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psutil.sensors_battery().power_plugged + + +def get_relative_humidity(): + try: + # Connect to a DHT22 sensor on pin 4 + from adafruit_dht import DHT22 + from board import D4 + except (ImportError, NotImplementedError): + # No DHT library results in an ImportError. + # Running on an unknown platform results in a NotImplementedError when getting the pin + return None + return DHT22(D4).humidity + + +@click.command(help="Displays the values of the sensors") +def show_sensors(): + click.echo("Python version: {0.major}.{0.minor}".format(python_version())) + for address in ip_addresses(): + click.echo("IP addresses: {0[1]} ({0[0]})".format(address)) + click.echo("CPU Load: {:.1%}".format(cpu_load())) + click.echo("RAM Available: {:.0f} MiB".format(ram_available() / 1024**2)) + click.echo("AC Connected: {!r}".format(ac_connected())) + click.echo("Humidity: {!r}".format(get_relative_humidity())) + + +if __name__ == '__main__': + show_sensors() \ No newline at end of file diff --git a/Ch01/figure01-01-fizzbuzz.ipynb b/Ch01/figure01-01-fizzbuzz.ipynb new file mode 100644 index 0000000..61c95a1 --- /dev/null +++ b/Ch01/figure01-01-fizzbuzz.ipynb @@ -0,0 +1,158 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1\n", + "2\n", + "Fizz\n", + "4\n", + "Buzz\n", + "Fizz\n", + "7\n", + "8\n", + "Fizz\n", + "Buzz\n", + "11\n", + "Fizz\n", + "13\n", + "14\n", + "FizzBuzz\n", + "16\n", + "17\n", + "Fizz\n", + "19\n", + "Buzz\n", + "Fizz\n", + "22\n", + "23\n", + "Fizz\n", + "Buzz\n", + "26\n", + "Fizz\n", + "28\n", + "29\n", + "FizzBuzz\n", + "31\n", + "32\n", + "Fizz\n", + "34\n", + "Buzz\n", + "Fizz\n", + "37\n", + "38\n", + "Fizz\n", + "Buzz\n", + "41\n", + "Fizz\n", + "43\n", + "44\n", + "FizzBuzz\n", + "46\n", + "47\n", + "Fizz\n", + "49\n", + "Buzz\n", + "Fizz\n", + "52\n", + "53\n", + "Fizz\n", + "Buzz\n", + "56\n", + "Fizz\n", + "58\n", + "59\n", + 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"name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Ch01/figure01-04-versioninfo.ipynb b/Ch01/figure01-04-versioninfo.ipynb new file mode 100644 index 0000000..828310c --- /dev/null +++ b/Ch01/figure01-04-versioninfo.ipynb @@ -0,0 +1,58 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "scrolled": false, + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "sys.version_info(major=3, minor=8, micro=0, releaselevel='final', serial=0)" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import sys\n", + "sys.version_info" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "advancedpython", + "language": "python", + "name": "advancedpython" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Ch01/figure01-05-ip-address.ipynb b/Ch01/figure01-05-ip-address.ipynb new file mode 100644 index 0000000..1049144 --- /dev/null +++ b/Ch01/figure01-05-ip-address.ipynb @@ -0,0 +1,137 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "scrolled": false, + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "sys.version_info(major=3, minor=8, micro=0, releaselevel='final', serial=0)" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import sys\n", + "sys.version_info" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'LAPTOP-IOJMBDVL'" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import socket\n", + "hostname = socket.gethostname()\n", + "hostname" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[(,\n", + " 0,\n", + " 0,\n", + " '',\n", + " ('fe80::xxxx:xxxx:ae23:fa5', 0, 0, 10)),\n", + " (,\n", + " 0,\n", + " 0,\n", + " '',\n", + " ('2001:xxxx:xxxx:xxxx:xxxx:xxxx:1321:a799', 0, 0, 0)),\n", + " (,\n", + " 0,\n", + " 0,\n", + " '',\n", + " ('2001:xxxx:xxxx:xxxx:xxxx:xxxx:ae23:fa5', 0, 0, 0)),\n", + " (, 0, 0, '', ('192.168.1.246', 0))]" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "addresses = socket.getaddrinfo(hostname, None)\n", + "addresses" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "AF_INET6 fe80::xxxx:xxxx:ae23:fa5\n", + "AF_INET6 2001:xxxx:xxxx:xxxx:xxxx:xxxx:1321:a799\n", + "AF_INET6 2001:xxxx:xxxx:xxxx:xxxx:xxxx:ae23:fa5\n", + "AF_INET 192.168.1.246\n" + ] + } + ], + "source": [ + "for address in addresses:\n", + " print(address[0].name, address[4][0])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "advancedpython", + "language": "python", + "name": "advancedpython" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Ch01/figure01-06-ip-address-joined.ipynb b/Ch01/figure01-06-ip-address-joined.ipynb new file mode 100644 index 0000000..010a2a6 --- /dev/null +++ b/Ch01/figure01-06-ip-address-joined.ipynb @@ -0,0 +1,85 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "scrolled": false, + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "sys.version_info(major=3, minor=8, micro=0, releaselevel='final', serial=0)" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import sys\n", + "sys.version_info" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "AF_INET6 fe80::xxxx:xxx:ae23:fa5\n", + "AF_INET6 2001:xxxx:xxxx:xxxx:xxx:xxxx:1321:a799\n", + "AF_INET6 2001:xxxx:xxxx:xxxx:xxx:xxxx:ae23:fa5\n", + "AF_INET 192.168.1.246\n" + ] + } + ], + "source": [ + "import socket\n", + "hostname = socket.gethostname()\n", + "\n", + "addresses = socket.getaddrinfo(hostname, None)\n", + "\n", + "for address in addresses:\n", + " print(address[0].name, address[4][0])\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "advancedpython", + "language": "python", + "name": "advancedpython" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Ch01/figure01-07-multiple-datapoints.ipynb b/Ch01/figure01-07-multiple-datapoints.ipynb new file mode 100644 index 0000000..21eb8a9 --- /dev/null +++ b/Ch01/figure01-07-multiple-datapoints.ipynb @@ -0,0 +1,154 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "scrolled": false, + "slideshow": { + "slide_type": "slide" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "sys.version_info(major=3, minor=8, micro=0, releaselevel='final', serial=0)" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import sys\n", + "sys.version_info" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "AF_INET6 fe80::xxxx:xxx:ae23:fa5\n", + "AF_INET6 2001:xxxx:xxxx:xxxx:xxx:xxxx:1321:a799\n", + "AF_INET6 2001:xxxx:xxxx:xxxx:xxx:xxxx:ae23:fa5\n", + "AF_INET 192.168.1.246\n" + ] + } + ], + "source": [ + "import socket\n", + "hostname = socket.gethostname()\n", + "\n", + "addresses = socket.getaddrinfo(hostname, None)\n", + "\n", + "for address in addresses:\n", + " print(address[0].name, address[4][0])\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "import psutil" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "60.8" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "psutil.cpu_percent()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "551014400" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "psutil.virtual_memory().available" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "psutil.sensors_battery().power_plugged" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "advancedpython", + "language": "python", + "name": "advancedpython" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Ch01/figure01-08-temperature_and_humidity_remote.ipynb b/Ch01/figure01-08-temperature_and_humidity_remote.ipynb new file mode 100644 index 0000000..e092c2f --- /dev/null +++ b/Ch01/figure01-08-temperature_and_humidity_remote.ipynb @@ -0,0 +1,54 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "44.6" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from adafruit_dht import DHT22\n", + "from board import D4\n", + "DHT22(D4).humidity" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "SSH pi@rpi4-office development-testing (office)", + "language": "python", + "name": "rik_ssh_pi_rpi4_office_developmenttestingoffice" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Ch01/figure01-09-temperature_and_humidity_local.ipynb b/Ch01/figure01-09-temperature_and_humidity_local.ipynb new file mode 100644 index 0000000..7dbcc39 --- /dev/null +++ b/Ch01/figure01-09-temperature_and_humidity_local.ipynb @@ -0,0 +1,43 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "ename": "ModuleNotFoundError", + "evalue": "No module named 'adafruit_dht'", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0madafruit_dht\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mDHT22\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mboard\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mD4\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[0mDHT22\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mD4\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhumidity\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'adafruit_dht'" + ] + } + ], + "source": [ + "from adafruit_dht import DHT22\n", + "from board import D4\n", + "DHT22(D4).humidity" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Ch01/listing01-01-fizzbuzz.py b/Ch01/listing01-01-fizzbuzz.py new file mode 100644 index 0000000..f737034 --- /dev/null +++ b/Ch01/listing01-01-fizzbuzz.py @@ -0,0 +1,9 @@ +for num in range(1, 101): + val = '' + if num % 3 == 0: + val += 'Fizz' + if num % 5 == 0: + val += 'Buzz' + if not val: + val = str(num) + print(val) diff --git a/Ch01/listing01-02-fizzbuzz_blank_lines.py b/Ch01/listing01-02-fizzbuzz_blank_lines.py new file mode 100644 index 0000000..25643f7 --- /dev/null +++ b/Ch01/listing01-02-fizzbuzz_blank_lines.py @@ -0,0 +1,11 @@ +for num in range(1, 101): + val = '' + if num % 3 == 0: + val += 'Fizz' + if num % 5 == 0: + val += 'Buzz' + + if not val: + val = str(num) + + print(val) diff --git a/Ch01/listing01-03-fizzbuzz_with_breakpoint.py b/Ch01/listing01-03-fizzbuzz_with_breakpoint.py new file mode 100644 index 0000000..e32ff6b --- /dev/null +++ b/Ch01/listing01-03-fizzbuzz_with_breakpoint.py @@ -0,0 +1,11 @@ +for num in range(1, 101): + val = '' + if num == 15: + breakpoint() + if num % 3 == 0: + val += 'Fizz' + if num % 5 == 0: + val += 'Buzz' + if not val: + val = str(num) + print(val) diff --git a/Ch01/listing01-04-converted.py b/Ch01/listing01-04-converted.py new file mode 100644 index 0000000..aaaac96 --- /dev/null +++ b/Ch01/listing01-04-converted.py @@ -0,0 +1,51 @@ +#!/usr/bin/env python +# coding: utf-8 + +# In[1]: + + +import sys +sys.version_info + + +# In[4]: + + +import socket +hostname = socket.gethostname() + +addresses = socket.getaddrinfo(hostname, None) + +for address in addresses: + print(address[0].name, address[4][0]) + + +# In[5]: + + +import psutil + + +# In[6]: + + +psutil.cpu_percent() + + +# In[7]: + + +psutil.virtual_memory().available + + +# In[8]: + + +psutil.sensors_battery().power_plugged + + +# In[ ]: + + + + diff --git a/Ch01/listing01-05-serverstatus.py b/Ch01/listing01-05-serverstatus.py new file mode 100644 index 0000000..1ad0002 --- /dev/null +++ b/Ch01/listing01-05-serverstatus.py @@ -0,0 +1,27 @@ +#!/usr/bin/env python +import sys +import socket + +import psutil + + +def python_version(): + return sys.version_info + +def ip_addresses(): + hostname = socket.gethostname() + + addresses = socket.getaddrinfo(hostname, None) + address_info = [] + for address in addresses: + address_info.append(address[0].name, address[4][0]) + return address_info + +def cpu_load(): + return psutil.cpu_percent() + +def ram_available(): + return psutil.virtual_memory().available + +def ac_connected(): + return psutil.sensors_battery().power_plugged diff --git a/Ch01/listing01-06-sensors_argv.py b/Ch01/listing01-06-sensors_argv.py new file mode 100644 index 0000000..9f17b4e --- /dev/null +++ b/Ch01/listing01-06-sensors_argv.py @@ -0,0 +1,63 @@ +#!/usr/bin/env python +# coding: utf-8 + +import psutil + +import sys + + +HELP_TEXT = """usage: python {program_name:s} + +Displays the values of the sensors + +Options and arguments: +--help: Display this message""" + + +def python_version(): + return sys.version_info + + +def ip_addresses(): + hostname = socket.gethostname() + addresses = socket.getaddrinfo(socket.gethostname(), None) + + address_info = [] + for address in addresses: + address_info.append((address[0].name, address[4][0])) + return address_info + + +def cpu_load(): + return psutil.cpu_percent(interval=0.1) + + +def ram_available(): + return psutil.virtual_memory().available + + +def ac_connected(): + return psutil.sensors_battery().power_plugged + + +def show_sensors(): + print("Python version: {}".format(python_version())) + for address in ip_addresses(): + print("IP addresses: {0[1]} ({0[0]})".format(address)) + print("CPU Load: {}".format(cpu_load())) + print("RAM Available: {}".format(ram_available())) + print("AC Connected: {}".format(ac_connected())) + + +def command_line(argv): + program_name, *arguments = argv + if not arguments: + show_sensors() + elif arguments and arguments[0] == '--help': + print(HELP_TEXT.format(program_name=program_name)) + return + else: + raise ValueError("Unknown arguments {}".format(arguments)) + +if __name__ == '__main__': + command_line(sys.argv) \ No newline at end of file diff --git a/Ch01/listing01-07-sensors_argparse.py b/Ch01/listing01-07-sensors_argparse.py new file mode 100644 index 0000000..185a19d --- /dev/null +++ b/Ch01/listing01-07-sensors_argparse.py @@ -0,0 +1,55 @@ +#!/usr/bin/env python +# coding: utf-8 + +import psutil + +import argparse +import sys + + +def python_version(): + return sys.version_info + + +def ip_addresses(): + hostname = socket.gethostname() + addresses = socket.getaddrinfo(socket.gethostname(), None) + + address_info = [] + for address in addresses: + address_info.append((address[0].name, address[4][0])) + return address_info + + +def cpu_load(): + return psutil.cpu_percent(interval=0.1) + + +def ram_available(): + return psutil.virtual_memory().available + + +def ac_connected(): + return psutil.sensors_battery().power_plugged + + +def show_sensors(): + print("Python version: {}".format(python_version())) + for address in ip_addresses(): + print("IP addresses: {0[1]} ({0[0]})".format(address)) + print("CPU Load: {}".format(cpu_load())) + print("RAM Available: {}".format(ram_available())) + print("AC Connected: {}".format(ac_connected())) + + +def command_line(argv): + parser = argparse.ArgumentParser( + description='Displays the values of the sensors', + add_help=True, + ) + arguments = parser.parse_args() + show_sensors() + + +if __name__ == '__main__': + command_line(sys.argv) \ No newline at end of file diff --git a/Ch01/listing01-08-sensors_click.py b/Ch01/listing01-08-sensors_click.py new file mode 100644 index 0000000..0854e7e --- /dev/null +++ b/Ch01/listing01-08-sensors_click.py @@ -0,0 +1,47 @@ +#!/usr/bin/env python +# coding: utf-8 +import socket +import sys + +import click +import psutil + + +def python_version(): + return sys.version_info + + +def ip_addresses(): + hostname = socket.gethostname() + addresses = socket.getaddrinfo(socket.gethostname(), None) + + address_info = [] + for address in addresses: + address_info.append((address[0].name, address[4][0])) + return address_info + + +def cpu_load(): + return psutil.cpu_percent(interval=0.1) / 100.0 + + +def ram_available(): + return psutil.virtual_memory().available + + +def ac_connected(): + return psutil.sensors_battery().power_plugged + + +@click.command(help="Displays the values of the sensors") +def show_sensors(): + click.echo("Python version: {0.major}.{0.minor}".format(python_version())) + for address in ip_addresses(): + click.echo("IP addresses: {0[1]} ({0[0]})".format(address)) + click.echo("CPU Load: {:.1%}".format(cpu_load())) + click.echo("RAM Available: {:.0f} MiB".format(ram_available() / 1024**2)) + click.echo("AC Connected: {!r}".format(ac_connected())) + + +if __name__ == '__main__': + show_sensors() \ No newline at end of file diff --git a/Ch01/listing01-09-sensors_click_bold.py b/Ch01/listing01-09-sensors_click_bold.py new file mode 100644 index 0000000..43847c7 --- /dev/null +++ b/Ch01/listing01-09-sensors_click_bold.py @@ -0,0 +1,39 @@ +#!/usr/bin/env python +# coding: utf-8 + +import click +import psutil + +import sys + + +def python_version(): + return sys.version_info + + +def cpu_load(): + return psutil.cpu_percent(interval=0.1) / 100.0 + + +def ram_available(): + return psutil.virtual_memory().available + + +def ac_connected(): + return psutil.sensors_battery().power_plugged + + +@click.command(help="Displays the values of the sensors") +def show_sensors(): + click.secho("Python version: ", bold=True, nl=False) + click.echo("{!r}".format(python_version())) + click.secho("CPU Load: ", bold=True, nl=False) + click.echo("{:.1%}".format(cpu_load())) + click.secho("RAM Available: ", bold=True, nl=False) + click.echo("{:d}".format(ram_available())) + click.secho("AC Connected: ", bold=True, nl=False) + click.echo("{!r}".format(ac_connected())) + + +if __name__ == '__main__': + show_sensors() \ No newline at end of file diff --git a/Ch01/listing01-10-final-sensors.py b/Ch01/listing01-10-final-sensors.py new file mode 100644 index 0000000..735d74f --- /dev/null +++ b/Ch01/listing01-10-final-sensors.py @@ -0,0 +1,59 @@ +#!/usr/bin/env python +# coding: utf-8 +import socket +import sys + +import click +import psutil + + +def python_version(): + return sys.version_info + + +def ip_addresses(): + hostname = socket.gethostname() + addresses = socket.getaddrinfo(socket.gethostname(), None) + + address_info = [] + for address in addresses: + address_info.append((address[0].name, address[4][0])) + return address_info + + +def cpu_load(): + return psutil.cpu_percent(interval=0.1) / 100.0 + + +def ram_available(): + return psutil.virtual_memory().available + + +def ac_connected(): + return psutil.sensors_battery().power_plugged + + +def get_relative_humidity(): + try: + from adafruit_dht import DHT11 + from board import D4 + except (ImportError, NotImplementedError): + # No DHT library results in an ImportError. + # Running on an unknown platform results in a NotImplementedError when getting the pin + return None + return DHT11(D4).humidity + + +@click.command(help="Displays the values of the sensors") +def show_sensors(): + click.echo("Python version: {0.major}.{0.minor}".format(python_version())) + for address in ip_addresses(): + click.echo("IP addresses: {0[1]} ({0[0]})".format(address)) + click.echo("CPU Load: {:.1%}".format(cpu_load())) + click.echo("RAM Available: {:.0f} MiB".format(ram_available() / 1024**2)) + click.echo("AC Connected: {!r}".format(ac_connected())) + click.echo("Humidity: {!r}".format(get_relative_humidity())) + + +if __name__ == '__main__': + show_sensors() \ No newline at end of file diff --git a/Ch02/apd.sensors-chapter02-ex01/Pipfile b/Ch02/apd.sensors-chapter02-ex01/Pipfile new file mode 100644 index 0000000..ec57aae --- /dev/null +++ b/Ch02/apd.sensors-chapter02-ex01/Pipfile @@ -0,0 +1,18 @@ +[[source]] +name = "pypi" +url = "https://pypi.org/simple" +verify_ssl = true + +[dev-packages] +ipykernel = "*" +remote-ikernel = "*" +pytest = "*" + +[packages] +psutil = "*" +click = "*" +adafruit-circuitpython-dht = {markers = "'arm' in platform_machine",version = "*"} +sysv-ipc = {markers = "platform_machine 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+#!/usr/bin/env python +# coding: utf-8 +import socket +import sys + +import click +import psutil + + +def python_version(): + return sys.version_info + + +def ip_addresses(): + hostname = socket.gethostname() + addresses = socket.getaddrinfo(socket.gethostname(), None) + + address_info = [] + for address in addresses: + address_info.append((address[0].name, address[4][0])) + return address_info + + +def cpu_load(): + return psutil.cpu_percent(interval=0.1) / 100.0 + + +def ram_available(): + return psutil.virtual_memory().available + + +def ac_connected(): + return psutil.sensors_battery().power_plugged + + +def get_relative_humidity(): + try: + from adafruit_dht import DHT11 + from board import D4 + except (ImportError, NotImplementedError): + # No DHT library results in an ImportError. + # Running on an unknown platform results in a NotImplementedError when getting the pin + return None + return DHT11(D4).humidity + + +@click.command()#help="Displays the values of the sensors") +def show_sensors(): + click.echo("Python version: {0.major}.{0.minor}".format(python_version())) + for address in ip_addresses(): + click.echo("IP addresses: {0[1]} ({0[0]})".format(address)) + click.echo("CPU Load: {:.1%}".format(cpu_load())) + click.echo("RAM Available: {:.0f} MiB".format(ram_available() / 1024**2)) + click.echo("AC Connected: {!r}".format(ac_connected())) + click.echo("Humidity: {!r}".format(get_relative_humidity())) + + +if __name__ == '__main__': + show_sensors() \ No newline at end of file diff --git a/Ch02/apd.sensors-chapter02-ex01/tests/__init__.py b/Ch02/apd.sensors-chapter02-ex01/tests/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/Ch02/apd.sensors-chapter02-ex01/tests/test_sensors.py b/Ch02/apd.sensors-chapter02-ex01/tests/test_sensors.py new file mode 100644 index 0000000..25ec9b3 --- /dev/null +++ b/Ch02/apd.sensors-chapter02-ex01/tests/test_sensors.py @@ -0,0 +1,4 @@ +import sensors + +def test_sensors(): + assert hasattr(sensors, 'python_version') \ No newline at end of file diff --git a/Ch02/apd.sensors-chapter02-pyi/.pre-commit-config.yaml b/Ch02/apd.sensors-chapter02-pyi/.pre-commit-config.yaml new file mode 100644 index 0000000..3ff6d15 --- /dev/null +++ b/Ch02/apd.sensors-chapter02-pyi/.pre-commit-config.yaml @@ -0,0 +1,23 @@ +repos: + +- repo: local + hooks: + - id: black + name: black + entry: pipenv run black + args: [--quiet] + language: system + types: [python] + + - id: mypy + name: mypy + entry: pipenv run mypy + args: ["--follow-imports=skip"] + language: system + types: [python] + + - id: flake8 + name: flake8 + entry: pipenv run flake8 + language: system + types: [python] diff --git a/Ch02/apd.sensors-chapter02-pyi/Pipfile b/Ch02/apd.sensors-chapter02-pyi/Pipfile new file mode 100644 index 0000000..c35d39c --- /dev/null +++ b/Ch02/apd.sensors-chapter02-pyi/Pipfile @@ -0,0 +1,29 @@ +[[source]] +name = "pypi" +url = "https://pypi.org/simple" +verify_ssl = true + +[[source]] +name = "pypi" +url = 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a/Ch02/apd.sensors-chapter02-pyi/sensors.py b/Ch02/apd.sensors-chapter02-pyi/sensors.py new file mode 100644 index 0000000..5db91fb --- /dev/null +++ b/Ch02/apd.sensors-chapter02-pyi/sensors.py @@ -0,0 +1,192 @@ +#!/usr/bin/env python +# coding: utf-8 +import math +import socket +import sys + + +import click +import psutil + + +class Sensor: + def value(self): + raise NotImplementedError + + @classmethod + def format(cls, value): + raise NotImplementedError + + def __str__(self): + return self.format(self.value()) + + +class PythonVersion(Sensor): + title = "Python Version" + + def value(self): + return sys.version_info + + @classmethod + def format(cls, value): + if value.micro == 0 and value.releaselevel == "alpha": + return "{0.major}.{0.minor}.{0.micro}a{0.serial}".format(value) + return "{0.major}.{0.minor}".format(value) + + +class IPAddresses(Sensor): + title = "IP Addresses" + FAMILIES = {"AF_INET": "IPv4", "AF_INET6": "IPv6"} + + def value(self): + hostname = socket.gethostname() + addresses = socket.getaddrinfo(hostname, None) + + address_info = [] + for address in addresses: + family, ip = (address[0].name, address[4][0]) + if family not in self.FAMILIES: + continue + value = (family, ip) + if value not in address_info: + address_info.append(value) + return address_info + + @classmethod + def format(cls, value): + return "\n".join( + "{0} ({1})".format(address[1], cls.FAMILIES.get(address[0], "Unknown")) + for address in value + ) + + +class CPULoad(Sensor): + title = "CPU Usage" + + def value(self): + return psutil.cpu_percent(interval=3) / 100.0 + + @classmethod + def format(cls, value): + return "{:.1%}".format(value) + + +class RAMAvailable(Sensor): + title = "RAM Available" + UNITS = ("B", "KiB", "MiB", "GiB", "TiB", "PiB", "EiB") + UNIT_SIZE = 2 ** 10 + + def value(self): + return psutil.virtual_memory().available + + @classmethod + def format(cls, value): + magnitude = math.floor(math.log(value, cls.UNIT_SIZE)) + max_magnitude = len(cls.UNITS) - 1 + magnitude = min(magnitude, max_magnitude) + scaled_value = value / (cls.UNIT_SIZE ** magnitude) + return "{:.1f} {}".format(scaled_value, cls.UNITS[magnitude]) + + +class ACStatus(Sensor): + title = "AC Connected" + + def value(self): + battery = psutil.sensors_battery() + if battery is not None: + return battery.power_plugged + else: + return None + + @classmethod + def format(cls, value): + if value is None: + return "Unknown" + elif value: + return "Connected" + else: + return "Not connected" + + +class Temperature(Sensor): + title = "Ambient Temperature" + + def value(self): + try: + # Connect to a DHT22 on pin 4 + from adafruit_dht import DHT22 + from board import D4 + except (ImportError, NotImplementedError): + # No DHT library results in an ImportError. + # Running on an unknown platform results in a + # NotImplementedError when getting the pin + return None + try: + return DHT22(D4).temperature + except RuntimeError: + return None + + @staticmethod + def celsius_to_fahrenheit(value): + return value * 9 / 5 + 32 + + @classmethod + def format(cls, value): + if value is None: + return "Unknown" + else: + return "{:.1f}C ({:.1f}F)".format(value, cls.celsius_to_fahrenheit(value)) + + def __str__(self): + return self.format(self.value()) + + +class RelativeHumidity(Sensor): + title = "Relative Humidity" + + def value(self): + try: + # Connect to a DHT22 on pin 4 + from adafruit_dht import DHT22 + from board import D4 + except (ImportError, NotImplementedError): + # No DHT library results in an ImportError. + # Running on an unknown platform results in a + # NotImplementedError when getting the pin + return None + try: + return DHT22(D4).humidity / 100 + except RuntimeError: + return None + + @classmethod + def format(cls, value): + if value is None: + return "Unknown" + else: + return "{:.1%}".format(value) + + +def get_sensors(): + return [ + PythonVersion(), + IPAddresses(), + CPULoad(), + RAMAvailable(), + ACStatus(), + Temperature(), + RelativeHumidity(), + ] + + +@click.command(help="Displays the values of the sensors") +def show_sensors(): + sensors = get_sensors() + for sensor in sensors: + click.secho(sensor.title, bold=True) + click.echo(str(sensor)) + click.echo("") + + +if __name__ == "__main__": + show_sensors() diff --git a/Ch02/apd.sensors-chapter02-pyi/sensors.pyi b/Ch02/apd.sensors-chapter02-pyi/sensors.pyi new file mode 100644 index 0000000..763c009 --- /dev/null +++ b/Ch02/apd.sensors-chapter02-pyi/sensors.pyi @@ -0,0 +1,59 @@ +from typing import Any, Iterable, List, Optional, Tuple, TypeVar, Generic + +T_value = TypeVar('T_value') + +class Sensor(Generic[T_value]): + title: str + def value(self) -> T_value: ... + @classmethod + def format(cls: Any, value: T_value) -> str: ... + +class PythonVersion(Sensor[Any]): + title: str = ... + def value(self) -> Any: ... + @classmethod + def format(cls: Any, value: Any) -> str: ... + +class IPAddresses(Sensor[Iterable[Tuple[str, str]]]): + title: str = ... + FAMILIES: Any = ... + def value(self) -> List[Tuple[str, str]]: ... + @classmethod + def format(cls: Any, value: Iterable[Tuple[str, str]]) -> str: ... + +class CPULoad(Sensor[float]): + title: str = ... + def value(self) -> float: ... + @classmethod + def format(cls: Any, value: float) -> str: ... + +class RAMAvailable(Sensor[int]): + title: str = ... + UNITS: Any = ... + UNIT_SIZE: Any = ... + def value(self) -> int: ... + @classmethod + def format(cls: Any, value: int) -> str: ... + +class ACStatus(Sensor[Optional[bool]]): + title: str = ... + def value(self) -> Optional[bool]: ... + @classmethod + def format(cls: Any, value: Optional[bool]) -> str: ... + +class Temperature(Sensor[Optional[float]]): + title: str = ... + def value(self) -> Optional[float]: ... + @staticmethod + def celsius_to_fahrenheit(value: float) -> float: ... + @classmethod + def format(cls: Any, value: Optional[float]) -> str: ... + +class RelativeHumidity(Sensor[Optional[float]]): + title: str = ... + def value(self) -> Optional[float]: ... + @classmethod + def format(cls: Any, value: Optional[float]) -> str: ... + +def get_sensors() -> Iterable[Sensor[Any]]: ... +def show_sensors() -> None: ... diff --git a/Ch02/apd.sensors-chapter02-pyi/setup.cfg b/Ch02/apd.sensors-chapter02-pyi/setup.cfg new file mode 100644 index 0000000..9aedcef --- /dev/null +++ b/Ch02/apd.sensors-chapter02-pyi/setup.cfg @@ -0,0 +1,5 @@ +[mypy] +ignore_missing_imports = True + +[flake8] +max-line-length = 88 \ No newline at end of file diff --git a/Ch02/apd.sensors-chapter02-pyi/tests/__init__.py b/Ch02/apd.sensors-chapter02-pyi/tests/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/Ch02/apd.sensors-chapter02-pyi/tests/test_acstatus.py b/Ch02/apd.sensors-chapter02-pyi/tests/test_acstatus.py new file mode 100644 index 0000000..7dab477 --- /dev/null +++ b/Ch02/apd.sensors-chapter02-pyi/tests/test_acstatus.py @@ -0,0 +1,55 @@ +from unittest import mock + +import pytest + +from sensors import ACStatus + + +@pytest.fixture +def sensor(): + return ACStatus() + + +class TestACStatusFormatter: + @pytest.fixture + def subject(self, sensor): + return sensor.format + + def test_format_True(self, subject): + assert subject(True) == "Connected" + + def test_format_False(self, subject): + assert subject(False) == "Not connected" + + def test_format_None(self, subject): + assert subject(None) == "Unknown" + + +class TestACStatusValue: + @pytest.fixture + def subject(self, sensor): + return sensor.value + + @pytest.fixture + def sensors_battery(self): + with mock.patch("psutil.sensors_battery") as sensors_battery: + yield sensors_battery + + def test_sensors_battery_called(self, subject, sensors_battery): + sensors_battery.return_value.power_plugged = True + assert subject() is True + assert sensors_battery.call_count == 1 + + def test_sensor_but_battery_unknown(self, subject, sensors_battery): + sensors_battery.return_value.power_plugged = None + assert subject() is None + assert sensors_battery.call_count == 1 + + def test_no_sensor(self, subject, sensors_battery): + sensors_battery.return_value = None + assert subject() is None + + def test_str_representation_is_formatted_value(self, sensor, sensors_battery): + sensors_battery.return_value.power_plugged = True + assert str(sensor) == "Connected" + assert sensors_battery.call_count == 1 diff --git a/Ch02/apd.sensors-chapter02-pyi/tests/test_cpuusage.py b/Ch02/apd.sensors-chapter02-pyi/tests/test_cpuusage.py new file mode 100644 index 0000000..504861b --- /dev/null +++ b/Ch02/apd.sensors-chapter02-pyi/tests/test_cpuusage.py @@ -0,0 +1,45 @@ +from unittest import mock + +import pytest + +from sensors import CPULoad + + +@pytest.fixture +def sensor(): + return CPULoad() + + +class TestCPULoadFormatter: + @pytest.fixture + def subject(self, sensor): + return sensor.format + + def test_format_simple_percentage(self, subject): + assert subject(0.05) == "5.0%" + + def test_format_multiple_decimal_places(self, subject): + assert subject(0.031415926) == "3.1%" + + def test_format_multiple_decimal_places_int(self, subject) -> None: + assert subject(1) == "100.0%" + + +class TestCPULoadValue: + @pytest.fixture + def subject(self, sensor): + return sensor.value + + @pytest.fixture + def cpuload(self): + with mock.patch("psutil.cpu_percent") as cpu_percent: + cpu_percent.return_value = 50.1 + yield cpu_percent + + def test_cpu_percent_called_With_interval(self, subject, cpuload): + assert subject() == 0.501 + assert cpuload.call_count == 1 + assert tuple(cpuload.call_args) == ((), {"interval": 3}) + + def test_str_representation_is_formatted_value(self, sensor, cpuload): + assert str(sensor) == "50.1%" diff --git a/Ch02/apd.sensors-chapter02-pyi/tests/test_dht.py b/Ch02/apd.sensors-chapter02-pyi/tests/test_dht.py new file mode 100644 index 0000000..c8391a3 --- /dev/null +++ b/Ch02/apd.sensors-chapter02-pyi/tests/test_dht.py @@ -0,0 +1,66 @@ +import pytest + +from sensors import Temperature, RelativeHumidity + + +@pytest.fixture +def temperature_sensor(): + return Temperature() + + +@pytest.fixture +def humidity_sensor(): + return RelativeHumidity() + + +class TestTemperatureFormatter: + @pytest.fixture + def subject(self, temperature_sensor): + return temperature_sensor.format + + def test_format_21c(self, subject): + assert subject(21.0) == "21.0C (69.8F)" + + def test_format_negative(self, subject): + assert subject(-32.0) == "-32.0C (-25.6F)" + + def test_format_unknown(self, subject): + assert subject(None) == "Unknown" + + +class TestTemperatureConversion: + @pytest.fixture + def subject(self, temperature_sensor): + return temperature_sensor.celsius_to_fahrenheit + + def test_celsius_to_fahrenheit(self, subject): + c = 21 + f = subject(c) + assert f == 69.8 + + def test_celsius_to_fahrenheit_equivlance_point(self, subject): + c = -40 + f = subject(c) + assert f == -40 + + def test_celsius_to_fahrenheit_float(self, subject): + c = 21.2 + f = subject(c) + assert f == 70.16 + + def test_celsius_to_fahrenheit_string(self, subject): + c = "21" + with pytest.raises(TypeError): + subject(c) + + +class TestHumidityFormatter: + @pytest.fixture + def subject(self, humidity_sensor): + return humidity_sensor.format + + def test_format_percentage(self, subject): + assert subject(0.035) == "3.5%" + + def test_format_unknown(self, subject): + assert subject(None) == "Unknown" diff --git a/Ch02/apd.sensors-chapter02-pyi/tests/test_ipaddresses.py b/Ch02/apd.sensors-chapter02-pyi/tests/test_ipaddresses.py new file mode 100644 index 0000000..f97d377 --- /dev/null +++ b/Ch02/apd.sensors-chapter02-pyi/tests/test_ipaddresses.py @@ -0,0 +1,59 @@ +import socket +from unittest import mock + +import pytest + +from sensors import IPAddresses + + +@pytest.fixture +def sensor(): + return IPAddresses() + + +class TestIPAddressFormatter: + @pytest.fixture + def subject(self, sensor): + return sensor.format + + def test_format_single_ipv4(self, subject): + ips = [("AF_INET", "192.0.2.1")] + assert subject(ips) == "192.0.2.1 (IPv4)" + + def test_format_single_ipv6(self, subject): + ips = [("AF_INET6", "2001:DB8::1")] + assert subject(ips) == "2001:DB8::1 (IPv6)" + + def test_format_mixed_list(self, subject): + ips = [("AF_INET", "192.0.2.1"), ("AF_INET6", "2001:DB8::1")] + assert subject(ips) == "192.0.2.1 (IPv4)\n2001:DB8::1 (IPv6)" + + def test_unusual_protocols_are_marked_as_unknown(self, subject): + ips = [("AF_IRDA", "ffff")] + assert subject(ips) == "ffff (Unknown)" + + +class TestIPAddressesValue: + @pytest.fixture + def subject(self, sensor): + return sensor.value + + @pytest.fixture + def gethostname(self): + with mock.patch("socket.gethostname") as gethostname: + gethostname.return_value = "localhost" + yield gethostname + + @pytest.fixture + def getaddrinfo(self, gethostname): + with mock.patch("socket.getaddrinfo") as getaddrinfo: + yield getaddrinfo + + def test_getaddrinfo_used_for_value_collection(self, getaddrinfo, subject): + getaddrinfo.return_value = [(socket.AF_INET, 0, 0, "", ("192.0.2.1", 0))] + assert subject() == [("AF_INET", "192.0.2.1")] + assert getaddrinfo.call_count == 1 + + def test_str_representation_is_formatted_value(self, getaddrinfo, sensor): + getaddrinfo.return_value = [(socket.AF_INET6, 0, 0, "", ("2001:DB8::1", 0))] + assert str(sensor) == "2001:DB8::1 (IPv6)" diff --git a/Ch02/apd.sensors-chapter02-pyi/tests/test_pythonversion.py b/Ch02/apd.sensors-chapter02-pyi/tests/test_pythonversion.py new file mode 100644 index 0000000..473eb62 --- /dev/null +++ b/Ch02/apd.sensors-chapter02-pyi/tests/test_pythonversion.py @@ -0,0 +1,61 @@ +from collections import namedtuple +from unittest import mock + +import pytest + +from sensors import PythonVersion + + +@pytest.fixture +def version(): + return namedtuple( + "sys_versioninfo", ("major", "minor", "micro", "releaselevel", "serial") + ) + + +@pytest.fixture +def sensor(): + return PythonVersion() + + +class TestPythonVersionFormatter: + @pytest.fixture + def subject(self, sensor): + return sensor.format + + def test_format_py38(self, subject, version): + py38 = version(3, 8, 0, "final", 0) + assert subject(py38) == "3.8" + + def test_format_large_version(self, subject, version): + large = version(255, 128, 0, "final", 0) + assert subject(large) == "255.128" + + def test_alpha_of_minor_is_marked(self, subject, version): + py39 = version(3, 9, 0, "alpha", 1) + assert subject(py39) == "3.9.0a1" + + def test_alpha_of_micro_is_unmarked(self, subject, version): + py39 = version(3, 9, 1, "alpha", 1) + assert subject(py39) == "3.9" + + +class TestPythonVersionValue: + @pytest.fixture + def subject(self, sensor): + return sensor.value + + @pytest.fixture + def python_version(self): + import sys + + return sys.version_info + + def test_data_value_is_sys_versioninfo(self, python_version, subject): + assert subject() == python_version + + +class TestPythonVersionSensor: + def test_str_representation_is_formatted_value(self, sensor, version): + with mock.patch("sys.version_info", new=version(3, 4, 1, "final", 1)): + assert str(sensor) == "3.4" diff --git a/Ch02/apd.sensors-chapter02-pyi/tests/test_ramusage.py b/Ch02/apd.sensors-chapter02-pyi/tests/test_ramusage.py new file mode 100644 index 0000000..b3c091a --- /dev/null +++ b/Ch02/apd.sensors-chapter02-pyi/tests/test_ramusage.py @@ -0,0 +1,47 @@ +from unittest import mock + +import pytest + +from sensors import RAMAvailable + + +@pytest.fixture +def sensor(): + return RAMAvailable() + + +class TestRAMAvailableFormatter: + @pytest.fixture + def subject(self, sensor): + return sensor.format + + def test_format_bytes(self, subject): + assert subject(15) == "15.0 B" + + def test_format_kibibytes(self, subject): + assert subject(15000) == "14.6 KiB" + + def test_format_mibibytes(self, subject): + assert subject(15000000) == "14.3 MiB" + + def test_format_gibibytes(self, subject): + assert subject(15000000000) == "14.0 GiB" + + +class TestRAMAvailableValue: + @pytest.fixture + def subject(self, sensor): + return sensor.value + + @pytest.fixture + def virtual_memory(self): + with mock.patch("psutil.virtual_memory") as virtual_memory: + virtual_memory.return_value.available = 1024 + yield virtual_memory + + def test_virtual_memory_called(self, subject, virtual_memory): + assert subject() == 1024 + assert virtual_memory.call_count == 1 + + def test_str_representation_is_formatted_value(self, sensor, virtual_memory): + assert str(sensor) == "1.0 KiB" diff --git a/Ch02/apd.sensors-chapter02-pyi/tests/test_sensors.py b/Ch02/apd.sensors-chapter02-pyi/tests/test_sensors.py new file mode 100644 index 0000000..6596ba1 --- /dev/null +++ b/Ch02/apd.sensors-chapter02-pyi/tests/test_sensors.py @@ -0,0 +1,17 @@ +from click.testing import CliRunner + +import pytest + +import sensors + + +def test_sensors(): + assert hasattr(sensors, "PythonVersion") + + +@pytest.mark.functional +def test_python_version_is_first_two_lines_of_cli_output(): + runner = CliRunner() + result = runner.invoke(sensors.show_sensors) + python_version = str(sensors.PythonVersion()) + assert ["Python Version", python_version] == result.stdout.split("\n")[:2] diff --git a/Ch02/listing02-01-temperature_sensor.py b/Ch02/listing02-01-temperature_sensor.py new file mode 100644 index 0000000..16bb8ac --- /dev/null +++ b/Ch02/listing02-01-temperature_sensor.py @@ -0,0 +1,73 @@ +#!/usr/bin/env python +# coding: utf-8 +import socket +import sys + +import click +import psutil + + +def python_version(): + return sys.version_info + + +def ip_addresses(): + hostname = socket.gethostname() + addresses = socket.getaddrinfo(socket.gethostname(), None) + + address_info = [] + for address in addresses: + address_info.append((address[0].name, address[4][0])) + return address_info + + +def cpu_load(): + return psutil.cpu_percent(interval=0.1) / 100.0 + + +def ram_available(): + return psutil.virtual_memory().available + + +def ac_connected(): + return psutil.sensors_battery().power_plugged + + +def get_relative_humidity(): + try: + # Connect a DHT22 sensor on GPIO pin 4 + from adafruit_dht import DHT22 + from board import D4 + except (ImportError, NotImplementedError): + # No DHT library results in an ImportError. + # Running on an unknown platform results in a NotImplementedError when getting the pin + return None + return DHT22(D4).humidity + + +def get_temperature(): + try: + # Connect a DHT22 sensor on GPIO pin 4 + from adafruit_dht import DHT22 + from board import D4 + except (ImportError, NotImplementedError): + # No DHT library results in an ImportError. + # Running on an unknown platform results in a NotImplementedError when getting the pin + return None + return DHT22(D4).temperature + + +@click.command(help="Displays the values of the sensors") +def show_sensors(): + click.echo("Python version: {0.major}.{0.minor}".format(python_version())) + for address in ip_addresses(): + click.echo("IP addresses: {0[1]} ({0[0]})".format(address)) + click.echo("CPU Load: {:.1%}".format(cpu_load())) + click.echo("RAM Available: {:.0f} MiB".format(ram_available() / 1024**2)) + click.echo("AC Connected: {!r}".format(ac_connected())) + click.echo("Humidity: {!r}".format(get_relative_humidity())) + click.echo("Temperature: {!r}".format(get_temperature())) + + +if __name__ == '__main__': + show_sensors() \ No newline at end of file diff --git a/Ch02/listing02-02-temperature_conversion.ipynb b/Ch02/listing02-02-temperature_conversion.ipynb new file mode 100644 index 0000000..29f8e8c --- /dev/null +++ b/Ch02/listing02-02-temperature_conversion.ipynb @@ -0,0 +1,85 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "def celsius_to_fahrenheit(celsius):\n", + " return celsius * 9 / 5 + 32\n", + "\n", + "def celsius_to_kelvin(celsius):\n", + " return 273.15 + celsius" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "69.8" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "celsius_to_fahrenheit(21)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "294.15" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "celsius_to_kelvin(21)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "advancedpython", + "language": "python", + "name": "advancedpython" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Ch02/listing02-03-temperature_conversion_invalid_types.ipynb b/Ch02/listing02-03-temperature_conversion_invalid_types.ipynb new file mode 100644 index 0000000..ab31649 --- /dev/null +++ b/Ch02/listing02-03-temperature_conversion_invalid_types.ipynb @@ -0,0 +1,128 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "def celsius_to_fahrenheit(celsius):\n", + " return celsius * 9 / 5 + 32\n", + "\n", + "def celsius_to_kelvin(celsius):\n", + " return 273.15 + celsius" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "69.8" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "celsius_to_fahrenheit(21)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "294.15" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "celsius_to_kelvin(21)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(32.18+3.6j)" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "celsius_to_fahrenheit(0.1+2j)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[33.8, 32. , 32. ],\n", + " [32. , 33.8, 32. ],\n", + " [32. , 32. , 33.8]])" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import numpy\n", + "celsius_to_fahrenheit(numpy.identity(3))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "advancedpython", + "language": "python", + "name": "advancedpython" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Ch02/listing02-04-temperature.py b/Ch02/listing02-04-temperature.py new file mode 100644 index 0000000..087592a --- /dev/null +++ b/Ch02/listing02-04-temperature.py @@ -0,0 +1,5 @@ +def celsius_to_fahrenheit(celsius): + return celsius * 9 / 5 + 32 + +def celsius_to_kelvin(celsius): + return 273.15 + celsius diff --git a/Ch02/listing02-05-unittest_temperature/__pycache__/temperature.cpython-37.pyc b/Ch02/listing02-05-unittest_temperature/__pycache__/temperature.cpython-37.pyc new file mode 100644 index 0000000..45c73af Binary files /dev/null and b/Ch02/listing02-05-unittest_temperature/__pycache__/temperature.cpython-37.pyc differ diff --git a/Ch02/listing02-05-unittest_temperature/temperature.py b/Ch02/listing02-05-unittest_temperature/temperature.py new file mode 100644 index 0000000..087592a --- /dev/null +++ b/Ch02/listing02-05-unittest_temperature/temperature.py @@ -0,0 +1,5 @@ +def celsius_to_fahrenheit(celsius): + return celsius * 9 / 5 + 32 + +def celsius_to_kelvin(celsius): + return 273.15 + celsius diff --git a/Ch02/listing02-05-unittest_temperature/test_unittest.py b/Ch02/listing02-05-unittest_temperature/test_unittest.py new file mode 100644 index 0000000..2655206 --- /dev/null +++ b/Ch02/listing02-05-unittest_temperature/test_unittest.py @@ -0,0 +1,22 @@ +import unittest +from temperature import celsius_to_fahrenheit + + +class TestTemperatureConversion(unittest.TestCase): + + def test_celsius_to_fahrenheit(self): + self.assertEqual(celsius_to_fahrenheit(21), 69.8) + + def test_celsius_to_fahrenheit_equivlance_point(self): + self.assertEqual(celsius_to_fahrenheit(-40), -40) + + def test_celsius_to_fahrenheit_float(self): + self.assertEqual(celsius_to_fahrenheit(21.2), 70.16) + + def test_celsius_to_fahrenheit_string(self): + with self.assertRaises(TypeError): + f = celsius_to_fahrenheit("21") + + +if __name__ == '__main__': + unittest.main() diff --git a/Ch02/listing02-06-pytest_temperature/temperature.py b/Ch02/listing02-06-pytest_temperature/temperature.py new file mode 100644 index 0000000..087592a --- /dev/null +++ b/Ch02/listing02-06-pytest_temperature/temperature.py @@ -0,0 +1,5 @@ +def celsius_to_fahrenheit(celsius): + return celsius * 9 / 5 + 32 + +def celsius_to_kelvin(celsius): + return 273.15 + celsius diff --git a/Ch02/listing02-06-pytest_temperature/test_pytest.py b/Ch02/listing02-06-pytest_temperature/test_pytest.py new file mode 100644 index 0000000..ad8dd48 --- /dev/null +++ b/Ch02/listing02-06-pytest_temperature/test_pytest.py @@ -0,0 +1,23 @@ +import pytest +from .temperature import celsius_to_fahrenheit + + +def test_celsius_to_fahrenheit(): + c = 21 + f = celsius_to_fahrenheit(c) + assert f == 69.8 + +def test_celsius_to_fahrenheit_equivlance_point(): + c = -40 + f = celsius_to_fahrenheit(c) + assert f == -40 + +def test_celsius_to_fahrenheit_float(): + c = 21.2 + f = celsius_to_fahrenheit(c) + assert f == 70.16 + +def test_celsius_to_fahrenheit_string(): + c = "21" + with pytest.raises(TypeError): + f = celsius_to_fahrenheit(c) diff --git a/Ch02/listing02-07-sensors.py b/Ch02/listing02-07-sensors.py new file mode 100644 index 0000000..20b6ef8 --- /dev/null +++ b/Ch02/listing02-07-sensors.py @@ -0,0 +1,198 @@ +#!/usr/bin/env python +# coding: utf-8 +import math +import socket +import sys +from typing import Any, Optional, List, Tuple, Iterable, TypeVar, Generic + + +import click +import psutil + + +T_value = TypeVar("T_value") + + +class Sensor(Generic[T_value]): + title: str + + def value(self) -> T_value: + raise NotImplementedError + + @classmethod + def format(cls, value: T_value) -> str: + raise NotImplementedError + + def __str__(self) -> str: + return self.format(self.value()) + + +class PythonVersion(Sensor[Any]): + title = "Python Version" + + def value(self) -> Any: + return sys.version_info + + @classmethod + def format(cls, value: Any) -> str: + if value.micro == 0 and value.releaselevel == "alpha": + return "{0.major}.{0.minor}.{0.micro}a{0.serial}".format(value) + return "{0.major}.{0.minor}".format(value) + + +class IPAddresses(Sensor[Iterable[Tuple[str, str]]]): + title = "IP Addresses" + FAMILIES = {"AF_INET": "IPv4", "AF_INET6": "IPv6"} + + def value(self) -> List[Tuple[str, str]]: + hostname = socket.gethostname() + addresses = socket.getaddrinfo(hostname, None) + + address_info: List[Tuple[str, str]] = [] + for address in addresses: + family, ip = (address[0].name, address[4][0]) + if family not in self.FAMILIES: + continue + value = (family, ip) + if value not in address_info: + address_info.append(value) + return address_info + + @classmethod + def format(cls, value: Iterable[Tuple[str, str]]) -> str: + return "\n".join( + "{0} ({1})".format(address[1], cls.FAMILIES.get(address[0], "Unknown")) + for address in value + ) + + +class CPULoad(Sensor[float]): + title = "CPU Usage" + + def value(self) -> float: + return psutil.cpu_percent(interval=3) / 100.0 + + @classmethod + def format(cls, value: float) -> str: + return "{:.1%}".format(value) + + +class RAMAvailable(Sensor[int]): + title = "RAM Available" + UNITS = ("B", "KiB", "MiB", "GiB", "TiB", "PiB", "EiB") + UNIT_SIZE = 2 ** 10 + + def value(self) -> int: + return psutil.virtual_memory().available + + @classmethod + def format(cls, value: int) -> str: + magnitude = math.floor(math.log(value, cls.UNIT_SIZE)) + max_magnitude = len(cls.UNITS) - 1 + magnitude = min(magnitude, max_magnitude) + scaled_value = value / (cls.UNIT_SIZE ** magnitude) + return "{:.1f} {}".format(scaled_value, cls.UNITS[magnitude]) + + +class ACStatus(Sensor[Optional[bool]]): + title = "AC Connected" + + def value(self) -> Optional[bool]: + battery = psutil.sensors_battery() + if battery is not None: + return battery.power_plugged + else: + return None + + @classmethod + def format(cls, value: Optional[bool]) -> str: + if value is None: + return "Unknown" + elif value: + return "Connected" + else: + return "Not connected" + + +class Temperature(Sensor[Optional[float]]): + title = "Ambient Temperature" + + def value(self) -> Optional[float]: + try: + # Connect to a DHT22 on pin 4 + from adafruit_dht import DHT22 + from board import D4 + except (ImportError, NotImplementedError): + # No DHT library results in an ImportError. + # Running on an unknown platform results in a + # NotImplementedError when getting the pin + return None + try: + return DHT22(D4).temperature + except RuntimeError: + return None + + @staticmethod + def celsius_to_fahrenheit(cls, value: float) -> float: + return value * 9 / 5 + 32 + + @classmethod + def format(cls, value: Optional[float]) -> str: + if value is None: + return "Unknown" + else: + return "{:.1f}C ({:.1f}F)".format(value, cls.celsius_to_fahrenheit(value)) + + def __str__(self) -> str: + return self.format(self.value()) + + +class RelativeHumidity(Sensor[Optional[float]]): + title = "Relative Humidity" + + def value(self) -> Optional[float]: + try: + # Connect to a DHT22 on pin 4 + from adafruit_dht import DHT22 + from board import D4 + except (ImportError, NotImplementedError): + # No DHT library results in an ImportError. + # Running on an unknown platform results in a + # NotImplementedError when getting the pin + return None + try: + return DHT22(D4).humidity / 100 + except RuntimeError: + return None + + @classmethod + def format(cls, value: Optional[float]) -> str: + if value is None: + return "Unknown" + else: + return "{:.1%}".format(value) + + +def get_sensors() -> Iterable[Sensor[Any]]: + return [ + PythonVersion(), + IPAddresses(), + CPULoad(), + RAMAvailable(), + ACStatus(), + Temperature(), + RelativeHumidity(), + ] + + 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+ title = "Python Version" + + def value(self): + return sys.version_info + + @classmethod + def format(cls, value): + if value.micro == 0 and value.releaselevel == "alpha": + return "{0.major}.{0.minor}.{0.micro}a{0.serial}".format(value) + return "{0.major}.{0.minor}".format(value) + + def __str__(self): + return self.format(self.value()) + + +class IPAddresses: + title = "IP Addresses" + + def value(self): + hostname = socket.gethostname() + addresses = socket.getaddrinfo(hostname, None) + + address_info = [] + for address in addresses: + value = (address[0].name, address[4][0]) + if value not in address_info: + address_info.append(value) + return address_info + + @classmethod + def format(cls, value): + return "\n".join( + "{0[1]} ({0[0]})".format(address) + for address in value + ) + + def __str__(self): + return self.format(self.value()) + + +class CPULoad: + title = "CPU Usage" + + def value(self): + return psutil.cpu_percent(interval=3) / 100.0 + + @classmethod + def format(cls, value): + return "{:.1%}".format(value) + + def __str__(self): + return self.format(self.value()) + + +class RAMAvailable: + title = "RAM Available" + UNITS = ('KiB', 'MiB', 'GiB', 'TiB', 'PiB', 'EiB') + UNIT_SIZE = 2**10 + + def value(self): + return psutil.virtual_memory().available + + @classmethod + def format(cls, value): + magnitude = math.floor(math.log(value, cls.UNIT_SIZE)) + max_magnitude = len(cls.UNITS) + magnitude = min(magnitude, max_magnitude) + scaled_value = value / cls.UNIT_SIZE ** magnitude + + return "{:.1f} {}".format(scaled_value, cls.UNITS[magnitude - 1]) + + def __str__(self): + return self.format(self.value()) + + +class ACStatus: + title = "AC Connected" + + def value(self): + battery = psutil.sensors_battery() + if battery is not None: + return battery.power_plugged + else: + return None + + @classmethod + def format(cls, value): + if value is None: + return "Unknown" + elif value: + return "Connected" + else: + return "Not connected" + + def __str__(self): + return self.format(self.value()) + + +class Temperature: + title = "Ambient Temperature" + + def value(self): + try: + # Connect to a DHT22 on pin 4 + from adafruit_dht import DHT22 + from board import D4 + except (ImportError, NotImplementedError): + # No DHT library results in an ImportError. + # Running on an unknown platform results in a NotImplementedError when getting the pin + return None + try: + return DHT22(D4).temperature + except RuntimeError: + return None + + @staticmethod + def celsius_to_fahrenheit(cls, value: float) -> float: + return value * 9 / 5 + 32 + + @classmethod + def format(cls, value): + if value is None: + return "Unknown" + else: + return "{:.1}C ({:.1}F)".format(value, self.celsius_to_fahrenheit(value)) + + def __str__(self): + return self.format(self.value()) + +class RelativeHumidity: + title = "Relative Humidity" + + def value(self): + try: + # Connect to a DHT22 on pin 4 + from adafruit_dht import DHT22 + from board import D4 + except (ImportError, NotImplementedError): + # No DHT library results in an ImportError. + # Running on an unknown platform results in a NotImplementedError when getting the pin + return None + try: + return DHT22(D4).humidity / 100 + except RuntimeError: + return None + + @classmethod + def format(cls, value): + if value is None: + return "Unknown" + else: + return "{:.1%}".format(value) + + def __str__(self): + return self.format(self.value()) + + +@click.command(help="Displays the values of the sensors") +def show_sensors(): + for sensor in [PythonVersion(), IPAddresses(), CPULoad(), RAMAvailable(), ACStatus(), Temperature(), RelativeHumidity()]: + click.secho(sensor.title, bold=True) + click.echo(sensor) + click.echo("") + + +if __name__ == '__main__': + show_sensors() \ No newline at end of file diff --git a/Ch02/listing02-08/tests/__init__.py b/Ch02/listing02-08/tests/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/Ch02/listing02-08/tests/test_pythonversion.py b/Ch02/listing02-08/tests/test_pythonversion.py new file mode 100644 index 0000000..abd2f49 --- /dev/null +++ b/Ch02/listing02-08/tests/test_pythonversion.py @@ -0,0 +1,32 @@ +from collections import namedtuple + +from sensors import PythonVersion + +import pytest + + +class TestPythonVersionFormatter: + + @pytest.fixture + def subject(self): + return PythonVersion().format + + @pytest.fixture + def version(self): + return namedtuple("sys_versioninfo", ("major", "minor", "micro", "releaselevel", "serial")) + + def test_format_py38(self, subject, version): + py38 = version(3, 8, 0, "final", 0) + assert subject(py38) == "3.8" + + def test_format_large_version(self, subject, version): + large = version(255, 128, 0, "final", 0) + assert subject(large) == "255.128" + + def test_alpha_of_minor_is_marked(self, subject, version): + py39 = version(3, 9, 0, "alpha", 1) + assert subject(py39) == "3.9.0a1" + + def test_alpha_of_micro_is_unmarked(self, subject, version): + py39 = version(3, 9, 1, "alpha", 1) + assert subject(py39) == "3.9" diff --git a/Ch02/listing02-08/tests/test_sensors.py b/Ch02/listing02-08/tests/test_sensors.py new file mode 100644 index 0000000..dec77f9 --- /dev/null +++ b/Ch02/listing02-08/tests/test_sensors.py @@ -0,0 +1,17 @@ +import sys + +from click.testing import CliRunner +import pytest + +import sensors + + +def test_sensors(): + assert hasattr(sensors, 'PythonVersion') + +@pytest.mark.functional +def test_python_version_is_first_two_lines_of_cli_output(): + runner = CliRunner() + result = runner.invoke(sensors.show_sensors) + python_version = str(sensors.PythonVersion()) + assert ["Python Version", python_version] == result.stdout.split("\n")[:2] \ No newline at end of file diff --git a/Ch02/listing02-09-sensors,cover.py b/Ch02/listing02-09-sensors,cover.py new file mode 100644 index 0000000..7c29f0e --- /dev/null +++ b/Ch02/listing02-09-sensors,cover.py @@ -0,0 +1,178 @@ + #!/usr/bin/env python + # coding: utf-8 +> import math +> import socket +> import sys + +> import click +> import psutil + + +> class PythonVersion: +> title = "Python Version" + +> def value(self): +! return sys.version_info + +> @classmethod +> def format(cls, value): +> if value.micro == 0 and value.releaselevel == "alpha": +> return "{0.major}.{0.minor}.{0.micro}a{0.serial}".format(value) +> return "{0.major}.{0.minor}".format(value) + +> def __str__(self): +! return self.format(self.value()) + + +> class IPAddresses: +> title = "IP Addresses" + +> def value(self): +! hostname = socket.gethostname() +! addresses = socket.getaddrinfo(hostname, None) + +! address_info = [] +! for address in addresses: +! value = (address[0].name, address[4][0]) +! if value not in address_info: +! address_info.append(value) +! return address_info + +> @classmethod +> def format(cls, value): +! return "\n".join( +! "{0[1]} ({0[0]})".format(address) +! for address in value +! ) + +> def __str__(self): +! return self.format(self.value()) + + +> class CPULoad: +> title = "CPU Usage" + +> def value(self): +! return psutil.cpu_percent(interval=3) / 100.0 + +> @classmethod +> def format(cls, value): +! return "{:.1%}".format(value) + +> def __str__(self): +! return self.format(self.value()) + + +> class RAMAvailable: +> title = "RAM Available" +> UNITS = ('KiB', 'MiB', 'GiB', 'TiB', 'PiB', 'EiB') +> UNIT_SIZE = 2**10 + +> def value(self): +! return psutil.virtual_memory().available + +> @classmethod +> def format(cls, value): +! magnitude = math.floor(math.log(value, cls.UNIT_SIZE)) +! max_magnitude = len(cls.UNITS) +! magnitude = min(magnitude, max_magnitude) +! scaled_value = value / cls.UNIT_SIZE ** magnitude + +! return "{:.1f} {}".format(scaled_value, cls.UNITS[magnitude - 1]) + +> def __str__(self): +! return self.format(self.value()) + + +> class ACStatus: +> title = "AC Connected" + +> def value(self): +! battery = psutil.sensors_battery() +! if battery is not None: +! return battery.power_plugged +! else: +! return None + +> @classmethod +> def format(cls, value): +! if value is None: +! return "Unknown" +! elif value: +! return "Connected" +! else: +! return "Not connected" + +> def __str__(self): +! return self.format(self.value()) + + +> class Temperature: +> title = "Ambient Temperature" + +> def value(self): +! try: + # Connect to a DHT22 on pin 4 +! from adafruit_dht import DHT22 +! from board import D4 +! except (ImportError, NotImplementedError): + # No DHT library results in an ImportError. + # Running on an unknown platform results in a NotImplementedError when getting the pin +! return None +! try: +! return DHT22(D4).temperature +! except RuntimeError: +! return None + +> @staticmethod +> def celsius_to_fahrenheit(cls, value: float) -> float: +! return value * 9 / 5 + 32 + +> @classmethod +> def format(cls, value): +! if value is None: +! return "Unknown" +! else: +! return "{:.1}C ({:.1}F)".format(value, self.celsius_to_fahrenheit(value)) + +> def __str__(self): +! return self.format(self.value()) + +> class RelativeHumidity: +> title = "Relative Humidity" + +> def value(self): +! try: + # Connect to a DHT22 on pin 4 +! from adafruit_dht import DHT22 +! from board import D4 +! except (ImportError, NotImplementedError): + # No DHT library results in an ImportError. + # Running on an unknown platform results in a NotImplementedError when getting the pin +! return None +! try: +! return DHT22(D4).humidity / 100 +! except RuntimeError: +! return None + +> @classmethod +> def format(cls, value): +! if value is None: +! return "Unknown" +! else: +! return "{:.1%}".format(value) + +> def __str__(self): +! return self.format(self.value()) + + +> @click.command(help="Displays the values of the sensors") +> def show_sensors(): +! for sensor in [PythonVersion(), IPAddresses(), CPULoad(), RAMAvailable(), ACStatus(), Temperature(), RelativeHumidity()]: +! click.secho(sensor.title, bold=True) +! click.echo(sensor) +! click.echo("") + + +> if __name__ == '__main__': +! show_sensors() diff --git a/Ch02/listing02-10/.pre-commit-config.yaml b/Ch02/listing02-10/.pre-commit-config.yaml new file mode 100644 index 0000000..3ff6d15 --- /dev/null +++ b/Ch02/listing02-10/.pre-commit-config.yaml @@ -0,0 +1,23 @@ +repos: + +- repo: local + hooks: + - id: black + name: black + entry: pipenv run black + args: [--quiet] + language: system + types: [python] + + - id: mypy + name: mypy + entry: pipenv run mypy + args: ["--follow-imports=skip"] + language: system + types: [python] + + - id: flake8 + name: flake8 + entry: pipenv run flake8 + language: system + types: [python] diff --git a/Ch02/listing02-10/Pipfile b/Ch02/listing02-10/Pipfile new file mode 100644 index 0000000..c35d39c --- /dev/null +++ b/Ch02/listing02-10/Pipfile @@ -0,0 +1,29 @@ +[[source]] +name = "pypi" +url = "https://pypi.org/simple" +verify_ssl = true + 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0000000..861f3d0 --- /dev/null +++ b/Ch02/listing02-10/pytest.ini @@ -0,0 +1,3 @@ +[pytest] +markers = + functional: these tests are significantly slower as they run the whole CLI script \ No newline at end of file diff --git a/Ch02/listing02-10/sensors.py b/Ch02/listing02-10/sensors.py new file mode 100644 index 0000000..0e46a9b --- /dev/null +++ b/Ch02/listing02-10/sensors.py @@ -0,0 +1,198 @@ +#!/usr/bin/env python +# coding: utf-8 +import math +import socket +import sys +from typing import Any, Optional, List, Tuple, Iterable, TypeVar, Generic + + +import click +import psutil + + +T_value = TypeVar("T_value") + + +class Sensor(Generic[T_value]): + title: str + + def value(self) -> T_value: + raise NotImplementedError + + @classmethod + def format(cls, value: T_value) -> str: + raise NotImplementedError + + def __str__(self) -> str: + return self.format(self.value()) + + +class PythonVersion(Sensor[Any]): + title = "Python Version" + + def value(self) -> Any: + return sys.version_info + + @classmethod + def format(cls, value: Any) -> str: + if value.micro == 0 and value.releaselevel == "alpha": + return "{0.major}.{0.minor}.{0.micro}a{0.serial}".format(value) + return "{0.major}.{0.minor}".format(value) + + +class IPAddresses(Sensor[Iterable[Tuple[str, str]]]): + title = "IP Addresses" + FAMILIES = {"AF_INET": "IPv4", "AF_INET6": "IPv6"} + + def value(self) -> List[Tuple[str, str]]: + hostname = socket.gethostname() + addresses = socket.getaddrinfo(hostname, None) + + address_info: List[Tuple[str, str]] = [] + for address in addresses: + family, ip = (address[0].name, address[4][0]) + if family not in self.FAMILIES: + continue + value = (family, ip) + if value not in address_info: + address_info.append(value) + return address_info + + @classmethod + def format(cls, value: Iterable[Tuple[str, str]]) -> str: + return "\n".join( + "{0} ({1})".format(address[1], cls.FAMILIES.get(address[0], "Unknown")) + for address in value + ) + + +class CPULoad(Sensor[float]): + title = "CPU Usage" + + def value(self) -> float: + return psutil.cpu_percent(interval=3) / 100.0 + + @classmethod + def format(cls, value: float) -> str: + return "{:.1%}".format(value) + + +class RAMAvailable(Sensor[int]): + title = "RAM Available" + UNITS = ("B", "KiB", "MiB", "GiB", "TiB", "PiB", "EiB") + UNIT_SIZE = 2 ** 10 + + def value(self) -> int: + return psutil.virtual_memory().available + + @classmethod + def format(cls, value: int) -> str: + magnitude = math.floor(math.log(value, cls.UNIT_SIZE)) + max_magnitude = len(cls.UNITS) - 1 + magnitude = min(magnitude, max_magnitude) + scaled_value = value / (cls.UNIT_SIZE ** magnitude) + return "{:.1f} {}".format(scaled_value, cls.UNITS[magnitude]) + + +class ACStatus(Sensor[Optional[bool]]): + title = "AC Connected" + + def value(self) -> Optional[bool]: + battery = psutil.sensors_battery() + if battery is not None: + return battery.power_plugged + else: + return None + + @classmethod + def format(cls, value: Optional[bool]) -> str: + if value is None: + return "Unknown" + elif value: + return "Connected" + else: + return "Not connected" + + +class Temperature(Sensor[Optional[float]]): + title = "Ambient Temperature" + + def value(self) -> Optional[float]: + try: + # Connect to a DHT22 on pin 4 + from adafruit_dht import DHT22 + from board import D4 + except (ImportError, NotImplementedError): + # No DHT library results in an ImportError. + # Running on an unknown platform results in a + # NotImplementedError when getting the pin + return None + try: + return DHT22(D4).temperature + except RuntimeError: + return None + + @staticmethod + def celsius_to_fahrenheit(value: float) -> float: + return value * 9 / 5 + 32 + + @classmethod + def format(cls, value: Optional[float]) -> str: + if value is None: + return "Unknown" + else: + return "{:.1f}C ({:.1f}F)".format(value, cls.celsius_to_fahrenheit(value)) + + def __str__(self) -> str: + return self.format(self.value()) + + +class RelativeHumidity(Sensor[Optional[float]]): + title = "Relative Humidity" + + def value(self) -> Optional[float]: + try: + # Connect to a DHT22 on pin 4 + from adafruit_dht import DHT22 + from board import D4 + except (ImportError, NotImplementedError): + # No DHT library results in an ImportError. + # Running on an unknown platform results in a + # NotImplementedError when getting the pin + return None + try: + return DHT22(D4).humidity / 100 + except RuntimeError: + return None + + @classmethod + def format(cls, value: Optional[float]) -> str: + if value is None: + return "Unknown" + else: + return "{:.1%}".format(value) + + +def get_sensors() -> Iterable[Sensor[Any]]: + return [ + PythonVersion(), + IPAddresses(), + CPULoad(), + RAMAvailable(), + ACStatus(), + Temperature(), + RelativeHumidity(), + ] + + +@click.command(help="Displays the values of the sensors") +def show_sensors() -> None: + sensors = get_sensors() + for sensor in sensors: + click.secho(sensor.title, bold=True) + click.echo(str(sensor)) + click.echo("") + + +if __name__ == "__main__": + show_sensors() diff --git a/Ch02/listing02-10/setup.cfg b/Ch02/listing02-10/setup.cfg new file mode 100644 index 0000000..9aedcef --- /dev/null +++ b/Ch02/listing02-10/setup.cfg @@ -0,0 +1,5 @@ +[mypy] +ignore_missing_imports = True + +[flake8] +max-line-length = 88 \ No newline at end of file diff --git a/Ch02/listing02-10/tests/__init__.py b/Ch02/listing02-10/tests/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/Ch02/listing02-10/tests/test_acstatus.py b/Ch02/listing02-10/tests/test_acstatus.py new file mode 100644 index 0000000..7dab477 --- /dev/null +++ b/Ch02/listing02-10/tests/test_acstatus.py @@ -0,0 +1,55 @@ +from unittest import mock + +import pytest + +from sensors import ACStatus + + +@pytest.fixture +def sensor(): + return ACStatus() + + +class TestACStatusFormatter: + @pytest.fixture + def subject(self, sensor): + return sensor.format + + def test_format_True(self, subject): + assert subject(True) == "Connected" + + def test_format_False(self, subject): + assert subject(False) == "Not connected" + + def test_format_None(self, subject): + assert subject(None) == "Unknown" + + +class TestACStatusValue: + @pytest.fixture + def subject(self, sensor): + return sensor.value + + @pytest.fixture + def sensors_battery(self): + with mock.patch("psutil.sensors_battery") as sensors_battery: + yield sensors_battery + + def test_sensors_battery_called(self, subject, sensors_battery): + sensors_battery.return_value.power_plugged = True + assert subject() is True + assert sensors_battery.call_count == 1 + + def test_sensor_but_battery_unknown(self, subject, sensors_battery): + sensors_battery.return_value.power_plugged = None + assert subject() is None + assert sensors_battery.call_count == 1 + + def test_no_sensor(self, subject, sensors_battery): + sensors_battery.return_value = None + assert subject() is None + + def test_str_representation_is_formatted_value(self, sensor, sensors_battery): + sensors_battery.return_value.power_plugged = True + assert str(sensor) == "Connected" + assert sensors_battery.call_count == 1 diff --git a/Ch02/listing02-10/tests/test_cpuusage.py b/Ch02/listing02-10/tests/test_cpuusage.py new file mode 100644 index 0000000..504861b --- /dev/null +++ b/Ch02/listing02-10/tests/test_cpuusage.py @@ -0,0 +1,45 @@ +from unittest import mock + +import pytest + +from sensors import CPULoad + + +@pytest.fixture +def sensor(): + return CPULoad() + + +class TestCPULoadFormatter: + @pytest.fixture + def subject(self, sensor): + return sensor.format + + def test_format_simple_percentage(self, subject): + assert subject(0.05) == "5.0%" + + def test_format_multiple_decimal_places(self, subject): + assert subject(0.031415926) == "3.1%" + + def test_format_multiple_decimal_places_int(self, subject) -> None: + assert subject(1) == "100.0%" + + +class TestCPULoadValue: + @pytest.fixture + def subject(self, sensor): + return sensor.value + + @pytest.fixture + def cpuload(self): + with mock.patch("psutil.cpu_percent") as cpu_percent: + cpu_percent.return_value = 50.1 + yield cpu_percent + + def test_cpu_percent_called_With_interval(self, subject, cpuload): + assert subject() == 0.501 + assert cpuload.call_count == 1 + assert tuple(cpuload.call_args) == ((), {"interval": 3}) + + def test_str_representation_is_formatted_value(self, sensor, cpuload): + assert str(sensor) == "50.1%" diff --git a/Ch02/listing02-10/tests/test_dht.py b/Ch02/listing02-10/tests/test_dht.py new file mode 100644 index 0000000..c8391a3 --- /dev/null +++ b/Ch02/listing02-10/tests/test_dht.py @@ -0,0 +1,66 @@ +import pytest + +from sensors import Temperature, RelativeHumidity + + +@pytest.fixture +def temperature_sensor(): + return Temperature() + + +@pytest.fixture +def humidity_sensor(): + return RelativeHumidity() + + +class TestTemperatureFormatter: + @pytest.fixture + def subject(self, temperature_sensor): + return temperature_sensor.format + + def test_format_21c(self, subject): + assert subject(21.0) == "21.0C (69.8F)" + + def test_format_negative(self, subject): + assert subject(-32.0) == "-32.0C (-25.6F)" + + def test_format_unknown(self, subject): + assert subject(None) == "Unknown" + + +class TestTemperatureConversion: + @pytest.fixture + def subject(self, temperature_sensor): + return temperature_sensor.celsius_to_fahrenheit + + def test_celsius_to_fahrenheit(self, subject): + c = 21 + f = subject(c) + assert f == 69.8 + + def test_celsius_to_fahrenheit_equivlance_point(self, subject): + c = -40 + f = subject(c) + assert f == -40 + + def test_celsius_to_fahrenheit_float(self, subject): + c = 21.2 + f = subject(c) + assert f == 70.16 + + def test_celsius_to_fahrenheit_string(self, subject): + c = "21" + with pytest.raises(TypeError): + subject(c) + + +class TestHumidityFormatter: + @pytest.fixture + def subject(self, humidity_sensor): + return humidity_sensor.format + + def test_format_percentage(self, subject): + assert subject(0.035) == "3.5%" + + def test_format_unknown(self, subject): + assert subject(None) == "Unknown" diff --git a/Ch02/listing02-10/tests/test_ipaddresses.py b/Ch02/listing02-10/tests/test_ipaddresses.py new file mode 100644 index 0000000..f97d377 --- /dev/null +++ b/Ch02/listing02-10/tests/test_ipaddresses.py @@ -0,0 +1,59 @@ +import socket +from unittest import mock + +import pytest + +from sensors import IPAddresses + + +@pytest.fixture +def sensor(): + return IPAddresses() + + +class TestIPAddressFormatter: + @pytest.fixture + def subject(self, sensor): + return sensor.format + + def test_format_single_ipv4(self, subject): + ips = [("AF_INET", "192.0.2.1")] + assert subject(ips) == "192.0.2.1 (IPv4)" + + def test_format_single_ipv6(self, subject): + ips = [("AF_INET6", "2001:DB8::1")] + assert subject(ips) == "2001:DB8::1 (IPv6)" + + def test_format_mixed_list(self, subject): + ips = [("AF_INET", "192.0.2.1"), ("AF_INET6", "2001:DB8::1")] + assert subject(ips) == "192.0.2.1 (IPv4)\n2001:DB8::1 (IPv6)" + + def test_unusual_protocols_are_marked_as_unknown(self, subject): + ips = [("AF_IRDA", "ffff")] + assert subject(ips) == "ffff (Unknown)" + + +class TestIPAddressesValue: + @pytest.fixture + def subject(self, sensor): + return sensor.value + + @pytest.fixture + def gethostname(self): + with mock.patch("socket.gethostname") as gethostname: + gethostname.return_value = "localhost" + yield gethostname + + @pytest.fixture + def getaddrinfo(self, gethostname): + with mock.patch("socket.getaddrinfo") as getaddrinfo: + yield getaddrinfo + + def test_getaddrinfo_used_for_value_collection(self, getaddrinfo, subject): + getaddrinfo.return_value = [(socket.AF_INET, 0, 0, "", ("192.0.2.1", 0))] + assert subject() == [("AF_INET", "192.0.2.1")] + assert getaddrinfo.call_count == 1 + + def test_str_representation_is_formatted_value(self, getaddrinfo, sensor): + getaddrinfo.return_value = [(socket.AF_INET6, 0, 0, "", ("2001:DB8::1", 0))] + assert str(sensor) == "2001:DB8::1 (IPv6)" diff --git a/Ch02/listing02-10/tests/test_pythonversion.py b/Ch02/listing02-10/tests/test_pythonversion.py new file mode 100644 index 0000000..473eb62 --- /dev/null +++ b/Ch02/listing02-10/tests/test_pythonversion.py @@ -0,0 +1,61 @@ +from collections import namedtuple +from unittest import mock + +import pytest + +from sensors import PythonVersion + + +@pytest.fixture +def version(): + return namedtuple( + "sys_versioninfo", ("major", "minor", "micro", "releaselevel", "serial") + ) + + +@pytest.fixture +def sensor(): + return PythonVersion() + + +class TestPythonVersionFormatter: + @pytest.fixture + def subject(self, sensor): + return sensor.format + + def test_format_py38(self, subject, version): + py38 = version(3, 8, 0, "final", 0) + assert subject(py38) == "3.8" + + def test_format_large_version(self, subject, version): + large = version(255, 128, 0, "final", 0) + assert subject(large) == "255.128" + + def test_alpha_of_minor_is_marked(self, subject, version): + py39 = version(3, 9, 0, "alpha", 1) + assert subject(py39) == "3.9.0a1" + + def test_alpha_of_micro_is_unmarked(self, subject, version): + py39 = version(3, 9, 1, "alpha", 1) + assert subject(py39) == "3.9" + + +class TestPythonVersionValue: + @pytest.fixture + def subject(self, sensor): + return sensor.value + + @pytest.fixture + def python_version(self): + import sys + + return sys.version_info + + def test_data_value_is_sys_versioninfo(self, python_version, subject): + assert subject() == python_version + + +class TestPythonVersionSensor: + def test_str_representation_is_formatted_value(self, sensor, version): + with mock.patch("sys.version_info", new=version(3, 4, 1, "final", 1)): + assert str(sensor) == "3.4" diff --git a/Ch02/listing02-10/tests/test_ramusage.py b/Ch02/listing02-10/tests/test_ramusage.py new file mode 100644 index 0000000..b3c091a --- /dev/null +++ b/Ch02/listing02-10/tests/test_ramusage.py @@ -0,0 +1,47 @@ +from unittest import mock + +import pytest + +from sensors import RAMAvailable + + +@pytest.fixture +def sensor(): + return RAMAvailable() + + +class TestRAMAvailableFormatter: + @pytest.fixture + def subject(self, sensor): + return sensor.format + + def test_format_bytes(self, subject): + assert subject(15) == "15.0 B" + + def test_format_kibibytes(self, subject): + assert subject(15000) == "14.6 KiB" + + def test_format_mibibytes(self, subject): + assert subject(15000000) == "14.3 MiB" + + def test_format_gibibytes(self, subject): + assert subject(15000000000) == "14.0 GiB" + + +class TestRAMAvailableValue: + @pytest.fixture + def subject(self, sensor): + return sensor.value + + @pytest.fixture + def virtual_memory(self): + with mock.patch("psutil.virtual_memory") as virtual_memory: + virtual_memory.return_value.available = 1024 + yield virtual_memory + + def test_virtual_memory_called(self, subject, virtual_memory): + assert subject() == 1024 + assert virtual_memory.call_count == 1 + + def test_str_representation_is_formatted_value(self, sensor, virtual_memory): + assert str(sensor) == "1.0 KiB" diff --git a/Ch02/listing02-10/tests/test_sensors.py b/Ch02/listing02-10/tests/test_sensors.py new file mode 100644 index 0000000..6596ba1 --- /dev/null +++ b/Ch02/listing02-10/tests/test_sensors.py @@ -0,0 +1,17 @@ +from click.testing import CliRunner + +import pytest + +import sensors + + +def test_sensors(): + assert hasattr(sensors, "PythonVersion") + + +@pytest.mark.functional +def test_python_version_is_first_two_lines_of_cli_output(): + runner = CliRunner() + result = runner.invoke(sensors.show_sensors) + python_version = str(sensors.PythonVersion()) + assert ["Python Version", python_version] == result.stdout.split("\n")[:2] diff --git a/Ch02/listing02-11/.pre-commit-config.yaml b/Ch02/listing02-11/.pre-commit-config.yaml new file mode 100644 index 0000000..3ff6d15 --- /dev/null +++ b/Ch02/listing02-11/.pre-commit-config.yaml @@ -0,0 +1,23 @@ +repos: + +- repo: local + hooks: + - id: black + name: black + entry: pipenv run black + args: [--quiet] + language: system + types: [python] + + - id: mypy + name: mypy + entry: pipenv run mypy + args: ["--follow-imports=skip"] + language: system + types: [python] + + - id: flake8 + name: flake8 + entry: pipenv run flake8 + language: system + types: [python] diff --git a/Ch02/listing02-11/Pipfile b/Ch02/listing02-11/Pipfile new file mode 100644 index 0000000..c35d39c --- /dev/null +++ b/Ch02/listing02-11/Pipfile @@ -0,0 +1,29 @@ +[[source]] +name = "pypi" +url = "https://pypi.org/simple" +verify_ssl = true + +[[source]] +name = "pypi" +url = "https://piwheels.org/simple" +verify_ssl = true + +[dev-packages] +ipykernel = "*" +remote-ikernel = "*" +pytest = "*" +pytest-cov = "*" +mypy = "*" +flake8 = "*" +black = "*" +pre-commit = "*" + +[packages] +psutil = "*" +click = "*" +adafruit-circuitpython-dht = {markers = "'arm' in platform_machine",version = "*"} + +[requires] + +[pipenv] +allow_prereleases = true 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NotImplementedError + + @classmethod + def format(cls, value: T_value) -> str: + raise NotImplementedError + + def __str__(self) -> str: + return self.format(self.value()) + + +class PythonVersion(Sensor[Any]): + title = "Python Version" + + def value(self) -> Any: + return sys.version_info + + @classmethod + def format(cls, value: Any) -> str: + if value.micro == 0 and value.releaselevel == "alpha": + return "{0.major}.{0.minor}.{0.micro}a{0.serial}".format(value) + return "{0.major}.{0.minor}".format(value) + + +class IPAddresses(Sensor[Iterable[Tuple[str, str]]]): + title = "IP Addresses" + FAMILIES = {"AF_INET": "IPv4", "AF_INET6": "IPv6"} + + def value(self) -> List[Tuple[str, str]]: + hostname = socket.gethostname() + addresses = socket.getaddrinfo(hostname, None) + + address_info: List[Tuple[str, str]] = [] + for address in addresses: + family, ip = (address[0].name, address[4][0]) + if family not in self.FAMILIES: + continue + value = (family, ip) + if value not in address_info: + address_info.append(value) + return address_info + + @classmethod + def format(cls, value: Iterable[Tuple[str, str]]) -> str: + return "\n".join( + "{0} ({1})".format(address[1], cls.FAMILIES.get(address[0], "Unknown")) + for address in value + ) + + +class CPULoad(Sensor[float]): + title = "CPU Usage" + + def value(self) -> float: + return psutil.cpu_percent(interval=3) / 100.0 + + @classmethod + def format(cls, value: float) -> str: + return "{:.1%}".format(value) + + +class RAMAvailable(Sensor[int]): + title = "RAM Available" + UNITS = ("B", "KiB", "MiB", "GiB", "TiB", "PiB", "EiB") + UNIT_SIZE = 2 ** 10 + + def value(self) -> int: + return psutil.virtual_memory().available + + @classmethod + def format(cls, value: int) -> str: + magnitude = math.floor(math.log(value, cls.UNIT_SIZE)) + max_magnitude = len(cls.UNITS) - 1 + magnitude = min(magnitude, max_magnitude) + scaled_value = value / (cls.UNIT_SIZE ** magnitude) + return "{:.1f} {}".format(scaled_value, cls.UNITS[magnitude]) + + +class ACStatus(Sensor[Optional[bool]]): + title = "AC Connected" + + def value(self) -> Optional[bool]: + battery = psutil.sensors_battery() + if battery is not None: + return battery.power_plugged + else: + return None + + @classmethod + def format(cls, value: Optional[bool]) -> str: + if value is None: + return "Unknown" + elif value: + return "Connected" + else: + return "Not connected" + + +class Temperature(Sensor[Optional[float]]): + title = "Ambient Temperature" + + def value(self) -> Optional[float]: + try: + # Connect to a DHT22 on pin 4 + from adafruit_dht import DHT22 + from board import D4 + except (ImportError, NotImplementedError): + # No DHT library results in an ImportError. + # Running on an unknown platform results in a + # NotImplementedError when getting the pin + return None + try: + return DHT22(D4).temperature + except RuntimeError: + return None + + @staticmethod + def celsius_to_fahrenheit(value: float) -> float: + return value * 9 / 5 + 32 + + @classmethod + def format(cls, value: Optional[float]) -> str: + if value is None: + return "Unknown" + else: + return "{:.1f}C ({:.1f}F)".format(value, cls.celsius_to_fahrenheit(value)) + + def __str__(self) -> str: + return self.format(self.value()) + + +class RelativeHumidity(Sensor[Optional[float]]): + title = "Relative Humidity" + + def value(self) -> Optional[float]: + try: + # Connect to a DHT22 on pin 4 + from adafruit_dht import DHT22 + from board import D4 + except (ImportError, NotImplementedError): + # No DHT library results in an ImportError. + # Running on an unknown platform results in a + # NotImplementedError when getting the pin + return None + try: + return DHT22(D4).humidity / 100 + except RuntimeError: + return None + + @classmethod + def format(cls, value: Optional[float]) -> str: + if value is None: + return "Unknown" + else: + return "{:.1%}".format(value) + + +def get_sensors() -> Iterable[Sensor[Any]]: + return [ + PythonVersion(), + IPAddresses(), + CPULoad(), + RAMAvailable(), + ACStatus(), + Temperature(), + RelativeHumidity(), + ] + + +@click.command(help="Displays the values of the sensors") +def show_sensors() -> None: + sensors = get_sensors() + for sensor in sensors: + click.secho(sensor.title, bold=True) + click.echo(str(sensor)) + click.echo("") + + +if __name__ == "__main__": + show_sensors() diff --git a/Ch02/listing02-11/setup.cfg b/Ch02/listing02-11/setup.cfg new file mode 100644 index 0000000..9aedcef --- /dev/null +++ b/Ch02/listing02-11/setup.cfg @@ -0,0 +1,5 @@ +[mypy] +ignore_missing_imports = True + +[flake8] +max-line-length = 88 \ No newline at end of file diff --git a/Ch02/listing02-11/tests/__init__.py b/Ch02/listing02-11/tests/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/Ch02/listing02-11/tests/test_acstatus.py b/Ch02/listing02-11/tests/test_acstatus.py new file mode 100644 index 0000000..7dab477 --- /dev/null +++ b/Ch02/listing02-11/tests/test_acstatus.py @@ -0,0 +1,55 @@ +from unittest import mock + +import pytest + +from sensors import ACStatus + + +@pytest.fixture +def sensor(): + return ACStatus() + + +class TestACStatusFormatter: + @pytest.fixture + def subject(self, sensor): + return sensor.format + + def test_format_True(self, subject): + assert subject(True) == "Connected" + + def test_format_False(self, subject): + assert subject(False) == "Not connected" + + def test_format_None(self, subject): + assert subject(None) == "Unknown" + + +class TestACStatusValue: + @pytest.fixture + def subject(self, sensor): + return sensor.value + + @pytest.fixture + def sensors_battery(self): + with mock.patch("psutil.sensors_battery") as sensors_battery: + yield sensors_battery + + def test_sensors_battery_called(self, subject, sensors_battery): + sensors_battery.return_value.power_plugged = True + assert subject() is True + assert sensors_battery.call_count == 1 + + def test_sensor_but_battery_unknown(self, subject, sensors_battery): + sensors_battery.return_value.power_plugged = None + assert subject() is None + assert sensors_battery.call_count == 1 + + def test_no_sensor(self, subject, sensors_battery): + sensors_battery.return_value = None + assert subject() is None + + def test_str_representation_is_formatted_value(self, sensor, sensors_battery): + sensors_battery.return_value.power_plugged = True + assert str(sensor) == "Connected" + assert sensors_battery.call_count == 1 diff --git a/Ch02/listing02-11/tests/test_cpuusage.py b/Ch02/listing02-11/tests/test_cpuusage.py new file mode 100644 index 0000000..504861b --- /dev/null +++ b/Ch02/listing02-11/tests/test_cpuusage.py @@ -0,0 +1,45 @@ +from unittest import mock + +import pytest + +from sensors import CPULoad + + +@pytest.fixture +def sensor(): + return CPULoad() + + +class TestCPULoadFormatter: + @pytest.fixture + def subject(self, sensor): + return sensor.format + + def test_format_simple_percentage(self, subject): + assert subject(0.05) == "5.0%" + + def test_format_multiple_decimal_places(self, subject): + assert subject(0.031415926) == "3.1%" + + def test_format_multiple_decimal_places_int(self, subject) -> None: + assert subject(1) == "100.0%" + + +class TestCPULoadValue: + @pytest.fixture + def subject(self, sensor): + return sensor.value + + @pytest.fixture + def cpuload(self): + with mock.patch("psutil.cpu_percent") as cpu_percent: + cpu_percent.return_value = 50.1 + yield cpu_percent + + def test_cpu_percent_called_With_interval(self, subject, cpuload): + assert subject() == 0.501 + assert cpuload.call_count == 1 + assert tuple(cpuload.call_args) == ((), {"interval": 3}) + + def test_str_representation_is_formatted_value(self, sensor, cpuload): + assert str(sensor) == "50.1%" diff --git a/Ch02/listing02-11/tests/test_dht.py b/Ch02/listing02-11/tests/test_dht.py new file mode 100644 index 0000000..c8391a3 --- /dev/null +++ b/Ch02/listing02-11/tests/test_dht.py @@ -0,0 +1,66 @@ +import pytest + +from sensors import Temperature, RelativeHumidity + + +@pytest.fixture +def temperature_sensor(): + return Temperature() + + +@pytest.fixture +def humidity_sensor(): + return RelativeHumidity() + + +class TestTemperatureFormatter: + @pytest.fixture + def subject(self, temperature_sensor): + return temperature_sensor.format + + def test_format_21c(self, subject): + assert subject(21.0) == "21.0C (69.8F)" + + def test_format_negative(self, subject): + assert subject(-32.0) == "-32.0C (-25.6F)" + + def test_format_unknown(self, subject): + assert subject(None) == "Unknown" + + +class TestTemperatureConversion: + @pytest.fixture + def subject(self, temperature_sensor): + return temperature_sensor.celsius_to_fahrenheit + + def test_celsius_to_fahrenheit(self, subject): + c = 21 + f = subject(c) + assert f == 69.8 + + def test_celsius_to_fahrenheit_equivlance_point(self, subject): + c = -40 + f = subject(c) + assert f == -40 + + def test_celsius_to_fahrenheit_float(self, subject): + c = 21.2 + f = subject(c) + assert f == 70.16 + + def test_celsius_to_fahrenheit_string(self, subject): + c = "21" + with pytest.raises(TypeError): + subject(c) + + +class TestHumidityFormatter: + @pytest.fixture + def subject(self, humidity_sensor): + return humidity_sensor.format + + def test_format_percentage(self, subject): + assert subject(0.035) == "3.5%" + + def test_format_unknown(self, subject): + assert subject(None) == "Unknown" diff --git a/Ch02/listing02-11/tests/test_ipaddresses.py b/Ch02/listing02-11/tests/test_ipaddresses.py new file mode 100644 index 0000000..f97d377 --- /dev/null +++ b/Ch02/listing02-11/tests/test_ipaddresses.py @@ -0,0 +1,59 @@ +import socket +from unittest import mock + +import pytest + +from sensors import IPAddresses + + +@pytest.fixture +def sensor(): + return IPAddresses() + + +class TestIPAddressFormatter: + @pytest.fixture + def subject(self, sensor): + return sensor.format + + def test_format_single_ipv4(self, subject): + ips = [("AF_INET", "192.0.2.1")] + assert subject(ips) == "192.0.2.1 (IPv4)" + + def test_format_single_ipv6(self, subject): + ips = [("AF_INET6", "2001:DB8::1")] + assert subject(ips) == "2001:DB8::1 (IPv6)" + + def test_format_mixed_list(self, subject): + ips = [("AF_INET", "192.0.2.1"), ("AF_INET6", "2001:DB8::1")] + assert subject(ips) == "192.0.2.1 (IPv4)\n2001:DB8::1 (IPv6)" + + def test_unusual_protocols_are_marked_as_unknown(self, subject): + ips = [("AF_IRDA", "ffff")] + assert subject(ips) == "ffff (Unknown)" + + +class TestIPAddressesValue: + @pytest.fixture + def subject(self, sensor): + return sensor.value + + @pytest.fixture + def gethostname(self): + with mock.patch("socket.gethostname") as gethostname: + gethostname.return_value = "localhost" + yield gethostname + + @pytest.fixture + def getaddrinfo(self, gethostname): + with mock.patch("socket.getaddrinfo") as getaddrinfo: + yield getaddrinfo + + def test_getaddrinfo_used_for_value_collection(self, getaddrinfo, subject): + getaddrinfo.return_value = [(socket.AF_INET, 0, 0, "", ("192.0.2.1", 0))] + assert subject() == [("AF_INET", "192.0.2.1")] + assert getaddrinfo.call_count == 1 + + def test_str_representation_is_formatted_value(self, getaddrinfo, sensor): + getaddrinfo.return_value = [(socket.AF_INET6, 0, 0, "", ("2001:DB8::1", 0))] + assert str(sensor) == "2001:DB8::1 (IPv6)" diff --git a/Ch02/listing02-11/tests/test_pythonversion.py b/Ch02/listing02-11/tests/test_pythonversion.py new file mode 100644 index 0000000..473eb62 --- /dev/null +++ b/Ch02/listing02-11/tests/test_pythonversion.py @@ -0,0 +1,61 @@ +from collections import namedtuple +from unittest import mock + +import pytest + +from sensors import PythonVersion + + +@pytest.fixture +def version(): + return namedtuple( + "sys_versioninfo", ("major", "minor", "micro", "releaselevel", "serial") + ) + + +@pytest.fixture +def sensor(): + return PythonVersion() + + +class TestPythonVersionFormatter: + @pytest.fixture + def subject(self, sensor): + return sensor.format + + def test_format_py38(self, subject, version): + py38 = version(3, 8, 0, "final", 0) + assert subject(py38) == "3.8" + + def test_format_large_version(self, subject, version): + large = version(255, 128, 0, "final", 0) + assert subject(large) == "255.128" + + def test_alpha_of_minor_is_marked(self, subject, version): + py39 = version(3, 9, 0, "alpha", 1) + assert subject(py39) == "3.9.0a1" + + def test_alpha_of_micro_is_unmarked(self, subject, version): + py39 = version(3, 9, 1, "alpha", 1) + assert subject(py39) == "3.9" + + +class TestPythonVersionValue: + @pytest.fixture + def subject(self, sensor): + return sensor.value + + @pytest.fixture + def python_version(self): + import sys + + return sys.version_info + + def test_data_value_is_sys_versioninfo(self, python_version, subject): + assert subject() == python_version + + +class TestPythonVersionSensor: + def test_str_representation_is_formatted_value(self, sensor, version): + with mock.patch("sys.version_info", new=version(3, 4, 1, "final", 1)): + assert str(sensor) == "3.4" diff --git a/Ch02/listing02-11/tests/test_ramusage.py b/Ch02/listing02-11/tests/test_ramusage.py new file mode 100644 index 0000000..b3c091a --- /dev/null +++ b/Ch02/listing02-11/tests/test_ramusage.py @@ -0,0 +1,47 @@ +from unittest import mock + +import pytest + +from sensors import RAMAvailable + + +@pytest.fixture +def sensor(): + return RAMAvailable() + + +class TestRAMAvailableFormatter: + @pytest.fixture + def subject(self, sensor): + return sensor.format + + def test_format_bytes(self, subject): + assert subject(15) == "15.0 B" + + def test_format_kibibytes(self, subject): + assert subject(15000) == "14.6 KiB" + + def test_format_mibibytes(self, subject): + assert subject(15000000) == "14.3 MiB" + + def test_format_gibibytes(self, subject): + assert subject(15000000000) == "14.0 GiB" + + +class TestRAMAvailableValue: + @pytest.fixture + def subject(self, sensor): + return sensor.value + + @pytest.fixture + def virtual_memory(self): + with mock.patch("psutil.virtual_memory") as virtual_memory: + virtual_memory.return_value.available = 1024 + yield virtual_memory + + def test_virtual_memory_called(self, subject, virtual_memory): + assert subject() == 1024 + assert virtual_memory.call_count == 1 + + def test_str_representation_is_formatted_value(self, sensor, virtual_memory): + assert str(sensor) == "1.0 KiB" diff --git a/Ch02/listing02-11/tests/test_sensors.py b/Ch02/listing02-11/tests/test_sensors.py new file mode 100644 index 0000000..6596ba1 --- /dev/null +++ b/Ch02/listing02-11/tests/test_sensors.py @@ -0,0 +1,17 @@ +from click.testing import CliRunner + +import pytest + +import sensors + + +def test_sensors(): + assert hasattr(sensors, "PythonVersion") + + +@pytest.mark.functional +def test_python_version_is_first_two_lines_of_cli_output(): + runner = CliRunner() + result = runner.invoke(sensors.show_sensors) + python_version = str(sensors.PythonVersion()) + assert ["Python Version", python_version] == result.stdout.split("\n")[:2] diff --git a/Ch02/listing02-12/.pre-commit-config.yaml b/Ch02/listing02-12/.pre-commit-config.yaml new file mode 100644 index 0000000..3ff6d15 --- /dev/null +++ b/Ch02/listing02-12/.pre-commit-config.yaml @@ -0,0 +1,23 @@ +repos: + +- repo: local + hooks: + - id: black + name: black + entry: pipenv run black + args: [--quiet] + language: system + types: [python] + + - id: mypy + name: mypy + entry: pipenv run mypy + args: ["--follow-imports=skip"] + language: system + types: [python] + + - id: flake8 + name: flake8 + entry: pipenv run flake8 + language: system + types: [python] diff --git a/Ch02/listing02-12/Pipfile b/Ch02/listing02-12/Pipfile new file mode 100644 index 0000000..c35d39c --- /dev/null +++ b/Ch02/listing02-12/Pipfile @@ -0,0 +1,29 @@ +[[source]] +name = "pypi" +url = "https://pypi.org/simple" +verify_ssl = true + +[[source]] +name = "pypi" +url = "https://piwheels.org/simple" +verify_ssl = true + +[dev-packages] +ipykernel = "*" +remote-ikernel = "*" +pytest = "*" +pytest-cov = "*" +mypy = "*" +flake8 = "*" +black = "*" +pre-commit = "*" + +[packages] +psutil = "*" +click = "*" +adafruit-circuitpython-dht = {markers = "'arm' in platform_machine",version = "*"} + +[requires] + +[pipenv] +allow_prereleases = true diff --git a/Ch02/listing02-12/Pipfile.lock b/Ch02/listing02-12/Pipfile.lock new file mode 100644 index 0000000..ff7db13 --- /dev/null +++ b/Ch02/listing02-12/Pipfile.lock @@ -0,0 +1,866 @@ +{ + "_meta": { + "hash": { + "sha256": "9bc7427fda5ec4bf8a1344d4f47036d9af341544ccce6f12273046f922d95df2" + }, + "pipfile-spec": 6, + "requires": {}, + "sources": [ + { + "name": "pypi", + "url": "https://pypi.org/simple", + "verify_ssl": true + }, + { + "name": "pypi", + "url": "https://piwheels.org/simple", + "verify_ssl": true + } + ] + }, + "default": { + "adafruit-blinka": { + "hashes": [ + "sha256:375a71854a52779a760c5b2f53f626c9217b523b77ac1c4648ee81c606ce4eec", + 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b/Ch02/listing02-12/sensors.py @@ -0,0 +1,192 @@ +#!/usr/bin/env python +# coding: utf-8 +import math +import socket +import sys + + +import click +import psutil + + +class Sensor: + def value(self): + raise NotImplementedError + + @classmethod + def format(cls, value): + raise NotImplementedError + + def __str__(self): + return self.format(self.value()) + + +class PythonVersion(Sensor): + title = "Python Version" + + def value(self): + return sys.version_info + + @classmethod + def format(cls, value): + if value.micro == 0 and value.releaselevel == "alpha": + return "{0.major}.{0.minor}.{0.micro}a{0.serial}".format(value) + return "{0.major}.{0.minor}".format(value) + + +class IPAddresses(Sensor): + title = "IP Addresses" + FAMILIES = {"AF_INET": "IPv4", "AF_INET6": "IPv6"} + + def value(self): + hostname = socket.gethostname() + addresses = socket.getaddrinfo(hostname, None) + + address_info = [] + for address in addresses: + family, ip = (address[0].name, address[4][0]) + if family not in self.FAMILIES: + continue + value = (family, ip) + if value not in address_info: + address_info.append(value) + return address_info + + @classmethod + def format(cls, value): + return "\n".join( + "{0} ({1})".format(address[1], cls.FAMILIES.get(address[0], "Unknown")) + for address in value + ) + + +class CPULoad(Sensor): + title = "CPU Usage" + + def value(self): + return psutil.cpu_percent(interval=3) / 100.0 + + @classmethod + def format(cls, value): + return "{:.1%}".format(value) + + +class RAMAvailable(Sensor): + title = "RAM Available" + UNITS = ("B", "KiB", "MiB", "GiB", "TiB", "PiB", "EiB") + UNIT_SIZE = 2 ** 10 + + def value(self): + return psutil.virtual_memory().available + + @classmethod + def format(cls, value): + magnitude = math.floor(math.log(value, cls.UNIT_SIZE)) + max_magnitude = len(cls.UNITS) - 1 + magnitude = min(magnitude, max_magnitude) + scaled_value = value / (cls.UNIT_SIZE ** magnitude) + return "{:.1f} {}".format(scaled_value, cls.UNITS[magnitude]) + + +class ACStatus(Sensor): + title = "AC Connected" + + def value(self): + battery = psutil.sensors_battery() + if battery is not None: + return battery.power_plugged + else: + return None + + @classmethod + def format(cls, value): + if value is None: + return "Unknown" + elif value: + return "Connected" + else: + return "Not connected" + + +class Temperature(Sensor): + title = "Ambient Temperature" + + def value(self): + try: + # Connect to a DHT22 on pin 4 + from adafruit_dht import DHT22 + from board import D4 + except (ImportError, NotImplementedError): + # No DHT library results in an ImportError. + # Running on an unknown platform results in a + # NotImplementedError when getting the pin + return None + try: + return DHT22(D4).temperature + except RuntimeError: + return None + + @staticmethod + def celsius_to_fahrenheit(value): + return value * 9 / 5 + 32 + + @classmethod + def format(cls, value): + if value is None: + return "Unknown" + else: + return "{:.1f}C ({:.1f}F)".format(value, cls.celsius_to_fahrenheit(value)) + + def __str__(self): + return self.format(self.value()) + + +class RelativeHumidity(Sensor): + title = "Relative Humidity" + + def value(self): + try: + # Connect to a DHT22 on pin 4 + from adafruit_dht import DHT22 + from board import D4 + except (ImportError, NotImplementedError): + # No DHT library results in an ImportError. + # Running on an unknown platform results in a + # NotImplementedError when getting the pin + return None + try: + return DHT22(D4).humidity / 100 + except RuntimeError: + return None + + @classmethod + def format(cls, value): + if value is None: + return "Unknown" + else: + return "{:.1%}".format(value) + + +def get_sensors(): + return [ + PythonVersion(), + IPAddresses(), + CPULoad(), + RAMAvailable(), + ACStatus(), + Temperature(), + RelativeHumidity(), + ] + + +@click.command(help="Displays the values of the sensors") +def show_sensors(): + sensors = get_sensors() + for sensor in sensors: + click.secho(sensor.title, bold=True) + click.echo(str(sensor)) + click.echo("") + + +if __name__ == "__main__": + show_sensors() diff --git a/Ch02/listing02-12/sensors.pyi b/Ch02/listing02-12/sensors.pyi new file mode 100644 index 0000000..763c009 --- /dev/null +++ b/Ch02/listing02-12/sensors.pyi @@ -0,0 +1,59 @@ +from typing import Any, Iterable, List, Optional, Tuple, TypeVar, Generic + +T_value = TypeVar('T_value') + +class Sensor(Generic[T_value]): + title: str + def value(self) -> T_value: ... + @classmethod + def format(cls: Any, value: T_value) -> str: ... + +class PythonVersion(Sensor[Any]): + title: str = ... + def value(self) -> Any: ... + @classmethod + def format(cls: Any, value: Any) -> str: ... + +class IPAddresses(Sensor[Iterable[Tuple[str, str]]]): + title: str = ... + FAMILIES: Any = ... + def value(self) -> List[Tuple[str, str]]: ... + @classmethod + def format(cls: Any, value: Iterable[Tuple[str, str]]) -> str: ... + +class CPULoad(Sensor[float]): + title: str = ... + def value(self) -> float: ... + @classmethod + def format(cls: Any, value: float) -> str: ... + +class RAMAvailable(Sensor[int]): + title: str = ... + UNITS: Any = ... + UNIT_SIZE: Any = ... + def value(self) -> int: ... + @classmethod + def format(cls: Any, value: int) -> str: ... + +class ACStatus(Sensor[Optional[bool]]): + title: str = ... + def value(self) -> Optional[bool]: ... + @classmethod + def format(cls: Any, value: Optional[bool]) -> str: ... + +class Temperature(Sensor[Optional[float]]): + title: str = ... + def value(self) -> Optional[float]: ... + @staticmethod + def celsius_to_fahrenheit(value: float) -> float: ... + @classmethod + def format(cls: Any, value: Optional[float]) -> str: ... + +class RelativeHumidity(Sensor[Optional[float]]): + title: str = ... + def value(self) -> Optional[float]: ... + @classmethod + def format(cls: Any, value: Optional[float]) -> str: ... + +def get_sensors() -> Iterable[Sensor[Any]]: ... +def show_sensors() -> None: ... diff --git a/Ch02/listing02-12/setup.cfg b/Ch02/listing02-12/setup.cfg new file mode 100644 index 0000000..9aedcef --- /dev/null +++ b/Ch02/listing02-12/setup.cfg @@ -0,0 +1,5 @@ +[mypy] +ignore_missing_imports = True + +[flake8] +max-line-length = 88 \ No newline at end of file diff --git a/Ch02/listing02-12/tests/__init__.py b/Ch02/listing02-12/tests/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/Ch02/listing02-12/tests/test_acstatus.py b/Ch02/listing02-12/tests/test_acstatus.py new file mode 100644 index 0000000..7dab477 --- /dev/null +++ b/Ch02/listing02-12/tests/test_acstatus.py @@ -0,0 +1,55 @@ +from unittest import mock + +import pytest + +from sensors import ACStatus + + +@pytest.fixture +def sensor(): + return ACStatus() + + +class TestACStatusFormatter: + @pytest.fixture + def subject(self, sensor): + return sensor.format + + def test_format_True(self, subject): + assert subject(True) == "Connected" + + def test_format_False(self, subject): + assert subject(False) == "Not connected" + + def test_format_None(self, subject): + assert subject(None) == "Unknown" + + +class TestACStatusValue: + @pytest.fixture + def subject(self, sensor): + return sensor.value + + @pytest.fixture + def sensors_battery(self): + with mock.patch("psutil.sensors_battery") as sensors_battery: + yield sensors_battery + + def test_sensors_battery_called(self, subject, sensors_battery): + sensors_battery.return_value.power_plugged = True + assert subject() is True + assert sensors_battery.call_count == 1 + + def test_sensor_but_battery_unknown(self, subject, sensors_battery): + sensors_battery.return_value.power_plugged = None + assert subject() is None + assert sensors_battery.call_count == 1 + + def test_no_sensor(self, subject, sensors_battery): + sensors_battery.return_value = None + assert subject() is None + + def test_str_representation_is_formatted_value(self, sensor, sensors_battery): + sensors_battery.return_value.power_plugged = True + assert str(sensor) == "Connected" + assert sensors_battery.call_count == 1 diff --git a/Ch02/listing02-12/tests/test_cpuusage.py b/Ch02/listing02-12/tests/test_cpuusage.py new file mode 100644 index 0000000..504861b --- /dev/null +++ b/Ch02/listing02-12/tests/test_cpuusage.py @@ -0,0 +1,45 @@ +from unittest import mock + +import pytest + +from sensors import CPULoad + + +@pytest.fixture +def sensor(): + return CPULoad() + + +class TestCPULoadFormatter: + @pytest.fixture + def subject(self, sensor): + return sensor.format + + def test_format_simple_percentage(self, subject): + assert subject(0.05) == "5.0%" + + def test_format_multiple_decimal_places(self, subject): + assert subject(0.031415926) == "3.1%" + + def test_format_multiple_decimal_places_int(self, subject) -> None: + assert subject(1) == "100.0%" + + +class TestCPULoadValue: + @pytest.fixture + def subject(self, sensor): + return sensor.value + + @pytest.fixture + def cpuload(self): + with mock.patch("psutil.cpu_percent") as cpu_percent: + cpu_percent.return_value = 50.1 + yield cpu_percent + + def test_cpu_percent_called_With_interval(self, subject, cpuload): + assert subject() == 0.501 + assert cpuload.call_count == 1 + assert tuple(cpuload.call_args) == ((), {"interval": 3}) + + def test_str_representation_is_formatted_value(self, sensor, cpuload): + assert str(sensor) == "50.1%" diff --git a/Ch02/listing02-12/tests/test_dht.py b/Ch02/listing02-12/tests/test_dht.py new file mode 100644 index 0000000..c8391a3 --- /dev/null +++ b/Ch02/listing02-12/tests/test_dht.py @@ -0,0 +1,66 @@ +import pytest + +from sensors import Temperature, RelativeHumidity + + +@pytest.fixture +def temperature_sensor(): + return Temperature() + + +@pytest.fixture +def humidity_sensor(): + return RelativeHumidity() + + +class TestTemperatureFormatter: + @pytest.fixture + def subject(self, temperature_sensor): + return temperature_sensor.format + + def test_format_21c(self, subject): + assert subject(21.0) == "21.0C (69.8F)" + + def test_format_negative(self, subject): + assert subject(-32.0) == "-32.0C (-25.6F)" + + def test_format_unknown(self, subject): + assert subject(None) == "Unknown" + + +class TestTemperatureConversion: + @pytest.fixture + def subject(self, temperature_sensor): + return temperature_sensor.celsius_to_fahrenheit + + def test_celsius_to_fahrenheit(self, subject): + c = 21 + f = subject(c) + assert f == 69.8 + + def test_celsius_to_fahrenheit_equivlance_point(self, subject): + c = -40 + f = subject(c) + assert f == -40 + + def test_celsius_to_fahrenheit_float(self, subject): + c = 21.2 + f = subject(c) + assert f == 70.16 + + def test_celsius_to_fahrenheit_string(self, subject): + c = "21" + with pytest.raises(TypeError): + subject(c) + + +class TestHumidityFormatter: + @pytest.fixture + def subject(self, humidity_sensor): + return humidity_sensor.format + + def test_format_percentage(self, subject): + assert subject(0.035) == "3.5%" + + def test_format_unknown(self, subject): + assert subject(None) == "Unknown" diff --git a/Ch02/listing02-12/tests/test_ipaddresses.py b/Ch02/listing02-12/tests/test_ipaddresses.py new file mode 100644 index 0000000..f97d377 --- /dev/null +++ b/Ch02/listing02-12/tests/test_ipaddresses.py @@ -0,0 +1,59 @@ +import socket +from unittest import mock + +import pytest + +from sensors import IPAddresses + + +@pytest.fixture +def sensor(): + return IPAddresses() + + +class TestIPAddressFormatter: + @pytest.fixture + def subject(self, sensor): + return sensor.format + + def test_format_single_ipv4(self, subject): + ips = [("AF_INET", "192.0.2.1")] + assert subject(ips) == "192.0.2.1 (IPv4)" + + def test_format_single_ipv6(self, subject): + ips = [("AF_INET6", "2001:DB8::1")] + assert subject(ips) == "2001:DB8::1 (IPv6)" + + def test_format_mixed_list(self, subject): + ips = [("AF_INET", "192.0.2.1"), ("AF_INET6", "2001:DB8::1")] + assert subject(ips) == "192.0.2.1 (IPv4)\n2001:DB8::1 (IPv6)" + + def test_unusual_protocols_are_marked_as_unknown(self, subject): + ips = [("AF_IRDA", "ffff")] + assert subject(ips) == "ffff (Unknown)" + + +class TestIPAddressesValue: + @pytest.fixture + def subject(self, sensor): + return sensor.value + + @pytest.fixture + def gethostname(self): + with mock.patch("socket.gethostname") as gethostname: + gethostname.return_value = "localhost" + yield gethostname + + @pytest.fixture + def getaddrinfo(self, gethostname): + with mock.patch("socket.getaddrinfo") as getaddrinfo: + yield getaddrinfo + + def test_getaddrinfo_used_for_value_collection(self, getaddrinfo, subject): + getaddrinfo.return_value = [(socket.AF_INET, 0, 0, "", ("192.0.2.1", 0))] + assert subject() == [("AF_INET", "192.0.2.1")] + assert getaddrinfo.call_count == 1 + + def test_str_representation_is_formatted_value(self, getaddrinfo, sensor): + getaddrinfo.return_value = [(socket.AF_INET6, 0, 0, "", ("2001:DB8::1", 0))] + assert str(sensor) == "2001:DB8::1 (IPv6)" diff --git a/Ch02/listing02-12/tests/test_pythonversion.py b/Ch02/listing02-12/tests/test_pythonversion.py new file mode 100644 index 0000000..473eb62 --- /dev/null +++ b/Ch02/listing02-12/tests/test_pythonversion.py @@ -0,0 +1,61 @@ +from collections import namedtuple +from unittest import mock + +import pytest + +from sensors import PythonVersion + + +@pytest.fixture +def version(): + return namedtuple( + "sys_versioninfo", ("major", "minor", "micro", "releaselevel", "serial") + ) + + +@pytest.fixture +def sensor(): + return PythonVersion() + + +class TestPythonVersionFormatter: + @pytest.fixture + def subject(self, sensor): + return sensor.format + + def test_format_py38(self, subject, version): + py38 = version(3, 8, 0, "final", 0) + assert subject(py38) == "3.8" + + def test_format_large_version(self, subject, version): + large = version(255, 128, 0, "final", 0) + assert subject(large) == "255.128" + + def test_alpha_of_minor_is_marked(self, subject, version): + py39 = version(3, 9, 0, "alpha", 1) + assert subject(py39) == "3.9.0a1" + + def test_alpha_of_micro_is_unmarked(self, subject, version): + py39 = version(3, 9, 1, "alpha", 1) + assert subject(py39) == "3.9" + + +class TestPythonVersionValue: + @pytest.fixture + def subject(self, sensor): + return sensor.value + + @pytest.fixture + def python_version(self): + import sys + + return sys.version_info + + def test_data_value_is_sys_versioninfo(self, python_version, subject): + assert subject() == python_version + + +class TestPythonVersionSensor: + def test_str_representation_is_formatted_value(self, sensor, version): + with mock.patch("sys.version_info", new=version(3, 4, 1, "final", 1)): + assert str(sensor) == "3.4" diff --git a/Ch02/listing02-12/tests/test_ramusage.py b/Ch02/listing02-12/tests/test_ramusage.py new file mode 100644 index 0000000..b3c091a --- /dev/null +++ b/Ch02/listing02-12/tests/test_ramusage.py @@ -0,0 +1,47 @@ +from unittest import mock + +import pytest + +from sensors import RAMAvailable + + +@pytest.fixture +def sensor(): + return RAMAvailable() + + +class TestRAMAvailableFormatter: + @pytest.fixture + def subject(self, sensor): + return sensor.format + + def test_format_bytes(self, subject): + assert subject(15) == "15.0 B" + + def test_format_kibibytes(self, subject): + assert subject(15000) == "14.6 KiB" + + def test_format_mibibytes(self, subject): + assert subject(15000000) == "14.3 MiB" + + def test_format_gibibytes(self, subject): + assert subject(15000000000) == "14.0 GiB" + + +class TestRAMAvailableValue: + @pytest.fixture + def subject(self, sensor): + return sensor.value + + @pytest.fixture + def virtual_memory(self): + with mock.patch("psutil.virtual_memory") as virtual_memory: + virtual_memory.return_value.available = 1024 + yield virtual_memory + + def test_virtual_memory_called(self, subject, virtual_memory): + assert subject() == 1024 + assert virtual_memory.call_count == 1 + + def test_str_representation_is_formatted_value(self, sensor, virtual_memory): + assert str(sensor) == "1.0 KiB" diff --git a/Ch02/listing02-12/tests/test_sensors.py b/Ch02/listing02-12/tests/test_sensors.py new file mode 100644 index 0000000..6596ba1 --- /dev/null +++ b/Ch02/listing02-12/tests/test_sensors.py @@ -0,0 +1,17 @@ +from click.testing import CliRunner + +import pytest + +import sensors + + +def test_sensors(): + assert hasattr(sensors, "PythonVersion") + + +@pytest.mark.functional +def test_python_version_is_first_two_lines_of_cli_output(): + runner = CliRunner() + result = runner.invoke(sensors.show_sensors) + python_version = str(sensors.PythonVersion()) + assert ["Python Version", python_version] == result.stdout.split("\n")[:2] diff --git a/Ch02/listing02-13-.pre-commit-config.yaml b/Ch02/listing02-13-.pre-commit-config.yaml new file mode 100644 index 0000000..3ff6d15 --- /dev/null +++ b/Ch02/listing02-13-.pre-commit-config.yaml @@ -0,0 +1,23 @@ +repos: + +- repo: local + hooks: + - id: black + name: black + entry: pipenv run black + args: [--quiet] + language: system + types: [python] + + - id: mypy + name: mypy + entry: pipenv run mypy + args: ["--follow-imports=skip"] + language: system + types: [python] + + - id: flake8 + name: flake8 + entry: pipenv run flake8 + language: system + types: [python] diff --git a/Ch03/apd.sensors-chapter03/.pre-commit-config.yaml b/Ch03/apd.sensors-chapter03/.pre-commit-config.yaml new file mode 100644 index 0000000..3ff6d15 --- /dev/null +++ b/Ch03/apd.sensors-chapter03/.pre-commit-config.yaml @@ -0,0 +1,23 @@ +repos: + +- repo: local + hooks: + - id: black + name: black + entry: pipenv run black + args: [--quiet] + language: system + types: [python] + + - id: mypy + name: mypy + entry: pipenv run mypy + args: ["--follow-imports=skip"] + language: system + types: [python] + + - id: flake8 + name: flake8 + entry: pipenv run flake8 + language: system + types: [python] diff --git a/Ch03/apd.sensors-chapter03/CHANGES.md b/Ch03/apd.sensors-chapter03/CHANGES.md new file mode 100644 index 0000000..569aeea --- /dev/null +++ b/Ch03/apd.sensors-chapter03/CHANGES.md @@ -0,0 +1,5 @@ +## Changes + +### 1.0.0 (2019-06-20) + +* Added initial sensors (Matthew Wilkes) diff --git a/Ch03/apd.sensors-chapter03/LICENCE b/Ch03/apd.sensors-chapter03/LICENCE new file mode 100644 index 0000000..ccaef50 --- /dev/null +++ b/Ch03/apd.sensors-chapter03/LICENCE @@ -0,0 +1,21 @@ +``` +Copyright (c) 2019 Matthew Wilkes + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. +``` \ No newline at end of file diff --git a/Ch03/apd.sensors-chapter03/Pipfile b/Ch03/apd.sensors-chapter03/Pipfile new file mode 100644 index 0000000..40413b8 --- /dev/null +++ b/Ch03/apd.sensors-chapter03/Pipfile @@ -0,0 +1,29 @@ +[[source]] +name = "pypi" +url = "https://pypi.org/simple" +verify_ssl = true + +[[source]] +name = "piwheels" +url = "https://piwheels.org/simple" +verify_ssl = true + +[dev-packages] +ipykernel = "*" +remote-ikernel = "*" +pytest = "*" +pytest-cov = "*" +mypy = "*" +flake8 = "*" +black = "*" +pre-commit = "*" +wheel = "*" +twine = "*" + +[packages] +apd-sensors = {editable = true,path = "."} + +[requires] + +[pipenv] +allow_prereleases = true diff --git a/Ch03/apd.sensors-chapter03/Pipfile.lock b/Ch03/apd.sensors-chapter03/Pipfile.lock new file mode 100644 index 0000000..e7e5bb8 --- /dev/null +++ b/Ch03/apd.sensors-chapter03/Pipfile.lock @@ -0,0 +1,897 @@ +{ + "_meta": { + "hash": { + "sha256": "b2def6fc1541c4ee04e68450c1818f456f7d3aac56b38ed1356d6bbcd13ffd8d" + }, + "pipfile-spec": 6, + "requires": {}, + "sources": [ + { + "name": "pypi", + "url": "https://pypi.org/simple", + "verify_ssl": true + }, + { + "name": "piwheels", + "url": "https://piwheels.org/simple", + "verify_ssl": true + } + ] + }, + "default": { + "apd-sensors": { + "editable": true, + "path": "." + }, + "click": { + "hashes": [ + "sha256:8a18b4ea89d8820c5d0c7da8a64b2c324b4dabb695804dbfea19b9be9d88c0cc", + "sha256:e345d143d80bf5ee7534056164e5e112ea5e22716bbb1ce727941f4c8b471b9a" + ], + "version": "==7.1.1" + }, + "psutil": { + "hashes": [ + "sha256:002bd856770037221256765a472f50d677074ad207276a39e2bccdb7394e333b", + "sha256:1413f4158eb50e110777c4f15d7c759521703bd6beb58926f1d562da40180058", + 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index 0000000..e6dcb45 --- /dev/null +++ b/Ch03/apd.sensors-chapter03/README.md @@ -0,0 +1,32 @@ +# Advanced Python Development Sensors + +This is the data collection package that forms part of the running example +for the book [Advanced Python Development](https://advancedpython.dev). + +## Usage + +This installs a console script called `sensors` that returns a report on +various aspects of the system. The available sensors are: + +* Python version +* IP Addresses +* CPU Usage +* RAM Available +* Battery charging state +* Ambient Temperature +* Ambient Humidity + +There are no command-line options, to view the report run `sensors` on the +command line. + +## Caveats + +The Ambient Temperature and Ambient Humidity sensors are only available on +RaspberryPi hosts and assume that a DHT22 sensor is connected to pin `D20`. + +If there is an entry in `/etc/hosts` for the current machine's hostname that +value will be the only result from the IP Addresses sensor. + +## Installation + +Install with `pip3 install apd.sensors` under Python 3.7 or higher diff --git a/Ch03/apd.sensors-chapter03/pytest.ini b/Ch03/apd.sensors-chapter03/pytest.ini new file mode 100644 index 0000000..861f3d0 --- /dev/null +++ b/Ch03/apd.sensors-chapter03/pytest.ini @@ -0,0 +1,3 @@ +[pytest] +markers = + functional: these tests are significantly slower as they run the whole CLI script \ No newline at end of file diff --git a/Ch03/apd.sensors-chapter03/setup.cfg b/Ch03/apd.sensors-chapter03/setup.cfg new file mode 100644 index 0000000..755ea35 --- /dev/null +++ b/Ch03/apd.sensors-chapter03/setup.cfg @@ -0,0 +1,38 @@ +[mypy] +ignore_missing_imports = True + +[flake8] +max-line-length = 88 + +[metadata] +name = apd.sensors +version = attr: apd.sensors.VERSION +description = APD Sensor package +long_description = file: README.md, CHANGES.md, LICENCE +long_description_content_type = text/markdown +keywords = iot +license = MIT +classifiers = + Programming Language :: Python :: 3 + Programming Language :: Python :: 3.7 + +[options] +zip_safe = False +include_package_data = True +package-dir = + =src +packages = find_namespace: +install_requires = + psutil + click + adafruit-circuitpython-dht ; 'arm' in platform_machine + +[options.package_data] +apd.sensors = py.typed + +[options.packages.find] +where = src + +[options.entry_points] +console_scripts = + sensors = apd.sensors.sensors:show_sensors diff --git a/Ch03/apd.sensors-chapter03/setup.py b/Ch03/apd.sensors-chapter03/setup.py new file mode 100644 index 0000000..6068493 --- /dev/null +++ b/Ch03/apd.sensors-chapter03/setup.py @@ -0,0 +1,3 @@ +from setuptools import setup + +setup() diff --git a/Ch03/apd.sensors-chapter03/src/apd/sensors/__init__.py b/Ch03/apd.sensors-chapter03/src/apd/sensors/__init__.py new file mode 100644 index 0000000..369b4e0 --- /dev/null +++ b/Ch03/apd.sensors-chapter03/src/apd/sensors/__init__.py @@ -0,0 +1 @@ +VERSION = "1.0.0" \ No newline at end of file diff --git a/Ch03/apd.sensors-chapter03/src/apd/sensors/py.typed b/Ch03/apd.sensors-chapter03/src/apd/sensors/py.typed new file mode 100644 index 0000000..e69de29 diff --git a/Ch03/apd.sensors-chapter03/src/apd/sensors/sensors.py b/Ch03/apd.sensors-chapter03/src/apd/sensors/sensors.py new file mode 100644 index 0000000..0e46a9b --- /dev/null +++ b/Ch03/apd.sensors-chapter03/src/apd/sensors/sensors.py @@ -0,0 +1,198 @@ +#!/usr/bin/env python +# coding: utf-8 +import math +import socket +import sys +from typing import Any, Optional, List, Tuple, Iterable, TypeVar, Generic + + +import click +import psutil + + +T_value = TypeVar("T_value") + + +class Sensor(Generic[T_value]): + title: str + + def value(self) -> T_value: + raise NotImplementedError + + @classmethod + def format(cls, value: T_value) -> str: + raise NotImplementedError + + def __str__(self) -> str: + return self.format(self.value()) + + +class PythonVersion(Sensor[Any]): + title = "Python Version" + + def value(self) -> Any: + return sys.version_info + + @classmethod + def format(cls, value: Any) -> str: + if value.micro == 0 and value.releaselevel == "alpha": + return "{0.major}.{0.minor}.{0.micro}a{0.serial}".format(value) + return "{0.major}.{0.minor}".format(value) + + +class IPAddresses(Sensor[Iterable[Tuple[str, str]]]): + title = "IP Addresses" + FAMILIES = {"AF_INET": "IPv4", "AF_INET6": "IPv6"} + + def value(self) -> List[Tuple[str, str]]: + hostname = socket.gethostname() + addresses = socket.getaddrinfo(hostname, None) + + address_info: List[Tuple[str, str]] = [] + for address in addresses: + family, ip = (address[0].name, address[4][0]) + if family not in self.FAMILIES: + continue + value = (family, ip) + if value not in address_info: + address_info.append(value) + return address_info + + @classmethod + def format(cls, value: Iterable[Tuple[str, str]]) -> str: + return "\n".join( + "{0} ({1})".format(address[1], cls.FAMILIES.get(address[0], "Unknown")) + for address in value + ) + + +class CPULoad(Sensor[float]): + title = "CPU Usage" + + def value(self) -> float: + return psutil.cpu_percent(interval=3) / 100.0 + + @classmethod + def format(cls, value: float) -> str: + return "{:.1%}".format(value) + + +class RAMAvailable(Sensor[int]): + title = "RAM Available" + UNITS = ("B", "KiB", "MiB", "GiB", "TiB", "PiB", "EiB") + UNIT_SIZE = 2 ** 10 + + def value(self) -> int: + return psutil.virtual_memory().available + + @classmethod + def format(cls, value: int) -> str: + magnitude = math.floor(math.log(value, cls.UNIT_SIZE)) + max_magnitude = len(cls.UNITS) - 1 + magnitude = min(magnitude, max_magnitude) + scaled_value = value / (cls.UNIT_SIZE ** magnitude) + return "{:.1f} {}".format(scaled_value, cls.UNITS[magnitude]) + + +class ACStatus(Sensor[Optional[bool]]): + title = "AC Connected" + + def value(self) -> Optional[bool]: + battery = psutil.sensors_battery() + if battery is not None: + return battery.power_plugged + else: + return None + + @classmethod + def format(cls, value: Optional[bool]) -> str: + if value is None: + return "Unknown" + elif value: + return "Connected" + else: + return "Not connected" + + +class Temperature(Sensor[Optional[float]]): + title = "Ambient Temperature" + + def value(self) -> Optional[float]: + try: + # Connect to a DHT22 on pin 4 + from adafruit_dht import DHT22 + from board import D4 + except (ImportError, NotImplementedError): + # No DHT library results in an ImportError. + # Running on an unknown platform results in a + # NotImplementedError when getting the pin + return None + try: + return DHT22(D4).temperature + except RuntimeError: + return None + + @staticmethod + def celsius_to_fahrenheit(value: float) -> float: + return value * 9 / 5 + 32 + + @classmethod + def format(cls, value: Optional[float]) -> str: + if value is None: + return "Unknown" + else: + return "{:.1f}C ({:.1f}F)".format(value, cls.celsius_to_fahrenheit(value)) + + def __str__(self) -> str: + return self.format(self.value()) + + +class RelativeHumidity(Sensor[Optional[float]]): + title = "Relative Humidity" + + def value(self) -> Optional[float]: + try: + # Connect to a DHT22 on pin 4 + from adafruit_dht import DHT22 + from board import D4 + except (ImportError, NotImplementedError): + # No DHT library results in an ImportError. + # Running on an unknown platform results in a + # NotImplementedError when getting the pin + return None + try: + return DHT22(D4).humidity / 100 + except RuntimeError: + return None + + @classmethod + def format(cls, value: Optional[float]) -> str: + if value is None: + return "Unknown" + else: + return "{:.1%}".format(value) + + +def get_sensors() -> Iterable[Sensor[Any]]: + return [ + PythonVersion(), + IPAddresses(), + CPULoad(), + RAMAvailable(), + ACStatus(), + Temperature(), + RelativeHumidity(), + ] + + +@click.command(help="Displays the values of the sensors") +def show_sensors() -> None: + sensors = get_sensors() + for sensor in sensors: + click.secho(sensor.title, bold=True) + click.echo(str(sensor)) + click.echo("") + + +if __name__ == "__main__": + show_sensors() diff --git a/Ch03/apd.sensors-chapter03/tests/__init__.py b/Ch03/apd.sensors-chapter03/tests/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/Ch03/apd.sensors-chapter03/tests/test_acstatus.py b/Ch03/apd.sensors-chapter03/tests/test_acstatus.py new file mode 100644 index 0000000..d2fe71e --- /dev/null +++ b/Ch03/apd.sensors-chapter03/tests/test_acstatus.py @@ -0,0 +1,55 @@ +from unittest import mock + +import pytest + +from apd.sensors.sensors import ACStatus + + +@pytest.fixture +def sensor(): + return ACStatus() + + +class TestACStatusFormatter: + @pytest.fixture + def subject(self, sensor): + return sensor.format + + def test_format_True(self, subject): + assert subject(True) == "Connected" + + def test_format_False(self, subject): + assert subject(False) == "Not connected" + + def test_format_None(self, subject): + assert subject(None) == "Unknown" + + +class TestACStatusValue: + @pytest.fixture + def subject(self, sensor): + return sensor.value + + @pytest.fixture + def sensors_battery(self): + with mock.patch("psutil.sensors_battery") as sensors_battery: + yield sensors_battery + + def test_sensors_battery_called(self, subject, sensors_battery): + sensors_battery.return_value.power_plugged = True + assert subject() is True + assert sensors_battery.call_count == 1 + + def test_sensor_but_battery_unknown(self, subject, sensors_battery): + sensors_battery.return_value.power_plugged = None + assert subject() is None + assert sensors_battery.call_count == 1 + + def test_no_sensor(self, subject, sensors_battery): + sensors_battery.return_value = None + assert subject() is None + + def test_str_representation_is_formatted_value(self, sensor, sensors_battery): + sensors_battery.return_value.power_plugged = True + assert str(sensor) == "Connected" + assert sensors_battery.call_count == 1 diff --git a/Ch03/apd.sensors-chapter03/tests/test_cpuusage.py b/Ch03/apd.sensors-chapter03/tests/test_cpuusage.py new file mode 100644 index 0000000..445f4e5 --- /dev/null +++ b/Ch03/apd.sensors-chapter03/tests/test_cpuusage.py @@ -0,0 +1,45 @@ +from unittest import mock + +import pytest + +from apd.sensors.sensors import CPULoad + + +@pytest.fixture +def sensor(): + return CPULoad() + + +class TestCPULoadFormatter: + @pytest.fixture + def subject(self, sensor): + return sensor.format + + def test_format_simple_percentage(self, subject): + assert subject(0.05) == "5.0%" + + def test_format_multiple_decimal_places(self, subject): + assert subject(0.031415926) == "3.1%" + + def test_format_multiple_decimal_places_int(self, subject) -> None: + assert subject(1) == "100.0%" + + +class TestCPULoadValue: + @pytest.fixture + def subject(self, sensor): + return sensor.value + + @pytest.fixture + def cpuload(self): + with mock.patch("psutil.cpu_percent") as cpu_percent: + cpu_percent.return_value = 50.1 + yield cpu_percent + + def test_cpu_percent_called_With_interval(self, subject, cpuload): + assert subject() == 0.501 + assert cpuload.call_count == 1 + assert tuple(cpuload.call_args) == ((), {"interval": 3}) + + def test_str_representation_is_formatted_value(self, sensor, cpuload): + assert str(sensor) == "50.1%" diff --git a/Ch03/apd.sensors-chapter03/tests/test_dht.py b/Ch03/apd.sensors-chapter03/tests/test_dht.py new file mode 100644 index 0000000..153c1db --- /dev/null +++ b/Ch03/apd.sensors-chapter03/tests/test_dht.py @@ -0,0 +1,66 @@ +import pytest + +from apd.sensors.sensors import Temperature, RelativeHumidity + + +@pytest.fixture +def temperature_sensor(): + return Temperature() + + +@pytest.fixture +def humidity_sensor(): + return RelativeHumidity() + + +class TestTemperatureFormatter: + @pytest.fixture + def subject(self, temperature_sensor): + return temperature_sensor.format + + def test_format_21c(self, subject): + assert subject(21.0) == "21.0C (69.8F)" + + def test_format_negative(self, subject): + assert subject(-32.0) == "-32.0C (-25.6F)" + + def test_format_unknown(self, subject): + assert subject(None) == "Unknown" + + +class TestTemperatureConversion: + @pytest.fixture + def subject(self, temperature_sensor): + return temperature_sensor.celsius_to_fahrenheit + + def test_celsius_to_fahrenheit(self, subject): + c = 21 + f = subject(c) + assert f == 69.8 + + def test_celsius_to_fahrenheit_equivlance_point(self, subject): + c = -40 + f = subject(c) + assert f == -40 + + def test_celsius_to_fahrenheit_float(self, subject): + c = 21.2 + f = subject(c) + assert f == 70.16 + + def test_celsius_to_fahrenheit_string(self, subject): + c = "21" + with pytest.raises(TypeError): + subject(c) + + +class TestHumidityFormatter: + @pytest.fixture + def subject(self, humidity_sensor): + return humidity_sensor.format + + def test_format_percentage(self, subject): + assert subject(0.035) == "3.5%" + + def test_format_unknown(self, subject): + assert subject(None) == "Unknown" diff --git a/Ch03/apd.sensors-chapter03/tests/test_ipaddresses.py b/Ch03/apd.sensors-chapter03/tests/test_ipaddresses.py new file mode 100644 index 0000000..bd61d85 --- /dev/null +++ b/Ch03/apd.sensors-chapter03/tests/test_ipaddresses.py @@ -0,0 +1,59 @@ +import socket +from unittest import mock + +import pytest + +from apd.sensors.sensors import IPAddresses + + +@pytest.fixture +def sensor(): + return IPAddresses() + + +class TestIPAddressFormatter: + @pytest.fixture + def subject(self, sensor): + return sensor.format + + def test_format_single_ipv4(self, subject): + ips = [("AF_INET", "192.0.2.1")] + assert subject(ips) == "192.0.2.1 (IPv4)" + + def test_format_single_ipv6(self, subject): + ips = [("AF_INET6", "2001:DB8::1")] + assert subject(ips) == "2001:DB8::1 (IPv6)" + + def test_format_mixed_list(self, subject): + ips = [("AF_INET", "192.0.2.1"), ("AF_INET6", "2001:DB8::1")] + assert subject(ips) == "192.0.2.1 (IPv4)\n2001:DB8::1 (IPv6)" + + def test_unusual_protocols_are_marked_as_unknown(self, subject): + ips = [("AF_IRDA", "ffff")] + assert subject(ips) == "ffff (Unknown)" + + +class TestIPAddressesValue: + @pytest.fixture + def subject(self, sensor): + return sensor.value + + @pytest.fixture + def gethostname(self): + with mock.patch("socket.gethostname") as gethostname: + gethostname.return_value = "localhost" + yield gethostname + + @pytest.fixture + def getaddrinfo(self, gethostname): + with mock.patch("socket.getaddrinfo") as getaddrinfo: + yield getaddrinfo + + def test_getaddrinfo_used_for_value_collection(self, getaddrinfo, subject): + getaddrinfo.return_value = [(socket.AF_INET, 0, 0, "", ("192.0.2.1", 0))] + assert subject() == [("AF_INET", "192.0.2.1")] + assert getaddrinfo.call_count == 1 + + def test_str_representation_is_formatted_value(self, getaddrinfo, sensor): + getaddrinfo.return_value = [(socket.AF_INET6, 0, 0, "", ("2001:DB8::1", 0))] + assert str(sensor) == "2001:DB8::1 (IPv6)" diff --git a/Ch03/apd.sensors-chapter03/tests/test_pythonversion.py b/Ch03/apd.sensors-chapter03/tests/test_pythonversion.py new file mode 100644 index 0000000..43ad806 --- /dev/null +++ b/Ch03/apd.sensors-chapter03/tests/test_pythonversion.py @@ -0,0 +1,61 @@ +from collections import namedtuple +from unittest import mock + +import pytest + +from apd.sensors.sensors import PythonVersion + + +@pytest.fixture +def version(): + return namedtuple( + "sys_versioninfo", ("major", "minor", "micro", "releaselevel", "serial") + ) + + +@pytest.fixture +def sensor(): + return PythonVersion() + + +class TestPythonVersionFormatter: + @pytest.fixture + def subject(self, sensor): + return sensor.format + + def test_format_py38(self, subject, version): + py38 = version(3, 8, 0, "final", 0) + assert subject(py38) == "3.8" + + def test_format_large_version(self, subject, version): + large = version(255, 128, 0, "final", 0) + assert subject(large) == "255.128" + + def test_alpha_of_minor_is_marked(self, subject, version): + py39 = version(3, 9, 0, "alpha", 1) + assert subject(py39) == "3.9.0a1" + + def test_alpha_of_micro_is_unmarked(self, subject, version): + py39 = version(3, 9, 1, "alpha", 1) + assert subject(py39) == "3.9" + + +class TestPythonVersionValue: + @pytest.fixture + def subject(self, sensor): + return sensor.value + + @pytest.fixture + def python_version(self): + import sys + + return sys.version_info + + def test_data_value_is_sys_versioninfo(self, python_version, subject): + assert subject() == python_version + + +class TestPythonVersionSensor: + def test_str_representation_is_formatted_value(self, sensor, version): + with mock.patch("sys.version_info", new=version(3, 4, 1, "final", 1)): + assert str(sensor) == "3.4" diff --git a/Ch03/apd.sensors-chapter03/tests/test_ramusage.py b/Ch03/apd.sensors-chapter03/tests/test_ramusage.py new file mode 100644 index 0000000..197ad06 --- /dev/null +++ b/Ch03/apd.sensors-chapter03/tests/test_ramusage.py @@ -0,0 +1,47 @@ +from unittest import mock + +import pytest + +from apd.sensors.sensors import RAMAvailable + + +@pytest.fixture +def sensor(): + return RAMAvailable() + + +class TestRAMAvailableFormatter: + @pytest.fixture + def subject(self, sensor): + return sensor.format + + def test_format_bytes(self, subject): + assert subject(15) == "15.0 B" + + def test_format_kibibytes(self, subject): + assert subject(15000) == "14.6 KiB" + + def test_format_mibibytes(self, subject): + assert subject(15000000) == "14.3 MiB" + + def test_format_gibibytes(self, subject): + assert subject(15000000000) == "14.0 GiB" + + +class TestRAMAvailableValue: + @pytest.fixture + def subject(self, sensor): + return sensor.value + + @pytest.fixture + def virtual_memory(self): + with mock.patch("psutil.virtual_memory") as virtual_memory: + virtual_memory.return_value.available = 1024 + yield virtual_memory + + def test_virtual_memory_called(self, subject, virtual_memory): + assert subject() == 1024 + assert virtual_memory.call_count == 1 + + def test_str_representation_is_formatted_value(self, sensor, virtual_memory): + assert str(sensor) == "1.0 KiB" diff --git a/Ch03/apd.sensors-chapter03/tests/test_sensors.py b/Ch03/apd.sensors-chapter03/tests/test_sensors.py new file mode 100644 index 0000000..2b5c009 --- /dev/null +++ b/Ch03/apd.sensors-chapter03/tests/test_sensors.py @@ -0,0 +1,17 @@ +from click.testing import CliRunner + +import pytest + +import apd.sensors.sensors + + +def test_sensors(): + assert hasattr(apd.sensors.sensors, "PythonVersion") + + +@pytest.mark.functional +def test_python_version_is_first_two_lines_of_cli_output(): + runner = CliRunner() + result = runner.invoke(apd.sensors.sensors.show_sensors) + python_version = str(apd.sensors.sensors.PythonVersion()) + assert ["Python Version", python_version] == result.stdout.split("\n")[:2] diff --git a/Ch03/listing03-01-setup.cfg b/Ch03/listing03-01-setup.cfg new file mode 100644 index 0000000..3d3667e --- /dev/null +++ b/Ch03/listing03-01-setup.cfg @@ -0,0 +1,30 @@ +[mypy] +ignore_missing_imports = True + +[flake8] +max-line-length = 88 + +[metadata] +name = apd.sensors +version = attr: apd.sensors.VERSION +description = APD Sensor package +long_description = file: README.md, CHANGES.md, LICENCE +keywords = iot +license = MIT +classifiers = + Programming Language :: Python :: 3 + Programming Language :: Python :: 3.7 + +[options] +zip_safe = False +include_package_data = True +package-dir = + =src +packages = find-namespace: +install_requires = + psutil + click + adafruit-circuitpython-dht ; 'arm' in platform_machine + +[options.packages.find] +where = src diff --git a/Ch03/listing03-02-indexserver.service b/Ch03/listing03-02-indexserver.service new file mode 100644 index 0000000..69b896f --- /dev/null +++ b/Ch03/listing03-02-indexserver.service @@ -0,0 +1,12 @@ +[Unit] +Description=Custom Index Server for Python distributions +After=multi-user.target + +[Service] +Type=idle +User=indexserver +WorkingDirectory=/home/indexserver/indexserver +ExecStart=/home/indexserver/.local/bin/pipenv run pypi-server -p 8080 -P htaccess ../packages + +[Install] +WantedBy=multi-user.target diff --git a/Ch03/listing03-03-cheatsheet.md b/Ch03/listing03-03-cheatsheet.md new file mode 100644 index 0000000..0074129 --- /dev/null +++ b/Ch03/listing03-03-cheatsheet.md @@ -0,0 +1,48 @@ +# Header 1 +## Header 2 +### Header 3 +#### Header 4 + +_italic_ **bold** **_bold and italic_** + +1. Numbered List +2. With more items + 1. Sublists are indented + 1. The numbers in any level of list need not be correct +3. It can be confusing if the numbers don't match the reader's expectation + +* Unordered lists +* Use asterisks in the first position + - Sublists are indented + - Hyphens can be used to visually differentiate sublists + + As with numbered lists, * - and + are interchangeable and do not need to be used consistently +* but it is best to use them consistently + +When referring to things that should be rendered in a monospace font, such as file names or the names of classes, these should be surrounded by `backticks`. + +Larger blocks of code should be surrounded with three backticks. They can optionally have a language following the first three backticks, to facilitate syntax highlighting +```python +def example(): + return True +``` + +> Quotations are declared with a leading right chevron +> and can cover multiple lines + +Links and images are handled similarly to each other, as a pair of square brackets that defines the text that should be shown followed by a pair of parentheses that contain the target URL. + +[Link to book's website](https://advancedpython.dev) + +Images are differentiated by having a leading exclamation mark: + +![Book's cover](https://advancedpython.dev/cover.png) + +Finally, tables use pipes to delimit columns and new lines to delimit rows. Hyphens are used to split the header row from the body, resulting in a very readable ASCII art style table: + + +| Multiplications | One | Two | +| --------------- | --- | --- | +| One | 1 | 2 | +| Two | 2 | 4 | + +However, the alignment is not important. The table will still render correctly even if the pipes are not aligned correctly. The row that contains the hyphens must include at least three hyphens per column, but otherwise, the format is relatively forgiving. diff --git a/Ch03/listing03-04-cheatsheet.rst b/Ch03/listing03-04-cheatsheet.rst new file mode 100644 index 0000000..faf0f5e --- /dev/null +++ b/Ch03/listing03-04-cheatsheet.rst @@ -0,0 +1,77 @@ +Header 1 +======== + +Header 2 +-------- + +Header 3 +++++++++ + +Header 4 +******** + +*italic* **bold** Combining bold and italic is not possible. + +1. Numbered List +2. With more items + + #. Sublists are indented with a blank line surrounding them + #. The # symbol can be used in place of the number to auto-number the list + +3. It can be confusing if the numbers don’t match the reader’s + expectation + +- Unordered lists +- Use asterisks in the first position + + - Sublists are indented with a blank line surrounding them + - Hyphens can be used to visually differentiate sublists + - As with numbered lists, \* - and + are interchangeable but must be used consistently + +- but it is best to use them consistently + +When referring to things that should be rendered in a monospace font, +such as file names or the names of classes. These should be surrounded +by ``double backticks``. + +Larger blocks of code are in a named block, starting with ``.. code ::``. They +can optionally have a language following the double colon, to +facilitate syntax highlighting + +.. code:: python + + def example(): + return True + +.. + + Quotations are declared with an unnamed block, declared with ``..`` + and can cover multiple lines. They must be surrounded by blank lines. + +Links have a confusing structure. The link definition is a pair of backticks +with a trailing underscore. Inside the backticks are the link text followed by +the target in angle brackets. + +`Link to book’s website `_ + +Images are handled similarly to code blocks, with a ``.. image::`` declaration +followed by the URL of the image. They can have indented arguments, such as +to define alt text. + +.. image:: https://advancedpython.dev/cover.png + :alt: Book’s cover + +Finally, tables use pipes to delimit columns and new lines to delimit +rows. Equals signs are used to delimit the columns as well as the top +and bottom of the table and the end of the header. + +=============== === === +Multiplications One Two +=============== === === +One 1 2 +Two 2 4 +=============== === === + +The alignment here is essential. The table will not render unless the equals signs +all match the extent of the column they define, with no discrepancy. Any text that extends +wider will also cause rendering to fail. diff --git a/Ch03/listing03-05-readme.md b/Ch03/listing03-05-readme.md new file mode 100644 index 0000000..6a2194a --- /dev/null +++ b/Ch03/listing03-05-readme.md @@ -0,0 +1,32 @@ +# Advanced Python Development Sensors + +This is the data collection package that forms part of the running example +for the book [Advanced Python Development](https://advancedpython.dev). + +## Usage + +This installs a console script called `sensors` that returns a report on +various aspects of the system. The available sensors are: + +* Python version +* IP Addresses +* CPU Usage +* RAM Available +* Battery charging state +* Ambient Temperature +* Ambient Humidity + +There are no command-line options, to view the report run `sensors` on the +command line. + +## Caveats + +The Ambient Temperature and Ambient Humidity sensors are only available on +RaspberryPi hosts and assume that a DHT22 sensor is connected to pin `D4`. + +If there is an entry in `/etc/hosts` for the current machine's hostname that +value will be the only result from the IP Addresses sensor. + +## Installation + +Install with `pip3 install apd.sensors` under Python 3.7 or higher diff --git a/Ch04/apd.sensors-chapter04-click-parsing/.pre-commit-config.yaml b/Ch04/apd.sensors-chapter04-click-parsing/.pre-commit-config.yaml new file mode 100644 index 0000000..3ff6d15 --- /dev/null +++ b/Ch04/apd.sensors-chapter04-click-parsing/.pre-commit-config.yaml @@ -0,0 +1,23 @@ +repos: + +- repo: local + hooks: + - id: black + name: black + entry: pipenv run black + args: [--quiet] + language: system + types: [python] + + - id: mypy + name: mypy + entry: pipenv run mypy + args: ["--follow-imports=skip"] + language: system + types: [python] + + - id: flake8 + name: flake8 + entry: pipenv run flake8 + language: system + types: [python] diff --git a/Ch04/apd.sensors-chapter04-click-parsing/CHANGES.md b/Ch04/apd.sensors-chapter04-click-parsing/CHANGES.md new file mode 100644 index 0000000..569aeea --- /dev/null +++ b/Ch04/apd.sensors-chapter04-click-parsing/CHANGES.md @@ -0,0 +1,5 @@ +## Changes + +### 1.0.0 (2019-06-20) + +* Added initial sensors (Matthew Wilkes) diff --git a/Ch04/apd.sensors-chapter04-click-parsing/LICENCE b/Ch04/apd.sensors-chapter04-click-parsing/LICENCE new file mode 100644 index 0000000..ccaef50 --- /dev/null +++ b/Ch04/apd.sensors-chapter04-click-parsing/LICENCE @@ -0,0 +1,21 @@ +``` +Copyright (c) 2019 Matthew Wilkes + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. +``` \ No newline at end of file diff --git a/Ch04/apd.sensors-chapter04-click-parsing/Pipfile b/Ch04/apd.sensors-chapter04-click-parsing/Pipfile new file mode 100644 index 0000000..40413b8 --- /dev/null +++ b/Ch04/apd.sensors-chapter04-click-parsing/Pipfile @@ -0,0 +1,29 @@ +[[source]] +name = "pypi" +url = "https://pypi.org/simple" +verify_ssl = true + +[[source]] +name = "piwheels" +url = "https://piwheels.org/simple" +verify_ssl = true + +[dev-packages] +ipykernel = "*" +remote-ikernel = "*" +pytest = "*" +pytest-cov = "*" +mypy = "*" +flake8 = "*" +black = "*" +pre-commit = "*" +wheel = "*" +twine = "*" + +[packages] +apd-sensors = {editable = true,path = "."} + +[requires] + +[pipenv] +allow_prereleases = true diff --git 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0000000..e6dcb45 --- /dev/null +++ b/Ch04/apd.sensors-chapter04-click-parsing/README.md @@ -0,0 +1,32 @@ +# Advanced Python Development Sensors + +This is the data collection package that forms part of the running example +for the book [Advanced Python Development](https://advancedpython.dev). + +## Usage + +This installs a console script called `sensors` that returns a report on +various aspects of the system. The available sensors are: + +* Python version +* IP Addresses +* CPU Usage +* RAM Available +* Battery charging state +* Ambient Temperature +* Ambient Humidity + +There are no command-line options, to view the report run `sensors` on the +command line. + +## Caveats + +The Ambient Temperature and Ambient Humidity sensors are only available on +RaspberryPi hosts and assume that a DHT22 sensor is connected to pin `D20`. + +If there is an entry in `/etc/hosts` for the current machine's hostname that +value will be the only result from the IP Addresses sensor. + +## Installation + +Install with `pip3 install apd.sensors` under Python 3.7 or higher diff --git a/Ch04/apd.sensors-chapter04-click-parsing/pyproject.toml b/Ch04/apd.sensors-chapter04-click-parsing/pyproject.toml new file mode 100644 index 0000000..4d3bb67 --- /dev/null +++ b/Ch04/apd.sensors-chapter04-click-parsing/pyproject.toml @@ -0,0 +1,5 @@ +[build-system] +requires = [ + "setuptools", +] +build-backend = "setuptools.build_meta" diff --git a/Ch04/apd.sensors-chapter04-click-parsing/pytest.ini b/Ch04/apd.sensors-chapter04-click-parsing/pytest.ini new file mode 100644 index 0000000..861f3d0 --- /dev/null +++ b/Ch04/apd.sensors-chapter04-click-parsing/pytest.ini @@ -0,0 +1,3 @@ +[pytest] +markers = + functional: these tests are significantly slower as they run the whole CLI script \ No newline at end of file diff --git a/Ch04/apd.sensors-chapter04-click-parsing/setup.cfg b/Ch04/apd.sensors-chapter04-click-parsing/setup.cfg new file mode 100644 index 0000000..db6d144 --- /dev/null +++ b/Ch04/apd.sensors-chapter04-click-parsing/setup.cfg @@ -0,0 +1,48 @@ +[mypy] +namespace_packages = True +mypy_path = src + +[mypy-psutil] +ignore_missing_imports = True + +[mypy-adafruit_dht] +ignore_missing_imports = True + +[mypy-board] +ignore_missing_imports = True + +[mypy-pytest] +ignore_missing_imports = True + +[flake8] +max-line-length = 88 + +[metadata] +name = apd.sensors +version = attr: apd.sensors.VERSION +description = APD Sensor package +long_description = file: README.md, CHANGES.md, LICENCE +long_description_content_type = text/markdown +keywords = iot +license = MIT +classifiers = + Programming Language :: Python :: 3 + Programming Language :: Python :: 3.7 + +[options] +zip_safe = False +include_package_data = True +package-dir = + =src +packages = find: +install_requires = + psutil + click + adafruit-circuitpython-dht ; 'arm' in platform_machine + +[options.packages.find] +where = src + +[options.entry_points] +console_scripts = + sensors = apd.sensors.cli:show_sensors diff --git a/Ch04/apd.sensors-chapter04-click-parsing/setup.py b/Ch04/apd.sensors-chapter04-click-parsing/setup.py new file mode 100644 index 0000000..6068493 --- /dev/null +++ b/Ch04/apd.sensors-chapter04-click-parsing/setup.py @@ -0,0 +1,3 @@ +from setuptools import setup + +setup() diff --git a/Ch04/apd.sensors-chapter04-click-parsing/src/apd/sensors/__init__.py b/Ch04/apd.sensors-chapter04-click-parsing/src/apd/sensors/__init__.py new file mode 100644 index 0000000..ec63ed1 --- /dev/null +++ b/Ch04/apd.sensors-chapter04-click-parsing/src/apd/sensors/__init__.py @@ -0,0 +1 @@ +VERSION = "1.1.0dev1" diff --git a/Ch04/apd.sensors-chapter04-click-parsing/src/apd/sensors/cli.py b/Ch04/apd.sensors-chapter04-click-parsing/src/apd/sensors/cli.py new file mode 100644 index 0000000..47358a8 --- /dev/null +++ b/Ch04/apd.sensors-chapter04-click-parsing/src/apd/sensors/cli.py @@ -0,0 +1,104 @@ +import functools +import importlib +import typing as t + +import click + +from .sensors import ( + Sensor, + ACStatus, + CPULoad, + IPAddresses, + PythonVersion, + RAMAvailable, + RelativeHumidity, + Temperature, +) + + +RETURN_CODES = {"OK": 0, "BAD_SENSOR_PATH": 17} + + +class PythonClass(click.types.ParamType): + name = "pythonclass" + + def __init__(self, superclass=type): + self.superclass = superclass + + def get_sensor_by_path( + self, sensor_path: str, fail: t.Callable[[str], None] + ) -> t.Any: + try: + module_name, sensor_name = sensor_path.split(":") + except ValueError: + return fail( + "Class path must be in the format dotted.path.to.module:ClassName" + ) + try: + module = importlib.import_module(module_name) + except ImportError: + return fail(f"Could not import module {module_name}") + try: + sensor_class = getattr(module, sensor_name) + except AttributeError: + return fail(f"Could not find attribute {sensor_name} in {module_name}") + if ( + isinstance(sensor_class, type) + and issubclass(sensor_class, self.superclass) + and sensor_class != self.superclass + ): + return sensor_class + else: + return fail( + f"Detected object {sensor_class!r} is" + f" not recognised as a {self.superclass.__name__} type" + ) + + def convert( + self, + value: str, + param: t.Optional[click.core.Parameter], + ctx: t.Optional[click.core.Context], + ) -> t.Any: + fail = functools.partial(self.fail, param=param, ctx=ctx) + return self.get_sensor_by_path(value, fail) + + def __repr__(self): + return "PythonClass" + + +def get_sensors() -> t.Iterable[Sensor[t.Any]]: + return [ + PythonVersion(), + IPAddresses(), + CPULoad(), + RAMAvailable(), + ACStatus(), + Temperature(), + RelativeHumidity(), + ] + + +@click.command(help="Displays the values of the sensors") +@click.option( + "--develop", + required=False, + metavar="path", + help="Load a sensor by Python path", + type=PythonClass(Sensor), +) +def show_sensors(develop: t.Callable[[], Sensor[t.Any]]) -> int: + sensors: t.Iterable[Sensor[t.Any]] + if develop: + sensors = [develop()] + else: + sensors = get_sensors() + for sensor in sensors: + click.secho(sensor.title, bold=True) + click.echo(str(sensor)) + click.echo("") + return RETURN_CODES["OK"] + + +if __name__ == "__main__": + show_sensors() diff --git a/Ch04/apd.sensors-chapter04-click-parsing/src/apd/sensors/sensors.py b/Ch04/apd.sensors-chapter04-click-parsing/src/apd/sensors/sensors.py new file mode 100644 index 0000000..47c2ab0 --- /dev/null +++ b/Ch04/apd.sensors-chapter04-click-parsing/src/apd/sensors/sensors.py @@ -0,0 +1,170 @@ +#!/usr/bin/env python +# coding: utf-8 +import math +import socket +import sys +from typing import Any, Optional, List, Tuple, Iterable, TypeVar, Generic + + +import psutil + + +T_value = TypeVar("T_value") + + +class Sensor(Generic[T_value]): + title: str + + def value(self) -> T_value: + raise NotImplementedError + + @classmethod + def format(cls, value: T_value) -> str: + raise NotImplementedError + + def __str__(self) -> str: + return self.format(self.value()) + + +class PythonVersion(Sensor[Any]): + title = "Python Version" + + def value(self) -> Any: + return sys.version_info + + @classmethod + def format(cls, value: Any) -> str: + if value.micro == 0 and value.releaselevel == "alpha": + return "{0.major}.{0.minor}.{0.micro}a{0.serial}".format(value) + return "{0.major}.{0.minor}".format(value) + + +class IPAddresses(Sensor[Iterable[Tuple[str, str]]]): + title = "IP Addresses" + FAMILIES = {"AF_INET": "IPv4", "AF_INET6": "IPv6"} + + def value(self) -> List[Tuple[str, str]]: + hostname = socket.gethostname() + addresses = socket.getaddrinfo(hostname, None) + + address_info: List[Tuple[str, str]] = [] + for address in addresses: + family, ip = (address[0].name, address[4][0]) + if family not in self.FAMILIES: + continue + value = (family, ip) + if value not in address_info: + address_info.append(value) + return address_info + + @classmethod + def format(cls, value: Iterable[Tuple[str, str]]) -> str: + return "\n".join( + "{0} ({1})".format(address[1], cls.FAMILIES.get(address[0], "Unknown")) + for address in value + ) + + +class CPULoad(Sensor[float]): + title = "CPU Usage" + + def value(self) -> float: + return psutil.cpu_percent(interval=3) / 100.0 + + @classmethod + def format(cls, value: float) -> str: + return "{:.1%}".format(value) + + +class RAMAvailable(Sensor[int]): + title = "RAM Available" + UNITS = ("B", "KiB", "MiB", "GiB", "TiB", "PiB", "EiB") + UNIT_SIZE = 2 ** 10 + + def value(self) -> int: + return psutil.virtual_memory().available + + @classmethod + def format(cls, value: int) -> str: + magnitude = math.floor(math.log(value, cls.UNIT_SIZE)) + max_magnitude = len(cls.UNITS) - 1 + magnitude = min(magnitude, max_magnitude) + scaled_value = value / (cls.UNIT_SIZE ** magnitude) + return "{:.1f} {}".format(scaled_value, cls.UNITS[magnitude]) + + +class ACStatus(Sensor[Optional[bool]]): + title = "AC Connected" + + def value(self) -> Optional[bool]: + battery = psutil.sensors_battery() + if battery is not None: + return battery.power_plugged + else: + return None + + @classmethod + def format(cls, value: Optional[bool]) -> str: + if value is None: + return "Unknown" + elif value: + return "Connected" + else: + return "Not connected" + + +class Temperature(Sensor[Optional[float]]): + title = "Ambient Temperature" + + def value(self) -> Optional[float]: + try: + from adafruit_dht import DHT22 + from board import D20 + except (ImportError, NotImplementedError): + # No DHT library results in an ImportError. + # Running on an unknown platform results in a + # NotImplementedError when getting the pin + return None + try: + return DHT22(D20).temperature + except RuntimeError: + return None + + @classmethod + def celsius_to_fahrenheit(cls, value: float) -> float: + return value * 9 / 5 + 32 + + @classmethod + def format(cls, value: Optional[float]) -> str: + if value is None: + return "Unknown" + else: + return "{:.1f}C ({:.1f}F)".format(value, cls.celsius_to_fahrenheit(value)) + + def __str__(self) -> str: + return self.format(self.value()) + + +class RelativeHumidity(Sensor[Optional[float]]): + title = "Relative Humidity" + + def value(self) -> Optional[float]: + try: + from adafruit_dht import DHT22 + from board import D20 + except (ImportError, NotImplementedError): + # No DHT library results in an ImportError. + # Running on an unknown platform results in a + # NotImplementedError when getting the pin + return None + try: + return DHT22(D20).humidity / 100 + except RuntimeError: + return None + + @classmethod + def format(cls, value: Optional[float]) -> str: + if value is None: + return "Unknown" + else: + return "{:.1%}".format(value) diff --git a/Ch04/apd.sensors-chapter04-click-parsing/tests/__init__.py b/Ch04/apd.sensors-chapter04-click-parsing/tests/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/Ch04/apd.sensors-chapter04-click-parsing/tests/test_acstatus.py b/Ch04/apd.sensors-chapter04-click-parsing/tests/test_acstatus.py new file mode 100644 index 0000000..1d10940 --- /dev/null +++ b/Ch04/apd.sensors-chapter04-click-parsing/tests/test_acstatus.py @@ -0,0 +1,55 @@ +from unittest import mock + +from apd.sensors.sensors import ACStatus + +import pytest + + +@pytest.fixture +def sensor(): + return ACStatus() + + +class TestACStatusFormatter: + @pytest.fixture + def subject(self, sensor): + return sensor.format + + def test_format_True(self, subject): + assert subject(True) == "Connected" + + def test_format_False(self, subject): + assert subject(False) == "Not connected" + + def test_format_None(self, subject): + assert subject(None) == "Unknown" + + +class TestACStatusValue: + @pytest.fixture + def subject(self, sensor): + return sensor.value + + @pytest.fixture + def sensors_battery(self): + with mock.patch("psutil.sensors_battery") as sensors_battery: + yield sensors_battery + + def test_sensors_battery_called(self, subject, sensors_battery): + sensors_battery.return_value.power_plugged = True + assert subject() is True + assert sensors_battery.call_count == 1 + + def test_sensor_but_battery_unknown(self, subject, sensors_battery): + sensors_battery.return_value.power_plugged = None + assert subject() is None + assert sensors_battery.call_count == 1 + + def test_no_sensor(self, subject, sensors_battery): + sensors_battery.return_value = None + assert subject() is None + + def test_str_representation_is_formatted_value(self, sensor, sensors_battery): + sensors_battery.return_value.power_plugged = True + assert str(sensor) == "Connected" + assert sensors_battery.call_count == 1 diff --git a/Ch04/apd.sensors-chapter04-click-parsing/tests/test_cpuusage.py b/Ch04/apd.sensors-chapter04-click-parsing/tests/test_cpuusage.py new file mode 100644 index 0000000..aa8a66a --- /dev/null +++ b/Ch04/apd.sensors-chapter04-click-parsing/tests/test_cpuusage.py @@ -0,0 +1,45 @@ +from unittest import mock + +from apd.sensors.sensors import CPULoad + +import pytest + + +@pytest.fixture +def sensor(): + return CPULoad() + + +class TestCPULoadFormatter: + @pytest.fixture + def subject(self, sensor): + return sensor.format + + def test_format_simple_percentage(self, subject): + assert subject(0.05) == "5.0%" + + def test_format_multiple_decimal_places(self, subject): + assert subject(0.031415926) == "3.1%" + + def test_format_multiple_decimal_places_int(self, subject) -> None: + assert subject(1) == "100.0%" + + +class TestCPULoadValue: + @pytest.fixture + def subject(self, sensor): + return sensor.value + + @pytest.fixture + def cpuload(self): + with mock.patch("psutil.cpu_percent") as cpu_percent: + cpu_percent.return_value = 50.1 + yield cpu_percent + + def test_cpu_percent_called_With_interval(self, subject, cpuload): + assert subject() == 0.501 + assert cpuload.call_count == 1 + assert tuple(cpuload.call_args) == ((), {"interval": 3}) + + def test_str_representation_is_formatted_value(self, sensor, cpuload): + assert str(sensor) == "50.1%" diff --git a/Ch04/apd.sensors-chapter04-click-parsing/tests/test_dht.py b/Ch04/apd.sensors-chapter04-click-parsing/tests/test_dht.py new file mode 100644 index 0000000..b546593 --- /dev/null +++ b/Ch04/apd.sensors-chapter04-click-parsing/tests/test_dht.py @@ -0,0 +1,66 @@ +from apd.sensors.sensors import Temperature, RelativeHumidity + +import pytest + + +@pytest.fixture +def temperature_sensor(): + return Temperature() + + +@pytest.fixture +def humidity_sensor(): + return RelativeHumidity() + + +class TestTemperatureFormatter: + @pytest.fixture + def subject(self, temperature_sensor): + return temperature_sensor.format + + def test_format_21c(self, subject): + assert subject(21.0) == "21.0C (69.8F)" + + def test_format_negative(self, subject): + assert subject(-32.0) == "-32.0C (-25.6F)" + + def test_format_unknown(self, subject): + assert subject(None) == "Unknown" + + +class TestTemperatureConversion: + @pytest.fixture + def subject(self, temperature_sensor): + return temperature_sensor.celsius_to_fahrenheit + + def test_celsius_to_fahrenheit(self, subject): + c = 21 + f = subject(c) + assert f == 69.8 + + def test_celsius_to_fahrenheit_equivlance_point(self, subject): + c = -40 + f = subject(c) + assert f == -40 + + def test_celsius_to_fahrenheit_float(self, subject): + c = 21.2 + f = subject(c) + assert f == 70.16 + + def test_celsius_to_fahrenheit_string(self, subject): + c = "21" + with pytest.raises(TypeError): + subject(c) + + +class TestHumidityFormatter: + @pytest.fixture + def subject(self, humidity_sensor): + return humidity_sensor.format + + def test_format_percentage(self, subject): + assert subject(0.035) == "3.5%" + + def test_format_unknown(self, subject): + assert subject(None) == "Unknown" diff --git a/Ch04/apd.sensors-chapter04-click-parsing/tests/test_ipaddresses.py b/Ch04/apd.sensors-chapter04-click-parsing/tests/test_ipaddresses.py new file mode 100644 index 0000000..e8a9690 --- /dev/null +++ b/Ch04/apd.sensors-chapter04-click-parsing/tests/test_ipaddresses.py @@ -0,0 +1,59 @@ +from unittest import mock +import socket + +from apd.sensors.sensors import IPAddresses + +import pytest + + +@pytest.fixture +def sensor(): + return IPAddresses() + + +class TestIPAddressFormatter: + @pytest.fixture + def subject(self, sensor): + return sensor.format + + def test_format_single_ipv4(self, subject): + ips = [("AF_INET", "192.0.2.1")] + assert subject(ips) == "192.0.2.1 (IPv4)" + + def test_format_single_ipv6(self, subject): + ips = [("AF_INET6", "2001:DB8::1")] + assert subject(ips) == "2001:DB8::1 (IPv6)" + + def test_format_mixed_list(self, subject): + ips = [("AF_INET", "192.0.2.1"), ("AF_INET6", "2001:DB8::1")] + assert subject(ips) == "192.0.2.1 (IPv4)\n2001:DB8::1 (IPv6)" + + def test_unusual_protocols_are_marked_as_unknown(self, subject): + ips = [("AF_IRDA", "ffff")] + assert subject(ips) == "ffff (Unknown)" + + +class TestIPAddressesValue: + @pytest.fixture + def subject(self, sensor): + return sensor.value + + @pytest.fixture + def gethostname(self): + with mock.patch("socket.gethostname") as gethostname: + gethostname.return_value = "localhost" + yield gethostname + + @pytest.fixture + def getaddrinfo(self, gethostname): + with mock.patch("socket.getaddrinfo") as getaddrinfo: + yield getaddrinfo + + def test_getaddrinfo_used_for_value_collection(self, getaddrinfo, subject): + getaddrinfo.return_value = [(socket.AF_INET, 0, 0, "", ("192.0.2.1", 0))] + assert subject() == [("AF_INET", "192.0.2.1")] + assert getaddrinfo.call_count == 1 + + def test_str_representation_is_formatted_value(self, getaddrinfo, sensor): + getaddrinfo.return_value = [(socket.AF_INET6, 0, 0, "", ("2001:DB8::1", 0))] + assert str(sensor) == "2001:DB8::1 (IPv6)" diff --git a/Ch04/apd.sensors-chapter04-click-parsing/tests/test_pythonversion.py b/Ch04/apd.sensors-chapter04-click-parsing/tests/test_pythonversion.py new file mode 100644 index 0000000..dff6bb0 --- /dev/null +++ b/Ch04/apd.sensors-chapter04-click-parsing/tests/test_pythonversion.py @@ -0,0 +1,61 @@ +from collections import namedtuple +from unittest import mock + +from apd.sensors.sensors import PythonVersion + +import pytest + + +@pytest.fixture +def version(): + return namedtuple( + "sys_versioninfo", ("major", "minor", "micro", "releaselevel", "serial") + ) + + +@pytest.fixture +def sensor(): + return PythonVersion() + + +class TestPythonVersionFormatter: + @pytest.fixture + def subject(self, sensor): + return sensor.format + + def test_format_py38(self, subject, version): + py38 = version(3, 8, 0, "final", 0) + assert subject(py38) == "3.8" + + def test_format_large_version(self, subject, version): + large = version(255, 128, 0, "final", 0) + assert subject(large) == "255.128" + + def test_alpha_of_minor_is_marked(self, subject, version): + py39 = version(3, 9, 0, "alpha", 1) + assert subject(py39) == "3.9.0a1" + + def test_alpha_of_micro_is_unmarked(self, subject, version): + py39 = version(3, 9, 1, "alpha", 1) + assert subject(py39) == "3.9" + + +class TestPythonVersionValue: + @pytest.fixture + def subject(self, sensor): + return sensor.value + + @pytest.fixture + def python_version(self): + import sys + + return sys.version_info + + def test_data_value_is_sys_versioninfo(self, python_version, subject): + assert subject() == python_version + + +class TestPythonVersionSensor: + def test_str_representation_is_formatted_value(self, sensor, version): + with mock.patch("sys.version_info", new=version(3, 4, 1, "final", 1)): + assert str(sensor) == "3.4" diff --git a/Ch04/apd.sensors-chapter04-click-parsing/tests/test_ramusage.py b/Ch04/apd.sensors-chapter04-click-parsing/tests/test_ramusage.py new file mode 100644 index 0000000..2a368c1 --- /dev/null +++ b/Ch04/apd.sensors-chapter04-click-parsing/tests/test_ramusage.py @@ -0,0 +1,47 @@ +from unittest import mock + +from apd.sensors.sensors import RAMAvailable + +import pytest + + +@pytest.fixture +def sensor(): + return RAMAvailable() + + +class TestRAMAvailableFormatter: + @pytest.fixture + def subject(self, sensor): + return sensor.format + + def test_format_bytes(self, subject): + assert subject(15) == "15.0 B" + + def test_format_kibibytes(self, subject): + assert subject(15000) == "14.6 KiB" + + def test_format_mibibytes(self, subject): + assert subject(15000000) == "14.3 MiB" + + def test_format_gibibytes(self, subject): + assert subject(15000000000) == "14.0 GiB" + + +class TestRAMAvailableValue: + @pytest.fixture + def subject(self, sensor): + return sensor.value + + @pytest.fixture + def virtual_memory(self): + with mock.patch("psutil.virtual_memory") as virtual_memory: + virtual_memory.return_value.available = 1024 + yield virtual_memory + + def test_virtual_memory_called(self, subject, virtual_memory): + assert subject() == 1024 + assert virtual_memory.call_count == 1 + + def test_str_representation_is_formatted_value(self, sensor, virtual_memory): + assert str(sensor) == "1.0 KiB" diff --git a/Ch04/apd.sensors-chapter04-click-parsing/tests/test_sensors.py b/Ch04/apd.sensors-chapter04-click-parsing/tests/test_sensors.py new file mode 100644 index 0000000..b31a026 --- /dev/null +++ b/Ch04/apd.sensors-chapter04-click-parsing/tests/test_sensors.py @@ -0,0 +1,64 @@ +import functools + +from click.testing import CliRunner +import pytest + +import apd.sensors.cli +import apd.sensors.sensors + + +def test_sensors(): + assert hasattr(apd.sensors.sensors, "PythonVersion") + + +@pytest.mark.functional +def test_python_version_is_first_two_lines_of_cli_output(): + runner = CliRunner() + result = runner.invoke(apd.sensors.cli.show_sensors) + python_version = str(apd.sensors.sensors.PythonVersion()) + assert ["Python Version", python_version] == result.stdout.split("\n")[:2] + + +class TestSensorFromPath: + @staticmethod + def fail(message): + raise RuntimeError(message) + + @pytest.fixture + def subject(self): + return functools.partial( + apd.sensors.cli.PythonClass(apd.sensors.sensors.Sensor).get_sensor_by_path, + fail=self.fail, + ) + + def test_get_sensor_by_path(self, subject): + assert ( + subject("apd.sensors.sensors:PythonVersion") + == apd.sensors.sensors.PythonVersion + ) + + def test_invalid_format(self, subject): + with pytest.raises(RuntimeError, match="dotted.path.to.module:ClassName"): + subject("apd.sensors.sensors.PythonVersion") + + def test_invalid_module(self, subject): + with pytest.raises(RuntimeError, match="Could not import module"): + subject("apd.nonsense.sensor:FakeSensor") + + def test_missing_sensor(self, subject): + with pytest.raises(RuntimeError, match="Could not find attribute"): + subject("apd.sensors.sensors:FakeSensor") + + def test_invalid_sensor(self, subject): + with pytest.raises(RuntimeError, match="is not recognised as a Sensor"): + subject("apd.sensors.sensors:Sensor") + + +@pytest.mark.functional +def test_develop_option_finds_sensor_by_path(): + runner = CliRunner() + result = runner.invoke( + apd.sensors.cli.show_sensors, ["--develop", "apd.sensors.sensors:PythonVersion"] + ) + python_version = str(apd.sensors.sensors.PythonVersion()) + assert ["Python Version", python_version, "", ""] == result.stdout.split("\n") diff --git a/Ch04/apd.sensors-chapter04-click-subcommands/.pre-commit-config.yaml b/Ch04/apd.sensors-chapter04-click-subcommands/.pre-commit-config.yaml new file mode 100644 index 0000000..3ff6d15 --- /dev/null +++ b/Ch04/apd.sensors-chapter04-click-subcommands/.pre-commit-config.yaml @@ -0,0 +1,23 @@ +repos: + +- repo: local + hooks: + - id: black + name: black + entry: pipenv run black + args: [--quiet] + language: system + types: [python] + + - id: mypy + name: mypy + entry: pipenv run mypy + args: ["--follow-imports=skip"] + language: system + types: [python] + + - id: flake8 + name: flake8 + entry: pipenv run flake8 + language: system + types: [python] diff --git a/Ch04/apd.sensors-chapter04-click-subcommands/CHANGES.md b/Ch04/apd.sensors-chapter04-click-subcommands/CHANGES.md new file mode 100644 index 0000000..569aeea --- /dev/null +++ b/Ch04/apd.sensors-chapter04-click-subcommands/CHANGES.md @@ -0,0 +1,5 @@ +## Changes + +### 1.0.0 (2019-06-20) + +* Added initial sensors (Matthew Wilkes) diff --git a/Ch04/apd.sensors-chapter04-click-subcommands/LICENCE b/Ch04/apd.sensors-chapter04-click-subcommands/LICENCE new file mode 100644 index 0000000..ccaef50 --- /dev/null +++ b/Ch04/apd.sensors-chapter04-click-subcommands/LICENCE @@ -0,0 +1,21 @@ +``` +Copyright (c) 2019 Matthew Wilkes + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. +``` \ No newline at end of file diff --git a/Ch04/apd.sensors-chapter04-click-subcommands/Pipfile b/Ch04/apd.sensors-chapter04-click-subcommands/Pipfile new file mode 100644 index 0000000..40413b8 --- /dev/null +++ b/Ch04/apd.sensors-chapter04-click-subcommands/Pipfile @@ -0,0 +1,29 @@ +[[source]] +name = "pypi" +url = "https://pypi.org/simple" +verify_ssl = true + +[[source]] +name = "piwheels" +url = "https://piwheels.org/simple" +verify_ssl = true + +[dev-packages] +ipykernel = "*" +remote-ikernel = "*" +pytest = "*" +pytest-cov = "*" +mypy = "*" +flake8 = "*" +black = "*" +pre-commit = "*" +wheel = "*" +twine = "*" + +[packages] +apd-sensors = {editable = true,path = "."} + +[requires] + +[pipenv] +allow_prereleases = true 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index 0000000..e6dcb45 --- /dev/null +++ b/Ch04/apd.sensors-chapter04-click-subcommands/README.md @@ -0,0 +1,32 @@ +# Advanced Python Development Sensors + +This is the data collection package that forms part of the running example +for the book [Advanced Python Development](https://advancedpython.dev). + +## Usage + +This installs a console script called `sensors` that returns a report on +various aspects of the system. The available sensors are: + +* Python version +* IP Addresses +* CPU Usage +* RAM Available +* Battery charging state +* Ambient Temperature +* Ambient Humidity + +There are no command-line options, to view the report run `sensors` on the +command line. + +## Caveats + +The Ambient Temperature and Ambient Humidity sensors are only available on +RaspberryPi hosts and assume that a DHT22 sensor is connected to pin `D20`. + +If there is an entry in `/etc/hosts` for the current machine's hostname that +value will be the only result from the IP Addresses sensor. + +## Installation + +Install with `pip3 install apd.sensors` under Python 3.7 or higher diff --git a/Ch04/apd.sensors-chapter04-click-subcommands/pyproject.toml b/Ch04/apd.sensors-chapter04-click-subcommands/pyproject.toml new file mode 100644 index 0000000..4d3bb67 --- /dev/null +++ b/Ch04/apd.sensors-chapter04-click-subcommands/pyproject.toml @@ -0,0 +1,5 @@ +[build-system] +requires = [ + "setuptools", +] +build-backend = "setuptools.build_meta" diff --git a/Ch04/apd.sensors-chapter04-click-subcommands/pytest.ini b/Ch04/apd.sensors-chapter04-click-subcommands/pytest.ini new file mode 100644 index 0000000..861f3d0 --- /dev/null +++ b/Ch04/apd.sensors-chapter04-click-subcommands/pytest.ini @@ -0,0 +1,3 @@ +[pytest] +markers = + functional: these tests are significantly slower as they run the whole CLI script \ No newline at end of file diff --git a/Ch04/apd.sensors-chapter04-click-subcommands/setup.cfg b/Ch04/apd.sensors-chapter04-click-subcommands/setup.cfg new file mode 100644 index 0000000..1675018 --- /dev/null +++ b/Ch04/apd.sensors-chapter04-click-subcommands/setup.cfg @@ -0,0 +1,35 @@ +[mypy] +ignore_missing_imports = True + +[flake8] +max-line-length = 88 + +[metadata] +name = apd.sensors +version = attr: apd.sensors.VERSION +description = APD Sensor package +long_description = file: README.md, CHANGES.md, LICENCE +long_description_content_type = text/markdown +keywords = iot +license = MIT +classifiers = + Programming Language :: Python :: 3 + Programming Language :: Python :: 3.7 + +[options] +zip_safe = False +include_package_data = True +package-dir = + =src +packages = find: +install_requires = + psutil + click + adafruit-circuitpython-dht ; 'arm' in platform_machine + +[options.packages.find] +where = src + +[options.entry_points] +console_scripts = + sensors = apd.sensors.sensors:sensors diff --git a/Ch04/apd.sensors-chapter04-click-subcommands/setup.py b/Ch04/apd.sensors-chapter04-click-subcommands/setup.py new file mode 100644 index 0000000..6068493 --- /dev/null +++ b/Ch04/apd.sensors-chapter04-click-subcommands/setup.py @@ -0,0 +1,3 @@ +from setuptools import setup + +setup() diff --git a/Ch04/apd.sensors-chapter04-click-subcommands/src/apd/sensors/__init__.py b/Ch04/apd.sensors-chapter04-click-subcommands/src/apd/sensors/__init__.py new file mode 100644 index 0000000..196b66d --- /dev/null +++ b/Ch04/apd.sensors-chapter04-click-subcommands/src/apd/sensors/__init__.py @@ -0,0 +1 @@ +VERSION = "2.0.0dev1" diff --git a/Ch04/apd.sensors-chapter04-click-subcommands/src/apd/sensors/sensors.py b/Ch04/apd.sensors-chapter04-click-subcommands/src/apd/sensors/sensors.py new file mode 100644 index 0000000..0e580b6 --- /dev/null +++ b/Ch04/apd.sensors-chapter04-click-subcommands/src/apd/sensors/sensors.py @@ -0,0 +1,248 @@ +#!/usr/bin/env python +# coding: utf-8 +import importlib +import math +import socket +import sys +from typing import Any, Optional, List, Tuple, Iterable, TypeVar, Generic + + +import click +import psutil + + +T_value = TypeVar("T_value") + + +class Sensor(Generic[T_value]): + title: str + + def value(self) -> T_value: + raise NotImplementedError + + @classmethod + def format(cls, value: T_value) -> str: + raise NotImplementedError + + def __str__(self) -> str: + return self.format(self.value()) + + +class PythonVersion(Sensor[Any]): + title = "Python Version" + + def value(self) -> Any: + return sys.version_info + + @classmethod + def format(cls, value: Any) -> str: + if value.micro == 0 and value.releaselevel == "alpha": + return "{0.major}.{0.minor}.{0.micro}a{0.serial}".format(value) + return "{0.major}.{0.minor}".format(value) + + +class IPAddresses(Sensor[Iterable[Tuple[str, str]]]): + title = "IP Addresses" + FAMILIES = {"AF_INET": "IPv4", "AF_INET6": "IPv6"} + + def value(self) -> List[Tuple[str, str]]: + hostname = socket.gethostname() + addresses = socket.getaddrinfo(hostname, None) + + address_info: List[Tuple[str, str]] = [] + for address in addresses: + family, ip = (address[0].name, address[4][0]) + if family not in self.FAMILIES: + continue + value = (family, ip) + if value not in address_info: + address_info.append(value) + return address_info + + @classmethod + def format(cls, value: Iterable[Tuple[str, str]]) -> str: + return "\n".join( + "{0} ({1})".format(address[1], cls.FAMILIES.get(address[0], "Unknown")) + for address in value + ) + + +class CPULoad(Sensor[float]): + title = "CPU Usage" + + def value(self) -> float: + return psutil.cpu_percent(interval=3) / 100.0 + + @classmethod + def format(cls, value: float) -> str: + return "{:.1%}".format(value) + + +class RAMAvailable(Sensor[int]): + title = "RAM Available" + UNITS = ("B", "KiB", "MiB", "GiB", "TiB", "PiB", "EiB") + UNIT_SIZE = 2 ** 10 + + def value(self) -> int: + return psutil.virtual_memory().available + + @classmethod + def format(cls, value: int) -> str: + magnitude = math.floor(math.log(value, cls.UNIT_SIZE)) + max_magnitude = len(cls.UNITS) - 1 + magnitude = min(magnitude, max_magnitude) + scaled_value = value / (cls.UNIT_SIZE ** magnitude) + return "{:.1f} {}".format(scaled_value, cls.UNITS[magnitude]) + + +class ACStatus(Sensor[Optional[bool]]): + title = "AC Connected" + + def value(self) -> Optional[bool]: + battery = psutil.sensors_battery() + if battery is not None: + return battery.power_plugged + else: + return None + + @classmethod + def format(cls, value: Optional[bool]) -> str: + if value is None: + return "Unknown" + elif value: + return "Connected" + else: + return "Not connected" + + +class Temperature(Sensor[Optional[float]]): + title = "Ambient Temperature" + + def value(self) -> Optional[float]: + try: + from adafruit_dht import DHT22 + from board import D20 + except (ImportError, NotImplementedError): + # No DHT library results in an ImportError. + # Running on an unknown platform results in a + # NotImplementedError when getting the pin + return None + try: + return DHT22(D20).temperature + except RuntimeError: + return None + + @classmethod + def celsius_to_fahrenheit(cls, value: float) -> float: + return value * 9 / 5 + 32 + + @classmethod + def format(cls, value: Optional[float]) -> str: + if value is None: + return "Unknown" + else: + return "{:.1f}C ({:.1f}F)".format(value, cls.celsius_to_fahrenheit(value)) + + def __str__(self) -> str: + return self.format(self.value()) + + +class RelativeHumidity(Sensor[Optional[float]]): + title = "Relative Humidity" + + def value(self) -> Optional[float]: + try: + from adafruit_dht import DHT22 + from board import D20 + except (ImportError, NotImplementedError): + # No DHT library results in an ImportError. + # Running on an unknown platform results in a + # NotImplementedError when getting the pin + return None + try: + return DHT22(D20).humidity / 100 + except RuntimeError: + return None + + @classmethod + def format(cls, value: Optional[float]) -> str: + if value is None: + return "Unknown" + else: + return "{:.1%}".format(value) + + +def get_sensors() -> Iterable[Sensor[Any]]: + return [ + PythonVersion(), + IPAddresses(), + CPULoad(), + RAMAvailable(), + ACStatus(), + Temperature(), + RelativeHumidity(), + ] + + +@click.group() +def show_sensors() -> None: + return + + +RETURN_CODES = {"OK": 0, "BAD_SENSOR_PATH": 17} + + +def get_sensor_by_path(sensor_path: str) -> Sensor[Any]: + try: + module_name, sensor_name = sensor_path.split(":") + except ValueError: + raise RuntimeError( + "Sensor path must be in the format dotted.path.to.module:ClassName" + ) + try: + module = importlib.import_module(module_name) + except ImportError: + raise RuntimeError(f"Could not import module {module_name}") + try: + sensor_class = getattr(module, sensor_name) + except AttributeError: + raise RuntimeError(f"Could not find attribute {sensor_name} in {module_name}") + if ( + isinstance(sensor_class, type) + and issubclass(sensor_class, Sensor) + and sensor_class != Sensor + ): + return sensor_class() + else: + raise RuntimeError( + f"Detected object {sensor_class!r} is not recognised as a Sensor type" + ) + + +@show_sensors.command(help="Displays the values of the sensors") +def show() -> int: + sensors = get_sensors() + for sensor in sensors: + click.secho(sensor.title, bold=True) + click.echo(str(sensor)) + click.echo("") + return RETURN_CODES["OK"] + + +@show_sensors.command(help="Displays the values of a specific sensor in development") +@click.argument("sensor_path", required=True, metavar="path") +def develop(sensor_path) -> int: + try: + sensor = get_sensor_by_path(sensor_path) + except RuntimeError as error: + click.secho(str(error), fg="red", bold=True) + return RETURN_CODES["BAD_SENSOR_PATH"] + + click.secho(sensor.title, bold=True) + click.echo(str(sensor)) + click.echo("") + return RETURN_CODES["OK"] + + +if __name__ == "__main__": + show_sensors() diff --git a/Ch04/apd.sensors-chapter04-click-subcommands/tests/__init__.py b/Ch04/apd.sensors-chapter04-click-subcommands/tests/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/Ch04/apd.sensors-chapter04-click-subcommands/tests/test_acstatus.py b/Ch04/apd.sensors-chapter04-click-subcommands/tests/test_acstatus.py new file mode 100644 index 0000000..1d10940 --- /dev/null +++ b/Ch04/apd.sensors-chapter04-click-subcommands/tests/test_acstatus.py @@ -0,0 +1,55 @@ +from unittest import mock + +from apd.sensors.sensors import ACStatus + +import pytest + + +@pytest.fixture +def sensor(): + return ACStatus() + + +class TestACStatusFormatter: + @pytest.fixture + def subject(self, sensor): + return sensor.format + + def test_format_True(self, subject): + assert subject(True) == "Connected" + + def test_format_False(self, subject): + assert subject(False) == "Not connected" + + def test_format_None(self, subject): + assert subject(None) == "Unknown" + + +class TestACStatusValue: + @pytest.fixture + def subject(self, sensor): + return sensor.value + + @pytest.fixture + def sensors_battery(self): + with mock.patch("psutil.sensors_battery") as sensors_battery: + yield sensors_battery + + def test_sensors_battery_called(self, subject, sensors_battery): + sensors_battery.return_value.power_plugged = True + assert subject() is True + assert sensors_battery.call_count == 1 + + def test_sensor_but_battery_unknown(self, subject, sensors_battery): + sensors_battery.return_value.power_plugged = None + assert subject() is None + assert sensors_battery.call_count == 1 + + def test_no_sensor(self, subject, sensors_battery): + sensors_battery.return_value = None + assert subject() is None + + def test_str_representation_is_formatted_value(self, sensor, sensors_battery): + sensors_battery.return_value.power_plugged = True + assert str(sensor) == "Connected" + assert sensors_battery.call_count == 1 diff --git a/Ch04/apd.sensors-chapter04-click-subcommands/tests/test_cpuusage.py b/Ch04/apd.sensors-chapter04-click-subcommands/tests/test_cpuusage.py new file mode 100644 index 0000000..aa8a66a --- /dev/null +++ b/Ch04/apd.sensors-chapter04-click-subcommands/tests/test_cpuusage.py @@ -0,0 +1,45 @@ +from unittest import mock + +from apd.sensors.sensors import CPULoad + +import pytest + + +@pytest.fixture +def sensor(): + return CPULoad() + + +class TestCPULoadFormatter: + @pytest.fixture + def subject(self, sensor): + return sensor.format + + def test_format_simple_percentage(self, subject): + assert subject(0.05) == "5.0%" + + def test_format_multiple_decimal_places(self, subject): + assert subject(0.031415926) == "3.1%" + + def test_format_multiple_decimal_places_int(self, subject) -> None: + assert subject(1) == "100.0%" + + +class TestCPULoadValue: + @pytest.fixture + def subject(self, sensor): + return sensor.value + + @pytest.fixture + def cpuload(self): + with mock.patch("psutil.cpu_percent") as cpu_percent: + cpu_percent.return_value = 50.1 + yield cpu_percent + + def test_cpu_percent_called_With_interval(self, subject, cpuload): + assert subject() == 0.501 + assert cpuload.call_count == 1 + assert tuple(cpuload.call_args) == ((), {"interval": 3}) + + def test_str_representation_is_formatted_value(self, sensor, cpuload): + assert str(sensor) == "50.1%" diff --git a/Ch04/apd.sensors-chapter04-click-subcommands/tests/test_dht.py b/Ch04/apd.sensors-chapter04-click-subcommands/tests/test_dht.py new file mode 100644 index 0000000..b546593 --- /dev/null +++ b/Ch04/apd.sensors-chapter04-click-subcommands/tests/test_dht.py @@ -0,0 +1,66 @@ +from apd.sensors.sensors import Temperature, RelativeHumidity + +import pytest + + +@pytest.fixture +def temperature_sensor(): + return Temperature() + + +@pytest.fixture +def humidity_sensor(): + return RelativeHumidity() + + +class TestTemperatureFormatter: + @pytest.fixture + def subject(self, temperature_sensor): + return temperature_sensor.format + + def test_format_21c(self, subject): + assert subject(21.0) == "21.0C (69.8F)" + + def test_format_negative(self, subject): + assert subject(-32.0) == "-32.0C (-25.6F)" + + def test_format_unknown(self, subject): + assert subject(None) == "Unknown" + + +class TestTemperatureConversion: + @pytest.fixture + def subject(self, temperature_sensor): + return temperature_sensor.celsius_to_fahrenheit + + def test_celsius_to_fahrenheit(self, subject): + c = 21 + f = subject(c) + assert f == 69.8 + + def test_celsius_to_fahrenheit_equivlance_point(self, subject): + c = -40 + f = subject(c) + assert f == -40 + + def test_celsius_to_fahrenheit_float(self, subject): + c = 21.2 + f = subject(c) + assert f == 70.16 + + def test_celsius_to_fahrenheit_string(self, subject): + c = "21" + with pytest.raises(TypeError): + subject(c) + + +class TestHumidityFormatter: + @pytest.fixture + def subject(self, humidity_sensor): + return humidity_sensor.format + + def test_format_percentage(self, subject): + assert subject(0.035) == "3.5%" + + def test_format_unknown(self, subject): + assert subject(None) == "Unknown" diff --git a/Ch04/apd.sensors-chapter04-click-subcommands/tests/test_ipaddresses.py b/Ch04/apd.sensors-chapter04-click-subcommands/tests/test_ipaddresses.py new file mode 100644 index 0000000..e8a9690 --- /dev/null +++ b/Ch04/apd.sensors-chapter04-click-subcommands/tests/test_ipaddresses.py @@ -0,0 +1,59 @@ +from unittest import mock +import socket + +from apd.sensors.sensors import IPAddresses + +import pytest + + +@pytest.fixture +def sensor(): + return IPAddresses() + + +class TestIPAddressFormatter: + @pytest.fixture + def subject(self, sensor): + return sensor.format + + def test_format_single_ipv4(self, subject): + ips = [("AF_INET", "192.0.2.1")] + assert subject(ips) == "192.0.2.1 (IPv4)" + + def test_format_single_ipv6(self, subject): + ips = [("AF_INET6", "2001:DB8::1")] + assert subject(ips) == "2001:DB8::1 (IPv6)" + + def test_format_mixed_list(self, subject): + ips = [("AF_INET", "192.0.2.1"), ("AF_INET6", "2001:DB8::1")] + assert subject(ips) == "192.0.2.1 (IPv4)\n2001:DB8::1 (IPv6)" + + def test_unusual_protocols_are_marked_as_unknown(self, subject): + ips = [("AF_IRDA", "ffff")] + assert subject(ips) == "ffff (Unknown)" + + +class TestIPAddressesValue: + @pytest.fixture + def subject(self, sensor): + return sensor.value + + @pytest.fixture + def gethostname(self): + with mock.patch("socket.gethostname") as gethostname: + gethostname.return_value = "localhost" + yield gethostname + + @pytest.fixture + def getaddrinfo(self, gethostname): + with mock.patch("socket.getaddrinfo") as getaddrinfo: + yield getaddrinfo + + def test_getaddrinfo_used_for_value_collection(self, getaddrinfo, subject): + getaddrinfo.return_value = [(socket.AF_INET, 0, 0, "", ("192.0.2.1", 0))] + assert subject() == [("AF_INET", "192.0.2.1")] + assert getaddrinfo.call_count == 1 + + def test_str_representation_is_formatted_value(self, getaddrinfo, sensor): + getaddrinfo.return_value = [(socket.AF_INET6, 0, 0, "", ("2001:DB8::1", 0))] + assert str(sensor) == "2001:DB8::1 (IPv6)" diff --git a/Ch04/apd.sensors-chapter04-click-subcommands/tests/test_pythonversion.py b/Ch04/apd.sensors-chapter04-click-subcommands/tests/test_pythonversion.py new file mode 100644 index 0000000..dff6bb0 --- /dev/null +++ b/Ch04/apd.sensors-chapter04-click-subcommands/tests/test_pythonversion.py @@ -0,0 +1,61 @@ +from collections import namedtuple +from unittest import mock + +from apd.sensors.sensors import PythonVersion + +import pytest + + +@pytest.fixture +def version(): + return namedtuple( + "sys_versioninfo", ("major", "minor", "micro", "releaselevel", "serial") + ) + + +@pytest.fixture +def sensor(): + return PythonVersion() + + +class TestPythonVersionFormatter: + @pytest.fixture + def subject(self, sensor): + return sensor.format + + def test_format_py38(self, subject, version): + py38 = version(3, 8, 0, "final", 0) + assert subject(py38) == "3.8" + + def test_format_large_version(self, subject, version): + large = version(255, 128, 0, "final", 0) + assert subject(large) == "255.128" + + def test_alpha_of_minor_is_marked(self, subject, version): + py39 = version(3, 9, 0, "alpha", 1) + assert subject(py39) == "3.9.0a1" + + def test_alpha_of_micro_is_unmarked(self, subject, version): + py39 = version(3, 9, 1, "alpha", 1) + assert subject(py39) == "3.9" + + +class TestPythonVersionValue: + @pytest.fixture + def subject(self, sensor): + return sensor.value + + @pytest.fixture + def python_version(self): + import sys + + return sys.version_info + + def test_data_value_is_sys_versioninfo(self, python_version, subject): + assert subject() == python_version + + +class TestPythonVersionSensor: + def test_str_representation_is_formatted_value(self, sensor, version): + with mock.patch("sys.version_info", new=version(3, 4, 1, "final", 1)): + assert str(sensor) == "3.4" diff --git a/Ch04/apd.sensors-chapter04-click-subcommands/tests/test_ramusage.py b/Ch04/apd.sensors-chapter04-click-subcommands/tests/test_ramusage.py new file mode 100644 index 0000000..2a368c1 --- /dev/null +++ b/Ch04/apd.sensors-chapter04-click-subcommands/tests/test_ramusage.py @@ -0,0 +1,47 @@ +from unittest import mock + +from apd.sensors.sensors import RAMAvailable + +import pytest + + +@pytest.fixture +def sensor(): + return RAMAvailable() + + +class TestRAMAvailableFormatter: + @pytest.fixture + def subject(self, sensor): + return sensor.format + + def test_format_bytes(self, subject): + assert subject(15) == "15.0 B" + + def test_format_kibibytes(self, subject): + assert subject(15000) == "14.6 KiB" + + def test_format_mibibytes(self, subject): + assert subject(15000000) == "14.3 MiB" + + def test_format_gibibytes(self, subject): + assert subject(15000000000) == "14.0 GiB" + + +class TestRAMAvailableValue: + @pytest.fixture + def subject(self, sensor): + return sensor.value + + @pytest.fixture + def virtual_memory(self): + with mock.patch("psutil.virtual_memory") as virtual_memory: + virtual_memory.return_value.available = 1024 + yield virtual_memory + + def test_virtual_memory_called(self, subject, virtual_memory): + assert subject() == 1024 + assert virtual_memory.call_count == 1 + + def test_str_representation_is_formatted_value(self, sensor, virtual_memory): + assert str(sensor) == "1.0 KiB" diff --git a/Ch04/apd.sensors-chapter04-click-subcommands/tests/test_sensors.py b/Ch04/apd.sensors-chapter04-click-subcommands/tests/test_sensors.py new file mode 100644 index 0000000..6885c41 --- /dev/null +++ b/Ch04/apd.sensors-chapter04-click-subcommands/tests/test_sensors.py @@ -0,0 +1,16 @@ +from click.testing import CliRunner +import pytest + +import apd.sensors.sensors + + +def test_sensors(): + assert hasattr(apd.sensors.sensors, "PythonVersion") + + +@pytest.mark.functional +def test_python_version_is_first_two_lines_of_cli_output(): + runner = CliRunner() + result = runner.invoke(apd.sensors.sensors.show_sensors) + python_version = str(apd.sensors.sensors.PythonVersion()) + assert ["Python Version", python_version] == result.stdout.split("\n")[:2] diff --git a/Ch04/apd.sensors-chapter04-configparser-local/.pre-commit-config.yaml b/Ch04/apd.sensors-chapter04-configparser-local/.pre-commit-config.yaml new file mode 100644 index 0000000..3ff6d15 --- /dev/null +++ b/Ch04/apd.sensors-chapter04-configparser-local/.pre-commit-config.yaml @@ -0,0 +1,23 @@ +repos: + +- repo: local + hooks: + - id: black + name: black + entry: pipenv run black + args: [--quiet] + language: system + types: [python] + + - id: mypy + name: mypy + entry: pipenv run mypy + args: ["--follow-imports=skip"] + language: system + types: [python] + + - id: flake8 + name: flake8 + entry: pipenv run flake8 + language: system + types: [python] diff --git a/Ch04/apd.sensors-chapter04-configparser-local/CHANGES.md b/Ch04/apd.sensors-chapter04-configparser-local/CHANGES.md new file mode 100644 index 0000000..569aeea --- /dev/null +++ b/Ch04/apd.sensors-chapter04-configparser-local/CHANGES.md @@ -0,0 +1,5 @@ +## Changes + +### 1.0.0 (2019-06-20) + +* Added initial sensors (Matthew Wilkes) diff --git a/Ch04/apd.sensors-chapter04-configparser-local/LICENCE b/Ch04/apd.sensors-chapter04-configparser-local/LICENCE new file mode 100644 index 0000000..ccaef50 --- /dev/null +++ b/Ch04/apd.sensors-chapter04-configparser-local/LICENCE @@ -0,0 +1,21 @@ +``` +Copyright (c) 2019 Matthew Wilkes + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. +``` \ No newline at end of file diff --git a/Ch04/apd.sensors-chapter04-configparser-local/Pipfile b/Ch04/apd.sensors-chapter04-configparser-local/Pipfile new file mode 100644 index 0000000..40413b8 --- /dev/null +++ b/Ch04/apd.sensors-chapter04-configparser-local/Pipfile @@ -0,0 +1,29 @@ +[[source]] +name = "pypi" +url = "https://pypi.org/simple" +verify_ssl = true + +[[source]] +name = "piwheels" +url = "https://piwheels.org/simple" +verify_ssl = true + +[dev-packages] +ipykernel = "*" +remote-ikernel = "*" +pytest = "*" +pytest-cov = "*" +mypy = "*" +flake8 = "*" +black = "*" +pre-commit = "*" +wheel = "*" +twine = "*" + +[packages] +apd-sensors = {editable = true,path = "."} + +[requires] + +[pipenv] +allow_prereleases = 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b/Ch04/apd.sensors-chapter04-configparser-local/README.md new file mode 100644 index 0000000..e6dcb45 --- /dev/null +++ b/Ch04/apd.sensors-chapter04-configparser-local/README.md @@ -0,0 +1,32 @@ +# Advanced Python Development Sensors + +This is the data collection package that forms part of the running example +for the book [Advanced Python Development](https://advancedpython.dev). + +## Usage + +This installs a console script called `sensors` that returns a report on +various aspects of the system. The available sensors are: + +* Python version +* IP Addresses +* CPU Usage +* RAM Available +* Battery charging state +* Ambient Temperature +* Ambient Humidity + +There are no command-line options, to view the report run `sensors` on the +command line. + +## Caveats + +The Ambient Temperature and Ambient Humidity sensors are only available on +RaspberryPi hosts and assume that a DHT22 sensor is connected to pin `D20`. + +If there is an entry in `/etc/hosts` for the current machine's hostname that +value will be the only result from the IP Addresses sensor. + +## Installation + +Install with `pip3 install apd.sensors` under Python 3.7 or higher diff --git a/Ch04/apd.sensors-chapter04-configparser-local/config.cfg b/Ch04/apd.sensors-chapter04-configparser-local/config.cfg new file mode 100644 index 0000000..a7e3d59 --- /dev/null +++ b/Ch04/apd.sensors-chapter04-configparser-local/config.cfg @@ -0,0 +1,32 @@ +[config] +plugins = + PythonVersion + IPAddress + CPULoad + RAMAvailable + ACStatus + Temperature + Humidity + +[IPAddress] +plugin = apd.sensors.sensors:IPAddresses + +[PythonVersion] +plugin = apd.sensors.sensors:PythonVersion + +[CPULoad] +plugin = apd.sensors.sensors:CPULoad + +[RAMAvailable] +plugin = apd.sensors.sensors:RAMAvailable + +[ACStatus] +plugin = apd.sensors.sensors:ACStatus + +[Temperature] +plugin = apd.sensors.sensors:Temperature +pin = D4 + +[Humidity] +plugin = apd.sensors.sensors:Temperature +pin = D4 diff --git a/Ch04/apd.sensors-chapter04-configparser-local/pytest.ini b/Ch04/apd.sensors-chapter04-configparser-local/pytest.ini new file mode 100644 index 0000000..861f3d0 --- /dev/null +++ b/Ch04/apd.sensors-chapter04-configparser-local/pytest.ini @@ -0,0 +1,3 @@ +[pytest] +markers = + functional: these tests are significantly slower as they run the whole CLI script \ No newline at end of file diff --git a/Ch04/apd.sensors-chapter04-configparser-local/setup.cfg b/Ch04/apd.sensors-chapter04-configparser-local/setup.cfg new file mode 100644 index 0000000..908c774 --- /dev/null +++ b/Ch04/apd.sensors-chapter04-configparser-local/setup.cfg @@ -0,0 +1,59 @@ +[mypy] +namespace_packages = True +mypy_path = src + +[mypy-psutil] +ignore_missing_imports = True + +[mypy-adafruit_dht] +ignore_missing_imports = True + +[mypy-board] +ignore_missing_imports = True + +[mypy-pytest] +ignore_missing_imports = True + +[flake8] +max-line-length = 88 + +[metadata] +name = apd.sensors +version = attr: apd.sensors.VERSION +description = APD Sensor package +long_description = file: README.md, CHANGES.md, LICENCE +long_description_content_type = text/markdown +keywords = iot +license = MIT +classifiers = + Programming Language :: Python :: 3 + Programming Language :: Python :: 3.7 + +[options] +zip_safe = False +include_package_data = True +package-dir = + =src +packages = find_namespace: +install_requires = + psutil + click + adafruit-circuitpython-dht ; 'arm' in platform_machine + +[options.package_data] +apd.sensors = py.typed + +[options.packages.find] +where = src + +[options.entry_points] +console_scripts = + sensors = apd.sensors.cli:show_sensors +apd_sensors = + PythonVersion = apd.sensors.sensors:PythonVersion + IPAddresses = apd.sensors.sensors:IPAddresses + CPULoad = apd.sensors.sensors:CPULoad + RAMAvailable = apd.sensors.sensors:RAMAvailable + ACStatus = apd.sensors.sensors:ACStatus + Temperature = apd.sensors.sensors:Temperature + RelativeHumidity = apd.sensors.sensors:RelativeHumidity \ No newline at end of file diff --git a/Ch04/apd.sensors-chapter04-configparser-local/setup.py b/Ch04/apd.sensors-chapter04-configparser-local/setup.py new file mode 100644 index 0000000..6068493 --- /dev/null +++ b/Ch04/apd.sensors-chapter04-configparser-local/setup.py @@ -0,0 +1,3 @@ +from setuptools import setup + +setup() diff --git a/Ch04/apd.sensors-chapter04-configparser-local/src/apd/sensors/__init__.py b/Ch04/apd.sensors-chapter04-configparser-local/src/apd/sensors/__init__.py new file mode 100644 index 0000000..ec63ed1 --- /dev/null +++ b/Ch04/apd.sensors-chapter04-configparser-local/src/apd/sensors/__init__.py @@ -0,0 +1 @@ +VERSION = "1.1.0dev1" diff --git a/Ch04/apd.sensors-chapter04-configparser-local/src/apd/sensors/cli.py b/Ch04/apd.sensors-chapter04-configparser-local/src/apd/sensors/cli.py new file mode 100644 index 0000000..343ca17 --- /dev/null +++ b/Ch04/apd.sensors-chapter04-configparser-local/src/apd/sensors/cli.py @@ -0,0 +1,142 @@ +import configparser +import enum +import importlib +import os +import sys +import typing as t + +import click + +from .sensors import Sensor +from .config_path_utils import user_config_dir, site_config_dirs + + +class ReturnCodes(enum.IntEnum): + OK = 0 + BAD_SENSOR_PATH = 17 + BAD_CONFIG = 18 + + +def parse_config_file( + path: t.Union[str, t.Iterable[str]] +) -> t.Dict[str, t.Dict[str, str]]: + parser = configparser.ConfigParser() + parser.read(path, encoding="utf-8") + try: + plugin_names = [ + name for name in parser.get("config", "plugins").split() if name + ] + except configparser.NoSectionError: + raise RuntimeError(f"Could not find [config] section in file") + except configparser.NoOptionError: + raise RuntimeError(f"Could not find plugins line in [config] section") + plugin_data = {} + for plugin_name in plugin_names: + try: + plugin_data[plugin_name] = dict(parser.items(plugin_name)) + except configparser.NoSectionError: + raise RuntimeError(f"Could not find [{plugin_name}] section in file") + return plugin_data + + +def get_sensor_by_path(sensor_path: str, **kwargs) -> Sensor[t.Any]: + try: + module_name, sensor_name = sensor_path.split(":") + except ValueError: + raise RuntimeError( + "Sensor path must be in the format dotted.path.to.module:ClassName" + ) + try: + module = importlib.import_module(module_name) + except ImportError: + raise RuntimeError(f"Could not import module {module_name}") + try: + sensor_class = getattr(module, sensor_name) + except AttributeError: + raise RuntimeError(f"Could not find attribute {sensor_name} in {module_name}") + if ( + isinstance(sensor_class, type) + and issubclass(sensor_class, Sensor) + and sensor_class != Sensor + ): + try: + return sensor_class(**kwargs) + except TypeError as error: + message = str(error) + if "got an unexpected" in message: + raise RuntimeError(f"Sensor {sensor_name} " + message.split(" ", 1)[1]) + raise + else: + raise RuntimeError( + f"Detected object {sensor_class!r} is not recognised as a Sensor type" + ) + + +def get_sensors(path: t.Iterable[str]) -> t.Iterable[Sensor[t.Any]]: + sensors = [] + for plugin_name, sensor_data in parse_config_file(path).items(): + try: + class_path = sensor_data.pop("plugin") + except TypeError: + raise RuntimeError( + f"Could not find plugin= line in [{plugin_name}] section" + ) + sensors.append(get_sensor_by_path(class_path, **sensor_data)) + return sensors + + +@click.command(help="Displays the values of the sensors") +@click.option( + "--develop", required=False, metavar="path", help="Load a sensor by Python path" +) +@click.option( + "--config", + required=False, + metavar="config_path", + help="Load the specified configuration file", +) +@click.option( + "--verbose", + "-v", + required=False, + is_flag=True, + help="Print additional programme information", +) +def show_sensors(develop: str, config: str, verbose: bool) -> None: + sensors: t.Iterable[Sensor[t.Any]] + if develop: + try: + sensors = [get_sensor_by_path(develop)] + except RuntimeError as error: + click.secho(str(error), fg="red", bold=True, err=True) + sys.exit(ReturnCodes.BAD_SENSOR_PATH) + else: + if config is None: + configs = ( + [user_config_dir("apd_sensors")] + + site_config_dirs("apd_sensors") + + [os.getcwd()] + ) + configs = [os.path.join(path, "config.cfg") for path in configs] + else: + configs = [os.path.abspath(config)] + if verbose: + click.secho( + "Looking for configuration in {}".format("; ".join(configs)), + fg="yellow", + err=True, + ) + try: + sensors = get_sensors(configs) + except RuntimeError as error: + click.secho(str(error), fg="red", bold=True, err=True) + sys.exit(ReturnCodes.BAD_CONFIG) + for sensor in sensors: + click.secho(sensor.title, bold=True) + click.echo(str(sensor)) + click.echo("") + sys.exit(ReturnCodes.OK) + + +if __name__ == "__main__": + show_sensors() diff --git a/Ch04/apd.sensors-chapter04-configparser-local/src/apd/sensors/config_path_utils.py b/Ch04/apd.sensors-chapter04-configparser-local/src/apd/sensors/config_path_utils.py new file mode 100644 index 0000000..4d9f3a0 --- /dev/null +++ b/Ch04/apd.sensors-chapter04-configparser-local/src/apd/sensors/config_path_utils.py @@ -0,0 +1,207 @@ +# Functions from Pip https://github.com/pypa/pip/blob/800f866600968997dd6d9e49076b401784195123/src/pip/_internal/utils/appdirs.py # noqa: E501 + + +# Copyright (c) 2008-2019 The pip developers (see AUTHORS.txt file) +# +# Permission is hereby granted, free of charge, to any person obtaining +# a copy of this software and associated documentation files (the +# "Software"), to deal in the Software without restriction, including +# without limitation the rights to use, copy, modify, merge, publish, +# distribute, sublicense, and/or sell copies of the Software, and to +# permit persons to whom the Software is furnished to do so, subject to +# the following conditions: +# +# The above copyright notice and this permission notice shall be +# included in all copies or substantial portions of the Software. +# +# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, +# EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF +# MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND +# NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE +# LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION +# OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION +# WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. + + +# Authors of the relevant code appear to be +# Chris Jerdonek +# cytolentino +# Donald Stufft +# Maxim Kurnikov +# Mickaël Schoentgen +# Monica Baluna +# Pradyun Gedam +# but thanks go to all contributors to pip + + +# This contains modifications to simplify dependencies, please do not +# report any bugs against the official version that only affect +# this implementation + +from __future__ import absolute_import + +import ctypes +import os +import sys + +from typing import List + + +WINDOWS = sys.platform.startswith("win") or (sys.platform == "cli" and os.name == "nt") + + +def expanduser(path): + # type: (str) -> str + """ + Expand ~ and ~user constructions. + Includes a workaround for https://bugs.python.org/issue14768 + """ + expanded = os.path.expanduser(path) + if path.startswith("~/") and expanded.startswith("//"): + expanded = expanded[1:] + return expanded + + +def user_data_dir(appname, roaming=False): + # type: (str, bool) -> str + r""" + Return full path to the user-specific data dir for this application. + "appname" is the name of application. + If None, just the system directory is returned. + "roaming" (boolean, default False) can be set True to use the Windows + roaming appdata directory. That means that for users on a Windows + network setup for roaming profiles, this user data will be + sync'd on login. See + + for a discussion of issues. + Typical user data directories are: + macOS: ~/Library/Application Support/ + if it exists, else ~/.config/ + Unix: ~/.local/share/ # or in + $XDG_DATA_HOME, if defined + Win XP (not roaming): C:\Documents and Settings\\ ... + ...Application Data\ + Win XP (roaming): C:\Documents and Settings\\Local ... + ...Settings\Application Data\ + Win 7 (not roaming): C:\\Users\\AppData\Local\ + Win 7 (roaming): C:\\Users\\AppData\Roaming\ + For Unix, we follow the XDG spec and support $XDG_DATA_HOME. + That means, by default "~/.local/share/". + """ + if WINDOWS: + const = roaming and "CSIDL_APPDATA" or "CSIDL_LOCAL_APPDATA" + path = os.path.join(os.path.normpath(_get_win_folder(const)), appname) + elif sys.platform == "darwin": + path = ( + os.path.join(expanduser("~/Library/Application Support/"), appname) + if os.path.isdir( + os.path.join(expanduser("~/Library/Application Support/"), appname) + ) + else os.path.join(expanduser("~/.config/"), appname) + ) + else: + path = os.path.join( + os.getenv("XDG_DATA_HOME", expanduser("~/.local/share")), appname + ) + + return path + + +def user_config_dir(appname, roaming=True): + # type: (str, bool) -> str + """Return full path to the user-specific config dir for this application. + "appname" is the name of application. + If None, just the system directory is returned. + "roaming" (boolean, default True) can be set False to not use the + Windows roaming appdata directory. That means that for users on a + Windows network setup for roaming profiles, this user data will be + sync'd on login. See + + for a discussion of issues. + Typical user data directories are: + macOS: same as user_data_dir + Unix: ~/.config/ + Win *: same as user_data_dir + For Unix, we follow the XDG spec and support $XDG_CONFIG_HOME. + That means, by default "~/.config/". + """ + if WINDOWS: + path = user_data_dir(appname, roaming=roaming) + elif sys.platform == "darwin": + path = user_data_dir(appname) + else: + path = os.getenv("XDG_CONFIG_HOME", expanduser("~/.config")) + path = os.path.join(path, appname) + + return path + + +# for the discussion regarding site_config_dirs locations +# see +def site_config_dirs(appname): + # type: (str) -> List[str] + r"""Return a list of potential user-shared config dirs for this application. + "appname" is the name of application. + Typical user config directories are: + macOS: /Library/Application Support// + Unix: /etc or $XDG_CONFIG_DIRS[i]// for each value in + $XDG_CONFIG_DIRS + Win XP: C:\Documents and Settings\All Users\Application ... + ...Data\\ + Vista: (Fail! "C:\ProgramData" is a hidden *system* directory + on Vista.) + Win 7: Hidden, but writeable on Win 7: + C:\ProgramData\\ + """ + if WINDOWS: + path = os.path.normpath(_get_win_folder("CSIDL_COMMON_APPDATA")) + pathlist = [os.path.join(path, appname)] + elif sys.platform == "darwin": + pathlist = [os.path.join("/Library/Application Support", appname)] + else: + # try looking in $XDG_CONFIG_DIRS + xdg_config_dirs = os.getenv("XDG_CONFIG_DIRS", "/etc/xdg") + if xdg_config_dirs: + pathlist = [ + os.path.join(expanduser(x), appname) + for x in xdg_config_dirs.split(os.pathsep) + ] + else: + pathlist = [] + + # always look in /etc directly as well + pathlist.append("/etc") + + return pathlist + + +# -- Windows support functions -- + + +def _get_win_folder(csidl_name): + # type: (str) -> str + # On Python 2, ctypes.create_unicode_buffer().value returns "unicode", + # which isn't the same as str in the annotation above. + csidl_const = { + "CSIDL_APPDATA": 26, + "CSIDL_COMMON_APPDATA": 35, + "CSIDL_LOCAL_APPDATA": 28, + }[csidl_name] + + buf = ctypes.create_unicode_buffer(1024) + ctypes.windll.shell32.SHGetFolderPathW(None, csidl_const, None, 0, buf) + + # Downgrade to short path name if have highbit chars. See + # . + has_high_char = False + for c in buf: + if ord(c) > 255: + has_high_char = True + break + if has_high_char: + buf2 = ctypes.create_unicode_buffer(1024) + if ctypes.windll.kernel32.GetShortPathNameW(buf.value, buf2, 1024): + buf = buf2 + + # The type: ignore is explained under the type annotation for this function + return buf.value # type: ignore diff --git a/Ch04/apd.sensors-chapter04-configparser-local/src/apd/sensors/py.typed b/Ch04/apd.sensors-chapter04-configparser-local/src/apd/sensors/py.typed new file mode 100644 index 0000000..e69de29 diff --git a/Ch04/apd.sensors-chapter04-configparser-local/src/apd/sensors/sensors.py b/Ch04/apd.sensors-chapter04-configparser-local/src/apd/sensors/sensors.py new file mode 100644 index 0000000..27f88a4 --- /dev/null +++ b/Ch04/apd.sensors-chapter04-configparser-local/src/apd/sensors/sensors.py @@ -0,0 +1,191 @@ +#!/usr/bin/env python +# coding: utf-8 +import math +import socket +import sys +from typing import Any, Optional, List, Tuple, Iterable, TypeVar, Generic + + +import psutil + + +T_value = TypeVar("T_value") + + +class Sensor(Generic[T_value]): + title: str + + def __init__(self, **kwargs): + if kwargs: + raise TypeError( + "Sensor got an unexpected keyword argument {}".format( + ", ".join(kwargs.keys()) + ) + ) + return + + def value(self) -> T_value: + raise NotImplementedError + + @classmethod + def format(cls, value: T_value) -> str: + raise NotImplementedError + + def __str__(self) -> str: + return self.format(self.value()) + + +class PythonVersion(Sensor[Any]): + title = "Python Version" + + def value(self) -> Any: + return sys.version_info + + @classmethod + def format(cls, value: Any) -> str: + if value.micro == 0 and value.releaselevel == "alpha": + return "{0.major}.{0.minor}.{0.micro}a{0.serial}".format(value) + return "{0.major}.{0.minor}".format(value) + + +class IPAddresses(Sensor[Iterable[Tuple[str, str]]]): + title = "IP Addresses" + FAMILIES = {"AF_INET": "IPv4", "AF_INET6": "IPv6"} + + def value(self) -> List[Tuple[str, str]]: + hostname = socket.gethostname() + addresses = socket.getaddrinfo(hostname, None) + + address_info: List[Tuple[str, str]] = [] + for address in addresses: + family, ip = (address[0].name, address[4][0]) + if family not in self.FAMILIES: + continue + value = (family, ip) + if value not in address_info: + address_info.append(value) + return address_info + + @classmethod + def format(cls, value: Iterable[Tuple[str, str]]) -> str: + return "\n".join( + "{0} ({1})".format(address[1], cls.FAMILIES.get(address[0], "Unknown")) + for address in value + ) + + +class CPULoad(Sensor[float]): + title = "CPU Usage" + + def value(self) -> float: + return psutil.cpu_percent(interval=3) / 100.0 + + @classmethod + def format(cls, value: float) -> str: + return "{:.1%}".format(value) + + +class RAMAvailable(Sensor[int]): + title = "RAM Available" + UNITS = ("B", "KiB", "MiB", "GiB", "TiB", "PiB", "EiB") + UNIT_SIZE = 2 ** 10 + + def value(self) -> int: + return psutil.virtual_memory().available + + @classmethod + def format(cls, value: int) -> str: + magnitude = math.floor(math.log(value, cls.UNIT_SIZE)) + max_magnitude = len(cls.UNITS) - 1 + magnitude = min(magnitude, max_magnitude) + scaled_value = value / (cls.UNIT_SIZE ** magnitude) + return "{:.1f} {}".format(scaled_value, cls.UNITS[magnitude]) + + +class ACStatus(Sensor[Optional[bool]]): + title = "AC Connected" + + def value(self) -> Optional[bool]: + battery = psutil.sensors_battery() + if battery is not None: + return battery.power_plugged + else: + return None + + @classmethod + def format(cls, value: Optional[bool]) -> str: + if value is None: + return "Unknown" + elif value: + return "Connected" + else: + return "Not connected" + + +class Temperature(Sensor[Optional[float]]): + title = "Ambient Temperature" + + def __init__(self, board="DHT22", pin="D4"): + self.board = board + self.pin = pin + + def value(self) -> Optional[float]: + try: + import adafruit_dht + import board + except (ImportError, NotImplementedError): + # No DHT library results in an ImportError. + # Running on an unknown platform results in a + # NotImplementedError when getting the pin + return None + try: + sensor_type = getattr(adafruit_dht, self.board) + pin = getattr(board, self.pin) + return sensor_type(pin).temperature + except RuntimeError: + return None + + @staticmethod + def celsius_to_fahrenheit(value: float) -> float: + return value * 9 / 5 + 32 + + @classmethod + def format(cls, value: Optional[float]) -> str: + if value is None: + return "Unknown" + else: + return "{:.1f}C ({:.1f}F)".format(value, cls.celsius_to_fahrenheit(value)) + + def __str__(self) -> str: + return self.format(self.value()) + + +class RelativeHumidity(Sensor[Optional[float]]): + title = "Relative Humidity" + + def __init__(self, board="DHT22", pin="D4"): + self.board = board + self.pin = pin + + def value(self) -> Optional[float]: + try: + import adafruit_dht + import board + except (ImportError, NotImplementedError): + # No DHT library results in an ImportError. + # Running on an unknown platform results in a + # NotImplementedError when getting the pin + return None + try: + sensor_type = getattr(adafruit_dht, self.board) + pin = getattr(board, self.pin) + return sensor_type(pin).humidity / 100 + except RuntimeError: + return None + + @classmethod + def format(cls, value: Optional[float]) -> str: + if value is None: + return "Unknown" + else: + return "{:.1%}".format(value) diff --git a/Ch04/apd.sensors-chapter04-configparser-local/tests/default_test_config.cfg b/Ch04/apd.sensors-chapter04-configparser-local/tests/default_test_config.cfg new file mode 100644 index 0000000..afefb5d --- /dev/null +++ b/Ch04/apd.sensors-chapter04-configparser-local/tests/default_test_config.cfg @@ -0,0 +1,10 @@ +[config] +plugins = + PythonVersion + IPAddress + +[PythonVersion] +plugin = apd.sensors.sensors:PythonVersion + +[IPAddress] +plugin = apd.sensors.sensors:IPAddresses diff --git a/Ch04/apd.sensors-chapter04-configparser-local/tests/test_acstatus.py b/Ch04/apd.sensors-chapter04-configparser-local/tests/test_acstatus.py new file mode 100644 index 0000000..d2fe71e --- /dev/null +++ b/Ch04/apd.sensors-chapter04-configparser-local/tests/test_acstatus.py @@ -0,0 +1,55 @@ +from unittest import mock + +import pytest + +from apd.sensors.sensors import ACStatus + + +@pytest.fixture +def sensor(): + return ACStatus() + + +class TestACStatusFormatter: + @pytest.fixture + def subject(self, sensor): + return sensor.format + + def test_format_True(self, subject): + assert subject(True) == "Connected" + + def test_format_False(self, subject): + assert subject(False) == "Not connected" + + def test_format_None(self, subject): + assert subject(None) == "Unknown" + + +class TestACStatusValue: + @pytest.fixture + def subject(self, sensor): + return sensor.value + + @pytest.fixture + def sensors_battery(self): + with mock.patch("psutil.sensors_battery") as sensors_battery: + yield sensors_battery + + def test_sensors_battery_called(self, subject, sensors_battery): + sensors_battery.return_value.power_plugged = True + assert subject() is True + assert sensors_battery.call_count == 1 + + def test_sensor_but_battery_unknown(self, subject, sensors_battery): + sensors_battery.return_value.power_plugged = None + assert subject() is None + assert sensors_battery.call_count == 1 + + def test_no_sensor(self, subject, sensors_battery): + sensors_battery.return_value = None + assert subject() is None + + def test_str_representation_is_formatted_value(self, sensor, sensors_battery): + sensors_battery.return_value.power_plugged = True + assert str(sensor) == "Connected" + assert sensors_battery.call_count == 1 diff --git a/Ch04/apd.sensors-chapter04-configparser-local/tests/test_cpuusage.py b/Ch04/apd.sensors-chapter04-configparser-local/tests/test_cpuusage.py new file mode 100644 index 0000000..445f4e5 --- /dev/null +++ b/Ch04/apd.sensors-chapter04-configparser-local/tests/test_cpuusage.py @@ -0,0 +1,45 @@ +from unittest import mock + +import pytest + +from apd.sensors.sensors import CPULoad + + +@pytest.fixture +def sensor(): + return CPULoad() + + +class TestCPULoadFormatter: + @pytest.fixture + def subject(self, sensor): + return sensor.format + + def test_format_simple_percentage(self, subject): + assert subject(0.05) == "5.0%" + + def test_format_multiple_decimal_places(self, subject): + assert subject(0.031415926) == "3.1%" + + def test_format_multiple_decimal_places_int(self, subject) -> None: + assert subject(1) == "100.0%" + + +class TestCPULoadValue: + @pytest.fixture + def subject(self, sensor): + return sensor.value + + @pytest.fixture + def cpuload(self): + with mock.patch("psutil.cpu_percent") as cpu_percent: + cpu_percent.return_value = 50.1 + yield cpu_percent + + def test_cpu_percent_called_With_interval(self, subject, cpuload): + assert subject() == 0.501 + assert cpuload.call_count == 1 + assert tuple(cpuload.call_args) == ((), {"interval": 3}) + + def test_str_representation_is_formatted_value(self, sensor, cpuload): + assert str(sensor) == "50.1%" diff --git a/Ch04/apd.sensors-chapter04-configparser-local/tests/test_dht.py b/Ch04/apd.sensors-chapter04-configparser-local/tests/test_dht.py new file mode 100644 index 0000000..153c1db --- /dev/null +++ b/Ch04/apd.sensors-chapter04-configparser-local/tests/test_dht.py @@ -0,0 +1,66 @@ +import pytest + +from apd.sensors.sensors import Temperature, RelativeHumidity + + +@pytest.fixture +def temperature_sensor(): + return Temperature() + + +@pytest.fixture +def humidity_sensor(): + return RelativeHumidity() + + +class TestTemperatureFormatter: + @pytest.fixture + def subject(self, temperature_sensor): + return temperature_sensor.format + + def test_format_21c(self, subject): + assert subject(21.0) == "21.0C (69.8F)" + + def test_format_negative(self, subject): + assert subject(-32.0) == "-32.0C (-25.6F)" + + def test_format_unknown(self, subject): + assert subject(None) == "Unknown" + + +class TestTemperatureConversion: + @pytest.fixture + def subject(self, temperature_sensor): + return temperature_sensor.celsius_to_fahrenheit + + def test_celsius_to_fahrenheit(self, subject): + c = 21 + f = subject(c) + assert f == 69.8 + + def test_celsius_to_fahrenheit_equivlance_point(self, subject): + c = -40 + f = subject(c) + assert f == -40 + + def test_celsius_to_fahrenheit_float(self, subject): + c = 21.2 + f = subject(c) + assert f == 70.16 + + def test_celsius_to_fahrenheit_string(self, subject): + c = "21" + with pytest.raises(TypeError): + subject(c) + + +class TestHumidityFormatter: + @pytest.fixture + def subject(self, humidity_sensor): + return humidity_sensor.format + + def test_format_percentage(self, subject): + assert subject(0.035) == "3.5%" + + def test_format_unknown(self, subject): + assert subject(None) == "Unknown" diff --git a/Ch04/apd.sensors-chapter04-configparser-local/tests/test_ipaddresses.py b/Ch04/apd.sensors-chapter04-configparser-local/tests/test_ipaddresses.py new file mode 100644 index 0000000..bd61d85 --- /dev/null +++ b/Ch04/apd.sensors-chapter04-configparser-local/tests/test_ipaddresses.py @@ -0,0 +1,59 @@ +import socket +from unittest import mock + +import pytest + +from apd.sensors.sensors import IPAddresses + + +@pytest.fixture +def sensor(): + return IPAddresses() + + +class TestIPAddressFormatter: + @pytest.fixture + def subject(self, sensor): + return sensor.format + + def test_format_single_ipv4(self, subject): + ips = [("AF_INET", "192.0.2.1")] + assert subject(ips) == "192.0.2.1 (IPv4)" + + def test_format_single_ipv6(self, subject): + ips = [("AF_INET6", "2001:DB8::1")] + assert subject(ips) == "2001:DB8::1 (IPv6)" + + def test_format_mixed_list(self, subject): + ips = [("AF_INET", "192.0.2.1"), ("AF_INET6", "2001:DB8::1")] + assert subject(ips) == "192.0.2.1 (IPv4)\n2001:DB8::1 (IPv6)" + + def test_unusual_protocols_are_marked_as_unknown(self, subject): + ips = [("AF_IRDA", "ffff")] + assert subject(ips) == "ffff (Unknown)" + + +class TestIPAddressesValue: + @pytest.fixture + def subject(self, sensor): + return sensor.value + + @pytest.fixture + def gethostname(self): + with mock.patch("socket.gethostname") as gethostname: + gethostname.return_value = "localhost" + yield gethostname + + @pytest.fixture + def getaddrinfo(self, gethostname): + with mock.patch("socket.getaddrinfo") as getaddrinfo: + yield getaddrinfo + + def test_getaddrinfo_used_for_value_collection(self, getaddrinfo, subject): + getaddrinfo.return_value = [(socket.AF_INET, 0, 0, "", ("192.0.2.1", 0))] + assert subject() == [("AF_INET", "192.0.2.1")] + assert getaddrinfo.call_count == 1 + + def test_str_representation_is_formatted_value(self, getaddrinfo, sensor): + getaddrinfo.return_value = [(socket.AF_INET6, 0, 0, "", ("2001:DB8::1", 0))] + assert str(sensor) == "2001:DB8::1 (IPv6)" diff --git a/Ch04/apd.sensors-chapter04-configparser-local/tests/test_pythonversion.py b/Ch04/apd.sensors-chapter04-configparser-local/tests/test_pythonversion.py new file mode 100644 index 0000000..43ad806 --- /dev/null +++ b/Ch04/apd.sensors-chapter04-configparser-local/tests/test_pythonversion.py @@ -0,0 +1,61 @@ +from collections import namedtuple +from unittest import mock + +import pytest + +from apd.sensors.sensors import PythonVersion + + +@pytest.fixture +def version(): + return namedtuple( + "sys_versioninfo", ("major", "minor", "micro", "releaselevel", "serial") + ) + + +@pytest.fixture +def sensor(): + return PythonVersion() + + +class TestPythonVersionFormatter: + @pytest.fixture + def subject(self, sensor): + return sensor.format + + def test_format_py38(self, subject, version): + py38 = version(3, 8, 0, "final", 0) + assert subject(py38) == "3.8" + + def test_format_large_version(self, subject, version): + large = version(255, 128, 0, "final", 0) + assert subject(large) == "255.128" + + def test_alpha_of_minor_is_marked(self, subject, version): + py39 = version(3, 9, 0, "alpha", 1) + assert subject(py39) == "3.9.0a1" + + def test_alpha_of_micro_is_unmarked(self, subject, version): + py39 = version(3, 9, 1, "alpha", 1) + assert subject(py39) == "3.9" + + +class TestPythonVersionValue: + @pytest.fixture + def subject(self, sensor): + return sensor.value + + @pytest.fixture + def python_version(self): + import sys + + return sys.version_info + + def test_data_value_is_sys_versioninfo(self, python_version, subject): + assert subject() == python_version + + +class TestPythonVersionSensor: + def test_str_representation_is_formatted_value(self, sensor, version): + with mock.patch("sys.version_info", new=version(3, 4, 1, "final", 1)): + assert str(sensor) == "3.4" diff --git a/Ch04/apd.sensors-chapter04-configparser-local/tests/test_ramusage.py b/Ch04/apd.sensors-chapter04-configparser-local/tests/test_ramusage.py new file mode 100644 index 0000000..197ad06 --- /dev/null +++ b/Ch04/apd.sensors-chapter04-configparser-local/tests/test_ramusage.py @@ -0,0 +1,47 @@ +from unittest import mock + +import pytest + +from apd.sensors.sensors import RAMAvailable + + +@pytest.fixture +def sensor(): + return RAMAvailable() + + +class TestRAMAvailableFormatter: + @pytest.fixture + def subject(self, sensor): + return sensor.format + + def test_format_bytes(self, subject): + assert subject(15) == "15.0 B" + + def test_format_kibibytes(self, subject): + assert subject(15000) == "14.6 KiB" + + def test_format_mibibytes(self, subject): + assert subject(15000000) == "14.3 MiB" + + def test_format_gibibytes(self, subject): + assert subject(15000000000) == "14.0 GiB" + + +class TestRAMAvailableValue: + @pytest.fixture + def subject(self, sensor): + return sensor.value + + @pytest.fixture + def virtual_memory(self): + with mock.patch("psutil.virtual_memory") as virtual_memory: + virtual_memory.return_value.available = 1024 + yield virtual_memory + + def test_virtual_memory_called(self, subject, virtual_memory): + assert subject() == 1024 + assert virtual_memory.call_count == 1 + + def test_str_representation_is_formatted_value(self, sensor, virtual_memory): + assert str(sensor) == "1.0 KiB" diff --git a/Ch04/apd.sensors-chapter04-configparser-local/tests/test_sensors.py b/Ch04/apd.sensors-chapter04-configparser-local/tests/test_sensors.py new file mode 100644 index 0000000..720c457 --- /dev/null +++ b/Ch04/apd.sensors-chapter04-configparser-local/tests/test_sensors.py @@ -0,0 +1,113 @@ +import os +from unittest import mock + +from click.testing import CliRunner + +import pytest + +import apd.sensors.cli +import apd.sensors.sensors + + +class MockTestingSensor(apd.sensors.sensors.Sensor[int]): + title = "Configured Number" + + def __init__(self, configured="0"): + self.configured = configured + + def value(self) -> int: + return int(self.configured) + + @classmethod + def format(cls, value: int) -> str: + return "{}".format(value) + + +def test_sensors(): + assert hasattr(apd.sensors.sensors, "PythonVersion") + + +@pytest.fixture +def default_config_path(): + return os.path.join(os.path.dirname(__file__), "default_test_config.cfg") + + +@pytest.mark.functional +def test_python_version_is_first_two_lines_of_cli_output(default_config_path): + runner = CliRunner() + result = runner.invoke( + apd.sensors.cli.show_sensors, ["--config", default_config_path] + ) + python_version = str(apd.sensors.sensors.PythonVersion()) + assert ["Python Version", python_version] == result.stdout.split("\n")[:2] + + +def test_parameter_for_sensor(): + with mock.patch("apd.sensors.cli.parse_config_file") as parse_config_file: + parse_config_file.return_value = { + "TestingSensor": { + "plugin": "tests.test_sensors:MockTestingSensor", + "configured": "42", + } + } + sensors = apd.sensors.cli.get_sensors("") + assert isinstance(sensors[0], MockTestingSensor) + assert sensors[0].value() == 42 + + +def test_spurious_parameters_raise_errors(): + with mock.patch("apd.sensors.cli.parse_config_file") as parse_config_file: + parse_config_file.return_value = { + "TestingSensor": { + "plugin": "apd.sensors.sensors:PythonVersion", + "magic": "42", + } + } + with pytest.raises(RuntimeError, match="unexpected keyword argument magic"): + apd.sensors.cli.get_sensors("") + + +def test_extract_plugins(default_config_path): + parsed = apd.sensors.cli.parse_config_file(default_config_path) + assert parsed == { + "IPAddress": {"plugin": "apd.sensors.sensors:IPAddresses"}, + "PythonVersion": {"plugin": "apd.sensors.sensors:PythonVersion"}, + } + + +class TestSensorFromPath: + @pytest.fixture + def subject(self): + return apd.sensors.cli.get_sensor_by_path + + def test_get_sensor_by_path(self, subject): + assert isinstance( + subject("apd.sensors.sensors:PythonVersion"), + apd.sensors.sensors.PythonVersion, + ) + + def test_invalid_format(self, subject): + with pytest.raises(RuntimeError, match="dotted.path.to.module:ClassName"): + subject("apd.sensors.sensors.PythonVersion") + + def test_invalid_module(self, subject): + with pytest.raises(RuntimeError, match="Could not import module"): + subject("apd.nonsense.sensor:FakeSensor") + + def test_missing_sensor(self, subject): + with pytest.raises(RuntimeError, match="Could not find attribute"): + subject("apd.sensors.sensors:FakeSensor") + + def test_invalid_sensor(self, subject): + with pytest.raises(RuntimeError, match="is not recognised as a Sensor"): + subject("apd.sensors.sensors:Sensor") + + +@pytest.mark.functional +def test_develop_option_finds_sensor_by_path(): + runner = CliRunner() + result = runner.invoke( + apd.sensors.cli.show_sensors, ["--develop", "apd.sensors.sensors:PythonVersion"] + ) + python_version = str(apd.sensors.sensors.PythonVersion()) + assert ["Python Version", python_version, "", ""] == result.stdout.split("\n") diff --git a/Ch04/apd.sensors-chapter04-configparser/.pre-commit-config.yaml b/Ch04/apd.sensors-chapter04-configparser/.pre-commit-config.yaml new file mode 100644 index 0000000..3ff6d15 --- /dev/null +++ b/Ch04/apd.sensors-chapter04-configparser/.pre-commit-config.yaml @@ -0,0 +1,23 @@ +repos: + +- repo: local + hooks: + - id: black + name: black + entry: pipenv run black + args: [--quiet] + language: system + types: [python] + + - id: mypy + name: mypy + entry: pipenv run mypy + args: ["--follow-imports=skip"] + language: system + types: [python] + + - id: flake8 + name: flake8 + entry: pipenv run flake8 + language: system + types: [python] diff --git a/Ch04/apd.sensors-chapter04-configparser/CHANGES.md b/Ch04/apd.sensors-chapter04-configparser/CHANGES.md new file mode 100644 index 0000000..569aeea --- /dev/null +++ b/Ch04/apd.sensors-chapter04-configparser/CHANGES.md @@ -0,0 +1,5 @@ +## Changes + +### 1.0.0 (2019-06-20) + +* Added initial sensors (Matthew Wilkes) diff --git a/Ch04/apd.sensors-chapter04-configparser/LICENCE b/Ch04/apd.sensors-chapter04-configparser/LICENCE new file mode 100644 index 0000000..ccaef50 --- /dev/null +++ b/Ch04/apd.sensors-chapter04-configparser/LICENCE @@ -0,0 +1,21 @@ +``` +Copyright (c) 2019 Matthew Wilkes + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. +``` \ No newline at end of file diff --git a/Ch04/apd.sensors-chapter04-configparser/Pipfile b/Ch04/apd.sensors-chapter04-configparser/Pipfile new file mode 100644 index 0000000..40413b8 --- /dev/null +++ b/Ch04/apd.sensors-chapter04-configparser/Pipfile @@ -0,0 +1,29 @@ +[[source]] +name = "pypi" +url = "https://pypi.org/simple" +verify_ssl = true + +[[source]] +name = "piwheels" +url = "https://piwheels.org/simple" +verify_ssl = true + +[dev-packages] +ipykernel = "*" +remote-ikernel = "*" +pytest = "*" +pytest-cov = "*" +mypy = "*" +flake8 = "*" +black = "*" +pre-commit = "*" +wheel = "*" +twine = "*" + +[packages] +apd-sensors = {editable = true,path = "."} + +[requires] + +[pipenv] +allow_prereleases = true diff --git 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new file mode 100644 index 0000000..e6dcb45 --- /dev/null +++ b/Ch04/apd.sensors-chapter04-configparser/README.md @@ -0,0 +1,32 @@ +# Advanced Python Development Sensors + +This is the data collection package that forms part of the running example +for the book [Advanced Python Development](https://advancedpython.dev). + +## Usage + +This installs a console script called `sensors` that returns a report on +various aspects of the system. The available sensors are: + +* Python version +* IP Addresses +* CPU Usage +* RAM Available +* Battery charging state +* Ambient Temperature +* Ambient Humidity + +There are no command-line options, to view the report run `sensors` on the +command line. + +## Caveats + +The Ambient Temperature and Ambient Humidity sensors are only available on +RaspberryPi hosts and assume that a DHT22 sensor is connected to pin `D20`. + +If there is an entry in `/etc/hosts` for the current machine's hostname that +value will be the only result from the IP Addresses sensor. + +## Installation + +Install with `pip3 install apd.sensors` under Python 3.7 or higher diff --git a/Ch04/apd.sensors-chapter04-configparser/pytest.ini b/Ch04/apd.sensors-chapter04-configparser/pytest.ini new file mode 100644 index 0000000..861f3d0 --- /dev/null +++ b/Ch04/apd.sensors-chapter04-configparser/pytest.ini @@ -0,0 +1,3 @@ +[pytest] +markers = + functional: these tests are significantly slower as they run the whole CLI script \ No newline at end of file diff --git a/Ch04/apd.sensors-chapter04-configparser/setup.cfg b/Ch04/apd.sensors-chapter04-configparser/setup.cfg new file mode 100644 index 0000000..908c774 --- /dev/null +++ b/Ch04/apd.sensors-chapter04-configparser/setup.cfg @@ -0,0 +1,59 @@ +[mypy] +namespace_packages = True +mypy_path = src + +[mypy-psutil] +ignore_missing_imports = True + +[mypy-adafruit_dht] +ignore_missing_imports = True + +[mypy-board] +ignore_missing_imports = True + +[mypy-pytest] +ignore_missing_imports = True + +[flake8] +max-line-length = 88 + +[metadata] +name = apd.sensors +version = attr: apd.sensors.VERSION +description = APD Sensor package +long_description = file: README.md, CHANGES.md, LICENCE +long_description_content_type = text/markdown +keywords = iot +license = MIT +classifiers = + Programming Language :: Python :: 3 + Programming Language :: Python :: 3.7 + +[options] +zip_safe = False +include_package_data = True +package-dir = + =src +packages = find_namespace: +install_requires = + psutil + click + adafruit-circuitpython-dht ; 'arm' in platform_machine + +[options.package_data] +apd.sensors = py.typed + +[options.packages.find] +where = src + +[options.entry_points] +console_scripts = + sensors = apd.sensors.cli:show_sensors +apd_sensors = + PythonVersion = apd.sensors.sensors:PythonVersion + IPAddresses = apd.sensors.sensors:IPAddresses + CPULoad = apd.sensors.sensors:CPULoad + RAMAvailable = apd.sensors.sensors:RAMAvailable + ACStatus = apd.sensors.sensors:ACStatus + Temperature = apd.sensors.sensors:Temperature + RelativeHumidity = apd.sensors.sensors:RelativeHumidity \ No newline at end of file diff --git a/Ch04/apd.sensors-chapter04-configparser/setup.py b/Ch04/apd.sensors-chapter04-configparser/setup.py new file mode 100644 index 0000000..6068493 --- /dev/null +++ b/Ch04/apd.sensors-chapter04-configparser/setup.py @@ -0,0 +1,3 @@ +from setuptools import setup + +setup() diff --git a/Ch04/apd.sensors-chapter04-configparser/src/apd/sensors/__init__.py b/Ch04/apd.sensors-chapter04-configparser/src/apd/sensors/__init__.py new file mode 100644 index 0000000..ec63ed1 --- /dev/null +++ b/Ch04/apd.sensors-chapter04-configparser/src/apd/sensors/__init__.py @@ -0,0 +1 @@ +VERSION = "1.1.0dev1" diff --git a/Ch04/apd.sensors-chapter04-configparser/src/apd/sensors/cli.py b/Ch04/apd.sensors-chapter04-configparser/src/apd/sensors/cli.py new file mode 100644 index 0000000..c49d2f2 --- /dev/null +++ b/Ch04/apd.sensors-chapter04-configparser/src/apd/sensors/cli.py @@ -0,0 +1,119 @@ +import configparser +import enum +import importlib +import sys +import typing as t + +import click + +from .sensors import Sensor + + +class ReturnCodes(enum.IntEnum): + OK = 0 + BAD_SENSOR_PATH = 17 + BAD_CONFIG = 18 + + +def parse_config_file( + path: t.Union[str, t.Iterable[str]] +) -> t.Dict[str, t.Dict[str, str]]: + parser = configparser.ConfigParser() + parser.read(path, encoding="utf-8") + try: + plugin_names = [ + name for name in parser.get("config", "plugins").split() if name + ] + except configparser.NoSectionError: + raise RuntimeError(f"Could not find [config] section in file") + except configparser.NoOptionError: + raise RuntimeError(f"Could not find plugins line in [config] section") + plugin_data = {} + for plugin_name in plugin_names: + try: + plugin_data[plugin_name] = dict(parser.items(plugin_name)) + except configparser.NoSectionError: + raise RuntimeError(f"Could not find [{plugin_name}] section in file") + return plugin_data + + +def get_sensor_by_path(sensor_path: str, **kwargs) -> Sensor[t.Any]: + try: + module_name, sensor_name = sensor_path.split(":") + except ValueError: + raise RuntimeError( + "Sensor path must be in the format dotted.path.to.module:ClassName" + ) + try: + module = importlib.import_module(module_name) + except ImportError: + raise RuntimeError(f"Could not import module {module_name}") + try: + sensor_class = getattr(module, sensor_name) + except AttributeError: + raise RuntimeError(f"Could not find attribute {sensor_name} in {module_name}") + if ( + isinstance(sensor_class, type) + and issubclass(sensor_class, Sensor) + and sensor_class != Sensor + ): + try: + return sensor_class(**kwargs) + except TypeError as error: + message = str(error) + if "got an unexpected" in message: + raise RuntimeError(f"Sensor {sensor_name} " + message.split(" ", 1)[1]) + raise + else: + raise RuntimeError( + f"Detected object {sensor_class!r} is not recognised as a Sensor type" + ) + + +def get_sensors(path: str) -> t.Iterable[Sensor[t.Any]]: + sensors = [] + for plugin_name, sensor_data in parse_config_file(path).items(): + try: + class_path = sensor_data.pop("plugin") + except TypeError: + raise RuntimeError( + f"Could not find plugin= line in [{plugin_name}] section" + ) + sensors.append(get_sensor_by_path(class_path, **sensor_data)) + return sensors + + +@click.command(help="Displays the values of the sensors") +@click.option( + "--develop", required=False, metavar="path", help="Load a sensor by Python path" +) +@click.option( + "--config", + required=False, + default="config.cfg", + metavar="config_path", + help="Load the specified configuration file", +) +def show_sensors(develop: str, config: str) -> None: + sensors: t.Iterable[Sensor[t.Any]] + if develop: + try: + sensors = [get_sensor_by_path(develop)] + except RuntimeError as error: + click.secho(str(error), fg="red", bold=True) + sys.exit(ReturnCodes.BAD_SENSOR_PATH) + else: + try: + sensors = get_sensors(config) + except RuntimeError as error: + click.secho(str(error), fg="red", bold=True) + sys.exit(ReturnCodes.BAD_CONFIG) + for sensor in sensors: + click.secho(sensor.title, bold=True) + click.echo(str(sensor)) + click.echo("") + sys.exit(ReturnCodes.OK) + + +if __name__ == "__main__": + show_sensors() diff --git a/Ch04/apd.sensors-chapter04-configparser/src/apd/sensors/py.typed b/Ch04/apd.sensors-chapter04-configparser/src/apd/sensors/py.typed new file mode 100644 index 0000000..e69de29 diff --git a/Ch04/apd.sensors-chapter04-configparser/src/apd/sensors/sensors.py b/Ch04/apd.sensors-chapter04-configparser/src/apd/sensors/sensors.py new file mode 100644 index 0000000..27f88a4 --- /dev/null +++ b/Ch04/apd.sensors-chapter04-configparser/src/apd/sensors/sensors.py @@ -0,0 +1,191 @@ +#!/usr/bin/env python +# coding: utf-8 +import math +import socket +import sys +from typing import Any, Optional, List, Tuple, Iterable, TypeVar, Generic + + +import psutil + + +T_value = TypeVar("T_value") + + +class Sensor(Generic[T_value]): + title: str + + def __init__(self, **kwargs): + if kwargs: + raise TypeError( + "Sensor got an unexpected keyword argument {}".format( + ", ".join(kwargs.keys()) + ) + ) + return + + def value(self) -> T_value: + raise NotImplementedError + + @classmethod + def format(cls, value: T_value) -> str: + raise NotImplementedError + + def __str__(self) -> str: + return self.format(self.value()) + + +class PythonVersion(Sensor[Any]): + title = "Python Version" + + def value(self) -> Any: + return sys.version_info + + @classmethod + def format(cls, value: Any) -> str: + if value.micro == 0 and value.releaselevel == "alpha": + return "{0.major}.{0.minor}.{0.micro}a{0.serial}".format(value) + return "{0.major}.{0.minor}".format(value) + + +class IPAddresses(Sensor[Iterable[Tuple[str, str]]]): + title = "IP Addresses" + FAMILIES = {"AF_INET": "IPv4", "AF_INET6": "IPv6"} + + def value(self) -> List[Tuple[str, str]]: + hostname = socket.gethostname() + addresses = socket.getaddrinfo(hostname, None) + + address_info: List[Tuple[str, str]] = [] + for address in addresses: + family, ip = (address[0].name, address[4][0]) + if family not in self.FAMILIES: + continue + value = (family, ip) + if value not in address_info: + address_info.append(value) + return address_info + + @classmethod + def format(cls, value: Iterable[Tuple[str, str]]) -> str: + return "\n".join( + "{0} ({1})".format(address[1], cls.FAMILIES.get(address[0], "Unknown")) + for address in value + ) + + +class CPULoad(Sensor[float]): + title = "CPU Usage" + + def value(self) -> float: + return psutil.cpu_percent(interval=3) / 100.0 + + @classmethod + def format(cls, value: float) -> str: + return "{:.1%}".format(value) + + +class RAMAvailable(Sensor[int]): + title = "RAM Available" + UNITS = ("B", "KiB", "MiB", "GiB", "TiB", "PiB", "EiB") + UNIT_SIZE = 2 ** 10 + + def value(self) -> int: + return psutil.virtual_memory().available + + @classmethod + def format(cls, value: int) -> str: + magnitude = math.floor(math.log(value, cls.UNIT_SIZE)) + max_magnitude = len(cls.UNITS) - 1 + magnitude = min(magnitude, max_magnitude) + scaled_value = value / (cls.UNIT_SIZE ** magnitude) + return "{:.1f} {}".format(scaled_value, cls.UNITS[magnitude]) + + +class ACStatus(Sensor[Optional[bool]]): + title = "AC Connected" + + def value(self) -> Optional[bool]: + battery = psutil.sensors_battery() + if battery is not None: + return battery.power_plugged + else: + return None + + @classmethod + def format(cls, value: Optional[bool]) -> str: + if value is None: + return "Unknown" + elif value: + return "Connected" + else: + return "Not connected" + + +class Temperature(Sensor[Optional[float]]): + title = "Ambient Temperature" + + def __init__(self, board="DHT22", pin="D4"): + self.board = board + self.pin = pin + + def value(self) -> Optional[float]: + try: + import adafruit_dht + import board + except (ImportError, NotImplementedError): + # No DHT library results in an ImportError. + # Running on an unknown platform results in a + # NotImplementedError when getting the pin + return None + try: + sensor_type = getattr(adafruit_dht, self.board) + pin = getattr(board, self.pin) + return sensor_type(pin).temperature + except RuntimeError: + return None + + @staticmethod + def celsius_to_fahrenheit(value: float) -> float: + return value * 9 / 5 + 32 + + @classmethod + def format(cls, value: Optional[float]) -> str: + if value is None: + return "Unknown" + else: + return "{:.1f}C ({:.1f}F)".format(value, cls.celsius_to_fahrenheit(value)) + + def __str__(self) -> str: + return self.format(self.value()) + + +class RelativeHumidity(Sensor[Optional[float]]): + title = "Relative Humidity" + + def __init__(self, board="DHT22", pin="D4"): + self.board = board + self.pin = pin + + def value(self) -> Optional[float]: + try: + import adafruit_dht + import board + except (ImportError, NotImplementedError): + # No DHT library results in an ImportError. + # Running on an unknown platform results in a + # NotImplementedError when getting the pin + return None + try: + sensor_type = getattr(adafruit_dht, self.board) + pin = getattr(board, self.pin) + return sensor_type(pin).humidity / 100 + except RuntimeError: + return None + + @classmethod + def format(cls, value: Optional[float]) -> str: + if value is None: + return "Unknown" + else: + return "{:.1%}".format(value) diff --git a/Ch04/apd.sensors-chapter04-configparser/tests/test_acstatus.py b/Ch04/apd.sensors-chapter04-configparser/tests/test_acstatus.py new file mode 100644 index 0000000..d2fe71e --- /dev/null +++ b/Ch04/apd.sensors-chapter04-configparser/tests/test_acstatus.py @@ -0,0 +1,55 @@ +from unittest import mock + +import pytest + +from apd.sensors.sensors import ACStatus + + +@pytest.fixture +def sensor(): + return ACStatus() + + +class TestACStatusFormatter: + @pytest.fixture + def subject(self, sensor): + return sensor.format + + def test_format_True(self, subject): + assert subject(True) == "Connected" + + def test_format_False(self, subject): + assert subject(False) == "Not connected" + + def test_format_None(self, subject): + assert subject(None) == "Unknown" + + +class TestACStatusValue: + @pytest.fixture + def subject(self, sensor): + return sensor.value + + @pytest.fixture + def sensors_battery(self): + with mock.patch("psutil.sensors_battery") as sensors_battery: + yield sensors_battery + + def test_sensors_battery_called(self, subject, sensors_battery): + sensors_battery.return_value.power_plugged = True + assert subject() is True + assert sensors_battery.call_count == 1 + + def test_sensor_but_battery_unknown(self, subject, sensors_battery): + sensors_battery.return_value.power_plugged = None + assert subject() is None + assert sensors_battery.call_count == 1 + + def test_no_sensor(self, subject, sensors_battery): + sensors_battery.return_value = None + assert subject() is None + + def test_str_representation_is_formatted_value(self, sensor, sensors_battery): + sensors_battery.return_value.power_plugged = True + assert str(sensor) == "Connected" + assert sensors_battery.call_count == 1 diff --git a/Ch04/apd.sensors-chapter04-configparser/tests/test_cpuusage.py b/Ch04/apd.sensors-chapter04-configparser/tests/test_cpuusage.py new file mode 100644 index 0000000..445f4e5 --- /dev/null +++ b/Ch04/apd.sensors-chapter04-configparser/tests/test_cpuusage.py @@ -0,0 +1,45 @@ +from unittest import mock + +import pytest + +from apd.sensors.sensors import CPULoad + + +@pytest.fixture +def sensor(): + return CPULoad() + + +class TestCPULoadFormatter: + @pytest.fixture + def subject(self, sensor): + return sensor.format + + def test_format_simple_percentage(self, subject): + assert subject(0.05) == "5.0%" + + def test_format_multiple_decimal_places(self, subject): + assert subject(0.031415926) == "3.1%" + + def test_format_multiple_decimal_places_int(self, subject) -> None: + assert subject(1) == "100.0%" + + +class TestCPULoadValue: + @pytest.fixture + def subject(self, sensor): + return sensor.value + + @pytest.fixture + def cpuload(self): + with mock.patch("psutil.cpu_percent") as cpu_percent: + cpu_percent.return_value = 50.1 + yield cpu_percent + + def test_cpu_percent_called_With_interval(self, subject, cpuload): + assert subject() == 0.501 + assert cpuload.call_count == 1 + assert tuple(cpuload.call_args) == ((), {"interval": 3}) + + def test_str_representation_is_formatted_value(self, sensor, cpuload): + assert str(sensor) == "50.1%" diff --git a/Ch04/apd.sensors-chapter04-configparser/tests/test_dht.py b/Ch04/apd.sensors-chapter04-configparser/tests/test_dht.py new file mode 100644 index 0000000..153c1db --- /dev/null +++ b/Ch04/apd.sensors-chapter04-configparser/tests/test_dht.py @@ -0,0 +1,66 @@ +import pytest + +from apd.sensors.sensors import Temperature, RelativeHumidity + + +@pytest.fixture +def temperature_sensor(): + return Temperature() + + +@pytest.fixture +def humidity_sensor(): + return RelativeHumidity() + + +class TestTemperatureFormatter: + @pytest.fixture + def subject(self, temperature_sensor): + return temperature_sensor.format + + def test_format_21c(self, subject): + assert subject(21.0) == "21.0C (69.8F)" + + def test_format_negative(self, subject): + assert subject(-32.0) == "-32.0C (-25.6F)" + + def test_format_unknown(self, subject): + assert subject(None) == "Unknown" + + +class TestTemperatureConversion: + @pytest.fixture + def subject(self, temperature_sensor): + return temperature_sensor.celsius_to_fahrenheit + + def test_celsius_to_fahrenheit(self, subject): + c = 21 + f = subject(c) + assert f == 69.8 + + def test_celsius_to_fahrenheit_equivlance_point(self, subject): + c = -40 + f = subject(c) + assert f == -40 + + def test_celsius_to_fahrenheit_float(self, subject): + c = 21.2 + f = subject(c) + assert f == 70.16 + + def test_celsius_to_fahrenheit_string(self, subject): + c = "21" + with pytest.raises(TypeError): + subject(c) + + +class TestHumidityFormatter: + @pytest.fixture + def subject(self, humidity_sensor): + return humidity_sensor.format + + def test_format_percentage(self, subject): + assert subject(0.035) == "3.5%" + + def test_format_unknown(self, subject): + assert subject(None) == "Unknown" diff --git a/Ch04/apd.sensors-chapter04-configparser/tests/test_ipaddresses.py b/Ch04/apd.sensors-chapter04-configparser/tests/test_ipaddresses.py new file mode 100644 index 0000000..bd61d85 --- /dev/null +++ b/Ch04/apd.sensors-chapter04-configparser/tests/test_ipaddresses.py @@ -0,0 +1,59 @@ +import socket +from unittest import mock + +import pytest + +from apd.sensors.sensors import IPAddresses + + +@pytest.fixture +def sensor(): + return IPAddresses() + + +class TestIPAddressFormatter: + @pytest.fixture + def subject(self, sensor): + return sensor.format + + def test_format_single_ipv4(self, subject): + ips = [("AF_INET", "192.0.2.1")] + assert subject(ips) == "192.0.2.1 (IPv4)" + + def test_format_single_ipv6(self, subject): + ips = [("AF_INET6", "2001:DB8::1")] + assert subject(ips) == "2001:DB8::1 (IPv6)" + + def test_format_mixed_list(self, subject): + ips = [("AF_INET", "192.0.2.1"), ("AF_INET6", "2001:DB8::1")] + assert subject(ips) == "192.0.2.1 (IPv4)\n2001:DB8::1 (IPv6)" + + def test_unusual_protocols_are_marked_as_unknown(self, subject): + ips = [("AF_IRDA", "ffff")] + assert subject(ips) == "ffff (Unknown)" + + +class TestIPAddressesValue: + @pytest.fixture + def subject(self, sensor): + return sensor.value + + @pytest.fixture + def gethostname(self): + with mock.patch("socket.gethostname") as gethostname: + gethostname.return_value = "localhost" + yield gethostname + + @pytest.fixture + def getaddrinfo(self, gethostname): + with mock.patch("socket.getaddrinfo") as getaddrinfo: + yield getaddrinfo + + def test_getaddrinfo_used_for_value_collection(self, getaddrinfo, subject): + getaddrinfo.return_value = [(socket.AF_INET, 0, 0, "", ("192.0.2.1", 0))] + assert subject() == [("AF_INET", "192.0.2.1")] + assert getaddrinfo.call_count == 1 + + def test_str_representation_is_formatted_value(self, getaddrinfo, sensor): + getaddrinfo.return_value = [(socket.AF_INET6, 0, 0, "", ("2001:DB8::1", 0))] + assert str(sensor) == "2001:DB8::1 (IPv6)" diff --git a/Ch04/apd.sensors-chapter04-configparser/tests/test_pythonversion.py b/Ch04/apd.sensors-chapter04-configparser/tests/test_pythonversion.py new file mode 100644 index 0000000..43ad806 --- /dev/null +++ b/Ch04/apd.sensors-chapter04-configparser/tests/test_pythonversion.py @@ -0,0 +1,61 @@ +from collections import namedtuple +from unittest import mock + +import pytest + +from apd.sensors.sensors import PythonVersion + + +@pytest.fixture +def version(): + return namedtuple( + "sys_versioninfo", ("major", "minor", "micro", "releaselevel", "serial") + ) + + +@pytest.fixture +def sensor(): + return PythonVersion() + + +class TestPythonVersionFormatter: + @pytest.fixture + def subject(self, sensor): + return sensor.format + + def test_format_py38(self, subject, version): + py38 = version(3, 8, 0, "final", 0) + assert subject(py38) == "3.8" + + def test_format_large_version(self, subject, version): + large = version(255, 128, 0, "final", 0) + assert subject(large) == "255.128" + + def test_alpha_of_minor_is_marked(self, subject, version): + py39 = version(3, 9, 0, "alpha", 1) + assert subject(py39) == "3.9.0a1" + + def test_alpha_of_micro_is_unmarked(self, subject, version): + py39 = version(3, 9, 1, "alpha", 1) + assert subject(py39) == "3.9" + + +class TestPythonVersionValue: + @pytest.fixture + def subject(self, sensor): + return sensor.value + + @pytest.fixture + def python_version(self): + import sys + + return sys.version_info + + def test_data_value_is_sys_versioninfo(self, python_version, subject): + assert subject() == python_version + + +class TestPythonVersionSensor: + def test_str_representation_is_formatted_value(self, sensor, version): + with mock.patch("sys.version_info", new=version(3, 4, 1, "final", 1)): + assert str(sensor) == "3.4" diff --git a/Ch04/apd.sensors-chapter04-configparser/tests/test_ramusage.py b/Ch04/apd.sensors-chapter04-configparser/tests/test_ramusage.py new file mode 100644 index 0000000..197ad06 --- /dev/null +++ b/Ch04/apd.sensors-chapter04-configparser/tests/test_ramusage.py @@ -0,0 +1,47 @@ +from unittest import mock + +import pytest + +from apd.sensors.sensors import RAMAvailable + + +@pytest.fixture +def sensor(): + return RAMAvailable() + + +class TestRAMAvailableFormatter: + @pytest.fixture + def subject(self, sensor): + return sensor.format + + def test_format_bytes(self, subject): + assert subject(15) == "15.0 B" + + def test_format_kibibytes(self, subject): + assert subject(15000) == "14.6 KiB" + + def test_format_mibibytes(self, subject): + assert subject(15000000) == "14.3 MiB" + + def test_format_gibibytes(self, subject): + assert subject(15000000000) == "14.0 GiB" + + +class TestRAMAvailableValue: + @pytest.fixture + def subject(self, sensor): + return sensor.value + + @pytest.fixture + def virtual_memory(self): + with mock.patch("psutil.virtual_memory") as virtual_memory: + virtual_memory.return_value.available = 1024 + yield virtual_memory + + def test_virtual_memory_called(self, subject, virtual_memory): + assert subject() == 1024 + assert virtual_memory.call_count == 1 + + def test_str_representation_is_formatted_value(self, sensor, virtual_memory): + assert str(sensor) == "1.0 KiB" diff --git a/Ch04/apd.sensors-chapter04-configparser/tests/test_sensors.py b/Ch04/apd.sensors-chapter04-configparser/tests/test_sensors.py new file mode 100644 index 0000000..720c457 --- /dev/null +++ b/Ch04/apd.sensors-chapter04-configparser/tests/test_sensors.py @@ -0,0 +1,113 @@ +import os +from unittest import mock + +from click.testing import CliRunner + +import pytest + +import apd.sensors.cli +import apd.sensors.sensors + + +class MockTestingSensor(apd.sensors.sensors.Sensor[int]): + title = "Configured Number" + + def __init__(self, configured="0"): + self.configured = configured + + def value(self) -> int: + return int(self.configured) + + @classmethod + def format(cls, value: int) -> str: + return "{}".format(value) + + +def test_sensors(): + assert hasattr(apd.sensors.sensors, "PythonVersion") + + +@pytest.fixture +def default_config_path(): + return os.path.join(os.path.dirname(__file__), "default_test_config.cfg") + + +@pytest.mark.functional +def test_python_version_is_first_two_lines_of_cli_output(default_config_path): + runner = CliRunner() + result = runner.invoke( + apd.sensors.cli.show_sensors, ["--config", default_config_path] + ) + python_version = str(apd.sensors.sensors.PythonVersion()) + assert ["Python Version", python_version] == result.stdout.split("\n")[:2] + + +def test_parameter_for_sensor(): + with mock.patch("apd.sensors.cli.parse_config_file") as parse_config_file: + parse_config_file.return_value = { + "TestingSensor": { + "plugin": "tests.test_sensors:MockTestingSensor", + "configured": "42", + } + } + sensors = apd.sensors.cli.get_sensors("") + assert isinstance(sensors[0], MockTestingSensor) + assert sensors[0].value() == 42 + + +def test_spurious_parameters_raise_errors(): + with mock.patch("apd.sensors.cli.parse_config_file") as parse_config_file: + parse_config_file.return_value = { + "TestingSensor": { + "plugin": "apd.sensors.sensors:PythonVersion", + "magic": "42", + } + } + with pytest.raises(RuntimeError, match="unexpected keyword argument magic"): + apd.sensors.cli.get_sensors("") + + +def test_extract_plugins(default_config_path): + parsed = apd.sensors.cli.parse_config_file(default_config_path) + assert parsed == { + "IPAddress": {"plugin": "apd.sensors.sensors:IPAddresses"}, + "PythonVersion": {"plugin": "apd.sensors.sensors:PythonVersion"}, + } + + +class TestSensorFromPath: + @pytest.fixture + def subject(self): + return apd.sensors.cli.get_sensor_by_path + + def test_get_sensor_by_path(self, subject): + assert isinstance( + subject("apd.sensors.sensors:PythonVersion"), + apd.sensors.sensors.PythonVersion, + ) + + def test_invalid_format(self, subject): + with pytest.raises(RuntimeError, match="dotted.path.to.module:ClassName"): + subject("apd.sensors.sensors.PythonVersion") + + def test_invalid_module(self, subject): + with pytest.raises(RuntimeError, match="Could not import module"): + subject("apd.nonsense.sensor:FakeSensor") + + def test_missing_sensor(self, subject): + with pytest.raises(RuntimeError, match="Could not find attribute"): + subject("apd.sensors.sensors:FakeSensor") + + def test_invalid_sensor(self, subject): + with pytest.raises(RuntimeError, match="is not recognised as a Sensor"): + subject("apd.sensors.sensors:Sensor") + + +@pytest.mark.functional +def test_develop_option_finds_sensor_by_path(): + runner = CliRunner() + result = runner.invoke( + apd.sensors.cli.show_sensors, ["--develop", "apd.sensors.sensors:PythonVersion"] + ) + python_version = str(apd.sensors.sensors.PythonVersion()) + assert ["Python Version", python_version, "", ""] == result.stdout.split("\n") diff --git a/Ch04/apd.sensors-chapter04-ex01/.pre-commit-config.yaml b/Ch04/apd.sensors-chapter04-ex01/.pre-commit-config.yaml new file mode 100644 index 0000000..3ff6d15 --- /dev/null +++ b/Ch04/apd.sensors-chapter04-ex01/.pre-commit-config.yaml @@ -0,0 +1,23 @@ +repos: + +- repo: local + hooks: + - id: black + name: black + entry: pipenv run black + args: [--quiet] + language: system + types: [python] + + - id: mypy + name: mypy + entry: pipenv run mypy + args: ["--follow-imports=skip"] + language: system + types: [python] + + - id: flake8 + name: flake8 + entry: pipenv run flake8 + language: system + types: [python] diff --git a/Ch04/apd.sensors-chapter04-ex01/CHANGES.md b/Ch04/apd.sensors-chapter04-ex01/CHANGES.md new file mode 100644 index 0000000..569aeea --- /dev/null +++ b/Ch04/apd.sensors-chapter04-ex01/CHANGES.md @@ -0,0 +1,5 @@ +## Changes + +### 1.0.0 (2019-06-20) + +* Added initial sensors (Matthew Wilkes) diff --git a/Ch04/apd.sensors-chapter04-ex01/LICENCE b/Ch04/apd.sensors-chapter04-ex01/LICENCE new file mode 100644 index 0000000..ccaef50 --- /dev/null +++ b/Ch04/apd.sensors-chapter04-ex01/LICENCE @@ -0,0 +1,21 @@ +``` +Copyright (c) 2019 Matthew Wilkes + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. +``` \ No newline at end of file diff --git a/Ch04/apd.sensors-chapter04-ex01/Pipfile b/Ch04/apd.sensors-chapter04-ex01/Pipfile new file mode 100644 index 0000000..40413b8 --- /dev/null +++ b/Ch04/apd.sensors-chapter04-ex01/Pipfile @@ -0,0 +1,29 @@ +[[source]] +name = "pypi" +url = "https://pypi.org/simple" +verify_ssl = true + +[[source]] +name = "piwheels" +url = "https://piwheels.org/simple" +verify_ssl = true + +[dev-packages] +ipykernel = "*" +remote-ikernel = "*" +pytest = "*" +pytest-cov = "*" +mypy = "*" +flake8 = "*" +black = "*" +pre-commit = "*" +wheel = "*" +twine = "*" + +[packages] +apd-sensors = {editable = true,path = "."} + +[requires] + +[pipenv] +allow_prereleases = true diff --git 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--- /dev/null +++ b/Ch04/apd.sensors-chapter04-ex01/README.md @@ -0,0 +1,32 @@ +# Advanced Python Development Sensors + +This is the data collection package that forms part of the running example +for the book [Advanced Python Development](https://advancedpython.dev). + +## Usage + +This installs a console script called `sensors` that returns a report on +various aspects of the system. The available sensors are: + +* Python version +* IP Addresses +* CPU Usage +* RAM Available +* Battery charging state +* Ambient Temperature +* Ambient Humidity + +There are no command-line options, to view the report run `sensors` on the +command line. + +## Caveats + +The Ambient Temperature and Ambient Humidity sensors are only available on +RaspberryPi hosts and assume that a DHT22 sensor is connected to pin `D20`. + +If there is an entry in `/etc/hosts` for the current machine's hostname that +value will be the only result from the IP Addresses sensor. + +## Installation + +Install with `pip3 install apd.sensors` under Python 3.7 or higher diff --git a/Ch04/apd.sensors-chapter04-ex01/pyproject.toml b/Ch04/apd.sensors-chapter04-ex01/pyproject.toml new file mode 100644 index 0000000..4d3bb67 --- /dev/null +++ b/Ch04/apd.sensors-chapter04-ex01/pyproject.toml @@ -0,0 +1,5 @@ +[build-system] +requires = [ + "setuptools", +] +build-backend = "setuptools.build_meta" diff --git a/Ch04/apd.sensors-chapter04-ex01/pytest.ini b/Ch04/apd.sensors-chapter04-ex01/pytest.ini new file mode 100644 index 0000000..861f3d0 --- /dev/null +++ b/Ch04/apd.sensors-chapter04-ex01/pytest.ini @@ -0,0 +1,3 @@ +[pytest] +markers = + functional: these tests are significantly slower as they run the whole CLI script \ No newline at end of file diff --git a/Ch04/apd.sensors-chapter04-ex01/setup.cfg b/Ch04/apd.sensors-chapter04-ex01/setup.cfg new file mode 100644 index 0000000..db6d144 --- /dev/null +++ b/Ch04/apd.sensors-chapter04-ex01/setup.cfg @@ -0,0 +1,48 @@ +[mypy] +namespace_packages = True +mypy_path = src + +[mypy-psutil] +ignore_missing_imports = True + +[mypy-adafruit_dht] +ignore_missing_imports = True + +[mypy-board] +ignore_missing_imports = True + +[mypy-pytest] +ignore_missing_imports = True + +[flake8] +max-line-length = 88 + +[metadata] +name = apd.sensors +version = attr: apd.sensors.VERSION +description = APD Sensor package +long_description = file: README.md, CHANGES.md, LICENCE +long_description_content_type = text/markdown +keywords = iot +license = MIT +classifiers = + Programming Language :: Python :: 3 + Programming Language :: Python :: 3.7 + +[options] +zip_safe = False +include_package_data = True +package-dir = + =src +packages = find: +install_requires = + psutil + click + adafruit-circuitpython-dht ; 'arm' in platform_machine + +[options.packages.find] +where = src + +[options.entry_points] +console_scripts = + sensors = apd.sensors.cli:show_sensors diff --git a/Ch04/apd.sensors-chapter04-ex01/setup.py b/Ch04/apd.sensors-chapter04-ex01/setup.py new file mode 100644 index 0000000..6068493 --- /dev/null +++ b/Ch04/apd.sensors-chapter04-ex01/setup.py @@ -0,0 +1,3 @@ +from setuptools import setup + +setup() diff --git a/Ch04/apd.sensors-chapter04-ex01/src/apd/sensors/__init__.py b/Ch04/apd.sensors-chapter04-ex01/src/apd/sensors/__init__.py new file mode 100644 index 0000000..ec63ed1 --- /dev/null +++ b/Ch04/apd.sensors-chapter04-ex01/src/apd/sensors/__init__.py @@ -0,0 +1 @@ +VERSION = "1.1.0dev1" diff --git a/Ch04/apd.sensors-chapter04-ex01/src/apd/sensors/cli.py b/Ch04/apd.sensors-chapter04-ex01/src/apd/sensors/cli.py new file mode 100644 index 0000000..aaaf33a --- /dev/null +++ b/Ch04/apd.sensors-chapter04-ex01/src/apd/sensors/cli.py @@ -0,0 +1,126 @@ +import functools +import importlib +import typing as t + +import click + +from .sensors import ( + Sensor, + ACStatus, + CPULoad, + IPAddresses, + PythonVersion, + RAMAvailable, + RelativeHumidity, + Temperature, +) + + +RETURN_CODES = {"OK": 0, "BAD_SENSOR_PATH": 17} + + +def is_valid_sensor_value(sensor_class, superclass): + return ( + isinstance(sensor_class, type) + and issubclass(sensor_class, superclass) + and sensor_class != superclass + ) + + +class PythonClass(click.types.ParamType): + name = "pythonclass" + + def __init__(self, superclass=type): + self.superclass = superclass + + def get_sensor_by_path( + self, sensor_path: str, fail: t.Callable[[str], None] + ) -> t.Any: + try: + module_name, sensor_name = sensor_path.split(":") + except ValueError: + return fail( + "Class path must be in the format dotted.path.to.module:ClassName" + ) + try: + module = importlib.import_module(module_name) + except ImportError: + return fail(f"Could not import module {module_name}") + try: + sensor_class = getattr(module, sensor_name) + except AttributeError: + return fail(f"Could not find attribute {sensor_name} in {module_name}") + if is_valid_sensor_value(sensor_class, self.superclass): + return sensor_class + else: + return fail( + f"Detected object {sensor_class!r} is not recognised" + f" as a {self.superclass} type" + ) + + def convert( + self, + value: str, + param: t.Optional[click.core.Parameter], + ctx: t.Optional[click.core.Context], + ) -> t.Any: + fail = functools.partial(self.fail, param=param, ctx=ctx) + return self.get_sensor_by_path(value, fail) + + def __repr__(self): + return "PythonClass" + + +def AutocompleteSensorPath( + ctx: click.core.Context, args: list, incomplete: str +) -> t.List[t.Tuple[str, str]]: + try: + module_name, sensor_name = incomplete.split(":") + module = importlib.import_module(module_name) + possibles = [ + (f"{module_name}:{name}", value.__doc__) + for (name, value) in vars(module).items() + if name.startswith(sensor_name) and is_valid_sensor_value(value, Sensor) + ] + except (ValueError, AttributeError): + return [] + else: + return possibles + + +def get_sensors() -> t.Iterable[Sensor[t.Any]]: + return [ + PythonVersion(), + IPAddresses(), + CPULoad(), + RAMAvailable(), + ACStatus(), + Temperature(), + RelativeHumidity(), + ] + + +@click.command(help="Displays the values of the sensors") +@click.option( + "--develop", + required=False, + metavar="path", + help="Load a sensor by Python path", + type=PythonClass(Sensor), + autocompletion=AutocompleteSensorPath, +) +def show_sensors(develop: t.Callable[[], Sensor[t.Any]]) -> int: + sensors: t.Iterable[Sensor[t.Any]] + if develop: + sensors = [develop()] + else: + sensors = get_sensors() + for sensor in sensors: + click.secho(sensor.title, bold=True) + click.echo(str(sensor)) + click.echo("") + return RETURN_CODES["OK"] + + +if __name__ == "__main__": + show_sensors() diff --git a/Ch04/apd.sensors-chapter04-ex01/src/apd/sensors/sensors.py b/Ch04/apd.sensors-chapter04-ex01/src/apd/sensors/sensors.py new file mode 100644 index 0000000..47c2ab0 --- /dev/null +++ b/Ch04/apd.sensors-chapter04-ex01/src/apd/sensors/sensors.py @@ -0,0 +1,170 @@ +#!/usr/bin/env python +# coding: utf-8 +import math +import socket +import sys +from typing import Any, Optional, List, Tuple, Iterable, TypeVar, Generic + + +import psutil + + +T_value = TypeVar("T_value") + + +class Sensor(Generic[T_value]): + title: str + + def value(self) -> T_value: + raise NotImplementedError + + @classmethod + def format(cls, value: T_value) -> str: + raise NotImplementedError + + def __str__(self) -> str: + return self.format(self.value()) + + +class PythonVersion(Sensor[Any]): + title = "Python Version" + + def value(self) -> Any: + return sys.version_info + + @classmethod + def format(cls, value: Any) -> str: + if value.micro == 0 and value.releaselevel == "alpha": + return "{0.major}.{0.minor}.{0.micro}a{0.serial}".format(value) + return "{0.major}.{0.minor}".format(value) + + +class IPAddresses(Sensor[Iterable[Tuple[str, str]]]): + title = "IP Addresses" + FAMILIES = {"AF_INET": "IPv4", "AF_INET6": "IPv6"} + + def value(self) -> List[Tuple[str, str]]: + hostname = socket.gethostname() + addresses = socket.getaddrinfo(hostname, None) + + address_info: List[Tuple[str, str]] = [] + for address in addresses: + family, ip = (address[0].name, address[4][0]) + if family not in self.FAMILIES: + continue + value = (family, ip) + if value not in address_info: + address_info.append(value) + return address_info + + @classmethod + def format(cls, value: Iterable[Tuple[str, str]]) -> str: + return "\n".join( + "{0} ({1})".format(address[1], cls.FAMILIES.get(address[0], "Unknown")) + for address in value + ) + + +class CPULoad(Sensor[float]): + title = "CPU Usage" + + def value(self) -> float: + return psutil.cpu_percent(interval=3) / 100.0 + + @classmethod + def format(cls, value: float) -> str: + return "{:.1%}".format(value) + + +class RAMAvailable(Sensor[int]): + title = "RAM Available" + UNITS = ("B", "KiB", "MiB", "GiB", "TiB", "PiB", "EiB") + UNIT_SIZE = 2 ** 10 + + def value(self) -> int: + return psutil.virtual_memory().available + + @classmethod + def format(cls, value: int) -> str: + magnitude = math.floor(math.log(value, cls.UNIT_SIZE)) + max_magnitude = len(cls.UNITS) - 1 + magnitude = min(magnitude, max_magnitude) + scaled_value = value / (cls.UNIT_SIZE ** magnitude) + return "{:.1f} {}".format(scaled_value, cls.UNITS[magnitude]) + + +class ACStatus(Sensor[Optional[bool]]): + title = "AC Connected" + + def value(self) -> Optional[bool]: + battery = psutil.sensors_battery() + if battery is not None: + return battery.power_plugged + else: + return None + + @classmethod + def format(cls, value: Optional[bool]) -> str: + if value is None: + return "Unknown" + elif value: + return "Connected" + else: + return "Not connected" + + +class Temperature(Sensor[Optional[float]]): + title = "Ambient Temperature" + + def value(self) -> Optional[float]: + try: + from adafruit_dht import DHT22 + from board import D20 + except (ImportError, NotImplementedError): + # No DHT library results in an ImportError. + # Running on an unknown platform results in a + # NotImplementedError when getting the pin + return None + try: + return DHT22(D20).temperature + except RuntimeError: + return None + + @classmethod + def celsius_to_fahrenheit(cls, value: float) -> float: + return value * 9 / 5 + 32 + + @classmethod + def format(cls, value: Optional[float]) -> str: + if value is None: + return "Unknown" + else: + return "{:.1f}C ({:.1f}F)".format(value, cls.celsius_to_fahrenheit(value)) + + def __str__(self) -> str: + return self.format(self.value()) + + +class RelativeHumidity(Sensor[Optional[float]]): + title = "Relative Humidity" + + def value(self) -> Optional[float]: + try: + from adafruit_dht import DHT22 + from board import D20 + except (ImportError, NotImplementedError): + # No DHT library results in an ImportError. + # Running on an unknown platform results in a + # NotImplementedError when getting the pin + return None + try: + return DHT22(D20).humidity / 100 + except RuntimeError: + return None + + @classmethod + def format(cls, value: Optional[float]) -> str: + if value is None: + return "Unknown" + else: + return "{:.1%}".format(value) diff --git a/Ch04/apd.sensors-chapter04-ex01/tests/__init__.py b/Ch04/apd.sensors-chapter04-ex01/tests/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/Ch04/apd.sensors-chapter04-ex01/tests/test_acstatus.py b/Ch04/apd.sensors-chapter04-ex01/tests/test_acstatus.py new file mode 100644 index 0000000..1d10940 --- /dev/null +++ b/Ch04/apd.sensors-chapter04-ex01/tests/test_acstatus.py @@ -0,0 +1,55 @@ +from unittest import mock + +from apd.sensors.sensors import ACStatus + +import pytest + + +@pytest.fixture +def sensor(): + return ACStatus() + + +class TestACStatusFormatter: + @pytest.fixture + def subject(self, sensor): + return sensor.format + + def test_format_True(self, subject): + assert subject(True) == "Connected" + + def test_format_False(self, subject): + assert subject(False) == "Not connected" + + def test_format_None(self, subject): + assert subject(None) == "Unknown" + + +class TestACStatusValue: + @pytest.fixture + def subject(self, sensor): + return sensor.value + + @pytest.fixture + def sensors_battery(self): + with mock.patch("psutil.sensors_battery") as sensors_battery: + yield sensors_battery + + def test_sensors_battery_called(self, subject, sensors_battery): + sensors_battery.return_value.power_plugged = True + assert subject() is True + assert sensors_battery.call_count == 1 + + def test_sensor_but_battery_unknown(self, subject, sensors_battery): + sensors_battery.return_value.power_plugged = None + assert subject() is None + assert sensors_battery.call_count == 1 + + def test_no_sensor(self, subject, sensors_battery): + sensors_battery.return_value = None + assert subject() is None + + def test_str_representation_is_formatted_value(self, sensor, sensors_battery): + sensors_battery.return_value.power_plugged = True + assert str(sensor) == "Connected" + assert sensors_battery.call_count == 1 diff --git a/Ch04/apd.sensors-chapter04-ex01/tests/test_cpuusage.py b/Ch04/apd.sensors-chapter04-ex01/tests/test_cpuusage.py new file mode 100644 index 0000000..aa8a66a --- /dev/null +++ b/Ch04/apd.sensors-chapter04-ex01/tests/test_cpuusage.py @@ -0,0 +1,45 @@ +from unittest import mock + +from apd.sensors.sensors import CPULoad + +import pytest + + +@pytest.fixture +def sensor(): + return CPULoad() + + +class TestCPULoadFormatter: + @pytest.fixture + def subject(self, sensor): + return sensor.format + + def test_format_simple_percentage(self, subject): + assert subject(0.05) == "5.0%" + + def test_format_multiple_decimal_places(self, subject): + assert subject(0.031415926) == "3.1%" + + def test_format_multiple_decimal_places_int(self, subject) -> None: + assert subject(1) == "100.0%" + + +class TestCPULoadValue: + @pytest.fixture + def subject(self, sensor): + return sensor.value + + @pytest.fixture + def cpuload(self): + with mock.patch("psutil.cpu_percent") as cpu_percent: + cpu_percent.return_value = 50.1 + yield cpu_percent + + def test_cpu_percent_called_With_interval(self, subject, cpuload): + assert subject() == 0.501 + assert cpuload.call_count == 1 + assert tuple(cpuload.call_args) == ((), {"interval": 3}) + + def test_str_representation_is_formatted_value(self, sensor, cpuload): + assert str(sensor) == "50.1%" diff --git a/Ch04/apd.sensors-chapter04-ex01/tests/test_dht.py b/Ch04/apd.sensors-chapter04-ex01/tests/test_dht.py new file mode 100644 index 0000000..b546593 --- /dev/null +++ b/Ch04/apd.sensors-chapter04-ex01/tests/test_dht.py @@ -0,0 +1,66 @@ +from apd.sensors.sensors import Temperature, RelativeHumidity + +import pytest + + +@pytest.fixture +def temperature_sensor(): + return Temperature() + + +@pytest.fixture +def humidity_sensor(): + return RelativeHumidity() + + +class TestTemperatureFormatter: + @pytest.fixture + def subject(self, temperature_sensor): + return temperature_sensor.format + + def test_format_21c(self, subject): + assert subject(21.0) == "21.0C (69.8F)" + + def test_format_negative(self, subject): + assert subject(-32.0) == "-32.0C (-25.6F)" + + def test_format_unknown(self, subject): + assert subject(None) == "Unknown" + + +class TestTemperatureConversion: + @pytest.fixture + def subject(self, temperature_sensor): + return temperature_sensor.celsius_to_fahrenheit + + def test_celsius_to_fahrenheit(self, subject): + c = 21 + f = subject(c) + assert f == 69.8 + + def test_celsius_to_fahrenheit_equivlance_point(self, subject): + c = -40 + f = subject(c) + assert f == -40 + + def test_celsius_to_fahrenheit_float(self, subject): + c = 21.2 + f = subject(c) + assert f == 70.16 + + def test_celsius_to_fahrenheit_string(self, subject): + c = "21" + with pytest.raises(TypeError): + subject(c) + + +class TestHumidityFormatter: + @pytest.fixture + def subject(self, humidity_sensor): + return humidity_sensor.format + + def test_format_percentage(self, subject): + assert subject(0.035) == "3.5%" + + def test_format_unknown(self, subject): + assert subject(None) == "Unknown" diff --git a/Ch04/apd.sensors-chapter04-ex01/tests/test_ipaddresses.py b/Ch04/apd.sensors-chapter04-ex01/tests/test_ipaddresses.py new file mode 100644 index 0000000..e8a9690 --- /dev/null +++ b/Ch04/apd.sensors-chapter04-ex01/tests/test_ipaddresses.py @@ -0,0 +1,59 @@ +from unittest import mock +import socket + +from apd.sensors.sensors import IPAddresses + +import pytest + + +@pytest.fixture +def sensor(): + return IPAddresses() + + +class TestIPAddressFormatter: + @pytest.fixture + def subject(self, sensor): + return sensor.format + + def test_format_single_ipv4(self, subject): + ips = [("AF_INET", "192.0.2.1")] + assert subject(ips) == "192.0.2.1 (IPv4)" + + def test_format_single_ipv6(self, subject): + ips = [("AF_INET6", "2001:DB8::1")] + assert subject(ips) == "2001:DB8::1 (IPv6)" + + def test_format_mixed_list(self, subject): + ips = [("AF_INET", "192.0.2.1"), ("AF_INET6", "2001:DB8::1")] + assert subject(ips) == "192.0.2.1 (IPv4)\n2001:DB8::1 (IPv6)" + + def test_unusual_protocols_are_marked_as_unknown(self, subject): + ips = [("AF_IRDA", "ffff")] + assert subject(ips) == "ffff (Unknown)" + + +class TestIPAddressesValue: + @pytest.fixture + def subject(self, sensor): + return sensor.value + + @pytest.fixture + def gethostname(self): + with mock.patch("socket.gethostname") as gethostname: + gethostname.return_value = "localhost" + yield gethostname + + @pytest.fixture + def getaddrinfo(self, gethostname): + with mock.patch("socket.getaddrinfo") as getaddrinfo: + yield getaddrinfo + + def test_getaddrinfo_used_for_value_collection(self, getaddrinfo, subject): + getaddrinfo.return_value = [(socket.AF_INET, 0, 0, "", ("192.0.2.1", 0))] + assert subject() == [("AF_INET", "192.0.2.1")] + assert getaddrinfo.call_count == 1 + + def test_str_representation_is_formatted_value(self, getaddrinfo, sensor): + getaddrinfo.return_value = [(socket.AF_INET6, 0, 0, "", ("2001:DB8::1", 0))] + assert str(sensor) == "2001:DB8::1 (IPv6)" diff --git a/Ch04/apd.sensors-chapter04-ex01/tests/test_pythonversion.py b/Ch04/apd.sensors-chapter04-ex01/tests/test_pythonversion.py new file mode 100644 index 0000000..dff6bb0 --- /dev/null +++ b/Ch04/apd.sensors-chapter04-ex01/tests/test_pythonversion.py @@ -0,0 +1,61 @@ +from collections import namedtuple +from unittest import mock + +from apd.sensors.sensors import PythonVersion + +import pytest + + +@pytest.fixture +def version(): + return namedtuple( + "sys_versioninfo", ("major", "minor", "micro", "releaselevel", "serial") + ) + + +@pytest.fixture +def sensor(): + return PythonVersion() + + +class TestPythonVersionFormatter: + @pytest.fixture + def subject(self, sensor): + return sensor.format + + def test_format_py38(self, subject, version): + py38 = version(3, 8, 0, "final", 0) + assert subject(py38) == "3.8" + + def test_format_large_version(self, subject, version): + large = version(255, 128, 0, "final", 0) + assert subject(large) == "255.128" + + def test_alpha_of_minor_is_marked(self, subject, version): + py39 = version(3, 9, 0, "alpha", 1) + assert subject(py39) == "3.9.0a1" + + def test_alpha_of_micro_is_unmarked(self, subject, version): + py39 = version(3, 9, 1, "alpha", 1) + assert subject(py39) == "3.9" + + +class TestPythonVersionValue: + @pytest.fixture + def subject(self, sensor): + return sensor.value + + @pytest.fixture + def python_version(self): + import sys + + return sys.version_info + + def test_data_value_is_sys_versioninfo(self, python_version, subject): + assert subject() == python_version + + +class TestPythonVersionSensor: + def test_str_representation_is_formatted_value(self, sensor, version): + with mock.patch("sys.version_info", new=version(3, 4, 1, "final", 1)): + assert str(sensor) == "3.4" diff --git a/Ch04/apd.sensors-chapter04-ex01/tests/test_ramusage.py b/Ch04/apd.sensors-chapter04-ex01/tests/test_ramusage.py new file mode 100644 index 0000000..2a368c1 --- /dev/null +++ b/Ch04/apd.sensors-chapter04-ex01/tests/test_ramusage.py @@ -0,0 +1,47 @@ +from unittest import mock + +from apd.sensors.sensors import RAMAvailable + +import pytest + + +@pytest.fixture +def sensor(): + return RAMAvailable() + + +class TestRAMAvailableFormatter: + @pytest.fixture + def subject(self, sensor): + return sensor.format + + def test_format_bytes(self, subject): + assert subject(15) == "15.0 B" + + def test_format_kibibytes(self, subject): + assert subject(15000) == "14.6 KiB" + + def test_format_mibibytes(self, subject): + assert subject(15000000) == "14.3 MiB" + + def test_format_gibibytes(self, subject): + assert subject(15000000000) == "14.0 GiB" + + +class TestRAMAvailableValue: + @pytest.fixture + def subject(self, sensor): + return sensor.value + + @pytest.fixture + def virtual_memory(self): + with mock.patch("psutil.virtual_memory") as virtual_memory: + virtual_memory.return_value.available = 1024 + yield virtual_memory + + def test_virtual_memory_called(self, subject, virtual_memory): + assert subject() == 1024 + assert virtual_memory.call_count == 1 + + def test_str_representation_is_formatted_value(self, sensor, virtual_memory): + assert str(sensor) == "1.0 KiB" diff --git a/Ch04/apd.sensors-chapter04-ex01/tests/test_sensors.py b/Ch04/apd.sensors-chapter04-ex01/tests/test_sensors.py new file mode 100644 index 0000000..b31a026 --- /dev/null +++ b/Ch04/apd.sensors-chapter04-ex01/tests/test_sensors.py @@ -0,0 +1,64 @@ +import functools + +from click.testing import CliRunner +import pytest + +import apd.sensors.cli +import apd.sensors.sensors + + +def test_sensors(): + assert hasattr(apd.sensors.sensors, "PythonVersion") + + +@pytest.mark.functional +def test_python_version_is_first_two_lines_of_cli_output(): + runner = CliRunner() + result = runner.invoke(apd.sensors.cli.show_sensors) + python_version = str(apd.sensors.sensors.PythonVersion()) + assert ["Python Version", python_version] == result.stdout.split("\n")[:2] + + +class TestSensorFromPath: + @staticmethod + def fail(message): + raise RuntimeError(message) + + @pytest.fixture + def subject(self): + return functools.partial( + apd.sensors.cli.PythonClass(apd.sensors.sensors.Sensor).get_sensor_by_path, + fail=self.fail, + ) + + def test_get_sensor_by_path(self, subject): + assert ( + subject("apd.sensors.sensors:PythonVersion") + == apd.sensors.sensors.PythonVersion + ) + + def test_invalid_format(self, subject): + with pytest.raises(RuntimeError, match="dotted.path.to.module:ClassName"): + subject("apd.sensors.sensors.PythonVersion") + + def test_invalid_module(self, subject): + with pytest.raises(RuntimeError, match="Could not import module"): + subject("apd.nonsense.sensor:FakeSensor") + + def test_missing_sensor(self, subject): + with pytest.raises(RuntimeError, match="Could not find attribute"): + subject("apd.sensors.sensors:FakeSensor") + + def test_invalid_sensor(self, subject): + with pytest.raises(RuntimeError, match="is not recognised as a Sensor"): + subject("apd.sensors.sensors:Sensor") + + +@pytest.mark.functional +def test_develop_option_finds_sensor_by_path(): + runner = CliRunner() + result = runner.invoke( + apd.sensors.cli.show_sensors, ["--develop", "apd.sensors.sensors:PythonVersion"] + ) + python_version = str(apd.sensors.sensors.PythonVersion()) + assert ["Python Version", python_version, "", ""] == result.stdout.split("\n") diff --git a/Ch04/apd.sensors-chapter04/.pre-commit-config.yaml b/Ch04/apd.sensors-chapter04/.pre-commit-config.yaml new file mode 100644 index 0000000..d3b6ec7 --- /dev/null +++ b/Ch04/apd.sensors-chapter04/.pre-commit-config.yaml @@ -0,0 +1,26 @@ +repos: + +- repo: local + hooks: + - id: black + name: black + entry: pipenv run black + args: [--quiet] + language: system + types: [python] + + - id: mypy + name: mypy + exclude: (?x)^( + (.*)/setup.py + )$ + entry: pipenv run mypy + args: ["--follow-imports=skip"] + language: system + types: [python] + + - id: flake8 + name: flake8 + entry: pipenv run flake8 + language: system + types: [python] diff --git a/Ch04/apd.sensors-chapter04/CHANGES.md b/Ch04/apd.sensors-chapter04/CHANGES.md new file mode 100644 index 0000000..569aeea --- /dev/null +++ b/Ch04/apd.sensors-chapter04/CHANGES.md @@ -0,0 +1,5 @@ +## Changes + +### 1.0.0 (2019-06-20) + +* Added initial sensors (Matthew Wilkes) diff --git a/Ch04/apd.sensors-chapter04/LICENCE b/Ch04/apd.sensors-chapter04/LICENCE new file mode 100644 index 0000000..053f694 --- /dev/null +++ b/Ch04/apd.sensors-chapter04/LICENCE @@ -0,0 +1,19 @@ +Copyright (c) 2019 Matthew Wilkes + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/Ch04/apd.sensors-chapter04/Pipfile b/Ch04/apd.sensors-chapter04/Pipfile new file mode 100644 index 0000000..a09fb90 --- /dev/null +++ b/Ch04/apd.sensors-chapter04/Pipfile @@ -0,0 +1,31 @@ +[[source]] +name = "pypi" +url = "https://pypi.org/simple" +verify_ssl = true + +[[source]] +name = "piwheels" +url = "https://piwheels.org/simple" +verify_ssl = true + +[dev-packages] +ipykernel = "*" +remote-ikernel = "*" +pytest = "*" +pytest-cov = "*" +mypy = "*" +flake8 = "*" +black = "*" +pre-commit = "*" +wheel = "*" +twine = "*" +webtest = "*" + +[packages] +apd-sensors = {editable = true,path = "."} +apd-sunnyboy-solar = {editable = true,path = "./plugins/apd.sunnyboy_solar"} + +[requires] + +[pipenv] +allow_prereleases 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the book [Advanced Python Development](https://advancedpython.dev). + +## Usage + +This installs a console script called `sensors` that returns a report on +various aspects of the system. The available sensors are: + +* Python version +* IP Addresses +* CPU Usage +* RAM Available +* Battery charging state +* Ambient Temperature +* Ambient Humidity + +There are no command-line options, to view the report run `sensors` on the +command line. + +## Caveats + +The Ambient Temperature and Ambient Humidity sensors are only available on +RaspberryPi hosts and assume that a DHT22 sensor is connected to pin `D20`. + +If there is an entry in `/etc/hosts` for the current machine's hostname that +value will be the only result from the IP Addresses sensor. + +## Installation + +Install with `pip3 install apd.sensors` under Python 3.7 or higher diff --git a/Ch04/apd.sensors-chapter04/plugins/apd.sunnyboy_solar/pyproject.toml b/Ch04/apd.sensors-chapter04/plugins/apd.sunnyboy_solar/pyproject.toml new file mode 100644 index 0000000..4d3bb67 --- /dev/null +++ b/Ch04/apd.sensors-chapter04/plugins/apd.sunnyboy_solar/pyproject.toml @@ -0,0 +1,5 @@ +[build-system] +requires = [ + "setuptools", +] +build-backend = "setuptools.build_meta" diff --git a/Ch04/apd.sensors-chapter04/plugins/apd.sunnyboy_solar/setup.cfg b/Ch04/apd.sensors-chapter04/plugins/apd.sunnyboy_solar/setup.cfg new file mode 100644 index 0000000..a24bdf9 --- /dev/null +++ b/Ch04/apd.sensors-chapter04/plugins/apd.sunnyboy_solar/setup.cfg @@ -0,0 +1,33 @@ +[mypy] +ignore_missing_imports = True + +[flake8] +max-line-length = 88 + +[metadata] +name = apd.sunnyboy_solar +version = attr: apd.sunnyboy_solar.VERSION +description = APD Sensor for reading data from Sunnyboy inverters +long_description = file: README.md, CHANGES.md, LICENCE +long_description_content_type = text/markdown +keywords = iot solar +license = MIT +classifiers = + Programming Language :: Python :: 3 + Programming Language :: Python :: 3.7 + +[options] +zip_safe = False +include_package_data = True +package-dir = + =src +packages = find: +install_requires = + apd.sensors + +[options.packages.find] +where = src + +[options.entry_points] +apd.sensors.sensors = + SolarCumulativeOutput = apd.sunnyboy_solar.sensor:SolarCumulativeOutput diff --git a/Ch04/apd.sensors-chapter04/plugins/apd.sunnyboy_solar/setup.py b/Ch04/apd.sensors-chapter04/plugins/apd.sunnyboy_solar/setup.py new file mode 100644 index 0000000..6068493 --- /dev/null +++ b/Ch04/apd.sensors-chapter04/plugins/apd.sunnyboy_solar/setup.py @@ -0,0 +1,3 @@ +from setuptools import setup + +setup() diff --git a/Ch04/apd.sensors-chapter04/plugins/apd.sunnyboy_solar/src/apd/sunnyboy_solar/Pipfile b/Ch04/apd.sensors-chapter04/plugins/apd.sunnyboy_solar/src/apd/sunnyboy_solar/Pipfile new file mode 100644 index 0000000..e69de29 diff --git a/Ch04/apd.sensors-chapter04/plugins/apd.sunnyboy_solar/src/apd/sunnyboy_solar/__init__.py b/Ch04/apd.sensors-chapter04/plugins/apd.sunnyboy_solar/src/apd/sunnyboy_solar/__init__.py new file mode 100644 index 0000000..3277f64 --- /dev/null +++ b/Ch04/apd.sensors-chapter04/plugins/apd.sunnyboy_solar/src/apd/sunnyboy_solar/__init__.py @@ -0,0 +1 @@ +VERSION = "1.0.0" diff --git a/Ch04/apd.sensors-chapter04/plugins/apd.sunnyboy_solar/src/apd/sunnyboy_solar/sensor.py b/Ch04/apd.sensors-chapter04/plugins/apd.sunnyboy_solar/src/apd/sunnyboy_solar/sensor.py new file mode 100644 index 0000000..232b46c --- /dev/null +++ b/Ch04/apd.sensors-chapter04/plugins/apd.sunnyboy_solar/src/apd/sunnyboy_solar/sensor.py @@ -0,0 +1,48 @@ +import os +import subprocess +import typing as t + +from apd.sensors.sensors import Sensor + + +class SolarCumulativeOutput(Sensor[t.Optional[float]]): + title = "Solar panel cumulative output" + + def __init__(self, path=None, bt_addr=None): + self.path = os.environ.get( + "APD_SUNNYBOYSOLAR_PATH", "/home/pi/opensunny-master/opensunny" + ) + self.bt_addr = os.environ.get("APD_SUNNYBOYSOLAR_BT_ADDRESS", None) + + def value(self) -> t.Optional[float]: + if self.bt_addr is None: + return None + try: + output: bytes = subprocess.check_output( + [self.path, "-i", self.bt_addr], stderr=subprocess.STDOUT, timeout=15 + ) + except (subprocess.CalledProcessError, FileNotFoundError): + return None + + lines = filter(None, output.split(b"\n")) + found = {} + for line in lines: + start, value = line.rsplit(b"=", 1) + start, key = start.rsplit(b" ", 1) + found[key] = value + + try: + yield_total = float(found[b"yield_total"][:-3].replace(b".", b"")) + power_dc_1 = int(found[b"power_dc_1"][:-1]) + power_dc_2 = int(found[b"power_dc_2"][:-1]) + if power_dc_1 > 1500 or power_dc_2 > 1500: + return None + except (ValueError, IndexError): + return None + return yield_total + + @classmethod + def format(cls, value: t.Optional[float]) -> str: + if value is None: + return "Unknown" + return "{} kW".format(value / 1000) diff --git a/Ch04/apd.sensors-chapter04/pytest.ini b/Ch04/apd.sensors-chapter04/pytest.ini new file mode 100644 index 0000000..861f3d0 --- /dev/null +++ b/Ch04/apd.sensors-chapter04/pytest.ini @@ -0,0 +1,3 @@ +[pytest] +markers = + functional: these tests are significantly slower as they run the whole CLI script \ No newline at end of file diff --git a/Ch04/apd.sensors-chapter04/setup.cfg b/Ch04/apd.sensors-chapter04/setup.cfg new file mode 100644 index 0000000..ecf9c2e --- /dev/null +++ b/Ch04/apd.sensors-chapter04/setup.cfg @@ -0,0 +1,62 @@ +[mypy] +namespace_packages = True +mypy_path = src + +[mypy-psutil] +ignore_missing_imports = True + +[mypy-adafruit_dht] +ignore_missing_imports = True + +[mypy-board] +ignore_missing_imports = True + +[mypy-pytest] +ignore_missing_imports = True + +[mypy-webtest] +ignore_missing_imports = True + +[flake8] +max-line-length = 88 + +[metadata] +name = apd.sensors +version = attr: apd.sensors.VERSION +description = APD Sensor package +long_description = file: README.md, CHANGES.md, LICENCE +long_description_content_type = text/markdown +keywords = iot +license = MIT +classifiers = + Programming Language :: Python :: 3 + Programming Language :: Python :: 3.7 + +[options] +zip_safe = False +include_package_data = True +package-dir = + =src +packages = find_namespace: +install_requires = + psutil + click + adafruit-circuitpython-dht ; 'arm' in platform_machine + +[options.package_data] +apd.sensors = py.typed + +[options.packages.find] +where = src + +[options.entry_points] +console_scripts = + sensors = apd.sensors.cli:show_sensors +apd.sensors.sensors = + PythonVersion = apd.sensors.sensors:PythonVersion + IPAddresses = apd.sensors.sensors:IPAddresses + CPULoad = apd.sensors.sensors:CPULoad + RAMAvailable = apd.sensors.sensors:RAMAvailable + ACStatus = apd.sensors.sensors:ACStatus + Temperature = apd.sensors.sensors:Temperature + RelativeHumidity = apd.sensors.sensors:RelativeHumidity \ No newline at end of file diff --git a/Ch04/apd.sensors-chapter04/setup.py b/Ch04/apd.sensors-chapter04/setup.py new file mode 100644 index 0000000..6068493 --- /dev/null +++ b/Ch04/apd.sensors-chapter04/setup.py @@ -0,0 +1,3 @@ +from setuptools import setup + +setup() diff --git a/Ch04/apd.sensors-chapter04/src/apd/sensors/__init__.py b/Ch04/apd.sensors-chapter04/src/apd/sensors/__init__.py new file mode 100644 index 0000000..848258f --- /dev/null +++ b/Ch04/apd.sensors-chapter04/src/apd/sensors/__init__.py @@ -0,0 +1 @@ +VERSION = "1.2.0" diff --git a/Ch04/apd.sensors-chapter04/src/apd/sensors/cli.py b/Ch04/apd.sensors-chapter04/src/apd/sensors/cli.py new file mode 100644 index 0000000..1ee27b9 --- /dev/null +++ b/Ch04/apd.sensors-chapter04/src/apd/sensors/cli.py @@ -0,0 +1,74 @@ +import enum +import importlib +import sys +import pkg_resources +import typing as t + +import click + +from .sensors import Sensor + + +class ReturnCodes(enum.IntEnum): + OK = 0 + BAD_SENSOR_PATH = 17 + + +def get_sensor_by_path(sensor_path: str) -> Sensor[t.Any]: + try: + module_name, sensor_name = sensor_path.split(":") + except ValueError: + raise RuntimeError( + "Sensor path must be in the format dotted.path.to.module:ClassName" + ) + try: + module = importlib.import_module(module_name) + except ImportError: + raise RuntimeError(f"Could not import module {module_name}") + try: + sensor_class = getattr(module, sensor_name) + except AttributeError: + raise RuntimeError(f"Could not find attribute {sensor_name} in {module_name}") + if ( + isinstance(sensor_class, type) + and issubclass(sensor_class, Sensor) + and sensor_class != Sensor + ): + return sensor_class() + else: + raise RuntimeError( + f"Detected object {sensor_class!r} is not recognised as a Sensor type" + ) + + +def get_sensors() -> t.Iterable[Sensor[t.Any]]: + sensors = [] + for sensor_class in pkg_resources.iter_entry_points("apd.sensors.sensors"): + class_ = sensor_class.load() + sensors.append(t.cast(Sensor[t.Any], class_())) + return sensors + + +@click.command(help="Displays the values of the sensors") +@click.option( + "--develop", required=False, metavar="path", help="Load a sensor by Python path" +) +def show_sensors(develop: str) -> None: + sensors: t.Iterable[Sensor[t.Any]] + if develop: + try: + sensors = [get_sensor_by_path(develop)] + except RuntimeError as error: + click.secho(str(error), fg="red", bold=True) + sys.exit(ReturnCodes.BAD_SENSOR_PATH) + else: + sensors = get_sensors() + for sensor in sensors: + click.secho(sensor.title, bold=True) + click.echo(str(sensor)) + click.echo("") + sys.exit(ReturnCodes.OK) + + +if __name__ == "__main__": + show_sensors() diff --git a/Ch04/apd.sensors-chapter04/src/apd/sensors/py.typed b/Ch04/apd.sensors-chapter04/src/apd/sensors/py.typed new file mode 100644 index 0000000..e69de29 diff --git a/Ch04/apd.sensors-chapter04/src/apd/sensors/sensors.py b/Ch04/apd.sensors-chapter04/src/apd/sensors/sensors.py new file mode 100644 index 0000000..c763370 --- /dev/null +++ b/Ch04/apd.sensors-chapter04/src/apd/sensors/sensors.py @@ -0,0 +1,181 @@ +#!/usr/bin/env python +# coding: utf-8 +import math +import os +import socket +import sys +from typing import Any, Optional, List, Tuple, Iterable, TypeVar, Generic + + +import psutil + + +T_value = TypeVar("T_value") + + +class Sensor(Generic[T_value]): + title: str + + def value(self) -> T_value: + raise NotImplementedError + + @classmethod + def format(cls, value: T_value) -> str: + raise NotImplementedError + + def __str__(self) -> str: + return self.format(self.value()) + + +class PythonVersion(Sensor[Any]): + title = "Python Version" + + def value(self) -> Any: + return sys.version_info + + @classmethod + def format(cls, value: Any) -> str: + if value.micro == 0 and value.releaselevel == "alpha": + return "{0.major}.{0.minor}.{0.micro}a{0.serial}".format(value) + return "{0.major}.{0.minor}".format(value) + + +class IPAddresses(Sensor[Iterable[Tuple[str, str]]]): + title = "IP Addresses" + FAMILIES = {"AF_INET": "IPv4", "AF_INET6": "IPv6"} + + def value(self) -> List[Tuple[str, str]]: + hostname = socket.gethostname() + addresses = socket.getaddrinfo(hostname, None) + + address_info: List[Tuple[str, str]] = [] + for address in addresses: + family, ip = (address[0].name, address[4][0]) + if family not in self.FAMILIES: + continue + value = (family, ip) + if value not in address_info: + address_info.append(value) + return address_info + + @classmethod + def format(cls, value: Iterable[Tuple[str, str]]) -> str: + return "\n".join( + "{0} ({1})".format(address[1], cls.FAMILIES.get(address[0], "Unknown")) + for address in value + ) + + +class CPULoad(Sensor[float]): + title = "CPU Usage" + + def value(self) -> float: + return psutil.cpu_percent(interval=3) / 100.0 + + @classmethod + def format(cls, value: float) -> str: + return "{:.1%}".format(value) + + +class RAMAvailable(Sensor[int]): + title = "RAM Available" + UNITS = ("B", "KiB", "MiB", "GiB", "TiB", "PiB", "EiB") + UNIT_SIZE = 2 ** 10 + + def value(self) -> int: + return psutil.virtual_memory().available + + @classmethod + def format(cls, value: int) -> str: + magnitude = math.floor(math.log(value, cls.UNIT_SIZE)) + max_magnitude = len(cls.UNITS) - 1 + magnitude = min(magnitude, max_magnitude) + scaled_value = value / (cls.UNIT_SIZE ** magnitude) + return "{:.1f} {}".format(scaled_value, cls.UNITS[magnitude]) + + +class ACStatus(Sensor[Optional[bool]]): + title = "AC Connected" + + def value(self) -> Optional[bool]: + battery = psutil.sensors_battery() + if battery is not None: + return battery.power_plugged + else: + return None + + @classmethod + def format(cls, value: Optional[bool]) -> str: + if value is None: + return "Unknown" + elif value: + return "Connected" + else: + return "Not connected" + + +class Temperature(Sensor[Optional[float]]): + title = "Ambient Temperature" + + def __init__(self, board=None, pin=None): + self.board = os.environ.get("APD_SENSORS_TEMPERATURE_BOARD", "DHT22") + self.pin = os.environ.get("APD_SENSORS_TEMPERATURE_PIN", "D4") + + def value(self) -> Optional[float]: + try: + # Connect to a DHT22 on pin 4 + from adafruit_dht import DHT22 + from board import D4 + except (ImportError, NotImplementedError): + # No DHT library results in an ImportError. + # Running on an unknown platform results in a + # NotImplementedError when getting the pin + return None + try: + return DHT22(D4).temperature + except RuntimeError: + return None + + @staticmethod + def celsius_to_fahrenheit(value: float) -> float: + return value * 9 / 5 + 32 + + @classmethod + def format(cls, value: Optional[float]) -> str: + if value is None: + return "Unknown" + else: + return "{:.1f}C ({:.1f}F)".format(value, cls.celsius_to_fahrenheit(value)) + + def __str__(self) -> str: + return self.format(self.value()) + + +class RelativeHumidity(Sensor[Optional[float]]): + title = "Relative Humidity" + + def __init__(self, board=None, pin=None): + self.board = os.environ.get("APD_SENSORS_TEMPERATURE_BOARD", "DHT22") + self.pin = os.environ.get("APD_SENSORS_TEMPERATURE_PIN", "D4") + + def value(self) -> Optional[float]: + try: + # Connect to a DHT22 on pin 4 + from adafruit_dht import DHT22 + from board import D4 + except (ImportError, NotImplementedError): + # No DHT library results in an ImportError. + # Running on an unknown platform results in a + # NotImplementedError when getting the pin + return None + try: + return DHT22(D4).humidity / 100 + except RuntimeError: + return None + + @classmethod + def format(cls, value: Optional[float]) -> str: + if value is None: + return "Unknown" + else: + return "{:.1%}".format(value) diff --git a/Ch04/apd.sensors-chapter04/src/apd/sensors/wsgi.py b/Ch04/apd.sensors-chapter04/src/apd/sensors/wsgi.py new file mode 100644 index 0000000..8c5b23c --- /dev/null +++ b/Ch04/apd.sensors-chapter04/src/apd/sensors/wsgi.py @@ -0,0 +1,31 @@ +import json +import typing as t + +from apd.sensors.cli import get_sensors + +if t.TYPE_CHECKING: + from wsgiref.types import StartResponse +else: + StartResponse = t.Callable + + +def sensor_values( + environ: t.Dict[str, str], start_response: StartResponse +) -> t.List[bytes]: + headers = [ + ("Content-type", "application/json; charset=utf-8"), + ("Content-Security-Policy", "default-src 'none';"), + ] + start_response("200 OK", headers) + data = {} + for sensor in get_sensors(): + data[sensor.title] = sensor.value() + encoded = json.dumps(data).encode("utf-8") + return [encoded] + + +if __name__ == "__main__": + import wsgiref.simple_server + + with wsgiref.simple_server.make_server("", 8000, sensor_values) as server: + server.serve_forever() diff --git a/Ch04/apd.sensors-chapter04/tests/test_acstatus.py b/Ch04/apd.sensors-chapter04/tests/test_acstatus.py new file mode 100644 index 0000000..d2fe71e --- /dev/null +++ b/Ch04/apd.sensors-chapter04/tests/test_acstatus.py @@ -0,0 +1,55 @@ +from unittest import mock + +import pytest + +from apd.sensors.sensors import ACStatus + + +@pytest.fixture +def sensor(): + return ACStatus() + + +class TestACStatusFormatter: + @pytest.fixture + def subject(self, sensor): + return sensor.format + + def test_format_True(self, subject): + assert subject(True) == "Connected" + + def test_format_False(self, subject): + assert subject(False) == "Not connected" + + def test_format_None(self, subject): + assert subject(None) == "Unknown" + + +class TestACStatusValue: + @pytest.fixture + def subject(self, sensor): + return sensor.value + + @pytest.fixture + def sensors_battery(self): + with mock.patch("psutil.sensors_battery") as sensors_battery: + yield sensors_battery + + def test_sensors_battery_called(self, subject, sensors_battery): + sensors_battery.return_value.power_plugged = True + assert subject() is True + assert sensors_battery.call_count == 1 + + def test_sensor_but_battery_unknown(self, subject, sensors_battery): + sensors_battery.return_value.power_plugged = None + assert subject() is None + assert sensors_battery.call_count == 1 + + def test_no_sensor(self, subject, sensors_battery): + sensors_battery.return_value = None + assert subject() is None + + def test_str_representation_is_formatted_value(self, sensor, sensors_battery): + sensors_battery.return_value.power_plugged = True + assert str(sensor) == "Connected" + assert sensors_battery.call_count == 1 diff --git a/Ch04/apd.sensors-chapter04/tests/test_api_server.py b/Ch04/apd.sensors-chapter04/tests/test_api_server.py new file mode 100644 index 0000000..4bdb983 --- /dev/null +++ b/Ch04/apd.sensors-chapter04/tests/test_api_server.py @@ -0,0 +1,25 @@ +import pytest +from webtest import TestApp + +from apd.sensors.wsgi import sensor_values +from apd.sensors.sensors import PythonVersion + + +@pytest.fixture(scope="session") +def subject(): + return sensor_values + + +@pytest.fixture(scope="session") +def api_server(subject): + return TestApp(subject) + + +@pytest.mark.functional +def test_sensor_values_returned_as_json(api_server): + value = api_server.get("/sensors/").json + python_version = PythonVersion().value() + + sensor_names = value.keys() + assert "Python Version" in sensor_names + assert value["Python Version"] == list(python_version) diff --git a/Ch04/apd.sensors-chapter04/tests/test_cpuusage.py b/Ch04/apd.sensors-chapter04/tests/test_cpuusage.py new file mode 100644 index 0000000..445f4e5 --- /dev/null +++ b/Ch04/apd.sensors-chapter04/tests/test_cpuusage.py @@ -0,0 +1,45 @@ +from unittest import mock + +import pytest + +from apd.sensors.sensors import CPULoad + + +@pytest.fixture +def sensor(): + return CPULoad() + + +class TestCPULoadFormatter: + @pytest.fixture + def subject(self, sensor): + return sensor.format + + def test_format_simple_percentage(self, subject): + assert subject(0.05) == "5.0%" + + def test_format_multiple_decimal_places(self, subject): + assert subject(0.031415926) == "3.1%" + + def test_format_multiple_decimal_places_int(self, subject) -> None: + assert subject(1) == "100.0%" + + +class TestCPULoadValue: + @pytest.fixture + def subject(self, sensor): + return sensor.value + + @pytest.fixture + def cpuload(self): + with mock.patch("psutil.cpu_percent") as cpu_percent: + cpu_percent.return_value = 50.1 + yield cpu_percent + + def test_cpu_percent_called_With_interval(self, subject, cpuload): + assert subject() == 0.501 + assert cpuload.call_count == 1 + assert tuple(cpuload.call_args) == ((), {"interval": 3}) + + def test_str_representation_is_formatted_value(self, sensor, cpuload): + assert str(sensor) == "50.1%" diff --git a/Ch04/apd.sensors-chapter04/tests/test_dht.py b/Ch04/apd.sensors-chapter04/tests/test_dht.py new file mode 100644 index 0000000..153c1db --- /dev/null +++ b/Ch04/apd.sensors-chapter04/tests/test_dht.py @@ -0,0 +1,66 @@ +import pytest + +from apd.sensors.sensors import Temperature, RelativeHumidity + + +@pytest.fixture +def temperature_sensor(): + return Temperature() + + +@pytest.fixture +def humidity_sensor(): + return RelativeHumidity() + + +class TestTemperatureFormatter: + @pytest.fixture + def subject(self, temperature_sensor): + return temperature_sensor.format + + def test_format_21c(self, subject): + assert subject(21.0) == "21.0C (69.8F)" + + def test_format_negative(self, subject): + assert subject(-32.0) == "-32.0C (-25.6F)" + + def test_format_unknown(self, subject): + assert subject(None) == "Unknown" + + +class TestTemperatureConversion: + @pytest.fixture + def subject(self, temperature_sensor): + return temperature_sensor.celsius_to_fahrenheit + + def test_celsius_to_fahrenheit(self, subject): + c = 21 + f = subject(c) + assert f == 69.8 + + def test_celsius_to_fahrenheit_equivlance_point(self, subject): + c = -40 + f = subject(c) + assert f == -40 + + def test_celsius_to_fahrenheit_float(self, subject): + c = 21.2 + f = subject(c) + assert f == 70.16 + + def test_celsius_to_fahrenheit_string(self, subject): + c = "21" + with pytest.raises(TypeError): + subject(c) + + +class TestHumidityFormatter: + @pytest.fixture + def subject(self, humidity_sensor): + return humidity_sensor.format + + def test_format_percentage(self, subject): + assert subject(0.035) == "3.5%" + + def test_format_unknown(self, subject): + assert subject(None) == "Unknown" diff --git a/Ch04/apd.sensors-chapter04/tests/test_ipaddresses.py b/Ch04/apd.sensors-chapter04/tests/test_ipaddresses.py new file mode 100644 index 0000000..bd61d85 --- /dev/null +++ b/Ch04/apd.sensors-chapter04/tests/test_ipaddresses.py @@ -0,0 +1,59 @@ +import socket +from unittest import mock + +import pytest + +from apd.sensors.sensors import IPAddresses + + +@pytest.fixture +def sensor(): + return IPAddresses() + + +class TestIPAddressFormatter: + @pytest.fixture + def subject(self, sensor): + return sensor.format + + def test_format_single_ipv4(self, subject): + ips = [("AF_INET", "192.0.2.1")] + assert subject(ips) == "192.0.2.1 (IPv4)" + + def test_format_single_ipv6(self, subject): + ips = [("AF_INET6", "2001:DB8::1")] + assert subject(ips) == "2001:DB8::1 (IPv6)" + + def test_format_mixed_list(self, subject): + ips = [("AF_INET", "192.0.2.1"), ("AF_INET6", "2001:DB8::1")] + assert subject(ips) == "192.0.2.1 (IPv4)\n2001:DB8::1 (IPv6)" + + def test_unusual_protocols_are_marked_as_unknown(self, subject): + ips = [("AF_IRDA", "ffff")] + assert subject(ips) == "ffff (Unknown)" + + +class TestIPAddressesValue: + @pytest.fixture + def subject(self, sensor): + return sensor.value + + @pytest.fixture + def gethostname(self): + with mock.patch("socket.gethostname") as gethostname: + gethostname.return_value = "localhost" + yield gethostname + + @pytest.fixture + def getaddrinfo(self, gethostname): + with mock.patch("socket.getaddrinfo") as getaddrinfo: + yield getaddrinfo + + def test_getaddrinfo_used_for_value_collection(self, getaddrinfo, subject): + getaddrinfo.return_value = [(socket.AF_INET, 0, 0, "", ("192.0.2.1", 0))] + assert subject() == [("AF_INET", "192.0.2.1")] + assert getaddrinfo.call_count == 1 + + def test_str_representation_is_formatted_value(self, getaddrinfo, sensor): + getaddrinfo.return_value = [(socket.AF_INET6, 0, 0, "", ("2001:DB8::1", 0))] + assert str(sensor) == "2001:DB8::1 (IPv6)" diff --git a/Ch04/apd.sensors-chapter04/tests/test_pythonversion.py b/Ch04/apd.sensors-chapter04/tests/test_pythonversion.py new file mode 100644 index 0000000..43ad806 --- /dev/null +++ b/Ch04/apd.sensors-chapter04/tests/test_pythonversion.py @@ -0,0 +1,61 @@ +from collections import namedtuple +from unittest import mock + +import pytest + +from apd.sensors.sensors import PythonVersion + + +@pytest.fixture +def version(): + return namedtuple( + "sys_versioninfo", ("major", "minor", "micro", "releaselevel", "serial") + ) + + +@pytest.fixture +def sensor(): + return PythonVersion() + + +class TestPythonVersionFormatter: + @pytest.fixture + def subject(self, sensor): + return sensor.format + + def test_format_py38(self, subject, version): + py38 = version(3, 8, 0, "final", 0) + assert subject(py38) == "3.8" + + def test_format_large_version(self, subject, version): + large = version(255, 128, 0, "final", 0) + assert subject(large) == "255.128" + + def test_alpha_of_minor_is_marked(self, subject, version): + py39 = version(3, 9, 0, "alpha", 1) + assert subject(py39) == "3.9.0a1" + + def test_alpha_of_micro_is_unmarked(self, subject, version): + py39 = version(3, 9, 1, "alpha", 1) + assert subject(py39) == "3.9" + + +class TestPythonVersionValue: + @pytest.fixture + def subject(self, sensor): + return sensor.value + + @pytest.fixture + def python_version(self): + import sys + + return sys.version_info + + def test_data_value_is_sys_versioninfo(self, python_version, subject): + assert subject() == python_version + + +class TestPythonVersionSensor: + def test_str_representation_is_formatted_value(self, sensor, version): + with mock.patch("sys.version_info", new=version(3, 4, 1, "final", 1)): + assert str(sensor) == "3.4" diff --git a/Ch04/apd.sensors-chapter04/tests/test_ramusage.py b/Ch04/apd.sensors-chapter04/tests/test_ramusage.py new file mode 100644 index 0000000..197ad06 --- /dev/null +++ b/Ch04/apd.sensors-chapter04/tests/test_ramusage.py @@ -0,0 +1,47 @@ +from unittest import mock + +import pytest + +from apd.sensors.sensors import RAMAvailable + + +@pytest.fixture +def sensor(): + return RAMAvailable() + + +class TestRAMAvailableFormatter: + @pytest.fixture + def subject(self, sensor): + return sensor.format + + def test_format_bytes(self, subject): + assert subject(15) == "15.0 B" + + def test_format_kibibytes(self, subject): + assert subject(15000) == "14.6 KiB" + + def test_format_mibibytes(self, subject): + assert subject(15000000) == "14.3 MiB" + + def test_format_gibibytes(self, subject): + assert subject(15000000000) == "14.0 GiB" + + +class TestRAMAvailableValue: + @pytest.fixture + def subject(self, sensor): + return sensor.value + + @pytest.fixture + def virtual_memory(self): + with mock.patch("psutil.virtual_memory") as virtual_memory: + virtual_memory.return_value.available = 1024 + yield virtual_memory + + def test_virtual_memory_called(self, subject, virtual_memory): + assert subject() == 1024 + assert virtual_memory.call_count == 1 + + def test_str_representation_is_formatted_value(self, sensor, virtual_memory): + assert str(sensor) == "1.0 KiB" diff --git a/Ch04/apd.sensors-chapter04/tests/test_sensors.py b/Ch04/apd.sensors-chapter04/tests/test_sensors.py new file mode 100644 index 0000000..f3f5f8e --- /dev/null +++ b/Ch04/apd.sensors-chapter04/tests/test_sensors.py @@ -0,0 +1,59 @@ +from click.testing import CliRunner + +import pytest +from unittest import mock + +import apd.sensors.cli +import apd.sensors.sensors + + +def test_sensors(): + assert hasattr(apd.sensors.sensors, "PythonVersion") + + +@pytest.mark.functional +def test_first_sensor_is_first_two_lines_of_cli_output(): + runner = CliRunner() + with mock.patch("apd.sensors.cli.get_sensors") as get_sensors: + get_sensors.return_value = [apd.sensors.sensors.PythonVersion()] + result = runner.invoke(apd.sensors.cli.show_sensors) + python_version = str(apd.sensors.sensors.PythonVersion()) + assert ["Python Version", python_version] == result.stdout.split("\n")[:2] + + +class TestSensorFromPath: + @pytest.fixture + def subject(self): + return apd.sensors.cli.get_sensor_by_path + + def test_get_sensor_by_path(self, subject): + assert isinstance( + subject("apd.sensors.sensors:PythonVersion"), + apd.sensors.sensors.PythonVersion, + ) + + def test_invalid_format(self, subject): + with pytest.raises(RuntimeError, match="dotted.path.to.module:ClassName"): + subject("apd.sensors.sensors.PythonVersion") + + def test_invalid_module(self, subject): + with pytest.raises(RuntimeError, match="Could not import module"): + subject("apd.nonsense.sensor:FakeSensor") + + def test_missing_sensor(self, subject): + with pytest.raises(RuntimeError, match="Could not find attribute"): + subject("apd.sensors.sensors:FakeSensor") + + def test_invalid_sensor(self, subject): + with pytest.raises(RuntimeError, match="is not recognised as a Sensor"): + subject("apd.sensors.sensors:Sensor") + + +@pytest.mark.functional +def test_develop_option_finds_sensor_by_path(): + runner = CliRunner() + result = runner.invoke( + apd.sensors.cli.show_sensors, ["--develop", "apd.sensors.sensors:PythonVersion"] + ) + python_version = str(apd.sensors.sensors.PythonVersion()) + assert ["Python Version", python_version, "", ""] == result.stdout.split("\n") diff --git a/Ch04/listing04-01-solar_prototype.ipynb b/Ch04/listing04-01-solar_prototype.ipynb new file mode 100644 index 0000000..929665b --- /dev/null +++ b/Ch04/listing04-01-solar_prototype.ipynb @@ -0,0 +1,104 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import subprocess\n", + "\n", + "bt_addr = \"00:80:25:00:00:00\"\n", + "output = subprocess.check_output(\n", + " [\"/home/pi/opensunny-master/opensunny\", \"-i\", bt_addr],\n", + " stderr=subprocess.STDOUT,\n", + " timeout=15,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "b'2020-04-04T12:34:45.914165:INFO:[Value] timestamp=1247525322 current_ac_l3=5.003A'" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "lines = [line for line in output.split(b\"\\n\") if line]\n", + "lines[-1]" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "dict_keys([b'model', b'power_ac', b'yield_total', b'power_dc_1', b'power_dc_2', b'voltage_dc_1', b'voltage_dc_2', b'power_ac_max_l1', b'power_ac_max_l2', b'power_ac_max_l3', b'power_ac_l1', b'power_ac_l2', b'power_ac_l3', b'voltage_ac_l1', b'voltage_ac_l2', b'voltage_ac_l3', b'current_ac_l1', b'current_ac_l2', b'current_ac_l3'])\n", + "b'15220.034kWh'\n" + ] + } + ], + "source": [ + "found = {}\n", + "for line in lines:\n", + " start, value = line.rsplit(b\"=\", 1)\n", + " _, key = start.rsplit(b\" \", 1)\n", + " found[key] = value\n", + "print(found.keys())\n", + "print(found[b\"yield_total\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "15220034.0\n" + ] + } + ], + "source": [ + "yield_total = float(found[b\"yield_total\"][:-3].replace(b\".\", b\"\"))\n", + "print(yield_total)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "SSH pi@rpi4 loft-sensor", + "language": "python", + "name": "rik_ssh_pi_rpi4_loftsensor" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Ch05/apd.sensors-chapter05-pintbased/.pre-commit-config.yaml b/Ch05/apd.sensors-chapter05-pintbased/.pre-commit-config.yaml new file mode 100644 index 0000000..d3b6ec7 --- /dev/null +++ b/Ch05/apd.sensors-chapter05-pintbased/.pre-commit-config.yaml @@ -0,0 +1,26 @@ +repos: + +- repo: local + hooks: + - id: black + name: black + entry: pipenv run black + args: [--quiet] + language: system + types: [python] + + - id: mypy + name: mypy + exclude: (?x)^( + (.*)/setup.py + )$ + entry: pipenv run mypy + args: ["--follow-imports=skip"] + language: system + types: [python] + + - id: flake8 + name: flake8 + entry: pipenv run flake8 + language: system + types: [python] diff --git a/Ch05/apd.sensors-chapter05-pintbased/CHANGES.md b/Ch05/apd.sensors-chapter05-pintbased/CHANGES.md new file mode 100644 index 0000000..dba741b --- /dev/null +++ b/Ch05/apd.sensors-chapter05-pintbased/CHANGES.md @@ -0,0 +1,29 @@ +## Changes + +### 2.0.0 (2019-09-08) + +* Add `to_json_compatible` and `from_json_compatible` methods to Sensor + to facilitate better HTTP API (Matthew Wilkes) + +* HTTP API is now versioned. The API from 1.3.0 is available at /v/1.0 + and an updated version is at /v/2.0 (Matthew Wilkes) + +### 1.3.0 (2019-08-20) + +* WSGI HTTP API support added (Matthew Wilkes) + +### 1.2.0 (2019-08-05) + +* Add external plugin support through `apd.sensors.sensors` entrypoint (Matthew Wilkes) + +### 1.1.0 (2019-07-12) + +* Add --develop argument (Matthew Wilkes) + +### 1.0.1 (2019-06-20) + +* Fix broken 1.0.0 release (Matthew Wilkes) + +### 1.0.0 (2019-06-20) + +* Added initial sensors (Matthew Wilkes) diff --git a/Ch05/apd.sensors-chapter05-pintbased/LICENCE b/Ch05/apd.sensors-chapter05-pintbased/LICENCE new file mode 100644 index 0000000..053f694 --- /dev/null +++ b/Ch05/apd.sensors-chapter05-pintbased/LICENCE @@ -0,0 +1,19 @@ +Copyright (c) 2019 Matthew Wilkes + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/Ch05/apd.sensors-chapter05-pintbased/Pipfile b/Ch05/apd.sensors-chapter05-pintbased/Pipfile new file mode 100644 index 0000000..f6cd446 --- /dev/null +++ b/Ch05/apd.sensors-chapter05-pintbased/Pipfile @@ -0,0 +1,34 @@ +[[source]] +name = "pypi" +url = "https://pypi.org/simple" +verify_ssl = true + +[[source]] +name = "piwheels" +url = "https://piwheels.org/simple" +verify_ssl = true + +[dev-packages] +ipykernel = "*" +remote-ikernel = "*" +pytest = "*" +pytest-cov = "*" +mypy = "*" +flake8 = "*" +black = "*" +pre-commit = "*" +wheel = "*" +twine = "*" +webtest = "*" +apd-sensors = {editable = true,extras = ["webapp"],path = "."} + +[packages] +apd-sensors = {editable = true,path = "."} +apd-sunnyboy-solar = {editable = true,path = "./plugins/apd.sunnyboy_solar"} +waitress = "*" +pint = "*" + +[requires] + +[pipenv] +allow_prereleases = true diff --git a/Ch05/apd.sensors-chapter05-pintbased/Pipfile.lock b/Ch05/apd.sensors-chapter05-pintbased/Pipfile.lock new file mode 100644 index 0000000..3b587bb --- /dev/null +++ b/Ch05/apd.sensors-chapter05-pintbased/Pipfile.lock @@ -0,0 +1,1018 @@ +{ + "_meta": { + "hash": { + "sha256": "b5f132c374a0a891097a70c6f3be719ec794dec7b855ed5f0ed9adad4d72b0e7" + }, + "pipfile-spec": 6, + "requires": {}, + "sources": [ + { + "name": "pypi", + "url": "https://pypi.org/simple", + "verify_ssl": true + }, + { + "name": "piwheels", + "url": "https://piwheels.org/simple", + "verify_ssl": true + } + ] + }, + "default": { + "apd-sensors": { + "editable": true, + "path": "." + }, + "apd-sunnyboy-solar": { + "editable": true, + "path": "./plugins/apd.sunnyboy_solar" + }, + "click": { + "hashes": [ + 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aspects of the system. The available sensors are: + +* Python version +* IP Addresses +* CPU Usage +* RAM Available +* Battery charging state +* Ambient Temperature +* Ambient Humidity + +There are no command-line options, to view the report run `sensors` on the +command line. + +## Caveats + +The Ambient Temperature and Ambient Humidity sensors are only available on +RaspberryPi hosts and assume that a DHT22 sensor is connected to pin `D20`. + +The location and type of the sensor can be controlled by setting a pair of +environment variables: `APD_SENSORS_TEMPERATURE_BOARD` and +`APD_SENSORS_TEMPERATURE_PIN`. + +If there is an entry in `/etc/hosts` for the current machine's hostname that +value will be the only result from the IP Addresses sensor. + +## Installation + +You can install with `pip3 install apd.sensors` under Python 3.7 or higher. + +We recommend using pipenv to manage your environment, in which case you would +install using `pipenv --three install apd.sensors` and run the programme using +`pipenv run sensors`. + +## API server + +There is an optional API server shipped with apd.sensors. To use this you +should install the `apd.sensors[webapp]` extra. The API can then be started +with the non-production quality wsgiref server using: + + python -m apd.sensors.wsgi.serve + +or through Waitress (if installed) using: + + waitress-serve --call apd.sensors.wsgi:set_up_config + +Other WSGI servers will also work, you should use set_up_config as a factory +function. + +An environment variable is required to use the API server, `APD_SENSORS_API_KEY` +should be set to the API key required to gain access. One can be generated +using: + + python -c "import uuid; print(uuid.uuid4().hex)" + +The following endpoints are supported: + +* /v/2.0/sensors +* /v/2.0/sensors/sensorid diff --git a/Ch05/apd.sensors-chapter05-pintbased/plugins/apd.sunnyboy_solar/pyproject.toml b/Ch05/apd.sensors-chapter05-pintbased/plugins/apd.sunnyboy_solar/pyproject.toml new file mode 100644 index 0000000..4d3bb67 --- /dev/null +++ b/Ch05/apd.sensors-chapter05-pintbased/plugins/apd.sunnyboy_solar/pyproject.toml @@ -0,0 +1,5 @@ +[build-system] +requires = [ + "setuptools", +] +build-backend = "setuptools.build_meta" diff --git a/Ch05/apd.sensors-chapter05-pintbased/plugins/apd.sunnyboy_solar/setup.cfg b/Ch05/apd.sensors-chapter05-pintbased/plugins/apd.sunnyboy_solar/setup.cfg new file mode 100644 index 0000000..0f5db10 --- /dev/null +++ b/Ch05/apd.sensors-chapter05-pintbased/plugins/apd.sunnyboy_solar/setup.cfg @@ -0,0 +1,33 @@ +[mypy] +ignore_missing_imports = True + +[flake8] +max-line-length = 88 + +[metadata] +name = apd.sunnyboy_solar +version = attr: apd.sunnyboy_solar.VERSION +description = APD Sensor for reading data from Sunnyboy inverters +long_description = file: README.md, CHANGES.md, LICENCE +long_description_content_type = text/markdown +keywords = iot solar +license = MIT +classifiers = + Programming Language :: Python :: 3 + Programming Language :: Python :: 3.7 + +[options] +zip_safe = False +include_package_data = True +package_dir = + =src +packages = find_namespace: +install_requires = + apd.sensors + +[options.packages.find] +where = src + +[options.entry_points] +apd.sensors.sensors = + SolarCumulativeOutput = apd.sunnyboy_solar.sensor:SolarCumulativeOutput diff --git a/Ch05/apd.sensors-chapter05-pintbased/plugins/apd.sunnyboy_solar/setup.py b/Ch05/apd.sensors-chapter05-pintbased/plugins/apd.sunnyboy_solar/setup.py new file mode 100644 index 0000000..6068493 --- /dev/null +++ b/Ch05/apd.sensors-chapter05-pintbased/plugins/apd.sunnyboy_solar/setup.py @@ -0,0 +1,3 @@ +from setuptools import setup + +setup() diff --git a/Ch05/apd.sensors-chapter05-pintbased/plugins/apd.sunnyboy_solar/src/apd/sunnyboy_solar/Pipfile b/Ch05/apd.sensors-chapter05-pintbased/plugins/apd.sunnyboy_solar/src/apd/sunnyboy_solar/Pipfile new file mode 100644 index 0000000..e69de29 diff --git a/Ch05/apd.sensors-chapter05-pintbased/plugins/apd.sunnyboy_solar/src/apd/sunnyboy_solar/__init__.py b/Ch05/apd.sensors-chapter05-pintbased/plugins/apd.sunnyboy_solar/src/apd/sunnyboy_solar/__init__.py new file mode 100644 index 0000000..3277f64 --- /dev/null +++ b/Ch05/apd.sensors-chapter05-pintbased/plugins/apd.sunnyboy_solar/src/apd/sunnyboy_solar/__init__.py @@ -0,0 +1 @@ +VERSION = "1.0.0" diff --git a/Ch05/apd.sensors-chapter05-pintbased/plugins/apd.sunnyboy_solar/src/apd/sunnyboy_solar/sensor.py b/Ch05/apd.sensors-chapter05-pintbased/plugins/apd.sunnyboy_solar/src/apd/sunnyboy_solar/sensor.py new file mode 100644 index 0000000..aa37a2e --- /dev/null +++ b/Ch05/apd.sensors-chapter05-pintbased/plugins/apd.sunnyboy_solar/src/apd/sunnyboy_solar/sensor.py @@ -0,0 +1,69 @@ +import os +import subprocess +import typing as t + +from pint import _DEFAULT_REGISTRY as ureg + +from apd.sensors.base import Sensor + + +class SolarCumulativeOutput(Sensor[t.Optional[t.Any]]): + name = "SolarCumulativeOutput" + title = "Solar panel cumulative output" + + def __init__( + self, path: t.Optional[str] = None, bt_addr: t.Optional[str] = None + ) -> None: + self.path = os.environ.get( + "APD_SUNNYBOYSOLAR_PATH", "/home/pi/opensunny-master/opensunny" + ) + self.bt_addr = os.environ.get("APD_SUNNYBOYSOLAR_BT_ADDRESS", None) + + def value(self) -> t.Optional[t.Any]: + if self.bt_addr is None: + return None + try: + output: bytes = subprocess.check_output( + [self.path, "-i", self.bt_addr], stderr=subprocess.STDOUT, timeout=15 + ) + except (subprocess.CalledProcessError, FileNotFoundError): + return None + + lines = filter(None, output.split(b"\n")) + found = {} + for line in lines: + start, value = line.rsplit(b"=", 1) + start, key = start.rsplit(b" ", 1) + found[key] = value + + try: + yield_total = float(found[b"yield_total"][:-3].replace(b".", b"")) + power_dc_1 = int(found[b"power_dc_1"][:-1]) + power_dc_2 = int(found[b"power_dc_2"][:-1]) + if power_dc_1 > 1500 or power_dc_2 > 1500: + return None + except (ValueError, IndexError, KeyError): + return None + return ureg.Quantity(yield_total, "watt") + + @classmethod + def format(cls, value: t.Optional[t.Any]) -> str: + if value is None: + return "Unknown" + return "{:~P}".format(value.to(ureg.kilowatt)) + + @classmethod + def to_json_compatible( + cls, value: t.Optional[t.Any] + ) -> t.Optional[t.Dict[str, t.Union[str, float]]]: + if value is not None: + return {"magnitude": value.magnitude, "unit": str(value.units)} + else: + return None + + @classmethod + def from_json_compatible(cls, json_version: t.Any) -> t.Optional[t.Any]: + if json_version: + return ureg.Quantity(json_version["magnitude"], ureg[json_version["unit"]]) + else: + return None diff --git a/Ch05/apd.sensors-chapter05-pintbased/pytest.ini b/Ch05/apd.sensors-chapter05-pintbased/pytest.ini new file mode 100644 index 0000000..861f3d0 --- /dev/null +++ b/Ch05/apd.sensors-chapter05-pintbased/pytest.ini @@ -0,0 +1,3 @@ +[pytest] +markers = + functional: these tests are significantly slower as they run the whole CLI script \ No newline at end of file diff --git a/Ch05/apd.sensors-chapter05-pintbased/setup.cfg b/Ch05/apd.sensors-chapter05-pintbased/setup.cfg new file mode 100644 index 0000000..fe8017c --- /dev/null +++ b/Ch05/apd.sensors-chapter05-pintbased/setup.cfg @@ -0,0 +1,66 @@ +[mypy] +namespace_packages = True +mypy_path = src + +[mypy-psutil] +ignore_missing_imports = True + +[mypy-adafruit_dht] +ignore_missing_imports = True + +[mypy-board] +ignore_missing_imports = True + +[mypy-pytest] +ignore_missing_imports = True + +[mypy-webtest] +ignore_missing_imports = True + +[flake8] +max-line-length = 88 + +[metadata] +name = apd.sensors +version = attr: apd.sensors.VERSION +description = APD Sensor package +long_description = file: README.md, CHANGES.md, LICENCE +long_description_content_type = text/markdown +keywords = iot +license = MIT +classifiers = + Programming Language :: Python :: 3 + Programming Language :: Python :: 3.7 + +[options] +zip_safe = False +include_package_data = True +package_dir = + =src +packages = find_namespace: +install_requires = + psutil + click + pint + adafruit-circuitpython-dht ; 'arm' in platform_machine + +[options.package_data] +apd.sensors = py.typed + +[options.packages.find] +where = src + +[options.entry_points] +console_scripts = + sensors = apd.sensors.cli:show_sensors +apd.sensors.sensors = + PythonVersion = apd.sensors.sensors:PythonVersion + IPAddresses = apd.sensors.sensors:IPAddresses + CPULoad = apd.sensors.sensors:CPULoad + RAMAvailable = apd.sensors.sensors:RAMAvailable + ACStatus = apd.sensors.sensors:ACStatus + Temperature = apd.sensors.sensors:Temperature + RelativeHumidity = apd.sensors.sensors:RelativeHumidity + +[options.extras_require] +webapp = flask \ No newline at end of file diff --git a/Ch05/apd.sensors-chapter05-pintbased/setup.py b/Ch05/apd.sensors-chapter05-pintbased/setup.py new file mode 100644 index 0000000..6068493 --- /dev/null +++ b/Ch05/apd.sensors-chapter05-pintbased/setup.py @@ -0,0 +1,3 @@ +from setuptools import setup + +setup() diff --git a/Ch05/apd.sensors-chapter05-pintbased/src/apd/sensors/__init__.py b/Ch05/apd.sensors-chapter05-pintbased/src/apd/sensors/__init__.py new file mode 100644 index 0000000..2101409 --- /dev/null +++ b/Ch05/apd.sensors-chapter05-pintbased/src/apd/sensors/__init__.py @@ -0,0 +1 @@ +VERSION = "2.0.0" diff --git a/Ch05/apd.sensors-chapter05-pintbased/src/apd/sensors/base.py b/Ch05/apd.sensors-chapter05-pintbased/src/apd/sensors/base.py new file mode 100644 index 0000000..74b32ae --- /dev/null +++ b/Ch05/apd.sensors-chapter05-pintbased/src/apd/sensors/base.py @@ -0,0 +1,51 @@ +#!/usr/bin/env python +# coding: utf-8 +import typing as t + + +T_value = t.TypeVar("T_value") + + +class Sensor(t.Generic[T_value]): + name: str + title: str + + def value(self) -> T_value: + raise NotImplementedError + + @classmethod + def format(cls, value: T_value) -> str: + raise NotImplementedError + + def __str__(self) -> str: + return self.format(self.value()) + + @classmethod + def to_json_compatible(cls, value: T_value) -> t.Any: + raise NotImplementedError() + + @classmethod + def from_json_compatible(cls, json_version: t.Any) -> T_value: + raise NotImplementedError() + + +class JSONSensor(Sensor[T_value]): + @classmethod + def to_json_compatible(cls, value: T_value) -> t.Any: + return value + + @classmethod + def from_json_compatible(cls, json_version: t.Any) -> T_value: + return t.cast(T_value, json_version) + + +version_info_type = t.NamedTuple( + "version_info_type", + [ + ("major", int), + ("minor", int), + ("micro", int), + ("releaselevel", str), + ("serial", int), + ], +) diff --git a/Ch05/apd.sensors-chapter05-pintbased/src/apd/sensors/cli.py b/Ch05/apd.sensors-chapter05-pintbased/src/apd/sensors/cli.py new file mode 100644 index 0000000..1ee27b9 --- /dev/null +++ b/Ch05/apd.sensors-chapter05-pintbased/src/apd/sensors/cli.py @@ -0,0 +1,74 @@ +import enum +import importlib +import sys +import pkg_resources +import typing as t + +import click + +from .sensors import Sensor + + +class ReturnCodes(enum.IntEnum): + OK = 0 + BAD_SENSOR_PATH = 17 + + +def get_sensor_by_path(sensor_path: str) -> Sensor[t.Any]: + try: + module_name, sensor_name = sensor_path.split(":") + except ValueError: + raise RuntimeError( + "Sensor path must be in the format dotted.path.to.module:ClassName" + ) + try: + module = importlib.import_module(module_name) + except ImportError: + raise RuntimeError(f"Could not import module {module_name}") + try: + sensor_class = getattr(module, sensor_name) + except AttributeError: + raise RuntimeError(f"Could not find attribute {sensor_name} in {module_name}") + if ( + isinstance(sensor_class, type) + and issubclass(sensor_class, Sensor) + and sensor_class != Sensor + ): + return sensor_class() + else: + raise RuntimeError( + f"Detected object {sensor_class!r} is not recognised as a Sensor type" + ) + + +def get_sensors() -> t.Iterable[Sensor[t.Any]]: + sensors = [] + for sensor_class in pkg_resources.iter_entry_points("apd.sensors.sensors"): + class_ = sensor_class.load() + sensors.append(t.cast(Sensor[t.Any], class_())) + return sensors + + +@click.command(help="Displays the values of the sensors") +@click.option( + "--develop", required=False, metavar="path", help="Load a sensor by Python path" +) +def show_sensors(develop: str) -> None: + sensors: t.Iterable[Sensor[t.Any]] + if develop: + try: + sensors = [get_sensor_by_path(develop)] + except RuntimeError as error: + click.secho(str(error), fg="red", bold=True) + sys.exit(ReturnCodes.BAD_SENSOR_PATH) + else: + sensors = get_sensors() + for sensor in sensors: + click.secho(sensor.title, bold=True) + click.echo(str(sensor)) + click.echo("") + sys.exit(ReturnCodes.OK) + + +if __name__ == "__main__": + show_sensors() diff --git a/Ch05/apd.sensors-chapter05-pintbased/src/apd/sensors/py.typed b/Ch05/apd.sensors-chapter05-pintbased/src/apd/sensors/py.typed new file mode 100644 index 0000000..e69de29 diff --git a/Ch05/apd.sensors-chapter05-pintbased/src/apd/sensors/sensors.py b/Ch05/apd.sensors-chapter05-pintbased/src/apd/sensors/sensors.py new file mode 100644 index 0000000..44d432f --- /dev/null +++ b/Ch05/apd.sensors-chapter05-pintbased/src/apd/sensors/sensors.py @@ -0,0 +1,192 @@ +#!/usr/bin/env python +# coding: utf-8 +import math +import os +import socket +import sys +import typing as t + +import psutil +from pint import _DEFAULT_REGISTRY as ureg + +from .base import Sensor, JSONSensor, version_info_type + + +class PythonVersion(JSONSensor[version_info_type]): + name = "PythonVersion" + title = "Python Version" + + def value(self) -> version_info_type: + return version_info_type(*sys.version_info) + + @classmethod + def format(cls, value: version_info_type) -> str: + if value.micro == 0 and value.releaselevel == "alpha": + return "{0.major}.{0.minor}.{0.micro}a{0.serial}".format(value) + return "{0.major}.{0.minor}".format(value) + + +class IPAddresses(JSONSensor[t.Iterable[t.Tuple[str, str]]]): + name = "IPAddresses" + title = "IP Addresses" + FAMILIES = {"AF_INET": "IPv4", "AF_INET6": "IPv6"} + + def value(self) -> t.List[t.Tuple[str, str]]: + hostname = socket.gethostname() + addresses = socket.getaddrinfo(hostname, None) + + address_info: t.List[t.Tuple[str, str]] = [] + for address in addresses: + family, ip = (address[0].name, address[4][0]) + if family not in self.FAMILIES: + continue + value = (family, ip) + if value not in address_info: + address_info.append(value) + return address_info + + @classmethod + def format(cls, value: t.Iterable[t.Tuple[str, str]]) -> str: + return "\n".join( + "{0} ({1})".format(address[1], cls.FAMILIES.get(address[0], "Unknown")) + for address in value + ) + + +class CPULoad(JSONSensor[float]): + name = "CPULoad" + title = "CPU Usage" + + def value(self) -> float: + return float(psutil.cpu_percent(interval=3)) / 100.0 + + @classmethod + def format(cls, value: float) -> str: + return "{:.1%}".format(value) + + +class RAMAvailable(JSONSensor[int]): + name = "RAMAvailable" + title = "RAM Available" + UNITS = ("B", "KiB", "MiB", "GiB", "TiB", "PiB", "EiB") + UNIT_SIZE = 2 ** 10 + + def value(self) -> int: + return int(psutil.virtual_memory().available) + + @classmethod + def format(cls, value: int) -> str: + magnitude = math.floor(math.log(value, cls.UNIT_SIZE)) + max_magnitude = len(cls.UNITS) - 1 + magnitude = min(magnitude, max_magnitude) + scaled_value = value / (cls.UNIT_SIZE ** magnitude) + return "{:.1f} {}".format(scaled_value, cls.UNITS[magnitude]) + + +class ACStatus(JSONSensor[t.Optional[bool]]): + name = "ACStatus" + title = "AC Connected" + + def value(self) -> t.Optional[bool]: + battery = psutil.sensors_battery() + if battery is not None: + value = battery.power_plugged + if value is None: + return None + else: + return bool(value) + else: + return None + + @classmethod + def format(cls, value: t.Optional[bool]) -> str: + if value is None: + return "Unknown" + elif value: + return "Connected" + else: + return "Not connected" + + +class Temperature(Sensor[t.Optional[t.Any]]): + name = "Temperature" + title = "Ambient Temperature" + + def __init__(self) -> None: + self.board = os.environ.get("APD_SENSORS_TEMPERATURE_BOARD", "DHT22") + self.pin = os.environ.get("APD_SENSORS_TEMPERATURE_PIN", "D20") + + def value(self) -> t.Optional[t.Any]: + try: + import adafruit_dht + import board + + sensor_type = getattr(adafruit_dht, self.board) + pin = getattr(board, self.pin) + except (ImportError, NotImplementedError, AttributeError): + # No DHT library results in an ImportError. + # Running on an unknown platform results in a + # NotImplementedError when getting the pin + return None + try: + return ureg.Quantity(sensor_type(pin).temperature, ureg.celsius) + except RuntimeError: + return None + + @classmethod + def format(cls, value: t.Optional[t.Any]) -> str: + if value is None: + return "Unknown" + else: + return "{:.3~P} ({:.3~P})".format(value, value.to(ureg.fahrenheit)) + + @classmethod + def to_json_compatible(cls, value: t.Optional[t.Any]) -> t.Any: + if value is not None: + return {"magnitude": value.magnitude, "unit": str(value.units)} + else: + return None + + @classmethod + def from_json_compatible(cls, json_version: t.Any) -> t.Optional[t.Any]: + if json_version: + return ureg.Quantity(json_version["magnitude"], ureg[json_version["unit"]]) + else: + return None + + def __str__(self) -> str: + return self.format(self.value()) + + +class RelativeHumidity(JSONSensor[t.Optional[float]]): + name = "RelativeHumidity" + title = "Relative Humidity" + + def __init__(self) -> None: + self.board = os.environ.get("APD_SENSORS_TEMPERATURE_BOARD", "DHT22") + self.pin = os.environ.get("APD_SENSORS_TEMPERATURE_PIN", "D20") + + def value(self) -> t.Optional[float]: + try: + import adafruit_dht + import board + + sensor_type = getattr(adafruit_dht, self.board) + pin = getattr(board, self.pin) + except (ImportError, NotImplementedError, AttributeError): + # No DHT library results in an ImportError. + # Running on an unknown platform results in a + # NotImplementedError when getting the pin + return None + + try: + return float(sensor_type(pin).humidity) + except RuntimeError: + return None + + @classmethod + def format(cls, value: t.Optional[float]) -> str: + if value is None: + return "Unknown" + else: + return "{:.1%}".format(value) diff --git a/Ch05/apd.sensors-chapter05-pintbased/src/apd/sensors/wsgi/__init__.py b/Ch05/apd.sensors-chapter05-pintbased/src/apd/sensors/wsgi/__init__.py new file mode 100644 index 0000000..a97c28f --- /dev/null +++ b/Ch05/apd.sensors-chapter05-pintbased/src/apd/sensors/wsgi/__init__.py @@ -0,0 +1,12 @@ +import flask + +from .base import set_up_config +from . import v10 +from . import v20 + + +__all__ = ["app", "set_up_config"] + +app = flask.Flask(__name__) +app.register_blueprint(v10.version, url_prefix="/v/1.0") +app.register_blueprint(v20.version, url_prefix="/v/2.0") diff --git a/Ch05/apd.sensors-chapter05-pintbased/src/apd/sensors/wsgi/base.py b/Ch05/apd.sensors-chapter05-pintbased/src/apd/sensors/wsgi/base.py new file mode 100644 index 0000000..c9be130 --- /dev/null +++ b/Ch05/apd.sensors-chapter05-pintbased/src/apd/sensors/wsgi/base.py @@ -0,0 +1,43 @@ +from hmac import compare_digest +import functools +import os +import typing as t + +import flask + + +ViewFuncReturn = t.TypeVar("ViewFuncReturn") +ErrorReturn = t.Tuple[t.Dict[str, str], int] +REQUIRED_CONFIG_KEYS = {"APD_SENSORS_API_KEY"} + + +def require_api_key( + func: t.Callable[[], ViewFuncReturn] +) -> t.Callable[[], t.Union[ViewFuncReturn, ErrorReturn]]: + @functools.wraps(func) + def wrapped() -> t.Union[ViewFuncReturn, ErrorReturn]: + api_key = flask.current_app.config["APD_SENSORS_API_KEY"] + headers = flask.request.headers + supplied_key = headers.get("X-API-Key", "") + if not compare_digest(api_key, supplied_key): + return {"error": "Supply API key in X-API-Key header"}, 403 + return func() + + return wrapped + + +def set_up_config( + environ: t.Optional[t.Dict[str, str]] = None, + to_configure: t.Optional[flask.Flask] = None, +) -> flask.Flask: + if environ is None: + environ = dict(os.environ) + if to_configure is None: + from apd.sensors.wsgi import app + + to_configure = app + missing_keys = REQUIRED_CONFIG_KEYS - environ.keys() + if missing_keys: + raise ValueError("Missing config variables: {}".format(", ".join(missing_keys))) + to_configure.config.from_mapping(environ) + return to_configure diff --git a/Ch05/apd.sensors-chapter05-pintbased/src/apd/sensors/wsgi/serve.py b/Ch05/apd.sensors-chapter05-pintbased/src/apd/sensors/wsgi/serve.py new file mode 100644 index 0000000..0b1f684 --- /dev/null +++ b/Ch05/apd.sensors-chapter05-pintbased/src/apd/sensors/wsgi/serve.py @@ -0,0 +1,10 @@ +from . import app +from .base import set_up_config + +if __name__ == "__main__": + import wsgiref.simple_server + + set_up_config(None, app) + + with wsgiref.simple_server.make_server("", 8000, app) as server: + server.serve_forever() diff --git a/Ch05/apd.sensors-chapter05-pintbased/src/apd/sensors/wsgi/v10.py b/Ch05/apd.sensors-chapter05-pintbased/src/apd/sensors/wsgi/v10.py new file mode 100644 index 0000000..7eee6dd --- /dev/null +++ b/Ch05/apd.sensors-chapter05-pintbased/src/apd/sensors/wsgi/v10.py @@ -0,0 +1,26 @@ +import json +import typing as t + +import flask + +from apd.sensors.cli import get_sensors +from .base import require_api_key + +version = flask.Blueprint(__name__, __name__) + + +@version.route("/sensors/") +@require_api_key +def sensor_values() -> t.Tuple[t.Dict[str, t.Any], int, t.Dict[str, str]]: + headers = {"Content-Security-Policy": "default-src 'none'"} + data = {} + for sensor in get_sensors(): + value = sensor.value() + try: + json.dumps(value) + except TypeError: + # This value isn't JSON serializable, skip it + continue + else: + data[sensor.title] = sensor.value() + return data, 200, headers diff --git a/Ch05/apd.sensors-chapter05-pintbased/src/apd/sensors/wsgi/v20.py b/Ch05/apd.sensors-chapter05-pintbased/src/apd/sensors/wsgi/v20.py new file mode 100644 index 0000000..fd2b5fc --- /dev/null +++ b/Ch05/apd.sensors-chapter05-pintbased/src/apd/sensors/wsgi/v20.py @@ -0,0 +1,32 @@ +import typing as t + +import flask + +from apd.sensors.cli import get_sensors +from .base import require_api_key + +version = flask.Blueprint(__name__, __name__) + + +@version.route("/sensors/") +@version.route("/sensors/") +@require_api_key +def sensor_values(sensor_id=None) -> t.Tuple[t.Dict[str, t.Any], int, t.Dict[str, str]]: + headers = {"Content-Security-Policy": "default-src 'none'"} + sensors = [] + for sensor in get_sensors(): + if sensor_id and sensor_id != sensor.name: + continue + try: + value = sensor.value() + sensor_data = { + "id": sensor.name, + "title": sensor.title, + "value": sensor.to_json_compatible(value), + "human_readable": sensor.format(value), + } + sensors.append(sensor_data) + except NotImplementedError: + pass + data = {"sensors": sensors} + return data, 200, headers diff --git a/Ch05/apd.sensors-chapter05-pintbased/tests/test_acstatus.py b/Ch05/apd.sensors-chapter05-pintbased/tests/test_acstatus.py new file mode 100644 index 0000000..d2fe71e --- /dev/null +++ b/Ch05/apd.sensors-chapter05-pintbased/tests/test_acstatus.py @@ -0,0 +1,55 @@ +from unittest import mock + +import pytest + +from apd.sensors.sensors import ACStatus + + +@pytest.fixture +def sensor(): + return ACStatus() + + +class TestACStatusFormatter: + @pytest.fixture + def subject(self, sensor): + return sensor.format + + def test_format_True(self, subject): + assert subject(True) == "Connected" + + def test_format_False(self, subject): + assert subject(False) == "Not connected" + + def test_format_None(self, subject): + assert subject(None) == "Unknown" + + +class TestACStatusValue: + @pytest.fixture + def subject(self, sensor): + return sensor.value + + @pytest.fixture + def sensors_battery(self): + with mock.patch("psutil.sensors_battery") as sensors_battery: + yield sensors_battery + + def test_sensors_battery_called(self, subject, sensors_battery): + sensors_battery.return_value.power_plugged = True + assert subject() is True + assert sensors_battery.call_count == 1 + + def test_sensor_but_battery_unknown(self, subject, sensors_battery): + sensors_battery.return_value.power_plugged = None + assert subject() is None + assert sensors_battery.call_count == 1 + + def test_no_sensor(self, subject, sensors_battery): + sensors_battery.return_value = None + assert subject() is None + + def test_str_representation_is_formatted_value(self, sensor, sensors_battery): + sensors_battery.return_value.power_plugged = True + assert str(sensor) == "Connected" + assert sensors_battery.call_count == 1 diff --git a/Ch05/apd.sensors-chapter05-pintbased/tests/test_api_server.py b/Ch05/apd.sensors-chapter05-pintbased/tests/test_api_server.py new file mode 100644 index 0000000..0ffbeaa --- /dev/null +++ b/Ch05/apd.sensors-chapter05-pintbased/tests/test_api_server.py @@ -0,0 +1,105 @@ +import os +import uuid +from unittest import mock + +import flask +import pytest +from webtest import TestApp + +from apd.sensors.wsgi import set_up_config +from apd.sensors.sensors import PythonVersion +from apd.sensors.wsgi import v10 +from apd.sensors.wsgi import v20 + + +@pytest.fixture(scope="session") +def api_key(): + return uuid.uuid4().hex + + +def test_api_key_is_required_config_option(): + app = mock.MagicMock() + with pytest.raises( + ValueError, match="Missing config variables: APD_SENSORS_API_KEY" + ): + set_up_config({}, to_configure=app) + + +def test_os_environ_is_default_for_config_values(api_key): + app = mock.MagicMock() + os.environ["APD_SENSORS_API_KEY"] = api_key + try: + assert app.config.from_mapping.call_count == 0 + set_up_config(None, to_configure=app) + assert app.config.from_mapping.call_count == 1 + assert app.config.from_mapping.call_args[0][0] == os.environ + finally: + del os.environ["APD_SENSORS_API_KEY"] + + +class CommonTests: + @pytest.mark.functional + def test_sensor_values_fails_on_missing_api_key(self, api_server): + response = api_server.get("/sensors/", expect_errors=True) + assert response.status_code == 403 + assert response.json["error"] == "Supply API key in X-API-Key header" + + @pytest.mark.functional + def test_sensor_values_require_correct_api_key(self, api_server): + response = api_server.get( + "/sensors/", headers={"X-API-Key": "wrong_key"}, expect_errors=True + ) + assert response.status_code == 403 + assert response.json["error"] == "Supply API key in X-API-Key header" + + +class Testv10API(CommonTests): + @pytest.fixture + def subject(self, api_key): + app = flask.Flask("testapp") + app.register_blueprint(v10.version) + set_up_config({"APD_SENSORS_API_KEY": api_key}, to_configure=app) + return app + + @pytest.fixture + def api_server(self, subject): + return TestApp(subject) + + @pytest.mark.functional + def test_sensor_values_returned_as_json(self, api_server, api_key): + value = api_server.get("/sensors/", headers={"X-API-Key": api_key}).json + python_version = PythonVersion().value() + + sensor_names = value.keys() + assert "Python Version" in sensor_names + assert value["Python Version"] == list(python_version) + + @pytest.mark.functional + def test_unserializable_sensor_is_omitted(self, api_server, api_key): + value = api_server.get("/sensors/", headers={"X-API-Key": api_key}).json + sensor_names = value.keys() + assert "Temperature" not in sensor_names + + +class Testv20API(CommonTests): + @pytest.fixture + def subject(self, api_key): + app = flask.Flask("testapp") + app.register_blueprint(v20.version) + set_up_config({"APD_SENSORS_API_KEY": api_key}, to_configure=app) + return app + + @pytest.fixture + def api_server(self, subject): + return TestApp(subject) + + @pytest.mark.functional + def test_sensor_values_returned_as_json(self, api_server, api_key): + value = api_server.get("/sensors/", headers={"X-API-Key": api_key}).json + sensors = value["sensors"] + + python_version = [ + sensor for sensor in sensors if sensor["id"] == "PythonVersion" + ][0] + assert python_version["title"] == "Python Version" + assert python_version["value"] == list(PythonVersion().value()) diff --git a/Ch05/apd.sensors-chapter05-pintbased/tests/test_cpuusage.py b/Ch05/apd.sensors-chapter05-pintbased/tests/test_cpuusage.py new file mode 100644 index 0000000..445f4e5 --- /dev/null +++ b/Ch05/apd.sensors-chapter05-pintbased/tests/test_cpuusage.py @@ -0,0 +1,45 @@ +from unittest import mock + +import pytest + +from apd.sensors.sensors import CPULoad + + +@pytest.fixture +def sensor(): + return CPULoad() + + +class TestCPULoadFormatter: + @pytest.fixture + def subject(self, sensor): + return sensor.format + + def test_format_simple_percentage(self, subject): + assert subject(0.05) == "5.0%" + + def test_format_multiple_decimal_places(self, subject): + assert subject(0.031415926) == "3.1%" + + def test_format_multiple_decimal_places_int(self, subject) -> None: + assert subject(1) == "100.0%" + + +class TestCPULoadValue: + @pytest.fixture + def subject(self, sensor): + return sensor.value + + @pytest.fixture + def cpuload(self): + with mock.patch("psutil.cpu_percent") as cpu_percent: + cpu_percent.return_value = 50.1 + yield cpu_percent + + def test_cpu_percent_called_With_interval(self, subject, cpuload): + assert subject() == 0.501 + assert cpuload.call_count == 1 + assert tuple(cpuload.call_args) == ((), {"interval": 3}) + + def test_str_representation_is_formatted_value(self, sensor, cpuload): + assert str(sensor) == "50.1%" diff --git a/Ch05/apd.sensors-chapter05-pintbased/tests/test_dht.py b/Ch05/apd.sensors-chapter05-pintbased/tests/test_dht.py new file mode 100644 index 0000000..52a5ee9 --- /dev/null +++ b/Ch05/apd.sensors-chapter05-pintbased/tests/test_dht.py @@ -0,0 +1,67 @@ +import pytest + +from apd.sensors.sensors import Temperature, RelativeHumidity, ureg + + +@pytest.fixture +def temperature_sensor(): + return Temperature() + + +@pytest.fixture +def humidity_sensor(): + return RelativeHumidity() + + +class TestTemperatureFormatter: + @pytest.fixture + def subject(self, temperature_sensor): + return temperature_sensor.format + + @staticmethod + def to_degc(magnitude): + return ureg.Quantity(magnitude, "degree_Celsius") + + def test_format_21c(self, subject): + assert subject(self.to_degc(21.0)) == "21.0 °C (69.8 °F)" + + def test_format_negative(self, subject): + assert subject(self.to_degc(-32.0)) == "-32.0 °C (-25.6 °F)" + + def test_format_unknown(self, subject): + assert subject(None) == "Unknown" + + +class TestTemperatureSerializer: + @pytest.fixture + def serialize(self, temperature_sensor): + return temperature_sensor.to_json_compatible + + @pytest.fixture + def deserialize(self, temperature_sensor): + return temperature_sensor.from_json_compatible + + @staticmethod + def to_degc(magnitude): + return ureg.Quantity(magnitude, "degree_Celsius") + + def test_serialize_21c(self, serialize): + assert serialize(self.to_degc(21.0)) == {"magnitude": 21.0, "unit": "degree_Celsius"} + + def test_serialize_negative(self, serialize): + assert serialize(self.to_degc(-32.3)) == {"magnitude": -32.3, "unit": "degree_Celsius"} + + def test_deserialize_21c(self, deserialize): + assert deserialize({"magnitude": 21.0, "unit": "degree_Celsius"}) == self.to_degc(21.0) + + +class TestHumidityFormatter: + @pytest.fixture + def subject(self, humidity_sensor): + return humidity_sensor.format + + def test_format_percentage(self, subject): + assert subject(0.035) == "3.5%" + + def test_format_unknown(self, subject): + assert subject(None) == "Unknown" diff --git a/Ch05/apd.sensors-chapter05-pintbased/tests/test_ipaddresses.py b/Ch05/apd.sensors-chapter05-pintbased/tests/test_ipaddresses.py new file mode 100644 index 0000000..bd61d85 --- /dev/null +++ b/Ch05/apd.sensors-chapter05-pintbased/tests/test_ipaddresses.py @@ -0,0 +1,59 @@ +import socket +from unittest import mock + +import pytest + +from apd.sensors.sensors import IPAddresses + + +@pytest.fixture +def sensor(): + return IPAddresses() + + +class TestIPAddressFormatter: + @pytest.fixture + def subject(self, sensor): + return sensor.format + + def test_format_single_ipv4(self, subject): + ips = [("AF_INET", "192.0.2.1")] + assert subject(ips) == "192.0.2.1 (IPv4)" + + def test_format_single_ipv6(self, subject): + ips = [("AF_INET6", "2001:DB8::1")] + assert subject(ips) == "2001:DB8::1 (IPv6)" + + def test_format_mixed_list(self, subject): + ips = [("AF_INET", "192.0.2.1"), ("AF_INET6", "2001:DB8::1")] + assert subject(ips) == "192.0.2.1 (IPv4)\n2001:DB8::1 (IPv6)" + + def test_unusual_protocols_are_marked_as_unknown(self, subject): + ips = [("AF_IRDA", "ffff")] + assert subject(ips) == "ffff (Unknown)" + + +class TestIPAddressesValue: + @pytest.fixture + def subject(self, sensor): + return sensor.value + + @pytest.fixture + def gethostname(self): + with mock.patch("socket.gethostname") as gethostname: + gethostname.return_value = "localhost" + yield gethostname + + @pytest.fixture + def getaddrinfo(self, gethostname): + with mock.patch("socket.getaddrinfo") as getaddrinfo: + yield getaddrinfo + + def test_getaddrinfo_used_for_value_collection(self, getaddrinfo, subject): + getaddrinfo.return_value = [(socket.AF_INET, 0, 0, "", ("192.0.2.1", 0))] + assert subject() == [("AF_INET", "192.0.2.1")] + assert getaddrinfo.call_count == 1 + + def test_str_representation_is_formatted_value(self, getaddrinfo, sensor): + getaddrinfo.return_value = [(socket.AF_INET6, 0, 0, "", ("2001:DB8::1", 0))] + assert str(sensor) == "2001:DB8::1 (IPv6)" diff --git a/Ch05/apd.sensors-chapter05-pintbased/tests/test_pythonversion.py b/Ch05/apd.sensors-chapter05-pintbased/tests/test_pythonversion.py new file mode 100644 index 0000000..43ad806 --- /dev/null +++ b/Ch05/apd.sensors-chapter05-pintbased/tests/test_pythonversion.py @@ -0,0 +1,61 @@ +from collections import namedtuple +from unittest import mock + +import pytest + +from apd.sensors.sensors import PythonVersion + + +@pytest.fixture +def version(): + return namedtuple( + "sys_versioninfo", ("major", "minor", "micro", "releaselevel", "serial") + ) + + +@pytest.fixture +def sensor(): + return PythonVersion() + + +class TestPythonVersionFormatter: + @pytest.fixture + def subject(self, sensor): + return sensor.format + + def test_format_py38(self, subject, version): + py38 = version(3, 8, 0, "final", 0) + assert subject(py38) == "3.8" + + def test_format_large_version(self, subject, version): + large = version(255, 128, 0, "final", 0) + assert subject(large) == "255.128" + + def test_alpha_of_minor_is_marked(self, subject, version): + py39 = version(3, 9, 0, "alpha", 1) + assert subject(py39) == "3.9.0a1" + + def test_alpha_of_micro_is_unmarked(self, subject, version): + py39 = version(3, 9, 1, "alpha", 1) + assert subject(py39) == "3.9" + + +class TestPythonVersionValue: + @pytest.fixture + def subject(self, sensor): + return sensor.value + + @pytest.fixture + def python_version(self): + import sys + + return sys.version_info + + def test_data_value_is_sys_versioninfo(self, python_version, subject): + assert subject() == python_version + + +class TestPythonVersionSensor: + def test_str_representation_is_formatted_value(self, sensor, version): + with mock.patch("sys.version_info", new=version(3, 4, 1, "final", 1)): + assert str(sensor) == "3.4" diff --git a/Ch05/apd.sensors-chapter05-pintbased/tests/test_ramusage.py b/Ch05/apd.sensors-chapter05-pintbased/tests/test_ramusage.py new file mode 100644 index 0000000..197ad06 --- /dev/null +++ b/Ch05/apd.sensors-chapter05-pintbased/tests/test_ramusage.py @@ -0,0 +1,47 @@ +from unittest import mock + +import pytest + +from apd.sensors.sensors import RAMAvailable + + +@pytest.fixture +def sensor(): + return RAMAvailable() + + +class TestRAMAvailableFormatter: + @pytest.fixture + def subject(self, sensor): + return sensor.format + + def test_format_bytes(self, subject): + assert subject(15) == "15.0 B" + + def test_format_kibibytes(self, subject): + assert subject(15000) == "14.6 KiB" + + def test_format_mibibytes(self, subject): + assert subject(15000000) == "14.3 MiB" + + def test_format_gibibytes(self, subject): + assert subject(15000000000) == "14.0 GiB" + + +class TestRAMAvailableValue: + @pytest.fixture + def subject(self, sensor): + return sensor.value + + @pytest.fixture + def virtual_memory(self): + with mock.patch("psutil.virtual_memory") as virtual_memory: + virtual_memory.return_value.available = 1024 + yield virtual_memory + + def test_virtual_memory_called(self, subject, virtual_memory): + assert subject() == 1024 + assert virtual_memory.call_count == 1 + + def test_str_representation_is_formatted_value(self, sensor, virtual_memory): + assert str(sensor) == "1.0 KiB" diff --git a/Ch05/apd.sensors-chapter05-pintbased/tests/test_sensors.py b/Ch05/apd.sensors-chapter05-pintbased/tests/test_sensors.py new file mode 100644 index 0000000..c67a180 --- /dev/null +++ b/Ch05/apd.sensors-chapter05-pintbased/tests/test_sensors.py @@ -0,0 +1,88 @@ +import json +from unittest import mock + +from click.testing import CliRunner + +import pytest + +import apd.sensors.cli +import apd.sensors.sensors + + +def test_sensors(): + assert hasattr(apd.sensors.sensors, "PythonVersion") + + +@pytest.mark.functional +def test_first_sensor_is_first_two_lines_of_cli_output(): + runner = CliRunner() + with mock.patch("apd.sensors.cli.get_sensors") as get_sensors: + get_sensors.return_value = [apd.sensors.sensors.PythonVersion()] + result = runner.invoke(apd.sensors.cli.show_sensors) + python_version = str(apd.sensors.sensors.PythonVersion()) + assert ["Python Version", python_version] == result.stdout.split("\n")[:2] + + +class TestSensorFromPath: + @pytest.fixture + def subject(self): + return apd.sensors.cli.get_sensor_by_path + + def test_get_sensor_by_path(self, subject): + assert isinstance( + subject("apd.sensors.sensors:PythonVersion"), + apd.sensors.sensors.PythonVersion, + ) + + def test_invalid_format(self, subject): + with pytest.raises(RuntimeError, match="dotted.path.to.module:ClassName"): + subject("apd.sensors.sensors.PythonVersion") + + def test_invalid_module(self, subject): + with pytest.raises(RuntimeError, match="Could not import module"): + subject("apd.nonsense.sensor:FakeSensor") + + def test_missing_sensor(self, subject): + with pytest.raises(RuntimeError, match="Could not find attribute"): + subject("apd.sensors.sensors:FakeSensor") + + def test_invalid_sensor(self, subject): + with pytest.raises(RuntimeError, match="is not recognised as a Sensor"): + subject("apd.sensors.sensors:Sensor") + + +@pytest.mark.functional +def test_develop_option_finds_sensor_by_path(): + runner = CliRunner() + result = runner.invoke( + apd.sensors.cli.show_sensors, ["--develop", "apd.sensors.sensors:PythonVersion"] + ) + python_version = str(apd.sensors.sensors.PythonVersion()) + assert ["Python Version", python_version, "", ""] == result.stdout.split("\n") + + +class TestDefaultSerializer: + @pytest.fixture + def python_version(self): + return apd.sensors.sensors.PythonVersion() + + @pytest.fixture + def serialize(self, python_version): + return python_version.to_json_compatible + + @pytest.fixture + def deserialize(self, python_version): + return python_version.from_json_compatible + + def test_serialize_deserialize_is_symmetric( + self, python_version, serialize, deserialize + ): + value = python_version.value() + serialized = serialize(value) + json_version = json.dumps(serialized) + assert ( + json_version == f"[{value.major}, {value.minor}, {value.micro}," + f' "{value.releaselevel}", {value.serial}]' + ) + deserialized = deserialize(serialized) + assert deserialized == value diff --git a/Ch05/apd.sensors-chapter05/.pre-commit-config.yaml b/Ch05/apd.sensors-chapter05/.pre-commit-config.yaml new file mode 100644 index 0000000..d3b6ec7 --- /dev/null +++ b/Ch05/apd.sensors-chapter05/.pre-commit-config.yaml @@ -0,0 +1,26 @@ +repos: + +- repo: local + hooks: + - id: black + name: black + entry: pipenv run black + args: [--quiet] + language: system + types: [python] + + - id: mypy + name: mypy + exclude: (?x)^( + (.*)/setup.py + )$ + entry: pipenv run mypy + args: ["--follow-imports=skip"] + language: system + types: [python] + + - id: flake8 + name: flake8 + entry: pipenv run flake8 + language: system + types: [python] diff --git a/Ch05/apd.sensors-chapter05/CHANGES.md b/Ch05/apd.sensors-chapter05/CHANGES.md new file mode 100644 index 0000000..dba741b --- /dev/null +++ b/Ch05/apd.sensors-chapter05/CHANGES.md @@ -0,0 +1,29 @@ +## Changes + +### 2.0.0 (2019-09-08) + +* Add `to_json_compatible` and `from_json_compatible` methods to Sensor + to facilitate better HTTP API (Matthew Wilkes) + +* HTTP API is now versioned. The API from 1.3.0 is available at /v/1.0 + and an updated version is at /v/2.0 (Matthew Wilkes) + +### 1.3.0 (2019-08-20) + +* WSGI HTTP API support added (Matthew Wilkes) + +### 1.2.0 (2019-08-05) + +* Add external plugin support through `apd.sensors.sensors` entrypoint (Matthew Wilkes) + +### 1.1.0 (2019-07-12) + +* Add --develop argument (Matthew Wilkes) + +### 1.0.1 (2019-06-20) + +* Fix broken 1.0.0 release (Matthew Wilkes) + +### 1.0.0 (2019-06-20) + +* Added initial sensors (Matthew Wilkes) diff --git a/Ch05/apd.sensors-chapter05/LICENCE b/Ch05/apd.sensors-chapter05/LICENCE new file mode 100644 index 0000000..053f694 --- /dev/null +++ b/Ch05/apd.sensors-chapter05/LICENCE @@ -0,0 +1,19 @@ +Copyright (c) 2019 Matthew Wilkes + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/Ch05/apd.sensors-chapter05/Pipfile b/Ch05/apd.sensors-chapter05/Pipfile new file mode 100644 index 0000000..f6cd446 --- /dev/null +++ b/Ch05/apd.sensors-chapter05/Pipfile @@ -0,0 +1,34 @@ +[[source]] +name = "pypi" +url = "https://pypi.org/simple" +verify_ssl = true + +[[source]] +name = "piwheels" +url = "https://piwheels.org/simple" +verify_ssl = true + +[dev-packages] +ipykernel = "*" +remote-ikernel = "*" +pytest = "*" +pytest-cov = "*" +mypy = "*" +flake8 = "*" +black = "*" +pre-commit = "*" +wheel = "*" +twine = "*" +webtest = "*" +apd-sensors = {editable = true,extras = ["webapp"],path = "."} + +[packages] +apd-sensors = {editable = true,path = "."} +apd-sunnyboy-solar = {editable = true,path = "./plugins/apd.sunnyboy_solar"} +waitress = "*" +pint = "*" + +[requires] + +[pipenv] +allow_prereleases = true diff --git a/Ch05/apd.sensors-chapter05/Pipfile.lock b/Ch05/apd.sensors-chapter05/Pipfile.lock new file mode 100644 index 0000000..3b587bb --- /dev/null +++ b/Ch05/apd.sensors-chapter05/Pipfile.lock @@ -0,0 +1,1018 @@ +{ + "_meta": { + "hash": { + "sha256": "b5f132c374a0a891097a70c6f3be719ec794dec7b855ed5f0ed9adad4d72b0e7" + }, + "pipfile-spec": 6, + "requires": {}, + "sources": [ + { + "name": "pypi", + "url": "https://pypi.org/simple", + "verify_ssl": true + }, + { + "name": "piwheels", + "url": "https://piwheels.org/simple", + "verify_ssl": true + } + ] + }, + "default": { + "apd-sensors": { + "editable": true, + "path": "." + }, + "apd-sunnyboy-solar": { + "editable": true, + "path": "./plugins/apd.sunnyboy_solar" + }, + "click": { + "hashes": [ + "sha256:8a18b4ea89d8820c5d0c7da8a64b2c324b4dabb695804dbfea19b9be9d88c0cc", + 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"==2.0.34" + }, + "werkzeug": { + "hashes": [ + "sha256:2de2a5db0baeae7b2d2664949077c2ac63fbd16d98da0ff71837f7d1dea3fd43", + "sha256:6c80b1e5ad3665290ea39320b91e1be1e0d5f60652b964a3070216de83d2e47c" + ], + "version": "==1.0.1" + }, + "wheel": { + "hashes": [ + "sha256:8788e9155fe14f54164c1b9eb0a319d98ef02c160725587ad60f14ddc57b6f96", + "sha256:df277cb51e61359aba502208d680f90c0493adec6f0e848af94948778aed386e" + ], + "index": "pypi", + "version": "==0.34.2" + } + } +} diff --git a/Ch05/apd.sensors-chapter05/README.md b/Ch05/apd.sensors-chapter05/README.md new file mode 100644 index 0000000..7f015bf --- /dev/null +++ b/Ch05/apd.sensors-chapter05/README.md @@ -0,0 +1,66 @@ +# Advanced Python Development Sensors + +This is the data collection package that forms part of the running example +for the book [Advanced Python Development](https://advancedpython.dev). + +## Usage + +This installs a console script called `sensors` that returns a report on +various aspects of the system. The available sensors are: + +* Python version +* IP Addresses +* CPU Usage +* RAM Available +* Battery charging state +* Ambient Temperature +* Ambient Humidity + +There are no command-line options, to view the report run `sensors` on the +command line. + +## Caveats + +The Ambient Temperature and Ambient Humidity sensors are only available on +RaspberryPi hosts and assume that a DHT22 sensor is connected to pin `D20`. + +The location and type of the sensor can be controlled by setting a pair of +environment variables: `APD_SENSORS_TEMPERATURE_BOARD` and +`APD_SENSORS_TEMPERATURE_PIN`. + +If there is an entry in `/etc/hosts` for the current machine's hostname that +value will be the only result from the IP Addresses sensor. + +## Installation + +You can install with `pip3 install apd.sensors` under Python 3.7 or higher. + +We recommend using pipenv to manage your environment, in which case you would +install using `pipenv --three install apd.sensors` and run the programme using +`pipenv run sensors`. + +## API server + +There is an optional API server shipped with apd.sensors. To use this you +should install the `apd.sensors[webapp]` extra. The API can then be started +with the non-production quality wsgiref server using: + + python -m apd.sensors.wsgi.serve + +or through Waitress (if installed) using: + + waitress-serve --call apd.sensors.wsgi:set_up_config + +Other WSGI servers will also work, you should use set_up_config as a factory +function. + +An environment variable is required to use the API server, `APD_SENSORS_API_KEY` +should be set to the API key required to gain access. One can be generated +using: + + python -c "import uuid; print(uuid.uuid4().hex)" + +The following endpoints are supported: + +* /v/2.0/sensors +* /v/2.0/sensors/sensorid diff --git a/Ch05/apd.sensors-chapter05/plugins/apd.sunnyboy_solar/pyproject.toml b/Ch05/apd.sensors-chapter05/plugins/apd.sunnyboy_solar/pyproject.toml new file mode 100644 index 0000000..4d3bb67 --- /dev/null +++ b/Ch05/apd.sensors-chapter05/plugins/apd.sunnyboy_solar/pyproject.toml @@ -0,0 +1,5 @@ +[build-system] +requires = [ + "setuptools", +] +build-backend = "setuptools.build_meta" diff --git a/Ch05/apd.sensors-chapter05/plugins/apd.sunnyboy_solar/setup.cfg b/Ch05/apd.sensors-chapter05/plugins/apd.sunnyboy_solar/setup.cfg new file mode 100644 index 0000000..0f5db10 --- /dev/null +++ b/Ch05/apd.sensors-chapter05/plugins/apd.sunnyboy_solar/setup.cfg @@ -0,0 +1,33 @@ +[mypy] +ignore_missing_imports = True + +[flake8] +max-line-length = 88 + +[metadata] +name = apd.sunnyboy_solar +version = attr: apd.sunnyboy_solar.VERSION +description = APD Sensor for reading data from Sunnyboy inverters +long_description = file: README.md, CHANGES.md, LICENCE +long_description_content_type = text/markdown +keywords = iot solar +license = MIT +classifiers = + Programming Language :: Python :: 3 + Programming Language :: Python :: 3.7 + +[options] +zip_safe = False +include_package_data = True +package_dir = + =src +packages = find_namespace: +install_requires = + apd.sensors + +[options.packages.find] +where = src + +[options.entry_points] +apd.sensors.sensors = + SolarCumulativeOutput = apd.sunnyboy_solar.sensor:SolarCumulativeOutput diff --git a/Ch05/apd.sensors-chapter05/plugins/apd.sunnyboy_solar/setup.py b/Ch05/apd.sensors-chapter05/plugins/apd.sunnyboy_solar/setup.py new file mode 100644 index 0000000..6068493 --- /dev/null +++ b/Ch05/apd.sensors-chapter05/plugins/apd.sunnyboy_solar/setup.py @@ -0,0 +1,3 @@ +from setuptools import setup + +setup() diff --git a/Ch05/apd.sensors-chapter05/plugins/apd.sunnyboy_solar/src/apd/sunnyboy_solar/Pipfile b/Ch05/apd.sensors-chapter05/plugins/apd.sunnyboy_solar/src/apd/sunnyboy_solar/Pipfile new file mode 100644 index 0000000..e69de29 diff --git a/Ch05/apd.sensors-chapter05/plugins/apd.sunnyboy_solar/src/apd/sunnyboy_solar/__init__.py b/Ch05/apd.sensors-chapter05/plugins/apd.sunnyboy_solar/src/apd/sunnyboy_solar/__init__.py new file mode 100644 index 0000000..3277f64 --- /dev/null +++ b/Ch05/apd.sensors-chapter05/plugins/apd.sunnyboy_solar/src/apd/sunnyboy_solar/__init__.py @@ -0,0 +1 @@ +VERSION = "1.0.0" diff --git a/Ch05/apd.sensors-chapter05/plugins/apd.sunnyboy_solar/src/apd/sunnyboy_solar/sensor.py b/Ch05/apd.sensors-chapter05/plugins/apd.sunnyboy_solar/src/apd/sunnyboy_solar/sensor.py new file mode 100644 index 0000000..aa37a2e --- /dev/null +++ b/Ch05/apd.sensors-chapter05/plugins/apd.sunnyboy_solar/src/apd/sunnyboy_solar/sensor.py @@ -0,0 +1,69 @@ +import os +import subprocess +import typing as t + +from pint import _DEFAULT_REGISTRY as ureg + +from apd.sensors.base import Sensor + + +class SolarCumulativeOutput(Sensor[t.Optional[t.Any]]): + name = "SolarCumulativeOutput" + title = "Solar panel cumulative output" + + def __init__( + self, path: t.Optional[str] = None, bt_addr: t.Optional[str] = None + ) -> None: + self.path = os.environ.get( + "APD_SUNNYBOYSOLAR_PATH", "/home/pi/opensunny-master/opensunny" + ) + self.bt_addr = os.environ.get("APD_SUNNYBOYSOLAR_BT_ADDRESS", None) + + def value(self) -> t.Optional[t.Any]: + if self.bt_addr is None: + return None + try: + output: bytes = subprocess.check_output( + [self.path, "-i", self.bt_addr], stderr=subprocess.STDOUT, timeout=15 + ) + except (subprocess.CalledProcessError, FileNotFoundError): + return None + + lines = filter(None, output.split(b"\n")) + found = {} + for line in lines: + start, value = line.rsplit(b"=", 1) + start, key = start.rsplit(b" ", 1) + found[key] = value + + try: + yield_total = float(found[b"yield_total"][:-3].replace(b".", b"")) + power_dc_1 = int(found[b"power_dc_1"][:-1]) + power_dc_2 = int(found[b"power_dc_2"][:-1]) + if power_dc_1 > 1500 or power_dc_2 > 1500: + return None + except (ValueError, IndexError, KeyError): + return None + return ureg.Quantity(yield_total, "watt") + + @classmethod + def format(cls, value: t.Optional[t.Any]) -> str: + if value is None: + return "Unknown" + return "{:~P}".format(value.to(ureg.kilowatt)) + + @classmethod + def to_json_compatible( + cls, value: t.Optional[t.Any] + ) -> t.Optional[t.Dict[str, t.Union[str, float]]]: + if value is not None: + return {"magnitude": value.magnitude, "unit": str(value.units)} + else: + return None + + @classmethod + def from_json_compatible(cls, json_version: t.Any) -> t.Optional[t.Any]: + if json_version: + return ureg.Quantity(json_version["magnitude"], ureg[json_version["unit"]]) + else: + return None diff --git a/Ch05/apd.sensors-chapter05/pytest.ini b/Ch05/apd.sensors-chapter05/pytest.ini new file mode 100644 index 0000000..861f3d0 --- /dev/null +++ b/Ch05/apd.sensors-chapter05/pytest.ini @@ -0,0 +1,3 @@ +[pytest] +markers = + functional: these tests are significantly slower as they run the whole CLI script \ No newline at end of file diff --git a/Ch05/apd.sensors-chapter05/setup.cfg b/Ch05/apd.sensors-chapter05/setup.cfg new file mode 100644 index 0000000..fe8017c --- /dev/null +++ b/Ch05/apd.sensors-chapter05/setup.cfg @@ -0,0 +1,66 @@ +[mypy] +namespace_packages = True +mypy_path = src + +[mypy-psutil] +ignore_missing_imports = True + +[mypy-adafruit_dht] +ignore_missing_imports = True + +[mypy-board] +ignore_missing_imports = True + +[mypy-pytest] +ignore_missing_imports = True + +[mypy-webtest] +ignore_missing_imports = True + +[flake8] +max-line-length = 88 + +[metadata] +name = apd.sensors +version = attr: apd.sensors.VERSION +description = APD Sensor package +long_description = file: README.md, CHANGES.md, LICENCE +long_description_content_type = text/markdown +keywords = iot +license = MIT +classifiers = + Programming Language :: Python :: 3 + Programming Language :: Python :: 3.7 + +[options] +zip_safe = False +include_package_data = True +package_dir = + =src +packages = find_namespace: +install_requires = + psutil + click + pint + adafruit-circuitpython-dht ; 'arm' in platform_machine + +[options.package_data] +apd.sensors = py.typed + +[options.packages.find] +where = src + +[options.entry_points] +console_scripts = + sensors = apd.sensors.cli:show_sensors +apd.sensors.sensors = + PythonVersion = apd.sensors.sensors:PythonVersion + IPAddresses = apd.sensors.sensors:IPAddresses + CPULoad = apd.sensors.sensors:CPULoad + RAMAvailable = apd.sensors.sensors:RAMAvailable + ACStatus = apd.sensors.sensors:ACStatus + Temperature = apd.sensors.sensors:Temperature + RelativeHumidity = apd.sensors.sensors:RelativeHumidity + +[options.extras_require] +webapp = flask \ No newline at end of file diff --git a/Ch05/apd.sensors-chapter05/setup.py b/Ch05/apd.sensors-chapter05/setup.py new file mode 100644 index 0000000..6068493 --- /dev/null +++ b/Ch05/apd.sensors-chapter05/setup.py @@ -0,0 +1,3 @@ +from setuptools import setup + +setup() diff --git a/Ch05/apd.sensors-chapter05/src/apd/sensors/__init__.py b/Ch05/apd.sensors-chapter05/src/apd/sensors/__init__.py new file mode 100644 index 0000000..2101409 --- /dev/null +++ b/Ch05/apd.sensors-chapter05/src/apd/sensors/__init__.py @@ -0,0 +1 @@ +VERSION = "2.0.0" diff --git a/Ch05/apd.sensors-chapter05/src/apd/sensors/base.py b/Ch05/apd.sensors-chapter05/src/apd/sensors/base.py new file mode 100644 index 0000000..74b32ae --- /dev/null +++ b/Ch05/apd.sensors-chapter05/src/apd/sensors/base.py @@ -0,0 +1,51 @@ +#!/usr/bin/env python +# coding: utf-8 +import typing as t + + +T_value = t.TypeVar("T_value") + + +class Sensor(t.Generic[T_value]): + name: str + title: str + + def value(self) -> T_value: + raise NotImplementedError + + @classmethod + def format(cls, value: T_value) -> str: + raise NotImplementedError + + def __str__(self) -> str: + return self.format(self.value()) + + @classmethod + def to_json_compatible(cls, value: T_value) -> t.Any: + raise NotImplementedError() + + @classmethod + def from_json_compatible(cls, json_version: t.Any) -> T_value: + raise NotImplementedError() + + +class JSONSensor(Sensor[T_value]): + @classmethod + def to_json_compatible(cls, value: T_value) -> t.Any: + return value + + @classmethod + def from_json_compatible(cls, json_version: t.Any) -> T_value: + return t.cast(T_value, json_version) + + +version_info_type = t.NamedTuple( + "version_info_type", + [ + ("major", int), + ("minor", int), + ("micro", int), + ("releaselevel", str), + ("serial", int), + ], +) diff --git a/Ch05/apd.sensors-chapter05/src/apd/sensors/cli.py b/Ch05/apd.sensors-chapter05/src/apd/sensors/cli.py new file mode 100644 index 0000000..1ee27b9 --- /dev/null +++ b/Ch05/apd.sensors-chapter05/src/apd/sensors/cli.py @@ -0,0 +1,74 @@ +import enum +import importlib +import sys +import pkg_resources +import typing as t + +import click + +from .sensors import Sensor + + +class ReturnCodes(enum.IntEnum): + OK = 0 + BAD_SENSOR_PATH = 17 + + +def get_sensor_by_path(sensor_path: str) -> Sensor[t.Any]: + try: + module_name, sensor_name = sensor_path.split(":") + except ValueError: + raise RuntimeError( + "Sensor path must be in the format dotted.path.to.module:ClassName" + ) + try: + module = importlib.import_module(module_name) + except ImportError: + raise RuntimeError(f"Could not import module {module_name}") + try: + sensor_class = getattr(module, sensor_name) + except AttributeError: + raise RuntimeError(f"Could not find attribute {sensor_name} in {module_name}") + if ( + isinstance(sensor_class, type) + and issubclass(sensor_class, Sensor) + and sensor_class != Sensor + ): + return sensor_class() + else: + raise RuntimeError( + f"Detected object {sensor_class!r} is not recognised as a Sensor type" + ) + + +def get_sensors() -> t.Iterable[Sensor[t.Any]]: + sensors = [] + for sensor_class in pkg_resources.iter_entry_points("apd.sensors.sensors"): + class_ = sensor_class.load() + sensors.append(t.cast(Sensor[t.Any], class_())) + return sensors + + +@click.command(help="Displays the values of the sensors") +@click.option( + "--develop", required=False, metavar="path", help="Load a sensor by Python path" +) +def show_sensors(develop: str) -> None: + sensors: t.Iterable[Sensor[t.Any]] + if develop: + try: + sensors = [get_sensor_by_path(develop)] + except RuntimeError as error: + click.secho(str(error), fg="red", bold=True) + sys.exit(ReturnCodes.BAD_SENSOR_PATH) + else: + sensors = get_sensors() + for sensor in sensors: + click.secho(sensor.title, bold=True) + click.echo(str(sensor)) + click.echo("") + sys.exit(ReturnCodes.OK) + + +if __name__ == "__main__": + show_sensors() diff --git a/Ch05/apd.sensors-chapter05/src/apd/sensors/py.typed b/Ch05/apd.sensors-chapter05/src/apd/sensors/py.typed new file mode 100644 index 0000000..e69de29 diff --git a/Ch05/apd.sensors-chapter05/src/apd/sensors/sensors.py b/Ch05/apd.sensors-chapter05/src/apd/sensors/sensors.py new file mode 100644 index 0000000..44d432f --- /dev/null +++ b/Ch05/apd.sensors-chapter05/src/apd/sensors/sensors.py @@ -0,0 +1,192 @@ +#!/usr/bin/env python +# coding: utf-8 +import math +import os +import socket +import sys +import typing as t + +import psutil +from pint import _DEFAULT_REGISTRY as ureg + +from .base import Sensor, JSONSensor, version_info_type + + +class PythonVersion(JSONSensor[version_info_type]): + name = "PythonVersion" + title = "Python Version" + + def value(self) -> version_info_type: + return version_info_type(*sys.version_info) + + @classmethod + def format(cls, value: version_info_type) -> str: + if value.micro == 0 and value.releaselevel == "alpha": + return "{0.major}.{0.minor}.{0.micro}a{0.serial}".format(value) + return "{0.major}.{0.minor}".format(value) + + +class IPAddresses(JSONSensor[t.Iterable[t.Tuple[str, str]]]): + name = "IPAddresses" + title = "IP Addresses" + FAMILIES = {"AF_INET": "IPv4", "AF_INET6": "IPv6"} + + def value(self) -> t.List[t.Tuple[str, str]]: + hostname = socket.gethostname() + addresses = socket.getaddrinfo(hostname, None) + + address_info: t.List[t.Tuple[str, str]] = [] + for address in addresses: + family, ip = (address[0].name, address[4][0]) + if family not in self.FAMILIES: + continue + value = (family, ip) + if value not in address_info: + address_info.append(value) + return address_info + + @classmethod + def format(cls, value: t.Iterable[t.Tuple[str, str]]) -> str: + return "\n".join( + "{0} ({1})".format(address[1], cls.FAMILIES.get(address[0], "Unknown")) + for address in value + ) + + +class CPULoad(JSONSensor[float]): + name = "CPULoad" + title = "CPU Usage" + + def value(self) -> float: + return float(psutil.cpu_percent(interval=3)) / 100.0 + + @classmethod + def format(cls, value: float) -> str: + return "{:.1%}".format(value) + + +class RAMAvailable(JSONSensor[int]): + name = "RAMAvailable" + title = "RAM Available" + UNITS = ("B", "KiB", "MiB", "GiB", "TiB", "PiB", "EiB") + UNIT_SIZE = 2 ** 10 + + def value(self) -> int: + return int(psutil.virtual_memory().available) + + @classmethod + def format(cls, value: int) -> str: + magnitude = math.floor(math.log(value, cls.UNIT_SIZE)) + max_magnitude = len(cls.UNITS) - 1 + magnitude = min(magnitude, max_magnitude) + scaled_value = value / (cls.UNIT_SIZE ** magnitude) + return "{:.1f} {}".format(scaled_value, cls.UNITS[magnitude]) + + +class ACStatus(JSONSensor[t.Optional[bool]]): + name = "ACStatus" + title = "AC Connected" + + def value(self) -> t.Optional[bool]: + battery = psutil.sensors_battery() + if battery is not None: + value = battery.power_plugged + if value is None: + return None + else: + return bool(value) + else: + return None + + @classmethod + def format(cls, value: t.Optional[bool]) -> str: + if value is None: + return "Unknown" + elif value: + return "Connected" + else: + return "Not connected" + + +class Temperature(Sensor[t.Optional[t.Any]]): + name = "Temperature" + title = "Ambient Temperature" + + def __init__(self) -> None: + self.board = os.environ.get("APD_SENSORS_TEMPERATURE_BOARD", "DHT22") + self.pin = os.environ.get("APD_SENSORS_TEMPERATURE_PIN", "D20") + + def value(self) -> t.Optional[t.Any]: + try: + import adafruit_dht + import board + + sensor_type = getattr(adafruit_dht, self.board) + pin = getattr(board, self.pin) + except (ImportError, NotImplementedError, AttributeError): + # No DHT library results in an ImportError. + # Running on an unknown platform results in a + # NotImplementedError when getting the pin + return None + try: + return ureg.Quantity(sensor_type(pin).temperature, ureg.celsius) + except RuntimeError: + return None + + @classmethod + def format(cls, value: t.Optional[t.Any]) -> str: + if value is None: + return "Unknown" + else: + return "{:.3~P} ({:.3~P})".format(value, value.to(ureg.fahrenheit)) + + @classmethod + def to_json_compatible(cls, value: t.Optional[t.Any]) -> t.Any: + if value is not None: + return {"magnitude": value.magnitude, "unit": str(value.units)} + else: + return None + + @classmethod + def from_json_compatible(cls, json_version: t.Any) -> t.Optional[t.Any]: + if json_version: + return ureg.Quantity(json_version["magnitude"], ureg[json_version["unit"]]) + else: + return None + + def __str__(self) -> str: + return self.format(self.value()) + + +class RelativeHumidity(JSONSensor[t.Optional[float]]): + name = "RelativeHumidity" + title = "Relative Humidity" + + def __init__(self) -> None: + self.board = os.environ.get("APD_SENSORS_TEMPERATURE_BOARD", "DHT22") + self.pin = os.environ.get("APD_SENSORS_TEMPERATURE_PIN", "D20") + + def value(self) -> t.Optional[float]: + try: + import adafruit_dht + import board + + sensor_type = getattr(adafruit_dht, self.board) + pin = getattr(board, self.pin) + except (ImportError, NotImplementedError, AttributeError): + # No DHT library results in an ImportError. + # Running on an unknown platform results in a + # NotImplementedError when getting the pin + return None + + try: + return float(sensor_type(pin).humidity) + except RuntimeError: + return None + + @classmethod + def format(cls, value: t.Optional[float]) -> str: + if value is None: + return "Unknown" + else: + return "{:.1%}".format(value) diff --git a/Ch05/apd.sensors-chapter05/src/apd/sensors/wsgi/__init__.py b/Ch05/apd.sensors-chapter05/src/apd/sensors/wsgi/__init__.py new file mode 100644 index 0000000..a97c28f --- /dev/null +++ b/Ch05/apd.sensors-chapter05/src/apd/sensors/wsgi/__init__.py @@ -0,0 +1,12 @@ +import flask + +from .base import set_up_config +from . import v10 +from . import v20 + + +__all__ = ["app", "set_up_config"] + +app = flask.Flask(__name__) +app.register_blueprint(v10.version, url_prefix="/v/1.0") +app.register_blueprint(v20.version, url_prefix="/v/2.0") diff --git a/Ch05/apd.sensors-chapter05/src/apd/sensors/wsgi/base.py b/Ch05/apd.sensors-chapter05/src/apd/sensors/wsgi/base.py new file mode 100644 index 0000000..c9be130 --- /dev/null +++ b/Ch05/apd.sensors-chapter05/src/apd/sensors/wsgi/base.py @@ -0,0 +1,43 @@ +from hmac import compare_digest +import functools +import os +import typing as t + +import flask + + +ViewFuncReturn = t.TypeVar("ViewFuncReturn") +ErrorReturn = t.Tuple[t.Dict[str, str], int] +REQUIRED_CONFIG_KEYS = {"APD_SENSORS_API_KEY"} + + +def require_api_key( + func: t.Callable[[], ViewFuncReturn] +) -> t.Callable[[], t.Union[ViewFuncReturn, ErrorReturn]]: + @functools.wraps(func) + def wrapped() -> t.Union[ViewFuncReturn, ErrorReturn]: + api_key = flask.current_app.config["APD_SENSORS_API_KEY"] + headers = flask.request.headers + supplied_key = headers.get("X-API-Key", "") + if not compare_digest(api_key, supplied_key): + return {"error": "Supply API key in X-API-Key header"}, 403 + return func() + + return wrapped + + +def set_up_config( + environ: t.Optional[t.Dict[str, str]] = None, + to_configure: t.Optional[flask.Flask] = None, +) -> flask.Flask: + if environ is None: + environ = dict(os.environ) + if to_configure is None: + from apd.sensors.wsgi import app + + to_configure = app + missing_keys = REQUIRED_CONFIG_KEYS - environ.keys() + if missing_keys: + raise ValueError("Missing config variables: {}".format(", ".join(missing_keys))) + to_configure.config.from_mapping(environ) + return to_configure diff --git a/Ch05/apd.sensors-chapter05/src/apd/sensors/wsgi/serve.py b/Ch05/apd.sensors-chapter05/src/apd/sensors/wsgi/serve.py new file mode 100644 index 0000000..0b1f684 --- /dev/null +++ b/Ch05/apd.sensors-chapter05/src/apd/sensors/wsgi/serve.py @@ -0,0 +1,10 @@ +from . import app +from .base import set_up_config + +if __name__ == "__main__": + import wsgiref.simple_server + + set_up_config(None, app) + + with wsgiref.simple_server.make_server("", 8000, app) as server: + server.serve_forever() diff --git a/Ch05/apd.sensors-chapter05/src/apd/sensors/wsgi/v10.py b/Ch05/apd.sensors-chapter05/src/apd/sensors/wsgi/v10.py new file mode 100644 index 0000000..7eee6dd --- /dev/null +++ b/Ch05/apd.sensors-chapter05/src/apd/sensors/wsgi/v10.py @@ -0,0 +1,26 @@ +import json +import typing as t + +import flask + +from apd.sensors.cli import get_sensors +from .base import require_api_key + +version = flask.Blueprint(__name__, __name__) + + +@version.route("/sensors/") +@require_api_key +def sensor_values() -> t.Tuple[t.Dict[str, t.Any], int, t.Dict[str, str]]: + headers = {"Content-Security-Policy": "default-src 'none'"} + data = {} + for sensor in get_sensors(): + value = sensor.value() + try: + json.dumps(value) + except TypeError: + # This value isn't JSON serializable, skip it + continue + else: + data[sensor.title] = sensor.value() + return data, 200, headers diff --git a/Ch05/apd.sensors-chapter05/src/apd/sensors/wsgi/v20.py b/Ch05/apd.sensors-chapter05/src/apd/sensors/wsgi/v20.py new file mode 100644 index 0000000..fd2b5fc --- /dev/null +++ b/Ch05/apd.sensors-chapter05/src/apd/sensors/wsgi/v20.py @@ -0,0 +1,32 @@ +import typing as t + +import flask + +from apd.sensors.cli import get_sensors +from .base import require_api_key + +version = flask.Blueprint(__name__, __name__) + + +@version.route("/sensors/") +@version.route("/sensors/") +@require_api_key +def sensor_values(sensor_id=None) -> t.Tuple[t.Dict[str, t.Any], int, t.Dict[str, str]]: + headers = {"Content-Security-Policy": "default-src 'none'"} + sensors = [] + for sensor in get_sensors(): + if sensor_id and sensor_id != sensor.name: + continue + try: + value = sensor.value() + sensor_data = { + "id": sensor.name, + "title": sensor.title, + "value": sensor.to_json_compatible(value), + "human_readable": sensor.format(value), + } + sensors.append(sensor_data) + except NotImplementedError: + pass + data = {"sensors": sensors} + return data, 200, headers diff --git a/Ch05/apd.sensors-chapter05/tests/test_acstatus.py b/Ch05/apd.sensors-chapter05/tests/test_acstatus.py new file mode 100644 index 0000000..d2fe71e --- /dev/null +++ b/Ch05/apd.sensors-chapter05/tests/test_acstatus.py @@ -0,0 +1,55 @@ +from unittest import mock + +import pytest + +from apd.sensors.sensors import ACStatus + + +@pytest.fixture +def sensor(): + return ACStatus() + + +class TestACStatusFormatter: + @pytest.fixture + def subject(self, sensor): + return sensor.format + + def test_format_True(self, subject): + assert subject(True) == "Connected" + + def test_format_False(self, subject): + assert subject(False) == "Not connected" + + def test_format_None(self, subject): + assert subject(None) == "Unknown" + + +class TestACStatusValue: + @pytest.fixture + def subject(self, sensor): + return sensor.value + + @pytest.fixture + def sensors_battery(self): + with mock.patch("psutil.sensors_battery") as sensors_battery: + yield sensors_battery + + def test_sensors_battery_called(self, subject, sensors_battery): + sensors_battery.return_value.power_plugged = True + assert subject() is True + assert sensors_battery.call_count == 1 + + def test_sensor_but_battery_unknown(self, subject, sensors_battery): + sensors_battery.return_value.power_plugged = None + assert subject() is None + assert sensors_battery.call_count == 1 + + def test_no_sensor(self, subject, sensors_battery): + sensors_battery.return_value = None + assert subject() is None + + def test_str_representation_is_formatted_value(self, sensor, sensors_battery): + sensors_battery.return_value.power_plugged = True + assert str(sensor) == "Connected" + assert sensors_battery.call_count == 1 diff --git a/Ch05/apd.sensors-chapter05/tests/test_api_server.py b/Ch05/apd.sensors-chapter05/tests/test_api_server.py new file mode 100644 index 0000000..0ffbeaa --- /dev/null +++ b/Ch05/apd.sensors-chapter05/tests/test_api_server.py @@ -0,0 +1,105 @@ +import os +import uuid +from unittest import mock + +import flask +import pytest +from webtest import TestApp + +from apd.sensors.wsgi import set_up_config +from apd.sensors.sensors import PythonVersion +from apd.sensors.wsgi import v10 +from apd.sensors.wsgi import v20 + + +@pytest.fixture(scope="session") +def api_key(): + return uuid.uuid4().hex + + +def test_api_key_is_required_config_option(): + app = mock.MagicMock() + with pytest.raises( + ValueError, match="Missing config variables: APD_SENSORS_API_KEY" + ): + set_up_config({}, to_configure=app) + + +def test_os_environ_is_default_for_config_values(api_key): + app = mock.MagicMock() + os.environ["APD_SENSORS_API_KEY"] = api_key + try: + assert app.config.from_mapping.call_count == 0 + set_up_config(None, to_configure=app) + assert app.config.from_mapping.call_count == 1 + assert app.config.from_mapping.call_args[0][0] == os.environ + finally: + del os.environ["APD_SENSORS_API_KEY"] + + +class CommonTests: + @pytest.mark.functional + def test_sensor_values_fails_on_missing_api_key(self, api_server): + response = api_server.get("/sensors/", expect_errors=True) + assert response.status_code == 403 + assert response.json["error"] == "Supply API key in X-API-Key header" + + @pytest.mark.functional + def test_sensor_values_require_correct_api_key(self, api_server): + response = api_server.get( + "/sensors/", headers={"X-API-Key": "wrong_key"}, expect_errors=True + ) + assert response.status_code == 403 + assert response.json["error"] == "Supply API key in X-API-Key header" + + +class Testv10API(CommonTests): + @pytest.fixture + def subject(self, api_key): + app = flask.Flask("testapp") + app.register_blueprint(v10.version) + set_up_config({"APD_SENSORS_API_KEY": api_key}, to_configure=app) + return app + + @pytest.fixture + def api_server(self, subject): + return TestApp(subject) + + @pytest.mark.functional + def test_sensor_values_returned_as_json(self, api_server, api_key): + value = api_server.get("/sensors/", headers={"X-API-Key": api_key}).json + python_version = PythonVersion().value() + + sensor_names = value.keys() + assert "Python Version" in sensor_names + assert value["Python Version"] == list(python_version) + + @pytest.mark.functional + def test_unserializable_sensor_is_omitted(self, api_server, api_key): + value = api_server.get("/sensors/", headers={"X-API-Key": api_key}).json + sensor_names = value.keys() + assert "Temperature" not in sensor_names + + +class Testv20API(CommonTests): + @pytest.fixture + def subject(self, api_key): + app = flask.Flask("testapp") + app.register_blueprint(v20.version) + set_up_config({"APD_SENSORS_API_KEY": api_key}, to_configure=app) + return app + + @pytest.fixture + def api_server(self, subject): + return TestApp(subject) + + @pytest.mark.functional + def test_sensor_values_returned_as_json(self, api_server, api_key): + value = api_server.get("/sensors/", headers={"X-API-Key": api_key}).json + sensors = value["sensors"] + + python_version = [ + sensor for sensor in sensors if sensor["id"] == "PythonVersion" + ][0] + assert python_version["title"] == "Python Version" + assert python_version["value"] == list(PythonVersion().value()) diff --git a/Ch05/apd.sensors-chapter05/tests/test_cpuusage.py b/Ch05/apd.sensors-chapter05/tests/test_cpuusage.py new file mode 100644 index 0000000..445f4e5 --- /dev/null +++ b/Ch05/apd.sensors-chapter05/tests/test_cpuusage.py @@ -0,0 +1,45 @@ +from unittest import mock + +import pytest + +from apd.sensors.sensors import CPULoad + + +@pytest.fixture +def sensor(): + return CPULoad() + + +class TestCPULoadFormatter: + @pytest.fixture + def subject(self, sensor): + return sensor.format + + def test_format_simple_percentage(self, subject): + assert subject(0.05) == "5.0%" + + def test_format_multiple_decimal_places(self, subject): + assert subject(0.031415926) == "3.1%" + + def test_format_multiple_decimal_places_int(self, subject) -> None: + assert subject(1) == "100.0%" + + +class TestCPULoadValue: + @pytest.fixture + def subject(self, sensor): + return sensor.value + + @pytest.fixture + def cpuload(self): + with mock.patch("psutil.cpu_percent") as cpu_percent: + cpu_percent.return_value = 50.1 + yield cpu_percent + + def test_cpu_percent_called_With_interval(self, subject, cpuload): + assert subject() == 0.501 + assert cpuload.call_count == 1 + assert tuple(cpuload.call_args) == ((), {"interval": 3}) + + def test_str_representation_is_formatted_value(self, sensor, cpuload): + assert str(sensor) == "50.1%" diff --git a/Ch05/apd.sensors-chapter05/tests/test_dht.py b/Ch05/apd.sensors-chapter05/tests/test_dht.py new file mode 100644 index 0000000..52a5ee9 --- /dev/null +++ b/Ch05/apd.sensors-chapter05/tests/test_dht.py @@ -0,0 +1,67 @@ +import pytest + +from apd.sensors.sensors import Temperature, RelativeHumidity, ureg + + +@pytest.fixture +def temperature_sensor(): + return Temperature() + + +@pytest.fixture +def humidity_sensor(): + return RelativeHumidity() + + +class TestTemperatureFormatter: + @pytest.fixture + def subject(self, temperature_sensor): + return temperature_sensor.format + + @staticmethod + def to_degc(magnitude): + return ureg.Quantity(magnitude, "degree_Celsius") + + def test_format_21c(self, subject): + assert subject(self.to_degc(21.0)) == "21.0 °C (69.8 °F)" + + def test_format_negative(self, subject): + assert subject(self.to_degc(-32.0)) == "-32.0 °C (-25.6 °F)" + + def test_format_unknown(self, subject): + assert subject(None) == "Unknown" + + +class TestTemperatureSerializer: + @pytest.fixture + def serialize(self, temperature_sensor): + return temperature_sensor.to_json_compatible + + @pytest.fixture + def deserialize(self, temperature_sensor): + return temperature_sensor.from_json_compatible + + @staticmethod + def to_degc(magnitude): + return ureg.Quantity(magnitude, "degree_Celsius") + + def test_serialize_21c(self, serialize): + assert serialize(self.to_degc(21.0)) == {"magnitude": 21.0, "unit": "degree_Celsius"} + + def test_serialize_negative(self, serialize): + assert serialize(self.to_degc(-32.3)) == {"magnitude": -32.3, "unit": "degree_Celsius"} + + def test_deserialize_21c(self, deserialize): + assert deserialize({"magnitude": 21.0, "unit": "degree_Celsius"}) == self.to_degc(21.0) + + +class TestHumidityFormatter: + @pytest.fixture + def subject(self, humidity_sensor): + return humidity_sensor.format + + def test_format_percentage(self, subject): + assert subject(0.035) == "3.5%" + + def test_format_unknown(self, subject): + assert subject(None) == "Unknown" diff --git a/Ch05/apd.sensors-chapter05/tests/test_ipaddresses.py b/Ch05/apd.sensors-chapter05/tests/test_ipaddresses.py new file mode 100644 index 0000000..bd61d85 --- /dev/null +++ b/Ch05/apd.sensors-chapter05/tests/test_ipaddresses.py @@ -0,0 +1,59 @@ +import socket +from unittest import mock + +import pytest + +from apd.sensors.sensors import IPAddresses + + +@pytest.fixture +def sensor(): + return IPAddresses() + + +class TestIPAddressFormatter: + @pytest.fixture + def subject(self, sensor): + return sensor.format + + def test_format_single_ipv4(self, subject): + ips = [("AF_INET", "192.0.2.1")] + assert subject(ips) == "192.0.2.1 (IPv4)" + + def test_format_single_ipv6(self, subject): + ips = [("AF_INET6", "2001:DB8::1")] + assert subject(ips) == "2001:DB8::1 (IPv6)" + + def test_format_mixed_list(self, subject): + ips = [("AF_INET", "192.0.2.1"), ("AF_INET6", "2001:DB8::1")] + assert subject(ips) == "192.0.2.1 (IPv4)\n2001:DB8::1 (IPv6)" + + def test_unusual_protocols_are_marked_as_unknown(self, subject): + ips = [("AF_IRDA", "ffff")] + assert subject(ips) == "ffff (Unknown)" + + +class TestIPAddressesValue: + @pytest.fixture + def subject(self, sensor): + return sensor.value + + @pytest.fixture + def gethostname(self): + with mock.patch("socket.gethostname") as gethostname: + gethostname.return_value = "localhost" + yield gethostname + + @pytest.fixture + def getaddrinfo(self, gethostname): + with mock.patch("socket.getaddrinfo") as getaddrinfo: + yield getaddrinfo + + def test_getaddrinfo_used_for_value_collection(self, getaddrinfo, subject): + getaddrinfo.return_value = [(socket.AF_INET, 0, 0, "", ("192.0.2.1", 0))] + assert subject() == [("AF_INET", "192.0.2.1")] + assert getaddrinfo.call_count == 1 + + def test_str_representation_is_formatted_value(self, getaddrinfo, sensor): + getaddrinfo.return_value = [(socket.AF_INET6, 0, 0, "", ("2001:DB8::1", 0))] + assert str(sensor) == "2001:DB8::1 (IPv6)" diff --git a/Ch05/apd.sensors-chapter05/tests/test_pythonversion.py b/Ch05/apd.sensors-chapter05/tests/test_pythonversion.py new file mode 100644 index 0000000..43ad806 --- /dev/null +++ b/Ch05/apd.sensors-chapter05/tests/test_pythonversion.py @@ -0,0 +1,61 @@ +from collections import namedtuple +from unittest import mock + +import pytest + +from apd.sensors.sensors import PythonVersion + + +@pytest.fixture +def version(): + return namedtuple( + "sys_versioninfo", ("major", "minor", "micro", "releaselevel", "serial") + ) + + +@pytest.fixture +def sensor(): + return PythonVersion() + + +class TestPythonVersionFormatter: + @pytest.fixture + def subject(self, sensor): + return sensor.format + + def test_format_py38(self, subject, version): + py38 = version(3, 8, 0, "final", 0) + assert subject(py38) == "3.8" + + def test_format_large_version(self, subject, version): + large = version(255, 128, 0, "final", 0) + assert subject(large) == "255.128" + + def test_alpha_of_minor_is_marked(self, subject, version): + py39 = version(3, 9, 0, "alpha", 1) + assert subject(py39) == "3.9.0a1" + + def test_alpha_of_micro_is_unmarked(self, subject, version): + py39 = version(3, 9, 1, "alpha", 1) + assert subject(py39) == "3.9" + + +class TestPythonVersionValue: + @pytest.fixture + def subject(self, sensor): + return sensor.value + + @pytest.fixture + def python_version(self): + import sys + + return sys.version_info + + def test_data_value_is_sys_versioninfo(self, python_version, subject): + assert subject() == python_version + + +class TestPythonVersionSensor: + def test_str_representation_is_formatted_value(self, sensor, version): + with mock.patch("sys.version_info", new=version(3, 4, 1, "final", 1)): + assert str(sensor) == "3.4" diff --git a/Ch05/apd.sensors-chapter05/tests/test_ramusage.py b/Ch05/apd.sensors-chapter05/tests/test_ramusage.py new file mode 100644 index 0000000..197ad06 --- /dev/null +++ b/Ch05/apd.sensors-chapter05/tests/test_ramusage.py @@ -0,0 +1,47 @@ +from unittest import mock + +import pytest + +from apd.sensors.sensors import RAMAvailable + + +@pytest.fixture +def sensor(): + return RAMAvailable() + + +class TestRAMAvailableFormatter: + @pytest.fixture + def subject(self, sensor): + return sensor.format + + def test_format_bytes(self, subject): + assert subject(15) == "15.0 B" + + def test_format_kibibytes(self, subject): + assert subject(15000) == "14.6 KiB" + + def test_format_mibibytes(self, subject): + assert subject(15000000) == "14.3 MiB" + + def test_format_gibibytes(self, subject): + assert subject(15000000000) == "14.0 GiB" + + +class TestRAMAvailableValue: + @pytest.fixture + def subject(self, sensor): + return sensor.value + + @pytest.fixture + def virtual_memory(self): + with mock.patch("psutil.virtual_memory") as virtual_memory: + virtual_memory.return_value.available = 1024 + yield virtual_memory + + def test_virtual_memory_called(self, subject, virtual_memory): + assert subject() == 1024 + assert virtual_memory.call_count == 1 + + def test_str_representation_is_formatted_value(self, sensor, virtual_memory): + assert str(sensor) == "1.0 KiB" diff --git a/Ch05/apd.sensors-chapter05/tests/test_sensors.py b/Ch05/apd.sensors-chapter05/tests/test_sensors.py new file mode 100644 index 0000000..c67a180 --- /dev/null +++ b/Ch05/apd.sensors-chapter05/tests/test_sensors.py @@ -0,0 +1,88 @@ +import json +from unittest import mock + +from click.testing import CliRunner + +import pytest + +import apd.sensors.cli +import apd.sensors.sensors + + +def test_sensors(): + assert hasattr(apd.sensors.sensors, "PythonVersion") + + +@pytest.mark.functional +def test_first_sensor_is_first_two_lines_of_cli_output(): + runner = CliRunner() + with mock.patch("apd.sensors.cli.get_sensors") as get_sensors: + get_sensors.return_value = [apd.sensors.sensors.PythonVersion()] + result = runner.invoke(apd.sensors.cli.show_sensors) + python_version = str(apd.sensors.sensors.PythonVersion()) + assert ["Python Version", python_version] == result.stdout.split("\n")[:2] + + +class TestSensorFromPath: + @pytest.fixture + def subject(self): + return apd.sensors.cli.get_sensor_by_path + + def test_get_sensor_by_path(self, subject): + assert isinstance( + subject("apd.sensors.sensors:PythonVersion"), + apd.sensors.sensors.PythonVersion, + ) + + def test_invalid_format(self, subject): + with pytest.raises(RuntimeError, match="dotted.path.to.module:ClassName"): + subject("apd.sensors.sensors.PythonVersion") + + def test_invalid_module(self, subject): + with pytest.raises(RuntimeError, match="Could not import module"): + subject("apd.nonsense.sensor:FakeSensor") + + def test_missing_sensor(self, subject): + with pytest.raises(RuntimeError, match="Could not find attribute"): + subject("apd.sensors.sensors:FakeSensor") + + def test_invalid_sensor(self, subject): + with pytest.raises(RuntimeError, match="is not recognised as a Sensor"): + subject("apd.sensors.sensors:Sensor") + + +@pytest.mark.functional +def test_develop_option_finds_sensor_by_path(): + runner = CliRunner() + result = runner.invoke( + apd.sensors.cli.show_sensors, ["--develop", "apd.sensors.sensors:PythonVersion"] + ) + python_version = str(apd.sensors.sensors.PythonVersion()) + assert ["Python Version", python_version, "", ""] == result.stdout.split("\n") + + +class TestDefaultSerializer: + @pytest.fixture + def python_version(self): + return apd.sensors.sensors.PythonVersion() + + @pytest.fixture + def serialize(self, python_version): + return python_version.to_json_compatible + + @pytest.fixture + def deserialize(self, python_version): + return python_version.from_json_compatible + + def test_serialize_deserialize_is_symmetric( + self, python_version, serialize, deserialize + ): + value = python_version.value() + serialized = serialize(value) + json_version = json.dumps(serialized) + assert ( + json_version == f"[{value.major}, {value.minor}, {value.micro}," + f' "{value.releaselevel}", {value.serial}]' + ) + deserialized = deserialize(serialized) + assert deserialized == value diff --git a/Ch05/listing05-01-helloworld.py b/Ch05/listing05-01-helloworld.py new file mode 100644 index 0000000..72e0590 --- /dev/null +++ b/Ch05/listing05-01-helloworld.py @@ -0,0 +1,13 @@ +import wsgiref.simple_server + +def hello_world(environ, start_response): + headers = [ + ("Content-type", "text/plain; charset=utf-8"), + ("Content-Security-Policy", "default-src 'none';"), + ] + start_response("200 OK", headers) + return [b"hello", b" ", b"world"] + +if __name__ == "__main__": + with wsgiref.simple_server.make_server("", 8000, hello_world) as server: + server.serve_forever() diff --git a/Ch05/listing05-02-helloworld-incremental.py b/Ch05/listing05-02-helloworld-incremental.py new file mode 100644 index 0000000..49df1ae --- /dev/null +++ b/Ch05/listing05-02-helloworld-incremental.py @@ -0,0 +1,19 @@ +import time +import wsgiref.simple_server + + +def hello_world(environ, start_response): + headers = [ + ("Content-type", "text/html; charset=utf-8"), + ("Content-Security-Policy", "default-src 'none';"), + ] + start_response("200 OK", headers) + yield b"" + for i in range(20): + yield b"

hello world

" + time.sleep(1) + yield b"" + +if __name__ == "__main__": + with wsgiref.simple_server.make_server("", 8000, hello_world) as server: + server.serve_forever() diff --git a/Ch05/listing05-03-apd_sensors_wsgi.py b/Ch05/listing05-03-apd_sensors_wsgi.py new file mode 100644 index 0000000..491f35a --- /dev/null +++ b/Ch05/listing05-03-apd_sensors_wsgi.py @@ -0,0 +1,30 @@ +import json +import typing as t +import wsgiref.simple_server + +from apd.sensors.cli import get_sensors + +if t.TYPE_CHECKING: + # Use the exact definition of StartResponse, of possible + from wsgiref.types import StartResponse +else: + StartResponse = t.Callable + + +def sensor_values( + environ: t.Dict[str, str], start_response: StartResponse +) -> t.List[bytes]: + headers = [ + ("Content-type", "application/json; charset=utf-8"), + ("Content-Security-Policy", "default-src 'none';"), + ] + start_response("200 OK", headers) + data = {} + for sensor in get_sensors(): + data[sensor.title] = sensor.value() + encoded = json.dumps(data).encode("utf-8") + return [encoded] + +if __name__ == "__main__": + with wsgiref.simple_server.make_server("", 8000, sensor_values) as server: + server.handle_request() diff --git a/Ch05/listing05-08-typing.py b/Ch05/listing05-08-typing.py new file mode 100644 index 0000000..62a72cf --- /dev/null +++ b/Ch05/listing05-08-typing.py @@ -0,0 +1,33 @@ +import functools +import random +import typing as t + +ViewFuncReturn = t.TypeVar("ViewFuncReturn") +ErrorReturn = int + +def result_or_number( + func: t.Callable[[], ViewFuncReturn] +) -> t.Callable[[], t.Union[ViewFuncReturn, ErrorReturn]]: + @functools.wraps(func) + def wrapped() -> t.Union[ViewFuncReturn, ErrorReturn]: + + pass_through = random.choice([True, False]) + if pass_through: + return func() + else: + return random.randint(0, 100) + + return wrapped + +@result_or_number +def hello() -> str: + return "Hello!" + +@result_or_number +def three() -> int: + return 3 + +if t.TYPE_CHECKING: + reveal_type(hello) +else: + print(hello()) diff --git a/Ch06/apd.aggregation-chapter06/.coveragerc b/Ch06/apd.aggregation-chapter06/.coveragerc new file mode 100644 index 0000000..e766c69 --- /dev/null +++ b/Ch06/apd.aggregation-chapter06/.coveragerc @@ -0,0 +1,3 @@ +[run] +branch = True +omit = tests/* diff --git a/Ch06/apd.aggregation-chapter06/.pre-commit-config.yaml b/Ch06/apd.aggregation-chapter06/.pre-commit-config.yaml new file mode 100644 index 0000000..d3b6ec7 --- /dev/null +++ b/Ch06/apd.aggregation-chapter06/.pre-commit-config.yaml @@ -0,0 +1,26 @@ +repos: + +- repo: local + hooks: + - id: black + name: black + entry: pipenv run black + args: [--quiet] + language: system + types: [python] + + - id: mypy + name: mypy + exclude: (?x)^( + (.*)/setup.py + )$ + entry: pipenv run mypy + args: ["--follow-imports=skip"] + language: system + types: [python] + + - id: flake8 + name: flake8 + entry: pipenv run flake8 + language: system + types: [python] diff --git a/Ch06/apd.aggregation-chapter06/CHANGES.md b/Ch06/apd.aggregation-chapter06/CHANGES.md new file mode 100644 index 0000000..8e424e5 --- /dev/null +++ b/Ch06/apd.aggregation-chapter06/CHANGES.md @@ -0,0 +1,5 @@ +## Changes + +### 1.0.0 (Unreleased) + +* Generated from skeleton diff --git a/Ch06/apd.aggregation-chapter06/LICENCE b/Ch06/apd.aggregation-chapter06/LICENCE new file mode 100644 index 0000000..86685d4 --- /dev/null +++ b/Ch06/apd.aggregation-chapter06/LICENCE @@ -0,0 +1,31 @@ +Copyright (c) 2019, Matthew Wilkes + + +All rights reserved. + +Redistribution and use in source and binary forms, with or without modification, +are permitted provided that the following conditions are met: + +* Redistributions of source code must retain the above copyright notice, this + list of conditions and the following disclaimer. + +* Redistributions in binary form must reproduce the above copyright notice, this + list of conditions and the following disclaimer in the documentation and/or + other materials provided with the distribution. + +* Neither the name of the copyright holder nor the names of its + contributors may be used to endorse or promote products derived from this + software without specific prior written permission. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND +ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED +WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. +IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, +INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, +BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, +DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY +OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE +OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED +OF THE POSSIBILITY OF SUCH DAMAGE. + + diff --git a/Ch06/apd.aggregation-chapter06/Pipfile b/Ch06/apd.aggregation-chapter06/Pipfile new file mode 100644 index 0000000..ba42fa3 --- /dev/null +++ b/Ch06/apd.aggregation-chapter06/Pipfile @@ -0,0 +1,22 @@ +[[source]] +name = "pypi" +url = "https://pypi.org/simple" +verify_ssl = true + +[dev-packages] +pytest = "*" +pytest-cov = "*" +mypy = "*" +flake8 = "*" +black = "*" +pre-commit = "*" +wheel = "*" +twine = "*" + +[packages] +apd-aggregation = {editable = true,path = "."} + +[requires] + +[pipenv] +allow_prereleases = true diff --git a/Ch06/apd.aggregation-chapter06/Pipfile.lock b/Ch06/apd.aggregation-chapter06/Pipfile.lock new file mode 100644 index 0000000..62333bb --- /dev/null +++ b/Ch06/apd.aggregation-chapter06/Pipfile.lock @@ -0,0 +1,466 @@ +{ + "_meta": { + "hash": { + "sha256": "6b1c63a96230c56a20e4bac5a8dfee64e295cb0c9240505e3d32018442533f74" + }, + "pipfile-spec": 6, + "requires": {}, + "sources": [ + { + "name": "pypi", + "url": "https://pypi.org/simple", + "verify_ssl": 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b/Ch06/apd.aggregation-chapter06/setup.cfg @@ -0,0 +1,44 @@ +[mypy] +namespace_packages = True +mypy_path = src +plugins = sqlmypy + +[mypy-alembic] +ignore_missing_imports = True + +[flake8] +max-line-length = 88 + +[metadata] +name = apd.aggregation +version = attr: apd.aggregation.VERSION +description = A programme that queries apd.sensor endpoints and aggregates their results. +long_description = file: README.md, CHANGES.md, LICENCE +long_description_content_type = text/markdown +keywords = +license = BSD +classifiers = + Programming Language :: Python :: 3 + Programming Language :: Python :: 3.7 + +[options] +zip_safe = False +include_package_data = True +package_dir = + =src +packages = find_namespace: +install_requires = + sqlalchemy + requests + psycopg2 + alembic + click + sqlalchemy-stubs + + +[options.packages.find] +where = src + +[options.entry_points] +console_scripts = + collect_sensor_data = apd.aggregation.cli:collect_sensor_data diff --git a/Ch06/apd.aggregation-chapter06/setup.py b/Ch06/apd.aggregation-chapter06/setup.py new file mode 100644 index 0000000..6068493 --- /dev/null +++ b/Ch06/apd.aggregation-chapter06/setup.py @@ -0,0 +1,3 @@ +from setuptools import setup + +setup() diff --git a/Ch06/apd.aggregation-chapter06/src/apd/aggregation/__init__.py b/Ch06/apd.aggregation-chapter06/src/apd/aggregation/__init__.py new file mode 100644 index 0000000..3277f64 --- /dev/null +++ b/Ch06/apd.aggregation-chapter06/src/apd/aggregation/__init__.py @@ -0,0 +1 @@ +VERSION = "1.0.0" diff --git a/Ch06/apd.aggregation-chapter06/src/apd/aggregation/alembic/README b/Ch06/apd.aggregation-chapter06/src/apd/aggregation/alembic/README new file mode 100644 index 0000000..97a87d5 --- /dev/null +++ b/Ch06/apd.aggregation-chapter06/src/apd/aggregation/alembic/README @@ -0,0 +1,12 @@ +Generic single-database configuration. + + +# Database setup + +To generate the required database tables you must create an alembic.ini file, as follows: + + [alembic] + script_location = apd.aggregation:alembic + sqlalchemy.url = postgresql+psycopg2://apd@localhost/apd + +and run `alembic upgrade head`. This should also be done after every upgrade of the software. \ No newline at end of file diff --git a/Ch06/apd.aggregation-chapter06/src/apd/aggregation/alembic/env.py b/Ch06/apd.aggregation-chapter06/src/apd/aggregation/alembic/env.py new file mode 100644 index 0000000..09beb9c --- /dev/null +++ b/Ch06/apd.aggregation-chapter06/src/apd/aggregation/alembic/env.py @@ -0,0 +1,73 @@ +from logging.config import fileConfig + +from sqlalchemy import engine_from_config +from sqlalchemy import pool + +from alembic import context + +# this is the Alembic Config object, which provides +# access to the values within the .ini file in use. +config = context.config + +# Interpret the config file for Python logging. +# This line sets up loggers basically. +fileConfig(config.config_file_name) + +from apd.aggregation.database import Base + +target_metadata = Base.metadata + +# other values from the config, defined by the needs of env.py, +# can be acquired: +# my_important_option = config.get_main_option("my_important_option") +# ... etc. + + +def run_migrations_offline(): + """Run migrations in 'offline' mode. + + This configures the context with just a URL + and not an Engine, though an Engine is acceptable + here as well. By skipping the Engine creation + we don't even need a DBAPI to be available. + + Calls to context.execute() here emit the given string to the + script output. + + """ + url = config.get_main_option("sqlalchemy.url") + context.configure( + url=url, + target_metadata=target_metadata, + literal_binds=True, + dialect_opts={"paramstyle": "named"}, + ) + + with context.begin_transaction(): + context.run_migrations() + + +def run_migrations_online(): + """Run migrations in 'online' mode. + + In this scenario we need to create an Engine + and associate a connection with the context. + + """ + connectable = engine_from_config( + config.get_section(config.config_ini_section), + prefix="sqlalchemy.", + poolclass=pool.NullPool, + ) + + with connectable.connect() as connection: + context.configure(connection=connection, target_metadata=target_metadata) + + with context.begin_transaction(): + context.run_migrations() + + +if context.is_offline_mode(): + run_migrations_offline() +else: + run_migrations_online() diff --git a/Ch06/apd.aggregation-chapter06/src/apd/aggregation/alembic/script.py.mako b/Ch06/apd.aggregation-chapter06/src/apd/aggregation/alembic/script.py.mako new file mode 100644 index 0000000..2c01563 --- /dev/null +++ b/Ch06/apd.aggregation-chapter06/src/apd/aggregation/alembic/script.py.mako @@ -0,0 +1,24 @@ +"""${message} + +Revision ID: ${up_revision} +Revises: ${down_revision | comma,n} +Create Date: ${create_date} + +""" +from alembic import op +import sqlalchemy as sa +${imports if imports else ""} + +# revision identifiers, used by Alembic. +revision = ${repr(up_revision)} +down_revision = ${repr(down_revision)} +branch_labels = ${repr(branch_labels)} +depends_on = ${repr(depends_on)} + + +def upgrade(): + ${upgrades if upgrades else "pass"} + + +def downgrade(): + ${downgrades if downgrades else "pass"} diff --git a/Ch06/apd.aggregation-chapter06/src/apd/aggregation/alembic/versions/6d2eacd5da3f_create_sensor_values_table.py b/Ch06/apd.aggregation-chapter06/src/apd/aggregation/alembic/versions/6d2eacd5da3f_create_sensor_values_table.py new file mode 100644 index 0000000..b4a3545 --- /dev/null +++ b/Ch06/apd.aggregation-chapter06/src/apd/aggregation/alembic/versions/6d2eacd5da3f_create_sensor_values_table.py @@ -0,0 +1,31 @@ +"""Create datapoints table + +Revision ID: 6d2eacd5da3f +Revises: N/A +Create Date: 2019-09-29 13:43:21.242706 + +""" +from alembic import op +import sqlalchemy as sa +from sqlalchemy.dialects import postgresql + +# revision identifiers, used by Alembic. +revision = "6d2eacd5da3f" +down_revision = None +branch_labels = None +depends_on = None + + +def upgrade(): + op.create_table( + "datapoints", + sa.Column("id", sa.Integer(), nullable=False), + sa.Column("sensor_name", sa.String(), nullable=True), + sa.Column("collected_at", postgresql.TIMESTAMP(), nullable=True), + sa.Column("data", postgresql.JSONB(astext_type=sa.Text()), nullable=True), + sa.PrimaryKeyConstraint("id"), + ) + + +def downgrade(): + op.drop_table("datapoints") diff --git a/Ch06/apd.aggregation-chapter06/src/apd/aggregation/cli.py b/Ch06/apd.aggregation-chapter06/src/apd/aggregation/cli.py new file mode 100644 index 0000000..3435e78 --- /dev/null +++ b/Ch06/apd.aggregation-chapter06/src/apd/aggregation/cli.py @@ -0,0 +1,52 @@ +import typing as t + +import click + +from .collect import standalone + + +@click.command() +@click.argument("server", nargs=-1) +@click.option( + "--db", + metavar="", + default="postgresql+psycopg2://localhost/apd", + help="The connection string to a PostgreSQL database", + envvar="APD_DB_URI", +) +@click.option("--api-key", metavar="", envvar="APD_API_KEY") +@click.option( + "--tolerate-failures", + "-f", + help="If provided, failure to retrieve some sensors' data will not abort the collection process", + is_flag=True, +) +@click.option("-v", "--verbose", is_flag=True, help="Enables verbose mode") +def collect_sensor_data( + db: str, server: t.Tuple[str], api_key: str, tolerate_failures: bool, verbose: bool +): + """This loads data from one or more sensors into the specified database. + + Only PostgreSQL databases are supported, as the column definitions use + multiple pg specific features. The database must already exist and be + populated with the required tables. + + The --api-key option is used to specify the access token for the sensors + being queried. + + You may specify any number of servers, the variable should be the full URL + to the sensor's HTTP interface, not including the /v/2.0 portion. Multiple + URLs should be separated with a space. + """ + if tolerate_failures: + attempts = [(s,) for s in server] + else: + attempts = [server] + success = True + for attempt in attempts: + try: + standalone(db, attempt, api_key, echo=verbose) + except ValueError as e: + click.secho(str(e), err=True, fg="red") + success = False + return success diff --git a/Ch06/apd.aggregation-chapter06/src/apd/aggregation/collect.py b/Ch06/apd.aggregation-chapter06/src/apd/aggregation/collect.py new file mode 100644 index 0000000..ecc2c41 --- /dev/null +++ b/Ch06/apd.aggregation-chapter06/src/apd/aggregation/collect.py @@ -0,0 +1,63 @@ +import datetime +import typing as t + +import requests +from sqlalchemy import create_engine +from sqlalchemy.orm import sessionmaker +from sqlalchemy.orm.session import Session + +from .database import DataPoint + + +def get_data_points(server: str, api_key: t.Optional[str]) -> t.Iterable[DataPoint]: + if not server.endswith("/"): + server += "/" + url = server + "v/2.0/sensors/" + headers = {} + if api_key: + headers["X-API-KEY"] = api_key + try: + result = requests.get(url, headers=headers) + except requests.ConnectionError as e: + raise ValueError(f"Error connecting to {server}") + now = datetime.datetime.now() + if result.ok: + for value in result.json()["sensors"]: + yield DataPoint( + sensor_name=value["id"], collected_at=now, data=value["value"] + ) + else: + raise ValueError( + f"Error loading data from {server}: " + + result.json().get("error", "Unknown") + ) + + +def add_data_from_sensors( + session: Session, servers: t.Tuple[str], api_key: t.Optional[str] +) -> t.Iterable[DataPoint]: + points: t.List[DataPoint] = [] + for server in servers: + for point in get_data_points(server, api_key): + session.add(point) + points.append(point) + return points + + +def standalone( + db_uri: str, servers: t.Tuple[str], api_key: t.Optional[str], echo: bool = False +) -> None: + engine = create_engine(db_uri, echo=echo) + sm = sessionmaker(engine) + Session = sm() + add_data_from_sensors(Session, servers, api_key) + Session.commit() + + +if __name__ == "__main__": + standalone( + db_uri="postgresql+psycopg2://apd@localhost/apd", + servers=("http://pvoutput:8080/",), + api_key="h3hdfjksfhwkjehnwekj", + ) + \ No newline at end of file diff --git a/Ch06/apd.aggregation-chapter06/src/apd/aggregation/database.py b/Ch06/apd.aggregation-chapter06/src/apd/aggregation/database.py new file mode 100644 index 0000000..af1cbde --- /dev/null +++ b/Ch06/apd.aggregation-chapter06/src/apd/aggregation/database.py @@ -0,0 +1,31 @@ +import sqlalchemy +from sqlalchemy.ext.declarative import declarative_base +from sqlalchemy.dialects.postgresql import JSONB, TIMESTAMP +from sqlalchemy.orm import sessionmaker + + +Base = declarative_base() + + +class DataPoint(Base): + __tablename__ = "datapoints" + id = sqlalchemy.Column(sqlalchemy.Integer, primary_key=True) + sensor_name = sqlalchemy.Column(sqlalchemy.String) + collected_at = sqlalchemy.Column(TIMESTAMP) + data = sqlalchemy.Column(JSONB) + + +def main(): + engine = sqlalchemy.create_engine( + "postgresql+psycopg2://apd@localhost/apd", echo=True + ) + sm = sessionmaker(engine) + Session = sm() + if False: + Base.metadata.create_all(engine) + print(Session.query(DataPoint).all()) + pass + + +if __name__ == "__main__": + main() diff --git a/Ch06/apd.aggregation-chapter06/tests/__init__.py b/Ch06/apd.aggregation-chapter06/tests/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/Ch06/apd.aggregation-chapter06/tests/conftest.py b/Ch06/apd.aggregation-chapter06/tests/conftest.py new file mode 100644 index 0000000..e69de29 diff --git a/Ch06/chapter06-ex1-generators.py b/Ch06/chapter06-ex1-generators.py new file mode 100644 index 0000000..b481f86 --- /dev/null +++ b/Ch06/chapter06-ex1-generators.py @@ -0,0 +1,27 @@ +from datetime import datetime +import random +import time + +from apd.aggregation.database import DataPoint + + +def generate_points(time_to_wait): + while True: + time.sleep(time_to_wait) + yield DataPoint( + sensor_name="Fake", + collected_at=datetime.now(), + data=random.choice([1, 2, 3]) + ) + +def get_points_on_odd_seconds(): + points = generate_points(1) + odd_seconds = filter(lambda point: point.collected_at.second % 2, points) + yield from odd_seconds + +def print_points(points): + for point in points: + print(point.sensor_name, point.collected_at, point.data) + +if __name__ == "__main__": + print_points(get_points_on_odd_seconds()) \ No newline at end of file diff --git a/Ch06/listing06-03-descriptors.py b/Ch06/listing06-03-descriptors.py new file mode 100644 index 0000000..4402911 --- /dev/null +++ b/Ch06/listing06-03-descriptors.py @@ -0,0 +1,27 @@ +class ExampleDescriptor: + + def __set_name__(self, instance, name): + self.name = name + + def __get__(self, instance, owner): + print(f"{self}.__get__({instance}, {owner})") + if not instance: + # We were called on the class available as `owner` + return self + else: + # We were called on the instance called `instance` + if self.name in instance.__dict__: + return instance.__dict__[self.name] + else: + raise AttributeError(self.name) + + def __set__(self, instance, value): + print(f"{self}.__set__({instance}, {value})") + instance.__dict__[self.name] = value + + def __delete__(self, instance): + print(f"{self}.__delete__({instance}") + del instance.__dict__[self.name] + +class A: + foo = ExampleDescriptor() diff --git a/Ch07/apd.aggregation-chapter07-aio/.coveragerc b/Ch07/apd.aggregation-chapter07-aio/.coveragerc new file mode 100644 index 0000000..e766c69 --- /dev/null +++ b/Ch07/apd.aggregation-chapter07-aio/.coveragerc @@ -0,0 +1,3 @@ +[run] +branch = True +omit = tests/* diff --git a/Ch07/apd.aggregation-chapter07-aio/.pre-commit-config.yaml b/Ch07/apd.aggregation-chapter07-aio/.pre-commit-config.yaml new file mode 100644 index 0000000..d3b6ec7 --- /dev/null +++ b/Ch07/apd.aggregation-chapter07-aio/.pre-commit-config.yaml @@ -0,0 +1,26 @@ +repos: + +- repo: local + hooks: + - id: black + name: black + entry: pipenv run black + args: [--quiet] + language: system + types: [python] + + - id: mypy + name: mypy + exclude: (?x)^( + (.*)/setup.py + )$ + entry: pipenv run mypy + args: ["--follow-imports=skip"] + language: system + types: [python] + + - id: flake8 + name: flake8 + entry: pipenv run flake8 + language: system + types: [python] diff --git a/Ch07/apd.aggregation-chapter07-aio/CHANGES.md b/Ch07/apd.aggregation-chapter07-aio/CHANGES.md new file mode 100644 index 0000000..8e424e5 --- /dev/null +++ b/Ch07/apd.aggregation-chapter07-aio/CHANGES.md @@ -0,0 +1,5 @@ +## Changes + +### 1.0.0 (Unreleased) + +* Generated from skeleton diff --git a/Ch07/apd.aggregation-chapter07-aio/LICENCE b/Ch07/apd.aggregation-chapter07-aio/LICENCE new file mode 100644 index 0000000..86685d4 --- /dev/null +++ b/Ch07/apd.aggregation-chapter07-aio/LICENCE @@ -0,0 +1,31 @@ +Copyright (c) 2019, Matthew Wilkes + + +All rights reserved. + +Redistribution and use in source and binary forms, with or without modification, +are permitted provided that the following conditions are met: + +* Redistributions of source code must retain the above copyright notice, this + list of conditions and the following disclaimer. + +* Redistributions in binary form must reproduce the above copyright notice, this + list of conditions and the following disclaimer in the documentation and/or + other materials provided with the distribution. + +* Neither the name of the copyright holder nor the names of its + contributors may be used to endorse or promote products derived from this + software without specific prior written permission. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND +ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED +WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. +IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, +INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, +BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, +DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY +OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE +OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED +OF THE POSSIBILITY OF SUCH DAMAGE. + + diff --git a/Ch07/apd.aggregation-chapter07-aio/Pipfile b/Ch07/apd.aggregation-chapter07-aio/Pipfile new file mode 100644 index 0000000..ba42fa3 --- /dev/null +++ b/Ch07/apd.aggregation-chapter07-aio/Pipfile @@ -0,0 +1,22 @@ +[[source]] +name = "pypi" +url = "https://pypi.org/simple" +verify_ssl = true + +[dev-packages] +pytest = "*" +pytest-cov = "*" +mypy = "*" +flake8 = "*" +black = "*" +pre-commit = "*" +wheel = "*" +twine = "*" + +[packages] 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"sha256:f06903e9f1f43b12d371004b4ac7b06ab39a44adc747266928ae6debfa7b3335" + ], + "version": "==0.6.0" + } + } +} diff --git a/Ch07/apd.aggregation-chapter07-aio/README.md b/Ch07/apd.aggregation-chapter07-aio/README.md new file mode 100644 index 0000000..85669dd --- /dev/null +++ b/Ch07/apd.aggregation-chapter07-aio/README.md @@ -0,0 +1,4 @@ +# APD Sensor aggregator + +A programme that queries apd.sensor endpoints and aggregates their results. + diff --git a/Ch07/apd.aggregation-chapter07-aio/pyproject.toml b/Ch07/apd.aggregation-chapter07-aio/pyproject.toml new file mode 100644 index 0000000..4d3bb67 --- /dev/null +++ b/Ch07/apd.aggregation-chapter07-aio/pyproject.toml @@ -0,0 +1,5 @@ +[build-system] +requires = [ + "setuptools", +] +build-backend = "setuptools.build_meta" diff --git a/Ch07/apd.aggregation-chapter07-aio/setup.cfg b/Ch07/apd.aggregation-chapter07-aio/setup.cfg new file mode 100644 index 0000000..949fb48 --- /dev/null +++ b/Ch07/apd.aggregation-chapter07-aio/setup.cfg @@ -0,0 +1,44 @@ +[mypy] +namespace_packages = True +mypy_path = src +plugins = sqlmypy + +[mypy-alembic] +ignore_missing_imports = True + +[flake8] +max-line-length = 88 + +[metadata] +name = apd.aggregation +version = attr: apd.aggregation.VERSION +description = A programme that queries apd.sensor endpoints and aggregates their results. +long_description = file: README.md, CHANGES.md, LICENCE +long_description_content_type = text/markdown +keywords = +license = BSD +classifiers = + Programming Language :: Python :: 3 + Programming Language :: Python :: 3.7 + +[options] +zip_safe = False +include_package_data = True +package_dir = + =src +packages = find_namespace: +install_requires = + sqlalchemy + aiohttp + psycopg2 + alembic + click + sqlalchemy-stubs + + +[options.packages.find] +where = src + +[options.entry_points] +console_scripts = + collect_sensor_data = apd.aggregation.cli:collect_sensor_data diff --git a/Ch07/apd.aggregation-chapter07-aio/setup.py b/Ch07/apd.aggregation-chapter07-aio/setup.py new file mode 100644 index 0000000..6068493 --- /dev/null +++ b/Ch07/apd.aggregation-chapter07-aio/setup.py @@ -0,0 +1,3 @@ +from setuptools import setup + +setup() diff --git a/Ch07/apd.aggregation-chapter07-aio/src/apd/aggregation/__init__.py b/Ch07/apd.aggregation-chapter07-aio/src/apd/aggregation/__init__.py new file mode 100644 index 0000000..3277f64 --- /dev/null +++ b/Ch07/apd.aggregation-chapter07-aio/src/apd/aggregation/__init__.py @@ -0,0 +1 @@ +VERSION = "1.0.0" diff --git a/Ch07/apd.aggregation-chapter07-aio/src/apd/aggregation/alembic/README b/Ch07/apd.aggregation-chapter07-aio/src/apd/aggregation/alembic/README new file mode 100644 index 0000000..97a87d5 --- /dev/null +++ b/Ch07/apd.aggregation-chapter07-aio/src/apd/aggregation/alembic/README @@ -0,0 +1,12 @@ +Generic single-database configuration. + + +# Database setup + +To generate the required database tables you must create an alembic.ini file, as follows: + + [alembic] + script_location = apd.aggregation:alembic + sqlalchemy.url = postgresql+psycopg2://apd@localhost/apd + +and run `alembic upgrade head`. This should also be done after every upgrade of the software. \ No newline at end of file diff --git a/Ch07/apd.aggregation-chapter07-aio/src/apd/aggregation/alembic/env.py b/Ch07/apd.aggregation-chapter07-aio/src/apd/aggregation/alembic/env.py new file mode 100644 index 0000000..09beb9c --- /dev/null +++ b/Ch07/apd.aggregation-chapter07-aio/src/apd/aggregation/alembic/env.py @@ -0,0 +1,73 @@ +from logging.config import fileConfig + +from sqlalchemy import engine_from_config +from sqlalchemy import pool + +from alembic import context + +# this is the Alembic Config object, which provides +# access to the values within the .ini file in use. +config = context.config + +# Interpret the config file for Python logging. +# This line sets up loggers basically. +fileConfig(config.config_file_name) + +from apd.aggregation.database import Base + +target_metadata = Base.metadata + +# other values from the config, defined by the needs of env.py, +# can be acquired: +# my_important_option = config.get_main_option("my_important_option") +# ... etc. + + +def run_migrations_offline(): + """Run migrations in 'offline' mode. + + This configures the context with just a URL + and not an Engine, though an Engine is acceptable + here as well. By skipping the Engine creation + we don't even need a DBAPI to be available. + + Calls to context.execute() here emit the given string to the + script output. + + """ + url = config.get_main_option("sqlalchemy.url") + context.configure( + url=url, + target_metadata=target_metadata, + literal_binds=True, + dialect_opts={"paramstyle": "named"}, + ) + + with context.begin_transaction(): + context.run_migrations() + + +def run_migrations_online(): + """Run migrations in 'online' mode. + + In this scenario we need to create an Engine + and associate a connection with the context. + + """ + connectable = engine_from_config( + config.get_section(config.config_ini_section), + prefix="sqlalchemy.", + poolclass=pool.NullPool, + ) + + with connectable.connect() as connection: + context.configure(connection=connection, target_metadata=target_metadata) + + with context.begin_transaction(): + context.run_migrations() + + +if context.is_offline_mode(): + run_migrations_offline() +else: + run_migrations_online() diff --git a/Ch07/apd.aggregation-chapter07-aio/src/apd/aggregation/alembic/script.py.mako b/Ch07/apd.aggregation-chapter07-aio/src/apd/aggregation/alembic/script.py.mako new file mode 100644 index 0000000..2c01563 --- /dev/null +++ b/Ch07/apd.aggregation-chapter07-aio/src/apd/aggregation/alembic/script.py.mako @@ -0,0 +1,24 @@ +"""${message} + +Revision ID: ${up_revision} +Revises: ${down_revision | comma,n} +Create Date: ${create_date} + +""" +from alembic import op +import sqlalchemy as sa +${imports if imports else ""} + +# revision identifiers, used by Alembic. +revision = ${repr(up_revision)} +down_revision = ${repr(down_revision)} +branch_labels = ${repr(branch_labels)} +depends_on = ${repr(depends_on)} + + +def upgrade(): + ${upgrades if upgrades else "pass"} + + +def downgrade(): + ${downgrades if downgrades else "pass"} diff --git a/Ch07/apd.aggregation-chapter07-aio/src/apd/aggregation/alembic/versions/6d2eacd5da3f_create_sensor_values_table.py b/Ch07/apd.aggregation-chapter07-aio/src/apd/aggregation/alembic/versions/6d2eacd5da3f_create_sensor_values_table.py new file mode 100644 index 0000000..b4a3545 --- /dev/null +++ b/Ch07/apd.aggregation-chapter07-aio/src/apd/aggregation/alembic/versions/6d2eacd5da3f_create_sensor_values_table.py @@ -0,0 +1,31 @@ +"""Create datapoints table + +Revision ID: 6d2eacd5da3f +Revises: N/A +Create Date: 2019-09-29 13:43:21.242706 + +""" +from alembic import op +import sqlalchemy as sa +from sqlalchemy.dialects import postgresql + +# revision identifiers, used by Alembic. +revision = "6d2eacd5da3f" +down_revision = None +branch_labels = None +depends_on = None + + +def upgrade(): + op.create_table( + "datapoints", + sa.Column("id", sa.Integer(), nullable=False), + sa.Column("sensor_name", sa.String(), nullable=True), + sa.Column("collected_at", postgresql.TIMESTAMP(), nullable=True), + sa.Column("data", postgresql.JSONB(astext_type=sa.Text()), nullable=True), + sa.PrimaryKeyConstraint("id"), + ) + + +def downgrade(): + op.drop_table("datapoints") diff --git a/Ch07/apd.aggregation-chapter07-aio/src/apd/aggregation/cli.py b/Ch07/apd.aggregation-chapter07-aio/src/apd/aggregation/cli.py new file mode 100644 index 0000000..3435e78 --- /dev/null +++ b/Ch07/apd.aggregation-chapter07-aio/src/apd/aggregation/cli.py @@ -0,0 +1,52 @@ +import typing as t + +import click + +from .collect import standalone + + +@click.command() +@click.argument("server", nargs=-1) +@click.option( + "--db", + metavar="", + default="postgresql+psycopg2://localhost/apd", + help="The connection string to a PostgreSQL database", + envvar="APD_DB_URI", +) +@click.option("--api-key", metavar="", envvar="APD_API_KEY") +@click.option( + "--tolerate-failures", + "-f", + help="If provided, failure to retrieve some sensors' data will not abort the collection process", + is_flag=True, +) +@click.option("-v", "--verbose", is_flag=True, help="Enables verbose mode") +def collect_sensor_data( + db: str, server: t.Tuple[str], api_key: str, tolerate_failures: bool, verbose: bool +): + """This loads data from one or more sensors into the specified database. + + Only PostgreSQL databases are supported, as the column definitions use + multiple pg specific features. The database must already exist and be + populated with the required tables. + + The --api-key option is used to specify the access token for the sensors + being queried. + + You may specify any number of servers, the variable should be the full URL + to the sensor's HTTP interface, not including the /v/2.0 portion. Multiple + URLs should be separated with a space. + """ + if tolerate_failures: + attempts = [(s,) for s in server] + else: + attempts = [server] + success = True + for attempt in attempts: + try: + standalone(db, attempt, api_key, echo=verbose) + except ValueError as e: + click.secho(str(e), err=True, fg="red") + success = False + return success diff --git a/Ch07/apd.aggregation-chapter07-aio/src/apd/aggregation/collect.py b/Ch07/apd.aggregation-chapter07-aio/src/apd/aggregation/collect.py new file mode 100644 index 0000000..b1c28d6 --- /dev/null +++ b/Ch07/apd.aggregation-chapter07-aio/src/apd/aggregation/collect.py @@ -0,0 +1,78 @@ +import asyncio +import datetime +import typing as t + +import aiohttp +from sqlalchemy import create_engine +from sqlalchemy.orm import sessionmaker +from sqlalchemy.orm.session import Session + +from .database import DataPoint + + +async def get_data_points( + server: str, api_key: t.Optional[str], http: aiohttp.ClientSession +) -> t.List[DataPoint]: + if not server.endswith("/"): + server += "/" + url = server + "v/2.0/sensors/" + headers = {} + if api_key: + headers["X-API-KEY"] = api_key + async with http.get(url) as request: + result = await request.json() + ok = request.status == 200 + now = datetime.datetime.now() + if ok: + points = [] + for value in result["sensors"]: + points.append( + DataPoint( + sensor_name=value["id"], collected_at=now, data=value["value"] + ) + ) + return points + else: + raise ValueError( + f"Error loading data from {server}: " + + result.json().get("error", "Unknown") + ) + + +async def handle_result( + result: t.List[DataPoint], session: Session +) -> t.List[DataPoint]: + for point in result: + session.add(point) + return result + + +async def add_data_from_sensors( + session: Session, servers: t.Tuple[str], api_key: t.Optional[str] +) -> t.List[DataPoint]: + tasks: t.List[t.Awaitable[t.List[DataPoint]]] = [] + points: t.List[DataPoint] = [] + async with aiohttp.ClientSession() as http: + for server in servers: + tasks.append(get_data_points(server, api_key, http)) + for a in await asyncio.gather(*tasks): + points += await handle_result(a, session) + return points + + +def standalone( + db_uri: str, servers: t.Tuple[str], api_key: t.Optional[str], echo: bool = False +) -> None: + engine = create_engine(db_uri, echo=echo) + sm = sessionmaker(engine) + Session = sm() + asyncio.run(add_data_from_sensors(Session, servers, api_key)) + Session.commit() + + +if __name__ == "__main__": + standalone( + db_uri="postgresql+psycopg2://apd@localhost/apd", + servers=("http://pvoutput:8080/",), + api_key="h3hdfjksfhwkjehnwekj", + ) diff --git a/Ch07/apd.aggregation-chapter07-aio/src/apd/aggregation/database.py b/Ch07/apd.aggregation-chapter07-aio/src/apd/aggregation/database.py new file mode 100644 index 0000000..af1cbde --- /dev/null +++ b/Ch07/apd.aggregation-chapter07-aio/src/apd/aggregation/database.py @@ -0,0 +1,31 @@ +import sqlalchemy +from sqlalchemy.ext.declarative import declarative_base +from sqlalchemy.dialects.postgresql import JSONB, TIMESTAMP +from sqlalchemy.orm import sessionmaker + + +Base = declarative_base() + + +class DataPoint(Base): + __tablename__ = "datapoints" + id = sqlalchemy.Column(sqlalchemy.Integer, primary_key=True) + sensor_name = sqlalchemy.Column(sqlalchemy.String) + collected_at = sqlalchemy.Column(TIMESTAMP) + data = sqlalchemy.Column(JSONB) + + +def main(): + engine = sqlalchemy.create_engine( + "postgresql+psycopg2://apd@localhost/apd", echo=True + ) + sm = sessionmaker(engine) + Session = sm() + if False: + Base.metadata.create_all(engine) + print(Session.query(DataPoint).all()) + pass + + +if __name__ == "__main__": + main() diff --git a/Ch07/apd.aggregation-chapter07-aio/tests/__init__.py b/Ch07/apd.aggregation-chapter07-aio/tests/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/Ch07/apd.aggregation-chapter07-aio/tests/conftest.py b/Ch07/apd.aggregation-chapter07-aio/tests/conftest.py new file mode 100644 index 0000000..e69de29 diff --git a/Ch07/apd.aggregation-chapter07-multiprocess/.coveragerc b/Ch07/apd.aggregation-chapter07-multiprocess/.coveragerc new file mode 100644 index 0000000..e766c69 --- /dev/null +++ b/Ch07/apd.aggregation-chapter07-multiprocess/.coveragerc @@ -0,0 +1,3 @@ +[run] +branch = True +omit = tests/* diff --git a/Ch07/apd.aggregation-chapter07-multiprocess/.pre-commit-config.yaml b/Ch07/apd.aggregation-chapter07-multiprocess/.pre-commit-config.yaml new file mode 100644 index 0000000..d3b6ec7 --- /dev/null +++ b/Ch07/apd.aggregation-chapter07-multiprocess/.pre-commit-config.yaml @@ -0,0 +1,26 @@ +repos: + +- repo: local + hooks: + - id: black + name: black + entry: pipenv run black + args: [--quiet] + language: system + types: [python] + + - id: mypy + name: mypy + exclude: (?x)^( + (.*)/setup.py + )$ + entry: pipenv run mypy + args: ["--follow-imports=skip"] + language: system + types: [python] + + - id: flake8 + name: flake8 + entry: pipenv run flake8 + language: system + types: [python] diff --git a/Ch07/apd.aggregation-chapter07-multiprocess/CHANGES.md b/Ch07/apd.aggregation-chapter07-multiprocess/CHANGES.md new file mode 100644 index 0000000..8e424e5 --- /dev/null +++ b/Ch07/apd.aggregation-chapter07-multiprocess/CHANGES.md @@ -0,0 +1,5 @@ +## Changes + +### 1.0.0 (Unreleased) + +* Generated from skeleton diff --git a/Ch07/apd.aggregation-chapter07-multiprocess/LICENCE b/Ch07/apd.aggregation-chapter07-multiprocess/LICENCE new file mode 100644 index 0000000..86685d4 --- /dev/null +++ b/Ch07/apd.aggregation-chapter07-multiprocess/LICENCE @@ -0,0 +1,31 @@ +Copyright (c) 2019, Matthew Wilkes + + +All rights reserved. + +Redistribution and use in source and binary forms, with or without modification, +are permitted provided that the following conditions are met: + +* Redistributions of source code must retain the above copyright notice, this + list of conditions and the following disclaimer. + +* Redistributions in binary form must reproduce the above copyright notice, this + list of conditions and the following disclaimer in the documentation and/or + other materials provided with the distribution. + +* Neither the name of the copyright holder nor the names of its + contributors may be used to endorse or promote products derived from this + software without specific prior written permission. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND +ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED +WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. +IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, +INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, +BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, +DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY +OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE +OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED +OF THE POSSIBILITY OF SUCH DAMAGE. + + diff --git a/Ch07/apd.aggregation-chapter07-multiprocess/Pipfile b/Ch07/apd.aggregation-chapter07-multiprocess/Pipfile new file mode 100644 index 0000000..ba42fa3 --- /dev/null +++ b/Ch07/apd.aggregation-chapter07-multiprocess/Pipfile @@ -0,0 +1,22 @@ +[[source]] +name = "pypi" +url = "https://pypi.org/simple" +verify_ssl = true + +[dev-packages] +pytest = "*" +pytest-cov = "*" +mypy = "*" +flake8 = "*" +black = "*" +pre-commit = "*" +wheel = "*" +twine = "*" + +[packages] +apd-aggregation = {editable = true,path = "."} + +[requires] + +[pipenv] +allow_prereleases = true diff --git a/Ch07/apd.aggregation-chapter07-multiprocess/Pipfile.lock b/Ch07/apd.aggregation-chapter07-multiprocess/Pipfile.lock new file mode 100644 index 0000000..62333bb --- /dev/null +++ b/Ch07/apd.aggregation-chapter07-multiprocess/Pipfile.lock @@ -0,0 +1,466 @@ +{ + 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+requires = [ + "setuptools", +] +build-backend = "setuptools.build_meta" diff --git a/Ch07/apd.aggregation-chapter07-multiprocess/setup.cfg b/Ch07/apd.aggregation-chapter07-multiprocess/setup.cfg new file mode 100644 index 0000000..6c2dc8a --- /dev/null +++ b/Ch07/apd.aggregation-chapter07-multiprocess/setup.cfg @@ -0,0 +1,44 @@ +[mypy] +namespace_packages = True +mypy_path = src +plugins = sqlmypy + +[mypy-alembic] +ignore_missing_imports = True + +[flake8] +max-line-length = 88 + +[metadata] +name = apd.aggregation +version = attr: apd.aggregation.VERSION +description = A programme that queries apd.sensor endpoints and aggregates their results. +long_description = file: README.md, CHANGES.md, LICENCE +long_description_content_type = text/markdown +keywords = +license = BSD +classifiers = + Programming Language :: Python :: 3 + Programming Language :: Python :: 3.7 + +[options] +zip_safe = False +include_package_data = True +package_dir = + =src +packages = find_namespace: +install_requires = + sqlalchemy + requests + psycopg2 + alembic + click + sqlalchemy-stubs + + +[options.packages.find] +where = src + +[options.entry_points] +console_scripts = + collect_sensor_data = apd.aggregation.cli:collect_sensor_data diff --git a/Ch07/apd.aggregation-chapter07-multiprocess/setup.py b/Ch07/apd.aggregation-chapter07-multiprocess/setup.py new file mode 100644 index 0000000..6068493 --- /dev/null +++ b/Ch07/apd.aggregation-chapter07-multiprocess/setup.py @@ -0,0 +1,3 @@ +from setuptools import setup + +setup() diff --git a/Ch07/apd.aggregation-chapter07-multiprocess/src/apd/aggregation/__init__.py b/Ch07/apd.aggregation-chapter07-multiprocess/src/apd/aggregation/__init__.py new file mode 100644 index 0000000..3277f64 --- /dev/null +++ b/Ch07/apd.aggregation-chapter07-multiprocess/src/apd/aggregation/__init__.py @@ -0,0 +1 @@ +VERSION = "1.0.0" diff --git a/Ch07/apd.aggregation-chapter07-multiprocess/src/apd/aggregation/alembic/README b/Ch07/apd.aggregation-chapter07-multiprocess/src/apd/aggregation/alembic/README new file mode 100644 index 0000000..97a87d5 --- /dev/null +++ b/Ch07/apd.aggregation-chapter07-multiprocess/src/apd/aggregation/alembic/README @@ -0,0 +1,12 @@ +Generic single-database configuration. + + +# Database setup + +To generate the required database tables you must create an alembic.ini file, as follows: + + [alembic] + script_location = apd.aggregation:alembic + sqlalchemy.url = postgresql+psycopg2://apd@localhost/apd + +and run `alembic upgrade head`. This should also be done after every upgrade of the software. \ No newline at end of file diff --git a/Ch07/apd.aggregation-chapter07-multiprocess/src/apd/aggregation/alembic/env.py b/Ch07/apd.aggregation-chapter07-multiprocess/src/apd/aggregation/alembic/env.py new file mode 100644 index 0000000..09beb9c --- /dev/null +++ b/Ch07/apd.aggregation-chapter07-multiprocess/src/apd/aggregation/alembic/env.py @@ -0,0 +1,73 @@ +from logging.config import fileConfig + +from sqlalchemy import engine_from_config +from sqlalchemy import pool + +from alembic import context + +# this is the Alembic Config object, which provides +# access to the values within the .ini file in use. +config = context.config + +# Interpret the config file for Python logging. +# This line sets up loggers basically. +fileConfig(config.config_file_name) + +from apd.aggregation.database import Base + +target_metadata = Base.metadata + +# other values from the config, defined by the needs of env.py, +# can be acquired: +# my_important_option = config.get_main_option("my_important_option") +# ... etc. + + +def run_migrations_offline(): + """Run migrations in 'offline' mode. + + This configures the context with just a URL + and not an Engine, though an Engine is acceptable + here as well. By skipping the Engine creation + we don't even need a DBAPI to be available. + + Calls to context.execute() here emit the given string to the + script output. + + """ + url = config.get_main_option("sqlalchemy.url") + context.configure( + url=url, + target_metadata=target_metadata, + literal_binds=True, + dialect_opts={"paramstyle": "named"}, + ) + + with context.begin_transaction(): + context.run_migrations() + + +def run_migrations_online(): + """Run migrations in 'online' mode. + + In this scenario we need to create an Engine + and associate a connection with the context. + + """ + connectable = engine_from_config( + config.get_section(config.config_ini_section), + prefix="sqlalchemy.", + poolclass=pool.NullPool, + ) + + with connectable.connect() as connection: + context.configure(connection=connection, target_metadata=target_metadata) + + with context.begin_transaction(): + context.run_migrations() + + +if context.is_offline_mode(): + run_migrations_offline() +else: + run_migrations_online() diff --git a/Ch07/apd.aggregation-chapter07-multiprocess/src/apd/aggregation/alembic/script.py.mako b/Ch07/apd.aggregation-chapter07-multiprocess/src/apd/aggregation/alembic/script.py.mako new file mode 100644 index 0000000..2c01563 --- /dev/null +++ b/Ch07/apd.aggregation-chapter07-multiprocess/src/apd/aggregation/alembic/script.py.mako @@ -0,0 +1,24 @@ +"""${message} + +Revision ID: ${up_revision} +Revises: ${down_revision | comma,n} +Create Date: ${create_date} + +""" +from alembic import op +import sqlalchemy as sa +${imports if imports else ""} + +# revision identifiers, used by Alembic. +revision = ${repr(up_revision)} +down_revision = ${repr(down_revision)} +branch_labels = ${repr(branch_labels)} +depends_on = ${repr(depends_on)} + + +def upgrade(): + ${upgrades if upgrades else "pass"} + + +def downgrade(): + ${downgrades if downgrades else "pass"} diff --git a/Ch07/apd.aggregation-chapter07-multiprocess/src/apd/aggregation/alembic/versions/6d2eacd5da3f_create_sensor_values_table.py b/Ch07/apd.aggregation-chapter07-multiprocess/src/apd/aggregation/alembic/versions/6d2eacd5da3f_create_sensor_values_table.py new file mode 100644 index 0000000..b4a3545 --- /dev/null +++ b/Ch07/apd.aggregation-chapter07-multiprocess/src/apd/aggregation/alembic/versions/6d2eacd5da3f_create_sensor_values_table.py @@ -0,0 +1,31 @@ +"""Create datapoints table + +Revision ID: 6d2eacd5da3f +Revises: N/A +Create Date: 2019-09-29 13:43:21.242706 + +""" +from alembic import op +import sqlalchemy as sa +from sqlalchemy.dialects import postgresql + +# revision identifiers, used by Alembic. +revision = "6d2eacd5da3f" +down_revision = None +branch_labels = None +depends_on = None + + +def upgrade(): + op.create_table( + "datapoints", + sa.Column("id", sa.Integer(), nullable=False), + sa.Column("sensor_name", sa.String(), nullable=True), + sa.Column("collected_at", postgresql.TIMESTAMP(), nullable=True), + sa.Column("data", postgresql.JSONB(astext_type=sa.Text()), nullable=True), + sa.PrimaryKeyConstraint("id"), + ) + + +def downgrade(): + op.drop_table("datapoints") diff --git a/Ch07/apd.aggregation-chapter07-multiprocess/src/apd/aggregation/cli.py b/Ch07/apd.aggregation-chapter07-multiprocess/src/apd/aggregation/cli.py new file mode 100644 index 0000000..3435e78 --- /dev/null +++ b/Ch07/apd.aggregation-chapter07-multiprocess/src/apd/aggregation/cli.py @@ -0,0 +1,52 @@ +import typing as t + +import click + +from .collect import standalone + + +@click.command() +@click.argument("server", nargs=-1) +@click.option( + "--db", + metavar="", + default="postgresql+psycopg2://localhost/apd", + help="The connection string to a PostgreSQL database", + envvar="APD_DB_URI", +) +@click.option("--api-key", metavar="", envvar="APD_API_KEY") +@click.option( + "--tolerate-failures", + "-f", + help="If provided, failure to retrieve some sensors' data will not abort the collection process", + is_flag=True, +) +@click.option("-v", "--verbose", is_flag=True, help="Enables verbose mode") +def collect_sensor_data( + db: str, server: t.Tuple[str], api_key: str, tolerate_failures: bool, verbose: bool +): + """This loads data from one or more sensors into the specified database. + + Only PostgreSQL databases are supported, as the column definitions use + multiple pg specific features. The database must already exist and be + populated with the required tables. + + The --api-key option is used to specify the access token for the sensors + being queried. + + You may specify any number of servers, the variable should be the full URL + to the sensor's HTTP interface, not including the /v/2.0 portion. Multiple + URLs should be separated with a space. + """ + if tolerate_failures: + attempts = [(s,) for s in server] + else: + attempts = [server] + success = True + for attempt in attempts: + try: + standalone(db, attempt, api_key, echo=verbose) + except ValueError as e: + click.secho(str(e), err=True, fg="red") + success = False + return success diff --git a/Ch07/apd.aggregation-chapter07-multiprocess/src/apd/aggregation/collect.py b/Ch07/apd.aggregation-chapter07-multiprocess/src/apd/aggregation/collect.py new file mode 100644 index 0000000..0d22164 --- /dev/null +++ b/Ch07/apd.aggregation-chapter07-multiprocess/src/apd/aggregation/collect.py @@ -0,0 +1,79 @@ +import datetime +from concurrent.futures import ThreadPoolExecutor, Future +import typing as t + +import requests +from sqlalchemy import create_engine +from sqlalchemy.orm import sessionmaker +from sqlalchemy.orm.session import Session + +from .database import DataPoint + + +def get_data_points(server: str, api_key: t.Optional[str]) -> t.List[DataPoint]: + if not server.endswith("/"): + server += "/" + url = server + "v/2.0/sensors/" + headers = {} + if api_key: + headers["X-API-KEY"] = api_key + try: + result = requests.get(url, headers=headers) + except requests.ConnectionError: + raise ValueError(f"Error connecting to {server}") + now = datetime.datetime.now() + if result.ok: + points = [] + for value in result.json()["sensors"]: + points.append( + DataPoint( + sensor_name=value["id"], collected_at=now, data=value["value"] + ) + ) + return points + else: + raise ValueError( + f"Error loading data from {server}: " + + result.json().get("error", "Unknown") + ) + + +def handle_result(execution: Future, session: Session) -> t.List[DataPoint]: + points: t.List[DataPoint] = [] + result = execution.result() + for point in result: + session.add(point) + points.append(point) + return points + + +def add_data_from_sensors( + session: Session, servers: t.Tuple[str], api_key: t.Optional[str] +) -> t.List[DataPoint]: + threads: t.List[Future] = [] + points: t.List[DataPoint] = [] + with ThreadPoolExecutor() as pool: + for server in servers: + points_future = pool.submit(get_data_points, server, api_key) + threads.append(points_future) + for thread in threads: + points += handle_result(thread, session) + return points + + +def standalone( + db_uri: str, servers: t.Tuple[str], api_key: t.Optional[str], echo: bool = False +) -> None: + engine = create_engine(db_uri, echo=echo) + sm = sessionmaker(engine) + Session = sm() + add_data_from_sensors(Session, servers, api_key) + Session.commit() + + +if __name__ == "__main__": + standalone( + db_uri="postgresql+psycopg2://apd@localhost/apd", + servers=("http://pvoutput:8080/",), + api_key="h3hdfjksfhwkjehnwekj", + ) diff --git a/Ch07/apd.aggregation-chapter07-multiprocess/src/apd/aggregation/database.py b/Ch07/apd.aggregation-chapter07-multiprocess/src/apd/aggregation/database.py new file mode 100644 index 0000000..af1cbde --- /dev/null +++ b/Ch07/apd.aggregation-chapter07-multiprocess/src/apd/aggregation/database.py @@ -0,0 +1,31 @@ +import sqlalchemy +from sqlalchemy.ext.declarative import declarative_base +from sqlalchemy.dialects.postgresql import JSONB, TIMESTAMP +from sqlalchemy.orm import sessionmaker + + +Base = declarative_base() + + +class DataPoint(Base): + __tablename__ = "datapoints" + id = sqlalchemy.Column(sqlalchemy.Integer, primary_key=True) + sensor_name = sqlalchemy.Column(sqlalchemy.String) + collected_at = sqlalchemy.Column(TIMESTAMP) + data = sqlalchemy.Column(JSONB) + + +def main(): + engine = sqlalchemy.create_engine( + "postgresql+psycopg2://apd@localhost/apd", echo=True + ) + sm = sessionmaker(engine) + Session = sm() + if False: + Base.metadata.create_all(engine) + print(Session.query(DataPoint).all()) + pass + + +if __name__ == "__main__": + main() diff --git a/Ch07/apd.aggregation-chapter07-multiprocess/tests/__init__.py b/Ch07/apd.aggregation-chapter07-multiprocess/tests/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/Ch07/apd.aggregation-chapter07-multiprocess/tests/conftest.py b/Ch07/apd.aggregation-chapter07-multiprocess/tests/conftest.py new file mode 100644 index 0000000..e69de29 diff --git a/Ch07/apd.aggregation-chapter07-nbio/.coveragerc b/Ch07/apd.aggregation-chapter07-nbio/.coveragerc new file mode 100644 index 0000000..e766c69 --- /dev/null +++ b/Ch07/apd.aggregation-chapter07-nbio/.coveragerc @@ -0,0 +1,3 @@ +[run] +branch = True +omit = tests/* diff --git a/Ch07/apd.aggregation-chapter07-nbio/.pre-commit-config.yaml b/Ch07/apd.aggregation-chapter07-nbio/.pre-commit-config.yaml new file mode 100644 index 0000000..d3b6ec7 --- /dev/null +++ b/Ch07/apd.aggregation-chapter07-nbio/.pre-commit-config.yaml @@ -0,0 +1,26 @@ +repos: + +- repo: local + hooks: + - id: black + name: black + entry: pipenv run black + args: [--quiet] + language: system + types: [python] + + - id: mypy + name: mypy + exclude: (?x)^( + (.*)/setup.py + )$ + entry: pipenv run mypy + args: ["--follow-imports=skip"] + language: system + types: [python] + + - id: flake8 + name: flake8 + entry: pipenv run flake8 + language: system + types: [python] diff --git a/Ch07/apd.aggregation-chapter07-nbio/CHANGES.md b/Ch07/apd.aggregation-chapter07-nbio/CHANGES.md new file mode 100644 index 0000000..8e424e5 --- /dev/null +++ b/Ch07/apd.aggregation-chapter07-nbio/CHANGES.md @@ -0,0 +1,5 @@ +## Changes + +### 1.0.0 (Unreleased) + +* Generated from skeleton diff --git a/Ch07/apd.aggregation-chapter07-nbio/LICENCE b/Ch07/apd.aggregation-chapter07-nbio/LICENCE new file mode 100644 index 0000000..86685d4 --- /dev/null +++ b/Ch07/apd.aggregation-chapter07-nbio/LICENCE @@ -0,0 +1,31 @@ +Copyright (c) 2019, Matthew Wilkes + + +All rights reserved. + +Redistribution and use in source and binary forms, with or without modification, +are permitted provided that the following conditions are met: + +* Redistributions of source code must retain the above copyright notice, this + list of conditions and the following disclaimer. + +* Redistributions in binary form must reproduce the above copyright notice, this + list of conditions and the following disclaimer in the documentation and/or + other materials provided with the distribution. + +* Neither the name of the copyright holder nor the names of its + contributors may be used to endorse or promote products derived from this + software without specific prior written permission. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND +ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED +WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. +IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, +INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, +BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, +DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY +OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE +OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED +OF THE POSSIBILITY OF SUCH DAMAGE. + + diff --git a/Ch07/apd.aggregation-chapter07-nbio/Pipfile b/Ch07/apd.aggregation-chapter07-nbio/Pipfile new file mode 100644 index 0000000..ba42fa3 --- /dev/null +++ b/Ch07/apd.aggregation-chapter07-nbio/Pipfile @@ -0,0 +1,22 @@ +[[source]] +name = "pypi" +url = "https://pypi.org/simple" +verify_ssl = true + +[dev-packages] +pytest = "*" +pytest-cov = "*" +mypy = "*" +flake8 = "*" +black = "*" +pre-commit = "*" +wheel = "*" +twine = "*" + +[packages] +apd-aggregation = {editable = true,path = "."} + +[requires] + +[pipenv] +allow_prereleases = true diff --git a/Ch07/apd.aggregation-chapter07-nbio/Pipfile.lock b/Ch07/apd.aggregation-chapter07-nbio/Pipfile.lock new file mode 100644 index 0000000..62333bb --- /dev/null +++ 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b/Ch07/apd.aggregation-chapter07-nbio/pyproject.toml new file mode 100644 index 0000000..4d3bb67 --- /dev/null +++ b/Ch07/apd.aggregation-chapter07-nbio/pyproject.toml @@ -0,0 +1,5 @@ +[build-system] +requires = [ + "setuptools", +] +build-backend = "setuptools.build_meta" diff --git a/Ch07/apd.aggregation-chapter07-nbio/setup.cfg b/Ch07/apd.aggregation-chapter07-nbio/setup.cfg new file mode 100644 index 0000000..acc9143 --- /dev/null +++ b/Ch07/apd.aggregation-chapter07-nbio/setup.cfg @@ -0,0 +1,48 @@ +[mypy] +namespace_packages = True +mypy_path = src +plugins = sqlmypy + +[mypy-alembic] +ignore_missing_imports = True + +[mypy-h11] +ignore_missing_imports = True + +[flake8] +max-line-length = 88 + +[metadata] +name = apd.aggregation +version = attr: apd.aggregation.VERSION +description = A programme that queries apd.sensor endpoints and aggregates their results. +long_description = file: README.md, CHANGES.md, LICENCE +long_description_content_type = text/markdown +keywords = +license = BSD +classifiers = + Programming Language :: Python :: 3 + Programming Language :: Python :: 3.7 + +[options] +zip_safe = False +include_package_data = True +package_dir = + =src +packages = find_namespace: +install_requires = + h11 + sqlalchemy + requests + psycopg2 + alembic + click + sqlalchemy-stubs + + +[options.packages.find] +where = src + +[options.entry_points] +console_scripts = + collect_sensor_data = apd.aggregation.cli:collect_sensor_data diff --git a/Ch07/apd.aggregation-chapter07-nbio/setup.py b/Ch07/apd.aggregation-chapter07-nbio/setup.py new file mode 100644 index 0000000..6068493 --- /dev/null +++ b/Ch07/apd.aggregation-chapter07-nbio/setup.py @@ -0,0 +1,3 @@ +from setuptools import setup + +setup() diff --git a/Ch07/apd.aggregation-chapter07-nbio/src/apd/aggregation/__init__.py b/Ch07/apd.aggregation-chapter07-nbio/src/apd/aggregation/__init__.py new file mode 100644 index 0000000..3277f64 --- /dev/null +++ b/Ch07/apd.aggregation-chapter07-nbio/src/apd/aggregation/__init__.py @@ -0,0 +1 @@ +VERSION = "1.0.0" diff --git a/Ch07/apd.aggregation-chapter07-nbio/src/apd/aggregation/alembic/README b/Ch07/apd.aggregation-chapter07-nbio/src/apd/aggregation/alembic/README new file mode 100644 index 0000000..97a87d5 --- /dev/null +++ b/Ch07/apd.aggregation-chapter07-nbio/src/apd/aggregation/alembic/README @@ -0,0 +1,12 @@ +Generic single-database configuration. + + +# Database setup + +To generate the required database tables you must create an alembic.ini file, as follows: + + [alembic] + script_location = apd.aggregation:alembic + sqlalchemy.url = postgresql+psycopg2://apd@localhost/apd + +and run `alembic upgrade head`. This should also be done after every upgrade of the software. \ No newline at end of file diff --git a/Ch07/apd.aggregation-chapter07-nbio/src/apd/aggregation/alembic/env.py b/Ch07/apd.aggregation-chapter07-nbio/src/apd/aggregation/alembic/env.py new file mode 100644 index 0000000..09beb9c --- /dev/null +++ b/Ch07/apd.aggregation-chapter07-nbio/src/apd/aggregation/alembic/env.py @@ -0,0 +1,73 @@ +from logging.config import fileConfig + +from sqlalchemy import engine_from_config +from sqlalchemy import pool + +from alembic import context + +# this is the Alembic Config object, which provides +# access to the values within the .ini file in use. +config = context.config + +# Interpret the config file for Python logging. +# This line sets up loggers basically. +fileConfig(config.config_file_name) + +from apd.aggregation.database import Base + +target_metadata = Base.metadata + +# other values from the config, defined by the needs of env.py, +# can be acquired: +# my_important_option = config.get_main_option("my_important_option") +# ... etc. + + +def run_migrations_offline(): + """Run migrations in 'offline' mode. + + This configures the context with just a URL + and not an Engine, though an Engine is acceptable + here as well. By skipping the Engine creation + we don't even need a DBAPI to be available. + + Calls to context.execute() here emit the given string to the + script output. + + """ + url = config.get_main_option("sqlalchemy.url") + context.configure( + url=url, + target_metadata=target_metadata, + literal_binds=True, + dialect_opts={"paramstyle": "named"}, + ) + + with context.begin_transaction(): + context.run_migrations() + + +def run_migrations_online(): + """Run migrations in 'online' mode. + + In this scenario we need to create an Engine + and associate a connection with the context. + + """ + connectable = engine_from_config( + config.get_section(config.config_ini_section), + prefix="sqlalchemy.", + poolclass=pool.NullPool, + ) + + with connectable.connect() as connection: + context.configure(connection=connection, target_metadata=target_metadata) + + with context.begin_transaction(): + context.run_migrations() + + +if context.is_offline_mode(): + run_migrations_offline() +else: + run_migrations_online() diff --git a/Ch07/apd.aggregation-chapter07-nbio/src/apd/aggregation/alembic/script.py.mako b/Ch07/apd.aggregation-chapter07-nbio/src/apd/aggregation/alembic/script.py.mako new file mode 100644 index 0000000..2c01563 --- /dev/null +++ b/Ch07/apd.aggregation-chapter07-nbio/src/apd/aggregation/alembic/script.py.mako @@ -0,0 +1,24 @@ +"""${message} + +Revision ID: ${up_revision} +Revises: ${down_revision | comma,n} +Create Date: ${create_date} + +""" +from alembic import op +import sqlalchemy as sa +${imports if imports else ""} + +# revision identifiers, used by Alembic. +revision = ${repr(up_revision)} +down_revision = ${repr(down_revision)} +branch_labels = ${repr(branch_labels)} +depends_on = ${repr(depends_on)} + + +def upgrade(): + ${upgrades if upgrades else "pass"} + + +def downgrade(): + ${downgrades if downgrades else "pass"} diff --git a/Ch07/apd.aggregation-chapter07-nbio/src/apd/aggregation/alembic/versions/6d2eacd5da3f_create_sensor_values_table.py b/Ch07/apd.aggregation-chapter07-nbio/src/apd/aggregation/alembic/versions/6d2eacd5da3f_create_sensor_values_table.py new file mode 100644 index 0000000..b4a3545 --- /dev/null +++ b/Ch07/apd.aggregation-chapter07-nbio/src/apd/aggregation/alembic/versions/6d2eacd5da3f_create_sensor_values_table.py @@ -0,0 +1,31 @@ +"""Create datapoints table + +Revision ID: 6d2eacd5da3f +Revises: N/A +Create Date: 2019-09-29 13:43:21.242706 + +""" +from alembic import op +import sqlalchemy as sa +from sqlalchemy.dialects import postgresql + +# revision identifiers, used by Alembic. +revision = "6d2eacd5da3f" +down_revision = None +branch_labels = None +depends_on = None + + +def upgrade(): + op.create_table( + "datapoints", + sa.Column("id", sa.Integer(), nullable=False), + sa.Column("sensor_name", sa.String(), nullable=True), + sa.Column("collected_at", postgresql.TIMESTAMP(), nullable=True), + sa.Column("data", postgresql.JSONB(astext_type=sa.Text()), nullable=True), + sa.PrimaryKeyConstraint("id"), + ) + + +def downgrade(): + op.drop_table("datapoints") diff --git a/Ch07/apd.aggregation-chapter07-nbio/src/apd/aggregation/cli.py b/Ch07/apd.aggregation-chapter07-nbio/src/apd/aggregation/cli.py new file mode 100644 index 0000000..3435e78 --- /dev/null +++ b/Ch07/apd.aggregation-chapter07-nbio/src/apd/aggregation/cli.py @@ -0,0 +1,52 @@ +import typing as t + +import click + +from .collect import standalone + + +@click.command() +@click.argument("server", nargs=-1) +@click.option( + "--db", + metavar="", + default="postgresql+psycopg2://localhost/apd", + help="The connection string to a PostgreSQL database", + envvar="APD_DB_URI", +) +@click.option("--api-key", metavar="", envvar="APD_API_KEY") +@click.option( + "--tolerate-failures", + "-f", + help="If provided, failure to retrieve some sensors' data will not abort the collection process", + is_flag=True, +) +@click.option("-v", "--verbose", is_flag=True, help="Enables verbose mode") +def collect_sensor_data( + db: str, server: t.Tuple[str], api_key: str, tolerate_failures: bool, verbose: bool +): + """This loads data from one or more sensors into the specified database. + + Only PostgreSQL databases are supported, as the column definitions use + multiple pg specific features. The database must already exist and be + populated with the required tables. + + The --api-key option is used to specify the access token for the sensors + being queried. + + You may specify any number of servers, the variable should be the full URL + to the sensor's HTTP interface, not including the /v/2.0 portion. Multiple + URLs should be separated with a space. + """ + if tolerate_failures: + attempts = [(s,) for s in server] + else: + attempts = [server] + success = True + for attempt in attempts: + try: + standalone(db, attempt, api_key, echo=verbose) + except ValueError as e: + click.secho(str(e), err=True, fg="red") + success = False + return success diff --git a/Ch07/apd.aggregation-chapter07-nbio/src/apd/aggregation/collect.py b/Ch07/apd.aggregation-chapter07-nbio/src/apd/aggregation/collect.py new file mode 100644 index 0000000..7f55943 --- /dev/null +++ b/Ch07/apd.aggregation-chapter07-nbio/src/apd/aggregation/collect.py @@ -0,0 +1,125 @@ +import datetime +import io +import json +import select +import socket +import typing as t +import urllib.parse + +import h11 +import requests +from sqlalchemy import create_engine +from sqlalchemy.orm import sessionmaker +from sqlalchemy.orm.session import Session + +from .database import DataPoint + + +def get_http(uri: str, headers: t.Dict[str, str]) -> socket.socket: + """Given a URI and a set of headers, make a HTTP request and return the + underlying socket. If there were a production-quality implementation of + nonblocking HTTP this function would be replaced with the relevant one + from that library.""" + parsed = urllib.parse.urlparse(uri) + sock = socket.socket() + if parsed.port: + port = parsed.port + else: + port = 80 + headers["Host"] = parsed.netloc + sock.connect((parsed.hostname, port)) + sock.setblocking(False) + + connection = h11.Connection(h11.CLIENT) + request = h11.Request(method="GET", target=parsed.path, headers=headers.items()) + + sock.send(connection.send(request)) + sock.send(connection.send(h11.EndOfMessage())) + return sock + + +def read_from_socket(sock: socket.socket) -> str: + """ If there were a production-quality implementation of nonblocking HTTP + this function would be replaced with the relevant one to get the body of + the response if it was a success or error otherwise. """ + data = sock.recv(2048) + connection = h11.Connection(h11.CLIENT) + connection.receive_data(data) + + response = connection.next_event() + headers = dict(response.headers) + body = connection.next_event() + eom = connection.next_event() + + try: + if ( + response.status_code == 200 + and headers.get(b"content-type", None) == b"application/json" + ): + return body.data.decode("utf-8") + else: + raise ValueError("Bad response") + finally: + sock.close() + + +def connect_to_server(server: str, api_key: t.Optional[str]) -> socket.socket: + if not server.endswith("/"): + server += "/" + url = server + "v/2.0/sensors/" + headers = {} + if api_key: + headers["X-API-KEY"] = api_key + + return get_http(url, headers=headers) + + +def prepare_datapoints_from_response(response: str) -> t.Iterator[DataPoint]: + now = datetime.datetime.now() + json_result = json.loads(response) + if "sensors" in json_result: + for value in json_result["sensors"]: + yield DataPoint( + sensor_name=value["id"], collected_at=now, data=value["value"] + ) + else: + raise ValueError( + f"Error loading data from stream: " + json_result.get("error", "Unknown") + ) + + +def add_data_from_sensors( + session: Session, servers: t.Tuple[str], api_key: t.Optional[str] +) -> t.Iterable[DataPoint]: + points: t.List[DataPoint] = [] + sockets = [connect_to_server(server, api_key) for server in servers] + while sockets: + readable, writable, exceptional = select.select(sockets, [], []) + for request in readable: + # In a production quality implementation there would be + # handling here for responses that have only partially been + # received. + value = read_from_socket(request) + for point in prepare_datapoints_from_response(value): + session.add(point) + points.append(point) + sockets.remove(request) + return points + + +def standalone( + db_uri: str, servers: t.Tuple[str], api_key: t.Optional[str], echo: bool = False +) -> None: + engine = create_engine(db_uri, echo=echo) + sm = sessionmaker(engine) + Session = sm() + add_data_from_sensors(Session, servers, api_key) + Session.commit() + + +if __name__ == "__main__": + standalone( + db_uri="postgresql+psycopg2://apd@localhost/apd", + servers=("http://pvoutput:8080/",), + api_key="h3hdfjksfhwkjehnwekj", + ) diff --git a/Ch07/apd.aggregation-chapter07-nbio/src/apd/aggregation/database.py b/Ch07/apd.aggregation-chapter07-nbio/src/apd/aggregation/database.py new file mode 100644 index 0000000..af1cbde --- /dev/null +++ b/Ch07/apd.aggregation-chapter07-nbio/src/apd/aggregation/database.py @@ -0,0 +1,31 @@ +import sqlalchemy +from sqlalchemy.ext.declarative import declarative_base +from sqlalchemy.dialects.postgresql import JSONB, TIMESTAMP +from sqlalchemy.orm import sessionmaker + + +Base = declarative_base() + + +class DataPoint(Base): + __tablename__ = "datapoints" + id = sqlalchemy.Column(sqlalchemy.Integer, primary_key=True) + sensor_name = sqlalchemy.Column(sqlalchemy.String) + collected_at = sqlalchemy.Column(TIMESTAMP) + data = sqlalchemy.Column(JSONB) + + +def main(): + engine = sqlalchemy.create_engine( + "postgresql+psycopg2://apd@localhost/apd", echo=True + ) + sm = sessionmaker(engine) + Session = sm() + if False: + Base.metadata.create_all(engine) + print(Session.query(DataPoint).all()) + pass + + +if __name__ == "__main__": + main() diff --git a/Ch07/apd.aggregation-chapter07-nbio/tests/__init__.py b/Ch07/apd.aggregation-chapter07-nbio/tests/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/Ch07/apd.aggregation-chapter07-nbio/tests/conftest.py b/Ch07/apd.aggregation-chapter07-nbio/tests/conftest.py new file mode 100644 index 0000000..e69de29 diff --git a/Ch07/apd.aggregation-chapter07-simple-threads/.coveragerc b/Ch07/apd.aggregation-chapter07-simple-threads/.coveragerc new file mode 100644 index 0000000..e766c69 --- /dev/null +++ b/Ch07/apd.aggregation-chapter07-simple-threads/.coveragerc @@ -0,0 +1,3 @@ +[run] +branch = True +omit = tests/* diff --git a/Ch07/apd.aggregation-chapter07-simple-threads/.pre-commit-config.yaml b/Ch07/apd.aggregation-chapter07-simple-threads/.pre-commit-config.yaml new file mode 100644 index 0000000..d3b6ec7 --- /dev/null +++ b/Ch07/apd.aggregation-chapter07-simple-threads/.pre-commit-config.yaml @@ -0,0 +1,26 @@ +repos: + +- repo: local + hooks: + - id: black + name: black + entry: pipenv run black + args: [--quiet] + language: system + types: [python] + + - id: mypy + name: mypy + exclude: (?x)^( + (.*)/setup.py + )$ + entry: pipenv run mypy + args: ["--follow-imports=skip"] + language: system + types: [python] + + - id: flake8 + name: flake8 + entry: pipenv run flake8 + language: system + types: [python] diff --git a/Ch07/apd.aggregation-chapter07-simple-threads/CHANGES.md b/Ch07/apd.aggregation-chapter07-simple-threads/CHANGES.md new file mode 100644 index 0000000..8e424e5 --- /dev/null +++ b/Ch07/apd.aggregation-chapter07-simple-threads/CHANGES.md @@ -0,0 +1,5 @@ +## Changes + +### 1.0.0 (Unreleased) + +* Generated from skeleton diff --git a/Ch07/apd.aggregation-chapter07-simple-threads/LICENCE b/Ch07/apd.aggregation-chapter07-simple-threads/LICENCE new file mode 100644 index 0000000..86685d4 --- /dev/null +++ b/Ch07/apd.aggregation-chapter07-simple-threads/LICENCE @@ -0,0 +1,31 @@ +Copyright (c) 2019, Matthew Wilkes + + +All rights reserved. + +Redistribution and use in source and binary forms, with or without modification, +are permitted provided that the following conditions are met: + +* Redistributions of source code must retain the above copyright notice, this + list of conditions and the following disclaimer. + +* Redistributions in binary form must reproduce the above copyright notice, this + list of conditions and the following disclaimer in the documentation and/or + other materials provided with the distribution. + +* Neither the name of the copyright holder nor the names of its + contributors may be used to endorse or promote products derived from this + software without specific prior written permission. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND +ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED +WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. +IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, +INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, +BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, +DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY +OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE +OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED +OF THE POSSIBILITY OF SUCH DAMAGE. + + diff --git a/Ch07/apd.aggregation-chapter07-simple-threads/Pipfile b/Ch07/apd.aggregation-chapter07-simple-threads/Pipfile new file mode 100644 index 0000000..ba42fa3 --- /dev/null +++ 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"sha256:f06903e9f1f43b12d371004b4ac7b06ab39a44adc747266928ae6debfa7b3335" + ], + "version": "==0.6.0" + } + } +} diff --git a/Ch07/apd.aggregation-chapter07-simple-threads/README.md b/Ch07/apd.aggregation-chapter07-simple-threads/README.md new file mode 100644 index 0000000..85669dd --- /dev/null +++ b/Ch07/apd.aggregation-chapter07-simple-threads/README.md @@ -0,0 +1,4 @@ +# APD Sensor aggregator + +A programme that queries apd.sensor endpoints and aggregates their results. + diff --git a/Ch07/apd.aggregation-chapter07-simple-threads/pyproject.toml b/Ch07/apd.aggregation-chapter07-simple-threads/pyproject.toml new file mode 100644 index 0000000..4d3bb67 --- /dev/null +++ b/Ch07/apd.aggregation-chapter07-simple-threads/pyproject.toml @@ -0,0 +1,5 @@ +[build-system] +requires = [ + "setuptools", +] +build-backend = "setuptools.build_meta" diff --git a/Ch07/apd.aggregation-chapter07-simple-threads/setup.cfg b/Ch07/apd.aggregation-chapter07-simple-threads/setup.cfg new file mode 100644 index 0000000..6c2dc8a --- /dev/null +++ b/Ch07/apd.aggregation-chapter07-simple-threads/setup.cfg @@ -0,0 +1,44 @@ +[mypy] +namespace_packages = True +mypy_path = src +plugins = sqlmypy + +[mypy-alembic] +ignore_missing_imports = True + +[flake8] +max-line-length = 88 + +[metadata] +name = apd.aggregation +version = attr: apd.aggregation.VERSION +description = A programme that queries apd.sensor endpoints and aggregates their results. +long_description = file: README.md, CHANGES.md, LICENCE +long_description_content_type = text/markdown +keywords = +license = BSD +classifiers = + Programming Language :: Python :: 3 + Programming Language :: Python :: 3.7 + +[options] +zip_safe = False +include_package_data = True +package_dir = + =src +packages = find_namespace: +install_requires = + sqlalchemy + requests + psycopg2 + alembic + click + sqlalchemy-stubs + + +[options.packages.find] +where = src + +[options.entry_points] +console_scripts = + collect_sensor_data = apd.aggregation.cli:collect_sensor_data diff --git a/Ch07/apd.aggregation-chapter07-simple-threads/setup.py b/Ch07/apd.aggregation-chapter07-simple-threads/setup.py new file mode 100644 index 0000000..6068493 --- /dev/null +++ b/Ch07/apd.aggregation-chapter07-simple-threads/setup.py @@ -0,0 +1,3 @@ +from setuptools import setup + +setup() diff --git a/Ch07/apd.aggregation-chapter07-simple-threads/src/apd/aggregation/__init__.py b/Ch07/apd.aggregation-chapter07-simple-threads/src/apd/aggregation/__init__.py new file mode 100644 index 0000000..3277f64 --- /dev/null +++ b/Ch07/apd.aggregation-chapter07-simple-threads/src/apd/aggregation/__init__.py @@ -0,0 +1 @@ +VERSION = "1.0.0" diff --git a/Ch07/apd.aggregation-chapter07-simple-threads/src/apd/aggregation/alembic/README b/Ch07/apd.aggregation-chapter07-simple-threads/src/apd/aggregation/alembic/README new file mode 100644 index 0000000..97a87d5 --- /dev/null +++ b/Ch07/apd.aggregation-chapter07-simple-threads/src/apd/aggregation/alembic/README @@ -0,0 +1,12 @@ +Generic single-database configuration. + + +# Database setup + +To generate the required database tables you must create an alembic.ini file, as follows: + + [alembic] + script_location = apd.aggregation:alembic + sqlalchemy.url = postgresql+psycopg2://apd@localhost/apd + +and run `alembic upgrade head`. This should also be done after every upgrade of the software. \ No newline at end of file diff --git a/Ch07/apd.aggregation-chapter07-simple-threads/src/apd/aggregation/alembic/env.py b/Ch07/apd.aggregation-chapter07-simple-threads/src/apd/aggregation/alembic/env.py new file mode 100644 index 0000000..09beb9c --- /dev/null +++ b/Ch07/apd.aggregation-chapter07-simple-threads/src/apd/aggregation/alembic/env.py @@ -0,0 +1,73 @@ +from logging.config import fileConfig + +from sqlalchemy import engine_from_config +from sqlalchemy import pool + +from alembic import context + +# this is the Alembic Config object, which provides +# access to the values within the .ini file in use. +config = context.config + +# Interpret the config file for Python logging. +# This line sets up loggers basically. +fileConfig(config.config_file_name) + +from apd.aggregation.database import Base + +target_metadata = Base.metadata + +# other values from the config, defined by the needs of env.py, +# can be acquired: +# my_important_option = config.get_main_option("my_important_option") +# ... etc. + + +def run_migrations_offline(): + """Run migrations in 'offline' mode. + + This configures the context with just a URL + and not an Engine, though an Engine is acceptable + here as well. By skipping the Engine creation + we don't even need a DBAPI to be available. + + Calls to context.execute() here emit the given string to the + script output. + + """ + url = config.get_main_option("sqlalchemy.url") + context.configure( + url=url, + target_metadata=target_metadata, + literal_binds=True, + dialect_opts={"paramstyle": "named"}, + ) + + with context.begin_transaction(): + context.run_migrations() + + +def run_migrations_online(): + """Run migrations in 'online' mode. + + In this scenario we need to create an Engine + and associate a connection with the context. + + """ + connectable = engine_from_config( + config.get_section(config.config_ini_section), + prefix="sqlalchemy.", + poolclass=pool.NullPool, + ) + + with connectable.connect() as connection: + context.configure(connection=connection, target_metadata=target_metadata) + + with context.begin_transaction(): + context.run_migrations() + + +if context.is_offline_mode(): + run_migrations_offline() +else: + run_migrations_online() diff --git a/Ch07/apd.aggregation-chapter07-simple-threads/src/apd/aggregation/alembic/script.py.mako b/Ch07/apd.aggregation-chapter07-simple-threads/src/apd/aggregation/alembic/script.py.mako new file mode 100644 index 0000000..2c01563 --- /dev/null +++ b/Ch07/apd.aggregation-chapter07-simple-threads/src/apd/aggregation/alembic/script.py.mako @@ -0,0 +1,24 @@ +"""${message} + +Revision ID: ${up_revision} +Revises: ${down_revision | comma,n} +Create Date: ${create_date} + +""" +from alembic import op +import sqlalchemy as sa +${imports if imports else ""} + +# revision identifiers, used by Alembic. +revision = ${repr(up_revision)} +down_revision = ${repr(down_revision)} +branch_labels = ${repr(branch_labels)} +depends_on = ${repr(depends_on)} + + +def upgrade(): + ${upgrades if upgrades else "pass"} + + +def downgrade(): + ${downgrades if downgrades else "pass"} diff --git a/Ch07/apd.aggregation-chapter07-simple-threads/src/apd/aggregation/alembic/versions/6d2eacd5da3f_create_sensor_values_table.py b/Ch07/apd.aggregation-chapter07-simple-threads/src/apd/aggregation/alembic/versions/6d2eacd5da3f_create_sensor_values_table.py new file mode 100644 index 0000000..b4a3545 --- /dev/null +++ b/Ch07/apd.aggregation-chapter07-simple-threads/src/apd/aggregation/alembic/versions/6d2eacd5da3f_create_sensor_values_table.py @@ -0,0 +1,31 @@ +"""Create datapoints table + +Revision ID: 6d2eacd5da3f +Revises: N/A +Create Date: 2019-09-29 13:43:21.242706 + +""" +from alembic import op +import sqlalchemy as sa +from sqlalchemy.dialects import postgresql + +# revision identifiers, used by Alembic. +revision = "6d2eacd5da3f" +down_revision = None +branch_labels = None +depends_on = None + + +def upgrade(): + op.create_table( + "datapoints", + sa.Column("id", sa.Integer(), nullable=False), + sa.Column("sensor_name", sa.String(), nullable=True), + sa.Column("collected_at", postgresql.TIMESTAMP(), nullable=True), + sa.Column("data", postgresql.JSONB(astext_type=sa.Text()), nullable=True), + sa.PrimaryKeyConstraint("id"), + ) + + +def downgrade(): + op.drop_table("datapoints") diff --git a/Ch07/apd.aggregation-chapter07-simple-threads/src/apd/aggregation/cli.py b/Ch07/apd.aggregation-chapter07-simple-threads/src/apd/aggregation/cli.py new file mode 100644 index 0000000..3435e78 --- /dev/null +++ b/Ch07/apd.aggregation-chapter07-simple-threads/src/apd/aggregation/cli.py @@ -0,0 +1,52 @@ +import typing as t + +import click + +from .collect import standalone + + +@click.command() +@click.argument("server", nargs=-1) +@click.option( + "--db", + metavar="", + default="postgresql+psycopg2://localhost/apd", + help="The connection string to a PostgreSQL database", + envvar="APD_DB_URI", +) +@click.option("--api-key", metavar="", envvar="APD_API_KEY") +@click.option( + "--tolerate-failures", + "-f", + help="If provided, failure to retrieve some sensors' data will not abort the collection process", + is_flag=True, +) +@click.option("-v", "--verbose", is_flag=True, help="Enables verbose mode") +def collect_sensor_data( + db: str, server: t.Tuple[str], api_key: str, tolerate_failures: bool, verbose: bool +): + """This loads data from one or more sensors into the specified database. + + Only PostgreSQL databases are supported, as the column definitions use + multiple pg specific features. The database must already exist and be + populated with the required tables. + + The --api-key option is used to specify the access token for the sensors + being queried. + + You may specify any number of servers, the variable should be the full URL + to the sensor's HTTP interface, not including the /v/2.0 portion. Multiple + URLs should be separated with a space. + """ + if tolerate_failures: + attempts = [(s,) for s in server] + else: + attempts = [server] + success = True + for attempt in attempts: + try: + standalone(db, attempt, api_key, echo=verbose) + except ValueError as e: + click.secho(str(e), err=True, fg="red") + success = False + return success diff --git a/Ch07/apd.aggregation-chapter07-simple-threads/src/apd/aggregation/collect.py b/Ch07/apd.aggregation-chapter07-simple-threads/src/apd/aggregation/collect.py new file mode 100644 index 0000000..a560a5a --- /dev/null +++ b/Ch07/apd.aggregation-chapter07-simple-threads/src/apd/aggregation/collect.py @@ -0,0 +1,102 @@ +from __future__ import annotations + +import datetime +import functools +import threading +import typing as t +import queue + +import requests +from sqlalchemy import create_engine +from sqlalchemy.orm import sessionmaker +from sqlalchemy.orm.session import Session + +from .database import DataPoint + + +T_Val = t.TypeVar("T_Val") + + +class return_via_queue(t.Generic[T_Val]): + def __init__(self, return_queue: queue.Queue[T_Val]) -> None: + self.return_queue = return_queue + + def __call__(self, func: t.Callable[..., T_Val]) -> t.Callable[..., T_Val]: + @functools.wraps(func) + def inner(*args, **kwargs): + value = func(*args, **kwargs) + self.return_queue.put(value) + return + + return inner + + +def get_data_points(server: str, api_key: t.Optional[str]) -> t.List[DataPoint]: + if not server.endswith("/"): + server += "/" + url = server + "v/2.0/sensors/" + headers = {} + if api_key: + headers["X-API-KEY"] = api_key + try: + result = requests.get(url, headers=headers) + except requests.ConnectionError: + raise ValueError(f"Error connecting to {server}") + now = datetime.datetime.now() + dps: t.List[DataPoint] = [] + if result.ok: + for value in result.json()["sensors"]: + dps.append( + DataPoint( + sensor_name=value["id"], collected_at=now, data=value["value"] + ) + ) + else: + raise ValueError( + f"Error loading data from {server}: " + + result.json().get("error", "Unknown") + ) + return dps + + +def add_data_from_sensors( + session: Session, servers: t.Tuple[str], api_key: t.Optional[str] +) -> t.Iterable[DataPoint]: + points: t.List[DataPoint] = [] + q: queue.Queue[t.List[DataPoint]] = queue.Queue() + wrap = return_via_queue(q) + threads = [ + threading.Thread(target=wrap(get_data_points), args=(server, api_key)) + for server in servers + ] + for thread in threads: + # Start all threads + thread.start() + for thread in threads: + # Wait for all threads to finish + thread.join() + while not q.empty(): + # So long as there's a return value in the queue, process one thread's results + found = q.get_nowait() + for point in found: + session.add(point) + points.append(point) + return points + + +def standalone( + db_uri: str, servers: t.Tuple[str], api_key: t.Optional[str], echo: bool = False +) -> None: + engine = create_engine(db_uri, echo=echo) + sm = sessionmaker(engine) + Session = sm() + add_data_from_sensors(Session, servers, api_key) + Session.commit() + + +if __name__ == "__main__": + standalone( + db_uri="postgresql+psycopg2://apd@localhost/apd", + servers=("http://pvoutput:8080/",), + api_key="h3hdfjksfhwkjehnwekj", + ) diff --git a/Ch07/apd.aggregation-chapter07-simple-threads/src/apd/aggregation/database.py b/Ch07/apd.aggregation-chapter07-simple-threads/src/apd/aggregation/database.py new file mode 100644 index 0000000..af1cbde --- /dev/null +++ b/Ch07/apd.aggregation-chapter07-simple-threads/src/apd/aggregation/database.py @@ -0,0 +1,31 @@ +import sqlalchemy +from sqlalchemy.ext.declarative import declarative_base +from sqlalchemy.dialects.postgresql import JSONB, TIMESTAMP +from sqlalchemy.orm import sessionmaker + + +Base = declarative_base() + + +class DataPoint(Base): + __tablename__ = "datapoints" + id = sqlalchemy.Column(sqlalchemy.Integer, primary_key=True) + sensor_name = sqlalchemy.Column(sqlalchemy.String) + collected_at = sqlalchemy.Column(TIMESTAMP) + data = sqlalchemy.Column(JSONB) + + +def main(): + engine = sqlalchemy.create_engine( + "postgresql+psycopg2://apd@localhost/apd", echo=True + ) + sm = sessionmaker(engine) + Session = sm() + if False: + Base.metadata.create_all(engine) + print(Session.query(DataPoint).all()) + pass + + +if __name__ == "__main__": + main() diff --git a/Ch07/apd.aggregation-chapter07-simple-threads/tests/__init__.py b/Ch07/apd.aggregation-chapter07-simple-threads/tests/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/Ch07/apd.aggregation-chapter07-simple-threads/tests/conftest.py b/Ch07/apd.aggregation-chapter07-simple-threads/tests/conftest.py new file mode 100644 index 0000000..e69de29 diff --git a/Ch07/listing07-01-nbioexample.py b/Ch07/listing07-01-nbioexample.py new file mode 100644 index 0000000..d0ed4df --- /dev/null +++ b/Ch07/listing07-01-nbioexample.py @@ -0,0 +1,79 @@ +import select +import socket +import typing as t +import urllib.parse + +import h11 + + + +def get_http(uri: str, headers: t.Dict[str, str]) -> socket.socket: + """Given a URI and a set of headers, make a HTTP request and return the + underlying socket. If there were a production-quality implementation of + nonblocking HTTP this function would be replaced with the relevant one + from that library.""" + parsed = urllib.parse.urlparse(uri) + sock = socket.socket() + if parsed.port: + port = parsed.port + else: + port = 80 + headers["Host"] = parsed.netloc + sock.connect((parsed.hostname, port)) + sock.setblocking(False) + + connection = h11.Connection(h11.CLIENT) + request = h11.Request(method="GET", target=parsed.path, headers=headers.items()) + + sock.send(connection.send(request)) + sock.send(connection.send(h11.EndOfMessage())) + return sock + + +def read_from_socket(sock: socket.socket) -> str: + """ If there were a production-quality implementation of nonblocking HTTP + this function would be replaced with the relevant one to get the body of + the response if it was a success or error otherwise. """ + data = sock.recv(1024000) + connection = h11.Connection(h11.CLIENT) + connection.receive_data(data) + + response = connection.next_event() + headers = dict(response.headers) + body = connection.next_event() + eom = connection.next_event() + + try: + if ( + response.status_code == 200 + and b"application/json" in headers.get(b"content-type", None) + ): + return body.data.decode("utf-8") + else: + raise ValueError("Bad response") + finally: + sock.close() + + +def show_responses(uris: t.Tuple[str]) -> None: + sockets = [] + for uri in uris: + print(f"Making request to {uri}") + sockets.append(get_http(uri, {})) + while sockets: + readable, writable, exceptional = select.select(sockets, [], []) + print(f"{ len(readable) } socket(s) ready") + for request in readable: + print(f"Reading from socket") + response = read_from_socket(request) + print(f"Got { len(response) } bytes") + sockets.remove(request) + + + +if __name__ == "__main__": + show_responses([ + "http://jsonplaceholder.typicode.com/posts?userId=1", + "http://jsonplaceholder.typicode.com/posts?userId=5", + "http://jsonplaceholder.typicode.com/posts?userId=8", + ]) diff --git a/Ch07/listing07-02-increment_dis.py b/Ch07/listing07-02-increment_dis.py new file mode 100644 index 0000000..16e37b0 --- /dev/null +++ b/Ch07/listing07-02-increment_dis.py @@ -0,0 +1,15 @@ +num = 0 + +def increment(): + global num + num += 1 # 5 0 LOAD_GLOBAL 0 (num) + # 2 LOAD_CONST 1 (1) + # 4 INPLACE_ADD + # 6 STORE_GLOBAL 0 (num) + + return None # 10 8 LOAD_CONST 0 (None) + # 10 RETURN_VALUE + +if __name__ == "__main__": + import dis + dis.dis(increment) \ No newline at end of file diff --git a/Ch07/listing07-05-threadpools-and-queues.py b/Ch07/listing07-05-threadpools-and-queues.py new file mode 100644 index 0000000..fd00dfa --- /dev/null +++ b/Ch07/listing07-05-threadpools-and-queues.py @@ -0,0 +1,80 @@ +from concurrent.futures import ThreadPoolExecutor +import queue +import requests +import textwrap + + +def print_column(text, column): + wrapped = textwrap.fill(text, 45) + indent_level = 50 * column + indented = textwrap.indent(wrapped, " " * indent_level) + print(indented) + + +def fetch(urls, responses, parsed): + while True: + url = urls.get() + if url is None: + print_column("Got instruction to finish", 0) + return + print_column(f"Getting {url}", 0) + response = requests.get(url) + print_column(f"Storing {response} from {url}", 0) + responses.put(response) + urls.task_done() + + +def parse(urls, responses, parsed): + # Wait for the initial URLs to be processed + print_column("Waiting for url fetch thread", 1) + urls.join() + + while not responses.empty(): + response = responses.get() + print_column(f"Starting processing of {response}", 1) + + if response.ok: + data = response.json() + for commit in data: + parsed.put(commit) + + links = response.headers["link"].split(",") + for link in links: + if "next" in link: + url = link.split(";")[0].strip("<>") + print_column(f"Discovered new url: {url}", 1) + urls.put(url) + + responses.task_done() + if responses.empty(): + # We have no responses left, so the loop will + # end. Wait for all queued urls to be fetched + # before continuing + print_column("Waiting for url fetch thread", 1) + urls.join() + + # We reach this point if there are no responses to process + # after waiting for the fetch thread to catch up. Tell the + # fetch thread that it can stop now, then exit this thread. + print_column("Sending instruction to finish", 1) + urls.put(None) + + +def get_commit_info(repos): + urls = queue.Queue() + responses = queue.Queue() + parsed = queue.Queue() + + for (username, repo) in repos: + urls.put(f"https://api.github.com/repos/{username}/{repo}/commits") + + with ThreadPoolExecutor() as pool: + fetcher = pool.submit(fetch, urls, responses, parsed) + parser = pool.submit(parse, urls, responses, parsed) + print(f"{parsed.qsize()} commits found") + + +if __name__ == "__main__": + get_commit_info( + [("MatthewWilkes", "apd.sensors"), ("MatthewWilkes", "apd.aggregation")] + ) diff --git a/Ch07/listing07-06-reentrantlocks.py b/Ch07/listing07-06-reentrantlocks.py new file mode 100644 index 0000000..995888f --- /dev/null +++ b/Ch07/listing07-06-reentrantlocks.py @@ -0,0 +1,24 @@ +from concurrent.futures import ThreadPoolExecutor +import threading + +num = 0 + +numlock = threading.RLock() + +def fiddle_with_num(): + global num + with numlock: + if num == 4: + num = -50 + +def increment(): + global num + with numlock: + num += 1 + fiddle_with_num() + +if __name__ == "__main__": + with ThreadPoolExecutor() as pool: + for i in range(8): + pool.submit(increment) + print(num) \ No newline at end of file diff --git a/Ch07/listing07-07-conditions.py b/Ch07/listing07-07-conditions.py new file mode 100644 index 0000000..f9356af --- /dev/null +++ b/Ch07/listing07-07-conditions.py @@ -0,0 +1,66 @@ +from concurrent.futures import ThreadPoolExecutor +import sys +import time +import threading + + +data = [] +results = [] +running = True +data_available = threading.Condition() +work_complete = threading.Condition() + + +def has_data(): + """ Return true if there is data in the data list """ + return bool(data) + + +def num_complete(n): + """Return a function that checks if the results list has the length specified by n""" + + def finished(): + return len(results) >= n + + return finished + + +def calculate(): + while running: + with data_available: + # Acquire the data_available lock and wait for has_data + print("Waiting for data") + data_available.wait_for(has_data) + time.sleep(1) + i = data.pop() + with work_complete: + if i % 2: + results.append(1) + else: + results.append(0) + # Acquire the work_complete lock and wake listeners + work_complete.notify_all() + + +if __name__ == "__main__": + with ThreadPoolExecutor() as pool: + # Schedule two worker functions + workers = [pool.submit(calculate), pool.submit(calculate)] + + for i in range(200): + with data_available: + data.append(i) + # After adding each piece of data wake the data_available lock + data_available.notify() + print("200 items submitted") + + with work_complete: + # Wait for at least 5 items to be complete through the work_complete lock + work_complete.wait_for(num_complete(5)) + + for worker in workers: + # Set a shared variable causing the threads to end their work + running = False + print("Stopping workers") + + print(f"{len(results)} items processed") diff --git a/Ch07/listing07-08-barriers.py b/Ch07/listing07-08-barriers.py new file mode 100644 index 0000000..4860465 --- /dev/null +++ b/Ch07/listing07-08-barriers.py @@ -0,0 +1,28 @@ +from concurrent.futures import ThreadPoolExecutor +import random +import time +import threading + + +barrier = threading.Barrier(5) + + +def wait_random(): + thread_id = threading.get_ident() + to_wait = random.randint(1, 10) + print(f"Thread {thread_id:5d}: Waiting {to_wait:2d} seconds") + start_time = time.time() + time.sleep(to_wait) + i = barrier.wait() + end_time = time.time() + elapsed = end_time - start_time + print( + f"Thread {thread_id:5d}: Resumed in position {i} after {elapsed:3.3f} seconds" + ) + + +if __name__ == "__main__": + with ThreadPoolExecutor() as pool: + # Schedule two worker functions + for i in range(5): + pool.submit(wait_random) diff --git a/Ch07/listing07-09-events.py b/Ch07/listing07-09-events.py new file mode 100644 index 0000000..356a95f --- /dev/null +++ b/Ch07/listing07-09-events.py @@ -0,0 +1,32 @@ +from concurrent.futures import ThreadPoolExecutor +import random +import time +import threading + + +event = threading.Event() + + +def wait_random(master): + thread_id = threading.get_ident() + to_wait = random.randint(1, 10) + print(f"Thread {thread_id:5d}: Waiting {to_wait:2d} seconds (Master: {master})") + start_time = time.time() + time.sleep(to_wait) + if master: + event.set() + else: + event.wait() + end_time = time.time() + elapsed = end_time - start_time + print( + f"Thread {thread_id:5d}: Resumed after {elapsed:3.3f} seconds" + ) + + +if __name__ == "__main__": + with ThreadPoolExecutor() as pool: + # Schedule two worker functions + for i in range(4): + pool.submit(wait_random, False) + pool.submit(wait_random, True) diff --git a/Ch07/listing07-10-semaphore.py b/Ch07/listing07-10-semaphore.py new file mode 100644 index 0000000..4aa0c78 --- /dev/null +++ b/Ch07/listing07-10-semaphore.py @@ -0,0 +1,29 @@ +from concurrent.futures import ThreadPoolExecutor +import random +import time +import threading + + +semaphore = threading.Semaphore(3) + + +def wait_random(): + thread_id = threading.get_ident() + to_wait = random.randint(1, 10) + with semaphore: + print(f"Thread {thread_id:5d}: Waiting {to_wait:2d} seconds") + start_time = time.time() + time.sleep(to_wait) + + end_time = time.time() + elapsed = end_time - start_time + print( + f"Thread {thread_id:5d}: Resumed after {elapsed:3.3f} seconds" + ) + + +if __name__ == "__main__": + with ThreadPoolExecutor() as pool: + # Schedule two worker functions + for i in range(5): + pool.submit(wait_random) diff --git a/Ch07/listing07-11-async-increment.py b/Ch07/listing07-11-async-increment.py new file mode 100644 index 0000000..2616da6 --- /dev/null +++ b/Ch07/listing07-11-async-increment.py @@ -0,0 +1,17 @@ +import asyncio + +async def increment(): + return 1 + +async def decrement(): + return -1 + +async def onehundred(): + num = 0 + for i in range(100): + num += await increment() + num += await decrement() + return num + +if __name__ == "__main__": + print(asyncio.run(onehundred())) diff --git a/Ch07/listing07-12-list_of_awaitables.py b/Ch07/listing07-12-list_of_awaitables.py new file mode 100644 index 0000000..85b2665 --- /dev/null +++ b/Ch07/listing07-12-list_of_awaitables.py @@ -0,0 +1,20 @@ +import asyncio +import typing as t + + +async def number(num: int) -> int: + return num + +def numbers() -> t.Iterable[t.Awaitable[int]]: + return [number(2), number(3)] + +async def add_all(numbers: t.Iterable[t.Awaitable[int]]) -> int: + total = 0 + for num in numbers: + total += await num + return total + +if __name__ == "__main__": + to_add = numbers() + result = asyncio.run(add_all(to_add)) + print(result) diff --git a/Ch07/listing07-13-awaitable_list.py b/Ch07/listing07-13-awaitable_list.py new file mode 100644 index 0000000..9a11492 --- /dev/null +++ b/Ch07/listing07-13-awaitable_list.py @@ -0,0 +1,20 @@ +import asyncio +import typing as t + + +async def number(num: int) -> int: + return num + +async def numbers() -> t.Iterable[int]: + return [await number(2), await number(3)] + +async def add_all(nums: t.Awaitable[t.Iterable[int]]) -> int: + total = 0 + for num in await nums: + total += num + return total + +if __name__ == "__main__": + to_add = numbers() + result = asyncio.run(add_all(to_add)) + print(result) \ No newline at end of file diff --git a/Ch07/listing07-14-async_for.py b/Ch07/listing07-14-async_for.py new file mode 100644 index 0000000..4398040 --- /dev/null +++ b/Ch07/listing07-14-async_for.py @@ -0,0 +1,21 @@ +import asyncio +import typing as t + + +async def number(num: int) -> int: + return num + +async def numbers() -> t.AsyncIterator[int]: + yield await number(2) + yield await number(3) + +async def add_all(nums: t.AsyncIterator[int]) -> int: + total = 0 + async for num in nums: + total += num + return total + +if __name__ == "__main__": + to_add = numbers() + result = asyncio.run(add_all(to_add)) + print(result) \ No newline at end of file diff --git a/Ch07/listing07-15-awaitable_gather.py b/Ch07/listing07-15-awaitable_gather.py new file mode 100644 index 0000000..98bb433 --- /dev/null +++ b/Ch07/listing07-15-awaitable_gather.py @@ -0,0 +1,23 @@ +import asyncio +import typing as t + + +async def number(num: int) -> int: + return num + +async def numbers() -> t.Iterable[int]: + return await asyncio.gather( + number(2), + number(3) + ) + +async def add_all(nums: t.Awaitable[t.Iterable[int]]) -> int: + total = 0 + for num in await nums: + total += num + return total + +if __name__ == "__main__": + to_add = numbers() + result = asyncio.run(add_all(to_add)) + print(result) \ No newline at end of file diff --git a/Ch07/listing07-16-async_increment_unsafe.py b/Ch07/listing07-16-async_increment_unsafe.py new file mode 100644 index 0000000..034b928 --- /dev/null +++ b/Ch07/listing07-16-async_increment_unsafe.py @@ -0,0 +1,22 @@ +import asyncio +import random + +num = 0 + +async def offset(): + await asyncio.sleep(0) + return 1 + +async def increment(): + global num + num += await offset() + +async def onehundred(): + tasks = [] + for i in range(100): + tasks.append(increment()) + await asyncio.gather(*tasks) + return num + +if __name__ == "__main__": + print(asyncio.run(onehundred())) diff --git a/Ch07/listing07-17-async_increment_safe.py b/Ch07/listing07-17-async_increment_safe.py new file mode 100644 index 0000000..0652978 --- /dev/null +++ b/Ch07/listing07-17-async_increment_safe.py @@ -0,0 +1,25 @@ +import asyncio +import random + +num = 0 + +async def offset(): + await asyncio.sleep(0) + return 1 + +async def increment(numlock): + global num + async with numlock: + num += await offset() + +async def onehundred(): + tasks = [] + numlock = asyncio.Lock() + + for i in range(100): + tasks.append(increment(numlock)) + await asyncio.gather(*tasks) + return num + +if __name__ == "__main__": + print(asyncio.run(onehundred())) diff --git a/Ch08/apd.aggregation-chapter08/.coveragerc b/Ch08/apd.aggregation-chapter08/.coveragerc new file mode 100644 index 0000000..e766c69 --- /dev/null +++ b/Ch08/apd.aggregation-chapter08/.coveragerc @@ -0,0 +1,3 @@ +[run] +branch = True +omit = tests/* diff --git a/Ch08/apd.aggregation-chapter08/.pre-commit-config.yaml b/Ch08/apd.aggregation-chapter08/.pre-commit-config.yaml new file mode 100644 index 0000000..d3b6ec7 --- /dev/null +++ b/Ch08/apd.aggregation-chapter08/.pre-commit-config.yaml @@ -0,0 +1,26 @@ +repos: + +- repo: local + hooks: + - id: black + name: black + entry: pipenv run black + args: [--quiet] + language: system + types: [python] + + - id: mypy + name: mypy + exclude: (?x)^( + (.*)/setup.py + )$ + entry: pipenv run mypy + args: ["--follow-imports=skip"] + language: system + types: [python] + + - id: flake8 + name: flake8 + entry: pipenv run flake8 + language: system + types: [python] diff --git a/Ch08/apd.aggregation-chapter08/CHANGES.md b/Ch08/apd.aggregation-chapter08/CHANGES.md new file mode 100644 index 0000000..8e424e5 --- /dev/null +++ b/Ch08/apd.aggregation-chapter08/CHANGES.md @@ -0,0 +1,5 @@ +## Changes + +### 1.0.0 (Unreleased) + +* Generated from skeleton diff --git a/Ch08/apd.aggregation-chapter08/LICENCE b/Ch08/apd.aggregation-chapter08/LICENCE new file mode 100644 index 0000000..86685d4 --- /dev/null +++ b/Ch08/apd.aggregation-chapter08/LICENCE @@ -0,0 +1,31 @@ +Copyright (c) 2019, Matthew Wilkes + + +All rights reserved. + +Redistribution and use in source and binary forms, with or without modification, +are permitted provided that the following conditions are met: + +* Redistributions of source code must retain the above copyright notice, this + list of conditions and the following disclaimer. + +* Redistributions in binary form must reproduce the above copyright notice, this + list of conditions and the following disclaimer in the documentation and/or + other materials provided with the distribution. + +* Neither the name of the copyright holder nor the names of its + contributors may be used to endorse or promote products derived from this + software without specific prior written permission. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND +ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED +WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. +IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, +INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, +BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, +DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY +OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE +OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED +OF THE POSSIBILITY OF SUCH DAMAGE. + + diff --git a/Ch08/apd.aggregation-chapter08/Pipfile b/Ch08/apd.aggregation-chapter08/Pipfile new file mode 100644 index 0000000..ef1fd86 --- /dev/null +++ b/Ch08/apd.aggregation-chapter08/Pipfile @@ -0,0 +1,25 @@ +[[source]] +name = "pypi" +url = "https://pypi.org/simple" +verify_ssl = true + +[dev-packages] +pytest = "*" +pytest-cov = "*" +pytest-asyncio = "*" +mypy = "*" +flake8 = "*" +black = "*" +pre-commit = "*" +wheel = "*" +twine = "*" +sqlalchemy-stubs = "*" + +[packages] +apd-aggregation = {editable = true,path = "."} +apd-sensors = {editable = true,extras = ["webapp"],path = "./../code"} + +[requires] + +[pipenv] +allow_prereleases = true diff --git a/Ch08/apd.aggregation-chapter08/Pipfile.lock b/Ch08/apd.aggregation-chapter08/Pipfile.lock new file mode 100644 index 0000000..37ebbba --- /dev/null +++ b/Ch08/apd.aggregation-chapter08/Pipfile.lock @@ -0,0 +1,751 @@ +{ + "_meta": { + "hash": { + "sha256": "9cbbc3af88a1213ed8f4f93c33e35a9f9d7e047077cd25ff02f9bde13e2b606b" + }, + 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+[build-system] +requires = [ + "setuptools", +] +build-backend = "setuptools.build_meta" diff --git a/Ch08/apd.aggregation-chapter08/pytest.ini b/Ch08/apd.aggregation-chapter08/pytest.ini new file mode 100644 index 0000000..c1d3e58 --- /dev/null +++ b/Ch08/apd.aggregation-chapter08/pytest.ini @@ -0,0 +1,3 @@ +[pytest] +markers = + functional: these tests are significantly slower as they start a HTTP server \ No newline at end of file diff --git a/Ch08/apd.aggregation-chapter08/setup.cfg b/Ch08/apd.aggregation-chapter08/setup.cfg new file mode 100644 index 0000000..06b7be6 --- /dev/null +++ b/Ch08/apd.aggregation-chapter08/setup.cfg @@ -0,0 +1,56 @@ +[mypy] +namespace_packages = True +mypy_path = src +plugins = sqlmypy + +[mypy-alembic] +ignore_missing_imports = True + +[mypy-alembic.config] +ignore_missing_imports = True + +[mypy-alembic.script] +ignore_missing_imports = True + +[mypy-alembic.runtime.environment] +ignore_missing_imports = True + +[mypy-pytest] +ignore_missing_imports = True + +[flake8] +max-line-length = 120 +ignore = E501 + +[metadata] +name = apd.aggregation +version = attr: apd.aggregation.VERSION +description = A programme that queries apd.sensor endpoints and aggregates their results. +long_description = file: README.md, CHANGES.md, LICENCE +long_description_content_type = text/markdown +keywords = +license = BSD +classifiers = + Programming Language :: Python :: 3 + Programming Language :: Python :: 3.7 + +[options] +zip_safe = False +include_package_data = True +package_dir = + =src +packages = find_namespace: +install_requires = + sqlalchemy + aiohttp + psycopg2 + alembic + click + + +[options.packages.find] +where = src + +[options.entry_points] +console_scripts = + collect_sensor_data = apd.aggregation.cli:collect_sensor_data diff --git a/Ch08/apd.aggregation-chapter08/setup.py b/Ch08/apd.aggregation-chapter08/setup.py new file mode 100644 index 0000000..6068493 --- /dev/null +++ b/Ch08/apd.aggregation-chapter08/setup.py @@ -0,0 +1,3 @@ +from setuptools import setup + +setup() diff --git a/Ch08/apd.aggregation-chapter08/src/apd/aggregation/__init__.py b/Ch08/apd.aggregation-chapter08/src/apd/aggregation/__init__.py new file mode 100644 index 0000000..3277f64 --- /dev/null +++ b/Ch08/apd.aggregation-chapter08/src/apd/aggregation/__init__.py @@ -0,0 +1 @@ +VERSION = "1.0.0" diff --git a/Ch08/apd.aggregation-chapter08/src/apd/aggregation/alembic/README b/Ch08/apd.aggregation-chapter08/src/apd/aggregation/alembic/README new file mode 100644 index 0000000..97a87d5 --- /dev/null +++ b/Ch08/apd.aggregation-chapter08/src/apd/aggregation/alembic/README @@ -0,0 +1,12 @@ +Generic single-database configuration. + + +# Database setup + +To generate the required database tables you must create an alembic.ini file, as follows: + + [alembic] + script_location = apd.aggregation:alembic + sqlalchemy.url = postgresql+psycopg2://apd@localhost/apd + +and run `alembic upgrade head`. This should also be done after every upgrade of the software. \ No newline at end of file diff --git a/Ch08/apd.aggregation-chapter08/src/apd/aggregation/alembic/env.py b/Ch08/apd.aggregation-chapter08/src/apd/aggregation/alembic/env.py new file mode 100644 index 0000000..4c8a2c2 --- /dev/null +++ b/Ch08/apd.aggregation-chapter08/src/apd/aggregation/alembic/env.py @@ -0,0 +1,80 @@ +from logging.config import fileConfig + +from sqlalchemy import engine_from_config +from sqlalchemy import pool +from alembic import context + +from apd.aggregation.database import metadata + + +# this is the Alembic Config object, which provides +# access to the values within the .ini file in use. +config = context.config + +# Interpret the config file for Python logging. +# This line sets up loggers basically. +if config.config_file_name: + fileConfig(config.config_file_name) + +target_metadata = metadata + + +def include_object(object, name, type_, reflected, compare_to): + if object.info.get("is_view", False): + return False + return True + + +def run_migrations_offline(): + """Run migrations in 'offline' mode. + + This configures the context with just a URL + and not an Engine, though an Engine is acceptable + here as well. By skipping the Engine creation + we don't even need a DBAPI to be available. + + Calls to context.execute() here emit the given string to the + script output. + + """ + url = config.get_main_option("sqlalchemy.url") + context.configure( + url=url, + include_object=include_object, + target_metadata=target_metadata, + literal_binds=True, + dialect_opts={"paramstyle": "named"}, + ) + + with context.begin_transaction(): + context.run_migrations() + + +def run_migrations_online(): + """Run migrations in 'online' mode. + + In this scenario we need to create an Engine + and associate a connection with the context. + + """ + connectable = engine_from_config( + config.get_section(config.config_ini_section), + prefix="sqlalchemy.", + poolclass=pool.NullPool, + ) + + with connectable.connect() as connection: + context.configure( + connection=connection, + target_metadata=target_metadata, + include_object=include_object, + ) + + with context.begin_transaction(): + context.run_migrations() + + +if context.is_offline_mode(): + run_migrations_offline() +else: + run_migrations_online() diff --git a/Ch08/apd.aggregation-chapter08/src/apd/aggregation/alembic/script.py.mako b/Ch08/apd.aggregation-chapter08/src/apd/aggregation/alembic/script.py.mako new file mode 100644 index 0000000..2c01563 --- /dev/null +++ b/Ch08/apd.aggregation-chapter08/src/apd/aggregation/alembic/script.py.mako @@ -0,0 +1,24 @@ +"""${message} + +Revision ID: ${up_revision} +Revises: ${down_revision | comma,n} +Create Date: ${create_date} + +""" +from alembic import op +import sqlalchemy as sa +${imports if imports else ""} + +# revision identifiers, used by Alembic. +revision = ${repr(up_revision)} +down_revision = ${repr(down_revision)} +branch_labels = ${repr(branch_labels)} +depends_on = ${repr(depends_on)} + + +def upgrade(): + ${upgrades if upgrades else "pass"} + + +def downgrade(): + ${downgrades if downgrades else "pass"} diff --git a/Ch08/apd.aggregation-chapter08/src/apd/aggregation/alembic/versions/4b2df8a6e1ce_add_indexes_to_datapoints.py b/Ch08/apd.aggregation-chapter08/src/apd/aggregation/alembic/versions/4b2df8a6e1ce_add_indexes_to_datapoints.py new file mode 100644 index 0000000..ef671bd --- /dev/null +++ b/Ch08/apd.aggregation-chapter08/src/apd/aggregation/alembic/versions/4b2df8a6e1ce_add_indexes_to_datapoints.py @@ -0,0 +1,32 @@ +"""Add indexes to datapoints + +Revision ID: 4b2df8a6e1ce +Revises: 6d2eacd5da3f +Create Date: 2019-12-02 16:07:41.123116 + +""" +from alembic import op + + +# revision identifiers, used by Alembic. +revision = "4b2df8a6e1ce" +down_revision = "6d2eacd5da3f" +branch_labels = None +depends_on = None + + +def upgrade(): + op.create_index( + op.f("ix_datapoints_collected_at"), + "datapoints", + ["collected_at"], + unique=False, + ) + op.create_index( + op.f("ix_datapoints_sensor_name"), "datapoints", ["sensor_name"], unique=False, + ) + + +def downgrade(): + op.drop_index(op.f("ix_datapoints_sensor_name"), table_name="datapoints") + op.drop_index(op.f("ix_datapoints_collected_at"), table_name="datapoints") diff --git a/Ch08/apd.aggregation-chapter08/src/apd/aggregation/alembic/versions/6962f8455a6d_add_daily_summary_view.py b/Ch08/apd.aggregation-chapter08/src/apd/aggregation/alembic/versions/6962f8455a6d_add_daily_summary_view.py new file mode 100644 index 0000000..eb02455 --- /dev/null +++ b/Ch08/apd.aggregation-chapter08/src/apd/aggregation/alembic/versions/6962f8455a6d_add_daily_summary_view.py @@ -0,0 +1,38 @@ +"""Add daily summary view + +Revision ID: 6962f8455a6d +Revises: 4b2df8a6e1ce +Create Date: 2019-12-03 11:50:24.403402 + +""" +from alembic import op + + +# revision identifiers, used by Alembic. +revision = "6962f8455a6d" +down_revision = "4b2df8a6e1ce" +branch_labels = None +depends_on = None + + +def upgrade(): + create_view = """ + CREATE VIEW daily_summary AS + SELECT + datapoints.sensor_name AS sensor_name, + datapoints.data AS data, + count(datapoints.id) AS count + FROM datapoints + WHERE + datapoints.collected_at >= CAST(CURRENT_DATE AS DATE) + AND + datapoints.collected_at < CAST(CURRENT_DATE AS DATE) + 1 + GROUP BY + datapoints.sensor_name, + datapoints.data; + """ + op.execute(create_view) + + +def downgrade(): + op.execute("""DROP VIEW daily_summary""") diff --git a/Ch08/apd.aggregation-chapter08/src/apd/aggregation/alembic/versions/6d2eacd5da3f_create_sensor_values_table.py b/Ch08/apd.aggregation-chapter08/src/apd/aggregation/alembic/versions/6d2eacd5da3f_create_sensor_values_table.py new file mode 100644 index 0000000..b4a3545 --- /dev/null +++ b/Ch08/apd.aggregation-chapter08/src/apd/aggregation/alembic/versions/6d2eacd5da3f_create_sensor_values_table.py @@ -0,0 +1,31 @@ +"""Create datapoints table + +Revision ID: 6d2eacd5da3f +Revises: N/A +Create Date: 2019-09-29 13:43:21.242706 + +""" +from alembic import op +import sqlalchemy as sa +from sqlalchemy.dialects import postgresql + +# revision identifiers, used by Alembic. +revision = "6d2eacd5da3f" +down_revision = None +branch_labels = None +depends_on = None + + +def upgrade(): + op.create_table( + "datapoints", + sa.Column("id", sa.Integer(), nullable=False), + sa.Column("sensor_name", sa.String(), nullable=True), + sa.Column("collected_at", postgresql.TIMESTAMP(), nullable=True), + sa.Column("data", postgresql.JSONB(astext_type=sa.Text()), nullable=True), + sa.PrimaryKeyConstraint("id"), + ) + + +def downgrade(): + op.drop_table("datapoints") diff --git a/Ch08/apd.aggregation-chapter08/src/apd/aggregation/alembic/versions/d8d4cf6a178f_add_deployment_id_to_datapoint.py b/Ch08/apd.aggregation-chapter08/src/apd/aggregation/alembic/versions/d8d4cf6a178f_add_deployment_id_to_datapoint.py new file mode 100644 index 0000000..88d5219 --- /dev/null +++ b/Ch08/apd.aggregation-chapter08/src/apd/aggregation/alembic/versions/d8d4cf6a178f_add_deployment_id_to_datapoint.py @@ -0,0 +1,34 @@ +"""Add deployment id to DataPoint + +Revision ID: d8d4cf6a178f +Revises: 6962f8455a6d +Create Date: 2019-12-03 20:58:11.285509 + +""" +from alembic import op +import sqlalchemy as sa +from sqlalchemy.dialects import postgresql + +# revision identifiers, used by Alembic. +revision = "d8d4cf6a178f" +down_revision = "6962f8455a6d" +branch_labels = None +depends_on = None + + +def upgrade(): + op.add_column( + "datapoints", + sa.Column("deployment_id", postgresql.UUID(as_uuid=True), nullable=True), + ) + op.create_index( + op.f("ix_datapoints_deployment_id"), + "datapoints", + ["deployment_id"], + unique=False, + ) + + +def downgrade(): + op.drop_index(op.f("ix_datapoints_deployment_id"), table_name="datapoints") + op.drop_column("datapoints", "deployment_id") diff --git a/Ch08/apd.aggregation-chapter08/src/apd/aggregation/cli.py b/Ch08/apd.aggregation-chapter08/src/apd/aggregation/cli.py new file mode 100644 index 0000000..3435e78 --- /dev/null +++ b/Ch08/apd.aggregation-chapter08/src/apd/aggregation/cli.py @@ -0,0 +1,52 @@ +import typing as t + +import click + +from .collect import standalone + + +@click.command() +@click.argument("server", nargs=-1) +@click.option( + "--db", + metavar="", + default="postgresql+psycopg2://localhost/apd", + help="The connection string to a PostgreSQL database", + envvar="APD_DB_URI", +) +@click.option("--api-key", metavar="", envvar="APD_API_KEY") +@click.option( + "--tolerate-failures", + "-f", + help="If provided, failure to retrieve some sensors' data will not abort the collection process", + is_flag=True, +) +@click.option("-v", "--verbose", is_flag=True, help="Enables verbose mode") +def collect_sensor_data( + db: str, server: t.Tuple[str], api_key: str, tolerate_failures: bool, verbose: bool +): + """This loads data from one or more sensors into the specified database. + + Only PostgreSQL databases are supported, as the column definitions use + multiple pg specific features. The database must already exist and be + populated with the required tables. + + The --api-key option is used to specify the access token for the sensors + being queried. + + You may specify any number of servers, the variable should be the full URL + to the sensor's HTTP interface, not including the /v/2.0 portion. Multiple + URLs should be separated with a space. + """ + if tolerate_failures: + attempts = [(s,) for s in server] + else: + attempts = [server] + success = True + for attempt in attempts: + try: + standalone(db, attempt, api_key, echo=verbose) + except ValueError as e: + click.secho(str(e), err=True, fg="red") + success = False + return success diff --git a/Ch08/apd.aggregation-chapter08/src/apd/aggregation/collect.py b/Ch08/apd.aggregation-chapter08/src/apd/aggregation/collect.py new file mode 100644 index 0000000..f745d69 --- /dev/null +++ b/Ch08/apd.aggregation-chapter08/src/apd/aggregation/collect.py @@ -0,0 +1,93 @@ +import asyncio +from contextvars import ContextVar +import datetime +import typing as t +import uuid + +import aiohttp +from sqlalchemy import create_engine +from sqlalchemy.orm import sessionmaker +from sqlalchemy.orm.session import Session + +from .database import DataPoint, datapoint_table + +http_session_var: ContextVar[aiohttp.ClientSession] = ContextVar("http_session") + + +async def get_data_points(server: str, api_key: t.Optional[str],) -> t.List[DataPoint]: + if not server.endswith("/"): + server += "/" + url = server + "v/2.1/sensors/" + deployment_id_url = server + "v/2.1/deployment_id" + headers = {} + if api_key: + headers["X-API-KEY"] = api_key + http = http_session_var.get() + + async with http.get(deployment_id_url) as request: + if request.status != 200: + raise ValueError(f"Error loading deployment id from {server}") + result = await request.json() + deployment_id = uuid.UUID(result["deployment_id"]) + + async with http.get(url, headers=headers) as request: + result = await request.json() + ok = request.status == 200 + now = datetime.datetime.now() + if ok: + points = [] + for value in result["sensors"]: + points.append( + DataPoint( + sensor_name=value["id"], + collected_at=now, + data=value["value"], + deployment_id=deployment_id, + ) + ) + return points + else: + raise ValueError( + f"Error loading data from {server}: " + result.get("error", "Unknown") + ) + + +def handle_result(result: t.List[DataPoint], session: Session) -> t.List[DataPoint]: + for point in result: + insert = datapoint_table.insert().values(**point._asdict()) + sql_result = session.execute(insert) + point.id = sql_result.inserted_primary_key[0] + return result + + +async def add_data_from_sensors( + session: Session, servers: t.Tuple[str], api_key: t.Optional[str] +) -> t.List[DataPoint]: + tasks: t.List[t.Awaitable[t.List[DataPoint]]] = [] + points: t.List[DataPoint] = [] + async with aiohttp.ClientSession() as http: + http_session_var.set(http) + tasks = [get_data_points(server, api_key) for server in servers] + for results in await asyncio.gather(*tasks): + points += results + loop = asyncio.get_running_loop() + await loop.run_in_executor(None, handle_result, points, session) + return points + + +def standalone( + db_uri: str, servers: t.Tuple[str], api_key: t.Optional[str], echo: bool = False +) -> None: + engine = create_engine(db_uri, echo=echo) + sm = sessionmaker(engine) + Session = sm() + asyncio.run(add_data_from_sensors(Session, servers, api_key)) + Session.commit() + + +if __name__ == "__main__": + standalone( + db_uri="postgresql+psycopg2://apd@localhost/apd", + servers=("http://pvoutput:8080/",), + api_key="h3hdfjksfhwkjehnwekj", + ) diff --git a/Ch08/apd.aggregation-chapter08/src/apd/aggregation/database.py b/Ch08/apd.aggregation-chapter08/src/apd/aggregation/database.py new file mode 100644 index 0000000..dd5d791 --- /dev/null +++ b/Ch08/apd.aggregation-chapter08/src/apd/aggregation/database.py @@ -0,0 +1,102 @@ +from __future__ import annotations + +from dataclasses import dataclass, field, asdict +import datetime +import typing as t +import uuid + +import sqlalchemy +from sqlalchemy.dialects.postgresql import JSONB, DATE, TIMESTAMP, UUID +from sqlalchemy.ext.hybrid import ExprComparator, hybrid_property +from sqlalchemy.orm import sessionmaker +from sqlalchemy.schema import Table + + +metadata = sqlalchemy.MetaData() + +datapoint_table = Table( + "datapoints", + metadata, + sqlalchemy.Column("id", sqlalchemy.Integer, primary_key=True), + sqlalchemy.Column("sensor_name", sqlalchemy.String, index=True), + sqlalchemy.Column("collected_at", TIMESTAMP, index=True), + sqlalchemy.Column("deployment_id", UUID(as_uuid=True), index=True), + sqlalchemy.Column("data", JSONB), +) + +daily_summary_view = Table( + "daily_summary", + metadata, + sqlalchemy.Column("sensor_name", sqlalchemy.String), + sqlalchemy.Column("data", JSONB), + sqlalchemy.Column("count", sqlalchemy.Integer), + info={"is_view": True}, +) + + +class DateEqualComparator(ExprComparator): + def __init__(self, fallback_expression, raw_expression): + # Do not try and find update expression from parent + super().__init__(None, fallback_expression, None) + self.raw_expression = raw_expression + + def __eq__(self, other): + """ Returns True iff on the same day as other """ + other_date = sqlalchemy.cast(other, DATE) + return sqlalchemy.and_( + self.raw_expression >= other_date, self.raw_expression < other_date + 1, + ) + + def operate(self, op, *other, **kwargs): + other = [sqlalchemy.cast(date, DATE) for date in other] + return op(self.expression, *other, **kwargs) + + def reverse_operate(self, op, other, **kwargs): + other = [sqlalchemy.cast(date, DATE) for date in other] + return op(other, self.expression, **kwargs) + + +@dataclass +class DataPoint: + sensor_name: str + data: t.Dict[str, t.Any] + deployment_id: uuid.UUID + id: t.Optional[int] = None + collected_at: datetime.datetime = field(default_factory=datetime.datetime.now) + + @classmethod + def from_sql_result(cls, result) -> DataPoint: + return cls(**result._asdict()) + + def _asdict(self) -> t.Dict[str, t.Any]: + data = asdict(self) + if data["id"] is None: + del data["id"] + return data + + @hybrid_property + def collected_on_date(self): + return self.collected_at.date() + + @collected_on_date.comparator # type: ignore + def collected_on_date(cls): + return DateEqualComparator( + fallback_expression=sqlalchemy.cast(datapoint_table.c.collected_at, DATE), + raw_expression=datapoint_table.c.collected_at, + ) + + +def main(): + engine = sqlalchemy.create_engine( + "postgresql+psycopg2://apd@localhost/apd", echo=True + ) + sm = sessionmaker(engine) + Session = sm() + if False: + metadata.create_all(engine) + print(Session.query(DataPoint).all()) + pass + + +if __name__ == "__main__": + main() diff --git a/Ch08/apd.aggregation-chapter08/src/apd/aggregation/query.py b/Ch08/apd.aggregation-chapter08/src/apd/aggregation/query.py new file mode 100644 index 0000000..512bd83 --- /dev/null +++ b/Ch08/apd.aggregation-chapter08/src/apd/aggregation/query.py @@ -0,0 +1,38 @@ +import asyncio +import contextlib +from contextvars import ContextVar +import typing as t + +from sqlalchemy import create_engine +from sqlalchemy.orm import sessionmaker +from sqlalchemy.orm.session import Session + + +from apd.aggregation.collect import datapoint_table, DataPoint + + +db_session_var: ContextVar[Session] = ContextVar("db_session") + + +@contextlib.contextmanager +def with_database(uri: t.Optional[str] = None) -> t.Iterator[Session]: + """Given a URI, set up a DB connection, and return a Session as a context manager """ + if uri is None: + uri = "postgresql+psycopg2://localhost/apd" + engine = create_engine(uri) + sm = sessionmaker(engine) + Session = sm() + token = db_session_var.set(Session) + yield Session + db_session_var.reset(token) + Session.commit() + Session.close() + + +async def get_data() -> t.AsyncIterator[DataPoint]: + db_session = db_session_var.get() + loop = asyncio.get_running_loop() + query = db_session.query(datapoint_table) + rows = await loop.run_in_executor(None, query.all) + for row in rows: + yield DataPoint.from_sql_result(row) diff --git a/Ch08/apd.aggregation-chapter08/tests/__init__.py b/Ch08/apd.aggregation-chapter08/tests/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/Ch08/apd.aggregation-chapter08/tests/conftest.py b/Ch08/apd.aggregation-chapter08/tests/conftest.py new file mode 100644 index 0000000..d377b56 --- /dev/null +++ b/Ch08/apd.aggregation-chapter08/tests/conftest.py @@ -0,0 +1,120 @@ +import datetime +from uuid import UUID + +from apd.aggregation.database import datapoint_table + +from alembic.config import Config +from alembic.script import ScriptDirectory +from alembic.runtime.environment import EnvironmentContext +import pytest + + +@pytest.fixture +def db_uri(): + return "postgresql+psycopg2://apd@localhost/apd-test" + + +@pytest.fixture +def db_session(db_uri): + from sqlalchemy import create_engine + from sqlalchemy.orm import sessionmaker + + engine = create_engine(db_uri, echo=True) + sm = sessionmaker(engine) + Session = sm() + yield Session + Session.close() + + +@pytest.fixture +def migrated_db(db_uri): + config = Config() + config.set_main_option("script_location", "apd.aggregation:alembic") + config.set_main_option("sqlalchemy.url", db_uri) + script = ScriptDirectory.from_config(config) + + def upgrade(rev, context): + return script._upgrade_revs(script.get_current_head(), rev) + + def downgrade(rev, context): + return script._downgrade_revs(None, rev) + + with EnvironmentContext(config, script, fn=upgrade): + script.run_env() + + yield + + with EnvironmentContext(config, script, fn=downgrade): + script.run_env() + + +@pytest.fixture +def populated_db(migrated_db, db_session): + datas = [ + { + "id": 1, + "sensor_name": "Test", + "data": "1", + "collected_at": datetime.datetime(2020, 4, 1, 12, 0, 1), + "deployment_id": UUID("b4c68905-b1e4-4875-940e-69e5d27730fd"), + }, + { + "id": 2, + "sensor_name": "Test", + "data": "2", + "collected_at": datetime.datetime(2020, 4, 1, 12, 1, 1), + "deployment_id": UUID("b4c68905-b1e4-4875-940e-69e5d27730fd"), + }, + { + "id": 3, + "sensor_name": "Test", + "data": "3", + "collected_at": datetime.datetime(2020, 4, 1, 12, 2, 1), + "deployment_id": UUID("b4c68905-b1e4-4875-940e-69e5d27730fd"), + }, + { + "id": 4, + "sensor_name": "Test", + "data": "4", + "collected_at": datetime.datetime(2020, 4, 1, 12, 3, 1), + "deployment_id": UUID("b4c68905-b1e4-4875-940e-69e5d27730fd"), + }, + { + "id": 5, + "sensor_name": "OtherTest", + "data": "5", + "collected_at": datetime.datetime(2020, 4, 1, 12, 4, 1), + "deployment_id": UUID("b4c68905-b1e4-4875-940e-69e5d27730fd"), + }, + { + "id": 6, + "sensor_name": "Test", + "data": "1", + "collected_at": datetime.datetime(2020, 4, 1, 12, 0, 1), + "deployment_id": UUID("6e887b81-57c3-4f3b-a7e7-bad159e05e78"), + }, + { + "id": 7, + "sensor_name": "Test", + "data": "1", + "collected_at": datetime.datetime(2020, 4, 1, 12, 1, 1), + "deployment_id": UUID("6e887b81-57c3-4f3b-a7e7-bad159e05e78"), + }, + { + "id": 8, + "sensor_name": "Test", + "data": "2", + "collected_at": datetime.datetime(2020, 4, 1, 12, 2, 1), + "deployment_id": UUID("6e887b81-57c3-4f3b-a7e7-bad159e05e78"), + }, + { + "id": 9, + "sensor_name": "OtherTest", + "data": "3", + "collected_at": datetime.datetime(2020, 4, 1, 12, 3, 1), + "deployment_id": UUID("6e887b81-57c3-4f3b-a7e7-bad159e05e78"), + }, + ] + for data in datas: + insert = datapoint_table.insert().values(**data) + db_session.execute(insert) diff --git a/Ch08/apd.aggregation-chapter08/tests/test_http_get.py b/Ch08/apd.aggregation-chapter08/tests/test_http_get.py new file mode 100644 index 0000000..d38fd4c --- /dev/null +++ b/Ch08/apd.aggregation-chapter08/tests/test_http_get.py @@ -0,0 +1,154 @@ +from concurrent.futures import ThreadPoolExecutor +import datetime +import typing as t +from unittest.mock import patch, MagicMock +import wsgiref.simple_server + +import aiohttp +from apd.sensors.base import Sensor +from apd.sensors.sensors import PythonVersion, ACStatus +from apd.sensors.wsgi import set_up_config +import flask +import pytest + +from apd.sensors.wsgi import v21 + +from apd.aggregation import collect + +pytestmark = [pytest.mark.functional] + + +@pytest.fixture +def sensors() -> t.Iterator[t.List[Sensor[t.Any]]]: + """ Patch the get_sensors method to return a known pair of sensors only """ + data: t.List[Sensor[t.Any]] = [PythonVersion(), ACStatus()] + with patch("apd.sensors.cli.get_sensors") as get_sensors: + get_sensors.return_value = data + yield data + + +def get_independent_flask_app(name: str) -> flask.Flask: + """ Create a new flask app with the v20 API blueprint loaded, so multiple copies + of the app can be run in parallel without conflicting configuration """ + app = flask.Flask(name) + app.register_blueprint(v21.version, url_prefix="/v/2.1") + return app + + +def run_server_in_thread( + name: str, config: t.Dict[str, t.Any], port: int +) -> t.Iterator[str]: + # Create a new flask app and load in required code, to prevent config conflicts + app = get_independent_flask_app(name) + flask_app = set_up_config(config, app) + server = wsgiref.simple_server.make_server("localhost", port, flask_app) + + with ThreadPoolExecutor() as pool: + pool.submit(server.serve_forever) + yield f"http://localhost:{port}/" + server.shutdown() + + +@pytest.fixture(scope="module") +def http_server(): + yield from run_server_in_thread( + "standard", + { + "APD_SENSORS_API_KEY": "testing", + "APD_SENSORS_DEPLOYMENT_ID": "a46b1d1207fd4cdcad39bbdf706dfe29", + }, + 12081, + ) + + +@pytest.fixture(scope="module") +def bad_api_key_http_server(): + yield from run_server_in_thread( + "alternate", + { + "APD_SENSORS_API_KEY": "penny", + "APD_SENSORS_DEPLOYMENT_ID": "38cf2bae9adb445fad946c82e290487a", + }, + 12082, + ) + + +class TestGetDataPoints: + @pytest.fixture + def mut(self): + return collect.get_data_points + + @pytest.mark.asyncio + async def test_get_data_points( + self, sensors: t.List[Sensor[t.Any]], mut, http_server: str + ) -> None: + # Get the data from the server, storing the time before and after + # as bounds for the collected_at value + async with aiohttp.ClientSession() as http: + collect.http_session_var.set(http) + time_before = datetime.datetime.now() + results = await mut(http_server, "testing") + time_after = datetime.datetime.now() + + assert len(results) == len(sensors) == 2 + + for (sensor, result) in zip(sensors, results): + assert sensor.from_json_compatible(result.data) == sensor.value() + assert result.sensor_name == sensor.name + assert time_before <= result.collected_at <= time_after + + @pytest.mark.asyncio + async def test_get_data_points_fails_with_bad_api_key( + self, sensors: t.List[Sensor[t.Any]], mut, http_server: str + ) -> None: + with pytest.raises( + ValueError, + match=f"Error loading data from {http_server}: Supply API key in X-API-Key header", + ): + async with aiohttp.ClientSession() as http: + collect.http_session_var.set(http) + await mut(http_server, "incorrect") + + +class TestAddDataFromSensors: + @pytest.fixture + def mut(self): + return collect.add_data_from_sensors + + @pytest.fixture + def mock_db_session(self): + return MagicMock() + + @pytest.mark.asyncio + async def test_get_get_data_from_sensors( + self, mock_db_session, sensors: t.List[Sensor[t.Any]], mut, http_server: str + ) -> None: + results = await mut(mock_db_session, [http_server], "testing") + assert mock_db_session.execute.call_count == len(sensors) + assert len(results) == len(sensors) + + @pytest.mark.asyncio + async def test_get_get_data_from_sensors_with_multiple_servers( + self, mock_db_session, sensors: t.List[Sensor[t.Any]], mut, http_server: str + ) -> None: + results = await mut(mock_db_session, [http_server, http_server], "testing") + assert mock_db_session.execute.call_count == len(sensors) * 2 + assert len(results) == len(sensors) * 2 + + @pytest.mark.asyncio + async def test_data_points_not_added_if_only_partial_success( + self, + mock_db_session, + sensors: t.List[Sensor[t.Any]], + mut, + http_server: str, + bad_api_key_http_server: str, + ) -> None: + with pytest.raises( + ValueError, + match=f"Error loading data from {bad_api_key_http_server}: Supply API key in X-API-Key header", + ): + await mut( + mock_db_session, [http_server, bad_api_key_http_server], "testing" + ) + assert mock_db_session.execute.call_count == 0 diff --git a/Ch08/apd.aggregation-chapter08/tests/test_sensor_aggregation.py b/Ch08/apd.aggregation-chapter08/tests/test_sensor_aggregation.py new file mode 100644 index 0000000..0f1872d --- /dev/null +++ b/Ch08/apd.aggregation-chapter08/tests/test_sensor_aggregation.py @@ -0,0 +1,174 @@ +from __future__ import annotations + +import contextlib +from dataclasses import dataclass +import json +import typing as t +from unittest.mock import patch, Mock + +import pytest +import sqlalchemy + +import apd.aggregation.collect + + +@pytest.fixture +def data() -> t.Any: + return { + "sensors": [ + { + "human_readable": "3.7", + "id": "PythonVersion", + "title": "Python Version", + "value": [3, 7, 2, "final", 0], + }, + { + "human_readable": "Not connected", + "id": "ACStatus", + "title": "AC Connected", + "value": False, + }, + ] + } + + +@dataclass +class FakeAIOHttpClient: + responses: t.Dict[str, str] + + @contextlib.asynccontextmanager + async def get( + self, url: str, headers: t.Optional[t.Dict[str, str]] = None + ) -> t.AsyncIterator[FakeAIOHttpResponse]: + if url in self.responses: + yield FakeAIOHttpResponse(body=self.responses[url]) + else: + yield FakeAIOHttpResponse(body="", status=404) + + +@dataclass +class FakeAIOHttpResponse: + body: str + status: int = 200 + + async def json(self) -> t.Any: + return json.loads(self.body) + + +@pytest.fixture +def mockclient(data) -> FakeAIOHttpClient: + return FakeAIOHttpClient( + { + "http://localhost/v/2.1/sensors/": json.dumps(data), + "http://localhost/v/2.1/deployment_id": json.dumps( + {"deployment_id": "b29ba0ee10f14552b6b21327bb96d3fb"} + ), + } + ) + + +class TestGetDataPoints: + @pytest.fixture + def mut(self): + return apd.aggregation.collect.get_data_points + + @pytest.mark.asyncio + async def test_get_data_points( + self, mut, mockclient: FakeAIOHttpClient, data + ) -> None: + token = apd.aggregation.collect.http_session_var.set(mockclient) + try: + datapoints = await mut("http://localhost", "") + finally: + apd.aggregation.collect.http_session_var.reset(token) + + assert len(datapoints) == len(data["sensors"]) + for sensor in data["sensors"]: + assert sensor["value"] in (datapoint.data for datapoint in datapoints) + assert sensor["id"] in (datapoint.sensor_name for datapoint in datapoints) + + +class TestAddDataFromSensors: + @pytest.fixture + def mut(self): + return apd.aggregation.collect.add_data_from_sensors + + @pytest.fixture(autouse=True) + def patch_aiohttp(self, mockclient): + with patch("aiohttp.ClientSession") as ClientSession: + ClientSession.return_value.__aenter__.return_value = mockclient + yield ClientSession + + @pytest.fixture + def db_session(self): + session = Mock() + sql_result = session.execute.return_value + sql_result.inserted_primary_key = [1] + return session + + @pytest.mark.asyncio + async def test_datapoints_are_added_to_the_session(self, mut, db_session) -> None: + assert db_session.execute.call_count == 0 + datapoints = await mut(db_session, ["http://localhost"], "") + assert db_session.execute.call_count == len(datapoints) + + +@pytest.mark.usefixtures("migrated_db") +class TestDatabaseConnection: + @pytest.fixture(autouse=True) + def patch_aiohttp(self, mockclient): + with patch("aiohttp.ClientSession") as ClientSession: + ClientSession.return_value.__aenter__.return_value = mockclient + yield ClientSession + + @pytest.fixture + def table(self): + return apd.aggregation.database.datapoint_table + + @pytest.fixture + def daily_summary_view(self): + return apd.aggregation.database.daily_summary_view + + @pytest.fixture + def model(self): + return apd.aggregation.database.DataPoint + + @pytest.fixture + def mut(self): + return apd.aggregation.collect.add_data_from_sensors + + @pytest.mark.asyncio + async def test_datapoints_are_added_to_the_session(self, db_session, table) -> None: + datapoints = await apd.aggregation.collect.add_data_from_sensors( + db_session, ["http://localhost"], "" + ) + num_points = db_session.query(table).count() + assert num_points == len(datapoints) == 2 + + @pytest.mark.asyncio + async def test_datapoints_can_be_mapped_back_to_DataPoints( + self, mut, db_session, table, model + ) -> None: + datapoints = await mut(db_session, ["http://localhost"], "") + db_points = [ + model.from_sql_result(result) for result in db_session.query(table) + ] + assert db_points == datapoints + + @pytest.mark.asyncio + async def test_daily_summary_view_matches_query( + self, db_session, model, table, daily_summary_view + ) -> None: + await apd.aggregation.collect.add_data_from_sensors( + db_session, ["http://localhost"], "" + ) + + headers = table.c.sensor_name, table.c.data + value_counts = ( + db_session.query(*headers, sqlalchemy.func.count(table.c.id)) + .filter(model.collected_on_date == sqlalchemy.func.current_date()) + .group_by(*headers) + ) + + daily_summary_view = db_session.query(daily_summary_view) + assert value_counts.all() == daily_summary_view.all() diff --git a/Ch08/apd.sensors-chapter08/.pre-commit-config.yaml b/Ch08/apd.sensors-chapter08/.pre-commit-config.yaml new file mode 100644 index 0000000..d3b6ec7 --- /dev/null +++ b/Ch08/apd.sensors-chapter08/.pre-commit-config.yaml @@ -0,0 +1,26 @@ +repos: + +- repo: local + hooks: + - id: black + name: black + entry: pipenv run black + args: [--quiet] + language: system + types: [python] + + - id: mypy + name: mypy + exclude: (?x)^( + (.*)/setup.py + )$ + entry: pipenv run mypy + args: ["--follow-imports=skip"] + language: system + types: [python] + + - id: flake8 + name: flake8 + entry: pipenv run flake8 + language: system + types: [python] diff --git a/Ch08/apd.sensors-chapter08/CHANGES.md b/Ch08/apd.sensors-chapter08/CHANGES.md new file mode 100644 index 0000000..dba741b --- /dev/null +++ b/Ch08/apd.sensors-chapter08/CHANGES.md @@ -0,0 +1,29 @@ +## Changes + +### 2.0.0 (2019-09-08) + +* Add `to_json_compatible` and `from_json_compatible` methods to Sensor + to facilitate better HTTP API (Matthew Wilkes) + +* HTTP API is now versioned. The API from 1.3.0 is available at /v/1.0 + and an updated version is at /v/2.0 (Matthew Wilkes) + +### 1.3.0 (2019-08-20) + +* WSGI HTTP API support added (Matthew Wilkes) + +### 1.2.0 (2019-08-05) + +* Add external plugin support through `apd.sensors.sensors` entrypoint (Matthew Wilkes) + +### 1.1.0 (2019-07-12) + +* Add --develop argument (Matthew Wilkes) + +### 1.0.1 (2019-06-20) + +* Fix broken 1.0.0 release (Matthew Wilkes) + +### 1.0.0 (2019-06-20) + +* Added initial sensors (Matthew Wilkes) diff --git a/Ch08/apd.sensors-chapter08/LICENCE b/Ch08/apd.sensors-chapter08/LICENCE new file mode 100644 index 0000000..053f694 --- /dev/null +++ b/Ch08/apd.sensors-chapter08/LICENCE @@ -0,0 +1,19 @@ +Copyright (c) 2019 Matthew Wilkes + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/Ch08/apd.sensors-chapter08/Pipfile b/Ch08/apd.sensors-chapter08/Pipfile new file mode 100644 index 0000000..f6cd446 --- /dev/null +++ b/Ch08/apd.sensors-chapter08/Pipfile @@ -0,0 +1,34 @@ +[[source]] +name = "pypi" +url = "https://pypi.org/simple" +verify_ssl = true + +[[source]] +name = "piwheels" +url = "https://piwheels.org/simple" +verify_ssl = true + +[dev-packages] +ipykernel = "*" +remote-ikernel = "*" +pytest = "*" +pytest-cov = "*" +mypy = "*" +flake8 = "*" +black = "*" +pre-commit = "*" +wheel = "*" +twine = "*" +webtest = "*" +apd-sensors = {editable = true,extras = ["webapp"],path = "."} + +[packages] +apd-sensors = {editable = true,path = "."} +apd-sunnyboy-solar = {editable = true,path = "./plugins/apd.sunnyboy_solar"} +waitress = "*" +pint = "*" + +[requires] + +[pipenv] +allow_prereleases = true diff --git a/Ch08/apd.sensors-chapter08/Pipfile.lock b/Ch08/apd.sensors-chapter08/Pipfile.lock new file mode 100644 index 0000000..1304857 --- /dev/null +++ b/Ch08/apd.sensors-chapter08/Pipfile.lock @@ -0,0 +1,872 @@ +{ + "_meta": { + "hash": { + "sha256": "34faaa40acc9d73922634e8c6bef3e8cdeffc6728a1c907283b70297d90b514b" + }, + "pipfile-spec": 6, + "requires": {}, + "sources": [ + { + "name": "pypi", + "url": "https://pypi.org/simple", + "verify_ssl": true + }, + { + "name": "piwheels", + "url": "https://piwheels.org/simple", + "verify_ssl": true + } + ] + }, + "default": { + "apd-sensors": { + "editable": true, + "path": "." + }, + "apd-sunnyboy-solar": { + "editable": true, + "path": "./plugins/apd.sunnyboy_solar" + }, + "click": { + "hashes": [ + "sha256:2335065e6395b9e67ca716de5f7526736bfa6ceead690adf616d925bdc622b13", + 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+ }, + "zipp": { + "hashes": [ + "sha256:4970c3758f4e89a7857a973b1e2a5d75bcdc47794442f2e2dd4fe8e0466e809a", + "sha256:8a5712cfd3bb4248015eb3b0b3c54a5f6ee3f2425963ef2a0125b8bc40aafaec" + ], + "version": "==0.5.2" + } + } +} diff --git a/Ch08/apd.sensors-chapter08/README.md b/Ch08/apd.sensors-chapter08/README.md new file mode 100644 index 0000000..7f015bf --- /dev/null +++ b/Ch08/apd.sensors-chapter08/README.md @@ -0,0 +1,66 @@ +# Advanced Python Development Sensors + +This is the data collection package that forms part of the running example +for the book [Advanced Python Development](https://advancedpython.dev). + +## Usage + +This installs a console script called `sensors` that returns a report on +various aspects of the system. The available sensors are: + +* Python version +* IP Addresses +* CPU Usage +* RAM Available +* Battery charging state +* Ambient Temperature +* Ambient Humidity + +There are no command-line options, to view the report run `sensors` on the +command line. + +## Caveats + +The Ambient Temperature and Ambient Humidity sensors are only available on +RaspberryPi hosts and assume that a DHT22 sensor is connected to pin `D20`. + +The location and type of the sensor can be controlled by setting a pair of +environment variables: `APD_SENSORS_TEMPERATURE_BOARD` and +`APD_SENSORS_TEMPERATURE_PIN`. + +If there is an entry in `/etc/hosts` for the current machine's hostname that +value will be the only result from the IP Addresses sensor. + +## Installation + +You can install with `pip3 install apd.sensors` under Python 3.7 or higher. + +We recommend using pipenv to manage your environment, in which case you would +install using `pipenv --three install apd.sensors` and run the programme using +`pipenv run sensors`. + +## API server + +There is an optional API server shipped with apd.sensors. To use this you +should install the `apd.sensors[webapp]` extra. The API can then be started +with the non-production quality wsgiref server using: + + python -m apd.sensors.wsgi.serve + +or through Waitress (if installed) using: + + waitress-serve --call apd.sensors.wsgi:set_up_config + +Other WSGI servers will also work, you should use set_up_config as a factory +function. + +An environment variable is required to use the API server, `APD_SENSORS_API_KEY` +should be set to the API key required to gain access. One can be generated +using: + + python -c "import uuid; print(uuid.uuid4().hex)" + +The following endpoints are supported: + +* /v/2.0/sensors +* /v/2.0/sensors/sensorid diff --git a/Ch08/apd.sensors-chapter08/plugins/apd.sunnyboy_solar/pyproject.toml b/Ch08/apd.sensors-chapter08/plugins/apd.sunnyboy_solar/pyproject.toml new file mode 100644 index 0000000..4d3bb67 --- /dev/null +++ b/Ch08/apd.sensors-chapter08/plugins/apd.sunnyboy_solar/pyproject.toml @@ -0,0 +1,5 @@ +[build-system] +requires = [ + "setuptools", +] +build-backend = "setuptools.build_meta" diff --git a/Ch08/apd.sensors-chapter08/plugins/apd.sunnyboy_solar/setup.cfg b/Ch08/apd.sensors-chapter08/plugins/apd.sunnyboy_solar/setup.cfg new file mode 100644 index 0000000..0f5db10 --- /dev/null +++ b/Ch08/apd.sensors-chapter08/plugins/apd.sunnyboy_solar/setup.cfg @@ -0,0 +1,33 @@ +[mypy] +ignore_missing_imports = True + +[flake8] +max-line-length = 88 + +[metadata] +name = apd.sunnyboy_solar +version = attr: apd.sunnyboy_solar.VERSION +description = APD Sensor for reading data from Sunnyboy inverters +long_description = file: README.md, CHANGES.md, LICENCE +long_description_content_type = text/markdown +keywords = iot solar +license = MIT +classifiers = + Programming Language :: Python :: 3 + Programming Language :: Python :: 3.7 + +[options] +zip_safe = False +include_package_data = True +package_dir = + =src +packages = find_namespace: +install_requires = + apd.sensors + +[options.packages.find] +where = src + +[options.entry_points] +apd.sensors.sensors = + SolarCumulativeOutput = apd.sunnyboy_solar.sensor:SolarCumulativeOutput diff --git a/Ch08/apd.sensors-chapter08/plugins/apd.sunnyboy_solar/setup.py b/Ch08/apd.sensors-chapter08/plugins/apd.sunnyboy_solar/setup.py new file mode 100644 index 0000000..6068493 --- /dev/null +++ b/Ch08/apd.sensors-chapter08/plugins/apd.sunnyboy_solar/setup.py @@ -0,0 +1,3 @@ +from setuptools import setup + +setup() diff --git a/Ch08/apd.sensors-chapter08/plugins/apd.sunnyboy_solar/src/apd/sunnyboy_solar/Pipfile b/Ch08/apd.sensors-chapter08/plugins/apd.sunnyboy_solar/src/apd/sunnyboy_solar/Pipfile new file mode 100644 index 0000000..e69de29 diff --git a/Ch08/apd.sensors-chapter08/plugins/apd.sunnyboy_solar/src/apd/sunnyboy_solar/__init__.py b/Ch08/apd.sensors-chapter08/plugins/apd.sunnyboy_solar/src/apd/sunnyboy_solar/__init__.py new file mode 100644 index 0000000..3277f64 --- /dev/null +++ b/Ch08/apd.sensors-chapter08/plugins/apd.sunnyboy_solar/src/apd/sunnyboy_solar/__init__.py @@ -0,0 +1 @@ +VERSION = "1.0.0" diff --git a/Ch08/apd.sensors-chapter08/plugins/apd.sunnyboy_solar/src/apd/sunnyboy_solar/sensor.py b/Ch08/apd.sensors-chapter08/plugins/apd.sunnyboy_solar/src/apd/sunnyboy_solar/sensor.py new file mode 100644 index 0000000..aa37a2e --- /dev/null +++ b/Ch08/apd.sensors-chapter08/plugins/apd.sunnyboy_solar/src/apd/sunnyboy_solar/sensor.py @@ -0,0 +1,69 @@ +import os +import subprocess +import typing as t + +from pint import _DEFAULT_REGISTRY as ureg + +from apd.sensors.base import Sensor + + +class SolarCumulativeOutput(Sensor[t.Optional[t.Any]]): + name = "SolarCumulativeOutput" + title = "Solar panel cumulative output" + + def __init__( + self, path: t.Optional[str] = None, bt_addr: t.Optional[str] = None + ) -> None: + self.path = os.environ.get( + "APD_SUNNYBOYSOLAR_PATH", "/home/pi/opensunny-master/opensunny" + ) + self.bt_addr = os.environ.get("APD_SUNNYBOYSOLAR_BT_ADDRESS", None) + + def value(self) -> t.Optional[t.Any]: + if self.bt_addr is None: + return None + try: + output: bytes = subprocess.check_output( + [self.path, "-i", self.bt_addr], stderr=subprocess.STDOUT, timeout=15 + ) + except (subprocess.CalledProcessError, FileNotFoundError): + return None + + lines = filter(None, output.split(b"\n")) + found = {} + for line in lines: + start, value = line.rsplit(b"=", 1) + start, key = start.rsplit(b" ", 1) + found[key] = value + + try: + yield_total = float(found[b"yield_total"][:-3].replace(b".", b"")) + power_dc_1 = int(found[b"power_dc_1"][:-1]) + power_dc_2 = int(found[b"power_dc_2"][:-1]) + if power_dc_1 > 1500 or power_dc_2 > 1500: + return None + except (ValueError, IndexError, KeyError): + return None + return ureg.Quantity(yield_total, "watt") + + @classmethod + def format(cls, value: t.Optional[t.Any]) -> str: + if value is None: + return "Unknown" + return "{:~P}".format(value.to(ureg.kilowatt)) + + @classmethod + def to_json_compatible( + cls, value: t.Optional[t.Any] + ) -> t.Optional[t.Dict[str, t.Union[str, float]]]: + if value is not None: + return {"magnitude": value.magnitude, "unit": str(value.units)} + else: + return None + + @classmethod + def from_json_compatible(cls, json_version: t.Any) -> t.Optional[t.Any]: + if json_version: + return ureg.Quantity(json_version["magnitude"], ureg[json_version["unit"]]) + else: + return None diff --git a/Ch08/apd.sensors-chapter08/pyproject.toml b/Ch08/apd.sensors-chapter08/pyproject.toml new file mode 100644 index 0000000..4d3bb67 --- /dev/null +++ b/Ch08/apd.sensors-chapter08/pyproject.toml @@ -0,0 +1,5 @@ +[build-system] +requires = [ + "setuptools", +] +build-backend = "setuptools.build_meta" diff --git a/Ch08/apd.sensors-chapter08/pytest.ini b/Ch08/apd.sensors-chapter08/pytest.ini new file mode 100644 index 0000000..861f3d0 --- /dev/null +++ b/Ch08/apd.sensors-chapter08/pytest.ini @@ -0,0 +1,3 @@ +[pytest] +markers = + functional: these tests are significantly slower as they run the whole CLI script \ No newline at end of file diff --git a/Ch08/apd.sensors-chapter08/setup.cfg b/Ch08/apd.sensors-chapter08/setup.cfg new file mode 100644 index 0000000..01be718 --- /dev/null +++ b/Ch08/apd.sensors-chapter08/setup.cfg @@ -0,0 +1,66 @@ +[mypy] +namespace_packages = True +mypy_path = src + +[mypy-psutil] +ignore_missing_imports = True + +[mypy-adafruit_dht] +ignore_missing_imports = True + +[mypy-board] +ignore_missing_imports = True + +[mypy-pint] +ignore_missing_imports = True + +[mypy-pytest] +ignore_missing_imports = True + +[mypy-webtest] +ignore_missing_imports = True + +[flake8] +max-line-length = 88 + +[metadata] +name = apd.sensors +version = attr: apd.sensors.VERSION +description = APD Sensor package +long_description = file: README.md, CHANGES.md, LICENCE +long_description_content_type = text/markdown +keywords = iot +license = MIT +classifiers = + Programming Language :: Python :: 3 + Programming Language :: Python :: 3.7 + +[options] +zip_safe = False +include_package_data = True +package_dir = + =src +packages = find_namespace: +install_requires = + psutil + click + pint + adafruit-circuitpython-dht ; 'arm' in platform_machine + +[options.packages.find] +where = src + +[options.entry_points] +console_scripts = + sensors = apd.sensors.cli:show_sensors +apd.sensors.sensors = + PythonVersion = apd.sensors.sensors:PythonVersion + IPAddresses = apd.sensors.sensors:IPAddresses + CPULoad = apd.sensors.sensors:CPULoad + RAMAvailable = apd.sensors.sensors:RAMAvailable + ACStatus = apd.sensors.sensors:ACStatus + Temperature = apd.sensors.sensors:Temperature + RelativeHumidity = apd.sensors.sensors:RelativeHumidity + +[options.extras_require] +webapp = flask \ No newline at end of file diff --git a/Ch08/apd.sensors-chapter08/setup.py b/Ch08/apd.sensors-chapter08/setup.py new file mode 100644 index 0000000..6068493 --- /dev/null +++ b/Ch08/apd.sensors-chapter08/setup.py @@ -0,0 +1,3 @@ +from setuptools import setup + +setup() diff --git a/Ch08/apd.sensors-chapter08/src/apd/sensors/__init__.py b/Ch08/apd.sensors-chapter08/src/apd/sensors/__init__.py new file mode 100644 index 0000000..2101409 --- /dev/null +++ b/Ch08/apd.sensors-chapter08/src/apd/sensors/__init__.py @@ -0,0 +1 @@ +VERSION = "2.0.0" diff --git a/Ch08/apd.sensors-chapter08/src/apd/sensors/base.py b/Ch08/apd.sensors-chapter08/src/apd/sensors/base.py new file mode 100644 index 0000000..75e7e27 --- /dev/null +++ b/Ch08/apd.sensors-chapter08/src/apd/sensors/base.py @@ -0,0 +1,61 @@ +#!/usr/bin/env python +# coding: utf-8 +import typing as t + + +JSON_0 = t.Union[str, int, float, bool, None] +JSON_1 = t.Union[t.Dict[str, JSON_0], t.Iterable[JSON_0], JSON_0] +JSON_2 = t.Union[t.Dict[str, JSON_1], t.Iterable[JSON_1], JSON_1] +JSON_3 = t.Union[t.Dict[str, JSON_2], t.Iterable[JSON_2], JSON_2] +JSON_4 = t.Union[t.Dict[str, JSON_3], t.Iterable[JSON_3], JSON_3] +JSON_5 = t.Union[t.Dict[str, JSON_4], t.Iterable[JSON_4], JSON_4] +JSON_like = JSON_5 + + +T_value = t.TypeVar("T_value") +JSONT_value = t.TypeVar("JSONT_value", bound=JSON_like) + + +class Sensor(t.Generic[T_value]): + name: str + title: str + + def value(self) -> T_value: + raise NotImplementedError + + @classmethod + def format(cls, value: T_value) -> str: + raise NotImplementedError + + def __str__(self) -> str: + return self.format(self.value()) + + @classmethod + def to_json_compatible(cls, value: T_value) -> JSON_like: + raise NotImplementedError() + + @classmethod + def from_json_compatible(cls, json_version: JSON_like) -> T_value: + raise NotImplementedError() + + +class JSONSensor(Sensor[JSONT_value]): + @classmethod + def to_json_compatible(cls, value: JSONT_value) -> JSONT_value: + return value + + @classmethod + def from_json_compatible(cls, json_version: JSON_like) -> JSONT_value: + return t.cast(JSONT_value, json_version) + + +version_info_type = t.NamedTuple( + "version_info_type", + [ + ("major", int), + ("minor", int), + ("micro", int), + ("releaselevel", str), + ("serial", int), + ], +) diff --git a/Ch08/apd.sensors-chapter08/src/apd/sensors/cli.py b/Ch08/apd.sensors-chapter08/src/apd/sensors/cli.py new file mode 100644 index 0000000..82b7faf --- /dev/null +++ b/Ch08/apd.sensors-chapter08/src/apd/sensors/cli.py @@ -0,0 +1,70 @@ +import importlib +import pkg_resources +import typing as t + +import click + +from .sensors import Sensor + + +RETURN_CODES = {"OK": 0, "BAD_SENSOR_PATH": 17} + + +def get_sensor_by_path(sensor_path: str) -> Sensor[t.Any]: + try: + module_name, sensor_name = sensor_path.split(":") + except ValueError: + raise RuntimeError( + "Sensor path must be in the format dotted.path.to.module:ClassName" + ) + try: + module = importlib.import_module(module_name) + except ImportError: + raise RuntimeError(f"Could not import module {module_name}") + try: + sensor_class = getattr(module, sensor_name) + except AttributeError: + raise RuntimeError(f"Could not find attribute {sensor_name} in {module_name}") + if ( + isinstance(sensor_class, type) + and issubclass(sensor_class, Sensor) + and sensor_class != Sensor + ): + return sensor_class() + else: + raise RuntimeError( + f"Detected object {sensor_class!r} is not recognised as a Sensor type" + ) + + +def get_sensors() -> t.Iterable[Sensor[t.Any]]: + sensors = [] + for sensor_class in pkg_resources.iter_entry_points("apd.sensors.sensors"): + class_ = sensor_class.load() + sensors.append(t.cast(Sensor[t.Any], class_())) + return sensors + + +@click.command(help="Displays the values of the sensors") +@click.option( + "--develop", required=False, metavar="path", help="Load a sensor by Python path" +) +def show_sensors(develop: str) -> int: + sensors: t.Iterable[Sensor[t.Any]] + if develop: + try: + sensors = [get_sensor_by_path(develop)] + except RuntimeError as error: + click.secho(str(error), fg="red", bold=True) + return RETURN_CODES["BAD_SENSOR_PATH"] + else: + sensors = get_sensors() + for sensor in sensors: + click.secho(sensor.title, bold=True) + click.echo(str(sensor)) + click.echo("") + return RETURN_CODES["OK"] + + +if __name__ == "__main__": + show_sensors() diff --git a/Ch08/apd.sensors-chapter08/src/apd/sensors/py.typed b/Ch08/apd.sensors-chapter08/src/apd/sensors/py.typed new file mode 100644 index 0000000..e69de29 diff --git a/Ch08/apd.sensors-chapter08/src/apd/sensors/sensors.py b/Ch08/apd.sensors-chapter08/src/apd/sensors/sensors.py new file mode 100644 index 0000000..caccbae --- /dev/null +++ b/Ch08/apd.sensors-chapter08/src/apd/sensors/sensors.py @@ -0,0 +1,196 @@ +#!/usr/bin/env python +# coding: utf-8 +import math +import os +import socket +import sys +import typing as t + +import psutil +from pint import _DEFAULT_REGISTRY as ureg + +from .base import Sensor, JSONSensor, version_info_type + + +class PythonVersion(JSONSensor[version_info_type]): + name = "PythonVersion" + title = "Python Version" + + def value(self) -> version_info_type: + return version_info_type(*sys.version_info) + + @classmethod + def from_json_compatible(cls, json_version: t.Any) -> version_info_type: + return version_info_type(*json_version) + + @classmethod + def format(cls, value: version_info_type) -> str: + if value.micro == 0 and value.releaselevel == "alpha": + return "{0.major}.{0.minor}.{0.micro}a{0.serial}".format(value) + return "{0.major}.{0.minor}".format(value) + + +class IPAddresses(JSONSensor[t.Iterable[t.Tuple[str, str]]]): + name = "IPAddresses" + title = "IP Addresses" + FAMILIES = {"AF_INET": "IPv4", "AF_INET6": "IPv6"} + + def value(self) -> t.List[t.Tuple[str, str]]: + hostname = socket.gethostname() + addresses = socket.getaddrinfo(hostname, None) + + address_info: t.List[t.Tuple[str, str]] = [] + for address in addresses: + family, ip = (address[0].name, address[4][0]) + if family not in self.FAMILIES: + continue + value = (family, ip) + if value not in address_info: + address_info.append(value) + return address_info + + @classmethod + def format(cls, value: t.Iterable[t.Tuple[str, str]]) -> str: + return "\n".join( + "{0} ({1})".format(address[1], cls.FAMILIES.get(address[0], "Unknown")) + for address in value + ) + + +class CPULoad(JSONSensor[float]): + name = "CPULoad" + title = "CPU Usage" + + def value(self) -> float: + return float(psutil.cpu_percent(interval=3)) / 100.0 + + @classmethod + def format(cls, value: float) -> str: + return "{:.1%}".format(value) + + +class RAMAvailable(JSONSensor[int]): + name = "RAMAvailable" + title = "RAM Available" + UNITS = ("B", "KiB", "MiB", "GiB", "TiB", "PiB", "EiB") + UNIT_SIZE = 2 ** 10 + + def value(self) -> int: + return int(psutil.virtual_memory().available) + + @classmethod + def format(cls, value: int) -> str: + magnitude = math.floor(math.log(value, cls.UNIT_SIZE)) + max_magnitude = len(cls.UNITS) - 1 + magnitude = min(magnitude, max_magnitude) + scaled_value = value / (cls.UNIT_SIZE ** magnitude) + return "{:.1f} {}".format(scaled_value, cls.UNITS[magnitude]) + + +class ACStatus(JSONSensor[t.Optional[bool]]): + name = "ACStatus" + title = "AC Connected" + + def value(self) -> t.Optional[bool]: + battery = psutil.sensors_battery() + if battery is not None: + value = battery.power_plugged + if value is None: + return None + else: + return bool(value) + else: + return None + + @classmethod + def format(cls, value: t.Optional[bool]) -> str: + if value is None: + return "Unknown" + elif value: + return "Connected" + else: + return "Not connected" + + +class Temperature(Sensor[t.Optional[t.Any]]): + name = "Temperature" + title = "Ambient Temperature" + + def __init__(self) -> None: + self.board = os.environ.get("APD_SENSORS_TEMPERATURE_BOARD", "DHT22") + self.pin = os.environ.get("APD_SENSORS_TEMPERATURE_PIN", "D20") + + def value(self) -> t.Optional[t.Any]: + try: + import adafruit_dht + import board + + sensor_type = getattr(adafruit_dht, self.board) + pin = getattr(board, self.pin) + except (ImportError, NotImplementedError, AttributeError): + # No DHT library results in an ImportError. + # Running on an unknown platform results in a + # NotImplementedError when getting the pin + return None + try: + return ureg.Quantity(sensor_type(pin).temperature, ureg.celsius) + except RuntimeError: + return None + + @classmethod + def format(cls, value: t.Optional[t.Any]) -> str: + if value is None: + return "Unknown" + else: + return "{:.3~P} ({:.3~P})".format(value, value.to(ureg.fahrenheit)) + + @classmethod + def to_json_compatible(cls, value: t.Optional[t.Any]) -> t.Any: + if value is not None: + return {"magnitude": value.magnitude, "unit": str(value.units)} + else: + return None + + @classmethod + def from_json_compatible(cls, json_version: t.Any) -> t.Optional[t.Any]: + if json_version: + return ureg.Quantity(json_version["magnitude"], ureg[json_version["unit"]]) + else: + return None + + def __str__(self) -> str: + return self.format(self.value()) + + +class RelativeHumidity(JSONSensor[t.Optional[float]]): + name = "RelativeHumidity" + title = "Relative Humidity" + + def __init__(self) -> None: + self.board = os.environ.get("APD_SENSORS_TEMPERATURE_BOARD", "DHT22") + self.pin = os.environ.get("APD_SENSORS_TEMPERATURE_PIN", "D20") + + def value(self) -> t.Optional[float]: + try: + import adafruit_dht + import board + + sensor_type = getattr(adafruit_dht, self.board) + pin = getattr(board, self.pin) + except (ImportError, NotImplementedError, AttributeError): + # No DHT library results in an ImportError. + # Running on an unknown platform results in a + # NotImplementedError when getting the pin + return None + + try: + return float(sensor_type(pin).humidity) + except RuntimeError: + return None + + @classmethod + def format(cls, value: t.Optional[float]) -> str: + if value is None: + return "Unknown" + else: + return "{:.1%}".format(value) diff --git a/Ch08/apd.sensors-chapter08/src/apd/sensors/wsgi/__init__.py b/Ch08/apd.sensors-chapter08/src/apd/sensors/wsgi/__init__.py new file mode 100644 index 0000000..b279326 --- /dev/null +++ b/Ch08/apd.sensors-chapter08/src/apd/sensors/wsgi/__init__.py @@ -0,0 +1,14 @@ +import flask + +from .base import set_up_config +from . import v10 +from . import v20 +from . import v21 + + +__all__ = ["app", "set_up_config"] + +app = flask.Flask(__name__) +app.register_blueprint(v10.version, url_prefix="/v/1.0") +app.register_blueprint(v20.version, url_prefix="/v/2.0") +app.register_blueprint(v21.version, url_prefix="/v/2.1") diff --git a/Ch08/apd.sensors-chapter08/src/apd/sensors/wsgi/base.py b/Ch08/apd.sensors-chapter08/src/apd/sensors/wsgi/base.py new file mode 100644 index 0000000..3224000 --- /dev/null +++ b/Ch08/apd.sensors-chapter08/src/apd/sensors/wsgi/base.py @@ -0,0 +1,43 @@ +from hmac import compare_digest +import functools +import os +import typing as t + +import flask + + +ViewFuncReturn = t.TypeVar("ViewFuncReturn") +ErrorReturn = t.Tuple[t.Dict[str, str], int] +REQUIRED_CONFIG_KEYS = {"APD_SENSORS_API_KEY", "APD_SENSORS_DEPLOYMENT_ID"} + + +def require_api_key( + func: t.Callable[[], ViewFuncReturn] +) -> t.Callable[[], t.Union[ViewFuncReturn, ErrorReturn]]: + @functools.wraps(func) + def wrapped() -> t.Union[ViewFuncReturn, ErrorReturn]: + api_key = flask.current_app.config["APD_SENSORS_API_KEY"] + headers = flask.request.headers + supplied_key = headers.get("X-API-Key", "") + if not compare_digest(api_key, supplied_key): + return {"error": "Supply API key in X-API-Key header"}, 403 + return func() + + return wrapped + + +def set_up_config( + environ: t.Optional[t.Dict[str, str]] = None, + to_configure: t.Optional[flask.Flask] = None, +) -> flask.Flask: + if environ is None: + environ = dict(os.environ) + if to_configure is None: + from apd.sensors.wsgi import app + + to_configure = app + missing_keys = REQUIRED_CONFIG_KEYS - environ.keys() + if missing_keys: + raise ValueError("Missing config variables: {}".format(", ".join(missing_keys))) + to_configure.config.from_mapping(environ) + return to_configure diff --git a/Ch08/apd.sensors-chapter08/src/apd/sensors/wsgi/serve.py b/Ch08/apd.sensors-chapter08/src/apd/sensors/wsgi/serve.py new file mode 100644 index 0000000..0b1f684 --- /dev/null +++ b/Ch08/apd.sensors-chapter08/src/apd/sensors/wsgi/serve.py @@ -0,0 +1,10 @@ +from . import app +from .base import set_up_config + +if __name__ == "__main__": + import wsgiref.simple_server + + set_up_config(None, app) + + with wsgiref.simple_server.make_server("", 8000, app) as server: + server.serve_forever() diff --git a/Ch08/apd.sensors-chapter08/src/apd/sensors/wsgi/v10.py b/Ch08/apd.sensors-chapter08/src/apd/sensors/wsgi/v10.py new file mode 100644 index 0000000..7eee6dd --- /dev/null +++ b/Ch08/apd.sensors-chapter08/src/apd/sensors/wsgi/v10.py @@ -0,0 +1,26 @@ +import json +import typing as t + +import flask + +from apd.sensors.cli import get_sensors +from .base import require_api_key + +version = flask.Blueprint(__name__, __name__) + + +@version.route("/sensors/") +@require_api_key +def sensor_values() -> t.Tuple[t.Dict[str, t.Any], int, t.Dict[str, str]]: + headers = {"Content-Security-Policy": "default-src 'none'"} + data = {} + for sensor in get_sensors(): + value = sensor.value() + try: + json.dumps(value) + except TypeError: + # This value isn't JSON serializable, skip it + continue + else: + data[sensor.title] = sensor.value() + return data, 200, headers diff --git a/Ch08/apd.sensors-chapter08/src/apd/sensors/wsgi/v20.py b/Ch08/apd.sensors-chapter08/src/apd/sensors/wsgi/v20.py new file mode 100644 index 0000000..fe7688f --- /dev/null +++ b/Ch08/apd.sensors-chapter08/src/apd/sensors/wsgi/v20.py @@ -0,0 +1,32 @@ +import typing as t + +import flask + +from apd.sensors import cli +from .base import require_api_key + +version = flask.Blueprint(__name__, __name__) + + +@version.route("/sensors/") +@version.route("/sensors/") +@require_api_key +def sensor_values(sensor_id=None) -> t.Tuple[t.Dict[str, t.Any], int, t.Dict[str, str]]: + headers = {"Content-Security-Policy": "default-src 'none'"} + sensors = [] + for sensor in cli.get_sensors(): + if sensor_id and sensor_id != sensor.name: + continue + try: + value = sensor.value() + sensor_data = { + "id": sensor.name, + "title": sensor.title, + "value": sensor.to_json_compatible(value), + "human_readable": sensor.format(value), + } + sensors.append(sensor_data) + except NotImplementedError: + pass + data = {"sensors": sensors} + return data, 200, headers diff --git a/Ch08/apd.sensors-chapter08/src/apd/sensors/wsgi/v21.py b/Ch08/apd.sensors-chapter08/src/apd/sensors/wsgi/v21.py new file mode 100644 index 0000000..9709a54 --- /dev/null +++ b/Ch08/apd.sensors-chapter08/src/apd/sensors/wsgi/v21.py @@ -0,0 +1,39 @@ +import typing as t + +import flask + +from apd.sensors import cli +from .base import require_api_key + +version = flask.Blueprint(__name__, __name__) + + +@version.route("/sensors/") +@version.route("/sensors/") +@require_api_key +def sensor_values(sensor_id=None) -> t.Tuple[t.Dict[str, t.Any], int, t.Dict[str, str]]: + headers = {"Content-Security-Policy": "default-src 'none'"} + sensors = [] + for sensor in cli.get_sensors(): + if sensor_id and sensor_id != sensor.name: + continue + try: + value = sensor.value() + sensor_data = { + "id": sensor.name, + "title": sensor.title, + "value": sensor.to_json_compatible(value), + "human_readable": sensor.format(value), + } + sensors.append(sensor_data) + except NotImplementedError: + pass + data = {"sensors": sensors} + return data, 200, headers + + +@version.route("/deployment_id") +def deployment_id() -> t.Tuple[t.Dict[str, t.Any], int, t.Dict[str, str]]: + headers = {"Content-Security-Policy": "default-src 'none'"} + data = {"deployment_id": flask.current_app.config["APD_SENSORS_DEPLOYMENT_ID"]} + return data, 200, headers diff --git a/Ch08/apd.sensors-chapter08/tests/__init__.py b/Ch08/apd.sensors-chapter08/tests/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/Ch08/apd.sensors-chapter08/tests/test_acstatus.py b/Ch08/apd.sensors-chapter08/tests/test_acstatus.py new file mode 100644 index 0000000..1d10940 --- /dev/null +++ b/Ch08/apd.sensors-chapter08/tests/test_acstatus.py @@ -0,0 +1,55 @@ +from unittest import mock + +from apd.sensors.sensors import ACStatus + +import pytest + + +@pytest.fixture +def sensor(): + return ACStatus() + + +class TestACStatusFormatter: + @pytest.fixture + def subject(self, sensor): + return sensor.format + + def test_format_True(self, subject): + assert subject(True) == "Connected" + + def test_format_False(self, subject): + assert subject(False) == "Not connected" + + def test_format_None(self, subject): + assert subject(None) == "Unknown" + + +class TestACStatusValue: + @pytest.fixture + def subject(self, sensor): + return sensor.value + + @pytest.fixture + def sensors_battery(self): + with mock.patch("psutil.sensors_battery") as sensors_battery: + yield sensors_battery + + def test_sensors_battery_called(self, subject, sensors_battery): + sensors_battery.return_value.power_plugged = True + assert subject() is True + assert sensors_battery.call_count == 1 + + def test_sensor_but_battery_unknown(self, subject, sensors_battery): + sensors_battery.return_value.power_plugged = None + assert subject() is None + assert sensors_battery.call_count == 1 + + def test_no_sensor(self, subject, sensors_battery): + sensors_battery.return_value = None + assert subject() is None + + def test_str_representation_is_formatted_value(self, sensor, sensors_battery): + sensors_battery.return_value.power_plugged = True + assert str(sensor) == "Connected" + assert sensors_battery.call_count == 1 diff --git a/Ch08/apd.sensors-chapter08/tests/test_api_server.py b/Ch08/apd.sensors-chapter08/tests/test_api_server.py new file mode 100644 index 0000000..7cac7ed --- /dev/null +++ b/Ch08/apd.sensors-chapter08/tests/test_api_server.py @@ -0,0 +1,148 @@ +import os +import uuid +from unittest import mock + +import flask +import pytest +from webtest import TestApp + +from apd.sensors.wsgi import set_up_config +from apd.sensors.sensors import PythonVersion +from apd.sensors.wsgi import v10 +from apd.sensors.wsgi import v20 +from apd.sensors.wsgi import v21 + + +@pytest.fixture(scope="session") +def api_key(): + return uuid.uuid4().hex + + +def test_api_key_is_required_config_option(): + app = mock.MagicMock() + with pytest.raises( + ValueError, match="Missing config variables: APD_SENSORS_API_KEY" + ): + set_up_config({"APD_SENSORS_DEPLOYMENT_ID": ""}, to_configure=app) + + +def test_deployment_id_is_required_config_option(): + app = mock.MagicMock() + with pytest.raises( + ValueError, match="Missing config variables: APD_SENSORS_DEPLOYMENT_ID" + ): + set_up_config({"APD_SENSORS_API_KEY": ""}, to_configure=app) + + +def test_os_environ_is_default_for_config_values(api_key): + app = mock.MagicMock() + os.environ["APD_SENSORS_API_KEY"] = api_key + os.environ["APD_SENSORS_DEPLOYMENT_ID"] = "8f1b57faa04b430c81decbbeee9e300c" + try: + assert app.config.from_mapping.call_count == 0 + set_up_config(None, to_configure=app) + assert app.config.from_mapping.call_count == 1 + assert app.config.from_mapping.call_args[0][0] == os.environ + finally: + del os.environ["APD_SENSORS_API_KEY"] + del os.environ["APD_SENSORS_DEPLOYMENT_ID"] + + +class CommonTests: + @pytest.mark.functional + def test_sensor_values_fails_on_missing_api_key(self, api_server): + response = api_server.get("/sensors/", expect_errors=True) + assert response.status_code == 403 + assert response.json["error"] == "Supply API key in X-API-Key header" + + @pytest.mark.functional + def test_sensor_values_require_correct_api_key(self, api_server): + response = api_server.get( + "/sensors/", headers={"X-API-Key": "wrong_key"}, expect_errors=True + ) + assert response.status_code == 403 + assert response.json["error"] == "Supply API key in X-API-Key header" + + +class Testv10API(CommonTests): + @pytest.fixture + def subject(self, api_key): + app = flask.Flask("testapp") + app.register_blueprint(v10.version) + set_up_config( + { + "APD_SENSORS_API_KEY": api_key, + "APD_SENSORS_DEPLOYMENT_ID": "8f1b57faa04b430c81decbbeee9e300c", + }, + to_configure=app, + ) + return app + + @pytest.fixture + def api_server(self, subject): + return TestApp(subject) + + @pytest.mark.functional + def test_sensor_values_returned_as_json(self, api_server, api_key): + value = api_server.get("/sensors/", headers={"X-API-Key": api_key}).json + python_version = PythonVersion().value() + + sensor_names = value.keys() + assert "Python Version" in sensor_names + assert value["Python Version"] == list(python_version) + + @pytest.mark.functional + def test_unserializable_sensor_is_omitted(self, api_server, api_key): + value = api_server.get("/sensors/", headers={"X-API-Key": api_key}).json + sensor_names = value.keys() + assert "Temperature" not in sensor_names + + +class Testv20API(CommonTests): + @pytest.fixture + def subject(self, api_key): + app = flask.Flask("testapp") + app.register_blueprint(v20.version) + set_up_config( + { + "APD_SENSORS_API_KEY": api_key, + "APD_SENSORS_DEPLOYMENT_ID": "8f1b57faa04b430c81decbbeee9e300c", + }, + to_configure=app, + ) + return app + + @pytest.fixture + def api_server(self, subject): + return TestApp(subject) + + @pytest.mark.functional + def test_sensor_values_returned_as_json(self, api_server, api_key): + value = api_server.get("/sensors/", headers={"X-API-Key": api_key}).json + sensors = value["sensors"] + + python_version = [ + sensor for sensor in sensors if sensor["id"] == "PythonVersion" + ][0] + assert python_version["title"] == "Python Version" + assert python_version["value"] == list(PythonVersion().value()) + + +class Testv21API(Testv20API): + @pytest.fixture + def subject(self, api_key): + app = flask.Flask("testapp") + app.register_blueprint(v21.version) + set_up_config( + { + "APD_SENSORS_API_KEY": api_key, + "APD_SENSORS_DEPLOYMENT_ID": "8f1b57faa04b430c81decbbeee9e300c", + }, + to_configure=app, + ) + return app + + @pytest.mark.functional + def test_deployment_id(self, api_server, api_key): + value = api_server.get("/deployment_id", headers={"X-API-Key": api_key}).json + assert value == {"deployment_id": "8f1b57faa04b430c81decbbeee9e300c"} diff --git a/Ch08/apd.sensors-chapter08/tests/test_cpuusage.py b/Ch08/apd.sensors-chapter08/tests/test_cpuusage.py new file mode 100644 index 0000000..aa8a66a --- /dev/null +++ b/Ch08/apd.sensors-chapter08/tests/test_cpuusage.py @@ -0,0 +1,45 @@ +from unittest import mock + +from apd.sensors.sensors import CPULoad + +import pytest + + +@pytest.fixture +def sensor(): + return CPULoad() + + +class TestCPULoadFormatter: + @pytest.fixture + def subject(self, sensor): + return sensor.format + + def test_format_simple_percentage(self, subject): + assert subject(0.05) == "5.0%" + + def test_format_multiple_decimal_places(self, subject): + assert subject(0.031415926) == "3.1%" + + def test_format_multiple_decimal_places_int(self, subject) -> None: + assert subject(1) == "100.0%" + + +class TestCPULoadValue: + @pytest.fixture + def subject(self, sensor): + return sensor.value + + @pytest.fixture + def cpuload(self): + with mock.patch("psutil.cpu_percent") as cpu_percent: + cpu_percent.return_value = 50.1 + yield cpu_percent + + def test_cpu_percent_called_With_interval(self, subject, cpuload): + assert subject() == 0.501 + assert cpuload.call_count == 1 + assert tuple(cpuload.call_args) == ((), {"interval": 3}) + + def test_str_representation_is_formatted_value(self, sensor, cpuload): + assert str(sensor) == "50.1%" diff --git a/Ch08/apd.sensors-chapter08/tests/test_dht.py b/Ch08/apd.sensors-chapter08/tests/test_dht.py new file mode 100644 index 0000000..4c2126e --- /dev/null +++ b/Ch08/apd.sensors-chapter08/tests/test_dht.py @@ -0,0 +1,67 @@ +from apd.sensors.sensors import Temperature, RelativeHumidity, ureg + +import pytest + + +@pytest.fixture +def temperature_sensor(): + return Temperature() + + +@pytest.fixture +def humidity_sensor(): + return RelativeHumidity() + + +class TestTemperatureFormatter: + @pytest.fixture + def subject(self, temperature_sensor): + return temperature_sensor.format + + @staticmethod + def to_degc(magnitude): + return ureg.Quantity(magnitude, "degC") + + def test_format_21c(self, subject): + assert subject(self.to_degc(21.0)) == "21.0 celsius (69.8 fahrenheit)" + + def test_format_negative(self, subject): + assert subject(self.to_degc(-32.0)) == "-32.0 celsius (-25.6 fahrenheit)" + + def test_format_unknown(self, subject): + assert subject(None) == "Unknown" + + +class TestTemperatureSerializer: + @pytest.fixture + def serialize(self, temperature_sensor): + return temperature_sensor.to_json_compatible + + @pytest.fixture + def deserialize(self, temperature_sensor): + return temperature_sensor.from_json_compatible + + @staticmethod + def to_degc(magnitude): + return ureg.Quantity(magnitude, "degC") + + def test_serialize_21c(self, serialize): + assert serialize(self.to_degc(21.0)) == {"magnitude": 21.0, "unit": "degC"} + + def test_serialize_negative(self, serialize): + assert serialize(self.to_degc(-32.3)) == {"magnitude": -32.3, "unit": "degC"} + + def test_deserialize_21c(self, deserialize): + assert deserialize({"magnitude": 21.0, "unit": "degC"}) == self.to_degc(21.0) + + +class TestHumidityFormatter: + @pytest.fixture + def subject(self, humidity_sensor): + return humidity_sensor.format + + def test_format_percentage(self, subject): + assert subject(0.035) == "3.5%" + + def test_format_unknown(self, subject): + assert subject(None) == "Unknown" diff --git a/Ch08/apd.sensors-chapter08/tests/test_ipaddresses.py b/Ch08/apd.sensors-chapter08/tests/test_ipaddresses.py new file mode 100644 index 0000000..e8a9690 --- /dev/null +++ b/Ch08/apd.sensors-chapter08/tests/test_ipaddresses.py @@ -0,0 +1,59 @@ +from unittest import mock +import socket + +from apd.sensors.sensors import IPAddresses + +import pytest + + +@pytest.fixture +def sensor(): + return IPAddresses() + + +class TestIPAddressFormatter: + @pytest.fixture + def subject(self, sensor): + return sensor.format + + def test_format_single_ipv4(self, subject): + ips = [("AF_INET", "192.0.2.1")] + assert subject(ips) == "192.0.2.1 (IPv4)" + + def test_format_single_ipv6(self, subject): + ips = [("AF_INET6", "2001:DB8::1")] + assert subject(ips) == "2001:DB8::1 (IPv6)" + + def test_format_mixed_list(self, subject): + ips = [("AF_INET", "192.0.2.1"), ("AF_INET6", "2001:DB8::1")] + assert subject(ips) == "192.0.2.1 (IPv4)\n2001:DB8::1 (IPv6)" + + def test_unusual_protocols_are_marked_as_unknown(self, subject): + ips = [("AF_IRDA", "ffff")] + assert subject(ips) == "ffff (Unknown)" + + +class TestIPAddressesValue: + @pytest.fixture + def subject(self, sensor): + return sensor.value + + @pytest.fixture + def gethostname(self): + with mock.patch("socket.gethostname") as gethostname: + gethostname.return_value = "localhost" + yield gethostname + + @pytest.fixture + def getaddrinfo(self, gethostname): + with mock.patch("socket.getaddrinfo") as getaddrinfo: + yield getaddrinfo + + def test_getaddrinfo_used_for_value_collection(self, getaddrinfo, subject): + getaddrinfo.return_value = [(socket.AF_INET, 0, 0, "", ("192.0.2.1", 0))] + assert subject() == [("AF_INET", "192.0.2.1")] + assert getaddrinfo.call_count == 1 + + def test_str_representation_is_formatted_value(self, getaddrinfo, sensor): + getaddrinfo.return_value = [(socket.AF_INET6, 0, 0, "", ("2001:DB8::1", 0))] + assert str(sensor) == "2001:DB8::1 (IPv6)" diff --git a/Ch08/apd.sensors-chapter08/tests/test_pythonversion.py b/Ch08/apd.sensors-chapter08/tests/test_pythonversion.py new file mode 100644 index 0000000..dff6bb0 --- /dev/null +++ b/Ch08/apd.sensors-chapter08/tests/test_pythonversion.py @@ -0,0 +1,61 @@ +from collections import namedtuple +from unittest import mock + +from apd.sensors.sensors import PythonVersion + +import pytest + + +@pytest.fixture +def version(): + return namedtuple( + "sys_versioninfo", ("major", "minor", "micro", "releaselevel", "serial") + ) + + +@pytest.fixture +def sensor(): + return PythonVersion() + + +class TestPythonVersionFormatter: + @pytest.fixture + def subject(self, sensor): + return sensor.format + + def test_format_py38(self, subject, version): + py38 = version(3, 8, 0, "final", 0) + assert subject(py38) == "3.8" + + def test_format_large_version(self, subject, version): + large = version(255, 128, 0, "final", 0) + assert subject(large) == "255.128" + + def test_alpha_of_minor_is_marked(self, subject, version): + py39 = version(3, 9, 0, "alpha", 1) + assert subject(py39) == "3.9.0a1" + + def test_alpha_of_micro_is_unmarked(self, subject, version): + py39 = version(3, 9, 1, "alpha", 1) + assert subject(py39) == "3.9" + + +class TestPythonVersionValue: + @pytest.fixture + def subject(self, sensor): + return sensor.value + + @pytest.fixture + def python_version(self): + import sys + + return sys.version_info + + def test_data_value_is_sys_versioninfo(self, python_version, subject): + assert subject() == python_version + + +class TestPythonVersionSensor: + def test_str_representation_is_formatted_value(self, sensor, version): + with mock.patch("sys.version_info", new=version(3, 4, 1, "final", 1)): + assert str(sensor) == "3.4" diff --git a/Ch08/apd.sensors-chapter08/tests/test_ramusage.py b/Ch08/apd.sensors-chapter08/tests/test_ramusage.py new file mode 100644 index 0000000..2a368c1 --- /dev/null +++ b/Ch08/apd.sensors-chapter08/tests/test_ramusage.py @@ -0,0 +1,47 @@ +from unittest import mock + +from apd.sensors.sensors import RAMAvailable + +import pytest + + +@pytest.fixture +def sensor(): + return RAMAvailable() + + +class TestRAMAvailableFormatter: + @pytest.fixture + def subject(self, sensor): + return sensor.format + + def test_format_bytes(self, subject): + assert subject(15) == "15.0 B" + + def test_format_kibibytes(self, subject): + assert subject(15000) == "14.6 KiB" + + def test_format_mibibytes(self, subject): + assert subject(15000000) == "14.3 MiB" + + def test_format_gibibytes(self, subject): + assert subject(15000000000) == "14.0 GiB" + + +class TestRAMAvailableValue: + @pytest.fixture + def subject(self, sensor): + return sensor.value + + @pytest.fixture + def virtual_memory(self): + with mock.patch("psutil.virtual_memory") as virtual_memory: + virtual_memory.return_value.available = 1024 + yield virtual_memory + + def test_virtual_memory_called(self, subject, virtual_memory): + assert subject() == 1024 + assert virtual_memory.call_count == 1 + + def test_str_representation_is_formatted_value(self, sensor, virtual_memory): + assert str(sensor) == "1.0 KiB" diff --git a/Ch08/apd.sensors-chapter08/tests/test_sensors.py b/Ch08/apd.sensors-chapter08/tests/test_sensors.py new file mode 100644 index 0000000..d6ca988 --- /dev/null +++ b/Ch08/apd.sensors-chapter08/tests/test_sensors.py @@ -0,0 +1,87 @@ +import json +from unittest import mock + +from click.testing import CliRunner +import pytest + +import apd.sensors.cli +import apd.sensors.sensors + + +def test_sensors(): + assert hasattr(apd.sensors.sensors, "PythonVersion") + + +@pytest.mark.functional +def test_first_sensor_is_first_two_lines_of_cli_output(): + runner = CliRunner() + with mock.patch("apd.sensors.cli.get_sensors") as get_sensors: + get_sensors.return_value = [apd.sensors.sensors.PythonVersion()] + result = runner.invoke(apd.sensors.cli.show_sensors) + python_version = str(apd.sensors.sensors.PythonVersion()) + assert ["Python Version", python_version] == result.stdout.split("\n")[:2] + + +class TestSensorFromPath: + @pytest.fixture + def subject(self): + return apd.sensors.cli.get_sensor_by_path + + def test_get_sensor_by_path(self, subject): + assert isinstance( + subject("apd.sensors.sensors:PythonVersion"), + apd.sensors.sensors.PythonVersion, + ) + + def test_invalid_format(self, subject): + with pytest.raises(RuntimeError, match="dotted.path.to.module:ClassName"): + subject("apd.sensors.sensors.PythonVersion") + + def test_invalid_module(self, subject): + with pytest.raises(RuntimeError, match="Could not import module"): + subject("apd.nonsense.sensor:FakeSensor") + + def test_missing_sensor(self, subject): + with pytest.raises(RuntimeError, match="Could not find attribute"): + subject("apd.sensors.sensors:FakeSensor") + + def test_invalid_sensor(self, subject): + with pytest.raises(RuntimeError, match="is not recognised as a Sensor"): + subject("apd.sensors.sensors:Sensor") + + +@pytest.mark.functional +def test_develop_option_finds_sensor_by_path(): + runner = CliRunner() + result = runner.invoke( + apd.sensors.cli.show_sensors, ["--develop", "apd.sensors.sensors:PythonVersion"] + ) + python_version = str(apd.sensors.sensors.PythonVersion()) + assert ["Python Version", python_version, "", ""] == result.stdout.split("\n") + + +class TestDefaultSerializer: + @pytest.fixture + def python_version(self): + return apd.sensors.sensors.PythonVersion() + + @pytest.fixture + def serialize(self, python_version): + return python_version.to_json_compatible + + @pytest.fixture + def deserialize(self, python_version): + return python_version.from_json_compatible + + def test_serialize_deserialize_is_symmetric( + self, python_version, serialize, deserialize + ): + value = python_version.value() + serialized = serialize(value) + json_version = json.dumps(serialized) + assert ( + json_version == f"[{value.major}, {value.minor}, {value.micro}," + f' "{value.releaselevel}", {value.serial}]' + ) + deserialized = deserialize(serialized) + assert deserialized == value diff --git a/Ch08/listing08-01-httpfixture.py b/Ch08/listing08-01-httpfixture.py new file mode 100644 index 0000000..068ca10 --- /dev/null +++ b/Ch08/listing08-01-httpfixture.py @@ -0,0 +1,45 @@ +from concurrent.futures import ThreadPoolExecutor +import typing as t +import wsgiref.simple_server + +import flask +import pytest + +from apd.sensors.wsgi import v20 +from apd.sensors.wsgi import set_up_config + + +def get_independent_flask_app(name: str) -> flask.Flask: + """ Create a new flask app with the v20 API blueprint loaded, so multiple copies + of the app can be run in parallel without conflicting configuration """ + app = flask.Flask(name) + app.register_blueprint(v20.version, url_prefix="/v/2.0") + return app + + +def run_server_in_thread( + name: str, config: t.Dict[str, t.Any], port: int +) -> t.Iterator[str]: + # Create a new flask app and load in required code, to prevent config conflicts + app = get_independent_flask_app(name) + flask_app = set_up_config(config, app) + server = wsgiref.simple_server.make_server("localhost", port, flask_app) + + with ThreadPoolExecutor() as pool: + pool.submit(server.serve_forever) + yield f"http://localhost:{port}/" + server.shutdown() + + +@pytest.fixture(scope="session") +def http_server(): + yield from run_server_in_thread( + "standard", {"APD_SENSORS_API_KEY": "testing"}, 12081 + ) + + +def test_http(http_server): + import requests + + response = requests.get(http_server + "v/2.0/sensors") + assert response.status_code == 403 diff --git a/Ch08/listing08-02-config_fixture.py b/Ch08/listing08-02-config_fixture.py new file mode 100644 index 0000000..110b166 --- /dev/null +++ b/Ch08/listing08-02-config_fixture.py @@ -0,0 +1,61 @@ +from concurrent.futures import ThreadPoolExecutor +import copy +import typing as t +import wsgiref.simple_server + +import flask +import pytest + +from apd.sensors.wsgi import v20 +from apd.sensors.wsgi import set_up_config + + +def get_independent_flask_app(name: str) -> flask.Flask: + """ Create a new flask app with the v20 API blueprint loaded, so multiple copies + of the app can be run in parallel without conflicting configuration """ + app = flask.Flask(name) + app.register_blueprint(v20.version, url_prefix="/v/2.0") + return app + + +def run_server_in_thread( + name: str, config: t.Dict[str, t.Any], port: int +) -> t.Iterator[str]: + # Create a new flask app and load in required code, to prevent config conflicts + app = get_independent_flask_app(name) + flask_app = set_up_config(config, app) + server = wsgiref.simple_server.make_server("localhost", port, flask_app) + + with ThreadPoolExecutor() as pool: + pool.submit(server.serve_forever) + yield f"http://localhost:{port}/" + server.shutdown() + + +@pytest.fixture(scope="session") +def config_defaults(): + return { + "APD_SENSORS_API_KEY": "testing", + "APD_SOME_VALUE": "example", + "APD_OTHER_THING": "off" + } + +@pytest.fixture(scope="session") +def http_server(config_defaults): + config = copy.copy(config_defaults) + yield from run_server_in_thread("standard", config, 12081) + +@pytest.fixture(scope="session") +def bad_api_key_http_server(config_defaults): + config = copy.copy(config_defaults) + config["APD_SENSORS_API_KEY"] = "penny" + yield from run_server_in_thread( + "alternate", config, 12082 + ) + + +def test_http(http_server): + import requests + + response = requests.get(http_server + "v/2.0/sensors") + assert response.status_code == 403 diff --git a/Ch08/listing08-03-mocking.py b/Ch08/listing08-03-mocking.py new file mode 100644 index 0000000..f22cbaf --- /dev/null +++ b/Ch08/listing08-03-mocking.py @@ -0,0 +1,33 @@ +from unittest.mock import Mock, MagicMock, AsyncMock + +import pytest + + +@pytest.fixture +def data() -> t.Any: + return { + "sensors": [ + { + "human_readable": "3.7", + "id": "PythonVersion", + "title": "Python Version", + "value": [3, 7, 2, "final", 0], + }, + { + "human_readable": "Not connected", + "id": "ACStatus", + "title": "AC Connected", + "value": False, + }, + ] + } + +@pytest.fixture +def mockclient(data): + client = MagicMock() + response = Mock() + response.json = AsyncMock(return_value=data) + response.status = 200 + client.get.return_value.__aenter__ = AsyncMock(return_value=response) + return client + diff --git a/Ch08/listing08-04-manual_mocks.py b/Ch08/listing08-04-manual_mocks.py new file mode 100644 index 0000000..41787b6 --- /dev/null +++ b/Ch08/listing08-04-manual_mocks.py @@ -0,0 +1,48 @@ +import contextlib +from dataclasses import dataclass +import typing as t + +import pytest + + +@pytest.fixture +def data() -> t.Any: + return { + "sensors": [ + { + "human_readable": "3.7", + "id": "PythonVersion", + "title": "Python Version", + "value": [3, 7, 2, "final", 0], + }, + { + "human_readable": "Not connected", + "id": "ACStatus", + "title": "AC Connected", + "value": False, + }, + ] + } + + +@dataclass +class FakeAIOHttpClient: + data: t.Any + + @contextlib.asynccontextmanager + async def get(self, url: str, headers: t.Optional[t.Dict[str, str]]=None) -> FakeAIOHttpResponse: + yield FakeAIOHttpResponse(json_data=self.data, status=200) + + +@dataclass +class FakeAIOHttpResponse: + json_data: t.Any + status: int + + async def json(self) -> t.Any: + return self.json_data + +@pytest.fixture +def mockclient(data) -> FakeAIOHttpClient: + return FakeAIOHttpClient(data) + diff --git a/Ch08/listing08-05-apdaggregation_mocks.py b/Ch08/listing08-05-apdaggregation_mocks.py new file mode 100644 index 0000000..e204598 --- /dev/null +++ b/Ch08/listing08-05-apdaggregation_mocks.py @@ -0,0 +1,46 @@ +from unittest.mock import patch, Mock, AsyncMock + +import pytest + +import apd.aggregation.collect + + +class TestGetDataPoints: + @pytest.fixture + def mut(self): + return apd.aggregation.collect.get_data_points + + @pytest.mark.asyncio + async def test_get_data_points( + self, mut, mockclient: FakeAIOHttpClient, data + ) -> None: + datapoints = await mut("http://localhost", "", mockclient) + + assert len(datapoints) == len(data["sensors"]) + for sensor in data["sensors"]: + assert sensor["value"] in (datapoint.data for datapoint in datapoints) + assert sensor["id"] in (datapoint.sensor_name for datapoint in datapoints) + +class TestAddDataFromSensors: + @pytest.fixture + def mut(self): + return apd.aggregation.collect.add_data_from_sensors + + @pytest.fixture(autouse=True) + def patch_aiohttp(self, mockclient): + # Ensure all tests in this class use the mockclient + with patch("aiohttp.ClientSession") as ClientSession: + ClientSession.return_value.__aenter__ = AsyncMock(return_value=mockclient) + yield ClientSession + + @pytest.fixture + def db_session(self): + return Mock() + + @pytest.mark.asyncio + async def test_datapoints_are_added_to_the_session(self, mut, db_session) -> None: + # The only times data should be added to the session are when running the MUT + assert db_session.add.call_count == 0 + datapoints = await mut(db_session, ["http://localhost"], "") + assert db_session.add.call_count == len(datapoints) + diff --git a/Ch08/listing08-06-classic_sqlalchemy.py b/Ch08/listing08-06-classic_sqlalchemy.py new file mode 100644 index 0000000..3997f2c --- /dev/null +++ b/Ch08/listing08-06-classic_sqlalchemy.py @@ -0,0 +1,27 @@ +from dataclasses import dataclass, field +import datetime +import typing as t + +import sqlalchemy +from sqlalchemy.dialects.postgresql import JSONB, TIMESTAMP +from sqlalchemy.schema import Table + + +metadata = sqlalchemy.MetaData() + +datapoint_table = Table( + "sensor_values", + metadata, + sqlalchemy.Column("id", sqlalchemy.Integer, primary_key=True), + sqlalchemy.Column("sensor_name", sqlalchemy.String), + sqlalchemy.Column("collected_at", TIMESTAMP), + sqlalchemy.Column("data", JSONB), +) + +@dataclass +class DataPoint: + sensor_name: str + data: t.Dict[str, t.Any] + id: int = None + collected_at: datetime.datetime = field(default_factory=datetime.datetime.now) + diff --git a/Ch08/listing08-07-datapoint_with_asdict.py b/Ch08/listing08-07-datapoint_with_asdict.py new file mode 100644 index 0000000..3a9cd8c --- /dev/null +++ b/Ch08/listing08-07-datapoint_with_asdict.py @@ -0,0 +1,18 @@ +from dataclasses import dataclass, field, asdict +import datetime +import typing as t + + +@dataclass +class DataPoint: + sensor_name: str + data: t.Dict[str, t.Any] + id: int = None + collected_at: datetime.datetime = field(default_factory=datetime.datetime.now) + + def _asdict(self): + data = asdict(self) + if data["id"] is None: + del data["id"] + return data + diff --git a/Ch08/listing08-08-database_integration.py b/Ch08/listing08-08-database_integration.py new file mode 100644 index 0000000..b518e3b --- /dev/null +++ b/Ch08/listing08-08-database_integration.py @@ -0,0 +1,25 @@ +import asyncio +import testing as t + + + +def handle_result(result: t.List[DataPoint], session: Session) -> t.List[DataPoint]: + for point in result: + insert = datapoint_table.insert().values(**point._asdict()) + sql_result = session.execute(insert) + point.id = sql_result.inserted_primary_key[0] + return result + +async def add_data_from_sensors( + session: Session, servers: t.Tuple[str], api_key: t.Optional[str] +) -> t.List[DataPoint]: + tasks: t.List[t.Awaitable[t.List[DataPoint]]] = [] + points: t.List[DataPoint] = [] + async with aiohttp.ClientSession() as http: + tasks = [get_data_points(server, api_key, http) for server in servers] + for results in await asyncio.gather(*tasks): + points += results + loop = asyncio.get_running_loop() + await loop.run_in_executor(None, handle_result, points, session) + return points + diff --git a/Ch08/listing08-09-full_datapoint.py b/Ch08/listing08-09-full_datapoint.py new file mode 100644 index 0000000..25efe10 --- /dev/null +++ b/Ch08/listing08-09-full_datapoint.py @@ -0,0 +1,22 @@ +from dataclasses import dataclass, field, asdict +import datetime +import typing as t + + +@dataclass +class DataPoint: + sensor_name: str + data: t.Dict[str, t.Any] + id: int = None + collected_at: datetime.datetime = field(default_factory=datetime.datetime.now) + + @classmethod + def from_sql_result(cls, result): + return cls(**result._asdict()) + + def _asdict(self): + data = asdict(self) + if data["id"] is None: + del data["id"] + return data + diff --git a/Ch08/listing08-10-comparator.py b/Ch08/listing08-10-comparator.py new file mode 100644 index 0000000..12c0ac2 --- /dev/null +++ b/Ch08/listing08-10-comparator.py @@ -0,0 +1,76 @@ +from __future__ import annotations + +from dataclasses import dataclass, field, asdict +import datetime +import typing as t + +import sqlalchemy +from sqlalchemy.dialects.postgresql import JSONB, DATE, TIMESTAMP +from sqlalchemy.ext.hybrid import ExprComparator, hybrid_property +from sqlalchemy.orm import sessionmaker +from sqlalchemy.schema import Table + +metadata = sqlalchemy.MetaData() + +datapoint_table = Table( + "sensor_values", + metadata, + sqlalchemy.Column("id", sqlalchemy.Integer, primary_key=True), + sqlalchemy.Column("sensor_name", sqlalchemy.String, index=True), + sqlalchemy.Column("collected_at", TIMESTAMP, index=True), + sqlalchemy.Column("data", JSONB), +) + +class DateEqualComparator(ExprComparator): + + def __init__(self, fallback_expression, raw_expression): + # Do not try and find update expression from parent + super().__init__(None, fallback_expression, None) + self.raw_expression = raw_expression + + def __eq__(self, other): + """ Returns True iff on the same day as other """ + other_date = sqlalchemy.cast(other, DATE) + return sqlalchemy.and_( + self.raw_expression >= other_date, + self.raw_expression < other_date + 1, + ) + + def operate(self, op, *other, **kwargs): + other = [sqlalchemy.cast(date, DATE) for date in other] + return op(self.expression, *other, **kwargs) + + def reverse_operate(self, op, other, **kwargs): + other = [sqlalchemy.cast(date, DATE) for date in other] + return op(other, self.expression, **kwargs) + + +@dataclass +class DataPoint: + sensor_name: str + data: t.Dict[str, t.Any] + id: t.Optional[int] = None + collected_at: datetime.datetime = field(default_factory=datetime.datetime.now) + + @classmethod + def from_sql_result(cls, result) -> DataPoint: + return cls(**result._asdict()) + + def _asdict(self) -> t.Dict[str, t.Any]: + data = asdict(self) + if data["id"] is None: + del data["id"] + return data + + @hybrid_property + def collected_on_date(self): + return self.collected_at.date() + + @collected_on_date.comparator + def collected_on_date(cls): + return DateEqualComparator( + cls, + sqlalchemy.cast(datapoint_table.c.collected_at, DATE), + datapoint_table.c.collected_at, + ) + diff --git a/Ch08/listing08-11-django.py b/Ch08/listing08-11-django.py new file mode 100644 index 0000000..b0c8963 --- /dev/null +++ b/Ch08/listing08-11-django.py @@ -0,0 +1,19 @@ +from django.db import models +from django.db.models.functions.datetime import TruncDate + +@TruncDate.register_lookup +class DateExact(models.Lookup): + lookup_name = 'exact' + + def as_sql(self, compiler, connection): + # self.lhs (left-hand-side of the comparison) is always TruncDate, we want its argument + underlying_dt = self.lhs.lhs + # Instead, we want to wrap the rhs with TruncDate + other_date = TruncDate(self.rhs) + # Compile both sides + lhs, lhs_params = compiler.compile(underlying_dt) + rhs, rhs_params = compiler.compile(other_date) + params = lhs_params + rhs_params + lhs_params + rhs_params + # Return ((lhs >= rhs) AND (lhs < rhs+1)) - compatible with postgresql only! + return '%s >= %s AND %s < (%s + 1)' % (lhs, rhs, lhs, rhs), params + diff --git a/Ch08/listing08-12-migration.py b/Ch08/listing08-12-migration.py new file mode 100644 index 0000000..607d47e --- /dev/null +++ b/Ch08/listing08-12-migration.py @@ -0,0 +1,36 @@ +"""Add daily summary view + +Revision ID: 6962f8455a6d +Revises: 4b2df8a6e1ce +Create Date: 2019-12-03 11:50:24.403402 + +""" +from alembic import op + +# revision identifiers, used by Alembic. +revision = "6962f8455a6d" +down_revision = "4b2df8a6e1ce" +branch_labels = None +depends_on = None + +def upgrade(): + create_view = """ + CREATE VIEW daily_summary AS + SELECT + datapoints.sensor_name AS sensor_name, + datapoints.data AS data, + count(datapoints.id) AS count + FROM datapoints + WHERE + datapoints.collected_at >= CAST(CURRENT_DATE AS DATE) + AND + datapoints.collected_at < CAST(CURRENT_DATE AS DATE) + 1 + GROUP BY + datapoints.sensor_name, + datapoints.data; + """ + op.execute(create_view) + +def downgrade(): + op.execute("""DROP VIEW daily_summary""") + diff --git a/Ch08/listing08-13-env.py b/Ch08/listing08-13-env.py new file mode 100644 index 0000000..e504735 --- /dev/null +++ b/Ch08/listing08-13-env.py @@ -0,0 +1,31 @@ +from logging.config import fileConfig + +from sqlalchemy import engine_from_config +from sqlalchemy import pool +from alembic import context + +from apd.aggregation.database import metadata as target_metadata + + +def include_object(object, name, type_, reflected, compare_to): + if object.info.get("is_view", False): + return False + return True + +def run_migrations_online(): + connectable = engine_from_config( + config.get_section(config.config_ini_section), + prefix="sqlalchemy.", + poolclass=pool.NullPool, + ) + + with connectable.connect() as connection: + context.configure( + connection=connection, + target_metadata=target_metadata, + include_object=include_object, + ) + + with context.begin_transaction(): + context.run_migrations() + diff --git a/Ch09/apd.aggregation-chapter09-ex01/.coveragerc b/Ch09/apd.aggregation-chapter09-ex01/.coveragerc new file mode 100644 index 0000000..e766c69 --- /dev/null +++ b/Ch09/apd.aggregation-chapter09-ex01/.coveragerc @@ -0,0 +1,3 @@ +[run] +branch = True +omit = tests/* diff --git a/Ch09/apd.aggregation-chapter09-ex01/.pre-commit-config.yaml b/Ch09/apd.aggregation-chapter09-ex01/.pre-commit-config.yaml new file mode 100644 index 0000000..d3b6ec7 --- /dev/null +++ b/Ch09/apd.aggregation-chapter09-ex01/.pre-commit-config.yaml @@ -0,0 +1,26 @@ +repos: + +- repo: local + hooks: + - id: black + name: black + entry: pipenv run black + args: [--quiet] + language: system + types: [python] + + - id: mypy + name: mypy + exclude: (?x)^( + (.*)/setup.py + )$ + entry: pipenv run mypy + args: ["--follow-imports=skip"] + language: system + types: [python] + + - id: flake8 + name: flake8 + entry: pipenv run flake8 + language: system + types: [python] diff --git a/Ch09/apd.aggregation-chapter09-ex01/CHANGES.md b/Ch09/apd.aggregation-chapter09-ex01/CHANGES.md new file mode 100644 index 0000000..8e424e5 --- /dev/null +++ b/Ch09/apd.aggregation-chapter09-ex01/CHANGES.md @@ -0,0 +1,5 @@ +## Changes + +### 1.0.0 (Unreleased) + +* Generated from skeleton diff --git a/Ch09/apd.aggregation-chapter09-ex01/LICENCE b/Ch09/apd.aggregation-chapter09-ex01/LICENCE new file mode 100644 index 0000000..86685d4 --- /dev/null +++ b/Ch09/apd.aggregation-chapter09-ex01/LICENCE @@ -0,0 +1,31 @@ +Copyright (c) 2019, Matthew Wilkes + + +All rights reserved. + +Redistribution and use in source and binary forms, with or without modification, +are permitted provided that the following conditions are met: + +* Redistributions of source code must retain the above copyright notice, this + list of conditions and the following disclaimer. + +* Redistributions in binary form must reproduce the above copyright notice, this + list of conditions and the following disclaimer in the documentation and/or + other materials provided with the distribution. + +* Neither the name of the copyright holder nor the names of its + contributors may be used to endorse or promote products derived from this + software without specific prior written permission. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND +ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED +WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. +IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, +INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, +BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, +DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY +OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE +OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED +OF THE POSSIBILITY OF SUCH DAMAGE. + + diff --git a/Ch09/apd.aggregation-chapter09-ex01/Pipfile b/Ch09/apd.aggregation-chapter09-ex01/Pipfile new file mode 100644 index 0000000..ef1fd86 --- /dev/null +++ b/Ch09/apd.aggregation-chapter09-ex01/Pipfile @@ -0,0 +1,25 @@ +[[source]] +name = "pypi" +url = "https://pypi.org/simple" +verify_ssl = true + +[dev-packages] +pytest = "*" +pytest-cov = "*" +pytest-asyncio = "*" +mypy = "*" +flake8 = "*" +black = "*" +pre-commit = "*" +wheel = "*" +twine = "*" +sqlalchemy-stubs = "*" + +[packages] +apd-aggregation = {editable = true,path = "."} +apd-sensors = {editable = true,extras = ["webapp"],path = "./../code"} + +[requires] + +[pipenv] +allow_prereleases = true diff --git a/Ch09/apd.aggregation-chapter09-ex01/Pipfile.lock b/Ch09/apd.aggregation-chapter09-ex01/Pipfile.lock new file mode 100644 index 0000000..37ebbba --- /dev/null +++ b/Ch09/apd.aggregation-chapter09-ex01/Pipfile.lock @@ -0,0 +1,751 @@ +{ + "_meta": { + "hash": { + "sha256": "9cbbc3af88a1213ed8f4f93c33e35a9f9d7e047077cd25ff02f9bde13e2b606b" + }, + "pipfile-spec": 6, + 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"zipp": { + "hashes": [ + "sha256:3718b1cbcd963c7d4c5511a8240812904164b7f381b647143a89d3b98f9bcd8e", + "sha256:f06903e9f1f43b12d371004b4ac7b06ab39a44adc747266928ae6debfa7b3335" + ], + "version": "==0.6.0" + } + } +} diff --git a/Ch09/apd.aggregation-chapter09-ex01/README.md b/Ch09/apd.aggregation-chapter09-ex01/README.md new file mode 100644 index 0000000..85669dd --- /dev/null +++ b/Ch09/apd.aggregation-chapter09-ex01/README.md @@ -0,0 +1,4 @@ +# APD Sensor aggregator + +A programme that queries apd.sensor endpoints and aggregates their results. + diff --git a/Ch09/apd.aggregation-chapter09-ex01/pyproject.toml b/Ch09/apd.aggregation-chapter09-ex01/pyproject.toml new file mode 100644 index 0000000..4d3bb67 --- /dev/null +++ b/Ch09/apd.aggregation-chapter09-ex01/pyproject.toml @@ -0,0 +1,5 @@ +[build-system] +requires = [ + "setuptools", +] +build-backend = "setuptools.build_meta" diff --git a/Ch09/apd.aggregation-chapter09-ex01/pytest.ini b/Ch09/apd.aggregation-chapter09-ex01/pytest.ini new file mode 100644 index 0000000..c1d3e58 --- /dev/null +++ b/Ch09/apd.aggregation-chapter09-ex01/pytest.ini @@ -0,0 +1,3 @@ +[pytest] +markers = + functional: these tests are significantly slower as they start a HTTP server \ No newline at end of file diff --git a/Ch09/apd.aggregation-chapter09-ex01/setup.cfg b/Ch09/apd.aggregation-chapter09-ex01/setup.cfg new file mode 100644 index 0000000..356d822 --- /dev/null +++ b/Ch09/apd.aggregation-chapter09-ex01/setup.cfg @@ -0,0 +1,63 @@ +[mypy] +namespace_packages = True +mypy_path = src +plugins = sqlmypy + +[mypy-alembic] +ignore_missing_imports = True + +[mypy-alembic.config] +ignore_missing_imports = True + +[mypy-alembic.script] +ignore_missing_imports = True + +[mypy-alembic.runtime.environment] +ignore_missing_imports = True + +[mypy-matplotlib.axes._base] +ignore_missing_imports = True + +[mypy-pint] +ignore_missing_imports = True + +[mypy-pytest] +ignore_missing_imports = True + +[flake8] +max-line-length = 120 +ignore = E501 + +[metadata] +name = apd.aggregation +version = attr: apd.aggregation.VERSION +description = A programme that queries apd.sensor endpoints and aggregates their results. +long_description = file: README.md, CHANGES.md, LICENCE +long_description_content_type = text/markdown +keywords = +license = BSD +classifiers = + Programming Language :: Python :: 3 + Programming Language :: Python :: 3.7 + +[options] +zip_safe = False +include_package_data = True +package_dir = + =src +packages = find_namespace: +install_requires = + sqlalchemy + aiohttp + pint + psycopg2 + alembic + click + + +[options.packages.find] +where = src + +[options.entry_points] +console_scripts = + collect_sensor_data = apd.aggregation.cli:collect_sensor_data diff --git a/Ch09/apd.aggregation-chapter09-ex01/setup.py b/Ch09/apd.aggregation-chapter09-ex01/setup.py new file mode 100644 index 0000000..6068493 --- /dev/null +++ b/Ch09/apd.aggregation-chapter09-ex01/setup.py @@ -0,0 +1,3 @@ +from setuptools import setup + +setup() diff --git a/Ch09/apd.aggregation-chapter09-ex01/src/apd/aggregation/__init__.py b/Ch09/apd.aggregation-chapter09-ex01/src/apd/aggregation/__init__.py new file mode 100644 index 0000000..3277f64 --- /dev/null +++ b/Ch09/apd.aggregation-chapter09-ex01/src/apd/aggregation/__init__.py @@ -0,0 +1 @@ +VERSION = "1.0.0" diff --git a/Ch09/apd.aggregation-chapter09-ex01/src/apd/aggregation/alembic/README b/Ch09/apd.aggregation-chapter09-ex01/src/apd/aggregation/alembic/README new file mode 100644 index 0000000..97a87d5 --- /dev/null +++ b/Ch09/apd.aggregation-chapter09-ex01/src/apd/aggregation/alembic/README @@ -0,0 +1,12 @@ +Generic single-database configuration. + + +# Database setup + +To generate the required database tables you must create an alembic.ini file, as follows: + + [alembic] + script_location = apd.aggregation:alembic + sqlalchemy.url = postgresql+psycopg2://apd@localhost/apd + +and run `alembic upgrade head`. This should also be done after every upgrade of the software. \ No newline at end of file diff --git a/Ch09/apd.aggregation-chapter09-ex01/src/apd/aggregation/alembic/env.py b/Ch09/apd.aggregation-chapter09-ex01/src/apd/aggregation/alembic/env.py new file mode 100644 index 0000000..4c8a2c2 --- /dev/null +++ b/Ch09/apd.aggregation-chapter09-ex01/src/apd/aggregation/alembic/env.py @@ -0,0 +1,80 @@ +from logging.config import fileConfig + +from sqlalchemy import engine_from_config +from sqlalchemy import pool +from alembic import context + +from apd.aggregation.database import metadata + + +# this is the Alembic Config object, which provides +# access to the values within the .ini file in use. +config = context.config + +# Interpret the config file for Python logging. +# This line sets up loggers basically. +if config.config_file_name: + fileConfig(config.config_file_name) + +target_metadata = metadata + + +def include_object(object, name, type_, reflected, compare_to): + if object.info.get("is_view", False): + return False + return True + + +def run_migrations_offline(): + """Run migrations in 'offline' mode. + + This configures the context with just a URL + and not an Engine, though an Engine is acceptable + here as well. By skipping the Engine creation + we don't even need a DBAPI to be available. + + Calls to context.execute() here emit the given string to the + script output. + + """ + url = config.get_main_option("sqlalchemy.url") + context.configure( + url=url, + include_object=include_object, + target_metadata=target_metadata, + literal_binds=True, + dialect_opts={"paramstyle": "named"}, + ) + + with context.begin_transaction(): + context.run_migrations() + + +def run_migrations_online(): + """Run migrations in 'online' mode. + + In this scenario we need to create an Engine + and associate a connection with the context. + + """ + connectable = engine_from_config( + config.get_section(config.config_ini_section), + prefix="sqlalchemy.", + poolclass=pool.NullPool, + ) + + with connectable.connect() as connection: + context.configure( + connection=connection, + target_metadata=target_metadata, + include_object=include_object, + ) + + with context.begin_transaction(): + context.run_migrations() + + +if context.is_offline_mode(): + run_migrations_offline() +else: + run_migrations_online() diff --git a/Ch09/apd.aggregation-chapter09-ex01/src/apd/aggregation/alembic/script.py.mako b/Ch09/apd.aggregation-chapter09-ex01/src/apd/aggregation/alembic/script.py.mako new file mode 100644 index 0000000..2c01563 --- /dev/null +++ b/Ch09/apd.aggregation-chapter09-ex01/src/apd/aggregation/alembic/script.py.mako @@ -0,0 +1,24 @@ +"""${message} + +Revision ID: ${up_revision} +Revises: ${down_revision | comma,n} +Create Date: ${create_date} + +""" +from alembic import op +import sqlalchemy as sa +${imports if imports else ""} + +# revision identifiers, used by Alembic. +revision = ${repr(up_revision)} +down_revision = ${repr(down_revision)} +branch_labels = ${repr(branch_labels)} +depends_on = ${repr(depends_on)} + + +def upgrade(): + ${upgrades if upgrades else "pass"} + + +def downgrade(): + ${downgrades if downgrades else "pass"} diff --git a/Ch09/apd.aggregation-chapter09-ex01/src/apd/aggregation/alembic/versions/4b2df8a6e1ce_add_indexes_to_datapoints.py b/Ch09/apd.aggregation-chapter09-ex01/src/apd/aggregation/alembic/versions/4b2df8a6e1ce_add_indexes_to_datapoints.py new file mode 100644 index 0000000..ef671bd --- /dev/null +++ b/Ch09/apd.aggregation-chapter09-ex01/src/apd/aggregation/alembic/versions/4b2df8a6e1ce_add_indexes_to_datapoints.py @@ -0,0 +1,32 @@ +"""Add indexes to datapoints + +Revision ID: 4b2df8a6e1ce +Revises: 6d2eacd5da3f +Create Date: 2019-12-02 16:07:41.123116 + +""" +from alembic import op + + +# revision identifiers, used by Alembic. +revision = "4b2df8a6e1ce" +down_revision = "6d2eacd5da3f" +branch_labels = None +depends_on = None + + +def upgrade(): + op.create_index( + op.f("ix_datapoints_collected_at"), + "datapoints", + ["collected_at"], + unique=False, + ) + op.create_index( + op.f("ix_datapoints_sensor_name"), "datapoints", ["sensor_name"], unique=False, + ) + + +def downgrade(): + op.drop_index(op.f("ix_datapoints_sensor_name"), table_name="datapoints") + op.drop_index(op.f("ix_datapoints_collected_at"), table_name="datapoints") diff --git a/Ch09/apd.aggregation-chapter09-ex01/src/apd/aggregation/alembic/versions/6962f8455a6d_add_daily_summary_view.py b/Ch09/apd.aggregation-chapter09-ex01/src/apd/aggregation/alembic/versions/6962f8455a6d_add_daily_summary_view.py new file mode 100644 index 0000000..eb02455 --- /dev/null +++ b/Ch09/apd.aggregation-chapter09-ex01/src/apd/aggregation/alembic/versions/6962f8455a6d_add_daily_summary_view.py @@ -0,0 +1,38 @@ +"""Add daily summary view + +Revision ID: 6962f8455a6d +Revises: 4b2df8a6e1ce +Create Date: 2019-12-03 11:50:24.403402 + +""" +from alembic import op + + +# revision identifiers, used by Alembic. +revision = "6962f8455a6d" +down_revision = "4b2df8a6e1ce" +branch_labels = None +depends_on = None + + +def upgrade(): + create_view = """ + CREATE VIEW daily_summary AS + SELECT + datapoints.sensor_name AS sensor_name, + datapoints.data AS data, + count(datapoints.id) AS count + FROM datapoints + WHERE + datapoints.collected_at >= CAST(CURRENT_DATE AS DATE) + AND + datapoints.collected_at < CAST(CURRENT_DATE AS DATE) + 1 + GROUP BY + datapoints.sensor_name, + datapoints.data; + """ + op.execute(create_view) + + +def downgrade(): + op.execute("""DROP VIEW daily_summary""") diff --git a/Ch09/apd.aggregation-chapter09-ex01/src/apd/aggregation/alembic/versions/6d2eacd5da3f_create_sensor_values_table.py b/Ch09/apd.aggregation-chapter09-ex01/src/apd/aggregation/alembic/versions/6d2eacd5da3f_create_sensor_values_table.py new file mode 100644 index 0000000..b4a3545 --- /dev/null +++ b/Ch09/apd.aggregation-chapter09-ex01/src/apd/aggregation/alembic/versions/6d2eacd5da3f_create_sensor_values_table.py @@ -0,0 +1,31 @@ +"""Create datapoints table + +Revision ID: 6d2eacd5da3f +Revises: N/A +Create Date: 2019-09-29 13:43:21.242706 + +""" +from alembic import op +import sqlalchemy as sa +from sqlalchemy.dialects import postgresql + +# revision identifiers, used by Alembic. +revision = "6d2eacd5da3f" +down_revision = None +branch_labels = None +depends_on = None + + +def upgrade(): + op.create_table( + "datapoints", + sa.Column("id", sa.Integer(), nullable=False), + sa.Column("sensor_name", sa.String(), nullable=True), + sa.Column("collected_at", postgresql.TIMESTAMP(), nullable=True), + sa.Column("data", postgresql.JSONB(astext_type=sa.Text()), nullable=True), + sa.PrimaryKeyConstraint("id"), + ) + + +def downgrade(): + op.drop_table("datapoints") diff --git a/Ch09/apd.aggregation-chapter09-ex01/src/apd/aggregation/alembic/versions/d8d4cf6a178f_add_deployment_id_to_datapoint.py b/Ch09/apd.aggregation-chapter09-ex01/src/apd/aggregation/alembic/versions/d8d4cf6a178f_add_deployment_id_to_datapoint.py new file mode 100644 index 0000000..88d5219 --- /dev/null +++ b/Ch09/apd.aggregation-chapter09-ex01/src/apd/aggregation/alembic/versions/d8d4cf6a178f_add_deployment_id_to_datapoint.py @@ -0,0 +1,34 @@ +"""Add deployment id to DataPoint + +Revision ID: d8d4cf6a178f +Revises: 6962f8455a6d +Create Date: 2019-12-03 20:58:11.285509 + +""" +from alembic import op +import sqlalchemy as sa +from sqlalchemy.dialects import postgresql + +# revision identifiers, used by Alembic. +revision = "d8d4cf6a178f" +down_revision = "6962f8455a6d" +branch_labels = None +depends_on = None + + +def upgrade(): + op.add_column( + "datapoints", + sa.Column("deployment_id", postgresql.UUID(as_uuid=True), nullable=True), + ) + op.create_index( + op.f("ix_datapoints_deployment_id"), + "datapoints", + ["deployment_id"], + unique=False, + ) + + +def downgrade(): + op.drop_index(op.f("ix_datapoints_deployment_id"), table_name="datapoints") + op.drop_column("datapoints", "deployment_id") diff --git a/Ch09/apd.aggregation-chapter09-ex01/src/apd/aggregation/analysis.py b/Ch09/apd.aggregation-chapter09-ex01/src/apd/aggregation/analysis.py new file mode 100644 index 0000000..6aff21a --- /dev/null +++ b/Ch09/apd.aggregation-chapter09-ex01/src/apd/aggregation/analysis.py @@ -0,0 +1,148 @@ +from __future__ import annotations + +import collections +import dataclasses +import datetime +import typing as t +from uuid import UUID + +from matplotlib.axes._base import _AxesBase + +from apd.aggregation.query import get_data_by_deployment +from apd.aggregation.database import DataPoint + + +@dataclasses.dataclass(frozen=True) +class Config: + title: str + sensor_name: str + clean: t.Callable[ + [t.AsyncIterator[DataPoint]], t.AsyncIterator[t.Tuple[datetime.datetime, float]] + ] + ylabel: str + + +async def clean_watthours_to_watts( + datapoints: t.AsyncIterator[DataPoint], +) -> t.AsyncIterator[t.Tuple[datetime.datetime, float]]: + async for date, data in clean_magnitude(datapoints): + yield (date, data) + + +async def clean_magnitude( + datapoints: t.AsyncIterator[DataPoint], +) -> t.AsyncIterator[t.Tuple[datetime.datetime, float]]: + async for datapoint in datapoints: + if datapoint.data is None: + continue + yield datapoint.collected_at, datapoint.data["magnitude"] + + +async def clean_temperature_fluctuations( + datapoints: t.AsyncIterator[DataPoint], +) -> t.AsyncIterator[t.Tuple[datetime.datetime, float]]: + allowed_jitter = 2.5 + allowed_range = (-40, 80) + window_datapoints: t.Deque[DataPoint] = collections.deque(maxlen=3) + + def datapoint_ok(datapoint: DataPoint) -> bool: + """Return False if this data point does not contain a valid temperature""" + if datapoint.data is None: + return False + elif datapoint.data["unit"] != "degC": + # This point is in a different temperature system. While it could be converted + # this cleaner is not yet doing that. + return False + elif not allowed_range[0] < datapoint.data["magnitude"] < allowed_range[1]: + return False + return True + + async for datapoint in datapoints: + if not datapoint_ok(datapoint): + # If the datapoint is invalid then skip directly to the next item + continue + + window_datapoints.append(datapoint) + if len(window_datapoints) == 3: + # Find the temperatures of the datapoints in the window, then average + # the first and last and compare that to the middle point. + window_temperatures = [dp.data["magnitude"] for dp in window_datapoints] + avr_first_last = (window_temperatures[0] + window_temperatures[2]) / 2 + diff_middle_avr = abs(window_temperatures[1] - avr_first_last) + if diff_middle_avr > allowed_jitter: + pass + else: + yield window_datapoints[1].collected_at, window_temperatures[1] + elif len(window_datapoints) == 1: + # The item in the iterator can't be compared to both neighbours + # so should be yielded + yield datapoint.collected_at, datapoint.data["magnitude"] + else: + # Otherwise, let the window fill up, it will be yieleded later + pass + # When the iterator ends the final item is not yet in the middle + # of the window, so the last item must be explicitly yielded. + if len(window_datapoints) > 1 and datapoint_ok(datapoint): + yield datapoint.collected_at, datapoint.data["magnitude"] + + +async def clean_passthrough( + datapoints: t.AsyncIterator[DataPoint], +) -> t.AsyncIterator[t.Tuple[datetime.datetime, float]]: + async for datapoint in datapoints: + if datapoint.data is None: + continue + else: + yield datapoint.collected_at, datapoint.data + + +configs = ( + Config( + sensor_name="SolarCumulativeOutput", + clean=clean_watthours_to_watts, + title="Solar generation", + ylabel="Watts", + ), + Config( + sensor_name="RAMAvailable", + clean=clean_passthrough, + title="RAM available", + ylabel="Bytes", + ), + Config( + sensor_name="RelativeHumidity", + clean=clean_passthrough, + title="Relative humidity", + ylabel="Percent", + ), + Config( + sensor_name="Temperature", + clean=clean_temperature_fluctuations, + title="Ambient temperature", + ylabel="Degrees C", + ), +) + + +def get_known_configs(): + return {config.title: config for config in configs} + + +async def plot_sensor( + config: Config, plot: _AxesBase, location_names: t.Dict[UUID, str], **kwargs +): + locations = [] + async for deployment, query_results in get_data_by_deployment( + sensor_name=config.sensor_name, **kwargs + ): + # Mypy currently doesn't understand callable fields on datatypes: https://github.com/python/mypy/issues/5485 + points = [dp async for dp in config.clean(query_results)] # type: ignore + if not points: + continue + locations.append(deployment) + x, y = zip(*points) + plot.set_title(config.title) + plot.set_ylabel(config.ylabel) + plot.plot_date(x, y, f"-", xdate=True) + plot.legend([location_names.get(l, l) for l in locations]) + return plot diff --git a/Ch09/apd.aggregation-chapter09-ex01/src/apd/aggregation/cli.py b/Ch09/apd.aggregation-chapter09-ex01/src/apd/aggregation/cli.py new file mode 100644 index 0000000..3435e78 --- /dev/null +++ b/Ch09/apd.aggregation-chapter09-ex01/src/apd/aggregation/cli.py @@ -0,0 +1,52 @@ +import typing as t + +import click + +from .collect import standalone + + +@click.command() +@click.argument("server", nargs=-1) +@click.option( + "--db", + metavar="", + default="postgresql+psycopg2://localhost/apd", + help="The connection string to a PostgreSQL database", + envvar="APD_DB_URI", +) +@click.option("--api-key", metavar="", envvar="APD_API_KEY") +@click.option( + "--tolerate-failures", + "-f", + help="If provided, failure to retrieve some sensors' data will not abort the collection process", + is_flag=True, +) +@click.option("-v", "--verbose", is_flag=True, help="Enables verbose mode") +def collect_sensor_data( + db: str, server: t.Tuple[str], api_key: str, tolerate_failures: bool, verbose: bool +): + """This loads data from one or more sensors into the specified database. + + Only PostgreSQL databases are supported, as the column definitions use + multiple pg specific features. The database must already exist and be + populated with the required tables. + + The --api-key option is used to specify the access token for the sensors + being queried. + + You may specify any number of servers, the variable should be the full URL + to the sensor's HTTP interface, not including the /v/2.0 portion. Multiple + URLs should be separated with a space. + """ + if tolerate_failures: + attempts = [(s,) for s in server] + else: + attempts = [server] + success = True + for attempt in attempts: + try: + standalone(db, attempt, api_key, echo=verbose) + except ValueError as e: + click.secho(str(e), err=True, fg="red") + success = False + return success diff --git a/Ch09/apd.aggregation-chapter09-ex01/src/apd/aggregation/collect.py b/Ch09/apd.aggregation-chapter09-ex01/src/apd/aggregation/collect.py new file mode 100644 index 0000000..f745d69 --- /dev/null +++ b/Ch09/apd.aggregation-chapter09-ex01/src/apd/aggregation/collect.py @@ -0,0 +1,93 @@ +import asyncio +from contextvars import ContextVar +import datetime +import typing as t +import uuid + +import aiohttp +from sqlalchemy import create_engine +from sqlalchemy.orm import sessionmaker +from sqlalchemy.orm.session import Session + +from .database import DataPoint, datapoint_table + +http_session_var: ContextVar[aiohttp.ClientSession] = ContextVar("http_session") + + +async def get_data_points(server: str, api_key: t.Optional[str],) -> t.List[DataPoint]: + if not server.endswith("/"): + server += "/" + url = server + "v/2.1/sensors/" + deployment_id_url = server + "v/2.1/deployment_id" + headers = {} + if api_key: + headers["X-API-KEY"] = api_key + http = http_session_var.get() + + async with http.get(deployment_id_url) as request: + if request.status != 200: + raise ValueError(f"Error loading deployment id from {server}") + result = await request.json() + deployment_id = uuid.UUID(result["deployment_id"]) + + async with http.get(url, headers=headers) as request: + result = await request.json() + ok = request.status == 200 + now = datetime.datetime.now() + if ok: + points = [] + for value in result["sensors"]: + points.append( + DataPoint( + sensor_name=value["id"], + collected_at=now, + data=value["value"], + deployment_id=deployment_id, + ) + ) + return points + else: + raise ValueError( + f"Error loading data from {server}: " + result.get("error", "Unknown") + ) + + +def handle_result(result: t.List[DataPoint], session: Session) -> t.List[DataPoint]: + for point in result: + insert = datapoint_table.insert().values(**point._asdict()) + sql_result = session.execute(insert) + point.id = sql_result.inserted_primary_key[0] + return result + + +async def add_data_from_sensors( + session: Session, servers: t.Tuple[str], api_key: t.Optional[str] +) -> t.List[DataPoint]: + tasks: t.List[t.Awaitable[t.List[DataPoint]]] = [] + points: t.List[DataPoint] = [] + async with aiohttp.ClientSession() as http: + http_session_var.set(http) + tasks = [get_data_points(server, api_key) for server in servers] + for results in await asyncio.gather(*tasks): + points += results + loop = asyncio.get_running_loop() + await loop.run_in_executor(None, handle_result, points, session) + return points + + +def standalone( + db_uri: str, servers: t.Tuple[str], api_key: t.Optional[str], echo: bool = False +) -> None: + engine = create_engine(db_uri, echo=echo) + sm = sessionmaker(engine) + Session = sm() + asyncio.run(add_data_from_sensors(Session, servers, api_key)) + Session.commit() + + +if __name__ == "__main__": + standalone( + db_uri="postgresql+psycopg2://apd@localhost/apd", + servers=("http://pvoutput:8080/",), + api_key="h3hdfjksfhwkjehnwekj", + ) diff --git a/Ch09/apd.aggregation-chapter09-ex01/src/apd/aggregation/database.py b/Ch09/apd.aggregation-chapter09-ex01/src/apd/aggregation/database.py new file mode 100644 index 0000000..3e8e794 --- /dev/null +++ b/Ch09/apd.aggregation-chapter09-ex01/src/apd/aggregation/database.py @@ -0,0 +1,102 @@ +from __future__ import annotations + +from dataclasses import dataclass, field, asdict +import datetime +import typing as t +import uuid + +import sqlalchemy +from sqlalchemy.dialects.postgresql import JSONB, DATE, TIMESTAMP, UUID +from sqlalchemy.ext.hybrid import ExprComparator, hybrid_property +from sqlalchemy.orm import sessionmaker +from sqlalchemy.schema import Table + + +metadata = sqlalchemy.MetaData() + +datapoint_table = Table( + "datapoints", + metadata, + sqlalchemy.Column("id", sqlalchemy.Integer, primary_key=True), + sqlalchemy.Column("sensor_name", sqlalchemy.String, index=True), + sqlalchemy.Column("collected_at", TIMESTAMP, index=True), + sqlalchemy.Column("deployment_id", UUID(as_uuid=True), index=True), + sqlalchemy.Column("data", JSONB), +) + +daily_summary_view = Table( + "daily_summary", + metadata, + sqlalchemy.Column("sensor_name", sqlalchemy.String), + sqlalchemy.Column("data", JSONB), + sqlalchemy.Column("count", sqlalchemy.Integer), + info={"is_view": True}, +) + + +class DateEqualComparator(ExprComparator): + def __init__(self, fallback_expression, raw_expression): + # Do not try and find update expression from parent + super().__init__(None, fallback_expression, None) + self.raw_expression = raw_expression + + def __eq__(self, other): + """ Returns True iff on the same day as other """ + other_date = sqlalchemy.cast(other, DATE) + return sqlalchemy.and_( + self.raw_expression >= other_date, self.raw_expression < other_date + 1, + ) + + def operate(self, op, *other, **kwargs): + other = [sqlalchemy.cast(date, DATE) for date in other] + return op(self.expression, *other, **kwargs) + + def reverse_operate(self, op, other, **kwargs): + other = [sqlalchemy.cast(date, DATE) for date in other] + return op(other, self.expression, **kwargs) + + +@dataclass +class DataPoint: + sensor_name: str + data: t.Any + deployment_id: uuid.UUID + id: t.Optional[int] = None + collected_at: datetime.datetime = field(default_factory=datetime.datetime.now) + + @classmethod + def from_sql_result(cls, result) -> DataPoint: + return cls(**result._asdict()) + + def _asdict(self) -> t.Dict[str, t.Any]: + data = asdict(self) + if data["id"] is None: + del data["id"] + return data + + @hybrid_property + def collected_on_date(self): + return self.collected_at.date() + + @collected_on_date.comparator # type: ignore + def collected_on_date(cls): + return DateEqualComparator( + fallback_expression=sqlalchemy.cast(datapoint_table.c.collected_at, DATE), + raw_expression=datapoint_table.c.collected_at, + ) + + +def main(): + engine = sqlalchemy.create_engine( + "postgresql+psycopg2://apd@localhost/apd", echo=True + ) + sm = sessionmaker(engine) + Session = sm() + if False: + metadata.create_all(engine) + print(Session.query(DataPoint).all()) + pass + + +if __name__ == "__main__": + main() diff --git a/Ch09/apd.aggregation-chapter09-ex01/src/apd/aggregation/query.py b/Ch09/apd.aggregation-chapter09-ex01/src/apd/aggregation/query.py new file mode 100644 index 0000000..6944e11 --- /dev/null +++ b/Ch09/apd.aggregation-chapter09-ex01/src/apd/aggregation/query.py @@ -0,0 +1,129 @@ +import asyncio +import contextlib +from contextvars import ContextVar +import datetime +import typing as t +from uuid import UUID + +from sqlalchemy import create_engine +from sqlalchemy.orm import sessionmaker +from sqlalchemy.orm.session import Session + + +from apd.aggregation.collect import datapoint_table, DataPoint + + +db_session_var: ContextVar[Session] = ContextVar("db_session") + + +@contextlib.contextmanager +def with_database(uri: t.Optional[str] = None) -> t.Iterator[Session]: + """Given a URI, set up a DB connection, and return a Session as a context manager """ + if uri is None: + uri = "postgresql+psycopg2://localhost/apd" + engine = create_engine(uri) + sm = sessionmaker(engine) + Session = sm() + token = db_session_var.set(Session) + yield Session + db_session_var.reset(token) + Session.commit() + Session.close() + + +async def get_data( + sensor_name: t.Optional[str] = None, + deployment_id: t.Optional[UUID] = None, + collected_before: t.Optional[datetime.datetime] = None, + collected_after: t.Optional[datetime.datetime] = None, +) -> t.AsyncIterator[DataPoint]: + db_session = db_session_var.get() + loop = asyncio.get_running_loop() + query = db_session.query(datapoint_table) + if sensor_name: + query = query.filter(datapoint_table.c.sensor_name == sensor_name) + if deployment_id: + query = query.filter(datapoint_table.c.deployment_id == deployment_id) + if collected_before: + query = query.filter(datapoint_table.c.collected_at < collected_before) + if collected_after: + query = query.filter(datapoint_table.c.collected_at > collected_after) + query = query.order_by( + datapoint_table.c.deployment_id, + datapoint_table.c.sensor_name, + datapoint_table.c.collected_at, + ) + + rows = await loop.run_in_executor(None, query.all) + for row in rows: + yield DataPoint.from_sql_result(row) + + +async def get_deployment_ids(): + db_session = db_session_var.get() + loop = asyncio.get_running_loop() + query = db_session.query(datapoint_table.c.deployment_id).distinct() + return [row.deployment_id for row in await loop.run_in_executor(None, query.all)] + + +async def get_data_by_deployment( + *args, **kwargs +) -> t.AsyncIterator[t.Tuple[UUID, t.AsyncIterator[DataPoint]]]: + """Return an Async Iterator that contains two-item pairs. + These pairs are a string (deployment_id), and an async iterator that contains + the datapoints with that deployment_id. + + Usage example: + + async for deployment_id, datapoints in get_data_by_deployment(): + print(deployment_id) + async for datapoint in datapoints: + print(datapoint) + print() + """ + # Get the data, using the arguments to this function as filters + data = get_data(*args, **kwargs) + + # The two levels of iterator share the item variable, initialise it with the + # first item from the iterator. Also set last_deployment_id to None, so the + # outer iterator knows to start a new group. + last_deployment_id: t.Optional[UUID] = None + try: + item = await data.__anext__() + except StopAsyncIteration: + # There were no items in the underlying query, return immediately + return + + async def subiterator(group_id: UUID) -> t.AsyncIterator[DataPoint]: + """Using a closure, create an iterator that yields the current + item, then yields all items from data while the deployment_id matches + group_id, leaving the first that doesn't match as item in the enclosing + scope.""" + # item is from the enclosing scope + nonlocal item + while item.deployment_id == group_id: + # yield items from data while they match the group_id this iterator represents + yield item + try: + # Advance the underlying iterator + item = await data.__anext__() + except StopAsyncIteration: + # The underlying iterator came to an end, so end the subiterator too + return + + while True: + while item.deployment_id == last_deployment_id: + # We are trying to advance the outer iterator while the underlying iterator + # is still part-way through a group. Speed through the underlying until we + # hit an item where the deployment_id is different to the last one (or, is not + # None, in the case of the start of the iterator) + try: + item = await data.__anext__() + except StopAsyncIteration: + # We hit the end of the underlying iterator: end this iterator too + return + last_deployment_id = item.deployment_id + # Don't yield an iterator for unclassified deployments + if last_deployment_id is not None: + # Instantiate a subiterator for this group + yield last_deployment_id, subiterator(last_deployment_id) diff --git a/Ch09/apd.aggregation-chapter09-ex01/tests/__init__.py b/Ch09/apd.aggregation-chapter09-ex01/tests/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/Ch09/apd.aggregation-chapter09-ex01/tests/conftest.py b/Ch09/apd.aggregation-chapter09-ex01/tests/conftest.py new file mode 100644 index 0000000..65db278 --- /dev/null +++ b/Ch09/apd.aggregation-chapter09-ex01/tests/conftest.py @@ -0,0 +1,123 @@ +import datetime +from uuid import UUID + +from apd.aggregation.database import datapoint_table + +from alembic.config import Config +from alembic.script import ScriptDirectory +from alembic.runtime.environment import EnvironmentContext +import pytest + + +@pytest.fixture +def db_uri(): + return "postgresql+psycopg2://apd@localhost/apd-test" + + +@pytest.fixture +def db_session(db_uri): + from sqlalchemy import create_engine + from sqlalchemy.orm import sessionmaker + + engine = create_engine(db_uri, echo=True) + sm = sessionmaker(engine) + Session = sm() + yield Session + Session.close() + + +@pytest.fixture +def migrated_db(db_uri, db_session): + config = Config() + config.set_main_option("script_location", "apd.aggregation:alembic") + config.set_main_option("sqlalchemy.url", db_uri) + script = ScriptDirectory.from_config(config) + + def upgrade(rev, context): + return script._upgrade_revs(script.get_current_head(), rev) + + def downgrade(rev, context): + return script._downgrade_revs(None, rev) + + with EnvironmentContext(config, script, fn=upgrade): + script.run_env() + + yield + + # Clear any pending work from the db_session connection + db_session.rollback() + + with EnvironmentContext(config, script, fn=downgrade): + script.run_env() + + +@pytest.fixture +def populated_db(migrated_db, db_session): + datas = [ + { + "id": 1, + "sensor_name": "Test", + "data": "1", + "collected_at": datetime.datetime(2020, 4, 1, 12, 0, 1), + "deployment_id": UUID("b4c68905-b1e4-4875-940e-69e5d27730fd"), + }, + { + "id": 2, + "sensor_name": "Test", + "data": "2", + "collected_at": datetime.datetime(2020, 4, 1, 12, 1, 1), + "deployment_id": UUID("b4c68905-b1e4-4875-940e-69e5d27730fd"), + }, + { + "id": 3, + "sensor_name": "Test", + "data": "3", + "collected_at": datetime.datetime(2020, 4, 1, 12, 2, 1), + "deployment_id": UUID("b4c68905-b1e4-4875-940e-69e5d27730fd"), + }, + { + "id": 4, + "sensor_name": "Test", + "data": "4", + "collected_at": datetime.datetime(2020, 4, 1, 12, 3, 1), + "deployment_id": UUID("b4c68905-b1e4-4875-940e-69e5d27730fd"), + }, + { + "id": 5, + "sensor_name": "OtherTest", + "data": "5", + "collected_at": datetime.datetime(2020, 4, 1, 12, 4, 1), + "deployment_id": UUID("b4c68905-b1e4-4875-940e-69e5d27730fd"), + }, + { + "id": 6, + "sensor_name": "Test", + "data": "1", + "collected_at": datetime.datetime(2020, 4, 1, 12, 0, 1), + "deployment_id": UUID("6e887b81-57c3-4f3b-a7e7-bad159e05e78"), + }, + { + "id": 7, + "sensor_name": "Test", + "data": "1", + "collected_at": datetime.datetime(2020, 4, 1, 12, 1, 1), + "deployment_id": UUID("6e887b81-57c3-4f3b-a7e7-bad159e05e78"), + }, + { + "id": 8, + "sensor_name": "Test", + "data": "2", + "collected_at": datetime.datetime(2020, 4, 1, 12, 2, 1), + "deployment_id": UUID("6e887b81-57c3-4f3b-a7e7-bad159e05e78"), + }, + { + "id": 9, + "sensor_name": "OtherTest", + "data": "3", + "collected_at": datetime.datetime(2020, 4, 1, 12, 3, 1), + "deployment_id": UUID("6e887b81-57c3-4f3b-a7e7-bad159e05e78"), + }, + ] + for data in datas: + insert = datapoint_table.insert().values(**data) + db_session.execute(insert) diff --git a/Ch09/apd.aggregation-chapter09-ex01/tests/test_analysis.py b/Ch09/apd.aggregation-chapter09-ex01/tests/test_analysis.py new file mode 100644 index 0000000..31d4d5d --- /dev/null +++ b/Ch09/apd.aggregation-chapter09-ex01/tests/test_analysis.py @@ -0,0 +1,283 @@ +import datetime +import uuid + +import pytest + +from apd.aggregation import analysis +from apd.aggregation.database import DataPoint + + +async def generate_datapoints(datas): + deployment_id = uuid.uuid4() + for i, (time, data) in enumerate(datas, start=1): + yield DataPoint( + id=i, + collected_at=time, + sensor_name="TestSensor", + data=data, + deployment_id=deployment_id, + ) + + +class TestPassThroughCleaner: + @pytest.fixture + def cleaner(self): + return analysis.clean_passthrough + + @pytest.mark.asyncio + async def test_float_passthrough(self, cleaner): + data = [ + (datetime.datetime(2020, 4, 1, 12, 0, 0), 65.0), + (datetime.datetime(2020, 4, 1, 13, 0, 0), 65.5), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert output == data + + @pytest.mark.asyncio + async def test_Nones_are_skipped(self, cleaner): + data = [ + (datetime.datetime(2020, 4, 1, 12, 0, 0), None), + (datetime.datetime(2020, 4, 1, 13, 0, 0), 65.5), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert output == [ + (datetime.datetime(2020, 4, 1, 13, 0, 0), 65.5), + ] + + +class TestTemperatureCleaner: + @pytest.fixture + def cleaner(self): + return analysis.clean_temperature_fluctuations + + @pytest.mark.asyncio + async def test_window_not_full(self, cleaner): + data = [ + ( + datetime.datetime(2020, 4, 1, 12, 0, 0), + {"magnitude": 21.0, "unit": "degC"}, + ), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert output == [(datetime.datetime(2020, 4, 1, 12, 0, 0), 21.0)] + + @pytest.mark.asyncio + async def test_window_exactly_full(self, cleaner): + data = [ + ( + datetime.datetime(2020, 4, 1, 12, 0, 0), + {"magnitude": 21.0, "unit": "degC"}, + ), + ( + datetime.datetime(2020, 4, 1, 12, 0, 1), + {"magnitude": 21.0, "unit": "degC"}, + ), + ( + datetime.datetime(2020, 4, 1, 12, 0, 2), + {"magnitude": 21.0, "unit": "degC"}, + ), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert output == [ + (datetime.datetime(2020, 4, 1, 12, 0, 0), 21.0), + (datetime.datetime(2020, 4, 1, 12, 0, 1), 21.0), + (datetime.datetime(2020, 4, 1, 12, 0, 2), 21.0), + ] + + @pytest.mark.asyncio + async def test_window_overfilled(self, cleaner): + data = [ + ( + datetime.datetime(2020, 4, 1, 12, 0, 0), + {"magnitude": 21.0, "unit": "degC"}, + ), + ( + datetime.datetime(2020, 4, 1, 12, 0, 1), + {"magnitude": 21.0, "unit": "degC"}, + ), + ( + datetime.datetime(2020, 4, 1, 12, 0, 2), + {"magnitude": 21.0, "unit": "degC"}, + ), + ( + datetime.datetime(2020, 4, 1, 12, 0, 3), + {"magnitude": 21.0, "unit": "degC"}, + ), + ( + datetime.datetime(2020, 4, 1, 12, 0, 4), + {"magnitude": 21.0, "unit": "degC"}, + ), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert output == [ + (datetime.datetime(2020, 4, 1, 12, 0, 0), 21.0), + (datetime.datetime(2020, 4, 1, 12, 0, 1), 21.0), + (datetime.datetime(2020, 4, 1, 12, 0, 2), 21.0), + (datetime.datetime(2020, 4, 1, 12, 0, 3), 21.0), + (datetime.datetime(2020, 4, 1, 12, 0, 4), 21.0), + ] + + @pytest.mark.asyncio + async def test_outlier_dropped(self, cleaner): + data = [ + ( + datetime.datetime(2020, 4, 1, 12, 0, 0), + {"magnitude": 21.0, "unit": "degC"}, + ), + ( + datetime.datetime(2020, 4, 1, 12, 0, 1), + {"magnitude": 61.0, "unit": "degC"}, + ), + ( + datetime.datetime(2020, 4, 1, 12, 0, 2), + {"magnitude": 21.0, "unit": "degC"}, + ), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert output == [ + (datetime.datetime(2020, 4, 1, 12, 0, 0), 21.0), + (datetime.datetime(2020, 4, 1, 12, 0, 2), 21.0), + ] + + @pytest.mark.asyncio + async def test_limited_to_DHT22_range(self, cleaner): + data = [ + ( + datetime.datetime(2020, 4, 1, 12, 0, 0), + {"magnitude": 91.0, "unit": "degC"}, + ), + ( + datetime.datetime(2020, 4, 1, 12, 0, 1), + {"magnitude": 91.0, "unit": "degC"}, + ), + ( + datetime.datetime(2020, 4, 1, 12, 0, 2), + {"magnitude": 91.0, "unit": "degC"}, + ), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert output == [] + + @pytest.mark.asyncio + async def test_Nones_dropped(self, cleaner): + data = [ + ( + datetime.datetime(2020, 4, 1, 12, 0, 0), + {"magnitude": 31.0, "unit": "degC"}, + ), + (datetime.datetime(2020, 4, 1, 12, 0, 1), None,), + ( + datetime.datetime(2020, 4, 1, 12, 0, 2), + {"magnitude": 32.0, "unit": "degC"}, + ), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert output == [ + (datetime.datetime(2020, 4, 1, 12, 0, 0), 31.0), + (datetime.datetime(2020, 4, 1, 12, 0, 2), 32.0), + ] + + +class TestWattHourCleaner: + @pytest.fixture + def cleaner(self): + return analysis.clean_watthours_to_watts + + @pytest.mark.asyncio + async def test_one_entry_insufficient_to_find_diff(self, cleaner): + data = [ + ( + datetime.datetime(2020, 4, 1, 12, 0, 0), + {"magnitude": 1.0, "unit": "watt_hour"}, + ), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert output == [] + + @pytest.mark.asyncio + async def test_two_entries_provides_diff_with_second_time(self, cleaner): + data = [ + ( + datetime.datetime(2020, 4, 1, 12, 0, 0), + {"magnitude": 1.0, "unit": "watt_hour"}, + ), + ( + datetime.datetime(2020, 4, 1, 13, 0, 0), + {"magnitude": 10.0, "unit": "watt_hour"}, + ), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert len(output) == 1 + assert output[0][0] == datetime.datetime(2020, 4, 1, 13, 0, 0) + assert output[0][1] == pytest.approx(9.0, 0.0001) + + @pytest.mark.asyncio + async def test_fractional_hour(self, cleaner): + data = [ + ( + datetime.datetime(2020, 4, 1, 12, 0, 0), + {"magnitude": 1.0, "unit": "watt_hour"}, + ), + ( + datetime.datetime(2020, 4, 1, 12, 23, 42), + {"magnitude": 10.0, "unit": "watt_hour"}, + ), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert len(output) == 1 + assert output[0][0] == datetime.datetime(2020, 4, 1, 12, 23, 42) + assert output[0][1] == pytest.approx(22.7848, 0.001) + + @pytest.mark.asyncio + async def test_diff_happens_pairwise(self, cleaner): + data = [ + ( + datetime.datetime(2020, 4, 1, 12, 0, 0), + {"magnitude": 1.0, "unit": "watt_hour"}, + ), + ( + datetime.datetime(2020, 4, 1, 12, 23, 42), + {"magnitude": 10.0, "unit": "watt_hour"}, + ), + ( + datetime.datetime(2020, 4, 1, 13, 23, 42), + {"magnitude": 13.0, "unit": "watt_hour"}, + ), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert len(output) == 2 + assert output[0][0] == datetime.datetime(2020, 4, 1, 12, 23, 42) + assert output[0][1] == pytest.approx(22.7848, 0.001) + assert output[1][0] == datetime.datetime(2020, 4, 1, 13, 23, 42) + assert output[1][1] == pytest.approx(3, 0.001) + + @pytest.mark.asyncio + async def test_None_ignored(self, cleaner): + data = [ + ( + datetime.datetime(2020, 4, 1, 12, 0, 0), + {"magnitude": 1.0, "unit": "watt_hour"}, + ), + (datetime.datetime(2020, 4, 1, 12, 58, 0), None,), + ( + datetime.datetime(2020, 4, 1, 13, 0, 0), + {"magnitude": 10.0, "unit": "watt_hour"}, + ), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert len(output) == 1 + assert output[0][0] == datetime.datetime(2020, 4, 1, 13, 0, 0) + assert output[0][1] == pytest.approx(9.0, 0.0001) diff --git a/Ch09/apd.aggregation-chapter09-ex01/tests/test_http_get.py b/Ch09/apd.aggregation-chapter09-ex01/tests/test_http_get.py new file mode 100644 index 0000000..d38fd4c --- /dev/null +++ b/Ch09/apd.aggregation-chapter09-ex01/tests/test_http_get.py @@ -0,0 +1,154 @@ +from concurrent.futures import ThreadPoolExecutor +import datetime +import typing as t +from unittest.mock import patch, MagicMock +import wsgiref.simple_server + +import aiohttp +from apd.sensors.base import Sensor +from apd.sensors.sensors import PythonVersion, ACStatus +from apd.sensors.wsgi import set_up_config +import flask +import pytest + +from apd.sensors.wsgi import v21 + +from apd.aggregation import collect + +pytestmark = [pytest.mark.functional] + + +@pytest.fixture +def sensors() -> t.Iterator[t.List[Sensor[t.Any]]]: + """ Patch the get_sensors method to return a known pair of sensors only """ + data: t.List[Sensor[t.Any]] = [PythonVersion(), ACStatus()] + with patch("apd.sensors.cli.get_sensors") as get_sensors: + get_sensors.return_value = data + yield data + + +def get_independent_flask_app(name: str) -> flask.Flask: + """ Create a new flask app with the v20 API blueprint loaded, so multiple copies + of the app can be run in parallel without conflicting configuration """ + app = flask.Flask(name) + app.register_blueprint(v21.version, url_prefix="/v/2.1") + return app + + +def run_server_in_thread( + name: str, config: t.Dict[str, t.Any], port: int +) -> t.Iterator[str]: + # Create a new flask app and load in required code, to prevent config conflicts + app = get_independent_flask_app(name) + flask_app = set_up_config(config, app) + server = wsgiref.simple_server.make_server("localhost", port, flask_app) + + with ThreadPoolExecutor() as pool: + pool.submit(server.serve_forever) + yield f"http://localhost:{port}/" + server.shutdown() + + +@pytest.fixture(scope="module") +def http_server(): + yield from run_server_in_thread( + "standard", + { + "APD_SENSORS_API_KEY": "testing", + "APD_SENSORS_DEPLOYMENT_ID": "a46b1d1207fd4cdcad39bbdf706dfe29", + }, + 12081, + ) + + +@pytest.fixture(scope="module") +def bad_api_key_http_server(): + yield from run_server_in_thread( + "alternate", + { + "APD_SENSORS_API_KEY": "penny", + "APD_SENSORS_DEPLOYMENT_ID": "38cf2bae9adb445fad946c82e290487a", + }, + 12082, + ) + + +class TestGetDataPoints: + @pytest.fixture + def mut(self): + return collect.get_data_points + + @pytest.mark.asyncio + async def test_get_data_points( + self, sensors: t.List[Sensor[t.Any]], mut, http_server: str + ) -> None: + # Get the data from the server, storing the time before and after + # as bounds for the collected_at value + async with aiohttp.ClientSession() as http: + collect.http_session_var.set(http) + time_before = datetime.datetime.now() + results = await mut(http_server, "testing") + time_after = datetime.datetime.now() + + assert len(results) == len(sensors) == 2 + + for (sensor, result) in zip(sensors, results): + assert sensor.from_json_compatible(result.data) == sensor.value() + assert result.sensor_name == sensor.name + assert time_before <= result.collected_at <= time_after + + @pytest.mark.asyncio + async def test_get_data_points_fails_with_bad_api_key( + self, sensors: t.List[Sensor[t.Any]], mut, http_server: str + ) -> None: + with pytest.raises( + ValueError, + match=f"Error loading data from {http_server}: Supply API key in X-API-Key header", + ): + async with aiohttp.ClientSession() as http: + collect.http_session_var.set(http) + await mut(http_server, "incorrect") + + +class TestAddDataFromSensors: + @pytest.fixture + def mut(self): + return collect.add_data_from_sensors + + @pytest.fixture + def mock_db_session(self): + return MagicMock() + + @pytest.mark.asyncio + async def test_get_get_data_from_sensors( + self, mock_db_session, sensors: t.List[Sensor[t.Any]], mut, http_server: str + ) -> None: + results = await mut(mock_db_session, [http_server], "testing") + assert mock_db_session.execute.call_count == len(sensors) + assert len(results) == len(sensors) + + @pytest.mark.asyncio + async def test_get_get_data_from_sensors_with_multiple_servers( + self, mock_db_session, sensors: t.List[Sensor[t.Any]], mut, http_server: str + ) -> None: + results = await mut(mock_db_session, [http_server, http_server], "testing") + assert mock_db_session.execute.call_count == len(sensors) * 2 + assert len(results) == len(sensors) * 2 + + @pytest.mark.asyncio + async def test_data_points_not_added_if_only_partial_success( + self, + mock_db_session, + sensors: t.List[Sensor[t.Any]], + mut, + http_server: str, + bad_api_key_http_server: str, + ) -> None: + with pytest.raises( + ValueError, + match=f"Error loading data from {bad_api_key_http_server}: Supply API key in X-API-Key header", + ): + await mut( + mock_db_session, [http_server, bad_api_key_http_server], "testing" + ) + assert mock_db_session.execute.call_count == 0 diff --git a/Ch09/apd.aggregation-chapter09-ex01/tests/test_query.py b/Ch09/apd.aggregation-chapter09-ex01/tests/test_query.py new file mode 100644 index 0000000..9b6e0aa --- /dev/null +++ b/Ch09/apd.aggregation-chapter09-ex01/tests/test_query.py @@ -0,0 +1,111 @@ +import datetime +from uuid import UUID + +import pytest + +from apd.aggregation.query import ( + get_data, + get_deployment_ids, + get_data_by_deployment, + db_session_var, +) + + +class TestGetData: + @pytest.fixture + def mut(self): + return get_data + + @pytest.mark.asyncio + @pytest.mark.parametrize( + "filter,num_items_expected", + [ + ({}, 9), + ({"sensor_name": "Test"}, 7), + ({"deployment_id": UUID("b4c68905-b1e4-4875-940e-69e5d27730fd")}, 5), + ({"collected_after": datetime.datetime(2020, 4, 1, 12, 2, 1)}, 3), + ({"collected_before": datetime.datetime(2020, 4, 1, 12, 2, 1)}, 4), + ( + { + "collected_after": datetime.datetime(2020, 4, 1, 12, 2, 1), + "collected_before": datetime.datetime(2020, 4, 1, 12, 3, 5), + }, + 2, + ), + ], + ) + async def test_iterate_over_items( + self, mut, db_session, populated_db, filter, num_items_expected + ): + db_session_var.set(db_session) + points = [dp async for dp in mut(**filter)] + assert len(points) == num_items_expected + + @pytest.mark.asyncio + async def test_empty_db(self, mut, db_session, migrated_db): + db_session_var.set(db_session) + points = [dp async for dp in mut()] + assert len(points) == 0 + + +class TestGetDeployments: + @pytest.fixture + def mut(self): + return get_deployment_ids + + @pytest.mark.asyncio + async def test_get_all_deployments(self, mut, db_session, populated_db): + db_session_var.set(db_session) + deployment_ids = await mut() + assert set(deployment_ids) == { + UUID("b4c68905-b1e4-4875-940e-69e5d27730fd"), + UUID("6e887b81-57c3-4f3b-a7e7-bad159e05e78"), + } + + @pytest.mark.asyncio + async def test_empty_db(self, mut, db_session, migrated_db): + db_session_var.set(db_session) + deployment_ids = await mut() + assert deployment_ids == [] + + +@pytest.mark.usefixtures("populated_db") +class TestGetDataByDeployment: + @pytest.fixture + def mut(self): + return get_data_by_deployment + + @pytest.mark.asyncio + @pytest.mark.parametrize( + "filter,num_items_expected", [({}, 9), ({"sensor_name": "Test"}, 7)] + ) + async def test_iterate_over_all_items( + self, mut, db_session, filter, num_items_expected + ): + db_session_var.set(db_session) + num_items = 0 + num_deployments = 0 + async for deployment_id, items in mut(**filter): + num_deployments += 1 + async for item in items: + num_items += 1 + assert num_deployments == 2 + assert num_items == num_items_expected + + @pytest.mark.asyncio + @pytest.mark.parametrize( + "filter,num_items_expected", [({}, 5), ({"sensor_name": "Test"}, 4)] + ) + async def test_skip_first_deployment( + self, mut, db_session, filter, num_items_expected + ): + db_session_var.set(db_session) + num_items = 0 + num_deployments = 0 + async for deployment_id, items in mut(**filter): + num_deployments += 1 + if num_deployments == 2: + async for item in items: + num_items += 1 + assert num_deployments == 2 + assert num_items == num_items_expected diff --git a/Ch09/apd.aggregation-chapter09-ex01/tests/test_sensor_aggregation.py b/Ch09/apd.aggregation-chapter09-ex01/tests/test_sensor_aggregation.py new file mode 100644 index 0000000..0f1872d --- /dev/null +++ b/Ch09/apd.aggregation-chapter09-ex01/tests/test_sensor_aggregation.py @@ -0,0 +1,174 @@ +from __future__ import annotations + +import contextlib +from dataclasses import dataclass +import json +import typing as t +from unittest.mock import patch, Mock + +import pytest +import sqlalchemy + +import apd.aggregation.collect + + +@pytest.fixture +def data() -> t.Any: + return { + "sensors": [ + { + "human_readable": "3.7", + "id": "PythonVersion", + "title": "Python Version", + "value": [3, 7, 2, "final", 0], + }, + { + "human_readable": "Not connected", + "id": "ACStatus", + "title": "AC Connected", + "value": False, + }, + ] + } + + +@dataclass +class FakeAIOHttpClient: + responses: t.Dict[str, str] + + @contextlib.asynccontextmanager + async def get( + self, url: str, headers: t.Optional[t.Dict[str, str]] = None + ) -> t.AsyncIterator[FakeAIOHttpResponse]: + if url in self.responses: + yield FakeAIOHttpResponse(body=self.responses[url]) + else: + yield FakeAIOHttpResponse(body="", status=404) + + +@dataclass +class FakeAIOHttpResponse: + body: str + status: int = 200 + + async def json(self) -> t.Any: + return json.loads(self.body) + + +@pytest.fixture +def mockclient(data) -> FakeAIOHttpClient: + return FakeAIOHttpClient( + { + "http://localhost/v/2.1/sensors/": json.dumps(data), + "http://localhost/v/2.1/deployment_id": json.dumps( + {"deployment_id": "b29ba0ee10f14552b6b21327bb96d3fb"} + ), + } + ) + + +class TestGetDataPoints: + @pytest.fixture + def mut(self): + return apd.aggregation.collect.get_data_points + + @pytest.mark.asyncio + async def test_get_data_points( + self, mut, mockclient: FakeAIOHttpClient, data + ) -> None: + token = apd.aggregation.collect.http_session_var.set(mockclient) + try: + datapoints = await mut("http://localhost", "") + finally: + apd.aggregation.collect.http_session_var.reset(token) + + assert len(datapoints) == len(data["sensors"]) + for sensor in data["sensors"]: + assert sensor["value"] in (datapoint.data for datapoint in datapoints) + assert sensor["id"] in (datapoint.sensor_name for datapoint in datapoints) + + +class TestAddDataFromSensors: + @pytest.fixture + def mut(self): + return apd.aggregation.collect.add_data_from_sensors + + @pytest.fixture(autouse=True) + def patch_aiohttp(self, mockclient): + with patch("aiohttp.ClientSession") as ClientSession: + ClientSession.return_value.__aenter__.return_value = mockclient + yield ClientSession + + @pytest.fixture + def db_session(self): + session = Mock() + sql_result = session.execute.return_value + sql_result.inserted_primary_key = [1] + return session + + @pytest.mark.asyncio + async def test_datapoints_are_added_to_the_session(self, mut, db_session) -> None: + assert db_session.execute.call_count == 0 + datapoints = await mut(db_session, ["http://localhost"], "") + assert db_session.execute.call_count == len(datapoints) + + +@pytest.mark.usefixtures("migrated_db") +class TestDatabaseConnection: + @pytest.fixture(autouse=True) + def patch_aiohttp(self, mockclient): + with patch("aiohttp.ClientSession") as ClientSession: + ClientSession.return_value.__aenter__.return_value = mockclient + yield ClientSession + + @pytest.fixture + def table(self): + return apd.aggregation.database.datapoint_table + + @pytest.fixture + def daily_summary_view(self): + return apd.aggregation.database.daily_summary_view + + @pytest.fixture + def model(self): + return apd.aggregation.database.DataPoint + + @pytest.fixture + def mut(self): + return apd.aggregation.collect.add_data_from_sensors + + @pytest.mark.asyncio + async def test_datapoints_are_added_to_the_session(self, db_session, table) -> None: + datapoints = await apd.aggregation.collect.add_data_from_sensors( + db_session, ["http://localhost"], "" + ) + num_points = db_session.query(table).count() + assert num_points == len(datapoints) == 2 + + @pytest.mark.asyncio + async def test_datapoints_can_be_mapped_back_to_DataPoints( + self, mut, db_session, table, model + ) -> None: + datapoints = await mut(db_session, ["http://localhost"], "") + db_points = [ + model.from_sql_result(result) for result in db_session.query(table) + ] + assert db_points == datapoints + + @pytest.mark.asyncio + async def test_daily_summary_view_matches_query( + self, db_session, model, table, daily_summary_view + ) -> None: + await apd.aggregation.collect.add_data_from_sensors( + db_session, ["http://localhost"], "" + ) + + headers = table.c.sensor_name, table.c.data + value_counts = ( + db_session.query(*headers, sqlalchemy.func.count(table.c.id)) + .filter(model.collected_on_date == sqlalchemy.func.current_date()) + .group_by(*headers) + ) + + daily_summary_view = db_session.query(daily_summary_view) + assert value_counts.all() == daily_summary_view.all() diff --git a/Ch09/apd.aggregation-chapter09-ex02/.coveragerc b/Ch09/apd.aggregation-chapter09-ex02/.coveragerc new file mode 100644 index 0000000..e766c69 --- /dev/null +++ b/Ch09/apd.aggregation-chapter09-ex02/.coveragerc @@ -0,0 +1,3 @@ +[run] +branch = True +omit = tests/* diff --git a/Ch09/apd.aggregation-chapter09-ex02/.pre-commit-config.yaml b/Ch09/apd.aggregation-chapter09-ex02/.pre-commit-config.yaml new file mode 100644 index 0000000..d3b6ec7 --- /dev/null +++ b/Ch09/apd.aggregation-chapter09-ex02/.pre-commit-config.yaml @@ -0,0 +1,26 @@ +repos: + +- repo: local + hooks: + - id: black + name: black + entry: pipenv run black + args: [--quiet] + language: system + types: [python] + + - id: mypy + name: mypy + exclude: (?x)^( + (.*)/setup.py + )$ + entry: pipenv run mypy + args: ["--follow-imports=skip"] + language: system + types: [python] + + - id: flake8 + name: flake8 + entry: pipenv run flake8 + language: system + types: [python] diff --git a/Ch09/apd.aggregation-chapter09-ex02/CHANGES.md b/Ch09/apd.aggregation-chapter09-ex02/CHANGES.md new file mode 100644 index 0000000..8e424e5 --- /dev/null +++ b/Ch09/apd.aggregation-chapter09-ex02/CHANGES.md @@ -0,0 +1,5 @@ +## Changes + +### 1.0.0 (Unreleased) + +* Generated from skeleton diff --git a/Ch09/apd.aggregation-chapter09-ex02/LICENCE b/Ch09/apd.aggregation-chapter09-ex02/LICENCE new file mode 100644 index 0000000..86685d4 --- /dev/null +++ b/Ch09/apd.aggregation-chapter09-ex02/LICENCE @@ -0,0 +1,31 @@ +Copyright (c) 2019, Matthew Wilkes + + +All rights reserved. + +Redistribution and use in source and binary forms, with or without modification, +are permitted provided that the following conditions are met: + +* Redistributions of source code must retain the above copyright notice, this + list of conditions and the following disclaimer. + +* Redistributions in binary form must reproduce the above copyright notice, this + list of conditions and the following disclaimer in the documentation and/or + other materials provided with the distribution. + +* Neither the name of the copyright holder nor the names of its + contributors may be used to endorse or promote products derived from this + software without specific prior written permission. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND +ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED +WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. +IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, +INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, +BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, +DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY +OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE +OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED +OF THE POSSIBILITY OF SUCH DAMAGE. + + diff --git a/Ch09/apd.aggregation-chapter09-ex02/Pipfile b/Ch09/apd.aggregation-chapter09-ex02/Pipfile new file mode 100644 index 0000000..b308175 --- /dev/null +++ b/Ch09/apd.aggregation-chapter09-ex02/Pipfile @@ -0,0 +1,25 @@ +[[source]] +name = "pypi" +url = "https://pypi.org/simple" +verify_ssl = true + +[dev-packages] +pytest = "*" +pytest-cov = "*" +pytest-asyncio = "*" +mypy = "*" +flake8 = "*" +black = "*" +pre-commit = "*" +wheel = "*" +twine = "*" +sqlalchemy-stubs = "*" + +[packages] +apd-aggregation = {editable = true,extras = ["jupyter"],path = "."} +apd-sensors = {editable = true,extras = ["webapp"],path = "./../code"} + +[requires] + +[pipenv] +allow_prereleases = true diff --git a/Ch09/apd.aggregation-chapter09-ex02/Pipfile.lock b/Ch09/apd.aggregation-chapter09-ex02/Pipfile.lock new file mode 100644 index 0000000..37ebbba --- /dev/null +++ b/Ch09/apd.aggregation-chapter09-ex02/Pipfile.lock @@ -0,0 +1,751 @@ +{ + "_meta": { + "hash": { + "sha256": "9cbbc3af88a1213ed8f4f93c33e35a9f9d7e047077cd25ff02f9bde13e2b606b" + }, + "pipfile-spec": 6, + "requires": {}, + "sources": [ + { + "name": "pypi", + "url": "https://pypi.org/simple", + "verify_ssl": true + } + ] + }, + "default": { + "aiohttp": { + "hashes": [ + "sha256:173267050501e1537293df06723bc5e719990889e2820ba3932969983892e960", + "sha256:438f1f1555c02c50894604d94944cff188fe138b46467b7fa99fdceb51ab5842", + "sha256:90bed250d1435aef33a1f8c439c5056d5d25a44fe6caf33fcafafed805bad4dc", + 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0000000..85669dd --- /dev/null +++ b/Ch09/apd.aggregation-chapter09-ex02/README.md @@ -0,0 +1,4 @@ +# APD Sensor aggregator + +A programme that queries apd.sensor endpoints and aggregates their results. + diff --git a/Ch09/apd.aggregation-chapter09-ex02/pyproject.toml b/Ch09/apd.aggregation-chapter09-ex02/pyproject.toml new file mode 100644 index 0000000..4d3bb67 --- /dev/null +++ b/Ch09/apd.aggregation-chapter09-ex02/pyproject.toml @@ -0,0 +1,5 @@ +[build-system] +requires = [ + "setuptools", +] +build-backend = "setuptools.build_meta" diff --git a/Ch09/apd.aggregation-chapter09-ex02/pytest.ini b/Ch09/apd.aggregation-chapter09-ex02/pytest.ini new file mode 100644 index 0000000..c1d3e58 --- /dev/null +++ b/Ch09/apd.aggregation-chapter09-ex02/pytest.ini @@ -0,0 +1,3 @@ +[pytest] +markers = + functional: these tests are significantly slower as they start a HTTP server \ No newline at end of file diff --git a/Ch09/apd.aggregation-chapter09-ex02/setup.cfg b/Ch09/apd.aggregation-chapter09-ex02/setup.cfg new file mode 100644 index 0000000..060cccc --- /dev/null +++ b/Ch09/apd.aggregation-chapter09-ex02/setup.cfg @@ -0,0 +1,76 @@ +[mypy] +namespace_packages = True +mypy_path = src +plugins = sqlmypy + +[mypy-alembic] +ignore_missing_imports = True + +[mypy-alembic.config] +ignore_missing_imports = True + +[mypy-alembic.script] +ignore_missing_imports = True + +[mypy-alembic.runtime.environment] +ignore_missing_imports = True + +[mypy-matplotlib] +ignore_missing_imports = True + +[mypy-matplotlib.axes._base] +ignore_missing_imports = True + +[mypy-matplotlib.figure] +ignore_missing_imports = True + +[mypy-matplotlib.pyplot] +ignore_missing_imports = True + + +[mypy-pint] +ignore_missing_imports = True + +[mypy-pytest] +ignore_missing_imports = True + +[flake8] +max-line-length = 120 +ignore = E501 + +[metadata] +name = apd.aggregation +version = attr: apd.aggregation.VERSION +description = A programme that queries apd.sensor endpoints and aggregates their results. +long_description = file: README.md, CHANGES.md, LICENCE +long_description_content_type = text/markdown +keywords = +license = BSD +classifiers = + Programming Language :: Python :: 3 + Programming Language :: Python :: 3.7 + +[options] +zip_safe = False +include_package_data = True +package_dir = + =src +packages = find_namespace: +install_requires = + sqlalchemy + aiohttp + pint + psycopg2 + alembic + click + + +[options.extras_require] +jupyter = ipywidgets + +[options.packages.find] +where = src + +[options.entry_points] +console_scripts = + collect_sensor_data = apd.aggregation.cli:collect_sensor_data diff --git a/Ch09/apd.aggregation-chapter09-ex02/setup.py b/Ch09/apd.aggregation-chapter09-ex02/setup.py new file mode 100644 index 0000000..6068493 --- /dev/null +++ b/Ch09/apd.aggregation-chapter09-ex02/setup.py @@ -0,0 +1,3 @@ +from setuptools import setup + +setup() diff --git a/Ch09/apd.aggregation-chapter09-ex02/src/apd/aggregation/__init__.py b/Ch09/apd.aggregation-chapter09-ex02/src/apd/aggregation/__init__.py new file mode 100644 index 0000000..3277f64 --- /dev/null +++ b/Ch09/apd.aggregation-chapter09-ex02/src/apd/aggregation/__init__.py @@ -0,0 +1 @@ +VERSION = "1.0.0" diff --git a/Ch09/apd.aggregation-chapter09-ex02/src/apd/aggregation/alembic/README b/Ch09/apd.aggregation-chapter09-ex02/src/apd/aggregation/alembic/README new file mode 100644 index 0000000..97a87d5 --- /dev/null +++ b/Ch09/apd.aggregation-chapter09-ex02/src/apd/aggregation/alembic/README @@ -0,0 +1,12 @@ +Generic single-database configuration. + + +# Database setup + +To generate the required database tables you must create an alembic.ini file, as follows: + + [alembic] + script_location = apd.aggregation:alembic + sqlalchemy.url = postgresql+psycopg2://apd@localhost/apd + +and run `alembic upgrade head`. This should also be done after every upgrade of the software. \ No newline at end of file diff --git a/Ch09/apd.aggregation-chapter09-ex02/src/apd/aggregation/alembic/env.py b/Ch09/apd.aggregation-chapter09-ex02/src/apd/aggregation/alembic/env.py new file mode 100644 index 0000000..4c8a2c2 --- /dev/null +++ b/Ch09/apd.aggregation-chapter09-ex02/src/apd/aggregation/alembic/env.py @@ -0,0 +1,80 @@ +from logging.config import fileConfig + +from sqlalchemy import engine_from_config +from sqlalchemy import pool +from alembic import context + +from apd.aggregation.database import metadata + + +# this is the Alembic Config object, which provides +# access to the values within the .ini file in use. +config = context.config + +# Interpret the config file for Python logging. +# This line sets up loggers basically. +if config.config_file_name: + fileConfig(config.config_file_name) + +target_metadata = metadata + + +def include_object(object, name, type_, reflected, compare_to): + if object.info.get("is_view", False): + return False + return True + + +def run_migrations_offline(): + """Run migrations in 'offline' mode. + + This configures the context with just a URL + and not an Engine, though an Engine is acceptable + here as well. By skipping the Engine creation + we don't even need a DBAPI to be available. + + Calls to context.execute() here emit the given string to the + script output. + + """ + url = config.get_main_option("sqlalchemy.url") + context.configure( + url=url, + include_object=include_object, + target_metadata=target_metadata, + literal_binds=True, + dialect_opts={"paramstyle": "named"}, + ) + + with context.begin_transaction(): + context.run_migrations() + + +def run_migrations_online(): + """Run migrations in 'online' mode. + + In this scenario we need to create an Engine + and associate a connection with the context. + + """ + connectable = engine_from_config( + config.get_section(config.config_ini_section), + prefix="sqlalchemy.", + poolclass=pool.NullPool, + ) + + with connectable.connect() as connection: + context.configure( + connection=connection, + target_metadata=target_metadata, + include_object=include_object, + ) + + with context.begin_transaction(): + context.run_migrations() + + +if context.is_offline_mode(): + run_migrations_offline() +else: + run_migrations_online() diff --git a/Ch09/apd.aggregation-chapter09-ex02/src/apd/aggregation/alembic/script.py.mako b/Ch09/apd.aggregation-chapter09-ex02/src/apd/aggregation/alembic/script.py.mako new file mode 100644 index 0000000..2c01563 --- /dev/null +++ b/Ch09/apd.aggregation-chapter09-ex02/src/apd/aggregation/alembic/script.py.mako @@ -0,0 +1,24 @@ +"""${message} + +Revision ID: ${up_revision} +Revises: ${down_revision | comma,n} +Create Date: ${create_date} + +""" +from alembic import op +import sqlalchemy as sa +${imports if imports else ""} + +# revision identifiers, used by Alembic. +revision = ${repr(up_revision)} +down_revision = ${repr(down_revision)} +branch_labels = ${repr(branch_labels)} +depends_on = ${repr(depends_on)} + + +def upgrade(): + ${upgrades if upgrades else "pass"} + + +def downgrade(): + ${downgrades if downgrades else "pass"} diff --git a/Ch09/apd.aggregation-chapter09-ex02/src/apd/aggregation/alembic/versions/4b2df8a6e1ce_add_indexes_to_datapoints.py b/Ch09/apd.aggregation-chapter09-ex02/src/apd/aggregation/alembic/versions/4b2df8a6e1ce_add_indexes_to_datapoints.py new file mode 100644 index 0000000..ef671bd --- /dev/null +++ b/Ch09/apd.aggregation-chapter09-ex02/src/apd/aggregation/alembic/versions/4b2df8a6e1ce_add_indexes_to_datapoints.py @@ -0,0 +1,32 @@ +"""Add indexes to datapoints + +Revision ID: 4b2df8a6e1ce +Revises: 6d2eacd5da3f +Create Date: 2019-12-02 16:07:41.123116 + +""" +from alembic import op + + +# revision identifiers, used by Alembic. +revision = "4b2df8a6e1ce" +down_revision = "6d2eacd5da3f" +branch_labels = None +depends_on = None + + +def upgrade(): + op.create_index( + op.f("ix_datapoints_collected_at"), + "datapoints", + ["collected_at"], + unique=False, + ) + op.create_index( + op.f("ix_datapoints_sensor_name"), "datapoints", ["sensor_name"], unique=False, + ) + + +def downgrade(): + op.drop_index(op.f("ix_datapoints_sensor_name"), table_name="datapoints") + op.drop_index(op.f("ix_datapoints_collected_at"), table_name="datapoints") diff --git a/Ch09/apd.aggregation-chapter09-ex02/src/apd/aggregation/alembic/versions/6962f8455a6d_add_daily_summary_view.py b/Ch09/apd.aggregation-chapter09-ex02/src/apd/aggregation/alembic/versions/6962f8455a6d_add_daily_summary_view.py new file mode 100644 index 0000000..eb02455 --- /dev/null +++ b/Ch09/apd.aggregation-chapter09-ex02/src/apd/aggregation/alembic/versions/6962f8455a6d_add_daily_summary_view.py @@ -0,0 +1,38 @@ +"""Add daily summary view + +Revision ID: 6962f8455a6d +Revises: 4b2df8a6e1ce +Create Date: 2019-12-03 11:50:24.403402 + +""" +from alembic import op + + +# revision identifiers, used by Alembic. +revision = "6962f8455a6d" +down_revision = "4b2df8a6e1ce" +branch_labels = None +depends_on = None + + +def upgrade(): + create_view = """ + CREATE VIEW daily_summary AS + SELECT + datapoints.sensor_name AS sensor_name, + datapoints.data AS data, + count(datapoints.id) AS count + FROM datapoints + WHERE + datapoints.collected_at >= CAST(CURRENT_DATE AS DATE) + AND + datapoints.collected_at < CAST(CURRENT_DATE AS DATE) + 1 + GROUP BY + datapoints.sensor_name, + datapoints.data; + """ + op.execute(create_view) + + +def downgrade(): + op.execute("""DROP VIEW daily_summary""") diff --git a/Ch09/apd.aggregation-chapter09-ex02/src/apd/aggregation/alembic/versions/6d2eacd5da3f_create_sensor_values_table.py b/Ch09/apd.aggregation-chapter09-ex02/src/apd/aggregation/alembic/versions/6d2eacd5da3f_create_sensor_values_table.py new file mode 100644 index 0000000..b4a3545 --- /dev/null +++ b/Ch09/apd.aggregation-chapter09-ex02/src/apd/aggregation/alembic/versions/6d2eacd5da3f_create_sensor_values_table.py @@ -0,0 +1,31 @@ +"""Create datapoints table + +Revision ID: 6d2eacd5da3f +Revises: N/A +Create Date: 2019-09-29 13:43:21.242706 + +""" +from alembic import op +import sqlalchemy as sa +from sqlalchemy.dialects import postgresql + +# revision identifiers, used by Alembic. +revision = "6d2eacd5da3f" +down_revision = None +branch_labels = None +depends_on = None + + +def upgrade(): + op.create_table( + "datapoints", + sa.Column("id", sa.Integer(), nullable=False), + sa.Column("sensor_name", sa.String(), nullable=True), + sa.Column("collected_at", postgresql.TIMESTAMP(), nullable=True), + sa.Column("data", postgresql.JSONB(astext_type=sa.Text()), nullable=True), + sa.PrimaryKeyConstraint("id"), + ) + + +def downgrade(): + op.drop_table("datapoints") diff --git a/Ch09/apd.aggregation-chapter09-ex02/src/apd/aggregation/alembic/versions/d8d4cf6a178f_add_deployment_id_to_datapoint.py b/Ch09/apd.aggregation-chapter09-ex02/src/apd/aggregation/alembic/versions/d8d4cf6a178f_add_deployment_id_to_datapoint.py new file mode 100644 index 0000000..88d5219 --- /dev/null +++ b/Ch09/apd.aggregation-chapter09-ex02/src/apd/aggregation/alembic/versions/d8d4cf6a178f_add_deployment_id_to_datapoint.py @@ -0,0 +1,34 @@ +"""Add deployment id to DataPoint + +Revision ID: d8d4cf6a178f +Revises: 6962f8455a6d +Create Date: 2019-12-03 20:58:11.285509 + +""" +from alembic import op +import sqlalchemy as sa +from sqlalchemy.dialects import postgresql + +# revision identifiers, used by Alembic. +revision = "d8d4cf6a178f" +down_revision = "6962f8455a6d" +branch_labels = None +depends_on = None + + +def upgrade(): + op.add_column( + "datapoints", + sa.Column("deployment_id", postgresql.UUID(as_uuid=True), nullable=True), + ) + op.create_index( + op.f("ix_datapoints_deployment_id"), + "datapoints", + ["deployment_id"], + unique=False, + ) + + +def downgrade(): + op.drop_index(op.f("ix_datapoints_deployment_id"), table_name="datapoints") + op.drop_column("datapoints", "deployment_id") diff --git a/Ch09/apd.aggregation-chapter09-ex02/src/apd/aggregation/analysis.py b/Ch09/apd.aggregation-chapter09-ex02/src/apd/aggregation/analysis.py new file mode 100644 index 0000000..664b45b --- /dev/null +++ b/Ch09/apd.aggregation-chapter09-ex02/src/apd/aggregation/analysis.py @@ -0,0 +1,226 @@ +from __future__ import annotations + +import asyncio +import collections +from concurrent.futures import ThreadPoolExecutor +import dataclasses +import datetime +import functools +import math +import typing as t +from uuid import UUID + +import matplotlib.pyplot as plt +from matplotlib.axes._base import _AxesBase +from matplotlib.figure import Figure +from pint import _DEFAULT_REGISTRY as ureg + +from apd.aggregation.query import get_data_by_deployment, with_database +from apd.aggregation.database import DataPoint + + +@dataclasses.dataclass(frozen=True) +class Config: + title: str + sensor_name: str + clean: t.Callable[ + [t.AsyncIterator[DataPoint]], t.AsyncIterator[t.Tuple[datetime.datetime, float]] + ] + ylabel: str + + +async def clean_watthours_to_watts( + datapoints: t.AsyncIterator[DataPoint], +) -> t.AsyncIterator[t.Tuple[datetime.datetime, float]]: + last_watthours = None + last_time = None + async for datapoint in datapoints: + if datapoint.data is None: + continue + time = datapoint.collected_at + watthours = ureg.Quantity(datapoint.data["magnitude"], datapoint.data["unit"]) + if last_watthours: + seconds_elapsed = (time - last_time).total_seconds() + time_elapsed = ureg.Quantity(seconds_elapsed, ureg.second) + additional_power = watthours - last_watthours + power = additional_power / time_elapsed + yield time, power.to(ureg.watt).magnitude + last_watthours = watthours + last_time = datapoint.collected_at + + +async def clean_magnitude( + datapoints: t.AsyncIterator[DataPoint], +) -> t.AsyncIterator[t.Tuple[datetime.datetime, float]]: + async for datapoint in datapoints: + if datapoint.data is None: + continue + yield datapoint.collected_at, datapoint.data["magnitude"] + + +async def clean_temperature_fluctuations( + datapoints: t.AsyncIterator[DataPoint], +) -> t.AsyncIterator[t.Tuple[datetime.datetime, float]]: + allowed_jitter = 2.5 + allowed_range = (-40, 80) + window_datapoints: t.Deque[DataPoint] = collections.deque(maxlen=3) + + def datapoint_ok(datapoint: DataPoint) -> bool: + """Return False if this data point does not contain a valid temperature""" + if datapoint.data is None: + return False + elif datapoint.data["unit"] != "degC": + # This point is in a different temperature system. While it could be converted + # this cleaner is not yet doing that. + return False + elif not allowed_range[0] < datapoint.data["magnitude"] < allowed_range[1]: + return False + return True + + async for datapoint in datapoints: + if not datapoint_ok(datapoint): + # If the datapoint is invalid then skip directly to the next item + continue + + window_datapoints.append(datapoint) + if len(window_datapoints) == 3: + # Find the temperatures of the datapoints in the window, then average + # the first and last and compare that to the middle point. + window_temperatures = [dp.data["magnitude"] for dp in window_datapoints] + avr_first_last = (window_temperatures[0] + window_temperatures[2]) / 2 + diff_middle_avr = abs(window_temperatures[1] - avr_first_last) + if diff_middle_avr > allowed_jitter: + pass + else: + yield window_datapoints[1].collected_at, window_temperatures[1] + elif len(window_datapoints) == 1: + # The item in the iterator can't be compared to both neighbours + # so should be yielded + yield datapoint.collected_at, datapoint.data["magnitude"] + else: + # Otherwise, let the window fill up, it will be yieleded later + pass + # When the iterator ends the final item is not yet in the middle + # of the window, so the last item must be explicitly yielded. + if len(window_datapoints) > 1 and datapoint_ok(datapoint): + yield datapoint.collected_at, datapoint.data["magnitude"] + + +async def clean_passthrough( + datapoints: t.AsyncIterator[DataPoint], +) -> t.AsyncIterator[t.Tuple[datetime.datetime, float]]: + async for datapoint in datapoints: + if datapoint.data is None: + continue + else: + yield datapoint.collected_at, datapoint.data + + +configs = ( + Config( + sensor_name="SolarCumulativeOutput", + clean=clean_watthours_to_watts, + title="Solar generation", + ylabel="Watts", + ), + Config( + sensor_name="RAMAvailable", + clean=clean_passthrough, + title="RAM available", + ylabel="Bytes", + ), + Config( + sensor_name="RelativeHumidity", + clean=clean_passthrough, + title="Relative humidity", + ylabel="Percent", + ), + Config( + sensor_name="Temperature", + clean=clean_temperature_fluctuations, + title="Ambient temperature", + ylabel="Degrees C", + ), +) + + +def get_known_configs() -> t.Dict[str, Config]: + return {config.title: config for config in configs} + + +async def plot_sensor( + config: Config, plot: _AxesBase, location_names: t.Dict[UUID, str], **kwargs: t.Any +) -> _AxesBase: + locations = [] + async for deployment, query_results in get_data_by_deployment( + sensor_name=config.sensor_name, **kwargs + ): + # Mypy currently doesn't understand callable fields on datatypes: https://github.com/python/mypy/issues/5485 + points = [dp async for dp in config.clean(query_results)] # type: ignore + if not points: + continue + locations.append(deployment) + x, y = zip(*points) + plot.set_title(config.title) + plot.set_ylabel(config.ylabel) + plot.plot_date(x, y, f"-", xdate=True) + plot.legend([location_names.get(l, l) for l in locations]) + return plot + + +_Coroutine_Result = t.TypeVar("_Coroutine_Result") + + +def wrap_coroutine( + f: t.Callable[..., t.Coroutine[t.Any, t.Any, _Coroutine_Result]] +) -> t.Callable[..., _Coroutine_Result]: + """Given a coroutine, return a function that runs that coroutine + in a new event loop in an isolated thread""" + + @functools.wraps(f) + def run_in_thread(*args: t.Any, **kwargs: t.Any) -> _Coroutine_Result: + loop = asyncio.new_event_loop() + wrapped = f(*args, **kwargs) + with ThreadPoolExecutor(max_workers=1) as pool: + task = pool.submit(loop.run_until_complete, wrapped) + # Mypy can get confused when nesting generic functions, like we do here + # The fact that Task is generic means we lose the association with + # _CoroutineResult. Adding an explicit cast restores this. + return t.cast(_Coroutine_Result, task.result()) + + return run_in_thread + + +async def plot_multiple_charts(*args: t.Any, **kwargs: t.Any) -> Figure: + # These parameters are pulled from kwargs to avoid confusing function + # introspection code in IPython widgets + location_names = kwargs.pop("location_names", None) + configs = kwargs.pop("configs", None) + dimensions = kwargs.pop("dimensions", None) + + with with_database("postgresql+psycopg2://apd@localhost/apd"): + coros = [] + if configs is None: + # If no configs are supplied, use all known configs + configs = get_known_configs().values() + if dimensions is None: + # If no dimensions are supplied, get the square root of the number + # of configs and round it to find a number of columns. This will + # keep the arrangement approximately square. Find rows by multiplying + # out rows. + total_configs = len(configs) + columns = round(math.sqrt(total_configs)) + rows = math.ceil(total_configs / columns) + figure = plt.figure(figsize=(10 * columns, 5 * rows), dpi=300) + for i, config in enumerate(configs, start=1): + plot = figure.add_subplot(columns, rows, i) + coros.append(plot_sensor(config, plot, location_names, *args, **kwargs)) + await asyncio.gather(*coros) + return figure + + +def interactable_plot_multiple_charts( + *args: t.Any, **kwargs: t.Any +) -> t.Callable[..., Figure]: + with_config = functools.partial(plot_multiple_charts, *args, **kwargs) + return wrap_coroutine(with_config) diff --git a/Ch09/apd.aggregation-chapter09-ex02/src/apd/aggregation/cli.py b/Ch09/apd.aggregation-chapter09-ex02/src/apd/aggregation/cli.py new file mode 100644 index 0000000..3435e78 --- /dev/null +++ b/Ch09/apd.aggregation-chapter09-ex02/src/apd/aggregation/cli.py @@ -0,0 +1,52 @@ +import typing as t + +import click + +from .collect import standalone + + +@click.command() +@click.argument("server", nargs=-1) +@click.option( + "--db", + metavar="", + default="postgresql+psycopg2://localhost/apd", + help="The connection string to a PostgreSQL database", + envvar="APD_DB_URI", +) +@click.option("--api-key", metavar="", envvar="APD_API_KEY") +@click.option( + "--tolerate-failures", + "-f", + help="If provided, failure to retrieve some sensors' data will not abort the collection process", + is_flag=True, +) +@click.option("-v", "--verbose", is_flag=True, help="Enables verbose mode") +def collect_sensor_data( + db: str, server: t.Tuple[str], api_key: str, tolerate_failures: bool, verbose: bool +): + """This loads data from one or more sensors into the specified database. + + Only PostgreSQL databases are supported, as the column definitions use + multiple pg specific features. The database must already exist and be + populated with the required tables. + + The --api-key option is used to specify the access token for the sensors + being queried. + + You may specify any number of servers, the variable should be the full URL + to the sensor's HTTP interface, not including the /v/2.0 portion. Multiple + URLs should be separated with a space. + """ + if tolerate_failures: + attempts = [(s,) for s in server] + else: + attempts = [server] + success = True + for attempt in attempts: + try: + standalone(db, attempt, api_key, echo=verbose) + except ValueError as e: + click.secho(str(e), err=True, fg="red") + success = False + return success diff --git a/Ch09/apd.aggregation-chapter09-ex02/src/apd/aggregation/collect.py b/Ch09/apd.aggregation-chapter09-ex02/src/apd/aggregation/collect.py new file mode 100644 index 0000000..f745d69 --- /dev/null +++ b/Ch09/apd.aggregation-chapter09-ex02/src/apd/aggregation/collect.py @@ -0,0 +1,93 @@ +import asyncio +from contextvars import ContextVar +import datetime +import typing as t +import uuid + +import aiohttp +from sqlalchemy import create_engine +from sqlalchemy.orm import sessionmaker +from sqlalchemy.orm.session import Session + +from .database import DataPoint, datapoint_table + +http_session_var: ContextVar[aiohttp.ClientSession] = ContextVar("http_session") + + +async def get_data_points(server: str, api_key: t.Optional[str],) -> t.List[DataPoint]: + if not server.endswith("/"): + server += "/" + url = server + "v/2.1/sensors/" + deployment_id_url = server + "v/2.1/deployment_id" + headers = {} + if api_key: + headers["X-API-KEY"] = api_key + http = http_session_var.get() + + async with http.get(deployment_id_url) as request: + if request.status != 200: + raise ValueError(f"Error loading deployment id from {server}") + result = await request.json() + deployment_id = uuid.UUID(result["deployment_id"]) + + async with http.get(url, headers=headers) as request: + result = await request.json() + ok = request.status == 200 + now = datetime.datetime.now() + if ok: + points = [] + for value in result["sensors"]: + points.append( + DataPoint( + sensor_name=value["id"], + collected_at=now, + data=value["value"], + deployment_id=deployment_id, + ) + ) + return points + else: + raise ValueError( + f"Error loading data from {server}: " + result.get("error", "Unknown") + ) + + +def handle_result(result: t.List[DataPoint], session: Session) -> t.List[DataPoint]: + for point in result: + insert = datapoint_table.insert().values(**point._asdict()) + sql_result = session.execute(insert) + point.id = sql_result.inserted_primary_key[0] + return result + + +async def add_data_from_sensors( + session: Session, servers: t.Tuple[str], api_key: t.Optional[str] +) -> t.List[DataPoint]: + tasks: t.List[t.Awaitable[t.List[DataPoint]]] = [] + points: t.List[DataPoint] = [] + async with aiohttp.ClientSession() as http: + http_session_var.set(http) + tasks = [get_data_points(server, api_key) for server in servers] + for results in await asyncio.gather(*tasks): + points += results + loop = asyncio.get_running_loop() + await loop.run_in_executor(None, handle_result, points, session) + return points + + +def standalone( + db_uri: str, servers: t.Tuple[str], api_key: t.Optional[str], echo: bool = False +) -> None: + engine = create_engine(db_uri, echo=echo) + sm = sessionmaker(engine) + Session = sm() + asyncio.run(add_data_from_sensors(Session, servers, api_key)) + Session.commit() + + +if __name__ == "__main__": + standalone( + db_uri="postgresql+psycopg2://apd@localhost/apd", + servers=("http://pvoutput:8080/",), + api_key="h3hdfjksfhwkjehnwekj", + ) diff --git a/Ch09/apd.aggregation-chapter09-ex02/src/apd/aggregation/database.py b/Ch09/apd.aggregation-chapter09-ex02/src/apd/aggregation/database.py new file mode 100644 index 0000000..0ffcb90 --- /dev/null +++ b/Ch09/apd.aggregation-chapter09-ex02/src/apd/aggregation/database.py @@ -0,0 +1,102 @@ +from __future__ import annotations + +from dataclasses import dataclass, field, asdict +import datetime +import typing as t +import uuid + +import sqlalchemy +from sqlalchemy.dialects.postgresql import JSONB, DATE, TIMESTAMP, UUID +from sqlalchemy.ext.hybrid import ExprComparator, hybrid_property +from sqlalchemy.orm import sessionmaker +from sqlalchemy.schema import Table + + +metadata = sqlalchemy.MetaData() + +datapoint_table = Table( + "datapoints", + metadata, + sqlalchemy.Column("id", sqlalchemy.Integer, primary_key=True), + sqlalchemy.Column("sensor_name", sqlalchemy.String, index=True), + sqlalchemy.Column("collected_at", TIMESTAMP, index=True), + sqlalchemy.Column("deployment_id", UUID(as_uuid=True), index=True), + sqlalchemy.Column("data", JSONB), +) + +daily_summary_view = Table( + "daily_summary", + metadata, + sqlalchemy.Column("sensor_name", sqlalchemy.String), + sqlalchemy.Column("data", JSONB), + sqlalchemy.Column("count", sqlalchemy.Integer), + info={"is_view": True}, +) + + +class DateEqualComparator(ExprComparator): + def __init__(self, fallback_expression, raw_expression): + # Do not try and find update expression from parent + super().__init__(None, fallback_expression, None) + self.raw_expression = raw_expression + + def __eq__(self, other): + """ Returns True iff on the same day as other """ + other_date = sqlalchemy.cast(other, DATE) + return sqlalchemy.and_( + self.raw_expression >= other_date, self.raw_expression < other_date + 1, + ) + + def operate(self, op, *other, **kwargs): + other = [sqlalchemy.cast(date, DATE) for date in other] + return op(self.expression, *other, **kwargs) + + def reverse_operate(self, op, other, **kwargs): + other = [sqlalchemy.cast(date, DATE) for date in other] + return op(other, self.expression, **kwargs) + + +@dataclass +class DataPoint: + sensor_name: str + data: t.Any + deployment_id: uuid.UUID + id: t.Optional[int] = None + collected_at: datetime.datetime = field(default_factory=datetime.datetime.now) + + @classmethod + def from_sql_result(cls, result) -> DataPoint: + return cls(**result._asdict()) + + def _asdict(self) -> t.Dict[str, t.Any]: + data = asdict(self) + if data["id"] is None: + del data["id"] + return data + + @hybrid_property + def collected_on_date(self): + return self.collected_at.date() + + @collected_on_date.comparator # type: ignore + def collected_on_date(cls): + return DateEqualComparator( + fallback_expression=sqlalchemy.cast(datapoint_table.c.collected_at, DATE), + raw_expression=datapoint_table.c.collected_at, + ) + + +def main() -> None: + engine = sqlalchemy.create_engine( + "postgresql+psycopg2://apd@localhost/apd", echo=True + ) + sm = sessionmaker(engine) + Session = sm() + if False: + metadata.create_all(engine) + print(Session.query(DataPoint).all()) + pass + + +if __name__ == "__main__": + main() diff --git a/Ch09/apd.aggregation-chapter09-ex02/src/apd/aggregation/query.py b/Ch09/apd.aggregation-chapter09-ex02/src/apd/aggregation/query.py new file mode 100644 index 0000000..5f22de5 --- /dev/null +++ b/Ch09/apd.aggregation-chapter09-ex02/src/apd/aggregation/query.py @@ -0,0 +1,129 @@ +import asyncio +import contextlib +from contextvars import ContextVar +import datetime +import typing as t +from uuid import UUID + +from sqlalchemy import create_engine +from sqlalchemy.orm import sessionmaker +from sqlalchemy.orm.session import Session + + +from apd.aggregation.database import datapoint_table, DataPoint + + +db_session_var: ContextVar[Session] = ContextVar("db_session") + + +@contextlib.contextmanager +def with_database(uri: t.Optional[str] = None) -> t.Iterator[Session]: + """Given a URI, set up a DB connection, and return a Session as a context manager """ + if uri is None: + uri = "postgresql+psycopg2://localhost/apd" + engine = create_engine(uri) + sm = sessionmaker(engine) + Session = sm() + token = db_session_var.set(Session) + yield Session + db_session_var.reset(token) + Session.commit() + Session.close() + + +async def get_data( + sensor_name: t.Optional[str] = None, + deployment_id: t.Optional[UUID] = None, + collected_before: t.Optional[datetime.datetime] = None, + collected_after: t.Optional[datetime.datetime] = None, +) -> t.AsyncIterator[DataPoint]: + db_session = db_session_var.get() + loop = asyncio.get_running_loop() + query = db_session.query(datapoint_table) + if sensor_name: + query = query.filter(datapoint_table.c.sensor_name == sensor_name) + if deployment_id: + query = query.filter(datapoint_table.c.deployment_id == deployment_id) + if collected_before: + query = query.filter(datapoint_table.c.collected_at < collected_before) + if collected_after: + query = query.filter(datapoint_table.c.collected_at > collected_after) + query = query.order_by( + datapoint_table.c.deployment_id, + datapoint_table.c.sensor_name, + datapoint_table.c.collected_at, + ) + + rows = await loop.run_in_executor(None, query.all) + for row in rows: + yield DataPoint.from_sql_result(row) + + +async def get_deployment_ids() -> t.List[UUID]: + db_session = db_session_var.get() + loop = asyncio.get_running_loop() + query = db_session.query(datapoint_table.c.deployment_id).distinct() + return [row.deployment_id for row in await loop.run_in_executor(None, query.all)] + + +async def get_data_by_deployment( + *args: t.Any, **kwargs: t.Any +) -> t.AsyncIterator[t.Tuple[UUID, t.AsyncIterator[DataPoint]]]: + """Return an Async Iterator that contains two-item pairs. + These pairs are a string (deployment_id), and an async iterator that contains + the datapoints with that deployment_id. + + Usage example: + + async for deployment_id, datapoints in get_data_by_deployment(): + print(deployment_id) + async for datapoint in datapoints: + print(datapoint) + print() + """ + # Get the data, using the arguments to this function as filters + data = get_data(*args, **kwargs) + + # The two levels of iterator share the item variable, initialise it with the + # first item from the iterator. Also set last_deployment_id to None, so the + # outer iterator knows to start a new group. + last_deployment_id: t.Optional[UUID] = None + try: + item = await data.__anext__() + except StopAsyncIteration: + # There were no items in the underlying query, return immediately + return + + async def subiterator(group_id: UUID) -> t.AsyncIterator[DataPoint]: + """Using a closure, create an iterator that yields the current + item, then yields all items from data while the deployment_id matches + group_id, leaving the first that doesn't match as item in the enclosing + scope.""" + # item is from the enclosing scope + nonlocal item + while item.deployment_id == group_id: + # yield items from data while they match the group_id this iterator represents + yield item + try: + # Advance the underlying iterator + item = await data.__anext__() + except StopAsyncIteration: + # The underlying iterator came to an end, so end the subiterator too + return + + while True: + while item.deployment_id == last_deployment_id: + # We are trying to advance the outer iterator while the underlying iterator + # is still part-way through a group. Speed through the underlying until we + # hit an item where the deployment_id is different to the last one (or, is not + # None, in the case of the start of the iterator) + try: + item = await data.__anext__() + except StopAsyncIteration: + # We hit the end of the underlying iterator: end this iterator too + return + last_deployment_id = item.deployment_id + # Don't yield an iterator for unclassified deployments + if last_deployment_id is not None: + # Instantiate a subiterator for this group + yield last_deployment_id, subiterator(last_deployment_id) diff --git a/Ch09/apd.aggregation-chapter09-ex02/tests/__init__.py b/Ch09/apd.aggregation-chapter09-ex02/tests/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/Ch09/apd.aggregation-chapter09-ex02/tests/conftest.py b/Ch09/apd.aggregation-chapter09-ex02/tests/conftest.py new file mode 100644 index 0000000..65db278 --- /dev/null +++ b/Ch09/apd.aggregation-chapter09-ex02/tests/conftest.py @@ -0,0 +1,123 @@ +import datetime +from uuid import UUID + +from apd.aggregation.database import datapoint_table + +from alembic.config import Config +from alembic.script import ScriptDirectory +from alembic.runtime.environment import EnvironmentContext +import pytest + + +@pytest.fixture +def db_uri(): + return "postgresql+psycopg2://apd@localhost/apd-test" + + +@pytest.fixture +def db_session(db_uri): + from sqlalchemy import create_engine + from sqlalchemy.orm import sessionmaker + + engine = create_engine(db_uri, echo=True) + sm = sessionmaker(engine) + Session = sm() + yield Session + Session.close() + + +@pytest.fixture +def migrated_db(db_uri, db_session): + config = Config() + config.set_main_option("script_location", "apd.aggregation:alembic") + config.set_main_option("sqlalchemy.url", db_uri) + script = ScriptDirectory.from_config(config) + + def upgrade(rev, context): + return script._upgrade_revs(script.get_current_head(), rev) + + def downgrade(rev, context): + return script._downgrade_revs(None, rev) + + with EnvironmentContext(config, script, fn=upgrade): + script.run_env() + + yield + + # Clear any pending work from the db_session connection + db_session.rollback() + + with EnvironmentContext(config, script, fn=downgrade): + script.run_env() + + +@pytest.fixture +def populated_db(migrated_db, db_session): + datas = [ + { + "id": 1, + "sensor_name": "Test", + "data": "1", + "collected_at": datetime.datetime(2020, 4, 1, 12, 0, 1), + "deployment_id": UUID("b4c68905-b1e4-4875-940e-69e5d27730fd"), + }, + { + "id": 2, + "sensor_name": "Test", + "data": "2", + "collected_at": datetime.datetime(2020, 4, 1, 12, 1, 1), + "deployment_id": UUID("b4c68905-b1e4-4875-940e-69e5d27730fd"), + }, + { + "id": 3, + "sensor_name": "Test", + "data": "3", + "collected_at": datetime.datetime(2020, 4, 1, 12, 2, 1), + "deployment_id": UUID("b4c68905-b1e4-4875-940e-69e5d27730fd"), + }, + { + "id": 4, + "sensor_name": "Test", + "data": "4", + "collected_at": datetime.datetime(2020, 4, 1, 12, 3, 1), + "deployment_id": UUID("b4c68905-b1e4-4875-940e-69e5d27730fd"), + }, + { + "id": 5, + "sensor_name": "OtherTest", + "data": "5", + "collected_at": datetime.datetime(2020, 4, 1, 12, 4, 1), + "deployment_id": UUID("b4c68905-b1e4-4875-940e-69e5d27730fd"), + }, + { + "id": 6, + "sensor_name": "Test", + "data": "1", + "collected_at": datetime.datetime(2020, 4, 1, 12, 0, 1), + "deployment_id": UUID("6e887b81-57c3-4f3b-a7e7-bad159e05e78"), + }, + { + "id": 7, + "sensor_name": "Test", + "data": "1", + "collected_at": datetime.datetime(2020, 4, 1, 12, 1, 1), + "deployment_id": UUID("6e887b81-57c3-4f3b-a7e7-bad159e05e78"), + }, + { + "id": 8, + "sensor_name": "Test", + "data": "2", + "collected_at": datetime.datetime(2020, 4, 1, 12, 2, 1), + "deployment_id": UUID("6e887b81-57c3-4f3b-a7e7-bad159e05e78"), + }, + { + "id": 9, + "sensor_name": "OtherTest", + "data": "3", + "collected_at": datetime.datetime(2020, 4, 1, 12, 3, 1), + "deployment_id": UUID("6e887b81-57c3-4f3b-a7e7-bad159e05e78"), + }, + ] + for data in datas: + insert = datapoint_table.insert().values(**data) + db_session.execute(insert) diff --git a/Ch09/apd.aggregation-chapter09-ex02/tests/test_analysis.py b/Ch09/apd.aggregation-chapter09-ex02/tests/test_analysis.py new file mode 100644 index 0000000..31d4d5d --- /dev/null +++ b/Ch09/apd.aggregation-chapter09-ex02/tests/test_analysis.py @@ -0,0 +1,283 @@ +import datetime +import uuid + +import pytest + +from apd.aggregation import analysis +from apd.aggregation.database import DataPoint + + +async def generate_datapoints(datas): + deployment_id = uuid.uuid4() + for i, (time, data) in enumerate(datas, start=1): + yield DataPoint( + id=i, + collected_at=time, + sensor_name="TestSensor", + data=data, + deployment_id=deployment_id, + ) + + +class TestPassThroughCleaner: + @pytest.fixture + def cleaner(self): + return analysis.clean_passthrough + + @pytest.mark.asyncio + async def test_float_passthrough(self, cleaner): + data = [ + (datetime.datetime(2020, 4, 1, 12, 0, 0), 65.0), + (datetime.datetime(2020, 4, 1, 13, 0, 0), 65.5), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert output == data + + @pytest.mark.asyncio + async def test_Nones_are_skipped(self, cleaner): + data = [ + (datetime.datetime(2020, 4, 1, 12, 0, 0), None), + (datetime.datetime(2020, 4, 1, 13, 0, 0), 65.5), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert output == [ + (datetime.datetime(2020, 4, 1, 13, 0, 0), 65.5), + ] + + +class TestTemperatureCleaner: + @pytest.fixture + def cleaner(self): + return analysis.clean_temperature_fluctuations + + @pytest.mark.asyncio + async def test_window_not_full(self, cleaner): + data = [ + ( + datetime.datetime(2020, 4, 1, 12, 0, 0), + {"magnitude": 21.0, "unit": "degC"}, + ), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert output == [(datetime.datetime(2020, 4, 1, 12, 0, 0), 21.0)] + + @pytest.mark.asyncio + async def test_window_exactly_full(self, cleaner): + data = [ + ( + datetime.datetime(2020, 4, 1, 12, 0, 0), + {"magnitude": 21.0, "unit": "degC"}, + ), + ( + datetime.datetime(2020, 4, 1, 12, 0, 1), + {"magnitude": 21.0, "unit": "degC"}, + ), + ( + datetime.datetime(2020, 4, 1, 12, 0, 2), + {"magnitude": 21.0, "unit": "degC"}, + ), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert output == [ + (datetime.datetime(2020, 4, 1, 12, 0, 0), 21.0), + (datetime.datetime(2020, 4, 1, 12, 0, 1), 21.0), + (datetime.datetime(2020, 4, 1, 12, 0, 2), 21.0), + ] + + @pytest.mark.asyncio + async def test_window_overfilled(self, cleaner): + data = [ + ( + datetime.datetime(2020, 4, 1, 12, 0, 0), + {"magnitude": 21.0, "unit": "degC"}, + ), + ( + datetime.datetime(2020, 4, 1, 12, 0, 1), + {"magnitude": 21.0, "unit": "degC"}, + ), + ( + datetime.datetime(2020, 4, 1, 12, 0, 2), + {"magnitude": 21.0, "unit": "degC"}, + ), + ( + datetime.datetime(2020, 4, 1, 12, 0, 3), + {"magnitude": 21.0, "unit": "degC"}, + ), + ( + datetime.datetime(2020, 4, 1, 12, 0, 4), + {"magnitude": 21.0, "unit": "degC"}, + ), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert output == [ + (datetime.datetime(2020, 4, 1, 12, 0, 0), 21.0), + (datetime.datetime(2020, 4, 1, 12, 0, 1), 21.0), + (datetime.datetime(2020, 4, 1, 12, 0, 2), 21.0), + (datetime.datetime(2020, 4, 1, 12, 0, 3), 21.0), + (datetime.datetime(2020, 4, 1, 12, 0, 4), 21.0), + ] + + @pytest.mark.asyncio + async def test_outlier_dropped(self, cleaner): + data = [ + ( + datetime.datetime(2020, 4, 1, 12, 0, 0), + {"magnitude": 21.0, "unit": "degC"}, + ), + ( + datetime.datetime(2020, 4, 1, 12, 0, 1), + {"magnitude": 61.0, "unit": "degC"}, + ), + ( + datetime.datetime(2020, 4, 1, 12, 0, 2), + {"magnitude": 21.0, "unit": "degC"}, + ), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert output == [ + (datetime.datetime(2020, 4, 1, 12, 0, 0), 21.0), + (datetime.datetime(2020, 4, 1, 12, 0, 2), 21.0), + ] + + @pytest.mark.asyncio + async def test_limited_to_DHT22_range(self, cleaner): + data = [ + ( + datetime.datetime(2020, 4, 1, 12, 0, 0), + {"magnitude": 91.0, "unit": "degC"}, + ), + ( + datetime.datetime(2020, 4, 1, 12, 0, 1), + {"magnitude": 91.0, "unit": "degC"}, + ), + ( + datetime.datetime(2020, 4, 1, 12, 0, 2), + {"magnitude": 91.0, "unit": "degC"}, + ), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert output == [] + + @pytest.mark.asyncio + async def test_Nones_dropped(self, cleaner): + data = [ + ( + datetime.datetime(2020, 4, 1, 12, 0, 0), + {"magnitude": 31.0, "unit": "degC"}, + ), + (datetime.datetime(2020, 4, 1, 12, 0, 1), None,), + ( + datetime.datetime(2020, 4, 1, 12, 0, 2), + {"magnitude": 32.0, "unit": "degC"}, + ), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert output == [ + (datetime.datetime(2020, 4, 1, 12, 0, 0), 31.0), + (datetime.datetime(2020, 4, 1, 12, 0, 2), 32.0), + ] + + +class TestWattHourCleaner: + @pytest.fixture + def cleaner(self): + return analysis.clean_watthours_to_watts + + @pytest.mark.asyncio + async def test_one_entry_insufficient_to_find_diff(self, cleaner): + data = [ + ( + datetime.datetime(2020, 4, 1, 12, 0, 0), + {"magnitude": 1.0, "unit": "watt_hour"}, + ), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert output == [] + + @pytest.mark.asyncio + async def test_two_entries_provides_diff_with_second_time(self, cleaner): + data = [ + ( + datetime.datetime(2020, 4, 1, 12, 0, 0), + {"magnitude": 1.0, "unit": "watt_hour"}, + ), + ( + datetime.datetime(2020, 4, 1, 13, 0, 0), + {"magnitude": 10.0, "unit": "watt_hour"}, + ), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert len(output) == 1 + assert output[0][0] == datetime.datetime(2020, 4, 1, 13, 0, 0) + assert output[0][1] == pytest.approx(9.0, 0.0001) + + @pytest.mark.asyncio + async def test_fractional_hour(self, cleaner): + data = [ + ( + datetime.datetime(2020, 4, 1, 12, 0, 0), + {"magnitude": 1.0, "unit": "watt_hour"}, + ), + ( + datetime.datetime(2020, 4, 1, 12, 23, 42), + {"magnitude": 10.0, "unit": "watt_hour"}, + ), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert len(output) == 1 + assert output[0][0] == datetime.datetime(2020, 4, 1, 12, 23, 42) + assert output[0][1] == pytest.approx(22.7848, 0.001) + + @pytest.mark.asyncio + async def test_diff_happens_pairwise(self, cleaner): + data = [ + ( + datetime.datetime(2020, 4, 1, 12, 0, 0), + {"magnitude": 1.0, "unit": "watt_hour"}, + ), + ( + datetime.datetime(2020, 4, 1, 12, 23, 42), + {"magnitude": 10.0, "unit": "watt_hour"}, + ), + ( + datetime.datetime(2020, 4, 1, 13, 23, 42), + {"magnitude": 13.0, "unit": "watt_hour"}, + ), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert len(output) == 2 + assert output[0][0] == datetime.datetime(2020, 4, 1, 12, 23, 42) + assert output[0][1] == pytest.approx(22.7848, 0.001) + assert output[1][0] == datetime.datetime(2020, 4, 1, 13, 23, 42) + assert output[1][1] == pytest.approx(3, 0.001) + + @pytest.mark.asyncio + async def test_None_ignored(self, cleaner): + data = [ + ( + datetime.datetime(2020, 4, 1, 12, 0, 0), + {"magnitude": 1.0, "unit": "watt_hour"}, + ), + (datetime.datetime(2020, 4, 1, 12, 58, 0), None,), + ( + datetime.datetime(2020, 4, 1, 13, 0, 0), + {"magnitude": 10.0, "unit": "watt_hour"}, + ), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert len(output) == 1 + assert output[0][0] == datetime.datetime(2020, 4, 1, 13, 0, 0) + assert output[0][1] == pytest.approx(9.0, 0.0001) diff --git a/Ch09/apd.aggregation-chapter09-ex02/tests/test_http_get.py b/Ch09/apd.aggregation-chapter09-ex02/tests/test_http_get.py new file mode 100644 index 0000000..d38fd4c --- /dev/null +++ b/Ch09/apd.aggregation-chapter09-ex02/tests/test_http_get.py @@ -0,0 +1,154 @@ +from concurrent.futures import ThreadPoolExecutor +import datetime +import typing as t +from unittest.mock import patch, MagicMock +import wsgiref.simple_server + +import aiohttp +from apd.sensors.base import Sensor +from apd.sensors.sensors import PythonVersion, ACStatus +from apd.sensors.wsgi import set_up_config +import flask +import pytest + +from apd.sensors.wsgi import v21 + +from apd.aggregation import collect + +pytestmark = [pytest.mark.functional] + + +@pytest.fixture +def sensors() -> t.Iterator[t.List[Sensor[t.Any]]]: + """ Patch the get_sensors method to return a known pair of sensors only """ + data: t.List[Sensor[t.Any]] = [PythonVersion(), ACStatus()] + with patch("apd.sensors.cli.get_sensors") as get_sensors: + get_sensors.return_value = data + yield data + + +def get_independent_flask_app(name: str) -> flask.Flask: + """ Create a new flask app with the v20 API blueprint loaded, so multiple copies + of the app can be run in parallel without conflicting configuration """ + app = flask.Flask(name) + app.register_blueprint(v21.version, url_prefix="/v/2.1") + return app + + +def run_server_in_thread( + name: str, config: t.Dict[str, t.Any], port: int +) -> t.Iterator[str]: + # Create a new flask app and load in required code, to prevent config conflicts + app = get_independent_flask_app(name) + flask_app = set_up_config(config, app) + server = wsgiref.simple_server.make_server("localhost", port, flask_app) + + with ThreadPoolExecutor() as pool: + pool.submit(server.serve_forever) + yield f"http://localhost:{port}/" + server.shutdown() + + +@pytest.fixture(scope="module") +def http_server(): + yield from run_server_in_thread( + "standard", + { + "APD_SENSORS_API_KEY": "testing", + "APD_SENSORS_DEPLOYMENT_ID": "a46b1d1207fd4cdcad39bbdf706dfe29", + }, + 12081, + ) + + +@pytest.fixture(scope="module") +def bad_api_key_http_server(): + yield from run_server_in_thread( + "alternate", + { + "APD_SENSORS_API_KEY": "penny", + "APD_SENSORS_DEPLOYMENT_ID": "38cf2bae9adb445fad946c82e290487a", + }, + 12082, + ) + + +class TestGetDataPoints: + @pytest.fixture + def mut(self): + return collect.get_data_points + + @pytest.mark.asyncio + async def test_get_data_points( + self, sensors: t.List[Sensor[t.Any]], mut, http_server: str + ) -> None: + # Get the data from the server, storing the time before and after + # as bounds for the collected_at value + async with aiohttp.ClientSession() as http: + collect.http_session_var.set(http) + time_before = datetime.datetime.now() + results = await mut(http_server, "testing") + time_after = datetime.datetime.now() + + assert len(results) == len(sensors) == 2 + + for (sensor, result) in zip(sensors, results): + assert sensor.from_json_compatible(result.data) == sensor.value() + assert result.sensor_name == sensor.name + assert time_before <= result.collected_at <= time_after + + @pytest.mark.asyncio + async def test_get_data_points_fails_with_bad_api_key( + self, sensors: t.List[Sensor[t.Any]], mut, http_server: str + ) -> None: + with pytest.raises( + ValueError, + match=f"Error loading data from {http_server}: Supply API key in X-API-Key header", + ): + async with aiohttp.ClientSession() as http: + collect.http_session_var.set(http) + await mut(http_server, "incorrect") + + +class TestAddDataFromSensors: + @pytest.fixture + def mut(self): + return collect.add_data_from_sensors + + @pytest.fixture + def mock_db_session(self): + return MagicMock() + + @pytest.mark.asyncio + async def test_get_get_data_from_sensors( + self, mock_db_session, sensors: t.List[Sensor[t.Any]], mut, http_server: str + ) -> None: + results = await mut(mock_db_session, [http_server], "testing") + assert mock_db_session.execute.call_count == len(sensors) + assert len(results) == len(sensors) + + @pytest.mark.asyncio + async def test_get_get_data_from_sensors_with_multiple_servers( + self, mock_db_session, sensors: t.List[Sensor[t.Any]], mut, http_server: str + ) -> None: + results = await mut(mock_db_session, [http_server, http_server], "testing") + assert mock_db_session.execute.call_count == len(sensors) * 2 + assert len(results) == len(sensors) * 2 + + @pytest.mark.asyncio + async def test_data_points_not_added_if_only_partial_success( + self, + mock_db_session, + sensors: t.List[Sensor[t.Any]], + mut, + http_server: str, + bad_api_key_http_server: str, + ) -> None: + with pytest.raises( + ValueError, + match=f"Error loading data from {bad_api_key_http_server}: Supply API key in X-API-Key header", + ): + await mut( + mock_db_session, [http_server, bad_api_key_http_server], "testing" + ) + assert mock_db_session.execute.call_count == 0 diff --git a/Ch09/apd.aggregation-chapter09-ex02/tests/test_query.py b/Ch09/apd.aggregation-chapter09-ex02/tests/test_query.py new file mode 100644 index 0000000..9b6e0aa --- /dev/null +++ b/Ch09/apd.aggregation-chapter09-ex02/tests/test_query.py @@ -0,0 +1,111 @@ +import datetime +from uuid import UUID + +import pytest + +from apd.aggregation.query import ( + get_data, + get_deployment_ids, + get_data_by_deployment, + db_session_var, +) + + +class TestGetData: + @pytest.fixture + def mut(self): + return get_data + + @pytest.mark.asyncio + @pytest.mark.parametrize( + "filter,num_items_expected", + [ + ({}, 9), + ({"sensor_name": "Test"}, 7), + ({"deployment_id": UUID("b4c68905-b1e4-4875-940e-69e5d27730fd")}, 5), + ({"collected_after": datetime.datetime(2020, 4, 1, 12, 2, 1)}, 3), + ({"collected_before": datetime.datetime(2020, 4, 1, 12, 2, 1)}, 4), + ( + { + "collected_after": datetime.datetime(2020, 4, 1, 12, 2, 1), + "collected_before": datetime.datetime(2020, 4, 1, 12, 3, 5), + }, + 2, + ), + ], + ) + async def test_iterate_over_items( + self, mut, db_session, populated_db, filter, num_items_expected + ): + db_session_var.set(db_session) + points = [dp async for dp in mut(**filter)] + assert len(points) == num_items_expected + + @pytest.mark.asyncio + async def test_empty_db(self, mut, db_session, migrated_db): + db_session_var.set(db_session) + points = [dp async for dp in mut()] + assert len(points) == 0 + + +class TestGetDeployments: + @pytest.fixture + def mut(self): + return get_deployment_ids + + @pytest.mark.asyncio + async def test_get_all_deployments(self, mut, db_session, populated_db): + db_session_var.set(db_session) + deployment_ids = await mut() + assert set(deployment_ids) == { + UUID("b4c68905-b1e4-4875-940e-69e5d27730fd"), + UUID("6e887b81-57c3-4f3b-a7e7-bad159e05e78"), + } + + @pytest.mark.asyncio + async def test_empty_db(self, mut, db_session, migrated_db): + db_session_var.set(db_session) + deployment_ids = await mut() + assert deployment_ids == [] + + +@pytest.mark.usefixtures("populated_db") +class TestGetDataByDeployment: + @pytest.fixture + def mut(self): + return get_data_by_deployment + + @pytest.mark.asyncio + @pytest.mark.parametrize( + "filter,num_items_expected", [({}, 9), ({"sensor_name": "Test"}, 7)] + ) + async def test_iterate_over_all_items( + self, mut, db_session, filter, num_items_expected + ): + db_session_var.set(db_session) + num_items = 0 + num_deployments = 0 + async for deployment_id, items in mut(**filter): + num_deployments += 1 + async for item in items: + num_items += 1 + assert num_deployments == 2 + assert num_items == num_items_expected + + @pytest.mark.asyncio + @pytest.mark.parametrize( + "filter,num_items_expected", [({}, 5), ({"sensor_name": "Test"}, 4)] + ) + async def test_skip_first_deployment( + self, mut, db_session, filter, num_items_expected + ): + db_session_var.set(db_session) + num_items = 0 + num_deployments = 0 + async for deployment_id, items in mut(**filter): + num_deployments += 1 + if num_deployments == 2: + async for item in items: + num_items += 1 + assert num_deployments == 2 + assert num_items == num_items_expected diff --git a/Ch09/apd.aggregation-chapter09-ex02/tests/test_sensor_aggregation.py b/Ch09/apd.aggregation-chapter09-ex02/tests/test_sensor_aggregation.py new file mode 100644 index 0000000..0f1872d --- /dev/null +++ b/Ch09/apd.aggregation-chapter09-ex02/tests/test_sensor_aggregation.py @@ -0,0 +1,174 @@ +from __future__ import annotations + +import contextlib +from dataclasses import dataclass +import json +import typing as t +from unittest.mock import patch, Mock + +import pytest +import sqlalchemy + +import apd.aggregation.collect + + +@pytest.fixture +def data() -> t.Any: + return { + "sensors": [ + { + "human_readable": "3.7", + "id": "PythonVersion", + "title": "Python Version", + "value": [3, 7, 2, "final", 0], + }, + { + "human_readable": "Not connected", + "id": "ACStatus", + "title": "AC Connected", + "value": False, + }, + ] + } + + +@dataclass +class FakeAIOHttpClient: + responses: t.Dict[str, str] + + @contextlib.asynccontextmanager + async def get( + self, url: str, headers: t.Optional[t.Dict[str, str]] = None + ) -> t.AsyncIterator[FakeAIOHttpResponse]: + if url in self.responses: + yield FakeAIOHttpResponse(body=self.responses[url]) + else: + yield FakeAIOHttpResponse(body="", status=404) + + +@dataclass +class FakeAIOHttpResponse: + body: str + status: int = 200 + + async def json(self) -> t.Any: + return json.loads(self.body) + + +@pytest.fixture +def mockclient(data) -> FakeAIOHttpClient: + return FakeAIOHttpClient( + { + "http://localhost/v/2.1/sensors/": json.dumps(data), + "http://localhost/v/2.1/deployment_id": json.dumps( + {"deployment_id": "b29ba0ee10f14552b6b21327bb96d3fb"} + ), + } + ) + + +class TestGetDataPoints: + @pytest.fixture + def mut(self): + return apd.aggregation.collect.get_data_points + + @pytest.mark.asyncio + async def test_get_data_points( + self, mut, mockclient: FakeAIOHttpClient, data + ) -> None: + token = apd.aggregation.collect.http_session_var.set(mockclient) + try: + datapoints = await mut("http://localhost", "") + finally: + apd.aggregation.collect.http_session_var.reset(token) + + assert len(datapoints) == len(data["sensors"]) + for sensor in data["sensors"]: + assert sensor["value"] in (datapoint.data for datapoint in datapoints) + assert sensor["id"] in (datapoint.sensor_name for datapoint in datapoints) + + +class TestAddDataFromSensors: + @pytest.fixture + def mut(self): + return apd.aggregation.collect.add_data_from_sensors + + @pytest.fixture(autouse=True) + def patch_aiohttp(self, mockclient): + with patch("aiohttp.ClientSession") as ClientSession: + ClientSession.return_value.__aenter__.return_value = mockclient + yield ClientSession + + @pytest.fixture + def db_session(self): + session = Mock() + sql_result = session.execute.return_value + sql_result.inserted_primary_key = [1] + return session + + @pytest.mark.asyncio + async def test_datapoints_are_added_to_the_session(self, mut, db_session) -> None: + assert db_session.execute.call_count == 0 + datapoints = await mut(db_session, ["http://localhost"], "") + assert db_session.execute.call_count == len(datapoints) + + +@pytest.mark.usefixtures("migrated_db") +class TestDatabaseConnection: + @pytest.fixture(autouse=True) + def patch_aiohttp(self, mockclient): + with patch("aiohttp.ClientSession") as ClientSession: + ClientSession.return_value.__aenter__.return_value = mockclient + yield ClientSession + + @pytest.fixture + def table(self): + return apd.aggregation.database.datapoint_table + + @pytest.fixture + def daily_summary_view(self): + return apd.aggregation.database.daily_summary_view + + @pytest.fixture + def model(self): + return apd.aggregation.database.DataPoint + + @pytest.fixture + def mut(self): + return apd.aggregation.collect.add_data_from_sensors + + @pytest.mark.asyncio + async def test_datapoints_are_added_to_the_session(self, db_session, table) -> None: + datapoints = await apd.aggregation.collect.add_data_from_sensors( + db_session, ["http://localhost"], "" + ) + num_points = db_session.query(table).count() + assert num_points == len(datapoints) == 2 + + @pytest.mark.asyncio + async def test_datapoints_can_be_mapped_back_to_DataPoints( + self, mut, db_session, table, model + ) -> None: + datapoints = await mut(db_session, ["http://localhost"], "") + db_points = [ + model.from_sql_result(result) for result in db_session.query(table) + ] + assert db_points == datapoints + + @pytest.mark.asyncio + async def test_daily_summary_view_matches_query( + self, db_session, model, table, daily_summary_view + ) -> None: + await apd.aggregation.collect.add_data_from_sensors( + db_session, ["http://localhost"], "" + ) + + headers = table.c.sensor_name, table.c.data + value_counts = ( + db_session.query(*headers, sqlalchemy.func.count(table.c.id)) + .filter(model.collected_on_date == sqlalchemy.func.current_date()) + .group_by(*headers) + ) + + daily_summary_view = db_session.query(daily_summary_view) + assert value_counts.all() == daily_summary_view.all() diff --git a/Ch09/apd.aggregation-chapter09-ex03-complete/.coveragerc b/Ch09/apd.aggregation-chapter09-ex03-complete/.coveragerc new file mode 100644 index 0000000..e766c69 --- /dev/null +++ b/Ch09/apd.aggregation-chapter09-ex03-complete/.coveragerc @@ -0,0 +1,3 @@ +[run] +branch = True +omit = tests/* diff --git a/Ch09/apd.aggregation-chapter09-ex03-complete/.pre-commit-config.yaml b/Ch09/apd.aggregation-chapter09-ex03-complete/.pre-commit-config.yaml new file mode 100644 index 0000000..d3b6ec7 --- /dev/null +++ b/Ch09/apd.aggregation-chapter09-ex03-complete/.pre-commit-config.yaml @@ -0,0 +1,26 @@ +repos: + +- repo: local + hooks: + - id: black + name: black + entry: pipenv run black + args: [--quiet] + language: system + types: [python] + + - id: mypy + name: mypy + exclude: (?x)^( + (.*)/setup.py + )$ + entry: pipenv run mypy + args: ["--follow-imports=skip"] + language: system + types: [python] + + - id: flake8 + name: flake8 + entry: pipenv run flake8 + language: system + types: [python] diff --git a/Ch09/apd.aggregation-chapter09-ex03-complete/CHANGES.md b/Ch09/apd.aggregation-chapter09-ex03-complete/CHANGES.md new file mode 100644 index 0000000..8e424e5 --- /dev/null +++ b/Ch09/apd.aggregation-chapter09-ex03-complete/CHANGES.md @@ -0,0 +1,5 @@ +## Changes + +### 1.0.0 (Unreleased) + +* Generated from skeleton diff --git a/Ch09/apd.aggregation-chapter09-ex03-complete/LICENCE b/Ch09/apd.aggregation-chapter09-ex03-complete/LICENCE new file mode 100644 index 0000000..86685d4 --- /dev/null +++ b/Ch09/apd.aggregation-chapter09-ex03-complete/LICENCE @@ -0,0 +1,31 @@ +Copyright (c) 2019, Matthew Wilkes + + +All rights reserved. + +Redistribution and use in source and binary forms, with or without modification, +are permitted provided that the following conditions are met: + +* Redistributions of source code must retain the above copyright notice, this + list of conditions and the following disclaimer. + +* Redistributions in binary form must reproduce the above copyright notice, this + list of conditions and the following disclaimer in the documentation and/or + other materials provided with the distribution. + +* Neither the name of the copyright holder nor the names of its + contributors may be used to endorse or promote products derived from this + software without specific prior written permission. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND +ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED +WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. +IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, +INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, +BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, +DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY +OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE +OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED +OF THE POSSIBILITY OF SUCH DAMAGE. + + diff --git a/Ch09/apd.aggregation-chapter09-ex03-complete/Pipfile b/Ch09/apd.aggregation-chapter09-ex03-complete/Pipfile new file mode 100644 index 0000000..b308175 --- /dev/null +++ b/Ch09/apd.aggregation-chapter09-ex03-complete/Pipfile @@ -0,0 +1,25 @@ +[[source]] +name = "pypi" +url = "https://pypi.org/simple" +verify_ssl = true + +[dev-packages] +pytest = "*" +pytest-cov = "*" +pytest-asyncio = "*" +mypy = "*" +flake8 = "*" +black = "*" +pre-commit = "*" +wheel = "*" +twine = "*" +sqlalchemy-stubs = "*" + +[packages] +apd-aggregation = {editable = true,extras = ["jupyter"],path = "."} +apd-sensors = {editable = true,extras = ["webapp"],path = "./../code"} + +[requires] + +[pipenv] +allow_prereleases = true diff --git a/Ch09/apd.aggregation-chapter09-ex03-complete/Pipfile.lock b/Ch09/apd.aggregation-chapter09-ex03-complete/Pipfile.lock new file mode 100644 index 0000000..37ebbba --- /dev/null +++ b/Ch09/apd.aggregation-chapter09-ex03-complete/Pipfile.lock @@ -0,0 +1,751 @@ +{ + "_meta": { + "hash": { + "sha256": "9cbbc3af88a1213ed8f4f93c33e35a9f9d7e047077cd25ff02f9bde13e2b606b" + }, + "pipfile-spec": 6, + "requires": {}, + "sources": [ + { + "name": "pypi", + "url": "https://pypi.org/simple", + "verify_ssl": true + } + ] + }, + "default": { + "aiohttp": { + "hashes": [ + "sha256:173267050501e1537293df06723bc5e719990889e2820ba3932969983892e960", + 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b/Ch09/apd.aggregation-chapter09-ex03-complete/README.md new file mode 100644 index 0000000..85669dd --- /dev/null +++ b/Ch09/apd.aggregation-chapter09-ex03-complete/README.md @@ -0,0 +1,4 @@ +# APD Sensor aggregator + +A programme that queries apd.sensor endpoints and aggregates their results. + diff --git a/Ch09/apd.aggregation-chapter09-ex03-complete/pyproject.toml b/Ch09/apd.aggregation-chapter09-ex03-complete/pyproject.toml new file mode 100644 index 0000000..4d3bb67 --- /dev/null +++ b/Ch09/apd.aggregation-chapter09-ex03-complete/pyproject.toml @@ -0,0 +1,5 @@ +[build-system] +requires = [ + "setuptools", +] +build-backend = "setuptools.build_meta" diff --git a/Ch09/apd.aggregation-chapter09-ex03-complete/pytest.ini b/Ch09/apd.aggregation-chapter09-ex03-complete/pytest.ini new file mode 100644 index 0000000..c1d3e58 --- /dev/null +++ b/Ch09/apd.aggregation-chapter09-ex03-complete/pytest.ini @@ -0,0 +1,3 @@ +[pytest] +markers = + functional: these tests are significantly slower as they start a HTTP server \ No newline at end of file diff --git a/Ch09/apd.aggregation-chapter09-ex03-complete/setup.cfg b/Ch09/apd.aggregation-chapter09-ex03-complete/setup.cfg new file mode 100644 index 0000000..73fc36a --- /dev/null +++ b/Ch09/apd.aggregation-chapter09-ex03-complete/setup.cfg @@ -0,0 +1,77 @@ +[mypy] +namespace_packages = True +mypy_path = src +plugins = sqlmypy + +[mypy-alembic] +ignore_missing_imports = True + +[mypy-alembic.config] +ignore_missing_imports = True + +[mypy-alembic.script] +ignore_missing_imports = True + +[mypy-alembic.runtime.environment] +ignore_missing_imports = True + +[mypy-matplotlib] +ignore_missing_imports = True + +[mypy-matplotlib.axes._base] +ignore_missing_imports = True + +[mypy-matplotlib.figure] +ignore_missing_imports = True + +[mypy-matplotlib.pyplot] +ignore_missing_imports = True + + +[mypy-pint] +ignore_missing_imports = True + +[mypy-pytest] +ignore_missing_imports = True + +[flake8] +max-line-length = 120 +ignore = E501 + +[metadata] +name = apd.aggregation +version = attr: apd.aggregation.VERSION +description = A programme that queries apd.sensor endpoints and aggregates their results. +long_description = file: README.md, CHANGES.md, LICENCE +long_description_content_type = text/markdown +keywords = +license = BSD +classifiers = + Programming Language :: Python :: 3 + Programming Language :: Python :: 3.7 + +[options] +zip_safe = False +include_package_data = True +package_dir = + =src +packages = find_namespace: +install_requires = + sqlalchemy + aiohttp + pint + psycopg2 + alembic + click + + +[options.extras_require] +jupyter = ipywidgets + +[options.packages.find] +where = src + +[options.entry_points] +console_scripts = + collect_sensor_data = apd.aggregation.cli:collect_sensor_data + sensor_deployments = apd.aggregation.cli:deployments diff --git a/Ch09/apd.aggregation-chapter09-ex03-complete/setup.py b/Ch09/apd.aggregation-chapter09-ex03-complete/setup.py new file mode 100644 index 0000000..6068493 --- /dev/null +++ b/Ch09/apd.aggregation-chapter09-ex03-complete/setup.py @@ -0,0 +1,3 @@ +from setuptools import setup + +setup() diff --git a/Ch09/apd.aggregation-chapter09-ex03-complete/src/apd/aggregation/__init__.py b/Ch09/apd.aggregation-chapter09-ex03-complete/src/apd/aggregation/__init__.py new file mode 100644 index 0000000..3277f64 --- /dev/null +++ b/Ch09/apd.aggregation-chapter09-ex03-complete/src/apd/aggregation/__init__.py @@ -0,0 +1 @@ +VERSION = "1.0.0" diff --git a/Ch09/apd.aggregation-chapter09-ex03-complete/src/apd/aggregation/alembic/README b/Ch09/apd.aggregation-chapter09-ex03-complete/src/apd/aggregation/alembic/README new file mode 100644 index 0000000..97a87d5 --- /dev/null +++ b/Ch09/apd.aggregation-chapter09-ex03-complete/src/apd/aggregation/alembic/README @@ -0,0 +1,12 @@ +Generic single-database configuration. + + +# Database setup + +To generate the required database tables you must create an alembic.ini file, as follows: + + [alembic] + script_location = apd.aggregation:alembic + sqlalchemy.url = postgresql+psycopg2://apd@localhost/apd + +and run `alembic upgrade head`. This should also be done after every upgrade of the software. \ No newline at end of file diff --git a/Ch09/apd.aggregation-chapter09-ex03-complete/src/apd/aggregation/alembic/env.py b/Ch09/apd.aggregation-chapter09-ex03-complete/src/apd/aggregation/alembic/env.py new file mode 100644 index 0000000..4c8a2c2 --- /dev/null +++ b/Ch09/apd.aggregation-chapter09-ex03-complete/src/apd/aggregation/alembic/env.py @@ -0,0 +1,80 @@ +from logging.config import fileConfig + +from sqlalchemy import engine_from_config +from sqlalchemy import pool +from alembic import context + +from apd.aggregation.database import metadata + + +# this is the Alembic Config object, which provides +# access to the values within the .ini file in use. +config = context.config + +# Interpret the config file for Python logging. +# This line sets up loggers basically. +if config.config_file_name: + fileConfig(config.config_file_name) + +target_metadata = metadata + + +def include_object(object, name, type_, reflected, compare_to): + if object.info.get("is_view", False): + return False + return True + + +def run_migrations_offline(): + """Run migrations in 'offline' mode. + + This configures the context with just a URL + and not an Engine, though an Engine is acceptable + here as well. By skipping the Engine creation + we don't even need a DBAPI to be available. + + Calls to context.execute() here emit the given string to the + script output. + + """ + url = config.get_main_option("sqlalchemy.url") + context.configure( + url=url, + include_object=include_object, + target_metadata=target_metadata, + literal_binds=True, + dialect_opts={"paramstyle": "named"}, + ) + + with context.begin_transaction(): + context.run_migrations() + + +def run_migrations_online(): + """Run migrations in 'online' mode. + + In this scenario we need to create an Engine + and associate a connection with the context. + + """ + connectable = engine_from_config( + config.get_section(config.config_ini_section), + prefix="sqlalchemy.", + poolclass=pool.NullPool, + ) + + with connectable.connect() as connection: + context.configure( + connection=connection, + target_metadata=target_metadata, + include_object=include_object, + ) + + with context.begin_transaction(): + context.run_migrations() + + +if context.is_offline_mode(): + run_migrations_offline() +else: + run_migrations_online() diff --git a/Ch09/apd.aggregation-chapter09-ex03-complete/src/apd/aggregation/alembic/script.py.mako b/Ch09/apd.aggregation-chapter09-ex03-complete/src/apd/aggregation/alembic/script.py.mako new file mode 100644 index 0000000..2c01563 --- /dev/null +++ b/Ch09/apd.aggregation-chapter09-ex03-complete/src/apd/aggregation/alembic/script.py.mako @@ -0,0 +1,24 @@ +"""${message} + +Revision ID: ${up_revision} +Revises: ${down_revision | comma,n} +Create Date: ${create_date} + +""" +from alembic import op +import sqlalchemy as sa +${imports if imports else ""} + +# revision identifiers, used by Alembic. +revision = ${repr(up_revision)} +down_revision = ${repr(down_revision)} +branch_labels = ${repr(branch_labels)} +depends_on = ${repr(depends_on)} + + +def upgrade(): + ${upgrades if upgrades else "pass"} + + +def downgrade(): + ${downgrades if downgrades else "pass"} diff --git a/Ch09/apd.aggregation-chapter09-ex03-complete/src/apd/aggregation/alembic/versions/4b2df8a6e1ce_add_indexes_to_datapoints.py b/Ch09/apd.aggregation-chapter09-ex03-complete/src/apd/aggregation/alembic/versions/4b2df8a6e1ce_add_indexes_to_datapoints.py new file mode 100644 index 0000000..ef671bd --- /dev/null +++ b/Ch09/apd.aggregation-chapter09-ex03-complete/src/apd/aggregation/alembic/versions/4b2df8a6e1ce_add_indexes_to_datapoints.py @@ -0,0 +1,32 @@ +"""Add indexes to datapoints + +Revision ID: 4b2df8a6e1ce +Revises: 6d2eacd5da3f +Create Date: 2019-12-02 16:07:41.123116 + +""" +from alembic import op + + +# revision identifiers, used by Alembic. +revision = "4b2df8a6e1ce" +down_revision = "6d2eacd5da3f" +branch_labels = None +depends_on = None + + +def upgrade(): + op.create_index( + op.f("ix_datapoints_collected_at"), + "datapoints", + ["collected_at"], + unique=False, + ) + op.create_index( + op.f("ix_datapoints_sensor_name"), "datapoints", ["sensor_name"], unique=False, + ) + + +def downgrade(): + op.drop_index(op.f("ix_datapoints_sensor_name"), table_name="datapoints") + op.drop_index(op.f("ix_datapoints_collected_at"), table_name="datapoints") diff --git a/Ch09/apd.aggregation-chapter09-ex03-complete/src/apd/aggregation/alembic/versions/6962f8455a6d_add_daily_summary_view.py b/Ch09/apd.aggregation-chapter09-ex03-complete/src/apd/aggregation/alembic/versions/6962f8455a6d_add_daily_summary_view.py new file mode 100644 index 0000000..eb02455 --- /dev/null +++ b/Ch09/apd.aggregation-chapter09-ex03-complete/src/apd/aggregation/alembic/versions/6962f8455a6d_add_daily_summary_view.py @@ -0,0 +1,38 @@ +"""Add daily summary view + +Revision ID: 6962f8455a6d +Revises: 4b2df8a6e1ce +Create Date: 2019-12-03 11:50:24.403402 + +""" +from alembic import op + + +# revision identifiers, used by Alembic. +revision = "6962f8455a6d" +down_revision = "4b2df8a6e1ce" +branch_labels = None +depends_on = None + + +def upgrade(): + create_view = """ + CREATE VIEW daily_summary AS + SELECT + datapoints.sensor_name AS sensor_name, + datapoints.data AS data, + count(datapoints.id) AS count + FROM datapoints + WHERE + datapoints.collected_at >= CAST(CURRENT_DATE AS DATE) + AND + datapoints.collected_at < CAST(CURRENT_DATE AS DATE) + 1 + GROUP BY + datapoints.sensor_name, + datapoints.data; + """ + op.execute(create_view) + + +def downgrade(): + op.execute("""DROP VIEW daily_summary""") diff --git a/Ch09/apd.aggregation-chapter09-ex03-complete/src/apd/aggregation/alembic/versions/6d2eacd5da3f_create_sensor_values_table.py b/Ch09/apd.aggregation-chapter09-ex03-complete/src/apd/aggregation/alembic/versions/6d2eacd5da3f_create_sensor_values_table.py new file mode 100644 index 0000000..b4a3545 --- /dev/null +++ b/Ch09/apd.aggregation-chapter09-ex03-complete/src/apd/aggregation/alembic/versions/6d2eacd5da3f_create_sensor_values_table.py @@ -0,0 +1,31 @@ +"""Create datapoints table + +Revision ID: 6d2eacd5da3f +Revises: N/A +Create Date: 2019-09-29 13:43:21.242706 + +""" +from alembic import op +import sqlalchemy as sa +from sqlalchemy.dialects import postgresql + +# revision identifiers, used by Alembic. +revision = "6d2eacd5da3f" +down_revision = None +branch_labels = None +depends_on = None + + +def upgrade(): + op.create_table( + "datapoints", + sa.Column("id", sa.Integer(), nullable=False), + sa.Column("sensor_name", sa.String(), nullable=True), + sa.Column("collected_at", postgresql.TIMESTAMP(), nullable=True), + sa.Column("data", postgresql.JSONB(astext_type=sa.Text()), nullable=True), + sa.PrimaryKeyConstraint("id"), + ) + + +def downgrade(): + op.drop_table("datapoints") diff --git a/Ch09/apd.aggregation-chapter09-ex03-complete/src/apd/aggregation/alembic/versions/d8cdc709086b_add_deployment_table.py b/Ch09/apd.aggregation-chapter09-ex03-complete/src/apd/aggregation/alembic/versions/d8cdc709086b_add_deployment_table.py new file mode 100644 index 0000000..204e096 --- /dev/null +++ b/Ch09/apd.aggregation-chapter09-ex03-complete/src/apd/aggregation/alembic/versions/d8cdc709086b_add_deployment_table.py @@ -0,0 +1,32 @@ +"""Add deployment table + +Revision ID: d8cdc709086b +Revises: d8d4cf6a178f +Create Date: 2019-12-17 16:28:58.585616 + +""" +from alembic import op +import sqlalchemy as sa +from sqlalchemy.dialects import postgresql + +# revision identifiers, used by Alembic. +revision = "d8cdc709086b" +down_revision = "d8d4cf6a178f" +branch_labels = None +depends_on = None + + +def upgrade(): + op.create_table( + "deployments", + sa.Column("id", postgresql.UUID(as_uuid=True), nullable=False), + sa.Column("uri", sa.String(), nullable=True), + sa.Column("name", sa.String(), nullable=True), + sa.Column("colour", sa.String(), nullable=True), + sa.Column("api_key", sa.String(), nullable=True), + sa.PrimaryKeyConstraint("id"), + ) + + +def downgrade(): + op.drop_table("deployments") diff --git a/Ch09/apd.aggregation-chapter09-ex03-complete/src/apd/aggregation/alembic/versions/d8d4cf6a178f_add_deployment_id_to_datapoint.py b/Ch09/apd.aggregation-chapter09-ex03-complete/src/apd/aggregation/alembic/versions/d8d4cf6a178f_add_deployment_id_to_datapoint.py new file mode 100644 index 0000000..88d5219 --- /dev/null +++ b/Ch09/apd.aggregation-chapter09-ex03-complete/src/apd/aggregation/alembic/versions/d8d4cf6a178f_add_deployment_id_to_datapoint.py @@ -0,0 +1,34 @@ +"""Add deployment id to DataPoint + +Revision ID: d8d4cf6a178f +Revises: 6962f8455a6d +Create Date: 2019-12-03 20:58:11.285509 + +""" +from alembic import op +import sqlalchemy as sa +from sqlalchemy.dialects import postgresql + +# revision identifiers, used by Alembic. +revision = "d8d4cf6a178f" +down_revision = "6962f8455a6d" +branch_labels = None +depends_on = None + + +def upgrade(): + op.add_column( + "datapoints", + sa.Column("deployment_id", postgresql.UUID(as_uuid=True), nullable=True), + ) + op.create_index( + op.f("ix_datapoints_deployment_id"), + "datapoints", + ["deployment_id"], + unique=False, + ) + + +def downgrade(): + op.drop_index(op.f("ix_datapoints_deployment_id"), table_name="datapoints") + op.drop_column("datapoints", "deployment_id") diff --git a/Ch09/apd.aggregation-chapter09-ex03-complete/src/apd/aggregation/analysis.py b/Ch09/apd.aggregation-chapter09-ex03-complete/src/apd/aggregation/analysis.py new file mode 100644 index 0000000..1a26a6d --- /dev/null +++ b/Ch09/apd.aggregation-chapter09-ex03-complete/src/apd/aggregation/analysis.py @@ -0,0 +1,426 @@ +from __future__ import annotations + +import asyncio +import collections +from concurrent.futures import ThreadPoolExecutor +import dataclasses +import datetime +import functools +import math +import typing as t +from uuid import UUID + +import matplotlib.pyplot as plt +from matplotlib.axes._base import _AxesBase +from matplotlib.figure import Figure +from pint import _DEFAULT_REGISTRY as ureg + +from apd.aggregation.query import ( + get_data, + get_data_by_deployment, + with_database, + get_deployment_by_id, +) +from apd.aggregation.database import DataPoint, deployment_table +from .utils import merc_x, merc_y + +plot_key = t.TypeVar("plot_key") +plot_value = t.TypeVar("plot_value") + +# Static UUID to represent aggregation of other deployments +GLOBAL = UUID("bd02526e-7619-4a59-b04b-1fafd1c262d1") + + +@dataclasses.dataclass +class Config(t.Generic[plot_key, plot_value]): + title: str + clean: t.Callable[ + [t.AsyncIterator[DataPoint]], t.AsyncIterator[t.Tuple[plot_key, plot_value]] + ] + draw: t.Optional[ + t.Callable[ + [t.Any, t.Iterable[plot_key], t.Iterable[plot_value], t.Optional[str]], None + ] + ] = None + get_data: t.Optional[ + t.Callable[..., t.AsyncIterator[t.Tuple[UUID, t.AsyncIterator[DataPoint]]]] + ] = None + ylabel: t.Optional[str] = None + sensor_name: dataclasses.InitVar[str] = None + + def __post_init__(self, sensor_name: t.Optional[str] = None) -> None: + if self.draw is None: + self.draw = draw_date # type: ignore + if self.get_data is None: + if sensor_name is None: + raise ValueError("You must specify either get_data or sensor_name") + self.get_data = get_one_sensor_by_deployment(sensor_name) + + +def draw_date( + plot: _AxesBase, + x: t.Iterable[datetime.datetime], + y: t.Iterable[float], + colour: t.Optional[str], +) -> None: + plot.plot_date(x, y, color=colour, linestyle="-", marker="", xdate=True) + + +def draw_bar( + plot: _AxesBase, + x: t.Iterable[datetime.datetime], + y: t.Iterable[float], + colour: t.Optional[str], +) -> None: + plot.bar(x, y, color=colour) + + +def draw_map( + plot: _AxesBase, + x: t.Iterable[t.Tuple[float, float]], + y: t.Iterable[float], + colour: t.Optional[str], +) -> None: + lon = [merc_y(coord[0]) for coord in x] + lat = [merc_x(coord[1]) for coord in x] + + for axis in "x", "y": + plt.tick_params( + axis=axis, + which="both", + bottom=False, + top=False, + left=False, + right=False, + labelbottom=False, + labelleft=False, + ) + + plot.tricontourf(lat, lon, y) + plot.plot(lat, lon, "wo", ms=3) + plot.set_aspect(1.0) + + +def get_map_cleaner_for( + sensor_name: str, +) -> t.Callable[ + [t.AsyncIterator[DataPoint]], t.AsyncIterator[t.Tuple[t.Tuple[float, float], float]] +]: + """Given a sensor_name that represents a float, return a coroutine that acts as a cleaner + extracting that sensor's data keyed by the value of a Location sensor.""" + + async def clean_latest_coord_and_value( + datapoints: t.AsyncIterator[DataPoint], + ) -> t.AsyncIterator[t.Tuple[t.Tuple[float, float], float]]: + + # We will be building a dictionary of UUID to dictionary + # That inner dictionary should contain coord (float, float) + # and value (float) only. Either or both can be None. + class IntermediateData(t.TypedDict): + coord: t.Optional[t.Tuple[float, float]] + value: t.Optional[float] + + # We will iterate over data points and build an entry in cleaned_data + # for each deployment. This lets newer data replace older data, as + # datapoints is assumed to be in date order + cleaned_data: t.Dict[UUID, IntermediateData] = {} + async for datapoint in datapoints: + # Get the existing data for this deployment, if we've seen it before + row_data = cleaned_data.get(datapoint.deployment_id, None) + if row_data is None: + # This is the first time we've seen this deployment + row_data = {"coord": None, "value": None} + if datapoint.sensor_name == "Location": + # Coord is a 2-tuple of floats + row_data["coord"] = ( + float(datapoint.data[0]), + float(datapoint.data[1]), + ) + elif datapoint.sensor_name == sensor_name: + # Value is a single float + row_data["value"] = float(datapoint.data) + # Store the info about this deployment back into the cleaned_data set + cleaned_data[datapoint.deployment_id] + + for data in cleaned_data.values(): + if data["coord"] is None or data["value"] is None: + # We only got a partial record, don't plot this + continue + yield data["coord"], data["value"] + + # Return the set up cleaner coroutine + return clean_latest_coord_and_value + + +async def clean_watthours_to_watts( + datapoints: t.AsyncIterator[DataPoint], +) -> t.AsyncIterator[t.Tuple[datetime.datetime, float]]: + last_watthours = None + last_time = None + async for datapoint in datapoints: + if datapoint.data is None: + continue + time = datapoint.collected_at + watthours = ureg.Quantity(datapoint.data["magnitude"], datapoint.data["unit"]) + if last_watthours: + seconds_elapsed = (time - last_time).total_seconds() + time_elapsed = ureg.Quantity(seconds_elapsed, ureg.second) + additional_power = watthours - last_watthours + power = additional_power / time_elapsed + yield time, power.to(ureg.watt).magnitude + last_watthours = watthours + last_time = datapoint.collected_at + + +async def clean_watthours_by_day( + datapoints: t.AsyncIterator[DataPoint], +) -> t.AsyncIterator[t.Tuple[datetime.datetime, float]]: + last_watthours = None + last_date = None + async for datapoint in datapoints: + if datapoint.data is None: + continue + date = datapoint.collected_at.date() + if date != last_date: + # We have a new date + watthours = ureg.Quantity( + datapoint.data["magnitude"], datapoint.data["unit"] + ) + if last_watthours is not None and last_date is not None: + midpoint = datetime.datetime.combine(last_date, datetime.time(12, 0, 0)) + diff = watthours - last_watthours + yield midpoint, diff.to(ureg.kilowatt_hours).magnitude + last_watthours = watthours + last_date = datapoint.collected_at.date() + if last_watthours is not None and last_date is not None: + watthours = ureg.Quantity(datapoint.data["magnitude"], datapoint.data["unit"]) + midpoint = datetime.datetime.combine( + datapoint.collected_at.date(), datetime.time(12, 0, 0) + ) + diff = watthours - last_watthours + yield midpoint, diff.to(ureg.kilowatt_hours).magnitude + + +async def clean_magnitude( + datapoints: t.AsyncIterator[DataPoint], +) -> t.AsyncIterator[t.Tuple[datetime.datetime, float]]: + async for datapoint in datapoints: + if datapoint.data is None: + continue + yield datapoint.collected_at, datapoint.data["magnitude"] + + +async def clean_temperature_fluctuations( + datapoints: t.AsyncIterator[DataPoint], +) -> t.AsyncIterator[t.Tuple[datetime.datetime, float]]: + allowed_jitter = 2.5 + allowed_range = (-40, 80) + window_datapoints: t.Deque[DataPoint] = collections.deque(maxlen=3) + + def datapoint_ok(datapoint: DataPoint) -> bool: + """Return False if this data point does not contain a valid temperature""" + if datapoint.data is None: + return False + elif datapoint.data["unit"] != "degC": + # This point is in a different temperature system. While it could be converted + # this cleaner is not yet doing that. + return False + elif not allowed_range[0] < datapoint.data["magnitude"] < allowed_range[1]: + return False + return True + + async for datapoint in datapoints: + if not datapoint_ok(datapoint): + # If the datapoint is invalid then skip directly to the next item + continue + + window_datapoints.append(datapoint) + if len(window_datapoints) == 3: + # Find the temperatures of the datapoints in the window, then average + # the first and last and compare that to the middle point. + window_temperatures = [dp.data["magnitude"] for dp in window_datapoints] + avr_first_last = (window_temperatures[0] + window_temperatures[2]) / 2 + diff_middle_avr = abs(window_temperatures[1] - avr_first_last) + if diff_middle_avr > allowed_jitter: + pass + else: + yield window_datapoints[1].collected_at, window_temperatures[1] + elif len(window_datapoints) == 1: + # The item in the iterator can't be compared to both neighbours + # so should be yielded + yield datapoint.collected_at, datapoint.data["magnitude"] + else: + # Otherwise, let the window fill up, it will be yieleded later + pass + # When the iterator ends the final item is not yet in the middle + # of the window, so the last item must be explicitly yielded. + if len(window_datapoints) > 1 and datapoint_ok(datapoint): + yield datapoint.collected_at, datapoint.data["magnitude"] + + +async def clean_passthrough( + datapoints: t.AsyncIterator[DataPoint], +) -> t.AsyncIterator[t.Tuple[datetime.datetime, float]]: + async for datapoint in datapoints: + if datapoint.data is None: + continue + else: + yield datapoint.collected_at, datapoint.data + + +def get_one_sensor_by_deployment( + sensor_name: str, +) -> t.Callable[..., t.AsyncIterator[t.Tuple[UUID, t.AsyncIterator[DataPoint]]]]: + return functools.partial(get_data_by_deployment, sensor_name=sensor_name) + + +def get_all_data() -> t.Callable[ + ..., t.AsyncIterator[t.Tuple[UUID, t.AsyncIterator[DataPoint]]] +]: + async def get_all_data_inner( + *args: t.Any, **kwargs: t.Any + ) -> t.AsyncIterator[t.Tuple[UUID, t.AsyncIterator[DataPoint]]]: + yield GLOBAL, get_data(*args, **kwargs) + + return get_all_data_inner + + +configs = ( + Config( + sensor_name="SolarCumulativeOutput", + clean=clean_watthours_to_watts, + title="Solar momentary output", + ylabel="Watts", + ), + Config( + sensor_name="SolarCumulativeOutput", + clean=clean_watthours_by_day, + title="Daily total generation", + ylabel="Kilowatt Hours", + draw=draw_bar, + ), + Config( + sensor_name="RAMAvailable", + clean=clean_passthrough, + title="RAM available", + ylabel="Bytes", + ), + Config( + sensor_name="RelativeHumidity", + clean=clean_passthrough, + title="Relative humidity", + ylabel="Percent", + ), + Config( + sensor_name="Temperature", + clean=clean_temperature_fluctuations, + title="Ambient temperature", + ylabel="Degrees C", + ), +) + + +def get_known_configs() -> t.Dict[str, Config[t.Any, t.Any]]: + return {config.title: config for config in configs} + + +async def plot_sensor( + config: Config[t.Any, t.Any], + plot: _AxesBase, + location_names: t.Dict[UUID, str], + **kwargs: t.Any +) -> _AxesBase: + locations = [] + if config.get_data is None: + raise ValueError("You must provide a get_data function") + async for deployment_id, query_results in config.get_data(**kwargs): + if deployment_id == GLOBAL: + name = "Global" + else: + try: + deployment = await get_deployment_by_id(deployment_id) + except IndexError: + name = str(deployment_id) + colour = None + else: + name = deployment.name or str(deployment_id) + colour = deployment.colour + # Mypy currently doesn't understand callable fields on datatypes: https://github.com/python/mypy/issues/5485 + points = [dp async for dp in config.clean(query_results)] # type: ignore + if not points: + continue + locations.append(name) + x, y = zip(*points) + plot.set_title(config.title) + plot.set_ylabel(config.ylabel) + if config.draw is None: + raise ValueError("You must provide a get_data function") + config.draw(plot, x, y, colour) + plot.legend(locations) + return plot + + +_Coroutine_Result = t.TypeVar("_Coroutine_Result") + + +def wrap_coroutine( + f: t.Callable[..., t.Coroutine[t.Any, t.Any, _Coroutine_Result]] +) -> t.Callable[..., _Coroutine_Result]: + """Given a coroutine, return a function that runs that coroutine + in a new event loop in an isolated thread""" + + @functools.wraps(f) + def run_in_thread(*args: t.Any, **kwargs: t.Any) -> _Coroutine_Result: + loop = asyncio.new_event_loop() + wrapped = f(*args, **kwargs) + with ThreadPoolExecutor(max_workers=1) as pool: + task = pool.submit(loop.run_until_complete, wrapped) + # Mypy can get confused when nesting generic functions, like we do here + # The fact that Task is generic means we lose the association with + # _CoroutineResult. Adding an explicit cast restores this. + return t.cast(_Coroutine_Result, task.result()) + + return run_in_thread + + +async def plot_multiple_charts(*args: t.Any, **kwargs: t.Any) -> Figure: + # These parameters are pulled from kwargs to avoid confusing function + # introspection code in IPython widgets + location_names = kwargs.pop("location_names", None) + configs = kwargs.pop("configs", None) + dimensions = kwargs.pop("dimensions", None) + + with with_database("postgresql+psycopg2://apd@localhost/apd") as session: + loop = asyncio.get_running_loop() + coros = [] + if configs is None: + # If no configs are supplied, use all known configs + configs = get_known_configs().values() + if dimensions is None: + # If no dimensions are supplied, get the square root of the number + # of configs and round it to find a number of columns. This will + # keep the arrangement approximately square. Find rows by multiplying + # out rows. + total_configs = len(configs) + columns = round(math.sqrt(total_configs)) + rows = math.ceil(total_configs / columns) + if location_names is None: + location_query = session.query( + deployment_table.c.id, deployment_table.c.name + ) + location_data = await loop.run_in_executor(None, location_query.all) + location_names = dict(location_data) + + figure = plt.figure(figsize=(10 * columns, 5 * rows), dpi=300) + for i, config in enumerate(configs, start=1): + plot = figure.add_subplot(columns, rows, i) + coros.append(plot_sensor(config, plot, location_names, *args, **kwargs)) + await asyncio.gather(*coros) + return figure + + +def interactable_plot_multiple_charts( + *args: t.Any, **kwargs: t.Any +) -> t.Callable[..., Figure]: + with_config = functools.partial(plot_multiple_charts, *args, **kwargs) + return wrap_coroutine(with_config) diff --git a/Ch09/apd.aggregation-chapter09-ex03-complete/src/apd/aggregation/cli.py b/Ch09/apd.aggregation-chapter09-ex03-complete/src/apd/aggregation/cli.py new file mode 100644 index 0000000..6e98436 --- /dev/null +++ b/Ch09/apd.aggregation-chapter09-ex03-complete/src/apd/aggregation/cli.py @@ -0,0 +1,184 @@ +import asyncio +import typing as t +import uuid + +import aiohttp +import click +from sqlalchemy import create_engine +from sqlalchemy.orm import sessionmaker + +from . import collect +from .database import Deployment, deployment_table + + +@click.command() +@click.argument("server", nargs=-1) +@click.option( + "--db", + metavar="", + default="postgresql+psycopg2://localhost/apd", + help="The connection string to a PostgreSQL database", + envvar="APD_DB_URI", +) +@click.option("--api-key", metavar="", envvar="APD_API_KEY") +@click.option( + "--tolerate-failures", + "-f", + help="If provided, failure to retrieve some sensors' data will not abort the collection process", + is_flag=True, +) +@click.option("-v", "--verbose", is_flag=True, help="Enables verbose mode") +def collect_sensor_data( + db: str, server: t.Tuple[str], api_key: str, tolerate_failures: bool, verbose: bool +): + """This loads data from one or more sensors into the specified database. + + Only PostgreSQL databases are supported, as the column definitions use + multiple pg specific features. The database must already exist and be + populated with the required tables. + + The --api-key option is used to specify the access token for the sensors + being queried. + + You may specify any number of servers, the variable should be the full URL + to the sensor's HTTP interface, not including the /v/2.0 portion. Multiple + URLs should be separated with a space. + """ + if tolerate_failures: + attempts = [(s,) for s in server] + else: + attempts = [server] + success = True + for attempt in attempts: + try: + collect.standalone(db, attempt, api_key, echo=verbose) + except ValueError as e: + click.secho(str(e), err=True, fg="red") + success = False + return success + + +@click.group() +def deployments(): + pass + + +@deployments.command() +@click.argument("uri") +@click.argument("name") +@click.option( + "--db", + metavar="", + default="postgresql+psycopg2://localhost/apd", + help="The connection string to a PostgreSQL database", + envvar="APD_DB_URI", +) +@click.option("--api-key", metavar="", envvar="APD_API_KEY") +@click.option("--colour") +def add( + db: str, uri: str, name: str, api_key: t.Optional[str], colour: t.Optional[str], +): + """This creates a record of a new deployment in the database. + """ + deployment = Deployment(id=None, uri=uri, name=name, api_key=api_key, colour=colour) + + async def http_get_deployment_id(): + async with aiohttp.ClientSession() as http: + collect.http_session_var.set(http) + return await collect.get_deployment_id(uri) + + deployment.id = asyncio.run(http_get_deployment_id()) + insert = deployment_table.insert().values(**deployment._asdict()) + + engine = create_engine(db) + sm = sessionmaker(engine) + Session = sm() + Session.execute(insert) + Session.commit() + return True + + +@deployments.command() +@click.option( + "--db", + metavar="", + default="postgresql+psycopg2://localhost/apd", + help="The connection string to a PostgreSQL database", + envvar="APD_DB_URI", +) +def list(db: str): + """This creates a record of a new deployment in the database. + """ + engine = create_engine(db) + sm = sessionmaker(engine) + Session = sm() + deployments = Session.query(deployment_table).all() + for deployment in deployments: + click.secho(deployment.name, bold=True) + click.echo(click.style("ID ", bold=True) + deployment.id.hex) + click.echo(click.style("URI ", bold=True) + deployment.uri) + click.echo(click.style("API key ", bold=True) + deployment.api_key) + click.echo(click.style("Colour ", bold=True) + str(deployment.colour)) + click.echo() + Session.rollback() + return True + + +@deployments.command() +@click.argument("id") +@click.option("--uri") +@click.option("--name") +@click.option( + "--db", + metavar="", + default="postgresql+psycopg2://localhost/apd", + help="The connection string to a PostgreSQL database", + envvar="APD_DB_URI", +) +@click.option("--api-key", metavar="", envvar="APD_API_KEY") +@click.option("--colour") +def edit( + db: str, + id, + uri: t.Optional[str], + name: t.Optional[str], + api_key: t.Optional[str], + colour: t.Optional[str], +): + """This creates a record of a new deployment in the database. + """ + update = {} + if uri is not None: + update["uri"] = uri + if name is not None: + update["name"] = name + if api_key is not None: + update["api_key"] = api_key + if colour is not None: + update["colour"] = colour + deployment_id = uuid.UUID(id) + + update = ( + deployment_table.update() + .where(deployment_table.c.id == deployment_id) + .values(**update) + ) + + engine = create_engine(db) + sm = sessionmaker(engine) + Session = sm() + Session.execute(update) + deployments = Session.query(deployment_table).filter( + deployment_table.c.id == deployment_id + ) + Session.commit() + + for deployment in deployments: + click.secho(deployment.name, bold=True) + click.echo(click.style("ID ", bold=True) + deployment.id.hex) + click.echo(click.style("URI ", bold=True) + deployment.uri) + click.echo(click.style("API key ", bold=True) + deployment.api_key) + click.echo(click.style("Colour ", bold=True) + str(deployment.colour)) + click.echo() + + return True diff --git a/Ch09/apd.aggregation-chapter09-ex03-complete/src/apd/aggregation/collect.py b/Ch09/apd.aggregation-chapter09-ex03-complete/src/apd/aggregation/collect.py new file mode 100644 index 0000000..1d9ea08 --- /dev/null +++ b/Ch09/apd.aggregation-chapter09-ex03-complete/src/apd/aggregation/collect.py @@ -0,0 +1,112 @@ +import asyncio +from contextvars import ContextVar +import datetime +import typing as t +import uuid + +import aiohttp +from sqlalchemy import create_engine +from sqlalchemy.orm import sessionmaker +from sqlalchemy.orm.session import Session + +from .database import DataPoint, datapoint_table, Deployment, deployment_table + +http_session_var: ContextVar[aiohttp.ClientSession] = ContextVar("http_session") + + +async def get_deployment_id(server): + http = http_session_var.get() + if not server.endswith("/"): + server += "/" + url = server + "v/2.1/deployment_id" + async with http.get(url) as request: + if request.status != 200: + raise ValueError(f"Error loading deployment id from {server}") + result = await request.json() + return uuid.UUID(result["deployment_id"]) + + +async def get_data_points(server: str, api_key: t.Optional[str],) -> t.List[DataPoint]: + if not server.endswith("/"): + server += "/" + url = server + "v/2.1/sensors/" + headers = {} + if api_key: + headers["X-API-KEY"] = api_key + http = http_session_var.get() + + # Get the deployment ID in parallel to the sensor data + deployment_id = asyncio.create_task(get_deployment_id(server)) + + async with http.get(url, headers=headers) as request: + result = await request.json() + ok = request.status == 200 + now = datetime.datetime.now() + if ok: + points = [] + for value in result["sensors"]: + points.append( + DataPoint( + sensor_name=value["id"], + collected_at=now, + data=value["value"], + deployment_id=await deployment_id, + ) + ) + return points + else: + raise ValueError( + f"Error loading data from {server}: " + result.get("error", "Unknown") + ) + + +def handle_result(result: t.List[DataPoint], session: Session) -> t.List[DataPoint]: + for point in result: + insert = datapoint_table.insert().values(**point._asdict()) + sql_result = session.execute(insert) + point.id = sql_result.inserted_primary_key[0] + return result + + +async def add_data_from_sensors( + session: Session, servers: t.Iterable[Deployment] +) -> t.List[DataPoint]: + tasks: t.List[t.Awaitable[t.List[DataPoint]]] = [] + points: t.List[DataPoint] = [] + async with aiohttp.ClientSession() as http: + http_session_var.set(http) + tasks = [get_data_points(server.uri, server.api_key) for server in servers] + for results in await asyncio.gather(*tasks): + points += results + loop = asyncio.get_running_loop() + await loop.run_in_executor(None, handle_result, points, session) + return points + + +def standalone( + db_uri: str, servers: t.Tuple[str], api_key: t.Optional[str], echo: bool = False +) -> None: + engine = create_engine(db_uri, echo=echo) + sm = sessionmaker(engine) + Session = sm() + deployments: t.Iterable[Deployment] + if servers: + deployments = tuple( + Deployment(id=None, name=None, colour=None, api_key=api_key, uri=server) + for server in servers + ) + else: + deployment_data = Session.query(deployment_table).all() + deployments = tuple( + Deployment.from_sql_result(deployment) for deployment in deployment_data + ) + asyncio.run(add_data_from_sensors(Session, deployments)) + Session.commit() + + +if __name__ == "__main__": + standalone( + db_uri="postgresql+psycopg2://apd@localhost/apd", + servers=("http://pvoutput:8080/",), + api_key="h3hdfjksfhwkjehnwekj", + ) diff --git a/Ch09/apd.aggregation-chapter09-ex03-complete/src/apd/aggregation/database.py b/Ch09/apd.aggregation-chapter09-ex03-complete/src/apd/aggregation/database.py new file mode 100644 index 0000000..f28b469 --- /dev/null +++ b/Ch09/apd.aggregation-chapter09-ex03-complete/src/apd/aggregation/database.py @@ -0,0 +1,129 @@ +from __future__ import annotations + +from dataclasses import dataclass, field, asdict +import datetime +import typing as t +import uuid + +import sqlalchemy +from sqlalchemy.dialects.postgresql import JSONB, DATE, TIMESTAMP, UUID +from sqlalchemy.ext.hybrid import ExprComparator, hybrid_property +from sqlalchemy.orm import sessionmaker +from sqlalchemy.schema import Table + + +metadata = sqlalchemy.MetaData() + +deployment_table = Table( + "deployments", + metadata, + sqlalchemy.Column("id", UUID(as_uuid=True), primary_key=True), + sqlalchemy.Column("uri", sqlalchemy.String, index=False), + sqlalchemy.Column("name", sqlalchemy.String, index=False, nullable=True), + sqlalchemy.Column("colour", sqlalchemy.String, index=False, nullable=True), + sqlalchemy.Column("api_key", sqlalchemy.String, index=False), +) + +datapoint_table = Table( + "datapoints", + metadata, + sqlalchemy.Column("id", sqlalchemy.Integer, primary_key=True), + sqlalchemy.Column("sensor_name", sqlalchemy.String, index=True), + sqlalchemy.Column("collected_at", TIMESTAMP, index=True), + sqlalchemy.Column("deployment_id", UUID(as_uuid=True), index=True), + sqlalchemy.Column("data", JSONB), +) + +daily_summary_view = Table( + "daily_summary", + metadata, + sqlalchemy.Column("sensor_name", sqlalchemy.String), + sqlalchemy.Column("data", JSONB), + sqlalchemy.Column("count", sqlalchemy.Integer), + info={"is_view": True}, +) + + +class DateEqualComparator(ExprComparator): + def __init__(self, fallback_expression, raw_expression): + # Do not try and find update expression from parent + super().__init__(None, fallback_expression, None) + self.raw_expression = raw_expression + + def __eq__(self, other): + """ Returns True iff on the same day as other """ + other_date = sqlalchemy.cast(other, DATE) + return sqlalchemy.and_( + self.raw_expression >= other_date, self.raw_expression < other_date + 1, + ) + + def operate(self, op, *other, **kwargs): + other = [sqlalchemy.cast(date, DATE) for date in other] + return op(self.expression, *other, **kwargs) + + def reverse_operate(self, op, other, **kwargs): + other = [sqlalchemy.cast(date, DATE) for date in other] + return op(other, self.expression, **kwargs) + + +@dataclass +class DataPoint: + sensor_name: str + data: t.Any + deployment_id: uuid.UUID + id: t.Optional[int] = None + collected_at: datetime.datetime = field(default_factory=datetime.datetime.now) + + @classmethod + def from_sql_result(cls, result) -> DataPoint: + return cls(**result._asdict()) + + def _asdict(self) -> t.Dict[str, t.Any]: + data = asdict(self) + if data["id"] is None: + del data["id"] + return data + + @hybrid_property + def collected_on_date(self): + return self.collected_at.date() + + @collected_on_date.comparator # type: ignore + def collected_on_date(cls): + return DateEqualComparator( + fallback_expression=sqlalchemy.cast(datapoint_table.c.collected_at, DATE), + raw_expression=datapoint_table.c.collected_at, + ) + + +@dataclass +class Deployment: + id: t.Optional[uuid.UUID] + uri: str + name: t.Optional[str] + colour: t.Optional[str] + api_key: t.Optional[str] + + @classmethod + def from_sql_result(cls, result) -> Deployment: + return cls(**result._asdict()) + + def _asdict(self) -> t.Dict[str, t.Any]: + data = asdict(self) + return data + + +def main() -> None: + engine = sqlalchemy.create_engine( + "postgresql+psycopg2://apd@localhost/apd", echo=True + ) + sm = sessionmaker(engine) + Session = sm() + if False: + metadata.create_all(engine) + print(Session.query(DataPoint).all()) + pass + + +if __name__ == "__main__": + main() diff --git a/Ch09/apd.aggregation-chapter09-ex03-complete/src/apd/aggregation/query.py b/Ch09/apd.aggregation-chapter09-ex03-complete/src/apd/aggregation/query.py new file mode 100644 index 0000000..3d2005d --- /dev/null +++ b/Ch09/apd.aggregation-chapter09-ex03-complete/src/apd/aggregation/query.py @@ -0,0 +1,146 @@ +import asyncio +import contextlib +from contextvars import ContextVar +import datetime +import typing as t +from uuid import UUID + +from sqlalchemy import create_engine +from sqlalchemy.orm import sessionmaker +from sqlalchemy.orm.session import Session + + +from apd.aggregation.database import ( + datapoint_table, + DataPoint, + deployment_table, + Deployment, +) + + +db_session_var: ContextVar[Session] = ContextVar("db_session") + + +@contextlib.contextmanager +def with_database(uri: t.Optional[str] = None) -> t.Iterator[Session]: + """Given a URI, set up a DB connection, and return a Session as a context manager """ + if uri is None: + uri = "postgresql+psycopg2://localhost/apd" + engine = create_engine(uri) + sm = sessionmaker(engine) + Session = sm() + token = db_session_var.set(Session) + yield Session + db_session_var.reset(token) + Session.commit() + Session.close() + + +async def get_data( + sensor_name: t.Optional[str] = None, + deployment_id: t.Optional[UUID] = None, + collected_before: t.Optional[datetime.datetime] = None, + collected_after: t.Optional[datetime.datetime] = None, +) -> t.AsyncIterator[DataPoint]: + db_session = db_session_var.get() + loop = asyncio.get_running_loop() + query = db_session.query(datapoint_table) + if sensor_name: + query = query.filter(datapoint_table.c.sensor_name == sensor_name) + if deployment_id: + query = query.filter(datapoint_table.c.deployment_id == deployment_id) + if collected_before: + query = query.filter(datapoint_table.c.collected_at < collected_before) + if collected_after: + query = query.filter(datapoint_table.c.collected_at > collected_after) + query = query.order_by( + datapoint_table.c.deployment_id, + datapoint_table.c.sensor_name, + datapoint_table.c.collected_at, + ) + + rows = await loop.run_in_executor(None, query.all) + for row in rows: + yield DataPoint.from_sql_result(row) + + +async def get_deployment_ids() -> t.List[UUID]: + db_session = db_session_var.get() + loop = asyncio.get_running_loop() + query = db_session.query(datapoint_table.c.deployment_id).distinct() + return [row.deployment_id for row in await loop.run_in_executor(None, query.all)] + + +async def get_deployment_by_id(deployment_id: UUID) -> Deployment: + db_session = db_session_var.get() + loop = asyncio.get_running_loop() + query = db_session.query(deployment_table).filter( + deployment_table.c.id == deployment_id + ) + return [ + Deployment.from_sql_result(row) + for row in await loop.run_in_executor(None, query.all) + ][0] + + +async def get_data_by_deployment( + *args: t.Any, **kwargs: t.Any +) -> t.AsyncIterator[t.Tuple[UUID, t.AsyncIterator[DataPoint]]]: + """Return an Async Iterator that contains two-item pairs. + These pairs are a string (deployment_id), and an async iterator that contains + the datapoints with that deployment_id. + + Usage example: + + async for deployment_id, datapoints in get_data_by_deployment(): + print(deployment_id) + async for datapoint in datapoints: + print(datapoint) + print() + """ + # Get the data, using the arguments to this function as filters + data = get_data(*args, **kwargs) + + # The two levels of iterator share the item variable, initialise it with the + # first item from the iterator. Also set last_deployment_id to None, so the + # outer iterator knows to start a new group. + last_deployment_id: t.Optional[UUID] = None + try: + item = await data.__anext__() + except StopAsyncIteration: + # There were no items in the underlying query, return immediately + return + + async def subiterator(group_id: UUID) -> t.AsyncIterator[DataPoint]: + """Using a closure, create an iterator that yields the current + item, then yields all items from data while the deployment_id matches + group_id, leaving the first that doesn't match as item in the enclosing + scope.""" + # item is from the enclosing scope + nonlocal item + while item.deployment_id == group_id: + # yield items from data while they match the group_id this iterator represents + yield item + try: + # Advance the underlying iterator + item = await data.__anext__() + except StopAsyncIteration: + # The underlying iterator came to an end, so end the subiterator too + return + + while True: + while item.deployment_id == last_deployment_id: + # We are trying to advance the outer iterator while the underlying iterator + # is still part-way through a group. Speed through the underlying until we + # hit an item where the deployment_id is different to the last one (or, is not + # None, in the case of the start of the iterator) + try: + item = await data.__anext__() + except StopAsyncIteration: + # We hit the end of the underlying iterator: end this iterator too + return + last_deployment_id = item.deployment_id + # Don't yield an iterator for unclassified deployments + if last_deployment_id is not None: + # Instantiate a subiterator for this group + yield last_deployment_id, subiterator(last_deployment_id) diff --git a/Ch09/apd.aggregation-chapter09-ex03-complete/src/apd/aggregation/utils.py b/Ch09/apd.aggregation-chapter09-ex03-complete/src/apd/aggregation/utils.py new file mode 100644 index 0000000..76f05ec --- /dev/null +++ b/Ch09/apd.aggregation-chapter09-ex03-complete/src/apd/aggregation/utils.py @@ -0,0 +1,27 @@ +import math + + +# Set up mercator transform from https://wiki.openstreetmap.org/wiki/Mercator#Python +# Thanks to Paulo Silva and the OSM contributors +def merc_x(lon: float) -> float: + r_major = 6378137.000 + return r_major * math.radians(lon) + + +def merc_y(lat: float) -> float: + if lat > 89.5: + lat = 89.5 + if lat < -89.5: + lat = -89.5 + r_major = 6378137.000 + r_minor = 6356752.3142 + temp = r_minor / r_major + eccent = math.sqrt(1 - temp ** 2) + phi = math.radians(lat) + sinphi = math.sin(phi) + con = eccent * sinphi + com = eccent / 2 + con = ((1.0 - con) / (1.0 + con)) ** com + ts = math.tan((math.pi / 2 - phi) / 2) / con + y = 0 - r_major * math.log(ts) + return y diff --git a/Ch09/apd.aggregation-chapter09-ex03-complete/tests/__init__.py b/Ch09/apd.aggregation-chapter09-ex03-complete/tests/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/Ch09/apd.aggregation-chapter09-ex03-complete/tests/conftest.py b/Ch09/apd.aggregation-chapter09-ex03-complete/tests/conftest.py new file mode 100644 index 0000000..328e95f --- /dev/null +++ b/Ch09/apd.aggregation-chapter09-ex03-complete/tests/conftest.py @@ -0,0 +1,124 @@ +import datetime +from uuid import UUID + +from apd.aggregation.database import datapoint_table + +from alembic.config import Config +from alembic.script import ScriptDirectory +from alembic.runtime.environment import EnvironmentContext +import pytest + + +@pytest.fixture +def db_uri(): + return "postgresql+psycopg2://apd@localhost/apd-test" + + +@pytest.fixture +def db_session(db_uri): + from sqlalchemy import create_engine + from sqlalchemy.orm import sessionmaker + + engine = create_engine(db_uri, echo=True) + sm = sessionmaker(engine) + Session = sm() + yield Session + Session.close() + + +@pytest.fixture +def migrated_db(db_uri, db_session): + config = Config() + config.set_main_option("script_location", "apd.aggregation:alembic") + config.set_main_option("sqlalchemy.url", db_uri) + script = ScriptDirectory.from_config(config) + + def upgrade(rev, context): + return script._upgrade_revs(script.get_current_head(), rev) + + def downgrade(rev, context): + return script._downgrade_revs(None, rev) + + with EnvironmentContext(config, script, fn=upgrade): + script.run_env() + + try: + yield + finally: + # Clear any pending work from the db_session connection + db_session.rollback() + + with EnvironmentContext(config, script, fn=downgrade): + script.run_env() + + +@pytest.fixture +def populated_db(migrated_db, db_session): + datas = [ + { + "id": 1, + "sensor_name": "Test", + "data": "1", + "collected_at": datetime.datetime(2020, 4, 1, 12, 0, 1), + "deployment_id": UUID("b4c68905-b1e4-4875-940e-69e5d27730fd"), + }, + { + "id": 2, + "sensor_name": "Test", + "data": "2", + "collected_at": datetime.datetime(2020, 4, 1, 12, 1, 1), + "deployment_id": UUID("b4c68905-b1e4-4875-940e-69e5d27730fd"), + }, + { + "id": 3, + "sensor_name": "Test", + "data": "3", + "collected_at": datetime.datetime(2020, 4, 1, 12, 2, 1), + "deployment_id": UUID("b4c68905-b1e4-4875-940e-69e5d27730fd"), + }, + { + "id": 4, + "sensor_name": "Test", + "data": "4", + "collected_at": datetime.datetime(2020, 4, 1, 12, 3, 1), + "deployment_id": UUID("b4c68905-b1e4-4875-940e-69e5d27730fd"), + }, + { + "id": 5, + "sensor_name": "OtherTest", + "data": "5", + "collected_at": datetime.datetime(2020, 4, 1, 12, 4, 1), + "deployment_id": UUID("b4c68905-b1e4-4875-940e-69e5d27730fd"), + }, + { + "id": 6, + "sensor_name": "Test", + "data": "1", + "collected_at": datetime.datetime(2020, 4, 1, 12, 0, 1), + "deployment_id": UUID("6e887b81-57c3-4f3b-a7e7-bad159e05e78"), + }, + { + "id": 7, + "sensor_name": "Test", + "data": "1", + "collected_at": datetime.datetime(2020, 4, 1, 12, 1, 1), + "deployment_id": UUID("6e887b81-57c3-4f3b-a7e7-bad159e05e78"), + }, + { + "id": 8, + "sensor_name": "Test", + "data": "2", + "collected_at": datetime.datetime(2020, 4, 1, 12, 2, 1), + "deployment_id": UUID("6e887b81-57c3-4f3b-a7e7-bad159e05e78"), + }, + { + "id": 9, + "sensor_name": "OtherTest", + "data": "3", + "collected_at": datetime.datetime(2020, 4, 1, 12, 3, 1), + "deployment_id": UUID("6e887b81-57c3-4f3b-a7e7-bad159e05e78"), + }, + ] + for data in datas: + insert = datapoint_table.insert().values(**data) + db_session.execute(insert) diff --git a/Ch09/apd.aggregation-chapter09-ex03-complete/tests/test_analysis.py b/Ch09/apd.aggregation-chapter09-ex03-complete/tests/test_analysis.py new file mode 100644 index 0000000..34a32b4 --- /dev/null +++ b/Ch09/apd.aggregation-chapter09-ex03-complete/tests/test_analysis.py @@ -0,0 +1,423 @@ +import datetime +import uuid + +import pytest + +from apd.aggregation import analysis +from apd.aggregation.database import DataPoint, datapoint_table +from apd.aggregation.query import db_session_var + + +async def generate_datapoints(datas): + deployment_id = uuid.uuid4() + for i, (time, data) in enumerate(datas, start=1): + yield DataPoint( + id=i, + collected_at=time, + sensor_name="TestSensor", + data=data, + deployment_id=deployment_id, + ) + + +class TestPassThroughCleaner: + @pytest.fixture + def cleaner(self): + return analysis.clean_passthrough + + @pytest.mark.asyncio + async def test_float_passthrough(self, cleaner): + data = [ + (datetime.datetime(2020, 4, 1, 12, 0, 0), 65.0), + (datetime.datetime(2020, 4, 1, 13, 0, 0), 65.5), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert output == data + + @pytest.mark.asyncio + async def test_Nones_are_skipped(self, cleaner): + data = [ + (datetime.datetime(2020, 4, 1, 12, 0, 0), None), + (datetime.datetime(2020, 4, 1, 13, 0, 0), 65.5), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert output == [ + (datetime.datetime(2020, 4, 1, 13, 0, 0), 65.5), + ] + + +class TestTemperatureCleaner: + @pytest.fixture + def cleaner(self): + return analysis.clean_temperature_fluctuations + + @pytest.mark.asyncio + async def test_window_not_full(self, cleaner): + data = [ + ( + datetime.datetime(2020, 4, 1, 12, 0, 0), + {"magnitude": 21.0, "unit": "degC"}, + ), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert output == [(datetime.datetime(2020, 4, 1, 12, 0, 0), 21.0)] + + @pytest.mark.asyncio + async def test_window_exactly_full(self, cleaner): + data = [ + ( + datetime.datetime(2020, 4, 1, 12, 0, 0), + {"magnitude": 21.0, "unit": "degC"}, + ), + ( + datetime.datetime(2020, 4, 1, 12, 0, 1), + {"magnitude": 21.0, "unit": "degC"}, + ), + ( + datetime.datetime(2020, 4, 1, 12, 0, 2), + {"magnitude": 21.0, "unit": "degC"}, + ), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert output == [ + (datetime.datetime(2020, 4, 1, 12, 0, 0), 21.0), + (datetime.datetime(2020, 4, 1, 12, 0, 1), 21.0), + (datetime.datetime(2020, 4, 1, 12, 0, 2), 21.0), + ] + + @pytest.mark.asyncio + async def test_window_overfilled(self, cleaner): + data = [ + ( + datetime.datetime(2020, 4, 1, 12, 0, 0), + {"magnitude": 21.0, "unit": "degC"}, + ), + ( + datetime.datetime(2020, 4, 1, 12, 0, 1), + {"magnitude": 21.0, "unit": "degC"}, + ), + ( + datetime.datetime(2020, 4, 1, 12, 0, 2), + {"magnitude": 21.0, "unit": "degC"}, + ), + ( + datetime.datetime(2020, 4, 1, 12, 0, 3), + {"magnitude": 21.0, "unit": "degC"}, + ), + ( + datetime.datetime(2020, 4, 1, 12, 0, 4), + {"magnitude": 21.0, "unit": "degC"}, + ), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert output == [ + (datetime.datetime(2020, 4, 1, 12, 0, 0), 21.0), + (datetime.datetime(2020, 4, 1, 12, 0, 1), 21.0), + (datetime.datetime(2020, 4, 1, 12, 0, 2), 21.0), + (datetime.datetime(2020, 4, 1, 12, 0, 3), 21.0), + (datetime.datetime(2020, 4, 1, 12, 0, 4), 21.0), + ] + + @pytest.mark.asyncio + async def test_outlier_dropped(self, cleaner): + data = [ + ( + datetime.datetime(2020, 4, 1, 12, 0, 0), + {"magnitude": 21.0, "unit": "degC"}, + ), + ( + datetime.datetime(2020, 4, 1, 12, 0, 1), + {"magnitude": 61.0, "unit": "degC"}, + ), + ( + datetime.datetime(2020, 4, 1, 12, 0, 2), + {"magnitude": 21.0, "unit": "degC"}, + ), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert output == [ + (datetime.datetime(2020, 4, 1, 12, 0, 0), 21.0), + (datetime.datetime(2020, 4, 1, 12, 0, 2), 21.0), + ] + + @pytest.mark.asyncio + async def test_limited_to_DHT22_range(self, cleaner): + data = [ + ( + datetime.datetime(2020, 4, 1, 12, 0, 0), + {"magnitude": 91.0, "unit": "degC"}, + ), + ( + datetime.datetime(2020, 4, 1, 12, 0, 1), + {"magnitude": 91.0, "unit": "degC"}, + ), + ( + datetime.datetime(2020, 4, 1, 12, 0, 2), + {"magnitude": 91.0, "unit": "degC"}, + ), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert output == [] + + @pytest.mark.asyncio + async def test_Nones_dropped(self, cleaner): + data = [ + ( + datetime.datetime(2020, 4, 1, 12, 0, 0), + {"magnitude": 31.0, "unit": "degC"}, + ), + (datetime.datetime(2020, 4, 1, 12, 0, 1), None,), + ( + datetime.datetime(2020, 4, 1, 12, 0, 2), + {"magnitude": 32.0, "unit": "degC"}, + ), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert output == [ + (datetime.datetime(2020, 4, 1, 12, 0, 0), 31.0), + (datetime.datetime(2020, 4, 1, 12, 0, 2), 32.0), + ] + + +class TestWattHourCleaner: + @pytest.fixture + def cleaner(self): + return analysis.clean_watthours_to_watts + + @pytest.mark.asyncio + async def test_one_entry_insufficient_to_find_diff(self, cleaner): + data = [ + ( + datetime.datetime(2020, 4, 1, 12, 0, 0), + {"magnitude": 1.0, "unit": "watt_hour"}, + ), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert output == [] + + @pytest.mark.asyncio + async def test_two_entries_provides_diff_with_second_time(self, cleaner): + data = [ + ( + datetime.datetime(2020, 4, 1, 12, 0, 0), + {"magnitude": 1.0, "unit": "watt_hour"}, + ), + ( + datetime.datetime(2020, 4, 1, 13, 0, 0), + {"magnitude": 10.0, "unit": "watt_hour"}, + ), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert len(output) == 1 + assert output[0][0] == datetime.datetime(2020, 4, 1, 13, 0, 0) + assert output[0][1] == pytest.approx(9.0, 0.0001) + + @pytest.mark.asyncio + async def test_fractional_hour(self, cleaner): + data = [ + ( + datetime.datetime(2020, 4, 1, 12, 0, 0), + {"magnitude": 1.0, "unit": "watt_hour"}, + ), + ( + datetime.datetime(2020, 4, 1, 12, 23, 42), + {"magnitude": 10.0, "unit": "watt_hour"}, + ), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert len(output) == 1 + assert output[0][0] == datetime.datetime(2020, 4, 1, 12, 23, 42) + assert output[0][1] == pytest.approx(22.7848, 0.001) + + @pytest.mark.asyncio + async def test_diff_happens_pairwise(self, cleaner): + data = [ + ( + datetime.datetime(2020, 4, 1, 12, 0, 0), + {"magnitude": 1.0, "unit": "watt_hour"}, + ), + ( + datetime.datetime(2020, 4, 1, 12, 23, 42), + {"magnitude": 10.0, "unit": "watt_hour"}, + ), + ( + datetime.datetime(2020, 4, 1, 13, 23, 42), + {"magnitude": 13.0, "unit": "watt_hour"}, + ), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert len(output) == 2 + assert output[0][0] == datetime.datetime(2020, 4, 1, 12, 23, 42) + assert output[0][1] == pytest.approx(22.7848, 0.001) + assert output[1][0] == datetime.datetime(2020, 4, 1, 13, 23, 42) + assert output[1][1] == pytest.approx(3, 0.001) + + @pytest.mark.asyncio + async def test_None_ignored(self, cleaner): + data = [ + ( + datetime.datetime(2020, 4, 1, 12, 0, 0), + {"magnitude": 1.0, "unit": "watt_hour"}, + ), + (datetime.datetime(2020, 4, 1, 12, 58, 0), None,), + ( + datetime.datetime(2020, 4, 1, 13, 0, 0), + {"magnitude": 10.0, "unit": "watt_hour"}, + ), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert len(output) == 1 + assert output[0][0] == datetime.datetime(2020, 4, 1, 13, 0, 0) + assert output[0][1] == pytest.approx(9.0, 0.0001) + + +class TestTemperatureMap: + @pytest.fixture + def temperature_data(self): + return { + (53.8667, -1.3333): -1, + (53.35, -2.2833): 1, + (52.45, -1.7333): 3, + (51.5, -0.1333): 6, + (51.55, -2.5667): 5, + (54.9667, -1.6167): -1, + (55.9667, -3.2167): -1, + (54.7667, -1.5833): 0, + (53.7667, -0.3): 1, + (53.7667, -3.0167): 0, + (51.4833, -3.1833): 4, + (53.2667, -3.5167): 2, + (52.0833, -2.8): 2, + (52.0167, -0.6): 2, + (52.6667, 1.2667): 5, + (50.4333, -4.9833): 9, + (50.35, -4.1167): 10, + (50.5167, -2.45): 9, + (50.8333, -1.1667): 9, + (56.45, -5.4333): 4, + (57.5333, -4.05): -2, + (54.5167, -3.6167): 3, + (53.0833, 0.2667): 4, + (51.35, 1.3333): 7, + (50.8833, 0.3167): 8, + (58.45, -3.1): 3, + (50.1, -5.6667): 9, + (58.2167, -6.3333): 4, + (55.6833, -6.25): 7, + } + + @pytest.fixture + def temperature_datapoints(self, migrated_db, db_session, temperature_data): + datas = [] + now = datetime.datetime.now() + for (coord, temp) in temperature_data.items(): + deployment_id = uuid.uuid4() + datas.append( + DataPoint( + sensor_name="Location", + deployment_id=deployment_id, + collected_at=now, + data=coord, + ) + ) + datas.append( + DataPoint( + sensor_name="Temperature", + deployment_id=deployment_id, + collected_at=now, + data=temp, + ) + ) + return datas + + @pytest.fixture + def populated_db(self, migrated_db, db_session, temperature_datapoints): + for data in temperature_datapoints: + insert = datapoint_table.insert().values(**data._asdict()) + db_session.execute(insert) + + @pytest.fixture + def cleaner(self): + return analysis.get_map_cleaner_for("Temperature") + + @pytest.fixture + def get_data(self, db_session): + db_session_var.set(db_session) + return analysis.get_all_data() + + @pytest.mark.asyncio + async def test_latest_coord_temp_cleaner( + self, temperature_data, populated_db, get_data, cleaner + ): + data = get_data() + deployments = 0 + async for deployment, data_points in cleaner(data): + deployments += 1 + cleaned = {a async for a in cleaner(data_points)} + # There should only be one deployment + assert deployments == 1 + expected = set(temperature_data.items()) + assert cleaned == expected + + @pytest.mark.asyncio + async def test_newer_items_superceed_older( + self, db_session, temperature_datapoints, populated_db, get_data, cleaner + ): + # Pick any of the deployment ids and find the pair of DataPoints in that deployment id + deployment_1 = temperature_datapoints[0].deployment_id + existing = [ + datapoint + for datapoint in temperature_datapoints + if datapoint.deployment_id == deployment_1 + ] + old_location = [ + datapoint for datapoint in existing if datapoint.sensor_name == "Location" + ][0] + old_temperature = [ + datapoint + for datapoint in existing + if datapoint.sensor_name == "Temperature" + ][0] + + new_location = DataPoint( + sensor_name="Location", + deployment_id=deployment_1, + collected_at=datetime.datetime(2031, 4, 1, 12, 0, 0), + data=(-10.5, 150.1), + ) + new_temperature = DataPoint( + sensor_name="Temperature", + deployment_id=deployment_1, + collected_at=datetime.datetime(2031, 4, 1, 12, 0, 0), + data=21, + ) + for data in [new_location, new_temperature]: + insert = datapoint_table.insert().values(**data._asdict()) + db_session.execute(insert) + + data = get_data() + deployments = 0 + async for deployment, data_points in cleaner(data): + deployments += 1 + cleaned = {a async for a in cleaner(data_points)} + # There should only be one deployment + assert deployments == 1 + + # We expect the newer one, not the older + assert (new_location.data, new_temperature.data) in cleaned + assert (old_location.data, old_temperature.data) not in cleaned + # The cleaner converts two data points to one plottable + assert len(cleaned) == len(temperature_datapoints) / 2 diff --git a/Ch09/apd.aggregation-chapter09-ex03-complete/tests/test_cli.py b/Ch09/apd.aggregation-chapter09-ex03-complete/tests/test_cli.py new file mode 100644 index 0000000..5cab441 --- /dev/null +++ b/Ch09/apd.aggregation-chapter09-ex03-complete/tests/test_cli.py @@ -0,0 +1,155 @@ +from unittest import mock +import uuid + +from click.testing import CliRunner +import pytest + +import apd.aggregation.cli +from apd.aggregation.database import Deployment, deployment_table + + +@pytest.mark.functional +def test_sensors_are_passed_to_get_data_points(db_uri): + runner = CliRunner() + with mock.patch("apd.aggregation.collect.get_data_points") as get_data_points: + runner.invoke( + apd.aggregation.cli.collect_sensor_data, + [ + "http://localhost", + "http://otherhost", + "--db", + db_uri, + "--api-key", + "key", + ], + ) + calls = get_data_points.call_args_list + assert len(calls) == 2 + details = {call[0] for call in get_data_points.call_args_list} + assert details == { + ("http://localhost", "key"), + ("http://otherhost", "key"), + } + + +@pytest.mark.functional +def test_add_deployment_to_db(db_uri, db_session, migrated_db): + runner = CliRunner() + deployment_id = uuid.UUID("76587cdd42dc489ea1d220e9b0bc62ca") + with mock.patch("apd.aggregation.collect.get_deployment_id") as get_deployment_id: + get_deployment_id.return_value = deployment_id + runner.invoke( + apd.aggregation.cli.deployments, + ["add", "http://otherhost", "Other", "--db", db_uri, "--api-key", "key"], + ) + deployments = db_session.query(deployment_table).all() + assert len(deployments) == 1 + deployment = Deployment.from_sql_result(deployments[0]) + assert deployment.uri == "http://otherhost" + assert deployment.api_key == "key" + assert deployment.id == deployment_id + + +@pytest.mark.functional +def test_use_stored_deployments(db_uri, migrated_db): + runner = CliRunner() + + deployment_id = uuid.UUID("76587cdd42dc489ea1d220e9b0bc62ca") + with mock.patch("apd.aggregation.collect.get_deployment_id") as get_deployment_id: + get_deployment_id.return_value = deployment_id + runner.invoke( + apd.aggregation.cli.deployments, + [ + "add", + "http://specifiedhost", + "Specified", + "--db", + db_uri, + "--api-key", + "an_api_key", + ], + ) + + with mock.patch("apd.aggregation.collect.get_data_points") as get_data_points: + runner.invoke( + apd.aggregation.cli.collect_sensor_data, ["--db", db_uri], + ) + calls = get_data_points.call_args_list + assert len(calls) == 1 + details = {call[0] for call in get_data_points.call_args_list} + assert details == { + ("http://specifiedhost", "an_api_key"), + } + + +@pytest.mark.functional +def test_list_deployments(db_uri, migrated_db): + runner = CliRunner() + + deployment_id = uuid.UUID("76587cdd42dc489ea1d220e9b0bc62ca") + with mock.patch("apd.aggregation.collect.get_deployment_id") as get_deployment_id: + get_deployment_id.return_value = deployment_id + runner.invoke( + apd.aggregation.cli.deployments, + [ + "add", + "http://specifiedhost", + "Specified", + "--db", + db_uri, + "--api-key", + "an_api_key", + ], + ) + + result = runner.invoke(apd.aggregation.cli.deployments, ["list", "--db", db_uri],) + expected = """Specified +ID 76587cdd42dc489ea1d220e9b0bc62ca +URI http://specifiedhost +API key an_api_key +Colour None + +""" + assert result.output == expected + + +@pytest.mark.functional +def test_edit_deployment(db_uri, migrated_db): + runner = CliRunner() + return + deployment_id = uuid.UUID("76587cdd42dc489ea1d220e9b0bc62ca") + with mock.patch("apd.aggregation.collect.get_deployment_id") as get_deployment_id: + get_deployment_id.return_value = deployment_id + runner.invoke( + apd.aggregation.cli.deployments, + [ + "add", + "http://specifiedhost", + "Specified", + "--db", + db_uri, + "--api-key", + "an_api_key", + ], + ) + + result = runner.invoke( + apd.aggregation.cli.deployments, + [ + "edit", + "--db", + db_uri, + deployment_id.hex(), + "--colour", + "red" "--name", + "New name", + ], + ) + expected = """New name +ID 76587cdd42dc489ea1d220e9b0bc62ca +URI http://specifiedhost +API key an_api_key +Colour red + +""" + assert result.output == expected diff --git a/Ch09/apd.aggregation-chapter09-ex03-complete/tests/test_http_get.py b/Ch09/apd.aggregation-chapter09-ex03-complete/tests/test_http_get.py new file mode 100644 index 0000000..df575c6 --- /dev/null +++ b/Ch09/apd.aggregation-chapter09-ex03-complete/tests/test_http_get.py @@ -0,0 +1,188 @@ +from concurrent.futures import ThreadPoolExecutor +import datetime +import typing as t +from unittest.mock import patch, MagicMock +import wsgiref.simple_server + +import aiohttp +from apd.sensors.base import Sensor +from apd.sensors.sensors import PythonVersion, ACStatus +from apd.sensors.wsgi import set_up_config +import flask +import pytest + +from apd.sensors.wsgi import v21 + +from apd.aggregation import collect +from apd.aggregation.database import Deployment + +pytestmark = [pytest.mark.functional] + + +@pytest.fixture +def sensors() -> t.Iterator[t.List[Sensor[t.Any]]]: + """ Patch the get_sensors method to return a known pair of sensors only """ + data: t.List[Sensor[t.Any]] = [PythonVersion(), ACStatus()] + with patch("apd.sensors.cli.get_sensors") as get_sensors: + get_sensors.return_value = data + yield data + + +def get_independent_flask_app(name: str) -> flask.Flask: + """ Create a new flask app with the v20 API blueprint loaded, so multiple copies + of the app can be run in parallel without conflicting configuration """ + app = flask.Flask(name) + app.register_blueprint(v21.version, url_prefix="/v/2.1") + return app + + +def run_server_in_thread( + name: str, config: t.Dict[str, t.Any], port: int +) -> t.Iterator[str]: + # Create a new flask app and load in required code, to prevent config conflicts + app = get_independent_flask_app(name) + flask_app = set_up_config(config, app) + server = wsgiref.simple_server.make_server("localhost", port, flask_app) + + with ThreadPoolExecutor() as pool: + pool.submit(server.serve_forever) + yield f"http://localhost:{port}/" + server.shutdown() + + +@pytest.fixture(scope="module") +def http_server(): + yield from run_server_in_thread( + "standard", + { + "APD_SENSORS_API_KEY": "testing", + "APD_SENSORS_DEPLOYMENT_ID": "a46b1d1207fd4cdcad39bbdf706dfe29", + }, + 12081, + ) + + +@pytest.fixture(scope="module") +def bad_api_key_http_server(): + yield from run_server_in_thread( + "alternate", + { + "APD_SENSORS_API_KEY": "penny", + "APD_SENSORS_DEPLOYMENT_ID": "38cf2bae9adb445fad946c82e290487a", + }, + 12082, + ) + + +class TestGetDataPoints: + @pytest.fixture + def mut(self): + return collect.get_data_points + + @pytest.mark.asyncio + async def test_get_data_points( + self, sensors: t.List[Sensor[t.Any]], mut, http_server: str + ) -> None: + # Get the data from the server, storing the time before and after + # as bounds for the collected_at value + async with aiohttp.ClientSession() as http: + collect.http_session_var.set(http) + time_before = datetime.datetime.now() + results = await mut(http_server, "testing") + time_after = datetime.datetime.now() + + assert len(results) == len(sensors) == 2 + + for (sensor, result) in zip(sensors, results): + assert sensor.from_json_compatible(result.data) == sensor.value() + assert result.sensor_name == sensor.name + assert time_before <= result.collected_at <= time_after + + @pytest.mark.asyncio + async def test_get_data_points_fails_with_bad_api_key( + self, sensors: t.List[Sensor[t.Any]], mut, http_server: str + ) -> None: + with pytest.raises( + ValueError, + match=f"Error loading data from {http_server}: Supply API key in X-API-Key header", + ): + async with aiohttp.ClientSession() as http: + collect.http_session_var.set(http) + await mut(http_server, "incorrect") + + +class TestAddDataFromSensors: + @pytest.fixture + def mut(self): + return collect.add_data_from_sensors + + @pytest.fixture + def mock_db_session(self): + return MagicMock() + + @pytest.mark.asyncio + async def test_get_get_data_from_sensors( + self, mock_db_session, sensors: t.List[Sensor[t.Any]], mut, http_server: str + ) -> None: + results = await mut( + mock_db_session, + [ + Deployment( + id=None, colour=None, name=None, uri=http_server, api_key="testing" + ) + ], + ) + assert mock_db_session.execute.call_count == len(sensors) + assert len(results) == len(sensors) + + @pytest.mark.asyncio + async def test_get_get_data_from_sensors_with_multiple_servers( + self, mock_db_session, sensors: t.List[Sensor[t.Any]], mut, http_server: str + ) -> None: + results = await mut( + mock_db_session, + [ + Deployment( + id=None, colour=None, name=None, uri=http_server, api_key="testing" + ), + Deployment( + id=None, colour=None, name=None, uri=http_server, api_key="testing" + ), + ], + ) + assert mock_db_session.execute.call_count == len(sensors) * 2 + assert len(results) == len(sensors) * 2 + + @pytest.mark.asyncio + async def test_data_points_not_added_if_only_partial_success( + self, + mock_db_session, + sensors: t.List[Sensor[t.Any]], + mut, + http_server: str, + bad_api_key_http_server: str, + ) -> None: + with pytest.raises( + ValueError, + match=f"Error loading data from {bad_api_key_http_server}: Supply API key in X-API-Key header", + ): + await mut( + mock_db_session, + [ + Deployment( + id=None, + colour=None, + name=None, + uri=http_server, + api_key="testing", + ), + Deployment( + id=None, + colour=None, + name=None, + uri=bad_api_key_http_server, + api_key="testing", + ), + ], + ) + assert mock_db_session.execute.call_count == 0 diff --git a/Ch09/apd.aggregation-chapter09-ex03-complete/tests/test_query.py b/Ch09/apd.aggregation-chapter09-ex03-complete/tests/test_query.py new file mode 100644 index 0000000..9b6e0aa --- /dev/null +++ b/Ch09/apd.aggregation-chapter09-ex03-complete/tests/test_query.py @@ -0,0 +1,111 @@ +import datetime +from uuid import UUID + +import pytest + +from apd.aggregation.query import ( + get_data, + get_deployment_ids, + get_data_by_deployment, + db_session_var, +) + + +class TestGetData: + @pytest.fixture + def mut(self): + return get_data + + @pytest.mark.asyncio + @pytest.mark.parametrize( + "filter,num_items_expected", + [ + ({}, 9), + ({"sensor_name": "Test"}, 7), + ({"deployment_id": UUID("b4c68905-b1e4-4875-940e-69e5d27730fd")}, 5), + ({"collected_after": datetime.datetime(2020, 4, 1, 12, 2, 1)}, 3), + ({"collected_before": datetime.datetime(2020, 4, 1, 12, 2, 1)}, 4), + ( + { + "collected_after": datetime.datetime(2020, 4, 1, 12, 2, 1), + "collected_before": datetime.datetime(2020, 4, 1, 12, 3, 5), + }, + 2, + ), + ], + ) + async def test_iterate_over_items( + self, mut, db_session, populated_db, filter, num_items_expected + ): + db_session_var.set(db_session) + points = [dp async for dp in mut(**filter)] + assert len(points) == num_items_expected + + @pytest.mark.asyncio + async def test_empty_db(self, mut, db_session, migrated_db): + db_session_var.set(db_session) + points = [dp async for dp in mut()] + assert len(points) == 0 + + +class TestGetDeployments: + @pytest.fixture + def mut(self): + return get_deployment_ids + + @pytest.mark.asyncio + async def test_get_all_deployments(self, mut, db_session, populated_db): + db_session_var.set(db_session) + deployment_ids = await mut() + assert set(deployment_ids) == { + UUID("b4c68905-b1e4-4875-940e-69e5d27730fd"), + UUID("6e887b81-57c3-4f3b-a7e7-bad159e05e78"), + } + + @pytest.mark.asyncio + async def test_empty_db(self, mut, db_session, migrated_db): + db_session_var.set(db_session) + deployment_ids = await mut() + assert deployment_ids == [] + + +@pytest.mark.usefixtures("populated_db") +class TestGetDataByDeployment: + @pytest.fixture + def mut(self): + return get_data_by_deployment + + @pytest.mark.asyncio + @pytest.mark.parametrize( + "filter,num_items_expected", [({}, 9), ({"sensor_name": "Test"}, 7)] + ) + async def test_iterate_over_all_items( + self, mut, db_session, filter, num_items_expected + ): + db_session_var.set(db_session) + num_items = 0 + num_deployments = 0 + async for deployment_id, items in mut(**filter): + num_deployments += 1 + async for item in items: + num_items += 1 + assert num_deployments == 2 + assert num_items == num_items_expected + + @pytest.mark.asyncio + @pytest.mark.parametrize( + "filter,num_items_expected", [({}, 5), ({"sensor_name": "Test"}, 4)] + ) + async def test_skip_first_deployment( + self, mut, db_session, filter, num_items_expected + ): + db_session_var.set(db_session) + num_items = 0 + num_deployments = 0 + async for deployment_id, items in mut(**filter): + num_deployments += 1 + if num_deployments == 2: + async for item in items: + num_items += 1 + assert num_deployments == 2 + assert num_items == num_items_expected diff --git a/Ch09/apd.aggregation-chapter09-ex03-complete/tests/test_sensor_aggregation.py b/Ch09/apd.aggregation-chapter09-ex03-complete/tests/test_sensor_aggregation.py new file mode 100644 index 0000000..311dd19 --- /dev/null +++ b/Ch09/apd.aggregation-chapter09-ex03-complete/tests/test_sensor_aggregation.py @@ -0,0 +1,199 @@ +from __future__ import annotations + +import contextlib +from dataclasses import dataclass +import json +import typing as t +from unittest.mock import patch, Mock + +import pytest +import sqlalchemy + +import apd.aggregation.collect +from apd.aggregation.database import Deployment + + +@pytest.fixture +def data() -> t.Any: + return { + "sensors": [ + { + "human_readable": "3.7", + "id": "PythonVersion", + "title": "Python Version", + "value": [3, 7, 2, "final", 0], + }, + { + "human_readable": "Not connected", + "id": "ACStatus", + "title": "AC Connected", + "value": False, + }, + ] + } + + +@dataclass +class FakeAIOHttpClient: + responses: t.Dict[str, str] + + @contextlib.asynccontextmanager + async def get( + self, url: str, headers: t.Optional[t.Dict[str, str]] = None + ) -> t.AsyncIterator[FakeAIOHttpResponse]: + if url in self.responses: + yield FakeAIOHttpResponse(body=self.responses[url]) + else: + yield FakeAIOHttpResponse(body="", status=404) + + +@dataclass +class FakeAIOHttpResponse: + body: str + status: int = 200 + + async def json(self) -> t.Any: + return json.loads(self.body) + + +@pytest.fixture +def mockclient(data) -> FakeAIOHttpClient: + return FakeAIOHttpClient( + { + "http://localhost/v/2.1/sensors/": json.dumps(data), + "http://localhost/v/2.1/deployment_id": json.dumps( + {"deployment_id": "b29ba0ee10f14552b6b21327bb96d3fb"} + ), + } + ) + + +class TestGetDataPoints: + @pytest.fixture + def mut(self): + return apd.aggregation.collect.get_data_points + + @pytest.mark.asyncio + async def test_get_data_points( + self, mut, mockclient: FakeAIOHttpClient, data + ) -> None: + token = apd.aggregation.collect.http_session_var.set(mockclient) + try: + datapoints = await mut("http://localhost", "") + finally: + apd.aggregation.collect.http_session_var.reset(token) + + assert len(datapoints) == len(data["sensors"]) + for sensor in data["sensors"]: + assert sensor["value"] in (datapoint.data for datapoint in datapoints) + assert sensor["id"] in (datapoint.sensor_name for datapoint in datapoints) + + +class TestAddDataFromSensors: + @pytest.fixture + def mut(self): + return apd.aggregation.collect.add_data_from_sensors + + @pytest.fixture(autouse=True) + def patch_aiohttp(self, mockclient): + with patch("aiohttp.ClientSession") as ClientSession: + ClientSession.return_value.__aenter__.return_value = mockclient + yield ClientSession + + @pytest.fixture + def db_session(self): + session = Mock() + sql_result = session.execute.return_value + sql_result.inserted_primary_key = [1] + return session + + @pytest.mark.asyncio + async def test_datapoints_are_added_to_the_session(self, mut, db_session) -> None: + assert db_session.execute.call_count == 0 + datapoints = await mut( + db_session, + [ + Deployment( + id=None, colour=None, name=None, uri="http://localhost", api_key="" + ) + ], + ) + assert db_session.execute.call_count == len(datapoints) + + +@pytest.mark.usefixtures("migrated_db") +class TestDatabaseConnection: + @pytest.fixture(autouse=True) + def patch_aiohttp(self, mockclient): + with patch("aiohttp.ClientSession") as ClientSession: + ClientSession.return_value.__aenter__.return_value = mockclient + yield ClientSession + + @pytest.fixture + def table(self): + return apd.aggregation.database.datapoint_table + + @pytest.fixture + def daily_summary_view(self): + return apd.aggregation.database.daily_summary_view + + @pytest.fixture + def model(self): + return apd.aggregation.database.DataPoint + + @pytest.fixture + def mut(self): + return apd.aggregation.collect.add_data_from_sensors + + @pytest.mark.asyncio + async def test_datapoints_are_added_to_the_session(self, db_session, table) -> None: + datapoints = await apd.aggregation.collect.add_data_from_sensors( + db_session, + [ + Deployment( + id=None, colour=None, name=None, uri="http://localhost", api_key="" + ) + ], + ) + num_points = db_session.query(table).count() + assert num_points == len(datapoints) == 2 + + @pytest.mark.asyncio + async def test_datapoints_can_be_mapped_back_to_DataPoints( + self, mut, db_session, table, model + ) -> None: + datapoints = await mut( + db_session, + [ + Deployment( + id=None, colour=None, name=None, uri="http://localhost", api_key="" + ) + ], + ) + db_points = [ + model.from_sql_result(result) for result in db_session.query(table) + ] + assert db_points == datapoints + + @pytest.mark.asyncio + async def test_daily_summary_view_matches_query( + self, db_session, model, table, daily_summary_view + ) -> None: + await apd.aggregation.collect.add_data_from_sensors( + db_session, + [ + Deployment( + id=None, colour=None, name=None, uri="http://localhost", api_key="" + ) + ], + ) + + headers = table.c.sensor_name, table.c.data + value_counts = ( + db_session.query(*headers, sqlalchemy.func.count(table.c.id)) + .filter(model.collected_on_date == sqlalchemy.func.current_date()) + .group_by(*headers) + ) + + daily_summary_view = db_session.query(daily_summary_view) + assert value_counts.all() == daily_summary_view.all() diff --git a/Ch09/apd.aggregation-chapter09-ex03/.coveragerc b/Ch09/apd.aggregation-chapter09-ex03/.coveragerc new file mode 100644 index 0000000..e766c69 --- /dev/null +++ b/Ch09/apd.aggregation-chapter09-ex03/.coveragerc @@ -0,0 +1,3 @@ +[run] +branch = True +omit = tests/* diff --git a/Ch09/apd.aggregation-chapter09-ex03/.pre-commit-config.yaml b/Ch09/apd.aggregation-chapter09-ex03/.pre-commit-config.yaml new file mode 100644 index 0000000..d3b6ec7 --- /dev/null +++ b/Ch09/apd.aggregation-chapter09-ex03/.pre-commit-config.yaml @@ -0,0 +1,26 @@ +repos: + +- repo: local + hooks: + - id: black + name: black + entry: pipenv run black + args: [--quiet] + language: system + types: [python] + + - id: mypy + name: mypy + exclude: (?x)^( + (.*)/setup.py + )$ + entry: pipenv run mypy + args: ["--follow-imports=skip"] + language: system + types: [python] + + - id: flake8 + name: flake8 + entry: pipenv run flake8 + language: system + types: [python] diff --git a/Ch09/apd.aggregation-chapter09-ex03/CHANGES.md b/Ch09/apd.aggregation-chapter09-ex03/CHANGES.md new file mode 100644 index 0000000..8e424e5 --- /dev/null +++ b/Ch09/apd.aggregation-chapter09-ex03/CHANGES.md @@ -0,0 +1,5 @@ +## Changes + +### 1.0.0 (Unreleased) + +* Generated from skeleton diff --git a/Ch09/apd.aggregation-chapter09-ex03/LICENCE b/Ch09/apd.aggregation-chapter09-ex03/LICENCE new file mode 100644 index 0000000..86685d4 --- /dev/null +++ b/Ch09/apd.aggregation-chapter09-ex03/LICENCE @@ -0,0 +1,31 @@ +Copyright (c) 2019, Matthew Wilkes + + +All rights reserved. + +Redistribution and use in source and binary forms, with or without modification, +are permitted provided that the following conditions are met: + +* Redistributions of source code must retain the above copyright notice, this + list of conditions and the following disclaimer. + +* Redistributions in binary form must reproduce the above copyright notice, this + list of conditions and the following disclaimer in the documentation and/or + other materials provided with the distribution. + +* Neither the name of the copyright holder nor the names of its + contributors may be used to endorse or promote products derived from this + software without specific prior written permission. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND +ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED +WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. +IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, +INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, +BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, +DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY +OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE +OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED +OF THE POSSIBILITY OF SUCH DAMAGE. + + diff --git a/Ch09/apd.aggregation-chapter09-ex03/Pipfile b/Ch09/apd.aggregation-chapter09-ex03/Pipfile new file mode 100644 index 0000000..b308175 --- /dev/null +++ b/Ch09/apd.aggregation-chapter09-ex03/Pipfile @@ -0,0 +1,25 @@ +[[source]] +name = "pypi" +url = "https://pypi.org/simple" +verify_ssl = true + +[dev-packages] +pytest = "*" +pytest-cov = "*" +pytest-asyncio = "*" +mypy = "*" +flake8 = "*" +black = "*" +pre-commit = "*" +wheel = "*" +twine = "*" +sqlalchemy-stubs = "*" + +[packages] +apd-aggregation = {editable = true,extras = ["jupyter"],path = "."} +apd-sensors = {editable = true,extras = ["webapp"],path = "./../code"} + +[requires] + +[pipenv] +allow_prereleases = true diff --git a/Ch09/apd.aggregation-chapter09-ex03/Pipfile.lock b/Ch09/apd.aggregation-chapter09-ex03/Pipfile.lock new file mode 100644 index 0000000..37ebbba --- /dev/null +++ b/Ch09/apd.aggregation-chapter09-ex03/Pipfile.lock @@ -0,0 +1,751 @@ +{ + "_meta": { + "hash": { + "sha256": "9cbbc3af88a1213ed8f4f93c33e35a9f9d7e047077cd25ff02f9bde13e2b606b" + }, + "pipfile-spec": 6, + "requires": {}, + "sources": [ + { + "name": "pypi", + "url": "https://pypi.org/simple", + "verify_ssl": true + } + ] + }, + "default": { + "aiohttp": { + "hashes": [ + "sha256:173267050501e1537293df06723bc5e719990889e2820ba3932969983892e960", + "sha256:438f1f1555c02c50894604d94944cff188fe138b46467b7fa99fdceb51ab5842", + 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file mode 100644 index 0000000..85669dd --- /dev/null +++ b/Ch09/apd.aggregation-chapter09-ex03/README.md @@ -0,0 +1,4 @@ +# APD Sensor aggregator + +A programme that queries apd.sensor endpoints and aggregates their results. + diff --git a/Ch09/apd.aggregation-chapter09-ex03/pyproject.toml b/Ch09/apd.aggregation-chapter09-ex03/pyproject.toml new file mode 100644 index 0000000..4d3bb67 --- /dev/null +++ b/Ch09/apd.aggregation-chapter09-ex03/pyproject.toml @@ -0,0 +1,5 @@ +[build-system] +requires = [ + "setuptools", +] +build-backend = "setuptools.build_meta" diff --git a/Ch09/apd.aggregation-chapter09-ex03/pytest.ini b/Ch09/apd.aggregation-chapter09-ex03/pytest.ini new file mode 100644 index 0000000..c1d3e58 --- /dev/null +++ b/Ch09/apd.aggregation-chapter09-ex03/pytest.ini @@ -0,0 +1,3 @@ +[pytest] +markers = + functional: these tests are significantly slower as they start a HTTP server \ No newline at end of file diff --git a/Ch09/apd.aggregation-chapter09-ex03/setup.cfg b/Ch09/apd.aggregation-chapter09-ex03/setup.cfg new file mode 100644 index 0000000..73fc36a --- /dev/null +++ b/Ch09/apd.aggregation-chapter09-ex03/setup.cfg @@ -0,0 +1,77 @@ +[mypy] +namespace_packages = True +mypy_path = src +plugins = sqlmypy + +[mypy-alembic] +ignore_missing_imports = True + +[mypy-alembic.config] +ignore_missing_imports = True + +[mypy-alembic.script] +ignore_missing_imports = True + +[mypy-alembic.runtime.environment] +ignore_missing_imports = True + +[mypy-matplotlib] +ignore_missing_imports = True + +[mypy-matplotlib.axes._base] +ignore_missing_imports = True + +[mypy-matplotlib.figure] +ignore_missing_imports = True + +[mypy-matplotlib.pyplot] +ignore_missing_imports = True + + +[mypy-pint] +ignore_missing_imports = True + +[mypy-pytest] +ignore_missing_imports = True + +[flake8] +max-line-length = 120 +ignore = E501 + +[metadata] +name = apd.aggregation +version = attr: apd.aggregation.VERSION +description = A programme that queries apd.sensor endpoints and aggregates their results. +long_description = file: README.md, CHANGES.md, LICENCE +long_description_content_type = text/markdown +keywords = +license = BSD +classifiers = + Programming Language :: Python :: 3 + Programming Language :: Python :: 3.7 + +[options] +zip_safe = False +include_package_data = True +package_dir = + =src +packages = find_namespace: +install_requires = + sqlalchemy + aiohttp + pint + psycopg2 + alembic + click + + +[options.extras_require] +jupyter = ipywidgets + +[options.packages.find] +where = src + +[options.entry_points] +console_scripts = + collect_sensor_data = apd.aggregation.cli:collect_sensor_data + sensor_deployments = apd.aggregation.cli:deployments diff --git a/Ch09/apd.aggregation-chapter09-ex03/setup.py b/Ch09/apd.aggregation-chapter09-ex03/setup.py new file mode 100644 index 0000000..6068493 --- /dev/null +++ b/Ch09/apd.aggregation-chapter09-ex03/setup.py @@ -0,0 +1,3 @@ +from setuptools import setup + +setup() diff --git a/Ch09/apd.aggregation-chapter09-ex03/src/apd/aggregation/__init__.py b/Ch09/apd.aggregation-chapter09-ex03/src/apd/aggregation/__init__.py new file mode 100644 index 0000000..3277f64 --- /dev/null +++ b/Ch09/apd.aggregation-chapter09-ex03/src/apd/aggregation/__init__.py @@ -0,0 +1 @@ +VERSION = "1.0.0" diff --git a/Ch09/apd.aggregation-chapter09-ex03/src/apd/aggregation/alembic/README b/Ch09/apd.aggregation-chapter09-ex03/src/apd/aggregation/alembic/README new file mode 100644 index 0000000..97a87d5 --- /dev/null +++ b/Ch09/apd.aggregation-chapter09-ex03/src/apd/aggregation/alembic/README @@ -0,0 +1,12 @@ +Generic single-database configuration. + + +# Database setup + +To generate the required database tables you must create an alembic.ini file, as follows: + + [alembic] + script_location = apd.aggregation:alembic + sqlalchemy.url = postgresql+psycopg2://apd@localhost/apd + +and run `alembic upgrade head`. This should also be done after every upgrade of the software. \ No newline at end of file diff --git a/Ch09/apd.aggregation-chapter09-ex03/src/apd/aggregation/alembic/env.py b/Ch09/apd.aggregation-chapter09-ex03/src/apd/aggregation/alembic/env.py new file mode 100644 index 0000000..4c8a2c2 --- /dev/null +++ b/Ch09/apd.aggregation-chapter09-ex03/src/apd/aggregation/alembic/env.py @@ -0,0 +1,80 @@ +from logging.config import fileConfig + +from sqlalchemy import engine_from_config +from sqlalchemy import pool +from alembic import context + +from apd.aggregation.database import metadata + + +# this is the Alembic Config object, which provides +# access to the values within the .ini file in use. +config = context.config + +# Interpret the config file for Python logging. +# This line sets up loggers basically. +if config.config_file_name: + fileConfig(config.config_file_name) + +target_metadata = metadata + + +def include_object(object, name, type_, reflected, compare_to): + if object.info.get("is_view", False): + return False + return True + + +def run_migrations_offline(): + """Run migrations in 'offline' mode. + + This configures the context with just a URL + and not an Engine, though an Engine is acceptable + here as well. By skipping the Engine creation + we don't even need a DBAPI to be available. + + Calls to context.execute() here emit the given string to the + script output. + + """ + url = config.get_main_option("sqlalchemy.url") + context.configure( + url=url, + include_object=include_object, + target_metadata=target_metadata, + literal_binds=True, + dialect_opts={"paramstyle": "named"}, + ) + + with context.begin_transaction(): + context.run_migrations() + + +def run_migrations_online(): + """Run migrations in 'online' mode. + + In this scenario we need to create an Engine + and associate a connection with the context. + + """ + connectable = engine_from_config( + config.get_section(config.config_ini_section), + prefix="sqlalchemy.", + poolclass=pool.NullPool, + ) + + with connectable.connect() as connection: + context.configure( + connection=connection, + target_metadata=target_metadata, + include_object=include_object, + ) + + with context.begin_transaction(): + context.run_migrations() + + +if context.is_offline_mode(): + run_migrations_offline() +else: + run_migrations_online() diff --git a/Ch09/apd.aggregation-chapter09-ex03/src/apd/aggregation/alembic/script.py.mako b/Ch09/apd.aggregation-chapter09-ex03/src/apd/aggregation/alembic/script.py.mako new file mode 100644 index 0000000..2c01563 --- /dev/null +++ b/Ch09/apd.aggregation-chapter09-ex03/src/apd/aggregation/alembic/script.py.mako @@ -0,0 +1,24 @@ +"""${message} + +Revision ID: ${up_revision} +Revises: ${down_revision | comma,n} +Create Date: ${create_date} + +""" +from alembic import op +import sqlalchemy as sa +${imports if imports else ""} + +# revision identifiers, used by Alembic. +revision = ${repr(up_revision)} +down_revision = ${repr(down_revision)} +branch_labels = ${repr(branch_labels)} +depends_on = ${repr(depends_on)} + + +def upgrade(): + ${upgrades if upgrades else "pass"} + + +def downgrade(): + ${downgrades if downgrades else "pass"} diff --git a/Ch09/apd.aggregation-chapter09-ex03/src/apd/aggregation/alembic/versions/4b2df8a6e1ce_add_indexes_to_datapoints.py b/Ch09/apd.aggregation-chapter09-ex03/src/apd/aggregation/alembic/versions/4b2df8a6e1ce_add_indexes_to_datapoints.py new file mode 100644 index 0000000..ef671bd --- /dev/null +++ b/Ch09/apd.aggregation-chapter09-ex03/src/apd/aggregation/alembic/versions/4b2df8a6e1ce_add_indexes_to_datapoints.py @@ -0,0 +1,32 @@ +"""Add indexes to datapoints + +Revision ID: 4b2df8a6e1ce +Revises: 6d2eacd5da3f +Create Date: 2019-12-02 16:07:41.123116 + +""" +from alembic import op + + +# revision identifiers, used by Alembic. +revision = "4b2df8a6e1ce" +down_revision = "6d2eacd5da3f" +branch_labels = None +depends_on = None + + +def upgrade(): + op.create_index( + op.f("ix_datapoints_collected_at"), + "datapoints", + ["collected_at"], + unique=False, + ) + op.create_index( + op.f("ix_datapoints_sensor_name"), "datapoints", ["sensor_name"], unique=False, + ) + + +def downgrade(): + op.drop_index(op.f("ix_datapoints_sensor_name"), table_name="datapoints") + op.drop_index(op.f("ix_datapoints_collected_at"), table_name="datapoints") diff --git a/Ch09/apd.aggregation-chapter09-ex03/src/apd/aggregation/alembic/versions/6962f8455a6d_add_daily_summary_view.py b/Ch09/apd.aggregation-chapter09-ex03/src/apd/aggregation/alembic/versions/6962f8455a6d_add_daily_summary_view.py new file mode 100644 index 0000000..eb02455 --- /dev/null +++ b/Ch09/apd.aggregation-chapter09-ex03/src/apd/aggregation/alembic/versions/6962f8455a6d_add_daily_summary_view.py @@ -0,0 +1,38 @@ +"""Add daily summary view + +Revision ID: 6962f8455a6d +Revises: 4b2df8a6e1ce +Create Date: 2019-12-03 11:50:24.403402 + +""" +from alembic import op + + +# revision identifiers, used by Alembic. +revision = "6962f8455a6d" +down_revision = "4b2df8a6e1ce" +branch_labels = None +depends_on = None + + +def upgrade(): + create_view = """ + CREATE VIEW daily_summary AS + SELECT + datapoints.sensor_name AS sensor_name, + datapoints.data AS data, + count(datapoints.id) AS count + FROM datapoints + WHERE + datapoints.collected_at >= CAST(CURRENT_DATE AS DATE) + AND + datapoints.collected_at < CAST(CURRENT_DATE AS DATE) + 1 + GROUP BY + datapoints.sensor_name, + datapoints.data; + """ + op.execute(create_view) + + +def downgrade(): + op.execute("""DROP VIEW daily_summary""") diff --git a/Ch09/apd.aggregation-chapter09-ex03/src/apd/aggregation/alembic/versions/6d2eacd5da3f_create_sensor_values_table.py b/Ch09/apd.aggregation-chapter09-ex03/src/apd/aggregation/alembic/versions/6d2eacd5da3f_create_sensor_values_table.py new file mode 100644 index 0000000..b4a3545 --- /dev/null +++ b/Ch09/apd.aggregation-chapter09-ex03/src/apd/aggregation/alembic/versions/6d2eacd5da3f_create_sensor_values_table.py @@ -0,0 +1,31 @@ +"""Create datapoints table + +Revision ID: 6d2eacd5da3f +Revises: N/A +Create Date: 2019-09-29 13:43:21.242706 + +""" +from alembic import op +import sqlalchemy as sa +from sqlalchemy.dialects import postgresql + +# revision identifiers, used by Alembic. +revision = "6d2eacd5da3f" +down_revision = None +branch_labels = None +depends_on = None + + +def upgrade(): + op.create_table( + "datapoints", + sa.Column("id", sa.Integer(), nullable=False), + sa.Column("sensor_name", sa.String(), nullable=True), + sa.Column("collected_at", postgresql.TIMESTAMP(), nullable=True), + sa.Column("data", postgresql.JSONB(astext_type=sa.Text()), nullable=True), + sa.PrimaryKeyConstraint("id"), + ) + + +def downgrade(): + op.drop_table("datapoints") diff --git a/Ch09/apd.aggregation-chapter09-ex03/src/apd/aggregation/alembic/versions/d8cdc709086b_add_deployment_table.py b/Ch09/apd.aggregation-chapter09-ex03/src/apd/aggregation/alembic/versions/d8cdc709086b_add_deployment_table.py new file mode 100644 index 0000000..204e096 --- /dev/null +++ b/Ch09/apd.aggregation-chapter09-ex03/src/apd/aggregation/alembic/versions/d8cdc709086b_add_deployment_table.py @@ -0,0 +1,32 @@ +"""Add deployment table + +Revision ID: d8cdc709086b +Revises: d8d4cf6a178f +Create Date: 2019-12-17 16:28:58.585616 + +""" +from alembic import op +import sqlalchemy as sa +from sqlalchemy.dialects import postgresql + +# revision identifiers, used by Alembic. +revision = "d8cdc709086b" +down_revision = "d8d4cf6a178f" +branch_labels = None +depends_on = None + + +def upgrade(): + op.create_table( + "deployments", + sa.Column("id", postgresql.UUID(as_uuid=True), nullable=False), + sa.Column("uri", sa.String(), nullable=True), + sa.Column("name", sa.String(), nullable=True), + sa.Column("colour", sa.String(), nullable=True), + sa.Column("api_key", sa.String(), nullable=True), + sa.PrimaryKeyConstraint("id"), + ) + + +def downgrade(): + op.drop_table("deployments") diff --git a/Ch09/apd.aggregation-chapter09-ex03/src/apd/aggregation/alembic/versions/d8d4cf6a178f_add_deployment_id_to_datapoint.py b/Ch09/apd.aggregation-chapter09-ex03/src/apd/aggregation/alembic/versions/d8d4cf6a178f_add_deployment_id_to_datapoint.py new file mode 100644 index 0000000..88d5219 --- /dev/null +++ b/Ch09/apd.aggregation-chapter09-ex03/src/apd/aggregation/alembic/versions/d8d4cf6a178f_add_deployment_id_to_datapoint.py @@ -0,0 +1,34 @@ +"""Add deployment id to DataPoint + +Revision ID: d8d4cf6a178f +Revises: 6962f8455a6d +Create Date: 2019-12-03 20:58:11.285509 + +""" +from alembic import op +import sqlalchemy as sa +from sqlalchemy.dialects import postgresql + +# revision identifiers, used by Alembic. +revision = "d8d4cf6a178f" +down_revision = "6962f8455a6d" +branch_labels = None +depends_on = None + + +def upgrade(): + op.add_column( + "datapoints", + sa.Column("deployment_id", postgresql.UUID(as_uuid=True), nullable=True), + ) + op.create_index( + op.f("ix_datapoints_deployment_id"), + "datapoints", + ["deployment_id"], + unique=False, + ) + + +def downgrade(): + op.drop_index(op.f("ix_datapoints_deployment_id"), table_name="datapoints") + op.drop_column("datapoints", "deployment_id") diff --git a/Ch09/apd.aggregation-chapter09-ex03/src/apd/aggregation/analysis.py b/Ch09/apd.aggregation-chapter09-ex03/src/apd/aggregation/analysis.py new file mode 100644 index 0000000..e17278d --- /dev/null +++ b/Ch09/apd.aggregation-chapter09-ex03/src/apd/aggregation/analysis.py @@ -0,0 +1,381 @@ +from __future__ import annotations + +import asyncio +import collections +from concurrent.futures import ThreadPoolExecutor +import dataclasses +import datetime +import functools +import math +import typing as t +from uuid import UUID + +import matplotlib.pyplot as plt +from matplotlib.axes._base import _AxesBase +from matplotlib.figure import Figure +from pint import _DEFAULT_REGISTRY as ureg + +from apd.aggregation.query import ( + get_data, + get_data_by_deployment, + with_database, + get_deployment_by_id, +) +from apd.aggregation.database import DataPoint, deployment_table +from .utils import merc_x, merc_y + +plot_key = t.TypeVar("plot_key") +plot_value = t.TypeVar("plot_value") + +# Static UUID to represent aggregation of other deployments +GLOBAL = UUID("bd02526e-7619-4a59-b04b-1fafd1c262d1") + + +@dataclasses.dataclass +class Config(t.Generic[plot_key, plot_value]): + title: str + clean: t.Callable[ + [t.AsyncIterator[DataPoint]], t.AsyncIterator[t.Tuple[plot_key, plot_value]] + ] + draw: t.Optional[ + t.Callable[ + [t.Any, t.Iterable[plot_key], t.Iterable[plot_value], t.Optional[str]], None + ] + ] = None + get_data: t.Optional[ + t.Callable[..., t.AsyncIterator[t.Tuple[UUID, t.AsyncIterator[DataPoint]]]] + ] = None + ylabel: t.Optional[str] = None + sensor_name: dataclasses.InitVar[str] = None + + def __post_init__(self, sensor_name: t.Optional[str] = None) -> None: + if self.draw is None: + self.draw = draw_date # type: ignore + if self.get_data is None: + if sensor_name is None: + raise ValueError("You must specify either get_data or sensor_name") + self.get_data = get_one_sensor_by_deployment(sensor_name) + + +def draw_date( + plot: _AxesBase, + x: t.Iterable[datetime.datetime], + y: t.Iterable[float], + colour: t.Optional[str], +) -> None: + plot.plot_date(x, y, color=colour, linestyle="-", marker="", xdate=True) + + +def draw_map( + plot: _AxesBase, + x: t.Iterable[t.Tuple[float, float]], + y: t.Iterable[float], + colour: t.Optional[str], +) -> None: + lon = [merc_y(coord[0]) for coord in x] + lat = [merc_x(coord[1]) for coord in x] + + for axis in "x", "y": + plt.tick_params( + axis=axis, + which="both", + bottom=False, + top=False, + left=False, + right=False, + labelbottom=False, + labelleft=False, + ) + + plot.tricontourf(lat, lon, y) + plot.plot(lat, lon, "wo", ms=3) + plot.set_aspect(1.0) + + +def get_map_cleaner_for( + sensor_name: str, +) -> t.Callable[ + [t.AsyncIterator[DataPoint]], t.AsyncIterator[t.Tuple[t.Tuple[float, float], float]] +]: + """Given a sensor_name that represents a float, return a coroutine that acts as a cleaner + extracting that sensor's data keyed by the value of a Location sensor.""" + + async def clean_latest_coord_and_value( + datapoints: t.AsyncIterator[DataPoint], + ) -> t.AsyncIterator[t.Tuple[t.Tuple[float, float], float]]: + + # We will be building a dictionary of UUID to dictionary + # That inner dictionary should contain coord (float, float) + # and value (float) only. Either or both can be None. + class IntermediateData(t.TypedDict): + coord: t.Optional[t.Tuple[float, float]] + value: t.Optional[float] + + # We will iterate over data points and build an entry in cleaned_data + # for each deployment. This lets newer data replace older data, as + # datapoints is assumed to be in date order + cleaned_data: t.Dict[UUID, IntermediateData] = {} + async for datapoint in datapoints: + # Get the existing data for this deployment, if we've seen it before + row_data = cleaned_data.get(datapoint.deployment_id, None) + if row_data is None: + # This is the first time we've seen this deployment + row_data = {"coord": None, "value": None} + if datapoint.sensor_name == "Location": + # Coord is a 2-tuple of floats + row_data["coord"] = ( + float(datapoint.data[0]), + float(datapoint.data[1]), + ) + elif datapoint.sensor_name == sensor_name: + # Value is a single float + row_data["value"] = float(datapoint.data) + # Store the info about this deployment back into the cleaned_data set + cleaned_data[datapoint.deployment_id] + + for data in cleaned_data.values(): + if data["coord"] is None or data["value"] is None: + # We only got a partial record, don't plot this + continue + yield data["coord"], data["value"] + + # Return the set up cleaner coroutine + return clean_latest_coord_and_value + + +async def clean_watthours_to_watts( + datapoints: t.AsyncIterator[DataPoint], +) -> t.AsyncIterator[t.Tuple[datetime.datetime, float]]: + last_watthours = None + last_time = None + async for datapoint in datapoints: + if datapoint.data is None: + continue + time = datapoint.collected_at + watthours = ureg.Quantity(datapoint.data["magnitude"], datapoint.data["unit"]) + if last_watthours: + seconds_elapsed = (time - last_time).total_seconds() + time_elapsed = ureg.Quantity(seconds_elapsed, ureg.second) + additional_power = watthours - last_watthours + power = additional_power / time_elapsed + yield time, power.to(ureg.watt).magnitude + last_watthours = watthours + last_time = datapoint.collected_at + + +async def clean_magnitude( + datapoints: t.AsyncIterator[DataPoint], +) -> t.AsyncIterator[t.Tuple[datetime.datetime, float]]: + async for datapoint in datapoints: + if datapoint.data is None: + continue + yield datapoint.collected_at, datapoint.data["magnitude"] + + +async def clean_temperature_fluctuations( + datapoints: t.AsyncIterator[DataPoint], +) -> t.AsyncIterator[t.Tuple[datetime.datetime, float]]: + allowed_jitter = 2.5 + allowed_range = (-40, 80) + window_datapoints: t.Deque[DataPoint] = collections.deque(maxlen=3) + + def datapoint_ok(datapoint: DataPoint) -> bool: + """Return False if this data point does not contain a valid temperature""" + if datapoint.data is None: + return False + elif datapoint.data["unit"] != "degC": + # This point is in a different temperature system. While it could be converted + # this cleaner is not yet doing that. + return False + elif not allowed_range[0] < datapoint.data["magnitude"] < allowed_range[1]: + return False + return True + + async for datapoint in datapoints: + if not datapoint_ok(datapoint): + # If the datapoint is invalid then skip directly to the next item + continue + + window_datapoints.append(datapoint) + if len(window_datapoints) == 3: + # Find the temperatures of the datapoints in the window, then average + # the first and last and compare that to the middle point. + window_temperatures = [dp.data["magnitude"] for dp in window_datapoints] + avr_first_last = (window_temperatures[0] + window_temperatures[2]) / 2 + diff_middle_avr = abs(window_temperatures[1] - avr_first_last) + if diff_middle_avr > allowed_jitter: + pass + else: + yield window_datapoints[1].collected_at, window_temperatures[1] + elif len(window_datapoints) == 1: + # The item in the iterator can't be compared to both neighbours + # so should be yielded + yield datapoint.collected_at, datapoint.data["magnitude"] + else: + # Otherwise, let the window fill up, it will be yieleded later + pass + # When the iterator ends the final item is not yet in the middle + # of the window, so the last item must be explicitly yielded. + if len(window_datapoints) > 1 and datapoint_ok(datapoint): + yield datapoint.collected_at, datapoint.data["magnitude"] + + +async def clean_passthrough( + datapoints: t.AsyncIterator[DataPoint], +) -> t.AsyncIterator[t.Tuple[datetime.datetime, float]]: + async for datapoint in datapoints: + if datapoint.data is None: + continue + else: + yield datapoint.collected_at, datapoint.data + + +def get_one_sensor_by_deployment( + sensor_name: str, +) -> t.Callable[..., t.AsyncIterator[t.Tuple[UUID, t.AsyncIterator[DataPoint]]]]: + return functools.partial(get_data_by_deployment, sensor_name=sensor_name) + + +def get_all_data() -> t.Callable[ + ..., t.AsyncIterator[t.Tuple[UUID, t.AsyncIterator[DataPoint]]] +]: + async def get_all_data_inner( + *args: t.Any, **kwargs: t.Any + ) -> t.AsyncIterator[t.Tuple[UUID, t.AsyncIterator[DataPoint]]]: + yield GLOBAL, get_data(*args, **kwargs) + + return get_all_data_inner + + +configs = ( + Config( + sensor_name="SolarCumulativeOutput", + clean=clean_watthours_to_watts, + title="Solar generation", + ylabel="Watts", + ), + Config( + sensor_name="RAMAvailable", + clean=clean_passthrough, + title="RAM available", + ylabel="Bytes", + ), + Config( + sensor_name="RelativeHumidity", + clean=clean_passthrough, + title="Relative humidity", + ylabel="Percent", + ), + Config( + sensor_name="Temperature", + clean=clean_temperature_fluctuations, + title="Ambient temperature", + ylabel="Degrees C", + ), +) + + +def get_known_configs() -> t.Dict[str, Config[t.Any, t.Any]]: + return {config.title: config for config in configs} + + +async def plot_sensor( + config: Config[t.Any, t.Any], + plot: _AxesBase, + location_names: t.Dict[UUID, str], + **kwargs: t.Any +) -> _AxesBase: + locations = [] + if config.get_data is None: + raise ValueError("You must provide a get_data function") + async for deployment_id, query_results in config.get_data(**kwargs): + if deployment_id == GLOBAL: + name = "Global" + else: + try: + deployment = await get_deployment_by_id(deployment_id) + except IndexError: + name = str(deployment_id) + colour = None + else: + name = deployment.name or str(deployment_id) + colour = deployment.colour + # Mypy currently doesn't understand callable fields on datatypes: https://github.com/python/mypy/issues/5485 + points = [dp async for dp in config.clean(query_results)] # type: ignore + if not points: + continue + locations.append(name) + x, y = zip(*points) + plot.set_title(config.title) + plot.set_ylabel(config.ylabel) + if config.draw is None: + raise ValueError("You must provide a get_data function") + config.draw(plot, x, y, colour) + plot.legend(locations) + return plot + + +_Coroutine_Result = t.TypeVar("_Coroutine_Result") + + +def wrap_coroutine( + f: t.Callable[..., t.Coroutine[t.Any, t.Any, _Coroutine_Result]] +) -> t.Callable[..., _Coroutine_Result]: + """Given a coroutine, return a function that runs that coroutine + in a new event loop in an isolated thread""" + + @functools.wraps(f) + def run_in_thread(*args: t.Any, **kwargs: t.Any) -> _Coroutine_Result: + loop = asyncio.new_event_loop() + wrapped = f(*args, **kwargs) + with ThreadPoolExecutor(max_workers=1) as pool: + task = pool.submit(loop.run_until_complete, wrapped) + # Mypy can get confused when nesting generic functions, like we do here + # The fact that Task is generic means we lose the association with + # _CoroutineResult. Adding an explicit cast restores this. + return t.cast(_Coroutine_Result, task.result()) + + return run_in_thread + + +async def plot_multiple_charts(*args: t.Any, **kwargs: t.Any) -> Figure: + # These parameters are pulled from kwargs to avoid confusing function + # introspection code in IPython widgets + location_names = kwargs.pop("location_names", None) + configs = kwargs.pop("configs", None) + dimensions = kwargs.pop("dimensions", None) + + with with_database("postgresql+psycopg2://apd@localhost/apd") as session: + loop = asyncio.get_running_loop() + coros = [] + if configs is None: + # If no configs are supplied, use all known configs + configs = get_known_configs().values() + if dimensions is None: + # If no dimensions are supplied, get the square root of the number + # of configs and round it to find a number of columns. This will + # keep the arrangement approximately square. Find rows by multiplying + # out rows. + total_configs = len(configs) + columns = round(math.sqrt(total_configs)) + rows = math.ceil(total_configs / columns) + if location_names is None: + location_query = session.query( + deployment_table.c.id, deployment_table.c.name + ) + location_data = await loop.run_in_executor(None, location_query.all) + location_names = dict(location_data) + + figure = plt.figure(figsize=(10 * columns, 5 * rows), dpi=300) + for i, config in enumerate(configs, start=1): + plot = figure.add_subplot(columns, rows, i) + coros.append(plot_sensor(config, plot, location_names, *args, **kwargs)) + await asyncio.gather(*coros) + return figure + + +def interactable_plot_multiple_charts( + *args: t.Any, **kwargs: t.Any +) -> t.Callable[..., Figure]: + with_config = functools.partial(plot_multiple_charts, *args, **kwargs) + return wrap_coroutine(with_config) diff --git a/Ch09/apd.aggregation-chapter09-ex03/src/apd/aggregation/cli.py b/Ch09/apd.aggregation-chapter09-ex03/src/apd/aggregation/cli.py new file mode 100644 index 0000000..6e98436 --- /dev/null +++ b/Ch09/apd.aggregation-chapter09-ex03/src/apd/aggregation/cli.py @@ -0,0 +1,184 @@ +import asyncio +import typing as t +import uuid + +import aiohttp +import click +from sqlalchemy import create_engine +from sqlalchemy.orm import sessionmaker + +from . import collect +from .database import Deployment, deployment_table + + +@click.command() +@click.argument("server", nargs=-1) +@click.option( + "--db", + metavar="", + default="postgresql+psycopg2://localhost/apd", + help="The connection string to a PostgreSQL database", + envvar="APD_DB_URI", +) +@click.option("--api-key", metavar="", envvar="APD_API_KEY") +@click.option( + "--tolerate-failures", + "-f", + help="If provided, failure to retrieve some sensors' data will not abort the collection process", + is_flag=True, +) +@click.option("-v", "--verbose", is_flag=True, help="Enables verbose mode") +def collect_sensor_data( + db: str, server: t.Tuple[str], api_key: str, tolerate_failures: bool, verbose: bool +): + """This loads data from one or more sensors into the specified database. + + Only PostgreSQL databases are supported, as the column definitions use + multiple pg specific features. The database must already exist and be + populated with the required tables. + + The --api-key option is used to specify the access token for the sensors + being queried. + + You may specify any number of servers, the variable should be the full URL + to the sensor's HTTP interface, not including the /v/2.0 portion. Multiple + URLs should be separated with a space. + """ + if tolerate_failures: + attempts = [(s,) for s in server] + else: + attempts = [server] + success = True + for attempt in attempts: + try: + collect.standalone(db, attempt, api_key, echo=verbose) + except ValueError as e: + click.secho(str(e), err=True, fg="red") + success = False + return success + + +@click.group() +def deployments(): + pass + + +@deployments.command() +@click.argument("uri") +@click.argument("name") +@click.option( + "--db", + metavar="", + default="postgresql+psycopg2://localhost/apd", + help="The connection string to a PostgreSQL database", + envvar="APD_DB_URI", +) +@click.option("--api-key", metavar="", envvar="APD_API_KEY") +@click.option("--colour") +def add( + db: str, uri: str, name: str, api_key: t.Optional[str], colour: t.Optional[str], +): + """This creates a record of a new deployment in the database. + """ + deployment = Deployment(id=None, uri=uri, name=name, api_key=api_key, colour=colour) + + async def http_get_deployment_id(): + async with aiohttp.ClientSession() as http: + collect.http_session_var.set(http) + return await collect.get_deployment_id(uri) + + deployment.id = asyncio.run(http_get_deployment_id()) + insert = deployment_table.insert().values(**deployment._asdict()) + + engine = create_engine(db) + sm = sessionmaker(engine) + Session = sm() + Session.execute(insert) + Session.commit() + return True + + +@deployments.command() +@click.option( + "--db", + metavar="", + default="postgresql+psycopg2://localhost/apd", + help="The connection string to a PostgreSQL database", + envvar="APD_DB_URI", +) +def list(db: str): + """This creates a record of a new deployment in the database. + """ + engine = create_engine(db) + sm = sessionmaker(engine) + Session = sm() + deployments = Session.query(deployment_table).all() + for deployment in deployments: + click.secho(deployment.name, bold=True) + click.echo(click.style("ID ", bold=True) + deployment.id.hex) + click.echo(click.style("URI ", bold=True) + deployment.uri) + click.echo(click.style("API key ", bold=True) + deployment.api_key) + click.echo(click.style("Colour ", bold=True) + str(deployment.colour)) + click.echo() + Session.rollback() + return True + + +@deployments.command() +@click.argument("id") +@click.option("--uri") +@click.option("--name") +@click.option( + "--db", + metavar="", + default="postgresql+psycopg2://localhost/apd", + help="The connection string to a PostgreSQL database", + envvar="APD_DB_URI", +) +@click.option("--api-key", metavar="", envvar="APD_API_KEY") +@click.option("--colour") +def edit( + db: str, + id, + uri: t.Optional[str], + name: t.Optional[str], + api_key: t.Optional[str], + colour: t.Optional[str], +): + """This creates a record of a new deployment in the database. + """ + update = {} + if uri is not None: + update["uri"] = uri + if name is not None: + update["name"] = name + if api_key is not None: + update["api_key"] = api_key + if colour is not None: + update["colour"] = colour + deployment_id = uuid.UUID(id) + + update = ( + deployment_table.update() + .where(deployment_table.c.id == deployment_id) + .values(**update) + ) + + engine = create_engine(db) + sm = sessionmaker(engine) + Session = sm() + Session.execute(update) + deployments = Session.query(deployment_table).filter( + deployment_table.c.id == deployment_id + ) + Session.commit() + + for deployment in deployments: + click.secho(deployment.name, bold=True) + click.echo(click.style("ID ", bold=True) + deployment.id.hex) + click.echo(click.style("URI ", bold=True) + deployment.uri) + click.echo(click.style("API key ", bold=True) + deployment.api_key) + click.echo(click.style("Colour ", bold=True) + str(deployment.colour)) + click.echo() + + return True diff --git a/Ch09/apd.aggregation-chapter09-ex03/src/apd/aggregation/collect.py b/Ch09/apd.aggregation-chapter09-ex03/src/apd/aggregation/collect.py new file mode 100644 index 0000000..1d9ea08 --- /dev/null +++ b/Ch09/apd.aggregation-chapter09-ex03/src/apd/aggregation/collect.py @@ -0,0 +1,112 @@ +import asyncio +from contextvars import ContextVar +import datetime +import typing as t +import uuid + +import aiohttp +from sqlalchemy import create_engine +from sqlalchemy.orm import sessionmaker +from sqlalchemy.orm.session import Session + +from .database import DataPoint, datapoint_table, Deployment, deployment_table + +http_session_var: ContextVar[aiohttp.ClientSession] = ContextVar("http_session") + + +async def get_deployment_id(server): + http = http_session_var.get() + if not server.endswith("/"): + server += "/" + url = server + "v/2.1/deployment_id" + async with http.get(url) as request: + if request.status != 200: + raise ValueError(f"Error loading deployment id from {server}") + result = await request.json() + return uuid.UUID(result["deployment_id"]) + + +async def get_data_points(server: str, api_key: t.Optional[str],) -> t.List[DataPoint]: + if not server.endswith("/"): + server += "/" + url = server + "v/2.1/sensors/" + headers = {} + if api_key: + headers["X-API-KEY"] = api_key + http = http_session_var.get() + + # Get the deployment ID in parallel to the sensor data + deployment_id = asyncio.create_task(get_deployment_id(server)) + + async with http.get(url, headers=headers) as request: + result = await request.json() + ok = request.status == 200 + now = datetime.datetime.now() + if ok: + points = [] + for value in result["sensors"]: + points.append( + DataPoint( + sensor_name=value["id"], + collected_at=now, + data=value["value"], + deployment_id=await deployment_id, + ) + ) + return points + else: + raise ValueError( + f"Error loading data from {server}: " + result.get("error", "Unknown") + ) + + +def handle_result(result: t.List[DataPoint], session: Session) -> t.List[DataPoint]: + for point in result: + insert = datapoint_table.insert().values(**point._asdict()) + sql_result = session.execute(insert) + point.id = sql_result.inserted_primary_key[0] + return result + + +async def add_data_from_sensors( + session: Session, servers: t.Iterable[Deployment] +) -> t.List[DataPoint]: + tasks: t.List[t.Awaitable[t.List[DataPoint]]] = [] + points: t.List[DataPoint] = [] + async with aiohttp.ClientSession() as http: + http_session_var.set(http) + tasks = [get_data_points(server.uri, server.api_key) for server in servers] + for results in await asyncio.gather(*tasks): + points += results + loop = asyncio.get_running_loop() + await loop.run_in_executor(None, handle_result, points, session) + return points + + +def standalone( + db_uri: str, servers: t.Tuple[str], api_key: t.Optional[str], echo: bool = False +) -> None: + engine = create_engine(db_uri, echo=echo) + sm = sessionmaker(engine) + Session = sm() + deployments: t.Iterable[Deployment] + if servers: + deployments = tuple( + Deployment(id=None, name=None, colour=None, api_key=api_key, uri=server) + for server in servers + ) + else: + deployment_data = Session.query(deployment_table).all() + deployments = tuple( + Deployment.from_sql_result(deployment) for deployment in deployment_data + ) + asyncio.run(add_data_from_sensors(Session, deployments)) + Session.commit() + + +if __name__ == "__main__": + standalone( + db_uri="postgresql+psycopg2://apd@localhost/apd", + servers=("http://pvoutput:8080/",), + api_key="h3hdfjksfhwkjehnwekj", + ) diff --git a/Ch09/apd.aggregation-chapter09-ex03/src/apd/aggregation/database.py b/Ch09/apd.aggregation-chapter09-ex03/src/apd/aggregation/database.py new file mode 100644 index 0000000..f28b469 --- /dev/null +++ b/Ch09/apd.aggregation-chapter09-ex03/src/apd/aggregation/database.py @@ -0,0 +1,129 @@ +from __future__ import annotations + +from dataclasses import dataclass, field, asdict +import datetime +import typing as t +import uuid + +import sqlalchemy +from sqlalchemy.dialects.postgresql import JSONB, DATE, TIMESTAMP, UUID +from sqlalchemy.ext.hybrid import ExprComparator, hybrid_property +from sqlalchemy.orm import sessionmaker +from sqlalchemy.schema import Table + + +metadata = sqlalchemy.MetaData() + +deployment_table = Table( + "deployments", + metadata, + sqlalchemy.Column("id", UUID(as_uuid=True), primary_key=True), + sqlalchemy.Column("uri", sqlalchemy.String, index=False), + sqlalchemy.Column("name", sqlalchemy.String, index=False, nullable=True), + sqlalchemy.Column("colour", sqlalchemy.String, index=False, nullable=True), + sqlalchemy.Column("api_key", sqlalchemy.String, index=False), +) + +datapoint_table = Table( + "datapoints", + metadata, + sqlalchemy.Column("id", sqlalchemy.Integer, primary_key=True), + sqlalchemy.Column("sensor_name", sqlalchemy.String, index=True), + sqlalchemy.Column("collected_at", TIMESTAMP, index=True), + sqlalchemy.Column("deployment_id", UUID(as_uuid=True), index=True), + sqlalchemy.Column("data", JSONB), +) + +daily_summary_view = Table( + "daily_summary", + metadata, + sqlalchemy.Column("sensor_name", sqlalchemy.String), + sqlalchemy.Column("data", JSONB), + sqlalchemy.Column("count", sqlalchemy.Integer), + info={"is_view": True}, +) + + +class DateEqualComparator(ExprComparator): + def __init__(self, fallback_expression, raw_expression): + # Do not try and find update expression from parent + super().__init__(None, fallback_expression, None) + self.raw_expression = raw_expression + + def __eq__(self, other): + """ Returns True iff on the same day as other """ + other_date = sqlalchemy.cast(other, DATE) + return sqlalchemy.and_( + self.raw_expression >= other_date, self.raw_expression < other_date + 1, + ) + + def operate(self, op, *other, **kwargs): + other = [sqlalchemy.cast(date, DATE) for date in other] + return op(self.expression, *other, **kwargs) + + def reverse_operate(self, op, other, **kwargs): + other = [sqlalchemy.cast(date, DATE) for date in other] + return op(other, self.expression, **kwargs) + + +@dataclass +class DataPoint: + sensor_name: str + data: t.Any + deployment_id: uuid.UUID + id: t.Optional[int] = None + collected_at: datetime.datetime = field(default_factory=datetime.datetime.now) + + @classmethod + def from_sql_result(cls, result) -> DataPoint: + return cls(**result._asdict()) + + def _asdict(self) -> t.Dict[str, t.Any]: + data = asdict(self) + if data["id"] is None: + del data["id"] + return data + + @hybrid_property + def collected_on_date(self): + return self.collected_at.date() + + @collected_on_date.comparator # type: ignore + def collected_on_date(cls): + return DateEqualComparator( + fallback_expression=sqlalchemy.cast(datapoint_table.c.collected_at, DATE), + raw_expression=datapoint_table.c.collected_at, + ) + + +@dataclass +class Deployment: + id: t.Optional[uuid.UUID] + uri: str + name: t.Optional[str] + colour: t.Optional[str] + api_key: t.Optional[str] + + @classmethod + def from_sql_result(cls, result) -> Deployment: + return cls(**result._asdict()) + + def _asdict(self) -> t.Dict[str, t.Any]: + data = asdict(self) + return data + + +def main() -> None: + engine = sqlalchemy.create_engine( + "postgresql+psycopg2://apd@localhost/apd", echo=True + ) + sm = sessionmaker(engine) + Session = sm() + if False: + metadata.create_all(engine) + print(Session.query(DataPoint).all()) + pass + + +if __name__ == "__main__": + main() diff --git a/Ch09/apd.aggregation-chapter09-ex03/src/apd/aggregation/query.py b/Ch09/apd.aggregation-chapter09-ex03/src/apd/aggregation/query.py new file mode 100644 index 0000000..3d2005d --- /dev/null +++ b/Ch09/apd.aggregation-chapter09-ex03/src/apd/aggregation/query.py @@ -0,0 +1,146 @@ +import asyncio +import contextlib +from contextvars import ContextVar +import datetime +import typing as t +from uuid import UUID + +from sqlalchemy import create_engine +from sqlalchemy.orm import sessionmaker +from sqlalchemy.orm.session import Session + + +from apd.aggregation.database import ( + datapoint_table, + DataPoint, + deployment_table, + Deployment, +) + + +db_session_var: ContextVar[Session] = ContextVar("db_session") + + +@contextlib.contextmanager +def with_database(uri: t.Optional[str] = None) -> t.Iterator[Session]: + """Given a URI, set up a DB connection, and return a Session as a context manager """ + if uri is None: + uri = "postgresql+psycopg2://localhost/apd" + engine = create_engine(uri) + sm = sessionmaker(engine) + Session = sm() + token = db_session_var.set(Session) + yield Session + db_session_var.reset(token) + Session.commit() + Session.close() + + +async def get_data( + sensor_name: t.Optional[str] = None, + deployment_id: t.Optional[UUID] = None, + collected_before: t.Optional[datetime.datetime] = None, + collected_after: t.Optional[datetime.datetime] = None, +) -> t.AsyncIterator[DataPoint]: + db_session = db_session_var.get() + loop = asyncio.get_running_loop() + query = db_session.query(datapoint_table) + if sensor_name: + query = query.filter(datapoint_table.c.sensor_name == sensor_name) + if deployment_id: + query = query.filter(datapoint_table.c.deployment_id == deployment_id) + if collected_before: + query = query.filter(datapoint_table.c.collected_at < collected_before) + if collected_after: + query = query.filter(datapoint_table.c.collected_at > collected_after) + query = query.order_by( + datapoint_table.c.deployment_id, + datapoint_table.c.sensor_name, + datapoint_table.c.collected_at, + ) + + rows = await loop.run_in_executor(None, query.all) + for row in rows: + yield DataPoint.from_sql_result(row) + + +async def get_deployment_ids() -> t.List[UUID]: + db_session = db_session_var.get() + loop = asyncio.get_running_loop() + query = db_session.query(datapoint_table.c.deployment_id).distinct() + return [row.deployment_id for row in await loop.run_in_executor(None, query.all)] + + +async def get_deployment_by_id(deployment_id: UUID) -> Deployment: + db_session = db_session_var.get() + loop = asyncio.get_running_loop() + query = db_session.query(deployment_table).filter( + deployment_table.c.id == deployment_id + ) + return [ + Deployment.from_sql_result(row) + for row in await loop.run_in_executor(None, query.all) + ][0] + + +async def get_data_by_deployment( + *args: t.Any, **kwargs: t.Any +) -> t.AsyncIterator[t.Tuple[UUID, t.AsyncIterator[DataPoint]]]: + """Return an Async Iterator that contains two-item pairs. + These pairs are a string (deployment_id), and an async iterator that contains + the datapoints with that deployment_id. + + Usage example: + + async for deployment_id, datapoints in get_data_by_deployment(): + print(deployment_id) + async for datapoint in datapoints: + print(datapoint) + print() + """ + # Get the data, using the arguments to this function as filters + data = get_data(*args, **kwargs) + + # The two levels of iterator share the item variable, initialise it with the + # first item from the iterator. Also set last_deployment_id to None, so the + # outer iterator knows to start a new group. + last_deployment_id: t.Optional[UUID] = None + try: + item = await data.__anext__() + except StopAsyncIteration: + # There were no items in the underlying query, return immediately + return + + async def subiterator(group_id: UUID) -> t.AsyncIterator[DataPoint]: + """Using a closure, create an iterator that yields the current + item, then yields all items from data while the deployment_id matches + group_id, leaving the first that doesn't match as item in the enclosing + scope.""" + # item is from the enclosing scope + nonlocal item + while item.deployment_id == group_id: + # yield items from data while they match the group_id this iterator represents + yield item + try: + # Advance the underlying iterator + item = await data.__anext__() + except StopAsyncIteration: + # The underlying iterator came to an end, so end the subiterator too + return + + while True: + while item.deployment_id == last_deployment_id: + # We are trying to advance the outer iterator while the underlying iterator + # is still part-way through a group. Speed through the underlying until we + # hit an item where the deployment_id is different to the last one (or, is not + # None, in the case of the start of the iterator) + try: + item = await data.__anext__() + except StopAsyncIteration: + # We hit the end of the underlying iterator: end this iterator too + return + last_deployment_id = item.deployment_id + # Don't yield an iterator for unclassified deployments + if last_deployment_id is not None: + # Instantiate a subiterator for this group + yield last_deployment_id, subiterator(last_deployment_id) diff --git a/Ch09/apd.aggregation-chapter09-ex03/src/apd/aggregation/utils.py b/Ch09/apd.aggregation-chapter09-ex03/src/apd/aggregation/utils.py new file mode 100644 index 0000000..76f05ec --- /dev/null +++ b/Ch09/apd.aggregation-chapter09-ex03/src/apd/aggregation/utils.py @@ -0,0 +1,27 @@ +import math + + +# Set up mercator transform from https://wiki.openstreetmap.org/wiki/Mercator#Python +# Thanks to Paulo Silva and the OSM contributors +def merc_x(lon: float) -> float: + r_major = 6378137.000 + return r_major * math.radians(lon) + + +def merc_y(lat: float) -> float: + if lat > 89.5: + lat = 89.5 + if lat < -89.5: + lat = -89.5 + r_major = 6378137.000 + r_minor = 6356752.3142 + temp = r_minor / r_major + eccent = math.sqrt(1 - temp ** 2) + phi = math.radians(lat) + sinphi = math.sin(phi) + con = eccent * sinphi + com = eccent / 2 + con = ((1.0 - con) / (1.0 + con)) ** com + ts = math.tan((math.pi / 2 - phi) / 2) / con + y = 0 - r_major * math.log(ts) + return y diff --git a/Ch09/apd.aggregation-chapter09-ex03/tests/__init__.py b/Ch09/apd.aggregation-chapter09-ex03/tests/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/Ch09/apd.aggregation-chapter09-ex03/tests/conftest.py b/Ch09/apd.aggregation-chapter09-ex03/tests/conftest.py new file mode 100644 index 0000000..328e95f --- /dev/null +++ b/Ch09/apd.aggregation-chapter09-ex03/tests/conftest.py @@ -0,0 +1,124 @@ +import datetime +from uuid import UUID + +from apd.aggregation.database import datapoint_table + +from alembic.config import Config +from alembic.script import ScriptDirectory +from alembic.runtime.environment import EnvironmentContext +import pytest + + +@pytest.fixture +def db_uri(): + return "postgresql+psycopg2://apd@localhost/apd-test" + + +@pytest.fixture +def db_session(db_uri): + from sqlalchemy import create_engine + from sqlalchemy.orm import sessionmaker + + engine = create_engine(db_uri, echo=True) + sm = sessionmaker(engine) + Session = sm() + yield Session + Session.close() + + +@pytest.fixture +def migrated_db(db_uri, db_session): + config = Config() + config.set_main_option("script_location", "apd.aggregation:alembic") + config.set_main_option("sqlalchemy.url", db_uri) + script = ScriptDirectory.from_config(config) + + def upgrade(rev, context): + return script._upgrade_revs(script.get_current_head(), rev) + + def downgrade(rev, context): + return script._downgrade_revs(None, rev) + + with EnvironmentContext(config, script, fn=upgrade): + script.run_env() + + try: + yield + finally: + # Clear any pending work from the db_session connection + db_session.rollback() + + with EnvironmentContext(config, script, fn=downgrade): + script.run_env() + + +@pytest.fixture +def populated_db(migrated_db, db_session): + datas = [ + { + "id": 1, + "sensor_name": "Test", + "data": "1", + "collected_at": datetime.datetime(2020, 4, 1, 12, 0, 1), + "deployment_id": UUID("b4c68905-b1e4-4875-940e-69e5d27730fd"), + }, + { + "id": 2, + "sensor_name": "Test", + "data": "2", + "collected_at": datetime.datetime(2020, 4, 1, 12, 1, 1), + "deployment_id": UUID("b4c68905-b1e4-4875-940e-69e5d27730fd"), + }, + { + "id": 3, + "sensor_name": "Test", + "data": "3", + "collected_at": datetime.datetime(2020, 4, 1, 12, 2, 1), + "deployment_id": UUID("b4c68905-b1e4-4875-940e-69e5d27730fd"), + }, + { + "id": 4, + "sensor_name": "Test", + "data": "4", + "collected_at": datetime.datetime(2020, 4, 1, 12, 3, 1), + "deployment_id": UUID("b4c68905-b1e4-4875-940e-69e5d27730fd"), + }, + { + "id": 5, + "sensor_name": "OtherTest", + "data": "5", + "collected_at": datetime.datetime(2020, 4, 1, 12, 4, 1), + "deployment_id": UUID("b4c68905-b1e4-4875-940e-69e5d27730fd"), + }, + { + "id": 6, + "sensor_name": "Test", + "data": "1", + "collected_at": datetime.datetime(2020, 4, 1, 12, 0, 1), + "deployment_id": UUID("6e887b81-57c3-4f3b-a7e7-bad159e05e78"), + }, + { + "id": 7, + "sensor_name": "Test", + "data": "1", + "collected_at": datetime.datetime(2020, 4, 1, 12, 1, 1), + "deployment_id": UUID("6e887b81-57c3-4f3b-a7e7-bad159e05e78"), + }, + { + "id": 8, + "sensor_name": "Test", + "data": "2", + "collected_at": datetime.datetime(2020, 4, 1, 12, 2, 1), + "deployment_id": UUID("6e887b81-57c3-4f3b-a7e7-bad159e05e78"), + }, + { + "id": 9, + "sensor_name": "OtherTest", + "data": "3", + "collected_at": datetime.datetime(2020, 4, 1, 12, 3, 1), + "deployment_id": UUID("6e887b81-57c3-4f3b-a7e7-bad159e05e78"), + }, + ] + for data in datas: + insert = datapoint_table.insert().values(**data) + db_session.execute(insert) diff --git a/Ch09/apd.aggregation-chapter09-ex03/tests/test_analysis.py b/Ch09/apd.aggregation-chapter09-ex03/tests/test_analysis.py new file mode 100644 index 0000000..34a32b4 --- /dev/null +++ b/Ch09/apd.aggregation-chapter09-ex03/tests/test_analysis.py @@ -0,0 +1,423 @@ +import datetime +import uuid + +import pytest + +from apd.aggregation import analysis +from apd.aggregation.database import DataPoint, datapoint_table +from apd.aggregation.query import db_session_var + + +async def generate_datapoints(datas): + deployment_id = uuid.uuid4() + for i, (time, data) in enumerate(datas, start=1): + yield DataPoint( + id=i, + collected_at=time, + sensor_name="TestSensor", + data=data, + deployment_id=deployment_id, + ) + + +class TestPassThroughCleaner: + @pytest.fixture + def cleaner(self): + return analysis.clean_passthrough + + @pytest.mark.asyncio + async def test_float_passthrough(self, cleaner): + data = [ + (datetime.datetime(2020, 4, 1, 12, 0, 0), 65.0), + (datetime.datetime(2020, 4, 1, 13, 0, 0), 65.5), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert output == data + + @pytest.mark.asyncio + async def test_Nones_are_skipped(self, cleaner): + data = [ + (datetime.datetime(2020, 4, 1, 12, 0, 0), None), + (datetime.datetime(2020, 4, 1, 13, 0, 0), 65.5), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert output == [ + (datetime.datetime(2020, 4, 1, 13, 0, 0), 65.5), + ] + + +class TestTemperatureCleaner: + @pytest.fixture + def cleaner(self): + return analysis.clean_temperature_fluctuations + + @pytest.mark.asyncio + async def test_window_not_full(self, cleaner): + data = [ + ( + datetime.datetime(2020, 4, 1, 12, 0, 0), + {"magnitude": 21.0, "unit": "degC"}, + ), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert output == [(datetime.datetime(2020, 4, 1, 12, 0, 0), 21.0)] + + @pytest.mark.asyncio + async def test_window_exactly_full(self, cleaner): + data = [ + ( + datetime.datetime(2020, 4, 1, 12, 0, 0), + {"magnitude": 21.0, "unit": "degC"}, + ), + ( + datetime.datetime(2020, 4, 1, 12, 0, 1), + {"magnitude": 21.0, "unit": "degC"}, + ), + ( + datetime.datetime(2020, 4, 1, 12, 0, 2), + {"magnitude": 21.0, "unit": "degC"}, + ), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert output == [ + (datetime.datetime(2020, 4, 1, 12, 0, 0), 21.0), + (datetime.datetime(2020, 4, 1, 12, 0, 1), 21.0), + (datetime.datetime(2020, 4, 1, 12, 0, 2), 21.0), + ] + + @pytest.mark.asyncio + async def test_window_overfilled(self, cleaner): + data = [ + ( + datetime.datetime(2020, 4, 1, 12, 0, 0), + {"magnitude": 21.0, "unit": "degC"}, + ), + ( + datetime.datetime(2020, 4, 1, 12, 0, 1), + {"magnitude": 21.0, "unit": "degC"}, + ), + ( + datetime.datetime(2020, 4, 1, 12, 0, 2), + {"magnitude": 21.0, "unit": "degC"}, + ), + ( + datetime.datetime(2020, 4, 1, 12, 0, 3), + {"magnitude": 21.0, "unit": "degC"}, + ), + ( + datetime.datetime(2020, 4, 1, 12, 0, 4), + {"magnitude": 21.0, "unit": "degC"}, + ), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert output == [ + (datetime.datetime(2020, 4, 1, 12, 0, 0), 21.0), + (datetime.datetime(2020, 4, 1, 12, 0, 1), 21.0), + (datetime.datetime(2020, 4, 1, 12, 0, 2), 21.0), + (datetime.datetime(2020, 4, 1, 12, 0, 3), 21.0), + (datetime.datetime(2020, 4, 1, 12, 0, 4), 21.0), + ] + + @pytest.mark.asyncio + async def test_outlier_dropped(self, cleaner): + data = [ + ( + datetime.datetime(2020, 4, 1, 12, 0, 0), + {"magnitude": 21.0, "unit": "degC"}, + ), + ( + datetime.datetime(2020, 4, 1, 12, 0, 1), + {"magnitude": 61.0, "unit": "degC"}, + ), + ( + datetime.datetime(2020, 4, 1, 12, 0, 2), + {"magnitude": 21.0, "unit": "degC"}, + ), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert output == [ + (datetime.datetime(2020, 4, 1, 12, 0, 0), 21.0), + (datetime.datetime(2020, 4, 1, 12, 0, 2), 21.0), + ] + + @pytest.mark.asyncio + async def test_limited_to_DHT22_range(self, cleaner): + data = [ + ( + datetime.datetime(2020, 4, 1, 12, 0, 0), + {"magnitude": 91.0, "unit": "degC"}, + ), + ( + datetime.datetime(2020, 4, 1, 12, 0, 1), + {"magnitude": 91.0, "unit": "degC"}, + ), + ( + datetime.datetime(2020, 4, 1, 12, 0, 2), + {"magnitude": 91.0, "unit": "degC"}, + ), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert output == [] + + @pytest.mark.asyncio + async def test_Nones_dropped(self, cleaner): + data = [ + ( + datetime.datetime(2020, 4, 1, 12, 0, 0), + {"magnitude": 31.0, "unit": "degC"}, + ), + (datetime.datetime(2020, 4, 1, 12, 0, 1), None,), + ( + datetime.datetime(2020, 4, 1, 12, 0, 2), + {"magnitude": 32.0, "unit": "degC"}, + ), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert output == [ + (datetime.datetime(2020, 4, 1, 12, 0, 0), 31.0), + (datetime.datetime(2020, 4, 1, 12, 0, 2), 32.0), + ] + + +class TestWattHourCleaner: + @pytest.fixture + def cleaner(self): + return analysis.clean_watthours_to_watts + + @pytest.mark.asyncio + async def test_one_entry_insufficient_to_find_diff(self, cleaner): + data = [ + ( + datetime.datetime(2020, 4, 1, 12, 0, 0), + {"magnitude": 1.0, "unit": "watt_hour"}, + ), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert output == [] + + @pytest.mark.asyncio + async def test_two_entries_provides_diff_with_second_time(self, cleaner): + data = [ + ( + datetime.datetime(2020, 4, 1, 12, 0, 0), + {"magnitude": 1.0, "unit": "watt_hour"}, + ), + ( + datetime.datetime(2020, 4, 1, 13, 0, 0), + {"magnitude": 10.0, "unit": "watt_hour"}, + ), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert len(output) == 1 + assert output[0][0] == datetime.datetime(2020, 4, 1, 13, 0, 0) + assert output[0][1] == pytest.approx(9.0, 0.0001) + + @pytest.mark.asyncio + async def test_fractional_hour(self, cleaner): + data = [ + ( + datetime.datetime(2020, 4, 1, 12, 0, 0), + {"magnitude": 1.0, "unit": "watt_hour"}, + ), + ( + datetime.datetime(2020, 4, 1, 12, 23, 42), + {"magnitude": 10.0, "unit": "watt_hour"}, + ), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert len(output) == 1 + assert output[0][0] == datetime.datetime(2020, 4, 1, 12, 23, 42) + assert output[0][1] == pytest.approx(22.7848, 0.001) + + @pytest.mark.asyncio + async def test_diff_happens_pairwise(self, cleaner): + data = [ + ( + datetime.datetime(2020, 4, 1, 12, 0, 0), + {"magnitude": 1.0, "unit": "watt_hour"}, + ), + ( + datetime.datetime(2020, 4, 1, 12, 23, 42), + {"magnitude": 10.0, "unit": "watt_hour"}, + ), + ( + datetime.datetime(2020, 4, 1, 13, 23, 42), + {"magnitude": 13.0, "unit": "watt_hour"}, + ), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert len(output) == 2 + assert output[0][0] == datetime.datetime(2020, 4, 1, 12, 23, 42) + assert output[0][1] == pytest.approx(22.7848, 0.001) + assert output[1][0] == datetime.datetime(2020, 4, 1, 13, 23, 42) + assert output[1][1] == pytest.approx(3, 0.001) + + @pytest.mark.asyncio + async def test_None_ignored(self, cleaner): + data = [ + ( + datetime.datetime(2020, 4, 1, 12, 0, 0), + {"magnitude": 1.0, "unit": "watt_hour"}, + ), + (datetime.datetime(2020, 4, 1, 12, 58, 0), None,), + ( + datetime.datetime(2020, 4, 1, 13, 0, 0), + {"magnitude": 10.0, "unit": "watt_hour"}, + ), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert len(output) == 1 + assert output[0][0] == datetime.datetime(2020, 4, 1, 13, 0, 0) + assert output[0][1] == pytest.approx(9.0, 0.0001) + + +class TestTemperatureMap: + @pytest.fixture + def temperature_data(self): + return { + (53.8667, -1.3333): -1, + (53.35, -2.2833): 1, + (52.45, -1.7333): 3, + (51.5, -0.1333): 6, + (51.55, -2.5667): 5, + (54.9667, -1.6167): -1, + (55.9667, -3.2167): -1, + (54.7667, -1.5833): 0, + (53.7667, -0.3): 1, + (53.7667, -3.0167): 0, + (51.4833, -3.1833): 4, + (53.2667, -3.5167): 2, + (52.0833, -2.8): 2, + (52.0167, -0.6): 2, + (52.6667, 1.2667): 5, + (50.4333, -4.9833): 9, + (50.35, -4.1167): 10, + (50.5167, -2.45): 9, + (50.8333, -1.1667): 9, + (56.45, -5.4333): 4, + (57.5333, -4.05): -2, + (54.5167, -3.6167): 3, + (53.0833, 0.2667): 4, + (51.35, 1.3333): 7, + (50.8833, 0.3167): 8, + (58.45, -3.1): 3, + (50.1, -5.6667): 9, + (58.2167, -6.3333): 4, + (55.6833, -6.25): 7, + } + + @pytest.fixture + def temperature_datapoints(self, migrated_db, db_session, temperature_data): + datas = [] + now = datetime.datetime.now() + for (coord, temp) in temperature_data.items(): + deployment_id = uuid.uuid4() + datas.append( + DataPoint( + sensor_name="Location", + deployment_id=deployment_id, + collected_at=now, + data=coord, + ) + ) + datas.append( + DataPoint( + sensor_name="Temperature", + deployment_id=deployment_id, + collected_at=now, + data=temp, + ) + ) + return datas + + @pytest.fixture + def populated_db(self, migrated_db, db_session, temperature_datapoints): + for data in temperature_datapoints: + insert = datapoint_table.insert().values(**data._asdict()) + db_session.execute(insert) + + @pytest.fixture + def cleaner(self): + return analysis.get_map_cleaner_for("Temperature") + + @pytest.fixture + def get_data(self, db_session): + db_session_var.set(db_session) + return analysis.get_all_data() + + @pytest.mark.asyncio + async def test_latest_coord_temp_cleaner( + self, temperature_data, populated_db, get_data, cleaner + ): + data = get_data() + deployments = 0 + async for deployment, data_points in cleaner(data): + deployments += 1 + cleaned = {a async for a in cleaner(data_points)} + # There should only be one deployment + assert deployments == 1 + expected = set(temperature_data.items()) + assert cleaned == expected + + @pytest.mark.asyncio + async def test_newer_items_superceed_older( + self, db_session, temperature_datapoints, populated_db, get_data, cleaner + ): + # Pick any of the deployment ids and find the pair of DataPoints in that deployment id + deployment_1 = temperature_datapoints[0].deployment_id + existing = [ + datapoint + for datapoint in temperature_datapoints + if datapoint.deployment_id == deployment_1 + ] + old_location = [ + datapoint for datapoint in existing if datapoint.sensor_name == "Location" + ][0] + old_temperature = [ + datapoint + for datapoint in existing + if datapoint.sensor_name == "Temperature" + ][0] + + new_location = DataPoint( + sensor_name="Location", + deployment_id=deployment_1, + collected_at=datetime.datetime(2031, 4, 1, 12, 0, 0), + data=(-10.5, 150.1), + ) + new_temperature = DataPoint( + sensor_name="Temperature", + deployment_id=deployment_1, + collected_at=datetime.datetime(2031, 4, 1, 12, 0, 0), + data=21, + ) + for data in [new_location, new_temperature]: + insert = datapoint_table.insert().values(**data._asdict()) + db_session.execute(insert) + + data = get_data() + deployments = 0 + async for deployment, data_points in cleaner(data): + deployments += 1 + cleaned = {a async for a in cleaner(data_points)} + # There should only be one deployment + assert deployments == 1 + + # We expect the newer one, not the older + assert (new_location.data, new_temperature.data) in cleaned + assert (old_location.data, old_temperature.data) not in cleaned + # The cleaner converts two data points to one plottable + assert len(cleaned) == len(temperature_datapoints) / 2 diff --git a/Ch09/apd.aggregation-chapter09-ex03/tests/test_cli.py b/Ch09/apd.aggregation-chapter09-ex03/tests/test_cli.py new file mode 100644 index 0000000..5cab441 --- /dev/null +++ b/Ch09/apd.aggregation-chapter09-ex03/tests/test_cli.py @@ -0,0 +1,155 @@ +from unittest import mock +import uuid + +from click.testing import CliRunner +import pytest + +import apd.aggregation.cli +from apd.aggregation.database import Deployment, deployment_table + + +@pytest.mark.functional +def test_sensors_are_passed_to_get_data_points(db_uri): + runner = CliRunner() + with mock.patch("apd.aggregation.collect.get_data_points") as get_data_points: + runner.invoke( + apd.aggregation.cli.collect_sensor_data, + [ + "http://localhost", + "http://otherhost", + "--db", + db_uri, + "--api-key", + "key", + ], + ) + calls = get_data_points.call_args_list + assert len(calls) == 2 + details = {call[0] for call in get_data_points.call_args_list} + assert details == { + ("http://localhost", "key"), + ("http://otherhost", "key"), + } + + +@pytest.mark.functional +def test_add_deployment_to_db(db_uri, db_session, migrated_db): + runner = CliRunner() + deployment_id = uuid.UUID("76587cdd42dc489ea1d220e9b0bc62ca") + with mock.patch("apd.aggregation.collect.get_deployment_id") as get_deployment_id: + get_deployment_id.return_value = deployment_id + runner.invoke( + apd.aggregation.cli.deployments, + ["add", "http://otherhost", "Other", "--db", db_uri, "--api-key", "key"], + ) + deployments = db_session.query(deployment_table).all() + assert len(deployments) == 1 + deployment = Deployment.from_sql_result(deployments[0]) + assert deployment.uri == "http://otherhost" + assert deployment.api_key == "key" + assert deployment.id == deployment_id + + +@pytest.mark.functional +def test_use_stored_deployments(db_uri, migrated_db): + runner = CliRunner() + + deployment_id = uuid.UUID("76587cdd42dc489ea1d220e9b0bc62ca") + with mock.patch("apd.aggregation.collect.get_deployment_id") as get_deployment_id: + get_deployment_id.return_value = deployment_id + runner.invoke( + apd.aggregation.cli.deployments, + [ + "add", + "http://specifiedhost", + "Specified", + "--db", + db_uri, + "--api-key", + "an_api_key", + ], + ) + + with mock.patch("apd.aggregation.collect.get_data_points") as get_data_points: + runner.invoke( + apd.aggregation.cli.collect_sensor_data, ["--db", db_uri], + ) + calls = get_data_points.call_args_list + assert len(calls) == 1 + details = {call[0] for call in get_data_points.call_args_list} + assert details == { + ("http://specifiedhost", "an_api_key"), + } + + +@pytest.mark.functional +def test_list_deployments(db_uri, migrated_db): + runner = CliRunner() + + deployment_id = uuid.UUID("76587cdd42dc489ea1d220e9b0bc62ca") + with mock.patch("apd.aggregation.collect.get_deployment_id") as get_deployment_id: + get_deployment_id.return_value = deployment_id + runner.invoke( + apd.aggregation.cli.deployments, + [ + "add", + "http://specifiedhost", + "Specified", + "--db", + db_uri, + "--api-key", + "an_api_key", + ], + ) + + result = runner.invoke(apd.aggregation.cli.deployments, ["list", "--db", db_uri],) + expected = """Specified +ID 76587cdd42dc489ea1d220e9b0bc62ca +URI http://specifiedhost +API key an_api_key +Colour None + +""" + assert result.output == expected + + +@pytest.mark.functional +def test_edit_deployment(db_uri, migrated_db): + runner = CliRunner() + return + deployment_id = uuid.UUID("76587cdd42dc489ea1d220e9b0bc62ca") + with mock.patch("apd.aggregation.collect.get_deployment_id") as get_deployment_id: + get_deployment_id.return_value = deployment_id + runner.invoke( + apd.aggregation.cli.deployments, + [ + "add", + "http://specifiedhost", + "Specified", + "--db", + db_uri, + "--api-key", + "an_api_key", + ], + ) + + result = runner.invoke( + apd.aggregation.cli.deployments, + [ + "edit", + "--db", + db_uri, + deployment_id.hex(), + "--colour", + "red" "--name", + "New name", + ], + ) + expected = """New name +ID 76587cdd42dc489ea1d220e9b0bc62ca +URI http://specifiedhost +API key an_api_key +Colour red + +""" + assert result.output == expected diff --git a/Ch09/apd.aggregation-chapter09-ex03/tests/test_http_get.py b/Ch09/apd.aggregation-chapter09-ex03/tests/test_http_get.py new file mode 100644 index 0000000..df575c6 --- /dev/null +++ b/Ch09/apd.aggregation-chapter09-ex03/tests/test_http_get.py @@ -0,0 +1,188 @@ +from concurrent.futures import ThreadPoolExecutor +import datetime +import typing as t +from unittest.mock import patch, MagicMock +import wsgiref.simple_server + +import aiohttp +from apd.sensors.base import Sensor +from apd.sensors.sensors import PythonVersion, ACStatus +from apd.sensors.wsgi import set_up_config +import flask +import pytest + +from apd.sensors.wsgi import v21 + +from apd.aggregation import collect +from apd.aggregation.database import Deployment + +pytestmark = [pytest.mark.functional] + + +@pytest.fixture +def sensors() -> t.Iterator[t.List[Sensor[t.Any]]]: + """ Patch the get_sensors method to return a known pair of sensors only """ + data: t.List[Sensor[t.Any]] = [PythonVersion(), ACStatus()] + with patch("apd.sensors.cli.get_sensors") as get_sensors: + get_sensors.return_value = data + yield data + + +def get_independent_flask_app(name: str) -> flask.Flask: + """ Create a new flask app with the v20 API blueprint loaded, so multiple copies + of the app can be run in parallel without conflicting configuration """ + app = flask.Flask(name) + app.register_blueprint(v21.version, url_prefix="/v/2.1") + return app + + +def run_server_in_thread( + name: str, config: t.Dict[str, t.Any], port: int +) -> t.Iterator[str]: + # Create a new flask app and load in required code, to prevent config conflicts + app = get_independent_flask_app(name) + flask_app = set_up_config(config, app) + server = wsgiref.simple_server.make_server("localhost", port, flask_app) + + with ThreadPoolExecutor() as pool: + pool.submit(server.serve_forever) + yield f"http://localhost:{port}/" + server.shutdown() + + +@pytest.fixture(scope="module") +def http_server(): + yield from run_server_in_thread( + "standard", + { + "APD_SENSORS_API_KEY": "testing", + "APD_SENSORS_DEPLOYMENT_ID": "a46b1d1207fd4cdcad39bbdf706dfe29", + }, + 12081, + ) + + +@pytest.fixture(scope="module") +def bad_api_key_http_server(): + yield from run_server_in_thread( + "alternate", + { + "APD_SENSORS_API_KEY": "penny", + "APD_SENSORS_DEPLOYMENT_ID": "38cf2bae9adb445fad946c82e290487a", + }, + 12082, + ) + + +class TestGetDataPoints: + @pytest.fixture + def mut(self): + return collect.get_data_points + + @pytest.mark.asyncio + async def test_get_data_points( + self, sensors: t.List[Sensor[t.Any]], mut, http_server: str + ) -> None: + # Get the data from the server, storing the time before and after + # as bounds for the collected_at value + async with aiohttp.ClientSession() as http: + collect.http_session_var.set(http) + time_before = datetime.datetime.now() + results = await mut(http_server, "testing") + time_after = datetime.datetime.now() + + assert len(results) == len(sensors) == 2 + + for (sensor, result) in zip(sensors, results): + assert sensor.from_json_compatible(result.data) == sensor.value() + assert result.sensor_name == sensor.name + assert time_before <= result.collected_at <= time_after + + @pytest.mark.asyncio + async def test_get_data_points_fails_with_bad_api_key( + self, sensors: t.List[Sensor[t.Any]], mut, http_server: str + ) -> None: + with pytest.raises( + ValueError, + match=f"Error loading data from {http_server}: Supply API key in X-API-Key header", + ): + async with aiohttp.ClientSession() as http: + collect.http_session_var.set(http) + await mut(http_server, "incorrect") + + +class TestAddDataFromSensors: + @pytest.fixture + def mut(self): + return collect.add_data_from_sensors + + @pytest.fixture + def mock_db_session(self): + return MagicMock() + + @pytest.mark.asyncio + async def test_get_get_data_from_sensors( + self, mock_db_session, sensors: t.List[Sensor[t.Any]], mut, http_server: str + ) -> None: + results = await mut( + mock_db_session, + [ + Deployment( + id=None, colour=None, name=None, uri=http_server, api_key="testing" + ) + ], + ) + assert mock_db_session.execute.call_count == len(sensors) + assert len(results) == len(sensors) + + @pytest.mark.asyncio + async def test_get_get_data_from_sensors_with_multiple_servers( + self, mock_db_session, sensors: t.List[Sensor[t.Any]], mut, http_server: str + ) -> None: + results = await mut( + mock_db_session, + [ + Deployment( + id=None, colour=None, name=None, uri=http_server, api_key="testing" + ), + Deployment( + id=None, colour=None, name=None, uri=http_server, api_key="testing" + ), + ], + ) + assert mock_db_session.execute.call_count == len(sensors) * 2 + assert len(results) == len(sensors) * 2 + + @pytest.mark.asyncio + async def test_data_points_not_added_if_only_partial_success( + self, + mock_db_session, + sensors: t.List[Sensor[t.Any]], + mut, + http_server: str, + bad_api_key_http_server: str, + ) -> None: + with pytest.raises( + ValueError, + match=f"Error loading data from {bad_api_key_http_server}: Supply API key in X-API-Key header", + ): + await mut( + mock_db_session, + [ + Deployment( + id=None, + colour=None, + name=None, + uri=http_server, + api_key="testing", + ), + Deployment( + id=None, + colour=None, + name=None, + uri=bad_api_key_http_server, + api_key="testing", + ), + ], + ) + assert mock_db_session.execute.call_count == 0 diff --git a/Ch09/apd.aggregation-chapter09-ex03/tests/test_query.py b/Ch09/apd.aggregation-chapter09-ex03/tests/test_query.py new file mode 100644 index 0000000..9b6e0aa --- /dev/null +++ b/Ch09/apd.aggregation-chapter09-ex03/tests/test_query.py @@ -0,0 +1,111 @@ +import datetime +from uuid import UUID + +import pytest + +from apd.aggregation.query import ( + get_data, + get_deployment_ids, + get_data_by_deployment, + db_session_var, +) + + +class TestGetData: + @pytest.fixture + def mut(self): + return get_data + + @pytest.mark.asyncio + @pytest.mark.parametrize( + "filter,num_items_expected", + [ + ({}, 9), + ({"sensor_name": "Test"}, 7), + ({"deployment_id": UUID("b4c68905-b1e4-4875-940e-69e5d27730fd")}, 5), + ({"collected_after": datetime.datetime(2020, 4, 1, 12, 2, 1)}, 3), + ({"collected_before": datetime.datetime(2020, 4, 1, 12, 2, 1)}, 4), + ( + { + "collected_after": datetime.datetime(2020, 4, 1, 12, 2, 1), + "collected_before": datetime.datetime(2020, 4, 1, 12, 3, 5), + }, + 2, + ), + ], + ) + async def test_iterate_over_items( + self, mut, db_session, populated_db, filter, num_items_expected + ): + db_session_var.set(db_session) + points = [dp async for dp in mut(**filter)] + assert len(points) == num_items_expected + + @pytest.mark.asyncio + async def test_empty_db(self, mut, db_session, migrated_db): + db_session_var.set(db_session) + points = [dp async for dp in mut()] + assert len(points) == 0 + + +class TestGetDeployments: + @pytest.fixture + def mut(self): + return get_deployment_ids + + @pytest.mark.asyncio + async def test_get_all_deployments(self, mut, db_session, populated_db): + db_session_var.set(db_session) + deployment_ids = await mut() + assert set(deployment_ids) == { + UUID("b4c68905-b1e4-4875-940e-69e5d27730fd"), + UUID("6e887b81-57c3-4f3b-a7e7-bad159e05e78"), + } + + @pytest.mark.asyncio + async def test_empty_db(self, mut, db_session, migrated_db): + db_session_var.set(db_session) + deployment_ids = await mut() + assert deployment_ids == [] + + +@pytest.mark.usefixtures("populated_db") +class TestGetDataByDeployment: + @pytest.fixture + def mut(self): + return get_data_by_deployment + + @pytest.mark.asyncio + @pytest.mark.parametrize( + "filter,num_items_expected", [({}, 9), ({"sensor_name": "Test"}, 7)] + ) + async def test_iterate_over_all_items( + self, mut, db_session, filter, num_items_expected + ): + db_session_var.set(db_session) + num_items = 0 + num_deployments = 0 + async for deployment_id, items in mut(**filter): + num_deployments += 1 + async for item in items: + num_items += 1 + assert num_deployments == 2 + assert num_items == num_items_expected + + @pytest.mark.asyncio + @pytest.mark.parametrize( + "filter,num_items_expected", [({}, 5), ({"sensor_name": "Test"}, 4)] + ) + async def test_skip_first_deployment( + self, mut, db_session, filter, num_items_expected + ): + db_session_var.set(db_session) + num_items = 0 + num_deployments = 0 + async for deployment_id, items in mut(**filter): + num_deployments += 1 + if num_deployments == 2: + async for item in items: + num_items += 1 + assert num_deployments == 2 + assert num_items == num_items_expected diff --git a/Ch09/apd.aggregation-chapter09-ex03/tests/test_sensor_aggregation.py b/Ch09/apd.aggregation-chapter09-ex03/tests/test_sensor_aggregation.py new file mode 100644 index 0000000..311dd19 --- /dev/null +++ b/Ch09/apd.aggregation-chapter09-ex03/tests/test_sensor_aggregation.py @@ -0,0 +1,199 @@ +from __future__ import annotations + +import contextlib +from dataclasses import dataclass +import json +import typing as t +from unittest.mock import patch, Mock + +import pytest +import sqlalchemy + +import apd.aggregation.collect +from apd.aggregation.database import Deployment + + +@pytest.fixture +def data() -> t.Any: + return { + "sensors": [ + { + "human_readable": "3.7", + "id": "PythonVersion", + "title": "Python Version", + "value": [3, 7, 2, "final", 0], + }, + { + "human_readable": "Not connected", + "id": "ACStatus", + "title": "AC Connected", + "value": False, + }, + ] + } + + +@dataclass +class FakeAIOHttpClient: + responses: t.Dict[str, str] + + @contextlib.asynccontextmanager + async def get( + self, url: str, headers: t.Optional[t.Dict[str, str]] = None + ) -> t.AsyncIterator[FakeAIOHttpResponse]: + if url in self.responses: + yield FakeAIOHttpResponse(body=self.responses[url]) + else: + yield FakeAIOHttpResponse(body="", status=404) + + +@dataclass +class FakeAIOHttpResponse: + body: str + status: int = 200 + + async def json(self) -> t.Any: + return json.loads(self.body) + + +@pytest.fixture +def mockclient(data) -> FakeAIOHttpClient: + return FakeAIOHttpClient( + { + "http://localhost/v/2.1/sensors/": json.dumps(data), + "http://localhost/v/2.1/deployment_id": json.dumps( + {"deployment_id": "b29ba0ee10f14552b6b21327bb96d3fb"} + ), + } + ) + + +class TestGetDataPoints: + @pytest.fixture + def mut(self): + return apd.aggregation.collect.get_data_points + + @pytest.mark.asyncio + async def test_get_data_points( + self, mut, mockclient: FakeAIOHttpClient, data + ) -> None: + token = apd.aggregation.collect.http_session_var.set(mockclient) + try: + datapoints = await mut("http://localhost", "") + finally: + apd.aggregation.collect.http_session_var.reset(token) + + assert len(datapoints) == len(data["sensors"]) + for sensor in data["sensors"]: + assert sensor["value"] in (datapoint.data for datapoint in datapoints) + assert sensor["id"] in (datapoint.sensor_name for datapoint in datapoints) + + +class TestAddDataFromSensors: + @pytest.fixture + def mut(self): + return apd.aggregation.collect.add_data_from_sensors + + @pytest.fixture(autouse=True) + def patch_aiohttp(self, mockclient): + with patch("aiohttp.ClientSession") as ClientSession: + ClientSession.return_value.__aenter__.return_value = mockclient + yield ClientSession + + @pytest.fixture + def db_session(self): + session = Mock() + sql_result = session.execute.return_value + sql_result.inserted_primary_key = [1] + return session + + @pytest.mark.asyncio + async def test_datapoints_are_added_to_the_session(self, mut, db_session) -> None: + assert db_session.execute.call_count == 0 + datapoints = await mut( + db_session, + [ + Deployment( + id=None, colour=None, name=None, uri="http://localhost", api_key="" + ) + ], + ) + assert db_session.execute.call_count == len(datapoints) + + +@pytest.mark.usefixtures("migrated_db") +class TestDatabaseConnection: + @pytest.fixture(autouse=True) + def patch_aiohttp(self, mockclient): + with patch("aiohttp.ClientSession") as ClientSession: + ClientSession.return_value.__aenter__.return_value = mockclient + yield ClientSession + + @pytest.fixture + def table(self): + return apd.aggregation.database.datapoint_table + + @pytest.fixture + def daily_summary_view(self): + return apd.aggregation.database.daily_summary_view + + @pytest.fixture + def model(self): + return apd.aggregation.database.DataPoint + + @pytest.fixture + def mut(self): + return apd.aggregation.collect.add_data_from_sensors + + @pytest.mark.asyncio + async def test_datapoints_are_added_to_the_session(self, db_session, table) -> None: + datapoints = await apd.aggregation.collect.add_data_from_sensors( + db_session, + [ + Deployment( + id=None, colour=None, name=None, uri="http://localhost", api_key="" + ) + ], + ) + num_points = db_session.query(table).count() + assert num_points == len(datapoints) == 2 + + @pytest.mark.asyncio + async def test_datapoints_can_be_mapped_back_to_DataPoints( + self, mut, db_session, table, model + ) -> None: + datapoints = await mut( + db_session, + [ + Deployment( + id=None, colour=None, name=None, uri="http://localhost", api_key="" + ) + ], + ) + db_points = [ + model.from_sql_result(result) for result in db_session.query(table) + ] + assert db_points == datapoints + + @pytest.mark.asyncio + async def test_daily_summary_view_matches_query( + self, db_session, model, table, daily_summary_view + ) -> None: + await apd.aggregation.collect.add_data_from_sensors( + db_session, + [ + Deployment( + id=None, colour=None, name=None, uri="http://localhost", api_key="" + ) + ], + ) + + headers = table.c.sensor_name, table.c.data + value_counts = ( + db_session.query(*headers, sqlalchemy.func.count(table.c.id)) + .filter(model.collected_on_date == sqlalchemy.func.current_date()) + .group_by(*headers) + ) + + daily_summary_view = db_session.query(daily_summary_view) + assert value_counts.all() == daily_summary_view.all() diff --git a/Ch09/apd.aggregation-chapter09/.coveragerc b/Ch09/apd.aggregation-chapter09/.coveragerc new file mode 100644 index 0000000..e766c69 --- /dev/null +++ b/Ch09/apd.aggregation-chapter09/.coveragerc @@ -0,0 +1,3 @@ +[run] +branch = True +omit = tests/* diff --git a/Ch09/apd.aggregation-chapter09/.pre-commit-config.yaml b/Ch09/apd.aggregation-chapter09/.pre-commit-config.yaml new file mode 100644 index 0000000..d3b6ec7 --- /dev/null +++ b/Ch09/apd.aggregation-chapter09/.pre-commit-config.yaml @@ -0,0 +1,26 @@ +repos: + +- repo: local + hooks: + - id: black + name: black + entry: pipenv run black + args: [--quiet] + language: system + types: [python] + + - id: mypy + name: mypy + exclude: (?x)^( + (.*)/setup.py + )$ + entry: pipenv run mypy + args: ["--follow-imports=skip"] + language: system + types: [python] + + - id: flake8 + name: flake8 + entry: pipenv run flake8 + language: system + types: [python] diff --git a/Ch09/apd.aggregation-chapter09/CHANGES.md b/Ch09/apd.aggregation-chapter09/CHANGES.md new file mode 100644 index 0000000..8e424e5 --- /dev/null +++ b/Ch09/apd.aggregation-chapter09/CHANGES.md @@ -0,0 +1,5 @@ +## Changes + +### 1.0.0 (Unreleased) + +* Generated from skeleton diff --git a/Ch09/apd.aggregation-chapter09/Connect to database.ipynb b/Ch09/apd.aggregation-chapter09/Connect to database.ipynb new file mode 100644 index 0000000..98a74f0 --- /dev/null +++ b/Ch09/apd.aggregation-chapter09/Connect to database.ipynb @@ -0,0 +1,577 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "53635\n" + ] + } + ], + "source": [ + "from apd.aggregation.query import with_database\n", + "from apd.aggregation.database import datapoint_table\n", + "\n", + "with with_database(\"postgresql+psycopg2://apd@localhost/apd\") as session:\n", + " print(session.query(datapoint_table).count())" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "53635\n" + ] + } + ], + "source": [ + "from apd.aggregation.query import with_database, get_data\n", + "\n", + "with with_database(\"postgresql+psycopg2://apd@localhost/apd\") as session:\n", + " count = 0\n", + " async for datapoint in get_data():\n", + " count += 1\n", + " print(count)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from apd.aggregation.query import with_database, get_data\n", + "\n", + "from matplotlib import pyplot as plt\n", + "\n", + "async def plot():\n", + " points = [(dp.collected_at, dp.data) async for dp in get_data() if dp.sensor_name==\"RelativeHumidity\"]\n", + " x, y = zip(*points)\n", + " plt.plot_date(x, y, \"o\", xdate=True)\n", + "\n", + "with with_database(\"postgresql+psycopg2://apd@localhost/apd\") as session:\n", + " await plot()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from apd.aggregation.query import with_database, get_data\n", + "\n", + "from matplotlib import pyplot as plt\n", + "\n", + "async def plot():\n", + " points = [(dp.collected_at, dp.data) async for dp in get_data(sensor_name=\"RelativeHumidity\")]\n", + " x, y = zip(*points)\n", + " plt.plot_date(x, y, \"o\", xdate=True)\n", + "\n", + "with with_database(\"postgresql+psycopg2://apd@localhost/apd\") as session:\n", + " await plot()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import collections\n", + "\n", + "from apd.aggregation.query import with_database, get_data\n", + "\n", + "from matplotlib import pyplot as plt\n", + "\n", + "async def plot():\n", + " legends = collections.defaultdict(list)\n", + " async for dp in get_data(sensor_name=\"RelativeHumidity\"):\n", + " legends[dp.deployment_id].append((dp.collected_at, dp.data))\n", + "\n", + " for deployment_id, points in legends.items():\n", + " x, y = zip(*points)\n", + " plt.plot_date(x, y, \"o\", xdate=True)\n", + "\n", + "with with_database(\"postgresql+psycopg2://apd@localhost/apd\") as session:\n", + " await plot()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import collections\n", + "\n", + "from apd.aggregation.query import with_database, get_data, get_deployment_ids\n", + "\n", + "from matplotlib import pyplot as plt\n", + "\n", + "async def plot(deployment_id):\n", + " points = []\n", + " async for dp in get_data(sensor_name=\"RelativeHumidity\", deployment_id=deployment_id):\n", + " points.append((dp.collected_at, dp.data))\n", + "\n", + " if points:\n", + " x, y = zip(*points)\n", + " plt.plot_date(x, y, \"o\", xdate=True)\n", + "\n", + "with with_database(\"postgresql+psycopg2://apd@localhost/apd\") as session:\n", + " deployment_ids = await get_deployment_ids()\n", + " for deployment in deployment_ids:\n", + " await plot(deployment)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import asyncio\n", + "\n", + "from matplotlib import pyplot as plt\n", + "\n", + "from apd.aggregation.query import with_database\n", + "from apd.aggregation.analysis import get_known_configs, plot_sensor\n", + "\n", + "with with_database(\"postgresql+psycopg2://apd@localhost/apd\") as session:\n", + " coros = []\n", + " figure = plt.figure(figsize = (20, 5), dpi=300)\n", + " configs = get_known_configs()\n", + " to_display = configs[\"Relative humidity\"], configs[\"RAM available\"]\n", + " for i, config in enumerate(to_display, start=1):\n", + " plot = figure.add_subplot(1, 2, i)\n", + " coros.append(plot_sensor(config, plot, {}))\n", + " await asyncio.gather(*coros)\n", + "\n", + "display(figure)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import asyncio\n", + "\n", + "from matplotlib import pyplot as plt\n", + "\n", + "from apd.aggregation.query import with_database\n", + "from apd.aggregation.analysis import get_known_configs, plot_sensor, Config\n", + "\n", + "async def clean_magnitude(datapoints):\n", + " async for datapoint in datapoints:\n", + " if datapoint.data is None:\n", + " continue\n", + " yield datapoint.collected_at, datapoint.data[\"magnitude\"]\n", + "\n", + "TemperatureConfig = Config(\n", + " sensor_name=\"Temperature\",\n", + " clean=clean_magnitude,\n", + " title=\"Ambient temperature\",\n", + " ylabel=\"Degrees C\",\n", + ")\n", + " \n", + "with with_database(\"postgresql+psycopg2://apd@localhost/apd\") as session:\n", + " coros = []\n", + " figure = plt.figure(figsize = (20, 5), dpi=300)\n", + " configs = get_known_configs()\n", + " to_display = configs[\"Relative humidity\"], TemperatureConfig\n", + " for i, config in enumerate(to_display, start=1):\n", + " plot = figure.add_subplot(1, 2, i)\n", + " coros.append(plot_sensor(config, plot, {}))\n", + " await asyncio.gather(*coros)\n", + "\n", + "display(figure)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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dv1ZJrqqq6sZuHVRV1Q1Jfj1u6IRunQULVn1FcvQftZ7/2onJltt708sPX5Fc8qeTB/nscWTyW+ckg8t70xezZ+kxyaPOal/z6w8l33hYMrKxNz3RueFbx/5srvl4sv6XycZrxx69tPh+yWP/s32A2Fz3oL9tPfej1/auDwDua/jm5Httgnz3OCJ53DnJ4LLm88uOTe7/5tbrb/nmhIEpBKUBAAAAAOym+me7AQAAAAAAYGFqTBpgNNijToD5YvL7imA0AACYYSvGfXxTD867KcnhOz5e2YPzYOE5+g3Jr/4x2XpH8/lrP50c9Zru9jB8y1h4UjulP/ndNUljVXd7YfdywO8kR7567O9oKxuuTq7/QnLoi3rXF5O75hPJ5lumvu4Rn0+WHT95Xf/ipBoZ+7j0JyObdp5ffMDUz59r9jqp9dyV/5Q88B3Jor171w8AY65u89x2YM/kqb+afI8jX5P8/K+az132rmS/0+69rrZPrT8AAAAAgN2QYDQAAAAAAGBW1CcLMKr5NgYwNfVao+38gGA0AACYacvHfTyNtJMpG3/Gsh6cBwvP4NLk/m9KLn5z8/nf/Ff3g9Fuv2jyMIf9nyQUbaHa44jJa37zX4LRdje/+a/prVv5qKQuC7Vjexzefn7dT5L9ntCbXgC4V7v/D654RGd7tHvuu+W2+16PjnS2JwAAAADAbqw22w0AAAAAAAALU2OyYDQBRsAUNWpDbef7a+4rAAAwwwbHfdyLn0ccf8aiHpwHC9ORbYLPrvtMctPXZ/7M7VuSqz6SXPiK5LynTV5/v9+b+R6YGw46ffKaX38w+Ux/ctm7k613dL8n7mv85/MFLxp73Pa9qe+z8jFC0aaqsSpZ8cjW87/8h6SqetcPwEK36Ybk5/87ufHLrWs6fV5baklfizcIWv/LZP2v7r2uBKMBAAAAAHOfYDQAAAAAAGBW1CcJRhsQjAZMUb3VC0J2ELgIAAAzbtO4j1f04LzxZ2xqWQXsmv7FyVGvaz1/7hOTS/9i5s7bvjn5zhOTC1+WXPXhyesPPSM5+Lkzdz5zy+IDkwe+a/K6anty6duSrxyZDN/S/b4YM/Hz+dpPjj223Da1fYZWJyd/sDs9zncnf6j13E1fTX7yht71ArCQ3fXz5D8fklz8J61ragPJIS/ofM9HfL713FePSm6/aOxjwWgAAAAAwDwgGA0AAAAAAJgVjb72wWj9ZbBHnQDzRX8ZyECbe4dgNAAAmHHjk2YO7sF548+4tQfnwcJ1wO+0n7/83cnwDH0arjkzWXvu5HW1geQpVySn/OvYxyxcx76189otv0muFLDVM51+Pt/j2Lclp3xk7PGwTyYP/WjyuO8kT7k82fOorrU5ry09OjnwGa3nf/m3yYare9cPwEJ1+XuSzZOEsz7u3Kk9r93nYe3nL3nb2K+j7YLRqs7PAwAAAACYRYLRAAAAAACAWVGvNdrO95f+HnUCzCft7i0DgtEAAGCmXbXj15Lk4FLK4d06qJRyWJJDmpwNdMOSQ9vPVyPJrefMzFk3/Wdndcf+2VjgTykzcy5zVylJfWXn9Vd/tHu9cF83f31q9Ue8MjnsjLHH6hcmh74k2fcxSX/77x8wiaX3bz9/8zd60wfAQnZzB89xJ3vOPdHg8mRgaev5284f+7VqF4wGAAAAADA3CEYDAAAAAABmRV/pz2BZ1HK+X4ARMA2N2lDLOfcVAACYcZck2Zqk2nH9tC6e9bvjPt6W5OIungUMrU72OLJ9zZX/lIxs2rVztqxLrv1kZ7X7P2nXzmJ+OfgFndduur57fXCvajRZ84XO65c+IGkc0L1+FrL9nth+/uqPJSPDPWkFYEHZekdyw5eTm76WbLm9fe3yE5LGqqntX0r758TbN4/9OioYDQAAAACY+wSjAQAAAAAAs6ZeW9xybkCAETAN9Vqj5ZxgNAAAmFlVVW1N8t0kZcfjT0oprdOKp2nHnm/MWABbleS8HWcD3VJKcuJfJ7XWb2yQ276f/PteyfpfTe+Mm7+ZnHVIZ7WrX5zs9eDpncP8dPQfJUsO67z+yg92rxfGQhLPe0YysrGz+r56cuL7x+41zLwVpyYHP7f1/O0XJl87Idlwde96Apjvbvl28qWDkvOenpz75Pa1fYuTE943vXOOfVubySqpqqQSjAYAAAAAzH2C0QAAAAAAgFnT6GsdjCbACJgO9xUAAOi5z+z4tUqyIsn7u3DGXyVZmbHwtST5dBfOACY68KnJEy9qXzO6JfnRq6e+9+i25L9emIzc3bqmvio58rXJI89MHvpRAUrc19BByWk/SE76x2Tfx+be/0W0cNHvJ5tv60lrC9KVH0xu/HLr+f2elBx6xtjjge9KfvtHyf5P6l1/C00pycM/1T488O5fJT/+o971BDCfjW5LvvfszgJCj3tH8sQfJfudNr2zlh2bPLzdl8RVMioYDQAAAACY+/pnuwEAAAAAAGDhqtcEGAEzq919ZcB9BQAAuuHTSd6dZFXGUmleVUq5saqq98zE5qWUNyf5w4wFr5Ukt0YwGvTOsuOSB30g+cnrW9fc8s1k6x3J4PLO9117frJ5bfuax30nWXp053uy8NT3SY78w7HH1juTf5/k7+CNX0kOe2lvelto1nyh/fyDPuDzuddKLTnmDclFf9C65qb/SEY2Jf2t/00VgA6sPT/Zum7yuqHVyXF/vuvnDd2v9Vw1mlSC0QAAAACAuU8wGgAAAAAAMGsagtGAGSZwEQAAequqqq2llLck+VjuDS97VynlgUl+v6qqO6azbyllWZJ/TvKccftWSf60qqqtM9E70KF9Tp285pZvJUsfkCxZnfTV7x0f3ZZsWZc09h0LaRi+JRndOhZQ1U59ZbLHYbvWNwvL4LJkz2OS9Ve0rrn5G8mhZySjW5IN17TeZ+tdGftfTpLFByYDe8x4u/PGtg3JpjXJby5oXePzefbs8/D289X2sc+FZcf2ph+A+WrNmZ3VrZjkvtyxWuspwWgAAAAAwDwhGA0AAAAAAJg19Vqj5dxATYARMHVtAxfdVwAAoCuqqvpEKeUZSZ6ee0PMfi/J40spH0ny4aqqruxkr1LK4UlekeSMJHvl3kC0KslXqqr62Mz/DoC29n5IsvzE5I6ftq753rPHfq0tSg57WfLgv0su+8vkF3+TjGyc+plH/3Hi63im6v5vSn7wktbz13927DEVpS858OnJQz8mIG28bRuSH5yR3PDFsXCtdnw+z57lxyf7PTG5+T9b15z9gOTBf58c9dre9QWwUB31upnZp5TWc+c8LjnyNa3nq9GZ6QEAAAAAoMsEowEAAAAAALOmURtqOddfvFAKmLp6u2A09xUAAOim/5HknCQn5d5wtL2SvCHJG0opNyf5YZIrkty545EkS5MsS3JMkpOT7L9j/J5Xet+z14+TvLDrvwtgZ6UkT/h+8rnWX3P/t9EtyZX/PBaWNHzz9M576EeTQ18yvbUsbIe+eCy87Pxnztye1fZkzZlJrf7/s3ff4XFddf7H32ekkVXcbcklbrHTnR7SExISUigJoZelZhd2YYEsS1nI0kLgt7CFXcrCshD6AqEEUoFU0kmB9B7HvUlx72rn98fISI7vHc2MZkYa+/16nvvo6nzPPeer2BnZ8tzPhZP/r3zr1rr73gdLfzn4vPkXwyEfrXw/Svfi38BjX4BHL02f86eLoGUOzDi/am1J0l5nv7/LBQ6XRSa91HFH7kjT21WmHiRJkiRJkiSpsgxGkyRJkiRJkiRJw6axrim1ZoCRpFI01aXfpJ31dUWSJEmqmBjj5hDCmcD3gVeTCzSD/oCz6cCr+o40YcD5wOuvBN4RY9xctoYlFac+/ed4iUoNRZt/saFoGpqZr4Hj/hfufU951136S+j6FmRHl3fdWtS9BZb8vLC5h36qsr1ocHWj4PDPweo/QMft6fMW/sBgNEmqpNlvKN9aIU8w2mBid/n6kCRJkiRJkqQKGsJPQiVJkiRJkiRJkoamKdOSWjMYTVIpGjPpwWi+rkiSJEmVFWPcFGN8LfBhYBu5ULM44KBvLOngBXMDsB34WIzx1THGjdX6OiSl2Pcdld9j/OGV30N7vvFHlH/N3k7Y/Gz5161FmxZA747B5405AOoaK9+PCjPY6+uGx6rThyTtiXo7B58zbn759htKMFqvwWiSJEmSJEmSakP9cDcgSZIkSZIkSZL2Xo2ZptRa1gAjSSVoMhhNkiRJGnYxxv8MIfwQ+AfgfcCEgeWUy8KA83XAN4CvxBifr0yXkoo270JY+IPKrT+qFfY5v3Lra+8x6VgYfxisf6S86/72qN3HRk3afSzTAJOOg8O/AOPLGIIynLatggc+Bh23wZbFhV0z768r25OKM+9CePabEHuT6xufgmVXw4zzqtuXJO0Jutbnr8+4ABrbClpq2faFXP38T1iy/Vm62T3ELBsamFs3lfNGtTBlx5bie40Go0mSJEmSJEmqDQajSZIkSZIkSZKkYdOUaUmtGWAkqRT5Ahd9XZEkSZKqJ8a4BvhUCOFzwPHAacDJwD7ARGBnksxaYA2wArgTuBW4J8bYWfWmJeXX9mI4+Wdw55vKu27IwMTj4MTvQ3363+ulgoUAL/k93P1OWHV9ZffasSZ5fNmVsPoP8PKHoWVWZXuotJ7tcMPJsPm5wuaPmgz7vxcO/khl+1JxJh4Np/4GbssTQHnb+XD6dTD9ZdXrS5L2BJ3r8tdP/FFBy7R3ruQ/lnyCHXF73nl/7l7DsweewsefuJXxXfnn7sZgNEmSJEmSJEk1wmA0SZIkSZIkSZI0bAwwklRujZnm1FrW1xVJkiSp6mKMXcAdfYekWjf7jbmjexv07oCeHfC7Y2Db8tLWO/abMOetkB1d3j6lpmlwxu+h465cqFea5lnQ2wnbV5W/h64N8Nz34LDPlH/talp2ZWGhaOPmw5k354LRQqbyfal4M86D1z4Pv5qcPueprxiMJknF6lyfv17gn3XvWP/7QUPRdtqYbeTB8dM4vWNhQfP/otdgNEmSJEmSJEm1wX9xlCRJkiRJkiRJw6apriW1ZoCRpFI01aUHoxm4KEmSJElSmdQ3QcN4aJoCYw8sfZ3pLzcUTZU1bn7+kK6xB8L8iyu3/5p7K7d2tTx/T2HzWk+FxjZD0Ua6hom5I82a+6rXiyTtKfKFBB92ScHLLNr+dFHbLm0eV9R8AKLBaJIkSZIkSZJqQ/1wNyBJkiRJkiRJkvZejZmm1JoBRpJK0ZgxGE2SJEmSpKqa/UZYfXPx1006AVpmlb8faaCGcTDtZbDi2uT67Dfm6n/+EMSe8u/fcSfcdsHu443TYMb5MP1l5d+z3Bb9qLB5s99Y2T5UHiHkfq2e+WZyvXMtdG02tFKSCtW9DbatTK/PflPBS7V35lknaetSwkh7DUaTJEmSJEmSVBt8HJMkSZIkSZIkSRo24+onJI5nyNBYlx5uJElpRteNJZPyz6Cj68ZWuRtJkiRJkvYCc98Fcy8s7pq6Zjjxh5XpR3qh4/4Hxh+2+/i8d8O+74Dm6XDijyHTUP69uzbAsit3P579H/jDy+Hxfyv/nuW08Eew4/nB5x3+eWg7rfL9qDyO+AI05wmm3LK4er1IUq3bsii9dvBHYewBBS2zvXcbG3vWFbV1d6auqPkAxK7ir5EkSZIkSZKkYVA/3A1IkiRJkiRJkqS916TsFKY1zGRl59Jdxuc1HUxjpmmYupJUy0ZlGjmg+TCe3PrQLuNt2elMbpgyTF1JkiRJkrQHy2ThhMvgsE/D2gcgdvfXenZA73bIjoXYC91bYNwhMPFFkPFtzKqS5hlw7gOw/mHYvABCPUw8Blpm9s+Z8ybY5+Ww5j4Ysx+0zIbt7XBFAT9POuar0LMdHvxY8b09+jnY/72QHV38tZXW2wMPXZxe3+c82O89MOk4aGyrXl8auoYJcN4zcPmo5PqWxTB+fnV7kqRatf7h9Nrhlxa8TEfnytTa3MaDeG77k7uNd4XkBwXl1ds9+BxJkiRJkiRJGgF8R4EkSZIkSZIkSRpW75j2D3x92efY3LMBgAn1k3nr1PcPc1eSatmbpvwdX1v2GdZ0tQPQnBnNhdM/PMxdSZIkSZK0h2uZnTukkShTBxOPyh1psmNh6pn9nze2wfGXwT1/nX/tue8qPRitezOsuWfXfUeKTU/D1mXp9YM/Bm2nVK8flVddQy40MOnXeOvi6vcjSbUo9sLjX0yuNc+AupQAygTtnSsSx+tDPXObkoPRujMlBKNFg9EkSZIkSZIk1QaD0SRJkiRJkiRJ0rCa1TiPS/b9Bgu2PUEm1LFf0yE0ZAp/k7gkvVBbwzQ+OeerLNz2FJ1xBwc0H0Zjpmm425IkSZIkSVKtmf5yCHUQe5LrU14C2dG5Y9LxuZCzYq28fmQGo21emF7LjoNJx1WvF1VGy+zkYLTl18L+761+P5JUa5b+CtY9mFxrO72opdq7ViaOT85OTf238+5QQjBar8FokiRJkiRJkmpDCT8BlSRJkiRJkiRJKq+muhYOHf0iDmk5ylA0SWUxKtPIQS1HcPjo4wxFkyRJkiRJUmmapsLhn0+uNUyEI77Y//lR/54LDCvWE/8KT329tP4qafNz6bVjvwF1DdXrRZXRPDt5fMW18NCnqtuLJNWa3h54+NPp9YP+oajlOjpXJI63NUynPmQTa92ZuqL2ACAajCZJkiRJkiSpNtQPdwOSJEmSJEmSJEmSJEmSJEmSJI1I8z8ObafB6ptgxxrIZKFlDuxzHrTM7J/Xdgq87EFYfg1sfnb37ZH24AAAIABJREFUdVbdABseT97jzxfBzFdD8z4V+RJKsmVh8njDBJjzlur2ospoSQlGA3js87nfkxOPrl4/klRLll8NG59Mrs18DUw8pqjl2jtXJo63ZqeRTQtGC5mi9gAMRpMkSZIkSZJUM2oqGC2EcHPfaQTeHGNsL3GdKcBPd64VYzyzHP1JkiRJkiRJkiRJkiRJkiRJkvYwrSfmjsGMngMHvj+59uDF6cFosReWXQkHvK/kFstuc0ow2j7nV7cPVU6+YDSApVcYjCZJaVbdkFIIcNjnil6uvWtF4nhbw3R6Y09irauUYLReg9EkSZIkSZIk1YYSfgI6rE4HTuv72DiEdRr71th5SJIkSZIkSZIkSZIkSZIkSZJUGW2n5q8v/ims/TP0bK9OP4PZ/Fzy+Oi51e1DlTPY78nNC6rThyTVorTXyJmvhfHzi1pqa89mNvdsTKy1ZadRH7KJte5MCbcFRoPRJEmSJEmSJNWGWgtGAwjD3YAkSZIkSZIkSZIkSZIkSZIkSQWbejZMOj693nEH/O4Y+MV4eOyLEGP1enuhGGH9Q8m10ftWtxdVzrhDcgE+aRb/DLa3V68fSaolK3+fPD7puKKXau9cmVprbcgTjBbqit6L3q7ir5EkSZIkSZKkYVCLwWiSJEmSJEmSJEmSJEmSJEmSJNWOTB2cedPg83p3wEOfgGVXVr6nNM98M73WYjDaHuXkn8GYA9Lrd721er1IUq1YeUN6rYQA0Y6uFYnj2dDA+PpJZDMpwWiZEm4LjN3FXyNJkiRJkiRJw2BvDUarH3DuT3QlSZIkSZIkSZIkSZIkSZIkSZVV3wInfL+wuYt+XNFWSt67hMAXjWCZejj2v9Prq26Abauq148k1YIFl6XXRs8tern2zpWJ463ZqWRChvqQEowWSrgtsLez+GskSZIkSZIkaRjsrcFokwecbxm2LiRJkiRJkiRJkiRJkiRJkiRJe48JRxU2b8Pjle0jn23JAS0ANE2rXh+qjnGH5a9vfKo6fUhSrdj8XHptzAFFL5cajNYwHSA1GK0rU8JtgVtXFH+NJEmSJEmSJA2DvTUY7cV9HyPgT3QlSZIkSZIkSZIkSZIkSZIkSZU34XCYfOLg8zY+ATeeBluXVb6ngXq7Ycui5Nr4IyDsrbcg7MGapsDM16bXtyyuXi+SNNJ1boC19yXXQgayo4tesqMr+da2toZcGGlaMFpvyNCb2Edd+mZbFua+10uSJEmSJEnSCFfL/yoZi5kcQsiGEGaFEP4G+OcBpUfK25YkSZIkSZIkSZIkSZIkSZIkSSlOvxZmvR7qx+Sf134bXH8S9GyvTl8AT301vXbiD6vXh6or369tWlCeJO1tYoQbT02vn3ZdScu2d65MHG/LTgcgmxKMBtCdFIJ22rX5w9Ge/lpR/UmSJEmSJEnScBhxwWghhJ60Y+A0YFG+uQnXbgcWAt8Cxg5Y66oqfnmSJEmSJEmSJEmSJEmSJGkQIYRjQgivCyGcF0LYb7j7kSSprBomwCk/h9etg1ctyj9361JYVsW3vD/1X+m10XOr14eqq74ZZlyQXDMYTZJy1j0A6x9Jr489oOglN/dsZGvv5sRaW0MuGK0+XzBaJuHWwOnnwOvWp2/66KVF9ShJkiRJkiRJw2HEBaORCz1LOwqdN9gR+9Z4Evhl5b4USZIkSZIkSZIkSZIkSZL2XiGExhDC3AFH3SDzzw8hLALuBS4HfgM8FUK4I4RwSBValiSpejJ10DwLGibmn7fgsur0A9C1MXk81EN2dPX6UPW1zEke37K4qm1I0oi19v70Wl0zNM8sesn2zhWptbaGaUD+YLSukHJrYHY0NO2TXOtcDzvWFtyjJEmSJEmSJA2H+uFuIEVk9yC0cgrA/cAbY4xdFdxHkiRJkiRJkiRJkiRJkqS92YeBz/WdLwPmpE0MIbwB+AnJD1M9CbgnhHB6jPFPFehTkqThEQLMfjM889/pc1ZdDz8J0DABOtf1j9c1Q8/W3PmUM5IWh/GHwry/yX1Ms+L3sORyWPcgdG1InjPxRYN+KapxacFoa+6Bm86E+tHQdioc8H6oaxzaXjHCc9+FlddD41SYdyFMOGJoa0pSpW1emF6b9XrIFH+bXnvnysTxUaGRsXUTAMjmCUbrzqQEowG0ngRLfpFQiLDydzDnLcW0KkmSJEmSJElVNRKD0W4jF4yW5LS+j5Hc0yC3F7hmBHYA64EngFtijLcPpUlJkiRJkiRJkiRJkiRJkjSoC8iFnEXgshhj4vsDQwgTgG8Bmb65Ax+wuvOaFuCKEMKBMcZC3z8oSdLId+S/5A9G22lgKBr0h6IBrL45+ZrVN8GC78IZN8Lk43avP/ttuPc9g+994g8Gn6PaNnpO8nj3lv7fX8uvguVXwxk3Q6au9L3u/VtY8O3+zxd8B864HlpPLn1NSaq0x7+YXjvmP0tasqNrReJ4a8M0Qsj9lbg+XzBayPNafML3UoLRgLv+Cma9oaQwN0mSJEmSJEmqhhH308sY4+lptRBCL/1vcHpjjHFJVZqSJEmSJEmSJEmSJEmSJElFCSE0AUfS/76/a/JM/wAwjv5AtOXAFUA38Bpgdt+8GcAHgX+tQMuSJA2P7BiYdi6s/F1l1u/eBE98CU791a7jvd3wyGcHvz7TAGP2q0hrGkFa5hQ2r/02WHU9TH9ZaftsXrhrKBrkQv4e+xc4Pd8fFyVpGG14Ir128MegYUJJy7Z3rkwcb2uY9pfzfMFoXZlM+uL1LTD3nfDc95PrK34LM84roEtJkiRJkiRJqr48P/0cscLgUyRJkiRJkiRJkiRJkiRJ0jA7DKgj976/LTHGP+eZ+1b6Q9GeAg6NMV4UY/xw3zr39c0LwDsr1rEkScNl7jsru/7qW3Yf2/QMbFsx+LUtcyDU4q0HKkqhwWiQ/PupUMt+kzy+4lqIMbkmScOt/Q/ptdFzSl+2M/n7cGt2+l/O6zPpwWjdg31/nnJmem0or+WSJEmSJEmSVGH1w91AkS4ZcL5+2LqQJEmSJEmSJEmSJEmSJEmD2bfvYwQeT5sUQjgI2K9vXgQ+HWPcsLMeY9wcQvgA8Me+oQNDCDNjjEsr07YkScNg2tnk8j8rFAzVuQ4e+iSEAbcQbFlY2LUzXlWZnjSyNIyDttOg/dbB524u8PdOkhW/Ta/t6IDGttLXlqRKWfun9Nr0V5a0ZIyRjq6VibW2hml/Oc+GIQSjTX95em3Lc/mvlSRJkiRJkqRhVFPBaDHGSwafJUmSJEmSJEmSJEmSJEmSRoApA86T7/bOObXvYwA2Ab9+4YQY470hhGXAjL6hwwGD0SRJe46GCXDsN+G+v6vcHo99ofhrJhwJB324/L1oZDr6y3DLubmAsnyW/hJ6eyBTV979Ny8yGE3SyLP+MVhwWXq9ZWZJy27q2cD23m2Jtbbs9L+cZ6gjEIgJ4andg70Oj5oILfsmh6EuuxJ6uyFTU7cXSpIkSZIkSdpLDPJYCEmSJEmSJEmSJEmSJEmSpJI0DzjflGfeyX0fI3BTjLE7Zd6jA85nDaUxSZJGpP3/Fl7+CBzwwdLDoeZ/EkIZAk4O/TSc+is46w5omjL4fO0ZJh6d+z14wvdzv5dmXJA+d/VNpe3RszW9lhTcI0nD7cF/Sq/N/+eSl23vXJFaa2uY9pfzEAL1IZs4rysUcGvg4Zek11ZcN/j1kiRJkiRJkjQMDEaTJEmSJEmSJEmSJEmSJEmVEAacJ9/FnXPSgPPb88xbM+B8bEkdSZI00o0/FF70FTh/UWnXH3EptMweWg/7vj0XojLzNVDfMrS1VHuapsDcd+R+Lx34wfR5y35T2vqbF+WpGYwmaYTp2Q4rf5teb5lT8tIdXSsTxxszzYyuG7fLWH1K6Gl3poBbA1v2Ta+V+louSZIkSZIkSRVmMJokSZIkSZIkSZIkSZIkSaqETQPOpyRNCCFMBfYbMHRXnvUG3gkeUmdJkrQnqG+CSScUd83+78t9nHL60PZuG+L12nNMfFF6bcFlsP353cc3L4KNT0GMu9d6dsC2Felrrvxd8nWSNFy2LIbYm15vO63kpVd3Jr8etmWnEcKuf+WtD8lZ492hgFsDJx6dXlv6a+jtGXwNSZIkSZIkSaqymg5GCyG8JIRwaQjhmhDCvSGEp0IIzxV5LBjur0OSJEmSJEmSJEmSJEmSpD3Q8r6PATgsZc7LB5zvAP6cZ73xA863DKEvSZJqw2GfhkxyEMpuRrXCgR/MnR/4IWiYWNqe4w+DWa8v7VrtebJjIDs2udbbCVe0wj3vht4u2LEGbjgFrtoXrjkIrjsMtizd9ZoNjwF5gs/ab81dt3lRub4CSSpdjHDve9Lrs94IY/cvefmOtGC0hum7jWVDQ+Lc7kzd4BvVN0PTPsm1rvVw5UxYc//g60iSJEmSJElSFdUPPmXkCSGcA3yVXZ8SWerTH32ckCRJkiRJkiRJkiRJkiRJ5ffwgPOJIYRzYoy/f8Gcd/V9jMC9McauPOvNHXC+qhwNSpI0ok1/GZx5Kyz8AWx6JhcqtX11rjZ6Low5AOoaYfQ82P+9MGZerjZ+Ppx9Nzz7LVj3IMSewfeqa4LJJ8KBF0F2dOW+JtWe4y+DO/KE5S34Dow7BNpvh447+8c3PJa77pw/9o/d9VeD77fhMbjzzXDO3aX3LEnlsPJ30H5bev3knwxp+faulYnjScFo9SE5KLUrZArb7Nhvwm3nJ9e2rYRbXwEXLCs8kFWSJEmSJEmSKqzmgtFCCB8FvrjzU/qDzUoJOCs1TE2SJEmSJEmSJEmSJEmSJOURY1wQQniG3ENQA/CNEMJLY4wLAUIIHwZOHnDJlWlrhRBGs+vDVBdUoGVJkkae1hNzR7HGHgBH/0f5+9HeZ/S+g89Z8N1ceN8LrbkHti6D5hkQI2x8srA91/wRNi8sbG9JqpRFP02vtZ4KhYaSJYgx0tGZHIzWmp2221h9SL4FsDtTYA+j5+avb2+HVTfB9HMLW0+SJEmSJEmSKqymgtFCCOcAX+r7NPYdO8PNtgLrgXxPi5QkSZIkSZIkSZIkSZIkSdXzHXLv+4vAvsCTIYSHgDZgJv3vA9wO/DjPOqfT/37BbuCxCvUrSZKkgcYeBHXN0LM1fc6GR9Nrq2+F2W+Aro3F7bvxqfzBaDFCCOl1SRqqjU+k1yYeM6SlN3SvpTPuSKy1NUzfbaw+ZBPndhcazjZmHmTH5n8t3vikwWiSJEmSJEmSRozSH00xPL7Y93HnG6GWAe8H5sQYR8cYZ8QY9y32GLavRpIkSZIkSZIkSZIkSZKkPdtXgCf7ziOQBY4BZtEfdBaBL8cYO/Ks8+oBcx+KMeUOckmSJJVXfQvMu7D06+9+K/ysAX41ubjrHv9i8viin8K18+Hno+Hmc2DzotJ7k6Q0z3wL1t6fXp/3N0Navr1rRWqtrWHabmPZTEPi3IKD0eoaYb/3DNLUbYWtJUmSJEmSJElVUDPBaCGEecAR5N7UBHAPcGiM8RsxxiXD15kkSZIkSZIkSZIkSZIkSUoSY+wEziEXjrYzCC3Q/17AAFwBfCZtjRDCaOC1A665qSLNSpIkKdnR/wXzL84F61RL+62w4re7ji2/Fu56C2x4HHq2wqrr4YZToGd79fqStOdb+CO47+/S64dfCuPnD2mL9s6VieMtmTG01I3Zbbw+1CfO78rUFb7pkV+CQ1P/6g3Lfg09nYWvJ0mSJEmSJEkVVDPBaMCJfR93viHq7THGTcPYjyRJkiRJkiRJkiRJkiRJGkSMcSlwJPBe4LfA48AT5ALRXhdjfH2MsTfPEu8ExpJ7/2AArq1ow5IkSdpVpg6O+AK8YQtkGqq37zP/s+vnz35r9znblsPya6rTj6S9wzPfzF+f89Yhb9HeuSJxvLVhWuJ4fcgmjneHIm4NDBk4/LNw+OfT56y4rvD1JEmSJEmSJKmCaikYra3vYwQeiDE+M5zNSJIkSZIkSZIkSZIkSZKkwsQYu2KM34oxviLGeGjf8boY4xUFXH4ZMGHnEWO8o7LdSpIkKVHIwNiDqrffugd3/Xz51cnzFlxW+V4k7R1i3P21Z6D60dA8Y8jbdHStTBxvKzYYLVPCrYHjDkmv5fvaJUmSJEmSJKmKaikYLQw4f3bYupAkSZIkSZIkSZIkSZIkSVUTY9wWY9yw8xjufiRJkvZq+76tvOvNe3d6besSuONNsOxquP+i9Hmrri9vT5L2XlsWQs+29PrsN0GmfsjbtHeuSBxvy05PHE8NRgsl3Bo47dz02ubnil9PkiRJkiRJkiqgloLRlg84rxu2LiRJkiRJkiRJkiRJkiRJkiRJkvZGB/4DzH5z+dY7/FI44gvp9SWXw23nw9NfTZ8Te6FnR/l6krR36u2Bq+al1yefBEf/59C3ib10dK1KrLU2TEscz6YEo3VlSrjFrr4J2k5Prm1ZWPx6kiRJkiRJklQBtRSM9tiA85nD1oUkSZIkSZIkSZIkSZIkSZIkSdLeKFMPJ/1f+dZrGA8HXjT0dZZdOfQ1JO3dVt2Qv37WHZAdPeRt1nU/T3fsSqy1NUxPHK9PCUbrDiXeGjjr9cnjGx6D7q2lrSlJkiRJkiRJZVQzwWgxxkeAR4EAHBNCmDDMLUmSJEmSJEmSJEmSJEmSpBKFEGaEEF4cQrgghPC2EMLbh7snSZIkFSAEGH/E0Nepa4S6UVDfAtmxQ1ur/bah9yNp75bvdWT8EbnXvjLo6FyZWmvLTkscTw1Gy5R4a+DoucnjnevgmW+UtqYkSZIkSZIklVH9cDdQpP8AvgfUAR8GPjm87UiSJEmSJEmSJEmSJEmSpEKFEGYDHwLOB2YnTPlhwjWnAi/p+3RdjPFrletQkiRJBZlxAax/aGhrZMf1nzfPhA2Plb7WM/8Nm56GcYf2j9U1wKTjYPorc+eSlE/H7em1ma/Je+nWns08vPlelu9YTCTmnbtqx9LE8TF142iqa0mspQajhRKD0VpPyYVT9mzfvfbAR2HSCdB2SmlrS5IkSZIkSVIZ1FQwWozxByGEVwKvBT4WQrgzxvjb4e5LkiRJkiRJkiRJkiRJkiSlCyFkgEuBj5J7OGpImJZ29/jzwGd31kMI18UYF1SgTUmSJBXqoItg9c35g4QG0zC+//y4b8MNJw2tp1U35I4XmnImnHYV1DcPbX1Je67n/wgdd6TXD/xgamlNVztfXfoZOrpWDqmFtobpqbVspszBaNnRsN974an/TK7feCocfxnMu7C09SVJkiRJkiRpiEr86eewegdwFblQtytDCJ8LIYwf5BpJkiRJkiRJkiRJkiRJkjQMQghZ4HfAx0l+oGtaIFquGOMTwC30h6m9pawNSpIkqXgNE+CMG+GMm+Cof4d9zhvaepNeVJ6+kqy+CRb/tHLrS6p9D3w0vXbMV3YNcnyB69dcMeRQNIDW7LTUWn1IDkbrytSVvuH8j0N9S3r9zx+C7q2lry9JkiRJkiRJQ5D0BqMRK4Tw6b7Th4CTgMnAPwP/GEK4G3gcWAf0FrNujPFz5exTkiRJkiRJkiRJkiRJkiT9xWXAS8kFoEVyAWe3kws76wQ+X8AavwJe0nd+NnBp+duUJElSUeoaYOoZueOAv4fLm4q7vnlm/3kmm/t869Ly9rjT8mtg3l9XZm1Jta1zPXTckV4fs3/eyx/Zcl9Z2mhrKD4YrTtkSt+wsQ0OvAge+3/J9a6N0H47TD+n9D0kSZIkSZIkqUQ1FYwGfJZdnwy58w1SzcAZfUcpDEaTJEmSJEmSJEmSJEmSJKnMQghnAm+l//1+zwJviTHe31efTWHBaNcCX+9b49gQQmOMcXtlupYkSVLR6hqh9VTouH3X8cwoaBgH29t3v2bKmbt+3jK7csFo6x+Bpb/OndePhsnHQ3ZsZfaSVFs2L8xfn3xSaqmzdwfru9eUpY2Dmo9MraUGo2WGEIwGcPBH4On/hq4NyfUn/wNiD0w6Fhpbh7aXJEmSJEmSJBVhiD/9HBF2PkGyFKGcjUiSJEmSJEmSJEmSJEmSpF18pu9jABYDJ+0MRStGjHExsL7v0yxwUHnakyRJUtkc9lmoa9p1bP7F8KL/hlC/6/jYA2Huu3Ydq2RQ2eYFcPtrcsctZ8OvWmHRTyq3n6TaseqG9Nphn8uFO6Z4vmtVWVo4esxJzG7cL7WeGowWhnhrYMMEOOnH6fVVN8Ctr4Ar2uDRL0As9RY+SZIkSZIkSSpO/eBTRhzDzCRJkiRJkiRJkiRJkiRJGuFCCBOBk+h/+OlFMcbnh7Dk433rARwAPDiEtSRJklRuU8+As+/OBY51roN9XgEzXpWrNe0DSy6HrUth0vG5ULTG1sLXDvUQu3PnDRNz6z73vdJ77e2Eu94KradCy8zS15FU+x78p/TaYZ/Ke2l758rE8UCGQ1uOGXTrprpm9m86lBPHnUEI6bfMZdOC0TJDDEYD2OeVMGZ/2PRM/nkPfxJaT4IpLxn6npIkSZIkSZI0iFoLRvMnp5IkSZIkSZIkSZIkSZIk1YZTgJ13abfHGK8a4noDQ9XahriWJEmSKmHCEbnjhVpPzB15rz0aVlyXXHvTDggvCADasQaWD+WPmBGW/hIO+tAQ1pBU07YsSa9NPXvQyzu6ViWOT85O4b0z/rnUrnZTnxKM1hXqyrPB7DfBo5cOPm/x5QajSZIkSZIkSaqKmgpGizHeOtw9SJIkSZIkSZIkSZIkSZKkgkzr+xiB+8uw3qYB56PLsJ4kSZJGklmvhcc+v/v45BN3D0UDmPvOIQajAWvu7T/v2Q5dA/7ImamHhglDW1/SyLbxqfTahCMHvbyjc0XieGvDtMTxUqUFo3VnEl4bSzHpuMLmbXq6PPupcD3bc0f9GMjU5c4zoyAE6OnMfa9K+h4pSZIkSZIk1biaCkaTJO3B1j8CT3899480s14P01+R+4caSZIkSZIkSZIkSZIk1aqJA87XlWG9pgHnXWVYT5IkSSPJhCOh7XRo/8Ou4wdelDx/+itg9H6w+dn+sUwWpp4NK64tbM/FP4PlV0P3Fsg0QG/nrvWWOXD4pbDvWwv8IiTVlDX3pNf2/7tBL+/oWpk43pqdWmpHiepD8i2A3eUKxJp2Low9GDY+kX/e6lvg2e/Afn9Tnn2VbsH34J4Lk2vZsdC1EeqaciFpc98JR/1bLiRNkiRJkiRJ2kP4OABJ0vBbdSP89kh49n9h4Q/h1vPg0UuHuytJkiRJkiRJkiRJkiQNzcYB52PKsN6UAedry7CeJEmSRprTr4H9/x7G7A+tp8LJl8PsNybPrWuAs++EOW+Dln1h6lnwkt/DaVfDMV+BiS+C7PjcUdeUvAbkQtFg91A0gC2L4O63wcobhvylSRphYoSHP5Vcqx8No/cddImOzlWJ460N04bS2W6yoSFxvDtTRyzHBpl6eOltsO87oGV2/rn3vhuW/rocuyrN8mvSQ9EgF4oG0LMNutbDU/8FD11cnd4kSZIkSZKkKvExAJKk4RUj3P8BiL27jj/2BZh3ITTPGJ6+JEmSJEmSJEmSJEmSNFQdA873H8pCIYQ64KgBQyuHsp4kSZJGqPoWOPbrhc9vbIOTfrj7+IEfzB07LfkF3PGG0vta8B2Ydlbp10saedY/kl6bccGgl3f1drKu+/nEWmu2vMFo9SGbWusOGbIvvB+jFI2T4cTv587XPwLXHZ4+d8F3YOarh76nkj377eKvWXAZHPEvkKkrfz+SJEmSJEnSMMgMdwOSpL3c+odg45O7j/d2wtIrqt+PJEmSJEmSJEmSJEmSymXnXeYBODCEMJQn5L0MaO47j8Afh9KYJEmS9jLjDh3a9esfLk8fkkaO9Q+l18bNH/Ty57tWE4mJtbaGMgejZfIHo5Xd6HmQGZVezxcqp6FbflXx13SuhW3Lyt+LJEmSJEmSNEzqh7uBoQohZIETgVOBecBEYAxAjPHMYWxNklSIFb9Lry35xa5PapMkSZIkSZIkSZIkSVLNiDE+EUJYDuxDLhztw8CHil0nhJABLt65LPBQjHF92RqVJEnSnm/cwTDxWFh7X2nXb3wSenZAXZ6gIO2ZOjfAI5dAx23QvRm2LIae7blaYxs0TNh1fmYUTDoODrsEmqdXv18VZtmVcPfb0+tz3jLoEh1dKxPHAxkmZdtK7SxRfUi/BbA7k4Hesm4H9c0w6/Ww6MfJ9a1LYfm1sM8ryrzxHqbjTnjqq31BcuX+RUpw/UmwbUXuvHkmnPBdmPrSyu8rac+w5n548su5QOBxh0J2NCz4LrwwBHTGq2Deuwf/HrD4clj4Y+jZBjNfA/u/F0KoWPuSJEmSpD1PzQajhRBayL1B6v1A6wvL7Pa37b9c92bgC32frgWOjTEmP55DklR5+Z4U9PzdsGMNjJpUvX4kSZIkSZIkSZIkSZJUTv8HfIzc+/reH0K4LsZ4Q5Fr/D/ghAGff7tczUmSJGkv8uLfwJ1vhI47Srv+hpPh3PvL25NGtt4uuPHU9Pe8b2/PHS+0/mFYcS284gloGFfZHlW8hT+Gu9+WXq9vgZZZgy7T0ZkcjDYx20p9yJbaXXJLedbrDpmy7vUXx34zFya58ank+q2vzL2uznhVZfavde23wc1nQW9n9fbcGYoGufC6m8+C064xwE7S4NbcBzeelgsxA9jwWPrcZVfCsqvglMtzIZpJnvkfuO+9/Z+vvikXLnvUl8rXsyRJkiRpj1ehn3xWVgjhcOBPwCVAG7k3TBXqamASMAc4Cjir3P1Jkoqw8cn0WuyB5ddUrxdJkiRJkiRJkiRJkiSV278CG8k97LQOuDKE8J5CLgwhTA4hfB/4KP0PS10FfLcCfUqSJGlP1zwdzrodXvZAadev/ROse7i8PWlkW35N/geB57NtJTzzzfL2o/J4/Iv56/v9bUHLtHclB6O1ZqcW29GgsqEhtVaxYLTsaDhnkDDIxw24SfXkl6sbipbmiX8b7g4k1YKnvtLBKQYhAAAgAElEQVQfilaQCI+lfD+NMfm15+mvQ/fWktqTJEmSJO2dai4YLYRwCHArsD+5QLSdb3YKFBCQFmPcDPxiwNBry92jJKlAsTd/MBrAst9UpxdJkiRJkiRJkiRJkiSVXYxxLfBB+t/v1wh8M4TwTAjhX4DzB84PIRwXQnhbCOFHwALgbfS/P7AHeFeMcQTcWSxJkqSaNe4wqGsq7drn7ypvLxrZhvrrvfK35elD5dO5ATY8ln/OmP0KWqqjMyUYrWFasV0Nqj5kU2tdmbqy7/cX2dHQOCW9vuYe6O2q3P61rGOEfL9ovzV3744k5dNxZ/HXrPszdCeEqW1fBZuf2328Zyusvrn4fSRJkiRJe6364W6gGCGERuAaYBz9gWiPAF8BbgFGAU8UsNSVwIV952eWuU1JUqG2rcr9UDMf3zwgSZIkSZIkSZIkSZJU02KMPwwh7Ad8ktx7/wIwD/jYC6YG4O4XfB4HXPOJGOP1le9YkiRJe7RMHcx4NSz+SfHXPvu/sPZP0DwTZrwKJhxR/v7Ub+2fYNnVuXCmGRcUHFg1JKtuhJU3QOdaWP2Hoa3VfhusvB6mnV2W1lQGWxYOPmfGBQUt1dG1KnG8LVuJYLT0WwC7Q6bs++1i5mvhmW8k12JvLvxm7IGV7aHWdG2CHR3D3UW/2y6Afc6H2W+A7Njh7kbSSLJpASz5OWxZVNr191wI9aMh1MGEI2HWG3KvgWm2JX/vlCRJkiQpSU0Fo5F7auQc+kPRvgr8Y4y5xxaEEGYXuM4t9L9Rat8QQluMsb3MvUpSabYshQc+nHvSwpj94NDPwNQzhruryti2fPA529uhcz00jK98P5IkSZIkSZIkSZIkSaqIGOOnQwgLgG8ATfS/DzAMON/5OewaiLYDeE+M8UdValeSJEl7uqO+BBsegfWP7Doe6uHwS+GhTyRft+6B3AHw2OfhpJ/ArNdVtte91cIfwx/fkQteAnj0Ujj9d9B6YuX2fPjTuX3K6ZZz4Ogvw0EfKu+6Ks3mAoLRmgYPNuuOXaztSg6+am2oRDBaNr2XTIWD0Q77LLT/ATY8nly/9lB4w2aoG1XZPmpFjHDj6cPdxa6WX507nv4anHETNE4e7o4kjQQdd8EfXg5dG0pfY/HPdv38yf+EI780tL4kSZIkSepTa8FoH6D/DVC/iTH+QymLxBg3hxAWAfv2DR0MGIwmafh1b4UbToGtS3Kfb1sBN58JJ/4Q5rwVQsh/fa3ZWkAwGsCmZ2DSsZXtRZIkSZIkSZIkSZIkSRUVY/xBCOEW4GPAu8gFpEF/GNpAAegB/g/4bIxxUVWalCRJ0t6heQaccx88/0fY+ERuLDse2k6F5n2gcx088a/51+jtggc+AjNfA6HCwUR7m95uuP8D/aFoAF0b4cF/grNuq8yeW5fDY18o7dpDPgHPfgs61ybXH7oY5l4IDeNK70/lMVgw2uGfL2iZNV3tRHoTa63ZKgejVfr1p7EVzn0ALk8JPovdudAtQyJz1twL6/6cXAt18KKvkfxjkASd63Pfj5r3gUxD7vvO1qXQMAEaxsPWZRCyuV+j+98/+HrrH4YF/wvzLy74y5G0B3vo4qGFoiXZ9DQ8+rn0euwu736SJEmSpD1azQSjhRAOAfbp+zQCHx3ikgvoD0abC9w6xPUkaeiWXdUfijbQ3W+HBz8OJ/8s94/te4ptBQajrX/EYDRJkiRJkiRJkiRJkqQ9QIxxCfD+EMLHgFP6jpnAJKABeB5YDdwF3BRjXD9cvUqSJGkPVzcKppyWO15o7IGFrbFlMWx4AsbPL29ve7tVN0JXwl8FOm7PPYy8vrkCe96waxBbMQ68CDY9BUuvSK73bIf2W2HG+aX3p/LYMkgw2uh989f7tHeuTBwPBCZnpxTb1aAyIUMd9fSwe6BMV6au/5MpZ5R9bwDqGmDScbnQryQrrjMYbacVv02vNc+E/d9bmX0LCUaD3K+VwWiSurfm/mxSCeseSK+VO4hNkiRJkrRHq5lgNODIvo8ReDTG+NwQ1xv4LyQ+ckXSyLD4p+m1bSvgxhfDYZfkwtHaTqv9J4ttLTAYbfk1MO/CyvYiSZIkSZIkSZIkSZKkqokxbgWu7zskSZKkkWXqS4FA7haWQTz4T9B6ErS+GCYfD5lsf61rM6y+Ofeg6MY2CHWwfRW0zIFp58CoSRX6AmrcxqfSaz3byhuM1rMD1twDj1xS2vUTjoSmKTDjNenBaABLfwWd63Lno1qh9WRo8HamqtucJxgt1MGUMwtapqMrORhtQv1kspmGUjobVH2opyfuHozWPfC+kkM/XZG9AZh2bnow2srfQ4wQQuX2rxWrb06vTTu3cvvOeRss+tHg8zruhCW/hMknQfP0yvUjaWSKETY+AYvy3MNYSSt/Dwd9GHo74fk/wtalufFQBwQYfxiMP7T275mUJEmSJJVFLQWjtQ44f6YM6+0YcF6BR8VIUglW3TD4nEc+k/s4/eVw6q9zT96pVdtWFDZv9c3+I5kkSZIkSZIkSZIkSZIkSZKk6miZBQd/BJ74t8Hnrrg2dwA0z4Kz786FzTx/D1x/Qv5rT70CZr566P3uafK9bzwWEFZXqE0L4A8vg00l3qZU1whH/EvufPabYMG3of3W5LkLf5g7dsqOzd0PMPWM0vZW8WJv//+rSeb/cy7krgAdncnBaK0NU0vprCD1mSw7erbvNt6d6QuPmfX6XOBepez/Xnj0c8m1bSvg7nfA8d+p7XtchiL2wp//ETpuT59z8Icrt//8jxcWjAZwx+uBAMd8BQ78QOV6kjSy9GyHu98JSy4fvh5W3wJXtOV66dmWPGf6K+GUn0F9S3V7kyRJkiSNOLUUm9044HxH6qzCDXysyqYyrCdJQ5cdW/jcFdfBs9+qXC/V0F3gy2/XBtiW/A+HkiRJkiRJkiRJkiRJkiRJklR2R/0rvPgqOOD9MPst0FhA4NHWJfDgx3IBOXe9ZfD5d7weOjcMvdc9Tr7bnXrLt8197xs8FG3c/Nyv/8EfgZfeBmfdAQd/FA79NJxzL0w/NzcvUwdnFPCg9J26NsKdb4LertL7V3FWXJde2/ftcPglBS/V0bUqcbw1O63YrgpWH7KJ493TzoGTfpI7MvUV25+mqXD8Zen1RT8qPJhrT7T8WnjqK+n1Qz4BY/ar3P7jDoFXLYGDP1bgBRH+9EHY8GTlepI0sjz7ncJC0Y78Irz4ylxgaKbve092HLSeAs0zYMoZuT8bzXxdaX10rksPRQNYcQ089dXS1pYkSZIk7VEq+NPOsnt+wPnkMqw3d8D5mjKsJ0lDV9c4+JyBnvtebT+dpTvPDzFfaOOTuSenSZIkSZIkSZIkSZIkSZIkSVI1zDgvdwDc+Vew+CeDX7P0V3DgRbD5ucHnxh5Yfg3s+1dD63NPE0J6LfaUZ4/OdbCqgCCzee+Ggy7adaz15OS5mSwc+il49NLCetjRAe23wtSXFjZfQ7Pop+m1/d9X1FIdnckPfm9tqFwwWjYtGG3Om2H8WRXbdxcTjspfX/ILmPfX1ellpFnyi/z1ySdUvoeWmXDUl3JHz3a4vGnwa5b8Ag77VOV7kzT8lg7yOgUw6Xg45J9y5zPOhyM+nz439sLlzdC7ozz9DbTk5zD/E+VfV5IkSZJUU2opGG3nozQCMMhPUfMLIUwCDh4w9OxQ1pOksogRdjw/+LyB1j0A6x6CCUdUpqdKy/d0hxfa+CRMPaNyvUiSJEmSJEmSJEmSJKkqQghZ4ETgVGAeMBEYAxBjPHMYW5MkSZLSTT6hsGC0nu3w1FcLX/fJf4fWEyHUQfNMCJnSe+zalHtYdyY5QGmP0Ntd+rVdmyAzCuoaYNMCIA5+TbFhRpNPLG7+yt/DuEOhaWru8+3t0L0ZGibkDpXPloXptfGHFrxMT+xmTVd7Yq01W7lgtPqUYLSu2FmxPXcz9iDIjoOuDcn1jruq18tIs+np9FrIwMQXVa8XyH0vmHA0rPtz/nmrroe5b899/+nakAuNbJoB9OaCKOtbqtKutIsYYduKCgRuhb7XsPXQND33/8merGvjrvcqbszzOrVTMX/uCRmYfDy031Z8b4NZ92Du9YgAnWsHmZyBlllD+zO0JEmSJGlEqqVgtLuAXiADTAohnBFjvLnEtS4kF7AGsAW4vwz9SdLQ7FgD3VuKv+63R8Kbe/M/FWuk6tle+NyNT1auD0mSJEmSJEmSJEmSJFVcCKEF+BDwfqD1hWVSUglCCG8GvtD36Vrg2BhjAQkGkiRJUhnNeQs8/iXYtnzwuYt+XPi66x6Eq+blzkdNgsMugQP+vrjetq2Cu94C7bdCXRPMfRcc/V+QqStunREjz3vjYwnBaNtWw11/Be235ILRJh4NHXcOfl3rqTDpuOL2mnoWjD8C1j9U2Pwn/j13JJl4LJzyMxg9t7gelOz5u5PHs+OKCn9a09VBLz2JtbaG6gejdceuiu25exNNcOAH4dFLk+vdm+Cq/eCkn8DkIv/fqWVblsCae9Lrc94GzdOr189OB38U7npz/jkdd8CVc9Lr018JJ/3QoEZVz+Kfw4Mfzx9mWQ6ZLMx6Axz37dxr255k4J97Ym/h19WPgf3+tri9DvpI7nWkmH0K9cuJhc9tmADzL4aDPlyb91hKkiRJkhLVTAR2jHEdcN+AoUtDKP5vqCGEfYCPk3sDVQRuiLESf+uWpCLlezrMYB65pHx9VFPPtsLnPv21yvUhSZIkSZIkSZIkSZKkigohHA78CbgEaCNv0sFurgYmAXOAo4Czyt2fJEmSNKhRk+Dsu2HuOysXVLVjDdz/flj6m+Ku+8MrYHVf+EX3Fnj66/DIpyvTYzXku12ot4RgtNsugNU35f779GwbPBRt/BG5YI2X/K74cI1MPbz0D3DA+2HcodA8K3fUjy6+77X3wU1nQm9yCJeKsPTX6bWTLy9qqY6ulam1ydmpRa1VjBERjAa58MZ5706vb14AN58BO9ZWr6fhFCPccEp6ve00OP471etnoDlvglOvgGnnlr7GimvgzkHC1aRyWX1rLtCr0qFoAL1dsOj/cn/u2tPcdn7/n3sKNeuNcNbtMO7g4vaacR68+GqY/gpomd3/557mWVDXWNxaQ9G5Dh74KCz+afX2lCRJkiRVXM0Eo/X5yoDzE4D/KebiEMIU4CpgAv1vqvpyeVqTpCHa9Ezp1z56CXRtLF8v1VJMMBrAmvsr04ckSZIkSZIkSZIkSZIqJoRwCHArsD+59+7FnSUKCEiLMW4GfjFg6LXl7lGSJEkqSMtMOOF7cP6Cyu7z3HcLn7vhCVj3593HF/64fP1UXZ6/JsQig9E2PAlr/lj4/GnnwssfhKP/Heqbi9trp4bx8KKvwSsegQsW546DPlTaWlsWQfsfSrtW/Z75ZnqtyKDDjs7kYLTx9ZNoyIwqaq1iZFOD0UoICxyK/8/efYfHUd1tH/8eraolW5Z777gXsKm2AYNN7wkQAk+AUEMq6SEJLaQ8SQhvypMQkkACCaEaTHXoBowxNsXghnvvkqtsWdqVzvvHSPFqvbN1drQr3Z/r2ks7c86c81Pbnd2duccY5/8jltB+WP+4P/W0tMp34cAG9/bj7ncCE1tK34vglJlw8gupj7HlJTiw0buaRKKxFj78VvLP8+la9wiEkjy/LZvtWQJV85Lb5nM1MPlRqBiX2py9z4Ypz8MFaw/t91y4zhn3cgujfpTauKlYlcQ+tIiIiIiIiGS9nApGs9Y+CixoXDTAdcaYt40xJ8bazhhTaoz5UuO2R+IcVGWBl621cS7zIiLik33L09t+/XRv6vBTssFoy36bmTpEREREREREREREREREREREJCOMMcXA80B52OqFwLXAIGAECYSjAc+E3Z/qWYEiIiIiIqnqdXbmxt69KPG+buFDB9ZDfa039fguxkuEhmByQ215Kbn+Hcck19+PcZP5e5DoYoU6lfZPaqgdwejBaF0LeiQ1TrLyXYPRkvyf8EJBh/g/t7byd7t7oXtbQQco7edfLbF0HJ3e9ttne1OHiJuNM6IHvWZafQ1Ur/Z/3kxJ9rG3/VAIFGemlibdT83s+OH2tJHnHhERERERkTaiBS83kLKLgblA58blScAsY8xWYGV4R2PMvcBQ4ASgiENXmjTAJuALPtUsInK4vcthzUNwcDv0PN1ZjqbiKAjuif8m63vXwoDLIZC5Kwx5LtlgtE0vOB9k50X/QE9EREREREREREREREREREREss7XgQE4x+4B/B74lrW2AcAYk+gZ6G9w6Pi/gcaYbtba7R7XKiIiIiKSuMHXwuYXMzP2/jXw8iTAgglA9SqoCQtjKunpfK2JHtD0X3W7oaR7ZmrMJBMjGM2GoKEelvzCCVGp2Rx7rHg/o2bzBmDglYn3T0avc6GoK9TuSH7bD2+Gpb+EDiNgyI3Q/1Lv62vNrIW9S6O3FXWGQGFSw+2o2xp1fdfCnslWlhS3YLSgrcvovK4GXQsLb3NvX/FHJ6Bx5Peh6yT/6vLL/nXw8Y9g7cPufQZ8IXvOfyntBz1Og62vpLb9nM/Dojuh+ykw7hdQWB5/G5F4DmyGj2+B7W87+z4t5dWTmp+PVzoABlwBR3w59j5JNln7KCz7LVS9l9x2g6/JTD3huk9xAmJjBUl65eA2CNVAfknm5xIREREREZGMy7lgNGvtamPMucDTQE8OHejUEwi/tIYBbgi7T1jfjcC51tpKX4oWEYlU9T68Ps0JPANY9Vf3vr3Pg9G3QtU8eCXWh0EWXp0Cp70NeTny8J5sMFpwN2x/C3roor8iIiIiIiIiIiIiIiIiIiIiOeJrHApFm2GtvTmVQay11caYtcDAxlUjgDYXjGaMKcC5oGw/nOMmq4HNwEfW2rUtWJqIiIhI29P3MzDhD/DB1zIzfuUc97ZEw76CORqMRowQkoYQzP8SrPqb99Oe9Ax0HO39uOAEdEybBXO+ALs+TH77mi3Obdvr0FAHA//H8xJbrXWPured+HTSw+0IRv//61rQMsFooYZQRud1NeqHENoLS+9277PpOdjyMkx7E7oc519tmVZbBS8dDwejh+T91/jf+FNPoiY/Bu9eBVv+Aw3B5Lff+6lzq3wXzngf8gLe1yhtR3AfvHw8HNjQ0pVA3c7myzVbnL/zul0w+sctU1MyVv8D5n4xuW0KyuGIm2DEdzNSUjMmD05+znn82f4Wh94qBkoHQmnfxvUeWf4HGPk978YTERERERGRFpMjyTnNWWvnGWPGAw8AZzWtjvjabBOcT0UM8ApwlbU2zjuPIiIZtOiuQ6Fo8bQf6gSddZ0I562A545w71s1F9b8EwYn+WZmS6k/GH39pEfhncuit216TsFoIiIiIiIiIiIiIiIiIiIiIjnAGDMS6N24aIF0z7RbxaFgtEHAm2mOlzZjzCDgGODoxq/jgfZhXdZZawd4ME9X4E7gc0Anlz5zgHustdPTnU9EREREEjTsqzD0KxDaB6H90YNm8kvB5ENhubMc3AvWOseTh/bDCyMzV1/drsyNnVExgtEObnECQLx26mvQ41Tvxw1XPhLO+gDqdjvH/X/w9dTGWXq3gtGSsfhn7m0dRiQ1VIOtp7JuW9S2boUtFIxmUwi48kJeAI76NVQcBXOucO/XUAvL/691BaOtfTh+KNqoH0OgyJ96ElVYASc/C6EDsG8lzByX2ji7FsC216Dn6d7WJ23L+scTC0Wb+gaUDUpvrmf6p7bdst/CiO9BoDC9+TNtaQIhjAOvhLF3NS4YKOnlb7hhaX8nIDa479D+aaAdFHdx7jeEnAC1R+LUdME656sNwbNDiHo6+ZJfKhhNRERERESklcjJYDQAa+024BxjzATgG8BUnKsfRrMHeA34g7W2xQ+EEpE2zjbApmcT798+LAit/RDnCglvnufef92juRGM1hAEWx+9raQ39LvUeZM70p7Fma1LRERERERERERERERERERERLxyZONXCyyy1q5Oc7zdYffL0xwrZcaYKcAtOGFoUUPKPJ7vLOAfQLc4XScCE40xDwM3Wmv3Z7o2EREREQGMgYIOzi0RTf2agtKKu8PB6EFLaavbHb9PNjIxgtGq5jlhGF6rSDEgKBWFHdO7WPjujyFUA/kl3tXUmtVWubcVdU5qqJ3BSuqJ/vfXtSCzwWgFeVkWjNYkkXC5qnmZr8NPle/F7xN+LlC2yW8HFWOhqAvUVqY2RtU8BaNJeirnxu/T71LoPiX9ubqcAJXvJr9dbRVUr4by4enXkCnBatizKH6/iiOhtF/m64mnoL1zi5TXeKr74Oth1V+jb9vnwubfQ0E5BKPs69YfdEKIY+1PioiIiIiISE7I2WC0JtbaD4Ar4b9XXuwLdAYKgUpgG7DYWtvQYkWKiITbvz7xvkVdoNNRzdd1OxnyyyBUHX2bba87H2AXdky9Rj/U17i35Zc4Vw2KFoyWs1ctExEREREREREREREREREREWlzuobdX+HBeLVh99t5MF6qjgR8Ofu3MYRtBs4xkU0s8CGwGugIHAV0CWu/AuhgjLlQx06KiIiI5IB+l8Dy/8vM2It+Aqv/7gTfbHvdCWUb/1sY+IVDARTZyFr3tn1evLSI0OvspAOy0tZhBJSPhD1LUtt+9qVOuBFA2SAnPCby3AOB0AE4uDV6W+nApENTdgS3uLZ1KeyR1FjJyjfRg9GCti6j88ZVcSSUDYHqle599i2Hd6+CrpNh4FUQKHTvmwt2L4jdnlcEfc7zp5Z09LsEVtyb2rarH4TdC2HrK855Pv0ugZLeENoPHUfDoKsTDwyVtmfLy7Dqb7H75BXBmDu8ma/fJakFowGsfgCO+pU3dWTC/rUJdDLQ97OZrsQb/S5xD0brd2nz5YpxsP3Nw/vVH4Btb0CPU72vT0RERNq2fatg7cON7+XEeO/K5EOnCc7roqKMX2NLRKRVy+JPMZLXeDXJdK8oKSKSWdteT7zv8G9D5FV9CtrD+N/AvBujb2NDsHkmDPh86jX6IRQjGC2v2D3YLVevWiYiIiIiIiIiIiIiIiIiIiLS9hSH3a917ZW48rD7+zwYz2u1wEZgsBeDGWP6AE/RPBTtHeB6a+3SsH5FwI3A3UDTwUbnAT8FfuhFLSIiIiKSQWN/Ars/ge1veT925btAWBBIcC+8dw1sfBpOmgEmz/s5vWDr3du8DkYrHwlH/8HbMRNhDEz8N8w6C2rcw7ZcbX6++fKn98CJ06H3ud7U11qse8y9bdK/kx5uR13031WHQAXFeSVJj5cMt2C0kA1ldN64jIHJj8Ksc+DgNvd+ax5ybuseg1NmHn6uTK6o2xU70DBQDBMfhsIK/2pK1difOuFmO2Ynv231yuZheOufaN6+6n6YNis3fg7iryW/ggXfj90nrwgmPw7lI7yZ84iboHIurH88+W2X/hra9YNhX/WmFq8lEq57/ANQ2i/ztXihxzQY9WNY/NPm64d8yQlNC3fsX+H5odHHeX0qnPqawtFERETEOzs/gNdPc14TJmLdv2HVX2Dqm1DSPbO1iYi0Yq0qGE2ygzGmAJgE9AN6AtXAZuAja+3aFixNpOXtXQ7vXZtY3/KRMPxb0duG3OAkBf/n6OjtW1/N/mC0+hjBaPklUOASjBZUMJqIiIiIiIiIiIiIiIiIiIhIjqgMu9/Fg/EGhd2v8mC8dASBxcD7wPzGrwtxjp17w6M57gTCzyCeA0yz1h4M72StrQV+b4xZDzwd1vQtY8x91tp1HtUjIiIiIplQWAFT33DCaWYe6c+cm56DbbOyNywilWC0bifBEJeLjzeprXLCmAo6OMtlg6BiPAQKY2+XKRXj4PzVUPke1Gxy1hX3cM4l2LUA6nbCnCsSG6uhDhbcAr3OcYKqxDH/Jve2ivFJD7cjGD0YrWthj6THSpZ7MFow43PH1WkCnL/GCeybfWnsvtteg00vQN8L/anNayvudW8b9k0YcxsUupwPk22KOsHUWbBnkRP2VlgBJT1h33KorQTb4ITZVc1Lfuzdn8Dqh2D4NzwvW3JY3R5YeEfsPkO+BOPvhvxS7+YNFMOkR2HsXVC9Ckp6O/sTDWHXcVj8M/fQww++Dt1PgY6jvKvJC9bCyvti9ykfBYOu9qUcTxgD4+6CI26ELa+ADUKP06B0wOH7N+2HOL/b+oNRh+KTW7N3X1dERERyz6K7Eg9Fa7J3mbO/Nua2zNQkItIGKBitFTLG/AO4yqPh1llrByQ4b1ecA7E+B3Ry6TMHuMdaO92j+kRyy7wbEu979J9if8DaaYJzxYpoH6qsfsC5mlfv82DAFZAXSL5Wr9RshZV/ca72cmA99D4fxt/jfPDqJlDi/kFQ3W7njVt9WCsiIiIiIiIiIiIiIiIiIiKS7bY2fjXAUekMZIzpDIwIW7UynfHS9CDw58iAMgDj0TEtxpgjaH4cYB1wdbQ5m1hrZxhjHgzbrgi4HbjGk6JEREREJHNMnhOS1ft82PSsP3Oufyx7wyJiBaOF9kdf3/UkGHB5ZurJpEAxdD/58PUlZzpfV94H299KbKw9i+DgVifYSKCh3v1vqV2flALxdgS3Rl3ftSDzP/OCbA5GA8gvgb4XO2FGbv+nTba8lLvBaNvfdm8b+pXcCUVrkhdwnn8qxh1aF36/XT946/zUxl77sILRpLnKOVBfE7vPETd5G4rWxBjoMNS5AVSMbd6+eaZ7MBoWFt0Jkx/3vq50HIz+nNTMsBz9H2zXBwZ/MXYfY5yQW7ffW+UcCFZDQZn39YmIiEjbYq0T2pqKrS8rGE1EJA15LV2AZL047zQ5jDFnAYuAm3AJRWs0EXjSGPMvY0wG3qESyWLBfbAjxgcg4TofG/3DzUgVMY4X3fAkzL0K3vmcs8PdEqrXwsxxsPB2JxQNnA/qnxvifJDlJlDiXGkmGlsf/0MyEREREREREREREREREREREckGc4CGxvudjTHppC5cgxOwBrAfeD+dwoi/y6YAACAASURBVNJhrd0VK6DMI5cD4VdDfMpauyKB7X4ZsXypMabYu7JEREREJKN6ne3fXCv/Agvvci7UveNdaAj5N3c8sYLR3JQN9L6ObJDs38S6x5xAMIGajWBd/q5L+6c05I66LVHXdyvMfDBafrYHo4ETUtPzrPj91ufw3+m+GDntZYP8q8Mv3U+GQLvUtt05H6pXe1uP5K7qtbDwJ7H7tOsH5aN8Kecwvc6J3b7+CfjgZtjzqT/1xFOzBRb/b+w+JgC9z/WnnpYS7/e2f60vZYiIiEgr1BB0grGX/wmW3g31B1IbZ8c7sOp+2L3Y2/pERNqInApGM8aMNsa83nh7zRjTLYUxujdu2zTO0EzU2opMj9fBGDMFmAGE/z4s8AHwBPAKUBmx2RXAI8aYnPobFElLbRXYhvj9ADofl1i/DsPj99kwHSrfTWw8ry28Aw5uj9724Tfdt8svi32VnAMb0ypLRERERERERERERERERERERDLPWrsLmB+26i5jjHHr78YY0xv4Ac5xaRZ4xdpED8TJWRdFLP89kY2stUuB98JWlQKne1WUiIiIiGTYgCug64nx+5X0hE7HpD/fwttg/pfhlYnw5rkQrE5/TC8oGO2QwddDpwmJ9//wm/D2RRBK8YTd1mTzf9zbjv5T0sM12AZ2BLdGbeta0HLBaMFsCkYDGHMHFPeI3aduF7z9mdz7Ow3ug2qXYLSBVznBcK1NQQc4MjKDPQnPDoZPf+tdPZKbVtwLzw2GqrnuffIKYfw9kBdw75NJfS+CnmfE7rPsd/DCCPj4VrDWn7qi2TADnh0Ey38fu9/o2519xtZs2M1QNti9/bVTsyv8V0RERHJD3S5nP+LVk+D9r8CC76U33nvXwYuj4aPvt+x+pIhIDspv6QKSdCMwBefAppettS5pO+6stduMMUGcA30scD3wXS+LzALfAe5IYbujcYLMmljggVgbGGP6AE8BhWGr3wGubzzAqqlfEc7v726g6Z3484CfAj9MoVaR3BPal3jfzgl+SN0+wWzHNf+ErhMTn98L9bVOKFuy8ttDXj4UxAhGe/l4uHhX6/zQSERERERERERERERERERERKR1+R3w78b7xwN/xjmWLCHGmO7As0BF4yoL3ONlgdnGGNMDGBe2KoRzXF6iZgHhV2Y8C+dnKCIiIiLZrqAMTn0Z1k+HqnnQUAeBIigb5LTvXQ7lI6DvZ5zgoU3PQeVcqN0O5MGWmZBfCt2mAAbWPQrB3YnNveUlJ7hkZBacYpNKMFrpAM/LyApFnWDqLOfY/J3vHwr32DAdandE32bTc7DyrzD8G76VmZXmf8m9rWJs0sPtDlURcgkh61roRzBa9NMA3WpqMR1HwZkfwIanYNFPYvydPgur7odhX/O3vnR8crt729Cv+leH34Z9FTofDZtnwsHG0zlX/jnx7T/8JvQ5/9BzmbQtBzbC+1+FeNc4OPN96DjGn5qiCRTByc/B+idhzuWx+y7+KfS5wPm/8FvoAMy9CuoPxu43dRZ0P9mXklpUu15w5nx4slP09todsPEZ6PdZf+sSERGR3Lbk17Bjdvx+eQUw6Frnfqga1v4rdv+lv4Li7jDiW+nXKCLSRuRaMNoFYfcfTGOcB3GC0QzOVRWz4FMb71hrK4HKZLczxvwgYtUb1trVcTa7k0MHnAHMAaZZa5u9s2KtrQV+b4xZDzwd1vQtY8x91tp1ydYrknOCSQSjJXr1rsKK+H0AVj8Ax96b+Pxe2Pa6sxOfrMLGQLTCcvc+wT2w6yPoND612kRERERERERERERERERERETEF9baR40x38MJ+jLAdcaYkcAPrbVvu21njCkFvgDcDnTDCUQD56KqyYSE5aLREcufWGv3J7H9nIjlUWnWIyIiIiJ+ChTDwCucWzx9zndubspHwQdJhA5teDI3g9FMANr1zUwt2aCgDAZd5dya1GxyAtDcbJjetoPRgjHOZeh8bEpD7qjb4trWtaBHSmMmI98URF2fdcFo4ATVDPsqdD0B/hMjOGjD9NwKRtv2mntb2UD/6mgJXY53bk2SCUYDJyhvxHe8rUlyw4YZ8UPRRv2wZUPRmuQVwIDPQ6AE3r4odt8N01smGG3b6xDcG7tP7/PaRihak8IK6HYybH8zevuG6QpGExERkeRseDKxfqUDD+U31B+MH4wGsOB70PkY6HZi6vWJiLQheS1dQKKMMYOAPo2LDcDzaQz3HND0KclAY0y/dGprDYwxJcBlEavvj7PNEUDYpyrUAVdHhqKFs9bOoHmoXRHOgWsirV+iwWgFHaDD0MT65hXgHC8aTyJ9PLZxRmrbNQWjBYqhqIt7v50fpja+iIiIiIiIiIiIiIiIiIiIiPjtYqCKQ+Fmk4BZxphNwEPhHY0x9xpjXgN2AH8Eujc1AZtxwtJau5ERyyuT3H5VnPFEREREpK3oc15y/fetgF0Lmt/2b8hMbbE0hJLrb+shLz8ztWSrrpNjt+94G6yN3ac127/Ova1sUEpD7ghGD0YrC5RTEihNacxkFOQVRl0fasjCYLQm5aOgoKN7+/Y3c+vvtGaTe1thJ//qyAaDr0uu//on4cDGzNQi2W3f8vh9ukzKfB3J6HMBVIyP3Wfvp/7UEmlfAm8Tds2yn6cfYu0XJfIzExERkdzVUA+h/VBbBQc2Oc/9uxdB3e7kxqmvdbbbu9x5fywR4ftdgWLodEz8bWw9vPM52PHu4e/Bhd/qdkP1mkMhw9Y673XU1yb3fYmI5Lhcete/6QqIFlhmrY1x6Y7YrLXVxphlHDrYZwywPs36ct3FQHnY8i7gqTjbXA4EwpafstYm8iz/S5oHql1qjPlyrEA1kVYhlGAw2sCrwSSYW2mMs6NcXxO7X4PPO7m2ATY+m9q2hRWH7vc+H1Y/EL1fKJmL4IqIiIiIiIiIiIiIiIiIiIhIS7HWrjbGnAs8DfTEOQ7QNN7vEdbVADeE3Ses70bgXGttpS9Ft6whEcvJHt8YmQDQ2RhTYa3dlUZNIiIiIpKLSvvDwCthzUPx+wLU7YKZRx2+vsMIOHE6lI/wtj43tj65/qNvz0wd2WzgVbDs97GDmp7qDpOfgO4n+1dXtlgY429i9K0pDfnhvjlR13ct6BF1vdfyTUHU9SGbZJCgnwLFMOLb8EmMn/nTveDEJ7M/yKdut3OifTSDrnbO72lLhn4NVv0t8f5V78GMvlA+2nk+6TA0c7VJ9lj7KCz/Q+w+nSZAz9P9qSdRxsBxf4NXT3Y/H3DjDFj+Rxj6FX9rq14Tu72kl7OP0NYMuR4W/yx62875TjBjuz7+1iQiItKW2AaoP9h4qzl0v+EghGqcr5Fthy3XHNrGrS1aX9ewcAPdpzjvixR1jl3/kl/Bgu8n9z3nlzmvi8KNugVmX3wozMxNzRZ4ZWKC87SHI26CdY/AgQ3O6+yRP4DRt7W916Ei0iblUjBa/7D7kVczTMUqDgWj9fNgvFx3bcTywwkElV0Usfz3RCay1i41xrwHHNe4qhQ4HUgxRUkkRwQTCEbrfiqMuS25cRMJRgPYvRg6jkpu7FRVzYODW1PbNvxqQOPvjhGMlnI+poiIiIiIiIiIiIiIiIiIiIj4zFo7zxgzHngAOKtpdcTXZpvgBKIZ4BXgKmttigek5JyOEcvbk9m48eKxB4HisNXlOBdMTYsxphvQNcnNBqc7r4iIiIik4bgHoOMY+Oi7qY+xdym8cTqcvwbyfDgVKdlgtGFfz0wd2aykO5wxF+ZcAdvfit6ndgfMOhMu2ADFXfytryXteAc2THdvLx/p3uYi2FDHpwc+jtrWrbBX0uOlIt9E/98L2jpf5k/Z6B87oWKf/iZ6+8Gt8PppcNEmKKzwt7ZkfHCze9voJM8Dag0qxsJZH8O8G6FqbuLb7VkEb5wB562EvEDm6pOWt2sBzPl87D4jvw+jfuTPvkWyOh0F096Al44HtwDK978K7Y/wN9htf4xgtCNugpG3QIk/gZ1ZpbQ/HPsXmHdD9PbXp8G5n/pbk4iIiN9sA9TXphYwlkww2WFhZwehIRtfl1rY9gbMvhSmvubebeOziYeilY8Ekw8VR8Hwm6HiyObtfS+CKTOdEOk9S2DP4tTLbxLaB0t/dWi5/iAsvANKB8KgK9MfX0Qky2XhOwau2ofd3+PBeHvD7nfwYLycZYwZDJwUsfr+ONv0AMaFrQoB7yQx7SwOBaOBc6CbgtGkdXO7QkTpQDjur1DcDTqMTP6N/UBx/D7gXInCr2C0Hck8HEQwYd9/YYUTFrft9cP7hfanPoeIiIiIiIiIiIiIiIiIiIiI+M5auw04xxgzAfgGMBXo6dJ9D/Aa8Adr7Zs+lZgtyiKWE7hq4mFqaB6M1t6tY5K+DNzu0VgiIiIi4oe8AIz4jnN7ug/UbEptnAMbYfss6DHN0/KiSjYYLZvDlDKpXR84+QV4Isbufv1BWP84DP2yf3W1tDX/dG/rOjmlIRfv/9B9yAJ/AmjyTUHU9SEb9GX+tIy53T0YDZyT7Dc8BYOv9a+mZG1+Ifp6E4B2ff2tJVtUjIUz3m2+bsMMePui2NvtXws7ZkP3kzNWmmSBWI/FACf8Ewb+jz+1pKrTBDj1ZXjtVPc+ax7yNxit2iUYbexPYfSP/KsjG8XaR927DPYshfIR/tUjIiJtk7XQUOt9GNlhfaO0ZWU4WRbY9gYcrHQPjA8PHYulsBOck0DQWc/Tm+8fzvsSrLwvsTmSseYhBaOJSJuQS8Fo4Qf2eBFkFv6uf5KfmLQ61+BcVbPJh9baBXG2GR2x/Im1NpmUojkRyz6lNYm0oKBLMFpxN+gxNfVx8xIMRtu/NvU5krVvefT13afCCQ/BjN7u29YfbL5c4PIhbag6tdpEREREREREREREREREREREpEVZaz8ArgQwxgwC+gKdgUKgEtgGLLbWNrRYkS0rMhjtYNResdUA4ekQkWOKiIiISFs04tvw4bdS337PkuwLRut6IhgTv19rVVAGZYOgerV7nz0JnLjbmsT6fjuOS2nILXUbXNv6FA9MacxkFZjCqOsbaKDB1pMXfpH6bFPQHkoHwn6XQB9wHl+ylW2A4N7obSYf8nLpFM0Mq0jwf2zvEgWjtXbxnnsqjvSnjnSVj3ICEN32Tfx87LLW/fzAMn+ei7Jau75OWG7drujtexWMJiLSZvw3nCyBgLFQTVgoWYxgskT7NtS29Hcvh7FQszF6MFr1atjxTmLDJPpaJ9KE38LO92HnB6lt72bba96OJyKSpXLpXbfKsPv9PRivX9j9Kg/Gy0nGmABwVcTq+xPYdGTE8sokp14VZzyR1ifkEozmFvyVqECCwWgHUryyVyr2Lou+vnykEwQXS5cTmi8HSqP3CyWTxSgiIiIiIiIiIiIiIiIiIiIifjPGtAfCz8pbFXkBTmvtaiDGGfwCWJ+2EREREZHWrv/l8MmtqR+L/cE3YOV90O8yGP0jMHne1tckmWC0ITdkpoZcMuQGWPAD9/aV98Gah6CgHLqfCuPviX5CcGsQOgA7Zru3D/5iSsNWBre6to0qHZ/SmMnKNwWubSEbojCbg9HA+Tv9+Bb39k/vgYFXQcVY/2pKxO7F8O4XoKEuevuAK/ytJ9uVDYQep8HWV2L3m/9l2PISHHU3tB/iT23ivWA1fPB1WP33Q+vyG7P5Y+1rdDkBOo7ObG1eKe4GfS6CDU9Gb9/1EWx6EXqfnfla9i6DUHX0ttIBmZ8/2+Xlw+BrYend0dvf/ixMfhz6XeJvXSIikll7lsJH34OquU4wmQ05X0XC1UcJrFv3GLxzWeJjDLkxtbkDxTD5SXjpaKj1ONbm8YiMivB9RZMHgXYQKIGukxpfew32dn4RER9k6BOIjFjf+NUAY4wxnVMdqHHb8HdJfUwLyjpnAr3DlmuAfyewXeQ7juuj9nK3LmK5szGmImpPkdYi6BKMlu9TMNqWmenNk6iNz8H2N6O3tR8a/0o4kW8EF7hcrNbtjVwRERERERERERERERERERERyRafBz5qvM0Dilq2nJwReWBMSQpjRG7j1cE2fwJGJ3m7wKO5RURERCRdJd1h2pvQ6WhINURpzxJYeBt8+B1vawuXaDDa0f8HA/8nc3XkihHfgzF3uLfbeuf4+5pNsPaf8NrJ0JBE+FwumXWWe9vQr0KnCSkNWxXcHnX9uLLjCJg450h4JD/GPEHrEtqVTUZ+H0bfHrvPK5Oheq0v5STkwEZ4ZZITfOTmyF/6V0+umPwE9PvcoYAsNxufgZdPgIOV/tQl3nvzvOahaOA834SqiZnZf/JzGS3Lcyc8CGWD3NvfPAe2vpbZGhpC8MII9/ayge5tbcm4X8Run30prJ/uTy0iIpJ5BzbDyxNh8/NQW+nsgygUTaKJDLpe/2TioWilA+CYe6H/51Kfv2wAnDYHup4IXr6H0LTv/d998DC2wVlXuwM2znBee9Xu9G5uERGf+PPOqzfmArVAIU442leAn6Q41pc5FAoXAt5Ju7rcdU3E8nRr7e4EtusYsRz9HX4X1tpqY8xBIDzRqRzYlcw4kYwx3YCuSW6maFPxh1swWkGawWh5SRwvunc5dBia3nyx7F4Mb53v3t5hmPO141jY/UmUDgYqIq6WFCiNPlaqVykTEREREREREREREREREREREb90wTneD2C+tVZHWycma4PRrLXbSfJ4QWNM/E4iIiIi4p9OE+DM+dAQhLwCsBaCe2kWYLL017D457HHWfVXGHcX5Lsc752ORILRKsbD0K94P3cuMgbG3A7lo2H2xfH771kCW1+FXmdkvjY/7VkC299ybz8i9b+XquC2qOuHthuT8pjJyjcFrm0hG/StjpQZA2PvgNJ+8N610fuE9jkhS2Pv9LU0V6sfhOAe9/b8Uijq7F89uaKwHCY/6gQ5ffQdWPY79761lbDu3zDs6/7VJ97Y9TFsn5X8dme+n3v/N/ntYOoseKafe5/l/wc9pmauhq2vuLcFiqG4R+bmziV5+TDoGlj9gHuf5b+Hfp/1ryYREcmcNQ9CMJFYDGl5BgIlkF8CecXO/kug2FkXeT8vRlu0vuFjzjySqAG9DbXNl2O9Rol0wZq0vvP/6jAUTnvLCe9zC/Cb0ffwgDOv1O6AdY/ovTQRyTk5E4xmra01xrwNTGtc9R1jzNPW2oXJjGOMGQ18l0PPaO9Ya9tkuo4xpitwXsTq+xPcPPKSDTUplFBD82C0NNOhACf0Ls7lQ0RaSMglGC0/zT/9RK+GBfD8MPh8g/OBUias+7d7W14BdD7Wud/7/OjBaBdXHV5bgcsVYjK1Yy8iIiIiIiIiIiIiIiIiIiIiXmk6e9gCG1uykBwTedZ1UhcLNcaUcXgwms4MEREREZHm8hpDloxxQmzCdRwbf/tQNexdBp3Gx++brESOkS+NEU7SVnUYnnjfnfNbXzBa1Xz3trxCKBuQ0rD1NsTOYGXUts4F3VIaMxUFuR6M1qTDsNjtO9/3p45E7IzxNwXQ/ojMnZ/TGuTlQ+fj4veL9b8r2WvD9OS3MXlQNtj7WvxQ0ssJQwy5nIqc6ceuqhjjtx+qx6JwHYbGbq963wkG1s9MRCT3Vc1r6QpyjHEPGGu6Hy+0LGrfOGMGisHk+/PcGyiKHjpWHxaMZi3smJ3YeMO+6U1d4Zp+NtF0PwU2Pef9nE302ktEclDOBKM1uhsnGM3iBHPNNMZcbK2dm8jGxphjgSeBUpyrUNrGMduqK4Hwd8VXAW8muG1kUpFLLGlMNUBFjDFFWpegSzBaQZrBaA1J/vu9ejKc+CQUZ+ADuF0L3Nu6TTn0gfmoW5w3fLf8x1kOtIOJ/4TCisO3c7uCmILRRERERERERERERERERERERLLdlrD7hS1WRe5ZEbHcP8ntI/vvtNbuSqMeEREREWlrep0D+WXxj9l+eSIc8ycYdLUTduIVG4rfZ+AXvJuvtSgf6YTaRbuIeaRPboXh33Q/Xj+X1B+EFffCh99y79PnQvcTj+PYFazE0hC1rUtBj5TGTEV+nvvL6lAi/zPZovPx0K4fHFgfvX3zizD7MueE8MHXQV7A3/oAdi+CNQ/Cxmdi9+t/mT/15LLe58YOkwJY+y8o7e/8vlMMMBQfhQ44j7mL7kp+255nQmFH72vyQ14A+l0Kq/8evf3ARidwI1Dk/dxbX4OFt7m367GouX6XwoIfuLfXH4CNM6DvRf7VJCIimZFouFW2iRYcFjVkzCVwLK8Y8hMMMQvvm1fQ+oNB81yC0RrqnK/bZ8PyPyQ+Xv/PeVNXwvNdltlgtDUPOq/R+l2cuTlERDyWU8Fo1tqXjTGzgCk4oWa9gLeMMf8E7gPmW2tt+DbGGAMcDdwIfAEnCMw23t621r7o2zeQfb4YsfxA5M8vCalsl+pcIrkplKFgtGg76LHseBteOg6mzXI+OPBKcJ/zAZSbkd87dD+/HUx5EfYsgZpN0OloKOoUfbt8l8zEnR/o6gwiIiIiIiIiIiIiIiIiIiIi2W1R2P2BLVZF7lkasTwkye0HRSwvSaMWEREREWmLCsrgpBnw9mchuMe9X0MtvHctbH8TTnjQu/ltffw+fRRmcRhjYNJjMOts2L8mfv/XToVpb6YcGJYVGoLw+mnxT4g/OokTnyNUBre5tnUuyMAF613kG/fTAIMNQd/qSFteAE56Gv4zwb3P+sec29ZXYPIT/p43smMOvHFG/GDI/pfDsJv9qSmXFbSHE5+G2RdDcK97v8U/g1V/g2lvQYeh/tUnyak/6Dx3VL2X/LYVR8Kxf/G+Jj8ddTdseh5qd0Rv3/Qs9LvE2zlXPQDvXRe7z/AYwaBtUdlAOOGf8G6MEN23PwOTn4R+n/WvLhER8db+DVBbmfr2foWRHdZWqPPiM8ktULy+FtY/Ae98PrH3nEweHHUPdDnO2/ri6X8ZVL0Py/5f5uaYfQmM+hGM+2nm5hAR8VBOBaM1ugz4EOiJE6yVD1zdeNtvjFkG7Gps6wQMBZpSdUzjegNsAC71se6sYow5HhgVtqoe+EcSQ0S+u1uSQhmR28R5xzghfwKeSHKbwUCcS3iIeCDoEoyW73MwGsD+tfDWRXDWh+nNHV7DU93d2zsdAz2mNV9nDHQc5dxiiXUFqj2LoOOYxOsUEREREREREREREREREREREd9Ya5cbYz4BxgJjjTG9rbWbWrquHLAoYnmsMaadtfZAgttPijOeiIiIiEh8PabCZ7bBzvfhlcmx+655CEb+AMpHeDN3vJNUu5+iE4ndlA+H81bA7gVwYLNz8fOVf47et2oebJgBAy7zt0YvbXohfija8G9DceoBZm7BaB0CHSnK8y9ULt8UuLaFbJ1vdXii03g4/V14+YTY/TZMh50fQOej/akLnICueKFokx6D/m32tMTk9TzNeT5Z9FPn5+vm4DZY9js45o/+1SbJ2fhM4qFoJz176H7ZIGcfweRlpi6/FHWCC9bC4y7nus3/irfBaA0h+ORWnFOiXUz4HQSKvJuztRj4P9DnQngixnmbn9wKfT+jfUoRkVy16C73tnE/h25TogecBYohr0iP/62V235RQy0s+kVioWhTXoQuE6Gw3NvaEmHyYMI9MOoHTkBacC/kt8OJxwHqaxrD9QLNt1vzD9jwVOLzLP01DP8mFHX2qnIRkYzJuWA0a+12Y8yZwLPAAA69qjc4AWgTItb9d1MOhaKtBC6w1m73o+YsdW3E8kxr7eYkts/KYLTG32lSv1ejHVfxS8glGK2gBYLRAHZ9BFtfhx6nRm9vqAcbgrqdzhupRV0A6+z011Y5ffIKoKQXvHm+szPtZsxtqdUIUNDRvW3bLAWjiYiIiLSk4F4IlDpXcBQREREREREREREREYnuD8BfcY7d+wmHH7smEay1W8IC5cA51nMy8HKCQ0yJWJ7pUWkiIiIi0tYEiqDrJBhyI6y8L3bfra95F4zWEOdE1dKB3szTWuUFoNME5wbuwWgA217L7WC0ba/H79N+cFpTuAWjdS6IcXH5DCiIGYwW9LESj7Qfmli/ra/6F4xmLWx7I36/bidlvpbWJlAMA6+MHYwGzmOSZK+tCf5+xv0M+pyX2VpaSn47KO4BB7ce3hbaB/W13gWV7V0GNXFO+dU+kbuCMigfBXsWR2/fu9T5PZb09LcuERHxxu6F7m2Dr4Pirv7VItkjz2U/rHqNs28VT1Fn6HWWtzWlorgb9D478f771yUXjNZQ54TM97kg+dpERHyWkxHr1tpFOAFoj3Io7MyG3f7bleaBaA3AQ8Ax1tqlftacTYwxpcDnIlbfn+QweyKWk9o7NMaUcXgw2u4kaxDJLUGXYLT8FgpGA3h9Krx3vfOma7gNM+DRfHisGJ7uBc/0g8fbOVe0eKIDPDvQuc3oA4/kwdZXYs9T0iv1Grud6N4W7yo8IiIiIpIZ+1bBS8fBEx3hqW6w8CfOAUEiIiIiIiIiIiIiIiIRrLX3Ay/gHMN3tTHmey1cUq54OmL5i4lsZIwZDhwXtmo/iQeqiYiIiIhE1zuBcJMPvg7b3vTmOCIbJxgtkXrE0X0K5Je6t6/6m3MCbS6yDbDmoTidDPQ6J61pqlyD0bqlNW6y8ghgXE4FDNmQr7V4oqgTdJkYv9+in8CBOMFAXrAWNkyH+prY/TodAyU9Ml9Pa9T+COgwLHafvctg6T1QOVfHpWajyjmJ9etzUWbraGkdx0RfX38wscDORG1/M3Z7fqnzPC/u4u0zfvxjqF7rSykiIuKh4D6omuverlC0tiuvMPr6RXcmtn2fC72rxU+9U3jfY/XfYfHPYfVDULPF+5pERDySk8FoANbaXdbay4GRwP8DmmJdTcQN4GPgbmCYtfZqa21kqFdbcwkQnsS0DXg+yTFWRCz3T3L7yP47rbW7khxDJHdY61z1IZqCNIPRGtIIRgPng8wPv3loec+n8LbHb0CXJvsQEaaos3tbOqFwIiIiIpKahiC8NgWq5gEW6nbCupZEtwAAIABJREFUwtthxb0tXZmIiIiIiIiIiIiIiGSvz+MEfRngF8aYl4wxp7RwTdnuYSA8DeIzxpgjEtju+xHLj1trdZCNiIiIiKSn5xnQ/7I4naxzXNG7V6YfZhMrGK3vxamd8NlWFbSHCb+L3ef54bDxGX/q8Up9Lcz+HATjnCI29idQ2i+tqSpdgtG6FPgbjmWMId/kR20L2qCvtXhm/D1Q2Cl2n/oaeGEEbHsjc3U0hJzHrtmXxO5X0BEm/L/M1dHaGQMT/hA7rBHgo2/DyyfA/JucAETJDtvegD2L4/cb+X0oH5H5elrSMTGOmZ51NlSvTn+Ojc/C+19xbzd5zvN7uucltnbDboaO49zbVz/g7AdteMq/mkREJD3Va+H5GPsaY+/yrRTJQoGi1LfNL4VRP/KuFj+VDYQxdyS3zcZn4OMfwdyrnP+pbXFCeUVEWkj0d0NziLV2OfBtAGNMGdAdaErRqQS2WWv3t1B52eraiOWHrE360iBLI5aHJLn9oIjlJUluL5Jb6mvc34zPT/MNyMIKOLg9etu5y+CTH8P6J2KPseJeGHIjVIyDpb9Or55IHUbEDjdLRI/TYWuUC9c21KY3roiIiIgkb/tbcGDj4evX/RuGftn/ekREREREREREREREJKsZYx5ovLsX2IdzUc9pwDRjzD6cC59ub2xLlLXWRh4H16pYa1cYYx4ErmlcVQj8wxgz1S3ozBhzAXB12Ko6IMFLoIuIiIiIxJCXDxMfhoFXw6wzY/dd+y/oMQ0GXZX6fG7BaKUDYPJjThiIJG7wtXBgMyy8LXp7/UF473roeRYECv2tLVXrn4QNT7q3l4+E4x6ALselPVWVSzBa54JuaY+drHxTQNDWHbY+FGVdTuhyHJz9CWx+IXYIVnAvvHcDnLfcCdfy2qbnnceuWI65F3qdA6V9vZ+/Lel5Gpy9EFb/HRbFCa1YeR/0uwR6TPWnNnFnLcyN81Zcnwtg+Heg22R/ampJ7QdD52MbLzIdxce3wqSHUx+/IQjvXRe7z5kfQcXY1OdoK0q6w2mz4YkY52821MK8G5zH+HTCVERExB+f3Ao1m9zb+3/ev1ok++Sl8FxeMd5536T/ZVAUJ7g7m4253bmwwbbXAQPdp0JxF3h2cPxtg3tg/o1wztLMvOYWEUlDzgSjGWN6AMeGrZptrd0Z3sdaWw1UA6v8rC2XGGOGApHvLt2fwlCLIpbHGmPaWWsPJLj9pDjjibQuwRjHbaZ7ZYZxP4/+ZueAL0CHoTD5cXjxSNj9cexxZh6ZXh1uTvTgigmB4ujr63UxWxERERHfLb07+vod7/hbh4iIiIiIiIiIiIiI5IqrARu2bIGmI6o7cPjxbPGYxjFaNBjNGNOH6Mdg9ohYzjfGDHAZptpaWxljmtuBi4CKxuWJwKvGmOustZ+G1VIE3AD8JmL731hr18UYX0REREQkcSYPep0Bx/4V5l0fu++iu2DAFU6gWircgtF6n6tQtFT1Od89GA2gdgdUvgvdT/avpnRsnBG7/Zj7PAlFO9hQQ3X93qhtXQojX/5lXoEpoCbK+pAN+l6LZ9r1hiE3QFEXePuz7v2qV8KexdBxtPc1bHw6dnvHsXDEl7yft60qGwhj7oSlv45/XtCGpxWMlg32LIH9a9zbz/oIKjJ0Xlq26nOBezDaxhlOmFyqoRI75jjPy24GXqVQtGQUlEHv82HTs+59aqtgx2w93oiIZDtrY78WNHlQ2s+/eiT75CUZ9t7rXJjyXGZqaQldjndu4cbe5QQKxrN3Gez9FMpHZKY2EZEU5dKnAZ8Bnm68PQzUtmw5OeuaiOXZ1tplyQ5ird0CfBK2Kp/kDlCbErE8M9kaRHJKKIPBaL3OhvwoYwy44tD9oV9Jb45UDf8WlA9PfxzXYDQ9FYiIiIj4rm5n/D4iIiIiIiIiIiIiIiKx2bBbrpoNrIlyeySiX2+XfmsAlyvSOKy1G3GOnawLWz0JWGKMmW+MecwY8x9gA/B7oCCs3/NAAke5i4iIiIgkqdtJ8ftUr3ICb7a/7dx2LYCGUOJzWJe+JpD4GNJchxFQ1DV2nw3Tk/s9tZSG+tgnw+eXeRbQU1m3zbWtS0E3T+ZIRr4piLo+5PY/k0u6TIz/P775BecxZf9652911ydQ+R7U7Up93oOVsOah2H0SedyT5BgDXRP4ua75B+xeBLYh4yVJhIYQ7F4IwWrY+YF7v8IK6DDSv7qyRa+z3dvqD8Cm552LTtftTn7sne/Hbu+WIyGm2SSRn9nWVw49x4iISHaq3QGhavf2LhMhL/prJmkjAkXJ9W8Lr/WS2XesXpW5OkREUpRLwWgdca70aID51tr9LVxPzjHGBIArI1bfn8aQkZfD+GKCdQwHwi+7sh94OY06RLJf6IB7W6A0vbFLesKpL0P5qEPLx93vXI2ryaBrYNzP05snWd2meDdnnssLkYY4V4YREREREW80hGDRT+GlE9yvbgbO1VdEREREREREREREREQOZzy8tSnW2lnARcCOsNUGOBq4FDgDiEw3eAS4zFpb70eNIiIiItLGdBgK/S+P3+/jH8KrJzm3mUfBU91g80uJzbF3WfT1Jj/xOqW5QCGM/nHsPsv/ANO7wuaZ/tSUiu1vwdM93cPzAEZ8DwrKPJmuMrg16vo8AnTM7+LJHMlwC0YL2qDPlWRASQ844iux+yz4gfOY8kx/eLQAZo6Dl4+HJzvB3GugIYmfQ6gGZl8GT8UJDCzqAkO/lvi4krhRP3A/X6hJaD+8OAZm9IWq+f7UJbDxOZjeBV4cC0+0h7lXufcdfZvzHNPWVBwJ3ae6t791Prwy2Xl8mndTYsGjDUF473r46DvufcpHQb9Lkq+3rRt4JZQNit1nyS8PPcf851ioib4PICIiLWjhHbHbR/3IlzIki8V7fRGudAAMirGf21p0nQw9piXW983zYO/yzNYjIpKkXApG29n41QJbWrKQHHY20DNseR/wRBrjPQyEHzj1GWPMEQls9/2I5cettUo3ktatvsa9Lb8k/fG7HA/nLIKLd8GFm2DwNc3b8wIw6ha43PpzVYgep8G0N5JPVnYTKI6+vl4PHSIiIiK+mPtF+ORWqJobu1+s/V4REREREREREREREWmrBmbgFucsttbFWvsiMBr4M7ArRte5wMXW2st18VkRERERyagTHoIJv4de5ya+Td0ueOs8OLg9dj9rYZ/LSZgmkPh8crhhX4cTn4rdJ7gb3roAarb5U1Mygnth1tlQu8O9z5AvwZhbPZuyKhj977VTQRcCLfD36BaMFmoNwWgAE34Lx/41tW1X/x2W3p14/0V3wvrHYvfpMBxOf9cJhBTvdT8Fpr0Fg6+P37dmM8w6S+cR+eHAJnj7IgjuSaz/8JszW082OyWRwFcLK/8My34fv+un98Cqv8Xuc9psz8I/25TiLs7j+cjIU5td7JwP71yW2ZpERCQ5uxbAinvd26e9Db3O9K8eyU55CQT2lg2CEd919g2Ku2W+ppZmDJz8PBz5v9DzTOhyAhR0dO//1gXOe3MiIlkil4LRwsPQSlusitx2bcTyo+kc/GStXQE8GLaqEPiHMcYlwQiMMRcAV4etqgPuTLUGkZwRKyAi4EEwWpPCjs4Oaiz9fXhT7uRnvR3PLWCtvtbbeURERETkcPvXwdqHE+sb0vk1IiIiIiIiIiIiIiLSnLV2XSZuWfB9DbDWmjRvVycx33Zr7U1AD+BU4IvALcDXgc8Cg6y1J1hrp2fi+xURERERaSYvAMO+BlOeg/NWgEnw9KSGIKx/InafXQvc2xSMlr6+FzknAMeSyO+pJWx8Nv4xaqN+6OmUlcGtUdd3Kejh6TyJys9zCUZraCXBaMbAkOtg8HWpbZ/osY6J9j3pWWg/JLVaJDFdjoXj/gJnzIvft7YKtryc+ZraunWPga1PrO+AL2S2lmyXF4Dy0Yn1TeQxJ16fEd91zh2U1BR3cwJBRt6SWP/tb8HBGGGsIiLir1jPk+WjoNtk/2qR7OWWR9AkrwDOXQ5H/QpKWuZ1fYsIFDkBsafMhNPnQJ8L3Pvu/RR2fehfbSIiceRSMNpHQFO0pC6zkCRjTHfgnIjVceLjE3I7za9AORF41RgzPGL+ImPM14DIT0Z+kw0HqYlknFswWl5h4h8Ce2XgldBxbOL985PIoiwbBBdugIBrPmJq3MbTlV5EREREMqt6LXz0fQ69HI9DwWgiIiIiIiIiIiIiIiIZZa2ts9a+Ya39h7X2f621f7DWPmWtXdPStYmIiIhIG9V+CPS/PPH+e5aCbTweyUY5LmnvUvdty0cmV5tEVzE+fp+9jb+naL+jlrJnSez24m5Q0svTKauC26Ou71zQzdN5ElVgXILRbCsJRmvSKYG/0Wj2LIaG0KG/XbdbcB8c2Bh7rMIKKO2fWh2SvA7DIVASv1+s5wjxxpJfJN6301GZqyNXVCT4M9j7KdgG98elhnrYuyzOXCk+NkpzCT/HWKjZnNFSREQkTLTnx3B7YuwH6jlSmuTFCUbrOM4Jt23r4u0Pxfp/ExHxWX5LF5Aoa+16Y8xc4ARgmDFmqLV2eUvXlUOuovnve5G1NoFLKcRmrd1ojPkM8BJQ2Lh6ErDEGPMBsBooB8YDXSM2fx64Nd0aRHKCWzBaIm/aey2/HZwxH1bcCx/e7N6v36XQ6yzoMAJePj56n/KRcNRvYNvrUNzDuTJPQQfva3Z7IdJQ6/1cIiIiIuJ8gLDop7DoDudD+EQpGE1EREREREREREREREREREREpO0ZeydsfAZC++L3XfFHWH2/c5HsQAm06wtHfAmG3QzGQHWMzN+eZ3pXc1vW90Io6Q01m9z7rPiTcwPnvIFh34AhN/hTX6Rts2DBLVA1N3a/ITd5foJzZXBr1PWdC7p7Ok+i8l2C0YKtLRit/2Xw8Y+gblfy2z4a/WeUtMHXQ6Awfj/xRkF7GHgVrPxz7H4LfuAE2425U4EGXqs/CB/cDLWVifUvKE8uGLW1OuJGWPdw/OOt6w/AI2n8zZb0gj4XpL69HNL7PGf/88CG+H1nHgkTH4EBl2W+LhGRtmrFfTD/S4evD5RA10kw5iew9Few+QX3MVrqtapkn7w4r+GO+LI/dWS7/pfDJ7dBcE/09ne/AAvvhCHXw4jvOu/XiYi0kLyWLiBJv3a5L/F9MWL5fq8GttbOAi4CdoStNsDRwKXAGRweivYIcJm1tt6rOkSyWiiLgtHA+XBm+Dfgwk1QOuDw9pOegcmPwaCrIb/MfRxrodeZcNSvYMS3MhOKBhAojr6+/mBm5hMRbwX3wsq/wUffg43PJRewIyIiLaPyXVh4W/KP2aHqzNQjIiIiIiIiIiIiIiIiIiIiIiLZq2wQnPISdD4eTH78/k3HgdfXwL7l8OG3YNnvnXX710bfpv1QKO7iSbltXqAYTn8Hep6VWP89S2DejbDq75mtK5pdH8Mbp8cPRRvzExhzm6dTW2upCm6P2taloIencyXKLRgt1NqC0Qor4LQ50H2q+/kkmVLSG0b9GMb93N95BY7+PQz/tvM7iGXxz+DjH/hTU1vy7lWw8r7E+lYcBafNhpKWCYnMKl0nwUnPQqcJYDJ0unbn4+C0dyC/hc5DbG0CRc7fb6+zIb80fv85n4dNz2e+LhGRtmjV/dFD0cB5v2Drq/DKRNg4w32MAVdAt8mZqU9yT6DIva24GwyOjFxpo4q7OPuXsVSvhAXfh6WK9RGRlpVTwWjW2hnAAzihW+caY/5oTCKfmLRtxphJwPCwVXXAv7ycw1r7IjAa+DMQ63Icc4GLrbWXW2v3e1mDSFarz7JgtCbtesF5K+HU12DkLTDpMfhsFfQ5/1CfmG+a2oyXCCgYTSSXHdwOr0yGedc7L4DfOh/eu07haCIi2W7tv1PbLqSXeSIiIiIiIiIiIiIiIiIiIiIibVLXE+CMd+GyWrgsBBMfTm77FX90vlavid7e84z06pPmSvvDKS/CxEcS36bpd+SnVX+DhjihX8feB2Nu9TyQZm/9boK2Lmpbl4Juns6VqDYTjAZQPhymvgqX7nceU3qemfk52/WDizbCuLsgL5D5+aS5vAIYfzdcuAHG/SJ235V/hfpaf+pqC2q2wvrHE+tbUA5nfQgdR2e2plzS+xw48324LOjcAu28Gzu/PZz+LpQN8G5MgdJ+MOUFuGSv8xwT7xzPFff6U5eISFuz/E/pjzH0q+mPIa1HXqF727j/9a+OXNBxFAz7Rvx+y/8I1qc8CRGRKHIxVOxGYB/wDeBLwMnGmN8Az1prq1q0sixlrX0HJ0wu0/NsB24yxnwDmAT0B3oA+4FNwEfWWpdPqERaObdgtGy4UkNeAHqc6tyiifnGnk87snkuCc0N+hBDJOutuA92L2y+bvXfYcgN0OX4lqlJRETi2/xiatspGE1ERERERERERHZ9Aot/DrWV0OkoGHNHYldbFxERERERERGR1sHkOWewlI9Mbrt9KyBYDftdTjspG5h2aRJFMr+nXR9DQ72/gVE7P4jfp8PwjExdGdzm2taloEdG5oynoC0FozVpekzpMBy2/Cezc5WPyuz4khhj4oduBffA/rXQYZgvJbV6299MvG+HEZmrI9eZPOfWYRjs+sibMctHOP8TkhlNzzFu53422fmhL+WIiLQpDSHYlebjq8mD9kd4U4+0Du36urdl6L2DnPb/2bvv8Diqe//j76Nmyb33XrCNbYwNphubTiB0QighIZWUm/IjJIGEm5BcEkKSG0hCSGghhBog9GqqaTaYYoptjLstNxV3SZa02vP7Y6SrlTWzO7s7O7srfV7Ps49Wc86c+dqWV7M7cz7Hz99J7Xpo2AZd+mW+HhERF3kVjGaMeSnm291AD2B/4Lbm9nKgornNL2utPS6wIgVrbQPwcrbrEMkpXh+OJVpNIBfkQo2Fpe7bm/aGW4eIJO+jn7tvX/+QgtFERHKZ182FiSgYTURyQbQJbAQKPUK2RUREREREJHN2rYB5h7Zex9v6Imx9GU5627khVUREOiVjzBcDHM7i3B+4E9gCfGKtlqgWEREREclJvac7oVs7l/rf58Ee3m3dFIyWEb2nQa+psPPjxH1tBJ4/AgpiwrmKusOA2TDp/0FR1+DqqnzDWYy5akH8fl1HQv8jgztujGqPYLQuppRuhXF+VjOoyLhPBWzsyMFoLUZfCMtvyPAxLsrs+OLf4BOgS39nERYvT06CCd9yFm3vc2B4tXU0lQvgjfP999f/k8RGXxRcMJr+vnPD3i3w2rkw7qsw9DPZrkZE8pmNwqp/wKanYceHsGdVa9u4rzsLz3UdmrXyQpXMZwVehnxGYU3S1tBT4f3LwTa13d59HPQ/NDs15bIR58C734doQ/x+1e/A0JPCqUlEZB95FYwGzMW5samFxcnjbok8H9H88Hujk0mir4hI6jpqMFpY95UWeExmj9aHc3wRCd6q22HmH7JdhYiI7MtaWHNX6vs3KRhNRLIo2gjvXQ5r/ulMwB9yEhx2hy52ioiIiIiIhGnxT9ovbrTtXdj8nCYJiIh0bv8kc/fp1RhjFgF3Av+21upmEhERERGRXGEMzH4E5p8Guz9Nf7zuo9MfQ9ozBo5+BOZ/FnYtT9y/+u322zY/B5ueguNfhYIApqptfBJePcsJYoun22iY8zgUFKZ/TBdVDVtct/cvGYwxxrUt04pMiev2SGcIRus3Cw65Bd75bvBzSUwhTP6xE74muaGwC8x9Gl49E+o2efdb8TdY8y849gUt3J6KzfOc39O+GJjwbSeMTuKb+D3nd+qq20j9Y1EDE77p/J1LbtjwH+dx6O0w7ivZrkZE8tU734UVN7m3rboVNjwEpy6BsiHh1hW2SB08Mz39cQ77R/pjSMfScwIc9QAsuAQiu51t3cfBnCe0oKOb0gFw9ONOUHLjDu9+r5wM526Dkj7h1SYi0izfgtHcKNhMRHJfPgejFbhfOHOE9BJcWOq+vbY8nOOLSGps1LutpHd4dYiIiH+b58HCL6W+f+Oe4GoREfHDWmelrF2fwuIfw84lrW0bn4DXzoHjX8laeSIiIiIiIp1Kw04of8S9besrCkYTERFoXQA1SN1xFlydC1xrjPmytXZeBo4jIiIiIiKp6LkffPYTJxjttXPaXtdPVrcxwdUlbfUYD6cug90roG6zs+3VM+NPit1X1QLY9AwM9xuwE8fH1yQORTv6MRh2mhPsliHVjRWu2/sVD8zYMRMpMsWu2ztFMBrA+K/DmIth+wfOIh0FRYBxfl5sivNbCoqg9wFQ3CPQUiUA/WbBmRvgwZ4QibNwb6QGlv4Ojn44vNo6io+vgWhD4n7Hvwq9p2keiF8FxXDoLXDgb51zn5a5NW99DfasdN/n2Oeh5TXeFEDvqQqeyFUf/wrGXqJwFRFJXt1W71C0Fg3bYdXtMPWqcGrKlg0BnLeZIijN3nszyWEjzoZhp8P2xVDUHXpOzOhnB3lv6ElwTiXs+Aienendb809MPG/wqtLRKRZPgaj6beOiOSfSB4Ho8U72Y8XehQkr2A0gI/+x3mTrzclIrln5S3ebbogJiKSmz79S3r7N8W58UREJGhN9c6NQmvv9u5TMd8JTeu5X3h1iYiIiIiIdFYf/ty7bcPDMOO68GoREZFcFHtjh/XYvq99ZzS79bUxbUOAZ4wx37PW/jX5EkVEREREJCOMcSahjv0KvP/D1MYo7AolvYKtS9oyxrm/ouUeix7jYds7yY1RMT/9YLRIHVS/Fb+PKYQhJ2V8DkFV4xbX7f2LB2f0uPEUGfepgJFoJwlGA2d+Sf9Ds12FhMUUOKEG6+6L36/ilVDK6VCaGqDqjcT9xn0NBs7OfD0dUZe+bf/uDvglvHlR+36lg2Hw8eHVJempWec8uiu0V0SSVP22v34rb+74wWhBnLsd8Mv0x5COq6AI+h2c7SryR0ER9J0BQz4Dm59x71MxX8FoIpIVeRWMZq1VhLaI5KemPA5GiyvFFXWS1WWAd9tHP4chJ0D/w8KpRUT8qd8Gi77l3V6sm1NERHLGjiVQ/ijsrYBNT/nbp+9BsO3d9tvjrcgnIhK0VbfFD0VrsXmegtFEREREREQybfdKWHFjnA4hLbgkIiK56svNX3sAPwf64QSZRYGFwCJgPbALKAH6AtOA2UDLbHML/Bt4FigDegP7N/cZRduAtOuNMcustS9l9E8lIiIiIiLJGX566sFopQODrUUSG3FW8sFom5+BxcWwaxlsfRlK+sGwU2HYaU7gi0kwLSzaCCt85FwPPQUKuyRXWwqqGytct/crzt7PY3FBsev2RtuJgtGk8xl+RuJgtIbtUP4EDP2MM6lfWkXqnNfn3Sug22ho3AUbHoHqhWB9XL8ZfmbGS+w0hpwMBV0gWt92u/6O88+u5QpGE5HkRff661dbDouvhB4TnPc+ZdkLZs6Y3SvTH0O/P0WCN+JM72C0DQ/B4p9Crykw7BQo6RNubSLSaelTHhGRMKy61X17Uddw6wiaDSkYre8MKOrmHbSx5m4Fo4nkmvLH4rcXdQunDhERiW/Do/DGec4NZX7Nfbb55jMFo4lIlq2911+/mrUZLUNERERERERw3qPFmzxTsxaa6kOZrCgiIrnHWnunMWYC8DhOKBrArcA11toNXvsZYwqAM4A/AGOAzwHLrLW/2qffKcCfgHE4AWlFwP8CMwL+o4iIiIiISDp6jIfpv4YPfpb8vof9I/h6JL4J34ZNz0Lla/732bnUebRo3AWf3ug8Rl0Ah98FBYXu+0Zq4dUzYcvz8Y/RdSQceJ3/mlIUsY1sj1S5tvUvHpTx43spMiWu2yMKRpOObPhZMPI8WP9A/H6vng6jLoQj7kocxNhZNGyHl09xQtBSMfYSJ8xLgtGlLxx8Iyy6tPW6Wq+pMPWq7NYlyXvlM/C53VDcPduViEg+iUb89136W+dr6SA45jnoMz0zNWVDUz1UvJLeGFP/G3rtH0g5IhJj9Bfg7Uu925de63ztPg6OfV5BsSISCgWjiYhkWuMe77bCsvDqyIiQgtEKS2HAbNj8rHv7ir/CrHgr0ItI6FbcFL+9yecKByIikjk2Cu9+N4lQNAOzboKhJ8Gaf7p3UTCaiISp6k1//eo2ZrYOERERERERSbxgio3C7hXQe2o49YiISE4xxnQFHgUmAo3AxdbaBDNZwVobBR4xxswDngGOAn5hjFlvrf1nTL+njTGvAa8ABzZvPsAYc4K1NsGMehERERERCdWUn8LQU+HlE2Fvhf/9+h+RuZrEXUlvZ5Lr1ldg+3tgm1rbdnyUOKBoX+vucybYDjvFvX3tPYlD0Y56AAYfDyV9kjt2CrY1VmE95kv0Lx6c8eN7KTLFrtsVjCYdWmEJHHkf7PcdqHwTPrjSu++6e6H/oTDxe+HVl8tW/C21ULTRF8P4b8CAI8GY4OvqzMZ/DQbOhq0vQ9lQGHSswrXy1Zo7ndclERG/fM/dibF3Kyz+CRzjMbc6H8V7Lzn6YjjkZifcdcvzUNu8vlK0AXZ8CF1HwNivQF+tjSSSEUVd4dgX4KXj4/fbswo+vgYOuz2cukSkU1MwmohIpsVbIam4R3h1ZMLgBCe2QSrL3sVDEUnSugdg2zvx+yg4R0Qk+3Yth9pyf30PuAamXNm6gl6RxwX4SJxQYBGRINVX+++7c5l3W7TJuXAKzs1FhaXp1SUiIiIiItIZ1WxwJkYmsmuZgtFERDqvXwGTcVbgu85PKFosa22NMeZs4BOgL3CjMeYpa21lTJ/dxphzm/u03Bd5IqBgNBERERGRXNNnOhx6B8w/1V//siFQ2CWzNYm7wi7OQppDT2q7veK15IPRADY+5h2MtvGJ+PsOOApGfi75Y6aoqnGLZ1u/4oGh1bGvIuM+FTBiIyFXIhIyUwADj3Yeq26FPau9+y65FsZ9HYrKwqsvV5U/nvw+A46CI/4VfC3SqudE5yG5o88M2P64iqP7AAAgAElEQVR+cvuUP65gNBFJTqphxpvnQaSu45zbxHvvN/ky589ZVAZjvxReTSLSqsd4f/02PgYoGE1EMq8g2wWIiHR4W1/ybhtwdHh1pGPi9z22h7iCSkm/+O0168OpQ0QSW/KbxH0UjCYikn3Vb/nvO+WnraFoAEXd3Pvp9V1EwrLrU/99d3wAD/aGxVdAU4OzrakB3v4W/KcvPDzAeTzYE148zpnQLyIiIiIiIv6VP+bdVlAMw06HyT+GHhPCq0lERHKGMaYI+GLzt/XAdamMY62tAm5u/rYMuNClzxrgQcA0bzoylWOJiIiIiEgIBh7tfQ/SvgYdm9laJHl9D4KiFBaJX3mL83nirhWwcylseBQ2PAIbn4KdS+LvO+i41GpNUXVjhev2noV9KCnIXlBfsSl23R6xDSFXIpJFiX4v7N0Cr57uvMZsfh4ad4dTV7ZE6qDyTajbAnsrYdNzzmvrhkeSu1e4hX7vSmc0+cfu23tO8t5nyzznHGb3SrA2M3WJSMcSTTEYDQtr74FNz0Ld1kBLyoqqBd5tvaaEV4eIuOs6ErqPS9yvvho2POycD218CioXtM7XEREJkPsyESIiEgwbhWV/8G4ffHx4taRj4vedD8RrY8LHxl4CvUJc0b1L//jtzx0KZ28OpxYR8daw3QmeSKSpNvO1iIhIe9Em53U62ggLv5y4f0lfOPYFMKbtdgWjiUi21W1Mrn/jTlh6nXOh5aA/wns/gJV/b9sn2uiEmz93MJy5CSK7Ydu7zgXWssHB1S4iIiIiItLRbPQIRus5ET77Sbi1iIhILjoK6A9Y4G1rbToXE+YBVzY/PxP4k0uf53BC0wwwPI1jiYiIiIhIJhV3hylXwQdXxu9X0gcm/yicmsS/oq4w7efwfgr/Nq+emfw+3UbB+K8nv18aqhq3uG7vVzww1Dr2VeQRjNZoUw1ZEMlDky6D8kehvsq7z5YXnEeLQ26G8d/IfG1h2/AwvPkFaKoLZrxuo2H8pcGMJZJPhp8G/Q6D6oWt23pOhjlPwBPjvfeb/1nn68A5MPth6NI3s3WKSH6LRlLf9+2Y90MTvg0H/RkKCtOvKUzRRnj7Uqgtd2/ve7Cz+J6IZJcxcMD/wIIvODkZ8bx2TtvvS/rA7Edg0JzM1ScinU7eB6MZYw4ETgdmA+OAvkAPwFpr2/35jDG9gZ7N39ZbaztANK6I5Kyqhd5t474OhSXh1ZKO7mPgxAWw7j5nFYOBc2DUee0DMjIp0Ypge7c4b4i76p5WkazatdxfPwXniIiEr2YdvHJq4pU1W0y6DPb7DnQf276tqLv7Pnp9F5Gw1Fentt/y62HcV2H1nd599lbA/cXOe96WCzmTfggzfh/u+2AREREREZF80LADtr7i3jbyvFBLERGRnDUy5vmmNMeKXTFvlEefZTHP+6R5PBERERERyaQpV0CvybDmbtjwkLOIY8M2p633NBh6Coz9CvTcL7t1irvJl0OPiVD+CNQ2L3C3ZV7wxxl8Ahz+r9AXtatudJ9u1r84u4vrFRn3OSgRm0bIgki+6TUZTnoLVt4KS3/rb5+3L4X+hzu/XzqKus3w+nlgm4IZb+ovYMI3tYiodE5F3eDYebDyFqh+G/pMh3HfgNL+cNzL8OIx8fevmO8Exh52ezj1ikh+ShhmbHDWGUpgxU3Qd6ZzT3w+WfE3WH2Hd/uBPs/rRCTzRl8AZUNh3f2w+1PY+pK//Rq2w6tnwFmbnFB9EZEA5G0wmjFmGnA9EPuO0s/MxGOAh5qf1xhjBltra4OuT0QEa+GT673b+80Kr5YgdB0Kk3+YxQJ8vMRveib0laBEZB+bnvXXT8E5IiLhe+sb/kPRxn4ZZv6vd7tXaG1kT/J1iYikoiHFYDSAp6f66GSd9/UtPvlf6H8ojPxc6scVERERERHpaHYua55w4zHhbviZ4dYjIiK5akjM8wSr4iXUcve0AbxmaG6Ped4lzeOJiIiIiEimDT/DeUh+Gn6a82hxbwYWnDvsjqyE9FQ1eASjlQwMuZK2ioz7VMBIwpAFkQ6m+1g48FqY+H14fCw01SXeZ939HSsYbf1/ggtFm/wjOODqYMYSyVfFPdznTvaZ4W//9f+GQ26BgsJg6xKRjiPqcc7e92A4eZHz/LnDoXph4rHW3pd/wWjr7o/f3n1MOHWIiD+D5jgPgMfGQM1af/s17oTNz8KIszNWmoh0LgXZLiAVxphLgIU4IWf7fmqeKAr3MWB9837dgHOCrk9EBGvhra86K1d50QrpySkblLhPtCHzdYiIt/LH4eNf+usbqWkbNCEiIplVvw22PO+//+QfxW/3DEZT8KWIhKQ+jWC0VJU/Fv4xRUREREREctWKm+GpKbDzY/f2riP8TxIQEZGOblfM8/3THGtKzHOv1VpKY577mBErIiIiIiIigRlxbrDjlQ1xHllQ3Vjhur1/cfghbbGKTbHr9ojVXArppMoGw4Rv+eu75DfQuAua6jNbU9AitU7d0Sbna8tj2e+CO0bQr98iHUlJL+g+PnG/SA3s3Zz5ekQkf3ktOhcbftzvYH9j7fqk9ZwgGlBQaiZFI7DjI+/2LgOg68jw6hGR5PSblVz/6kXO+xgRkQDkXTCaMeYc4HagLHYzsAFYTPugtDastVHg3zGbTg+6RhERqhbA6ju82wuKnQ/FxL/Bxzt/b/EUdo3fLpKrmvbCh7+AF4+FhV+Gbe9mu6LkRWrh1WRW7LNO8reIiIRj51IS54jH6DU5fnuhgtFEJMsatoV/zLX3hH9MERERERGRXFS3Bd6/nLifNw07HUzc2zdERKTzKG/+aoCxxphD0xjr4uavNmbcfQ2O6VOZxrFEREREREQkWft9O+Dxvgsm/KlvdU011ER3u7b1Kx4YcjVtFXkGo0WwWrRaOqspP/UXWgTwYC94sAe8MAd2rchsXena9Aw8NRUe6ObUfX9Rc/3Nj9oNwRyn32HJBx2IdDYTv++v3+o7M1uHiOS3aKP79th50+O+DoWl7v1i1W1sPSf4Tz9nTmouhhBVLYR/l8H9xRDxWvMI2O87UFDk3S4i2TXhO2AK/fdf+lvnfcwTE6H88czVJSKdQl4FoxljhgAt7wxbPq29CRhnrR0NnO1zqMdahgTmBFagiEiLTc/Ebx/ymXDq6EhK+sCYL8bvkyg4TSQXWQsvnwQf/wq2vgyr/+lcZKt+J9uVJefJScnvU18dfB0iIuIumdWnxl+auE9xd/ftEfebwUREAqdzSRERERERkezZ8HD8G1YBhiezmIqIiHRw84FGnPv9DHCTMSbple+MMZ8HTqT1vsHnPbrOjHm+NtnjiIiIiIiISBoGHQNznkpvjMKu0Hs6zPwj7H9FMHUlqapxq2dbv+JBIVbSXlFBiWdbxEZCrEQkh3TpB8fOg9EX+esfbYSKV+GFo3IzQARg23sw/zTYuSSzxxn3VTj2OS12I5LIxP+CWTdBn5nOuYqXD6+CXZ+GV5eI5Bc/wWh9DoBjX4BBx0FxL38haY07nTmpCxLMvw5bzXqYdzg07Y3fb8K3YOrPw6lJRFIzaA7MeRIGHg1FPZzzocKuYBIEGu7+FF49I//my4tITsm36NSfAy3vGpuA8621/4lp97u0xSKcm62KgX7GmDHW2jXBlSkind6Sa+K3l/QOp46OZtbfYdXt3u01a0MrRSQwFfOdi2qxIjXw6Y1w+D+zUlLSdq9KbbWh+mroMS74ekREpL09SbzlHXpq4j7Fvdy3N+2Fpnoo7OL/eCIiqVAwmoiIiIiISPYs+U389uKeMFBr1ImIiMNau8sY8yRwFs79fQcCzxljLrTW+rrQbIz5GnAjreFqUeBuj+4nxTz/IOXCRUREREREJDXDToEL95neda/PwJ1Dbobx3wi+piRVN1a4bi+gkD5F/UKupq3iOJOOI7aRYrTYvHRS3cfAEXdDnwPh/R/522dvBZQ/BqMvyGxtqVj1D7BNwY139OMw/LTgxhPpjCZ8y3kAPDMDti9277f6n3BgguupItI5+QlGAxhwJBz3gvM8UgsPdPM3fvkjULcVyrIb5vx/1tzlo5OBmTcopFUkHww92XnEWvZHeP+Hiff95Ho48p7M1CUiHV5BtgvwyxhTCFyAc3OTBa7bJxTNN2ttBPgkZtOk9CsUEYmRKIW7WMFoKSlIkOf54X+D9ZuRKZIjPv2L+/Y1d4ZbR4umvVD1Nuz4KP7/JxuFbe87KxF9dHVqx6qvSm0/ERFJXjIBskNOTNwnXtBv407/xxIRSVXjjuwcd+OT2TmuiIiIiIhIrmjcA3Ub4/cZegoUloRTj4iI5IvLgdjl4I8Elhpj/maMOdYY03PfHYwx+xljLjXGLAJuBkpwQtEscLu19iOXfUYAc2ldYPW1YP8YIiIiIiIikpIBs/31G3RsZuvwqapxi+v2fsUDKDCFIVfTVpHxDj6LWI+gBZHOZMjJifvE8go2yrYdQeb9G+g3K8DxRISe+3u3Bfr/V0Q6FBtx3x4n/JiirtBtjM/xo7BzSfJ1ZcrS3ybu02O87i8RyWe94pwTxdr6kvIfRCRlCRJmcsphQMsNUA3A79IcrxyY1vx8RJpjiYi0VdTDCRfyEi9IQtJTvQj6H5LtKkT82/BwtitoVfkGvPmF1vCcwcfDUQ9ASZ+2/Wo3wssnpf9BWUN1evuLiIh/e9b663f6Kijskrjfvr8bYjVsh9KB/o4nIpKqSE3iPtN+BR/9PNjjfnoTDPtssGOKiIiIiIjkk/d/lLjP8DMzX4eIiOQVa+0aY8xXgLtwFnO1QDfgG80PjDG7gN04AWi9mr+CE4ZG8z4GeAu4zONQV9C6WOxe4PlA/yAiIiIiIiKSmjFfhMoE2dX9D3cmxeeAzfUbXLf3Kx4UciXtxQtGa7QNIVYikqN6TYE+B/oPPFv2O2jYBpN+CL0mZba2aBMsvwE2PQP1lW3buvSDvgdBUz1Uvw3VbwV33CEnQtng4MYTERhzMay7171t09Owazn0nBhuTSKS+6IeQcYF3uf4gPOa8/Gv/B3jpeNg1Pkw/lIYNDep8gJRtdC5337nRxDZk7j/6IszX5OIZM7g46BsCNRtjt9v7xZ4ehqYQigscz4DmvIzKO0fTp0dSd1mWPIbqHoLovXt26ddDSPOCr0skUzKp2C0lk+3LbDIWrsrzfFi92+34qSISFq83qC28Er2lsSmXwsfXOndvuo2BaNJ/tj1afx2GwVTEL9Pumo3wcbHYfsHsPLvbdu2vACLfwqH/K3t9gVf8h+K1qUf1HsEoHltFxGRYFW+AZufSdzv4Buh+1h/YxbHCfpt2OFvDBGRdERqE/eZ9t9Q9SZsfja44/p5PRUREREREelIok2w4T9QuwG6jmh/LWFfBcUw5ORwahMRkbxirb3fGBMFbsG5X69lSeiW4LNezY92u8b0ew4431rrtXLCXcADzc/3xOknIiIiIiIiYRr3FdizGpZe697e/3A46qFwa/IQtVEW7HrRta1/jgejRTRPRQSMgaMfgxfmtC4Yn8iq25xrIScuyGyQ0VtfgTX/8m7f+nLwxxw4Fw6/K/hxRTq7oSfDgCOd+/TdzDscTlwIPfcLty4RyW2pBqNNvQrqNsHqO8A2JT7Ouvth/UPOOdGwU5KvM1UVr8NLx7sH9bgZfylMiTNXXERyX0ExHDMPXjsHdieYLx87J736Ldj0FJz8LhT3yGyNHUl9tXOeWbPOu0/D9vDqEQlJPgWjDYh57r70RnKiMc/z6e9BRHJd4x5oTBAG0T03VjLKSyPOjh+MVrcpvFpE0rXxyfjt710OB/0xc8ff9j68fFL71YZirf4HzPhd65vLhh1Q4fOC27RfwX7fhtfOhopX27fXVyVfs4iIJOejX8JHVyfuZ4pgv+/4H7eom7NKg9tFFQWjiUgYIj7nM3bJwAoyDTugJE5ApIiIiIiISEfR1OBMHKpe6H+fsV+BErdMGxEREbDWPmCMeRP4A3A2rfftWZfuJubrGuDX1tp/JBg/iV9aIiIiIiIiEhpTAAf+Bqb9HGo2QFMdFBRB6SAnHKBscLYr/D+f1n7k2da/OPt1FscNRvMIWhDpbLqNhDPWOIvIN9VC4y5n7sZHv/Dep2E7fHoTHPynzNRUsw7WBBxQdvK7rc+LujffU2ehxwSINoCNQukAz91FJE0z/gjzDnVva9gOK/6e2TlhIpJ/vM7X45zjA07w0KG3wszrYfcKwMIHP4u/eLiNwLLrwg1G++QP/kPRpv3SeX8oIvmv91Q4bbnznqe+Gp47xF+I4+4VsP5BJ0xf/Flzd/xQNJEOKp8CwWJvfioMYLy+Mc81a1tEgpMo0Rag38GZr6OjSrRSglY5kkQad8HS66BqIfScDJN+AD2yFFZYtzF++/LrYfIPoesw/2PaKHz6V1jxV9i13Nm2/09g/yvbT0T64KfxQ9HAuSD2YE/n+bivwoCjnWMk0mM/ZzUCY6Ckn3uf+urE44iISOpW3e4vFA1g1AXJjW2MEwrk9lqulQVEJNNs1LlhzY9+h8Dau4M9/o4PYeDRwY4pIiIiIiKSi5bfkFwo2ohz4cDrMlePiIh0CNbacuB8Y8wQ4FzgCGA60B/oDdQD24F1wELgBWCetdYtPE1ERERERETySWEp9JyQ7SriWlm31LNtQEn2g9GKFIwm4l/Xoa3PI3vAO/fQUfl65mqpfBP3tQFSNPh46DszuPFEJHndx8Zvr3ojnDpEJH9EPeY+F/iM+yjuDn1nOM/7HhQ/GA2gagFEm6AgiFgOH5I5lxp2aubqEJHs6DbKeUy6DJb93t8+la8rGC0ZmXzPKpLD8ikYLTa1Y6hnL/+mxjxXKoeIBKcliMjLgNnQe1o4tXRUfQ+Gbe+4t0V1MU/iaKqHF+bC9ved77e+BOsfgBMXQI9x4dcT2ZO4z8YnYfw3nACaFvE+kHr7m7Dq1rbbll7nJGefusS5oSAacYIkNj+XXL2rbncefky7urXmLv3d+zToFExExJO1bV/7k7Xmbnjra/76miKYcGnyxyju4x6M1qjscRHJsKY6/31HXeCERDZsi9+vdDAccTe8dHziMV+YAxdE03udFhERERERyVWx1yBW3+F/v3OqoUvfxP1ERESaWWs3A39pfoiIiIiIiIjkhMqGzZ5tk7vNCLESdwpGE0lR/8OhdBDs3erdZ9cnsOg7bbdtexdq10P/I6F0YOt2UwC9D4ARZ0MXl4Xkd6+CdfdDUTcY9XnYszqYP0eLEWcHO56IJK+0PwycAxXz3dur34at82HQnHDrEpHc5TX3ucD7HN/TiLNhya8TH++N82DYGTDyHOe8JEhVC2HTs1D5GlS/BZEaf/t1Gw19sv/eSkQyZPRF/oPRVt8BZcNgyIkwcHZm68pVteWw4RFo2A5DTob+h4CNQvnjznvKipeh+3jocyBseCjb1YpkRT4Fo61v/mqAGcaYYmtT+8TWGLMfMCxm04fpFici8n92feLdNuHbMMPnyZx4i9Z7t+1cEl4dkn82Pd0aitaivhJW3gwzfhd+PX4+7Fn0TecBMPJzUP2OczFu8HFw6G2tF9dq1sMrp3j/H9izGv5dFkzdiRx+N4y+oPV7twt9APVV4dQjIpJPbBQ+/G/nd1NTAww7DQ66AUoHJDGGdUIx/Rg4F6ZfAwOOTL7Wkt7u2xsUjCYiGeb3oik4N56c/C4s/BJUvOrdb9Axzjn2ye/ABz9LHCJc9WZqr50iIiIiIiK5quI1eO8y2PEB9D4Q9vuv+Nc9Yw0/S6FoIiIiIiIiIiIi0iFUNroHo/UvHkRpQUj3YsdRaLynAjYqGE3EW0ExHHYnvH6u9wL3TbWw4ib3Nq8J6Euvg+NehG6jWretvR8WXAw24ny/5Br3hYhTNfwsGHNJcOOJSOoOvhGenubd/uJcmPZLmPbz0EoSkRzmdb4eJ/zYU9+Z/vpteNh5fPpnOGZecPd2LP8zvPv91PY97J9OyKyIdEx9psMB/+PMj/RjyTXOY/pvYMqVma0t12x7H14+sXWu/UdXO+eXFa/A+gdb++2tcOYwiXRS+XTWsACoAyxQBlwQv3tc34t5vtVauzydwkRE2qhZ6759xDkw669Q1DXUcjqkpjjBaHu3QuUCJ1REZF8rb3bf7jd9OmjJBDqA80amZo1zwW3jE/D8bCf8JhqBZw7MjWDAox6CMRe13eYZjBbgxT0RkY7io1/Ckt84r5GR3bDuXljwRef13q+G7bDzY399j3859WCf4p7u2xt3pTaeiIhfkdrk+ncfDcfPh5Pfg3Ffg8J9blIt6QP7X+E873sQHPMsHPtC/DFX/zO5GkRERERERHJRpBa2vATL/ggvHA3b3nFWDN62yAmY9qvXlMzVKCIiIiIiIiIiIhKiyoYtrttP6ntuyJW4KzAFFHmEo0UUjCYS39CT4LSVMCPA+SN7VsHSmPEa98B7P2gNRYP05k0MPgHmPAUnLXJCRE58C456EIqyH9QoIkDvqc7/z3g+/iXUbgynHhHJbVGP8/WCFILRAIp7+++77V1YdVtqx9lXww5Y/BP//addDeO/AYfcAmdXwKA5wdQhIrlr6lVw6lI45FaYeYPzGpDIhz+Huq2Zry2XfHhVaygaABbe+U7bUDQ/hp/p/D3PvAH6HRpoiSK5wHuZiBxjra03xrwIfLZ506+NMY9ba3ckM44x5kjgUpyANYCHAyxTRMT7g6rY1T8kPdE4wWgAzx8BPSc6H/73GBdOTZIfNj+X7Qra8lppyK/dnzrhOdvfc0Jwsq10EAw9uf32Lv3d+ysYTUSkLWth9T/ab9/8rDMptd8sf+O0+UAsgwq6uG/XzVUikmnJBgy36DsDDr3VubFt3X1Q/Y5zDjv2y9BzQtu+g4+DsqFQt8l9rFW3OWOJiIiIiIjkqzX3wIIvBDNW2ZBgxhERERERERERERHJopqm3dREd7u2DSjJnc9Bi0wxkdjQpWYRr6AFEWlVNggm/j/44Kfe4STJ2vR06/Mtz8PeACfzH3QD9Nrfed7v4ODGFZHg9J4av91Gnfls474STj0ikrui7c/hAfAIPk6oIMn9Nj0F+/84tWPF2voyNO3117eoB0z7RfrHFJH802uy8wDY9j6svCV+fxuBLfNgzMWZry0XRBvbvpdMx0E3KMdEOrS8CUZr9mucYDQLDAPmGWM+a62t8LOzMeYY4CGgADBABPhDhmoVkc6q4hX37WVDQy2jQ2tKEIwGsGu5s5L9Ca9nvh7pGGwUTEG4x0w10CHWh1elP0ZQDr0Nirq1317Sz71/fZUTAmRMZusSEckXTXuhtty9bcmv4ehH/Y0TVjBaYYn79qaGcI4vIp1XuufRJb1hwrdgQoJ+vad7B6MB7FzaetOZiIiIiIhIPtm9MrhQNIAeid5giYiIuDPGFAOHAOOAvkAPwFhrf5XVwkRERERERKRTqmzY7Nk2sDi3gtGgrt32iBY1FfGnoBAGHecsXByE2g2w9j7AwLLfBTMmQNeR0GNicOOJSGYUlsKA2VD5mnef9Q/BmC8mH2KUDbXlUP2287zvLOg2Irv1iHQkXufrBcWpjZfsXNidy2Dt/e5tvSZD72lgm2Dbu7Bnrfc4yZxDlfRJqkQR6aB6T4PSQYlDpJdcCz32g74zU39tzFU2CjuXOPOQrIWG6uDG7joyuLFEclAevItqZa19yxhzP3A+TjjawcAnxpjrgQeAdrOvjTGFwFzg68DncALRaN7/T9batZmvXEQ6jfInnDd+bsqGhVtLR9ZjAuzdkrhf5RvOG/DuozNdkXQE9dVQOiDcYwYRjBa0ISc5K5Eka+BcGPZZ97YuHsFo0XrnA3N9SC4i4ojs8W4rf8z/OH6D0frO8j+mmwKPYLSogtFEJMOaQjqPjiYI5X5qCpzf0PEuuIiIiIiISMe34ZHgxiodDIOOCW48ERHpFIwxRwGXAycCXVy6tAtGM8acDJzX/O02a+3lmatQREREREREOqPKRvdgtGJTQs+i3JnQ7wSjtdeoYDQR/6b9AqrehMZd6Y9lI/Dmhantu9934dO/tN9uCuHAa50QNxHJfQf8Cl45FZpq3ds3PwPPHgxzn4auQ8OtzS8bhcVXtg94nHQZzPh98gFMItJeNOBgtGTVV8KbF3i3lw115gP5nZPkx/4/CW4sEclfBUUw/Vp466s4MT8edi2DeYdBz0kw95mOkw/RsANePQsqXgl+7J6TwZjE/UTyWF4FozX7KjARmIHzqtcbuLr50Wb2tTFmGTAGaDkjNM37GOBN4IowChaRTiLaBG99xbs9Vz+0ykf7/xjmx1lFIVb12x3nxFfSU7MufnvdxiwEo8UJwMmGqb9wLvAt/xO89/+S23fYqd5t3cd4t216EiZ8K7ljiYh0VIkCM3evgh7jEo/j9yLExO/56+fFKxhNN1eJSKZFPG4cCVrT3sR9lv0vTNFHjCIiIiIiksPKH4O19wFRGHEu9D0IFv84mLFLB8GxL+THyuYiIpITjDHdgFtwFkaF1kVOY3ndCb0EuBgoaB7rLmvtB4EXKSIiIiIiIp1WZYP74u0DigdTkENhIEXG/TPZiO7dE/Gv/2Fw0tuw9l7YubR9+4aHMl/DxB/AzD/CwKNhy/NQv83Z3m0UjDgHBhye+RpEJBiD5jqvKU9P9e6z4wN49/sw+8HQykrKxqfah6IBfPJHGDAbRpwZfk0iHU3gwWgBB+HUbQp2PIBe+wc/pojkp3Ffhh7jnQU9l18fv++uT2DRN+GYZ8OpLdM++mVmQtEARn0+M+OK5JC8uzvVWltnjDkJuB84ltYboQzO6pEtwWcGJ0Dt/3aNaZsHnGetbQqrbhHpBCpfjx9CUaZgtMAMOhZ6TIDdKxL3fePzMOq8xP2kY6vbDE9Pj9+nthz6HBhOPS0SBeCEab/vwgFXO8/HXAxLroH6an/7lvSFUed7t5cNgd7TYMdH7du2L066VBGRDivR74Xyx2DyZaxdtZsAACAASURBVInHqa9M3KdsGAxP8+Kk18WXpgb37SIiQfFaUS9oQ050VgWN5+NfwegLodvIcGoSERERERFJxoqbnZvEWqwP8Cb74WfC7Ie14qSIiPhmjOkJvAZMpXWB01gt9/a5stZuMMY8DZzW3Pd8QMFoIiIiIiIiEpiKxs2u2weUDAm5kviKjfuipgpGE0lSz4lwwC/d2+4N4fpHt9HOdZaR5zoPEclvvafArL/Bom9599n4ODTVQ2GX8Orya/0D8dsUjCaSPhtx3+4RfNwhdOQ/m4gkb+Bs59F9DLz7vfh9N8+Dhu1Q0iec2jJp/b8zN7ZeZ6UTyMufcmttlTHmBODy5seAlqZ9vrZoCUrbAfwe+J1C0UQkUHVb4MW58ft0hBOvXFHUFY57Bd6/HNbdl7h/3VYoG5TxsiSHrf4nNO6M32f+aTDgKDjoT9B3ZihlEdkTznHi6TkRRl0IU37auq1LPzhhgfN/bOPj8fcf8hmY+QfoOjx+v36HugejNexIvmYRkY4q0e+F8kcTB6NFamHxFd7tZcOcVe5mXg/F3ZOvMVaB+81VRBWMJiIZ1rQ3nOOM/Dx8dHX8Pk11sPzPzjmxiIiIiIj4s/0DePtS2LUcek2G6b+GQcdku6qOx1pY8uvMjD39Wpj8I4WiiYhIsh4CptF6b18D8ADwMhAF/uljjEdwgtEATgCuDLZEERERERER6cwqGzyC0YpzKxityLgvaqpgNJEAjTof1t2f2WP0PzSz44tI+Pol+H8dbYDaDdBjfDj1xNNUD427oLR5ev7uT737bn8/nJpSsbcKoiHdVyySrojH4uAF7uf3HUJH/rOJSOoSnTMBYKFqIfSe1rqpdFBuva407oai7u730DXscOaKRuqgzv3zpkAU5GVklEhS8van3Fprgd8bY/4CXIBzo9NRwFCgIKbrduBN4DngLmttglQUEZEk2Si8dFzifoVdM19LZ9J1KBx5L4y+0Am0imfdvTDxB5qc0ZlVL/LXr/J1ePFYOOUj6DYiszVZ6/1hVhD2vwIOvDb1/XtOgDmPwaOjoHZ9+/ZDb4NxX/U/XnFP9+2Nu1OrT0SkI4rUxG+vfA32VkDpQPf2PavhlVO99x99ERxxd+r17UvBaCKSLU314Ryn1yQYe4kTtBzPhv/AjN/rPaeIiIiIiB+1G2He4U7IMEDVAuc6zykfQ/fRWS2tw9m9wrmpPmgzr4dJPwh+XBER6dCMMecCx9MairYA+Ly1try5fZTPoZ5tGRKYbozpbq3NgRXJREREREREpCOobNziun1giYLRRDqd/b7r3BcWjfl/1WsqTP8NvHamM5csHf2P8BkGICJ5pe8MGHQsbH3Ju8+8w+D0tekvcp6qxj3w1tecRduj9dBtNHQfC9Vve++z6xOYd6QzF6H7mNBKjWvzPFj0bdizKtuViKQv5ZCfOPeuT/4xLPtdiuMGSIE9IuKm3ywYMNuZKxnPK6e0/b6oG4y6AA6+EQq7ZK6+RCpeg7e/7iwM23UEzPgDjDrPadvxESz4UnjBskavs9LxFSTuktustXuttXdYay+01o4EioH+OAFpXay1/ay1p1lrb1QomohkRMV82Lk0cb/C0szX0hkNPh7KhsXv895l8MLRTrqudE7lj/jv27gzuf6paqqj9Z7vfQycA72npz72ft9LLxQt1oiz2m8rKIbhZyY3TlEP9+2RXcnXJCLSUUV8zNt5eJAzgThW01547XPw+DjnoqOXvgenV9++FIwmItkSDSkYDeDQ2+GwO2D0F7z71KyFHR+GVpKIiIiISF5be09rKFqLSA0suSY79XRkfj5rSkW/WZkZV0REOrqfxjz/GDihJRQtGdbaLUBF87cFwOQAahMRERERERGhrqmGPU3u084GFOdaMJr7pNdGBaOJBGfAEXDM8zD0VOg5CcZ9HY6fD8NPg6OfgMEnQulgKB3k/ogVu73nZCd07ZjntBCnSEc15wmY+P+82+urYeEloZXTzsIvw/p/t96LW7M2fpBbi6o34cVjINqU0fJ82bXcWVBeoWjSUXgEH6dl3FfgiPtg4Nw45ys+zkVMoff++57zuO6vwB4RcWEMHPOMMxe+ZxKX/CM1sOo2JzciW2o2wEvHO+cj4Cxc+sbnofJNaNwNzx/tPxStdFD7+Zl+X19b6HVWOoGc/yk3xkwHTgT2xwk8A6gClgHPW2vbvCpYay2wLdQiRSR51kLlG1C3EWrLnXCHuubHQX9x0vHzRfU7/vrpA+vMKCyFQ2+F18+LP8Gj8nVYfAUc8vfEY+5cCpuedSbmDDgKBh6tf798V9wTGpMI4PITdpiuSK1320F/di6e/aev80YtWVOuSL2udmP9zEmv3v6e870phFl/gy79khunuKf79sbd6dUnItKR+H3Nf+e/4OiYEM+P/wc2PJR4v+Gnp1aXF69VaaK6uUpEMqwpxGA0UwBjL3EeB/0JHh4I1uWmjg2PQJ80wo1FRERERDqL7Yvdt6+6HWbdDAWF4dbTkbm9d0lXl/7Q77DgxxURkQ7NGDMEODBm03ettXEumCf0CTCw+fkEYFEaY4mIiIiIiIgAUNm4xbNtQEluBaMVG/dFTSMKRhMJ1qA5zmNfw05xHiIiboq6wkF/hMrXYJvHvNPyR52AtGTnZqWrvhrKH0ncz0vNOtj6Igw5MbiaUrHmLrCR7NYgEqSCDMR9mCIYfb7z8PLkZNj1SfxxBh0Dxz4fv8+9BYD1rkNExE1RNzj4T87zl0+Gzc/533fNXTDzeih0/3wko9beA9GG9ttX3wEDZkPjDn/jdBsDZ6z21/eNi2Ddve5tmfgdIpJjcvan3BgzE7geOCpOt2uNMW8Al1lrfSYTiUjOePnE9ivCg5PUnk/BaIt/nO0KZOhn4LPL4JkDnQ/ovKy9B2bd5Exs97L6X/DWV9t+ODb+G04QVLz9JLeVDUsuGC2VMLJkReOEORR2cd6QDT8b1t6V/Nilg1Ovq91YA+DEN2DrfKgrh4FzoMf45Mcp7uG+XcFoIiKtNj/rr1/5o06wcNdhTjjQ8r/426/bmNRrc7PvigQt3D7cExEJUrxz6Uzq0tc5H3ZbGa/ildDLERERERHJS+vu827bvQJ6TQqvlo4uE9c6Jv1Q4XUiIpKKw5u/WmCDtfbVNMeLXTQ15FljIiIiIiIi0lFVNmx23V5kiuldlFtvP4s8FjVVMJqIiEgO6TXFOxjNNjlhRAOODLemncvSX2Br7b3ZD0bb8WF2jy8StO5jgx/TT1BOrymJg9F6Tcl8HSIivaYmF4wW2e0EtvackLmavCz7nfv2VbdBcS//4/Se6r9vvNdSo3v5pOPLybMJY8wZwL1AKWBimlriYmO3HQW8aoy50Fr7aEgliki6jHGCivasbN9WuzH8elL18a+zXYG06DrcCS97/TzvPpE9ULMeuo92b99bBQu/1H77yltg1IXuK73kOmud5OONj0NxTxhzsZPS3tkkuwpEZE9m6ogVNxit1Pk666/QUA2bnk5ubGMS90lGYSkMPSm9MYo8gtEiCkYTEfk/a/7lv++jw51gsmRCyIL+/aBgNBHJlqYsBaMBDPusezDarmXh1yIiIiIikm9q1sdv37tFwWhBCvpax8C5MOmyYMcUEZHOInZlrw8CGC/2l1z3AMYTERERERERobLRPRitf/EgCnJsgfUi4z4dMBJN8p55ERERyZyxl8CaO73bnz+q7fcTvg0H/I+ziG+Q6rfBh/8NFa/Czo/TH2/Nnc5j1AUw9SrotX/6Y/pV8Tp88kfY+ER4xxTJtNJBMPDo4Mf1eM/Qxtgvw4b/xBsExnzRx8Gsd5OfOkRExnzR+R0f7/VkXy8c7dxv2GL0RTDzeigdkF4tVW/Bst/D9sXugbIN2733Xfl3/8cZ+2X/feO9lup1VjqB3PpkFjDGTALuA8pwAtAsbQPRWmZy25hHKXCvMWZyuNWKSFq6DnPfXpcnwWir74QPr8p2FRJr2OmJ+8RLMH84zsluMknDueSDnzlhbxv+A6vvgJdOgPXxPqzooCI1SfbPcDCatfHDHAq6OF+Le8Dcp+DsCjhzAxz1UOKxR10YTI1BK/YIRmtUMJqICAC1m5LfJ5kAsuFnJj9+IgpGE5FsiRcy3KLHfpk5du8D3LfvrXBuHhEREREREXd1m+H52fH71FeFU0tnkey1kXj6zoLjX4ZCj8+DRERE4otdFnlXAOPFhqHtDWA8ERERERERESobtrhuH1A8JORKEisyxa7bG63u3RMREckZg+bCYXf477/iJicsLcjFg6ONzpgrbgomFC3Wuvtg3pGwe2Ww43qpfANeOg7KHwnneCJh6DMDjnsFCkuDH9sUJu4z7FQ4+K9Q4hLIWDYEjnoQ+s5Mr44CBfaIiA99DoDZDzuvPX7t3edznLX3wLzDIVKXeh3V78CLc51Mhj2roGZt+0c8fu7XK+kDB/0JRpzlv654r6V6nZVOIBd/yv+OE3QWG4bWCLwDbGj+fjhwEFBC23C0m4EMxOKKSEZ0He6+vTYPgtG2vgILL8l2FbKvwi5wxjp4bJR3n51LYOjJ7bdXvR1/7PrK9GrLhoYdzQnJMWwTLLkGRp6TnZqyJVKbXP8tL8CbX4RZN0Jxz+DqWHkrLL/BCb+Jt4JHYZe237ckVHdzX4msjSEnpV5fJnkFo0XroalBk6lERGrXZ3b8kecFP2aB+81VRBuDP5aISCw/N31M/01mjt1zknfbzqUw8CjvdhERERGRzmzVPxJ//rFzCXBuKOV0CkEtAlPSF45/JZixRESks4pdMrmXZy//hsY812oFIiIiIiIiEojKRvf7tAeU5E8wWsTq3j0REZGcMvYSWPcAbH7GX/9dy2DTUzDi7GCOX/6YM2amNO5w5srNuC5zx2jxyQ2JF3A/6iHoMz3ztYgEobg3lPbP3PjGZ4TIft+GCd+EmvVgI862gi7O/H9jwqtDRGTEmTD8DKjbCE0x66O9/nnY/p6/MfasckJUR1+YWg2f/qXtsYMy848w7DQntLLbKDAFye3v8TmQ06bXWen4cuqn3BgzFSfYzOIEoFngf4HfWGu379O3N3AlcHnM5iONMQdYaz8MqWQRSUfZMPftdTkejLZzGbx4TLarEC/dRkKvqd6rGGx5ESb/sP329Q/EH7dhR/q1hW3zPCd0al/bF0Pjbu+gqo6oyUfK8r7W3gU1a+CE14KpYc3d8PY3Wr9vjPMzVdDFfXu8EAaAnpNh1PnJ1xaGeAFzla/B4OPCq0VEJBfVbcrs+EFdHI1V4BFqmeiCo4hIutze58QacCQM/Uxmjl02FIp6QGR3+7bNzykYTURERETEy7Z3E/f56GqY+vNgbqwUaAwoGO2EN6CoazBjiYhIZxW7Et2UdAYyxnQBDozZVJ7OeCIiIiIiIiItKhs8gtGKB4dcSWLFxv3ePQWjiYiI5KAeE/wHowGsvjO4e/8rApoTF8/mZ8IJRqtakLjPwNlQOjDztYjkg4IkIkRMAXQfnZk6FNgjIskwxglmjNVriv9gNIBVt6cejObnfCMV/Y+EHuNT3z/ea7peZ6UTSDJKMOPOaf7aEor2PWvtj/YNRQOw1u6w1v4E+E5Mf4AMzPYWkYzo6hGMVrMu3DqSYaPw1P7ZrkISiffh39aXILJPSJa1UP5o/DHjhVjlKq9wOMh8+EouiTY6j1RUvg4rbg6mjlW3++/rFYyWKMzuM+9BoUdITbYVxal91W3h1SEikqtqMxgOPOx0KPT43ZIOBaOJSLbEC0ab8QeY+0zmJu0bA/0Pd29L9L5SRERERKQzK3/EX7+K+Zmto7OwFt77Qfrj9J4GvRIs2iIiIpJYy13KBhhtjEnnl8s5QMsFigiwMJ3CRERERERERADqo3vZ2dRu6hoAA0qGhFxNYkUek14jNhJyJSIiIpLQyHOT67/xcfj0Jmjclf6x480tDMqOj9pv2/gUvHsZLP091GxIb/ztH8DiK6AuwXyLgXMViiYSK1eCcpIJaBMRcTPyc8n13/oSvPlF5/Ha5+Cx0fDyKc4c/yaXuUjb3oMProI3LoLdKwIpuY1uo6HfwemNEe81Xa+z0gnkWjDarOavFlhorf1roh2stX8H3sC5cQrgkAzVJiJB6zrSfXvNWqjbEmopvr16VrYrED8mft9JKXcTrYctL7TdtnMJ7FkVf8wG9wudOc1a77ZtSaQj57t9g/CSteib0Lg7/ToqXvHXzxRBQaF3+9BT3bcf+FsoLE26rNCUxVktrUr3qouIJLxQl47Bx2VmXK9gtKa6zBxPRKRFk0cA49hLYPIPEwcKp2v4Ge7bd34Mu1dm9tgiIiIiIvnK7w3I5Y9lto7OYv0D6Y9RWAaz/p7+OCIi0ulZa9cAsR+cXZnKOMaYLsDPWoYFFllr07whQERERERERAQqG7znrwwszsVgtGLX7RGb4mLiIiIikjkDjoKpv0hun3e+A8/Phvrq9I4dRjAawJ7Vrc8XXwHzPwvLr4fFP4bnZsGOJamNu/FJeO5QWHpd/H7dxsAhurYtnZAx3m25EpRT4P7eRUTEt2GnwsQkFwhde5fz2PAQ1KyDzc/AW1+Dl0+Cpr2t/dY/6JxrLPk1rLs32LoBuvSDI+7xzrzwK95req4EYYpkUK4Fo02OeX5nEvv9K+a5lisWyRf9Znm3Vb4WXh1+LfqOk7gvua9LX/hcnCCrqgVtv694NfGY296FaFN6dYWtYZt325sXwqbnwqslmyK16Y/xZJqnF/FC6vZV2CV++9gvtd9mCmHUhcnVFLaibt5tNWth1R2hlSIikpNqN7lvT3fVIlMII89LbwwvXhco6jZBVCtPikgGRV1WaQEoSHAuHZThp3u3KcRBRERERMRd7wP89Vt3f3KfqYu7DY8k7jP2EmexIS+nr4YBRwRWkoiIdHotF4QN8AVjjMuFb2/GmALgVtreX5hw0VURERERERERPyob3e/fK6CQPsUDQq4mMa9gtEbrsdigiIiIZI8xcMDVcMqHye2340NY9Y/Uj7u3EvZWpL7/8fPhxLdg5g2J+77/E+drzQZY+rt96tgKS69N/vjWwuKfeN8z3GLuM3DqEug5MfljiHRkuRKUkyt1iEj+MgVw0PVw2gonZOzQf0Dv6amNVTG/9b46G4X3fwTWxxzIWX93jtvyiKelz9xngrv/Lt5raa4EYYpkUK4Fo/WOef5eEvu19DX7jCEiuazrMOg+zr1t09Ph1pLI8hthxU2J++33Xfftfj4AkmAVdYUR57q37btaQs06f2NuzLMJ7pufjd/+9jfaJht3VJEAFoiu2wTbP0h9/2T+nhOFOYw4F6Zd3fpGprgXzP4PdBuRcnmhmfrf3m2LvgWNcQINRUQ6uvpK9+29pqU+ZkkfmPMklA1OfYx4Ckq828ofzcwxRaRzqlkH7/0Q5p/h3DDRuNO9X1jBaF2HQ1+PsPf3Lw+nBhERERGRfGN9Lj6zdwvsTHG1aGlV8XLiPlN/ARO+7d7We3rmPlMSEZHO6k9ABWBx7vG73RjzG2NM10Q7GmP2B+YBFzXvb4GVwP2ZK1dEREREREQ6k8qGLa7b+xcPotAUhlxNYsXG/d69iJ/JvCIiIpIdvac59/cnI9HcwHjSve5e2BX6HwKT4iy21WL3p87X8sdwPsLfx9p7kj9+bTnsXBq/T+lgGHoyFJUlP75Ih2DiNOXI+xgFo4lIUHqMh9EXwrgvw9SrUh9nXfNtBrs+9ZcvUdwLJlzqHLfl4TVXvmxIa5+hJ0Nxz9TrjBXvtVSvs9IJ5NpPea+Y59WevdrbHvO8R0C1iEgYBs6BPavab19zF0z+MfSa3L4tbOsfhHc9As9iTfi281j9j7ZBTCV9YcTZmatPvJX0ct++b1BWnfsKT+2suz9//i2rFsKe1fH71K6HLS/CsFPDqSlbmmqDGWft3dAnxRRpr+AGN4UJwhyMgWm/gMmXw5410HNS/iQ6dx3p3Rath/LHYcxF4dUjIpJL6qvct/faH7a+mNxY034JYy9xPkwrcF8ZMhBxg9Eeh5EeIbUiIsnYvQrmHd4aILnxce++ic6lgzTiTNi2yL1t+Z9h4vfCq0VEREREJB/4DUYD57y/99TM1dIZJFr5e+ip0H2083zQsbD1pbbtEy7NSFkiItJ5WWtrjTFfAp7EWcy1APgJ8B1jzNPA+tj+xpjPA/sBJwKH48zsaJndsRe4wFrrMrtKREREREREJHmVjZtdtw8oGRJyJf4UeUx6jdjGkCsRERGRpAz5DKy713//rS/Bir85c8f6HZZcANiOj5OvL1Yy9+TuWQ0rb4EPf+bdZ9F/QY8JUNwD+s50FpDf+TFsexdsBGo3OfcVdB8NZcNg01OJjzv0FP81inQ2Jk5oWpjyZd6riOSXwcc7gWCpBMRvfBx2/n/27js8jure//h7dleSVVzl3ns3pts0B2x6MaYGCEloCSUhjSTkF3IhnRsCCUlubgoJkBB6B5tmbAOmmG7ce+/dkiVb0u7O749BV21mdnZ3Zov0eT2PHmvnnDnnK1lazc7O+cxSqFrnrX9hp5bb+l0Mi37RcvuAK5Kvxwu351I9z0obkGs/5aFGnydxZXSTviHHXiKSe3qdbgWJNWfGYN0jMN7moCCTYjXw9qXe+h75O+uEz+TX4bOfwL750OVoOPwuKO0XbJ1iL1xqv339ozDxfgi3sx57vXvChif9qSsTFtzhrd/m6W0gGO2gP+MsvRt2f2D9rnc5Krl9kwlGC7Xz1i9Smn8LwxKlW2+ZoWA0EWm7nILROo2FsqFwYJX3sUr6QqlLGKVf3ILR1j0Ex/87+BpEpPVb/oeGULREQhkMRus7DT5zuIBjwR0w/ObceUNZRERERCQXJBWMNh3G/Di4WtqCSHuIVtq3FXSEExu95zXpefjkO7DlZSgqh6E3wLAbM1OniIi0KaZpvmoYxk3A/9JwjV97oPmFOQbwSLPH9SFoUeBa0zQ/CbJWERERERERaVt21DoEoxXkajCa/Q1TFYwmIiKS48beZoWdHdrmfZ8Pb7L+7TASTn654QZYiexfnHR5TbitFWguegA+SHDzrZV/Tq+e5or7wOhb/R1TRPxnKPZDRAJQ2AmOuAs++V5q+88Y7b1v2GbNf+fDYOjXrWDYemVDYcS3U6snEYeA/IRtIq2EfspFJLv6ToOibvaLnHd/kPl6mvvo5sR9jAhMeq4hBb/rRJjyerB1iTcRh2A0gPe+Cic+Dod2OQeR2IlHcz891zRh9/ve+lauCLaWXBA75Nw2bSM8l0Rw4Y634JWjrQVD7YdDxzEQCife75DHIAdI7o4a+aagfbYrEBHJXU7HI0Xd4LCfwbtX0rDmJwG3YyA/JfNmp4hIqlb8yXvfTB5LdxgFpQPt7xJTtw8OboGSPpmrR0REREQk18WTuDvj7g/AjOviyHSE7BfFAdZ7I43vJF5QBhP+EXxNIiIigGma9xmGsRp4GOhB0zc/Gn/eOAzN/PzxLuCLpmnOyUStIiIiIiIi0nbsrHMIRivsmeFKvIk4nANWMJqIiEiO6zgazvwINjwJ22bBttcgXutt34pl8OktcNLT3vrvX+TcNvhqa13cop9D7KB9n0zerDhZR94LAy6F4twMsRUREZEMGPld6HIULPoF7HwbyidC2SCrbc0D/s0TsglGAzjmr9D7bNgxF8oGQ/9LoV1X/+ZtzO06SgWjSRugn3IRya5wEfQ5B9Y82LLN60mdoNTuhdX3uffpdxGM/Ql0PjwzNUly3EJBNj4FB7c2TeP14sAa6DA8vbqCdmg71O331nf7bIjHvIV75Su3E7QlfaHDCKhYntyYb19i/duuJ5z0DHQ7zrnv2ofgva94HzuXTxynK6JgNBERW7FD1l2S7BSVQ/dJ0K4HrP239WGErMXBTjIWjOaywFZExA/JBCdAZo+lDcM6J7DsHvv2yhUKRhMRERERacyMJdd36d0w+ofB1dOamSbUVdi3Hf+obmIiIiJZZ5rmbMMwhgI3At8E+jt0NT7/dxfwv8A9pmlWZqBEERERERERaUNq4zXsi+62betekJthGxHD/tq9uriC0URERHJeSR8Y+R3ro15dBTzZMfG+m16w1h6EHQI66pkm7HMIRjvmLzDsBuvz9Y/Bvs/s+zW+WbERATPJa3qD0u9CGPntbFchIiIiuaD7JJg8s+X23e/D/iX+zBEutt9uGND3fOsjm0KKjJLWTz/lIpJ9pQMdGlzCHjJh8wz39on/gsFJhB1J5rmFgphx2PMpLP9DcmMe2pH7wWgVS5Prv/IvMOKbwdSSC2KH7LfXnwSe8ADMPhVi1dbjSHuIeryW+tA2ePNcmLYJIjYvbvYtTC4UDZqeOG5tEi62MhK0i4i0UjX2F1UBUPT5nQJ6TrE+jvuX9XjzdHjzPPt9ImX+1ufEKXxURMQv+xYk1z/Tx9KH/dw5GG3WZCtEufc5EC7MbF357uB22DIdMKzQ76Lu0HUCtB+a7cpEREREJB3JBKMBzL/VuqC5rR8H1lXA1ldh32Lr8+4nQc9T3c+3x2ucL0ov7RdMnSIiIkkyTbMKuBu42zCM4cCJQD+gHCjECkPbDrwLfGKappmtWkVERERERKR121W33bGtW2FuBqMVOASjRU0Fo4mIiOSlgg7QcSzsdwgzq2dGYdd70GEk1FVa7xsXdIRISdN+B7dC3T77MTqOafjcCDnPFWp07evhv4FPb3GvLVO6HpftCkRERCTX9TjVx2C0HF/zb4SzXYFI4HIxGK3+IqaJhmEM9LhPz8YPDMM4iSSSNUzTfMtrXxEJgsMJlB1Z/tXc8YZzW6S9QtHygVswGsAn34HavcmNGT2Qej2ZUrkquf4bn2zlwWgOoS31wWjdjoPz18Gm56yTtr3OgFcnQPUGb+PX7oGtL1sLtJrb8GTy9YZy/EVSOhIFo61/BE54ODO1iIjkkppdzm31wWgtuL0Jmam/JS4vu93er5MFkAAAIABJREFUJBUR8WL/MnjlqOT2yfSxdKQEyo+F3R/Yt8+9ELocbd2BprBTZmvLVzvfhTlntHztHS62wkH7X5KdukREREQkfckGowGsexjG3eF/Lfmiaj3MmgIHVjdsW/576DgavjAdygY57Ofy/kZBB39rFBER8YFpmiuAFdmuQ0RERERERNqmnbVbbbeHCNGloFuGq/EmomA0ERGR1mf0rfDelxP3mzW55baBV8KEfzQEd2yf47y/52C0RtfkDrwclv0ODm5OXF+QinvDIK3pFRERkQSG3Qhr/wV1+9Mfy8jFSKZGcr0+ER/k6k+5ATyaxr5vJNHfJHe/DyJtQ8gliXTvZ9B5fOZqqWeasPqfzu2DPJxkkuxLFIxWuTL5MaOVqdWSSXUVyfXPdghh0GKH7LeHixs+b9cNhn6t4XH3SbDuP97nmHsRdD4c9s5PrcYmdbVLf4xcFUkQjAZQtRFK+wVfi4hILolWObelsljV8JwTnp6uE53bzDhUrIAOwzNTi7RuB9ZYr892f2gtpC8dCAMug16nZbsyCdKC/0p+n8YXa2RKl6Ocg9EA9nwET3WG4TfDqO9Daf/M1ZaP3r/WPpA8dhA+vBH6nA/hRnchPLAWlt5j3dmwxykw6Cr380x+MuOw6j7YPhuKe8GQa6HTuMzMLSIiIpKPUglG2zHX/zryyfwfNw1Fq7d/CSz6JUx0eC9zyW+cx/Rynl5ERERERERERESkDdlZZx+M1qWgm2MAWbYpGE1ERKQVGnQlxOvg/WuS33fdf6CoHI6613r83pX2/Yp7Q1GXRhvcgtEaXatZ3AtOexuW3Al7PoXaPVDY+fM1jGbLfVNZs5nIkOtg7O3Qrrv/Y4uIiEjr0nHk58cud8G6h9IbK1PrNFMVUlSStH65+lNuYgWcJbtPvRx/dhGRJuJR57aNz2QnGG3+re7tw7+RmTokPYmC0Zx0PgJqdkP1hpZtdXkQjBavyXYFucUxGM0lgGzEt5ILRgN/QtEA2xPCrYWXcJ/n+8NldXoxJiJti9PfKmh6p6XG2rncibKgU3r1eFWQYCHtzONhyhvQaWxGypFWqmIlvD4JDm1run3NAzDxfhh8VVbKkoDFDsGmZ5Pbp+No98DGoAy5Dlb9PXHIw4o/WQF/Z38G7YdmprZ8U70ZKpY5t9fshp1vQ8/P73a4fym8fETDa+B1D8P2N+D4NN+48mre1bD23w2PV98Pk1+HrsdmZn4RERGRfON0zNx3Gmx6zr5t+yyoOwAFZcHVlcu2vuzctvkF57Y19zu3pRLCLyIiIiIiIiIiItKK7azdZru9W0GvDFfinVMwWpw4MTNG2MjQTeVERETEX4OvSi0YDazrGY/8HVRvdO7T/AbEbkEfoWbHG2UD4di/JVfTIz4t9T/8NzD6h/6MJdKqKE5DRMRRp7Fw/L+tD4BoFTw/EGp2ZbUs3xlaiy+tn0ucc9aZSX6ksq+I5AIz7ty2f3Hm6qi3byEs/a1z+/g7rQXXkvtSDUbrf6lz0Ec+BKO5has4iVb7X0euiB203x4udt6n/BiYPCuYehIp6JideTMh7BDu09zOd4KtQ0Qk17iFeDq92dj5SPu7HZX0hw4j/KstkeMfcW6r2Q3L/5i5WqR1Wvm/LUPRADBhwe0QTxBGJcnZ+S7MOhWe6w+zT4OKFdmpo2J54qCxekYE+l8CU+ZkJ1y3y5FwzF+99Y1Vw4vD4NGI9YbKpz+07u4nlupNifscWAWf/cS6WGbG6JbB4Ov+Y7XNmuxjeLWN/UubhqIBRCvdzyeJiIiItHVOx/i9zoR+FzvvN/fCYOrJdbFDULvXub1ml/1r4kSvkxMF3YuIiIiIiIiIiIi0MTvrttpu71aYu8FoBQ7BaABRU9eiiIiI5C3DgG4npbZv7V44uA32L3Hu0+WoZhtcIgbcQtO8Gv7N9McA6HSYP+OIiIhI2xUphVGpBK3meAilgtGkDci1n/INKLBMpO0p6e3cdtD+TabAmCa8lOBEyeCrM1OL+CDF/M/h34RNz9m3RQ+kXk6mxGoS92muZhdE+vtfSy5wC5tx03MyXGHCoV3wTDf/63JS2Clzc2VD/0thwxPufdY9BD2+kJl6RERyQdzhb1XI5W9VKAxjb4ePmr1ZOO52f96E9Kqws3v76vtgwt8zU4u0Tm6BqdUb4cBq6DA8c/W0ZlUbYM4ZDa95qjfCK0fD1NXQLoPHwwC75iXuc+JT0P8i63V8Jp/37Ay9Dur2wac/8NbfjEHVeitEq3ojnPBosPXlC7fg/HofXO9trO1zYOaJcM5SKO2XXl121j1sv33jUxCrhXCh/3OKiGTagTVwcDsU94TSgdn/eysi+S8etd9uhGHMj61jKTvbZlqht50PD662XFSzO3GfaEXLczNV69z3aX5XbxERkQwyDCMEjAXGA/2BbkAx1vWCB4EdWNcPfgYsNk1T1xGKiIiIiIhI4ByD0QpyNxgtkiAYrYgE18mLiIhI7hpyHeycm9q+VeuhYpn72I0ZKa699GrwVdZNsr1cH+qkdAD0PNW3kkRERKQNG34TrH3QPUi2hVy4ftqlhlCuRUaJ+C+nfspN0xyY7RpEJAvcTkxkOoRq20z39q7HQXGPzNQi6YuncLejHlOgoAwK2tu3RyvTqykT4ikGo5W21mC0g/bbw8Xe9m/XFbqd4B7K4aceUzIzT7aM+kHiYLTV/4Txd2Y+gENEJFtSDfEc/g0o6QcbnrKC0vpdAn3O9r8+N5GSzM4nbY/TsVy9imUKRvPLol+2fA0erYR1/4GR381MDWbcChdb9jv3fic9C/2mWZ/nSkjLqO9Dp/Ew5/Tk9lv/GETaw7F/Df4Ck1zn9zmgaBWsuR/G3eHvuAAbn3Zue6IEjr0PhihYX0Ty1MFtMPMkOLCqYVvpQJg8E9oPzVpZItIKmDH77aEIdB7vvu/Kv1rHzG2Jl2C02v0tg9HcLnLveVp6NYmIiKTIMIxJwPXAWUBHj7vtNQxjBnCfaZpvB1aciIiIiIiItGl18Tr21O2ybetW2DPD1XiXKBhNRERE8tigL0PtblhyFxzalty+H98Mez52bm8/pOnjoK9b7XKUdc3vJ7c0vRapsUiZdf1ouARi1Y1qC1vr+Sb+S4EfIiIi4o9IKZzyKsy7Fna8AfHabFeUPkPHSdL66adcRLKvbDB0GAUVS1u2HdyS2VrW/se9/ch7M1OH+KPLUVgpuEncRLjbCda/EYdgtLo8CEaLpRCMtnkGdD4id0IF/JRq2ExjR/4e3jjbCpDzQ1E5nLcSPvo2rHuoYXv/S6D/xf7MkavKj4YxP4bFv3bv9/F34ISHM1OTiEi2pfO3qu9U6yNbwh6C0eJRvRkpqUt0krliGZDF34HWIh6D1ffZt7ldIOG3VfclDkU76o8NoWi5ptdpcN4qeDHJ0JjV90FhRzjit8HUlS+iVf6PufHpYILR3M4NmDF4/xrY+goM/BL0OgPCRf7XICISlHcua3khYtU6eHEY9DoTCrtApzFQ3Nt6jivulZUyRSQPOQWjGWHrYutj/wYfXG/fZ/eHwdWVq7y8H1G3v+W2qvXO/dtauJyIiGSdYRijgT8Dk+o3JbF7F+BK4ErDMOYAN5mmucLnEkVERERERKSN2xPdgUnctq17Qe8MV+OdgtFERERaMcOwbmg84jvWOoNIMTzi8fS62zW/A6+0myylEpNSv94hetD6Wsw4HNrZ0N6um3XNQDxqtYUKoGanFZimm7iLiIiI30r6wuRXrWOTugprW7gdPNUpu3WlSms2pQ0IOM5ZRMSjo/9ov71mV2ohT6mo3tQ0oKi5gVdC12MzU4v4o11X6HZicvsM/JL1b0EeB6PFHcJV3Cy8HT76hnUCsbVxDJsp9j5G+TFw9kKY+AAc85f0a5q2GQo7w3EPwpQ34Ih7YMpsOP4R6wRuazf+V1A+0b3PhseanugWEWnN/PhblS1e3mys3Rt8HdJ6xRO8HqxYlpk6WrtKl/WE6zIYVrv+Eff2/pfAiJszU0uq2g+BC1M4jl16t/XRlgURjLZvIWye7v+4Xmrd8AS8dT7MPAFq9vhfg4hIEGr2wI43ndu3vmL9vf7sNph3NUwfDdtd+ouINOYWjAYw9OvO+1YuBzOJm+C0Bl6C0Wr3tdx2cLN9307jrJtViYiIZIhhGJcCH2CFohk03NWu+Uc9u7b6/SYDHxuGcVGm6hcREREREZG2YUftVtvtBgblBT0yXI13EZfrzeviCkYTERFpFQzDChIDOPy/0x+v/fCW24bdaN+3uE/68zVX/7UYISju0fBhfB5zEIpAuND6utt1VyiaiBejfmC/vf2wzNYhIpKPIsUNxyORUud+RgaCZBNxq8FQMJq0fgpGE5HcUOxyN51D2zJTw4c3ubf3nZaZOsRfJzwGpQO89T3uIejw+Um+SJl9n3UPwRvnwvvXwTtXwCvHWHddeP0UWPFn6+4E2ZZqmODKv8D2N3wtJSfEDtpvD7dLbpzinjD4Khh2A/S7OPV6hnwNwkXW50YIenwBRn0PepzStpKZO9icUG/MjAcTYCAikoscg9GS/FuVDWEPbzh6Wcgr4iRe696+5gE4mKHXjK3ZwS3ObSX9MlfHjrfc2wdcnpk60tWuK1y8D/qen9x+n/4AZoyFJXdZd59pa6IHghl33lX+fz+TCSTf8zEs/rW/84uIBOWg/cIPR3X74IOvQdwh7EhEpLFEwWgAJz5l3ydaZd3gqC3xcj6l8XuoG5+Dedc4H3t2GO1PXSIiIh4YhnEJ8AhQQtNAtPqgM4CdwApgHlaA2kpgV6M+jfcDKAUeNQzjgsx8FSIiIiIiItIW7Ky1v2amc6QrBTl8s+sCw7m2qKlgNBERkVan/yXpjzHg0pbb+pxnfzP3IdekP5+IBG/AF+0DcZxCD0VExF4+r+3P59pFPFIwmojkBrdgtIPbM1PDno+c2wo7J7+gWXJDSW+YujZxv3OXwaArGx4XtHfuu2UGrP4nrH+04edmxxvw0Tdh7oVgms77ZkI8xWA0gM0v+ldHrggibGbYjTRcr50EIwKjvp/6vK1Jl6MS99n1bvB1iIjkAqdwlXwIRvNyJ6YtLwdfh7ReiYLRAF4aCxUrg6+lNavd49xWtz8zNWx9LXGfjmOCr8MvhR3hpGdh2iY45VU45TUo6Zt4v/2LYf6tMOsUiHn4+W9NolXBjFuzG7bP8m+8aLXz60wn6x6ywp9FRHJdKoskKlfC7vf9r0VEWh8vwWg9pzjvv3Ouv/Xkuv2LEvd55zLrZjmL74S5F1jh4U5KArijt4iIiA3DMEYAD2Bdl9g4EK0CuBc4B+hqmmZP0zRHmaZ5vGmaE03THGmaZg+gG3Ae8EegkqYBaRHgX4Zh6Db3IiIiIiIi4ouddfY3ZOxW2CvDlSQnomA0ERGRtqVsMEx0eT/Yiw4jWm4rKIMvvACRsoZtfc+HMbelN5eIZEZxTzjxSQgVNWwb+GUYfnP2ahIRkcyyC8gUaWUUjCYiuaGgg3NbLKCFsY3FY3DIJYDt7AVKTM1nhgHdv+DcXj6h5cm9ou6pzbX5Rdg2M7V9/eK0QHvk9xLvu/xef2vJBRuesN9ud0cLr3pOhpOehrKh9u2DvgonPQOdDwcjZC3s6nwkTH4dOgxPfd7WpN+FiftULA++DhGRXBBEiGemFJYn7rPgJ8HXIa2Xl2Comt2w7HfB19Ka1ex2bqurgLoDwdcwN8HxYXEvKBsSfB1+MgwrfKDX6dDrNDj8N9733f0+LLy94XE8CsvuhRdHwAtDYMHtwQWJZUuQX4+fIZ01u5Lf59AO2DvfvxpERIIST3GRxMwT4PFiePkIWPeYvzWJtEa1+2DetfBsX3h1Aqx9OLn9Nzxt7fd4CTzWDp7sBLNPg30egrSyyUswWmEnKB1k3+/dL8Ghnf7Xlau2vOKt3wfXw2c/TtyvWMFoIiKSMf8DlNAQiGYCPwP6mab5PdM0XzZNc6/TzqZp7jZNc4Zpmt8B+gG/+HyMemXAnwKrXkRERERERNqUnbVbbbd3K1AwmoiIiOSYwVfBxftg0nMw8V/J7TvsJue2nqfCRTvh1Ddh6hpr/HCRc38RyS39psHFu2HKbDh/Axz/b62FFxHxlZHtAtwZioyS1k9HNiKSG4yQFVIUO9iyLVod/Px1+8CM27dN+AeU9A2+BgnW6B/BrndbLu4zQnC0zTWzJWksEPnku3DO4tT3T1esxn57qAj6TIXNL7jvX7ka2vscOBCtgopl1gKnjmMg5Pxm7P8xTahaD6FCqNlhfV1GGDqMtO5I4cX2OWBG7dsiHsdw0u8C68P8/Brs+ucQI2QFINT3iUcBA0Jh22HaLC/PqxXLgq9DRCQXOAWjhfIgGM3L37eC9sHNH4/BgVVQOlBvwDZWtcH6vhd2znYl6Ys7HNs2t9XjgnGx5xaMBlYQlNdj8FRUrEgcijX85vw/pu5/CSy+E/Z7DKxY8hsY8W2ItId5V8HGpxvaFv3CCts69q+BlJoV0QAD+PwMJUslGA1g/1LocqR/dYiIBCHuIZTWSeyQ9Xz77uVWyHO/af7VJdKamCa8cTbses96fHAzvHelde574GWJ99/0Arx9cdNt8RrY9roVUnjeSmiX4o1fguZ0rt5odpzf5xxY8T/2fWedDGctyP/XBonEo1C11lvftR4veNd7nSIikgGGYZwATKEhFK0SuNA0zVmpjGeaZiVwh2EYc4FngNLPxz3NMIzjTdN815/KRUREREREpK3aWecQjFaY28FoYSOMQQiTlmtg6hSMJiIi0noVdoS+51ufb3oONj3rbb+ywe7t4XbQfVJ6tYlI9kRKoccp2Zt/wGWwXjdUFZFWquep2a4AOo5zbnMJzxdpLRT/JyK5I1Jivz3R4mg/uC1q7Xl68PNL8HqfCVPmwJBrG7YN+iqcNR/Kj2nZvziNYLT9S2D1A6nvny6n8IhwO2/BJOse8a8W04SFP4cnO8IrR8PLR8BTnWFNgoU6lavhpcPghUHwXB9rv9cmwqvHwFMd4bPbnMMM68UOwazJzu2dDkv+67FjGNZHKGx9GM3Sn0OR1r9IK1VH3uveXrMTavZkphYRkWxyCkYL50EwGlgBx26CCjrePB2e6QbTR8JTXWDpPcHMk0+q1sNLh8PzA+Cpcph7MURtwqfzhWl6D+eoWpffX2u2JQpGiwUcWL71Nff2DqNg5C3B1pAJoQI4430Y+1/e93m2NzzZvmkoWr1Vf4MDa/yrL9uCPP9Tudy/sRL9vjh570qIpRE4JCKSCc1vKpGquRdAXYCBlyL5bP+ihlC0xlb+b+J9Nz0Pb53v3F5XActtbgSTK8yY/Xaj2b3c+roEK+5fAltm+FdTrnIKkUtH+dH+jykiItLSTZ//a2CFo12faihaY6Zpvg5c32hcgBvTHVdERERERETatpgZZXfdDtu2bgU9M1xN8gocFr5GFYwmIiLSNvQ513vf3ucEV4eIyIjv2m8ff2dm6xARSUfj/In/Y8Cgr2S8lBZ6ToHCzi2395gC4cLM1yOSYQpGE5HcEXYIRgt6ATa4B6MVlQc/v2RGtxNgwj/gCtP6OO5B6OSQklvSN7253r8G3rkcFtwO866Gj26G3R9a4Q5BizkFoxVBxEMw2rYEoQRe1FXAhqfgteNg4R1NFzxFq2DeVdYiLjumCW9NsxaI2bbHYfGv4a0LIO6wkKp2L8wY41yfEYHeZ3n6UiRAzUPk7FT4GGAgIpKr8j0YbfSt7u3RKv+PgQ6stY4Xavdaj2PV8On3YdOL/s6TT0wT3jgX9n1Wv8EKUvo0j8Okkl0Ivnd+MHW0BbUJgp6CCqwyTdg2C+b/0L3fSc+0npP1kRI47OdwqU/f0zlnwr6F/oyVbTGH78nAK6HvBfZtw78Fx/2nZZhGczW7YadNAEkq3M4hJbLkN/7UICISFLdQ2lCSdxR7sj2seRCqN6VVkkirs/Yh++0751r/Rqth8wxY+TdYdZ8VAr7kLvjwm9br4EQW/9K6McmmF5zPN2SLYzBasxuLdJ8EBZ2cx/noZuv7s+uDxDdQyVdxn4PROo5NfAdwERGRNBmGUQSchxVcZgJPm6bp263hTdN8FHgaKxzNAKYahtFKThqKiIiIiIhINuyu20kc+/PM3Qt7Zbia5EUUjCYiItK2DbjMCsNIZNT3oePI4OsRkbar/BgY9o1m2ybAsBuyU4+ISCrG/heUDmq67YjfQrtu2amnsVAEjv1b02sti8rhyLuzV5NIBiVYMSYikkERh2C0aAaC0Xa+Y789XOxcl7Ruxb3TH2N9s2t8V/wPDPkaHPMXCIXt9/FD3GGxU6gICjok3n/n29bCqz4p3g2ichXMPh2q1rr3e2sanPQ09Luw6fa9nziHojW2+QV4ri+cs6hpgOG+hTBrCtTsdN63+0lQ6LKwSjLDaSFcY5XLodtxwdciIpJN+R6MNvBLsPCnLh1M62uMFPs358an7f+ObHoG+p7n3zz5pHKl/THUhqfg6D97CyTNNU6Bv05mHg/nr4PSAYGU06olCnoK4nV5tBrevhS2zHDvN/7XrfOCjEiJ9TqmJkEoXSKVK+Gl8TD+VzDm//lTW7Y4BfBFSmHAF2HTs023h4pg+Degw3DociRsfc16PTz/R/bjzDwejvkrDLs+vTrTCUZb+y8Y91/pzS8iEqS4wyKJcDGcvQC2vAwff8v7ePOuhkgZnPA49DnbnxpF8p3bscSBdfDW1PSDb+ddZf3baRxMegHKBqY3nl+czgc3f78kVABDvw5L77LvX70BPvz8wslBX4EJ/7Qu/GlNkg0KT2Skw515RURE/DURKPv8cxP4XQBz3ANc9PnnZcBxwJsBzCMiIiIiIiJtwM66rY5tXQt6ZrCS1CgYTUREpI2LlMDJL8HmF2H3+y1viFjQEXqcDD1OyUp5ItKGGAYc/Sfod4G1Vr/jKOh1FhSUJd5XRCRXlA6AMz+EzdOtm0L3PBW6Tsh2VQ36XwIdRsPWlyHS3sqAKOmb7apEMiKU7QJERP5P2CGALJaBYLT5t9pvL+oa/NySm8KFTcO2/LL6PuugM0hOARLhIjA8/ul/+1Ko2pjcvBufg3nXwovDEoei1Zt7EdRVNN225RXvcx7aBm+c23TbBze4h6KBtRhTsi/uYXFXxbLg6xARyTanUNN8CUZrPxSOe8i9j1PYTao+/YH99jUP+jtPPtnwpP32mp3O4Xu5rvkb9F7MOcv/OtqCA6vd22edDHPOhP1L0psnWg1LfguPGPBEaeJQNICRt6Q3Zy7rNsmngUxY8BPY5yFgOpc5BoUWQ++zYdxPG+5wU9gFjv2rFYoG1hvoI78No35oBfA4+fAGWPDT5J8XqzbCwp/Bu1fCsnuS27exA6v9/5soIuInp2C0UIF13D/iZjh/A3Q5xvuY0QPw5jkQPehPjSL5zu3mJR9/O/1QtMb2LYTFv/RvvHSYJphx+zbD5kYyY3/ibdy1/4ZNz6deV65yej5O1eCr/B1PRETEXv3drkxgqWma8/ye4PMxG5+k1B22REREREREJGW763bYbu8UKacwVJThapIXMexvGhL1++YbIiIikrvChdD/IjjiLjjq3qYfh/1MoWgikjmGAT2nwLjbrfAehaKJSD4qKofBX4Wxt+VWKFq9TmNg1Pdh2PUKRZM2RcFoIpI7IqX226MBB6O5ja9gtLatMIBgNIBVfw9m3Hpxh2C0UDuoq/Q2RqzaOXDEzsJfwNwLYM393vep92yfpo8XeFzwVG/3vIYwtdp9sOs99/5H/h7adUtuDglGaf/EfQ6sCb4OEZFscwpnCeVJMBrAoCvh7AXO7TGFwGRVvobwpBKMVrG0dS6KD1I8CpUJgtEAtr4KM8bA9jdTmyd2CGafBvN/6H2fk1+2LtporUbc7N9YZhzWJgipzHWJQr7H3QEXbIGzPoPz19qHOxgGdBjhPs+in8Gb53kPmziwFmYeDwt/Cusehqr13vZzUrEivf1FRILkdPwVanS3+dJ+cMY8OGeJ9bd60Fe9jf1aDl4gIJINBR2d2za/4P986x51DiTLJLca7ILRCtrDJR7fz9jyUmo15TI/F62dNd/7TXNERETSM6bR5+8EOM/bDnOKiIiIiIiIJKUqVmG7vVOkS4YrSU3EKLDdXpfKNVciIiIiIiIiIiIiOUhXwIpI7giX2G+PBRyMVrHcua1sSLBzS24L2d9FKW2bX4QPrvceUlYvdgg+/SE8XgyPGNbHm1Nhzyct+9kJF0E0iTk3PA4HtyfuV1cBi3/lfdzmogfg6e5QsdLbfHbeOMtaWFWzG+sG1C5GfDu1OcR/vc+BUIKgi9q9malFRCQTNj4Dr50Azw2A974KNXus7bGD9v3DeRSMBlDkEjy6b3Hm6mir7BaS1/vom94DgHJJqhfpfXCDv3W0dlXrklt0/8l3U5tn83TY9a73/qECKD8mtbnyRY9TYNLzVpBXouNiL5belRuhF6lyDPludBfmdt2h82FQ0MF5nPbDE8+17XV4rBBePwXm/whmngjTR8E7X4JDO5v2XfFnqN6UeEyvKpb5N5aIiN+cjhmb/50yQtBxFPQ+EyY+4G3sfQvTD5cUaQ3cjmOCEKuGR8PW+wkvjoDZp8Ou9zNbA4AZc25zej1bUAadDks89pr7oe5AanXlqriPwWidx/s3loiIiLvGF9gEecDReGxd1CMiIiIiIiIpq4rZn1suDbfPcCWpKXC41iRq5uF1YiIiIiIiIiIiIiI2FIwmIrkj4hCMFg04GO2tac5t424Pdm5pu1b9Hd48F0yXEK9oFRzcBrEaK0xkxhhY+tumwWebX4TZpzYNFIu5LCbvfU5ydXoJLtg1z3kBu1c1O2H6cNj8QupjfPztxMFvZy8Cw0h9DvFXQRmMTBCsUbsvM7WIiARt04sw92Lrb2v1Blj7b5h1ihVg0zyApV64OLM1pitS6tz25jnJh8JKctzuag3sAAAgAElEQVSC0TY8DvOuzVwtfnE6rgUoP9a57dA2qFjhfz2t1cEtyfXf+2ny31/ThJV/TW6fgV+CovLk9slHfafCucvgshr44qH0A9Le/ZI/dWWDUxhist+TDiO8993xBiz5Dex8xwosW/8IzDwB4o2CO5bdk9z8iRzc7O94IiJ+cnoudrjbvNVmwNTVEPGwQOSz26zznH4G/oiId5UrYNtMmD0l80GFbmHMhstNao7x+DrincuSqyfXJRNe7ab3uf6MIyIi4k2PRp8HebDReOyeAc4jIiIiIiIirVx1zP56tpJQfgSjRRzew4v6dY5ZREREREREREREJMsUjCYiuSPsEIwWCzAYLXrQCqawY4Sh07jg5hbZ8RbsW2jftvxP8Fx/eLYXPN4OXhoLB9bY963dC4t/3fDYKaQs3A56ne6+yKi5RCEJO9+BOWd4Hy+RD76e+r6r/gY1u5zbz5oPncakPr4EY/ydMOF+53YFo4lIa7Hyz0CzQNR9C2Db61C53H6fsoFBV+Uvp+P5eusfzUwdbZVbMBpY33+nEL5c5RTMAXDSs+77bnnZ31pas3gKd4nd+pr3vvuXwDPdYfus5OY4/K7k+rcG4SK4cJt7n67HQZejnNs3PBl8wHxQnMIQw0XJjVM+Ib06KlemF9qdSPRgcGOLiKTL6e7xiUIqywbDWZ/CkK+591v3sHWe8+lyWPdIajWK5LtUjr/rlQ5s+r5V1+NSGydaBQt/mnodqTBjzm1ur2e7efwat8yAPZ8kV1Mu8ytActgN/owjIiLiTeO7HAT5Jm/92AbQJcB5REREREREpJWrih2w3V4aLstwJamJOKwJiDq95yciIiIiIiIiIiKSZxSMJiK5I+IQpBDkgt6KZc5tfXQXdcmAj29u+BmPHrQCBuZeBB9/C2r3eB9nxR/BNKFmD8QcFllHyqCwExz9R+/jVm+0/q07YIVLrH0IDn6+UL92H8w+3ftY6ep1lvU1OInXwaYX7dtCRdB5fDB1SXoMA4ZcDcc7LIat25/ZekREghCtgq2v2rfNOQNih+zbOowKrqYghMJWEKuTNQ9mrJQ2yTDc280obHwqM7X4xS0YLVICF7mE4u5f5H89rVUqwQyf3gJbZzZ9/qraAGv+BUt/B5/9Fyy4HeZeAjPGuAcY2xl8DbTrlnxdrUFhZ5i6Fkr6N9powBH3wBUmnP4unPkRlE+039+MwfY5GSnVd06/84nCeJrrcYr7a0cvdr0H+xbBpz9Mbf+icuc2p9fsIiK5wOm4IGR/t/km2g+BCX+Hy+OJ+9ZVwLtfgvevs44dVv4N9i1OrlaRfJVqMFpRV5i6Bs5eYB0XXmHCqW9CuDi18dY86F/4lhepBqMB9DzV2xyL7/ReT64zffi/6X8J9Doz/XFERES8a5xuH2QwWuM3kF3elBARERERERFxVx23D0YryZtgNPv38OoUjCYiIiIiIiIiIiKthP3tIUREsiHsEIwWCzAYrXKFc9ugq4KbV/JD6SDYvyTYOXa8Ba8eCxMfsBYC7luQ+lgr/mwFkjhpP9T6d9iN0PU4WPJbWO8QRlVvyW9gwOXwzmUNQYKhQjjpWajZ6f33s9cZMPQGqFwO83/kbZ/GinvBKS9ZwQuvHOX8/7L5BfvtBe2Tn1Myq7CT/fa6fVboX6KwFxGRXHVgLcz2uIC3uQ4j/a0lE8LFzkFvu97zZ46oAmVsuYWI1TuwJvg6/OT2NYUKrXC0IdfB6n+0bHcLwZamUglmiNfCnNOhyzFw8nTY9jq891V/Fu8bIRj+zfTHyWdlA+Hs+bDxGSuQuvskKD+maZ9hN8Luefb7v3kuXB6zvpf5JF5jvz1UZL/dSbjI+v4s/W3qtSz9bXr7n/mp9dq1ZmfLNgWjiUgu8yOk0jCs4KaXDkvcd/U/G+8I438JY37sfS6RfJTqYqQBV7Q8RxoqsM7fr7k/tTF3vQfdT0pt32SlE4w28EvWa45ENj4FS++BUbckV1suSuW11ZG/h8Iu1g1vOh8Bvc/SeXUREcm0xidxglyB3fgPpYcUZxERERERERF7VbFK2+2l4fy47rzAsH8PL2p6uI5MREREREREREREJA/k2eo4EWnVIg7BaNEgg9FWOrf1nRrcvJIfMrUYf/9iKxwtnVA0gI9vhg9vsm+LtIfi3g2POx8Oh/3c27gvH940WCJeC+9eAfsXea9twj+g3zQYfSu06+l9v3r9L7P+DbeDsz5z7le1zn57QYfk55TMKnAIRovXKThARPLbrMmphVGVDoKC/LjzZBO1e93bTTP9OaL2d+ps87wExh2yCejJZU4hSdAQlNTlSPv2nW9DzR6oq4Sld8Pbl8IHN8C22bDxWZh3LbxzBaz8K8Rc5mkL0gkz2/MhfPZjmHe1P6FoAEf+Aboc4c9Y+aywMwy51gp1aB6KBjDoSvf9NzwZTF1BcvpdDCcZjAYw7o70aklVuAROfAJK+0H3L9j3SfS3UkQkm5wCU0NJZg10HAtlQ5Oc3ITPfgJ70zxHKpLrUgkm7nUGjP+FfdsRd6Vey+uT4OPvwJ5PUh/Dq7SC0b7s/f2aT78P+5J47yJXxVN4fTXyOzD4KzD2NuhztkLRRERERERERERERBJwCkYrCeXHdXsRI2K7PerXNTwiIiIiIiIiIiIiWaZgNBHJHZFS++3RquDm3Pm2/fa+08DQU2Sb12MylA7IdhX+6DCy5SIYp985L+r2w8ZnvPXtOBpK+jY8Hv+r5Ocr7tXweSgCo36Y3P6R/LhzV5tW6BCMBvBRhkIKRUT8tvZh59DORHqf5WspGdN+mHu7H2EwsUPu7aksHm4N4gm+LwCHdgRfh5/iDncvNUIQ+nzhfIeRzvs/XQ4zRsOnP7BColb9DWZPgbkXwpr7Yf2j8OGN8OZUiLXhO6WmEszQ2Op/Ov9fJevsRTBCx36eGCE4/hHn9tX3Z64Wvzj9HIXs73DsKlIKR/9PevUkq+fpcMEW6H+J9ThcbN9v7b8UyCgiucvxuTjJYDTDgDE/SqEA03qeFGnNkj3+nroaTn7Z+eYfReUw5rbU61n+B3htImx5OfUxvEgnGC0UhqP/BGd6DHB7aRyYce+15aJkF601vjGOiIiIiIiIiIiIiCRkmiZVMfsbdJaF8+O684hh/x5e1EzzWiARERERERERERGRHKHUHxHJHeES++2x6mDmi1bB9tn2bZ0OC2ZOyS/hQjj1LWtxc77rPqnltnY9oGxI6mMeWOOtX79Lmj7udXrywYPdTmz6uKRPcvsX5Mcb1G2aWzDamgf8CdIREcm0T7+X+r4DvuhfHZnU7ST39ppd6c8RO+je3laDFKIJvi8ANTuDr8NPTmFljUOSOh0GGPb9AKo3JZ5n22vOrw3bgnSD0fzSYzJ0GpPtKvJL7zOd23bMgdr9mavFD3GHsLBQUWrjZTpktOsEKOzY8DjiEIwGsG1W8PWIiKTC6bgglZDKwdfAkb+D4iTP4y37HTxiNHxMHwXrn0h+fpFclczx99DroWxwy5ueNDfwivRreuNsiLuEl6XLLcQ8UTBavc6HQ+lAb323z/HWL1clG/o+9vZg6hARERERERERERFppWrMQ8SxPy9ekufBaHUKRhMREREREREREZFWQsFoIpI7Ig7BaNGAgtH2LXZegNLn3GDmlPxT2h8mvwqXRRMHfeSy4d9ouc0wYMz/C3bekr4w6paW24Zc532MHpOh68Sm25JdUFnQIbn+knkFLsFoANvfzEwdIiJ+qd4Eh3akvn++HncM/6b7gubqjenPkSgY7f3r2mZwQvxQ4j75FjQadwhGa3xRX1F5y2PFVGx9Lf0x8lUuXAxphOGIu7NdRf4p7Ax9zrNvi9fB1lcyW0+6nH7nwykGo5UNhgGXp15PssLtmj12CUZb/odgaxERSZXTc3HIflGFK8OAkd+FCzbBJZUwOsXzkBXL4J0vwpoHU9tfJNckc/w94lve+nUcDX0vSK2ext6+2LktWg3Vm1Mf23QJXQtFvI1hGDDmNm99t8301i9XmS7BaCX9mz4uGwr9Xf7vREREMsv8/N+JhmFMCuIDmJDNL1BERERERERah6pYpWNbabgsg5WkzikYLWo6vOcnIiIiIiIiIiIikmc8XmUsIpIBYYdgtFhAwWg7HO4WHymFLkcFM6fkr1AYht0IO+dmu5LkDb4GygbZtw25Foq6wtqHYOPT/s7b+1yY+E8osLlr1jF/gY7jYMsMKOwC7bpB1QaoWtfQp7CTFYo26vvWgqfGinsnV0skP+7c1aZFiq2fhdo99u2NfzZERHKZacKiX8DCO1IfY+rqln/78kWXI+DMj+Hlw+3bZ59qLWI+7Bepf42JgtEAVv4ZBlya2vj5Kurh++IUDJ2rnBaCNw/mGPRl2PVeenMt/70VpjvudjDa2H0E4i4L7oPSYwpEq6xgu4KOMPpW6HxY5utoDSY9D486/Mxueh4GfDGz9aQjVmO/PVSY+pjH/RsqlsPeT1Ifw6tQEsFo29pwGKOI5Dan48V0nosBCspg/K+g4xhY/xgc3Aw1O61Aaa+W3g2Dr0qvDpFc4PV12eCrrMAzr058HJb9Hra9DiV9YOj1VoDWkv+G3R/C7nmJx9j0HBxY2/T9hHgUPvkerPqrVXuHkXDik9BprPfawD0YzS1gvbmh11nvaax7CDY+S0P2SjNLfgOH/3dSJeYUt5+T09+znhP3zbfezxx5i/XaSkREJHcYwKMBz2F+Po+IiIiIiIhISqpjBxzbSvIkGK3A4eZGUbebb4iIiIiIiIiIiIjkEQWjiUjuiDgEo0UDCkab/yP77e2Htb2F8OJNvwuhz3mw+cXs1VDcx1o4mIwRN7u39z3f+qjeBM/1S722xkbf6r7oyAjBiG9aH6ko7Z9c/8JOqc0jmTXkGmtBl53YoczWIiKSqvWPpReKdsTdUDbYv3qyofN4KOnrHHKw+FdQOtBazJwKL8Foez5Nbex8Fvfwt7J6A1SuhvZDgq/HD06BXaFmp7OGXAcf3pT+fIt+ZgUADP5q+mPlEzPDgXmX1bX8P5TUGYYV4r3yLy3btsyAWC2E0wyzcVO7D7bPgeqNYESg8xFQfqwVLp4MM+4ShliUen2hCBz/MMwYlfoYXoWbB6O1s+8nIpLLHIPR7BdVJMUwYNCXrI96Myd5vxHF/sWw6j7rPGa77unXI5ItXoLRyo+FI3+f3LihAhj9Q+ujsaP/aP27+E747MeJx3lhMFwWbTieW3YPrPhTQ3vFMnhpHFxWm9xzg1/BaAD9plkfAM/0hEPb7ftVroL2Q5MbO1c4HRsbESjpDUf9LrP1iIiIJCcToWUO6agiIiIiIiIi3lTFKm23GxiUhEozXE1qIoZDMFq+3TxTRERERERERERExIGSf0Qkd4QdgtFiAQSjLbnLua39cP/nk9YhXAQnPgVfeBFG/dChj8PPsV9OSPLGyl2Pg07jvfUt6QtT1yZfU3NTZruHovmhuLcVqOKVfq/zw9DrndtqdmWuDhGRdCzzsHB5wv1w6QH4wnToMcX6OzXup3D6PBh1S+AlZkRRV/f2DU+kPnbUQzBatNIKA2pLvHxfAKaPgEW/CrYWvzgtnDeahWqFCmDqan/mnHcV1OzxZ6x8kemLIRWK5r++0+y311XAno+Dm3fvfJgxBuZeCB9/Gz76Bsw8Ht6alnzIfdzlOTuUZrBbhxGZeU3YIhit2LmvHwFDIiJBcHo+dlhUkbZhNybX/4Ovw/SRsG12MPWIZILT8XdxHxj3MzjhMescu983+xj0VYi099Z37b8aPl/zoH2fN85Nbn4/g9EacwuQe3EYmHmameI1KFxERCR3mQF/iIiIiIiIiKTFKRitOFRKKJ3z1hnkFIxWl+mbJIqIiIiIiIiIiIgERFfOikjuiDgFox0CMw6GT1mOsRqYf6tze+cj/JlHWqdwIfQ51/rofRa8db612Byg7wVWcNnci2HLdP/nHn8ndDsxuX0m/BOMJG7GXNInufEbM0JwxkfQJQO/Q4ZhLf5ffq+3/h1GBluP+KP9UOc2BaOJSD7Y9jrs+dC9z+BrYMjV1ud9zrE+WqNEwWjbZqY+dsxjAFjtbijulfo8+SZ+yFs/MwYLb4deZ0D50cHWlC7TYSF482A0sEJzi7pBzc705517AZz6Zvrj5ItMBqP1PD1zc7Ul3U+GSClEq1q2VSyFbsf5P6dpwvtfh4NbWrZtmQ7P9oYzPoAOHgPJYjXObeGi1GqsZxgw6VmYfToc3JzeWG6SCUZzaxMRySanRRLphlQ6GfBF2DUPVvzR+z61e2H2FPhijXWuViTfOP2e9Z0K424Pbt6S3jDxfvjwBqjZ7d73/Wth0/PWa4WKZfZ9tr0G+xZDpzHe5g8qGC3Re3qPhqDfhRApg24nweCrIZQHC9ocXw8rYFdERHLaBhRaJiIiIiIiInmiKn7AdntpuCzDlaTOKRgtqmA0ERERERERERERaSUUjCYiuSPsEIwGVvhBpNSfebbPcW8f9BV/5pHWr8fJcOEO2PMRlPSF0gHW9iHX2gejTZ4F7YfBkjth5V8Sjz/xAeh0GBzaAeXHQFF5cvVN22wtdkpGqAAi7SFqfxcsRyX9Yeoqa/9MSSYYreOoYGsR/wz/lv1iWAWjiUiu2/U+zD4tcb/eZwZfSy4o6hbc2F6D0WraWDBazGMwGljB06v/kcfBaDaL2I0QDLsBFv0i/Xl3vAU73obuSYYS5yun73MQhl6XubnaknAhdBhtH875/rUw5Br/59y/2D0MtG4/vHosTH7d23NNvNa5LZRmMBpAx9Fw/nrr9fvye2H9Y+mP2VzzYDTXvgpGE5EcFXN4Pg7qnJ8RgqP/AKO+D/sWwLJ7Ep+/r/fmeTD51WDqEgmSUzBxJgKv+l8MPU+FihVQuwfeOMu57+YXEo/30li4PObtxkZBBaN19HBTlI3PWP+u/TdsmQEnPZPcDWWywel1WkiXd4iISO4yTXNgtmsQERERERER8ao6Zn+tfEm4fYYrSV2BgtFERERERERERESklfNwlbKISIa4BZ9Fq/2bZ/8S9/Zkg6SkbQsXQbcTGkLRAPpNgwn3Q9kQMCLQ/Qtw7groORlK+1mPvRh8FXQ50gpQSSYUrctRcObHqf8sF3VJfp/T3s5sKBpY3/d2PRL3K+nX9P9HcltRV/vtmQxG2zwdZp8O00fChzdBXUXm5haR/LXwZ4n7RMqgVxsJRisdGNzYyQSjtSVuoUJ2Vv3NCkjLZfEkF4KPvQNG3+rP3J98z59x8oFTMEO6Jj0H3U+2XieUDoJj/wb9LwlmLoEOLoEQ+xb7O9fuj+ClcYn71e2Hxb+ybzuwDuZeBC8dBnMvhj2fOI8TKkypzJbjhKHrBDj+YRhzm/V6MlIK/S5yfu7oMML6OfY0frNgNLfASgWjiUiuclok4ddzsZPSftDnHDjS400QALa9Bi+OgE3PB1eXSBCcjr8zdX69sBN0PdZ63+HY+9If79EwVKxM3K92j3NbOsFoAOd5mL/epufg0RDMPsM6rs1VTq+HDQWjiYiIiIiIiIiIiPihKnbAdntpqCzDlaQuomA0ERERERERERERaeV05ayI5I5IiXNbzMdgtNq9zm2j/59/80jbNuRq6yNe13JBk5eQro5jndsGfhnWPWTfdvp70HWi9zrtFHaGqvXe+5dPsBYvZlooAuN/De9f695v7E/AUBZs3mjnEIxWvTEz8295Bd6aBmbMelyxHPbOt8L/9HMkIm52vp24z2G/gIL8uaNkWsqPDm5st6CZxmrbWjCaw6JpN7s/shbE5yozyYXgoTAc/t8w/k6IVkKoqOG1ZEFH62/5snvhk+8mnnvPhxCtcg/wbi2CCEbrfQ70mQp9z7d/TST+6+gSjLbhCejkIcDTi8pV8Oox3vtveq7ltpo98MKghsf7FsLGp53HCBd5n88LIwTjf2n9XTZj1mvLmj2w5kE4tL2hX9lQOPNTiBRbX/fME5u2N1fQoenjeI1zXwWjiUiucgrbzdTf8s6HWccQm1/w1r9yhXUe5wsvQp9zg61NxC/ZDkZrbOh1sH8RLP9DeuNMHw7nb2h4n6Bmj/X1FLQH04TqDTBrsvP+TuHXXrUfagXRb33F+z7bXoMdc+CsBe7H0tni9Ho43e+ViIiIiIiIiIiIiABQ7RCMVhJWMJqIiIiIiIiIiIhIrlC6g4jkjrBLMFq0yr95Nj/v3Nb/Iv/mEQH7xUxdjobiXu77DXEJ+xr6NfvtA7+cfigaQGGX5Pp3cglxC9qQa9zbT3gMhn49M7WIP0oH2W8/uAW2vBr8/Kv+2hCKVm/Xe7D30+DnFpH85hbkWzoATnoGRnw7c/VkWxcPwWjzrk4tkCl20Fu/mjYWjJbKBW2vTYDqzf7X4pfmf5PrJVoIbhhWQFG4yAr9LezcEHDqFsjd3BNlsOZf3vvnKz+D0bpPgsN+aT3nGYa1TaFomdHbJQxm0c/9+39emELA2paXGz4/tBOeLk9u/1Bh8nN6YRgNzydFXeDsBTD0BitUY+QtcPq7VigaWGEbp70DJX3tx4qUQnmzwDincCFQMJqI5K6YUzCazyGVbk562gq67TDC+z4LfxpYOSK+c3rtlq3j5jE/aRnwmoo198PB7TBrinW891RneLobPN0Vnh/ovq8RTn/+k2ckv0+8Dhb/Ov25g+AUfu4UFC4iIiIiIiIiIiIiSamKV9puLw3nz41PnYLR6hSMJiIiIiIiIiIiIq2EgtFEJHe4LVCPugRNJKOuEvYtdG7vcpQ/84i4CUXgiHucFxSWT3QP/Op2Agy+qum2rsfB0X/0p75kg9E6jPRn3lRNfMB+e4dRMOCLma1F0uf28/TGmXBoV7Dzb3IIz1xyV7Dzikh+M03nAKeux8H566DfBQ0hQW1BSb/EfdY8CJ/emvzYXoPR6iqSHzufOS2aTmTuhf7W4acgFoK7BXLbmXcV7Pk49flySfVm2PgsbJ9jPW/VMx2+z50Ph4jHu+D2vQAuj8Opb8LY2yAcUJCVOOt8WEMAoJ0Pv5H+HPEobH4x+f3eONsKRAN45/Lk9w9nKIynXXc49i9wystw5N3QrlvT9vZD4PwN0P+SptuNEBz955Z19r3AeS63/ysRkWyK19hvz+Tf9lAExvwIzl0GV5jWMUYiez6G6k32bfuXWMdAez7xt06RVDkF1josXgpcu64wOoXX5s3tfBee6wvbZ1uPzRjU7ILaPYn39SMI1wjB5NetwNpkrHsIFtwBH94E6x+H2KH0awEw47D3M1h1H2yebr0/mdT+CkYTERERERERERERCVJVrPUGo0UVjCYiIiIiIiIiIiKthK6cFZHcES52botW+TPHK0c7t42/0585RLwYeDl0GmctEorXQlF3qFwJHUZA/4sh3M55XyMEE/4J/S+FXe9Bh9HQ97zkF/w4KeycXP9uk/yZN1V9p0H4ppYhKQOvyE49kp7S/tbPv9MCtG0zrd+fTDu4NfNzikj+cApFAzi8jQYrGoZ1TFG7173fhifgqN8lN7bXYLR4bXLj5junxfWJ7P4A9i2CTmP9rccPjgvBw6mPmcox85LfwIlPpD5nLlj9AHz0jYbfn24nwMmvQEGZ889OST+YMhuesglO7n02DLgCqjdAx9HQZ2rbCn/MVVPXwfP97du2vpr++PsWQN3+1Pbd8CT0OQe2z0p+X6dQ8WwwDDjhcRhwufV6vLCT9fvQ+fCWfTuPdx7HKXhIRCTbnJ6fsvlcbBhwzlKYMcq938xJcP6aptvm/+jzsPvPQ2EHXwUT7tdxi2SX0/F3KEvBaAAjvgMLf+pcW98LrGN/t9Doba+lNnf5RP9CY3tOgbM+g43PWPXGDsHqfyTeb9HPrX9X/sX697yV0H5o6nXEo/DulbDh8YZtpQPgCzOg0xhvYzi9Hg7p8g4RERERERERERERP1THDthuLwl7vIlgDihQMJqIiIiIiIiIiIi0crpyVkRyRyhihaPZBR3U7Ut//IrlULnCub2jx8UIIn7pNDb1AAojBL3Psj78VmQTfOCkuA+UuwQOZkJhJzjhMXjncohVW9v6ToOR381uXZIaIwTtekHVWvv2fQuALASjacGsiKVqI6z7D1RvhO4nQ/9L9PsB7oFUbXnB6shbYMFP3Psc3AybnoetM6GoHAZ+CToMd98n6jEYzW3BdmvktGjai/WP51kwWhq/V26B3E42PAnP9oUj77HCifPpeS8egwX/BUuaBYHvfAfe+zJMetY9mKGwM5y7DGafZj33A3Q6DI69D0p6B1u7JK+0H7TrCYe2tWyr3gC1+6GwY8u2eAzWPQx7P4XinjDsG1ZoXnP7l6ReW8VyK2wvFan83gbJMKDfBdaHa78QDP06rPp7y7aYgtFEJEfFHMKFw1kOqew4Eo79O3z0TecA5Kq1sHcBdD7Merx9jhVw29iaB62Psbdbz9ElfYKsWsReLgajRUrg6P+FD77Wsq1dDzjpaesYaOtrMOcMf+fud6G/47UfAqN/0PB48FUw92L7Y2QnLw6zXvsNviq192Devtg619FY1Xp4aSyMuQ1G/8j+eLuxeACvh0VERERERERERETk/1Q5BKOVhvInGC3i8N6CgtFERERERERERESktfDp9ssiIj4pKrffXrM7/bE3POXe3svnxRwi+aowiWC0cXdYi72zre9UuHAbTJ4F562Ek56BSGm2q5JU9b/Eua16U+bqaCKPAlBEglKxHF49Fj77Maz8C7zzRfjwhmxXlRs2v+jc1pYXrA7z+PPx1jRY+WdY9HN49RjYNc+9f/yQt3E3Pg1bXvXWtzVIJxitar1/dfjJaSF4WoGDZmq7HdwM71wG730ljbkzLB6D2VNahqLV2/ScFZDmdDFk/V1lO4yAqWvg9PfgrM/gzE8UipbLRn3fua1yZcttpglzL4B5X4Xl98L8H8GMMVBrE1BfsSz1ug5ugsrlye83/BfDx+AAACAASURBVFv5FUbYXPlE++1xBaOJSI5yen4KFWa2DjtDvwbTNkHnI5z7rHmg4fOVf3Put+jn8NI42POJf/WJeOV0/J3NYDSwQsDsjl2G3tBwPNbrdCgd5N+chZ2tkMIgdTsBpq6CKW8kt9+GJ+CNs2HhL5Lbb8FPW4aiNbb4V/BcX/vj7cYcA/Ta8HkmEREREREREREREZ+Ypkl1vNK2rSTcPsPVpC5iOAWjRTHNFK+REhEREREREREREckhOZBkIiLSSGGAwWhugRUnPAbhHFjcJZILCjt76zf+ThhybbC1JKOgPfScDO2H5vfCeXEPRmvXPXN1NKGfKRGW3g2HtjXdturvsH9pdurJFWbcPSAu2wubs6mo3DrOTkZdBSz8qXufeK338Rbcltz8+cxp0bQXNbv8q8NPZsx+ezqBg05ha16t+w/seCu9MTJl8wuw4033Pu9f5y2ALhSBrhP/P3v3GeZWfedt/JY0fdx7t3HFBoyNwfTeHXpCKMmmV9iQPEvabsqm955N2M1uCmwCaSS0UBIILfSOKabZuPfumfF4RtLz4uB101GXRtLcn+vS5dG//kbWaCTNOV/BwJkQjRWvRhXftI+F9215I5isux2evBKujcB10X3fL2lfAn8c+EZ/DK6rC75+/qvha5/xRPq6lv4JnvpEdt/D7g75bu5zKkmsMXV73GA0SRUqNBgt5PGs3JqGwtyfhfe/9IPgNVqiG5b8Lv1aOzbCc18Jvl51F9x1Etx2CDzxL9C5oXg1S3sLDbzq4fcPonVwwi0w5vwgDLFxMMz4Vzjwc3uOOybDz1a2GgbBSX+Dhv7FWS+dulYYfjwcdV3uc+d/Pnh82L4m89jt69I/Z96pa3PwfHvZTeFjwsLPe3MAvyRJkiRJkiQVyY5kJ90h78O2xvqUuZr8hQWjAaHfnyRJkiRJkiRVE4PRJFWWxpBgtB0FBqN1rof1j4T3j7+osPWlWtIwKPOY0x+DAz4NEZ9KqAQGHxre192+6+vta4JLPIeAnHTSfTqaYXsSLA458fX1X5e3jkqz5v7ghPowvf2E1eEn5j5n5R3pQ75yCQDb8ER2Jy/XgkIOZutcW7w6iinse4oW8HPVZ0J435TLsltjcY6Bfz1leZqT7HfasgC2vZa6r6eDGZSfaAz6H5i6b/Vd0LYYbj0IFnwvu/WSifCQwp0O/BwMOgTOfCa3WjPZ/18K+3mvBGFBQonO4L2qYr2WkaRiCXtcCgt67Anp3jcCuO882PpKdmst+3MQ+P33U2D13bDxKXjp+3D/+enfJ5IKEfaaNs3JS2XTOBiO+xNcuBUuWAuzvrZvMHK/afmvX98f3toGlybhLeth0JzC6s3VuAth4CG5z1t9N9x9JiQyPC9ee19ur83vOxeWhXyok8FokiRJkiRJklQybfGtoX2tsb5lrKQw9WmD0TweQZIkSZIkSVL1M81EUmUJC0brLDAY7aUfh/cd/vPC1pZqTWOGYLTmkcFJ71Ipjb84dXt3G2x8Fq6rhz8NDy6/a4SnPhGENhQibeCDT5vVyyWT0B1yMNDCq8tbS6VZcUv6/t4eLBRrzm/etoXhfYkcA8C2vJxfDdUml8C4vXWuK14dxRT2fx2JpW7PRv8DoXX8vu0DDoJD/wPGvjnzGq9clf/+5fLaz2Hhr7Ibu+be1O2VEMyg/Ayem7p94S/hxgnpH2ML2W/gTJj55eKtO+HS4q3VU8KChHZshOuHwJ+GwvwvGr4jqXIkOlO3hwU99pTzV4b3Lb8ZHn5P9ms9+sF929bcB8szvNaT8hX22q2S3j+INYR/UER9v/xC0CEIvq1ryb+uQkVjcMrdsP+VMOiw3OZufBKWhHxowE7ty3Kv6b5zUodShr0erqT7iSRJkiRJkiRVqbTBaNHqCUarSxuMVsCxZJIkSZIkSZJUIUx4kFRZGkoQjJZMwHNfDO8fOCv/taVa1Dwmff+cH0PEpxAqsYaBqds718Gdx0JyrxPDXvwOLPh+YXumC5QJOxFQ6i12bAzvCwtM6y02L0jfH60rTx2VKtaU37wtaW7XXA/aSrdWLck1MG53lRqMtvfv+50iBfxcRSLB89low662WAsc8r2g74hf7tkXpqtCH/val8HTn4ZH3lf4Wr398auajT67vPvtHowx5tzirDnt/8GgOcVZqydlChLq2gLzvxCEGUpSJQgNRsvi+VE5NY+AYceH969/uPA9lv1pz+vJJGx6Hhb/HtoWF76+eq94W+r2SgsgTCefMNx++8P0TxS/llzV94NDvgNnPAoXbYeBs7Ofu/RP6fs7VuRX06Pv37ct7PWwr9MkSZIkSZIkqWDt8W2hfc2x1jJWUph0wWhdBqNJkiRJkiRJqgGmmkiqLI2DUrfne6J+dwf85cD0Y3I56UHqDfpOhn7TU/cd9lMY9+by1qPeKdaSun3lbUF4QCqL/rewPRM7Cpsv1bKOleF9XVuCINreatsr6fvTHHzUK0Tz/P7XPx7ely7IMpXeEoxWyMFs3dsgvr14tRRLWNhboSeCjzkbznwKDv46zP42nPkkjDgl6KvvCxPelnmNrS8XVkMpLPpfuGkyvPDN4qzX2x+/qtmQw8u314hToG63g4L7Z3gPJpUZn4Lpn4T93hGEbJx0J8z5XvFq7EnZBoQu/m1p65CkbMVD3huJVWBg0+QPlnb9Rdfs+jrRHQTP3nogPHAR3DgBnvtqafdXbUrEoXND6r6mIeWtpRBDj4Zzl4T3R+qgdUJw6TMZDrsK5j0Ldc3lqjA7sUY4/ZEgIDsbS69P/9q5Pc9gtBW3BeGLuwt7PVxIULgkSZIkSZIkCYC2ROpgtOZoC7FIrMzV5K8uzXvG3WEfwCFJkiRJkiRJVcQjZyVVlqYRqdu3vZbfenedAFteDO8/7gaIRPJbW6pVkQgc83u4+3To2O1EnoO/DlM+3HN1qXepy+MT1zY9E5xAlu/jetpgNPOE1ct1ZDixc90jMPTI8tRSSRJdsDXD89RCA5x6q+U3wcFfTt1nMFpqYSdNZ6tzHbSMKU4txZKMp24vxong/WcEl1RmfQO2vATrHgyfv3kBDJpTeB3F0rkeHv0gJDqLt2a+wYbqeU0joK5PEHpYaqPP3fN6JALTPw4vfie7+fUDgteatfreTDTLIKHVd5W2DknKVthziWwfz8pp/MXw4KWlWz+ZCAKsGgcFAZYLf7Fn/7OfhRGnwpC5patBtWfHBiCZuq+xioLRAFrHwls2wf0XwOq/B21Nw+GIq2HU6T1bWy6i9TDxXTD85OB72ZAmqB3glunQOh76TIR4JyS7Ycnvgw/6iLfnV0PnWti+CppH7moLO2HNYDRJkiRJkiRJKlh7fGvK9pZY3zJXUpj6NB962F3Ih2xKkiRJkiRJUoUw4UFSZek7NXV7xwroSv0HqFCPXQbrHw3v7z8Dxpwb3i/1ZgMOhHMWwSn3BwGCF6yBAz7d01WpN6lryW/ejo3575kuGK1WgxqkbG1fnb5/+U3lqaPStC0OP1F1J09Yzc+mZ2HbwtR9uX6aZW8IRkvECT25Plud64pSSlGFnghe4k9mbRoGp9wLZ6Q5IX7tP0pbQ66W/gniHcVd02C06hWJQN8ppd+nZUwQIrG3cRdlv8bIU2v7uXasAoOEJCmd0GC0hvLWkY1IBM56qbR73DItCGbeOxRtp+U3lnZ/1Z50r7uqLRgNoKE/nHQnnPFE8Brq7FeqKxRtd61j4fRHgr+JpNP2Oqy5Fxb+EhZfG4SiQf6haDtt2evxLOy96lK/HpYkSZIkSZKkXqAtnvqD5lqjfcpcSWHqIuF/w+tOpvuwaEmSJEmSJEmqDp6hLamy9JsW3rf1FRg4C7a+ChueCC4bnwxCcFonwMyvwIADgrFbXoFXrkq/1/hLi1a2VJNiDTDsmJ6uQr1VLM9gtI7l0Dgov7npgtGo4bAGKRuZwnaW3Qizvl6eWirJ9rWZxxgsBCPPgJW35z5v+S0w7Yp92xM5fppl2yKIb4dYU+41VItsw+JOvgfuOiF1322zYcLb4eCvBSeEV4LQYLQyvJ0VrYNBc2C/d8Cia/btf/U/g5/vQ75bGT/nS/+UeUzfqXDiHXDTftmtabBjdRt6NGx8Kv/5kbrgZ7DPJGgeBRse3/P5wJAj4dCfQH2Kg4L7HwD1A6BrU+Z9xr45/xqrQdOw7Mc++XGY8SloGlq6eiQpnUQckonUfZUa9NhvKkx6L7z289Ks37kOVt4Bq+9O3f/81+Dgr5Zmb5VWMgHxziAMcOe/iR37tqXq26d/R+o5qebuSPP8qGFw+b7/YopEYNAhPV1FcUSiwd9EzloAt+xf/PWP+CU8/O7UfXedCPOehQEHBdfDPgCkoX/x65IkSZIkSZKkXqYtvjVle0us2oLRwo9Z6sr1w0clSZIkSZIkqQJ5hqOkytIyFqKNwQkie3vwUuhYCV1b9u3b+DSs/Cuc9SK0joMVt6bfJ1oPU/+5ODVLkoqvLs9gtPYVu04ey1W6oJ1INL81pVoRT/HcbHdbXgyCaftNKU89laJzXeYxBgvBtI/BqjuzD+/aadNzqdtzDUZLJqBtaW3fP7O9TYYfDw2DYMeG1P2v/xpW3wVveh4aBhavvnwlQu4z0TL+XI16U+pgNICXfwzdW4OT23tSvDO78MEznghCrObNh1uzeL5U11x4beo5Uy4P7rup3kMBmPQ+eO1/wudfuCV4b6ZhQO571zXD9Cvh2c+lHzdwNox9S+7rV5OmEdmPXfBdWPsAnPoPiMZKV5MkhUn1nvxO0QoNRoMg3DebYLTxl8JRvw5eH+xYD099EhZdnXney/9ReI07da4Pnmf3tveZEt2ZA8MK7cs1pKzSTgaqa/X5dyXpNw2O+SP8o8jPVaMNQYDx2gdS9986E057KHgO2bEi9ZjGIcWtSZIkSZIkSZJ6obZE6mC0PrF+Za6kMHVpjk3sTuZ4jJ0kSZIkSZIkVSDP0JZUWaIx6DsFNqcIQdjyUvq58XZ47itw+M9g3UPpx56zyE9Vl6RKFmvNb17H8vz3TOwI79v4VP7rSrUg3QnyOy2/Efp9vPS1VJJsgtGi4Z/K2GuMOh1OvA3+fmpu83ZsTN2eazAaBMEH1HAwWi4n9TcOCQ9GgyCMetGvYdpHCq+rUMl46vZyBg6OOhPq+kD3ttT9C38FRODw/+m5gItlN2QeM+K0IBQNoP8B0GcibFuYfk6/GYXXpp7Tf3849QF44Vuw4fFdjxPNo2H8xTD5A+mD0eqagQLCOQ74TBDosPi3sH0VdLdBfd8giKSuFYYdDwd9ofYDwCKR3MavfxiW/AEmXFyaeiQpnbTBaA3lqyNX2YYETbg0eFyOxKBpGEz5cHbBaCvvKKw+gA1PwoNvD0LF6wfAAf8G0z+e+++JTJLJ4PVSysCwTojvSNFWypCyN/qTieJ+n7XIsKvKM/L04q8ZrYdBc8OD0QD+emT6NbyvSJIkSZIkSVLB2uOpjwNqifUpcyWFiUQi1EXq6E5x7JjBaJIkSZIkSZJqgcFokipPv2mpg9GysfxGSP4nJLaHj5n9XWgZnd/6kqTyqGvJb17Hivz3TBeM1rESdmyChgH5r1+pti0Kgt8GHQqt43q6GlWqdD8fO73+m+DE7lqX6IY190J8Oyy+LvP4cgY4VbIRp0DDoPSBXHvr2pS6PZcQsJ22r819TjXJJixuzHnBv01DYevL6ccuurpCgtFC/q+jZfy5qu8L0z8B8/89fMzCX8LQo2HSe8tX1+7WPph5zMwv7fo6EgnuDwu+Fz4+2hj83Kq6DTgQjromvH/ax+ClH+zbPvE9he8dicDk9wWX3m78Jdk9Z9jp5R8ZjCapZ8TTvO6LNZavjlxlExI09s0wat6ebUMOh/3emV04WiG2r4Xb5+y63rUJnv4kLL0exl4QHiaWb0iZqlPLmJ6uQHur7wP7XwkLvlu8NSP1MOYceOn7+a9hMJokSZIkSZIkFawtJBittcqC0QDqIg0pg9G6/LuRJEmSJEmSpBrgGdqSKk+/afnP3b4GVt4By24MHzPhbfmvL0kqj0oLRgNYdhNMfEf+61eaZDI4EffF7+xqO+AzMPPLQZCFtLt4Z+YxG5+GFbfBqDNLX09P2bYQ7j0HNj+f/ZxorHT1VJtoQ27jd4QEo2UTAra3znW5z6km2YTF7Qw6yuYk6g1PFFZPsSRCvq9ImX+uZnwqCDXasiB8zCPvg9HnQlMPnKSeLuhu8ofgoM9D88g92zMFo+33T0EQgGrbuLekDkYbc275a6llk96TWzDauodKV4skpZNI87ovWsnBaIPT9x92FUz+QOr3Oo74ZfC86P7z89s7Ek3fv+VluCXk7x3rHwkuEgSvJVR5Zn87CDFLJoqzXrQehh4D/abDlhfzW8NgNEmSJEmSJEkqWHt8a8r2lmjfMldSuLpIfcr2VGFpkiRJkiRJklRtDEaTVHkGHlLY/HvmhfcNPQaahxe2viSp9GKt+c1rX57/npmC0Z76eG0Fo624dc9QNIDnvwrDjoeRp/ZMTapc6U6Q39098+CSRG2G68W3w02TerqK6hbLMUxhw+Op25MGo+0jLEBsp+mfhNFnBV83ZAiO2OnxK2Ds+TD8xPzr2vIyvH4trH84CFsbejQ0jwnWHHtB5seKsAP0ImV+OyvWCCfcBjftl37cPWfCGY+Vp6bdhQWjjTgN5l6Vum/IUdA4FDrXpuiMwMFfK1p5qmBDjoKZX4FnP7urbfrHYfTZPVdTLRp+cu5z1j8Ogw8tfi0qv/YVsOhq2PISkOzpaqT0ulKfAALk/ly+nKKpT/b4P5M/GP68MxKBsefBQV+A+V/IY+8M4c+Pvj/3NdX7jH0zTLuip6tQKpEInPl0EJLf9nrh60XrIVoHR14Nd58OOzbmvobBaJIkSZIkSZJUsLbEtpTtrbHq+xDBupDjqLrzOcZOkiRJkiRJkiqMwWiSKs+Yc6BlDLQvCx8Tieb3Ce2eXCJJ1aGuJb95HSvy3zOR4SCAlMEhVeyVkKCUhb8yGE37yhQcuLuFv4JJ7y5ZKT0i3gk3ju/pKqpfNI8whZd/ClMv27Mt0+N1KrUejJbuQLZTH4ChR+26nu3t9/KPg8vsbwdBSblacx/cfSbE23e1Lbsx+PeVn8Ck98PhP0u/RljgW7mD0QD6TIDzlsMNo8PHbHgcVv0dRpxUtrJIdIefoL/3z87uojE48PPwxEf27bu4K+hX7YtE4MDPwMR3wcanYMBMaB3X01XVnkgEmkdCx8rs59xxGBx1HUy4uHR1qfQ2L4A7j6395yHqHTIFgPW0gbOD32V7a92vtMHd6W6X7WuD58SqbpFY8Fo21pji34bUfdFGiDXs9nXI3FgzDD4M+k6tzYD5WjHgIHjT83D3abD2gX37j/oNvPBN2PRs5rV2BjkOPgzOXQJ3Hpf6sSudxizDziVJkiRJkiRJKSWTSdriqT8wqDXWt8zVFK4+kvpDhAxGkyRJkiRJklQLDEaTVHmi9XDSnfDQu2DDo0AE+s+AgYfAoDnBZeDBcPthsOXF3NYePLcUFUuSii3WE8FoOQQ/1YIVf0ndvvhaOPo35a1FlS/emf3YR96TWzBaojs4gXTV34Kwo+lXwqgzc6+xlF79GWxf09NVVL98whSe/iSMews0DdvVFhaWlU6tB5Kku01a9wr127Eht7Wf+gT0nQZjzt53z0XXwPNfhW0Lg7bm0dCxPLt1X/vv4LKzvvr+MPxkmPklqH/j01eT8dRzoz30dlbLqCBo7m9Hh495+F1w3pKylcSOjeGh4S0ZAq6mfBg618CCHwQBdsNPhMN/YShab9QyOriodPL53fXMp4PfgT31mKfcJROw4Huw9E/Ba9O2xT1dkVQ8+YQcl9OBn4X737xv+9A0z9uKId3ju48Bufu/oLE0oWLRhpCgsRz6sg44a/C5sQJ1LXDyPfDUx+G1X0D3VqgfAAd9HsZfAmPOh6euhIW/hPj28HV2Pzmtvk/w+vKpK8M/wGKfOvpCv/0L+lYkSZIkSZIkqbfrSu4IDQ1rqcJgtDqD0SRJkiRJkiTVMM+qklSZ+k2D0x8KTihMdAcnwuytcUhua467aN9QAElSZarLMxht+2pIJiESyX1ubwtGqwWdG6B7GzQM3HXirEojkUMwGkDbEmjNEMiz00PvgMXX7bq++i44/mYYfVZue5bS4t/2dAW1IZZHmEJ3Gyy7ESa/f1dbPgdtbV+d+5xqkkhzm0T3Ovht7JvDwzHD3HcOnHIvDDsu+D2b6IIXvwXPfm7PcdmGou1u98CITc/ChseDvSIRSIYETUR68O2soUfBcTcFt0kq7Uth84tQ1xpc4tuhcTDEmopXQ6I7eK0ca4DO9eHjGgenXycaC4LoDvxccJvm8/xJUnbGnBeEQeaibXHwuNh3chCCCEGYRl1LECbpz2zleeJj8PKPe7oKqfgiUYg193QV6Y05Pwh5XX33rrZoI0z9SHbzBx2a377xdkjEU4dnFRLeX3KR9MFgxQgiSxc4lnLPBn+3qbJF62DOD2D2d4Lw/OYRweMjQF0zHPZTmPNDePQDsPBXIWvs9fp893m3zYLNL6SvYeo/F/e1pSRJkiRJkiT1Qm3xraF9rdE+ZaykOMKC0boMRpMkSZIkSZJUAwxGk1TZItHwkJNcg9GO+t/C65EklUdda37zkvEg4Cyf8B2D0arHhifh4XfBpvl7tk/7KMz+buoTklWYXH8+lt0I07I4AX3ZTXuGou1079lw3I0wJiR4qJy2r4N1D/V0FbUhmsdjM8CyG/YMRksXAhamokMJiiAsQAz2DREbeWp+ezz7eTjke/DYh2H9o/mtkY2198PKO2DUGWmC0Xr4cX7M2dAyNghBS+UvM/Zt2+8dcOh/QH0Bnywb74Rn/i0I3Ul0waizYMIl4eMzBaPttPfJ+ZKKb9S83IPRAG6fA0SA5L59c34YBP4YIlMZNj1vKJpq15CjKj+IPBKBY6+HBT+ElbdD8yg46PMwcFZ280ecDPX9oGtL7nt3b4OG/vu2z/9C5rn1A6D/jPSBY4WEjYX1GYor5S9aBy2jQvrqg9eKoXNDXntF6+G0R+CpK+HVn6UeM+fHMPXy3GqVJEmSJEmSJO2jLb4ttK81VjvBaO3xrWzt3vzGmDqaY3kely1JkiRJkiRJPchgNEnVK5dgtFPu92RvSaomsZb853a35ReMFjcY7f8suwmGHp19oEohkskg4KxjBTQMgu4t0GdicNnb1tdg45Pwj7emXuulHwYnFc/8QklL7pXinbmNTxWMtmMTrP0HdG4I7lsDZ8F954avcd+5cNYC6Dct93o3Pg2r7wnuU8NPgNZx2c3r2gLrHoaOVcGJrk3D4aUfkTKMRLnL9/n4iluhfRm0jAmu5xOMtvEp2LERGgbmV0OlS3eb7H27t4yBGf8KL3w9tz3W3PtGQE8ZLPlDEIyWCAlGi1bA21mnPQw3jM5+/KJrINYEc/8r/z1f+CYs+N6u6ytuCS6pRBsLez4lqbhGzYMRp8Gqv+YxOeR5yBMfhebRMO7NBZWmIrn1wJ6uQCqNur4w6xs9XUV2GgYG7wfk855ArAlmfQse+1Duc1MFoy2/NXgNks7BX4UD/i33/SRVuDShg+neF6nvE7xe7H8gPHHFrva61uBvnINmF69ESZIkSZIkSerF2hNbQ/taaigY7Y4N13PHhuv/7/rwhjG8Zei7OaBPmY7/kiRJkiRJkqQiqIAzSSUpT9kGo9X1gSFHlrYWSVJxReshEoNkPPe53W3QOCj3ecksgnaSSYikObmtVtx3LhCBY34H4y4s3T7dbfDwe2HJ7/Zsj9QFJwfP/GJwPZmAZz6bXYjPomsMRiuFRI7BaGvuhfj24ORyCILS7jsv931v2R8uiUMkmt34RDc8cDEsvX7P9tnfgelXpp+77hG4/3zoWJl9feMvgUGHwlMZ1tYb0gTMxZqC+0yYG8YGJwhP/gAkQ8KyMrl+CBz7JxiTJpCvWqW7TSIp3vo5+Ksw7HhYfhO88tPS1ZWvhb8IfgesvT91f6rvqdxaRsH0T8CL385+zms/h4O/ll/waHcHvPit7Mc3Du4dz1mkahFrgBNuhdd/Aw+/s3jrvnKVwWiVYNP83MZPfFdJypCKrt/+MOZ86De1pyspjykfhAEHwd+Ozm1ed9u+ba/9T/o5pz0EQ47IbR9J1S/k5LQ9TPtI8DfNZX+GaBNMfj80jyh9bZIkSZIkSZLUS7TFt6Vsb4o2E6uEY5JyVJ/Ne8/A6h3LuGr51/jU+G8ztinFBxdLkiRJkiRJUgWqvndtJWmnpqHZjRv3VojGSluLJKm4IhGoa4WuLbnPTXVC6k6JOKy8A1b/HVpGw6T3Qn2/zPN2SnZndwJbTUjCQ++EEadAw8DiLr1tIbx+HTz72ZCtu+G5L0HX1iAQa8kfoH1Jdmu3LYJEVxCup+JJ7EjdPvxEWH33vu3J7iAAaOrlwc/xAxfnv/d1Meg3Pfh6yBHQOgHGnh+csL5T2xKY/8UgTCmVpz4Or18LA2ft1piAtQ9C/wNg5GlBuFEuoWhTr4BDf/jG+gajZaX/QbDmvn3bow1BMMBts9PPf/SDwWNSIosgy1SSCfjHRXDBamjon98alWbdo7DsBlhxS/iYVI+HkQiMOj24dKwMTriuNDeMDe+LVsjbWRPfnVswWjIOj/8zHH1d7nutviu75yo75RO+Jqm0ojGY+I4gkHL9I8VZc/Vdwe+3bENkVRobn8l+7Fu3Ba91JVWmoUfB1I/Ayz/Ofk68fd+2dM+vp/0/Q9Gk3irb9ysHHxpcJEmSJEmSJElF1xbfmrK9Nda3zJUUR10Ox8omiPPYlvsMRpMkSZIkSZJUNSrkTFJJykPz6OzGzfhkaeuQJJVGrCW/YLR4SGhIMgmPXw6v/teutgXfg9Mfg+YRQQhXJokdvStwK94By26Cie8s3pprH4S7T4fubqWuyAAAIABJREFU1J+6t4eXvp/fHot/D/u9Lb+5Si3embp90BxYc38QhLa3x/85CPFpnQDx7YXtv+XFPf99/mtwzO9hzDmw+l6464TMa2x8MrjsbevLeYRCRWDy+3KcI8a/NQiDIbln+7iLgtC6t7bB7zMEhSy5PjwY7bgbg/vcg5eGz090wqq/wrgLcyq9Ii38FTzy3iAQJ51IhpDobAOnCzH3v4PwtmU3Fme9TN9zufTbH/pOga2vZD9n8W9h4OzcX6cuuyG38Q0Go0kVa/wlxQtGA9j6GvSbUrz1lLv2pdmNO/luQ9GkatA0PLfxe4fXdqcISttdv2m5rS+pykTCu3rT+8qSJEmSJEmSVKHa46mPXW2J9ilzJcUxuH5YTuNX7VhWokokSZIkSZIkqfiiPV2AJOWteVTmMcf+2RONJKlaxZrzm3f7ofCPi2HT/D3bn/7knqFoAO3LYP6/B18vvjbz2mFhPLVs8wuFrxHfAa/8J/ztWPjb0dmFohXiUQOrii6xI3V7XR/oOyl83tOfggcuKkE9nfDUx2HjM9mFohXblMtgwEHl37faDTsOjvxfaBkTXI82BOEwc68Krte1BEFT6ax7iH2C1XZqGADjL4a+U9Ov0V4DB3d1roeH351FKFodRNKclA3QmNvBcTmb9tEgSPDQnwT1FEPHyuKsU6hIBMa+Jfd5878A29dkHrf2QbjnLPjjIHjt57ntMfiw3OuSVB7TroADP1e89W6fDX87Lvi90La4eOsqOzs2wzP/ln5M49AgJHT4CWUpSVKB6vvlNn7vYLStL6cf3396butLqh0Go0mSJEmSJElSj2tLpP4Q5dZY3zJXUhyz+xxFJN2HduwlLBhOkiRJkiRJkiqRwWiSqlfL6PC+ie+Ci7th7HllK0eSVGSxhvznLvkd3HE4bFsUXH/pR/Did1KPffVnsPDqfYPUUumNwWjZ3C57i2/f87Z65l/hsQ/D2n8Ur65M+3esLs9evUWiM3V7tDFzkFWpbH0FbptV/n37HwgHf7X8+9aK/d4G5y6B85bBhZvh6GuhrnVXf+v49PO3p/nZ3hkCNv3K9Gt0rMq+3koS3x4EoiW64foh2c2JZhFE1lTiYLTmN163tYyG/d5RnDX7H1CcdYphxieCwJtcxDtg8W+D/0sIAu6SCUgmIREPfoetuS8IE13xF9ixMfe6Jr4n9zmSyiMSgZlfCt63mvTewtfrboO198PCX8Hth+X3mKH8JLrhrpPSj7lwK5y/IggJlVQd6nM86WXvYLTNC9KP76nX0JLKI104ecRgNEmSJEmSJEnqaWHBYC2xPmWupDgmt8zgn0ZcQZ9Y/6zGtyUMRpMkSZIkSZJUPbI4Q1aSKlTzyPT90Vh56pAklUakwKeq8Q64aSIc8Fl49b/Sj334Xdmt2RuD0VbeDtvXQVMWITxd2+CR98KyGyEShXEXwoS3w4Lvlb7OvT33RTjsp+Xft1bFw4LRGmDkGcH/eW8w8nQ46jfQkN1BRAoRiYSHHGcKRos1h/dF3zjBePIHoK4PPPi21ONe/BYsvi54jBh9VuZ6e9qOjfDwe2DZDbnPzeak68YsQ9byNfK0XV/P/Rn0mwYrbg2uT/sobHgCns8xbHDIEcWrr1ANA+H0h+HZf4fXf539vCc+Ck/+CyTjwXOeZHfxapr0Xuhv4IZU8aIx6DuluGt2roVbZ8F5i4u7rlJb/XfY+GR4/+QPQn11Hjwv9Wp1uQajte95fdmfw8cOmpN7qK6k2hE1GE2SJEmSJEmSelpbfGvK9tZojn8jqiBH9D+Ruf2OZ13XarqTwXHOz217nBvWXbPP2PaQ71+SJEmSJEmSKlG0pwuQpLzFmiDWkrpv3MXlrUWSVHzFOlHs+a8EAQHFkNhRnHV6WiKX8JUkPPu57IY+/E5Y8ntIdAbBdIuugbtPyzyvFF65CtY92jN716Kw+36sMQi/G3x44Xu0jIUjfgWRCg23PeAzcOLt0Di4pyupbZmC0dI9nu/+e2PCpTD5Q+Fj25fCvWfDpudyq68nPHBpfqFokN39tS7kNVUxTHw3DDx41/VoDGZ8Ek65J7iMPR+mfiS3NSd/EAbMLGaVheszEY76X7g0ueflopBQyZ2S8Tf+LWIo2qA5cOhPireepNIaNS99f8tYeMtGOOX+7NdsXwKr7ymoLGVp6fXp+/sfWJ46JBVXfY4nvcTbdvt6e/C+SJhZ3wiCoiX1TgajSZIkSZIkSVKPa4tvS9neEqvuD72KRqIMaxjJqMZxjGocx+jG1MfhtcW3kUwmy1ydJEmSJEmSJOXHYDRJ1W3iu/dtq+sDw48vfy2SpOKqq8CDDBJdPV1BccS35zZ+0dXQ3Z5+TOcGWHZj/jWVwoLv9HQFtSMREu4TbYT6PnDy3TD94/mvP+ZcOPNpmPhOOPOZ/NdJZdjxQXjbzkusOfu5E94OU6+A4/8CB3+luHUptdYJ6fs3PRveF6nb83rjkMz7vfLTzGN6UttiWHl7/vNHnp55TF2RP+20zySYcjkc8wc4/OeZxzcPD0J/MmkaAcfdGIR+VUuYRKwBzniidOvP+SEc8n2Y+C6Y+B447Kdw6gNBaKWk6tD/QOh/QOq+Q74Hb3oBGgbAsGNgzo+yX/fV/y5OfQrXthhe/Vn6MS2jylOLpOLK9flx927BaMtvDh839gIYcUp+NUmqHnu/N7E7g9EkSZIkSZIkqce1xbembO8TK/IxVD2sJeT7SRBne6KjzNVIkiRJkiRJUn7SHJkrSVXgoC/Axidh3UPB9VhTcAJ+rKlHy5IkFUGxg1qKIVkrwWg5HtQQ74CXfwwzPhU+ZuPTkIwXVlcu6voEgT9Lrw8fs+QP0LkeGgeXr65aFQ8LRmsI/q1rhtnfhtb94PHLc1v7wH+HmV/YdX3AATDoMNjwWF6lAkEY2sl/h0iKLPDkNfDYZfDqf6Zf47yl0DImu/3m/jc8+v592ye+K7v52qU19SdVhlnUMYEb153Dpu4BnL2iD3MG7NaZVTDaVUGYVKVadWdh82d9I/OYwYcFP8uJHYXtBTDtY3DId1P/7KXTMABO/Qfcdz50rt2rb1DwGm/ESYXX1xMGHRKELL7+6+KvPf4SaBpa/HUllU8kAkf8Eu47FzpWBm2tE4LnMX3223PsmHPgiSuyW3fxtTD+Ihh9dvWESVabxy7LPKZ5dOnrkFR89Tm+H9W5YdfXG54KHzf+0vzqkVRdJrwNnv3svu0tY9KHpkmSJEmSJEmSyqI9sS1le0usAj/MuQCtab6f9sRWmmMtZaxGkiRJkiRJkvLj0beSqlvTEDjl3uCEo+0rYdhx0DCwp6uSJBVDfZqDDA67Ch77cPlq2akYoTGVINdgNICnPw19JsG4t6Tuf/FbhdWUq/EXwZgL0gejAVw/BM5fCc0jylNXrQq77+8MRttp6mVB+EY2QREAk967ZyjaTrmGKu1u4Gw45Z7w/kgE5l4V3IfuOjH1mAtWQ9Ow7PccNQ/qWqG7bc/2sRdmv4YCOQSjPbz5cObNv4VN3cHz/y//MMkv35XgHUe+cf/JJhgNYP6X4aDP5Vpp6W17HR55X/7zL1gdBI5lUt8XxpwHS36f/14AZ70E/abmP3/o0XD2S7D2AehYFbQ1DYOhx0DjoMJq62mzvw3Lb4KuLcVbc8plhqJJtWLwYXDWAlj3SPAcaOgxEGvcd1zLWIg1Z/9c/r5zYdL74fCfFbdewdbXYMWtmcc1jyp9LZKKL9dgtOe/Agd8OnhNuGVB+LjRZxVWl6Tq0GcCDJ4L6x/ds33cRQbWSpIkSZIkSVIFaItvTdneGqvAD3MuQLqgt7b4NgbXDy9jNZIkSZIkSZKUnwLONpekChGthyFzYcy5hqJJUi2pSxOMNvbNMPWK8tWyU6Kr/HuWwuYX8pv31CchEd+zrWsLPHY5rLyjsJrq+kLLuMzjog0w4W0w58cwel4QkpfJ058urDaF3/djDfu2TfkwHPIDqO+ffs39r4S5IUEd+Qaj7fcOOPmu7MYOPwFOuQ/6TNy5KQw+HM59PbdQNICWUXDiX6HvG6FQTcNh7n8F91Hlpmlk1kM/s+gr/xeKBpBMRvjIdUm6upNBw+BDs1to/udh28JcqiytjlXwyAfgpv0KWyeX+/HhPw9+t0brg8uY82D8xdnPH3lmYaFoOzUMDAIjJr8vuIw5p/pD0SAI55z5lSIuGIFZXy/iepJ6XH0/GHkqjDg5dSgaBM+PhhyZ27qv/Tf8rgUWXg3JZOF1KrD85uzGGc4sVae6PE56+euRcG0Elv05df+4i8If3yXVnuNvhuEnBc/fYi1BWO2sb/R0VZIkSZIkSZLU6+1IdNKVTP0hsS3RNMcsV6GWaGtoX3t8WxkrkSRJkiRJkqT81fV0AZIkSVJK6U5EjTbApPfCwl9Adxn/QF8rwWgv/SC/eW2LYONTu8KGkkm473xY/ff81qvvD2e/DJE6qO8LiW74fUv4+HnPBiFWdbsdsDHlQzD5A3D3mbDqr6nnLfl9EKBW15xfnYJkyH0/Up+6ff+PBgFpiR3Byd+JHdC5Pvi/i8Qg1pzhpPBI9rUNPAROvQ+I5v5/POxYOPtV6FgJsabCApiGHgVnvwTb10HjYIjk8D1ol2gsq2GdiQbu3nTiPu1bt8M9L8OpM4B+07Lfd/Hv4YAKCFHs2gq3HwodywtbZ9BhuY2v7wPH/hG62yGZCK5veAoW/zbz3EgMpvVAWGm1mfIheOYz0J36U2ez1mcSnPViEGAnqffZ/0pYc0/wWJ2teAc8/C7YvgZmfKJUlfUum5/LPKZpuI/VUrWqzyMYbdP89P3j3pxfLZKqU9OwILh/x+bg/SaDESVJkiRJkiSpIrQn2kL7WmN5/I2ogkUjMZqjrXSk+J7b4gUevyRJkiRJkiRJZRLt6QIkSZKklOrTfPpatB4GzoST7ixfPVAbwWidG2DV3/Kff8dhsOD7QSja8pvzC0VrnQBj3wynPxacKNg4KPg/zRRq1Tx6z1C0nSJRmPy+8HnxDlh9V+51apdEd+r2aJqs7VhD8HMcrQ/+31rHBYFhDQMynxCaLlRs7s9g2HHQ/0CY8Sk49R/B+vkG30Ui0DKqsFC03TUNMRStUFM+nHHI5u7+oX0LViV3XZnwT9ntueyG7MaV0o7N8Id+uYWiDT0mdfuYc/Oroa5l1+/fQbPhtIeh3/67+kecCvOeg2kfC34GR54Jx/8FRp2R3369SbQe9nt7bnNaxgS/J5uGw6A5QRDoqQ8YtCP1ZqPnwXE3w8jToXkUNI/Mfu7Tn4S2paWrrTd57eeZx4x6U+nrkFQa0XoYemxx1+ybQ2izpNrR0N9QNEmSJEmSJEmqIO1pAsFaYmmOWa5SfULC3gxGkyRJkiRJklQt0pzFLkmSJPWgkafD819L3RdrCv4dcjhM/wS8+O3y1JTYUZ59Smnjk5BMFLbGk/8C21fDhidzm3fg52Dml9KPiTaE3871/cLnjTwDIjFIxlP3L7sBRp+VXZ3aVzIkFLBk4TxpgsUmvB0mv79E+6oi7P8vsOymtAFhW7rDHw/67H7OcfOI7PZc/wi8/NMgUKxldJaFFtmDb8t9zsT3BMGAK+/Y1dZvOkz+YHFqGnI4nPXivu1zvl+c9XubmV+Bhb8KAjt3F6mDo6+DcW/pkbIkVZnR84LLTmv+AffMg+4sDly+ZX+4KPwTsCtadwdsehZIwuDDey6IduMzmcc0j4TpV5a+Fkmlc/BX4c7jirde3ynFW0uSJEmSJEmSJEl52ZYmEKy1BoPRWmJ9oWvVPu1tiW09UI0kSZIkSZIk5S7a0wVIkiRJKQ09BlrG7Ns+5jyI7PY0tpxhV4mQcKhqUqzv4YVvwqq/ZTd2ymVw/C2ZQ9EgCIcJE03TV98X9ntHeP+quzLvrdSSifAwvXT/XwVJE3SxMxhRtavvZDj90bT3r83x/qF9rbsHozUMyH7fxy+HG8bAM5+FZDL7ecXQvgxW/CX3efV9g8fXw38Bkz8Eh/wATnsQmoYUv0YVrnEQXLAKDvhs8FgWrQ8CXk9/2FA0Sfkbdgyc8RhM/3jmsfF22Jwi8LLSbXgSbjsY/noE/PVIuGMutIcHqJbU/C+m7+8zGc58GvrPKE89kkpj2LHQlGXIcib1A6CuuThrSZIkSZIkSZIkKW/t8dSBYI2RJuoipfqQ2J7TGk0d9taeJiBOkiRJkiRJkiqJwWiSJEmqTJEoHHVdcALpTn2nwqE/2XPc0GPhgH/bs23S+0pTU7IWgtG6y7fXzC/DpUk47Ccw+k3ZzUkXfpbJrG+F97W9Xv6go1qRjIf3lSoYLZLmpWokTWiaakfLKDjws6Hdm7vDg9FaGna7j9TnEIy20/NfhVV35j6vEJuey3NiJHjcnPRumHsV7P/R3MLgVH71/eDgL8NFHXDxDpj9LRg0p6erklTt+k2D2d+G4/8SPM6k8+zny1NTsSST8Mj7Yesru9o2PA6Pf6T8taz8Kyz7c/oxZ78MTcPKU4+k0hpyZHHWGX5icdaRJEmSJEmSJElSQdpCAsFaY33LXEl5tMRSB6O1hQTESZIkSZIkSVKlMRhNkiRJlWvYMXDuQjj+Zjj5bpg3PwjL2V0kAgd/Fc5+NQhSO/MZmPuz0tST2FGadcspXchVMY16U9pQo1CDDs1/z6YhcNpD4f0r78h/7d4skSYQMFqiT0nsM6k066q6RBtCuzalCUbbIwMxXcheOouvzW9evjpW5jfPEDRJ0u5Gz4OzX4EDPxc+Zukf4cXvlq+mQm14AjY+uW/7sj9D29Ly1TH/y3D36enHHP5zQ3ylWlKs59pTLyvOOpIkSZIkSZIkSSpIeyJ1IFhYgFi1Cwt8C7sdJEmSJEmSJKnSGIwmSZKkytYwEEafBcNPgFh4SA59J8GEi2HgzOBk9BmfLn4t6QKiqkWyuzz75Hv7T/946vb93pnd/KZh4X2Pvj/3epT+PhOtK82eU/85dfuoeaXZT5Up2hjatTlNMFp3YrcryUTouLSWXJ/fvHy1L8t9Tl0rDDmq+LVIkqpb0zCY+SUYODt8zFMfh62vlq+mQqx/JLxvxS3lqWHRb2D+5zOPq6/NTxGXeq36IgSjzf0vGH5y4etIkiRJkiRJkiSpYG3x1IFgrb0sGK0tvrXMlUiSJEmSJElSfgxGkyRJUm2a+K7ir1kLwWiJLILRhp9Y2B6Dj4ChR+c3d8Qp0P+APduijTD5A9nNbxwS3te+DLo8oCNn6e73kfrS7DlwFgw9dq+9ojDlw6XZT5Upll8wWnz3LLR+0/Lbu3treQNjsglb2dukD0Bdc/FrkSTVhqN/m77/5inlqaNQG54I79v4dOn3X3oDPPT27MbWGYwm1ZSGAoPRRp0VvJcRiRSnHkmSJEmSJEmSJBWkLb4lZXtLtDb/1tsSEvjWHhIQJ0mSJEmSJEmVxmA0SZIk1aZ8w3DSqYVgtGQ8fX/rBDjxbzDrm1DfL/f1hx0PJ/01/xN/o/Vwyr0w6X3B/+GoN8GJt8PQo7KbnymMYOsr+dXVm6UL04vWlWbPSAROuBWmfgT6z4ARp8FxN8Los0qznypTtCG0a3M8PBitO57cdWXosVCfZ6DBzVMgmcw8rlCbnkvfP3guTP8kzPpWEDw56FCY9Q045Dulr02SVL36Tc383Hj94+WppRDrHwvv61xXun3jnfDoB+H+87OfU1+bB8tLvVahwWiT3lucOiRJkiRJkiRJklQUYYFgrbHa/FtvazR1MFpb3A8YliRJkiRJklQdSnQWuyRJklSDEjt6uoLCJdOEXEXq4OCvQzQGMz4Jkz8AfxyY2/rH/qnwQIDGwXD4f+c3N1Mg29LroWsL9JkErWPz26O3SaYJBIzUl27f+j5w6I9Kt74qX7QxtKst3hrat3suGrEGmPV1eOzDe6476BBY91DmGl65CqZelkWxBVjyx/C+g74IB31+1/UZnyhtLZKk2jL1Mnjhm+H9K2+HwYeWr55sdayGtkXQOBS2vBA+rpTBaI9/BF7L8TVJXeqDyiVVqaYR+c8dfhKMmle8WiRJkiRJkiRJklSwtkRYMFpt/q23JSTwrS2+jWQySSTfD0CWJEmSJEmSpDIxGE2SJEnKViJNQFS1SMbD+069H4Ycset6ff/c12/IMUit3J7/WnABmPQ+OOw/gyA4hUukCdOL+pJSJRQLD0ZLJKOhfd17P8xN+RD02x+W3Qh1LTDuQhg4C1bcDk9/GjY9E17D45dD8ygYe16Oxedg1d/C+8aUcF9JUu2b+O70wWjPfi74HTnuLeWrKZ1kEl7+CTz5/9IHOu9UqmC0rm3w+v/mPi/ic2Oppow8Nfi53vvxqHkkTHg7dG2GV3+277xIHZxwaxDSLEmSJEmSJEmSpIrRHt+asj0sQKzahQW+xemmM7mdpkhzmSuSJEmSJEmSpNyEn0ksSZIkVbvpnyjuepueLe56PSEsYKDP5D1D0QAiEeg3Lbf1K+ET5LINEnrtf4LQhRe+CS/9CNoWl7auapUulMLwB5VSNDxIIF4/ILwvkaJx+Akw5/tw8FeDUDSAUWfAvKfhzKfT13H/+dCxOnO9+dq2MHV741AYOLN0+0qSal+/aTDnR+nH/ONCePwK2PxieWpKZ9HV8MRHsgtFg9IFo21ZAPHtuc2JNUOfiaWpR1LPaBgIR16z5+veIUfBm16E2d+Cuf8FR/8OorsFOg86FM5bljbkWZIkSZIkSZIkST2jLb4tZXtYgFi1a00T+NYecltIkiRJkiRJUiUxGE2SJEm1a+xbgCIGdS38BSy9oXjr9YRkPHV7NCTgavylpaulVMZfkv3Yl38MT38anvgo3DoL1j5QurqqVaIrvC9aX7461PtEw8ME4tHwg9G6UwWjpTPwYBh3UfoxK2/PcdEsJbpge0jo2pHXlGZPSVLvMu0jMPWK9GNe/jHcNhuW/rk8NaWy8Bp4+N25zelcD8lcf/FnYcuC3OeMPgfq/DRtqeZMuATOXQxH/xZOfQBOuQca+u/qH/9WOOc1OOo6OPluOPUf0Dy8x8qVJEmSJEmSJElSuLAwsJY0x6JVs9ZoeDBaW3xrGSuRJEmSJEmSpPwYjCZJkqTaNWQuHPUbaByyZ3tda/6BTo9fVpqT78sl0Z26PRISjHbAZ2DKZbsCigYcHLSlMuXywusrhvFvheEn5T6vaxM89Yni11PtkiH3GQi/30jFEAsPRktEW0P74vk8RB/xi/T9HSvyWDSNba/DvefAbxuAZOoxLaOLu6ckqfcanyEAFCDRCfdfAPHO0tezt64tQVBxrpJxuO88WP9Y4TWsexgefDv8/XR46J9yn3/4/xReg6TK1DIqeBwdelTq95JaRsOEi2H4CWlfw0iSJEmSJEmSJKnndCW66ExuT9nXJxYeIFbNmmPhx9iFhcRJkiRJkiRJUiUxGE2SJEm1bcIlcMFqOG8pXBIP/n3LZpj3PMSa9xzbPBrOfAaGHRe+XsdK2PRcaWsupbCQq0gsdXs0Bof9BC7cBOevhHlPwwH/GtxWu4s1w+T3F7fWQhzz+/zmrXsoCCzaXTUH4RVDoiu8L9+AQSkb0YbQrni0Obwvnx/ZuhY44bbw/u4iHgjWsRJungzLb04/bu/HWUmS8jXkyOCSjTuPL20tqSz/SxBSnNfcm4Oa1z6U//4rboM7j4PXfwOr/pr7/KlXQH1tfoK4JEmSJEmSJEmSJNWC9sTW0L6WGg1Gi0ViNEdbUva1pbk9JEmSJEmSJKlSGIwmSZKk2heJQsuYXf9GY9BvCpz2IIw8A1r3g/GXwCn3wMCZ+wa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ec0p3wdlzr8XSgefxROB+5b5+XFu4FTduOR+HVB2Bo/0nodLNZUmUD1TX5hWuapS5yrNcGyJnYjAaERERERERmbLaGTNo8znSiCI85L7XBH70aXvzymeyzr8yQ64qGNCgJza86XHGw9RsUgajJXnG41c8q11rI4jHyQyL65Fqu3OimEn4kFsHGhTP6qNxoDcI1Fl/yVnR85i8PMqp4YFhi8Fobf2ZrQelz5oOe+kPbf05CkYzWffMQtOIiCh/Jdu/MxiNyBpVMFpFKdAv6VsRLOJgNNVvNS4WF3C78rPzFREREREREREREZFKWIwoy0r00izWhAqd3ysPg/gotApb9A3yafI4EEoV5tMbDeDBbbemPF8X3JhXthAfr/wkg2Icbjg+iLcHX0VbuBUGzBubhuLqBoOqdYmoGPg98vW/PbxpWvtSu2rd9fhYxYGYU7rLhOFBxbbrsxGmYRaiFjSGUIGdwWhCCHwQXI61oZXKMMVMGjVCWBdahaH4QFrnW+2egd19+2BJ+b5Y4tsHVW4Hvlma8ppXL8Ex/u/i3o4bpeV/6rwDc0t3VU6vIx3BaOrjeWMehwEXKl3TcVjN57FPxUF4ovt+vD7w76TTvD74b6wYXoaj/Sfi0OrPQU9DoF4hyvWxLJ08mhfzS3fDAVX/BbfmyXV1yKYuRdBuPt+HIEo3BqMRERERERGRKavBaAPqdklTBMPqmSYLuCo0ZuFPkwX1clQmvuVjoAciEobmLZ5GJaoAMD3J85f6Sg2ytzne+6rA3SeKvH+AY3U7DaX3RVgZFTULRnMBM01eYNMxwGA0O+ImbZ16gtmrhx1m4VSJ2hmMljdaOu2Nn6tla7ZvsrpeEhFRfjHb9wNj59hxQ8CV7KKEqMipLlsrSuTBaPl0/Zpuya7xg2Gg2peduhARERERERERERFlS9hQv42GwWiUTn5Ps3T4YLwfiMsbpDR45dPkA1X4hYDA0oHnpzXvVwaAV/v/idNmX4YyFx9eOFFftBu/3XIxuqLt05qPVytBlas2TbUiyj+qYMCwGJ32vtSuf/Q8iu83n4X9Kw/dMSwUH5KO69PVYWdTxnWpG15PDk18pOv3eKn/acvzdqoSrRSLfHthSfk+2N23L5q8s/O+TwE53wGVh+HFvqewcbRlSllvLIDe4YBy2nQEXJkFnTZ4Z017/pQZle5qnNh0Gg6tOhJ/6rwD7ZFNpuOHjGH8sfN2LB34N05o/NGUQE0CHu66Gy/3P5PraqTNy3gGywZfxI9n/RIeneFo+SQQ3SYdzmBqop2KrLs5ERERERER2WU1cKl32OKIADoH1WWuInuWlKyTfaJBvWrqwO7pNVbIN6r1MVkGgVlI1ivrUq+PU1jdToPhzNYjnVTbhlsHNE1Do2RzGNeR3heAFbwRk8CBbhv79myKWNx3Mhgtf6y1GYzW1p+bddMs/CzMYDQiooIjhLAUfJlP59lETlOp6Ms2POrMa5FsSPbNizk0joiIiIiIiIiIiAqXKhhNgwaP5s1ybaiQpdK5WBUulg/qPI3QkLmGua2j6/DG4AsZmz9Nz4t9T007FA0A6j1NDAuioub3NOW6CjvEEcNjXX+AIXY2pA1OCi4bV24SdjZZiVYKF9zSsmBC8FpHeEvehqJp0DG/dBG+OOM4nDHnSly/6AH8ZPYvcXjtVzGzZA73c5QVmqbhuIYfpDStDte0P9/svLYxj8OAi8Wuvt1x/vyb8HX//6JESx4g3jq6FtduOhsPd96NkXgwCzXMD+3hzQUVijZuTWgFVgy/ketqkE2BSId0uJPOP4lyjcFoREREREREZMowrI0XkL9oSMo0GK3IrlRjFn9fABjSJQ8nu+U3wApVqsFoC+rUZZc+aWMhOJTVYLR86jyt2jbc25/ned0a6hXP6zsGircDfSpGTcI+uuVtJXLOSkAJAASGuC7kix6bIXy9OXo2axbKx2A0IqLCE7MYxjok76dDRAmE4nSvokQ+PJhH16/pluxeXDH/NkRERERERERERFS4VMFoJXopQyoorRpSCDlrSCFMzSk8uge17vqMfsaq4DsZnT+lblXw7bTMx+9lp3wqbjXuOkcFtfbGAmgPb97xd8iQd+TwuSotz1PTNGWQWmLw2od5ts+v9zTisOov4IfN5+D6hffjnHnX42v+b2M3315wa55cV4+K1Pyy3XBg1WdsTaNBh1uThxfaUeupV4YgNngYjJYPXJoLR8z4Gi5ZcBsOqDws6fgCBl7qfxqXbTwVbw2+DKFqxFVECvn65YPg8lxXgWzqiwWkw+sZjEa0Q5F1NyciIiIiIiK7rAYu2QnP6TIJUSu2UBGrHe0BYEiXPJzs2pq+yuSBVIPRjtlPPcJLa4FL8jwczep2GjOAaCw/HmREFduGJ+FFRzOr5eN0DKS/PoVsxKRTfY9Dg9GsHit6+GKjvGH3+J+rABrVvgmwHthHRET5wywQMxGD0YiSU12JKoPRwhmriuMlu8Yv5t+GiIiIiIiIiIiICpcyGE0rzXJNqNBVuqsxp2QXy+NXuKoxp9T6+E60R/n+GZ1/IFJcL/jNF4YwEIhuS8u8Mr0OETmdrunYvXzfXFdjgq5o+45/JwaXJVIFnan4FOOHjJ3z74o6e59fppdjv4pDcELjj3H5gjtw+S534n+afoz9Kg9Vfj+iXPjv+u/AqykazUjMK10Ijz79gEaX5sKS8n2mDHdrbiz2fWza86fsqfHU4QfNZ+O02ZdaCrUbjPfjDx2/xm+3XoyO8JYs1NC5Cvn6JRBJz/k/ZUdcxDFihKRl1e7aLNeGyLkYjEZERERERESm4hbzogI2wnM6B9U9PIstxMYsYGWyQVkwWtv69FUmDyiD0ZLc4Zhfr+FrU5/f7HDFUwLvbs6PwDAZw0auW8gkBMtJVPsed8KybqqSj2MnqJGA0ai6zKnBaFYDqIrtmJLPzNZDmVwF0Jite239QDiav8cSIiKayuo5R76cYxPlkuplo6pgtOEiDv9KdkYZ5D6HiIiIiIiIiIiIClBYKILR9LIs14SKwdH+k+DRkgdLaNBxjP8kuDVPFmqVOZ+bcTRq3HUZm39PtAtxwTcKOk1/rAcxYbNRlsT80kX4RNWn0lAjovx2VN23UC5ry58jnZG2Hf8OKYLRfLq9ILByl/z7BeNDO/7ttDAZF9xYVLYnvlr/bZwz9zpcv/B+/HDWufhkzRdQ723KdfWIlGo99fjuzNOhW4j68GolONp/Yto++8t1x6NML58w7Et130KFW9E5gxxt9/J9ceH83+Kr9f9j6RpnbWglrm49E38LPKAMKC903WkKD3aiQv5uhWgkru7wpDovIypG7lxXgIiIiIiIiJxNFUQ1WV8IiMUF3C4t6bidg+qy1m6gPyRQ40s+n0IQsxFoNSx5mCq2rENx/FJjVAFguoUf4dKv6XhyhfoH/3+vC+w3Nz9/TavbKTAW2lDty1xd0kX1nbSERVRfqUHWZdypYV5ONWrSJq0n6LyQJyEEIhZDJXuGx8bXtPzctotJ2GbbyJwFoyVZ94693cBTp+lc54iICoTVIOuR6bclJyp4ymC0Uvl1XbCIg9GShZ8X829DREREREREREREhUvVIbtEL81yTagY7F6+L86ddz3eGVqKQETecbzGMwP7VByEXcqWZLl26ef3zsS5867HW4P/wdZwK4TqwU0SYTGC94ffnDLcQBy90QD83pnTrSqlUcAkFOHjlZ+EliSIxa27Mb90NxxU9Rl4dcXbjoiKyNzSXXHuvOuxfOhVbItsUT4DT7cNo6vRE+2aMjwxGC0xuCyR3UANVZBaYvCaat8yv3QR/J5mW583HdXuWizy7YVFvj1RyiBdylP7VR6KX8y7Du8PL0N3ZOp2DgBNJbOwb8UhmFkyJ22fO79sEc6bdwOWD72KYHwIe5Tvhz3K90vb/Cn7PLoHX6r7Jj5R+Sn8peturAq+bTp+HDH8s/eveGvwPziu4f+wT+VBWaqpMwSi8pDPbB/LpmM4PoDVofemDB+KD2DUGOGxMU8EDfk5HAD4XPYCbokKGYPRiIiIiIiIyJTVwCUhxsLR/Baen3Wp79sgZgDPfiBwwoH5HSjyziaBsx8xsHwTUOMDjt1fwzXHaCjzTvxedoLRBmUPJ7esm2ZN84tqfbQSjLZbg3n57S8J/OZb9uvkBHEb61Eokrl6pJOhaDGQuKzrFPd5u4edF+blVEIIjJisE90ODJmLG+pQhcnCsbF1vpztshxPFYzmcclDaYZGc7OdJwvIefYD4In3gGPYPoCIqCBYDWPNl3NsolxSnb2pztWDRbxdJTvTNbuGIyIiIiIiIiIiIspXYWNEOpzBaJQpzSXz0FwyL9fVyJpq9wwcOePoac0jbIzizHXHS8sC0W0MRnOYQEQe+FDhqsL/Nv88y7UhKgz13iZ8se4bWf3Mx7ruw/N9T0wZ3hVpBwDERBRhIQ+YtRuooRo/ZAzv+KzeaEA6zhfrjsPHKg609XlEBMwrXYh5pQuz/rl+70x8qe64rH8uZVa9twk/mfVLrBhehke6fo++WLfp+L2xAO5svwZ7l38CxzX8H+q9jVmqae6MHcvkv0s+Hcu6I524eOMpirJtmF26IMs1olQkhs9OZjfglqiQmce6ExERERERUdGzGowGAL1Ba+N1DZqX/+Z5kfIb6ZygZZvAx68y8NJaYDgMbO0Dbv63wMkPTP1OyQJWEvW4Zkwd2NGacj3z0XSC0XwlGubVqcujcSAYzs/1zs52mi+hDapdQOKyrlc8r+9xYJiXU8Xi5uuPE4PRVAFaKlwf8kM4Kh+uClwdkrfjybiIhfXv0idtpFUSEZGjWdnvAwwpIrJCdY1XqejPNjACxO1c7BaQZF87FCnO34WIiIiIiIiIiIgKW9iQNwRgMBqRc5Topah21UrLVCFclDuBqHyZ+D0MsCPKJ43eWdLhnZE2CCEQiqs7cJTbDEZTjR/cHtrRE+2CgLx9JPctRETOoGka9q08GBcvuBWfn/F16HAlnWZl8C1c0Xoanu15BFFD0ai/QPRGA8pjWb2nKcu1SV2tpx4uuKVlgei2LNeGUhWMD0mH63ChROP9MKJxDEYjIiIiIiIiU3b6oAbD1sZ75G3zmb7VCux1qYF1nfnV0dMwBM551MDuF8tvkj60TGB918TvFLMRjNbhltxk7Q9AxGymBOWx6QSjAcB/72s+4tubbFbIIQoxGM3KslYFozkxzMupRpI8t3JiqJjVgJJx25KEcZIzqALvVNu5k4PRVrYhrwNeiYhoJ6vnHQwpIkrdzGr58LgBBOTtfgqekSRnN1+u64mIiIiIiIiIiIjsYDAaUX7we+XBN6oQLsodVVidahkSkTOpgtFGjBCG4gPKQA0AKNcVb6ZV8OnyBpuh7cFoqv2KBg31nkZbn0VERJlVopfiaP+JuHD+b7CobK+k40dFBH/vfghXtZ6ONcEVWahhbpgFOufTscyluVDnaZCWMbQ6f4yHz05W7qqAplnsKElUBBiMRkRERERERKaSdcZMFEzSMVMIgT8uszbD1R3A4osMrO7Inw7m974mcMNz5vV94r1JwWg2ft8Ot6QxghBAX6f1meS5uOL30i3e4TjnCxp2N2nT8ZVbbCwQBymqYLSEZV1XLh+HwWjWjSYJRhscBSIxZ+2HIzYCJQHgxRZn1Z8mWrlV4J5XDbT1y8uVwWgWw1jTLWpx/XNiqCAREdln9bwjWdgsEQGqs/LZNepp2hXniIUu2RVMvlzXExEREREREREREdmhDEbTyrJcEyIy4/fIG2B2sfO946iWSYNiGRKRMzV6m5VlXZE2hIygstznUjTAVCh3yYPUxsPXAtFt0vJadz08utfWZxERUXbMLJmDR3HcowAAIABJREFUM+Zcge/NPBNVLpOGWtt1Rdtx89ZLcG/7jeiP9WahhtmlOpbVuOvg1UuyXJvpqfc2SYd3K74jOU/IkHc68SnOyYiKFYPRiIiIiIiIyJSdwKWgSUjJSETgm3ca+M499gJq9rzEwO9eyo+wqvtfT/7dXlwzcRyrASsA0C4LRgOA7uJpUKIMy7L4IoTmGg1vnK++HTIcBsLR/AtRKshgNFUIXsKyrq+QL/i+kPPCvJwqWTAaAPSq20zkRCRmb/xX13FdcKornjKwz+UGfni/ehn5Fdv54EimamXOakBO+0Bm60FERNlh9XotX86xiXJJKE75/JUa3IrLdFV4bqFLdo3PfQ4REREREREREREVorCQNwQo0UuzXBMiMuNXdL5XBQxQbgghEIjK2xarliEROVOFqxpluvxN0p2RdoS2h5ZN5tG8tgNeVEFq46EdAUXgIvcrRETOpmkaDqz6NC5ecCs+XXMUNAsRM8uHXsHlG0/FC31/R1zYfLO9g6lCw+o9+Xcs8yvqzGuz/BGKy4PRynV74bZEhY7BaERERERERGTKTuDSSfca+Pdq+QQPLxf46zup1eGnfxRTAsWcRgiBlW3Jx1s96f5izFYwmuKNTz0MRrMajAYAlaUavry3uvyjgL06ZYoQAg++YeDEewz84lEDH7artwFViJhMvnSgtrKsZ1arp1/vkOXodCMWgtG65ffacyZsMxgtIG/zQTn27maBy/6e/Nhep3imMyR/UXTGWQ1dbOvLcEWIiCgrrAayWjmnIiI5l66+tmvvd/a9oExRhciN4z6HiIiIiIiIiIiIClHYkDcE8DIYjchRGrzydqw90W0wCigwId8NxPsQFfLGon6P4iXNRORImqah0TtLWtYZaUNQEajhU4SpmSl3VUqHB+PDMIShDlzkfoWIKC/4XBX4VuPJOHfedZhXuijp+KPGCB7tuge/2vRzbBhZk4UaZl5XAYV8qsLcVOFv5DzK8zhFWC1RsWIwGhEREREREZmK2whc6h4GPv8bA/e+OnWiR9+eXmfWI24y8FarczvEdg4CA/KXVk7Q2gOMRnd+j5iN37fdrXho2FM8Ny1VAWB2gtEA4KzPqW+JrHZIztxpfxI46V6Bh5YJ3PicwMHXGHhjg3wbsBNgGIw4dztKZCUYbVe/etmvKZ7NYlpG8zAYzWpAybihcGbqQdPzh6XC0r7LL29ng5EoMDiS/f1Z1GL7zbYiDfEgIio0Vs878iV8mChXRJKkr1m18uEdAxmoTB5Idp7MfQ4REREREREREREVooghb+BRwmA0IkdRhd/ERAx9sZ4s14ZUAorABwDwexlgRJRvVKGUnZE2hOLytwf7FCFnZny6PIRDwEDYGEEgIm+czf0KEVF+mVu6EL+Yey1OaPyxct+faGu4FTdsPg8PbrsVw7HBLNQwc1ShYX5FyJiTqcLceqPdiAm+eTMfhAz5eZwqrJaoWDEYjYiIiIiIiEzZCVwCACGAi58UiMUnTvj0yunX5cy/2EgRS4PBEYFfPmFg1wviOPCquDTwbZzVECYhgLWdO/+2GrACAMOuSgxJbjqLbockeWWBlbAsKz67RD3BPSbLOVu29Ar87qWJX3Y4DBx6rSHtUG5nOx2Sv1zVcVTfSUtYdCUeDbv45eOt2cZQIitGLDzv6HFaMJrNF4vmyzpfbH7/irVtdLdGddmKrWmqjA1WA3LaizTEg4io0Fg9zx5hSBGRKbNcNA2AX9G+rt9CAH0hYjAaERERERERERERFaOwIb8pzGA0ImepNwkMMAvjouwKROXLwqdXsJM9UR5q9M6SDu+KtiNoyBv5lruSB93YmWYoPoDuaKe0TBWaSUREzqVrLnyy5gu4ZMGtOLjqcEvTLB14HpdtPBWv9f8Lhsh9vyu7DGEoj2X1eXgsUx1/BQz0RLuyXBtKRTAuP4/zucqzXBMiZ2MwGhEREREREZmyG4wGAO39wGqb7Rv+fHLyZKul68cCo7LltD8JXP2MwMZuYPkm4P/uF7jtRfnN243d1uu1pXfnvycHyCXT7pbcuOwpnsYkqvXRlcIdjqP3lQ//5ypgXWduQ7We+1D9+Tf+q7iD0SaH4C1WhCZtCKS3PoUqbCEYrXvYWSFzVuqcaLBIwxScLBgWGLW4HHdr1FDjk5e9szn766bVYL6N3AcRERUEq+fZDCkiMme2KWkaUOOT3xMaCGWmPk5nFiQHcJ9DREREREREREREhSks5I2aSjQGoxE5SZnLh0pXtbQsELX4hmHKOFVInd+bf4EPRKQORgtEtmEo1i8t86UUjKYOTtwa3ggD8gaUfq86NJOIiJyt0l2Dk2b+DGfNuRrN3rlJxw8aQ3io8zbcuPl8bBndkIUapk9/rAcxIe/E0JCHx7I6T4OyrDvCa7N8EFIEo5XrDLMmSsRgNCIiIiIiIjKVSjAaAKzetnPCkYj5TJqqgGP21fD5PZLPd955BroGMx+EsmKLwANvTP2c370k/+w2+TNFqYGRnfOI2XxJRpu7eerAbgajTQ7LsmLJTPVEiy8yYKS68qfB8x+qyy54TEBM6iVtp6qD+RKMptg2Ji/ruXXy5djR76wwL6eysu70BDNfDzusBlONGw5jyjZDudVu45hZ5gH2VzxjfXdzeupjR9Ti+teS44BNIiJKD6unECM2g1uJaCdNA6rK5GX9oeI8p0p2nTbCYDQiIiIiIiIiIiIqQGFDEYymK24iE1HO+D3ycC1VGBdlXyAqXxYNimVHRM7W6JX0HwBgII7No+ulZakEapTqPmiQt8veOLJWOV29J//CZIiIaKKFvj1w/vyb8HX/9y0FlG8cbcG1m87GI12/x0g8P95+2W0S5JyPxzKvXoIad520jKHV+SEYH5IOTyXglqiQMRiNiIiIiIiITKnCiZJZnfBM/YqnzXt03nKCDo9bw2+P19Fg4RncnpfsDK1atkHgj8sMvLkxvZ1lf/28fH6rO4BQeGqZnZCXgZGd/7YasDKuwy1plNBTPDcslcFoKdzh2D3Jfeu7XsldB+wKk+cIMQMYSmgHKISwHNgATJzWyVRfaXIw2qwa+Xh2wgqLmZVgtG75S0hyJhKzN74hgBCDAxzFzjGzxA3sN1fe0OadTdnfT1td/1q2MZCPiLKjPzR2PfTEuwI9w9zvpJvVX5QhRUTmzE6LNAA1PnlZ/4h8eKFLdhqZ7AUERERERERERERERPlIHYyWvEM2EWWX36sIRlOEcVH2BSLydsWqZUdEzub3zFQGlm0Nt0qHpxKooWs6fLp8uk2j66TDq90zeL5GRFQgXJobR8z4b1y84FbsX3lo0vEFDLzY9xQu33gqlg++4vi286pz5HJXZd4GUfkVgW5mIXDkHCFD3lmrPE/XR6JMYTAaERERERERmbISmiOztnPnv59aoZ7J8gt1fP2AsQd1i5s0rLtKx6IG83n3BIH9rzTw7d8bOORaA9+5R+Dgawz88H4jbTdSn1ulnk9PcOqw9n7rn5vYsTdmM3iuXRaM1r4BImYzKShPqYL6JodlWbH7TPOJHnojdzflh5OEl/UmrIN2V/mhPOlYbjUETxWM1j6Q3voUKivrT0+eB6MB+RMIWCzaB6zvuEo8wP5z5WWrtwGRWHb31RGLgaZ9ISAgf4EPEVHaLG8VmHfe2PXQsbcb2OdyIyehkYXM6vVwiCFFRKZMg9E0oKZMXtafHy8TTbtk+x4GPxMREREREREREVGhiYs4okJ+85NBG0TOo+p8H4gwGM0JhBDoirRLy1TLjoiczauXoNZdLy0zIG/UmGqghioYplURjOb3MHCRiKjQ1Hrq8X/N5+Cnsy+xtJ8fiPfh3o4bcfPWS9AZactCDVMTUISF5fM5cr1XXvcuXps5niEMBOPyzlo+V2WWa0PkbAxGIyIiIiIiIlOpBqNt6d054ahJiM3+8yaGU1WWavjgUh3HHWAeWvX+VuBPb06s3D2vCrhOGQtMu/NlA/FUKw8gbFLnbsl9p/Z+6/MeSAimiloMWBnXIbupPDwArFxqb0bTIAwD4sW/wrj2FBh3/BKiJ3tvklCGZaUQjLbHTMDrVpe/th45e2NJstCgvoTO4XZX86HR/AhtsBqC11wjX/iBISAczY/vmktW1p+BEWf9jmb7Z5VBBqM5SpuNY2aJG9h7lnw7jxvAtiyHIMZtBJq2dCYfh4hoOk68x5gQ/tneD5z+Z5vJy2TK6uUAQ1iJzCXblGp88uH9eRLsnW4MRiMiIiIiIiIiIqJiEzHUD1tKGYxG5Dh+rzwcIRDdBkPwmXWuDcUHEBby/apq2RGR8zV6Z9kaP9VADVWgWkxEpcP9ikAWIiLKf3uU74dfzv8tvlJ3AtyaJ+n4LaH3ceXG0/G3wIOIGOEs1NCe7qg8LKw+j4PRVKFu3YoQOHKOsDECAfn1c7meWsAtUaFiMBoRERERERGZSjVbLDH0ZEDRkXWvZvlwj1vDX07RceeJKaRdYSww7ccPCRx3h5FysJVZ8IksGG1zr/V5J/4eMZvBaO3e2dLhYunT9mY0DeK6H0Nc/D/A0/cBD10P8d39ITatycpnpzMYzVei4SefMZ/wyRX255sOydanaQWjOe/5gpTVZT2rRj2PbYPpq0+hsrL+DDosjCASl1fabXKnk2ElzmInTLTEbb6dd2Q5GM3OPnfNNmeFChJRYWnvF9IAxtfWA+u7uP9JF6v7/f5Q8nGIipnZrRlNA2rK5NfmxbptJbuVxWA0IiIiIiIiIiIiKjRhk2C0Er0sizUhIiv8shf8AoiKCAZjfVmuDU0WiMgDHwD1siMi52vwKjpeKKQaqGE3UI37FSKiwubRvTiq/lu4aP7N2LN8/6TjxxHDP3sfxeUbf4r3h9/MQg2tC0TkYWH5HB5crzgOd0c7GVrtcCFD0jl1u1QDbokKFYPRiIiIiIiIyJSqI/g1x2r4hsk9zfZ+7AglUwWjXX2s+WXpDz+p4xPzLVRS4Yn3ANcpBhZdGMc5jxoYjVrr1R6LCwyahOj0DE+cT19QoGvIer0SQ4ZiNu8zvlB5hLzg1b+nHAJnh3j+L2OBaIkGeiD+9OuMfzaQ3mA0ALj2WA0/OEw98TG/M3DDcwaMVBMCUxAKi+TBaMGd/zYL8ZNxWsiVitVl3WwSmNRmI3ypWFkKRnNYqJhqna/xqafJl/W+WHTY2DZLPWPLtlTxkqn2bAej2djntvBFS0SUQWbnOW9sZDBauli9xOrnuQaRKbNNSYP6XH5wFFm9HneKZF+ZwWhERERERERERERUaMLCJBhNK81iTYjIigaT4ICuqDqUi7IjoFgGpboPFa6qLNeGiNKl0TvL1vjlKQZq2A1UMzsmEBFR4fB7Z+Insy7Cyc3nocZdl3T83lgAd7RdjTvarkZPVPIW4CwTQiAQVQSjeZqyXJv08XvldY+JKAZiSTqmUU4F4+pgtHJXagG3RIWKwWhERERERERkShVAU18BPPwjF147V35pGY4BPcPAaFQgEpPPo8bCyxyfO2P6l67rA8ANzwn817UGVrULvL9V4MN2gVhc3tO0NygdvEP3pHtPLTbv0Q6Edn5uNG5v2l5RiYCrfmrB1vXA5hZ7M7NIDPVBrH0PYsWrEJedJB/p9X9k5LMnU4ZlpbiaeN0a7j5Jh9/k2e85jwpc8XT2OmKv60oewNCXsA7Z7SM+5LCQKxWrwWjVZYDPKx+3ncFoSeVjMJqqzl43UFEiL3uztfjCFJysrd/68vC4AE3T0FwtL2+3Ma90sPNpW/gskYgyqEwRGAkA63LfhqRgWD3X7g9lth5EhUzT1MFoQiS/R1OIkt0TGIlmpx5ERERERERERERE2RI21I1TvLqiMQgR5YzPVYFyXd7oMhBhMFquqYLR/J4maFqKbyEmopyzG4zmSzFQw+50fg+D0YiIioWmadi38mBcvOBWfG7GMdDhSjrN+8Nv4vKNp+EfPY8iJnLX6Gk4PohRQ97Qsz6fg9FM6q4KgiNnCCmC0TRoKNMVDSqJihSD0YiIiIiIiMhUsnCiuTPU07b1AwMj6vJqC8Fo1T4NHTek5/L13S3A3pca2PdyA3tdaqD+TAMPvD41+a1ryHw+gUn3nt7fai+UpT/hN4nZDEYDgJtnnCovWPac/ZmZEMFBGJd8B+KoJogfHATx0yPUI/dugxjKfAqVoQjqmxyWZddndjOfwWV/F/ioKzvhO2u2Jf+cvoT78XaD0ZwWcqWi+l6T2+VomobmGvm4dsKXipVqm0o0aLIfzwVVnV0asM9sedmzK7kuOInV0MLyEuxojDdTEYzWMZCmSllkZ587NMr1jogyx2x/9FFX9upR6JKFE43rC429UZCI5Mw2Dw1Ak8mL6duzfL7nBMnOOUOR7NSDiIiIiIiIiIiIKFvMgtFK9NIs1oSIrPJ75R3w2fk+9wIR+TLwexleRJTPGrzNtsYvTzEYrdxl8rZxiXwOkyEiotSU6mU4xv9dXDD/11hYtmfS8aMigie7H8RVrWeiJfh+Fmo4VbfJdUo+nycztDp/BePyzqtlejl0LXnoIFExYTAaERERERERmUoWRNVYpQ6lShaMVmMxwL6xSsO7F6X/EnZwFPjuHwT+9z4D5z1m4Du/N3DNswY+dpl5UtCmnol/P2MzcCeQcO8qpvio2nivcvrH6k+QDhcbVtmqh4oQAuK9VyC+6AdeeMT6hJvXpuXzzSQL6kvVtV9PPoMDrrSQIJUGk9cvmd7gzn9bCbZKlC+hDXZC8GYpgtGshi8VMyshT04L01PuB3Tgq/vIt+WVbfmx3mfK4IjAI8sFnnxPoDeY299BCGE53KLcu/PfzTXyZZvt7dzOPnfIYdsOERUWs2P4tsHiPealm9VAzLgBBMOZrQtRPjMNRtPM7y0V43Udg9GIiIiIiIiIiIio2IQNeSNDt+aBS3NnuTZEZIXfIw8PYOf73OuKypeBapkRUX6oddfDo3mTj7idT08tGM1nI1Ct0lWNMpfFDiFERFRwmkvm4sw5V+K7Taej0qV4C3qCzshW/HbrxfhD+00YiKn7zGWCKsC5RCtFlUvRISlP1CtCq83C4Cj3gsawdHiq4bZEhYzBaERERERERGRK1RnTtf2K0u3S0KS4f7m1T5iGPFWXWa/HPnM0rL48M5ex9y0VuO4fAn98U+DCx5P3fG/ZtnMcIQReapGPN69OPrytf2dATzQuH2ff0RXqz8dcbHM1Ti3YpKiIDSIagTjvWIjTjrQ/8aY10/78ZDIVjLagXsMFR5nPZGgUWNOR+ZCJNgudvvtCO/9tNaxh3Gg0P0Ib7CxrpwQm5SMrq89oFIjEnBOwEjcJzTt4F/m60BcCuuXPDQreu5sFFpxv4Ft3GTj6d2Phn+9syt3y7A2OrVNWVJTs/PdMxfPGjoHsfhc7+1ynhQoSUWFRHQ+BiSG6ND12jjL9JqHgRMUu2bbkdmlorJKXtfU751okW5JlOocixR38TERERERERERERIUnbMgfsJfopVmuCRFZ5fcqgtEUoVyUHUIIBCLt0rIGxTIjovygazoavM3WxoWOUj21wLJyG4FqDFwkIiJN03BQ9WdxyYLb8KmaL0FD8s5dbw39B5dt/Cle7HsKcaHoVJdmqgDnem8TNG2aHdJyzO+RB6OpwuDIGULxIenwMgajEU3BYDQiIiIiIiIyZSWcaLYirKStH7jwcXmPfU2bGHhixeImDf88Q0ddub3p0q2lc2cH1GBYHXzyg8PkN0dDEWBwe6f5mCLQwCNieGbzV5V1aPNIHmxuWmO7Y+yyDQKn/tHAOXd1YMV1N0AcXgksfcbWPMaJzdMPZkvGLBBpui76soZvH2Q+oz0uMfBRl73f+NV1Aqf/2cDP/mzglXXJp2230Om7fxrBaAAQyIOAKNXX0iV3s5oV+6CHlgl2Fk/CMAlVSTTooKAPs+PSEvkzHQDAmiJ9rvPde40JYYrt/cDP/mxxwWfA2k7r45YnnCc0K0JYOwamVx+77OxzhxiMRkQZZLY/Stzv0/RYPVcCJp6jE9FEZpdl4+3KVNd1xRh4beWc02rYMBEREREREREREVE+CAtFMJrGYDQip1J2vo90sM1eDgWNIYwY8ofXDDAiyn8Nsv4DEj5XRcohLz5XpeVxVSGZRERUfHyuChzfeArOmXc95pYuTDr+qBHCI12/x682nY2NI5nvi6YKCatXXNfkE79X/h26I0XagSZPBOPyjn12QmqJigWD0YiIiIiIiMiUlWC0WbXycdr6gfUBeVlVKaCnkGb1uT00tF6r46WzdTzxEx0fXKojcruOlZeO/X3jcZl/U0N/CAhsD+bvNgmYOniBui5t2zv2xhQvt3CLGI4MvqCcPuCqnzpwqA9oXa2u0CR3v2LgkGsN3P6SwA3LG3Dw2h/jqYovWZ5+ik1rUp/WIuX6mIY7HCUeDff/r4anTzOf2QFXGnh3s7WGOw8tM/CZGwzc8oLArS8IfPYGA/e/bp6u0Gah03dfcOfnpxKM1jVof5psU4VQyHYbsxQd6AHgnL+ykZUZq+uPKgAyF1R1dumAvxKoUbzkbn2g+NaFjn6BDyQvAF26HtjYnZvfo6XT+ucmbu8zFcFo2Q7KYDAaETmFWWBXXzB79Sh0do6WvfzdiZTMtqXxUz7VdV17loNwncBKX6ERBqMRERERERERERFRAQkbimA0vSzLNSEiq1RhOGExiqF4ET7gcYhApENZxgAjovzX6J1laTzfNAI1yl3Wp1WFZBIRUfGaV7oQ58z9FY5v/BHK9PKk428Nb8T1m8/FQ9tuw3A8cx2dVCFhhXAsU4W7BaIMrXaykCoYzUZILVGxYDAaERERERERmbISRNVcIw8Aa+8TUL1saGAk9TqVl2j41G4avravhj2aNbhdGvZsHvv7zM/pePKnOhY3pj5/K1o6x/5vFoy292x12XiQS1QVjIYYdAg0xDql5d0ev3zCV59Sf2iCtj6BUx6YuHDDeiku8V9iq/P/BDZC2VKlDupLTyCepmn40t4aPre7epyh0bFwtMCQ+S8Viwtc8JiYUGdDABc+LhCLy6cVQuCNDcnr2ZfwQr9UgtECJuutU1gJZRzXbBKMduNzAvVnxnHcHXGsap84065BgVP/aGDPS+I47Fdx/OOD4rvpb1h80JEPwWi6NrYNz1GEdRZjSMzmXnXZGxtys76vsfHioVhC6I/qXKN7GIjEsvddbAWjhTNXDyIixekkgLHjNhszpIedn7FjgL85kYrZtjR+OT/T5N5SsbFyzhmKZL4eRERERERERERERNkSNuSNCUv00izXhIis8nvUIVtm4VyUWV2K375EK0WVy6ShJRHlhUZvs6XxphOoYSdUjYGLREQko2sufKrmi7h0wW04uOqzlqZ5beBfuGzjqVg68DwMYfLm4BQFovLz5EI4lqnC3UaMEILGUJZrQ1aplo3PRkgtUbFgMBoRERERERGZMhT3ExODqGYpnpVv6ZsY4JTonC+mJ8hK5isf07D6CheMu1x44/zMXPq2bBvrpaoKRnPpgL8C8CueK27sGZs+pvh9PSIKAPDHuqXlgdmfkA4XLe8oajzRnHPlH7yi9GPocCtu7GoatAfeg3benRjRSrHKuztGtITGZ1vXQ7RvtPT5qbITljUdt307+XrT+HMDQ6Pq3sqrO8a2gcna+oFV7fJp/u9+ax2+exMCnlTbqJlkoW5OYGdZz1J0oB/XGwT++g7w2RsM9AyPzdgwBI75nYHbXxJY3QEsXQ8cdbOBl1uc/9ukk9WQp8FphFmmW7J1o9YnL1cdjwqZ2fLdYhKalkmt8sOaVHxCMJp6vE09qdfHLjv73EgMCEeLa59CRNmTbH80nSBq2slqiCwwdp5PRPaNX82p7i21D2StKo7BYDQiIiIiIiIiIiIqNmFD/sY+BqMROVeFqwqluryhlip0gDJPHfjQBC1NLyAmotxp8M6yNN50AjXKbUxrFpJJRERU6a7BSTNPx5lzrsJM79yk4wfjQ3hw2624afMF2DramrZ6jBojGIrLG6GpQsXySb1JuFt3xMZb7SmrQnF5h1Q752JExYLBaERERERERGTKSjiRqvPqqnZA1Y/8Wx/PzgP2AxdoePuXOr6x/8ThlaXAPrPNp/3c7uqyls6x/6sCpuorAF3XsKtfPv267dPH4vJyt4iNzScuT5DpbtxLPuHmFvnwBLe9aJ6i0OJdNHXgl78P7S9rEJu9BD9qOxZ1u3Vgn13fRu3iTpzTcBVicI2N99pTST9/OtRBfen9nIUNGlwW7ppU/8zA3a/IK7WqXd2T+QNJWSwu8Mhya8ELiQFPZh2my0vkwwN58NIPO8FoZoFJibqHgev+OTbjMx8WeH3D1HE+e6OB0SIKMrIcjCZvf5oT8ST7gRpFMFp/EQbEBMPqstYshokl6hiwvn0lhofOrwNUbfPGj8nZYHfvMOSgbYeICkuyY/jPHxEQNkK9SM7OT8hgNCI1s21p/ByvuVpe3iYJHC90VvY9DEYjIiIiIiIiIiKiQsJgNKL8o2maMkSAwWi5E1AEHzC8iKgwNHqbLY03nUANO6FqDSZBLEREROMW+fbEBfNvwjH+76FES36dv2F0Da7ddBYe7boXI/FQ0vGTCUTU1yd+b/4Ho1W7auHRvNKyQJTBaE4VVASj+fTKLNeEyPkYjEZERERERESmkgXQAMCsWvupVPVZDLDfb66Gh3/kgnHXzv8Gbnbh3YtdGLxZx56TnhGe/CkNxl0u/PNMF048WP7dtvaO/b9bfh9qx/db1CCffl3nWC/XqCoYDePBaPLkmu4SxYPNjR9CxGLyMgBDowKn/cm8h+1a724T/tbuXgr9vDugzZyPy58WuHt5OSL6WOJWTPPgprozUbr7EB6sOgHDr/zLdN7TZScsa7qszvKUBwSueMpAJDaxcusD6mk2SMrWB4BhkxCjRP0jgLH9xzALxWhU3A8NKNZbJ1F9L1kwkqoDvcz1/xTNJWnxAAAgAElEQVToHBS45QX1D3fOX4snREQVNjjZ4IhzfhPlfmD7nc4an3zr7Q9mqEIOZhaWsLYzN8u0Q/6yJanE8NAyr4Z5M+TjrdmWve9iNUxw3JDF/ToRkV3J9kd/eE3g8XezU5dCZme/38FgNCIls01pRzBajfw8vnNoLEi8mFjZ90wnGM0wBN7ZJPDQMgMbAsX12xIREREREREREZEzKYPRtLIs14SI7PArAnFU4VyUeapQOtWyIqL84nNVoNKVvNH0dAI1XJobpXryc7ByvdJWiBoRERU3l+bG52YcjYsW3IL9Kg5JOr4BAy/0PYnLW3+KtwdfndaLgrsV4WAuuFHrrk95vk5hFlqt+u6UeyFD3rFvOgG3RIWKwWhERERERERkykoQ1awa+/OtK0+tPulWUarhnV/quPd7Gq45VsNzZ+i44zs7L5ebFd+tfWDsh+kakpfvCEZrlJev6xr7vyp4ziPGws388W5p+ca4Xz4hADxwrbLoT7c+p55uuxbvop1/fOHb0JYcsOPPB99Q30z+3qx7UD36ON5aZSP1xibV+ujKwB2OT++WfJxxlzwp8KnrDPQM76zg+DKWWd469YustvGSRCGAwe3tAU2D0arkw7sV662TqAK7ZCF4JR4NPvkLTqSuedb8ociflomi6Xhv9VsOjGS0Grao1g3X9nWjxicv73dQuFu2BCPq77wmB8+4hBBotxEaE5u0rBcrXsjUksXvYjVMcJyTth0iKiyq64hE1z5rc6dFU9g5e2jrL75zDSKrrLRLU91bEsJeuG4hsLI3CaUYwBuJCZxwt8DHrzJw4j0CCy80eLwgIiIiIiIiIiKinAsLRTDa9pd3EpEz+T3ysK0uRTgXZZ4yGE2xrIgo/zR4FS9XTzDdQA2fnnx6v1fRoJOIiMjEDI8fP5x1Lk6dfbEyzCvRQKwX93TcgFu2XorOSFtKn6kKbq7zNEDXXCnN02nqFcflQITXZk4VisuD0Rg8SzQVg9GIiIiIiIjIlKrDfWIQld1gNJ8X8JVI0o1yxOPW8L1DdZz7RR1H7jGxXjMVL1Ua75S7vkveXbWpamw+8+vk0/cEx/4/OfRlnHt7MNrc6GZp+Stt5RjwzJCWiXuvgAhPTWIR6z/Ak+/E5B+Y4PYZp4z945AvQTvrtzuGD4QENvUknRw/vC+KuFla1zRYCepLl5MOsTfTN1sB/1nGjtCzwJD6N/jXaiAUnli+Zpu936wvNPb/VILRzOrmFHaX9cVfsb68bv63+ffvCQJL11ueXV6zuqkOytuf5oRy3dh+XKpRvKiuL5iZ+jhZ0CQsob0fGMxyWNzQKBCKWB9/8jnIwgb5dt7Wl73vYffwtiGQmXoQEVnZHy3fBLQzrGta7ARiFuO5BpFVZnui8TO8OfJbHADMg8cLkZV9z0g0tXn/4TWBR96euEQueFzgvS08XhAREREREREREVHuhA1VMFpplmtCRHY0eOVhW4FIO4SVN+dQWoXiwwjG5W/NZYARUeFo9M5KOs50AzXKXZVJx2HgIhERTcee5fvjl/Nvxpfrjodb8yQdf01oBa5qPR1/734IEcPeGyWV4cEFdI6sCpkLRLP4BnqyLGKEERXyjjXlevLzMKJiw2A0IiIiIiIiMqUM7kp4KUJFqYZqRRCNTH0ehdfP3PCydHhHv4AQAmsU9wgXb7+nWN3fKi0fGBq7gRWNyRt/uDEWYHZE8EVpeTSu4c09vquoNYDlL0wZZPz9XiwtO0g9zfi8NS9uPq0V2q8eh+ar3FHP+edbSwV4f6AWb2ywNKptyQKR0unr+2sprasHXm3g8XcF+kPqcUajwDuTMu9abN5v7t0evKAKLwSAhip5iFBA/mIJR7G7rE/5VHrT8Z54rzgaZlkN+xicmrWYM6p1fjw0r7ZcXt7voO+QLcEkIWTrsxza9UG7vfE/MX/i36og1vaBlKqTErvBaHZDL4mIrLK6P3p6JfdD02Hn1+szOf8nKnZm/V607efxNT4NfkWbnlMesJFSWACs7OOD4dT277e9KJ/uwTd4vCAiIiIiIiIiIqLcUQej2WiUSERZp+p8P2KEEDTkAV2UOWahBw2e5izWhIgyyUowmpVgMzNWgtX8inBMIiIiqzy6F1+uPx6/nH8z9vDtl3T8mIjh2Z5HcEXrz7ByeLnlz+lWnCfXK65n8pHqu3RHGIzmRKG4ulPfdANuiQoRg9GIiIiIiIjIlCqAxj3pilIVViKTL8FoIhpB0z9vlpYFIxr6hmJY2yWfdsn2e4pVL94vLR8VXowu/ZcyeM4jogCA/UffhT8m/5B3Fhyjrnzr6gl/io/eR+cTj6LfVaueJsH5LzVgePtLNIJhAd9PDQzYCBU66+GJX+y5VQKH3xBH89lxfOq6OB5allqnZlWIk57eTCwAgK9Ew39+oeOQXexP+/XbDbyeJBxucljOn9+y1wm5b3swmlmH6cYq+fCuPGhzpOo4r1rW1T4NVx6dvhXhN88LGHYTkPKQ1a84KG9/mhPK0LzxQAVFm9hiDCsJJQlGC2R5X/DsB/a2qTOOnHiy0aw412jrS7VG9tndLaztzEw9iIjMwnETPfN+4Z/PZJKd/X4xhrASpUPiVdwSRVuz9QEgMFQ8+zMr39TKPZql6wW+8OuxezGNP49j/yviyrDim/5VPL8vEREREREREREROU/YkN/0LNFLs1wTIrLDLBQnwA74WReIdEiHezQvqtzW2g4TkfNZCTos16fXWaPcSjCah8FoRESUHg3emTh19sX4YfM5qHHXJR2/J9qJ29uuxJ1t16AnqujUl0B1bVJIIZ+q7zIQ71OG0VPumAWJTzfglqgQMRiNiIiIiIiIlAxDKDuCF0MwGtavRHPvKmXxy7+6E5GYvGxxkwYhBKpXvaCcvv+C7yMak//ALjE2Yw3AHuHV0nEubPk4BMY6zA7pFRjVSnaUiU0tO//d0Qrx/U/g+NkPKOsyWTgGPPCGwIftAntfalgOXhj3VutYB1wRGsIL7w7ji7818NJaYNsg8OpHwIn3CNy3dGymXYMCW/usdcBNFoiUbktmanjtPBdGf6fj9CPS+yFrEu6tr+0UCCvWJZXxkCdVWBwANCmC0bIdhpSKVJb18Z9I7zI65nepBfjlE6thH0MOehaiqrNr+3FpRrl8PegahHKfW6iCYfPy7uHs/h4fbJV/3uxaoGpSW+ZDdwUOWzhx2Kwa+bLtCQLhaHa+i91gtE09xbXOEVH2WN0fvb3Z/rx7gwKrOwQ2BASEKq22SNj5+sFw8Z1rEFllti1pCad4uzWqr+n+vqJ4ti+z6/xxyYKf39si8OnrDfxr9di9mMAQ8N6W9NSPiIiIiIiIiIiIKN1UnXRLNAajETlZlasW3oR2q4kCUXlIF2VOV0T+hhy/pwm6xm7ERIWi0Tsr6Tg+C8FmptPryQM5GgooTIaIiHJP0zTsV3koLl5wK46sPRq6hRicFcPLcMXG0/Bcz2OIiah0nKgRRV+sW1rm9yje4pmHzL5Ld5Sh1U4TjA8ry3yu8izWhCg/8I4GERERERERKZmFYbkmB6PVWg8kqqvIUIpVum1qwexoG9yKG6RPrlM/NNytEUBPB6qD8oYGADCgVyG6Xh565hY7U7KWRNYq53Hzofdh4a6rUbu4C/MXrsXvak8ZK1i/EgAghMDGb38Oh8x/Ga/6DlPOR+anfxTY61IDrT22JtvhsF8ZCB81G0feXiYt/9GDAgdcEUfT2Qbmnmtg4QVxvLHBvKNztoPRxnndGm76poZbTtBQ4k7PPFu27fwyt7xgv4N3X2hsGrNQjMYq+Q8TigChsLM7laeyrOclfzmMLX9/HxgIOft3mi6roSqDI875HZKtG/MV60HMADamuD/LR63dAlc/Y77cutXPUzKirV8+/AeHaXjpFzq+/18aPrkIuOAoDf84XYfbNXGDbzYJYX3k7SwFo9nMS+zJ8m9MRMXD6v5oax8wPGptH9nWJ/CZ6+OoP9PAnpcYWHihgaazDfw1S/tYJ7IbiDkwkpl6EOU7s00pMRjtKx9TX/D944Pi2RdZ+ab9SfY3Vz1tP+SeiIiIiIiIiIiIKFfCQhGMpjMYjcjJNE2DXxGME4gwGC3bAorAA9UyIqL8VO9tTBoWM91gtHIL0/s93LcQEVH6leplOLbhe7hg/q+xa9nuScePiDCe6L4fV7eeibWhlVPKe2NdEIrWWPUFFIw2w+NXnh8EIgxGc5qQIhitRCuFW/NkuTZEzpemrrxERERERERUiGImHSjdrol/m4WVTFY3vWdtWSM2t8CDGBZG1mNNyZIp5X+r/Ip0urkzgPISDeKDFlQbg8r5D7iqEeveBpTvMaXMg53BaHuGP1TO4+d93wS8Y//udvvxs6ZfY0l4DQ5f+zLE0/8PhmHgC3Ofxnrvrsp5ZFL5IvmbNQAgEgPe3bLz7w3dwJE3Gdj8Kx0zyqd2hhZC3TU408FowFgjnlM/q+HUzwKnPGDg7lem1yl7TcK95TUd6nnNr4M0nK4vNPZ/82A0dVlgGJgnf1mjI6QSjObSNezVDHygziO07V+rgW8ckL75OY3lYDR5+9OcUHXuH183FjWOhSvIdhlrOrYHVxY4IQS+fEvyFIRsB6O1D8iHz6oB9p2j4Z7vmu/M59SOBbPK1oErnhL49kECmpbZA4LJoUiqJ5iZehAR2QnsOusRgf3nmk8gBHDqH6eOExgCjrvTwIqLdew9O08CrtPI7n6/fwSoT/7SXKKiY3Vb+vLe6rJ1XempSz6wso/vD6nLRqMCz35g7zOzcV+FiIiIiIiIiIiISCVsqILR5C/kJCLn8Hua0BZunTI8EGUwWrapwugYXkRUWNyaB3WeRtP9bLk+vcYrPpf59GW6D+VJxiEiIpqO5pJ5OGvO1Vg2+CIeC/w/DMcVHRG22xbZit9suQifqPw0jm34HqrdtQCALsU5sgYN9Z7C6Vji0tyY4fGjO9o5paxbEaBMuROMD0mHTzfclqhQmcdCExERERERUVFThc8AgHvSFeUsG8Fo9flyn2ZTCwBgcWSttLjfVSsdvrhiLAxNXPJtVBmD0IT8hxzQqxCFPMnfLXYGox07+ITlKgPA1fXnjn3+tSfjgj905SwUDQCEZu/WQygCPP6uvAewWcdgPct3OO48Ucc/Ttfx88+n3nN4Y/dYZ2VAHRYEALv65cN7t4ftpByMJr+P6hjKYLQky/r4A9Pbm/uFNdMLwHM6I3l2FgBgcCSz9bAjWWheqUfDgjr5OC2dhb08x134hMBqC20LsxmMFjcEtimD0axttxWlGo5UvPhpXRewfFOKlbNBtQapzm16g+bBnkREqTK7Vpvs968I/OQh8/9koWiJ7nu9OPdldgLoAPOgIqJiZrYpJZ4Jul3qsFyz6+ZCY+U6zWx/81br2P0VO1xsOUJEREREREREREQ5IoQwCUYrzXJtiMguv1ceuhWIsPN9tqlCklTLiIjyV6N3lml5mat8WvMvTxLK4ffMzPhLbImIiDRNw8HVh+PSBbfhkzVfhIbkx563hl7GZRtPxUt9T8MQcWUoWI27Dh7dm+4q51S9p0k6PMBgNMcJGfKOPMnOwYiKFZu3EhERERERkVLMLBjNNfFvq6EmQB4Fo20eC0bbI7za1mTzN70Isf4DYKAHOgQqDXkC1aCrGjHNLS3ziOiOfzfFp76xwcxL5Z9Bj2sG1nh3w/X1Zycd/9b/Sf3B5Ml1b+KhtpNSnl5mjeKeq1nHYD0Hz1Y/v6eG67+hw7jLlVJAmiGAj7rG/t2h6OB91TEaan3ysr7QzvmozChXd252fDCaYnkn+6XPOELDsfulrx7LNhR2CIjVbzeQB8Foiev6LopAwc7B9NfHaQZHBK591tqS7cliMFrnoHrZNdsIV730q+pb2u9vzfz2qto3+RUvPwzH7IdSEBFZYTewa7peWVvY50QqdrMtGYxGJGe2LU1uKz2nVn7VFxgCwtHi2BdZ+Zb9IfVYq9rt/06J91Xe2yJw0d8MnPtXA0vXF8dvTkRERERERERERLkTFREIyB/GMxiNyPn8HkUwmiKkizJjJB7CUFzeENWvCEggovzV6G1WlpXpPrg0l7LcCp+eJBiNgYtERJRFPlcFTmj8EX4x9zrMLdk16fijRggPd92NX236BVYNvy0dRxUils9Ux+duhlY7TjAu78jjcyk6pRAVOQajERERERERkVIsri5zT7qinFVrfb75EIwm4nFg60cAgMODL9qatqp3PcR9V+34u9qQJ/H06TWIah5pmRuxCX9/cy97qURLyw7GT5puTjremxfo+M5B9kO9/vA9Da3X6Lj9jNk4cOQt29Obae+XD4+b9MXNRTBaouu/oeN7h9qvxKsfCYxEhDJA4ZBdNNSUy+fbFxz7QcwC49y6ensLDDu7c7Mq7CPZsvaVaHjkRzpartDxyCk6/iv5cw8AwGcXy4e/u2Xnb12IrIaqDMpfzJsTqnU+cd2or5CvKL3BDFTIYfa7wmSnMEkqgQmpUu3bAXvBaAftomEPRZualiw8s1NtM36Tc5tsBtARUfEw7CZ2TdPyTUCPw88fM8HuN+53UJgskZOYBqNN+tvs/tK2Igg6Bqzt482CGFWB82bGX4Dw7EqBg68xcNXTAtf/U+BT1xl48A3r1xhEREREREREREREdoUNdaMUBqMROZ+q8/1wfBAhRWdvSr/uqPoBEQOMiApPo3eWsiwdgRrlSeahCsUkIiLKpPlli3DOvOvwrYaTUab7ko6/JbwBH4belZY1FOA5sioQmaHVzqO6Vi5PEk5LVKzcua4AEREREREROVfMpN+ja1Iw2mwbwWizanKcYmXFtlYgGgEAHBZaihJjFGGLDa0qjWHgpcd2/O2PdWOLZ87Uj3A3IqiXS+dRbkzs4VpXaS/b/Jg5jyYd5y8n6/j4/LFlcdM3NZz1sHnHW00DDt0F+P13dSxuGl+Gc9D8+S8AG2xVz1Rbn7weZgFguQ5GA4A7vqOhrgK46z8CQxZDpJ58T+Dze6grP7NaHWw2HrRjFmyla2NhPZ2SzuNdQ9bqmCvKYDQLC1vTNCxqBBY1An0hDa+tT96p/LTDdbzYIl/JPnODgRWXTO/taU5ltl0lGnRQyId63dj57xnyXWvBBrps6hE4/zGBP79l7/t92AFsCAjs4s/8TlQVjOZxAXWK5aXy8fkaPuyY+l1btmV++arWP7PQ194QMLcuM/WZLiEEHl4ucMsLAuEY8PX9NZxxpIZSj/V14sE3DNzzqsBwGDhqbw0XfVmD2+WAAzNRgbMabppOv/23wOX/XVzbt93feTgsMDXmiYjMaJM2meZq9bjt/cA8h55XpZOV7Ms+k2C0VM6Lx0/fznvMQCQhq98QwC8eFfifA4Wl63EiIiIiIiIiIiIqXBEjjLbwJsRENK3zHYz1KctKNAajETmdqvM9ALw3/EZGwnNmePyo8zSkPL0QAoHoNgzEeqXlDd6ZqHbPSHn+2TBqjKAtvAmGGHsD9kcjq6TjuTU3at1F8ICNqMg0eJuVZekI1Ch3mc/D71Xv+4mIiDJJ11z4dO1R2K/yUDwWuA9vDr6U0nzqTa5j8pXqO/VGA1gbWgkN5v0Sq90z4Pc0QZvcoI/SLmTIO/T5kpyDERUrBqMRERERERGRklkwmnvS/TB/xVi4STSefL5L8uH+4aaWHf/0IIaFkfVYVbqnpUmr4hNTqJpi8jexbXM3YVCvks/DmDiPuur0XcJrEAjf7poQWnL6ERqeWSnw/Oqp49/0TQ0nHKihogQoL5l6g7P01MvhPz2AgNuflvq1KcJzTAPA7OXGZYTXreH6b2j41bEC/1kHHH5j8sSpF1qAlW3qcrNgtG6rwWiKl3YF8jUYzeY99qP21gCYdwr/yWc0HDBPXb6yDXh9vcAhuxbeDX6rYR8jUSAaE/C4c/8bxBWbVuK6UafYbnqD6a9Prg2EBA6+xpAGIFqxfFN2gtHa+uUrW3ONtcDDRLs1yoe3dNqtlX2qbWZGhQZNE9IQi7Y+YN+p+ag5MRIR0LWx7SUaBx5+W+B/79tZ6bc3CawPADceB1SWIumDzd+/YuDkByZOvzEA3P+D3O8riAqd6niYSa+uK8yAUTNWwokSBcOZqQdRvjPblCafblSVAeUl8u2ptacwr8sms3Kd1m8SXr06hZd8unRg24CQ3qPoHASWrgcOW2R/vkRERERERERERFQYXu57Bn8N3IuYiCUfOY1KLL7IlIhyp8ZdB7fmkYYmPrjt1ox97oLSxThl1vmoctfYmq4n2ok72q5BW7jVdLwlvn3ww+ZzUebyTaOW6SeEwNM9f8Y/eh6BgeQNB+o9TdC1wnwpLFExa/TOUpalI1DD51I0Pt8uE6GXREREdlS5a/C9mWfg0Ooj8ZfOO9ER2WJrer+38I5lquBSAwZ+s+UiS/No8s7GT2ZdhHqvosMGpUUwPiwdXp7kHIyoWDmg2zARERERERE5lVlne/ek5+S6rmFmdfJ5+iuBuoo86MS6uWXCn4siH1metNKYeINqpiIYrcPdhEFdftOqKj4xtaquKn3BaIHT104IRQPGgk/+dqqOC47ScOB8YHEjcNRewN9O1XHGkToaqzRpKBoAaJW1+IJ3Rdrq19Y/FsA0WbIAMKfQdQ2fWazhvYt1HHcAsIexHjOj8l7JkRjwm+flG5q/Eqgq06YXjKYD/kr5j+P0YDRVCIXdZd1co2Fxknvyt5ygYXYtUGXSlvKiv+UgfSQLrAajAcCQQ4I+VHV2JdzprCuXj9NTgMFot70kUg5FA4AW+SEq7doH5MObLZw7TLakSb4jWB8AIpLjRzoZihXQ4wLm1MqnaenMfZCQEAJXPm2g/KcGyk41UPITAxWnGRNC0cbd86pAzekGDrrawLIN5nW/+d9Ty//4pkCHIgiPiNJHdTxsqAQu+oqGg3cZO6e3+58qfBIA1nVl5rs4mZ1zJQAYdsj5EpHTmIUMTj6z0zQNu9TLx12bhSBcJ7ASytgfGjvHmywYFtjca/8zXTowYBK2tqmX53dERERERERERETF6qPQh/hL111ZD0UDgBK9LOufSUT26JoOvyf7b0veONqSUvDaPe03JA1FA4A1oRX4S9edKdQss94dXopnev5iKRQNYHgRUaGqctWiVHGelI5ADZ+uaIS7XSGGyRARUX7azbcXzp9/E46uPwlercTydLm4hsm0+jR8p22Rrbi97co01IbMhBTBaD59+gG3RIUofb2qiYiIiIiIqODE4uoytyRqe1YNkna+XJIn9w7Fhg8n/L1bZK3laSuNiYlTTYpgtDZPM4ZcVYp5JKTclPowZ4YGYHqdUEuMUby7x+2YsefZ0vIyr4Yrj9Zw5dH2533ByQvx4B3Tqt4Oo1HglY+Aw5dMHJ4vwWjjPjZbw59PGIB4cG8Y0DB/4Tq0e5qnjPeyYtUa31bqK+TLvnt4rBN0st9FHazm7E7Nqu+VyrI+6VANFz4un+GyC3Ro2thM7zpJw/F3ycd7YQ3wxLsCZV7g8MWAx+3AlS4FdsI+BkeAGeZtHbLCyrpRp1jvnRqM1jMs8P5WYFW7wIJ6DXNnAL1BYF2XQFUZcOiuGmbXTl3neoMCv3zC2kI8Zj/g8XenDs9WMFpbn3x4s72XtgIAFivOJeIGsCEALMlgmxuz9W9Jk/w8aE2WfmMzN/5L4OK/2dvvL98EHHengQ8u1VFVNnX9i8YEPmifOp0hgIffFjj9iMLYTxI5lWp/VOYFLvuajsu+lvq8z3rYwG+en/oBbf3A8KhARWnxbN9WwokSBRmMRiRl9+pzt0ZgZdvU4evyJBhtaFTg5bVATRnwiflAicfeftPKdVo0DoQiQPmk9nyphsd1DwOPvq3+YKMws8KJiIiIiIiIiIjIgjcHX87J52rQ4NG8OflsIrLH752JjsiWrH/uquDbGI4NosItb4c7WVekHa2j6yzPf8XQMsSbYnBpzumCu2zgJVvjM7yIqDBpmoYGTzM2h9dPKfO5ph+o4dXNg2WqXCk0/CQiIsoQt+bB5+uOxcerPolHu+7Be8NvJJ0mHSFiTlOil6LaVYuBuKLjhkUdkS3oinSggdcSGROMD0mHpyPglqgQSbqxExEREREREY2JmXR6dCmC0ZL55CLnd6IXAz3As/+fvfsOc6Ja2AD+nsn2hS1soxepSlERuSoWELuIyrXfa0Gxo9jrp2LvXa5eK/aGXkVsiAURVLChUhaQKtv7bpYtyZzvj7DsbnLOZCab7Gbh/T0PD5uZM2dOksnJzGTOO6+0mnZg7RLby/sHo/XQBKOtiRusrSOlZR3xSTh8D9ur17rC+waGXaUORWurYaMH4u+hd+L2otvDUt8XKwMH43a2YDQAQLEvMcaAxHE1HztadGh335PSBZt5TF9QldXgZEMAWZrzosXq86hRQxdCYYRwNuus/QS6KH6jP+9AgX37N288p4wxkG1xHnnK0yaOftzEsFtNrMyL7mA5uxwFo9VFrh1O2ApGS1Z3CmVuwHTypNvBwlyJ4beZmPiIicvfkjjuKRN73mFiwsMmLnhV4rRnJQbdbGL2ktYf9m9yJTKvtJdOsOV+A0Ny1K9JbmH7vB75ler19Exz3oEPytL3+7kRDsuw2v6GdFc3ak1Bx25zLy02cd2c0Nrwdznw0hL1stUW4T+hBnIQkX1ezVdAOPaLp0/QV/K7IqhoZ+Z0t8HdEJl2EHV2ViGDQtHlDNbsu65pp33Xtli8TqLPdSYmP2Xi4AdNjL3HRF6Fs3bb7XsqagOn5bZh3/MWiyDdKDuMIiIiIiIiIiIionaU37C5Q9bbI67PjpsdElF06xs/sEPWKyFR2GD/R+y8emf9Wb2sQ41mwHpHKXAYQNc3oWPeGyKKvH4J6nEIPeP6hqX+/pr6ByUO5z4aERFFpW6xWbig1w24pNf/ITM2R1suO7YnEl1J7diy9tMnTPv/FZ7SsNRDarVmjeHAQ9YAACAASURBVHJ6OAJuiXZGDEajsBJCxAohxgshzhJCXC+EuFQIcaIQon9Ht42IiIiIaGclK0shX7kP5mNXwrxhCsz/3Aj52euQXm+b67YKRotRHFH2TA/+I9exI6P7hzBpmpBTdguYfmjtN0gy3bbq8A9G6+VRX3hR4UrX1pFiVjU/SEhC1wSBCw9p22t35iGJbVo+mB6X34Sbh6/G6ZVvtbmuVfmKYLQgAWBRqSRvx5+Tqz9ytOhuwheopws2A4Dv1gFnvah/YVwGtEFf0R6MZif8yq5e6QLPnimQ2uIjMHlP4KnTAyv75Zbgp8s2lADnv2JCWo3u7yScPIXKbZFrhxN2gmB0nxuvCZSof0PoEKYpMXW2iaIgn8cGD3DubAnjAi/2usOLM54zcejDwUPRkuKA16cJ9EoXGKa5qVFuAdplWy7VvO459m7Y2kp8rECfbup5hVWRfS5WfZPuNV6tzkcNm9X5EnfMMzH1JRMPzTdR7m5u5PwVEue93LbX5I6P1MvXWIQlVkdJf0G0MwvnvpK/fhlAYqx63qd/dv79HyecfkVa9Y1EuzLLYDTFtCGa69JyC9tn3zVUpilx8jNmq1DpP7YC17zrrM12n2KFYp8rUsc7DEYjIiIiIiIiIiLadRU3RPhHb41xaUd0yHqJyLn9Ug9FnFDcubQdFDfmOyjrvD+r9UbPxWZe6UFpY5Ht8qmudOzVZb8ItoiIOtJBaUfCgKvVtGSjK/bqun9Y6h+Xqt4XOyTt6LDUT0REFCkjuozB//V/AsdknIoYERMw/5D0YzqgVe3j4LSjwlKPlMHHqlBovNKDOlM92CKZwWhESoE9OXV6QoiZAG5rQxUvSynPcbjOLAC3AzgVgHJYohBiCYBHpJTvtaFtRERERETUgszbADltf6C6vHni4o8hAWDBW8CDc9t0Rx5d+AwAxLgCp/VKC17n4OyQmxNxUkrIW08HGgJHsyfIehxRswAfpBwftJ6ufsn9w+pzHbclpeVd5hJ8aU5Pnibw34WhjUQdUr8Gw3bX3/EiHERsHMTd7+CG79fgzZfaVpcqRMZqEK4RrdHvLYLRxtd+i67eKlS77KUApc+5F3LAUcjcR3/S/binrE82CyG2B0QFvnjF0XO9jpJVEF4oThtr4LDdJf7YCqQnA3v1UfeNPdMEjhwOfL7Cur7v1wO/bQH2Ds+N1TqMk8HtFbWRa4cTdoJgeqbql99aAWSHEMYVCT9uADY6vJnO738Dv/8d/I2bO93AfgOAzK6+F2ZodwFVX1BTD+RVAL30WZ1hUa7ZftJDvNlSVhdgk+K1i3TwnS6kwjCAoTnq17ioGih3S6Qnhz/F85tcieOeMuGub5721FcSP9xoILcAOOrxtnem5bW+fST/fcrqes0CAKrqmJxBFGm6fSVXGPaLXYbA+KHAp38Gzluybtf6fDsNAqptiEw7iDo7q4+S6rTVEM1+VXUdUFgFdLfY3+9Iv24BCqoCp7+1TOK18yQMm+mVdvueckV+f1WEAhoZjEZERERERERERLRrqjO3ocpbHrxgGHWP641xqUdgfNqx7bpeIgpdt9gsXNX3bswpehEb69bAIz3ttu6iBgfBaA7KNnF7o+cOtKWNxTAR/FqgeJGAgUl74LTsCxFndExgHRFFXu+EAbi8z0z8r/gVFNRvwYDEoZiSNRWpMeG5GHRc2uHwSg8WVnyKgoYt6BHXBxO7HY99Ug4MS/1ERESRFGfEY1Lm6Ribcgg+LX0Xf7p/QhdXKg5KO3KnPt8wossYnNvjGswvm4Ot9ZsgLa/c07Nz3EGhsQrfTjIYjEakwmA0ajMhxNEAZgMIFm9wAIADhBCvA7hQSqm4VJuIiIiIiJyQ/7mxdShaSz/OB76aA0w8OeT6PV79vBjFgPtgwWhxMUBGlJ6jkV4v5EPTgYUfaMtMjluGD2AnGK31hRADGjciVjagUcTZbk+K2WIkbbwvNSbGJfDBJQZO+I/zE4z/3PYRxIiLHC8XipH7D8GceImTngn9ROiaQqC2XiIpvnnQsNUgXFf4s2bCo7g5GC1eNmCi+2tb4XoAkN5QDPn0TUh+5WjkpPgGfzvRNN46S/OZq64D6holEmKj88XThl+1Iewjs6vAhGHBy43oJfD5iuA/AMz5WWLvvtH5+tnlZHB7fqUE0PHPV9dmV4uQgewUXzCMKuAzryJ6Au1yCyOTLnDbcQKTRrV+r4ZaZGOuLuh8wWiZmr4t0sFoVsF8w7rrl8stBPbbLfztufl/rUPRAGBzGTD+IRNrCsO3nvJaoFty62k1FqEbVeqbGBFRGNkJCm2Lw3YX+PTPwJV8nQtUbZNISWzffYJXvzcxe4lEXSMwaU+B648UtgOG2sLpN3UNgyGJHFMGo1n86rymMHqD0VT9ZpMyN5DZ1V49ujBefxWKfS4GoxEREREREREREVE4lTQo7m653e0DnkZGbPhv1GmIaL1DJhFZ6ZswCFf1vcd3g+IQB99beTH/YfxSvThgenGjg2A0TdmD047G95VfolEG3gmr1oyeO9BaPddHBr+JOOELQRMQbbqpNhF1HkOSRuL6fg8qb3waDgenH42D04+GKU3uoxERUaeUHdcTZ/eYEbHvymg0JuVAjEk5EKYMPqbvirWnwiMbA6YzGC1y3FbBaK4oHXRL1MF4JEJtIoQYD+ADtA5FkwB+BvAugC8AlPgt9i8AbwrBI2EiIiIioraQNZXA4nnWZea/0aZ1eCzOY7kUe/S9061PEvZMRfSeSHz3CWDei5ZFjv33QbaCBrr6XQgRAy8GN6xz1JxE2WJ0a0JzaszkvQROH6tuRGoisO5uAymu1gkp3T0FuPqgOojkFEdtaIspowW8/zXQeMyLKFjTB9Wru2FK1f8c1THy9tYboNUg3LaEZUWSLMlr9Xh4/Urby6aalcCm1RAFG3HkQOfZ4juC0SwGXpdGzzU7ASId9mHFKtyoJTvhadHOdPB7RV5F5NrhhK7NLfsBlyHQQxOWsLUiet63sgjcNiC7K3D5oYEflPRkgWxNfxCpgLYmpilRoQtGSw7tQ53ZRb1cJF7Tlqz6pp5pQBfNTVbXF4f3NfZ4JYqqJL5fr54fzlA0AMivDJxWbRG6UVQNSClR5paQdpM9iMgRVfgnEL59pRG99BVlX23CXd9+n+1nFpo4+yWJr3OB79cDN/9P4uLX22f9TvaVAMAdeJ08EcE66EvV22R2FQGhrE3WRHjftS3iLW5LV+zg+NtuCFlFbWDByggF1NrtD+sbJbaWcx+QiIiIiIiIiIhoZ6EL4XEhBt1is2EII+z/iKhzE0JEpG/Iju2pXF9xg4NgNE3Z7NgeSHapL6xye6uV0zuCrv1pMRlIMBJ3vFZRe502EUVMpD/33EcjIqLOblfcR7Z1rKWJGzKlt51bu+twW4Rv645LiXZ1PBrZNZwOYICDf9fYqVQI0RvA+wDiWkxeDGC4lHKMlPIUKeURAHoDmAGgZVzocQDuasNzIiIiIiKipV8AnsBU/laWfAJZG/qP8lbBaDGKI8qRvawH4vdKC7kpESU350LOusG6UEYPZB1xDPbsbV3MkF6keQPTg8Zs+9lRm1q9jPGJrebNPkfgjuMFurfIOZs0Clh1h4HdsgSW35WIc/qtx4HGnzjH+BxLDv0S6dNvcbT+cBBCwOg1AJneUiTKOkyutg7y87ehBNhQ0jyQ1jIYLVrPUfsFow1ryLW9aJrXl0IjT90dx300zfGqm0KiUhP1ZaxCbTpaRwajHTlcKMMf/f2yGfh2Tece7G13wD0AbI2SYDSvzW1D950TLc8DUIdNtUVOCrDsZkMbNqYL/Vutv8FzWFTX6be19CT19GAyNDfDKamOcMibpnohfN97fbup54frvc4tkNj/Xi/iLjbR/Zr2uxNTvuJzU1MfOK3Jynyg17UmMq800e8GE/N+79x9JVE00vVHdvZh7LAKim3wAF0vM3H6syZq6iL/+X5sQeA6Zi+RKK2J/LqdrsGqbyTalYXyaR2So54e7gDYcEqI1c8rqrJfj93jtHJF+HC1Jhht3ED761fRHYc1qWuUOPtFE92uMNHnehN9rzfxxo+8cygREREREREREVFnV9ygvqAgIzYbLuFq59YQ0a4sK079I3ZRY76tG7Y0mg0o95Ro6u6pHYBe642eu8/qwiqzYm3eCZaIiIiIiKgFXfipKXndV6TUasK3Y0QM4kR8O7eGqHNgMNquoUBKudHBP/VZvkC3A0hv8XgJgMOklKtaFpJS1kspnwBwit/yVwkh+oX+tIiIiIiIdg3SNCFXLIX84bNWIWfyO5shUwveCXndHouA/xjFdU3pyQIHD9Yv0zs9+tKrpJSQ/xoVtJx4ZiEAYPce1s+hm7cMLgSeAJxc87HtNu1ev6r1hITWqTGxMQL/d6yBvIdcMJ/1/Zs73YXuqb629csQePHmwfj2mT3x4jPHoP+/z+q4u1v0Hbrjz1Oq5uCA2iWOFv/g1xbBaBbnVaMxGE1KCSxb0GrasHonwWjNKTSH1yxAvOksxazpNUmxCEariuJgNN21Su3xXvdOF7jxaHsrGv+QCa+TdLEQbGuQ+OQPiW9yJRo84V2Xk6bnVURHsJGuL/DfNnqnq8tttHvmqx0UhDkYLe9BA3266bfdId3V89YURPa9VQU2NAk1GC1TE4z28+bQ6rMr2PbXPVU9P1gwmrte4ouVEq//aGJlnvr9qGuU2P1WEz9usNlYB2JdwD4WZ2rzKwPbVB0kDKlge/jH3+XACbP0z4uIQhPpENleaUDXBOsyb/8kcdFrkf1s19RJZQhSoxf436+R71ec7ua5GYxGpGQ1FkV3umJIjmbftTB69ykaPPp5xQ7GzNgYu+OrU3GNVJVmH22f/gKXTwz9S6I+yL0Zpr8p8eoPEtu2l9taAZz5osSitdH7fhEREREREREREVFwRY15yunZcT3buSVEtKvLiu2hnF5n1qLGG/wONSWNhZCa2/lkx/VAkqG+GKlGM2i9I+jCKrPi1K8NERERERGRFaGJGzIV4yIpPHTh20lG144b+0kU5RiMRiERQgwGcHaLSQ0AzpFSaodUSyk/APByi0nxAG6LTAuJiIiIiHYOsrwY8sqjIS86CPLa4yFPGQq55BPfzF8X2qvjwUual3HIG0IQ1Ql760/C7D8wpGZElHzh9qBlxJWPQXT3pYUMCXJjtUxvqXL64TULkOCySJpr4aiaz1tPSAgxNSYaZPcGEpMBAHFoxNebjsCzeRejf8NGpHvLcFXpo7il+G7t4gtWtQhGsxhLG23BaLK+DvL2s4CG1skII+pXIM1bbquONLM5RaeLdOP0Kmchh02vSXKcvkx1FAejmZqR4O31Xt9xvIG50+2dOrvk9cgN9F62UaL3dSYmPWni0IdNjLnLxIaS8K3PSdjH1orgZdqDLiTA5fd27ZYV/UEK64vD15YN9xpBfwgZpvkO+3ULYEYw4C8SwWgZmmC0wirgkS8i90NcsCCiHqnq98AqBG/5Fonht5k48jETZ74gMWKmiQtfNVvdzbaoSiLp0vA8r/12A2ZMFHjgJIFbJgncOklg5e0Glt2sv5v3ekWgYI2D8B9TAi8tiZ7PHtHOQHesFq59JcMQOGVM8Mre/VmiojZyn293g37e2qKIrXYHu+FETZz0jUS7EstgNM30wdnq6b9tQav9pGhidYxdVG2/zXZ3zfMU+5hV29RlUxOBR08RmHdZaJeI1FuEvjV6JOb8FNhoKYG3l0Xne0VERERERERERET2FDfkK6dnxQW5iI6IKMysAhmLG9V9lZ0yAga6xWQj2aW+GEk3aL0j6J6DLjSOiIiIiIjIiiHU15JJBqNFjNtUH2PqjkmJiMFoFLozALQcKfe+lHKtjeXu93t8ihAiIXzNIiIiIiLaucjHrgB++aZ5QmUp5PUnQn74HFCivhujsp7rT4Ssdp5o49Gcx4oxoA1fOX4v/eB5q3kdQa5dDrx8b/CCYw7d8efwHtbPIUMTjJb8z6k4bLg+cKTJuNrFmFl8V+uJ8Z03GE0IAfQduuOxCybOrXwZ6/7aA8VreuOBoptxW4k+GG1li+s4OlMwGj55GfgyMMgsFh4cVTPfVhWp3tYjnO8uuhX7bltmuwlNr4lhCHTVHHlXRXUwmnq60Y5nsyaNElh4rYGsrtblnlskUexgkLtdUkqc/IzZKlTqzzzg8jfD9yODkzyBMnfYVtsmum3D/2tpSI66XG5hdAQplLslvl9vXaZ3OvDd9QauPVJo+7mEWGD2VIF+GcE7wj0032HF1cDSjUEXD5lVMFpaiF9x3VP0z/eadyV+2RSZ9zhYMFr3VPX8/MrWC5a7JR6ab+L690zsfaeJzWWtyz+3SMJ1oYlr3jVR7pa4dW74ns+SG1x49FQD1xxh4PbJBmZONjAw2/cEJo1SLzN/ReD6nYZrPjy/4z93RDuT9thXuuuE4N8tjV5g/3tNXDvHxIKV4f+c11oEo1VVRT6FzOkug1V7iXZlVh8lXbbv7pp9181lwAr7p8TaVbVFt1Rcbb8eu11PfkVgyUpNMFpKgu8czTEjBf7zL+cnUeoa9fNKavTnF75cxX1AIiIiIiIiIiKizqy4sUA5nSE8RNTeurpSEa8ZgqgLcWxdRt2fdYvNRKwRi2SX+iJBt+ngR54IMqUXJQ2FynlZceyTiYiIiIjIOUMTN2RKBqNFii58O4nBaERaDEajUJ3o9/glOwtJKVcB+LHFpGQAR4SrUUREREREOxP5+2LgqznqeQ9Nd17flAGOl/F41dNdFkeT/TIEzt4/cIDlWfvbC21pL3L1z5Dnjg1ecM8DIVoEe40fav38M+MUo2CPPQfGjEewT7/gz/+bTYcjWfolyMR38jzpfsOCFvlwyxTl9E2lgLveN4jWMhgtys5wSEUoWpPRdb8GXT7JdCMOrUcd53iL8O3GiVi0cTxeypuGl47abFlHyxAlXTBadV30DlA2NefR2zsE76DBAmvuNPD8WdYrzrk6/Cf+V+YjICwJAD7+A9hSFp73zupz5a/CItyqPQULpmoyJEf9nlXUAuuKwtyoEIy9R7/NPHWGwPwrDKy+w8ABAwXu/6eBVXcYmH+F7//vbzDw8lSB9y82sP4eA2ftb68TPHAQEB+jnvfhb5HrD8o1oXpdE4AYV2gf6qFBbj495m4TjZ7wP6dg218PTTDa3+XNf68rktjrDhPXzZF48HPrNj7yhUTGlSae/TY8zyUnxXr+IUPU78cPG4ASvwDImsjnERGRBV1/FGK3qpSTIlA7K/h3TG6hL/zwiMdM3DkvvPtEbou+pnLhfMiS4Be4t4WTfSUA2MZgNCLHdMFoVudf5i6PzmPZKk0oGQAUORgzozse9penuAeCLqAsJbH577REdRkr9R79PKtQSDf7RSIiIiIiIiIiok6rwaxHhUd9k1CG8BBRexNCaPueokYbwWiaMk1Bj7pB6LpB6+2t3FMCL9Q/2GTFBrmQioiIiIiISEEbjAYGo0WK26u+kFAX1k1EDEajEAghugPYs8UkD4DFDqr4xu/x0W1tExERERHRzkg+eGl4K6yrhdy4ytEiHs15rBiX9XKPnCJwzgECQvgGuJ5zgMAjp0RHKJos+hvmnVMhzz8geOF/HAFx51utJmV0ETh4sH6RzH+MBcYd63sQGwecOgPiqicAAENzrFd3RuWbUL5KsfHB2xrFRL+hQcsMq8/Vzluz/SZ3lsFo0bF5NVv+nXbWUGNr0MWzPCXK6bHwYP9tS3Fm5Rs4s+xlXHm4/om3DItL0QajBW1Kh7EbftUeUpMEzj3QwMXjrVd+yesmpAzfAH3VIPcmdoIA/i6XmPqSieG3eXHAfV48PN+E1++FdRL2UVMPeLwdH0DgtRmaN7ynvo7nv2vf5+HxStz/mYne13lhXOD791exumyfdODiQwQO20MgKb75SQ3O8U0bsvUb7DvrOPzr1Yk4/o/7kRNvkb7gp0uCwERNVmVEg9Fq1XWnJ4Ve58Cs4GXmLg+9fh1t37S9z+3bTd1PbChp/vw8+LnElnJlsYg7d5x1P3bsSPV8KYHPV7Z+8tsalUUtRXMgJ1FnE6w/CpeEWIG1dxk4cJC98nfOkyisCt9n3TIYrSEG8tX7wrYuFafPpM4iOIhoV2Z1mKTbO+mWLDBuoHreJ39E5z5FjcUx9vIt9tts9zgtvzJwmi6cLbVFGFp6svMD6zqLfT+rYLQwHiITERERERERERFROytpLNDOawoSIiJqT9maYLTihuDBaEUNecrpTWFryYZ6ELpu0Hp7K26w6JMZVklERERERCEwhHqAqCkZjBYpuvDtJEMd1k1EDEaj0Izwe/y7lNLtYPklfo+Ht7E9REREREQ7HVlTCTgMMbPlh88dFdeFz8QEOZpMTxZ48RwDdbN8/148x0C3EAZdhpusKIGcui8w/42gZcXH+TAe+ggiPTB55fi99M8lK7srjPveh/i6BmJ+OYzpD0DE+YLNhvUIEkZS86l6hitIEl206xs8GK1/4ybEm+oRxKsLfKNoddsjEF3BaPLr9yzn737YfkHr6OlRX4TTyqK5yLI472m2eL26aoLRqjphMJrowPf6idOsV/7MQokb/yfDFo5mNbj8p43Wy1bWSoycaeLl7yVW5QM/rAeunSNx5TuhB6MB0bHN6Nrs8vtu6pYsMLa/uuxjCyRW5bffCP2LX5e48X1pGXbX5JhRAkKzoculX0BecRSw9AvgjyWQz90Gefd5geVME7LWd1GebPRtSLJ+G2ThFhy3m7oRqwuANYWReU0qNOEMbQlGi4sRGJxtXeaTP8P/fHQf76bvoSGaEFSPCWzcfiPtj2wEG0ZCelLwYLSh3YHdMtXzft7U+rFVOIbOt2ucL0NEanaDQsNhYLbAt9e5cNyo4GU9JvDlqvD1c1b7QxVGKvDVe5Bm5C7AcFp1gwcBQbREdkmPB7LefuhtZxLqp+K4PdWd2h/B88Y7hFUI7JK/gBqbIbF2X6+iaqDR01xaSolKzSaUktD8WqYlqstYqbcIfrQMRnO+KiIiIiIiIiIiIooSRZqgIQMGMmJt3M2MiCjMdKGMxRZBjsHKZG+vM8mlvhiz1lQPWm9vRY3qPjnFlYYEI4Qff4iIiIiIaJdnCPUAUQkGo0WKW3OMmaw5JiUiBqPtKi4UQiwQQmwVQtQJIaqFEBuFEAuFEHcLIQ5yWN8efo/XOVz+ryD1ERERERHR5sikRsiPZzsq7wkxGK1JbIxAbEwUJVbNfR6oKgtaTDw8DyKlm3b+lNECcTHqeYft7nu+IiYWIqZ1oRE9gd7p6uVS4704qma+eqZLs7LOYvd9ghZxwcSQhrXKeau3X49ilS8QVcFoj1+tn3nOzRhw0XRkBjlf2ctOMNr6FRhZ+5t2dssAqxRNMFp1FIRc6eje7458r12GwLfXWneAD3wmkTbDxDML2/5DgNWg+tU/roIsDkwDKK6W2O8eL9KvMJUD4p/6SuKnjc31Og37qKh1Vj4STM3Godo2poxWbzCNXmD6G2bYQux0TFPigldNvPCd/fWcOiawzXKbG+bd50FePSlwgW/eh/zwOV85KSFffwhycm/IIzNhHhQPeWhX3/+HpUGeNAiTHh6tXfeHv0Xm9SjX3FKhLcFoADDZIqgUAF5aHP7nE6xvGmRx7ffaQqBqm0RBVdibBQDoEg/00exnnDFW4NvrDAzMtn7NhBA4eIi6zDMLWz/5UILRPl/JaAyicLEbFBpOE4bZ2xH79wuyVVBPW7gtwnYKY3KAimJgrX6fuK10zyIpTr9MKP0j7dqk1wvziashj+sJeVQWzKsnQVaUdHSzwspqt9sq/Hp0X/XM6jrA442+/Yrqeuv5T35tr81OjtNa7lvWNerP5aW0GA+TFsJ+uFXfZhWMZhVyT0RERERERERERNFNFyKUEZsNl+jk17MRUaeUFacORitqyLO8DswjG1HWWGxZZ7Krq3K+2xsdwWjFmrBK3WtCREREREQUjKGJG/JKbzu3ZNdR661WTk/SHJMSEYPRdhWnAZgIoCeAeABdAPQDcDCAmwB8K4RYJoQ4zGZ9g/web3bYnk1+jzOEEJohe0REREREu6jNuaEtl9ULOPRk/fyNqyDLi2xXpxtkGsnB9pEkf1sUvNCUiyDGHm5ZpHe6wMzjAgfn/nM0cMgQ/XIxLoEH/ikCguWEAO4/tASppiYppZMHo4mcvsCgUUHLDW1QBwLOX+HbDq0GBkdLMJosyQNK1RegoGs6xLm3wOUyMGmUdYN7NrYIRuuSpi034fHxttrVVROMVqUIzooW0RiMBgAHDhbISLYuU10HXPK6xNSXTORVBB/4bpoSK/Ik3lxq4of1zUEiNRaD6nMbc2D+a1TABV0nP2Ni6Ubr9Y29pzkQzCpwUCUqgtF024biu+nCgwVyUtTlv84F3lwa2TCFl5ZIPL/I2TrGD229kcttbsjz/gF89pp2GfnQdMhtbuCTVyCfuRmoLNWW7eEpwNhtS5XzQnk9TFNi0VqJJX9JuOvVy5drtpv0IJ+lYK6YKLBbpnWZlkGA4aDb/poCPZLihTYEdU2RxFr7u2GOHDsSqHzCwKb7XTCfDfz32jQDw3va60AH56in1zUCm0tlq8dOrS2MvgATos5Kt28cyX2lY0bar3zATSb+u9DEyry2fe513y0AkBfTwxdc9tefbVqHFV2/nxyvX2abRUAQkYp8fibw7lNATSXgaQSWfgF5w5SOblZYWQajWSxnFeBVFYVB36pw6pbe/8Ven+ik58yraP7b6jVpGZieFcI1VA0e/TyrYDT2iURERERERERERJ2XPoSnZzu3hIjIJztWHQK2zXTDbaoHlwNAaWMRJNQ/smfFNgWjqe92W2fWwistfihpJ8WNmj45tns7t4SIiIiIiHYWQhM3ZGqOn6jtdOHbyYb6mJSIGIxGzcYAmC+EuFsIq/tyAwD8R2Q7Gs4nt3QxeQAAIABJREFUpawB4H9ZdqqTOoiIiIiIdnZyzW+hLZjSDWLmq8Do8foyc1+wXZ1Hcx4rxuWsWVFj2QLr+YP3hLjiMVtV3XC0gS+uNHDFYQIXjxd4Y5rAWxcYcAVJIjhtrIElNxi46RiBs/YXuOYIgUXXGTh/X33akOjkwWgAgIMmBy0ytF4djPbjBuCOeaZlgJMqEKkjyDvO0c/cdyKaDrlP2Mt6O+l9+ETgjKshZjwC8cEmYPd9leUSpEVyVgspier1VUfhQPImHRH2Yddf99jb4F7+XqL3dSae/kb/o4C7XmLK0yZGzjTxr+clDrjPxIEPmMivkJbvT4UrHRs8mZAn9Id0+0IV1xZKfLvW3nP4aXtsveNgtCgI0/M62DZSkwQePEm/0VzzrkRlbWSCmipqJc5/xVndWx9ovW3JX7+FPLE/sMXGG/vFm5DzXrS1nsnV85TTf9sCfPqH/TZvKpUYfZeJQx40ceD9Jva6w8QvmwKX1wXqpSW17QPdK13g11sNHGeRvfnfb8McjGZj+xuiCRZbWwisKwpvey48RODlqQIfXGog+Glde4Zk6+t56uuWwWjOn0vL4A4iahtNhnVE95WG5Aj00mf2tpJXAVz8usSImSamvWJa3p3birtIfdduAKgzElFppEKGGixug67ZSXH6ZbaFEBxJu7gv3w6ctuJHyL/XtX9bIsSqB7DahbEKRouG0GZ/ZW7r+T9v8h0nBOPkOC2vsvlvq2C21MTmv7slCwzKtr8OANhmse9nFYzmbkDI3wFERERERERERETUsYpb3tixBYbwEFFHyYpTB6MBQHFDgcU8daiYgEBmrO9CnyRDf2eZWs3A9fake35WrwkREREREZEVQ6jHRknJYLRI0R1fJmnCuomIwWg7u60AngNwPoADAewBYBiAcQAuA/C5X3kB4CYA9wSp179XDWVIrP8yIdyXOpAQIlsIMdzJPwADw7FuIiIiIqJwkVICi9WBJUFl9YQQAmLaTH39331kuzptMFonPJqU9cEToMSNzzkKFZm4u8AjpxiYdYaB08YGD0VrMqa/wF0nGJg91cADJxk4YKAAYi1G1u8EwWjCRjDanvW/a+fNnCux/G/9QNqEKHiJZN564NeF2vniikd3/H34HtZhCsMOGAHj4nsgTroUIj4B4uDjtWVf3XqOcvrY/s1/6waTF1VH7+BkXcuiIRgtJVFg3mX2O8JL35AYOdOLmAu9OORBLxava352T34lMXd56/LLNgK9rjNx/XvW789HXY4FygqA954GACz/23aT8NsWX91Ox6dHQ/iALiRAt2386x8ChwxRzyuoAm6dG5nPwVXv2K83MRZ49ZhNyHnvTpiPzoB8/SGYz8+EvPxwYHvwXTDywUuBP3+wVVYXjAYAxz5pwmszieGwR0z83mK7+6sYuPC1wPCbck34Q7pF0IVdXRMEXjhb/3lckRfmYDQb298gTbDY2kKJQntvpy21sww8/S8DZ+5vfx/Ejj376OfNXd4yGM153fmVwcsQkT0dFSJ78hjnK3jxO4lZX9vvj+WP82HOuh7115yIJ99WD7hpkhfTA9isDlgOB12/z2A0Chfp9QL5m9TzPnutnVsTOVbHHZbBaIn6edFwbOKv3EabttoIitX18Sp5Fc0vbpXFr/YpCa0fHx8ksN1fjcVptdoG/RvsNYF6j6NVERERERERERERUZRgCA8RRZsUVzriRYJyXlGD/rflokZ1MFpaTAZiDd+Pv8kWg9DdHRyMZkoTJY2aPjmWfTIREREREYXG0MQNmWAwWiSY0kStyWA0Iqc64VB2smEpgCMB9JFSXiClfF5KuVhKuUpKmSulXCKlfEpKeRSAfQGs9Vv+BiGEfsR1YDBa8HSBQP6XZYerp74EwJ8O/30YpnUTEREREYXH+hVA3oaQFhWjx/v+GLGfvtDqnyGLt9qqz7szBaPdebZ1gX0mAINGtU9jVGLj9fOMTviC+xs0Cujez7LIUTXzke4t085/eYl6oG1iLJAU3/FpWfLjl/Uzx0+BSM/e8TAxTuCS8eo2d08BjtjDb+Kkqdqqp1R/gMGJgSOrT923uf6eqeplt5brm9zRtOFDUfJxOGakwKcz7DdmRZ7vOS1aCxz0gIk75vk62LeXhR7atCjpQACA/NS37W2tsF/X6u3XSdnMv9qhQhNw1Z50bXZp3g4hBGadYWi/u2Z9LfHr5vA/ry9W2qvz3QsNrDv8A5z+2Ahg9t3A+89APnMz8PK9YW9Tk90bVmNQwzrt/CV/Ba/jmYUm/ioOnP7zJt/23lK5W11HOILRACCzq/47ILcAAUFtbWEnGG1IjrrMumKgVPNaNOkSD2y5P3jf8uNNBhJiI/Pdpwt2A4A1hcDqfN+LEEowWkkNUN/Y8f0I0c7A6fdhuFwxMbS+5/K3JIqqgn/+zadvgrzmONS//R/sX3QLlidYH6Ntie0NbM4NqU126L5CLIPRGiLTFtpJeSy+UGt2jURRq14lxSoYLZRbd0VQg0fCXR+8XEl18DJO9pbyWpwOqLL41b6L3xih6RME4h2EzFu93rVB+j07rwsRERERERERERFFl0azAeWeEuU8hvAQUUcRQiArrrtyXrEm/AwAihvU87JbBD0mu7pql3drBq63l0pPGRql+gcZhlUSEREREVGoDKEJRpMMRouEOrMWUnN1oNUxKdGuLkqGklI4SSk/kVLOlzZG/EkpfwKwH4A1frPuE0K47K7SaRtDXIaIiIiIaNewaG7oyx40GYDvx3/xxHx9ucUf26rO41VPj7F7tBAl5IJ3gIUf6AuMHg/xfy9BiA4M13JZjEa1mtdJCCGAg46zLJMkt+Guopna+b//rZ6eKSphXjwe5q1nQP66sA2tbKN5s/XzsnoFTLp9ssC0gwRiW3ye9u4DfH2NgbiY1tuiSMsETp6urDpeNuDdxuswtr/vcZd44PqjBK44rLmOXunqZm3Nr4H571EwrzsB8tvoyg03NefRjY7PwNvhyOECr08TyAwh7n3mXAnjAi+Wa7ZrO3Ljh/j++PsvVFfV4cq37Z9uyd0ebOQ0GK0mCga02wmm8rdHT4GrjlAXMCXw5FfhOVVVWStx/XsmRt/pxdbAvMJWztxPoPhBEyf+egdyHj4D8Gq+dCNAADihSv+Zn7vc+vWQUuLSN/Rl/tzael55rbpcerLlahz5aLr6VHd5LVBsI3zCLjvb34BM9baWVwGUWlwnuVsmMHe6gV7pAvOv0J+6f/4sgX37R7Yz/GOmfv1N28e2EILRAKCgKrTliKi1UL4Pw6FvhsDHl4X282L3a0xU1wU2XEqJF74zceg1f2HUD6djt0GrkTysAr8l7Bm0zr/iBgKbciG3h0t5vBK3fGjCuMAL4wIvsq/yYt7voX/P615ny2C0EPtH2kV5LTaYuihL/moDq0+h1akYlyHQNUE9r0Kzj9lRdPu8/optjJtxcpyW3yI/r0qzyXRN8L2WLfXLEPjfJQb6bD9fEOz7w+r1ZjAaERERERERERHRzqeksVA7UDObITxE1IF04YzFDQXaZYob1fNa1hUr4hAjYpXlar1hvPgoBFahb1mx6qA4IiIiIiKiYIQmbsgEg9Eiodarv3gw2QhhYBrRLoLBaAQpZRmA09H6mvRhACZoFvHvcS3u1a3lv0zH3jqBiIiIiCiKyFCD0Q6aDNF70I6HYu9DgAF7qNfx3UfWbajzjXb0aM5juUI4mpT122Ajvzns5E9fQt5+pr7A6PEwHv8cIrODL9iK04z0BYC+Q9qvHREktgf3WZlW8aJ2XqlbPT2zagPw5/fA1+9BXj0J8uevQ21iSOQ2N2TeBqBMf2GNGLZPwLTEOIFnzzRQ9YSBtXcZKH7EwM+3uDC0u3o0srj0AW39I1a8ge8vLkXNkwYKHzZw7xSjVdBfz1R1neWyC7Zt3gR8/ynkzadAfv6Gdh3traPCPpw6fazvNb9vSvs3bF3cQDQiBiYEjphZ5GjZv4p9/3fGYDRviKF5txwrdgz69/fV6rZ/P5mmxJGPmXjwc4nftujLPXqqQO0sAy+fayD9sfOAl+9p87pDcWXZk9p5P220fj02lgJWX+lr/DZHbTBakuVqHNm3v37eevVNrEOi7Zta7Bv1SlOXqfcA64rUFRw9Alh3jwvjh/o25MP2ELjs0MCN+snTBc49MPKn9Yf3FDhgoHpe0/ZRF2LwT16Q0EAisifU78NwOHqkQOMzBmqeNFD+mIFlN9vvlw57xERpTeu+8N5PJc5/ReKbqv5YGb8HNsf2tV3furjtndWcpwAAJz9j4u6Pm+svqQEmP2Xi5SUmtjU4/77Xfd9ZBqMFCQgiaqXRYoOpj7LkrzZoy+mgNM0v0RW10XUfrjLNOQt/U2cHv2jMyeuVX9lcuHKbesEUzSmno0YIbLzPwN8PGCh91Lovr7DI6QsWjBYNx5FERERERERERETkjC6ER8BAt5jsdm4NEVGzLE04Y3FjnnaZ4gZ1n9ayLiGEdiC622LwenvQhb4lu7oiycXB80REREREFBqXcCmnS8lgtEhwm/pjSx7bEekxGI0AAFLKXwDM95t8lKZ4NAej/QfACIf/jg/TuomIiIiI2kwWbgbW/BrSsmLGw4ETDzxOXfiXbyBrA+9gJj94FuaJAyCP6AbzooPhKS5ULh7j4GhSbv0L5iUTII/MgJyyG+Tbj9tfOAzkczMt54vJ09qnIUGIxGRg+D8CZ3RNB/Y9rP0bFAkjxwHZvS2LuGDi3zXvOKo2w9Mi8aaxAXLOrFBa55jM3whz+kTIo7MgTx1mXdjiPYyPFRiYLZDRxTrFQrhcELM0oW+mCfnYlUiKF0iMC6ynlyYMCgDyYpov7pGv3m/ZhvakCx8SURaMBvguirruKAP3/7N9G9co4rAhrj++TJ6AH2t6OVp26/ZgIqfBaO4oGNAeamhecrzA7cerCxVUoc3hndfMkVi6MXi5yyYIJMQKyL/+BBa83aZ1tkWOtwiXlan7y9x86x+yVutvQgoAWNniOkMppUUwWvg+M1ldgQT1TVuRXxm21cC0EUTUUxOMBgB/bFVP36Nn4Gvx4EkCj54qMLY/MG4g8Po0gYsPab9+ZsIw9brm/OL7XxeMdt6BAifvo29nON8Pol2Z7vswlBDrULgMgaR4gdQkgX36CSy+3sCRw4Mvt2wjkHWVid7XefHRcoknvzLxfx+E/h28Jm4wAEDOvge5BRIfLleXmzpbIm2GiWMe96Koyv76dCVdAoiPUc/bFmJwJO2iPBYbTN2uEYwWbO8mTROm+9Hy6ApG0+3z+nPXIyAg0p+T47SWobNVdeoyKRa/5gsh0DPN159bqajVHzMFC0ZzMzCSiIiIiIiIiIio09GF8HSLzUSsoflxnoioHWTFaoLRNP2WV3pQ2qi+6ah/Xcmurspytd7A663bky6sUvdaEBERERER2SE0cUMmvO3ckl2DW3NsKWAgwdBcKElEDEajVj7zezxKU85/6FqWk5UIIbogMBitQlXWKSllkZRyhZN/AP4Kx7qJiIiIiMKicAvQo7/jxcQNz0Lk9A2crgtGa2wAfmydjSy/fh/y4cuAkjzfiNUVP8Lzv+eUi9sNRpMN9ZCXHQ78sQTweoGSPMinroP87DV7FYRA1tVC/rYIctVPkH/+AKxcar3AhH9GrC1OiQvvBBJanMgSAuLieyBido6LyURMDMSMRwCXJkFgu+x6TWqMRqa3tPWE7z5y2jRHZN56yK/fgzxlKLD8O9+2beXsmyDSHR06643cH+g9SD3vqzmQeeuVs3qm6qssbHkX102rIStL9YXbUajhVx3p2iMN3D65fRuYGzcUixMPcLxcTT1QXSe1IU86HT2g3eOV+HGDep6dIJjdu6vfnwYPULkt9HblVUg8tiB4esHovoBhCEgpIZ+7NfQV9h4Y2nL7Hw0xZ92Oh8dWf6IsVlBtoKJW/3xWF1g/1y9WSni8vjI19YBXs52lh/G3GyEEemj6uvzK8IVm6Gpq2Td1T9GHOOpCwbolB06LixGYMdHADze5sOh6F04fa8Box05waI5+3n8XmtoAjH8MAN6+0MBAzVdfXkV0hZgQdVbRtq+0/0CBT2e48NkMeweLeRXA8bNMzHirbX3CjpDfulp8t8ZjWbbRC3y2AjjuKfs7QFZhvYlx6nnbGABETlgFo9W3YQc1ylh90oOFX2dqboT44XLgnZ9MNHiiY9+izG2/7E+brOeHHIym2WRSEuzXp9Po1fdvwYLRKnaejD8iIiIiIiIiIqJdBkN4iChaZcWp+yG3Wa0cZF7aWKwd1J8V173V4yRNMJrbrHHYyvAqbmCfTERERERE4WcITTCadDjQiGyp9aqPLZNcydr3gogYjEatbfR7rBu1vdbvcT+H6/EvXyalLHdYBxERERHRTkmMGgfx9mqI2T9DnHcbMGTv4AslJkMce7Z63rB9gAz1D9/y569aP/5kdkCZRxOmKpeNcQVvFgDg9++A4sCQK/nFWzYrcEb+tgjyjBGQlx0GecE4yIsPsSwvXlsOYUTPobHY+xCIZxdDnHsrcMbVEE99CXHcuR3drLASBx8P8d/vgH9dAxx8vLJMmleTGqMREIwWQeasGyBP3R3y1jNsL2NMuy1s6xdCAAdN1s6Xp+4OKQNHUCfHAwmafL1SV2brCZvXtKWJYRNtYR923TLJwFdXGxiz/ezHbpnW5dtqddwQ5MYPCWnZreW+HEwnaupDWlVY/F0uMeZuE9V16vl2to2cFP28wqrQ2gUAby+z90KOGyQgK0shLzsMWPyx8xXtOxHi2lkQs3+BmPGwo0XFebdB3Pc+RE4fiAvuBAAMbdB/3nPVN1EFAPy2xXpd5bXAou1nEMstAiLSFWFgbaELRpv7W/jCMqwCcprEuARy1NdJamWE+bUIh8HZ+g/Vxa9LbNR8/TZ93+jejzxnX/NEpKHdV+rgw5sjhgusuav9GlHqyvD94fVg5Tp7X+bLNgJ/brX33aDbVzIEkKjZv97WGB0hTdRJeCwSpZYtaL92RJjVcUewYLR9+ukLnPasxNBbTPy4vuM/d2Vu+2145Xvrsk6O00rdwLYG3wK6sOdU/1uWhahQfcPKoMFopTUd//4QERERERERERGRM0UNecrpukAiIqL2km0RBqYKENMFPQKBwWLJLvUde1SBa+1JG1bpF+xGRERERETkhKGJGzLBYLRI0B1bJhsOB58Q7WKiZ/Q3RQP/S6V1l0iv8ns8yOF6dvN7vNLh8kREREREOzUhBMTAERDn3ATjhR8grnzMuvyLS/XzDAPY7yj1zM/fgPnsrTCfug5y4QfAD5+3mv1TwmjkxfZULuqyeTQpX7hDPWPpF/YqcEDW1ULefZ4yiE1FXPU4RL9hYW9HW4kBe0BMvRnGxfdAjBrX0c2JCDF0bxgX3Q3j7neAA44JmJ9uVjiqr6cn8EI86VXf4a8tzKdvAt561NlCh54U9nYITaDcDh+9ELiMEMhUX7ODkqYwie3kkk9CbVpYmZrz6NEejAYA44cKLL3ZBfNZF9bd48KzZ0au0bnxQ5Ebpw9Ge/RU/brzKgGnw9PdNfWQ7z8Nc8puMC+ZAPn9p8owvkiY8ZaJ3//Wz7cTBJNt8XtBW4LRVuivn2tlcu8CyEk9geXfOV6HuPE5GI98AjF5GkR8AjDlEvvLXvU4xDk3NYeBnn4VMPEU9PLkIVlzR9PVD94P89x/wHzoUsiX7oJcuxwA4DUlPvkj+Hv+4XJfmfJafZn0JNtPwRZdENf8lYCpSxByyG7f1DvdWb290qKvcxvWHYi1G4bbQkKs77n01DynfGdf80Sk4Y3ifaVB2QIvnN0+DSlxZezYn6mvsP9lvmCVve8Fq0BMfTCa7WYQAR7rDUY2Bkmc2gkE6y0mjbIusakUOOclEw2ejg3fstrv9ffmUmm5f+p013V9ie//Kk2IdEqCvXpmTrZ+re/6WN2wbUE20xL1IQcRERERERERERFFseJG9d3UrAKJiIjaQ2pMN8SKOOU8VYCYKiwNANJiMhBnxLealqQJRqv1dtyPHVJKFDeo++SsWAajERERERFR6AyhCUaTDEaLhFrN2B3dsSgR+TAYjVrK9Htcoin3p9/jUUIIJ8MY/Uf2+9dHREREREQtTT4f2G24et7x0yB6W2cViz6a+XW1wKv3A28/Dvl/pwbM/qzLEdo6Y+weTdY5GBXaVt9/ChRsslf28NMgTrwosu0he7r3C5iU6q10VMUE98LAidvCeyGKfOsx4I2HHS8njj0nrO0AAAz/h+Vs+eClkOVFAdPtBqPhtQcgF38cauvCRjcQPBrCPpyadpCBzfcbePpfApdPFLjhaP2TeOoMgc9mGHj8NIFnp1TjmfxL8WDh9cjxFCrL/xm/B9bFDVTOu/d4EzMmGuiWrF7X5jKpDXnSqflxEeSjV/hCKP9YAnndCZAz/x3xcDR3vcTc5dZl7GwbSfECXeLV84racGPPvPLgz39k3R845Eb/+wXYI+ashTjmrNbTDANi9s/BF558XsB3noiJgbjtFbhmPIwhDeuUi60uBLD2N+DD5yFfvBPy/P0hP38DawqBUnfw1X63NngwWprutgwhyknRbwQ/2dxFCMZu3zS0u/3OKsYAxjm99UQ7SE0S6J8RvJy/pqAgXVBdfmXHhpYQ7Sx0/ZErSnaWTh0j0D0lfPWNrPtDOb3eSEDt9p+o8gvtH3/a/d7X7eIYAkjQBaPt/DlWFE4ej/X8XxXHu52Q1be/CNJtHWhjPym3EPiig2/FVWZjH7klq/1Tp4dXudvHwlRrgtG6Jtr7bjhptHW5d3+SymM/d711gxmMRkRERERERERE1Ll4ZCPKGouV87LiGIxGRB1LCIEsTUijKkBMFZYGqEPFkg31XTfd3jZcWNZGVd4K1Ev1j0Dsk4mIiIiIqC2EJm7IBIPRIsGtCd1OZjAakSUGo1FL/iOr81SFpJT5AH5vMSkGwIEO1jPe7/GnDpYlIiIiItrliJgYiDvfBHr0bz3j6LMgZjwavIKsXiGtt8iVrZ23d1+bg+0tRnLK78N7KCAfvtx2WXHS9LCum0In0vwzuoF6oUkt0hhTpwgGqg1+IYrM2wDzzqkwD4r3/TtvP5hn7wNzxpEwbzwJcsWPvnLffgg563pHbUKXNIhrnoIYe7iz5WwQQkC89JNlGTm5D6Tfa6ALRiuOCXwP5H9ugHSamBVmut7D6KRns3qnC1x4iIHHTjVwz4kG5l1moGda8/zEWOCuEwQuOljgiOEClx1q4LzdCzCt4iVcWfYk7iyaqaz3p8QxqDXUyWcHpPwNAOjXTd2mNYX6UBUdt8cVOPGrOcDiec4qcmhtEeANskm6bG4bOZqQlsKq0MOa8oLkOSaatXgh/0IYlpEQCnsd7AtFy+mrnC0GjoC47RUgu4+2CnHqDPV0IYATL8JQqU5kWBM3uPUErxfyrqnY8luurab/shk48wUTU55Wv3Fd4oHYmPCG9+zbXz/vle/DE8ZlNxhtSI79OscOANKSoiPIyN8PNzrvdJuCgnSftWgIxthQInH2iybG3efFFW+byKtgWBt1PrpdtSjJRUNSvMDHl7dtxy3e8OLqmqdRt6or3t76b225pqDf/LJG23U3BMmiaqLr94UAkjWHLbUMRiMnPEE2mE2r26cdEdaWHGXDEPjmmuD9Sbj290JlFQissqVMP8/pcVpuoW+Bylr1gikJ9urZo6fAR9P1r3VNve840l+wfi8a9v+IiIiIiIiIiIjIvtLGIkjNAFhdGBERUXvSBYIVNQYOhVSFpenqSNIMRnebHfdjR3GDOtgNYJ9MRERERERtYwjF+CAApvS2c0t2DbWa0O0kTUg3Efl00qGkFG5CiAQAU/wmf2OxyP/8Hk+1uZ5haB3A5gYw386yRERERES7MtF3KIx3ciE+K4aYsw7iyyoYNz0HERsXfOHM0H74bhD6us85wG4wmj7FRt4wBfLr95w2S13Xdx8BlSX2Cu9/NMQe+4ZlvRQGqRkBk4Y0rLG9+OWlT0K5NQYJRpN5GyCn7gvMf6N54ppfgfV/Ar98A3z3EeRFB0O++gDkzafYaou47mmI13+HeHsVxMf5EMefb/t5OCUGjQTGHWtZRk6fCOltPhmd2UX9uS11BQajYfMaYLO98KNIcRL2IRvqtfVYzetIx4wU2HK/gY33Gsi900DF4wZuOsaA0fIJlhXt+HOful8cryN9w1IAwLAe6vc+t0A6HnBfY6gv/pKfvuqsIocuejV4UJ/dIJhszW8GhVUOGuRng8VXkEt68ELehRhd91vwio76N8S3dRDvroH4OB/Gk19oQ9GaiMNOhXhrJcQNzwbO3O8oiL5D9cu6XBjSU/1jVm78EOX0/Fn3W7anpdd/lKjQBESkJ9muxrYpo/UbwX++kVi6oe1hGdqAHL/Qu2Hd7ScT9c+IkhQjhfRkgSdOc9a+pmC0DE0gZ6m7jY0KUXWdRF2jxJ9bJfa6w8SrP0h8vx544kuJiQ+bqNAEiQQjpYTptDMlCgOvzaDGjrR3X4E/Zjr7KfKJgiuxcsxLWH2ngcqnYvHAa5cg9u0VyHxziXaZ0pgM1Is4/CEH2F5PfpBQ0ya6MCcBIFlzyFwTnbufFK081oF+csvadmpIZFkFo9nptg4eIrC3PgsYALBwTcd+J5c73Mcpdevb6vRprNk+nqeqTj0/NdF+XceOErh9sv5d+XVzYOPcQYLRShmMRkRERERERERE1KnoQngEBDJjHdwljIgoQrI1wWiqELSihsCwNEAdKpbsUl9Yphu83h5KGtXBbglGErq4NHctJCIiIiIissHQxA3pAvOpbdxe9YV0Sa7kdm4JUefCYDRqcj2AXi0eewF8bFH+9e1lmkwRQgy2uZ6W3pFSai7RJiIiIiIifyI5BSKnD0RcvP2FMnuGtK4GEaucPiTHN8DdFl2y0fZ58u3HQ2hZM1lbA/P+iyHaf04KAAAgAElEQVRvPMneAidPh7jzzTatk8IsNTCUa4yDAKi7imeqZ7iDBKPNmRU0PA0A5LO32GvIvhMhjjsXou9QiJ67QRiRP+Ui7pljXWDtcsjnbt3xUBdQU6wKRtu+fEfSDQRvGfYhP54N85QhkIenwbz4EMiNq5rnff2eb95hqTDPPwBy9c8RbrFzQgj0zRAYnCMQGxPYr8qnb9rx9/D6lUg0NQlTGmm/zAMADNVck7q6wPmA+1pDk2b17YeQQUIdQlVQKbF0Y/BydoNgcjTXg4USjFZUJXHUY15Ua85uuaQHCzdOxCnV9oJAxakzIISA6N4PIqWb7XaI2DiIY8+GuPMtYJ8JwG4jgNOuhLjr7aDLDhutDpFZFzsQHgSGpuXHdLfdLiuDssNSTStdEwQOtjhDedvctv9Ap+2bHrwY5qReMP97C6TXizH97dfZK73NzYqoSycIPH+W/aSl+Bjf/xnJmkDOdg7G+GG9xKiZXqRebiLpUhOjbjcDPrO5hcCHvzkPUvl5k8SEh0zEXWyiz3VevL2MPwJT+3ESItuRhvcUWHJpJbp7Ai/WzvEU7vh3QO0SvFJyCS6dOhxDzz8PQ3IE4mIEhGFA9ByA9Myu2udW4srA94n/gFsT4KqSV2HvM2+1T9olQT3PzWA0ciLYPvTOEoxmMU/Y7Lcm7m5dsKgato4bIqXcIuhMxSos1ipITiW30LeALhgtxUEwGgDcMkl/TmNrReC0YP1eSQ1DZImIiIiIiIiIiDqTYk0IT1pMBmINGzdyJSKKMFWoGQAUN7YOdvRKL0obi5RlVeFqyS71b866wevtwf85NcmK7Q5h94c2IiIiIiIiBUOorxMzGYwWEbWm+thSF9JNRD4xHd0ACi8hxJkA5kspCx0scz6A2/wmz5ZSbtItI6VcK4R4GcC52yfFAZgthJioCzoTQhwP4JwWkxoA3G63nUREREREFKLs3iEt1iDUFzEdNNjBD+kyyImwFT86aFGLaqUEtqyBvGQCUFkafAGXC+LWVyAOtRmgRu0nNSNgUrxswEHuRViUfJDlov0bNiJJblPP3PoXMHxswGRZVgjk/gK8+2RIzVXqM9hW+FC4CcMA7nsP8oZ/6gu9/hDkyZdBZHRHluY86ZZYdR8h7zgb4vDTwtDS0OhCKMTKHyHjioDyIsiHpjfP+PMHyMuPAN5c4QuFu/WM5nmrf4a88mjgtd8hMsIT6hQOsrTAtz3WB55KkUu/AFYt2/E4Bl7sVbcc3yftb7v+9F/mQtZUYnDRHwACl9taLjFcfY0Y4mOAek/g9BqrwJEt64ABu9tun119r7f3o4rtYLRUAVU0w+YyZ4P1pZQ4/TkTX+fqy/yxfjSGNKyzV+EJF0AMGuWoDf7E+BMhxp/oaJmh+48Avgx87g1GPDbG9sOgxvWtpheEKRhtTP/IXJg3fqjAt2vV7+XnK4CKWom0pNDXrQ0iMhuByhLgtQcghcCAC+7AHj2AleprE1vplRZyc9qFEALnHihw0GCJcfebKAlyrWdG1XrI3wvQbT0A7Bcwv6YeqG+UiI+N3MWZ7nqJ5X8DK/IkLnzV3md72Ubg7APsr6OyVuL4WSbytoeDbK0ATn9OIrurxIRhAmVuiV82AUO7A3268UJUCj9daI4rym6JJNf9jrHPXIa/1/5gXfCUy2Fc9l/tbJchkJ6kDhIqc3XDNuEs9aeg0l45XQ8ihEBynLpEDYPRyInGBuv5W2zuS0Y5q6Avu+M1htnYDf3wN4n9duuY790yZznWKLMIRnMaYL1m+5UBVZpTJCmaIEcrh+0OLFgVOH1FHrBgpcTI3kBOiu+1rg2yGZc7fG2IiIiIiIiIiIioYxU3qH/oVoUIERF1hCxNf1TjrUKttwZJ2wPOyhtL4IXiIjiow9WSDfVFlttMN0zphSECbzIZacUN6rBK3WtARERERERklwFNMFqw8aAUklpN6HaSJqSbiHwYjLbzOQ/Af4UQ7wJ4B8A3UkrlZdVCiDEAbgLgP1pyK4D/s7Gu27Yvm7798QEAFgghpkkpV7dYTzyACwA87Lf8w1bha0REREREFB4iIQly4Ejgrz8cLdcoYpXTY538rm818rWpiNcL4bJfqWyoh7xnGvDlO/YWOO0KiAknQeyxr+11UDtKy1JOvrH0gaDBaCdVvaedJ+88B3BXQZx4YfO095+GfPJawNMYSkuVxCX3AsefD5HUMXdnEOMmQY7YD/jTImjih8+BY8/GIPVLjTVxg+GFAZfijh6yJB8is2MuoPFqzqOLBW9BvvuMemZ5EbB0AeS3HwTOq6kEFs8DJk8LXyPbQL7zBOR/bgS86guvVPat+9l2MFq8WYdEWQd5zzRkbu4CKJarrpPwmOrQgJREoLg6cLpbJOlXuml12IPRtpZLeGz+pmI3CGag5rOQq76GTOunTbAMRcvwlGBgw3p9gRbE7a8DEyxCDiNoaI46KA4AcuOHBASj5YcrGK1fZAIrTh4jcMc8/f7HmS+Y+Oiy0C9S1NVstPzx79NXIafNxN59BVbmB98X6pXWOUKzBucI/HqLgRcWS6zOB95aFvjcBjWsQ99LR0EC6Ba/B7DbT8q6St1AzwgFwi1YKXHWiyYKqpwt994vEk+dEbxck2cXyR2haC3N+trE2iKBS9+QO77LLh4v8PipAjGuzvFeU+egDZGNks1M1tZAXncCsHxR8ML7ToSY/kDQYhld1MFoJa7AsOVgVPWoaAMxBZCsCRoKFhBE1Io3yPFp4WbI+m0Q8c7C/6KN1R6R3W5raHf9fmuTucsl7p1it1XhVW6zX2lSahE2qzudlhwPuBXhi2VuoKRaolITjJaa6PzLoWea+vWevURi9hLf9JuPFbhjsoA7SL+3LXynYYiIiIiIiIiIiKgdFDWqg9FUIUJERB0h26I/Km4sQD/XoO1/6+9omBkXeA2U1WD0WtONLq4UB60MD/bJREREREQUKYZgMFp7cmuC0ZINBqMRWWEw2s4pEcBZ2/+ZQoi1ADYCqATgBZABYE8AOYplywAcJaUMOhRUSvm3EGIKgM8BxG2fPA7ASiHEzwDWA0gFMBqA/5DTeQBucfa0iIiIiIgoZAdOchyM1iDilNPjnBxJmt7gZTwNgMv+AF85+27boWjio60QaZm266YOkNVTOTnFa51mMq52Ma4te9SyjHzkcmCf8RB9h0Ku+x3y0StCbmaAjB4Q76+HMGwmMUWQuOddyMl9tPPl3+sgAAzTDCKvNxKwKbYvdmvcGLjwX38A7RSMVt8o8f6vEj9t8g0CX6m5JqmLaT3aXL71KLByqXreZ69DdHAwmly5DPLV+4Dv5jledp9tv9gum25uT+tZNBdpCXsDAxRtgYGK8m3wnUpqrUu8Ohit1ggSjBZms74JHirVxLA51n9ItvqzsKkM2NYgkRhnr6IPfrVu26SaT5WBg60LnQtx3X8gOjDFJileoG83YHNZ4LzcuCE4Fv/P3n2HR1Htfxx/n9n0QhKSUIKA0ouI2EWxYBd7vfZesKBe61WvXq+/a0WvDXvvHbH3ih0vYgEJiNIJSSC975zfH5uQsufszm42Db+v5+Fhd+a0bJmd2Z3zmXdbLVvrsyTLRWjbTWPSTJCxeYobDlVcOdP8/Lz1Mywr1gzKju4xtwYRtXxNFa2CylL6Z2R4arOjAsI6woAsxTUHBB67q6ZoDry+gD/9gddEpn89z6w8aUO4SU5DsbWd4orwf/faMs2seYHwsb3HKHYcGv45K6/RnPx45KFoACNN35aHcN0b5hfDq3Ph1Tbbh/s+1Ww1CE7buZskVokNXDcQYOfXgQAsvw6Es7b6F2aZG3EdHVw3wjZcDXOWml+D3WD3FAB981neQtEAdcvrnj4Lc9IgvyB4ebEvmyRdE9H41lUGngtfmB0I26e9UpBqPmSmstb7/osQnoK7V/wOQzfv+LF0Ea+7wmPzIM4hZHDygtWQX6AZ0bfzP3Nt4V9piVBhDDOzbyts+5yj+wUCmk0WFkCZZVPYK4pcvf4edmX/85Zmh81U2EBICYwUQgghhBBCCCGEEKJnKayzhPAkSAiPEKJ7yIjrTbxKoF4H/whRWLeawUnDNtw26eXLIskJ/gEl1We/QG6lv7zTg9G01hTWrTKu6yPbZCGEEEIIIUQ7OZgvOO/iYT6oiIjWmirXMEkLSAlxLCqEkGC0vwIHGNn4L5yPgJO11iu8Nq61/lQpdSjwOM3hZwrYpvGfyXPAGVpr+UQUQgghhBCik6iDz0A/cWNEdazBaObvvMxcD1cIaKiHRG8zNPXK3+Gpmz2VVVc8KKFoPYDKyEYPGQtLfm21PE3bw6/+ud0qrnxiP+JpCNu+fv95OPkq9CnbtnusLanjL+kWoWgAKqsPfFiK3tMya7nxSh0jQgS+5CcMNwejLV8E2+/d/kGGUVGj2f8ul9mLw5cdVhem0NoQX2ss+SWygcWYfuVe9J1/DyS/RWF0nffgsSz/+g23M/0l1nLr15ZiCkazBX1UOylowBQzoH/52rg8WlOfcXngs9gHo40MvtgnEHhafi+EzQd4a2fWj/axKTTT1t0Tsr665TXUjvt566yDjepnCUZL2yJwGYUWinzt/2zNToXB2e1uxuqK/Ry+WOTnHctb/qlvNFdNiTIYzbJr47QNwSspYkCWt2C03B76O9aY/jDvz+35lC2pU4nsXfEBqbpqw/refsOLqlFx6IxLFhVo9vqvu+F1ed0bmluPUFy8d+jP3lf+FwhS62gFZTrigI8zntQcsZUmIyWy157W2hi81Z5AraB1xjo6dPiXx35DjTN4LJqwY/WwrFW9MOU3Rr5ukL+nf/0WPn7ZU1n1+A+oOG8/V2anmpcX+7Jb7ft44WooqYLsMBdZs4UTOQpSE83rTAFIQlg1hD+uZcXiHh+MFuoQyOtmKzNFcdhWihfnhD5GmPWj5tJ9On9jaPtc6ZNu3i6sNZ/jBNi3Pf0zID0Jyg0BaHOXaesY0pPsfdkM8Bje+/hXLpVhtnsSjCaEEEIIIYQQQgghRM/h1w0U1681rsuNlxAeIUT34CiHnPh+rK5bFrSusH618XZLuQnmE8hSfPYfkKv8FRGOsv0q3XKq3Srjutx4y0lwQgghhBBCCOGRwnxuvtt2boRotzpdS4M2ny+aGuJYVAghwWgbozuBlcBOwGAP5SuB94EZWuuPoulQa/22Umpz4DrgaCDLUvQbYLrW+pVo+hFCCCGEEEJET+UOQE85Bd56zHOdOhVvXJ4QyZGk9vBFWH342ZG6aBX6qZvh1fs9datOvQb2O8FTWdEN7LhfUDDaiNpFZPhLKPW1nombngSXjs73FIoGwLzZ8O5TsRppwA77wiFnx7bNdlKJSehBI2BZfvBKfyCXPC1J0T8DVpcGF1kZH5wEpYGHfurNOwWB+udPdthjtHly+TdLNNPfc6msg303V0zdVZEQFyj7eb7mwc81v6zSaA3D+8C5uzvsPqq5rae+0Z5C0QBG1Rn+xpaKzFdIBCC7c04G0n4/vPs0+qYzmxdm9YH15hNHvRpRu8hz2Qx/8xOd6Rqe9EbrqjAmEdiCPgBqVSJJ2jDz/Zv30FXlqJT2pzw9/11koWgAXrMKh+SAzzEHFyxc4y0YLb9AM9983hwATw6dxfgFP5tX5g5A/eNB1LZ7ehtwJxjRT/H+/ODHO3+Lv0H5la1eu4Vx7Q9G230kKNWxYRWX7O3wzi/m/ZA35mmumuK9rfoGzRNfa16ao1lTZi7jtN3nKS2mf8ZQT+3bgn66K718EfrmqTDvC1KBKbxrLJdAPen+MsoNV8stCnOu6H/e1kFhfVe8qjl5oiY7zf7aef676IInAUqrvZd966fo+sm60GVITmTBX7ZgFCFsujq7V69agj57F09l1dWPoSIIewq8/4PfFMW+3iSY9k3CKKoIH4xmC3NSCtIs+0vhAoKEaMXDdyIUruz4cXSwKLOhgzxwvKK6TvPWz/bPyMtf0Vy6T2z6i4RtPAN7w5Ki4OWLCuxt2R4vR8HIvjBnafC6H0NkhNu2V6Fss6l5m9vWK/8L35YEowkhhBBCCCGEEEII0XOsqy/ExW9cZwsSEkKIrtAnob8xGG1t3Wrj7VZ14/OMyxNVEj7i8BvOT630h7jqTQcprFtjXZebIGGVQgghhBBCiPZxlPmkY+1lPqiISKhjylRf++dgCbExk2C0jYzWeiYwE0AplQmMBQYCfYEUwAFKgPXAAuAnrbX5V4vI+l0LTFVKXUBzKFs/AsFrK4G5Wus/2tuPEEIIIYQQInrq0hnoJT/DgjmeytfHIhjNS5JDQ+jZkbq0GH3WJFgbYoZnC+rlxai+Az2VFd2DGrJ50FTbBOo5c/3D3JpzSavlU3dTpNaVeJia2+jPBej3n4vFMAN23A/nltdi114sDRxuDkZzmw/7B2RagtHi8vDjUK2SSdOVAFzc52buWnMUNJ5b8/o8l0dPVhy7naKmPhBSp5TirZ80B97T/KX3e79q/rcUnjhV8cF8zQF3u9S3+Obh55Uw80eXN89z2G9cINzmTY8BM70bisnxF3sqa5SZG33dCOgH/wnP3tZ6YTtD0QDSdCUDkytZXh0+RSnLLdlwu2VIWlvrdFrEwWjVKtkcjAbo8/dCPfJN2PGFsq5Sc+zDkScnOJasJK01Sil0Qz0qLp74OMWQ5FIWVWYElV1YoGl6QJrqmcz60T6+ZTc75D35pXnlkLGox3/o8FCwSI2ynLv821oH9cJv6L17A+CiKPZlG8v+39pruLbPtfjxhexLKfjH/h2f2rPbSPu67/6ENaWafhnenoeLX9bc83Ho16TT9qpIpUUM7Bc+TMLnQEayp2F0C3r1n+hjIwgx8q8zBqMVVzS/10ye/Dr4cfO78NqPmtN2NterrtN8GiY7M5RIgtFenxd9uospGEWIWErqwl/+dHkJ+ujRnsqqadNR+xwbUfu2ELNiX29rEOx2mwa2+8Z6leH7DBVOZNtfqpBgNBGJhvqwRXTRqhCfmj1DqE/OSHaNM1IUs87zUVKlueldzS3vmlu++2OX8yd3blKkKXgZYFR/xWf5hlDHSigq1+SkBz8Atq/TlIKR/RRzlgYXWLDK/ignmb/iC2n7zSKvY1NVF/r4SgghhBBCCCGEEEII0X0U1ttDeHLiJRhNCNF95Mabg8EKW4ShFdabg9FsQY9KKVJ9aZT5S4LWVfrDXAWwA9jGn6AS6eXL6uTRCCGEEEIIITY2DuZz7Ny2cyNEu1WFOKZMccJc5VmIv7guvm686Eha6xKt9Zda6+e11ndqrW/UWv9Haz1Da/2s1npuLELR2vRZp7X+RGv9uNb6Jq313VrrVyUUTQghhBBCiK6nfD7Uza/BIWfCkM1hy11Q/30H9jZPRq9TCcblCaHzTlqzzSJvqT50MBrvPOktFG2nKain50koWk80aIRx8Q2F13BT2nNsORC23RRuOUJx46EKKuwhT0EcB+Z94b38VrvB+EnBywePhOMuQV3/vPe2OpvlSh20uFLHgExzkX/nXk3i6AoyRxUyeNginsw4jgeyzggqd+rjmqRzXDIvcPGd5eKc6W8VitbkqW80PyzV3PZ+61C0DUPScNVrzfXyC0L/aU22qpnrraBNtYf0i3bSBcuDQ9G8ik+AjOzmfwlJrVara59k5KbhQ9EAsvzrN9yOw0+a5eoilZYv0FPMHwEAVDshUpzy56I9BlnabH9DdD+i+Fq8BXTBctx/HoM7KRG9S1Lg/93TcCcl4j54DSPWmIPL8n8v4YP5GudM/4bXuHOmn49/a/15ZgtG234z2CQrxHZq2PhuGQYwsq95TIXlUNSQAjtNAWC9Lwu/Mifu7Fn5Me+5FzJ5FIwbEFg2MKs56MJRMHkUfHW5w4RBHf8YKKX44Wr7199eAxl/W62Z8Un4skHBaCXFbD0Y0kKEDAJkp4JjS/XrhvRD/4qofLZ/nXF5qDAiN0Sw7ofz7fU+XAB1wRfs9cxrMFp9g+aDEOMQoqvtNKzjtyn6py9xz5qEu0cG7rGbo+d8hF6/Fr1/X0/11RUPwBHnRdxvji0YLS6HSpViXDeqvyLZEghU5OGC3rZDWqXs2/hKCUYTkfCHD0ajyDzhoScJ9fVQNLvHmSmKE3ewV7QFpnUkWzDaGPOcHAB+s8wvtI3eUTDYnFPM74X2fqIJzVRKcfLE2HymaA217dhPE0IIIYQQQgghhBBCdJ6WgUItZcT1JtFJMq4TQoiukJtgCUZrDBNztZ8iS9ijLVQNIMVn/mG6yu2CYDTLNjk3oX+3PAdNCCGEEEII0bM4lnlorpZgtFirDHFMaTsOFUIEdOF144UQQgghhBBCdDaVlYu6+O7WC7feHV1fC5+80mpxvSUYLT6iYDQPX4Q1hJ4ErH8MH2qlHv4aNXIrr6MS3Y0lGE0BlxRO57Lbj2+1XFdGEIy2zmPiVlw86q3VqJR07213Nz7Lm9PfnEyWl6WwT7EOWBk/gFPzHmr3cK6Z5fJ+iOCYH5fDkkLNJlnwZ7G3NveveLd9g6oIvpJjzH0xK6pq6pJ7UAcHh9G1NarI5cMF4Sf5Z7W5amWmW0qFz/vrOzXB/lqpUWGSnr54HQ4/x3NfLX22UIec0B9KU7aUdl30dSfCz1+ZCz51MyP63MBb7B+06t25NTz5c/Bn1563u8y/zmFUf0VBmebrJeamD96ycRC2YLS0jHB/RpcYFeKizmc86TJz0kHoL9+iyGdJYQByGwrZZvET7P7QjG5z4t2EQYpR/cyBE2/+pDndkIPZ1hNfa085r07b90tpEQlxipsPV5z7rL0BW8hPd6TrauGD5yKq09tv3sCHCkYrCRFQlmQJNwKYOdf+OPscePN8h33GKt77VbPfncHv87Ia0FqHff2uKIFqDxk2QnSF3UfClHEd24deV4A+d3LzguWL0BcFf6baqOfnowYMjarvbEs+bLGvN1WOORgtJQGy02DF+uB1RRWawFGHXahwolRLkGyFBKOJSIQLiwcoXNnx4+hgoXanot1zHN0fBvWGZYYc1pUlUFKlyUzpvP1SW7ZrXoaiV5KmrCZ43dJ1mp0Nj4AtZE0pe+D52hBhj6H2oUIZGeI4IVIzPtFcvHf3OE4QQgghhBBCCCGEEELYNQUKtdUnRIiQEEJ0Bdt2qdxfSrW/imq3kgZtvnKLLVQNINVyjl2l5cKkHcm2Tc6Nj+GPOEIIIYQQQoi/LAfzPDS37UXjRbtVWY4pk5wUfCqSybpC/PVIMJoQQgghhBBC/MUppeBfT6PbBKPVYZ41mRDJkaTb/mA0luWHXr/VbhKK1sOplHT7JOk/F6CrylsFlulij2FnkdjtsJ4digZguVJHy4BC2wTqjvDOL+HLvPGTZt+xyjrpu6Vkt4ojy19t36BsYVUxomuq0HdeHFmlbfdAHX8ZaqvdPBUf2ddbs5ltgtEy/KWsiN/E87BSQmSfVavkkHX1z1+jogxGu+W96H9AqWrKlJjzkT0UrdHIukXG5Wvj7A/wjE81dx+j+GiBPSjrkHDBaOmd+CaMQF4mpCWag1xenwcNxx2ML+1yCv051jZy/UWgG+DzWbDrIR042sgcOF7x25rgJ+yD+VBdp0lOCB3O8OIcD6logNMmDFaXFKGAqbs5fP27y9PfmtvJ7knBaI/+O+I6OZZgtKI/VgHmbdIC8zmdACRaQj1WlWge/8r+XM25ymH8wMBznWHZhPndwHsgPcxFzos6/wLAoofyOY3/VIvbjf8cZV8XapljWZeaqNhlBBy3vQq7XWsvffCg6CrudhjqojtQvT3uzBhkp5mDW4t82VQ65tS01MRACKU5GC18n6EOaVMTzeOplGA0EYlw34kArFziKbyzOwsVNBvtn6WU4sDxihmfmBv/aAEcvnV0bUfDdlzrc2Bgb/h1VfC6lYZtU+i2FAMywweetxVtMFpeDHOd7/9Mc/HesWtPCCGEEEIIIYQQQgjRMdbWWUJ4QoQICSFEVwi1XSqsX021337VvlDBYrZgtCp/558wUlhnuBoksk0WQgghhBBCxIZjmYfman8nj2TjV2k5pkz19aAJJUJ0EQlGE0IIIYQQQgiBchz05CPh45c2LKtTCcayCZGE0GsPATf1ddZVurwElpvDa5qoY/8ewYBEd6X+/Sz6mmON6/R/L0Jd9XDzgu/ej23nQ8aizv5PbNvsCo7lzek2fyHdmcFoXlz0gmbw1PCz4BPcWu5ZcyH9G8wn+nhWWYp2XZRjCZFrB712BfrYzb1XcBzUuTejjpoWUT+j+nmbBJ/ltp5h39u/LqJ+UkMFozmhg9Goju4ksPIazUe/RVUVgN//KMW971Z49rawZYdbgtFCmb0o8LibAg0AhveBUf2bgtFKjGVUWgyTBWJIKcUWm8BXv5vXf1mQya6X3UfRf18xrk9xK0nR1QDoq4+GT6tQvu5x1ZoDt1Dc+l7we6a6Ht6fDwdvGZt+VNv35ao/Nty86XBlDUYbmtszAk50VQU8Mz3ietmWbU/x3B/RFenG98S5z9r3H5MtoR6HzLDXeWVqcygaBAKSbP4sgnFhMiSLOv8CwB1GqfDBXEFBXBEEeTnGdap10FcE4V/hlrWqp+x9GscZRUhZqMfHcXrGeztSes7HUdVT/3kRtcvB7e7f9v6tclIp9vU2rktJsNcrtp8Hv4Ftr8txAqGiJtX14Hc1vo30dSBizEswWsEy+HMBbDam48fTBdrzTrn5MHsw2pEPuLgPdt4+qWvZYPicwLG46ThilSVPOVTI2iZZkY8t2mC0AVmRh7DZ/F4Yk2aEEEIIIYQQQgghhBAdrLDeEowWLyE8QojuJTMumzgVT4MO/r2tsG41Va75B+E0XwbJPvOFtwBSHPMPzJX+zj9hRLbJQgghhBBCiI6ksASj4WE+qIiILWzbdgwqhGgmwWhCCCGEEEIIIQLaBJjUK/OsyYRIjiS1h8mT//sURgSnkujSYvQBeaHrjtwHVC0AACAASURBVNwKttkzggGJbmvH/ezr/vfJhpu6sgyW/Nr+/jJzUSdfCZsMhS13QSWGCXrqCWxhX27zF9J5GbGZ0BxLh91n/8L8v2suJtMtZVLVbDatX9b+zlw3EFjVyxycES09/3v0xVOgtjp0wUPPQg0aAcnpMG7HwO0IjbJfrLKVTH/rGfZ5DeaTpGxSzNmYAFSrpNCVi6MLsHv/V6hriKoqAP3euAWKb/dUdmRt5MFo81aA1pqFa8zvo202bRHpUGFJOEjrZumELRy7veKr381/26wfNbsdfRiFVbvCrOD1uQ1Fre7rG05H/fOxjhhmxHYcCtmp5vCbQ+91qbvPIc5nj+NY5yE0B0C1/fFv2cINN/MyFUdto3hxTvDje8x2PSQ053PDE++BLZSx2NcbPpsJU05utdx1NT+tsLdneoXW1GvmWeokxcPebTJkBveGeB/UGy5k9dsaD8FoFZF9lh6yZWD7kJkSLvhLeQrlsod/RR5SplQPef2JbkHX1qAvCrHPbrPN5JiEokFge26zIt785k358QNy+u+FaQtS5CHL1RZ0pAgdJFtVB+lhdpmEALwFowH8+HmPDkbz8vVQNFISFZvnwS+W8OL7P3M5e9fYB2ObhAozy8s0B4ytWm9+YKxtKRgQYTCaUoF9n2jkdc9cZyGEEEIIIYQQQgghRAdxtZ+iugLjutwECeERQnQvjnLIje/H6rrlQevW1q+m2m8+6adPmFCxVJ8lGM2N7mKh0aryV1DhLzOuk22yEEIIIYQQIhYcZQlG0xKMFmtVlmPKFMsxqBCimQSjCSGEEEIIIYQIcFrPkqxT5lSctsFoWmv46i30G49CYgpqjyObJ7674b8I0zMuR/3twsDtL99Ev/M0VJXD9x+Grjh6W9TVj6B8Uc7uFN2KSkpBJyRBXU3wyrUr0HW1qIREWL82Nv2deDnq8HNi0la34VjeCy3ehwN6+YGe8Z7Zq+IDzl9/X+wb/u4D2PPodjej/X54ZQb663dgzsdhy6vrnkFNPqLd/eZlQloiVNSGLpflL2l1v3+EwWiJcYEJ/KYJ+dVOmCDBZflorSMO3Hnjp/BpCQOzYPl687p9Kj7w3FdffwGD65ayNGGw5zoAa8thgSX3bWRjaJ3WGsotg0ztvskCZ++iOO9Z83PwxjzNf4+GIrIwBTrk+lsHo/H+s+jJh6N2OqADRmqmi1ah774UPn45sGDPo1HTbsOXlcuULRRPfm3+2xKmukwZB3uPVfRKgplzNWvLYevBiiv2VZSGyTts4rT98W/FYrTfv2E/5Z5jFBU1mrd/CazulQQ3HqbYa0z3DqbS5evRj/0fvHRPVPVtwWilvgz07DdRbYLRTAF2LVUatn2LCswhZwBbD4LUxNaPcXycYmhuIAStrYUFmkDckV24Mbb0zOmKY7brnDAWITrczPujqqaueTJmQ8gJ8dv/mjhzemzKnLfpvf/WQHCSULGHoENbmJNSgX0ym8paCUYT3ug1S72VW74ozCdU9xbq3dbenM7thyh+WWXu4ZxnNKfupEmI6/hHzxak6KjAcZzJKkuesm3b43MgNw0ykvG8n5oUF30YaqQhbEIIIYQQQgghhBBCiJ5tfUMRfsxXtMuN93glPyGE6ES5Cf2NwWiFdaupds0neIQLFUvxpRuXV/nLIx9gOxTV2y9OKttkIYQQQgghRCw4mM9z120vGi/ardJyTGkL5xZCNJNgNCGEEEIIIYQQAW1S/utUvLFYgq/NZMrXHkTfPm3DXf3xSzBtOurI88G1pFS0odevha/fRd94hrexjtkOdf/nUU/sFN3UUdPg6VvM6169D/52YSD0Jhb2PjY27XQnjiV4pWUwWloDPSUYbUTd4g5pV193IozfGZU7oH3t/OdU+OB5T2XVebfEJBQNAhPaR/WDOWHyEzLd1sFcAxpWRdaP1iTHmwPYalSYYLTqCiheDTl5EfX51e/m2f+nT1LMOEaxuhQG9oZZP8Jh97X+oWVi1VeMq/3Fc18KOGf9/Vze98aIxvjst5oFloy5UU3nm83/HirNV+sk3ZKI0A04juKxkxWnPB78PCwpgqJyTaHlwqfZ/uKgZfqKw+Gu91ETdo31UFv3U18HxavRR45oveLDF9DfvAcvLODALbKswWgAb/0Mb/3cev03SzQPfBY+MKeJ0/bHv/o6WPMnDBgKQE664s1pPoorNAVlgSA9n9O992N0VQX6lO2gYFlkFTNyAvuA5evJ9JvTPkp8mYH3ShsrSwyFW6iqC1620HzBcgBuPdL82TiqnzkYbUlh6P4BijxeAHhwNhw6oXs/x0JEQr/9RGQV0jJQry1DJcYuHax3auR1Ut1Kstf8BAR/Hnl5P4cKOkoNEYwWLsRWCGj8LmTWQ94KL1/UsYPpYLagL2h/MNq5uysemW3v4LN82GtM+/rwwhQqDYEwM1sw2hpLMJqtLccJ7LfvM1bx4hxv+6pJ5q/3PElPiu2+TDQB2kIIIYQQQgghhBBC/FXNLnmfd4tf6tQ+G7Q5FA3CBwkJIURXsAWE/VA+2zqRP1yoWKpjnpRe6fd4wkgI+VW/8PH611lZuxTd9iKMbdRrw0kyQJyKJzMuu91jEUIIIYQQQghHmc+1z6/6hat/9zjHs5PFqXiGJI9k3+yj6NNNv6/6ueJ7Pi95hzV1K9CNJ09W+M1zjFIcczi3EKKZBKMJIYQQQgghhAjwtQ5LqlMJxmLxLYrpyjL0/VcFldHP3AaHToUwP9xvUFKMfupmz0NVh02ViZQbIXXK1WhLMJqecTkcdDp89Xb7+7nkHlTGRnhiiGMJPGsRUNgroYE0fx0VlqsaRiojGQb1hhN2VIzoozjk3thdFWTLmnkxayvIu8/ACZdFVVUvW4g+ZzKUFnmuo46+IKq+bCYMUsxZGnoSfE5D66CqvAZLmpeFem46yVlnUUHwa6Xa8RBw8sf8iILRyms0i9ea1+0zRhEfpxjU+LY9ZALMPMfh9g9cVq6HfdT3XD/7cBy8h1gx+UgunvscN/ovo8SX5bnaxS/Z+9h6kEJrjT57krmAUjB0c+9j7AJ7jVFgeRz7XOySZgmAyW0wp0npaXvDY9+jhm0RoxG2aPvPBejp58Ev34DfcnJ2RQnMepi9j4wuVLMhgk1aUDAawNKFG4LRmmSnKbJ7ykV93nky8lA0QB0+FXY9FP3QtWQsNe9Pljq9YN0adEUpKi1jw/JVYYLRKmuDX5+/rbG/L3cYYt5fzMs0v9bXVYbfjoQKUtosB+oaYJcRiulHKJLiZX9V9Hx6+SL0pQfByiXeKw0Zi7rtzZiGogHE+RSZKVBS5b1OiltFzrLvIC66YDRbmJNSkGrexAFQKcFowov3n/Nedll+x42jE4QMRmtn21sOVEwZFwi7Nfnbgy7Fd8QuIFy/+Rj6iRuhohRGb4O6/D5U30H4QwQp9k4xryutNi+3teVrPA9tt5Hw4hxv421PMFqs1fshQc5QEUIIIYQQQgghhBDCkxq3inWW3+I7Wy9fJklOmIvoCSFEF8hNMJ+fZgsVC9QJPXE+xXJuZVU7g9Hyq37h7uX/wo89hNKL3Ph+1vACIYQQQgghhIiEwnxsUa/rus33UiZr61cxv/JHLht8C73jc7t6OK3MLf+Kh1fdivY4vynV11MmlwjRdeS0UyGEEEIIIYQQAU7rL7NswWitJjB+PguqyoMLFa+GFYtCz3xtqbQIViz2VnboONjzaG9lRY+iEhLRA4fD8kXG9fpvo0M3cOhZMPOB0GV2Owx1cPe8akW72U52cVuE9fgbGFy/gl99YyNq+tSSx3lw9TmoM6+H3AEwYAgMH49Kaj27+6O/O+xxe/vD0ZR22b/i3Xa3Y6Mf/CcMGQP9N4NNR6Mc+4lCuqEeFs6FkkIoKULfdKb3jtIyUfd/FoMRt3bgeMVDX4Tevmb717W6n1e/KqI+nJpKkmvWQ7whGE2FP9lVf/s+ats9Pff380r7ugmDgpcdNKqag+J+gvVr0Vce6bkffHGoa59C7X4YWmt+nrwpQ4cuoM6xJH55NLo/DO2j0M9Mtxfadk9UVp929dPR+mdAryQoqzGvr7CEvOT47UGB+pRt4e0CVHpmxOPRtdUw/3soWA6JyZDQ/DzpKw7z1sZ7T5N+wmVM3U1x36cRhOdFyDGFwS7Lh4n7d1if7aHr62DF74EQ283GBAXO6toa9It3Rd7wgCFw4GmonP6oG1+m9yINtwY/NtVOCrUqAf/vi5mXuhUj+kJuumJlSejnyPQazF9jLnvweHs7tnC6dZUhuweguMI8xtMnKR48QU48FRsP3dAAC/9nD/y06TMQ9fgPHRZk3Sc9wmA0XUV2wS8wIHhdsYfz1l3LMa2jIC1E7lul/Tx7ITbQL9zpvfCqP9CVZajUXh03oA5QVav5/k/48nf7Z3wsNhfPneHQa5r5WHR9Ffxzlstl+yjSk9rXmf7qbfTNZzcv+P5D9Pl7oZ+db/0KzOdARrI5lLWsBrTWQdtMv+WwuikYbZMse6BxW+0NRtthCHwTQTZmKDX1EowmhBBCCCGEEEIIIURPFC5ESAghukqf+Mi3T7lh6tgmpVe5FbjajTqU7OP1r7c7FA1kmyyEEEIIIYSIHZ+K3QVHO1uZfz0/lM9mr96HdvVQWvlg3WueQ9HAHs4thGgmp50KIYQQQgghhAhwWn+ZVa/MMycTWhTTX75pb69oFVSWeepaf/Oep3IA6qGvUL6e+8WbCGPH/azBaKxfG7Kq2vlAdIhgNHXpvTDl5HYMrpvz2YLR/M23/Q0cWPEWvyaZg9GG5MABNR/wx+pA8kyGv5Q9Kz/muLLngMZAsZb++zZqmz023J00HIbmwu+GC4OM7g/PnO6w1fXhg9P2q3iPvv7Qz3d76SsOD9wYtgXc9Cqq78DgMvO/R//jCFhnSd0JJSMb9fDXqH6D2znSYHuMgtREqLSEVAFk+4tb3c9rWB1RHw4uyW61cV2NCpEC0uS7DyLq7+cV5i/+05Ng0+zWy/Tns9D/dwpUe0gwarLrodBnE9TkI1Cb7wCAUor+Bx/G6bMf497eZ4dpILSDxgeCDPQ7T1rL9IRQRqUUo/rBd39GVi83RDAaAG8+Csf8PaI29eez0FcdFdlATJYuxL19GndP+y/3fdr+5mwcgrdtetlCOiYWqH30H/PR/zoBlvwSWDB+Elz3NCq7X2B9/o/o07b31JZ65Fv49Rv0gh9Qg0fCvsdvaAcgI0SO4oOZp3HFfeOobQzwnLaHIjNM7qJpu/fbGvP2Y2R/+6PfO9W8vNhTMJp5eY5crElsRPTqPwPb4EXzIq+83/EdFooGsO2mivwC7ycMpLqVxGnzyeWl1eZAopZsQUcKSIwLBKS5hjJLCjUTh3bHTwHRrRSGSAc2WTQPtowwrLALvTjH5aRHNbVh5nfEYpuRlqS47zjF1GfMb9r/vKW56yPNoyc5HL519P3ph64NXrj6T9yv3gbMgbiOgl6WfZx6P9Q2BIeX+U0bFsDXOPQBEWQOJ7czGO34HRTfLIlNwHBNvf2xEEIIIYQQQgghhBBCdF99EvK6eghCCGEU6fZJoegTJlgs1TIpXaOpcatIsQSnhbOkemFU9drqEy/bZCGEEEIIIURs2I5/eoqlNZb5h12kQdeztGZxRHUy43p30GiE2HhEF1EvhBBCCCGEEGLj0yJszI+DX5mztBMaF+v6OpjzsbU5/c7T3vt+5lZPxdTLi1DxCd7bFT2OOukf0VfeZg84zBBulJyKuvFl1EGnbdyherYrEeoWYT0N9VxcfAc7VX0ZVGxYVh3vXejw339uxcwVRzFzxVE8vvoMji97zhrsoy/aH/e+K9HFgeCwOJ/iob2WkhVX06rccfGf8dOf2zF+zgP8cOJi+idYUmUa3V5wacj16tIZOF/UwuY7hiznyeKf0LeeE7RYNzSgrz46ulA0QL22rENC0QCSExT7mrPtAOiVBPH7HdtqWd4++0TUh4NLsq4xrqt2PASjrf4TbUsSMVi2zrx88zxwnOZXoF5fiP7X8ZGFoo2biPN/z+NMm74hFK2JOv4yblp7FdtXfbthWZyu5+QSe8CZyUHlb6ILlsNSywlscfEwcUpEbXaVcZtEHhSRO2mXkOv1vf9A+/0hy7QqX7wmNqFoTWY+AMeO5tODf45dm22YgtFYlt9h/UVD19ag330afeKE5lA0gHlfoA8ZjDvjCvQz0z2HoqEUDB6FOvRsnCsfQh13SatQNCBk0NlF/W6j1m3+XL7rI82/3wy93WgbjKa1ZmGBuezIvvZ2sm3BaKE/ngAokmA08Regp58XXSjaFjuhIgzCjNQBW0RWPsWtJsMtNa5rcKG6LnR921bJcQJhTqmJ5vUnPhqbECEhWsmf29Uj8GxpseaYh8KHosXS0dsq4kKc/VBeA0c+4PLdH9G9P92Vf/DpqgxuyL6MF9MPp0o17+j4f7M/Nz4ncIxmU2rIo/Zb8sSb8tDzIghGaxu6FqlTJsYu5LGmPmZNCSGEEEIIIYQQQgghOtHW6Tt39RCEEMKod3wumyWN9Fx+dOqEsMFmqSHWV/jLPffVUrW/kgq/+XfrSG3dS7bJQgghhBBCiNgYkzqhq4fQLjWuec5TVymuX4s2zSux8BHH2NStOnBEQmwczLPchRBCCCGEEEL89TjNwRT1yj5rsikYjSW/QGWZvb33n43RwALUy4tRfQfGtE3R/aheveHvd6JvvyDyuo4DF94BB5wKC75Hr1mGGrU1jN4GlTugA0bbzdhC31qGEfkbyHJL+GDp/nydsj0/Jo7HVQ4j6hax6z/uoFefzYA+6BMuh6du9tbvs7ehn70NZnwCq/5gl5vO5Fey+Dh1N4p92WxV8z92qP4OBejbL2A88JOTyQEDZ/JtSnD4zur8QeT6i+z9JSbD7ocDoI79O/rKI72NM5TvPkCXFKEyc5qXzfsCCldG1Zx65FtUXMd+7XbQeMUr/zNP6C+rAefKh9CHnAmr/oBNhpI6amuyLvSzvspb+wpNkmuYoQ9UtwgAICER6mqDC9VUQVU5pPby1N9qy3lfm2S1mYT/2UyoD5Ng0oY63h60p3L6k3rOv/hixmQ+St2dYl82E2p+ZGD9Cp7IOB5tCxxsoV/DGrZ9+Cj0wyECFg46vcNfE7Fy8kTFI7MjC4vos8c+kHgBvHCntYw+cyd4YLanx0Ef0gGhgquXsvNN23PsxK94dv2WMW9emaJzulEwmi5ahb74AFjyq73Q8/+1BgAZ9RuMSgwdlJiZEkmD4ZW32dwUlAVCTkxG9bOHeGSnKkxxR+sqA2FrStnr2oLRbGFrQvQ0etUS+O4DT2XVtU9Bckpgf2PT0TBhtw7/vNtqkPn9a5OqK/G59nDOkmpIsYSbAbiW8xOathK2bRBAVa0mJTF2gUJi46JtL65QdRbOtYZWdzezftREkJMcE5kpiolD4fMwF4Hc4UaX2Zc7TBwa2aN50aMl3D34vQ33t67+H28vP4hs/zr8P3wGXGWs5yjICBEWW1YNfdscNoULRstNg3gf1HvIHm5vMFpyguKlsxyOfCDy12xbNZ0YlCeEEEIIIYQQQgghhIiNPbMOYVTK+K4ehhBCWB3X71zuXXE96xoKQ5brE5/H0X3OCNteipNuXVflLwf6RzpECuujuzhrWwfmHMegxKExaUsIIYQQQggh+iYM4Ni+U3mu4H50ZDMJuoVay5ynrrK2brXnsj7iODXv4rDh3UIICUYTQgghhBBCCNGkRfhLnUqwFov79CXc+56Gb96zlom5CbtKKNpfySFnQRTBaEAgyGT4eBg+vsdMGI8ZW4CTbjF52R+YhZxAPbtWzWbXqtnN1VOag9XUnkejvQajNXVz7u4bbvehkL+VvWQtm+WW8OnSvbi0703cl3UmfhXH+JqfeGD1OaFD0QA1/Q1Uelbgzs4HRjRGK61h7me482ZDwTLYdAz8uSDydlLSURf+FzUi9qFLbU3ZInwoiBqzLYzZdsP9AZl4DkbL9q8jRZsLVzot0n/6bAIrfjc3UrQqgmA089/SP7P5tq6qQN92vqf2WlIT9w9d4KhpODMuZ6/Kj1stHl63mPzEEWHbP6D8bZxwz8V5t4Rtp7vYaZjiwRMU057X1NR7q5ObDmrqjejli+Crt82F8ufCO0+iJ+6PnnEFfPBcYPkBp6LO/j9URjYA+pt3Y/BX2N3zzT7UTZnPy4uzY9qui2EbvH4t7jXHok64HDW8a08U1y/eHToULRpjtgtbJC0xEAbixuh30rVlrYPLVpbYyw7Nta/Ltvx+2OAGQo56WcJLtNbWYLSctL/cnofYCGmt0Vf/zXuFEVuiBoX/rIylTbMDwUC20KC2UtwqkrU9vWx1Kbw4x+Xj3zR9eymu3F+xWU7z+9m2+XI8vOWLKmBQiNA18Re3fm3kdfLnxn4cHWTpuq7pd+Y5DtkXhd9A7HxzoMxuI+De4xxG9Q/9pp67THP38i1aLfsheSvuzTqLfxbdiFtTDZb3u8+x71sAlBrOzQoXjOY4ioFZsCT04TPQ/mA0gBT714QR8Xp8IYQQQgghhBBCCCGEgDGpE0j12cN5OlqcimezpBHkJPTrsjEIIYQXeYmDuGaze/ijJp919ebf4HLj+zE4aTjxTvgfPZKcZBwcXIJ/sKl0LSeNhFFomRwfp+I4pu/UsPUTVCKbJY+kd3yIk2GEEEIIIYQQIgo7Z+7D+LQdWFz9KzXdLGisyYLKH5lT/kXQ8u423sJ687Ffui+DQ3JP3HA/xUljWMqYLv3uT4ieRILRhBBCCCGEEEIE+JpDkeqVfdZk/LM3QW2MgzXCUGf+u1P7E11LKYXedk/4/kPvlTbfoeMG1FM4PvNy1998uzEYzcjX/DWRGjIWvesh8NlrMRpcsHgauKPgEv5VeD1VTgp9GwrwGU4m2qDfYNQLv6Gc5vAhpRQ8+wv62M3bPR59zbHNd2a/GVFd9VI+VFXAoBGouBjMOvegd6qiby8oKAteN2WcuU5eJvyyylv7I2sXkuZWGtdVOSnNd/oMtAejFa+BwaM89bfKEm6UlxH4X9fXoc+c6KmtltRjc8KXcRy45B709PNaLT93/f1c0O/2sPUPrAjzehk0AhUfoySBTnL6JIeTJ2q2u8Hlx+Xhy+ekgfL54KZX0YcMhnUFxnL6udvhtvPA32K79Oaj6C9mwcuLUUkp6LefjG7QvfvCuInw2cyQxXq55Tz/xkDWv1XFH0Uax23g1Gfi+WlldN02SbH9qPbJK+hv3oM730ON3qZ9nURB19cFbnz0Ymwb9sWhDj8nbDHHUWQkew9lDKe6HsqqIaNxM2TbdsT7Aq9Lm96p9nWrS+3hJeU1gfA0k1D9CdFT6If/BYvmea+Qt1mHjcUmPk6xWQ4s9pgplepW0cs17DA12uM2P2U1TYFImhe+13zzD4cxeYFltmBH5SEYrSbErrcQ/Ppt5HWWLUTX1aISun/iXrHHOSGJMT5bIStV8chJitOe8JbK+mk+jLnW5bsrHcbmtV6XEAe+xhTEl34wt/d6+oH8s+hG/CuXwhBzHz4H0pPsYygzZDf6LcP3tcjiHd3fYzBaDB7jhBg9TxKMJoQQQgghhBBCCCGEd3mJg8lLHNzVwxBCiB4hwUlkZIrlpLkIKaVI8aVT4S8NWlflL4+qTdvk+Jz4fuyYsUdUbQohhBBCCCFErKTHZTAhPfL5Op3Fr/09IxjNEoqdlzhIjv2EaAcnfBEhhBBCCCGEEH8JLUKV6pQ9wCVB13XGaDZQ099ASejVX8+ICREVVwee1kED6UEcy9c8bosEl5ZhRG35Ws90Vlc/BkecG4OBhZbplpLXsDp0KNq+J6AenN0qFK2JGjgc9eCXHTjCMI48H9VvMGrI2E4LRWvyf4eYEzn2GmNenpfpIcGj0ci6RaRagtEqnBbpP0kp0Ku3uZG13pOmVgefRwZA/8ZgND56CZYu9NwegDrtWtQwjye87XRA0KJz198ftlp2QxF7VH4SutBmY7yNoZuJ8yku3NPbaya38UI1SinY7wR7weWLzNuh0mL0Xlnol2fAJ69EPtiDT0c9vwB1/XOo82/1VCVrSgpbnZTKlqdk8NXsPC5OmsWmWdEl2CS4tWxR+7O9QHUF+qW7o2o7WvqHT3BP3gY9OR09OR3Wrohd4xN2RU1/HTVuR0/F+/aKXdcAWRe6XDnTpbpOs7LEnBrSPyMQymYzINMeahQqbKkoRMiLBKOJrqJ/+hL33Mm4+/XBPW0H9Pzvo2unpgqev8N7hW0md/q+T5OtBnnfp0nRVSTpGuItx7HNoWgBFbUw/f3mbYu2hBOF2MRsUNW5h86ih9Ffvxt5JdeFlUtiP5gOUFzhLZhsR0uYWHucPDEQjpaZEr5sk+1ucEk9r/W/jGkuqef6yb3Iz03vmP+euUlbogF/vT3xy1GBgLU0S55dmeHcLL/l8NjXYtszsp+3bWFSDDbV8ZYc9khVy3ZRCCGEEEIIIYQQQgghhBA9QKrPfBJIpd/j1YHaKKxbY1yeG98/qvaEEEIIIYQQ4q8kyTFf9by2uwWj1cuxnxAdQYLRhBBCCCGEEEIEtAxGwz5rsjOD0dQDs1Hb791p/YnuQ+0cHFIU0q6HdMxAehLHMlPZbRFC5A8R+tMm2EIlpeBccDvq1ln20LVOoq58CJXVx75+9DZw3KWdOKJG2f1Rp1zV+f02OnmiYqehrZeN6gfHbmcLRvPWbi9/KTn+IlLdKuP6StUiYcAXBzl5xnJ63mxP/VXUaGvYUF6mQtdWo5+80VNbrRzkPTBR5fRHnXl90PL/LdmOdH+Ztd5Na68mSdeGbnvSwZ7H0d2M7h8+bCEhDjJa/M6kjvl71P3pOyOsqxTqqkdxLpmBSk5FKYU6ahqccHlEzSTVlHDz3GNYvGwCtx0RuuwRW/rxqdbBGNcV/ptkXRO64rzOC3DUS35FX7gv/B4irC0aY7dHfVqFc9f7C1GGMgAAIABJREFUqG28X7HI67YnEje9o7n2dc2SQvP6AWH6TE5QDMw0J47kF9iDXJats7cpwWiiK+jClejLDoGfvoSKUsifiz5rZ/SqKIKTfv8Z6sJsy5okp6JOuTryPmLkgC28l01xq1BApr/Ec52Pf2veDriWTUJTuOK+Y+3tVNXB2jLNzys06yq9hUSJv5BFP1pXqQtutx+DLc/voAG1X2295rs/NJ/na/4oCl8+KR6uOTD2x5pKKU7ZyWHdHT4u2dt7kGJbVXVQXQ/F5rzoDVbGDcANcdqFr3FVL/O5WZRWB28frMFoLboZ7fFcqaT46B+DJsPsh+MRqYkuh1gIIYQQQgghhBBCCCGEEKJTpTjmk0Cqog1Gq19tXJ6bIJPjhRBCCCGEECKcREswWk13C0ark2M/ITpCXFcPQAghhBBCCCFEN+FrDlVK0rX8rfQF6lQCdZn9qR+9A3VLFlJXbA/KiblhW8CorTunL9H9jNnOc1F1x7uo1F4dOJgewjZx3m0xozpUMJrP/DWR2mFfePR79Mz7YNbD7Rhg9JQKP5FbHX8p+plbO2E0wNBxMOkg1FHno9KzOqdPA5+j+ODvDk98pfn2DxiSCxfsoUhPMj9e4YKCmmxavxQFpLrmE7kqndQWg4iD0dvAkl+CC875yFN/+QX2dUNWfo6+5G9QFiKNyGT8zqjefSOqok64DEZOCIRzLQsETmxR+wvf/zGRB7NO47eEUejG1+Im9Ss5quwldq/6PHSjaZmw05TIxt6NjPTwEI7u1/o9qjKy4c1V6APMgXntctBpqJwB6EU/Qu8+qD2ORk3YJaiY2vd49HO3Q0N9ZO2v+J0L5p7HdUn3UGbIBkpwa7n3zfH8Y/I5PPJNApVOKoeVz2T/infDt71uDVprT9uz9tAL56JP3yH2DR94KurCO1A+SwhnCAMyFRD7QKDp79vbDBXGprWGx29g+PJtWZYWHPAWKhjttzXmddmpkJHSsc+tECb6melQGRzgqY8eDZ/XRLbNCRWKNnQc6vjL0N++D1m5qH2PRw0JkQjWwSYN97ZdSXKr8RHYF870l1IY5y3ZZ9k6KCrX5KQrtC0YrfH/KVso3v3VXOiKV1zmLIWaekhJgDv/pjhtZ7lmlWj8LFq20Lxyr2NQR5yLfvkeWGkIOVzWPYPRZi/SHP2gy+rS8GX3GAUTBimO2U4xYVDHfn7efLji11WadwyHK7HyW8IIxtfaA2mdxj8xIxlWGTIaTfudXoLRBvX2ti1MSQxbJKy8TMV2m8J3f7avnZoId8+FEEIIIYQQQgghhBBCCCG6Qqov3bi80i2Pqr3C+jXG5bnx/aJqTwghhBBCCCH+SpIswWgNuh6/bsCnuj42ya8bKK5fa1yXGy/BaEK0R9e/w4UQQgghhBBCdA9Oc9BFX/9anl51SuBOziScCz7EvetR+N/dnTYcdfn9KFvQk9joKcdBn3QlPHFD+MJb7dbh4+kRHEtYjetvvt0QeTAagBq6OeqSGXDJDHRDPXp38xURu5JKy0D3GwxrlnZcH1NvQB17cYe1H42keMVZuyrO2jV82TyP4URPrDoNgFRtDsKsaBOMpnbYF/3W48EFC5ahG+pRcfEh+7OFECXGaTa5/RgojzAULbs/atptkdVppLbbC/VMINRAz3wAffs0htUv4Za1V0XX3uX3o9IyoqrbHfRKVvRJh7UhzukzhVmojGz0UdPgxbtiOh516FTUsHGEi89Qg0bA1BvQ9/4jdCCkyZuP8v0+OWy+9ErqVUKrVY+vOp3e5cvoPesKIv7LGuqhtBgycyKtGZIuWg1fvxMINNp+744JRes/GOey+6Kv7jGUMZYGZYd4lXz5JvrRfzO0312Y4hvXLC8GzOFJC83nqTIyqxr9wiOQ3Q8mHYxKTIp4zEJESmsNr9xrL/D2kzDlJO8N1tdZV6lL70WN3Q6151ERjLDjDMyC5HioDhOwk9Ii1DvLXR9RH6c94TLrPB+uZdep6VD19J0V5z9nLjR7cfPtqjo48ynNpOGaEX0lSPEvr3AlVFcaV6nDpwZuDBxhDEbTy/LD7gt1tuo6zTEPeQtFe+4MxdHbdt53PUopXj/PYZ87XD7+rWP6WJg4knG1v1rXN4WZ9bLsHpQZLlppC0ZzWjz5XoOv02IQjAZw/wmBx7Gw8dggKwXev8hh/CZwyAyXtz2Ez9XUa+h2r2AhhBBCCCGEEEIIIYQQQojWrMFofvOFRkOpdWsobTCf/5abIJPjhRBCCCGEECIcWzAaQI1bbT2G60zr6gtx8RvX9ZFjPyHaRWaYCyGEEEIIIYQAQCnLIaJunI1ZFmEwTXtsviNq1Nad15/oltQBJ4cvtO0eKCWTaoHmdIa23BYzqkMFBIUIRmtJxcWjHvs+fLlbZ6GuewbSs+yF+g1G/f0u1FQPAXhe7Ht8bNoxOezsbheKFikvE+dT3YoNk/pTXXNYQ2WbYDQGDjc3pjWsKwjb5+JC8/JhSevxlReHrb9BYjLq+udRT/yAGrGl93o2ex4NifYfUMJRN76M2u3Q9o+ji/UJ8xvRVoPMy9UhZ3rerniy6WgYurnn4uqoaagn56KueDDiroa+dwvrF/blqZUnc2jZa9xacDlLFw3jqPJXIm6rlWJLqlaU9I9foE/eGn3LVPQdF6GPGRvT9puoR8Nv80PxGtoRS7sMt+8b6C/eACDHX2RcX7zaHp60yBLkOPKXF9H3XIa+7kT01F3QRasiGK0QUSpYFnK1vulMdHkEYWChgtHGbue9nU7gOIqRHi6c3TLkNa9+dUR9NIWaaUswWtNWJjFekZ1qLtOW1vDol+FDasVfwPJF9nWDRrT+P6hufuzH005v/KRZWeKtbE5a5x+/+xzFBxc5jOmgc4vyE4bjV5agcprDzGzBaKWmYDTLpsLX4rDf6z5WaoyC0bYcqJh/ncNLZzk8d4Yi//8cth6siPMp3jjf4cO/OzxwgmLeNQ4DLV8D1IQJtBRCCCGEEEIIIYQQQgghhOgOUn3mC8dW+kNcXdKiqN5+vlCfeJkcL4QQQgghhBDhJIYJRusO1oY4Tzkn3sNJz0IIKwlGE0IIIYQQQggR4LNM4nQb0+qLI5tI3i7JKZ3Xl+i++gwMXyYjp+PH0VM4tvdwi2C0UOEYtvoGatgWqCsfNq8cMAT18NeoHfZFTT4C9cZKezsnXoE69KyYBY6pEy6H/U+CprA823Ytmran3R6ztrrKyH7Nk/JttqyZt+F2mtdgtNw8e4OF4cOB1pm7YeCayMKY1J3voXY7FJWRHVE9a3vpmajpb4QvOOkgGDex+X5iMurSe1E7HxiTcXS1HPN5fhtsNdj8olIDh6OueQLSMto/iCGbo26eGXEQpho0AjXlJNj72Ii7TNK1HFP2Ii+tPJaL1t3NgAaPQVehXn8x3JfSWqPP3xNKIwgPDEFd+xT07tt6Ye9+qLs/RLXzORzUu/MDUPYaHWLlbz8AkNNgCUarsv9sYgt9GVK3pPnOonnol2eEG6IQ7bd2RfgyL9/rvT1bgG5aF6QbejCyb/htS4qv+W/Ka4hsG7y+CiprNa4lnKjlPlW6JezI5MP5EowmgPWWZOC0TFRjsLSyBaMty0fbEvu6yLu/eC+bHWbfsqMopZh9ucOBW8S+7d8SRuCGOO2iKcwsw3JuVllN8DK/G7ysZVsAvZIhJSH8+NJiFIwGkJ2mOHxrxdHbOmS3CLlTSjF5lOKMSQ7jNlEkxZvrSzCaEEIIIYQQQgghhBBCCCF6ghTH/KNWlb8i4rYK68zBaA4+suJzI25PCCGEEEIIIf5qkkIEo9V2k2C0wjrzecqZcdkkODE8iU+Iv6C4rh6AEEIIIYQQQohuwhaK5G8MRvMy8T5W1q3tvL5Et6UcBz1sC1j8k71QjAKQNgZKORinx+vAjGpdV4u+4jBzZcdBOZHl56v9ToDJRwSCftYXAgp6ZcEmw1qFFymfD+7/An32pNYNDB4F+x4fUZ9hx5SQiPrHg+jzb4UVi2HQCPRBA6G2fV90q8fmBP6OHi49STE2D362Z9UxqjZ/w+1Ur8Fo6VmQkAR1hhn9ReHDpEqqzMuz/CGC/NoauRWM2c57eY/UlpPgno/Q5+1hL3PAKaiJ+6OLVsP6tTB4FCph4/nhIlQwmlKwxYAQ6ycfAbscDH/+Fvw+jItHf/AcvHBnyP7VI9+iRmwZwYgNbZx8Jfr9Z9vVhqd+TrsWjrsEPTndXCAG+1La7wet0Xdc0O62WtntsMDztWwhVJZDajoMGhnxZ4PJyL7hy8TSweMhJTFEYFLj9jzHbw6VK9K90Fobg/hswWibNLTZsH72Gpz9H0/jFSJqxfarSjfRH7+EOuUqb+3V15mXx3tI3ekCIz1cPC110CBYmQEVpeR5DbhsIb8A8/41zTm84C2YqMn/lkU8DLExKjWHc5LZIvjbFoxWtg7+mA9DxsZ+XFGat9x7UFu40N2OlJmimHWej4IyzaoSiHOgqs2mz69h55stqWQW+Ykj8HsIRktPVpi2KmWGw1UvwWhKKQZkwqIwX6HFMhjNK2swmiWDUwghhBBCCCGEEEIIIYQQojtJ9ZnP/an0l0fcVmG9eXJ8dnwffKrnn5MohBBCCCGEEB0tVDBaTXcJRrMc++XGezjhWQgRkgSjCSGEEEIIIYQIsAVfuH50QwOs+sNed8hYGDcRZj0Um7HUmMN4xF+POuVq9FVH2ddn5FjX/eXYgruawg1/+DhE3ei+IlKJyZA3JPAvVLmx28H0N9CP/BtKCmHs9qizrkd1UNCGSsuAUVsDoA3BNp5tvgPqzH+jho2L0ci63ndXOiSfa5/oP7Ju4Ybb1mA0ldJ8x+eglELn5sHKJcGFC0OksDUqqTIHKWT6S8PWBWDf41HTbjOGGMWCGr8z/Ps59DXHBK/MyIatJwfK5fSHnP4dMoau1DvNHOAA0Ccd0pJCP+4qLh5s76ERW8KQzdE3nmGue94t7Q5FA1ADh8OMT9CXHAjVkV+51bNt90DFJ6BHbwML5gSt1t9/iDrglIia1LXV6KduhidubN/Y+g2GbfaANx8NXjdhV1Rc4+fApqPb14/BkNxAiIct4CPWjt42zLagoR6AbEswWrGThVu8Fl9O60S32npNkeXl07/tD5krFlvD1f6KtNaB/RG3xT+/5Xar+27jvwjrNN3WbsR19IZ+I+in5TrtWsYa5u9oNVZbG4b74fy5wPsT1cOC0UZ5OE8gN8OHevBL9N2XkrekLOI+fl2lcS3bLqfF29sWAGSiFFTWalJDBTiKjV/ZOvPylsFow7YIfE9jehF++Va3CkYrieC8ouzU8GU6Wt9eir697OtXT3e46JH1fPxzDaVOBgCb1i+lf8NqPk3dLaj88viBlPnsDTZtLzIs52aVVQfva1uD0dpsOjbLCR+MltqdgtHqO3ccQgghhBBCCCGEEEIIIYQQ0Ujxma/2U+VGft5RYZ1lcnzCxneemRBCCCGEEEJ0hHiVgMJBE3xiXbcJRqszX/Bajv2EaD8JRhNCCCGEEEIIEeBYQpVcPxQs3RAi0ZZ6bA5q2Di066K/eB3WFbR7KOqQs9rdhthITDoo9PpsuXLCBsoSbqhdtNboZ6bb60YZjBYJtf3eqO337vB+gvo97lL0I9dFXu/M61EnXNYBI+paifGKkycqHv/KHHQ1si5/w21bMFqF0+LEr6bXTo45GE3/OZ9wsR8lVeblGa49GE3dPBM1cf8wLceO2v0wuOJB9E1ntl5+3i2oxKROG0dXyDGf5wdApv3CO54opWD/E2HbPdAnToCKFs/5xP3hqGnt66BlX1tMRL0fHITl3n0pvHhXbDoZPCrw/ybDjMFofPwy+spHInrN6IeuhRfujH5MY7ZD3fcZqjEA183oDS0/DxKSUCf9I/r2PUiIUwzNhfz27yJ6ctQ2YbY6VYGTVHMswWiu8lGyeAnZbYLRVofIahzQsCp44fzv0CMmwKJ5UFFiCPyyBGBZA7Gay2pr+FbL+7ZwrxDLw42jVb8ext50X/QMDZZgtLgIUr860aj+9uDOJnmZCjVwOOqW1xj8m4bbI0to/GC+vYeWuYehtg9taR1o95AJEQ1FbGR0SZF5RUb2hpsqPQs9bieY90Vw/fy5YfexO1N5jbdyKQmQnNCdRt6ariyDxT/Rp66Gp1+dErR+SfymjBg231h3YcIIa7u+xkP1XpZdwDLD42cNRmtz2D+8r+L9+aG3hWldEMQowWhCCCGEEEIIIYQQQgghhOjJUn3pxuWV/gpc7eLYztU0KGx7sb1GufFy7qsQQgghhBBCeKGUIslJotoNnnxU212C0azHfhKMJkR7STCaEEIIIYQQQogAnyUYze+HFYvt9TYZCoByHPR+J7QO24hWF4Qnie5JKQWvLkEfNsRcYId9OndA3ZljOdlm+SL0sZuHfh9HcKJOjzNxfzAFox02FV69z15vzLYdN6YudtKO5mC0TFXJ5FOmoFIPQ990JqnaHIxW6yTRgI84/M3BaJm55s5mPfz/7N13eFvl3cbx+zmSLHkkTmI7w9khgwwCgbDCCJuwd6CMQmhLgQ7a0pc9CqVQKB28BUrZ0EIZYa9SRtn0BcreOwkhyxmOR7yk5/1DSWxZ50hHsuT5/VxXrljPOj8nWkc65z6yg0dKx50Zfzy3Y5ct0pqPayRnUnI90TWuS5qbX5OZsLnHb5c/Zr/jpZETZB+/TZKR2e94mc227/Q6OlvKYLSi3GzDVAyX7nhP9q4/SWuqZKZsLe1/out9JtfMj6+Q/fxd6c3nOrbQwMEyJaXxNUdO8I7qeeaeeBicD3b1cmn+1R0qy/z56Y2haJJkfniJNGEL2VefkPoPktn7GJlJ+U/n2XRo5wSjfXSxI8dJc79ZtlCSVOYRjCZJK75cpLLtEh/fi92fkiRJlS3JX2Tak3dOXQfQ3TR7BKOFCjq3Dp+m+DhOoHJA6887jM98G4+9Z7Wpx7HoTpbBaJL0xPtWB8/ovuFQ6ATVHq9BpeWJt7fYyTUYTc/dL7tskcyQkbmvLQu1jf7GlRXnt46OsPddK3v1GZ4XBZCk0c0LFY41qNFJTjj7MDzZc96G54v+HqHC1S7HZUU93ky2D0abNMR9XFvFXfA0HvE4CoVgNAAAAAAAAAAA0BMUOe4HTFnF1Bhbp8KA/y++VjQtdW2vKODkeAAAAADwK+wUugajNXSDYLSYjaqqyf1kCfb9gI7rxWe9AgAAAAAy4hWMZGNSlfsX8yobJhNpTUUxx54hTZ7ZsTJOvEBm7JQOrYHexVQMl/nl1VK7gBzzk9/JDB7RRVV1Q17BaGtXpQ5Fk6R1tbmvJ1MnnOveftipHVt3wubSsWcktm29ezwcaNKW7nNKBkib79ix7XZjsycZnbZ74uOpICj95fslKp57klQQliT1j9Z4rrEmsD5pZGMwWpnnWHvDhdLrTye3f/2R7OHjtaYl7DpvQCw5ZcQ8ubJLQtE2bn/6LDln/VXOWdf1iVA0SapwvwCqpNwFo0mSGTREzqmXyTnnBpmDT5IJds41PYwxMn94XNpx/+TOfgNl/vy0tNOB6Rcav1nrz2Oneg6zLz7suzZ779XxgNpsjJsm89BCmYLEx5cxRmb3I+Scd7Ocn17ZKaFokjRxSPYBQJ98PlWHrH0w7bhLDzGaNDT1duxLj2z8eXDLCs9xX36d/Pzz5Qr3hJLiWK36x9amrQ/oKvaTt2T9PJe0eASjBUK5LShHCoJGc2emfsxXtDlWvSBodPY+mT0XraqTXvnCva8j2Z0ffOsZn4m+orrKvb008T21GZ0cHryB/dWxuawoay1R6zvsKlXgbley7/9H9k8/TxmKJkkBxbRJ85eufYuDw73nrd9VL/UIRlvbkNwWjaVea4PJw9I/GZUk57jlXaFHGBvBaAAAAAAAAAAAoCcoCXgfMFUX9X+sZXOsSatb3L8brAhxcjwAAAAA+BVx3A/A6w7BaKtbqhRVi2vfYPb9gA7rnLPLAAAAAADdXyDg3h6LSmtXuvcNKE+4aUpKpWv+Lb32L+nrj6UhI6VvvpC96SLX6ebM66S9jpbefkFa/KU0fQeZTaZ15LdAL2UO+oE0Y7b05nPxsL4Zs2XGTO7qsroXx+Mx3EOYnQ+Sve1SySYGRZjZB3dsXWNkfvhr2V0Pkz74jzRivDRjl3jo0jXPyu4xIHnO3J/IBLtnCEiu/PFIR0dtbfXql1bFYWmPyUZjy9efVL8+6aM86hHYIGl5oELl0ZWtwWil5Z5jJck+fJPMNnu23n78dtnLfiBJWhModZ0zILom4bY59TKZom6aptCLVZYaSe4BLuFe8umyCQSkS+dL//239NnbUnOzVFEpbbOnTNlQqaEubaCZ2btNOMn2c7wHvv2CrLUyaRJ17OO3S3+7PJNfI27OsTI7HShtu5dM2CN9owtsOjT7uZs0f6W7Fh+re2sO07HDb0vqnz5Cuv1ER9NHpA8GsX+7YuPPRXadhjcv1uJQcpDJJwsbtG+7ts+Wu685oekLdSAfCcg7+/3tpKJ+stO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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import asyncio\n", + "\n", + "from matplotlib import pyplot as plt\n", + "\n", + "from apd.aggregation.query import with_database\n", + "from apd.aggregation.analysis import get_known_configs, plot_sensor, Config\n", + "\n", + "with with_database(\"postgresql+psycopg2://apd@localhost/apd\") as session:\n", + " coros = []\n", + " figure = plt.figure(figsize = (20, 5), dpi=300)\n", + " configs = get_known_configs()\n", + " to_display = configs[\"Relative humidity\"], configs[\"Ambient temperature\"]\n", + " for i, config in enumerate(to_display, start=1):\n", + " plot = figure.add_subplot(1, 2, i)\n", + " coros.append(plot_sensor(config, plot, {}))\n", + " await asyncio.gather(*coros)\n", + "\n", + "display(figure)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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34ddrFjNvLdi2bRtPPPEEQ0NDjIyMEIlE6OvrY8uWLZx66qmEwxW8HHAecrkcDz/8MDt37mRgYIB0Ok1fXx8bN27k7LPPJhJp4EuiBEEQBMEnBMq80+W3e+C1N7o8ea3Fxl55kZEgCIIgCIIgCIIgCIIgCIKw/JCn/AVB8C8FMZpXR1btmk8tO0UKS4+X4GGpRVclO5WUuXMsNIZSAhnlkxv4IkbzJ6WEDX7Aq4PiTPy+DkcCXvIGv3WULiV0EDGav2i4GK0Eyf2NjkCYj3L/4UYeO+XEaHu+A+vf7p82m1BetFfgmRvhhMsXtoyR/y09/sCd8Pg1cOrWheUvNJ6S59V1aCNVs4zhR6H3ZUsXi1AdnmK0KkUdIkZbHPFdsO0G2HkTuFUIcpQF698BJ10NnacsXXxC9XiK0USKXBKv+uxILBeE5Yflcd3RlevVvqeU1HIx97CqvXawUFG2IAiCIDQ/W4qGn6ty/ueZLUY7EfjpoiKa5l7gGeCE/PDvK6VO1Vo/XmompdRa4C0zkrLArTWKSRCaihtvvBE943pJb28v7373uxeV5+WXX85nP/tZsllz/yWRSPC1r32Nq666CoDdu3ezadMmz/nPPffcedNvuukmgJLxKY/7b7t27WLjxo1Tw+eccw4///nPp4Z1FdeM9u7dyw033MBtt91Gf7+35zEajXLuuefyzne+k7e85S3Ydvnzl+3bt3Pdddfxox/9iPHxcc983/jGN7J161aOP/74iuMWBEEQhOVGJdX3ZAaO/pjL3/6hIhqE1vxlYqWgJWSGlYJkBnIudERhPAnpHGQcCNmwrgtetVnR1ybP+QiCIAiCIAiCIAiCIAiCIAj+Qp7yFwShgZS5eday1nyXEma52coEM4J/aJRopmQHbpHr+RKvzlp+EMgUEDGaP/G7DKqSY9jv63Ak4CWn81tH6VLxDP4auotfBC80DK+OpdUKQRZMmbb3vjtg3RvrE4pQGeXqgkaWR+XEaA9cDKOPw+99qj7xCOWpVLqaPACPXbV0cTz5STjmLyG2fumWISwdpcqlegjHq5Et3nUGvOFpaD+h/LTC0uNVBlV9fl9LMVqZY9bN+U+KvFDGtsNT/wh7bilfh8/ECsKmd8GJV0HbsUsWnrAIPK9plpDrCN71mbw8Q1gOeF6vrkKIKTSIUmK0RbSBqm1vPfcVOPUTC1+eIAiCIDQhSqluoLso+YUqsyme/riFRzQbrbWrlPoLjGgtjLlA8l2l1Pla693zzaOUWgncDrTMSL5Oa32gVnEJQjPx4x//eNbwn//5nxMKhRaVZ19fHxdeeCHf//73Zy2nIEZrVrTWfOpTn+KTn/wkmUym7PTJZJI777yTO++8c46YrRjHcbjyyiv5whe+gOuWvn6VTCb5zne+w/e+9z0+97nPcdlll1W7KoIgCIKwLHhiX+Vi03++e3Evzulq0Xz/AxavPEHkaIIgCIIgCIIgCIIgCIIgCIJ/WCY9ewRBWJZE15hvEaMtLxolmuk+3XtcNZ1ChfrhJRfzkxjN8ojFS+om1Ae//6etCuotEaM1nkaJPKulVDyPfAiO/6v6xSKUxu/12mNXiRjNb5QTWVUjCKo1ldS12z4DW66CUMfSxyOUR2cbHcE0z30Nfu+TjY5CWAhuiePIb20kgOe/Di/6bKOjEMC7bV1tO6iW0qJyx6yTAqu1dstrBMO/hac+DXv/G6iiI4QdhWPfB1uugJZ1SxaeUAs8RDlaxGgl8RSj+bAuE4Rq8ZKvJ/fXNw6hetxSL/dZRBuo2vZWqt/UI/JCIUEQBOHIorNoeFJrnagyj8NFwzW9MK61flAp9QbgFqAPI157XCn1DeDHwB7Myf864DzgUqBnRhZfAeSi7AxyjmbfSKOjODJY1wUBu3H3tPbt28fu3btnpZ1//vk1yfv888+fJUZ76KGHyGazBIMlnnX0Mblcjosvvpjvfe97c8atWrWKU045hd7eXtLpNP39/fzud78jHo9XlHcymeSP/uiPuOuuu2alB4NBTjvtNNatW0c4HObQoUM8/PDDTE5OTsV0+eWXMzIywrXXXrvodRQEQRCEZuMlGxU/+N3ihGeVMjIJ7/2Wy/atFrbl3X5zXI2lQDXyuSVBEARBKCKZ0RwYhawDjgbXBVfDxh7oaFlYnZXKalwXoiH/13ta65Ixam22zwvDkMrC6g5zzeaFYbN+azogaIOVbwMk0prJDIRsaI/OXv9EWjM6Cd0xiAQhnYPBOPSPQyxk8g7aZj9obZaXc8HJ7xPHhUPj0BqGgGUuauYcyDhm2tFJaI/AizeArSCVg4NjMDgBqzshmYGJlIn70BhYCk5ZB2s7a7efMjlNOmfi2jNstsO6LhP/gVHzrTWsaDfrEQvPXW4mpwnaJqZsTjOegrH8O9Yd16x3LAQB22yH9ggEA2pqf2Uds/yADaGAv48/QRAEQRAEQRCEpUae8hcEwb9UJEYr/2ZCwWc0quPZUW+GX3mMKyedEBqDk5w/3UtG1gi8OhV5yW+E+lDpf3r925c2Di8qEXpKudR4GiXyrJaSHSM1ZMZESuQXvKSZ9arX1r4BDv7Ye/z4M5AahEhvfeIRyuNnSWbn75WfRjsw8iisPGfJwxEqwE/nzofuEjFas+KkvcdVIv+tBa3HQvy5yqYd/PXSxiJUjlfbutp2UC3FaOXyclIQbFIx2uH74alPwcGfVDdfsB2O/2s44TKI9C1NbEJt8ZLWiBitNF6S31qWMYLQKFqPmT99ch8kXoDY+vrGI1RBibJbWXD8h+DZf60+24UI+Seeh/bjqp9PEARBEJqX4gsAHg8qlKR4nrYFxuKJ1voepdQW4HLgEmBT/vflJWZ7GrhGa31breNRSq3ASNqqwaPBWn/2jcDRH5Pz53qw89MWGxt4+/GBBx6Yk3b66SVerlkFL3nJS2YNJ5NJHnvsMV760peybt06du3aNTXuX/7lX7jxxhunhm+99VZe/vKXz8mzt9dsrHPOOWcq7eKLL+bXv56+3j0z35msW7e4lxxcccUVc6Ror3vd67j22mt56UtfOmd613V56KGH+Pa3v83NN99cMu8PfvCDs6RoHR0dXHvttbznPe+hrW12kZlMJvnyl7/MP/zDP5BKmXvsW7du5YwzzuCCCy5Y4NoJgiAIQnNy+gZFVS9AWiTPHYbg+11OXQfpLDzTP/90loKWkHmno86H19sKtmWEJlkHwgEjRUnljChlfTd0RI0IJJGGgbiRffS2mumzDrRFjFwknjbzn3G04oa3KHpbIZmFyYwRr7RFYF2XQmtNKmukboNxM6+rzXiATM7E0BMzsVmWmSfnGEFLJGhiGU7AUMKsz6p2IyZ5YRj2j0IsbGLOOia9JWTkLLZl4pxIaXpbFas7oLPF5DeaNNuoIHtxXCNUCQfNekWCZl0Oj5v8bMtIT9rCZvp42myzeAo6WmDzKuiOiRBFEARhPh58XnPtHS73bC893dnHwNouRdYxgk/bUqSymkNjpg5J5Ux5nXNMORwKGPlWgaBtPq42ZXpvq6nXNKYu1Jh6J5GeFn31T8AJK0391NkCazsVsXxdNZaE/nEj7g9Ypi4IBUz9YCsYT00LxUYn4fiVZtzopKmTlJqWjOXc6fq4JWTyiwTNZzxlYspW8C7kcnS1QCJj1rOAlY/DL7SG4eKXKSxl4g3aRriWyeU/+d+WAssyAr2sY7ZjPK3Z0W/q/3iJxyTnY0UbrGw3++zgmMkzvcBHwCNBk89M1nVBX6tp44QD5ng9pk8xmX8seGOv2e/94zA6qUlmpsV0WQcOT5h13DVotsExfWacq818sbykLuuYbRgNwVAcJtJwxiZF0DbjtqyGaNAc2zp//A1MmPbQZAZWdShWtBmZ3lDcLCsaNNMVhIXpnDl+V3eYZR6egAOjmo29inDA/Bdslf+e+ck3hQ5PmGMeoNA6Umr6XeOWMulK5X/P+FbMTVvacWr+6a355ys5Tk2vW/E01Y6bE2uNxvldICk0H66rp87hCseX1hrHNeWCHHOCIAiCcGThs179giAIMyiI0VQpMVrWe5zgT7z2mbXEVVKgBWIbILFn7jg/SyeOZLzkYgvpyLNUiBjNn3h1cC3m+A8tbRxeVCKNkHKp8TRK5FktXh3hC2RHRYzmF3Ie/WgC0fosf8MfwyNlyr1cHBAxmm8oJclcVZs32S+Y1a+BQGv+mClBLlGfeITy+Kl9qn30JIxQHW6JJ34qkf/WgrUXwjP/XNm0XrJtof54ta2rPb+v6fWAMg9FeElt/YrWRoL71Kdh4JfVzRvug81/C8f9lZw7NBue54PSsbsknrJGn53vC8JC6D3Te9zAAyJG8zOlrmkrGzZ/GPb9ACb3Tqf3ngmD87wRaP07pn/bC7ju5Mi5vCAIgnDEUSxGW8hFgeILUUtlWy+cuFTSNe9B4FrgniWK5a+Ajy9R3oJQM/bt2zdreOXKlfT09NQk75NPPnne5b30pS8lEAiwcePGqfTOzs5Z061atWrW+GJaW6eLkUhk9nXRUvMtlLvuuosvfOELs9Kuv/56PvrRj3rOY1kWZ511FmeddRZbt26dE2eB2267jZtuumlqeMOGDdx3332e6xGNRrniiis488wzOe+880ilUmit+Zu/+RueeeYZLKvMMxKCIAiCsIz4g+PgpDXw1IH6LvfxfaXHu3quMKScQGTX4Pzpe0emfw8VXZp8pl/zn7/yfsZkppitEgpSj9qztM/BBCz44h8r3vdKaQcJgiDMZNeg5g1fdBmdLD/tA8/D7PK6dNldLLUqSDzBSKvGK7yC+PShmUMLry9m5+NNQZRVaXzVMDLPdvaTFA1Me+Tr99c/qMMT5lMLiqVoYF7ysG9kdtr9Oxa+ns8drmbaapazmG3vs4OpJizHdSpNpUK1+URySzFuqWR44ykjuJzMmLZ6d8xIDR13WlpZ7vtgXsxZ4HNvU6RzJt/WMHRGjQRxJGHmsZSph8ZT5rwmnRc+FtY9aBvJYSEOMBLPgQl45pAmkTGi54EJk09vK8TCakoUaSsjHUykNas6FBt7jUB62wFN1oFNvYpjVxjZZkfULKd/3IgSbWVEiCMJzYFRI/rMOuZcLZeXRIcC05LRcMDUKenctIQ6k8t/O2YZxWVqa14inZhx3rm2E/raTB7pnCk/07np34X5WsPTEu6p3xEjK52THoa2iJo7XwRiISPbFgRBEAShMchT/sKSoJTaBJwGrME87HUQ2AM8qLUWk5WQp8yJQMta822FvKeRw6n58OoMa5UQ4NWK4z4Aj/3d3PRKJUpCfWl2MZrW06+dEOpLpVKxvrOXNg4vrIDpzFaq7CklwxHqg2d91WSnUFo6xPsGL7mGVad6LdwNL/sKPPw+72n8JE4SStdnp36yfnHMhx2Cl34ZfvXnpaeTY8o/LHZfdL8Ejnkv/Ob9NQjmyLvZv2woKUarU3128t/D8MNG7FEOKYP8gy/FaGVoluNHu7D3+0aINvJodfO2rIMtH4Fj/tK8UEBoQjw6P8h5YGm8rsf4TYQuCAsh0gvtm2H86bnjBh6AjX9c/5iEyihZdlvQugnOfwj2fBviO2HlOXDURXDf640ctYAdmf1CkIW0n7zk/oIgCIJw5LCQC5hLftFTKfVe4J+BWIWznAXcBTyplHq/1rqCC2qCsPwYHh6eNdzV1VWzvCORCOFwmHR6+tp58fKaha1bt84afv/7319SilZMsfitgNZ6Vt6BQIA77rijIrlbQbh21VVXAfDcc89x++23c9FFF1UclyAIgiA0Oy1hxY8vs/g/P9D89GnNUV3w8QstbnlYc/OD8uxFte/lWxop2tKTc+ED/6U58xjNqetq/1y662osS5HJaYK2ESB0RMlLEipbntaanANPHYTdgxBPa9ojivaoETQMxY2wYEW7ESfYFrRHze9kFlZ3QHvUxLB3GLpiRhSxe9Dst64YrGo3wpt4Gjb1QEvIyCJSWSOkGIibY2I8CcOTMJHS9LUqzjoGOlpKr0cyoxlOmFiDtslDKSO7CNpGuBAOgFIKrTWuBrdIcFHJb8eFJ/eb70jQbOdUFjpbzIWFTF5w0RYxy48EjRRjNAk5x+QxGIeJFKxsNxKOVNaIIyIBM5+jzT4M2bCuy+xHjRlO5mURXS1mnJJ+DsIi0Fo3/Bj6xA91RVI0QRAEoT5obdoigLzXs0quvK0R53dey5wvvbHnn/OJuPePmk8pUtnZArppSq2P97jYHIlaQaymaJ1HstYagbYZ44rniwSlTS4IgiAIlSJP+Qs1RSn1VuDDgNcryYeVUt8BrtFae7z3RRDyRNeY71LCLFfEaE2Hl+ynHh3PvJYhAiJ/0sxiNLQpn+wSYkdh6ahEdrj6AlANfHuaHYFcwnt8pXI3YeloZH1VS5xKXhgv1AU/1GvHXgodJ8Hdr5h/vJe8TWgMpeqCji31i8OLTX8Gu74Fh+72nqZZpDJHAovZF+0nwKt/YaQ5yoaH31u7uITmolS7wgrXJ4ZwD7zqXhj6DUzsgI4TYejX8NvL5k4rZZB/8GpbN1SMVuZBDb8fP24Wdt8C266fX35TitZj4aS/g41/Jtctmh2v6xoiRiuNVztb2fWNQxCWir6z568bBsWD4WtKld2F8r5lDWz58Oxxf3A7bPsM9N8LLRvguPdD31nT4xci5JfrQ4IgCMKRR3GXiOgC8iieZ95uFgtFKfX3wHVFyY8AXwbuBw5guhmtAl4OXAqcm5/uZODnSqn3aK3/o5ZxCUIzUCwq8xJ4LZTOzk76+/unhoeGhmqafz14/PHHeeCB6XPGtrY2PvOZz9Qk75/97Gc8+eSTU8OXXHIJp556asXzf/CDH+Saa64hlTLnKXfccYeI0QRBEIQlQY+bNoNq725wJHNZ26X45rtmd5Q+bwu89mS44zF4+pCRNAVtI3GKhcyd0KE4jCWNPEopI1JK5yAUgHjKyLaE5uK0rS6v3mL2YzIDARtWtpljZCgOP3tGY1tGhLWhx8gD9o1MS7k25A/vnGs+WcdIuA6Ne0vmWkLTcq9MzhxLazrMuPGUyXsy4xWxH+R9JoZoEE5YBaOTRkKQSMOBMbN+YERilaBU9UI+P/OqzXBMnyKZAatwKyJkyolo0IjU2iJw+gZFNGj2dThgypuJNChgOKEJ2EZOt2sQoiEjfEvnzPQHx4xELhQwx4vjGmFKOAB9rWYPzRTNacxvR5v95LjmmG6LTM+rMMtQysQ5ljTLS+W7liWzZj+lsuZz3EozfTIzXfYl85KMglCut9UI8MBMl8yafFe0wYvXK1683iwjnjYiukKpfGjcbI+sY7adbUE6L6Cb/miSGfOfcbWJbWW7Wa+gbfJ3XHhivxH6jadMmT0yaY5ZxzXyvn2jZrktITO+IMzb0GO2Qzhotlkya7ahxixLY7ZDW8TkN5Y08a7pNLEOxk15ErLNfrItk98je6aPlZPzXfsKZUf/OGQcs91sZdKtvOiwI3+VrLCNCq4PpUy8kxmzXdoiZntEAmY+pfJCRmX+ky0hIwuxlNnug3EjA9zYY2SKL1qv+M9fLaM/pCAIgiAIvieRNp/+8eIxCxOt2da0TG2OUC2iiM2Sr80Wsc2WrzElXwsFRLQmCIIgLE+arFe/4FeUUq3A14CLy0zaDXwAuEgp9U6t9U+WPDihObGjEMzfNREx2vLCs+NZPcRoHp3bKpEoCfXHUyCzkOeQl4hSnbLdlHQwbhSVyA4b/b+3woCI0XyN1z6wmuwUykk2OgKhgNe+qHe91vVi73F+F4AcaZQ61/GLpLH3zNJiNOlM7R8W8/8+6m1GigZw7F9CYjc89alFBCMPJDUtpY4jq47nPnYYVrzCfADGn5l/OimD/INX27qhYrQy+LVd5KTg+W/C9hsgsaf89DPpPAVO/BisfxtYIoBaFngK36UnS0m8rhs12/m+IHjRezY8/4256aOPQ3YCgm31j0koj+f1ajXdY2Q+7DCcco35zDt+Ae0nv7aDBEEQBGHp8LUYTSn1KuCTRcnXAlu1ntMde3f+822l1KXAv3wJIP4AACAASURBVGP6odrAN5RSz2mta2XM/TJwW5XzHAP8oEbLFwRfoEq115uEe++9d9bwn/zJn9De3l6TvO++e/Z9xHe84x1Vzd/S0sLLXvYyfvGLXwBw//331yQuQRAEQQDQrguP/QL931+B+74Pb/pL1JVfanRYFaGU4u2nK95++sLzePQFzUuuk3tKzcY92+dLnfsczv7RuVM9eaD65RVLz7SeP2+/k8zCY3vnpqeq7Iq0nKRoAD99Gn76dCUrtcxWvGqWev0Xl//OwYXNd3Cs8mm9yo8D85QHlYoGx8o83h6f5/2dw4np/P/fE0f6cSkIgiAIQrPjuKZNNH+7aGGytVCgWKI2W6jWOo9QzXyreeeLhcG2mv8+iCAIgtD8yFP+wqJRStnAd4DXFY0aAB4FxjAPNr2Iaen/SuAHSqlXa61/Wa9YBZ9R6sGg6Nrp8SJGW1547bN6dDzzEkiIgMifeHVir2dH6HJYJWJxUhCszYOCQpVU8p9utBjNDpceX4ncTVhavPaBX2RElSIdGf2Dp/CzzvVaqfJHRHr+olRd4BdpQ7njV8ogf6D14gRRgaI+fX4SFQv1xZ3niTMwUrRGdv7yKoukDPIPnhKiKttB1U6/GPx2/GQnYMe/w9Ofh1R/dfP2nAEn/T2sfUNj/6vCEuCxP7V0YimJ1zWhZjvfFwQv+s6eP127MPgQrP7D+sYjVIhH2e0pwayQBYnR5PqQIAiCcMRR3A20RSkV01pX2I0TgBVFw7XsJv4pZp8A/ofW+hPlZtJaf1UpdRTwD/kkG7gRWIQ6YVb+h4HD1cyzHARSQvPR3d09a3hsrIqe3xUwOjr77168vGbgwQcfnDV8zjnn1CzvX/5y9qPB3d3d7N69u6o8Zkradu/ejeu6WNYiz5UEQRCEIxo9dAj+51voH30T9u+cHvGDr+NOJlAf+xoqUKLvQpOjXReGDnJabzuffFOMa+7Qy072JAiCIAiCIAiCIAhHIpkcDOe8ZLULk61FgzOEafMI1WLzithmiNZmzNMahpaQ3DMUBEEQqkee8hdqwfXMlqJlgQ8DX9VaT70jRCl1IvB14Mx8Uhi4XSl1itb6YL2CFZqEljXTv0uJ0bSI0ZoOL2FRPTqeeQkkRIzmT/wikClFqVj81on6SKIS6Vmj//flOvQ3Oj7Bu43RbB2lpSOjf/BLvaYsI7BxM3PHSd3lL0rVBX4pi0SM1hzo3OIEKcXtFj+1x4X64niI0Rp9TIgYzf94ta2rPXbqeawtRihZS9JD8MwX4dkvQGakunlXngcn/z2sOEeEaMsVL1mO9F4pjef1abu+cQjCUtF2HIT7ID0wd9zAAyJG8yuul7RxkWXTQsTW0o4WBEEQjjC01kNKqRGga0byemB7FdlsKBresejAAKXUWuDlRcllpWgzuB64Aig0Cl6ilDpVa/14LeJrZtZ1wc5Pi1ipHqzrKj/NUlIsKhsZqfIaWwlSqRSp1Oz2c09PT83yrxcHD85+dPekk06qWd579+6dNfzylxcXadXhui6jo6NNKaATBEEQfMTPvof+yj/MP+7uW9F334p+218DGoYPm/su0RiMDpjfXStQr/wj1JkX1CwkrTU8+yg89WuYjKOHD6G6VqJTCVQoAoGgiSHaCvFRaGmHzl7IpGBi1KS1dprrjLF2sAPgupBKwNgQemwIDuyEvTvgwC5ITQJwdWsHb1PreaTvVWRPOpvoOa9nMmcxGIeABeMpeHyvJhI0HZ1ftB5cDavaFbZlNodSpuP1QFwTtCESAEfDRApCNhyegMkMhAMQtCEWNt/7RkyH6FgY2iOQysFIvuP2//lBc9zrspTZHoIgCIJ/+MA5imsvVAzG4Z/u1uwd1gQs6IopgrYRcWjAcU0ZHrBgfTes7YSDYzAQN9OcsBLWdiqCARhPQjwNg3FNOgepLNiWeZOBUqY+UMrkFQtDOgs/f9bUk7YFqazm0Dj0tZphS4FtKQI2rGiHFW0m/3QWAjb0tprYfvQ7TTQEK9sVHVH4zW5NNAhvOk2xvluRdUz9e2jM5NvXZmIIWGbYcaE9qmiLmLo3HIDVHeb3YNzUz1lnOibHhXQOBiY0QVsRDZnfh8ZNjB1RRSxk1jEcgIwDyYzZXp0t0BOD4UmIp0z8hW0TC0/HpLUZt6INklkTQ9A2bYGgDaGAme7J/TAwYeZtCUE0ZPIfnYSca6bV2sR/y8Oae7ZrhuJm31oKOqMmn3DA5BkKmHZJKKBwXIinNcmM2baRINi2mW9jDxzTB7GworfVLKM7ZrZLIm2W3R4x+6uw3caTsHdEM5wwca3uMHkentBkcmYZfW2m3dOef/QtYJvjJ5k1271/HJ46oIkEzfZZ3aEIBczynzus2T9qYvjWrzRDCThuBXS1wIYehaWgf1xj5dd3XZci50LOKRwDcFQ3xEJmOVnHHAdtEbMPhxNmfTqiZhtNpOCxvZqHd8GhcRO3paCn1eSRccyx7GpIZOD4FdAZUwzHNWNJk3/OnW7/hQMm/6EErGjVtEYUEymz3FQODoyabfmqE6b/m1MfPXu4sP27Wqb1PVoX/c4fY5oZv4u/fTJOM//0giAIfiOZNZ/DE/ON9Sq4vAs0pYolarOFarE58rXCeOU5XyggsjVBEITljk960grNilLqaOCyouS3aa1/UDyt1nqbUuo84F6m5Wg9wMeB9y9poELzEZ0hRlMlxGiuiNGaDtej41kpAV6t8OpA4tXhRGgsXp1vygml6omI0fyJVzkzk0aLx+xw6fGVrIOwtHjWV012CiViNP/gtS8W0kF1sdhREaM1A6WEDX65aF/u+JUyyB8s9r8dKNrPixUTiayleXE9xGhWmbbtUuNVFkm95h+82tZVi9HqeKw1+vhJHoSn/wl2/Bvk5n11mzdr3wgnfQx6z1ia2AT/4CVGYxFC1COB5XK+LwheKAV9Z8G+ObdqYfCB+scjVIhH2e1Z1lfIQs7f5FxeEARBODLZDpw1Y/hYqhOjHT1PfrXgtKLhnVrrXZXOrLVOKKUeAs6dkXwGcMSL0QK2YmNvo6MQ6sHatWtnDR86dIihoaGaCMyeeuqpsstrBoaGhmYNd3XVzmZXnHctmJiYEDGaIAiCsDjO/2P4t6sh43H/G+C2L5bMQv+/m+FDN6DeUdyNpnp0Love+k742fdmpxd9LwnxMY7lCY6deAJ23giJt6I++hWIxirqUKxzORgfgmTcXJseGTDStY5e82x+Mg72CCQmIBSGjh4jcItEIZeDbGZK3kZyHCYGIZ3i6hMP82x2Jb9yNnOo92RWbd7EsSstOqNGsnFo3Ag0osFpWUosZKQdGiNp0Ri5x/5RIynJ5IxcI5R/7OrgmBGiHLtCsaLNSGgcDU8fhH2jmjUdipcfbZY3OmmEL4NxiATNtCHbSE3AiD0mMzCWNJ2yO6Jm+em88KUrZjx1BZnOSF7qclSXkYU42kw3lJfx/N9HNFt/JM/WCIIgVEuQLFfv/Dg9n9hGz/gIXwmGIBiCYBjiYzA5bn5n0+YZxnDUDA/sh5Y2kx6JmZfQFuyfw/0mcztg2g7hKERbIBQ14zt6INICI4chFMkvL8R7O3pNHsk4tLShNm6BjZtNXZnLwuggenwYUhr6C5YkDUz//nAgb0kayhnLVcCGkSH4wbCpZ7U2FQz572jMxHlgF/SugeFD0N5jrF9ODlYcZUSpuSxrXdesZ3s3tHWCZUMyYerrcBQS4zB0CBJjZl27VsDgAdj3nNker/wjs6zU5PQ2CrewEqC1HeLjRirrZDEKMMz2KnxCUTrau8w2beuEWIcRwSbjkE1zSiBk5k+Mm/bCxAhkUnTlsmZd27uNINbJcW0wzLWtHWYdQxGzXq5jpnNn/nbAcaAlBl0roWcVjMyIDUweKLOP4qP5/DTseMxsg2NOgfFh80mMm22glBHTKpV/ebll8rAsE3sgaI6LwjEUyBvPHCcfm8Nm1+GVuZzJN5UwMtxkHCyb82NtsO5YOOp4Pn/GOujsM/vIyZlPLgt9SdPeau+GFWvNcTYxYtZbKZjIx5PLoUcHYdew2eeZtFmP8WHoWwtdfSafkX7zUtEV7SZ+yzax2gEIKBgahtFBM3wwf8yOHDbrm0mb/R4Mm+PDts12SSXM+EiL+b+5rjlWUklIJ2Gvmt6GSpll9qwyyz3rdai+tWZ9Xcd8a40eGYDBg9CzEtW7xvwfC8dboRWti75n/Z4nrdrpZj0PXSKPUtPNM712NS7K/M0BjcJ1NRplhGpuPq0gVMuna62mZGxT06DQ+XnzJQauzuetFVrr6em0NmkwvXw9+/f08hQaPZ1XPs0sd/Z00zI4NXvczHjzy52afuZ3fvkzpyuOc/Z8am6ezIhz5jrM2H5m+ul8CmlMza/QloUVsAkph925XhI5m82hfgKWNsJES2FbCluBbSssy8gwrfzHthVXbPN+rrM7mKLLThEOaCzbJulYrAoniagsGRXG0ZBzNBuicaIB1+wzRzOSDTGci5JIQzAArrKxlSZsa1ZEUnQF0uQIEM1N0J4bIaMDZCbTuOkUTjCKG4zgpNOMpgOM2e1MEiHkpFin+2kJwZ7QBgZ1B0kVoT/XSjpnDqZowEEDm9rSqEAAhSadcWmxsqyMpNnUMk5YZwjbmmwmSzYYI6kihFSOaEDjBCL0xjSR9lYCTopQNm72w1A/dmqC5+Jt7E628mJnG2sy+4mPJ9keO4XDsY1sWhWkw0oRJkPYSRAeOUCEDOEghMMBtLJIBDuJuyHiToi4EyTuhJjIBc1v1/xOuCEmcoXxQSacEGktzxEuJVobEedEyohpi8aWmtNzTACHVjtDm5Wm1crQapvvoHIYcyKMu1GUbdETyrCqzWFdKM6G4DDtgTSJlhXE3TCZeJJuZ4icCmJForwwbrM5OkRrSLNvRHMwtJZwOEDOVeS0heO4ONrCsYM4KoDjanKOecFLKylaoxYHnQ72JyK0B7O84eVtvPsPgthWZX3BdGLc1IeFdkogBKOHzW87aOroQN7JkBg3v13H1LV2ABWOorWeur6jXdfU+WODpv4NRaC9y7SpEuOm3s5lYfVG094Y6YfJuKmb9z4Lh/aYunzL6Wb8yqNQsfbK1kVrM28wBJSX2OlczrQhtEZZFtpxYOgg9O81bYpA0GyDjm5YdxzKlpciC8KRgNTOwmL5ODDTZnTzfFK0AlrrpFLqXcATQCif/B6l1A1a651LF6bgT0o0XmaK0UoJs0SM1nx4Ch7qUCV5LaPRgiRhfrw6IS9WxFBLRIzmT3QFssNGCxHLySOkXGosunDJfx7qUV/VEimL/IOf6jU7Atk5V5DlePEbXsIGP5VD5Y5fOab8wWL3Q7F0yk/tcaG+OB4PhtdTVjXv8j2OSZ0zZamIbhqP1/lNteWJFSo/Ta1olBAkvgu2fxae/6a3jHA+lAXr3wEnXQ2dpyxdfILP8JDlaBGjlcTrupHXSzUEoRnpPXt+MdrEc/WPRagMz7K7EWI0OZcXBEEQjkieZLYY7Uzgh5XMqJSKAafOk18t6CwaPrSAPIrnER2YcERx1llnzUl75JFHeM1rXrPovB955JFZw9FolNNOK/YZNh+VSFAqJZOZ52Vdi0TLC3gEQRCERaLau9GvfDPc/e1F5aP/9Sr0w3cbCVgwBLE2I3XIZKCzx8gbCvKVvBRDx8dMR07LguHDRsTy9G9rtGY14KffRf/0uwDojl5YvQGOOj4vwQgaQcnooOlEOzpohBtLxPH5zxSBoOmgC/Cqt5mOvsm4EYeEIzA+Mi0dKUhCLJvjV61Hnfgy6F1txtkB07n2qDWolrap7LXW4ORYsTFnOgUnEzBuE0nG6c5mIBOm+7knzDqv2QSbTjQxpJN0h1voDmZZlxyGXbvMfu9eaeJKTcJY2khCsvlPtNUITYbywp10EhLjbOzqgw2bOS0EsG7Jtm29CFguOXeR17gFQRCq4NP9/8Ca7aXlpo2i7mey++a7L/zQwvPb8/Ts4Z/fvvC8asHggdnDowOVzzs2CAf3VL/M+Bjs3VH9fAthbIZofuQw7HseHvpJ/Y+jpSA1aT5Qfr8NHTTfzz1edt2XxbYpotCKkqeZlh/f2XgfD0dfNid9XXYfu7cfP88cQr3JEiBhxZiwWonnPxNWK4n8t0mLMWG1Ebdi86YnrNjUfBNWG46f+gAtQ3LYjDpRRh2Pl60XSABLdymjJHfshEtvcQnoLO06TpsbJ6aTREgzqaIcsvr4/czDdOpxAtkkvzf5v1wy9m263NEFLW9K7znzekqN0a2dRs6rLMhljCw2l4WDu6cnCoWnX06Qv2aj27rMdZ7C9ZvCd0H+O3MZeZkaWe/7TXrDZtQ7r0b94cVzxjmuZjIDbREjGR1PGel9MgPtUSPSH09BT8y8WGs+UlmNAkKBuffQhhMarY0MvyVkhPm2ZfLP5FenJWTSXA2hQF5UpzUTKYjlu+EcHoeV7UawKQjC/EhNKiwYpVQUeGtR8mfKzae1flYpdTvw9nxSAPgT4LraRig0NdEZb08UMdrywmuf1aOjstcyREDkT/wkkPFCxGj+pJL/dKP/9+WOY6nfGouXjAiaT6zRKKGDMBc/1Wtey5S6y1941VV+KocsEaM1BbUWo5Xb72VZjo8gHCF4SZrKSX+XmnLnZVZr/WIR5serfV21GK3ENcKqKVMW1bsOG9sOT/0j7LmlMtl3ASsIm94FJ14FbccuWXiCT1EiRlsQjXxxhyDUi9ZN86dnFvaglFAHlkraKGI0QRAEQaiUHwOXzhg+p4p5f5/Zz2A+qrXur0VQQHEDLraAPIovjsUXGIsgNCXr169n/fr1vPDCC1Npd911V03EaHffffes4TPOOINQqI4vd6gRvb2zfYnDw8OsXbvWY+rq8z5wwHRWjkQiTE5O1lS8JgiCIAgLRb3h3ehFitEAePhuz1FN/2TEWF6A5hdx28xOvD+9rapZvfaFDkXMfTUnB65/7q/1Rs+AjT/zHP++ka+RUC08FTmJntwgMT1JRoUYsHtZn92LjUPYTbMh+wIbs3uYtFoYsrtpcycI6BwBHAI6R1BnyakAe4LrWZnrp88ZZFK18HzoaE7IPEubGyeoM1jaRaOIW61kVRBH2bhYdDmj2OQYszrYFdqIRvGa+N2cnH6KoM4SIEeOAJNWCxkVosMZI6NCOMomo0IcCqxkd3AD98ZehYNNtzPMutx+2t1x1mQPENYZVuUOkVVBFJq41UpEp+h0xngudDRJFSWkM9g4pFSEVbl+VuX6sXFod8e5s/W13B89mwm7nZgbpzfq0umMMKDbcRzNsG4j6k5yWvpxOp1RYm4CR9mEdIZ2ZwJXWUTdJAcDqxgK9BJz41jaxd5wHFasFdvNYY0exhrYi51NYeFiawcL10xH0W/tYuNCIMjqwBjJRIaArXEjbWQDLdihEKHDO7FwGbU7Cegcw3YXw3Y3q3L9tLiTWLiEdIZgKMBgoBflOqBderKDOChSVhS7s4eIypHRNvvUCrI5TXtmiEzWJUmY29rfwuPhU0hYLbS4SVr0JBE3RX9gJQOBPlblDqG05s62C6o6bm2dw8UipDP0usPEnDgtOkmbM06rTuBiYWtnarsM2j2kVRiFxtIuFi6qoxsrGMTKplHZFNFcHEtBVgVIOTbKdQi6GSatKINWj1keWWztgOswYncR1mna3DgOFk9GTkZply5nhHZ3gg53nDZ3HJSF0i5t7gRB5TJutzOouhm0u7G0S1QnCeosI3YXhwKrFvQ/DpAjrHKELdd8qxwdKkHAzZLOap5Ux3jOe3J6G63uBG0kCekMVsAiSYS4asFysvRkB4joFONWO+lgjKy26Qyk6F6/mhUtDrHMKFZmEmXbWJaFApI6iKvBSifIpjKEQhYDVg/doTRHtyXJpTJsy61lx2Q7q9Uo4YBmV6qDDivJCmucdePPEHGTBNZuIKA0oWyCqJVh8vAQWlkETjubiZWbyWVztGRGUW4OtEbnP7ianAOj4xnCbopw2CblBnCwSLs20SC0tEdp7Wgh58BkBsbTCjfn8Mg+m10TUU7uGOfn/T10hh1yLsQzFrGJAwR1lqMzu/irkX/n/MQ9C9pfgiAIglBP7t3zWto2D89Jv2Ts1gZEI8xHkByd7hid7lhN8tNAWoXnCNXM8PTvmSK2uNVKXMWYsNuIq7nytbjVivZ6flTwNTkVZFh1MWx1zRn3w8gfmh9RoP1PuXzVPwGwJb2duNVKtzNCxE2ywhlgwO5ld2gjSms6XHO+vTN09FReR2X3YmsHR9nsDR4FwOnJRwjpDA42WilyBBmz23GwcZTN2ux+1mf3EtUpwjptzke0Q1aFmLRaGLPacZRNSoUZsbuJWzGyKkirmyCjgoR0FgLwwvHrUFozHOgB4NzEz3CwmbRiuFhopViffYHe3BDrsy9g4xDSGRSaiE4zZHezJ7iBcasNWzvsD67hsL2CDdk9HJXbTw6bnAqYDwFyTgDnmza5Bw7htPeRcyHnwKFxeL4Kf27QhlXt5n0GrgtjSSNNK54mGgSlIOdCoop3oAP0tkLAgsG4mb+Y1jC89iQjVTs0Zs6NbMtI2QrLSmRMHK5rfp+yFo5ZoTg0qtk1BPtH4NDnLWyRrAnLDHnKX1gMrwFaZgz/Smv9tNfERdzEtBgN4CJEjCbMJLpm+reyzGe+Dl1axDFNRyM7nnl1IKmmw6lQP7xkPn4So5USALjSeahhVCRGa/D/3i4jj2i0uO1Ip9T2b7aO0tKR0T941mtl3kixFHjVpVJ3+QsviYyfyqFy7TIpg/zBosVoRfs5sNhyq+kf/z1y8ZR8+lyMFhQxWsPxlH1WeX6vailGK0O96rDh38JTn4a9/01V5aMdhWPfB1uugJbmf1u4sEA8H2zxT8cNX+IpH/JRO1sQFkuwY/703Li51yYPxvkPL6nlYvfVQsTW8qIFQRAE4cjkJ0AS85g5wJlKqc0VPgf3rqLh/65hXAeKhk9QSrVorSeryOPFRcOHFhmTIDQdr33ta/nqV786Nfytb32L66+/nmBw4dcbBwYGuOOOO+YspxlZvXr1rOFt27Zxyimn1CTvlStXTonRUqkUL7zwAhs2bKhJ3oIgCIKwKF70Sjj1bHj8gUZHIjSSjD+fa1qV83Zt/37ifr506LI6RrM4guTocMenhkM6O3VbvM8Z5JT0U1wYv7PqfFcky/dsfn38x7w+/uOq8y7m2OzO2Qnb71tchvnHJ1oLv+NzT/F7nSEAOt0xjs7unptHGlanJ2YlBYGIk4SB4anhE3huzqynDDxVcahpFWJ3cAO2dtiY3YONYwR1booAOeJWKy3upBG+LVMG7R6eDJ9IjgCtbpyITk9JByJuiqhO0erGieokCRUzYkKdxqrg+Y9nQsdxd+w8RuwuVucOcW7iPo7J7lp4sDsWPmvFeB0+v6vDsgVBEARhmRDVKe7ffQ5vXncbg4E+AN48fjsfG/xMgyMTlgoFRHSaiJOeausvFhdFUkVnCdUK34ki+dqE1UqiaJppMdt0WtJqKb9goSFsD28BmBKcFXOQ1XPS5pv2kejpJZezN3gUD/HyBURYmp/Fzp2T9mjktKrz2RE+rvQEB5h7d70Ksg7sHSk/TXYR3dIHy7zCLJ6G7/5vdXn2j8M922efg+4fgfU9VQYnCD5HnvIXFkPxkxz3VTHv/ZjLuIVj8EVKqZU1fGOm0AzkEt7jWtbMHlZB0POoU10RozUdXoIHqw6dW706t3nFJDQWz073fhKjBcxxNV8nbxGBNA63grPLRovHynVIk3KpsZQ6PqwmO4WSjoz+wU/1mlcZJHWXv/CUyPioHCp3/Ipszx8sdj8UCxwX0rFeWB64Hq+0afQxUWr5Ug75A686rdp2UD2uHRVY6nbR4fvhqU/BwZ9UN1+wHY7/azjhMoj0LU1sQhPhIcvxkusIBs92tsdLNQShGfESo2kXcnFTnwj+wlPauMiyaSFia7k+JAiCIByBaK0nlVLfBf5sRvJHgXeXmk8pdTzw5hlJOeCWGob2ODACFF4ZHsnH+JVKZlZKvQFYW5T8y5pFJwhNwmWXXcbXvvY1tDadAgYGBrjpppu49NJLF5znjTfeSDY7/dxiLBbjve9976JjbQRnn302t91229Twfffdxzve8Y6a5H3WWWfx6KOPTg3fddddTbudBEEQhOWFUgq23oJ+x2ZIy/N1gr/YlN3Nxsxudoc2zhl3YqYSf7cg1IawznBCZrZtq82Nz/t7udLrDHHO5P0VTduqS/SPm4cTMjvmbF9BEARBEI4Mzkw+zL4dR/O7yKmszB1mXW5/o0MSmgwLTUxPEnMmWekcrkmeDtaULC1eJFcrlqiZ9NnytbjVSkLFmLDbiOe/sypUk9gEQaiOXYMiRhOWHz7qTSs0IScXDf+q0hm11gml1BPAi2YknwSIGO1IIllCvRotei7PCs7fAVfEaM2HV8czL2lZLfGSSDRakCTMj1cH9kZ3ui/GjpjOZMVI56HGUcl/utHiMTtceryUS42l1PFRj/qqlkhZ5A+09pbJNEKMViw5KiAiPX/hVRb5qRwqd/zm5JjyBYutC4rLjMWWW7r8GzEFn+J41WVl2rZLTaljUtpCjUe73pKmqsVodbxBvhTHjtZGhPbUp2Cgyj7I4T7Y/Ldw3F9ByEN2Ixx5KBGjLYhmaGcLwmIJdXqPy4yJGM2XeJTdXmV9pZS8n6KAec7PpA0tCIIgHLlcC1wMFMzs71JK/bfW+o75JlZKRYCbgJkXLL6htX6+1EKUUsUV8Lla6/vmm1Zr7eSFbTMtQtcrpR7QWj9ZZjnrgX8vSn5Aa32w1HyCsBw58cQTueCCC7jzzjun0j760Y/ypje9iZUrV1ad37Zt2/jsZz87K+3d73433d3di461Ebz61a+eNXzLLbdwww030NbWtui8X/Oa1/ClL31pavjrX/+6iNEEQRAE36B6VqHuGUVvfwR9963QxJW0ZAAAIABJREFUv3f2S3lzOdj/PHT2wbZfg1PBC3trybGnglIwOQGTccikjMQtGIZIC8RHIZuZnj7SAqlJM0+0FSIxsCywA9DaAUefhDrqOFh3LDg5OLwf/bVr6rtOQkUo4IuHLufC9bfPGXfJWC1d3IIgCIIgCIIgNIoADi9JPVp+QkGoEzYuHe44He54zfLMECwSqpnfiXnka4X0cvI1d7EvmRSEI4Cdg5pXnqAaHYYg1BR5yl9YDFuKhp+rcv7nmS1GOxH46aIiEpqLkmK01bOHreD804kYrfnw2mde0rJa4nXSo+t8s1qoDK/ONwEPmUujEDGa/6jkP93o/70lYjRfU2r7N1tHaRFd+YNSdYKXpGwp8ZKQSN3lL7RHu9lP5VA5oY2X6FaoL74To0k7p2nxknyWa9suNSJG8zelzr2qFqN5XB9cCmp57GgX9n4fnvo0jFT5IEvLOtjyETjmLyHQUruYhOWBpyxHxGgl8SqX5IEZYTkRLCHRzI4BR9UtFKFCvKSWixajlRDLhrogMzw3Xa4nCoIgCEcoWuudSqkbgStnJH9XKfVh4Kta6ynjgFJqC/B14KwZ0w4Bn1iC0LYCfwoULtR2Ag8qpT4GfFNrPTlzYqVUCPhj4HNAb1FeVy9BfILQFHz+85/nvvvuY3LS/GVGR0e56KKL+MlPfkJra2vF+QwMDPDWt76VTGZaQrJ69WquuaZ5pSInnXQSr3zlK/n5z38OwPj4OFdffTX/+q//uui8L7jgAo455hief944Ix9++GG++c1v8hd/8ReLzlsQBEEQaoXacjpqy+klp9Gui77yQvjNPfWJaeutqHMvKjuddhywLJQyHR11OgmhyNRwWVZvQP/TZUayttRYFrge10EjLRCOQs8qCEXg6d8ufTw+54LEXXx/79u5dPWXGAz00ZMb5LOHr+as5K8bHZogCIK/CUfhlDONSLRvjRGFatfUQeEoKhID24aWNpicQMfHjBg1lwXXQXWtABQ6k0K1dhi5qOtCIGjEo5mUkZamk6A1enQAsmlAmTpMKUjGYegQvPAM7HlmdnwdvRDOPy/V2QfdK8z9QKVMXYkyvwsflIk/k4JwC7R2QlcfKhSenk8p9NgQjI9AcgLiY0aMatkmFq1h7w7zu2cV7N9ptsEr3ggbN8PEKKCNcDWVNL9bWs16B8NmGU7OtINiHSaPzj4IhSGdgvgIdOfF845jpm3tgFgHqrXdbDet8y/U1fntNmiEruPDkMjLV4JhiLbA2LDZJx29qHXHmG3f0Wu2T/9ek38wZPZ1IGjSM2mTlsua9bZts31m/rZtGB00y06MTb/gd+rbNdsuFDHxR1vNsgYOwOB+GD4MG7fAinVm3Q/vN3nG2lGF9dfabDftguugJ0Zh6CC0dUIgZGS33SshYPaPsuzpGC3LxJlMQCQK7T3mWOnfi37+CXj2MXNcoc3+LXyUBYf3zj7OAkFYedS82x5lQXu32cfjI5BKmG2/Yq2JLxwxx1qkxaz/6ICJKdpqtkswbGJuaTX/o0zKbM+OXlRvvi/w2JCZPhCA7lVmmaGQOT7Ghsx+yqSm91t7t5m+EKPron/4Ddj+iDkWIjHIZcz/rqXNbMNQJL/dbBNfenK2NJgZ7eFC23hmG1lVOp2aPd1842a1vRc4XalllptuqfKteF2K56tDbEu9zvNNp5Q51iZGTPkVjppjMhw1//FC+ec607+nhnNFn/w0EyOmbA4ETfp8Lx4PhU0ZOD5slgfm/5mvh4jGTJk8ctjE1bsGYm1wcI/5nxWItZv/YvcK6Flt/p+TcfOfPLQHNmw2/8fEuPmvbzzRlAfBEBzeBz+fK22uiEJ9FgxPxz9zXCUvWw9FTJk2eNBsrwLRVujoMdsvGDLbpVBv9e81ZYvt0e+m1Dmz17iS59kLmGdB42ocd6XXDhaxnBDQrRRzXyszmf8MVpWf1pBSYSZ0lDgR4joy/ZsW4npmWpSEjpDNaXpSB7BzaYacGM+oDfxv5EVopYi5k7TqSRIqyoTVyqjdNWeZvbkBbFzGrHZiboIXZZ8kqDPY2iHgmm9bO9jKxVIwrDoYsToJkOP54DEcDK6eZ00EYWnZ5fHXEoRmxke9aYVmQinVDXPaIi9UmU3x9MctPCKhKUn1e48r7ujn1fFx580w9FDNQhLqwMSO+dPrIXjwWkZuEh79yNIvX6iOxJ75061FihhqjVdH7p03w/Bv6hqKkGf0ifLTNFrIUU4AMLlfyqVGkp1HdligHiLPWnLgTshNNDoKwfEQycDiBUMLwWuZB34snV/9xOiT86f7qRwqd/yOPCb1mR/waldXSvF+Xmy5Je2c5uXwL+ZPt30sRtt2A0RX1i8WYS5uiXOvRorRyj1QceBOyNXgrWfaNXmNP13dfK3Hwkl/Bxv/DOwSQhPhCMdDljP4a6lrS+F1zuMnAbEgLJZQCTHaU/8ILWvqF4tQGWPb509frLSx1EOMwbb5xWgD9y9tPfJ715sH1AVBEATBn/wdcBJwQX44CHwR+D9Kqf8FJoCjgRczu/dBBniz1vpgrQPSWu9TSl0C3AYUKtG2fFw3KKV+CxzAWLJXAacD81me/l5rfX+t4xOEZmHz5s188Ytf5D3vec9U2oMPPsgFF1zArbfeyrp168rmsWPHDt7ylrewfft0+92yLL71rW/R19e3JHHXi2uuuYbzzjtvavhLX/oSmzZt4oorrqho/rGxMcLhMJHI7Gu+gUCArVu3cskll0ylfeADH6Czs5OLLiove5nJPffcw9FHH83RRx9d1XyCIAiCUAuUZcENP4D/+U/0U3kxVTRm5AsFscP4MCQmTOfxsSHTsT0xAQP7zL3JQNBMGwgamUNHD+z43ewF2QHUp/4v6uzXVxaXPfs6mwpX94JO9YcXw9mvh0MvwM4nTcdxrc26ZFLoHb+D/c+bDvMdPSburhWozl7TOb8z/4m1mw7/yjK/hw9DZw+0dZltFGlBKYXO5YyELZMyHdaDIQiGUeG59431ru3w65+gD+9Dda9E73wKxgaNPCASM1KWaMzElkkZmYgdADtovlMJsx92PGYkJ37GDpjO/snZz62+Mf4jLtzxIw4E1rA6dxCL/D3uQNBIQQJB87ED5tgaHSgSgsygs89IS5Q1LWSolIJoINZhvgNBc413T5X3wAvMFCCEwmafFdJdxwgdCkRaoKXd7OOO7rxQKGr2aVsn9K01x2syAYMHpiUrrmOO533PLSwuQRCakze/D+vDX6hqlgVoR6qaThfqVdeFjh7TplgCFqA1aSiNjLeey67VsirJR7uukQ7lMhBtQwWa+xkY9eq3NzoEQagLOjUJgRAqEEAPHjRt0oJsMZkw51Iz6g6t9fS5lVJzzglnTsfAftN271pRuTy7VKwD++HALiN6jMbybfU206ZPjJtzkYK4UlkmXVlG5lnII5c1UriZYsvEGIwOmnPJjh6zDUIRc46TGIfWzqkyTWcz8Nzj0N4Fa46uyXoJzUcs/1ko2nWnxKAq2DcrfTKjsSwLSwHZDKGQBfQaAWe0BXT0/7N35+GWpXV96L/vqTo1dA1d1d3V88xMM2iDE6IQwAkjoBghqKHVaKIxN0aMEUwuiT5ouKKJCRi5EUGJKFeUSeMQEFAwDtgo0LTQDT0ATTc9VHfX0F3je/9Yp6rO2Wfa++xh7eHzeZ71VK111vDutd/zXfu8e+3fTtl05r2UenyhAOL8ljNF648eaQqj7rs0eehADt7xqfz+Xx3KnQc356qdhzM/dyIHj2/OgWOb88DR+Vy//+z87f492VxO5nidyyce3N3Ho4PGLV88kVXvMYcJNdl/4dCmPR3zh2uth3rcxxc75te4S5+ZV1b54OMdv9dMTL5RFHhY7cNt9Xhy42uGf3wGo40CMmtZrVDbF/6gmRhPa304fxTWKx5x9D65NK4m7YPS9/5lMzG+2ij4udq19L6/VtRzEqz2t1Eb1vuA9sHPuJ5Ng03b157v1bH79YtpMzfGhdFuffPo2kHvei6MNsICYff+RTtfxrDnicnjX5Fc/o8UDGF9q93Y88DHm4nejFMBYujXpm3NdfPkCh+Auu0to28PfRjiDUqrvRa7/2PdffnIRj35Z3KmpgsAjJda64lSynck+ZUkL1r0o/OTfOMqm30xyUuHWXSs1vr2Usrzk7whyeJvAdie5OnrbH4oyU/UWl87rPbBpPje7/3eXH/99Xnd6153etkHP/jBPP7xj88rXvGKfOd3fmcuu+yyZdvdfPPNedOb3pTXvOY1OXJk6RdivfrVr15SUGxSPetZz8rLXvay/PzP//zpZT/2Yz+WD3zgA3nlK1+ZpzzlKcu2OXnyZP7yL/8yv/Vbv5U3vvGN+ehHP5orr7xy2XoveclL8t73vje/+qu/miQ5evRoXvjCF+YlL3lJfvRHf3TFfSfJiRMn8tGPfjTvete78ta3vjU33nhj3ve+9ymMBkBryubNybd8b8q3fG9P29Xjx5JaU+ab9zpPfZD91P+bok4Hk/MuaaWARTlrV3L1Nc3U+bON7vTsc8/8f/OZ+5zK5s1NIbVu2nXV45KrHne6Df183L0eeehMMbDjx5MH701u+2TzgfyzdpwppnZq2jzfFLirtfmg/4H9TaGR7TubfTx8uPlQ9LGjpwvdZOv2pkDA7r3N/++7qzn4qUIBW7Y1xeCOH03uuDXZtr05zu5zUrZub/rC/Xcn93yhOfZFVyWHHkw5fjSX1toUBNi6vTnW9p0rFgA4XSRhoTjaqT63bL0TJ5p+d3iheMHe85vHfargwuGDTRu2bG8+3L1GEZ96YH9y+6eaPrzj7KYfbVk4d8eONudr83xy9OGUnWefbmdqTU4cX9bGWmuy/4vJ4QNNu87a1Vexg3rnbcnffaj5QrNjR5u2bdnWPCdbtyVbz0r2XZycd3FzDo481JznA/ubIg0njjUFGT7656m3f6p53stc817+pk1NoYcdu5PHf1my94Jm/VP97NS2Bx9oigRu2doUbjvyUHLk4abQxalibAf3JwcfTPbuS65+QtPegw8kB+5vttt9TvP8nDje9LmjDzdtmJtbaM/Cv4cPNNvVk822u/Y0heW2NEUIc86Fzf4euLcpZpE0+9u2PTlxMjl2pCleePJEctGVzT5Lac7JFz/X7P/Y0eYxPHy4KSZw+EBy1eObgoWb55u+cOhA8/NzL0h27mnO81xJjh1r9n3q92v/F1N/6780bT52pDne7nOaYiD7Lk45+9zUAwvFFE+cSDlrZ1Pk79wLlz7RRx9uzul5FzaP98F7m/mzFurGL/Sl3PXZpr0H7m+Kdmw768zzvlB4JOdf2pzbv/rjpiDj9e9vHtupfnj8WNM/6snmsaQkF13RFO3bu+9Mcb3tO5tz9eB9zbG2bGv2v21H87v38OFm+YkTzeM/+9zkysel7N2X+tmbkh27Uy5e+Nvn5ImmnQ/c0/zs7z6YPPFpTbvf89Yz56GU5rGee1Ezv3nzmd/HUzbPNwUFHz7cFCF56FBTdHLX3iZnjh5p+vauvc05S5rnfv8Xz+z//Eub/dz8sabgw1rmFvpQmTtz/k5lZq1N/6snm3Y8tMaXmq9UuHDnnqaPHTuanH9ZU1T0Rf9q7fa0oJSy9LoIQ1Lm5hayvZ9SMcColW1nnfn/eRct/eFZy78D59Rr4/X+diylNNfsASr7LmleR6zk1OuG9fZxqrjzKVu3NdM5K3wB9vyWZX8/lvktyeOe2m2TYUVlbm7FPlvm5rJj8a1U84s+H7F55WJlze/i0t/HsmVr8/dUkuzam12P2ZsXP6b79h07XvORzyYf+FTNHfcnO7Ym+3Yl9xxMDjyc3H0g2b092b0tufq8ZNt8ctt9yYdvrfnTTyXPflxy1Xklj74geeCh5Of/uOboieTYieTyc5LnPblk17ZmP3c92Gz/mAuTHVuS2+9L7j2UHD+RHDmeHD1ec/xksnVzsm2+ZPf2ZMvCLV/n7EjuPpjcek/NFeeWXLC72W7/4eTSvclr/6Tms/uTay5OHrkvOXdXySV7kpM1+fz+5PP7ax4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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import asyncio\n", + "from uuid import UUID\n", + "\n", + "from matplotlib import pyplot as plt\n", + "\n", + "from apd.aggregation.query import with_database\n", + "from apd.aggregation.analysis import get_known_configs, plot_sensor\n", + "\n", + "\n", + "location_names = {\n", + " UUID('53998a51-60de-48ae-b71a-5c37cd1455f2'): \"Loft\",\n", + " UUID('1bc63cda-e223-48bc-93c2-c1f651779d69'): \"Living Room\",\n", + " UUID('ea0683de-6772-4678-bfe7-6014f54ffc8e'): \"Office\",\n", + " UUID('5aaa901a-7564-41fb-8eba-50cdd6fe9f80'): \"Outside\",\n", + "}\n", + "\n", + "with with_database(\"postgresql+psycopg2://apd@localhost/apd\") as session:\n", + " coros = []\n", + " figure = plt.figure(figsize = (20, 10), dpi=300)\n", + " configs = get_known_configs().values()\n", + " for i, config in enumerate(configs, start=1):\n", + " plot = figure.add_subplot(2, 2, i)\n", + " coros.append(plot_sensor(config, plot, location_names))\n", + " await asyncio.gather(*coros)\n", + "\n", + "display(figure)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "9f576f5bb8e34cb8ab1d3c09f770d027", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "interactive(children=(DatePicker(value=None, description='Start date'), DatePicker(value=None, description='En…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import asyncio\n", + "from uuid import UUID\n", + "import functools\n", + "from concurrent.futures import ThreadPoolExecutor\n", + "\n", + "import ipywidgets as widgets\n", + "from matplotlib import pyplot as plt\n", + "\n", + "from apd.aggregation.query import with_database\n", + "from apd.aggregation.analysis import get_known_configs, plot_sensor\n", + "\n", + "\n", + "def wrap_coroutine(f):\n", + " @functools.wraps(f)\n", + " def run_in_thread(*args, **kwargs):\n", + " loop = asyncio.new_event_loop()\n", + " wrapped = f(*args, **kwargs)\n", + " with ThreadPoolExecutor(max_workers=1) as pool:\n", + " task = pool.submit(loop.run_until_complete, wrapped)\n", + " return task.result()\n", + " return run_in_thread\n", + "\n", + "@wrap_coroutine\n", + "async def plot(*args, **kwargs):\n", + " location_names = {\n", + " UUID('53998a51-60de-48ae-b71a-5c37cd1455f2'): \"Loft\",\n", + " UUID('1bc63cda-e223-48bc-93c2-c1f651779d69'): \"Living Room\",\n", + " UUID('ea0683de-6772-4678-bfe7-6014f54ffc8e'): \"Office\",\n", + " UUID('5aaa901a-7564-41fb-8eba-50cdd6fe9f80'): \"Outside\",\n", + " }\n", + "\n", + " with with_database(\"postgresql+psycopg2://apd@localhost/apd\") as session:\n", + " coros = []\n", + " figure = plt.figure(figsize = (20, 10), dpi=300)\n", + " configs = get_known_configs().values()\n", + " for i, config in enumerate(configs, start=1):\n", + " plot = figure.add_subplot(2, 2, i)\n", + " coros.append(plot_sensor(config, plot, location_names, *args, **kwargs))\n", + " await asyncio.gather(*coros)\n", + " return figure\n", + "\n", + "\n", + "start = widgets.DatePicker(\n", + " description='Start date',\n", + ")\n", + "end = widgets.DatePicker(\n", + " description='End date',\n", + ")\n", + "out = widgets.interactive(plot, collected_after=start, collected_before=end)\n", + "display(out)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "60091fa7d9ea415985ec53937e422b4a", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "interactive(children=(DatePicker(value=None, description='Start date'), DatePicker(value=None, description='En…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from uuid import UUID\n", + "import ipywidgets as widgets\n", + "from apd.aggregation.analysis import interactable_plot_multiple_charts, configs\n", + "\n", + "location_names = {\n", + " UUID('53998a51-60de-48ae-b71a-5c37cd1455f2'): \"Loft\",\n", + " UUID('1bc63cda-e223-48bc-93c2-c1f651779d69'): \"Living Room\",\n", + " UUID('ea0683de-6772-4678-bfe7-6014f54ffc8e'): \"Office\",\n", + " UUID('5aaa901a-7564-41fb-8eba-50cdd6fe9f80'): \"Outside\",\n", + "}\n", + "\n", + "start = widgets.DatePicker(\n", + " description='Start date',\n", + ")\n", + "end = widgets.DatePicker(\n", + " description='End date',\n", + ")\n", + "\n", + "plot = interactable_plot_multiple_charts(location_names=location_names)\n", + "out = widgets.interactive(plot, collected_after=start, collected_before=end)\n", + "display(out)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "08a4d937037b45bda2298879df7f80b6", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "interactive(children=(DatePicker(value=None, description='Start date'), DatePicker(value=None, description='En…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import datetime\n", + "import uuid\n", + "import ipywidgets as widgets\n", + "from apd.aggregation.analysis import Config, configs, interactable_plot_multiple_charts, draw_map, get_map_cleaner_for\n", + "from apd.aggregation.database import DataPoint\n", + "from apd.aggregation.utils import merc_x, merc_y\n", + "\n", + "start = widgets.DatePicker(description='Start date')\n", + "end = widgets.DatePicker(description='End date')\n", + "\n", + "def get_literal_data():\n", + " # Get manually entered temperature data, as our particular deployment\n", + " # does not contain data of this shape\n", + " raw_data = {(53.8667, -1.3333): -1, (53.35, -2.2833): 1, (52.45, -1.7333): 3, (51.5, -0.1333): 6, (51.55, -2.5667): 5, (54.9667, -1.6167): -1, (55.9667, -3.2167): -1, (54.7667, -1.5833): 0, (53.7667, -0.3): 1, (53.7667, -3.0167): 0, (51.4833, -3.1833): 4, (53.2667, -3.5167): 2, (52.0833, -2.8): 2, (52.0167, -0.6): 2, (52.6667, 1.2667): 5, (50.4333, -4.9833): 9, (50.35, -4.1167): 10, (50.5167, -2.45): 9, (50.8333, -1.1667): 9, (56.45, -5.4333): 4, (57.5333, -4.05): -2, (54.5167, -3.6167): 3, (53.0833, 0.2667): 4, (51.35, 1.3333): 7, (50.8833, 0.3167): 8, (58.45, -3.1): 3, (50.1, -5.6667): 9, (58.2167, -6.3333): 4, (55.6833, -6.25): 7}\n", + " now = datetime.datetime.now()\n", + " async def points():\n", + " for (coord, temp) in raw_data.items():\n", + " deployment_id = uuid.uuid4()\n", + " yield DataPoint(sensor_name=\"Location\", deployment_id=deployment_id, collected_at=now, data=coord)\n", + " yield DataPoint(sensor_name=\"Temperature\", deployment_id=deployment_id, collected_at=now, data=temp)\n", + " async def deployments(*args, **kwargs):\n", + " yield None, points()\n", + " return deployments\n", + "\n", + "def draw_map_with_gb(plot, x, y, colour):\n", + " # Draw the map and add an explicit coastline\n", + " gb_boundary = [(-2.6653539699999556, 51.617254950000074), (-2.6937556629999335, 51.59617747600004), (-2.71312415299991, 51.58661530200004), (-2.760487433999913, 51.579779364000046), (-2.7980850899999155, 51.56350332200009), (-2.816395636999914, 51.555568752000056), (-2.8587133449999556, 51.546128648000035), (-2.943348761999914, 51.54254791900007), (-2.9562882149999155, 51.54572174700007), (-2.9641007149999155, 51.552801825000074), (-2.969471808999913, 51.559881903000075), (-2.9746801419999542, 51.56297435100004), (-2.984038865999935, 51.55853913000004), (-3.0102432929999168, 51.53803131700005), (-3.0156957669999542, 51.53164297100005), (-3.0737198559999115, 51.50836823100008), (-3.0764867829999503, 51.50775788000004), (-3.088937954999949, 51.505072333000044), (-3.105620897999927, 51.49721914300005), (-3.1317032539999445, 51.48041413000004), (-3.1350805329999503, 51.47630442900004), (-3.1409399079999503, 51.46478913000004), (-3.1453751289999445, 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51.49408600500004), (-2.716420050999943, 51.49957916900007), (-2.6961563789999445, 51.51264069200005), (-2.6078181629999335, 51.60736725500004), (-2.599598761999914, 51.611395575000074), (-2.5892227859999366, 51.61424388200004), (-2.580433722999942, 51.61863841400009), (-2.5768123039999296, 51.62750885600008), (-2.57445227799991, 51.63572825700004), (-2.568959113999938, 51.644964911000045), (-2.562245245999918, 51.65338776200008), (-2.556385870999918, 51.65924713700008), (-2.499175584999932, 51.69676341400009), (-2.464995897999927, 51.725897528000075), (-2.4477432929999168, 51.73187897300005), (-2.404896613999938, 51.74115631700005), (-2.3885391919999392, 51.750433661000045), (-2.380767381999931, 51.76190827000005), (-2.3833715489999463, 51.77326080900008), (-2.398060675999943, 51.782131252000056), (-2.4024145169999542, 51.76357656500005), (-2.4203995429999168, 51.753119208000044), (-2.4434301419999542, 51.74843984600005), (-2.4632869129999335, 51.747300523000035), (-2.482085740999935, 51.74286530200004), (-2.495838995999918, 51.73200104400007), (-2.507964647999927, 51.71857330900008), (-2.5221248039999296, 51.706366278000075), (-2.581695115999935, 51.681708075000074), (-2.5911352199999556, 51.67536041900007), (-2.600209113999938, 51.66559479400007), (-2.6653539699999556, 51.617254950000074)]\n", + " draw_map(plot, x, y, colour)\n", + " plot.plot(\n", + " [merc_x(coord[0]) for coord in gb_boundary],\n", + " [merc_y(coord[1]) for coord in gb_boundary],\n", + " \"k-\",\n", + " )\n", + "\n", + "country = Config(\n", + " get_data=get_literal_data(),\n", + " clean=get_map_cleaner_for(\"Temperature\"),\n", + " title=\"Country wide temperature\",\n", + " ylabel=\"\",\n", + " draw=draw_map_with_gb,\n", + ")\n", + "\n", + "out = widgets.interactive(interactable_plot_multiple_charts(configs=configs + (country, )), collected_after=start, collected_before=end)\n", + "display(out)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "apd.aggregation", + "language": "python", + "name": "apd.aggregation" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Ch09/apd.aggregation-chapter09/LICENCE b/Ch09/apd.aggregation-chapter09/LICENCE new file mode 100644 index 0000000..86685d4 --- /dev/null +++ b/Ch09/apd.aggregation-chapter09/LICENCE @@ -0,0 +1,31 @@ +Copyright (c) 2019, Matthew Wilkes + + +All rights reserved. + +Redistribution and use in source and binary forms, with or without modification, +are permitted provided that the following conditions are met: + +* Redistributions of source code must retain the above copyright notice, this + list of conditions and the following disclaimer. + +* Redistributions in binary form must reproduce the above copyright notice, this + list of conditions and the following disclaimer in the documentation and/or + other materials provided with the distribution. + +* Neither the name of the copyright holder nor the names of its + contributors may be used to endorse or promote products derived from this + software without specific prior written permission. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND +ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED +WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. +IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, +INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, +BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, +DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY +OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE +OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED +OF THE POSSIBILITY OF SUCH DAMAGE. + + diff --git a/Ch09/apd.aggregation-chapter09/Mapping.ipynb b/Ch09/apd.aggregation-chapter09/Mapping.ipynb new file mode 100644 index 0000000..916e704 --- /dev/null +++ b/Ch09/apd.aggregation-chapter09/Mapping.ipynb @@ -0,0 +1,254 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "datapoints = {\n", + " (53.8667, -1.3333): -1,\n", + " (53.35, -2.2833): 1,\n", + " (52.45, -1.7333): 3,\n", + " (51.5, -0.1333): 6,\n", + " (51.55, -2.5667): 5,\n", + " (54.9667, -1.6167): -1,\n", + " (55.9667, -3.2167): -1,\n", + " (54.7667, -1.5833): 0,\n", + " (53.7667, -0.3): 1,\n", + " (53.7667, -3.0167): 0,\n", + " (51.4833, -3.1833): 4,\n", + " (53.2667, -3.5167): 2,\n", + " (52.0833, -2.8): 2,\n", + " (52.0167, -0.6): 2,\n", + " (52.6667, 1.2667): 5,\n", + " (50.4333, -4.9833): 9,\n", + " (50.35, -4.1167): 10,\n", + " (50.5167, -2.45): 9,\n", + " (50.8333, -1.1667): 9,\n", + " (56.45, -5.4333): 4,\n", + " (57.5333, -4.05): -2,\n", + " (54.5167, -3.6167): 3,\n", + " (53.0833, 0.2667): 4,\n", + " (51.35, 1.3333): 7,\n", + " (50.8833, 0.3167): 8,\n", + " (58.45, -3.1): 3,\n", + " (50.1, -5.6667): 9,\n", + " (58.2167, -6.3333): 4,\n", + " (55.6833, -6.25): 7,\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "fig, ax = plt.subplots()\n", + "lats = [ll[0] for ll in datapoints.keys()]\n", + "lons = [ll[1] for ll in datapoints.keys()]\n", + "ax.plot(lons, lats, \"o\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "fig, ax = plt.subplots()\n", + "lats = [ll[0] for ll in datapoints.keys()]\n", + "lons = [ll[1] for ll in datapoints.keys()]\n", + "# Don't draw lines\n", + "ax.plot(lons, lats, \"o\")\n", + "# This is a map of the UK. Here, 1 degree latitude is \n", + "# 1.7x as far as 1 degree longitude\n", + "ax.set_aspect(1.7)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# Set up mercator transform from https://wiki.openstreetmap.org/wiki/Mercator#Python\n", + "# Thanks to Paulo Silva and the OSM contributors\n", + "import math\n", + "\n", + "def merc_x(lon):\n", + " r_major=6378137.000\n", + " return r_major*math.radians(lon)\n", + "\n", + "def merc_y(lat):\n", + " if lat>89.5:lat=89.5\n", + " if lat<-89.5:lat=-89.5\n", + " r_major=6378137.000\n", + " r_minor=6356752.3142\n", + " temp=r_minor/r_major\n", + " eccent=math.sqrt(1-temp**2)\n", + " phi=math.radians(lat)\n", + " sinphi=math.sin(phi)\n", + " con=eccent*sinphi\n", + " com=eccent/2\n", + " con=((1.0-con)/(1.0+con))**com\n", + " ts=math.tan((math.pi/2-phi)/2)/con\n", + " y=0-r_major*math.log(ts)\n", + " return y\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots()\n", + "\n", + "x = tuple(map(merc_x, lons))\n", + "y = tuple(map(merc_y, lats))\n", + "ax.plot(x, y, 'o')\n", + "ax.set_aspect(1.0)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots()\n", + "\n", + "x = tuple(map(merc_x, lons))\n", + "y = tuple(map(merc_y, lats))\n", + "\n", + "ax.tricontourf(x, y, tuple(datapoints.values()))\n", + "ax.plot(x, y, 'wo', ms=3)\n", + "ax.set_aspect(1.0)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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--git a/Ch09/apd.aggregation-chapter09/pyproject.toml b/Ch09/apd.aggregation-chapter09/pyproject.toml new file mode 100644 index 0000000..4d3bb67 --- /dev/null +++ b/Ch09/apd.aggregation-chapter09/pyproject.toml @@ -0,0 +1,5 @@ +[build-system] +requires = [ + "setuptools", +] +build-backend = "setuptools.build_meta" diff --git a/Ch09/apd.aggregation-chapter09/pytest.ini b/Ch09/apd.aggregation-chapter09/pytest.ini new file mode 100644 index 0000000..c1d3e58 --- /dev/null +++ b/Ch09/apd.aggregation-chapter09/pytest.ini @@ -0,0 +1,3 @@ +[pytest] +markers = + functional: these tests are significantly slower as they start a HTTP server \ No newline at end of file diff --git a/Ch09/apd.aggregation-chapter09/setup.cfg b/Ch09/apd.aggregation-chapter09/setup.cfg new file mode 100644 index 0000000..73fc36a --- /dev/null +++ b/Ch09/apd.aggregation-chapter09/setup.cfg @@ -0,0 +1,77 @@ +[mypy] +namespace_packages = True +mypy_path = src +plugins = sqlmypy + +[mypy-alembic] +ignore_missing_imports = True + +[mypy-alembic.config] +ignore_missing_imports = True + +[mypy-alembic.script] +ignore_missing_imports = True + +[mypy-alembic.runtime.environment] +ignore_missing_imports = True + +[mypy-matplotlib] +ignore_missing_imports = True + +[mypy-matplotlib.axes._base] +ignore_missing_imports = True + +[mypy-matplotlib.figure] +ignore_missing_imports = True + +[mypy-matplotlib.pyplot] +ignore_missing_imports = True + + +[mypy-pint] +ignore_missing_imports = True + +[mypy-pytest] +ignore_missing_imports = True + +[flake8] +max-line-length = 120 +ignore = E501 + +[metadata] +name = apd.aggregation +version = attr: apd.aggregation.VERSION +description = A programme that queries apd.sensor endpoints and aggregates their results. +long_description = file: README.md, CHANGES.md, LICENCE +long_description_content_type = text/markdown +keywords = +license = BSD +classifiers = + Programming Language :: Python :: 3 + Programming Language :: Python :: 3.7 + +[options] +zip_safe = False +include_package_data = True +package_dir = + =src +packages = find_namespace: +install_requires = + sqlalchemy + aiohttp + pint + psycopg2 + alembic + click + + +[options.extras_require] +jupyter = ipywidgets + +[options.packages.find] +where = src + +[options.entry_points] +console_scripts = + collect_sensor_data = apd.aggregation.cli:collect_sensor_data + sensor_deployments = apd.aggregation.cli:deployments diff --git a/Ch09/apd.aggregation-chapter09/setup.py b/Ch09/apd.aggregation-chapter09/setup.py new file mode 100644 index 0000000..6068493 --- /dev/null +++ b/Ch09/apd.aggregation-chapter09/setup.py @@ -0,0 +1,3 @@ +from setuptools import setup + +setup() diff --git a/Ch09/apd.aggregation-chapter09/src/apd/aggregation/__init__.py b/Ch09/apd.aggregation-chapter09/src/apd/aggregation/__init__.py new file mode 100644 index 0000000..3277f64 --- /dev/null +++ b/Ch09/apd.aggregation-chapter09/src/apd/aggregation/__init__.py @@ -0,0 +1 @@ +VERSION = "1.0.0" diff --git a/Ch09/apd.aggregation-chapter09/src/apd/aggregation/alembic/README b/Ch09/apd.aggregation-chapter09/src/apd/aggregation/alembic/README new file mode 100644 index 0000000..97a87d5 --- /dev/null +++ b/Ch09/apd.aggregation-chapter09/src/apd/aggregation/alembic/README @@ -0,0 +1,12 @@ +Generic single-database configuration. + + +# Database setup + +To generate the required database tables you must create an alembic.ini file, as follows: + + [alembic] + script_location = apd.aggregation:alembic + sqlalchemy.url = postgresql+psycopg2://apd@localhost/apd + +and run `alembic upgrade head`. This should also be done after every upgrade of the software. \ No newline at end of file diff --git a/Ch09/apd.aggregation-chapter09/src/apd/aggregation/alembic/env.py b/Ch09/apd.aggregation-chapter09/src/apd/aggregation/alembic/env.py new file mode 100644 index 0000000..4c8a2c2 --- /dev/null +++ b/Ch09/apd.aggregation-chapter09/src/apd/aggregation/alembic/env.py @@ -0,0 +1,80 @@ +from logging.config import fileConfig + +from sqlalchemy import engine_from_config +from sqlalchemy import pool +from alembic import context + +from apd.aggregation.database import metadata + + +# this is the Alembic Config object, which provides +# access to the values within the .ini file in use. +config = context.config + +# Interpret the config file for Python logging. +# This line sets up loggers basically. +if config.config_file_name: + fileConfig(config.config_file_name) + +target_metadata = metadata + + +def include_object(object, name, type_, reflected, compare_to): + if object.info.get("is_view", False): + return False + return True + + +def run_migrations_offline(): + """Run migrations in 'offline' mode. + + This configures the context with just a URL + and not an Engine, though an Engine is acceptable + here as well. By skipping the Engine creation + we don't even need a DBAPI to be available. + + Calls to context.execute() here emit the given string to the + script output. + + """ + url = config.get_main_option("sqlalchemy.url") + context.configure( + url=url, + include_object=include_object, + target_metadata=target_metadata, + literal_binds=True, + dialect_opts={"paramstyle": "named"}, + ) + + with context.begin_transaction(): + context.run_migrations() + + +def run_migrations_online(): + """Run migrations in 'online' mode. + + In this scenario we need to create an Engine + and associate a connection with the context. + + """ + connectable = engine_from_config( + config.get_section(config.config_ini_section), + prefix="sqlalchemy.", + poolclass=pool.NullPool, + ) + + with connectable.connect() as connection: + context.configure( + connection=connection, + target_metadata=target_metadata, + include_object=include_object, + ) + + with context.begin_transaction(): + context.run_migrations() + + +if context.is_offline_mode(): + run_migrations_offline() +else: + run_migrations_online() diff --git a/Ch09/apd.aggregation-chapter09/src/apd/aggregation/alembic/script.py.mako b/Ch09/apd.aggregation-chapter09/src/apd/aggregation/alembic/script.py.mako new file mode 100644 index 0000000..2c01563 --- /dev/null +++ b/Ch09/apd.aggregation-chapter09/src/apd/aggregation/alembic/script.py.mako @@ -0,0 +1,24 @@ +"""${message} + +Revision ID: ${up_revision} +Revises: ${down_revision | comma,n} +Create Date: ${create_date} + +""" +from alembic import op +import sqlalchemy as sa +${imports if imports else ""} + +# revision identifiers, used by Alembic. +revision = ${repr(up_revision)} +down_revision = ${repr(down_revision)} +branch_labels = ${repr(branch_labels)} +depends_on = ${repr(depends_on)} + + +def upgrade(): + ${upgrades if upgrades else "pass"} + + +def downgrade(): + ${downgrades if downgrades else "pass"} diff --git a/Ch09/apd.aggregation-chapter09/src/apd/aggregation/alembic/versions/4b2df8a6e1ce_add_indexes_to_datapoints.py b/Ch09/apd.aggregation-chapter09/src/apd/aggregation/alembic/versions/4b2df8a6e1ce_add_indexes_to_datapoints.py new file mode 100644 index 0000000..ef671bd --- /dev/null +++ b/Ch09/apd.aggregation-chapter09/src/apd/aggregation/alembic/versions/4b2df8a6e1ce_add_indexes_to_datapoints.py @@ -0,0 +1,32 @@ +"""Add indexes to datapoints + +Revision ID: 4b2df8a6e1ce +Revises: 6d2eacd5da3f +Create Date: 2019-12-02 16:07:41.123116 + +""" +from alembic import op + + +# revision identifiers, used by Alembic. +revision = "4b2df8a6e1ce" +down_revision = "6d2eacd5da3f" +branch_labels = None +depends_on = None + + +def upgrade(): + op.create_index( + op.f("ix_datapoints_collected_at"), + "datapoints", + ["collected_at"], + unique=False, + ) + op.create_index( + op.f("ix_datapoints_sensor_name"), "datapoints", ["sensor_name"], unique=False, + ) + + +def downgrade(): + op.drop_index(op.f("ix_datapoints_sensor_name"), table_name="datapoints") + op.drop_index(op.f("ix_datapoints_collected_at"), table_name="datapoints") diff --git a/Ch09/apd.aggregation-chapter09/src/apd/aggregation/alembic/versions/6962f8455a6d_add_daily_summary_view.py b/Ch09/apd.aggregation-chapter09/src/apd/aggregation/alembic/versions/6962f8455a6d_add_daily_summary_view.py new file mode 100644 index 0000000..eb02455 --- /dev/null +++ b/Ch09/apd.aggregation-chapter09/src/apd/aggregation/alembic/versions/6962f8455a6d_add_daily_summary_view.py @@ -0,0 +1,38 @@ +"""Add daily summary view + +Revision ID: 6962f8455a6d +Revises: 4b2df8a6e1ce +Create Date: 2019-12-03 11:50:24.403402 + +""" +from alembic import op + + +# revision identifiers, used by Alembic. +revision = "6962f8455a6d" +down_revision = "4b2df8a6e1ce" +branch_labels = None +depends_on = None + + +def upgrade(): + create_view = """ + CREATE VIEW daily_summary AS + SELECT + datapoints.sensor_name AS sensor_name, + datapoints.data AS data, + count(datapoints.id) AS count + FROM datapoints + WHERE + datapoints.collected_at >= CAST(CURRENT_DATE AS DATE) + AND + datapoints.collected_at < CAST(CURRENT_DATE AS DATE) + 1 + GROUP BY + datapoints.sensor_name, + datapoints.data; + """ + op.execute(create_view) + + +def downgrade(): + op.execute("""DROP VIEW daily_summary""") diff --git a/Ch09/apd.aggregation-chapter09/src/apd/aggregation/alembic/versions/6d2eacd5da3f_create_sensor_values_table.py b/Ch09/apd.aggregation-chapter09/src/apd/aggregation/alembic/versions/6d2eacd5da3f_create_sensor_values_table.py new file mode 100644 index 0000000..b4a3545 --- /dev/null +++ b/Ch09/apd.aggregation-chapter09/src/apd/aggregation/alembic/versions/6d2eacd5da3f_create_sensor_values_table.py @@ -0,0 +1,31 @@ +"""Create datapoints table + +Revision ID: 6d2eacd5da3f +Revises: N/A +Create Date: 2019-09-29 13:43:21.242706 + +""" +from alembic import op +import sqlalchemy as sa +from sqlalchemy.dialects import postgresql + +# revision identifiers, used by Alembic. +revision = "6d2eacd5da3f" +down_revision = None +branch_labels = None +depends_on = None + + +def upgrade(): + op.create_table( + "datapoints", + sa.Column("id", sa.Integer(), nullable=False), + sa.Column("sensor_name", sa.String(), nullable=True), + sa.Column("collected_at", postgresql.TIMESTAMP(), nullable=True), + sa.Column("data", postgresql.JSONB(astext_type=sa.Text()), nullable=True), + sa.PrimaryKeyConstraint("id"), + ) + + +def downgrade(): + op.drop_table("datapoints") diff --git a/Ch09/apd.aggregation-chapter09/src/apd/aggregation/alembic/versions/d8cdc709086b_add_deployment_table.py b/Ch09/apd.aggregation-chapter09/src/apd/aggregation/alembic/versions/d8cdc709086b_add_deployment_table.py new file mode 100644 index 0000000..204e096 --- /dev/null +++ b/Ch09/apd.aggregation-chapter09/src/apd/aggregation/alembic/versions/d8cdc709086b_add_deployment_table.py @@ -0,0 +1,32 @@ +"""Add deployment table + +Revision ID: d8cdc709086b +Revises: d8d4cf6a178f +Create Date: 2019-12-17 16:28:58.585616 + +""" +from alembic import op +import sqlalchemy as sa +from sqlalchemy.dialects import postgresql + +# revision identifiers, used by Alembic. +revision = "d8cdc709086b" +down_revision = "d8d4cf6a178f" +branch_labels = None +depends_on = None + + +def upgrade(): + op.create_table( + "deployments", + sa.Column("id", postgresql.UUID(as_uuid=True), nullable=False), + sa.Column("uri", sa.String(), nullable=True), + sa.Column("name", sa.String(), nullable=True), + sa.Column("colour", sa.String(), nullable=True), + sa.Column("api_key", sa.String(), nullable=True), + sa.PrimaryKeyConstraint("id"), + ) + + +def downgrade(): + op.drop_table("deployments") diff --git a/Ch09/apd.aggregation-chapter09/src/apd/aggregation/alembic/versions/d8d4cf6a178f_add_deployment_id_to_datapoint.py b/Ch09/apd.aggregation-chapter09/src/apd/aggregation/alembic/versions/d8d4cf6a178f_add_deployment_id_to_datapoint.py new file mode 100644 index 0000000..88d5219 --- /dev/null +++ b/Ch09/apd.aggregation-chapter09/src/apd/aggregation/alembic/versions/d8d4cf6a178f_add_deployment_id_to_datapoint.py @@ -0,0 +1,34 @@ +"""Add deployment id to DataPoint + +Revision ID: d8d4cf6a178f +Revises: 6962f8455a6d +Create Date: 2019-12-03 20:58:11.285509 + +""" +from alembic import op +import sqlalchemy as sa +from sqlalchemy.dialects import postgresql + +# revision identifiers, used by Alembic. +revision = "d8d4cf6a178f" +down_revision = "6962f8455a6d" +branch_labels = None +depends_on = None + + +def upgrade(): + op.add_column( + "datapoints", + sa.Column("deployment_id", postgresql.UUID(as_uuid=True), nullable=True), + ) + op.create_index( + op.f("ix_datapoints_deployment_id"), + "datapoints", + ["deployment_id"], + unique=False, + ) + + +def downgrade(): + op.drop_index(op.f("ix_datapoints_deployment_id"), table_name="datapoints") + op.drop_column("datapoints", "deployment_id") diff --git a/Ch09/apd.aggregation-chapter09/src/apd/aggregation/analysis.py b/Ch09/apd.aggregation-chapter09/src/apd/aggregation/analysis.py new file mode 100644 index 0000000..60d352c --- /dev/null +++ b/Ch09/apd.aggregation-chapter09/src/apd/aggregation/analysis.py @@ -0,0 +1,381 @@ +from __future__ import annotations + +import asyncio +import collections +from concurrent.futures import ThreadPoolExecutor +import dataclasses +import datetime +import functools +import math +import typing as t +from uuid import UUID + +import matplotlib.pyplot as plt +from matplotlib.axes._base import _AxesBase +from matplotlib.figure import Figure +from pint import _DEFAULT_REGISTRY as ureg + +from apd.aggregation.query import ( + get_data, + get_data_by_deployment, + with_database, + get_deployment_by_id, +) +from apd.aggregation.database import DataPoint, deployment_table +from .utils import merc_x, merc_y + +plot_key = t.TypeVar("plot_key") +plot_value = t.TypeVar("plot_value") + +# Static UUID to represent aggregation of other deployments +GLOBAL = UUID("bd02526e-7619-4a59-b04b-1fafd1c262d1") + + +@dataclasses.dataclass +class Config(t.Generic[plot_key, plot_value]): + title: str + clean: t.Callable[ + [t.AsyncIterator[DataPoint]], t.AsyncIterator[t.Tuple[plot_key, plot_value]] + ] + draw: t.Optional[ + t.Callable[ + [t.Any, t.Iterable[plot_key], t.Iterable[plot_value], t.Optional[str]], None + ] + ] = None + get_data: t.Optional[ + t.Callable[..., t.AsyncIterator[t.Tuple[UUID, t.AsyncIterator[DataPoint]]]] + ] = None + ylabel: t.Optional[str] = None + sensor_name: dataclasses.InitVar[str] = None + + def __post_init__(self, sensor_name: t.Optional[str] = None) -> None: + if self.draw is None: + self.draw = draw_date # type: ignore + if self.get_data is None: + if sensor_name is None: + raise ValueError("You must specify either get_data or sensor_name") + self.get_data = get_one_sensor_by_deployment(sensor_name) + + +def draw_date( + plot: _AxesBase, + x: t.Iterable[datetime.datetime], + y: t.Iterable[float], + colour: t.Optional[str], +) -> None: + plot.plot_date(x, y, color=colour, linestyle="-", marker="", xdate=True) + + +def draw_map( + plot: _AxesBase, + x: t.Iterable[t.Tuple[float, float]], + y: t.Iterable[float], + colour: t.Optional[str], +) -> None: + lon = [merc_y(coord[0]) for coord in x] + lat = [merc_x(coord[1]) for coord in x] + + for axis in "x", "y": + plt.tick_params( + axis=axis, + which="both", + bottom=False, + top=False, + left=False, + right=False, + labelbottom=False, + labelleft=False, + ) + + plot.tricontourf(lat, lon, y) + plot.plot(lat, lon, "wo", ms=3) + plot.set_aspect(1.0) + + +def get_map_cleaner_for( + sensor_name: str, +) -> t.Callable[ + [t.AsyncIterator[DataPoint]], t.AsyncIterator[t.Tuple[t.Tuple[float, float], float]] +]: + """Given a sensor_name that represents a float, return a coroutine that acts as a cleaner + extracting that sensor's data keyed by the value of a Location sensor.""" + + async def clean_latest_coord_and_value( + datapoints: t.AsyncIterator[DataPoint], + ) -> t.AsyncIterator[t.Tuple[t.Tuple[float, float], float]]: + + # We will be building a dictionary of UUID to dictionary + # That inner dictionary should contain coord (float, float) + # and value (float) only. Either or both can be None. + class IntermediateData(t.TypedDict): + coord: t.Optional[t.Tuple[float, float]] + value: t.Optional[float] + + # We will iterate over data points and build an entry in cleaned_data + # for each deployment. This lets newer data replace older data, as + # datapoints is assumed to be in date order + cleaned_data: t.Dict[UUID, IntermediateData] = {} + async for datapoint in datapoints: + # Get the existing data for this deployment, if we've seen it before + row_data = cleaned_data.get(datapoint.deployment_id, None) + if row_data is None: + # This is the first time we've seen this deployment + row_data = {"coord": None, "value": None} + if datapoint.sensor_name == "Location": + # Coord is a 2-tuple of floats + row_data["coord"] = ( + float(datapoint.data[0]), + float(datapoint.data[1]), + ) + elif datapoint.sensor_name == sensor_name: + # Value is a single float + row_data["value"] = float(datapoint.data) + # Store the info about this deployment back into the cleaned_data set + cleaned_data[datapoint.deployment_id] = row_data + + for data in cleaned_data.values(): + if data["coord"] is None or data["value"] is None: + # We only got a partial record, don't plot this + continue + yield data["coord"], data["value"] + + # Return the set up cleaner coroutine + return clean_latest_coord_and_value + + +async def clean_watthours_to_watts( + datapoints: t.AsyncIterator[DataPoint], +) -> t.AsyncIterator[t.Tuple[datetime.datetime, float]]: + last_watthours = None + last_time = None + async for datapoint in datapoints: + if datapoint.data is None: + continue + time = datapoint.collected_at + watthours = ureg.Quantity(datapoint.data["magnitude"], datapoint.data["unit"]) + if last_watthours: + seconds_elapsed = (time - last_time).total_seconds() + time_elapsed = ureg.Quantity(seconds_elapsed, ureg.second) + additional_power = watthours - last_watthours + power = additional_power / time_elapsed + yield time, power.to(ureg.watt).magnitude + last_watthours = watthours + last_time = datapoint.collected_at + + +async def clean_magnitude( + datapoints: t.AsyncIterator[DataPoint], +) -> t.AsyncIterator[t.Tuple[datetime.datetime, float]]: + async for datapoint in datapoints: + if datapoint.data is None: + continue + yield datapoint.collected_at, datapoint.data["magnitude"] + + +async def clean_temperature_fluctuations( + datapoints: t.AsyncIterator[DataPoint], +) -> t.AsyncIterator[t.Tuple[datetime.datetime, float]]: + allowed_jitter = 2.5 + allowed_range = (-40, 80) + window_datapoints: t.Deque[DataPoint] = collections.deque(maxlen=3) + + def datapoint_ok(datapoint: DataPoint) -> bool: + """Return False if this data point does not contain a valid temperature""" + if datapoint.data is None: + return False + elif datapoint.data["unit"] != "degC": + # This point is in a different temperature system. While it could be converted + # this cleaner is not yet doing that. + return False + elif not allowed_range[0] < datapoint.data["magnitude"] < allowed_range[1]: + return False + return True + + async for datapoint in datapoints: + if not datapoint_ok(datapoint): + # If the datapoint is invalid then skip directly to the next item + continue + + window_datapoints.append(datapoint) + if len(window_datapoints) == 3: + # Find the temperatures of the datapoints in the window, then average + # the first and last and compare that to the middle point. + window_temperatures = [dp.data["magnitude"] for dp in window_datapoints] + avr_first_last = (window_temperatures[0] + window_temperatures[2]) / 2 + diff_middle_avr = abs(window_temperatures[1] - avr_first_last) + if diff_middle_avr > allowed_jitter: + pass + else: + yield window_datapoints[1].collected_at, window_temperatures[1] + elif len(window_datapoints) == 1: + # The item in the iterator can't be compared to both neighbours + # so should be yielded + yield datapoint.collected_at, datapoint.data["magnitude"] + else: + # Otherwise, let the window fill up, it will be yieleded later + pass + # When the iterator ends the final item is not yet in the middle + # of the window, so the last item must be explicitly yielded. + if len(window_datapoints) > 1 and datapoint_ok(datapoint): + yield datapoint.collected_at, datapoint.data["magnitude"] + + +async def clean_passthrough( + datapoints: t.AsyncIterator[DataPoint], +) -> t.AsyncIterator[t.Tuple[datetime.datetime, float]]: + async for datapoint in datapoints: + if datapoint.data is None: + continue + else: + yield datapoint.collected_at, datapoint.data + + +def get_one_sensor_by_deployment( + sensor_name: str, +) -> t.Callable[..., t.AsyncIterator[t.Tuple[UUID, t.AsyncIterator[DataPoint]]]]: + return functools.partial(get_data_by_deployment, sensor_name=sensor_name) + + +def get_all_data() -> t.Callable[ + ..., t.AsyncIterator[t.Tuple[UUID, t.AsyncIterator[DataPoint]]] +]: + async def get_all_data_inner( + *args: t.Any, **kwargs: t.Any + ) -> t.AsyncIterator[t.Tuple[UUID, t.AsyncIterator[DataPoint]]]: + yield GLOBAL, get_data(*args, **kwargs) + + return get_all_data_inner + + +configs = ( + Config( + sensor_name="SolarCumulativeOutput", + clean=clean_watthours_to_watts, + title="Solar generation", + ylabel="Watts", + ), + Config( + sensor_name="RAMAvailable", + clean=clean_passthrough, + title="RAM available", + ylabel="Bytes", + ), + Config( + sensor_name="RelativeHumidity", + clean=clean_passthrough, + title="Relative humidity", + ylabel="Percent", + ), + Config( + sensor_name="Temperature", + clean=clean_temperature_fluctuations, + title="Ambient temperature", + ylabel="Degrees C", + ), +) + + +def get_known_configs() -> t.Dict[str, Config[t.Any, t.Any]]: + return {config.title: config for config in configs} + + +async def plot_sensor( + config: Config[t.Any, t.Any], + plot: _AxesBase, + location_names: t.Dict[UUID, str], + **kwargs: t.Any +) -> _AxesBase: + locations = [] + if config.get_data is None: + raise ValueError("You must provide a get_data function") + async for deployment_id, query_results in config.get_data(**kwargs): + if deployment_id == GLOBAL: + name = "Global" + else: + try: + deployment = await get_deployment_by_id(deployment_id) + except IndexError: + name = str(deployment_id) + colour = None + else: + name = deployment.name or str(deployment_id) + colour = deployment.colour + # Mypy currently doesn't understand callable fields on datatypes: https://github.com/python/mypy/issues/5485 + points = [dp async for dp in config.clean(query_results)] # type: ignore + if not points: + continue + locations.append(name) + x, y = zip(*points) + plot.set_title(config.title) + plot.set_ylabel(config.ylabel) + if config.draw is None: + raise ValueError("You must provide a get_data function") + config.draw(plot, x, y, colour) + plot.legend(locations) + return plot + + +_Coroutine_Result = t.TypeVar("_Coroutine_Result") + + +def wrap_coroutine( + f: t.Callable[..., t.Coroutine[t.Any, t.Any, _Coroutine_Result]] +) -> t.Callable[..., _Coroutine_Result]: + """Given a coroutine, return a function that runs that coroutine + in a new event loop in an isolated thread""" + + @functools.wraps(f) + def run_in_thread(*args: t.Any, **kwargs: t.Any) -> _Coroutine_Result: + loop = asyncio.new_event_loop() + wrapped = f(*args, **kwargs) + with ThreadPoolExecutor(max_workers=1) as pool: + task = pool.submit(loop.run_until_complete, wrapped) + # Mypy can get confused when nesting generic functions, like we do here + # The fact that Task is generic means we lose the association with + # _CoroutineResult. Adding an explicit cast restores this. + return t.cast(_Coroutine_Result, task.result()) + + return run_in_thread + + +async def plot_multiple_charts(*args: t.Any, **kwargs: t.Any) -> Figure: + # These parameters are pulled from kwargs to avoid confusing function + # introspection code in IPython widgets + location_names = kwargs.pop("location_names", None) + configs = kwargs.pop("configs", None) + dimensions = kwargs.pop("dimensions", None) + + with with_database("postgresql+psycopg2://apd@localhost/apd") as session: + loop = asyncio.get_running_loop() + coros = [] + if configs is None: + # If no configs are supplied, use all known configs + configs = get_known_configs().values() + if dimensions is None: + # If no dimensions are supplied, get the square root of the number + # of configs and round it to find a number of columns. This will + # keep the arrangement approximately square. Find rows by multiplying + # out rows. + total_configs = len(configs) + columns = round(math.sqrt(total_configs)) + rows = math.ceil(total_configs / columns) + if location_names is None: + location_query = session.query( + deployment_table.c.id, deployment_table.c.name + ) + location_data = await loop.run_in_executor(None, location_query.all) + location_names = dict(location_data) + + figure = plt.figure(figsize=(10 * columns, 5 * rows), dpi=300) + for i, config in enumerate(configs, start=1): + plot = figure.add_subplot(columns, rows, i) + coros.append(plot_sensor(config, plot, location_names, *args, **kwargs)) + await asyncio.gather(*coros) + return figure + + +def interactable_plot_multiple_charts( + *args: t.Any, **kwargs: t.Any +) -> t.Callable[..., Figure]: + with_config = functools.partial(plot_multiple_charts, *args, **kwargs) + return wrap_coroutine(with_config) diff --git a/Ch09/apd.aggregation-chapter09/src/apd/aggregation/cli.py b/Ch09/apd.aggregation-chapter09/src/apd/aggregation/cli.py new file mode 100644 index 0000000..6e98436 --- /dev/null +++ b/Ch09/apd.aggregation-chapter09/src/apd/aggregation/cli.py @@ -0,0 +1,184 @@ +import asyncio +import typing as t +import uuid + +import aiohttp +import click +from sqlalchemy import create_engine +from sqlalchemy.orm import sessionmaker + +from . import collect +from .database import Deployment, deployment_table + + +@click.command() +@click.argument("server", nargs=-1) +@click.option( + "--db", + metavar="", + default="postgresql+psycopg2://localhost/apd", + help="The connection string to a PostgreSQL database", + envvar="APD_DB_URI", +) +@click.option("--api-key", metavar="", envvar="APD_API_KEY") +@click.option( + "--tolerate-failures", + "-f", + help="If provided, failure to retrieve some sensors' data will not abort the collection process", + is_flag=True, +) +@click.option("-v", "--verbose", is_flag=True, help="Enables verbose mode") +def collect_sensor_data( + db: str, server: t.Tuple[str], api_key: str, tolerate_failures: bool, verbose: bool +): + """This loads data from one or more sensors into the specified database. + + Only PostgreSQL databases are supported, as the column definitions use + multiple pg specific features. The database must already exist and be + populated with the required tables. + + The --api-key option is used to specify the access token for the sensors + being queried. + + You may specify any number of servers, the variable should be the full URL + to the sensor's HTTP interface, not including the /v/2.0 portion. Multiple + URLs should be separated with a space. + """ + if tolerate_failures: + attempts = [(s,) for s in server] + else: + attempts = [server] + success = True + for attempt in attempts: + try: + collect.standalone(db, attempt, api_key, echo=verbose) + except ValueError as e: + click.secho(str(e), err=True, fg="red") + success = False + return success + + +@click.group() +def deployments(): + pass + + +@deployments.command() +@click.argument("uri") +@click.argument("name") +@click.option( + "--db", + metavar="", + default="postgresql+psycopg2://localhost/apd", + help="The connection string to a PostgreSQL database", + envvar="APD_DB_URI", +) +@click.option("--api-key", metavar="", envvar="APD_API_KEY") +@click.option("--colour") +def add( + db: str, uri: str, name: str, api_key: t.Optional[str], colour: t.Optional[str], +): + """This creates a record of a new deployment in the database. + """ + deployment = Deployment(id=None, uri=uri, name=name, api_key=api_key, colour=colour) + + async def http_get_deployment_id(): + async with aiohttp.ClientSession() as http: + collect.http_session_var.set(http) + return await collect.get_deployment_id(uri) + + deployment.id = asyncio.run(http_get_deployment_id()) + insert = deployment_table.insert().values(**deployment._asdict()) + + engine = create_engine(db) + sm = sessionmaker(engine) + Session = sm() + Session.execute(insert) + Session.commit() + return True + + +@deployments.command() +@click.option( + "--db", + metavar="", + default="postgresql+psycopg2://localhost/apd", + help="The connection string to a PostgreSQL database", + envvar="APD_DB_URI", +) +def list(db: str): + """This creates a record of a new deployment in the database. + """ + engine = create_engine(db) + sm = sessionmaker(engine) + Session = sm() + deployments = Session.query(deployment_table).all() + for deployment in deployments: + click.secho(deployment.name, bold=True) + click.echo(click.style("ID ", bold=True) + deployment.id.hex) + click.echo(click.style("URI ", bold=True) + deployment.uri) + click.echo(click.style("API key ", bold=True) + deployment.api_key) + click.echo(click.style("Colour ", bold=True) + str(deployment.colour)) + click.echo() + Session.rollback() + return True + + +@deployments.command() +@click.argument("id") +@click.option("--uri") +@click.option("--name") +@click.option( + "--db", + metavar="", + default="postgresql+psycopg2://localhost/apd", + help="The connection string to a PostgreSQL database", + envvar="APD_DB_URI", +) +@click.option("--api-key", metavar="", envvar="APD_API_KEY") +@click.option("--colour") +def edit( + db: str, + id, + uri: t.Optional[str], + name: t.Optional[str], + api_key: t.Optional[str], + colour: t.Optional[str], +): + """This creates a record of a new deployment in the database. + """ + update = {} + if uri is not None: + update["uri"] = uri + if name is not None: + update["name"] = name + if api_key is not None: + update["api_key"] = api_key + if colour is not None: + update["colour"] = colour + deployment_id = uuid.UUID(id) + + update = ( + deployment_table.update() + .where(deployment_table.c.id == deployment_id) + .values(**update) + ) + + engine = create_engine(db) + sm = sessionmaker(engine) + Session = sm() + Session.execute(update) + deployments = Session.query(deployment_table).filter( + deployment_table.c.id == deployment_id + ) + Session.commit() + + for deployment in deployments: + click.secho(deployment.name, bold=True) + click.echo(click.style("ID ", bold=True) + deployment.id.hex) + click.echo(click.style("URI ", bold=True) + deployment.uri) + click.echo(click.style("API key ", bold=True) + deployment.api_key) + click.echo(click.style("Colour ", bold=True) + str(deployment.colour)) + click.echo() + + return True diff --git a/Ch09/apd.aggregation-chapter09/src/apd/aggregation/collect.py b/Ch09/apd.aggregation-chapter09/src/apd/aggregation/collect.py new file mode 100644 index 0000000..1d9ea08 --- /dev/null +++ b/Ch09/apd.aggregation-chapter09/src/apd/aggregation/collect.py @@ -0,0 +1,112 @@ +import asyncio +from contextvars import ContextVar +import datetime +import typing as t +import uuid + +import aiohttp +from sqlalchemy import create_engine +from sqlalchemy.orm import sessionmaker +from sqlalchemy.orm.session import Session + +from .database import DataPoint, datapoint_table, Deployment, deployment_table + +http_session_var: ContextVar[aiohttp.ClientSession] = ContextVar("http_session") + + +async def get_deployment_id(server): + http = http_session_var.get() + if not server.endswith("/"): + server += "/" + url = server + "v/2.1/deployment_id" + async with http.get(url) as request: + if request.status != 200: + raise ValueError(f"Error loading deployment id from {server}") + result = await request.json() + return uuid.UUID(result["deployment_id"]) + + +async def get_data_points(server: str, api_key: t.Optional[str],) -> t.List[DataPoint]: + if not server.endswith("/"): + server += "/" + url = server + "v/2.1/sensors/" + headers = {} + if api_key: + headers["X-API-KEY"] = api_key + http = http_session_var.get() + + # Get the deployment ID in parallel to the sensor data + deployment_id = asyncio.create_task(get_deployment_id(server)) + + async with http.get(url, headers=headers) as request: + result = await request.json() + ok = request.status == 200 + now = datetime.datetime.now() + if ok: + points = [] + for value in result["sensors"]: + points.append( + DataPoint( + sensor_name=value["id"], + collected_at=now, + data=value["value"], + deployment_id=await deployment_id, + ) + ) + return points + else: + raise ValueError( + f"Error loading data from {server}: " + result.get("error", "Unknown") + ) + + +def handle_result(result: t.List[DataPoint], session: Session) -> t.List[DataPoint]: + for point in result: + insert = datapoint_table.insert().values(**point._asdict()) + sql_result = session.execute(insert) + point.id = sql_result.inserted_primary_key[0] + return result + + +async def add_data_from_sensors( + session: Session, servers: t.Iterable[Deployment] +) -> t.List[DataPoint]: + tasks: t.List[t.Awaitable[t.List[DataPoint]]] = [] + points: t.List[DataPoint] = [] + async with aiohttp.ClientSession() as http: + http_session_var.set(http) + tasks = [get_data_points(server.uri, server.api_key) for server in servers] + for results in await asyncio.gather(*tasks): + points += results + loop = asyncio.get_running_loop() + await loop.run_in_executor(None, handle_result, points, session) + return points + + +def standalone( + db_uri: str, servers: t.Tuple[str], api_key: t.Optional[str], echo: bool = False +) -> None: + engine = create_engine(db_uri, echo=echo) + sm = sessionmaker(engine) + Session = sm() + deployments: t.Iterable[Deployment] + if servers: + deployments = tuple( + Deployment(id=None, name=None, colour=None, api_key=api_key, uri=server) + for server in servers + ) + else: + deployment_data = Session.query(deployment_table).all() + deployments = tuple( + Deployment.from_sql_result(deployment) for deployment in deployment_data + ) + asyncio.run(add_data_from_sensors(Session, deployments)) + Session.commit() + + +if __name__ == "__main__": + standalone( + db_uri="postgresql+psycopg2://apd@localhost/apd", + servers=("http://pvoutput:8080/",), + api_key="h3hdfjksfhwkjehnwekj", + ) diff --git a/Ch09/apd.aggregation-chapter09/src/apd/aggregation/database.py b/Ch09/apd.aggregation-chapter09/src/apd/aggregation/database.py new file mode 100644 index 0000000..f28b469 --- /dev/null +++ b/Ch09/apd.aggregation-chapter09/src/apd/aggregation/database.py @@ -0,0 +1,129 @@ +from __future__ import annotations + +from dataclasses import dataclass, field, asdict +import datetime +import typing as t +import uuid + +import sqlalchemy +from sqlalchemy.dialects.postgresql import JSONB, DATE, TIMESTAMP, UUID +from sqlalchemy.ext.hybrid import ExprComparator, hybrid_property +from sqlalchemy.orm import sessionmaker +from sqlalchemy.schema import Table + + +metadata = sqlalchemy.MetaData() + +deployment_table = Table( + "deployments", + metadata, + sqlalchemy.Column("id", UUID(as_uuid=True), primary_key=True), + sqlalchemy.Column("uri", sqlalchemy.String, index=False), + sqlalchemy.Column("name", sqlalchemy.String, index=False, nullable=True), + sqlalchemy.Column("colour", sqlalchemy.String, index=False, nullable=True), + sqlalchemy.Column("api_key", sqlalchemy.String, index=False), +) + +datapoint_table = Table( + "datapoints", + metadata, + sqlalchemy.Column("id", sqlalchemy.Integer, primary_key=True), + sqlalchemy.Column("sensor_name", sqlalchemy.String, index=True), + sqlalchemy.Column("collected_at", TIMESTAMP, index=True), + sqlalchemy.Column("deployment_id", UUID(as_uuid=True), index=True), + sqlalchemy.Column("data", JSONB), +) + +daily_summary_view = Table( + "daily_summary", + metadata, + sqlalchemy.Column("sensor_name", sqlalchemy.String), + sqlalchemy.Column("data", JSONB), + sqlalchemy.Column("count", sqlalchemy.Integer), + info={"is_view": True}, +) + + +class DateEqualComparator(ExprComparator): + def __init__(self, fallback_expression, raw_expression): + # Do not try and find update expression from parent + super().__init__(None, fallback_expression, None) + self.raw_expression = raw_expression + + def __eq__(self, other): + """ Returns True iff on the same day as other """ + other_date = sqlalchemy.cast(other, DATE) + return sqlalchemy.and_( + self.raw_expression >= other_date, self.raw_expression < other_date + 1, + ) + + def operate(self, op, *other, **kwargs): + other = [sqlalchemy.cast(date, DATE) for date in other] + return op(self.expression, *other, **kwargs) + + def reverse_operate(self, op, other, **kwargs): + other = [sqlalchemy.cast(date, DATE) for date in other] + return op(other, self.expression, **kwargs) + + +@dataclass +class DataPoint: + sensor_name: str + data: t.Any + deployment_id: uuid.UUID + id: t.Optional[int] = None + collected_at: datetime.datetime = field(default_factory=datetime.datetime.now) + + @classmethod + def from_sql_result(cls, result) -> DataPoint: + return cls(**result._asdict()) + + def _asdict(self) -> t.Dict[str, t.Any]: + data = asdict(self) + if data["id"] is None: + del data["id"] + return data + + @hybrid_property + def collected_on_date(self): + return self.collected_at.date() + + @collected_on_date.comparator # type: ignore + def collected_on_date(cls): + return DateEqualComparator( + fallback_expression=sqlalchemy.cast(datapoint_table.c.collected_at, DATE), + raw_expression=datapoint_table.c.collected_at, + ) + + +@dataclass +class Deployment: + id: t.Optional[uuid.UUID] + uri: str + name: t.Optional[str] + colour: t.Optional[str] + api_key: t.Optional[str] + + @classmethod + def from_sql_result(cls, result) -> Deployment: + return cls(**result._asdict()) + + def _asdict(self) -> t.Dict[str, t.Any]: + data = asdict(self) + return data + + +def main() -> None: + engine = sqlalchemy.create_engine( + "postgresql+psycopg2://apd@localhost/apd", echo=True + ) + sm = sessionmaker(engine) + Session = sm() + if False: + metadata.create_all(engine) + print(Session.query(DataPoint).all()) + pass + + +if __name__ == "__main__": + main() diff --git a/Ch09/apd.aggregation-chapter09/src/apd/aggregation/query.py b/Ch09/apd.aggregation-chapter09/src/apd/aggregation/query.py new file mode 100644 index 0000000..f3a8c95 --- /dev/null +++ b/Ch09/apd.aggregation-chapter09/src/apd/aggregation/query.py @@ -0,0 +1,148 @@ +import asyncio +import contextlib +from contextvars import ContextVar +import datetime +import typing as t +from uuid import UUID + +from sqlalchemy import create_engine +from sqlalchemy.orm import sessionmaker +from sqlalchemy.orm.session import Session + + +from apd.aggregation.database import ( + datapoint_table, + DataPoint, + deployment_table, + Deployment, +) + + +db_session_var: ContextVar[Session] = ContextVar("db_session") + + +@contextlib.contextmanager +def with_database(uri: t.Optional[str] = None) -> t.Iterator[Session]: + """Given a URI, set up a DB connection, and return a Session as a context manager """ + if uri is None: + uri = "postgresql+psycopg2://localhost/apd" + engine = create_engine(uri) + sm = sessionmaker(engine) + Session = sm() + token = db_session_var.set(Session) + try: + yield Session + Session.commit() + finally: + db_session_var.reset(token) + Session.close() + + +async def get_data( + sensor_name: t.Optional[str] = None, + deployment_id: t.Optional[UUID] = None, + collected_before: t.Optional[datetime.datetime] = None, + collected_after: t.Optional[datetime.datetime] = None, +) -> t.AsyncIterator[DataPoint]: + db_session = db_session_var.get() + loop = asyncio.get_running_loop() + query = db_session.query(datapoint_table) + if sensor_name: + query = query.filter(datapoint_table.c.sensor_name == sensor_name) + if deployment_id: + query = query.filter(datapoint_table.c.deployment_id == deployment_id) + if collected_before: + query = query.filter(datapoint_table.c.collected_at < collected_before) + if collected_after: + query = query.filter(datapoint_table.c.collected_at > collected_after) + query = query.order_by( + datapoint_table.c.deployment_id, + datapoint_table.c.sensor_name, + datapoint_table.c.collected_at, + ) + + rows = await loop.run_in_executor(None, query.all) + for row in rows: + yield DataPoint.from_sql_result(row) + + +async def get_deployment_ids() -> t.List[UUID]: + db_session = db_session_var.get() + loop = asyncio.get_running_loop() + query = db_session.query(datapoint_table.c.deployment_id).distinct() + return [row.deployment_id for row in await loop.run_in_executor(None, query.all)] + + +async def get_deployment_by_id(deployment_id: UUID) -> Deployment: + db_session = db_session_var.get() + loop = asyncio.get_running_loop() + query = db_session.query(deployment_table).filter( + deployment_table.c.id == deployment_id + ) + return [ + Deployment.from_sql_result(row) + for row in await loop.run_in_executor(None, query.all) + ][0] + + +async def get_data_by_deployment( + *args: t.Any, **kwargs: t.Any +) -> t.AsyncIterator[t.Tuple[UUID, t.AsyncIterator[DataPoint]]]: + """Return an Async Iterator that contains two-item pairs. + These pairs are a string (deployment_id), and an async iterator that contains + the datapoints with that deployment_id. + + Usage example: + + async for deployment_id, datapoints in get_data_by_deployment(): + print(deployment_id) + async for datapoint in datapoints: + print(datapoint) + print() + """ + # Get the data, using the arguments to this function as filters + data = get_data(*args, **kwargs) + + # The two levels of iterator share the item variable, initialise it with the + # first item from the iterator. Also set last_deployment_id to None, so the + # outer iterator knows to start a new group. + last_deployment_id: t.Optional[UUID] = None + try: + item = await data.__anext__() + except StopAsyncIteration: + # There were no items in the underlying query, return immediately + return + + async def subiterator(group_id: UUID) -> t.AsyncIterator[DataPoint]: + """Using a closure, create an iterator that yields the current + item, then yields all items from data while the deployment_id matches + group_id, leaving the first that doesn't match as item in the enclosing + scope.""" + # item is from the enclosing scope + nonlocal item + while item.deployment_id == group_id: + # yield items from data while they match the group_id this iterator represents + yield item + try: + # Advance the underlying iterator + item = await data.__anext__() + except StopAsyncIteration: + # The underlying iterator came to an end, so end the subiterator too + return + + while True: + while item.deployment_id == last_deployment_id: + # We are trying to advance the outer iterator while the underlying iterator + # is still part-way through a group. Speed through the underlying until we + # hit an item where the deployment_id is different to the last one (or, is not + # None, in the case of the start of the iterator) + try: + item = await data.__anext__() + except StopAsyncIteration: + # We hit the end of the underlying iterator: end this iterator too + return + last_deployment_id = item.deployment_id + # Don't yield an iterator for unclassified deployments + if last_deployment_id is not None: + # Instantiate a subiterator for this group + yield last_deployment_id, subiterator(last_deployment_id) diff --git a/Ch09/apd.aggregation-chapter09/src/apd/aggregation/utils.py b/Ch09/apd.aggregation-chapter09/src/apd/aggregation/utils.py new file mode 100644 index 0000000..76f05ec --- /dev/null +++ b/Ch09/apd.aggregation-chapter09/src/apd/aggregation/utils.py @@ -0,0 +1,27 @@ +import math + + +# Set up mercator transform from https://wiki.openstreetmap.org/wiki/Mercator#Python +# Thanks to Paulo Silva and the OSM contributors +def merc_x(lon: float) -> float: + r_major = 6378137.000 + return r_major * math.radians(lon) + + +def merc_y(lat: float) -> float: + if lat > 89.5: + lat = 89.5 + if lat < -89.5: + lat = -89.5 + r_major = 6378137.000 + r_minor = 6356752.3142 + temp = r_minor / r_major + eccent = math.sqrt(1 - temp ** 2) + phi = math.radians(lat) + sinphi = math.sin(phi) + con = eccent * sinphi + com = eccent / 2 + con = ((1.0 - con) / (1.0 + con)) ** com + ts = math.tan((math.pi / 2 - phi) / 2) / con + y = 0 - r_major * math.log(ts) + return y diff --git a/Ch09/apd.aggregation-chapter09/tests/__init__.py b/Ch09/apd.aggregation-chapter09/tests/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/Ch09/apd.aggregation-chapter09/tests/conftest.py b/Ch09/apd.aggregation-chapter09/tests/conftest.py new file mode 100644 index 0000000..328e95f --- /dev/null +++ b/Ch09/apd.aggregation-chapter09/tests/conftest.py @@ -0,0 +1,124 @@ +import datetime +from uuid import UUID + +from apd.aggregation.database import datapoint_table + +from alembic.config import Config +from alembic.script import ScriptDirectory +from alembic.runtime.environment import EnvironmentContext +import pytest + + +@pytest.fixture +def db_uri(): + return "postgresql+psycopg2://apd@localhost/apd-test" + + +@pytest.fixture +def db_session(db_uri): + from sqlalchemy import create_engine + from sqlalchemy.orm import sessionmaker + + engine = create_engine(db_uri, echo=True) + sm = sessionmaker(engine) + Session = sm() + yield Session + Session.close() + + +@pytest.fixture +def migrated_db(db_uri, db_session): + config = Config() + config.set_main_option("script_location", "apd.aggregation:alembic") + config.set_main_option("sqlalchemy.url", db_uri) + script = ScriptDirectory.from_config(config) + + def upgrade(rev, context): + return script._upgrade_revs(script.get_current_head(), rev) + + def downgrade(rev, context): + return script._downgrade_revs(None, rev) + + with EnvironmentContext(config, script, fn=upgrade): + script.run_env() + + try: + yield + finally: + # Clear any pending work from the db_session connection + db_session.rollback() + + with EnvironmentContext(config, script, fn=downgrade): + script.run_env() + + +@pytest.fixture +def populated_db(migrated_db, db_session): + datas = [ + { + "id": 1, + "sensor_name": "Test", + "data": "1", + "collected_at": datetime.datetime(2020, 4, 1, 12, 0, 1), + "deployment_id": UUID("b4c68905-b1e4-4875-940e-69e5d27730fd"), + }, + { + "id": 2, + "sensor_name": "Test", + "data": "2", + "collected_at": datetime.datetime(2020, 4, 1, 12, 1, 1), + "deployment_id": UUID("b4c68905-b1e4-4875-940e-69e5d27730fd"), + }, + { + "id": 3, + "sensor_name": "Test", + "data": "3", + "collected_at": datetime.datetime(2020, 4, 1, 12, 2, 1), + "deployment_id": UUID("b4c68905-b1e4-4875-940e-69e5d27730fd"), + }, + { + "id": 4, + "sensor_name": "Test", + "data": "4", + "collected_at": datetime.datetime(2020, 4, 1, 12, 3, 1), + "deployment_id": UUID("b4c68905-b1e4-4875-940e-69e5d27730fd"), + }, + { + "id": 5, + "sensor_name": "OtherTest", + "data": "5", + "collected_at": datetime.datetime(2020, 4, 1, 12, 4, 1), + "deployment_id": UUID("b4c68905-b1e4-4875-940e-69e5d27730fd"), + }, + { + "id": 6, + "sensor_name": "Test", + "data": "1", + "collected_at": datetime.datetime(2020, 4, 1, 12, 0, 1), + "deployment_id": UUID("6e887b81-57c3-4f3b-a7e7-bad159e05e78"), + }, + { + "id": 7, + "sensor_name": "Test", + "data": "1", + "collected_at": datetime.datetime(2020, 4, 1, 12, 1, 1), + "deployment_id": UUID("6e887b81-57c3-4f3b-a7e7-bad159e05e78"), + }, + { + "id": 8, + "sensor_name": "Test", + "data": "2", + "collected_at": datetime.datetime(2020, 4, 1, 12, 2, 1), + "deployment_id": UUID("6e887b81-57c3-4f3b-a7e7-bad159e05e78"), + }, + { + "id": 9, + "sensor_name": "OtherTest", + "data": "3", + "collected_at": datetime.datetime(2020, 4, 1, 12, 3, 1), + "deployment_id": UUID("6e887b81-57c3-4f3b-a7e7-bad159e05e78"), + }, + ] + for data in datas: + insert = datapoint_table.insert().values(**data) + db_session.execute(insert) diff --git a/Ch09/apd.aggregation-chapter09/tests/test_analysis.py b/Ch09/apd.aggregation-chapter09/tests/test_analysis.py new file mode 100644 index 0000000..34a32b4 --- /dev/null +++ b/Ch09/apd.aggregation-chapter09/tests/test_analysis.py @@ -0,0 +1,423 @@ +import datetime +import uuid + +import pytest + +from apd.aggregation import analysis +from apd.aggregation.database import DataPoint, datapoint_table +from apd.aggregation.query import db_session_var + + +async def generate_datapoints(datas): + deployment_id = uuid.uuid4() + for i, (time, data) in enumerate(datas, start=1): + yield DataPoint( + id=i, + collected_at=time, + sensor_name="TestSensor", + data=data, + deployment_id=deployment_id, + ) + + +class TestPassThroughCleaner: + @pytest.fixture + def cleaner(self): + return analysis.clean_passthrough + + @pytest.mark.asyncio + async def test_float_passthrough(self, cleaner): + data = [ + (datetime.datetime(2020, 4, 1, 12, 0, 0), 65.0), + (datetime.datetime(2020, 4, 1, 13, 0, 0), 65.5), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert output == data + + @pytest.mark.asyncio + async def test_Nones_are_skipped(self, cleaner): + data = [ + (datetime.datetime(2020, 4, 1, 12, 0, 0), None), + (datetime.datetime(2020, 4, 1, 13, 0, 0), 65.5), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert output == [ + (datetime.datetime(2020, 4, 1, 13, 0, 0), 65.5), + ] + + +class TestTemperatureCleaner: + @pytest.fixture + def cleaner(self): + return analysis.clean_temperature_fluctuations + + @pytest.mark.asyncio + async def test_window_not_full(self, cleaner): + data = [ + ( + datetime.datetime(2020, 4, 1, 12, 0, 0), + {"magnitude": 21.0, "unit": "degC"}, + ), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert output == [(datetime.datetime(2020, 4, 1, 12, 0, 0), 21.0)] + + @pytest.mark.asyncio + async def test_window_exactly_full(self, cleaner): + data = [ + ( + datetime.datetime(2020, 4, 1, 12, 0, 0), + {"magnitude": 21.0, "unit": "degC"}, + ), + ( + datetime.datetime(2020, 4, 1, 12, 0, 1), + {"magnitude": 21.0, "unit": "degC"}, + ), + ( + datetime.datetime(2020, 4, 1, 12, 0, 2), + {"magnitude": 21.0, "unit": "degC"}, + ), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert output == [ + (datetime.datetime(2020, 4, 1, 12, 0, 0), 21.0), + (datetime.datetime(2020, 4, 1, 12, 0, 1), 21.0), + (datetime.datetime(2020, 4, 1, 12, 0, 2), 21.0), + ] + + @pytest.mark.asyncio + async def test_window_overfilled(self, cleaner): + data = [ + ( + datetime.datetime(2020, 4, 1, 12, 0, 0), + {"magnitude": 21.0, "unit": "degC"}, + ), + ( + datetime.datetime(2020, 4, 1, 12, 0, 1), + {"magnitude": 21.0, "unit": "degC"}, + ), + ( + datetime.datetime(2020, 4, 1, 12, 0, 2), + {"magnitude": 21.0, "unit": "degC"}, + ), + ( + datetime.datetime(2020, 4, 1, 12, 0, 3), + {"magnitude": 21.0, "unit": "degC"}, + ), + ( + datetime.datetime(2020, 4, 1, 12, 0, 4), + {"magnitude": 21.0, "unit": "degC"}, + ), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert output == [ + (datetime.datetime(2020, 4, 1, 12, 0, 0), 21.0), + (datetime.datetime(2020, 4, 1, 12, 0, 1), 21.0), + (datetime.datetime(2020, 4, 1, 12, 0, 2), 21.0), + (datetime.datetime(2020, 4, 1, 12, 0, 3), 21.0), + (datetime.datetime(2020, 4, 1, 12, 0, 4), 21.0), + ] + + @pytest.mark.asyncio + async def test_outlier_dropped(self, cleaner): + data = [ + ( + datetime.datetime(2020, 4, 1, 12, 0, 0), + {"magnitude": 21.0, "unit": "degC"}, + ), + ( + datetime.datetime(2020, 4, 1, 12, 0, 1), + {"magnitude": 61.0, "unit": "degC"}, + ), + ( + datetime.datetime(2020, 4, 1, 12, 0, 2), + {"magnitude": 21.0, "unit": "degC"}, + ), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert output == [ + (datetime.datetime(2020, 4, 1, 12, 0, 0), 21.0), + (datetime.datetime(2020, 4, 1, 12, 0, 2), 21.0), + ] + + @pytest.mark.asyncio + async def test_limited_to_DHT22_range(self, cleaner): + data = [ + ( + datetime.datetime(2020, 4, 1, 12, 0, 0), + {"magnitude": 91.0, "unit": "degC"}, + ), + ( + datetime.datetime(2020, 4, 1, 12, 0, 1), + {"magnitude": 91.0, "unit": "degC"}, + ), + ( + datetime.datetime(2020, 4, 1, 12, 0, 2), + {"magnitude": 91.0, "unit": "degC"}, + ), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert output == [] + + @pytest.mark.asyncio + async def test_Nones_dropped(self, cleaner): + data = [ + ( + datetime.datetime(2020, 4, 1, 12, 0, 0), + {"magnitude": 31.0, "unit": "degC"}, + ), + (datetime.datetime(2020, 4, 1, 12, 0, 1), None,), + ( + datetime.datetime(2020, 4, 1, 12, 0, 2), + {"magnitude": 32.0, "unit": "degC"}, + ), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert output == [ + (datetime.datetime(2020, 4, 1, 12, 0, 0), 31.0), + (datetime.datetime(2020, 4, 1, 12, 0, 2), 32.0), + ] + + +class TestWattHourCleaner: + @pytest.fixture + def cleaner(self): + return analysis.clean_watthours_to_watts + + @pytest.mark.asyncio + async def test_one_entry_insufficient_to_find_diff(self, cleaner): + data = [ + ( + datetime.datetime(2020, 4, 1, 12, 0, 0), + {"magnitude": 1.0, "unit": "watt_hour"}, + ), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert output == [] + + @pytest.mark.asyncio + async def test_two_entries_provides_diff_with_second_time(self, cleaner): + data = [ + ( + datetime.datetime(2020, 4, 1, 12, 0, 0), + {"magnitude": 1.0, "unit": "watt_hour"}, + ), + ( + datetime.datetime(2020, 4, 1, 13, 0, 0), + {"magnitude": 10.0, "unit": "watt_hour"}, + ), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert len(output) == 1 + assert output[0][0] == datetime.datetime(2020, 4, 1, 13, 0, 0) + assert output[0][1] == pytest.approx(9.0, 0.0001) + + @pytest.mark.asyncio + async def test_fractional_hour(self, cleaner): + data = [ + ( + datetime.datetime(2020, 4, 1, 12, 0, 0), + {"magnitude": 1.0, "unit": "watt_hour"}, + ), + ( + datetime.datetime(2020, 4, 1, 12, 23, 42), + {"magnitude": 10.0, "unit": "watt_hour"}, + ), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert len(output) == 1 + assert output[0][0] == datetime.datetime(2020, 4, 1, 12, 23, 42) + assert output[0][1] == pytest.approx(22.7848, 0.001) + + @pytest.mark.asyncio + async def test_diff_happens_pairwise(self, cleaner): + data = [ + ( + datetime.datetime(2020, 4, 1, 12, 0, 0), + {"magnitude": 1.0, "unit": "watt_hour"}, + ), + ( + datetime.datetime(2020, 4, 1, 12, 23, 42), + {"magnitude": 10.0, "unit": "watt_hour"}, + ), + ( + datetime.datetime(2020, 4, 1, 13, 23, 42), + {"magnitude": 13.0, "unit": "watt_hour"}, + ), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert len(output) == 2 + assert output[0][0] == datetime.datetime(2020, 4, 1, 12, 23, 42) + assert output[0][1] == pytest.approx(22.7848, 0.001) + assert output[1][0] == datetime.datetime(2020, 4, 1, 13, 23, 42) + assert output[1][1] == pytest.approx(3, 0.001) + + @pytest.mark.asyncio + async def test_None_ignored(self, cleaner): + data = [ + ( + datetime.datetime(2020, 4, 1, 12, 0, 0), + {"magnitude": 1.0, "unit": "watt_hour"}, + ), + (datetime.datetime(2020, 4, 1, 12, 58, 0), None,), + ( + datetime.datetime(2020, 4, 1, 13, 0, 0), + {"magnitude": 10.0, "unit": "watt_hour"}, + ), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert len(output) == 1 + assert output[0][0] == datetime.datetime(2020, 4, 1, 13, 0, 0) + assert output[0][1] == pytest.approx(9.0, 0.0001) + + +class TestTemperatureMap: + @pytest.fixture + def temperature_data(self): + return { + (53.8667, -1.3333): -1, + (53.35, -2.2833): 1, + (52.45, -1.7333): 3, + (51.5, -0.1333): 6, + (51.55, -2.5667): 5, + (54.9667, -1.6167): -1, + (55.9667, -3.2167): -1, + (54.7667, -1.5833): 0, + (53.7667, -0.3): 1, + (53.7667, -3.0167): 0, + (51.4833, -3.1833): 4, + (53.2667, -3.5167): 2, + (52.0833, -2.8): 2, + (52.0167, -0.6): 2, + (52.6667, 1.2667): 5, + (50.4333, -4.9833): 9, + (50.35, -4.1167): 10, + (50.5167, -2.45): 9, + (50.8333, -1.1667): 9, + (56.45, -5.4333): 4, + (57.5333, -4.05): -2, + (54.5167, -3.6167): 3, + (53.0833, 0.2667): 4, + (51.35, 1.3333): 7, + (50.8833, 0.3167): 8, + (58.45, -3.1): 3, + (50.1, -5.6667): 9, + (58.2167, -6.3333): 4, + (55.6833, -6.25): 7, + } + + @pytest.fixture + def temperature_datapoints(self, migrated_db, db_session, temperature_data): + datas = [] + now = datetime.datetime.now() + for (coord, temp) in temperature_data.items(): + deployment_id = uuid.uuid4() + datas.append( + DataPoint( + sensor_name="Location", + deployment_id=deployment_id, + collected_at=now, + data=coord, + ) + ) + datas.append( + DataPoint( + sensor_name="Temperature", + deployment_id=deployment_id, + collected_at=now, + data=temp, + ) + ) + return datas + + @pytest.fixture + def populated_db(self, migrated_db, db_session, temperature_datapoints): + for data in temperature_datapoints: + insert = datapoint_table.insert().values(**data._asdict()) + db_session.execute(insert) + + @pytest.fixture + def cleaner(self): + return analysis.get_map_cleaner_for("Temperature") + + @pytest.fixture + def get_data(self, db_session): + db_session_var.set(db_session) + return analysis.get_all_data() + + @pytest.mark.asyncio + async def test_latest_coord_temp_cleaner( + self, temperature_data, populated_db, get_data, cleaner + ): + data = get_data() + deployments = 0 + async for deployment, data_points in cleaner(data): + deployments += 1 + cleaned = {a async for a in cleaner(data_points)} + # There should only be one deployment + assert deployments == 1 + expected = set(temperature_data.items()) + assert cleaned == expected + + @pytest.mark.asyncio + async def test_newer_items_superceed_older( + self, db_session, temperature_datapoints, populated_db, get_data, cleaner + ): + # Pick any of the deployment ids and find the pair of DataPoints in that deployment id + deployment_1 = temperature_datapoints[0].deployment_id + existing = [ + datapoint + for datapoint in temperature_datapoints + if datapoint.deployment_id == deployment_1 + ] + old_location = [ + datapoint for datapoint in existing if datapoint.sensor_name == "Location" + ][0] + old_temperature = [ + datapoint + for datapoint in existing + if datapoint.sensor_name == "Temperature" + ][0] + + new_location = DataPoint( + sensor_name="Location", + deployment_id=deployment_1, + collected_at=datetime.datetime(2031, 4, 1, 12, 0, 0), + data=(-10.5, 150.1), + ) + new_temperature = DataPoint( + sensor_name="Temperature", + deployment_id=deployment_1, + collected_at=datetime.datetime(2031, 4, 1, 12, 0, 0), + data=21, + ) + for data in [new_location, new_temperature]: + insert = datapoint_table.insert().values(**data._asdict()) + db_session.execute(insert) + + data = get_data() + deployments = 0 + async for deployment, data_points in cleaner(data): + deployments += 1 + cleaned = {a async for a in cleaner(data_points)} + # There should only be one deployment + assert deployments == 1 + + # We expect the newer one, not the older + assert (new_location.data, new_temperature.data) in cleaned + assert (old_location.data, old_temperature.data) not in cleaned + # The cleaner converts two data points to one plottable + assert len(cleaned) == len(temperature_datapoints) / 2 diff --git a/Ch09/apd.aggregation-chapter09/tests/test_cli.py b/Ch09/apd.aggregation-chapter09/tests/test_cli.py new file mode 100644 index 0000000..5cab441 --- /dev/null +++ b/Ch09/apd.aggregation-chapter09/tests/test_cli.py @@ -0,0 +1,155 @@ +from unittest import mock +import uuid + +from click.testing import CliRunner +import pytest + +import apd.aggregation.cli +from apd.aggregation.database import Deployment, deployment_table + + +@pytest.mark.functional +def test_sensors_are_passed_to_get_data_points(db_uri): + runner = CliRunner() + with mock.patch("apd.aggregation.collect.get_data_points") as get_data_points: + runner.invoke( + apd.aggregation.cli.collect_sensor_data, + [ + "http://localhost", + "http://otherhost", + "--db", + db_uri, + "--api-key", + "key", + ], + ) + calls = get_data_points.call_args_list + assert len(calls) == 2 + details = {call[0] for call in get_data_points.call_args_list} + assert details == { + ("http://localhost", "key"), + ("http://otherhost", "key"), + } + + +@pytest.mark.functional +def test_add_deployment_to_db(db_uri, db_session, migrated_db): + runner = CliRunner() + deployment_id = uuid.UUID("76587cdd42dc489ea1d220e9b0bc62ca") + with mock.patch("apd.aggregation.collect.get_deployment_id") as get_deployment_id: + get_deployment_id.return_value = deployment_id + runner.invoke( + apd.aggregation.cli.deployments, + ["add", "http://otherhost", "Other", "--db", db_uri, "--api-key", "key"], + ) + deployments = db_session.query(deployment_table).all() + assert len(deployments) == 1 + deployment = Deployment.from_sql_result(deployments[0]) + assert deployment.uri == "http://otherhost" + assert deployment.api_key == "key" + assert deployment.id == deployment_id + + +@pytest.mark.functional +def test_use_stored_deployments(db_uri, migrated_db): + runner = CliRunner() + + deployment_id = uuid.UUID("76587cdd42dc489ea1d220e9b0bc62ca") + with mock.patch("apd.aggregation.collect.get_deployment_id") as get_deployment_id: + get_deployment_id.return_value = deployment_id + runner.invoke( + apd.aggregation.cli.deployments, + [ + "add", + "http://specifiedhost", + "Specified", + "--db", + db_uri, + "--api-key", + "an_api_key", + ], + ) + + with mock.patch("apd.aggregation.collect.get_data_points") as get_data_points: + runner.invoke( + apd.aggregation.cli.collect_sensor_data, ["--db", db_uri], + ) + calls = get_data_points.call_args_list + assert len(calls) == 1 + details = {call[0] for call in get_data_points.call_args_list} + assert details == { + ("http://specifiedhost", "an_api_key"), + } + + +@pytest.mark.functional +def test_list_deployments(db_uri, migrated_db): + runner = CliRunner() + + deployment_id = uuid.UUID("76587cdd42dc489ea1d220e9b0bc62ca") + with mock.patch("apd.aggregation.collect.get_deployment_id") as get_deployment_id: + get_deployment_id.return_value = deployment_id + runner.invoke( + apd.aggregation.cli.deployments, + [ + "add", + "http://specifiedhost", + "Specified", + "--db", + db_uri, + "--api-key", + "an_api_key", + ], + ) + + result = runner.invoke(apd.aggregation.cli.deployments, ["list", "--db", db_uri],) + expected = """Specified +ID 76587cdd42dc489ea1d220e9b0bc62ca +URI http://specifiedhost +API key an_api_key +Colour None + +""" + assert result.output == expected + + +@pytest.mark.functional +def test_edit_deployment(db_uri, migrated_db): + runner = CliRunner() + return + deployment_id = uuid.UUID("76587cdd42dc489ea1d220e9b0bc62ca") + with mock.patch("apd.aggregation.collect.get_deployment_id") as get_deployment_id: + get_deployment_id.return_value = deployment_id + runner.invoke( + apd.aggregation.cli.deployments, + [ + "add", + "http://specifiedhost", + "Specified", + "--db", + db_uri, + "--api-key", + "an_api_key", + ], + ) + + result = runner.invoke( + apd.aggregation.cli.deployments, + [ + "edit", + "--db", + db_uri, + deployment_id.hex(), + "--colour", + "red" "--name", + "New name", + ], + ) + expected = """New name +ID 76587cdd42dc489ea1d220e9b0bc62ca +URI http://specifiedhost +API key an_api_key +Colour red + +""" + assert result.output == expected diff --git a/Ch09/apd.aggregation-chapter09/tests/test_http_get.py b/Ch09/apd.aggregation-chapter09/tests/test_http_get.py new file mode 100644 index 0000000..df575c6 --- /dev/null +++ b/Ch09/apd.aggregation-chapter09/tests/test_http_get.py @@ -0,0 +1,188 @@ +from concurrent.futures import ThreadPoolExecutor +import datetime +import typing as t +from unittest.mock import patch, MagicMock +import wsgiref.simple_server + +import aiohttp +from apd.sensors.base import Sensor +from apd.sensors.sensors import PythonVersion, ACStatus +from apd.sensors.wsgi import set_up_config +import flask +import pytest + +from apd.sensors.wsgi import v21 + +from apd.aggregation import collect +from apd.aggregation.database import Deployment + +pytestmark = [pytest.mark.functional] + + +@pytest.fixture +def sensors() -> t.Iterator[t.List[Sensor[t.Any]]]: + """ Patch the get_sensors method to return a known pair of sensors only """ + data: t.List[Sensor[t.Any]] = [PythonVersion(), ACStatus()] + with patch("apd.sensors.cli.get_sensors") as get_sensors: + get_sensors.return_value = data + yield data + + +def get_independent_flask_app(name: str) -> flask.Flask: + """ Create a new flask app with the v20 API blueprint loaded, so multiple copies + of the app can be run in parallel without conflicting configuration """ + app = flask.Flask(name) + app.register_blueprint(v21.version, url_prefix="/v/2.1") + return app + + +def run_server_in_thread( + name: str, config: t.Dict[str, t.Any], port: int +) -> t.Iterator[str]: + # Create a new flask app and load in required code, to prevent config conflicts + app = get_independent_flask_app(name) + flask_app = set_up_config(config, app) + server = wsgiref.simple_server.make_server("localhost", port, flask_app) + + with ThreadPoolExecutor() as pool: + pool.submit(server.serve_forever) + yield f"http://localhost:{port}/" + server.shutdown() + + +@pytest.fixture(scope="module") +def http_server(): + yield from run_server_in_thread( + "standard", + { + "APD_SENSORS_API_KEY": "testing", + "APD_SENSORS_DEPLOYMENT_ID": "a46b1d1207fd4cdcad39bbdf706dfe29", + }, + 12081, + ) + + +@pytest.fixture(scope="module") +def bad_api_key_http_server(): + yield from run_server_in_thread( + "alternate", + { + "APD_SENSORS_API_KEY": "penny", + "APD_SENSORS_DEPLOYMENT_ID": "38cf2bae9adb445fad946c82e290487a", + }, + 12082, + ) + + +class TestGetDataPoints: + @pytest.fixture + def mut(self): + return collect.get_data_points + + @pytest.mark.asyncio + async def test_get_data_points( + self, sensors: t.List[Sensor[t.Any]], mut, http_server: str + ) -> None: + # Get the data from the server, storing the time before and after + # as bounds for the collected_at value + async with aiohttp.ClientSession() as http: + collect.http_session_var.set(http) + time_before = datetime.datetime.now() + results = await mut(http_server, "testing") + time_after = datetime.datetime.now() + + assert len(results) == len(sensors) == 2 + + for (sensor, result) in zip(sensors, results): + assert sensor.from_json_compatible(result.data) == sensor.value() + assert result.sensor_name == sensor.name + assert time_before <= result.collected_at <= time_after + + @pytest.mark.asyncio + async def test_get_data_points_fails_with_bad_api_key( + self, sensors: t.List[Sensor[t.Any]], mut, http_server: str + ) -> None: + with pytest.raises( + ValueError, + match=f"Error loading data from {http_server}: Supply API key in X-API-Key header", + ): + async with aiohttp.ClientSession() as http: + collect.http_session_var.set(http) + await mut(http_server, "incorrect") + + +class TestAddDataFromSensors: + @pytest.fixture + def mut(self): + return collect.add_data_from_sensors + + @pytest.fixture + def mock_db_session(self): + return MagicMock() + + @pytest.mark.asyncio + async def test_get_get_data_from_sensors( + self, mock_db_session, sensors: t.List[Sensor[t.Any]], mut, http_server: str + ) -> None: + results = await mut( + mock_db_session, + [ + Deployment( + id=None, colour=None, name=None, uri=http_server, api_key="testing" + ) + ], + ) + assert mock_db_session.execute.call_count == len(sensors) + assert len(results) == len(sensors) + + @pytest.mark.asyncio + async def test_get_get_data_from_sensors_with_multiple_servers( + self, mock_db_session, sensors: t.List[Sensor[t.Any]], mut, http_server: str + ) -> None: + results = await mut( + mock_db_session, + [ + Deployment( + id=None, colour=None, name=None, uri=http_server, api_key="testing" + ), + Deployment( + id=None, colour=None, name=None, uri=http_server, api_key="testing" + ), + ], + ) + assert mock_db_session.execute.call_count == len(sensors) * 2 + assert len(results) == len(sensors) * 2 + + @pytest.mark.asyncio + async def test_data_points_not_added_if_only_partial_success( + self, + mock_db_session, + sensors: t.List[Sensor[t.Any]], + mut, + http_server: str, + bad_api_key_http_server: str, + ) -> None: + with pytest.raises( + ValueError, + match=f"Error loading data from {bad_api_key_http_server}: Supply API key in X-API-Key header", + ): + await mut( + mock_db_session, + [ + Deployment( + id=None, + colour=None, + name=None, + uri=http_server, + api_key="testing", + ), + Deployment( + id=None, + colour=None, + name=None, + uri=bad_api_key_http_server, + api_key="testing", + ), + ], + ) + assert mock_db_session.execute.call_count == 0 diff --git a/Ch09/apd.aggregation-chapter09/tests/test_query.py b/Ch09/apd.aggregation-chapter09/tests/test_query.py new file mode 100644 index 0000000..9b6e0aa --- /dev/null +++ b/Ch09/apd.aggregation-chapter09/tests/test_query.py @@ -0,0 +1,111 @@ +import datetime +from uuid import UUID + +import pytest + +from apd.aggregation.query import ( + get_data, + get_deployment_ids, + get_data_by_deployment, + db_session_var, +) + + +class TestGetData: + @pytest.fixture + def mut(self): + return get_data + + @pytest.mark.asyncio + @pytest.mark.parametrize( + "filter,num_items_expected", + [ + ({}, 9), + ({"sensor_name": "Test"}, 7), + ({"deployment_id": UUID("b4c68905-b1e4-4875-940e-69e5d27730fd")}, 5), + ({"collected_after": datetime.datetime(2020, 4, 1, 12, 2, 1)}, 3), + ({"collected_before": datetime.datetime(2020, 4, 1, 12, 2, 1)}, 4), + ( + { + "collected_after": datetime.datetime(2020, 4, 1, 12, 2, 1), + "collected_before": datetime.datetime(2020, 4, 1, 12, 3, 5), + }, + 2, + ), + ], + ) + async def test_iterate_over_items( + self, mut, db_session, populated_db, filter, num_items_expected + ): + db_session_var.set(db_session) + points = [dp async for dp in mut(**filter)] + assert len(points) == num_items_expected + + @pytest.mark.asyncio + async def test_empty_db(self, mut, db_session, migrated_db): + db_session_var.set(db_session) + points = [dp async for dp in mut()] + assert len(points) == 0 + + +class TestGetDeployments: + @pytest.fixture + def mut(self): + return get_deployment_ids + + @pytest.mark.asyncio + async def test_get_all_deployments(self, mut, db_session, populated_db): + db_session_var.set(db_session) + deployment_ids = await mut() + assert set(deployment_ids) == { + UUID("b4c68905-b1e4-4875-940e-69e5d27730fd"), + UUID("6e887b81-57c3-4f3b-a7e7-bad159e05e78"), + } + + @pytest.mark.asyncio + async def test_empty_db(self, mut, db_session, migrated_db): + db_session_var.set(db_session) + deployment_ids = await mut() + assert deployment_ids == [] + + +@pytest.mark.usefixtures("populated_db") +class TestGetDataByDeployment: + @pytest.fixture + def mut(self): + return get_data_by_deployment + + @pytest.mark.asyncio + @pytest.mark.parametrize( + "filter,num_items_expected", [({}, 9), ({"sensor_name": "Test"}, 7)] + ) + async def test_iterate_over_all_items( + self, mut, db_session, filter, num_items_expected + ): + db_session_var.set(db_session) + num_items = 0 + num_deployments = 0 + async for deployment_id, items in mut(**filter): + num_deployments += 1 + async for item in items: + num_items += 1 + assert num_deployments == 2 + assert num_items == num_items_expected + + @pytest.mark.asyncio + @pytest.mark.parametrize( + "filter,num_items_expected", [({}, 5), ({"sensor_name": "Test"}, 4)] + ) + async def test_skip_first_deployment( + self, mut, db_session, filter, num_items_expected + ): + db_session_var.set(db_session) + num_items = 0 + num_deployments = 0 + async for deployment_id, items in mut(**filter): + num_deployments += 1 + if num_deployments == 2: + async for item in items: + num_items += 1 + assert num_deployments == 2 + assert num_items == num_items_expected diff --git a/Ch09/apd.aggregation-chapter09/tests/test_sensor_aggregation.py b/Ch09/apd.aggregation-chapter09/tests/test_sensor_aggregation.py new file mode 100644 index 0000000..311dd19 --- /dev/null +++ b/Ch09/apd.aggregation-chapter09/tests/test_sensor_aggregation.py @@ -0,0 +1,199 @@ +from __future__ import annotations + +import contextlib +from dataclasses import dataclass +import json +import typing as t +from unittest.mock import patch, Mock + +import pytest +import sqlalchemy + +import apd.aggregation.collect +from apd.aggregation.database import Deployment + + +@pytest.fixture +def data() -> t.Any: + return { + "sensors": [ + { + "human_readable": "3.7", + "id": "PythonVersion", + "title": "Python Version", + "value": [3, 7, 2, "final", 0], + }, + { + "human_readable": "Not connected", + "id": "ACStatus", + "title": "AC Connected", + "value": False, + }, + ] + } + + +@dataclass +class FakeAIOHttpClient: + responses: t.Dict[str, str] + + @contextlib.asynccontextmanager + async def get( + self, url: str, headers: t.Optional[t.Dict[str, str]] = None + ) -> t.AsyncIterator[FakeAIOHttpResponse]: + if url in self.responses: + yield FakeAIOHttpResponse(body=self.responses[url]) + else: + yield FakeAIOHttpResponse(body="", status=404) + + +@dataclass +class FakeAIOHttpResponse: + body: str + status: int = 200 + + async def json(self) -> t.Any: + return json.loads(self.body) + + +@pytest.fixture +def mockclient(data) -> FakeAIOHttpClient: + return FakeAIOHttpClient( + { + "http://localhost/v/2.1/sensors/": json.dumps(data), + "http://localhost/v/2.1/deployment_id": json.dumps( + {"deployment_id": "b29ba0ee10f14552b6b21327bb96d3fb"} + ), + } + ) + + +class TestGetDataPoints: + @pytest.fixture + def mut(self): + return apd.aggregation.collect.get_data_points + + @pytest.mark.asyncio + async def test_get_data_points( + self, mut, mockclient: FakeAIOHttpClient, data + ) -> None: + token = apd.aggregation.collect.http_session_var.set(mockclient) + try: + datapoints = await mut("http://localhost", "") + finally: + apd.aggregation.collect.http_session_var.reset(token) + + assert len(datapoints) == len(data["sensors"]) + for sensor in data["sensors"]: + assert sensor["value"] in (datapoint.data for datapoint in datapoints) + assert sensor["id"] in (datapoint.sensor_name for datapoint in datapoints) + + +class TestAddDataFromSensors: + @pytest.fixture + def mut(self): + return apd.aggregation.collect.add_data_from_sensors + + @pytest.fixture(autouse=True) + def patch_aiohttp(self, mockclient): + with patch("aiohttp.ClientSession") as ClientSession: + ClientSession.return_value.__aenter__.return_value = mockclient + yield ClientSession + + @pytest.fixture + def db_session(self): + session = Mock() + sql_result = session.execute.return_value + sql_result.inserted_primary_key = [1] + return session + + @pytest.mark.asyncio + async def test_datapoints_are_added_to_the_session(self, mut, db_session) -> None: + assert db_session.execute.call_count == 0 + datapoints = await mut( + db_session, + [ + Deployment( + id=None, colour=None, name=None, uri="http://localhost", api_key="" + ) + ], + ) + assert db_session.execute.call_count == len(datapoints) + + +@pytest.mark.usefixtures("migrated_db") +class TestDatabaseConnection: + @pytest.fixture(autouse=True) + def patch_aiohttp(self, mockclient): + with patch("aiohttp.ClientSession") as ClientSession: + ClientSession.return_value.__aenter__.return_value = mockclient + yield ClientSession + + @pytest.fixture + def table(self): + return apd.aggregation.database.datapoint_table + + @pytest.fixture + def daily_summary_view(self): + return apd.aggregation.database.daily_summary_view + + @pytest.fixture + def model(self): + return apd.aggregation.database.DataPoint + + @pytest.fixture + def mut(self): + return apd.aggregation.collect.add_data_from_sensors + + @pytest.mark.asyncio + async def test_datapoints_are_added_to_the_session(self, db_session, table) -> None: + datapoints = await apd.aggregation.collect.add_data_from_sensors( + db_session, + [ + Deployment( + id=None, colour=None, name=None, uri="http://localhost", api_key="" + ) + ], + ) + num_points = db_session.query(table).count() + assert num_points == len(datapoints) == 2 + + @pytest.mark.asyncio + async def test_datapoints_can_be_mapped_back_to_DataPoints( + self, mut, db_session, table, model + ) -> None: + datapoints = await mut( + db_session, + [ + Deployment( + id=None, colour=None, name=None, uri="http://localhost", api_key="" + ) + ], + ) + db_points = [ + model.from_sql_result(result) for result in db_session.query(table) + ] + assert db_points == datapoints + + @pytest.mark.asyncio + async def test_daily_summary_view_matches_query( + self, db_session, model, table, daily_summary_view + ) -> None: + await apd.aggregation.collect.add_data_from_sensors( + db_session, + [ + Deployment( + id=None, colour=None, name=None, uri="http://localhost", api_key="" + ) + ], + ) + + headers = table.c.sensor_name, table.c.data + value_counts = ( + db_session.query(*headers, sqlalchemy.func.count(table.c.id)) + .filter(model.collected_on_date == sqlalchemy.func.current_date()) + .group_by(*headers) + ) + + daily_summary_view = db_session.query(daily_summary_view) + assert value_counts.all() == daily_summary_view.all() diff --git a/Ch09/chapter09-analysis.ipynb b/Ch09/chapter09-analysis.ipynb new file mode 100644 index 0000000..98a74f0 --- /dev/null +++ b/Ch09/chapter09-analysis.ipynb @@ -0,0 +1,577 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "53635\n" + ] + } + ], + "source": [ + "from apd.aggregation.query import with_database\n", + "from apd.aggregation.database import datapoint_table\n", + "\n", + "with with_database(\"postgresql+psycopg2://apd@localhost/apd\") as session:\n", + " print(session.query(datapoint_table).count())" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "53635\n" + ] + } + ], + "source": [ + "from apd.aggregation.query import with_database, get_data\n", + "\n", + "with with_database(\"postgresql+psycopg2://apd@localhost/apd\") as session:\n", + " count = 0\n", + " async for datapoint in get_data():\n", + " count += 1\n", + " print(count)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from apd.aggregation.query import with_database, get_data\n", + "\n", + "from matplotlib import pyplot as plt\n", + "\n", + "async def plot():\n", + " points = [(dp.collected_at, dp.data) async for dp in get_data() if dp.sensor_name==\"RelativeHumidity\"]\n", + " x, y = zip(*points)\n", + " plt.plot_date(x, y, \"o\", xdate=True)\n", + "\n", + "with with_database(\"postgresql+psycopg2://apd@localhost/apd\") as session:\n", + " await plot()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from apd.aggregation.query import with_database, get_data\n", + "\n", + "from matplotlib import pyplot as plt\n", + "\n", + "async def plot():\n", + " points = [(dp.collected_at, dp.data) async for dp in get_data(sensor_name=\"RelativeHumidity\")]\n", + " x, y = zip(*points)\n", + " plt.plot_date(x, y, \"o\", xdate=True)\n", + "\n", + "with with_database(\"postgresql+psycopg2://apd@localhost/apd\") as session:\n", + " await plot()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import collections\n", + "\n", + "from apd.aggregation.query import with_database, get_data\n", + "\n", + "from matplotlib import pyplot as plt\n", + "\n", + "async def plot():\n", + " legends = collections.defaultdict(list)\n", + " async for dp in get_data(sensor_name=\"RelativeHumidity\"):\n", + " legends[dp.deployment_id].append((dp.collected_at, dp.data))\n", + "\n", + " for deployment_id, points in legends.items():\n", + " x, y = zip(*points)\n", + " plt.plot_date(x, y, \"o\", xdate=True)\n", + "\n", + "with with_database(\"postgresql+psycopg2://apd@localhost/apd\") as session:\n", + " await plot()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import collections\n", + "\n", + "from apd.aggregation.query import with_database, get_data, get_deployment_ids\n", + "\n", + "from matplotlib import pyplot as plt\n", + "\n", + "async def plot(deployment_id):\n", + " points = []\n", + " async for dp in get_data(sensor_name=\"RelativeHumidity\", deployment_id=deployment_id):\n", + " points.append((dp.collected_at, dp.data))\n", + "\n", + " if points:\n", + " x, y = zip(*points)\n", + " plt.plot_date(x, y, \"o\", xdate=True)\n", + "\n", + "with with_database(\"postgresql+psycopg2://apd@localhost/apd\") as session:\n", + " deployment_ids = await get_deployment_ids()\n", + " for deployment in deployment_ids:\n", + " await plot(deployment)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import asyncio\n", + "\n", + "from matplotlib import pyplot as plt\n", + "\n", + "from apd.aggregation.query import with_database\n", + "from apd.aggregation.analysis import get_known_configs, plot_sensor\n", + "\n", + "with with_database(\"postgresql+psycopg2://apd@localhost/apd\") as session:\n", + " coros = []\n", + " figure = plt.figure(figsize = (20, 5), dpi=300)\n", + " configs = get_known_configs()\n", + " to_display = configs[\"Relative humidity\"], configs[\"RAM available\"]\n", + " for i, config in enumerate(to_display, start=1):\n", + " plot = figure.add_subplot(1, 2, i)\n", + " coros.append(plot_sensor(config, plot, {}))\n", + " await asyncio.gather(*coros)\n", + "\n", + "display(figure)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import asyncio\n", + "\n", + "from matplotlib import pyplot as plt\n", + "\n", + "from apd.aggregation.query import with_database\n", + "from apd.aggregation.analysis import get_known_configs, plot_sensor, Config\n", + "\n", + "async def clean_magnitude(datapoints):\n", + " async for datapoint in datapoints:\n", + " if datapoint.data is None:\n", + " continue\n", + " yield datapoint.collected_at, datapoint.data[\"magnitude\"]\n", + "\n", + "TemperatureConfig = Config(\n", + " sensor_name=\"Temperature\",\n", + " clean=clean_magnitude,\n", + " title=\"Ambient temperature\",\n", + " ylabel=\"Degrees C\",\n", + ")\n", + " \n", + "with with_database(\"postgresql+psycopg2://apd@localhost/apd\") as session:\n", + " coros = []\n", + " figure = plt.figure(figsize = (20, 5), dpi=300)\n", + " configs = get_known_configs()\n", + " to_display = configs[\"Relative humidity\"], TemperatureConfig\n", + " for i, config in enumerate(to_display, start=1):\n", + " plot = figure.add_subplot(1, 2, i)\n", + " coros.append(plot_sensor(config, plot, {}))\n", + " await asyncio.gather(*coros)\n", + "\n", + "display(figure)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import asyncio\n", + "\n", + "from matplotlib import pyplot as plt\n", + "\n", + "from apd.aggregation.query import with_database\n", + "from apd.aggregation.analysis import get_known_configs, plot_sensor, Config\n", + "\n", + "with with_database(\"postgresql+psycopg2://apd@localhost/apd\") as session:\n", + " coros = []\n", + " figure = plt.figure(figsize = (20, 5), dpi=300)\n", + " configs = get_known_configs()\n", + " to_display = configs[\"Relative humidity\"], configs[\"Ambient temperature\"]\n", + " for i, config in enumerate(to_display, start=1):\n", + " plot = figure.add_subplot(1, 2, i)\n", + " coros.append(plot_sensor(config, plot, {}))\n", + " await asyncio.gather(*coros)\n", + "\n", + "display(figure)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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Q2KRpdDJsWrJ6Xuc1U19RPe+kCoeb+9lk47r28aaxybQz2p/TIf+a/P6stN+PVSSHXDrw57c5k17UczDafq9qfzz9rJ6Dp+qHJI3Dq8cjn5o8Z07v5xzx0eraXGBUa0vyxO+SuqYqBK5+cNK6MWldn5StyYKrk7/+KFn0m2SvY5OppyfL/5z8+bKe95vwzOr7tuDbPYfRwUD9+mUDXzP++Op9Yc2C5PFfdR5rGlO9py27PRl7ZBVutnFdMupp1XvC3icnrc3Joz+rvrZuSJ74bbL0tiqYbUs8/sukeVn1fgUAAAAAAOw0BKMBAAAAAAAAAAAAAMCuYPi05IV/SZb8sQrHGntkUj+o85yirj14bCCKIjnwHckBb6/2rqvf/Jq6xuSYL1ThZasfSsYc2l5PUZc885rk+hckaxdWfeNPTA7/SPv60U9Lnv3T5I73VaFeY45Mjvx4Mmhc+5zpr02G7ZfMvypJXTL15cnezx7489ucUQck089JHvhSe1/T2OSgf2pv73NqUjQk5YbOa6edOfDz+gpFS6rv7YRndumrT+qGVo+nn1VdHa1b1Hsw2gEXJhNOrK7D/yNZckuy9pFk4ilJ44hqTtmaLL6xOvuRnyR/+ufOexzx8apv45r+PEPobtFvq6snzUurK6ne4560+qFk4Q+3X02r5yVNh22//QEAAAAAgAETjAYAAAAA/H/27js8zurO2/g9Rb1LtmxLstwbtjHYmF4NobcACYGQTkJ62c2mbMiml01v+242bbPJBkgjpIcNSSiB0AnVNmCMe2+SrK6Z949jZTSaGVnFtlzuz3Wda+Y5v3POc55pBlvzlSRJkiRJkiRJkqRDRTQOY07cf+tHIhAZRChaX0XjQuuvagFctiqEcOVXQtmMzECw2lPh7D8PvP64M0Lb3074DtSeAZv+DMUNIXisbHqqXjg2BLs9+u5UX+l0mHvj/t/bYBSMgfLZ0LQsvT9WBLVnpo6jeTDmhMz5kWjqtVW9CCZeAZvvgqI6GH8OxAoh0QF/f3/28x/zOTjqX8L97jbYei889QnYfDdMegXM+ReoXhhqT9wIy76UfZ3iRigcB8ke2PFoZr32zHBN2+5PBWlJw7VrKVQZjCZJkiRJkiRJ0sHEYDRJkiRpv0mO9gYkSZIkSZIkSZIkSZIkaXRF4zDm+NHexeBEIjD11aHlMvtdUHsabPxTCAyrvxDyqw7cHgcSicC8f4P7Xknaz6/NeS/kVwx9vfJZofVVMjn3+OpjU/fjRSFMbfw5mePiRTDtDbmD0c5/CAprw/2eDtj4xxDWN/YUyCtPH5tMwu5V4X5+JbSth542+MNx2dcunQbHfQPuvCD3dZxyC9z7itz18jnQtDR3XYeW+66FydeM9i4kSZIkSZIkSVIfBqNJkiRJkiRJkiRJkiRJkiRJkiQNVvXC0A5Gk6+BgjGw8ofQ0woNl8PkV+679SecC9F8SHSm99ecALVnDn6d8jlQNgOan0vvH3tKKhQNIFYA9RfnXicSgdLJqeP8Smjfknv8/I9B3fnhPFvuzT5m0tW5g9GO/SLM+adw/6ZIjj3F4Zou2PEELP8q7HwyXNPs90DrWrj/tbn3p9HRthGKxo/2LiRJkiRJkiRJ0h7R0d6AJEmSJEmSJEmSJEmSJEmSJEmS9pEJL4GTfwCn/QymXBfCw/aV/Eo4+X9D+FevsafA6b+AaDz3vP4iETj+W5BXnuorHAeLvjbyPRaMgZJJWc4ZD48NwLQ3ZZ9bf2m4bbw6SzECjVemDuOl2deYfkO4rToaTvwunP8gnPkbGH82TH1N+mPX15iTYMkfs9cAzn0Aznswd724Ea5N5q4DnHPPwHWAhpfufczhpmXFaO9AkiRJkiRJkiT1YTCaJEmStL8k9/IDRpIkSZIkSZIkSZIkSZIkHWoaXwYv3QCn/hTOuRvOvguKJgx9nXFnwsXL4aQfwCm3wIVPQfXCke8vEoFZ78nsn/IqKKwN9ydeDnmVmWOmXR9u534QCmrSa7PelR641ju2v8mvHHh/M9+RvX/GW6B6EUTzMmvxEqicB6VTc69bc1y4PfoT2euTroHaU8N15FLcCKfclLs+5mSomJe7PljRgpGv0V9eJRz9SXjZLqhcMLS5Xc37fj+SJEmSJEmSJGnYhvDreCRJkiRlMPxMkiRJkiRJkiRJkiRJknSkKRwDjVeNfJ2i8SGwbF+b/a4QJrby+9C9G+ovg3k3pup55fCSu+H+18H2R0Ow2/yPQsMloV61AM57EFb+CNrWw/hzYOIV6eeY9U5Y8zNoXZvqa3gpjDlx4L3NuAFe+B507Ur1lU6HiVdBvAgaXwEv/jB9ztQ3QLw4tAnnwYbbM9edcH64nXQNPPkRSCbS69NeH27LZ+XeW+U8iBXmrk+8MgS3PfLO7PVrEiGYLtEDt+T4ylJxI8z/CDzwhuz1M38PpVNgy725x0w4D8aeCj0dkOyCuotCqFy8ONSP/gT89UpIdKXmNF4Nq3+cfb3uluz9kiRJkiRJkiRpVBiMJkmSJEmSJEmSJEmSJEmSJEmSpMPL9OtDy6VyPpz/MHS3QqwoBHr1VToV5n849/zSKXDu/fDC96HleRhzCkx9XeY6/ZXPgpfcA898DnY9A2NOgPkfD6FoACd8OwS3rfkpRPJg8rWw4NOp+Yu+Dve8FHY9neqrPSOMAyibBqfdBg+8Hjq2QqwYjv1cCHeDEMD2yHsg0ZG5t6l7wtMmXglrfp5ei8Rg0tWQ7IZH3gX0+8WyE69KXXs0FoLaNvwh8xxzPwBjT8v+2MSKYdwSiOXD7lXZxwAc8zmoOjp3veESeMm98OLN0NMGDZeG/eQKRlv5g1DPK8295sGkuw1aXoDdK8Prt3J+eL0mE+F5a98EdRdAfjU0LQ21kknQ0w6dO6FgDERzfKUs0QORaOq57OkIz0WiI5y3pxW6mqBtQzhvXnl4L8RLDtz1S5IkSZIkSZIOewajSZIkSftNcu9DJEmSJEmSJEmSJEmSJEnS6IkXD39ucT3M+9DQ51XOh5N/mL0WK4DF34Djvh6O+wetlc+A8x6Etb8MwWqV80PwV6wwNabhEqjfBLtfhOLG9BCswrGw5I9w37XQujbVv+hr0HhluD/vw7D5TujYlqrPeV+4XoBZ74TlX03V8qtg7r+m73PytZnBaNECmPgyKBwD1Ytg+yP95lwTQtEAao6HaD4kOtPH5FWEcLm9qVkcWl8VR4XHrL91v4afloX7jS+HCeeFELHVP4GmZTD+JSFobPw5sOF2WPrF8Dy1roGyWVA+M+yzqwnW3Jq+9rTroXgiNFwOFXMgmrf3vSeTsO5XsOonsOlP0NUM098UXqtPf3rv8wEee+/gxgFMegWsuiWzv+FyWP/77CF62VQvCo/dzLdD0QTo6QyvgaZl0Lk9hLmVzQghdOWzYcffQ2Dbzidg55N7HqdLobA2PP6RvBCyJ0mSJEmSJEk64hiMJkmSJI2I4WeSJEmSJEmSJEmSJEmSJGkf6x+I1le8OISIDTg/CqVTs9dqT4PLVodgtLxyyCsL43tVLQjhay/eDO2bQyBYwyWp+sIvw9hTYMMfQwDWlFdB2fT0c0y+Dpqfg2f+PYSGFdbCyTeHUDSA034B91wB2x8Oxw2Xw8KvpObnV8Kka2Dl/6SvO+0NIZRsOOKlex+z+ieh9fXi/4aWTduGECKXy4rvhNsnPxJuy+dA6TSY9jqoXgwlEzPnPPFhePpT6X3Lv5I5bl/JFooGsPa2oa2z/ZHQ9hbeNlD9wTemH0ficNrPQ6Bc9aLwOuqvuzWE7kVj4floehYiMSidAokuKBwH8aKhXYskSZIkSZIkaVQZjCZJkiRJkiRJkiRJkiRJkiRJkiQdSSKR7KFcvUqnwrwP5Z7b+LLQBlr/6I/DUR8MAWxl09LD10omwvkPhVq8BPKrMtc4/luhf/VPIZoPk6+F+R8b3PVlM5hgtP2taWlo638z2js5NCS74e7LDuw5y2ZC41XhtdfdBsu+AF1Nqfr8j8GU63IHD0qSJEmSJEmSRsxgNEmSJGl/SSZHeweSJEmSJEmSJEmSJEmSJEmjJ14E5TNy14sbctdi+bDoy7DwSyFobaQKx498DR3+mp+Fpz+du/7kR0IDGH8OFNVDrACqF0NPK6z/PWz4Q6hXL4LaM2HGm6FsOnTuhFgxJLugYxsUT9w3r21JkiRJkiRJOswYjCZJkiSNhOFnkiRJkiRJkiRJkiRJkiRJ+8++Co6a+FJYddO+WUsC2HhHn4NvZda3PxLasi/ufa3iBpj/Uag5HqKF0LQ0hPmVToa2DZBXDiWTQrha925Y+QPYfBdsvQ/KZkDJZJhwPhRNgPaN0NUM064P9zffBXmV4T0QzQs//2wgmyRJkiRJkqSDmMFokiRJkiRJkiRJkiQdISJZvuiU9BdASKMikUhk9GV7j0qSJEmSJGkfmXglTH09vPC90d6JlKl1LTxw/fDm7vh7aGtvS+9/7L2ZY/MqoacVEp3huOGyELRWcyJULYCieiABRXUhbA2gYytE4xArgqVfgA3/B+WzYdLLYexp4Xj3Kqg7H8qmQzIBkejwrqVXTyfE8ke2hiRJkiRJkqRDlsFokiRJ0n7jlwklSZIkSZIkHVyi0cwvInV1dZGXlzcKu5GObN3d3Rl92d6jkiRJkiRJ2kciETjhO1B7Oqz5eQii2vHYaO9q8IomQLwUYoUhlGr1T4c2v+ZEiBdDcQOs/MH+2aMOfl0704/X/jLcbrxjaOtsuQdWfDu975Es4wrHQfumcL98NjQ/D8l+fzcaK4Se9tznGnsKJHrCa3f82dBwORSNH9w+ezohmhfe/5IkSZIkSZIOGQajSZIkSSNi+JkkSZIkSZKkQ0ckEiE/P5/Ozs5/9LW0tFBcXDyKu5KOTC0tLWnH+fn5RPxyniRJkiRJ0v4VicDU14QG0NUEP63IPrZkCsy7ESKxEKLWtBy23Q+l00JQ0xMfzjFvElz2Yghy2ngHPPx2aHl++HuuPQPO/ktmsNOvZuRe96KlUD4LundDNB6Cp/o66X8gmYSWF0JIWncLTDgftvwVnvp49jVP/Rn89arM/uJGuHQF3DLAL+C4dAXsegb+/gHo3A6Vx0DZNCACz3499zwd+npD0QCalmUfM1AoGsCWe8PtNmDNz+Cht2SOqVwQAtC2P5xZK50aggW7W8NrvXt3eB32tENRHdRdBJEotG2AWAFMflUIdCuohq5dkOgK7+uC2rBeNBbe34lOiBelnyuZNIRNkiRJkiRJ2gcMRpMkSZIkSZIkSZIk6QhSVlbGtm3b/nHc1NTE2LFjDWSSDqBkMklTU1NaX1lZ2SjtRpIkSZIk6QiWV567NvcDMO31ueu5gtEaXx5uozGoOw8ufW7P+H+Dpz4xtP3N/xgc9f7sQUuz3x1C1/qb+XaomB3u55XmXjsSCeFkR38s1ZdfmT0YbdobYcJ5EC8JoVJ9TXlVCF/Lpf7SEExVOhXqL86sT3k13L44+9ySSbB7Ve61pV47H89da3khtGza1sOKb6f3rf7p4M8biUOye+/jiifCxKtgxyOw+e7sY07+EVTOh9Z1IYyxa1cIeysYA2NOgvbNUDY9BLVtvAOi+VB3YXif93TA2l/CM5+B5ufDZ9vY02Duv0LV0ennaV0H638LRGH8kvDe7JXoguYVUDYjfIb1Z/CbJEmSJEmSDhCD0SRJkqT9JjnaG5AkSZIkSZKkDP2D0bq6uli3bh319fWGo0kHQDKZZN26dXR1daX1l5cP8CVcSZIkSZIk7T+TroFVN6f3RaJQf8nA86a9MTNQCWDyddnHz/sIrP4JNC0feN1rB/nzp41Xh7C1zu2pvmg+TLt+cPOzqT4uhDet+Vmqr7AW5rw3hC+ddivccyV0t4Ra3UVw1PtS+1n948w1p71hL+dcCMWN0Lo6vX/CeXDWH+CmIf699bizYfF/wG9mD20eQOE4aN80tDmRGCR7hn4uHR4GE4oG0LoGln954DH3vXLk++nV3RLej73vyWyhhn01XAY7/p49iLDmRNh2f7hfMhmIwO6V4XjMSTDjLeFzLxKBRA+0PA9F9dC8HLbcC9GC8HlaMAZ2PQn5VelhbJIkSZIkSVIWBqNJkiRJI2L4mSRJkiRJkqRDS2FhIXl5eWmhTM3NzaxYsYLy8nJKS0uJx+NEo9FR3KV0eEkkEnR3d9PS0kJTU1NGKFpeXh4FBQWjtDtJkiRJkqQj3NwPwaa/QPvG9L6iCQPPm/1PsO5X6UFak6+DqqOzj4/G4Ow74aG3wtpfZB8z4y2D33fhGHjJvfDIu2D7Q1AxFxZ8CqoWDH6N/iIROOUWeOG/YfPdUDolBJuVNIb6hHPhik2w9X4oqoPymSFEDmDm22DNz9ODospnQd2FezlnFE78b7j7cuhuDn3FjbBwT4jUjLfCc/8vc16sEHraM/snvzLsbajGngLlR2UPu8uldBpcvAx6WqG7FTb8AWJFUHsmPPbP8OKPhr4PaX8YKBQNYO0vc9d6Q9EAdr+YXtv6t9D+9uqB13/ozQPX8yohryy02jOhcDy0rYeCaogWQtdOSHSFcLWJV0Ll3OzrJJPQsSWst/Nx6GqCinlQNG7g80uSJEmSJOmgYzCaJEmSJEmSJEmSJElHkEgkQl1dHatXryaZTP3yh66uLrZt28a2bdtGcXfSkaf3PRmJREZ7K5IkSZIkSUemyrlw/kOw+qchiGf8S0IA2N5UzIZz74eVPwhhQbVnwJRXDTynaDycfiss/wY88o7MeuPVQ9t7xWxYcvvQ5uxNNAbTrw8tm3gxjF+S2V97Gpz1e3jmc9DyAow9FY79AkQH8fW18Uvgkudg058hXhIey/yKUGu8Knsw2kk/DAFu63/XZw9nwqSrwx7HngJb7u03KQInfBceeH3mehOvguqF2YPRGi6D9X+AREd6f+NV4fqi5ZBXDlNfm6pVLgCGGIxWNAEmviy8nvIqYOzJUDIJnvn38Npsfi41duY7oGAsdO2CbQ9C8cTw+kp0wbNfz32OxpfD6p8MbV/S/ta1MzSAXc8MPPbJj8DY08JnzIbbYceje1+/oCa8Z8aeFoIwW56HLfdB57ZUaNyEC6B8NtSeGgIOi+pD4GQkCp3bQ9haNDay65QkSZIkSdKgRfr+kLN0JIpEInOBp3qPn3rqKebOzfFbIyRJkvpLdMEt+dlrk66FU/xNb5IkSZIkSZIOTq2trRnhaJIOrEgkQmNjI8XFxftszaeffpp58+b17ZqXTCaf3mcnkCRpiPwZPUmSJCmLrha4+9IQ0NNr6uvhhO+AAfrpkkl4/EPwzGdSfTPfDgu/AiRg7W2w/VGonB/CzWJ7fq5328Pw53NCcBhAtACO+xpMfxMs/SI89t7UenUXw6m3hDCkPy2BzXelavEyOOeuEKR033WQ6Az9tWfA6bdBfmX2fe9aCr89KrO/dFoI3nvuPzNrJ3wXpmUJbevV1RLOX1Cde0zHdvh5Tfba6b+EhkvT+5LJ8Bi1roP7roGdTwIRGHdWCFF76M25zyUdiWKFUDIFWtdC+UxIJqBlJZCEwnEhLDFWEgIaJ18HHZth8z2w4Q9hfsVcqLsQJl0DpZOBSFizY1uY37EFtv4NYsUhHDGvfBQvVpIkSZIk9efP5x04BqPpiOcPXUmSpBHp6YQfF2SvGYwmSZIkSZIk6SDX2trK+vXr6erqGu2tSEecvLw86urq9mkoGviDV5Kkg48/oydJkiTl0NMBG26HXU9DzfEwbomhaANpfh52PAaVR0PZzME9Vm0bYfXPgARMOD+EGP1jvRUhfKhsOlQvhmgs9He3wdLPw+a7oXQKzHwHVB0dau2bYctfoagBqheGAKSB3HFGWKevY78A48+G/zsJetpT/UX1cPEyyCvd+3Xtze+OgZ2Pp/fFS+ClGyCvbOC5XU0hkKn32v76Clj94+xjr+3zvcSu5nCOSDQcv3hLCFrL5pRboGpheMy3PgjJHmi4BNbcCve/LvucCx6HrffBQ2/JrJVOg5YVua+pqD48X1ULobsZln0p91jpYFQwFqJ5kFcBkT2fVXUXwJTXhPfP8q/A7lVhTP2lMOak8Bm5/OvQtBTat8C8G6H2tDA/vzoEsCWT0LQcttwD2x4IIW9l06HhpSG8zT+TJEmSJEnK4M/nHTgGo+mI5w9dSZKkETEYTZIkSZIkSdIhLplM0tHRQVNTE83NzXR2do72lqTDVn5+PmVlZZSXl1NQUEBkP3ypyB+8kiQdbPwZPUmSJElHrM5d8NBbYf3voKAapr8J5rwvhA1tuQ+e+mQILao5ARZ+AYob9s151/4S7rkKkt2pvgWfhrkfHPpa634Hd12U2T/+HFjyx9zzOrbBL+og0e/fHKoWwgWPZJ/T8iL8akpmfzQPrtoRgtcAEl3Q/ByUToVYYWrczidh+6NQOA5qz4B4Ufbz/KQ8BKRJGrqCMdCxNXstvxoKx4YxBWMgXgata2HznaFefwmUzwnhbD2tUD4rhIRGolAyCfJrINkFRXVQNiMVtNhXMhHmRPNDPde/syS6obslfEb0/ZyQJEmSJGmE/Pm8A2cvv5ZCkiRJ0vAZQixJkiRJkiTp4BeJRCgsLKSwsJDa2lqSySSJRAJ/0Zq070QiEaLR6H4JQpMkSZIkSZJ0kMqvCL9kOZnMDO8ZezKc9bv9c96Gy+Ccu+HFH0HPbqi/DCZePry1JpwbAshaXkjvn37DwPMKamD2e+CZf0/1ReIw/99yzymdHELitj2Q3l9/aSoUDUJQWsVRmfMr54e2N3M/AI9/KLN/zMmw9b69zx+MyvkhqC2Xy9dCcX24n0zAjsdg9yooHA9Vx8CLN8GDb8w+N5qfGTjXa/y5MOlqeOANuc+9t+ucfB3sXglb7s09RkeuXKFoAJ3bQ2N59vq6X4c2FEUTINkTgs5IQvfu9Nf/mJOgejGs/D50NYXwxYGUl8UAACAASURBVB2PZl9r/LlQvQialsGYE8J7r3I+7FoagtrKZkHlXOhqCe+B4onhMynZBbGiEMTWvhnW3gadO6H+4uyfRYkuIAJRv74tSZIkSdJI+H/WkiRJ0oj4xUBJkiRJkiRJh5dIJEIsFhvtbUiSJEmSJEmSdHgYjV+YMPak0EYqGg8haw/eAJvvhJJJMOdfoPGqvc9d8BkoPwrW/wbyymHKq6H29IHnnPpjuPMi2PV0OB57Ciz+5ogvI039ZdmD0SZdHQKNtj+UWcsadBaBeHEIaupv9j/Dyh/Cpj9l1qIFIQDtH8tEQ1hT9aJU35gTcu//kmfht/Ohuzm9P14Kp98KrWtzzz3uG1B3AfxqWvb69DfB8f8VAqNuzvFvRQs+BdOuhyc/Cmt/Be2bwuti7Cmw7SFoWpr7/NJQtW0YuL71b6H1yhWKBrDx/0IDWPuLke/t7+9P3a89M4TC7Xwi1Tf2NJh8DTRcEcLVIrEQ9DZciZ7weTHQnynJJJAM4yRJkiRJOsQZjCZJkiRJkiRJkiRJkiRJkiRJkiRJkjIV18OZvwmBO0MJeYtEYOqrQxuskklw4ZPQ/GwIECudPOTt7lXl3BDu1Tccre4imPYGyKuE+1+TPn7sKXD8t+FPS6B9Y6p/9nsgVgxPfzJ9fDQf6i8GktmD0eovguhefkFNxVwomQK7V6b315wYHqOZb4NnPptem/FmiJdAcWPYQ6Izc91xZ0PhAMFMZTPCbSQaxmbbf8PlUFgLi/9faNm0bwmBVuWzIZYPNw3wupnxNnjuP7LXJl0Lq27KPVc6WGy+M7Nvyz2hPfTW4a0ZKwaSkF8Nbesy66VToageelph+yOZ9VN/Ej4POrZCwVioOhq6miC/Jnw+xApHJ7hTkiRJkqRBMhhNkiRJ2m+So70BSZIkSZIkSZIkSZIkSZIkSRq5AxWgE4lA+az9e465/woTr4Qt90H5zBA4Fo2FELdkTwjqat8EE86DhV+CvHI470FYdUsIKBq3BOovge4W2Pq3VIBYNB9O/D4U1MDk62DXM7D086nzFjfCgs9m3VKaSBSO/ybccwV07w59BTWw6Kvh/oJPh7CkVbcACWi8Go56X6jFi0Iw25pb09esOgYqZof75bOhaVnmeRtemrp/1Pthy92Q6Er1TbwSKo7a+/4Lx4bWq/FlsPqnmeN6H6tcwWjHfn54wWjnPQg1i2H3anj8Rtj59/DY1yyG0mnQshKqFoSxOx4PYXPVC8NzXtwAd14ILS+krxnNS38spP2tpzXcZgtFg/Aa7f867euvLx/ceSrmhvdETyvES0MYZtsmaLwqhCBu+CNsfyjUzrkrfM6tuTV8VpbPgp522PU0FE+E6uMg0RHGjDk5fK42LYdtD0PJnnq8eGiPgyRJkiTpiBVJJg1r0JEtEonMBZ7qPX7qqaeYO3fuKO5IkiQdUnra4cdF2WuTXgGn3Hxg9yNJkiRJkiRJko5oTz/9NPPmzevbNS+ZTD49WvuRJMmf0ZMkSZIkHdYSPSF4a/fqEARUNC69vvPpEJ4WL4G6CyC/cvBr714FG24PIUMTzoei8YOb174Z7roEtj0YjkunwZm/TQXOPfef8NBb0+fUXQxn/jq9b/M98Nw3oX0jjH8JzHkvROOD33+v9bfDneen9xXUwKUvQrIbbh0fwpT6Kp0GlzwHf/8ALP1c5przPgIv/i+0rEjvLxwHl68JQWbD1dMRgp+6dsK4s0N4HsCtE8Jj0d+ir8Gsd4T7bRthwx+gcALUHBfWIgG3TRz+fqTDXXEDtK5NHY85GWKF0N0Ku18MQYYtL4SQxTEnQVEddGyFkikhhDGvPMzrboFoIZAI8yVJkiRpP/Dn8w6cYfwtlCRJkiRJkiRJkiRJkiRJkiRJkiRJ0hEuGoPqRaFlUzk3tOEomQTT3zT0eYW1cO790Pxs+EXgFfPCPnvNeAsQhee/BZ07oP4iOCZL+FjtaaGNVN15cPKP4KmPh3CjmhPghO9AXmmoz3wbLPtS+px5N0IkAlNeBcu/AonOVC1aAFNfEx7Xe68N4WoARGDBp0YWigYQK4DJ12S5jgvhhe+l90Wi0HBZ6rhoPEx9bebchpfC2l+MbF8A1yRgx2Ow8ofhsSyZDBMvh6pj4GfVueeddTtsfzQ8NvlV4Xnv3g0Vc6G4Hrqaw7XsegY23w1rfja8/dWeDrVnhtCqjq2w8wloWja8tXTk6BuKBrD1vvTjDXsCCZ/+9NDXnvVumP+RoYVSSpIkSZIOCgajSZIkSftLMjnaO5AkSZIkSZIkSZIkSZIkSZIkHWkiESiflbs+44bQDpTJ14aW6IZov6+1HvsFKJsOa24LYWlTXgMNl4Za5Tw487fw2L/Azqegcj4c9w0onRJaUUMI8Up0wcQrYNyZ++8a5n4QNt4Brav79N0IJY17nzvt9ZnBaPFSaHwZvPDfgzv/Ue8Pz2v1wtD6m/XuECLX38QrYMK5oe3N+LNh1jvgF/XQtj77mKpjQzhbNmf/JQSsZfPUp+CJG7PXZr4Tnv1a9tr0N0HtGSE8q2MbbH8INv0l+9h4CYxbEgLeWlZkH6Mjy/KvhPft2X8KoZGSJEmSpEOGwWiSJEnSSBh+JkmSJEmSJEmSJEmSJEmSJEnS3vUPRYMQ9jXjLaFlM/4cuOCx7KFqY08K7UAomw7nPwRrboXWdTB+CYw7a3Bz6y+Gxd+EJz8C7ZugYi6c9D8QLYBVN0NP+97XaLx64PrEl+YIRrtqcHvsa+6H4OG3ZfZXHQsz3wYPXJ9ZK5qQOxQNoO787MFoeZVQs3jgvfQPn+vpDIFyO58Ij+WkV0BBdebcnnaIxEOg2rYHoaAmhMr1tMGTn4C2tSFcb8K5UDoNWp6HOy/MvZdJr4BVt+Su6+C06ylY8d0QbihJkiRJOmQYjCZJkiRJkiRJkiRJkiRJkiRJkiRJkqSDV7ZQtQOtsBZmvHl4c2fcANPfBN3NkFee6l9yBzx+Ywhvqj4OFn4JXvgeLP3CngERWPhlqD524PXHngZz3ttnHjD5Oph45dD32nAZPPIOSCb69V8OdReFALT+tUnXDLxm1UIonwVNy9P7p1y3J2AuAvT7xfUFY6GoPnOtWH54PPcmVhhui8ZBwyXp/Yu+lDm+fEZ4jts3Z9ZO/hFMvjYE9K35WfbzXd0RHpvVPw3Pw45HIV4WnvOieujYAonOve9b+97GPxqMJkmSJEmHmIPgb4IkSZKkw1Vy70MkSZIkSZIkSZIkSZIkSZIkSdLhLxJJD0UDGHsKnPOX9L5jPw+z3gO7noaqY6FwzODWPvbzMOU1sP0RqDgqBK1FIkPfZ3E9HPf/4OG3pgLQxi2B2e8O+1/4FXjknanxVcfAnPftfX9n/AbuuhSaloa+iVfAMZ+FeAnUXwLrfpU+Z8abIRob+v5Hov5SWPGd9L5oAUw4P9yf+prswWhVC0NgG8Dka0IbSMe2EJ7WshJqjoNpb4T8ilT9pgGet4uXw29m5a6fcze0b4I1P4fVP4Nk98B7ORJ0bB3tHUiSJEmShshgNEmSJGlEDD+TJEmSJEmSJEmSJEmSJEmSJEn7UHFdaENVOS+0kZpxA0w4D7beByWToeZ4iO75SvKsd4SgtM13QnEjjF8Sws32pmw6XPQ0tLwAeRXpgW+n3BzC1tbeBvEymPpamPfhkV/HUM37N9jyV2haFo4jUTjua1BQHY7HvwQKa6F9c/q8qa8Z2nkKauCYz+SuT3sjrPh2Zv/cD0H5TJj5dnj2G5n1ya+C2tPC/car4NgvwK5noHAsVB4drmf3GvhlY/bzzv4naLgc7jg9997m3gid26CrGV7839zjDiadO0Z7B5IkSZKkITIYTZIkSZIkSZIkSZIkSZIkSZIkSZIkSVJK6eTQsqmcG9pQRSJQNi2zP14MJ3wHjv92GDNaSibCeQ/Cxj9C24YQAFcxJ1WPFcCSP8G9V4fAsVgRzHpnCCrbl6ZclyUYLQKTXxnuNrw0ezDapKvTj4vrQ+uraALEiqGnNXP+5FdC9UI46X/hb9dl1iecDws+kTrOFYxWvQhO/Rk8+RFY+YPsY16+G6L5IXAvmYTdL4bAvObngQSUTodYfhiz8c+w/KvQ/GwYByFcbsprQlBdsge2PwJrbs1+LoPRJEmSJOmQYzCaJEmStN8kR3sDkiRJkiRJkiRJkiRJkiRJkiRJh4bRDEXrlVcGE6/IXa+cBxc9DW0bIb86hHfta7Wnw/Hfgkf/GbqbIb8qBMf1hrSNOwuO+iA885nUnFnvDsFlexONQ+NVmYFlZTOg6thwv+GycM7+gWLT3pB+XLkAdj6eeY5Z7w6herP/OXswWtnMEIbXKxKB0inhfsHxmePrLwxtbzbdBX86M7O/ezfseBwqjoJo3t7XkSRJkiSNOoPRJEmSpBEx/EySJEmSJEmSJEmSJEmSJEmSJOmIUjR+/64//Y0w9XXQugZKJkEkmqpFInDMp8OY7Y9C5Xwonzn4tRd+GXa/CJvvDsfFE+G0W1PBdHmlcM5d8LdXw46/Q+E4mPfhEKjW18QrMoPRogVQf3G4Xzkfak6Ebfenj5nx1sHvdSjyq3LXfn9MuK27KDye2x6E4voQBldzIuRXQF55CLsrGAvRWGruqp/A8/8FXU3h2ub+KyQT0LYOihtD2NxgJLqBSPrayWTqcU90Q7IHYgVDumxJkiRJOhwZjCZJkiRJkiRJkiRJkiRJkiRJkiRJkiRJB5NoHEqn5K6XThm4nktBNZx9J7SsCGFflQvSw7oghJpd8Bh0NUO8NBXe1dec98L2h2Hdr8NxrBBOvhnyK8NxJAJn/Q4efDNs+AMUjIEZb4FZ7xz6ngdjoGC0Xut/m7q//WFY+8uhnWP7w/DkR9P78sqhYh5E88Nj0L0bonnhudn6AOx6au/rxsuguzm9b+Y7wmNVOi398U8m0oPyevsSXWEPXbugZSVUHJU7ZC1bSNvedLeF57qnFSZcAEXjBj9XkiRJkobIYDRJkiRpf0kmR3sHkiRJkiRJkiRJkiRJkiRJkiRJUrpIBMqm731cXlnuWrwYTv8lND8Lu1dBzQmQX5E+Jr8KTv1x9jCvfW0wwWj7Q1cTbL0vs3/TENboH4oG8OzXQxuJSDQ89gBFE6BqEbRvgqal0N0SQu9mvh3aN0PReGjbCKtugspjoP4iqL8ENt4BT308XGevaD7M/1hYs2kpxEpgzIlQOS/09T7fiZ5wG4nsCW/rhp62EBwXLx7ZtUmSJEk6rBmMJkmSJI2E4WeSJEmSJEmSJEmSJEmSJEmSJEk6EkUiUD4rtAHH7edQNIB4CeSVpwd4Hel6Q9EA2jZA22/S690t8MxnM+dtuz+0Jz6cfd1EJzz+wZHtrXwWVB8PhbXh9VF1LDRcDvGizLEHIlhPkiRJ0kHFYDRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJ0qErEoG6i2HVTaO9Ew1G0/LQ+iuZAoXjoLghhNz1tMHW+yDZA7FCqF4cbiecD+POgEQP5FeGllcBsYIDfy2SJEmS9jmD0SRJkqT9JjnaG5AkSZIkSZIkSZIkSZIkSZIkSZKODIu+DDseg6alo70TDdfulaFty1LraYct94T7G/+YfX6sEPIqIb9iz+2ewLT8ysH1x0tCyJ4kSZKkUWUwmiRJkjQihp9JkiRJkiRJkiRJkiRJkiRJkiRJo66wFi58ErbcDY/fCB1bofnZ0d6VDqSedujZCO0bhzc/EusTmDaEQLXe/rwKiMb27TVJkiRJRyCD0SRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJh75oDMadBefeG45bXoBfTcs+9pjPwriz4fbFudcrmQy7X9zXu9TBKtkDndtDG6542eCD1DL6KyBWuO+uR5IkSTpEGYwmSZIk7TfJ0d6AJEmSJEmSJEmSJEmSJEmSJEmSdOQqboT8KujckVmbdC2UTITF/wkPvSWzXjEXLnoKkkl44sOw7MvQ0wrVx8EJ34aqY8K4nk74cUH281ceDRc+njpe8T144A3Zx16xBbpbYPWP4e8fyKzXnAhta0NYW7wENtwOkSgkE6kxeeU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n2rNI/wAmhmv8MjK8P9P+vvQpxj0GSA4tI4f6Mys5tCbjHr2RQ8vIof5Mew7pH0Mgh5bRz/oz7WOvDIEcWkYO9UcOrcw9euuQRcvIov7MShYZqx4gObSMHOrPrOTQmoxV90YOLSOH+jPtOTSV/UNhtAlQSrk8TQdcHJK3JfmuWmtdeas1jWqboam1Hkjyix2Lf6yU8q9W26aUcmmSP0xy9mq7HVDzxlop5aeTfHfH4pfXWv90xE0Zej9c+KO1s2r3Sus9XGt9eZJ/2fGja5P8416OOa3k0HJyaONmKYe68Pwk5y6afyDJ2wZ8jKkhi5aTRRs3C1mkfwyHLFpOX9u4McoiJogcWk4ObdwM59CDSa5aND0izTjQtyX5z0nuXljvsiQ/leRjpZQva6GdY0sWLSeLNm6WssiY9eDIoeXk0MbNUg51wZh1D2TRcrJo42Yhi/QPYFK4xi8nwzdujK7x7tWbIHJoOTm0cbOUQ10w7tElObScHNq4Wcgh/WPw5NBy+tnGjVEOMUHk0HJyaONmOIfco9cnWbScLNq4WcoiY9WDI4eWk0MbN0s51AVj1V2SQ8vJoY2bhRya1v6xue0GsLZSyvlJ/neSSxYtvjPJ19Va7155q2UOdsxvpCJf5zad+2zDzyT5ppypyFiS/JdSyrenqYr6t2kqcF68sN4P5sxF73NJLl20r4drrcsqfJZSruy2MbXWW3tqfQtKKT+Sptr1Yr9Qa/25Lre/sttjrXA+xr4f1lpfV0r5+iTPW7T4h5K8ZZDHmTRyaE1yqEdyaJnv65h/S621s3o0kUXrkEU9mrEsGnr/mCWyaE2yqEctZ1QVJ7cAACAASURBVBETSg6tSQ71aJZzqNZ6MsmtK/zoI0neXkr5d0leneSHF5ZfnuQ9pZSvrrV+fDStHF+yaE2yqEeznEXdMGa9Mjm0JjnUIzm0jDHrLsmiNcmiHs1YFhmzBsaaa/yaXON7NGPvS/fMuMfK5NCa5FCP5NAyxj26IIfWJId6NGM5ZMxjQOTQmuRQj2Zs7JUBkUNrkkM9muUcco9ef2TRmmRRj2Y5i7phrHplcmhNcqhHcmgZY9VdkENrkkM9mrEcmrqxaoXRxlgp5Zwk70ny6EWL70nynFrrTT3saioDu9Z6tJTybUn+V5InLfrR0xem1dyb5gXDHy1adv8q697SQ5NKD+uOXCnl+5P8Qsfi/15rfVkPu+nnfExKP/zZLP0D9itLKXtqrav1kakmh9Ymh3ojh5YqpVyW5Os6Fr9ho/ubZrJobbKoN7OWRSPqHzNBFq1NFvVmDLKICSSH1iaHeiOH1lZrPZzkX5ZSjiX51wuLdyf59VLKUzb47UJTQRatTRb1RhZ1zZj1InJobXKoN3JoKWPW3ZNFa5NFvZm1LDJmDYwz1/i1ucb3Zgyu8ZPSD417LCKH1iaHeiOHljLu0R05tDY51JtZyyFjHoMhh9Ymh3ozBjnEBJJDa5NDvZFDa3OP3upk0dpkUW9kUdeMVS8ih9Ymh3ojh5YyVt0dObQ2OdSbWcuhaRyrnmu7AayslHJ2kj9O8sRFi/enqWB5Q4+7e6Bjfl+PbdmZ5YE9Fh241vr5JE9L8vokx7rY5H1JnprkUMfyOwfctLFSSvnuJL+cpSH6xiT/YoTN6OyHZ5VSdvS4j/M75ofRD/8qze/aKZuSPH4Ixxl7cqg7cqg7cmhF12Xpa7G/q7X+TR/7m0qyqDuyqDuzmkX6R/9kUXf0te6MSRYxYeRQd+RQd+RQT34yyR2L5r80yXNaakvrZFF3ZFF3ZFFPjFkvkEPdkUPdkUMrui7GrNcli7oji7ozq1mkfwDjyDW+OzK8O2NyjR+3e2RWY9xjgRzqjhzqjhxa0XUx7rEmOdQdOdSdWc0h/aM/cqg7+ll3xiSHmDByqDtyqDtyqCfu0VtEFnVHFnVHFvXEWPUCOdQdOdQdObSi62Ksek1yqDtyqDuzmkPT1j8URhtDpZRdSf4wyVMWLX4wyTfWWv92A7vsrHp5RY/bd65/X611/4prtqDWeqjW+s+TPCbNQMj7knwuyUNJDiS5Mcmvpame+uxa661JHtexmw+PrMEjVkp5cZpwXvz7/htJ/ukoK+fXWu/N0j8Mk+TyHnfT2Rd7qejalVrrySS3dyzu6UXONJBDvZFDa5NDy5VSSpLv6VisqncHWdQbWbS2Wc8i/WPjZFFv9LW1jUsWMVnkUG/k0NrkUG9qrQ8leUfH4m9soy1tk0W9kUVrk0W9MWbdkEO9kUNrk0PLGbPujizqjSxa26xnkf4BjBPX+N7I8LWNyzV+nO6RWYtxj4Yc6o0cWpscWs64x/rkUG/k0NpmPYf0j42RQ73Rz9Y2LjnEZJFDvZFDa5NDvXGP3hmyqDeyaG2yqDfGqhtyqDdyaG1yaDlj1euTQ72RQ2ub9Ryapv6xue0GsNTCt9D8ryRfuWjxwSTfVGv9qw3u9saO+Uf2uP3VHfOf2GA7hqrWekuSn1mY1vNVHfN/uco+y0rLJ0Up5YVJ3pymOvUpv53kpQt/qPVkAOfjxjSVJU95ZJb3z7V09sVetu3FQx3znZVcp5oc2jg5tJwcWtWzkly1aP5ImhfTLJBFGyeLlpNFZwyjf0wzWbRxsmi5McwiJoAc2jg5tJwc2rBPdsz3+jsz8WTRxsmi5WTRhhmzlkMbIoeWk0OrMma9Dlm0cbJoOVl0hjFroG2u8RvnGr/cGF7jx+UemfUY95BDGyKHlpNDqzLusQY5tHFyaDk5dIYxj+7JoY2TQ8uNYQ4xAeTQxsmh5eTQhrlHTxZtmCxaThZtmLFqObQhcmg5ObQqY9VrkEMbJ4eWk0NnTMNY9dz6qzAqpZTtSX4vydMXLT6c5JtrrX/ex64/3jH/pFLKWT1s/9Xr7G+iLFRTfVbH4g+00ZZhKqU8L8lvZmkBxHckeUmt9UQ7rVrWdzqDcVULL2aetM7+BuW8jvl7hnScsSOHRkMOyaEk39sx/7u11vs2uK+pI4tGQxbJonWOMxP9Yy2yaDRmpa+NaRYx5uTQaMghOdSFYx3zW1tpRUtk0WjIIlnUBWPWcmio5JAcijHrNcmi0ZBFsmgts9I/gNFyjR+NWcnwMb3Gj/370guMe8ihoZJDcijGPVYlh0ZDDsmhdY4zE/1jNXJoNGaln41pDjHm5NBoyCE51AX36MmioZNFsqgLxqrl0FDJITkUY9WrkkOjIYfk0FrGuX8ojDYmSinbkrwryTMXLX44yfNqrX/az75rrV9I8tFFizZn6UVhPc/smP+DftozBp6V5MpF8x+otd7UUluGopTy3DQVK+cXLf79JC+qtR5vp1VJkj/smH9mD9t+TZZefD5Sa72r7xZ1KKWcl+XVW+8Y9HHGkRwaKTnUntZzqJSyJ8m3dSx+Q6/7mVayaKRkUXtaz6IuTH3/WIssGqmp72tjnEWMMTk0UnKI9VzaMT+M115jSRaNlCxiVcas5dCIyKEZZsx6bbJopGQRa5n6/gGMlmv8SE19ho/xNX7s35c27iGHRkQOtaf1HDLusTo5NFJyqD2t51AXpr5/rEYOjdTU97MxziHGmBwaKTnEetyjJ4tGQRaxKmPVcmhE5NAMM1a9Ojk0UnKItYxt/1AYbQyUUrYk+d0kz1m0+EiSF9Ra3zugw7y9Y/57umzbY5N8xaJFh5L88YDa1JZ/2zH/+lZaMSSllK9L8jtJtixa/MdJXlhrPdpOq077oyQPLZr/qoU+1o3rOuY7+/SgvDhLs/GuJDcO6VhjQw6NnBxqzzjk0Hcm2bZo/tYkf7LBfU0VWTRysuj/Z+/Ow2S7ynrxf98QCEMgYQqjJMyDyhiUgJiDgAIKGFBAZAjkyiDOIALXIaAXLj9F8YqCoJAwKoMEZFTEwxAIMo+KTIkQmQmEEDKv3x+7jqfO7urumrqquvvzeZ5+yF6119qrqtZ+O+x+867lWYVYtJkdvT42IhYt3I5eaysei1hR4tDCiUNs5id7xyvxYH+riUULJxaxEc+s9xOHto44tLt5Zr0OsWjhxCI2sqPXB7BYfscv3I6O4Sv+O347/F3ac4/9xKGtIw4tzyrEIc89RhCHFk4cWp5ViEOb2dHrYz3i0MLt6HW24nGIFSUOLZw4xGbk6O0nFm0dsYiNeFa9nzi0dcSh3c2z6hHEoYUTh9jIyq4PhdGWrKoOTvLKJPccar4wyc+11t46x0u9LMnFQ8f3q6obj9Gvv3hf2Vo7b37TWqyqeniSuw81fSRdxccdoaqOTfK6HPgvRm9P98v//OXMar/W2rlJXt1r7q+xNarqJkmOG2q6KMnL5zi1fde5RpLf7TX/Y2utzftaq0QcWixxaLlWJA49snf8wp0eZ8YhFi2WWLRcKxKLNrrOjl4fGxGLFmunr7VVj0WsJnFoscQhNlNVP53k6F7z65Yxl0USixZLLGIjnlmLQ4sgDhHPrEcSixZLLGIjO319AIvld/xi7fQYvuq/47fB36U999hPHNoi4tByrUgc8tyjRxxaLHFouVYkDm10nR29PtYjDi3WTl9nqx6HWE3i0GKJQ2xGjp5YtAhiERvxrFocWgRxiHhWvYY4tFjiEBtZ9fWhMNoSVdWl0gXS+w41X5Tkga21N8zzWq21zyQ5eajpMklOqqrLrtMlVXXfHLjTzQVJnjrPec1q8Atv3HPvl+QFQ00XJXlka+2iuU9sCarqmCRvSHK5oeZ3Jrl3a+37o3stxYnp/qVkn+Or6j7rnTxYoy/KgZU5/7a19rkN+ty0qu49yaSq6prpPr9rDDVfkOQZk4yz3YhDsxOH9hOHNldVt05y26GmS5KcNOk4O41YNDuxaD+xaGRf62MMYtHsrLX9tlEsYoWIQ7MTh/YTh/arqqOr6rjNz1zT7/ZJXtJrfmdr7ePzmdlqEotmJxbtJxbt55n1+MSh2YlD+4lDm/PMejSxaHZi0X5i0VrWB7AsfsfPTgzfbxv9jj8xcvVWhjg0O3FoP3Foc557rCUOzU4c2k8cGtn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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import asyncio\n", + "from uuid import UUID\n", + "\n", + "from matplotlib import pyplot as plt\n", + "\n", + "from apd.aggregation.query import with_database\n", + "from apd.aggregation.analysis import get_known_configs, plot_sensor\n", + "\n", + "\n", + "location_names = {\n", + " UUID('53998a51-60de-48ae-b71a-5c37cd1455f2'): \"Loft\",\n", + " UUID('1bc63cda-e223-48bc-93c2-c1f651779d69'): \"Living Room\",\n", + " UUID('ea0683de-6772-4678-bfe7-6014f54ffc8e'): \"Office\",\n", + " UUID('5aaa901a-7564-41fb-8eba-50cdd6fe9f80'): \"Outside\",\n", + "}\n", + "\n", + "with with_database(\"postgresql+psycopg2://apd@localhost/apd\") as session:\n", + " coros = []\n", + " figure = plt.figure(figsize = (20, 10), dpi=300)\n", + " configs = get_known_configs().values()\n", + " for i, config in enumerate(configs, start=1):\n", + " plot = figure.add_subplot(2, 2, i)\n", + " coros.append(plot_sensor(config, plot, location_names))\n", + " await asyncio.gather(*coros)\n", + "\n", + "display(figure)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "9f576f5bb8e34cb8ab1d3c09f770d027", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "interactive(children=(DatePicker(value=None, description='Start date'), DatePicker(value=None, description='En…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import asyncio\n", + "from uuid import UUID\n", + "import functools\n", + "from concurrent.futures import ThreadPoolExecutor\n", + "\n", + "import ipywidgets as widgets\n", + "from matplotlib import pyplot as plt\n", + "\n", + "from apd.aggregation.query import with_database\n", + "from apd.aggregation.analysis import get_known_configs, plot_sensor\n", + "\n", + "\n", + "def wrap_coroutine(f):\n", + " @functools.wraps(f)\n", + " def run_in_thread(*args, **kwargs):\n", + " loop = asyncio.new_event_loop()\n", + " wrapped = f(*args, **kwargs)\n", + " with ThreadPoolExecutor(max_workers=1) as pool:\n", + " task = pool.submit(loop.run_until_complete, wrapped)\n", + " return task.result()\n", + " return run_in_thread\n", + "\n", + "@wrap_coroutine\n", + "async def plot(*args, **kwargs):\n", + " location_names = {\n", + " UUID('53998a51-60de-48ae-b71a-5c37cd1455f2'): \"Loft\",\n", + " UUID('1bc63cda-e223-48bc-93c2-c1f651779d69'): \"Living Room\",\n", + " UUID('ea0683de-6772-4678-bfe7-6014f54ffc8e'): \"Office\",\n", + " UUID('5aaa901a-7564-41fb-8eba-50cdd6fe9f80'): \"Outside\",\n", + " }\n", + "\n", + " with with_database(\"postgresql+psycopg2://apd@localhost/apd\") as session:\n", + " coros = []\n", + " figure = plt.figure(figsize = (20, 10), dpi=300)\n", + " configs = get_known_configs().values()\n", + " for i, config in enumerate(configs, start=1):\n", + " plot = figure.add_subplot(2, 2, i)\n", + " coros.append(plot_sensor(config, plot, location_names, *args, **kwargs))\n", + " await asyncio.gather(*coros)\n", + " return figure\n", + "\n", + "\n", + "start = widgets.DatePicker(\n", + " description='Start date',\n", + ")\n", + "end = widgets.DatePicker(\n", + " description='End date',\n", + ")\n", + "out = widgets.interactive(plot, collected_after=start, collected_before=end)\n", + "display(out)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "60091fa7d9ea415985ec53937e422b4a", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "interactive(children=(DatePicker(value=None, description='Start date'), DatePicker(value=None, description='En…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from uuid import UUID\n", + "import ipywidgets as widgets\n", + "from apd.aggregation.analysis import interactable_plot_multiple_charts, configs\n", + "\n", + "location_names = {\n", + " UUID('53998a51-60de-48ae-b71a-5c37cd1455f2'): \"Loft\",\n", + " UUID('1bc63cda-e223-48bc-93c2-c1f651779d69'): \"Living Room\",\n", + " UUID('ea0683de-6772-4678-bfe7-6014f54ffc8e'): \"Office\",\n", + " UUID('5aaa901a-7564-41fb-8eba-50cdd6fe9f80'): \"Outside\",\n", + "}\n", + "\n", + "start = widgets.DatePicker(\n", + " description='Start date',\n", + ")\n", + "end = widgets.DatePicker(\n", + " description='End date',\n", + ")\n", + "\n", + "plot = interactable_plot_multiple_charts(location_names=location_names)\n", + "out = widgets.interactive(plot, collected_after=start, collected_before=end)\n", + "display(out)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "08a4d937037b45bda2298879df7f80b6", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "interactive(children=(DatePicker(value=None, description='Start date'), DatePicker(value=None, description='En…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import datetime\n", + "import uuid\n", + "import ipywidgets as widgets\n", + "from apd.aggregation.analysis import Config, configs, interactable_plot_multiple_charts, draw_map, get_map_cleaner_for\n", + "from apd.aggregation.database import DataPoint\n", + "from apd.aggregation.utils import merc_x, merc_y\n", + "\n", + "start = widgets.DatePicker(description='Start date')\n", + "end = widgets.DatePicker(description='End date')\n", + "\n", + "def get_literal_data():\n", + " # Get manually entered temperature data, as our particular deployment\n", + " # does not contain data of this shape\n", + " raw_data = {(53.8667, -1.3333): -1, (53.35, -2.2833): 1, (52.45, -1.7333): 3, (51.5, -0.1333): 6, (51.55, -2.5667): 5, (54.9667, -1.6167): -1, (55.9667, -3.2167): -1, (54.7667, -1.5833): 0, (53.7667, -0.3): 1, (53.7667, -3.0167): 0, (51.4833, -3.1833): 4, (53.2667, -3.5167): 2, (52.0833, -2.8): 2, (52.0167, -0.6): 2, (52.6667, 1.2667): 5, (50.4333, -4.9833): 9, (50.35, -4.1167): 10, (50.5167, -2.45): 9, (50.8333, -1.1667): 9, (56.45, -5.4333): 4, (57.5333, -4.05): -2, (54.5167, -3.6167): 3, (53.0833, 0.2667): 4, (51.35, 1.3333): 7, (50.8833, 0.3167): 8, (58.45, -3.1): 3, (50.1, -5.6667): 9, (58.2167, -6.3333): 4, (55.6833, -6.25): 7}\n", + " now = datetime.datetime.now()\n", + " async def points():\n", + " for (coord, temp) in raw_data.items():\n", + " deployment_id = uuid.uuid4()\n", + " yield DataPoint(sensor_name=\"Location\", deployment_id=deployment_id, collected_at=now, data=coord)\n", + " yield DataPoint(sensor_name=\"Temperature\", deployment_id=deployment_id, collected_at=now, data=temp)\n", + " async def deployments(*args, **kwargs):\n", + " yield None, points()\n", + " return deployments\n", + "\n", + "def draw_map_with_gb(plot, x, y, colour):\n", + " # Draw the map and add an explicit coastline\n", + " gb_boundary = [(-2.6653539699999556, 51.617254950000074), (-2.6937556629999335, 51.59617747600004), (-2.71312415299991, 51.58661530200004), (-2.760487433999913, 51.579779364000046), (-2.7980850899999155, 51.56350332200009), (-2.816395636999914, 51.555568752000056), (-2.8587133449999556, 51.546128648000035), (-2.943348761999914, 51.54254791900007), (-2.9562882149999155, 51.54572174700007), (-2.9641007149999155, 51.552801825000074), (-2.969471808999913, 51.559881903000075), (-2.9746801419999542, 51.56297435100004), (-2.984038865999935, 51.55853913000004), (-3.0102432929999168, 51.53803131700005), (-3.0156957669999542, 51.53164297100005), (-3.0737198559999115, 51.50836823100008), (-3.0764867829999503, 51.50775788000004), (-3.088937954999949, 51.505072333000044), (-3.105620897999927, 51.49721914300005), (-3.1317032539999445, 51.48041413000004), (-3.1350805329999503, 51.47630442900004), (-3.1409399079999503, 51.46478913000004), (-3.1453751289999445, 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51.49408600500004), (-2.716420050999943, 51.49957916900007), (-2.6961563789999445, 51.51264069200005), (-2.6078181629999335, 51.60736725500004), (-2.599598761999914, 51.611395575000074), (-2.5892227859999366, 51.61424388200004), (-2.580433722999942, 51.61863841400009), (-2.5768123039999296, 51.62750885600008), (-2.57445227799991, 51.63572825700004), (-2.568959113999938, 51.644964911000045), (-2.562245245999918, 51.65338776200008), (-2.556385870999918, 51.65924713700008), (-2.499175584999932, 51.69676341400009), (-2.464995897999927, 51.725897528000075), (-2.4477432929999168, 51.73187897300005), (-2.404896613999938, 51.74115631700005), (-2.3885391919999392, 51.750433661000045), (-2.380767381999931, 51.76190827000005), (-2.3833715489999463, 51.77326080900008), (-2.398060675999943, 51.782131252000056), (-2.4024145169999542, 51.76357656500005), (-2.4203995429999168, 51.753119208000044), (-2.4434301419999542, 51.74843984600005), (-2.4632869129999335, 51.747300523000035), (-2.482085740999935, 51.74286530200004), (-2.495838995999918, 51.73200104400007), (-2.507964647999927, 51.71857330900008), (-2.5221248039999296, 51.706366278000075), (-2.581695115999935, 51.681708075000074), (-2.5911352199999556, 51.67536041900007), (-2.600209113999938, 51.66559479400007), (-2.6653539699999556, 51.617254950000074)]\n", + " draw_map(plot, x, y, colour)\n", + " plot.plot(\n", + " [merc_x(coord[0]) for coord in gb_boundary],\n", + " [merc_y(coord[1]) for coord in gb_boundary],\n", + " \"k-\",\n", + " )\n", + "\n", + "country = Config(\n", + " get_data=get_literal_data(),\n", + " clean=get_map_cleaner_for(\"Temperature\"),\n", + " title=\"Country wide temperature\",\n", + " ylabel=\"\",\n", + " draw=draw_map_with_gb,\n", + ")\n", + "\n", + "out = widgets.interactive(interactable_plot_multiple_charts(configs=configs + (country, )), collected_after=start, collected_before=end)\n", + "display(out)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "apd.aggregation", + "language": "python", + "name": "apd.aggregation" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Ch09/chapter09-database.ipynb b/Ch09/chapter09-database.ipynb new file mode 100644 index 0000000..98a74f0 --- /dev/null +++ b/Ch09/chapter09-database.ipynb @@ -0,0 +1,577 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "53635\n" + ] + } + ], + "source": [ + "from apd.aggregation.query import with_database\n", + "from apd.aggregation.database import datapoint_table\n", + "\n", + "with with_database(\"postgresql+psycopg2://apd@localhost/apd\") as session:\n", + " print(session.query(datapoint_table).count())" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "53635\n" + ] + } + ], + "source": [ + "from apd.aggregation.query import with_database, get_data\n", + "\n", + "with with_database(\"postgresql+psycopg2://apd@localhost/apd\") as session:\n", + " count = 0\n", + " async for datapoint in get_data():\n", + " count += 1\n", + " print(count)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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qZkRxdc8uWM32AuC1DUtGvtuVhMnRk2fQ29dPbwtCSN1SFyOKdpuSAAA1tvth1c4XY5eJEELywrhXFPd863nP3+ev21P1HINDF+MShxBCcse4VhS9ff2uU0omJ947n5I0hBCST8a1ovjjb/ubMpqz5qmEJSGEkPwSWlGIyGwROWj5e1dEVohIi4jsEZGjxueUOAUOwrkL/qaM3j03XH0nQgipU0IrClU9oqrzVHUegF8EcBbA3wPoAbBXVWcB2Gt8J4QQklPimnq6GcCrqvoGgNsBbDW2bwVQjukaNaW3r7/WItQVvX39WLjhaVzdswsLNzzN8iekhsSlKL4IYLvx/3RVPQ4Axuc0pwNE5H4ROSAiB06dOhWTGKOZNW1SbOdiZq30WLTpWazYcRD9A4NQVNJgrthxkMqCkBoRecGdiDQCuA3AqiDHqeoWAFuASvTYqHI4cfZ8fG6tWcisVQ/J5u/51vM4evKM428rHzmYi/uth+dEorO69zC2738Tw6ooiODu+Vdhbbmj1mI5EsfK7M8C+IGqnjC+nxCRGap6XERmADgZwzVCEWfj/uFSMbZzhWHOmqdGGd3Ha7J5L3fmi7WPiF+VRZueHaXozNEQML6eE4mGvZ4Mq+LhfccAIJPKIo6pp7txadoJAJ4AcK/x/70AHo/hGqFonBCf9++7g0OxnSso89ftcfTMGm/J5q994MlaixAJr9HQih0Hcc2qXZw+I1jde9i1npjKImtEGlGISBOARQD+q2XzBgCPiMiXABwDsDTKNcLS29fv2z3WD0mvzXaKXLu0qw0bdx/xXBTYn4EpsThY3XsYHwznYMjgQbXFnRcVHF2QzCoDLyIpClU9C+AK27afouIFVVO6Hz1YaxEccZq/fvTAMcfItdUaHqAS3DCvWMsi3yoiGH/02CEqijplde/hWosQinEbPbZaeKZJjQWcOZ/uQrvevn78/o6DI41i/8DgqO9hyGsD29vXj1U7D2NwqP4WO57P+ciJBKe3r39kNOlFVjt+uVYUUbxL1t3RMaahKhULng1Xe8+uSN4Jf/TYIccotvXIxt1HAiuJgmT1NQrO6t7DmTRaEm/CeCr5VRIAcM+CtjjEjJ3cKorevn50P3oIQ4YrTP/AILofPQQAOPCG95RNqdgwolDsiqbaA3XyTvCrsNiTvEQY28qwjp/ye3jfMSqKnBHWU+nBJ17yfY2s1oncKooHn3hpREmYDF1UPPjESxio4qG0/s45ACoGRXuD7lfzbzNedHtvge6Q/hAEH021NpeSEIWQqlTzVPJq4Ku1RyaTJxZCyZYGuVUUboXv56F4NeClYoOv/BNmI+emWFbsGL04jG6RowkzNmi/Il5FYe8hzpo2CXtW3hjqXEk8X7sn3MQJDfja5yudHC7oS5ft+98MdVy1fDhWXvzyZ0JdIw1yqyiSYv2dc3yPKqphTaGa5HqHepnvfu7V07GlpbUrCaCS9nbRpmerKguneeowLo/tPbuwfEGb47NzyuF+7sLFMXXTHMH+ae/hTDc0Tlz7wJNjXKInTyxk8j6qTXs6vYNOdcyNUjHbGR9EMzDv29XVpQcOHAh0jN80pnYmNAhe+eotnvvMXv09X2swFs5s8eXCmgavW/J/54Gwz880Z1frSVezG3ld36ssV/ceTsQP3q4wwpTPZQXBy+u863ZWcFISVtKsz15u2hMEeGX9Esxc9WRVZWGVOYgBGwA2L5sXqgMkIi+oalfgAwOSbTWWAF9fOrfqPubwvhppKYnNy+alcp08oMafGcLEacrHdL21BhX8/R0HY/FhT2qx1MP7jkWW74NhzY2ffrXFlVeH7EgExV5X7FzQitJecE2wtDpf/o5/A/bCmS2ZnzqsK0WxfEGbrweSlYfW2lwK3dPIMkHmbb1wC2Hi5HqrqDTGfmwJ7T27atLgxqGE8rjq1wlF+FFnEPy6afvpFFqV2ztn/Rmwly9ow7b7rve1by3JraJoLAT3qc/TPP5lBcFzPZ8eURILZ7bUWKJ4WN17uOpL1xDg0Tq52XoFg/TrqhhHDz8M7T27Umkg80LSSjvOwKGK4Cuv89Im5VZRTJoYzA4fdPomzlwWYbDPNVfrdeQl0Y+fHu+mLwR7VvZ79YoF6ddVEYindz5r2iQsz+giqlpyWYCOXpJK+8qYXa7NOtPsI9q0n32yQm4Vhd+hnUnQ6Zs9K2+sibKYPLEQypC38pHRiX7c5u+TIG4lFfRZ2UcJ1bybg8j386su9e4XbXo2iFgAKvVobbmDdiYbG+6qbiu08vC+Y7i6J/7ou92LZ8d6PqAyCvLTIXnwtutiv3ZS5NY9tiCS+ErdPStvxMINTyceoTUODw97rgZz/j5p+0aQBYdJ9QqDjBKAimJpbS75eq4XtGJTeeXk+55RfJ2YfnnjyP9mWcTlep13wriLKyrl94ePHsLXl87Nte1u1rRJuZI/tyOKtMI5JNHjsBIkflFTQF9r+/xrEtNTKz0WHNrZ5mMqx2xcJ8aYS8TOwOBQoOf63KunAysJANj/wKJR38udramMLPLg+RTFNnDhomLFjoNo79mF2au/F6ke1yKfS5SFnbUit4oiCFHmiMudrYnOMd89/yrf+371Tn9uuybWrHxOLqNxTE95zfK0WxRSb19/1dXY0y9vHGlc/booO+FnyjDpnr3bKLHc2Zr4GoGwtpVrH3hyxJje3rMr0URScdkGzl24iJWPhM+nXot8LnlTEkCdKIqongXmHHPcsYYWzmwJJFvQoap1sOLkBjg4NDzSM3P6i6Nnak5FVWuYly9oG9UDjzIs//f3g/f+42LWtEmZWPw4f92eQPtf3bNrzNqGD4Y1MWVx07VTYzvXRQ0/MhhPEYmTJLeKIu0HXO5sxXM9n8brG5Zg+YK2Mdf3WoK/edm8EUUjuLQ+Imn/aavBP0zPKU0X0ajK3JRzde/hwI4OcbF52TzfvcVJjcEDwF1WkJH6V40T750fZYj3YnXvYdfRXlJZB595+ZTj9rChLMJOZXlNYdMB4RK5NWb7ja+TxLTR2nKHY8O2uvcwtu07NvLSTWosYN0dHSO947SNV0HWI7hRLTLm9MsbQ83f24kaw+nhfcfQ9bGWmi04a7KErvfDujs6Ak9/mS7Ta8sdvu7zglZGCq9VGeHUoszcGvYPfATkdCLsVJabU0Nrc2kkuvScNU855qw3FUk9OCjkVlGsLXfgtVPvV128leaCFjcFUivsnlBh8WrEJxTiCY0ch4dWrV5YQXD7URAvKKvtxqSp2ICzPqMcz1+3Z+R4p4CGteBKlwb6yuYSbrp2amDlFdbppHvxbMcEZtbzVQtSeOCN0+NmRbwbuVUUQGURGlexJs+qnS+6NuJxrWx1Ok+YnBVpM6WpiDW3XhdKyZk91jCZGr8aIMqxOeL7+VW7cMFSoNbEO2nj1UCb924qND+E7WS4JTALcj6zc+inLPM6nZVrRQEAxQb3BVb1viI2rsjFg0MXMWfNU449K7eeYVCcpg7uWdCWekM2QTCqMfVi8sQC+v701yJf0ymBlp9j/vDRQ7jgc9gYtkOVVMNWrYE2R+dpdATDlL+dteUOPPPyKdd3oTXneUNya8w22bjUuSIH9SjKC1Oa/C/7H7oYX0Kdd88NY86ap8Zs7148G6VitOkn+1DfZG25I1VlP3liAa+sX+I7wX2t8yZ8fencWOxQXiTZsJkOIq9tWDIqrpkVPz4rWekQOr0LpWIBm5fNc72/vJB7RWEuYkrbo6hWrLn1OhQDxMl58ImXsHDD07Fc+91zw2MUT7mzFXoxuAFyQoOMPK/1d3a4vkRryx2hAkAGZfrljSMN/2sbloxaVW0nbJiVuCl3tmLTF+J3284S98yvrgSy0iEsd7Zi/Z0do9oir7qdJ3KbuKie6e3r95UbPAlam0t4rufTgbJ3AZWeVdiX5uqeXZFtFW6Z5LywZ5lbOLMl0x2QuKdpzGdda7ySHGU1I15apJW4iIoixzilywzLpMYCzpyvHpcf8O8S29pciiWvc9R4W+Mxp4cTQbOqVSNL5TZ/3Z4xda7elQSQE0UhIs0A/gLAJ1BxUPkNAEcA7ADQDuB1AF9Q1Xe8zkNFEZ6oqTmtaz06v/L9WBerxTU909vXj+5HD2EohL+vmcqyXohrVJH10ROpkJdUqH8G4ClVvRbAXAA/BtADYK+qzgKw1/hOEiLq/GxzU+NIr3HNrdclbhwNQ7mzFR+6LJyDXj0piTihkiBWQisKEZkM4AYAfwkAqnpeVQcA3A5gq7HbVgDlqEISb6I07tb1C6ZxNA4mT4xnIZ7JQIiRTpDkOOOFOAzbWfEiItkhyojiGgCnAPy1iPSJyF+IyCQA01X1OAAYn9OcDhaR+0XkgIgcOHXKOe4L8UeUFdj29QtxhcKOe+74wwGzgV1WkDFZAuuB7sWzfbv3OiHIjhcRyQ5RFtxNAPBJAL+jqvtF5M8QYJpJVbcA2AJUbBQR5Kh7/CbhccJp/UK5szWSUTQJ19GhYW8X3Cy4q2aBcmdr6JAS9apcSXWiKIq3ALylqvuN74+hoihOiMgMVT0uIjMAnIwqJPGme/Hs0A173F4tSTXYfj2ySGVE0PWxFnQ/etA1akHeVwrXgjChVsYLoRWFqv6biLwpIrNV9QiAmwH8yPi7F8AG4/PxWCQlrmQlzWaW4tg4Bb+rpymVOMJSkEuYib/M2FRm4i8g/ajQtSCq19PvANgmIi8CmAfgq6goiEUichTAIuM7SZhyZ6uvcAdW4ooFBSTvc98cwEZhugybAeXM4Hd5SBFKsolb4q9apFKtBZGaClU9qKpdqjpHVcuq+o6q/lRVb1bVWcZnPCvCSFX8hDuw4hYnKyiTJxYS71U9eNt1rr/ZkwBt3/+m435u2wmphluU5FqkUq0FuY/1RC6xttzhK180UHGB9Grcg3iWprE61ktWu/3CLTS135DVhNhp9gjGGVfgzSxDRTHO2LPyRixf0DbKRbKxIJjSVBwVNLHafP03fK6nSNPn3k131d9qCZI2Xn2Meph+yn0+CjKWODLtmT14a/DBYgMwrJV1G7UwELu9q2HHCfbAhrOmTfKd85rUFz/zCMBZD9NPVBTEFb+eM1nzMCqIOE4zWUceTtFvj548g0WbnqWyIGPwStBVCOpFkkM49UQikUUPIzdbhOLSfLJbiPQgodNJ/eCVk7sebF9UFCQS21xWALttTwOveEf1MJ9M4qfc2eoaU40jCkKqELfdICxWzxOv3p+bmyMh1XCLqcYRBSEJ0tvXj4UbnsbVPbuwcMPTVd0MvaLkWkcK5c5W19zi9iCIhJDq0JhNakJvXz9WPnJwpJfWPzCIlY9UQpC4GdC9ouTaDY1L5sxwDIx307VTfckWdgFh3tKnEuIHjihIYniNEP5k54tjGv6LWtnuhpftwT7YeOZl59D15na3EQdQcQkOg1Nq2udePY17vvV8qPMRkhU4oiCRcHNFBSrTQW4987MuYU3dtgPeUXLtEri5Mprb19x6neu5Bjx85r1wy18eNq951tyOawnLorZQUZBI3D3/KtfcB2EXIkWZ+jFpEOepKtNDJWrOjaAEvSd7LnTT7RgAuj7WUlfhrr3KIk1lIXB20nAznY0n5capJxKJteUOzxAafozUdpymn8wwz37o7etP1ENlde9hzFz1JNp7dmHmqid9rRkJ6pbrpnwf3ncMq3YeRv/AIBSXwl2P53hDXmWRJkE8/LK4vigKVBQkMl5Nr1tDZo/4asVp+skpzLMbXo1ykHDlToRtAOIM81DP4a7zwniLYExFQSJTrfF1asjW3RFsCB5k/YPXvlHXRnk1ACWPBB9JL8mqh3hDeWK8RTCmoiCROX+hek/f2pDNWfNUVfuAvYfuZ/3Dok3PVt134GzFUB12qsarAfjAwxCfRvMwnqef8obXau08PicqChIZL08lK6t7D6O9ZxfePVddsdhDgHQvno1S0X26CqjEabrnW8979q4/bIx+kpiqiWsxX9h57LBuvVlngstKS7ftWeDu+Ve5/pbHaUJ6PZHUCGJ8VADtPbtGvi+c2YL1d3ZUHYlUc0U1O3pJhPLwct8NQlgjbVi33jQJ4wl0wcUzwW17Flhb7nB9jnkMI0NFQXKBqQAmNRbGZLQLwjGz98wAAA/USURBVDvG1JNX2Og06e3rH+PqOl7JiptrEji5PzeXio7K+8MRHSpqAaeeSG547tXTgY3gdsy542oNcnvPrsBTQEGnFHr7+rFyx8FRrq4rU1zbkTZJeAKlOd8fdDW/m5liYHAolNt4LaGiIJFIu7JHndoxjdF+FqgF9XsPOqWwaueLsFt3/Fl78omXI0B7zy7MX7fH8fckwq2EYc2t17n+5jRyMEevTuRt/QsVBYlE3gxzQc2fQewFQY3Zgz6dAIJijco778vfR+dXvu87Qm8tOfHeeUdlEbSBToqgq9+r5akYHBrOjQMCbRQkMFaDZN6IKrFXmlU/xuw5a57y5fUVFqsDADC6Ie0fGET3o4cABG/00uLEe+fHbEs73EoUrDYnP3VtYHAolpA1SUNFQQLhFCE1r7jFg7Jz7QNP4orLL/N8+f00CkkrCT8MXVSs2vliphsmJwO/27PKkoesGWbGbwQBkxU7DuLAG6czbdCPNPUkIq+LyGEROSgiB4xtLSKyR0SOGp9T4hGV1Jrevv5xoyQA4Nfnt/na74NhHTE4e1FtGqHWSsJkcOhipqegVtgM/Ct2HHRV6HF5yAZNouVEkDAzdrIeByoOG8VNqjpPVbuM7z0A9qrqLAB7je9kHJCX4b9f4u7B5WEdg8kfPJL+s5w4IX6TaBz5qs2RQNRAi1HdrbMcByoJY/btALYa/28FUE7gGiRlstwDDYo1kdDyBf5GFX7x8tDJEsM1MC997fNzYp8qisNO5jQSCBNoMeq9ZdnmF9VGoQC+LyIK4P+o6hYA01X1OACo6nERmRZVSFJ78ubd5IU5fbZo07M4evJMrOf2conMGnbDt5VJjQWsu6MjVluGea6Nu4/EttgxajRgoHqSK79keKF4ZKKOKBaq6icBfBbAb4vIDX4PFJH7ReSAiBw4dco5bSXJDmmHHZg80TuuU1Tae3bFriTGE2fOD2PFjoOZH0nGMPMUCK/w+HGQ1fKOpChU9W3j8ySAvwfwSwBOiMgMADA+T7ocu0VVu1S1a+rU6gnvSW2JK+BdNVqbS3h9wxK8+OXPpHI94k2cdimrLSAukh7B2RvuqJEBqpHVdRWhFYWITBKRy83/AfwagB8CeALAvcZu9wJ4PKqQpPakEYOoVCyMuk7c9gNSW6J4BbkRhzHbi1WWbIs/v2pX4g4dWXWIiDKimA7gn0XkEIB/AbBLVZ8CsAHAIhE5CmCR8Z3knHJnKxbObIntfJuXzcPmZfPQ2lyCoDKSWH/n6DnxLPuVk+AkMX2ZtAHYXD3f3rMLF8axDaIaoY3ZqvoTAHMdtv8UwM1RhCLZZNt918eyKnvhzJYRhVDNWOqW0J7kj8YJDTh3If6wJUmvbM7y+oa0YKwnEoi15Q68uv4WvL5hSajjZ02bhG33Xe97/3sCTj8tX9CG1zcswfTLG4OKVhM2L5tXaxE8mRDjzE4SSgJIfk1I2PwgYfBKp1tLsikVyQVBlYUA2LPyxkDH+J1+EkMec/9F1/1coOvUiiyH0sgLUdaEZM3L6LIqWRxrBRUFSY3XQo5CqjF5YmHMudPsBY5n8jIvH7bBz5qX0UBG1+FQUZDQBHk5Gwvh5zCqhX5I2pV28sRCrIZ8k1nTJgHI/vRTHvjyd8I1+FnzMspq9jsqChKaIKu1H7prjN+Db772+TmuvyXtQrtwZgte/PJnsO2+62NVFtMvbxyZhit3tkZSpOMJQfCcIUC+VsR7kfYCQr9QUZDQ+HV3XL6gLdJcfLmzFZuXzRtl6GuQynnDuNCWig2jXHObS0XHF2H5grZRhvcwysLJDXjzsnnY/8CiUfs9dNdc11hB5jFhlGJrc2lk5JIHrmwu4Zt1PMLK6tQT81GQ0FzZXPJcZdtq5BKIw2Bb7mz1fZ5qU2Lr75wT6HxWtt13vWeMJDt+3YCtcZCseRisx5U7W0cUox8ZrM4GQWS2Eue02PIFbZ62I3PBZZiYUHHEfMoCaUVACAoVBQnNTddO9Xzxn+v5dIrSXKLalFhankZBG9kgymtSYwFnzruvcraPfESAoEtfNi+bF2tZmUrOXIcjAJoaCzh7fniMYjTLwm+irAdvc0+Xmiduujab4YyoKEhonnnZPZhjaw17RmkHMLQzpamINbdel6hCWndHh2c4CftalXvme/fmnUhC/rXljkDThX5GcFEUmt8sh2mxff+xTEYkoI2ChMarQa5lz8hr+J50bCAA6PvTX0t81OJ1fqc7XFvuCGTjSDp6b5xEKessKQmgNnlC/EBFQULj1SB7jTaSxiuAYdKxgdI0HLuN2tyey9pyh6/psMkTC3UTvbeWI988QUVBQuM1aqj19I8bcTQMXpnMgq48j0L34tko2Vby2iPw2jE9yOxeWK9vWDLyVy9KAog3KvKUpuK4jXhMGwUJjdeooZbeG27GbEE8DUNWpiv8eEq5HcfQIRXKna2xhA4vFmTELrVt/7HAjgMmWXVlpqIgofFyXUwjf4UbbqMZxfiLrVTvjX4aUyJOMc16+/pdFXQYxwFg9CLMrEFFQUJTEHGd869l4+W2viOu+Wi30OcZXVSbe7zq2aaEF+e5TSV5Kei15Y5AimJCg+DrS+dmWuFTUZDQeBmGk84R4EX34tlYtfPwqGxq1ebug+B21xmZkRp33D3/KseG15rXJCnCuqq6LS4MG02g1tCYTULj1UMPEgcqbsqdrVh/Z4dn9rwouLnYpuF6W4+Yrr1m+RZExoRXyRpuMudRSQAcUZAIdC+e7WoI9Bt6ISmSnLt3G0kl7XpbzwRdqBeEhTNbHFd/Rw0CmaTMacMRBQlNludUk8RtJEWf/HziFOxx4cyWTI9Y0oYjCkICkrQNhKQPlYI3VBQkEvXoARR2/QIheYWKgkSicUIDzl246Lh9PFPv6xdIdbzWWuQNKgoSCScl4bWdkHqgt69/1PRk/8AgVu08DCCftr3x3e0jhJAasHH3kVE2LAAYHBquqdt4FKgoSCTcMouNl4xjhITBLYxMVoNlViOyohCRgoj0ich3je8tIrJHRI4an1Oii0myyoO3XYeiLZxqsUHGTcYxQsLgFhQzq6lOqxHHiOL3APzY8r0HwF5VnQVgr/GdjFPKna3YuHTuqFXQGzMet4aQpAkTAj7LRDJmi8hHASwBsA7ASmPz7QBuNP7fCuBZAH8c5Tok29ADiJDRjDcX6qheT5sB/BGAyy3bpqvqcQBQ1eMiMs3pQBG5H8D9ANDWNj6TfRBC6pfx1IEKPfUkIp8DcFJVXwhzvKpuUdUuVe2aOrV2+ZUJIYR4E2VEsRDAbSJyC4DLAEwWkYcBnBCRGcZoYgaAk3EISgghpDaEHlGo6ipV/aiqtgP4IoCnVXU5gCcA3Gvsdi+AxyNLSQghpGYksY5iA4BFInIUwCLjOyGEkJwSSwgPVX0WFe8mqOpPAdwcx3kJIYTUHtEMJFsRkVMA3jC+fgTAv9dQnLBQ7nSh3OlCudPFr9wfU9XEvYEyoSisiMgBVe2qtRxBodzpQrnThXKnS9bkZqwnQgghnlBREEII8SSLimJLrQUICeVOF8qdLpQ7XTIld+ZsFIQQQrJFFkcUhBBCMgQVBSGEEG9U1fMPwFUAnkEl58RLAH7P2N4CYA+Ao8bnFGP7Fcb+7wP4c9u5lgF40TjPQx7XXAfgTQDv27avBPAj4xx7UfEhdjp+IiqhRM4CGATwrxa5hwG8B+AcKnGoMi83gJsAHLbIPQzgnqzLbfz2Z4Zs54zzZKm8bzDK9SKAtzC6fu8FMATgDLJXvx3lBvAxAAct9eTHeZA7B++lW3ln/b28AcAPAFwAcJftt68B+KHxt8xNhpH9q+4AzADwSeP/y1FpBD4O4CEAPcb2HgBfM/6fBOCXAfymtYCMgjsGYKrxfSuAm12uucC4rr2AbgLQZPz/WwB2uBz/3wD8LYBPohKH6tsWuc/nVO6HDHlbUGmQv5EDuX8TwOsA/sSQ8y0A38yQ3O0APg3guwDuwuj6/XcA/sb4LWv1xE3uuQC+Ycj7IQDvAPifOZA76++ll9xZfi/bAcxB5d28y7J9CSqdnwmGnAcATHY6h/lXdepJVY+r6g+M/99DpZfSikqCoq3GblsBlI19zqjqPwP4wHaqawD8q6qeMr7/A4DPu1xznxo5LWzbn1HVs8bXfQA+6iL27QD+lyH3YwB+xSL3hJzKbZb3XQC+B+BzOZD7k6hUxL9W1TMA/gmV3lQm5FbV11X1aRgrYG31uxOVURKQsXriIfc0VOrFVlRGeWcALM6B3Jl+L6vIndn30pD7RVRGQlY+DuAfVfWC8V4eAvAZp3OYBLJRiEg7Ki/QftgSFKFSSb14BcC1ItIuIhNQqQhXBbm+jS+h8mCcaEVlyAZVvYDKC/OLhtwC4Dsisg/A/BzJbZb3FwH8dU7kfhLAFAA/E5GPoNJDas6Q3KOw128Ap4FM1u9R2OT+OQC7UXke61HpwXqRFbmz/F6OwqUdzOJ76cYhAJ8VkSbjvbypmgy+gwKKyIdQmVJYoarvikggyVT1HRH5LQA7UNFw/w8V7RoYEVkOoAuVnqvjLja5fw7AfYbc76pql4hcA+BpVFGWGZIbRn6PDlQaAk8yIneviAwZ1z4F4HkAd2RIbiuXIT/124pdblXVOSJyJYBeWJ5NxuXO8nvpJXeW30s3Gb4vIp/C6PfygtcxvkYUIlJEpXC2qepOY/MJo4DMgqqaoEhVv6Oq81X1egBHABwVkYKIHDT+vuJDll8F8ACA21T1nLFtnXkOY7e3AFxlyL0TFaPk/zV++zcjsdJPUOkRvJ8TuU8A+C8A/h6VgGF5Ke9jAD6rqosAlGD00jMi98juAP4QtvqNyrxzFuu3p9xG/X4bwE9QGd3lQe4sv5decmf5vfSSYZ2qzjPeS0HFKcnzAM8/4yR/C2CzbftGjDY+PWT7/T9jrLV/mvE5BRXvjF+ocm27EacTwKsAZlU57rcB/G9D7icBPGK57iZDXjM6419mXW5LeR9DZZiYl/IuAPgfhrxzAPwbgI1ZkdtSv18B8F2H+r0Fl4zZmSlvN7lRmav+piHvFFR6i3+VA7kz/V76qCeZfC8t+/8NRhuzCwCuMP6fg4rn0wTPc/i4yC8DUFRcsQ4af7egMve5FxVNtBdAi+WY11HpOb6PSm/z48b27ai4df0IwBc9rvmQcZzpjvagsf0fUNHgphxPuBx/GSrDV0XFE+FHxv5/YPxvurP9KCdy3wJgHiqGsTyV9+2o9JjOoOI2uz9jcn8KlR6gojL0HrSU9/OoeOJcNMr9rhzI/QAqLpdW99g8lHfW30uvepLl9/JTxnFnAPwUwEuW99W8/j4A87x0gKoyhAchhBBvuDKbEEKIJ1QUhBBCPKGiIIQQ4gkVBSGEEE+oKAghhHhCRUEIIcQTKgpCCCGe/H9DmsqiQBIg1AAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from apd.aggregation.query import with_database, get_data\n", + "\n", + "from matplotlib import pyplot as plt\n", + "\n", + "async def plot():\n", + " points = [(dp.collected_at, dp.data) async for dp in get_data() if dp.sensor_name==\"RelativeHumidity\"]\n", + " x, y = zip(*points)\n", + " plt.plot_date(x, y, \"o\", xdate=True)\n", + "\n", + "with with_database(\"postgresql+psycopg2://apd@localhost/apd\") as session:\n", + " await plot()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from apd.aggregation.query import with_database, get_data\n", + "\n", + "from matplotlib import pyplot as plt\n", + "\n", + "async def plot():\n", + " points = [(dp.collected_at, dp.data) async for dp in get_data(sensor_name=\"RelativeHumidity\")]\n", + " x, y = zip(*points)\n", + " plt.plot_date(x, y, \"o\", xdate=True)\n", + "\n", + "with with_database(\"postgresql+psycopg2://apd@localhost/apd\") as session:\n", + " await plot()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import collections\n", + "\n", + "from apd.aggregation.query import with_database, get_data\n", + "\n", + "from matplotlib import pyplot as plt\n", + "\n", + "async def plot():\n", + " legends = collections.defaultdict(list)\n", + " async for dp in get_data(sensor_name=\"RelativeHumidity\"):\n", + " legends[dp.deployment_id].append((dp.collected_at, dp.data))\n", + "\n", + " for deployment_id, points in legends.items():\n", + " x, y = zip(*points)\n", + " plt.plot_date(x, y, \"o\", xdate=True)\n", + "\n", + "with with_database(\"postgresql+psycopg2://apd@localhost/apd\") as session:\n", + " await plot()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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dv2kfeF9/zhymfve7ZZRIURSl/OiMwqZQSQB0/6adFxcsKJNEiqIo6aBqZhQdM06BXMe9CDM6nh94W6gksvS8+BIH2toYtXBh0iIqiqKkkqqYUXScPCNfSQAYY7X7oPPGVQlIpSiKUhkMeUWx+8orPT/fOff84gfp1rQBiqJUL0NaURxoa3M1KWXp37u3RNIoiqJUJkNaUXRdv9LXdh1nfTRhSRRFUSqX0IpCRKaLyNM5f++KyDUiMkZEHhWRXfbr6DgFDoI5csTfhu+9l6wgiqIoFUxoRWGM2WmMOd0YczrwEeAQ8GNgBbDVGHMSsNV+ryiKolQocZme5gEvGWNeAS4BNtjtG4BFMZ2jrBxoayu3CFXFgbY2dn1yHh0zTmHXJ+dp/ytKGYlLUSwB7rX/n2CM6QKwX8c77SAiV4nINhHZtm/fvpjEyCdz4rTYjrX3zrtiO5bizYsLFtC5/Gv0dnaCMfR2dtK5/GuqLBSlTERWFCIyDLgYuD/IfsaYe4wxrcaY1nHjxkUVw/kch+ILa+3t6ortWGGphlH27iuvpOfFlxw/6/x6ZVgxq+E6KdHpWrOGjlNPo+PkGXScehpda9aUWyRX4liZ/SngSWPMm/b7N0Wk2RjTJSLNQNniT+N8uMuoUbEdKwwdZ300z+ne29lJ1w03AgypVeOe4cz9/aUTJCQvLliQp+iysyEYWtdJiUbhfUJfH/vv/SEAzavSt8A3DtPTFRw1OwE8BCy1/18K/CSGc4RCMpnYjmXefTe2YwVl59zzHSOzzOHDQ8ok1jH79HKLEAnP2dDyr9Fxyqk6u1DoWrPG9T7JKou0EWlGISINwAXAX+U0rwPuE5EvAq8Cl0U5R1gOtLX5D4/1Q8KjWafMtaM/+1n23nmX56LA3s7OROUqFV1r1sAHH5RbjEgUW9xJf7/OLpTUKgMvIikKY8wh4NiCtrewoqDKSud115dbBEcOtLWx98676O3qoq65mfFfuYZ3HnzQMXNt0QcPgEhCkiZPbl8MysU1hOm87npVFFVKmv0QXgzd7LE9PZ4fS0MD5tChEgljcaCtjc6vfX3godjb2Zn3PhQV+oA90NZG1w03Yg4fLrcopafIvakMPQ60tQ3MJj1J6cCvolN4RIkuaV6zGhkxIq+t8H0hUaMTOq+73jGLbTWy9867giuJ2tpkhCkDlTqyrHbCRCr5VhJA05LFUUVMhIpVFAfa2ui89rr8WPtrr7NGqsUuXn09oxYupPnmm6hraQER6lpaaL75puIntqMTcs/hW2HpSHKAUL6Vvr74BSkTlWinrnZeXLDAum7Z+9DhWeBE1y23+j5HGiOeoIIVRdctt0Jvb35jby9dt9xa9EfYcpN1YUctXMhJP9/KjI7nOennWwPZjff/cCNwdLSgi8MCEmKKXdfSkoAgilKcKJFKZv9+fycZOTKoWCWjYn0Ubp3v56J4KoT6en/1J2yTkduUsnP51/LOo4qjgBAmt8zxU2IVoTCWPXPiNE7cvDnUsZK4voWRcDJsGM23rAUYFBChzvFk2X9foPXEAxSrh5PLjP/321DnKAUVO6NIiuxsIw5yHx5JrneoFnt392/aY3sgD1rwhFX21k+NdCc7tV8bdC4dJ89wvXZONdzNkSN0Lv+a4wy2ElPld8w+3erD3L+0fo8iZk+n6/jiggX+IhfBGqCmGDEpcKa2traabdu2BdrHbxnTQdTVMePZHZ6bvDBrtq81GPXnzPF/IyTMjBc6yi1CIEJfP9tkVWwk7RSGnLut1/m9+rJrzZpE/AtNVyzJs0+H6p/hw5nxzNMxSpUcHbNP91w3U8r72TNMu7aWGc89S8eppxVVFrkyB3FgA7SsvyPUrFBEnjDGtAbeMSBVN6Noua24Yyk7vS9GqZREy/o7SnKeisCYgZF01w03Os4wsqG3haPuOGZeSTmh/ThFi/LBB5UzuyyyuLJjxiklEaPwXhlEXx8dJ8+g/qNnBTrumwEc2PXnzEm96bCqFEXTFUt8XZC0XLS6lpbQI400E8Ru64VbChO30Nv99/7Ql+nKyySUJHEooSETTWVM+FlnAPyGafsZFOYqtz6fDuymK5Yw9bvf9bVtOalcRREij1NaQ88cGT48LxKr/pw5ZRYoHrrWrCn+o6vxf1s6hdl6JYP0G6oYywg/BFlbvWKRtNKONSu0MYFlrZRnUsUqitrGxkDbBzXfxFnLIgyFtuZio45KSW3tZ8Tbcvu6QMcc9F3r3IP5fIcqEs/oPHPiNJquWBL5OEOO4cN9b5qk0q5rbo71eNl7Rpqaim7rZ5u0ULGKwu/ULktQ882JmzeXR1mMHBnKkdf59RV5Nnk3+30SxK2kgl6rQbOEIgsbg8jXceppA//7iYgq5MTNm2letUr9TAW0rL050Pb77/0hHTNOif2eHv+Va2I9HlizID8Dkubrr4v93ElRsYqiFOkcTty8uSSLvGa80HH0L2wsdUF221KlIA+y4DCpUWGQWQJYisX3de3rY/eVV7Jz7vmuC67cqBl/tLjjqIULVVnkEOreNMYKBT5tZmpnzH7JnDitonyPlasoSpTOIYkRRx4BFJ4EjLUutL8mYZ5yqzrnFBqYXc3uRfbhKsOGRRPMA7N/f6Dr2v2bds9U725M/9Uv896XSllUQuRTJN9Ab6+lME6ewQuzZke6j8tRzyXKws5yUbmKIgBRbMSjFi5M1MbcdLn/ch3NARcD5lblcwoZjcU85VGno+PkGQMK6UBbW9HV2DXjxw88XP2GKDvhx2QYZoFcENzMh6MWLkx8jUBY38qgBXAJFpKKyzdgjhyh8+srQt/H5ajnUmlKAqpEUUSNLMjamOM2Q9WfMyeQbEGnqrkX1ykM0Bw+PDAyc/qLY2SaNUUVezA3XbEkbwQeZVre/4e3Qu8blcyJ01Kx+HHn3PMDbd8x45TBaxs++CAxZfGh8+fGd7D+/vAzgyGUkThJKldRlPgCDyQQfKHDmmEUnt/DLNSy/o6jisbOVNuy/o7E46dzHf5hRk6lDBGNqsyzcnatWRM40CEuWtbf4Xu0KA0NwU8wfPjR+68I/Xv35jnivehas8Z9tpdQ1cH3f/kr5w9CprIIbcryMGGrT+koFaso/JpskjAbNa9axYznns13Qj/1pHWunKyo0tAwsGAuSqba0MRQBKWYGSPXYRuFqCaw7GK6ci04Ezt1vV+a16wOfI5syLRvpdrX52uFczn6zPXBHrKQVVhTlpuVoK6l5aiZ0CWra3YAWA1UbPbY5lWr+GD37qKLt0q5oKV51ap0LaCJKY/XgbY214dgTV0dcVQT33vnXZGVZ9J+B1dEAvuPst/Vj8y5vpuBU9bXY3xmOd459/yB/bvWrLEyofb1QW1tIB9ZnNQ1NzvOcuuam/nQ+XMDK6+wQSfjv3LNoEqLMmJE3vGKRSIeevLJobMi3oWKnVFA8UVoSjx03uiu/OJa2ep4nJSWhcyltqmJljtuD6XksiNWJ7Nk7my1UElAsMCGbMRWx6mnORbeKQfjv3KNY4XJ8V+5huZVq5zNux6EHWS4FTALNDvMyuuDSp2BVGz22CwdM2e5LrAqzMg5FAiU3iGTYcaO7cH3c2LkSMeR1a5PzoslcqSupYWTfr41ry2pTK2e1Nb6D7126ZNS0XHazMHFu2ImyVxjxTL8gv/7Ng0BBF6/hbqWlkTqhmj2WJ+03HqLY3vQiKJKoTbIsv+envgWJr33nmOtAKeRYVAKp/pZgozUYmHkSGY896zvmUy5C8203HZroLxYYUjSl+bLb+fjWqQlRYrbLKll/R2l80smRMUriuwiplJHFJWLCddfhwRIiNh1y63s+uS8eE7+3nuDFM+ohQsJNSutq/M11W9etSpUAsig1IwfP/Dgn9HxvLeTPmSalbgZtXAhLbevG9IlYpuWLC66TVoGhHGYsdJKxZueqpEDbW103XJr4NQVcZA1ETlViPNCRowI/aPpmHFKZMd8GDNkYZW5+nPmpHoAEnfWWSdzYDnwLHJUZvNfuSmV6UkVRQXjVC4zLNLQgDl0yNe2NePH+0ppUdfSEktd56h+kKFY08OJoFXVipGmfts59/zB91yVKwmoEEUhIk3APwOnAQb4C2AnsBGYCuwGLjfGvON1HFUU4Ynq8JWGBprXrGbUwoX8bs45sS5Wi8s8c6Ctja7rr8ccyQ1aMIAPX4JdyrJaiGtWkfbZk2JRKc7su4F/M8acDMwGOoAVwFZjzEnAVvu9khBR7bO1TU0Do8YJ11+XuHM0DKMWLqT5Y73UNfQCZuDV+vOmmpREnKiSUHIJveBORI4B5gJfADDGHAGOiMglwB/bm20A/gP4ehQhlSKIhLbh565fCLIIrCguq1nDMmp8J6Muzv+OHT88zv7PZWYRoDjOUKGupSVyuHJaooiU9BBl+HgCsA/4rog8JSL/LCKNwARjTBeA/eoYPiIiV4nINhHZtm/fvghiKFEcvYWpD+JKhR277bh+9OBzLHmDpmkHcZxZDB8+qEpgNRA5Lb5IaqKIlPQQRVHUAWcC/2SMOQM4SAAzkzHmHmNMqzGmddy4cRHEUKKERzo9WKI6MBMJHe1zjnppPutdZizpys+79UJHVSoJiJgWf/hwZnQ8H69AypAgiqLYA+wxxjxuv38AS3G8KSLNAPZr8IovSiCijCLjjmpJbH3BkYPJHHcIMlB61WP9iVOqkGpVrr7Zfh/ceRqsbrJet99XbolKRmgfhTHmDRF5TUSmG2N2AvOA5+2/pcA6+/UnsUiquBKrbyECqcpjs/mr8MT3wPSB1MJHvgALvlluqUpGNmOxEhPb74O2L0OPnYjxwGvWe4BZl5dPrhIRNcTl74EfiMh24HTgViwFcYGI7AIusN8rCTNq4cLgSfRiXPGceMx9/Rj/227+Kmz7jqUkwHrd9h2rXVHCsPWmo0oiS0+31V4FRFIUxpinbT/DLGPMImPMO8aYt4wx84wxJ9mvb8clrOKNn3QHubjlyQrMyJHJj14/dbv7Z8Ma898/8T3n7dzaFaUYB/a4tL9WWjnKRPqC5pXQNK9a5ateNFghkJ4P9wApnkuyOtZrel/ovzAu2V/d2hWlGA5RdwNUga9CFcUQ48TNmwdV2iOTsbLO5iRNLBYC2bLuNl/nK23MvZtpLf11K5QhTBWYnyq2wp3iThyV9rKzjbzkg5mMVf/AmIHqaKWNuXdbLxJyHcm3zoY/vHD0/diT4e8ed99eqV66PbIQVYH5SRWF4orvyJm0RRhJrYuZKWfmUagkwHr/rbNVWSiDGTXJXSGIfzNtpaKmJyUaaYwwcvVFmKP25EIlkcWtXalu5t3o/lkV+L5UUSjR2PYvwdpLwajJ7p9VgT1ZSYBZl4O4PC51RqEoxYjZbxCW3MgTr9GfW5ijohTD9Lu064xCUZIjaEoEtxEd5M8UZl3uvkBv1KTgcipKlaOKQikP2++DH3/JdhAa6/XHX/JWFm4jOhjsaDz1M87bnXShP9nCsuFiWD3q6N+Gi8MfS1FSgioKJTm8Hrht1wyesps+q90NL99D4VqKXY84b5Zt90oJ8rOQ5VM2XAy//2V+2+9/qcpCqXhUUSjR8HLkeTmOe1yywbq1g7fvodAn4hbKmG33SgnSHTLrTKGSKNZejM1fhTVjrJnJmjHVnatK+6KsqKJQovGRL7h/5mMh0pbGBi6c1MKsqZO5cFILWxob4kmJUCxCpdQZP4N+J6+w42pLd52aEOyAmQGGkHJTRaFEY8E38Uyh4fEg29LYwLXjjqUrU4cRoStTx7XjjmXLL64fvHE2zbMftt+XbIRKmAdA0LDcbd9xb2/7cr5vp+3LQ1tZePVFSQkQ4Zca5RYPqiiUGPAIhXV7kA1r5IaxYzAFqdGNCDccM2zwcZzSPLvh9VAOkq7cibAPgDjTPFRxuuuKYYhlMFZFoUSn2MPX6UG24C56XOpnOLYHWf+Q5FoJrwdAptH5MyDxxIVVkG+oohhiGYxVUSjR6XWuZ51H7oPstinw4DLPzde2r81v8LP+4VtnF982m9wtrKnG6wHQc8hrx3DnC8JQNj9VGl5BHhV4nVRRKNHxilTKZfNXLbv+Bwe8txNh486N+W3zboRMvfd+f3jBCkX1Gl1n6wokYaqJazFfWDt22HXEKREAABUoSURBVLDetFPjkrvUrT0NeAV5VKCZUBWFUjoCOh9nbpg58Les61FY+D+L7+Q3FDUJ85Rn+G4Awjppw4b1lpIwgQD9vcHa04BX9uQKTCOjikJJJybfVNP+RrulLArLngYl+zBNSyqPagp1HWKRQHk4XTc3351XtbyUoopCSScODu32N9phwV0Rj2vbjouN/lePCv4AC2pS2H4fPPhX+aGuD/5VsGNUEklEApVSsca1mr/77YobFKiiUKIR4GY/d3ILM6dOHvgLjDFFneDFj2GPZv0suAs62g1qUmi7Bihc7+GRz6rS8QoEWD0K/uFk58+TSLcShqCr+b1MgRW2/kUVhRINn6Pocye38F5trTVTyP0LyOnHO5uMHFd4OxLwnEH8BUHNWX6DAIKSa866/cPWXyWYtt7vclYWSaRbCUPQ1fzF6lT0dFdMAIIqCiUwa9vXMvv7s5m5YSazR8PaMU1F9xlQElEQoU+ERS0T8pq3NDawomCF94pxxzJz6mSWTRhbcJCIYaquP37x58y+bcrRzLJJsHqUNevKmrO637YfprZpa9PfpF9ZFFLqdCtRyFXSftZMdL+d7uthk+L4MiWNLHt4meUrsOkXYeMxI/lpYwOPvdbpuI/76D4EIrw0LH/l9oqxYwYrIft9e3098yY1s3WPwwNIarxTl2e5+Tj40FjbtBShUNNtU4qHBidNf49l8krzw3f7fdZM9cAea5Y270b3a+VVo6TUZNPM+M0gkOXBZfBqe3nrzBchUi+LyG4R2SEiT4vINrttjIg8KiK77NfKc/Erjmx5eUuekhhAhPdqaweN9LOsGHds9NlEoSy5ysfr2CLsratznvV85Epf51o7ajizR8PMqZOYPXWy+wyqmBkhR0kU+muyf26mtVjpOZjqUeyi9pXMtPt75mhY1H6DR+6ueHw6W17ewoUPXMisDbO48IEL2fLyluAHCZJmppCUR3/FoY4/YYw53RjTar9fAWw1xpwEbLXfK0OAFb/2uJQOI32AeZOaY1cSiLBi3LFcOKnFl9kLe9Yz6CHvYwS3dkwTG48ZSb/tU8nOoOZNah68sQ97+bxJzcycOtnZX2Ob1maWQln8+EvJn6OQ2uFFN5l5/CTrPsrpk5eGZVwHIXHUq97y8hZWP7aaroNdGAxdB7tY/djq4MoiahqVFOeBSmLedgmwwf5/A7AogXMoJcbvj+bs4yflOZT31kWwbhoPc47ti9h4zEh/iijnIb928xeOtrd+0XM3x+Pbs5R5k5qZbc8EBpSQR4TOzOMnWf3h5ci3P0tcWZQj59Al3/I0Fc08fpJz37gMQoBYvsfdT97N4b7DeW2H+w5z95N3BztQVDNYivNARfVRGOARETHA/zHG3ANMMMZ0ARhjukRkfFQhlfLj60cjwiH7R551KIfGS0kUnDMQImz8wzZWAsseWEj7+7+HnFDdpv5+Vrz1DvMPHnJwhOcfZ+ChjxXUuvGYkcB7rHSwPpw7ucV/pJe9zazjJ7H9FeeQ27Vjmrj/mJH0Y432Lnv3PVa+vX/QdssmjKW9Pj/1ybQjR9jU+aa3Q31Yo7VmJU5fRvZYW28aNPp2VRI5bGlsYP7BgnxaUbMBA10HHfxXHu2uxGQGSyNRZxTnGWPOBD4F/K2IzPW7o4hcJSLbRGTbvn37IoqhJM0bB98IvlOYEFhjaO7pZd17ffGbrHKY988nW0qiwPSzv7aW68cdy8zjJ1kP2CL+j8L3G48ZyZbGBs48flKe7yFw1JcIRsRRWbmZwwrNcOdObjn6HfJMOcPcTTlZjhy0nKwJ+zK2NDb4UhKIcMPY6EohMlEzAxQjpb6jSIrCGNNpv+4Ffgx8FHhTRJoB7Ne9LvveY4xpNca0jhs3LooYSgk4rvG4eA/oMmNo/lALj/xlB/P/viPe8+VSMBsopC/COg+wnPc9NTWR14wgQnt9PWvHNOWZuNzMYdaMxmLZhLHuysnLlFNI1AWOuWSjguzZRDasmWxfFcEx/XzC6ygGmVyjZgYoRkrXVYRWFCLSKCIjs/8DFwLPAg8BS+3NlgI/iSqkUn6uPvPqeA9oDCP686fqI2pH5J1n8fTF8Z4zl2IPpiizmZhnQoWzh2K0TplYfDZEzGHLfiiICoolGi4GZ7YXax5bk/NmrKfiXNQyIW8WWXTW5kRKEztGmVFMAP5TRJ4BfgtsMcb8G7AOuEBEdgEX2O+VCmf+CfOZc9yc2I637vw7WH3+HTQ3NiMIzY3NrD53NfNPmD+wzco5K2M7X8Xi4tx1Y+bxk/jAzwhdhBVjx0R/sAXhwB6WTRgbPoULDivzE3YAd/fZim31KDA9rtvNm9TsEK3lw8RXIYR2ZhtjXgZmO7S/BcyLIpSSTr590bdZ276W+393P/0RHHdzjpszoBByFYMTgmBKUfQnLkL4ZELv63Z+v8cp2PalYcMGHuAj+/pcF1CGZdnESbRnCP89RejDGrlv6nzzaPv2+5JdQFhkfcNAdF+QaK0KI0XLGpVKYOWclTzz58+wY+kOq8FvdJLNtGOm8e2Lvu17+8unB3sALJ6+mB1LdzB+xHhLtoDylZodZ97Ijt0xlTEN6gtxmqnYf+/V1tpO5kw8sgHtw8L7fQawH755+bySXhNSJN9XMRNaIBOfZznd8qGKQgnNgLLwiSBs+symQPv4NT8Jwo6lOwa2/8Txn7A/iMlfkJTSsUfC6/a9lS6lZiuM06eWoW5Hsb7Oyee1bMLYSOanUCuwcyi63sVeHJo1t3mGXAPUFV+UWA5UUSjRCPAg3r50eyIijKwbOejYG3dujNWpXGsMtYnNUIT5Bw8xsq8vdcqijzIsAguwhqa9vt56+IYMK727/baisjitws86rn3N4nJmagPyupGt6Z4yVFEooQkyGstEMGEMq/G28z72ucdCH9sPI+tG8nTNNJ5+ZU/wB7nH9tOOmWb989l7ACyfQAWYy5Jmxyt7GN7f768f7Ifvll+Hq0PddWTwIsXC4++tq2PW8ZPY0tjAbHt9TJ7jOgi2vK6ktPqdKgolNEFSHNz8sZtDn+em89wfAomG0GI53h/73GOw9CH48Pms+8PbwR7kLg+S8SPGHzXDzbocai1luCOrjOJUFhWkfAQBhG2vvh5gJ+Hu4eFmPr4e8/bixxXjjqU/d31MBD5SkOom7aiiUELjd7X24umLi0Y3eTH/hPms+/g66muPjsQEYfH0xaFCaOtr61n38XUDobmjho2ixuGnsHj64nzH+9KHmD/+LBr8PnSNYd3H1+Wdq7mxmXUfX8fWxVvzt73kHwfWBOx4ZQ/r9r1Fc28vAgP7hFGKzY3NrJPKWdB6XONxAzOsOd3dvhXcG3Xh1lP4Vp8xKIfcYx2pqRmonbJ67JijyiKlpietR6GE5rjG4zzz4TQ3NnP1mVdHUhJZ5p8w3/dxipnEVp27KtDx8lj6EIe+d5qvTTMG32HA+XmQ9jC/7ljmn3VjXtjn/BPmDyjGmRtmFj1/brDBCh/bO7Hu4/Etg1o8fbHlO3JhYMGl3Vff3noTMz2sNLkcN8xHFuGUcrimhuvsvGjz6yLkR0sQVRRKaOZOmuv5w3/k0kdKKM1RipnE4lBcxagxhpvP9yjh6cSsy32vB2ioa+BQ7yHXzwsXRwZej2IM6+beHmtfZZVc7jqchroGunu7Oa7xuPxBhd0Xcx5eRnvXb4qO5q+ec20omZr6+9lfG+PqbmNCzTz6bdMWUz9D8ndncNT0pITmV3t+5fpZc6NDvYYSESqBYRzYvoCm4U3cGvNDtpAbz/Euu1q4ViXoehREEpE/dx3OjqU7ePxzj7N96XYeufQRx/N9+6JvF33wrvv4utCyrnj7AJm4/DdRjyPCda88FI8sMaOKQgmN1wN57iTfiYRjxyuBYU3CpTN37H6NXy/5deKzFq/ji4OLduWclYF8HCPrRhbfKCVE8n+9/z4373uL5p5exBjwG22VEP2kM1W5KgolNF4PZK/ZRtJ4JTCMknpkAK8R7tiTox/fJ26zNrfrsnLOSl8+h5F1IxMPOU4NoyYz/+AhHtnTyfbdr7HjlT1MO3IkXKSYCIvffa9iIsyCoIpCCY3XrKFs5p8ixGEScxqxD7T/3eORj++Xq8+8mhG1I/LaCjPwFpKNICuMwsqagnYs3VE9SgJg3mAT3qbON9mx+zXG9/YGeug3DW9i5Ql/Fqd0qUGd2UpovGYNsdevCICXMzuOdOluTmGTXJ0lR7Iml7ufvJs3Dr4x2CHssV8pHPoVwazLXVOHb93TxaKWCbw0vHhajUxNhhUfXQEnzEc2zAqdyHJgEWbKUEWhhMYrNDb2+hUB8JrNDLUHZLU/9J3Wv8SJVTL2wKD2LS9vcVXQl0+/3DMa0I28RZgpQxWFEpoaqXG1+Zfz4eW2vqOckVhKeLzus1s/fmuyJ2/9omOzl4JeOWdlIEVRJ3Ws/djaVCt89VEoofFyDEfNyhmFMLZ7Jb1c9keXObbn1jVJjAXfDLWbW4RZNg1+7t9Tf/5UqpUEqKJQIuA1Qg+SBypu5p8wn9XnrvasnhcFtxDbpENvq5VsaG+2f2ukZnB6lZThJnOlVm0Uk4JQrtbWVrNt27Zyi6EEZMvLW1jx6xWunwetV1EpeKXPGKrfeUiz4WL4/S8Ht3/4fCsZZIoRkSeMMa1Jn0eHQEpo0j5dTgq3mZT6QCoUOzNwHhWgJEqJOrMVJSBXn3k1qx9bzeG+wwNt6gOpcFQpeKKKQlECEnb9gqJUKqoolEgMqxnGkf4jju1DmWpfv6AUx2utRaWhikKJhJOS8GpXlGpgy8tb8syTXQe7WP3YaqAyfXvqzFYURYmZu5+8O8+HBXC473BZw8ajoIpCicSoYaMCtStKNeCWRiatyTKLEVlRiEitiDwlIpvt92NE5FER2WW/jo4uppJWrj37Wuok34JZJ3Vce3a4imOKMhRwS4pZzmSZUYhjRnE10JHzfgWw1RhzErDVfq8MUeafMJ+1H1ubtwo67XlrFCVphloamUjObBGZBMwHbgG+ajdfAvyx/f8G4D+Ar0c5j5JuNAJIUfIZaiHUUaOe7gK+BuTWTZxgjOkCMMZ0ich4px1F5CrgKoApU6ZEFENRFCVdDKUBVGjTk4gsAPYaY54Is78x5h5jTKsxpnXcuHFhxVAURVESJsqM4jzgYhH5NDACOEZE/hV4U0Sa7dlEM7A3DkEVRVGU8hB6RmGMudYYM8kYMxVYAvzcGPN54CFgqb3ZUuAnkaVUFEVRykYS6yjWAReIyC7gAvu9oiiKUqHEksLDGPMfWNFNGGPeAubFcVxFURSl/KSicJGI7ANesd+OBf5QRnHConKXFpW7tKjcpcWv3McbYxKPBkqFoshFRLaVomJT3KjcpUXlLi0qd2lJm9ya60lRFEXxRBWFoiiK4kkaFcU95RYgJCp3aVG5S4vKXVpSJXfqfBSKoihKukjjjEJRFEVJEaooFEVRFG+MMZ5/wGTgF1g1J54DrrbbxwCPArvs19F2+7H29u8D3yo41mJgu32cOzzOeQvwGvB+QftXgeftY2zFiiF22n84ViqRQ0A38LscufuA94APsPJQpV5u4BPAjhy5+4DPpV1u+7O7bdk+sI+Tpv6ea/drP7CH/Pt7K9ADHCR997ej3MDxwNM590lHJchdAb9Lt/5O++9yLvAk0AtcWvDZ7cCz9t9iNxkGti+6ATQDZ9r/j8R6CJwC3AGssNtXALfb/zcCHwO+lNtBdse9Coyz328A5rmcc4593sIO+gTQYP//18BGl/3/Bvg+cCZWHqof5ch9pELlvsOWdwzWA/kbFSD3l4DdwHW2nHuAO1Mk91Tgk8Bm4FLy7+8fAt+zP0vbfeIm92zgG7a8HwLeAf6xAuRO++/SS+40/y6nArOwfpuX5rTPxxr81NlybgOOcTpG9q+o6ckY02WMedL+/z2sUcpErAJFG+zNNgCL7G0OGmP+EzhccKgTgN8ZY/bZ7/8d+DOXc7Ybu6ZFQfsvjDGH7LftwCQXsS8B/smW+wHg/By56ypU7mx/Xwr8DFhQAXKfiXUjftcYcxD4FdZoKhVyG2N2G2N+jr0CtuD+PgNrlgQpu0885B6PdV9swJrlHQQuqgC5U/27LCJ3an+XttzbsWZCuZwC/NIY02v/Lp8B/tTpGFkC+ShEZCrWD+hxCgoUYd2kXrwInCwiU0WkDutGmBzk/AV8EevCODERa8qGMaYX6wfzEVtuAdpEpB04u4Lkzvb3EuC7FSL3T4HRwAERGYs1QmpKkdx5FN7fwNuQyvs7jwK5jwMexroet2GNYL1Ii9xp/l3m4fIcTOPv0o1ngE+JSIP9u/xEMRl8JwUUkQ9hmRSuMca8KyKBJDPGvCMifw1sxNJwj2Fp18CIyOeBVqyRq+MmBXIfByyz5X7XGNMqIicAP6eIskyR3Nj1PWZiPQg8SYncm0Skxz73PuA3wGdSJHcuI6ic+zuXQrmNMWaWiLQAm8i5NimXO82/Sy+50/y7dJPhERE5i/zfZa/XPr5mFCKSweqcHxhjHrSb37Q7KNtRRQsUGWPajDFnG2POAXYCu0SkVkSetv9u8iHLnwDXAxcbYz6w227JHsPebA8w2Zb7QSyn5P+1P3vDLqz0MtaI4P0KkftN4C+BH2MlDKuU/n4V+JQx5gKgHnuUnhK5BzYH/gcF9zeW3TmN97en3Pb93Qm8jDW7qwS50/y79JI7zb9LLxluMcacbv8uBSsoyXMHzz/7IN8H7ipoX0++8+mOgs+/wGBv/3j7dTRWdMYfFTl3oRPnDOAl4KQi+/0t8L9tuX8K3Jdz3m/a8mazM34n7XLn9PerWNPESunvWuB/2fLOAt4A1qdF7pz7+0Vgs8P9fQ9Hndmp6W83ubFs1Xfa8o7GGi3+SwXInerfpY/7JJW/y5ztv0e+M7sWONb+fxZW5FOd5zF8nORjgMEKxXra/vs0lu1zK5Ym2gqMydlnN9bI8X2s0eYpdvu9WGFdzwNLPM55h71fNhxttd3+71gaPCvHQy77j8CavhqsSITn7e3/u/1/Npzt+QqR+9PA6ViOsUrq70uwRkwHscJmH0+Z3GdhjQAN1tS7O6e/f4MVidNv9/ulFSD39Vghl7nhsZXQ32n/XXrdJ2n+XZ5l73cQeAt4Luf3mj1/O3C6lw4wxmgKD0VRFMUbXZmtKIqieKKKQlEURfFEFYWiKIriiSoKRVEUxRNVFIqiKIonqigURVEUT1RRKIqiKJ78f6S57F/6hNcgAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import collections\n", + "\n", + "from apd.aggregation.query import with_database, get_data, get_deployment_ids\n", + "\n", + "from matplotlib import pyplot as plt\n", + "\n", + "async def plot(deployment_id):\n", + " points = []\n", + " async for dp in get_data(sensor_name=\"RelativeHumidity\", deployment_id=deployment_id):\n", + " points.append((dp.collected_at, dp.data))\n", + "\n", + " if points:\n", + " x, y = zip(*points)\n", + " plt.plot_date(x, y, \"o\", xdate=True)\n", + "\n", + "with with_database(\"postgresql+psycopg2://apd@localhost/apd\") as session:\n", + " deployment_ids = await get_deployment_ids()\n", + " for deployment in deployment_ids:\n", + " await plot(deployment)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import asyncio\n", + "\n", + "from matplotlib import pyplot as plt\n", + "\n", + "from apd.aggregation.query import with_database\n", + "from apd.aggregation.analysis import get_known_configs, plot_sensor\n", + "\n", + "with with_database(\"postgresql+psycopg2://apd@localhost/apd\") as session:\n", + " coros = []\n", + " figure = plt.figure(figsize = (20, 5), dpi=300)\n", + " configs = get_known_configs()\n", + " to_display = configs[\"Relative humidity\"], configs[\"RAM available\"]\n", + " for i, config in enumerate(to_display, start=1):\n", + " plot = figure.add_subplot(1, 2, i)\n", + " coros.append(plot_sensor(config, plot, {}))\n", + " await asyncio.gather(*coros)\n", + "\n", + "display(figure)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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0ku7ZwHnAwcAR1BCOBlxacn560wqUJEmSJI0sE+7/LFzxLPjfo2H2pyAHO11V/dKPnnaN9Qvh2lPhkQthxT0w95tw7Smw4clOVyZJ0taW3Aw3v67w76TffQA2ryz0b1wCt78X/vcY+PUbYdkdna1TkiRJkjSu9HW6gDF4PXArMLPYPgm4MSIWAQ+XToyIrwGHAS8ApjD89MwAFgJvbVPNkrSt1Q/Bo98t/ABvn5cU2iOZcTxsWQVrH6m+323nwYFvgd4pza+1VeoNRlv4vzC4BXomtaYeSZIkSZIkSZIkSZKkzvoAcCDDD0H+D+BDmYVP00fEATXucwPD98odFBF7ZuaSJtcqSZIkSRry+3+EP3x2uD37fNi8Ap79pc7VpInt8Ytgw8Kt+9bPh/k/h8P+sjM1SZJUbumtcMOZw58jXHkvLP0tnHYVXHcarPpDoX/VffDElfDiX8OMYztXryRJkjSerX0E7v4IPPUb2PVoOOofYa9TO12V1DI9nS6gXpn5CPBKYBFbB53tA7ywZGoA7wJOBaaWzV0AvCIzl7atcEkqtex3cNXz4P5/gbnfgF+/Aeb/dOS5+70KXvkgvPiWUTbNwtN+BvubXW3r1BuMtmUlLPlVa2qRJEmSJEmSJEmSJEnqvPczHIp2SWb+9VAoWj0ycy0wr6TriCbUJkmSJEkaSQ7C3P/etn/urMKDwaVWuOtvRu7/3V+1tw5Jkqp5+BvbfoZw+e/gvk8Ph6IN6V8Dj36nfbVJkiRJ3WTT8kKeyPyLYeMiWHQt3PhyWH5npyuTWqbrgtEAMvN24ATgSgpBZzB8M1iWHJSNBXAN8LzMvLcNpUrSyO77Z9iyqra5Ox8GPX2wx5/Aq+ZUn7vsVnj0e43X1y4DG0fuP+lHldcs/EVrapEkSZIkSZIkSZIkSeqgiDgS2I/he+I+0uCWc0vOD25wL0mSJElSJSt+D5uWbdvfv8YHg0uSpO3bIxeO3P/gl0bu/+MXW1eLJEmS1M2evArWz9+6b2ADPPqDztQjtUFXBqMBZObizHwF8Fzg+8AiCjeEjXSsBi4GTsvMMzNzUWeqliQKT4NaeFnt83d+Rsn5oXDKKMFgj1UJFRtPBrdADow8tsN+sP/ZI4+tur91NUmSJEmSJEmSJEmSJHXOccXXBO7LzEca3G9lyfn0BveSJEmSJFUyuHlsY5IkSZIkSZJUi9veOXJ/pdBhaQLo63QBjcrMO4G3AUTEwcDTgZnAZGApsBi4PzMHO1akJJVa93jtc6fsDrsdv3XfnqdA307Qv3bkNYuvh80rYfKuY6+xHQY2VB7r2wFmHA+P/2Tbsc0rWleTJEmSJEmSJEmSJElS5+xRcj6nCfttKjnfsQn7SZIkSZIkSZIkSZIkqd0G1ne6Aqntuj4YrVTxCZmNPiVTklpr8fW1zz38b6Fn0tZ9k3aGE74At7975DXZD09cCQe+eew1tkN/lWC0nqmVg902rxy5X5IkSZIkSZIkSZIkqbtNLTnfVHFW7aaXnK9pwn6SJEmSJEmSJEmSJEmS1HITKhhN40NETAJOAvYH9gHWAk8Ad2fmvA6WJnXe6ofgtvNqmzv9SDj8QyOPHfou2O3Z8MvnjDy+6NrxH4w2UCUYrW8HmFQhGG2LwWiSJEmSJEmSJEmSJGlCWlpyvnsT9ju45HxZE/aTJEmSJE142ekCJEmSJEmSNMFllR9BVRuTtH0xGG0CiohvA29v0naPZeaBNV53D+AC4I3AbhXm/Ab4Ymb+rEn1Sd3l9nfVPvc5X4XeyZXHd3s2POO9MOdr2449ciFsWQ37vQoO/HPo6a2/1mbZsAge/m+YOwvWPw77nQUnfBEGN1de07sDTK4QjLZ5ZeFfsxGtqVeSJEmSJEmSJEmSJKkzFhVfAzi+kY0iYiZwREnXw43sJ0mSJEmSJEmSJEmSJEnt0tPpAjTubahlUkS8DLgPeC8VQtGK/gT4aUR8PyKmNaE+qXtsWQNP3Vzb3JnPg71OGX3ejCr3wM7/Kdz6drjljZ2LxV07D658Fsw+vxCKBrDwMvjFofDkVZXX9e4Ak2eMPJYD0L+u6aVKkiRJkiRJkiRJkiR12G+AweL5zIh4UQN7nUshYA1gHfC7RgqTJEmSJEmSJEmSJEmSpHbpqmC0iDg6Iq4vHtdFxJ5j2GOv4tqhfQ5rRa0TyM9GmxARpwKXAKV/HgncCVwEXAMsLVv258D/RERX/TcoNWTTMsjB0ecBzHx+bfN2OXz0OfN/Bkt/W9t+zTb7k7Bxychjd/1N5XV9O8HkXSuPr1/QUFmSJEmSJEmSJEmSJEnjTWauAO4o6frniIhK8yuJiP2Aj1G4hyuBazJrvWlFkiRJkiRJkiRJkiRJkjqr20Kp3g2cCpwCbM7MCmk7lWXmYmBLyT5/0cT6xosPAweN4XhD2T4JXFjtQhHxNOBiYHJJ9y3AUZn5nMw8OzNfAjwN+CCF/+2HvAr49Bi+Pqk79a+pfe7M59Y2b+casx0f/V7t126WgU2FULZ69e0MPX0wqUow2tUnQubYa5MkSZIkSZIkSZIkSRqf/r3k/ETg6/Usjoi9gMuAGcBQqNoXm1OaJEmSJEmSJEmSJEmSJLVetwWjvbrk/DsN7DO0NoA/a2CfcSkzl2bmvHoP4IyyrW7IzEdGudwFFG6iG/Ib4IzMfKCspk2Z+R/A2WXrPxQRB4zhy5S6z5Y6gtF2qzEYbfKM0ecAPFI147A1Fl8P/WvrXze5GIg2eXrlOVtWwYq7x1aXJEmSJEmSJEmSJEnSOJWZPwLuKTYDeGdE3BwRJ1dbFxHTIuI9xbXHUXgoZgJXZ+YtraxZkiRJkiRJkiRJkiRJkpqpa4LRIuJg4GnF5iBweQPb/QIYKJ4fFBH7N1LbRBAROwBvKuueNcqaZwBvL+naDLwjMzdWWpOZl7B1qN0U4Pz6qpW6VK3BaJN2gV0Oq21uzySGH+5bTS1zmmzBJWNbNxSM1jsVpuxeed7yu8a2vyRJkiRJkiRJkiRJ0vj2emAZhWAzgJOAGyNiIfDd0okR8bWIuA54CvgKsNfQEPAE8Na2VCxJkiRJ27UcfYokSZIkSZIkSapZ1wSjAUcXXxN4MDPXjnWj4toHS7qOaaSwCeL1wPSS9grg4lHWvAXoLWlfnJlzarjW58raZ0fE1BrWSd2tv8ZgtIPeAVHj23NEIUBsNIObatuvWXIQFlw2trWTZwyf73dW5Xn968a2vyRJkiRJkiRJkiRJ0jiWmY8ArwQWUQg4y+LrPsALS6YG8C7gVGBq2dwFwCsyc2nbCpckSZIkTQCGvEmSJEmSJKm1ssqPoKqNSdq+dFMw2gEl53ObsF/pHvs3Yb9ud15Z+weZuXGUNX9W1v5WLRfKzAeA20q6pgEvqWWt1NW21BCMtteL4JhP1LdvLcFoACvvr2/fRiy7HTYuGtvaSbsOn5/w+crz+secjylJkiRJkiRJkiRJkjSuZebtwAnAlRSCzmD40+lZclA2FsA1wPMy8942lCpJkiRJ+v/ftkmSJEmSJEmSpGbopmC0nUvOVzVhv9Ul57s0Yb+uFRGHAH9a1j1rlDV7A88q6eoHbqnjsjeWtV9Wx1qpO/VXCEabdhC86Fp4+b1w2tUwZWZ9+9YajLbgkvr2bcRT9bwdlIne4fPJMwphcSPpXzf2a0iSJEmSJEmSJEmSJI1zmbk4M18BPBf4PrCIwqftRzpWAxcDp2XmmZk5xifaSZIkSZIkSZIkSZIkSVJn9XW6gDpsKDlvRpBZadDaQBP262bnsvXjae7KzHtGWXN0WfvezKwnpeg3Ze2j6lgrdactFYLRpu4Je58+9n17agxGWzdv7Neo15qHRu7f63R4wXfhkv0qrx3YuHV70s4jz+tfO7baJEmSJEmSJEmSJEmSukhm3gm8DSAiDgaeDswEJgNLgcXA/Zk52LEiJUmSJEmSJEmSJEmSJKlJuikYbWnJ+QFN2G//kvNlTdivK0VEL/D2su5ZNSw9sqz9cJ2XnjvKftLE018hGK1S8FetemsMRlu/sLHr1GP1gyP3Tz+yEARXze4v2LrdO23kef31ZDFKkiRJkiRJkiRJkiSNbxGxM3BQSdfc8odVZuYjwCNtLUySJEmSJEmSJEmSJEmS2qin0wXU4fHiawDHRMTMsW5UXHtsSVcb04LGnZcC+5W0NwA/rGHdoWXtx0ecVdljZe2ZETGjzj2k7rKlQjBaX5uC0Z68srHr1GrBL2DJTSOP7XwY9IySybnfy7duT9pp5Hn9a+uvTZIkSZIkSZIkSZIkafx6M3B38bgdmNLZciRJkiRJkiRJkiRJkiSp/UZJpxlXbgU2AZMphKP9FfCpMe71lwyHwvUDtzRcXfc6t6z9s8xcWcO6XcvaS+q5aGaujYiNQGmi03RgRT37lIuIPYE96lx2SCPXlGpWKRhtUoPBaD113AO7+iHY5bDGrlfNyvvhV2dVHt/lmYXXXY+FlfeOMCFgxglbd/VOG3mv/nUj90uSJEmSJEmSJEmSJHWn3SncGwdwR2Yu72QxkiRJkiRJkiRJkiRJktQJXROMlpmbIuJm4Ixi14cj4ueZObuefSLiaOAjQBa7bsnM7TJdJyL2AF5V1j2rxuU7lbU3jKGEDWwdjNZgOhRQCL07vwn7SM3XXyEYra/B//RzoPa5lz8T3jwIEaPPHYvHflh5rGcSzHxe4Xy/s0YORnv9sm1rm1T+dlPUv3ZsNUqSJEmSJEmSJEmSJI1Pq4qvCSzoZCGSJEmSpO1U5uhzJEmSJEmSJElqsZ5OF1Cnzxdfk0Iw15URcWKtiyPiecAVwDSGn6z5+corJry3AZNK2nOBm2pcW55UtHEM1y8PU6uQfiRNEFsqBKNNajAYbbDO//tdewpsXNLYNStZcU/lsT1PhcnTC+dH/T3s89Lhsd4d4eSfweQZ267rmzbyfgajSZIkSZIkSZIkSZKkieXJkvPJHatCkiRJkiRJkiRJkiSpRapF8xvbL2lIVwWjZebVwI0UQs0S2Bf4VUTMiojnRUSUr4mC50bEN4GbgacNbQfcnJlXtKf6cemcsvaFmWN+tMtY1vn3kbYv/S0KRhuoMxjtqZvhqufDuscau265LWvgiSpvqUd+dPi8b0c49Qp4+X1w2lXwmvnw9NeOvK6vQmbi8jt9GpUkSZIkSZIkSZIkSZpI7is5P6hjVUiSJEmSJEmSJEmSJElSB/V1uoAxeBNwF7APhWCtPuAdxWNdRDwIrCiO7QYcBgyl6gwFqgUwHzi7jXWPKxFxInBUSdcA8O06tlhb1t5hDGWUrynfcyy+ClxU55pDgEubcG2pui0VgtH62hyMBrBuHvzqz+BldzV27dIaLt6r8vhuz4W9z9i6LwJ2PapwVNM3rfLYqvtg12Nqr1OSJEmSJEmSJEmSJGmcysyHIuJe4Fjg2IjYLzMXdrouSZIkSdJofOC3JEmSJEmSJEnN1HXBaJm5JCJeClwGHMjwbw+CQgDas8v6/v9ShkPRHgZenZlL2lHzOHVeWfvKzHyijvXjMhit+Gda159rRIw+SWqG/grBaJM6EIwGsOJuWHQ97P2ikccHByD7YfNyGOyHKbsDCTkAm5YV5vRMgh32hZvOgoENla91zCfGViPApF0rjy2+0WA0SZKkTtqyGnqnQU9vpyuRJEmSJEmSJGmi+E/gGxTuc/sU297nJUmSJEmSJEmSJEmSJEkTWtcFowFk5n0R8WzgK8DZQA8jP16ltC+AAeAHwAczc1XLCx2nImIa8May7ll1blP+v98eddawE9sGo62sswapu2ypEIzW16FgNIDrT4dD3gnP+TL0Thnun38J3PxnjdVVaod9x752z5Mrj/U3nKcoSZKksVgzF37zFlh2B0yeAc/8IBz9T2DwtCRJkiRJkiRJDcnMWRHxGuAVwDsi4sHM/NdO1yVJkiRJqsb7piRJkiRJkiRJaqaeThcwVpm5IjPfAhwJfAmYXRyKsgPg98DngWdm5ju251C0ojcApUlMi4HL69xjTln7gDrXl89fnpkr6txD6h6Z0F8hGG1Sg8Fogw0EowHM/Sbc9TfD7VV/bG4oGsC0et8iSkyZWXmskVA4SZIkjc3gFrjuVFh2O5CweTnMPh/mfK3TlUmSJEmSJEmSNFG8Gfg5hfvfPhsRV0XEaR2uSZIkSZIkSZIkSZIkSZLaoq/TBTQqMx8C/hYgInYC9gKGUnSWAoszc12Hyhuvzitrfzcz++vc44Gy9qF1rj+4rP2HOtdL3WVgA+TgyGN9DQajTZ4BG5eMPPbKB+Hej8PjF1XfY87X4NB3w4xnwQP/1lg95XY5onq4WS32fgksunrb/sFNje0rSZKk+i35FaxfsG3/Yz+Ew/6y/fVIkiRJkiRJkjSBRMSFxdPVwBoKD8A8AzgjItZQeEjokuJYrTIzy+8ZkyRJkiRpBNnpAiRJkiRJkiRJ6p5gtIjYG3heSdevM3N56ZzMXAusBea2s7ZuEhGHAS8s6541hq3uK2sfGxE7Zub6GtefNMp+0sSypcq9qJMaDEZ71mfgtndu23/gW2GXw+CFP4ErjoOVv6++z5XHNVZHJSdf3PgevVNH7h/Y2PjekiRJqs8Dnx+5/6lb2luHJEmSJEmSJEkT0zvY+lPoCUTxfBe2vfdrNFHcw2A0SZIkSZIkSZIkSZIkSV2hp9MF1OG1wM+Lxw+ATZ0tp2udW9b+dWY+WO8mmfkkcG9JVx/13XR3aln7ynprkLpKfwuD0fZ9OfSNsMeBfz58fthfNXaNsTr8QzD98Mb3qRiM5l8FkiRJbbd5+ehzJEmSJEmSJElSM2XJIUmSJEmSJEmSJEmS1LWyyt0P1cYkbV+6KRhtVwpPrwzgjsxc1+F6uk5E9AJvK+ue1cCWPy9rn1NjHYcDzy/pWgdc3UAd0vjXv77yWO+0xvbeYR940dUw/ajh9vNnwb5nDs85+Fx41mcau0699jy1edfsmTJy/+DG5uwvSZKk6gb74b5Pw1UvgGW3V57nTx0lSZIkSZIkSWqGaOIhSZIkSWo575uSJEmSJEmSJKmZ+jpdQB2WF18TeLKThXSxlwP7lLTXABc1sN8PgI8DvcX2ayPiGZk5Z5R1f1fW/klmmm6kiW1gQ+Wxvh0a33/3E+EV98HmlTBpOkTZfa09vXDU3xeOa0+FJTc1fs1q9n5xIaytWXqnjtw/4FuHJElSW9x6Dsz7/ujzBjZA346tr0eSJEmSJEmSpInroE4XIEmSJEmSJEmSJEmSJEmd1E3BaKVhaNM6VkV3O6+s/aPMXDfWzTJzTkR8Bzi32DUZ+HZEnF4p6CwiXg28o6RrM3DBWGuQuka1YLTeJgSjDZm86+hzDnhT64PRTrmsufv1Thm5f2BTc68jSZKkba17DOb9oLa5/esMRpMkSZIkSZIkqQGZ+Vina5AkSZIk1StGnyJJkiRJkiRJkmrW0+kC6nA3kMXzwzpZSDeKiL2AV5R1f7MJW58PrChp/wlwbUQcXnb9KRHxfuCisvVf8GY+bRcqBaP1TIZo81vxQW+DXY+tfX5fHVmUOx0Mr5kPvVPrr6uaSvsNjJjBKEmSpGZZOw/u/juGvx0fRf+Ys7clSZIkSZIkSZIkSZIkSZIkSZIkSZIkib5OF1CrzHw8Im4FXgA8MyIOy8yHOl1XF3k7W/9535eZtze6aWYuiIjXAlcBk4vdJwF/iIg7gUeA6cAJwB5lyy8H/qnRGqSuUCkYrXeH9tYB0LcjnHkHzPka3PXXleftfzbs+zLY5Qi4+sSR50w/Eo7/Aiy+HqbuDYe+Eybt0vyae6aM3D+4qfnXkiRJEmTCfZ+G+z4JOVj7OoPRJEmSJEmSJEmSJEmSJG13anzwpCRJkiRJkiRJqknXBKMV/Rtwccn5qztYS7c5p6w9q1kbZ+aNEfFnwLcZDj8L4DnFYyT/A/xFZg40qw5pXOsfR8FoAL2T4fAPwv5vgGtOgnXzth7/00vhaWcVzlfeX3mfTNj3pYWjlXqnjtw/sLG115XUHFtWw2M/gTUPwR4nw36vgOjpdFWSpGqW/hZmf6L+df1rm1+LJEmSJEmSJEmSJEmSJKlNDHmTJEmSJEmSJHVeVwWjZeYlEXEhcC7wyoj4CvDBzOzvcGnjWkScBBxe0rUZ+H4zr5GZV0TE0cAFwBuBGRWm3gp8PjN/1szrS+PewDgLRhuy477wqodhyU2w6FqYcRzsfQZM2W14Tl+1Gtv0S0+D0aTutXEJXH8GrJxdaD/wb3DwOfD8bxqOJknj2bwfjm1d/7rm1iFJkiRJkiRJkqStRMRBwHHAvsBOwJPAY8BvMnNLJ2uTJEmStl/R6QIkSZIkSZKkrlEtpcLYfklDuioYrejdwBrgg8B7gFMi4gvAZZm5rKOVjVOZeQtt+C1LZi4B3hsRHwROAg4A9gbWAQuBuzPz0VbXIY1LlYLRqoaOtUlPL+z9osIxkqrhbW36Z2XPlJH7Bze15/qSxm7Ofw2Hog155Ftw6Ltg9xM7U5MkaXRPXDG2dQajSZIkSZIkacW9cP9nYNNS2O14OOaT0Det01VJkia4iPg28PYmbfdYZh5Y5VqN3jBzUGbOq3dRRLwe+BDwggpTlkfEj4FPZObSBuqTJEmSJEmSJEmSJEmSOqqrgtEi4vqS5hpgZ+BI4JvF8QXAkuJYrTIzT29akSIzNwM3dLoOaVypFIxWNXRsnBgPNfZOHbl/YGN765BUv9mfGLn/8Z8ajCZJ49m6MWZaG4wmaTwYHIDsh94KIduSJEmSpNZZPQeufv7w7/EWXweLb4Azb4fo6WxtktQlIuJtTdwuKdxLtwpYBPwxM3248ugq3OjTGRGxE/AN4E2jTN0NeC/w2oh4e2Ze1fLiJEmSJEmSJEmSJEmSpBboqmA04FQKN2sNSSCKB8DTi0etN29FHXMlaewmajBau+6V7anwYfbBTe25vqTmmzsLTvh8p6uQJJXLhEe/N/b1AwajSeqgwS1w14fh0W8XPoC/z5lw4rdgysxOVyZJkiRJ2497/m7bhxstvxOevAr2fVlnapKk7vNtWndP27qIuAP4DvDjzPTGi5H9rNMFDImIXuDHwMvLhp4C7qYQencIcDzD91HuBVwaEWdk5q/bVaskSZIkSZIkSZIkSZLULN0WjDYSg80kjX/dHIzWM7nKYJvegnunjty/fkF7ri9pbHKw8tjkXdtXhySpdk9eDbe+fezrt6xtXi2SVItMWDsXVj8E93wUVt0/PLbwF3Dz6+CMGztWniRJkiRtVzavggU/H3ls8Y0Go0lS/WL5V3KCAAAgAElEQVT0KXXbicLDSU8FPhsR52Tm1S24Tid8GPjkGNY9B7iopJ3AhXWsvw14U53XrOeGl//D1qFoW4APAf+dmZuHOiPiSOCbwAuKXVOASyLimMx8ss76JEmSJEmSJEmSJEmSpI7qxmC0VtzwJUmt1d/FwWhR5W23WuhRM1UKRgOY/c9w9Mer1ympMx7+78pjBqNJ0vj00H82tn5gXXPqkKRaDGyC294J875fec6Smwqhabsc1r66JEmSJGl7de8nKo/NvxiO/1z7apGk7ld6E0RW6C9X/nS7keZmydg+wJUR8YHM/Er9JY4vmbkUWFrvuoj4WFnXDZn5SB1bbMzMefVetxYRcTDwwbLuN2TmpeVzM/MPEXE6cB3D4WgzgfOB97SiPkmSJEml2vTAcUmSJEmSJEmSthNdFYyWmT2drkGSxmSgi4PRqmrTL3Cn7FF5bPYnYJ8Xw+4ntqcWSbXZtBzueG/l8UnT21eLJKm6lffDgktg4xJ44n9rW7Pbs2H5ndv29xuMJqmN5n6zeijakCevNhhNkiRJklptzcMw58tVJrTpgUuSNDGcU3zdGfgEhXCroPBmeitwB/A4sBqYDOwGHAOcDOxdXJvAj4FfAjsAuwJHFuccwNYBaV+KiAcy8/qWflXjUETsALyprHtWJ2qp4HxgUkn72yOFog3JzA0R8Q5gNoX/NgDOi4h/rTPsTZIkSdL2LA15kyRJkiRJkiR1XlcFo0lS15r7jZH7+3Zsbx3N1q5feu52PPRNqxy08ej3DUaTxpsFFe/FLuib1p46JEnVzb8EbjkbBrfUvubUX8KcrwAGo0nqsHk/rG3eunktLUOSJEmSROF7tKwSfrZuHgxsgt4pbStJkrpVZn4nIp4BXEYhFA3gG8CnM3N+pXUR0QO8Gvg8cBDwBuCBzPxU2byXA/8OHEIhIK0P+AJwfJO/lG7weqD0qV4rgIs7VMtWiqFtry/r/txo6zLzoYi4BDi72NUHvAX4dHMrlCRJkrS16HQBkiRJkiRJkiRNKD2dLkCSJrwtayuP9e7Qvjpaok3BaL1TYY+TK4/P+Up76pBUuzlfrT4+sLE9dUiSKstBuPP9dYSiBTz3a7DvmZUDLg1Gk9ROS39T27wNC1tbhyRJkiRp9Aem5CCsmdOeWiSpy0XEjsAlwDOBfuDNmfnuaqFoAJk5mJk/B44Ffk3h3sDzI+IdZfOuAE4A7i7pPjYiXty8r6JrnFfW/kFmjpdfZp8JlD5x8beZ+cca136rrP3a5pQkSZIkSZIkSZIkSVLjskpMRbUxSdsXg9EkqdWeurny2KSd21dHK+x9RvuutcPe7buWpMY89hNY/rvqcwzOkaTOW/0grF9Q29xjPw1v7odnvKfQ7ttp5Hn9VUKBJamZNi2rfe6qByqPDQ7AxqWFw/BeSZIkSRqbdfNhxV2jz1td5fszSVKpTwFHUHha3ecy8yf1LM7MdRSCsJYDAXw5IvYom7MGeD2F4LWhW4pf0mDdXSUiDgH+tKx7VidqqeClZe0b61h7M4U/2yHHR8ReDVckSZIkSZIkSZIkSZIktYnBaJLUaouvrzy2R/n9lePUMz9Yof8D7ath8szq4+seb08dkkZ3/2dGn2MwmiR13rLbap971D9AlPwIoW/ayPN8f5fULqsfqn3uyt/DRbvCPR+Dgc2FvoHNcPt74We7wcV7FI6LdoHrTi98oF+SJEmSVLsFl1Ye65kE+50FR3wUdn5G+2qSpC4VEX3A24rNTcDnxrJPZi4F/qvY3AF4ywhzHgUuohCeBnDSWK7Vxc5l+GsHuCsz7+lUMSM4uqz921oXFsPxZpd1H9VwRZIkSZIkSZIkSZIkSVKbGIwmSa2Ug/DA5yuP731G+2ppxDM/CDvuv3Xfwe+A6eX3YLbQlN2rj1/1/PbUIam6zSsKwROjGVjf+lokSdsaHIDld8HS2+DWc0afP3k3eOldELF1v8Fokjptw8L65m9ZBX/4XCEcDeCuv4aHvw5bVg/PGdxSCDe/6jmF98vNK2HRdbBhUfPqliRJkqSJaGGFYLRdnglv2gynXArHfw5mHNfeuiSpO70Q2B1I4PZiwNVYXV1y/poKc64qvgbwtAau1VUiohd4e1n3rDFut39EfCsi7o+IFRGxOSIWF9vfj4h3RcRuY9j3iLL2w3Wun1vWPnIMNUiSJEmSJEmSJEmSJEkd0dfpAhoVEccBZwEnA4cAuwE7A5mZ23x9EbErsEuxuSkzF7erVknboaW3Vh475C+gd3L7amnETgfBS34Lj/0PrHkY9jwFDjh724CMVqoUvjFk4yJYvwB23G7u05XGp9UP1jbP4BxJar91j8GNr4BV99c2//APwWF/BTsdvO1Y304jr/H9XVK7bFo2tnUPfgkOOQ8e+U7lORuXwI8mFb7nzcFC3+F/C8f/W3u/D5YkSZKkbrB5JSy+ceSx/c9uaymSNEGUPrXuiQb3erLk/IAKcx4oOZ/R4PW6yUuB/UraG4AfjnGvg4pHqT2Lx5HAnwNfjIhvAP+UmWtH27AYpFYepvZ4nXWVz39GneslSZIk1SU7XYAkSZIkSZIkSRNK1wajRcQxwJeA00q7a1h6GvDT4vm6iNg7M9c3uz5JIhP++KXK4zOf275ammHHfeGIv+1gATW8xT9xJRz6F60vRVJlT/yytnkG50hS+932rtpD0Q4+B074QuXxSqG1/aN+lkeSmmPzGIPRAK44uoZJWfi+fsgfvwC7Px/2f8PYrytJkiRJE82qB+DXZ0P2jzz+tNe0tx5Jmhj2KTkf5Qlyo9qx+BrA3hXmrCg5n9Lg9brJuWXtn2XmyhZebxrw18DLI+K1mTnaL2x2LWuvz8x6f8m+pKw9vc71FUXEnsAedS47pFnXlyRJktRqhrxJkiRJkiRJkjqvK4PRIuIdwFeAqRRu3Cr9qXtSPT3nUgpPQzyAwg1HrwO+15JCJW2/MuG282D+TyvP8Qnp9dlhr9HnDG5ufR2SKltwGdx3QW1z+9cV3iujllxbSVLDNi2HRdfUPv+Ij1QfrxiMZvClpDbZ1EAw2lgtuNRgNEmSJEkaMue/4I73UvFDkjs+HWYc39aSJGmCWF1yfmSDex1Vcl7pySZTS843NHi9rhARewCvKuueNYat+oFfA9cC9wILgDXATsD+wMnA24A9S9YcBlwbESdm5mNV9t6prD2WP5vyNTuPYY9K/hI4v4n7SZIkSROA98NKkiRJkiRJktRMPZ0uoF4R8ToKNyLtUNoNzAfuYZTfJmTmIPDjkq6zml2jJLH0t/DItyqP90yCyU17EOv2Ye8zCv+7VdO7Y/Vxabwa2Aj3ng/XvQhuPQeW39npiurXvx5+9eo6FiRsWdWyciRJZVb9gbqe5Dn9iOrjvQajSeqwzcvbf815P2j/NSVJkiRpPNqwCO7+MFV/3rTfWT4cRZLGZkHxNYCDI+L5Dez11uJrluxbbu+SOU81cK1u8jag9AaUucBNde7xcWC/zDwtM/8lM3+RmXdn5sOZeU9mXpaZH6Hw8Nb/w9Z/ae4NXBxR9S/K8mC0jXXWB9sGo5XvKUmSJEmSJEmSJEmSJI1bXRWMFhH7AN8pNoduFvoqcEhmHgi8tsatLh3aEjilaQVK0pAnrqw+vs/L2lPHRDJ5Bhz0tupzRgtOk8ajTLjhTLjvU7D4Bnjk23DtKbDsd52urD6XH17/mk3Lml+HJGlkG5+sfe6h7x59zqQKn53pX1P7dSSpEf5bUpIkSZI6Z/7F0L+2+pyn1fMwFUlSiZuALRTujQvgqxFR91PiIuKNwEsYvsfumgpTTyg5n1fvdbrUOWXtCzOzjqfLQDEMbUkN8zZm5t8D7y8bOgF4cz2XrKe+BtZIkiRJGjP/CS5JkiRJkiTVqtpv6ev7Db6kiayrgtGATwA7UrjpaxA4OzPfl5mPFsdrfXu7g8INZAAzI+Kg5pYpabt3/6erj0/etT11TDTP/Xr18XXz2lKG1FRLboIlv9q6r38dPPTlztQzFmvmwvr59a8zzEKS2mfto6PPGbLvK0afM2n6yP0DG2FgU+3XkqSx8t+SkiRJktQ593+m+vikXWBPn1EnSWORmauByyncH5fAccBVEfH0WveIiHdSePjoULhaAt+vMP3MkvPfj6XmbhIRJwJHlXQNAN9u9XUz8yvAZWXdf1llSXkC6Q5juGz5mlFSTevyVeDoOg9TUyVJkiRJkiRJkiRJklSzrglGi4heCk9JzOLxucz82Vj2ysx+4I8lXYc3XqEkleidWn18ksFoY9LTV3383n8yAljd56H/HLn/0e+0t44hAxth6e2wcvYocduDsPxuWH4XzP7k2K61aenY1kmS6ldPgOw+Lxl9TrWg3y2rar+WJI3VlpWdue7CyztzXUmSJEkaL7ashQ0Lq8/Z9+XQO7k99UjSxPRhYGNJ+yTgDxHxtYh4UUTsUr4gIg6LiHdHxB3AfwGTGQ5Fm5WZs0dY83TgVIYfRnpzc7+Mcem8svaVmflEm6792bL2iRFR6Rcu4zoYLTOXZOb99RzA3GZdX5IkSRqfotMFSJIkSZIkSZI0oXRNMBpwIrALhd8WbAH+tcH9FpSc1/xETUmqSd/O1cerBUmoMcvu6HQFUn3mX9zpCoY9dQtcfgRc/Xy44li44SWwecW289YvLIz/8gT45bNhXqWHi49i87LG6pUk1W7tvNrmnTUXeqeMPm/yjMpjI/3dIUnN1r9u9DnHfKr5133oq83fU5IkSZK6yd0fGX3O017T+jokaQLLzEeBc4HBoS5gGvAu4BpgRUSsiIjHI2JRRGwAHgC+Cjyb4UA0gNuAD1W41Mco3D8YwKbi3hNWREwD3ljWPauNJdwOlP4SpRc4ssLc8qfQ7Fisvx57lrU79LQJSZIkSZIkSZIkSZIkqX7dFIx2aPE1gTsyc3WD+5Wu3+YpmpLUkMEt1cezvz11TETPKn+Abpm532xPHVIzrH6o+ngOVh9vhvVPwJyvw+3vhWteCOvmDY8tuhbu+Ydt1/z27bDq/tr2nzKz8tgmg9EkqS2eugWevHL0ec/5Mux0cG17TqoS9LvZz9VIaoP+9aPPOeafYJ+XNve6tbyfSpIkSdJEMjgAj/0EHvhC4fXhr1ef3zOp+d+LSdJ2KDN/BPw5sIatg86ieEwHnkYh/GpKSX8WjwCuAl6WmZWeMvA94LTicXKVeRPFG4DSJx0uBi5v18UzcxB4vKx7jwpzl7F1iBrA/nVe8oCy9pw610uSJEnabuXoUyRJkiRJkiRJarFuCkYrvQlofhP2K00a6WvCfpJUsGUtbBklDGKnQ6uPq7Knv7b6+IYn2lOH1AwLR7nH+q4Pt/b6y++GK4+DO95b+YNMj1wIW9YMtzevhCU31Lb/MZ+CVz4Ie/7pyOObltZXrySpfrMvKARfjib64LC/qn3fvmkQvSOPGYwmqR36a/yM5pTdm39t3+ckSZIkbS8GNhd+tnTLG+HuDxdeR3PwuTB5eutrk6TtQGb+BDga+AkwQCHsDIbDz0qPIQHMA96ZmS/LzFVV9r81M28qHne24EsYb84ra383s+1PNtxQ1t6hytwHytr13mxU/jSc8v0kSZIkSZIkSZIkSZKkcaubgtFKb+Cq8OnruuxWcu6nGSU1z5qHRp8z8zmtr2Oi2uWw6uNtv2dVXWfLavj9P8J1p8Md74M1D3eulg0Lq48/+CVYP8qccjkID/4nXH44/DAKxz0fg80j3O/++3+ATU9V329wM1y0S2Gf294JCy4rXGM0Ox8GR38cpsyEyTNHnrNp2ej7SJLGbu4smP3J2uYe8Ob69o6AybuOPLZ5RX17SVK9chAG1tc2d+bzmn/9lfc2f09JkiRJGo8e/L+w7Nba5z/99XDc51pXjyRthzJzQWa+CTgA+CDwY+BBYBmFsLT1wELgFuALwMuAQzPzws5UPD5FxGFA+ZNkZnWglPInOVR7mth9Ze0X1HqRiJgGHDvKfpIkSZIkSZIkSZIkSdK41U3BaKWpHfs2Yb+jS85N5ZDUPKsfrD6+x8mw6zHtqWWi2q1KsNzglvbVoe4zsAmuPRXu/wwsvh7mfAWu/hNYM7cz9fSvHX3Owsshc+u+wYHK829/D9z5ga3fi/7wOfjlCTCwsbi+vxAQ9+RV9dU7dxb8P/buOzyO8l77+PdRtST33jEYXDBgmunV9N4J7QQSckIaSU4K5KSQHt4USM9JQghJCL33YmxjbIPpxtjYYNx7r2qWtM/7x1jRSp6Znd2dmS26P9elS7tP/UkYbZu5Z9a1wcYe+AMnNAegsuOx3bvt0lMwERFPHf/2p2vJv51AyyBMGex3Q/p7lPdyb29S9riIRKylPvjYva6Eit6px3UZCBNfCrbmSydm/3daREREREQkXyV/BrH4ruDzLtkExz8EFT3Cr0lERLDWrrHW/t5ae6W1dn9rbX9rbYW1tpu1dri19nhr7TettS9YqzevXHy6w/0Z1toUB/iEyxjTF9inQ/NqnynPd7h/UhrbHQ+UJd1/11q7Lo35IiIiIiKSNr0UExERERERERERCcrv3TS90yYirQopGG357u8GOMQYU57pQruvADkkqWlONoWJiLSzfYF3335fgJM7HrcoaUs0evdtmxdfHVJ4Vj8LW95t39a4AT7+S27qaa5NPebNz8F9JXCvgRmXwxP7wEPdYdr50LC+bVztcnjmAFh0h/s6OxfDA1XOOveXw0M9iOyl4dH/hhFXtt2v7OM+rtHv4tciIp2UTcB734FH+8GD3WHm1dCwIfW8dmtYJxQziP4nwakvQ79j060UKnq6t+9SMJqIRCzI8+hWXfrCmW9D/xP8xw04GQaeAme+BYPOSL3uxleD1yAiIiIiIlII1k+H5yfAg1Xw/BGw+F/+n3smG3oRVAYIpRYREckBY0wp8MkOzXfmoJQraH+85jpgvs/4F4Dkq0QcbYwZE3Cv6zrcfyzgPBERERERERERERERERERERGRvFBIwWiv4RzoY4Eq4Er/4b6+nHR7XdxXfxSRIle71L192CUw4Y9QVh1rOUWpxScYrWEdbHjNCRUR6cgrAG3+L+Oto1U6gQ4Ayx+C2iXQUgernoJJxzvhN4lmeO7g/AgGPO5h2Pvq9m2ewWiboq9HRKTQvP9DmPcz529k8w5Ydi+89knn731Qu7bAtrnBxp46NbNQNIDy7u7tTdszW09EJKjmuvTGdx0Bp06DM9+BkZ+B0qr2/RW9YP9vObd7H+YEmk98yX/Nxf9IrwYREREREZF81FwHa6fA/NvhpRNg81uQaILNb8Ksa4Ov02NcdDWKiIhk72xgUNL9HcBDcRZgjBkAfLdD81PWen8AZK2tAx7u0HxzgL1GARclNTUD9wYsVUREREREMmZyXYCIiIiIiIiIiIiISFEpmGA0a20jMBnn0wID/NQY0zPddYwxxwI34ASsWeDRMOsUEaFulXt7zV7x1lHMEj7BaACTjoFn9ocdi+KpRwrHmhdyXUF7zTuzm7/jIyc8Z+YnnBCcXOsyAAafuWd7ZV/38QpGExFpz1pY/Pc929c875yUGlTjxvBq8lNS6d5um+LZX0Q6r3QDhlv1PgSOvAMuWg0T/gT7fBr2/184/XXodVD7sQNPgarB3mst+ltmNYiIiIiIiOSLJffAgzUw5RR49+vZrVU1KPUYERGR3Lm+w/37rbUZvclojBltjDkvzTkDgaeBAUnNu4BbA0z/AZD8wct1xpjzffbqAtwFVCQ132mt1QE0IiIiIiIiIiIiIiIiIiIiIlJQCiYYbbef7v5ugSHAi8aY/kEnG2NOBp7E+bkN0AL8KuwiRaSTW/+ye7vfCdWSnpYUwWgA2z9M70r2IjYR/56ZBjokm/NdWJEnOa9H/g3KavZsr+jjPr5xoxMCJCIijpYGqFvp3jfvp+7tbuIKRiutcG9v2RXP/iLSeWX7PLqiJ+z3eTjqTjj4Z9B9P/dxPcf7r7Ptg+zqEBERERERyZUdH8Nr14S3XjeP11UiIhIqY0y5MeZYY8wnjTFfNcZ8zxhzS67rymfGmAHAOR2as7nqwSDgSWPMHGPMTcYYzwdBY0w3Y8yXgNnA4R26f2KtXZxqs91jftuh+WFjzJeMMe0+qDHGjMW58OwxSc2bgB+m2kdERERERMJQRMfD6theEREREREREREREckDZbkuIB3W2teNMfcDV+B8anA4sMAY82vgQZwrKbZjjCkFTgL+G7gMJxCN3fN/a61dGn3lItJprHwKbIt7X9WQeGspZt32g4a1qcdtmAk7l0LXEVFXJMWgcRN06RfvnmEEo4Vt0Bmw5oX05/U/CYac695X6RGMlmh0AoBqhqW/n4hIMWre6d238ong6wQNRus9Ifiabko8gtESCkYTkYi1xPQ8OpEilPuZcXDFLigpj6ceERERERGRsKx4LLy1ugyEASeHt56IiOzBGHMc8A3gdKDSZciPXOacCVy+++5ma+03oqswr11L+2Mk51pr3whh3QOBnwM/N8ZsA+YCG4EdQFdgGDAe9+Mz/2qt/XEae30LGAectft+OfB74HvGmHd277kPcChtx0aCcyzlRdbaNWnsJSIiIiIiIiIiIiIiIiIiIiKSF0pyXUAGrgfexTmIxwI9gR8AH+z++g9jzHygFniRtlC01kuXvIpz0JCISDgSLfD6p737qwfHV0ux2/+m4GM3hXE8qxSF2mX+/fWr4qkjmV8ATi4c8H046Tk49Nfpzx3S8SLbSbru7d23+un09xIRKVapAjN3LAq2TtBgtNFfDjbOi1cwmm3Kbl0RkVSa6+LZp6Uh9Zj5t0Vfh4iIiIiISDZWPgEzroAZl8OyB533mGan8Vmbny4DYOJLUFJQ1+QTESkYxpgaY8w9wDTgPKALzvFvyV9e5gH/hRMM9j/GmPERl5uvPtXh/p0R7NEDOBa4ALgGuBA4jD1D0WqBz1prb0hncWttC07I3QMduvoDZ+IcF3kY7f89rAcusNZOT2cvERERERHJht9LNBERERERERERERERSVfBBaNZa+uBM4AptA86MzhXxEy+PxqooO0TBrv79ovAObsPGhIRCceGGf4hFFUKRgvNgInQbb9gY2d+ItpapDDUr4FnUxznXbcynlqSpQrAidOoG+GgH4AxsPd/QWWf4HMresNeV3j3Vw2Cnge6922ZnVaZIiJFLdXjwsongq3TuCH1mKohMPTCYOt5KSl3b2/Zld26IiKptMQUjDbo9NRj5v4IapdHX4uIiIiIiEgmFv4FXrkQlj8Ayx9yPjd7at9w1h56IVy0BnqOC2c9ERFpxxjTHefCn1fgfna9dWlr67R2BfBs0lyfD3SLkzHmWGBMUtMu4N9ZLjsf+BkwE6gPOOcj4NvACGvtHZlsaq3daa29AicEbZbP0M3A/wEHWGufz2QvERERERHJlO/LNBERERERERERERERSVPBBaMBWGs3AqcBNwMbaR981vo9+YvdY7YB38EJRdseW8EiUvzq18Lkk/zHVPSKpZROoawaTnkZ9roy2Pj6dZGWIwVg8T+gaZv/mGnnwaTjYfM7sZQEQPPO+Pby0n00HPhDOPT2trbKPnDaazDk/NTzB50Fp02H6qH+4/oc6d6+a2vwWkVEil2qx4WVjwdYow5mf8u7v2oIDLsETn8NyrumV19HJRXu7QkFo4lIxFoa4tlneICg7ZZ6+PB30dciIiIiIlJMtrwHLxwFD/WCF4+BdVNzXVFxshbm/TSatcffCsc97FxsRUREovIwkHz1qV3A3cCngetwD0vr6LGk26eFVlmBsNbOtNaapK/K3ccdZrPmOmvtd6y1xwFdcYLXzgE+C9wE3AJ8E/jM7vb+1trR1tpbs9179/4PW2uPBvYBLgW+DPwv8ClgIjDIWvsFa22Aq+iIiIiIiIiIiIiIiIiIiIjkhvW5zoBfn4h0LmW5LiBT1loL/NIY83vgSpyDt44DBtM+8G0LztUzXwDuttamSEUREUmTTcCUU1KPK62OvpbOpHowHHsvjLjKCbTys+xeGP1VnZzRmW16M9i4DTNg8kQ4+32oGRZtTdY64TVR2f9bcPCtmc/vvh+c+AQ8vhfULd+z/8i/wcjrg69X3t29vWlHZvWJiBSj5lr//g3ToWE9dOnv3r9zMbx8jvf8EVfDMf/OvL6OFIwmIrnS0hjPPj3GwD7XOUHLflY8Aof8Uq85RURERESCqFsFLx7thAwDbHzN+Zzn7LnQdUROSys6OxZC3Yrw1z301zDmq+GvKyIi/2GMuRQ4lbYLgr4GfMJau3J3/14Bl3q+dUlgvDGmq7U2D67eVRystQngw91fce+9BFgS974iIiIiIuJHxwyIiIiIiIiIiIiIiISpJPWQ/GatbbDW3mWtvcpaOxwoB/riBKRVWmv7WGvPs9b+QaFoIhKJ9dNg2wepx5V2ib6WzmjgqVA1xH/MO1+Dl06AXVvjqUnyz8rHUo9p1bQtvfGZaqmn7Tj2DvqfCD3HZ772qC9nF4qWbNhFe7aVlMPQC9Nbp6ybe3vz9vRrEhEpVs0BzkV6dIBzAnGylgaYfhk8ORK2L/Ce2/vw7OrrSMFoIpIriZiC0QCOvBOOugtGXOM9pnYpbJ0TW0kiIiIiIgVt6T1toWitmmth3k9yU08xC/JeUyb6TIhmXRERSfbtpNtzgdNaQ9HSYa1dC6zffbcEGBtCbSIiIiIiIiIiIiIiIiIiIiIiIpHL+2A0Y8x4Y8w3jTF3GWOe2v11lzHmJmPMIR3HW8dma+1aa21TLmoWkQCshfUzYNkDMP82ePtrMOMTMOk42PxurqtLz6a3go0zugpUJEq7wJF3QFlX/3EbZsDsbwVbc9sHMP92mPtTWDfN+fcqha28e3rjg4QdZqu5zrvvsN/BGW9AWU1ma48L+G890FrfgV6Htt03pTDh/6CyT3rreP03aNqReW0iIsWmuTbYuLe+1P7+3KxeMQcAACAASURBVB/DiodTzxt6fvo1+Skpd29P6KW4iESsJcZgNFMC+1wHx9wNl2xyng+7WRFDuLKIiIiISDHYMtu9fdGdkGiJt5ZiZyP4fVb2hT5Hhb+uiIj8hzFmEHBwUtON1lqfD5dTSr6iyn5ZrCMiIiIiIiK+iul482L6WURERERERERERESkUJXlugAvxphDgV8Dx/kMu9UYMxP4mrU2YDKRiOSNqafveUV4gJ2LoPceuYf5a/ZNua5ABp8F586H5w6Gxk3e45beAxP+5JzY7mXxv+D168E2t7Xt+1knCMpvnuS3qiHQtD34+KDBNNlI+IQ5lFZCaQUMvRiW3p3+2l0GZl7XHmv1g9NnOiGB9Suh/4nQbd/01ynv5t6uYDQRkTZrng82buXjULcKqoc44UAf/j7YvJq9M6/NTUmFe3tiV7j7iIh05PdcOkqVvZ3nw+um7Nm3/uXYyxERERERKUjL7vPu27EQeoyJr5ZiF8VnHWO+DiUegdEiIhKWo3d/t8AKa+0rWa63Oel2mle/EhERERERERERERERERERERERyY28THgxxlwATMcJRTNJX/8ZkvR1HPCKMebCuOsUkSwY4wQVualbFW8t2Zj701xXIK2qhzrhZX6ad0Ltcu/+ho0w69r2oWgAH/8V1k/PvsZcsNYJe5t+Kcz6NKybmuuKcqPjf9NUmndGU0cy32C0Ls73CX+EwWenv7Yxqceko7QLDD4DRl6fWSgaQJlHMFqzgtFERP5jyb+Cj318KNxfCQ90Cf63NOzHBwWjiUiutOQoGA1gyLnu7dvnx1uHiIiIiEgh8vuMBqBhbTx1dBZhf9bR/yQY87Vw1xQRETfJV8F6L4T1kh8QuoawnoiIiIiIiLgK+dgsEREREREREREREZFOLu+C0YwxY4D7gCqcTwbs7i9oH5Bmk766APcaY8bGW62IZKXaIxitvkCC0Rb/E+Z8N9dVSLIh56ces32Bd9+j/bz71ryQfj354L3vOGFvKx6BxXfBlNNg+SO5rip+zbVpjo84GM1a/zCHkkrne3k3OOkZuHg9XLgCjns49dp7XRVOjWEr9whGa1IwmogIAHWr05+TTgDZ0AiyxBWMJiK54hcy3KrbqGj27nmQe3vDemjcHM2eIiIiIiLFoH4NTDref0zjxnhq6SzS/WzET+8JcOpUKPV4P0hERMLUI+n29hDWSw5DawhhPRERERERERERERERERERERERkcjlXTAa8GecoLPW0DMDNAOvAQ8CD+2+3UT7kLQuwF/iLlZEslA91L29rgCC0da9DLOuy3UV0lFpJVywzH/Mtnnu7Rvf8J/XuCGzmnJp11ZYcHv7NtsC836Sm3pyqbkuvfFrX4JXPwlNYRxnnuTjO+CZcfBwb5h2rve40sr297v0c/5m1gxPvcegM7KrMSpewWiJRmhRgI6ICHXLo11/+OXhr1lS7t6eaAp/LxGRZH4hw63G/yyavbuP8e7b9kE0e4qIiIiIFINFf0/9/ofXZziSmbAuAlPRG059OZy1REQkiC1Jt3t4jgpucNJtJfuLiIiIiIhExua6ABERERERERERkYKhd9NEJIi8CkYzxhwAnEBbIBrAbcBAa+2x1torrLWfsNYeCwwEftlhiWONMQfFV7GIZKVqiHt7fZ4Ho22bD5NPznUV4qVmOPQ4wLt/7WT39uUP+q+7a2vmNeXKmhed0KmOtsyGph3x15NLLbXpz1l6N7x8Tng1LPk3vPFZJyyhaSvsXOw9tqTSvd0vhAGg+1jY64rMa4xSeXfvvg3T46tDRCRf1a+Odv1hF4e/ZkmFe3tCgZciEjG31znJ+h0Lg8+KZu+qwVDmEfq75oVo9hQRERERKQab30495v0fgNXhTqFpCikY7bSZUFYdzloiIhJE8lXbxmWzkDGmEjg4qWllNuuJiIiIiIiIiIiIiIiIiIiIRE1HEYpIq7wKRgMu2f3d4Pyt+rK19pvW2i0dB1prt1prbwa+mDQeIIKzvUUkEtUewWi1y+KtIx02Ac/sn+sqJBW/4I91U6C5Q0iWtbDycf81mwowGG3bXO++qMNX8kmiyfnKxIYZsPAv4dSx6M7gY72C0co9AhhanfUOlHqE1OSaV3gEwKK/xVeHiEi+qoswHHjI+VDq8diSDQWjiUiu+AWjHfIrOOm56E7aNwb6Hu3el+p1pYiIiIhIZ7bysWDj1k+Lto7Owlp456vZr9PzQOiR4qItIiIStnd2fzfACGNMNn+ILwFa38xvBmZlU5iIiIiIiIj4MbkuQERERERERERERESkqORbMNqE3d8tMMta+8dUE6y1fwZm0vYpwhER1SYiYase7t5euxTq18ZaSmCvXJTrCiSI0V8B4/EQl2iEtS+1b9s2D3Yu8l9z1x4ZnfnP+uQhb37Hu6/YdAzCS9ebn4OmHdnXsf7lYONMGZSUevcPPse9/eD/B6Vd0i4rNlUDvfs26vh7ERHqIwxGG3hKNOt6BaO11Eezn4hIqxaPAMZ9roOxX08dKJytoRe4t2+bCzs+jnZvEREREZFC1aV/sHErn4i2js5i+YPZr1FaBRP+nP06IiKSFmvtEiD5Tab/zWQdY0wl8J3WZYE3rbVZfnguIiIiIiIi3nyO2y44xfSziIiIiIiIiIiIiEihyrdgtLFJt/+Zxrx/Jd3W5YpFCkWfCd59G6bHV0dQb34RVj2Z6yokiMrecJlPkNXG19rfX/9K6jU3vw2Jluzqituuzd59r14Fq1+Ir5Zcaq7Lfo2ns3x64RdS11FppX//Ptfu2WZKYa+r0qspbmU13n21S2HRXbGVIiKSl+pWu7cHPWnYiymF4Zdnt4aXknL39vrVkGiOZk8REXACr92UpHguHZah53v3KcRBRERERMRdz4OCjVt2f3rvqYu7FY+lHrPPdc7Fhrycvxj6HRNaSSIikpbWD08NcI0xxuVDYm/GmBLgDtofi5fyAqUiIiIiIiIiIiIiIiIiIiIiIiL5It+C0Xom3X4njXmtY02HNUQkn1UPga4j3ftWPxtvLal8+AdY+KfU40bd6N5+6G/CrUdSK6uGYZe69zVuan+/dlmwNVcV2Anua57373/js9DSEE8tudQcwkWv61fDlvcyn5/O7zlVmMOwS+HAH4Apc+6X94DjH4GaYRmXF5sDvufd9+bnockn0FBEpNg1bnBv73Fg5mtW9IITn4aqgZmv4aekwrtv5ePR7CkinVPtMnjn6zDtAvjgF9C0zX1cXMFo1UOht0fY+7vfiKcGEREREZFCYwNefKZhLWybF20tncH6qanHHPB92O8L7n09x0f3npKIiATxW2A9YHGOh7vTGPMzY0x1qonGmP2BF4Grd8+3wMfA/dGVKyIiIiIiIs7LNxERERERERERERERCUu+BaP1SLq9yXPUnrYk3e4WUi0iEof+J7q3L7kbts2PtxYvyx+Ctz0Cz5Lt9wXnq6ymfXtFbxh2cTS1ib+KHu7tHYOy6lcHW29ZAR0nvHEW7FzsP6ZuOaydHE89udRSF846S/+d+Vyv4AY3pSnCHIyBA78Pl22Fs9+HSzbC0Asyry1O1cO9+xKNsPLJ+GoREck3jRvd23vsn/5aB/4QLlgGF6+DwWdmV5cf32A0/U0XkZDsWATPT4AFt8OqJ2H2zbDmBfexqZ5Lh2nYhd59H/4uvjpERERERApF0GA0cJ77S3Ya1vv3Dz4Huo6A7qNgwMQ9+/e7IZKyREQkGGttHXAtkMAJNisBbgbWGGPuA9olWxpjPmGM+Z4xZjowBzgZ54x8AzQCV1prbYw/goiIiIiIiIiIiIiIiIiIiIiISFbyLRgtuZ40joxuNzbffiYR8TPodPd22wJL7423FjctjTDj8mBjD70deoyBiS/BgFOgsg8MOgNOmQo1w6KtU9yV1ri3L7sPWhra7q95Pth6yx/Kvqa4zPl+sHGrno62jnzQUh/OOvN/BS+dCJvfTn9uOsFoJV2CjSurgZ4HQElZ+vXkSnl3//7Vz8RTh4hIPvIKRut5AHTdN721qodCzXAoKc++Lj9+wWhL7452bxHpPD78LTRuCDa2JMZgtKE+wWhzvg86z1REREREpL20gtE6wWcXUSvzuZ5ceQ84LukzrxOegJHXQ9Vg6HkgHP5H2O/z0dcoIiK+rLUv4ASgtYajgXPB0MuBbyQNNcC9wA+AY2h/7FwzcL219p2o6xUREREREREdJyAiIiIiIiIiIiIiEqYCStIQkaI09EKo7Od+kvOmN+Kvp6O3bkw9xpTBCY9D6e4TsPseBae8FG1dEkyZRzAawGvXwnEPQMNG7yASN4nm/A+ishY2vR5s7I6Poq0lHySH4HV04Qp4PI3gwvWvwPOHOycMdRsFPcZBSWnqeQ0Bgxyg7W9JMSr3ORFLRKSz83o+UtkPDvohvHoNgQ+e83sOFCa/YDQRkbB89PvgY+N8Lt19LNSMgNqle/Y1bYX61VA9JL56RERERETyXaI5+NhNb4BNgNE10TLmF5h/4Qooq2q7X94Vjvxb9DWJiEjarLV3GGMWAfcAA2j/QUHybZN03+6+vxH4hLV2ahy1ioiIiIiIiIiIiIiIiIiIiARlfU6V9OsTkc5FRxKLSG6VVsKQc9z7ErviraWjXVtg0R3+Y4ZdAme+6f0zSG75hYKseBjq18DCP6a35s7F2dUUh4Z10LQt2Nh1UyDREm09udZS795eUgnVQ6H76PTXnHEZPDceHh8KG17zH7vkbnjp+OBrlxRxMFqZgtFERFy1NEDzTve+yj4w4iqYOAn2/qTTluqk4NiC0XxOsBURCUM6wQkQ73NpY5z3BLx0hhBqEREREZF02DQ+i7AtMP9X0dVS7KyFpu3ufcfcp4uYiIgUGGvtFGBf4CZgBU7oWccvkm5vAn4EjFQomoiIiIiISJxM6iEiIiIiIiIiIiIiIhJYWa4LEBGhZoRHRyLOKva06hn//qP+Cft8Mp5aJDN+oSA2AZvfhQ9/m96aDeuh+6js6ora9vnpjV/4fzD6S9HUkg9aGtzbS7s434+8C6acCi11zv2ybtC8I9jaDWth2rlw4Uooq9qzf+v78FqafydKizgYLeXJVjooREQ6qcZN3n2VfZ3vA09xvo7+p3N/1dMw7Tz3OWVdw63Pi1f4qIhIWLbOSW983M+lD/oRLLjNvW/yRDj+URh8DpRWxFtXoatfB6ufBowT+l3ZH/oeCd32zXVlIiIiIpKNdILRAGbfDMMu1vPApu2w5gXYOs+53f94GHiq//vtiUawHkHTNcOiqVNERCJlra0FfgX8yhgzCjgOGAb0ASqAjcA64FXgHWt17WQREREREZH46aWYiIiIiIiIiIiIiEiY8jEYrfXTgKOMMSMCzhmYfMcYczxpJGtYa18JOlZEolDi3rw+x/9rrn/Zu6+sm0LRCoFfMBrAO1+FXVvSW7N5Z+b1xGXHx+mNX/FQkQejeYS2tAaj9TsaLlgKKx+HkgoYdAa8cCTULQ+2/q7NsOY55wStjpY/lH69JZ04GG3ZvXDsPfHUIiKSTxo3eve1BqPtweM5NMT4WOLzstv41CciEsS2BfD8YenNifu5dFk19DkCNr3h3j/9Yuh9OEycBBU9462tUG14Faaesedr79IqJxx0+GW5qUtEREREspduMBrA0nvgwO+HX0uhqF0Gk0+BnYva2j78NfTYH058Grru7THP5/ON8u7h1igiIrGz1n4EfJTrOkRERERERCQdBXbhYOVti4iIiIiIiIiIiEgeyMdgNHDe9b8vi7kvpzHekr+/B5HOoaTUu2/Le9BrfHy1tLIWFt3p3b/3f8VXi2QuVTDajoXpr9m8I7Na4tS0Pb3xuQ4hjFpLg3t7aVXb7S79YN//brvf/wRY+u/ge0y/BHodDFtmZ1Zju7q6ZL9GvipLEYwGULsCaoZFX4uISD5prvXuy+RkVRPTgXR9j/LuswnY/hF0HxVPLVLcdi52Xp9tetM5kb5mBOx1BQw6LdeVSZTmfC/9OT3GhV9HKr0P8w5GA9j8FjzcC0bdCGO/ATXD46utEL1+vXsgeUs9vPl5GHIBlFa0te9cAvNvg6atMOBk2Ps6//eZwmQT8PEdsG4KVA2CkddDzwPj2VtERESkEGUSjLZ+evh1FJLZ324fitZq2wcw9ydwlMdnmR/83HvNIO/Ti4iIiIiIiIiISMgUNCYiIiIiIiIiIiIikq6SXBfgweIEnKXzZZO+0p0rIrmUaPbuW/FofHUkm32zf/+oL8ZTh2QnVTCal16HQLXHyepNBRCMlmjMdQX5xTMYzSeAbPSX098njFA0oKgPfggS7vPEcP/HBRGRYuT1WAVQUune3qWf95zyntnVE1R5ihNpJx0DW+fGU4sUr+0L4cVjYd7PYO0kJ4Bo8d9h6hmw+B+5rk6i0tIAKx9Lb06P/f0DG6My8jNgAgRxffR7eHos7Pg4+poKVd0q2L7Au79xE2yY0XZ/23znd7rwj7D0Hnj9MzDrusjL/I9Zn4I3PwfLH4QPf+v8rdroE5InIiIi0tl5BaMNvdB7zrrJ0OQSnNtZrHnOu2/Vk959i//u3ZdJCL+IiIiIiIiIiIgEoFOTRERERERERERERETClK/BaNA+6CzIVyZzRSQf2IR337Z58dXRauv7MP+X3v3jb3VOuJb8l2kw2vDLvYM+CiEYzS9cxUtzXfh15IuWevf20irvOX0mwMTJ0dSTSnmP3Owbh1KPcJ+ONsyMtg4RkXzjF+JpPA6Y63UodOm/Z3v1cOg+OrzaUjnmXu++xk3w4e/iq0WK08I/QcNalw4Lc26BhMeJ9ZKZDa/C5FPh8eEw5TTY/lFu6tj+oXdoQkemDIZfBqdMhZKyaOty0/tQmPDnYGNb6uCp/eC+MnhiBLx7EySaIi2voNStTD1m58fw3nfhXgPP7L9nMPjSfzt9kyeGGF7tYtt8WPKv9m3NO/zfTxIRERHp7Lye4w86E4Zd6j1v+sXR1JPvWhpg1xbv/saN7q+JU71OThV0LyIiIiIiIiIiIhnSKUoiIiIiIiIiIiIiImHKwdmCvpajTwNEOp/qwd599WviqwPAWnj2IP8x+3wqnlokBBnmf476Eqx83L2veWfm5cSlpTH1mI4aN0LZ8PBryQd+YTN+Bk6Eqyw0bIRH+4Vfl5eKnvHtlQvDL4flD/qPWXo3DDgxnnpERPJBwuOxqsTnsaqkFA64Bd76Uvv2A2/xDlOLQkUv//5Fd8CRf42nFilOfoGpdStg5yLoPiq+eopZ7XKYekbba566FfD84XD+IugS4/NhgI2zUo857mEYfonzOj7Ov3tu9v0MNG2Fd78ZbLxtgdplTohW3Qo49r5o6ysUfsH5rd64Idha66bCpOPgnPlQMyy7utwsvce9fcXD0LILSivC31NEJG47F0P9OqgaCDUjcv94KyKFL9Hs3m5KYdy3nedSbtZOckJvex0cXW35qHFT6jHN2/d8b6Z2qf+ckvKMSxIRkXgZY0qAA4DxwHCgH1CFc2xdPbAe51i794B51lodcyciIiIiIiIiIiIiIiIiIiIiIkUjr4LRrLUjcl2DiOTAwFO9++IOoVo7yb+/79FQNSCeWiR7iab05ww4Bcq7Qnk39/7mHdnVFIdEhsFoNcUajFbv3l5aFWx+l77Q71j/UI4wDTglnn1yZew3UwejLboTxt8afwCHiEiuZBriOeqLUD0Mlj/sBKUNuwyGnB1+fX7KquPdTzofr+dyrbYvUDBaWOb+ZM/X4M07YOm/Ycz/xFODTTjhYgtu9x93/GMw7ELndr6EtIz9BvQcD1NPT2/esvuhrBsc8WcwGYZ7F4uw3wNqroXFf4cDvx/uugArHvHue7AajrgDRipYX0QKVP1amHQ87Py4ra1mBEycBN32zVlZIlIEbIt7e0kZ9BrvP3fhn53nzJ1JkGC0Xdv2DEbbvsB7/MDTsqtJRERiYYw5AbgBOAvoEXDaFmPMM8Ad1toZkRUnIiIiIiIiPvLk+AUREREREREREZEC4Hf5N10aTkRadfKz7UQkL3TdB7qPde+rXx1vLUv+7d9/6G/iqUPC0fsw0v6Qud+xzvcyj2C0pgIIRmvJIBht1TPF+yoh07CZZIf+Gir7hlMPQGUfuHQzjPiv9u3DL4Phl4a3Tz7qcziM+3bqcW9/NfpaRETyRTaPVUPPh2P+BUfdFX8oGkBpgGC0RHP0dUjxSuzy7/c74VuCS7TAojvc+za/HV8dH9+ROhTtsN+1haLlm0GnwXkfpx7X0aI7YPbN4ddTaJprw1/TL8AsG37vDdgWeP3TMOMTsPLJzF6ji4jk0swr2oeiAdQuhaf2g6lnwcyrYd7PYPE/oH5NLioUkULlFYxmSp2Q4CP+4j1305vR1JTPGjemHtO0bc+22mXe4ztbuJyISIExxuxvjJkKTAWuAHriHPAQ5Ks3cA0wzRjzkjFGV5MQEREREREREREREREREREREZGCpmA0EckPh//Ovb1xY3wnkNathKV3e/ePuAb6HhFPLRKOLn2h33HpzRlxtfO9vICD0RIe4Sp+3r8F3voi2ET49eSaZ9hMVfA1+kyAs993Qmcm/F/2NV24Cip6wdH/gFNehkNug1OmwDH3Qkl59uvnu/E/hT5H+Y9Zfj80bIinHhGRXAvjsSpXygIEo+3aEn0dUrwSKV4PKhgtHDs+8u5bek98dSy7179/+GUw+sZ4aslUt5FwcQbPY+f/yvnqzKIIRtv6Pqx6Ovx1g9S6/EF45QKYdCw0bg6/BhGRKDRuhvXTvPvXPO88Xr/3HZj1KXh6f1jnM15EJJlfMBrAvp/1nrvjw+K9uIuXIMFou7bu2Va/yn1szwOdi1WJiEheMsZcDrwBnEBb2Jl1+Wrl1tc6byLwtjHmkrjqFxEREREREWj/sq3QFdPPIiIiIiIiIiIiIiKFSsFoIpIfqgZ79zWsjaeGN7/g3z/0wnjqkHAdez/U7BVs7NF3Q/fdF04u6+o+Zund8PK58PpnYOZV8PwEuNfASyfDR3+ERHM4dWcj0zDBhf8H614OtZS80FLv3l7aJb11qgbCPtfBfp+DYZdmXs/I/4bSSue2KYEBJ8LYr8GAk6GkLPN1C033FBcpt4loAgxERPKRZzBamo9VuVAaIBgtyIm8Il4Su/z7F98F9TG9Zixm9au9+6qHxVfH+lf8+/e6Mp46stWlL1y6FYZekN68d78JzxwAH/wCmj1exxSz5p3RrDvruvB/n+kEkm9+G+b9LNz9RUSiUr8mvfFNW+GN/4aER9iRiEiyVMFoAMc97D6muda5wFFnEuT9lOTPUFc8DrM+7f3cs/v+4dQlIiKhM8ZcBtwLVNM+EK016AxgA/ARMAsnQG0hsDFpTPI8gBrgPmPMRfH8FCIiIiIiIiIiIiIiIiIiIiIiIuFSMJqI5Ae/YLT6dfHUsPkt776KXumf0Cz5oXownL8k9bhzF8De17TdL+/mPXb1M7DoTlh2X9u/m/Uvw1tfgukXg83xVbISGQajAax6Krw68kUUYTP7fZ62Y9DTYMpg7Dcy37eY9D4s9ZiNr0Zfh4hIPvAKVymEYLSyAMFoq5+Lvg4pXqmC0QCePQC2L4y+lmK2a7N3X9O2eGpY82LqMT3GRV9HWCp6wPGPwYUr4eQX4OQXoXpo6nnb5sHsm2HyydAS4N9/MWmujWbdxk2wbnJ46zXXeb/O9LL0bif8WUQk39mm9OfsWAibXg+/FhEpPkGC0Qae4j1/w/Rw68l32+amHjPzCudiOfNuhekXOeHhXqqHhFebiIiExhgzGrgL5xi+5EC07cBvgHOAvtbagdbasdbaY6y1R1lrx1hrBwD9gPOA3wE7aB+QVgb80xizX9w/l4iIiIiISOeUwXHFIiIiIiIiIiIiIiLiScFoIpIfyrt797VEdGJsskQLNPgEsJ09B0rKoq9DomEM9D/Ru7/PkdB9dPu2yv6Z7bXqKVg7KbO5YfE6QXvM11LP/fA34daSD5Y/6N5eWpX5mgMnwvGPQNd93fv3vhaOfxR6HQymxDmxq9ehMPEl6D4q832LybCLU4/Z/mH0dYiI5IMoQjzjUtEn9Zg5342+DileQYKhGjfBgtujr6WYNW7y7mvaDk07o69heornh1WDoOvI6OsIkzFO+MCg02HQaXDwz4PP3fQ6vH9L2/1EMyz4DTw1Gp4cCXNuiS5ILFei/HnCDOls3Jj+nIb1sGV2eDWIiEQlkUEwGsCkY+GBKnjuEFh6f7g1iRSjXVth1vXw2FB44UhYck9685c/4sx7oBru7wIP9YQpp8HWAEFauRQkGK2iJ9Ts7T7u1auhYUP4deWr1c8HG/fGDfDet1OPq1IwmohInvoDUE1bIJoFfggMs9Z+zVr7nLV2i9dka+0ma+0z1tqvAsOAH+9eo1VX4PeRVS8iIiIiIiJJcnxhbRERERERERERERGRIqOUHxHJD6bECSlqqd+zr7ku+v2btoJNuPcd+TeoHhp9DRKt/b8FG1/d8+Q+UwKHuxwHXJ3FCSLv/A+cMy/z+dlqaXRvL6mEIefDqif95+9YBN1CDhxoroXtC5wTnHqMg5Ly1HOshdplUFIBjeudn8uUQvcxUN412L7rpoJtdu8rC7iGl2EXOV9294EMrX9DTIkTgNA6JtEMGCgpdV2m0wryd3X7gujrEBHJB17BaCUFEIwW5PGtvFt0+ydaYOfHUDMCSiuj26fQ1C53fu8VvXJdSfYSHs9tO1oT8IRxcecXjAZOEFTQ5+CZ2P5R6lCsUTcW/nPq4ZfBvFthW8DAig9+DqO/AmXdYNZ1sOKRtr65P3bCto74cySl5kRzhAF8YYaSZRKMBrBtPvQ+NLw6RESikAgQSuulpcH5e/vqlU7I87ALw6tLpJhYCy+fDRtfc+7Xr4LXrnHe+x5xRer5K5+EGZe2b0s0wtqXnJDC8xZClwwv/BI1r/fqTYfn+UPOgY/+iyAqkQAAIABJREFU4D528klw1pzCf22QSqIZapcEG7vkn8HG6bNOEZG8Y4w5FjiFtlC0HcDF1trJmaxnrd0BfN8YMx14FKjZve5pxphjrLWvhlO5iIiIiIiIpM/kugARERERERERERERkYJTkusCRET+o6zavT3VydFh8DupdeDp0e8v0Rt8JpwyFUZe39a297Vw1mzoM2HP8VVZBKNt+wAW3ZX5/Gx5hUeUdgkWTLL03vBqsRbe/xE81AOePxyeOwQe7gWLU5yos2MRPHsQPLk3PD7EmffiUfDCBHi4B7z3He8ww1YtDTB5ond/z4PS/3ncGON8lZQ6X6bDwQslZcV/klamDv2Nf3/jBmjcHE8tIiK55BWMVloAwWjgBBz7iSroeNXT8Gg/eHoMPNwb5t8WzT6FpHYZPHswPLEXPNwHpl8KzS7h04XC2uDhHLVLC/tnzbVUwWgtEQeWr3nRv7/7WBjz9WhriENJOZzxOhzwveBzHhsMD3VrH4rW6uO/wM7F4dWXa1G+/7Pjw/DWSvX/i5fXroGWLAKHRETi0PGiEpmafhE0RRh4KVLIts1tC0VLtvBPqeeufAJeucC7v2k7fOhyIZh8YVvc202Ha7kN9QlW3PYBrH4mvJrylVeIXDb6HB7+miIikq0v7P5ucMLRbsg0FC2ZtfYl4IakdQE+n+26IiIiIiIikg2beoiIiIiIiIiIiEgn4veOmd5NE5FWCkYTkfxR6hGMFvUJ2OAfjFbZJ/r9JR79joUj/wZXWefr6H9AzwPdx1YPzW6v1z8NM6+EObfArE/BWzfCpjedcIeotXgFo1VCWYBgtLUpQgmCaNoOyx+GF4+G97/f/oSn5lqYdZ1zEpcba+GVC50TxFz7EzDvZ/DKRZDwOJFq1xZ4Zpx3faYMBp8V6EeRCHUMkXOzPcQAAxGRfFXowWj73+zf31wb/nOgnUuc5wu7tjj3W+rg3W/AyqfC3aeQWAsvnwtb32ttcIKU3i3gMKl0TwTfMjuaOjqDXSmCnqIKrLIW1k6G2Tf5jzv+USitiKaGuJVVw0E/gstD+p1OPRO2vh/OWrnW4vE7GXENDL3IvW/Ul+Hof+8ZptFR4ybY4BJAkgm/95BS+eDn4dQgIhIVv1DakvL01nqoGyz+B9StzKokkaKz5G739g3Tne/NdbDqGVj4F/j4DicE/INfwJtfcl4HpzLvJ86FSVY+6f1+Q654BqN1uLBI/xOgvKf3Om/d6Px+Nr6R+gIqhSoRcjBajwOg6z7hrikiIlkxxlQC5+Ecy2uBR6y194e1vrX2PuARnHA0A5xvjCmSN9hERERERETyVYBjYkVEREREREREREREJDAFo4lI/ijzCEZrjiEYbcNM9/bSKu+6pLhVDc5+jWX3w9wfOycAfvQHeOEIeOMG7zCvsCQ8TnYqqYTy7qnnb5jhnHiVqR0fw7MHw4zLYNPr3uNeuRBWPLpn+5Z3vEPRkq16Eh4f6pzgnmzr+/DUaNi52Htu/+OhwufEKomH14lwyXYoGE1EOoFCD0YbcXWKATb8k7FXPOL+OLLS5blFZ7FjoftzqOUPxxPOGwWvwF8vk46B2mXR1FLsUgU9RfG6vLkOpp0HU06FlnrvceN/Bj3GhL9/rpVVhxPEvmMhPDse5t2a/Vq55hXAV1YDIz+9Z3tJJYz6Iux9NZw9Bw79DRz8/7zXn3SME6CRrWyC0Zb8M/v9RUSilGhyby+tgnM+gMN+l956sz4FT4+FVc9mX5tIsfB7LrFzKbx4FEw7F978HLzxWScEfPbNsPCPwfeYdR28coHzmcTOpVkWHCKv94NLOgSjlZTDvp/1XqduufP7efFI5+9M2CFi+SDdoPBUxvxPuOuJiEgYjgK60nbW/O0R7HFb0u2uwNER7CEiIiIiIiL/UaDH57gp1GONRERERERERERERKSoKBhNRPJHqUcAWUsMwWizb3Zvr+wb/d6Sn0orwjlJvaNFd8Ca58JfN5lXgERpJZiAD/0zLofaFentu+JxmHU9PLUf1C4JNmf6JdC0vX3b6ueD79mwFl4+t33bG5+Dxg3+8459IPgeEp0gJ6xtXxB9HSIiueYValoowWjd9oWj7/Yf4xV2k6l3v+nevvgf4e5TSJY/5N7euCH8YLq4JHalP2fqWeHX0RnsXOTfP/kkmHombPsgu32a6+CDX8K9Bh6sgdUBApnHfD27PfNZvxNCWsjCnO/C1gAB0/nMMyi0CgafDQf+AMzu0IyK3nDEn6H7KOd+j7Ew5isw9iYo6+q9x5ufgzk/SP/vYu0KeP+H8Oo1sOC21OO97FwU/mOiiEiYvILRSsqd5/2jb4QLlkPvCcHXbN4J086BZp8gVJHOxO/iJW9/xbnwR1i2vg/zfhLeetmwFmzCvc+U7tl2wHeDrbvkX7Dyiczryldef48ztc914a4nIiJhaA0ps8B8a+2ssDfYvWbyG3oKRhMRERERERERERERERERERERkYKhYDQRyR9lNe7tzREHo/mtr2C0zq0igmA0gI//Gs26rRIewWglXaBpR7A1Wuq8A0fcvP9jmH4RLP578DmtHhvS/v6cgCc8tdo0qy1MbddW2Pia//hDfw1d+qW3h0SjZnjqMTsXR1+HiEiueYWzlBRIMBrA3tfA2XO8+1sUApNThRrCk0kw2vb5xXlSfJQSzbAjRTAawJoX4JlxsG5aZvu0NMCU02D2TcHnnPScE1pdrEbfGN5aNgFLUoRU5rtUId8Hfh8uWg1nvQcXLHEPdzAGuo/232fuD2HaecHDJnYugUnHwPs/gKX3QO2yYPO8bP8ou/kiIlHyev5VUt52u2YYnDELzvnAeaze+9pga794ZPb1iRSD8h7efaueDH+/pfd5B5LFya8Gt2C08m5wWcDPM1Y/m1lN+cwGuKhIUGfNDn7RHBERidO4pNszI9xnhseeIiIiIiIiEjqT6wJERERERERERERERIqKjoAVkfxRWu3e3hJxMNr2D737uo6Mdm/JbyVl0ay76il444bgIWWtWhrg3ZvggSq41zhf086Hze/sOc5NaSU0p7Hn8gegfl3qcU3bYd5Pg6/bUfNOeKQ/bF8YbD83L5/lnFjVuAnnoto+Rn8lsz0kfIPPgZIUQRe7tsRTi4hIHFY8Ci8eC4/vBa9dC42bnfaWevfxpQUUjAZQ6RM8unVefHV0Vm4nkrd660vBA4DySSbBaABvfC7cOopd7dL0Trp/538y22fV07Dx1eDjS8qhz4TM9ioUA06GE55wgrxSPS8OYv4v8iP0IlOeId+Vbbe79IdeB0F5d+91uo1Kvdfal+D+CnjpZJj9LZh0HDw9FmZeDQ0b2o/96I9QtzL1mkFtXxDeWiIiYfN6ztjxccqUQI+xMPhMOOquYGtvfT/7cEmRYuD3PCYKLXVwX6nzecJTo2HK6bDx9XhrALAt3n1er2fLu0LPg1Kvvfjv0LQzs7ryVSLEYLRe48NbS0REwpR8MEqUD87Ja+sAGBERERERkUilOH5ZRERERERERERERETSomA0EckfZR7BaM0RB6O9cqF334G3RLu3dF4f/xWmnQvW50Pw5lqoXwstjU6YyDPjYP4v2wefrXoKppzaPlCsxedk8sHnpFdnkOCCjbO8T2APqnEDPD0KVj2Z+RpvfyV18NvZc8Hoimx5o7wrjEkRrLFrazy1iIhEbeVTMP1S57G1bjks+RdMPtkJsOkYwNKqtCreGrNVVuPdN+2c9ENhJT1+wWjLH4BZ18dXS1i8ntcC9DnCu69hLWz/KPx6ilX96vTGb3k3/d+vtbDwz+nNGXE1VPZJb04hGno+nLsArmiETzRkH5D26tXh1JULXmGI6f5Ouo8OPnb9y/DBz2HDTCewbNm9MOlYSCQFdyy4Lb39U6lfFe56IiJh8vpbbMq95xgD5y+Csm6p13/vO877nGEG/ohIcDs+grWTYMop8QcV+oUxG5+L1EwI+Dpi5hXp1ZPv0gmv9jP43HDWERGRKAxIuh3lA3Py2gMj3EdERERERER86dhlEREREREREREREZF0KRhNRPJHqUcwWkuEwWjN9U4whRtTCj0PjG5vkfWvwNb33fs+/D08PhweGwQPdIFnD4Cdi93H7toC837Wdt8rpKy0Cww63f8ko45ShSRsmAlTzwi+XipvfDbzuR//BRo3evefNRt6jst8fYnG+FvhyL979ysYTUSKxcI/ssdVQbfOgbUvwY4P3ed0HRF1VeHyej7fatl98dTRWfkFo4Hz+/cK4ctXXsEcAMc/5j939XPh1lLMEk3pz1nzYvCx2z6AR/vDusnp7XHwL9IbXwxKK+Hitf5j+h4NvQ/z7l/+UPQB81HxCkMsrUxvnT5HZlfHjoXZhXan0lwf3doiItmyHs8LUoVUdt0HznoXRv63/7il9zjvcz7SB5bem1mNIoUuk+ffrWpGtP/cqu/Rma3TXAvv/yDzOjJhW7z7/F7P9gv4M65+Bja/k15N+SysAMn9PhfOOiIiEoXkKwJE+YFo69oG6B3hPiIiIiIiIuLL50LaIiIiIiIiIiIiIiLiSsFoIpI/yjyCFKI8oXf7Au++IbqKusTg7Rvb/o031zsBA9Mvgbe/DLs2B1/no9+BtdC4GVo8TrIu6woVPeHw3wVft26F871ppxMuseRuqN99ov6urTDl9OBrZWvQWc7P4CXRBCufcu8rqYRe46OpS7JjDIz8FBzjcTJs07Z46xERiUJzLax5wb1v6hnQ0uDe131sdDVFoaTUCWL1svgfsZXSKZkUV5a1zbDi4XhqCYtfMFpZNVziE4q7bW749RSrTIIZ3v06rJnU/u9X7XJY/E+Yfzu89z2YcwtMvwyeGecfYOxmn09Dl37p11UMKnrB+UugenhSo4FDboOrLJz+Kpz5FvQ5yn2+bYF1U2MpNXRe/8+nCuPpaMDJ/q8dg9j4GmydC+/elNn8yj7efV6v2UVE8oHX84KS8tRzu42EI/8KVyZSj23aDq9eDa9/xnnusPAvsHVeerWKFKpMg9Eq+8L5i+HsOc7zwqssnDoNSqsyW2/xP8IL3woi02A0gIGnBttj3q3B68l3NoT/NsMvg0FnZr+OiIhEJTkJPspgtOQPW33ewBcRERERERERERERERERERGJj/W5loBfn4h0LmW5LkBE5D9KPYLRWiIMRtvxkXff3tdFt68Uhpq9YdsH0e6x/hV44Qg46i7nRMCtczJf66M/OoEkXrrt63zf7/PQ92j44JewzCOMqtUHP4e9roSZV7QFCZZUwPGPQeOG4P9/DjoD9v0c7PgQZn8r2JxkVYPg5Ged4IXnD/P+77LqSff28m7p7ynxqujp3t601XkFmyrsRUQkX+1cAlMCnsDbUfcx4dYSh9Iq76C3ja+Fs0ezAmVc+YWItdq5OPo6wuT3M5VUOOFoIz8Di/62Z79fCLa0l0kwQ2IXTD0dek+Ak56GtS/Ba9eGc/K+KYFRX8p+nULWdQScPRtWPOoEUvc/AfpMaD9mv8/Dplnu86edC1e2OL/LQpJodG8vqXRv91Ja6fx+5v8y81rm/zK7+We+67x2bdywZ5+C0UQkn4URUmmME9z07EGpxy66M3kijP8JjPt28L1ECpHNMBhtr6v2fI+0pNx5/37x3zNbc+Nr0P/4zOamK5tgtBFXO685UlnxMMy/DcZ+Pb3a8lEmr60O/TVU9HYueNPrEBh8lt5XFxHJb8lveGT4BCGQ5AeVAInHIiIiIiIikrGiOluzmH4WERERERERERERESlUBXZ2nIgUtTKPYLTmKIPRFnr3DT0/un2lMMR1Mv62eU44WjahaABv3whvfsG9r6wbVA1uu9/rYDjoR8HWfe7g9sESiV3w6lWwbW7w2o78Gwy7EPa/GboMDD6v1fArnO+lXeCs97zH1S51by/vnv6eEq9yj2C0RJOCA0SksE2emFkYVc3eUN41/HqitmuLf38YBwA278x+jWIUJDCuwSWgJ595hSRBW1BS70Pd+zfMgMbN0LQD5v8KZlwOb3wO1k6BFY/BrOth5lWw8M/Q4rNPZ5BNmNnmN+G9b8OsT4UTigZw6G+h9yHhrFXIKnrByOudUIeOoWgAe1/jP3/5Q9HUFSWv/xdL0wxGAzjw+9nVkqnSajjuQagZBv1PdB+T6rFSRCSXvAJTS9LMT+hxAHTdN83NLbz3XdiS5XukIvkuk2DiQWfA+B+79x3yi8xreekEePursPmdzNcIKqtgtP8K/nnNu9+ArWl8dpGvEhm8vhrzVdjnk3DAd2DI2QpFExERERERERERySt6v05EREREREREREREJF0KRhOR/FFW497eXBvdnhtmuLcPvRCM/kR2egMmQs1eua4iHN3H7HkSjNf/c0E0bYMVjwYb22N/qB7adn/8T9Pfr2pQ2+2SMhh7U3rzy7qlv6fEq8IjGA3grZhCCkVEwrbkHu/QzlQGnxVqKbHptp9/fxhhMC0N/v2ZnDxcDBIpfi8ADeujryNMiV3u7aYESnafON99jPf8R/rAM/vDu990QqI+/gtMOQWmXwyL/w7L7oM3Pw/TzocWj706g0yCGZItutP7v1W6zp4Lo/XcLxBTAsfc692/6O/x1RIWr39HJRXpr1VWA4f/Ibt60jXwdLhoNQy/zLlfWuU+bsk/FcgoIvnL829xmsFoxsC4b2VQgHX+TooUs3Sff5+/CE56zvviH5V9YNx3Mq/nw9/Ci0fB6ucyXyOIbILRSkrh8N/DmQED3J49EGwieG35KN3g6eQL44iIiIiIiIiIiEgeCuFikiIiIiIiIiIiIiIinYxSf0Qkf5RWu7e31EWzX3MtrJvi3tfzoGj2lMJSWgGnvuKc3Fzo+p+wZ1uXAdB1ZOZr7lwcbNywy9rfH3R6+sGD/Y5rf796SHrzyxWMlvf8gtEW3xVOkI6ISNze/Vrmc/f6RHh1xKnf8f79jRuz36Ol3r+/swYpNKf4vQA0boi+jjB5hZUlhyT1PAjfq+rWrUy9z9oXvV8bdgbZBqOFZcBE6Dku11UUlsFnevetnwq7tsVXSxgSHmFhJZWZrRd3yGjfI6GiR9v9Mo9gNIC1k6OvR0QkE17PCzIJqdzn03Do7VCV5vt4C26He03b19NjYdmD6e8vkq/Sef697w3QdZ89L3rS0Yirsq/p5bMh4RNeli2/EPNUwWiteh0MNSOCjV03Ndi4fJVu6PsBt0RTh4iIiIiIiIiIiIiIiIiIiIiIiIiISI4oGE1E8keZRzBac0TBaFvneZ+AMuTcaPaUwlMzHCa+AFc0pw76yGejvrhnmzEw7n+j3bd6KIz9+p5tIz8TfI0BE6HvUe3b0j2hsrx7euMlfuU+wWgA66bFU4eISFjqVkLD+sznF+rzjlFf8j+huW5F9nukCkZ7/TOdMzgh0ZB6TKEFjSY8gtFMedvtyj57PlfMxJoXs1+jUNk8CEYzpXDIr3JdReGp6AVDznPvSzTBmufjrSdbXv/Pl2YYjNZ1H9jryszrSVdplw73fYLRPvxttLWIiGTK629xSfn/Z+++49uq7/2PvyR5z9jZibP3TsgCwl6BkJCEUUZ7KWW1hZb2Fkq5LaW0QCfQ9Wtp6aXQCaVlBMIegbASRgIJIZNMZyfO9oot/f744uulc3SOdDT9fj4eesQ63/WxI8lHsr5vhT9ux+eD4f8NcyvhokMwMsrXIQ+ugrcuhvUPRTdeJNW4Of8edoOzfqUjoWJudPW09OaF1m0N1VC9Nfq5Qzaha/4sZ3P4fDDqe8767njJWb9UFbIJRivo2/p60WDoa/N/JyIiqSz02b/H+ny+k+JxAaYm8xsUERERERHpWEKRu4iIiIiIiIiIiIiIiGMO32UsIpIAAYtgtMY4BaPtsvi0+KxCKJ8YnzUlffkDMOSrsPuNZFfi3sAroWhA+LZBV0FuF9jwN9jymLfr9poJxz4A2cXt2ybfB6VjYNszkFMOeV3hyGY4srG5T04nE4o24iaz4aml/F7uaskKU4Oklqx8c1uorwrf3vK2ISKSykIh+PgOWP6D6Oc479P2v/vSRfkEOPsDeG58+PZXzzCbmMfeEf33GCkYDWDt76Df56KbP101OPi5WAVDpyqrjeBtgzkG/BfseSe2tVb/0oTpjrkNfB3scwSCNhvu46X76dBwxATbZZfCyO9A2djE15EJTpoHD1vcZivnQb+LE1tPLBrrwh/350Q/53F/hYOrYd+S6Odwyu8iGG1HBw5jFJHUZnW+GMtjMUB2EYy7C0pHwaZHoGYr1O02gdJOrbwbBl4RWx0iqcDp87KBV5jAM6dO+Bes+iXseBkKesPgL5sArU9+Cnvfg72LIs9R+SQc3tD67wnBBljyLVj3B1N7yXA44d/QabTz2sA+GM0uYL2twVebv2ls/BtseQLLjYaf/AzG/9RViSnF7nZy1jvmMXH/h+bvmcNvNM+tREQkXfmAh+O8RuizdURERERERERERERERERERERERNKGgtFEJHVkWQSjNcQpGO3DW8IfLx7S8TbCizN9zofes2Dr08mrIb+32TjoxrCv27dXzDaX6kp4sk/0tbU08jv2m458fhj2NXOJRmFfd/1zOkW3jiTWoCvNhq5wGmsTW4uISLQ2PRJbKNqEu6FooHf1JEPZOCiosA45WHEXFPY3m5mj4SQYrWppdHOns6CD35XVm+HQp1A8KP71eMEqsMvf5uWsQVfDe9fFvt7HPzQBAAO/GPtc6SSU4MC8S462/z+U6Pl8JsR77X3t27Y9A431EIgxzMZO/X7YuQCqt4AvC8omQOcpJlzcjVDQJgwxN/r6/Flw/D/gmRHRz+FUoG0wWl74fiIiqcwyGC07/HE3fD4Y8HlzafLSSc4/iOLAClj3J/M6Zl632OsRSRYnwWidp8Axv3Q3rz8bRt5sLi1N+o35d8VP4KPvRp7nqYFwSUPz+dyqe2DNb5vbD66CZ8fAJfXuHhu8CkYD6DPHXAAe7wG1O8P3O7QOige7mztVWJ0b+7KgoBdMvDex9YiISDwlIrTMIklUREREREREREREREREREREREQkdSn5R0RSR8AiGK0xDsFon/zcuq14qPfrSWYI5MIJ/4GTn4YRN1v0sbgde2Wayw+L7nIcdBrnrG9BBZy3wX1NbZ3+qn0omhfye5lAFad0v04Pg79s3Va3J3F1iIjEYpWDjctT/wyfOwwnz4fup5vfU2Nuh7MWwYgb415iQuR2sW/f/Gj0czc4CEZrOGTCgDoSJz8XgPnD4OO74luLV6w2zvvahGr5s+G8T71Zc9EVUFflzVzpwkkwg5cUiua9ijnhjx89CFUfxG/dfR/CM6PgjfPhg2/A+9fDS8fDwjnuQ+6DNo/Z/hiD3UqGJeY5YbtgtHzrvl4EDImIxIPV47EvTo9bQ77qrv+718L84bDj1fjUI5IIVuff+b1hzA9h2iPmNXavP+xjwBchq9hZ3w1/af56/UPh+7w20936XgajtWQXIPf0EAilaQ6M06BwERHJFKE4X0RERERERCQhMukpWCZ9LyIiIiIiIiIikorsXoHSq1Mi0kTvnBWR1JFlFYxWC6Eg+DzKcmysgw+/Y91eNsGbdSQzBXKg90xz6XUOLJxtNpsDVMw1wWVvXAjb5nu/9rifQNcT3I2Z+gD4XHzAdEFvd/O35PPD9PehPAH3IZ/PbP5f/Stn/UuGx7ce8UbxYOs2BaOJSDrY8TJUvWffZ+CVMOhL5uve55pLJooUjLbjpejnbnQYAFa/F/J7Rr9OugnWOusXaoTlt0HP6dB5UnxrilXIYiN422A0MKG5uV2hbnfs674xF854PfZ50kUig9F6nJW4tTqSbqdAViE0HGnfdnAldD3O+zVDIVh8LdRsa9+2bT480QumvwslDgPJGuus2wK50dXYxOeDk56AV8+Cmq2xzWXHTTCaXZuISDKFLM4LYg2ptNLvYtizCNb8xvmY+n3w6ulwcZ15rVYk3VjdzyrOgzG3xW/dgl5w7J/hva9A3V77vouvgsp55rnCwVXh++x4EfavgE6jnK0fr2C0SH/Te9gPfc6HrCLoeiIM/BL4Y1gvUSyfDytgV0Qkg2xG7+MVERERERHpQFy8lzvVuXlfuoiIiIiIiIiIiIhIDBSMJiKpI2ARjAYm/CCr0Jt1di6wbx9wuTfrSObrfgqcvwuq3oeCCijsZ44Puip8MNppr0DxEPjkJ7D2vsjzH/sgdBoLtbug82TI7eyuvjlbzWYnN/zZkFUMDYfcjSvoC+etM+MTxU0wWumI+NYi3hl6Q/jNsApGE5FUt2cxvHpm5H69zo5/Lakgt2v85nYajFbXwYLRGh0Go4EJnv70f9M4GC3MJnafH4Z8BT6+I/Z1dy2EXW9CN5ehxOnK6uccD4OvTtxaHUkgB0pGhg/nXHwVDLrS+zUPrLAPAz16AF6YAqe97OyxJlhv3eaPMRgNoHQkzN5knr+v/hVseiT2OdtqG4xm21fBaCKSohotHo/j9Zqfzw+Tfg0jboL9y2DVPZFfv2/y+iw47YX41CUST1bBxIkIvOp7IfQ4Aw6ugfoqeO0c675bn4o837Oj4dJGZx9sFK9gtFIHH4qy5XHz74a/wrZn4MTHU3/jntXzNL/e3iEikilCoVD/ZNcgIiIiIiIiiZRB2dihDPpeRERERERERERERCSlOXiXsohIgtgFnzVUe7fOgU/s290GSUnHFsiFrtOaQ9EA+syBqX+GokHgy4JuJ8PMNdDjNCjsY647MfAKKD/GBKi4CUUrnwhnfxD9bTm33P2YM99MbCgamJ97XvfI/Qr6tP7/kdSW2yX88UQGo22dD6+eBfOHw3vXwdGDiVtbRNLX8h9G7pNVBD07SDBaYf/4ze0mGK0jsQsVCmfdH01AWioLutwIPvoHMPI73qy95FvezJMOrIIZYnXSk9DtFPM8oXAATPkj9L0oPmsJlNgEQuxf4e1ae9+HZ8dE7ndnMFE6AAAgAElEQVT0AKy4K3zb4Y3wxgXw7Fh440KoWmI9jz8nqjLbzxOALlPh+H/AqO+Z55NZhdDnAuvHjpJh5nbsaP42wWh2gZUKRhORVBWyOC/w6rHYSmEf6H0uHOPwQxAAdrwITw+Dynnxq0skHqzOvxP1+npOJ+gyxfzdYcqfYp/v4QAcXBu5X32VdVsswWgAsxys36TySXjYD69ON+e1qcrq+bBPwWgiIiIiIiIiIiIiIiIiIiIiIiIiItIx6J2zIpI6sgqs2xo9DEar32fdNvJ/vFtHOrZBXzKX4NH2G5qchHSVjrZu6/9fsPFv4dvOege6HOu8znByyuDIJuf9O081mxcTzZ8F434Mi6+y7zf6VvApCzZt5FkEo1VvScz6256HhXMg1GiuH1wN+z404X+6HYmInd1vRu4z9g7ILo5/Lamg86T4zW0XNNNSfUcLRrPYNG1n7/tmQ3yqCrncCO4PwPifwrifQMMh8Oc2P5fMLjW/y1f9Cpb8d+S1q96DhiP2Ad6ZIh7BaL3Ohd7nQcXs8M+JxHulNsFomx+FTg4CPJ04tA5emOy8f+WT7Y/VVcFTA5qv718OWx6zniOQ63w9J3x+GHen+b0cajTPLeuqYP1DULuzuV/RYDh7KWTlm+/7pRNat7eVXdL6erDOuq+C0UQkVVmF7Sbqd3nZWHMOsfUpZ/0PrTGv45z8NPSeGd/aRLyS7GC0lgZfDQc+htW/jm2e+UNh9ubmvxPUVZnvJ7sYQiGo3gyvnGY93ir82qniwSaIfvvzzsfseBF2LYBzltmfSyeL1fPhWH9WIiIiIiIiIiIiEj+hULIrEBERERERERERERHJKEp3EJHUEbAJRms44t06W+dZt/W9wLt1RCD8ZqbySZDf037cIJuwr8HXhD/e/79iD0UDyCl317+TTYhbvA260r592iMw+NrE1CLeKBwQ/njNNtj2QvzXX/eH5lC0JnvegX1L47+2iKQ3uyDfwn5w4uMw7BuJqyfZyh0Eoy36UnSBTI01zvrVdbBgtFAUP8sXp0L1Vu9r8Urb38lNIm0E9/lMQFEg14T+5pQ1B5zaBXK39WgRrP+L8/7pystgtG4nwdg7zWOez2eOKRQtMXrZhMF8/CPv/p+XRxGwtu255q9rd8Njnd2N9+e4X9MJn6/58SS3HGYsg8FfMaEaw2+Es942oWhgwjbOfAsKKsLPlVUIndsExlmFC4GC0UQkdTVaBaN5HFJp58THTNBtyTDnY5bfHrdyRDxn9dwtWefNo25tH/AajfV/hpqd8Mrp5nzvP2XwWFd4rAvM628/1heIff1TnnE/JngUVvw49rXjwSr83CooXERERERERERERFKcL9kFiIiIiIiIiIiIiIikHQWjiUjqsNug3mATNOHG0UOwf7l1e/lEb9YRsePPggn3WG8o7HysfeBX12kw8IrWx7ocB5N+4019boPRSoZ7s260jn0w/PGSEdDv4sTWIrGzuz29djbU7onv+pUW4Zmf/Dy+64pIeguFrAOcuhwHszdCn7nNIUEdQUGfyH3WPwRLv+N+bqfBaEcPup87nVltmo7kjfO9rcNL8dgIbhfIHc6iK6Dqg+jXSyXVW2HLE7BzQetPKQ5Z/JzLxkNWkbO5K+bCpUE443UY/T0IxCnISqyVjW0OAAznvetjXyPYAFufdj/utRkmEA3grUvdjw8kKIwnrxtMuQ9OfQ6OuRvyurZuLx4EszdD34taH/f5YdLv2tdZMdd6Lbv/KxGRZArWhT+eyN/t/iwYdQvMXAWXhcw5RiRVH0B1Zfi2A5+Yc6CqJd7WKRItq8BaX5KC0fK6wMgonpu3tftteLICdr5qrocaoW4P1FdFHutFEK7PD6e9bAJr3dj4N1j2A3jvOtj0L2isjb0WgFAQ9n0E6/4EW+ebv0+6Gq9gNBERERERERERkcwSitxFRERERERERERERERa0TtnRSR1BPKt2xqOeLPG85Os28b9xJs1RJzofyl0GmM2CQXrIbcbHFoLJcOg74UQyLMe6/PD1Aeg7+dgzztQMhIqZrnf8GMlp8xd/64nebNutCrmQOC69iEp/S9LTj0Sm8K+5vZvtQFtx0vm/pNoNdsTv6aIpA+rUDSA8R00WNHnM+cU9fvs+21+FCbe625up8FowXp386Y7q831kex9F/Z/DJ1Ge1uPFyw3ggeinzOac+ZPfgYnPBr9mqng0wfh/eub7z9dp8Epz0N2kfVtp6APnP4q/CdMcHKvGdDvMqjeDKUjofd5HSv8MVWdtxHm9Q3ftv2F2OffvwyOHohu7OZ/Q+9zYecr7sdahYong88H0/4F/S41z8dzOpn7Q9n49n3LxlnPYxU8JCKSbFaPT8l8LPb54NyV8MwI+34vnQSz17c+9uEtn4Xdf7bZaOAVMPXPOm+R5LI6//YnKRgNYNg3Yfnt1rVVzDXn/nah0TtejG7tzsd6Fxrb43Q45yPY8ript7EWPv3fyOM+/pH5d+195t9Za6F4cPR1BBvg7S/A5n81HyvsByc/A51GOZvD6vmwX2/vEBERERERERERSV0ZFH4WyqDvRUREREREREREUpLdS1B6eUpEmuidsyKSOvxZJhwtXNDB0f2xz39wNRxaY91e6nAzgohXOo2OPoDC54de55iL13LDBB9Yye8NnW0CBxMhpxNMewTeuhQaq82xijkw/L+TW5dEx+eHvJ5wZEP49v3LgCQEo2nDrIhxZAts/DtUb4Fup0Dfi3T/APtAqo68YXX4jbDsVvs+NVuhch5sfwlyO0P/z0PJUPsxDQ6D0ew2bGciq03TTmz6V5oFo8Vwv7IL5Lay+d/wRAUcc48JJ06nx71gIyz7PnzSJgh891vwzn/BSU/YBzPklMHMVfDqmeaxH6DTWJjyJyjoFd/axb3CPpDXA2p3tG+r3gz1ByCntH1bsBE2/gP2LYX8HjDkehOa19aBT6Kv7eBqE7YXjWjut/Hk80GfueZi288Pg6+Fdfe3b2tUMJqIpKhGi3DhQJJDKkuHw5T74f2vWQcgH9kA+5ZB2VhzfecCE3Db0vqHzGX0beYxuqB3PKsWCS8Vg9GyCmDS7+Hda9q35XWHEx8z50DbX4QF071du8/53s5XPAhGfrv5+sAr4I0Lw58jW3l6iHnuN/CK6P4G8+aF5rWOlo5sgmdHw6jvwchbwp9vtxSMw/NhERERERERERERSaI0eq9JJOn0vhkRERERERERERERSWseffyyiIhHcjuHP163N/a5N//Hvr2nx5s5RNJVjotgtDE/MJu9k63iPDh/B5z2CsxaCyc+DlmFya5KotX3Iuu26srE1dGK3sghwsHV8MIU+Oi7sPY+eOtieO8rya4qNWx92rqtI29YHeLw9rFwDqz9HXz8I3hhMuxZZN8/WOts3i2PwbYXnPXNBLEEox3Z5F0dXrLaCB5T4GCUHxlSsxXeugTeuTyGtRMs2Aivnt4+FK1J5ZMmIC1kEczg+yyYoWQYnLceznoHzvkIzl6iULRUNuIm67ZDa9sfC4Xgjbmw6Iuw+lfw4S3wzCioDxNQf3BV9HXVVMKh1e7HDb0hvd9U3fnY8MeDCkYTkRRl9fjkz0lsHeEMvgbmVELZBOs+6x9s/nrtH637ffwjeHYMVC3xrj4Rp6zOv5MZjAYmBCzcucvgrzSfj/U8CwoHeLdmTpkJKYynrtPgvHVw+mvuxm1+FF6bAcvvcDdu2e3tQ9FaWnEXPFkR/ny7JcsAvQ78OpOIiIiIiIiIiEhai/L9KqkolEHfi4iIiIiIiIiIiIiktBRIMhERaSEnjsFodoEV0x6BQAps7hJJBTllzvqN+wkMuiq+tbiRXQw9ToPiwem9cV7sg9HyuiWujlZ0mxJh5d1Qu6P1sXX3w4GVyaknVYSC9gFxyd7YnEy5nc15thtHD8Ly2+37BOudz7fse+7WT2dWm6adqNvjXR1eCjWGPx5L4KBV2JpTG/8OuxbGNkeibH0Kdr1u32fx1c4C6PxZ0OVYKBsL/oB3NYr3hn3Tuu3gZ8FkDdWw5Eb4pw8e9rd/vaR6M/yn7LP2ADycZb5ecZf13Gd/YF/Xlsdh6bedfQ8tHXOP+zGpJJAb/nijgtFEJEVZBqNZPJ4lWl5XmHK/dfvqX5nnaMEG2Pwv+7nq98HHd5qvd7wCr5wGzx0DH3wL6qq8q1mkLcvAqyS/fuDPglPmQ8VcE4aY2xlG/g+M/n7rfidEuG85lVMOp70EOaXezGcnqxC6nwzHP+x+7PLbzOND7a7IfWv32J8zNzl6wJxvVz5l3ccq/LwjB/CLiIiIiIiIiIiIiIiIiIiIiIiIiEiHomA0EUktuRbBaPUxBqPV7YW9i63b+10c2/wimSSnPHKf6e/BqFvAp1MJiYPOk6zbGqqbv67dZS6NLgJy7Nh9ip3C9kRgk8XG141/T2wdqWbXG2ZDvZWOvmG1+6nux2x/wT7ky00AWNUHzjYvZwKrTdNO1O32rg4vWX1P/hjuV0X9rduGXOdsjk0uA/+SZavNJvsmB1fB4U/DtyU7mEGi4w9A6ejwbTtfgSOb4NkxsOpeZ/OFgtYhhU1Gfx/Kj4FzPnJXayTDvxXb/T0VWAUJBevMa1VePZcREfGK1eOSVdBjMti9bgSwcA4cWutsrsonTOD3q2fAzgWwbyms/iW8Mdf+dSKRWFg9p/WlwPl3bmc46XG46BCcvxvG/7h9MHLJsOjnzy6Fzx2By0Jw4V4onxhbvW71vQjKjnE/bucCWHAOBCOcF+9e6O65+cLZUGnxoU4KRhMREREREREREUlD+tuCiIiIiIiIiIiIiIiXlGYiIqnFKhitLsZgtNW/tW6b+kBsc4tkmtwIwWj5Pc2md5F46ndJ+OMNR2DfMng4Gx7vbi7/yoWl3zahDbGwDXzQabN0cKEQNBwK37b+L4mtJdVsm2/f3tGDhQL50Y07vN66LegyAOzgmuhqSDduAuPaqtvjXR1esvq/9gXCH3eidDQU9mt/vNMYmPT/oM8FkedYe1/06yfKpw/A+oec9d31evjjqRDMINHpPCX88fUPwrz+9o+xsaxXNhbG3uHdvP0v826uZLEKEqrfB491gce7wvIfKnxHRFJHsC78caugx2SZu926bevTsOhK53O9++X2x3YthK0RnuuJRMvquVsqvX4QyLH+oIjskuhC0MEE32YVRF9XrPwBOGMBDL8Ryie7G7tvCWy2+NCAJtWV7mtaeF74UEqr58OpdDsRERERERERERERF/ThvCIiIiIiIiIiIiIibinhQURSS04cgtFCQfj4h9btZeOjn1skE+VX2LdP/C34dAohcZZTFv543R54+UQItdkYtvJuWPXL2Na0C5Sx2ggo0lHU77NuswpM6ygOrLJv92clpo5UFciLbtxBm59ryGUAmN1cmcRtYFxLqRqM1vb3fRNfDPcrn8+cz/pzmo8FCuCYe03bsQ+2brNyNEUf+6or4cNbYPHVsc/V0R+/0lnvWYldr2UwRsVsb+Yc9t9QPtGbuZIpUpDQ0YOw/HYTZigikgosg9EcnB8lUn4P6HaydfveRbGvUfl46+uhEOxfAZsehSObYp9fOq7GI+GPp1oAoZ1ownBLhsOIb3tfi1vZJXDM3XD2u3BxLZRNcD52y+P27TXboqvp3WvaH7N6PqznaSIiIiIiIiIiImlKH5YlIiIiIiIiIiIiIuKWUk1EJLXkloc/Hu1G/YYaeGa0fR83mx5EOoLiwVAyInzb5N9D3wsSW490TIGC8Me3P2fCA8LZ8LfY1gzWxzZeJJPVbLduO3rQBNF2VIfX2rf7shNTR6ryR/n9733fus0uyDKcjhKM5jYwrqWGw9BY610tXrEKe4t1I3jFLDhnKYz7CUz4BZyzBHqcYdqyi6H/5yPPcWhNbDXEw4a/wVOD4ZOfeTNfR3/8SmddpiZurR5nQFZh8/XSCK/BhDPyOzDiZhhwuQnZOO1lmHivdzUmk9OA0E2PxLcOERGnGi1eGwmkYGDT4C/Hd/4Nf23+OthggmefHQ1vXQzz+sPHd8V3fclMwUaoqwrfltclsbXEous0mL3Zut2XBYX9zaVoMEy+D2Ysg6z8RFXoTCAXpi82AdlObHnM/rlzdZTBaNueM+GLLVk9H44lKFxERERERERERETEMQW5iYiIiIiIiIiIiEjy6Z2zIpJa8nqEP3740+jme+UUOLjSuv2kJ8Hni25ukUzl88EJj8KC6VDTYiPPuJ/AkK8mry7pWFqGKzi1/yOzgSzax3XbYDTlCUsHVxNhY+eexdD1uMTUkkqCR+FQhPPUWAOcOqqtT8G4O8K3KRgtPKtN007V7YGCCm9q8UqoMfxxLzaCl440l3DG/xQOroY9b1uPP7AKyifGXodX6vbCu1+GYJ13c0YbbCjJl9cDsopM6GG89Z7d+rrPByNugpV3Oxuf3ck818zU12b8DoOEdr4S3zpERJyyOpdw+niWSP0ugbcvi9/8oaAJsMotNwGW6//cun3ZrdDjTOgyJX41SOapr8JyM1luGgWjART2gQv3wxvnw85XzbG87nDsX6DX9OTW5oY/GwZeAd1PN99LlU1QO8D8EVDYD4oGQmMdhBpg86Pmgz4aq6OroW431O6A/J7Nx0IKRhMREREREREREUk7bT8AIVNl6t/3RUREREREREQkoexeTusoL7WJSGRKeBCR1FI8NPzxmm1w9JC7ud67Dva+a91eOhIqZlu3i3RknUbDeRvgjDdMgOD5u2DULcmuSjqSrILoxtXvi35Nu2A0vZFDOrranfbtW59KTB2p5sgm642qTbRhNTr7l8Hh9eHbIv3M2+oIwWjBRmL+pNa6PZ6U4inLjeCB+K6b1w3OeB3OttkQv/vN+Nbg1pbHobHG2zkVjJa+fD4oHhL/dQoqTIhEW30vdj5HzzMz+1w7kIJBQiIidiyD0XISW4cTPh/MXB3fNeYPM8HMbUPRmmydF9/1JfPYPe9Kt2A0gJxSOO1lOPsD8xxq1tr0CkVrqbAPTF9s/iZi58hG2PU6rH8QNv3ThKJB9KFoTQ62eTyzeq063s+HRUREREREREREJE4y6O/i2pUqIiIiIiIiIiIiIgmiHdoiklpKhlm3HVoLZePh0Dqo+sBc9i0xITiF/WHsndBplOl7cC2svc9+rX6XeVa2SEYK5EC3E5JdhXRUgSiD0Wq2Qm55dGPtgtEy6U0pItGIFLZTOQ/G/yQxtaSS2t2R+yhYCHqeDdufdz9u63wYdkP748Gj7uY5sgEaayGQ576GdOE0LO701+CVU8K3PTcB+n8Bxv3YbAhPBZbBaAl4OcufBeUTYcDlsOGv7dvX/cHcv4+5JzXu51sej9yneCic+gI8NcDZnAp2TG9dp8G+pdGP92WZ+2DRIMjvBVXvtz4f6HIcTPodZBe1H1s6CrI7wdH9kdfpc0H0NaaDvG7O+y65CUZ+B/K6xq8eERE7wUYIBcO3pWrQY8lQGHQVfPpAfOav2wPbX4CdC8K3r/gxjLsrPmtLfIWC0FhnwgCb/g3Wtz8Wrq1de334MeHG1tucH+V0Ttz37yWfD8qPSXYV3vD5zd9EZq6C+cO9n//YB2HRl8K3vXIqzFgGncaY61YfAJJT6n1dIiIiIiIiIiIikgAKExMRERERERERERERcUs7HEUktRT0AX+u2SDS1tuXQc12OHqwfdu+D2H7izBzJRT2hW3P2q/jz4ahX/OmZhER8V5WlMFo1duaN4+5ZRe04/NHN6dIpmgMc27W0sGVJpi2ZEhi6kkVdXsi91GwEAz7Jux42Xl4V5P9H4c/7jYYLRSEI1sy+/bp9GfS/WTIKYf6qvDtG/8OO1+Bc1dATpl39UUraHGb8SfwftXr3PDBaABrfgsNh8zm9mRqrHMWPnj2BybEasZyeNbB+VJWfuy1SfIMud7cdsO9hgIw6Gr49H+tx1900Lw2k9PJ/dpZ+TDiRlj2fft+ZROgz4Xu508neT2c9111D+x+C858E/yB+NUkImIl3GvyTfwpGowGJtzXSTBav8vg+L+b5wf1e2HpzbDhL5HHrfl/sdfYpG6vOc/uaK8zBRsiB4bF2uY2pMzt89N4yyrU+XcqKRkGJ/wH3vT4XNWfYwKMd78Vvv3ZsXDWO+YcsmZb+D65XbytSURERERERERERDyk8DMRERERERERERERES9ph7aIpBZ/AIqHwIEwIQgHV9uPbayGj++EqffDnnfs+563QZ+qLiKSygKF0Y2r2Rr9msF667Z9S6OfVyQT2G2Qb7J1HpTcFP9aUomTYDR/dvzrSHW9psOpz8GrZ7obV78v/HG3wWhggg/I4GA0N5v6c7tYB6OBCaPe8HcY9vXY64pVqDH88UQGDvY6B7KKoOFw+Pb1DwE+mPq/yQu4qHwycp8eZ5lQNIDSUVA0EA6vtx9TMjL22iR5SofDmW/BJz+HqvebHyfye0O/S2DwtfbBaFn5QAzhHKO+ZwIdNj0CtTug4QhkF5sgkqxC6HYyjLk98wPAfD53/fcugs3/hv6XxKceERE7tsFoOYmrwy2nIUH9LzOPy74A5HWDIV91Foy2/YXY6gOoWgJvf8GEimd3glHfhRE3uf89EUkoZJ4vhQ0Mq4PG+jDH4hlS9ll7KOjt95mJFHaVenpO935OfzaUT7EORgN48Tj7OXRbERERERERERERSVMe/01ARERERERERERERKQDUDCaiKSekmHhg9Gc2DoPQn+AYK11nwn3QEHv6OYXEZHEyCqIblzNtujXtAtGq9kO9fshp1P086eqwxtM8Fv5JCjsm+xqJFXZ3T+abPyH2did6YINsOt1aKyFTQ9H7p/IAKdU1uMMyCm3D+Rq6+j+8MfdhIA1qd3tfkw6cRIWVzHH/JvXFQ6tse+74S8pEoxm8X/tT+D9KrsYRnwblv/Aus/6B6HrNBh0VeLqamn325H7jP1R89c+n7k9rLrXur8/19xvJb11Gg3H/9W6fdg3YfWv2h8feGXsa/t8MPhqc+no+l3q7JyhyZrfKBhNRJKj0eZ5XyA3cXW45SQkqM8F0GtG62NdpsKALzoLR4tF7W54fmLz9aP74cObYctj0Od86zCxaEPKJD0VVCS7AmkruwiG3wir7vFuTl82VJwHq38Z/RwKRhMREREREREREUlToWQXICIiIiIiIiIiIiKSdrRDW0RST8mw6MfW7oLtL0DlPOs+/T8f/fwiIpIYqRaMBlD5FAy8PPr5U00oZDbirry7+dio78HYO0yQhUhLjXWR++z7ELY9B73OiX89yXJ4Pbx+HhxY4XyMPxC/etKNP8dd/3qLYDQnIWBt1e1xPyadOAmLawo6crKJuuqD2OrxStDi+/Il+H418jsm1OjgKus+i6+G3rMhLwmb1O2C7gZ/BcbcBvk9Wx+PFIw24L9MEIBktr4Xhg9Gq5id+Foy2aAr3QWj7XknfrWIiNgJ2jzv86dyMFpn+/bJ98Hga8O/1nHsg+a86I250a3t89u3H1wD8y3+3rF3sbmIgHkuIalnwi9MiFko6M18/mzoegKUjICDK6ObQ8FoIiIiIiIiIiIiIiIiIiIiIiIiIiLSQSgYTURST9kxsY1/bYZ1W9cTIL97bPOLiEj8BQqjG1e9Nfo1IwWjLb0ps4LRtj3bOhQNYMVd0O1k6HlmcmqS1GW3Qb6l12bApcHMDNdrrIWnBiW7ivQWcBmmUPV++OMhBaO1YxUg1mTEzdB7pvk6J0JwRJP3b4A+c6H7qdHXdXANbPwn7F1kwta6ToP8CjNnn/MjP1ZYBb75EvxyViAXTnkOnhpg3++1c+Ds9xJTU0tWwWg9zoIp94Vv63I85HaFut1hGn0w7seelScprMvxMPZOWHZr87ERN0HvWcmrKRN1P939mL3vQ+dJ3tciiVe9DTb8BQ6uRp8CLynv6CHrNrfn8onkz7ZvH/xl6/NOnw/6zIExt8Py26NYO0L487vXuJ9TOp4+F8CwG5JdhYTj88E5H5qQ/CMbY5/Pnw3+LDjuL7BgOtTvcz+HgtFERERERERERERSWCb9PTCTvhcRERERERERERERSVcKRhOR1FNxHhRUQHWldR+fP7pPaNfmEhGR9JBVEN24mm3RrxmMELQTNjgkja21CEpZ/5CC0aS9SMGBLa1/CAZ9KW6lJEVjHczrl+wq0p8/ijCFNb+Hode1Phbp8TqcTA9GswuLO/Mt6Hp883WnP781vzWXCb8wQUlu7VoIC86BxurmY5XzzL9rfweDroGp99vPYRX4luhgNICi/jBnKzzZ27pP1fuw41XocVrCyiLYYL1Bv+19pyV/AEbfBh98vX3bJUdNu2Q+nw9Gfw8GXgH7lkKnsVDYN9lVZR6fD/J7Qs1252NemAzHPwz9L4lfXRJ/B1bByydm/nmIdAyRAsCSrWyC+V3WVuGA+AZ32/1canebc2JJb76AeS4byA3zb074Nn8uBHJafG0xNpAPnSdD8dDMDJjPFJ3GwLkrYMFZsPut9u3H/wM++RnsXxZ5rqYgx86TYfZmePmk8I9ddnIdhp2LiIiIiIiIiIiIiIiIiIiIiIiIpDC7aH7F9otIEwWjiUjq8WfDaS/DO1dA1buAD0pHQtkxUD7RXMrGwfOT4eBKd3N3nhKPikVExGuBZASjuQh+ygTbngl/fNM/Ydo/EluLpL7GOud9F1/pLhgt2GA2kO54yYQdjbgRep3jvsZ4Wnc/1O5KdhXpL5owhQ9vhr4XQl635mNWYVl2Mj2QxO5nUtgm1K++yt3cS78NxcOgYlb7NTf8FVbcBYfXm2P5vaFmq7N5P/2TuTTVl10K3U+HsT+C7CJzLNQYfqw/SS9nFfQyQXMvTbPus+gKmLM5YSVRv886NLwgQsDVkK9C3S5Y9SsTYNf9VJj6Z4WidUQFvc1F4iea310f3WJ+BybrMU/cCwVh1b2w5XHz3PTIpmRXJOKdaEKOE2n0rfDGBYn/YfoAACAASURBVO2Pd7U5b/OC3eO7HgPc+7+gMZtQMX+ORdCYizbHAWc5OjcWI6sATn8Nlt4En/4ZGg5BdicYcxv0uxQq5sLSG2H9g9BYaz2PL7v56+wi8/xy6Y3WH2DRro5iKBke07ciIiIiIiIiIiIiSRLSVk4REREREREREREREbe0q0pEUlPJMJj+jtlQGGwwG2Hayu3ibs6+F7cPBRARkdSUFWUwWu1O8wYSn8/92I4WjJYJ6qqg4TDklDVvnJX4CLoIRgM4shkKIwTyNHnnctj0cPP1na/AyU9D75nu1oynTY8ku4LMEIgiTKHhCFTOg8HXNB8LHXU/T+1O92PSSdDmZ+LPbn29zwXW4ZhWFp4HZ7wO3U4yv2eDR2Hlz2HZ91v3cxqK1lLLwIj9y6DqfbOWzwchi6AJXxJfzup6PJz0lPmZhFO9BQ6shKxCc2mshdzOEMjzroZgg3muHMiBur3W/XI728/jD5ggutHfNz/TaM6fRMSZijkmDNKNI5vM42LxYBOCCCZMI6vAhEnqPpt6PvgmrPltsqsQ8Z7PD4H8ZFdhr2KuCXnduaD5mD8Xhn7d2fjySdGt21gNwcbw4VmxhPfHnc8+GMyLIDK7wLGwa+bod5ukNn8WTPwVTLjbhOfn9zCPjwBZ+TD59zDx1/DutbD+IYs52jw/bznuufFw4BP7GoZ+zdvnliIiIiIiIiIiIuIthZ+JiIiIiIiIiIiIiHhKwWgiktp8fuuQE7fBaMf/LfZ6REQkMbIKoxsXajQBZ9GE7ygYLX1ULYFFV8D+5a2PD/sGTLgn/IZkiY3b+0flPBjmYAN65VOtQ9GavD4LTpoHFRbBQ4lUuwf2vJPsKjKDP4rHZoDKJ1sHo9mFgFlJ6VACD1gFiEH7ELGeZ0a3xrLb4Jh74b2vwt53o5vDid1vwPYXoNfZNsFoSX6cr5gFBX1MCFo4z4xsf2zA5TDp/0F2cfTrNtbBR981oTvBo9BrJvS/1Lp/pGC0Jm0354uI93rNcB+MBvD8RMAHhHkD/cRfm8Afhcikhv0rFIommavL8akfRO7zwYmPwapfw/bnIb8XjLkNysY7G9/jdMgugaMH3a/dcBhyStsfX3575LHZnaB0pH3gWCxhY1ZtCsUViZ4/Cwp6WbRlm+eKlmMtnnv5s+GsxbD0Rlh3f/g+E38LQ693V6uIiIiIiIiIiIiIiIiIiIiIiIiIiEgaUzCaiKQvN8FoZ7yhzd4iIukkUBD92IYj0QWjNSoY7f9UPgVdpzkPVIlFKGQCzmq2QU45NByEooHm0tahT2HfEnjzc+HnWv1rs6l47O1xLblDaqxz1z9cMFr9ftj9JtRVmdtW2XhYONt6joWzYeYqKBnmvt59H8LO18xtqvspUNjX2bijB2HPIqjZYTa65nWH1b8hbBiJuBft+fi2Z6G6EgoqzPVogtH2LYX6fZBTFl0Nqc7uZ9L2515QASP/Bz75ibs1dr3+WUBPAmz+twlGC1oEo/lT4OWssxbBk72d99/wVwjkwZQ/Rr/mJz+DVfc2X98231zC8efGdj4lIt7qNQN6nAU7XoxisMV5yAffgPze0PeCmEoTjzw7OtkViMRHVjGM/2myq3Amp8y8HhDNawKBPBj/c3jvK+7HhgtG2/qseQ5iZ9xdMOq77tcTkRRnEzpo97pIdpF5vlg6Gj64ofl4VqH5G2f5BO9KFBERERERERERERERERERERERERERSQMpsJNURCRKToPRsoqgy3HxrUVERLzlzwZfAEKN7sc2HIHccvfjQg6CdkIh8NlsbssUC2cDPjjhX9D3ovit03AEFl0Fm//V+rgvy2wOHvtDcz0UhI9udRbis+GvCkaLh6DLYLRdr0NjrdlcDiYobeEc9+vOHw6XNoLP76x/sAHeugS2PNb6+IS7YcSN9mP3LIY35kLNduf19bsUyifB0ghzy2dsAuYCeeY2Y+XJPmaD8OBrIWQRlhXJY13gxMehwiaQL13Z/Ux8YV76GXcXdDsZtj4Fa38fv7qitf7P5nfA7jfCt4f7nhKtoBeM+Das/IXzMZ8+AON+HF3waEMNrPy58/65nTvGOYtIugjkwCnPwsZ/wKIvejfv2vsUjJYK9i9313/gFXEpQ8RzJcOhYi6UDE12JYkx5MvQaQy8NM3duIYj7Y99+r/2Y856B7oc624dEUl/PgeB8cO+bv6mWfkE+PNg8DWQ3yP+tYmIiIiIiIiIiEic6YM5RURERERERERERETcSoGdpCIiUcrr6qxf38+BPxDfWkRExFs+H2QVwtGD7seG25DaJNgI21+Ana9CQW8YdBVkl0Qe1yTU4GwDW0YIwTtfhB5nQE6Zt1MfXg8bH4Zlt1os3QAf/wiOHjKBWJv/DdWbnc19ZAMEj5pwPfFOsD788e6nws4F7Y+HGkwA0NDrzf34rUuiX/vhAJSMMF93ORYK+0OfuWbDepMjm2H5D02YUjhLb4KN/4Sy8S0OBmH321A6CnqeZcKN3ISiDb0BJv36s/kVjOZI6RjYtbD9cX+OCQZ4boL9+He/bB6Tgg6CLMMJBeHNi+H8nZBTGt0cqWbPu1D5JGybb90n3OOhzwe9pptLzXaz4TrVPNnHus2fIi9nDfySu2C0UCO8/zWY9rD7tXa+4uxcpUk04WsiEl/+AAy83ARS7l3szZw7XzG/35yGyEp87PvIed/PHTbPdUUkNXU9HoZ+Hdb81vmYxur2x+zOr4f9t0LRRDoqp69Xdp5kLiIiIiIiIiIiIpJmMij8LJRB34uIiIiIiIiIiIiIpK0U2UkqIhKF/N7O+o28Ob51iIhIfAQKogtGa7QIDQmF4P3rYd0fm4+tuhemvwf5PUwIVyTB+o4VuNVYA5VPwcAvejfn7rdhwXRoOBy57+pfRrfGpkdhwOejGyvhNdaFP14+EXa9YYLQ2nr/aybEp7A/NNbGtv7Bla3/XfFjOOFRqDgPdr4Or5wSeY59S8ylrUNrogiF8sHgq12OEfp9zoTBtH0TYN+LTWjd547AoxGCQjY/Zh2MdtI8c5t7+zLr8cE62PEi9L3IVekpaf1DsPgqE4hjxxchJNpp4HQspvzJhLdVzvNmvkjfc6KUDIfiIXBorfMxmx6Bsgnun6dWPumuf46C0URSVr9LvQtGAzj0KZQM8W4+ca96i7N+py9QKJpIOsjr7q5/2/DahjBBaS2VDHM3v4ikGZ91U0d6XVlEREREREREREQyl8/mdVARERERERERERGH7LL5ldsvIk38yS5ARCRq+b0i9znxCW00EhFJV4H86MY9PwnevAT2L299/MObW4eiAVRXwvIfmK83/TPy3FZhPJnswCexz9FYD2v/AC+dCC9NcxaKFot3FVjluWB9+ONZRVA8yHrch9+Bty6OQz11sPQm2PeRs1A0rw25DjqNSfy66a7bSXDc36Cgwlz355hwmCn3metZBSZoys6ed7D8dNWcTtDvEigeaj9HdaWrslNS3V5Y9CUHoWhZkd+MmNvNu7rCGfYNEyQ46XemHi/UbPdmnlj5fNDnQvfjlt8Otbsi99v9Nrw2E/5TDp8+4G6NzpPd1yUiiTHsBhj9fe/me34CvHSS+b1wZJN384oz9Qfgo+/a98ntakJCu5+SkJJEJEbZJe76tw1GO7TGvn/pCHfzi0jmUDCaiIiIiIiIiIhIB5ZBOzm1K1VEREREREREREREEkTBaCKSvgp6W7cNvAIuaYA+cxJWjoiIeCyQE/3Yzf+CF6bC4Q3m+urfwMq7w/dddz+s/0v7ILVwOmIwmpOfS1uNta1/Vh/9D7z3Vdj9pnd1RVq/Zmdi1uoognXhj/tzIwdZxcuhtfDc+MSvWzoaxt2V+HUzxYDPw+zNMKcSLjoA0/4JWYXN7YX97MfX2ty3m0LARtxoP0fNDuf1ppLGWhOIFmyAx7o4G+N3EESWF+dgtPzPnrcV9IYBl3szZ+kob+bxwshvm8AbNxprYNMj5v8STMBdKGjeOBpsNL/Ddi00YaLbnoH6fe7rGnil+zEikhg+H4z9kXndatBVsc/XcAR2vwHrH4LnJ0f3mCHRCTbAK6fZ97noEMzdZkJCRSQ9ZBe76982GO3AKvv+yXoOLSKJYRdO7lMwmoiIiIiIiIiIiIiIiIiIiIiIiIiIiFMOdsiKiKSo/J727f5AYuoQEZH48MV4qtpYA08NhFG3wro/2vdddIWzOTtiMNr256F2D+Q5COE5ehgWXwWV88Dnh74XQf8vwKp7419nWx//ECb/PvHrZqpGq2C0HOh5tvk/7wh6Tofj/wE5pcmuJL35fNYhx5GC0QL51m3+zzYYD74Wsorg7c+H77fy57DpYfMY0Xtm5HqTrX4fLLoSKp90P9bJputchyFr0ep5VvPXU+6HkmGw7Vlzfdg3oOoDWOEybLDLsd7VF6ucMpi+CJb9ADb+3fm4D74BS74FoUZzzhNq8K6mQVdBqQI3RFKePwDFQ7yds243PDse5mzydl4Jb+ersG+JdfvgL0N2UeLqERFvZLkNRqtufb3yCeu+5RPdh+qKSObwKxhNREREREREREQks4WSXYCIiIiIiIiIiIiISEbxJ7sAEZGoBfIgUBC+re8lia1FRES859VGsRV3moAALwTrvZkn2YJuwldCsOz7zrou+iJsfhSCdSaYbsNfYcFZkcfFw9r7YM+7yVk7E1nd9gO5Jvyu89TY1yjoA8c+BL4UDbcd9T049XnI7ZzsSjJbpGA0u8fzlr83+l8Gg79i3bd6C7w+C/Z/7K6+ZHjrsuhC0cDZ7TXL4jmVFwZ+CcrGNV/3B2DkzXDGa+bSZy4M/bq7OQd/GTqN9bLK2BUNhOP/BpeFWl8utgiVbBJq/OxfD0PRyifCpN95N5+IxFevGfbtBX3gwn1wxhvO56zeDDtfi6kscWjLY/btpaMTU4eIeCvbZTBa45EWX9ea10WsjP+pCYoWkY5JwWgiIiIiIiIiIiIiIiIiIiIiIiIiIiKOKRhNRNLbwC+1P5ZVBN1PTnwtIiLirayiZFfQXvBosivwRmOtu/4b/gIN1fZ96qqgcl70NcXDqruTXUHmCFqE+/hzIbsITl8AI26Kfv6K2XDOhzDwi3DOR9HPE063k014W9MlkO98bP8vwNAb4ORnYNyd3tYl4RX2t2/fv8y6zZfV+npul8jrrf195D7JdGQTbH8++vE9p0fuk+Uy+CGSokEw5Ho44d8w9YHI/fO7m9CfSPJ6wEnzTOhXuoRJBHLg7A/iN//EX8Mxv4SBV8DAK2Hy7+HMt0xopYikh9LRUDoqfNsx98K5n0BOJ+h2Akz8jfN51/3Jm/rE2pFNsO5++z4FvRJTi4h4y+35cUOLYLStT1v363M+9DgjuppEJH20fW2iJQWjiYiIiIiIiIiIdFyhULIrEBERERERERERERFJOzbvzBURSQNjbod9S2DPO+Z6IM9swA/kJbUsERHxgNdBLV4IZUowWo37/mt+CyO/Y91n34cQaoytLjeyikzgz5bHrPts/jfU7YXczomrK1M1WgWj5Zh/s/Jhwi+gcAC8f727uUf/AMbe3ny90ygonwxV70VVKmDC0E5/FXxhssBDf4X3roN1f7CfY84WKKhwtt6UP8G717Q/PvAKZ+OlWWE/V9031PRn3p7z2N/QiVnbipjYqUWjo2C0+0yYVKra8XJs48f/NHKfzpPNfTlYH9taAMO+CcfcE/6+ZyenE5z5JiycC3W727SVm+d4PU6Lvb5kKD/GhCxu/Lv3c/e7FPK6ej+viCSOzwfHPggLZ0PNdnOssL85jyka0LpvxXnwwQ3O5t30T+h3MfSelT5hkunmvesi98nvHf86RMR72S5fj6qrav66aql1v36XRVePiKSX/p+HZbe2P15QYR+aJiIiIiIiIiIiIukvo8LPMul7EREREREREREREZF0pXffikh6y+sCZ7xuNhzVboduJ0FOWbKrEhERL2QXWbdNvg/e+2riamniRWhMKnAbjAbw4S1QNAj6Xhi+feXPY6vJrX4XQ8X59sFoAI91gbnbIb9HYurKVFa3/aZgtCZDrzPhG06CIgAGXdU6FK2J21CllsomwBmvWbf7fDDlPnMbeuXU8H3O3wl53Zyv2WsGZBVCw5HWx/tc5HwOMVwEoy06MJUZy+ezv8Gc/9/x6xAPXhHk8uM+u/04CUYDWH4HjPm+20rj7/BGWHx19OPP32kCxyLJLoaKObD50ejXApi5GkqGRj++6zSYtRp2vwU1O8yxvG7Q9QTILY+ttmSb8AvY+hQcPejdnEOuUyiaSKboPBlmroI9i805UNcTIJDbvl9BHwjkOz+XXzgbBl0DU+/3tl6BQ5/Ctmcj98vvFf9aRMR7boPRVtwJo24xzwkPrrLu13tmbHWJSHoo6g+dp8Ded1sf73uxAmtFREREREREREQkM+i1ThERERERERERERFJkBh2m4uIpAh/NnSZAhWzFYomIpJJsmyC0fpcAENvSFwtTYJHE79mPBz4JLpxS2+GYGPrY0cPwnvXw/YXYqspqxgK+kbu58+B/p+Hib+F3jNMSF4kH94SW21ifdsP5LQ/NuSrcMyvILvUfs7hN8IUi6COaIPRBlwOp7/irG/3U+CMhVA0sGlR6DwVZm90F4oGUNALTn0Rij8LhcrrDlP+aG6j4k5eT8ddv7fhzv8LRQMIhXx8/eEQRxs++8TSzpOcTbT8Nji83k2V8VWzAxZfC08NiG0eN7fjqQ+Y363+bHOpmAP9LnE+vuc5sYWiNckpM4ERg682l4rz0j8UDUw459g7PZzQB+N/4uF8IpJ02SXQ80zocXr4UDQw50ddjnM376d/gn8VwPq/ZNinkyfZ1qed9VM4s0h6ynIZjAbw4nHwTx9UPhG+ve/F1o/vIpJ5Tn4aup9mzt8CBSasdvxPk12ViIiIiIiIiIiIiDf0t2cREREREREREfGA3atMIdtWEelIspJdgIiIiIhIWHYbUf05MOgqWP9naDicuJoyJRht9a+iG3dkA+xb2hw2FArBwrmw89Xo5ssuhVlrwJcF2cUQbIBHC6z7z1hmQqyyCpuPDfkKDL4WFpwDO14MP27zoyZALSs/ujoFQha3fV92+OPDv2EC0oL1ZvN3sB7q9pr/O18AAvkRNoW7+FTJsmPgzIWA3/3/cbcTYdY6qNkOgbzYApi6Hg+zVkPtHsjtrE/GjJY/4KhbXTCHBftPbXf8UC28tgbOHAmUDHO+7qZHYVQKhCgePQTPT4KarbHNUz7ZXf/sIjjxP9BQDaGguV61FDY9EnmsLwDDkhBWmm6GfAU++h40HIptnqJBMHOlCbATkY5n+I2w6zXzWO1UYw0sugJqd8HIb8erso7lwMeR++R112O1SLrKjiIYbf9y+/a+F0RXi4ikp7xuJri//oB5vUnBiCIiIiIiIiIiIqKNnCIiIiIiIiIiIiIirvmTXYCIiIiISFjZRdZt/mwoGwunvZy4eiAzgtHqqmDHS9GPf2EyrPqlCUXb+nR0oWiF/aHPBTD9PbNRMLfc/J9GCrXK7906FK2Jzw+Dr7Ye11gDO19xX6c0CzaEP+63ydoO5Jj7sT/b/L8V9jWBYTmdIm8ItQsVm3I/dDsJSkfDyO/AmW+a+aMNvvP5oKBXbKFoLeV1USharIZ8NWKXAw2llm2rdrR4I2H//3K2ZuWTzvrFU/0B+HeJu1C0rieEP14xO7oasgqaf/+WT4CzFkHJ8Ob2HmfCjI9h2DfNfbDnOXDyM9Dr7OjW60j82TDgC+7GFFSY35N53aF8ogkCPfMtBe2IdGS9Z8BJT0PP6ZDfC/J7Oh/74c1wZEv8autIPn0gcp9e58a/DhGJD382dD3R2zmLXYQ2i0jmyClVKJqIiIiIiIiIiEiHovAzEREREREREREREREv2exiFxERERFJop7TYcWPw7cF8sy/XabCiG/Dyl8kpqZgfWLWiad9SyAUjG2OJd+C2p1QtcTduNHfh7E/su/jz7H+OWeXWI/reTb4AhBqDN9e+ST0numsTmkvZBEKGLdwHptgsf5fgMHXxGldSQnDvwWVT9kGhB1ssH48KGq55zi/h7M19y6GNb83gWIFvR0W6rG3P+9+zMArTTDg9heaj5WMgMFf9qamLlNh5sr2xyf+0pv5O5qxd8L6h0xgZ0u+LJj2MPS9MClliUia6T3DXJrsehNemwENhyKPnT8cLj4Sv9riqaEG9i8DQtB5avKCaPd9FLlPfk8YcWP8axGR+Bl3F7x8knfzFQ/xbi4RERERERERERERERERERERERERERERkQ7An+wCRERERETC6noCFFS0P14xB3wtTmMTGXYVtAiHSidefQ+f/Ax2vOSs75Dr4OT5kUPRwITDWPHbtGUXw4DLrdt3vBJ5bQkvFLQO07P7/4qJTdBFUzCiZK7iwTD9Xdvb14HGUsu2wpbBaDmdnK/7/vXwZAV8dCuEEvwJrtWVsO0Z9+Oyi83j69Q/w+CvwDG/grPehrwu3tcoscsth/N3wKhbzWOZP9sEvE5fpFA0EYletxPg7PdgxE2R+zZWw4EwgZeprmoJPDcOXjwWXjwOXpgC1dYBqnG1/If27UWD4ZwPoXRkYuoRkfjodiLkOQxZjiS7E2TlezOXiIiIiIiIiIiIiIikqQS/F0lEREREREREREREJAMoGE1EREREUpPPD8c/bDaQNikeCpN+17pf1xNh1HdbHxt0dXxqCmVCMFpD4tYaewdcFoLJv4Pe5zobYxd+Fsn4n1u3HdmY+KCjTBFqtG6LVzCaz+apqs8mNE0yR0EvGH2rZfOBButgtIKcFreRbBfBaE1W3AU7XnY/Lhb7P45yoM88bg76Eky5D4Z/w10YnCRedgmMuwMuroFL6mHCz6F8YrKrEpF0VzIMJvwCTn7GPM7YWXZbYmrySigEi6+BQ2ubj1W9D+9/PfG1bH8RKp+w7zNrDeR1S0w9IhJfXY7zZp7up3ozj4iIiIiIiIiIiIiIpLaMen9iJn0vIiIiIiIiIiIiIpKuFIwmIiIiIqmr2wkwez2c/DScvgBmLDdhOS35fDDuLpi1zgSpnfMRTLk/PvUE6+MzbyLZhVx5qde5tqFGlsonRb9mXhc46x3r9u0vRD93Rxa0CQT0Z8dnzaJB8ZlX0os/x7Jpv00wWqv3GNqF7NnZ9M/oxkWrZnt04xSCJiIiLfWeAbPWwujvW/fZ8h9YeU/iaopV1Qewb0n745VPwJEtiatj+R2wYLp9n6kPKMRXJJN4da499Dpv5hEREREREREREREREUkF+puoiIiIiIiIiIiIiCSIgtFEREREJLXllEHvmdD9FAhYh+RQPAj6XwJlY80bL0be4n0tdgFR6SLUkJh1ov35j7gp/PEBX3Q2Pq+bddu717ivR+xvM/6s+Kw59Gvhj/eaEZ/1JDX5cy2bDtgEozUEW1wJBS372dr8WHTjolVd6X5MViF0Od77WkREJL3ldYOxP4KyCdZ9lt4Eh9YlrqZY7F1s3bZtfmJq2PAPWH5b5H7ZxfGvRUQSJ9uDYLQpf4Tup8c+j4iIiIiIiIiIiIiISKpo9amVIiIiIiIiIiIi0bF7mUkvQYlIEwWjiYiIiEhmGniF93NmQjBa0EEwWvdTY1uj87HQdVp0Y3ucAaWjWh/z58Lga52Nz+1i3VZdCUcPRVdXR2Z3u/dlx2fNsvHQ9cQ2a/lhyFfjs56kpkB0wWiNLbPQSoZFt3bDocQGxjgJW2lr0LWQle99LSIikhmmPWLf/vSQxNQRq6oPrNv2fRj/9bc8Ce98wVnfLAWjiWSUnBiD0XrNNK9l+Hze1CMiIiIiIiIiIv+fvfuOl7Os8///uk5NTnovhAQSQu9VAghSpLgrRVDBhthx7bvquiqiu2v5Ka5lbasifBcLIGADpCiKFKlK7ySUBEJCek6Sc2au3x+TbOZMZu65p50y5/V8POaRXP2TcHKYOXPP+5Ykaejyk5ySJEmSJEmSJFXMYDRJkiQ1p2rDcJI0QzBazCSPj9oBXnU97PtlaB9b+f5Tj4Sjr6v+g78t7XDsn2DeO3P/DWe+Bl51LUxZkG59uTCCNY9XV9dwlhSm19LWmDNDgKOuhp0/AON2h+mvhlf+Crb7h8acp8GppaPk0KpM6WC03kzehYRTjoD2KgMNfjO/fy5KXPlA8vikg2G3j8O+X8kFT048EPb9Euz/1cbXJkkausbuXP658fK7+qeWWiy/s/TYxmWNOzezEe54D9x8avo17QajSU2l1mC0ee+oTx2SJEmSJEmSJGmIMPxMkiRJkiRJkqR6atCn2CVJkqQmlN000BXULiaEXIU22OeL0NIKu38cdno3XD6hsv2PuKL2QIDOSXDI/1S3tlwg27O/hJ7VMHoejNq+ujOGm5gQCBjaG3du+2g48JuN21+DX0tnyaF1mVElx/Jz0WjtgH2/CHe+r+++E/eHZbeVr+Hx78LO56YotgbPXF56bK/zYa/Pbm3v/i+NrUWS1Fx2Phce+nLp8SXXwqQD+6+etLpfhHVPQ+cUWP1Q6XmNDEa76wPwZIWvSdpGN6YWSQNjxPTq1047GmaeVL9aJEmSJEmSJEmSJEmSJEmSJEkaZgxGkyRJktLKJgREDRUxU3rsuJth8iu2ttvHVb5/R4VBav3twf/MPQDmvRMO+l4uCE6lZRPC9Fp8SakGai0djJaNLSXHegu/zc1/L4zdFZ77FbR1wewzYMK+sPha+NsnYeXfS9dw1/th5EzY/pQKi6/AC9eXHpvVwHMlSc1v7tuTg9Hu+0zu/5GzT++/mpLECI/9N9zzkeRA5y0aFYzWsxYW/r/K1wWfG0tNZcZxuX/Xhd+PRs6AHd4MPavgiR9suy60wVFX50KaJUmSJEmSJEmSAIjlp0iSJEmSJEmSpD5Kf5JYkiRJGup2+5f67rfyvvruNxBKBQyM3qlvKBpACDB2l8r2D6G6uuophHMn4gAAIABJREFUbZDQkz/MhS489GV49JuwblFj6xqqkkIpDH9QI7WUDhLItI8vPZYt0jntKDjg67DPf+RC0QBmngAn/Q1O/FtyHTefCt0vlq+3WmufKt7fOQUm7N24cyVJzW/sLnDAN5Pn/OUMuOuDsOrh/qkpydMXwd0fSBeKBo0LRlv9CGQ2VLamdSSMntuYeiQNjI4JcOjFfV/3Tl4Ar3kY9vsKHPx9OOwX0JIX6DzxQDjlucSQZ0mSJEmSJEmSpEEvGuQmSZIkSZIkSRp4BqNJkiSpeW1/OlDHoK6nfgzPXlW//QZCzBTvbykRcDXnrMbV0ihzzkw/97Fvwd8+CXd/CK7eF166pXF1DVXZntJjLe39V4eGn5bSYQKZltElx3qLBaMlmbAPzH5D8pwl11a4aUrZHthQInTt0Isbc6YkaXjZ5QOw8weT5zz2LbhmP3j2yv6pqZinLobb317Zmo3LIVb6P/4UVj9S+ZrtXgttI+tfi6SBtcOZcPIiOOzncNwtcOxN0DFu6/ic18Nrn4QFP4Nj/gjH/QVGThuwciVJkiRJkiRJ0kAaJmFig+HmuZIkSZIkSZKkYcFgNEmSJDWvyQfDgkugc3Lf/rZR1Qc63XVuYz5831+yvcX7Q4lgtD3+DeafuzWgaPw+ub5i5r+/9vrqYc7rYdrRla/rWQn3/kv96xnqYomvGSj9dSPVQ2vpYLRsy6iSY5lqvkW/4sfJ492Lq9g0wdqF8KfXws87KHlRZNd29T1TkjR8zSkTAAqQ3Qg3nwaZjY2vp1DP6lxQcaViBv58Ciy/s/Yalt0Ot74Z/nA83PaWytcf8sPaa5A0OHXNzH0fnbKg+M+SuraDHd4I045KfA0jSZIkSZIkSZLUFOIwCYCTJEmSJEmSJA04g9EkSZLU3HY4E057EU55Fs7M5H49fRWc9CC0juw7d+R2cOLfYeorS+/XvQRWPtDYmhupVMhVaC3e39IKB/03nLESTl0CJ/0N9vjX3N9VvtaRsNO76ltrLQ6/tLp1y27LBRblG8pBePWQ7Sk9Vm3AoJRGS0fJoUzLyNJj1fyTbeuCo64pPd67topNS+heAr/ZCZ7/TfK8wu+zkiRVa/KhuUcaNxzZ2FqKef53uZDiqtb+JlfzS7dVf/7ia+CGV8LCS+CF6ypfv/MHoX109edLkiRJkiRJkiRJanKGiUmSJEmSJEn5kvL3zeaXtIXBaJIkSWp+oQW6Zm39taUVxs6HV98KM06AUTvCnDPh2Jtgwt7bBqYVumYfuPFoWPNkv5RfVzFTvL+lLXld6wgYOT33+7ZRcPxfYc4bc393M18DR98IE/apb6216JxU/dpf7wiXjoGbz4Df7g4/74BrDoAXbqxffUNJtkSYHpT/upFq0dJZcigTRpQc6602y3DmCUAoPlbPf//3n1/6e/EWrSOgY0L9zpQkDW8hwKt+n27u8r9Cz+rG1lNo8dW1rc90w6Nfr379IxckhwGX0z6m+rWSJEmSJEmSJEmSmoOf1pQkSZIkSZIkqa4MRpMkSdLwNWFfeNU1cPJTcNhPYcxOuf6EMJ7/8+If4YYjoXd9Y2ust1IhV6G1sn26toPDfpb7uzvqtzDl0Nprq7fZb6h+be9aePZyWP1wLsBoxT3wp9fCqkfqV99QERNCIkJ7/9Wh4ac1KRgtYazaYDTIBT4Ws/yvsHF5DRtvlu2Fhf9bft6I6bkQG0mS6qV9DMx7Z7q5L99T//NjhLVPw0u3QGZD37E1j9W+/zOXVV/X0j/XdvaIqbWtlyRJkiRJkiRJkiRJkiRJkiRJktSHwWiSJElSoc5J6eZ1Pw8v3NjYWuotZor3h7b+raM/zH1bfffLrM+FpQ03pcL0AFqa8OtGg0dLR8mhmBCM1ltLMFrb6NJjj36jho03u/9z0Luu/LzJgzBsUpI09LWNSTdvdZ3DgHvXwc2nwa/nwvWHwxXT4YUbto5vXFafc6oJMe1dC9lN5ecd8C1oH1d8bMaJlZ8rSZIkSZIkSZIkSZIkSZIkSZIkqSSD0SRJkqRCaYPRAB78z8bV0QixRMhVMwZczTgBJuxX3z1XPVjf/YaCUl8zBAi+pFQDtbSXHMpQOhgtU1Mw2qjSYw98AV6+Fx78EtzzMXj027BuUfq9e9bCg/+Rbu6cN6bfV5KktNrTBqM9Wt9zH/oKPHfV1nbPKvjDcbD497l2vYLR7vlY5WvSnj33rXDoxdA6om///l+HsfMrP1eSJEmSJEmSJEnS8BHjQFdQoaFWryRJkiRJkiSpGTVh+oEkSZJUo87J6eduWt64OhohZor3h9b+raM/hAALfgq/261+ez5zGRz2s/rtNxRke4r3J4RWSY2WaekqOdZbUzDa6OTxa/fv277v03Dkb2Hq4eX3vnrPdDWM3wtmnphuriRJlUgbjLbm8fqeu/CS4v03nQA7vg16VtfnnKcvgkN/UtmaNMFobWOgfSzMei289il48Y+Q2QBTj4Qx86oqVZIkSZIkSZIkSVKzGSZhYiEMdAWSJEmSJEmSpGHCYDRJkiSpUCXBaEMtHCrbW7w/NOlLg45x9d0vZnJ3bxxOF/fEYfY1o8Fj9Nzc9+PCwJLWLrIjppdclqkpGG1UZfN7VsENR8A/Pg5jdio9b/WjsG5R+f1GTIcFPxt6/2+RJA0N7WPTzVtxD9z+Dnjqx7l2axccfhlsd1Lx+b3d8NCXYemfoHdNrq9tDEw7Cua/H9Y+Wfqspy9KXX4qG5dD56Tcc/ZFP4dFm/+/Ov9cmH5MkfkpgtHyA+VGzoAdzqpfvZIkSZIkSZIkSZIkSZIkSZIkSZK20TLQBUiSJEmDTsfE9HPDEAuvKRly1dq/dfSX9vH13/O+T9d/z8Es21O83+AmNVpogZ3eu23/vHPIxNLBfL2ZGs5s66pu3W/mw5onSo8/e0Xy+pMegFOXwKmLYfwe1dUgSVI5LZ3p5nUv2RqKBpBZD396DTx9ybZzY4Q/Hg8PnA9Lb4KX7849lt4E938Ofj23DoVX4KEv5Wq6/3Nw61nw/G9y/x/+w7Gw8Gfbzt+4vPye+cFokiRJkiRJkiRJklSxONAF1E9soj+LJEmSJEmSJGlQMxhNkiRJKhSz6ee2dDSujkaIJRKDQumQoSGtbSRMPLD42Pz3V7fnIxdAb3f1NQ01pcL0Wpr0a0aDy96fh32/AuP3zj32Oh8O+AaZbOkL7DK1XHuXLfH1nsZv5sONR8Pap7YdW3x16XVzzsyFoY2cDiFUf74kSeX0rKlt/YP/vm3f87+Fl24uvaa3xjPzjZxRfs7DX4VfdMIDn9927NazoHd9376Ny8rv2WYwmiRJkiRJkiRJkqRyDAyTJEmSJEmS0kr6aZo/aZO0hcFokiRJUqGJ+6Wf2zrEgtFKhf40c8jV3l/YNsBu3y/DQd+ubr/MBnjxj7XXNVRseKl4f2jv3zo0PIUAu/8LnPT33GOvz0JoISEXjd4S+Y+pZGoMPXzxj3D94ZDZ2Ld/04rSa2adUtuZkiSlNeng2tavfqRvsNiGpXDfp2vbsxLTjk03L9tTeuyaffu21y0qv1+7wWiSJEmSJEmSJEmSJEmSJEmSJElSfzIYTZIkSSo0akcYu1u6uUMtHCqWSAwKrf1bR3+aeQIcdwvs/EGY/z448new+8dr23PjsvrUNpjFCA99Be56f/HxZg7T06CXyVY3Vlaow49JupfAs7/c2o4R1j9bev7Mk2o/U5KkNCbuD52TattjSzDa/Z+Hq2bByvtqryut1g6Y947a9ljzOLx899b26kfLryn1GkqSJEmSJEmSJEmSJEmSJEmSJElSQxiMJkmSJBUKAV5xYbq5LUMtGK23eH9o8pCrSQfCgd+Ag74D2+WFEO3xqer2ax9Tn7oGs6V/gr99ovT4UAsFVFNJCj/rrSUYbdbJNSzOc+ub4M73w+/2gGv2gZ7VxefNfz+0j67PmZIkldPSBgt+Ci2d1e+R6YYl18H950G2p361pdHSAXt9DsbvVds+1x4Iy+/M/X71I+Xn96yp7TxJkiRJkiRJkiRJw1wc6AIqNNTqlSRJkiRJkiQ1I4PRJEmSpGImHwKvuKj8vKEWKBYzxftDa//WMVjMPgMIla+rJUxiqFh4SfJ4yxD72ldTySZce5cUmlbW2N1g3O41bJDn8e/Aqodg5f2l5+x9fn3OkiQprRmvhlOegUMvhl0+XPn6TDc8ckH960qjpQO6ZsGrb4ejrqltr98fDPd/HtYtLD+3VMCpJEmSJEmSJEmSJG0RDROTJEmSJEmSJKmeDEaTJEmSShm7c/k5safxdeTrWQsPfxX+dDLceS6sf66y9bG3eP9wDbmasC8s+Cm0j6tsXXZTY+oZTJ78YfL4UAsFVFNJCj/rrSUYLQQ46urc94ZG65wCnZMaf44kSYVGTIUd3wKzT6987W93gSW/r39NabS0535t64KZJ8DYXWvb7/7zSHWn8961tZ0jSZIkSZIkSZIkSZIkSZIkSZIkqSJ+kl2SJEkqpXNy+TmZDY2vY4uYhT/9Ayz909a+Rb+AE+6E0XPT7ZEtEYw2nEOudngjzD4DHvgCPHB+ujXNHoyW5u6VW4IppAGQSfgSTQpNS2XUHDjxXlj3LHSMg/axsHE5rFuUC5Jp6YC2MfDgf8KD/179ObWGuUiSVKvWroGuoDItHX3bI2fC6kcaf+6skxt/hiRJkiRJkiRJkiQNCSmuLZQkSZIkSZIkqQ6GcfrB8BZC2BXYB5gFjAQ2AEuBJ4C/xxjX1bB3O3AYMBuYAawFFgP3xhgX1la5JElSPxpswWiLLu0bigaw6WV47L9h/6+l2yNmiveH1tpqG+paWmHeOc0ZjPb81XDfZ2D9Ihi3Bxz8Qxg7P3nNE98vv+9wDtPTgEsKP6s5GG2LUdtv/X3npNwj3z5fgBHT4O4PVLf/9GOqr02SpHpoG+LBaKPm9M+5O7y5f86RJEmSJEmSJEmS1JzS3KhUkiSpGfk8SJIkSSUkPVX0aaSkLfwk+zASQhgPfAg4h1xoWSmZEMLfgMtjjF+qYP8pwPnAG4CJJebcClwQY/xl6sIlSZIGSvu43Ifvk0Kw+jMY7ZGvFu9/8Q/p94i9xftbfGnAqNmw03vhie+VnztUgtFWPgg3nwLZnlx76Z/h9wfDyQuhY1zxNc9cDne+r/zeHRPqVqZUqWzCDzfrFoyWxi7/BJuWw/2fq3ztTu+pezmSJFWkdYCD0Vo6c8Gj3YtTzm/v255zJjx1Yf3ryjfzNTB5QWPPkCRJkiRJkiRJktQE/LSmJEmSJEmSJEn1ZPrBMBFCOAP4LjApxfRW4ABgFpAqGC2EcCLwE2BqmakLgAUhhEuA98QY16XZX5IkaUCEABMPhGW3lp7TX8FoMcLLdxcfW/G3CvbJFO8PrZXX1IwO+g5MWQBP/ABe+kvpeUMlGO3+z24NRduiZyU8eznMe0ff/mwP3H4OLPzfdHtPP6Y+NUpVSAo/6y3xba5h9joPxu8Nz/8aul+AdQth9SPJaw78Noyc3i/lSZJUUtsABqPt8hHY8U0wYT946iJ48Ubomp17Dv7SzcXXtHT0bU87CsbsDGseq399c86EqUfA3HdAi6+VJEmSJEmSJEmSJEmSJEmSJEmSpP5kMNowEEI4D/hckaFngMeAl4ARwAxgL2BUhfsfBVwF5H8yLQL3AE8B44H9gMl5428CxoYQTokxJnykXZIkaYDNPDE5GC3bT8FoPauTxzetgo5x5ffJ9hbvD740AHJheDu+BbZ/HVw+fttQsS2GQjBa73p49oriY399J0x7FYyeC2ufgsXXwl3vT7/3tGNglw/Vp06pCknBaJnsANx9dftTc48tHvsO3PPh4t9DdngTzD+3/2qTJKmU1gEKRtvuH+GAC7a257099wC4432lg9FCe992Szsc8iO44Yj61rfvl2D3T9R3T0mSJEmSJEmSJEnD2ABczyRJkjQo+DxIkiRJklQ90w+aXAjhY2wbivYz4IsxxvuLzG8BDgVeBxyfYv9ZwBX0DUW7BXhXjPHhvHmdwHuArwJbPsH2j8C/A59K+ceRJEnqfyOmJ49n+iEYLbMR/ljmqdm6RdCxd/m9YolgtBZfGvTR1gUzToTnf118fCgEo/355OTxX8+DuWfDokshsz79vsfcBFMW5IIopAGSlH2WGQzvn+98Lsx4Nbx0K2TWQUtHLuBy4oEw5fBcCKMkSQOtdcTAnLvj20qPtXYmjHVs2zf1cDh9JTx1IdzzkdprAxg5sz77SJIkSZIkSZIkSRpGBsNFS3USm+jPIkmSJEmSJEkaskw/aGIhhH2AL+V19QBnxRgvL7UmxpglF2x2SwghzdfH+cCEvPatwLExxj4JITHGjcA3QwjPAFfmDX00hPD9GOOiFGdJkiT1v7au5PGNy6BnLbSPblwNz1wKy/+aPGfdQpiQJhgtU7w/tFZcVtN7xYXwy0nFx/ojEK8WG16CF24oP++pn1S270Hfg2lHVlWSVE+ZbOmx3hLf5vrdmJ1yD0mSBqsQYPxesHKb+2dAaIGY8D/cQi0dMPUoeOG68nO3PzV5n0rHOsbBrh+GSYfA9QvKn1/OyBm17yFJkiRJkiRJkiRJkiRJkiRJkiSpai0DXYAaY3Oo2Y/pG373nqRQtEIxxt4yZ8wH3pbXtQk4uzAUrWDPq4CL8ro6gfPS1iRJktTvWssEowFcdwj0djfuLnmP/Ff5OetS5sxmSzzFS5WJO8x0ToSpJULA7vtM/9ZSqTShaJXqnAI7vqX++0pVSApGSxqTJEkF5r17274ZJ6Z7HZTv9JWw12fLz9vt47nQtVJaR5QeC+3Je08+BCbsW76GckZuV/sekiRJkiRJkiRJktSUGnSdrCRJkiRJkiRJBQxGa15nAPvntW+MMV5Y5zPOAlrz2lfEGB9Pse7LBe3XhxASPvEmSZI0gNpSBAKseggu7YLLx8PNZ8CGpfWtYcU95ecs/2v5OeuegRdvLD4WWov3D3ctHaXH6v3fuZ42vFDf/Wb+A5xwZ7p/D1I/yCZcX9drMJokSent8k9w4Ldh/D7QNRt2ei8ccTn0rk2/R9dsaBsJE/YrP3fkjDLjM0uPJT03h1zg2tE3wA5vgRHTYeKBsOBncOpimHUKjJhWvr6u2TBm5/LzJEmSJEmSJEmSJCmtRt10V5IkabDzeZAkSZIkqQZtA12AGuY9Be3/bMAZpxa0UwWvxRgfDiH8FThkc9co4NXAr+tYmyRJUn20jkw/t2c1PHs5LL8DTn4698H8WmV70s1beAm84iJoKRFwltkI1x9Wen3wpUFRSeELi34Bu3wgeX1mI6x6EMbu2r+hYhuX1b7Hrh+D/b9a+z5SA2QSws+SxiRJUhE7vz/3qFVbF3ROgY0vlZ5TLhht7K6lx3rXlK+hcxIsuHjb/ldeuW3f05fAbW/O6wiw12dLv6aSJEmSJEmSJEmSpFIM/ZAkSZIkSZJSS/ppmj9pk7SF6QdNKISwE3BkXtdC4I91PmM6sE9eVy9wSwVb3MTWYDSAEzEYTZIkDUbVhFmtfwaumAqnLIbWhGCtNNYuTD/3pZth2lHFx57/Nax/rvRaP/xfQsKPUNY8kbz0qYvhzvdAZkMuYG2f/4TdPlbf8kqpNRhtwU9hzhvrU4vUAEnhZ70Go0mSNHAOvRhuOrH0eNlgtF1Kj2W6q6uplB3fBGN2gie+D61dMPv00q+nJEmSJEmSygghtAOHAbOBGcBaYDFwb4xxYZ3P2hHYF5gJjAaWAIuAW2OMKe+8JUmSJEmSJEmSJEmSJA1eBqM1p1cVtG+Mse63n9mzoH1fjHFdBetvLWjvUWM9kiRJjdFaRTAawMbl8MAXYJ8v1Hb+xqXp5750S+kP8i+/M3ntmifTnzOcJIUvJIXmrfg73P62re3sJrj3n2H8XjDj1fWrL9+qh2DJ9bDpZXjiB9Xvs9fnYYcz61eX1ACZhFe4SaFpkiQppfnnwuPfqXzd+MIfGxcYPS95fMR0aB8PPSu3HZtyeOX1lDP5kNxDkiRJkiQ1hRDC54Dzatjiohjj2RWeOQU4H3gDMLHEnFuBC2KMv6yhNkIIpwMfBQ4tMeXlEMIvgM/GGGu8k5IkSZIkSZIkSZIkSZI0cFoGugA1xMEF7dsAQs6xIYQLQwgPhRBWhRDWhRAWhRBuCCF8MoSwQ8ozdi9oP1FhjYXJG4X7SZIkDQ5J4VflPHtZ7ef3rEk/975Pw+rHt+2PER7+/5LXjvPpWFG960uPtY4sPfbcVcX7n72ytnpKeeKHcPXecM+H4YHP17BRgB3fXLeypEbJJoSf9WT6rw5JkprWrFPSzx2z09bfd82CaUcXnzflCOjaLnmvEGCnd27bP2oOTNg3fU2SJEmSJEn9IIRwIvAA8D5KhKJttgC4PITwvyGEUVWcMzqE8DPgMkqHorG5hvcBD4QQjq/0HEmSJEm1SLjTY+LYYDTU6pUkSYOXzyskSZIkSdUzGK05HVjQfnhz4NkNwPXA2cBuwFigC5gNHAN8EXgshPDfIYRyCSA7FbSfqbDGRQXtSSGECRXuIUmS1HitNQSjrV2YCyWrxPO/g9vOhtvPgcW/h57Vla3/7c7wl9fDjcfCvR+HNU/CstvLrxu7a2XnDBeZKoPR7v9c8f4nvldTOUWtXwx3vAtiHdKgDr8URu9Y+z5Sg2USvrVu6Om/OiRJalozjoP9L0j3emi3f+7bPvRimHhQ376JB8BhP0939p7nwewztrbHzIejroXg2xmSJEmSJGnwCCEcBVwFTM3rjsDd5ALMrgeWFSx7E/CzENL/oCOE0Ar8AnhjwdBLwHWbz7qHvp8wnAb8KoRweNpzJEmSJEmSJEmSJEmSpMGkbaALUEPMKGh3AXcCk1OsbQfOBQ4NIbwmxrikxLzxBe2llRQYY1wbQtgAjMjrHgesqGSfQiGEqcCUCpfNq+VMSZLU5NpqCEbLboTeddA+Ot38x78Hd75va/upC2HKYZWf+8xluV9fvBGevgimvDJ5/pj5MPXIys8ZDnoTgtFa2vuvjiRXbVeffV6/rravd6kfZbKlx9Zt6r86JElqart+BHZ6L6x9ElY/Cn85fds5o+fBtGP69nVtByfckQuKXv8sdG0Po3dIf2776Fxg78blsHEZjNkZQqjlTyJJkiRJkoavM4EUd5H6P2vTTAohzAKuADryum8B3hVjfDhvXifwHuCr5K7LA/hH4N+BT6Ws6UvASXntHuCjwA9ijP/3rkgIYXfgh8Chm7s6gatCCHslXAMoSZIkSZWp9GbBkiRJkiRJkiRVyWC05lQYWnYhW0PR1gHfA64BngNGAfsA5wD5d4jcD/hlCOHIGGNPkTMK0z26q6izm77BaGOq2KPQucB5ddhHkiQpp3Vkbes3LU8XjBazcP/52/a/dEtt529YCs9eXnp8yhFw6E/Sh7cNN5mEYLRHvwm7frjImg2Nq6fQ8ruqWxfagCy0jYFpr4IDv20omoaUbML1dU9UFNstSZIStY2E8XvmHodeDLe9devY9GPhFRdBa0fxtaN3qCwQrVDnpNxDkiRJkiSpei/EGBc2YN/zgQl57VuBY2OMfd4ojDFuBL4ZQngGuDJv6KMhhO/HGBclHRJCmAt8qKD7jBjjrwrnxhgfCiEcA9zI1nC0SeSupXtvij+TJEmSpIYxTEySJA1XPg+SJEmSJFXPYLQms/kuk50F3bM2//oQcEKM8dmC8XuAC0MIHyN3d8otDgU+Qe4OlYUKkzOqSX/opu8FYqZxSJKkwSe01LZ+4zIYNaf8vOV3wYYXajurGsf9uf/PHEp6E4LR1j0NT/4Y5p3Tt//WtzS2pnwv/qGy+ae9CCOmNqYWqR9lssnjv7gzyxsOqvH7tyRJ6mvHt+QekiRJkiRJw1gIYT7wtryuTcDZhaFo+WKMV4UQLspb10kusOycUms2Ow9oz2v/pFgoWt453SGEs4H7gS1p9u8IIXwlxvhUmbMkSZIk1cTQD0mSJEmSJEmS6slPCTef1hL9qygeivZ/YoxfA75e0P2REEKawLJq3sXxnR9JktT8NixLN2/j0sbWUcx+X+v/M4eaWCZ96ckf9m1vWAbPXZG8JrOptpryPfjF9HM7JkDnlPqdLQ2gcsFoP/qLLzclSZIkSZIkSVJDnEXfa/SuiDE+nmLdlwvarw8hjCg1OYQwEji9zB7biDE+BlyV19VGrmZJkiRJkiRJkiRJkgaFmPDxv6QxScOLwWhNJsa4Hij2EfELkkLR8nyGXIjaFhOBE4vMW1vQHpmuwsQ1hXtW4zvAnhU+Tq7DuZIkqZmN3aX6teufSTdvIF6pd23X/2cONft9JXl82W1926sfLh+mduOroHtJbXUBrHsWelamnz9yBoRQ+7nSIJAt8y3zhof7pw5JkiRJkiRJkjTsnFrQvjDNohjjw8Bf87pGAa9OWHI80JXXvi3G+EiqCret6bSU6yRJkiRJkiRJkiRJkqRBwWC05rSuSN/FaRbGGNcBVxR0H1Vk6qAMRosxLo0xPljJA3iy1nMlSVKTm/3Gbfs6JsLrlsEhP4IFl0D72OJr73g3rLiv/BkxU1uN1RhpMFpZM4tlBCfoWVN+zrJb4doD4KVbq6tpiyd/VNn8qUfVdp40iGTK5A9KkiRJkiRJkiTVWwhhOrBPXlcvcEsFW9xU0E56M/KEMmuT3Eyuti32CyFMq2C9JEmSpHoaiBvn1mSo1StJkgatIfc8SJIkSZI0mBiM1pxWFrRfjDEurGD97QXt3YrMWVXQnlLB/oQQRrNtMFph3ZIkSYPDnv8GO7wFCLn2yJlw9HXQOQnmnQM7nAXj9ii9/q/vhKcvgVvOgrs+CMvv2nbOQASjjZ7b/2cONaPmQOuI5Dn5b9b1pghGA+heAjceBfd8DO79OPzlDfD4dyFbwdfBA+ennwsw7x2VzZcGMYPRJEmSJEmSJEnSANizoH3f5huRplV456Sk2cPnAAAgAElEQVSEN5m3Oeu2tIdsrun+Cs6SJEmSVCtDPyRJkiRJkiRJqqu2gS5ADfEYsH1ee0mF6xcXtCcVmfN4QXtOhWcUzn85xriiwj0kSZL6R0s7LLgYDvh6LtBq3O4QCjKG28aUXv/ynXDbm7e2n/wfOPI3MP3YrX2Z7vrWXM64PaFrZv+eOVRt/zpYeEnp8dgLoT33+56UwWgA2R545IKt7WcuhRf/CIf9AkJIXnvfeenPAZj9epi4f2VrpEEsm+I6wt8/GDl+jzL/liRJkiRJkiRJUjN7Twjh0+RuDDoJ6AGWA4uAvwDXxhhvrmC/3QvaT1RYz5Nl9stXeDPTas7ar+CsP1S4hyRJkiRJkiRJkiRJkjQgWspP0RD0YEF7Y4XrC+ePKDLn4YL2ThWeMbeg/VCF6yVJkvpf5yQYv+e2oWgA7QnBaIUyG+APx8GVs+CuD+TavZXcSLwOZry6f88bylo6k8ezm7b+vreCYLRinrkMVj1QfOzZq+DqfeGnAR74fOk9TvwbHPBNGD0PRkyFXT4MB323trqkQSaTLT/nM1elmCRJkiRJkiRJkprZG4FjgJlAJzCa3A09Xwl8CvhzCOHOEMKxpbfoo/AauWcqrGdRQXtSCGFC4aQQwkRgYo1nFc6fX+F6SZIkSSoixR0tJUmSJEmSJEmqg7aBLkANcV9Be3yF6wvnLy8ypzCtYe8QQleMcX3KMw4rs58kSdLQUkkw2hbdz8Nj34bVj+ZCrCrxiovg0W/AinsqPxdg3B7VrRuOWjqSxzMboW1U7vc9q2s/78kfwQH/1bdvyfXwl9dBLBP0NH4fmLD5scsHaq9FGqTSBKPdtQiy2UhLS2h8QZIkSZIkSZIkaag6ELguhPBF4NMxxqRP+RdeV7e0koNijGtDCBvoe6PSccCKMuesjzFWeqetwtrGVbi+pBDCVGBKhcsqfENckiRJGmqSXkoYJiZJkoYrnwdJkiRJkqpnMFpzuobcTwy2fPp7bghhRIxxQ8r1exa0nyucEGNcEkK4D9h7c1cbcDhwXcozjipoX5NynSRJ0uDUVkUw2hYvXA9cX9mazskw7xy4q8pgtLG7VLduOIo9yePZjVt/37Om9vNeKPK18MQPyoeiAez41trPl4aATMr3yJethaljG1uLJEmSJEmSJEkadJ4HrgbuAB4GXgaywCRgf+AfgOPz5gfgU0AL8K8J+44uaHdXUVs3fYPRir3RXK9z8tXwhvY2zgXOq+N+kiRJkiRJkiRJkqRhJOmWZYm3M5M0rLQMdAGqvxjjYuC2vK524JgKtjihoH1ziXlXFrTfnmbzEMKuwCF5XetIH6gmSZI0OLXX8zryFDonw9yzq1wcYOxu9aymufWW+ZxBJi8YrbcOwWirHtr2JzfPXp5u7c7n1n6+NMjFGFP/cHPJqsbWIkmSJEmSJEmSBpU7yAWebR9jfHeM8YcxxltijA/HGB+NMd4aY/x2jPEE4CDg8YL1nwwhnJywf2FgWdobleYrfPOxcM/+PEeSJEmSJEmSJEmSJEkalAxGa14XFrQ/mmZRCOEI4OC8riy5u2cWcwmQyWufFkKYn+KYTxS0L40xVnPxliRJ0uDRPrZ/zxsxGdpGwZG/rXztzNdA58T619SsMmWC0bJ5wWg9ZYLRJh4Ax/2l/Jl/fSdkM+Xn5Tv2ZmgdUX6eNMRlK7jjw+KVjatDkiRJkiRJkiQNLjHGq2OM18VY/hYrMca7gFcAjxUMfSmE0Jr2yEprHORrJEmSJDXEEHt6nvaulZIkSWX5vEKSJEmSVL22gS5ADXMhuTC03Ta3jw4hfDTGeEGpBSGEqWwbqHZpjPHJYvNjjI+HEC4Cztnc1QH8JIRwTKmgs8131Dw7r2sTcH65P4wkSdKg1zamf8/rmLT51/GVrz3kB/WtpdmVC0bLpAxGG7srHH4pjJ4LI2dA95LSc5/6MUw+BNY/DyvvL1/j/v8FUw8vP09qApls+rlLVkUgNKwWSZIkSZIkSZI0dMUYXw4hnAncxdY3FHYFXgXcUGTJ2oL2yCqOLVxTuGd/nlOt7wCXVbhmHvCrOtYgSZIkDTKGfkiSJEmSJEmSVE8GozWpGGMmhPAh4FqgZXP310IIc4DPxRhX5M8PIRwLfJfcBUhbrAA+Veao84BTgQmb2wuAG0II74wxPpK3fyfwbuBrBeu/FmNclP5PJkmSNEiFfg7eaR+b+7W1wmvgu2blQrmUXrlgtOymrb/ftLz4nNmvh8N+vvXrJLSXP/eO96Srb+45sOuH0s2VmkC2gmsIF69qXB2SJEmSJEmSJGnoizHeE0K4Djg+r/sEDEYrKca4FFhayZrQ3++nS5IkSZIkSZIkSZIkaUgzGK2JxRiv3xyO9q287g8C7wsh3A48T+4CqH2BOQXLNwFnxhifLnPGcyGE04DfAx2buw8DHgoh3A08BYwD9gemFCz/LfCZiv9gkiRJg9HGEoFYjbLlwvHWrsrWlQv50rbKBqNthEe+AU/9GFbeV3zOtKP7hue1dBSfV42pr6zfXtIQkMmmn7vEYDRJkiRJkiRJklTetfQNRtu7xLzCdx4Kr4dLFEIYzbaBZStTnNMVQhgVY1xXwXFTU5wjSZIkSRWq4K6WkiRJkiRJkiTVwGC0Jhdj/HYIIQN8FdiSmtEOHJGw7EXgtBjjrSnPuCmEcCrwE7Ze7BWAAzc/ivkZ8K4YYybNGZIkSYPeQIVTtVUYjLbzBxtTRzPb/nRYfkfp8Qe/CIt/l7zH2F37tnd6F/ztE7XXBjDj+PJzpCZSSTDaC6u8EE+SJEmSJEmSJJW1sKBdKvDs8YJ24c1Iyymc/3KMcUXhpBjj8hDCCmBCXvds4OEaziqsXZIkSVI9xYTrlJLGJEmSmpnPgyRJkiRJNWgZ6ALUeDHG75K7i+X/AmsSpr4AfA7YJW0oWt4ZVwN7At8DtrlYK8/twOkxxrMqvIOlJEnS4DbpEGgb0z9ntY/d+vsRhTf6LmP70+pby3Aw5w3J4+VC0QDG7lKw5xurryffjBNh5PT67CUNEdkK3h9fvLJxdUiSJEmSJEmSpKbRXdAeWWJeYTDZThWeM7eg/VDC3HqfVUmomiRJkiRJkiRJkiRJkjSg2ga6APWPGOOTwFtCCCOBw4BZwHRgE/AS8PcY4301nrEUeF8I4UObz5iz+Yx1wPPAvTHGp2s5Q5IkadBq7YRD/gdufRPETGPP2u9reeeOSL9ut3+BcXvUv55mN2o27PNF+Pu/Vre+fTyMmFZkz/+Av/9bbbUd9O3a1ktDUCabfu6SVY2rQ5IkSZIkSZIkNY3JBe1lJeY9UNDeO4TQFWNcn/Kcw8rsVzi2IK99KPCbNIeEEEaRu5Fq2rMkSZIkSZIkSZIkSRoU4kAXIGnQMBhtmIkxdgM3NPiMTcAfG3mGJEnSoDTnDTBhX7jjvbD0psac0doFs1+Xfv4Rv4Q1j8OUw2HyAgihMXU1uz0+WX0w2rQji/+97/EpmH5c7utlxT2V7zvpYBhdeKN3qflVGowWYyT4vU+SJEmSJEmSJJV2SEF7cbFJMcYlIYT72Bo61gYcDlyX8pyjCtrXJMy9Fnh3wtokR9D32tB7Y4wvVrBekiRJ0rDmR08lSVK9+LxCkiRJklS9loEuQJIkSWoqY3eBff69MXu3j4XDL4OOCenXbH8a7P4JmHKYoWi16ppV3boZJ5Yem3QQHPdnaOmofN/dqwxqk4a4bAXvj/dkYFNv42qRJEmSJEmSJElDWwhhBHBaQfdNCUuuLGi/PeU5u9I3gG0dyYFqvwe689qHbt4jjbML2oU1S5IkSaq7pIuaDASRJEmSJEmSJKlSBqNJkiRJ9dYxqbb1rV1wzB/g9BVwVoRTl8Dxd8CpL8B2J207v3NK8X26tq+tDvWV7alu3Zh5yeNto2B0mTmFJh0C2/1jdfVIQ1ys8DrB7ir/6UqSJEmSJEmSpGHhE8B2ee0M8LuE+ZdsnrPFaSGE+SnPyXdpjHFDqckxxvXA5WX22EYIYWfg1LyuXuCnKeqTJEmSJEmSJEmSJEmSBg2D0SRJkqR665xcfs74fUqPnfR3mPYq6Bifa4+cDpMOgraRxefv/YXi/bv9c/k6lN7GZdWtGzmz/JwT7k65WYA5Z8Hhv4CW1urqkYa4bKXBaJsaU4ckSZIkSZIkSRo8QghvCSFMq3DNu4DzCrp/EmNcVGpNjPFx4KK8rg7gJyGEEQnnnAycnde1CTg/RYmfA/JvAXN2COG1CeeMAC7cXNMWP4oxPpniLEmSJEkqr9K7WkqSJEmSJEmSVCWD0SRJkqR665hQfs7xd8CME/r2tXbl+sfsVNl5c14PYwpuQj56Lsx5Y2X7KNmkg6tbN3K78nPaRsKOb02e09IJZ2bgsEtg1JzqapGaQKWX1nX3lJ8jSZIkSZIkSZKGvHcAT4cQLgohvCaEMKrUxBDCgSGEK4AfACFv6Hng0ynOOg9YkddeANwQQti14JzOEMIHgMsK1n8tKXxtixjjU8A3CrovDyH8UwghP/yMEMJuwI2ba9liOekC2CRJkiQ1lGFikiRpuPJ5kCRJkiSpem0DXYAkSZLUdFpaYeZJsPjq0nNaO+CVv4JHLoCX/gIjpsDOH4CJ+1d+XscEOO5WePgrsOJemLAf7PJhGDG1+j+DtjXtaFh2W+Xr2semmzfrFHj64tLjnZMghNLj0jCRzVY232A0SZIkSZIkSZKGjZHAWzc/siGEx4GFwCogA0wC9gGmFVn7MnBCjPGFcofEGJ8LIZwG/B7YElB2GPBQCOFu4ClgHLA/MKVg+W+Bz1TwZ/oksAdw4uZ2O/At4DMhhHuANcDczWflv5m4CTg1xrikgrMkSZIkVSsa+iFJkiRJkiRJUj0ZjCZJkiQ1wr5fKh2MNm7P3K+tHbDHJ+tz3ojJsN9X6rOXitv1I7n/pivuTb9m/N7pw8xmvJrcZxVKXCDVMSH9uVITy1Z4DWH3psbUIUmSJEmSJEmSBrUWYJfNj3JuBM6OMT6XdvMY400hhFOBn7A1/CwAB25+FPMz4F0xxkwF52RCCK8Hfgi8IW9oKnBCiWVLgbfFGG9Oe44kSZIkSZIkSZIkSZI0mLQMdAGSJEl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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import asyncio\n", + "\n", + "from matplotlib import pyplot as plt\n", + "\n", + "from apd.aggregation.query import with_database\n", + "from apd.aggregation.analysis import get_known_configs, plot_sensor, Config\n", + "\n", + "async def clean_magnitude(datapoints):\n", + " async for datapoint in datapoints:\n", + " if datapoint.data is None:\n", + " continue\n", + " yield datapoint.collected_at, datapoint.data[\"magnitude\"]\n", + "\n", + "TemperatureConfig = Config(\n", + " sensor_name=\"Temperature\",\n", + " clean=clean_magnitude,\n", + " title=\"Ambient temperature\",\n", + " ylabel=\"Degrees C\",\n", + ")\n", + " \n", + "with with_database(\"postgresql+psycopg2://apd@localhost/apd\") as session:\n", + " coros = []\n", + " figure = plt.figure(figsize = (20, 5), dpi=300)\n", + " configs = get_known_configs()\n", + " to_display = configs[\"Relative humidity\"], TemperatureConfig\n", + " for i, config in enumerate(to_display, start=1):\n", + " plot = figure.add_subplot(1, 2, i)\n", + " coros.append(plot_sensor(config, plot, {}))\n", + " await asyncio.gather(*coros)\n", + "\n", + "display(figure)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import asyncio\n", + "\n", + "from matplotlib import pyplot as plt\n", + "\n", + "from apd.aggregation.query import with_database\n", + "from apd.aggregation.analysis import get_known_configs, plot_sensor, Config\n", + "\n", + "with with_database(\"postgresql+psycopg2://apd@localhost/apd\") as session:\n", + " coros = []\n", + " figure = plt.figure(figsize = (20, 5), dpi=300)\n", + " configs = get_known_configs()\n", + " to_display = configs[\"Relative humidity\"], configs[\"Ambient temperature\"]\n", + " for i, config in enumerate(to_display, start=1):\n", + " plot = figure.add_subplot(1, 2, i)\n", + " coros.append(plot_sensor(config, plot, {}))\n", + " await asyncio.gather(*coros)\n", + "\n", + "display(figure)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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Q2KRpdDJsWrJ6Xuc1U19RPe+kCoeb+9lk47r28aaxybQz2p/TIf+a/P6stN+PVSSHXDrw57c5k17UczDafq9qfzz9rJ6Dp+qHJI3Dq8cjn5o8Z07v5xzx0eraXGBUa0vyxO+SuqYqBK5+cNK6MWldn5StyYKrk7/+KFn0m2SvY5OppyfL/5z8+bKe95vwzOr7tuDbPYfRwUD9+mUDXzP++Op9Yc2C5PFfdR5rGlO9py27PRl7ZBVutnFdMupp1XvC3icnrc3Joz+rvrZuSJ74bbL0tiqYbUs8/sukeVn1fgUAAAAAAOw0BKMBAAAAAAAAAAAAAMCuYPi05IV/SZb8sQrHGntkUj+o85yirj14bCCKIjnwHckBb6/2rqvf/Jq6xuSYL1ThZasfSsYc2l5PUZc885rk+hckaxdWfeNPTA7/SPv60U9Lnv3T5I73VaFeY45Mjvx4Mmhc+5zpr02G7ZfMvypJXTL15cnezx7489ucUQck089JHvhSe1/T2OSgf2pv73NqUjQk5YbOa6edOfDz+gpFS6rv7YRndumrT+qGVo+nn1VdHa1b1Hsw2gEXJhNOrK7D/yNZckuy9pFk4ilJ44hqTtmaLL6xOvuRnyR/+ufOexzx8apv45r+PEPobtFvq6snzUurK6ne4560+qFk4Q+3X02r5yVNh22//QEAAAAAgAETjAYAAAAA/H/27js8zurO2/g9Rb1LtmxLstwbtjHYmF4NobcACYGQTkJ62c2mbMiml01v+242bbPJBkgjpIcNSSiB0AnVNmCMe2+SrK6Z949jZTSaGVnFtlzuz3Wda+Y5v3POc55pBlvzlSRJkiRJkiRJkqRDRTQOY07cf+tHIhAZRChaX0XjQuuvagFctiqEcOVXQtmMzECw2lPh7D8PvP64M0Lb3074DtSeAZv+DMUNIXisbHqqXjg2BLs9+u5UX+l0mHvj/t/bYBSMgfLZ0LQsvT9WBLVnpo6jeTDmhMz5kWjqtVW9CCZeAZvvgqI6GH8OxAoh0QF/f3/28x/zOTjqX8L97jbYei889QnYfDdMegXM+ReoXhhqT9wIy76UfZ3iRigcB8ke2PFoZr32zHBN2+5PBWlJw7VrKVQZjCZJkiRJkiRJ0sHEYDRJkiRpv0mO9gYkSZIkSZIkSZIkSZIkaXRF4zDm+NHexeBEIjD11aHlMvtdUHsabPxTCAyrvxDyqw7cHgcSicC8f4P7Xknaz6/NeS/kVwx9vfJZofVVMjn3+OpjU/fjRSFMbfw5mePiRTDtDbmD0c5/CAprw/2eDtj4xxDWN/YUyCtPH5tMwu5V4X5+JbSth542+MNx2dcunQbHfQPuvCD3dZxyC9z7itz18jnQtDR3XYeW+66FydeM9i4kSZIkSZIkSVIfBqNJkiRJkiRJkiRJkiRJkiRJkiQNVvXC0A5Gk6+BgjGw8ofQ0woNl8PkV+679SecC9F8SHSm99ecALVnDn6d8jlQNgOan0vvH3tKKhQNIFYA9RfnXicSgdLJqeP8Smjfknv8/I9B3fnhPFvuzT5m0tW5g9GO/SLM+adw/6ZIjj3F4Zou2PEELP8q7HwyXNPs90DrWrj/tbn3p9HRthGKxo/2LiRJkiRJkiRJ0h7R0d6AJEmSJEmSJEmSJEmSJEmSJEmS9pEJL4GTfwCn/QymXBfCw/aV/Eo4+X9D+FevsafA6b+AaDz3vP4iETj+W5BXnuorHAeLvjbyPRaMgZJJWc4ZD48NwLQ3ZZ9bf2m4bbw6SzECjVemDuOl2deYfkO4rToaTvwunP8gnPkbGH82TH1N+mPX15iTYMkfs9cAzn0Aznswd724Ea5N5q4DnHPPwHWAhpfufczhpmXFaO9AkiRJkiRJkiT1YTCaJEmStL8k9/IDRpIkSZIkSZIkSZIkSZIkHWoaXwYv3QCn/hTOuRvOvguKJgx9nXFnwsXL4aQfwCm3wIVPQfXCke8vEoFZ78nsn/IqKKwN9ydeDnmVmWOmXR9u534QCmrSa7PelR641ju2v8mvHHh/M9+RvX/GW6B6EUTzMmvxEqicB6VTc69bc1y4PfoT2euTroHaU8N15FLcCKfclLs+5mSomJe7PljRgpGv0V9eJRz9SXjZLqhcMLS5Xc37fj+SJEmSJEmSJGnYhvDreCRJkiRlMPxMkiRJkiRJkiRJkiRJknSkKRwDjVeNfJ2i8SGwbF+b/a4QJrby+9C9G+ovg3k3pup55fCSu+H+18H2R0Ow2/yPQsMloV61AM57EFb+CNrWw/hzYOIV6eeY9U5Y8zNoXZvqa3gpjDlx4L3NuAFe+B507Ur1lU6HiVdBvAgaXwEv/jB9ztQ3QLw4tAnnwYbbM9edcH64nXQNPPkRSCbS69NeH27LZ+XeW+U8iBXmrk+8MgS3PfLO7PVrEiGYLtEDt+T4ylJxI8z/CDzwhuz1M38PpVNgy725x0w4D8aeCj0dkOyCuotCqFy8ONSP/gT89UpIdKXmNF4Nq3+cfb3uluz9kiRJkiRJkiRpVBiMJkmSJEmSJEmSJEmSJEmSJEmSpMPL9OtDy6VyPpz/MHS3QqwoBHr1VToV5n849/zSKXDu/fDC96HleRhzCkx9XeY6/ZXPgpfcA898DnY9A2NOgPkfD6FoACd8OwS3rfkpRPJg8rWw4NOp+Yu+Dve8FHY9neqrPSOMAyibBqfdBg+8Hjq2QqwYjv1cCHeDEMD2yHsg0ZG5t6l7wtMmXglrfp5ei8Rg0tWQ7IZH3gX0+8WyE69KXXs0FoLaNvwh8xxzPwBjT8v+2MSKYdwSiOXD7lXZxwAc8zmoOjp3veESeMm98OLN0NMGDZeG/eQKRlv5g1DPK8295sGkuw1aXoDdK8Prt3J+eL0mE+F5a98EdRdAfjU0LQ21kknQ0w6dO6FgDERzfKUs0QORaOq57OkIz0WiI5y3pxW6mqBtQzhvXnl4L8RLDtz1S5IkSZIkSZIOewajSZIkSftNcu9DJEmSJEmSJEmSJEmSJEnS6IkXD39ucT3M+9DQ51XOh5N/mL0WK4DF34Djvh6O+wetlc+A8x6Etb8MwWqV80PwV6wwNabhEqjfBLtfhOLG9BCswrGw5I9w37XQujbVv+hr0HhluD/vw7D5TujYlqrPeV+4XoBZ74TlX03V8qtg7r+m73PytZnBaNECmPgyKBwD1Ytg+yP95lwTQtEAao6HaD4kOtPH5FWEcLm9qVkcWl8VR4XHrL91v4afloX7jS+HCeeFELHVP4GmZTD+JSFobPw5sOF2WPrF8Dy1roGyWVA+M+yzqwnW3Jq+9rTroXgiNFwOFXMgmrf3vSeTsO5XsOonsOlP0NUM098UXqtPf3rv8wEee+/gxgFMegWsuiWzv+FyWP/77CF62VQvCo/dzLdD0QTo6QyvgaZl0Lk9hLmVzQghdOWzYcffQ2Dbzidg55N7HqdLobA2PP6RvBCyJ0mSJEmSJEk64hiMJkmSJI2I4WeSJEmSJEmSJEmSJEmSJGkf6x+I1le8OISIDTg/CqVTs9dqT4PLVodgtLxyyCsL43tVLQjhay/eDO2bQyBYwyWp+sIvw9hTYMMfQwDWlFdB2fT0c0y+Dpqfg2f+PYSGFdbCyTeHUDSA034B91wB2x8Oxw2Xw8KvpObnV8Kka2Dl/6SvO+0NIZRsOOKlex+z+ieh9fXi/4aWTduGECKXy4rvhNsnPxJuy+dA6TSY9jqoXgwlEzPnPPFhePpT6X3Lv5I5bl/JFooGsPa2oa2z/ZHQ9hbeNlD9wTemH0ficNrPQ6Bc9aLwOuqvuzWE7kVj4floehYiMSidAokuKBwH8aKhXYskSZIkSZIkaVQZjCZJkiRJkiRJkiRJkiRJkiRJkiQdSSKR7KFcvUqnwrwP5Z7b+LLQBlr/6I/DUR8MAWxl09LD10omwvkPhVq8BPKrMtc4/luhf/VPIZoPk6+F+R8b3PVlM5hgtP2taWlo638z2js5NCS74e7LDuw5y2ZC41XhtdfdBsu+AF1Nqfr8j8GU63IHD0qSJEmSJEmSRsxgNEmSJGl/SSZHeweSJEmSJEmSJEmSJEmSJEmjJ14E5TNy14sbctdi+bDoy7DwSyFobaQKx498DR3+mp+Fpz+du/7kR0IDGH8OFNVDrACqF0NPK6z/PWz4Q6hXL4LaM2HGm6FsOnTuhFgxJLugYxsUT9w3r21JkiRJkiRJOswYjCZJkiSNhOFnkiRJkiRJkiRJkiRJkiRJ+8++Co6a+FJYddO+WUsC2HhHn4NvZda3PxLasi/ufa3iBpj/Uag5HqKF0LQ0hPmVToa2DZBXDiWTQrha925Y+QPYfBdsvQ/KZkDJZJhwPhRNgPaN0NUM064P9zffBXmV4T0QzQs//2wgmyRJkiRJkqSDmMFokiRJkiRJkiRJkiQdISJZvuiU9BdASKMikUhk9GV7j0qSJEmSJGkfmXglTH09vPC90d6JlKl1LTxw/fDm7vh7aGtvS+9/7L2ZY/MqoacVEp3huOGyELRWcyJULYCieiABRXUhbA2gYytE4xArgqVfgA3/B+WzYdLLYexp4Xj3Kqg7H8qmQzIBkejwrqVXTyfE8ke2hiRJkiRJkqRDlsFokiRJ0n7jlwklSZIkSZIkHVyi0cwvInV1dZGXlzcKu5GObN3d3Rl92d6jkiRJkiRJ2kciETjhO1B7Oqz5eQii2vHYaO9q8IomQLwUYoUhlGr1T4c2v+ZEiBdDcQOs/MH+2aMOfl0704/X/jLcbrxjaOtsuQdWfDu975Es4wrHQfumcL98NjQ/D8l+fzcaK4Se9tznGnsKJHrCa3f82dBwORSNH9w+ezohmhfe/5IkSZIkSZIOGQajSZIkSSNi+JkkSZIkSZKkQ0ckEiE/P5/Ozs5/9LW0tFBcXDyKu5KOTC0tLWnH+fn5RPxyniRJkiRJ0v4VicDU14QG0NUEP63IPrZkCsy7ESKxEKLWtBy23Q+l00JQ0xMfzjFvElz2Yghy2ngHPPx2aHl++HuuPQPO/ktmsNOvZuRe96KlUD4LundDNB6Cp/o66X8gmYSWF0JIWncLTDgftvwVnvp49jVP/Rn89arM/uJGuHQF3DLAL+C4dAXsegb+/gHo3A6Vx0DZNCACz3499zwd+npD0QCalmUfM1AoGsCWe8PtNmDNz+Cht2SOqVwQAtC2P5xZK50aggW7W8NrvXt3eB32tENRHdRdBJEotG2AWAFMflUIdCuohq5dkOgK7+uC2rBeNBbe34lOiBelnyuZNIRNkiRJkiRJ2gcMRpMkSZIkSZIkSZIk6QhSVlbGtm3b/nHc1NTE2LFjDWSSDqBkMklTU1NaX1lZ2SjtRpIkSZIk6QiWV567NvcDMO31ueu5gtEaXx5uozGoOw8ufW7P+H+Dpz4xtP3N/xgc9f7sQUuz3x1C1/qb+XaomB3u55XmXjsSCeFkR38s1ZdfmT0YbdobYcJ5EC8JoVJ9TXlVCF/Lpf7SEExVOhXqL86sT3k13L44+9ySSbB7Ve61pV47H89da3khtGza1sOKb6f3rf7p4M8biUOye+/jiifCxKtgxyOw+e7sY07+EVTOh9Z1IYyxa1cIeysYA2NOgvbNUDY9BLVtvAOi+VB3YXif93TA2l/CM5+B5ufDZ9vY02Duv0LV0ennaV0H638LRGH8kvDe7JXoguYVUDYjfIb1Z/CbJEmSJEmSDhCD0SRJkqT9JjnaG5AkSZIkSZKkDP2D0bq6uli3bh319fWGo0kHQDKZZN26dXR1daX1l5cP8CVcSZIkSZIk7T+TroFVN6f3RaJQf8nA86a9MTNQCWDyddnHz/sIrP4JNC0feN1rB/nzp41Xh7C1zu2pvmg+TLt+cPOzqT4uhDet+Vmqr7AW5rw3hC+ddivccyV0t4Ra3UVw1PtS+1n948w1p71hL+dcCMWN0Lo6vX/CeXDWH+CmIf699bizYfF/wG9mD20eQOE4aN80tDmRGCR7hn4uHR4GE4oG0LoGln954DH3vXLk++nV3RLej73vyWyhhn01XAY7/p49iLDmRNh2f7hfMhmIwO6V4XjMSTDjLeFzLxKBRA+0PA9F9dC8HLbcC9GC8HlaMAZ2PQn5VelhbJIkSZIkSVIWBqNJkiRJI2L4mSRJkiRJkqRDS2FhIXl5eWmhTM3NzaxYsYLy8nJKS0uJx+NEo9FR3KV0eEkkEnR3d9PS0kJTU1NGKFpeXh4FBQWjtDtJkiRJkqQj3NwPwaa/QPvG9L6iCQPPm/1PsO5X6UFak6+DqqOzj4/G4Ow74aG3wtpfZB8z4y2D33fhGHjJvfDIu2D7Q1AxFxZ8CqoWDH6N/iIROOUWeOG/YfPdUDolBJuVNIb6hHPhik2w9X4oqoPymSFEDmDm22DNz9ODospnQd2FezlnFE78b7j7cuhuDn3FjbBwT4jUjLfCc/8vc16sEHraM/snvzLsbajGngLlR2UPu8uldBpcvAx6WqG7FTb8AWJFUHsmPPbP8OKPhr4PaX8YKBQNYO0vc9d6Q9EAdr+YXtv6t9D+9uqB13/ozQPX8yohryy02jOhcDy0rYeCaogWQtdOSHSFcLWJV0Ll3OzrJJPQsSWst/Nx6GqCinlQNG7g80uSJEmSJOmgYzCaJEmSJEmSJEmSJElHkEgkQl1dHatXryaZTP3yh66uLrZt28a2bdtGcXfSkaf3PRmJREZ7K5IkSZIkSUemyrlw/kOw+qchiGf8S0IA2N5UzIZz74eVPwhhQbVnwJRXDTynaDycfiss/wY88o7MeuPVQ9t7xWxYcvvQ5uxNNAbTrw8tm3gxjF+S2V97Gpz1e3jmc9DyAow9FY79AkQH8fW18Uvgkudg058hXhIey/yKUGu8Knsw2kk/DAFu63/XZw9nwqSrwx7HngJb7u03KQInfBceeH3mehOvguqF2YPRGi6D9X+AREd6f+NV4fqi5ZBXDlNfm6pVLgCGGIxWNAEmviy8nvIqYOzJUDIJnvn38Npsfi41duY7oGAsdO2CbQ9C8cTw+kp0wbNfz32OxpfD6p8MbV/S/ta1MzSAXc8MPPbJj8DY08JnzIbbYceje1+/oCa8Z8aeFoIwW56HLfdB57ZUaNyEC6B8NtSeGgIOi+pD4GQkCp3bQ9haNDay65QkSZIkSdKgRfr+kLN0JIpEInOBp3qPn3rqKebOzfFbIyRJkvpLdMEt+dlrk66FU/xNb5IkSZIkSZIOTq2trRnhaJIOrEgkQmNjI8XFxftszaeffpp58+b17ZqXTCaf3mcnkCRpiPwZPUmSJCmLrha4+9IQ0NNr6uvhhO+AAfrpkkl4/EPwzGdSfTPfDgu/AiRg7W2w/VGonB/CzWJ7fq5328Pw53NCcBhAtACO+xpMfxMs/SI89t7UenUXw6m3hDCkPy2BzXelavEyOOeuEKR033WQ6Az9tWfA6bdBfmX2fe9aCr89KrO/dFoI3nvuPzNrJ3wXpmUJbevV1RLOX1Cde0zHdvh5Tfba6b+EhkvT+5LJ8Bi1roP7roGdTwIRGHdWCFF76M25zyUdiWKFUDIFWtdC+UxIJqBlJZCEwnEhLDFWEgIaJ18HHZth8z2w4Q9hfsVcqLsQJl0DpZOBSFizY1uY37EFtv4NYsUhHDGvfBQvVpIkSZIk9efP5x04BqPpiOcPXUmSpBHp6YQfF2SvGYwmSZIkSZIk6SDX2trK+vXr6erqGu2tSEecvLw86urq9mkoGviDV5Kkg48/oydJkiTl0NMBG26HXU9DzfEwbomhaANpfh52PAaVR0PZzME9Vm0bYfXPgARMOD+EGP1jvRUhfKhsOlQvhmgs9He3wdLPw+a7oXQKzHwHVB0dau2bYctfoagBqheGAKSB3HFGWKevY78A48+G/zsJetpT/UX1cPEyyCvd+3Xtze+OgZ2Pp/fFS+ClGyCvbOC5XU0hkKn32v76Clj94+xjr+3zvcSu5nCOSDQcv3hLCFrL5pRboGpheMy3PgjJHmi4BNbcCve/LvucCx6HrffBQ2/JrJVOg5YVua+pqD48X1ULobsZln0p91jpYFQwFqJ5kFcBkT2fVXUXwJTXhPfP8q/A7lVhTP2lMOak8Bm5/OvQtBTat8C8G6H2tDA/vzoEsCWT0LQcttwD2x4IIW9l06HhpSG8zT+TJEmSJEnK4M/nHTgGo+mI5w9dSZKkETEYTZIkSZIkSdIhLplM0tHRQVNTE83NzXR2do72lqTDVn5+PmVlZZSXl1NQUEBkP3ypyB+8kiQdbPwZPUmSJElHrM5d8NBbYf3voKAapr8J5rwvhA1tuQ+e+mQILao5ARZ+AYob9s151/4S7rkKkt2pvgWfhrkfHPpa634Hd12U2T/+HFjyx9zzOrbBL+og0e/fHKoWwgWPZJ/T8iL8akpmfzQPrtoRgtcAEl3Q/ByUToVYYWrczidh+6NQOA5qz4B4Ufbz/KQ8BKRJGrqCMdCxNXstvxoKx4YxBWMgXgata2HznaFefwmUzwnhbD2tUD4rhIRGolAyCfJrINkFRXVQNiMVtNhXMhHmRPNDPde/syS6obslfEb0/ZyQJEmSJGmE/Pm8A2cvv5ZCkiRJ0vAZQixJkiRJkiTp4BeJRCgsLKSwsJDa2lqSySSJRAJ/0Zq070QiEaLR6H4JQpMkSZIkSZJ0kMqvCL9kOZnMDO8ZezKc9bv9c96Gy+Ccu+HFH0HPbqi/DCZePry1JpwbAshaXkjvn37DwPMKamD2e+CZf0/1ReIw/99yzymdHELitj2Q3l9/aSoUDUJQWsVRmfMr54e2N3M/AI9/KLN/zMmw9b69zx+MyvkhqC2Xy9dCcX24n0zAjsdg9yooHA9Vx8CLN8GDb8w+N5qfGTjXa/y5MOlqeOANuc+9t+ucfB3sXglb7s09RkeuXKFoAJ3bQ2N59vq6X4c2FEUTINkTgs5IQvfu9Nf/mJOgejGs/D50NYXwxYGUl8UAACAASURBVB2PZl9r/LlQvQialsGYE8J7r3I+7FoagtrKZkHlXOhqCe+B4onhMynZBbGiEMTWvhnW3gadO6H+4uyfRYkuIAJRv74tSZIkSdJI+H/WkiRJ0oj4xUBJkiRJkiRJh5dIJEIsFhvtbUiSJEmSJEmSdHgYjV+YMPak0EYqGg8haw/eAJvvhJJJMOdfoPGqvc9d8BkoPwrW/wbyymHKq6H29IHnnPpjuPMi2PV0OB57Ciz+5ogvI039ZdmD0SZdHQKNtj+UWcsadBaBeHEIaupv9j/Dyh/Cpj9l1qIFIQDtH8tEQ1hT9aJU35gTcu//kmfht/Ohuzm9P14Kp98KrWtzzz3uG1B3AfxqWvb69DfB8f8VAqNuzvFvRQs+BdOuhyc/Cmt/Be2bwuti7Cmw7SFoWpr7/NJQtW0YuL71b6H1yhWKBrDx/0IDWPuLke/t7+9P3a89M4TC7Xwi1Tf2NJh8DTRcEcLVIrEQ9DZciZ7weTHQnynJJJAM4yRJkiRJOsQZjCZJkiRJkiRJkiRJkiRJkiRJkiRJkjIV18OZvwmBO0MJeYtEYOqrQxuskklw4ZPQ/GwIECudPOTt7lXl3BDu1Tccre4imPYGyKuE+1+TPn7sKXD8t+FPS6B9Y6p/9nsgVgxPfzJ9fDQf6i8GktmD0eovguhefkFNxVwomQK7V6b315wYHqOZb4NnPptem/FmiJdAcWPYQ6Izc91xZ0PhAMFMZTPCbSQaxmbbf8PlUFgLi/9faNm0bwmBVuWzIZYPNw3wupnxNnjuP7LXJl0Lq27KPVc6WGy+M7Nvyz2hPfTW4a0ZKwaSkF8Nbesy66VToageelph+yOZ9VN/Ej4POrZCwVioOhq6miC/Jnw+xApHJ7hTkiRJkqRBMhhNkiRJ2m+So70BSZIkSZIkSZIkSZIkSZIkSRq5AxWgE4lA+az9e465/woTr4Qt90H5zBA4Fo2FELdkTwjqat8EE86DhV+CvHI470FYdUsIKBq3BOovge4W2Pq3VIBYNB9O/D4U1MDk62DXM7D086nzFjfCgs9m3VKaSBSO/ybccwV07w59BTWw6Kvh/oJPh7CkVbcACWi8Go56X6jFi0Iw25pb09esOgYqZof75bOhaVnmeRtemrp/1Pthy92Q6Er1TbwSKo7a+/4Lx4bWq/FlsPqnmeN6H6tcwWjHfn54wWjnPQg1i2H3anj8Rtj59/DY1yyG0mnQshKqFoSxOx4PYXPVC8NzXtwAd14ILS+krxnNS38spP2tpzXcZgtFg/Aa7f867euvLx/ceSrmhvdETyvES0MYZtsmaLwqhCBu+CNsfyjUzrkrfM6tuTV8VpbPgp522PU0FE+E6uMg0RHGjDk5fK42LYdtD0PJnnq8eGiPgyRJkiTpiBVJJg1r0JEtEonMBZ7qPX7qqaeYO3fuKO5IkiQdUnra4cdF2WuTXgGn3Hxg9yNJkiRJkiRJko5oTz/9NPPmzevbNS+ZTD49WvuRJMmf0ZMkSZIkHdYSPSF4a/fqEARUNC69vvPpEJ4WL4G6CyC/cvBr714FG24PIUMTzoei8YOb174Z7roEtj0YjkunwZm/TQXOPfef8NBb0+fUXQxn/jq9b/M98Nw3oX0jjH8JzHkvROOD33+v9bfDneen9xXUwKUvQrIbbh0fwpT6Kp0GlzwHf/8ALP1c5przPgIv/i+0rEjvLxwHl68JQWbD1dMRgp+6dsK4s0N4HsCtE8Jj0d+ir8Gsd4T7bRthwx+gcALUHBfWIgG3TRz+fqTDXXEDtK5NHY85GWKF0N0Ku18MQYYtL4SQxTEnQVEddGyFkikhhDGvPMzrboFoIZAI8yVJkiRpP/Dn8w6cYfwtlCRJkiRJkiRJkiRJkiRJkiRJkiRJ0hEuGoPqRaFlUzk3tOEomQTT3zT0eYW1cO790Pxs+EXgFfPCPnvNeAsQhee/BZ07oP4iOCZL+FjtaaGNVN15cPKP4KmPh3CjmhPghO9AXmmoz3wbLPtS+px5N0IkAlNeBcu/AonOVC1aAFNfEx7Xe68N4WoARGDBp0YWigYQK4DJ12S5jgvhhe+l90Wi0HBZ6rhoPEx9bebchpfC2l+MbF8A1yRgx2Ow8ofhsSyZDBMvh6pj4GfVueeddTtsfzQ8NvlV4Xnv3g0Vc6G4Hrqaw7XsegY23w1rfja8/dWeDrVnhtCqjq2w8wloWja8tXTk6BuKBrD1vvTjDXsCCZ/+9NDXnvVumP+RoYVSSpIkSZIOCgajSZIkSftLMjnaO5AkSZIkSZIkSZIkSZIkSZIkHWkiESiflbs+44bQDpTJ14aW6IZov6+1HvsFKJsOa24LYWlTXgMNl4Za5Tw487fw2L/Azqegcj4c9w0onRJaUUMI8Up0wcQrYNyZ++8a5n4QNt4Brav79N0IJY17nzvt9ZnBaPFSaHwZvPDfgzv/Ue8Pz2v1wtD6m/XuECLX38QrYMK5oe3N+LNh1jvgF/XQtj77mKpjQzhbNmf/JQSsZfPUp+CJG7PXZr4Tnv1a9tr0N0HtGSE8q2MbbH8INv0l+9h4CYxbEgLeWlZkH6Mjy/KvhPft2X8KoZGSJEmSpEOGwWiSJEnSSBh+JkmSJEmSJEmSJEmSJEmSJEnS3vUPRYMQ9jXjLaFlM/4cuOCx7KFqY08K7UAomw7nPwRrboXWdTB+CYw7a3Bz6y+Gxd+EJz8C7ZugYi6c9D8QLYBVN0NP+97XaLx64PrEl+YIRrtqcHvsa+6H4OG3ZfZXHQsz3wYPXJ9ZK5qQOxQNoO787MFoeZVQs3jgvfQPn+vpDIFyO58Ij+WkV0BBdebcnnaIxEOg2rYHoaAmhMr1tMGTn4C2tSFcb8K5UDoNWp6HOy/MvZdJr4BVt+Su6+C06ylY8d0QbihJkiRJOmQYjCZJkiRJkiRJkiRJkiRJkiRJkiRJkqSDV7ZQtQOtsBZmvHl4c2fcANPfBN3NkFee6l9yBzx+Ywhvqj4OFn4JXvgeLP3CngERWPhlqD524PXHngZz3ttnHjD5Oph45dD32nAZPPIOSCb69V8OdReFALT+tUnXDLxm1UIonwVNy9P7p1y3J2AuAvT7xfUFY6GoPnOtWH54PPcmVhhui8ZBwyXp/Yu+lDm+fEZ4jts3Z9ZO/hFMvjYE9K35WfbzXd0RHpvVPw3Pw45HIV4WnvOieujYAonOve9b+97GPxqMJkmSJEmHmIPgb4IkSZKkw1Vy70MkSZIkSZIkSZIkSZIkSZIkSdLhLxJJD0UDGHsKnPOX9L5jPw+z3gO7noaqY6FwzODWPvbzMOU1sP0RqDgqBK1FIkPfZ3E9HPf/4OG3pgLQxi2B2e8O+1/4FXjknanxVcfAnPftfX9n/AbuuhSaloa+iVfAMZ+FeAnUXwLrfpU+Z8abIRob+v5Hov5SWPGd9L5oAUw4P9yf+prswWhVC0NgG8Dka0IbSMe2EJ7WshJqjoNpb4T8ilT9pgGet4uXw29m5a6fcze0b4I1P4fVP4Nk98B7ORJ0bB3tHUiSJEmShshgNEmSJGlEDD+TJEmSJEmSJEmSJEmSJEmSJEn7UHFdaENVOS+0kZpxA0w4D7beByWToeZ4iO75SvKsd4SgtM13QnEjjF8Sws32pmw6XPQ0tLwAeRXpgW+n3BzC1tbeBvEymPpamPfhkV/HUM37N9jyV2haFo4jUTjua1BQHY7HvwQKa6F9c/q8qa8Z2nkKauCYz+SuT3sjrPh2Zv/cD0H5TJj5dnj2G5n1ya+C2tPC/car4NgvwK5noHAsVB4drmf3GvhlY/bzzv4naLgc7jg9997m3gid26CrGV7839zjDiadO0Z7B5IkSZKkITIYTZIkSZIkSZIkSZIkSZIkSZIkSZIkSVJK6eTQsqmcG9pQRSJQNi2zP14MJ3wHjv92GDNaSibCeQ/Cxj9C24YQAFcxJ1WPFcCSP8G9V4fAsVgRzHpnCCrbl6ZclyUYLQKTXxnuNrw0ezDapKvTj4vrQ+uraALEiqGnNXP+5FdC9UI46X/hb9dl1iecDws+kTrOFYxWvQhO/Rk8+RFY+YPsY16+G6L5IXAvmYTdL4bAvObngQSUTodYfhiz8c+w/KvQ/GwYByFcbsprQlBdsge2PwJrbs1+LoPRJEmSJOmQYzCaJEmStN8kR3sDkiRJkiRJkiRJkiRJkiRJkiRJh4bRDEXrlVcGE6/IXa+cBxc9DW0bIb86hHfta7Wnw/Hfgkf/GbqbIb8qBMf1hrSNOwuO+iA885nUnFnvDsFlexONQ+NVmYFlZTOg6thwv+GycM7+gWLT3pB+XLkAdj6eeY5Z7w6herP/OXswWtnMEIbXKxKB0inhfsHxmePrLwxtbzbdBX86M7O/ezfseBwqjoJo3t7XkSRJkiSNOoPRJEmSpBEx/EySJEmSJEmSJEmSJEmSJEmSJOmIUjR+/64//Y0w9XXQugZKJkEkmqpFInDMp8OY7Y9C5Xwonzn4tRd+GXa/CJvvDsfFE+G0W1PBdHmlcM5d8LdXw46/Q+E4mPfhEKjW18QrMoPRogVQf3G4Xzkfak6Ebfenj5nx1sHvdSjyq3LXfn9MuK27KDye2x6E4voQBldzIuRXQF55CLsrGAvRWGruqp/A8/8FXU3h2ub+KyQT0LYOihtD2NxgJLqBSPrayWTqcU90Q7IHYgVDumxJkiRJOhwZjCZJkiRJkiRJkiRJkiRJkiRJkiRJkiRJB5NoHEqn5K6XThm4nktBNZx9J7SsCGFflQvSw7oghJpd8Bh0NUO8NBXe1dec98L2h2Hdr8NxrBBOvhnyK8NxJAJn/Q4efDNs+AMUjIEZb4FZ7xz6ngdjoGC0Xut/m7q//WFY+8uhnWP7w/DkR9P78sqhYh5E88Nj0L0bonnhudn6AOx6au/rxsuguzm9b+Y7wmNVOi398U8m0oPyevsSXWEPXbugZSVUHJU7ZC1bSNvedLeF57qnFSZcAEXjBj9XkiRJkobIYDRJkiRpf0kmR3sHkiRJkiRJkiRJkiRJkiRJkiRJUrpIBMqm731cXlnuWrwYTv8lND8Lu1dBzQmQX5E+Jr8KTv1x9jCvfW0wwWj7Q1cTbL0vs3/TENboH4oG8OzXQxuJSDQ89gBFE6BqEbRvgqal0N0SQu9mvh3aN0PReGjbCKtugspjoP4iqL8ENt4BT308XGevaD7M/1hYs2kpxEpgzIlQOS/09T7fiZ5wG4nsCW/rhp62EBwXLx7ZtUmSJEk6rBmMJkmSJI2E4WeSJEmSJEmSJEmSJEmSJEmSJEk6EkUiUD4rtAHH7edQNIB4CeSVpwd4Hel6Q9EA2jZA22/S690t8MxnM+dtuz+0Jz6cfd1EJzz+wZHtrXwWVB8PhbXh9VF1LDRcDvGizLEHIlhPkiRJ0kHFYDRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJ0qErEoG6i2HVTaO9Ew1G0/LQ+iuZAoXjoLghhNz1tMHW+yDZA7FCqF4cbiecD+POgEQP5FeGllcBsYIDfy2SJEmS9jmD0SRJkqT9JjnaG5AkSZIkSZIkSZIkSZIkSZIkSZKODIu+DDseg6alo70TDdfulaFty1LraYct94T7G/+YfX6sEPIqIb9iz+2ewLT8ysH1x0tCyJ4kSZKkUWUwmiRJkjQihp9JkiRJkiRJkiRJkiRJkiRJkiRJo66wFi58ErbcDY/fCB1bofnZ0d6VDqSedujZCO0bhzc/EusTmDaEQLXe/rwKiMb27TVJkiRJRyCD0SRJkiRJkiRJkiRJkiRJkiRJkiRJkiRJh75oDMadBefeG45bXoBfTcs+9pjPwriz4fbFudcrmQy7X9zXu9TBKtkDndtDG6542eCD1DL6KyBWuO+uR5IkSTpEGYwmSZIk7TfJ0d6AJEmSJEmSJEmSJEmSJEmSJEmSdOQqboT8KujckVmbdC2UTITF/wkPvSWzXjEXLnoKkkl44sOw7MvQ0wrVx8EJ34aqY8K4nk74cUH281ceDRc+njpe8T144A3Zx16xBbpbYPWP4e8fyKzXnAhta0NYW7wENtwOkSgkE6kxeeU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n2rNI/wAmhmv8MjK8P9P+vvQpxj0GSA4tI4f6Mys5tCbjHr2RQ8vIof5Mew7pH0Mgh5bRz/oz7WOvDIEcWkYO9UcOrcw9euuQRcvIov7MShYZqx4gObSMHOrPrOTQmoxV90YOLSOH+jPtOTSV/UNhtAlQSrk8TQdcHJK3JfmuWmtdeas1jWqboam1Hkjyix2Lf6yU8q9W26aUcmmSP0xy9mq7HVDzxlop5aeTfHfH4pfXWv90xE0Zej9c+KO1s2r3Sus9XGt9eZJ/2fGja5P8416OOa3k0HJyaONmKYe68Pwk5y6afyDJ2wZ8jKkhi5aTRRs3C1mkfwyHLFpOX9u4McoiJogcWk4ObdwM59CDSa5aND0izTjQtyX5z0nuXljvsiQ/leRjpZQva6GdY0sWLSeLNm6WssiY9eDIoeXk0MbNUg51wZh1D2TRcrJo42Yhi/QPYFK4xi8nwzdujK7x7tWbIHJoOTm0cbOUQ10w7tElObScHNq4Wcgh/WPw5NBy+tnGjVEOMUHk0HJyaONmOIfco9cnWbScLNq4WcoiY9WDI4eWk0MbN0s51AVj1V2SQ8vJoY2bhRya1v6xue0GsLZSyvlJ/neSSxYtvjPJ19Va7155q2UOdsxvpCJf5zad+2zDzyT5ppypyFiS/JdSyrenqYr6t2kqcF68sN4P5sxF73NJLl20r4drrcsqfJZSruy2MbXWW3tqfQtKKT+Sptr1Yr9Qa/25Lre/sttjrXA+xr4f1lpfV0r5+iTPW7T4h5K8ZZDHmTRyaE1yqEdyaJnv65h/S621s3o0kUXrkEU9mrEsGnr/mCWyaE2yqEctZ1QVJ7cAACAASURBVBETSg6tSQ71aJZzqNZ6MsmtK/zoI0neXkr5d0leneSHF5ZfnuQ9pZSvrrV+fDStHF+yaE2yqEeznEXdMGa9Mjm0JjnUIzm0jDHrLsmiNcmiHs1YFhmzBsaaa/yaXON7NGPvS/fMuMfK5NCa5FCP5NAyxj26IIfWJId6NGM5ZMxjQOTQmuRQj2Zs7JUBkUNrkkM9muUcco9ef2TRmmRRj2Y5i7phrHplcmhNcqhHcmgZY9VdkENrkkM9mrEcmrqxaoXRxlgp5Zwk70ny6EWL70nynFrrTT3saioDu9Z6tJTybUn+V5InLfrR0xem1dyb5gXDHy1adv8q697SQ5NKD+uOXCnl+5P8Qsfi/15rfVkPu+nnfExKP/zZLP0D9itLKXtqrav1kakmh9Ymh3ojh5YqpVyW5Os6Fr9ho/ubZrJobbKoN7OWRSPqHzNBFq1NFvVmDLKICSSH1iaHeiOH1lZrPZzkX5ZSjiX51wuLdyf59VLKUzb47UJTQRatTRb1RhZ1zZj1InJobXKoN3JoKWPW3ZNFa5NFvZm1LDJmDYwz1/i1ucb3Zgyu8ZPSD417LCKH1iaHeiOHljLu0R05tDY51JtZyyFjHoMhh9Ymh3ozBjnEBJJDa5NDvZFDa3OP3upk0dpkUW9kUdeMVS8ih9Ymh3ojh5YyVt0dObQ2OdSbWcuhaRyrnmu7AayslHJ2kj9O8sRFi/enqWB5Q4+7e6Bjfl+PbdmZ5YE9Fh241vr5JE9L8vokx7rY5H1JnprkUMfyOwfctLFSSvnuJL+cpSH6xiT/YoTN6OyHZ5VSdvS4j/M75ofRD/8qze/aKZuSPH4Ixxl7cqg7cqg7cmhF12Xpa7G/q7X+TR/7m0qyqDuyqDuzmkX6R/9kUXf0te6MSRYxYeRQd+RQd+RQT34yyR2L5r80yXNaakvrZFF3ZFF3ZFFPjFkvkEPdkUPdkUMrui7GrNcli7oji7ozq1mkfwDjyDW+OzK8O2NyjR+3e2RWY9xjgRzqjhzqjhxa0XUx7rEmOdQdOdSdWc0h/aM/cqg7+ll3xiSHmDByqDtyqDtyqCfu0VtEFnVHFnVHFvXEWPUCOdQdOdQdObSi62Ksek1yqDtyqDuzmkPT1j8URhtDpZRdSf4wyVMWLX4wyTfWWv92A7vsrHp5RY/bd65/X611/4prtqDWeqjW+s+TPCbNQMj7knwuyUNJDiS5Mcmvpame+uxa661JHtexmw+PrMEjVkp5cZpwXvz7/htJ/ukoK+fXWu/N0j8Mk+TyHnfT2Rd7qejalVrrySS3dyzu6UXONJBDvZFDa5NDy5VSSpLv6VisqncHWdQbWbS2Wc8i/WPjZFFv9LW1jUsWMVnkUG/k0NrkUG9qrQ8leUfH4m9soy1tk0W9kUVrk0W9MWbdkEO9kUNrk0PLGbPujizqjSxa26xnkf4BjBPX+N7I8LWNyzV+nO6RWYtxj4Yc6o0cWpscWs64x/rkUG/k0NpmPYf0j42RQ73Rz9Y2LjnEZJFDvZFDa5NDvXGP3hmyqDeyaG2yqDfGqhtyqDdyaG1yaDlj1euTQ72RQ2ub9Ryapv6xue0GsNTCt9D8ryRfuWjxwSTfVGv9qw3u9saO+Uf2uP3VHfOf2GA7hqrWekuSn1mY1vNVHfN/uco+y0rLJ0Up5YVJ3pymOvUpv53kpQt/qPVkAOfjxjSVJU95ZJb3z7V09sVetu3FQx3znZVcp5oc2jg5tJwcWtWzkly1aP5ImhfTLJBFGyeLlpNFZwyjf0wzWbRxsmi5McwiJoAc2jg5tJwc2rBPdsz3+jsz8WTRxsmi5WTRhhmzlkMbIoeWk0OrMma9Dlm0cbJoOVl0hjFroG2u8RvnGr/cGF7jx+UemfUY95BDGyKHlpNDqzLusQY5tHFyaDk5dIYxj+7JoY2TQ8uNYQ4xAeTQxsmh5eTQhrlHTxZtmCxaThZtmLFqObQhcmg5ObQqY9VrkEMbJ4eWk0NnTMNY9dz6qzAqpZTtSX4vydMXLT6c5JtrrX/ex64/3jH/pFLKWT1s/9Xr7G+iLFRTfVbH4g+00ZZhKqU8L8lvZmkBxHckeUmt9UQ7rVrWdzqDcVULL2aetM7+BuW8jvl7hnScsSOHRkMOyaEk39sx/7u11vs2uK+pI4tGQxbJonWOMxP9Yy2yaDRmpa+NaRYx5uTQaMghOdSFYx3zW1tpRUtk0WjIIlnUBWPWcmio5JAcijHrNcmi0ZBFsmgts9I/gNFyjR+NWcnwMb3Gj/370guMe8ihoZJDcijGPVYlh0ZDDsmhdY4zE/1jNXJoNGaln41pDjHm5NBoyCE51AX36MmioZNFsqgLxqrl0FDJITkUY9WrkkOjIYfk0FrGuX8ojDYmSinbkrwryTMXLX44yfNqrX/az75rrV9I8tFFizZn6UVhPc/smP+DftozBp6V5MpF8x+otd7UUluGopTy3DQVK+cXLf79JC+qtR5vp1VJkj/smH9mD9t+TZZefD5Sa72r7xZ1KKWcl+XVW+8Y9HHGkRwaKTnUntZzqJSyJ8m3dSx+Q6/7mVayaKRkUXtaz6IuTH3/WIssGqmp72tjnEWMMTk0UnKI9VzaMT+M115jSRaNlCxiVcas5dCIyKEZZsx6bbJopGQRa5n6/gGMlmv8SE19ho/xNX7s35c27iGHRkQOtaf1HDLusTo5NFJyqD2t51AXpr5/rEYOjdTU97MxziHGmBwaKTnEetyjJ4tGQRaxKmPVcmhE5NAMM1a9Ojk0UnKItYxt/1AYbQyUUrYk+d0kz1m0+EiSF9Ra3zugw7y9Y/57umzbY5N8xaJFh5L88YDa1JZ/2zH/+lZaMSSllK9L8jtJtixa/MdJXlhrPdpOq077oyQPLZr/qoU+1o3rOuY7+/SgvDhLs/GuJDcO6VhjQw6NnBxqzzjk0Hcm2bZo/tYkf7LBfU0VWTRysuj/Z+/Ow2S7ynrxf98QCEMgYQqjJMyDyhiUgJiDgAIKGFBAZAjkyiDOIALXIaAXLj9F8YqCoJAwKoMEZFTEwxAIMo+KTIkQmQmEEDKv3x+7jqfO7urumrqquvvzeZ5+yF6119qrqtZ+O+x+867lWYVYtJkdvT42IhYt3I5eaysei1hR4tDCiUNs5id7xyvxYH+riUULJxaxEc+s9xOHto44tLt5Zr0OsWjhxCI2sqPXB7BYfscv3I6O4Sv+O347/F3ac4/9xKGtIw4tzyrEIc89RhCHFk4cWp5ViEOb2dHrYz3i0MLt6HW24nGIFSUOLZw4xGbk6O0nFm0dsYiNeFa9nzi0dcSh3c2z6hHEoYUTh9jIyq4PhdGWrKoOTvLKJPccar4wyc+11t46x0u9LMnFQ8f3q6obj9Gvv3hf2Vo7b37TWqyqeniSuw81fSRdxccdoaqOTfK6HPgvRm9P98v//OXMar/W2rlJXt1r7q+xNarqJkmOG2q6KMnL5zi1fde5RpLf7TX/Y2utzftaq0QcWixxaLlWJA49snf8wp0eZ8YhFi2WWLRcKxKLNrrOjl4fGxGLFmunr7VVj0WsJnFoscQhNlNVP53k6F7z65Yxl0USixZLLGIjnlmLQ4sgDhHPrEcSixZLLGIjO319AIvld/xi7fQYvuq/47fB36U999hPHNoi4tByrUgc8tyjRxxaLHFouVYkDm10nR29PtYjDi3WTl9nqx6HWE3i0GKJQ2xGjp5YtAhiERvxrFocWgRxiHhWvYY4tFjiEBtZ9fWhMNoSVdWl0gXS+w41X5Tkga21N8zzWq21zyQ5eajpMklOqqrLrtMlVXXfHLjTzQVJnjrPec1q8Atv3HPvl+QFQ00XJXlka+2iuU9sCarqmCRvSHK5oeZ3Jrl3a+37o3stxYnp/qVkn+Or6j7rnTxYoy/KgZU5/7a19rkN+ty0qu49yaSq6prpPr9rDDVfkOQZk4yz3YhDsxOH9hOHNldVt05y26GmS5KcNOk4O41YNDuxaD+xaGRf62MMYtHsrLX9tlEsYoWIQ7MTh/YTh/arqqOr6rjNz1zT7/ZJXtJrfmdr7ePzmdlqEotmJxbtJxbt55n1+MSh2YlD+4lDm/PMejSxaHZi0X5i0VrWB7AsfsfPTgzfbxv9jj8xcvVWhjg0O3FoP3Foc557rCUOzU4c2k8cGtn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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import asyncio\n", + "from uuid import UUID\n", + "\n", + "from matplotlib import pyplot as plt\n", + "\n", + "from apd.aggregation.query import with_database\n", + "from apd.aggregation.analysis import get_known_configs, plot_sensor\n", + "\n", + "\n", + "location_names = {\n", + " UUID('53998a51-60de-48ae-b71a-5c37cd1455f2'): \"Loft\",\n", + " UUID('1bc63cda-e223-48bc-93c2-c1f651779d69'): \"Living Room\",\n", + " UUID('ea0683de-6772-4678-bfe7-6014f54ffc8e'): \"Office\",\n", + " UUID('5aaa901a-7564-41fb-8eba-50cdd6fe9f80'): \"Outside\",\n", + "}\n", + "\n", + "with with_database(\"postgresql+psycopg2://apd@localhost/apd\") as session:\n", + " coros = []\n", + " figure = plt.figure(figsize = (20, 10), dpi=300)\n", + " configs = get_known_configs().values()\n", + " for i, config in enumerate(configs, start=1):\n", + " plot = figure.add_subplot(2, 2, i)\n", + " coros.append(plot_sensor(config, plot, location_names))\n", + " await asyncio.gather(*coros)\n", + "\n", + "display(figure)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "9f576f5bb8e34cb8ab1d3c09f770d027", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "interactive(children=(DatePicker(value=None, description='Start date'), DatePicker(value=None, description='En…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import asyncio\n", + "from uuid import UUID\n", + "import functools\n", + "from concurrent.futures import ThreadPoolExecutor\n", + "\n", + "import ipywidgets as widgets\n", + "from matplotlib import pyplot as plt\n", + "\n", + "from apd.aggregation.query import with_database\n", + "from apd.aggregation.analysis import get_known_configs, plot_sensor\n", + "\n", + "\n", + "def wrap_coroutine(f):\n", + " @functools.wraps(f)\n", + " def run_in_thread(*args, **kwargs):\n", + " loop = asyncio.new_event_loop()\n", + " wrapped = f(*args, **kwargs)\n", + " with ThreadPoolExecutor(max_workers=1) as pool:\n", + " task = pool.submit(loop.run_until_complete, wrapped)\n", + " return task.result()\n", + " return run_in_thread\n", + "\n", + "@wrap_coroutine\n", + "async def plot(*args, **kwargs):\n", + " location_names = {\n", + " UUID('53998a51-60de-48ae-b71a-5c37cd1455f2'): \"Loft\",\n", + " UUID('1bc63cda-e223-48bc-93c2-c1f651779d69'): \"Living Room\",\n", + " UUID('ea0683de-6772-4678-bfe7-6014f54ffc8e'): \"Office\",\n", + " UUID('5aaa901a-7564-41fb-8eba-50cdd6fe9f80'): \"Outside\",\n", + " }\n", + "\n", + " with with_database(\"postgresql+psycopg2://apd@localhost/apd\") as session:\n", + " coros = []\n", + " figure = plt.figure(figsize = (20, 10), dpi=300)\n", + " configs = get_known_configs().values()\n", + " for i, config in enumerate(configs, start=1):\n", + " plot = figure.add_subplot(2, 2, i)\n", + " coros.append(plot_sensor(config, plot, location_names, *args, **kwargs))\n", + " await asyncio.gather(*coros)\n", + " return figure\n", + "\n", + "\n", + "start = widgets.DatePicker(\n", + " description='Start date',\n", + ")\n", + "end = widgets.DatePicker(\n", + " description='End date',\n", + ")\n", + "out = widgets.interactive(plot, collected_after=start, collected_before=end)\n", + "display(out)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "60091fa7d9ea415985ec53937e422b4a", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "interactive(children=(DatePicker(value=None, description='Start date'), DatePicker(value=None, description='En…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from uuid import UUID\n", + "import ipywidgets as widgets\n", + "from apd.aggregation.analysis import interactable_plot_multiple_charts, configs\n", + "\n", + "location_names = {\n", + " UUID('53998a51-60de-48ae-b71a-5c37cd1455f2'): \"Loft\",\n", + " UUID('1bc63cda-e223-48bc-93c2-c1f651779d69'): \"Living Room\",\n", + " UUID('ea0683de-6772-4678-bfe7-6014f54ffc8e'): \"Office\",\n", + " UUID('5aaa901a-7564-41fb-8eba-50cdd6fe9f80'): \"Outside\",\n", + "}\n", + "\n", + "start = widgets.DatePicker(\n", + " description='Start date',\n", + ")\n", + "end = widgets.DatePicker(\n", + " description='End date',\n", + ")\n", + "\n", + "plot = interactable_plot_multiple_charts(location_names=location_names)\n", + "out = widgets.interactive(plot, collected_after=start, collected_before=end)\n", + "display(out)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "08a4d937037b45bda2298879df7f80b6", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "interactive(children=(DatePicker(value=None, description='Start date'), DatePicker(value=None, description='En…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import datetime\n", + "import uuid\n", + "import ipywidgets as widgets\n", + "from apd.aggregation.analysis import Config, configs, interactable_plot_multiple_charts, draw_map, get_map_cleaner_for\n", + "from apd.aggregation.database import DataPoint\n", + "from apd.aggregation.utils import merc_x, merc_y\n", + "\n", + "start = widgets.DatePicker(description='Start date')\n", + "end = widgets.DatePicker(description='End date')\n", + "\n", + "def get_literal_data():\n", + " # Get manually entered temperature data, as our particular deployment\n", + " # does not contain data of this shape\n", + " raw_data = {(53.8667, -1.3333): -1, (53.35, -2.2833): 1, (52.45, -1.7333): 3, (51.5, -0.1333): 6, (51.55, -2.5667): 5, (54.9667, -1.6167): -1, (55.9667, -3.2167): -1, (54.7667, -1.5833): 0, (53.7667, -0.3): 1, (53.7667, -3.0167): 0, (51.4833, -3.1833): 4, (53.2667, -3.5167): 2, (52.0833, -2.8): 2, (52.0167, -0.6): 2, (52.6667, 1.2667): 5, (50.4333, -4.9833): 9, (50.35, -4.1167): 10, (50.5167, -2.45): 9, (50.8333, -1.1667): 9, (56.45, -5.4333): 4, (57.5333, -4.05): -2, (54.5167, -3.6167): 3, (53.0833, 0.2667): 4, (51.35, 1.3333): 7, (50.8833, 0.3167): 8, (58.45, -3.1): 3, (50.1, -5.6667): 9, (58.2167, -6.3333): 4, (55.6833, -6.25): 7}\n", + " now = datetime.datetime.now()\n", + " async def points():\n", + " for (coord, temp) in raw_data.items():\n", + " deployment_id = uuid.uuid4()\n", + " yield DataPoint(sensor_name=\"Location\", deployment_id=deployment_id, collected_at=now, data=coord)\n", + " yield DataPoint(sensor_name=\"Temperature\", deployment_id=deployment_id, collected_at=now, data=temp)\n", + " async def deployments(*args, **kwargs):\n", + " yield None, points()\n", + " return deployments\n", + "\n", + "def draw_map_with_gb(plot, x, y, colour):\n", + " # Draw the map and add an explicit coastline\n", + " gb_boundary = [(-2.6653539699999556, 51.617254950000074), (-2.6937556629999335, 51.59617747600004), (-2.71312415299991, 51.58661530200004), (-2.760487433999913, 51.579779364000046), (-2.7980850899999155, 51.56350332200009), (-2.816395636999914, 51.555568752000056), (-2.8587133449999556, 51.546128648000035), (-2.943348761999914, 51.54254791900007), (-2.9562882149999155, 51.54572174700007), (-2.9641007149999155, 51.552801825000074), (-2.969471808999913, 51.559881903000075), (-2.9746801419999542, 51.56297435100004), (-2.984038865999935, 51.55853913000004), (-3.0102432929999168, 51.53803131700005), (-3.0156957669999542, 51.53164297100005), (-3.0737198559999115, 51.50836823100008), (-3.0764867829999503, 51.50775788000004), (-3.088937954999949, 51.505072333000044), (-3.105620897999927, 51.49721914300005), (-3.1317032539999445, 51.48041413000004), (-3.1350805329999503, 51.47630442900004), (-3.1409399079999503, 51.46478913000004), (-3.1453751289999445, 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51.49408600500004), (-2.716420050999943, 51.49957916900007), (-2.6961563789999445, 51.51264069200005), (-2.6078181629999335, 51.60736725500004), (-2.599598761999914, 51.611395575000074), (-2.5892227859999366, 51.61424388200004), (-2.580433722999942, 51.61863841400009), (-2.5768123039999296, 51.62750885600008), (-2.57445227799991, 51.63572825700004), (-2.568959113999938, 51.644964911000045), (-2.562245245999918, 51.65338776200008), (-2.556385870999918, 51.65924713700008), (-2.499175584999932, 51.69676341400009), (-2.464995897999927, 51.725897528000075), (-2.4477432929999168, 51.73187897300005), (-2.404896613999938, 51.74115631700005), (-2.3885391919999392, 51.750433661000045), (-2.380767381999931, 51.76190827000005), (-2.3833715489999463, 51.77326080900008), (-2.398060675999943, 51.782131252000056), (-2.4024145169999542, 51.76357656500005), (-2.4203995429999168, 51.753119208000044), (-2.4434301419999542, 51.74843984600005), (-2.4632869129999335, 51.747300523000035), (-2.482085740999935, 51.74286530200004), (-2.495838995999918, 51.73200104400007), (-2.507964647999927, 51.71857330900008), (-2.5221248039999296, 51.706366278000075), (-2.581695115999935, 51.681708075000074), (-2.5911352199999556, 51.67536041900007), (-2.600209113999938, 51.66559479400007), (-2.6653539699999556, 51.617254950000074)]\n", + " draw_map(plot, x, y, colour)\n", + " plot.plot(\n", + " [merc_x(coord[0]) for coord in gb_boundary],\n", + " [merc_y(coord[1]) for coord in gb_boundary],\n", + " \"k-\",\n", + " )\n", + "\n", + "country = Config(\n", + " get_data=get_literal_data(),\n", + " clean=get_map_cleaner_for(\"Temperature\"),\n", + " title=\"Country wide temperature\",\n", + " ylabel=\"\",\n", + " draw=draw_map_with_gb,\n", + ")\n", + "\n", + "out = widgets.interactive(interactable_plot_multiple_charts(configs=configs + (country, )), collected_after=start, collected_before=end)\n", + "display(out)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "apd.aggregation", + "language": "python", + "name": "apd.aggregation" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Ch09/chapter09-mapping.ipynb b/Ch09/chapter09-mapping.ipynb new file mode 100644 index 0000000..916e704 --- /dev/null +++ b/Ch09/chapter09-mapping.ipynb @@ -0,0 +1,254 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "datapoints = {\n", + " (53.8667, -1.3333): -1,\n", + " (53.35, -2.2833): 1,\n", + " (52.45, -1.7333): 3,\n", + " (51.5, -0.1333): 6,\n", + " (51.55, -2.5667): 5,\n", + " (54.9667, -1.6167): -1,\n", + " (55.9667, -3.2167): -1,\n", + " (54.7667, -1.5833): 0,\n", + " (53.7667, -0.3): 1,\n", + " (53.7667, -3.0167): 0,\n", + " (51.4833, -3.1833): 4,\n", + " (53.2667, -3.5167): 2,\n", + " (52.0833, -2.8): 2,\n", + " (52.0167, -0.6): 2,\n", + " (52.6667, 1.2667): 5,\n", + " (50.4333, -4.9833): 9,\n", + " (50.35, -4.1167): 10,\n", + " (50.5167, -2.45): 9,\n", + " (50.8333, -1.1667): 9,\n", + " (56.45, -5.4333): 4,\n", + " (57.5333, -4.05): -2,\n", + " (54.5167, -3.6167): 3,\n", + " (53.0833, 0.2667): 4,\n", + " (51.35, 1.3333): 7,\n", + " (50.8833, 0.3167): 8,\n", + " (58.45, -3.1): 3,\n", + " (50.1, -5.6667): 9,\n", + " (58.2167, -6.3333): 4,\n", + " (55.6833, -6.25): 7,\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "fig, ax = plt.subplots()\n", + "lats = [ll[0] for ll in datapoints.keys()]\n", + "lons = [ll[1] for ll in datapoints.keys()]\n", + "ax.plot(lons, lats, \"o\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "fig, ax = plt.subplots()\n", + "lats = [ll[0] for ll in datapoints.keys()]\n", + "lons = [ll[1] for ll in datapoints.keys()]\n", + "# Don't draw lines\n", + "ax.plot(lons, lats, \"o\")\n", + "# This is a map of the UK. Here, 1 degree latitude is \n", + "# 1.7x as far as 1 degree longitude\n", + "ax.set_aspect(1.7)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# Set up mercator transform from https://wiki.openstreetmap.org/wiki/Mercator#Python\n", + "# Thanks to Paulo Silva and the OSM contributors\n", + "import math\n", + "\n", + "def merc_x(lon):\n", + " r_major=6378137.000\n", + " return r_major*math.radians(lon)\n", + "\n", + "def merc_y(lat):\n", + " if lat>89.5:lat=89.5\n", + " if lat<-89.5:lat=-89.5\n", + " r_major=6378137.000\n", + " r_minor=6356752.3142\n", + " temp=r_minor/r_major\n", + " eccent=math.sqrt(1-temp**2)\n", + " phi=math.radians(lat)\n", + " sinphi=math.sin(phi)\n", + " con=eccent*sinphi\n", + " com=eccent/2\n", + " con=((1.0-con)/(1.0+con))**com\n", + " ts=math.tan((math.pi/2-phi)/2)/con\n", + " y=0-r_major*math.log(ts)\n", + " return y\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots()\n", + "\n", + "x = tuple(map(merc_x, lons))\n", + "y = tuple(map(merc_y, lats))\n", + "ax.plot(x, y, 'o')\n", + "ax.set_aspect(1.0)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots()\n", + "\n", + "x = tuple(map(merc_x, lons))\n", + "y = tuple(map(merc_y, lats))\n", + "\n", + "ax.tricontourf(x, y, tuple(datapoints.values()))\n", + "ax.plot(x, y, 'wo', ms=3)\n", + "ax.set_aspect(1.0)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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(-2.4434301419999542, 51.74843984600005), (-2.4632869129999335, 51.747300523000035), (-2.482085740999935, 51.74286530200004), (-2.495838995999918, 51.73200104400007), (-2.507964647999927, 51.71857330900008), (-2.5221248039999296, 51.706366278000075), (-2.581695115999935, 51.681708075000074), (-2.5911352199999556, 51.67536041900007), (-2.600209113999938, 51.66559479400007), (-2.6653539699999556, 51.617254950000074)]\n", + "fig, ax = plt.subplots()\n", + "\n", + "x = tuple(map(merc_x, lons))\n", + "y = tuple(map(merc_y, lats))\n", + "\n", + "ax.tricontourf(x, y, tuple(datapoints.values()))\n", + "ax.plot(x, y, 'wo', ms=3)\n", + "ax.plot(\n", + " [merc_x(coord[0]) for coord in gb_boundary],\n", + " [merc_y(coord[1]) for coord in gb_boundary],\n", + " 'k-'\n", + ")\n", + "ax.set_aspect(1.0)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "apd.aggregation", + "language": "python", + "name": "apd.aggregation" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Ch09/listing09-01-query_contextmanager.py b/Ch09/listing09-01-query_contextmanager.py new file mode 100644 index 0000000..5565acc --- /dev/null +++ b/Ch09/listing09-01-query_contextmanager.py @@ -0,0 +1,27 @@ +import contextlib +from contextvars import ContextVar +import functools +import typing as t + +from sqlalchemy import create_engine +from sqlalchemy.orm import sessionmaker +from sqlalchemy.orm.session import Session + +db_session_var: ContextVar[Session] = ContextVar("db_session") + +@contextlib.contextmanager +def with_database(uri: t.Optional[str] = None) -> t.Iterator[Session]: + """Given a URI, set up a DB connection, and return a Session as a context manager """ + if uri is None: + uri = "postgresql+psycopg2://localhost/apd" + engine = create_engine(uri) + sm = sessionmaker(engine) + Session = sm() + token = db_session_var.set(Session) + try: + yield Session + Session.commit() + finally: + db_session_var.reset(token) + Session.close() + diff --git a/Ch09/listing09-02-getdata.py b/Ch09/listing09-02-getdata.py new file mode 100644 index 0000000..1fb77d9 --- /dev/null +++ b/Ch09/listing09-02-getdata.py @@ -0,0 +1,15 @@ +import testing as t + +from apd.aggregation.database import DataPoint + + +# Partial code from apd.aggregation package + +async def get_data() -> t.AsyncIterator[DataPoint]: + db_session = db_session_var.get() + loop = asyncio.get_running_loop() + query = db_session.query(datapoint_table) + rows = await loop.run_in_executor(None, query.all) + for row in rows: + yield DataPoint.from_sql_result(row) + diff --git a/Ch09/listing09-03-count-datapoints.py b/Ch09/listing09-03-count-datapoints.py new file mode 100644 index 0000000..3be24f0 --- /dev/null +++ b/Ch09/listing09-03-count-datapoints.py @@ -0,0 +1,8 @@ +from apd.aggregation.query import with_database, get_data + +with with_database("postgresql+psycopg2://apd@localhost/apd") as session: + count = 0 + async for datapoint in get_data(): + count += 1 + print(count) + diff --git a/Ch09/listing09-04-plot.py b/Ch09/listing09-04-plot.py new file mode 100644 index 0000000..153aa47 --- /dev/null +++ b/Ch09/listing09-04-plot.py @@ -0,0 +1,17 @@ +from apd.aggregation.query import with_database, get_data + +from matplotlib import pyplot as plt + +async def plot(): + points = [ + (dp.collected_at, dp.data) + async for dp in get_data() + if dp.sensor_name=="RelativeHumidity" + ] + x, y = zip(*points) + plt.plot_date(x, y, "o", xdate=True) + +with with_database("postgresql+psycopg2://apd@localhost/apd") as session: + await plot() +plt.show() + diff --git a/Ch09/listing09-05-filtering.py b/Ch09/listing09-05-filtering.py new file mode 100644 index 0000000..7f567ef --- /dev/null +++ b/Ch09/listing09-05-filtering.py @@ -0,0 +1,13 @@ +from apd.aggregation.query import with_database, get_data + +from matplotlib import pyplot as plt + +async def plot(): + points = [(dp.collected_at, dp.data) async for dp in get_data(sensor_name="RelativeHumidity")] + x, y = zip(*points) + plt.plot_date(x, y, "o", xdate=True) + +with with_database("postgresql+psycopg2://apd@localhost/apd") as session: + await plot() +plt.show() + diff --git a/Ch09/listing09-06-multiplot.py b/Ch09/listing09-06-multiplot.py new file mode 100644 index 0000000..c628674 --- /dev/null +++ b/Ch09/listing09-06-multiplot.py @@ -0,0 +1,19 @@ +import collections + +from apd.aggregation.query import with_database, get_data + +from matplotlib import pyplot as plt + +async def plot(): + legends = collections.defaultdict(list) + async for dp in get_data(sensor_name="RelativeHumidity"): + legends[dp.deployment_id].append((dp.collected_at, dp.data)) + + for deployment_id, points in legends.items(): + x, y = zip(*points) + plt.plot_date(x, y, "o", xdate=True) + +with with_database("postgresql+psycopg2://apd@localhost/apd") as session: + await plot() +plt.show() + diff --git a/Ch09/listing09-07-more_filtering.py b/Ch09/listing09-07-more_filtering.py new file mode 100644 index 0000000..346da88 --- /dev/null +++ b/Ch09/listing09-07-more_filtering.py @@ -0,0 +1,21 @@ +async def get_deployment_ids(): + db_session = db_session_var.get() + loop = asyncio.get_running_loop() + query = db_session.query(datapoint_table.c.deployment_id).distinct() + return [row.deployment_id for row in await loop.run_in_executor(None, query.all)] + +async def get_data( + sensor_name: t.Optional[str] = None, + deployment_id: t.Optional[UUID] = None, +) -> t.AsyncIterator[DataPoint]: + db_session = db_session_var.get() + loop = asyncio.get_running_loop() + query = db_session.query(datapoint_table) + if sensor_name: + query = query.filter(datapoint_table.c.sensor_name == sensor_name) + if deployment_id: + query = query.filter(datapoint_table.c.deployment_id == deployment_id) + query = query.order_by( + datapoint_table.c.collected_at, + ) + diff --git a/Ch09/listing09-08-plot_with_helpers.py b/Ch09/listing09-08-plot_with_helpers.py new file mode 100644 index 0000000..fc0286a --- /dev/null +++ b/Ch09/listing09-08-plot_with_helpers.py @@ -0,0 +1,20 @@ +import collections + +from apd.aggregation.query import with_database, get_data, get_deployment_ids + +from matplotlib import pyplot as plt + +async def plot(deployment_id): + points = [] + async for dp in get_data(sensor_name="RelativeHumidity", deployment_id=deployment_id): + points.append((dp.collected_at, dp.data)) + + x, y = zip(*points) + plt.plot_date(x, y, "o", xdate=True) + +with with_database("postgresql+psycopg2://apd@localhost/apd") as session: + deployment_ids = await get_deployment_ids() + for deployment in deployment_ids: + await plot(deployment) +plt.show() + diff --git a/Ch09/listing09-09-async_groupby.py b/Ch09/listing09-09-async_groupby.py new file mode 100644 index 0000000..630a240 --- /dev/null +++ b/Ch09/listing09-09-async_groupby.py @@ -0,0 +1,67 @@ +import typing as t +from uuid import UUID + +from apd.aggregation.database import DataPoint +from apd.aggregation.query import get_data + + +async def get_data_by_deployment( + *args, **kwargs +) -> t.AsyncIterator[t.Tuple[UUID, t.AsyncIterator[DataPoint]]]: + """Return an Async Iterator that contains two-item pairs. + These pairs are a string (deployment_id), and an async iterator that contains + the datapoints with that deployment_id. + + Usage example: + + async for deployment_id, datapoints in get_data_by_deployment(): + print(deployment_id) + async for datapoint in datapoints: + print(datapoint) + print() + """ + # Get the data, using the arguments to this function as filters + data = get_data(*args, **kwargs) + + # The two levels of iterator share the item variable, initialise it with the + # first item from the iterator. Also set last_deployment_id to None, so the + # outer iterator knows to start a new group. + last_deployment_id: t.Optional[UUID] = None + try: + item = await data.__anext__() + except StopAsyncIteration: + # There were no items in the underlying query, return immediately + return + + async def subiterator(group_id: UUID) -> t.AsyncIterator[DataPoint]: + """Using a closure, create an iterator that yields the current + item, then yields all items from data while the deployment_id matches + group_id, leaving the first that doesn't match as item in the enclosing + scope.""" + # item is from the enclosing scope + nonlocal item + while item.deployment_id == group_id: + # yield items from data while they match the group_id this iterator represents + yield item + try: + # Advance the underlying iterator + item = await data.__anext__() + except StopAsyncIteration: + # The underlying iterator came to an end, so end the subiterator too + return + + while True: + while item.deployment_id == last_deployment_id: + # We are trying to advance the outer iterator while the underlying iterator + # is still part-way through a group. Speed through the underlying until we + # hit an item where the deployment_id is different to the last one (or, is not + # None, in the case of the start of the iterator) + try: + item = await data.__anext__() + except StopAsyncIteration: + # We hit the end of the underlying iterator: end this iterator too + return + last_deployment_id = item.deployment_id + # Instantiate a subiterator for this group + yield last_deployment_id, subiterator(last_deployment_id) + diff --git a/Ch09/listing09-10-new_get_data.py b/Ch09/listing09-10-new_get_data.py new file mode 100644 index 0000000..83c666b --- /dev/null +++ b/Ch09/listing09-10-new_get_data.py @@ -0,0 +1,35 @@ +import asyncio +import datetime +import typing as t +from uuid import UUID + +from apd.aggregation.database import DataPoint + + +async def get_data( + sensor_name: t.Optional[str] = None, + deployment_id: t.Optional[UUID] = None, + collected_before: t.Optional[datetime.datetime] = None, + collected_after: t.Optional[datetime.datetime] = None, +) -> t.AsyncIterator[DataPoint]: + db_session = db_session_var.get() + loop = asyncio.get_running_loop() + query = db_session.query(datapoint_table) + if sensor_name: + query = query.filter(datapoint_table.c.sensor_name == sensor_name) + if deployment_id: + query = query.filter(datapoint_table.c.deployment_id == deployment_id) + if collected_before: + query = query.filter(datapoint_table.c.collected_at < collected_before) + if collected_after: + query = query.filter(datapoint_table.c.collected_at > collected_after) + query = query.order_by( + datapoint_table.c.deployment_id, + datapoint_table.c.sensor_name, + datapoint_table.c.collected_at, + ) + + rows = await loop.run_in_executor(None, query.all) + for row in rows: + yield DataPoint.from_sql_result(row) + diff --git a/Ch09/listing09-11-database_fixtures.py b/Ch09/listing09-11-database_fixtures.py new file mode 100644 index 0000000..517f8df --- /dev/null +++ b/Ch09/listing09-11-database_fixtures.py @@ -0,0 +1,70 @@ +import datetime +from uuid import UUID + +from apd.aggregation.database import datapoint_table + +from alembic.config import Config +from alembic.script import ScriptDirectory +from alembic.runtime.environment import EnvironmentContext +import pytest + + +@pytest.fixture +def db_uri(): + return "postgresql+psycopg2://apd@localhost/apd-test" + + +@pytest.fixture +def db_session(db_uri): + from sqlalchemy import create_engine + from sqlalchemy.orm import sessionmaker + + engine = create_engine(db_uri, echo=True) + sm = sessionmaker(engine) + Session = sm() + yield Session + Session.close() + + +@pytest.fixture +def migrated_db(db_uri, db_session): + config = Config() + config.set_main_option("script_location", "apd.aggregation:alembic") + config.set_main_option("sqlalchemy.url", db_uri) + script = ScriptDirectory.from_config(config) + + def upgrade(rev, context): + return script._upgrade_revs(script.get_current_head(), rev) + + def downgrade(rev, context): + return script._downgrade_revs(None, rev) + + with EnvironmentContext(config, script, fn=upgrade): + script.run_env() + + try: + yield + finally: + # Clear any pending work from the db_session connection + db_session.rollback() + + with EnvironmentContext(config, script, fn=downgrade): + script.run_env() + + +@pytest.fixture +def populated_db(migrated_db, db_session): + datas = [ + { + "id": 1, + "sensor_name": "Test", + "data": "1", + "collected_at": datetime.datetime(2020, 4, 1, 12, 0, 1), + "deployment_id": UUID("b4c68905-b1e4-4875-940e-69e5d27730fd"), + }, + # Additional sample data omitted from listing for brevity’s sake + ] + for data in datas: + insert = datapoint_table.insert().values(**data) + db_session.execute(insert) + diff --git a/Ch09/listing09-12-parameterisation.py b/Ch09/listing09-12-parameterisation.py new file mode 100644 index 0000000..fba6e2b --- /dev/null +++ b/Ch09/listing09-12-parameterisation.py @@ -0,0 +1,34 @@ +import datetime +import pytest + + +class TestGetData: + @pytest.fixture + def mut(self): + return get_data + + @pytest.mark.asyncio + @pytest.mark.parametrize( + "filter,num_items_expected", + [ + ({}, 9), + ({"sensor_name": "Test"}, 7), + ({"deployment_id": UUID("b4c68905-b1e4-4875-940e-69e5d27730fd")}, 5), + ({"collected_after": datetime.datetime(2020, 4, 1, 12, 2, 1),}, 3), + ({"collected_before": datetime.datetime(2020, 4, 1, 12, 2, 1),}, 4), + ( + { + "collected_after": datetime.datetime(2020, 4, 1, 12, 2, 1), + "collected_before": datetime.datetime(2020, 4, 1, 12, 3, 5), + }, + 2, + ), + ], + ) + async def test_iterate_over_items( + self, mut, db_session, populated_db, filter, num_items_expected + ): + db_session_var.set(db_session) + points = [dp async for dp in mut(**filter)] + assert len(points) == num_items_expected + diff --git a/Ch09/listing09-13-configs.py b/Ch09/listing09-13-configs.py new file mode 100644 index 0000000..4b88d88 --- /dev/null +++ b/Ch09/listing09-13-configs.py @@ -0,0 +1,50 @@ +import dataclasses +import datetime +import typing as t + + +# Additional functions in main codebase + +@dataclasses.dataclass(frozen=True) +class Config: + title: str + sensor_name: str + clean: t.Callable[[t.AsyncIterator[DataPoint]], t.AsyncIterator[t.Tuple[datetime.datetime, float]]] + ylabel: str + + +configs = ( + Config( + sensor_name="RAMAvailable", + clean=clean_passthrough, + title="RAM available", + ylabel="Bytes", + ), + Config( + sensor_name="RelativeHumidity", + clean=clean_passthrough, + title="Relative humidity", + ylabel="Percent", + ), +) + + +def get_known_configs() -> t.Dict[str, Config]: + return {config.title: config for config in configs} + + +async def plot_sensor(config: Config, plot: t.Any, location_names: t.Dict[UUID,str], **kwargs) -> t.Any: + locations = [] + async for deployment, query_results in get_data_by_deployment(sensor_name=config.sensor_name, + **kwargs): + points = [dp async for dp in config['clean'](query_results)] + if not points: + continue + locations.append(deployment) + x, y = zip(*points) + plot.set_title(config['title']) + plot.set_ylabel(config['ylabel']) + plot.plot_date(x, y, "-", xdate=True) + plot.legend([location_names.get(l, l) for l in locations]) + return plot + diff --git a/Ch09/listing09-14-two_plots.py b/Ch09/listing09-14-two_plots.py new file mode 100644 index 0000000..e991f8c --- /dev/null +++ b/Ch09/listing09-14-two_plots.py @@ -0,0 +1,19 @@ +import asyncio + +from matplotlib import pyplot as plt + +from apd.aggregation.query import with_database +from apd.aggregation.analysis import get_known_configs, plot_sensor + +with with_database("postgresql+psycopg2://apd@localhost/apd") as session: + coros = [] + figure = plt.figure(figsize = (20, 5), dpi=300) + configs = get_known_configs() + to_display = configs["Relative humidity"], configs["RAM available"] + for i, config in enumerate(to_display, start=1): + plot = figure.add_subplot(1, 2, i) + coros.append(plot_sensor(config, plot, {})) + await asyncio.gather(*coros) + +display(figure) + diff --git a/Ch09/listing09-15-temperature_cleaner.py b/Ch09/listing09-15-temperature_cleaner.py new file mode 100644 index 0000000..305da0f --- /dev/null +++ b/Ch09/listing09-15-temperature_cleaner.py @@ -0,0 +1,51 @@ +import collections +import datetime +import typing as t + +from apd.aggregation.database import DataPoint + + +async def clean_temperature_fluctuations( + datapoints: t.AsyncIterator[DataPoint], +) -> t.AsyncIterator[t.Tuple[datetime.datetime, float]]: + allowed_jitter = 2.5 + allowed_range = (-40, 80) + window_datapoints: t.Deque[DataPoint] = collections.deque(maxlen=3) + + def datapoint_ok(datapoint: DataPoint) -> bool: + """Return False if this data point does not contain a valid temperature""" + if datapoint.data is None: + return False + elif datapoint.data["unit"] != "degC": + # This point is in a different temperature system. While it could be converted + # this cleaner is not yet doing that. + return False + elif not allowed_range[0] < datapoint.data["magnitude"] < allowed_range[1]: + return False + return True + + async for datapoint in datapoints: + if not datapoint_ok(datapoint): + # If the datapoint is invalid then skip directly to the next item + continue + + window_datapoints.append(datapoint) + if len(three_temperatures) == 3: + # Find the temperatures of the datapoints in the window, then average + # the first and last and compare that to the middle point. + window_temperatures = [dp.data["magnitude"] for dp in window_datapoints] + avg_first_last = (window_temperatures[0] + window_temperatures[2]) / 2 + diff_middle_avg = abs(window_temperatures[1] - avg_first_last) + if diff_middle_avg > allowed_jitter: + pass + else: + yield window_datapoints[1].collected_at, window_temperatures[1] + else: + # The first two items in the iterator can't be compared to both neighbours + # so they should be yielded + yield datapoint.collected_at, datapoint.data["magnitude"] + # When the iterator ends the final item is not yet in the middle + # of the window, so the last item must be explicitly yielded + if datapoint_ok(datapoint): + yield datapoint.collected_at, datapoint.data["magnitude"] + diff --git a/Ch09/listing09-16-chart_grid.py b/Ch09/listing09-16-chart_grid.py new file mode 100644 index 0000000..e59dcad --- /dev/null +++ b/Ch09/listing09-16-chart_grid.py @@ -0,0 +1,27 @@ +import asyncio +from uuid import UUID + +from matplotlib import pyplot as plt + +from apd.aggregation.query import with_database +from apd.aggregation.analysis import get_known_configs, plot_sensor + + +location_names = { + UUID('53998a51-60de-48ae-b71a-5c37cd1455f2'): "Loft", + UUID('1bc63cda-e223-48bc-93c2-c1f651779d69'): "Living Room", + UUID('ea0683de-6772-4678-bfe7-6014f54ffc8e'): "Office", + UUID('5aaa901a-7564-41fb-8eba-50cdd6fe9f80'): "Outside", +} + +with with_database("postgresql+psycopg2://apd@localhost/apd") as session: + coros = [] + figure = plt.figure(figsize = (20, 10), dpi=300) + configs = get_known_configs().values() + for i, config in enumerate(configs, start=1): + plot = figure.add_subplot(2, 2, i) + coros.append(plot_sensor(config, plot, location_names)) + await asyncio.gather(*coros) + +display(figure) + diff --git a/Ch09/listing09-17-sync_from_async.py b/Ch09/listing09-17-sync_from_async.py new file mode 100644 index 0000000..28cec82 --- /dev/null +++ b/Ch09/listing09-17-sync_from_async.py @@ -0,0 +1,10 @@ +import typing as t + +def add_number_from_callback(a: t.Callable[[], int], b: t.Callable[[], int]) -> int: + return a() + b() + +def constant() -> int: + return 5 + +print(add_number_from_callback(constant, constant)) + diff --git a/Ch09/listing09-18-wrap_coroutine.py b/Ch09/listing09-18-wrap_coroutine.py new file mode 100644 index 0000000..879a043 --- /dev/null +++ b/Ch09/listing09-18-wrap_coroutine.py @@ -0,0 +1,23 @@ +import asyncio +from concurrent.futures import ThreadPoolExecutor +import functools + + +def wrap_coroutine(f): + @functools.wraps(f) + def run_in_thread(*args, **kwargs): + loop = asyncio.new_event_loop() + wrapped = f(*args, **kwargs) + with ThreadPoolExecutor(max_workers=1) as pool: + task = pool.submit(loop.run_until_complete, wrapped) + return task.result() + return run_in_thread + +async def main() -> None: + print( + add_number_from_callback( + constant, wrap_coroutine(async_get_number_from_HTTP_request) + ) + ) + + diff --git a/Ch09/listing09-19-interactable.py b/Ch09/listing09-19-interactable.py new file mode 100644 index 0000000..c5f2573 --- /dev/null +++ b/Ch09/listing09-19-interactable.py @@ -0,0 +1,39 @@ +import asyncio +from uuid import UUID + +import ipywidgets as widgets +from matplotlib import pyplot as plt + +from apd.aggregation.query import with_database +from apd.aggregation.analysis import get_known_configs, plot_sensor, wrap_coroutine + + +@wrap_coroutine +async def plot(*args, **kwargs): + location_names = { + UUID('53998a51-60de-48ae-b71a-5c37cd1455f2'): "Loft", + UUID('1bc63cda-e223-48bc-93c2-c1f651779d69'): "Living Room", + UUID('ea0683de-6772-4678-bfe7-6014f54ffc8e'): "Office", + UUID('5aaa901a-7564-41fb-8eba-50cdd6fe9f80'): "Outside", + } + + with with_database("postgresql+psycopg2://apd@localhost/apd") as session: + coros = [] + figure = plt.figure(figsize = (20, 10), dpi=300) + configs = get_known_configs().values() + for i, config in enumerate(configs, start=1): + plot = figure.add_subplot(2, 2, i) + coros.append(plot_sensor(config, plot, location_names, *args, **kwargs)) + await asyncio.gather(*coros) + return figure + + +start = widgets.DatePicker( + description='Start date', +) +end = widgets.DatePicker( + description='End date', +) +out = widgets.interactive(plot, collected_after=start, collected_before=end) +display(out) + diff --git a/Ch09/listing09-20-genericised_plots.py b/Ch09/listing09-20-genericised_plots.py new file mode 100644 index 0000000..db8e1bc --- /dev/null +++ b/Ch09/listing09-20-genericised_plots.py @@ -0,0 +1,34 @@ +async def plot_multiple_charts(*args: t.Any, **kwargs: t.Any) -> Figure: + # These parameters are pulled from kwargs to avoid confusing function + # introspection code in IPython widgets + location_names = kwargs.pop("location_names", None) + configs = kwargs.pop("configs", None) + dimensions = kwargs.pop("dimensions", None) + db_uri = kwargs.pop("db_uri", "postgresql+psycopg2://apd@localhost/apd") + + with with_database(db_uri): + coros = [] + if configs is None: + # If no configs are supplied, use all known configs + configs = get_known_configs().values() + if dimensions is None: + # If no dimensions are supplied, get the square root of the number + # of configs and round it to find a number of columns. This will + # keep the arrangement approximately square. Find rows by multiplying + # out rows. + total_configs = len(configs) + columns = round(math.sqrt(total_configs)) + rows = math.ceil(total_configs / columns) + figure = plt.figure(figsize=(10 * columns, 5 * rows), dpi=300) + for i, config in enumerate(configs, start=1): + plot = figure.add_subplot(columns, rows, i) + coros.append(plot_sensor(config, plot, location_names, *args, **kwargs)) + await asyncio.gather(*coros) + return figure + +def interactable_plot_multiple_charts( + *args: t.Any, **kwargs: t.Any +) -> t.Callable[..., Figure]: + with_config = functools.partial(plot_multiple_charts, *args, **kwargs) + return wrap_coroutine(with_config) + diff --git a/Ch09/listing09-21-contours_and_scatter.py b/Ch09/listing09-21-contours_and_scatter.py new file mode 100644 index 0000000..a9ae67e --- /dev/null +++ b/Ch09/listing09-21-contours_and_scatter.py @@ -0,0 +1,14 @@ +fig, ax = plt.subplots() + +lats = [ll[0] for ll in datapoints.keys()] +lons = [ll[1] for ll in datapoints.keys()] +temperatures = tuple(datapoints.values()) + +x = tuple(map(merc_x, lons)) +y = tuple(map(merc_y, lats)) + +ax.tricontourf(x, y, temperatures) +ax.plot(x, y, 'wo', ms=3) +ax.set_aspect(1.0) +plt.show() + diff --git a/Ch09/listing09-22-get_data_config.py b/Ch09/listing09-22-get_data_config.py new file mode 100644 index 0000000..1d59534 --- /dev/null +++ b/Ch09/listing09-22-get_data_config.py @@ -0,0 +1,19 @@ +@dataclasses.dataclass +class Config: + title: str + clean: t.Callable[[t.AsyncIterator[DataPoint]], t.AsyncIterator[t.Tuple[datetime.datetime, float]]] + get_data: t.Optional[ + t.Callable[..., t.AsyncIterator[t.Tuple[UUID, t.AsyncIterator[DataPoint]]]] + ] = None + ylabel: str + sensor_name: dataclasses.InitVar[str] = None + + def __post_init__(self, sensor_name=None): + if self.get_data is None: + if sensor_name is None: + raise ValueError("You must specify either get_data or sensor_name") + self.get_data = get_one_sensor_by_deployment(sensor_name) + +def get_one_sensor_by_deployment(sensor_name): + return functools.partial(get_data_by_deployment, sensor_name=sensor_name) + diff --git a/Ch09/listing09-23-generic_config.py b/Ch09/listing09-23-generic_config.py new file mode 100644 index 0000000..05e930d --- /dev/null +++ b/Ch09/listing09-23-generic_config.py @@ -0,0 +1,36 @@ +import dataclasses +import typing as t +from uuid import UUID + +from apd.aggregation.database import DataPoint + + + +plot_key = t.TypeVar("plot_key") +plot_value = t.TypeVar("plot_value") + +@dataclasses.dataclass +class Config(t.Generic[plot_key, plot_value]): + title: str + clean: t.Callable[ + [t.AsyncIterator[DataPoint]], t.AsyncIterator[t.Tuple[plot_key, plot_value]] + ] + draw: t.Optional[ + t.Callable[ + [t.Any, t.Iterable[plot_key], t.Iterable[plot_value], t.Optional[str]], None + ] + ] = None + get_data: t.Optional[ + t.Callable[..., t.AsyncIterator[t.Tuple[UUID, t.AsyncIterator[DataPoint]]]] + ] = None + ylabel: t.Optional[str] = None + sensor_name: dataclasses.InitVar[str] = None + + def __post_init__(self, sensor_name=None): + if self.draw is None: + self.draw = draw_date + if self.get_data is None: + if sensor_name is None: + raise ValueError("You must specify either get_data or sensor_name") + self.get_data = get_one_sensor_by_deployment(sensor_name) + diff --git a/Ch09/listing09-24-custom_map_chart.py b/Ch09/listing09-24-custom_map_chart.py new file mode 100644 index 0000000..95b202c --- /dev/null +++ b/Ch09/listing09-24-custom_map_chart.py @@ -0,0 +1,36 @@ +def get_literal_data(): + # Get manually entered temperature data, as our particular deployment + # does not contain data of this shape + raw_data = {...} + now = datetime.datetime.now() + async def points(): + for (coord, temp) in raw_data.items(): + deployment_id = uuid.uuid4() + yield DataPoint(sensor_name="Location", deployment_id=deployment_id, + collected_at=now, data=coord) + yield DataPoint(sensor_name="Temperature", deployment_id=deployment_id, + collected_at=now, data=temp) + async def deployments(*args, **kwargs): + yield None, points() + return deployments + +def draw_map_with_gb(plot, x, y, colour): + # Draw the map and add an explicit coastline + gb_boundary = [...] + draw_map(plot, x, y, colour) + plot.plot( + [merc_x(coord[0]) for coord in gb_boundary], + [merc_y(coord[1]) for coord in gb_boundary], + "k-", + ) + +country = Config( + get_data=get_literal_data(), + clean=get_map_cleaner_for("Temperature"), + title="Country wide temperature", + ylabel="", + draw=draw_map_with_gb, +) + +out = widgets.interactive(interactable_plot_multiple_charts(configs=configs + (country, )), collected_after=start, collected_before=end) + diff --git a/Ch10/apd.aggregation-chapter10-ex01/.coveragerc b/Ch10/apd.aggregation-chapter10-ex01/.coveragerc new file mode 100644 index 0000000..e766c69 --- /dev/null +++ b/Ch10/apd.aggregation-chapter10-ex01/.coveragerc @@ -0,0 +1,3 @@ +[run] +branch = True +omit = tests/* diff --git a/Ch10/apd.aggregation-chapter10-ex01/.pre-commit-config.yaml b/Ch10/apd.aggregation-chapter10-ex01/.pre-commit-config.yaml new file mode 100644 index 0000000..d3b6ec7 --- /dev/null +++ b/Ch10/apd.aggregation-chapter10-ex01/.pre-commit-config.yaml @@ -0,0 +1,26 @@ +repos: + +- repo: local + hooks: + - id: black + name: black + entry: pipenv run black + args: [--quiet] + language: system + types: [python] + + - id: mypy + name: mypy + exclude: (?x)^( + (.*)/setup.py + )$ + entry: pipenv run mypy + args: ["--follow-imports=skip"] + language: system + types: [python] + + - id: flake8 + name: flake8 + entry: pipenv run flake8 + language: system + types: [python] diff --git a/Ch10/apd.aggregation-chapter10-ex01/CHANGES.md b/Ch10/apd.aggregation-chapter10-ex01/CHANGES.md new file mode 100644 index 0000000..8e424e5 --- /dev/null +++ b/Ch10/apd.aggregation-chapter10-ex01/CHANGES.md @@ -0,0 +1,5 @@ +## Changes + +### 1.0.0 (Unreleased) + +* Generated from skeleton diff --git a/Ch10/apd.aggregation-chapter10-ex01/Connect to database.ipynb b/Ch10/apd.aggregation-chapter10-ex01/Connect to database.ipynb new file mode 100644 index 0000000..98a74f0 --- /dev/null +++ b/Ch10/apd.aggregation-chapter10-ex01/Connect to database.ipynb @@ -0,0 +1,577 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "53635\n" + ] + } + ], + "source": [ + "from apd.aggregation.query import with_database\n", + "from apd.aggregation.database import datapoint_table\n", + "\n", + "with with_database(\"postgresql+psycopg2://apd@localhost/apd\") as session:\n", + " print(session.query(datapoint_table).count())" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "53635\n" + ] + } + ], + "source": [ + "from apd.aggregation.query import with_database, get_data\n", + "\n", + "with with_database(\"postgresql+psycopg2://apd@localhost/apd\") as session:\n", + " count = 0\n", + " async for datapoint in get_data():\n", + " count += 1\n", + " print(count)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from apd.aggregation.query import with_database, get_data\n", + "\n", + "from matplotlib import pyplot as plt\n", + "\n", + "async def plot():\n", + " points = [(dp.collected_at, dp.data) async for dp in get_data() if dp.sensor_name==\"RelativeHumidity\"]\n", + " x, y = zip(*points)\n", + " plt.plot_date(x, y, \"o\", xdate=True)\n", + "\n", + "with with_database(\"postgresql+psycopg2://apd@localhost/apd\") as session:\n", + " await plot()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from apd.aggregation.query import with_database, get_data\n", + "\n", + "from matplotlib import pyplot as plt\n", + "\n", + "async def plot():\n", + " points = [(dp.collected_at, dp.data) async for dp in get_data(sensor_name=\"RelativeHumidity\")]\n", + " x, y = zip(*points)\n", + " plt.plot_date(x, y, \"o\", xdate=True)\n", + "\n", + "with with_database(\"postgresql+psycopg2://apd@localhost/apd\") as session:\n", + " await plot()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import collections\n", + "\n", + "from apd.aggregation.query import with_database, get_data\n", + "\n", + "from matplotlib import pyplot as plt\n", + "\n", + "async def plot():\n", + " legends = collections.defaultdict(list)\n", + " async for dp in get_data(sensor_name=\"RelativeHumidity\"):\n", + " legends[dp.deployment_id].append((dp.collected_at, dp.data))\n", + "\n", + " for deployment_id, points in legends.items():\n", + " x, y = zip(*points)\n", + " plt.plot_date(x, y, \"o\", xdate=True)\n", + "\n", + "with with_database(\"postgresql+psycopg2://apd@localhost/apd\") as session:\n", + " await plot()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import collections\n", + "\n", + "from apd.aggregation.query import with_database, get_data, get_deployment_ids\n", + "\n", + "from matplotlib import pyplot as plt\n", + "\n", + "async def plot(deployment_id):\n", + " points = []\n", + " async for dp in get_data(sensor_name=\"RelativeHumidity\", deployment_id=deployment_id):\n", + " points.append((dp.collected_at, dp.data))\n", + "\n", + " if points:\n", + " x, y = zip(*points)\n", + " plt.plot_date(x, y, \"o\", xdate=True)\n", + "\n", + "with with_database(\"postgresql+psycopg2://apd@localhost/apd\") as session:\n", + " deployment_ids = await get_deployment_ids()\n", + " for deployment in deployment_ids:\n", + " await plot(deployment)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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O6ONrr3SfHJJDHSGHpnN/3rzJIlnUESssi1yr7iw5JIc6YoXl0Om4Vj1/ckgOdUQf51Bfzg+F0ZapUsraJK9o2fyXZzistWLhnfPs9o6W9bNKKZvm2Uan7W5ZP2eex7fu/21tjGU5++kka6as70mXqutPfJPY+o3ifOdi6/6XzOPYC5O8LE0Ib0wynGTbxPpLk/xhkjtLKf/vxPuMU5BDk+RQZ6ykHDqd1vfUH9daj3ao7b4kiybJos7o1ywyP7pIDk0yzzpj0XKI/iGHJsmhzljROVRKGSulfFsp5cdLKZ9L8o6WXV5Xa72+F2NbyuTQJDnUGSsph1yr7iBZNEkWdcZKyqLTcb16HuTQJDnUGf2aQ+YHsOz4jJ8kwzujX38ePRvXPTpEDk2SQ52xknLodFzzmAc5NEkOdUa/5pD50UVyaJJ51hn9eu2VLpJDk+RQZ6zoHHJ/3sLJokmyqDNWUha5Vt0hcmiSHOqMlZRDp+Na9TzIoUlyqDP6NYf6cn4ojLZ8/VaSs6esP5zkj85wzMaW9fvn02GtdX+SQy2bN8ynjS64uWX9u+d6YCnl/CQ7Wjb3+vksulLKziS/1rL57bXW1mqQndI6Dx+ptR6YZxutc7fTr9tYkp9L8uVSyuUdbrufyKGGHGqTHGqUUq5IcmXL5ve02+4KIIsasqhNfZ5F5kd3yaGGedamHuQQ/UMONeRQm1ZaDpVSNpZS6tQlyf4kX0vy/iRPm7L7/iQ/VWv9nR4MdTmQQw051KaVlkNz5Fr13MmihixqkyxquF69IHKoIYfa1Oc5ZH4Ay5HP+IYMb1Of/zx6oVz3mBs51JBDbZJDDdc8FkQONeRQm/o8h8yP7pJDDfOsTX1+7ZXukkMNOdSmlZZD7s/rOFnUkEVtWmlZNEeuVc+NHGrIoTbJoYZr1QsihxpyqE19nkN9OT8URluGSinPT/KvWjb/Sq31wTMc2lqt+OACum89Zt0C2uikz7Wsv7CUsmbWPWf6F7Ns6/XzWVSllJEkH8n05/3NJP+ui932ah4eTXJ1kl9N8iNJnpzmrzY9Kclzk/xOZn4x85gkny6lXLCAMfY1OTSNHGrDCsuhM2mtVP25WuutHWi3b8miaWRRG1ZAFpkfXSKHpjHP2tCjHKIPyKFp5FAb5NAp3ZfkV5JcWGt9d68HsxTJoWnkUBtWWA65Vt1hsmgaWdSGFZZFZ+J69TzIoWnkUBtWQA6ZH8Cy4jN+GhnehhXw8+ipXPfoIDk0jRxqwwrLoTNxzWMe5NA0cqgNKyCHzI8ukUPTmGdtWAHXXukSOTSNHGqDHDol9+fNgSyaRha1YYVlkWvVHSSHppFDbVhhOXQmrlXPgxyaRg61YQXkUF/OD4XRlplSyhOS/MeWzZ9K8u/ncHhrcLdWp5yL1uBubXOx/VWaap4nbEzyxjMdVEo5L8lrZ3losJSyujNDWxb+KMl3Tlk/luTHF/DXkOajF/PwV5M8qtb6fbXWN9daP1lr/Uqt9dZa6z/WWj9Ra/1/klyQ5LeT1CnHnp3kL0opZQHj7EtyaAY51J6VkkOnNfGF9I+1bFbd+zRk0QyyqD39nkXmRxfIoRnMs/b0IodY5uTQDHKoPXJodtuT/EySV5VS1vd6MEuNHJpBDrVnpeSQa9UdJotmkEXtWSlZdFquV8+PHJpBDrWn33PI/ACWDZ/xM8jw9vT7z6NPcN2jg+TQDHKoPSslh07LNY/5kUMzyKH29HsOmR9dIIdmMM/a0+/XXukCOTSDHGqPHJqd+/POQBbNIIvas1KyyLXqDpJDM8ih9qyUHDot16rnRw7NIIfa0+851JfzQ2G0ZaSUcn6aiTg1LO9I8mO11jr7Uae1WMd0Ta11X5K3t2x+bSnl1ac6ppRybpK/SbLhVM12aHhLWinlN5L885bNr6u1/v0iD6Xr83Dim9fW6t2z7Xeo1vq6JP+65aEnJ/ln8+mzX8mhmeTQwq2kHJqD5yY5a8r6nv/d3v3HWpMX9B3/fJctIIJSi0AVdSn+wlBSFFMBTTcUDNSAsqAQmoYVtLbYJrVt0pSadGsbTNNo7R+NUotiKdBSBEREQMSVVuOvZlGoW0oqawVd5JdF9vey3/5x7tN77px75sycHzNzzrxeyflj5p4zZ57zfJ/33Od7J9+b5A17fo+ToUWrtGh7c2iR8bF/OrTKONvehDrEEdGhVTq0vRl36NNJHr30eEwWc0DXJfnXST529rwvSfIDSd5XSvn6Ec5zknRolQ5tb04dMle9X1q0Sou2N6cWdWC+uiMdWqVD25tDh4wP4Fi4xq/S8O1N6BrvHr0jokOrdGh7c+pQB+Y8OtKhVTq0vTl0yPjYPx1aZZxtb0Id4ojo0Cod2t6MO+T+vB1p0Sot2t6cWmSuen90aJUObW9OHerAXHVHOrRKh7Y3hw6d6vi4euwToJtSysOT/EKSL17afWuSp9daP3b5q1Z8prG9zcp8zdc0jzmGlyd5Zs5XZixJfqSU8rwsVkd9bxYrcX7R2fP+ds4vfh9O8qilY91Za11Z6bOUck3Xk6m13tLr7EdQSvl7Wax6veyHa63/quPrr+n6Xpd8HpMfh7XWf1tK+eYkz17a/dIkr93n+xwbHWqlQz3p0IqXNLZfW2ttriJNtGgDLeppZi06+PiYCx1qpUM9jdwhjpQOtdKhnubcoVrrfUluueRLNyV5Uynl+5P8yyR/52z/lyZ5VynlKbXW9w9zltOkQ610qKc5d6gLc9XraVErLepJi1aYr+5Ah1rpUE8z65C5amDSXONbucb3NLOfR/dm3uNyOtRKh3rSoRXmPDrQoVY61NPMOmTOY090qJUO9TSzuVf2RIda6VBPc+6Q+/N2o0WttKinObeoC3PVl9OhVjrUkw6tMFfdgQ610qGeZtahk5urtjDaESilfEGSdyX5yqXdH0/ytFrrB3sc6iTDXWu9u5RyXZK3JXn80pe+8eyxziey+MbhHUv7/mTNcz/U45RKj+cOrpTy3Ul+uLH7R2ut/6DHYXb5PI5lHP5gLv5H9htKKQ+tta4bIydNh9rpUD86dFEp5UuSPL2x+5XbHu+UaVE7Lepnbi0aaHycPB1qp0P9TKBDHCEdaqdD/ehQu1rr7Un+binlniTfd7b785L8h1LK1235G4aOng6106F+dKgzc9UNWtROi/rRoovMV3ejQ+10qJ+5dchcNTBlrvHtXOP7mcA1/ljGoXmPJTrUTof60aGLzHl0o0PtdKifuXXInMd+6FA7HepnAh3iCOlQOx3qR4fauT9vPS1qp0X9aFFn5qqX6FA7HepHhy4yV92NDrXToX7m1qFTnKu+auwToF0p5fOTvDPJX1za/aksVrL8Hz0P938b21/Y81wenNVwT2Ig11o/kuTJSV6R5J4OL/mlJE9Mcltj/617PrVJKaX8jSQ/losx/ckk3zvgaTTH4YNKKZ/b8xgPb2wfYhz+Rhb/1q64X5KvOcD7TJ4OdaND3ejQpa7Pxe/JfrvW+t93ON5J0qJutKibubbI+NiNDnVjnHUzkQ5xZHSoGx3qRod6+SdJ/nBp+wlJnjbSuYxKh7rRoW50qBdz1Uu0qBst6kaLLnV9zFe30qFudKibuXbI+ACmyDW+Gw3vZiLX+KndG7OOeY8zOtSNDnWjQ5e6PuY8WulQNzrUzVw7ZHzsRoe6Mc66mUiHODI61I0OdaNDvbg/b4kWdaNF3WhRL+aqz+hQNzrUjQ5d6vqYq26lQ93oUDdz7dCpjQ8Lo01YKeUhSd6e5OuWdn86yTNqre/d4pDN1S+/rOfrm8//ZK31U5c+cwS11ttqrX8ryVdlMSHyS0k+nOSOJH+a5OYkP5XFKqp/tdZ6S5LHNg7zW4Od8MBKKS/IItLL/+5fk+S7hlxBv9b6iVz8D2KSfGnPwzTHYp+VXTuptd6X5P80dvf6ZucU6FA/OtROh1aVUkqS72zstrp3gxb1o0Xt5t4i42M7OtSPcdY3m3gKAAARjElEQVRuKh3iuOhQPzrUTof6qbXekeTNjd3PGONcxqRD/ehQOx3qx1z1OS3qR4vaadEq89Wb6VA/OtRu7h0yPoApcY3vR8PbTeUaP6V7Y9qY91jQoX50qJ0OrTLnsZkO9aND7ebeIeNjOzrUj3HWbiod4rjoUD861E6H+nF/3jkt6keL2mlRP+aqF3SoHx1qp0OrzFVvpkP96FC7uXfolMbH1WOfAJc7+200b0vyDUu7P5PkmbXW39jysDc3tr+85+v/QmP7d7c8j4OqtX4oycvPHps8qbH962uOWS7bfyxKKc9N8uosVqm+4r8kedHZf9h62cPncXMWK0xe8eVZHZ9tmmOxz2v7uKOx3VzR9aTp0PZ0aJUOrfXUJI9e2r4ri2+qOaNF29OiVVp07hDj41Tp0PZ0aNUEO8QR0KHt6dAqHdraBxrbff/NHDUd2p4OrdKhrc16rjrRol1o0SotWst8dQsd2p4OrdKhc+aqgbG5xm/PNX7VBK/xU7k3ZpNZz3vo0PZ0aJUOrWXOo4UObU+HVunQOXMe3enQ9nRo1QQ7xBHQoe3p0Cod2tqs789LtGgXWrRKi7ZmrlqHtqJDq3RoLXPVLXRoezq0SofOncJc9VWbn8LQSimfk+StSb5xafftSb6l1vqrOxz6/Y3tx5dSHtTj9U/ZcLyjcraq6lMbu395jHM5pFLKs5O8LhcXQnxzkhfWWj87zlmtjJ1mINc6+6bm8RuOty8Pa2x//EDvMzk6NAwd0qEkL25sv7HW+sktj3VytGgYWqRFG95nFuNjHR0axlzG2UQ7xMTp0DB0SIc6uKex/YBRzmIEOjQMHdKhDmY7V51o0VC0SItivnotHRqGDulQm7mMD2BYrvHDmEvDJ3qNn/zPo8/Mdt5Dh4ahQzoUcx5r6dAwdEiHNrzPLMbHOjo0jLmMs4l2iInToWHokA51MNv78xItGooWaVEH5qp16KB0SIdirnotHRqGDulQmymPDwujTUwp5YFJ3pLk2qXddyZ5dq31Pbscu9b6R0l+Z2nX1bl4cdjk2sb2z+9yPhPw1CTXLG3/cq31gyOdy0GUUv5aFitX/pml3T+X5Pm11nvHOaskydsb29f2eO035eJF6KZa60d3PqOGUsrDsrqK6x/u+32mSIcGpUPjGb1DpZSHJrmusfuVfY9zqrRoUFo0ntFb1MHJj491dGhQJz/OJtwhJkyHBqVDbPKoxvYhvu+aHB0alA6x1pznqhMtGpgWzZj56vV0aFA6RJuTHx/AsFzjB3XyDZ/wNX7yP4+e87yHDg1Kh8YzeofMeaynQ4PSofGM3qEOTn58rKNDgzr5cTbhDjFhOjQoHWKTWd6fl2jRwLSItcxV69BAdGjGzFWvp0OD0iHaTHZ8WBhtQkop90/yxiRPW9p9V5Jvq7X+4p7e5k2N7e/seG5fneQvL+26Lck793ROY/lHje1XjHIWB1JKeXqSn05y/6Xd70zy3Frr3eOc1f/3jiR3LG0/6WyMdXF9Y7s5pvflBbnYyI8muflA7zUZOjQ4HRrPFDr015M8cGn7liTv3vJYJ0WLBqdF45lCizY56fGxjg4N7qTH2cQ7xETp0OB0iE2+ubE9icn9Q9KhwekQbWY5V51o0Qi0aN7MV19ChwanQ7Q56fEBDMs1fnAn3fCJX+OP4efRs5z30KHB6dB4ptAhcx6X0KHB6dB4ptChTU56fKyjQ4M76XE28Q4xUTo0OB1ik9ndn5do0Qi0iDbmqs/p0OHo0LyZq76EDg1Oh2gz2fFhYbSJKKVcneT1SZ65tPueJM+rtb5jj2/1miSfXdq+rpTyFR1e1xzEr6+13rm/0xpWKeVFSZ6+tOu9Waz8eBJKKX8lyc/k4jdI787im4C7xjmrc7XW25O8obG7OcZWlFK+Mslzlnbdm+S1ezy1K+/ziCTf39j9s7XWuu/3mhIdGpYOjWsiHXpxY/snTr0zXWjRsLRoXBNpUdv7nPT4WEeHhnXq42zqHWKadGhYOsQmpZRvSfLExu6fGeNchqJDw9Ih2sx1rjrRoqFpETFfvUKHhqVDtDn18QEMyzV+WKfe8Klf44/g59GznPfQoWHp0Lgm0iFzHg06NCwdGtdEOtT2Pic9PtbRoWGd+jibeoeYJh0alg6xyRzvz0u0aGhaRBtz1To0BB0i5qpX6NCwdIg2Ux8fFkabgFLK/bII6rcu7b43yfNrrW/d53vVWj+Y5KeWdt0/yatKKQ9c85KUUr41F3/jzd1J/tk+z2tXZxe+rs+9LsmPL+26N8mLa6337v3ERlBKeVKStyb5nKXd70nyrFrrHZe/ahQ3ZPHNyRXXl1Keve7JZ2P0J3Nxhc5X1lr/d8trvqqU8qw+J1VKeWQWn98jlnbfneQH+xzn2OjQ7nTonA5tVkr5S0m+dmnXfUle1fc4p0aLdqdF57To0tcaHxvo0O6Ms3NH1CEmRId2p0PndOhcKeWJpZTnbH7myuu+PsmrG7vfU2t9337ObHp0aHc6dE6Hzpmr7keLdqdF57RoM/PVq3Rodzp0TodWGR/AWFzjd6fh547oGn9D3KM3GTq0Ox06p0ObmfNYpUO706FzOnTpa42PDXRod8bZuSPqEBOiQ7vToXM6dM79ef1o0e606JwWnTNX3Z0O7U6HzunQZuaqV+nQ7nTonA6tOrXx0fkPw0H9RJLvaOx7WZKbSinX9DzWrR1WmvynWfwGmz97tv3kJO8qpXxXrfV/XnlSKeUBSf5mkh9qvP6Haq2/3+VkSimPyuXj7JGN7atb/qyfqbV+fMNbva+U8nNJfjrJr9da77vkXB6X5B8neWHjSy+rtd604fh7cejPo5TyhCQ/n+TBS7s/kOR7kzy8lNLndO+std7a5wV91Fp/r5Tyb5L8w6Xdbyil/P0k/67WeveVnaWUxyb591mM1Ss+kc3fQPz5JG8ppbwvyX9M8qazb15WlFIekuRFWazs/YjGl/9FrfX3OvyxjpkO6ZAOLey7Q+u8pLH9jlrrH2x5rFOiRVqkRQuHatFRjI+R6ZAOza5DyXDjo5Ty4CQPW/Pl5oTyw1re68NTmlzbMx3SIR26aF/j41FJ3lhKeX8WP0B7c5IP1Hr5b1kqpXxNku9J8tLGed15tu+U6ZAO6dBF+xof5qr70SIt0qKL9j0+msxXr9IhHdKhi2Y5PoCT5Bo/k4a7xp9zj97k6JAO6dCCe/TGo0M6pEML7s8bjw7p0Ow6lLg/b2J0SId06CL3541Di7RIiy5yj97wdEiHdOgi9+cNT4d0SIcumuX46KzW6jHyI0nd4+Paju95bZK7Gq+9L8lvJvnPSd6e5I8vOf7PJrlfjz/bLXv4M72qw/t8fOn5f5rkV7P4R/qaJO9sOY9/PvDf9UE/jyx+o9G+xtKNA3we90vytkve+6NZXIBen+S3zsbm8tfvSvJNHcd589h/kuS/ZTHB9uokbzp7j3vWfA6vGLsRA41NHdIhHTpAh9a85wOyuFFi+XjPHbMBU3nscexokRbdsMexdOMAn8cgLTqW8THmY4/jRocmPs4O/Xnk+Do01Pi4fk+fyTVj9+KAfxc6pEM6dJjP49suef6nz8bHW7K4AeL1Sd6V5NY1x789ydPG7sQAfxc6pEM6dJjP49pLnm+uev3npUVapEUHHB+N9zRfffnnokM6pEPGh4eHxwk+9thk1/iJN/zQn0eO7xrvHr2JPPY4bnRIh27Y41i6cYDPwz16E3nscdzokA7dsMexdOMAn4f78yby2OO40aGJj7NDfx45vg4NNT6u39Nncs3YvTjg34UO6ZAOHebzcH9ev78PLdIiLTrM53HtJc83V335Z6VDOqRDBxwfjfc0V33556JDOqRDxkfnx2UryTEDtdYbSynPSfKqJF94trskeeLZ4zKvS/LdtdbPHv4Md/LgJE/a8JxPJXlprfU/DXA+rFFr/Wwp5Tuy+M1Kz1/60sOTPGPNy/44yYtqrf91y7f9/CRP6fC825J8X631x7d8HzbQIR2agpE69JwkX7C0/bEsJvoZgRZp0RSM1CLjYyJ0yDiDsemQDs3YQ7J5fFzxa0m+p9b6Owc8n9nSIR2aMXPVE6JFWjRj5qsnQod0aMaMD+CkucZr+BS4R2/edEiHpsA9evOmQzo0Be7PmzcdMs5gbDqkQzPm/rwJ0SItmjFz1ROhQzo0Y+aqJ0KHdGjGjn58XDX2CTCeWuvbkjwuyY9lMVDX+bUkz6u1vrDWetsgJ9ffjyS5KYtVOdv8QZIfSPKYqf6jnJta62dqrS9I8u1ZjLV1PpnkR5M8rtb69o6HvznJy5P8SpI7Or7mfyV5WRa/4cR/Yg9Mh3RoCg7cocu8pLH96lrrPTscjx1pkRZNwUAtMj4mSoeMMxibDunQDLw7i9+K+7okH+74mtuTvCHJs5I82U1Xh6VDOjQD5qqPgBZp0UyZr54QHdKhGTE+gFlxjdfwKXCP3rzpkA5NgXv05k2HdGgK3J83bzpknMHYdEiHZsD9eUdAi7RoBsxVT5wO6dBMmaueEB3SoRk5qfFRaq1jnwMTUEq5fxarHn9ZkkdmsbrxR5LcVGv90Jjn1kcp5fOSPCHJo7NYqfOBWfwH5iNJfrvW+rsjnh4dlFIeneRrk3xRks9NcmuS30/yK7XWu3c47lVJviLJY5J8cZKH5nx8fCrJHyX5zVrrx3b6A7A1HWIqDtUhjoMWMRWHbJHxMW06BIxNh5iDUsojkjw2i3H+55I8KMk9ST6d5BNJ3p/kA0fwm31Okg5x6sxVHwctAsamQ8yB8QHMkWs8U+EevfnSIabCPXrzpUNMhfvz5kuHgLHpEHPg/rzp0yJOnbnq6dMhYGw6xBycyviwMBoAAAAAAAAAAAAAAAAAAAAAAAAwuqvGPgEAAAAAAAAAAAAAAAAAAAAAAAAAC6MBAAAAAAAAAAAAAAAAAAAAAAAAo7MwGgAAAAAAAAAAAAAAAAAAAAAAADA6C6MBAAAAAAAAAAAAAAAAAAAAAAAAo7MwGgAAAAAAAAAAAAAAAAAAAAAAADA6C6MBAAAAAAAAAAAAAAAAAAAAAAAAo7MwGgAAAAAAAAAAAAAAAAAAAAAAADA6C6MBAAAAAAAAAAAAAAAAAAAAAAAAo7MwGgAAAAAAAAAAAAAAAAAAAAAAADA6C6MBAAAAAAAAAAAAAAAAAAAAAAAAo7MwGgAAAAAAAAAAAAAAAAAAAAAAADA6C6MBAAAAAAAAAAAAAAAAAAAAAAAAo7MwGgAAAAAAAAAAAAAAAAAAAAAAADA6C6MBAAAAAAAAAAAAAAAAAAAAAAAAo7MwGgAAAAAAAAAAAAAAAAAAAAAAADA6C6MBAAAAAAAAAAAAAAAAAAAAAAAAo7MwGgAAAAAAAAAAAAAAAAAAAAAAADA6C6MBAAAAAAAAAAAAAAAAAAAAAAAAo7MwGgAAAAAAAAAAAAAAAAAAAAAAADA6C6MBAAAAAAAAAAAAAAAAAAAAAAAAo7MwGgAAAAAAAAAAAAAAAAAAAAAAADA6C6MBAAAAAAAAAAAAAAAAAAAAAAAAo7MwGgAAAAAAAAAAAAAAAAAAAAAAADA6C6MBAAAAAAAAAAAAAAAAAAAAAAAAo7MwGgAAAAAAAAAAAAAAAAAAAAAAADA6C6MBAAAAAAAAAAAAAAAAAAAAAAAAo7MwGgAAAAAAAAAAAAAAAAAAAAAAADA6C6MBAAAAAAAAAAAAAAAAAAAAAAAAo7MwGgAAAAAAAAAAAAAAAAAAAAAAADA6C6MBAAAAAAAAAAAAAAAAAAAAAAAAo7MwGgAAAAAAAAAAAAAAAAAAAAAAADA6C6MBAAAAAAAAAAAAAAAAAAAAAAAAo7MwGgAAAAAAAAAAAAAAAAAAAAAAADA6C6MBAAAAAAAAAAAAAAAAAAAAAAAAo7MwGgAAAAAAAAAAAAAAAAAAAAAAADA6C6MBAAAAAAAAAAAAAAAAAAAAAAAAo7MwGgAAAAAAAAAAAAAAAAAAAAAAADC6/wfh0VgquWOTFwAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import asyncio\n", + "\n", + "from matplotlib import pyplot as plt\n", + "\n", + "from apd.aggregation.query import with_database\n", + "from apd.aggregation.analysis import get_known_configs, plot_sensor\n", + "\n", + "with with_database(\"postgresql+psycopg2://apd@localhost/apd\") as session:\n", + " coros = []\n", + " figure = plt.figure(figsize = (20, 5), dpi=300)\n", + " configs = get_known_configs()\n", + " to_display = configs[\"Relative humidity\"], configs[\"RAM available\"]\n", + " for i, config in enumerate(to_display, start=1):\n", + " plot = figure.add_subplot(1, 2, i)\n", + " coros.append(plot_sensor(config, plot, {}))\n", + " await asyncio.gather(*coros)\n", + "\n", + "display(figure)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import asyncio\n", + "\n", + "from matplotlib import pyplot as plt\n", + "\n", + "from apd.aggregation.query import with_database\n", + "from apd.aggregation.analysis import get_known_configs, plot_sensor, Config\n", + "\n", + "async def clean_magnitude(datapoints):\n", + " async for datapoint in datapoints:\n", + " if datapoint.data is None:\n", + " continue\n", + " yield datapoint.collected_at, datapoint.data[\"magnitude\"]\n", + "\n", + "TemperatureConfig = Config(\n", + " sensor_name=\"Temperature\",\n", + " clean=clean_magnitude,\n", + " title=\"Ambient temperature\",\n", + " ylabel=\"Degrees C\",\n", + ")\n", + " \n", + "with with_database(\"postgresql+psycopg2://apd@localhost/apd\") as session:\n", + " coros = []\n", + " figure = plt.figure(figsize = (20, 5), dpi=300)\n", + " configs = get_known_configs()\n", + " to_display = configs[\"Relative humidity\"], TemperatureConfig\n", + " for i, config in enumerate(to_display, start=1):\n", + " plot = figure.add_subplot(1, 2, i)\n", + " coros.append(plot_sensor(config, plot, {}))\n", + " await asyncio.gather(*coros)\n", + "\n", + "display(figure)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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Nb3xj1PGf/umfxqJFiwqN56KLLipULyJi27Zt8dWvfnX4+JBDDol3vetdhdsfd9xxcdJJJw0ff/e73y3cFgAAaC+DaaBh1+pL4ze791bYAM9ojbpHfam3Kf02Sl+58vgrfV8BABBx88brc8+ZQz2v3jdt5c1L65U3P68UElZNq83bJ6LSZ6j3PgAAQLuxPu951ucBwOSkZ5+ocrLen5ACAAAAAEB7qf6aLWYawWgR/zMirq6zzaER8Z0G9Q9Nd9ttt406/sM//MPCbY8++ug49thjh99o2dvbG3fffXcsX768oWOcjLlz58app55auP5RRx0Vjz76aEREbN++PbZu3Rrz588fV++OO+4YdXz22WcX7mPFihWx//77x1NPPVWz7m233TbqbZRvfvObCy0iG+k1r3lN3HrrrRER8dhjj8Xq1avjhS98YV3XAAAAZr68YLQ50RFdpa5x5SkiesvbK7bprRAgUC2sa25pXrFBtomB1B+DaXBcecOCF+q8TqP6bZS88QymwRhMA9GRdU7ziAAAWte2oS3x88235p5vtblec9W38a9RgV15gWaNCkbLe2ZrlhSpymce//2YV7cUpegu9Ywrr1QGAAAzmfV5o1mfBwCTsLZKMNrQ0PSNAwAAAAAAWpBgtPayaczx0noaZ1k2P8YHlm0s0M+8LMv2SCltq6O7ZQX6qVtKaU1ErKmnTZZljegaWsKGDRvi4YcfHj5evHhxHHXUUXVdY/ny5cMLryIi7rrrrpZaeHXooYdGV1fxzSJLliwZdbxp06aKC6/+4z/+Y/jvixcvjsMOO6yucb3qVa8q9HbIsQvj9t9//+GFYUWN/fy//vWvLbwCAIBZqFJQV0TE8sWnx1v2+ZNx5SmleO8DvxflKI87V2mDf95m96Wd+8VHD/mHOkc7s3137T/HDevHZ/Hn3aN65QUsdGQdUxrI1ijV7kNfuTc65ghGAwDY7SebfhQDqT/3fKPCvdpBSvUGo01tcHG9188Lpj5lyZnxe8v+uO5xTZXe8o54/4NvqXiu0vdj3n04ao9j4rwDL27o2AAAoNVYnzee9XkAMAlrn8w/l8av7QAAAAAAgNlEMFp7eXDM8YvqbD+2/vqU0oaxlVJKz2VZtiEiRq5meGFE3D+JvsaOHZiAtWvXjjo+/PDD6w7/O/LII0cdr1lTV9bglBu7kKqWzs7Rm68HBgbG1dm2bVv09j6/iWMii5iKtnn88cdHHb/vfe+L973vfXX3N9L69esn1R4AAJiZBtP455uIiM6scghVlmXRXZobO8rjs+3r2ezeUxqbq9/+8j5zX2pM8EJesNjCOUti/eDaceWtFpZRLSCir9wbe8xZMI2jAQBoXeVUjls33lC1TquF4M4kjQguTik1MBit8ni6W+yZqivrjiyySDE+iK7SZ857Hmm1zwUAAFPB+rzxrM8DgEnY9Fz+ufLQ9I0DAAAAAABakGC09jI2mKy+16lFHDLmeFWNvka+ou6wCv3X01c9bdvb0gMj+7dfNXsU7W/pgc0ewZTYsGF0luGiRYvqvsbYNq22qKdUKjX8mhs3bhx1vGBB/Ru2Fy5cWKjec89V+QX2BG3ZsqXh1wQAAFpfXjDanCz/R37dpZ6cYLR6Nrv3FBxh+8j7zI0KKMsLWFjUsWdOMFprhWX0pfz70GpjBQBopvu33RtrB56pWsf86XmVgrqqacS9G0j9kaJc8Vy9wWt59eeW5tU9rqlUykrRlXVXDH6u9Bny7vNsfFYEAGYo6/OmTxuu0bM+b2KszwOAHNs2558bEowGAAAAAMDsJhitvfznmOOXZVk2L6W0vWD7E2tcb+y5kcFoJ0TEdUU6ybJsj4h4WR19zSpZR0fEfgc1exjMUCmN3iBS79soK2nENVpdd3f3qOP+/v66r1G0zUSuXcvYrzsAADA75AWjdWSduW26S3MrltcXjFb5Gu2snvtWr5RS7nUWdiypWF5vIMNUq3YfWm2sAADNdPPG62vWEYz2vLxgtFKUolwhvKxaYG9R1cKPKwWHVZM3F27FZ6ru0tzoG6rwXFjhM+fdh+6s9T4XAEAl1ucxGdbnTYz1eQCQo6/KzzRT5Rc4AAAAAADAbCEYrY2klJ7OsuwX8XzoWEdErIiIHxa8xCljjqutzL8hIt5ZpW01J8Xo7717U0rP1tEeyLHnnnuOOt60aVPd1xjbZsmSypuwJ2Ooxd5gNfYzjn2zZxFF39y59957jzq+44474oQTTqi7PwAAgME0WLG8WjBaT84G/Eob9vM38fcUGF17yfvMjQj9GkyDUY7Kz8kLOxZXLG+1sIxq46kWLAEAMJus6382/mvb3TXrNSLcq911l+bGjvK2ceWNmCc3cm7bW678/rKe0ry6rjMdekpzY/PQ+N+P1fOsmPe8CQAA7cT6vImxPg8AJqDcWv8/BwAAAACA6VZq9gBouGvGHL+tSKMsy46MiN8cUbQtqgeq/SAiRq54PmHXNYp465jjsWMGJmjp0qWjjh944IG6r/GrX/1q1PGyZcsq1uvoGJ2tOThYeUN+JRNZ2DSV5syZEwcccMDw8a9//evYvr3yZpU8K1euLFRvn332GXU8ka8RAABARMRgGqhYXi0YLS/gq9Lm/7xAgNm42T3vM/eVeyOlNKlrVwu+WDSn8maoVgsbqxYQ12ohbgAAzXLLxusjRe2542AazJ3rzzZ596uewOd6VbtGb51z27x5e08Lhk034llxNoZoAwAw+1ifNzHW5wHAeKmvxs8zy+XpGQgAAAAAALQowWjt558jYuSrYd6UZdnhBdp9cMzxv6WUcld2p5S2R8Q3a1xjnCzLXhwRvzuiaDAi/qXA+IAClixZEoceeujw8caNG+P++++v6xp33HHHqOPjjjuuYr2FCxeOOt64cWPhPv7rv/6rrjFNh+OPP3747+VyOW6++ebCbdevXx//8R//Uaju8uXLRx3/8IfVMigBAADyDabKG2A6so6K5RH1BQjkBQLMxs3ueZ85RTkGUv+krl0t5GxRx56V26TeKKfWWQRdLfysEeEUAAAzXX+5L36y6UeF6wuX3SUnhLhacPFkVbtGPQHFA+WB3Ge2ntK8usc11fKD0cZ/5rz7MBufFQEAmH2sz5s46/MAYIwtNYJMy0PVzwMAAAAAQJsTjNZmUkoPRsSVI4q6IuKKLMtyVyFnWfbGiHjriKL+iPhoge4ujYiRryt/a5Zlv1Oln56I+NquMe12eUrp4QJ9AQWtWLFi1PE///M/F257//33x9133z183NPTE6985Ssr1h37pspVq1YV7uf73/9+4brT5fTTTx91/JWvfKVw2yuvvDL6+4tthD/ttNNizpw5w8ff/e53Y82aNYX7AgAA2G0oNxitM7dNd86PiPpSpc3ulQMB8kII2lm1zzzZ4K9qwQsLOxbnnutPfZPqt5GqBUT05b97AQBg1rhny+2xrbylcH3BaDtVjkXLDxZrSDBalfnrQOqPciq2GbHaHLkVn6m6c8Pmiodot+LnAgCAqWB93sRYnwcAY/zq3urnh1rnZWkAAAAAANAMgtGmWZZlB2ZZdtDYPxGx75iqHZXq7fqzd41uLomIka+PWR4R/yfLsiPHjKU7y7L3RsTVY9p/NqX0WK3PklL6dUT8f2OKv5ll2Z9mWTYy/CyyLDsqIn60ayy7PRfFAtiAOpx77rmjjr/whS/EM888U6jthz/84VHHf/AHfxDd3d0V6x577LGjjq+77rpCffzgBz+IO++8s1Dd6XTOOefEggULho+vueaa+MEPflCz3ZNPPhl/9Vd/VbifJUuWxDnnnDN8vHXr1rjgggvqGywAAEBEDKaBiuUdWUdum+5STjBahQCBvI383dns2+yed98iqgceFFEtvGFRx5Ip67eRqoVHCPUAAIi4eeP1ddU3h9opReWNf/nPNZOfI9cKPi76tal2nbxgt2aq71mx8j2o9twEAADtxPq8ibE+DwBGS1+5pEYFwWgAAAAAAMxugtGm320R8UiFP98YU++AnHqPRMRnqnWQUnoiIt4UESNfj3ZiRKzKsuyuLMuuyrLshoh4PCL+LiI6R9T794i4qI7P86GIGLmSvzMi/j4iHs+y7Posy/4ty7KfR8R/xehQtP6I+N2U0tN19AUUcOqpp8YxxxwzfLxp06Z4y1veEjt2VN/I8fnPfz6+853vDB9nWRZ//ud/nlv/hBNOiHnznt+4cc0118TPf/7zqn08+OCD8Ud/9Ee1PkJTLFiwIM4///xRZWeffXbceOONuW0effTReO1rXxsbN26sq69LL7101IK2f/qnf4oPfvCDMTQ0VNd1Vq1aFbfccktdbQAAgPaRH4zWWbE8IqK7VDnUrFKAQF7Y1Wzc7J533yJqByfUkhcqkEUW8+csqrtdM1QbSysFuAEANMOjOx6Mx3ofrKvNZOeY7SLllPfkPtdMfo5ca/5aPBhte+65vPE3U9FgtJRSfoh2C34uAACYCtbnTYz1eQAwxvpnq58v1/f/LQAAAAAAaDeC0dpUSummiPjdiFg7ojiLiFdFxNkR8fqIWDqm2Tci4g9SSoV/g7Kr7tkRcdWYU8si4oyI+G8R8cpdfe+2JiLemFK6tWg/MJs888wz8eijj07oz26XX355dHV1DR/fdNNNcdJJJ8XPfvazcf2tW7cuzjvvvHj/+98/qvzCCy+Ml73sZbnjXLBgQfz+7//+8PHQ0FC84Q1viB/+8Ifj6vb398dXvvKVOP744+PZZ5+NJUuW1HNLps1FF10UL33pS4ePN2/eHKeddlqcffbZ8c1vfjN+8YtfxC9/+cv44Q9/GO973/vi6KOPjvvvvz96enrijW98Y+F+Dj744Pjyl788quxv/uZvYsWKFXHdddfF4OBgbttHH300Lrvssjj11FPj6KOPjh//+Mf1f1AAAKAtDOQGo3Xktim62T0iP4xhNm52787yP/Nkwxfy73NP1cCEVgnLGEqDuSF9EYLRAABu2fj9utuYQ+1WORqtpzSvYnlfmvx9qzXPzguQruc6rfhMlffsMfZzDKaBKEe5Yt1W/FwAAFCJ9XnNY30eAOyUhoYiNq+vXqlc+edwAAAAAAAwW+TvkmTGSyl9P8uy34iIj0bE70dE3kqHn0bEZ1JK35pgP1sj4g+yLPtmRPy/EXF8TtX1sTNA7ZKU0tqcOjDrveUtb5lw25R2bhA59thj4wtf+EL8yZ/8SZR3/VL07rvvjuOPPz4OO+ywOProo6Onpycef/zxuPPOO8ct9Hnta18bH/vYx2r297GPfSyuueaa4TcyrlmzJl7/+tfHYYcdFi972cuiu7s7nn322fjZz34W27Zti4iIfffdNz71qU+15Jspu7q64nvf+16ceuqp8dBDD0XEznt69dVXx9VXX12xTZZlcdlll8Xq1avHvdGzmnPPPTeeeeaZ+PCHPzz8NfrpT38av/M7vxPz5s2LV7ziFbHPPvvE3LlzY8uWLbFu3bpYtWpV3W+/BAAA2tdQqrxpoyPrzG1TdLN7RH7gV7WwrnbVWeqMOdERQzH+nk82tCKvfXdpbm6Q3c52kwtka5Ra42iVcQIANMPWwc3x8y23VTx35LyXxyM7flUxaMscaqdUZzDa4K7Q3mrPRLXUnt8Wm//3lrdXLO/IOqKzNPHxTZW8MOix35/V7k93lv/8AgAArcT6vOaxPg8Adnnu6Yih/KDOiIgoD03PWAAAAAAAoEUJRptmKaWDprm/NRHx7izLzo+IEyPiRRGxb0Rsi4gnI+LelNIjDerrmxHxzSzLDo6IYyNi/4jYIyKeiYjHIuL2lFJ/I/oCanvHO94RS5Ysibe97W2xdevW4fKHHnpoeFFRJX/8x38cX/ziF6Ozs/amjAMOOCC+9a1vxVlnnRVbtmyp2cfBBx8c3/ve9+LZZ5+t89NMnxe84AVx6623xnve85645pprqtbda6+94sorr4w3vOEN8cEPfnDUuQULFtTsa/dbP9/2trfFM888M1y+ffv2uP322wuNt1Xf7gkAAEy9wTRQsbxaCEB3TqjZ2M3tKaXcDe/VwrraWU9pbmwrbxlXXinIoh659znriTlZR3RknRW/1pXC7Jqh1jiEegAAs9kdm/5P7rz95MW/FU/1rY6+oQrBaJOcY7aNyrloVcOa+8q90TFn6oLRis7De3Ofp1ozaDrvno69H9U+/2wM0QYAYHazPm9irM8DgIh45rHadYYEowEAAAAAMLuVmj0ApkdKqT+ldGNK6YqU0idTSn+fUvp2o0LRxvT1SErpW7v6+OSuPm8UigbT781vfnM8/PDDcf7558fee++dW6+zszNe97rXxe233x6XX355oUVXu5166qlx5513xhvf+MbctzAuXbo0PvCBD8R9990XRx11VN2fY7rtu+++8e1vf3t4AdZLXvKSWLx4cfT09MQhhxwSp59+enzpS1+Khx9+ON7whjdERIx7U+SiRYsK9XXGGWfEI488Epdddlkcc8wxNd9k2dnZGcuXL49LL700HnjggTj//PMn9iEBAIAZrZyGohzliueqB6NVDjUbu9l9IPVHyrn+bN3snnfvJhtQlhd6sbu/ogEFzdKo4AgAgHZTTkNx66YbKp5b0rF3vHT+cYXn57NVyklGqxWMNhl9DQr+7S1vr1jeU5pX95imQ9Hnnb6Uf39ma4g2AACzm/V5E2N9HgCz3pona9dJlddsAAAAAADAbNHR7AEAzHaPPvrolF5/2bJl8bd/+7fxuc99Lu6+++745S9/GWvXro2+vr7Ye++948ADD4wVK1YUeoNiniOPPDKuvfbaWLduXdx8883xxBNPxPbt22OfffaJgw8+OE466aTo6Hj+fzmnnHJKpFR5M0sl9dQd64orrogrrrhiQm1XrFgRK1asKFR31apVw3/PsiyWLVtWuJ+enp54z3veE+95z3ti/fr18dOf/jSefvrpWL9+fQwMDMT8+fNj2bJl8eIXvziOPPLImDevNTfNAAAA02cwDeae68jyf+SXH7I1enN7tTCr2brZvXuKAsry2u/ur7vUE1uHNldo1xqBY7U+f17wGwBAu/uvbffEcwNrKp5bsfj1MSebM2Xhu+2iOcFoNea3Ba+fN19v1aDp/JC+McFoVT7/bH1WBACg9VmfV5v1edbnATDNerfVrlMemvpxAAAAAAAz1rotKbb0PX+8dH7E/J7qLwmCmUYwGsAsUSqV4rjjjovjjjtuyvrYe++94/d+7/em7Pqtatu2bXHPPfcMH7/4xS+e8EK2PffcM84888xGDQ0AAGhTg2kg91xH1pl7rjvL2eyeeqOcylHKSjuPqwajteZG/qk2VaEVefd6d3/dWbEwu2apNY7JBlMAAMxUN2+8vmL5nOiIExe9NiKKBxfPXpU35ld7Jpn0/DxVb1/0+jvK2yuWt24wWrEg6LzPX4pSdGZdDR8XAADMJNbnTR3r8wBoO/0Ffo9eLk/9OAAAAACAGeeex1Kc+9VyrHp6dPnX357Ff/9NwWi0F8FoADBJV155ZWzf/vwGlxNOOKGJowEAAGaDwTSYe65qMFqVTfj9qS96doVwVdvsnxcQ1u7yQysmF/yVd69395d3v/tSawSO1RrHZIMpAABmorX9T8eqbfdUPHfsguWxsGNxRFSZ6wmXjYi8WLSd9y2LLFKFGpMNleutce+Lfm3yxtG6wWh534s7IqUUWZbtOq78+btLPcN1AAAAGs36PADaTqFgtKGpHwcAAAAAMKNs3pHi1M+WY7NlpswSpWYPAABmsieeeCIuuuiiUWXnnntuk0YDAADMFoNpIPfcnCz/XQjVQs1Gbtyvttm/VTfyT7VqQQGTUS1YoFq/rRI4VuvzT/b+AADMRLdsvCH33MlLzhz+u2C0WipHo2WRTdm9a9T8Nj8AeV7dY5oOeSHa5SiPev7Mf36Znc+JAADA1LM+D4C21N9fu45gNAAAAABgjG/fm4SiMasIRgOAEb71rW/FX/zFX8TatWtr1r333nvj5JNPjvXr1w+XvfyA4QheAAAgAElEQVTlL4/XvOY1UzlEAACAqsFoHRMORusd8ffKm/jnREd0ZJ0FRth+8jb6T3UwWl4QXauEZdQaR6uMEwBguvSX++Inm35U8dyB3QfHIT1HDB93Z60912u2lHKC0bIsurOpCRCude+LXj8/GK01A8R6JvmsKBgNAAAoyvo8AIhI/QV+BlwuT/1AAAAAAIAZZeWTzR4BTK/8XZIAMAtt2bIlPvGJT8RnPvOZOOOMM+K0006Ll7/85bFs2bLo6OiI9evXx8qVK+Pf//3f47rrrhu1KaerqyuuvPLKJo4eAACYLQbTYO65zirBZdU24Y/cuN+XKi/CbdVN/NMhL1Ru0sELOfd6d0hGXsDAZPttlFrj6Cv3RkopsiybphEBADTXz7fcGtvLWyuee/XiM0fNi3LDd1NrzPVaWXdpbsTQhnHlefPromoFHxe9/kwLRssL6YvY+Vnmx8KIqB3sDAAAUIv1eQAQEYWC0YamfhwAAAAAwIzy6LrKL5yFdiUYDQAqGBgYiOuuuy6uu+66QvXnzp0b//iP/xgvf/nLp3hkAAAAEYNpIPdcR5VgtLzghYjRG9zzNvHP5s3ueQEGecEARdW611PVb6PUGkc5hmIwDURn1jVNIwIAaJ6UUty84fsVz80tzYvjFp48qixvfl0rnGu2KEe5YnkWWZV7N9lgtOrti16/t7y9Ynm1Z7JmqvasNzKoL/f5JZu9z4oAAMDEWJ8HwKxWKBit8s9HAQAAAIDZ65F1zR4BTK9SswcAAK1k8eLFMWfOnLranHjiiXHLLbfEm9/85ikaFQAAwGjVgtHmZPnvQpiTzckNqBq5wT1vs3+rbuKfDnkb/fOCAYrKu9e7A9FaPSyjSDBEq4S4AQBMtUd7H4jH+35d8dwJi06LrlL3qLKpCvdqf1nus8lk58m1g9GKXT+v3tzSvLrHNB3yApkjRt+TkSFpRdsDAACMZH0eAETBYLShqR8HAAAAADCj5AWjffR3snjjMdn0DgamQf4uSQCYhc4666x49tln44Ybbojbb789Vq5cGY899lisX78+ent7Y+7cubHnnnvGi170ojjppJPizDPPjBNPPLHZwwYAAGaZasFoHVln1bbdpZ4YGOofVz5ys3te2FdecMNskLfRf7KhFXmBCbvvdX7gQ2uEZRQKRks7Yn4snIbRAAA0180bv5977uTFvzWuTDBadSlSxfJSZFMSIFxO5ehLtYLRin1tduTO81szQKwj64xSlKIc5XHnioVoz95nRQAAoD7W5wFARAyMX7MxTnn8z+oAAAAAgNlrw7YUm3KWSJ5yRBZ7dAtGo/0IRgOAMfbaa68455xz4pxzzmn2UAAAACoaTIMVy0sxJ0pZqWrb7lJPbB3aPK68r8Bm97xwsNkgN6AsTTx4IaJ2sMBUBD40UpHPnxe0BwDQTrYMbox7ttxe8dxR846JZV37jyvPm1+bP+1WORgtsmrBaBMPletPfTXrFL1+3ny9VZ+psl33dEd5+7hzQrQBAIBGsz4PgFmvv8DPGQWjAQAAAAAjPLIu/9zBe0/fOGA6Vd8lCQAAAAC0nME0ULG8M+us2bY7ywn4GhWMZrP7WLnBaJMIXkgpVQlG29lfd1b5nrdKWEaRcUzmHgEAzBS3b/o/uQHGr15yZsXyqZhjtpOUl4sW2ZSEyhUJHy56/d4KAWMRrRuMFlHt+1GINgAAAAA0VKFgtKGpHwcAAAAAMGP8OicYrasjYv9F0zsWmC6C0QAAAABghskLXJiTddRsmxdu1peeX3ibH4w2eze75923yQQvDKaBKEflxcy773VewMDIr1czFQntEOwBALS7chqK2zb+oOK5PTuWxm/s8cqK53Ln5uZPERGRIicZLabm3hUL/a1dZ6A8kPvM1soBYkWC+moFOwMAAAAABfQVCUYrT/04AAAAAIAZ45F1lddUvmjPiFIpm+bRwPQQjAYAAAAAM8xgGqhY3pF11mybtxF/ZAhAXiBAXvj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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import asyncio\n", + "\n", + "from matplotlib import pyplot as plt\n", + "\n", + "from apd.aggregation.query import with_database\n", + "from apd.aggregation.analysis import get_known_configs, plot_sensor, Config\n", + "\n", + "with with_database(\"postgresql+psycopg2://apd@localhost/apd\") as session:\n", + " coros = []\n", + " figure = plt.figure(figsize = (20, 5), dpi=300)\n", + " configs = get_known_configs()\n", + " to_display = configs[\"Relative humidity\"], configs[\"Ambient temperature\"]\n", + " for i, config in enumerate(to_display, start=1):\n", + " plot = figure.add_subplot(1, 2, i)\n", + " coros.append(plot_sensor(config, plot, {}))\n", + " await asyncio.gather(*coros)\n", + "\n", + "display(figure)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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CYy+b5NyJPDWN2rb6bq31112cD0zg6quvHrP9ile8ouNzDz/88Dz+8Y/ftr1hw4Z8+9vf7lltvbDTTjvl+OOP7/j4Rz7ykdv+f/369Vm3bl3L46655pox2yeeeGLHcxxzzDEdN7ddffXVY1ajfNGLXtRRE9loxx133Lb/v+WWW7Jy5cquzgcAAOaGdsFo7UICkvahZq1udm9303+7cLW5rJuQgG4N1Y1tAx/2WLhXy/F2QWr9MlFAhGAPAICG6++7OvduvnvS4zYMD2a4Ds9ARbNfu/fJ7YLFevH+fHCC97bdXr/d8TsN7NLVdWZCN89pu8e1dBYGvgEAwPagP28s/XkAMDV1yyTBZ4LRAAAAAACY5xZOfgg7mIuTPHbU9quSfGmyk0opj0jypFFD909y3uVJBpNsvSv0qFLKI2qtP+6gxpOati/u4BygA6tXr87Pfvazbdt77LHHmMajThx99NH5zne+s237uuuuy9FHH92zGqfrkEMOyeLFizs+fvny5WO216xZk1133XXccd///ve3/f8ee+yRQw89tKu6jjjiiI5Wh2xujNt///3zy1/+squ5mh//z3/+8zz4wQ/u6hoAAMCOb9MUgtGWtAn4anXzf7uwq6UL5mMwWusb/DfVoWyumyZ8ziczUbjCHgv3bDk+OLw+tdaUUqY8by9N9BgGBaMBAKTWmi/f0/mvRIfqxiydh4HE49TWwWhLB3bO2i1rxo1PFNjbqYmCfbsJRhuuw22v1S54uZ/afVbsLhht9j0uAADoNf154+nPA4ApWnfvxPsnC04DAAAAAIA5TjDa3POpJH+TZMHI9gtLKYfVWm+a5Ly/atr+l1rrhnYH11rXl1L+NckfN13jVRNNUkp5WJIXjBranOTTk9QGdGjVqlVjtg877LCub5J+xCMeMWb7rrvumnZdvdTcSDWZRYvG3py+adP44ID7778/GzY88CNvKk1MnZ5z6623jtl+wxvekDe84Q1dzzfaPffcM63zAQCAHdPmNsFoiyYI6Wp3o3qrG/bb3ey+U5uQsLlsohv8NwwPZtcFUw9Gmyh4YY9Fe7UcH86WbKpDWVyWTHneXproMUy0DwBgvrjx/u/kjqGVHR+/YXhQyFSS1rFoyU4LdklafBzqxXvPicLPNg4PdhxQvHF4MLXNI9hpwS5Trm97aff91ipsrm2I9jz8rAgAwPyjP288/XkAMEW3/GTi/cOC0QAAAAAAmN8Eo80xtdabSikXJPnTkaHFST5RSnlGu6CzUsofJDlp1NBQkrd1MN3ZSV6SZGtXw0mllItrrS2XZCulLE3y8ZGatvpYrfVnrY6ft1YcmPIvk/ySi+lbcWC/K9guVq9ePWZ799137/oazefMtqaegYGBnl/z3nvHrri1bNmyrq+x2267dXTc3Xff3fW1J7N27dqeXxMAAJj9Ng0PtRxfONA+pKtdqFmrm//bBQLMx4CGicLgNg4PZtcFnX0mbGVwS+tQgSTZfWHrYLSt8y4emB3BaIMThEe0C00AAJhPvnTPxV0dv2HL+mThntupmh1Jm2CxNu/PJ3pf2qmJgtFqajbWDVlaJv9MNDjB++DZGDbdPhitxWfFLa2foyXz8LMiALCD0p83c+Zgj57+vKnRnwcA49UP/83EBwwPz0whAAAAAAAwSwlGm2GllAPT+nl/UNP2wlLKQW0us67W+psJpjkryQuSbF227egkXy6l/Fmt9cejalmS5LVJzm06/9xa6y0TXD9JUmv9eSnlH5OcNmr4X0spf5nkI7XWbXfnllIemeSjI7VsdXc6C2CbV8rChcl+B/W7DHZQtY69QaTb1Shb6cU1ZrslS8beSD401DpcYCKdnjOVa0+m+esOAADMD5vr5pbji0r7YLR2N6q3umm/XaDVfLzZfekEwQWDWwYfWDZgCjZOELywx8LlbfcNDg9mWfaY+sQ9NNFjmChYAgBgPvjl4E25afC/ujrHe6iG2iYYrd37816E8k52jQ3Dgx2FRQ8O39923+wMRmvznDaFoNVa235/7jQPPysCADsm/XlMh/68qdGfBwAt3PLjifcPb5mZOgAAAAAAYJYSjDbzrk7ykA6OOyDJL9rsuyDJSe1OrLXeVkp5YZLLkyweGX5KkhtLKd9O8vMkuyd5fJIVTaf/e5IzOqhvq7ckOTzJ745sL0ryviRnlFK+k2RtkoNH5hrdvTGU5AW11ju7mAuYxJ577jlme82aNV1fo/mc5cvb34Q9VVu2zK5f1DY/xuaVPTvR6cqde++995jta665JkcddVTX8wEAAGyum1qOL5wgGK3dDfytgq3a3+w++27i394mCoObbvhCq1C6JFlSlmangV2227y9sqVuzqba/iaj2VInAEC/fHn1xV2f4z1UQ7tgtHafSSYK7O1Uu/fn3c7RHCg22tIJ3uf3S6dhc5vrpmxJ65DuiQKlAQBgrtCfNzX68wBgrLplS7JukvcRw8MzUwwAAAAAAMxSA/0ugO2j1npVkhckWTVquCQ5IsmJSZ6T8aFon0nyklprxx0RI8eemOSipl37JHlukj9K8oSMDUW7K8kf1Fq/1uk8QGdWrBj7sv7pT3/a9TV+8pOfjNneZ599Wh63cOHYbM3Nm1vfBNHKVBqbtqcFCxbkgAMO2Lb985//POvXd3fT0Q033NDRcfvuu++Y7al8jQAAAJK0DaOaKBitXYBAq+CFdmEM8/Fm90UDi7KwtF5jYrqhFe2CFZYO7DRJINv0Qx96YbI6ZkudAAD9sGroznx37Tdb7jt0p8OzpCxtuc97qIZ2wWjtPpNsrpuzabh1gHSnJgs+G9zS2fv/weH7W44vLIuyaKD9Z7Z+aRei3fy9ONH35kSfXwAAYK7Qnzc1+vMAoMldtyZbJvm7fXh2BZ0CAAAAAMBME4w2h9Vav5jk0Uk+lGSiLodvJnlRrfVltdbWHdoTz7Ou1vqSNELQWnf2N9yT5INJHl1rvazbeYDJLV++PIcccsi27XvvvTc/+tGPurrGNddcM2b7yCOPbHncbrvtNmb73nvv7XiOH/7wh13VNBOe/OQnb/v/4eHh/Od//mfH595zzz35/ve/39GxRx999JjtL33pSx3PAwAAsFWtNZtr6ybZRWVx2/Pa3ajefHP7cB3OxuENLY9td8P8XNcufGFDm+epU+2CBZYu2DkLyoIsLkvanDe9QLZemawOoR4AwHx2xerPp2a45b5n7XlCx2FU81brXLTstKB9WPNkwWaTmey57/T6gztY0HT778X1E26PttOC+flZEQCA+UV/3tTpzwOAUe74xeTHbBGMBgAAAADA/CYYbYbVWg+qtZZp/jmpi/nuqrWekuRBSY5P8qokb03yF0n+MMnBtdajaq2f68Fj+9da61FJDk7yopE53joy5/FJ9qu1/nmtddV05wLaO+aYY8Zsf+pTn+r43B/96Ef59re/vW176dKlecITntDy2OaVKm+88caO5/niF7/Y8bEz5ZnPfOaY7X/+53/u+NwLLrggQ0NDHR37jGc8IwsWLNi2/fnPfz533XVXx3MBAAAkyXC2tA1YWFgWtj2vfbjX2Jv7h+rG1DYpBPM3GK2zoIButQte2BpiN9vDMiarY7bUCQAw09ZuXpNvrLmi5b79Fv9WDt/lCVnaJuBruuFec0W7zyQ7TRAutr3en2/VLvCs0+Mmqr2f2n3uaA7Mnuj5aRfEDQAAc43+vKnRnwcAo6y6Y/JjhgWjAQAAAAAwvwlGmydqrUO11itrrZ+otf59rfV9tdZ/q7V2sNRM13P9otb6uZE5/n5kzitrrZ11JQDT8id/8idjtt///vfnV7/6VUfnvvWtbx2z/ZKXvCRLlixpeezjH//4Mdtf+MIXOprj8ssvz7XXXtvRsTPp5S9/eZYtW7Zt++KLL87ll18+6Xm333573v72t3c8z/Lly/Pyl7982/a6dety2mmndVcsAAAw722qm9ruW1gWt923dGBpy/Hm8IANW9rf7N8uXG2um+lgtJ22BaN1FmbXLxuaghLG75/e8wMAsKP6z3u/mE1tfj36zD1PyEAZaBsk1Wn41lzXPhhtl7bnTPd98mTvXzsNrWv3mWrWBqO1Cekb91lxgsc/X0O0AQCYf/TnTY3+PAAYZaiDf2cUjAYAAAAAtLBhU83f/t/hPPu8LTnqXQ/8ufyHrXsuYUcmGA1gjjn++OPzuMc9btv2mjVr8tKXvjSDgxP/AvW8887LpZdeum27lJI3vvGNbY8/6qijsvPOD9wkcfHFF+f666+fcI6bbropr3zlKyd7CH2xbNmyvP71rx8zduKJJ+bKK69se87/z96dh0lSFXi//53IzKrM3rt6VRqGpUGWEZARQWiYpllGQQFHXhWdYUa9jo6+iuMdF1zxOtdtZoSZC47ggOAyyAVZbNEWkIaWRkAaEBQa6JamAemNql4rMysz47x/FFVdWRUnMqJyz/x+nofHjjgnIk5GllXnZJ7ziw0bNuj000/X9u3bY13r4osvLpvQ9oMf/ECf/vSnVSrF+wL7iSee0KpVq2IdAwAAAKAzFP2wYLSUs8wVslW0RRXGnDMsiMG1YL7T1SugzBW80DsajOYIZAsJr2ukSsERrRLgBgAA0EhDfl73bP95YNnMZJ9eP/1kSXvDcMeLGr7V6VzBaGEBXPUKLo5aPiLr7wncn2nR8ZTrno4fG7rub8r0hI5FAQAAgE7C/LzJYX4eAABj5AlGAwAAAAAAABCf71u96VJfX7zV6s4npQee3fvfy7sJRkPnIRgNAFrMpk2btGHDhkn9N+Kqq65ST0/P6Pbdd9+tk046SQ888MCE623btk0f+chH9IlPfKJs/6c+9SkdeeSRznZOnz5d73znO0e3S6WSzjrrLN1+++0T6g4NDem73/2ujj/+eG3evFmzZ8+Oc0sa5gtf+IJe+9rXjm7v3LlTp556qt7xjnfoxhtv1GOPPaa1a9fq9ttv18c//nEdccQRevLJJ5VOp3XOOedEvs4BBxygK6+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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import asyncio\n", + "from uuid import UUID\n", + "\n", + "from matplotlib import pyplot as plt\n", + "\n", + "from apd.aggregation.query import with_database\n", + "from apd.aggregation.analysis import get_known_configs, plot_sensor\n", + "\n", + "\n", + "location_names = {\n", + " UUID('53998a51-60de-48ae-b71a-5c37cd1455f2'): \"Loft\",\n", + " UUID('1bc63cda-e223-48bc-93c2-c1f651779d69'): \"Living Room\",\n", + " UUID('ea0683de-6772-4678-bfe7-6014f54ffc8e'): \"Office\",\n", + " UUID('5aaa901a-7564-41fb-8eba-50cdd6fe9f80'): \"Outside\",\n", + "}\n", + "\n", + "with with_database(\"postgresql+psycopg2://apd@localhost/apd\") as session:\n", + " coros = []\n", + " figure = plt.figure(figsize = (20, 10), dpi=300)\n", + " configs = get_known_configs().values()\n", + " for i, config in enumerate(configs, start=1):\n", + " plot = figure.add_subplot(2, 2, i)\n", + " coros.append(plot_sensor(config, plot, location_names))\n", + " await asyncio.gather(*coros)\n", + "\n", + "display(figure)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "9f576f5bb8e34cb8ab1d3c09f770d027", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "interactive(children=(DatePicker(value=None, description='Start date'), DatePicker(value=None, description='En…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import asyncio\n", + "from uuid import UUID\n", + "import functools\n", + "from concurrent.futures import ThreadPoolExecutor\n", + "\n", + "import ipywidgets as widgets\n", + "from matplotlib import pyplot as plt\n", + "\n", + "from apd.aggregation.query import with_database\n", + "from apd.aggregation.analysis import get_known_configs, plot_sensor\n", + "\n", + "\n", + "def wrap_coroutine(f):\n", + " @functools.wraps(f)\n", + " def run_in_thread(*args, **kwargs):\n", + " loop = asyncio.new_event_loop()\n", + " wrapped = f(*args, **kwargs)\n", + " with ThreadPoolExecutor(max_workers=1) as pool:\n", + " task = pool.submit(loop.run_until_complete, wrapped)\n", + " return task.result()\n", + " return run_in_thread\n", + "\n", + "@wrap_coroutine\n", + "async def plot(*args, **kwargs):\n", + " location_names = {\n", + " UUID('53998a51-60de-48ae-b71a-5c37cd1455f2'): \"Loft\",\n", + " UUID('1bc63cda-e223-48bc-93c2-c1f651779d69'): \"Living Room\",\n", + " UUID('ea0683de-6772-4678-bfe7-6014f54ffc8e'): \"Office\",\n", + " UUID('5aaa901a-7564-41fb-8eba-50cdd6fe9f80'): \"Outside\",\n", + " }\n", + "\n", + " with with_database(\"postgresql+psycopg2://apd@localhost/apd\") as session:\n", + " coros = []\n", + " figure = plt.figure(figsize = (20, 10), dpi=300)\n", + " configs = get_known_configs().values()\n", + " for i, config in enumerate(configs, start=1):\n", + " plot = figure.add_subplot(2, 2, i)\n", + " coros.append(plot_sensor(config, plot, location_names, *args, **kwargs))\n", + " await asyncio.gather(*coros)\n", + " return figure\n", + "\n", + "\n", + "start = widgets.DatePicker(\n", + " description='Start date',\n", + ")\n", + "end = widgets.DatePicker(\n", + " description='End date',\n", + ")\n", + "out = widgets.interactive(plot, collected_after=start, collected_before=end)\n", + "display(out)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "60091fa7d9ea415985ec53937e422b4a", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "interactive(children=(DatePicker(value=None, description='Start date'), DatePicker(value=None, description='En…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from uuid import UUID\n", + "import ipywidgets as widgets\n", + "from apd.aggregation.analysis import interactable_plot_multiple_charts, configs\n", + "\n", + "location_names = {\n", + " UUID('53998a51-60de-48ae-b71a-5c37cd1455f2'): \"Loft\",\n", + " UUID('1bc63cda-e223-48bc-93c2-c1f651779d69'): \"Living Room\",\n", + " UUID('ea0683de-6772-4678-bfe7-6014f54ffc8e'): \"Office\",\n", + " UUID('5aaa901a-7564-41fb-8eba-50cdd6fe9f80'): \"Outside\",\n", + "}\n", + "\n", + "start = widgets.DatePicker(\n", + " description='Start date',\n", + ")\n", + "end = widgets.DatePicker(\n", + " description='End date',\n", + ")\n", + "\n", + "plot = interactable_plot_multiple_charts(location_names=location_names)\n", + "out = widgets.interactive(plot, collected_after=start, collected_before=end)\n", + "display(out)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "08a4d937037b45bda2298879df7f80b6", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "interactive(children=(DatePicker(value=None, description='Start date'), DatePicker(value=None, description='En…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import datetime\n", + "import uuid\n", + "import ipywidgets as widgets\n", + "from apd.aggregation.analysis import Config, configs, interactable_plot_multiple_charts, draw_map, get_map_cleaner_for\n", + "from apd.aggregation.database import DataPoint\n", + "from apd.aggregation.utils import merc_x, merc_y\n", + "\n", + "start = widgets.DatePicker(description='Start date')\n", + "end = widgets.DatePicker(description='End date')\n", + "\n", + "def get_literal_data():\n", + " # Get manually entered temperature data, as our particular deployment\n", + " # does not contain data of this shape\n", + " raw_data = {(53.8667, -1.3333): -1, (53.35, -2.2833): 1, (52.45, -1.7333): 3, (51.5, -0.1333): 6, (51.55, -2.5667): 5, (54.9667, -1.6167): -1, (55.9667, -3.2167): -1, (54.7667, -1.5833): 0, (53.7667, -0.3): 1, (53.7667, -3.0167): 0, (51.4833, -3.1833): 4, (53.2667, -3.5167): 2, (52.0833, -2.8): 2, (52.0167, -0.6): 2, (52.6667, 1.2667): 5, (50.4333, -4.9833): 9, (50.35, -4.1167): 10, (50.5167, -2.45): 9, (50.8333, -1.1667): 9, (56.45, -5.4333): 4, (57.5333, -4.05): -2, (54.5167, -3.6167): 3, (53.0833, 0.2667): 4, (51.35, 1.3333): 7, (50.8833, 0.3167): 8, (58.45, -3.1): 3, (50.1, -5.6667): 9, (58.2167, -6.3333): 4, (55.6833, -6.25): 7}\n", + " now = datetime.datetime.now()\n", + " async def points():\n", + " for (coord, temp) in raw_data.items():\n", + " deployment_id = uuid.uuid4()\n", + " yield DataPoint(sensor_name=\"Location\", deployment_id=deployment_id, collected_at=now, data=coord)\n", + " yield DataPoint(sensor_name=\"Temperature\", deployment_id=deployment_id, collected_at=now, data=temp)\n", + " async def deployments(*args, **kwargs):\n", + " yield None, points()\n", + " return deployments\n", + "\n", + "def draw_map_with_gb(plot, x, y, colour):\n", + " # Draw the map and add an explicit coastline\n", + " gb_boundary = [(-2.6653539699999556, 51.617254950000074), (-2.6937556629999335, 51.59617747600004), (-2.71312415299991, 51.58661530200004), (-2.760487433999913, 51.579779364000046), (-2.7980850899999155, 51.56350332200009), (-2.816395636999914, 51.555568752000056), (-2.8587133449999556, 51.546128648000035), (-2.943348761999914, 51.54254791900007), (-2.9562882149999155, 51.54572174700007), (-2.9641007149999155, 51.552801825000074), (-2.969471808999913, 51.559881903000075), (-2.9746801419999542, 51.56297435100004), (-2.984038865999935, 51.55853913000004), (-3.0102432929999168, 51.53803131700005), (-3.0156957669999542, 51.53164297100005), (-3.0737198559999115, 51.50836823100008), (-3.0764867829999503, 51.50775788000004), (-3.088937954999949, 51.505072333000044), (-3.105620897999927, 51.49721914300005), (-3.1317032539999445, 51.48041413000004), (-3.1350805329999503, 51.47630442900004), (-3.1409399079999503, 51.46478913000004), (-3.1453751289999445, 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51.49408600500004), (-2.716420050999943, 51.49957916900007), (-2.6961563789999445, 51.51264069200005), (-2.6078181629999335, 51.60736725500004), (-2.599598761999914, 51.611395575000074), (-2.5892227859999366, 51.61424388200004), (-2.580433722999942, 51.61863841400009), (-2.5768123039999296, 51.62750885600008), (-2.57445227799991, 51.63572825700004), (-2.568959113999938, 51.644964911000045), (-2.562245245999918, 51.65338776200008), (-2.556385870999918, 51.65924713700008), (-2.499175584999932, 51.69676341400009), (-2.464995897999927, 51.725897528000075), (-2.4477432929999168, 51.73187897300005), (-2.404896613999938, 51.74115631700005), (-2.3885391919999392, 51.750433661000045), (-2.380767381999931, 51.76190827000005), (-2.3833715489999463, 51.77326080900008), (-2.398060675999943, 51.782131252000056), (-2.4024145169999542, 51.76357656500005), (-2.4203995429999168, 51.753119208000044), (-2.4434301419999542, 51.74843984600005), (-2.4632869129999335, 51.747300523000035), (-2.482085740999935, 51.74286530200004), (-2.495838995999918, 51.73200104400007), (-2.507964647999927, 51.71857330900008), (-2.5221248039999296, 51.706366278000075), (-2.581695115999935, 51.681708075000074), (-2.5911352199999556, 51.67536041900007), (-2.600209113999938, 51.66559479400007), (-2.6653539699999556, 51.617254950000074)]\n", + " draw_map(plot, x, y, colour)\n", + " plot.plot(\n", + " [merc_x(coord[0]) for coord in gb_boundary],\n", + " [merc_y(coord[1]) for coord in gb_boundary],\n", + " \"k-\",\n", + " )\n", + "\n", + "country = Config(\n", + " get_data=get_literal_data(),\n", + " clean=get_map_cleaner_for(\"Temperature\"),\n", + " title=\"Country wide temperature\",\n", + " ylabel=\"\",\n", + " draw=draw_map_with_gb,\n", + ")\n", + "\n", + "out = widgets.interactive(interactable_plot_multiple_charts(configs=configs + (country, )), collected_after=start, collected_before=end)\n", + "display(out)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "apd.aggregation", + "language": "python", + "name": "apd.aggregation" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Ch10/apd.aggregation-chapter10-ex01/LICENCE b/Ch10/apd.aggregation-chapter10-ex01/LICENCE new file mode 100644 index 0000000..86685d4 --- /dev/null +++ b/Ch10/apd.aggregation-chapter10-ex01/LICENCE @@ -0,0 +1,31 @@ +Copyright (c) 2019, Matthew Wilkes + + +All rights reserved. + +Redistribution and use in source and binary forms, with or without modification, +are permitted provided that the following conditions are met: + +* Redistributions of source code must retain the above copyright notice, this + list of conditions and the following disclaimer. + +* Redistributions in binary form must reproduce the above copyright notice, this + list of conditions and the following disclaimer in the documentation and/or + other materials provided with the distribution. + +* Neither the name of the copyright holder nor the names of its + contributors may be used to endorse or promote products derived from this + software without specific prior written permission. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND +ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED +WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. +IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, +INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, +BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, +DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY +OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE +OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED +OF THE POSSIBILITY OF SUCH DAMAGE. + + diff --git a/Ch10/apd.aggregation-chapter10-ex01/Mapping.ipynb b/Ch10/apd.aggregation-chapter10-ex01/Mapping.ipynb new file mode 100644 index 0000000..916e704 --- /dev/null +++ b/Ch10/apd.aggregation-chapter10-ex01/Mapping.ipynb @@ -0,0 +1,254 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "datapoints = {\n", + " (53.8667, -1.3333): -1,\n", + " (53.35, -2.2833): 1,\n", + " (52.45, -1.7333): 3,\n", + " (51.5, -0.1333): 6,\n", + " (51.55, -2.5667): 5,\n", + " (54.9667, -1.6167): -1,\n", + " (55.9667, -3.2167): -1,\n", + " (54.7667, -1.5833): 0,\n", + " (53.7667, -0.3): 1,\n", + " (53.7667, -3.0167): 0,\n", + " (51.4833, -3.1833): 4,\n", + " (53.2667, -3.5167): 2,\n", + " (52.0833, -2.8): 2,\n", + " (52.0167, -0.6): 2,\n", + " (52.6667, 1.2667): 5,\n", + " (50.4333, -4.9833): 9,\n", + " (50.35, -4.1167): 10,\n", + " (50.5167, -2.45): 9,\n", + " (50.8333, -1.1667): 9,\n", + " (56.45, -5.4333): 4,\n", + " (57.5333, -4.05): -2,\n", + " (54.5167, -3.6167): 3,\n", + " (53.0833, 0.2667): 4,\n", + " (51.35, 1.3333): 7,\n", + " (50.8833, 0.3167): 8,\n", + " (58.45, -3.1): 3,\n", + " (50.1, -5.6667): 9,\n", + " (58.2167, -6.3333): 4,\n", + " (55.6833, -6.25): 7,\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "fig, ax = plt.subplots()\n", + "lats = [ll[0] for ll in datapoints.keys()]\n", + "lons = [ll[1] for ll in datapoints.keys()]\n", + "ax.plot(lons, lats, \"o\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "fig, ax = plt.subplots()\n", + "lats = [ll[0] for ll in datapoints.keys()]\n", + "lons = [ll[1] for ll in datapoints.keys()]\n", + "# Don't draw lines\n", + "ax.plot(lons, lats, \"o\")\n", + "# This is a map of the UK. Here, 1 degree latitude is \n", + "# 1.7x as far as 1 degree longitude\n", + "ax.set_aspect(1.7)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# Set up mercator transform from https://wiki.openstreetmap.org/wiki/Mercator#Python\n", + "# Thanks to Paulo Silva and the OSM contributors\n", + "import math\n", + "\n", + "def merc_x(lon):\n", + " r_major=6378137.000\n", + " return r_major*math.radians(lon)\n", + "\n", + "def merc_y(lat):\n", + " if lat>89.5:lat=89.5\n", + " if lat<-89.5:lat=-89.5\n", + " r_major=6378137.000\n", + " r_minor=6356752.3142\n", + " temp=r_minor/r_major\n", + " eccent=math.sqrt(1-temp**2)\n", + " phi=math.radians(lat)\n", + " sinphi=math.sin(phi)\n", + " con=eccent*sinphi\n", + " com=eccent/2\n", + " con=((1.0-con)/(1.0+con))**com\n", + " ts=math.tan((math.pi/2-phi)/2)/con\n", + " y=0-r_major*math.log(ts)\n", + " return y\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots()\n", + "\n", + "x = tuple(map(merc_x, lons))\n", + "y = tuple(map(merc_y, lats))\n", + "ax.plot(x, y, 'o')\n", + "ax.set_aspect(1.0)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots()\n", + "\n", + "x = tuple(map(merc_x, lons))\n", + "y = tuple(map(merc_y, lats))\n", + "\n", + "ax.tricontourf(x, y, tuple(datapoints.values()))\n", + "ax.plot(x, y, 'wo', ms=3)\n", + "ax.set_aspect(1.0)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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0000000..85669dd --- /dev/null +++ b/Ch10/apd.aggregation-chapter10-ex01/README.md @@ -0,0 +1,4 @@ +# APD Sensor aggregator + +A programme that queries apd.sensor endpoints and aggregates their results. + diff --git a/Ch10/apd.aggregation-chapter10-ex01/pyproject.toml b/Ch10/apd.aggregation-chapter10-ex01/pyproject.toml new file mode 100644 index 0000000..4d3bb67 --- /dev/null +++ b/Ch10/apd.aggregation-chapter10-ex01/pyproject.toml @@ -0,0 +1,5 @@ +[build-system] +requires = [ + "setuptools", +] +build-backend = "setuptools.build_meta" diff --git a/Ch10/apd.aggregation-chapter10-ex01/pytest.ini b/Ch10/apd.aggregation-chapter10-ex01/pytest.ini new file mode 100644 index 0000000..8c26fbc --- /dev/null +++ b/Ch10/apd.aggregation-chapter10-ex01/pytest.ini @@ -0,0 +1,4 @@ +[pytest] +markers = + functional: these tests are significantly slower as they start a HTTP server + performance: very slow tests that provide performance guarantees \ No newline at end of file diff --git a/Ch10/apd.aggregation-chapter10-ex01/setup.cfg b/Ch10/apd.aggregation-chapter10-ex01/setup.cfg new file mode 100644 index 0000000..3ee0436 --- /dev/null +++ b/Ch10/apd.aggregation-chapter10-ex01/setup.cfg @@ -0,0 +1,83 @@ +[mypy] +namespace_packages = True +mypy_path = src +plugins = sqlmypy + +[mypy-alembic] +ignore_missing_imports = True + +[mypy-alembic.config] +ignore_missing_imports = True + +[mypy-alembic.script] +ignore_missing_imports = True + +[mypy-alembic.runtime.environment] +ignore_missing_imports = True + +[mypy-matplotlib] +ignore_missing_imports = True + +[mypy-matplotlib.axes._base] +ignore_missing_imports = True + +[mypy-matplotlib.figure] +ignore_missing_imports = True + +[mypy-matplotlib.pyplot] +ignore_missing_imports = True + +[mypy-IPython.core] +ignore_missing_imports = True + +[mypy-yappi] +ignore_missing_imports = True + +[mypy-pint] +ignore_missing_imports = True + +[mypy-pytest] +ignore_missing_imports = True + +[flake8] +max-line-length = 120 +ignore = E501 + +[metadata] +name = apd.aggregation +version = attr: apd.aggregation.VERSION +description = A programme that queries apd.sensor endpoints and aggregates their results. +long_description = file: README.md, CHANGES.md, LICENCE +long_description_content_type = text/markdown +keywords = +license = BSD +classifiers = + Programming Language :: Python :: 3 + Programming Language :: Python :: 3.7 + +[options] +zip_safe = False +include_package_data = True +package_dir = + =src +packages = find_namespace: +install_requires = + sqlalchemy + aiohttp + pint + psycopg2 + alembic + click + + +[options.extras_require] +jupyter = ipywidgets +yappi = yappi + +[options.packages.find] +where = src + +[options.entry_points] +console_scripts = + collect_sensor_data = apd.aggregation.cli:collect_sensor_data + sensor_deployments = apd.aggregation.cli:deployments diff --git a/Ch10/apd.aggregation-chapter10-ex01/setup.py b/Ch10/apd.aggregation-chapter10-ex01/setup.py new file mode 100644 index 0000000..6068493 --- /dev/null +++ b/Ch10/apd.aggregation-chapter10-ex01/setup.py @@ -0,0 +1,3 @@ +from setuptools import setup + +setup() diff --git a/Ch10/apd.aggregation-chapter10-ex01/src/apd/aggregation/__init__.py b/Ch10/apd.aggregation-chapter10-ex01/src/apd/aggregation/__init__.py new file mode 100644 index 0000000..3277f64 --- /dev/null +++ b/Ch10/apd.aggregation-chapter10-ex01/src/apd/aggregation/__init__.py @@ -0,0 +1 @@ +VERSION = "1.0.0" diff --git a/Ch10/apd.aggregation-chapter10-ex01/src/apd/aggregation/alembic/README b/Ch10/apd.aggregation-chapter10-ex01/src/apd/aggregation/alembic/README new file mode 100644 index 0000000..97a87d5 --- /dev/null +++ b/Ch10/apd.aggregation-chapter10-ex01/src/apd/aggregation/alembic/README @@ -0,0 +1,12 @@ +Generic single-database configuration. + + +# Database setup + +To generate the required database tables you must create an alembic.ini file, as follows: + + [alembic] + script_location = apd.aggregation:alembic + sqlalchemy.url = postgresql+psycopg2://apd@localhost/apd + +and run `alembic upgrade head`. This should also be done after every upgrade of the software. \ No newline at end of file diff --git a/Ch10/apd.aggregation-chapter10-ex01/src/apd/aggregation/alembic/env.py b/Ch10/apd.aggregation-chapter10-ex01/src/apd/aggregation/alembic/env.py new file mode 100644 index 0000000..4c8a2c2 --- /dev/null +++ b/Ch10/apd.aggregation-chapter10-ex01/src/apd/aggregation/alembic/env.py @@ -0,0 +1,80 @@ +from logging.config import fileConfig + +from sqlalchemy import engine_from_config +from sqlalchemy import pool +from alembic import context + +from apd.aggregation.database import metadata + + +# this is the Alembic Config object, which provides +# access to the values within the .ini file in use. +config = context.config + +# Interpret the config file for Python logging. +# This line sets up loggers basically. +if config.config_file_name: + fileConfig(config.config_file_name) + +target_metadata = metadata + + +def include_object(object, name, type_, reflected, compare_to): + if object.info.get("is_view", False): + return False + return True + + +def run_migrations_offline(): + """Run migrations in 'offline' mode. + + This configures the context with just a URL + and not an Engine, though an Engine is acceptable + here as well. By skipping the Engine creation + we don't even need a DBAPI to be available. + + Calls to context.execute() here emit the given string to the + script output. + + """ + url = config.get_main_option("sqlalchemy.url") + context.configure( + url=url, + include_object=include_object, + target_metadata=target_metadata, + literal_binds=True, + dialect_opts={"paramstyle": "named"}, + ) + + with context.begin_transaction(): + context.run_migrations() + + +def run_migrations_online(): + """Run migrations in 'online' mode. + + In this scenario we need to create an Engine + and associate a connection with the context. + + """ + connectable = engine_from_config( + config.get_section(config.config_ini_section), + prefix="sqlalchemy.", + poolclass=pool.NullPool, + ) + + with connectable.connect() as connection: + context.configure( + connection=connection, + target_metadata=target_metadata, + include_object=include_object, + ) + + with context.begin_transaction(): + context.run_migrations() + + +if context.is_offline_mode(): + run_migrations_offline() +else: + run_migrations_online() diff --git a/Ch10/apd.aggregation-chapter10-ex01/src/apd/aggregation/alembic/script.py.mako b/Ch10/apd.aggregation-chapter10-ex01/src/apd/aggregation/alembic/script.py.mako new file mode 100644 index 0000000..2c01563 --- /dev/null +++ b/Ch10/apd.aggregation-chapter10-ex01/src/apd/aggregation/alembic/script.py.mako @@ -0,0 +1,24 @@ +"""${message} + +Revision ID: ${up_revision} +Revises: ${down_revision | comma,n} +Create Date: ${create_date} + +""" +from alembic import op +import sqlalchemy as sa +${imports if imports else ""} + +# revision identifiers, used by Alembic. +revision = ${repr(up_revision)} +down_revision = ${repr(down_revision)} +branch_labels = ${repr(branch_labels)} +depends_on = ${repr(depends_on)} + + +def upgrade(): + ${upgrades if upgrades else "pass"} + + +def downgrade(): + ${downgrades if downgrades else "pass"} diff --git a/Ch10/apd.aggregation-chapter10-ex01/src/apd/aggregation/alembic/versions/4b2df8a6e1ce_add_indexes_to_datapoints.py b/Ch10/apd.aggregation-chapter10-ex01/src/apd/aggregation/alembic/versions/4b2df8a6e1ce_add_indexes_to_datapoints.py new file mode 100644 index 0000000..85f4019 --- /dev/null +++ b/Ch10/apd.aggregation-chapter10-ex01/src/apd/aggregation/alembic/versions/4b2df8a6e1ce_add_indexes_to_datapoints.py @@ -0,0 +1,35 @@ +"""Add indexes to datapoints + +Revision ID: 4b2df8a6e1ce +Revises: 6d2eacd5da3f +Create Date: 2019-12-02 16:07:41.123116 + +""" +from alembic import op + + +# revision identifiers, used by Alembic. +revision = "4b2df8a6e1ce" +down_revision = "6d2eacd5da3f" +branch_labels = None +depends_on = None + + +def upgrade(): + op.create_index( + op.f("ix_sensor_values_collected_at"), + "sensor_values", + ["collected_at"], + unique=False, + ) + op.create_index( + op.f("ix_sensor_values_sensor_name"), + "sensor_values", + ["sensor_name"], + unique=False, + ) + + +def downgrade(): + op.drop_index(op.f("ix_sensor_values_sensor_name"), table_name="sensor_values") + op.drop_index(op.f("ix_sensor_values_collected_at"), table_name="sensor_values") diff --git a/Ch10/apd.aggregation-chapter10-ex01/src/apd/aggregation/alembic/versions/6962f8455a6d_add_daily_summary_view.py b/Ch10/apd.aggregation-chapter10-ex01/src/apd/aggregation/alembic/versions/6962f8455a6d_add_daily_summary_view.py new file mode 100644 index 0000000..e9b1a81 --- /dev/null +++ b/Ch10/apd.aggregation-chapter10-ex01/src/apd/aggregation/alembic/versions/6962f8455a6d_add_daily_summary_view.py @@ -0,0 +1,38 @@ +"""Add daily summary view + +Revision ID: 6962f8455a6d +Revises: 4b2df8a6e1ce +Create Date: 2019-12-03 11:50:24.403402 + +""" +from alembic import op + + +# revision identifiers, used by Alembic. +revision = "6962f8455a6d" +down_revision = "4b2df8a6e1ce" +branch_labels = None +depends_on = None + + +def upgrade(): + create_view = """ + CREATE VIEW daily_summary AS + SELECT + sensor_values.sensor_name AS sensor_name, + sensor_values.data AS data, + count(sensor_values.id) AS count + FROM sensor_values + WHERE + sensor_values.collected_at >= CAST(CURRENT_DATE AS DATE) + AND + sensor_values.collected_at < CAST(CURRENT_DATE AS DATE) + 1 + GROUP BY + sensor_values.sensor_name, + sensor_values.data; + """ + op.execute(create_view) + + +def downgrade(): + op.execute("""DROP VIEW daily_summary""") diff --git a/Ch10/apd.aggregation-chapter10-ex01/src/apd/aggregation/alembic/versions/6d2eacd5da3f_create_sensor_values_table.py b/Ch10/apd.aggregation-chapter10-ex01/src/apd/aggregation/alembic/versions/6d2eacd5da3f_create_sensor_values_table.py new file mode 100644 index 0000000..38fbb26 --- /dev/null +++ b/Ch10/apd.aggregation-chapter10-ex01/src/apd/aggregation/alembic/versions/6d2eacd5da3f_create_sensor_values_table.py @@ -0,0 +1,31 @@ +"""Create sensor_values table + +Revision ID: 6d2eacd5da3f +Revises: +Create Date: 2019-09-29 13:43:21.242706 + +""" +from alembic import op +import sqlalchemy as sa +from sqlalchemy.dialects import postgresql + +# revision identifiers, used by Alembic. +revision = "6d2eacd5da3f" +down_revision = None +branch_labels = None +depends_on = None + + +def upgrade(): + op.create_table( + "sensor_values", + sa.Column("id", sa.Integer(), nullable=False), + sa.Column("sensor_name", sa.String(), nullable=True), + sa.Column("collected_at", postgresql.TIMESTAMP(), nullable=True), + sa.Column("data", postgresql.JSONB(astext_type=sa.Text()), nullable=True), + sa.PrimaryKeyConstraint("id"), + ) + + +def downgrade(): + op.drop_table("sensor_values") diff --git a/Ch10/apd.aggregation-chapter10-ex01/src/apd/aggregation/alembic/versions/d8cdc709086b_add_deployment_table.py b/Ch10/apd.aggregation-chapter10-ex01/src/apd/aggregation/alembic/versions/d8cdc709086b_add_deployment_table.py new file mode 100644 index 0000000..204e096 --- /dev/null +++ b/Ch10/apd.aggregation-chapter10-ex01/src/apd/aggregation/alembic/versions/d8cdc709086b_add_deployment_table.py @@ -0,0 +1,32 @@ +"""Add deployment table + +Revision ID: d8cdc709086b +Revises: d8d4cf6a178f +Create Date: 2019-12-17 16:28:58.585616 + +""" +from alembic import op +import sqlalchemy as sa +from sqlalchemy.dialects import postgresql + +# revision identifiers, used by Alembic. +revision = "d8cdc709086b" +down_revision = "d8d4cf6a178f" +branch_labels = None +depends_on = None + + +def upgrade(): + op.create_table( + "deployments", + sa.Column("id", postgresql.UUID(as_uuid=True), nullable=False), + sa.Column("uri", sa.String(), nullable=True), + sa.Column("name", sa.String(), nullable=True), + sa.Column("colour", sa.String(), nullable=True), + sa.Column("api_key", sa.String(), nullable=True), + sa.PrimaryKeyConstraint("id"), + ) + + +def downgrade(): + op.drop_table("deployments") diff --git a/Ch10/apd.aggregation-chapter10-ex01/src/apd/aggregation/alembic/versions/d8d4cf6a178f_add_deployment_id_to_datapoint.py b/Ch10/apd.aggregation-chapter10-ex01/src/apd/aggregation/alembic/versions/d8d4cf6a178f_add_deployment_id_to_datapoint.py new file mode 100644 index 0000000..9e34b3f --- /dev/null +++ b/Ch10/apd.aggregation-chapter10-ex01/src/apd/aggregation/alembic/versions/d8d4cf6a178f_add_deployment_id_to_datapoint.py @@ -0,0 +1,34 @@ +"""Add deployment id to DataPoint + +Revision ID: d8d4cf6a178f +Revises: 6962f8455a6d +Create Date: 2019-12-03 20:58:11.285509 + +""" +from alembic import op +import sqlalchemy as sa +from sqlalchemy.dialects import postgresql + +# revision identifiers, used by Alembic. +revision = "d8d4cf6a178f" +down_revision = "6962f8455a6d" +branch_labels = None +depends_on = None + + +def upgrade(): + op.add_column( + "sensor_values", + sa.Column("deployment_id", postgresql.UUID(as_uuid=True), nullable=True), + ) + op.create_index( + op.f("ix_sensor_values_deployment_id"), + "sensor_values", + ["deployment_id"], + unique=False, + ) + + +def downgrade(): + op.drop_index(op.f("ix_sensor_values_deployment_id"), table_name="sensor_values") + op.drop_column("sensor_values", "deployment_id") diff --git a/Ch10/apd.aggregation-chapter10-ex01/src/apd/aggregation/analysis.py b/Ch10/apd.aggregation-chapter10-ex01/src/apd/aggregation/analysis.py new file mode 100644 index 0000000..0ac429a --- /dev/null +++ b/Ch10/apd.aggregation-chapter10-ex01/src/apd/aggregation/analysis.py @@ -0,0 +1,384 @@ +from __future__ import annotations + +import asyncio +import collections +from concurrent.futures import ThreadPoolExecutor +import dataclasses +import datetime +import functools +import math +import typing as t + +from uuid import UUID + +import matplotlib.pyplot as plt +from matplotlib.axes._base import _AxesBase +from matplotlib.figure import Figure +from pint import _DEFAULT_REGISTRY as ureg + +from apd.aggregation.query import ( + get_data, + get_data_by_deployment, + with_database, + get_deployment_by_id, +) +from apd.aggregation.database import DataPoint, deployment_table +from .utils import merc_x, merc_y, convert_temperature +from .typing import IntermediateMapData, T_key, T_value, CleanerFunc, Cleaned +from .typing import ( + CLEANED_COORD_FLOAT, + CLEANED_DT_FLOAT, + COORD_FLOAT_CLEANER, + DT_FLOAT_CLEANER, +) + +# Static UUID to represent aggregation of other deployments +GLOBAL = UUID("bd02526e-7619-4a59-b04b-1fafd1c262d1") + + +@dataclasses.dataclass +class Config(t.Generic[T_key, T_value]): + title: str + clean: CleanerFunc[Cleaned[T_key, T_value]] + draw: t.Optional[ + t.Callable[ + [t.Any, t.Iterable[T_key], t.Iterable[T_value], t.Optional[str]], None + ] + ] = None + get_data: t.Optional[ + t.Callable[..., t.AsyncIterator[t.Tuple[UUID, t.AsyncIterator[DataPoint]]]] + ] = None + ylabel: t.Optional[str] = None + sensor_name: dataclasses.InitVar[str] = None + + def __post_init__(self, sensor_name: t.Optional[str] = None) -> None: + if self.draw is None: + self.draw = draw_date # type: ignore + if self.get_data is None: + if sensor_name is None: + raise ValueError("You must specify either get_data or sensor_name") + self.get_data = get_one_sensor_by_deployment(sensor_name) + + +def draw_date( + plot: _AxesBase, + x: t.Iterable[datetime.datetime], + y: t.Iterable[float], + colour: t.Optional[str], +) -> None: + plot.plot_date(x, y, color=colour, linestyle="-", marker="", xdate=True) + + +def draw_map( + plot: _AxesBase, + x: t.Iterable[t.Tuple[float, float]], + y: t.Iterable[float], + colour: t.Optional[str], +) -> None: + lon = [merc_y(coord[0]) for coord in x] + lat = [merc_x(coord[1]) for coord in x] + + for axis in "x", "y": + plt.tick_params( + axis=axis, + which="both", + bottom=False, + top=False, + left=False, + right=False, + labelbottom=False, + labelleft=False, + ) + + plot.tricontourf(lat, lon, y) + plot.plot(lat, lon, "wo", ms=3) + plot.set_aspect(1.0) + + +def get_map_cleaner_for(sensor_name: str,) -> COORD_FLOAT_CLEANER: + """Given a sensor_name that represents a float, return a coroutine that acts as a cleaner + extracting that sensor's data keyed by the value of a Location sensor.""" + + async def clean_latest_coord_and_value( + datapoints: t.AsyncIterator[DataPoint], + ) -> CLEANED_COORD_FLOAT: + + # We will iterate over data points and build an entry in cleaned_data + # for each deployment. This lets newer data replace older data, as + # datapoints is assumed to be in date order + # IntermediateMapData is a typing hint for a dictionary with specific key/values + cleaned_data: t.Dict[UUID, IntermediateMapData] = {} + async for datapoint in datapoints: + # Get the existing data for this deployment, if we've seen it before + row_data = cleaned_data.get(datapoint.deployment_id, None) + if row_data is None: + # This is the first time we've seen this deployment + row_data = {"coord": None, "value": None} + if datapoint.sensor_name == "Location": + # Coord is a 2-tuple of floats + row_data["coord"] = ( + float(datapoint.data[0]), + float(datapoint.data[1]), + ) + elif datapoint.sensor_name == sensor_name: + # Value is a single float + row_data["value"] = float(datapoint.data) + # Store the info about this deployment back into the cleaned_data set + cleaned_data[datapoint.deployment_id] = row_data + + for data in cleaned_data.values(): + if data["coord"] is None or data["value"] is None: + # We only got a partial record, don't plot this + continue + yield data["coord"], data["value"] + + # Return the set up cleaner coroutine + return clean_latest_coord_and_value + + +async def clean_watthours_to_watts( + datapoints: t.AsyncIterator[DataPoint], +) -> CLEANED_DT_FLOAT: + last_watthours = None + last_time = None + async for datapoint in datapoints: + if datapoint.data is None: + continue + time = datapoint.collected_at + watthours = ureg.Quantity(datapoint.data["magnitude"], datapoint.data["unit"]) + if last_watthours: + seconds_elapsed = (time - last_time).total_seconds() + time_elapsed = ureg.Quantity(seconds_elapsed, ureg.second) + additional_power = watthours - last_watthours + power = additional_power / time_elapsed + yield time, power.to(ureg.watt).magnitude + last_watthours = watthours + last_time = datapoint.collected_at + + +async def clean_magnitude(datapoints: t.AsyncIterator[DataPoint],) -> CLEANED_DT_FLOAT: + async for datapoint in datapoints: + if datapoint.data is None: + continue + yield datapoint.collected_at, datapoint.data["magnitude"] + + +def convert_temperature_system( + cleaner: DT_FLOAT_CLEANER, temperature_unit: str, +) -> DT_FLOAT_CLEANER: + async def converter(datapoints: t.AsyncIterator[DataPoint],) -> CLEANED_DT_FLOAT: + results = cleaner(datapoints) + async for date, temp_c in results: + yield date, convert_temperature(temp_c, "degC", temperature_unit) + + return converter + + +async def clean_temperature_fluctuations( + datapoints: t.AsyncIterator[DataPoint], +) -> CLEANED_DT_FLOAT: + allowed_jitter = 2.5 + allowed_range = (-40, 80) + window_datapoints: t.Deque[DataPoint] = collections.deque(maxlen=3) + + def datapoint_ok(datapoint: DataPoint) -> bool: + """Return False if this data point does not contain a valid temperature""" + if datapoint.data is None: + return False + elif datapoint.data["unit"] != "degC": + # This point is in a different temperature system. While it could be converted + # this cleaner is not yet doing that. + return False + elif not allowed_range[0] < datapoint.data["magnitude"] < allowed_range[1]: + return False + return True + + async for datapoint in datapoints: + if not datapoint_ok(datapoint): + # If the datapoint is invalid then skip directly to the next item + continue + + window_datapoints.append(datapoint) + if len(window_datapoints) == 3: + # Find the temperatures of the datapoints in the window, then average + # the first and last and compare that to the middle point. + window_temperatures = [dp.data["magnitude"] for dp in window_datapoints] + avr_first_last = (window_temperatures[0] + window_temperatures[2]) / 2 + diff_middle_avr = abs(window_temperatures[1] - avr_first_last) + if diff_middle_avr > allowed_jitter: + pass + else: + yield window_datapoints[1].collected_at, window_temperatures[1] + elif len(window_datapoints) == 1: + # The item in the iterator can't be compared to both neighbours + # so should be yielded + yield datapoint.collected_at, datapoint.data["magnitude"] + else: + # Otherwise, let the window fill up, it will be yieleded later + pass + # When the iterator ends the final item is not yet in the middle + # of the window, so the last item must be explicitly yielded. + if len(window_datapoints) > 1 and datapoint_ok(datapoint): + yield datapoint.collected_at, datapoint.data["magnitude"] + + +async def clean_passthrough( + datapoints: t.AsyncIterator[DataPoint], +) -> CLEANED_DT_FLOAT: + async for datapoint in datapoints: + if datapoint.data is None: + continue + else: + yield datapoint.collected_at, datapoint.data + + +def get_one_sensor_by_deployment( + sensor_name: str, +) -> t.Callable[..., t.AsyncIterator[t.Tuple[UUID, t.AsyncIterator[DataPoint]]]]: + return functools.partial(get_data_by_deployment, sensor_name=sensor_name) + + +def get_all_data() -> t.Callable[ + ..., t.AsyncIterator[t.Tuple[UUID, t.AsyncIterator[DataPoint]]] +]: + async def get_all_data_inner( + *args: t.Any, **kwargs: t.Any + ) -> t.AsyncIterator[t.Tuple[UUID, t.AsyncIterator[DataPoint]]]: + yield GLOBAL, get_data(*args, **kwargs) + + return get_all_data_inner + + +configs = ( + Config( + sensor_name="SolarCumulativeOutput", + clean=clean_watthours_to_watts, + title="Solar generation", + ylabel="Watts", + ), + Config( + sensor_name="RAMAvailable", + clean=clean_passthrough, + title="RAM available", + ylabel="Bytes", + ), + Config( + sensor_name="RelativeHumidity", + clean=clean_passthrough, + title="Relative humidity", + ylabel="Percent", + ), + Config( + sensor_name="Temperature", + clean=clean_temperature_fluctuations, + title="Ambient temperature", + ylabel="Degrees C", + ), +) + + +def get_known_configs() -> t.Dict[str, Config[t.Any, t.Any]]: + return {config.title: config for config in configs} + + +async def plot_sensor( + config: Config[t.Any, t.Any], + plot: _AxesBase, + location_names: t.Dict[UUID, str], + **kwargs: t.Any +) -> _AxesBase: + locations = [] + if config.get_data is None: + raise ValueError("You must provide a get_data function") + async for deployment_id, query_results in config.get_data(**kwargs): + if deployment_id == GLOBAL: + name = "Global" + else: + try: + deployment = await get_deployment_by_id(deployment_id) + except IndexError: + name = str(deployment_id) + colour = None + else: + name = deployment.name or str(deployment_id) + colour = deployment.colour + # Mypy currently doesn't understand callable fields on datatypes: https://github.com/python/mypy/issues/5485 + points = [dp async for dp in config.clean(query_results)] # type: ignore + if not points: + continue + locations.append(name) + x, y = zip(*points) + plot.set_title(config.title) + plot.set_ylabel(config.ylabel) + if config.draw is None: + raise ValueError("You must provide a get_data function") + config.draw(plot, x, y, colour) + plot.legend(locations) + return plot + + +async def plot_multiple_charts(*args: t.Any, **kwargs: t.Any) -> Figure: + # These parameters are pulled from kwargs to avoid confusing function + # introspection code in IPython widgets + location_names = kwargs.pop("location_names", None) + configs = kwargs.pop("configs", None) + dimensions = kwargs.pop("dimensions", None) + db_uri = kwargs.pop("db_uri", "postgresql+psycopg2://apd@localhost/apd") + + with with_database(db_uri) as session: + loop = asyncio.get_running_loop() + coros = [] + if configs is None: + # If no configs are supplied, use all known configs + configs = get_known_configs().values() + if dimensions is None: + # If no dimensions are supplied, get the square root of the number + # of configs and round it to find a number of columns. This will + # keep the arrangement approximately square. Find rows by multiplying + # out rows. + total_configs = len(configs) + columns = round(math.sqrt(total_configs)) + rows = math.ceil(total_configs / columns) + if location_names is None: + location_query = session.query( + deployment_table.c.id, deployment_table.c.name + ) + location_data = await loop.run_in_executor(None, location_query.all) + location_names = dict(location_data) + + figure = plt.figure(figsize=(10 * columns, 5 * rows), dpi=300) + for i, config in enumerate(configs, start=1): + plot = figure.add_subplot(columns, rows, i) + coros.append(plot_sensor(config, plot, location_names, *args, **kwargs)) + await asyncio.gather(*coros) + return figure + + +_Coroutine_Result = t.TypeVar("_Coroutine_Result") + + +def wrap_coroutine( + f: t.Callable[..., t.Coroutine[t.Any, t.Any, _Coroutine_Result]] +) -> t.Callable[..., _Coroutine_Result]: + """Given a coroutine, return a function that runs that coroutine + in a new event loop in an isolated thread""" + + @functools.wraps(f) + def run_in_thread(*args: t.Any, **kwargs: t.Any) -> _Coroutine_Result: + loop = asyncio.new_event_loop() + wrapped = f(*args, **kwargs) + with ThreadPoolExecutor(max_workers=1) as pool: + task = pool.submit(loop.run_until_complete, wrapped) + # Mypy can get confused when nesting generic functions, like we do here + # The fact that Task is generic means we lose the association with + # _CoroutineResult. Adding an explicit cast restores this. + return t.cast(_Coroutine_Result, task.result()) + + return run_in_thread + + +def interactable_plot_multiple_charts( + *args: t.Any, **kwargs: t.Any +) -> t.Callable[..., Figure]: + with_config = functools.partial(plot_multiple_charts, *args, **kwargs) + return wrap_coroutine(with_config) diff --git a/Ch10/apd.aggregation-chapter10-ex01/src/apd/aggregation/cli.py b/Ch10/apd.aggregation-chapter10-ex01/src/apd/aggregation/cli.py new file mode 100644 index 0000000..d1b0fbc --- /dev/null +++ b/Ch10/apd.aggregation-chapter10-ex01/src/apd/aggregation/cli.py @@ -0,0 +1,184 @@ +import asyncio +import typing as t +import uuid + +import aiohttp +import click +from sqlalchemy import create_engine +from sqlalchemy.orm import sessionmaker + +from . import collect +from .database import Deployment, deployment_table + + +@click.command() +@click.argument("server", nargs=-1) +@click.option( + "--db", + metavar="", + default="postgresql+psycopg2://localhost/apd", + help="The connection string to a PostgreSQL database", + envvar="APD_DB_URI", +) +@click.option("--api-key", metavar="", envvar="APD_API_KEY") +@click.option( + "--tolerate-failures", + "-f", + help="If provided, failure to retrieve some sensors' data will not abort the collection process", + is_flag=True, +) +@click.option("-v", "--verbose", is_flag=True, help="Enables verbose mode") +def collect_sensor_data( + db: str, server: t.Tuple[str], api_key: str, tolerate_failures: bool, verbose: bool +): + """This loads data from one or more sensors into the specified database. + + Only PostgreSQL databases are supported, as the column definitions use + multiple pg specific features. The database must already exist and be + populated with the required tables. + + The --api-key option is used to specify the access token for the sensors + being queried. + + You may specify any number of servers, the variable should be the full URL + to the sensor's HTTP interface, not including the /v/2.0 portion. Muliple + URLs should be separated with a space. + """ + if tolerate_failures: + attempts = [(s,) for s in server] + else: + attempts = [server] + success = True + for attempt in attempts: + try: + collect.standalone(db, attempt, api_key, echo=verbose) + except ValueError as e: + click.secho(str(e), err=True, fg="red") + success = False + return success + + +@click.group() +def deployments(): + pass + + +@deployments.command() +@click.argument("uri") +@click.argument("name") +@click.option( + "--db", + metavar="", + default="postgresql+psycopg2://localhost/apd", + help="The connection string to a PostgreSQL database", + envvar="APD_DB_URI", +) +@click.option("--api-key", metavar="", envvar="APD_API_KEY") +@click.option("--colour") +def add( + db: str, uri: str, name: str, api_key: t.Optional[str], colour: t.Optional[str], +): + """This creates a record of a new deployment in the database. + """ + deployment = Deployment(id=None, uri=uri, name=name, api_key=api_key, colour=colour) + + async def http_get_deployment_id(): + async with aiohttp.ClientSession() as http: + collect.http_session_var.set(http) + return await collect.get_deployment_id(uri) + + deployment.id = asyncio.run(http_get_deployment_id()) + insert = deployment_table.insert().values(**deployment._asdict()) + + engine = create_engine(db) + sm = sessionmaker(engine) + Session = sm() + Session.execute(insert) + Session.commit() + return True + + +@deployments.command() +@click.option( + "--db", + metavar="", + default="postgresql+psycopg2://localhost/apd", + help="The connection string to a PostgreSQL database", + envvar="APD_DB_URI", +) +def list(db: str): + """This creates a record of a new deployment in the database. + """ + engine = create_engine(db) + sm = sessionmaker(engine) + Session = sm() + deployments = Session.query(deployment_table).all() + for deployment in deployments: + click.secho(deployment.name, bold=True) + click.echo(click.style("ID ", bold=True) + deployment.id.hex) + click.echo(click.style("URI ", bold=True) + deployment.uri) + click.echo(click.style("API key ", bold=True) + deployment.api_key) + click.echo(click.style("Colour ", bold=True) + str(deployment.colour)) + click.echo() + Session.rollback() + return True + + +@deployments.command() +@click.argument("id") +@click.option("--uri") +@click.option("--name") +@click.option( + "--db", + metavar="", + default="postgresql+psycopg2://localhost/apd", + help="The connection string to a PostgreSQL database", + envvar="APD_DB_URI", +) +@click.option("--api-key", metavar="", envvar="APD_API_KEY") +@click.option("--colour") +def edit( + db: str, + id, + uri: t.Optional[str], + name: t.Optional[str], + api_key: t.Optional[str], + colour: t.Optional[str], +): + """This creates a record of a new deployment in the database. + """ + update = {} + if uri is not None: + update["uri"] = uri + if name is not None: + update["name"] = name + if api_key is not None: + update["api_key"] = api_key + if colour is not None: + update["colour"] = colour + deployment_id = uuid.UUID(id) + + update_stmt = ( + deployment_table.update() + .where(deployment_table.c.id == deployment_id) + .values(**update) + ) + + engine = create_engine(db) + sm = sessionmaker(engine) + Session = sm() + Session.execute(update_stmt) + deployments = Session.query(deployment_table).filter( + deployment_table.c.id == deployment_id + ) + Session.commit() + + for deployment in deployments: + click.secho(deployment.name, bold=True) + click.echo(click.style("ID ", bold=True) + deployment.id.hex) + click.echo(click.style("URI ", bold=True) + deployment.uri) + click.echo(click.style("API key ", bold=True) + deployment.api_key) + click.echo(click.style("Colour ", bold=True) + str(deployment.colour)) + click.echo() + + return True diff --git a/Ch10/apd.aggregation-chapter10-ex01/src/apd/aggregation/collect.py b/Ch10/apd.aggregation-chapter10-ex01/src/apd/aggregation/collect.py new file mode 100644 index 0000000..1d9ea08 --- /dev/null +++ b/Ch10/apd.aggregation-chapter10-ex01/src/apd/aggregation/collect.py @@ -0,0 +1,112 @@ +import asyncio +from contextvars import ContextVar +import datetime +import typing as t +import uuid + +import aiohttp +from sqlalchemy import create_engine +from sqlalchemy.orm import sessionmaker +from sqlalchemy.orm.session import Session + +from .database import DataPoint, datapoint_table, Deployment, deployment_table + +http_session_var: ContextVar[aiohttp.ClientSession] = ContextVar("http_session") + + +async def get_deployment_id(server): + http = http_session_var.get() + if not server.endswith("/"): + server += "/" + url = server + "v/2.1/deployment_id" + async with http.get(url) as request: + if request.status != 200: + raise ValueError(f"Error loading deployment id from {server}") + result = await request.json() + return uuid.UUID(result["deployment_id"]) + + +async def get_data_points(server: str, api_key: t.Optional[str],) -> t.List[DataPoint]: + if not server.endswith("/"): + server += "/" + url = server + "v/2.1/sensors/" + headers = {} + if api_key: + headers["X-API-KEY"] = api_key + http = http_session_var.get() + + # Get the deployment ID in parallel to the sensor data + deployment_id = asyncio.create_task(get_deployment_id(server)) + + async with http.get(url, headers=headers) as request: + result = await request.json() + ok = request.status == 200 + now = datetime.datetime.now() + if ok: + points = [] + for value in result["sensors"]: + points.append( + DataPoint( + sensor_name=value["id"], + collected_at=now, + data=value["value"], + deployment_id=await deployment_id, + ) + ) + return points + else: + raise ValueError( + f"Error loading data from {server}: " + result.get("error", "Unknown") + ) + + +def handle_result(result: t.List[DataPoint], session: Session) -> t.List[DataPoint]: + for point in result: + insert = datapoint_table.insert().values(**point._asdict()) + sql_result = session.execute(insert) + point.id = sql_result.inserted_primary_key[0] + return result + + +async def add_data_from_sensors( + session: Session, servers: t.Iterable[Deployment] +) -> t.List[DataPoint]: + tasks: t.List[t.Awaitable[t.List[DataPoint]]] = [] + points: t.List[DataPoint] = [] + async with aiohttp.ClientSession() as http: + http_session_var.set(http) + tasks = [get_data_points(server.uri, server.api_key) for server in servers] + for results in await asyncio.gather(*tasks): + points += results + loop = asyncio.get_running_loop() + await loop.run_in_executor(None, handle_result, points, session) + return points + + +def standalone( + db_uri: str, servers: t.Tuple[str], api_key: t.Optional[str], echo: bool = False +) -> None: + engine = create_engine(db_uri, echo=echo) + sm = sessionmaker(engine) + Session = sm() + deployments: t.Iterable[Deployment] + if servers: + deployments = tuple( + Deployment(id=None, name=None, colour=None, api_key=api_key, uri=server) + for server in servers + ) + else: + deployment_data = Session.query(deployment_table).all() + deployments = tuple( + Deployment.from_sql_result(deployment) for deployment in deployment_data + ) + asyncio.run(add_data_from_sensors(Session, deployments)) + Session.commit() + + +if __name__ == "__main__": + standalone( + db_uri="postgresql+psycopg2://apd@localhost/apd", + servers=("http://pvoutput:8080/",), + api_key="h3hdfjksfhwkjehnwekj", + ) diff --git a/Ch10/apd.aggregation-chapter10-ex01/src/apd/aggregation/database.py b/Ch10/apd.aggregation-chapter10-ex01/src/apd/aggregation/database.py new file mode 100644 index 0000000..d7f0d37 --- /dev/null +++ b/Ch10/apd.aggregation-chapter10-ex01/src/apd/aggregation/database.py @@ -0,0 +1,129 @@ +from __future__ import annotations + +from dataclasses import dataclass, field, asdict +import datetime +import typing as t +import uuid + +import sqlalchemy +from sqlalchemy.dialects.postgresql import JSONB, DATE, TIMESTAMP, UUID +from sqlalchemy.ext.hybrid import ExprComparator, hybrid_property +from sqlalchemy.orm import sessionmaker +from sqlalchemy.schema import Table + + +metadata = sqlalchemy.MetaData() + +deployment_table = Table( + "deployments", + metadata, + sqlalchemy.Column("id", UUID(as_uuid=True), primary_key=True), + sqlalchemy.Column("uri", sqlalchemy.String, index=False), + sqlalchemy.Column("name", sqlalchemy.String, index=False, nullable=True), + sqlalchemy.Column("colour", sqlalchemy.String, index=False, nullable=True), + sqlalchemy.Column("api_key", sqlalchemy.String, index=False), +) + +datapoint_table = Table( + "sensor_values", + metadata, + sqlalchemy.Column("id", sqlalchemy.Integer, primary_key=True), + sqlalchemy.Column("sensor_name", sqlalchemy.String, index=True), + sqlalchemy.Column("collected_at", TIMESTAMP, index=True), + sqlalchemy.Column("deployment_id", UUID(as_uuid=True), index=True), + sqlalchemy.Column("data", JSONB), +) + +daily_summary_view = Table( + "daily_summary", + metadata, + sqlalchemy.Column("sensor_name", sqlalchemy.String), + sqlalchemy.Column("data", JSONB), + sqlalchemy.Column("count", sqlalchemy.Integer), + info={"is_view": True}, +) + + +class DateEqualComparator(ExprComparator): + def __init__(self, fallback_expression, raw_expression): + # Do not try and find update expression from parent + super().__init__(None, fallback_expression, None) + self.raw_expression = raw_expression + + def __eq__(self, other): + """ Returns True iff on the same day as other """ + other_date = sqlalchemy.cast(other, DATE) + return sqlalchemy.and_( + self.raw_expression >= other_date, self.raw_expression < other_date + 1, + ) + + def operate(self, op, *other, **kwargs): + other = [sqlalchemy.cast(date, DATE) for date in other] + return op(self.expression, *other, **kwargs) + + def reverse_operate(self, op, other, **kwargs): + other = [sqlalchemy.cast(date, DATE) for date in other] + return op(other, self.expression, **kwargs) + + +@dataclass +class DataPoint: + sensor_name: str + data: t.Any + deployment_id: uuid.UUID + id: t.Optional[int] = None + collected_at: datetime.datetime = field(default_factory=datetime.datetime.now) + + @classmethod + def from_sql_result(cls, result) -> DataPoint: + return cls(**result._asdict()) + + def _asdict(self) -> t.Dict[str, t.Any]: + data = asdict(self) + if data["id"] is None: + del data["id"] + return data + + @hybrid_property + def collected_on_date(self): + return self.collected_at.date() + + @collected_on_date.comparator # type: ignore + def collected_on_date(cls): + return DateEqualComparator( + fallback_expression=sqlalchemy.cast(datapoint_table.c.collected_at, DATE), + raw_expression=datapoint_table.c.collected_at, + ) + + +@dataclass +class Deployment: + id: t.Optional[uuid.UUID] + uri: str + name: t.Optional[str] + colour: t.Optional[str] + api_key: t.Optional[str] + + @classmethod + def from_sql_result(cls, result) -> Deployment: + return cls(**result._asdict()) + + def _asdict(self) -> t.Dict[str, t.Any]: + data = asdict(self) + return data + + +def main() -> None: + engine = sqlalchemy.create_engine( + "postgresql+psycopg2://apd@localhost/apd", echo=True + ) + sm = sessionmaker(engine) + Session = sm() + if False: + metadata.create_all(engine) + print(Session.query(DataPoint).all()) + pass + + +if __name__ == "__main__": + main() diff --git a/Ch10/apd.aggregation-chapter10-ex01/src/apd/aggregation/query.py b/Ch10/apd.aggregation-chapter10-ex01/src/apd/aggregation/query.py new file mode 100644 index 0000000..3d2005d --- /dev/null +++ b/Ch10/apd.aggregation-chapter10-ex01/src/apd/aggregation/query.py @@ -0,0 +1,146 @@ +import asyncio +import contextlib +from contextvars import ContextVar +import datetime +import typing as t +from uuid import UUID + +from sqlalchemy import create_engine +from sqlalchemy.orm import sessionmaker +from sqlalchemy.orm.session import Session + + +from apd.aggregation.database import ( + datapoint_table, + DataPoint, + deployment_table, + Deployment, +) + + +db_session_var: ContextVar[Session] = ContextVar("db_session") + + +@contextlib.contextmanager +def with_database(uri: t.Optional[str] = None) -> t.Iterator[Session]: + """Given a URI, set up a DB connection, and return a Session as a context manager """ + if uri is None: + uri = "postgresql+psycopg2://localhost/apd" + engine = create_engine(uri) + sm = sessionmaker(engine) + Session = sm() + token = db_session_var.set(Session) + yield Session + db_session_var.reset(token) + Session.commit() + Session.close() + + +async def get_data( + sensor_name: t.Optional[str] = None, + deployment_id: t.Optional[UUID] = None, + collected_before: t.Optional[datetime.datetime] = None, + collected_after: t.Optional[datetime.datetime] = None, +) -> t.AsyncIterator[DataPoint]: + db_session = db_session_var.get() + loop = asyncio.get_running_loop() + query = db_session.query(datapoint_table) + if sensor_name: + query = query.filter(datapoint_table.c.sensor_name == sensor_name) + if deployment_id: + query = query.filter(datapoint_table.c.deployment_id == deployment_id) + if collected_before: + query = query.filter(datapoint_table.c.collected_at < collected_before) + if collected_after: + query = query.filter(datapoint_table.c.collected_at > collected_after) + query = query.order_by( + datapoint_table.c.deployment_id, + datapoint_table.c.sensor_name, + datapoint_table.c.collected_at, + ) + + rows = await loop.run_in_executor(None, query.all) + for row in rows: + yield DataPoint.from_sql_result(row) + + +async def get_deployment_ids() -> t.List[UUID]: + db_session = db_session_var.get() + loop = asyncio.get_running_loop() + query = db_session.query(datapoint_table.c.deployment_id).distinct() + return [row.deployment_id for row in await loop.run_in_executor(None, query.all)] + + +async def get_deployment_by_id(deployment_id: UUID) -> Deployment: + db_session = db_session_var.get() + loop = asyncio.get_running_loop() + query = db_session.query(deployment_table).filter( + deployment_table.c.id == deployment_id + ) + return [ + Deployment.from_sql_result(row) + for row in await loop.run_in_executor(None, query.all) + ][0] + + +async def get_data_by_deployment( + *args: t.Any, **kwargs: t.Any +) -> t.AsyncIterator[t.Tuple[UUID, t.AsyncIterator[DataPoint]]]: + """Return an Async Iterator that contains two-item pairs. + These pairs are a string (deployment_id), and an async iterator that contains + the datapoints with that deployment_id. + + Usage example: + + async for deployment_id, datapoints in get_data_by_deployment(): + print(deployment_id) + async for datapoint in datapoints: + print(datapoint) + print() + """ + # Get the data, using the arguments to this function as filters + data = get_data(*args, **kwargs) + + # The two levels of iterator share the item variable, initialise it with the + # first item from the iterator. Also set last_deployment_id to None, so the + # outer iterator knows to start a new group. + last_deployment_id: t.Optional[UUID] = None + try: + item = await data.__anext__() + except StopAsyncIteration: + # There were no items in the underlying query, return immediately + return + + async def subiterator(group_id: UUID) -> t.AsyncIterator[DataPoint]: + """Using a closure, create an iterator that yields the current + item, then yields all items from data while the deployment_id matches + group_id, leaving the first that doesn't match as item in the enclosing + scope.""" + # item is from the enclosing scope + nonlocal item + while item.deployment_id == group_id: + # yield items from data while they match the group_id this iterator represents + yield item + try: + # Advance the underlying iterator + item = await data.__anext__() + except StopAsyncIteration: + # The underlying iterator came to an end, so end the subiterator too + return + + while True: + while item.deployment_id == last_deployment_id: + # We are trying to advance the outer iterator while the underlying iterator + # is still part-way through a group. Speed through the underlying until we + # hit an item where the deployment_id is different to the last one (or, is not + # None, in the case of the start of the iterator) + try: + item = await data.__anext__() + except StopAsyncIteration: + # We hit the end of the underlying iterator: end this iterator too + return + last_deployment_id = item.deployment_id + # Don't yield an iterator for unclassified deployments + if last_deployment_id is not None: + # Instantiate a subiterator for this group + yield last_deployment_id, subiterator(last_deployment_id) diff --git a/Ch10/apd.aggregation-chapter10-ex01/src/apd/aggregation/typing.py b/Ch10/apd.aggregation-chapter10-ex01/src/apd/aggregation/typing.py new file mode 100644 index 0000000..5db1337 --- /dev/null +++ b/Ch10/apd.aggregation-chapter10-ex01/src/apd/aggregation/typing.py @@ -0,0 +1,48 @@ +import datetime +import typing as t +from typing_extensions import Protocol + +from apd.aggregation.database import DataPoint + + +# Aliases for common types +# These type variables allow for generic functions. T_key represents the place +# in a chart that an item will be placed, and T_value the kind of data that is plotted. +T_key = t.TypeVar("T_key") +T_value = t.TypeVar("T_value") + +# Cleaned is a placeholder type, representing an async iterator of key and value functions +# Cleaned[float, float] is equivalent to typing.AsyncIterator[Tuple[builtins.float, builtins.float]] +Cleaned = t.AsyncIterator[t.Tuple[T_key, T_value]] + +# T_cleaned represents a placeholder for the result of a cleaner function, and is *covariant* +# because it can accept compatible types (such as ints where floats were declared). +# It is bound to Cleaned, so only items that match the specification for Cleaned (for any values +# of T_key or T_value) are valid +T_cleaned = t.TypeVar("T_cleaned", covariant=True, bound=Cleaned) + + +# CleanerFunc is a generic protocol, it matches any Callable that converts an async iterator +# of datapoints to its type. So, CleanerFunc[float] would be equivalent to t.Callable[[t.AsyncIterator[DataPoint]], float] +class CleanerFunc(Protocol[T_cleaned]): + def __call__(self, datapoints: t.AsyncIterator[DataPoint]) -> T_cleaned: + ... + + +# CLEANED_DT_FLOAT is a Cleaned represents datetime/float pairs, for simple charts +CLEANED_DT_FLOAT = Cleaned[datetime.datetime, float] +# and CLEANED_COORD_FLOAT represents (lat/lon), float pairs +CLEANED_COORD_FLOAT = Cleaned[t.Tuple[float, float], float] + +# The _CLEANER variants are functions that return their matching iterators from above +DT_FLOAT_CLEANER = CleanerFunc[CLEANED_DT_FLOAT] +COORD_FLOAT_CLEANER = CleanerFunc[CLEANED_COORD_FLOAT] + + +# When drawing a map we will be building a dictionary of UUID to dictionary +# That inner dictionary should contain coord (float, float) +# and value (float) only. Either or both can be None. +# This class is abstract, it's just for type checking. +class IntermediateMapData(t.TypedDict): + coord: t.Optional[t.Tuple[float, float]] + value: t.Optional[float] diff --git a/Ch10/apd.aggregation-chapter10-ex01/src/apd/aggregation/utils.py b/Ch10/apd.aggregation-chapter10-ex01/src/apd/aggregation/utils.py new file mode 100644 index 0000000..da90a12 --- /dev/null +++ b/Ch10/apd.aggregation-chapter10-ex01/src/apd/aggregation/utils.py @@ -0,0 +1,82 @@ +from __future__ import annotations + +import contextlib +import functools +import importlib +import io +import math +import os +import typing as t + +from pint import _DEFAULT_REGISTRY as ureg + + +# Set up mercator transform from https://wiki.openstreetmap.org/wiki/Mercator#Python +# Thanks to Paulo Silva and the OSM contributors +def merc_x(lon: float) -> float: + r_major = 6378137.000 + return r_major * math.radians(lon) + + +def merc_y(lat: float) -> float: + if lat > 89.5: + lat = 89.5 + if lat < -89.5: + lat = -89.5 + r_major = 6378137.000 + r_minor = 6356752.3142 + temp = r_minor / r_major + eccent = math.sqrt(1 - temp ** 2) + phi = math.radians(lat) + sinphi = math.sin(phi) + con = eccent * sinphi + com = eccent / 2 + con = ((1.0 - con) / (1.0 + con)) ** com + ts = math.tan((math.pi / 2 - phi) / 2) / con + y = 0 - r_major * math.log(ts) + return y + + +@functools.lru_cache +def convert_temperature(magnitude: float, origin_unit: str, target_unit: str) -> float: + # if origin_unit == "degC" and target_unit == "degF": + # return (magnitude * 1.8) + 32 + temp = ureg.Quantity(magnitude, origin_unit) + return temp.to(target_unit).magnitude + + +@contextlib.contextmanager +def jupyter_page_file() -> t.Iterator[io.StringIO]: + from IPython.core import page + + output = io.StringIO() + yield output + output.seek(0) + page.page(output.read()) + + +@contextlib.contextmanager +def profile_with_yappi() -> t.Iterator[None]: + import yappi + + yappi.clear_stats() + yappi.start() + yield None + yappi.stop() + + +class YappiPackageFilter: + """ This object can be passed to yappi's modname filter to limit + by Python package rather than module filename""" + + def __init__(self, package: str) -> None: + mod = importlib.import_module(package) + self.fn = mod.__file__ + if self.fn.endswith("__init__.py"): + self.fn = os.path.dirname(self.fn) + + def __eq__(self, other: object) -> t.Union[bool, NotImplemented]: + if isinstance(other, str): + return other.startswith(self.fn) + else: + return NotImplemented diff --git a/Ch10/apd.aggregation-chapter10-ex01/tests/__init__.py b/Ch10/apd.aggregation-chapter10-ex01/tests/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/Ch10/apd.aggregation-chapter10-ex01/tests/conftest.py b/Ch10/apd.aggregation-chapter10-ex01/tests/conftest.py new file mode 100644 index 0000000..328e95f --- /dev/null +++ b/Ch10/apd.aggregation-chapter10-ex01/tests/conftest.py @@ -0,0 +1,124 @@ +import datetime +from uuid import UUID + +from apd.aggregation.database import datapoint_table + +from alembic.config import Config +from alembic.script import ScriptDirectory +from alembic.runtime.environment import EnvironmentContext +import pytest + + +@pytest.fixture +def db_uri(): + return "postgresql+psycopg2://apd@localhost/apd-test" + + +@pytest.fixture +def db_session(db_uri): + from sqlalchemy import create_engine + from sqlalchemy.orm import sessionmaker + + engine = create_engine(db_uri, echo=True) + sm = sessionmaker(engine) + Session = sm() + yield Session + Session.close() + + +@pytest.fixture +def migrated_db(db_uri, db_session): + config = Config() + config.set_main_option("script_location", "apd.aggregation:alembic") + config.set_main_option("sqlalchemy.url", db_uri) + script = ScriptDirectory.from_config(config) + + def upgrade(rev, context): + return script._upgrade_revs(script.get_current_head(), rev) + + def downgrade(rev, context): + return script._downgrade_revs(None, rev) + + with EnvironmentContext(config, script, fn=upgrade): + script.run_env() + + try: + yield + finally: + # Clear any pending work from the db_session connection + db_session.rollback() + + with EnvironmentContext(config, script, fn=downgrade): + script.run_env() + + +@pytest.fixture +def populated_db(migrated_db, db_session): + datas = [ + { + "id": 1, + "sensor_name": "Test", + "data": "1", + "collected_at": datetime.datetime(2020, 4, 1, 12, 0, 1), + "deployment_id": UUID("b4c68905-b1e4-4875-940e-69e5d27730fd"), + }, + { + "id": 2, + "sensor_name": "Test", + "data": "2", + "collected_at": datetime.datetime(2020, 4, 1, 12, 1, 1), + "deployment_id": UUID("b4c68905-b1e4-4875-940e-69e5d27730fd"), + }, + { + "id": 3, + "sensor_name": "Test", + "data": "3", + "collected_at": datetime.datetime(2020, 4, 1, 12, 2, 1), + "deployment_id": UUID("b4c68905-b1e4-4875-940e-69e5d27730fd"), + }, + { + "id": 4, + "sensor_name": "Test", + "data": "4", + "collected_at": datetime.datetime(2020, 4, 1, 12, 3, 1), + "deployment_id": UUID("b4c68905-b1e4-4875-940e-69e5d27730fd"), + }, + { + "id": 5, + "sensor_name": "OtherTest", + "data": "5", + "collected_at": datetime.datetime(2020, 4, 1, 12, 4, 1), + "deployment_id": UUID("b4c68905-b1e4-4875-940e-69e5d27730fd"), + }, + { + "id": 6, + "sensor_name": "Test", + "data": "1", + "collected_at": datetime.datetime(2020, 4, 1, 12, 0, 1), + "deployment_id": UUID("6e887b81-57c3-4f3b-a7e7-bad159e05e78"), + }, + { + "id": 7, + "sensor_name": "Test", + "data": "1", + "collected_at": datetime.datetime(2020, 4, 1, 12, 1, 1), + "deployment_id": UUID("6e887b81-57c3-4f3b-a7e7-bad159e05e78"), + }, + { + "id": 8, + "sensor_name": "Test", + "data": "2", + "collected_at": datetime.datetime(2020, 4, 1, 12, 2, 1), + "deployment_id": UUID("6e887b81-57c3-4f3b-a7e7-bad159e05e78"), + }, + { + "id": 9, + "sensor_name": "OtherTest", + "data": "3", + "collected_at": datetime.datetime(2020, 4, 1, 12, 3, 1), + "deployment_id": UUID("6e887b81-57c3-4f3b-a7e7-bad159e05e78"), + }, + ] + for data in datas: + insert = datapoint_table.insert().values(**data) + db_session.execute(insert) diff --git a/Ch10/apd.aggregation-chapter10-ex01/tests/test_analysis.py b/Ch10/apd.aggregation-chapter10-ex01/tests/test_analysis.py new file mode 100644 index 0000000..6b3f31b --- /dev/null +++ b/Ch10/apd.aggregation-chapter10-ex01/tests/test_analysis.py @@ -0,0 +1,503 @@ +import collections.abc +import datetime +import functools +import uuid + +import pytest + +from apd.aggregation import analysis +from apd.aggregation.database import DataPoint, datapoint_table +from apd.aggregation.query import db_session_var + + +async def generate_datapoints(datas): + deployment_id = uuid.uuid4() + for i, (time, data) in enumerate(datas, start=1): + yield DataPoint( + id=i, + collected_at=time, + sensor_name="TestSensor", + data=data, + deployment_id=deployment_id, + ) + + +@functools.singledispatch +def consume(input_iterator): + items = [item for item in input_iterator] + + def inner_iterator(): + for item in items: + yield item + + return inner_iterator() + + +@consume.register +async def consume_async(input_iterator: collections.abc.AsyncIterator): + items = [item async for item in input_iterator] + + async def inner_iterator(): + for item in items: + yield item + + return inner_iterator() + + +class TestPassThroughCleaner: + @pytest.fixture + def cleaner(self): + return analysis.clean_passthrough + + @pytest.mark.asyncio + async def test_float_passthrough(self, cleaner): + data = [ + (datetime.datetime(2020, 4, 1, 12, 0, 0), 65.0), + (datetime.datetime(2020, 4, 1, 13, 0, 0), 65.5), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert output == data + + @pytest.mark.asyncio + async def test_Nones_are_skipped(self, cleaner): + data = [ + (datetime.datetime(2020, 4, 1, 12, 0, 0), None), + (datetime.datetime(2020, 4, 1, 13, 0, 0), 65.5), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert output == [ + (datetime.datetime(2020, 4, 1, 13, 0, 0), 65.5), + ] + + +class TestTemperatureCleaner: + @pytest.fixture + def cleaner(self): + return analysis.clean_temperature_fluctuations + + @pytest.mark.asyncio + async def test_window_not_full(self, cleaner): + data = [ + ( + datetime.datetime(2020, 4, 1, 12, 0, 0), + {"magnitude": 21.0, "unit": "degC"}, + ), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert output == [(datetime.datetime(2020, 4, 1, 12, 0, 0), 21.0)] + + @pytest.mark.asyncio + async def test_window_exactly_full(self, cleaner): + data = [ + ( + datetime.datetime(2020, 4, 1, 12, 0, 0), + {"magnitude": 21.0, "unit": "degC"}, + ), + ( + datetime.datetime(2020, 4, 1, 12, 0, 1), + {"magnitude": 21.0, "unit": "degC"}, + ), + ( + datetime.datetime(2020, 4, 1, 12, 0, 2), + {"magnitude": 21.0, "unit": "degC"}, + ), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert output == [ + (datetime.datetime(2020, 4, 1, 12, 0, 0), 21.0), + (datetime.datetime(2020, 4, 1, 12, 0, 1), 21.0), + (datetime.datetime(2020, 4, 1, 12, 0, 2), 21.0), + ] + + @pytest.mark.asyncio + async def test_window_overfilled(self, cleaner): + data = [ + ( + datetime.datetime(2020, 4, 1, 12, 0, 0), + {"magnitude": 21.0, "unit": "degC"}, + ), + ( + datetime.datetime(2020, 4, 1, 12, 0, 1), + {"magnitude": 21.0, "unit": "degC"}, + ), + ( + datetime.datetime(2020, 4, 1, 12, 0, 2), + {"magnitude": 21.0, "unit": "degC"}, + ), + ( + datetime.datetime(2020, 4, 1, 12, 0, 3), + {"magnitude": 21.0, "unit": "degC"}, + ), + ( + datetime.datetime(2020, 4, 1, 12, 0, 4), + {"magnitude": 21.0, "unit": "degC"}, + ), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert output == [ + (datetime.datetime(2020, 4, 1, 12, 0, 0), 21.0), + (datetime.datetime(2020, 4, 1, 12, 0, 1), 21.0), + (datetime.datetime(2020, 4, 1, 12, 0, 2), 21.0), + (datetime.datetime(2020, 4, 1, 12, 0, 3), 21.0), + (datetime.datetime(2020, 4, 1, 12, 0, 4), 21.0), + ] + + @pytest.mark.asyncio + async def test_outlier_dropped(self, cleaner): + data = [ + ( + datetime.datetime(2020, 4, 1, 12, 0, 0), + {"magnitude": 21.0, "unit": "degC"}, + ), + ( + datetime.datetime(2020, 4, 1, 12, 0, 1), + {"magnitude": 61.0, "unit": "degC"}, + ), + ( + datetime.datetime(2020, 4, 1, 12, 0, 2), + {"magnitude": 21.0, "unit": "degC"}, + ), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert output == [ + (datetime.datetime(2020, 4, 1, 12, 0, 0), 21.0), + (datetime.datetime(2020, 4, 1, 12, 0, 2), 21.0), + ] + + @pytest.mark.asyncio + async def test_limited_to_DHT22_range(self, cleaner): + data = [ + ( + datetime.datetime(2020, 4, 1, 12, 0, 0), + {"magnitude": 91.0, "unit": "degC"}, + ), + ( + datetime.datetime(2020, 4, 1, 12, 0, 1), + {"magnitude": 91.0, "unit": "degC"}, + ), + ( + datetime.datetime(2020, 4, 1, 12, 0, 2), + {"magnitude": 91.0, "unit": "degC"}, + ), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert output == [] + + @pytest.mark.asyncio + async def test_Nones_dropped(self, cleaner): + data = [ + ( + datetime.datetime(2020, 4, 1, 12, 0, 0), + {"magnitude": 31.0, "unit": "degC"}, + ), + (datetime.datetime(2020, 4, 1, 12, 0, 1), None,), + ( + datetime.datetime(2020, 4, 1, 12, 0, 2), + {"magnitude": 32.0, "unit": "degC"}, + ), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert output == [ + (datetime.datetime(2020, 4, 1, 12, 0, 0), 31.0), + (datetime.datetime(2020, 4, 1, 12, 0, 2), 32.0), + ] + + +class TestWattHourCleaner: + @pytest.fixture + def cleaner(self): + return analysis.clean_watthours_to_watts + + @pytest.fixture(scope="class") + def huge_data_set(self): + date = datetime.datetime(2020, 4, 1, 12, 0, 0) + power = 500 + data = [] + for i in range(50_000): + date += datetime.timedelta(hours=1) + power += i + data.append((date, {"magnitude": power, "unit": "watt_hour"},)) + return data + + @pytest.mark.asyncio + async def test_one_entry_insufficient_to_find_diff(self, cleaner): + data = [ + ( + datetime.datetime(2020, 4, 1, 12, 0, 0), + {"magnitude": 1.0, "unit": "watt_hour"}, + ), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert output == [] + + @pytest.mark.asyncio + async def test_two_entries_provides_diff_with_second_time(self, cleaner): + data = [ + ( + datetime.datetime(2020, 4, 1, 12, 0, 0), + {"magnitude": 1.0, "unit": "watt_hour"}, + ), + ( + datetime.datetime(2020, 4, 1, 13, 0, 0), + {"magnitude": 10.0, "unit": "watt_hour"}, + ), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert len(output) == 1 + assert output[0][0] == datetime.datetime(2020, 4, 1, 13, 0, 0) + assert output[0][1] == pytest.approx(9.0, 0.0001) + + @pytest.mark.asyncio + async def test_nonstandard_units_can_be_used(self, cleaner): + data = [ + ( + datetime.datetime(2020, 4, 1, 12, 0, 0), + {"magnitude": 1.0, "unit": "watt_hour"}, + ), + ( + datetime.datetime(2020, 4, 1, 13, 0, 0), + {"magnitude": 10000.0, "unit": "joule"}, + ), + ( + datetime.datetime(2020, 4, 1, 14, 0, 0), + {"magnitude": 3.0, "unit": "watt_hour"}, + ), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert len(output) == 2 + assert output[0][0] == datetime.datetime(2020, 4, 1, 13, 0, 0) + assert output[0][1] == pytest.approx(1.7777, 0.001) + assert output[1][0] == datetime.datetime(2020, 4, 1, 14, 0, 0) + assert output[1][1] == pytest.approx(0.22222, 0.001) + + @pytest.mark.asyncio + async def test_fractional_hour(self, cleaner): + data = [ + ( + datetime.datetime(2020, 4, 1, 12, 0, 0), + {"magnitude": 1.0, "unit": "watt_hour"}, + ), + ( + datetime.datetime(2020, 4, 1, 12, 23, 42), + {"magnitude": 10.0, "unit": "watt_hour"}, + ), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert len(output) == 1 + assert output[0][0] == datetime.datetime(2020, 4, 1, 12, 23, 42) + assert output[0][1] == pytest.approx(22.7848, 0.001) + + @pytest.mark.asyncio + async def test_diff_happens_pairwise(self, cleaner): + data = [ + ( + datetime.datetime(2020, 4, 1, 12, 0, 0), + {"magnitude": 1.0, "unit": "watt_hour"}, + ), + ( + datetime.datetime(2020, 4, 1, 12, 23, 42), + {"magnitude": 10.0, "unit": "watt_hour"}, + ), + ( + datetime.datetime(2020, 4, 1, 13, 23, 42), + {"magnitude": 13.0, "unit": "watt_hour"}, + ), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert len(output) == 2 + assert output[0][0] == datetime.datetime(2020, 4, 1, 12, 23, 42) + assert output[0][1] == pytest.approx(22.7848, 0.001) + assert output[1][0] == datetime.datetime(2020, 4, 1, 13, 23, 42) + assert output[1][1] == pytest.approx(3, 0.001) + + @pytest.mark.asyncio + async def test_None_ignored(self, cleaner): + data = [ + ( + datetime.datetime(2020, 4, 1, 12, 0, 0), + {"magnitude": 1.0, "unit": "watt_hour"}, + ), + (datetime.datetime(2020, 4, 1, 12, 58, 0), None,), + ( + datetime.datetime(2020, 4, 1, 13, 0, 0), + {"magnitude": 10.0, "unit": "watt_hour"}, + ), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert len(output) == 1 + assert output[0][0] == datetime.datetime(2020, 4, 1, 13, 0, 0) + assert output[0][1] == pytest.approx(9.0, 0.0001) + + @pytest.mark.asyncio + @pytest.mark.performance + async def test_performance_of_cleaner(self, cleaner, huge_data_set): + import cProfile + + # Generate data point objects before profiling starts + datapoints = await consume(generate_datapoints(huge_data_set)) + profiler = cProfile.Profile() + profiler.enable() + + # Run the cleaner to completion + await consume(cleaner(datapoints)) + + profiler.disable() + cleaner_stat = [ + stat for stat in profiler.getstats() if stat.code == cleaner.__code__ + ][0] + # Assert that the cleaner may take no more than 0.5 seconds to process + # these 50k data points + assert cleaner_stat.totaltime < 0.5 + + +class TestTemperatureMap: + @pytest.fixture + def temperature_data(self): + return { + (53.8667, -1.3333): -1, + (53.35, -2.2833): 1, + (52.45, -1.7333): 3, + (51.5, -0.1333): 6, + (51.55, -2.5667): 5, + (54.9667, -1.6167): -1, + (55.9667, -3.2167): -1, + (54.7667, -1.5833): 0, + (53.7667, -0.3): 1, + (53.7667, -3.0167): 0, + (51.4833, -3.1833): 4, + (53.2667, -3.5167): 2, + (52.0833, -2.8): 2, + (52.0167, -0.6): 2, + (52.6667, 1.2667): 5, + (50.4333, -4.9833): 9, + (50.35, -4.1167): 10, + (50.5167, -2.45): 9, + (50.8333, -1.1667): 9, + (56.45, -5.4333): 4, + (57.5333, -4.05): -2, + (54.5167, -3.6167): 3, + (53.0833, 0.2667): 4, + (51.35, 1.3333): 7, + (50.8833, 0.3167): 8, + (58.45, -3.1): 3, + (50.1, -5.6667): 9, + (58.2167, -6.3333): 4, + (55.6833, -6.25): 7, + } + + @pytest.fixture + def temperature_datapoints(self, migrated_db, db_session, temperature_data): + datas = [] + now = datetime.datetime.now() + for (coord, temp) in temperature_data.items(): + deployment_id = uuid.uuid4() + datas.append( + DataPoint( + sensor_name="Location", + deployment_id=deployment_id, + collected_at=now, + data=coord, + ) + ) + datas.append( + DataPoint( + sensor_name="Temperature", + deployment_id=deployment_id, + collected_at=now, + data=temp, + ) + ) + return datas + + @pytest.fixture + def populated_db(self, migrated_db, db_session, temperature_datapoints): + for data in temperature_datapoints: + insert = datapoint_table.insert().values(**data._asdict()) + db_session.execute(insert) + + @pytest.fixture + def cleaner(self): + return analysis.get_map_cleaner_for("Temperature") + + @pytest.fixture + def get_data(self, db_session): + db_session_var.set(db_session) + return analysis.get_all_data() + + @pytest.mark.asyncio + async def test_latest_coord_temp_cleaner( + self, temperature_data, populated_db, get_data, cleaner + ): + data = get_data() + deployments = 0 + async for deployment, data_points in data: + deployments += 1 + cleaned = {a async for a in cleaner(data_points)} + # There should only be one deployment + assert deployments == 1 + expected = set(temperature_data.items()) + assert cleaned == expected + + @pytest.mark.asyncio + async def test_newer_items_superceed_older( + self, db_session, temperature_datapoints, populated_db, get_data, cleaner + ): + # Pick any of the deployment ids and find the pair of DataPoints in that deployment id + deployment_1 = temperature_datapoints[0].deployment_id + existing = [ + datapoint + for datapoint in temperature_datapoints + if datapoint.deployment_id == deployment_1 + ] + old_location = [ + datapoint for datapoint in existing if datapoint.sensor_name == "Location" + ][0] + old_temperature = [ + datapoint + for datapoint in existing + if datapoint.sensor_name == "Temperature" + ][0] + + new_location = DataPoint( + sensor_name="Location", + deployment_id=deployment_1, + collected_at=datetime.datetime(2031, 4, 1, 12, 0, 0), + data=(-10.5, 150.1), + ) + new_temperature = DataPoint( + sensor_name="Temperature", + deployment_id=deployment_1, + collected_at=datetime.datetime(2031, 4, 1, 12, 0, 0), + data=21, + ) + for data in [new_location, new_temperature]: + insert = datapoint_table.insert().values(**data._asdict()) + db_session.execute(insert) + + data = get_data() + deployments = 0 + async for deployment, data_points in data: + deployments += 1 + cleaned = {a async for a in cleaner(data_points)} + # There should only be one deployment + assert deployments == 1 + + # We expect the newer one, not the older + assert (new_location.data, new_temperature.data) in cleaned + assert (old_location.data, old_temperature.data) not in cleaned + # The cleaner converts two data points to one plottable + assert len(cleaned) == len(temperature_datapoints) / 2 diff --git a/Ch10/apd.aggregation-chapter10-ex01/tests/test_cli.py b/Ch10/apd.aggregation-chapter10-ex01/tests/test_cli.py new file mode 100644 index 0000000..5cab441 --- /dev/null +++ b/Ch10/apd.aggregation-chapter10-ex01/tests/test_cli.py @@ -0,0 +1,155 @@ +from unittest import mock +import uuid + +from click.testing import CliRunner +import pytest + +import apd.aggregation.cli +from apd.aggregation.database import Deployment, deployment_table + + +@pytest.mark.functional +def test_sensors_are_passed_to_get_data_points(db_uri): + runner = CliRunner() + with mock.patch("apd.aggregation.collect.get_data_points") as get_data_points: + runner.invoke( + apd.aggregation.cli.collect_sensor_data, + [ + "http://localhost", + "http://otherhost", + "--db", + db_uri, + "--api-key", + "key", + ], + ) + calls = get_data_points.call_args_list + assert len(calls) == 2 + details = {call[0] for call in get_data_points.call_args_list} + assert details == { + ("http://localhost", "key"), + ("http://otherhost", "key"), + } + + +@pytest.mark.functional +def test_add_deployment_to_db(db_uri, db_session, migrated_db): + runner = CliRunner() + deployment_id = uuid.UUID("76587cdd42dc489ea1d220e9b0bc62ca") + with mock.patch("apd.aggregation.collect.get_deployment_id") as get_deployment_id: + get_deployment_id.return_value = deployment_id + runner.invoke( + apd.aggregation.cli.deployments, + ["add", "http://otherhost", "Other", "--db", db_uri, "--api-key", "key"], + ) + deployments = db_session.query(deployment_table).all() + assert len(deployments) == 1 + deployment = Deployment.from_sql_result(deployments[0]) + assert deployment.uri == "http://otherhost" + assert deployment.api_key == "key" + assert deployment.id == deployment_id + + +@pytest.mark.functional +def test_use_stored_deployments(db_uri, migrated_db): + runner = CliRunner() + + deployment_id = uuid.UUID("76587cdd42dc489ea1d220e9b0bc62ca") + with mock.patch("apd.aggregation.collect.get_deployment_id") as get_deployment_id: + get_deployment_id.return_value = deployment_id + runner.invoke( + apd.aggregation.cli.deployments, + [ + "add", + "http://specifiedhost", + "Specified", + "--db", + db_uri, + "--api-key", + "an_api_key", + ], + ) + + with mock.patch("apd.aggregation.collect.get_data_points") as get_data_points: + runner.invoke( + apd.aggregation.cli.collect_sensor_data, ["--db", db_uri], + ) + calls = get_data_points.call_args_list + assert len(calls) == 1 + details = {call[0] for call in get_data_points.call_args_list} + assert details == { + ("http://specifiedhost", "an_api_key"), + } + + +@pytest.mark.functional +def test_list_deployments(db_uri, migrated_db): + runner = CliRunner() + + deployment_id = uuid.UUID("76587cdd42dc489ea1d220e9b0bc62ca") + with mock.patch("apd.aggregation.collect.get_deployment_id") as get_deployment_id: + get_deployment_id.return_value = deployment_id + runner.invoke( + apd.aggregation.cli.deployments, + [ + "add", + "http://specifiedhost", + "Specified", + "--db", + db_uri, + "--api-key", + "an_api_key", + ], + ) + + result = runner.invoke(apd.aggregation.cli.deployments, ["list", "--db", db_uri],) + expected = """Specified +ID 76587cdd42dc489ea1d220e9b0bc62ca +URI http://specifiedhost +API key an_api_key +Colour None + +""" + assert result.output == expected + + +@pytest.mark.functional +def test_edit_deployment(db_uri, migrated_db): + runner = CliRunner() + return + deployment_id = uuid.UUID("76587cdd42dc489ea1d220e9b0bc62ca") + with mock.patch("apd.aggregation.collect.get_deployment_id") as get_deployment_id: + get_deployment_id.return_value = deployment_id + runner.invoke( + apd.aggregation.cli.deployments, + [ + "add", + "http://specifiedhost", + "Specified", + "--db", + db_uri, + "--api-key", + "an_api_key", + ], + ) + + result = runner.invoke( + apd.aggregation.cli.deployments, + [ + "edit", + "--db", + db_uri, + deployment_id.hex(), + "--colour", + "red" "--name", + "New name", + ], + ) + expected = """New name +ID 76587cdd42dc489ea1d220e9b0bc62ca +URI http://specifiedhost +API key an_api_key +Colour red + +""" + assert result.output == expected diff --git a/Ch10/apd.aggregation-chapter10-ex01/tests/test_http_get.py b/Ch10/apd.aggregation-chapter10-ex01/tests/test_http_get.py new file mode 100644 index 0000000..df575c6 --- /dev/null +++ b/Ch10/apd.aggregation-chapter10-ex01/tests/test_http_get.py @@ -0,0 +1,188 @@ +from concurrent.futures import ThreadPoolExecutor +import datetime +import typing as t +from unittest.mock import patch, MagicMock +import wsgiref.simple_server + +import aiohttp +from apd.sensors.base import Sensor +from apd.sensors.sensors import PythonVersion, ACStatus +from apd.sensors.wsgi import set_up_config +import flask +import pytest + +from apd.sensors.wsgi import v21 + +from apd.aggregation import collect +from apd.aggregation.database import Deployment + +pytestmark = [pytest.mark.functional] + + +@pytest.fixture +def sensors() -> t.Iterator[t.List[Sensor[t.Any]]]: + """ Patch the get_sensors method to return a known pair of sensors only """ + data: t.List[Sensor[t.Any]] = [PythonVersion(), ACStatus()] + with patch("apd.sensors.cli.get_sensors") as get_sensors: + get_sensors.return_value = data + yield data + + +def get_independent_flask_app(name: str) -> flask.Flask: + """ Create a new flask app with the v20 API blueprint loaded, so multiple copies + of the app can be run in parallel without conflicting configuration """ + app = flask.Flask(name) + app.register_blueprint(v21.version, url_prefix="/v/2.1") + return app + + +def run_server_in_thread( + name: str, config: t.Dict[str, t.Any], port: int +) -> t.Iterator[str]: + # Create a new flask app and load in required code, to prevent config conflicts + app = get_independent_flask_app(name) + flask_app = set_up_config(config, app) + server = wsgiref.simple_server.make_server("localhost", port, flask_app) + + with ThreadPoolExecutor() as pool: + pool.submit(server.serve_forever) + yield f"http://localhost:{port}/" + server.shutdown() + + +@pytest.fixture(scope="module") +def http_server(): + yield from run_server_in_thread( + "standard", + { + "APD_SENSORS_API_KEY": "testing", + "APD_SENSORS_DEPLOYMENT_ID": "a46b1d1207fd4cdcad39bbdf706dfe29", + }, + 12081, + ) + + +@pytest.fixture(scope="module") +def bad_api_key_http_server(): + yield from run_server_in_thread( + "alternate", + { + "APD_SENSORS_API_KEY": "penny", + "APD_SENSORS_DEPLOYMENT_ID": "38cf2bae9adb445fad946c82e290487a", + }, + 12082, + ) + + +class TestGetDataPoints: + @pytest.fixture + def mut(self): + return collect.get_data_points + + @pytest.mark.asyncio + async def test_get_data_points( + self, sensors: t.List[Sensor[t.Any]], mut, http_server: str + ) -> None: + # Get the data from the server, storing the time before and after + # as bounds for the collected_at value + async with aiohttp.ClientSession() as http: + collect.http_session_var.set(http) + time_before = datetime.datetime.now() + results = await mut(http_server, "testing") + time_after = datetime.datetime.now() + + assert len(results) == len(sensors) == 2 + + for (sensor, result) in zip(sensors, results): + assert sensor.from_json_compatible(result.data) == sensor.value() + assert result.sensor_name == sensor.name + assert time_before <= result.collected_at <= time_after + + @pytest.mark.asyncio + async def test_get_data_points_fails_with_bad_api_key( + self, sensors: t.List[Sensor[t.Any]], mut, http_server: str + ) -> None: + with pytest.raises( + ValueError, + match=f"Error loading data from {http_server}: Supply API key in X-API-Key header", + ): + async with aiohttp.ClientSession() as http: + collect.http_session_var.set(http) + await mut(http_server, "incorrect") + + +class TestAddDataFromSensors: + @pytest.fixture + def mut(self): + return collect.add_data_from_sensors + + @pytest.fixture + def mock_db_session(self): + return MagicMock() + + @pytest.mark.asyncio + async def test_get_get_data_from_sensors( + self, mock_db_session, sensors: t.List[Sensor[t.Any]], mut, http_server: str + ) -> None: + results = await mut( + mock_db_session, + [ + Deployment( + id=None, colour=None, name=None, uri=http_server, api_key="testing" + ) + ], + ) + assert mock_db_session.execute.call_count == len(sensors) + assert len(results) == len(sensors) + + @pytest.mark.asyncio + async def test_get_get_data_from_sensors_with_multiple_servers( + self, mock_db_session, sensors: t.List[Sensor[t.Any]], mut, http_server: str + ) -> None: + results = await mut( + mock_db_session, + [ + Deployment( + id=None, colour=None, name=None, uri=http_server, api_key="testing" + ), + Deployment( + id=None, colour=None, name=None, uri=http_server, api_key="testing" + ), + ], + ) + assert mock_db_session.execute.call_count == len(sensors) * 2 + assert len(results) == len(sensors) * 2 + + @pytest.mark.asyncio + async def test_data_points_not_added_if_only_partial_success( + self, + mock_db_session, + sensors: t.List[Sensor[t.Any]], + mut, + http_server: str, + bad_api_key_http_server: str, + ) -> None: + with pytest.raises( + ValueError, + match=f"Error loading data from {bad_api_key_http_server}: Supply API key in X-API-Key header", + ): + await mut( + mock_db_session, + [ + Deployment( + id=None, + colour=None, + name=None, + uri=http_server, + api_key="testing", + ), + Deployment( + id=None, + colour=None, + name=None, + uri=bad_api_key_http_server, + api_key="testing", + ), + ], + ) + assert mock_db_session.execute.call_count == 0 diff --git a/Ch10/apd.aggregation-chapter10-ex01/tests/test_query.py b/Ch10/apd.aggregation-chapter10-ex01/tests/test_query.py new file mode 100644 index 0000000..9b6e0aa --- /dev/null +++ b/Ch10/apd.aggregation-chapter10-ex01/tests/test_query.py @@ -0,0 +1,111 @@ +import datetime +from uuid import UUID + +import pytest + +from apd.aggregation.query import ( + get_data, + get_deployment_ids, + get_data_by_deployment, + db_session_var, +) + + +class TestGetData: + @pytest.fixture + def mut(self): + return get_data + + @pytest.mark.asyncio + @pytest.mark.parametrize( + "filter,num_items_expected", + [ + ({}, 9), + ({"sensor_name": "Test"}, 7), + ({"deployment_id": UUID("b4c68905-b1e4-4875-940e-69e5d27730fd")}, 5), + ({"collected_after": datetime.datetime(2020, 4, 1, 12, 2, 1)}, 3), + ({"collected_before": datetime.datetime(2020, 4, 1, 12, 2, 1)}, 4), + ( + { + "collected_after": datetime.datetime(2020, 4, 1, 12, 2, 1), + "collected_before": datetime.datetime(2020, 4, 1, 12, 3, 5), + }, + 2, + ), + ], + ) + async def test_iterate_over_items( + self, mut, db_session, populated_db, filter, num_items_expected + ): + db_session_var.set(db_session) + points = [dp async for dp in mut(**filter)] + assert len(points) == num_items_expected + + @pytest.mark.asyncio + async def test_empty_db(self, mut, db_session, migrated_db): + db_session_var.set(db_session) + points = [dp async for dp in mut()] + assert len(points) == 0 + + +class TestGetDeployments: + @pytest.fixture + def mut(self): + return get_deployment_ids + + @pytest.mark.asyncio + async def test_get_all_deployments(self, mut, db_session, populated_db): + db_session_var.set(db_session) + deployment_ids = await mut() + assert set(deployment_ids) == { + UUID("b4c68905-b1e4-4875-940e-69e5d27730fd"), + UUID("6e887b81-57c3-4f3b-a7e7-bad159e05e78"), + } + + @pytest.mark.asyncio + async def test_empty_db(self, mut, db_session, migrated_db): + db_session_var.set(db_session) + deployment_ids = await mut() + assert deployment_ids == [] + + +@pytest.mark.usefixtures("populated_db") +class TestGetDataByDeployment: + @pytest.fixture + def mut(self): + return get_data_by_deployment + + @pytest.mark.asyncio + @pytest.mark.parametrize( + "filter,num_items_expected", [({}, 9), ({"sensor_name": "Test"}, 7)] + ) + async def test_iterate_over_all_items( + self, mut, db_session, filter, num_items_expected + ): + db_session_var.set(db_session) + num_items = 0 + num_deployments = 0 + async for deployment_id, items in mut(**filter): + num_deployments += 1 + async for item in items: + num_items += 1 + assert num_deployments == 2 + assert num_items == num_items_expected + + @pytest.mark.asyncio + @pytest.mark.parametrize( + "filter,num_items_expected", [({}, 5), ({"sensor_name": "Test"}, 4)] + ) + async def test_skip_first_deployment( + self, mut, db_session, filter, num_items_expected + ): + db_session_var.set(db_session) + num_items = 0 + num_deployments = 0 + async for deployment_id, items in mut(**filter): + num_deployments += 1 + if num_deployments == 2: + async for item in items: + num_items += 1 + assert num_deployments == 2 + assert num_items == num_items_expected diff --git a/Ch10/apd.aggregation-chapter10-ex01/tests/test_sensor_aggregation.py b/Ch10/apd.aggregation-chapter10-ex01/tests/test_sensor_aggregation.py new file mode 100644 index 0000000..311dd19 --- /dev/null +++ b/Ch10/apd.aggregation-chapter10-ex01/tests/test_sensor_aggregation.py @@ -0,0 +1,199 @@ +from __future__ import annotations + +import contextlib +from dataclasses import dataclass +import json +import typing as t +from unittest.mock import patch, Mock + +import pytest +import sqlalchemy + +import apd.aggregation.collect +from apd.aggregation.database import Deployment + + +@pytest.fixture +def data() -> t.Any: + return { + "sensors": [ + { + "human_readable": "3.7", + "id": "PythonVersion", + "title": "Python Version", + "value": [3, 7, 2, "final", 0], + }, + { + "human_readable": "Not connected", + "id": "ACStatus", + "title": "AC Connected", + "value": False, + }, + ] + } + + +@dataclass +class FakeAIOHttpClient: + responses: t.Dict[str, str] + + @contextlib.asynccontextmanager + async def get( + self, url: str, headers: t.Optional[t.Dict[str, str]] = None + ) -> t.AsyncIterator[FakeAIOHttpResponse]: + if url in self.responses: + yield FakeAIOHttpResponse(body=self.responses[url]) + else: + yield FakeAIOHttpResponse(body="", status=404) + + +@dataclass +class FakeAIOHttpResponse: + body: str + status: int = 200 + + async def json(self) -> t.Any: + return json.loads(self.body) + + +@pytest.fixture +def mockclient(data) -> FakeAIOHttpClient: + return FakeAIOHttpClient( + { + "http://localhost/v/2.1/sensors/": json.dumps(data), + "http://localhost/v/2.1/deployment_id": json.dumps( + {"deployment_id": "b29ba0ee10f14552b6b21327bb96d3fb"} + ), + } + ) + + +class TestGetDataPoints: + @pytest.fixture + def mut(self): + return apd.aggregation.collect.get_data_points + + @pytest.mark.asyncio + async def test_get_data_points( + self, mut, mockclient: FakeAIOHttpClient, data + ) -> None: + token = apd.aggregation.collect.http_session_var.set(mockclient) + try: + datapoints = await mut("http://localhost", "") + finally: + apd.aggregation.collect.http_session_var.reset(token) + + assert len(datapoints) == len(data["sensors"]) + for sensor in data["sensors"]: + assert sensor["value"] in (datapoint.data for datapoint in datapoints) + assert sensor["id"] in (datapoint.sensor_name for datapoint in datapoints) + + +class TestAddDataFromSensors: + @pytest.fixture + def mut(self): + return apd.aggregation.collect.add_data_from_sensors + + @pytest.fixture(autouse=True) + def patch_aiohttp(self, mockclient): + with patch("aiohttp.ClientSession") as ClientSession: + ClientSession.return_value.__aenter__.return_value = mockclient + yield ClientSession + + @pytest.fixture + def db_session(self): + session = Mock() + sql_result = session.execute.return_value + sql_result.inserted_primary_key = [1] + return session + + @pytest.mark.asyncio + async def test_datapoints_are_added_to_the_session(self, mut, db_session) -> None: + assert db_session.execute.call_count == 0 + datapoints = await mut( + db_session, + [ + Deployment( + id=None, colour=None, name=None, uri="http://localhost", api_key="" + ) + ], + ) + assert db_session.execute.call_count == len(datapoints) + + +@pytest.mark.usefixtures("migrated_db") +class TestDatabaseConnection: + @pytest.fixture(autouse=True) + def patch_aiohttp(self, mockclient): + with patch("aiohttp.ClientSession") as ClientSession: + ClientSession.return_value.__aenter__.return_value = mockclient + yield ClientSession + + @pytest.fixture + def table(self): + return apd.aggregation.database.datapoint_table + + @pytest.fixture + def daily_summary_view(self): + return apd.aggregation.database.daily_summary_view + + @pytest.fixture + def model(self): + return apd.aggregation.database.DataPoint + + @pytest.fixture + def mut(self): + return apd.aggregation.collect.add_data_from_sensors + + @pytest.mark.asyncio + async def test_datapoints_are_added_to_the_session(self, db_session, table) -> None: + datapoints = await apd.aggregation.collect.add_data_from_sensors( + db_session, + [ + Deployment( + id=None, colour=None, name=None, uri="http://localhost", api_key="" + ) + ], + ) + num_points = db_session.query(table).count() + assert num_points == len(datapoints) == 2 + + @pytest.mark.asyncio + async def test_datapoints_can_be_mapped_back_to_DataPoints( + self, mut, db_session, table, model + ) -> None: + datapoints = await mut( + db_session, + [ + Deployment( + id=None, colour=None, name=None, uri="http://localhost", api_key="" + ) + ], + ) + db_points = [ + model.from_sql_result(result) for result in db_session.query(table) + ] + assert db_points == datapoints + + @pytest.mark.asyncio + async def test_daily_summary_view_matches_query( + self, db_session, model, table, daily_summary_view + ) -> None: + await apd.aggregation.collect.add_data_from_sensors( + db_session, + [ + Deployment( + id=None, colour=None, name=None, uri="http://localhost", api_key="" + ) + ], + ) + + headers = table.c.sensor_name, table.c.data + value_counts = ( + db_session.query(*headers, sqlalchemy.func.count(table.c.id)) + .filter(model.collected_on_date == sqlalchemy.func.current_date()) + .group_by(*headers) + ) + + daily_summary_view = db_session.query(daily_summary_view) + assert value_counts.all() == daily_summary_view.all() diff --git a/Ch10/apd.aggregation-chapter10/.coveragerc b/Ch10/apd.aggregation-chapter10/.coveragerc new file mode 100644 index 0000000..e766c69 --- /dev/null +++ b/Ch10/apd.aggregation-chapter10/.coveragerc @@ -0,0 +1,3 @@ +[run] +branch = True +omit = tests/* diff --git a/Ch10/apd.aggregation-chapter10/.pre-commit-config.yaml b/Ch10/apd.aggregation-chapter10/.pre-commit-config.yaml new file mode 100644 index 0000000..d3b6ec7 --- /dev/null +++ b/Ch10/apd.aggregation-chapter10/.pre-commit-config.yaml @@ -0,0 +1,26 @@ +repos: + +- repo: local + hooks: + - id: black + name: black + entry: pipenv run black + args: [--quiet] + language: system + types: [python] + + - id: mypy + name: mypy + exclude: (?x)^( + (.*)/setup.py + )$ + entry: pipenv run mypy + args: ["--follow-imports=skip"] + language: system + types: [python] + + - id: flake8 + name: flake8 + entry: pipenv run flake8 + language: system + types: [python] diff --git a/Ch10/apd.aggregation-chapter10/CHANGES.md b/Ch10/apd.aggregation-chapter10/CHANGES.md new file mode 100644 index 0000000..8e424e5 --- /dev/null +++ b/Ch10/apd.aggregation-chapter10/CHANGES.md @@ -0,0 +1,5 @@ +## Changes + +### 1.0.0 (Unreleased) + +* Generated from skeleton diff --git a/Ch10/apd.aggregation-chapter10/Connect to database.ipynb b/Ch10/apd.aggregation-chapter10/Connect to database.ipynb new file mode 100644 index 0000000..98a74f0 --- /dev/null +++ b/Ch10/apd.aggregation-chapter10/Connect to database.ipynb @@ -0,0 +1,577 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "53635\n" + ] + } + ], + "source": [ + "from apd.aggregation.query import with_database\n", + "from apd.aggregation.database import datapoint_table\n", + "\n", + "with with_database(\"postgresql+psycopg2://apd@localhost/apd\") as session:\n", + " print(session.query(datapoint_table).count())" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "53635\n" + ] + } + ], + "source": [ + "from apd.aggregation.query import with_database, get_data\n", + "\n", + "with with_database(\"postgresql+psycopg2://apd@localhost/apd\") as session:\n", + " count = 0\n", + " async for datapoint in get_data():\n", + " count += 1\n", + " print(count)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from apd.aggregation.query import with_database, get_data\n", + "\n", + "from matplotlib import pyplot as plt\n", + "\n", + "async def plot():\n", + " points = [(dp.collected_at, dp.data) async for dp in get_data() if dp.sensor_name==\"RelativeHumidity\"]\n", + " x, y = zip(*points)\n", + " plt.plot_date(x, y, \"o\", xdate=True)\n", + "\n", + "with with_database(\"postgresql+psycopg2://apd@localhost/apd\") as session:\n", + " await plot()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from apd.aggregation.query import with_database, get_data\n", + "\n", + "from matplotlib import pyplot as plt\n", + "\n", + "async def plot():\n", + " points = [(dp.collected_at, dp.data) async for dp in get_data(sensor_name=\"RelativeHumidity\")]\n", + " x, y = zip(*points)\n", + " plt.plot_date(x, y, \"o\", xdate=True)\n", + "\n", + "with with_database(\"postgresql+psycopg2://apd@localhost/apd\") as session:\n", + " await plot()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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p/vudEwsfo2NXNLIIgiCkkP6tKDatdDYpWXzcUhxZBEEQUkr/VhRPfN3bdreMj1cOQRCEFBNYUSilDldKvZj1+lApdaVSaphS6mml1Kvm+4FRCuyLrr3ettvbFq8cgiAIKSawotBav6K1nqy1ngxMA3YDPwUWAmu11hOAteZ3QRAEIaVEZXqaCbyutX4TmAusMMtXAA0RnaO0bFpZagnKi00r4TtHQ2ON8S71LwglIypFcR7wkPl5lNa6BcB8H2m3g1LqMqXUeqXU+p07d0YkRh7DPXg0eWXtt6I7luDOPcfD45dC29uANt4fv1SUhSCUiNCKQik1EDgTeNTPflrr+7TW9Vrr+hEjRoQVw54o3Vrb3onuWEEph172ijONRY52/PTviitLUMrhOgnhWXMVLBlmrOdaMsz4nlCiWJl9OrBRa73d/L5dKVWrtW5RStUCOyI4RzCibNyrSjcnDxieWdmT7m1vw+pvGJ/706pxN3dmnb9oMoHcc3yuorNGQ9C/rpMQjvz7RHfB+u8bn2ffVRqZXIjC9HQ+vWYngCeBC83PFwJPRHCOYGQGRnes9hJ6Rt050d4zq6O9f5nEbk55mku30dDjl8KSA2V0IRgjB6f7xFIWCSPUiEIpNQQ4Bci2CdwKrFRKXQK8BZwb5hyB2bTSu3usJ7oiPJYNdpFrp1xgKAK3RYFtb8crV7FYcxV0tZdainAUWtypu2V0ISRWGbgRSlForXcDB+WVvYfhBVVaVl1Ragns2bTSaPzb3oHqg2HmTfDCj+wj1xZqeABQsYhZFLLrgtJnWiwaT1whiqJcSfA8hBv9N3ps9z733wcOhX1FjuG0aSU8fhk9jWLb27nfA5HSBnbTSmOOpSPlo4ggdBW4N4X+R/6z70gyO37pDuERxrtk9t1QWZVblv89n7DeCU9cQd8bJaUNfVjWfsu/klCZeGQpBSntWZY9QTyVNq00TY4envX6r4QWMQ7Sqyg2rYRVX8v1tV/1NaO80MWrHGoM/ef8M1SPA5TxPuefC5/X8k7IPodXhSU9yV6CzK3omOeJikkK7dRlzz3HG9fNug/t2gI7nrrO+zkS6PEEaTY9PXUddHfklnV3GOXt77vvO+du433SvL62YmuysRDrf2Bc1J7egom4Q3pE4Xs0VT0uFkkEoSCFPJXcGvhC7ZHFII/5cUpAehWFU+V7uShuDXjlUI8L9cxGzkmxPH5p7nnELTKPACa3YYdFK0K+L/vwifD154MdK47rm+8JlxkEc+8xPuc7REinJF42PBhsv0L5cLK5/q1g5ygC6TU9xYU12oiC7MYjzvUO5WLvfuPX0TXI+UoCjO9ecqTb2am9jkSzaax2vnZ2Ody79hrnsQtvksZQ+TePNuog+5XU/1HI7Gl3He853qPnIkYHNcEorUs/mVpfX6/Xr1/vbyevaUzzqRgAN73nvs3NI72twTj0ZO83Qtw0pixUetDrZ3mFFOpJ27khZ2/rdn63ulxzVTzzC/WX5JovgtRPpgpu3BadTHFy82j3dTPFvJ/d3LRVJSz+q9EhKKQssmXON0kX4qz7A40KlVIbtNb1vnf0SfmNKBruLbyNNbwvRLGUxFn3F+c8qUDT05Ne/Q37EYblepvT674smpFXXJPQXiZFC9HVnp7RZaHFlY1FCpmTf6/kozsMpV13or/j+pnAPvTkxJsOy0tR1F/i7YIk5aJVjwvc00g0fuy2bjiFMLF1vdVGY+zFdOVmEoqTKJRQv/Gm6g4x6vSBVzdtL53CbOXmdQK7/hK48Elv25aQ9CqKIHGcEup6ZkumCr75x14lcejJpZUnKtZcVfih87News7N1i0YpNeeXhQ9/CBYtnrBIG6lHWlU6G7/sqakTUqvohi4n7/t/ZpvosxlEYR8W3OhXkdaQlt76fF+6Xv+jpn/Xysqnbf12tODaHrnwycavUYhl0yBxa3ZxKm0qw+O9njWPVM1rPC2XrZJCOlVFH4eePBvvvn686VRFoOqg03k/fTvcm3yTvb7OIhaSfm9VvmjhELhW/zIt2R472cvHlH5fP15o9co80y5zPWwuDWb9d837q+o7+mZN0V7PDBGQV7ap9Nvi/7cMZHedRQqE/9K3a8/bzR8cUdojcLDIz9Xg2W/j3t+w8+Cw7h6hX47DU9dZ8z/eLmuusOYU9n5F/covnbsV9v72aqLIG60/ZFA7uLaqL9VXzWcUtI8dzd8YqrkT++IoljhHOLocWTjxx7v19c63/4ah3nq8csdym0axPU/KHw8q3HNDAouUyHa3/d3Xd/4tX8lAXB13jqNSfOKM7JIg+dTmLmB7k7j/mqsNlzZw9zHpcjnEmZhZ4lIr6LwQxgb8aR58dqYp13kfVu/iwGzs/LZuYxGYp5yUdiN1b0KadNKCq7G3q+2t3H16qJshxeTYdw9e6dR4qR58a8RCDq3kr8ALs5EUlHNDXTtNcyuQe/jUuRzSZmSgHJRFGE9Cywbc9Sxhg492Z9sYYaqdm6AHe29PTO7VxQ9U8sUVahhrr8ktwce5r/uKl32XYZPTMbixzt9zq81Hth3bUNXe3zKYsKp0R1LdwcfGfSniMQxkl5FUewLPGme4a7a2GY0avnndzMLnXV/lqJRvesj4vafzrbdB+k5FdNFNKwyt+Rcc5X/OYuoOOt+773FgQFCNmSqeu+/QnzckjsR78aaqwCHfORxZR189Zf25UFDWQQ1ZbmZsMUBoYf0TmZPu8jbEDsOs9Hsu+wbtjVXmXZ408QycKiR98LqHRd78kpF0A8oFBlzv9pg9vt8Nq0sWD9tW6vYsWl/OndnGDCki5GTPqK6rr1XzvHTS7fgzApd75XZd/s3f1ku07Pv8vY/dYcxUmj8wH27UtSZU8PesTvY8YKaspycGqrH9UaXvmW8fc56S5GUgYNCehXF7LvgvdcKL94q5oIWJwVSKvI9oYLi1ohnIrqFCnhotW2touX3NehuI9ZT5+4BtPy+BqBXWZTsgVX+54/8eEFlz91YeI5y3G2Yoaz911xlRELVXcao2M8cWZRUH+zQQB9smKX8Kq+gTiczb+qbabGyKvd4haK6vrWuH62Itye9pidIxdL3fsHqK51/i2plq+1xetNCbt94QI+SsNDdiu0bD4jm/EGpGgZn3RdstGhNbNuZJRvbel/5SgL8KSZrxLdkuH3inVIw8yb7DJMzbzI6W3bmXTeCjtadEpj5Gh3e5d1ykVJzVnpHFBYVA50XWJX7itiKAGFO7OjYZQy/7XpWTj1Dv9iZDuq/0tOQde2z79M4lQdGVRomGy8Mqobr3gh/TrsEWl72WfVVw1XUC0HDgsTVsFn/1ynCrzU6L0Y4kyD1n8/su4x5F6dnoXpcqvOGpHtEAdDwXWwTkvv1KEoLfpb9d++LbiXr3jb7XAF2PUO/5A/1Lfz01KJgULURUtrrY1HqRDMN90YzD+VGnA1bj4NIa25cs2y8/L+kdAidRkln3e/8/1JC+hXFpHnG0L/YHkWl4vTb/AVEfOo6Yy1DFOxt66t4Js1zdJhxpWIAnob6s++CzEBUpf1JnMp9s19tb8Pf+EHuqup8goZZiZpJ8+BL/9a/U8ROu7jwNknpEEZhxkoo6U1cVM5sWuktN3gcVI8zekd2GeLcqKwK/tA01tC2dTDNz1eDzurbqG7GHN/WO5ntRn5iIC/kZ5k79ORkd0CiNtNY17rUuCU5GlRd+pFdCSlW4iJRFGnGLl1mUAYOhX1evGjw7hJbPS6avM5mvK2W/z2A1i1DDe9jBTWH7aL20x8W3r8/5vSww29WtUIkqd7unNj3nitzJQEpURRKqRrg34GjMR7frwCvAI8AdcBWYJ7W2tWRWxRFCMKm5sxe63HbodGOUqIyz2xaSdvyf6Bl3RB0V7a1VFPziQLKwkplWS5ENapI+uhJANKTCnU58HOt9UTgWGAzsBBYq7WeAKw1vwtxEdY+WzWst9d4+m3JDGkwaR47XhmbpyQAFK2vD6Vtq8tkejkpiSgRJSFkEVhRKKUOAE4Cvg+gtd6ntW4F5gIrzM1WAA1hhRQKEMbzJXv9wqR5/pMGOTEoWnt553tOowZFywaHtRR+kuP0F6KY2E6KF5GQGMKMKA4DdgIPKKVeUEr9u1JqKDBKa90CYL6PtNtZKXWZUmq9Umr9zp07Q4ghhFqBnb9+IapQ2BHbjlW1s+LRHTa3caaqb5bAcmDmTdi6i3umIjleREJiCLPgbgAwFfh7rfXzSqnl+DAzaa3vA+4DY44ihByC1yQ8dtitX5g0L9ykaByuo/vcstapZLirJoFJ84KHlChX5SoUJMyI4h3gHa21FS7zMQzFsV0pVQtgvpcw5nOZECa5UtReLTE12Hp3wGBx5YgVFt9tZb5dqBBREq60rV7Nq5+fyeYjjuTVz8+kbfXqUotUNAKPKLTW25RSbyulDtdavwLMBF42XxcCt5rvT0QiqeBMUtJsJiiOTcuSJbSufBS6uiCToWbeudQuXlxqsYpHFGEphB7aVq+m5cab0Hv2ANDZ3EzLjUYHrXrOnFKKVhTCej39PfBjpdQmYDLwbQwFcYpS6lXgFPO7EDeT5vmf1I4qFhTE7nOvamo8b9uyZAmtDz1sKAmAri5aH3qYliVLYpJO6O/s+M7dPUrCQu/Zw47v+IwanFJCKQqt9Yta63qt9SStdYPW+gOt9Xta65la6wnme4myyJQhXsIdZNPw3WjOO6g69t5r7Q3/6PibGjIk53vrykdtt3MqF4RCdLbYLzDtbG4usiSlIf2xnoReZt/lLV80GC6Qbo27n/UURVgd6za87zN/YY0k8nEqF4QCZFy87sphrkIURX/j68+bfvBZLpKZgWbU2aygiYVcIL2upyimz71ycPt0KheEiHBzQC8H81P681EIfYki05412sgOPlgxEHSnsW7Dyo5WTJ97p3AzAcPQvDZ7Nh2vvd7zvfKTn+CTa9YEOpbQv9Ftzt585WB+EkUhOOPRcyZxHkaZjL2ZKWvkka8kADpee53XZs8WZSH0YUBtrbNCyCQw7E3EiOlJCEUiPYyc5iK07rEn5ysJC6dyobwZ+U2XdMBlMPclikIIRevDj/gqLwYDxoxx/K0c7MlC9FTPmeM8FyYjCkEoQMTzBkHJ9jxx6/05uTkKQkGc7mkZUQhCfPgOieDi3ZQ9UqieM4eMwwK9AbUuKU4FQbBFFIVQEtpWr6b5uoXGBKHWdDY303zdQndl4TJKyZ9o3P/0L9put9/JJ3mSLShbL76YzROP6HltvdjnIkhBSCCiKITYcGtwW25aDN153und3Ua5A25zD/mjjY9//RvbzaxypxEHQMuybzufx4WtF19M+3Prcsran1snykJIPaIohHC4TOS5TRzr9nZf5VDA8yRvtOHkymiVj3IJCaJbW53P40K+kihUXoiWJUvYfNTRxujkqKPLOlaV1EVpEUUhhKJm3rmOvwVZiKSJKCRCAQ+VYkf89Puf3NyOyy3cdWJcsH1GBuhPyk0UhRCK2sWLXSeZ/TZkCnhryc19yq0wz15oW706Vg+VIA2AX7fc1ocedixvufGmnLmdlhtv6tfKwq0uiooPD7/EKLeIEEUhhKfAJLNdQ5Yf8TWbzMcf9SmzC/PshFuj7CdcuR1BG4AowzyUc7jrtNDfIhiLohBCU6jxtWvIapc04melhZ/1D27bhr3hXRuAqirnHWMOXFgO8YZSRT+LYCyKQgjP3r0FN8luyDZ/+jiar7nWdftFq17K+e5l/cNrs2cX3LbLDO4W2FTj1gC4jXiKsACxP5ufUoeLk0car5MoCiE0bp5K2bQsWcLmiUfAR4ZpyamPrYEfr8vNcTHym1eiBg92PX7Ha6+z9eKLXXvXyswrEIepJqrFfEHt2EHdehPPAIfYpU7lCcDNySONZkJRFELR8Dr5qDCURd3Cpp7X17YNp/bmbxXct5ArqnXDxxHKw9V91wdBJ2mDuvUWk0CeQJ2d/soTgFv05DSGkRFFISSSplVX8+AvlvLZtzcA8NvX3+dr24a7ToJ7octsTJMSyqOcXF37mydQNnbXzWnuTrlky0sqoiiExKEwbsxR7a1cu+EhfrbqappWXU3lM09Tu6Qx3MFN23Gh3v/miUf4bsD8mhTaVq+m+drrchv9cwIAABT/SURBVMOYXHudr2OkiTg8gYqpWP2u5ndqXHVra+o6BaIohFDEfbMrehXHtRseKjgJXhCzN+tlwZ3f3q5fk0LzTYv7TnIXOepuUXFxBNg88QheOelk25/jCLcSBL+r+btcTIFpW/8iikIIRTEn5iJxMPXppupnvsC3OcujE4Bfss1Zf54+g79Mn5EK01b3jh22yiKOcCtB8L2av0CeCr1nT2ocEJLrNiAklkWrXuKh59+mS2uampvT1dsI22N3SbM68ptXFhzxbP70cT1eX3GweeIROd91ayuWtJ3NzTRfbzS6xQ5h4pXuHTv6lFXPmRN+JFkk2lavZsd37jZGlx7uNd3aStvq1Ym9HhaiKARffPn+5/jt6+/3fN9ZVcOo9uR729iilKeHefOxkxlw0EHuD7+X48SsJDzR2UnzTYsT3TBlN7YDamuN+SSnaxXzQkY/WGFmvEYQsGi+5lp2b9xY2jzzBQjVGVRKbVVKvaSUelEptd4sG6aUelop9ar5fmA0ogqlZtUL7+YoCYAHjzydPZlK1/20+YqSKI5Xc958b+fau7dnwtmNgmaEUisJi/b2RJug3r3m2pwJ/reuvT72TIqrXniXE279FYcubOKEW3/Fqhfe9X0MP2Fm8km691cUVoPPaa0na63rze8LgbVa6wnAWvO70A+48pEX+5Q9M24ayyef49hwa+CMhjtpzwyMVBYFdONfYWRv77UH57XPmoZ1DBbN1xX/sVQDvd0D+fU9QHc5X+cI8lWveuFdrn/8Jd5tbUcD77a2c/3jL/lWFmHDqCQ5DlQc5uW5wArz8wqgIYZzCEXG7aH5wpv/W3D/f5l8duSjigrgw8oq3wrj/sX/2vO55vzzIpNH4+6hkyjyk0YVgdplS6EiWJPjqKwjiJ10xy9eob0j9zjtHV3c8YtX/B0orBkswXGgwioKDfxSKbVBKXWZWTZKa90CYL6PDHkOIQE4PTRLn/0eU//6WsFe9zPjpvk6n1dz1QEd7cxquNPzcRUw4cn/AODbV93FK6t+3qNowioyDXQWaVQRhdLNTtma//rz1GmRm6eq58xhzG23umcq9EnYaMBgjCD8lDvSj12bwyqKE7TWU4HTgSuUUoUTEpsopS5TSq1XSq3fuXNnSDGEuGl2eGjclIQGNg7/ZM53N3TWq+PAg7h92vmBzEuFGNneyssTj6DhZ/czqr2VCnrDhoQ5l7XmIw0UvBa7d9N8zbWJnsuA4vv3h40MUIik1neoetZaN5vvO4CfAscB25VStQDme19/N2Of+7TW9Vrr+hEjRoQRQygCY2pcQmjbYCmJRSde3lO2pm6G61zGh5lBXHrRdznyz5s59rlneWbcNM+jhR2D9vfcyCvsG/WwjU4QJRFUMRU6V6HjepU1SrdUyyso25YfthPgtqgtCvJNrqEjAxQgqesqAj8bSqmhSqn9rc/AqcAfgSeBC83NLgSeCCukUHquOe1w3/tkKwmAeyefzeq6GXQplTN6sJTKxWfdmnOeC6aP93yui05f3KMssl9+icNDy+1caTpuWOy8gqyRXGAimMx24/rHN/V83nzU0bGv50iqQ0SYdRSjgJ8qYwJnAPCfWuufK6X+F1iplLoEeAtwjrcrpIaGKWN5dP1bfdxj39hvJId+vCOnh6rNcjvunXw2904+m7vnTwaMuY/m1nbG1FRxy2mH0zBlbM+2SxuO4Ud54cbzyW5kLjo914vpZ6uuLvi/8tlZVcODR57ONRseSpUpKZ8uIEOy5HcKcWJdw0CyxjwB3N5hTPrnL2QsNwIrCq31FuBYm/L3gJlhhBKSyY8vnZGzKhtgaNe+Pg+4MsudOOETw3oUQrZisENhr4zAaGDW1M3w9R/c0BjrQp4ZN41nxk3rmai35IgSaxT1yQ+bqd63u+C2fs7/YWUV5826mTWrru6Zf0kCrfsNo+aj9/qU76yqYf89H1GljUbfr7xxr2xO8vqGYpGq6AtC6VnacAyv33IGW2+dBcAIh1XZTuUTRg7lx5d6b9y/PH08V3zhWt7Yb2Qfs9LquhncO/nsnO0vmD6erbfOYtT+/tdtaHK9sxadeDln+PCo8suiEy/niGWNBWXyQzfwvUmGR/rskLJriNS082+fOrXP4sw9mUoePPJ0zp57W+C6jntNSND8IIFwS6dbQiSEhxCYrbfO4plfLLUN4bGzqq/bogKevuqzvs5hmZ+u+IK7bVgBb5jKC+CUo0azY9D+jNz7kaceqgbumHa+L9miwEsco43DP1nQBVkDnaqCu6bOz1F2Hw4cUnDE4sacM2/jtcB752LJddHLTzGivbXHzJctr0ah/KrHEGtCVr3wLp99e4OjTEuf/V7gYwchM2hQUc/nFRlRCKGwC+Fh9RLzyW7Io+SAQZk+x/7RurccJ7jzmyFrdOK01mPj8E9GOjHuNodjx6ITL3edZLeOd+bc2/v8h+8dMzfwZHG3UnRGPDP+zLhpXHTaImY13MlFpy3qI++auumBVtsHdSv9zb/+BwtefKzHTXpUeyvXmDlQfrbq6h7TY7GwcronDVEUQmBWvfBuTwiP7VU1dAPbq2pYPvmcPg3AwExwS/mgAe636aYlX3T87aLTF3NGw505r9unnZ8j7+3Tzu9jwsrm9plXMGTG9ECyf5gZ1EdJvbHfSK74wrVMGDkUgDF33F7wOLMa7rRdU2K5FTuNuPwudMw+btMhwf5zGO6dfLbvOQoFvHlbMLPVmeufYHBXR06ZNa9TyJkhDg+5pGa/E9OTEBhrtbY1+evG7ef08XvwzG1nT7KNMwX+XGgtvMhrccInhplzKl9k68UXs/u5db4asvPmLLMtH7X/wB4zXPWcOTT/4w3Q0dFnu/as0drshjv56os/Ydab66jQmm6laDpkuquSC0Ncx3VDgeeovtkM+Kvtcq2COM2lecUyb4K/SXgnB4Wk9tyTKpeQApxWa+dzwfTxBb2b3GiYMpa750+mqrL3dq1QxnGXNhzj+3hVlRXcPX8yY2uqUEBNVaXtg3DB9PE5E+91DzzAkBn+TCN3z5+cc66xNVXcPX8yz99wSs52Y769rE8cpE4U90w+p2efC6aP597JZzN77h2c0XAns+feUbAxH1tTxa6jpzqawJLGmJoqxtx+m+/9dtjMiXnho8rgk8ea3hGr3/2cSKrpSUYUQmDG1FS5xsMZW1PFNXlrI4LSMGWs5+MUivp5y1mTfB0vm7oHHuBlDz71GvjroP09uwFb7p3ZeRjGfPNKHshy+2yYMrZHMdYtbCoow9aeeZvP86MT5+TY2/dVDGBgd2fBY1jrXaLggunjXdfFVFVmuOa0w6k262rHd+5mX3NzQRNQF4on6+fy2SBCBQzkl++a7XUS3jI9Du3aZ+sE4jtLYpEQRSEE5nMTR7g++L9d+PkiStNLoaifUSguJ6ymYseg/Wl98Ke+9q2eM8fzeoChAzPs2ue82OyETwzL+X7j31zex5pTaEHi3fMnR1pXlpKz1uEoYMjADLv3dTEmr1Nh1cWX73+Oa/7pEgai+yzqBEPh3T3lXBq+9n8DybR/CI+w7NHcmrrpzNn6nCfz0xVfuJbPvr2BBS8+ljM/sidTyftfupAJgSWKD1EUQmD++8/OwRzH+owNFSVeTWJx8eX/s5zFc46KVSEt+9IxjvM2QJ+1Kl8+vm9v3sl92PICuy4G+Zc2HOPLXPjjS2dQ9/odrnMzYRSaqqjw7V6rMeoum3snn82crc952hecXYX/p/kgXvclTXEQRSEExq1B/tzE0gV6dDOJZSJIndmtFBmHyVYFvHDTqaHPUYiGKWMdFYXdP7Qa52xlcdHpi3nwqSU9k7EWq+tm8OPj53FdZNKGxwr9YkcYhawKKAm7q7xj0P59wsVY2xbykspeq2PrVJHEiSNEUQghcGuQ3UYbcXPNaYc7NqJdEeQMaDrE3syggYGf/ETo43tlrEP9O0X6XdpwDPWHDMupG7sG74BBGVeX4/7EgDFjCmam8zpZvaZuhqP5qdBanaQjikIIjNscRanNP05EYRL7tylGz3Z2XqPwxn4jmbVmTejje+Wa0w7n+sdfysnOZk0IO2H1vrODMUblcJBGRn7zysgiwj78mfP42xmH8N7Dj1CR1SHZYbMCPW2IohAC4zZq8Ju/IkqcJrMVwcKl59OtnU0h8aw9tydoox/U46s/4iWEihcqM4rFc46idsqpfGZPfeBkd9YizKQhikIIjJtrbBQNclCcRjOaeD2eSkG5N/pxLwRTZLsZ97LqhXcdFbSd44AXshdhJg1RFEJgMko52vxL2Xg5zZ1E5YnllGwnKeG8+xtu99ldEa7zsKPm/PNsy90UtJc8KtkMqFDcee6xiVb4oiiEwLhNDK964d2S3fhBbPd+cAvOJ0TP+cePs214s/OaxEXt4r6T/V5wWlwYNJpAqZEQHkJg3HrohRa9xUnDlLHcctYxOWEzbjnrmMgaFScX2yhcb4W+LG04hgumj++p34xSfcKrJA0nmdOoJACUjsBdMCz19fV6/fr1pRZD8MmqF951XfRlZ9vtD7iFz+iv/7k/s/Xii2l/bl2f8qoZ06l74IESSOQdpdQGrXV93OeREYUQmCTbVOPEaSRVytXoQnDqHniAqrww8mlQEsVE5igEwSdxz4EIxUeUgjuiKIRQlKMHkCxaE8oNURRCKAYOqGBvZ994OQMLZKVLO+W+fkEojNtai7QhikIIhZ2ScCsXhHJg1Qvv5pgn321t5/rHXwLSObfXv7t9giAIJeCOX7ySM4cF0N7RVVK38TCIohBCUVNV6atcEMoBpzAySQ2WWYjQikIplVFKvaCUWmN+H6aUelop9ar5fmB4MYWk0njmUVRW5E5dV1YoGs88qkQSCULpcQqKWcpgmWGIYkSxANic9X0hsFZrPQFYa34X+ikNU8Zyx7nH5qyCviPhcWsEIW6uOe1wqiozOWVpdqEONZmtlDoYI7LyMuAqs3gu9OQ5XwE8A4lKliVEjHgACUIu/c2FOqzX093AtUB2AtlRWusWAK11i1JqpN2OSqnLgMsAxo8fH1IMQRCEZNGfOlCBTU9KqdnADq31hiD7a63v01rXa63rR4woXX5lQRAEwZ0wI4oTgDOVUmcAg4EDlFI/ArYrpWrN0UQtsCMKQQVBEITSEHhEobW+Xmt9sNa6DjgP+JXW+gLgSeBCc7MLgSdCSykIgiCUjDjWUdwKnKKUehU4xfwuCIIgpJRIQnhorZ/B8G5Ca/0eMDOK4wqCIAilJxGJi5RSO4E3za/Dgb+WUJygiNzFReQuLiJ3cfEq9yFa69i9gRKhKLJRSq0vRsamqBG5i4vIXVxE7uKSNLkl1pMgCILgiigKQRAEwZUkKor7Si1AQETu4iJyFxeRu7gkSu7EzVEIgiAIySKJIwpBEAQhQYiiEARBENzRWru+gHHAf2PknPgTsMAsHwY8Dbxqvh9olh9kbv8xcE/eseYDm8zj3O5yzmXA28DHeeVXAS+bx1iL4UNst/8gjFAiu4F24C9ZcncBHwF7MeJQJV5u4HPAS1lydwFfTrrc5m/LTdn2msdJUn2fZNZrN/AOuff3WqAD2EXy7m9buYFDgBez7pPNaZA7Bc+lU30n/bk8CdgIdALn5P12G/BH8zXfSYae7QtuALXAVPPz/hiNwJHA7cBCs3whcJv5eShwInB5dgWZFfcWMML8vgKY6XDO6eZ58yvoc8AQ8/NXgUcc9v8a8ENgKkYcqp9kyb0vpXLfbso7DKNB/qcUyH05sBX4R1POd4DvJEjuOuDzwBrgHHLv74eBB83fknafOMl9LPBPprz7AR8A302B3El/Lt3kTvJzWQdMwng2z8kqn4XR+RlgyrkeOMDuGNaroOlJa92itd5ofv4Io5cyFiNB0QpzsxVAg7nNLq31s8CevEMdBvxFa73T/P5fwNkO51ynzZwWeeX/rbXebX5dBxzsIPZc4F5T7seAk7PkHpBSua36Pgd4CpidArmnYtyID2itdwG/wehNJUJurfVWrfWvMFfA5t3fUzBGSZCw+8RF7pEY98UKjFHeLuC0FMid6OeygNyJfS5NuTdhjISyORL4tda603wu/wB80e4YFr7mKJRSdRgP0PPkJSjCuEndeA2YqJSqU0oNwLgRxvk5fx6XYFwYO8ZiDNnQWndiPDDTTLkVsFoptQ44PkVyW/V9HvBASuT+GXAg0KaUGo7RQ6pJkNw55N/fwPuQyPs7hzy5RwO/wLget2D0YN1IitxJfi5zcGgHk/hcOvEH4HSl1BDzufxcIRk8BwVUSu2HYVK4Umv9oVLKl2Ra6w+UUl8FHsHQcL/D0K6+UUpdANRj9FxtN8mTezRwqSn3h1rreqXUYcCvKKAsEyQ3Zn6PYzAaAlcSIvcqpVSHee6dwHPAlxIkdzaDSc/9nU2+3FprPUkpNQZYRda1SbjcSX4u3eRO8nPpJMMvlVKfJve57HTbx9OIQilViVE5P9ZaP24WbzcryKqoggmKtNartdbHa61nAK8AryqlMkqpF83XtzzI8gXgBuBMrfVes2yZdQxzs3eAcabcj2NMSv6H+ds2M7HSFowewccpkXs78P+An2IEDEtLfb8FnK61PgWowuylJ0Tuns2Bq8m7vzHszkm8v13lNu/vZmALxuguDXIn+bl0kzvJz6WbDMu01pPN51JhOCW57uD6Mg/yQ+DuvPI7yJ18uj3v94voO9s/0nw/EMM741MFzp0/iTMFeB2YUGC/K4DvmXL/DFiZdd67THmt6IzfT7rcWfX9FsYwMS31nQH+xZR3ErANuCMpcmfd368Ba2zu7/voncxOTH07yY1hq/6OKe+BGL3FH6RA7kQ/lx7uk0Q+l1nbP0juZHYGOMj8PAnD82mA6zE8nOREQGO4Yr1ovs7AsH2uxdBEa4FhWftsxeg5fozR2zzSLH8Iw63rZeA8l3Pebu5nuaM1muX/haHBLTmedNh/MMbwVWN4Irxsbv8P5mfLne3llMh9BjAZY2IsTfU9F6PHtAvDbfb5hMn9aYweoMYYerdn1fdzGJ443Wa9n5MCuW/AcLnMdo9NQ30n/bl0u0+S/Fx+2txvF/Ae8Kes59U6/zpgspsO0FpLCA9BEATBHVmZLQiCILgiikIQBEFwRRSFIAiC4IooCkEQBMEVURSCIAiCK6IoBEEQBFdEUQiCIAiu/H8KgxuCBe2ajgAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import collections\n", + "\n", + "from apd.aggregation.query import with_database, get_data\n", + "\n", + "from matplotlib import pyplot as plt\n", + "\n", + "async def plot():\n", + " legends = collections.defaultdict(list)\n", + " async for dp in get_data(sensor_name=\"RelativeHumidity\"):\n", + " legends[dp.deployment_id].append((dp.collected_at, dp.data))\n", + "\n", + " for deployment_id, points in legends.items():\n", + " x, y = zip(*points)\n", + " plt.plot_date(x, y, \"o\", xdate=True)\n", + "\n", + "with with_database(\"postgresql+psycopg2://apd@localhost/apd\") as session:\n", + " await plot()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import collections\n", + "\n", + "from apd.aggregation.query import with_database, get_data, get_deployment_ids\n", + "\n", + "from matplotlib import pyplot as plt\n", + "\n", + "async def plot(deployment_id):\n", + " points = []\n", + " async for dp in get_data(sensor_name=\"RelativeHumidity\", deployment_id=deployment_id):\n", + " points.append((dp.collected_at, dp.data))\n", + "\n", + " if points:\n", + " x, y = zip(*points)\n", + " plt.plot_date(x, y, \"o\", xdate=True)\n", + "\n", + "with with_database(\"postgresql+psycopg2://apd@localhost/apd\") as session:\n", + " deployment_ids = await get_deployment_ids()\n", + " for deployment in deployment_ids:\n", + " await plot(deployment)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import asyncio\n", + "\n", + "from matplotlib import pyplot as plt\n", + "\n", + "from apd.aggregation.query import with_database\n", + "from apd.aggregation.analysis import get_known_configs, plot_sensor\n", + "\n", + "with with_database(\"postgresql+psycopg2://apd@localhost/apd\") as session:\n", + " coros = []\n", + " figure = plt.figure(figsize = (20, 5), dpi=300)\n", + " configs = get_known_configs()\n", + " to_display = configs[\"Relative humidity\"], configs[\"RAM available\"]\n", + " for i, config in enumerate(to_display, start=1):\n", + " plot = figure.add_subplot(1, 2, i)\n", + " coros.append(plot_sensor(config, plot, {}))\n", + " await asyncio.gather(*coros)\n", + "\n", + "display(figure)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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0ku7ZwHnAwcAR1BCOBlxacn560wqUJEmSJI0sE+7/LFzxLPjfo2H2pyAHO11V/dKPnnaN9Qvh2lPhkQthxT0w95tw7Smw4clOVyZJ0taW3Aw3v67w76TffQA2ryz0b1wCt78X/vcY+PUbYdkdna1TkiRJkjSu9HW6gDF4PXArMLPYPgm4MSIWAQ+XToyIrwGHAS8ApjD89MwAFgJvbVPNkrSt1Q/Bo98t/ABvn5cU2iOZcTxsWQVrH6m+323nwYFvgd4pza+1VeoNRlv4vzC4BXomtaYeSZIkSZIkSZIkSZKkzvoAcCDDD0H+D+BDmYVP00fEATXucwPD98odFBF7ZuaSJtcqSZIkSRry+3+EP3x2uD37fNi8Ap79pc7VpInt8Ytgw8Kt+9bPh/k/h8P+sjM1SZJUbumtcMOZw58jXHkvLP0tnHYVXHcarPpDoX/VffDElfDiX8OMYztXryRJkjSerX0E7v4IPPUb2PVoOOofYa9TO12V1DI9nS6gXpn5CPBKYBFbB53tA7ywZGoA7wJOBaaWzV0AvCIzl7atcEkqtex3cNXz4P5/gbnfgF+/Aeb/dOS5+70KXvkgvPiWUTbNwtN+BvubXW3r1BuMtmUlLPlVa2qRJEmSJEmSJEmSJEnqvPczHIp2SWb+9VAoWj0ycy0wr6TriCbUJkmSJEkaSQ7C3P/etn/urMKDwaVWuOtvRu7/3V+1tw5Jkqp5+BvbfoZw+e/gvk8Ph6IN6V8Dj36nfbVJkiRJ3WTT8kKeyPyLYeMiWHQt3PhyWH5npyuTWqbrgtEAMvN24ATgSgpBZzB8M1iWHJSNBXAN8LzMvLcNpUrSyO77Z9iyqra5Ox8GPX2wx5/Aq+ZUn7vsVnj0e43X1y4DG0fuP+lHldcs/EVrapEkSZIkSZIkSZIkSeqgiDgS2I/he+I+0uCWc0vOD25wL0mSJElSJSt+D5uWbdvfv8YHg0uSpO3bIxeO3P/gl0bu/+MXW1eLJEmS1M2evArWz9+6b2ADPPqDztQjtUFXBqMBZObizHwF8Fzg+8AiCjeEjXSsBi4GTsvMMzNzUWeqliQKT4NaeFnt83d+Rsn5oXDKKMFgj1UJFRtPBrdADow8tsN+sP/ZI4+tur91NUmSJEmSJEmSJEmSJHXOccXXBO7LzEca3G9lyfn0BveSJEmSJFUyuHlsY5IkSZIkSZJUi9veOXJ/pdBhaQLo63QBjcrMO4G3AUTEwcDTgZnAZGApsBi4PzMHO1akJJVa93jtc6fsDrsdv3XfnqdA307Qv3bkNYuvh80rYfKuY6+xHQY2VB7r2wFmHA+P/2Tbsc0rWleTJEmSJEmSJEmSJElS5+xRcj6nCfttKjnfsQn7SZIkSZIkSZIkSZIkqd0G1ne6Aqntuj4YrVTxCZmNPiVTklpr8fW1zz38b6Fn0tZ9k3aGE74At7975DXZD09cCQe+eew1tkN/lWC0nqmVg902rxy5X5IkSZIkSZIkSZIkqbtNLTnfVHFW7aaXnK9pwn6SJEmSJEmSJEmSJEmS1HITKhhN40NETAJOAvYH9gHWAk8Ad2fmvA6WJnXe6ofgtvNqmzv9SDj8QyOPHfou2O3Z8MvnjDy+6NrxH4w2UCUYrW8HmFQhGG2LwWiSJEmSJEmSJEmSJGlCWlpyvnsT9ju45HxZE/aTJEmSJE142ekCJEmSJEmSNMFllR9BVRuTtH0xGG0CiohvA29v0naPZeaBNV53D+AC4I3AbhXm/Ab4Ymb+rEn1Sd3l9nfVPvc5X4XeyZXHd3s2POO9MOdr2449ciFsWQ37vQoO/HPo6a2/1mbZsAge/m+YOwvWPw77nQUnfBEGN1de07sDTK4QjLZ5ZeFfsxGtqVeSJEmSJEmSJEmSJKkzFhVfAzi+kY0iYiZwREnXw43sJ0mSJEmSJEmSJEmSJEnt0tPpAjTubahlUkS8DLgPeC8VQtGK/gT4aUR8PyKmNaE+qXtsWQNP3Vzb3JnPg71OGX3ejCr3wM7/Kdz6drjljZ2LxV07D658Fsw+vxCKBrDwMvjFofDkVZXX9e4Ak2eMPJYD0L+u6aVKkiRJkiRJkiRJkiR12G+AweL5zIh4UQN7nUshYA1gHfC7RgqTJEmSJEmSJEmSJEmSpHbpqmC0iDg6Iq4vHtdFxJ5j2GOv4tqhfQ5rRa0TyM9GmxARpwKXAKV/HgncCVwEXAMsLVv258D/RERX/TcoNWTTMsjB0ecBzHx+bfN2OXz0OfN/Bkt/W9t+zTb7k7Bxychjd/1N5XV9O8HkXSuPr1/QUFmSJEmSJEmSJEmSJEnjTWauAO4o6frniIhK8yuJiP2Aj1G4hyuBazJrvWlFkiRJkiRJkiRJkiRJkjqr20Kp3g2cCpwCbM7MCmk7lWXmYmBLyT5/0cT6xosPAweN4XhD2T4JXFjtQhHxNOBiYHJJ9y3AUZn5nMw8OzNfAjwN+CCF/+2HvAr49Bi+Pqk79a+pfe7M59Y2b+casx0f/V7t126WgU2FULZ69e0MPX0wqUow2tUnQubYa5MkSZIkSZIkSZIkSRqf/r3k/ETg6/Usjoi9gMuAGcBQqNoXm1OaJEmSJEmSJEmSJEmSJLVetwWjvbrk/DsN7DO0NoA/a2CfcSkzl2bmvHoP4IyyrW7IzEdGudwFFG6iG/Ib4IzMfKCspk2Z+R/A2WXrPxQRB4zhy5S6z5Y6gtF2qzEYbfKM0ecAPFI147A1Fl8P/WvrXze5GIg2eXrlOVtWwYq7x1aXJEmSJEmSJEmSJEnSOJWZPwLuKTYDeGdE3BwRJ1dbFxHTIuI9xbXHUXgoZgJXZ+YtraxZkiRJkiRJkiRJkiRJkpqpa4LRIuJg4GnF5iBweQPb/QIYKJ4fFBH7N1LbRBAROwBvKuueNcqaZwBvL+naDLwjMzdWWpOZl7B1qN0U4Pz6qpW6VK3BaJN2gV0Oq21uzySGH+5bTS1zmmzBJWNbNxSM1jsVpuxeed7yu8a2vyRJkiRJkiRJkiRJ0vj2emAZhWAzgJOAGyNiIfDd0okR8bWIuA54CvgKsNfQEPAE8Na2VCxJkiRJ27UcfYokSZIkSZIkSapZ1wSjAUcXXxN4MDPXjnWj4toHS7qOaaSwCeL1wPSS9grg4lHWvAXoLWlfnJlzarjW58raZ0fE1BrWSd2tv8ZgtIPeAVHj23NEIUBsNIObatuvWXIQFlw2trWTZwyf73dW5Xn968a2vyRJkiRJkiRJkiRJ0jiWmY8ArwQWUQg4y+LrPsALS6YG8C7gVGBq2dwFwCsyc2nbCpckSZIkTQCGvEmSJEmSJKm1ssqPoKqNSdq+dFMw2gEl53ObsF/pHvs3Yb9ud15Z+weZuXGUNX9W1v5WLRfKzAeA20q6pgEvqWWt1NW21BCMtteL4JhP1LdvLcFoACvvr2/fRiy7HTYuGtvaSbsOn5/w+crz+secjylJkiRJkiRJkiRJkjSuZebtwAnAlRSCzmD40+lZclA2FsA1wPMy8942lCpJkiRJ+v/ftkmSJEmSJEmSpGbopmC0nUvOVzVhv9Ul57s0Yb+uFRGHAH9a1j1rlDV7A88q6eoHbqnjsjeWtV9Wx1qpO/VXCEabdhC86Fp4+b1w2tUwZWZ9+9YajLbgkvr2bcRT9bwdlIne4fPJMwphcSPpXzf2a0iSJEmSJEmSJEmSJI1zmbk4M18BPBf4PrCIwqftRzpWAxcDp2XmmZk5xifaSZIkSZIkSZIkSZIkSVJn9XW6gDpsKDlvRpBZadDaQBP262bnsvXjae7KzHtGWXN0WfvezKwnpeg3Ze2j6lgrdactFYLRpu4Je58+9n17agxGWzdv7Neo15qHRu7f63R4wXfhkv0qrx3YuHV70s4jz+tfO7baJEmSJEmSJEmSJEmSukhm3gm8DSAiDgaeDswEJgNLgcXA/Zk52LEiJUmSJEmSJEmSJEmSJKlJuikYbWnJ+QFN2G//kvNlTdivK0VEL/D2su5ZNSw9sqz9cJ2XnjvKftLE018hGK1S8FetemsMRlu/sLHr1GP1gyP3Tz+yEARXze4v2LrdO23kef31ZDFKkiRJkiRJkiRJkiSNbxGxM3BQSdfc8odVZuYjwCNtLUySJEmSJEmSJEmSJEmS2qin0wXU4fHiawDHRMTMsW5UXHtsSVcb04LGnZcC+5W0NwA/rGHdoWXtx0ecVdljZe2ZETGjzj2k7rKlQjBaX5uC0Z68srHr1GrBL2DJTSOP7XwY9IySybnfy7duT9pp5Hn9a+uvTZIkSZIkSZIkSZIkafx6M3B38bgdmNLZciRJkiRJkiRJkiRJkiSp/UZJpxlXbgU2AZMphKP9FfCpMe71lwyHwvUDtzRcXfc6t6z9s8xcWcO6XcvaS+q5aGaujYiNQGmi03RgRT37lIuIPYE96lx2SCPXlGpWKRhtUoPBaD113AO7+iHY5bDGrlfNyvvhV2dVHt/lmYXXXY+FlfeOMCFgxglbd/VOG3mv/nUj90uSJEmSJEmSJEmSJHWn3SncGwdwR2Yu72QxkiRJkiRJkiRJkiRJktQJXROMlpmbIuJm4Ixi14cj4ueZObuefSLiaOAjQBa7bsnM7TJdJyL2AF5V1j2rxuU7lbU3jKGEDWwdjNZgOhRQCL07vwn7SM3XXyEYra/B//RzoPa5lz8T3jwIEaPPHYvHflh5rGcSzHxe4Xy/s0YORnv9sm1rm1T+dlPUv3ZsNUqSJEmSJEmSJEmSJI1Pq4qvCSzoZCGSJEmSpO1U5uhzJEmSJEmSJElqsZ5OF1Cnzxdfk0Iw15URcWKtiyPiecAVwDSGn6z5+corJry3AZNK2nOBm2pcW55UtHEM1y8PU6uQfiRNEFsqBKNNajAYbbDO//tdewpsXNLYNStZcU/lsT1PhcnTC+dH/T3s89Lhsd4d4eSfweQZ267rmzbyfgajSZIkSZIkSZIkSZKkieXJkvPJHatCkiRJkiRJkiRJkiSpRapF8xvbL2lIVwWjZebVwI0UQs0S2Bf4VUTMiojnRUSUr4mC50bEN4GbgacNbQfcnJlXtKf6cemcsvaFmWN+tMtY1vn3kbYv/S0KRhuoMxjtqZvhqufDuscau265LWvgiSpvqUd+dPi8b0c49Qp4+X1w2lXwmvnw9NeOvK6vQmbi8jt9GpUkSZIkSZIkSZIkSZpI7is5P6hjVUiSJEmSJEmSJEmSJElSB/V1uoAxeBNwF7APhWCtPuAdxWNdRDwIrCiO7QYcBgyl6gwFqgUwHzi7jXWPKxFxInBUSdcA8O06tlhb1t5hDGWUrynfcyy+ClxU55pDgEubcG2pui0VgtH62hyMBrBuHvzqz+BldzV27dIaLt6r8vhuz4W9z9i6LwJ2PapwVNM3rfLYqvtg12Nqr1OSJEmSJEmSJEmSJGmcysyHIuJe4Fjg2IjYLzMXdrouSZIkSdJofOC3JEmSJEmSJEnN1HXBaJm5JCJeClwGHMjwbw+CQgDas8v6/v9ShkPRHgZenZlL2lHzOHVeWfvKzHyijvXjMhit+Gda159rRIw+SWqG/grBaJM6EIwGsOJuWHQ97P2ikccHByD7YfNyGOyHKbsDCTkAm5YV5vRMgh32hZvOgoENla91zCfGViPApF0rjy2+0WA0SZKkTtqyGnqnQU9vpyuRJEmSJEmSJGmi+E/gGxTuc/sU297nJUmSJEmSJEmSJEmSJEkTWtcFowFk5n0R8WzgK8DZQA8jP16ltC+AAeAHwAczc1XLCx2nImIa8May7ll1blP+v98eddawE9sGo62sswapu2ypEIzW16FgNIDrT4dD3gnP+TL0Thnun38J3PxnjdVVaod9x752z5Mrj/U3nKcoSZKksVgzF37zFlh2B0yeAc/8IBz9T2DwtCRJkiRJkiRJDcnMWRHxGuAVwDsi4sHM/NdO1yVJkiRJqsb7piRJkiRJkiRJaqaeThcwVpm5IjPfAhwJfAmYXRyKsgPg98DngWdm5ju251C0ojcApUlMi4HL69xjTln7gDrXl89fnpkr6txD6h6Z0F8hGG1Sg8Fogw0EowHM/Sbc9TfD7VV/bG4oGsC0et8iSkyZWXmskVA4SZIkjc3gFrjuVFh2O5CweTnMPh/mfK3TlUmSJEmSJEmSNFG8Gfg5hfvfPhsRV0XEaR2uSZIkSZIkSZIkSZIkSZLaoq/TBTQqMx8C/hYgInYC9gKGUnSWAoszc12Hyhuvzitrfzcz++vc44Gy9qF1rj+4rP2HOtdL3WVgA+TgyGN9DQajTZ4BG5eMPPbKB+Hej8PjF1XfY87X4NB3w4xnwQP/1lg95XY5onq4WS32fgksunrb/sFNje0rSZKk+i35FaxfsG3/Yz+Ew/6y/fVIkiRJkiRJkjSBRMSFxdPVwBoKD8A8AzgjItZQeEjokuJYrTIzy+8ZkyRJkiRpBNnpAiRJkiRJkiRJ6p5gtIjYG3heSdevM3N56ZzMXAusBea2s7ZuEhGHAS8s6541hq3uK2sfGxE7Zub6GtefNMp+0sSypcq9qJMaDEZ71mfgtndu23/gW2GXw+CFP4ErjoOVv6++z5XHNVZHJSdf3PgevVNH7h/Y2PjekiRJqs8Dnx+5/6lb2luHJEmSJEmSJEkT0zvY+lPoCUTxfBe2vfdrNFHcw2A0SZIkSZIkSZIkSZIkSV2hp9MF1OG1wM+Lxw+ATZ0tp2udW9b+dWY+WO8mmfkkcG9JVx/13XR3aln7ynprkLpKfwuD0fZ9OfSNsMeBfz58fthfNXaNsTr8QzD98Mb3qRiM5l8FkiRJbbd5+ehzJEmSJEmSJElSM2XJIUmSJEmSJEmSJEmS1LWyyt0P1cYkbV+6KRhtVwpPrwzgjsxc1+F6uk5E9AJvK+ue1cCWPy9rn1NjHYcDzy/pWgdc3UAd0vjXv77yWO+0xvbeYR940dUw/ajh9vNnwb5nDs85+Fx41mcau0699jy1edfsmTJy/+DG5uwvSZKk6gb74b5Pw1UvgGW3V57nTx0lSZIkSZIkSWqGaOIhSZIkSWo575uSJEmSJEmSJKmZ+jpdQB2WF18TeLKThXSxlwP7lLTXABc1sN8PgI8DvcX2ayPiGZk5Z5R1f1fW/klmmm6kiW1gQ+Wxvh0a33/3E+EV98HmlTBpOkTZfa09vXDU3xeOa0+FJTc1fs1q9n5xIaytWXqnjtw/4FuHJElSW9x6Dsz7/ujzBjZA346tr0eSJEmSJEmSpInroE4XIEmSJEmSJEmSJEmSJEmd1E3BaKVhaNM6VkV3O6+s/aPMXDfWzTJzTkR8Bzi32DUZ+HZEnF4p6CwiXg28o6RrM3DBWGuQuka1YLTeJgSjDZm86+hzDnhT64PRTrmsufv1Thm5f2BTc68jSZKkba17DOb9oLa5/esMRpMkSZIkSZIkqQGZ+Vina5AkSZIk1StGnyJJkiRJkiRJkmrW0+kC6nA3kMXzwzpZSDeKiL2AV5R1f7MJW58PrChp/wlwbUQcXnb9KRHxfuCisvVf8GY+bRcqBaP1TIZo81vxQW+DXY+tfX5fHVmUOx0Mr5kPvVPrr6uaSvsNjJjBKEmSpGZZOw/u/juGvx0fRf+Ys7clSZIkSZIkSZIkSZIkSZIkSZIkSZIkib5OF1CrzHw8Im4FXgA8MyIOy8yHOl1XF3k7W/9535eZtze6aWYuiIjXAlcBk4vdJwF/iIg7gUeA6cAJwB5lyy8H/qnRGqSuUCkYrXeH9tYB0LcjnHkHzPka3PXXleftfzbs+zLY5Qi4+sSR50w/Eo7/Aiy+HqbuDYe+Eybt0vyae6aM3D+4qfnXkiRJEmTCfZ+G+z4JOVj7OoPRJEmSJEmSJEmSJEmSJG13anzwpCRJkiRJkiRJqknXBKMV/Rtwccn5qztYS7c5p6w9q1kbZ+aNEfFnwLcZDj8L4DnFYyT/A/xFZg40qw5pXOsfR8FoAL2T4fAPwv5vgGtOgnXzth7/00vhaWcVzlfeX3mfTNj3pYWjlXqnjtw/sLG115XUHFtWw2M/gTUPwR4nw36vgOjpdFWSpGqW/hZmf6L+df1rm1+LJEmSJEmSJEmSJEmSJKlNDHmTJEmSJEmSJHVeVwWjZeYlEXEhcC7wyoj4CvDBzOzvcGnjWkScBBxe0rUZ+H4zr5GZV0TE0cAFwBuBGRWm3gp8PjN/1szrS+PewDgLRhuy477wqodhyU2w6FqYcRzsfQZM2W14Tl+1Gtv0S0+D0aTutXEJXH8GrJxdaD/wb3DwOfD8bxqOJknj2bwfjm1d/7rm1iFJkiRJkiRJkqStRMRBwHHAvsBOwJPAY8BvMnNLJ2uTJEmStl/R6QIkSZIkSZKkrlEtpcLYfklDuioYrejdwBrgg8B7gFMi4gvAZZm5rKOVjVOZeQtt+C1LZi4B3hsRHwROAg4A9gbWAQuBuzPz0VbXIY1LlYLRqoaOtUlPL+z9osIxkqrhbW36Z2XPlJH7Bze15/qSxm7Ofw2Hog155Ftw6Ltg9xM7U5MkaXRPXDG2dQajSZIkSZIkacW9cP9nYNNS2O14OOaT0Det01VJkia4iPg28PYmbfdYZh5Y5VqN3jBzUGbOq3dRRLwe+BDwggpTlkfEj4FPZObSBuqTJEmSJEmSJEmSJEmSOqqrgtEi4vqS5hpgZ+BI4JvF8QXAkuJYrTIzT29akSIzNwM3dLoOaVypFIxWNXRsnBgPNfZOHbl/YGN765BUv9mfGLn/8Z8ajCZJ49m6MWZaG4wmaTwYHIDsh94KIduSJEmSpNZZPQeufv7w7/EWXweLb4Azb4fo6WxtktQlIuJtTdwuKdxLtwpYBPwxM3248ugq3OjTGRGxE/AN4E2jTN0NeC/w2oh4e2Ze1fLiJEmSJEmSJEmSJEmSpBboqmA04FQKN2sNSSCKB8DTi0etN29FHXMlaewmajBau+6V7anwYfbBTe25vqTmmzsLTvh8p6uQJJXLhEe/N/b1AwajSeqgwS1w14fh0W8XPoC/z5lw4rdgysxOVyZJkiRJ2497/m7bhxstvxOevAr2fVlnapKk7vNtWndP27qIuAP4DvDjzPTGi5H9rNMFDImIXuDHwMvLhp4C7qYQencIcDzD91HuBVwaEWdk5q/bVaskSZIkSZIkSZIkSZLULN0WjDYSg80kjX/dHIzWM7nKYJvegnunjty/fkF7ri9pbHKw8tjkXdtXhySpdk9eDbe+fezrt6xtXi2SVItMWDsXVj8E93wUVt0/PLbwF3Dz6+CMGztWniRJkiRtVzavggU/H3ls8Y0Go0lS/WL5V3KCAAAgAElEQVT0KXXbicLDSU8FPhsR52Tm1S24Tid8GPjkGNY9B7iopJ3AhXWsvw14U53XrOeGl//D1qFoW4APAf+dmZuHOiPiSOCbwAuKXVOASyLimMx8ss76JEmSJEmSJEmSJEmSpI7qxmC0VtzwJUmt1d/FwWhR5W23WuhRM1UKRgOY/c9w9Mer1ympMx7+78pjBqNJ0vj00H82tn5gXXPqkKRaDGyC294J875fec6Smwqhabsc1r66JEmSJGl7de8nKo/NvxiO/1z7apGk7ld6E0RW6C9X/nS7keZmydg+wJUR8YHM/Er9JY4vmbkUWFrvuoj4WFnXDZn5SB1bbMzMefVetxYRcTDwwbLuN2TmpeVzM/MPEXE6cB3D4WgzgfOB97SiPkmSJEml2vTAcUmSJEmSJEmSthNdFYyWmT2drkGSxmSgi4PRqmrTL3Cn7FF5bPYnYJ8Xw+4ntqcWSbXZtBzueG/l8UnT21eLJKm6lffDgktg4xJ44n9rW7Pbs2H5ndv29xuMJqmN5n6zeijakCevNhhNkiRJklptzcMw58tVJrTpgUuSNDGcU3zdGfgEhXCroPBmeitwB/A4sBqYDOwGHAOcDOxdXJvAj4FfAjsAuwJHFuccwNYBaV+KiAcy8/qWflXjUETsALyprHtWJ2qp4HxgUkn72yOFog3JzA0R8Q5gNoX/NgDOi4h/rTPsTZIkSdL2LA15kyRJkiRJkiR1XlcFo0lS15r7jZH7+3Zsbx3N1q5feu52PPRNqxy08ej3DUaTxpsFFe/FLuib1p46JEnVzb8EbjkbBrfUvubUX8KcrwAGo0nqsHk/rG3eunktLUOSJEmSROF7tKwSfrZuHgxsgt4pbStJkrpVZn4nIp4BXEYhFA3gG8CnM3N+pXUR0QO8Gvg8cBDwBuCBzPxU2byXA/8OHEIhIK0P+AJwfJO/lG7weqD0qV4rgIs7VMtWiqFtry/r/txo6zLzoYi4BDi72NUHvAX4dHMrlCRJkrS16HQBkiRJkiRJkiRNKD2dLkCSJrwtayuP9e7Qvjpaok3BaL1TYY+TK4/P+Up76pBUuzlfrT4+sLE9dUiSKstBuPP9dYSiBTz3a7DvmZUDLg1Gk9ROS39T27wNC1tbhyRJkiRp9Aem5CCsmdOeWiSpy0XEjsAlwDOBfuDNmfnuaqFoAJk5mJk/B44Ffk3h3sDzI+IdZfOuAE4A7i7pPjYiXty8r6JrnFfW/kFmjpdfZp8JlD5x8beZ+cca136rrP3a5pQkSZIkSZIkSZIkSVLjskpMRbUxSdsXg9EkqdWeurny2KSd21dHK+x9RvuutcPe7buWpMY89hNY/rvqcwzOkaTOW/0grF9Q29xjPw1v7odnvKfQ7ttp5Hn9VUKBJamZNi2rfe6qByqPDQ7AxqWFw/BeSZIkSRqbdfNhxV2jz1td5fszSVKpTwFHUHha3ecy8yf1LM7MdRSCsJYDAXw5IvYom7MGeD2F4LWhW4pf0mDdXSUiDgH+tKx7VidqqeClZe0b61h7M4U/2yHHR8ReDVckSZIkSZIkSZIkSZIktYnBaJLUaouvrzy2R/n9lePUMz9Yof8D7ath8szq4+seb08dkkZ3/2dGn2MwmiR13rLbap971D9AlPwIoW/ayPN8f5fULqsfqn3uyt/DRbvCPR+Dgc2FvoHNcPt74We7wcV7FI6LdoHrTi98oF+SJEmSVLsFl1Ye65kE+50FR3wUdn5G+2qSpC4VEX3A24rNTcDnxrJPZi4F/qvY3AF4ywhzHgUuohCeBnDSWK7Vxc5l+GsHuCsz7+lUMSM4uqz921oXFsPxZpd1H9VwRZIkSZIkSZIkSZIkSVKbGIwmSa2Ug/DA5yuP731G+2ppxDM/CDvuv3Xfwe+A6eX3YLbQlN2rj1/1/PbUIam6zSsKwROjGVjf+lokSdsaHIDld8HS2+DWc0afP3k3eOldELF1v8Fokjptw8L65m9ZBX/4XCEcDeCuv4aHvw5bVg/PGdxSCDe/6jmF98vNK2HRdbBhUfPqliRJkqSJaGGFYLRdnglv2gynXArHfw5mHNfeuiSpO70Q2B1I4PZiwNVYXV1y/poKc64qvgbwtAau1VUiohd4e1n3rDFut39EfCsi7o+IFRGxOSIWF9vfj4h3RcRuY9j3iLL2w3Wun1vWPnIMNUiSJEmSJEmSJEmSJEkd0dfpAhoVEccBZwEnA4cAuwE7A5mZ23x9EbErsEuxuSkzF7erVknboaW3Vh475C+gd3L7amnETgfBS34Lj/0PrHkY9jwFDjh724CMVqoUvjFk4yJYvwB23G7u05XGp9UP1jbP4BxJar91j8GNr4BV99c2//APwWF/BTsdvO1Y304jr/H9XVK7bFo2tnUPfgkOOQ8e+U7lORuXwI8mFb7nzcFC3+F/C8f/W3u/D5YkSZKkbrB5JSy+ceSx/c9uaymSNEGUPrXuiQb3erLk/IAKcx4oOZ/R4PW6yUuB/UraG4AfjnGvg4pHqT2Lx5HAnwNfjIhvAP+UmWtH27AYpFYepvZ4nXWVz39GneslSZIk1SU7XYAkSZIkSZIkSRNK1wajRcQxwJeA00q7a1h6GvDT4vm6iNg7M9c3uz5JIhP++KXK4zOf275ammHHfeGIv+1gATW8xT9xJRz6F60vRVJlT/yytnkG50hS+932rtpD0Q4+B074QuXxSqG1/aN+lkeSmmPzGIPRAK44uoZJWfi+fsgfvwC7Px/2f8PYrytJkiRJE82qB+DXZ0P2jzz+tNe0tx5Jmhj2KTkf5Qlyo9qx+BrA3hXmrCg5n9Lg9brJuWXtn2XmyhZebxrw18DLI+K1mTnaL2x2LWuvz8x6f8m+pKw9vc71FUXEnsAedS47pFnXlyRJktRqhrxJkiRJkiRJkjqvK4PRIuIdwFeAqRRu3Cr9qXtSPT3nUgpPQzyAwg1HrwO+15JCJW2/MuG282D+TyvP8Qnp9dlhr9HnDG5ufR2SKltwGdx3QW1z+9cV3iujllxbSVLDNi2HRdfUPv+Ij1QfrxiMZvClpDbZ1EAw2lgtuNRgNEmSJEkaMue/4I73UvFDkjs+HWYc39aSJGmCWF1yfmSDex1Vcl7pySZTS843NHi9rhARewCvKuueNYat+oFfA9cC9wILgDXATsD+wMnA24A9S9YcBlwbESdm5mNV9t6prD2WP5vyNTuPYY9K/hI4v4n7SZIkSROA98NKkiRJkiRJktRMPZ0uoF4R8ToKNyLtUNoNzAfuYZTfJmTmIPDjkq6zml2jJLH0t/DItyqP90yCyU17EOv2Ye8zCv+7VdO7Y/Vxabwa2Aj3ng/XvQhuPQeW39npiurXvx5+9eo6FiRsWdWyciRJZVb9gbqe5Dn9iOrjvQajSeqwzcvbf815P2j/NSVJkiRpPNqwCO7+MFV/3rTfWT4cRZLGZkHxNYCDI+L5Dez11uJrluxbbu+SOU81cK1u8jag9AaUucBNde7xcWC/zDwtM/8lM3+RmXdn5sOZeU9mXpaZH6Hw8Nb/w9Z/ae4NXBxR9S/K8mC0jXXWB9sGo5XvKUmSJEmSJEmSJEmSJI1bXRWMFhH7AN8pNoduFvoqcEhmHgi8tsatLh3aEjilaQVK0pAnrqw+vs/L2lPHRDJ5Bhz0tupzRgtOk8ajTLjhTLjvU7D4Bnjk23DtKbDsd52urD6XH17/mk3Lml+HJGlkG5+sfe6h7x59zqQKn53pX1P7dSSpEf5bUpIkSZI6Z/7F0L+2+pyn1fMwFUlSiZuALRTujQvgqxFR91PiIuKNwEsYvsfumgpTTyg5n1fvdbrUOWXtCzOzjqfLQDEMbUkN8zZm5t8D7y8bOgF4cz2XrKe+BtZIkiRJGjP/CS5JkiRJkiTVqtpv6ev7Db6kiayrgtGATwA7UrjpaxA4OzPfl5mPFsdrfXu7g8INZAAzI+Kg5pYpabt3/6erj0/etT11TDTP/Xr18XXz2lKG1FRLboIlv9q6r38dPPTlztQzFmvmwvr59a8zzEKS2mfto6PPGbLvK0afM2n6yP0DG2FgU+3XkqSx8t+SkiRJktQ593+m+vikXWBPn1EnSWORmauByyncH5fAccBVEfH0WveIiHdSePjoULhaAt+vMP3MkvPfj6XmbhIRJwJHlXQNAN9u9XUz8yvAZWXdf1llSXkC6Q5juGz5mlFSTevyVeDoOg9TUyVJkiRJkiRJkiRJklSzrglGi4heCk9JzOLxucz82Vj2ysx+4I8lXYc3XqEkleidWn18ksFoY9LTV3383n8yAljd56H/HLn/0e+0t44hAxth6e2wcvYocduDsPxuWH4XzP7k2K61aenY1kmS6ldPgOw+Lxl9TrWg3y2rar+WJI3VlpWdue7CyztzXUmSJEkaL7ashQ0Lq8/Z9+XQO7k99UjSxPRhYGNJ+yTgDxHxtYh4UUTsUr4gIg6LiHdHxB3AfwGTGQ5Fm5WZs0dY83TgVIYfRnpzc7+Mcem8svaVmflEm6792bL2iRFR6Rcu4zoYLTOXZOb99RzA3GZdX5IkSRqfotMFSJIkSZIkSZI0oXRNMBpwIrALhd8WbAH+tcH9FpSc1/xETUmqSd/O1cerBUmoMcvu6HQFUn3mX9zpCoY9dQtcfgRc/Xy44li44SWwecW289YvLIz/8gT45bNhXqWHi49i87LG6pUk1W7tvNrmnTUXeqeMPm/yjMpjI/3dIUnN1r9u9DnHfKr5133oq83fU5IkSZK6yd0fGX3O017T+jokaQLLzEeBc4HBoS5gGvAu4BpgRUSsiIjHI2JRRGwAHgC+Cjyb4UA0gNuAD1W41Mco3D8YwKbi3hNWREwD3ljWPauNJdwOlP4SpRc4ssLc8qfQ7Fisvx57lrU79LQJSZIkSZIkSZIkSZIkqX7dFIx2aPE1gTsyc3WD+5Wu3+YpmpLUkMEt1cezvz11TETPKn+Abpm532xPHVIzrH6o+ngOVh9vhvVPwJyvw+3vhWteCOvmDY8tuhbu+Ydt1/z27bDq/tr2nzKz8tgmg9EkqS2eugWevHL0ec/5Mux0cG17TqoS9LvZz9VIaoP+9aPPOeafYJ+XNve6tbyfSpIkSdJEMjgAj/0EHvhC4fXhr1ef3zOp+d+LSdJ2KDN/BPw5sIatg86ieEwHnkYh/GpKSX8WjwCuAl6WmZWeMvA94LTicXKVeRPFG4DSJx0uBi5v18UzcxB4vKx7jwpzl7F1iBrA/nVe8oCy9pw610uSJEnabuXoUyRJkiRJkiRJarFuCkYrvQlofhP2K00a6WvCfpJUsGUtbBklDGKnQ6uPq7Knv7b6+IYn2lOH1AwLR7nH+q4Pt/b6y++GK4+DO95b+YNMj1wIW9YMtzevhCU31Lb/MZ+CVz4Ie/7pyOObltZXrySpfrMvKARfjib64LC/qn3fvmkQvSOPGYwmqR36a/yM5pTdm39t3+ckSZIkbS8GNhd+tnTLG+HuDxdeR3PwuTB5eutrk6TtQGb+BDga+AkwQCHsDIbDz0qPIQHMA96ZmS/LzFVV9r81M28qHne24EsYb84ra383s+1PNtxQ1t6hytwHytr13mxU/jSc8v0kSZIkSZIkSZIkSZKkcaubgtFKb+Cq8OnruuxWcu6nGSU1z5qHRp8z8zmtr2Oi2uWw6uNtv2dVXWfLavj9P8J1p8Md74M1D3eulg0Lq48/+CVYP8qccjkID/4nXH44/DAKxz0fg80j3O/++3+ATU9V329wM1y0S2Gf294JCy4rXGM0Ox8GR38cpsyEyTNHnrNp2ej7SJLGbu4smP3J2uYe8Ob69o6AybuOPLZ5RX17SVK9chAG1tc2d+bzmn/9lfc2f09JkiRJGo8e/L+w7Nba5z/99XDc51pXjyRthzJzQWa+CTgA+CDwY+BBYBmFsLT1wELgFuALwMuAQzPzws5UPD5FxGFA+ZNkZnWglPInOVR7mth9Ze0X1HqRiJgGHDvKfpIkSZIkSZIkSZIkSdK41U3BaKWpHfs2Yb+jS85N5ZDUPKsfrD6+x8mw6zHtqWWi2q1KsNzglvbVoe4zsAmuPRXu/wwsvh7mfAWu/hNYM7cz9fSvHX3Owsshc+u+wYHK829/D9z5ga3fi/7wOfjlCTCwsbi+vxAQ9+RV9dU7dxb8P/buOzyO8l77+PdRtST33jEYXDBgmunV9N4J7QQSckIaSU4K5KSQHt4USM9JQghJCL33YmxjbIPpxtjYYNx7r2qWtM/7x1jRSp6Znd2dmS26P9elS7tP/UkYbZu5Z9a1wcYe+AMnNAegsuOx3bvt0lMwERFPHf/2p2vJv51AyyBMGex3Q/p7lPdyb29S9riIRKylPvjYva6Eit6px3UZCBNfCrbmSydm/3daREREREQkXyV/BrH4ruDzLtkExz8EFT3Cr0lERLDWrrHW/t5ae6W1dn9rbX9rbYW1tpu1dri19nhr7TettS9YqzevXHy6w/0Z1toUB/iEyxjTF9inQ/NqnynPd7h/UhrbHQ+UJd1/11q7Lo35IiIiIiKSNr0UExERERERERERCcrv3TS90yYirQopGG357u8GOMQYU57pQruvADkkqWlONoWJiLSzfYF3335fgJM7HrcoaUs0evdtmxdfHVJ4Vj8LW95t39a4AT7+S27qaa5NPebNz8F9JXCvgRmXwxP7wEPdYdr50LC+bVztcnjmAFh0h/s6OxfDA1XOOveXw0M9iOyl4dH/hhFXtt2v7OM+rtHv4tciIp2UTcB734FH+8GD3WHm1dCwIfW8dmtYJxQziP4nwakvQ79j060UKnq6t+9SMJqIRCzI8+hWXfrCmW9D/xP8xw04GQaeAme+BYPOSL3uxleD1yAiIiIiIlII1k+H5yfAg1Xw/BGw+F/+n3smG3oRVAYIpRYREckBY0wp8MkOzXfmoJQraH+85jpgvs/4F4Dkq0QcbYwZE3Cv6zrcfyzgPBERERERERERERERERERERGRvFBIwWiv4RzoY4Eq4Er/4b6+nHR7XdxXfxSRIle71L192CUw4Y9QVh1rOUWpxScYrWEdbHjNCRUR6cgrAG3+L+Oto1U6gQ4Ayx+C2iXQUgernoJJxzvhN4lmeO7g/AgGPO5h2Pvq9m2ewWiboq9HRKTQvP9DmPcz529k8w5Ydi+89knn731Qu7bAtrnBxp46NbNQNIDy7u7tTdszW09EJKjmuvTGdx0Bp06DM9+BkZ+B0qr2/RW9YP9vObd7H+YEmk98yX/Nxf9IrwYREREREZF81FwHa6fA/NvhpRNg81uQaILNb8Ksa4Ov02NcdDWKiIhk72xgUNL9HcBDcRZgjBkAfLdD81PWen8AZK2tAx7u0HxzgL1GARclNTUD9wYsVUREREREMmZyXYCIiIiIiIiIiIiISFEpmGA0a20jMBnn0wID/NQY0zPddYwxxwI34ASsWeDRMOsUEaFulXt7zV7x1lHMEj7BaACTjoFn9ocdi+KpRwrHmhdyXUF7zTuzm7/jIyc8Z+YnnBCcXOsyAAafuWd7ZV/38QpGExFpz1pY/Pc929c875yUGlTjxvBq8lNS6d5um+LZX0Q6r3QDhlv1PgSOvAMuWg0T/gT7fBr2/184/XXodVD7sQNPgarB3mst+ltmNYiIiIiIiOSLJffAgzUw5RR49+vZrVU1KPUYERGR3Lm+w/37rbUZvclojBltjDkvzTkDgaeBAUnNu4BbA0z/AZD8wct1xpjzffbqAtwFVCQ132mt1QE0IiIiIiIiIiIiIiIiIiIiIlJQCiYYbbef7v5ugSHAi8aY/kEnG2NOBp7E+bkN0AL8KuwiRaSTW/+ye7vfCdWSnpYUwWgA2z9M70r2IjYR/56ZBjokm/NdWJEnOa9H/g3KavZsr+jjPr5xoxMCJCIijpYGqFvp3jfvp+7tbuIKRiutcG9v2RXP/iLSeWX7PLqiJ+z3eTjqTjj4Z9B9P/dxPcf7r7Ptg+zqEBERERERyZUdH8Nr14S3XjeP11UiIhIqY0y5MeZYY8wnjTFfNcZ8zxhzS67rymfGmAHAOR2as7nqwSDgSWPMHGPMTcYYzwdBY0w3Y8yXgNnA4R26f2KtXZxqs91jftuh+WFjzJeMMe0+qDHGjMW58OwxSc2bgB+m2kdERERERMJQRMfD6theEREREREREREREckDZbkuIB3W2teNMfcDV+B8anA4sMAY82vgQZwrKbZjjCkFTgL+G7gMJxCN3fN/a61dGn3lItJprHwKbIt7X9WQeGspZt32g4a1qcdtmAk7l0LXEVFXJMWgcRN06RfvnmEEo4Vt0Bmw5oX05/U/CYac695X6RGMlmh0AoBqhqW/n4hIMWre6d238ong6wQNRus9Ifiabko8gtESCkYTkYi1xPQ8OpEilPuZcXDFLigpj6ceERERERGRsKx4LLy1ugyEASeHt56IiOzBGHMc8A3gdKDSZciPXOacCVy+++5ma+03oqswr11L+2Mk51pr3whh3QOBnwM/N8ZsA+YCG4EdQFdgGDAe9+Mz/2qt/XEae30LGAectft+OfB74HvGmHd277kPcChtx0aCcyzlRdbaNWnsJSIiIiIiIiIiIiIiIiIiIiKSF0pyXUAGrgfexTmIxwI9gR8AH+z++g9jzHygFniRtlC01kuXvIpz0JCISDgSLfD6p737qwfHV0ux2/+m4GM3hXE8qxSF2mX+/fWr4qkjmV8ATi4c8H046Tk49Nfpzx3S8SLbSbru7d23+un09xIRKVapAjN3LAq2TtBgtNFfDjbOi1cwmm3Kbl0RkVSa6+LZp6Uh9Zj5t0Vfh4iIiIiISDZWPgEzroAZl8OyB533mGan8Vmbny4DYOJLUFJQ1+QTESkYxpgaY8w9wDTgPKALzvFvyV9e5gH/hRMM9j/GmPERl5uvPtXh/p0R7NEDOBa4ALgGuBA4jD1D0WqBz1prb0hncWttC07I3QMduvoDZ+IcF3kY7f89rAcusNZOT2cvERERERHJht9LNBERERERERERERERSVfBBaNZa+uBM4AptA86MzhXxEy+PxqooO0TBrv79ovAObsPGhIRCceGGf4hFFUKRgvNgInQbb9gY2d+ItpapDDUr4FnUxznXbcynlqSpQrAidOoG+GgH4AxsPd/QWWf4HMresNeV3j3Vw2Cnge6922ZnVaZIiJFLdXjwsongq3TuCH1mKohMPTCYOt5KSl3b2/Zld26IiKptMQUjDbo9NRj5v4IapdHX4uIiIiIiEgmFv4FXrkQlj8Ayx9yPjd7at9w1h56IVy0BnqOC2c9ERFpxxjTHefCn1fgfna9dWlr67R2BfBs0lyfD3SLkzHmWGBMUtMu4N9ZLjsf+BkwE6gPOOcj4NvACGvtHZlsaq3daa29AicEbZbP0M3A/wEHWGufz2QvERERERHJlO/LNBERERERERERERERSVPBBaMBWGs3AqcBNwMbaR981vo9+YvdY7YB38EJRdseW8EiUvzq18Lkk/zHVPSKpZROoawaTnkZ9roy2Pj6dZGWIwVg8T+gaZv/mGnnwaTjYfM7sZQEQPPO+Pby0n00HPhDOPT2trbKPnDaazDk/NTzB50Fp02H6qH+4/oc6d6+a2vwWkVEil2qx4WVjwdYow5mf8u7v2oIDLsETn8NyrumV19HJRXu7QkFo4lIxFoa4tlneICg7ZZ6+PB30dciIiIiIlJMtrwHLxwFD/WCF4+BdVNzXVFxshbm/TSatcffCsc97FxsRUREovIwkHz1qV3A3cCngetwD0vr6LGk26eFVlmBsNbOtNaapK/K3ccdZrPmOmvtd6y1xwFdcYLXzgE+C9wE3AJ8E/jM7vb+1trR1tpbs9179/4PW2uPBvYBLgW+DPwv8ClgIjDIWvsFa22Aq+iIiIiIiIiIiIiIiIiIiIjkhvW5zoBfn4h0LmW5LiBT1loL/NIY83vgSpyDt44DBtM+8G0LztUzXwDuttamSEUREUmTTcCUU1KPK62OvpbOpHowHHsvjLjKCbTys+xeGP1VnZzRmW16M9i4DTNg8kQ4+32oGRZtTdY64TVR2f9bcPCtmc/vvh+c+AQ8vhfULd+z/8i/wcjrg69X3t29vWlHZvWJiBSj5lr//g3ToWE9dOnv3r9zMbx8jvf8EVfDMf/OvL6OFIwmIrnS0hjPPj3GwD7XOUHLflY8Aof8Uq85RURERESCqFsFLx7thAwDbHzN+Zzn7LnQdUROSys6OxZC3Yrw1z301zDmq+GvKyIi/2GMuRQ4lbYLgr4GfMJau3J3/14Bl3q+dUlgvDGmq7U2D67eVRystQngw91fce+9BFgS974iIiIiIuJHxwyIiIiIiIiIiIiIiISpJPWQ/GatbbDW3mWtvcpaOxwoB/riBKRVWmv7WGvPs9b+QaFoIhKJ9dNg2wepx5V2ib6WzmjgqVA1xH/MO1+Dl06AXVvjqUnyz8rHUo9p1bQtvfGZaqmn7Tj2DvqfCD3HZ772qC9nF4qWbNhFe7aVlMPQC9Nbp6ybe3vz9vRrEhEpVs0BzkV6dIBzAnGylgaYfhk8ORK2L/Ce2/vw7OrrSMFoIpIriZiC0QCOvBOOugtGXOM9pnYpbJ0TW0kiIiIiIgVt6T1toWitmmth3k9yU08xC/JeUyb6TIhmXRERSfbtpNtzgdNaQ9HSYa1dC6zffbcEGBtCbSIiIiIiIiIiIiIiIiIiIiIiIpHL+2A0Y8x4Y8w3jTF3GWOe2v11lzHmJmPMIR3HW8dma+1aa21TLmoWkQCshfUzYNkDMP82ePtrMOMTMOk42PxurqtLz6a3go0zugpUJEq7wJF3QFlX/3EbZsDsbwVbc9sHMP92mPtTWDfN+fcqha28e3rjg4QdZqu5zrvvsN/BGW9AWU1ma48L+G890FrfgV6Htt03pTDh/6CyT3rreP03aNqReW0iIsWmuTbYuLe+1P7+3KxeMQcAACAASURBVB/DiodTzxt6fvo1+Skpd29P6KW4iESsJcZgNFMC+1wHx9wNl2xyng+7WRFDuLKIiIiISDHYMtu9fdGdkGiJt5ZiZyP4fVb2hT5Hhb+uiIj8hzFmEHBwUtON1lqfD5dTSr6iyn5ZrCMiIiIiIiK+iul482L6WURERERERERERESkUJXlugAvxphDgV8Dx/kMu9UYMxP4mrU2YDKRiOSNqafveUV4gJ2LoPceuYf5a/ZNua5ABp8F586H5w6Gxk3e45beAxP+5JzY7mXxv+D168E2t7Xt+1knCMpvnuS3qiHQtD34+KDBNNlI+IQ5lFZCaQUMvRiW3p3+2l0GZl7XHmv1g9NnOiGB9Suh/4nQbd/01ynv5t6uYDQRkTZrng82buXjULcKqoc44UAf/j7YvJq9M6/NTUmFe3tiV7j7iIh05PdcOkqVvZ3nw+um7Nm3/uXYyxERERERKUjL7vPu27EQeoyJr5ZiF8VnHWO+DiUegdEiIhKWo3d/t8AKa+0rWa63Oel2mle/EhERERERERERERERERERERERyY28THgxxlwATMcJRTNJX/8ZkvR1HPCKMebCuOsUkSwY4wQVualbFW8t2Zj701xXIK2qhzrhZX6ad0Ltcu/+ho0w69r2oWgAH/8V1k/PvsZcsNYJe5t+Kcz6NKybmuuKcqPjf9NUmndGU0cy32C0Ls73CX+EwWenv7Yxqceko7QLDD4DRl6fWSgaQJlHMFqzgtFERP5jyb+Cj318KNxfCQ90Cf63NOzHBwWjiUiutOQoGA1gyLnu7dvnx1uHiIiIiEgh8vuMBqBhbTx1dBZhf9bR/yQY87Vw1xQRETfJV8F6L4T1kh8QuoawnoiIiIiIiLgK+dgsEREREREREREREZFOLu+C0YwxY4D7gCqcTwbs7i9oH5Bmk766APcaY8bGW62IZKXaIxitvkCC0Rb/E+Z8N9dVSLIh56ces32Bd9+j/bz71ryQfj354L3vOGFvKx6BxXfBlNNg+SO5rip+zbVpjo84GM1a/zCHkkrne3k3OOkZuHg9XLgCjns49dp7XRVOjWEr9whGa1IwmogIAHWr05+TTgDZ0AiyxBWMJiK54hcy3KrbqGj27nmQe3vDemjcHM2eIiIiIiLFoH4NTDref0zjxnhq6SzS/WzET+8JcOpUKPV4P0hERMLUI+n29hDWSw5DawhhPRERERERERERERERERERERERkcjlXTAa8GecoLPW0DMDNAOvAQ8CD+2+3UT7kLQuwF/iLlZEslA91L29rgCC0da9DLOuy3UV0lFpJVywzH/Mtnnu7Rvf8J/XuCGzmnJp11ZYcHv7NtsC836Sm3pyqbkuvfFrX4JXPwlNYRxnnuTjO+CZcfBwb5h2rve40sr297v0c/5m1gxPvcegM7KrMSpewWiJRmhRgI6ICHXLo11/+OXhr1lS7t6eaAp/LxGRZH4hw63G/yyavbuP8e7b9kE0e4qIiIiIFINFf0/9/ofXZziSmbAuAlPRG059OZy1REQkiC1Jt3t4jgpucNJtJfuLiIiIiIhExua6ABERERERERERkYKhd9NEJIi8CkYzxhwAnEBbIBrAbcBAa+2x1torrLWfsNYeCwwEftlhiWONMQfFV7GIZKVqiHt7fZ4Ho22bD5NPznUV4qVmOPQ4wLt/7WT39uUP+q+7a2vmNeXKmhed0KmOtsyGph3x15NLLbXpz1l6N7x8Tng1LPk3vPFZJyyhaSvsXOw9tqTSvd0vhAGg+1jY64rMa4xSeXfvvg3T46tDRCRf1a+Odv1hF4e/ZkmFe3tCgZciEjG31znJ+h0Lg8+KZu+qwVDmEfq75oVo9hQRERERKQab30495v0fgNXhTqFpCikY7bSZUFYdzloiIhJE8lXbxmWzkDGmEjg4qWllNuuJiIiIiIiIiIiIiIiIiIiIRE1HEYpIq7wKRgMu2f3d4Pyt+rK19pvW2i0dB1prt1prbwa+mDQeIIKzvUUkEtUewWi1y+KtIx02Ac/sn+sqJBW/4I91U6C5Q0iWtbDycf81mwowGG3bXO++qMNX8kmiyfnKxIYZsPAv4dSx6M7gY72C0co9AhhanfUOlHqE1OSaV3gEwKK/xVeHiEi+qoswHHjI+VDq8diSDQWjiUiu+AWjHfIrOOm56E7aNwb6Hu3el+p1pYiIiIhIZ7bysWDj1k+Lto7Owlp456vZr9PzQOiR4qItIiIStnd2fzfACGNMNn+ILwFa38xvBmZlU5iIiIiIiIj4MbkuQERERERERERERESkqORbMNqE3d8tMMta+8dUE6y1fwZm0vYpwhER1SYiYase7t5euxTq18ZaSmCvXJTrCiSI0V8B4/EQl2iEtS+1b9s2D3Yu8l9z1x4ZnfnP+uQhb37Hu6/YdAzCS9ebn4OmHdnXsf7lYONMGZSUevcPPse9/eD/B6Vd0i4rNlUDvfs26vh7ERHqIwxGG3hKNOt6BaO11Eezn4hIqxaPAMZ9roOxX08dKJytoRe4t2+bCzs+jnZvEREREZFC1aV/sHErn4i2js5i+YPZr1FaBRP+nP06IiKSFmvtEiD5Tab/zWQdY0wl8J3WZYE3rbVZfnguIiIiIiIi3nyO2y44xfSziIiIiIiIiIiIiEihyrdgtLFJt/+Zxrx/Jd3W5YpFCkWfCd59G6bHV0dQb34RVj2Z6yokiMrecJlPkNXG19rfX/9K6jU3vw2Jluzqituuzd59r14Fq1+Ir5Zcaq7Lfo2ns3x64RdS11FppX//Ptfu2WZKYa+r0qspbmU13n21S2HRXbGVIiKSl+pWu7cHPWnYiymF4Zdnt4aXknL39vrVkGiOZk8REXACr92UpHguHZah53v3KcRBRERERMRdz4OCjVt2f3rvqYu7FY+lHrPPdc7Fhrycvxj6HRNaSSIikpbWD08NcI0xxuVDYm/GmBLgDtofi5fyAqUiIiIiIiIiIiIiIiIiIiIiIiL5It+C0Xom3X4njXmtY02HNUQkn1UPga4j3ftWPxtvLal8+AdY+KfU40bd6N5+6G/CrUdSK6uGYZe69zVuan+/dlmwNVcV2Anua57373/js9DSEE8tudQcwkWv61fDlvcyn5/O7zlVmMOwS+HAH4Apc+6X94DjH4GaYRmXF5sDvufd9+bnockn0FBEpNg1bnBv73Fg5mtW9IITn4aqgZmv4aekwrtv5ePR7CkinVPtMnjn6zDtAvjgF9C0zX1cXMFo1UOht0fY+7vfiKcGEREREZFCYwNefKZhLWybF20tncH6qanHHPB92O8L7n09x0f3npKIiATxW2A9YHGOh7vTGPMzY0x1qonGmP2BF4Grd8+3wMfA/dGVKyIiIiIiIs7LNxERERERERERERERCUu+BaP1SLq9yXPUnrYk3e4WUi0iEof+J7q3L7kbts2PtxYvyx+Ctz0Cz5Lt9wXnq6ymfXtFbxh2cTS1ib+KHu7tHYOy6lcHW29ZAR0nvHEW7FzsP6ZuOaydHE89udRSF846S/+d+Vyv4AY3pSnCHIyBA78Pl22Fs9+HSzbC0Asyry1O1cO9+xKNsPLJ+GoREck3jRvd23vsn/5aB/4QLlgGF6+DwWdmV5cf32A0/U0XkZDsWATPT4AFt8OqJ2H2zbDmBfexqZ5Lh2nYhd59H/4uvjpERERERApF0GA0cJ77S3Ya1vv3Dz4Huo6A7qNgwMQ9+/e7IZKyREQkGGttHXAtkMAJNisBbgbWGGPuA9olWxpjPmGM+Z4xZjowBzgZ54x8AzQCV1prbYw/goiIiIiIiIiIiIiIiIiIiIiISFbyLRgtuZ40joxuNzbffiYR8TPodPd22wJL7423FjctjTDj8mBjD70deoyBiS/BgFOgsg8MOgNOmQo1w6KtU9yV1ri3L7sPWhra7q95Pth6yx/Kvqa4zPl+sHGrno62jnzQUh/OOvN/BS+dCJvfTn9uOsFoJV2CjSurgZ4HQElZ+vXkSnl3//7Vz8RTh4hIPvIKRut5AHTdN721qodCzXAoKc++Lj9+wWhL7452bxHpPD78LTRuCDa2JMZgtKE+wWhzvg86z1REREREpL20gtE6wWcXUSvzuZ5ceQ84LukzrxOegJHXQ9Vg6HkgHP5H2O/z0dcoIiK+rLUv4ASgtYajgXPB0MuBbyQNNcC9wA+AY2h/7FwzcL219p2o6xUREREREREdJyAiIiIiIiIiIiIiEqYCStIQkaI09EKo7Od+kvOmN+Kvp6O3bkw9xpTBCY9D6e4TsPseBae8FG1dEkyZRzAawGvXwnEPQMNG7yASN4nm/A+ishY2vR5s7I6Poq0lHySH4HV04Qp4PI3gwvWvwPOHOycMdRsFPcZBSWnqeQ0Bgxyg7W9JMSr3ORFLRKSz83o+UtkPDvohvHoNgQ+e83sOFCa/YDQRkbB89PvgY+N8Lt19LNSMgNqle/Y1bYX61VA9JL56RERERETyXaI5+NhNb4BNgNE10TLmF5h/4Qooq2q7X94Vjvxb9DWJiEjarLV3GGMWAfcAA2j/QUHybZN03+6+vxH4hLV2ahy1ioiIiIiIiIiIiIiIiIiIiARlfU6V9OsTkc5FRxKLSG6VVsKQc9z7ErviraWjXVtg0R3+Y4ZdAme+6f0zSG75hYKseBjq18DCP6a35s7F2dUUh4Z10LQt2Nh1UyDREm09udZS795eUgnVQ6H76PTXnHEZPDceHh8KG17zH7vkbnjp+OBrlxRxMFqZgtFERFy1NEDzTve+yj4w4iqYOAn2/qTTluqk4NiC0XxOsBURCUM6wQkQ73NpY5z3BLx0hhBqEREREZF02DQ+i7AtMP9X0dVS7KyFpu3ufcfcp4uYiIgUGGvtFGBf4CZgBU7oWccvkm5vAn4EjFQomoiIiIiISJxM6iEiIiIiIiIiIiIiIhJYWa4LEBGhZoRHRyLOKva06hn//qP+Cft8Mp5aJDN+oSA2AZvfhQ9/m96aDeuh+6js6ora9vnpjV/4fzD6S9HUkg9aGtzbS7s434+8C6acCi11zv2ybtC8I9jaDWth2rlw4Uooq9qzf+v78FqafydKizgYLeXJVjooREQ6qcZN3n2VfZ3vA09xvo7+p3N/1dMw7Tz3OWVdw63Pi1f4qIhIWLbOSW983M+lD/oRLLjNvW/yRDj+URh8DpRWxFtXoatfB6ufBowT+l3ZH/oeCd32zXVlIiIiIpKNdILRAGbfDMMu1vPApu2w5gXYOs+53f94GHiq//vtiUawHkHTNcOiqVNERCJlra0FfgX8yhgzCjgOGAb0ASqAjcA64FXgHWt17WQREREREZH46aWYiIiIiIiIiIiIiEiY8jEYrfXTgKOMMSMCzhmYfMcYczxpJGtYa18JOlZEolDi3rw+x/9rrn/Zu6+sm0LRCoFfMBrAO1+FXVvSW7N5Z+b1xGXHx+mNX/FQkQejeYS2tAaj9TsaLlgKKx+HkgoYdAa8cCTULQ+2/q7NsOY55wStjpY/lH69JZ04GG3ZvXDsPfHUIiKSTxo3eve1BqPtweM5NMT4WOLzstv41CciEsS2BfD8YenNifu5dFk19DkCNr3h3j/9Yuh9OEycBBU9462tUG14Faaesedr79IqJxx0+GW5qUtEREREspduMBrA0nvgwO+HX0uhqF0Gk0+BnYva2j78NfTYH058Grru7THP5/ON8u7h1igiIrGz1n4EfJTrOkRERERERCQdBXbhYOVti4iIiIiIiIiIiEgeyMdgNHDe9b8vi7kvpzHekr+/B5HOoaTUu2/Le9BrfHy1tLIWFt3p3b/3f8VXi2QuVTDajoXpr9m8I7Na4tS0Pb3xuQ4hjFpLg3t7aVXb7S79YN//brvf/wRY+u/ge0y/BHodDFtmZ1Zju7q6ZL9GvipLEYwGULsCaoZFX4uISD5prvXuy+RkVRPTgXR9j/LuswnY/hF0HxVPLVLcdi52Xp9tetM5kb5mBOx1BQw6LdeVSZTmfC/9OT3GhV9HKr0P8w5GA9j8FjzcC0bdCGO/ATXD46utEL1+vXsgeUs9vPl5GHIBlFa0te9cAvNvg6atMOBk2Ps6//eZwmQT8PEdsG4KVA2CkddDzwPj2VtERESkEGUSjLZ+evh1FJLZ324fitZq2wcw9ydwlMdnmR/83HvNIO/Ti4iIiIiIiIiISMgUNCYiIiIiIiIiIiIikq6SXBfgweIEnKXzZZO+0p0rIrmUaPbuW/FofHUkm32zf/+oL8ZTh2QnVTCal16HQLXHyepNBRCMlmjMdQX5xTMYzSeAbPSX098njFA0oKgPfggS7vPEcP/HBRGRYuT1WAVQUune3qWf95zyntnVE1R5ihNpJx0DW+fGU4sUr+0L4cVjYd7PYO0kJ4Bo8d9h6hmw+B+5rk6i0tIAKx9Lb06P/f0DG6My8jNgAgRxffR7eHos7Pg4+poKVd0q2L7Au79xE2yY0XZ/23znd7rwj7D0Hnj9MzDrusjL/I9Zn4I3PwfLH4QPf+v8rdroE5InIiIi0tl5BaMNvdB7zrrJ0OQSnNtZrHnOu2/Vk959i//u3ZdJCL+IiIiIiIiIiIgEoFOTRERERERERERERETClK/BaNA+6CzIVyZzRSQf2IR337Z58dXRauv7MP+X3v3jb3VOuJb8l2kw2vDLvYM+CiEYzS9cxUtzXfh15IuWevf20irvOX0mwMTJ0dSTSnmP3Owbh1KPcJ+ONsyMtg4RkXzjF+JpPA6Y63UodOm/Z3v1cOg+OrzaUjnmXu++xk3w4e/iq0WK08I/QcNalw4Lc26BhMeJ9ZKZDa/C5FPh8eEw5TTY/lFu6tj+oXdoQkemDIZfBqdMhZKyaOty0/tQmPDnYGNb6uCp/eC+MnhiBLx7EySaIi2voNStTD1m58fw3nfhXgPP7L9nMPjSfzt9kyeGGF7tYtt8WPKv9m3NO/zfTxIRERHp7Lye4w86E4Zd6j1v+sXR1JPvWhpg1xbv/saN7q+JU71OThV0LyIiIiIiIiIiIhnSKUoiIiIiIiIiIiIiImHKwdmCvpajTwNEOp/qwd599WviqwPAWnj2IP8x+3wqnlokBBnmf476Eqx83L2veWfm5cSlpTH1mI4aN0LZ8PBryQd+YTN+Bk6Eqyw0bIRH+4Vfl5eKnvHtlQvDL4flD/qPWXo3DDgxnnpERPJBwuOxqsTnsaqkFA64Bd76Uvv2A2/xDlOLQkUv//5Fd8CRf42nFilOfoGpdStg5yLoPiq+eopZ7XKYekbba566FfD84XD+IugS4/NhgI2zUo857mEYfonzOj7Ov3tu9v0MNG2Fd78ZbLxtgdplTohW3Qo49r5o6ysUfsH5rd64Idha66bCpOPgnPlQMyy7utwsvce9fcXD0LILSivC31NEJG47F0P9OqgaCDUjcv94KyKFL9Hs3m5KYdy3nedSbtZOckJvex0cXW35qHFT6jHN2/d8b6Z2qf+ckvKMSxIRkXgZY0qAA4DxwHCgH1CFc2xdPbAe51i794B51lodcyciIiIiIiIiIiIiIiIiIiIiIkUjr4LRrLUjcl2DiOTAwFO9++IOoVo7yb+/79FQNSCeWiR7iab05ww4Bcq7Qnk39/7mHdnVFIdEhsFoNcUajFbv3l5aFWx+l77Q71j/UI4wDTglnn1yZew3UwejLboTxt8afwCHiEiuZBriOeqLUD0Mlj/sBKUNuwyGnB1+fX7KquPdTzofr+dyrbYvUDBaWOb+ZM/X4M07YOm/Ycz/xFODTTjhYgtu9x93/GMw7ELndr6EtIz9BvQcD1NPT2/esvuhrBsc8WcwGYZ7F4uw3wNqroXFf4cDvx/uugArHvHue7AajrgDRipYX0QKVP1amHQ87Py4ra1mBEycBN32zVlZIlIEbIt7e0kZ9BrvP3fhn53nzJ1JkGC0Xdv2DEbbvsB7/MDTsqtJRERiYYw5AbgBOAvoEXDaFmPMM8Ad1toZkRUnIiIiIiIiPvLk+AUREREREREREZEC4Hf5N10aTkRadfKz7UQkL3TdB7qPde+rXx1vLUv+7d9/6G/iqUPC0fsw0v6Qud+xzvcyj2C0pgIIRmvJIBht1TPF+yoh07CZZIf+Gir7hlMPQGUfuHQzjPiv9u3DL4Phl4a3Tz7qcziM+3bqcW9/NfpaRETyRTaPVUPPh2P+BUfdFX8oGkBpgGC0RHP0dUjxSuzy7/c74VuCS7TAojvc+za/HV8dH9+ROhTtsN+1haLlm0GnwXkfpx7X0aI7YPbN4ddTaJprw1/TL8AsG37vDdgWeP3TMOMTsPLJzF6ji4jk0swr2oeiAdQuhaf2g6lnwcyrYd7PYPE/oH5NLioUkULlFYxmSp2Q4CP+4j1305vR1JTPGjemHtO0bc+22mXe4ztbuJyISIExxuxvjJkKTAWuAHriHPAQ5Ks3cA0wzRjzkjFGV5MQEREREREREREREREREREREZGCpmA0EckPh//Ovb1xY3wnkNathKV3e/ePuAb6HhFPLRKOLn2h33HpzRlxtfO9vICD0RIe4Sp+3r8F3voi2ET49eSaZ9hMVfA1+kyAs993Qmcm/F/2NV24Cip6wdH/gFNehkNug1OmwDH3Qkl59uvnu/E/hT5H+Y9Zfj80bIinHhGRXAvjsSpXygIEo+3aEn0dUrwSKV4PKhgtHDs+8u5bek98dSy7179/+GUw+sZ4aslUt5FwcQbPY+f/yvnqzKIIRtv6Pqx6Ovx1g9S6/EF45QKYdCw0bg6/BhGRKDRuhvXTvPvXPO88Xr/3HZj1KXh6f1jnM15EJJlfMBrAvp/1nrvjw+K9uIuXIMFou7bu2Va/yn1szwOdi1WJiEheMsZcDrwBnEBb2Jl1+Wrl1tc6byLwtjHmkrjqFxEREREREWj/sq3QFdPPIiIiIiIiIiIiIiKFSsFoIpIfqgZ79zWsjaeGN7/g3z/0wnjqkHAdez/U7BVs7NF3Q/fdF04u6+o+Zund8PK58PpnYOZV8PwEuNfASyfDR3+ERHM4dWcj0zDBhf8H614OtZS80FLv3l7aJb11qgbCPtfBfp+DYZdmXs/I/4bSSue2KYEBJ8LYr8GAk6GkLPN1C033FBcpt4loAgxERPKRZzBamo9VuVAaIBgtyIm8Il4Su/z7F98F9TG9Zixm9au9+6qHxVfH+lf8+/e6Mp46stWlL1y6FYZekN68d78JzxwAH/wCmj1exxSz5p3RrDvruvB/n+kEkm9+G+b9LNz9RUSiUr8mvfFNW+GN/4aER9iRiEiyVMFoAMc97D6muda5wFFnEuT9lOTPUFc8DrM+7f3cs/v+4dQlIiKhM8ZcBtwLVNM+EK016AxgA/ARMAsnQG0hsDFpTPI8gBrgPmPMRfH8FCIiIiIiIiIiIiIiIiIiIiIiIuFSMJqI5Ae/YLT6dfHUsPkt776KXumf0Cz5oXownL8k9bhzF8De17TdL+/mPXb1M7DoTlh2X9u/m/Uvw1tfgukXg83xVbISGQajAax6Krw68kUUYTP7fZ62Y9DTYMpg7Dcy37eY9D4s9ZiNr0Zfh4hIPvAKVymEYLSyAMFoq5+Lvg4pXqmC0QCePQC2L4y+lmK2a7N3X9O2eGpY82LqMT3GRV9HWCp6wPGPwYUr4eQX4OQXoXpo6nnb5sHsm2HyydAS4N9/MWmujWbdxk2wbnJ46zXXeb/O9LL0bif8WUQk39mm9OfsWAibXg+/FhEpPkGC0Qae4j1/w/Rw68l32+amHjPzCudiOfNuhekXOeHhXqqHhFebiIiExhgzGrgL5xi+5EC07cBvgHOAvtbagdbasdbaY6y1R1lrx1hrBwD9gPOA3wE7aB+QVgb80xizX9w/l4iIiIiISOeUwXHFIiIiIiIiIiIiIiLiScFoIpIfyrt797VEdGJsskQLNPgEsJ09B0rKoq9DomEM9D/Ru7/PkdB9dPu2yv6Z7bXqKVg7KbO5YfE6QXvM11LP/fA34daSD5Y/6N5eWpX5mgMnwvGPQNd93fv3vhaOfxR6HQymxDmxq9ehMPEl6D4q832LybCLU4/Z/mH0dYiI5IMoQjzjUtEn9Zg5342+DileQYKhGjfBgtujr6WYNW7y7mvaDk07o69heornh1WDoOvI6OsIkzFO+MCg02HQaXDwz4PP3fQ6vH9L2/1EMyz4DTw1Gp4cCXNuiS5ILFei/HnCDOls3Jj+nIb1sGV2eDWIiEQlkUEwGsCkY+GBKnjuEFh6f7g1iRSjXVth1vXw2FB44UhYck9685c/4sx7oBru7wIP9YQpp8HWAEFauRQkGK2iJ9Ts7T7u1auhYUP4deWr1c8HG/fGDfDet1OPq1IwmohInvoDUE1bIJoFfggMs9Z+zVr7nLV2i9dka+0ma+0z1tqvAsOAH+9eo1VX4PeRVS8iIiIiIiJJcnxhbRERERERERERERGRIqOUHxHJD6bECSlqqd+zr7ku+v2btoJNuPcd+TeoHhp9DRKt/b8FG1/d8+Q+UwKHuxwHXJ3FCSLv/A+cMy/z+dlqaXRvL6mEIefDqif95+9YBN1CDhxoroXtC5wTnHqMg5Ly1HOshdplUFIBjeudn8uUQvcxUN412L7rpoJtdu8rC7iGl2EXOV9294EMrX9DTIkTgNA6JtEMGCgpdV2m0wryd3X7gujrEBHJB17BaCUFEIwW5PGtvFt0+ydaYOfHUDMCSiuj26fQ1C53fu8VvXJdSfYSHs9tO1oT8IRxcecXjAZOEFTQ5+CZ2P5R6lCsUTcW/nPq4ZfBvFthW8DAig9+DqO/AmXdYNZ1sOKRtr65P3bCto74cySl5kRzhAF8YYaSZRKMBrBtPvQ+NLw6RESikAgQSuulpcH5e/vqlU7I87ALw6tLpJhYCy+fDRtfc+7Xr4LXrnHe+x5xRer5K5+EGZe2b0s0wtqXnJDC8xZClwwv/BI1r/fqTYfn+UPOgY/+iyAqkQAAIABJREFU4D528klw1pzCf22QSqIZapcEG7vkn8HG6bNOEZG8Y4w5FjiFtlC0HcDF1trJmaxnrd0BfN8YMx14FKjZve5pxphjrLWvhlO5iIiIiIiIpM/kugARERERERERERERkYJTkusCRET+o6zavT3VydFh8DupdeDp0e8v0Rt8JpwyFUZe39a297Vw1mzoM2HP8VVZBKNt+wAW3ZX5/Gx5hUeUdgkWTLL03vBqsRbe/xE81AOePxyeOwQe7gWLU5yos2MRPHsQPLk3PD7EmffiUfDCBHi4B7z3He8ww1YtDTB5ond/z4PS/3ncGON8lZQ6X6bDwQslZcV/klamDv2Nf3/jBmjcHE8tIiK55BWMVloAwWjgBBz7iSroeNXT8Gg/eHoMPNwb5t8WzT6FpHYZPHswPLEXPNwHpl8KzS7h04XC2uDhHLVLC/tnzbVUwWgtEQeWr3nRv7/7WBjz9WhriENJOZzxOhzwveBzHhsMD3VrH4rW6uO/wM7F4dWXa1G+/7Pjw/DWSvX/i5fXroGWLAKHRETi0PGiEpmafhE0RRh4KVLIts1tC0VLtvBPqeeufAJeucC7v2k7fOhyIZh8YVvc202Ha7kN9QlW3PYBrH4mvJrylVeIXDb6HB7+miIikq0v7P5ucMLRbsg0FC2ZtfYl4IakdQE+n+26IiIiIiIikg2beoiIiIiIiIiIiEgn4veOmd5NE5FWCkYTkfxR6hGMFvUJ2OAfjFbZJ/r9JR79joUj/wZXWefr6H9AzwPdx1YPzW6v1z8NM6+EObfArE/BWzfCpjedcIeotXgFo1VCWYBgtLUpQgmCaNoOyx+GF4+G97/f/oSn5lqYdZ1zEpcba+GVC50TxFz7EzDvZ/DKRZDwOJFq1xZ4Zpx3faYMBp8V6EeRCHUMkXOzPcQAAxGRfFXowWj73+zf31wb/nOgnUuc5wu7tjj3W+rg3W/AyqfC3aeQWAsvnwtb32ttcIKU3i3gMKl0TwTfMjuaOjqDXSmCnqIKrLIW1k6G2Tf5jzv+USitiKaGuJVVw0E/gstD+p1OPRO2vh/OWrnW4vE7GXENDL3IvW/Ul+Hof+8ZptFR4ybY4BJAkgm/95BS+eDn4dQgIhIVv1DakvL01nqoGyz+B9StzKokkaKz5G739g3Tne/NdbDqGVj4F/j4DicE/INfwJtfcl4HpzLvJ86FSVY+6f1+Q654BqN1uLBI/xOgvKf3Om/d6Px+Nr6R+gIqhSoRcjBajwOg6z7hrikiIlkxxlQC5+Ecy2uBR6y194e1vrX2PuARnHA0A5xvjCmSN9hERERERETyVYBjYkVEREREREREREREJDAFo4lI/ijzCEZrjiEYbcNM9/bSKu+6pLhVDc5+jWX3w9wfOycAfvQHeOEIeOMG7zCvsCQ8TnYqqYTy7qnnb5jhnHiVqR0fw7MHw4zLYNPr3uNeuRBWPLpn+5Z3vEPRkq16Eh4f6pzgnmzr+/DUaNi52Htu/+OhwufEKomH14lwyXYoGE1EOoFCD0YbcXWKATb8k7FXPOL+OLLS5blFZ7FjoftzqOUPxxPOGwWvwF8vk46B2mXR1FLsUgU9RfG6vLkOpp0HU06FlnrvceN/Bj3GhL9/rpVVhxPEvmMhPDse5t2a/Vq55hXAV1YDIz+9Z3tJJYz6Iux9NZw9Bw79DRz8/7zXn3SME6CRrWyC0Zb8M/v9RUSilGhyby+tgnM+gMN+l956sz4FT4+FVc9mX5tIsfB7LrFzKbx4FEw7F978HLzxWScEfPbNsPCPwfeYdR28coHzmcTOpVkWHCKv94NLOgSjlZTDvp/1XqduufP7efFI5+9M2CFi+SDdoPBUxvxPuOuJiEgYjgK60nbW/O0R7HFb0u2uwNER7CEiIiIiIiL/UaDH57gp1GONRERERERERERERKSoKBhNRPJHqUcAWUsMwWizb3Zvr+wb/d6Sn0orwjlJvaNFd8Ca58JfN5lXgERpJZiAD/0zLofaFentu+JxmHU9PLUf1C4JNmf6JdC0vX3b6ueD79mwFl4+t33bG5+Dxg3+8459IPgeEp0gJ6xtXxB9HSIiueYValoowWjd9oWj7/Yf4xV2k6l3v+nevvgf4e5TSJY/5N7euCH8YLq4JHalP2fqWeHX0RnsXOTfP/kkmHombPsgu32a6+CDX8K9Bh6sgdUBApnHfD27PfNZvxNCWsjCnO/C1gAB0/nMMyi0CgafDQf+AMzu0IyK3nDEn6H7KOd+j7Ew5isw9iYo6+q9x5ufgzk/SP/vYu0KeP+H8Oo1sOC21OO97FwU/mOiiEiYvILRSsqd5/2jb4QLlkPvCcHXbN4J086BZp8gVJHOxO/iJW9/xbnwR1i2vg/zfhLeetmwFmzCvc+U7tl2wHeDrbvkX7Dyiczryldef48ztc914a4nIiJhaA0ps8B8a+2ssDfYvWbyG3oKRhMRERERERERERERERERERERkYKhYDQRyR9lNe7tzREHo/mtr2C0zq0igmA0gI//Gs26rRIewWglXaBpR7A1Wuq8A0fcvP9jmH4RLP578DmtHhvS/v6cgCc8tdo0qy1MbddW2Pia//hDfw1d+qW3h0SjZnjqMTsXR1+HiEiueYWzlBRIMBrA3tfA2XO8+1sUApNThRrCk0kw2vb5xXlSfJQSzbAjRTAawJoX4JlxsG5aZvu0NMCU02D2TcHnnPScE1pdrEbfGN5aNgFLUoRU5rtUId8Hfh8uWg1nvQcXLHEPdzAGuo/232fuD2HaecHDJnYugUnHwPs/gKX3QO2yYPO8bP8ou/kiIlHyev5VUt52u2YYnDELzvnAeaze+9pga794ZPb1iRSD8h7efaueDH+/pfd5B5LFya8Gt2C08m5wWcDPM1Y/m1lN+cwGuKhIUGfNDn7RHBERidO4pNszI9xnhseeIiIiIiIiEjqT6wJERERERERERERERIqKjoAVkfxRWu3e3hJxMNr2D737uo6Mdm/JbyVl0ay76il444bgIWWtWhrg3ZvggSq41zhf086Hze/sOc5NaSU0p7Hn8gegfl3qcU3bYd5Pg6/bUfNOeKQ/bF8YbD83L5/lnFjVuAnnoto+Rn8lsz0kfIPPgZIUQRe7tsRTi4hIHFY8Ci8eC4/vBa9dC42bnfaWevfxpQUUjAZQ6RM8unVefHV0Vm4nkrd660vBA4DySSbBaABvfC7cOopd7dL0Trp/538y22fV07Dx1eDjS8qhz4TM9ioUA06GE55wgrxSPS8OYv4v8iP0IlOeId+Vbbe79IdeB0F5d+91uo1Kvdfal+D+CnjpZJj9LZh0HDw9FmZeDQ0b2o/96I9QtzL1mkFtXxDeWiIiYfN6ztjxccqUQI+xMPhMOOquYGtvfT/7cEmRYuD3PCYKLXVwX6nzecJTo2HK6bDx9XhrALAt3n1er2fLu0LPg1Kvvfjv0LQzs7ryVSLEYLRe48NbS0REwpR8MEqUD87Ja+sAGBERERERkUilOH5ZRERERERERERERETSomA0EckfZR7BaM0RB6O9cqF334G3RLu3dF4f/xWmnQvW50Pw5lqoXwstjU6YyDPjYP4v2wefrXoKppzaPlCsxedk8sHnpFdnkOCCjbO8T2APqnEDPD0KVj2Z+RpvfyV18NvZc8Hoimx5o7wrjEkRrLFrazy1iIhEbeVTMP1S57G1bjks+RdMPtkJsOkYwNKqtCreGrNVVuPdN+2c9ENhJT1+wWjLH4BZ18dXS1i8ntcC9DnCu69hLWz/KPx6ilX96vTGb3k3/d+vtbDwz+nNGXE1VPZJb04hGno+nLsArmiETzRkH5D26tXh1JULXmGI6f5Ouo8OPnb9y/DBz2HDTCewbNm9MOlYSCQFdyy4Lb39U6lfFe56IiJh8vpbbMq95xgD5y+Csm6p13/vO877nGEG/ohIcDs+grWTYMop8QcV+oUxG5+L1EwI+Dpi5hXp1ZPv0gmv9jP43HDWERGRKAxIuh3lA3Py2gMj3EdERERERER86dhlEREREREREREREZF0KRhNRPJHqUcwWkuEwWjN9U4whRtTCj0PjG5vkfWvwNb33fs+/D08PhweGwQPdIFnD4Cdi93H7toC837Wdt8rpKy0Cww63f8ko45ShSRsmAlTzwi+XipvfDbzuR//BRo3evefNRt6jst8fYnG+FvhyL979ysYTUSKxcI/ssdVQbfOgbUvwY4P3ed0HRF1VeHyej7fatl98dTRWfkFo4Hz+/cK4ctXXsEcAMc/5j939XPh1lLMEk3pz1nzYvCx2z6AR/vDusnp7XHwL9IbXwxKK+Hitf5j+h4NvQ/z7l/+UPQB81HxCkMsrUxvnT5HZlfHjoXZhXan0lwf3doiItmyHs8LUoVUdt0HznoXRv63/7il9zjvcz7SB5bem1mNIoUuk+ffrWpGtP/cqu/Rma3TXAvv/yDzOjJhW7z7/F7P9gv4M65+Bja/k15N+SysAMn9PhfOOiIiEoXkKwJE+YFo69oG6B3hPiIiIiIiIuLL50LaIiIiIiIiIiIiIiLiSsFoIpI/yjyCFKI8oXf7Au++IbqKusTg7Rvb/o031zsBA9Mvgbe/DLs2B1/no9+BtdC4GVo8TrIu6woVPeHw3wVft26F871ppxMuseRuqN99ov6urTDl9OBrZWvQWc7P4CXRBCufcu8rqYRe46OpS7JjDIz8FBzjcTJs07Z46xERiUJzLax5wb1v6hnQ0uDe131sdDVFoaTUCWL1svgfsZXSKZkUV5a1zbDi4XhqCYtfMFpZNVziE4q7bW749RSrTIIZ3v06rJnU/u9X7XJY/E+Yfzu89z2YcwtMvwyeGecfYOxmn09Dl37p11UMKnrB+UugenhSo4FDboOrLJz+Kpz5FvQ5yn2+bYF1U2MpNXRe/8+nCuPpaMDJ/q8dg9j4GmydC+/elNn8yj7efV6v2UVE8oHX84KS8tRzu42EI/8KVyZSj23aDq9eDa9/xnnusPAvsHVeerWKFKpMg9Eq+8L5i+HsOc7zwqssnDoNSqsyW2/xP8IL3woi02A0gIGnBttj3q3B68l3NoT/NsMvg0FnZr+OiIhEJTkJPspgtOQPW33ewBcRERERERERERERERERERGJj/W5loBfn4h0LmW5LkBE5D9KPYLRWiIMRtvxkXff3tdFt68Uhpq9YdsH0e6x/hV44Qg46i7nRMCtczJf66M/OoEkXrrt63zf7/PQ92j44JewzCOMqtUHP4e9roSZV7QFCZZUwPGPQeOG4P9/DjoD9v0c7PgQZn8r2JxkVYPg5Ged4IXnD/P+77LqSff28m7p7ynxqujp3t601XkFmyrsRUQkX+1cAlMCnsDbUfcx4dYSh9Iq76C3ja+Fs0ezAmVc+YWItdq5OPo6wuT3M5VUOOFoIz8Di/62Z79fCLa0l0kwQ2IXTD0dek+Ak56GtS/Ba9eGc/K+KYFRX8p+nULWdQScPRtWPOoEUvc/AfpMaD9mv8/Dplnu86edC1e2OL/LQpJodG8vqXRv91Ja6fx+5v8y81rm/zK7+We+67x2bdywZ5+C0UQkn4URUmmME9z07EGpxy66M3kijP8JjPt28L1ECpHNMBhtr6v2fI+0pNx5/37x3zNbc+Nr0P/4zOamK5tgtBFXO685UlnxMMy/DcZ+Pb3a8lEmr60O/TVU9HYueNPrEBh8lt5XFxHJb8lveGT4BCGQ5AeVAInHIiIiIiIikrGiOluzmH4WERERERERERERESlUBXZ2nIgUtTKPYLTmKIPRFnr3DT0/un2lMMR1Mv62eU44WjahaABv3whvfsG9r6wbVA1uu9/rYDjoR8HWfe7g9sESiV3w6lWwbW7w2o78Gwy7EPa/GboMDD6v1fArnO+lXeCs97zH1S51by/vnv6eEq9yj2C0RJOCA0SksE2emFkYVc3eUN41/HqitmuLf38YBwA278x+jWIUJDCuwSWgJ595hSRBW1BS70Pd+zfMgMbN0LQD5v8KZlwOb3wO1k6BFY/BrOth5lWw8M/Q4rNPZ5BNmNnmN+G9b8OsT4UTigZw6G+h9yHhrFXIKnrByOudUIeOoWgAe1/jP3/5Q9HUFSWv/xdL0wxGAzjw+9nVkqnSajjuQagZBv1PdB+T6rFSRCSXvAJTS9LMT+hxAHTdN83NLbz3XdiS5XukIvkuk2DiQWfA+B+79x3yi8xreekEePursPmdzNcIKqtgtP8K/nnNu9+ArWl8dpGvEhm8vhrzVdjnk3DAd2DI2QpFExERERERERERySt6v05EREREREREREREJF0KRhOR/FFW497eXBvdnhtmuLcPvRCM/kR2egMmQs1eua4iHN3H7HkSjNf/c0E0bYMVjwYb22N/qB7adn/8T9Pfr2pQ2+2SMhh7U3rzy7qlv6fEq8IjGA3grZhCCkVEwrbkHu/QzlQGnxVqKbHptp9/fxhhMC0N/v2ZnDxcDBIpfi8ADeujryNMiV3u7aYESnafON99jPf8R/rAM/vDu990QqI+/gtMOQWmXwyL/w7L7oM3Pw/TzocWj706g0yCGZItutP7v1W6zp4Lo/XcLxBTAsfc692/6O/x1RIWr39HJRXpr1VWA4f/Ibt60jXwdLhoNQy/zLlfWuU+bsk/FcgoIvnL829xmsFoxsC4b2VQgHX+TooUs3Sff5+/CE56zvviH5V9YNx3Mq/nw9/Ci0fB6ucyXyOIbILRSkrh8N/DmQED3J49EGwieG35KN3g6eQL44iIiIiIiIiIiEgeCuFikiIiIiIiIiIiIiIinYxSf0Qkf5RWu7e31EWzX3MtrJvi3tfzoGj2lMJSWgGnvuKc3Fzo+p+wZ1uXAdB1ZOZr7lwcbNywy9rfH3R6+sGD/Y5rf796SHrzyxWMlvf8gtEW3xVOkI6ISNze/Vrmc/f6RHh1xKnf8f79jRuz36Ol3r+/swYpNKf4vQA0boi+jjB5hZUlhyT1PAjfq+rWrUy9z9oXvV8bdgbZBqOFZcBE6Dku11UUlsFnevetnwq7tsVXSxgSHmFhJZWZrRd3yGjfI6GiR9v9Mo9gNIC1k6OvR0QkE17PCzIJqdzn03Do7VCV5vt4C26He03b19NjYdmD6e8vkq/Sef697w3QdZ89L3rS0Yirsq/p5bMh4RNeli2/EPNUwWiteh0MNSOCjV03Ndi4fJVu6PsBt0RTh4iIiIiIiIiIiIiIiIiIiIiIiIiISI4oGE1E8keZRzBac0TBaFvneZ+AMuTcaPaUwlMzHCa+AFc0pw76yGejvrhnmzEw7n+j3bd6KIz9+p5tIz8TfI0BE6HvUe3b0j2hsrx7euMlfuU+wWgA66bFU4eISFjqVkLD+sznF+rzjlFf8j+huW5F9nukCkZ7/TOdMzgh0ZB6TKEFjSY8gtFMedvtyj57PlfMxJoXs1+jUNk8CEYzpXDIr3JdReGp6AVDznPvSzTBmufjrSdbXv/Pl2YYjNZ1H9jryszrSVdplw73fYLRPvxttLWIiGTK629xSfn/Z+++49uq7/2PvyR5z9jZibP3TsgCwl6BkJCEUUZ7KWW1hZb2Fkq5LaW0QCfQ9Wtp6aXQCaVlBMIegbASRgIJIZNMZyfO9oot/f744uulc3SOdDT9fj4eesQ63/WxI8lHsr5vhT9ux+eD4f8NcyvhokMwMsrXIQ+ugrcuhvUPRTdeJNW4Of8edoOzfqUjoWJudPW09OaF1m0N1VC9Nfq5Qzaha/4sZ3P4fDDqe8767njJWb9UFbIJRivo2/p60WDoa/N/JyIiqSz02b/H+ny+k+JxAaYm8xsUERERERHpWEKRu4iIiIiIiIiIiIiIiGMO32UsIpIAAYtgtMY4BaPtsvi0+KxCKJ8YnzUlffkDMOSrsPuNZFfi3sAroWhA+LZBV0FuF9jwN9jymLfr9poJxz4A2cXt2ybfB6VjYNszkFMOeV3hyGY4srG5T04nE4o24iaz4aml/F7uaskKU4Oklqx8c1uorwrf3vK2ISKSykIh+PgOWP6D6Oc479P2v/vSRfkEOPsDeG58+PZXzzCbmMfeEf33GCkYDWDt76Df56KbP101OPi5WAVDpyqrjeBtgzkG/BfseSe2tVb/0oTpjrkNfB3scwSCNhvu46X76dBwxATbZZfCyO9A2djE15EJTpoHD1vcZivnQb+LE1tPLBrrwh/350Q/53F/hYOrYd+S6Odwyu8iGG1HBw5jFJHUZnW+GMtjMUB2EYy7C0pHwaZHoGYr1O02gdJOrbwbBl4RWx0iqcDp87KBV5jAM6dO+Bes+iXseBkKesPgL5sArU9+Cnvfg72LIs9R+SQc3tD67wnBBljyLVj3B1N7yXA44d/QabTz2sA+GM0uYL2twVebv2ls/BtseQLLjYaf/AzG/9RViSnF7nZy1jvmMXH/h+bvmcNvNM+tREQkXfmAh+O8RuizdURERERERERERERERERERERERNKGgtFEJHVkWQSjNcQpGO3DW8IfLx7S8TbCizN9zofes2Dr08mrIb+32TjoxrCv27dXzDaX6kp4sk/0tbU08jv2m458fhj2NXOJRmFfd/1zOkW3jiTWoCvNhq5wGmsTW4uISLQ2PRJbKNqEu6FooHf1JEPZOCiosA45WHEXFPY3m5mj4SQYrWppdHOns6CD35XVm+HQp1A8KP71eMEqsMvf5uWsQVfDe9fFvt7HPzQBAAO/GPtc6SSU4MC8S462/z+U6Pl8JsR77X3t27Y9A431EIgxzMZO/X7YuQCqt4AvC8omQOcpJlzcjVDQJgwxN/r6/Flw/D/gmRHRz+FUoG0wWl74fiIiqcwyGC07/HE3fD4Y8HlzafLSSc4/iOLAClj3J/M6Zl632OsRSRYnwWidp8Axv3Q3rz8bRt5sLi1N+o35d8VP4KPvRp7nqYFwSUPz+dyqe2DNb5vbD66CZ8fAJfXuHhu8CkYD6DPHXAAe7wG1O8P3O7QOige7mztVWJ0b+7KgoBdMvDex9YiISDwlIrTMIklUREREREREREREREREREREREQkdSn5R0RSR8AiGK0xDsFon/zcuq14qPfrSWYI5MIJ/4GTn4YRN1v0sbgde2Wayw+L7nIcdBrnrG9BBZy3wX1NbZ3+qn0omhfye5lAFad0v04Pg79s3Va3J3F1iIjEYpWDjctT/wyfOwwnz4fup5vfU2Nuh7MWwYgb415iQuR2sW/f/Gj0czc4CEZrOGTCgDoSJz8XgPnD4OO74luLV6w2zvvahGr5s+G8T71Zc9EVUFflzVzpwkkwg5cUiua9ijnhjx89CFUfxG/dfR/CM6PgjfPhg2/A+9fDS8fDwjnuQ+6DNo/Z/hiD3UqGJeY5YbtgtHzrvl4EDImIxIPV47EvTo9bQ77qrv+718L84bDj1fjUI5IIVuff+b1hzA9h2iPmNXavP+xjwBchq9hZ3w1/af56/UPh+7w20936XgajtWQXIPf0EAilaQ6M06BwERHJFKE4X0RERERERCQhMukpWCZ9LyIiIiIiIiIikorsXoHSq1Mi0kTvnBWR1JFlFYxWC6Eg+DzKcmysgw+/Y91eNsGbdSQzBXKg90xz6XUOLJxtNpsDVMw1wWVvXAjb5nu/9rifQNcT3I2Z+gD4XHzAdEFvd/O35PPD9PehPAH3IZ/PbP5f/Stn/UuGx7ce8UbxYOs2BaOJSDrY8TJUvWffZ+CVMOhL5uve55pLJooUjLbjpejnbnQYAFa/F/J7Rr9OugnWOusXaoTlt0HP6dB5UnxrilXIYiN422A0MKG5uV2hbnfs674xF854PfZ50kUig9F6nJW4tTqSbqdAViE0HGnfdnAldD3O+zVDIVh8LdRsa9+2bT480QumvwslDgPJGuus2wK50dXYxOeDk56AV8+Cmq2xzWXHTTCaXZuISDKFLM4LYg2ptNLvYtizCNb8xvmY+n3w6ulwcZ15rVYk3VjdzyrOgzG3xW/dgl5w7J/hva9A3V77vouvgsp55rnCwVXh++x4EfavgE6jnK0fr2C0SH/Te9gPfc6HrCLoeiIM/BL4Y1gvUSyfDytgV0Qkg2xG7+MVERERERHpQFy8lzvVuXlfuoiIiIiIiIiIiIhIDBSMJiKpI2ARjAYm/CCr0Jt1di6wbx9wuTfrSObrfgqcvwuq3oeCCijsZ44Puip8MNppr0DxEPjkJ7D2vsjzH/sgdBoLtbug82TI7eyuvjlbzWYnN/zZkFUMDYfcjSvoC+etM+MTxU0wWumI+NYi3hl6Q/jNsApGE5FUt2cxvHpm5H69zo5/Lakgt2v85nYajFbXwYLRGh0Go4EJnv70f9M4GC3MJnafH4Z8BT6+I/Z1dy2EXW9CN5ehxOnK6uccD4OvTtxaHUkgB0pGhg/nXHwVDLrS+zUPrLAPAz16AF6YAqe97OyxJlhv3eaPMRgNoHQkzN5knr+v/hVseiT2OdtqG4xm21fBaCKSohotHo/j9Zqfzw+Tfg0jboL9y2DVPZFfv2/y+iw47YX41CUST1bBxIkIvOp7IfQ4Aw6ugfoqeO0c675bn4o837Oj4dJGZx9sFK9gtFIHH4qy5XHz74a/wrZn4MTHU3/jntXzNL/e3iEikilCoVD/ZNcgIiIiIiIiiZRB2dihDPpeRERERERERERERCSlOXiXsohIgtgFnzVUe7fOgU/s290GSUnHFsiFrtOaQ9EA+syBqX+GokHgy4JuJ8PMNdDjNCjsY647MfAKKD/GBKi4CUUrnwhnfxD9bTm33P2YM99MbCgamJ97XvfI/Qr6tP7/kdSW2yX88UQGo22dD6+eBfOHw3vXwdGDiVtbRNLX8h9G7pNVBD07SDBaYf/4ze0mGK0jsQsVCmfdH01AWioLutwIPvoHMPI73qy95FvezJMOrIIZYnXSk9DtFPM8oXAATPkj9L0oPmsJlNgEQuxf4e1ae9+HZ8dE7ndnMFE6AAAgAElEQVT0AKy4K3zb4Y3wxgXw7Fh440KoWmI9jz8nqjLbzxOALlPh+H/AqO+Z55NZhdDnAuvHjpJh5nbsaP42wWh2gZUKRhORVBWyOC/w6rHYSmEf6H0uHOPwQxAAdrwITw+Dynnxq0skHqzOvxP1+npOJ+gyxfzdYcqfYp/v4QAcXBu5X32VdVsswWgAsxys36TySXjYD69ON+e1qcrq+bBPwWgiIiIiIiIiIiIiIiIiIiIiIiIiItIx6J2zIpI6sgqs2xo9DEar32fdNvJ/vFtHOrZBXzKX4NH2G5qchHSVjrZu6/9fsPFv4dvOege6HOu8znByyuDIJuf9O081mxcTzZ8F434Mi6+y7zf6VvApCzZt5FkEo1VvScz6256HhXMg1GiuH1wN+z404X+6HYmInd1vRu4z9g7ILo5/Lamg86T4zW0XNNNSfUcLRrPYNG1n7/tmQ3yqCrncCO4PwPifwrifQMMh8Oc2P5fMLjW/y1f9Cpb8d+S1q96DhiP2Ad6ZIh7BaL3Ohd7nQcXs8M+JxHulNsFomx+FTg4CPJ04tA5emOy8f+WT7Y/VVcFTA5qv718OWx6zniOQ63w9J3x+GHen+b0cajTPLeuqYP1DULuzuV/RYDh7KWTlm+/7pRNat7eVXdL6erDOuq+C0UQkVVmF7Sbqd3nZWHMOsfUpZ/0PrTGv45z8NPSeGd/aRLyS7GC0lgZfDQc+htW/jm2e+UNh9ubmvxPUVZnvJ7sYQiGo3gyvnGY93ir82qniwSaIfvvzzsfseBF2LYBzltmfSyeL1fPhWH9WIiIiIiIiIiIiEj+hULIrEBERERERERERERHJKEp3EJHUEbAJRms44t06W+dZt/W9wLt1RCD8ZqbySZDf037cIJuwr8HXhD/e/79iD0UDyCl317+TTYhbvA260r592iMw+NrE1CLeKBwQ/njNNtj2QvzXX/eH5lC0JnvegX1L47+2iKQ3uyDfwn5w4uMw7BuJqyfZyh0Eoy36UnSBTI01zvrVdbBgtFAUP8sXp0L1Vu9r8Urb38lNIm0E9/lMQFEg14T+5pQ1B5zaBXK39WgRrP+L8/7pystgtG4nwdg7zWOez2eOKRQtMXrZhMF8/CPv/p+XRxGwtu255q9rd8Njnd2N9+e4X9MJn6/58SS3HGYsg8FfMaEaw2+Es942oWhgwjbOfAsKKsLPlVUIndsExlmFC4GC0UQkdTVaBaN5HFJp58THTNBtyTDnY5bfHrdyRDxn9dwtWefNo25tH/AajfV/hpqd8Mrp5nzvP2XwWFd4rAvM628/1heIff1TnnE/JngUVvw49rXjwSr83CooXERERERERERERFKcL9kFiIiIiIiIiIiIiIikHQWjiUjqsNug3mATNOHG0UOwf7l1e/lEb9YRsePPggn3WG8o7HysfeBX12kw8IrWx7ocB5N+4019boPRSoZ7s260jn0w/PGSEdDv4sTWIrGzuz29djbU7onv+pUW4Zmf/Dy+64pIeguFrAOcuhwHszdCn7nNIUEdQUGfyH3WPwRLv+N+bqfBaEcPup87nVltmo7kjfO9rcNL8dgIbhfIHc6iK6Dqg+jXSyXVW2HLE7BzQetPKQ5Z/JzLxkNWkbO5K+bCpUE443UY/T0IxCnISqyVjW0OAAznvetjXyPYAFufdj/utRkmEA3grUvdjw8kKIwnrxtMuQ9OfQ6OuRvyurZuLx4EszdD34taH/f5YdLv2tdZMdd6Lbv/KxGRZArWhT+eyN/t/iwYdQvMXAWXhcw5RiRVH0B1Zfi2A5+Yc6CqJd7WKRItq8BaX5KC0fK6wMgonpu3tftteLICdr5qrocaoW4P1FdFHutFEK7PD6e9bAJr3dj4N1j2A3jvOtj0L2isjb0WgFAQ9n0E6/4EW+ebv0+6Gq9gNBERERERERERkcwSitxFRERERERERERERERa0TtnRSR1BPKt2xqOeLPG85Os28b9xJs1RJzofyl0GmM2CQXrIbcbHFoLJcOg74UQyLMe6/PD1Aeg7+dgzztQMhIqZrnf8GMlp8xd/64nebNutCrmQOC69iEp/S9LTj0Sm8K+5vZvtQFtx0vm/pNoNdsTv6aIpA+rUDSA8R00WNHnM+cU9fvs+21+FCbe625up8FowXp386Y7q831kex9F/Z/DJ1Ge1uPFyw3ggeinzOac+ZPfgYnPBr9mqng0wfh/eub7z9dp8Epz0N2kfVtp6APnP4q/CdMcHKvGdDvMqjeDKUjofd5HSv8MVWdtxHm9Q3ftv2F2OffvwyOHohu7OZ/Q+9zYecr7sdahYong88H0/4F/S41z8dzOpn7Q9n49n3LxlnPYxU8JCKSbFaPT8l8LPb54NyV8MwI+34vnQSz17c+9uEtn4Xdf7bZaOAVMPXPOm+R5LI6//YnKRgNYNg3Yfnt1rVVzDXn/nah0TtejG7tzsd6Fxrb43Q45yPY8ript7EWPv3fyOM+/pH5d+195t9Za6F4cPR1BBvg7S/A5n81HyvsByc/A51GOZvD6vmwX2/vEBERERERERERSV0ZFH4WyqDvRUREREREREREUpLdS1B6eUpEmuidsyKSOvxZJhwtXNDB0f2xz39wNRxaY91e6nAzgohXOo2OPoDC54de55iL13LDBB9Yye8NnW0CBxMhpxNMewTeuhQaq82xijkw/L+TW5dEx+eHvJ5wZEP49v3LgCQEo2nDrIhxZAts/DtUb4Fup0Dfi3T/APtAqo68YXX4jbDsVvs+NVuhch5sfwlyO0P/z0PJUPsxDQ6D0ew2bGciq03TTmz6V5oFo8Vwv7IL5Lay+d/wRAUcc48JJ06nx71gIyz7PnzSJgh891vwzn/BSU/YBzPklMHMVfDqmeaxH6DTWJjyJyjoFd/axb3CPpDXA2p3tG+r3gz1ByCntH1bsBE2/gP2LYX8HjDkehOa19aBT6Kv7eBqE7YXjWjut/Hk80GfueZi288Pg6+Fdfe3b2tUMJqIpKhGi3DhQJJDKkuHw5T74f2vWQcgH9kA+5ZB2VhzfecCE3Db0vqHzGX0beYxuqB3PKsWCS8Vg9GyCmDS7+Hda9q35XWHEx8z50DbX4QF071du8/53s5XPAhGfrv5+sAr4I0Lw58jW3l6iHnuN/CK6P4G8+aF5rWOlo5sgmdHw6jvwchbwp9vtxSMw/NhERERERERERERSaI0eq9JJOn0vhkRERERERERERERSWseffyyiIhHcjuHP163N/a5N//Hvr2nx5s5RNJVjotgtDE/MJu9k63iPDh/B5z2CsxaCyc+DlmFya5KotX3Iuu26srE1dGK3sghwsHV8MIU+Oi7sPY+eOtieO8rya4qNWx92rqtI29YHeLw9rFwDqz9HXz8I3hhMuxZZN8/WOts3i2PwbYXnPXNBLEEox3Z5F0dXrLaCB5T4GCUHxlSsxXeugTeuTyGtRMs2Aivnt4+FK1J5ZMmIC1kEczg+yyYoWQYnLceznoHzvkIzl6iULRUNuIm67ZDa9sfC4Xgjbmw6Iuw+lfw4S3wzCioDxNQf3BV9HXVVMKh1e7HDb0hvd9U3fnY8MeDCkYTkRRl9fjkz0lsHeEMvgbmVELZBOs+6x9s/nrtH637ffwjeHYMVC3xrj4Rp6zOv5MZjAYmBCzcucvgrzSfj/U8CwoHeLdmTpkJKYynrtPgvHVw+mvuxm1+FF6bAcvvcDdu2e3tQ9FaWnEXPFkR/ny7JcsAvQ78OpOIiIiIiIiIiEhai/L9KqkolEHfi4iIiIiIiIiIiIiktBRIMhERaSEnjsFodoEV0x6BQAps7hJJBTllzvqN+wkMuiq+tbiRXQw9ToPiwem9cV7sg9HyuiWujlZ0mxJh5d1Qu6P1sXX3w4GVyaknVYSC9gFxyd7YnEy5nc15thtHD8Ly2+37BOudz7fse+7WT2dWm6adqNvjXR1eCjWGPx5L4KBV2JpTG/8OuxbGNkeibH0Kdr1u32fx1c4C6PxZ0OVYKBsL/oB3NYr3hn3Tuu3gZ8FkDdWw5Eb4pw8e9rd/vaR6M/yn7LP2ADycZb5ecZf13Gd/YF/Xlsdh6bedfQ8tHXOP+zGpJJAb/nijgtFEJEVZBqNZPJ4lWl5XmHK/dfvqX5nnaMEG2Pwv+7nq98HHd5qvd7wCr5wGzx0DH3wL6qq8q1mkLcvAqyS/fuDPglPmQ8VcE4aY2xlG/g+M/n7rfidEuG85lVMOp70EOaXezGcnqxC6nwzHP+x+7PLbzOND7a7IfWv32J8zNzl6wJxvVz5l3ccq/LwjB/CLiIiIiIiIiIiIiIiIiIiIiIiIiEiHomA0EUktuRbBaPUxBqPV7YW9i63b+10c2/wimSSnPHKf6e/BqFvAp1MJiYPOk6zbGqqbv67dZS6NLgJy7Nh9ip3C9kRgk8XG141/T2wdqWbXG2ZDvZWOvmG1+6nux2x/wT7ky00AWNUHzjYvZwKrTdNO1O32rg4vWX1P/hjuV0X9rduGXOdsjk0uA/+SZavNJvsmB1fB4U/DtyU7mEGi4w9A6ejwbTtfgSOb4NkxsOpeZ/OFgtYhhU1Gfx/Kj4FzPnJXayTDvxXb/T0VWAUJBevMa1VePZcREfGK1eOSVdBjMti9bgSwcA4cWutsrsonTOD3q2fAzgWwbyms/iW8Mdf+dSKRWFg9p/WlwPl3bmc46XG46BCcvxvG/7h9MHLJsOjnzy6Fzx2By0Jw4V4onxhbvW71vQjKjnE/bucCWHAOBCOcF+9e6O65+cLZUGnxoU4KRhMREREREREREUlD+tuCiIiIiIiIiIiIiIiXlGYiIqnFKhitLsZgtNW/tW6b+kBsc4tkmtwIwWj5Pc2md5F46ndJ+OMNR2DfMng4Gx7vbi7/yoWl3zahDbGwDXzQabN0cKEQNBwK37b+L4mtJdVsm2/f3tGDhQL50Y07vN66LegyAOzgmuhqSDduAuPaqtvjXR1esvq/9gXCH3eidDQU9mt/vNMYmPT/oM8FkedYe1/06yfKpw/A+oec9d31evjjqRDMINHpPCX88fUPwrz+9o+xsaxXNhbG3uHdvP0v826uZLEKEqrfB491gce7wvIfKnxHRFJHsC78caugx2SZu926bevTsOhK53O9++X2x3YthK0RnuuJRMvquVsqvX4QyLH+oIjskuhC0MEE32YVRF9XrPwBOGMBDL8Ryie7G7tvCWy2+NCAJtWV7mtaeF74UEqr58OpdDsRERERERERERERF/ThvCIiIiIiIiIiIiIibinhQURSS04cgtFCQfj4h9btZeOjn1skE+VX2LdP/C34dAohcZZTFv543R54+UQItdkYtvJuWPXL2Na0C5Sx2ggo0lHU77NuswpM6ygOrLJv92clpo5UFciLbtxBm59ryGUAmN1cmcRtYFxLqRqM1vb3fRNfDPcrn8+cz/pzmo8FCuCYe03bsQ+2brNyNEUf+6or4cNbYPHVsc/V0R+/0lnvWYldr2UwRsVsb+Yc9t9QPtGbuZIpUpDQ0YOw/HYTZigikgosg9EcnB8lUn4P6HaydfveRbGvUfl46+uhEOxfAZsehSObYp9fOq7GI+GPp1oAoZ1ownBLhsOIb3tfi1vZJXDM3XD2u3BxLZRNcD52y+P27TXboqvp3WvaH7N6PqznaSIiIiIiIiIiImlKH5YlIiIiIiIiIiIiIuKWUk1EJLXkloc/Hu1G/YYaeGa0fR83mx5EOoLiwVAyInzb5N9D3wsSW490TIGC8Me3P2fCA8LZ8LfY1gzWxzZeJJPVbLduO3rQBNF2VIfX2rf7shNTR6ryR/n9733fus0uyDKcjhKM5jYwrqWGw9BY610tXrEKe4t1I3jFLDhnKYz7CUz4BZyzBHqcYdqyi6H/5yPPcWhNbDXEw4a/wVOD4ZOfeTNfR3/8SmddpiZurR5nQFZh8/XSCK/BhDPyOzDiZhhwuQnZOO1lmHivdzUmk9OA0E2PxLcOERGnGi1eGwmkYGDT4C/Hd/4Nf23+OthggmefHQ1vXQzz+sPHd8V3fclMwUaoqwrfltclsbXEous0mL3Zut2XBYX9zaVoMEy+D2Ysg6z8RFXoTCAXpi82AdlObHnM/rlzdZTBaNueM+GLLVk9H44lKFxERERERERERETEMQW5iYiIiIiIiIiIiEjy6Z2zIpJa8nqEP3740+jme+UUOLjSuv2kJ8Hni25ukUzl88EJj8KC6VDTYiPPuJ/AkK8mry7pWFqGKzi1/yOzgSzax3XbYDTlCUsHVxNhY+eexdD1uMTUkkqCR+FQhPPUWAOcOqqtT8G4O8K3KRgtPKtN007V7YGCCm9q8UqoMfxxLzaCl440l3DG/xQOroY9b1uPP7AKyifGXodX6vbCu1+GYJ13c0YbbCjJl9cDsopM6GG89Z7d+rrPByNugpV3Oxuf3ck818zU12b8DoOEdr4S3zpERJyyOpdw+niWSP0ugbcvi9/8oaAJsMotNwGW6//cun3ZrdDjTOgyJX41SOapr8JyM1luGgWjART2gQv3wxvnw85XzbG87nDsX6DX9OTW5oY/GwZeAd1PN99LlU1QO8D8EVDYD4oGQmMdhBpg86Pmgz4aq6OroW431O6A/J7Nx0IKRhMREREREREREUk7bT8AIVNl6t/3RUREREREREQkoexeTusoL7WJSGRKeBCR1FI8NPzxmm1w9JC7ud67Dva+a91eOhIqZlu3i3RknUbDeRvgjDdMgOD5u2DULcmuSjqSrILoxtXvi35Nu2A0vZFDOrranfbtW59KTB2p5sgm642qTbRhNTr7l8Hh9eHbIv3M2+oIwWjBRmL+pNa6PZ6U4inLjeCB+K6b1w3OeB3OttkQv/vN+Nbg1pbHobHG2zkVjJa+fD4oHhL/dQoqTIhEW30vdj5HzzMz+1w7kIJBQiIidiyD0XISW4cTPh/MXB3fNeYPM8HMbUPRmmydF9/1JfPYPe9Kt2A0gJxSOO1lOPsD8xxq1tr0CkVrqbAPTF9s/iZi58hG2PU6rH8QNv3ThKJB9KFoTQ62eTyzeq063s+HRUREREREREREJE4y6O/i2pUqIiIiIiIiIiIiIgmiHdoiklpKhlm3HVoLZePh0Dqo+sBc9i0xITiF/WHsndBplOl7cC2svc9+rX6XeVa2SEYK5EC3E5JdhXRUgSiD0Wq2Qm55dGPtgtEy6U0pItGIFLZTOQ/G/yQxtaSS2t2R+yhYCHqeDdufdz9u63wYdkP748Gj7uY5sgEaayGQ576GdOE0LO701+CVU8K3PTcB+n8Bxv3YbAhPBZbBaAl4OcufBeUTYcDlsOGv7dvX/cHcv4+5JzXu51sej9yneCic+gI8NcDZnAp2TG9dp8G+pdGP92WZ+2DRIMjvBVXvtz4f6HIcTPodZBe1H1s6CrI7wdH9kdfpc0H0NaaDvG7O+y65CUZ+B/K6xq8eERE7wUYIBcO3pWrQY8lQGHQVfPpAfOav2wPbX4CdC8K3r/gxjLsrPmtLfIWC0FhnwgCb/g3Wtz8Wrq1de334MeHG1tucH+V0Ttz37yWfD8qPSXYV3vD5zd9EZq6C+cO9n//YB2HRl8K3vXIqzFgGncaY61YfAJJT6n1dIiIiIiIiIiIikgAKExMRERERERERERERcUs7HEUktRT0AX+u2SDS1tuXQc12OHqwfdu+D2H7izBzJRT2hW3P2q/jz4ahX/OmZhER8V5WlMFo1duaN4+5ZRe04/NHN6dIpmgMc27W0sGVJpi2ZEhi6kkVdXsi91GwEAz7Jux42Xl4V5P9H4c/7jYYLRSEI1sy+/bp9GfS/WTIKYf6qvDtG/8OO1+Bc1dATpl39UUraHGb8SfwftXr3PDBaABrfgsNh8zm9mRqrHMWPnj2BybEasZyeNbB+VJWfuy1SfIMud7cdsO9hgIw6Gr49H+tx1900Lw2k9PJ/dpZ+TDiRlj2fft+ZROgz4Xu508neT2c9111D+x+C858E/yB+NUkImIl3GvyTfwpGowGJtzXSTBav8vg+L+b5wf1e2HpzbDhL5HHrfl/sdfYpG6vOc/uaK8zBRsiB4bF2uY2pMzt89N4yyrU+XcqKRkGJ/wH3vT4XNWfYwKMd78Vvv3ZsXDWO+YcsmZb+D65XbytSURERERERERERDyk8DMRERERERERERERES9ph7aIpBZ/AIqHwIEwIQgHV9uPbayGj++EqffDnnfs+563QZ+qLiKSygKF0Y2r2Rr9msF667Z9S6OfVyQT2G2Qb7J1HpTcFP9aUomTYDR/dvzrSHW9psOpz8GrZ7obV78v/HG3wWhggg/I4GA0N5v6c7tYB6OBCaPe8HcY9vXY64pVqDH88UQGDvY6B7KKoOFw+Pb1DwE+mPq/yQu4qHwycp8eZ5lQNIDSUVA0EA6vtx9TMjL22iR5SofDmW/BJz+HqvebHyfye0O/S2DwtfbBaFn5QAzhHKO+ZwIdNj0CtTug4QhkF5sgkqxC6HYyjLk98wPAfD53/fcugs3/hv6XxKceERE7tsFoOYmrwy2nIUH9LzOPy74A5HWDIV91Foy2/YXY6gOoWgJvf8GEimd3glHfhRE3uf89EUkoZJ4vhQ0Mq4PG+jDH4hlS9ll7KOjt95mJFHaVenpO935OfzaUT7EORgN48Tj7OXRbERERERERERERSVMe/01ARERERERERERERKQDUDCaiKSekmHhg9Gc2DoPQn+AYK11nwn3QEHv6OYXEZHEyCqIblzNtujXtAtGq9kO9fshp1P086eqwxtM8Fv5JCjsm+xqJFXZ3T+abPyH2did6YINsOt1aKyFTQ9H7p/IAKdU1uMMyCm3D+Rq6+j+8MfdhIA1qd3tfkw6cRIWVzHH/JvXFQ6tse+74S8pEoxm8X/tT+D9KrsYRnwblv/Aus/6B6HrNBh0VeLqamn325H7jP1R89c+n7k9rLrXur8/19xvJb11Gg3H/9W6fdg3YfWv2h8feGXsa/t8MPhqc+no+l3q7JyhyZrfKBhNRJKj0eZ5XyA3cXW45SQkqM8F0GtG62NdpsKALzoLR4tF7W54fmLz9aP74cObYctj0Od86zCxaEPKJD0VVCS7AmkruwiG3wir7vFuTl82VJwHq38Z/RwKRhMREREREREREUlToWQXICIiIiIiIiIiIiKSdrRDW0RST8mw6MfW7oLtL0DlPOs+/T8f/fwiIpIYqRaMBlD5FAy8PPr5U00oZDbirry7+dio78HYO0yQhUhLjXWR++z7ELY9B73OiX89yXJ4Pbx+HhxY4XyMPxC/etKNP8dd/3qLYDQnIWBt1e1xPyadOAmLawo6crKJuuqD2OrxStDi+/Il+H418jsm1OjgKus+i6+G3rMhLwmb1O2C7gZ/BcbcBvk9Wx+PFIw24L9MEIBktr4Xhg9Gq5id+Foy2aAr3QWj7XknfrWIiNgJ2jzv86dyMFpn+/bJ98Hga8O/1nHsg+a86I250a3t89u3H1wD8y3+3rF3sbmIgHkuIalnwi9MiFko6M18/mzoegKUjICDK6ObQ8FoIiIiIiIiIiIiIiIiIiIiIiIiIiLSQSgYTURST9kxsY1/bYZ1W9cTIL97bPOLiEj8BQqjG1e9Nfo1IwWjLb0ps4LRtj3bOhQNYMVd0O1k6HlmcmqS1GW3Qb6l12bApcHMDNdrrIWnBiW7ivQWcBmmUPV++OMhBaO1YxUg1mTEzdB7pvk6J0JwRJP3b4A+c6H7qdHXdXANbPwn7F1kwta6ToP8CjNnn/MjP1ZYBb75EvxyViAXTnkOnhpg3++1c+Ds9xJTU0tWwWg9zoIp94Vv63I85HaFut1hGn0w7seelScprMvxMPZOWHZr87ERN0HvWcmrKRN1P939mL3vQ+dJ3tciiVe9DTb8BQ6uRp8CLynv6CHrNrfn8onkz7ZvH/xl6/NOnw/6zIExt8Py26NYO0L487vXuJ9TOp4+F8CwG5JdhYTj88E5H5qQ/CMbY5/Pnw3+LDjuL7BgOtTvcz+HgtFERERERERERERSWCb9PTCTvhcRERERERERERERSVcKRhOR1FNxHhRUQHWldR+fP7pPaNfmEhGR9JBVEN24mm3RrxmMELQTNjgkja21CEpZ/5CC0aS9SMGBLa1/CAZ9KW6lJEVjHczrl+wq0p8/ijCFNb+Hode1Phbp8TqcTA9GswuLO/Mt6Hp883WnP781vzWXCb8wQUlu7VoIC86BxurmY5XzzL9rfweDroGp99vPYRX4luhgNICi/jBnKzzZ27pP1fuw41XocVrCyiLYYL1Bv+19pyV/AEbfBh98vX3bJUdNu2Q+nw9Gfw8GXgH7lkKnsVDYN9lVZR6fD/J7Qs1252NemAzHPwz9L4lfXRJ/B1bByydm/nmIdAyRAsCSrWyC+V3WVuGA+AZ32/1canebc2JJb76AeS4byA3zb074Nn8uBHJafG0xNpAPnSdD8dDMDJjPFJ3GwLkrYMFZsPut9u3H/wM++RnsXxZ5rqYgx86TYfZmePmk8I9ddnIdhp2LiIiIiIiIiIiIiIiIiIiIiIiIpDC7aH7F9otIEwWjiUjq8WfDaS/DO1dA1buAD0pHQtkxUD7RXMrGwfOT4eBKd3N3nhKPikVExGuBZASjuQh+ygTbngl/fNM/Ydo/EluLpL7GOud9F1/pLhgt2GA2kO54yYQdjbgRep3jvsZ4Wnc/1O5KdhXpL5owhQ9vhr4XQl635mNWYVl2Mj2QxO5nUtgm1K++yt3cS78NxcOgYlb7NTf8FVbcBYfXm2P5vaFmq7N5P/2TuTTVl10K3U+HsT+C7CJzLNQYfqw/SS9nFfQyQXMvTbPus+gKmLM5YSVRv886NLwgQsDVkK9C3S5Y9SsTYNf9VJj6Z4WidUQFvc1F4iea310f3WJ+BybrMU/cCwVh1b2w5XHz3PTIpmRXJOKdaEKOE2n0rfDGBYn/YfoAACAASURBVO2Pd7U5b/OC3eO7HgPc+7+gMZtQMX+ORdCYizbHAWc5OjcWI6sATn8Nlt4En/4ZGg5BdicYcxv0uxQq5sLSG2H9g9BYaz2PL7v56+wi8/xy6Y3WH2DRro5iKBke07ciIiIiIiIiIiIiSRLSVk4REREREREREREREbe0q0pEUlPJMJj+jtlQGGwwG2Hayu3ibs6+F7cPBRARkdSUFWUwWu1O8wYSn8/92I4WjJYJ6qqg4TDklDVvnJX4CLoIRgM4shkKIwTyNHnnctj0cPP1na/AyU9D75nu1oynTY8ku4LMEIgiTKHhCFTOg8HXNB8LHXU/T+1O92PSSdDmZ+LPbn29zwXW4ZhWFp4HZ7wO3U4yv2eDR2Hlz2HZ91v3cxqK1lLLwIj9y6DqfbOWzwchi6AJXxJfzup6PJz0lPmZhFO9BQ6shKxCc2mshdzOEMjzroZgg3muHMiBur3W/XI728/jD5ggutHfNz/TaM6fRMSZijkmDNKNI5vM42LxYBOCCCZMI6vAhEnqPpt6PvgmrPltsqsQ8Z7PD4H8ZFdhr2KuCXnduaD5mD8Xhn7d2fjySdGt21gNwcbw4VmxhPfHnc8+GMyLIDK7wLGwa+bod5ukNn8WTPwVTLjbhOfn9zCPjwBZ+TD59zDx1/DutbD+IYs52jw/bznuufFw4BP7GoZ+zdvnliIiIiIiIiIiIuIthZ+JiIiIiIiIiIiIiHhKwWgiktp8fuuQE7fBaMf/LfZ6REQkMbIKoxsXajQBZ9GE7ygYLX1ULYFFV8D+5a2PD/sGTLgn/IZkiY3b+0flPBjmYAN65VOtQ9GavD4LTpoHFRbBQ4lUuwf2vJPsKjKDP4rHZoDKJ1sHo9mFgFlJ6VACD1gFiEH7ELGeZ0a3xrLb4Jh74b2vwt53o5vDid1vwPYXoNfZNsFoSX6cr5gFBX1MCFo4z4xsf2zA5TDp/0F2cfTrNtbBR981oTvBo9BrJvS/1Lp/pGC0Jm0354uI93rNcB+MBvD8RMAHhHkD/cRfm8Afhcikhv0rFIommavL8akfRO7zwYmPwapfw/bnIb8XjLkNysY7G9/jdMgugaMH3a/dcBhyStsfX3575LHZnaB0pH3gWCxhY1ZtCsUViZ4/Cwp6WbRlm+eKlmMtnnv5s+GsxbD0Rlh3f/g+E38LQ693V6uIiIiIiIiIiIiIiIiIiIiIiIiIiEgaUzCaiKQvN8FoZ7yhzd4iIukkUBD92IYj0QWjNSoY7f9UPgVdpzkPVIlFKGQCzmq2QU45NByEooHm0tahT2HfEnjzc+HnWv1rs6l47O1xLblDaqxz1z9cMFr9ftj9JtRVmdtW2XhYONt6joWzYeYqKBnmvt59H8LO18xtqvspUNjX2bijB2HPIqjZYTa65nWH1b8hbBiJuBft+fi2Z6G6EgoqzPVogtH2LYX6fZBTFl0Nqc7uZ9L2515QASP/Bz75ibs1dr3+WUBPAmz+twlGC1oEo/lT4OWssxbBk72d99/wVwjkwZQ/Rr/mJz+DVfc2X98231zC8efGdj4lIt7qNQN6nAU7XoxisMV5yAffgPze0PeCmEoTjzw7OtkViMRHVjGM/2myq3Amp8y8HhDNawKBPBj/c3jvK+7HhgtG2/qseQ5iZ9xdMOq77tcTkRRnEzpo97pIdpF5vlg6Gj64ofl4VqH5G2f5BO9KFBERERERERERERERERERERERERERSQMpsJNURCRKToPRsoqgy3HxrUVERLzlzwZfAEKN7sc2HIHccvfjQg6CdkIh8NlsbssUC2cDPjjhX9D3ovit03AEFl0Fm//V+rgvy2wOHvtDcz0UhI9udRbis+GvCkaLh6DLYLRdr0NjrdlcDiYobeEc9+vOHw6XNoLP76x/sAHeugS2PNb6+IS7YcSN9mP3LIY35kLNduf19bsUyifB0ghzy2dsAuYCeeY2Y+XJPmaD8OBrIWQRlhXJY13gxMehwiaQL13Z/Ux8YV76GXcXdDsZtj4Fa38fv7qitf7P5nfA7jfCt4f7nhKtoBeM+Das/IXzMZ8+AON+HF3waEMNrPy58/65nTvGOYtIugjkwCnPwsZ/wKIvejfv2vsUjJYK9i9313/gFXEpQ8RzJcOhYi6UDE12JYkx5MvQaQy8NM3duIYj7Y99+r/2Y856B7oc624dEUl/PgeB8cO+bv6mWfkE+PNg8DWQ3yP+tYmIiIiIiIiIiEic6YM5RURERERERERERETcSoGdpCIiUcrr6qxf38+BPxDfWkRExFs+H2QVwtGD7seG25DaJNgI21+Ana9CQW8YdBVkl0Qe1yTU4GwDW0YIwTtfhB5nQE6Zt1MfXg8bH4Zlt1os3QAf/wiOHjKBWJv/DdWbnc19ZAMEj5pwPfFOsD788e6nws4F7Y+HGkwA0NDrzf34rUuiX/vhAJSMMF93ORYK+0OfuWbDepMjm2H5D02YUjhLb4KN/4Sy8S0OBmH321A6CnqeZcKN3ISiDb0BJv36s/kVjOZI6RjYtbD9cX+OCQZ4boL9+He/bB6Tgg6CLMMJBeHNi+H8nZBTGt0cqWbPu1D5JGybb90n3OOhzwe9pptLzXaz4TrVPNnHus2fIi9nDfySu2C0UCO8/zWY9rD7tXa+4uxcpUk04WsiEl/+AAy83ARS7l3szZw7XzG/35yGyEp87PvIed/PHTbPdUUkNXU9HoZ+Hdb81vmYxur2x+zOr4f9t0LRRDoqp69Xdp5kLiIiIiIiIiIiIpJmMij8LJRB34uIiIiIiIiIiIiIpK0U2UkqIhKF/N7O+o28Ob51iIhIfAQKogtGa7QIDQmF4P3rYd0fm4+tuhemvwf5PUwIVyTB+o4VuNVYA5VPwcAvejfn7rdhwXRoOBy57+pfRrfGpkdhwOejGyvhNdaFP14+EXa9YYLQ2nr/aybEp7A/NNbGtv7Bla3/XfFjOOFRqDgPdr4Or5wSeY59S8ylrUNrogiF8sHgq12OEfp9zoTBtH0TYN+LTWjd547AoxGCQjY/Zh2MdtI8c5t7+zLr8cE62PEi9L3IVekpaf1DsPgqE4hjxxchJNpp4HQspvzJhLdVzvNmvkjfc6KUDIfiIXBorfMxmx6Bsgnun6dWPumuf46C0URSVr9LvQtGAzj0KZQM8W4+ca96i7N+py9QKJpIOsjr7q5/2/DahjBBaS2VDHM3v4ikGZ91U0d6XVlEREREREREREQyl8/mdVARERERERERERGH7LL5ldsvIk38yS5ARCRq+b0i9znxCW00EhFJV4H86MY9PwnevAT2L299/MObW4eiAVRXwvIfmK83/TPy3FZhPJnswCexz9FYD2v/AC+dCC9NcxaKFot3FVjluWB9+ONZRVA8yHrch9+Bty6OQz11sPQm2PeRs1A0rw25DjqNSfy66a7bSXDc36Cgwlz355hwmCn3metZBSZoys6ed7D8dNWcTtDvEigeaj9HdaWrslNS3V5Y9CUHoWhZkd+MmNvNu7rCGfYNEyQ46XemHi/UbPdmnlj5fNDnQvfjlt8Otbsi99v9Nrw2E/5TDp8+4G6NzpPd1yUiiTHsBhj9fe/me34CvHSS+b1wZJN384oz9Qfgo+/a98ntakJCu5+SkJJEJEbZJe76tw1GO7TGvn/pCHfzi0jmUDCaiIiIiIiIiIhIB5ZBOzm1K1VEREREREREREREEkTBaCKSvgp6W7cNvAIuaYA+cxJWjoiIeCyQE/3Yzf+CF6bC4Q3m+urfwMq7w/dddz+s/0v7ILVwOmIwmpOfS1uNta1/Vh/9D7z3Vdj9pnd1RVq/Zmdi1uoognXhj/tzIwdZxcuhtfDc+MSvWzoaxt2V+HUzxYDPw+zNMKcSLjoA0/4JWYXN7YX97MfX2ty3m0LARtxoP0fNDuf1ppLGWhOIFmyAx7o4G+N3EESWF+dgtPzPnrcV9IYBl3szZ+kob+bxwshvm8AbNxprYNMj5v8STMBdKGjeOBpsNL/Ddi00YaLbnoH6fe7rGnil+zEikhg+H4z9kXndatBVsc/XcAR2vwHrH4LnJ0f3mCHRCTbAK6fZ97noEMzdZkJCRSQ9ZBe76982GO3AKvv+yXoOLSKJYRdO7lMwmoiIiIiIiIiIiIiIiIiIiIiIiIiIiFMOdsiKiKSo/J727f5AYuoQEZH48MV4qtpYA08NhFG3wro/2vdddIWzOTtiMNr256F2D+Q5COE5ehgWXwWV88Dnh74XQf8vwKp7419nWx//ECb/PvHrZqpGq2C0HOh5tvk/7wh6Tofj/wE5pcmuJL35fNYhx5GC0QL51m3+zzYYD74Wsorg7c+H77fy57DpYfMY0Xtm5HqTrX4fLLoSKp90P9bJputchyFr0ep5VvPXU+6HkmGw7Vlzfdg3oOoDWOEybLDLsd7VF6ucMpi+CJb9ADb+3fm4D74BS74FoUZzzhNq8K6mQVdBqQI3RFKePwDFQ7yds243PDse5mzydl4Jb+ersG+JdfvgL0N2UeLqERFvZLkNRqtufb3yCeu+5RPdh+qKSObwKxhNREREREREREQks4WSXYCIiIiIiIiIiIiISEbxJ7sAEZGoBfIgUBC+re8lia1FRES859VGsRV3moAALwTrvZkn2YJuwldCsOz7zrou+iJsfhSCdSaYbsNfYcFZkcfFw9r7YM+7yVk7E1nd9gO5Jvyu89TY1yjoA8c+BL4UDbcd9T049XnI7ZzsSjJbpGA0u8fzlr83+l8Gg79i3bd6C7w+C/Z/7K6+ZHjrsuhC0cDZ7TXL4jmVFwZ+CcrGNV/3B2DkzXDGa+bSZy4M/bq7OQd/GTqN9bLK2BUNhOP/BpeFWl8utgiVbBJq/OxfD0PRyifCpN95N5+IxFevGfbtBX3gwn1wxhvO56zeDDtfi6kscWjLY/btpaMTU4eIeCvbZTBa45EWX9ea10WsjP+pCYoWkY5JwWgiIiIiIiIiIiIiIiIiIiIiIiIiIiKOKRhNRNLbwC+1P5ZVBN1PTnwtIiLirayiZFfQXvBosivwRmOtu/4b/gIN1fZ96qqgcl70NcXDqruTXUHmCFqE+/hzIbsITl8AI26Kfv6K2XDOhzDwi3DOR9HPE063k014W9MlkO98bP8vwNAb4ORnYNyd3tYl4RX2t2/fv8y6zZfV+npul8jrrf195D7JdGQTbH8++vE9p0fuk+Uy+CGSokEw5Ho44d8w9YHI/fO7m9CfSPJ6wEnzTOhXuoRJBHLg7A/iN//EX8Mxv4SBV8DAK2Hy7+HMt0xopYikh9LRUDoqfNsx98K5n0BOJ+h2Akz8jfN51/3Jm/rE2pFNsO5++z4FvRJTi4h4y+35cUOLYLStT1v363M+9DgjuppEJH20fW2iJQWjiYiIiIiIiIiIdFyhULIrEBERERERERERERFJOzbvzBURSQNjbod9S2DPO+Z6IM9swA/kJbUsERHxgNdBLV4IZUowWo37/mt+CyO/Y91n34cQaoytLjeyikzgz5bHrPts/jfU7YXczomrK1M1WgWj5Zh/s/Jhwi+gcAC8f727uUf/AMbe3ny90ygonwxV70VVKmDC0E5/FXxhssBDf4X3roN1f7CfY84WKKhwtt6UP8G717Q/PvAKZ+OlWWE/V9031PRn3p7z2N/QiVnbipjYqUWjo2C0+0yYVKra8XJs48f/NHKfzpPNfTlYH9taAMO+CcfcE/6+ZyenE5z5JiycC3W727SVm+d4PU6Lvb5kKD/GhCxu/Lv3c/e7FPK6ej+viCSOzwfHPggLZ0PNdnOssL85jyka0LpvxXnwwQ3O5t30T+h3MfSelT5hkunmvesi98nvHf86RMR72S5fj6qrav66aql1v36XRVePiKSX/p+HZbe2P15QYR+aJiIiIiIiIiIiIukvo8LPMul7EREREREREREREZF0pXffikh6y+sCZ7xuNhzVboduJ0FOWbKrEhERL2QXWbdNvg/e+2riamniRWhMKnAbjAbw4S1QNAj6Xhi+feXPY6vJrX4XQ8X59sFoAI91gbnbIb9HYurKVFa3/aZgtCZDrzPhG06CIgAGXdU6FK2J21CllsomwBmvWbf7fDDlPnMbeuXU8H3O3wl53Zyv2WsGZBVCw5HWx/tc5HwOMVwEoy06MJUZy+ezv8Gc/9/x6xAPXhHk8uM+u/04CUYDWH4HjPm+20rj7/BGWHx19OPP32kCxyLJLoaKObD50ejXApi5GkqGRj++6zSYtRp2vwU1O8yxvG7Q9QTILY+ttmSb8AvY+hQcPejdnEOuUyiaSKboPBlmroI9i805UNcTIJDbvl9BHwjkOz+XXzgbBl0DU+/3tl6BQ5/Ctmcj98vvFf9aRMR7boPRVtwJo24xzwkPrrLu13tmbHWJSHoo6g+dp8Ded1sf73uxAmtFREREREREREQkM+i1ThERERERERERERFJkBh2m4uIpAh/NnSZAhWzFYomIpJJsmyC0fpcAENvSFwtTYJHE79mPBz4JLpxS2+GYGPrY0cPwnvXw/YXYqspqxgK+kbu58+B/p+Hib+F3jNMSF4kH94SW21ifdsP5LQ/NuSrcMyvILvUfs7hN8IUi6COaIPRBlwOp7/irG/3U+CMhVA0sGlR6DwVZm90F4oGUNALTn0Rij8LhcrrDlP+aG6j4k5eT8ddv7fhzv8LRQMIhXx8/eEQRxs++8TSzpOcTbT8Nji83k2V8VWzAxZfC08NiG0eN7fjqQ+Y363+bHOpmAP9LnE+vuc5sYWiNckpM4ERg682l4rz0j8UDUw459g7PZzQB+N/4uF8IpJ02SXQ80zocXr4UDQw50ddjnM376d/gn8VwPq/ZNinkyfZ1qed9VM4s0h6ynIZjAbw4nHwTx9UPhG+ve/F1o/vIpJ5Tn4aup9mzt8CBSasdvxPk12ViIiIiIiIiIiIiDf0t2cREREREREREfGA3atMIdtWEelIspJdgIiIiIhIWHYbUf05MOgqWP9naDicuJoyJRht9a+iG3dkA+xb2hw2FArBwrmw89Xo5ssuhVlrwJcF2cUQbIBHC6z7z1hmQqyyCpuPDfkKDL4WFpwDO14MP27zoyZALSs/ujoFQha3fV92+OPDv2EC0oL1ZvN3sB7q9pr/O18AAvkRNoW7+FTJsmPgzIWA3/3/cbcTYdY6qNkOgbzYApi6Hg+zVkPtHsjtrE/GjJY/4KhbXTCHBftPbXf8UC28tgbOHAmUDHO+7qZHYVQKhCgePQTPT4KarbHNUz7ZXf/sIjjxP9BQDaGguV61FDY9EnmsLwDDkhBWmm6GfAU++h40HIptnqJBMHOlCbATkY5n+I2w6zXzWO1UYw0sugJqd8HIb8erso7lwMeR++R112O1SLrKjiIYbf9y+/a+F0RXi4ikp7xuJri//oB5vUnBiCIiIiIiIiIiIqKNnCIiIiIiIiIiIiIirvmTXYCIiIiISFjZRdZt/mwoGwunvZy4eiAzgtHqqmDHS9GPf2EyrPqlCUXb+nR0oWiF/aHPBTD9PbNRMLfc/J9GCrXK7906FK2Jzw+Dr7Ye11gDO19xX6c0CzaEP+63ydoO5Jj7sT/b/L8V9jWBYTmdIm8ItQsVm3I/dDsJSkfDyO/AmW+a+aMNvvP5oKBXbKFoLeV1USharIZ8NWKXAw2llm2rdrR4I2H//3K2ZuWTzvrFU/0B+HeJu1C0rieEP14xO7oasgqaf/+WT4CzFkHJ8Ob2HmfCjI9h2DfNfbDnOXDyM9Dr7OjW60j82TDgC+7GFFSY35N53aF8ogkCPfMtBe2IdGS9Z8BJT0PP6ZDfC/J7Oh/74c1wZEv8autIPn0gcp9e58a/DhGJD382dD3R2zmLXYQ2i0jmyClVKJqIiIiIiIiIiEiHovAzEREREREREREREREv2exiFxERERFJop7TYcWPw7cF8sy/XabCiG/Dyl8kpqZgfWLWiad9SyAUjG2OJd+C2p1QtcTduNHfh7E/su/jz7H+OWeXWI/reTb4AhBqDN9e+ST0numsTmkvZBEKGLdwHptgsf5fgMHXxGldSQnDvwWVT9kGhB1ssH48KGq55zi/h7M19y6GNb83gWIFvR0W6rG3P+9+zMArTTDg9heaj5WMgMFf9qamLlNh5sr2xyf+0pv5O5qxd8L6h0xgZ0u+LJj2MPS9MClliUia6T3DXJrsehNemwENhyKPnT8cLj4Sv9riqaEG9i8DQtB5avKCaPd9FLlPfk8YcWP8axGR+Bl3F7x8knfzFQ/xbi4RERERERERERERERERERERERERERERkQ7An+wCRERERETC6noCFFS0P14xB3wtTmMTGXYVtAiHSidefQ+f/Ax2vOSs75Dr4OT5kUPRwITDWPHbtGUXw4DLrdt3vBJ5bQkvFLQO07P7/4qJTdBFUzCiZK7iwTD9Xdvb14HGUsu2wpbBaDmdnK/7/vXwZAV8dCuEEvwJrtWVsO0Z9+Oyi83j69Q/w+CvwDG/grPehrwu3tcoscsth/N3wKhbzWOZP9sEvE5fpFA0EYletxPg7PdgxE2R+zZWw4EwgZeprmoJPDcOXjwWXjwOXpgC1dYBqnG1/If27UWD4ZwPoXRkYuoRkfjodiLkOQxZjiS7E2TlezOXiIiIiIiIiIiIiIikqQS/F0lEREREREREREREJAMoGE1EREREUpPPD8c/bDaQNikeCpN+17pf1xNh1HdbHxt0dXxqCmVCMFpD4tYaewdcFoLJv4Pe5zobYxd+Fsn4n1u3HdmY+KCjTBFqtG6LVzCaz+apqs8mNE0yR0EvGH2rZfOBButgtIKcFreRbBfBaE1W3AU7XnY/Lhb7P45yoM88bg76Eky5D4Z/w10YnCRedgmMuwMuroFL6mHCz6F8YrKrEpF0VzIMJvwCTn7GPM7YWXZbYmrySigEi6+BQ2ubj1W9D+9/PfG1bH8RKp+w7zNrDeR1S0w9IhJfXY7zZp7up3ozj4iIiIiIiIiIiIiIpLaMen9iJn0vIiIiIiIiIiIiIpKuFIwmIiIiIqmr2wkwez2c/DScvgBmLDdhOS35fDDuLpi1zgSpnfMRTLk/PvUE6+MzbyLZhVx5qde5tqFGlsonRb9mXhc46x3r9u0vRD93Rxa0CQT0Z8dnzaJB8ZlX0os/x7Jpv00wWqv3GNqF7NnZ9M/oxkWrZnt04xSCJiIiLfWeAbPWwujvW/fZ8h9YeU/iaopV1Qewb0n745VPwJEtiatj+R2wYLp9n6kPKMRXJJN4da499Dpv5hEREREREREREREREUkF+puoiIiIiIiIiIiIiCSIgtFEREREJLXllEHvmdD9FAhYh+RQPAj6XwJlY80bL0be4n0tdgFR6SLUkJh1ov35j7gp/PEBX3Q2Pq+bddu717ivR+xvM/6s+Kw59Gvhj/eaEZ/1JDX5cy2bDtgEozUEW1wJBS372dr8WHTjolVd6X5MViF0Od77WkREJL3ldYOxP4KyCdZ9lt4Eh9YlrqZY7F1s3bZtfmJq2PAPWH5b5H7ZxfGvRUQSJ9uDYLQpf4Tup8c+j4iIiIiIiIiIiIiISKpo9amVIiIiIiIiIiIi0bF7mUkvQYlIEwWjiYiIiEhmGniF93NmQjBa0EEwWvdTY1uj87HQdVp0Y3ucAaWjWh/z58Lga52Nz+1i3VZdCUcPRVdXR2Z3u/dlx2fNsvHQ9cQ2a/lhyFfjs56kpkB0wWiNLbPQSoZFt3bDocQGxjgJW2lr0LWQle99LSIikhmmPWLf/vSQxNQRq6oPrNv2fRj/9bc8Ce98wVnfLAWjiWSUnBiD0XrNNK9l+Hze1CMiIiIiIiIiIv+fvfuOl7Os8///uk5NTnovhAQSQu9VAghSpLgrRVDBhthx7bvquiqiu2v5Ka5lbasifBcLIGADpCiKFKlK7ySUBEJCek6Sc2au3x+TbOZMZu65p50y5/V8POaRXP2TcHKYOXPP+5Ykaejyk5ySJEmSJEmSJFXMYDRJkiQ1p2rDcJI0QzBazCSPj9oBXnU97PtlaB9b+f5Tj4Sjr6v+g78t7XDsn2DeO3P/DWe+Bl51LUxZkG59uTCCNY9XV9dwlhSm19LWmDNDgKOuhp0/AON2h+mvhlf+Crb7h8acp8GppaPk0KpM6WC03kzehYRTjoD2KgMNfjO/fy5KXPlA8vikg2G3j8O+X8kFT048EPb9Euz/1cbXJkkausbuXP658fK7+qeWWiy/s/TYxmWNOzezEe54D9x8avo17QajSU2l1mC0ee+oTx2SJEmSJEmSJGmIMPxMkiRJkiRJkqR6atCn2CVJkqQmlN000BXULiaEXIU22OeL0NIKu38cdno3XD6hsv2PuKL2QIDOSXDI/1S3tlwg27O/hJ7VMHoejNq+ujOGm5gQCBjaG3du+2g48JuN21+DX0tnyaF1mVElx/Jz0WjtgH2/CHe+r+++E/eHZbeVr+Hx78LO56YotgbPXF56bK/zYa/Pbm3v/i+NrUWS1Fx2Phce+nLp8SXXwqQD+6+etLpfhHVPQ+cUWP1Q6XmNDEa76wPwZIWvSdpGN6YWSQNjxPTq1047GmaeVL9aJEmSJEmSJEmSJEmSJEmSJEkaZgxGkyRJktLKJgREDRUxU3rsuJth8iu2ttvHVb5/R4VBav3twf/MPQDmvRMO+l4uCE6lZRPC9Fp8SakGai0djJaNLSXHegu/zc1/L4zdFZ77FbR1wewzYMK+sPha+NsnYeXfS9dw1/th5EzY/pQKi6/AC9eXHpvVwHMlSc1v7tuTg9Hu+0zu/5GzT++/mpLECI/9N9zzkeRA5y0aFYzWsxYW/r/K1wWfG0tNZcZxuX/Xhd+PRs6AHd4MPavgiR9suy60wVFX50KaJUmSJEmSJEmSAIjlp0iSJEmSJEmSpD5Kf5JYkiRJGup2+5f67rfyvvruNxBKBQyM3qlvKBpACDB2l8r2D6G6uuophHMn4gAAIABJREFUbZDQkz/MhS489GV49JuwblFj6xqqkkIpDH9QI7WUDhLItI8vPZYt0jntKDjg67DPf+RC0QBmngAn/Q1O/FtyHTefCt0vlq+3WmufKt7fOQUm7N24cyVJzW/sLnDAN5Pn/OUMuOuDsOrh/qkpydMXwd0fSBeKBo0LRlv9CGQ2VLamdSSMntuYeiQNjI4JcOjFfV/3Tl4Ar3kY9vsKHPx9OOwX0JIX6DzxQDjlucSQZ0mSJEmSJEmSpEEvGuQmSZIkSZIkSRp4BqNJkiSpeW1/OlDHoK6nfgzPXlW//QZCzBTvbykRcDXnrMbV0ihzzkw/97Fvwd8+CXd/CK7eF166pXF1DVXZntJjLe39V4eGn5bSYQKZltElx3qLBaMlmbAPzH5D8pwl11a4aUrZHthQInTt0Isbc6YkaXjZ5QOw8weT5zz2LbhmP3j2yv6pqZinLobb317Zmo3LIVb6P/4UVj9S+ZrtXgttI+tfi6SBtcOZcPIiOOzncNwtcOxN0DFu6/ic18Nrn4QFP4Nj/gjH/QVGThuwciVJkiRJkiRJ0kAaJmFig+HmuZIkSZIkSZKkYcFgNEmSJDWvyQfDgkugc3Lf/rZR1Qc63XVuYz5831+yvcX7Q4lgtD3+DeafuzWgaPw+ub5i5r+/9vrqYc7rYdrRla/rWQn3/kv96xnqYomvGSj9dSPVQ2vpYLRsy6iSY5lqvkW/4sfJ492Lq9g0wdqF8KfXws87KHlRZNd29T1TkjR8zSkTAAqQ3Qg3nwaZjY2vp1DP6lxQcaViBv58Ciy/s/Yalt0Ot74Z/nA83PaWytcf8sPaa5A0OHXNzH0fnbKg+M+SuraDHd4I045KfA0jSZIkSZIkSZLUFOIwCYCTJEmSJEmSJA04g9EkSZLU3HY4E057EU55Fs7M5H49fRWc9CC0juw7d+R2cOLfYeorS+/XvQRWPtDYmhupVMhVaC3e39IKB/03nLESTl0CJ/0N9vjX3N9VvtaRsNO76ltrLQ6/tLp1y27LBRblG8pBePWQ7Sk9Vm3AoJRGS0fJoUzLyNJj1fyTbeuCo64pPd67topNS+heAr/ZCZ7/TfK8wu+zkiRVa/KhuUcaNxzZ2FqKef53uZDiqtb+JlfzS7dVf/7ia+CGV8LCS+CF6ypfv/MHoX109edLkiRJkiRJkiRJanKGiUmSJEmSJEn5kvL3zeaXtIXBaJIkSWp+oQW6Zm39taUVxs6HV98KM06AUTvCnDPh2Jtgwt7bBqYVumYfuPFoWPNkv5RfVzFTvL+lLXld6wgYOT33+7ZRcPxfYc4bc393M18DR98IE/apb6216JxU/dpf7wiXjoGbz4Df7g4/74BrDoAXbqxffUNJtkSYHpT/upFq0dJZcigTRpQc6602y3DmCUAoPlbPf//3n1/6e/EWrSOgY0L9zpQkDW8hwKt+n27u8r9Cz+rG1lNo8dW1rc90w6Nfr379IxckhwGX0z6m+rWSJEmSJEmSJEmSmoOf1pQkSZIkSZIkqa4MRpMkSdLwNWFfeNU1cPJTcNhPYcxOuf6EMJ7/8+If4YYjoXd9Y2ust1IhV6G1sn26toPDfpb7uzvqtzDl0Nprq7fZb6h+be9aePZyWP1wLsBoxT3wp9fCqkfqV99QERNCIkJ7/9Wh4ac1KRgtYazaYDTIBT4Ws/yvsHF5DRtvlu2Fhf9bft6I6bkQG0mS6qV9DMx7Z7q5L99T//NjhLVPw0u3QGZD37E1j9W+/zOXVV/X0j/XdvaIqbWtlyRJkiRJkiRJkiRJkiRJkiRJktSHwWiSJElSoc5J6eZ1Pw8v3NjYWuotZor3h7b+raM/zH1bfffLrM+FpQ03pcL0AFqa8OtGg0dLR8mhmBCM1ltLMFrb6NJjj36jho03u/9z0Luu/LzJgzBsUpI09LWNSTdvdZ3DgHvXwc2nwa/nwvWHwxXT4YUbto5vXFafc6oJMe1dC9lN5ecd8C1oH1d8bMaJlZ8rSZIkSZIkSZIkSZIkSZIkSZIkqSSD0SRJkqRCaYPRAB78z8bV0QixRMhVMwZczTgBJuxX3z1XPVjf/YaCUl8zBAi+pFQDtbSXHMpQOhgtU1Mw2qjSYw98AV6+Fx78EtzzMXj027BuUfq9e9bCg/+Rbu6cN6bfV5KktNrTBqM9Wt9zH/oKPHfV1nbPKvjDcbD497l2vYLR7vlY5WvSnj33rXDoxdA6om///l+HsfMrP1eSJEmSJEmSJEnS8BHjQFdQoaFWryRJkiRJkiSpGTVh+oEkSZJUo87J6eduWt64OhohZor3h9b+raM/hAALfgq/261+ez5zGRz2s/rtNxRke4r3J4RWSY2WaekqOdZbUzDa6OTxa/fv277v03Dkb2Hq4eX3vnrPdDWM3wtmnphuriRJlUgbjLbm8fqeu/CS4v03nQA7vg16VtfnnKcvgkN/UtmaNMFobWOgfSzMei289il48Y+Q2QBTj4Qx86oqVZIkSZIkSZIkSVKzGSZhYiEMdAWSJEmSJEmSpGHCYDRJkiSpUCXBaEMtHCrbW7w/NOlLg45x9d0vZnJ3bxxOF/fEYfY1o8Fj9Nzc9+PCwJLWLrIjppdclqkpGG1UZfN7VsENR8A/Pg5jdio9b/WjsG5R+f1GTIcFPxt6/2+RJA0N7WPTzVtxD9z+Dnjqx7l2axccfhlsd1Lx+b3d8NCXYemfoHdNrq9tDEw7Cua/H9Y+Wfqspy9KXX4qG5dD56Tcc/ZFP4dFm/+/Ov9cmH5MkfkpgtHyA+VGzoAdzqpfvZIkSZIkSZIkSZIkSZIkSZIkSZK20TLQBUiSJEmDTsfE9HPDEAuvKRly1dq/dfSX9vH13/O+T9d/z8Es21O83+AmNVpogZ3eu23/vHPIxNLBfL2ZGs5s66pu3W/mw5onSo8/e0Xy+pMegFOXwKmLYfwe1dUgSVI5LZ3p5nUv2RqKBpBZD396DTx9ybZzY4Q/Hg8PnA9Lb4KX7849lt4E938Ofj23DoVX4KEv5Wq6/3Nw61nw/G9y/x/+w7Gw8Gfbzt+4vPye+cFokiRJkiRJkiRJklSxONAF1E9soj+LJEmSJEmSJGlQMxhNkiRJKhSz6ee2dDSujkaIJRKDQumQoSGtbSRMPLD42Pz3V7fnIxdAb3f1NQ01pcL0Wpr0a0aDy96fh32/AuP3zj32Oh8O+AaZbOkL7DK1XHuXLfH1nsZv5sONR8Pap7YdW3x16XVzzsyFoY2cDiFUf74kSeX0rKlt/YP/vm3f87+Fl24uvaa3xjPzjZxRfs7DX4VfdMIDn9927NazoHd9376Ny8rv2WYwmiRJkiRJkiRJkqRyDAyTJEmSJEmS0kr6aZo/aZO0hcFokiRJUqGJ+6Wf2zrEgtFKhf40c8jV3l/YNsBu3y/DQd+ubr/MBnjxj7XXNVRseKl4f2jv3zo0PIUAu/8LnPT33GOvz0JoISEXjd4S+Y+pZGoMPXzxj3D94ZDZ2Ld/04rSa2adUtuZkiSlNeng2tavfqRvsNiGpXDfp2vbsxLTjk03L9tTeuyaffu21y0qv1+7wWiSJEmSJEmSJEmSJEmSJEmSJElSfzIYTZIkSSo0akcYu1u6uUMtHCqWSAwKrf1bR3+aeQIcdwvs/EGY/z448new+8dr23PjsvrUNpjFCA99Be56f/HxZg7T06CXyVY3Vlaow49JupfAs7/c2o4R1j9bev7Mk2o/U5KkNCbuD52TattjSzDa/Z+Hq2bByvtqryut1g6Y947a9ljzOLx899b26kfLryn1GkqSJEmSJEmSJEmSJEmSJEmSJElSQxiMJkmSJBUKAV5xYbq5LUMtGK23eH9o8pCrSQfCgd+Ag74D2+WFEO3xqer2ax9Tn7oGs6V/gr99ovT4UAsFVFNJCj/rrSUYbdbJNSzOc+ub4M73w+/2gGv2gZ7VxefNfz+0j67PmZIkldPSBgt+Ci2d1e+R6YYl18H950G2p361pdHSAXt9DsbvVds+1x4Iy+/M/X71I+Xn96yp7TxJkiRJkiRJkiRJw1wc6AIqNNTqlSRJkiRJkiQ1I4PRJEmSpGImHwKvuKj8vKEWKBYzxftDa//WMVjMPgMIla+rJUxiqFh4SfJ4yxD72ldTySZce5cUmlbW2N1g3O41bJDn8e/Aqodg5f2l5+x9fn3OkiQprRmvhlOegUMvhl0+XPn6TDc8ckH960qjpQO6ZsGrb4ejrqltr98fDPd/HtYtLD+3VMCpJEmSJEmSJEmSJG0RDROTJEmSJEmSJKmeDEaTJEmSShm7c/k5safxdeTrWQsPfxX+dDLceS6sf66y9bG3eP9wDbmasC8s+Cm0j6tsXXZTY+oZTJ78YfL4UAsFVFNJCj/rrSUYLQQ46urc94ZG65wCnZMaf44kSYVGTIUd3wKzT6987W93gSW/r39NabS0535t64KZJ8DYXWvb7/7zSHWn8961tZ0jSZIkSZIkSZIkSZIkSZIkSZIkqSJ+kl2SJEkqpXNy+TmZDY2vY4uYhT/9Ayz909a+Rb+AE+6E0XPT7ZEtEYw2nEOudngjzD4DHvgCPHB+ujXNHoyW5u6VW4IppAGQSfgSTQpNS2XUHDjxXlj3LHSMg/axsHE5rFuUC5Jp6YC2MfDgf8KD/179ObWGuUiSVKvWroGuoDItHX3bI2fC6kcaf+6skxt/hiRJkiRJkiRJkiQNCSmuLZQkSZIkSZIkqQ6GcfrB8BZC2BXYB5gFjAQ2AEuBJ4C/xxjX1bB3O3AYMBuYAawFFgP3xhgX1la5JElSPxpswWiLLu0bigaw6WV47L9h/6+l2yNmiveH1tpqG+paWmHeOc0ZjPb81XDfZ2D9Ihi3Bxz8Qxg7P3nNE98vv+9wDtPTgEsKP6s5GG2LUdtv/X3npNwj3z5fgBHT4O4PVLf/9GOqr02SpHpoG+LBaKPm9M+5O7y5f86RJEmSJEmSJEmS1JzS3KhUkiSpGfk8SJIkSSUkPVX0aaSkLfwk+zASQhgPfAg4h1xoWSmZEMLfgMtjjF+qYP8pwPnAG4CJJebcClwQY/xl6sIlSZIGSvu43Ifvk0Kw+jMY7ZGvFu9/8Q/p94i9xftbfGnAqNmw03vhie+VnztUgtFWPgg3nwLZnlx76Z/h9wfDyQuhY1zxNc9cDne+r/zeHRPqVqZUqWzCDzfrFoyWxi7/BJuWw/2fq3ztTu+pezmSJFWkdYCD0Vo6c8Gj3YtTzm/v255zJjx1Yf3ryjfzNTB5QWPPkCRJkiRJkiRJktQE/LSmJEmSJEmSJEn1ZPrBMBFCOAP4LjApxfRW4ABgFpAqGC2EcCLwE2BqmakLgAUhhEuA98QY16XZX5IkaUCEABMPhGW3lp7TX8FoMcLLdxcfW/G3CvbJFO8PrZXX1IwO+g5MWQBP/ABe+kvpeUMlGO3+z24NRduiZyU8eznMe0ff/mwP3H4OLPzfdHtPP6Y+NUpVSAo/6y3xba5h9joPxu8Nz/8aul+AdQth9SPJaw78Noyc3i/lSZJUUtsABqPt8hHY8U0wYT946iJ48Ubomp17Dv7SzcXXtHT0bU87CsbsDGseq399c86EqUfA3HdAi6+VJEmSJEmSJEmSJEmSJEmSJEmSpP5kMNowEEI4D/hckaFngMeAl4ARwAxgL2BUhfsfBVwF5H8yLQL3AE8B44H9gMl5428CxoYQTokxJnykXZIkaYDNPDE5GC3bT8FoPauTxzetgo5x5ffJ9hbvD740AHJheDu+BbZ/HVw+fttQsS2GQjBa73p49oriY399J0x7FYyeC2ufgsXXwl3vT7/3tGNglw/Vp06pCknBaJnsANx9dftTc48tHvsO3PPh4t9DdngTzD+3/2qTJKmU1gEKRtvuH+GAC7a257099wC4432lg9FCe992Szsc8iO44Yj61rfvl2D3T9R3T0mSJEmSJEmSJEnD2ABczyRJkjQo+DxIkiRJklQ90w+aXAjhY2wbivYz4IsxxvuLzG8BDgVeBxyfYv9ZwBX0DUW7BXhXjPHhvHmdwHuArwJbPsH2j8C/A59K+ceRJEnqfyOmJ49n+iEYLbMR/ljmqdm6RdCxd/m9YolgtBZfGvTR1gUzToTnf118fCgEo/355OTxX8+DuWfDokshsz79vsfcBFMW5IIopAGSlH2WGQzvn+98Lsx4Nbx0K2TWQUtHLuBy4oEw5fBcCKMkSQOtdcTAnLvj20qPtXYmjHVs2zf1cDh9JTx1IdzzkdprAxg5sz77SJIkSZIkSZIkSRpGBsNFS3USm+jPIkmSJEmSJEkaskw/aGIhhH2AL+V19QBnxRgvL7UmxpglF2x2SwghzdfH+cCEvPatwLExxj4JITHGjcA3QwjPAFfmDX00hPD9GOOiFGdJkiT1v7au5PGNy6BnLbSPblwNz1wKy/+aPGfdQpiQJhgtU7w/tFZcVtN7xYXwy0nFx/ojEK8WG16CF24oP++pn1S270Hfg2lHVlWSVE+ZbOmx3hLf5vrdmJ1yD0mSBqsQYPxesHKb+2dAaIGY8D/cQi0dMPUoeOG68nO3PzV5n0rHOsbBrh+GSYfA9QvKn1/OyBm17yFJkiRJkiRJkiRJkiRJkiRJkiSpai0DXYAaY3Oo2Y/pG373nqRQtEIxxt4yZ8wH3pbXtQk4uzAUrWDPq4CL8ro6gfPS1iRJktTvWssEowFcdwj0djfuLnmP/Ff5OetS5sxmSzzFS5WJO8x0ToSpJULA7vtM/9ZSqTShaJXqnAI7vqX++0pVSApGSxqTJEkF5r17274ZJ6Z7HZTv9JWw12fLz9vt47nQtVJaR5QeC+3Je08+BCbsW76GckZuV/sekiRJkiRJkiRJktSUGnSdrCRJkiRJkiRJBQxGa15nAPvntW+MMV5Y5zPOAlrz2lfEGB9Pse7LBe3XhxASPvEmSZI0gNpSBAKseggu7YLLx8PNZ8CGpfWtYcU95ecs/2v5OeuegRdvLD4WWov3D3ctHaXH6v3fuZ42vFDf/Wb+A5xwZ7p/D1I/yCZcX9drMJokSent8k9w4Ldh/D7QNRt2ei8ccTn0rk2/R9dsaBsJE/YrP3fkjDLjM0uPJT03h1zg2tE3wA5vgRHTYeKBsOBncOpimHUKjJhWvr6u2TBm5/LzJEmSJEmSJEmSJCmtRt10V5IkabDzeZAkSZIkqQZtA12AGuY9Be3/bMAZpxa0UwWvxRgfDiH8FThkc9co4NXAr+tYmyRJUn20jkw/t2c1PHs5LL8DTn4698H8WmV70s1beAm84iJoKRFwltkI1x9Wen3wpUFRSeELi34Bu3wgeX1mI6x6EMbu2r+hYhuX1b7Hrh+D/b9a+z5SA2QSws+SxiRJUhE7vz/3qFVbF3ROgY0vlZ5TLhht7K6lx3rXlK+hcxIsuHjb/ldeuW3f05fAbW/O6wiw12dLv6aSJEmSJEmSJEmSpFIM/ZAkSZIkSZJSS/ppmj9pk7SF6QdNKISwE3BkXtdC4I91PmM6sE9eVy9wSwVb3MTWYDSAEzEYTZIkDUbVhFmtfwaumAqnLIbWhGCtNNYuTD/3pZth2lHFx57/Nax/rvRaP/xfQsKPUNY8kbz0qYvhzvdAZkMuYG2f/4TdPlbf8kqpNRhtwU9hzhvrU4vUAEnhZ70Go0mSNHAOvRhuOrH0eNlgtF1Kj2W6q6uplB3fBGN2gie+D61dMPv00q+nJEmSJEmSygghtAOHAbOBGcBaYDFwb4xxYZ3P2hHYF5gJjAaWAIuAW2OMKe+8JUmSJEmSJEmSJEmSJA1eBqM1p1cVtG+Mse63n9mzoH1fjHFdBetvLWjvUWM9kiRJjdFaRTAawMbl8MAXYJ8v1Hb+xqXp5750S+kP8i+/M3ntmifTnzOcJIUvJIXmrfg73P62re3sJrj3n2H8XjDj1fWrL9+qh2DJ9bDpZXjiB9Xvs9fnYYcz61eX1ACZhFe4SaFpkiQppfnnwuPfqXzd+MIfGxcYPS95fMR0aB8PPSu3HZtyeOX1lDP5kNxDkiRJkiQ1hRDC54Dzatjiohjj2RWeOQU4H3gDMLHEnFuBC2KMv6yhNkIIpwMfBQ4tMeXlEMIvgM/GGGu8k5IkSZIkSZIkSZIkSZI0cFoGugA1xMEF7dsAQs6xIYQLQwgPhRBWhRDWhRAWhRBuCCF8MoSwQ8ozdi9oP1FhjYXJG4X7SZIkDQ5J4VflPHtZ7ef3rEk/975Pw+rHt+2PER7+/5LXjvPpWFG960uPtY4sPfbcVcX7n72ytnpKeeKHcPXecM+H4YHP17BRgB3fXLeypEbJJoSf9WT6rw5JkprWrFPSzx2z09bfd82CaUcXnzflCOjaLnmvEGCnd27bP2oOTNg3fU2SJEmSJEn9IIRwIvAA8D5KhKJttgC4PITwvyGEUVWcMzqE8DPgMkqHorG5hvcBD4QQjq/0HEmSJEm1SLjTY+LYYDTU6pUkSYOXzyskSZIkSdUzGK05HVjQfnhz4NkNwPXA2cBuwFigC5gNHAN8EXgshPDfIYRyCSA7FbSfqbDGRQXtSSGECRXuIUmS1HitNQSjrV2YCyWrxPO/g9vOhtvPgcW/h57Vla3/7c7wl9fDjcfCvR+HNU/CstvLrxu7a2XnDBeZKoPR7v9c8f4nvldTOUWtXwx3vAtiHdKgDr8URu9Y+z5Sg2USvrVu6Om/OiRJalozjoP9L0j3emi3f+7bPvRimHhQ376JB8BhP0939p7nwewztrbHzIejroXg2xmSJEmSJGnwCCEcBVwFTM3rjsDd5ALMrgeWFSx7E/CzENL/oCOE0Ar8AnhjwdBLwHWbz7qHvp8wnAb8KoRweNpzJEmSJEmSJEmSJEmSpMGkbaALUEPMKGh3AXcCk1OsbQfOBQ4NIbwmxrikxLzxBe2llRQYY1wbQtgAjMjrHgesqGSfQiGEqcCUCpfNq+VMSZLU5NpqCEbLboTeddA+Ot38x78Hd75va/upC2HKYZWf+8xluV9fvBGevgimvDJ5/pj5MPXIys8ZDnoTgtFa2vuvjiRXbVeffV6/rravd6kfZbKlx9Zt6r86JElqart+BHZ6L6x9ElY/Cn85fds5o+fBtGP69nVtByfckQuKXv8sdG0Po3dIf2776Fxg78blsHEZjNkZQqjlTyJJkiRJkoavM4EUd5H6P2vTTAohzAKuADryum8B3hVjfDhvXifwHuCr5K7LA/hH4N+BT6Ws6UvASXntHuCjwA9ijP/3rkgIYXfgh8Chm7s6gatCCHslXAMoSZIkSZWp9GbBkiRJkiRJkiRVyWC05lQYWnYhW0PR1gHfA64BngNGAfsA5wD5d4jcD/hlCOHIGGNPkTMK0z26q6izm77BaGOq2KPQucB5ddhHkiQpp3Vkbes3LU8XjBazcP/52/a/dEtt529YCs9eXnp8yhFw6E/Sh7cNN5mEYLRHvwm7frjImg2Nq6fQ8ruqWxfagCy0jYFpr4IDv20omoaUbML1dU9UFNstSZIStY2E8XvmHodeDLe9devY9GPhFRdBa0fxtaN3qCwQrVDnpNxDkiRJkiSpei/EGBc2YN/zgQl57VuBY2OMfd4ojDFuBL4ZQngGuDJv6KMhhO/HGBclHRJCmAt8qKD7jBjjrwrnxhgfCiEcA9zI1nC0SeSupXtvij+TJEmSpIYxTEySJA1XPg+SJEmSJFXPYLQms/kuk50F3bM2//oQcEKM8dmC8XuAC0MIHyN3d8otDgU+Qe4OlYUKkzOqSX/opu8FYqZxSJKkwSe01LZ+4zIYNaf8vOV3wYYXajurGsf9uf/PHEp6E4LR1j0NT/4Y5p3Tt//WtzS2pnwv/qGy+ae9CCOmNqYWqR9lssnjv7gzyxsOqvH7tyRJ6mvHt+QekiRJkiRJw1gIYT7wtryuTcDZhaFo+WKMV4UQLspb10kusOycUms2Ow9oz2v/pFgoWt453SGEs4H7gS1p9u8IIXwlxvhUmbMkSZIk1cTQD0mSJEmSJEmS6slPCTef1hL9qygeivZ/YoxfA75e0P2REEKawLJq3sXxnR9JktT8NixLN2/j0sbWUcx+X+v/M4eaWCZ96ckf9m1vWAbPXZG8JrOptpryPfjF9HM7JkDnlPqdLQ2gcsFoP/qLLzclSZIkSZIkSVJDnEXfa/SuiDE+nmLdlwvarw8hjCg1OYQwEji9zB7biDE+BlyV19VGrmZJkiRJkiRJkiRJkgaFmPDxv6QxScOLwWhNJsa4Hij2EfELkkLR8nyGXIjaFhOBE4vMW1vQHpmuwsQ1hXtW4zvAnhU+Tq7DuZIkqZmN3aX6teufSTdvIF6pd23X/2cONft9JXl82W1926sfLh+mduOroHtJbXUBrHsWelamnz9yBoRQ+7nSIJAt8y3zhof7pw5JkiRJkiRJkjTsnFrQvjDNohjjw8Bf87pGAa9OWHI80JXXvi3G+EiqCret6bSU6yRJkiRJkiRJkiRJkqRBwWC05rSuSN/FaRbGGNcBVxR0H1Vk6qAMRosxLo0xPljJA3iy1nMlSVKTm/3Gbfs6JsLrlsEhP4IFl0D72OJr73g3rLiv/BkxU1uN1RhpMFpZM4tlBCfoWVN+zrJb4doD4KVbq6tpiyd/VNn8qUfVdp40iGTK5A9KkiRJkiRJkiTVWwhhOrBPXlcvcEsFW9xU0E56M/KEMmuT3Eyuti32CyFMq2C9JEmSpHoaiBvn1mSo1StJkgatIfc8SJIkSZI0mBiM1pxWFrRfjDEurGD97QXt3YrMWVXQnlLB/oQQRrNtMFph3ZIkSYPDnv8GO7wFCLn2yJlw9HXQOQnmnQM7nAXj9ii9/q/vhKcvgVvOgrs+CMvv2nbOQASjjZ7b/2cONaPmQOuI5Dn5b9b1pghGA+heAjceBfd8DO79OPzlDfD4dyFbwdfBA+ennwsw7x2VzZcGMYPRJEmSJEmSJEnSANizoH3f5huRplV456Sk2cPnAAAgAElEQVSEN5m3Oeu2tIdsrun+Cs6SJEmSVCtDPyRJkiRJkiRJqqu2gS5ADfEYsH1ee0mF6xcXtCcVmfN4QXtOhWcUzn85xriiwj0kSZL6R0s7LLgYDvh6LtBq3O4QCjKG28aUXv/ynXDbm7e2n/wfOPI3MP3YrX2Z7vrWXM64PaFrZv+eOVRt/zpYeEnp8dgLoT33+56UwWgA2R545IKt7WcuhRf/CIf9AkJIXnvfeenPAZj9epi4f2VrpEEsm+I6wt8/GDl+jzL/liRJkiRJkiRJUjN7Twjh0+RuDDoJ6AGWA4uAvwDXxhhvrmC/3QvaT1RYz5Nl9stXeDPTas7ar+CsP1S4hyRJkiRJkiRJkiRJkjQgWspP0RD0YEF7Y4XrC+ePKDLn4YL2ThWeMbeg/VCF6yVJkvpf5yQYv+e2oWgA7QnBaIUyG+APx8GVs+CuD+TavZXcSLwOZry6f88bylo6k8ezm7b+vreCYLRinrkMVj1QfOzZq+DqfeGnAR74fOk9TvwbHPBNGD0PRkyFXT4MB323trqkQSaTLT/nM1elmCRJkiRJkiRJkprZG4FjgJlAJzCa3A09Xwl8CvhzCOHOEMKxpbfoo/AauWcqrGdRQXtSCGFC4aQQwkRgYo1nFc6fX+F6SZIkSSoixR0tJUmSJEmSJEmqg7aBLkANcV9Be3yF6wvnLy8ypzCtYe8QQleMcX3KMw4rs58kSdLQUkkw2hbdz8Nj34bVj+ZCrCrxiovg0W/AinsqPxdg3B7VrRuOWjqSxzMboW1U7vc9q2s/78kfwQH/1bdvyfXwl9dBLBP0NH4fmLD5scsHaq9FGqTSBKPdtQiy2UhLS2h8QZIkSZIkSZIkaag6ELguhPBF4NMxxqRP+RdeV7e0koNijGtDCBvoe6PSccCKMuesjzFWeqetwtrGVbi+pBDCVGBKhcsqfENckiRJGmqSXkoYJiZJkoYrnwdJkiRJkqpnMFpzuobcTwy2fPp7bghhRIxxQ8r1exa0nyucEGNcEkK4D9h7c1cbcDhwXcozjipoX5NynSRJ0uDUVkUw2hYvXA9cX9mazskw7xy4q8pgtLG7VLduOIo9yePZjVt/37Om9vNeKPK18MQPyoeiAez41trPl4aATMr3yJethaljG1uLJEmSJEmSJEkadJ4HrgbuAB4GXgaywCRgf+AfgOPz5gfgU0AL8K8J+44uaHdXUVs3fYPRir3RXK9z8tXwhvY2zgXOq+N+kiRJkiRJkiRJkqRhJOmWZYm3M5M0rLQMdAGqvxjjYuC2vK524JgKtjihoH1ziXlXFrTfnmbzEMKuwCF5XetIH6gmSZI0OLXX8zryFDonw9yzq1wcYOxu9aymufWW+ZxBJi8YrbcOwWirHtr2JzfPXp5u7c7n1n6+NMjFGFP/cHPJqsbWIkmSJEmSJEmSBpU7yAWebR9jfHeM8YcxxltijA/HGB+NMd4aY/x2jPEE4CDg8YL1nwwhnJywf2FgWdobleYrfPOxcM/+PEeSJEmSJEmSJEmSJEkalAxGa14XFrQ/mmZRCOEI4OC8riy5u2cWcwmQyWufFkKYn+KYTxS0L40xVnPxliRJ0uDRPrZ/zxsxGdpGwZG/rXztzNdA58T619SsMmWC0bJ5wWg9ZYLRJh4Ax/2l/Jl/fSdkM+Xn5Tv2ZmgdUX6eNMRlK7jjw+KVjatDkiRJkiRJkiQNLjHGq2OM18VY/hYrMca7gFcAjxUMfSmE0Jr2yEprHORrJEmSJDXEEHt6nvaulZIkSWX5vEKSJEmSVL22gS5ADXMhuTC03Ta3jw4hfDTGeEGpBSGEqWwbqHZpjPHJYvNjjI+HEC4Cztnc1QH8JIRwTKmgs8131Dw7r2sTcH65P4wkSdKg1zamf8/rmLT51/GVrz3kB/WtpdmVC0bLpAxGG7srHH4pjJ4LI2dA95LSc5/6MUw+BNY/DyvvL1/j/v8FUw8vP09qApls+rlLVkUgNKwWSZIkSZIkSZI0dMUYXw4hnAncxdY3FHYFXgXcUGTJ2oL2yCqOLVxTuGd/nlOt7wCXVbhmHvCrOtYgSZIkDTKGfkiSJEmSJEmSVE8GozWpGGMmhPAh4FqgZXP310IIc4DPxRhX5M8PIRwLfJfcBUhbrAA+Veao84BTgQmb2wuAG0II74wxPpK3fyfwbuBrBeu/FmNclP5PJkmSNEiFfg7eaR+b+7W1wmvgu2blQrmUXrlgtOymrb/ftLz4nNmvh8N+vvXrJLSXP/eO96Srb+45sOuH0s2VmkC2gmsIF69qXB2SJEmSJEmSJGnoizHeE0K4Djg+r/sEDEYrKca4FFhayZrQ3++nS5IkSZIkSZIkSZIkaUgzGK2JxRiv3xyO9q287g8C7wsh3A48T+4CqH2BOQXLNwFnxhifLnPGcyGE04DfAx2buw8DHgoh3A08BYwD9gemFCz/LfCZiv9gkiRJg9HGEoFYjbLlwvHWrsrWlQv50rbKBqNthEe+AU/9GFbeV3zOtKP7hue1dBSfV42pr6zfXtIQkMmmn7vEYDRJkiRJkiRJklTetfQNRtu7xLzCdx4Kr4dLFEIYzbaBZStTnNMVQhgVY1xXwXFTU5wjSZIkSRWq4K6WkiRJkiRJkiTVwGC0Jhdj/HYIIQN8FdiSmtEOHJGw7EXgtBjjrSnPuCmEcCrwE7Ze7BWAAzc/ivkZ8K4YYybNGZIkSYPeQIVTtVUYjLbzBxtTRzPb/nRYfkfp8Qe/CIt/l7zH2F37tnd6F/ztE7XXBjDj+PJzpCZSSTDaC6u8EE+SJEmSJEmSJJW1sKBdKvDs8YJ24c1Iyymc/3KMcUXhpBjj8hDCCmBCXvds4OEaziqsXZIkSVI9xYTrlJLGJEmSmpnPgyRJkiRJNWgZ6ALUeDHG75K7i+X/AmsSpr4AfA7YJW0oWt4ZVwN7At8DtrlYK8/twOkxxrMqvIOlJEnS4DbpEGgb0z9ntY/d+vsRhTf6LmP70+pby3Aw5w3J4+VC0QDG7lKw5xurryffjBNh5PT67CUNEdkK3h9fvLJxdUiSJEmSJEmSpKbRXdAeWWJeYTDZThWeM7eg/VDC3HqfVUmomiRJkiRJkiRJkiRJkjSg2ga6APWPGOOTwFtCCCOBw4BZwHRgE/AS8PcY4301nrEUeF8I4UObz5iz+Yx1wPPAvTHGp2s5Q5IkadBq7YRD/gdufRPETGPP2u9reeeOSL9ut3+BcXvUv55mN2o27PNF+Pu/Vre+fTyMmFZkz/+Av/9bbbUd9O3a1ktDUCabfu6SVY2rQ5IkSZIkSZIkNY3JBe1lJeY9UNDeO4TQFWNcn/Kcw8rsVzi2IK99KPCbNIeEEEaRu5Fq2rMkSZIkSZIkSZIkSRoU4kAXIGnQMBhtmIkxdgM3NPiMTcAfG3mGJEnSoDTnDTBhX7jjvbD0psac0doFs1+Xfv4Rv4Q1j8OUw2HyAgihMXU1uz0+WX0w2rQji/+97/EpmH5c7utlxT2V7zvpYBhdeKN3qflVGowWYyT4vU+SJEmSJEmSJJV2SEF7cbFJMcYlIYT72Bo61gYcDlyX8pyjCtrXJMy9Fnh3wtokR9D32tB7Y4wvVrBekiRJ0rDmR08lSVK9+LxCkiRJklS9loEuQJIkSWoqY3eBff69MXu3j4XDL4OOCenXbH8a7P4JmHKYoWi16ppV3boZJ5Yem3QQHPdnaOmofN/dqwxqk4a4bAXvj/dkYFNv42qRJEmSJEmSJElDWwhhBHBaQfdNCUuuLGi/PeU5u9I3gG0dyYFqvwe689qHbt4jjbML2oU1S5IkSaq7pIuaDASRJEmSJEmSJKlSBqNJkiRJ9dYxqbb1rV1wzB/g9BVwVoRTl8Dxd8CpL8B2J207v3NK8X26tq+tDvWV7alu3Zh5yeNto2B0mTmFJh0C2/1jdfVIQ1ys8DrB7ir/6UqSJEmSJEmSpGHhE8B2ee0M8LuE+ZdsnrPFaSGE+SnPyXdpjHFDqckxxvXA5WX22EYIYWfg1LyuXuCnKeqTJEmSJEmSJEmSJEmSBg2D0SRJkqR665xcfs74fUqPnfR3mPYq6Bifa4+cDpMOgraRxefv/YXi/bv9c/k6lN7GZdWtGzmz/JwT7k65WYA5Z8Hhv4CW1urqkYa4bKXBaJsaU4ckSZIkSZIkSRo8QghvCSFMq3DNu4DzCrp/EmNcVGpNjPFx4KK8rg7gJyGEEQnnnAycnde1CTg/RYmfA/JvAXN2COG1CeeMAC7cXNMWP4oxPpniLEmSJEkqr9K7WkqSJEmSJEmSVCWD0SRJkqR665hQfs7xd8CME/r2tXbl+sfsVNl5c14PYwpuQj56Lsx5Y2X7KNmkg6tbN3K78nPaRsKOb02e09IJZ2bgsEtg1JzqapGaQKWX1nX3lJ8jSZIkSZIkSZKGvHcAT4cQLgohvCaEMKrUxBDCgSGEK4AfACFv6Hng0ynOOg9YkddeANwQQti14JzOEMIHgMsK1n8tKXxtixjjU8A3CrovDyH8UwghP/yMEMJuwI2ba9liOekC2CRJkiQ1lGFikiRpuPJ5kCRJkiSpem0DXYAkSZLUdFpaYeZJsPjq0nNaO+CVv4JHLoCX/gIjpsDOH4CJ+1d+XscEOO5WePgrsOJemLAf7PJhGDG1+j+DtjXtaFh2W+Xr2semmzfrFHj64tLjnZMghNLj0jCRzVY232A0SZIkSZIkSZKGjZHAWzc/siGEx4GFwCogA0wC9gGmFVn7MnBCjPGFcofEGJ8LIZwG/B7YElB2GPBQCOFu4ClgHLA/MKVg+W+Bz1TwZ/oksAdw4uZ2O/At4DMhhHuANcDczWflv5m4CTg1xrikgrMkSZIkVSsa+iFJkiRJkiRJUj0ZjCZJkiQ1wr5fKh2MNm7P3K+tHbDHJ+tz3ojJsN9X6rOXitv1I7n/pivuTb9m/N7pw8xmvJrcZxVKXCDVMSH9uVITy1Z4DWH3psbUIUmSJEmSJEmSBrUWYJfNj3JuBM6OMT6XdvMY400hhFOBn7A1/CwAB25+FPMz4F0xxkwF52RCCK8Hfgi8IW9oKnBCiWVLgbfFGG9Oe44kSZIkSZIkSZIkSZI0mLQMdAGSJEl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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import asyncio\n", + "\n", + "from matplotlib import pyplot as plt\n", + "\n", + "from apd.aggregation.query import with_database\n", + "from apd.aggregation.analysis import get_known_configs, plot_sensor, Config\n", + "\n", + "async def clean_magnitude(datapoints):\n", + " async for datapoint in datapoints:\n", + " if datapoint.data is None:\n", + " continue\n", + " yield datapoint.collected_at, datapoint.data[\"magnitude\"]\n", + "\n", + "TemperatureConfig = Config(\n", + " sensor_name=\"Temperature\",\n", + " clean=clean_magnitude,\n", + " title=\"Ambient temperature\",\n", + " ylabel=\"Degrees C\",\n", + ")\n", + " \n", + "with with_database(\"postgresql+psycopg2://apd@localhost/apd\") as session:\n", + " coros = []\n", + " figure = plt.figure(figsize = (20, 5), dpi=300)\n", + " configs = get_known_configs()\n", + " to_display = configs[\"Relative humidity\"], TemperatureConfig\n", + " for i, config in enumerate(to_display, start=1):\n", + " plot = figure.add_subplot(1, 2, i)\n", + " coros.append(plot_sensor(config, plot, {}))\n", + " await asyncio.gather(*coros)\n", + "\n", + "display(figure)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import asyncio\n", + "\n", + "from matplotlib import pyplot as plt\n", + "\n", + "from apd.aggregation.query import with_database\n", + "from apd.aggregation.analysis import get_known_configs, plot_sensor, Config\n", + "\n", + "with with_database(\"postgresql+psycopg2://apd@localhost/apd\") as session:\n", + " coros = []\n", + " figure = plt.figure(figsize = (20, 5), dpi=300)\n", + " configs = get_known_configs()\n", + " to_display = configs[\"Relative humidity\"], configs[\"Ambient temperature\"]\n", + " for i, config in enumerate(to_display, start=1):\n", + " plot = figure.add_subplot(1, 2, i)\n", + " coros.append(plot_sensor(config, plot, {}))\n", + " await asyncio.gather(*coros)\n", + "\n", + "display(figure)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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n2rNI/wAmhmv8MjK8P9P+vvQpxj0GSA4tI4f6Mys5tCbjHr2RQ8vIof5Mew7pH0Mgh5bRz/oz7WOvDIEcWkYO9UcOrcw9euuQRcvIov7MShYZqx4gObSMHOrPrOTQmoxV90YOLSOH+jPtOTSV/UNhtAlQSrk8TQdcHJK3JfmuWmtdeas1jWqboam1Hkjyix2Lf6yU8q9W26aUcmmSP0xy9mq7HVDzxlop5aeTfHfH4pfXWv90xE0Zej9c+KO1s2r3Sus9XGt9eZJ/2fGja5P8416OOa3k0HJyaONmKYe68Pwk5y6afyDJ2wZ8jKkhi5aTRRs3C1mkfwyHLFpOX9u4McoiJogcWk4ObdwM59CDSa5aND0izTjQtyX5z0nuXljvsiQ/leRjpZQva6GdY0sWLSeLNm6WssiY9eDIoeXk0MbNUg51wZh1D2TRcrJo42Yhi/QPYFK4xi8nwzdujK7x7tWbIHJoOTm0cbOUQ10w7tElObScHNq4Wcgh/WPw5NBy+tnGjVEOMUHk0HJyaONmOIfco9cnWbScLNq4WcoiY9WDI4eWk0MbN0s51AVj1V2SQ8vJoY2bhRya1v6xue0GsLZSyvlJ/neSSxYtvjPJ19Va7155q2UOdsxvpCJf5zad+2zDzyT5ppypyFiS/JdSyrenqYr6t2kqcF68sN4P5sxF73NJLl20r4drrcsqfJZSruy2MbXWW3tqfQtKKT+Sptr1Yr9Qa/25Lre/sttjrXA+xr4f1lpfV0r5+iTPW7T4h5K8ZZDHmTRyaE1yqEdyaJnv65h/S621s3o0kUXrkEU9mrEsGnr/mCWyaE2yqEctZ1QVJ7cAACAASURBVBETSg6tSQ71aJZzqNZ6MsmtK/zoI0neXkr5d0leneSHF5ZfnuQ9pZSvrrV+fDStHF+yaE2yqEeznEXdMGa9Mjm0JjnUIzm0jDHrLsmiNcmiHs1YFhmzBsaaa/yaXON7NGPvS/fMuMfK5NCa5FCP5NAyxj26IIfWJId6NGM5ZMxjQOTQmuRQj2Zs7JUBkUNrkkM9muUcco9ef2TRmmRRj2Y5i7phrHplcmhNcqhHcmgZY9VdkENrkkM9mrEcmrqxaoXRxlgp5Zwk70ny6EWL70nynFrrTT3saioDu9Z6tJTybUn+V5InLfrR0xem1dyb5gXDHy1adv8q697SQ5NKD+uOXCnl+5P8Qsfi/15rfVkPu+nnfExKP/zZLP0D9itLKXtqrav1kakmh9Ymh3ojh5YqpVyW5Os6Fr9ho/ubZrJobbKoN7OWRSPqHzNBFq1NFvVmDLKICSSH1iaHeiOH1lZrPZzkX5ZSjiX51wuLdyf59VLKUzb47UJTQRatTRb1RhZ1zZj1InJobXKoN3JoKWPW3ZNFa5NFvZm1LDJmDYwz1/i1ucb3Zgyu8ZPSD417LCKH1iaHeiOHljLu0R05tDY51JtZyyFjHoMhh9Ymh3ozBjnEBJJDa5NDvZFDa3OP3upk0dpkUW9kUdeMVS8ih9Ymh3ojh5YyVt0dObQ2OdSbWcuhaRyrnmu7AayslHJ2kj9O8sRFi/enqWB5Q4+7e6Bjfl+PbdmZ5YE9Fh241vr5JE9L8vokx7rY5H1JnprkUMfyOwfctLFSSvnuJL+cpSH6xiT/YoTN6OyHZ5VSdvS4j/M75ofRD/8qze/aKZuSPH4Ixxl7cqg7cqg7cmhF12Xpa7G/q7X+TR/7m0qyqDuyqDuzmkX6R/9kUXf0te6MSRYxYeRQd+RQd+RQT34yyR2L5r80yXNaakvrZFF3ZFF3ZFFPjFkvkEPdkUPdkUMrui7GrNcli7oji7ozq1mkfwDjyDW+OzK8O2NyjR+3e2RWY9xjgRzqjhzqjhxa0XUx7rEmOdQdOdSdWc0h/aM/cqg7+ll3xiSHmDByqDtyqDtyqCfu0VtEFnVHFnVHFvXEWPUCOdQdOdQdObSi62Ksek1yqDtyqDuzmkPT1j8URhtDpZRdSf4wyVMWLX4wyTfWWv92A7vsrHp5RY/bd65/X611/4prtqDWeqjW+s+TPCbNQMj7knwuyUNJDiS5Mcmvpame+uxa661JHtexmw+PrMEjVkp5cZpwXvz7/htJ/ukoK+fXWu/N0j8Mk+TyHnfT2Rd7qejalVrrySS3dyzu6UXONJBDvZFDa5NDy5VSSpLv6VisqncHWdQbWbS2Wc8i/WPjZFFv9LW1jUsWMVnkUG/k0NrkUG9qrQ8leUfH4m9soy1tk0W9kUVrk0W9MWbdkEO9kUNrk0PLGbPujizqjSxa26xnkf4BjBPX+N7I8LWNyzV+nO6RWYtxj4Yc6o0cWpscWs64x/rkUG/k0NpmPYf0j42RQ73Rz9Y2LjnEZJFDvZFDa5NDvXGP3hmyqDeyaG2yqDfGqhtyqDdyaG1yaDlj1euTQ72RQ2ub9Ryapv6xue0GsNTCt9D8ryRfuWjxwSTfVGv9qw3u9saO+Uf2uP3VHfOf2GA7hqrWekuSn1mY1vNVHfN/uco+y0rLJ0Up5YVJ3pymOvUpv53kpQt/qPVkAOfjxjSVJU95ZJb3z7V09sVetu3FQx3znZVcp5oc2jg5tJwcWtWzkly1aP5ImhfTLJBFGyeLlpNFZwyjf0wzWbRxsmi5McwiJoAc2jg5tJwc2rBPdsz3+jsz8WTRxsmi5WTRhhmzlkMbIoeWk0OrMma9Dlm0cbJoOVl0hjFroG2u8RvnGr/cGF7jx+UemfUY95BDGyKHlpNDqzLusQY5tHFyaDk5dIYxj+7JoY2TQ8uNYQ4xAeTQxsmh5eTQhrlHTxZtmCxaThZtmLFqObQhcmg5ObQqY9VrkEMbJ4eWk0NnTMNY9dz6qzAqpZTtSX4vydMXLT6c5JtrrX/ex64/3jH/pFLKWT1s/9Xr7G+iLFRTfVbH4g+00ZZhKqU8L8lvZmkBxHckeUmt9UQ7rVrWdzqDcVULL2aetM7+BuW8jvl7hnScsSOHRkMOyaEk39sx/7u11vs2uK+pI4tGQxbJonWOMxP9Yy2yaDRmpa+NaRYx5uTQaMghOdSFYx3zW1tpRUtk0WjIIlnUBWPWcmio5JAcijHrNcmi0ZBFsmgts9I/gNFyjR+NWcnwMb3Gj/370guMe8ihoZJDcijGPVYlh0ZDDsmhdY4zE/1jNXJoNGaln41pDjHm5NBoyCE51AX36MmioZNFsqgLxqrl0FDJITkUY9WrkkOjIYfk0FrGuX8ojDYmSinbkrwryTMXLX44yfNqrX/az75rrV9I8tFFizZn6UVhPc/smP+DftozBp6V5MpF8x+otd7UUluGopTy3DQVK+cXLf79JC+qtR5vp1VJkj/smH9mD9t+TZZefD5Sa72r7xZ1KKWcl+XVW+8Y9HHGkRwaKTnUntZzqJSyJ8m3dSx+Q6/7mVayaKRkUXtaz6IuTH3/WIssGqmp72tjnEWMMTk0UnKI9VzaMT+M115jSRaNlCxiVcas5dCIyKEZZsx6bbJopGQRa5n6/gGMlmv8SE19ho/xNX7s35c27iGHRkQOtaf1HDLusTo5NFJyqD2t51AXpr5/rEYOjdTU97MxziHGmBwaKTnEetyjJ4tGQRaxKmPVcmhE5NAMM1a9Ojk0UnKItYxt/1AYbQyUUrYk+d0kz1m0+EiSF9Ra3zugw7y9Y/57umzbY5N8xaJFh5L88YDa1JZ/2zH/+lZaMSSllK9L8jtJtixa/MdJXlhrPdpOq077oyQPLZr/qoU+1o3rOuY7+/SgvDhLs/GuJDcO6VhjQw6NnBxqzzjk0Hcm2bZo/tYkf7LBfU0VWTRysuj/Z+/Ow2S7ynrxf98QCEMgYQqjJMyDyhiUgJiDgAIKGFBAZAjkyiDOIALXIaAXLj9F8YqCoJAwKoMEZFTEwxAIMo+KTIkQmQmEEDKv3x+7jqfO7urumrqquvvzeZ5+yF6119qrqtZ+O+x+867lWYVYtJkdvT42IhYt3I5eaysei1hR4tDCiUNs5id7xyvxYH+riUULJxaxEc+s9xOHto44tLt5Zr0OsWjhxCI2sqPXB7BYfscv3I6O4Sv+O347/F3ac4/9xKGtIw4tzyrEIc89RhCHFk4cWp5ViEOb2dHrYz3i0MLt6HW24nGIFSUOLZw4xGbk6O0nFm0dsYiNeFa9nzi0dcSh3c2z6hHEoYUTh9jIyq4PhdGWrKoOTvLKJPccar4wyc+11t46x0u9LMnFQ8f3q6obj9Gvv3hf2Vo7b37TWqyqeniSuw81fSRdxccdoaqOTfK6HPgvRm9P98v//OXMar/W2rlJXt1r7q+xNarqJkmOG2q6KMnL5zi1fde5RpLf7TX/Y2utzftaq0QcWixxaLlWJA49snf8wp0eZ8YhFi2WWLRcKxKLNrrOjl4fGxGLFmunr7VVj0WsJnFoscQhNlNVP53k6F7z65Yxl0USixZLLGIjnlmLQ4sgDhHPrEcSixZLLGIjO319AIvld/xi7fQYvuq/47fB36U999hPHNoi4tByrUgc8tyjRxxaLHFouVYkDm10nR29PtYjDi3WTl9nqx6HWE3i0GKJQ2xGjp5YtAhiERvxrFocWgRxiHhWvYY4tFjiEBtZ9fWhMNoSVdWl0gXS+w41X5Tkga21N8zzWq21zyQ5eajpMklOqqrLrtMlVXXfHLjTzQVJnjrPec1q8Atv3HPvl+QFQ00XJXlka+2iuU9sCarqmCRvSHK5oeZ3Jrl3a+37o3stxYnp/qVkn+Or6j7rnTxYoy/KgZU5/7a19rkN+ty0qu49yaSq6prpPr9rDDVfkOQZk4yz3YhDsxOH9hOHNldVt05y26GmS5KcNOk4O41YNDuxaD+xaGRf62MMYtHsrLX9tlEsYoWIQ7MTh/YTh/arqqOr6rjNz1zT7/ZJXtJrfmdr7ePzmdlqEotmJxbtJxbt55n1+MSh2YlD+4lDm/PMejSxaHZi0X5i0VrWB7AsfsfPTgzfbxv9jj8xcvVWhjg0O3FoP3Foc557rCUOzU4c2k8cGtn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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import asyncio\n", + "from uuid import UUID\n", + "\n", + "from matplotlib import pyplot as plt\n", + "\n", + "from apd.aggregation.query import with_database\n", + "from apd.aggregation.analysis import get_known_configs, plot_sensor\n", + "\n", + "\n", + "location_names = {\n", + " UUID('53998a51-60de-48ae-b71a-5c37cd1455f2'): \"Loft\",\n", + " UUID('1bc63cda-e223-48bc-93c2-c1f651779d69'): \"Living Room\",\n", + " UUID('ea0683de-6772-4678-bfe7-6014f54ffc8e'): \"Office\",\n", + " UUID('5aaa901a-7564-41fb-8eba-50cdd6fe9f80'): \"Outside\",\n", + "}\n", + "\n", + "with with_database(\"postgresql+psycopg2://apd@localhost/apd\") as session:\n", + " coros = []\n", + " figure = plt.figure(figsize = (20, 10), dpi=300)\n", + " configs = get_known_configs().values()\n", + " for i, config in enumerate(configs, start=1):\n", + " plot = figure.add_subplot(2, 2, i)\n", + " coros.append(plot_sensor(config, plot, location_names))\n", + " await asyncio.gather(*coros)\n", + "\n", + "display(figure)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "9f576f5bb8e34cb8ab1d3c09f770d027", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "interactive(children=(DatePicker(value=None, description='Start date'), DatePicker(value=None, description='En…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import asyncio\n", + "from uuid import UUID\n", + "import functools\n", + "from concurrent.futures import ThreadPoolExecutor\n", + "\n", + "import ipywidgets as widgets\n", + "from matplotlib import pyplot as plt\n", + "\n", + "from apd.aggregation.query import with_database\n", + "from apd.aggregation.analysis import get_known_configs, plot_sensor\n", + "\n", + "\n", + "def wrap_coroutine(f):\n", + " @functools.wraps(f)\n", + " def run_in_thread(*args, **kwargs):\n", + " loop = asyncio.new_event_loop()\n", + " wrapped = f(*args, **kwargs)\n", + " with ThreadPoolExecutor(max_workers=1) as pool:\n", + " task = pool.submit(loop.run_until_complete, wrapped)\n", + " return task.result()\n", + " return run_in_thread\n", + "\n", + "@wrap_coroutine\n", + "async def plot(*args, **kwargs):\n", + " location_names = {\n", + " UUID('53998a51-60de-48ae-b71a-5c37cd1455f2'): \"Loft\",\n", + " UUID('1bc63cda-e223-48bc-93c2-c1f651779d69'): \"Living Room\",\n", + " UUID('ea0683de-6772-4678-bfe7-6014f54ffc8e'): \"Office\",\n", + " UUID('5aaa901a-7564-41fb-8eba-50cdd6fe9f80'): \"Outside\",\n", + " }\n", + "\n", + " with with_database(\"postgresql+psycopg2://apd@localhost/apd\") as session:\n", + " coros = []\n", + " figure = plt.figure(figsize = (20, 10), dpi=300)\n", + " configs = get_known_configs().values()\n", + " for i, config in enumerate(configs, start=1):\n", + " plot = figure.add_subplot(2, 2, i)\n", + " coros.append(plot_sensor(config, plot, location_names, *args, **kwargs))\n", + " await asyncio.gather(*coros)\n", + " return figure\n", + "\n", + "\n", + "start = widgets.DatePicker(\n", + " description='Start date',\n", + ")\n", + "end = widgets.DatePicker(\n", + " description='End date',\n", + ")\n", + "out = widgets.interactive(plot, collected_after=start, collected_before=end)\n", + "display(out)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "60091fa7d9ea415985ec53937e422b4a", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "interactive(children=(DatePicker(value=None, description='Start date'), DatePicker(value=None, description='En…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from uuid import UUID\n", + "import ipywidgets as widgets\n", + "from apd.aggregation.analysis import interactable_plot_multiple_charts, configs\n", + "\n", + "location_names = {\n", + " UUID('53998a51-60de-48ae-b71a-5c37cd1455f2'): \"Loft\",\n", + " UUID('1bc63cda-e223-48bc-93c2-c1f651779d69'): \"Living Room\",\n", + " UUID('ea0683de-6772-4678-bfe7-6014f54ffc8e'): \"Office\",\n", + " UUID('5aaa901a-7564-41fb-8eba-50cdd6fe9f80'): \"Outside\",\n", + "}\n", + "\n", + "start = widgets.DatePicker(\n", + " description='Start date',\n", + ")\n", + "end = widgets.DatePicker(\n", + " description='End date',\n", + ")\n", + "\n", + "plot = interactable_plot_multiple_charts(location_names=location_names)\n", + "out = widgets.interactive(plot, collected_after=start, collected_before=end)\n", + "display(out)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "08a4d937037b45bda2298879df7f80b6", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "interactive(children=(DatePicker(value=None, description='Start date'), DatePicker(value=None, description='En…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import datetime\n", + "import uuid\n", + "import ipywidgets as widgets\n", + "from apd.aggregation.analysis import Config, configs, interactable_plot_multiple_charts, draw_map, get_map_cleaner_for\n", + "from apd.aggregation.database import DataPoint\n", + "from apd.aggregation.utils import merc_x, merc_y\n", + "\n", + "start = widgets.DatePicker(description='Start date')\n", + "end = widgets.DatePicker(description='End date')\n", + "\n", + "def get_literal_data():\n", + " # Get manually entered temperature data, as our particular deployment\n", + " # does not contain data of this shape\n", + " raw_data = {(53.8667, -1.3333): -1, (53.35, -2.2833): 1, (52.45, -1.7333): 3, (51.5, -0.1333): 6, (51.55, -2.5667): 5, (54.9667, -1.6167): -1, (55.9667, -3.2167): -1, (54.7667, -1.5833): 0, (53.7667, -0.3): 1, (53.7667, -3.0167): 0, (51.4833, -3.1833): 4, (53.2667, -3.5167): 2, (52.0833, -2.8): 2, (52.0167, -0.6): 2, (52.6667, 1.2667): 5, (50.4333, -4.9833): 9, (50.35, -4.1167): 10, (50.5167, -2.45): 9, (50.8333, -1.1667): 9, (56.45, -5.4333): 4, (57.5333, -4.05): -2, (54.5167, -3.6167): 3, (53.0833, 0.2667): 4, (51.35, 1.3333): 7, (50.8833, 0.3167): 8, (58.45, -3.1): 3, (50.1, -5.6667): 9, (58.2167, -6.3333): 4, (55.6833, -6.25): 7}\n", + " now = datetime.datetime.now()\n", + " async def points():\n", + " for (coord, temp) in raw_data.items():\n", + " deployment_id = uuid.uuid4()\n", + " yield DataPoint(sensor_name=\"Location\", deployment_id=deployment_id, collected_at=now, data=coord)\n", + " yield DataPoint(sensor_name=\"Temperature\", deployment_id=deployment_id, collected_at=now, data=temp)\n", + " async def deployments(*args, **kwargs):\n", + " yield None, points()\n", + " return deployments\n", + "\n", + "def draw_map_with_gb(plot, x, y, colour):\n", + " # Draw the map and add an explicit coastline\n", + " gb_boundary = [(-2.6653539699999556, 51.617254950000074), (-2.6937556629999335, 51.59617747600004), (-2.71312415299991, 51.58661530200004), (-2.760487433999913, 51.579779364000046), (-2.7980850899999155, 51.56350332200009), (-2.816395636999914, 51.555568752000056), (-2.8587133449999556, 51.546128648000035), (-2.943348761999914, 51.54254791900007), (-2.9562882149999155, 51.54572174700007), (-2.9641007149999155, 51.552801825000074), (-2.969471808999913, 51.559881903000075), (-2.9746801419999542, 51.56297435100004), (-2.984038865999935, 51.55853913000004), (-3.0102432929999168, 51.53803131700005), (-3.0156957669999542, 51.53164297100005), (-3.0737198559999115, 51.50836823100008), (-3.0764867829999503, 51.50775788000004), (-3.088937954999949, 51.505072333000044), (-3.105620897999927, 51.49721914300005), (-3.1317032539999445, 51.48041413000004), (-3.1350805329999503, 51.47630442900004), (-3.1409399079999503, 51.46478913000004), (-3.1453751289999445, 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51.49408600500004), (-2.716420050999943, 51.49957916900007), (-2.6961563789999445, 51.51264069200005), (-2.6078181629999335, 51.60736725500004), (-2.599598761999914, 51.611395575000074), (-2.5892227859999366, 51.61424388200004), (-2.580433722999942, 51.61863841400009), (-2.5768123039999296, 51.62750885600008), (-2.57445227799991, 51.63572825700004), (-2.568959113999938, 51.644964911000045), (-2.562245245999918, 51.65338776200008), (-2.556385870999918, 51.65924713700008), (-2.499175584999932, 51.69676341400009), (-2.464995897999927, 51.725897528000075), (-2.4477432929999168, 51.73187897300005), (-2.404896613999938, 51.74115631700005), (-2.3885391919999392, 51.750433661000045), (-2.380767381999931, 51.76190827000005), (-2.3833715489999463, 51.77326080900008), (-2.398060675999943, 51.782131252000056), (-2.4024145169999542, 51.76357656500005), (-2.4203995429999168, 51.753119208000044), (-2.4434301419999542, 51.74843984600005), (-2.4632869129999335, 51.747300523000035), (-2.482085740999935, 51.74286530200004), (-2.495838995999918, 51.73200104400007), (-2.507964647999927, 51.71857330900008), (-2.5221248039999296, 51.706366278000075), (-2.581695115999935, 51.681708075000074), (-2.5911352199999556, 51.67536041900007), (-2.600209113999938, 51.66559479400007), (-2.6653539699999556, 51.617254950000074)]\n", + " draw_map(plot, x, y, colour)\n", + " plot.plot(\n", + " [merc_x(coord[0]) for coord in gb_boundary],\n", + " [merc_y(coord[1]) for coord in gb_boundary],\n", + " \"k-\",\n", + " )\n", + "\n", + "country = Config(\n", + " get_data=get_literal_data(),\n", + " clean=get_map_cleaner_for(\"Temperature\"),\n", + " title=\"Country wide temperature\",\n", + " ylabel=\"\",\n", + " draw=draw_map_with_gb,\n", + ")\n", + "\n", + "out = widgets.interactive(interactable_plot_multiple_charts(configs=configs + (country, )), collected_after=start, collected_before=end)\n", + "display(out)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "apd.aggregation", + "language": "python", + "name": "apd.aggregation" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Ch10/apd.aggregation-chapter10/LICENCE b/Ch10/apd.aggregation-chapter10/LICENCE new file mode 100644 index 0000000..86685d4 --- /dev/null +++ b/Ch10/apd.aggregation-chapter10/LICENCE @@ -0,0 +1,31 @@ +Copyright (c) 2019, Matthew Wilkes + + +All rights reserved. + +Redistribution and use in source and binary forms, with or without modification, +are permitted provided that the following conditions are met: + +* Redistributions of source code must retain the above copyright notice, this + list of conditions and the following disclaimer. + +* Redistributions in binary form must reproduce the above copyright notice, this + list of conditions and the following disclaimer in the documentation and/or + other materials provided with the distribution. + +* Neither the name of the copyright holder nor the names of its + contributors may be used to endorse or promote products derived from this + software without specific prior written permission. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND +ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED +WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. +IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, +INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, +BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, +DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY +OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE +OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED +OF THE POSSIBILITY OF SUCH DAMAGE. + + diff --git a/Ch10/apd.aggregation-chapter10/Mapping.ipynb b/Ch10/apd.aggregation-chapter10/Mapping.ipynb new file mode 100644 index 0000000..916e704 --- /dev/null +++ b/Ch10/apd.aggregation-chapter10/Mapping.ipynb @@ -0,0 +1,254 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "datapoints = {\n", + " (53.8667, -1.3333): -1,\n", + " (53.35, -2.2833): 1,\n", + " (52.45, -1.7333): 3,\n", + " (51.5, -0.1333): 6,\n", + " (51.55, -2.5667): 5,\n", + " (54.9667, -1.6167): -1,\n", + " (55.9667, -3.2167): -1,\n", + " (54.7667, -1.5833): 0,\n", + " (53.7667, -0.3): 1,\n", + " (53.7667, -3.0167): 0,\n", + " (51.4833, -3.1833): 4,\n", + " (53.2667, -3.5167): 2,\n", + " (52.0833, -2.8): 2,\n", + " (52.0167, -0.6): 2,\n", + " (52.6667, 1.2667): 5,\n", + " (50.4333, -4.9833): 9,\n", + " (50.35, -4.1167): 10,\n", + " (50.5167, -2.45): 9,\n", + " (50.8333, -1.1667): 9,\n", + " (56.45, -5.4333): 4,\n", + " (57.5333, -4.05): -2,\n", + " (54.5167, -3.6167): 3,\n", + " (53.0833, 0.2667): 4,\n", + " (51.35, 1.3333): 7,\n", + " (50.8833, 0.3167): 8,\n", + " (58.45, -3.1): 3,\n", + " (50.1, -5.6667): 9,\n", + " (58.2167, -6.3333): 4,\n", + " (55.6833, -6.25): 7,\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "fig, ax = plt.subplots()\n", + "lats = [ll[0] for ll in datapoints.keys()]\n", + "lons = [ll[1] for ll in datapoints.keys()]\n", + "ax.plot(lons, lats, \"o\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "fig, ax = plt.subplots()\n", + "lats = [ll[0] for ll in datapoints.keys()]\n", + "lons = [ll[1] for ll in datapoints.keys()]\n", + "# Don't draw lines\n", + "ax.plot(lons, lats, \"o\")\n", + "# This is a map of the UK. Here, 1 degree latitude is \n", + "# 1.7x as far as 1 degree longitude\n", + "ax.set_aspect(1.7)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# Set up mercator transform from https://wiki.openstreetmap.org/wiki/Mercator#Python\n", + "# Thanks to Paulo Silva and the OSM contributors\n", + "import math\n", + "\n", + "def merc_x(lon):\n", + " r_major=6378137.000\n", + " return r_major*math.radians(lon)\n", + "\n", + "def merc_y(lat):\n", + " if lat>89.5:lat=89.5\n", + " if lat<-89.5:lat=-89.5\n", + " r_major=6378137.000\n", + " r_minor=6356752.3142\n", + " temp=r_minor/r_major\n", + " eccent=math.sqrt(1-temp**2)\n", + " phi=math.radians(lat)\n", + " sinphi=math.sin(phi)\n", + " con=eccent*sinphi\n", + " com=eccent/2\n", + " con=((1.0-con)/(1.0+con))**com\n", + " ts=math.tan((math.pi/2-phi)/2)/con\n", + " y=0-r_major*math.log(ts)\n", + " return y\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots()\n", + "\n", + "x = tuple(map(merc_x, lons))\n", + "y = tuple(map(merc_y, lats))\n", + "ax.plot(x, y, 'o')\n", + "ax.set_aspect(1.0)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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u/6iq/tiEYWliak0mPybCZBKhNmlvZipNweeBk3ESrWwXkS3GLXVpdEK8ILJQk4wSMpMuXhhJU6CqN6jqzHJGxqCVwqrxqaXfjWZ1imRBYLaRt/j7MF2rTEJg9VQAKrF4461xzalJ04osjbTrfmz0YEE/oqTH3lklsrQbZW2e6u8nCQEn5c2y81Qzhi1eLGwFIElvlossAUwLzJsbI8n7JEUmRZZEM4apUJmEwG6782qu+51zue3OqycJLQxpVwAyKTJbScKz1MqNEfeejQyZucjqpOe0OVzzobM5+x0nJXL9oNwYUek5Ldp8BdPezLopcR+Y9QyPvrjUyLWKp4yE+nXGDZU9p83hrvJs74tX15ztHYdauTFqDWb02jhaKnHDugeODcJs1LS53JPVgX+299LzTguV1yIq5SlyUQXmtzHMfIXyj9KkN7POk2WJ8mxvUMbGxnl618EJ+/0iMFVmiyJgr42jpfFI8xVMkVmRmRyKHTfJSt++IW78iw2ccfpcnt51cMJk3CCCxBFVeFE9ZNnGntXB8xWCQubY89OM9phkVmS20LdvqKa4qhHF28UNwZuPHmLz96I3+yzeeCs/vuyWWPf0kpfJLKNSeS6uwGxYuzN9CxLGho7yOIRNjleNsAKr1mZmogJgjci8Y8rS7ihvBmzwYGXssSQDZGVMfxyBJenNcpG5NKJRstw70HNaMglcXutss8qDlTGVpkCAO4CLgBHgOlV92rCtk2jkjHKY6CGierUJvQNjJW78iw111Uqr2WYbNS1T1T3lsfvAmTgi8qcpuBBY5L7WAnebNjQtlncH9/uVvUbYL3dC70CxjTNOn2vMRlMCSypkRm0nC0xTAFwMfFVVFdgsIieKyBxVNfdTTYHl3XNYv/ZS2guT+/28lL/kat6tVu9AXOIKzF88SHJUsKk0BZ2A96kNuNsmECVNgQ0zys9a2OXkKQvZ71eNcsv7uo2PGwmV9ZS/gsqfI13jNXs+4nqz0J7Mk6bgc0G7A7bppA2q63Ay/tDb2ztpv208tX+A0VIJIFSeslorlNTbO+C9T1xqVXCC9tfbzWQqTcEA4C1kdAHPx7bKEnb0D3HDugc4a2FXpDxlSZKkwMIQp6spisgqpikAHgJuFJH7cPKSvdqo8ljSHeU7+oeaXlwrO+awumsumwcOsm04+LPW481CicyTpuB3Pds+Ds5McuA/cJov9uHUPq+PZU0TEGZRr6j5/pMW2L2XXH6scnP1NzdUFFpcTKUpUFX9pKouVNVfUNXEcmFknSDBVCvEJx0eJyZhbmN1V+2mlagVAHtb8AzTyI7yuMLwi60R5a+JSZjH2TxQuWkl7kQTq0XWbB3lYUVTb/NElAJ+OQnz32x+PFKojOLN8kGLDaIR3T5xa4/bhodCiytOBcBqT5YGpjrKG92XmMZaUGG9mVUii5unzPSMctNfWJKCixoe41A8ZeTYKw5WicwmbFnGrxqNstFb4I8jtFxkCZLk+C5bfgRhQqb1Ikuzo9yWL9KPrXZVwnqR5bxNI8pflajWRnbFo18BYMaMGe8I2p+LrAa2eA1b7PCz4qRO/ul9HwVg0aJFi4OOaRqR2ZB6PSlsEViQN1s1ex7tbYXyv0FDvppHZEmS1pecZngMyxOHf8LoeKn8b+AYwVxklmK7uMpsf2mQax/7GgB79+4NXI2m5bqVwuYs8xM3KUsUlnfP4ayFXfzgSH/dw20q2VqPeCvVzre/5KS0OnLkyOuB58W+YwMxmRjPVo5NWikW+N3Sqqqd1Wmkgm/U8OuWJ0lvtuKMTtqLzrguFN47ozvSysFJUu8wqbxMZgEjXeO+cV21J63EJeqPxMQ4vKbyZI2eUW6CchmpPK6rPNZ+T3/6XszUQM+wY/xPxFkwtQenmvrbqrrJs/8E4F6g273mX6vqPxix0DJMhkx/Idw7rmtaykHG5EjisJ/kDuBhVV0KLAd2+/Z/EtilqsuBc4EvuvM0I9PoZQnrGcJSD3HmP5qi1o/E9POoKTIReSfwPuDvAVT1LVV9xXeYAjPcxCvTgZeAMZOGmu4or0dc9QrA5jawJH5wYTzZAuAw8A8isk1E1ouIvyP0LmAZzoTencCnVHXSk4ySpqDRNMqbRRFYUmJstMjDiKwInAHcraorgdeBP/YdcwGwHTgFWAHc5XrACajqOlXtVdXe2bNn12e5hVTKAFTGBg9WzYakfmhhRDYADKjqE+7/G3FE5+V64EF3/uU+4ACQSutpPR3lUR6y/8sqN6beeP4a1q+9dILQ6umDNCnMNAQG4fKTDQMH3WR44KSP2uU7rN/djoi8C1gCPGvQTuuplAGo0d6rkjdNS2AQvp3sJuCf3Rrjs8D1vjQFtwL/KCI7cYZ7fFZVX0zC4KSJ27cZlAHIlMDCNptUyqeWtMCWdRzi4v+9sfI9wlxEVbcDvb7N93j2Pw+cH8fALOP98v0ZgDaNm13IKwxeb1r+v5od9QosbNEkU91KjZpRHvfh7+gfYv33nkpEYGG8YtmbjpXGGS2V+MGR/orHNkpg0GTdSjaQZg3S602rDReqR2BxKla5yOqkHDIbIa4w99g0PsimveZDZD219kyFy7CYGO9fT3OGrcQR2LKOQ3U/z6YUmY1kcY0nU5Nz8nBZhbjNGf5rmLyeCTtqYXrmV+ZElqWh2DZ4rzTFVaalwmWc8kXsZLwVzmuE8OKMMkly3qqVnuxTS7/LHc/8srHr+R9g0iNoi6eMsOKkTlbNnscTh39ybDaPd78tNGJStJUiM0FZSKZqmmHKUmXxlKfut7cVGB0vce1jX5sktDTwCv/NKYmvr3aMphUZJPcrDcp77/VO5an7xbY2QFk1e17qIisLf0qhjbHx93BL38vsOXKgIfduapFVI2rILHuzoLz3O9v2Tzj27an7yuj4OE8c/olZ42PwawtmMaXQRkEKqEDPCYtykdmKN+89KGuWdbBzz0SRlafuVyqTNQK/F+97dS9j4yVUoKQl+l7d2zBbMikyU80YcbzZU2/sZXR8NbW81PaXBhsurmrFgz1HDnBL35foOWERfa/uNe7FlsyYT2dnZ0fQvkyKLE3q8VKmy4hRKzZ7jhxIJEQumTGfW3tu4tGOr09afhJykcVqzojipZJsIrAlJ1vPCYsovp2jbBItL7IksUUESXPNvA8BzhpbQftbqsW/EqbFYGLkQlb49nvuOvb+0KFDgWuc5iIzTKuICyYKDGBwcHA46DgjuTDcY84FbgfagRdV9ZciWx0B0x3lJrqa0hRYpaHpSQwm8IurFmHLZOVcGJe5M5Ym9LG4Ivwy8EFV7ReRn4tkhaWkIRq/WKqJJMychyjXq3X+oy8ujSwwCCEyTy6M68DJhQG85TvsKpzJvf3uMS9EtsQC0vJE1cRievJMNdHVulccgUE4T+bNhbEc2IqT68KbH3Qx0C4i3wdmAHeo6lf9FxKRtcBagO7u7lgGh6X8wGwee2bDep5hbKg301IYkZVzYdykqk+IyB04uTBu8R1zJs4s8qnAJhHZrKoTsiGr6jpgHUBvb29gdbde/A/NtkGONggrCiZSeYURWVAuDH/ClQGcwv7rwOsi8hhOHrPAlNthiDOmzOYv0GbbADqOX0bXtBUMjGxn+I3dRvPE1RSZqg6LyEERWaKqewjOhfFtnEw+RWAKsAr4G2NWhqBWuSYtb2ZaXH4xmLrmb3Z/gYK0U9JR2tuON3LdMkZyYajqbhF5GPghMA6sV9U+o5YGUBaPjV4iCZv8Yniw/9NGhNY1bQUFaadNCrRJ5e6huBjJheEe8wXgC4bsCk3YLzOMN7NRrF68YgCla9qKukTWqNSpLdl3abuYKjEwsp2SjgJKSccYGNke6fxG5+Mt01Iiy6q4ygy/sZsH+z8dqkyWlqCCaCmRNQPDb+yeJC6bBBVELrKMYbuggshFZjFZFFQQucgSJkq7VrOIyk/Liuyi6f72ZPMcP+VMOmfdBjJlUrtWswoqiKYRWSWP0QgxVWLqcecg0o64jZy/Ne9LqdmSJk0hso7jl3F5922ItKN6FYMvXsEbb21NxZZFXYEjkFuaphDZeTPnuB6jCChTjzsnlMiOn3ImU487h6NvboolylxQ4ci8yC6avoujb05F3ZZw1TGOvrmp5nlOeel+1/uN1vR+uaDik1mRectab7y1lcEXr4jkld4uL032frmgzCIVpsolTm9vr27ZsqXmcUFjykwU5suerK1tat3XynEQka2q6h9IkT1PVo/Acg+VDpkRWVRx5YKyB+tF1nvSh+nmW7zhnx/lIxeVvVgvsjWzr53U9pULKlukVvAXkcPAc5X2d3Z2dnR0dHSCk8jj0KFDz1eaBh+SWUAml0dMiCSex6mqOmlJ5tRE1mhEZEtQzadVaeTzyBOu5CROLrKcxGklka1L2wDLaNjzaJkyWU56tJIny0mJXGQ5iZMpkYnIn4nIoIhsd18XefZ9TkT2icgeEbnAs/1MEdnp7rtTRMTdfpyIfMPd/oSIzPOcc62I7HVf13q2z3eP3eueO6Uxn9wsIvJB9zntExF/8hzzqGpmXsCfAX8UsP10YAdwHDAf2A8U3H1PAucAAvwncKG7/feAe9z3VwLfcN+fhJPv4yRgpvt+prvvfuBK9/09wCfSfiYxnmHBfT4LcJLj7ABOT/KemfJkVbgYuE9V31TVA8A+4GwRmQO8U1U3qfOEvwr8huecf3LfbwTOc73cBcB/qepLqvoy8F/AB919H3CPxT23fK0scTawT1WfVSdr5n04zyIxsiiyG0XkhyLyFRGZ6W7rBA56jhlwt3W67/3bJ5yjqmPAq8DJVa51MvCKe6z/Wlmi0udLDOtEJiLfFZG+gNfFwN3AQmAFMAR8sXxawKW0yvY451S7VpZo+OewbhSGqoZKrygifwf8m/vvADDXs7sLeN7d3hWw3XvOgJu87wTgJXf7ub5zvo/TmXyiiBRdb+a9Vpao9KwSwzpPVg23jFXmEqCcaO8h4Eq3xjgfWAQ8qapDwBERWe2Wqa7ByQpZPqdcc7wMeNQttz0CnC8iM91wfD7wiLvve+6xuOeWr5UlngIWuTXlKTiVnocSvWPatZ2INaOvATtxMjo+BMzx7PtTnFrTHtwapLu9F0eM+4G7eLuX43hgA04l4Ulggeec33a37wOu92xf4B67zz33uLSfSczneBFOPt/9wJ8mfb+8WykncTIVLnOySS6ynMTJRZaTOLnIchInF1lO4uQiy0mcXGQ5ifP/zauNAradx70AAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots()\n", + "\n", + "x = tuple(map(merc_x, lons))\n", + "y = tuple(map(merc_y, lats))\n", + "\n", + "ax.tricontourf(x, y, tuple(datapoints.values()))\n", + "ax.plot(x, y, 'wo', ms=3)\n", + "ax.set_aspect(1.0)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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"execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from apd.aggregation.analysis import interactable_plot_multiple_charts, configs\n", + "from apd.aggregation.utils import jupyter_page_file, profile_with_yappi, yappi_package_matches\n", + "import yappi\n", + "\n", + "with profile_with_yappi():\n", + " plot = interactable_plot_multiple_charts()\n", + " plot()\n", + "\n", + "with jupyter_page_file() as output:\n", + " yappi.get_func_stats(filter_callback=lambda stat:\n", + " yappi_package_matches(stat, [\"apd.aggregation\"])\n", + " ).print_all(output)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r\n", + "Clock type: WALL\r\n", + "Ordered by: totaltime, desc\r\n", + "\r\n", + "name ncall tsub ttot tavg \r\n", + "..futures\\thread.py:52 _WorkItem.run 7 0.000092 3.204091 0.457727\r\n", + "..tion\\analysis.py:373 run_in_thread 1 0.000053 1.844382 1.844382\r\n", + "..38\\Lib\\threading.py:859 Thread.run 2/1 0.000025 1.839877 0.919939\r\n", + "..rrent\\futures\\thread.py:66 _worker 2/1 0.000022 1.839852 0.919926\r\n", + "..lectorEventLoop.run_until_complete 1 0.000027 1.839745 1.839745\r\n", + "..ndowsSelectorEventLoop.run_forever 1 0.000088 1.839611 1.839611\r\n", + "..WindowsSelectorEventLoop._run_once 17 0.000408 1.839497 0.108206\r\n", + "..alysis.py:327 plot_multiple_charts 1 1.643447 1.839309 1.839309\r\n", + "..py:635 ThreadPoolExecutor.__exit__ 1 0.000005 1.837286 1.837286\r\n", + "..py:230 ThreadPoolExecutor.shutdown 1 0.000009 1.837281 1.837281\r\n", + "..8\\Lib\\threading.py:979 Thread.join 1 0.000006 1.837272 1.837272\r\n", + "..:1017 Thread._wait_for_tstate_lock 1 0.000011 1.837263 1.837263\r\n", + "..gation\\analysis.py:291 plot_sensor 1 0.000000 1.407986 1.407986\r\n", + "..query.py:88 get_data_by_deployment 1 1.397146 1.397188 1.397188\r\n", + "..d\\aggregation\\query.py:41 get_data 1 1.283592 1.375576 1.375576\r\n", + "..lchemy\\orm\\query.py:3232 Query.all 6 0.004333 1.362649 0.227108\r\n", + "..ctors.py:319 SelectSelector.select 17 0.000177 1.362418 0.080142\r\n", + "..tors.py:313 SelectSelector._select 17 0.000069 1.362216 0.080130\r\n", + "..y\\orm\\query.py:3400 Query.__iter__ 6 0.000054 0.958316 0.159719\r\n", + "..:3425 Query._execute_and_instances 6 0.000073 0.956631 0.159439\r\n", + "..ine\\base.py:916 Connection.execute 9 0.000062 0.740051 0.082228\r\n", + "..:589 PGDialect_psycopg2.do_execute 11 0.000042 0.738361 0.067124\r\n", + "..y:1159 Connection._execute_context 9 0.000307 0.734166 0.081574\r\n", + "..:291 Select._execute_on_connection 6 0.000047 0.731436 0.121906\r\n", + ".. 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8cFO2tZ133jn33Xdfq+2GDh1aUZ41a1bmzZvXVJ40aVK+9rWv5dhjj01d3ZZJBcqyzMMPP5y777473/rWt2pwJButW7cuZ555ZkWg3U477ZSZM2fm5JNPrpjLypUr85WvfCVf/vKXs2zZshaX8W3LvHnzctppp1UE2h177LG59NJL8853vnOL/X/729/m05/+dH71q18lSV544YWcfvrpmT17durrfafTEsF2/ds/JdkzyXGbygOTXJfki0VRPJJkRZLdkuyfZPNFrdcm+duyLF/qwbkCANCH1LeV2S5tBdu5BQEAAAAAgM56/vnnK8rjx4/P6NGja9L3Xnvt1eJ4PR1sN2bMmIwZMyZJMmDAn79XGDBgQCZNmtThfu64446Ktvfcc08mT57c6v5FUWT69OmZPn16vvjFL6axsbH6ybfguuuuy6OPPtpU3mmnnXLfffdlt91222LfoUOH5uKLL85ee+2V97///Vm6dGmHx2lsbMypp55akZ3vkksuycUXX9xqm/322y+/+MUvcuqppzZlNbzvvvty00035YMf/GCHx96WtPwNGf1CWZYbkrw/yc3NHhqX5Ngk70vyjlQG2v0pyXvLsvxVj0wSAIA+qShavpXYULazjGz8FRQAAAAAAHTWq6++WlEeOXJkzfoePHhwGhoa2hyvL3n22Web/v32t7+9zUC75urr62uyfG5jY2Ouu+66irpvfvObLQbabe7kk0/OOeecU9VYt912W5544omm8vvf//42A+3eNGDAgHz729/OuHHjmuquvfbaqsbelgi26+fKslxZluVp2RhY90Abu76a5F+T7FWW5U96ZHIAAPRZrQXNNabtZWRbW34WAAAAAABoX/PgtxEjRtS0/+b9bb70aV/2pz/9qVfG/e///u8888wzTeUZM2bkPe95T4fafulLX6oq4O+rX/1q07+LoshVV13V4bZDhw7Nxz7256WoH3/88Yp582eC7bYRZVneUpblgdm4bOwpST6V5KIkZyf5yyQ7lWV5TlmWi3txmgAA9BF1rWS2a2wvs51lZAEAAAAAYKtVFEX7O/URe+yxR9O/Fy5c2CvZ2u67776K8umnn97htmPHjs3RRx/doX1XrVqVBx74cw6uGTNmZNddd+3wWElyxBFHVJR/9SuLYrbEN13bmLIsn07ydG/PAwCAvq21DHXtZbarl9kOAAAAAAA6bdSoURXl1157rab9L1u2rM3x+pIzzjgjt912W1P5M5/5TG6//facffbZOf7447PTTjt1+xzmzJlTUT7ggAOqan/AAQfkP//zP9vd74EHHsi6deuayrvttlvVmekaGxsryn/4wx+qar+tEGwHAABUra61ZWTLxjSmjcx2bkEAAAAAAGpuxIAxuXy3b/T2NPq9EQPG9PYUtgh+W7p0ac36Xr16dVavXl1RN3r06Jr139NOOumknHTSSRUBd7/+9a/z61//OkkyefLkHHTQQXn3u9+dQw45JFOnTq35HBYtWlRR3n333atqP2XKlA7tt3Dhwory97///Xz/+9+vaqzmmi9ZzEa+6QIAAKrW+jKyMtsBAAAAAPS0+qI+oweO7+1p0AN23nnnivLLL7+cJUuW1CQobu7cue2O15cURZGbb745F198cf75n/95i0DCBQsWZMGCBfm///f/JtkYfPeBD3wg5557bs0y+jUPhhw+fHhV7XfYYYcO7bdkyZKq+u2IFStW1LzP/qDlb8gAAADa0Fpmuw1pzIayjcx2hb/3AQAAAACAzjrooIO2qGu+VGlnNe9nu+22y7777luTvnvLgAED8uUvfznPPPNMrr322hxyyCFpaGhocd8FCxbkkksuyW677Zabb765h2faNWvXrq15n2VZ1rzP/kCwHQAAULXWMtuVZWObme3q3IIAAAAAAECnTZw4MRMnTqyo++lPf1qTvu+5556K8gEHHJBBgwbVpO83bdjQ+ncI3Wn8+PE5//zz89///d957bXXcv/99+faa6/Ne9/73gwdOrRi39deey2nn356br/99i6PO3LkyIry8uXLq2r/2muvdWi/MWMqlzi+8sorU5Zll7Ybb7yxqrluK3zTBQAAVK21oLkNm/5rSX0GpCiK7pwWAAAAAAD0e8cee2xF+Tvf+U7WrVvXpT4XL16cO++8s81x3jRgQOUqNuvXt77iTXPNl1XtDQ0NDTnwwANz/vnn5/bbb8+SJUvy/e9/P1OmTGnapyzLfOpTn0pjY2OXxho/vnJ55/nz51fV/qmnnurUOB1tR/UE2wEAAFWrL1peRrax3NDqMrKttQEAAAAAADru05/+dMUfty9evDizZs3qUp8zZ86sCNjbfvvt85GPfKTFfYcPH15RXrZsWYfHmTt3blXz6ok/4h80aFBOPfXUPPjgg9l5552b6hcuXJiHH364S31Pnz69ovzAAw9U1f7BBx/s0H4HHnhgxbm65557LAPbTQTbAQAAVWsts11jWl9GVrAdAAAAAAB03bRp03LcccdV1H32s5/NokWLOtXfvHnzcs0111TUnX322Rk1alSL+48bN26L9h31ox/9qKq5NTQ0NP17zZo1VbWt1ogRI3LSSSdV1D399NNd6vPggw+uKH/ve9/rcNvFixd3eIngsWPHZr/99msqv/DCC/nxj3/c4bHoOMF2AABA1eo6ldluQIv1AAAAAABAdb7yla9kyJAhTeVly5blpJNOysqVK6vqZ/HixTnllFOydu3aprqddtopX/rSl1pts//++1eU77rrrg6N9V//9V956KGHqprfiBEjmv79yiuvdHm53PY0XyJ382C/zjj00EMzadKkpvKcOXNy9913d6jtZZddVtXxfvKTn6woX3DBBVW/HmifYDsAAKBqrQbbpTEb0kpmu8hsBwAAAAAAtbDHHnvkuuuuq6i7//77c9xxx+X555/vUB/z58/PkUcemSeffLKprq6uLt/5zncyduzYVtsdeOCBFYF+P/zhDzNnzpx2x/rQhz7UoXltburUqU3/Xr9+fX75y192qN3rr7+e6667LitWrOjwWCtXrsxtt93W6vidUVdXt0UQ3Mc+9rF2M+bddttt+frXv17VWB/84Aezxx57NJWffPLJ/O3f/m2WLl1aVT+LFy/e4jzwZ4LtAACAqrW2jGySrGtc22K9zHYAAAAAAFA7H/7wh/OJT3yiou6+++7LtGnTctVVV2XhwoUttluwYEG+8IUvZO+9987jjz9e8djVV1+dI488ss1xhw0bllNPPbWpvGHDhvz1X/91i0uerl27NjfccEPe9a53ZdGiRRk5cmRHDy9JcsQRR1SUzz777Hz961/Pww8/nD/+8Y955plnmrZXXnmlYtxPfepTmTBhQj784Q/nrrvuajPw7qGHHsqRRx6ZZ599tqnuXe96V6ZMmVLVfFvyqU99Km9/+9ubyi+++GLe/e5355ZbbkljY2PFvqtWrcpll12W0047LY2NjVWdr/r6+txyyy0ZPnx4U93Pfvaz7LPPPvnXf/3XNo//1Vdfzc0335zTTz89u+yyS7761a9WcYTbFt92AQAAVWsts12SrC9bTmle30YbAAAAAACgetdff31GjhyZL3/5yynLMkmyYsWKXHTRRfnc5z6XadOmZZdddsnIkSOzZMmSPPvss/n973+/RT8DBw7MzJkz8/GPf7xD415++eX54Q9/mGXLliVJ/vSnP+WYY47J5MmTs88++6ShoSGLFi3Kgw8+mFWrViVJdtxxx1x99dVVZbh73/vel89//vNN2fpefPHFLQIM3/ShD30oN954Y0Xd8uXLM2vWrMyaNStFUWTy5MnZbbfdMmLEiAwYMCBLlizJE088sUU2wCFDhuSb3/xmh+fZloEDB+amm27KYYcdliVLliRJXnrppbzvfe/L+PHj8453vCM77LBDFi1alN/85jd54403kiQ77LBDrr766nz0ox/t8Fh77rlnbr311pxyyil57bXXkiTPP/98zjnnnJx77rnZe++9M3HixAwfPjyvv/56li1blqeeeqrD2RARbAcAAHRCW5nt1pYtZ7ark9kOAAAAAABq7vLLL89hhx2Wc845J/Pnz2+qL8syc+fOzdy5c9tsv//+++cb3/hGpk+f3uExd95559x666058cQTKzKmLViwIAsWLNhi/1133TX/+Z//mUWLFnV4jCTZbrvt8sMf/jAnnnhiXnjhharaNleWZebPn19xjlqy884757bbbsvee+/dpfE2t+eee+ZnP/tZjj/++Lz00ktN9YsWLcqPfvSjLfYfMWJE7rzzzmzYsKHqsY466qjMmTMnp59+esXyvhs2bMijjz6aRx99tN0+qs1AuC2xjCwAAFC1+qL1W4n1rQTb1UdmOwAAAAAA6A5HHXVU5s2bl5tuuilHHnlkBgxo+w/gGxoacsIJJ+SOO+7InDlzqgq0e9Nf/uVf5qGHHsp73/veFEXR4j5jx47NZz7zmTz66KOZOnVq1WMkyfTp0zNv3rz827/9W0488cRMnjw5w4cPT31969877LDDDrn33ntz4YUX5h3veEe75yNJ/uIv/iJXXnllnnrqqbzzne/s1Fzbsu++++bJJ5/Mueeem2HDhrW4z9ChQ3PWWWflscceyyGHHNLpsSZPnpyHHnood911V4466qg0NDS022bq1Kk599xz86tf/Sq33XZbp8fu74o3U0jC1qYoij2TPPFm+Yknnsiee+7ZizMCAOBNz69+Olc++w8tPnbMqFPyX6/eskX9Lg275aJJ/9zdUwMAAAAA6HPWr1+/Rbat3XffvUMBQtCSVatW5eGHH86CBQuyePHirF27Ng0NDRk/fnymTJmS/fffv0MBWB31yiuv5N57783zzz+f119/PePHj8+uu+6aQw45ZKt4Hb/xxhuZO3du/vCHP+Tll1/OqlWrUhRFhg8fnokTJ2afffbJW9/61h6bz5o1azJ79uw8/fTTWbp0acaOHZsJEybkkEMOyfbbb1/z8VavXp0HH3wwzz7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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import yappi\n", + "\n", + "from apd.aggregation.analysis import interactable_plot_multiple_charts, Config\n", + "from apd.aggregation.analysis import clean_temperature_fluctuations, get_one_sensor_by_deployment\n", + "from apd.aggregation.utils import profile_with_yappi\n", + "\n", + "yappi.set_clock_type(\"wall\")\n", + "\n", + "filter_in_db = Config(\n", + " clean=clean_temperature_fluctuations,\n", + " title=\"Ambient temperature\",\n", + " ylabel=\"Degrees C\",\n", + " get_data=get_one_sensor_by_deployment(\"Temperature\"),\n", + ")\n", + "\n", + "with profile_with_yappi():\n", + " plot = interactable_plot_multiple_charts(configs=[filter_in_db])\n", + " plot()\n", + "\n", + "yappi.get_func_stats().print_all()" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r\n", + "Clock type: CPU\r\n", + "Ordered by: totaltime, desc\r\n", + "\r\n", + "name ncall tsub ttot tavg \r\n", + "..futures\\thread.py:52 _WorkItem.run 7 0.000000 9.515625 1.359375\r\n", + "..lchemy\\orm\\query.py:3232 Query.all 6 0.187500 6.390625 1.065104\r\n", + "..lchemy\\orm\\loading.py:35 instances 67353 0.046875 6.140625 0.000091\r\n", + "..esult.py:1257 ResultProxy.fetchall 6 0.000000 5.109375 0.851562\r\n", + "..py:1217 ResultProxy._fetchall_impl 6 0.000000 5.078125 0.846354\r\n", + "..38\\Lib\\threading.py:859 Thread.run 2/1 0.000000 3.125000 1.562500\r\n", + "..rrent\\futures\\thread.py:66 _worker 2/1 0.000000 3.125000 1.562500\r\n", + "..b\\asyncio\\events.py:79 Handle._run 29 0.000000 3.125000 0.107759\r\n", + "..WindowsSelectorEventLoop._run_once 17 0.000000 3.125000 0.183824\r\n", + "..lectorEventLoop.run_until_complete 1 0.000000 3.125000 3.125000\r\n", + "..ndowsSelectorEventLoop.run_forever 1 0.000000 3.125000 3.125000\r\n", + "..gation\\analysis.py:291 plot_sensor 1 0.031250 3.046875 3.046875\r\n", + "..egation\\analysis.py:313 4 0.015625 2.703125 0.675781\r\n", + "..and_clean_temperature_fluctuations 4 0.046875 2.687500 0.671875\r\n", + "..184 clean_temperature_fluctuations 4 0.062500 2.640625 0.660156\r\n", + "..sycopg2\\_json.py:164 typecast_json 67339 0.312500 2.562500 0.000038\r\n", + "..-input-1-879b7ebf06b8>:7 4 0.234375 2.515625 0.628906\r\n", + "..gregation\\query.py:116 subiterator 4 0.390625 2.281250 0.570312\r\n", + "..n38\\Lib\\json\\__init__.py:299 loads 67339 0.515625 2.250000 0.000033\r\n", + "..es\\psycopg2\\extras.py:686 67347 0.156250 1.796875 0.000027\r\n", + "..on38\\Lib\\uuid.py:130 UUID.__init__ 67347 1.000000 1.640625 0.000024\r\n", + "..\\decoder.py:332 JSONDecoder.decode 67339 0.718750 1.546875 0.000023\r\n", + "..d\\aggregation\\query.py:41 get_data 1 0.312500 1.531250 1.531250\r\n", + "..ion\\database.py:77 from_sql_result 67339 0.468750 1.218750 0.000018\r\n", + "..chemy\\orm\\loading.py:83 6 0.328125 0.984375 0.164062\r\n", + ".._collections.py:121 result._asdict 67343 0.375000 0.515625 0.000008\r\n", + "..thon38\\Lib\\uuid.py:231 UUID.__eq__ 67351 0.281250 0.359375 0.000005\r\n", + "..chemy\\orm\\loading.py:84 67347 0.343750 0.343750 0.000005\r\n", + "..y\\util\\_collections.py:112 __new__ 67347 0.234375 0.312500 0.000005\r\n", + "..\\matplotlib\\__init__.py:1535 inner 4 0.000000 0.296875 0.074219\r\n", + "..axes.py:1651 AxesSubplot.plot_date 4 0.000000 0.296875 0.074219\r\n", + "..xes\\_axes.py:1412 AxesSubplot.plot 4 0.000000 0.296875 0.074219\r\n", + "..gregation\\analysis.py:63 draw_date 4 0.000000 0.296875 0.074219\r\n", + ".._base.py:1842 AxesSubplot.add_line 4 0.000000 0.281250 0.070312\r\n", + "..oder.py:343 JSONDecoder.raw_decode 67339 0.265625 0.265625 0.000004\r\n", + "..s\\matplotlib\\dates.py:1911 convert 6 0.000000 0.265625 0.044271\r\n", + "..on_base.py:2063 vectorize.__call__ 4 0.000000 0.265625 0.066406\r\n", + "..b\\axis.py:1562 XAxis.convert_units 12 0.000000 0.265625 0.022135\r\n", + "...py:168 AxesSubplot.convert_xunits 48 0.000000 0.265625 0.005534\r\n", + "..lotlib\\lines.py:664 Line2D.recache 10 0.000000 0.265625 0.026562\r\n", + "..s\\matplotlib\\dates.py:401 date2num 6 0.000000 0.265625 0.044271\r\n", + "..tlib\\lines.py:1018 Line2D.get_path 10 0.000000 0.265625 0.026562\r\n", + "..68 AxesSubplot._update_line_limits 4 0.000000 0.265625 0.066406\r\n", + "...py:2154 vectorize._vectorize_call 4 0.046875 0.265625 0.066406\r\n", + ":1 DataPoint.__init__ 67339 0.234375 0.234375 0.000003\r\n", + "..tplotlib\\dates.py:210 _to_ordinalf 8467 0.140625 0.218750 0.000026\r\n", + "..alysis.py:327 plot_multiple_charts 1 0.000000 0.078125 0.078125\r\n", + "..ine\\base.py:916 Connection.execute 9 0.000000 0.062500 0.006944\r\n", + "..:291 Select._execute_on_connection 6 0.000000 0.062500 0.010417\r\n", + "..y\\orm\\query.py:3400 Query.__iter__ 6 0.000000 0.062500 0.010417\r\n", + ".. 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import yappi\n", + "\n", + "from apd.aggregation.analysis import interactable_plot_multiple_charts, Config, clean_temperature_fluctuations, get_data_by_deployment\n", + "from apd.aggregation.utils import profile_with_yappi\n", + "\n", + "async def filter_and_clean_temperature_fluctuations(datapoints):\n", + " filtered = (item async for item in datapoints if item.sensor_name==\"Temperature\")\n", + " cleaned = clean_temperature_fluctuations(filtered)\n", + " async for item in cleaned:\n", + " yield item\n", + "\n", + "filter_in_python = Config(\n", + " clean=filter_and_clean_temperature_fluctuations,\n", + " title=\"Ambient temperature\",\n", + " ylabel=\"Degrees C\",\n", + " get_data=get_data_by_deployment,\n", + ")\n", + "\n", + "with profile_with_yappi():\n", + " plot = interactable_plot_multiple_charts(configs=[filter_in_python])\n", + " plot()\n", + "\n", + "yappi.get_func_stats().print_all()" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from apd.aggregation.utils import yappi_package_matches\n", + "import yappi\n", + "\n", + "yappi.get_func_stats().save(\"callgrind.filter_in_python\", \"callgrind\") " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "apd.aggregation", + "language": "python", + "name": "apd.aggregation" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Ch10/apd.aggregation-chapter10/pyproject.toml b/Ch10/apd.aggregation-chapter10/pyproject.toml new file mode 100644 index 0000000..4d3bb67 --- /dev/null +++ b/Ch10/apd.aggregation-chapter10/pyproject.toml @@ -0,0 +1,5 @@ +[build-system] +requires = [ + "setuptools", +] +build-backend = "setuptools.build_meta" diff --git a/Ch10/apd.aggregation-chapter10/pytest.ini b/Ch10/apd.aggregation-chapter10/pytest.ini new file mode 100644 index 0000000..8c26fbc --- /dev/null +++ b/Ch10/apd.aggregation-chapter10/pytest.ini @@ -0,0 +1,4 @@ +[pytest] +markers = + functional: these tests are significantly slower as they start a HTTP server + performance: very slow tests that provide performance guarantees \ No newline at end of file diff --git a/Ch10/apd.aggregation-chapter10/setup.cfg b/Ch10/apd.aggregation-chapter10/setup.cfg new file mode 100644 index 0000000..3ee0436 --- /dev/null +++ b/Ch10/apd.aggregation-chapter10/setup.cfg @@ -0,0 +1,83 @@ +[mypy] +namespace_packages = True +mypy_path = src +plugins = sqlmypy + +[mypy-alembic] +ignore_missing_imports = True + +[mypy-alembic.config] +ignore_missing_imports = True + +[mypy-alembic.script] +ignore_missing_imports = True + +[mypy-alembic.runtime.environment] +ignore_missing_imports = True + +[mypy-matplotlib] +ignore_missing_imports = True + +[mypy-matplotlib.axes._base] +ignore_missing_imports = True + +[mypy-matplotlib.figure] +ignore_missing_imports = True + +[mypy-matplotlib.pyplot] +ignore_missing_imports = True + +[mypy-IPython.core] +ignore_missing_imports = True + +[mypy-yappi] +ignore_missing_imports = True + +[mypy-pint] +ignore_missing_imports = True + +[mypy-pytest] +ignore_missing_imports = True + +[flake8] +max-line-length = 120 +ignore = E501 + +[metadata] +name = apd.aggregation +version = attr: apd.aggregation.VERSION +description = A programme that queries apd.sensor endpoints and aggregates their results. +long_description = file: README.md, CHANGES.md, LICENCE +long_description_content_type = text/markdown +keywords = +license = BSD +classifiers = + Programming Language :: Python :: 3 + Programming Language :: Python :: 3.7 + +[options] +zip_safe = False +include_package_data = True +package_dir = + =src +packages = find_namespace: +install_requires = + sqlalchemy + aiohttp + pint + psycopg2 + alembic + click + + +[options.extras_require] +jupyter = ipywidgets +yappi = yappi + +[options.packages.find] +where = src + +[options.entry_points] +console_scripts = + collect_sensor_data = apd.aggregation.cli:collect_sensor_data + sensor_deployments = apd.aggregation.cli:deployments diff --git a/Ch10/apd.aggregation-chapter10/setup.py b/Ch10/apd.aggregation-chapter10/setup.py new file mode 100644 index 0000000..6068493 --- /dev/null +++ b/Ch10/apd.aggregation-chapter10/setup.py @@ -0,0 +1,3 @@ +from setuptools import setup + +setup() diff --git a/Ch10/apd.aggregation-chapter10/src/apd/aggregation/__init__.py b/Ch10/apd.aggregation-chapter10/src/apd/aggregation/__init__.py new file mode 100644 index 0000000..3277f64 --- /dev/null +++ b/Ch10/apd.aggregation-chapter10/src/apd/aggregation/__init__.py @@ -0,0 +1 @@ +VERSION = "1.0.0" diff --git a/Ch10/apd.aggregation-chapter10/src/apd/aggregation/alembic/README b/Ch10/apd.aggregation-chapter10/src/apd/aggregation/alembic/README new file mode 100644 index 0000000..97a87d5 --- /dev/null +++ b/Ch10/apd.aggregation-chapter10/src/apd/aggregation/alembic/README @@ -0,0 +1,12 @@ +Generic single-database configuration. + + +# Database setup + +To generate the required database tables you must create an alembic.ini file, as follows: + + [alembic] + script_location = apd.aggregation:alembic + sqlalchemy.url = postgresql+psycopg2://apd@localhost/apd + +and run `alembic upgrade head`. This should also be done after every upgrade of the software. \ No newline at end of file diff --git a/Ch10/apd.aggregation-chapter10/src/apd/aggregation/alembic/env.py b/Ch10/apd.aggregation-chapter10/src/apd/aggregation/alembic/env.py new file mode 100644 index 0000000..4c8a2c2 --- /dev/null +++ b/Ch10/apd.aggregation-chapter10/src/apd/aggregation/alembic/env.py @@ -0,0 +1,80 @@ +from logging.config import fileConfig + +from sqlalchemy import engine_from_config +from sqlalchemy import pool +from alembic import context + +from apd.aggregation.database import metadata + + +# this is the Alembic Config object, which provides +# access to the values within the .ini file in use. +config = context.config + +# Interpret the config file for Python logging. +# This line sets up loggers basically. +if config.config_file_name: + fileConfig(config.config_file_name) + +target_metadata = metadata + + +def include_object(object, name, type_, reflected, compare_to): + if object.info.get("is_view", False): + return False + return True + + +def run_migrations_offline(): + """Run migrations in 'offline' mode. + + This configures the context with just a URL + and not an Engine, though an Engine is acceptable + here as well. By skipping the Engine creation + we don't even need a DBAPI to be available. + + Calls to context.execute() here emit the given string to the + script output. + + """ + url = config.get_main_option("sqlalchemy.url") + context.configure( + url=url, + include_object=include_object, + target_metadata=target_metadata, + literal_binds=True, + dialect_opts={"paramstyle": "named"}, + ) + + with context.begin_transaction(): + context.run_migrations() + + +def run_migrations_online(): + """Run migrations in 'online' mode. + + In this scenario we need to create an Engine + and associate a connection with the context. + + """ + connectable = engine_from_config( + config.get_section(config.config_ini_section), + prefix="sqlalchemy.", + poolclass=pool.NullPool, + ) + + with connectable.connect() as connection: + context.configure( + connection=connection, + target_metadata=target_metadata, + include_object=include_object, + ) + + with context.begin_transaction(): + context.run_migrations() + + +if context.is_offline_mode(): + run_migrations_offline() +else: + run_migrations_online() diff --git a/Ch10/apd.aggregation-chapter10/src/apd/aggregation/alembic/script.py.mako b/Ch10/apd.aggregation-chapter10/src/apd/aggregation/alembic/script.py.mako new file mode 100644 index 0000000..2c01563 --- /dev/null +++ b/Ch10/apd.aggregation-chapter10/src/apd/aggregation/alembic/script.py.mako @@ -0,0 +1,24 @@ +"""${message} + +Revision ID: ${up_revision} +Revises: ${down_revision | comma,n} +Create Date: ${create_date} + +""" +from alembic import op +import sqlalchemy as sa +${imports if imports else ""} + +# revision identifiers, used by Alembic. +revision = ${repr(up_revision)} +down_revision = ${repr(down_revision)} +branch_labels = ${repr(branch_labels)} +depends_on = ${repr(depends_on)} + + +def upgrade(): + ${upgrades if upgrades else "pass"} + + +def downgrade(): + ${downgrades if downgrades else "pass"} diff --git a/Ch10/apd.aggregation-chapter10/src/apd/aggregation/alembic/versions/4b2df8a6e1ce_add_indexes_to_datapoints.py b/Ch10/apd.aggregation-chapter10/src/apd/aggregation/alembic/versions/4b2df8a6e1ce_add_indexes_to_datapoints.py new file mode 100644 index 0000000..ef671bd --- /dev/null +++ b/Ch10/apd.aggregation-chapter10/src/apd/aggregation/alembic/versions/4b2df8a6e1ce_add_indexes_to_datapoints.py @@ -0,0 +1,32 @@ +"""Add indexes to datapoints + +Revision ID: 4b2df8a6e1ce +Revises: 6d2eacd5da3f +Create Date: 2019-12-02 16:07:41.123116 + +""" +from alembic import op + + +# revision identifiers, used by Alembic. +revision = "4b2df8a6e1ce" +down_revision = "6d2eacd5da3f" +branch_labels = None +depends_on = None + + +def upgrade(): + op.create_index( + op.f("ix_datapoints_collected_at"), + "datapoints", + ["collected_at"], + unique=False, + ) + op.create_index( + op.f("ix_datapoints_sensor_name"), "datapoints", ["sensor_name"], unique=False, + ) + + +def downgrade(): + op.drop_index(op.f("ix_datapoints_sensor_name"), table_name="datapoints") + op.drop_index(op.f("ix_datapoints_collected_at"), table_name="datapoints") diff --git a/Ch10/apd.aggregation-chapter10/src/apd/aggregation/alembic/versions/6962f8455a6d_add_daily_summary_view.py b/Ch10/apd.aggregation-chapter10/src/apd/aggregation/alembic/versions/6962f8455a6d_add_daily_summary_view.py new file mode 100644 index 0000000..eb02455 --- /dev/null +++ b/Ch10/apd.aggregation-chapter10/src/apd/aggregation/alembic/versions/6962f8455a6d_add_daily_summary_view.py @@ -0,0 +1,38 @@ +"""Add daily summary view + +Revision ID: 6962f8455a6d +Revises: 4b2df8a6e1ce +Create Date: 2019-12-03 11:50:24.403402 + +""" +from alembic import op + + +# revision identifiers, used by Alembic. +revision = "6962f8455a6d" +down_revision = "4b2df8a6e1ce" +branch_labels = None +depends_on = None + + +def upgrade(): + create_view = """ + CREATE VIEW daily_summary AS + SELECT + datapoints.sensor_name AS sensor_name, + datapoints.data AS data, + count(datapoints.id) AS count + FROM datapoints + WHERE + datapoints.collected_at >= CAST(CURRENT_DATE AS DATE) + AND + datapoints.collected_at < CAST(CURRENT_DATE AS DATE) + 1 + GROUP BY + datapoints.sensor_name, + datapoints.data; + """ + op.execute(create_view) + + +def downgrade(): + op.execute("""DROP VIEW daily_summary""") diff --git a/Ch10/apd.aggregation-chapter10/src/apd/aggregation/alembic/versions/6d2eacd5da3f_create_sensor_values_table.py b/Ch10/apd.aggregation-chapter10/src/apd/aggregation/alembic/versions/6d2eacd5da3f_create_sensor_values_table.py new file mode 100644 index 0000000..b4a3545 --- /dev/null +++ b/Ch10/apd.aggregation-chapter10/src/apd/aggregation/alembic/versions/6d2eacd5da3f_create_sensor_values_table.py @@ -0,0 +1,31 @@ +"""Create datapoints table + +Revision ID: 6d2eacd5da3f +Revises: N/A +Create Date: 2019-09-29 13:43:21.242706 + +""" +from alembic import op +import sqlalchemy as sa +from sqlalchemy.dialects import postgresql + +# revision identifiers, used by Alembic. +revision = "6d2eacd5da3f" +down_revision = None +branch_labels = None +depends_on = None + + +def upgrade(): + op.create_table( + "datapoints", + sa.Column("id", sa.Integer(), nullable=False), + sa.Column("sensor_name", sa.String(), nullable=True), + sa.Column("collected_at", postgresql.TIMESTAMP(), nullable=True), + sa.Column("data", postgresql.JSONB(astext_type=sa.Text()), nullable=True), + sa.PrimaryKeyConstraint("id"), + ) + + +def downgrade(): + op.drop_table("datapoints") diff --git a/Ch10/apd.aggregation-chapter10/src/apd/aggregation/alembic/versions/d8cdc709086b_add_deployment_table.py b/Ch10/apd.aggregation-chapter10/src/apd/aggregation/alembic/versions/d8cdc709086b_add_deployment_table.py new file mode 100644 index 0000000..204e096 --- /dev/null +++ b/Ch10/apd.aggregation-chapter10/src/apd/aggregation/alembic/versions/d8cdc709086b_add_deployment_table.py @@ -0,0 +1,32 @@ +"""Add deployment table + +Revision ID: d8cdc709086b +Revises: d8d4cf6a178f +Create Date: 2019-12-17 16:28:58.585616 + +""" +from alembic import op +import sqlalchemy as sa +from sqlalchemy.dialects import postgresql + +# revision identifiers, used by Alembic. +revision = "d8cdc709086b" +down_revision = "d8d4cf6a178f" +branch_labels = None +depends_on = None + + +def upgrade(): + op.create_table( + "deployments", + sa.Column("id", postgresql.UUID(as_uuid=True), nullable=False), + sa.Column("uri", sa.String(), nullable=True), + sa.Column("name", sa.String(), nullable=True), + sa.Column("colour", sa.String(), nullable=True), + sa.Column("api_key", sa.String(), nullable=True), + sa.PrimaryKeyConstraint("id"), + ) + + +def downgrade(): + op.drop_table("deployments") diff --git a/Ch10/apd.aggregation-chapter10/src/apd/aggregation/alembic/versions/d8d4cf6a178f_add_deployment_id_to_datapoint.py b/Ch10/apd.aggregation-chapter10/src/apd/aggregation/alembic/versions/d8d4cf6a178f_add_deployment_id_to_datapoint.py new file mode 100644 index 0000000..88d5219 --- /dev/null +++ b/Ch10/apd.aggregation-chapter10/src/apd/aggregation/alembic/versions/d8d4cf6a178f_add_deployment_id_to_datapoint.py @@ -0,0 +1,34 @@ +"""Add deployment id to DataPoint + +Revision ID: d8d4cf6a178f +Revises: 6962f8455a6d +Create Date: 2019-12-03 20:58:11.285509 + +""" +from alembic import op +import sqlalchemy as sa +from sqlalchemy.dialects import postgresql + +# revision identifiers, used by Alembic. +revision = "d8d4cf6a178f" +down_revision = "6962f8455a6d" +branch_labels = None +depends_on = None + + +def upgrade(): + op.add_column( + "datapoints", + sa.Column("deployment_id", postgresql.UUID(as_uuid=True), nullable=True), + ) + op.create_index( + op.f("ix_datapoints_deployment_id"), + "datapoints", + ["deployment_id"], + unique=False, + ) + + +def downgrade(): + op.drop_index(op.f("ix_datapoints_deployment_id"), table_name="datapoints") + op.drop_column("datapoints", "deployment_id") diff --git a/Ch10/apd.aggregation-chapter10/src/apd/aggregation/analysis.py b/Ch10/apd.aggregation-chapter10/src/apd/aggregation/analysis.py new file mode 100644 index 0000000..dfa852b --- /dev/null +++ b/Ch10/apd.aggregation-chapter10/src/apd/aggregation/analysis.py @@ -0,0 +1,391 @@ +from __future__ import annotations + +import asyncio +import collections +from concurrent.futures import ThreadPoolExecutor +import dataclasses +import datetime +import functools +import math +import typing as t + +from uuid import UUID + +import matplotlib.pyplot as plt +from matplotlib.axes._base import _AxesBase +from matplotlib.figure import Figure +from pint import _DEFAULT_REGISTRY as ureg + +from apd.aggregation.query import ( + get_data, + get_data_by_deployment, + with_database, + get_deployment_by_id, +) +from apd.aggregation.database import DataPoint, deployment_table +from .utils import merc_x, merc_y, convert_temperature +from .typing import IntermediateMapData, T_key, T_value, CleanerFunc, Cleaned +from .typing import ( + CLEANED_COORD_FLOAT, + CLEANED_DT_FLOAT, + COORD_FLOAT_CLEANER, + DT_FLOAT_CLEANER, +) + +# Static UUID to represent aggregation of other deployments +GLOBAL = UUID("bd02526e-7619-4a59-b04b-1fafd1c262d1") + + +@dataclasses.dataclass +class Config(t.Generic[T_key, T_value]): + title: str + clean: CleanerFunc[Cleaned[T_key, T_value]] + draw: t.Optional[ + t.Callable[ + [t.Any, t.Iterable[T_key], t.Iterable[T_value], t.Optional[str]], None + ] + ] = None + get_data: t.Optional[ + t.Callable[..., t.AsyncIterator[t.Tuple[UUID, t.AsyncIterator[DataPoint]]]] + ] = None + ylabel: t.Optional[str] = None + sensor_name: dataclasses.InitVar[str] = None + + def __post_init__(self, sensor_name: t.Optional[str] = None) -> None: + if self.draw is None: + self.draw = draw_date # type: ignore + if self.get_data is None: + if sensor_name is None: + raise ValueError("You must specify either get_data or sensor_name") + self.get_data = get_one_sensor_by_deployment(sensor_name) + + +def draw_date( + plot: _AxesBase, + x: t.Iterable[datetime.datetime], + y: t.Iterable[float], + colour: t.Optional[str], +) -> None: + plot.plot_date(x, y, color=colour, linestyle="-", marker="", xdate=True) + + +def draw_map( + plot: _AxesBase, + x: t.Iterable[t.Tuple[float, float]], + y: t.Iterable[float], + colour: t.Optional[str], +) -> None: + lon = [merc_y(coord[0]) for coord in x] + lat = [merc_x(coord[1]) for coord in x] + + for axis in "x", "y": + plt.tick_params( + axis=axis, + which="both", + bottom=False, + top=False, + left=False, + right=False, + labelbottom=False, + labelleft=False, + ) + + plot.tricontourf(lat, lon, y) + plot.plot(lat, lon, "wo", ms=3) + plot.set_aspect(1.0) + + +def get_map_cleaner_for(sensor_name: str,) -> COORD_FLOAT_CLEANER: + """Given a sensor_name that represents a float, return a coroutine that acts as a cleaner + extracting that sensor's data keyed by the value of a Location sensor.""" + + async def clean_latest_coord_and_value( + datapoints: t.AsyncIterator[DataPoint], + ) -> CLEANED_COORD_FLOAT: + + # We will iterate over data points and build an entry in cleaned_data + # for each deployment. This lets newer data replace older data, as + # datapoints is assumed to be in date order + # IntermediateMapData is a typing hint for a dictionary with specific key/values + cleaned_data: t.Dict[UUID, IntermediateMapData] = {} + async for datapoint in datapoints: + # Get the existing data for this deployment, if we've seen it before + row_data = cleaned_data.get(datapoint.deployment_id, None) + if row_data is None: + # This is the first time we've seen this deployment + row_data = {"coord": None, "value": None} + if datapoint.sensor_name == "Location": + # Coord is a 2-tuple of floats + row_data["coord"] = ( + float(datapoint.data[0]), + float(datapoint.data[1]), + ) + elif datapoint.sensor_name == sensor_name: + # Value is a single float + row_data["value"] = float(datapoint.data) + # Store the info about this deployment back into the cleaned_data set + cleaned_data[datapoint.deployment_id] = row_data + + for data in cleaned_data.values(): + if data["coord"] is None or data["value"] is None: + # We only got a partial record, don't plot this + continue + yield data["coord"], data["value"] + + # Return the set up cleaner coroutine + return clean_latest_coord_and_value + + +async def clean_watthours_to_watts( + datapoints: t.AsyncIterator[DataPoint], +) -> CLEANED_DT_FLOAT: + last_watthours = None + last_time = None + async for datapoint in datapoints: + if datapoint.data is None: + continue + time = datapoint.collected_at + if datapoint.data["unit"] == "watt_hour": + watt_hours = datapoint.data["magnitude"] + else: + watt_hours = ( + ureg.Quantity(datapoint.data["magnitude"], datapoint.data["unit"]) + .to(ureg.watt_hour) + .magnitude + ) + if last_watthours: + seconds_elapsed = (time - last_time).total_seconds() + hours_elapsed = seconds_elapsed / (60.0 * 60.0) + additional_power = watt_hours - last_watthours + power = additional_power / hours_elapsed + yield time, power + last_watthours = watt_hours + last_time = datapoint.collected_at + + +async def clean_magnitude(datapoints: t.AsyncIterator[DataPoint],) -> CLEANED_DT_FLOAT: + async for datapoint in datapoints: + if datapoint.data is None: + continue + yield datapoint.collected_at, datapoint.data["magnitude"] + + +def convert_temperature_system( + cleaner: DT_FLOAT_CLEANER, temperature_unit: str, +) -> DT_FLOAT_CLEANER: + async def converter(datapoints: t.AsyncIterator[DataPoint],) -> CLEANED_DT_FLOAT: + results = cleaner(datapoints) + async for date, temp_c in results: + yield date, convert_temperature(temp_c, "degC", temperature_unit) + + return converter + + +async def clean_temperature_fluctuations( + datapoints: t.AsyncIterator[DataPoint], +) -> CLEANED_DT_FLOAT: + allowed_jitter = 2.5 + allowed_range = (-40, 80) + window_datapoints: t.Deque[DataPoint] = collections.deque(maxlen=3) + + def datapoint_ok(datapoint: DataPoint) -> bool: + """Return False if this data point does not contain a valid temperature""" + if datapoint.data is None: + return False + elif datapoint.data["unit"] != "degC": + # This point is in a different temperature system. While it could be converted + # this cleaner is not yet doing that. + return False + elif not allowed_range[0] < datapoint.data["magnitude"] < allowed_range[1]: + return False + return True + + async for datapoint in datapoints: + if not datapoint_ok(datapoint): + # If the datapoint is invalid then skip directly to the next item + continue + + window_datapoints.append(datapoint) + if len(window_datapoints) == 3: + # Find the temperatures of the datapoints in the window, then average + # the first and last and compare that to the middle point. + window_temperatures = [dp.data["magnitude"] for dp in window_datapoints] + avr_first_last = (window_temperatures[0] + window_temperatures[2]) / 2 + diff_middle_avr = abs(window_temperatures[1] - avr_first_last) + if diff_middle_avr > allowed_jitter: + pass + else: + yield window_datapoints[1].collected_at, window_temperatures[1] + elif len(window_datapoints) == 1: + # The item in the iterator can't be compared to both neighbours + # so should be yielded + yield datapoint.collected_at, datapoint.data["magnitude"] + else: + # Otherwise, let the window fill up, it will be yieleded later + pass + # When the iterator ends the final item is not yet in the middle + # of the window, so the last item must be explicitly yielded. + if len(window_datapoints) > 1 and datapoint_ok(datapoint): + yield datapoint.collected_at, datapoint.data["magnitude"] + + +async def clean_passthrough( + datapoints: t.AsyncIterator[DataPoint], +) -> CLEANED_DT_FLOAT: + async for datapoint in datapoints: + if datapoint.data is None: + continue + else: + yield datapoint.collected_at, datapoint.data + + +def get_one_sensor_by_deployment( + sensor_name: str, +) -> t.Callable[..., t.AsyncIterator[t.Tuple[UUID, t.AsyncIterator[DataPoint]]]]: + return functools.partial(get_data_by_deployment, sensor_name=sensor_name) + + +def get_all_data() -> t.Callable[ + ..., t.AsyncIterator[t.Tuple[UUID, t.AsyncIterator[DataPoint]]] +]: + async def get_all_data_inner( + *args: t.Any, **kwargs: t.Any + ) -> t.AsyncIterator[t.Tuple[UUID, t.AsyncIterator[DataPoint]]]: + yield GLOBAL, get_data(*args, **kwargs) + + return get_all_data_inner + + +configs = ( + Config( + sensor_name="SolarCumulativeOutput", + clean=clean_watthours_to_watts, + title="Solar generation", + ylabel="Watts", + ), + Config( + sensor_name="RAMAvailable", + clean=clean_passthrough, + title="RAM available", + ylabel="Bytes", + ), + Config( + sensor_name="RelativeHumidity", + clean=clean_passthrough, + title="Relative humidity", + ylabel="Percent", + ), + Config( + sensor_name="Temperature", + clean=clean_temperature_fluctuations, + title="Ambient temperature", + ylabel="Degrees C", + ), +) + + +def get_known_configs() -> t.Dict[str, Config[t.Any, t.Any]]: + return {config.title: config for config in configs} + + +async def plot_sensor( + config: Config[t.Any, t.Any], + plot: _AxesBase, + location_names: t.Dict[UUID, str], + **kwargs: t.Any +) -> _AxesBase: + locations = [] + if config.get_data is None: + raise ValueError("You must provide a get_data function") + async for deployment_id, query_results in config.get_data(**kwargs): + if deployment_id == GLOBAL: + name = "Global" + else: + try: + deployment = await get_deployment_by_id(deployment_id) + except IndexError: + name = str(deployment_id) + colour = None + else: + name = deployment.name or str(deployment_id) + colour = deployment.colour + # Mypy currently doesn't understand callable fields on datatypes: https://github.com/python/mypy/issues/5485 + points = [dp async for dp in config.clean(query_results)] # type: ignore + if not points: + continue + locations.append(name) + x, y = zip(*points) + plot.set_title(config.title) + plot.set_ylabel(config.ylabel) + if config.draw is None: + raise ValueError("You must provide a get_data function") + config.draw(plot, x, y, colour) + plot.legend(locations) + return plot + + +async def plot_multiple_charts(*args: t.Any, **kwargs: t.Any) -> Figure: + # These parameters are pulled from kwargs to avoid confusing function + # introspection code in IPython widgets + location_names = kwargs.pop("location_names", None) + configs = kwargs.pop("configs", None) + dimensions = kwargs.pop("dimensions", None) + db_uri = kwargs.pop("db_uri", "postgresql+psycopg2://apd@localhost/apd") + + with with_database(db_uri) as session: + loop = asyncio.get_running_loop() + coros = [] + if configs is None: + # If no configs are supplied, use all known configs + configs = get_known_configs().values() + if dimensions is None: + # If no dimensions are supplied, get the square root of the number + # of configs and round it to find a number of columns. This will + # keep the arrangement approximately square. Find rows by multiplying + # out rows. + total_configs = len(configs) + columns = round(math.sqrt(total_configs)) + rows = math.ceil(total_configs / columns) + if location_names is None: + location_query = session.query( + deployment_table.c.id, deployment_table.c.name + ) + location_data = await loop.run_in_executor(None, location_query.all) + location_names = dict(location_data) + + figure = plt.figure(figsize=(10 * columns, 5 * rows), dpi=300) + for i, config in enumerate(configs, start=1): + plot = figure.add_subplot(columns, rows, i) + coros.append(plot_sensor(config, plot, location_names, *args, **kwargs)) + await asyncio.gather(*coros) + return figure + + +_Coroutine_Result = t.TypeVar("_Coroutine_Result") + + +def wrap_coroutine( + f: t.Callable[..., t.Coroutine[t.Any, t.Any, _Coroutine_Result]] +) -> t.Callable[..., _Coroutine_Result]: + """Given a coroutine, return a function that runs that coroutine + in a new event loop in an isolated thread""" + + @functools.wraps(f) + def run_in_thread(*args: t.Any, **kwargs: t.Any) -> _Coroutine_Result: + loop = asyncio.new_event_loop() + wrapped = f(*args, **kwargs) + with ThreadPoolExecutor(max_workers=1) as pool: + task = pool.submit(loop.run_until_complete, wrapped) + # Mypy can get confused when nesting generic functions, like we do here + # The fact that Task is generic means we lose the association with + # _CoroutineResult. Adding an explicit cast restores this. + return t.cast(_Coroutine_Result, task.result()) + + return run_in_thread + + +def interactable_plot_multiple_charts( + *args: t.Any, **kwargs: t.Any +) -> t.Callable[..., Figure]: + with_config = functools.partial(plot_multiple_charts, *args, **kwargs) + return wrap_coroutine(with_config) diff --git a/Ch10/apd.aggregation-chapter10/src/apd/aggregation/cli.py b/Ch10/apd.aggregation-chapter10/src/apd/aggregation/cli.py new file mode 100644 index 0000000..64b1246 --- /dev/null +++ b/Ch10/apd.aggregation-chapter10/src/apd/aggregation/cli.py @@ -0,0 +1,184 @@ +import asyncio +import typing as t +import uuid + +import aiohttp +import click +from sqlalchemy import create_engine +from sqlalchemy.orm import sessionmaker + +from . import collect +from .database import Deployment, deployment_table + + +@click.command() +@click.argument("server", nargs=-1) +@click.option( + "--db", + metavar="", + default="postgresql+psycopg2://localhost/apd", + help="The connection string to a PostgreSQL database", + envvar="APD_DB_URI", +) +@click.option("--api-key", metavar="", envvar="APD_API_KEY") +@click.option( + "--tolerate-failures", + "-f", + help="If provided, failure to retrieve some sensors' data will not abort the collection process", + is_flag=True, +) +@click.option("-v", "--verbose", is_flag=True, help="Enables verbose mode") +def collect_sensor_data( + db: str, server: t.Tuple[str], api_key: str, tolerate_failures: bool, verbose: bool +): + """This loads data from one or more sensors into the specified database. + + Only PostgreSQL databases are supported, as the column definitions use + multiple pg specific features. The database must already exist and be + populated with the required tables. + + The --api-key option is used to specify the access token for the sensors + being queried. + + You may specify any number of servers, the variable should be the full URL + to the sensor's HTTP interface, not including the /v/2.0 portion. Multiple + URLs should be separated with a space. + """ + if tolerate_failures: + attempts = [(s,) for s in server] + else: + attempts = [server] + success = True + for attempt in attempts: + try: + collect.standalone(db, attempt, api_key, echo=verbose) + except ValueError as e: + click.secho(str(e), err=True, fg="red") + success = False + return success + + +@click.group() +def deployments(): + pass + + +@deployments.command() +@click.argument("uri") +@click.argument("name") +@click.option( + "--db", + metavar="", + default="postgresql+psycopg2://localhost/apd", + help="The connection string to a PostgreSQL database", + envvar="APD_DB_URI", +) +@click.option("--api-key", metavar="", envvar="APD_API_KEY") +@click.option("--colour") +def add( + db: str, uri: str, name: str, api_key: t.Optional[str], colour: t.Optional[str], +): + """This creates a record of a new deployment in the database. + """ + deployment = Deployment(id=None, uri=uri, name=name, api_key=api_key, colour=colour) + + async def http_get_deployment_id(): + async with aiohttp.ClientSession() as http: + collect.http_session_var.set(http) + return await collect.get_deployment_id(uri) + + deployment.id = asyncio.run(http_get_deployment_id()) + insert = deployment_table.insert().values(**deployment._asdict()) + + engine = create_engine(db) + sm = sessionmaker(engine) + Session = sm() + Session.execute(insert) + Session.commit() + return True + + +@deployments.command() +@click.option( + "--db", + metavar="", + default="postgresql+psycopg2://localhost/apd", + help="The connection string to a PostgreSQL database", + envvar="APD_DB_URI", +) +def list(db: str): + """This creates a record of a new deployment in the database. + """ + engine = create_engine(db) + sm = sessionmaker(engine) + Session = sm() + deployments = Session.query(deployment_table).all() + for deployment in deployments: + click.secho(deployment.name, bold=True) + click.echo(click.style("ID ", bold=True) + deployment.id.hex) + click.echo(click.style("URI ", bold=True) + deployment.uri) + click.echo(click.style("API key ", bold=True) + deployment.api_key) + click.echo(click.style("Colour ", bold=True) + str(deployment.colour)) + click.echo() + Session.rollback() + return True + + +@deployments.command() +@click.argument("id") +@click.option("--uri") +@click.option("--name") +@click.option( + "--db", + metavar="", + default="postgresql+psycopg2://localhost/apd", + help="The connection string to a PostgreSQL database", + envvar="APD_DB_URI", +) +@click.option("--api-key", metavar="", envvar="APD_API_KEY") +@click.option("--colour") +def edit( + db: str, + id, + uri: t.Optional[str], + name: t.Optional[str], + api_key: t.Optional[str], + colour: t.Optional[str], +): + """This creates a record of a new deployment in the database. + """ + update = {} + if uri is not None: + update["uri"] = uri + if name is not None: + update["name"] = name + if api_key is not None: + update["api_key"] = api_key + if colour is not None: + update["colour"] = colour + deployment_id = uuid.UUID(id) + + update_stmt = ( + deployment_table.update() + .where(deployment_table.c.id == deployment_id) + .values(**update) + ) + + engine = create_engine(db) + sm = sessionmaker(engine) + Session = sm() + Session.execute(update_stmt) + deployments = Session.query(deployment_table).filter( + deployment_table.c.id == deployment_id + ) + Session.commit() + + for deployment in deployments: + click.secho(deployment.name, bold=True) + click.echo(click.style("ID ", bold=True) + deployment.id.hex) + click.echo(click.style("URI ", bold=True) + deployment.uri) + click.echo(click.style("API key ", bold=True) + deployment.api_key) + click.echo(click.style("Colour ", bold=True) + str(deployment.colour)) + click.echo() + + return True diff --git a/Ch10/apd.aggregation-chapter10/src/apd/aggregation/collect.py b/Ch10/apd.aggregation-chapter10/src/apd/aggregation/collect.py new file mode 100644 index 0000000..1d9ea08 --- /dev/null +++ b/Ch10/apd.aggregation-chapter10/src/apd/aggregation/collect.py @@ -0,0 +1,112 @@ +import asyncio +from contextvars import ContextVar +import datetime +import typing as t +import uuid + +import aiohttp +from sqlalchemy import create_engine +from sqlalchemy.orm import sessionmaker +from sqlalchemy.orm.session import Session + +from .database import DataPoint, datapoint_table, Deployment, deployment_table + +http_session_var: ContextVar[aiohttp.ClientSession] = ContextVar("http_session") + + +async def get_deployment_id(server): + http = http_session_var.get() + if not server.endswith("/"): + server += "/" + url = server + "v/2.1/deployment_id" + async with http.get(url) as request: + if request.status != 200: + raise ValueError(f"Error loading deployment id from {server}") + result = await request.json() + return uuid.UUID(result["deployment_id"]) + + +async def get_data_points(server: str, api_key: t.Optional[str],) -> t.List[DataPoint]: + if not server.endswith("/"): + server += "/" + url = server + "v/2.1/sensors/" + headers = {} + if api_key: + headers["X-API-KEY"] = api_key + http = http_session_var.get() + + # Get the deployment ID in parallel to the sensor data + deployment_id = asyncio.create_task(get_deployment_id(server)) + + async with http.get(url, headers=headers) as request: + result = await request.json() + ok = request.status == 200 + now = datetime.datetime.now() + if ok: + points = [] + for value in result["sensors"]: + points.append( + DataPoint( + sensor_name=value["id"], + collected_at=now, + data=value["value"], + deployment_id=await deployment_id, + ) + ) + return points + else: + raise ValueError( + f"Error loading data from {server}: " + result.get("error", "Unknown") + ) + + +def handle_result(result: t.List[DataPoint], session: Session) -> t.List[DataPoint]: + for point in result: + insert = datapoint_table.insert().values(**point._asdict()) + sql_result = session.execute(insert) + point.id = sql_result.inserted_primary_key[0] + return result + + +async def add_data_from_sensors( + session: Session, servers: t.Iterable[Deployment] +) -> t.List[DataPoint]: + tasks: t.List[t.Awaitable[t.List[DataPoint]]] = [] + points: t.List[DataPoint] = [] + async with aiohttp.ClientSession() as http: + http_session_var.set(http) + tasks = [get_data_points(server.uri, server.api_key) for server in servers] + for results in await asyncio.gather(*tasks): + points += results + loop = asyncio.get_running_loop() + await loop.run_in_executor(None, handle_result, points, session) + return points + + +def standalone( + db_uri: str, servers: t.Tuple[str], api_key: t.Optional[str], echo: bool = False +) -> None: + engine = create_engine(db_uri, echo=echo) + sm = sessionmaker(engine) + Session = sm() + deployments: t.Iterable[Deployment] + if servers: + deployments = tuple( + Deployment(id=None, name=None, colour=None, api_key=api_key, uri=server) + for server in servers + ) + else: + deployment_data = Session.query(deployment_table).all() + deployments = tuple( + Deployment.from_sql_result(deployment) for deployment in deployment_data + ) + asyncio.run(add_data_from_sensors(Session, deployments)) + Session.commit() + + +if __name__ == "__main__": + standalone( + db_uri="postgresql+psycopg2://apd@localhost/apd", + servers=("http://pvoutput:8080/",), + api_key="h3hdfjksfhwkjehnwekj", + ) diff --git a/Ch10/apd.aggregation-chapter10/src/apd/aggregation/database.py b/Ch10/apd.aggregation-chapter10/src/apd/aggregation/database.py new file mode 100644 index 0000000..f28b469 --- /dev/null +++ b/Ch10/apd.aggregation-chapter10/src/apd/aggregation/database.py @@ -0,0 +1,129 @@ +from __future__ import annotations + +from dataclasses import dataclass, field, asdict +import datetime +import typing as t +import uuid + +import sqlalchemy +from sqlalchemy.dialects.postgresql import JSONB, DATE, TIMESTAMP, UUID +from sqlalchemy.ext.hybrid import ExprComparator, hybrid_property +from sqlalchemy.orm import sessionmaker +from sqlalchemy.schema import Table + + +metadata = sqlalchemy.MetaData() + +deployment_table = Table( + "deployments", + metadata, + sqlalchemy.Column("id", UUID(as_uuid=True), primary_key=True), + sqlalchemy.Column("uri", sqlalchemy.String, index=False), + sqlalchemy.Column("name", sqlalchemy.String, index=False, nullable=True), + sqlalchemy.Column("colour", sqlalchemy.String, index=False, nullable=True), + sqlalchemy.Column("api_key", sqlalchemy.String, index=False), +) + +datapoint_table = Table( + "datapoints", + metadata, + sqlalchemy.Column("id", sqlalchemy.Integer, primary_key=True), + sqlalchemy.Column("sensor_name", sqlalchemy.String, index=True), + sqlalchemy.Column("collected_at", TIMESTAMP, index=True), + sqlalchemy.Column("deployment_id", UUID(as_uuid=True), index=True), + sqlalchemy.Column("data", JSONB), +) + +daily_summary_view = Table( + "daily_summary", + metadata, + sqlalchemy.Column("sensor_name", sqlalchemy.String), + sqlalchemy.Column("data", JSONB), + sqlalchemy.Column("count", sqlalchemy.Integer), + info={"is_view": True}, +) + + +class DateEqualComparator(ExprComparator): + def __init__(self, fallback_expression, raw_expression): + # Do not try and find update expression from parent + super().__init__(None, fallback_expression, None) + self.raw_expression = raw_expression + + def __eq__(self, other): + """ Returns True iff on the same day as other """ + other_date = sqlalchemy.cast(other, DATE) + return sqlalchemy.and_( + self.raw_expression >= other_date, self.raw_expression < other_date + 1, + ) + + def operate(self, op, *other, **kwargs): + other = [sqlalchemy.cast(date, DATE) for date in other] + return op(self.expression, *other, **kwargs) + + def reverse_operate(self, op, other, **kwargs): + other = [sqlalchemy.cast(date, DATE) for date in other] + return op(other, self.expression, **kwargs) + + +@dataclass +class DataPoint: + sensor_name: str + data: t.Any + deployment_id: uuid.UUID + id: t.Optional[int] = None + collected_at: datetime.datetime = field(default_factory=datetime.datetime.now) + + @classmethod + def from_sql_result(cls, result) -> DataPoint: + return cls(**result._asdict()) + + def _asdict(self) -> t.Dict[str, t.Any]: + data = asdict(self) + if data["id"] is None: + del data["id"] + return data + + @hybrid_property + def collected_on_date(self): + return self.collected_at.date() + + @collected_on_date.comparator # type: ignore + def collected_on_date(cls): + return DateEqualComparator( + fallback_expression=sqlalchemy.cast(datapoint_table.c.collected_at, DATE), + raw_expression=datapoint_table.c.collected_at, + ) + + +@dataclass +class Deployment: + id: t.Optional[uuid.UUID] + uri: str + name: t.Optional[str] + colour: t.Optional[str] + api_key: t.Optional[str] + + @classmethod + def from_sql_result(cls, result) -> Deployment: + return cls(**result._asdict()) + + def _asdict(self) -> t.Dict[str, t.Any]: + data = asdict(self) + return data + + +def main() -> None: + engine = sqlalchemy.create_engine( + "postgresql+psycopg2://apd@localhost/apd", echo=True + ) + sm = sessionmaker(engine) + Session = sm() + if False: + metadata.create_all(engine) + print(Session.query(DataPoint).all()) + pass + + +if __name__ == "__main__": + main() diff --git a/Ch10/apd.aggregation-chapter10/src/apd/aggregation/query.py b/Ch10/apd.aggregation-chapter10/src/apd/aggregation/query.py new file mode 100644 index 0000000..f3a8c95 --- /dev/null +++ b/Ch10/apd.aggregation-chapter10/src/apd/aggregation/query.py @@ -0,0 +1,148 @@ +import asyncio +import contextlib +from contextvars import ContextVar +import datetime +import typing as t +from uuid import UUID + +from sqlalchemy import create_engine +from sqlalchemy.orm import sessionmaker +from sqlalchemy.orm.session import Session + + +from apd.aggregation.database import ( + datapoint_table, + DataPoint, + deployment_table, + Deployment, +) + + +db_session_var: ContextVar[Session] = ContextVar("db_session") + + +@contextlib.contextmanager +def with_database(uri: t.Optional[str] = None) -> t.Iterator[Session]: + """Given a URI, set up a DB connection, and return a Session as a context manager """ + if uri is None: + uri = "postgresql+psycopg2://localhost/apd" + engine = create_engine(uri) + sm = sessionmaker(engine) + Session = sm() + token = db_session_var.set(Session) + try: + yield Session + Session.commit() + finally: + db_session_var.reset(token) + Session.close() + + +async def get_data( + sensor_name: t.Optional[str] = None, + deployment_id: t.Optional[UUID] = None, + collected_before: t.Optional[datetime.datetime] = None, + collected_after: t.Optional[datetime.datetime] = None, +) -> t.AsyncIterator[DataPoint]: + db_session = db_session_var.get() + loop = asyncio.get_running_loop() + query = db_session.query(datapoint_table) + if sensor_name: + query = query.filter(datapoint_table.c.sensor_name == sensor_name) + if deployment_id: + query = query.filter(datapoint_table.c.deployment_id == deployment_id) + if collected_before: + query = query.filter(datapoint_table.c.collected_at < collected_before) + if collected_after: + query = query.filter(datapoint_table.c.collected_at > collected_after) + query = query.order_by( + datapoint_table.c.deployment_id, + datapoint_table.c.sensor_name, + datapoint_table.c.collected_at, + ) + + rows = await loop.run_in_executor(None, query.all) + for row in rows: + yield DataPoint.from_sql_result(row) + + +async def get_deployment_ids() -> t.List[UUID]: + db_session = db_session_var.get() + loop = asyncio.get_running_loop() + query = db_session.query(datapoint_table.c.deployment_id).distinct() + return [row.deployment_id for row in await loop.run_in_executor(None, query.all)] + + +async def get_deployment_by_id(deployment_id: UUID) -> Deployment: + db_session = db_session_var.get() + loop = asyncio.get_running_loop() + query = db_session.query(deployment_table).filter( + deployment_table.c.id == deployment_id + ) + return [ + Deployment.from_sql_result(row) + for row in await loop.run_in_executor(None, query.all) + ][0] + + +async def get_data_by_deployment( + *args: t.Any, **kwargs: t.Any +) -> t.AsyncIterator[t.Tuple[UUID, t.AsyncIterator[DataPoint]]]: + """Return an Async Iterator that contains two-item pairs. + These pairs are a string (deployment_id), and an async iterator that contains + the datapoints with that deployment_id. + + Usage example: + + async for deployment_id, datapoints in get_data_by_deployment(): + print(deployment_id) + async for datapoint in datapoints: + print(datapoint) + print() + """ + # Get the data, using the arguments to this function as filters + data = get_data(*args, **kwargs) + + # The two levels of iterator share the item variable, initialise it with the + # first item from the iterator. Also set last_deployment_id to None, so the + # outer iterator knows to start a new group. + last_deployment_id: t.Optional[UUID] = None + try: + item = await data.__anext__() + except StopAsyncIteration: + # There were no items in the underlying query, return immediately + return + + async def subiterator(group_id: UUID) -> t.AsyncIterator[DataPoint]: + """Using a closure, create an iterator that yields the current + item, then yields all items from data while the deployment_id matches + group_id, leaving the first that doesn't match as item in the enclosing + scope.""" + # item is from the enclosing scope + nonlocal item + while item.deployment_id == group_id: + # yield items from data while they match the group_id this iterator represents + yield item + try: + # Advance the underlying iterator + item = await data.__anext__() + except StopAsyncIteration: + # The underlying iterator came to an end, so end the subiterator too + return + + while True: + while item.deployment_id == last_deployment_id: + # We are trying to advance the outer iterator while the underlying iterator + # is still part-way through a group. Speed through the underlying until we + # hit an item where the deployment_id is different to the last one (or, is not + # None, in the case of the start of the iterator) + try: + item = await data.__anext__() + except StopAsyncIteration: + # We hit the end of the underlying iterator: end this iterator too + return + last_deployment_id = item.deployment_id + # Don't yield an iterator for unclassified deployments + if last_deployment_id is not None: + # Instantiate a subiterator for this group + yield last_deployment_id, subiterator(last_deployment_id) diff --git a/Ch10/apd.aggregation-chapter10/src/apd/aggregation/typing.py b/Ch10/apd.aggregation-chapter10/src/apd/aggregation/typing.py new file mode 100644 index 0000000..5db1337 --- /dev/null +++ b/Ch10/apd.aggregation-chapter10/src/apd/aggregation/typing.py @@ -0,0 +1,48 @@ +import datetime +import typing as t +from typing_extensions import Protocol + +from apd.aggregation.database import DataPoint + + +# Aliases for common types +# These type variables allow for generic functions. T_key represents the place +# in a chart that an item will be placed, and T_value the kind of data that is plotted. +T_key = t.TypeVar("T_key") +T_value = t.TypeVar("T_value") + +# Cleaned is a placeholder type, representing an async iterator of key and value functions +# Cleaned[float, float] is equivalent to typing.AsyncIterator[Tuple[builtins.float, builtins.float]] +Cleaned = t.AsyncIterator[t.Tuple[T_key, T_value]] + +# T_cleaned represents a placeholder for the result of a cleaner function, and is *covariant* +# because it can accept compatible types (such as ints where floats were declared). +# It is bound to Cleaned, so only items that match the specification for Cleaned (for any values +# of T_key or T_value) are valid +T_cleaned = t.TypeVar("T_cleaned", covariant=True, bound=Cleaned) + + +# CleanerFunc is a generic protocol, it matches any Callable that converts an async iterator +# of datapoints to its type. So, CleanerFunc[float] would be equivalent to t.Callable[[t.AsyncIterator[DataPoint]], float] +class CleanerFunc(Protocol[T_cleaned]): + def __call__(self, datapoints: t.AsyncIterator[DataPoint]) -> T_cleaned: + ... + + +# CLEANED_DT_FLOAT is a Cleaned represents datetime/float pairs, for simple charts +CLEANED_DT_FLOAT = Cleaned[datetime.datetime, float] +# and CLEANED_COORD_FLOAT represents (lat/lon), float pairs +CLEANED_COORD_FLOAT = Cleaned[t.Tuple[float, float], float] + +# The _CLEANER variants are functions that return their matching iterators from above +DT_FLOAT_CLEANER = CleanerFunc[CLEANED_DT_FLOAT] +COORD_FLOAT_CLEANER = CleanerFunc[CLEANED_COORD_FLOAT] + + +# When drawing a map we will be building a dictionary of UUID to dictionary +# That inner dictionary should contain coord (float, float) +# and value (float) only. Either or both can be None. +# This class is abstract, it's just for type checking. +class IntermediateMapData(t.TypedDict): + coord: t.Optional[t.Tuple[float, float]] + value: t.Optional[float] diff --git a/Ch10/apd.aggregation-chapter10/src/apd/aggregation/utils.py b/Ch10/apd.aggregation-chapter10/src/apd/aggregation/utils.py new file mode 100644 index 0000000..861d4c4 --- /dev/null +++ b/Ch10/apd.aggregation-chapter10/src/apd/aggregation/utils.py @@ -0,0 +1,86 @@ +from __future__ import annotations + +import contextlib +import functools +import importlib +import io +import math +import os +import typing as t + +from pint import _DEFAULT_REGISTRY as ureg + + +# Set up mercator transform from https://wiki.openstreetmap.org/wiki/Mercator#Python +# Thanks to Paulo Silva and the OSM contributors +def merc_x(lon: float) -> float: + r_major = 6378137.000 + return r_major * math.radians(lon) + + +def merc_y(lat: float) -> float: + if lat > 89.5: + lat = 89.5 + if lat < -89.5: + lat = -89.5 + r_major = 6378137.000 + r_minor = 6356752.3142 + temp = r_minor / r_major + eccent = math.sqrt(1 - temp ** 2) + phi = math.radians(lat) + sinphi = math.sin(phi) + con = eccent * sinphi + com = eccent / 2 + con = ((1.0 - con) / (1.0 + con)) ** com + ts = math.tan((math.pi / 2 - phi) / 2) / con + y = 0 - r_major * math.log(ts) + return y + + +@functools.lru_cache +def convert_temperature(magnitude: float, origin_unit: str, target_unit: str) -> float: + # if origin_unit == "degC" and target_unit == "degF": + # return (magnitude * 1.8) + 32 + temp = ureg.Quantity(magnitude, origin_unit) + return temp.to(target_unit).magnitude + + +@contextlib.contextmanager +def jupyter_page_file() -> t.Iterator[io.StringIO]: + from IPython.core import page + + output = io.StringIO() + yield output + output.seek(0) + page.page(output.read()) + + +@contextlib.contextmanager +def profile_with_yappi() -> t.Iterator[None]: + import yappi + + yappi.clear_stats() + yappi.start() + try: + yield None + finally: + yappi.stop() + + +@functools.lru_cache +def get_package_prefix(package: str) -> str: + mod = importlib.import_module(package) + prefix = mod.__file__ + if prefix.endswith("__init__.py"): + prefix = os.path.dirname(prefix) + return prefix + + +def yappi_package_matches(stat, packages: t.List[str]): + """ This object can be passed to yappi's filter_callback to limit + by Python package.""" + for package in packages: + prefix = get_package_prefix(package) + if stat.full_name.startswith(prefix): + return True + return False diff --git a/Ch10/apd.aggregation-chapter10/tests/__init__.py b/Ch10/apd.aggregation-chapter10/tests/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/Ch10/apd.aggregation-chapter10/tests/conftest.py b/Ch10/apd.aggregation-chapter10/tests/conftest.py new file mode 100644 index 0000000..328e95f --- /dev/null +++ b/Ch10/apd.aggregation-chapter10/tests/conftest.py @@ -0,0 +1,124 @@ +import datetime +from uuid import UUID + +from apd.aggregation.database import datapoint_table + +from alembic.config import Config +from alembic.script import ScriptDirectory +from alembic.runtime.environment import EnvironmentContext +import pytest + + +@pytest.fixture +def db_uri(): + return "postgresql+psycopg2://apd@localhost/apd-test" + + +@pytest.fixture +def db_session(db_uri): + from sqlalchemy import create_engine + from sqlalchemy.orm import sessionmaker + + engine = create_engine(db_uri, echo=True) + sm = sessionmaker(engine) + Session = sm() + yield Session + Session.close() + + +@pytest.fixture +def migrated_db(db_uri, db_session): + config = Config() + config.set_main_option("script_location", "apd.aggregation:alembic") + config.set_main_option("sqlalchemy.url", db_uri) + script = ScriptDirectory.from_config(config) + + def upgrade(rev, context): + return script._upgrade_revs(script.get_current_head(), rev) + + def downgrade(rev, context): + return script._downgrade_revs(None, rev) + + with EnvironmentContext(config, script, fn=upgrade): + script.run_env() + + try: + yield + finally: + # Clear any pending work from the db_session connection + db_session.rollback() + + with EnvironmentContext(config, script, fn=downgrade): + script.run_env() + + +@pytest.fixture +def populated_db(migrated_db, db_session): + datas = [ + { + "id": 1, + "sensor_name": "Test", + "data": "1", + "collected_at": datetime.datetime(2020, 4, 1, 12, 0, 1), + "deployment_id": UUID("b4c68905-b1e4-4875-940e-69e5d27730fd"), + }, + { + "id": 2, + "sensor_name": "Test", + "data": "2", + "collected_at": datetime.datetime(2020, 4, 1, 12, 1, 1), + "deployment_id": UUID("b4c68905-b1e4-4875-940e-69e5d27730fd"), + }, + { + "id": 3, + "sensor_name": "Test", + "data": "3", + "collected_at": datetime.datetime(2020, 4, 1, 12, 2, 1), + "deployment_id": UUID("b4c68905-b1e4-4875-940e-69e5d27730fd"), + }, + { + "id": 4, + "sensor_name": "Test", + "data": "4", + "collected_at": datetime.datetime(2020, 4, 1, 12, 3, 1), + "deployment_id": UUID("b4c68905-b1e4-4875-940e-69e5d27730fd"), + }, + { + "id": 5, + "sensor_name": "OtherTest", + "data": "5", + "collected_at": datetime.datetime(2020, 4, 1, 12, 4, 1), + "deployment_id": UUID("b4c68905-b1e4-4875-940e-69e5d27730fd"), + }, + { + "id": 6, + "sensor_name": "Test", + "data": "1", + "collected_at": datetime.datetime(2020, 4, 1, 12, 0, 1), + "deployment_id": UUID("6e887b81-57c3-4f3b-a7e7-bad159e05e78"), + }, + { + "id": 7, + "sensor_name": "Test", + "data": "1", + "collected_at": datetime.datetime(2020, 4, 1, 12, 1, 1), + "deployment_id": UUID("6e887b81-57c3-4f3b-a7e7-bad159e05e78"), + }, + { + "id": 8, + "sensor_name": "Test", + "data": "2", + "collected_at": datetime.datetime(2020, 4, 1, 12, 2, 1), + "deployment_id": UUID("6e887b81-57c3-4f3b-a7e7-bad159e05e78"), + }, + { + "id": 9, + "sensor_name": "OtherTest", + "data": "3", + "collected_at": datetime.datetime(2020, 4, 1, 12, 3, 1), + "deployment_id": UUID("6e887b81-57c3-4f3b-a7e7-bad159e05e78"), + }, + ] + for data in datas: + insert = datapoint_table.insert().values(**data) + db_session.execute(insert) diff --git a/Ch10/apd.aggregation-chapter10/tests/test_analysis.py b/Ch10/apd.aggregation-chapter10/tests/test_analysis.py new file mode 100644 index 0000000..6b3f31b --- /dev/null +++ b/Ch10/apd.aggregation-chapter10/tests/test_analysis.py @@ -0,0 +1,503 @@ +import collections.abc +import datetime +import functools +import uuid + +import pytest + +from apd.aggregation import analysis +from apd.aggregation.database import DataPoint, datapoint_table +from apd.aggregation.query import db_session_var + + +async def generate_datapoints(datas): + deployment_id = uuid.uuid4() + for i, (time, data) in enumerate(datas, start=1): + yield DataPoint( + id=i, + collected_at=time, + sensor_name="TestSensor", + data=data, + deployment_id=deployment_id, + ) + + +@functools.singledispatch +def consume(input_iterator): + items = [item for item in input_iterator] + + def inner_iterator(): + for item in items: + yield item + + return inner_iterator() + + +@consume.register +async def consume_async(input_iterator: collections.abc.AsyncIterator): + items = [item async for item in input_iterator] + + async def inner_iterator(): + for item in items: + yield item + + return inner_iterator() + + +class TestPassThroughCleaner: + @pytest.fixture + def cleaner(self): + return analysis.clean_passthrough + + @pytest.mark.asyncio + async def test_float_passthrough(self, cleaner): + data = [ + (datetime.datetime(2020, 4, 1, 12, 0, 0), 65.0), + (datetime.datetime(2020, 4, 1, 13, 0, 0), 65.5), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert output == data + + @pytest.mark.asyncio + async def test_Nones_are_skipped(self, cleaner): + data = [ + (datetime.datetime(2020, 4, 1, 12, 0, 0), None), + (datetime.datetime(2020, 4, 1, 13, 0, 0), 65.5), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert output == [ + (datetime.datetime(2020, 4, 1, 13, 0, 0), 65.5), + ] + + +class TestTemperatureCleaner: + @pytest.fixture + def cleaner(self): + return analysis.clean_temperature_fluctuations + + @pytest.mark.asyncio + async def test_window_not_full(self, cleaner): + data = [ + ( + datetime.datetime(2020, 4, 1, 12, 0, 0), + {"magnitude": 21.0, "unit": "degC"}, + ), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert output == [(datetime.datetime(2020, 4, 1, 12, 0, 0), 21.0)] + + @pytest.mark.asyncio + async def test_window_exactly_full(self, cleaner): + data = [ + ( + datetime.datetime(2020, 4, 1, 12, 0, 0), + {"magnitude": 21.0, "unit": "degC"}, + ), + ( + datetime.datetime(2020, 4, 1, 12, 0, 1), + {"magnitude": 21.0, "unit": "degC"}, + ), + ( + datetime.datetime(2020, 4, 1, 12, 0, 2), + {"magnitude": 21.0, "unit": "degC"}, + ), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert output == [ + (datetime.datetime(2020, 4, 1, 12, 0, 0), 21.0), + (datetime.datetime(2020, 4, 1, 12, 0, 1), 21.0), + (datetime.datetime(2020, 4, 1, 12, 0, 2), 21.0), + ] + + @pytest.mark.asyncio + async def test_window_overfilled(self, cleaner): + data = [ + ( + datetime.datetime(2020, 4, 1, 12, 0, 0), + {"magnitude": 21.0, "unit": "degC"}, + ), + ( + datetime.datetime(2020, 4, 1, 12, 0, 1), + {"magnitude": 21.0, "unit": "degC"}, + ), + ( + datetime.datetime(2020, 4, 1, 12, 0, 2), + {"magnitude": 21.0, "unit": "degC"}, + ), + ( + datetime.datetime(2020, 4, 1, 12, 0, 3), + {"magnitude": 21.0, "unit": "degC"}, + ), + ( + datetime.datetime(2020, 4, 1, 12, 0, 4), + {"magnitude": 21.0, "unit": "degC"}, + ), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert output == [ + (datetime.datetime(2020, 4, 1, 12, 0, 0), 21.0), + (datetime.datetime(2020, 4, 1, 12, 0, 1), 21.0), + (datetime.datetime(2020, 4, 1, 12, 0, 2), 21.0), + (datetime.datetime(2020, 4, 1, 12, 0, 3), 21.0), + (datetime.datetime(2020, 4, 1, 12, 0, 4), 21.0), + ] + + @pytest.mark.asyncio + async def test_outlier_dropped(self, cleaner): + data = [ + ( + datetime.datetime(2020, 4, 1, 12, 0, 0), + {"magnitude": 21.0, "unit": "degC"}, + ), + ( + datetime.datetime(2020, 4, 1, 12, 0, 1), + {"magnitude": 61.0, "unit": "degC"}, + ), + ( + datetime.datetime(2020, 4, 1, 12, 0, 2), + {"magnitude": 21.0, "unit": "degC"}, + ), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert output == [ + (datetime.datetime(2020, 4, 1, 12, 0, 0), 21.0), + (datetime.datetime(2020, 4, 1, 12, 0, 2), 21.0), + ] + + @pytest.mark.asyncio + async def test_limited_to_DHT22_range(self, cleaner): + data = [ + ( + datetime.datetime(2020, 4, 1, 12, 0, 0), + {"magnitude": 91.0, "unit": "degC"}, + ), + ( + datetime.datetime(2020, 4, 1, 12, 0, 1), + {"magnitude": 91.0, "unit": "degC"}, + ), + ( + datetime.datetime(2020, 4, 1, 12, 0, 2), + {"magnitude": 91.0, "unit": "degC"}, + ), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert output == [] + + @pytest.mark.asyncio + async def test_Nones_dropped(self, cleaner): + data = [ + ( + datetime.datetime(2020, 4, 1, 12, 0, 0), + {"magnitude": 31.0, "unit": "degC"}, + ), + (datetime.datetime(2020, 4, 1, 12, 0, 1), None,), + ( + datetime.datetime(2020, 4, 1, 12, 0, 2), + {"magnitude": 32.0, "unit": "degC"}, + ), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert output == [ + (datetime.datetime(2020, 4, 1, 12, 0, 0), 31.0), + (datetime.datetime(2020, 4, 1, 12, 0, 2), 32.0), + ] + + +class TestWattHourCleaner: + @pytest.fixture + def cleaner(self): + return analysis.clean_watthours_to_watts + + @pytest.fixture(scope="class") + def huge_data_set(self): + date = datetime.datetime(2020, 4, 1, 12, 0, 0) + power = 500 + data = [] + for i in range(50_000): + date += datetime.timedelta(hours=1) + power += i + data.append((date, {"magnitude": power, "unit": "watt_hour"},)) + return data + + @pytest.mark.asyncio + async def test_one_entry_insufficient_to_find_diff(self, cleaner): + data = [ + ( + datetime.datetime(2020, 4, 1, 12, 0, 0), + {"magnitude": 1.0, "unit": "watt_hour"}, + ), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert output == [] + + @pytest.mark.asyncio + async def test_two_entries_provides_diff_with_second_time(self, cleaner): + data = [ + ( + datetime.datetime(2020, 4, 1, 12, 0, 0), + {"magnitude": 1.0, "unit": "watt_hour"}, + ), + ( + datetime.datetime(2020, 4, 1, 13, 0, 0), + {"magnitude": 10.0, "unit": "watt_hour"}, + ), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert len(output) == 1 + assert output[0][0] == datetime.datetime(2020, 4, 1, 13, 0, 0) + assert output[0][1] == pytest.approx(9.0, 0.0001) + + @pytest.mark.asyncio + async def test_nonstandard_units_can_be_used(self, cleaner): + data = [ + ( + datetime.datetime(2020, 4, 1, 12, 0, 0), + {"magnitude": 1.0, "unit": "watt_hour"}, + ), + ( + datetime.datetime(2020, 4, 1, 13, 0, 0), + {"magnitude": 10000.0, "unit": "joule"}, + ), + ( + datetime.datetime(2020, 4, 1, 14, 0, 0), + {"magnitude": 3.0, "unit": "watt_hour"}, + ), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert len(output) == 2 + assert output[0][0] == datetime.datetime(2020, 4, 1, 13, 0, 0) + assert output[0][1] == pytest.approx(1.7777, 0.001) + assert output[1][0] == datetime.datetime(2020, 4, 1, 14, 0, 0) + assert output[1][1] == pytest.approx(0.22222, 0.001) + + @pytest.mark.asyncio + async def test_fractional_hour(self, cleaner): + data = [ + ( + datetime.datetime(2020, 4, 1, 12, 0, 0), + {"magnitude": 1.0, "unit": "watt_hour"}, + ), + ( + datetime.datetime(2020, 4, 1, 12, 23, 42), + {"magnitude": 10.0, "unit": "watt_hour"}, + ), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert len(output) == 1 + assert output[0][0] == datetime.datetime(2020, 4, 1, 12, 23, 42) + assert output[0][1] == pytest.approx(22.7848, 0.001) + + @pytest.mark.asyncio + async def test_diff_happens_pairwise(self, cleaner): + data = [ + ( + datetime.datetime(2020, 4, 1, 12, 0, 0), + {"magnitude": 1.0, "unit": "watt_hour"}, + ), + ( + datetime.datetime(2020, 4, 1, 12, 23, 42), + {"magnitude": 10.0, "unit": "watt_hour"}, + ), + ( + datetime.datetime(2020, 4, 1, 13, 23, 42), + {"magnitude": 13.0, "unit": "watt_hour"}, + ), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert len(output) == 2 + assert output[0][0] == datetime.datetime(2020, 4, 1, 12, 23, 42) + assert output[0][1] == pytest.approx(22.7848, 0.001) + assert output[1][0] == datetime.datetime(2020, 4, 1, 13, 23, 42) + assert output[1][1] == pytest.approx(3, 0.001) + + @pytest.mark.asyncio + async def test_None_ignored(self, cleaner): + data = [ + ( + datetime.datetime(2020, 4, 1, 12, 0, 0), + {"magnitude": 1.0, "unit": "watt_hour"}, + ), + (datetime.datetime(2020, 4, 1, 12, 58, 0), None,), + ( + datetime.datetime(2020, 4, 1, 13, 0, 0), + {"magnitude": 10.0, "unit": "watt_hour"}, + ), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert len(output) == 1 + assert output[0][0] == datetime.datetime(2020, 4, 1, 13, 0, 0) + assert output[0][1] == pytest.approx(9.0, 0.0001) + + @pytest.mark.asyncio + @pytest.mark.performance + async def test_performance_of_cleaner(self, cleaner, huge_data_set): + import cProfile + + # Generate data point objects before profiling starts + datapoints = await consume(generate_datapoints(huge_data_set)) + profiler = cProfile.Profile() + profiler.enable() + + # Run the cleaner to completion + await consume(cleaner(datapoints)) + + profiler.disable() + cleaner_stat = [ + stat for stat in profiler.getstats() if stat.code == cleaner.__code__ + ][0] + # Assert that the cleaner may take no more than 0.5 seconds to process + # these 50k data points + assert cleaner_stat.totaltime < 0.5 + + +class TestTemperatureMap: + @pytest.fixture + def temperature_data(self): + return { + (53.8667, -1.3333): -1, + (53.35, -2.2833): 1, + (52.45, -1.7333): 3, + (51.5, -0.1333): 6, + (51.55, -2.5667): 5, + (54.9667, -1.6167): -1, + (55.9667, -3.2167): -1, + (54.7667, -1.5833): 0, + (53.7667, -0.3): 1, + (53.7667, -3.0167): 0, + (51.4833, -3.1833): 4, + (53.2667, -3.5167): 2, + (52.0833, -2.8): 2, + (52.0167, -0.6): 2, + (52.6667, 1.2667): 5, + (50.4333, -4.9833): 9, + (50.35, -4.1167): 10, + (50.5167, -2.45): 9, + (50.8333, -1.1667): 9, + (56.45, -5.4333): 4, + (57.5333, -4.05): -2, + (54.5167, -3.6167): 3, + (53.0833, 0.2667): 4, + (51.35, 1.3333): 7, + (50.8833, 0.3167): 8, + (58.45, -3.1): 3, + (50.1, -5.6667): 9, + (58.2167, -6.3333): 4, + (55.6833, -6.25): 7, + } + + @pytest.fixture + def temperature_datapoints(self, migrated_db, db_session, temperature_data): + datas = [] + now = datetime.datetime.now() + for (coord, temp) in temperature_data.items(): + deployment_id = uuid.uuid4() + datas.append( + DataPoint( + sensor_name="Location", + deployment_id=deployment_id, + collected_at=now, + data=coord, + ) + ) + datas.append( + DataPoint( + sensor_name="Temperature", + deployment_id=deployment_id, + collected_at=now, + data=temp, + ) + ) + return datas + + @pytest.fixture + def populated_db(self, migrated_db, db_session, temperature_datapoints): + for data in temperature_datapoints: + insert = datapoint_table.insert().values(**data._asdict()) + db_session.execute(insert) + + @pytest.fixture + def cleaner(self): + return analysis.get_map_cleaner_for("Temperature") + + @pytest.fixture + def get_data(self, db_session): + db_session_var.set(db_session) + return analysis.get_all_data() + + @pytest.mark.asyncio + async def test_latest_coord_temp_cleaner( + self, temperature_data, populated_db, get_data, cleaner + ): + data = get_data() + deployments = 0 + async for deployment, data_points in data: + deployments += 1 + cleaned = {a async for a in cleaner(data_points)} + # There should only be one deployment + assert deployments == 1 + expected = set(temperature_data.items()) + assert cleaned == expected + + @pytest.mark.asyncio + async def test_newer_items_superceed_older( + self, db_session, temperature_datapoints, populated_db, get_data, cleaner + ): + # Pick any of the deployment ids and find the pair of DataPoints in that deployment id + deployment_1 = temperature_datapoints[0].deployment_id + existing = [ + datapoint + for datapoint in temperature_datapoints + if datapoint.deployment_id == deployment_1 + ] + old_location = [ + datapoint for datapoint in existing if datapoint.sensor_name == "Location" + ][0] + old_temperature = [ + datapoint + for datapoint in existing + if datapoint.sensor_name == "Temperature" + ][0] + + new_location = DataPoint( + sensor_name="Location", + deployment_id=deployment_1, + collected_at=datetime.datetime(2031, 4, 1, 12, 0, 0), + data=(-10.5, 150.1), + ) + new_temperature = DataPoint( + sensor_name="Temperature", + deployment_id=deployment_1, + collected_at=datetime.datetime(2031, 4, 1, 12, 0, 0), + data=21, + ) + for data in [new_location, new_temperature]: + insert = datapoint_table.insert().values(**data._asdict()) + db_session.execute(insert) + + data = get_data() + deployments = 0 + async for deployment, data_points in data: + deployments += 1 + cleaned = {a async for a in cleaner(data_points)} + # There should only be one deployment + assert deployments == 1 + + # We expect the newer one, not the older + assert (new_location.data, new_temperature.data) in cleaned + assert (old_location.data, old_temperature.data) not in cleaned + # The cleaner converts two data points to one plottable + assert len(cleaned) == len(temperature_datapoints) / 2 diff --git a/Ch10/apd.aggregation-chapter10/tests/test_cli.py b/Ch10/apd.aggregation-chapter10/tests/test_cli.py new file mode 100644 index 0000000..5cab441 --- /dev/null +++ b/Ch10/apd.aggregation-chapter10/tests/test_cli.py @@ -0,0 +1,155 @@ +from unittest import mock +import uuid + +from click.testing import CliRunner +import pytest + +import apd.aggregation.cli +from apd.aggregation.database import Deployment, deployment_table + + +@pytest.mark.functional +def test_sensors_are_passed_to_get_data_points(db_uri): + runner = CliRunner() + with mock.patch("apd.aggregation.collect.get_data_points") as get_data_points: + runner.invoke( + apd.aggregation.cli.collect_sensor_data, + [ + "http://localhost", + "http://otherhost", + "--db", + db_uri, + "--api-key", + "key", + ], + ) + calls = get_data_points.call_args_list + assert len(calls) == 2 + details = {call[0] for call in get_data_points.call_args_list} + assert details == { + ("http://localhost", "key"), + ("http://otherhost", "key"), + } + + +@pytest.mark.functional +def test_add_deployment_to_db(db_uri, db_session, migrated_db): + runner = CliRunner() + deployment_id = uuid.UUID("76587cdd42dc489ea1d220e9b0bc62ca") + with mock.patch("apd.aggregation.collect.get_deployment_id") as get_deployment_id: + get_deployment_id.return_value = deployment_id + runner.invoke( + apd.aggregation.cli.deployments, + ["add", "http://otherhost", "Other", "--db", db_uri, "--api-key", "key"], + ) + deployments = db_session.query(deployment_table).all() + assert len(deployments) == 1 + deployment = Deployment.from_sql_result(deployments[0]) + assert deployment.uri == "http://otherhost" + assert deployment.api_key == "key" + assert deployment.id == deployment_id + + +@pytest.mark.functional +def test_use_stored_deployments(db_uri, migrated_db): + runner = CliRunner() + + deployment_id = uuid.UUID("76587cdd42dc489ea1d220e9b0bc62ca") + with mock.patch("apd.aggregation.collect.get_deployment_id") as get_deployment_id: + get_deployment_id.return_value = deployment_id + runner.invoke( + apd.aggregation.cli.deployments, + [ + "add", + "http://specifiedhost", + "Specified", + "--db", + db_uri, + "--api-key", + "an_api_key", + ], + ) + + with mock.patch("apd.aggregation.collect.get_data_points") as get_data_points: + runner.invoke( + apd.aggregation.cli.collect_sensor_data, ["--db", db_uri], + ) + calls = get_data_points.call_args_list + assert len(calls) == 1 + details = {call[0] for call in get_data_points.call_args_list} + assert details == { + ("http://specifiedhost", "an_api_key"), + } + + +@pytest.mark.functional +def test_list_deployments(db_uri, migrated_db): + runner = CliRunner() + + deployment_id = uuid.UUID("76587cdd42dc489ea1d220e9b0bc62ca") + with mock.patch("apd.aggregation.collect.get_deployment_id") as get_deployment_id: + get_deployment_id.return_value = deployment_id + runner.invoke( + apd.aggregation.cli.deployments, + [ + "add", + "http://specifiedhost", + "Specified", + "--db", + db_uri, + "--api-key", + "an_api_key", + ], + ) + + result = runner.invoke(apd.aggregation.cli.deployments, ["list", "--db", db_uri],) + expected = """Specified +ID 76587cdd42dc489ea1d220e9b0bc62ca +URI http://specifiedhost +API key an_api_key +Colour None + +""" + assert result.output == expected + + +@pytest.mark.functional +def test_edit_deployment(db_uri, migrated_db): + runner = CliRunner() + return + deployment_id = uuid.UUID("76587cdd42dc489ea1d220e9b0bc62ca") + with mock.patch("apd.aggregation.collect.get_deployment_id") as get_deployment_id: + get_deployment_id.return_value = deployment_id + runner.invoke( + apd.aggregation.cli.deployments, + [ + "add", + "http://specifiedhost", + "Specified", + "--db", + db_uri, + "--api-key", + "an_api_key", + ], + ) + + result = runner.invoke( + apd.aggregation.cli.deployments, + [ + "edit", + "--db", + db_uri, + deployment_id.hex(), + "--colour", + "red" "--name", + "New name", + ], + ) + expected = """New name +ID 76587cdd42dc489ea1d220e9b0bc62ca +URI http://specifiedhost +API key an_api_key +Colour red + +""" + assert result.output == expected diff --git a/Ch10/apd.aggregation-chapter10/tests/test_http_get.py b/Ch10/apd.aggregation-chapter10/tests/test_http_get.py new file mode 100644 index 0000000..df575c6 --- /dev/null +++ b/Ch10/apd.aggregation-chapter10/tests/test_http_get.py @@ -0,0 +1,188 @@ +from concurrent.futures import ThreadPoolExecutor +import datetime +import typing as t +from unittest.mock import patch, MagicMock +import wsgiref.simple_server + +import aiohttp +from apd.sensors.base import Sensor +from apd.sensors.sensors import PythonVersion, ACStatus +from apd.sensors.wsgi import set_up_config +import flask +import pytest + +from apd.sensors.wsgi import v21 + +from apd.aggregation import collect +from apd.aggregation.database import Deployment + +pytestmark = [pytest.mark.functional] + + +@pytest.fixture +def sensors() -> t.Iterator[t.List[Sensor[t.Any]]]: + """ Patch the get_sensors method to return a known pair of sensors only """ + data: t.List[Sensor[t.Any]] = [PythonVersion(), ACStatus()] + with patch("apd.sensors.cli.get_sensors") as get_sensors: + get_sensors.return_value = data + yield data + + +def get_independent_flask_app(name: str) -> flask.Flask: + """ Create a new flask app with the v20 API blueprint loaded, so multiple copies + of the app can be run in parallel without conflicting configuration """ + app = flask.Flask(name) + app.register_blueprint(v21.version, url_prefix="/v/2.1") + return app + + +def run_server_in_thread( + name: str, config: t.Dict[str, t.Any], port: int +) -> t.Iterator[str]: + # Create a new flask app and load in required code, to prevent config conflicts + app = get_independent_flask_app(name) + flask_app = set_up_config(config, app) + server = wsgiref.simple_server.make_server("localhost", port, flask_app) + + with ThreadPoolExecutor() as pool: + pool.submit(server.serve_forever) + yield f"http://localhost:{port}/" + server.shutdown() + + +@pytest.fixture(scope="module") +def http_server(): + yield from run_server_in_thread( + "standard", + { + "APD_SENSORS_API_KEY": "testing", + "APD_SENSORS_DEPLOYMENT_ID": "a46b1d1207fd4cdcad39bbdf706dfe29", + }, + 12081, + ) + + +@pytest.fixture(scope="module") +def bad_api_key_http_server(): + yield from run_server_in_thread( + "alternate", + { + "APD_SENSORS_API_KEY": "penny", + "APD_SENSORS_DEPLOYMENT_ID": "38cf2bae9adb445fad946c82e290487a", + }, + 12082, + ) + + +class TestGetDataPoints: + @pytest.fixture + def mut(self): + return collect.get_data_points + + @pytest.mark.asyncio + async def test_get_data_points( + self, sensors: t.List[Sensor[t.Any]], mut, http_server: str + ) -> None: + # Get the data from the server, storing the time before and after + # as bounds for the collected_at value + async with aiohttp.ClientSession() as http: + collect.http_session_var.set(http) + time_before = datetime.datetime.now() + results = await mut(http_server, "testing") + time_after = datetime.datetime.now() + + assert len(results) == len(sensors) == 2 + + for (sensor, result) in zip(sensors, results): + assert sensor.from_json_compatible(result.data) == sensor.value() + assert result.sensor_name == sensor.name + assert time_before <= result.collected_at <= time_after + + @pytest.mark.asyncio + async def test_get_data_points_fails_with_bad_api_key( + self, sensors: t.List[Sensor[t.Any]], mut, http_server: str + ) -> None: + with pytest.raises( + ValueError, + match=f"Error loading data from {http_server}: Supply API key in X-API-Key header", + ): + async with aiohttp.ClientSession() as http: + collect.http_session_var.set(http) + await mut(http_server, "incorrect") + + +class TestAddDataFromSensors: + @pytest.fixture + def mut(self): + return collect.add_data_from_sensors + + @pytest.fixture + def mock_db_session(self): + return MagicMock() + + @pytest.mark.asyncio + async def test_get_get_data_from_sensors( + self, mock_db_session, sensors: t.List[Sensor[t.Any]], mut, http_server: str + ) -> None: + results = await mut( + mock_db_session, + [ + Deployment( + id=None, colour=None, name=None, uri=http_server, api_key="testing" + ) + ], + ) + assert mock_db_session.execute.call_count == len(sensors) + assert len(results) == len(sensors) + + @pytest.mark.asyncio + async def test_get_get_data_from_sensors_with_multiple_servers( + self, mock_db_session, sensors: t.List[Sensor[t.Any]], mut, http_server: str + ) -> None: + results = await mut( + mock_db_session, + [ + Deployment( + id=None, colour=None, name=None, uri=http_server, api_key="testing" + ), + Deployment( + id=None, colour=None, name=None, uri=http_server, api_key="testing" + ), + ], + ) + assert mock_db_session.execute.call_count == len(sensors) * 2 + assert len(results) == len(sensors) * 2 + + @pytest.mark.asyncio + async def test_data_points_not_added_if_only_partial_success( + self, + mock_db_session, + sensors: t.List[Sensor[t.Any]], + mut, + http_server: str, + bad_api_key_http_server: str, + ) -> None: + with pytest.raises( + ValueError, + match=f"Error loading data from {bad_api_key_http_server}: Supply API key in X-API-Key header", + ): + await mut( + mock_db_session, + [ + Deployment( + id=None, + colour=None, + name=None, + uri=http_server, + api_key="testing", + ), + Deployment( + id=None, + colour=None, + name=None, + uri=bad_api_key_http_server, + api_key="testing", + ), + ], + ) + assert mock_db_session.execute.call_count == 0 diff --git a/Ch10/apd.aggregation-chapter10/tests/test_query.py b/Ch10/apd.aggregation-chapter10/tests/test_query.py new file mode 100644 index 0000000..9b6e0aa --- /dev/null +++ b/Ch10/apd.aggregation-chapter10/tests/test_query.py @@ -0,0 +1,111 @@ +import datetime +from uuid import UUID + +import pytest + +from apd.aggregation.query import ( + get_data, + get_deployment_ids, + get_data_by_deployment, + db_session_var, +) + + +class TestGetData: + @pytest.fixture + def mut(self): + return get_data + + @pytest.mark.asyncio + @pytest.mark.parametrize( + "filter,num_items_expected", + [ + ({}, 9), + ({"sensor_name": "Test"}, 7), + ({"deployment_id": UUID("b4c68905-b1e4-4875-940e-69e5d27730fd")}, 5), + ({"collected_after": datetime.datetime(2020, 4, 1, 12, 2, 1)}, 3), + ({"collected_before": datetime.datetime(2020, 4, 1, 12, 2, 1)}, 4), + ( + { + "collected_after": datetime.datetime(2020, 4, 1, 12, 2, 1), + "collected_before": datetime.datetime(2020, 4, 1, 12, 3, 5), + }, + 2, + ), + ], + ) + async def test_iterate_over_items( + self, mut, db_session, populated_db, filter, num_items_expected + ): + db_session_var.set(db_session) + points = [dp async for dp in mut(**filter)] + assert len(points) == num_items_expected + + @pytest.mark.asyncio + async def test_empty_db(self, mut, db_session, migrated_db): + db_session_var.set(db_session) + points = [dp async for dp in mut()] + assert len(points) == 0 + + +class TestGetDeployments: + @pytest.fixture + def mut(self): + return get_deployment_ids + + @pytest.mark.asyncio + async def test_get_all_deployments(self, mut, db_session, populated_db): + db_session_var.set(db_session) + deployment_ids = await mut() + assert set(deployment_ids) == { + UUID("b4c68905-b1e4-4875-940e-69e5d27730fd"), + UUID("6e887b81-57c3-4f3b-a7e7-bad159e05e78"), + } + + @pytest.mark.asyncio + async def test_empty_db(self, mut, db_session, migrated_db): + db_session_var.set(db_session) + deployment_ids = await mut() + assert deployment_ids == [] + + +@pytest.mark.usefixtures("populated_db") +class TestGetDataByDeployment: + @pytest.fixture + def mut(self): + return get_data_by_deployment + + @pytest.mark.asyncio + @pytest.mark.parametrize( + "filter,num_items_expected", [({}, 9), ({"sensor_name": "Test"}, 7)] + ) + async def test_iterate_over_all_items( + self, mut, db_session, filter, num_items_expected + ): + db_session_var.set(db_session) + num_items = 0 + num_deployments = 0 + async for deployment_id, items in mut(**filter): + num_deployments += 1 + async for item in items: + num_items += 1 + assert num_deployments == 2 + assert num_items == num_items_expected + + @pytest.mark.asyncio + @pytest.mark.parametrize( + "filter,num_items_expected", [({}, 5), ({"sensor_name": "Test"}, 4)] + ) + async def test_skip_first_deployment( + self, mut, db_session, filter, num_items_expected + ): + db_session_var.set(db_session) + num_items = 0 + num_deployments = 0 + async for deployment_id, items in mut(**filter): + num_deployments += 1 + if num_deployments == 2: + async for item in items: + num_items += 1 + assert num_deployments == 2 + assert num_items == num_items_expected diff --git a/Ch10/apd.aggregation-chapter10/tests/test_sensor_aggregation.py b/Ch10/apd.aggregation-chapter10/tests/test_sensor_aggregation.py new file mode 100644 index 0000000..311dd19 --- /dev/null +++ b/Ch10/apd.aggregation-chapter10/tests/test_sensor_aggregation.py @@ -0,0 +1,199 @@ +from __future__ import annotations + +import contextlib +from dataclasses import dataclass +import json +import typing as t +from unittest.mock import patch, Mock + +import pytest +import sqlalchemy + +import apd.aggregation.collect +from apd.aggregation.database import Deployment + + +@pytest.fixture +def data() -> t.Any: + return { + "sensors": [ + { + "human_readable": "3.7", + "id": "PythonVersion", + "title": "Python Version", + "value": [3, 7, 2, "final", 0], + }, + { + "human_readable": "Not connected", + "id": "ACStatus", + "title": "AC Connected", + "value": False, + }, + ] + } + + +@dataclass +class FakeAIOHttpClient: + responses: t.Dict[str, str] + + @contextlib.asynccontextmanager + async def get( + self, url: str, headers: t.Optional[t.Dict[str, str]] = None + ) -> t.AsyncIterator[FakeAIOHttpResponse]: + if url in self.responses: + yield FakeAIOHttpResponse(body=self.responses[url]) + else: + yield FakeAIOHttpResponse(body="", status=404) + + +@dataclass +class FakeAIOHttpResponse: + body: str + status: int = 200 + + async def json(self) -> t.Any: + return json.loads(self.body) + + +@pytest.fixture +def mockclient(data) -> FakeAIOHttpClient: + return FakeAIOHttpClient( + { + "http://localhost/v/2.1/sensors/": json.dumps(data), + "http://localhost/v/2.1/deployment_id": json.dumps( + {"deployment_id": "b29ba0ee10f14552b6b21327bb96d3fb"} + ), + } + ) + + +class TestGetDataPoints: + @pytest.fixture + def mut(self): + return apd.aggregation.collect.get_data_points + + @pytest.mark.asyncio + async def test_get_data_points( + self, mut, mockclient: FakeAIOHttpClient, data + ) -> None: + token = apd.aggregation.collect.http_session_var.set(mockclient) + try: + datapoints = await mut("http://localhost", "") + finally: + apd.aggregation.collect.http_session_var.reset(token) + + assert len(datapoints) == len(data["sensors"]) + for sensor in data["sensors"]: + assert sensor["value"] in (datapoint.data for datapoint in datapoints) + assert sensor["id"] in (datapoint.sensor_name for datapoint in datapoints) + + +class TestAddDataFromSensors: + @pytest.fixture + def mut(self): + return apd.aggregation.collect.add_data_from_sensors + + @pytest.fixture(autouse=True) + def patch_aiohttp(self, mockclient): + with patch("aiohttp.ClientSession") as ClientSession: + ClientSession.return_value.__aenter__.return_value = mockclient + yield ClientSession + + @pytest.fixture + def db_session(self): + session = Mock() + sql_result = session.execute.return_value + sql_result.inserted_primary_key = [1] + return session + + @pytest.mark.asyncio + async def test_datapoints_are_added_to_the_session(self, mut, db_session) -> None: + assert db_session.execute.call_count == 0 + datapoints = await mut( + db_session, + [ + Deployment( + id=None, colour=None, name=None, uri="http://localhost", api_key="" + ) + ], + ) + assert db_session.execute.call_count == len(datapoints) + + +@pytest.mark.usefixtures("migrated_db") +class TestDatabaseConnection: + @pytest.fixture(autouse=True) + def patch_aiohttp(self, mockclient): + with patch("aiohttp.ClientSession") as ClientSession: + ClientSession.return_value.__aenter__.return_value = mockclient + yield ClientSession + + @pytest.fixture + def table(self): + return apd.aggregation.database.datapoint_table + + @pytest.fixture + def daily_summary_view(self): + return apd.aggregation.database.daily_summary_view + + @pytest.fixture + def model(self): + return apd.aggregation.database.DataPoint + + @pytest.fixture + def mut(self): + return apd.aggregation.collect.add_data_from_sensors + + @pytest.mark.asyncio + async def test_datapoints_are_added_to_the_session(self, db_session, table) -> None: + datapoints = await apd.aggregation.collect.add_data_from_sensors( + db_session, + [ + Deployment( + id=None, colour=None, name=None, uri="http://localhost", api_key="" + ) + ], + ) + num_points = db_session.query(table).count() + assert num_points == len(datapoints) == 2 + + @pytest.mark.asyncio + async def test_datapoints_can_be_mapped_back_to_DataPoints( + self, mut, db_session, table, model + ) -> None: + datapoints = await mut( + db_session, + [ + Deployment( + id=None, colour=None, name=None, uri="http://localhost", api_key="" + ) + ], + ) + db_points = [ + model.from_sql_result(result) for result in db_session.query(table) + ] + assert db_points == datapoints + + @pytest.mark.asyncio + async def test_daily_summary_view_matches_query( + self, db_session, model, table, daily_summary_view + ) -> None: + await apd.aggregation.collect.add_data_from_sensors( + db_session, + [ + Deployment( + id=None, colour=None, name=None, uri="http://localhost", api_key="" + ) + ], + ) + + headers = table.c.sensor_name, table.c.data + value_counts = ( + db_session.query(*headers, sqlalchemy.func.count(table.c.id)) + .filter(model.collected_on_date == sqlalchemy.func.current_date()) + .group_by(*headers) + ) + + daily_summary_view = db_session.query(daily_summary_view) + assert value_counts.all() == daily_summary_view.all() diff --git a/Ch10/apd.sensors-chapter10/.pre-commit-config.yaml b/Ch10/apd.sensors-chapter10/.pre-commit-config.yaml new file mode 100644 index 0000000..d3b6ec7 --- /dev/null +++ b/Ch10/apd.sensors-chapter10/.pre-commit-config.yaml @@ -0,0 +1,26 @@ +repos: + +- repo: local + hooks: + - id: black + name: black + entry: pipenv run black + args: [--quiet] + language: system + types: [python] + + - id: mypy + name: mypy + exclude: (?x)^( + (.*)/setup.py + )$ + entry: pipenv run mypy + args: ["--follow-imports=skip"] + language: system + types: [python] + + - id: flake8 + name: flake8 + entry: pipenv run flake8 + language: system + types: [python] diff --git a/Ch10/apd.sensors-chapter10/CHANGES.md b/Ch10/apd.sensors-chapter10/CHANGES.md new file mode 100644 index 0000000..3334453 --- /dev/null +++ b/Ch10/apd.sensors-chapter10/CHANGES.md @@ -0,0 +1,39 @@ +## Changes + +### 2.1.1 (2020-01-06) + +* Cache DHT sensor connections (Matthew Wilkes) +* Force use of bitbang interface for DHT sensors, to improve reliability on Linux (Matthew Wilkes) + +### 2.1.0 (2019-12-09) + +* Add optional APD_SENSORS_DEPLOYMENT_ID parameter and v2.1 API which allows + users to find a unique identifier for a webapp sensor deployment (Matthew Wilkes) + +### 2.0.0 (2019-09-08) + +* Add `to_json_compatible` and `from_json_compatible` methods to Sensor + to facilitate better HTTP API (Matthew Wilkes) + +* HTTP API is now versioned. The API from 1.3.0 is available at /v/1.0 + and an updated version is at /v/2.0 (Matthew Wilkes) + +### 1.3.0 (2019-08-20) + +* WSGI HTTP API support added (Matthew Wilkes) + +### 1.2.0 (2019-08-05) + +* Add external plugin support through `apd.sensors.sensors` entrypoint (Matthew Wilkes) + +### 1.1.0 (2019-07-12) + +* Add --develop argument (Matthew Wilkes) + +### 1.0.1 (2019-06-20) + +* Fix broken 1.0.0 release (Matthew Wilkes) + +### 1.0.0 (2019-06-20) + +* Added initial sensors (Matthew Wilkes) diff --git a/Ch10/apd.sensors-chapter10/LICENCE b/Ch10/apd.sensors-chapter10/LICENCE new file mode 100644 index 0000000..053f694 --- /dev/null +++ b/Ch10/apd.sensors-chapter10/LICENCE @@ -0,0 +1,19 @@ +Copyright (c) 2019 Matthew Wilkes + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/Ch10/apd.sensors-chapter10/Pipfile b/Ch10/apd.sensors-chapter10/Pipfile new file mode 100644 index 0000000..f6cd446 --- /dev/null +++ b/Ch10/apd.sensors-chapter10/Pipfile @@ -0,0 +1,34 @@ +[[source]] +name = "pypi" +url = "https://pypi.org/simple" +verify_ssl = true + +[[source]] +name = "piwheels" +url = "https://piwheels.org/simple" +verify_ssl = true + +[dev-packages] +ipykernel = "*" +remote-ikernel = "*" +pytest = "*" +pytest-cov = "*" +mypy = "*" +flake8 = "*" +black = "*" +pre-commit = "*" +wheel = "*" +twine = "*" +webtest = "*" +apd-sensors = {editable = true,extras = ["webapp"],path = "."} + +[packages] +apd-sensors = {editable = true,path = "."} +apd-sunnyboy-solar = {editable = true,path = 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"==2.0.34" + }, + "werkzeug": { + "hashes": [ + "sha256:2de2a5db0baeae7b2d2664949077c2ac63fbd16d98da0ff71837f7d1dea3fd43", + "sha256:6c80b1e5ad3665290ea39320b91e1be1e0d5f60652b964a3070216de83d2e47c" + ], + "version": "==1.0.1" + }, + "wheel": { + "hashes": [ + "sha256:8788e9155fe14f54164c1b9eb0a319d98ef02c160725587ad60f14ddc57b6f96", + "sha256:df277cb51e61359aba502208d680f90c0493adec6f0e848af94948778aed386e" + ], + "index": "pypi", + "version": "==0.34.2" + } + } +} diff --git a/Ch10/apd.sensors-chapter10/README.md b/Ch10/apd.sensors-chapter10/README.md new file mode 100644 index 0000000..7f015bf --- /dev/null +++ b/Ch10/apd.sensors-chapter10/README.md @@ -0,0 +1,66 @@ +# Advanced Python Development Sensors + +This is the data collection package that forms part of the running example +for the book [Advanced Python Development](https://advancedpython.dev). + +## Usage + +This installs a console script called `sensors` that returns a report on +various aspects of the system. The available sensors are: + +* Python version +* IP Addresses +* CPU Usage +* RAM Available +* Battery charging state +* Ambient Temperature +* Ambient Humidity + +There are no command-line options, to view the report run `sensors` on the +command line. + +## Caveats + +The Ambient Temperature and Ambient Humidity sensors are only available on +RaspberryPi hosts and assume that a DHT22 sensor is connected to pin `D20`. + +The location and type of the sensor can be controlled by setting a pair of +environment variables: `APD_SENSORS_TEMPERATURE_BOARD` and +`APD_SENSORS_TEMPERATURE_PIN`. + +If there is an entry in `/etc/hosts` for the current machine's hostname that +value will be the only result from the IP Addresses sensor. + +## Installation + +You can install with `pip3 install apd.sensors` under Python 3.7 or higher. + +We recommend using pipenv to manage your environment, in which case you would +install using `pipenv --three install apd.sensors` and run the programme using +`pipenv run sensors`. + +## API server + +There is an optional API server shipped with apd.sensors. To use this you +should install the `apd.sensors[webapp]` extra. The API can then be started +with the non-production quality wsgiref server using: + + python -m apd.sensors.wsgi.serve + +or through Waitress (if installed) using: + + waitress-serve --call apd.sensors.wsgi:set_up_config + +Other WSGI servers will also work, you should use set_up_config as a factory +function. + +An environment variable is required to use the API server, `APD_SENSORS_API_KEY` +should be set to the API key required to gain access. One can be generated +using: + + python -c "import uuid; print(uuid.uuid4().hex)" + +The following endpoints are supported: + +* /v/2.0/sensors +* /v/2.0/sensors/sensorid diff --git a/Ch10/apd.sensors-chapter10/plugins/apd.sunnyboy_solar/pyproject.toml b/Ch10/apd.sensors-chapter10/plugins/apd.sunnyboy_solar/pyproject.toml new file mode 100644 index 0000000..4d3bb67 --- /dev/null +++ b/Ch10/apd.sensors-chapter10/plugins/apd.sunnyboy_solar/pyproject.toml @@ -0,0 +1,5 @@ +[build-system] +requires = [ + "setuptools", +] +build-backend = "setuptools.build_meta" diff --git a/Ch10/apd.sensors-chapter10/plugins/apd.sunnyboy_solar/setup.cfg b/Ch10/apd.sensors-chapter10/plugins/apd.sunnyboy_solar/setup.cfg new file mode 100644 index 0000000..0f5db10 --- /dev/null +++ b/Ch10/apd.sensors-chapter10/plugins/apd.sunnyboy_solar/setup.cfg @@ -0,0 +1,33 @@ +[mypy] +ignore_missing_imports = True + +[flake8] +max-line-length = 88 + +[metadata] +name = apd.sunnyboy_solar +version = attr: apd.sunnyboy_solar.VERSION +description = APD Sensor for reading data from Sunnyboy inverters +long_description = file: README.md, CHANGES.md, LICENCE +long_description_content_type = text/markdown +keywords = iot solar +license = MIT +classifiers = + Programming Language :: Python :: 3 + Programming Language :: Python :: 3.7 + +[options] +zip_safe = False +include_package_data = True +package_dir = + =src +packages = find_namespace: +install_requires = + apd.sensors + +[options.packages.find] +where = src + +[options.entry_points] +apd.sensors.sensors = + SolarCumulativeOutput = apd.sunnyboy_solar.sensor:SolarCumulativeOutput diff --git a/Ch10/apd.sensors-chapter10/plugins/apd.sunnyboy_solar/setup.py b/Ch10/apd.sensors-chapter10/plugins/apd.sunnyboy_solar/setup.py new file mode 100644 index 0000000..6068493 --- /dev/null +++ b/Ch10/apd.sensors-chapter10/plugins/apd.sunnyboy_solar/setup.py @@ -0,0 +1,3 @@ +from setuptools import setup + +setup() diff --git a/Ch10/apd.sensors-chapter10/plugins/apd.sunnyboy_solar/src/apd/sunnyboy_solar/Pipfile b/Ch10/apd.sensors-chapter10/plugins/apd.sunnyboy_solar/src/apd/sunnyboy_solar/Pipfile new file mode 100644 index 0000000..e69de29 diff --git a/Ch10/apd.sensors-chapter10/plugins/apd.sunnyboy_solar/src/apd/sunnyboy_solar/__init__.py b/Ch10/apd.sensors-chapter10/plugins/apd.sunnyboy_solar/src/apd/sunnyboy_solar/__init__.py new file mode 100644 index 0000000..3277f64 --- /dev/null +++ b/Ch10/apd.sensors-chapter10/plugins/apd.sunnyboy_solar/src/apd/sunnyboy_solar/__init__.py @@ -0,0 +1 @@ +VERSION = "1.0.0" diff --git a/Ch10/apd.sensors-chapter10/plugins/apd.sunnyboy_solar/src/apd/sunnyboy_solar/sensor.py b/Ch10/apd.sensors-chapter10/plugins/apd.sunnyboy_solar/src/apd/sunnyboy_solar/sensor.py new file mode 100644 index 0000000..aa37a2e --- /dev/null +++ b/Ch10/apd.sensors-chapter10/plugins/apd.sunnyboy_solar/src/apd/sunnyboy_solar/sensor.py @@ -0,0 +1,69 @@ +import os +import subprocess +import typing as t + +from pint import _DEFAULT_REGISTRY as ureg + +from apd.sensors.base import Sensor + + +class SolarCumulativeOutput(Sensor[t.Optional[t.Any]]): + name = "SolarCumulativeOutput" + title = "Solar panel cumulative output" + + def __init__( + self, path: t.Optional[str] = None, bt_addr: t.Optional[str] = None + ) -> None: + self.path = os.environ.get( + "APD_SUNNYBOYSOLAR_PATH", "/home/pi/opensunny-master/opensunny" + ) + self.bt_addr = os.environ.get("APD_SUNNYBOYSOLAR_BT_ADDRESS", None) + + def value(self) -> t.Optional[t.Any]: + if self.bt_addr is None: + return None + try: + output: bytes = subprocess.check_output( + [self.path, "-i", self.bt_addr], stderr=subprocess.STDOUT, timeout=15 + ) + except (subprocess.CalledProcessError, FileNotFoundError): + return None + + lines = filter(None, output.split(b"\n")) + found = {} + for line in lines: + start, value = line.rsplit(b"=", 1) + start, key = start.rsplit(b" ", 1) + found[key] = value + + try: + yield_total = float(found[b"yield_total"][:-3].replace(b".", b"")) + power_dc_1 = int(found[b"power_dc_1"][:-1]) + power_dc_2 = int(found[b"power_dc_2"][:-1]) + if power_dc_1 > 1500 or power_dc_2 > 1500: + return None + except (ValueError, IndexError, KeyError): + return None + return ureg.Quantity(yield_total, "watt") + + @classmethod + def format(cls, value: t.Optional[t.Any]) -> str: + if value is None: + return "Unknown" + return "{:~P}".format(value.to(ureg.kilowatt)) + + @classmethod + def to_json_compatible( + cls, value: t.Optional[t.Any] + ) -> t.Optional[t.Dict[str, t.Union[str, float]]]: + if value is not None: + return {"magnitude": value.magnitude, "unit": str(value.units)} + else: + return None + + @classmethod + def from_json_compatible(cls, json_version: t.Any) -> t.Optional[t.Any]: + if json_version: + return ureg.Quantity(json_version["magnitude"], ureg[json_version["unit"]]) + else: + return None diff --git a/Ch10/apd.sensors-chapter10/pytest.ini b/Ch10/apd.sensors-chapter10/pytest.ini new file mode 100644 index 0000000..861f3d0 --- /dev/null +++ b/Ch10/apd.sensors-chapter10/pytest.ini @@ -0,0 +1,3 @@ +[pytest] +markers = + functional: these tests are significantly slower as they run the whole CLI script \ No newline at end of file diff --git a/Ch10/apd.sensors-chapter10/setup.cfg b/Ch10/apd.sensors-chapter10/setup.cfg new file mode 100644 index 0000000..cf99b51 --- /dev/null +++ b/Ch10/apd.sensors-chapter10/setup.cfg @@ -0,0 +1,69 @@ +[mypy] +namespace_packages = True +mypy_path = src + +[mypy-psutil] +ignore_missing_imports = True + +[mypy-adafruit_dht] +ignore_missing_imports = True + +[mypy-board] +ignore_missing_imports = True + +[mypy-pint] +ignore_missing_imports = True + +[mypy-pytest] +ignore_missing_imports = True + +[mypy-webtest] +ignore_missing_imports = True + +[flake8] +max-line-length = 88 + +[metadata] +name = apd.sensors +version = attr: apd.sensors.VERSION +description = APD Sensor package +long_description = file: README.md, CHANGES.md, LICENCE +long_description_content_type = text/markdown +keywords = iot +license = MIT +classifiers = + Programming Language :: Python :: 3 + Programming Language :: Python :: 3.7 + +[options] +zip_safe = False +include_package_data = True +package_dir = + =src +packages = find_namespace: +install_requires = + psutil + click + pint + adafruit-circuitpython-dht ; 'arm' in platform_machine + +[options.package_data] +apd.sensors = py.typed + +[options.packages.find] +where = src + +[options.entry_points] +console_scripts = + sensors = apd.sensors.cli:show_sensors +apd.sensors.sensors = + PythonVersion = apd.sensors.sensors:PythonVersion + IPAddresses = apd.sensors.sensors:IPAddresses + CPULoad = apd.sensors.sensors:CPULoad + RAMAvailable = apd.sensors.sensors:RAMAvailable + ACStatus = apd.sensors.sensors:ACStatus + Temperature = apd.sensors.sensors:Temperature + RelativeHumidity = apd.sensors.sensors:RelativeHumidity + +[options.extras_require] +webapp = flask \ No newline at end of file diff --git a/Ch10/apd.sensors-chapter10/setup.py b/Ch10/apd.sensors-chapter10/setup.py new file mode 100644 index 0000000..6068493 --- /dev/null +++ b/Ch10/apd.sensors-chapter10/setup.py @@ -0,0 +1,3 @@ +from setuptools import setup + +setup() diff --git a/Ch10/apd.sensors-chapter10/src/apd/sensors/__init__.py b/Ch10/apd.sensors-chapter10/src/apd/sensors/__init__.py new file mode 100644 index 0000000..127c148 --- /dev/null +++ b/Ch10/apd.sensors-chapter10/src/apd/sensors/__init__.py @@ -0,0 +1 @@ +VERSION = "2.1.0" diff --git a/Ch10/apd.sensors-chapter10/src/apd/sensors/base.py b/Ch10/apd.sensors-chapter10/src/apd/sensors/base.py new file mode 100644 index 0000000..74b32ae --- /dev/null +++ b/Ch10/apd.sensors-chapter10/src/apd/sensors/base.py @@ -0,0 +1,51 @@ +#!/usr/bin/env python +# coding: utf-8 +import typing as t + + +T_value = t.TypeVar("T_value") + + +class Sensor(t.Generic[T_value]): + name: str + title: str + + def value(self) -> T_value: + raise NotImplementedError + + @classmethod + def format(cls, value: T_value) -> str: + raise NotImplementedError + + def __str__(self) -> str: + return self.format(self.value()) + + @classmethod + def to_json_compatible(cls, value: T_value) -> t.Any: + raise NotImplementedError() + + @classmethod + def from_json_compatible(cls, json_version: t.Any) -> T_value: + raise NotImplementedError() + + +class JSONSensor(Sensor[T_value]): + @classmethod + def to_json_compatible(cls, value: T_value) -> t.Any: + return value + + @classmethod + def from_json_compatible(cls, json_version: t.Any) -> T_value: + return t.cast(T_value, json_version) + + +version_info_type = t.NamedTuple( + "version_info_type", + [ + ("major", int), + ("minor", int), + ("micro", int), + ("releaselevel", str), + ("serial", int), + ], +) diff --git a/Ch10/apd.sensors-chapter10/src/apd/sensors/cli.py b/Ch10/apd.sensors-chapter10/src/apd/sensors/cli.py new file mode 100644 index 0000000..1ee27b9 --- /dev/null +++ b/Ch10/apd.sensors-chapter10/src/apd/sensors/cli.py @@ -0,0 +1,74 @@ +import enum +import importlib +import sys +import pkg_resources +import typing as t + +import click + +from .sensors import Sensor + + +class ReturnCodes(enum.IntEnum): + OK = 0 + BAD_SENSOR_PATH = 17 + + +def get_sensor_by_path(sensor_path: str) -> Sensor[t.Any]: + try: + module_name, sensor_name = sensor_path.split(":") + except ValueError: + raise RuntimeError( + "Sensor path must be in the format dotted.path.to.module:ClassName" + ) + try: + module = importlib.import_module(module_name) + except ImportError: + raise RuntimeError(f"Could not import module {module_name}") + try: + sensor_class = getattr(module, sensor_name) + except AttributeError: + raise RuntimeError(f"Could not find attribute {sensor_name} in {module_name}") + if ( + isinstance(sensor_class, type) + and issubclass(sensor_class, Sensor) + and sensor_class != Sensor + ): + return sensor_class() + else: + raise RuntimeError( + f"Detected object {sensor_class!r} is not recognised as a Sensor type" + ) + + +def get_sensors() -> t.Iterable[Sensor[t.Any]]: + sensors = [] + for sensor_class in pkg_resources.iter_entry_points("apd.sensors.sensors"): + class_ = sensor_class.load() + sensors.append(t.cast(Sensor[t.Any], class_())) + return sensors + + +@click.command(help="Displays the values of the sensors") +@click.option( + "--develop", required=False, metavar="path", help="Load a sensor by Python path" +) +def show_sensors(develop: str) -> None: + sensors: t.Iterable[Sensor[t.Any]] + if develop: + try: + sensors = [get_sensor_by_path(develop)] + except RuntimeError as error: + click.secho(str(error), fg="red", bold=True) + sys.exit(ReturnCodes.BAD_SENSOR_PATH) + else: + sensors = get_sensors() + for sensor in sensors: + click.secho(sensor.title, bold=True) + click.echo(str(sensor)) + click.echo("") + sys.exit(ReturnCodes.OK) + + +if __name__ == "__main__": + show_sensors() diff --git a/Ch10/apd.sensors-chapter10/src/apd/sensors/py.typed b/Ch10/apd.sensors-chapter10/src/apd/sensors/py.typed new file mode 100644 index 0000000..e69de29 diff --git a/Ch10/apd.sensors-chapter10/src/apd/sensors/sensors.py b/Ch10/apd.sensors-chapter10/src/apd/sensors/sensors.py new file mode 100644 index 0000000..e45c047 --- /dev/null +++ b/Ch10/apd.sensors-chapter10/src/apd/sensors/sensors.py @@ -0,0 +1,189 @@ +#!/usr/bin/env python +# coding: utf-8 +import math +import os +import socket +import sys +import typing as t + +import psutil +from pint import _DEFAULT_REGISTRY as ureg + +from .base import Sensor, JSONSensor, version_info_type + + +class PythonVersion(JSONSensor[version_info_type]): + name = "PythonVersion" + title = "Python Version" + + def value(self) -> version_info_type: + return version_info_type(*sys.version_info) + + @classmethod + def from_json_compatible(cls, json_version: t.Any) -> version_info_type: + return version_info_type(*json_version) + + @classmethod + def format(cls, value: version_info_type) -> str: + if value.micro == 0 and value.releaselevel == "alpha": + return "{0.major}.{0.minor}.{0.micro}a{0.serial}".format(value) + return "{0.major}.{0.minor}".format(value) + + +class IPAddresses(JSONSensor[t.Iterable[t.Tuple[str, str]]]): + name = "IPAddresses" + title = "IP Addresses" + FAMILIES = {"AF_INET": "IPv4", "AF_INET6": "IPv6"} + + def value(self) -> t.List[t.Tuple[str, str]]: + hostname = socket.gethostname() + addresses = socket.getaddrinfo(hostname, None) + + address_info: t.List[t.Tuple[str, str]] = [] + for address in addresses: + family, ip = (address[0].name, address[4][0]) + if family not in self.FAMILIES: + continue + value = (family, ip) + if value not in address_info: + address_info.append(value) + return address_info + + @classmethod + def format(cls, value: t.Iterable[t.Tuple[str, str]]) -> str: + return "\n".join( + "{0} ({1})".format(address[1], cls.FAMILIES.get(address[0], "Unknown")) + for address in value + ) + + +class CPULoad(JSONSensor[float]): + name = "CPULoad" + title = "CPU Usage" + + def value(self) -> float: + return float(psutil.cpu_percent(interval=3)) / 100.0 + + @classmethod + def format(cls, value: float) -> str: + return "{:.1%}".format(value) + + +class RAMAvailable(JSONSensor[int]): + name = "RAMAvailable" + title = "RAM Available" + UNITS = ("B", "KiB", "MiB", "GiB", "TiB", "PiB", "EiB") + UNIT_SIZE = 2 ** 10 + + def value(self) -> int: + return int(psutil.virtual_memory().available) + + @classmethod + def format(cls, value: int) -> str: + magnitude = math.floor(math.log(value, cls.UNIT_SIZE)) + max_magnitude = len(cls.UNITS) - 1 + magnitude = min(magnitude, max_magnitude) + scaled_value = value / (cls.UNIT_SIZE ** magnitude) + return "{:.1f} {}".format(scaled_value, cls.UNITS[magnitude]) + + +class ACStatus(JSONSensor[t.Optional[bool]]): + name = "ACStatus" + title = "AC Connected" + + def value(self) -> t.Optional[bool]: + battery = psutil.sensors_battery() + if battery is not None: + value = battery.power_plugged + if value is None: + return None + else: + return bool(value) + else: + return None + + @classmethod + def format(cls, value: t.Optional[bool]) -> str: + if value is None: + return "Unknown" + elif value: + return "Connected" + else: + return "Not connected" + + +class DHTSensor: + def __init__(self) -> None: + self.board = os.environ.get("APD_SENSORS_TEMPERATURE_BOARD", "DHT22") + self.pin = os.environ.get("APD_SENSORS_TEMPERATURE_PIN", "D20") + + @property + def sensor(self) -> t.Any: + try: + import adafruit_dht + import board + + # Force using legacy interface + adafruit_dht._USE_PULSEIO = False + + sensor_type = getattr(adafruit_dht, self.board) + pin = getattr(board, self.pin) + return sensor_type(pin) + except (ImportError, NotImplementedError, AttributeError): + # No DHT library results in an ImportError. + # Running on an unknown platform results in a + # NotImplementedError when getting the pin + return None + + +class Temperature(Sensor[t.Optional[t.Any]], DHTSensor): + name = "Temperature" + title = "Ambient Temperature" + + def value(self) -> t.Optional[t.Any]: + try: + return ureg.Quantity(self.sensor.temperature, ureg.celsius) + except (RuntimeError, AttributeError): + return None + + @classmethod + def format(cls, value: t.Optional[t.Any]) -> str: + if value is None: + return "Unknown" + else: + return "{:.3~P} ({:.3~P})".format(value, value.to(ureg.fahrenheit)) + + @classmethod + def to_json_compatible(cls, value: t.Optional[t.Any]) -> t.Any: + if value is not None: + return {"magnitude": value.magnitude, "unit": str(value.units)} + else: + return None + + @classmethod + def from_json_compatible(cls, json_version: t.Any) -> t.Optional[t.Any]: + if json_version: + return ureg.Quantity(json_version["magnitude"], ureg[json_version["unit"]]) + else: + return None + + def __str__(self) -> str: + return self.format(self.value()) + + +class RelativeHumidity(JSONSensor[t.Optional[float]], DHTSensor): + name = "RelativeHumidity" + title = "Relative Humidity" + + def value(self) -> t.Optional[float]: + try: + return float(self.sensor.humidity) + except (RuntimeError, AttributeError): + return None + + @classmethod + def format(cls, value: t.Optional[float]) -> str: + if value is None: + return "Unknown" + else: + return "{:.1%}".format(value) diff --git a/Ch10/apd.sensors-chapter10/src/apd/sensors/wsgi/__init__.py b/Ch10/apd.sensors-chapter10/src/apd/sensors/wsgi/__init__.py new file mode 100644 index 0000000..b279326 --- /dev/null +++ b/Ch10/apd.sensors-chapter10/src/apd/sensors/wsgi/__init__.py @@ -0,0 +1,14 @@ +import flask + +from .base import set_up_config +from . import v10 +from . import v20 +from . import v21 + + +__all__ = ["app", "set_up_config"] + +app = flask.Flask(__name__) +app.register_blueprint(v10.version, url_prefix="/v/1.0") +app.register_blueprint(v20.version, url_prefix="/v/2.0") +app.register_blueprint(v21.version, url_prefix="/v/2.1") diff --git a/Ch10/apd.sensors-chapter10/src/apd/sensors/wsgi/base.py b/Ch10/apd.sensors-chapter10/src/apd/sensors/wsgi/base.py new file mode 100644 index 0000000..c9be130 --- /dev/null +++ b/Ch10/apd.sensors-chapter10/src/apd/sensors/wsgi/base.py @@ -0,0 +1,43 @@ +from hmac import compare_digest +import functools +import os +import typing as t + +import flask + + +ViewFuncReturn = t.TypeVar("ViewFuncReturn") +ErrorReturn = t.Tuple[t.Dict[str, str], int] +REQUIRED_CONFIG_KEYS = {"APD_SENSORS_API_KEY"} + + +def require_api_key( + func: t.Callable[[], ViewFuncReturn] +) -> t.Callable[[], t.Union[ViewFuncReturn, ErrorReturn]]: + @functools.wraps(func) + def wrapped() -> t.Union[ViewFuncReturn, ErrorReturn]: + api_key = flask.current_app.config["APD_SENSORS_API_KEY"] + headers = flask.request.headers + supplied_key = headers.get("X-API-Key", "") + if not compare_digest(api_key, supplied_key): + return {"error": "Supply API key in X-API-Key header"}, 403 + return func() + + return wrapped + + +def set_up_config( + environ: t.Optional[t.Dict[str, str]] = None, + to_configure: t.Optional[flask.Flask] = None, +) -> flask.Flask: + if environ is None: + environ = dict(os.environ) + if to_configure is None: + from apd.sensors.wsgi import app + + to_configure = app + missing_keys = REQUIRED_CONFIG_KEYS - environ.keys() + if missing_keys: + raise ValueError("Missing config variables: {}".format(", ".join(missing_keys))) + to_configure.config.from_mapping(environ) + return to_configure diff --git a/Ch10/apd.sensors-chapter10/src/apd/sensors/wsgi/serve.py b/Ch10/apd.sensors-chapter10/src/apd/sensors/wsgi/serve.py new file mode 100644 index 0000000..0b1f684 --- /dev/null +++ b/Ch10/apd.sensors-chapter10/src/apd/sensors/wsgi/serve.py @@ -0,0 +1,10 @@ +from . import app +from .base import set_up_config + +if __name__ == "__main__": + import wsgiref.simple_server + + set_up_config(None, app) + + with wsgiref.simple_server.make_server("", 8000, app) as server: + server.serve_forever() diff --git a/Ch10/apd.sensors-chapter10/src/apd/sensors/wsgi/v10.py b/Ch10/apd.sensors-chapter10/src/apd/sensors/wsgi/v10.py new file mode 100644 index 0000000..7eee6dd --- /dev/null +++ b/Ch10/apd.sensors-chapter10/src/apd/sensors/wsgi/v10.py @@ -0,0 +1,26 @@ +import json +import typing as t + +import flask + +from apd.sensors.cli import get_sensors +from .base import require_api_key + +version = flask.Blueprint(__name__, __name__) + + +@version.route("/sensors/") +@require_api_key +def sensor_values() -> t.Tuple[t.Dict[str, t.Any], int, t.Dict[str, str]]: + headers = {"Content-Security-Policy": "default-src 'none'"} + data = {} + for sensor in get_sensors(): + value = sensor.value() + try: + json.dumps(value) + except TypeError: + # This value isn't JSON serializable, skip it + continue + else: + data[sensor.title] = sensor.value() + return data, 200, headers diff --git a/Ch10/apd.sensors-chapter10/src/apd/sensors/wsgi/v20.py b/Ch10/apd.sensors-chapter10/src/apd/sensors/wsgi/v20.py new file mode 100644 index 0000000..fe7688f --- /dev/null +++ b/Ch10/apd.sensors-chapter10/src/apd/sensors/wsgi/v20.py @@ -0,0 +1,32 @@ +import typing as t + +import flask + +from apd.sensors import cli +from .base import require_api_key + +version = flask.Blueprint(__name__, __name__) + + +@version.route("/sensors/") +@version.route("/sensors/") +@require_api_key +def sensor_values(sensor_id=None) -> t.Tuple[t.Dict[str, t.Any], int, t.Dict[str, str]]: + headers = {"Content-Security-Policy": "default-src 'none'"} + sensors = [] + for sensor in cli.get_sensors(): + if sensor_id and sensor_id != sensor.name: + continue + try: + value = sensor.value() + sensor_data = { + "id": sensor.name, + "title": sensor.title, + "value": sensor.to_json_compatible(value), + "human_readable": sensor.format(value), + } + sensors.append(sensor_data) + except NotImplementedError: + pass + data = {"sensors": sensors} + return data, 200, headers diff --git a/Ch10/apd.sensors-chapter10/src/apd/sensors/wsgi/v21.py b/Ch10/apd.sensors-chapter10/src/apd/sensors/wsgi/v21.py new file mode 100644 index 0000000..9709a54 --- /dev/null +++ b/Ch10/apd.sensors-chapter10/src/apd/sensors/wsgi/v21.py @@ -0,0 +1,39 @@ +import typing as t + +import flask + +from apd.sensors import cli +from .base import require_api_key + +version = flask.Blueprint(__name__, __name__) + + +@version.route("/sensors/") +@version.route("/sensors/") +@require_api_key +def sensor_values(sensor_id=None) -> t.Tuple[t.Dict[str, t.Any], int, t.Dict[str, str]]: + headers = {"Content-Security-Policy": "default-src 'none'"} + sensors = [] + for sensor in cli.get_sensors(): + if sensor_id and sensor_id != sensor.name: + continue + try: + value = sensor.value() + sensor_data = { + "id": sensor.name, + "title": sensor.title, + "value": sensor.to_json_compatible(value), + "human_readable": sensor.format(value), + } + sensors.append(sensor_data) + except NotImplementedError: + pass + data = {"sensors": sensors} + return data, 200, headers + + +@version.route("/deployment_id") +def deployment_id() -> t.Tuple[t.Dict[str, t.Any], int, t.Dict[str, str]]: + headers = {"Content-Security-Policy": "default-src 'none'"} + data = {"deployment_id": flask.current_app.config["APD_SENSORS_DEPLOYMENT_ID"]} + return data, 200, headers diff --git a/Ch10/apd.sensors-chapter10/tests/test_acstatus.py b/Ch10/apd.sensors-chapter10/tests/test_acstatus.py new file mode 100644 index 0000000..d2fe71e --- /dev/null +++ b/Ch10/apd.sensors-chapter10/tests/test_acstatus.py @@ -0,0 +1,55 @@ +from unittest import mock + +import pytest + +from apd.sensors.sensors import ACStatus + + +@pytest.fixture +def sensor(): + return ACStatus() + + +class TestACStatusFormatter: + @pytest.fixture + def subject(self, sensor): + return sensor.format + + def test_format_True(self, subject): + assert subject(True) == "Connected" + + def test_format_False(self, subject): + assert subject(False) == "Not connected" + + def test_format_None(self, subject): + assert subject(None) == "Unknown" + + +class TestACStatusValue: + @pytest.fixture + def subject(self, sensor): + return sensor.value + + @pytest.fixture + def sensors_battery(self): + with mock.patch("psutil.sensors_battery") as sensors_battery: + yield sensors_battery + + def test_sensors_battery_called(self, subject, sensors_battery): + sensors_battery.return_value.power_plugged = True + assert subject() is True + assert sensors_battery.call_count == 1 + + def test_sensor_but_battery_unknown(self, subject, sensors_battery): + sensors_battery.return_value.power_plugged = None + assert subject() is None + assert sensors_battery.call_count == 1 + + def test_no_sensor(self, subject, sensors_battery): + sensors_battery.return_value = None + assert subject() is None + + def test_str_representation_is_formatted_value(self, sensor, sensors_battery): + sensors_battery.return_value.power_plugged = True + assert str(sensor) == "Connected" + assert sensors_battery.call_count == 1 diff --git a/Ch10/apd.sensors-chapter10/tests/test_api_server.py b/Ch10/apd.sensors-chapter10/tests/test_api_server.py new file mode 100644 index 0000000..7ce8dc3 --- /dev/null +++ b/Ch10/apd.sensors-chapter10/tests/test_api_server.py @@ -0,0 +1,140 @@ +import os +import uuid +from unittest import mock + +import flask +import pytest +from webtest import TestApp + +from apd.sensors.wsgi import set_up_config +from apd.sensors.sensors import PythonVersion +from apd.sensors.wsgi import v10 +from apd.sensors.wsgi import v20 +from apd.sensors.wsgi import v21 + + +@pytest.fixture(scope="session") +def api_key(): + return uuid.uuid4().hex + + +def test_api_key_is_required_config_option(): + app = mock.MagicMock() + with pytest.raises( + ValueError, match="Missing config variables: APD_SENSORS_API_KEY" + ): + set_up_config({"APD_SENSORS_DEPLOYMENT_ID": ""}, to_configure=app) + + +def test_os_environ_is_default_for_config_values(api_key): + app = mock.MagicMock() + os.environ["APD_SENSORS_API_KEY"] = api_key + os.environ["APD_SENSORS_DEPLOYMENT_ID"] = "8f1b57faa04b430c81decbbeee9e300c" + try: + assert app.config.from_mapping.call_count == 0 + set_up_config(None, to_configure=app) + assert app.config.from_mapping.call_count == 1 + assert app.config.from_mapping.call_args[0][0] == os.environ + finally: + del os.environ["APD_SENSORS_API_KEY"] + del os.environ["APD_SENSORS_DEPLOYMENT_ID"] + + +class CommonTests: + @pytest.mark.functional + def test_sensor_values_fails_on_missing_api_key(self, api_server): + response = api_server.get("/sensors/", expect_errors=True) + assert response.status_code == 403 + assert response.json["error"] == "Supply API key in X-API-Key header" + + @pytest.mark.functional + def test_sensor_values_require_correct_api_key(self, api_server): + response = api_server.get( + "/sensors/", headers={"X-API-Key": "wrong_key"}, expect_errors=True + ) + assert response.status_code == 403 + assert response.json["error"] == "Supply API key in X-API-Key header" + + +class Testv10API(CommonTests): + @pytest.fixture + def subject(self, api_key): + app = flask.Flask("testapp") + app.register_blueprint(v10.version) + set_up_config( + { + "APD_SENSORS_API_KEY": api_key, + "APD_SENSORS_DEPLOYMENT_ID": "8f1b57faa04b430c81decbbeee9e300c", + }, + to_configure=app, + ) + return app + + @pytest.fixture + def api_server(self, subject): + return TestApp(subject) + + @pytest.mark.functional + def test_sensor_values_returned_as_json(self, api_server, api_key): + value = api_server.get("/sensors/", headers={"X-API-Key": api_key}).json + python_version = PythonVersion().value() + + sensor_names = value.keys() + assert "Python Version" in sensor_names + assert value["Python Version"] == list(python_version) + + @pytest.mark.functional + def test_unserializable_sensor_is_omitted(self, api_server, api_key): + value = api_server.get("/sensors/", headers={"X-API-Key": api_key}).json + sensor_names = value.keys() + assert "Temperature" not in sensor_names + + +class Testv20API(CommonTests): + @pytest.fixture + def subject(self, api_key): + app = flask.Flask("testapp") + app.register_blueprint(v20.version) + set_up_config( + { + "APD_SENSORS_API_KEY": api_key, + "APD_SENSORS_DEPLOYMENT_ID": "8f1b57faa04b430c81decbbeee9e300c", + }, + to_configure=app, + ) + return app + + @pytest.fixture + def api_server(self, subject): + return TestApp(subject) + + @pytest.mark.functional + def test_sensor_values_returned_as_json(self, api_server, api_key): + value = api_server.get("/sensors/", headers={"X-API-Key": api_key}).json + sensors = value["sensors"] + + python_version = [ + sensor for sensor in sensors if sensor["id"] == "PythonVersion" + ][0] + assert python_version["title"] == "Python Version" + assert python_version["value"] == list(PythonVersion().value()) + + +class Testv21API(Testv20API): + @pytest.fixture + def subject(self, api_key): + app = flask.Flask("testapp") + app.register_blueprint(v21.version) + set_up_config( + { + "APD_SENSORS_API_KEY": api_key, + "APD_SENSORS_DEPLOYMENT_ID": "8f1b57faa04b430c81decbbeee9e300c", + }, + to_configure=app, + ) + return app + + @pytest.mark.functional + def test_deployment_id(self, api_server, api_key): + value = api_server.get("/deployment_id", headers={"X-API-Key": api_key}).json + assert value == {"deployment_id": "8f1b57faa04b430c81decbbeee9e300c"} diff --git a/Ch10/apd.sensors-chapter10/tests/test_cpuusage.py b/Ch10/apd.sensors-chapter10/tests/test_cpuusage.py new file mode 100644 index 0000000..445f4e5 --- /dev/null +++ b/Ch10/apd.sensors-chapter10/tests/test_cpuusage.py @@ -0,0 +1,45 @@ +from unittest import mock + +import pytest + +from apd.sensors.sensors import CPULoad + + +@pytest.fixture +def sensor(): + return CPULoad() + + +class TestCPULoadFormatter: + @pytest.fixture + def subject(self, sensor): + return sensor.format + + def test_format_simple_percentage(self, subject): + assert subject(0.05) == "5.0%" + + def test_format_multiple_decimal_places(self, subject): + assert subject(0.031415926) == "3.1%" + + def test_format_multiple_decimal_places_int(self, subject) -> None: + assert subject(1) == "100.0%" + + +class TestCPULoadValue: + @pytest.fixture + def subject(self, sensor): + return sensor.value + + @pytest.fixture + def cpuload(self): + with mock.patch("psutil.cpu_percent") as cpu_percent: + cpu_percent.return_value = 50.1 + yield cpu_percent + + def test_cpu_percent_called_With_interval(self, subject, cpuload): + assert subject() == 0.501 + assert cpuload.call_count == 1 + assert tuple(cpuload.call_args) == ((), {"interval": 3}) + + def test_str_representation_is_formatted_value(self, sensor, cpuload): + assert str(sensor) == "50.1%" diff --git a/Ch10/apd.sensors-chapter10/tests/test_dht.py b/Ch10/apd.sensors-chapter10/tests/test_dht.py new file mode 100644 index 0000000..52a5ee9 --- /dev/null +++ b/Ch10/apd.sensors-chapter10/tests/test_dht.py @@ -0,0 +1,67 @@ +import pytest + +from apd.sensors.sensors import Temperature, RelativeHumidity, ureg + + +@pytest.fixture +def temperature_sensor(): + return Temperature() + + +@pytest.fixture +def humidity_sensor(): + return RelativeHumidity() + + +class TestTemperatureFormatter: + @pytest.fixture + def subject(self, temperature_sensor): + return temperature_sensor.format + + @staticmethod + def to_degc(magnitude): + return ureg.Quantity(magnitude, "degree_Celsius") + + def test_format_21c(self, subject): + assert subject(self.to_degc(21.0)) == "21.0 °C (69.8 °F)" + + def test_format_negative(self, subject): + assert subject(self.to_degc(-32.0)) == "-32.0 °C (-25.6 °F)" + + def test_format_unknown(self, subject): + assert subject(None) == "Unknown" + + +class TestTemperatureSerializer: + @pytest.fixture + def serialize(self, temperature_sensor): + return temperature_sensor.to_json_compatible + + @pytest.fixture + def deserialize(self, temperature_sensor): + return temperature_sensor.from_json_compatible + + @staticmethod + def to_degc(magnitude): + return ureg.Quantity(magnitude, "degree_Celsius") + + def test_serialize_21c(self, serialize): + assert serialize(self.to_degc(21.0)) == {"magnitude": 21.0, "unit": "degree_Celsius"} + + def test_serialize_negative(self, serialize): + assert serialize(self.to_degc(-32.3)) == {"magnitude": -32.3, "unit": "degree_Celsius"} + + def test_deserialize_21c(self, deserialize): + assert deserialize({"magnitude": 21.0, "unit": "degree_Celsius"}) == self.to_degc(21.0) + + +class TestHumidityFormatter: + @pytest.fixture + def subject(self, humidity_sensor): + return humidity_sensor.format + + def test_format_percentage(self, subject): + assert subject(0.035) == "3.5%" + + def test_format_unknown(self, subject): + assert subject(None) == "Unknown" diff --git a/Ch10/apd.sensors-chapter10/tests/test_ipaddresses.py b/Ch10/apd.sensors-chapter10/tests/test_ipaddresses.py new file mode 100644 index 0000000..bd61d85 --- /dev/null +++ b/Ch10/apd.sensors-chapter10/tests/test_ipaddresses.py @@ -0,0 +1,59 @@ +import socket +from unittest import mock + +import pytest + +from apd.sensors.sensors import IPAddresses + + +@pytest.fixture +def sensor(): + return IPAddresses() + + +class TestIPAddressFormatter: + @pytest.fixture + def subject(self, sensor): + return sensor.format + + def test_format_single_ipv4(self, subject): + ips = [("AF_INET", "192.0.2.1")] + assert subject(ips) == "192.0.2.1 (IPv4)" + + def test_format_single_ipv6(self, subject): + ips = [("AF_INET6", "2001:DB8::1")] + assert subject(ips) == "2001:DB8::1 (IPv6)" + + def test_format_mixed_list(self, subject): + ips = [("AF_INET", "192.0.2.1"), ("AF_INET6", "2001:DB8::1")] + assert subject(ips) == "192.0.2.1 (IPv4)\n2001:DB8::1 (IPv6)" + + def test_unusual_protocols_are_marked_as_unknown(self, subject): + ips = [("AF_IRDA", "ffff")] + assert subject(ips) == "ffff (Unknown)" + + +class TestIPAddressesValue: + @pytest.fixture + def subject(self, sensor): + return sensor.value + + @pytest.fixture + def gethostname(self): + with mock.patch("socket.gethostname") as gethostname: + gethostname.return_value = "localhost" + yield gethostname + + @pytest.fixture + def getaddrinfo(self, gethostname): + with mock.patch("socket.getaddrinfo") as getaddrinfo: + yield getaddrinfo + + def test_getaddrinfo_used_for_value_collection(self, getaddrinfo, subject): + getaddrinfo.return_value = [(socket.AF_INET, 0, 0, "", ("192.0.2.1", 0))] + assert subject() == [("AF_INET", "192.0.2.1")] + assert getaddrinfo.call_count == 1 + + def test_str_representation_is_formatted_value(self, getaddrinfo, sensor): + getaddrinfo.return_value = [(socket.AF_INET6, 0, 0, "", ("2001:DB8::1", 0))] + assert str(sensor) == "2001:DB8::1 (IPv6)" diff --git a/Ch10/apd.sensors-chapter10/tests/test_pythonversion.py b/Ch10/apd.sensors-chapter10/tests/test_pythonversion.py new file mode 100644 index 0000000..43ad806 --- /dev/null +++ b/Ch10/apd.sensors-chapter10/tests/test_pythonversion.py @@ -0,0 +1,61 @@ +from collections import namedtuple +from unittest import mock + +import pytest + +from apd.sensors.sensors import PythonVersion + + +@pytest.fixture +def version(): + return namedtuple( + "sys_versioninfo", ("major", "minor", "micro", "releaselevel", "serial") + ) + + +@pytest.fixture +def sensor(): + return PythonVersion() + + +class TestPythonVersionFormatter: + @pytest.fixture + def subject(self, sensor): + return sensor.format + + def test_format_py38(self, subject, version): + py38 = version(3, 8, 0, "final", 0) + assert subject(py38) == "3.8" + + def test_format_large_version(self, subject, version): + large = version(255, 128, 0, "final", 0) + assert subject(large) == "255.128" + + def test_alpha_of_minor_is_marked(self, subject, version): + py39 = version(3, 9, 0, "alpha", 1) + assert subject(py39) == "3.9.0a1" + + def test_alpha_of_micro_is_unmarked(self, subject, version): + py39 = version(3, 9, 1, "alpha", 1) + assert subject(py39) == "3.9" + + +class TestPythonVersionValue: + @pytest.fixture + def subject(self, sensor): + return sensor.value + + @pytest.fixture + def python_version(self): + import sys + + return sys.version_info + + def test_data_value_is_sys_versioninfo(self, python_version, subject): + assert subject() == python_version + + +class TestPythonVersionSensor: + def test_str_representation_is_formatted_value(self, sensor, version): + with mock.patch("sys.version_info", new=version(3, 4, 1, "final", 1)): + assert str(sensor) == "3.4" diff --git a/Ch10/apd.sensors-chapter10/tests/test_ramusage.py b/Ch10/apd.sensors-chapter10/tests/test_ramusage.py new file mode 100644 index 0000000..197ad06 --- /dev/null +++ b/Ch10/apd.sensors-chapter10/tests/test_ramusage.py @@ -0,0 +1,47 @@ +from unittest import mock + +import pytest + +from apd.sensors.sensors import RAMAvailable + + +@pytest.fixture +def sensor(): + return RAMAvailable() + + +class TestRAMAvailableFormatter: + @pytest.fixture + def subject(self, sensor): + return sensor.format + + def test_format_bytes(self, subject): + assert subject(15) == "15.0 B" + + def test_format_kibibytes(self, subject): + assert subject(15000) == "14.6 KiB" + + def test_format_mibibytes(self, subject): + assert subject(15000000) == "14.3 MiB" + + def test_format_gibibytes(self, subject): + assert subject(15000000000) == "14.0 GiB" + + +class TestRAMAvailableValue: + @pytest.fixture + def subject(self, sensor): + return sensor.value + + @pytest.fixture + def virtual_memory(self): + with mock.patch("psutil.virtual_memory") as virtual_memory: + virtual_memory.return_value.available = 1024 + yield virtual_memory + + def test_virtual_memory_called(self, subject, virtual_memory): + assert subject() == 1024 + assert virtual_memory.call_count == 1 + + def test_str_representation_is_formatted_value(self, sensor, virtual_memory): + assert str(sensor) == "1.0 KiB" diff --git a/Ch10/apd.sensors-chapter10/tests/test_sensors.py b/Ch10/apd.sensors-chapter10/tests/test_sensors.py new file mode 100644 index 0000000..c67a180 --- /dev/null +++ b/Ch10/apd.sensors-chapter10/tests/test_sensors.py @@ -0,0 +1,88 @@ +import json +from unittest import mock + +from click.testing import CliRunner + +import pytest + +import apd.sensors.cli +import apd.sensors.sensors + + +def test_sensors(): + assert hasattr(apd.sensors.sensors, "PythonVersion") + + +@pytest.mark.functional +def test_first_sensor_is_first_two_lines_of_cli_output(): + runner = CliRunner() + with mock.patch("apd.sensors.cli.get_sensors") as get_sensors: + get_sensors.return_value = [apd.sensors.sensors.PythonVersion()] + result = runner.invoke(apd.sensors.cli.show_sensors) + python_version = str(apd.sensors.sensors.PythonVersion()) + assert ["Python Version", python_version] == result.stdout.split("\n")[:2] + + +class TestSensorFromPath: + @pytest.fixture + def subject(self): + return apd.sensors.cli.get_sensor_by_path + + def test_get_sensor_by_path(self, subject): + assert isinstance( + subject("apd.sensors.sensors:PythonVersion"), + apd.sensors.sensors.PythonVersion, + ) + + def test_invalid_format(self, subject): + with pytest.raises(RuntimeError, match="dotted.path.to.module:ClassName"): + subject("apd.sensors.sensors.PythonVersion") + + def test_invalid_module(self, subject): + with pytest.raises(RuntimeError, match="Could not import module"): + subject("apd.nonsense.sensor:FakeSensor") + + def test_missing_sensor(self, subject): + with pytest.raises(RuntimeError, match="Could not find attribute"): + subject("apd.sensors.sensors:FakeSensor") + + def test_invalid_sensor(self, subject): + with pytest.raises(RuntimeError, match="is not recognised as a Sensor"): + subject("apd.sensors.sensors:Sensor") + + +@pytest.mark.functional +def test_develop_option_finds_sensor_by_path(): + runner = CliRunner() + result = runner.invoke( + apd.sensors.cli.show_sensors, ["--develop", "apd.sensors.sensors:PythonVersion"] + ) + python_version = str(apd.sensors.sensors.PythonVersion()) + assert ["Python Version", python_version, "", ""] == result.stdout.split("\n") + + +class TestDefaultSerializer: + @pytest.fixture + def python_version(self): + return apd.sensors.sensors.PythonVersion() + + @pytest.fixture + def serialize(self, python_version): + return python_version.to_json_compatible + + @pytest.fixture + def deserialize(self, python_version): + return python_version.from_json_compatible + + def test_serialize_deserialize_is_symmetric( + self, python_version, serialize, deserialize + ): + value = python_version.value() + serialized = serialize(value) + json_version = json.dumps(serialized) + assert ( + json_version == f"[{value.major}, {value.minor}, {value.micro}," + f' "{value.releaselevel}", {value.serial}]' + ) + deserialized = deserialize(serialized) + assert deserialized == value diff --git a/Ch10/chapter10-yappi.ipynb b/Ch10/chapter10-yappi.ipynb new file mode 100644 index 0000000..153122f --- /dev/null +++ b/Ch10/chapter10-yappi.ipynb @@ -0,0 +1,3140 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from apd.aggregation.analysis import interactable_plot_multiple_charts, configs\n", + "from apd.aggregation.utils import jupyter_page_file, profile_with_yappi, yappi_package_matches\n", + "import yappi\n", + "\n", + "with profile_with_yappi():\n", + " plot = interactable_plot_multiple_charts()\n", + " plot()\n", + "\n", + "with jupyter_page_file() as output:\n", + " yappi.get_func_stats(filter_callback=lambda stat:\n", + " yappi_package_matches(stat, [\"apd.aggregation\"])\n", + " ).print_all(output)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r\n", + "Clock type: WALL\r\n", + "Ordered by: totaltime, desc\r\n", + "\r\n", + "name ncall tsub ttot tavg \r\n", + "..futures\\thread.py:52 _WorkItem.run 7 0.000092 3.204091 0.457727\r\n", + "..tion\\analysis.py:373 run_in_thread 1 0.000053 1.844382 1.844382\r\n", + "..38\\Lib\\threading.py:859 Thread.run 2/1 0.000025 1.839877 0.919939\r\n", + "..rrent\\futures\\thread.py:66 _worker 2/1 0.000022 1.839852 0.919926\r\n", + "..lectorEventLoop.run_until_complete 1 0.000027 1.839745 1.839745\r\n", + "..ndowsSelectorEventLoop.run_forever 1 0.000088 1.839611 1.839611\r\n", + "..WindowsSelectorEventLoop._run_once 17 0.000408 1.839497 0.108206\r\n", + "..alysis.py:327 plot_multiple_charts 1 1.643447 1.839309 1.839309\r\n", + "..py:635 ThreadPoolExecutor.__exit__ 1 0.000005 1.837286 1.837286\r\n", + "..py:230 ThreadPoolExecutor.shutdown 1 0.000009 1.837281 1.837281\r\n", + "..8\\Lib\\threading.py:979 Thread.join 1 0.000006 1.837272 1.837272\r\n", + "..:1017 Thread._wait_for_tstate_lock 1 0.000011 1.837263 1.837263\r\n", + "..gation\\analysis.py:291 plot_sensor 1 0.000000 1.407986 1.407986\r\n", + "..query.py:88 get_data_by_deployment 1 1.397146 1.397188 1.397188\r\n", + "..d\\aggregation\\query.py:41 get_data 1 1.283592 1.375576 1.375576\r\n", + "..lchemy\\orm\\query.py:3232 Query.all 6 0.004333 1.362649 0.227108\r\n", + "..ctors.py:319 SelectSelector.select 17 0.000177 1.362418 0.080142\r\n", + "..tors.py:313 SelectSelector._select 17 0.000069 1.362216 0.080130\r\n", + "..y\\orm\\query.py:3400 Query.__iter__ 6 0.000054 0.958316 0.159719\r\n", + "..:3425 Query._execute_and_instances 6 0.000073 0.956631 0.159439\r\n", + "..ine\\base.py:916 Connection.execute 9 0.000062 0.740051 0.082228\r\n", + "..:589 PGDialect_psycopg2.do_execute 11 0.000042 0.738361 0.067124\r\n", + "..y:1159 Connection._execute_context 9 0.000307 0.734166 0.081574\r\n", + "..:291 Select._execute_on_connection 6 0.000047 0.731436 0.121906\r\n", + ".. 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import yappi\n", + "\n", + "from apd.aggregation.analysis import interactable_plot_multiple_charts, Config\n", + "from apd.aggregation.analysis import clean_temperature_fluctuations, get_one_sensor_by_deployment\n", + "from apd.aggregation.utils import profile_with_yappi\n", + "\n", + "yappi.set_clock_type(\"wall\")\n", + "\n", + "filter_in_db = Config(\n", + " clean=clean_temperature_fluctuations,\n", + " title=\"Ambient temperature\",\n", + " ylabel=\"Degrees C\",\n", + " get_data=get_one_sensor_by_deployment(\"Temperature\"),\n", + ")\n", + "\n", + "with profile_with_yappi():\n", + " plot = interactable_plot_multiple_charts(configs=[filter_in_db])\n", + " plot()\n", + "\n", + "yappi.get_func_stats().print_all()" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r\n", + "Clock type: CPU\r\n", + "Ordered by: totaltime, desc\r\n", + "\r\n", + "name ncall tsub ttot tavg \r\n", + "..futures\\thread.py:52 _WorkItem.run 7 0.000000 9.515625 1.359375\r\n", + "..lchemy\\orm\\query.py:3232 Query.all 6 0.187500 6.390625 1.065104\r\n", + "..lchemy\\orm\\loading.py:35 instances 67353 0.046875 6.140625 0.000091\r\n", + "..esult.py:1257 ResultProxy.fetchall 6 0.000000 5.109375 0.851562\r\n", + "..py:1217 ResultProxy._fetchall_impl 6 0.000000 5.078125 0.846354\r\n", + "..38\\Lib\\threading.py:859 Thread.run 2/1 0.000000 3.125000 1.562500\r\n", + "..rrent\\futures\\thread.py:66 _worker 2/1 0.000000 3.125000 1.562500\r\n", + "..b\\asyncio\\events.py:79 Handle._run 29 0.000000 3.125000 0.107759\r\n", + "..WindowsSelectorEventLoop._run_once 17 0.000000 3.125000 0.183824\r\n", + "..lectorEventLoop.run_until_complete 1 0.000000 3.125000 3.125000\r\n", + "..ndowsSelectorEventLoop.run_forever 1 0.000000 3.125000 3.125000\r\n", + "..gation\\analysis.py:291 plot_sensor 1 0.031250 3.046875 3.046875\r\n", + "..egation\\analysis.py:313 4 0.015625 2.703125 0.675781\r\n", + "..and_clean_temperature_fluctuations 4 0.046875 2.687500 0.671875\r\n", + "..184 clean_temperature_fluctuations 4 0.062500 2.640625 0.660156\r\n", + "..sycopg2\\_json.py:164 typecast_json 67339 0.312500 2.562500 0.000038\r\n", + "..-input-1-879b7ebf06b8>:7 4 0.234375 2.515625 0.628906\r\n", + "..gregation\\query.py:116 subiterator 4 0.390625 2.281250 0.570312\r\n", + "..n38\\Lib\\json\\__init__.py:299 loads 67339 0.515625 2.250000 0.000033\r\n", + "..es\\psycopg2\\extras.py:686 67347 0.156250 1.796875 0.000027\r\n", + "..on38\\Lib\\uuid.py:130 UUID.__init__ 67347 1.000000 1.640625 0.000024\r\n", + "..\\decoder.py:332 JSONDecoder.decode 67339 0.718750 1.546875 0.000023\r\n", + "..d\\aggregation\\query.py:41 get_data 1 0.312500 1.531250 1.531250\r\n", + "..ion\\database.py:77 from_sql_result 67339 0.468750 1.218750 0.000018\r\n", + "..chemy\\orm\\loading.py:83 6 0.328125 0.984375 0.164062\r\n", + ".._collections.py:121 result._asdict 67343 0.375000 0.515625 0.000008\r\n", + "..thon38\\Lib\\uuid.py:231 UUID.__eq__ 67351 0.281250 0.359375 0.000005\r\n", + "..chemy\\orm\\loading.py:84 67347 0.343750 0.343750 0.000005\r\n", + "..y\\util\\_collections.py:112 __new__ 67347 0.234375 0.312500 0.000005\r\n", + "..\\matplotlib\\__init__.py:1535 inner 4 0.000000 0.296875 0.074219\r\n", + "..axes.py:1651 AxesSubplot.plot_date 4 0.000000 0.296875 0.074219\r\n", + "..xes\\_axes.py:1412 AxesSubplot.plot 4 0.000000 0.296875 0.074219\r\n", + "..gregation\\analysis.py:63 draw_date 4 0.000000 0.296875 0.074219\r\n", + ".._base.py:1842 AxesSubplot.add_line 4 0.000000 0.281250 0.070312\r\n", + "..oder.py:343 JSONDecoder.raw_decode 67339 0.265625 0.265625 0.000004\r\n", + "..s\\matplotlib\\dates.py:1911 convert 6 0.000000 0.265625 0.044271\r\n", + "..on_base.py:2063 vectorize.__call__ 4 0.000000 0.265625 0.066406\r\n", + "..b\\axis.py:1562 XAxis.convert_units 12 0.000000 0.265625 0.022135\r\n", + "...py:168 AxesSubplot.convert_xunits 48 0.000000 0.265625 0.005534\r\n", + "..lotlib\\lines.py:664 Line2D.recache 10 0.000000 0.265625 0.026562\r\n", + "..s\\matplotlib\\dates.py:401 date2num 6 0.000000 0.265625 0.044271\r\n", + "..tlib\\lines.py:1018 Line2D.get_path 10 0.000000 0.265625 0.026562\r\n", + "..68 AxesSubplot._update_line_limits 4 0.000000 0.265625 0.066406\r\n", + "...py:2154 vectorize._vectorize_call 4 0.046875 0.265625 0.066406\r\n", + ":1 DataPoint.__init__ 67339 0.234375 0.234375 0.000003\r\n", + "..tplotlib\\dates.py:210 _to_ordinalf 8467 0.140625 0.218750 0.000026\r\n", + "..alysis.py:327 plot_multiple_charts 1 0.000000 0.078125 0.078125\r\n", + "..ine\\base.py:916 Connection.execute 9 0.000000 0.062500 0.006944\r\n", + "..:291 Select._execute_on_connection 6 0.000000 0.062500 0.010417\r\n", + "..y\\orm\\query.py:3400 Query.__iter__ 6 0.000000 0.062500 0.010417\r\n", + ".. 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import yappi\n", + "\n", + "from apd.aggregation.analysis import interactable_plot_multiple_charts, Config, clean_temperature_fluctuations, get_data_by_deployment\n", + "from apd.aggregation.utils import profile_with_yappi\n", + "\n", + "async def filter_and_clean_temperature_fluctuations(datapoints):\n", + " filtered = (item async for item in datapoints if item.sensor_name==\"Temperature\")\n", + " cleaned = clean_temperature_fluctuations(filtered)\n", + " async for item in cleaned:\n", + " yield item\n", + "\n", + "filter_in_python = Config(\n", + " clean=filter_and_clean_temperature_fluctuations,\n", + " title=\"Ambient temperature\",\n", + " ylabel=\"Degrees C\",\n", + " get_data=get_data_by_deployment,\n", + ")\n", + "\n", + "with profile_with_yappi():\n", + " plot = interactable_plot_multiple_charts(configs=[filter_in_python])\n", + " plot()\n", + "\n", + "yappi.get_func_stats().print_all()" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from apd.aggregation.utils import yappi_package_matches\n", + "import yappi\n", + "\n", + "yappi.get_func_stats().save(\"callgrind.filter_in_python\", \"callgrind\") " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "apd.aggregation", + "language": "python", + "name": "apd.aggregation" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Ch10/listing10-01-profiling_wrapper.py b/Ch10/listing10-01-profiling_wrapper.py new file mode 100644 index 0000000..8c416c2 --- /dev/null +++ b/Ch10/listing10-01-profiling_wrapper.py @@ -0,0 +1,48 @@ +_Coroutine_Result = t.TypeVar("_Coroutine_Result") + +def wrap_coroutine( + f: t.Callable[..., t.Coroutine[t.Any, t.Any, _Coroutine_Result]], debug: bool=False, +) -> t.Callable[..., _Coroutine_Result]: + """Given a coroutine, return a function that runs that coroutine + in a new event loop in an isolated thread""" + + @functools.wraps(f) + def run_in_thread(*args: t.Any, **kwargs: t.Any) -> _Coroutine_Result: + loop = asyncio.new_event_loop() + wrapped = f(*args, **kwargs) + + if debug: + # Create a new function that runs the loop inside a cProfile + # session, so it can be profiled transparently + + def fn(): + import cProfile + + return cProfile.runctx( + "loop.run_until_complete(wrapped)", + {}, + {"loop": loop, "wrapped": wrapped}, + sort="cumulative", + ) + + task_callable = fn + else: + # If not debugging just submit the loop run function with the desired + # coroutine + task_callable = functools.partial(loop.run_until_complete, wrapped) + with ThreadPoolExecutor(max_workers=1) as pool: + task = pool.submit(task_callable) + # Mypy can get confused when nesting generic functions, like we do here + # The fact that Task is generic means we lose the association with + # _CoroutineResult. Adding an explicit cast restores this. + return t.cast(_Coroutine_Result, task.result()) + + return run_in_thread + + +def interactable_plot_multiple_charts( + *args: t.Any, debug: bool=False, **kwargs: t.Any +) -> t.Callable[..., Figure]: + with_config = functools.partial(plot_multiple_charts, *args, **kwargs) + return wrap_coroutine(with_config, debug=debug) + diff --git a/Ch10/listing10-02-profile_with_yappi.py b/Ch10/listing10-02-profile_with_yappi.py new file mode 100644 index 0000000..b06fcfb --- /dev/null +++ b/Ch10/listing10-02-profile_with_yappi.py @@ -0,0 +1,13 @@ +from apd.aggregation.analysis import interactable_plot_multiple_charts, configs +from apd.aggregation.utils import jupyter_page_file, profile_with_yappi, yappi_package_matches +import yappi + +with profile_with_yappi(): + plot = interactable_plot_multiple_charts() + plot() + +with jupyter_page_file() as output: + yappi.get_func_stats(filter_callback=lambda stat: + yappi_package_matches(stat, ["apd.aggregation"]) + ).print_all(output) + diff --git a/Ch10/listing10-03-memory_profiler.py b/Ch10/listing10-03-memory_profiler.py new file mode 100644 index 0000000..3261a22 --- /dev/null +++ b/Ch10/listing10-03-memory_profiler.py @@ -0,0 +1,12 @@ +import tracemalloc + +from apd.aggregation.analysis import interactable_plot_multiple_charts + + +tracemalloc.start() +plot = interactable_plot_multiple_charts()() +snapshot = tracemalloc.take_snapshot() +tracemalloc.stop() +for line in snapshot.statistics("lineno", cumulative=True): + print(line) + diff --git a/Ch10/listing10-04-sql_filtering.py b/Ch10/listing10-04-sql_filtering.py new file mode 100644 index 0000000..f8fafa1 --- /dev/null +++ b/Ch10/listing10-04-sql_filtering.py @@ -0,0 +1,21 @@ +import yappi + +from apd.aggregation.analysis import interactable_plot_multiple_charts, Config +from apd.aggregation.analysis import clean_temperature_fluctuations, get_one_sensor_by_deployment +from apd.aggregation.utils import profile_with_yappi + +yappi.set_clock_type("wall") + +filter_in_db = Config( + clean=clean_temperature_fluctuations, + title="Ambient temperature", + ylabel="Degrees C", + get_data=get_one_sensor_by_deployment("Temperature"), +) + +with profile_with_yappi(): + plot = interactable_plot_multiple_charts(configs=[filter_in_db]) + plot() + +yappi.get_func_stats().print_all() + diff --git a/Ch10/listing10-05-python_filtering.py b/Ch10/listing10-05-python_filtering.py new file mode 100644 index 0000000..1f04818 --- /dev/null +++ b/Ch10/listing10-05-python_filtering.py @@ -0,0 +1,24 @@ +import yappi + +from apd.aggregation.analysis import interactable_plot_multiple_charts, Config, clean_temperature_fluctuations, get_data_by_deployment +from apd.aggregation.utils import jupyter_page_file, profile_with_yappi, YappiPackageFilter + +async def filter_and_clean_temperature_fluctuations(datapoints): + filtered = (item async for item in datapoints if item.sensor_name=="Temperature") + cleaned = clean_temperature_fluctuations(filtered) + async for item in cleaned: + yield item + +filter_in_python = Config( + clean=filter_and_clean_temperature_fluctuations, + title="Ambient temperature", + ylabel="Degrees C", + get_data=get_data_by_deployment, +) + +with profile_with_yappi(): + plot = interactable_plot_multiple_charts(configs=[filter_in_python]) + plot() + +yappi.get_func_stats().print_all() + diff --git a/Ch10/listing10-06-consume_iterators.py b/Ch10/listing10-06-consume_iterators.py new file mode 100644 index 0000000..1051b69 --- /dev/null +++ b/Ch10/listing10-06-consume_iterators.py @@ -0,0 +1,14 @@ +def consume(input_iterator): + items = [item for item in input_iterator] + def inner_iterator(): + for item in items: + yield item + return inner_iterator() + +async def consume_async(input_iterator): + items = [item async for item in input_iterator] + async def inner_iterator(): + for item in items: + yield item + return inner_iterator() + diff --git a/Ch10/listing10-07-consume_iterators_singledispatch.py b/Ch10/listing10-07-consume_iterators_singledispatch.py new file mode 100644 index 0000000..e55a28a --- /dev/null +++ b/Ch10/listing10-07-consume_iterators_singledispatch.py @@ -0,0 +1,18 @@ +import functools + +@functools.singledispatch +def consume(input_iterator): + items = [item for item in input_iterator] + def inner_iterator(): + for item in items: + yield item + return inner_iterator() + +@consume.register +async def consume_async(input_iterator: collections.abc.AsyncIterator): + items = [item async for item in input_iterator] + async def inner_iterator(): + for item in items: + yield item + return inner_iterator() + diff --git a/Ch10/listing10-08-typed_conversion.py b/Ch10/listing10-08-typed_conversion.py new file mode 100644 index 0000000..8f154a7 --- /dev/null +++ b/Ch10/listing10-08-typed_conversion.py @@ -0,0 +1,24 @@ +CLEANED_DT_FLOAT = t.AsyncIterator[t.Tuple[datetime.datetime, float]] +CLEANED_COORD_FLOAT = t.AsyncIterator[t.Tuple[t.Tuple[float, float], float]] + +DT_FLOAT_CLEANER = t.Callable[[t.AsyncIterator[DataPoint]], CLEANED_DT_FLOAT] +COORD_FLOAT_CLEANER = t.Callable[[t.AsyncIterator[DataPoint]], CLEANED_COORD_FLOAT] + + +def convert_temperature(magnitude: float, origin_unit: str, target_unit: str) -> float: + temp = ureg.Quantity(magnitude, origin_unit) + return temp.to(target_unit).magnitude + + +def convert_temperature_system( + cleaner: DT_FLOAT_CLEANER, temperature_unit: str, +) -> DT_FLOAT_CLEANER: + async def converter(datapoints: t.AsyncIterator[DataPoint],) -> CLEANED_DT_FLOAT: + results = cleaner(datapoints) + reveal_type(temperature_unit) + reveal_type(convert_temperature) + async for date, temp_c in results: + yield date, convert_temperature(temp_c, "degC", temperature_unit) + + return converter + diff --git a/Ch10/listing10-09-fahrenheit_chart.py b/Ch10/listing10-09-fahrenheit_chart.py new file mode 100644 index 0000000..a54a81c --- /dev/null +++ b/Ch10/listing10-09-fahrenheit_chart.py @@ -0,0 +1,13 @@ +import yappi +from apd.aggregation.analysis import interactable_plot_multiple_charts, Config +from apd.aggregation.analysis import convert_temperature_system, clean_temperature_fluctuations +from apd.aggregation.analysis import get_one_sensor_by_deployment + +filter_in_db = Config( + clean=convert_temperature_system(clean_temperature_fluctuations, "degF"), + title="Ambient temperature", + ylabel="Degrees F", + get_data=get_one_sensor_by_deployment("Temperature"), +) +display(interactable_plot_multiple_charts(configs=[filter_in_db])()) + diff --git a/Ch10/listing10-10-minimal_cache.py b/Ch10/listing10-10-minimal_cache.py new file mode 100644 index 0000000..c5b1680 --- /dev/null +++ b/Ch10/listing10-10-minimal_cache.py @@ -0,0 +1,16 @@ +def convert_temperature_system( + cleaner: DT_FLOAT_CLEANER, temperature_unit: str, +) -> DT_FLOAT_CLEANER: + async def converter(datapoints: t.AsyncIterator[DataPoint],) -> CLEANED_DT_FLOAT: + temperatures = {} + results = cleaner(datapoints) + async for date, temp_c in results: + if temp_c in temperatures: + temp_f = temperatures[temp_c] + else: + temp_f = temperatures[temp_c] = convert_temperature(temp_c, "degC", temperature_unit) + yield date, temp_f + + return converter + + diff --git a/Ch11/apd.aggregation-chapter11/.coveragerc b/Ch11/apd.aggregation-chapter11/.coveragerc new file mode 100644 index 0000000..e766c69 --- /dev/null +++ b/Ch11/apd.aggregation-chapter11/.coveragerc @@ -0,0 +1,3 @@ +[run] +branch = True +omit = tests/* diff --git a/Ch11/apd.aggregation-chapter11/.pre-commit-config.yaml b/Ch11/apd.aggregation-chapter11/.pre-commit-config.yaml new file mode 100644 index 0000000..d3b6ec7 --- /dev/null +++ b/Ch11/apd.aggregation-chapter11/.pre-commit-config.yaml @@ -0,0 +1,26 @@ +repos: + +- repo: local + hooks: + - id: black + name: black + entry: pipenv run black + args: [--quiet] + language: system + types: [python] + + - id: mypy + name: mypy + exclude: (?x)^( + (.*)/setup.py + )$ + entry: pipenv run mypy + args: ["--follow-imports=skip"] + language: system + types: [python] + + - id: flake8 + name: flake8 + entry: pipenv run flake8 + language: system + types: [python] diff --git a/Ch11/apd.aggregation-chapter11/CHANGES.md b/Ch11/apd.aggregation-chapter11/CHANGES.md new file mode 100644 index 0000000..8e424e5 --- /dev/null +++ b/Ch11/apd.aggregation-chapter11/CHANGES.md @@ -0,0 +1,5 @@ +## Changes + +### 1.0.0 (Unreleased) + +* Generated from skeleton diff --git a/Ch11/apd.aggregation-chapter11/Connect to database.ipynb b/Ch11/apd.aggregation-chapter11/Connect to database.ipynb new file mode 100644 index 0000000..98a74f0 --- /dev/null +++ b/Ch11/apd.aggregation-chapter11/Connect to database.ipynb @@ -0,0 +1,577 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "53635\n" + ] + } + ], + "source": [ + "from apd.aggregation.query import with_database\n", + "from apd.aggregation.database import datapoint_table\n", + "\n", + "with with_database(\"postgresql+psycopg2://apd@localhost/apd\") as session:\n", + " print(session.query(datapoint_table).count())" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "53635\n" + ] + } + ], + "source": [ + "from apd.aggregation.query import with_database, get_data\n", + "\n", + "with with_database(\"postgresql+psycopg2://apd@localhost/apd\") as session:\n", + " count = 0\n", + " async for datapoint in get_data():\n", + " count += 1\n", + " print(count)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from apd.aggregation.query import with_database, get_data\n", + "\n", + "from matplotlib import pyplot as plt\n", + "\n", + "async def plot():\n", + " points = [(dp.collected_at, dp.data) async for dp in get_data() if dp.sensor_name==\"RelativeHumidity\"]\n", + " x, y = zip(*points)\n", + " plt.plot_date(x, y, \"o\", xdate=True)\n", + "\n", + "with with_database(\"postgresql+psycopg2://apd@localhost/apd\") as session:\n", + " await plot()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from apd.aggregation.query import with_database, get_data\n", + "\n", + "from matplotlib import pyplot as plt\n", + "\n", + "async def plot():\n", + " points = [(dp.collected_at, dp.data) async for dp in get_data(sensor_name=\"RelativeHumidity\")]\n", + " x, y = zip(*points)\n", + " plt.plot_date(x, y, \"o\", xdate=True)\n", + "\n", + "with with_database(\"postgresql+psycopg2://apd@localhost/apd\") as session:\n", + " await plot()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import collections\n", + "\n", + "from apd.aggregation.query import with_database, get_data\n", + "\n", + "from matplotlib import pyplot as plt\n", + "\n", + "async def plot():\n", + " legends = collections.defaultdict(list)\n", + " async for dp in get_data(sensor_name=\"RelativeHumidity\"):\n", + " legends[dp.deployment_id].append((dp.collected_at, dp.data))\n", + "\n", + " for deployment_id, points in legends.items():\n", + " x, y = zip(*points)\n", + " plt.plot_date(x, y, \"o\", xdate=True)\n", + "\n", + "with with_database(\"postgresql+psycopg2://apd@localhost/apd\") as session:\n", + " await plot()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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dv2kfeF9/zhymfve7ZZRIURSl/OiMwqZQSQB0/6adFxcsKJNEiqIo6aBqZhQdM06BXMe9CDM6nh94W6gksvS8+BIH2toYtXBh0iIqiqKkkqqYUXScPCNfSQAYY7X7oPPGVQlIpSiKUhkMeUWx+8orPT/fOff84gfp1rQBiqJUL0NaURxoa3M1KWXp37u3RNIoiqJUJkNaUXRdv9LXdh1nfTRhSRRFUSqX0IpCRKaLyNM5f++KyDUiMkZEHhWRXfbr6DgFDoI5csTfhu+9l6wgiqIoFUxoRWGM2WmMOd0YczrwEeAQ8GNgBbDVGHMSsNV+ryiKolQocZme5gEvGWNeAS4BNtjtG4BFMZ2jrBxoayu3CFXFgbY2dn1yHh0zTmHXJ+dp/ytKGYlLUSwB7rX/n2CM6QKwX8c77SAiV4nINhHZtm/fvpjEyCdz4rTYjrX3zrtiO5bizYsLFtC5/Gv0dnaCMfR2dtK5/GuqLBSlTERWFCIyDLgYuD/IfsaYe4wxrcaY1nHjxkUVw/kch+ILa+3t6ortWGGphlH27iuvpOfFlxw/6/x6ZVgxq+E6KdHpWrOGjlNPo+PkGXScehpda9aUWyRX4liZ/SngSWPMm/b7N0Wk2RjTJSLNQNniT+N8uMuoUbEdKwwdZ300z+ne29lJ1w03AgypVeOe4cz9/aUTJCQvLliQp+iysyEYWtdJiUbhfUJfH/vv/SEAzavSt8A3DtPTFRw1OwE8BCy1/18K/CSGc4RCMpnYjmXefTe2YwVl59zzHSOzzOHDQ8ok1jH79HKLEAnP2dDyr9Fxyqk6u1DoWrPG9T7JKou0EWlGISINwAXAX+U0rwPuE5EvAq8Cl0U5R1gOtLX5D4/1Q8KjWafMtaM/+1n23nmX56LA3s7OROUqFV1r1sAHH5RbjEgUW9xJf7/OLpTUKgMvIikKY8wh4NiCtrewoqDKSud115dbBEcOtLWx98676O3qoq65mfFfuYZ3HnzQMXNt0QcPgEhCkiZPbl8MysU1hOm87npVFFVKmv0QXgzd7LE9PZ4fS0MD5tChEgljcaCtjc6vfX3godjb2Zn3PhQV+oA90NZG1w03Yg4fLrcopafIvakMPQ60tQ3MJj1J6cCvolN4RIkuaV6zGhkxIq+t8H0hUaMTOq+73jGLbTWy9867giuJ2tpkhCkDlTqyrHbCRCr5VhJA05LFUUVMhIpVFAfa2ui89rr8WPtrr7NGqsUuXn09oxYupPnmm6hraQER6lpaaL75puIntqMTcs/hW2HpSHKAUL6Vvr74BSkTlWinrnZeXLDAum7Z+9DhWeBE1y23+j5HGiOeoIIVRdctt0Jvb35jby9dt9xa9EfYcpN1YUctXMhJP9/KjI7nOennWwPZjff/cCNwdLSgi8MCEmKKXdfSkoAgilKcKJFKZv9+fycZOTKoWCWjYn0Ubp3v56J4KoT6en/1J2yTkduUsnP51/LOo4qjgBAmt8zxU2IVoTCWPXPiNE7cvDnUsZK4voWRcDJsGM23rAUYFBChzvFk2X9foPXEAxSrh5PLjP/321DnKAUVO6NIiuxsIw5yHx5JrneoFnt392/aY3sgD1rwhFX21k+NdCc7tV8bdC4dJ89wvXZONdzNkSN0Lv+a4wy2ElPld8w+3erD3L+0fo8iZk+n6/jiggX+IhfBGqCmGDEpcKa2traabdu2BdrHbxnTQdTVMePZHZ6bvDBrtq81GPXnzPF/IyTMjBc6yi1CIEJfP9tkVWwk7RSGnLut1/m9+rJrzZpE/AtNVyzJs0+H6p/hw5nxzNMxSpUcHbNP91w3U8r72TNMu7aWGc89S8eppxVVFrkyB3FgA7SsvyPUrFBEnjDGtAbeMSBVN6Noua24Yyk7vS9GqZREy/o7SnKeisCYgZF01w03Os4wsqG3haPuOGZeSTmh/ThFi/LBB5UzuyyyuLJjxiklEaPwXhlEXx8dJ8+g/qNnBTrumwEc2PXnzEm96bCqFEXTFUt8XZC0XLS6lpbQI400E8Ru64VbChO30Nv99/7Ql+nKyySUJHEooSETTWVM+FlnAPyGafsZFOYqtz6fDuymK5Yw9bvf9bVtOalcRREij1NaQ88cGT48LxKr/pw5ZRYoHrrWrCn+o6vxf1s6hdl6JYP0G6oYywg/BFlbvWKRtNKONSu0MYFlrZRnUsUqitrGxkDbBzXfxFnLIgyFtuZio45KSW3tZ8Tbcvu6QMcc9F3r3IP5fIcqEs/oPHPiNJquWBL5OEOO4cN9b5qk0q5rbo71eNl7Rpqaim7rZ5u0ULGKwu/ULktQ882JmzeXR1mMHBnKkdf59RV5Nnk3+30SxK2kgl6rQbOEIgsbg8jXceppA//7iYgq5MTNm2letUr9TAW0rL050Pb77/0hHTNOif2eHv+Va2I9HlizID8Dkubrr4v93ElRsYqiFOkcTty8uSSLvGa80HH0L2wsdUF221KlIA+y4DCpUWGQWQJYisX3de3rY/eVV7Jz7vmuC67cqBl/tLjjqIULVVnkEOreNMYKBT5tZmpnzH7JnDitonyPlasoSpTOIYkRRx4BFJ4EjLUutL8mYZ5yqzrnFBqYXc3uRfbhKsOGRRPMA7N/f6Dr2v2bds9U725M/9Uv896XSllUQuRTJN9Ab6+lME6ewQuzZke6j8tRzyXKws5yUbmKIgBRbMSjFi5M1MbcdLn/ch3NARcD5lblcwoZjcU85VGno+PkGQMK6UBbW9HV2DXjxw88XP2GKDvhx2QYZoFcENzMh6MWLkx8jUBY38qgBXAJFpKKyzdgjhyh8+srQt/H5ajnUmlKAqpEUUSNLMjamOM2Q9WfMyeQbEGnqrkX1ykM0Bw+PDAyc/qLY2SaNUUVezA3XbEkbwQeZVre/4e3Qu8blcyJ01Kx+HHn3PMDbd8x45TBaxs++CAxZfGh8+fGd7D+/vAzgyGUkThJKldRlPgCDyQQfKHDmmEUnt/DLNSy/o6jisbOVNuy/o7E46dzHf5hRk6lDBGNqsyzcnatWRM40CEuWtbf4Xu0KA0NwU8wfPjR+68I/Xv35jnivehas8Z9tpdQ1cH3f/kr5w9CprIIbcryMGGrT+koFaso/JpskjAbNa9axYznns13Qj/1pHWunKyo0tAwsGAuSqba0MRQBKWYGSPXYRuFqCaw7GK6ci04Ezt1vV+a16wOfI5syLRvpdrX52uFczn6zPXBHrKQVVhTlpuVoK6l5aiZ0CWra3YAWA1UbPbY5lWr+GD37qKLt0q5oKV51ap0LaCJKY/XgbY214dgTV0dcVQT33vnXZGVZ9J+B1dEAvuPst/Vj8y5vpuBU9bXY3xmOd459/yB/bvWrLEyofb1QW1tIB9ZnNQ1NzvOcuuam/nQ+XMDK6+wQSfjv3LNoEqLMmJE3vGKRSIeevLJobMi3oWKnVFA8UVoSjx03uiu/OJa2ep4nJSWhcyltqmJljtuD6XksiNWJ7Nk7my1UElAsMCGbMRWx6mnORbeKQfjv3KNY4XJ8V+5huZVq5zNux6EHWS4FTALNDvMyuuDSp2BVGz22CwdM2e5LrAqzMg5FAiU3iGTYcaO7cH3c2LkSMeR1a5PzoslcqSupYWTfr41ry2pTK2e1Nb6D7126ZNS0XHazMHFu2ImyVxjxTL8gv/7Ng0BBF6/hbqWlkTqhmj2WJ+03HqLY3vQiKJKoTbIsv+envgWJr33nmOtAKeRYVAKp/pZgozUYmHkSGY896zvmUy5C8203HZroLxYYUjSl+bLb+fjWqQlRYrbLKll/R2l80smRMUriuwiplJHFJWLCddfhwRIiNh1y63s+uS8eE7+3nuDFM+ohQsJNSutq/M11W9etSpUAsig1IwfP/Dgn9HxvLeTPmSalbgZtXAhLbevG9IlYpuWLC66TVoGhHGYsdJKxZueqpEDbW103XJr4NQVcZA1ETlViPNCRowI/aPpmHFKZMd8GDNkYZW5+nPmpHoAEnfWWSdzYDnwLHJUZvNfuSmV6UkVRQXjVC4zLNLQgDl0yNe2NePH+0ppUdfSEktd56h+kKFY08OJoFXVipGmfts59/zB91yVKwmoEEUhIk3APwOnAQb4C2AnsBGYCuwGLjfGvON1HFUU4Ynq8JWGBprXrGbUwoX8bs45sS5Wi8s8c6Ctja7rr8ccyQ1aMIAPX4JdyrJaiGtWkfbZk2JRKc7su4F/M8acDMwGOoAVwFZjzEnAVvu9khBR7bO1TU0Do8YJ11+XuHM0DKMWLqT5Y73UNfQCZuDV+vOmmpREnKiSUHIJveBORI4B5gJfADDGHAGOiMglwB/bm20A/gP4ehQhlSKIhLbh565fCLIIrCguq1nDMmp8J6Muzv+OHT88zv7PZWYRoDjOUKGupSVyuHJaooiU9BBl+HgCsA/4rog8JSL/LCKNwARjTBeA/eoYPiIiV4nINhHZtm/fvghiKFEcvYWpD+JKhR277bh+9OBzLHmDpmkHcZxZDB8+qEpgNRA5Lb5IaqKIlPQQRVHUAWcC/2SMOQM4SAAzkzHmHmNMqzGmddy4cRHEUKKERzo9WKI6MBMJHe1zjnppPutdZizpys+79UJHVSoJiJgWf/hwZnQ8H69AypAgiqLYA+wxxjxuv38AS3G8KSLNAPZr8IovSiCijCLjjmpJbH3BkYPJHHcIMlB61WP9iVOqkGpVrr7Zfh/ceRqsbrJet99XbolKRmgfhTHmDRF5TUSmG2N2AvOA5+2/pcA6+/UnsUiquBKrbyECqcpjs/mr8MT3wPSB1MJHvgALvlluqUpGNmOxEhPb74O2L0OPnYjxwGvWe4BZl5dPrhIRNcTl74EfiMh24HTgViwFcYGI7AIusN8rCTNq4cLgSfRiXPGceMx9/Rj/227+Kmz7jqUkwHrd9h2rXVHCsPWmo0oiS0+31V4FRFIUxpinbT/DLGPMImPMO8aYt4wx84wxJ9mvb8clrOKNn3QHubjlyQrMyJHJj14/dbv7Z8Ma898/8T3n7dzaFaUYB/a4tL9WWjnKRPqC5pXQNK9a5ateNFghkJ4P9wApnkuyOtZrel/ovzAu2V/d2hWlGA5RdwNUga9CFcUQ48TNmwdV2iOTsbLO5iRNLBYC2bLuNl/nK23MvZtpLf11K5QhTBWYnyq2wp3iThyV9rKzjbzkg5mMVf/AmIHqaKWNuXdbLxJyHcm3zoY/vHD0/diT4e8ed99eqV66PbIQVYH5SRWF4orvyJm0RRhJrYuZKWfmUagkwHr/rbNVWSiDGTXJXSGIfzNtpaKmJyUaaYwwcvVFmKP25EIlkcWtXalu5t3o/lkV+L5UUSjR2PYvwdpLwajJ7p9VgT1ZSYBZl4O4PC51RqEoxYjZbxCW3MgTr9GfW5ijohTD9Lu064xCUZIjaEoEtxEd5M8UZl3uvkBv1KTgcipKlaOKQikP2++DH3/JdhAa6/XHX/JWFm4jOhjsaDz1M87bnXShP9nCsuFiWD3q6N+Gi8MfS1FSgioKJTm8Hrht1wyesps+q90NL99D4VqKXY84b5Zt90oJ8rOQ5VM2XAy//2V+2+9/qcpCqXhUUSjR8HLkeTmOe1yywbq1g7fvodAn4hbKmG33SgnSHTLrTKGSKNZejM1fhTVjrJnJmjHVnatK+6KsqKJQovGRL7h/5mMh0pbGBi6c1MKsqZO5cFILWxob4kmJUCxCpdQZP4N+J6+w42pLd52aEOyAmQGGkHJTRaFEY8E38Uyh4fEg29LYwLXjjqUrU4cRoStTx7XjjmXLL64fvHE2zbMftt+XbIRKmAdA0LDcbd9xb2/7cr5vp+3LQ1tZePVFSQkQ4Zca5RYPqiiUGPAIhXV7kA1r5IaxYzAFqdGNCDccM2zwcZzSPLvh9VAOkq7cibAPgDjTPFRxuuuKYYhlMFZFoUSn2MPX6UG24C56XOpnOLYHWf+Q5FoJrwdAptH5MyDxxIVVkG+oohhiGYxVUSjR6XWuZ51H7oPstinw4DLPzde2r81v8LP+4VtnF982m9wtrKnG6wHQc8hrx3DnC8JQNj9VGl5BHhV4nVRRKNHxilTKZfNXLbv+Bwe8txNh486N+W3zboRMvfd+f3jBCkX1Gl1n6wokYaqJazFfWDt22HXEKREAABUoSURBVLDetFPjkrvUrT0NeAV5VKCZUBWFUjoCOh9nbpg58Les61FY+D+L7+Q3FDUJ85Rn+G4Awjppw4b1lpIwgQD9vcHa04BX9uQKTCOjikJJJybfVNP+RrulLArLngYl+zBNSyqPagp1HWKRQHk4XTc3351XtbyUoopCSScODu32N9phwV0Rj2vbjouN/lePCv4AC2pS2H4fPPhX+aGuD/5VsGNUEklEApVSsca1mr/77YobFKiiUKIR4GY/d3ILM6dOHvgLjDFFneDFj2GPZv0suAs62g1qUmi7Bihc7+GRz6rS8QoEWD0K/uFk58+TSLcShqCr+b1MgRW2/kUVhRINn6Pocye38F5trTVTyP0LyOnHO5uMHFd4OxLwnEH8BUHNWX6DAIKSa866/cPWXyWYtt7vclYWSaRbCUPQ1fzF6lT0dFdMAIIqCiUwa9vXMvv7s5m5YSazR8PaMU1F9xlQElEQoU+ERS0T8pq3NDawomCF94pxxzJz6mSWTRhbcJCIYaquP37x58y+bcrRzLJJsHqUNevKmrO637YfprZpa9PfpF9ZFFLqdCtRyFXSftZMdL+d7uthk+L4MiWNLHt4meUrsOkXYeMxI/lpYwOPvdbpuI/76D4EIrw0LH/l9oqxYwYrIft9e3098yY1s3WPwwNIarxTl2e5+Tj40FjbtBShUNNtU4qHBidNf49l8krzw3f7fdZM9cAea5Y270b3a+VVo6TUZNPM+M0gkOXBZfBqe3nrzBchUi+LyG4R2SEiT4vINrttjIg8KiK77NfKc/Erjmx5eUuekhhAhPdqaweN9LOsGHds9NlEoSy5ysfr2CLsratznvV85Epf51o7ajizR8PMqZOYPXWy+wyqmBkhR0kU+muyf26mtVjpOZjqUeyi9pXMtPt75mhY1H6DR+6ueHw6W17ewoUPXMisDbO48IEL2fLyluAHCZJmppCUR3/FoY4/YYw53RjTar9fAWw1xpwEbLXfK0OAFb/2uJQOI32AeZOaY1cSiLBi3LFcOKnFl9kLe9Yz6CHvYwS3dkwTG48ZSb/tU8nOoOZNah68sQ97+bxJzcycOtnZX2Ob1maWQln8+EvJn6OQ2uFFN5l5/CTrPsrpk5eGZVwHIXHUq97y8hZWP7aaroNdGAxdB7tY/djq4MoiahqVFOeBSmLedgmwwf5/A7AogXMoJcbvj+bs4yflOZT31kWwbhoPc47ti9h4zEh/iijnIb928xeOtrd+0XM3x+Pbs5R5k5qZbc8EBpSQR4TOzOMnWf3h5ci3P0tcWZQj59Al3/I0Fc08fpJz37gMQoBYvsfdT97N4b7DeW2H+w5z95N3BztQVDNYivNARfVRGOARETHA/zHG3ANMMMZ0ARhjukRkfFQhlfLj60cjwiH7R551KIfGS0kUnDMQImz8wzZWAsseWEj7+7+HnFDdpv5+Vrz1DvMPHnJwhOcfZ+ChjxXUuvGYkcB7rHSwPpw7ucV/pJe9zazjJ7H9FeeQ27Vjmrj/mJH0Y432Lnv3PVa+vX/QdssmjKW9Pj/1ybQjR9jU+aa3Q31Yo7VmJU5fRvZYW28aNPp2VRI5bGlsYP7BgnxaUbMBA10HHfxXHu2uxGQGSyNRZxTnGWPOBD4F/K2IzPW7o4hcJSLbRGTbvn37IoqhJM0bB98IvlOYEFhjaO7pZd17ffGbrHKY988nW0qiwPSzv7aW68cdy8zjJ1kP2CL+j8L3G48ZyZbGBs48flKe7yFw1JcIRsRRWbmZwwrNcOdObjn6HfJMOcPcTTlZjhy0nKwJ+zK2NDb4UhKIcMPY6EohMlEzAxQjpb6jSIrCGNNpv+4Ffgx8FHhTRJoB7Ne9LvveY4xpNca0jhs3LooYSgk4rvG4eA/oMmNo/lALj/xlB/P/viPe8+VSMBsopC/COg+wnPc9NTWR14wgQnt9PWvHNOWZuNzMYdaMxmLZhLHuysnLlFNI1AWOuWSjguzZRDasmWxfFcEx/XzC6ygGmVyjZgYoRkrXVYRWFCLSKCIjs/8DFwLPAg8BS+3NlgI/iSqkUn6uPvPqeA9oDCP686fqI2pH5J1n8fTF8Z4zl2IPpiizmZhnQoWzh2K0TplYfDZEzGHLfiiICoolGi4GZ7YXax5bk/NmrKfiXNQyIW8WWXTW5kRKEztGmVFMAP5TRJ4BfgtsMcb8G7AOuEBEdgEX2O+VCmf+CfOZc9yc2I637vw7WH3+HTQ3NiMIzY3NrD53NfNPmD+wzco5K2M7X8Xi4tx1Y+bxk/jAzwhdhBVjx0R/sAXhwB6WTRgbPoULDivzE3YAd/fZim31KDA9rtvNm9TsEK3lw8RXIYR2ZhtjXgZmO7S/BcyLIpSSTr590bdZ276W+393P/0RHHdzjpszoBByFYMTgmBKUfQnLkL4ZELv63Z+v8cp2PalYcMGHuAj+/pcF1CGZdnESbRnCP89RejDGrlv6nzzaPv2+5JdQFhkfcNAdF+QaK0KI0XLGpVKYOWclTzz58+wY+kOq8FvdJLNtGOm8e2Lvu17+8unB3sALJ6+mB1LdzB+xHhLtoDylZodZ97Ijt0xlTEN6gtxmqnYf+/V1tpO5kw8sgHtw8L7fQawH755+bySXhNSJN9XMRNaIBOfZznd8qGKQgnNgLLwiSBs+symQPv4NT8Jwo6lOwa2/8Txn7A/iMlfkJTSsUfC6/a9lS6lZiuM06eWoW5Hsb7Oyee1bMLYSOanUCuwcyi63sVeHJo1t3mGXAPUFV+UWA5UUSjRCPAg3r50eyIijKwbOejYG3dujNWpXGsMtYnNUIT5Bw8xsq8vdcqijzIsAguwhqa9vt56+IYMK727/baisjitws86rn3N4nJmagPyupGt6Z4yVFEooQkyGstEMGEMq/G28z72ucdCH9sPI+tG8nTNNJ5+ZU/wB7nH9tOOmWb989l7ACyfQAWYy5Jmxyt7GN7f768f7Ifvll+Hq0PddWTwIsXC4++tq2PW8ZPY0tjAbHt9TJ7jOgi2vK6ktPqdKgolNEFSHNz8sZtDn+em89wfAomG0GI53h/73GOw9CH48Pms+8PbwR7kLg+S8SPGHzXDzbocai1luCOrjOJUFhWkfAQBhG2vvh5gJ+Hu4eFmPr4e8/bixxXjjqU/d31MBD5SkOom7aiiUELjd7X24umLi0Y3eTH/hPms+/g66muPjsQEYfH0xaFCaOtr61n38XUDobmjho2ixuGnsHj64nzH+9KHmD/+LBr8PnSNYd3H1+Wdq7mxmXUfX8fWxVvzt73kHwfWBOx4ZQ/r9r1Fc28vAgP7hFGKzY3NrJPKWdB6XONxAzOsOd3dvhXcG3Xh1lP4Vp8xKIfcYx2pqRmonbJ67JijyiKlpietR6GE5rjG4zzz4TQ3NnP1mVdHUhJZ5p8w3/dxipnEVp27KtDx8lj6EIe+d5qvTTMG32HA+XmQ9jC/7ljmn3VjXtjn/BPmDyjGmRtmFj1/brDBCh/bO7Hu4/Etg1o8fbHlO3JhYMGl3Vff3noTMz2sNLkcN8xHFuGUcrimhuvsvGjz6yLkR0sQVRRKaOZOmuv5w3/k0kdKKM1RipnE4lBcxagxhpvP9yjh6cSsy32vB2ioa+BQ7yHXzwsXRwZej2IM6+beHmtfZZVc7jqchroGunu7Oa7xuPxBhd0Xcx5eRnvXb4qO5q+ec20omZr6+9lfG+PqbmNCzTz6bdMWUz9D8ndncNT0pITmV3t+5fpZc6NDvYYSESqBYRzYvoCm4U3cGvNDtpAbz/Euu1q4ViXoehREEpE/dx3OjqU7ePxzj7N96XYeufQRx/N9+6JvF33wrvv4utCyrnj7AJm4/DdRjyPCda88FI8sMaOKQgmN1wN57iTfiYRjxyuBYU3CpTN37H6NXy/5deKzFq/ji4OLduWclYF8HCPrRhbfKCVE8n+9/z4373uL5p5exBjwG22VEP2kM1W5KgolNF4PZK/ZRtJ4JTCMknpkAK8R7tiTox/fJ26zNrfrsnLOSl8+h5F1IxMPOU4NoyYz/+AhHtnTyfbdr7HjlT1MO3IkXKSYCIvffa9iIsyCoIpCCY3XrKFs5p8ixGEScxqxD7T/3eORj++Xq8+8mhG1I/LaCjPwFpKNICuMwsqagnYs3VE9SgJg3mAT3qbON9mx+zXG9/YGeug3DW9i5Ql/Fqd0qUGd2UpovGYNsdevCICXMzuOdOluTmGTXJ0lR7Iml7ufvJs3Dr4x2CHssV8pHPoVwazLXVOHb93TxaKWCbw0vHhajUxNhhUfXQEnzEc2zAqdyHJgEWbKUEWhhMYrNDb2+hUB8JrNDLUHZLU/9J3Wv8SJVTL2wKD2LS9vcVXQl0+/3DMa0I28RZgpQxWFEpoaqXG1+Zfz4eW2vqOckVhKeLzus1s/fmuyJ2/9omOzl4JeOWdlIEVRJ3Ws/djaVCt89VEoofFyDEfNyhmFMLZ7Jb1c9keXObbn1jVJjAXfDLWbW4RZNg1+7t9Tf/5UqpUEqKJQIuA1Qg+SBypu5p8wn9XnrvasnhcFtxDbpENvq5VsaG+2f2ukZnB6lZThJnOlVm0Uk4JQrtbWVrNt27Zyi6EEZMvLW1jx6xWunwetV1EpeKXPGKrfeUiz4WL4/S8Ht3/4fCsZZIoRkSeMMa1Jn0eHQEpo0j5dTgq3mZT6QCoUOzNwHhWgJEqJOrMVJSBXn3k1qx9bzeG+wwNt6gOpcFQpeKKKQlECEnb9gqJUKqoolEgMqxnGkf4jju1DmWpfv6AUx2utRaWhikKJhJOS8GpXlGpgy8tb8syTXQe7WP3YaqAyfXvqzFYURYmZu5+8O8+HBXC473BZw8ajoIpCicSoYaMCtStKNeCWRiatyTKLEVlRiEitiDwlIpvt92NE5FER2WW/jo4uppJWrj37Wuok34JZJ3Vce3a4imOKMhRwS4pZzmSZUYhjRnE10JHzfgWw1RhzErDVfq8MUeafMJ+1H1ubtwo67XlrFCVphloamUjObBGZBMwHbgG+ajdfAvyx/f8G4D+Ar0c5j5JuNAJIUfIZaiHUUaOe7gK+BuTWTZxgjOkCMMZ0ich4px1F5CrgKoApU6ZEFENRFCVdDKUBVGjTk4gsAPYaY54Is78x5h5jTKsxpnXcuHFhxVAURVESJsqM4jzgYhH5NDACOEZE/hV4U0Sa7dlEM7A3DkEVRVGU8hB6RmGMudYYM8kYMxVYAvzcGPN54CFgqb3ZUuAnkaVUFEVRykYS6yjWAReIyC7gAvu9oiiKUqHEksLDGPMfWNFNGGPeAubFcVxFURSl/KSicJGI7ANesd+OBf5QRnHConKXFpW7tKjcpcWv3McbYxKPBkqFoshFRLaVomJT3KjcpUXlLi0qd2lJm9ya60lRFEXxRBWFoiiK4kkaFcU95RYgJCp3aVG5S4vKXVpSJXfqfBSKoihKukjjjEJRFEVJEaooFEVRFG+MMZ5/wGTgF1g1J54DrrbbxwCPArvs19F2+7H29u8D3yo41mJgu32cOzzOeQvwGvB+QftXgeftY2zFiiF22n84ViqRQ0A38LscufuA94APsPJQpV5u4BPAjhy5+4DPpV1u+7O7bdk+sI+Tpv6ea/drP7CH/Pt7K9ADHCR997ej3MDxwNM590lHJchdAb9Lt/5O++9yLvAk0AtcWvDZ7cCz9t9iNxkGti+6ATQDZ9r/j8R6CJwC3AGssNtXALfb/zcCHwO+lNtBdse9Coyz328A5rmcc4593sIO+gTQYP//18BGl/3/Bvg+cCZWHqof5ch9pELlvsOWdwzWA/kbFSD3l4DdwHW2nHuAO1Mk91Tgk8Bm4FLy7+8fAt+zP0vbfeIm92zgG7a8HwLeAf6xAuRO++/SS+40/y6nArOwfpuX5rTPxxr81NlybgOOcTpG9q+o6ckY02WMedL+/z2sUcpErAJFG+zNNgCL7G0OGmP+EzhccKgTgN8ZY/bZ7/8d+DOXc7Ybu6ZFQfsvjDGH7LftwCQXsS8B/smW+wHg/By56ypU7mx/Xwr8DFhQAXKfiXUjftcYcxD4FdZoKhVyG2N2G2N+jr0CtuD+PgNrlgQpu0885B6PdV9swJrlHQQuqgC5U/27LCJ3an+XttzbsWZCuZwC/NIY02v/Lp8B/tTpGFkC+ShEZCrWD+hxCgoUYd2kXrwInCwiU0WkDutGmBzk/AV8EevCODERa8qGMaYX6wfzEVtuAdpEpB04u4Lkzvb3EuC7FSL3T4HRwAERGYs1QmpKkdx5FN7fwNuQyvs7jwK5jwMexroet2GNYL1Ii9xp/l3m4fIcTOPv0o1ngE+JSIP9u/xEMRl8JwUUkQ9hmRSuMca8KyKBJDPGvCMifw1sxNJwj2Fp18CIyOeBVqyRq+MmBXIfByyz5X7XGNMqIicAP6eIskyR3Nj1PWZiPQg8SYncm0Skxz73PuA3wGdSJHcuI6ic+zuXQrmNMWaWiLQAm8i5NimXO82/Sy+50/y7dJPhERE5i/zfZa/XPr5mFCKSweqcHxhjHrSb37Q7KNtRRQsUGWPajDFnG2POAXYCu0SkVkSetv9u8iHLnwDXAxcbYz6w227JHsPebA8w2Zb7QSyn5P+1P3vDLqz0MtaI4P0KkftN4C+BH2MlDKuU/n4V+JQx5gKgHnuUnhK5BzYH/gcF9zeW3TmN97en3Pb93Qm8jDW7qwS50/y79JI7zb9LLxluMcacbv8uBSsoyXMHzz/7IN8H7ipoX0++8+mOgs+/wGBv/3j7dTRWdMYfFTl3oRPnDOAl4KQi+/0t8L9tuX8K3Jdz3m/a8mazM34n7XLn9PerWNPESunvWuB/2fLOAt4A1qdF7pz7+0Vgs8P9fQ9Hndmp6W83ubFs1Xfa8o7GGi3+SwXInerfpY/7JJW/y5ztv0e+M7sWONb+fxZW5FOd5zF8nORjgMEKxXra/vs0lu1zK5Ym2gqMydlnN9bI8X2s0eYpdvu9WGFdzwNLPM55h71fNhxttd3+71gaPCvHQy77j8CavhqsSITn7e3/u/1/Npzt+QqR+9PA6ViOsUrq70uwRkwHscJmH0+Z3GdhjQAN1tS7O6e/f4MVidNv9/ulFSD39Vghl7nhsZXQ32n/XXrdJ2n+XZ5l73cQeAt4Luf3mj1/O3C6lw4wxmgKD0VRFMUbXZmtKIqieKKKQlEURfFEFYWiKIriiSoKRVEUxRNVFIqiKIonqigURVEUT1RRKIqiKJ78f6S57F/6hNcgAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import collections\n", + "\n", + "from apd.aggregation.query import with_database, get_data, get_deployment_ids\n", + "\n", + "from matplotlib import pyplot as plt\n", + "\n", + "async def plot(deployment_id):\n", + " points = []\n", + " async for dp in get_data(sensor_name=\"RelativeHumidity\", deployment_id=deployment_id):\n", + " points.append((dp.collected_at, dp.data))\n", + "\n", + " if points:\n", + " x, y = zip(*points)\n", + " plt.plot_date(x, y, \"o\", xdate=True)\n", + "\n", + "with with_database(\"postgresql+psycopg2://apd@localhost/apd\") as session:\n", + " deployment_ids = await get_deployment_ids()\n", + " for deployment in deployment_ids:\n", + " await plot(deployment)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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QTeJcjaE2xvRX8L5qoPy0t7BrfTvkr22GMXn/c9VH34eq74B2k3BrE5ZVlyQztdrj3qWrHbeNN3OBrWuhdm3ByZ3dZ4vvC0azmXidVccBQrVR12EfduzCjapyEp4W7kwX+YWpOXU3Djd0Y656HPAYCq00YQk7c8LnddtTB/82oEVT4KYT/d8oiU0UWmiOB3UV0FbJNAU5umC0JsG9qZNjrdvVxTatyu7Y1DoBiI22Lt+8O7TbuMIryMgT/LbZujyUoWgAkJbrvyzfJnQjI983EXVPoUCcJnsQkStW4Z9A6K6V+rTRd9TiNhOFpfX33n7lZxOXvS2Yuw74bTNw3/8E49+vn/W7uVYCgELOuSeyZHc5YHW0SW6q/EJZK62v42vX2oi2+bd0u13cfzsNIcsp8q+YW0cBtU6Ph6Xlgp3ZvAYkIiKi+ldUVIRNmzZhy5YtyMrKYigaEYWFsrIyZGVlYcuWLdi0aROKijS/LCEiIiIiIiIiIiIiIiIiIj9hOm2YQuwCAIe5+LrdSadKqbYAPgcQVWXxrwB6i8gAETlXRE4C0BbABADlVeqdAeDRWjwnIiIiIiJa/ANQUW5fZ+E3kKL8oFdhF4wWYXFH2beN/UT8NglBD6VOybZ1kKl321dKaoXmJ52Gw9vaVzPEiwSvf3rQgOI/XY2p2maMblStbNpYhYfPVGhZJefs9H7AmocNdGqusOLRRhjbYTOGGH9jrPE9Fp74IxJvuN/V+kNBKQWjzWFI9mahkZRgdL59kF9N/2QC/2Tun0hrG4wWpnl7qBGM1qNsneOmCV5fCo2c1xNnfHml61VXhkTFN9LXsQu1aWgNGYx2cm9lGf5Y09JtwC/rD+zJ3k4n3APAzjAJRvM63Dd055xweR6AddhUbaTEAX/cZ2jDxnShf2t3hXYcNeWX6Pe1xMbWywNJirVenplfxyFvmu6V8p332jezLg/Va71ul+CYx72IGm+i5e0u03pqIc3ifVNQ6r+s0uo0oM0dJpJvMdHhbhNf/XVgHyuJwpHueOTkGsYJu6DYsgqg6Y0mLnjNREFJ3b+/n5vjv45pCwVZBXW/brdrsDs2Eh3Kgnm3dkuxXh7qANhQionUl2XkOe/H6X1atsV8+nxNMNqxnZ2v34ruPqxSSbngsrdMNLvZRLu7TLS/y8QHv9ff9SoREREd2oqKirBt2zaUlwf4vSkRUQMqLy/Htm3bGI5GREREREREREREREREROQQg9EODbtEZIuLr0yH/T4EILHK44UARojImqqVRKRURF4AcG6N9rcqpToE/7SIiIiIiA4NYpqQVYshi76rFnImCxyGTM35OOh1V3j1ZREe/2WJTRSO76pv0zYx/NKrRARyUb+A9dQrPwMAerayfw7NvHvggf/Ez9EFXzseU8/SNdUXxFRPjYmMUPi/UQZSn/LAfM33NesGD1rG+8bWIUnhrfu64pdXDsdbr5yGjhdfCqUaaNu3777vx3PzPsXgooWumn+xrEowms182nAMRhMR4I851Zb1KHUTjLY/hWZkwRxEm+5SzCq3SZxNMFpeGAejSQMGo7VNVLjnVGcrGvqUCa+bdLEgFJcJvlkpmLdOUFYR2nW5GXpqTngEG+mOBTX3jbaJ1vW2OP3kqx7sCnEwWuoUA+2a6ffdbi2ty9bvqtvX1iqwoVKwwWjJmmC0P7cF159Tgfa/lvHW5YGC0QpLBT+sFrz/u4nVqdavR0m5oOcDJn7/x+FgXYj0AEfafFKblus/pvwAYUi79oZ/7MgGzpqqf15EFJy6DpFtkwA0jbGvM2OJ4Nr36va9XVAiliFI5V7gf8vq/rji9jKvkMFoRJZ093eAL2DWSrcUzbVrevheU5RV6Mt2Fzjvx257VevT4n8h5Gmu0Y7sqHDT8OBPEqUBMkZu+FDw7iJB8d56O3OAS94SzN8Qvq8XERERHRwqQ9HE6UUUEVEDEhGGoxERERERERERERERERERORTR0AOgA5NSqiuAy6osKgMwVkS0U6pF5Aul1PQq7aIBPAjg8jobKBERERHRAU6yd0MmXgwsnedbEJ8E3PsG1ODTgGU/O+tjynVAcitfG5e8QQRRnfUvhXnrrScfHNPZ9RDqnLz5UMA66pbnoFr60kK6tbSvm+zNslw+smAOYjxelHgtEuVqOKXg++oLYoJMjQkHLdoCjZoAxYWIQjnmbj0J0+MvwaTku5DricO4nOloYhbhkeb3WTafs0Zwy0jfz3ahBOEWjCalJZDHrwLKqicj9CldhQRvNnI8msSmKhLM/Sk6sVKIC/I+xrSESx2PoXKbNInS18kP42A0UzOJqb5e64fPNDDoMMHol2wOhHtd977g1UvqZmB/bBGc8py5L1iqT2tg5g0GDksOzfrchH3szAlcpz7o5rd5avwLiE7NFQD/yuEUpLB5d+jG8s/jRsAQzB6ac9iy7YBpCow6eoPVRTBakiYYLT0PeOYHE7eOrJv/CRIoiKhVvPV+ZxeCt2K74MypJrbtqVwiuOo4hVcuVvte04w8QcvbAx+PnDi6E3DUYQptEn3nAQXgkqMVOrdQMK62TsXdbBEoWOAi/McU4O2FginnhNkJm+gAprtXC9Wh3DAUzh2g8OYC+3PVJ38KXrpQkNC4bt7fhWX6sg0ZdbLKatzOq3dzbCQ6lNgGo2mWd21hvXz5dt9E8gYLgLdhd4+dkS/QP9vqnN6npVpcY+YVW9eNbwRMPEPhpF4Kp7/o/rqy1Cb0rbxC8OkS/0GLADP+EBzXNfxeKyIiIjo4iAhSU1P9QtEiIyMRFxeH2NhYREZGhuW1IxEd3EQE5eXlKCgoQF5eHsrLy6uVpaamonPnzjw+ERERERERERERERERERHZYDAaBetCAFVn838uIhsctJuM6oFq5yqlrrMLVCMiIiIiOpTJczfvD0UDgNwsyF1nA7e/BGSmOu/nrrOBb9Khmia4Wn+FZp5khAHtH+meeYTCzTOsZ3CeeUR4/WGvbFgBTH88cMUBJ+77sXcr68CTSkmaYLQm/x6HER4PvvrLflXHFv2Kibsfrb4w+sANRlNKQdp3B9YtBQB4YOLy3Om4PHd6tXq6YLTVaft/PpCC0fDNdODHj/0WR6ICpxTMxkfx5wXsIt5bfYbzYxkPYFV0T/zRaKCjIVRuE8NQaBpjPUE7L4zvxrXhQ3WTdWTp9H4KP99h4JxXTOzO19d7fb7g0bMEzZuGdkcUEfznFbNaqNTfqcBNH5r48sbAIYvO1uG87p7CkKyy1nT7Rs3TUrcU63rr0sMjSCG7UPDbZvs6bROBj642MHO54OnZYvncYyKBVy5W6JAU+Pn00pzDducDi7f4ArPqgl0wWkKQp7iWcfrz8e2fCIZ2E/TvEPrXOFAwWst46/K03OoNswsFb/4q2J0PTPnev9PX5wteny+4daTCfacpPDArdCF6C+/WHz9O7wfLa5XZqwQPnlF9mdtwzadnC6ac464NEenVx7XSo2cFDkYr9wLHPG7i9MMVTu6lMKJXaI+9RTbBaHl5pQAahXR9NbkNRrMbL9GhzO6tpLss76m5dt22B1iVCvRpE5KhhVS+TTii3T1lTU4PPWk5/jVzNcFocTG+z2hO6wv89yKF6953d4ArKdeXZRboP1/4cU34BFMTERHRwaekpKRa2BAANG3aFG3atGnwz3+JiCIjI9G4cWM0b94cO3fuRH7+/hvD8vJylJaWIiYmpgFHSEREREREREREREREREQU3upxKikdZM6u8fhtJ41EZA2A36ssagLgpFANioiIiIjoYCJ//Qr89Kl12VM3uO9vzGGu21R4rZd7bO4mOyQpXHaM/2SDS49xFtpSX2Ttn5DLBwWuePgQqPbd9z0c2t3++SdHWcyCHTUWxoRncKSDgJZ5W0eiidRIkIk+wP8gukOPgFVmbh9juXxrFlBY6ptEaxuMFmafcIhFKFql/iXLArZvbBYiCtUn86R4M/DLluGYv2Uo3k69Em+fss22j6phcU01u1B+SfhOUDY1wYz1HYJ3XFeF9Y8YeONS+xWn3KYZcC2sTvNN+q/p65XA9j2hee3s3lc15diEW9WnQMFUlbqlWL9mOUXAxowQDyoIgybp95mXLlSYfbOBtQ8bGNxZYfK/Dax52MDsm33ff7vbwPRxCp+PN7B5koFLj3F2EBzSBYjW/KuMmcvr7niQrQnVaxoDRHiCe1N3b2lfPuAxE+UVoX9Ogfa/VppgtB3Z+3/emCE44mETd34qlqFoVT3zgyDpFhOv/RKa55ISZ19+Qjfr12PRP0BmfvUxFNgEfxBR3dMdj4I8rFpKiVMomhr4HLMu3Rd+eNJzJh75KrTXRIU2x5rcn2dDMtP0FULAzbUSABQzGI3INV1ehd3nL7NWhOe9bJ4mlAwAMlwEo+nuh2tKzbEYgyagLK5KjmRCEJmSpRX6MrtQyEIeF4mIiKgOVQ0ZAnwhRAxFI6Jwo5RCmzZtEBkZWW15Xl5eA42IiIiIiIiIiIiIiIiIiOjAEGbThulAoJRqCeDwKosqAPzqoot5NR6fWtsxEREREREdjGTK9aHtsKQIsmWNqyYVmomYER77ds+cqzB2sIJSvgmuYwcreNMYbgAAIABJREFUPHNueExCkIwdMB8ZB7lqcODKR50E9chH1RYlxSoc31XfJPmoQcCxo3wPIqOA8yZA3foCAKB7iv3qLsz9EJZbKTI68FjDmOrQPWCdHqXrtGXr033fbYPRwmP32m/FAm1Rd2NnwObNKzItl0eiAscUL8YluR/gkj3TcctI/ROvGhYXpw1GCziUBuM0/Ko+xDdWuHyIgfFD7Vd+3fsmREI3Qd9qknslJ0EAO7IF49420ftBLwY/4cXTs014a2xYN2EfBaVAhbfhAwi8DkPzerfW9/HGgvp9HhVeweTvTLS90wvjat/Xpt3WddslAuNPUBjRS6Fx9P4n1TXFt6zbznkYOPUMXPTucJy5cjJSom3SF2qIjVEYrsmqrNNgtCLrvhMbB99n5+aB68xaEXz/Otpj095jbvtm1seJfzL3v3+mfC/Ynm1Zrc5dfqz9cWxUX+tyEeD71dWffHG5ZVVb4RzISXSgCXQ8CpWYSIUNjxoY0sVZ/Ue+EqTnhe69bhuMVhYBefeJkK3LittnUmITHER0KLO7TdJdnTRronBsZ+uyb1aG5zVFgc099ortzsfs9D4tLdd/mS6cLb5KGFpiE/c31iU21352wWghvEUmIiIi8lMzGC0uLo6haEQUlpRSiIur/p9Lah7DiIiIiIiIiIiIiIiIiIioOgajUTD61Hj8l4gUumi/sMbj3rUcDxERERHRQUcKcgGXIWaOLPreVXVd+ExEgLvJxCYKb401UDLV9/XWWAPNgph0GWqSkwkZNxCY/UHAuurrNBhPfQmV6J+8cuYR+ufSvEVTGE98DjW3AGp2NowbnoSK8gWb9WgVIIyk4FvrAk+AJLpw1z5wMFrH8q2INq1nEK/d5ZtFq9sfgfAKRpO5n9mW9xxxdMA+WlekBl7R/FloHqsvNqtsr6aaYLS8AzAYrSHnNL1wvv3KX/lZcM//JGThaHaTy5dssW+bWyToO9HE9N8Ea9KARZuBOz4V3PJx8MFoQHjsM7oxe2qcm5o1URjU0bruc3MEa9Lqb4b++PcF93wutmF3lU7rp7ST92TxD5CbTwEW/wCsXAh5/UHIY1f41zNNSJFvQo2U+3YkKS2GpG/HGZ2sB7F2F7A+vW62SY4mnKE2wWhREQpdW9jX+ebv0D8f3du78jzUTROCWmECW7J8P3/pINiwLiQ2DhyM1r0l0CnZuuzPrdUf24Vj6Pyy3n0bIrLmNCg0FDq3UPjlTg/O6Be4boUJ/LgmdMc5u+uhHCMe+OkziGlzo1BLbrsuq4BfEC2RU1JRASl1Hnp7IAn2XXHG4dYHtZWB88YbhF0I7MJNQIHDkFin2ysjHyiv2F9bRJCr2YXiYvZvy4RG1nXslNoEP9oGo7lfFREREZEjIoKysuoXIrGxNr80ISJqYDWPUWVlZSH9h0tERERERERERERERERERAcbBqMdGq5RSs1RSu1USpUopfKVUluUUj8rpR5TSh3nsr9eNR5vdNl+U4D+iIiIiIhoW92kRsjX01zVrwgyGK1SZIRCZEQYJVbNegPI2xOwmnr6K6i4ZtryMf0VoiKsy0b09D1fFREJFVG9Up/WQNtE63bx0V6cUjDbutCjWdmBoueRAat4YKJb2QbLsrW7fN/t8gXCKhjt+dv0hWPvw2HX3oDkAHNz2jgJRtu8Cn2LlmuLqwZYxWmC0fLDIORKR/d6N+Rr7TEUfrnD/gD45HeChAkmXvm59gEddpPq1/6+BrLbPw1gd77g6EleJN5sWk6If+knwZIt+/t1G/aRU+Sufl0wNTuH1b4xpr/1DlPuBW74wKzzCSemKbj6XRNvLnC+nvMG+I9ZigthPnYF5LbT/RvM+xwy83VfPRHI+09BRreFnJwM87hoyIlNfd9HJEDO6YLTn+6vXffM5XWzPbI1/1KhNsFoADDaJqgUAN7+NfTPJ9CxqYt/nuo+G9KBvGLBrryQDwsAEBsNtNNcZ1w4SOGXOw10bmG/zZRSOL6bdZ1Xfq7+5IMJRvt+NSd5EYWK06DQUBrWw9mF2MVvSrWgntootAnbSY9IAXJ2Axv018S1pXsWjaP0bYI5PtKhTbxemC/cBjmjNeSU5jBvOx2Sk9nQwwopu8tuu/Dr/u2tC/NLgApv+F1X5Jfal78419mY3dynVb22LCnXf5YXVyUMLSGI63C7Y5tdMJpdyD0RERFRbZgWF02RkZENMBIiImciIvx/5291LCMiIiIiIiIiIiIiIiIiIh8Gox0azgcwHEBrANEAYgF0AHA8gHsB/KKU+kMpNcJhf11qPN7mcjxbazxOUkpppuwRERERER2itq0Lrl3zNsCJ/9GXb1kDyc5w3J1ukmldTravS7J8fuBKY66FGjTStkrbRIWJZ/hPzv13f+CEbvp2ER6FJ/+t/ILllAImn5iJeFOTlHKAB6OplPZAl34B63Uvsw4EnL3Ktx/a/V14uASjSWYqkJVmXdg0Eery++HxGDi9n/2AW5dXCUaLTdDWG/b8UEfjaqoJRsuzCM4KF+EYjAYAQ7oqJDWxr5NfAlz3vmDc2yZScwJPfDdNwapUwYeLTSzavD9IpMBmUv268hSYF/XzC/b6zysmFm+xX9+gSfsDwewCB62ERTCabt+wODddc7xCSpx1/bnrgA8X122YwtsLBW/Md7eOod2r7+RSXAi54ijgu/e0beSpGyDFhcA370BeuQ/IzdLWbVWxC4OKF1uWBbM9TFMwf4Ng4SZBYal1+2zNfpMY4L0UyM3DFTol29epGgQYCrr9rzLQo3G00oagrs8QbHB+GebKqL5A7gsGtk72wHzN/+u9Kw30bu3sANo1xXp5STmwLUuqPXZrQ3r4BZgQHah018Z1ea10Wl/nnR92r4lXfzaxOrV273vduQUAUiNa+YLLNv1dq3XY0R33m0Tr2xTbBAQRWZE3JgKfvAQU5AIV5cDiHyB3j2noYYWUbTCaTTu7AK+8MAz6tgqnrurzpc6OiW6OnKk5+3+22yZVA9ObN3Wxgr3KKvRldsFoPCYSERFRXbH6pxfKLnWXiKiBGRa/yKrrf+BDRERERERERERERERERHQgO0CnslMdGABgtlLqMRX4L4Rqzsh2NZ1PRAoA1Pyz7Hg3fRARERERHexk/fLgGsY1g5r4LtB/qL7OrDcdd1ehmWwf4XE3rLDxxxz78q6HQ938nKOu7j7VwA+3GLh5hML4oQofXKnw0dUGPAGSCM4fZGDh3QbuPU3h0mMUbj9JYf6dBq4aqE8bUgd4MBoA4LjRAat0L7UORvv9H+Dhr0zbACerQKSGIA+P1RcOHL5vUs5ZR9jvJ21HDgcuvA1qwjNQX2wFeg60rBcjNslZVcQ1sl5ffhhOJK/UEGEfTm2a5GyHm/6boO2dJl6ep0/1KywVjHnZRN+JJi56QzD4CRNDnjSRliO2r0+OJxH/VCRDzuoIKfSFKm5IF/yywdlzWLI3tt51MFoYhOl5Xewb8Y0Vppyj32lu/0SQW1Q3k05yigRXveOu751PVt+3ZNkvkLM7AtsdvLA/fAj56i1H6xmd/5Xl8uXbgW9XOh/z1ixB/0dNnDDFxJDJJo542MTSrf7tdYF6CY1r94Zuk6iw7AEDZ9hkb776S4iD0Rzsf900wWIb0oGNGaEdzzUnKEwfp/DF9UbIJn52a6Hv56W5VYPR3D+XqsEdRFQ7mgzrOr1W6pai0Eaf2VtNag4w/n1Bn4kmrnzHDHqSZ2HGbm1ZidEIuUY8JNhgcQd0w24cpW9THERwJB3ifpzhv2zV75AdG+t/LHXE7ghgdwljF4wWDqHNNe0ptC//c6vvPiEQN/dpqbn7f7YLZotvtP/nZk0UurRwvg4AKLa59rMLRiss40R/IiIiIiIiIiIiIiIiIiIiIiIiIiJyL0ymDVMd2QngdQBXARgCoBeAHgCOBXAjgO9r1FcA7gUwKUC/sTUeBzMltmabIP4vtT+lVAulVG83XwA6h2LdREREREShIiLAr9aBJQE1bw2lFNSVE/X9L/jScXfaYLQD8G5SSgMnQKl7XncVKjK8p8Iz5xqYeqGB8wcFDkWrNKCjwqNnGZg2zsCT5xgY3FkBkTYz6w+CYDTlIBjt8NK/tGUTZwlW7NBPpI0Jg00kqZuBZT9ry9XNz+77eWQv+zCFHoP7wBg/Ceqc66GiY6COP1Nb992dYy2XD+q4/2fdZPKM/PCdnKwbWTgEo8U1UvjqRucHwus/EPSd6EXENV6cMMWLXzfuf3Yv/iSYtaJ6/T+2AG3uNHHXZ/avz5exo4A9u4DPXgYArNjheEhYvt3Xt9v56eEQPqALCdDtGxcdpXBCN+uyXXnAA7Pq5n1w68fO+20UCbx72lakfPYIzGcnQN5/CuYbEyE3jQT2Bt8FIlOuB/5e5KiuLhgNAEa9aMLrMIlhxDMm/qqy323aDVzznn/4TbYm/CHRJujCqaYxCm9epn8/rkoNcTCag/2viyZYbEO6IN3Zy+lI0VQDL19k4JJjnF+DOHF4O33ZrBVVg9Hc952WG7gOETnTUCGy/xngfgVvLRBMnev8eCy/z4Y59S6U3n42XpyRals3NaIVsM06YDkUdMd9BqNRqIjXC6RttS777r16Hk3dsbvvsA1Ga6QvC4d7k5qyHYxpp4OgWN0x3kpqzv6Nm2fzW/u4mOqPzwwQ2F5Tgc3HakVl+hfYawKlFa5WRURERERERERERERERERERERERERExGC0g9RiACcDaCciV4vIGyLyq4isEZF1IrJQRF4SkVMADASwoUb7u5VS+hnX/sFogdMF/NX8s+yafQbrOgB/u/yaGaJ1ExERERGFxuZVQOo/QTVV/Yf6fuhztL7S2j8hu3c66s97MAWjPXKZfYUjhwFd+tXPYKxERuvLjANwg9fUpR/QsoNtlVMKZiPRu0dbPn2h9UTbRpFA4+iGT8uSr6frC4eOgUpsse9hoyiF64Zaj7llHHBSrxoLTx+n7XpM/hfo2sh/ZvV5A/f33zreuu3ObP2QG5o2fChM3g6n9VX4doLzwaxK9T2n+RuA45408fBXvgPsjD+CD22a33gIAEC+9e17O3Oc97V2l++7w/yrfXI0AVf1STdmj+blUEph6oWG9tw1da5g2bbQP68fVjvr85NrDGwc+QUueK4PMO0x4PNXIK/cB0x/PORjqtSzbC26lG3Uli/cFLiPV342sWm3//I/t/r296qyC637CEUwGgAkN9WfA9btgl9QW204CUbrlmJdZ+NuIEuzLSrFRgPbJwc+tvx+r4GYyLo59+mC3QBgfTqwNs23EYIJRsssAErLG/44QnQwcHs+DJWbhwd37LnpI0FGXuD3v/nyvZDbz0DpjP/imIz7sSLG/h5te2RbYNu6oMbkhO4UYhuMVlY3Y6GDVIXNCbXg0EgUtTuqxNkFowXzr7vqUFmFoLA0cL3M/MB13FwtpVb5OCDP5rf2sTWC0W4YphDtImTebnsXBTjuOdkuREREREREREREREREREREREREREREVYXJVFIKJRH5RkRmi4MZfyKyBMDRANbXKHpCKeVxukq3YwyyDRERERHRoWH+rODbHjcagC8ERr0wW1/v168ddVfhtV4e4fRuIUzInI+Bn7/QV+g/FOr/3oZSDRiu5bGZjWpXdoBQSgHHnWFbp7EU49GMidryv3ZYL09WuTDHD4X5wIWQZT/XYpS19NU0fVnzNn6LHhqtcOVxCpFV3k//agfMvd1AVET1fVElJAP/ucGy62gpwyfld2JQR9/j2GjgrlMUbh6xv482idbD2plWAPPifjDvPAvyS3jlhpuaYEaj4TPw9jm5t8L7VyokBxH3PnGWwLjaixWa/dqJddHdfD/s2IT8vBLcMsP5xy3r9gYbuQ1GKwiDCe1Ogqlq6tVa4daTrCuYArz4U2g+qsotEtz1mYn+j3ix0z+vsJpLjlbYPcXE2cseRsrTFwJezUm3DigAZ+Xp3/OzVthvDxHB9R/o6/y9s3pZdpF1vcQmtqtx5csbrD/qzi4CdjsIn3DKyf53WLL1vpaaA2QV6PvulAzMusFAm0SF2TfrP7p/41KFgR3r9mC4cqJ+/ZX7R3EQwWgAsCsvuHZEVF0w58NQaJ+k8PWNwf16seXtJvJL/AcuInhzgYkTb9+EfosuQKcua9GkRw6WxxwesM9NUZ2Bresge8OlKryC+2eaMK72wrjaixa3evHVX8Gf53Xb2TYYLcjjIx2ivDY7TEmYJX/Vgt270O6jGI+h0DTGuixHc43ZUHTXvDXttrkerOTmPi2tSn5enmaXaRrj25ZVdUhS+N91Btrt/bwg0PnDbnszGI2IiIiIiIiIiIiIiIiIiIiIiIiIiEKNwWgEEdkD4AJU/5v0HgCGaZrU/HNtm//VrVWzjYM/ASciIiIiOjRIsMFox42Gattl30P1rxOAw3pZr2PBl/ZjKPHNdqzQBBN5griblNJiOMhvDjlZ8iPkoUv0FfoPhfH891DJrepvUFaiNDN9AaB9t/obRx1Se4P77FyZ85a2LKvQenly3j/A378Bcz+D3HY65M+5wQ4xKFJcCEn9B9izS1tH9TjSb1mjKIXXLjGQ94KBDY8a2P2MgT/v96B7S+vZyOr6J7X991n1AX4bn4WCFw2kP23g8TFGtaC/1vHWfWZLLIq3bQV++xZy37mQ7z/QrqO+NVTYh1sXDPJt8yfG1P/ANkZ1RjkiYELhpIkZrtpu2u37fiAGo3mDDM27f5TaN+m/pp/W1v78ZJqCk58zMeV7wfLt+nrPnqdQNNXA9MsNJD53BTB9Uq3XHYxb9ryoLVuyxX57bMkC7E7p62vsjtpgtMa2q3FlYEd92ebM0K1He2yqcm3UJsG6TmkFsDHDuoNT+wAbJ3kwtLtvRx7RS+HGE/136hcvULh8SN1/rN+7tcLgztZllftHSZDBP6kBQgOJyJlgz4ehcGpfhfJXDBS8aCD7OQN/3Of8uDTiGRNZBdWPhY9/K7jqHcG8vI5YHd0L2yLbO+5vY9Teg9WnLwEA/vOKice+3t9/ZgEw+iUT0xeaKC5zf77Xne9sg9ECBAQRVVNus8OUhlnyVy3U5uOgBM1vonOKwuv/cO3RfGZR07hpmgN4FW62V1ru/sq5xdYN4zQfOZ3SR2HLEwZ2PGkg61n7Y3mOTU5foGC0cLiPJCIiIiIiIiIiIiIiIiIiIiIiIiKiAwuD0QgAICJLAcyusfgUTfVwDkb7L4A+Lr/ODNG6iYiIiIhqTdK3AeuXBdVWTXjaf+GQM6wrL50HKcr3X/8Xr8E8+zDISc1gXns8KnanWzaPcHE3KTs3wbxuGOTkJMiYTpAZzztvHALy+kTbcjX6yvoZSACqUROg91H+BU0TgYEj6n9AdaHvsUCLtrZVPDBxccHHrrpNqqiSeFNeBvl0ajCjc03StsC8YTjk1OaQ83rYV7Z5DaMjFTq3UEiKtU+xUB4P1FRN6JtpQp67BY2jFRpF+ffTRhMGBQCpEftDAeXdybZjqE+68CEVZsFoAKCUwp2nGJj87/odXLmKwj9RHfFjk2H4vaCNq7Y79wYTuQ1GKwyDCe3BhuY1iVZ46EzrSrvyUOvwzts/FSzeErjejcMUYiIVZNPfwJwZtVpnbaR4M3DjHuvj5bo0+7CGtWn2fa9O3f+ziNgEo4XuPdO8KRATaV2Wlhuy1cB0EETUWhOMBgArd1ov79Xaf1tMOUfh2fMUBnUEju0MvH+lwvgT6u84M6yH9bo+Xer7rgtGu2KIwn+O1I8zlK8H0aFMdz4MJsQ6GB5DoXG0QnxjhSM7KPx6l4GTewdu98cWoPmtJtre6cWXKwQv/mTi/74I/hy8PqorAECmTcK6XYKZK6zrjZsmSJhg4rTnvcjIc74+XU2PAqIjrMuKgwyOpENUhc0OU3JoBKMFurpJ0ITpfrkivILRdNe8NRWWwi8gsiY392lVQ2fzSqzrxNn8Nl8phdYJvuO5nZwi/T1ToGC0QgZGEhERERERERERERERERERERERERGRSwxGo6q+q/G4n6Zezalrzd2sRCkVC/9gtByrum6JSIaIrHLzBWBTKNZNRERERBQS6duBVh1dN1N3vwaV0t5/uS4YrbwM+L16NrLM/Rzy9I1AZqpvxuqq31Hxv9ctmzsNRpOyUsiNI4GVCwGvF8hMhbx0J+S795x1EAQpKYIsnw9ZswTy9yJg9WL7BsP+XWdjcUtd8wgQU2XGr1JQ4ydBRWiSXg4wKiICasIzgEeTILBXi1JNaoxGsjer+oIFX7odmiuSuhky9zPIud2BFQt8+7ady+6FSnR166zX9xigbRfrsp8+haRutixqHa/vMj2ixf4HW9dCcrP0letRsOFXDemOkw08NLp+B7guqjt+bTTYdbuCUiC/RLQhTzoNPaG9wiv4/R/rMidBMD1bWr8+ZRVAbnHw40rNETw3J3B6Qf/2gGEoiAjk9QeCX2HbzsG1O+ZUqE837ns4Kv8by2q78g3kFOmfz9pd9s/1h9WCCq+vTkEp4NXsZ4makItgKKXQSnOsS8sNXWiGrqeqx6aWcfoQR10oWLMm/suiIhQmDDew6F4P5t/lwQWDDBj1eBDsnqIve/VnUxuAcdRhwIxrDHTWnPpSc8IrxIToQBVu10rHdFb4doIH301wdrOYmgOcOdXEhI9qd0zYF/JbUoQF6yts65Z7ge9WAWe85PwCyC6st1GUdVkxA4DIDbtgtNJaXKCGGbt3eqDw6+RY6+UzVwAfLzFRVhEe1xZ7Cp3XXbLVvjzoYDTNLhMX47w/nXKv/vgWKBgt5+DJ+CMiIiIiIiIiIiIiIiIiIiIiIiIionrCYDSqakuNx7pZ2xtqPO7gcj016+8RkWyXfRARERERHZRUv2OhZqyFmvYn1BUPAt3+FbhRoyZQoy6zLutxJJDUyrJI/vyp+uNvpvnVeTZmnGXbCE/gYQEA/loA7PYPuZIfPnLYgTuyfD7kwj6QG0dArj4WMv4E2/rqvRVQRvjcGqt/nQD12q9Qlz8AXHgb1Es/Qp1xeUMPK6TU8WdCvboAuOh24PgzLeskeDWpMRp+wWh1yJx6N+S8npAHLnTcxrjywZCtXykFHDdaWy7n9YSI/wzqJtFAjCZfL8uTXH3BtvW1GWLIhFvYh1P3n27gp9sMDNj76UenZPv6tbU2qhvWRXcLqu3ObF8OphsFpUGtKiR2ZAsGPGYiv8S63Mm+kRKnL0vPC25cADDjD2cb8tguCpKbBblxBPDr1+5XNHA41B1ToaYthZrwtKum6ooHoZ74HCqlHdTVjwAAupfp3+/rdun7Wr7dfl3ZRcD8vZ8gZtsERCRahIHVhi4Ybdby0IVl2AXkVIrwKKQ0dddvUoi3RSh0baF/U41/X7BFc/qtPN/oXo9Ud6d5ItLQXis18O3NSb0V1j9af4PI8iT5fvBWYPVGZyfzP7YAf+90dm7QXSsZCmikub4uLg+PkCY6QFTYJEr9Maf+xlHH7O47AgWjHdlBX+H81wTd7zfx++aGf9/tKXQ+hnd+s6/r5j4tqxAoLvM10IU9x9f8l2VBSs+3Xh4oGC2roOFfHyIiIiIiIiIiIiIiIiIiIiIiIiIiOrCEz+xvCgc1/1Ra9yfSa2o87uJyPZ1qPF7tsj0RERER0UFNKQXVuQ/U2HthvLkI6pbn7Ou/tVhfZhjA0adYF37/AczXHoD50p2Qn78AFn1frXhJTH+kRra2bOpxeDcpbz5sXbD4B2cduCAlRZDHrrAMYrOibn0eqkOPkI+jttRhvaDG3Qdj/CSofsc29HDqhOr+LxjXPgbjsY+Bwaf5lSeaOa76a12R6rdMvN6gx6djvnwv8NGz7hqdeE7Ix6E0gXL7fPmmfxulkBxrXT2zMkxiL1n4TbBDCynTtF4e7sFoADC0u8Li+zwwX/Ng4yQPXruk7ga9Lro71kXpg9GePU+/7tRcwO309MKCUsjnL8Mc0wnmdcMgv31rGcZXFyZ8ZOKvHfpyJ0EwLWzCqmoTjLYqzVm90W13QU5vDaxY4Hod6p7XYTzzDdToK6GiY4Ax1zlve+vzUGPv3R8GesGtwPBz0aYiFU3MAss2a6dMhnn5UTCfuh7y9qOQDSsAAF5T8M3KwK/5zBW+OtlF+jqJjR0/BUd0QVyzVwOmLkHIJafHpraJ7vptkxB+B7ceLYFIp2G4VcRE+p5La81zSnN3miciDW8YXyt1aaHw5mX1M5BMT9K+65nSHOcn8zlrnJ0X7AIx9cFojodBBFTY7zBSHiBx6iAQ6Ghxej/7GluzgLFvmyiraNjwLbvr3po+XCy216duL103Z/q+52lCpONinPUzcbT9tn70a+uBFQfYTTOtbzmIiIiIiIiIiIiIiIiIiIiIiIiIiIi0GIxGVSXXeJypqfd3jcf9lFJupjHWnNlfsz8iIiIiIqpq9FVAp97WZWdeCdXWPqtYtdOUlxQB704GZjwP+b/z/Iq/iz1J22eE07vJEhezQmvrt2+BXVud1R15PtTZ19bteMiZlh38FsV7c111MazwZ/+FxaGddSsfPQd88LTrdmrU2JCOAwDQ+yjbYplyPSQ7w2+502A0vPck5Nevgx1dyOgmgodD2IdbVx5nYNtkAy9fpHDTcIW7T9U/iZcuVPhugoHnz1d4bUw+Xkm7HlPS70JKRbpl/b+je2FjVGfLssfPNDFhuIFmTazXtW2PaEOedAp+nw959mZfCOXKhZA7z4JMvLjOw9EKSwWzVtjXcbJvNI5WiI22LsvIdz+uSqnZgZ9/35KVOOGemv8vwBn16Qao0y6tvswwoKb9Gbjx6Cv8znkqIgLqwXfgmfA0upVttGy2Nh3AhuXAzDcgbz0CueoYyPcfYH06kFUYeLULNgQORkvQ/VuGIKXE6XeCJQ4vEQJxemzq3tL5wSrCAI51+68n6kF8Y4WOSYHr1VQZFKQLqkvLbdjQEqKDhe545AmTi6XzBii0jAt23je8AAAgAElEQVRdf31LVlouLzViULT3V1Rp6c7vP52e93WXOIYCYnTBaAd/jhWFUkWFffkyi/vdA5Dd2V8FOGwNcXCdtC4d+KGB/xXXHgfXyFXZXZ+6vb1at8v3PV8TjNa0kbNzwzn97et9skQs7/0KS+0HzGA0IiIiImpoHTt29P1zsr1f8+bNa+ghERERERERERERERERERERUQAMRqOqas6sTrWqJCJpAP6qsigCwBAX6xla4/G3LtoSERERER1yVEQE1CMfAq06Vi849VKoCc8G7qB5m6DWm+FpoS37V3uHk+1tZnLKb6G9FZCnb3JcV51zQ0jXTcFTCTUzuoFSpUkt0hhQYhEMVBQ46UBS/4H5yDiYx0X7vq44GuZlR8KccDLMe86BrPrdV++XmZCpd7kaE2IToG5/CWrQSHftHFBKQb29xLaOjG4HqbENdMFouyP8XwP5790Qt4lZIaY7ehgH6KdZbRMVrjnBwHPnGZh0toGvbjTQOmF/eaNI4NGzFK49XuGk3go3nmjgip67cGXO27hlz4t4JGOiZb9LGg1AkWGdfDY4bgcAoEMz6zGtT9eHqugUVnj8F/70KfDrV+46cmlDBuANsEt6HO4bKZqQlvS84MOaUgPkOTYyi/Bm2jUwbCMhLBxxvC8ULaW9ZbHq3AfqwXeAFu20XajzJlgvVwo4+1p0F+tEhvVRXasv8Hohj47D9uXrHA196TbgkjdNjHnZ+oWLjQYiI0Ib3jOwo77snd9CE8blNBitW4rzPgcdBiQ0Do8go5oW3eP+oFsZFKR7r4VDMMY/mYLL3jJx7BNe3DzDRGoOw9rowKO7VAuTXDQ0jlb4+qbaXbhFG17cVvAyStY0xYydF2vrVQb9pu0pd9x3WYAsqkq6475SQBPNbUsRg9HIjYoAO8zWtfUzjjpWmxxlw1CYd3vg40morveCZRcIbGX7Hn2Z2/u0dem+BrlF1g3jYpz106u1wpc36Ld1QanvPrKmQMe9cLj+IyIiIiIiIiIiIiIiIiIiIiIiIiKiA8sBOpWUQk0pFQNgTI3F82ya/K/G43EO19MD1QPYCgHMdtKWiIiIiOhQptp3h/HxOqjvdkN9uhHqxzwY974OFRkVuHFyq6DWWab0fY8d7DQYTZ9iI3ePgcz9zO2wrPta8CWQm+ms8jGnQvUaGJL1UgjEJ/kt6la23nHzm7JehOXeGCAYTVL/gYwbCMz+YP/C9cuAzX8DS+cBC76EXHs85N0nIfed62gs6s6Xod7/C2rGGqiv06DOvMrx83BLdekLHDvKto7cMBzi9e57nBxr/b7N8vgHo2HbemCbs/CjuuIm7EPKSrX92JU1pNP6KmyfbGDL4wbWPWIg53kD955mwKj6BPdk7PvxyJKlrteR+M9iAECPVtav/bpd4nrCfYFhnbAn377rriOXrn03cFCf0yCYFk2tl6fnuRhQDf/YnII8UoE3U69B/5LlgTs65WKoX0qgPlkP9XUajBd/0IaiVVIjzoP6aDXU3a/5Fx59ClT77vq2Hg+6tbYIuwOwLrqb5fK0qZNtx1PV+78LcjQBEYmNHXfj2Jj++p3gv/MEi/+pfViGNiCnRuhdj5bOk4k6JoVJipGFxCYKL5zvbnyVwWhJmkDOrMJaDipI+SWCknLB3zsFRzxs4t1Fgt82Ay/8KBj+tIkcTZBIICIC0+3BlCgEvA6DGhvSv9orrJzo7leRL+y6BasHvI21jxjIfSkST753HSJnrELyhwu1bbIiklCqorBSDnO8nrQAoaaVdGFOCkATzS1zQXheflK4qrAP9JPtG+ppIHXLLhjNyWHr+G4K/9JnAQMAfl7fsOfkbJfXOFmF+rG6fRrrd/m+55VYl8c3ct7XqH4KD43WvyrLtvkPrjBAMFoWg9GIiIiIiIiIiIiIiIiIiIiIiIiIiMglBqNRpbsAtKny2Avga5v67++tU2mMUqqrw/VU9bGIaP5Em4iIiIiIalJN4qBS2kFFRTtvlNw6qHWVqUjL5d1SfBPcHdElG+0tkxnPBzGy/aSoAObk8ZB7znHW4D83QD3yYa3WSSEW7x/KNcBFANSjuydaFxQGCEb7dGrA8DQAkNfudzaQgcOhzrgcqn13qNadoIy6/8hFTfrUvsKGFZDXH9j3UBdQs9sqGG1v+4akmwheNexDvp4G89xukJEJMMefANmyZn/Z3M98ZSPiYV41GLL2zzoesXtKKbRPUuiaohAZ4X9clZfv3fdz79LVaGRqEqY0EpZ+BQDonmJdvnaX+wn3RYYmzeqXmZAAoQ7B2pUrWLwlcD2nQTApcdbLgwlGy8gTnPKcF/maT7c8UoGftwzHufnOgkDVeROglIJq2QEqrpnjcajIKKhRl0E98hFw5DCgUx/g/FugHp0RsG2P/tYhMhsjO6MC/qFpaREtHY/LTpcWIemmmqYxCsfbfEL54KzAAXuBaI9NU8bDPL0NzFfvh3i9GNDReZ9tEms9rDp1/TCFNy51nrQUHeH7ntREE8hZz8EYizYL+k30Iv4mE42vN9HvIdPvPbsuHZi53H2Qyp9bBcOeMhE13kS7O72Y8Uft9zEip9yEyDak3q0VFl6fi5YVu/zKUirS930NLlqIdzKvw/XjeqP7VVegW4pCVISCMgyo1ochMbmp9rllepLwW6OjUKgJcLWSmuPsPW93TRobY11WyGA0ciPQNfTBEoxmU6YcHreG97SvmJEPR/cNdSXbJujMil1YrF2QnJV16b4GumC0OBfBaABw/+n6zzR25vgvC3TcyyxgiCwREREREREREREREREREREREREREbkT0dADoNBSSl0CYLaIpLtocxWAB2ssniYiW3VtRGSDUmo6gMv3LooCME0pNVwXdKaUOhPA2CqLygA85HScREREREQUpBZtg2pWpqIslx/X1cVMewkQDrHqdxcjqtKtCLB9PeS6YUBuVuAGHg/UA+9AnegwQI3qT3yS36JoKcNxhfMxv8lxtk07lm1BYym2Lty5Ceg9yG+x7EkH1i0FPnkxqOFaatfVUfhQqCnDAJ74DHL3v/WV3n8K8p8boZJaonlT6yrbI62PEfLwZVAjzw/BSIOjC6FQq3+HRGUA2RmQp27YX/D3IshNJwEfrvKFwj1w4f6ytX9CbjkVeO8vqKTQhDqFgmTt8u2Ppf4fpcjiH4A1f+x7HAEvjihZgd8aH+O4/8SlsyAFueiasRKAf7ud2YLerazbRkcApRX+ywvsAke2bwQO6+l4fE61v8tZ0JDjYLR4Batohm173E3WFxFc8LqJuev0dVZu7o9uZRuddXjW1VBd+rkaQ01q6NlQQ8921ab7MX2AH/2fe5kRjS2RHdClfHO15btCFIw2oGPdJPcM7a7wywbr1/L7VUBOkSChcfDr1gYRmeVAbibw3pMQpXDY1Q+jVytgdVrgPtskBD2ceqGUwuVDFI7rKjh2sonMAMFmSXmbIX/tQrPNAHC0X3lBKVBaLoiOrLv0psJSwYodwKpUwTXvOntv/7EFuGyw83XkFgnOnGoidW84yM4c4ILXBS2aCob1UNhTKFi6FejeEmjXLMySquigoAvN8YTZv0SSjX9h0Cs3YseGRfYVz70Jxo2vaos9hkJiY+sgoT2eZihW7lJ/duU6q6c7giil0CTKukYBg9HIjfIy+/LtDq8lw5xd0JfTYLQeDi5DZy4XHN2pYc67e9zlWGOPTTCa2wDr9Xv/MiBP8xFJnCbI0c6InsCcNf7LV6UCc1YL+rYFUuJ827oowG6c7XLbEBERERERERERERERERERERERERERMRjt4HMFgFeVUp8A+BjAPBGx/LNqpdQAAPcCqDlbcieA/3Owrgf3tk3c+3gwgDlKqStFZG2V9UQDuBrA0zXaP20XvkZERERERKGhYhpDOvcFNq101a5cRVouj/S46MRu5mtlFa8XyuO8UykrhUy6EvjxY2cNzr8Zatg5UL0GOl4H1aOE5paL78l6MmAw2jl5n2nL5JGxQGEe1NnX7F/2+cuQF+8AKsqDGakldd3jwJlXQTXWpI7VMXXs6ZA+RwN/2wRNLPoeGHUZulhvaqyP6govDHjgn/YjmWlQyZrkrDrm1YQPqTkfQT55xbowOwNYPAfyyxf+ZQW5wK9fAaOvDN0ga0E+fgHy33sAr0X6mMbAkj8dB6NFmyVoJCWQSVcieVssYNEuv0RQYVqHBsQ1Anbn+y8vVI31K926NuTBaDuzBRXOctEcB8F01rwX1u1y1r7Skq2wDUVLqshE57LN+gpVqIfeB4bZhBzWoe4p1kFxALAuuptfMFpaqILROtRNYMV/Big8/JX++uOSN018eaObi5nqdD0bVcNgv30XcuVE/Ku9wuq0wNdCbRIOjNCsrikKy+438OavgrVpwEd/+D+3LmUb0f76fhAAzaJ7AZ2WWPaVVQi0rqNAuDmrBZe+ZWJXnrt2ny0VvHRh4HqVXpsv+0LRqpo618SGDIXrP5B957LxQxWeP08hwnNgvNZ0YNCGyIbJbiZFBZA7zwJWzA9ceeBwqBueDFgtKdY6GC3T4x+2HIhVP1a0gZgKaKIJGgoUEERUjTfA/Wn6NkhpMVS0u/C/cGN3ReT0sNW9pf66tdKsFYLHxzgdVWhlOzyuVMqyCZvVfZzWJBootAhf3FMIZOYLcjXBaPGN3J8cWidYb+9pCwXTFvqW3zdK4eHRCoUBjnvFofsYhoiIiIiIiIiIiIiIiIiIiIiIiIiIDhEMRjs4NQJw6d4vUym1AcAWALkAvACSABwOIMWi7R4Ap4hIwKmgIrJDKTUGwPcAovYuPhbAaqXUnwA2A4gH0B9AzSmnXwG4393TIiIiIiKioA053XUwWpmKslwe5eZO0vQGrlNRBnicT/CVaY85DkVTX+6ESkh23Dc1gOatLRfHee3TTI4t+hV37HnWto48cxNw5FCo9t0hG/+CPHtz0MP0k9QK6vPNUIbDJKY6pCZ9AhndTlsuOzZCAeihmUReasRga2R7dCrf4t9400qgnoLRSssFny8TLNnqmwS+Os26XqxpP9tcPnoWWL3Yuuy796EaOBhNVv8BefcJYMFXrtseWbzUcd1Ec29az/xZSIj5F3CYxVhgICe7GL6PkqqLjbYORisyAgSjhdjUeYFDpSoZDuf6d2th/V7YugcoLhM0inLW0RfL7Md2esG3loGD1StdDnXnf6EaMMWmcbRC+2bAtj3+ZeuiumEUvqu2LMOjSZZzaWDHkHTjp3drhUlnK9z7P+vX5+uVwLYsQfuk4La5Noio6j6VmQoU5qJVfLyjPusqIKwutElUeOB037a7b5TgjEfSscXr2ycSvNl4f+dl+8JNkiuytP1kFQR+3hl5gpkrfOFjJ/VSOKZz4Ncsv0Qwdpr7UDQA6G71abmNh7603hk+XwZ8XuP48PI8Qf/2wBVDwiSxivYxTV+AnVd8AVhe8YWzVvsKsMx03Ub827rswxRgyVbrfTAMLk8BADL5GmehaADUk7McnQuTY4H16f7LszxJiJESV+PbU+h7LTwBLiB0Z3ulgCbWt8woLHV+/ULkKLh7xyagc5+6H0sDcXop3Ls1EGHANjh5TRqwPl3QLaX+z7m68K/YaKDAMsxMf6zQXXP2bOkLaLayLh3I0xwK44LI1Wvl4FL2sa8FRx+mAgZCMjCSiIiIiIiIiIiIiIiIiIiIiIiIiIjcYjDawc8A0H3vVyA/AhgrIjucdi4i85RSZwOYhv3hZwrAgL1fVj4EcJWIOEhIICIiIiKiUFBnXgWZ/rirNtpgNI+LTswAgTCAbxJwtLMZmrJzE/DuZEd11d2vMRTtAKDikyCdegObV1VbHiv68Kv7B6Xi3umnIhIVAfuX2R8BY++DjBtY67FWpS6+PSxC0QBAJbYA5uRCRmhmLYvvfdjNJvBlfVRX62C07RuAo06q/SADKCgRnPaCiQUbA9ftUhagUobNxxqb/3Y3sBCTz/4Lef5WX/JbEHqWOQ8eS/Rm7/s5wZujrZedkQurYDRd0Eex0RgCwCpmQP7+zXJ5sMa/b+LVn0MfjNa9pfVyEWDTbqBPG2f9zFyuH5uC4KY9L9m2V09+AXXMqc5WVsd6tNQEo8X28/0bhSoyPbU/tyY1ATok1bobrbtPNTB/gxffat7y7y4S3DcqyGA0zaWNUTMELycTbRKdBaM1bxrUUBpcr1bAii1HYR6OQJmKxkkFP6CJFO0rb+a12Kn2yrLPuMSGdMHIZ819++VDXwqmnKNw20n2597PlvqC1Opaep64Dvi46h3BOf0F8Y3d7XsiYhm8VZtALb8yyzZiH/7lcL124/QfiyDgWB0sq9YuQP2DkScM8vdk1e/AT586qqum/QkV4ezXlUlNrJdneZKqXfs4YQqQUwQkxQauZ8VQQJNo6zKrACQirYrA97XYsfGAD0azuwVyethKaKwwpr/Cx0vs7xFmLhfccXL9Hwx155UWTa2PCxkWQdSVdMeeVvFA0xgg3yIAbdk20Y6haYx+XTptHIb3TltoojDAcY/BaERERER0qBARLF26FGvXrkVGRgZKS0vRvHlztGnTBkOGDEFsbIAPIsLY9u3b8ccff2DHjh0oLi5GcnIy+vbtiwEDBsCo5e9MMzIyMH/+fKSmpqK4uBitW7dGp06dcPTRR9e6byurV6/GypUrsXv3buTl5aFZs2Zo1aoVhgwZgqSkOvzlBRERERERERERERERERERucJgtIPP8wB2AjgWQAcH9QsBzAYwVUR+DGaFIvKNUqoPgIcAnAcgUVN1EYCnROSzYNZDRERERETBU83bQEaNA75+23GbMhVpuTzKzZ2kOEgbKA88O1IyUyHvTgY+f8XRatXlDwCnXuKoLoWBY071C0brVroB8d4c5Hqqz8RtGgPc0XO9o1A0AMCKBcB374ZqpD5HnwKcdW1o+6wlFR0Dad8N2Lbev9DryyWPjVFoFQ+k5fpX2RnpnwQlAF7/qxm+Tfe1v/FEA8N7Wk8uX7RZ8NT3JgrLgFP6KIw/QSEqwlf3l/WC134R/J0qEAG6tgCuH2ZgWI/9fb27SByFogFAjzKL51hVZqq+LEmTiBVi4vUC370HeeLq/QsTWwDZGbXqt1vpBsd14737X+gE0+JF32tPESyTCHRBHwBQqqIRIxYz3xd9DynKh2pc+5Snjxa7C0UDAKdzYzolAx7DOrhg3S5nwWjr0wWr0/Tl73SeicPXrLQubN4G6p7XoAaOcDbgetCtpcLs1f7be32/84H8e6vtu7sjah+MNqw7oFTdhlXcfpKBb/+2vg75coXgvlHO+yqvEEz/TfDJEsGuPOs6Rs1rntwstIrv7Kh/XdBPuJLtGyCTxwMr5qMJgFH4zrJeFMrR1JuHfE+cX1lmgf06HvtG/ML67v5cMHawIClWv+98tDi44EkAyC12Xvfrv4JbT+LNJjoluwv+0gWjEOk0dHavpG6GXHu8o7rq/96GchH25Hv/+78psjzNEGV1bRJAZkHgYDRdmJNSQKzmeilQQBBRNQ4+E8HunXU/jjoWZDa0n1cvViguE3y9Un+OvOszwR0nh2Z9bujG064ZsDnTf/mGdH1fuu1lKKB7CrBkq3/ZcpuMcN3xys6AjtbH3Jo+Wxq4LwajEREREdHBLjMzE5MmTcJ7772H3bt3W9aJiorCiSeeiIkTJ+Koo45y1O/YsWMxffr0fY//+ecfdOzY0VHbefPmYdiwYfseP/jgg5g4caK2ftXP7E844QTMmzcPALBw4UI8+OCD+Omnn2Ba/OeQlJQU3Hfffbj++utdh5gtW7YMd9xxB+bOnWvZd9u2bXHNNdfg7rvvRkREBCZOnIiHHnpoX/ncuXMxdOhQR+vKysrClClT8N5772HnTuv7bMMwMHjwYDz44IMYMSJ8fodDRERERERERERERERERHSoauDpERRqIvI/EblIRDrCF1A2BMAFAG4GcC+A/wNwA4CLAPQHEC8iY4INRauy3gwRGQ+gJYATAYwDcA+AmwD8G0AnETmGoWhERERERA1H3TEV6DnAcf3yUASjOUlyqLCfHSm5WZBrjnMeivbpRqhx90E1dCIAOaY6+YchRKEcV2e/4bd8/FCFJmU5zjvfsgYy+8PaDK+6Y06FMWUmVEQYZs2362q93PTu+7FNgnWVnRGt4YWBArU/nee2FpMxfte5mLUCmLUCGPmsiWkLTZRVCPKKBbJ3pvbXfwkGP2Hi82XA96uAW2YIrnrHV/bDasHIZ018sFjw1w5g5U7g82XAiGdNfLty//HhK4cBM80qspDszXJU11JC8+DbuiCv3V89FA2odSgaAMRKIdo1KnRUN9Hc/z6pGpJW0x6xTgOxC0YrVo20ZXLjyMCDC2BPoeDCN9wnJxiarKTKfVUqygEAkREKnRpZb5N16eLXzsrM5fqybZMNXBDzq3Vhp95Qn20Kq1A0AOihyQxcm2FAzVi777EJhSxPkmXdRzMegAdey7KqlALuOa3uz9FDu+vLFm8BduU638du+1Rw9buCH9bo6xioGYyWiXaJgcPfPAYQr39LhR1J2wK5sA+wYr6j+knePZbLswrst/87v/mXe03gC5v3XnGZYF6A7Ew7boLRZq0IPt1lcyawNQvYkQ2k5gDpeb5wpuwiIK/EF6hUUg6UexmKRsGJacDLVMnPgZzX01FdddNTUCdf6Kp/XYhZlqcZio3GlmWDOur7y3JwWWUXTqS7XipgMBq5sfca1Y7YhT8fIOxOaW7ycuMbK8y8wYPMZw3ceYq+4Ys/OQjqDzGr4GUA6NHKepxZhUBmvvWW0V0DKAV0b2nd35pU/VaOsf6Iz9ZRh7lvo1NUZn9/RURERER0IPviiy/QqVMnPPvss9pQNAAoKyvDd999h6OPPhrXXHMNKioc/hOoBjRp0iQcf/zxmDNnjmVwGQCkp6fjpptuwjnnnIOyMuepyM888wwGDhyIH3/8Udv3jh07cP/99+OEE05AerpNunQA77zzDjp16oTJkydrQ9EAwDRNLFiwACNHjsQll1zi6vkQEREREREREREREREREVHocZb4QUxEckTkVxH5SESeF5HHReQxEZkqIh+IyDIRCTxb0d06y0RkrohME5EnRORFEflcRP4J5XqIiIiIiMg95fFATf4COOtqoFMf4IjjoZ79FjjJejJ6mYqyXB7lcbFSJ5MeywP8QfG37wAZOwL3c+woqPdWQKW0czY2Ch/tu1kunrT7ATwR+yGOaAcM7Ag8eY7C42croEAf8uTHMByHtwAA+g8FDj/Of3mH7sBFt0M98pHzvuqb0nzMI/snE+iC0R5u/n+I7lmAhB670aHLBrwTfxFeTbzKr97l0wQx15lImGDCc83/s3ff8U3V+x/HX9+ke9CWtoyydwGZiiKCIkNRZAiK4p4o4h7g1qu/K6I4roqgXlCc14kILtyKqCiKomyQPbso3W3y/f0ROtKeb3LSpgP8PB8PHuSc70yanJyc9vuOG8ckF6OeqbpY4ZUfNSu2ah5b4qbY4sqD1nDX++Xt1ttcy9C34Dd7FU3y7YWK1YTeux1ef6x6jUPDIC6x/F9YhFexuu9lurSNNjT2luDKLLsdgosY10HLerkO64SRKOu3AADyHT5SnNb/hrZzzPbhuIeqF2LgrPAS0Hu3475nIu5B4egTIzz/nxyDe1A47ufvpfMe6+Cy9Zuy+Gy1xjHJVfYcd0xy8eVa7/czUzDace2gZYKP41THXqhAkh/qSJem1nPafxDSSqLghJEAZDoTcCnrxJ1huV/yqftGhqRCjxaefa0SyoMuHAqGpMKyaQ76tK79x0ApxYq7zZe/7QYyrt2tmfWV/7pVgtGy0jm6DcT4CBkESIwGhynVrwHSL9wfUH1jMJqPw7HbRxrY56vN7T5fA0U1WEtoNxituETzmY95CFHfTuhY+8cU/cf3uK8ahHtoHO7zjkL/8gU6cx/69Ka22qvbn4Ozrg143CRTMFpIErnKOhgttbki0hAIlGZ9euTF9JFWKfMxPleC0UQgXP6D0UjbXfvzqGW+Lg9V5/Q4PkpxUX9zw0c+qfsQLlMwWrfm5jZr91jvN83eoaCNdU4xm8z5C9UKzVRKccmA4LynaA2FDT/zQQghhBBCiIDNmzeP8ePHc/Cg90WGDh06MGrUKM455xyOP/54nE7vX7A///zzjBo1qkGHo82cOZO77roLl8vzi74uXbowevRoJk6cyODBg4mI8P4d1oIFC7jnnnts9f3YY49xyy23lPVdqlu3bowZM4YJEybQv3//ssdt2bJlTJgwoUp9O+69914uvvhisrOzy/YppUhNTWXUqFGcd955nHbaaSQne3+50quvvsrpp5/eoH9GQgghhBBCCCGEEEIIIYQQQghxpKvH740XQgghhBBCCFHXVEIy6panvXcefTK6uBC+etdrd7EhGC00oGA0G+E2Jb4XAeuV/kOt1H9/QHXpa3dWoqExBKMp4Nb9M5n6+AVe+3VuAMFoGTYTt0JCUR/uRkXF2u+7oXEaXpwVFgmkJCjMS6w9doa24LKUF2o8nXsXulniIzhm5XbYvF/TMgG2pNvr8/ScT2o2qZysmrW347uF1Wqmbn0GNaZqGF1lqWluPl/jf5F/gsv7vsa7D5DjtP/8jg4zP1cKlJ+kp+8+gPHX2B6rom/WaZ8L+n0pzZbSbjf6XxfBqmXWFV+ZQecmD/Ehp1cp+uS3Al5eVfW9a9jjblb/y0Fqc8XebM0Pm627HtP70CRMwWgxcf7uRr1IbWYuu/JlNwsGjUZ//yFpTkMKA5Bcsp9jNs7n5BdmNZjwtz6tFanNrAMnFv+hucIiB7Oy+T9oWzmvjsqvlwNphIUoZoxXTHnd3IEp5Kch0kWF8NkbAbVp7LI+wPsKRsvyEVAWYQg3Aljwm/lxdjpg8XUOTu2u+PQvzWn/qfo6zy4ArbXf5++OLMi3kWEjRH04uQuM7FG7Y+iMvXqme70AACAASURBVOgpQ8p3bN+Avqnqe6qJ+t9qVIsO1Ro70ZAPm+5sTJ7DOhgtKgwSY2BHZtWytByN51OHma9womhDkGyOBKOJQPgLiwfYv7P251HLfJ1OVffMsWtzaN0YtlnksO7Mgqw8TXxU3Z2XmrJdU+IUjSI02QVVy7ZmaAZaPAKmkDWlzIHn+3yEPfo6h/Kli4/PCYGa9ZXmllMaxucEIYQQQghdUgL7a/YFH8Km5JaokCPzz5RXrlzJ5MmTcbvLT+B79+7NrFmzGDBggFfd/fv3c8899/Dcc8+V7fvkk0+49957eeihh+psznatWrWK777z/I5+7NixTJ8+ndTUVK86mZmZ3Hzzzbz00ktl+x577DEmT55M27ZtjX2vWLGC22+/3Wvf4MGDeeaZZ+jevbvX/v3793PvvfcyZ84cvv32W1avDuwbK+bPn8+DDz5Ytu1wOJgyZQq33norrVu39qqrtWbhwoXccMMNbNu2DYAvvviCe+65h+nTpwc0rhBCCCGEEEIIIYQQQgghhBBCiOA4Mv/iQAghhBBCCCGEbUopuP9VdKVgtCKsV02GBfJJ0l3zYDS2rfdd3newhKId5lRUrHmR9JY16LyDXoFlOt1m2FkgBo87vEPRAJTDen+FgELTAura8PGf/uss+kMzorsyLvquKNKdx9kH36vZpExhVUGiC/LQ/7klsEb9hqIumIrqO9hW9S5N7XUbXykYLc51gB2hLW1PK8pH9lm+ivTZVq/6AVXNYLRHPrXxZDDIK82U+OULcyjaIV2KNlju3xdifoBnfa15eqLiizXmoKyx/oLRYuvwRRiAlHiICbcOcvngdyg5fwzOmGnsdyUZ+0h2pYEugW8Xwklja3G2gRnVS7F2T9Uf2GerIb9IExnmO5zhrV9spKIBjkphsDorDQVMHuzgh01uXv3Jup/EwykYbd4DAbdJMgSjpf29C7A+Jq3Zbe4v3BDqsStL89Iy88/ql7sc9Grl+VnHGQ5hLrfnNRAbYR4fIC3Hd7kQpZyOQ/9UhduH/jmUuczXPoehLDpccWJnOP845fe4VlN6TGv/lawMHoe66UlUY5snMxYSY6yDW9OcieQ6rFPTosM9IZTWwWj+x/T1kTY63Ho+uRKMJgLh75oIwM7NtsI7GzJfQbPVvVtKKUb1Usz6yrrzL9bA+KOr13d1mD7XOh3QqjH8tatq2U6LY5PvvhQt4v0HnldW3WC0lCDmOs/5RnPLKcHrTwghhBCiRvbvQE/oUt+z+EdQb62D5m3rexq14vLLL6eoqDzseuDAgXz66adERVUNb09OTmbOnDl07NiR2267rWz/jBkzmDhxIj161HLSfYAyMjwJ1FOnTmXGjBmWdRISEnjxxRfJzMxk4ULPlwa5XC7mzp3rFUZW2ZQpUygpKSnbHjduHG+++SYhFgF6ycnJzJ49m/bt2zN16lTS0tJs34etW7cyefLksu3w8HDef/99RowYYVlfKcXYsWMZMGAAJ5xwAhs3bgTg0UcfZdKkSbRr18722EIIIYQQQgghhBBCCCGEEEIIIYJDgtGEEEIIIYQQQqAcDvSQs+HLt8v2Fakwy7phzgA61jYCboqLjEX6YBZstw6vKaXOuzmACYmGSj3wOvre8yzL9BM3oe76b/mO5UuCO3j77qir/x3cPuuDw/DidLvKbtZlMJodN72paTPZ/yr4MHchz+y5keYle2o2YO4BtNuNchhC5GpA79uBPu8o+w0cDtSUGagJ1wc0Tmoze4vgE9zeK+wbuzICGifaVzCaw3cwGvnVSw06WKD5Ym21mgKw6e8DuGc/Cq8/5rduJ0Mwmi9LN3ged6tAA4BOTSC1eWkwWpZlHRUTxGSBIFJK0bMlLNtkXf793nhOmjqbtCfetSyPcucSpfMB0HefA1/noZyBnDDUnlE9FY9+WvU1k18MS1bDmN7BGUdVfl3u+rvs5sPjlTEYrUPy4RFwovNy4LWZAbdLNBx70n9bic6JtXxNTHndfP4YaQj1GDvL3ObdyeWhaOAJSDLZkgY9/GRIph30XX44Ucp/MFeVIK4AgrwclmXKO+grgPAvf/u82inzmJbzrEZIma/Hx+E4PF7bgdK/fFmtdurfb6FOHFPj8U2v3zxHNOnOxpZlUWHmdum5/sc0nXU5HJ5QUSv5xeBya5xH6PNABJmdYLS922DLGmjXrfbnUw9q8kqZMc4cjHb2c27cz9fdOanbcMBwOjyfxa0+R+wy5Cn7CllrmRD43KobjNYiIfAQNpNN+4PSjRBCCCGEEA3CV199xa+//lq23ahRI958803LULSKbr31Vr755hsWL14MgNvt5oknnmDevHm1Ot/qGDhwINOnT/db79///ndZMBrAl19+aQxG+/nnn/npp5/Ktps3b868efMsQ9Equu222/j8889ZssT+74gfffRR8vPzy7afeOIJYyhaRU2aNOH111/n2GOPBTxhb0888QRPPfWU7bGFEEIIIYQQQgghhBBCCCGEEEIEhwSjCSGEEEIIIYTwqBRgUqysV02GBfJJUttYPPnr19C5aiqJPpCOPiPFd9sufeGYYQFMSDRYx59mLvv1q7KbOjcbNv9V8/Hik1GX3AktO0DvE1HhfoKeDgemsC93+YrqlLjgLGgOpnGzzWE2T+y5hXj3AQblLaVt8baaD+Z2ewKrGlkHZ1SXXv0z+paRUJjvu+KZV6Fad4bIWOhxvOd2gFKb2asX7/JeYZ9SsjugcaKsszEByFcRvhunVy/AbslfUFRSraYANFv0CKQ/bqtul8LAg9F+3wFaa9btsX4dHdO2QqRDjiHhIKaBpRNWcN5ximWbrO/bwpWaweeMY3/eSbCwanlySZrXtn7oCtQ9L9bGNAN2fAdIjLYOvznzWTdFsx2EOM1xHBk2QnMAFJWOZdvWld1MiVdMOEbx1i9VH9+Jxx4moTnfWvzgbTCFMqY7G8M3C2DkJV773W7NHzvM/Vk9QwuKNb8b2kSEwimVMmTaNIZQJxS7qtZfu8dGMFpOYO+lY3t7jg/xUf6Cv5StUC5z+FfgIWVKHSbPP9Eg6MIC9E0+ztlNjhkSlFA08BzPTXaEWr94o1Z+RlLz4VgdQdJsZLmago4UvoNk84og1s8pkxCAvWA0gJXfHtbBaHYuD1VHVLjiqBT40xBePOcbN1efFPxgbCu+wsxS4q0DxnZlWj8wxr4UtAgwGE0pz7lPdaQ0zFxnIYQQQggh6t38+fO9tqdMmUJKip/fax/y8MMPlwWjAbzxxhvMnj2b8HAfFxrqwV133YXDxhcNde/enbZt27JlyxYAVq5caaz7xhtveG1fe+21xMXZ++Bxzz332A5Gy83N9Qqba9++PVdddZWttgD9+vVj0KBBfPfddwB88MEHEowmhBBCCCGEEEIIIYQQQgghhBD1QILRhBBCCCGEEEJ4OLxXSRYp61ScysFoWmtY9iF60TwIj0INPbt84bvbHHhU1n7WNNS5N3puf78Y/fGrkHcQfv7cd8Ou/VB3z0U5q7m6UzQoKiIKHRYBRQVVC/ftQBcVosLCIXNfcMa7aBpq/DVB6avBcBheCxVehy0auYDD4zUzPOczrsucHfyOl38Gw86pcTfa5YJ3Z6F/+Bh++dJvffWv11BDzqrxuCnxEBMOOYW+6yW4sry2mwcYjBYe4lnAb7UgP9/hJ0hw23q01gEH7iz6w39aQqsE2J5pXXZqzme2x2rq2kuboq1sDWtjuw3AvoOwxpD71uVQaJ3WGg4aJhndcJMFrj5Rce3r1j+DRb9rnjgH0kjAKtAh2eUdjMaS19FDxqNOOKMWZmpNp+1CP30bfPmOZ8ewc1DXP4YzIZmRPRUv/2B938ImuxnZA07prmgUAQt+0+w7CEe3Udw+QnHAT95hKYeu9GLZsRHtcpWdpzwzUZFToPnoT09xowiYPk4xvFvDDqbSBzPRL/4fvP1MtdqbgtEOOOPQSxejKgWjWQXYVZRrcezbsNc65Azg6NYQHe79GIeGKDoke0LQKlu3V+OJOzLzN8eKXrtCMfHYugljEaLWLZhTrWbq3peDNoWkGHPZnhDr9NioXz6i8elHA1WThNJtBB2awpyU8pyTmeQWSjCasEfv2Wqv3vYNft6hGjZfr7aa5nQe117x5y7rEa55TXPZCZqwkNp/9ExBig7l+RxnZZchT9l07HE6IDkG4iKxfZ4aEVL9MNRAQ9iEEEIIIYT4p1i6dKnX9gUXXGC7bffu3enbty+//vorAAUFBaxYsYIBAwYEdY41ERkZyZAhQ2zX79q1a1kwWl5eHjk5OcTEVL2Qs2zZMq/tCRMm2B5j4MCBpKSksGuXIRm7gqVLl5KfX/6h6ayzzrIV8lbRySefXBaMtnXrVrZt20br1q0D6kMIIYQQQgghhBBCCCGEEEIIIUTNSDCaEEIIIYQQQggP5f3HwEUq1LJamLPSYsr3n0c/fn3Zpv7ybbh+Jurs68BtSKmoRGfugx8+QU+/0t5cux2LmvNttRd2igZqwvXw6iPWZe/NhnNv9ITeBMMp5wWnn4bE9Af9FYPRYko4XILROhdtrJV+9b8ugl4DUcktatbPvy+Dz/5nq6669pGghKKBZ0F7ajP4xU9+QrzbO5irRYn/hSJe42hNZKh1AFuB8hOMlp8D6bshKSWgMZdtsl79f8UgxayJit0HoFVjWLgSxs32DqEakLeMHoV/2h5LAddkzmFa0+kBzfH1nzRrDBlzqaV5LKt/htxs60qxhkSEBsDhULx4ieLSl6r+HDanQdpBzf4c67aJrvQq+/Tt4+GpJag+JwV7qt7jFBdB+m702Z29Cz5/E/3jp/DmGkb1TDAGowF8uAo+XOVd/uNmzXPf+A/MKeWgUjBacRHs2QItOgCQFKtYfL2T9BzN3mxPkJ7T0bDPY3ReDvrSY2HvtsAaxiV5zgEPZhLvsk77yHLGe14rlezMsqhcQV5R1X3r9prrP3q29XtjajPrYLTN+32PD5BmeB1U1iYRzuzTsH/GQgRCfzQ/sAYxcaj3t6HCg5cO1jg68DbR7lwS9/wBVH0/svN69hV0FO0jGM1fiK0QcOhayMIX7FXevqF2J1PLTEFfUPNgtCknK+YuNQ/wzXoY3q1mY9hhFSoNnjAzUzDaHkMwmqkvh8Nz3n5qd8Vbv9g7V42wvrxnS2xEcM9lqhOgLYQQQgghREOTmZnJpk2byrbj4+Pp2rVrQH0MGDCgLBgN4Oeff25QwWgdOnQgLMz6S9SsJCR4pyofOHDAMhjt999/L7sdHx9Px44dA5rXMcccwwcffOC3XuXgupSUlLLgNrsq3//NmzdLMJoQQgghhBBCCCGEEEIIIYQQQtSxwL4CSwghhBBCCCHEkcvpHZZUpKz/2Dm0QjWdm42ec1eVOvq1x9AlJaANKzkry0pHvzLD9lTVuMmykPIIpC6921imZ01D5+XAso9qPs6tz6DiEmvcT4PjMASeVQgobBRWQozrYNCGjIuEHi3gkbMU718T3MtMvQt+91+puj55rdpN9bZ1uM9oYTsUDUCdc0O1x7PSp7X/419SiXdQVUqJIc3LQL0xk8gS6+dKvsNGwMnfqwMa72CBZuM+67JTuylCQxStExVKKcb2USy4xsGgTtA+CSYn/8wH28fjwH6IFUPO5hb9BvGuTP91K7jlbfMYR7dWaK3RVw+yrqAUdDgqoPHq2vBu5udWk1vczDMETiSXWKdJ6etPQW/8Iyhzq9L3ljW4rx2KHp5QNRStVE4WLPwvp3Sv3hglNk9jwCIYDWDruiq7EmMU3VJUgw9FA+DjlwMPRQPU+MmoZ76AgaOIi7M+nzzgaAQZe9A53mkgu/wEo+UWVn0Ort1jfl32b2/9OKfEW+/PyPV/HPEVpNQuCVrEw8RjFcumOYgIPQx+zkL4obdvwH1u18De29t3R72yMqihaAAhTkV8VGBtotx5JG1bbllmJxjNFOakFET7WB+cK8Fowo4lb9ivu2197c2jDvgMRqth371bKUb2MJef+3wAJ3U26MUv4j67M+7TmuK+eST60PmSy0eQYmPDsetAvvV+U1/OQx+7B3exP9+aBKMFW7G9708QQgghhBCiQdu/3/t6eKdOnQL+vXVqaqrX9r59hl+Q1JPKQWf+hIZ6f/AoLi6uUic3N5eCgoKy7eqEjNlts337dq/tG2+8kXbt2gX07+67vX9vnZGREfB8hRBCCCGEEEIIIYQQQgghhBBC1ExIfU9ACCGEEEIIIUQD4fAONTIFo4VV/CT57ULIswjOSd8NOzb4Xvla0YE02LHRXt0OPWDYOfbqisOKCgtHt+oE2zdYlutz/Xzb+plXwYLnfNcZPA415spqzrCBU4ZgMneFReCuEtoU7+AvZ2ApQZdlvcTzu69BTXoQkltAi/bQqRcqwnt19xc3Oxj6eM0XnSvt5vScT2rcj4l+/h5o3w2at4O2XVEOc6ibLimGdb9B1n7ISkM/PMn+QDHxqDnfBGHG3kb1Urzwne/ja6LLe4FGSvGugMZwFOQSWZAJobFVyvJVpN/2+qclqH7DbI+3aqe5rI/FOpfRqfmMDvkDMveh7zzb9jg4Q1D3vYI6eRxaa1YNaUuHDmsocoTb78NC1+bQoYlCvzbTXKnfMFRCkxqNU9uax0GjCMgusC7PMYS8JLnSjH3qS/vBR3tRsfEBz0cX5sPqn2HvdgiPhLDyn5O+fZy9Pj59ldgLpzJ5sGL21wGE5wXIYRUGu209DDi91sasCV1cBDs2eUJs23WrsnBPFxag33oq8I5btIdRl6OSmqOmv0PjDRoerfrY5DuiKFRhuDZt5PfovnRuCsmxip1Zvn9GVs/B9Xus647pZe4nMcZ6f0auz+EBSM+xnuMVgxTPXyjfRSOOHLqkBNb9ag78NGnSCvXSiloLsm4SC1l59utH6TwS9/4JLaqWpdsIRnMbPtM6FMT4yH3LLbI5QfGPpt/8j/3Ku/5G52ajohvV3oRqQV6h5uct8P0m83t8MA4Xb1zpoNH11p9FM/PgnoVupp6qiI2o2WB62UfoGVeX7/j5c/R1w9GvrzZeAnM6IC5SgUWQc3YBaK2rHDNdho/VpcFoLROs+7NS02C0/u3hx80166NUQXGl64pCCCGEEPUluSXqrapfaiBqQXLL+p5B0GVmen/hSlxcXMB9VG7T0EK3HD5+b1ZdWVne34oRG1v190/+NGpk7zNxenq6/0oBOngweF88JYQQQgghhBBCCCGEEEIIIYQQwh75s1MhhBBCCCGEEB4Op9dmsbJeORlWoZr+frG5v7RdkJtta2j946e26gGoF5ahnE7/FcXh6fjTjMFoZPr+tnQ1cBTaRzCauu1ZGHlJDSbXwDlNwWiu8tuuEkblfMhfEdbBaO2T4IyCz/h7tyd5Js51gGG5X3J+9hvAoUCxip74CHXM0LLNQZ2gQzJs2l+1767N4bUrHPR90H9w2mk5n9LU5fvnXVP69vGeGx17wsPvoZq2qlpn9c/oO86CDEPqji9xiaj//oBq1qaGM61qaCpEh0OuIaQKINHlvegjpWR3QGM4cBPpzrcsK1A+UkBKLf8soPFW7bBe1B8bAW0Tvffpbxei/+9SyLeRYFTqpDOhSUvUkLNQR/UHQClF8zHjuGLpizzb+Go/Hfg2upcnyEB//LKxzuEQyqiUIrUZLN8SWLtkH8FoACyeBxNvDqhP/e1C9F0TApuIla3rcD9+PU9f/wSzv655dyYOqh7b9LZ11E4sUM3ov1ej778QNv/p2dFrEPzrVVRiM0/5+pXoy4+z1Zea+xP89SN6zQpUmy4w4oKyfgDifOQoPh9/ObfP7kHhoQDP64cq4v3kLlod99busT5+dGlufvQbR1vvT7cVjGa9P8kQtibE4Ujv3uI5Bm/4PfDGp11Qa6FoAP3aKtbvtR90Ge3OJUSXWJYdyLcOJKrIFHSkgPAQT0Ca26LO5v2aAR0a4ruAaFD2+0gHtrLhd+gdYFhhPXrrFzcXz9MUWr8EywTjmBEToZh9vmLya9Yv2n9/qHnqC828ix2MP7r64+kX7qu6c/cW3Ms+AqwDcR0KGhnOcYpdUFhSNbzMZXVgAZyHpt4igMzhyBoGo13QX/Hj5uAEDBcUmx8LIYQQQoi6pEJCoHnb+p6GOEzpShcLgvGZpjavpTQU4eHeX1BTVBR4qrzdNtXp25/KP3chhBBCCCGEEEIIIYQQQgghhBC1L/hf6yWEEEIIIYQQ4vBUIWzMhQOXss7SDju0WxcXwS9fGrvTH79qf+zXHrVVTb2zARUaZr9fcdhRF99R/cbHDIVxFuFGkdGo6e+gRl9+ZIfqKcNlHl0hrKekmFvSn+SEvO+rVOuYUMSnNzp44p6+LNgxgQU7JvDS7iu5IPsNY7CPvul03LPvRKd7gsNCnIoXhm8lIaTAq975od/wx5Zj6fXLc6y4aCPNwwypMoc8vvc2n+Xqtlk4viuEo473Wc+WjX+gH72mym5dUoK++5zqhaIB6v1ttRKKBhAZphhhnW0HQKMICD3tPK99KaeeGtAYDtxE6gLLsnyHjWC03VsCWiSyLcN6/1Ep4HCUPwN15n70/RcEForWYwCO//sfjutnloWilVIXTOXhfXdxXN5PZftCdDGXZJkDzqyMPrgYvXc7bF1nXSEkFAaMDKjP+tKjZeALsJIHneizXD97B9rl8lnHq376nuCEopVa8Byc15Wvx6wKXp+VWAWjsW19rY1XHbqwAP3Jq+iL+pSHogH8/h16bBvcs25HvzbTdigaSkGbVNSZV+O48wXU+bd6haIBPoPObmr2GIXu8vflp77QPLDY93GjcjCa1pp1e63rdmlq7ifRFIzm++0JgDQJRhP/AHrmtdULRet5AirAIMxAndEzsPpR7nzi3Acsy0rckO9nnazpqORweBYtR4dbl180TxbLilqw/rf6noFtW9M1E1/wH4oWTOf0U4T4+OuHgwVw9nNulv9dvdene+fffL0rjocSp/JW7HjyVPmJjmut+WfjdHg+o5kcsMijdhnyxEvz0FMCCEarHLoWqEsHBC+goaA4aF0JIYQQQghRbxo3buy1feCA9XUHXyq3SUhIqNGcrLgCuCZfFyrfx8zMzID7yMgw/DKpkqSkJK/tZcuWobWu0b9LLrkk4PkKIYQQQgghhBBCCCGEEEIIIYSoGetV7kIIIYQQQggh/nkc5cEUxcq8arI0GI3Nf0Jutrm/Ja8HaWIe6p2NqKatgtqnaHhUo8Zw83/Qj98QeFuHA258Es64DNb8jN6zDZV6NHQ9BpXcohZm28CYQt8qLnxwlZDgzuKzrafzQ9RxrAzvhVs56Fy0gZPueJJGTdoBTdAXToNXZtgb9/XH0K8/BrO+gl1/c+LDk/iLBL6MHky6M5G+Bb/SP385CtCP30Av4A9HPGe0WsBPUVXDd3avb02yK808XngknDweAHXezeg7z7Y3T1+Wf4bOSkPFV1go8ft3sH9ntbpTc39ChdTuZbfRvRTv/mq9oD+7ABx3voAeOwl2/Q0tOxCdejQJN7rIzLPXv0IT4bZYoQ/kVwgAICwcigqrVirIg7yDEN3I1ni7DeuGWiZUWoT/zQIo9pNgUom6wBy0p5KaE33N/Xw3awhfRJ9MujORPgUraVW8g/lxF6BNgYMVNCvZQ7//TkD/10fAwugrav05ESyXDFDMXRpYWESToadC+A3w5n+MdfSkE+C5pbYeBz22FkIFd29l4MPHcd6AZbye2Tvo3Sur6JwGFIym03ahbzkDNv9lrvS/J4wBQJaatUGF+w5KjI8KpEP/DlY63OzN9oScWEltZg7xSIxWWMUdZeR6wtaUMrc1BaOZwtaEONzoXZth+We26qr7XoHIKM/5Rtuu0Gdwrb/f9W1t/fo1ida5ON3mhcBZ+RBlCDcDcBvCiUqPEqZjEEBeoSYqPHiBQuLIok1PLl9t1v1mDK1uaBau1ASQkxwU8VGKAR3g2w2+6/Wf7mbpNAcDOgT2aN40L4un23xatn10/q98tH00ia4MXCu+Ae6ybOdQEOcjLDY7H5pW+tjkLxgtOQZCnVBsI+egpsFokWGKt69ycPZzgT9nKyuow6A8IYQQQgghaktycrLX9vr1gV8HXrfO+0tWmjRpYlkvpNJ1lpIS+yfV1Qkeq01Op5MWLVqwc6fnd2+bN28mLy+PqCj7F5FXrbL35SdNm3p/a8b69es5/vggfNmSEEIIIYQQQgghhBBCCCGEEEKIOuV/ZZ8QQgghhBBCiH+GCuEvRSrMWC3k67dx3zYafUUd/vFwn5MkFO2fZOxV1W6qlEJ16oUafQWOSQ+gThzzzwhFA6/XsBddYfGyy7NgIoxiTspbyg2Zs7gp42lG5nxCbFR5sJoadk7Aw+spJ6P/fRm4Smji2s+52W8zJXMOxx8KRasowZ3F11uHc23Gszi1Z069Cv7gx78H+g5FA9TMRajYQ98qP3BUwPO0nryG377B/eRNuO8Yj/u5e9DvzAq8n6hY1J3/RXUOfuhSZSN7+l/Er7r1Qw2b4AkIBFrE2+8/0ZVBlLZOUct1VEj/adLS3EnaLtvj7T5gnZrQvMKcdV4O+rHrbPdZSg043XeFCdfjQDM890vOzX6bLkUbiNL5dCraaKv/Mw5+hMNPQIu69hG70613J3RUPH+hCihAITkW1OTp4OuxXv8bfPwyOn0P7gcuwT0o3PNvxmT0gfSyavrHT2owe/+e+fFUzuqY7r9igNxWl9oz9+G+9zz0ht+DPl6g9FtP+w5Fq45ux/qtEhPuCQMJln3ZnuCyUjuzzHU7JJvLEmOs95e4fYccaa2NwWhJMYdLVI0QZlpr9N3n2m/QuTfqhDNQZ1+H6jesTkJA2yaWBwPZEeXOI95tSGDFE8765OduRj/j4sqX3fyd5v2ebnqHt3NsMx0vhAAgc1/gbdb/Fvx51JKtGfUz7oJr7B0gBs5w45jkYshMJflFWQAAIABJREFUF2t3+09w+22b5untPb32rYjsy7MJnusX7gLrUGnwHLMa+QhGO2DR1F8wmsOhaJXgc8plahqMBhBlvkwYkILi4PQjhBBCCCFEfUpISKBDhw5l21lZWaxZsyagPpYtW+a13a9fP8t6jRp5pyhnZfm4IFrJX38F+XpwEPTv37/sttvt5ptvvrHdNiMjg99/t3etfcCAAV7bS5YssT2OEEIIIYQQQgghhBBCCCGEEEKIhkOC0YQQQgghhBBCeDjLQ5GKlXnVZOjrD8OPn9bFjMqoSQ/U6XiifimloN+wwBod1d9/nSOdw2m93+0qv+3y8U3yzvIgC9W+O5w0NkgTsxZKCU/uvZW961uxbUMHlv89gGMKfjU3aNYG9U0+qveg8nkqhXr9z6DMR997Hrz7LCxdDK8+AksX2W6r3l6Pmv8r6sPdqNMuDMp8/GkcrWjayLpsZA/r/SkBBKN1KVxHjDvXsizPEVW+0cRHaGX6Htvj7TKs5UmJ8/yvi4vQkwZYV/JBvfiL/zoOB+rWZ6rsn5I5x9YYo3IW+67QujMqNEhJAnXkikEOsp9y0NtmJmlSDCinE/Xwe9C4qbGefuNx9Pj28Nkb5TsXz0Of3wNd4Ani0x+9XL1JN24KJ53pt1oj90H+t6gV+2fC8ts1v0wtpmcQ8jOj3IYwjK/eRU8Zgl7j/7lYG3RxEbq4CL54K7gdO0NQ46/xW83hUMT5CAIJVH4xZFd4qE3HjlCn53lp0jjaXLbbnJ/EwQJPeJoVX+MJcbjQ/70fAglzTGlXa3MxCQ1RtEuyXz/anUe8y7xoeOhjLm5+S7P4D5i7VNPrX25W7yoPSXIb8pKUjWC0Ah+n3kLw10+Bt9m2Dl1UGPy51IJ0m8GA4UHOU0yIVsy92H5Y6dfrodt9bn7Zoskv8v7nqnAAeHuF9cHgg1hPWLdr51bjGE4HxEaY55BtEcrqMhx7KgZDdm1u7rOiiCA8xmFB+jlJMJoQQgghhDhSDBw40Gv7tddes912zZo1rFixomw7IiKCo48+2rJukyZNvLZXr15te5yPPvrIdt26MmyY9+9/X3jhBdtt58+fT1FRka26Q4cOxVnhbx8++OAD9u2rRkC5EEIIIYQQQgghhBBCCCGEEEKIeiXBaEIIIYQQQgghPCqEKhUpc4BLmLb3B8fBomYuQkno1T9P5z4BVVejLq+liRxGHIbLPO4KCS4ul3Ud8ApGA1B3vwhnTQnCxHyLdx8gpWQ3TgxJMwAjLkQ9vxRlcR9Vq06o57+vxRn6cfZ1qGZtUO27o0LMoZK14f/GWi/4H97Nen9KvP2AgC5FG4g2BKPlOCqk/0REQaPG1p3s22l7PFMQUfNDwWh88TZsXWe7PwB1+X2ojoaUuMpOOKPKLjvBaIklaQzN/cp3pXbd7M2hgQlxKm4cZu85kxzr+V8pBb7CAbdvsD4OHUhHD09AvzMLvno38MmOuQL1vzWoB99AXfeorSYJI6Poe3E0vS+NY9nSFG6JWEjbhOol2IS5C+lZuMpcIT8H/fbT1eq7uvSKr3Bfcgx6SCx6SCzs2xG8zvuchJr5AarH8baqm0IcqyvhRjd3LnCTX6TZmWWdGtI8zhPKZtIi3hxqtNHH+rg0HyEvEowm6ov+43vcU4bgPq0J7sv7o1f/XL1+CvLgf0/ab3DMkDo/9ynVt7X9c5oonUeELiDU8Dk2u8C7r5xCmLmk/NiiDeFEPg4xZfLq9qOzOMzoHz4JvJHbDTs3B38ytSA9x/DiqeT49sEf+5IBnnC0+Cj/dUsd+5Cb6Gu9/8Vd7yZ6iovkm1w8/LH1/fktojcacBWbE78cCpwORUy4dXm2Rb6uy/Dx2Fnh2NOlmb1jYUQQDtWhhhz2QOXLcVEIIYQQQhwhLrroIq/tZ555hj177H1ZzB133OG1fe655xIebv2BoW/fvl7bixbZ+1KfTz/9lOXLl9uqW5fOP/98YmNjy7YXLFjAp5/6/0K2nTt38sAD9r9ILSEhgfPPP79sOycnh1tvvTWwyQohhBBCCCGEEEIIIYQQQgghhKh3EowmhBBCCCGEEMKjYjAa5lWTdRmMpp5bijrulDobTzQcamDVkCKfThpbOxM5nDgMK5XdFUKIXD5CfyoFW6iIKBw3PI56dKE5dK2OqDtfQCU0MZd3PQbOv60OZ3RIYnPUpXfV/biHXDJAcUIH732pzeC8Y03BaPb6beQ6QJIrjWh3nmV5rqqQMOAMgaQUy3r696W2xssp0MawoZR4hS7MR7883VZfXkbbD0xUSc1Rkx6ssv/XzccS68o2tnt4391E6ELffQ8aY3seDU3X5v7DFsJCIC6yfFtNvLna4+n/BNhWKdRd83DcOgsVGY1SCjXherhwWkDdRBRkMeO3iWzc1ofHzvJd96zeLpzKOxjjX/sfIFIX+G74e90FOOrNf6FvHAGbfIS1VUf341Bf5+F4agnqmKG2m9k99gTi4Y81932g2bzfuryFnzEjwxSt4q0TR9bvNQe5bMsw9ynBaKI+6P070VPHwh/fQ84BWP8b+qqB6F3VCE7atAqK/BzLSkVGoy69O/AxguSMnvbrRrnzUEC8K8t2my/Xlh8H3IZDQmm44oju5n7yimBftmbVDk1Grr2QKPEPsmGlsUjd8Lj5M9j29bU0oZorLNYs/1vz7XrN32n+60eEwr2jgv9ZUynFpSc4yHjSya2n2A9SrCyvCPKLId06L7rMzpAWuH382YXzUFGjSOvyA/lVjw/GYLQKw3Rt7ntepSJCq/8YlOpo/jgekILq5RALIYQQQgjR4AwZMoTevXuXbR84cICJEyeSn2+RfFzBE088wcKFC8u2lVLcdNNNxvrHH388UVHlv5NZsGABv/zyi88xNmzYwMUXX+zvLtSL2NhYbrjhBq99EyZM4KuvzF9As2XLFoYPH05Wlv1rOwD333+/V+DcK6+8wrRp03D5+hIpC6tXr+bbb78NqI0QQgghhBBCCCGEEEIIIYQQQojgkGA0IYQQQgghhBAezvJQpQhdyLkH3mRc9gLOcPzIqd3h5Mh1nJD3vTEoJ+g69oTUo+tmLNHwdDvWdlX15Ceo6Ea1OJnDhGnhvLvCimpfwWjOEMvdqv8I1LyfYcwVNZhczSjlfyG3uqAOg9E69IBL7kK98hsqNqHuxq3E6VB8drOD2ecrLhmgeGCM4qc7HSTFWj9e/oKCSrUt3ooCot3WaWW5jugKkwiBrsdYd/TLF7bGW7/XXNZ+57foce1h+wZbfZXpNRDVuGlATdSFU1GPLYbWncv29Sz8k5//HsDN6U9w+sGPOS3nE07L+YQrM+fy2dYRXHrgZd+dxsTDCSMDm3sD0sXGQ9i1mfdrVMUlohbvqp0Jjb4cddm9MGg0jLkC9Z8lqBHnV6mmRlxQJezRlh2buOG3a2kUYV0c5i7k2cXdWd7paa7JmMPFWa+wcPs4bs14wn/fGXvQuvZDcfS639AX9w1+x6MuQz31GcppCOH0oUV8zcM4rMxcopm5xPox9RXGprVGv/hvOm23XmznKxht7R7rssRoiIuqnfsphC/6tZmQWzXAU5/TNfBjjq9QtA49UPe9AiMuhIk3o+Z8h+p5QoCzDZ5Bney93iLc+TjxnAvHuw7Y7n9bBqQd9Dx+poexdAYje5rncvu7btre4abXA25aT3Mzd6kh6Uj842itYds668LhE1FnTYHmba3LtzXMYLSlGzTt73TTf7qbwTPdrN5trjs0FW49RfH9NAeDu9Tu++eM8YrTjqrVIVgb1hmXMp8jOQ7dxThDMFq2xeHXTjBa68b2HruocP91/EmJVxzbtub9FBTXvA8hhBBCCCGCYc+ePWzZsqVa/0rNnTuXsLCwsu2vv/6aQYMG8dNPP1UZLy0tjSlTpnDzzd5fDjJ16lR69jQnwMfGxnLOOeeUbbtcLkaOHMmSJUuq1C0qKuKFF16gf//+7N27l4SE+vv9lS/33HMPPXr0KNvOzs5m6NChTJgwgXfeeYc//viDtWvXsmTJEm688Ua6d+/OmjVriIiIYMwY+19E065dO55//nmvfY888ggDBw5k0aJFlJSYf2e6ZcsWZs2axZAhQ+jevTtffvll4HdUCCGEEEIIIYQQQgghhBBCCCFEjVmveBVCCCGEEEII8c/jKF/E2dS1j1d3XerZSBqE44bPcT81D359us6mo6bNQZmCnsQRTzkc6IvvhPkP+a/cd3Ctz+ew4DAsxHZX+OZzH3/kbwpGA1AdjkLdOgtunYUuKUafHFPNSdYeFROHbtYG9mytvTEmP4Q675Za6786IkIVV52kuOok/3VT4hXgPyRl/q7LAYjW1kGYOZWC0VT/EegPX6pace82dEkxyk9AlSmEKDxE0/LxiXAww++cvSQ2R13/WGBtDlHHDke9tgoAveA59OPX07F4M4/su6t6/U2bg4qJq1bbhqBRpKJJLOw7aK7Tp3XVQAYVl4iecD289VRQ56POnIzq2AN/ERCqdWeY/BD62Tt8B0JaWTyPn09N4qitd1KswryKXtp1BY0PbqPxwtsJ+J6VFMOBdIhPCrSlTzptN/zwsSfQ6LhT0Ff0D2r/ADRvg2Pq7Oo3txnKGEytE308S75fjJ73AB2aPYVVfOOe7elAE8um6/ZYd9klIR/95lxIbAaDxqDCDel6QgSR1hrefdZc4aOXYeTF9jssLjIWqdueRXU/FjVsQgAzrD2tEiAyFPL9BOxEVQj1TnBnBjTG5fPdLLzWidtw6lT6UfWKgYrr3rCutHRj+e28Ipj0imZQJ03nphKk+I+3fyfk51oWqfGTPTdadYadm6uU623r/Z4L1bX8Is3EF9zstpE/+MaVinP61d21HqUUH1zr4NQn3Xy5tnbGWBfehR6FfxnLS8PMTOG72flV95mC0RwVfvh2g69jghCMBjDnQs/juP/QZ4OEKFhyk4NeLWHsLDcf/em/j4JiDQ3uGSyEEEIIIf6JJk6cWO22pWH0ffv25ZlnnuHqq6/GfehLilasWEH//v3p2LEj3bt3JyIigu3bt7N8+fIqQVzDhw/nwQcf9Dvegw8+yIIFC8jKygJg3759nHrqqXTs2JGePXsSHh7O3r17+emnn8jN9XzWbNasGTNmzODiiwO4NlRHwsLC+PDDDxkyZAgbN3ounmitefvtt3n77bct2yilmDVrFtu2bWPhwoVe+3256KKL2LNnD3fccUfZz+jHH39k9OjRREVF0adPH5o2bUpkZCQHDx4kLS2N1atXlz3WQgghhBBCCCGEEEIIIYQQQggh6pcEowkhhBBCCCGEAEAph3Vcjj60GjM7wGCamjjqeFTq0XU3nmiQ1BmXoP0Fo/Ub6veP3v8xTEGC7gorqn0FBPkIRqtIhYTCiz+jL+3nu96jCyEvBz3zWjhoCKJo1sYTNJafg559p63xfRpxAbz075r3Y2Xc1Q0uFC1QdhbOR7tzyhb1R7utwxpyKwWj0aqTdWdaQ8ZeaNLS55gb91vv7xiRifNgut85lwmPRN39IvQ5ERWXaL+dybBzYNY0KLRIKrBBTX8HNXBUzedRz/wFo/Vtbb1fjZ2EfvfZwIPJTNp2hQ5H2a6uJlwP/UfAqh/QD08KaKgOnz5CpvoPC2LH8F7sWAbk/8CE7HdpUbIr0Fl7S98T1GA0vfI79N3neALXapGa93ON2tsN7QimEzuZzw30d4sASHKlWZan787EFIy2wRDk2OXPt9CfTfVsdOoFj7yPSkqxP2EhqmPvNp/F+uFJcOJoVGyCvf58BaN1PzaQmdU6h0PRpRms3O67XsWQ15Ti3RBpf4zSUDNtCEYrPcqEhyoSoyHd+rTJi9Yw73vNw+Pk88s/3vYN5rLWncv///ETi7bra2dONbDoD81Om2vGk2Lq/vnvdCg+u8lBj/vdrN4d/P7Xh3XCpQxB5ZSHmZmC0Q5YBaMZjj3OCh/77Z5jRQcpGK13K8Xqfzn4eh2UuDXDuioSD/08F13n4Kt1sGm/pn87xRlPu9lucRmgwE+gpRBCCCGEEIebK6+8koSEBC699FJycnLK9m/cuLEs9MvKZZddxpw5cwgN9f3FMgAtWrTg3XffZezYsRw8WH6x3jRGu3bt+PDDD9m7d2+A96butGrViu+++45rrrmGBQsW+KybmJjI/PnzGTlyJNOmTfMqi42N9TvW1KlT6dmzJ5deeil79pR/80VeXh7ff/+9rfkmJNi8viaEEEIIIYQQQgghhBBCCCGEECKo6u7rmIUQQgghhBBCNGxOwyJOt8vzf3otrB41iYyqu7FEw9Wklf86ccELmDnsOUyv4QrBaKaAMl/tLaiOPVF3/te6sEV71H9/QPUfgRpyFmrRTnM/F92OOvOqoAWOqQunwekXQ2lYnum4Vp2+r388aH3Vly7Nyhflm/Qu+L3sdozdYLRkH+E/+/0HSWUYgkRa7QksjEn951PU4DODE4oGqNh41MxF/isOGg09BpRvh0eibnv2iAhFA0iK8V3et431k0q16oS6dz7ExNV8Eu2PQs1YEHAQpmrdGTXyYjjlvICHjNCFTMx+i7d3nsdNGU/bD0Xz9fwL4rmU1hp93bCghaKp+16Bxk29dzZuhnr6c1QNf4atG9d9AMrwrj4K164AIKnEEIyWZ/61iSn0pX3R5vKNDb+j35nlb4pC1Ny+Hf7rvPOs/f5MQZYx9ZBuaEOXpv6PLVHO8vuUUhLYMTgzD3ILNW5DOFHFc6pYQ9iRlc9XGzoU/yyZhmTgmPiyMENVGpBW2bb1aFNiXz355E/7dRP9nFvWFqUUS6c5GNUz+H2vDeuM28efXZSGmcUZwhmzC6ruc7mr7qvYF0CjSIgK8z+/mCAFowEkxijGH604p5+jLBQNPI/vkFTFlYMc9GipiDBkO0gwmhBCCCGEOBKdddZZbNq0iRtuuIGkJPPvLUNDQznllFP4/vvvmTt3rq1QtFJDhgxh+fLljBkzxnidPDk5mdtuu42VK1fStauvC6QNQ7NmzXjvvffKAtK6detGfHw8ERERtG/fnmHDhvHcc8+xadMmRo4cCUBWlvcF2rg4e9euR4wYwd9//82sWbPo3bu33981hIaGMmDAAO6//37Wr1/PDTfcUL07KYQQQgghhBBCCCGEEEIIIYQQokZC6nsCQgghhBBCCCEaCFMokutQMJqdhffBkrGv7sYSDZZyONAde8LGP8yVghSAdCRQyoHl8njtWVGtiwrRt4+zbuxwoByB5eer0y6EIWd5gn4y9wMKGiVAy45eCwqU0wlzvkNfPci7gzapMOKCgMb0O6ewcNQdz6OvexR2bITWndGjW0Fhfs36ffEXz/04zMVGKLqnwCpzVh2phevLbkfbDUaLTYCwCCiyWNGf5j9MKivPen+Cy0eQX2Vd+kK3Y+3Xt0n1HgTPfIG+dqi5zhmXogacjk7bDZn7oE0qKiyI6QP1zFcwmlLQs4WP8iFnwYljYMvaqq/DkFD0Z2/Am//xOb6a+xOqc+8AZmzRxyV3ope8XqM+bI1z+X1w/q3oIbHWFYJwLqVdLtAa/WSQF2INHuf5eW1bB7kHIToWWncJ+L3BSpem/usE05heEBXuY2HboeN5kss6VC5NN0Jrbbk4zhSM1rKk0oH1m/fh6n/bmq8Q1Za+x28V/eXbqEvvstdfcZH1/lAbqTv1oEsz/3WiW7eGnXGQc4AUuwGXFazfi/X5NeU5vGAvmKjUr9sCnoY4Eh2wDuckvsICelMwWnYG/L0a2ncP/ryq6fft9oPa/IXu1qb4KMXCa53szdbsyoIQB+RVOvS5NAycYUglM1gf3hmXjWC02EiF1VEl2+Ljqp1gNKUULeJhg59LaMEMRrPLGIxmyOAUQgghhBCitm3ZsqVW+2/SpAlPPvkkjz/+OCtWrGDt2rXs37+fwsJCkpKSaNmyJQMHDiQ21nDt2IbU1FTef/990tLS+Oabb9ixYwd5eXk0bdqUdu3aMWjQIEJCyv8kfPDgwQEFa9ckhPull17ipZdeqlbbgQMHMnDgQFt1V69eXXZbKUWTJk1sjxMREcE111zDNddcQ0ZGBj/++CO7d+8mIyOD4uJiYmJiaNKkCZ07dyY1NZWoKPkiNyGEEEIIIYQQQgghhBBCCCGEqG8SjCaEEEIIIYQQwsMUfOF2oUtKYNff5rbtu0OPAbDwheDMpcA6jEf886hL70bfNcFcHmf+5vV/HFNwV2m44YovfbSt3iUiFR4JKe09/3zV634szFyEnvsAZO2H7sehrnoQVUtBGyomDlKPBkD7+dZ3n47qj5r0AKpjjyDNrP4tv9NB5BTzQv8uRevKbhuD0VSFxSBOB0opdHIK7NxctfJ+Hylsh2TlWS+2iXcd8NsWgBEXoK5/zDLEKBhUr4HwwBvoeydWLYxLhKOHeOolNYek5rUyh/rUOMY6wAGgSSzERPh+3FVIKJheQ517Q/uj0NOvtG577SM1DkUDUK06wayv0LeOgvycGvdn1G8oKjQM3fUYWPNLlWL98+eoMy4NqEtdmI9+ZQbMn16zuTVrA8cMhcXzqpb1OQlVumCubdeajWOhfbInxMMU8BFs5/TzcywoKQYg0RCMlu5IwJ2+D2eSd6JbYbEmzfD0aV6823vHjo3GcLV/Iq2153zEXeGfy3Dba9t96F+AbUpva3fAbXTZuAGMU7FMuw1z9XM/vOZq6sNi258ta+z/oA6zYLRUG8FoyXFO1PPfo5++jZTN2QGP8dcujdtw7HJUeHmbAoCsKAW5hZpoXwGO4siXnWG9v2IwWseenus0Vk/C7z9sUMFoWQHkUCdG+69T25o2UjRtZC7fPdPBTXMz+XJVAQcccQC0Ld5K85LdfB09uEr97aGtyHaaOyw9XsRFWpdn51c91zYGo1U6dLRL8h+MFt2QgtGK63YeQgghhBBC1DWHw0G/fv3o169frY2RlJTE+PHja63/hio3N5dff/21bLtz587VDppr3Lgxp59+erCmJoQQQgghhBBCCCGEEEIIIYQQopZIMJoQQgghhBBCCA+HIVTJ7YK9W8tCJCpTL/6C6tgD7Xajv/sAMvbWeCpq7FU17kMcIQaN9l2eaCMR4Z9CGcINtRutNfq1mea21QxGC4Q67hTUcafU+jhVxj3/NvTcfwXebtKDqAun1sKM6ld4qOKSAYqXllkHXXUpWl922xSMluOIKd8ofe4kWQej6S2r8Rf7kZVnvT/ObQ5GUzMWoAbU3aIVdfI4uP159MOTvPdf+wgqPKLO5lEfkmLMZfGGcAe7lFJw+kXQbyj6oj6QU+FnPuB0mHB9zQaoOFbPAaglVYOw3E/fBm89FZxB2qR6/m/Z0TIYjS/fQd85N6DnjH7hPnjzP9WfU7djUbO/QR0KwHXHNYaK7wdhEaiL76h+/zaEhSg6JMP6mp8i2jLhGD9HnTxPulmSIRjNrZxkbdxMYqVgtN0+shpblOyqunP1cnTnPrDhd8jJsgj8MgRgGQOxyutqY/hWxW1TuJeP/f7m4TWujbmXbovDQ4khGC0kgNSvOpTa3BzcWSolXqFadUI98j5t1mp4PLCExs9Wm0eomHvo6/hQmdaefsf2CWgq4gijs9KsC+ISy26q2AR0jxPg9++qtl//m99z7Lp0sMBevagwiAxrSDP3pnOzYeMfNCkq4NX3RlYp3xzals4dV1u2XRfW2div89BH9UaGU8Bsi8fPGIxW6WN/p6aKJat9Hwtj6iGIUYLRhBBCCCGEEME2f/588vLKf6F0/PHH1+NshBBCCCGEEEIIIYQQQgghhBBC1AUJRhNCCCGEEEII4eE0BKO5XLBjo7ldyw4AKIcDfdqF3mEb1VUP4UmiYVJKwXub0ePaW1fof2rdTqghcxiC0bZvQJ93lO/XsSlU7Ugw4HSwCkYbNxnem21u161f7c2pnl18vHUwWrzKZcilI1HR49APTyJaWwejFToiKMFJCK7yYLT4ZOvBFv4X3aQVXDjN83quRO/dTtbag+DoUnU+rizLLtW85ahOvQz3rvaokRdDq07oj+YDCjXyYlSPI3/hjc9gtKjgjKGSW8Brq9D/exKy0lDd+sEZl1k+Z4JNXfsIeuMf8OvXNesooQkqJs7TZ6tO5qieL97yhMHZoDP3wTvP1Gha6unPy0LRANRV/wedeqN/+BgaNUadej6qS+2n86Q2q5tgtDUPOHA4/Dxv9m4DINEQjAawf/N2Evt7v753Wh+SAEgp2V1ln776RN/zEKKhKTYEo4WG1e08bOrW3H+dlPjy2yd0DHyMD1dpUg05zI5qBqMBfPynZmyfhhsOJerAAcN7UFyS93bvQZbBaHz9HnrvdlTTVsGfWzXkFNqrlxhdu/OoCf3us+hnphq/FACgTfE2wt0FFDqqJpytDu9qbFd6vGhkCBU+kF91n8twMlk5GK1LU+t6FUXXw2E8wvBXKBKMJoQQQgghhKiOHTt2cM8993jtu+gie9fZhRBCCCGEEEIIIYQQQgghhBBCHL6O4FWvQgghhBBCCCECYgpG0m5I22NdltgcFVGeiqIumApdj6nZNC67F9WuW436EEcWldwCdeszUCkgR133KKpJy3qaVQNkCkbLzvAdigaQnxP8+QTqkrus94+/pmb9duoFF0z13tdvqCccqEtf6zYx8dBrYM3GbcBO6qK4Yaj36yksBGZfEUP0hEkQFg5AI9dBYx9ZzkNJI2XBaInGuvqF++Dnz6vu37IGfVZHskrCLdvFu6umjKhP0+slFK1s/J4DcNz+HI7b5/wjQtEAkmPNZcEKRgNQjZviuGY6jjtfQI2dhAqpm+/0UEqhHv8IBp5RtTA2AfX05zBotP+OOvYov92uu7Ga/u4D23PTbz/jCaitjvZHoRZuQ4V5v76UUqihZ+O4ex6O62fWSSgaQOem1Q8AWrexO2dmv++33kNnKro08z2OXrqo7HaTkv3Gepu3VD3+bN5vnVAS7c6hkTvn4DNdAAAgAElEQVTb7/yEqC963W9oO8eSEkMwmjM0uBMKkrAQxYRjfL/mkyuEe4aFKO44LbBjUUYuLNtkXVaT7M6/dhnjM8U/xYE06/1x3ufUqk3V8OBS+v4LgjmjaitxadthV74Cd+uT/vNH9JM3+QxFA3DipkPxZsuynSEtzO0OfVSPMwSjZRdU3edy++6rVNfm/g9GMVVz3GpdpCGMTYLRhBBCCCGEEADvvvsud955J/v3m6/Rlvrtt9848cQTycjIKNvXq1cvTj755NqcohBCCCGEEEIIIYQQQgghhBBCiAagblaXCSGEEEIIIYRo+JxO6/1uF2SnW5fFJ3ltqpg4mPUVLF8CW9ZC01awYxN67r8sm6tpc+CU82Dlt7BzM/Q8AdXhqJrcC3GEUmOuhD4nwa9fe8L6+pyEatu1vqfVsDgMr+HDhDpxDHr+Q6C9gyLUSWNr1q9SqKseRJ88Hv76EVp2hD6DPaFLs75ED4uv2mbCdaiQhhkCEixPnOPg3H6aHzZrosNhWFdFu6RDi+oPJX0kuQyBDcA+ZzJJrvTyYLS4JGNdAP3BXNSxw8u3P3oZPf1KALKccZZt4l1ZXtvqmumoqAaapnAES4lTgHWAS/gRcnVZOZ3w0Duw4ivYsBKKiyE5BY4djkpsBgW5fgPN1KkVwkmOH2GuuPJbtNYoP4k6+qOX4ZUZgdwNjxEXoAaNhuNOQYUb0jfqQWqz6rftUPw3/9t5AW8fHM8FLeZXKe/ZEl6+zEHPlv6DQfQrj5TdjtL5tCjeyc7QqkEm67YVcHqlfRv2WffZqWgTNchHEqLW6Sv6Q1Qs+qj+qF4DoecJ0LUfKrxSUk6xIRgt1JBu0wDcPFzx1i/mkLG2id6vzv8bq/jfz5q/K53ixLmyKFah5DmibY9dsedOTczHCCtrdmPrvUAcwTKsnzCqUjAarc3BaPz5I3rfjnoPC88ptF83sQGeyuviIvTkk2zXTy7ZDxa5zvtCko1tHIde6o0irM+rD+RXbWM3GK1/e885eWGJcXii6+EwHhFqfV8LfMxTCCGEEEII8c9x8OBBpk+fzsyZMxkxYgRDhw6lV69eNGnShJCQEDIyMli1ahWLFy9m0aJF6Aq/OwwLC2P+/KrXiYUQQgghhBBCCCGEEEIIIYQQQhx5jpCla0IIIYT4f/buOzyqKv/j+OfMTHoggSSU0HuVJoiAIIK42FgLKthWdC3outZ117ViXV3Xsquua8Gy9rVXXAvqCvpDsaBrwUWaSi+hhbQ5vz8Gkgxz78ydSSb1/XoeHnLP+Z5zv0kmk5lJ7icAANSYW6hSRYVs0UbnuT0v2pVkUlKlMYeF/kmy3y6UnILROvaQDj0ldDF4tbAcwI3p3Fvq3Lu+22i4fL7YNQ2Y6TVYuvxB2Vt+IxVvkzKyZGbeKDPU+0XqUffvPUTqPSR8LC1D+scHsjf8Wlr+bSgQbPJJ0gm/q5VzNnQjuxuN7O4QyGFCt6WCKMFo6wL5Uqkqg9FMy9Yu0Vm7vPe8Xp/1Nz2de5xKUlvo1y88ovGSgjIq8jkHo+UEi8IHeuwV7QxIksLI7MBKO1wydBojY4w0fELo355G/kJm5g2y910llZeFz/n90vEXS5OmVe2VliGddb3sPZdF7rWtSNqwWspv79qL3bC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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import asyncio\n", + "\n", + "from matplotlib import pyplot as plt\n", + "\n", + "from apd.aggregation.query import with_database\n", + "from apd.aggregation.analysis import get_known_configs, plot_sensor\n", + "\n", + "with with_database(\"postgresql+psycopg2://apd@localhost/apd\") as session:\n", + " coros = []\n", + " figure = plt.figure(figsize = (20, 5), dpi=300)\n", + " configs = get_known_configs()\n", + " to_display = configs[\"Relative humidity\"], configs[\"RAM available\"]\n", + " for i, config in enumerate(to_display, start=1):\n", + " plot = figure.add_subplot(1, 2, i)\n", + " coros.append(plot_sensor(config, plot, {}))\n", + " await asyncio.gather(*coros)\n", + "\n", + "display(figure)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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mOldHr5heSOP7/2glvvbFzXjyS5rxlJc048tjzocBAAAAoL703wEAbOQcCQCg9ziHA+pDMBoAAACFygYuzbYYLE6+RouAn2YacWr8ZKTXXB3pe/8s0ofu6U5hNdRsZk/Pi8m6ZHf29BOz/dVJu5KzXM7VS8FoRYGBees1IuKUYLSBsLjcep5pn0s96YO35R+f7z3WxUIA4LRjLUKVe+kcG3rF+OR2V7D9fuHqND554My/7zka8Z/e0IzUT2ECAAAAQA/SrwcAAACujwE6yTGW/jSy3QUAAABQb2WD0WYWIy7e1dla+lWjRKjV8Rf8RDz68HWr/xh+UcRv/Z9InvGszhbWQ4Zyot8vuTB7+okW4Qa9pux+ulAiTKouioLRLtgRsWdnxPTCxrZTs52rifpYahEoGRExNd/5Omivqfk0vvRwfvsX7vdFDQDdd3ii+PNnNXQ5L6oZWFPlTG68xX7X79I0jY/cvnEZ3DEWccO9Ed/xNdtQFAAAAAAwQAa7jxYAqAPnIwAA28aPdwI1kjNsGAAAAFaVDVyaXepsHf2sKABqzfHJdQt4ZSXSP/j5SJcWO1dUTeVtj3kxBHnBaAePtKWc2lgpEa4XEbHY6Gwd7VS0X+wYjnhUThDjqTkd8INgucS2nBWcR709dKq4/c6xiIOH67GPf+H+NH7+7c141utX4o3XNSP15R9A35prca3bb6HL0ClVTpfGJjtXRy9YXok4mRP6/dYbnHcCAAAAQC35zhgAAABacO0MPUE/F1AjgtEAAAAo1CwZuDQjgGbTGiWW8bHhy8+eMDsVceeNnSmoxvK2xyQnGe2S3dkNC8sRk30UoFU2wHCph4LRGgXBaCOFwWidqYd6WSoRKDk9eNmRPW+0RTBaRMSTX1LyxKSD/vFgGt/9B814/T+m8bc3Rzz3bWn8/NX985kCwNlanXecyAkvAs5W5WzpgRMdK6MnFN1Xduio804AAAAA6D369QCAQeU8CGDw5AzuiRC2BNBRjrH0J8FoAAAAFCrb7zy71Nk6+llRANSasR37Nk68/XPtL6bm8jbHoZwejkdfmP9cr/xY/3T4lQ0wXFjubB3ttFywX+wYzl+3gikGw+Jy6/13WmBnzxmbLHdcvvmB7T1+v/qa5obj6Rv+bxrjE/3zuQLAGa3ChZ1/QjlV7mscn4yYmh/cc6ui8PN7jnavDgAAAAAAAAAAAAaMEEOgRgSjAQAAUKhoMOZ6s4udraOfFQVArRkd2b9x4s4L2l9MzeVtj0M5Pyqz7+L85/q7W/uno3al5FtZbPTOe24VjHZJXjDaTGfqGQRpmsaB8TQOT6aR1vyLjKUSx83phYhm2Q8xamH0VLn5Pn339q7Xj96xcVqaRrzhOtsbQD9qdd4xIRgNSql6iXHwSGfq6AVFlzGHJ7tXBwAAAADARr4XBwC6oOb3sAKwWY7vwBl1H7cE1INgNAAAAAqVzZSZEYy2Kc1mWmoZj43s2zhxdqr9BdXcSjN7el4w2tdclv9cBw5HjJ7qj07UZs5yOdficmfraKdWwWiP3p290k/N9sc67ba7j6Txz1/ZjKe8tBn7/1czfvTPmrVelkuNcvPNLnW2DtprdKLcfAcOd7aOzfry2HZXAEAntDrvmF6o7zkT1EnVPeXg4cHdt4ru91oseS0EAAAAAHTb4PZpAgAAANBHahBYtv0VAHUhGA0AAIBCeUFU55pdrN7lNLOQxgduSeNPP9mM2x8ezC6rsst3dGT/hmnpxLE2V1N/zZwUubxgtCRJ4ur/mtMYEQ+cbEdV269sgOFCDw2gbhTsGyNDEZdcmN12YrYz9fSzZjONn31LM26878y0j9we8cvvqe9xuWww2vRCZ+ugvQ4dLbfN3V3TkIylRj3rAmBrigJ7IyKmhYRDRxyb3u4Ktk/Za3wAAAAAoEfUYDApAMC2cB4EwHo+FwA6xzGWPiUYDQAAgEJlB2POVBwMfmQqjX/56mY883XN+J/vTONpv9OMP/lEyZSwPtJqkP2a8R37Nk6cONHeYnpA3vaYF4wWEfET35rfeLxPBlqX3U8XlztbRzsV7Rs7hiMu2Z3ddmKmM/X0s8/dG/HZQxunv/eLaW2DnhZLBqNNzXe2Dtrr4JFy891dcr5OyAvojCi/XQLQW1oFss4IYoVSqt5zM7vUmTp6gfuTAAAAAIBtpZMSANh2zkcAALaPczGgPgSjAQAAUKgoAGS92YrBaK/6WBq3PXz2tF9+dxqHJwer86xRMgtuYmjvxomTx9tbTA/I2xyHC3o4kiSJxzwqu+34TH9sb6WD0XootKdlMNqF2W0nZjtTTz977SeyN6CZxYjjNQ2aWyoZKnl4qrN10D6zi2k8eLLcvEemI5a3KbSv6NhUdrsEoLe0Or5PV7wWhkFV9eytaj9TPyl7jQ8AAAAAAAAAAL3DTTEAQDWC0QAAAChUdjDmTMUBq3/5jxufuJlGfODWweroLgpYWW96aPfGiaeOtbeYHpC3PQ4lxY+7NGPxRdQ39KmqsvvpwnJn62inlsFou7NX+vGZiNQv11Zy7/H85XWipvvIUsmQv7EJ20KvuOdo+XnTdPtC74rCcRZ76BgLQHmtzjumF7pTB/S6vMu0kZw7Fqr2M/UTwWgAAAAA0It07AEAbOQcCWDwOPZDzzMmC6gRwWgAAAAUKjsYc3ap2vPO54SHvOWzg9V51miWm296+KJoxjlBUCfG219QzeUGo7Xo4bgsJxjt7gpBPHXWLLkd5e13ddQoCB8aGYq4fE922/JKxMnZztTUr3YM57fVNTxwsWQw2uhEZ+ugfQ6MV/v83651WxSOUxSaBkDvanV8F4wG5eTdK7V7Z/b02QEORmt1X1ljZbD6zgAAAACg9+nTAwAAANfHAJ3kGEt/EowGAABAobLBaJPz5Z9zdjH/SVsFXPWbovCnc80OXXj2hMkTkS4N1kjhvACwoSR7+ppL92TP8ObPpJH2wS9ZlN1P5yoGGG6n5aJgtOGIfRfnt49Ptr+efrZSEKx3oqYhc0XhVOuNCUbrGQePVJt/u9Zt0bGp7HYJQG8pOvZHrJ5jr5Q9IYcBlreX7D4/e3ovXb+2W6tDyiCHxgEAAAAA2813IgBAF/TBvd0Ag6tgcI/jO/QG+ypQIwM23BwAAICqyo7vPjlTvtPryFR+23CLgKt+02qQ/XpTQxdtnHh8rH3F9IC87bFVMNolu/Pbrrtn8/XURdn9tJcGT+ftGyNDEUmSxBUZu8MawWjVzBcEDhyvcGzvpqWSx07BaL3j7orBaKMT27NtFoWfLQpGA+g7aZqWCr7spfNsqJs9O7OnzyzU81qkG1q980EOjQMAAACA2jJgFAAAAIB+0KVurqKX0dUGrBGMBgAAQKFms9x8x6bLP2dhMNqAXak2Si7fiIjpoYx0r+Pj7SumB2w2GO3xl+S3veyDFVZCTZUNRuulwdN5+8bI8Or/zxtJ4tKcwLvxST3gVSwUhH0cn+leHVWUCSiJiDg2bVvoFScqhvCdnO1QIS0UhfIJRgPoP42SYazTC52tA/pB3o1Ku8/Pnj7bQ9ev7daqL26Qlw0AAAAA9Cb3LgAAA0qiBQBn8bkA0DmOsfSnARtuDgAAQFVlA5eqhOccLQhRG7RQkbID7SMipof2bJx49OH2FdMDNhuM9mP/NH+GT98d8dIeD0cru582mhHLjd7o6FzO2Td2DJ/5e9/F2fOMT7a/nn42XzCo/kRNg9HKflac2KbwLKqr+vm/XQE0ecemiPKBfQD0jqJAzPUEo0FreVeiucFoix0rpfZaXeMP8rIBAAAAALqhN+4tAgAAAKAT9A0B9SEYDQAAgEIrJfOijlUIzzkyld9BNmghNkUBK+eaygpGGz3UvmJ6QG4wWosejsddmsSPPi2//eUfTuOWB3u347ZZIddtriAEq07yjj0j69b1lRdlz1MlqJGIheX8troGo5UNoBq0z5ReVrQdZtmuAJqibW90ImJxuXc/SwDYqOw5R6+cY8N2yvsh8LxgtJkBDv9qdUY565gDAAAAAAAA9DX34QH0J8d3AKAawWgAAAAUyguiOtepuYjGSrmZj0zlt91/PGJibnA6uxsVAq1mMoLR0ofuaWM19ZcXADaUtH7sy360uBvk/3yud7e7svtpRO+ENuS9p2Tdur50T/aKr2uYV10tFAR+nJit336RpmkslQyVPDGzOj/1t1gyeGbNtgWjtdj2nvm6pm0OoI+UDbKerxjwCYMoNxhtZ/Z13ewAB6O1Cj8f5GUDAAAAAPXle2IAAAAA+oDxEECNCEYDAACgUNnApTRdDUcr4+h0flujGfHRO3q/A+3mB9L4vtesxEXPX4nH/upKvOBdzZhf2vi+qgSjTQ1vDEaLQQtGy9k0ygSjPeHy4vbXfbp3t7uVCttR7wSjZa+P9ev6kt3Zjz0+07vrstvSNI35gm3ieA1D5laa5b9nWWz0zjY/6PKC0XYMZ0+fXtie/bxVQM5H74j4wK3dqQWAzisbxup8A1rLO3u78Pzs6bMDvF+1OtMtuoYDAAAAAGrIYFIAYGA5DwJgHdfHAJ3jGEufEowGAABAobLBaBERJ2fLzXd0qrj9jz+eRtrDnTEHD6fxLb/XjE/fHTGzGPHwqYjXfiKN//62je+pVcDKeieGH71x4vj9m66zF20lGG3X+Ul89SX57csrEbOLvbndVdlPeyW0Ie8QsH5dX5oTjHaihmFeddVYKd5+6hiMlheglcf20BsWl7OnX5aRCRoRMb3QuVqKLJXY/l72wQpplQDUWpnjfoSQIigj7xpvz87s6ZPzEStVLnb7SKu3PZcRvA8AAAAAAADQP3wnCgCwbXp4TCfQfwSjAQAAUKjKGNTZxXLzveeLxU/6hfsjvuFlzbjnSG91pDWbabzovc148kuyA1HefmMah46e/Z4aFYLRxkeu3Dhx4likjYopQT1sK8FoERH/7puKZ/ziAxULqol+DEYrs67zgtHqGOZVV/M5YVRr6hgqVjagZM3hFmGc1ENe4F3efl7nYLTbR6OnA14BOKPseYeQIti8fRdnT19pRhyb7m4tddFskbPbK9f1AAAAADBYBuS7At+FAwAAsGmuKQGAagSjAQAAUKjVYMz1ZlsMzEzTNK6+sdwT3jUe8cTfasZd473T8f3mz6bxmmuL6/3ArecEo1VYvuMj+zZOTNOIU0fKP0mPW8lZXkMlezhe9K+SeHLGYlzzI39aYYXUyEAFo61b15dcmD2PYLTyFloEo00tRCw16nUcXqoQKBkR8amD9aqfs93+cBpv+kwzRiey23OD0UqGsbbbcsntr46hggBUV/a8o1XYLJB/W+Nj9uY/ZiznHLHftbqC6ZXregAAAAAAAGDQuX8TgPV8LkBP6FIwftHLyObfDAuN/iQYDQAAgEJVApdmC0JK5pfS+I9/2YyfflO1Tpavf2kz/uLTvRFW9dbPtX5vnzpw9jxlA1YiIsaygtEiIo6Pl3+SHpcblpWUe/z+vUnc8Ov53SEzixGLy73XEdiXwWh5IXjr1vWlu7NX/Km5+oV51VWrYLSIiJOzna+jiqVGtfk/c49toa5e/uFmPO13mvHf3pq/ji7L2c+n5jtVVbGyATljk52tA4DuKHu91ivn2LCd8m5UumxPEiM5l+l54bn9rtU1vmMOAAAAAPSaXrtvodfqBQAAAKB99A0B9SEYDQAAgEJVApee8+ZmfOKu7Ae8+6Y0/vbmzdXwC1enGwLF6iZN07h9tPV8dx0++9+NSsFo+7MbTghGKxuMFhGxZ2cSP/zU/PavHKtWU6ekaRp/c0Mznv2mZvyv9zbjy2P5+0BeiFiWXhlAXWZd77s4//GHarIe626+RDDa8ZnO11HFYsVgtGPTnamDrbnlwTR++0OtP9sv2Z09fXqhzQWVVDZ0cfRUhwsBoCvKBrKWOacCsg0P5V/bjU3Uuy+oU1r92qVjDgAAAAAAANDXWn1pCkBvcnwHACoSjAYAAEChlQqBS8dnIn7wj5vx5s9sfNB7v7i1Duxn/FEzvnB/fTvBj0xFTM63nu/+ExELy2feR6PC8h0b2ZfdcOJw9vQ+lBcAViUYLSLil34gv0vkrprkzD3/HWk8581pvP3GNP7w2jS+/RXNuOHe7H2gSoDh7FJ996P1ygSjfc1l+ev+wODsFluy0IPBaGUDStZML3amDrbmr69PSx27LtuTPX1+OWJqvvvHs+WSgaajAxriAdBvyp539Er4MGyXtMVNjVc9Knv6+GQHiukBrc6THXMAAAAAoIYM7gYAAACgH+jnAmpEMBoAAACFqgQuRaz2fb3kg2k0Vs5+4Edu33otL3xXhRSxNpiaT+M3P9CMr/mNlfi231vJDHxbUzaEKU0j7j5y5t9lA1YiImaG98T00O6Nz3m8JkleXVAmLKuM731S/gPeVLCeu+Whk2n8xafPfrMzixH//JXNzAHlVfbT6YWtVtcdee8pWbfqzt+RxD+5LHu+A4d1xJcxXyIY7UTdgtEqHDcjemebHzR/dV25ffQJV+S33fZwm4qpoGxAztiAhngA9Juy59nzQoqgUNF9UklEXLaxqyMiIiZKBND3I8FoAAAAANBnDCYFAAaV8yAAzuJzAaBjnHvTpwSjAQAAUKhqMFpExNhExF0Vs7re+d9bJ1tdf2g1MKpbnv+ONH7/79O473jETQ9E/Ne3pvHnn8oOzbrvePm6Hjp55u9zA+RaGRvZt3HiCcFow5vo4fj335Q9/Zo7I+45sr2dgdd+Of/1//AfBjsY7dwQvCfmhCbde6y99fSrxRLBlfGueQAAIABJREFUaMdn6tU5Xqbm9aYGNEyhzmYX01gouR6fcEUSe3dlt938YPe3zbLBfPc5BgH0hbLn2UKKoFjRrpQkEXt3ZfcJTc51pp66a3V/kmMOAAAAAAAA0N/qdd8qAMBAEbAF1IhgNAAAAAptJhgtIuKuw2ceOL9U/CRXXhTxY9+UxA8+pfXzfvWvNePoVOc72G57KI233bDxdf7i09mvPTpR/rkn5888RyM7Zy3X6Mj+jROPC0Y7NyyrjCfty3/QE3+rGc3Nbvxt8PEv57f9xvvSSM/pZK5S6lSvBKPl7BvnruvHXpK9HscndMSXUWbbOTHb+TqqKBtMtWZmMTbsM2yvsQqfmRfsiHj6Y7PbbnmwPfVUsVxy+zu4zQGbALRH2VOI+YrBrcAZSRJx0QXZbRNzg3lO1eo6bV4wGgAAAACwbQaz3xYAAIB2cE0JAFQjGA0AAIBCeeFErdy1Lqvr5R8p7rz+058cih0jSfzJfxqKy/e0fu6vf+mZ0Kob703j6hub8fn72ttB/r8/nv18d41HzC1ubKsS8jI5f+bvsgEra8ZH9m2ceOJwtSfpYbnBaJvo4XjylcXtb7hu+7502b0zv63RjJheF26WpmmlH+OY7pFgtLy3dG4w2lV7s+erElY4yMoEox2f6XwdVSw1qs3fTCPmBAfUSpXPzPNHIv7pY7MDEG9+oPvH6bLb38HDAvmA7piYW70e+sAtaZyYcdxpt7JLVEgRFCs6LUoiYu+u7LaJ+ezp/a7VaWSrHyAAAAAAALaDfjsAAAAA+kCXxkEUvYqeNmDNyHYXAAAAQL2VCc3JcveRM39/+Lb8J7npxUPx9K9eDTx54pVJ3PN7Q/Etv9uMe47mP/eJ2Yin/24zvn5/Eu/4/Npzp/Gz35nEG56dRJJkB6hUce2d+TWfmI3Ydf7Z08Ymyi+o9QN7GxWD58aygtHG7o200YhkpP8v8/OC+s4NyyrjyfuSKOoqffsNafzcd1d/3naYaRFednI24qILVv+u2t883SMDy8uG4OUFo41NtreeflVm+znR48FoEauBgBee33o+umNssvyB6/wdEU9/bHbbXYcjlhppnDey9c/9spZKBpqemos4Nh1x+UWdrQcYbDfdn8Yz/qh5Ovg2jf17Iz74vDPXV2xd2evhOSFFUKgwGC2J2HtBdtvEXGfqqbtWxx7BzwAAAADQa3yPAAAMKudBAKzjR6cBOsgxlv401HoWAAAABtlmg9EeOnnmgQsFITbnDtrfszOJO142FM/65uLB/F96ONaFoq1602fSGH5uM37qr5rxl//YjJXNFh8RiwU1H88ICRqbKP/ck+uCqZZLBqysGd+REYw2Mxlx+/XVnmgL0mYz0k/9bTRf+dxovv43Iz1xuGuvnRuWtYnsh6fsizivIEvus4ci0m364qVVaNCpdYPDq27m0wu90dFZNgRv/97slX9sOmJxuTfe63Yqs/1MztdrORYdn/NMtQgbpLtGK3xmnj8S8dSrsvfzlWbE4S6HIK5UCDQ9eKT1PABb8ew3rYWirRqbiPif76yYvEyhspcD0841oFCrXWnvruzpEz0S7N1ugtEAAAAAgG1loDoAsO2cjwD0rqLBPY7v0Bvsq0B9CEYDAACg0GazxdaHnkzmDGT9hv3Z03eMJPGu5w7FXz57E2lXsRqY9j/ensazXt/cdLBVUfBJVjDagyfLP/f65dGoGIw2dt5jMqen13+k2hNtQfrq/xHpS/6/iI+8JeLtfxDpzzw90gcOdOW12xmMtuv8JH7+e4of+MHbqj9vO7TanrYUjLZYvZ7tUHZdX7U3/zkOT7Wvnn5VZvuZqlkYwdJKdtEjBT2dwkrqpUqY6Pkjxfv5eJeD0aoccw8c9oUg0DljE2lmAONnD0UcOur40y5lj/sTc63ngUFW1DWTJBF7L8i+Nh/UfatVV5ZgNAAAAACoI9/PAAAAANAHhOYDNSIYDQAAgEJ5A8Ff8cwk/sPT8x83NhGPhJLlBaP9/jOLL0v/278cim99XIkic3zg1ojh5zbj6168Ei96bzMWlst1zDVW0pgqCNE5MXP285yaTePodPm61ocMNQoC2LJ8cs8zshs+86FNh8BVkX78XauBaOtNnoj0Hf+7468d0d5gtIiIVz4ziZ/9zvwH/9hfNOM11zajudmEwE2YW0xbB6PNnvm7KMQvS91CrvKUXdf7CwKTRiuELw2qUsFoNQsVy9vm9+7Kf0yvbPeDYrzCvrlzx+q63bkju32s28FoFY65Bw93rg6AovOcG+5zQ0K7lL3EmnCuAYWKdqUk8s/lpxaiq9fjddHqLQtGAwAAAAAAAHrD4H3fC0ARnwsAneMYS38SjAYAAEChvACaS3dHvPvnhuOzv5p9abnYiDgxE7GwnMZSI/s59l7Q+vWvfcHWL10PHYt4zbVp/ItXNuPOsTS+9HAaXx5Lo7GS3eFzcjZz8iOOz5z974NHqtUzOXfmdZdXqj32ZLonjg1furHh4UMRDx6s9mQlpdOnIr371khv+0ykv/2c7Jk+97GOvPa5csOyNrmZnDeSxBufMxSX7cmf50XvTePlH+le5+A9R1sHMJxatw1VHSM+XbOQqzxlg9EuviBi13nZ844JRmupF4PR8mo+byRi9/nZbZ+/Xwd/nYxOlF8fO4YjkiSJ/Rdnt49VeK52qPJqD7UIuQTYigtyAiMjIu6peH1CvrLn2hNzna0D+lmS5AejpWnrPpp+1KpPYH65O3UAAAAAAO3ingUAAAAAekTZXxUG6ALBaAAAABRqFU702EfnP3Z0ImJyPr/94hLBaBfvSmL8Ne25fL3loYinvqwZ3/Q7zfiGlzXj0hc2422f25j8dnS6+HmOnROM9qWHq3X4TaxbJo2KwWgREa999POyG268tvqTFUhnp6L50p+O9IeujPRn/1mkv/CM/JlPHo50uvMpVM2coL5zw7Kq+p4nFD/Bb38oja8c7U7H7oHDrV/n1LrgharBaHULucqT976S5Nx/J7F/b/a8VcKXBlXePrXeVMFxfDvk1TycRDztMdltH73dtlAnZUMLLzx/dR+PiNiXE4w2PtmmokqqcsydXrDdAZ1TdDz6ytHu1dHvyt7bcWouInUjCOQq2j2SiLjyovz2sS6f79VBq3POuaXu1AEAAAAAsJHvQwCALnAPBkB/cnwHACoSjAYAAEChVkFUV1yUH0rVKhht765yNVxxURK3/Fb7L2GnFiJ+5q/T+C9vacavva8ZP/1XzXjFR5vxjb9dnBT0wImz//33FQN3jq0LXmvkvNSjVk7mPv59l/5k5vT03jsr1ZEnTdNIb70u0n99WcQn31P+gQ/e3ZbXL9IqqG+zXvnjrZ/gm3+3RIJUG5y7fWU5OXvm7zLBVuv1SmhDlRC8q3KC0cqGLw2yMiFPdQvTyz0ODEX826dl78u3j/bGdt8pU/NpvOemND54axonZ7d3OaRpWjrc4sLzzvy9f2/2uu32fl7lmDtds30H6C9Fn+GHpwb3M6/dygZirjQjZhc7Wwv0ssJgtKS4b2kQr+sEowEAAABADxrgexIAAADgDNfH0PP0cwE1IhgNAACAQnmDMYdPX1GODCdx5cXZ8zx8Ki0Mebr4gvJ1PO2rkrjrdzpzGfuW69N49cfSuPrzabz4/a077w4ePjNPmqbx6YPZ8331JdnTRyfOBPQsr2TP800Lt+W/fjw2Dg9fsbHhgZxCKkiXlyL9tWdG+vzvr/7gBw5s+fVb6VQw2uMvTeI3fqj4SaYXIg6Md75zd7TEoO9Tc2f+LhvWsGZhuTdCG6qs67oEJvWiMpvPwnLEUqM+X2ysFITmffs/yd4WTs1FHJ/pYFE1dsuDaTz+15vxE29oxr//i9Xwz5sf2L71eXJ2dZsqY/f5Z/7elxOAOD7Z3fdS5Zhbt1BBoL/kfR5GnB2iy9ZU+ZSZKAgFh0HXal8aGU7iiouy20Yn6nMt0i2t7iubWxrs4GcAAAAA6Dn68+gE2xUAvcDnFQBn8bkAnFF0qug0chMsNPqUYDQAAAAKlQknekxOWMnoRMSL3589Yj9Jzg48KeOJVyZxzQuG4pILqz2u3Q4eOTMAdXYxP/jkZ78zO6Bnbili6vSg+UZOoMGOtBF//+C/za1hdMf+jRMfOFB5YOyN96bxvKub8aI3jMdtr35NpN+3J+L6v6/0HGvSB7cezNZKUSDSVv3WDyfxU/+s+Ime8tJmfOVotWX8mXvS+J/vbMYvvrMZ193T+rFjJQZ9T2whGC0i4lgPBETlva2hjN6s/TnHoLffmBos3kKzIFRlvakaBX0UfS496cr8xx043Jl66u5n3tw8K0xxbCLiF99ZcsV3wN1Hys974brzhP05Iazjk1urp6oqx9xpwWhABxUdj9Yf99masudKEWefowNnK7osS05fhudd1w1i4HWZc86yYcMAAAAAANW51wYA2G7ORwAAto1xWECNCEYDAACgUJlgtKselT3P6ETEoWPZbRftjBjaRJrVDzwliftfORSf/pWh+MDPD8UdLxuKpdcNxe0vW/33Hz6rDQlZLUzMRRybXv37eEHA1Lc/Pr+W0dMDexsr2e0jaSO+f/aTuY8/NnzpxonTpyLuvyu/oHO88bpmfMcrm/G6T6fxmpsuj2+/+3/Eh3f/m9KP3+CBA5t/bEm522MbejjO35HEW/9LEh95fvGTffPvNuOWB8t18r79xmZ8z2ua8aefTOPPPpnG976mGW/9XHG6wmiJQd+nZs+8/maC0Y5OVX9Mt+WFUGQdNq7KGUAfEfGiv9UhX6Ts9pMXALkd8moeHoq4bE/E3l3Z7YeODd62MD6Rxh1jG6dffyjivuPbszwOHin/uuv39305wWjdDsoQjAbURVFg16nZ7tXR76p8Wp603CFX0b60dsqXd1031uUg3Dooc1/ZvGA0AAAAAKiZwbsnAQAAACoRtgQAVCQYDQAAgEJlgqj2780OABs7lUaSkw02Ob/5mi48P4nvekISP/pNSTxlfxIjw0l8/f7Vf7/wB4big78wFE+8YvPPX8bBI6v/LwpGe+pj8tvWglyW84LRohFDkcbljSOZ7cd3XJb9wM98OP9F1xk9lcZz33b2yl0c2hkvveylm79Nr0Io22blB/W1JxAvSZL4N09N4geenD/P9MJqONqx6eIl1VhJ4zfel55VczONePH702isZD82TdO44d7WdZ6aO/s5qzpWsN3WRZlQxjX7C4LR/vDaNC594Uo86/UrcefY2U96dCqN513djK9/6Up856tW4mN3DN4Xbc2SXy72QjDaULK6D39VTljnIIbEPHgyv+2Ge7dnez9wuPy8jXWhP3nnGsdnIpYa3XsvlYLRFjtXB0DO6WRErH5up24gaosqi3F80jKHPEX70trl/L6CvqVBU+acc26p83UAAAAAAAAAAEA1RWN7Bu8+IOhN9tXeZL3RnwSjAQAAUKjZzJ6+PojqqpxQoodOnR3gtN6L/nV7gqyy/Mg3JnHXy4ej+YbhuOHXO3Ppe/DwamdRXjDa8FDEZbsjLtuT3X7fidXHN3KW7450OSIiLmscz2w/9phvzZyeHrw5p+KzfdWvZr/wbTu/McZH9mU/KEkiedutkfzaX8Z8sjPuPO/JMZ/sPNP+8KFIx+4r9fqbVSUsayv+/KdabzdX/HIzphfyOw3vGl/dB841OhFx51j2Y/7rW8t1Qp5cF/CUt48WaRXqVgdV1vVVOQPo15ycjfjbmyO+9zXNODGz+sTNZho/9hfNeN2n07hrPOL6QxE/9Npm/OPB+i+bdiob8jS1hTDLdmu1bTxqV3Z73udRPytavw8VhKZ10v3ZH2uZVs4KRsuf74ETm6+nqirH3KVGxOLyYB1TgO5pdTzaShA1Z5QNkY1YPc8Hqlu7msvrWxqb7FoptSEYDQAAAAD6je+N6QTbFQC9wOcVAAAAmycYDQAAgEJlwonyBq/eORaRN478J76lc8Fo633b45P44m8OxX94+tnT9+yMeNpjih/7A0/Obzt4ZPX/eQFTl+6OGBpK4msuy378Pacf31jJbh9JG6vPs5KdIHP8im/IfuCDB7Onr/PnnypOUTh43tdtnPjD/38k7zoQjcc8KX5u9JlxyRPG42lf88V41BOPxIsu/71oxPDqfJ/9cMvX34r8oL72vs7XXp7EcIlek4t/sRlvvC67qDvH8r/MvyOjrbGSxntuKncDwPqAp6IB0xeenz392HSpl9lWVYLRigKT1js+E/Hqa1af+IXvTuNz926c53v/sBkLAxRkVDoYbaGzdVSx0uI4sDcnGG1iAANiZhfz2+7vYpjYeuOT5fev9eGhj7skIsk51q99JndD1aPDdI32HaC/tPoM/+X3pJFWCPUiW5VFKBgN8hXtS2vnePsvzm4fzQgc73dljj2C0QAAAACAbeH7JwCgK5xzAPQnx3foCfp/gBoRjAYAAEChVgE0ERFXPap6KtWluzdZ0Cb808cm8e6fG47mG878N/na4bjlJcMx9dqh+Pr9Z8//378rieYbhuOaFw7Hs789+709fHL1/8dnsl9z7f193eXZj7/nyGon4XJeMFqsBaNlJ9ccP39/5vS478uRNhrZbRExvZDG899R3EF593lPOOvfyRuvj6Ffe30k+x4Xv/ORNN5404WxNLSauNVIdsQfXfLC2Pnk6fibi34yZq77h8Ln3qoqYVlbVfYpn/u2NF7+4WYsNc4u7tCx/Mfcm9F26FjETEGI0XoT8xHN0wujKBTjij3Z04/lbLd1kve+soKR8gbQZ/mDa9I4MpXGn34yf8G96G8HpxM/L2zwXFPz9VkmuceB0z2de3dl770Tsx0qqMaKwhLuPrI963R8svy868NDLzgvia9+dPZ8Bw53772UDRNcM13yuA5QVavj0V9/No3339KdWvpZleP+uGA0yFW0Kz0SjLY3+zz+yPRqkPggKXPs2UowWrOZxs0PpPH2G5tx77HBWrYAAAAA0Dn62gAAAADoA4LRgBoRjAYAAEChMkFUV+2t/ryXXLi5etpt984kbv7NoXjzf07iFc9M4toXDMXrf/rM5fL+nPc2Nrm6YI5OZ7c/Eox2RXb7PUdX/58XPLcjXQ03u2zleGb7fSuXZT8wIuJtr8xtesefXZv/uNMOnvd1Z/7xr34qkid98yP//Jsb8js3//NVb4qLF94fX7izQupNRXnb43AHeji++wmt51nz0g+m8V2vbsaJmTMFrq3jLDfdv/GN3DVe/vXSNGJqYfXvwmC0i7KnH8/ZbuskL7ArKwTv/B1J7Dqv/HO/4qPFnfTvuDEdmIH3Zd/l5HxHy6gkb9sYPr1t7N2V3T5Ro3C3bpldyn/PBw53sZDT0jSNsQqhMY1z1vUTr8ye72AX30vZMME1ddp3gP6Sdx2x3is/WvGgxQZVzh5GJwbvXAPKKnOfVF7fUppWC9ftB2WOJnObDOBdaqTxk29M41t+rxnPflMaX/vips8LAAAAAIBeZJAyAD3B5xUA67iOAeggx1j6k2A0AAAACuUNuF8fRFU1GG3XeRG7zs9IN9omO0aS+M//fCh+9V8Pxfc/5ey69l2c/Zi1QbmHjmZ3Gl150erzPO6S7MefmF39/7mhL2tGTgejPXb5wcz260YvjMkdj85sS9/88kgXNyaxpIfuiA/e3Mh+wXVe9+jnrv7xHf8mkl/6k0emT86l8cCJlg+P//aW5VgpSuvagjJBfe3ynO+o9qSfvz/isl9qPhJ6dmw6fxn8w10Rc4tntx84XG2ZnZpb/f9mgtGKaquLquv6JT9Sfn299hPF7//EbMT1h0o/XU8ru6uuBfHVQe62cfpzae8F2e2nZjtTT53NFoQljE1ETHU5LG56IWJuqfz8556DfO3l2fv56KnuvY+qH2/3HutMHQBljkc3PRAxJqxrS6oEYg7iuQaUVXQkWjvD+6rsLo6IKA4e70dljj3zy5t77r/+bBrv+eLZa+Q33p/GrQ/5vAAAAACAzumx/jcD1fuD9QgAAABsRpf6FIpeRq8GsEYwGgAAAIVyg7uGz/y9e2cSF+cE0WS5dPfWauqmfff+Y+b08Yk00jSNA4ezH/fEK1f/f/HE/Zntk9OryTDLjeyuupFYDTB7xuynMtuXV5L4/FN+JqfqiLjpkxsmNT/05rj+gn+W/5i1507Oi9c+//5IXvX+SHbteaTOx/16uVSAL00+Km64t9SslbUKRGqnH396sqlt9dt+vxnvvyWNibn8eRaWI24+J/PuYM62lOfk6eCFvPDCiIjLL8oOETo2U+21tkPVdf3c72pvOt4Hbh2MbvSyYR9TG7MWt03eNr8WmveoC7PbJ2r0HrpltkUI2aEuh3bdMVZt/m993Nn/zgtiHZvcVDmbUjUYrWroJUBZZY9HH7ndcWgrqiy9UwXn/zDoim5gSk6fx+/dlcRle7Lnee7bKqQU9oEyx/jZxc0d3//8U9mP+5sbfF4A0H+SJBlOkuSJSZL8WJIkz0uS5DeSJPnlJEn+S5Ik350kSU5PYm9IkuTx697bryZJ8pzT72vHdtcGAAAAALAlAj4B+pTjOwBQjWA0AAAACuUF0Iycc0WZF1aSpVeC0dLlpbjymtdmts0uJXFquhF3H81+7JNOB6Nd9Km3ZrYvpOfFwvX/kBs8tyNdjoiIpy/cEpc1sl/k5sf/WH7x99911j/Tr3wpjnzgvTEx/Kj8x6zz65++PGYWV/+eXUxj1y80Y7JCqNAvvfvsN3btnWl832tWYv+vrMR3vXol3n7j5gY154U4DbU3EysiInadn8T//V9D8R3/pPpjf/x1zfhci3C4c8Ny3vmFal/ynDodjFY0YPqKi7KnH52u9FLbIu+ehrx1ffGuJH7337dvQ/jjj6fRrJqA9P/Yu+/wKKr9DeDvmU2DkEAgkFAEpEvRaxdFRcEuqFy71+5Pr+1i16tXxYINe+/itVwLKmJBEQuCqKACSgu9hkAI6SFt5/v7YwnZ7M6ZndnsbjbJ+3keHjIzZ86cnZ2+e95thpy+xJLK6LbDDW1oXl2ggiaoszWGlVSECEbLj/GxYPpid/vUdaMbXmx001xrbC4Mt0XuuT0srNganXYQEdmF4/r78s+Wfz0TTW6O+60xhJUoEvzv4uqepQRanQ/kl7ae45mTV+rkGc3c1YLjnvA9i8m60Yv97vNqw4of/6b1rF8iImrZlFI9lVLXKaU+B7ADwHIAHwN4FsBEAI8CeA3ADwCKlVLTlVInhbksaeS/3mEu93Sl1FwAa/xe20MA3tz1uvKUUs8rpTLDqZ+IiIiIiMLE8A4iIiIiIiIiImoR+JyLiOIHg9GIiIiIiIhIyzRF2xG8NQSjYfVf6LZjiXbyrIdfQnWt9bSB2QoigvZLvtPOX3T7xaiptV7BHvFVrAAMrlpmWeaOnAMg8D1uLDXaoVIl754m63Pq/96yDnLxgTi7x1vatgSqqgXe+kWwNFcwbILpOHihzvx1vg64UlGK7xaU4finTPywAsgrAeasAs5/TTB5rq/SbSWCTYXOHpqGCkSKtEFdFX66zYPK5w2MHxXZhSzPq/97xVZBlWZb0qkLedKFxQFAtiYYLdZhSOEI570++8DIvkenPR9egF9z4jTso7QZBKN5dp2XOqZabwfbSqA95rZU5VX207eXxXZ9LN5kvbweGUB6SsNxh/YFRvRrOK57B+v3tqAcqKqJzWtxG4y2vqB1bXNEFDtOj0e/b3Bf945ywbItgjX5AmnlnXjcvPzyqtZ3rUHklN2+pPwu8QZk6e/pPlvUevYvu/v8OqGCnxduFBw5ycQ3y3zPYvJLgYUbI9M+IiKieKWUehfAegBPADgJgOYJ+W4eAMcD+Fwp9ZlSKivKTWwUpVQ7pdT/AHwIYLhN0Y4ArgSwWCl1XEwaR0RERERE9lr55y0ULdyuiIioGeB1EBERNcDzAhFR1PDam1ooBqMRERERERGRll0YlicwGC3DeSBRp3ZRSrGKtPU56FGzGQlSYzl52kp9wtuALAAFW9C+PFdbpthIR81q69CzBKlPyRpUvUJbx9OHTka/vsuQMXAbevdbgeczrvBNWP0XAEBEsPa8YzC89yzMaTtCW4+Va94VDJ1gYl2Bq9l2G/GwiaoTe2D0C20sp//zbcH+93mRfZOJnrea6He7F7+ssX8IF+tgtDpJCQqPn6nwzDkKyQmRqTMnr/7FPPOd+4ePhRW+eexCMbLSrVdMRTVQURXfDzzDea97dYpsGz77EyiuiO/11FhOQ1VKdsbPegi1bfTWbAe1JrA2zONZc7Ruu+CBL+3ft+1lMWrMLpuLrMdfOkLhh5sNXHyYwuH9gdtPVPhqvIEET8MdvptNCOuHv8coGM1lXmJBjNcxEbUeTo9HmwqBskpnx8jNhYKRk7zIvN7EkLtN9LvDRPZNJj6K0TE2HrkNxCzeGZ12EDV3druSfzDayXvrb/i+Wtx6jkVOXmlRiOPNxC/ch9wTERG1AAM04zcD+AHA+wA+ArAAQOCZ8mQAPyqlsqPWukZQSnnga//ZAZPyAcyALyztDzS8lMgC8KlSyt2HQ0RERERERNRCtJ7n6kRE1BLxPEZE1CIxtIeoeeC+SkRxJEJdeYmIiIiIiKglqrXpQJngaThsF1YSqJM+TyyuyIYcJKIW/apXY3nyoKDpn6adbDlfz45AarKCLM5Be7NEW3+xpz1qt+cBqYODpiWiPhhtSNVSbR03Fp4JJPn+3p7QGf/KfgKDqpbj6BWzIF+8CdM0cVzPL7A6qa+2jmhK7b9dO626FliwsX54zXZg9OMmNjxsoGNqcGdosXmwGu1gNABQSuHqoxSuPgq44i0Tr8xu3IPe5Xl+f2/R19W7EyzD6QorfP/bB6Ppp+WXAb2SQzSyCYUTjOYxFIZ2Axbr8whd+2YZcPr+kasv3jgORquMbjvc0HXur9s2+mf5whWsDhnLt+wKrmzhRAQnPRM6BSHh++xxAAAgAElEQVTWwWi5xdbju3cA/raHwmsX2h/M98jwBbNabQP3fS4472CBUtE9Ibj9jK+gPDrtICJyE9h1w4eC/XrazyACXP1ucJn8UuCMl0wsusvAsB7NJOA6gtwe94t2Aplp0WkLUXPmdF86aZh+2sptkWlLc+DkGF9UoZ9WWSOYvtjdMmPxXIWIiCjGFgB4HcB0EVkdOFEp1R3AXQAu9xs9AMCHSqkjxO4DCWu/Iji0LJRNLso+BOBEv+EaADcAeFlEqutGKqUGA3gVwPBdo5IBTFVKDRORLS7bR0RERERErrSWDqOt5XUSERERERERERERUVNjMBoRERERERFp6cJnACDBaDjc3UUwWmYzCUbD+hwAwMDqFZbBaEWeDMvZBrYrAZABufs8pJslUGJClBFUrthIRw0SLetIkPpgtHElU/Gv7CccN/uBzFtx9IZZkIcux+2d78XqzKYJRQNg+brtVFQDnywQXDoiuEeuXcdgw91iGu2l8w38fT/BN8sEj80I7wt/a7f7OiunJCptWBAA9O1sHYy2Y1fYTtjBaKVAr07O2toUtMFoId7rsw9S+M/UyH0J87vlgtP3b7k9xM3Q2VkAgJKd0W2HG6FC81ISFfbs5AtbDJSzVQC03Pezzh1TBcscdHOMZTCa1xTkaYPRnL0n7VIURu8FfL0keNrKbcBv64EDe4ffRid0R5fMdtbrc0e5L6gu2oFtRNT62N2rBXq1kYG+ADD5Z8FjZ7S+Y5mbADrAPqiIqDWz25X8jywJHl9Y7qVvBs9hd9/c0ji5T7M73sxf53u+4oYnxs9ViIiIokQAfAFggoj8ZltQZDOAK5RSiwA85zdpBICzALznctmVIrLO5TyOKKX6ABgfMPoMEfk0sKyILFVKjQLwLerD0ToBuBvAP6PRPiIiIiIiImoqDKkjIiIiIqLmhvcxRM2C698Ro/jA941aJn69lYiIiIiIiLRq7YLRPA2HnYaaAM0oGG2DLxhtcNUyV7P1Xv89ZPVioLgABgRpZqlluRJPe9Qq68zyRKnZ/Xe2d6ur5f+QOhIFno5YnjQAkzJvCln+2XPDDzi4vNM8vLP5grDnt7I8z3q8XcdgowkyGo4dojDpdAPmyx7ceKz7BpgCrNrm+3uLpoP3xNMUMtpaTyusqK9Hp2OqvnNzvvVmGTd073eoNX3dKIVx+0auHb+uadkPhp2+uuJmEIzmv6336WxdZmtJ5NsTb0p2Ch6a7uydLYhhMNrWEv17181FuOqEMfpH2n9uiv7+qjs2dU6zHl9V6z6UgojICbeBXY01e0XLvibScfvdDgajEVmz25cC82P3yLC+68svBapqWsexyMmrLKrQl1qS6349+T9XWbhRcOenJm79yMTc1a1jnRMRUYtxhoicHCoUzZ+IPA/go4DR50e2WY12N9DgV3YmW4Wi1RGRnQAuAuD/VOrSXQFrRERERETUJJrbc7bm1l6yxE7MREQUF3g+IiIiImp2YvRMwW4pfKxBRHUYjEZERERERERatV79tISAO8ruGc7rbQ7BaOL1AptWAQCOLv/e1bzpO1ZDJk/cPdzetE7iKTQ6oEYlWk5LQG2D4TOHukslmtvmEFyV/XTIcvNuN/CPg92Her1xkcK6Bw28cF0PHLRzvuv57eQWWY/32jzUbIpgNH+TTjdw0aHuGzFnlWBntWgDFIb3UeiQal1vYblvhdgFxiUY+v0tvyy+nxLrwj5CvddtkxU+/KeBnPsMfHiFgcP6OlveUQOtxy/YWL+uWyKnoSolldFthxu6bd5/28hsZ72h7CiPQoPizL732RwUAoQTmBAu3bEdcBeMdnAfhcFdraflaII1I0m3z3S2ubaJZQAdEbUeZow/8f9tPVAQ59eP0eD2FRfFUZgsUTyxDUYLGLZ7vpTXCoKOAWfHeLsgRl3gvJ26H0CY/pfgkAdNTPxCMOlrwRGPmHj7F+f3GERERE1JRNaFOetzAcNHNbIpEaOUagPg9IDRD4eaT0RWAJjqNyoBwLkRbBoRERERERERERFRFLW+76gQEbUOPL4TERGROwxGIyIiIiIiIq1am36PnoA7yh4ugtG6d2jiFCsn8tYBNdUAgBEVc5FsOk8FSjPLgB8+3j3cuXa79SISslBupFpOSzUb9nDtlObuFv60Pabgx9QjbMu8f7mBA3orpLdRePzM0O+JUsBhfYFl9xq48FADPTspqKw90O3Y41y1LZTNhdYfdtgFgDV1MBoAvPgPhRuPVUhLcT7PtIWCLcX66V3b64PN6oJ27IKtDKUP69lW6qyNTUUbjObgzVZKoX+Wwt/3V7jQYWDdtUfr97GRj7bcTuB2+5W/kjgK+dBvG/V/d7Q+tLbYQJf1BYJzXzFhXO7FWutTjqWlW4A1+bFZJ7pgtEQP0Enzfukc0Nt6v87Ji/5r0W1/dqGvO2xCK5qaiOD9+SZGPOzFgRO9eGi6icoad+vx7V9MHPWob/67p5motUsyJaKIcRpuGklPfdv69m+367msqvWtI6LGUgGXdt3a68vahe22JE6yLwttrjHDuS727HofbvvYRLVfVr8pwM1TBGZTnHiIiIhiZ0HAcBullIso/6g6DkBbv+GfRWS5w3nfCBgeF5kmERERERGRpRj/qA0REREREREREVF08DkXEcWPhKZuABEREREREcUvu2C0hIAMoc7tfOEmNd7Q9Q7Kbly7YmJ9zu4/E1GLftWrsSRliKNZ070lDYaza/Msy+UlZKPESLeuw2xYR6f2kbuFVxBUveBBgqe+9/H4UQpf/iWYuSy4/ONnKpxzkEK7ZCA1OTiMJuXqe9F5fD7yEzpHpH2bNR2dbQPA4iD6PSlBYdLpCg+PE/y4Ejj6sdCJU9/lAH9t1k+3C0bb7jQYLc16Wn5zDUZzGYJ34jCFUA/lrxqpsH8v/fS/NgM/rxYM7xsHCXwR5rRv+84aoKZWkJjQ9OvAq9m1/LeNTpr9Zkd55NvT1IorBIc8aGJrSeiyVn5bL+jTOfrv6+Yi642tWwdngYf+BmRZj8/Z6rZV7un2mY7tFJQSy++6by4E/rZHdNvl1M5qgaF8+0uNF/jgd8Elk+sb/ft6wep84LEzgLQUX9CknVdnm7j8rYbzr80H/ntp0x8riFo63fkwmuasbH1fdHDbh6m8KjrtIGru7HalwMuN9DZAarL1/rSuoGXelwVycp9WZBNevWyL+2V6DCCvWCyfUWwtAeauBkb0d18vERFRM1FrMS4p5q2wdnzA8A8u5p0N32ur+4BpX6VUlojE4CkaERERERERRR3D+IiIqFng+YqIiPzwPoaIKIp4jKWWicFoREREREREpGXX2T7B03DYMBS6tgc27LCvs3Ma0KldM+jEuiGnwWD/6lWOg9HSzLIGw101wWhbErJRYlinVqV7G6ZWdUpPQKQeUOWPX4EEz+AG45RS+PRqAxO/FMxcKijeCfTtDFxxpIEx+9i/XyotA8clfY23zdERad/mIusAplABYPHCMBRGDgQW3mVg4hcmlsxfjUJvW2xJ7BpUtroWeHKm9Y7WOQ1Ib6OQ2c76hTsKRjOAzmnWwWDxHoym+8zL7XvdrYPCwCz7wKRnzvFVmp4ClFRal7nzUxMzb/BYT2zGnAajAUBpFdAxDp4m6trs8QtI7JRqXaagBQajPfeDhB2KBgA51qeoiMstth7frb37ugZlWx/XVucD1bWCpCgG+JmaDTDRA+yRYX0dlLNVcBKa9kQlIpj4peCuT0Pv9K/NEbw2R3BAL+CZcwwc3Eff9qe/Da7v3XmCh/8u6Nohjk7ORC2Q7nzYJQ244kiFb5YKCsM47wmAFZrrppXb3NfX3Lm5VgKAMgajEVmy+05j4BWDUgp9Mq1DxHXHp5bGyXdAiyp813iBQbblVRLy2ZwVjwEU24Strd8hGNHE17RERERR1C9guBbA9qZoiIWhAcM/O51RRMqVUn8B2Ndv9BAAreSqioiIiIgonrBTIjUFbndERNSMMTiHiIiIqOnYXYvxOo2IYiwOujISERERERFRvKr16qclGMHjuncIHYw2KLtxbYoVWbO0wfCA6hWO500zGyZOZWuC0TYndkOpJ11Th1/KTUpb7NHROgTGjWSzEgsGv4COQ26ynN4mSeH+UxXuP9V93bdf3g9vv9io5u1WWQPMXgUcPajh+OYSjFZn7x4K751TDHl7GEwo9O63ErmJ3YLKzdJsWnX7SmY76/d+e5mvE3So9ZLZznra9rL4fhite13hvNcXHKpwxyfWFf56u7G7I/nLFyic/bJ1ue+WA1MXCNokAUcPRFBwX3PlJuyjZCfQURM4FktOto1Omu0+XoPRCsoEf24CluQK9sxU6NkR2FEOrNwmSG8DHNpXoUdG8Da3o1zwn6nO3sTT9gU+WRA8PlbBaJsLrcd36+C+roGaawmvCazJBwYF51BGjN32Nyjb+jpoeYzWsZ3HvnEWiubvt/XAGS+ZWDzBQHqb4O2vplawODd4PlOAD34XjB/VMo6TRPFKdzxqkwTcM9bAPWPDr/uGD0w8OTN4AZuLgLJKQbuU1rN/u/3+RjmD0Ygsub37HJBlHYy2splEeJRWCmatADq0AQ7sDSQnujtuOrlPq/ECFdVAanLD8eGGx20vA6b8rl+wafPjCURERC3A6QHDv4mI27NfT6XUGwAOAtANQCqAQvgC1hYA+BHAFBFxG2G6V8DwKpfzr0bDYLTBAL5zWQcRERERETkS39/DiZzW8jqJiIiIiIgo8nhPSURERO5YdGMnIiIiIiIi8qm16fbh0QSjhXJ4//jvRC/FBcD0/zYYN6JiruP5A4PRumqC0VYk9dfWke5fR3JbHDPY8eK1rvO+i0E3WIeiNdag/fpi08D7cM+2eyJS3zdLgz/waG7BaACAfF9ijAHBmLIvXM06MNv3onTBZrWmL6jKrnOyoYDOaZqmlVqPjxe6EAojjKdZFxyi0C45ePylIxQO7F2/8Zx5gIEumvUFAONeMHHCUyYG3WViaW7L+FDOVTBaZfTa4YajYLRU64PCjnLAdPOiY2BWjmDI3SZGPW7iX+8JxjxrYp97TRz1mInL3xKc/bKg3x0mJs9tuLP/kCPIvN5Z/8yNDxsYkGW9TnK2xmZ9bCm2Xk63Du4P4P0664/7OVEOy7Db/gZkWzdqRV7TbnNv/GTilinhtWFTIfDGXOt5S23Cf8IN5CAi57yaU0AkrouvOUpfyZ8WQUUtmdvLhvLq6LSDqLmzCxlUFoec/ppr1xUxunZtjJ9WCfa4xcTYZ00cMcnEQQ+YyC1y126nx56iiuBxOY249rzTJkg3zm6jiIiIIkYp1Q7ApQGjPwmjqj0BXARf8FgHAIkAuuwaPg/ASwA2KKWe2LVMJ23rCKBjwOgNLtsVWF7/wRQRERERERERERERERFRVPELKETNgu2vCnM/jltufw2aqJlgMBpFlFIqUSk1Uil1gVLqVqXU1Uqp05RSvZu6bURERERELZUUF0D++xDMJ6+Heds4mM//G/LVOxCvt9F12wWjJVjcUXbLCN0L/6Rh8Zpg5SOmCRnXJ2j80RU/oK1Z7qiOwGC07rXW6QFFngxtHelmSf1ASlukpShccWTj1t35R7Zp1PyhdP3X7bhjyHKcU/xeo+tatsUiGC1EAFhc2p67+8+xpZ+5mrWP8gXq6YLNAGDOKuCC1/UrxmNAG/QV78FoTsKvnOqeofDy+Qrt/XaBsfsAz54TXNkfd4Z+XLZ2O/B//zUhLeChsZuXULwzeu1ww0kQjG6/8ZrA9rLItylcpim4eLKJbSH2x+pa4JLJAuNyL/52rxfnvmLi6MdCh6K1TQLeuUyhe4bCoGzrMjl5iMm2XKBZ71np7utKTlTYI7BL6C5bS6L7WuyOTbp1vNw6HzVilm8R3Pu5iYvfMPHoDBOF5fWNnLFEcOmbjVsn935mPX+ZTVhiaZwcL4haskheKwXq1Qlok2g9bfri5n/944bbU6TdsZGoNbMNRrMYNyDLumzO1thcu4bLNAVnvGg2CJX+azNw04fu2uz0JRZZXHNF636HwWhERNSCPQjA/6lOEYBXo7SsVADXAfhdKTXEQfnAnyOqEBFnH1TV2xYw3N7l/EREREREFAlx/FzTWnNrb2sV6n3i+0hERHGg2V0HERERERERUTxJaOoGUOQppSYAuLsRVbwpIhe5XGZnAPcAOAvBv1RZV2YugMdF5KNGtI2IiIiIiPxI7lrIZcOB0sL6kT994ftKy8z3gEnToFT4PeN14TMAkOAJHtc9sIuGhf5dwm5O1IkI5K5zgOrg3uwpUoVjy2ZiavopIetJMxv2Qh1UleO6Lelev5ScFF+a0zNnK7w0K7wPiAdUrcCgvTQ9iyNEJSZBTfwAt/28Av97o3F1WYXI2HXCNeI1+t0vGG1kxY9I85ag1OMsBShjyoOQPY9H5v4nasuMedY+GEkptSsgKnjl5cdROJQVuyC8cJx9kIHRewn+2gxkpAJ/28P62Nitg8JxQ4Cvl9jX9/MaYOFGYN+ekW1nrLnp3F5UEb12uOEkCKabTfe+zUVAlzDCuKLh17XAugJ38/y5CfhzU+g3bto1Bg7ZE8hM862YgdkKVseCsiogtwjors/qjIhCzfaT0Ta8+jq3A9ZbrLtoB9/pvqdlGMDALOt1vK0UKCwXZKRGPsXzhxzBmGdNlFfVj3v2O8Ev/zaQkwcc/1TjD6aFFb5rpMBrytIqzQwASir5hTaiaNNdK3kicF3sMRRGDgSmLw6eNndV69q/3QYBVVRHpx1EzZ3drmT12GqA5rqqtBLYWgJkx2mcx4KNQF5J8Pj35gvevlRgOEyvdHrsKbSIRSmJUkAjg9GIiKglUkqdBuCagNF3iMgOF9XUApgDYCaAPwFsAlAKoB2AngAOB3ABAP9PxwYAmKmUOkRE1tvU3S5gOJwo+sB5bH6KxTmlVBcAnV3O1jcSyyYiIiIiIiIiIqLWgB9QEhE1XzbHcAZmEjUPdvtqBPfjGC2GiJq5eO02TM2IUuoEAIsBXAlNKNouhwKYopR6WymVGpPGERERERG1cPL8vxuGovn7dQbw3ZRG1V/r1U9LsLijDBWMlpQAdArsxhEnxOuFPHIVMGuqtszYpPmO6kozSxsM71mzDonirod8uunXkzbZlxqT4FGYelV4t/J/3/kZ1NCDw5rXrWHDB2DKPxv3yGHFVqCiquFTTLtOuJ7IZ81ERn59MFqyVGNU+feOZ82ozoe8cDtSkwRZYYQ41fW37qzZ50orgcqa+H1SrA2/asSmlZmmcNQgpQ1FqzO0u7MNasrv8bv+nHLTuX1LcXy8Xl2bPX4hA13S9cEwuUVRaFSYcrZGZ53ePUbh5L3V7lA0ABhok41pFUYZaZEORsvUHNuiHYxmF8w3KFs/X87W6LTnjk8ahqIBwIYdwMhHTRz1WOQSJq3evzKb0I2ScLrpEpErToJCG2P0XtYVfZ8DlOyM/TXBWz+bGPWYF4c95MWD002YMUrocbuUMgZDErlmGYxmE6q/IkrXVZEwfbH+GLDDIsRMx+mXuoosrrkYjEZEROSMUmofAP8NGD0DwAsuqvkPgO4icpSITBSRz0RkgYisEpGFIjJNRG4G0AvAQ2h4i5EN4GNl/+tGgU/AwjnTB14xROpTuqvg+76gm3+fRmjZRERERERxig/RiIiIiIiIiHh/TNQSMLGMiOIHg9GoUZRSIwFMRcNftRQAvwP4EMA3ALYHzHYegP8ppbj9ERERERE1gpQVAz99bl9mxruNWkatTZ6GVeBMjwz7Xvjd2gP2fTya0IdPA5+/blvkpH8c7ihoIM1smMqSAC/6V69y1Zw24tdXJaU+NWbs3xTOOci6Ee3bAKsmGkj3NExIya7Nw42HV0KlhpGuFaZx+yl4XzJQc+LryFuxB0qXd8S4kk9c1THsnoYboF0n3MaEZUWTbM9tMDykaqnjedubxcD65VB563BcXxc9qHfZHYyWpi9TEOUAocaIdtiHHbtwI39fL2n+D/RNF7lJ8RIopmuz/3HAYyh0bW9dbnNR/LxvbsIRnOqSBvzr6OAdJSNVoYvmeBCtgLY6piko0gWjpYa3U2e2s54vGuvUn92xqVsHoF2y9fQ1+ZFdx7VewbYSwc9rrKdHOjBkS3HwuFKbrrjbSgERwY5ygfDDT6Ko8OrOhxG6VrILiu1yo4nyqtjt2y/OMnHhG4Lvc4Cf1wB3fCK48p3YLN/NtRIAlLvLwyZqNewuB6yONplpCh01P7O1IsrXro2RnKCflu/i/ttpCFlRRXDB4igF1Do9HlbVCDYX8hqQiIjim1KqJ4Av0DAkbD2Af4iLk9iuMLRtDspVisi/AVwbMGk/AOc4XR7C60XCkzIREREREVGLxVs+IiIiIiJqbngfQ0QUPTzGUssUp92GKcLOAbCni383OalUKdUDwMcAkvxG/wRgiIgcICJnisixAHoAGA+gxq/cGAD3N+I1ERERERHRvG+A2hr7MnO/hFSUhr0Iu2C0BIs7ymHd7Tvid+8QdlOiSjbkQJ67zb5Qp67ofOyJ2KeHfTFDvOjgDU4POmDn767a1GA1JrdpMG3yRQr3nqKQ7ZdzdvLewLJ7DfTprLDo/ja4qNcajDAW4yLja8w9+ltkXHOnq+VHglIKRvc9kektQBupxNhS+yC/QGu3A2u31z+Usw1Gi9O8PQQEow2qznE8awevL4VGztoLYz67zPWi60Ki2rfRl7ELtWlqTRmMdtwQZRn+GOiPDcCPK5r3g2OnHe4BYHOcBKN5HW4bunNOvLwOwDpsqjGy0oH5dxjasDFd6N/yvMi2I1BppX5by2hrPT6UTu2sx28vjXLIm6Z6pXznvZ4dradH6r3OyRMMf9CLpCtNZN/kMq2nEbZY7DdlVcHj6izdAnS/2UTm9SZ63Wbi8z+b97GSKB7pjkdOrmGcsAuKra4F0q41cc7LJsoqo79/PzkzeBmT5woKyqK/bLdLsDs2ErVm4eytA7Ksx0c6ADaSUhL107aVOK/H6X1aoUX4cKkmGO2wvs6Xb0V3H1anskZw4esmOl5nYo9bTfS81cS7v8buepWIiMgppVQX+H7os7vf6DwAx4hIfjSXLSLPAZgWMPoqm1kCo1VtnvZrBc4Txz+XQkRERETUkvHzUmoC/AELIiKKCzwfERG1OrwXIWr+7PZj7uNEFGMMRmsd8kRknYt/2x3Wew+ADL/huQBGi8gy/0IiUiUiTwM4M2D+G5RSvcJ/WURERERErYOYJmTJPMgvXzUIOZM5DkOmZn4Q9rJrvfppCZ7gcRmpCkf018/TIyP+0qtEBHLe3iHLqRdnAQD26mr/Gjp6d8CD4I6fY8u+cNymvaqWNRyR0jA1JjFB4T8nGch91APzZd+/add4kN3e17ZenRRev6M/fnxxH7z+4ono/Y8LoFQTrfueA3f/eWbJFBxaMdfV7FMX+AWj2fSnjcdgNBEB5s9sMG5QlZtgtPoUmmPKZiLZdJdiVrdO0m26SpXEcTCa7ll5LN7rHhkK/z7B2YJGPmrC6yZdLAw7qwVf/iX4IUdQXRvZZblpem5RfHyAoTsWBG4bPTKsy61z+uQrBvIiHIyWO8nAHh312+6AbOtpK/Ki+95aBTbUCTcYLVMTjPb7hvDqcyrU9pfd3np6qGC08irBN0sF7/xqYmmu9ftRWSPY6y4Tv6512FgXEj3A/jZParcUB7epNEQYUt6u8I9NhcCpz+lfFxGFJ9ohst07AGkp9mXe/03wz7eju2+XVYplCFKNF/hkQfSPK24v88oZjEZkye67ULrHFQOyNNeuW+P3mqK6Vj8t30UEidPvjuVb/BZCieYabf/eCv8aFf5JoirEbzNc8z/BW78Idu4qt7kIOP91weyV8ft+ERFR66OU6ghgJoABfqO3w/d9t5UxasaDAcOHKKV0PysUz8FozwMY6vLfKRFaNhERERERNSV2fiUiIqKY4DUHEVHLxOM7ERERucNgNAqLUqo/gAv9RlUDuEhEtF2qRWQqgDf9RiUDuDs6LSQiIiIiahmkMB9y/QmQfx4OufkUyJkDIXO/9E1cMMtZHZOuqp/HJW8YQVSn7qvvZDm8b1jNiCp57Z6QZdT1T0Jl+9JCBmTbl830FliOP6ZsJlI8Nklzfo4v+7rhiJQwU2PiQZceQJtUAEASavD9+mPxcu6V6F29DhneHbih4AncmT9RO/vMZX7BaDafgcRbMJpUVULuuQCobpiMMLRqCTp4Cx3V0cGsT9FpJ+U4p8RdyGHdOklN0pcpjeNgNFPzRcpYvdf3nmJg2jXOHp1d9U70PqCbv07Q4xYTJz9j4ujHTBxwv4m12yO3PDdhH5uLQpeJBd13bD0Bb1efzvEfpLAmP3JtWfugETIEc5DmHLZgI2BGMeAvGsFonTTBaFtLgMe/sbmAaaRQQURd21u/B3YheIs2CobcbeK4J02c/5pg6AQTV7xl+gI2d9lWImh7dWRe1yF9gPGjFB45XeHOkxXuOllh6T0G5t9hkXq7yxqLQMEyF+E/pgBvzI2ffY+oJdDdq0XqWskwFM48IHRlH/4uKKqI3v5dXq2ftnJb1Ba7m9u+PW6OjUStiW0wmmZ8/y7W4xduRIPrpHhid4+9rdR5m51emudaXGOW7LQu274N8MSZCp9fG95XRKpsQt9qagVTfgtutAjw/vz4fK+IiKj1UUq1BzADwDC/0YUAjhGRJTFsyrxdy63jATBYUzbwbN9WKZXqcnmBV1URecorIttEZImbfwBWR2LZRERERERxK06fWxIRERERERHFFu+PiZo9PuciojjCYDQK17nwfTGrzscOfznz4YDhM5VSKZFrFhERERFRyyJPXgf88UP9iOICyK2nQT59Bdie67yeW0+DlLrv61Cr6WyfYEAbvnLK3/Sd5+2mNQVZuQh488HQBQ84evefQ7rav4ZOmmC01L9fjNFD9IEjdfrzLNQAACAASURBVA6r+AkT8u9vODK5+QajKaWAngN3D3tg4pLiN7Fq9WDkr+iBR7bdgbu364PRlm6p/7s5BaPhyzeBb4ODzBJRi+PLZjiqor23YZ+nidvuwoE75ztuQt06MQyFNM2dd0lcB6NZjzdi+DTr5L0VZt1soHOafblXZgvyXXRyd0pEcMaLZoNQqcW5wL/+F7nQJzefV+woj9hiG0W3bQSelgZkWZfL2RofQQqF5YKf19iX6ZEBzLnVwM3HKe1xLiURmHyxQq9OoQ+EgzXnsPxSYN66kLOHzS4YrUOYp7jsdP3rvelDwR/ro/MehwpGy25vPX1LccMZC8sFj84wcetHJva9z8SGHQ3LvzJb4LnCxE0fmigsF9w1LXKvZ+5tHjxxloGbjjVwz1gDE8Ya6NvF9wJO3tt6nhlLgpfvNlzzsRlNv98RtSSxuFa6/9TQ55YaLzD8QRM3TzExc2nk9/MKm2C0kpLop5C5vWSway9Ra2a3K+myfffSXLtu2AEscf5ILKZKbQ5L+aXO63F66NlSFFyyWBOMlp7ie0Zz4jCF589z/xClskY/bXuZ/vnCt8t4DUhERE1PKZUG4CsA+/uNLgFwvIgsjGVbRMQEsCFgdGdN2QI0DFEDgJ4uF9krYNjJ9/uIiIiIiKi1i4PvVJADId8nvo9ERBQPeD4iIiI/vN8kIooiHmOpZWIwGoXrtIDhN5zMJCLLAPzqNyoVwLGRahQRERERUUsif/4EfDfFetqj17ivb9yeruep9VqP99jcTfbqpHDh8OAOlhcMdxbaEiuy/HfIJQeFLrjPCCi/YK+RA+1ff2aSRS/Yky6CMf5x7N8r9Ov/Yf0xSJWABJnkZp4n3WtQyCKfbhxnOX59AVBe5XswZxuMFmdPOMQiFK3OfpULQs7f1ixHEhr2Os7ybsOP60Zh9rqReCP3MrxxfGDfqYb8Q5R0wWillfH70NPUZH/FOgTv8P4KK+4z8OoF9gvOujFyYWV1lm5BUFgSAHzxF7BxR2TeO7v9KlCRTbhVLIUKpqozIMv6PSuqAFZti3CjwnDQA/pt5tlzFWZcZ2D5vQYO7avw8N8NLLvXwIzrfP//fJuBNy9W+PhKA2seMHDBcGcHwRH9gOQE62mfLoze8aBQE6qXlgIkeMLbqQdm208/YKKJmtrIv6ZQ219XTTDaJr8urKu2Cf52r4lbpggmfW3fxse/EXS63sTLP0bmtWSl208/coD1+/HLWmB7QABkWfTziIjIhu54FOZh1VJWukLFc6HPMTlbfeGHxz5p4r7PI3tNVG5zrCmeNQOyfYu+QAS4uVYCgJ0MRiNyTReMZvf8Zdqi+LyXLdGEkgHANhfBaLr74UC5Fr+BoAsoS29T/3eHNtZl7FTV6qfZhUKW87hIRERNTCmVCuBLAIf4jS4DcIKIzGuaViHwqsHu7LwsYLify2X1CVEfERERERHFRHw+0yQiIiKKWwzOISJqxuyO4Ty+EzULdtdivE4johiLs27D1BwopbIB7OM3qhbATy6q+CFg+ITGtomIiIiIqCWSSVdHtsLKCsg6d/0dajUdMRM89vM9fqbCRYcqKOXr4HrRoQqPnxkfoWiybRPM+y6G/N+hoQsffCzUfe81GNWpncIR/fWzZB58EHDYSb6BxCTgrPFQNzwNABiYZb+4c4v/B8u1lJgcuq1xTPUaGLLMoKoc7bQVW33/2wajxcfmVW/RHO2kgcbmkLN3rt1uOT4RtRi+cx7OL34X5+94E9cfo3/h/mFx6dpgtJBNaTJOw69ioX1bhUtGGLhypP3Cr3rHhETwIb9VJ/c6ToIANhUKLn7DxJC7vTj0IS8em2HCG7Bi3YR9lFUBtd6m/xDD6zA0b0g3fR2vzont66j1Ch7+ykSPW7wwLvf9W51vXXaPDODKIxVGD1Zom1z/ovpn+cYN2PwDDnxuDM57axRO+ethZCXbpC8EaJeiMEqTVRnVYLQK67oz2oZfZ9/OoctMWxR+/TraY9OuY27PjtbHibXb6/efSV8LNhZaFou6Sw6zP46dNMx6ugjw9dKGL35njWVRW/EcyEnU3IQ6HkVKSqLCyvsNjHDY9f6+zwVbSyK3r9sGo1UnQN56KGLLsuL2lVTaBAcRtWZ2t0m6q5OOqQqH9bWe9uVf8XlNUWZzj71oo/M2O71P21IcPE4XztbeL24lI9X9jXWlzbWfXTAavwdHRERNSSnVBsDnAEb4ja4AcJKIzG2aVgEAMgOGrT8Q8FkcMDzc6UJ2hcLtHaI+IiIiIiKKGD4MIyIiIiIiIiIicooxikTkBIPRKBxDA4b/FJFyF/MHfrFsSCPbQ0RERETU4khZMeAyxMyRX752VVwXPpMQ4m4yI1Xh9YsMVD7n+/f6RQY6htHpMtKkaDvk4gOBGe+GLKu+2ALj0c+gMoKTV075m/61dO6SBuOhj6G+L4OaUQjjmkegknzBZoO6hggjKZtuPcETIoku3vUMHYzWu2Y9kk3rHsTL83yPM3XbIxBfwWjy/Ue20/cafUjIOrrV5oZe0Oxp6NxOP9n0W19pmmC0kmYYjKaa8L1++mz7hb84S/DvTyRi4Wh2nct/W2c/b3GFYNgEE2/+LFi2BfhlDXDzFMH1H4QfjAbExzaja7Mn4NzUMVXhoN7WZZ+cKVi2JXYflVz5juDfH4tt2F2dE/dWUJoNXeZ9A7nueGDeN8BfcyGv3A2ZeGlwOdOEVJT6/q7xbUhStROydSPG9LFuxPI8YMXW6KyTIk04Q2OC0ZISFPp3sS/z5eLIvx7d7l13HhqgCUGtNYF1Bb6/P3MQbBgNGW1DB6MNzAb6BHbN3eX39Q2H7cIxdH5c4X4eIrLmNCg0Evp2UfjxFg/GBHant1BrAt8ui9xxzu56qMhoD3z3EcS0uVFoJLdVV9ciKIiWyCmprYVUOQ+9bU7C3SvG7GN9UPsrdN54k7ALgZ27GihzGBLrdH1tKwVqautLiwiKNZtQekr9uuzQxrqMnSqb4EfbYDT3iyIiIooIpVQKgGkARvqNrgQwVkR+bJJGAVBKZQLoEzDa7gOBrwKGR7pY3OEAEvyGF4jIVhfzExERERERUVzjE1giIiIiIopDdn0p+At7RM0E9+Nmie8NtVAMRmsdrlBKzVRKbVZKVSqlSpVS65RSs5RSE5VSh7usb3DA8CqX868OUR8REREREW2ITmqEfDHZVfnaMIPR6iQmKCQmxFFi1bRXgZIdIYupxz6HSu+onT5uP4WkBOtpo/fyvV6VkAiV0LDQ0G5Ajwzr+done3F82QzriR7NwpqLvfYPWcQDEwOqV1pOW57n+98uXyCugtGeulE/8aI7sOc/r0GmTaAZAHR3Eoy2ZgmGVSzUTvYPsErXBKOVxkHIlY7u/W7K99pjKPx4s/0B8JGvBB3Gm3hxVuMDOuw61S//dRkkPzgNIL9UcMgDXmRcZ1p2iH/2O8Fv6+rrdRv2UVThrnw0mJqNw2rbGLef9QZT4wWuedeMWIidjmkKLn/LxGtznC/nrAOC2yw7y2FOvBRy48nBM/zwMeTTV3zlRCDvPAoZ2wNyXCbMw5MhR6f5/h/dAXJ6P5z82H7aZX+6MDrro1DzkwqNCUYDgLE2QaUA8MZPkX89oY5N/YLzVHdbuRUo2SnIK4l4swAA7ZKBPTTXGecepPDjLQb6drFfZ0opHDHAusyLsxq++HCC0b5eyg/biCLFaVBoJB01yNmF2D9ekwZBPY1RbhO2szUhCyjKB1bqr4kbS/cq2ibp5wnn+Eitm3i9MJ++ETKmG+T4zjBvPBlStL2pmxVRdpfdduHX+/W0nlhaCdR64++6orTKfvoz3ztrs5v7NP9ry8oa/bO8dL8wtA5hXIfbHdvsgtHsQu6JiIiiRSmVBOBjAKP9RlcBOFVEvm2aVu12Nhp+Z3MrALtfSvoagP+T3uFKqUEOl3VRwPAnDucjIiIiIqJIY6dEahLc7oiIKA7wOoiIiIiIiIgagcForcPZAEYB6AYgGUA7AL0AHAHgdgA/KqXmK6VG66tooF/A8AaX7VkfMNxJKaXpskdERERE1EptyAlvvs7dgaPP0E9ftwxSuM1xdbpOptHsbB9NsnB26ELj/gl10DG2RXpkKEwYE9w59+/7AUcO0M+X4FF45O8qKFhOKeDho7ejvalJSmnmwWgqqyfQb++Q5QZWWwcCzlji2w7tOgbHSzCabM8FCrZYT0zLgLrkTng8Bk7e277B3Wr8gtHaddCWO+qpkY7alaYJRiuxCM6KF/EYjAYAI/ordEq1L1NaCVz1juDiN0zkFoX+UodpCpbkCv43z8Qva+qDRMpsOtXn1GTBPG/voGCvM140MW+d/fIOeqA+EMwucNBKXASj6bYNi3PTFUcoZKVbl/8+B/jfvOh+6eaNuYJXZ7tbxsiBDTdy2VkOufRg4Ku3tfPIo9dAdpYDX/4X8uIdQHGBtmzX2jwctHOe5bRw1odpCmavFMxdLSivsp6/ULPdZITYl0K5bpRCn0z7Mv5BgJGg2/7qAj3aJittCOqKbYKVzi/DXDlpGFD8tIH1D3tgvhz87+3LDAzp5uwA2j/LenxlDbChQBoMu7VyK7/oRhQpumvjaF4rnTjMeeV73m7ipVkmluY2br/XnVsAIDehq687x+rFjVqGHd1xPzVZP89Om4AgIivy6gTgw2eBsmKgtgaY9w3ktnFN3ayIsg1Gs5nPLsCrJA6Dvq3Cqf19/IezY6KbI2duUf3fduvEPzC9c5qLBexSXaufZheMxmMiERHFmlIqAcAHAE7wG10D4HQR+bppWuWjlMoC8J+A0Z+Jza83iEgFgCkBo291sKwBAE7zG1UL4F2HTSUiIiIiIrLBz3yJiIgoFnjNQUTUfNkdw3l8J2oW7L7wx+BbIoqxZtqVnaLgAAAzlFITlbL7XW4AQGCPbFfd+USkDEDg17Lbu6mDiIiIiKilkxULw5sxvSPUhLeA/Ubqy0x7zXF1tZrO9gked82KG/Nn2k/vvw/UdU86quq2Ewx8c72B60YrXDlS4d3LFN673IAnRBLB2QcZmHubgdtPVLhguMJNxyrMvsXA/x2oTxtSzTwYDQBw+NiQRQZWWQej/boWuPdz0zbAySoQqSnIvRfpJx44CnW33Kf+zX476XHMKODcG6HGPw41dT2w14GW5VLEJjnLT3ob6+WVxmFH8jpNEfbh1OoHnG1wb/4s6HGLiRd+0Kf6lVcJxr1gYtgEE+e9Kjj0IRMjHjGxpUhs358iTwbW1mZCTu0NKfeFKq7cKvhxpbPX8Nuu2HrXwWhxEKbndbFttG+rMOl0/UZz04eC4orofDBTVCH4v/+6q3vzIw23LVnwI+S03sBGB2/sN/+DfP66o+WMLf3ccvzCjcD0v5y3eX2BYL/7TRw5ycSIh0387V4Tf6wPnl8XqNehbeN26O4ZCgvuMjDGJnvzpR8jHIzmYPsboAkWW7kVWLUtsu254kiFNy9WmHq1gdCPdZ0Z0EVfz7Pf+wejuX8t/sEdRNQ4mgzrqF4rDchS6K7P7G0gtwi48h3B0AkmLvuvGRTm6lT5tnzttEqjDYqN9pBwg8Ud0DW7bZJ+np1hBEdSK/ft+8HjlvwK2bQq9m2JErsjgN0ljF0wWjyENgfaUW4//ff1vvuEUNzcp+UW1/9tF8zWvk393x1TFfp1cb4MANhpc+1nF4xWXo2wzwFERERuKaU8AN4BcIrf6FoAZ4mI9QOx8JYzUCk1xuU82QA+B+D/5KgawIMOZp8AX7hbnYuUUtoPXZRSKQDeAOB/5/KaiKx23GAiIiIiIgoDn4MREREREREREVELYPt9Lz4DI6LYipNuwxQlmwG8AuD/AIwAMBjAIACHAbgWQOCvYCoAtwN4IES97QKGw+kSGzhPGL9LHUwp1UUpNcTNPwB9I7FsIiIiIqJIERHgpzD7Z3TuBqUU1GUT9PXP+cxxddpgtGZ4NylVoROg1L9fcRUqMmovhcfPNPDcuQbOPih0KFqdA3or3H+qgckXG3jkdAOH9lVAok3P+hYQjKYcBKPtU/WndtqEaYJFm/QPT1PiYBVJ7hpgwSztdHXdE7v/PmawfZjCoEOHwrjyAajTr4ZKToE64hRt2bc2X2Q5/qDe9X/rOpNvK43fB9K6lsVDMFp6G4XPr3V+ILz6XcGwCV4kXOHFkZO8+GlV/at75jvBtEUNy89fB3S/xcStH9m/P5+1OwnYkQd89AIAYNEmx03Cwo2+ut32T4+H8AFdSIBu2zjvYIUjB1hPyysB7poWnf3ghg+c19smEXjrxPXI+ug+mE+Mh7zzKMxXJ0D+dQywK/guFJl0NbD4F0dldcFoAHDSMya8DpMYRj9u4k+/7W51PnDF28HhN4Wa8IcMm6ALp9JSFF67UL8/LsmNcDCag+2vnyZYbOVWwVZnb6cjFc8ZeOE8A+cPd34N4sQ+e+inTVvkH4zmvu4txaHLEJEzTRUie8YB7hfw+hzBc987Px7LrzNgPncrqm46Dc+8n2tbNjehK7DBOmA5EnTHfQajUaSI1wtsWW897au3Y9ya6LG777ANRmujnxYP9yaBCh20abODoFjdMd5KblH9yi2x+dQ+PaXh8CkhAtsDldk8Vquo1r/BXhOoqnW1KCIiosZ4HcCZAeNuB7BAKdXb5b8Ui/rrdAUwTSn1p1LqFqVUf11BpVSaUuoaAAvh++FSf/eLyJpQL2pXmacCRk9RSl2jlGpwd6KU2gvAtwAO9RtdAOCeUMshIiIiIiKqF7/fJyJ/Id4n/mgFERHFBZ6PiIhaHwYqERE1DR5jqWVqhl3ZyYF5AI4DsIeIXC4ir4rITyKyTERyRGSuiDwrIscDOBDAyoD5b1NK6XtcBwejhU4XCBb4tezAOsN1FYDFLv99GqFlExERERFFxpolQO7asGZV+430/TH0EH2h5b9D8jc7qs/bkoLR7rvQvsD+RwH99o5NY6wkJuunGc1whQfqtzeQ3cu2yPFlM5Dh3aGd/uZc6wd0bRKBtslNn5YlX7ypnzhyHFRGl92DbZIUrhpp3ebsdODYwQEjT75YW/W40qno3ya4Z/VZB9bX36299bybC/VNbmra8KE42R1OHKYwfbzzxizJ9b2m2SuBwx8xce/nvgPs+/PDf/A8u+0IAIBM9217m4uc17U8z/e/w/yr3Yo0AVexpGuzR/N2KKXw3LmG9tz13PeCBRsi/7q+Weqszg+vMLDqmKk458mhwOSJwMcvQl68A3jzwYi3qc5e1cvRr3qVdvrc1aHreHGWidX5weN/X+/b3v0VllvXEYlgNADITNOfA3LyEBTU1hhOgtEGZFmXWZUPFGjWRZ12ycDGh0MfW3693UBKYnTOfbpgNwBYsRVYvsW3EsIJRtteBlTVNP1xhKglcHs+jJTrRoV37PnXe4JtJaH3f/OF2yE3jUHV+89j+LY7sSjF/h5tY2IPYENOWG1yQncKsQ1Gq45OW6iFqrU5oZa1jkRRu6NKul0wWjg/3RVF1bWC8qrQ5baXhi7j5mop1+9xQInNp/btAqJdrjlKIdlFyLzd+q4Icdxzsl6IiIgi5AKLcY8AWBvGP5sP2nYbBuBhACuUUkVKqTlKqalKqbeUUp8opX4DsAPAMwACnxi9LCL3uXhttwGY7jecuKvejUqp6UqpD3YtbwkahqJVAzhNRLa4WBYREREREUUcPyMlIiIiIiIiIqJmwq7/BYPYiSjG4qQrKUWSiHwpIjPEQY8/EfkNvi9yrQiY9JBSyuN0kW7bGOY8REREREStw+xp4c97+FgAvhAY9fQMfbmfvnBUXa3XenyC07uFOCEzPwBmTdUX2G8k1H/egFJNGK7lsemNajetmVBKAYePsS3TVnbi/m0TtNP/3GQ9PlMVw7xyJMy7zoUsmNWIVjbS55P10zp3Dxp1z1iFyw5XSPTbn/bdA/j+JgNJCQ23RdUhEzjjGsuqk6UaH9bcgoN6+4bbJQO3Hq9w3ej6OrpnWDdr85YymP/YG+Ytp0J+jK/ccFMTzGg0fQbebscNUXjnMoXMMOLeJ0wTGJd7sUizXTuRkzzA98em1SgtqcT17zt/3JKzK9jIbTBaWRx0aHcSTBVocDeFG461LmAK8Mx3kXlUVVwhuPUjE/vd58Xm4LzCBs4/RCF/konTFtyLrMfOBbyak24UKACnluj3+WmL7NeHiODqd/VlFm9uOK2wwrpcRqrtYlz57BrrR92FFUC+g/AJp5xsf3tmWm9ruUVAQZm+7j6ZwLRrDHTPUJhxnf7R/asXKBzYO7oHw78m6Jdft33sDCMYDQDySsKbj4gaCud8GAk9Oyl8cW14Hy9m32SitDK44SKC1+aYOPqm1dj7l3PQp99ypA4qwsKUfULWuTqpL7A+B7IrXKrWK7jzUxPG5V4Yl3vR5QYvPv8z/PO8bj3bBqOFeXykVsprs8FUxlnyVyPY7YV2j2I8hkJaivW0Is01ZlPRXfMGyre5Hqzj5j5ti19+Xolmk0lL8a1Lf706KXxylYE9dj0vCHX+sFvfDEYjIiICALQHcBiAUwD8A8CpAPYHEPgBTzmAy0XkCjeVi4gXwJkA3g+Y1AXA8QDO2LU8/7P6NgCniMhsN8siIiIiIqIwsVMoERERUeTw2oqIqPlioBIROcTDBRE5wWA0gojsAHAOGn4nfRCAozSzBH5d2+a3urUC53HwFXAiIiIiotZBwg1GO3wsVI9+uwfVvkcCew62Xsacz+zbUOnr7VirCSbyhHE3KVU74SC/OeLkt28h95yvL7DfSBhPfQ2V2TV2jbKSpOnpCwA9B8SuHVGkdgX32bms6HXttIJy6/GZJWuBxT8D338EufFkyO/fh9vEsMjOckjuWmBHnraMGrR/0Lg2SQovn2+g5GkDK+83kP+4gd/v9GBgtnVvZHX1I9r6hy55Fz9fWYCyZwxsfczAg+OMBkF/3dpb11ko7bBzw3rg5+mQO86EfP2udhmx1lRhH26dc5BvnT80LvYNW5XUFzVIgAmFYydsczXv6nzf/80xGM0bZmjenSep3Z3+A323vPHnJ9MUHPekiUlfCxZu1Jd74iyFiucMvHmJgYwnLwXefKDRyw7H9Tue0U77bZ39+lhXYP9B04qAzVEbjNbWdjGuHNhbP23N9sgtR3ts8rs26t7BukxVLbBqm3UFJwwFVj3gwciBvg159GCFa48O3qifOUfhkhHRf6w/pJvCoX2tp9VtH5VhBv/khggNJCJnwj0fRsIJwxRqXjRQ9oyBwicNzL/D+XFp9OMmCsoaHgsfnC74v/8KfijpjaXJg7Ehsafj+lYl7TpYTXkWAHDGiyYmflFf//YyYOyzJt6ca2Jntfvzve58ZxuMFiIgiKiBGpsNpirOkr8aoTGPgzpoPokuqoivbz7t0DyzCHTxZM0B3I+b9bWluL5w8U7rGdM1j5yOH6qw7iEDmx4xUPCE/bG8yCanL1QwWjzcRxIREUXYMgAPAPgJgNM02xUAbgfQW0ReCWehIlImImfDF4L2i03RHQBeADBURL4KZ1lERERERETUDIR8mBxfz9GJiIiIiIh4n0LUTDCxrJnie0MtE4PRCAAgIn8AmBEw+nhN8XgORnsewFCX/06J0LKJiIiIiBpNtm4AViwIa141/rHgkSPGWBf+4wdIRWnw8qe+DPO0PSHHdoT5zyNQm7/VcvYEF3eTsnk1zKuOghzXCTKuD+T9p5zPHAHyygTb6WrsZbFpSAiqTSow5ODgCWkZwIGjY9+gaBh2GNClh20RD0z8o+wDV9V2qvVLvKmphkx5LpzWuSZb1sG8ZhTkhM6QswbZF7Z5D5MTFfp2UejUzj7FQnk8UM9pQt9ME/Lk9WibrNAmKbie7powKADITagPBZS3HrZtQyzpwodUnAWjAYBSCrccb+Dhv8e2cTUqCWuTeuPb1KPwa1l3V/Nu3hVM5DYYrTwOOrSHG5qXmqxwzynWhfJK0OjwzpumCOatC13u2qMUUhIVZPViYOb7jVpmY2R5t+HaHdbHy5wt9mENy7fY1700t/5vEbEJRovcPtM5DUhJtJ62pThii4HpIIiomyYYDQD+2mw9fnC34HUx6XSFJ85SOKg3cFhf4J3LFK48MnbHmaMGWS9ryh++/3XBaJeOUDhjf307I/l+ELVmuvNhOCHW4fAYCm2TFdq3Vdi/l8JPtxo4bkjo+eavAzrfYKLHLV58tkjwzHcm/jM1/HPwiqT+AACZ/ABy8gSfLrIud/FkQYfxJk58yottJc6XpyvpUUBygvW0nWEGR1IrVWuzwVS2jmC0UFc3HTRhup8tiq8v8eiueQOVVyEoIDKQm/s0/9DZkkrrMuk2n+YrpdCtg+94bqeoQn/PFCoYrZyBkUREFCMioiL47web5WwVkTtEZASAdvD9AOlJAC4HcAuAuwDcDOCyXeO7iMhAEXlQRBod4S8iU0RkOIA+AE4H8C8A/wZwMYCjAXQVkatEJL+xyyIiIiIiokiJr+eZRERERDHD4AwiolaIx34iIiKKHAajkb/AX4jcW1MusOtaZzcLUUq1Q3AwWpFVWbdEZJuILHHzD8DqSCybiIiIiCgitm4EuvZ2PZu67WWorJ7B43XBaDXVwK8Ns5Hl+48hj10LbM/1fQi55FfUfmL9o/VOg9Gkugpy7THAX3MBrxfYngt59hbIV287qyAMUlkBWTgbsuw3yOJfgKXz7Gc46u9Ra4tb6or7gBS/Hr9KQV35AFSCJumlmVEJCVDjHwc8mgSBXbpUaVJjNDK9BQ1HzPnMbdNckdw1kO8/gpw5EFg0x7dt27nwdqgMV7fOesOGAz36WU/7bgokd43lpG7t9VVuTehSP7B+OaS4QF84hsINv2pKNx9n4J6xsW1gTtJA/NTmUNfzlVUBpZWiDXnSaeoO7bVewa9rrac5CYLZK9v6/amuBYp3ht+u3CLBkzNDf4i7X0/AMBREBPLKXeEvsEffri6bOgAAIABJREFU8OYbfgLUlFW7B08q/dKyWF6pgaIK/etZnmf/Wr9ZKqj1+sqUVQFezXaWoQm5CIdSCl01x7otxZH7gF1Xk/+xKTtdH+KoCwXrmBo8LilBYfwoA7/c7sHsWz045yADRgwPggOz9NNemmVqAzAO3hN4/woDfTWnvtwifuGBKBLi7VppeF+F6eM9+Gq8s5vF3CLglOdMjH+vcceE3SG/lRWYs6LWtmyNF/hqCTDmWecXQHZhvW2SrKftZAAQuWEXjFbViAvUOGO3p4cKv85sZz3+00XAB7+ZqK6Nj2uLHeXOy/623n562MFomk0mPcV5fTo1Xv3xLVQwWlHLyfgjIiIKIiKmiOSIyJci8oqITBKR+0TkURF5bdf4qASUichaEflIRJ4RkYdEZLKIfC8ivCshIiIiIiIiIiKiZi4+PgcmIqJI4/GdqHmw2VcZfEtEMcZgNPK3LmBY12t7ZcBwL5fLCSy/Q0QKXdZBRERERNQiqb0Pg3p/OdTk36EuvRsYsG/omdqkQp10ofW0QfsDnbpaTpLfv2s4/OXkoDJPpFxsOW+CJ3SzAAB/zgHyg0Ou5Jv3HFbgjiycDTl3KOTa0ZDLD4NceaRtefX2Iigjfm6N1b5HQr38E9QldwHn3gj17LdQYy5p6mZFlDriFKiX5gDn3QQccYplmQ5eTWqMRlAwWhSZz90GOWsvyF3nOp7HuOzuiC1fKQUcPlY7Xc7aC2LxkDk1GUjR5OsVeDIbjtiwojFNjJh4C/tw6s6TDXx3o4EDdj396JNpX76xlicNQE7ygLDm3Vzo/jOJsqqwFhURmwoFB0w0UVppPd3JtpGVrp+2tSS8dgHA+/OdrcjD+ilIcQHk2tHAT1+4X9CBo6Bufg5q8h9Q4x9zNau69G6ohz6GytoD6vL7AAADq/X7e06evq6FG+2XVVgBzN71BLHQJiAiwyIMrDF0wWjTFkbuwze7gJw6CR6FrDR39XaK8LqIhP5d9DvVle8I1mlOv3XnG937kevuNE9EGtprpSa+vTl2iMKK+2PXiAJPJ98f3losXeXsZD5/HbB4s7Nzg+5ayVBAG8319c4afumDXKi1yayYPzN27Ygyu/uOUMFo+/fSFzj7ZcHAO038uqbp97sd5c7b8N+f7cu6uU8rKAd2Vvtm0IU9tw/8ybIwbS21Hh8qGK2grOnfHyIiIiIiIiIiouhrLc/BWsvrJIoR0wvUlDV1K4iIiIiIIoj3jURERBQ58dP7m+JB4FeldV+RXhYw3M/lcvoEDC91OT8RERERUYumlILqOxTqotthvPYL1PVP2pd/fZ5+mmEAhxxvPfHrd2G+fBfMZ2+BzJoK/PJ1g8m/peyH3MRulrN6HN5Nymv3Wk+Y942zClyQygrIxEstg9isqBueguo1KOLtaCy152Coi++AceUDUHsf1tTNiQo1cF8Y/5wIY+IHwKEnBk3PMItc1detNjdonHi9YbdPx3zhduC9J9zNdPTpEW+H0gTK7fbZa8HzKIXMdtbFt9eFSewic78Mt2kRZZrW4+M9GA0ARg5UmHeHB+bLHqx6wIOXz49eo3OSByInSR+M9sRZ+mXnFrv/2LG8rAry8Qswx/WBedVRkJ+nW4bxRcP4/2fvvqOjqP42gD93Nj2BJIQapAjSBQTBgqIodqzYey9YsPdeXhV7w94rVn4o2LuIiCi9hd7SSEIS0pOd7/vHElJ27uzMZjdswvM5h0N25rYtc3dmd+bZKSYWbtKvdxIE09EmrKopwWhLspyVO363bMix6cCCma77ULe/BuOpr6GOvwQqNg4Yf6Xzujc8C3XBHXVhoGfeAIw9DV1rMpFoWp9cufzxSTAv2hfmE1dB3noIsnIBAMBrCr5eFPg5n7bAV2Zrmb5MaoLju+CILojr+6WAqUsQcsnp3LRbqrt2u6ZE3uTWvzMQ7TQMt564aN99Sdfcpyx3b/NEpOGN4H2lPToqvHF+8wwkz5O2Y3+mstD5m/mPy5y9L9gFYuqD0RwPgwiosX/BSHWAxKlWINBscewQ+xLr84EL3jJRVbNzT6q02+9t7KM5Yrt/6nbXdU2e7/9iTYh02zhn7dx3vP1j/dAM64GVB3iZ5vF6LiIiIiIiIiIiIqIwCPRhMsMImkVlATB3IvDNMODPM4E8/fm8DYgA824FPm8PfJYC/DgGKPM/D5KItsv+EfjlKN82893+wKpXd/aIiIiIKBjNdN0BETWR7bbK7ThicY6lVorBaFRf+0a38zTlFje6PUQp5eYyxsZX9jduj4iIiIiI6jv+UqDXIOt1J1wCtZt9VrHqpllfUQa8Nwn4+FnIXaf7rf426Qhtm1FOjyYrXFwV2lR/fQNkr3dW9vAzoE66IrzjIWc69/BblOwtctXEIaW/+S8sD+1VtzLlGeDDJ13XU+MuCOk4AACD9rVdLY9fBdma67fcaTAa3n8M8ueMYEcXMroLwSMh7MOtS0Yb2DDJwEtnK0wcq3Db0fo78cJZCt9ea+DZMxReHb8NL2ddhcdzbkWnmhzL8otjB2JVTG/LdY+cYOLasQbaJVr3taFAtCFPOiV//wF5+jpfCOWiWZBbToTcd07Yw9FKKwVfLrAv4+S1kRCrkBRrvS53m/tx1crcGvj+D65YhINvb/x7Ac6oz1ZCHXNew2WGAfX2v4ErH3+x33ueioqCuvddeK59En2rVllWW54DYOV8YNrrkDcfhFy6P+S7D5GRA+SXBu525srAwWgpup9lCFKntvoXwVyHuwiBOJ2b+nV2PllFGcABbn96ohkkJyj0TAtcrrHaoCBdUF1WEb9wIwoF3XzkiZCdpdNHKHRuG7r2BlcsslxeacShbPtXVFk5zo8/nb7v63ZxDAXE6YLRWn+OFYVSTY39+nkWx7stkN27vwowbR3oYD9pRQ7ww07+Ka4CB/vI9dntn7o9vFqR7ft/myYYrU28s/eGU4bbl/t0rlge+5VW2g+YwWhERERERERERLTLa3EXJba08RLtJDXlwM9jgYznga3zgfVTfLe3zg9cd+kkYNljQHUhIF4g9zfg58Na4HxB1AxyZwK/jgOyvgMq84H82cCcy4GMyTt7ZOQI5zUiol0O92mJWj6b7Tjc1w4RETXGYDSqr/GV1ZY/NSEiWQAW1lsUBeBAF/2MaXT7Gxd1iYiIiIh2OSoqCurBj4AuPRuuOPo8qGufDtxAh65B9Zvr6ahdN6y7w4vt7T4I+yu0hwLy5ETHZdUpV4e0bwqeSmmc0Q1UKk1qkcaICotgoLLASQeSuRbmgxfCHB3r+3fxfjDP3xvmtUfCvP0UyJK/feV+nwaZfKurMSEpBeqmF6D2OdxdPQeUUlBvzbUtI8d3gzR6DHTBaFui/J8DefE2iNvErBDTzR5GC/00a7dUhcsPNvDM6QYePsnA9GsMpKfUrY+PBh46UeGKgxSOGKRwzaEGLh6QjUsK38L1Bc/jwdz7LNudGz8CZYZ18tmotpsAAD3aWY8pI0cfqqJTWuPxX/jzZ8Cf09015NLKXMAb4CXpcfja6KQJackpDv4LmswAeY7xZhneyLochtuTfPY6yBeK1qm75WrVe0+oe98FOnbTNqFOv9Z6uVLASVegn1gnMmTE9Gm4wOuFPHQhNs5f4Wjo/20Azn3DxPiXrJ+4pFggOiq04T0je+rXvftXaL6AcxqM1reT8zb32R1ISYiMIKPGZt/uftKtDQrSbWuREIyxNk9w/psmDnjUi+s+NpFZyC9oqeXR7apFSC4aEmIVZkxs2o5brOHFjSUvoWJZG3y8+Rxtudqg36yCasdtVwXIoqqlm/eVAhI1hy1lDEYjN2oCvGDWL2+ecYRZU86FMgyFX28KPJ+Ean8vWHaBwFY2FujXuT1OW5Hjq1BUZl2xbZyzdgamK3x1tf6xLqn0HUc2Fmjei4T9PyIiIiIiIiIiIiKikMv9zT8EraYEWP1m4LobPvZfVrwMKFocmrERtSZr3gJMiy+kVr7c/GOhZsTzuYiIWifO70REROROC72UlEJNKRUHYHyjxb/aVJna6PaFDvvpj4YBbKUAvndSl4iIiIhoV6a694PxyQqob7dAfbYK6qdiGHe8BhUdE7hy+y5B9Vml9G1fMMppMJo+xUZuGw/55XO3w7Jua+ZXQFGes8L7Hw01cGRI+qUQSE7zW9S3KsNx9Yn5z8Py1RggGE0y10IuHAl8/2Hdwox5wJrFwH+/AjO/glxxEOS9xyB3nuZoLOqWl6A+WAj18TKoGVlQJ1zq+H64pfYYDBwwzraMXD0W4vXuuN0+yXq7zff4B6NhQwawwVn4Ubi4CfuQqkptO3brdqZjBitsnGRg3SMGVjxooPBZA3ccY8CofwcLcnf8uXfFf677SF07BwDQv4v1c78iW1xfcF9iWCfsyTfvuWvIpSveCxzU5zQIpmMb6+U5xS4G1Mham7cgj9TgjczLMbzCwa/BHnUO1O8VUJ9mQM3IgvH8D9pQtFrqsNOhpiyFuu1V/5X7HQXVvZ++rseDvukWYXcAVsT2tVyeNXmS7Xjq++BvQaEmICI1wXEzjo0frn8RvPirYM7apn+Zrg3IafRFff/OzpOJeqZFSIqRhdREhefOcDe+2mC0NE0gZ35pEwcVpG0VgopqweLNgr0eMPHebMFfa4DnfhKMfdJEoSZIJBARgel2MiUKAa/DoMadaVh3hUX3ufsq8rns67F0xFtY/qCBohei8dj7VyL64yVo/9EsbZ38qDRUqhgskt0d95MVINS0li7MSQFI1Bwyl0Tm7idFqhr7QD/ZuLKZBhJedsFoTqatg/oqDNNnAQMAfsvYue/JW13u4+SX6sfq9m5kZPv+L66wXp8c77ytcUMU7j9e/6zM2+A/uNIAwWj5DEYjIiIiIiIiIqJdAr8zpOYW4DXXlF8tIWfm3Wy9POP5wHUbB6rVWv5M8OMhaq3WT7FeXrTY9jx1IiIi2ll4LELU4tl9phDCzxtsuwlZL0TU0jEYjWrdCqBrvdteADNsyn+wvUyt8UqpPg77qe8TEdGcok1ERERERI2pxLZQnbpBxcQ6r9Q+Pai+qlS05fK+nXwXuDuiSzbavk4+fjaIkdWRshKYkyZAbj/FWYVTr4Z68KMm9UkhluwfyjXCRQDUQ1vus15RGiAY7bPJAcPTAEBevdvZQEaOhTruIqju/aDSe0EZ4f/IRT38mX2BlQsgr92z46YuoGaLVTDa9vo7k+5C8PphHzLjbZin9YUcngJzwsGQdcvq1v3yuW/dYckwLx0FWf5vmEfsnlIK3dMU+nRSiI7yn1flpTt2/D2ociniTU3ClEbKf9MBAP06Wa9fnu3+gvsyQ5Nm9fs0SIBQh2BlFwnmrAtczmkQTKe21suDCUbLLRYc9YwX2zSfbnmkBr+tG4vTtjkLAlWnXwulFFTnHlBt2zkeh4qOgRp3PtSDU4C9DwF67QmccT3UQxa/LttI/+HWITKronujBv6haVlRnR2Py84eHUPSTANt4hQOsvmE8t4vm34inHZuenwCzGO7wnzlbojXixE9nbfZNbXJwwqrqw5ReP0850lLsVG+/9MSNYGczRyMMXuNYMh9XiRPNJFwlYkh95t+2+yKHGDafPdfn/67XnDIEyZiJpjodosXH//Dky2p+bgJkd2ZBqUrzLqqCJ1rsv3WdarJ2fFvVNksvJt3Ja66cBD6XXox+nZSiIlSUIYBlb47Utu30d63PE8a/orfF6WaAFcrmYXOtnm7fdKkOOt1pQxGIzcC7UO3lmA0m3XK4bw1doB9wdxtcHTcEC5bbYLOrNiFxbo9d2xFjq+CLhitrYtgNAC4+1j9ZxqbC/2XBZr38kp4mhoRERERERERERERtUIla3b2CIh2DV6b8yYZAtkC8DkiIqL6+L5ARBQ+nGOpdYra2QOg0FJKnQvgexHJcVHnUgD3Nlr8tois19URkZVKqXcAXLR9UQyAt5VSY3VBZ0qpEwBcUG9RFYD7nY6TiIiIiIiC1HG3oKpVqRjL5aP7uLjSPtAvcS3528WI6jUrAmzMgFx5CFCUH7iCxwN1z7tQhzoMUKPmk5zmtyhWqjC69A/8kTjatmrPqnVIkHLrlZtXA4P28VssBTnAiv+ATx38KqNT3fo4Ch8KNWUYwKOfQ247WV/ogycgp14DldYZHdpYF9kYbT1HyAPnQx1+RghGGhxdCIVa+jckJhfYmgt54uq6FYtnQyYeAXy0xBcKd89ZdeuW/wu5/mjg/YVQaaEJdQoFyc/2vR4r/T9KkTk/AMv+2XE7Cl7sVbEAfyXs77j91P++hJQUoU/uIgD+9TZvFQzqYl03NgqorPFfXmIXOLJxFbD7AMfjc6r7rc6ChhwHoyUrWH3gv6HA3ZcAIoIzXzPxywp9mUVrhqNv1SpnDZ54GdQeQ1yNoTE15iSoMSe5qtNv/z2Bn/zve5URi3XRPbBHdcOTOLNDFIw2omd4knvG9FP4faX1c/ndEqCwTJCSEHzf2iAisxooygPefwyiFHa/7AEM7AIszQrcZteUoIfTLJRSuOhAhdF9BAdMMpEXINgsrXgNZGE22q0BgP381pdUApXVgtjo8KU3lVYKFmwClmQKLn/P2bb9zzrg/FHO+ygqE5ww2UTm9nCQzYXAma8JOrYRHNJfoaBU8N96oF9noFu7CEuqolZBd26vJ8J+EklWLcQ+L1+DTStn2xc8bSKMa17RrvYYCqkJ1kFCBZ52KFfuUn+yi5yV080gSikkxliXKGEwGrlRXWW/fqPDfckIZ3c9gtNgtP4OdkOnzRfs12vnvO8WuMuxRoFNMJrbAOuM7WcGFGs+ImmrCXK0c9gA4Mdl/suXZAI/LhUM3g3o1Nb3WJcFeBlvdfnYEBERERERERERtT68KJGIiCg8TMDixz+pFWDoHRFRC2Yzh3N+J2r5uB0TUTNjMFrrczGAV5RSnwL4BMCvImJ5WrVSagSAOwA0vlpyM4C7HPR17/a6qdtvjwLwo1LqEhFZXq+fWACXAXiyUf0n7cLXiIiIiIgoNFRcAqT3YGD1Ilf1qlW05fJoN98fO/iwS7xeKI/zRqWqEvLwJcBPnzircMZ1UIecAjVwpOM+qBmldLBcfHv+YwGD0U4p/ly7Th68ACgthjrp8rplX7wEef5moKY6mJFaUlc+ApxwKVSCJnUszNQBx0L23A9YbBM0Mfs7YNz52MP6oUZGTB94YcAD/7QfycuCaq9JzgozryZ8SP04BfLpy9Yrt+YCc36E/P4//3UlRcCf04HjLwndIJtAPnkO8uLtgNcifUxjZMW/joPRYs0KxEsF5OFL0H5DEmBRb1uFoMa0Dg1oGw9s2ea/vFQl6DtdvzzkwWibtwpqnOWiOQ6C6a3ZFlZkO6tfa+562IaipdXkoXeVs1+GVfd/ABxiE3IYRv06WQfFAcCK2L5+wWhZoQpG6xGewIpTRyg8MF2//3HuGya+uib4k+F0LRv1w2C/eQ9yyX0Y1l1haVbgfaGuKS0jNKtPJ4V5dxt440/B8ixgyj/+922PqlXoftUQCIB2sQOBXnMt28ovBdLDFAj341LBeW+ayC52V+/z/wQvnBW4XK1X/5AdoWj1Tf7FxMpchas+lB3vZRPGKDx7ukKUp2U819QyaENkI+RlJmUlkFtOBBb8EbjwyLFQVz8WsFhaknUwWp7HP2w5EKt2rGgDMRWQqAkaChQQRNSAN8Dxac4GSGU5VKy78L9IY7dH5HTa6tdZv99a68sFgkfGOx1VaG11OK/UyrcJm9V9nJYYC5RahC8WlAJ52wRFmmC05Hj3bw7pKdaP99uzBG/P8i2/c5zCA8crlAaY98pD9zEMERERERERERFR5NpVLgrdVe4nERG1DHxfIiIiijx8fyYiIqIQYjBa6xQP4Lzt/0yl1EoA6wAUAfACSAMwFEAni7oFAI4SkYCXgorIJqXUeADfAYjZvvgAAEuVUv8CWAMgGcBwAI0vOZ0O4G53d4uIiIiIiIJ24LGug9GqVIzl8hg3R5KmN3CZmirA4/wCX3n7/xyHoqmvNkOltHfcNu0EHdItF7f12qeZHFD2J24ueNq2jDw1Edh7DFT3fpBVCyFPXxf0MP2kdYH6Yg2U4TCJKYzUw59Cju+mXS+bVkEB6K+5iLzSiMP66O7oVb3Ov/LqRUAzBaNVVgu+mCeYu973XdjSLOtySab91eYy5Wlg6Rzrdd9+ALWTg9Fk6T+Q9x4FZk53XXfv8v8cl001t6f1/PElUuKGAbtbjAUGCreWw/dRUkNJsdbBaGVGgGC0EJv8q/MvRg2H1/r37Wi9LawvAMqrBPExzhr63zz7sR1b8o1l4GDDQhdB3fIi1E5MsUmIVejeDthQ4L9uRUxfjMO3DZblejTJci6N7BmSZvwMSld4+CSFO6ZaPz8zFgEb8gXd04J7zLVBRPVfU3mZQGkRuiQnO2ozXAFh4dA1VeGeY32P3Z3jBMc9mIN1Xt9rIsW7FR9sPn9HuEn7mnxtO/klge93brFg2gJf+NgRAxX27x34OdtWIbjgbfehaADQz+rTchv3f2X9YvhiHvBFo/nhpV8Fw7sDFx8YIYlVtINp+gLsvOILwPKKL5y1wb8Ay0zXdcS/rss2TAHmrrd+DUbA7ikAQCZd7iwUDYB67EtH74Xtk4CMHP/l+Z40xEmFq/EVlPqeC0+AHQjdu71SQKL1ITNKK3liF7ngJLh702qg957hH8tO4nRXeFA6EGXANjh5WRaQkSPo26n533N14V9JsUCJZZiZfq7Q7XMO6OwLaLayIgco1kyFbYPI1eviYFf2/2YI9ttdBQyEZGAkERERERERERERURgEDBvgd1ZEtCvgXEdERNSy8L2bqEWw/cyB23HEYjAltVIMRmv9DAD9tv8L5CcAF4jIJqeNi8ivSqmTALyNuvAzBWDE9n9WPgJwqYg4SEggIiIiIqJQUCdcCnnnEVd1tMFoHheNmAECYQDfRcCxzq7QlM2rgfcmOSqrbnuVoWgtgEpOg/QaBKxZ0mB5kujDr+7eJxN3vHM0olETsH35fgpwwZ2QC0c2eaz1qXNuiohQNABQqR2BH4sgh2muWhbfdtjXJvAlI6aPdTDaxpXAvkc0fZABlFQIjnnOxMxVgcvuURWgUK7NxxprFrsbWIjJ5y9Cnr0h6A+bB1Q5Dx5L9W7d8XeKt1BbbmtuEayC0XRBH+VGAgSAVcyALP7LcnmwJnxg4pXfQh+M1q+z9XIRYPUWYM+uztqZNl8/NgXBxIIXbOurx/4Htf/RzjoLs/6dNcFoSUN8P6NQT56n6e+taYlAj7QmN6N129EG/ljpxTeaTf692YI7xwUZjKbZtTEah+AV5qFrqrNgtA5tghrKTjewC7Bg3b74FXuhSsXiiJIfkChlO9a381q8qLbLt8+4xMocweFPmztel/d/JXj8FIUbj7B/7/38P1+QWrjlFIvrgI9L3xWcMlyQnODutScilsFbTQnU8ltnWUfsw78c9ms3Tv+xCAKO1cGyBvUClG+NPBGQvydL/gZ+/sxRWfX2v1BRzr6uTEu0Xp7vSWuw7+OEKUBhGZCWFLicFUMBibHW66wCkIi0agIf12LTqhYfjGZ3COR02kpJUBg/XOGTufbHCNPmC24+svknQ937Ssc21vNCrkUQdS3d3NMlGWgTB2yzCECbt0G0Y2gTp+9Lp6vD8N63Z5koDTDvMRiNiIiIiIiIiIh2eS3tosSWNl4iItqF8T0r4nG/gohoF8S5n4iIiEKHwWitz7MANgM4AEAPB+VLAXwPYLKI/BRMhyLytVJqTwD3AzgdQKqm6GwAT4jI58H0Q0REREREwVMdukLGXQjMeMtxnSoVbbk8xs2RpDhIG6gOfHWk5GVC3psEfPGyo27VRfcAR5/rqCxFgP2P9gtG61u5EsneQhR5Gl6J2yYOuHlAhqNQNADAgpnAt++FaqQ++x0FnHhFaNtsIhUbB+neF9iQ4b/S68slT4pT6JIMZBX5F9kc7Z8EJQBeW9gO3+T46l9zqIGxA6wvLp+9RvDEdyZKq4Cj9lSYcLBCTJSv7O8Zgld/FyzOFIgAfToCVx1i4JD+dW29N1schaIBQP8qi/tYX16mfl2aJhErxMTrBb59H/LoZXULUzsCW3Ob1G7fypWOyyZ7657oFNPiSd+uoAyWSQS6oA8AqFSxiBOLK99nfwcp2waV0PSUpylz3IWiAYDTrMJe7QGPYR1csCLbWTBaRo5gaZZ+/bu9p2HoskXWKzt0hbr9VaiRhzkbcDPo21nh+6X+j3fGkDOAbXc0eO1uiWp6MNoh/QClwhtWcdMRBr5ZbL0f8tUCwZ3jnLdVXSN45y/Bp3MF2cXWZYzG+zxF+eiS3NtR+7qgn0glG1dCJk0AFvyBRADj8K1luRhUo423GNs8bf3W5ZXY9/F/X4tfWN9tXwguGCVIS9K/dqbMCf5kiqJy52VnLAyun9TrTPRq7y74SxeMQqSzs7N7JXMN5IqDHJVVd70F5SLsybf9+28U+Z52iLHaNwkgryRwMJru/FylgCTN/lKggCCiBhx8JoItm8M/jjAL1bnur5yjUF4lmLFI/x556+eCm48MTX9u6MbTrR2wJs9/+cocfVu6x8tQQL9OwNz1/uvm22SE6+YrOyN6Ws+5jX3+X+C2GIxGREREREREREREREREYcHQrVaMzy0RUevE+Z2oRbDbz+Y+OBE1MwajtTIiMhXAVABQSqUAGASgG4BOABIAGAAKAWwFsAzAQhHxhqDfXAATlFLXoi6UrTN8wWubAcwTkbVN7YeIiIiIiIKnbp4MWbMIWDbXUfnqUASjOUlyqLG/OlKK8iGXjwZyba7wrEd9tgqqUzdHZSkyqF57+n29EYNqXLb1dTze/qYGyyeMUUisKnT+dci6ZZDvPwrFMH32PxrGY/8LXXuh1K2PdTCaWXfY3zVFE4wWlQ4vDJSreCRJKQDgxo6T8Fz2aUDBxMzBAAAgAElEQVS2r8yXC0y8eYHCWfsoVFT7QuqUUpixUHDcC3WBQN8tEfy3HnjnIoUflgqOfd5Edb1PHhZtBqbONzH9agNHD/aF20x3GDDTriYf7b35jspaSukQfF0X5NW7gQ+fbLiwiaFoAJAkpegWX4qN5YFTlFLNwh1/1w9Ja6xAklwHo5WreOtgNAByzeFQb8wOOD47BaWCs153/2WJoclKEhEopSA11VBR0YiOUugVX4SVpcl+ZVfkCGofkNp6VqbN149vwyQD6e/+ab2y1yCot/8NeyiYW/01mYHLcw2oj5dDjmgHADChkO9Jsyz7UO49uLfjvfDCY9uXUsDtx4Q/tWdMP/26OeuA7CJB52Rnz8ONnwle+Nn+NWmgcTBaHrp1Dhwm4TGA5HhHw4gIkrUOcpaLECNvgWUwWn5J3bZm5d2//B83rwn8b77g4gOt65VXCX4NkJ1px00w2pcLgv9C1yoYhSiU4nbiN3+yrRBy+gBHZdXEJ6COPMtV+7oQs3xPO20Q7D49ffO+Zb3SwH3ahRPp9pdKGIxGbtRUBywieZk275otg907p5td4+QEhWlXe1BYJnj0W8Fj31q3/PzPJq45tHmTIq2ClwGgfxeF3zIsQh1LgbxtgvZt/B8A3cdpSgH9OivMXe9fYFmm/lGOs/6Iz9a+u7uvo1NWZX98RURERERERERE1DrwolCKNHxNEtGugHMdERFRxGFoElHL10zBaJwtiMiJnfy78RROIlIoIn+KyBQReVZEHhGR/xORySLyoYjMC0UoWqM+q0TkFxF5W0QeFZHnReQLhqIREREREe18yuOBmvQ/4MTLgF57AnsdBPX0N8AR1hejV6kYy+Ux9nknDTn5sKvaPhgN37zrLBTtgHFQ7y9gKFpL1L2v5eKHt9yDR5M+wl7dgJE9gcdOUXjkJAWU6EOe/BgGsOAP5+WHjwGGjvZf3qMfcPZNUA9Ocd5Wc1Oaj3mk7ursrinWRR7ocBdiB5Qgpf8W9NhjJd5NPhuvpF7qV+6itwVxV5pIudaE53ITxmXeBqFotd6bLfh3veDJ7xuGou0YkgB3/q+uXkaO/V2rNbxinrOCOuUO0i+aSHI2+oeiORUdAySn1f2LiWuwWt37Lvr1DByKBgCp3q07/o6CF0nebZblSg3rhJEE67cAAEC5YZPilDEP4jDIUmffhzWJAgF46m0CkrMR5t1nwhwdCzkozvf/IUkwR8fCfPUe9M22Di7LWF2IH5YKjMu8O17jxmVe/Ly84fuZLhht392B3VJt5qk9hkZkGEC/TtZj2rINyKtJAA4YBwDY6kmFV1kn7hxW+jO+M6/Dof2BwV19y7ql1gVdGAo4tD8w61YDw7qH/zFQSuHfu/QffzsNZFyeJZj8S+CyfsFohfnYuweQZBMyCABpiYChS/WLQPLafa7Kp3kLLJfbhRGZNsG6Py7V1/txGVBV43Rk/pwGo1XXCH6wGQfRznbAHuGfU2ThnzAvHw1zbDLMs/aEzP0JsjUXckwnR/XVba8Ap1ztut/2umC0qPYoVQmW6/p3UYjXBALlWe8eNaA7pFVKP8eXMhiN3PAGDkZDXlb4xxFmdh8PBbN7nJKgcN5++oq6wLRw0gWjDeyir7M823q5bvSGAnpY5xRj9RZ9P8GEZiqlcMGo0LyniACVTdhPIyIiIiIiIiIiIiIrvHy4deLzSuQOtxkiIqIWhaFpROQQp4tg8EGj1mkn/m48ERERERERNTeV2gHqxucbLtz7EEh1JfDL5w0WV2uC0aJdBaM5CLepsb8IWOYHDrVSr/8F1W+401FRpNEEoykAN215Arc8dU6D5VLqIhitwGHiVlQ01IwsqIQ2ztuONB7NxumtSyZLT1UI9EHn5uiuuCj9tSYP555pJr63CY6ZvxFYs0WwWyqwLt9Zm8eUfNu0QZUUNq2+E39MC6qauukFqBP8w+ga659n4sdlgT+sTvU2vK8pZhFKPM5f34kx+tdKhQqQ9PTHl8DJVzruq77fVojtBf12arOlxDQh958HLJplXfC9Sejb8WHMwDF+q76dV4F3F/m/dx32lIml9xvo30Uhp1jw1xrrpk/Ya/sgdMFoScmB7sZO0b+zft2l75qYOvp4yJ8zkOfRpDAA6FCzBSNWvYNDXpscMeFvw7or9O9sHTgxfaHgEosczMbe+UscfalmNN5eivIQE6Uw6WSFqz7UN6AL+YlEUlUJ/PCRqzrtvNYTvF0wWqFNQFmcJtwIAKbO0z/OHgOYfo2BIwcpfLdEcPSz/tt5cQUgIgFfv5sKgXIHGTZEO8Mh/YBxg8PbhxTkQK46tG7BxpWQ6/3fU3XUlKVQXXsH1XeaJh8239MOZYZ1MFpCDJCWBGza6r8ur0TgO+rQswsnStQEyZYwGI3cCBQWDwBbNod/HGFmtzsV7J7jgC5A93bABosc1s2FQGGZICWh+fZLddmu6ckKbeMExRX+69YXCA60eAR0IWtK6QPPc23CHu32oez0szlOcGvyL4Ibj4iM4wQiIiIiIiIiIqLmx4sSiVonbttEOx3TEloAPkdERLsezv1ELZ/Ndsx9cCJqZgxGIyIiIiIi2sUppYD73oc0CkargvVVkzFujiTNpgejYUOG/frhYxiK1sKphDb6j0zXLYOUbWsQWCb5DsPO3BgzvmWHogGAMqyX1wso1F1AHQ7fLA5c5quFgqMGKe1F3/XFm2U4ddsXTRuULqwqRKSiDPLsje4qjRwLdc4tUMPHOCrer5OzZlMaBaMle4uwKXo3x8NKsMk+K1fxtnVl0V9QQQajPfadgxeDRlltpsTcn/ShaNv1q1ppuTw3Sv8AT/5V8PyZCj8t0wdlnRgoGK1NM26ELqSnAEmx1kEuXy4Aas4+AZ6kW7HF217bRgdvHiA1wO/TgINPDONo3TluqMLybP8n7IelQHmVID7GPpzhk7nOvrgzGoXBSmEeFIAJYwz8tdrE+39bt5PWkoLR3nzAdZ32mmC0vLWZAKznpGVZ+vZiNaEemYWCt2fpn6u5dxoY2s33XCdrpjCv6dsG2sTp+weAvBL79US1PMb2f6re39v/GUq/zm6ZoVmXGKtwUF/g7H1VwHmtqeSE7sFVHDMe6vpnoNo53JmxkJZkHdya50lDqWGdmpYY6wuhtA5GC9yn3SFtYqz1eEoZjEZuBPpMBAA2r3EU3hnJ7M6FCvZuKaVw3FCFyb9YN/7TMuDkvYNrOxi641qPAXRrByzJ9F+32WJusm9LoWtK4MDzxoINRksPYa7zy78JbjwidO0RERERERERERFFnl3lotBd5X4S7Swt9/sgop2D70utF59bIqLWifM7ERERucNgNCIiIiIiIoIyDMihpwI/f7pjWZWKsSwb43HRsDgIuKmu0q6SbYXARuvwmlrqrBtcDIgilXrgQ8g9Z1muk6evh7rz9boFc74Pbee9BkFd8X+hbXNnMDQbp+nd8WdzBqM5cf3Hgh4TAp/MFWNW4oXs69ClJrtpHZYWQUwTytCEyDWB5G6CnLWn8wqGAXXVJKjTJrrqp39nZxfBp5oNr7Bv5y1w1U+iXTCaYR+MhvLgUoO2VQh+Wh5UVQDA6rVFMF96HPjwyYBl+2iC0ezMXOl73K0CDQCgT0egf5faYLRCyzIqKYTJAiGklMKQ3YBZq63X/5mTgoNveQl5T39uuT7BLEWClAMA5K7TgV/LoDxudhjC57ghCo9/57/NlFcD3y8FTtgrNP2oxttl5todfz56stIGo/Xu0DJOaJWyEuCDJ1zXS9PMPfnz5kNK2lhuE1d9qN9/jNeEepw4WV/n8wl1oWiALyBJZ10eMDhAhmTeNvv1LYlSgYO5/IK4XAR5GZbrVMOgLxfhX4GWNain9H1ajjOIkDK7x8cwWsa27ZbM/Tmoeur/PoE66IQm96/bfsuMROR72lmuS4jR18svDdynbq/LMHyholbKqwGvKfC00tcBhZiTYLScDcC6ZcDuA8M/np2gKVvKpPH6YLRTXzFhvtp8+6SmZsLwGL5jcavjiExNnrJdyNpuqe7HFmwwWtdU9yFsOqu3hKQZIiIiIiIiIiIiItohwOe3dr9aQiHC7wOJdjon56kTERFRM7M7FuFxClGLYPeZAj9viGB8bqh1YjAaERERERER+TQKMKlW1ldNxrg5knTyYdd/vwJ9/VNJpCgfcmy6fd1+w4ERh7kYEEWs/Y/Wr/vvlx1/SmkxsGZJ0/tL6QB1wR3Abr2BvQ6Cig0Q9NQS6MK+zLoTP9KTI+9DzvEv6U9MeTr7RqSYRRhdNhM9qzc0vTPT9AVWtbUOzgiWLP0HcuM4oLLcvuBJl0N17wvEtwEG7+/726X+nZ2VS/E2vMI+vSbLVT8J1tmYAIByFWdfOT+4ALvvlwBVNUFVBQB0/uoxIP8pR2X7VboPRluwCRARrMi23o5G9Kx3smOJJuEgKcLSCes5a1+FWaut79u0+YIxp4/HlrKDgWn+6zvU5DW4LQ9fAnX3W+EYpmv79wbSEq3Db0560UTVSwaiPPoTVQschOYAgEKjuWzDih1/pqconDZC4ZO5/o/vmfu0kJNkf7d44h3QhTLme9oBv00Fxl3QYLlpChZu0rdn9QqtqBYs0NSJiwaOaJQh06MdEO0Bqr3+5ZdnOwhGK3H3XnriXr75ISUhUPCXchTKpQ//ch9SplQLef1RRJDKCsj1NvvsOiMODUkoGuCbz3U2RVtvvAnzf0D7LofDagbJc5Dlqgs6UrAPki2rAtoE2GUiAuAsGA0A5v/eooPRwnUuVEKswp7pwGJNePHLv5m44uDQB2NbsQszS0+xDhjL3Gr9wGjbUkBXl8FoSvn2fYKRHpm5zkRERERERERERBR2kXd+ERERkTW+Z0U+PkdEREREREQUPAajERERERERkY/R8CrJKmWditM4GE1EgFkzIF+9CcQmQI09te7CdzPwL3HJ5FuhzrjO9/ef0yHfvA+UbQP++dG+4oCRUHe9AeUJ8upOiigqLgESEwdUVfivzN0EqaqEiokFtuaGpr/zboU6+cqQtBUxDM22UG877NrWC6BlbDOHl/yAa7a+FPqG5/wAHHZ6k5sRrxf4fDLkr2+AuT8HLK/u/wDq0FOa3G96CpAUC5RU2pdL9RY2uN3FZTBabJTvAn6rC/LLjQBBghsyICKuA3e+Whj4BJhuqcDGrdbrjiz5wXFfnbw56FG1HutjejiuAwC524Blmty3fttD60QE2KYZZGLkJgtccZDC1R9aPwdfLRA8fTqQh1RYnajUwdswGA3ffwg59GSoA44Nw0itSV4m5PmbgZ8/8y047HSoiU/Ck9oB44YovPuX9X2LmWBi3GDgiEEKbeOAqfMEuduAvXso3HaUQlGAvMNaRuNfH920CuL17thPeeFMhZIKwdeLfavbxgGPjFc4fGBkB1PJtq2Qtx4CPn0hqPq6YLQiTzJk5nSoRsFoVgF29ZVazH0rc6xDzgBg7+5AYmzDxzg6SqF3B18IWmMrcgSBftE50Bjr++AShTP3aZ4wFqKwm/pyUNXUPe+GbAjtk/TrsqOs02MT5n6NdsfsDcA/SSjfQdChLsxJKd8+mU5pJYPRyBnJXu+s3MaVAd6hIpvd1tbUnM59eykszrTu4coPBBcdIIiJCv+jpwtSNJTvOM5KpiZPWTf3eAygQxKQHA/H+6lxUcGHoboNYSMiIiIiIiIiIiIdBoIQUT3h+kUZol0St6dWi3MlEVHLZTeHc34nahlst1Vux0TUvHhFDhEREREREfmohoeIVSrasliMp9HFlP97FXLbycCfM4CfP4XceRrk0+d960xNSkUjsjUX8vW7vnZ+mxo4FG3gPlCv/AHVvZ+j9qmFOG2ift0XvoAsef7m0PR1xFmhaSeSGJqPeeoHoyXVNNNgmq5v1aqwtCv3nwfZsrnp7fzfRb7Xo5NQtKsfC0koGuC7oL2/de5HAylmw2CurjWZ7voRQbz12wAqVIBgtPISIN9dEBsAzFpt/QXJJaMVKl80sO4RA+seNfDFBP/X+qiyWRhcudhxXwrAlVvdB7x8+Ldgmeau7Xhelv4DlBZbF2qjSUSIAIah8NYF1oEJa/KAvG2CLSXWddO8+X7L5LaTIfN+C+UQLUl1FSR7PeSk3etC0QDgx48hZ+0JKS7AcUPsgyBmLAKunSK48G3BlwuA2WuAyb8Idr89cMBrLQONylZXAdnrdtxs30Zh+kQPtjxlYPF9BvKfMTBhTGR/PC9lJZAL93EfipbcHmjjS9FI8VqnfRR6UnzbSiObCy0K11NW5b9sRY6+/OOnWj/Gunl0zRb7/gEgT7MdNNYjDThpWEuOsCFqSL5+x12FpGSoH4ugUjuEbAztEt3XSTRLkZa90HKdk+3ZLugo0SYYLVCILRHg+ywE015zVnjjyvAOJszszpNqajDaVYfYN/BbRtPad8oqVBrwhZnpgtGyNcFourYMw7fffuQg5w9anOa4zok2caHdlxGe3EpERERERERERK0ZP/+iiMPXZOTic0MUMnz/JSIiIiIiImrVIvvKKyIiIiIiImo+Hk+Dm1UqxrJYdL1iUloMeflOvzLywZOQmhpAHAaKFOZD3pvkeKhq/ASopl45SxFHXXiXdp1MvhVSVgLM+rrp/dz0AlRyWpPbiTiGx3p5vYDCtjE1SPJuC1mXyfHA4K7AY6co/O/K0H7MtFfFgpC218C3HwRdVTasgHlsV+CHKY7rqNOvDbo/K8O6B57/2tc0DKpKr3EXVKY+egLxNdavlXIjLnADa5e66m9bhWBVrvW6IwcqREcpdE9TUErhxGEKU680MLoP0Ks9MKHDP/hy48kw3Jw0eOipuFE+Qop3a+Cy9dz4qb6PvbsriAjkitHWBZQCeu/pqr/mdvhA/Wur440m3pxpff871FinScnEIyCrrMNomkrWLYN59VjI4amQU/taFyopBKa9jiMGBddHjfNcNP9gNABYv8JvUVqSwsB0BY/RAvZjvnkXyNngupo6eQLUCz8BBx6H5GTr/ckioy1QkA0paZgGkhkgGK200v81uDxbv13u18v6cU5PsV5eUBp4HrELUtq9PdA1BThzH4VZtxqIi24BzzNRALJxJcwzBrh7b+81COq9+VCxDvYZXIjyKKQkuKuTYJah/YY5luucBKPpzqFWCki0nuIAAKUMRiMnvv/IedkNzZTuFSa2wWhNbHuvbgrjBuvXn/Gqi506B2T6WzBP7Qvz6E4wbxgH2b6/5LUJUmynmbuKyq2X69rybD/sHuPidwKaEowWatXOfj+BiIiIiIiIiIiIiJxgGFDLxeeOKIS4PREREUUeu/dnvncTtQh2x60hPKZtpm52HXzQqJWK2tkDICIiIiIioghhNAw10gWjxdQ/kvx9GlBmEZyTnwVsWun8A5WiPGDTKmdlew8GDjvdWVlqUVRMLKRbH2DjSsv1csYA+wZOuhyY+op9mTHjoU64NMgRRjilCSYz610E7q1Bj+pNWOJxlxJ0UeHbeDXrSqjLHgQ6dAW69gL6DIWKa3h19083GBj7VNMvOldi4piSb5vcjo68ejfQayDQZXeg5wAoQx/qJjXVwIp5QOEWoDAP8uhlzjtKSoF6+bcQjLih44YqvPaH/fya5i1ocDu9OtNVH0ZFKeIrtgLRbfzWlav4gPXl7++hRh7muL9Fm/XrhnX3X3Z8/3IcH7UQ2JoLueNUx/3AEwV173tQh4yHiGDRoT3Ru/cyVBmxztuwMKAL0LujgnzwhL7QyMOgUjs2qZ9w65IMtI0Diius15doQl7ae/O0bcqFI4Gvc6DapLgej1SWA0v/AXI2ArHxQEzd8yS3jXfWxnfvo825t2DCGIWXfg3fFz2GVRjshgxg1DFh67MppLoK2LTaF2K7+0C/wFmprIB88pz7hrv2Ao67GKp9F6hHPkO7lQI87v/YlBsJqFQx8K5ehQWJw9G3E9ChjcLmQvvnyOo1mJFtXfaEofp20pKslxeU2nYPAMgvsR7jJaMVXj2Xv0VDrYfU1AAr/tMHfup07Ab19r9hC7Lu2AYoLHNePkHKkJazGOjqvy7fQTCaqTmmNRSQZJP7VlrlcIC0S5OPn3VeOHMtpLQYKrFt+AYUBmWVgn/WAX+u1r/Hh2K6+OhSA20nWh+Lbi0D7p5m4pYjFdrENa0zmfU1ZNIVdQv++RFyzeGQD5dqPwLzGEByvILViZ3FFYCI+M2ZXs1hdW0w2m6p1u1ZaWow2n69gNlrmtZGrYrqRp8rEhERERERERER7Sp4USIRNcA5gShk+B4b+fgcERERERERURPwtFMiIiIiIiLyMTwNblYr6ysnY+oVkz+n69vLywRKix11LbO/c1QOANRrs6A8nsAFqWXa/2htMBq25tpWVQceB7EJRlM3vwiMu6AJg4twHl0wmrfub28NjiuZgSVx1sFovdoDx1b8gLVZvuSZZG8RDiv9GWcXfwRge6BYfU9/DTVi7I6bo/sAvTsAq7f4tz2gC/DBJQaGPxg4OO3oku/QyWv/fDeV3Hay7489hgCPfgHVqZt/maX/QG4/BSjQpO7YSU6Dev0vqM49mjhSf2P7A4mxQKkmpAoA0rz5DW6n12S56sOAiXiz3HJdhbJJAak15wdX/S3aZH3yS5s4oGdaw2Xy+zTIQxcC5Q4SjGodfBLQcTeoQ0+B2nM/AIBSCl1OGI9LZr6FF9tdEaABe8cP9QUZyDfvasu0hFBGpRT6dwbmrHNXr4NNMBoAYPqbwJk3uGpTfp8GufM0dwOxsn4FzKcm4vmJT+OlX5venI4B/7lNNqxAeGKBmkbWLoXcdy6wZrFvwdDRwP3vQ6V19q3PmA+5eF9Hbak3/gaWzIYs+xeqRz/gqHN2tAMAyTY5iq+mXIzbXhqMyu0BnhPHKqQEyF20mveWZ1vPH/266B/9donWy/MdBaNZL2+vCVsjaokka51vDl65wH3lo88JWygaAIzsqZCR4/yk2USzFFFSY7muqNw6kKg+3fm5CkBslC8gzbQos2aLYFTvSHwXoIiyxSYd2MrKBcBeLsMKd6JP5po4/01BpfUmuEMo5oykOIWXzlaY8IH1Rvt/MwTP/SR483wDJ+8dfH/y2r3+C7PWwZz1NQDrQFxDAW01+zjVXqCyxj+8zGs1sQDwbB96VxeZw/FNDEY7Zz+F2WtCc7FCRbX+sSAiIiIiIiIiIqIWguEmRPUEuz3Y1eM2RuQOt5nWi88tEVHLZTOH85iSqIXgdkxEkUNzxSwRERERERHtcuqFjXlhwKuss7Rjti+W6ipg7s/a5uSb9533/cHjjoqpz1ZCRcc4b5daHHX+7cFXHjEWGG8RbhSfCPXIZ1DHX9y6Q/WU5mMeqRfWU1ONG/OfwQFlf/oV2yO1Ct9dZ+Dpu4dj6qbTMHXTaXg761KcU/yRNthHrj8G5kt3QPJ9wWFRHoXXDl+P1KiKBuXOjv4NC9ftg6FzX8G/561ClxhNqsx2T+XcbLte3TwZxh+VwJ7725ZzZNVCyONX+i2WmhrIXacHF4oGQP1vQ1hC0QAgPkbhKOtsOwBA2zgg+uizGixLP/JIV30YMBEvFZbryg0HwWhZ6yAuvvDYUGC9fM90wDDqXoGydQvkvnPchaINHgXjoSkwJj6xIxStljrnFjyaeyf2Lft7x7IoqcYFhfqAMyvHb5sOydkIrF9hXSAqGhg1zlWbO8vg3dwHRXQYfZDtennxdojXa1umQfn87NCEotWa+gpw1gD8esKi0LXZiFUwGjZkhK2/YEhlBeTb9yHnDasLRQOABX9ATuwBc/JtkA+ecByKBqWAHv2hTroCxh2vQZ19U4NQNAC2QWfXd34SlWbd+/JzPwkemG4/bzQORhMRrMixLtuvk76dNF0wmv3bEwAgj8FotAuQJ64OLhRtyAFQLoMw3Tp2iLvyCWY5ks0iy3U1JlBeZV9fNysZhi/MKTHWev15b/LEDwqDjHk7ewSOrc8XnPla4FC0UDp9pEKUzdkP2yqAU18xMWdtcNunuXktfs1MxsNpt+CTNiejTNXt6HiX658bj+E7RtMpssij9mryxGvz0NNdBKM1Dl1z68JRoQt5rKgOWVNEREREREREREQRiN8NUHML9JrjazJi8SJyohDi9kREREREFHJ2x608piWiZmZ9lTsRERERERHteoy6YIpqpb9qsjYYDWsWA6XF+va+/zBEA/NRn62C6tQtpG1S5FFt2wE3PAt56lr3dQ0DuO4Z4NiLgGX/QLI3QPXfGxgwAqpD1zCMNsLoQt/qhxF5a5BqFuKH9cfgr4R9MT92KExloG/VShx8+zNo23F3AB0h594KvDfJWb8fPgn58Elg8i9A5loc9OhlWIJU/Jw4BvmeNAyv+A/7lc+BAiBPXYuhABYaKTi221T8neAfvpOV0R0dvHn6/mLjgUNOBgCos26A3HGqs3HamfMDpDAPKqV93bIFfwBbNgfVnHrjb6io8H7sdvxQhc//s/5CobgCMO54DXLiZUDmWmC33kjsvzdSr/Nia5mz9hUEcabFFfoAyusFACAmFqiq9C9UUQaUbQMS2zrqL8s6rwS7pTa6CP+3qUB1gASTRtQ5+qA91b4LEq+8D39MPhQ/JR6CfE8ahlXMR7fqTXgn+RyILnCwns412Rj5+mmQ122+4Dn+krC/JkLlglEKb8x092VVx7FHArHXAh8/qy0jlx0AvDLT0eMgJ4YhVDBrPQ58dF+cNWoWPty6V8ibV1Yn2UVQMJrkZUJuPBZYs0RfaMrT7k4V7NwDKtY+KDElwU2DgW1rNN3kFPtCTqz076wP8UhLVLA6MbKg1Be2ppS+ri4YTRe2RtTSSOYaYM4Pjsqqe98D4hN8+xs9BwDDxoT9/W54d+vtVydRSuEx9eGcheVAgibcDABMTThR7Syhm4MAoKxSkBAbukAhal1E9+Kyq7Ninja0OtJMmy/Nfv5TSoLCqN7A7yvtyze4V0AAACAASURBVO33iImZtxoY1dvdo3n9m4V4vsd3O27vXf4fvt54PNK8BfD++xuAOy3rGQpItgmLLS4HOjU6bAoUjNYhCYj2ANUOsoebGowWH6Pw6eUGTn3F/Wu2sYpmDMojIiIiIiIiIiKipuAFrkThZfeZe0v5NogoQjCUgYiIKPLYvj/zvZuIKHw4x1Lr1DKuxiMiIiIiIqLwqxf+UqVitMWifv0U5kvvA7O/05YJuWEHMxRtV3Li5UAQwWgAfEEmfYYCfYbueqcI6QKcpN6JVF7fVcgxqMbBZTNxcNnMuuoJdcFq6rDTIU6D0Wq7ueqQHX93xBacUfyptmyqWYhf1x+Omzs9ipdSL4NXRWFoxUK8knWlfSgaAPXEV1BtUn03DjzO1Ri1RIB5v8FcMBPI2QD0HAisW+a+nYQ2UNc9DdU39KFLjY0bEjgURA0cCQwcueN21xQ4DkZL8xYgQawLlxr10n867gZsWm3dSF6mi2A06/vSJaXubykrgTx5jaP26lOjjrEvcNpEGJNvxeGlPzdY3KdqFTJi+wZs/9htX8MI9Fxc/VjAdiLFAXsovHquwsQpgopqZ3U6tAHUhEcgG1cCs762LpQxD/jmXcioYyCTbwN++Mi3/NiLoK54CCo5DQAgs78Nwb3Qe2H2kagatxSfrUoLabsmLObgrbkw7zkL6txbofoMDWl/bsknz9uHogVj4D4BiyTF+sJAzBB9x5Zb3DC4bHOhvmzvDvp1aUnWy2tMX8hRW014iYhog9HaJ+1yex7UCokI5K4znFfouxdU98DvlaHUM80XDKQLDWoswSxDvOjTy7KKgE/mmvh5uaBTW4U7jlHYvX3d9qybvgwHm3xeCdDdJnSNdnFbc93XyZgX+nGEyfqCndPv1CsNpF0feII4cJKvzJi+wItnG+jfxX6jnrdB8PzGIQ2W/Rs/HC+mXo678x6BWVEOaLZ3j6HftwCAIos86kDBaIah0C0VWGN/+Ayg6cFoAJCg/5jQFafHF0RERERERERERK0PL0okap2CPE+AQU5EIdT0H/ehcOOcR0RE9fF9gahFYMAhEUUQzRWzREREREREtMvx1IUiVSv9VZPRHz7avKFoANRlDzRrf7RzKaWAkYe5q7TnfuEZTEtieKyXm966v7cHo1ny1OXnq16DgINPDNHArEWjBs/k3IScjG7YsLI35qwdhREV/+krdO4B9Vs51F6j68apFNSHi0MyHrnnLODzF4GZ04H3HwNmfuW4rvo0A+qd/6BmZEEdfW5IxhNIu0SFTprMsXGDrZenp1gvt9KvcgWSzFLLdWVGQt2NjjahlfnZjvvL1IQbpSf7/pfqKshloxy3V0u9NTdwGcOAuukFv+VXbX3ZUR/HlUy3L9C9L1R0iJIEmsklow0UP2dgL4eZpO2TAOXxQD36BdCuk7acfPQU5ORedaFoADD9TcjZgyEVviA++frd4AbdrhNw8EkBi7U1t2HKV92w5Qlgzm2CubdUY0jX4LqsL8G0SLQAgF8+h1x1KGRZ4NdiOEh1FaS6Cvjpk9A27ImCOvnKgMUMQyHZJgjErfJqoLjeQ62bO6I9vtelTrtE/bqsIv26bRW+8DQrdv0RtRTy+n3AygXOK6TvHrax6ERHKeze3nn5RLMMKV59iuLYJ7244RPB9IXAGzMFQ+83sTSz7qQNXbCjcnCNQ4XNrjcRlvztvs6GFZCqytCPJQzyNUGijcWG+GfcUhMV3jjf+UVIv2YAA+81MXedoLyq4T9vvQng03+tJ4Mv2/jCur2b12v78BhAmzj9GIotshu9mrnHU+/sjgFd9G3WFxeCxzgmRM8Tg9GIiIiIiIiIiKh140WhFGEYvhXB+NwQhQznulaMzy0RUcvFOZyIiIhCh8FoRERERERE5FMvVKlK6QNcYqSqOUazg3riKyiGXu16+g5zVVwdd3GYBtKCGJqPecx6CS5er3UZoEEwGgCou94CTrkqBAOzl2IWIb0mCx67X+476lyoV2dCWdxH1a0P1Kt/hnGEAZx6DVTnHlC9BkFF6UMlw+GhE60v+D98oPXy9BTnAQH9qlYiUROMVmLUS/+JSwDatrNuJHez4/50QURdtgej4adPgfUrHLcHAOrie6H20KTENXbAsX6LnASjpdXkYWzpL/aFdh/obAwRJsqjcN1hzl4zHdr4/ldKAXbhgBtXWs9DRfmQw1Mhn00Gfvnc/WBPuARqyjKoBz+CuuZxR1VSxyVg+PmJ2OvCZMyamY4b46ahZ2pwCTYxZiWGVC7SFygvgXz6fFBtB0v+/QXmBSMgh7aBHNoGyN0UusaHHQz1xJdQg/d3VFwX4his1OtM3DHVRHmVYHOh9ckTXZJ9oWw6XVP0oUarcvV959mEvDAYjXYWWfgnzKsOhXl0R5gX7wdZ+k9w7VSUAVOecV5hxKHNvu9Ta3h35/s0CVKGOKlAtOY4triiYVsllcAT39fNLbpzqG2mmB3KmvfQmVoY+etb95VME9i8JvSDCYP8EmcnOO7fK/R9XzDKF46WkhC4bK19HjaReHXDf8kTTSRe5UWH67149Bvr+zMvbi8IAG+1PvHLUIDHUEiKtV5fbJGv69UcHnvqzT39OjubC+NCMFVHa3LY3SrnvEhEREREREREREQUOgwDasH43BGFDrcnIiKiFoXHMUQtg922yu04gvG5odaJwWhERERERETkUz8YDfqrJpszGE29MhNq3yOarT+KHOpA/5AiWwefGJ6BtCSG5kpls14Ikdcm9KdRsIWKS4Bx7VNQj0/Th641E3XHa1CpHfXrB4wAzr65GUe0XVoXqAvvbP5+t7tglMIBvRsu698ZOGsfXTCas3bbeovQ3puHRLPMcn2pqpcw4IkC2qdblpMFMx31V1Ih2rCh9BQFqSyHvPuIo7YaON55YKJq3wXqsgf9lv+3Zh+08RZr6z2aexfipNK+7dEnOB5HpBnQJXDYQkwUkBxfd1udeUPQ/cmzLusqBXXnmzBumgwVnwilFNRpE4Fzb3XVTFxFISbNOxOrNgzDk6fYlz1lLy88quEXRvdveQDxUmFfcUHzBTjKmiWQ644CVtuEtQVj0L5Qv5bBeO57qBFjHVdzOve48eg3gnu/FKzZYr2+a4A+42MUuqVYJ45k5Oi/ENxQoG+TwWi0M8iWzZBbTgQW/gmUFAEZ8yCXHwjJDCI4afUioCrAXFYrPhHqwrvc9xEixw5xXjbBLIMCkOItdFzn5+V184CpmRJqwxWPGqRvp6wKyC0WLNokKCjlyQbUyMr52lXq2qf0x2AbM8I0oKarrBbMWSv4PUOwNi9w+bho4J7jQn+sqZTChQcYKHjGg5uOcB6k2FhZFVBeDeRb50XvsDmqK0yb0y4821e1jbdeX1TuPz9og9HqdTOgi/24asVFB/8Y1NpDfzjuSkVwOcRERERERERERERERK0LLyInCiFuT5GPzxER0a6Hcz8ROWM3W3AmIaJaUTt7AERERERERBQhPHWhSnFSiTOKPkaVikFVShdUD9gPVWtWoCpfH5QTcnsMAfrv3Tx9UeQZuI/jouqZb6ES24ZxMC2E7sJ5s94V1XbBaB7rj4nUfkcBb/4DmfoSMO31JgwweEoFvpBbnXMz5IPHm2E0AHoPBkYfD3XaNVBtUpunTwseQ+GHGwy8M0vw91qgVwfg2rEKbeKsH69AQUG1elavhwKQaFqnlZUaifUGEQUMGAGsWexfcO5PjvrLyNGv67X5d8hNZwDFNmlEVoYeCNWuk6sq6txbgH7DfOFcG3yBE0MqF+OftaPwaurFWB7TH7L9tbhb9WacVvwpDin73b7RpBTggHHuxh5B+jl4CAd0briNquQ0YHom5FjrwLwmOf5iqPZdISvnA+06Qo09HWrYQX7F1FHnQD56Cqipdtf+ptW4dt7VuD/uBRRbZAPFmJV4cfpQ3H7olXhjdgxKjUSM3zYVx5R8G7jtgmyIiKP5rClkxTzIJfuFvuHjLoK67hkojyaE00bXFIVwfDX5xPf6Nu3C2EQEePth9Nk4EhuS/APe7ILRlmdbr0tLBJITwvvcElmRD54ASv0DPOX0AcDvFe7mHLtQtN6Doc65BfL390BqB6ijzoHqZZMIFmaj+zibV+LMcnjg2xdO8RZhS5SzZJ8NBUDeNkH7Nkp7TULtIztuiMK3S6wL3fa5ibnrgYpqICEGePYMhYsP5G9W0fb3og0rrFcefibUKVdBPnsB2GwRcrghMoPRZq4UnP6qiayiwGXH9geGdVc4cx+FYd3D+/456WSFJZmCbywOV0JleUxfDK3UB9Ia2+9icjyQaZHRaLXf6SQYrXs7Z3NhQmzAIgGlpyjs0xOYs65p7VS43D0nIiIiIiIiIiJqPXgpJxHVxzmBKGQYNNh68bklImqlOL8TtQh2+2LcTyOiZsZgNCIiIiIiIvIx6oIuOnlz8X7mhb4b7UfDuPZHmM+9Cfz3fLMNR936MpQu6IlaPWUYkPPvAN55OHDh4WPCPp4WwdCE1Zjeur9r3AejAYDqvSfUTZOBmyZDaqohhyQFOcjwUUnJkM49gOz14etjwsNQZ90YtvaDERetcPnBCpcfHLhsusNwoncyLwYAJIp1EGZJo2A0td9RkBlv+xfM2QCpqYaKirbtTxdCFBsl2O2pM4FtLkPR0rpATXzSXZ3t1D6HQ33gCzWQqa9AnpqIParX4LHcO4Nr79aXoZKSg6obCdrGq/9n777Do6jzP4C/P7ObnpCEJAQCoZPQixQFBRQsqKBiwQYKnqJYz3L208N6ep5n1xPFdnbsXRF7+WEFESlKlR4ggZC6u9/fH5u22ZnZmc3uZnfzfj0PD9n5lvkkszM7u5l5Bx0ygO17jfvohVlIZg7UtIuBl+4LaT0ydQ6k9yAEis+QrkXAnNugHrrGPBBSz9vz8d0RuRi4/lrUSqJP05Obz0b7vRvQ/o2rYfs7c9UCZTuBrFy7I02pki3AN+95A432Pzw8oWidukG78uHgh1sMZQylrjkmz5Kv3oaafxN6dbwPevGNWzfuBKAfnrRyq/6UxdmVUC8+DuR0BMYeC0lKtl0zkV1KKeCVh4w7vPs0cPSZ1iesrTFskr89BBkwCnLoNBsVhk9hNpCSAFQGCNhJbRLqne3ZbWsdf3nKgzcudMBjcOpU/1b17IMEFz2v3+nL3xu/rqgBZj+jMLaPQlE+gxTbvB2bgMp9uk1ywhzvF4VFusFoasOqgOdCkVZZo3DqPGuhaM+fIzh5ZOQ+6xERvHmhhiPu8WDRivCsY2VSMQZV/2rYXh9m1s7g9GBPpf8yo2A0rcnGtxp8nR6CYDQAeGSG9+e4o+69QXYq8OGlGoZ0AY570IN3LYTPVdUqIOqewURERERERERERCHSZm4KbSvfZywItC24rcIv2J+x2ThuNyJ7uM8QERFFnTbz/piIiIgigXeYExEREREREQBAxOAtoqq7G3OPzWCalhg4GtJ3eOTWR1FJJs8M3GnkRIjwploAjekMzXma3FFtFhBkEozWlDgTIE98F7jfv96AzH0WyMg27tSxG+Sy+yBzLATgWTFpemjm0XP8eVEXimaXlRvn0zzlDTf1p3n0wxr2NQtGQ2Ef/cmUAnZtC7jO33foL++dvBuOvTsDjm+QlAK5+QXIUz9AioZaH2fk0JOBpJSgh8vtCyAHT215Ha2sQ4Z5+35d9ZfLcbMtH1cs6d4P6DXQcneZdjHk6Z8gVz9qe1W9PrgTu1fm45lNMzF1z+v417arsH51b0zb+4rtuXzsNEjVCpL6+QuomcOh7pwDdc+lUKcOCOn89WR+4GO+GauhHaE0ro/xuYH64i0AQK67RLd95xbj8KTVBkGOxctegnrgSqi5Z0DNGQdVstlGtURB2rbBtFn9czbUXhthYGbBaANGWZ8nAjRNUNwxcL+mIa8FtVtsraM+1MzoGq36o0xSgiAnTb9Pc0oB87/iRV8EYONq47auRb7/+41dFfp6WuitpQqbSq31zU2P/Pt3hyb46FIN/TuFZ/5ViX3gFoOgcjSGmRkFo5XpBaMZHCocTd72Wz3HSgtRMNrQQsHyuRpePlfD8+cIVt2iYXg3gdMheOsiDQsv0/DfGYIlN2goNPgYoCpAoCURERERERERERFFCd7EThRe3MeIQoj7ExERUWzhazdRbDDZVyP4nlbx/bM9/HlRnGIwGhEREREREXk5DG7i9Li9/++0dyN5i6SkRm5dFL06FAbuk5kb/jpihWa0DzcJRjMLxzAar0N6D4Zc+5h+Y+eekMe+gRwwCTLhRMhbm4znOeNqyNRzQxY4JjOuAo46E6gPyzM6rgUz98V3h2yu1lLcsfGmfCNDq5Y0fJ1uNRgtr8B4wh2Bw4F26a8GhVvthTHJvR9ADp4KycyxNc5wvowsyF1vBe449hhg0JjGx0kpkL89BDloSkjqaG256ebt+3XTf1JJYR/IDU8B6ZktL6LnQMgdr9kOwpSuRZCjzwQOP832KpNVNU7d8xJe3nQaLt11Pzq7LAZdmT3/QngupZSCuuhQoMxGeKAJufEZoH2+78L2HSH3L4S0cBt2bR/5AJTD+pk0rvgBAJDrMghGqzD+tYlR6EvPmjWND1YvgVrwYKASiVpu+5+B+yx4yPp8RgG66a2QbmhBcX7gY0uqo/F7KnDZOwbvrgD2VSt4DK4RaHpOlWEQdqRn4XJedEAAdhskA6dnQeqCpcUoGG3Dqqi72Of9Zdb75gQ4twwXEcGXV2mYMjj0c69ILILH5LKL+jCzTIPM5T1V/svcHv9lTecCgHYpQGpi4PrSQxSMBgA56YIThgtOHqkhp0nInYhgQl/BOWM1DOoiSE7QH89gNCIiIiIiIiIiarOi7HNdImptPCYQhQxfY6MftxERURvEYz9RzDM9h+M+TkSR5WztAoiIiIiIiChKGIUiueuC0azceB8qu7ZHbl0UtUTToHoPBn5fatwpRAFI8UBE0/94WXnvqFY11VBXH68/WNMgmr38fDlyBjDhRG/Qz+4dAARolw106e0TXiQOB/DIF1DnjfWdoFtfYNJ0W+sMWFNiEuSaR6Eu+hfw5+9A1yKoYwqB6sqWzfvE997vI8ZlJAsGFAC/GGfVoW/1qoav06wGo2VkA4nJQI3OHf0lgcOkSiv0l2e7TYL8miveD+g/ynp/i2ToWOCBj6EunGjcZ/IsyJijoEq2ALu3A936QhJDmD7QysyC0USAwZ1N2iecCIw7Fli3wn8/dCZAffQ88OK9puuXx/8PUjTURsU6c8y8FurD51o0h6X1/OVG4PQroCZk6HcIwbmUcrsBpaDuuaTFc/k4+Hjv9tqwEti3F0jLALoW235t0FOcH7hPKB07BEhNMglMqjue57r1Q+VKVDsopXSD+IyC0bq4mh1YP3sdOO9WS/USBW3n1oBd1KKXIbOuszZfbY3+8gQLqTutoLhj4D5pXbsCmzKB8jIUWA24bGLVNuPLN5oeIqwEE9X7cYPtMigelemHcyKrSfC3UTDanl3A2uVAzwGhrytISzZav9ApUOhuOGWlCt640IFtexQ2lwJODahoduhzK+CgOwxSyQysSiqC20IwWkaKQO+oskfn7aqVYDQRQecsYHWAj9BCGYxmlWEwmkEGJxERERERERERUXzgTaEUZRhEEwFB/qE4Zfa7iMj/8Tmi2Gbvd3sUS/g6RkQUu0yO4XyfQkQ28JBBRACD0YiIiIiIiKieUfCFxw3lcgGb1xqP7TkAGDQGeGNeaGqp0g/jobZHZl0Pdd004/bMXMO2NscouKs+3PCHRSZjg/uISJJSgIKe3n9m/QaMAu56C+rxm4DSHcCA/SHn3gwJU9CGpGcCfYcDAJROsI1lAw+AzL4J0ntQiCprfYuv1ZBygfHFQMU1Kxu+NgxGk9TGBw4NIgKVVwBsWuPfeYdJClud0gr931ZkucsCjgUATJoOufjfuiFGoSBDDgJueh7qhlP9GzNzgOETvP1yOwG5ncJSQ2tqn64f4AAAHTKA9GTzn7s4EwCjfahoKNBzINTt5+iPvfDOFoeiAYAU9gEe/ATqiilAZXmL5zM0ciIkIRGq3wjgt+/9mtV3CyGTZ9maUlVXQj1zB/DU7S2rrWM3YMRE4O35/m3DxkOcda8D3fu1bD06euZ5QzyMAj5C7eSRAY4FrloAQI5BMNpOLRuendvhyPVNdKuuVSgxePp0qt3iu+DP3w3D1doipZT3fMTT5J/b4Gufx566fzbH1H+tPLbHqIb12lhP0zblMag1wPfhU6vRHDqPA1n3m/UNFWPBaH0tBKPlZTogj34Fdf/fULBmj+11/LpZwWNw7NKa7N5GAUB6RIB91QppZgGOFP/27NJf3jQYrfdg7+c0ek/Cr96JqmC0Uhs51DlpgfuEW347QX474/Ytd2m49PHdWPRLFcq0TABA99r16OTagk/TDvbrvzGhEHscxhPWHy8yU/Tb91T6n2sbBqM1O3T0yA0cjJYWTcFotZGtg4iIiIiIiIiIiCi+8e7g2MVtRxQyTEogIiKKMXztJooJZufZETwH5xHDLv7EKD4xGI2IiIiIiIi8NINQJY8b2La+IUSiOXnie0jvQVAeD9QXbwK7trW4FDnu3BbPQXFi7DHm7TkWEhHaCjEIN1QeKKWgnr3LeGyQwWh2yP6HQ/Y/POzr8Vvv6X+Denyu/XGzb4bMuDIMFbWupATBzDGCJ7/W/8C7uGZVw9dGwWjlWnrjg/rnTq5+MJpatzzg3zEtrdBfnukxDkaTO16DjDkqwMyhI4ccD1z9KNQ/Z/suv/BOSFJyxOpoDbnpxm1ZBuEOVokIcNQZwMiJUGcMA8qbbPMxRwHTLm7ZCpqua/AYyIf+QVie+/8GvHRfaFbSra/3/y69dYPRsGgB1LWP23rOqHk3Ai/eG3xN/UdBHv4MUheA68lsDzR9PUhMhpx5TfDzW5DoFPTKA1a1/BTRkmkjAhx1KrzpZrkGwWgecaD09zXIaRaMtsUkq7Gza7P/wuWLoYqGAauXAOWlOoFfBgFYhoFYjX2VYfhW08dG4V4mywPV4bNeC7XXP6bY4DIIRnPaSP2KoL6djIM76xVkCaSwD+TO19FthQLutpfQ+NFy4zU0zT00Oz40p5R33uOG2SqF4owqLdFvyMxp+FIysqEGHQgs+cJ//KqfAp5jR9LeKmv9UhOBlMRoqtyX2rcH+H0pOtRU4X+vHu3XviahO4p6L9cduzKxyHBeR91b9XYGp4B7dH5+hsFozd7298kXfLjc/FiY3gpBjAxGIyIiIiIiIiIiIiIywSAnohDi/kRERBR1eL5LRBZFSf4aEUU5BqMRERERERGRl8MgGM3tBv783Xhcl14AANE0qCNn+IZtBKsVwpMoOokI8OoaqON76nc44IjIFhTNNINgtI2roU4baL4fG4WqxYMxRwF6wWjHzwFefdh4XP+R4auplZ05Wj8YLUv2YcKsoyFpx0P9czbSlH4wWrWWDBcccMLdGIyWlae/sjceg+pQCMy4yrs/N6O2bUTpir2AVuxfj7tUd0qZvxjSZ4jBdxc+cvSZQGEfqHefAiCQo8+EDBod8ToizTQYLTU065C8zsCzv0C9cA9QWgLpPxKYfJbucybU5MI7oX5fCvz4acsmyu4ASc/0zlnYx/iSu49f8obBWaB2bwcWPNCisuT+hQ2haAAg594C9BkK9c17QLv2kCNOhxSHP52nb8fIBKP9dpMGTQvwvNm2AQCQYxCMBgA71mxEzgG++/cm/UMSAKDAtcVvmTpvnHkdRNGm1iAYLSExsnVY1L9T4D4FWY1fH9jb/jre+UWhr0EOsxZkMBoAvLdM4bhh0RsORRFQZvAalJnr+3joWN1gNHz6KtS2jZD8wtDXFoTyamv9ctLCW0dLqFcegnrgSsM/CgAA3Wo3IMlThWrNP+FseVI/w3H1x4t2BqHCZZX+y9wGJ5PNg9GK8/X7NZXWCofxZIOrUBiMRkREREREREREbRfv5KTWwOdd9OK2IQoZpiXEgGC3EbctEVF84vGdKCZESWIZT/eJCADi+K5XIiIiIiIissUoGEl5gJKt+m05nSDJjakoMv1KoN+IlpVx1g2QHv1bNAfFF8nrDLniAaBZQI5c9C9Ihy6tVFUUMgpG27PLPBQNACrLQ1+PXTOv019+wvktm7fPEGD6lb7LRk70hgMV76c/Jj0LGHJQy9YbxcYXCy6Z6Ls/JTqBh89OR9q02UBiEgCgnXuv4RyljrqkkYZgtBzDvmrejcB3C/2Xr/sN6sTeKHUl6Y7L8vinjMgHO1slFK1h/YPHQLv6v9CufqRNhKIBQF6GcVuogtEAQNrnQzv/dmjXzoMcNxvijMzf9BARyN3vAgdN9m/MyIbcvxAYe0zgiXoPavy6xwDDbuqLNy3Xpl5+wBtQG4yeAyFvbIAk+u5fIgKZeBK06+dDu/iuiISiAUBRfvABQCt/H4Cpe14P2O+2qYLijubrUV++1fB1B9cOw35r1vkff9bs0P/NapqnHO08ewLWR9Ra1MqfoKwcS1wGwWiOhNAWFCKJTsG0Eeb7fF6TcM9Ep+CaI+0di3btA77+Q7+tJdmdv27mlRptXlmJ/vJM33Nq6eYfHlxP/WN6KCsKmsutLIddmQXutia17Fuoey41DUUDAAc86FW7Rrdtk7Oz8bi6t+qZBsFoe6r8l7k95nPV69cp8MEo3T/HLexSDMLYGIxGRERERERERERxrc3cqdlWvs8Y0Gaec/GI244odLg/ERERRR++PhMREVHoRObuMiIiIiIiIop+Dof+co8b2LNTvy0r1+ehpGcCD34CLP4QWLcCyC8E/vwD6vG5usPlqkeAw08Dfv4c2LQGGHwgpNfAlnwXFKfk2HOAYeOBHz/1hvUNGw/p3q+1y4oumsE+HCNk3LFQT93md9GejD+uZfOKQM69GeqQE4BfvwW69AaGHewNXXpwEdShHzoXrAAAIABJREFUWf5jpl0EcUZnCEio/OdkDaeMVPhmjUJaEnBoP0GP3Lqb6uuSPnLdBoENALY78pDr3tkYjJaZa9gXANSbj0NGHdb4+N2noW4/BwBQ6sjUHZPlLvV5LOffDkmN0jSFOFaQKTD6BXVSnHy6LA4HcNsC4IdPgNU/A7W1QF4BMOowSE5HoGpfwEAzOaJJOMnoScYdf/4cSilIgEQd9e7TwDN32Pk2vCZNh4w9Btj/cEiSQfpGK+jbMfixvWrX4oVN0/Hy3hMwvfNTfu2DuwBPn6VhcJfAwSDqmTsbvk5VlehcuwmbEvyDTFZuqMJRzZat3q4/Z5+aP9CCfCSisFNnHwCkZkANPAAy5CBg8IFAv5GQpGZJObUGwWgJBuk2UeCywwQvfW98EVX3HN+985bjBC98p7C22SlOprsUtZKACi3N8rqbztyng/ExQs9vW2DptYDi2C79J4w0C0ZDV+NgNCz7Fmr7n60eFl5ebb1vThSeyqvaGqg54y33z3PtAHRynbc78wzHaHW7ertk/fPqskr/MVaD0Q7o6T0nr3YZrh5prXAYT07Q/16rTOokIiIiIiIiIiKiaMKb2ImsCXJfYagdUQhxfyIiIoopPBcmihEm+2oE92MeMuziD4ziU5zcukZEREREREQtZhSq5HZDle3Sb2t+0y4ASUgEDpzs/QdArfgB0AtG69ILOHqm92bwJmE5REakaxHQtai1y4hemha4TxSTPkOA65+AuutCoLIcSEmDzLkdMsz6Teqm8xcNBYqG+i5LSgH++yXUbWcD61d4A8EmzQBO/1tI1hnt9u8p2L+nTiCHeJ9LeSbBaDucuUANGoLRpF1784/QP3sN7829Hy9lnYzqxAyc/frTOBiAB4IyTT8YLdNT5rug1yCzNVCYFPhnBzaoMMjQiUUiAoyY4P3X3P5HQObcBjXvRsBV69vmcACnXQEcdkrjXEkpwHm3Qj1ynf9c5WXAzq1AbifDWtTOrQ3Bgba+h2vmQY46w/a4SCjuaBywZ+bVjdMAAA54cMqel5HqqcSsgkdR5siCU9Xi7z2/x/VXH2gpXEiVlgDLF/vWVbNSNxht1a5EqNISSJMQ4FWbagD4h2b2rvnd5ndF1Aoq9gKLP4Ja/JH3cUIiVN8RwOAxkMEHAoPG+B/f6kVxMNqoHoJ7TxFc8oL/8UUEGFrYfJngo0s1zHzCgy/rdt2j976LeVvm4LxOD+DNjCmW153c5HAw60DBta9ZP8btrgC27wXy21keQnFEeTzApj/0G3OanR90K/bug0bBhZvXAq0cjLa3ynrfgqzoCQNUbjfwxqNQ//mrrXG5bv0/HLDdYRyMVh9mlp2q315WCbjcCk5H48/HMBit2Y8wLUkwoS/w3jLD1SM92bgtXJINcsarDV5qiIiIiIiIiIiIiIjaFrPfLfImZiJbmJQQA7iNiIjaHh77iYiIKHQYjEZEREREREReRsFoHjewR//GT7RrH3Ba6TscavCBwNKvfJefd5ulEAsisshoH44hcvipwIQTgY2rgS69vUGL4V5n/5HAkz8A2zcCSSmQnI5hX2fUqwvZS1bVyHDvwV6Hf2pHiaMuLKguGA2ZuX59mno4ezYu2nQ+sMn7+IVuH2D+5nMwqfxDKNEP9ctyNwtG697P+vdAIWMWjLavOnJ1tCYRAU67HDhuNlC6A8gpAPbuArb9CfToD0lN9x909ExALxgNADasNA9GO66b/RrPuSlqQ9EAoG+Qh9bxFZ/7PD6m/G1sW1WIlYlF6FG7DqkFkyFykLXJVnzvX1f1KixK8w/DW+Ps5g1RG3NUw7J168sA+B/retcYhNtQZDgc3nMgzeF9/dKaPG7a5jBrc3hDQX2WN3/c7Gu/cZp5P80BadomEnhuv6813++xSR/1378Dy76x/nOrrQF++Rr45WuoZ+/y1pNmkNLljN5gNAC4aIKGapcHVy7wvZjqmMFA1xz/95s98wSf/U3D+p2A470n0PnROQCAgdW/2gpGS0tq/Pqk/uW49rU0W3V/ulLh5JF8P9wmbd8IVFfqt3Ur9nkoyalQx88BXrxXv//OrSEuzr5yG+eDRfnhq8MOpZQ3HPvD56wP6tQd8szPyH1sL7DEv3m7s4PhUK1uV8/NMKrHG5iY16TdMBhN563TlCGC95YZX1CanmTYFDZJBlehVDEYjYiIiIiIiIiI4hqDjija8HkXvbhtiEKH+1PcYugdEVGc4vGdKCaYnotFbj/mEYOIAAajERERERERUT3DYDQPsGeXflu7bEtTyx2vQT1wFbDkc8CZCDn9CmDcsUEWSkS6NP1wqVgjzgSgR/8Ir9MJFPSI6DqjWpOgsjx3iW4w2g5HnrdrXTDaW3uKcX2Pxdjm7IAJ+z7FzTv+gZ616wAA1ZKIW3Ov9pvjrIJ5pmV0rd3Y+KCwD5DX2e53QiGQkWwc2lJRE8FCooCkZgCpdYkVSQVAboFx36xcqMwcoEwnXHbdb8B+B+uOU//3gf3Cjp4JzLjS/rgIykkX5KQBO/dZHzO88kdkevb4LXfCjQE1v3kflGy2PuH6lX6Lurj+1O2605ELbPjSJxht8/YqIMG/b/fa9dZrALzPoeYhXk2DrwIFZPkEeBmEcenO0SxUy2CM+I0xWY/uHJpBrRbHaHXjLISGSZyc+4TEna9BHdWCcFelgPIy/bYEnSd+lLnsUEFqInDfxwoC4PABgjtOMH79EhF0zwXU9FlQ678EPngWRdWrba0zrUleXM/vnsFpZZl4LvNUy+NPnacw8wk3HJo36MjZ/H8H4BDfr50O3z664xraxGecZnmcfw316/WvQcznNBwXoJYm37umxWF4nM7rUYOuRX6LZM7tUEbBaBtWhaio4O2tst63OL91t6da9TPUvZf5BfebysgG9hsPueRuSFIKcrskAUv8L7faZhKMVh9mlquTpVuvpNw3GM1jcEWX9t/roKpHQA45oWHZ5MGC8581vgSso0HuZTglG7x0VNXyUjUiIiIiIiIiIiKi0OFnrq0vyN99MOyHKIS4PxEREUUdnu8SxT7ux0QURRiMRkRERERERF5GwQIeN1BmFIyWY2lqSc+EXP1IkIURkSVG4YZW9Bkaujoo9knjRXu57hKsQU+/Ljucud4vNAc+Wq5w3MJhQLJ30YuZ07A8qR8Wrz0QCXBhWdIAbHXaC2vJdu9CB/f2unVokLP/AZE4DMaIEUlOoNrlv3z2OG4TU12LgV++9lusvlsIOX6O/3JXLdQVx9hbx/HnQbvUICwlyvTtCHz1h//y/HbASSMEDyxq/AVqgqrB30tuDTypjWA0tdE/PCbXpRNcB6DEmQO1YSWqaxV+3ghkLfsIW5zjdft2cm0Bpp4LvPbfgDXIOTdBzrjKcs1ElqVkBO4TLGdi4D6tTNME5x8sOP9ge+NEBHL9fHgA9P/sV1tj05MaXwPVogU4vSzVVjAaoP/aGjqRuCgl/OsQCSYUzk64G+DUpKGv1qIwOrP1SuPXvwJa2gQkeaoxtHop2nn2er/Z9h0haf4JVuJwQA0bD/z0mV+bmn8TZNZ1If+5l1UobCoFeuYByQnm53vl1dbn7dephYW1gNq5Feov+1sfcMpfoV1wh99io3CzCi3NcCpNeQA4kGPcBSXlvo/dHv1+jpKNUDfcDdz9DmTkoQCALtmCYYXATxv9+588QlolYNA4GC2ydRARERERERERERERRSez37PxWhwiW5TBL9aIiIgoSjFsiSjmRTA0jflsNvEHRnGKwWhERERERETkZRSq5PEAe/RDIySzfRgLIiJbxCDc0Ir99MNeqI1qEkCW5yrR7bLDkef9wuHAvQv9Ly76JXkQns88GWeUPYuViUW2SyiuXuW9zO+oMyGTpkOGjbM9B4XOpYcJ/vme/y9JJg/mxZimBh6gG4yG7xdBuVwQZ7OP5xctCDilXPc41G/fA3t2QcYdC4yfGqJiw+/4/QRf/eH/PLp7muCUkYIxPYG3PliLdks/wvSyZzG6cnHgSUs2QykVMDhRVVcCbzzmtzzXbRCM5sjBE78X4qK/euoCPCYaXnvcec7lkCljgcEHQd16FuAySfxITjGtkyhY4nSG73Ihp0G6TRyRax/DsKIHgYXWx6QlNXnwy9coTuga8rrIe42GSwGusF7LHukQuYlA14kAAKeqxcW7HsAd268z/3wl1zhRTP2xDNJrYEiq9HgU7vhA4e+vK3gU0CkT+M/JgmkjjN9r7iw3bPIxuEsrB6Od0MtWf71QNACm4WaGc339NjDxWKQkCtKSgH06YXJ+wWgGT0uHcgMA1PxbGoLRAOCsgwQXPe8/6KyDWud83SgYrZLBaERERERERERE1FbxpkRqDXzeRS8GORGFEI91UY+vR0REbZDJsZ+vC0SxwWxfDeF+bLqakK2FiGJdC+6YJSIiIiIiorjiMApGcwO7tuu3ZeaGrx4issdoHw4kMRly5jWhrYVim9b4kaFRaNBuR5b3C4cTH6/Qn+asgnk4rsvLuD33StslDKxeDjnrBmjXPMpQtChw2aGCkd19l911kqCwPYPRzMiBR+s3VFUA29b7LVafv24+4WmXQyZNh3bpPdBufBpyyAkQLXY+4j97rGBCX99lV07yhqKJCE4ZpeF/B/2MB7deYi0UDQBqqoG9uwP3e+Uh3cXtDY5xFVoazsFVdaFo5grG7A8RgRw6Ddon5ZBPK4CUdP3OwxhESmHUpbdxW5+hwAGTgPRM+/O2zw++phghmgbHtIswP+Nhy2PS64LRVF0YYtfajWjnLgtHeRTHXJKAu3MuxZOZZwDJJolbnY1DvdTTt4esnreWAte95g1FA4AtZcApjyqsLTG+xGjhb4EvP8pNB+45WQsYZBpqylULtfAleGaNBNwua4Oy8iDzdIJt6+Rl2P8etGdua/jaKFitpNz35+g2uB/KAW8wGpZ9A7VrW8Pyc8cJjhjg23fOwYLD+kdXMJqVcysiIiIiIiIiIqLY1UZu1+RN7FGE2yJ2cdsRhQxfl+IYty0RERFRW8czQiICAGdrF0BERERERERRQjMIVaquBPbt0W/L7RS+eojIHgkuHEfe2QJJTg1xMRTTmgQWZLn1A4d2O7IBAEsq8lFtkjHwdoZBMFQA08ueBVKODWoshV5uhuDTKzQsWgGsLVE4uFgwsDND0QIqGmbctmGVT8iJqq4CPjMJRjv0ZGhzbjNujwEZyYL3Ltaw8Ddg9XaFA3sL9usK35CUhET7E6/7DRh8oGkX9cI9usuNwh+tcigXOrT3rVkcDqjDTwHeeMy3c7dioPfgFq2PyNT4qcCz//JfnpIOefgzSFIylNsNrP0VWPIl1NKvgCVfATu3mE4rY6eEqeDoc+bEdrjklT3Y62gXsG9aXTAatm0AAGhQGF/xBd7KmBzGCilevZExBbNSNhi2y4QToZ68Vb9x0QKofiMhp/y1xXU8841+Glevaz1w/9c/2MzjUXh7qf6lR8cOAY4ZKkh0AhP7CjpmRjgUrbQE6oJDvOdcFsnFdwGHngzJ7mDYpyCIfEntj6UNX+emAxt2+fcpKfd9bBiMppo0fPUOMOUsAIDTIXj3Yg0fLgc27lIYUCAY3av1zteTDa5CYTAaERERERERERFRrOBtp0ThxX2MKHS4PxEREUUd0+BSvnYTxQaTfZXhxFGM24biE4PRiIiIiIiIyMsoGM0oFA0AcjuHpxYiss9oHzYz5SyGopG/JiF7WZ4y3S6lmjcR4JSlh4V89R1c23BQ5TdA8mkhn5uCl5IoOHowADAQzSpJSYPqUAhs3+jfuHE11AGTgPeegXr/f8BPn5nPNfW8MFUZWQlOwZGDgCONnkcJSfrLTaiv34WYBKOpr98Fdm/XbWtpMFrHdDc0zf97kTm3Q23fBHzznndBYR/I7a/4BcoQhZL85QaozWuAT17xXX7Xm5CkZO/XDoc3oK/3YMgJ50MpBWxZCyz5CmrJV8DSL4GNq70Dk1Igs66H7H9EpL+V1jP6SJz7+GO4K+eygF3T6w9X5Y3vl5/efBayi/WPN4W1G7ExoTAUVVIc2pTQGUhJM2yXHv2h+gwFVv+s264evAoYPQnSra/P8l37FO76UOHJrxS27gFGdQd65Aqm7ucNqHpnKVBS7r0QprC94NWfjGt0nOvBzDGCg4uBGQcIRAQ/bAC26L9dwOkHaDhxeOu87qmaaqgp9j6vkv+8BxkxIWC/ztn269HggVIKIoLcdP0+VoPRNDQ2qK/ehtQFowHesNkjBgDRcL6enKC/nMFoRERERERERERERBRfgrzhmDeRE4UQ9yciIiIionjFt89EBDAYjYiIiIiIiOppWuA+zeV2Cn0dRBQcu/twSjrkzGvCUwvFtibPpWz3bt0uZY5MVEgK1lZkhHTVyZ5K/LJmuPeB0+BueqJY0rWPbjCa2rAKuPcy4JWHAs/RZwgwaHQYiotCifaD0bDs/wyb1IfPQ90807A9270bIsH/0rSgvX4oqaS1g9z5OtTOrUB5KdC1mKFoFHaSkAi56TmoHZuATWuA9vlAx24Qk/1KRICCnkBBT8iRMwAAavd2oLQEyOkIadc+UuVHBcnOwzEd1uMud+C+afU/1urKhmUZnnI89+cMnNblGZ++Q6qWYvHaMdju7IDVCb3gnjQDnkkz4fYALo83AMnlBtxKNfm67v8mfXz6e0zamo33+MyldPuGar0ugzAnMlcpyUCycTAaAMg/noY6fbBhu5o+BPi8quH1pqxCYchcDzaVNvZZvA5YvE7hxe91ZwhY55NfKzz5NfB/a4EHTxO8uUR/TKITdQFdkaeUgrrEXqCjPPI5ZMD+lvpmpwJJTqDaZXFu5fHGlFXsBdLaITddoPez3mk5GK3JAeq7j6Eq90FMQvVai2EwmsWfGxERERERERERUfzhXZzUGvi8i17cNkQhw6SEGBDsNuK2JSKKXWbHcB7fiWKC6Xk292MiiiwGoxEREREREZGX3VCl5FQgPTM8tRCRfZp+OIuuIWMhs+dC8ruGrx6KXU3Ce7LcZbpddjuy8WnqONQqG887E8meSuxfuRj3brscOe5dIZmTKCoU9gG+X+S//I15lqeQe95vO6Faicn2x2xcpbtYuVxQD5kHgDrGTEJxErBiq/3VAkDH9ua/YpGcjkBOx+AmJwqS5HUG8joHPz67A5DdIYQVxZbRE/qi8N2N2JhQaNovLbHui5pKn+Un7X0FlZtTcEfOFdjhzMXh5Qvx721XwQEPOrm2opNrK/DaV8Br5wUXTh4DPBC44YBbHHDB2ex/73I3HHCJs/F/0fz6NvbRGvq6G/5vnKthXH0bHHCJ/vr15vJfh3+9vnM31uvxq1fne4OjoX+1JKFG8w8rrJQUICXd/AdbWAT06A+sXW7c57l/A6dfAQC47T3lE4oWSo9+rnDlEQpv/qx/gdOEYiAjOfTnLqpyH9Tjc4HPXgcAyNEzgTOuhjTZl9T8m4Fl31ibsLAP5MI7LYeiAd5AyYIsYG2Jtf6O+iCzsp1AWjvkGGzmneW+P0u3wbVjDtUkGK2mCvhuITDuWGvFRJBhMFptZOsgIiIiIiIiIiKKKAazUKTxORfDeIM5UehwnyEiIiIiilf86IOIAAajERERERERUT07oUoA0D6/7YR0EMUCi8EK8pcbITOvDXMxFNOk8bmU6dEPRivVMvFz8pCgV3Hv1stwwe5HgInTgI9fCnoeomgnXYtadPmd3PIipF37kNUT9RL8w2IC2rUNau9uSEa27/JfvgZ2bjEdKmdcgyOXC1ZsDW4rFWTxXJgo3mjHnI07n5iFU7v8z7RfWv3hqto3GE0AnFn2P5xZZj4eAODxBFdklNMAaHDDIBepTXuh3UmY3vkpv+WVWjKQkmo6VkSAObdBXXmcYR/1yHXA5Fn4qbQ9/vVB+K4IcnuAhz5V+GWTfvuUIeF5fVQ3zwS+eLPx8eNzgdpqyDlzoTweYPli4MlbLc0lf38ScvipQdXR2U4wmmoSjFbQA7kGwWgl5b6P3QaHh4agtTrqq3cgURmMJtC7CYXBaERERERERERERERE4J3dRCHF/YmIiCj6mLw+81yYKDaY7avcj6MYtw3Fp/j8U+RERERERERkn8NmMFpau/DUQUTBsRpuOGxceOug2NckZC/LXarbpUpLwdLkQUFNP7zyR5y3+1HIDU9BZt8U1BxEMaOwqGXjRx4amjpiRTDBaACwYZXfIvXlW+ZjDjkBMmAURnUPbpUAUJAZ/Fgiik6SlIxp18/AR+snmfZzOuqCn6qrIlAVxYsUj/7zpVJSgOS0gONl9JGQm543bHdDwxV3r8KIW8MfumcWvBbqYDTl8cBz/Sk+oWgN3nwc6o9lUEflQ80Zb2k+ufiuoEPRAKB3B+vfn4a6bbF1AwC0PBhN+Qaj4et3odxu/c6tKNngz/MxGI2IiIiIiIiIiIgokngzbPgF+zsRbhuikGEoQwzgNiIiIiKi4PBMkogABqMRERERERFRPYfBXYtGklLDUwcRBUez+DFPt77hrYPiQONFe1meMsNei1NG6i4/abjxRX89a9bgxU2nwfnMj5DDTgHyOgPJBq8nbS0QiuJTt+Lgx445CpJqkJ4RrxITgxu3fiUAQLlqoT55BeqjF4CX7jPuX7wf5NJ7AQCds4IPb+mVF/RQIopiMuYoHFLxOYZX/qjb3tuxrfFBdWWEqqJ4kKz0ny+VWgokxdprvhxyPHCAf3DfT0lDcHC3j/CfLaNaVGNL7dcV6JIdumA05XJBXX8y8Nlr+h1Kd0DNHA7s22NtwlnXQ066qEU1FeVb71sfZKZu+wsAIDdd/2fTNBjN4zG+nMuBZiFopTuAX7+1XlCEJCfoL692AYo3pxARERERERERUdwy++wrnj4Xi6fvhaiVmH5WHto/QEMU91T4/2gUtRL+XpGIKHaZHsN5fCeKCWb7cQjP00yPFjxcEFEdm3e9ExERERERUdxy2gyiSE4JTx1EFBwrwWiZOZCs3PDXQrGtyXMp211q2G1DQlfd5UcMAJ6YqWH+g5/hpR8F3WvWo3vtegys/hWH7luE7AU/QfI6AwAkIRFqzFHAogW+kxTvB8kvbPn3QtTa8rsCHbsBW9fbHirTLg5DQVEuISmoYWrDKmDXNqg544HNa807jz0GcsNTkLpQxoKsoFYJAJg8mBckE8UrefNPnDDrbvyQsp9f27G7F0DVngdJSGQwGtmS4tF/vrgkAS5oMMiS8iO3vQw1IaPh8c2512Bu3t9DUGHLTRkSwlC0sp1QF04E1v0Wkvnk/oWQoWNbPE/fjgKrF2lqqLsRo6oCyuMxDEYrqwSqahWSEwTvLTOerz5ozcdPnwODD7RUT6QYBaMB3nA0s3YiIiIiIiIiIiIisop3CMcuBjkRhQ6PhURERLGFr91EscEsGC2CVfCQYQ9/YBSnLNwxS0RERERERG1Cgt1gtNTw1EFEwRELH/P0GBD+Oij2NXku5bh32h7et5MgNUlwwSVj8emwBXhy62z8o+QWnIhPkX33iw2haA2ru+JBYEiTgILu/SC3vBB0+UTRRESAMUfaH3j4aZDhh4S+oGiXmBzcuPUroO67InAoGgC57L6GUDQA6JQZ3CpHZWxFuxQGoxHFK8nOw2WHuDGz9Gmf5SfseRU3bb4O+Okz7wKjYLRegyAvrQQGjg5zpRRLklW1YVvF9hLL80hCIuS2lwEA92ZfEJJQtDcv1DBpACAtfGk7dmjjBKqmGmrBA/DceDo8j82F2rbBu7y0BJ4HroRnbJL33yPXQa1eAs9D18Bz4+lQrz8K5aqFmndD6ELRHvk8JKFoADBMPx9alwNNgsz27DI97/hjB1BRrXDWU8Y3Q/nMV0e9/ID1giIkxeQjxqrayNVBREREREREREREQeJNlEThxX2MKIS4PxEREUUfvj4TERFR6DhbuwAiIiIiIiKKEk6bwWhJDEYjiioOR8Ausv/hESiEYl6TNIQUVYX2rp3Y5cyxPLw4v24ahwNy+f1Qs28CtqwHeg6EOP0/jpSMLMgDC71BCdVVQGEfb5gUUZyQMUdDvfqIvTGX/DtM1US5xKTgxn3zLuD2Dwrx43AA7fN9FqUkCtqnKuyqsHfcmV28AUDngP2IKHYlnH8LHnszF3dsvxa/JA1Av+qVyHdv9zb+vhQYdRhQYxB0lZQC6dQdeOgTYOMqb7/qKqCyPGL1U/RJLUkGXtVvq+4xxNZcMvYY3Ft0Ky53XGqp/5drx+PB9nPwfOYpfm3pUomjB6Vh8mAHdu1TWPqn95z+ucUKf1tg/ULFIwcCQwu9r6fK5YK6+njgu4UN7eq9p4HbXoa6ciqwa2vjwGfvgnr2rsZ+ixYA37wH/Pip5XUbSk6FvLcd4kxo+Vx1uuUIJvYFPl4RuK+mmoSc7dyKnt1y4NAAt0722YotwOptwI69xvM5lM7AshKoNx+HHPOXwAVFSPcc4IXZgmSnIDkBPv/SgzzdIyIiIiIiIiIiimkMQaLWwOddBAT7M+a2IQoZHuuiH7cRERE1xdcFothgtq9GcD/mEYOIAAajERERERERUb0Em8FoyQxGI4oqogXuM2h0+Oug2Kf5Ppc6uzZbDkbLTQdy0n3DhSQjG8jIDjhW8rtar5Eolgwbb69/936W9pm4lGCelCF/uRHq8bn+DVZC0QCgXQ5E83+93K+bYOFv1qaoN64PAxyJ4p0kJEL1G4mcJV/i4IovfNrUw9cCo4+Eqq7UH5yU4p1DBOhaHO5SKUakblfAqzrBVgAqB4yzNdeeSoUbEi8ALLwEVv7WDglwAbugG4x2aNmHwG+FQP+RaJ8mOLjuKXvJRO//VsLRpo0Q/Hd6k9fGJV/4hKIBALb/CXX+IUBNVeCiv343cB8L5Ky/hzQUrd6dJ2oYfov+tmzK0XQD7dyChNxO6KlVY7Un36/vSf+1OV8T6tEbgMmzdM9zWkNWqmDaCJ4rERERERGML+8QAAAgAElEQVQRERFRW8NbNSnS+JyLXdx2RJYFDF3g/hS/uG2JiGIXj+FEFBrMUiQiAIiOK2OJiIiIiIio9dm9UbTuRm8iihIOR+A+aZnhr4NiX7OQvQLXFstD+3YMdTFEsU8Sk4AZV1nvf+a13iCdtsjsfDQ9Czjlry07B83UD3n8+2R7vyqZWfo0evVqH3wdRBQ7HMZ/Y0qdfwjw5+/6jUnJYSqIYlmKyctcVWaBrbneW6ZQ7g4ccP/axpO8oWgADqj6DjNK/+fTnurZhyt3/htY/JHfWKdDcPnhGn643vx1cs7Bghdma8hMbTx/US/co9/ZSihaqHTuCRx/flimHtZV0MnC22ut6YWeO7dC3TwTRbt/CHq9DmWQhFdWAmxeE/S8REREREREREREREQUQaZ3dvOubyJfDEYjIiKKL3ztJooJfN8ao7htKD4xGI2IiIiIiIi8EgLfUOsjOTU8dRBRcMTCxzxpGeGvg2Kf5vtc6uzabHloUcc2GuZEFIA2+yZL/eQ/70IOnRbmaqKXiAAHTNJve20tJDkV6D8q+BVk5uouHttHMC5huW7bmIqvMXf7XBxW/hGm7H0bD2y5BI9umQPk2guwIaIYZRY+XF4KfPySfhuDxElHisnHLpW11udxexROnRf4Apb+1csxpfwdn2WPbjkf8zafh+P2vIGzd8/H92tHY1TV91CPzzWcZ1hXwfHDjNfz4Gk670W/fT9gfWF10kWQx76FhDGksF+nwH2aBpmpRQuA//sQ3Wo3BL1OBwyC0QBgb2nQ8xIRERERERERERFRPOLNsK2KN5EThYbpvmShnYiIiCKPr89EFCI8nBARABj/mXMiIiIiIiJqW2wGowmD0Yiii2YS2lAvlcFoZIVvuFlBrfVgtL4dQ10LUfyQx76BOnu0cftdb0FGTIxgRdFJLroTatXPwK6t3gUJiZBrH2889+zaB/jps+Amz8oxbHp5wNuY+K0Hy5IHNizr6NqKh7ZejIHVy4GdTTqntYOkpgdXAxHFFivn2HoSGYxG/lISjNsqa6zN8eN6hTPmeyz1fX/DFL9lCXBhVtnTmFX2tF+b2rgaUthHd675MzWs3+XBD+sbl+WmA+9cHF1/h03+/iTk8FMjsq6ifMGiFeZXXvkEmdWFxXVybQl6nU2D1vzsKwt6XiIiIiIiIiIiIgoBhiARtUFmf0BSmbTzmEBkXaD9hftT9AtyGzEFg4gohpkcw3l8J4oNZvtqCPfjCK2GiGIcg9GIiIiIiIjIy2kvGA1JvNGbKKpoFm5IZzAaWdHsuZTnLrE8tDjf7II/orZNivczv8wrtyBSpUQ16VoMPPWDNzykYi8w/BBIt76N7YVFwV/SmGkcjJbbsxDfvDAOb2RMwfKkfuhesx5Tyt/RPwbmdgq2AiKKNY4gf5XK98ukI9ksGK028PirX/XgzvetvQo+vnk2CmwGcKnTBgKfV0HE/5y+XYrgq6s0vP6zwrJNQGF7YMpgQcfMKDv/H3tMxFZVlB+4j6b8Q+w6u6wHTzfnE7TWXPmeoOclIiIiIiIiIiIiso53pUYN3iEc3ZQyyUXjtiOyLsD+ovP7OCIiIiIiig9890xEAIPRiIiIiIiIqF6CzWC05NTw1EFEwZEAwWiJSRC7+zm1Tc2C0XJtBKMNYK4TkbnMHKBsp34bj9ENJCsXmDRdv7HngODn7dHfuLHHAKSoKpyy5+XAE/UIvgYiijFBB6Mlh7YOigsiguQEoEonBK2yxnzsN38oy6Foojw4bN/HQVQIqHHJUBOnQfYbDxw9C+JwNLQlOgXTRgimjQhq6rCT82+HpKRFbH3eUGjzbaIXZNbJZmBdU8rwDioA+8p8+5Zshnr4OuDD57wLppwFmTQdMvjA4Nf/0+dQn74CeDyQg4+HDD8k6LmIiIiIiIiIiIhiidqyDvjqHaC2Ghh9JKR7v1auKNx42ylRy5ntR2ZtUfZHaYhaW8DgM75mERERRZ9gz4WJKHqY7KsM+45i3DYUnxiMRkRERERERF5Om2EcSQxGI4oqTW5Y15WSEZk6KA74XmCX67IWjFaUD/TIDUc9RHFEMzlWMxjNmkFjgKQUoLrS/tiDjzdu6z0YyMoDSncEnEamnGV/3UQUm8yO22YSGYxG+lKMgtF0ljX11DfWL1iZXP4uCloQvoWPX4L6+CVg8ULILS8EP0+kTZoR0dUV5Qfuo+ncqNG5dnPQ60xQJk+U8sZgNLV5LdTJfX3b35oP9c6TwI3PQCacaHvd6qMXoG6ZBXi835N6Yx5w1X8hR59pey4iIiIiIiIiIqJYopZ9C3X5ZKBir3fBozcAt7wAOXBy6xZGFBBvhm1dZjeRBwp6IqJGgY5lPNZFPQZnxL23lii8uUQhMwU4dZRgeDeGfBKRGb4uEMUEs3O4CJ7f8VSSiABAa+0CiIiIiIiIKEo4E+z1T2YwGlFUkQAf86SmR6YOin2a73Mp173T0rDDBwhEeEEDkSkGo7WYJKcCY46yP+6fr0JyC4zbHQ5g/HGBJ+rQBTLqMNvrJ6IY5Qjyb0wxGI0MpBq83O+rNr6Cx+NRePTzwFf4tEsGZvZYh//JLcad0rMgT3wfcC4AwGevwTM2CeqJW6BcroDd1U+fwXPhofCc2t/a/KHkTAAycyK6yu653p+5GQfcfst61q5Foqfa9vo6uLahR+06w3ZV2hhorR6+Vr+TxwN14+lQNfbWr5SCmndjQyha3UKox/4BxavPiIiIiIiIiIgozqn7rmgMRQMAVy3UXRfpfDZm9lkZP0ejcODzqvUFe6M4tx2RZYF+F8XfVcUxbttYcMf7Hhz7oAePf6lw90cKB93hwcLl3HZEbR5fn4mIiCiEGIxGREREREREAOrCIBwmYR3NJfFGb6KoEmj/Tc2ITB0U+yS4YLSBxnlDRFTPLGDHyWA0q2TW3+0PGn1k4HnPuBpIzzLukJUHmb/Y/rqJKHbZeY/chDDskgykJ+kv31djPGbKAx7jRgBODfjoUg277tEw/5peyHjuO8iivZDzb/frK7NvgvQeBIw81HLNav7NUI8YBG3V91nzK9SlRwFLvgD+/MPy3LbkdTZuy+kE0SJ76YNDExweIAMuWfkHkCWrakys+MR0XP9O/suuK7kDmtnF//+7EwCgKvYCX7xpOr86qQ9UVYVpHx8bVwNb1vkvL9kMrPrJ+jxEREREREREREQxRu3ZBfz2nX9DyWbg1/+LfEFEFEMYjEYUGoH2F+5PRK2lskZh7lu++2C1C5j7lvk1DkTUxjE0jSg2REnYN48YNvEYS3GKwWhERERERETUyE4gR1JK+OogIvskwMc8ae0iUwfFPhGfh7nuEkvD+nWSwJ2I2jqHybE60SAphfxIj36Qp360PuCwUyyFpUiHLpAnFgPHzQYGjgZGHQb0HgyMPQY44XzIE99BMnNaUDkRxRyzQEszzoTQ1kFxI83g5b68Sn95rUvhs1XG8x03FPj8Sg0T+wk0rfF8XBISgVMuhdz5OjD+OGD8VMhdb0Kmnuttv+ste4W/8pD3BkADat6NgNtlb047UjOAdu2N23N1ksQi4MQR5u+BBlYt011+zN63Tcd9fLmGV+ZoOEX7BFP3vI6X/zwVF+x+JGA9qmQz8M37gbfFrm1Qh2XD8+DVUB4LF6WbbHvs3h54PBERERERERERUazau9u47c/fG7/2uICa0vDXQ2QHb4ZtZSY/f24bIhsYjEYUrV77SaGq1n/5V38ALjf3TaK2jccAIiIiCp0gr+YnIiIiIiKiuJSQCFRXWuubmBzeWojInkChDanpkamDYl+z8KAkVYP2rp3Y5TQPA+rfOlkERLFFcxi32QmoJUjPAcA5N0HNu8G8Y8dukJnXWZ+3YzfI5fe3sDoiihvBBqMl8JhO+tINgtH21egv37gbqDBo278H8Or5xucWIgKMPhIy+kj/Nk0D7v0A6poTgYq9gcoGXLVQ/7rAG7LWfySkoGdDk9pbCnxpM2itqeGHAD98Yt4nt5N5iGz7/ODX3wLHDxMM7qKw9E/99snl7xoun2Mw57QRgvx2gqnDgGO1W4FNX1sv6JdvoD5/w3r/F/4DVbkXGDoeKOgB6T9Sv5/ZDVrCgGwiIiIiIiIiImqrxPvZ2ZLrgNUPAbVlxl0ZgkTU9pju98G2EbVBgV5DW/s1tnYvsOVDYN86IH8C0H5Y69YTlYLdRjweRru1Jn9z2cPNR0SGeIAgiglm59kRPAdv7dN9IooOWuAuRERERERE1GbYCeRgMBpRdHEGCkbLiEwdFPt0bmwfU/mt6ZCBBUBOOm+IJwrINBgtIXJ1xIsZVwKde/ovb98Rct6tkOsehzz2DaRrUeRrI6L4oAX5q9QEkwAnatPSjILRqvWXbzG5j+78g1t2/i37HQx56gfIJXcDGdmBB3z6KtTcM6BOHwz16sMAALV7O9RRLQslk3HHAh26mHfKLTDfr9IyW1RDsJwOwYOnadB0NkWXjFpMnjFWd1wn11acXPaS33KBB//7S5PJ3C5b9agbTgM+fcXWGLzxGNTcGVDnHgTP9adAeTz2xhMREREREREREbVlK+8Blt9uHooWb3hHahThtohuJp+3cz8isiHQ/tKK+1PVduDD0cCXJwI/XQG8vx+w4j+tVw9RFOFLHVFbx4MAEVnD2HAisoLBaERERERERNQogcFoRDEr0P6b2i4ydVDs0wlGm7r3DdMhxw1jKBqRJSbBaBJs+E4bJiKQe94H+o9qXNhvBOShRZDTr4BMmg7JzGm9Aoko9jkChA8bsfPemtqUNIOnRnkQwWjTD2j5Obh07AY58QLIfR9ZH+SqhfrPX+EZmwR1TGGLa0BeZ8g/XzXv02sgkGgSjJaa3vI6gnRgb8Fz5wiSm2TcdsoEnp+ThNRTL4C8+ScwbLzfuH9vuwrj9n3e8HhA1a/4IelCOB3BB6MBAFoSbPbZa8Db8/2Xm9bB94JERERERERERNSGrff/Awjxgbeexgdux/Az+YzcNBGG24bIugD7S2umLy27FSj71XfZj5cBFZtbpx6iKOLhSx0RGeIBgigmmJ1nR/AcnGGrdvEHRvEpyKv5iYiIiIiIKC45EwL3qWd2QyoRRZ4jwP7bijeKU4wR/3CmyeXvwqFccIv+x4lTGYxGZE2wATtkSDp2Ax75HPhjGZCYCBT0hNg5pyUiMmMSaGnKyWA00peeLNC7+KTCMBhN/0KVXnnegNBQkd6DoIaMBZZ8EbI5LcstgPQZAiwsg7r4MGD5Yt92hxMyaQbUY/8wniM1I6wlBjJthIYpgxW+WwckOoGhhUBygnf7SHYecM/7wJ+rgS3rgR79gU9eQccHrsTHGyZheWI/1EoCBlb/CmfXXr4TGwWSaVrLAtBMqJfuhxxztu/CWoMnKBBceBsREREREREREVFcUMDOb1u7CCKKWsEGo/EaLCIfAZMQwvM7M0tW3ae//I/HgEE3RLYWoijjbsVdk4iiAJOMiIiIKIT873IkIiIiIiKitivBxs3bicnhq4OI7AsUAtPKN4pTDNH8PzLMce/CxH2f6Hbv3cF74z8RWeAIMmCHTIkIpPcgSNdihqIRUWgFG2hp5701tSmpBk+NfTX6FwRuLdPv3ykzRAU1Ibe9BBx6MpCZG/rJzeQVeNeflAy59wNg6rlAehYgAvQZArnzdUjRUNOAfomC97spiYJxRYIDekpDKFo90TTvecr+h0M6dAHyOnuXAxhQ8xuGVi+FE25gwyrfSY1Cx4ZPCMN3UGf9Cqgt63yX1VQZ968xCU0jIiIiIiIiIiKKdSH7AxW8KZzCgGEDUSDI8DNuOyIbAuwv0bg/bX6vtSuIMkFuo2jctmSZh5uPiIzw+E4UI6LjPS2PGEQEMBiNiIiIiIiImnIyGI0oZgUIX5DU9AgVQjHP4MLem3bMRaKn2q/rHSdokJBdDEwU5zQGoxERxRQGo1GIpRt8lFJukDu1xSAYrWO70NTTlLRrD+3Gp6G9vQny4orQr0CPwwlk5zfWkJwK7bL7IO9sgXxUCm3+Ysiow7yNiSnG88Ta+93cToZN6qfPGx8YBKPJQZN1A61DZuVPvo/Nws9q9duU2w21ezvUvj0hLIyIiIiIiIiIiIiIKEaY3ijOW7uJrAu0v0Tj/hSNNRFFFoPRiNo6ngsTxTyGGEanQNuF243iFIPRiIiIiIiIqJGdm7eTGIxGFFWcCebtaWG4c57ik+h/ZDii6kd8vW48ppc+ixFVP+Kk4YIP/6ph6jCGohFZxmA0IqLY4gjyuB3o3JzarDSDj1321egv31qmf6FKx6zwnoNLQQ+gQ2FoJus9GBiwv37b/odDdPYz0TRI88+dsvOM1xFr73dzCwyb1MPXND4wCEZDQhLkuWUhLqqJ9c2C8aoNkvsAoMa/Tf32PdRf9oc6phBqcgE8D1wJ5XaHuEgiIiIiIiIiIqII4M2EFNP4/G1dJj9/5YlcGUSxLuBrMY91RNGIwWhERETxLHIv9PxojogABqMRERERERFRUwlJ1vsmMhiNKKo4AoQvpGZEpg6KfZrxR4ZDq5fiyS3n4Ntdx+LFczVM7MdQNCI75KQL9RsK+0S2ECIissbhDG6cndBxalPSDT52Ka/WX76lTH95p8zQ1GNGbnyq5ZOkZ0Iu+Cfk9leAbsW+be3zIefcZL2ePOM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dNbyml8ej7kkOySE51PPXQ+1jvauXt2s9T7JIFsmi3mbRCmrbdO9XyyE5JIecHyaTaWNOnuM3R4Z7jp+zX5/RW0OTHJJDcqi3ObSC2rznsbRt5ND5/cih8/uQQ+v0/Kh7kkNyaJPmkM/nraFJDskhOeTzeWthkkWySBb5jF7dkxySQ3LI5/PqnuSQHJJDzo9l3aa6CzBVSXJwhdu9pePk+h9L2ObajifGM0muXcJ2b+zY168tYZtdCz0hdDGork6r42D7WE9f4rbv7NjuO3p8P/fjeOxdamB3bHfTPMfxH/X4ePxGx/5+fQnbPG76nD23zXiSq5ewXft+3tXL27VeJzkkh+RQb3NoBbX9647aPtXLY7FWJlkki2RRb7JoPZ0fdU9ySA5t0hzq+fFoG2/Bv6AbH7w6dxwOrnA7OSSHOseRQ92r79921PDeftfQ59t7cIXbySE51DmOHJp/vPax3tXL27WeJ1kki2RRb47HKmrbdO9XyyE5JIecHyaTaWNOnuM3R4Z7jp+zT5/RW0OTHJJDcqi3ObSC2rznsfTt5JAc6hxHDq3T86PuSQ7JoU2aQz6ft4YmOSSH5FBvjscq69tUn8+bvs0HV7idLJJFnePIovnHax/rXb28Xet1kkNySA715nisojbvVS99OzkkhzrHkUPr9PxYztQItauq6u4VbvqLHfNfsYRtXp5koG3+96uq+vQStvsPHfMvLaWMLLZBVVWHq6o6vYSxV+OFyazz+L1VVf3NErf9fzvmX9GdkubXj+NRVdVDVVWdWMF2/ztJ53Fbyvm0IqWULUm+oWNx5zk2R1VVn0ry1rZFzbTOaVZJDq2KHJq9Dzm0SqWUkrnnwhu6uY+1ShatiiyavQ9ZNNu6OT/qJodWZd2cZ3Jozj77cX6c29cX+rGf9UwOrYocmr0POdQ9f9wxf00tVfSJHFoVOTR7H3KIFZNFqyKLZu9DFq3SZn2/Wg6tihyavQ85NNu6OT+Ajclz/Kqsmwz3HH/eWv5szGYlh1ZFDs3ehxxaJe95LJsckkOd+5BDs62b86NucmhV1s15Jofm7NPn89YQObQqcmj2PuRQ92yqz+clsmiVZNHsfcgiVkQOrYocmr0PObRK3qteNjkkhzr3IYdmWzfnx3JojLa+fbBjfkspZewC27y4Y/7Xl7Kjqqo+keR9bYu2JXneUrbtsWd2zL9jGdv+ZZKzbfO3llL2r76kdavzfLq0h/t6fpKtbfN/+3/au/dgafK6vuOfHyD3y6oIiAhLUAmKGhAT8RI3BiyIJcgl3pVVvF8qmphKoaRCNGIuXquSUqIoipdIEBQFgRBdMBqvtQgoElFBUZY74i677C788secfc6cnjNzpmd6unu6X6+q80f3M9PT5zzf5z3n+Z2uPrXWP9nyuc2ZfXw3p8SOdEiHuqRDC5+Z5AFL2zdn8RvrWE+LtKhLU2yR+Tg8HTJnXeqzQ0yHDulQl3TorHc2tu8yyFmMnw7pUJd0iF1pkRZ1SYsWrFe3o0M61KUpdsh8AMfKe7yGd2mKP4/m8HRIh7qkQwvWPNrRIR3q0hQ7ZD4OT4fMWZemuPbK4emQDnVJh85yfd72tEiLuqRF7EKHdKhLOrRgrbodHdKhLk2xQ5OcDzdGO243n7PvtuseXEq5V5JPbDz/N1u83lWN7Ue3eO6h3Kex/Zptn1hrfV+S1y/tulXG8TkNpTlPa2epA49qbF/V4rm/kbPn+pBSyj33PiN2pUM61CUdWnhyY/uFtdZrOjz+FGmRFnVpii0yH4enQ+asS312iOnQIR3qkg6ddb/G9t8Mchbjp0M61CUdYldapEVd0qIF69Xt6JAOdWmKHTIfwLHyHq/hXZriz6M5PB3SoS7p0II1j3Z0SIe6NMUOmY/D0yFz1qUprr1yeDqkQ13SobNcn7c9LdKiLmkRu9AhHeqSDi1Yq25Hh3SoS1Ps0CTnw43RjttHNbZvTvL2DY9/cGP7VbXW61q83m81tj+uxXMP5UMa2+9u+fzm4z9+j3M5ds15evMBX6s5i/932yeezOyrG7vHMItzpUM61KXZd6iUcrckT2jsfmYXx544LdKiLk2xRebj8HTInHWpzw4xHTqkQ13SobO+vLH964OcxfjpkA51SYfYlRZpUZdm3yLr1TvRIR3q0hQ7ZD6AY+U9XsO7NMWfR3N4OqRDXZp9h6x57ESHdKhLU+yQ+Tg8HTJnXZri2iuHp0M61CUdOsv1edvTIi3qkhaxCx3SoS7NvkPWqneiQzrUpSl2aJLz4cZox+2Jje3fr7V+YMPjP7ax/fpzH7Xen11wvCHc2Ni+XcvnNx8/hs+pd6WUuyZ5ZGP37x7wJR/U2O5zFu9bSvmJUsoflVLeVUq5sZTylpPtny6lfE0ppRl81tMhHerEzDq0yRclucPS9puT/GpHx54yLdKiTky4Rebj8HTInHVigA4xHTqkQ53QobNKKd+Y5EuXdt2c5AcHOp2x0yEd6sTMOmStuntapEWdmFmLNrFe3Z4O6VAnJtwh8wEcK+/xGt6JCf88+jzWPbqlQzrUiZl1aBNrHu3pkA51YsIdMh+Hp0PmrBMTXnvl8HRIhzqhQ2e5Pq81LdKiTsysRdaqu6VDOtSJmXVoE2vV7emQDnViwh2a5Hy4MdqRKqXcOcmTG7uff8HTmncs/MuWL/vGxvaHllI+uOUxuvaOxvaHt3x+8/EP3ONcjtnXJrnj0vbf5kB31z/5T2LzP4ptZ7H5+I9u8dz7J7kyiwhfluSDktzjZPtLkjwjyV+WUn7g5N8Za+jQJTrUjTl1aJPmv6mfrLXe3NGxJ0mLLtGibky1RebjgHToEnPWjd46xHTo0CU61I1Zd6iUcqdSygNLKU8qpbw8yX9tPOQptdZXDXFuY6ZDl+hQN+bUIWvVHdKiS7SoG3Nq0SbWq1vQoUt0qBtT7ZD5AI6O9/hLNLwbU/159Hmse3REhy7RoW7MqUObWPNoQYcu0aFuTLVD5uOAdOgSc9aNqa69ckA6dIkOdWPWHXJ93u606BIt6sacWmStuiM6dIkOdWNOHdrEWnULOnSJDnVjqh2a5Hy4Mdrx+p4k91rafneSH7vgOZc1tt/a5gVrrdcmuaGx+25tjnEAr21sf8q2Tyyl3DfJvRu7h/58eldKuTzJv23s/qFaa/NukF1pzuF7a63XtTxGc3a7/nu7U5JvSfIHpZSP6/jYU6JDCzq0Jx1aKKV8fJKHNXY/c9/jzoAWLWjRnibeIvNxWDq0YM72NECHmA4dWtChPc2tQ6WUy0opdfkjybVJ/iTJs5L846WHX5vka2qt3zvAqR4DHVrQoT3NrUNbsla9PS1a0KI9adGC9eqd6NCCDu1p4h0yH8Ax8h6/oOF7mvjPo3dl3WM7OrSgQ3vSoQVrHjvRoQUd2tPEO2Q+DkuHFszZnia+9sph6dCCDu1pbh1yfV7ntGhBi/Y0txZtyVr1dnRoQYf2pEML1qp3okMLOrSniXdokvPhxmhHqJTyuCTf1Nj9HbXWd17w1Obdiq/f4eWbz7nLDsfo0ssb208opdzx3Eeu+vJz9g39+fSqlHLbJD+fs5/3G5L85wO+7FBzeHOSq5I8Ncljkjw0i9/a9JAkj03yvVn9ZuZjkryslHK/Hc5x0nToDB3aw8w6dJHmnapfXmt9fQfHnSwtOkOL9jCDFpmPA9GhM8zZHgbqEBOgQ2fo0B50aK23JPmOJPevtf7o0CczRjp0hg7tYWYdslbdMS06Q4v2MLMWXcR6dQs6dIYO7WEGHTIfwFHxHn+Ghu9hBj+PXmbdo0M6dIYO7WFmHbqINY8WdOgMHdrDDDpkPg5Eh84wZ3uYwdorB6JDZ+jQHnRoLdfnbUGLztCiPcysRdaqO6RDZ+jQHmbWoYtYq25Bh87QoT3MoEOTnA83RjsypZRPTPJTjd0vTfLDWzy9Ge7m3Sm30Qx385h9e2EWd/O8xWVJnnbRk0opH5nk2875o1uXUu7QzakdhR9L8g+Xtt+f5Ek7/DakNoaYw6cm+Yha6z+ptX53rfWXa61X11pfX2t9Za31BbXWf53kfkn+Y5K69Nx7JXleKaXscJ6TpEMrdGg/c+nQRiffSH9pY7e7e2+gRSu0aD9Tb5H5OAAdWmHO9jNEhzhyOrRCh/ajQ+e7Z5KvS/L1pZS7Dn0yY6NDK3RoP3PpkLXqjmnRCi3az1xatJH16nZ0aIUO7WfqHTIfwNHwHr9Cw/cz9Z9H38K6R4d0aIUO7WcuHdrImkc7OrRCh/Yz9Q6ZjwPQoRXmbD9TX3vlAHRohQ7tR4fO5/q8C2jRCi3az1xaZK26Qzq0Qof2M5cObWStuh0dWqFD+5l6hyY5H26MdkRKKffNYhCXY/nGJF9aa63nP2ujvp5zMLXWv0vyQ43d31ZK+RfrnlNKuU+SFye527rDdnR6o1ZK+a4kX9bY/ZRa6yt6PpWDz+HJf16bd+8+73E31FqfkuSbG3/00CRf1OY1p0qHVunQ7ubUoS08NsmHLm3/bZLndvwak6FFq7Rod3Nokfnong6tMme7G1GHOCI6tEqHdjfjDr0nyf2XPh6QxRrQ45P8QJK3nTzuI5N8Z5JXl1I+eYDzHCUdWqVDu5tTh6xVd0uLVmnR7ubUoi1Yr96SDq3Sod3NoUPmAzgW3uNXafjuRvQe7xq9I6JDq3Rod3Pq0BaseWxJh1bp0O7m0CHz0T0dWmXOdjeiDnFEdGiVDu1uxh1yfd6etGiVFu1uTi2yVt0dHVqlQ7ubU4e2YK16Szq0Sod2N4cOTXU+bjP0CbCdUso9kvyvJB+xtPuaJI+stb7t/GetuLaxvcud+ZrPaR5zCE9P8uic3pmxJPnBUsoTs7g76iuzuBPnvU8e9/U5ffN7U5L7LB3rhlrryp0+SymXb3sytdY3tDr7AZRSviWLu14v+/5a63/Z8vmXb/ta53w9Rj+Htdb/Vkr57CSPWdr9DUl+tsvXOTY6tJEOtaRDK57c2P7ZWmvzLtJEiy6gRS3NrEUHn4+50KGNdKilgTvEkdKhjXSopTl3qNb6gSRvOOePrk7y/FLKU5P8pyTfdLL/vkleVkr5tFrra/o5y3HSoY10qKU5d2gb1qrX06KNtKglLVphvXoLOrSRDrU0sw5ZqwZGzXv8Rt7jW5rZz6Nbs+5xPh3aSIda0qEV1jy2oEMb6VBLM+uQNY+O6NBGOtTSzNZe6YgObaRDLc25Q67P248WbaRFLc25RduwVn0+HdpIh1rSoRXWqregQxvpUEsz69Dk1qrdGO0IlFI+JMnLknzM0u63J3lErfVPWxxqkuGutd5YSnl8khcl+YSlP/r0k4913pHFNw4vWdr37jWP/YsWp1RaPLZ3pZSvTvL9jd0/XGv9Vy0Os8/X41jm8Hty9j+yn1JKuazWum5GJk2HNtOhdnTorFLKRyZ5ZGP3M3c93pRp0WZa1M7cWtTTfEyeDm2mQ+2MoEMcIR3aTIfa0aHNaq3vTfLNpZSbknzrye67JvmpUson7fgbho6eDm2mQ+3o0NasVTdo0WZa1I4WnWW9ejs6tJkOtTO3DlmrBsbMe/xm3uPbGcF7/LHMoXWPJTq0mQ61o0NnWfPYjg5tpkPtzK1D1jy6oUOb6VA7I+gQR0iHNtOhdnRoM9fnradFm2lRO1q0NWvVS3RoMx1qR4fOsla9HR3aTIfamVuHprhWfauhT4DNSil3S/LSJB+/tPtdWdzJ8o9aHu5vG9sf1vJc7pzVcI9ikGutf53kU5M8I8lNWzzl15M8LMl1jf3XdHxqo1JK+bIkP5KzMf2JJN/Y42k05/COpZQ7tTzGPRrbh5jD383i39otbp3kYw/wOqOnQ9vRoe3o0LmuzNnvyf6w1voHexxvkrRoO1q0nbm2yHzsR4e2Y862M5IOcWR0aDs6tB0dauU7kvzN0vZDkjxioHMZlA5tR4e2o0OtWKteokXb0aLtaNG5roz16o10aDs6tJ25dsh8AGPkPX47Gr6dkbzHj+3amHWse5zQoe3o0HZ06FxXxprHRjq0HR3azlw7ZD72o0PbMWfbGUmHODI6tB0d2o4OteL6vCVatB0t2o4WtWKt+oQObUeHtqND57oy1qo30qHt6NB25tqhqc2HG6ONWCnlLklenOSTlna/J8mjaq2v3OGQzbtf3q/l85uPf2et9V3nPnIAtdbraq1fl+SBWSyI/HqSNyW5PsnfJXltkp/M4i6q/7TW+oYkD2oc5vd7O+GelVK+MItIL/+7/5kkX9XnHfRrre/I2f8gJsl9Wx6mOYtt7uy6lVrrB5L8ZWN3q292pkCH2tGhzXRoVSmlJPmKxm53927Qona0aLO5t8h87EaH2jFnm42lQxwXHWpHhzbToXZqrdcn+cXG7kcNcS5D0qF2dGgzHWrHWvUpLWpHizbTolXWqy+mQ+3o0GZz75D5AMbEe3w7Gr7ZWN7jx3RtzCbWPRZ0qB0d2kyHVlnzuJgOtaNDm829Q+ZjNzrUjjnbbCwd4rjoUDs6tJkOteP6vFNa1I4WbaZF7VirXtChdnRoMx1aZa36YjrUjg5tNvcOTWk+bjP0CXC+k99G86Ikn7K0+9okj661/u6Oh31tY/ujWj7/7zW2/3jH8zioWutfJHn6ycdFHt7Y/p01xyzn7T8WpZQnJHl2FnepvsX/TPKkk/+wtdLB1+O1Wdxh8hYfldX53KQ5i22e28b1je3mHV0nTYd2p0OrdGitz0py/6Xt92XxTTUntGh3WrRKi04dYj6mSod2p0OrRtghjoAO7U6HVunQzl7X2G77b+ao6dDudGiVDu1s1mvViRbtQ4tWadFa1qs30KHd6dAqHTplrRoYmvf43XmPXzXC9/ixXBtzkVmve+jQ7nRolQ6tZc1jAx3anQ6t0qFT1jy2p0O706FVI+wQR0CHdqdDq3RoZ7O+Pi/Ron1o0Sot2pm1ah3aiQ6t0qG1rFVvoEO706FVOnRqCmvVt7r4IfStlHKHJL+S5NOXdr83yefUWn9rj0O/prH9CaWUO7Z4/qddcLyjcnJX1c9q7H75EOdySKWUxyT5uZy9EeIvJvniWuv7hzmrldlpBnKtk29qPuGC43Xl7o3ttx/odUZHh/qhQzqU5Csb28+rtb5zx2NNjhb1Q4u06ILXmcV8rKND/ZjLnI20Q4ycDvVDh3RoCzc1tm83yFkMQIf6oUM6tIXZrlUnWtQXLdKiWK9eS4f6oUM6tMlc5gPol/f4fsyl4SN9jx/9z6NPzHbdQ4f6oUM6FGsea+lQP3RIhy54nVnMxzo61I+5zNlIO8TI6VA/dEiHtjDb6/MSLeqLFmnRFqxV69BB6ZAOxVr1WjrUDx3SoU3GPB9ujDYypZTbJ3lBkiuWdt+Q5DG11lfsc+xa65uTvGpp121y9s3hIlc0tn91n/MZgc9KcvnS9strrX860LkcRCnln2Vx58oPWtr9wiRfUGu9eZizSpK8uLF9RYvnfkbOvgldXWt9y95n1FBKuXtW7+L6N12/zhjpUK90aDiDd6iUclmSxzd2P7PtcaZKi3qlRcMZvEVbmPx8rKNDvZr8nI24Q4yYDvVKh7jIfRrbh/i+a3R0qFc6xFpzXqtOtKhnWjRj1qvX06Fe6RCbTH4+gH55j+/V5Bs+4vf40f88es7rHjrUKx0azuAdsuaxng71SoeGM3iHtjD5+VhHh3o1+TkbcYcYMR3qlQ5xkVlen5doUc+0iLWsVetQT3RoxqxVr6dDvdIhNhntfLgx2oiUUm6b5HlJHrG0+31JPq/W+r87epnnN7a/Ystz+/tJ/tHSruuSvLSjcxrKv2lsP2OQsziQUsojk/xCktsu7X5pkifUWm8c5qwueUmS65e2H34yY9u4srHdnOmufGHONvItSV57oNcaDR3qnQ4NZwwd+pIkt1/afkOSX9vxWJOiRb3TouGMoUUXmfR8rKNDvZv0nI28Q4yUDvVOh7jIZze2R7G4f0g61DsdYpNZrlUnWjQALZo369Xn0KHe6RCbTHo+gH55j+/dpBs+8vf4Y/h59CzXPXSodzo0nDF0yJrHOXSoxYhGTgAADKBJREFUdzo0nDF06CKTno91dKh3k56zkXeIkdKh3ukQF5nd9XmJFg1Ai9jEWvUpHTocHZo3a9Xn0KHe6RCbjHY+3BhtJEopt0nynCSPXtp9U5In1lpf0uFL/UyS9y9tP76U8tFbPK85xM+ptd7Q3Wn1q5TypCSPXNr1yizu/DgJpZTPTPJLOfsN0q9l8U3A+4Y5q1O11vcmeW5jd3PGVpRSPibJ45Z23ZzkZzs8tVte555JntrY/cu11tr1a42JDvVLh4Y1kg59ZWP7x6femW1oUb+0aFgjadGm15n0fKyjQ/2a+pyNvUOMkw71S4e4SCnlc5I8rLH7l4Y4l77oUL90iE3muladaFHftIhYr16hQ/3SITaZ+nwA/fIe36+pN3zs7/FH8PPoWa576FC/dGhYI+mQNY8GHeqXDg1rJB3a9DqTno91dKhfU5+zsXeIcdKhfukQF5nj9XmJFvVNi9jEWrUO9UGHiLXqFTrULx1ik7HPhxujjUAp5dZZBPWxS7tvTvIFtdZf6fK1aq1/muQnl3bdNsmzSim3X/OUlFIem7O/8ebGJP++y/Pa18kb37aPfXySH13adXOSr6y13tz5iQ2glPLwJL+S5A5Lu1+R5HNrrdef/6xBPC2Lb05ucWUp5THrHnwyoz+Rs3fofGat9c82POeBpZTPbXNSpZR7ZfH1u+fS7huTfE+b4xwbHdqfDp3SoYuVUv5Bkocu7fpAkme1Pc7UaNH+tOiUFp37XPNxAR3anzk7dUQdYkR0aH86dEqHTpVSHlZKedzFj1x53icneXZj9ytqra/u5szGR4f2p0OndOiUtep2tGh/WnRKiy5mvXqVDu1Ph07p0CrzAQzFe/z+NPzUEb3HPy2u0RsNHdqfDp3SoYtZ81ilQ/vToVM6dO5zzccFdGh/5uzUEXWIEdGh/enQKR065fq8drRof1p0SotOWaveng7tT4dO6dDFrFWv0qH96dApHVo1tfnY+pPhoH48yec39n17kqtLKZe3PNY1W9xp8t9l8RtsPvhk+1OTvKyU8lW11j+55UGllNsl+Zok39d4/vfVWt+4zcmUUu6T8+fsXo3t22z4XK+ttb79gpd6dSnlhUl+Icnv1Fo/cM65PDjJU5J8ceOPvr3WevUFx+/Eob8epZSHJPnVJHde2v26JN+Y5B6llDane0Ot9Zo2T2ij1vrnpZQfSvJtS7ufW0r5l0n+e631xlt2llIelOTHspjVW7wjF38D8eFJXlBKeXWSn07y/JNvXlaUUu6S5ElZ3Nn7no0//g+11j/f4tM6ZjqkQzq00HWH1nlyY/sltda/2vFYU6JFWqRFC4dq0VHMx8B0SIdm16Gkv/kopdw5yd3X/HFzQfnuG17rTWNaXOuYDumQDp3V1XzcJ8nzSimvyeIHaL+Y5HW1nv9blkopH5vka5N8Q+O8bjjZN2U6pEM6dFZX82Gtuh0t0iItOqvr+WiyXr1Kh3RIh86a5XwAk+Q9fiYN9x5/yjV6o6NDOqRDC67RG44O6ZAOLbg+bzg6pEOz61Di+ryR0SEd0qGzXJ83DC3SIi06yzV6/dMhHdKhs1yf1z8d0iEdOmuW87G1WquPgT+S1A4/rtjyNa9I8r7Gcz+Q5PeS/HySFyd56znH/+Ukt27xub2hg8/pWVu8ztuXHv93SX4ri3+kP5PkpRvO47t6/rs+6Ncji99o1NUsXdXD1+PWSV50zmu/JYs3oOck+f2T2Vz+8/cl+Ywt57x57Hcn+T9ZLLA9O8nzT17jpjVfh2cM3YieZlOHdEiHDtChNa95uywulFg+3hOGbMBYPjqcHS3Soqd1OEtX9fD16KVFxzIfQ350ODc6NPI5O/TXI8fXob7m48qOviaXD92LA/5d6JAO6dBhvh6fd87j33MyHy/I4gKI5yR5WZJr1hz/vUkeMXQnevi70CEd0qHDfD2uOOfx1qrXf72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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import asyncio\n", + "\n", + "from matplotlib import pyplot as plt\n", + "\n", + "from apd.aggregation.query import with_database\n", + "from apd.aggregation.analysis import get_known_configs, plot_sensor, Config\n", + "\n", + "async def clean_magnitude(datapoints):\n", + " async for datapoint in datapoints:\n", + " if datapoint.data is None:\n", + " continue\n", + " yield datapoint.collected_at, datapoint.data[\"magnitude\"]\n", + "\n", + "TemperatureConfig = Config(\n", + " sensor_name=\"Temperature\",\n", + " clean=clean_magnitude,\n", + " title=\"Ambient temperature\",\n", + " ylabel=\"Degrees C\",\n", + ")\n", + " \n", + "with with_database(\"postgresql+psycopg2://apd@localhost/apd\") as session:\n", + " coros = []\n", + " figure = plt.figure(figsize = (20, 5), dpi=300)\n", + " configs = get_known_configs()\n", + " to_display = configs[\"Relative humidity\"], TemperatureConfig\n", + " for i, config in enumerate(to_display, start=1):\n", + " plot = figure.add_subplot(1, 2, i)\n", + " coros.append(plot_sensor(config, plot, {}))\n", + " await asyncio.gather(*coros)\n", + "\n", + "display(figure)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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6dsopNa7NWITLFnJ3RjPKfyriSIBZodDtcxvcPk+Y4sFrWZtxhlh3Ntg1VdhrxUYLfAMAoML2ccx/QdIySWs03DdzT0mHKP8BtmdIusMYc4K19uWwOzTGzJf0F0kdo2ZbSUskrZDULek1knpGLX+fpMnGmLdaa72w+wIAAABQRV6EpjnBaDX3rQX+96afLrh625Kyum+FlMlJR82VuruM73aeZ/XkS9KTL1lZK+030+jVu0mxmNHmlNX9K6RpE6Utaekfa6w8KyXi0sCQFDNSsl06aT+jWVP9yweAUm1OWd27XPKsdNSe0hTH51ip+iv0E+eKvsqUgx2stXr2FenR56WcZ7XndKPXvEqKt9XXd823Hd/Jj72w4985z2rp89KKtdK8PaQ5PfX1GgAAQO396Db/NkQqI12z2CrrDS/frdvosNlSop32A2pvw4D/fILRAH8EowEAAAAAAAANxDXYPm7aI5XTZtrUbjqUsUOB67kCGlqZ65i4AsPGrOcKXnAEOhTfb62D0fz3N1K/oDC9tJdWZxu/2AAAgNpan1mrx7Y85LvskIlHqrt9Wo1rNJY7fDdcG7PZ2IBgNBUGo5UZKldq+3Z0+UH7arQAsbDh2IRoAwCw3cOSfi3pRmvt8sKFxpjdJH1V0r+Mmr2PpD8ZY4631hZ9PLkxZpaka5Ufina3pHOttU+NWi8h6TxJ35U0csP4zZK+Juk/o7woAAAAAFUSJbPYy1WvHojsukes/ulQo1uftHrHzzxt3nb7viMu/eZDRu85PJa3fv+A1Tt+6mnhM6PnWh09V/rYfKOP/t5qazrMnq0+f4rRN99uFIsxYBtA+a5favXuX3ga2NZtMBGXfndOTGe+tnKfMf2Ogf5RrSQYraLSGasPXWZ15UOjb0laHbCLdP2nYtpjWmN8z2xNW21JS2f8r6fFq3bM/8CRRpf+s6m7kDcAAFA73V3utug7fz76nozVrKnSDZ+K6aDdaDugtlzv0W6G2QC+YsVXAQAAAAAAAFAvctb/icBxE/0ZCGEG6DfaIP5acB2T8MELjmC0IiF0rv3WOiyjWP2D3jO1DnEDAACQpDv7F8jKf7DZCVPfWOPa+EsYR1vPEarb7ILyQUxBXzRnqFyIY2etdbZvR8p1BhSPKj+Vc7dzO2ON1WOp3XTIqHiHP0K0AQAtzkq6XtJh1tp51tof+4WiSZK19gVr7XmSPlGw6FhJ7w65v4skTR01fY+kk0eHom3bV9pa+yNJ7yrY/nxjzB4h9wUAAACgmqKEneX8+4egOp54MTi3+m2XeNqw1epdv9gRiiZJQ1npg7+2WrMpf/uL/mYLQtGG3bNc+sClYUPRhn33ZqsbHw+/PgC4bBrMD0WTpHRWev+lnvo2F83vD61/oDJl9fZVrk6QfrqoMBRt2JMvSZ/8Y4Tw1nG2cp30uatsXiiaJP3uPqufLeI9AwBAK5vVHX7d5zcMt4NDPMcKqKgNjmC0qY3VzRCoGYLRAAAAAAAAgAaStRnf+XHTHrmsMAPZGew+VsI4ghFCBpS5wsGKhdC5zkXYQLZKcdd/WzCa4/hIrRvsAQAAxk/Gy+jujbf6Ltul41Xau/OgGtfInzN8q8YhuPXCKiAYrSC0q5xjl7FDzn0ligWjjWoXB7XJGy1s2hjDtSIAAMW901p7hrX2obAbWGsvkXRNwewPFNvOGLO3pH8eNWtI0oesdd9os9b+RdLlo2YlJF0Qtq4AAAAAqsiLEDqS9e8fgur466PFB0J/4Rqrfp/Bq1lPunZJ/vZ/WlzZgdVXPshAbQDlu+ExmxeKNmIoK10X4nMwrP4KdWfr7atMORh2lU8o2ogbHx8OzqsHqUxwPZatka592H+dv1XwfQwAABrPblOLrzPa0uelf6ypTl0Alw1b/ed3dxV/mCnQighGAwAAAAAAABpIxhmMFo9cVlCA1Yhkgw3irwXXMXEFhoVdr1iwgHu/tQ3LcO1vR3CE+z0T9hgBAABUypLNd2tLbqPvsuO73yhj6qMzSZjwrdYS1GG9MBjNv/0ZJkA4ONBs+JyEaf8HnadkrPEe5RgmzI1rRQBAK7PWrixx058UTJ8YYpv3SmobNX2ttXZZiO2+VTD9LmNC3BAGAAAAUF22csFoGwesXuy38jwCQCrh+Q3F17nhMfexHj2QekvK6sX+ClRqlN/fz3kGUL5lr7iXPfJc5fbjFyJZinVbpc0pPv8qZVlA6IdnpRV1EkS3en3w8ruXW6UczaS7l1e+PgAAoHFMSkTfJqiNDFTDynX+1zg7T65xRYAGQTAaAAAAAAAA0ECyzmC09shlhRnIXiysqxU5gxFsSl6RTszWWmewWLHzUU7gQyWlrSMYzYwEo7l/Uax1XQEAAO7ov9F3fsIkdfjk+bWtTACC0fKFj0ULOnbFA4SDju9IuWHKT3n+ozvaFFd7LPq12ngLE6LNtSIAACV5uGC60xjTXWSbtxVMXxZmR9bapyTdP2rWBEmnhNkWAAAAQBXlcuHXzfj3D3niRavD/iennT7radYXPPWc7+lLf/YISCtTmBCfl/yfwyJJ+uFtVqnM8DlgUDWAenX9Uvd3xU8WWp3xo5z6Npf/fbKxgj9x9tZJWFej25yyWrc1eJ3v3FQfbYli5/x7N9dHPQEAQP3JRLjtMmJlH20L1JarvTunp7b1ABoFwWgAAAAAAABAA8nZrO/8UoLRwgxkDxOe1mqCjtuQTQdum7UZefIPTyt2PoICGayt3Q9yrvCIkfrFTJvaTYdj2+LhFAAAAJWyOrVcvalnfJcdMeVEdbZ11bhGbq4Q3Fq39eqFjRCN5gwuDhEqF9Q+HSk3XDCa/76SbY15PRXmWnEkGBkAAETid3PX/0aaJGPMTEmHFGx/d4T93V4w/cYI2wIAAACohiIPW8uTGdv/YGvaav53PC1eJY3cOu4fkL5xo9V3CAkpS/9A+cfvk1cMl3HWLyOc5wg2pzjHAEq3JWX1wMrgdW54XHr7T8v/DKtkMNqKtZUrq5WFCZi74gGre5aP/3dNbxnhJOTEAgDQ2oZKCEbrXVf5egAug0PWGbw/p6fwkbEAJILRAAAAAAAAgIbh2ZwzVKuUYLQwoWdhBsS3GldohVQ8fCEoeKHYsU46ggc85ZS1/k+KrgbXaxhdf3c4BcFoAACgdhb13+Bcdnx3fWVCuNqYnryatvXqh7vHuikIRgsTXObiCjQbXW5QaF2xcho1aJprRQAAqmavgumspKAhiQcVTC+11m6NsL97CqYPjLAtAAAAgGrwygtGu+4Rq3WOq4JL7yIJpBwbBsov44r7rbakrP6xpvyy/IQJtQEAlz8/HO574q5npadfKu87ZcPWyn0nlROShR3Cfodcdvf4H+9yvu9SGSmVGf/XAAAAxkemhGC0VbQ3UUOr17uXzempXT2ARkIwGgAAAAAAANAgsjbrXBY38cjlhRnInnCEcbWyoOMWFKxQbHmxYx2031oGjrlew+j6lRNOAQAAUAkDuS16aNOdvsv27jxIuyZ2r3GNgiVMfbT16kVQdzNjCoLRHMeuWGixJKWt/7FtU3x7+LQzGM2m5NnhQYzONnKDXk8FhUFHWQcAAIxxZsH0Q9baoFSEAwqmn424v+VFygMAAABQa16EEbqZoTGzlr7gXv3ZV6TBIQbzlqq/AsFogxnpzmXll+OyYm31ygbQ/IK+Q8auW973yQv9ZW2ep3dd5cpqZWED5h57fvzbEivLDALdWPxnYgAA0KSG3MNtnAghRy2tD3gM2m7dtasH0Eiij5YEAAAAAAAAMC6yNuNcNjJoP4pwg92Lh6e1mmTAcSsWWpG2AcFoRY514H7toCZqcuD2leJ6jaPr53pvFQuOAwAAqJR7N96mjB07aEySTph6eo1rU1yySPhurdp6dcP6d7g3MmPmBQWXFeMKT8tr2waE1g3ZtJKmUynPf8RcZ1tX0TrUo6BrjyjrAACAHYwxEyWdUzD7z0U226tgenXE3a4qmJ5mjJlqrd0QsRwAAAAAFWCtdd779JVJj5n17QXB269cJ+2/S9SaQZI2VCAYTZLe9L9B+dfleWaNlXzukxey1uqHt1n94g6rp18enjenR+rulF6/v9GFbzbqShgtesbqh7d5euwFybPSXtOlDx1jdNbhsaq9BgDjp3dt+O+g5Wul/gGrr/7V6o5/WG0e9bPbvjtLHzkuprfPG/48um+F1fdv9vTI81Ju20fg6vWVq/d9y8c/qKsR+J2vA3aRzjshpi0pq/OvCnccH1gpxf4lp50nS10dw/NGh4XsvpPUtu1rYk6P9L4jjM4+prLfGyvXlXfON2yVdm6xn9cBAMCwTIQ8+hErCeJFDQ34d2mVtKP9DSAfwWgAAAAAAABAg8ha9yNsqhWMxmD3sYKOW7Hgr6DgtGLHOig4rZaBY67XkAgRHuEKngAAAKgkz3q6o/9G32VT4jvpkImH17hGxQW1MYPCdZuVlauze4RgtCKhxUHrjG57BwcjDyoZ63S2x8Ncc9WjMAHZhGgDABDZNyTNHDXdL+lXRbYpfCb0K1F2aK3dYoxJSRr9xT1FUlnBaMaYGZKmR9xsbjn7BAAAAJpClFA0Scrkj5T87s3FA7dWrCUYrRTW2ooFo1XTF6+1+vfTiq/3+autLr4l//02Emrz8HNWD/RaXfiWmE79gZc3aLy3T7rlKauNg54+egLhaECzWdFXfJ0Ry9ZIJ37X06PPj13W2ycteMLT5Wcb7TvT6KTveUq5n/datodWSRsHrKZ0FQ+GbFWZrNUJ3xkOuhytt0+6/rHSAjvXbPKfPzr0rrdP+r+nrdZt9fT5Uyr3vdEb4b3q55olVl96E+8XAABa0VAJwWgbBmhvonZcwWhdHZIxvAcBPwSjAQAAAAAAAA0iLliMLgAAIABJREFUa909iOIm+q2+cIPdG3MgfzW1mTa1mw5l7NhfJYqFL7iWxxQrGm4XGJYRIvShUsoJj0jb2tUTAAC0rqcGHtHazMu+y46bcqraSmg7V1tQ27y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ec0p3wdlzr8XSgefxROB+5b5+XFu4FTduOR+HVB2Bo/0nodLNZUmUD1TX5hWuapS5yrNcGyJnYjAaERERERERmbLaGTNo8znSiCI85L7XBH70aXvzymeyzr8yQ64qGNCgJza86XHGw9RsUgajJXnG41c8q11rI4jHyQyL65Fqu3OimEn4kFsHGhTP6qNxoDcI1Fl/yVnR85i8PMqp4YFhi8Fobf2ZrQelz5oOe+kPbf05CkYzWffMQtOIiCh/Jdu/MxiNyBpVMFpFKdAv6VsRLOJgNNVvNS4WF3C78rPzFREREREREREREZFKWIwoy0r00izWhAqd3ysPg/gotApb9A3yafI4EEoV5tMbDeDBbbemPF8X3JhXthAfr/wkg2Icbjg+iLcHX0VbuBUGzBubhuLqBoOqdYmoGPg98vW/PbxpWvtSu2rd9fhYxYGYU7rLhOFBxbbrsxGmYRaiFjSGUIGdwWhCCHwQXI61oZXKMMVMGjVCWBdahaH4QFrnW+2egd19+2BJ+b5Y4tsHVW4Hvlma8ppXL8Ex/u/i3o4bpeV/6rwDc0t3VU6vIx3BaOrjeWMehwEXKl3TcVjN57FPxUF4ovt+vD7w76TTvD74b6wYXoaj/Sfi0OrPQU9DoF4hyvWxLJ08mhfzS3fDAVX/BbfmyXV1yKYuRdBuPt+HIEo3BqMRERERERGRKavBaAPqdklTBMPqmSYLuCo0ZuFPkwX1clQmvuVjoAciEobmLZ5GJaoAMD3J85f6Sg2ytzne+6rA3SeKvH+AY3U7DaX3RVgZFTULRnMBM01eYNMxwGA0O+ImbZ16gtmrhx1m4VSJ2hmMljdaOu2Nn6tla7ZvsrpeEhFRfjHb9wNj59hxQ8CV7KKEqMipLlsrSuTBaPl0/Zpuya7xg2Gg2peduhARERERERERERFlS9hQv42GwWiUTn5Ps3T4YLwfiMsbpDR45dPkA1X4hYDA0oHnpzXvVwaAV/v/idNmX4YyFx9eOFFftBu/3XIxuqLt05qPVytBlas2TbUiyj+qYMCwGJ32vtSuf/Q8iu83n4X9Kw/dMSwUH5KO69PVYWdTxnWpG15PDk18pOv3eKn/acvzdqoSrRSLfHthSfk+2N23L5q8s/O+TwE53wGVh+HFvqewcbRlSllvLIDe4YBy2nQEXJkFnTZ4Z017/pQZle5qnNh0Gg6tOhJ/6rwD7ZFNpuOHjGH8sfN2LB34N05o/NGUQE0CHu66Gy/3P5PraqTNy3gGywZfxI9n/RIeneFo+SQQ3SYdzmBqop2KrLs5ERERERER2WU1cKl32OKIADoH1WWuInuWlKyTfaJBvWrqwO7pNVbIN6r1MVkGgVlI1ivrUq+PU1jdToPhzNYjnVTbhlsHNE1Do2RzGNeR3heAFbwRk8CBbhv79myKWNx3Mhgtf6y1GYzW1p+bddMs/CzMYDQiooIjhLAUfJlP59lETlOp6Ms2POrMa5FsSPbNizk0joiIiIiIiIiIiAqXKhhNgwaP5s1ybaiQpdK5WBUulg/qPI3QkLmGua2j6/DG4AsZmz9Nz4t9T007FA0A6j1NDAuioub3NOW6CjvEEcNjXX+AIXY2pA1OCi4bV24SdjZZiVYKF9zSsmBC8FpHeEvehqJp0DG/dBG+OOM4nDHnSly/6AH8ZPYvcXjtVzGzZA73c5QVmqbhuIYfpDStDte0P9/svLYxj8OAi8Wuvt1x/vyb8HX//6JESx4g3jq6FtduOhsPd96NkXgwCzXMD+3hzQUVijZuTWgFVgy/ketqkE2BSId0uJPOP4lyjcFoREREREREZMowrI0XkL9oSMo0GK3IrlRjFn9fABjSJQ8nu+U3wApVqsFoC+rUZZc+aWMhOJTVYLR86jyt2jbc25/ned0a6hXP6zsGircDfSpGTcI+uuVtJXLOSkAJAASGuC7kix6bIXy9OXo2axbKx2A0IqLCE7MYxjok76dDRAmE4nSvokQ+PJhH16/pluxeXDH/NkRERERERERERFS4VMFoJXopQyoorRpSCDlrSCFMzSk8uge17vqMfsaq4DsZnT+lblXw7bTMx+9lp3wqbjXuOkcFtfbGAmgPb97xd8iQd+TwuSotz1PTNGWQWmLw2od5ts+v9zTisOov4IfN5+D6hffjnHnX42v+b2M3315wa55cV4+K1Pyy3XBg1WdsTaNBh1uThxfaUeupV4YgNngYjJYPXJoLR8z4Gi5ZcBsOqDws6fgCBl7qfxqXbTwVbw2+DKFqxFVECvn65YPg8lxXgWzqiwWkw+sZjEa0Q5F1NyciIiIiIiK7rAYu2QnP6TIJUSu2UBGrHe0BYEiXPJzs2pq+yuSBVIPRjtlPPcJLa4FL8jwczep2GjOAaCw/HmREFduGJ+FFRzOr5eN0DKS/PoVsxKRTfY9Dg9GsHit6+GKjvGH3+J+rABrVvgmwHthHRET5wywQMxGD0YiSU12JKoPRwhmriuMlu8Yv5t+GiIiIiIiIiIiICpcyGE0rzXJNqNBVuqsxp2QXy+NXuKoxp9T6+E60R/n+GZ1/IFJcL/jNF4YwEIhuS8u8Mr0OETmdrunYvXzfXFdjgq5o+45/JwaXJVIFnan4FOOHjJ3z74o6e59fppdjv4pDcELjj3H5gjtw+S534n+afoz9Kg9Vfj+iXPjv+u/AqykazUjMK10Ijz79gEaX5sKS8n2mDHdrbiz2fWza86fsqfHU4QfNZ+O02ZdaCrUbjPfjDx2/xm+3XoyO8JYs1NC5Cvn6JRBJz/k/ZUdcxDFihKRl1e7aLNeGyLkYjEZERERERESm4hbzogI2wnM6B9U9PIstxMYsYGWyQVkwWtv69FUmDyiD0ZLc4Zhfr+FrU5/f7HDFUwLvbs6PwDAZw0auW8gkBMtJVPsed8KybqqSj2MnqJGA0ai6zKnBaFYDqIrtmJLPzNZDmVwF0Jite239QDiav8cSIiKayuo5R76cYxPlkuplo6pgtOEiDv9KdkYZ5D6HiIiIiIiIiIiIClBYKILR9LIs14SKwdH+k+DRkgdLaNBxjP8kuDVPFmqVOZ+bcTRq3HUZm39PtAtxwTcKOk1/rAcxYbNRlsT80kX4RNWn0lAjovx2VN23UC5ry58jnZG2Hf8OKYLRfLq9ILByl/z7BeNDO/7ttDAZF9xYVLYnvlr/bZwz9zpcv/B+/HDWufhkzRdQ723KdfWIlGo99fjuzNOhW4j68GolONp/Yto++8t1x6NML58w7Et130KFW9E5gxxt9/J9ceH83+Kr9f9j6RpnbWglrm49E38LPKAMKC903WkKD3aiQv5uhWgkru7wpDovIypG7lxXgIiIiIiIiJxNFUQ1WV8IiMUF3C4t6bidg+qy1m6gPyRQ40s+n0IQsxFoNSx5mCq2rENx/FJjVAFguoUf4dKv6XhyhfoH/3+vC+w3Nz9/TavbKTAW2lDty1xd0kX1nbSERVRfqUHWZdypYV5ONWrSJq0n6LyQJyEEIhZDJXuGx8bXtPzctotJ2GbbyJwFoyVZ94693cBTp+lc54iICoTVIOuR6bclJyp4ymC0Uvl1XbCIg9GShZ8X829DREREREREREREhUvVIbtEL81yTagY7F6+L86ddz3eGVqKQETecbzGMwP7VByEXcqWZLl26ef3zsS5867HW4P/wdZwK4TqwU0SYTGC94ffnDLcQBy90QD83pnTrSqlUcAkFOHjlZ+EliSIxa27Mb90NxxU9Rl4dcXbjoiKyNzSXXHuvOuxfOhVbItsUT4DT7cNo6vRE+2aMjwxGC0xuCyR3UANVZBaYvCaat8yv3QR/J5mW583HdXuWizy7YVFvj1RyiBdylP7VR6KX8y7Du8PL0N3ZOp2DgBNJbOwb8UhmFkyJ22fO79sEc6bdwOWD72KYHwIe5Tvhz3K90vb/Cn7PLoHX6r7Jj5R+Sn8peturAq+bTp+HDH8s/eveGvwPziu4f+wT+VBWaqpMwSi8pDPbB/LpmM4PoDVofemDB+KD2DUGOGxMU8EDfk5HAD4XPYCbokKGYPRiIiIiIiIyJTVwCUhxsLR/Baen3Wp79sgZgDPfiBwwoH5HSjyziaBsx8xsHwTUOMDjt1fwzXHaCjzTvxedoLRBmUPJ7esm2ZN84tqfbQSjLZbg3n57S8J/OZb9uvkBHEb61Eokrl6pJOhaDGQuKzrFPd5u4edF+blVEIIjJisE90ODJmLG+pQhcnCsbF1vpztshxPFYzmcclDaYZGc7OdJwvIefYD4In3gGPYPoCIqCBYDWPNl3NsolxSnb2pztWDRbxdJTvTNbuGIyIiIiIiIiIiIspXYWNEOpzBaJQpzSXz0FwyL9fVyJpq9wwcOePoac0jbIzizHXHS8sC0W0MRnOYQEQe+FDhqsL/Nv88y7UhKgz13iZ8se4bWf3Mx7ruw/N9T0wZ3hVpBwDERBRhIQ+YtRuooRo/ZAzv+KzeaEA6zhfrjsPHKg609XlEBMwrXYh5pQuz/rl+70x8qe64rH8uZVa9twk/mfVLrBhehke6fo++WLfp+L2xAO5svwZ7l38CxzX8H+q9jVmqae6MHcvkv0s+Hcu6I524eOMpirJtmF26IMs1olQkhs9OZjfglqiQmce6ExERERERUdGzGowGAL1Ba+N1DZqX/+Z5kfIb6ZygZZvAx68y8NJaYDgMbO0Dbv63wMkPTP1OyQJWEvW4Zkwd2NGacj3z0XSC0XwlGubVqcujcSAYzs/1zs52mi+hDapdQOKyrlc8r+9xYJiXU8Xi5uuPE4PRVAFaKlwf8kM4Kh+uClwdkrfjybiIhfXv0idtpFUSEZGjWdnvAwwpIrJCdY1XqejPNjACxO1c7BaQZF87FCnO34WIiIiIiIiIiIgKW9iQNwRgMBqRc5Topah21UrLVCFclDuBqHyZ+D0MsCPKJ43eWdLhnZE2CCEQiqs7cJTbDEZTjR/cHtrRE+2CgLx9JPctRETOoGka9q08GBcvuBWfn/F16HAlnWZl8C1c0Xoanu15BFFD0ai/QPRGA8pjWb2nKcu1SV2tpx4uuKVlgei2LNeGUhWMD0mH63ChROP9MKJxDEYjIiIiIiIiU3b6oAbD1sZ75G3zmb7VCux1qYF1nfnV0dMwBM551MDuF8tvkj60TGB918TvFLMRjNbhltxk7Q9AxGymBOWx6QSjAcB/72s+4tubbFbIIQoxGM3KslYFozkxzMupRpI8t3JiqJjVgJJx25KEcZIzqALvVNu5k4PRVrYhrwNeiYhoJ6vnHQwpIkrdzGr58LgBBOTtfgqekSRnN1+u64mIiIiIiIiIiIjsYDAaUX7we+XBN6oQLsodVVidahkSkTOpgtFGjBCG4gPKQA0AKNcVb6ZV8OnyBpuh7cFoqv2KBg31nkZbn0VERJlVopfiaP+JuHD+b7CobK+k40dFBH/vfghXtZ6ONcEVWahhbpgFOufTscyluVDnaZCWMbQ6f4yHz05W7qqAplnsKElUBBiMRkRERERERKaSdcZMFEzSMVMIgT8uszbD1R3A4osMrO7Inw7m974mcMNz5vV94r1JwWg2ft8Ot6QxghBAX6f1meS5uOL30i3e4TjnCxp2N2nT8ZVbbCwQBymqYLSEZV1XLh+HwWjWjSYJRhscBSIxZ+2HIzYCJQHgxRZn1Z8mWrlV4J5XDbT1y8uVwWgWw1jTLWpx/XNiqCAREdln9bwjWdgsEQGqs/LZNepp2hXniIUu2RVMvlzXExEREREREREREdmhDEbTyrJcEyIy4/fIG2B2sfO946iWSYNiGRKRMzV6m5VlXZE2hIygstznUjTAVCh3yYPUxsPXAtFt0vJadz08utfWZxERUXbMLJmDR3HcowAAIABJREFUM+Zcge/NPBNVLpOGWtt1Rdtx89ZLcG/7jeiP9WahhtmlOpbVuOvg1UuyXJvpqfc2SYd3K74jOU/IkHc68SnOyYiKFYPRiIiIiIiIyJSdwKWgSUjJSETgm3ca+M499gJq9rzEwO9eyo+wqvtfT/7dXlwzcRyrASsA0C4LRgOA7uJpUKIMy7L4IoTmGg1vnK++HTIcBsLR/AtRKshgNFUIXsKyrq+QL/i+kPPCvJwqWTAaAPSq20zkRCRmb/xX13FdcKornjKwz+UGfni/ehn5Fdv54EimamXOakBO+0Bm60FERNlh9XotX86xiXJJKE75/JUa3IrLdFV4bqFLdo3PfQ4REREREREREREVorCQNwQo0UuzXBMiMuNXdL5XBQxQbgghEIjK2xarliEROVOFqxpluvxN0p2RdoS2h5ZN5tG8tgNeVEFq46EdAUXgIvcrRETOpmkaDqz6NC5ecCs+XXMUNAsRM8uHXsHlG0/FC31/R1zYfLO9g6lCw+o9+Xcs8yvqzGuz/BGKy4PRynV74bZEhY7BaERERERERGTKTuDSSfca+Pdq+QQPLxf46zup1eGnfxRTAsWcRgiBlW3Jx1s96f5izFYwmuKNTz0MRrMajAYAlaUavry3uvyjgL06ZYoQAg++YeDEewz84lEDH7artwFViJhMvnSgtrKsZ1arp1/vkOXodCMWgtG65ffacyZsMxgtIG/zQTn27maBy/6e/Nhep3imMyR/UXTGWQ1dbOvLcEWIiCgrrAayWjmnIiI5l66+tmvvd/a9oExRhciN4z6HiIiIiIiIiIiIClHYkDcE8DIYjchRGrzydqw90W0wCigwId8NxPsQFfLGon6P4iXNRORImqah0TtLWtYZaUNQEajhU4SpmSl3VUqHB+PDMIShDlzkfoWIKC/4XBX4VuPJOHfedZhXuijp+KPGCB7tuge/2vRzbBhZk4UaZl5XAYV8qsLcVOFv5DzK8zhFWC1RsWIwGhEREREREZmK2whc6h4GPv8bA/e+OnWiR9+eXmfWI24y8FarczvEdg4CA/KXVk7Q2gOMRnd+j5iN37fdrXho2FM8Ny1VAWB2gtEA4KzPqW+JrHZIztxpfxI46V6Bh5YJ3PicwMHXGHhjg3wbsBNgGIw4dztKZCUYbVe/etmvKZ7NYlpG8zAYzWpAybihcGbqQdPzh6XC0r7LL29ng5EoMDiS/f1Z1GL7zbYiDfEgIio0Vs878iV8mChXRJKkr1m18uEdAxmoTB5Idp7MfQ4REREREREREREVooghb+BRwmA0IkdRhd/ERAx9sZ4s14ZUAorABwDwexlgRJRvVKGUnZE2hOLytwf7FCFnZny6PIRDwEDYGEEgIm+czf0KEVF+mVu6EL+Yey1OaPyxct+faGu4FTdsPg8PbrsVw7HBLNQwc1ShYX5FyJiTqcLceqPdiAm+eTMfhAz5eZwqrJaoWDEYjYiIiIiIiEzZCVwCACGAi58UiMUnTvj0yunX5cy/2EgRS4PBEYFfPmFg1wviOPCquDTwbZzVECYhgLWdO/+2GrACAMOuSgxJbjqLbockeWWBlbAsKz67RD3BPSbLOVu29Ar87qWJX3Y4DBx6rSHtUG5nOx2Sv1zVcVTfSUtYdCUeDbv45eOt2cZQIitGLDzv6HFaMJrNF4vmyzpfbH7/irVtdLdGddmKrWmqjA1WA3LaizTEg4io0Fg9zx5hSBGRKbNcNA2AX9G+rt9CAH0hYjAaERERERERERERFaOwIb8pzGA0ImepNwkMMAvjouwKROXLwqdXsJM9UR5q9M6SDu+KtiNoyBv5lruSB93YmWYoPoDuaKe0TBWaSUREzqVrLnyy5gu4ZMGtOLjqcEvTLB14HpdtPBWv9f8Lhsh9vyu7DGEoj2X1eXgsUx1/BQz0RLuyXBtKRTAuP4/zucqzXBMiZ2MwGhEREREREZmyG4wGAO39wGqb7Rv+fHLyZKul68cCo7LltD8JXP2MwMZuYPkm4P/uF7jtRfnN243d1uu1pXfnvycHyCXT7pbcuOwpnsYkqvXRlcIdjqP3lQ//5ypgXWduQ7We+1D9+Tf+q7iD0SaH4C1WhCZtCKS3PoUqbCEYrXvYWSFzVuqcaLBIwxScLBgWGLW4HHdr1FDjk5e9szn766bVYL6N3AcRERUEq+fZDCkiMme2KWkaUOOT3xMaCGWmPk5nFiQHcJ9DREREREREREREhSks5I2aSjQGoxE5SZnLh0pXtbQsELX4hmHKOFVInd+bf4EPRKQORgtEtmEo1i8t86UUjKYOTtwa3ggD8gaUfq86NJOIiJyt0l2Dk2b+DGfNuRrN3rlJxw8aQ3io8zbcuPl8bBndkIUapk9/rAcxIe/E0JCHx7I6T4OyrDvCa7N8EFIEo5XrDLMmSsRgNCIiIiIiIjKVSjAaAKzetnPCkYj5TJqqgGP21fD5PZLPd955BroGMx+EsmKLwANvTP2c370k/+w2+TNFqYGRnfOI2XxJRpu7eerAbgajTQ7LsmLJTPVEiy8yYKS68qfB8x+qyy54TEBM6iVtp6qD+RKMptg2Ji/ruXXy5djR76wwL6eysu70BDNfDzusBlONGw5jyjZDudVu45hZ5gH2VzxjfXdzeupjR9Ti+teS44BNIiJKD6unECM2g1uJaCdNA6rK5GX9oeI8p0p2nTbCYDQiIiIiIiIiIiIqQGFDEYymK24iE1HO+D3ycC1VGBdlXyAqXxYNimVHRM7W6JX0HwBgII7No+ulZakEapTqPmiQt8veOLJWOV29J//CZIiIaKKFvj1w/vyb8HX/9y0FlG8cbcG1m87GI12/x0g8P95+2W0S5JyPxzKvXoIad520jKHV+SEYH5IOTyXglqiQMRiNiIiIiIiITKnCiZJZnfBM/YqnzXt03nKCDo9bw2+P19Fg4RncnpfsDK1atkHgj8sMvLkxvZ1lf/28fH6rO4BQeGqZnZCXgZGd/7YasDKuwy1plNBTPDcslcFoKdzh2D3Jfeu7XsldB+wKk+cIMQMYSmgHKISwHNgATJzWyVRfaXIw2qwa+Xh2wgqLmZVgtG75S0hyJhKzN74hgBCDAxzFzjGzxA3sN1fe0OadTdnfT1td/1q2MZCPiLKjPzR2PfTEuwI9w9zvpJvVX5QhRUTmzE6LNAA1PnlZ/4h8eKFLdhqZ7AUERERERERERERERPlIHYyWvEM2EWWX36sIRlOEcVH2BSLydsWqZUdEzub3zFQGlm0Nt0qHpxKooWs6fLp8uk2j66TDq90zeL5GRFQgXJobR8z4b1y84FbsX3lo0vEFDLzY9xQu33gqlg++4vi286pz5HJXZd4GUfkVgW5mIXDkHCFD3lmrPE/XR6JMYTAaERERERERmbISmiOztnPnv59aoZ7J8gt1fP2AsQd1i5s0rLtKx6IG83n3BIH9rzTw7d8bOORaA9+5R+Dgawz88H4jbTdSn1ulnk9PcOqw9n7rn5vYsTdmM3iuXRaM1r4BImYzKShPqYL6JodlWbH7TPOJHnojdzflh5OEl/UmrIN2V/mhPOlYbjUETxWM1j6Q3voUKivrT0+eB6MB+RMIWCzaB6zvuEo8wP5z5WWrtwGRWHb31RGLgaZ9ISAgf4EPEVHaLG8VmHfe2PXQsbcb2OdyIyehkYXM6vVwiCFFRKZMg9E0oKZMXtafHy8TTbtk+x4GPxMREREREREREVGhiYs4okJ+85NBG0TOo+p8H4gwGM0JhBDoirRLy1TLjoiczauXoNZdLy0zIG/UmGqghioYplURjOb3MHCRiKjQ1Hrq8X/N5+Cnsy+xtJ8fiPfh3o4bcfPWS9AZactCDVMTUISF5fM5cr1XXvcuXps5niEMBOPyzlo+V2WWa0PkbAxGIyIiIiIiIlOpBqNt6d054ahJiM3+8yaGU1WWavjgUh3HHWAeWvX+VuBPb06s3D2vCrhOGQtMu/NlA/FUKw8gbFLnbsl9p/Z+6/MeSAimiloMWBnXIbupPDwArFxqb0bTIAwD4sW/wrj2FBh3/BKiJ3tvklCGZaUQjLbHTMDrVpe/th45e2NJstCgvoTO4XZX86HR/AhtsBqC11wjX/iBISAczY/vmktW1p+BEWf9jmb7Z5VBBqM5SpuNY2aJG9h7lnw7jxvAtiyHIMZtBJq2dCYfh4hoOk68x5gQ/tneD5z+Z5vJy2TK6uUAQ1iJzCXblGp88uH9eRLsnW4MRiMiIiIiIiIiIqJiEzHUD1tKGYxG5Dh+rzwcIRDdBkPwmXWuDcUHEBby/apq2RGR8zV6Z9kaP9VADVWgWkxEpcP9ikAWIiLKf3uU74dfzv8tvlJ3AtyaJ+n4LaH3ceXG0/G3wIOIGOEs1NCe7qg8LKw+j4PRVKFu3YoQOHKOsDECAfn1c7meWsAtUaFiMBoRERERERGZSjVbLDH0ZEDRkXWvZvlwj1vDX07RceeJKaRdYSww7ccPCRx3h5FysJVZ8IksGG1zr/V5J/4eMZvBaO3e2dLhYunT9mY0DeK6H0Nc/D/A0/cBD10P8d39ITatycpnpzMYzVei4SefMZ/wyRX255sOydanaQWjOe/5gpTVZT2rRj2PbYPpq0+hsrL+DDosjCASl1fabXKnk2ElzmInTLTEbb6dd2Q5GM3OPnfNNmeFChJRYWnvF9IAxtfWA+u7uP9JF6v7/f5Q8nGIipnZrRlNA2rK5NfmxbptJbuVxWA0IiIiIiIiIiIiKjRhk2C0Er0sizUhIiv8shf8AoiKCAZjfVmuDU0WiMgDHwD1siMi52vwKjpeKKQaqGE3UI37FSKiwubRvTiq/lu4aP7N2LN8/6TjxxHDP3sfxeUbf4r3h9/MQg2tC0TkYWH5HB5crzgOd0c7GVrtcCFD0jl1u1QDbokKFYPRiIiIiIiIyJSqI/g1x2r4hsk9zfZ+7AglUwWjXX2s+WXpDz+p4xPzLVRS4Yn3ANcpBhZdGMc5jxoYjVrr1R6LCwyahOj0DE+cT19QoGvIer0SQ4ZiNu8zvlB5hLzg1b+nHAJnh3j+L2OBaIkGeiD+9OuMfzaQ3mA0ALj2WA0/OEw98TG/M3DDcwaMVBMCUxAKi+TBaMGd/zYL8ZNxWsiVitVl3WwSmNRmI3ypWFkKRnNYqJhqna/xqafJl/W+WHTY2DZLPWPLtlTxkqn2bAej2djntvBFS0SUQWbnOW9sZDBauli9xOrnuQaRKbNNSYP6XH5wFFm9HneKZF+ZwWhERERERERERERUaMLCJBhNK81iTYjIigaT4ICuqDqUi7IjoFgGpboPFa6qLNeGiNKl0TvL1vjlKQZq2A1UMzsmEBFR4fB7Z+Insy7Cyc3nocZdl3T83lgAd7RdjTvarkZPVPIW4CwTQiAQVQSjeZqyXJv08XvldY+JKAZiSTqmUU4F4+pgtHJXagG3RIWKwWhERERERERkShVAU18BPPwjF147V35pGY4BPcPAaFQgEpPPo8bCyxyfO2P6l67rA8ANzwn817UGVrULvL9V4MN2gVhc3tO0NygdvEP3pHtPLTbv0Q6Edn5uNG5v2l5RiYCrfmrB1vXA5hZ7M7NIDPVBrH0PYsWrEJedJB/p9X9k5LMnU4ZlpbiaeN0a7j5Jh9/k2e85jwpc8XT2OmKv60oewNCXsA7Z7SM+5LCQKxWrwWjVZYDPKx+3ncFoSeVjMJqqzl43UFEiL3uztfjCFJysrd/68vC4AE3T0FwtL2+3Ma90sPNpW/gskYgyqEwRGAkA63LfhqRgWD3X7g9lth5EhUzT1MFoQiS/R1OIkt0TGIlmpx5ERERERERERERE2RI21I1TvLqiMQgR5YzPVYFyXd7oMhBhMFquqYLR/J4maFqKbyEmopyzG4zmSzFQw+50fg+D0YiIioWmadi38mBcvOBWfG7GMdDhSjrN+8Nv4vKNp+EfPY8iJnLX6Gk4PohRQ97Qsz6fg9FM6q4KgiNnCCmC0TRoKNMVDSqJihSD0YiIiIiIiMhUsnCiuTPU07b1AwMj6vJqC8Fo1T4NHTek5/L13S3A3pca2PdyA3tdaqD+TAMPvD41+a1ryHw+gUn3nt7fai+UpT/hN4nZDEYDgJtnnCovWPac/ZmZEMFBGJd8B+KoJogfHATx0yPUI/dugxjKfAqVoQjqmxyWZddndjOfwWV/F/ioKzvhO2u2Jf+cvoT78XaD0ZwWcqWi+l6T2+VomobmGvm4dsKXipVqm0o0aLIfzwVVnV0asM9sedmzK7kuOInV0MLyEuxojDdTEYzWMZCmSllkZ587NMr1jogyx2x/9FFX9upR6JKFE43rC429UZCI5Mw2Dw1Ak8mL6duzfL7nBMnOOUOR7NSDiIiIiIiIiIiIKFvMgtFK9NIs1oSIrPJ75R3w2fk+9wIR+TLwexleRJTPGrzNtsYvTzEYrdxl8rZxiXwOkyEiotSU6mU4xv9dXDD/11hYtmfS8aMigie7H8RVrWeiJfh+Fmo4VbfJdUo+nycztDp/BePyzqtlejl0LXnoIFExYTAaERERERERmUoWRNVYpQ6lShaMVmMxwL6xSsO7F6X/EnZwFPjuHwT+9z4D5z1m4Du/N3DNswY+dpl5UtCmnol/P2MzcCeQcO8qpvio2nivcvrH6k+QDhcbVtmqh4oQAuK9VyC+6AdeeMT6hJvXpuXzzSQL6kvVtV9PPoMDrrSQIJUGk9cvmd7gzn9bCbZKlC+hDXZC8GYpgtGshi8VMyshT04L01PuB3Tgq/vIt+WVbfmx3mfK4IjAI8sFnnxPoDeY299BCGE53KLcu/PfzTXyZZvt7dzOPnfIYdsOERUWs2P4tsHiPealm9VAzLgBBMOZrQtRPjMNRtPM7y0V43Udg9GIiIiIiIiIiIio2IQNeSNDt+aBS3NnuTZEZIXfIw8PYOf73OuKypeBapkRUX6oddfDo3mTj7idT08tGM1nI1Ct0lWNMpfFDiFERFRwmkvm4sw5V+K7Taej0qV4C3qCzshW/HbrxfhD+00YiKn7zGWCKsC5RCtFlUvRISlP1CtCq83C4Cj3gsawdHiq4bZEhYzBaERERERERGRK1RnTtf2K0u3S0KS4f7m1T5iGPFWXWa/HPnM0rL48M5ex9y0VuO4fAn98U+DCx5P3fG/ZtnMcIQReapGPN69OPrytf2dATzQuH2ff0RXqz8dcbHM1Ti3YpKiIDSIagTjvWIjTjrQ/8aY10/78ZDIVjLagXsMFR5nPZGgUWNOR+ZCJNgudvvtCO/9tNaxh3Gg0P0Ib7CxrpwQm5SMrq89oFIjEnBOwEjcJzTt4F/m60BcCuuXPDQreu5sFFpxv4Ft3GTj6d2Phn+9syt3y7A2OrVNWVJTs/PdMxfPGjoHsfhc7+1ynhQoSUWFRHQ+BiSG6ND12jjL9JqHgRMUu2bbkdmlorJKXtfU751okW5JlOocixR38TERERERERERERIUnbMgfsJfopVmuCRFZ5fcqgtEUoVyUHUIIBCLt0rIGxTIjovygazoavM3WxoWOUj21wLJyG4FqDFwkIiJN03BQ9WdxyYLb8KmaL0FD8s5dbw39B5dt/Cle7HsKcaHoVJdmqgDnem8TNG2aHdJyzO+RB6OpwuDIGULxIenwMgajEU3BYDQiIiIiIiIyZSWcaLYirKStH7jwcXmPfU2bGHhixeImDf88Q0ddub3p0q2lc2cH1GBYHXzyg8PkN0dDEWBwe6f5mCLQwCNieGbzV5V1aPNIHmxuWmO7Y+yyDQKn/tHAOXd1YMV1N0AcXgksfcbWPMaJzdMPZkvGLBBpui76soZvH2Q+oz0uMfBRl73f+NV1Aqf/2cDP/mzglXXJp2230Om7fxrBaAAQyIOAKNXX0iV3s5oV+6CHlgl2Fk/CMAlVSTTooKAPs+PSEvkzHQDAmiJ9rvPde40JYYrt/cDP/mxxwWfA2k7r45YnnCc0K0JYOwamVx+77OxzhxiMRkQZZLY/Stzv0/RYPVcCJp6jE9FEZpdl4+3KVNd1xRh4beWc02rYMBEREREREREREVE+CAtFMJrGYDQip1J2vo90sM1eDgWNIYwY8ofXDDAiyn8Nsv4DEj5XRcohLz5XpeVxVSGZRERUfHyuChzfeArOmXc95pYuTDr+qBHCI12/x682nY2NI5nvi6YKCatXXNfkE79X/h26I0XagSZPBOPyjn12QmqJigWD0YiIiIiIiMiUlWC0WbXycdr6gfUBeVlVKaCnkGb1uT00tF6r46WzdTzxEx0fXKojcruOlZeO/X3jcZl/U0N/CAhsD+bvNgmYOniBui5t2zv2xhQvt3CLGI4MvqCcPuCqnzpwqA9oXa2u0CR3v2LgkGsN3P6SwA3LG3Dw2h/jqYovWZ5+ik1rUp/WIuX6mIY7HCUeDff/r4anTzOf2QFXGnh3s7WGOw8tM/CZGwzc8oLArS8IfPYGA/e/bp6u0Gah03dfcOfnpxKM1jVof5psU4VQyHYbsxQd6AHgnL+ykZUZq+uPKgAyF1R1dumAvxKoUbzkbn2g+NaFjn6BDyQvAF26HtjYnZvfo6XT+ucmbu8zFcFo2Q7KYDAaETmFWWBXXzB79Sh0do6WvfzdiZTMtqXxUz7VdV17loNwncBKX6ERBqMRERERERERERFRAQkbimA0vSzLNSEiq1RhOGExiqF4ET7gcYhApENZxgAjovzX6J1laTzfNAI1yl3Wp1WFZBIRUfGaV7oQ58z9FY5v/BHK9PKk428Nb8T1m8/FQ9tuw3A8cx2dVCFhhXAsU4W7BaIMrXaykCoYzUZILVGxYDAaERERERERmbISRNVcIw8Aa+8TUL1saGAk9TqVl2j41G4avravhj2aNbhdGvZsHvv7zM/pePKnOhY3pj5/K1o6x/5vFoy292x12XiQS1QVjIYYdAg0xDql5d0ev3zCV59Sf2iCtj6BUx6YuHDDeiku8V9iq/P/BDZC2VKlDupLTyCepmn40t4aPre7epyh0bFwtMCQ+S8Viwtc8JiYUGdDABc+LhCLy6cVQuCNDcnr2ZfwQr9UgtECJuutU1gJZRzXbBKMduNzAvVnxnHcHXGsap84065BgVP/aGDPS+I47Fdx/OOD4rvpb1h80JEPwWi6NrYNz1GEdRZjSMzmXnXZGxtys76vsfHioVhC6I/qXKN7GIjEsvddbAWjhTNXDyIixekkgLHjNhszpIedn7FjgL85kYrZtjR+OT/T5N5SsbFyzhmKZL4eRERERERERERERNkSNuSNCUv00izXhIis8nvUIVtm4VyUWV2K375EK0WVy6ShJRHlhUZvs6XxphOoYSdUjYGLREQko2sufKrmi7h0wW04uOqzlqZ5beBfuGzjqVg68DwMYfLm4BQFovLz5EI4lqnC3UaMEILGUJZrQ1aplo3PRkgtUbFgMBoRERERERGZMhT3ExODqGYpnpVv6ZsY4JTonC+mJ8hK5isf07D6CheMu1x44/zMXPq2bBvrpaoKRnPpgL8C8CueK27sGZs+pvh9PSIKAPDHuqXlgdmfkA4XLe8oajzRnHPlH7yi9GPocCtu7GoatAfeg3benRjRSrHKuztGtITGZ1vXQ7RvtPT5qbITljUdt307+XrT+HMDQ6Pq3sqrO8a2gcna+oFV7fJp/u9+ax2+exMCnlTbqJlkoW5OYGdZz1J0oB/XGwT++g7w2RsM9AyPzdgwBI75nYHbXxJY3QEsXQ8cdbOBl1uc/9ukk9WQp8FphFmmW7J1o9YnL1cdjwqZ2fLdYhKalkmt8sOaVHxCMJp6vE09qdfHLjv73EgMCEeLa59CRNmTbH80nSBq2slqiCwwdp5PRPaNX82p7i21D2StKo7BYDQiIiIiIiIiIiIqNmFD/sY+BqMROVeFqwqluryhlip0gDJPHfjQBC1NLyAmotxp8M6yNN50AjXKbUxrFpJJRERU6a7BSTNPx5lzrsJM79yk4wfjQ3hw2624afMF2DramrZ6jBojGIrLG6GpQsXySb1JuFt3xMZb7SmrQnF5h1Q752JExYLBaERERERERGTKSjiRqvPqqnZA1Y/8Wx/PzgP2AxdoePuXOr6x/8ThlaXAPrPNp/3c7uqyls6x/6sCpuorAF3XsKtfPv267dPH4vJyt4iNzScuT5DpbtxLPuHmFvnwBLe9aJ6i0OJdNHXgl78P7S9rEJu9BD9qOxZ1u3Vgn13fRu3iTpzTcBVicI2N99pTST9/OtRBfen9nIUNGlwW7ppU/8zA3a/IK7WqXd2T+QNJWSwu8Mhya8ELiQFPZh2my0vkwwN58NIPO8FoZoFJibqHgev+OTbjMx8WeH3D1HE+e6OB0SIKMrIcjCZvf5oT8ST7gRpFMFp/EQbEBMPqstYshokl6hiwvn0lhofOrwNUbfPGj8nZYHfvMOSgbYeICkuyY/jPHxEQNkK9SM7OT8hgNCI1s21p/ByvuVpe3iYJHC90VvY9DEYjIiIiIiIiIiKiQsJgNKL8o2maMkSAwWi5E1AEHzC8iKgwNHqbLY03nUANO6FqDSZBLEREROMW+fbEBfNvwjH+76FES36dv2F0Da7ddBYe7boXI/FQ0vGTCUTU1yd+b/4Ho1W7auHRvNKyQJTBaE4VVASj+fTKLNeEyPkYjEZERERERESmkgXQAMCsWvupVPVZDLDfb66Gh3/kgnHXzv8Gbnbh3YtdGLxZx56TnhGe/CkNxl0u/PNMF048WP7dtvaO/b9bfh9qx/db1CCffl3nWC/XqCoYDePBaPLkmu4SxYPNjR9CxGLyMgBDowKn/cm8h+1a724T/tbuXgr9vDugzZyPy58WuHt5OSL6WOJWTPPgprozUbr7EB6sOgHDr/zLdN7TZScsa7qszvKUBwSueMpAJDaxcusD6mk2SMrWB4BhkxCjRP0jgLH9xzALxWhU3A8NKNZbJ1F9L1kwkqoDvcz1/xTNJWnxAAAgAElEQVToHBS45QX1D3fOX4snREQVNjjZ4IhzfhPlfmD7nc4an3zr7Q9mqEIOZhaWsLYzN8u0Q/6yJanE8NAyr4Z5M+TjrdmWve9iNUxw3JDF/ToRkV3J9kd/eE3g8XezU5dCZme/38FgNCIls01pRzBajfw8vnNoLEi8mFjZ90wnGM0wBN7ZJPDQMgMbAsX12xIREREREREREZEzKYPRtLIs14SI7PArAnFU4VyUeapQOtWyIqL84nNVoNKVvNH0dAI1XJobpXryc7ByvdJWiBoRERU3l+bG52YcjYsW3IL9Kg5JOr4BAy/0PYnLW3+KtwdfndaLgrsV4WAuuFHrrk95vk5hFlqt+u6UeyFD3rFvOgG3RIWKwWhERERERERkykoQ1awa+/OtK0+tPulWUarhnV/quPd7Gq45VsNzZ+i44zs7L5ebFd+tfWDsh+kakpfvCEZrlJev6xr7vyp4ziPGws388W5p+ca4Xz4hADxwrbLoT7c+p55uuxbvop1/fOHb0JYcsOPPB99Q30z+3qx7UD36ON5aZSP1xibV+ujKwB2OT++WfJxxlzwp8KnrDPQM76zg+DKWWd469YustvGSRCGAwe3tAU2D0arkw7sV662TqAK7ZCF4JR4NPvkLTqSuedb8ociflomi6Xhv9VsOjGS0Grao1g3X9nWjxicv73dQuFu2BCPq77wmB8+4hBBotxEaE5u0rBcrXsjUksXvYjVMcJyTth0iKiyq64hE1z5rc6dFU9g5e2jrL75zDSKrrLRLU91bEsJeuG4hsLI3CaUYwBuJCZxwt8DHrzJw4j0CCy80eLwgIiIiIiIiIiKinAsLRTDa9pd3EpEz+T3ysK0uRTgXZZ4yGE2xrIgo/zR4FS9XTzDdQA2fnnx6v1fRoJOIiMjEDI8fP5x1Lk6dfbEyzCvRQKwX93TcgFu2XorOSFtKn6kKbq7zNEDXXCnN02nqFcflQITXZk4VisuD0Rg8SzQVg9GIiIiIiIjIlKrDfWIQld1gNJ8X8JVI0o1yxOPW8L1DdZz7RR1H7jGxXjMVL1Ua75S7vkveXbWpamw+8+vk0/cEx/4/OfRlnHt7MNrc6GZp+Stt5RjwzJCWiXuvgAhPTWIR6z/Ak+/E5B+Y4PYZp4z945AvQTvrtzuGD4QENvUknRw/vC+KuFla1zRYCepLl5MOsTfTN1sB/1nGjtCzwJD6N/jXaiAUnli+Zpu936wvNPb/VILRzOrmFHaX9cVfsb68bv63+ffvCQJL11ueXV6zuqkOytuf5oRy3dh+XKpRvKiuL5iZ+jhZ0CQsob0fGMxyWNzQKBCKWB9/8jnIwgb5dt7Wl73vYffwtiGQmXoQEVnZHy3fBLQzrGta7ARiFuO5BpFVZnui8TO8OfJbHADMg8cLkZV9z0g0tXn/4TWBR96euEQueFzgvS08XhAREREREREREVHuhA1VMFpplmtCRHY0eOVhW4FIO4SVN+dQWoXiwwjG5W/NZYARUeFo9M5KOs50AzXKXZVJx2HgIhERTcee5fvjl/Nvxpfrjodb8yQdf01oBa5qPR1/734IEcPeGyWV4cEFdI6sCpkLRLP4BnqyLGKEERXyjjXlevLzMKJiw2A0IiIiIiIiMqUM7kp4KUJFqYZqRRCNTH0ehdfP3PCydHhHv4AQAmsU9wgXb7+nWN3fKi0fGBq7gRWNyRt/uDEWYHZE8EVpeTSu4c09vquoNYDlL0wZZPz9XiwtO0g9zfi8NS9uPq0V2q8eh+ar3FHP+edbSwV4f6AWb2ywNKptyQKR0unr+2sprasHXm3g8XcF+kPqcUajwDuTMu9abN5v7t0evKAKLwSAhip5iFBA/mIJR7G7rE/5VHrT8Z54rzgaZlkN+xicmrWYM6p1fjw0r7ZcXt7voO+QLcEkIWTrsxza9UG7vfE/MX/i36og1vaBlKqTErvBaHZDL4mIrLK6P3p6JfdD02Hn1+szOf8nKnZm/V607efxNT4NfkWbnlMesJFSWACs7OOD4dT277e9KJ/uwTd4vCAiIiIiIiIiIqLcUQej2WiUSERZp+p8P2KEEDTkAV2UOWahBw2e5izWhIgyyUowmpVgMzNWgtX8inBMIiIiqzy6F1+uPx6/nH8z9vDtl3T8mIjh2Z5HcEXrz7ByeLnlz+lWnCfXK65n8pHqu3RHGIzmRKG4ulPfdANuiQoRg9GIiIiIiIjIlCqAxj3pilIVViKTL8FoIhpB0z9vlpYFIxr6hmJY2yWfdsn2e4pVL94vLR8VXowu/ZcyeM4jogCA/UffhT8m/5B3Fhyjrnzr6gl/io/eR+cTj6LfVaueJsH5LzVgePtLNIJhAd9PDQzYCBU66+GJX+y5VQKH3xBH89lxfOq6OB5allqnZlWIk57eTCwAgK9Ew39+oeOQXexP+/XbDbyeJBxucljOn9+y1wm5b3swmlmH6cYq+fCuPGhzpOo4r1rW1T4NVx6dvhXhN88LGHYTkPKQ1a84KG9/mhPK0LzxQAVFm9hiDCsJJQlGC2R5X/DsB/a2qTOOnHiy0aw412jrS7VG9tndLaztzEw9iIjMwnETPfN+4Z/PZJKd/X4xhrASpUPiVdwSRVuz9QEgMFQ8+zMr39TKPZql6wW+8OuxezGNP49j/yviyrDim/5VPL8vEREREREREREROU/YkN/0LNFLs1wTIrLDLBQnwA74WReIdEiHezQvqtzW2g4TkfNZCTos16fXWaPcSjCah8FoRESUHg3emTh19sX4YfM5qHHXJR2/J9qJ29uuxJ1t16AnqujUl0B1bVJIIZ+q7zIQ71OG0VPumAWJTzfglqgQMRiNiIiIiIiIlAxDKDuCF0MwGtavRHPvKmXxy7+6E5GYvGxxkwYhBKpXvaCcvv+C7yMak//ALjE2Yw3AHuHV0nEubPk4BMY6zA7pFRjVSnaUiU0tO//d0Qrx/U/g+NkPKOsyWTgGPPCGwIftAntfalgOXhj3VutYB1wRGsIL7w7ji7818NJaYNsg8OpHwIn3CNy3dGymXYMCW/usdcBNFoiUbktmanjtPBdGf6fj9CPS+yFrEu6tr+0UCCvWJZXxkCdVWBwANCmC0bIdhpSKVJb18Z9I7zI65nepBfjlE6thH0MOehaiqrNr+3FpRrl8PegahHKfW6iCYfPy7uHs/h4fbJV/3uxaoGpSW+ZDdwUOWzhx2Kwa+bLtCQLhaHa+i91gtE09xbXOEVH2WN0fvb3Z/rx7gwKrOwQ2BASEKq22SNj5+sFw8Z1rEFllti1pCad4uzWqr+n+vqJ4ti+z6/xxyYKf39si8OnrDfxr9di9mMAQ8N6W9NSPiIiIiIiIiIiIKN1UnXRLNAajETlZlasW3oR2q4kCUXlIF2VOV0T+hhy/pwm6xm7ERIWi0Tsr6Tg+C8FmptPryQM5GgooTIaIiHJP0zTsV3koLl5wK46sPRq6hRicFcPLcMXG0/Bcz2OIiah0nKgRRV+sW1rm9yje4pmHzL5Ld5Sh1U4TjA8ry3yu8izWhCg/8I4GERERERERKZmFYbkmB6PVWg8kqqvIUIpVum1qwexoG9yKG6RPrlM/NNytEUBPB6qD8oYGADCgVyG6Xh565hY7U7KWRNYq53Hzofdh4a6rUbu4C/MXrsXvak8ZK1i/EgAghMDGb38Oh8x/Ga/6DlPOR+anfxTY61IDrT22JtvhsF8ZCB81G0feXiYt/9GDAgdcEUfT2Qbmnmtg4QVxvLHBvKNztoPRxnndGm76poZbTtBQ4k7PPFu27fwyt7xgv4N3X2hsGrNQjMYq+Q8TigChsLM7laeyrOclfzmMLX9/HxgIOft3mi6roSqDI875HZKtG/MV60HMADamuD/LR63dAlc/Y77cutXPUzKirV8+/AeHaXjpFzq+/18aPrkIuOAoDf84XYfbNXGDbzYJYX3k7SwFo9nMS+zJ8m9MRMXD6v5oax8wPGptH9nWJ/CZ6+OoP9PAnpcYWHihgaazDfw1S/tYJ7IbiDkwkpl6EOU7s00pMRjtKx9TX/D944Pi2RdZ+ab9SfY3Vz1tP+SeiIiIiIiIiIiIKFfCQhGMpjMYjcjJNE2DXxGME4gwGC3bAorAA9UyIqL8VO9tTBoWM91gtHIL0/s93LcQEVH6leplOLbhe7hg/q+xa9nuScePiDCe6L4fV7eeibWhlVPKe2NdEIrWWPUFFIw2w+NXnh8EIgxGc5qQIhitRCuFW/NkuTZEzpemrrxERERERERUiGImHSjdrol/m4WVTFY3vWdtWSM2t8CDGBZG1mNNyZIp5X+r/Ip0urkzgPISDeKDFlQbg8r5D7iqEeveBpTvMaXMg53BaHuGP1TO4+d93wS8Y//udvvxs6ZfY0l4DQ5f+zLE0/8PhmHgC3Ofxnrvrsp5ZFL5IvmbNQAgEgPe3bLz7w3dwJE3Gdj8Kx0zyqd2hhZC3TU408FowFgjnlM/q+HUzwKnPGDg7lem1yl7TcK95TUd6nnNr4M0nK4vNPZ/82A0dVlgGJgnf1mjI6QSjObSNezVDHygziO07V+rgW8ckL75OY3lYDR5+9OcUHXuH183FjWOhSvIdhlrOrYHVxY4IQS+fEvyFIRsB6O1D8iHz6oB9p2j4Z7vmu/M59SOBbPK1oErnhL49kECmpbZA4LJoUiqJ5iZehAR2QnsOusRgf3nmk8gBHDqH6eOExgCjrvTwIqLdew9O08CrtPI7n6/fwSoT/7SXKKiY3Vb+vLe6rJ1XempSz6wso/vD6nLRqMCz35g7zOzcV+FiIiIiIiIiIiISCVsqILR5C/kJCLn8Hua0BZunTI8EGUwWrapwugYXkRUWNyaB3WeRtP9bLk+vcYrPpf59GW6D+VJxiEiIpqO5pJ5OGvO1Vg2+CIeC/w/DMcVHRG22xbZit9suQifqPw0jm34HqrdtQCALsU5sgYN9Z7C6Vji0tyY4fGjO9o5paxbEaBMuROMD0mHTzfclqhQmcdCExERERERUVFThc8AgHvSFeUsG8Fo9flyn2ZTCwBgcWSttLjfVSsdvrhiLAxNXPJtVBmD0IT8hxzQqxCFPMnfLXYGox07+ITlKgPA1fXnjn3+tSfjgj905SwUDQCEZu/WQygCPP6uvAewWcdgPct3OO48Ucc/Ttfx88+n3nN4Y/dYZ2VAHRYEALv65cN7t4ftpByMJr+P6hjKYLQky/r4A9Pbm/uFNdMLwHM6I3l2FgBgcCSz9bAjWWheqUfDgjr5OC2dhb08x134hMBqC20LsxmMFjcEtimD0axttxWlGo5UvPhpXRewfFOKlbNBtQapzm16g+bBnkREqTK7Vpvs968I/OQh8/9koWiJ7nu9OPdldgLoAPOgIqJiZrYpJZ4Jul3qsFyz6+ZCY+U6zWx/81br2P0VO1xsOUJEREREREREREQ5IoQwCUYrzXJtiMguv1ceuhWIsPN9tqlCklTLiIjyV6N3lml5mat8WvMvTxLK4ffMzPhLbImIiDRNw8HVh+PSBbfhkzVfhIbkx563hl7GZRtPxUt9T8MQcWUoWI27Dh7dm+4q51S9p0k6PMBgNMcJGfKOPMnOwYiKFZu3EhERERERkVLMLBjNNfFvq6EmQB4Fo20eC0bbI7za1mTzN70Isf4DYKAHOgQqDXkC1aCrGjHNLS3ziOiOfzfFp76xwcxL5Z9Bj2sG1nh3w/X1Zycd/9b/Sf3B5Ml1b+KhtpNSnl5mjeKeq1nHYD0Hz1Y/v6eG67+hw7jLlVJAmiGAj7rG/t2h6OB91TEaan3ysr7QzvmozChXd252fDCaYnkn+6XPOELDsfulrx7LNhR2CIjVbzeQB8Foiev6LopAwc7B9NfHaQZHBK591tqS7cliMFrnoHrZNdsIV730q+pb2u9vzfz2qto3+RUvPwzH7IdSEBFZYTewa7peWVvY50QqdrMtGYxGJGe2LU1uKz2nVn7VFxgCwtHi2BdZ+Zb9IfVYq9rt/06J91Xe2yJw0d8MnPtXA0vXF8dvTkRERERERERERLkTFREIyB/GMxiNyPn8HkUwmiKkizJjJB7CUFzeENWvCEggovzV6G1WlpXpPrg0l7LcCp+eJBiNgYtERJRFPlcFTmj8EX4x9zrMLdk16fijRggPd92NX236BVYNvy0dRxUils9Ux+duhlY7TjAu78jjcyk6pRAVOQajERERERERkVIsri5zT7qinFVrfb75EIwm4nFg60cAgMODL9qatqp3PcR9V+34u9qQJ/H06TWIah5pmRuxCX9/cy97qURLyw7GT5puTjremxfo+M5B9kO9/vA9Da3X6Lj9jNk4cOQt29Obae+XD4+b9MXNRTBaouu/oeN7h9qvxKsfCYxEhDJA4ZBdNNSUy+fbFxz7QcwC49y6ensLDDu7c7Mq7CPZsvaVaHjkRzpartDxyCk6/iv5cw8AwGcXy4e/u2Xnb12IrIaqDMpfzJsTqnU+cd2or5CvKL3BDFTIYfa7wmSnMEkqgQmpUu3bAXvBaAftomEPRZualiw8s1NtM36Tc5tsBtARUfEw7CZ2TdPyTUCPw88fM8HuN+53UJgskZOYBqNN+tvs/tK2Igg6Bqzt482CGFWB82bGX4Dw7EqBg68xcNXTAtf/U+BT1xl48A3r1xhEREREREREREREdoUNdaMUBqMROZ+q8/1wfBAhRWdvSr/uqPoBEQOMiApPo3eWsiwdgRrlSeahCsUkIiLKpPlli3DOvOvwrYaTUab7ko6/JbwBH4belZY1FOA5sioQmaHVzqO6Vi5PEk5LVKzcua4AEREREREROVfMpN+ja1Iw2mwbwWizanKcYmXFtlYgGgEAHBZaihJjFGGLDa0qjWHgpcd2/O2PdWOLZ87Uj3A3IqiXS+dRbkzs4VpXaS/b/Jg5jyYd5y8n6/j4/LFlcdM3NZz1sHnHW00DDt0F+P13dSxuGl+Gc9D8+S8AG2xVz1Rbn7weZgFguQ5GA4A7vqOhrgK46z8CQxZDpJ58T+Dze6grP7NaHWw2HrRjFmyla2NhPZ2SzuNdQ9bqmCvKYDQLC1vTNCxqBBY1An0hDa+tT96p/LTDdbzYIl/JPnODgRWXTO/taU5ltl0lGnRQyId63dj57xnyXWvBBrps6hE4/zGBP79l7/t92AFsCAjs4s/8TlQVjOZxAXWK5aXy8fkaPuyY+l1btmV++arWP7PQ194QMLcuM/WZLiEEHl4ucMsLAuEY8PX9NZxxpIZSj/V14sE3DNzzqsBwGDhqbw0XfVmD2+WAAzNRgbMabppOv/23wOX/XVzbt93feTgsMDXmiYjMaJM2meZq9bjt/cA8h55XpZOV7Ms+k2C0VM6Lx0/fznvMQCQhq98QwC8eFfifA4Wl63EiIiIiIiIiIiIqXBEjjLbwJsRENK3zHYz1KctKNAajETmdqvM9ALw3/EZGwnNmePyo8zSkPL0QAoHoNgzEeqXlDd6ZqHbPSHn+2TBqjKAtvAmGGHsD9kcjq6TjuTU3at1F8ICNqMg0eJuVZekI1Ch3mc/D71Xv+4mIiDJJ11z4dO1R2K/yUDwWuA9vDr6U0nzqTa5j8pXqO/VGA1gbWgkN5v0Sq90z4Pc0QZvcoI/SLmTIO/T5kpyDERUrBqMRERERERGRklkwmnvS/TB/xVi4STSefL5L8uH+4aaWHf/0IIaFkfVYVbqnpUmr4hNTqJpi8jexbXM3YVCvks/DmDiPuur0XcJrEAjf7poQWnL6ERqeWSnw/Oqp49/0TQ0nHKihogQoL5l6g7P01MvhPz2AgNuflvq1KcJzTAPA7OXGZYTXreH6b2j41bEC/1kHHH5j8sSpF1qAlW3qcrNgtG6rwWiKl3YF8jUYzeY99qP21gCYdwr/yWc0HDBPXb6yDXh9vcAhuxbeDX6rYR8jUSAaE/C4c/8bxBWbVuK6UafYbnqD6a9Prg2EBA6+xpAGIFqxfFN2gtHa+uUrW3ONtcDDRLs1yoe3dNqtlX2qbWZGhQZNE9IQi7Y+YN+p+ag5MRIR0LWx7SUaBx5+W+B/79tZ6bc3CawPADceB1SWIumDzd+/YuDkByZOvzEA3P+D3O8riAqd6niYSa+uK8yAUTNWwokSBcOZqQdRvjPblCafblSVAeUl8u2ptacwr8sms3Kd1m8SXr06hZd8unRg24CQ3qPoHASWrgcOW2R/vkRERERERERERFQYXu57Bn8N3IuYiCUfOY1KLL7IlIhyp8ZdB7fmkYYmPrjt1ox97oLSxThl1vmoctfYmq4n2ok72q5BW7jVdLwlvn3ww+ZzUebyTaOW6SeEwNM9f8Y/eh6BgeQNB+o9TdC1wnwpLFExa/TOUpalI1DD51I0Pt8uE6GXREREdlS5a/C9mWfg0Ooj8ZfOO9ER2WJrer+38I5lquBSAwZ+s+UiS/No8s7GT2ZdhHqvosMGpUUwPiwdXp7kHIyoWDmg2zARERERERE5lVlne/ek5+S6rmFmdfJ5+iuBuoo86MS6uWXCn4siH1metNKYeINqpiIYrcPdhEFdftOqKj4xtaquKn3BaIHT104IRQPGgk/+dqqOC47ScOB8YHEjcNRewN9O1XHGkToaqzRpKBoAaJW1+IJ3Rdrq19Y/FsA0WbIAMKfQdQ2fWazhvYt1HHcAsIexHjOj8l7JkRjwm+flG5q/Eqgq06YXjKYD/kr5j+P0YDRVCIXdZd1co2Fxknvyt5ygYXYtUGXSlvKiv+UgfSQLrAajAcCQQ4I+VHV2JdzprCuXj9NTgMFot70kUg5FA4AW+SEq7doH5MObLZw7TLakSb4jWB8AIpLjRzoZihXQ4wLm1MqnaenMfZCQEAJXPm2g/KcGyk41UPITAxWnGRNC0cbd86pAzekGDrrawLIN5nW/+d9Ty//4pkCHIgiPiNJHdTxsqAQu+oqGg3cZO6e3+58qfBIA1nVl5rs4mZ1zJQAYdsj5EpHTmIUMTj6z0zQNu9TLx12bhSBcJ7ASytgfGjvHmywYFtjca/8zXTowYBK2tqmX53dERERERERERETF6qPQh/hL111ZD0UDgBK9LOufSUT26JoOvyf7b0veONqSUvDaPe03JA1FA4A1oRX4S9edKdQss94dXopnev5iKRQNYHgRUaGqctWiVHGelI5ADZ+uaIS7XSGGyRARUX7azbcXzp9/E46uPwlercTydLm4hsm0+jR8p22Rrbi97co01IbMhBTBaD59+gG3RIUofb2qiYiIiIiIqODE4uoytyRqe1YNkna+XJIn9w7Fhg8n/L1bZK3laSuNiYlTTYpgtDZPM4ZcVYp5JKTclPowZ4YGYHqdUEuMUby7x+2YsefZ0vIyr4Yrj9Zw5dH2533ByQvx4B3Tqt4Oo1HglY+Aw5dMHJ4vwWjjPjZbw59PGIB4cG8Y0DB/4Tq0e5qnjPeyYtUa31bqK+TLvnt4rBN0st9FHazm7E7Nqu+VyrI+6VANFz4un+GyC3Ro2thM7zpJw/F3ycd7YQ3wxLsCZV7g8MWAx+3AlS4FdsI+BkeAGeZtHbLCyrpRp1jvnRqM1jMs8P5WYFW7wIJ6DXNnAL1BYF2XQFUZcOiuGmbXTl3neoMCv3zC2kI8Zj/g8XenDs9WMFpbn3x4s72XtgIAFivOJeIGsCEALMlgmxuz9W9Jk/w8aE2WfmMzN/5L4OK/2dvvL98EHHengQ8u1VFVNnX9i8YEPmifOp0hgIffFjj9iMLYTxI5lWp/VOYFLvuajsu+lvq8z3rYwG+en/oBbf3A8KhARWnxbN9WwokSBRmMRiRl9+pzt0ZgZdvU4evyJBhtaFTg5bVATRnwiflAicfeftPKdVo0DoQiQPmk9nyphsd1DwOPvq3+YKMws8KJiIiIiIiIiIjIgjcHX87J52rQ4NG8OflsIrLH752JjsiWrH/uquDbGI4NosItb4c7WVekHa2j6yzPf8XQMsSbYnBpzumCu2zgJVvjM7yIqDBpmoYGTzM2h9dPKfO5ph+o4dXNg2WqXCk0/CQiIsoQt+bB5+uOxcerPolHu+7Be8NvJJ0mHSFiTlOil6LaVYuBuKLjhkUdkS3oinSggdcSGROMD0mHpyPglqgQSbqxExEREREREY2JmXR6dCmC0ZL55CLnd6IXAz3As/+fvfsOc6Ja2AD+nsn2hS1soxepSlERuSoWELuIyrXfa0Gxo9jrp2LvXa5eK/aGXkVsiAURVLChUhaQKtv7bpYtyZzvj7DsbnLOZCab7Gbh/T0PD5uZM2dOksnJzGTOO6+0mnZg7RLby/sHo/XQBKOtiRusrSOlZR3xSTh8D9ur17rC+waGXaUORWurYaMH4u+hd+L2otvDUt8XKwMH43a2YDQAQLEvMcaAxHE1HztadGh335PSBZt5TF9QldXgZEMAWZrzosXq86hRQxdCYYRwNuus/QS6KH6jP+9AgX37N288p4wxkG1xHnnK0yaOftzEsFtNrMyL7mA5uxwFo9VFrh1O2ApGS1Z3CmVuwHTypNvBwlyJ4beZmPiIicvfkjjuKRN73mFiwsMmLnhV4rRnJQbdbGL2ktYf9m9yJTKvtJdOsOV+A0Ny1K9JbmH7vB75ler19Exz3oEPytL3+7kRDsuw2v6GdFc3ak1Bx25zLy02cd2c0Nrwdznw0hL1stUW4T+hBnIQkX1ezVdAOPaLp0/QV/K7IqhoZ+Z0t8HdEJl2EHV2ViGDQtHlDNbsu65pp33Xtli8TqLPdSYmP2Xi4AdNjL3HRF6Fs3bb7XsqagOn5bZh3/MWiyDdKDuMIiIiIiIiIiIionaU37C5Q9bbI67PjpsdElF06xs/sEPWKyFR2GD/R+y8emf9Wb2sQ41mwHpHKXAYQNc3oWPeGyKKvH4J6nEIPeP6hqX+/pr6ByUO5z4aERFFpW6xWbig1w24pNf/ITM2R1suO7YnEl1J7diy9tMnTPv/FZ7SsNRDarVmjeHAQ9YAACAASURBVHJ6OAJuiXZGDEajsBJCxAohxgshzhJCXC+EuFQIcaIQon9Ht42IiIiIaGclK0shX7kP5mNXwrxhCsz/3Aj52euQXm+b67YKRotRHFH2TA/+I9exI6P7hzBpmpBTdguYfmjtN0gy3bbq8A9G6+VRX3hR4UrX1pFiVjU/SEhC1wSBCw9p22t35iGJbVo+mB6X34Sbh6/G6ZVvtbmuVfmKYLQgAWBRqSRvx5+Tqz9ytOhuwheopws2A4Dv1gFnvah/YVwGtEFf0R6MZif8yq5e6QLPnimQ2uIjMHlP4KnTAyv75Zbgp8s2lADnv2JCWo3u7yScPIXKbZFrhxN2gmB0nxuvCZSof0PoEKYpMXW2iaIgn8cGD3DubAnjAi/2usOLM54zcejDwUPRkuKA16cJ9EoXGKa5qVFuAdplWy7VvO459m7Y2kp8rECfbup5hVWRfS5WfZPuNV6tzkcNm9X5EnfMMzH1JRMPzTdR7m5u5PwVEue93LbX5I6P1MvXWIQlVkdJf0G0MwvnvpK/fhlAYqx63qd/dv79HyecfkVa9Y1EuzLLYDTFtCGa69JyC9tn3zVUpilx8jNmq1DpP7YC17zrrM12n2KFYp8rUsc7DEYjIiIiIiIiIiLadRU3RPhHb41xaUd0yHqJyLn9Ug9FnFDcubQdFDfmOyjrvD+r9UbPxWZe6UFpY5Ht8qmudOzVZb8ItoiIOtJBaUfCgKvVtGSjK/bqun9Y6h+Xqt4XOyTt6LDUT0REFCkjuozB//V/AsdknIoYERMw/5D0YzqgVe3j4LSjwlKPlMHHqlBovNKDOlM92CKZwWhESoE9OXV6QoiZAG5rQxUvSynPcbjOLAC3AzgVgHJYohBiCYBHpJTvtaFtRERERETUgszbADltf6C6vHni4o8hAWDBW8CDc9t0Rx5d+AwAxLgCp/VKC17n4OyQmxNxUkrIW08HGgJHsyfIehxRswAfpBwftJ6ufsn9w+pzHbclpeVd5hJ8aU5Pnibw34WhjUQdUr8Gw3bX3/EiHERsHMTd7+CG79fgzZfaVpcqRMZqEK4RrdHvLYLRxtd+i67eKlS77KUApc+5F3LAUcjcR3/S/binrE82CyG2B0QFvnjF0XO9jpJVEF4oThtr4LDdJf7YCqQnA3v1UfeNPdMEjhwOfL7Cur7v1wO/bQH2Ds+N1TqMk8HtFbWRa4cTdoJgeqbql99aAWSHEMYVCT9uADY6vJnO738Dv/8d/I2bO93AfgOAzK6+F2ZodwFVX1BTD+RVAL30WZ1hUa7ZftJDvNlSVhdgk+K1i3TwnS6kwjCAoTnq17ioGih3S6Qnhz/F85tcieOeMuGub5721FcSP9xoILcAOOrxtnem5bW+fST/fcrqes0CAKrqmJxBFGm6fSVXGPaLXYbA+KHAp38Gzluybtf6fDsNAqptiEw7iDo7q4+S6rTVEM1+VXUdUFgFdLfY3+9Iv24BCqoCp7+1TOK18yQMm+mVdvueckV+f1WEAhoZjEZERERERERERLRrqjO3ocpbHrxgGHWP641xqUdgfNqx7bpeIgpdt9gsXNX3bswpehEb69bAIz3ttu6iBgfBaA7KNnF7o+cOtKWNxTAR/FqgeJGAgUl74LTsCxFndExgHRFFXu+EAbi8z0z8r/gVFNRvwYDEoZiSNRWpMeG5GHRc2uHwSg8WVnyKgoYt6BHXBxO7HY99Ug4MS/1ERESRFGfEY1Lm6Ribcgg+LX0Xf7p/QhdXKg5KO3KnPt8wossYnNvjGswvm4Ot9ZsgLa/c07Nz3EGhsQrfTjIYjEakwmA0ajMhxNEAZgMIFm9wAIADhBCvA7hQSqm4VJuIiIiIiJyQ/7mxdShaSz/OB76aA0w8OeT6PV79vBjFgPtgwWhxMUBGlJ6jkV4v5EPTgYUfaMtMjluGD2AnGK31hRADGjciVjagUcTZbk+K2WIkbbwvNSbGJfDBJQZO+I/zE4z/3PYRxIiLHC8XipH7D8GceImTngn9ROiaQqC2XiIpvnnQsNUgXFf4s2bCo7g5GC1eNmCi+2tb4XoAkN5QDPn0TUh+5WjkpPgGfzvRNN46S/OZq64D6holEmKj88XThl+1Iewjs6vAhGHBy43oJfD5iuA/AMz5WWLvvtH5+tnlZHB7fqUE0PHPV9dmV4uQgewUXzCMKuAzryJ6Au1yCyOTLnDbcQKTRrV+r4ZaZGOuLuh8wWiZmr4t0sFoVsF8w7rrl8stBPbbLfztufl/rUPRAGBzGTD+IRNrCsO3nvJaoFty62k1FqEbVeqbGBFRGNkJCm2Lw3YX+PTPwJV8nQtUbZNISWzffYJXvzcxe4lEXSMwaU+B648UtgOG2sLpN3UNgyGJHFMGo1n86rymMHqD0VT9ZpMyN5DZ1V49ujBefxWKfS4GoxEREREREREREVE4lTQo7m653e0DnkZGbPhv1GmIaL1DJhFZ6ZswCFf1vcd3g+IQB99beTH/YfxSvThgenGjg2A0TdmD047G95VfolEG3gmr1oyeO9BaPddHBr+JOOELQRMQbbqpNhF1HkOSRuL6fg8qb3waDgenH42D04+GKU3uoxERUaeUHdcTZ/eYEbHvymg0JuVAjEk5EKYMPqbvirWnwiMbA6YzGC1y3FbBaK4oHXRL1MF4JEJtIoQYD+ADtA5FkwB+BvAugC8AlPgt9i8AbwrBI2EiIiIioraQNZXA4nnWZea/0aZ1eCzOY7kUe/S9061PEvZMRfSeSHz3CWDei5ZFjv33QbaCBrr6XQgRAy8GN6xz1JxE2WJ0a0JzaszkvQROH6tuRGoisO5uAymu1gkp3T0FuPqgOojkFEdtaIspowW8/zXQeMyLKFjTB9Wru2FK1f8c1THy9tYboNUg3LaEZUWSLMlr9Xh4/Urby6aalcCm1RAFG3HkQOfZ4juC0SwGXpdGzzU7ASId9mHFKtyoJTvhadHOdPB7RV5F5NrhhK7NLfsBlyHQQxOWsLUiet63sgjcNiC7K3D5oYEflPRkgWxNfxCpgLYmpilRoQtGSw7tQ53ZRb1cJF7Tlqz6pp5pQBfNTVbXF4f3NfZ4JYqqJL5fr54fzlA0AMivDJxWbRG6UVQNSClR5paQdpM9iMgRVfgnEL59pRG99BVlX23CXd9+n+1nFpo4+yWJr3OB79cDN/9P4uLX22f9TvaVAMAdeJ08EcE66EvV22R2FQGhrE3WRHjftS3iLW5LV+zg+NtuCFlFbWDByggF1NrtD+sbJbaWcx+QiIiIiIiIiIhoZ6EL4XEhBt1is2EII+z/iKhzE0JEpG/Iju2pXF9xg4NgNE3Z7NgeSHapL6xye6uV0zuCrv1pMRlIMBJ3vFZRe502EUVMpD/33EcjIqLOblfcR7Z1rKWJGzKlt51bu+twW4Rv645LiXZ1PBrZNZwOYICDf9fYqVQI0RvA+wDiWkxeDGC4lHKMlPIUKeURAHoDmAGgZVzocQDuasNzIiIiIiKipV8AnsBU/laWfAJZG/qP8lbBaDGKI8qRvawH4vdKC7kpESU350LOusG6UEYPZB1xDPbsbV3MkF6keQPTg8Zs+9lRm1q9jPGJrebNPkfgjuMFurfIOZs0Clh1h4HdsgSW35WIc/qtx4HGnzjH+BxLDv0S6dNvcbT+cBBCwOg1AJneUiTKOkyutg7y87ehBNhQ0jyQ1jIYLVrPUfsFow1ryLW9aJrXl0IjT90dx300zfGqm0KiUhP1ZaxCbTpaRwajHTlcKMMf/f2yGfh2Tece7G13wD0AbI2SYDSvzW1D950TLc8DUIdNtUVOCrDsZkMbNqYL/Vutv8FzWFTX6be19CT19GAyNDfDKamOcMibpnohfN97fbup54frvc4tkNj/Xi/iLjbR/Zr2uxNTvuJzU1MfOK3Jynyg17UmMq800e8GE/N+79x9JVE00vVHdvZh7LAKim3wAF0vM3H6syZq6iL/+X5sQeA6Zi+RKK2J/LqdrsGqbyTalYXyaR2So54e7gDYcEqI1c8rqrJfj93jtHJF+HC1Jhht3ED761fRHYc1qWuUOPtFE92uMNHnehN9rzfxxo+8cygREREREREREVFnV9ygvqAgIzYbLuFq59YQ0a4sK079I3ZRY76tG7Y0mg0o95Ro6u6pHYBe642eu8/qwiqzYm3eCZaIiIiIiKgFXfipKXndV6TUasK3Y0QM4kR8O7eGqHNgMNquoUBKudHBP/VZvkC3A0hv8XgJgMOklKtaFpJS1kspnwBwit/yVwkh+oX+tIiIiIiIdg3SNCFXLIX84bNWIWfyO5shUwveCXndHouA/xjFdU3pyQIHD9Yv0zs9+tKrpJSQ/xoVtJx4ZiEAYPce1s+hm7cMLgSeAJxc87HtNu1ev6r1hITWqTGxMQL/d6yBvIdcMJ/1/Zs73YXuqb629csQePHmwfj2mT3x4jPHoP+/z+q4u1v0Hbrjz1Oq5uCA2iWOFv/g1xbBaBbnVaMxGE1KCSxb0GrasHonwWjNKTSH1yxAvOksxazpNUmxCEariuJgNN21Su3xXvdOF7jxaHsrGv+QCa+TdLEQbGuQ+OQPiW9yJRo84V2Xk6bnVURHsJGuL/DfNnqnq8tttHvmqx0UhDkYLe9BA3266bfdId3V89YURPa9VQU2NAk1GC1TE4z28+bQ6rMr2PbXPVU9P1gwmrte4ouVEq//aGJlnvr9qGuU2P1WEz9usNlYB2JdwD4WZ2rzKwPbVB0kDKlge/jH3+XACbP0z4uIQhPpENleaUDXBOsyb/8kcdFrkf1s19RJZQhSoxf436+R71ec7ua5GYxGpGQ1FkV3umJIjmbftTB69ykaPPp5xQ7GzNgYu+OrU3GNVJVmH22f/gKXTwz9S6I+yL0Zpr8p8eoPEtu2l9taAZz5osSitdH7fhEREREREREREVFwRY15yunZcT3buSVEtKvLiu2hnF5n1qLGG/wONSWNhZCa2/lkx/VAkqG+GKlGM2i9I+jCKrPi1K8NERERERGRFaGJGzIV4yIpPHTh20lG144b+0kU5RiMRiERQgwGcHaLSQ0AzpFSaodUSyk/APByi0nxAG6LTAuJiIiIiHYOsrwY8sqjIS86CPLa4yFPGQq55BPfzF8X2qvjwUual3HIG0IQ1Ql760/C7D8wpGZElHzh9qBlxJWPQXT3pYUMCXJjtUxvqXL64TULkOCySJpr4aiaz1tPSAgxNSYaZPcGEpMBAHFoxNebjsCzeRejf8NGpHvLcFXpo7il+G7t4gtWtQhGsxhLG23BaLK+DvL2s4CG1skII+pXIM1bbquONLM5RaeLdOP0Kmchh02vSXKcvkx1FAejmZqR4O31Xt9xvIG50+2dOrvk9cgN9F62UaL3dSYmPWni0IdNjLnLxIaS8K3PSdjH1orgZdqDLiTA5fd27ZYV/UEK64vD15YN9xpBfwgZpvkO+3ULYEYw4C8SwWgZmmC0wirgkS8i90NcsCCiHqnq98AqBG/5Fonht5k48jETZ74gMWKmiQtfNVvdzbaoSiLp0vA8r/12A2ZMFHjgJIFbJgncOklg5e0Glt2sv5v3ekWgYI2D8B9TAi8tiZ7PHtHOQHesFq59JcMQOGVM8Mre/VmiojZyn293g37e2qKIrXYHu+FETZz0jUS7EstgNM30wdnq6b9tQav9pGhidYxdVG2/zXZ3zfMU+5hV29RlUxOBR08RmHdZaJeI1FuEvjV6JOb8FNhoKYG3l0Xne0VERERERERERET2FDfkK6dnxQW5iI6IKMysAhmLG9V9lZ0yAga6xWQj2aW+GEk3aL0j6J6DLjSOiIiIiIjIiiHU15JJBqNFjNtUH2PqjkmJiMFoFLozALQcKfe+lHKtjeXu93t8ihAiIXzNIiIiIiLaucjHrgB++aZ5QmUp5PUnQn74HFCivhujsp7rT4Ssdp5o49Gcx4oxoA1fOX4v/eB5q3kdQa5dDrx8b/CCYw7d8efwHtbPIUMTjJb8z6k4bLg+cKTJuNrFmFl8V+uJ8Z03GE0IAfQduuOxCybOrXwZ6/7aA8VreuOBoptxW4k+GG1li+s4OlMwGj55GfgyMMgsFh4cVTPfVhWp3tYjnO8uuhX7bltmuwlNr4lhCHTVHHlXRXUwmnq60Y5nsyaNElh4rYGsrtblnlskUexgkLtdUkqc/IzZKlTqzzzg8jfD9yODkzyBMnfYVtsmum3D/2tpSI66XG5hdAQplLslvl9vXaZ3OvDd9QauPVJo+7mEWGD2VIF+GcE7wj0032HF1cDSjUEXD5lVMFpaiF9x3VP0z/eadyV+2RSZ9zhYMFr3VPX8/MrWC5a7JR6ab+L690zsfaeJzWWtyz+3SMJ1oYlr3jVR7pa4dW74ns+SG1x49FQD1xxh4PbJBmZONjAw2/cEJo1SLzN/ReD6nYZrPjy/4z93RDuT9thXuuuE4N8tjV5g/3tNXDvHxIKV4f+c11oEo1VVRT6FzOkug1V7iXZlVh8lXbbv7pp9181lwAr7p8TaVbVFt1Rcbb8eu11PfkVgyUpNMFpKgu8czTEjBf7zL+cnUeoa9fNKavTnF75cxX1AIiIiIiIiIiKizqy4sUA5nSE8RNTeurpSEa8ZgqgLcWxdRt2fdYvNRKwRi2SX+iJBt+ngR54IMqUXJQ2FynlZceyTiYiIiIjIOUMTN2RKBqNFii58O4nBaERaDEajUJ3o9/glOwtJKVcB+LHFpGQAR4SrUUREREREOxP5+2LgqznqeQ9Nd17flAGOl/F41dNdFkeT/TIEzt4/cIDlWfvbC21pL3L1z5Dnjg1ecM8DIVoEe40fav38M+MUo2CPPQfGjEewT7/gz/+bTYcjWfolyMR38jzpfsOCFvlwyxTl9E2lgLveN4jWMhgtys5wSEUoWpPRdb8GXT7JdCMOrUcd53iL8O3GiVi0cTxeypuGl47abFlHyxAlXTBadV30DlA2NefR2zsE76DBAmvuNPD8WdYrzrk6/Cf+V+YjICwJAD7+A9hSFp73zupz5a/CItyqPQULpmoyJEf9nlXUAuuKwtyoEIy9R7/NPHWGwPwrDKy+w8ABAwXu/6eBVXcYmH+F7//vbzDw8lSB9y82sP4eA2ftb68TPHAQEB+jnvfhb5HrD8o1oXpdE4AYV2gf6qFBbj495m4TjZ7wP6dg218PTTDa3+XNf68rktjrDhPXzZF48HPrNj7yhUTGlSae/TY8zyUnxXr+IUPU78cPG4ASvwDImsjnERGRBV1/FGK3qpSTIlA7K/h3TG6hL/zwiMdM3DkvvPtEbou+pnLhfMiS4Be4t4WTfSUA2MZgNCLHdMFoVudf5i6PzmPZKk0oGQAUORgzozse9penuAeCLqAsJbH577REdRkr9R79PKtQSDf7RSIiIiIiIiIiok6rwaxHhUd9k1CG8BBRexNCaPueokYbwWiaMk1Bj7pB6LpB6+2t3FMCL9Q/2GTFBrmQioiIiIiISEEbjAYGo0WK26u+kFAX1k1EDEajEAghugPYs8UkD4DFDqr4xu/x0W1tExERERHRzkg+eGl4K6yrhdy4ytEiHs15rBiX9XKPnCJwzgECQvgGuJ5zgMAjp0RHKJos+hvmnVMhzz8geOF/HAFx51utJmV0ETh4sH6RzH+MBcYd63sQGwecOgPiqicAAENzrFd3RuWbUL5KsfHB2xrFRL+hQcsMq8/Vzluz/SZ3lsFo0bF5NVv+nXbWUGNr0MWzPCXK6bHwYP9tS3Fm5Rs4s+xlXHm4/om3DItL0QajBW1Kh7EbftUeUpMEzj3QwMXjrVd+yesmpAzfAH3VIPcmdoIA/i6XmPqSieG3eXHAfV48PN+E1++FdRL2UVMPeLwdH0DgtRmaN7ynvo7nv2vf5+HxStz/mYne13lhXOD791exumyfdODiQwQO20MgKb75SQ3O8U0bsvUb7DvrOPzr1Yk4/o/7kRNvkb7gp0uCwERNVmVEg9Fq1XWnJ4Ve58Cs4GXmLg+9fh1t37S9z+3bTd1PbChp/vw8+LnElnJlsYg7d5x1P3bsSPV8KYHPV7Z+8tsalUUtRXMgJ1FnE6w/CpeEWIG1dxk4cJC98nfOkyisCt9n3TIYrSEG8tX7wrYuFafPpM4iOIhoV2Z1mKTbO+mWLDBuoHreJ39E5z5FjcUx9vIt9tts9zgtvzJwmi6cLbVFGFp6svMD6zqLfT+rYLQwHiITERERERERERFROytpLNDOawoSIiJqT9maYLTihuDBaEUNecrpTWFryYZ6ELpu0Hp7K26w6JMZVklERERERCEwhHqAqCkZjBYpuvDtJEMd1k1EDEaj0Izwe/y7lNLtYPklfo+Ht7E9REREREQ7HVlTCTgMMbPlh88dFdeFz8QEOZpMTxZ48RwDdbN8/148x0C3EAZdhpusKIGcui8w/42gZcXH+TAe+ggiPTB55fi99M8lK7srjPveh/i6BmJ+OYzpD0DE+YLNhvUIEkZS86l6hitIEl206xs8GK1/4ybEm+oRxKsLfKNoddsjEF3BaPLr9yzn737YfkHr6OlRX4TTyqK5yLI472m2eL26aoLRqjphMJrowPf6idOsV/7MQokb/yfDFo5mNbj8p43Wy1bWSoycaeLl7yVW5QM/rAeunSNx5TuhB6MB0bHN6Nrs8vtu6pYsMLa/uuxjCyRW5bffCP2LX5e48X1pGXbX5JhRAkKzoculX0BecRSw9AvgjyWQz90Gefd5geVME7LWd1GebPRtSLJ+G2ThFhy3m7oRqwuANYWReU0qNOEMbQlGi4sRGJxtXeaTP8P/fHQf76bvoSGaEFSPCWzcfiPtj2wEG0ZCelLwYLSh3YHdMtXzft7U+rFVOIbOt2ucL0NEanaDQsNhYLbAt9e5cNyo4GU9JvDlqvD1c1b7QxVGKvDVe5Bm5C7AcFp1gwcBQbREdkmPB7LefuhtZxLqp+K4PdWd2h/B88Y7hFUI7JK/gBqbIbF2X6+iaqDR01xaSolKzSaUktD8WqYlqstYqbcIfrQMRnO+KiIiIiIiIiIiIooSRZqgIQMGMmJt3M2MiCjMdKGMxRZBjsHKZG+vM8mlvhiz1lQPWm9vRY3qPjnFlYYEI4Qff4iIiIiIaJdnCPUAUQkGo0WKW3OMmaw5JiUiBqPtKi4UQiwQQmwVQtQJIaqFEBuFEAuFEHcLIQ5yWN8efo/XOVz+ryD1ERERERHR5sikRsiPZzsq7wkxGK1JbIxAbEwUJVbNfR6oKgtaTDw8DyKlm3b+lNECcTHqeYft7nu+IiYWIqZ1oRE9gd7p6uVS4704qma+eqZLs7LOYvd9ghZxwcSQhrXKeau3X49ilS8QVcFoj1+tn3nOzRhw0XRkBjlf2ctOMNr6FRhZ+5t2dssAqxRNMFp1FIRc6eje7458r12GwLfXWneAD3wmkTbDxDML2/5DgNWg+tU/roIsDkwDKK6W2O8eL9KvMJUD4p/6SuKnjc31Og37qKh1Vj4STM3Godo2poxWbzCNXmD6G2bYQux0TFPigldNvPCd/fWcOiawzXKbG+bd50FePSlwgW/eh/zwOV85KSFffwhycm/IIzNhHhQPeWhX3/+HpUGeNAiTHh6tXfeHv0Xm9SjX3FKhLcFoADDZIqgUAF5aHP7nE6xvGmRx7ffaQqBqm0RBVdibBQDoEg/00exnnDFW4NvrDAzMtn7NhBA4eIi6zDMLWz/5UILRPl/JaAyicLEbFBpOE4bZ2xH79wuyVVBPW7gtwnYKY3KAimJgrX6fuK10zyIpTr9MKP0j7dqk1wvziashj+sJeVQWzKsnQVaUdHSzwspqt9sq/Hp0X/XM6jrA442+/Yrqeuv5T35tr81OjtNa7lvWNerP5aW0GA+TFsJ+uFXfZhWMZhVyT0RERERERERERNFNFyKUEZsNl+jk17MRUaeUFacORitqyLO8DswjG1HWWGxZZ7Krq3K+2xsdwWjFmrBK3WtCREREREQUjKGJG/JKbzu3ZNdR661WTk/SHJMSEYPRdhWnAZgIoCeAeABdAPQDcDCAmwB8K4RYJoQ4zGZ9g/web3bYnk1+jzOEEJohe0REREREu6jNuaEtl9ULOPRk/fyNqyDLi2xXpxtkGsnB9pEkf1sUvNCUiyDGHm5ZpHe6wMzjAgfn/nM0cMgQ/XIxLoEH/ikCguWEAO4/tASppiYppZMHo4mcvsCgUUHLDW1QBwLOX+HbDq0GBkdLMJosyQNK1RegoGs6xLm3wOUyMGmUdYN7NrYIRuuSpi034fHxttrVVROMVqUIzooW0RiMBgAHDhbISLYuU10HXPK6xNSXTORVBB/4bpoSK/Ik3lxq4of1zUEiNRaD6nMbc2D+a1TABV0nP2Ni6Ubr9Y29pzkQzCpwUCUqgtF024biu+nCgwVyUtTlv84F3lwa2TCFl5ZIPL/I2TrGD229kcttbsjz/gF89pp2GfnQdMhtbuCTVyCfuRmoLNWW7eEpwNhtS5XzQnk9TFNi0VqJJX9JuOvVy5drtpv0IJ+lYK6YKLBbpnWZlkGA4aDb/poCPZLihTYEdU2RxFr7u2GOHDsSqHzCwKb7XTCfDfz32jQDw3va60AH56in1zUCm0tlq8dOrS2MvgATos5Kt28cyX2lY0bar3zATSb+u9DEyry2fe513y0AkBfTwxdc9tefbVqHFV2/nxyvX2abRUAQkYp8fibw7lNATSXgaQSWfgF5w5SOblZYWQajWSxnFeBVFYVB36pw6pbe/8Ven+ik58yraP7b6jVpGZieFcI1VA0e/TyrYDT2iURERERERERERJ2XPoSnZzu3hIjIJztWHQK2zXTDbaoHlwNAaWMRJNQ/smfFNgWjqe92W2fWwistfihpJ8WNmj45tns7t4SIiIiIiHYWQhM3ZGqOn6jtdOHbyYb6mJSIGIxGzcYAmC+EuFsIq/tyAwD8R2Q7Gs4nt3QxeQAAIABJREFUpawB4H9ZdqqTOoiIiIiIdnZyzW+hLZjSDWLmq8Do8foyc1+wXZ1Hcx4rxuWsWVFj2QLr+YP3hLjiMVtV3XC0gS+uNHDFYQIXjxd4Y5rAWxcYcAVJIjhtrIElNxi46RiBs/YXuOYIgUXXGTh/X33akOjkwWgAgIMmBy0ytF4djPbjBuCOeaZlgJMqEKkjyDvO0c/cdyKaDrlP2Mt6O+l9+ETgjKshZjwC8cEmYPd9leUSpEVyVgspier1VUfhQPImHRH2Yddf99jb4F7+XqL3dSae/kb/o4C7XmLK0yZGzjTxr+clDrjPxIEPmMivkJbvT4UrHRs8mZAn9Id0+0IV1xZKfLvW3nP4aXtsveNgtCgI0/M62DZSkwQePEm/0VzzrkRlbWSCmipqJc5/xVndWx9ovW3JX7+FPLE/sMXGG/vFm5DzXrS1nsnV85TTf9sCfPqH/TZvKpUYfZeJQx40ceD9Jva6w8QvmwKX1wXqpSW17QPdK13g11sNHGeRvfnfb8McjGZj+xuiCRZbWwisKwpvey48RODlqQIfXGog+Glde4Zk6+t56uuWwWjOn0vL4A4iahtNhnVE95WG5Aj00mf2tpJXAVz8usSImSamvWJa3p3birtIfdduAKgzElFppEKGGixug67ZSXH6ZbaFEBxJu7gv3w6ctuJHyL/XtX9bIsSqB7DahbEKRouG0GZ/ZW7r+T9v8h0nBOPkOC2vsvlvq2C21MTmv7slCwzKtr8OANhmse9nFYzmbkDI3wFERERERERERETUsYpb3tixBYbwEFFHyYpTB6MBQHFDgcU8daiYgEBmrO9CnyRDf2eZWs3A9fake35WrwkREREREZEVQ6jHRknJYLRI0R1fJmnCuomIwWg7u60AngNwPoADAewBYBiAcQAuA/C5X3kB4CYA9wSp179XDWVIrP8yIdyXOpAQIlsIMdzJPwADw7FuIiIiIqJwkVICi9WBJUFl9YQQAmLaTH39331kuzptMFonPJqU9cEToMSNzzkKFZm4u8AjpxiYdYaB08YGD0VrMqa/wF0nGJg91cADJxk4YKAAYi1G1u8EwWjCRjDanvW/a+fNnCux/G/9QNqEKHiJZN564NeF2vniikd3/H34HtZhCsMOGAHj4nsgTroUIj4B4uDjtWVf3XqOcvrY/s1/6waTF1VH7+BkXcuiIRgtJVFg3mX2O8JL35AYOdOLmAu9OORBLxava352T34lMXd56/LLNgK9rjNx/XvW789HXY4FygqA954GACz/23aT8NsWX91Ox6dHQ/iALiRAt2386x8ChwxRzyuoAm6dG5nPwVXv2K83MRZ49ZhNyHnvTpiPzoB8/SGYz8+EvPxwYHvwXTDywUuBP3+wVVYXjAYAxz5pwmszieGwR0z83mK7+6sYuPC1wPCbck34Q7pF0IVdXRMEXjhb/3lckRfmYDQb298gTbDY2kKJQntvpy21sww8/S8DZ+5vfx/Ejj376OfNXd4yGM153fmVwcsQkT0dFSJ78hjnK3jxO4lZX9vvj+WP82HOuh7115yIJ99WD7hpkhfTA9isDlgOB12/z2A0Chfp9QL5m9TzPnutnVsTOVbHHZbBaIn6edFwbOKv3EabttoIitX18Sp5Fc0vbpXFr/YpCa0fHx8ksN1fjcVptdoG/RvsNYF6j6NVERERERERERERUZRgCA8RRZsUVzriRYJyXlGD/rflokZ1MFpaTAZiDd+Pv8kWg9DdHRyMZkoTJY2aPjmWfTIREREREYXG0MQNmWAwWiSY0kStyWA0Iqc64VB2smEpgCMB9JFSXiClfF5KuVhKuUpKmSulXCKlfEpKeRSAfQGs9Vv+BiGEfsR1YDBa8HSBQP6XZYerp74EwJ8O/30YpnUTEREREYXH+hVA3oaQFhWjx/v+GLGfvtDqnyGLt9qqz7szBaPdebZ1gX0mAINGtU9jVGLj9fOMTviC+xs0Cujez7LIUTXzke4t085/eYl6oG1iLJAU3/FpWfLjl/Uzx0+BSM/e8TAxTuCS8eo2d08BjtjDb+Kkqdqqp1R/gMGJgSOrT923uf6eqeplt5brm9zRtOFDUfJxOGakwKcz7DdmRZ7vOS1aCxz0gIk75vk62LeXhR7atCjpQACA/NS37W2tsF/X6u3XSdnMv9qhQhNw1Z50bXZp3g4hBGadYWi/u2Z9LfHr5vA/ry9W2qvz3QsNrDv8A5z+2Ahg9t3A+89APnMz8PK9YW9Tk90bVmNQwzrt/CV/Ba/jmYUm/ioOnP7zJt/23lK5W11HOILRACCzq/47ILcAAUFtbWEnGG1IjrrMumKgVPNaNOkSD2y5P3jf8uNNBhJiI/Pdpwt2A4A1hcDqfN+LEEowWkkNUN/Y8f0I0c7A6fdhuFwxMbS+5/K3JIqqgn/+zadvgrzmONS//R/sX3QLlidYH6Ntie0NbM4NqU126L5CLIPRGiLTFtpJeSy+UGt2jURRq14lxSoYLZRbd0VQg0fCXR+8XEl18DJO9pbyWpwOqLL41b6L3xih6RME4h2EzFu93rVB+j07rwsRERERERERERFFl0azAeWeEuU8hvAQUUcRQiArrrtyXrEm/AwAihvU87JbBD0mu7pql3drBq63l0pPGRql+gcZhlUSEREREVGoDKEJRpMMRouEOrMWUnN1oNUxKdGuLkqGklI4SSk/kVLOlzZG/EkpfwKwH4A1frPuE0K47K7SaRtDXIaIiIiIaNewaG7oyx40GYDvx3/xxHx9ucUf26rO41VPj7F7tBAl5IJ3gIUf6AuMHg/xfy9BiA4M13JZjEa1mtdJCCGAg46zLJMkt+Guopna+b//rZ6eKSphXjwe5q1nQP66sA2tbKN5s/XzsnoFTLp9ssC0gwRiW3ye9u4DfH2NgbiY1tuiSMsETp6urDpeNuDdxuswtr/vcZd44PqjBK44rLmOXunqZm3Nr4H571EwrzsB8tvoyg03NefRjY7PwNvhyOECr08TyAwh7n3mXAnjAi+Wa7ZrO3Ljh/j++PsvVFfV4cq37Z9uyd0ebOQ0GK0mCga02wmm8rdHT4GrjlAXMCXw5FfhOVVVWStx/XsmRt/pxdbAvMJWztxPoPhBEyf+egdyHj4D8Gq+dCNAADihSv+Zn7vc+vWQUuLSN/Rl/tzael55rbpcerLlahz5aLr6VHd5LVBsI3zCLjvb34BM9baWVwGUWlwnuVsmMHe6gV7pAvOv0J+6f/4sgX37R7Yz/GOmfv1N28e2EILRAKCgKrTliKi1UL4Pw6FvhsDHl4X282L3a0xU1wU2XEqJF74zceg1f2HUD6djt0GrkTysAr8l7Bm0zr/iBgKbciG3h0t5vBK3fGjCuMAL4wIvsq/yYt7voX/P615ny2C0EPtH2kV5LTaYuihL/moDq0+h1akYlyHQNUE9r0Kzj9lRdPu8/optjJtxcpyW3yI/r0qzyXRN8L2WLfXLEPjfJQb6bD9fEOz7w+r1ZjAaERERERERERHRzqeksVA7UDObITxE1IF04YzFDQXaZYob1fNa1hUr4hAjYpXlar1hvPgoBFahb1mx6qA4IiIiIiKiYIQmbsgEg9Eiodarv3gw2QhhYBrRLoLBaAQpZRmA09H6mvRhACZoFvHvcS3u1a3lv0zH3jqBiIiIiCiKyFCD0Q6aDNF70I6HYu9DgAF7qNfx3UfWbajzjXb0aM5juUI4mpT122Ajvzns5E9fQt5+pr7A6PEwHv8cIrODL9iK04z0BYC+Q9qvHREktgf3WZlW8aJ2XqlbPT2zagPw5/fA1+9BXj0J8uevQ21iSOQ2N2TeBqBMf2GNGLZPwLTEOIFnzzRQ9YSBtXcZKH7EwM+3uDC0u3o0srj0AW39I1a8ge8vLkXNkwYKHzZw7xSjVdBfz1R1neWyC7Zt3gR8/ynkzadAfv6Gdh3traPCPpw6fazvNb9vSvs3bF3cQDQiBiYEjphZ5GjZv4p9/3fGYDRviKF5txwrdgz69/fV6rZ/P5mmxJGPmXjwc4nftujLPXqqQO0sAy+fayD9sfOAl+9p87pDcWXZk9p5P220fj02lgJWX+lr/DZHbTBakuVqHNm3v37eevVNrEOi7Zta7Bv1SlOXqfcA64rUFRw9Alh3jwvjh/o25MP2ELjs0MCN+snTBc49MPKn9Yf3FDhgoHpe0/ZRF2LwT16Q0EAisifU78NwOHqkQOMzBmqeNFD+mIFlN9vvlw57xERpTeu+8N5PJc5/ReKbqv5YGb8HNsf2tV3furjtndWcpwAAJz9j4u6Pm+svqQEmP2Xi5SUmtjU4/77Xfd9ZBqMFCQgiaqXRYoOpj7LkrzZoy+mgNM0v0RW10XUfrjLNOQt/U2cHv2jMyeuVX9lcuHKbesEUzSmno0YIbLzPwN8PGCh91Lovr7DI6QsWjBYNx5FERERERERERETkjC6ER8BAt5jsdm4NEVGzLE04Y3FjnnaZ4gZ1n9ayLiGEdiC622LwenvQhb4lu7oiycXB80REREREFBqXcCmnS8lgtEhwm/pjSx7bEekxGI0AAFLKXwDM95t8lKZ4NAej/QfACIf/jg/TuomIiIiI2kwWbgbW/BrSsmLGw4ETDzxOXfiXbyBrA+9gJj94FuaJAyCP6AbzooPhKS5ULh7j4GhSbv0L5iUTII/MgJyyG+Tbj9tfOAzkczMt54vJ09qnIUGIxGRg+D8CZ3RNB/Y9rP0bFAkjxwHZvS2LuGDi3zXvOKo2w9Mi8aaxAXLOrFBa55jM3whz+kTIo7MgTx1mXdjiPYyPFRiYLZDRxTrFQrhcELM0oW+mCfnYlUiKF0iMC6ynlyYMCgDyYpov7pGv3m/ZhvakCx8SURaMBvguirruKAP3/7N9G9co4rAhrj++TJ6AH2t6OVp26/ZgIqfBaO4oGNAeamhecrzA7cerCxVUoc3hndfMkVi6MXi5yyYIJMQKyL/+BBa83aZ1tkWOtwiXlan7y9x86x+yVutvQgoAWNniOkMppUUwWvg+M1ldgQT1TVuRXxm21cC0EUTUUxOMBgB/bFVP36Nn4Gvx4EkCj54qMLY/MG4g8Po0gYsPab9+ZsIw9brm/OL7XxeMdt6BAifvo29nON8Pol2Z7vswlBDrULgMgaR4gdQkgX36CSy+3sCRw4Mvt2wjkHWVid7XefHRcoknvzLxfx+E/h28Jm4wAEDOvge5BRIfLleXmzpbIm2GiWMe96Koyv76dCVdAoiPUc/bFmJwJO2iPBYbTN2uEYwWbO8mTROm+9Hy6ApG0+3z+nPXIyAg0p+T47SWobNVdeoyKRa/5gsh0DPN159bqajVHzMFC0ZzMzCSiIiIiIiIiIio09GF8HSLzUSsoflxnoioHWTFaoLRNP2WV3pQ2qi+6ah/Xcmurspytd7A663bky6sUvdaEBERERER2SE0cUMmvO3ckl2DW3NsKWAgwdBcKElEDEajVj7zezxKU85/6FqWk5UIIbogMBitQlXWKSllkZRyhZN/AP4Kx7qJiIiIiMKicAvQo7/jxcQNz0Lk9A2crgtGa2wAfmydjSy/fh/y4cuAkjzfiNUVP8Lzv+eUi9sNRpMN9ZCXHQ78sQTweoGSPMinroP87DV7FYRA1tVC/rYIctVPkH/+AKxcar3AhH9GrC1OiQvvBBJanMgSAuLieyBido6LyURMDMSMRwCXJkFgu+x6TWqMRqa3tPWE7z5y2jRHZN56yK/fgzxlKLD8O9+2beXsmyDSHR06643cH+g9SD3vqzmQeeuVs3qm6qssbHkX102rIStL9YXbUajhVx3p2iMN3D65fRuYGzcUixMPcLxcTT1QXSe1IU86HT2g3eOV+HGDep6dIJjdu6vfnwYPULkt9HblVUg8tiB4esHovoBhCEgpIZ+7NfQV9h4Y2nL7Hw0xZ92Oh8dWf6IsVlBtoKJW/3xWF1g/1y9WSni8vjI19YBXs52lh/G3GyEEemj6uvzK8IVm6Gpq2Td1T9GHOOpCwbolB06LixGYMdHADze5sOh6F04fa8Box05waI5+3n8XmtoAjH8MAN6+0MBAzVdfXkV0hZgQdVbRtq+0/0CBT2e48NkMeweLeRXA8bNMzHirbX3CjpDfulp8t8ZjWbbRC3y2AjjuKfs7QFZhvYlx6nnbGABETlgFo9W3YQc1ylh90oOFX2dqboT44XLgnZ9MNHiiY9+izG2/7E+brOeHHIym2WRSEuzXp9Po1fdvwYLRKnaejD8iIiIiIiIiIqJdBkN4iChaZcWp+yG3Wa0cZF7aWKwd1J8V173V4yRNMJrbrHHYyvAqbmCfTERERERE4WcITTCadDjQiGyp9aqPLZNcydr3gogYjEatbfR7rBu1vdbvcT+H6/EvXyalLHdYBxERERHRTkmMGgfx9mqI2T9DnHcbMGTv4AslJkMce7Z63rB9gAz1D9/y569aP/5kdkCZRxOmKpeNcQVvFgDg9++A4sCQK/nFWzYrcEb+tgjyjBGQlx0GecE4yIsPsSwvXlsOYUTPobHY+xCIZxdDnHsrcMbVEE99CXHcuR3drLASBx8P8d/vgH9dAxx8vLJMmleTGqMREIwWQeasGyBP3R3y1jNsL2NMuy1s6xdCAAdN1s6Xp+4OKQNHUCfHAwmafL1SV2brCZvXtKWJYRNtYR923TLJwFdXGxiz/ezHbpnW5dtqddwQ5MYPCWnZreW+HEwnaupDWlVY/F0uMeZuE9V16vl2to2cFP28wqrQ2gUAby+z90KOGyQgK0shLzsMWPyx8xXtOxHi2lkQs3+BmPGwo0XFebdB3Pc+RE4fiAvuBAAMbdB/3nPVN1EFAPy2xXpd5bXAou1nEMstAiLSFWFgbaELRpv7W/jCMqwCcprEuARy1NdJamWE+bUIh8HZ+g/Vxa9LbNR8/TZ93+jejzxnX/NEpKHdV+rgw5sjhgusuav9GlHqyvD94fVg5Tp7X+bLNgJ/brX33aDbVzIEkKjZv97WGB0hTdRJeCwSpZYtaL92RJjVcUewYLR9+ukLnPasxNBbTPy4vuM/d2Vu+2145Xvrsk6O00rdwLYG3wK6sOdU/1uWhahQfcPKoMFopTUd//4QERERERERERGRM0UNecrpukAiIqL2km0RBqYKENMFPQKBwWLJLvUde1SBa+1JG1bpF+xGRERERETkhKGJGzLBYLRI0B1bJhsOB58Q7WKiZ/Q3RQP/S6V1l0iv8ns8yOF6dvN7vNLh8kREREREOzUhBMTAERDn3ATjhR8grnzMuvyLS/XzDAPY7yj1zM/fgPnsrTCfug5y4QfAD5+3mv1TwmjkxfZULuqyeTQpX7hDPWPpF/YqcEDW1ULefZ4yiE1FXPU4RL9hYW9HW4kBe0BMvRnGxfdAjBrX0c2JCDF0bxgX3Q3j7neAA44JmJ9uVjiqr6cn8EI86VXf4a8tzKdvAt561NlCh54U9nYITaDcDh+9ELiMEMhUX7ODkqYwie3kkk9CbVpYmZrz6NEejAYA44cKLL3ZBfNZF9bd48KzZ0au0bnxQ5Ebpw9Ge/RU/brzKgGnw9PdNfWQ7z8Nc8puMC+ZAPn9p8owvkiY8ZaJ3//Wz7cTBJNt8XtBW4LRVuivn2tlcu8CyEk9geXfOV6HuPE5GI98AjF5GkR8AjDlEvvLXvU4xDk3NYeBnn4VMPEU9PLkIVlzR9PVD94P89x/wHzoUsiX7oJcuxwA4DUlPvkj+Hv+4XJfmfJafZn0JNtPwRZdENf8lYCpSxByyG7f1DvdWb290qKvcxvWHYi1G4bbQkKs77n01DynfGdf80Sk4Y3ifaVB2QIvnN0+DSlxZezYn6mvsP9lvmCVve8Fq0BMfTCa7WYQAR7rDUY2Bkmc2gkE6y0mjbIusakUOOclEw2ejg3fstrv9ffmUmm5f+p013V9ie//Kk2IdEqCvXpmTrZ+re/6WN2wbUE20xL1IQcRERERERERERFFseJG9d3UrAKJiIjaQ2pMN8SKOOU8VYCYKiwNANJiMhBnxLealqQJRqv1dtyPHVJKFDeo++SsWAajERERERFR6AyhCUaTDEaLhFrN2B3dsSgR+TAYjVrK9Htcoin3p9/jUUIIJ8MY/Uf2+9dHREREREQtTT4f2G24et7x0yB6W2cViz6a+XW1wKv3A28/Dvl/pwbM/qzLEdo6Y+weTdY5GBXaVt9/ChRsslf28NMgTrwosu0he7r3C5iU6q10VMUE98LAidvCeyGKfOsx4I2HHS8njj0nrO0AAAz/h+Vs+eClkOVFAdPtBqPhtQcgF38cauvCRjcQPBrCPpyadpCBzfcbePpfApdPFLjhaP2TeOoMgc9mGHj8NIFnp1TjmfxL8WDh9cjxFCrL/xm/B9bFDVTOu/d4EzMmGuiWrF7X5jKpDXnSqflxEeSjV/hCKP9YAnndCZAz/x3xcDR3vcTc5dZl7GwbSfECXeLV84racGPPvPLgz39k3R845Eb/+wXYI+ashTjmrNbTDANi9s/BF558XsB3noiJgbjtFbhmPIwhDeuUi60uBLD2N+DD5yFfvBPy/P0hP38DawqBUnfw1X63NngwWprutgwhyknRbwQ/2dxFCMZu3zS0u/3OKsYAxjm99UQ7SE0S6J8RvJy/pqAgXVBdfmXHhpYQ7Sx0/ZErSnaWTh0j0D0lfPWNrPtDOb3eSEDt9p+o8gvtH3/a/d7X7eIYAkjQBaPt/DlWFE4ej/X8XxXHu52Q1be/CNJtHWhjPym3EPiig2/FVWZjH7klq/1Tp4dXudvHwlRrgtG6Jtr7bjhptHW5d3+SymM/d711gxmMRkRERERERERE1Ll4ZCPKGouV87LiGIxGRB1LCIEsTUijKkBMFZYGqEPFkg31XTfd3jZcWNZGVd4K1Ev1j0Dsk4mIiIiIqC2EJm7IBIPRIsGtCd1OZjAakSUGo1FL/iOr81SFpJT5AH5vMSkGwIEO1jPe7/GnDpYlIiIiItrliJgYiDvfBHr0bz3j6LMgZjwavIKsXiGtt8iVrZ23d1+bg+0tRnLK78N7KCAfvtx2WXHS9LCum0In0vwzuoF6oUkt0hhTpwgGqg1+IYrM2wDzzqkwD4r3/TtvP5hn7wNzxpEwbzwJcsWPvnLffgg563pHbUKXNIhrnoIYe7iz5WwQQkC89JNlGTm5D6Tfa6ALRiuOCXwP5H9ugHSamBVmut7D6KRns3qnC1x4iIHHTjVwz4kG5l1moGda8/zEWOCuEwQuOljgiOEClx1q4LzdCzCt4iVcWfYk7iyaqaz3p8QxqDXUyWcHpPwNAOjXTd2mNYX6UBUdt8cVOPGrOcDiec4qcmhtEeANskm6bG4bOZqQlsKq0MOa8oLkOSaatXgh/0IYlpEQCnsd7AtFy+mrnC0GjoC47RUgu4+2CnHqDPV0IYATL8JQqU5kWBM3uPUErxfyrqnY8luurab/shk48wUTU55Wv3Fd4oHYmPCG9+zbXz/vle/DE8ZlNxhtSI79OscOANKSoiPIyN8PNzrvdJuCgnSftWgIxthQInH2iybG3efFFW+byKtgWBt1PrpdtSjJRUNSvMDHl7dtxy3e8OLqmqdRt6or3t76b225pqDf/LJG23U3BMmiaqLr94UAkjWHLbUMRiMnPEE2mE2r26cdEdaWHGXDEPjmmuD9Sbj290JlFQissqVMP8/pcVpuoW+Bylr1gikJ9urZo6fAR9P1r3VNve840l+wfi8a9v+IiIiIiIiIiIjIvtLGIkjNAFhdGBERUXvSBYIVNQYOhVSFpenqSNIMRnebHfdjR3GDOtgNYJ9MRERERERtYwjF+CAApvS2c0t2DbWa0O0kTUg3Efl00qGkFG5CiAQAU/wmf2OxyP/8Hk+1uZ5haB3A5gYw386yRERERES7MtF3KIx3ciE+K4aYsw7iyyoYNz0HERsXfOHM0H74bhD6us85wG4wmj7FRt4wBfLr95w2S13Xdx8BlSX2Cu9/NMQe+4ZlvRQGqRkBk4Y0rLG9+OWlT0K5NQYJRpN5GyCn7gvMf6N54ppfgfV/Ar98A3z3EeRFB0O++gDkzafYaou47mmI13+HeHsVxMf5EMefb/t5OCUGjQTGHWtZRk6fCOltPhmd2UX9uS11BQajYfMaYLO98KNIcRL2IRvqtfVYzetIx4wU2HK/gY33Gsi900DF4wZuOsaA0fIJlhXt+HOful8cryN9w1IAwLAe6vc+t0A6HnBfY6gv/pKfvuqsIocuejV4UJ/dIJhszW8GhVUOGuRng8VXkEt68ELehRhd91vwio76N8S3dRDvroH4OB/Gk19oQ9GaiMNOhXhrJcQNzwbO3O8oiL5D9cu6XBjSU/1jVm78EOX0/Fn3W7anpdd/lKjQBESkJ9muxrYpo/UbwX++kVi6oe1hGdqAHL/Qu2Hd7ScT9c+IkhQjhfRkgSdOc9a+pmC0DE0gZ6m7jY0KUXWdRF2jxJ9bJfa6w8SrP0h8vx544kuJiQ+bqNAEiQQjpYTptDMlCgOvzaDGjrR3X4E/Zjr7KfKJgiuxcsxLWH2ngcqnYvHAa5cg9u0VyHxziXaZ0pgM1Is4/CEH2F5PfpBQ0ya6MCcBIFlzyFwTnbufFK081oF+csvadmpIZFkFo9nptg4eIrC3PgsYALBwTcd+J5c73Mcpdevb6vRprNk+nqeqTj0/NdF+XceOErh9sv5d+XVzYOPcQYLRShmMRkRERERERERE1KnoQngEBDJjHdwljIgoQrI1wWiqELSihsCwNEAdKpbsUl9Yphu83h5KGtXBbglGErq4NHctJCIiIiIissHQxA3pAvOpbdxe9YV0Sa7kdm4JUefCYDRqcj2AXi0eewF8bFH+9e1lmkwRQgy2uZ6W3pFSai7RJiIiIiIifyI5BSKnD0RcvP2FMnuGtK4GEaucPiTHN8DdFl2y0fZ58u3HQ2hZM1lbA/P+iyHaf04KAAAgAElEQVRvPMneAidPh7jzzTatk8IsNTCUa4yDAKi7imeqZ7iDBKPNmRU0PA0A5LO32GvIvhMhjjsXou9QiJ67QRiRP+Ui7pljXWDtcsjnbt3xUBdQU6wKRtu+fEfSDQRvGfYhP54N85QhkIenwbz4EMiNq5rnff2eb95hqTDPPwBy9c8RbrFzQgj0zRAYnCMQGxPYr8qnb9rx9/D6lUg0NQlTGmm/zAMADNVck7q6wPmA+1pDk2b17YeQQUIdQlVQKbF0Y/BydoNgcjTXg4USjFZUJXHUY15Ua85uuaQHCzdOxCnV9oJAxakzIISA6N4PIqWb7XaI2DiIY8+GuPMtYJ8JwG4jgNOuhLjr7aDLDhutDpFZFzsQHgSGpuXHdLfdLiuDssNSTStdEwQOtjhDedvctv9Ap+2bHrwY5qReMP97C6TXizH97dfZK73NzYqoSycIPH+W/aSl+Bjf/xnJmkDOdg7G+GG9xKiZXqRebiLpUhOjbjcDPrO5hcCHvzkPUvl5k8SEh0zEXWyiz3VevL2MPwJT+3ESItuRhvcUWHJpJbp7Ai/WzvEU7vh3QO0SvFJyCS6dOhxDzz8PQ3IE4mIEhGFA9ByA9Myu2udW4srA94n/gFsT4KqSV2HvM2+1T9olQT3PzWA0ciLYPvTOEoxmMU/Y7Lcm7m5dsKgato4bIqXcIuhMxSos1ipITiW30LeALhgtxUEwGgDcMkl/TmNrReC0YP1eSQ1DZImIiIiIiIiIiDqTYk0IT1pMBmINGzdyJSKKMFWoGQAUN7YOdvRKL0obi5RlVeFqyS71b866wevtwf85NcmK7Q5h94c2IiIiIiIiBUOorxMzGYwWEbWm+thSF9JNRD4xHd0ACi8hxJkA5kspCx0scz6A2/wmz5ZSbtItI6VcK4R4GcC52yfFAZgthJioCzoTQhwP4JwWkxoA3G63nUREREREFKLs3iEt1iDUFzEdNNjBD+kyyImwFT86aFGLaqUEtqyBvGQCUFkafAGXC+LWVyAOtRmgRu0nNSNgUrxswEHuRViUfJDlov0bNiJJblPP3PoXMHxswGRZVgjk/gK8+2RIzVXqM9hW+FC4CcMA7nsP8oZ/6gu9/hDkyZdBZHRHluY86ZZYdR8h7zgb4vDTwtDS0OhCKMTKHyHjioDyIsiHpjfP+PMHyMuPAN5c4QuFu/WM5nmrf4a88mjgtd8hMsIT6hQOsrTAtz3WB55KkUu/AFYt2/E4Bl7sVbcc3yftb7v+9F/mQtZUYnDRHwACl9taLjFcfY0Y4mOAek/g9BqrwJEt64ABu9tun119r7f3o4rtYLRUAVU0w+YyZ4P1pZQ4/TkTX+fqy/yxfjSGNKyzV+EJF0AMGuWoDf7E+BMhxp/oaJmh+48Avgx87g1GPDbG9sOgxvWtpheEKRhtTP/IXJg3fqjAt2vV7+XnK4CKWom0pNDXrQ0iMhuByhLgtQcghcCAC+7AHj2AleprE1vplRZyc9qFEALnHihw0GCJcfebKAlyrWdG1XrI3wvQbT0A7Bcwv6YeqG+UiI+N3MWZ7nqJ5X8DK/IkLnzV3md72Ubg7APsr6OyVuL4WSbytoeDbK0ATn9OIrurxIRhAmVuiV82AUO7A3268UJUCj9daI4rym6JJNf9jrHPXIa/1/5gXfCUy2Fc9l/tbJchkJ6kDhIqc3XDNuEs9aeg0l45XQ8ihEBynLpEDYPRyInGBuv5W2zuS0Y5q6Avu+M1htnYDf3wN4n9duuY790yZznWKLMIRnMaYL1m+5UBVZpTJCmaIEcrh+0OLFgVOH1FHrBgpcTI3kBOiu+1rg2yGZc7fG2IiIiIiIiIiIioYxU3qH/oVoUIERF1hCxNf1TjrUKttwZJ2wPOyhtL4IXiIjiow9WSDfVFlttMN0zphSECbzIZacUN6rBK3WtARERERERklwFNMFqw8aAUklpN6HaSJqSbiHwYjLbzOQ/Af4UQ7wJ4B8A3UkrlZdVCiDEAbgLgP1pyK4D/s7Gu27Yvm7798QEAFgghpkkpV7dYTzyACwA87Lf8w1bha0REREREFB4iIQly4Ejgrz8cLdcoYpXTY538rm818rWpiNcL4bJfqWyoh7xnGvDlO/YWOO0KiAknQeyxr+11UDtKy1JOvrH0gaDBaCdVvaedJ+88B3BXQZx4YfO095+GfPJawNMYSkuVxCX3AsefD5HUMXdnEOMmQY7YD/jTImjih8+BY8/GIPVLjTVxg+GFAZfijh6yJB8is2MuoPFqzqOLBW9BvvuMemZ5EbB0AeS3HwTOq6kEFs8DJk8LXyPbQL7zBOR/bgS86guvVPat+9l2MFq8WYdEWQd5zzRkbu4CKJarrpPwmOrQgJREoLg6cLpbJOlXuml12IPRtpZLeGz+pmI3CGag5rOQq76GTOunTbAMRcvwlGBgw3p9gRbE7a8DEyxCDiNoaI46KA4AcuOHBASj5YcrGK1fZAIrTh4jcMc8/f7HmS+Y+Oiy0C9S1NVstPzx79NXIafNxN59BVbmB98X6pXWOUKzBucI/HqLgRcWS6zOB95aFvjcBjWsQ99LR0EC6Ba/B7DbT8q6St1AzwgFwi1YKXHWiyYKqpwt994vEk+dEbxck2cXyR2haC3N+trE2iKBS9+QO77LLh4v8PipAjGuzvFeU+egDZGNks1M1tZAXncCsHxR8ML7ToSY/kDQYhld1MFoJa7AsOVgVPWoaAMxBZCsCRoKFhBE1Io3yPFp4WbI+m0Q8c7C/6KN1R6R3W5raHf9fmuTucsl7p1it1XhVW6zX2lSahE2qzudlhwPuBXhi2VuoKRaolITjJaa6PzLoWea+vWevURi9hLf9JuPFbhjsoA7SL+3LXynYYiIiIiIiIiIiKgdFDWqg9FUIUJERB0h26I/Km4sQD/XoO1/6+9omBkXeA2U1WD0WtONLq4UB60MD/bJREREREQUKYZgMFp7cmuC0ZINBqMRWWEw2s4pEcBZ2/+ZQoi1ADYCqATgBZABYE8AOYplywAcJaUMOhRUSvm3EGIKgM8BxG2fPA7ASiHEzwDWA0gFMBqA/5DTeQBucfa0iIiIiIgoZAdOchyM1iDilNPjnBxJmt7gZTwNgMv+AF85+27boWjio60QaZm266YOkNVTOTnFa51mMq52Ma4te9SyjHzkcmCf8RB9h0Ku+x3y0StCbmaAjB4Q76+HMGwmMUWQuOddyMl9tPPl3+sgAAzTDCKvNxKwKbYvdmvcGLjwX38A7RSMVt8o8f6vEj9t8g0CX6m5JqmLaT3aXL71KLByqXreZ69DdHAwmly5DPLV+4Dv5jledp9tv9gum25uT+tZNBdpCXsDAxRtgYGK8m3wnUpqrUu8Ohit1ggSjBZms74JHirVxLA51n9ItvqzsKkM2NYgkRhnr6IPfrVu26SaT5WBg60LnQtx3X8gOjDFJileoG83YHNZ4LzcuCE4Fv/P3n2HR1Htfxx/n9n0QhKSUIKA0ouI2EWxYBd7vfZesKBe61WvXq+/a0WvDXvvHbH3ih0vYgEJiNIJSSC975zfH5uQsufszm42Db+v5+Fhd+a0bJmd2Z3zmXdbLVvrsyTLRWjbTWPSTJCxeYobDlVcOdP8/Lz1Mywr1gzKju4xtwYRtXxNFa2CylL6Z2R4arOjAsI6woAsxTUHBB67q6ZoDry+gD/9gddEpn89z6w8aUO4SU5DsbWd4orwf/faMs2seYHwsb3HKHYcGv45K6/RnPx45KFoACNN35aHcN0b5hfDq3Ph1Tbbh/s+1Ww1CE7buZskVokNXDcQYOfXgQAsvw6Es7b6F2aZG3EdHVw3wjZcDXOWml+D3WD3FAB981neQtEAdcvrnj4Lc9IgvyB4ebEvmyRdE9H41lUGngtfmB0I26e9UpBqPmSmstb7/osQnoK7V/wOQzfv+LF0Ea+7wmPzIM4hZHDygtWQX6AZ0bfzP3Nt4V9piVBhDDOzbyts+5yj+wUCmk0WFkCZZVPYK4pcvf4edmX/85Zmh81U2EBICYwUQgghhBBCCCGEEKJnKayzhPAkSAiPEKJ7yIjrTbxKoF4H/whRWLeawUnDNtw26eXLIskJ/gEl1We/QG6lv7zTg9G01hTWrTKu6yPbZCGEEEIIIUQ7OZgvOO/iYT6oiIjWmirXMEkLSAlxLCqEkGC0vwIHGNn4L5yPgJO11iu8Nq61/lQpdSjwOM3hZwrYpvGfyXPAGVpr+UQUQgghhBCik6iDz0A/cWNEdazBaObvvMxcD1cIaKiHRG8zNPXK3+Gpmz2VVVc8KKFoPYDKyEYPGQtLfm21PE3bw6/+ud0qrnxiP+JpCNu+fv95OPkq9CnbtnusLanjL+kWoWgAKqsPfFiK3tMya7nxSh0jQgS+5CcMNwejLV8E2+/d/kGGUVGj2f8ul9mLw5cdVhem0NoQX2ss+SWygcWYfuVe9J1/DyS/RWF0nffgsSz/+g23M/0l1nLr15ZiCkazBX1UOylowBQzoH/52rg8WlOfcXngs9gHo40MvtgnEHhafi+EzQd4a2fWj/axKTTT1t0Tsr665TXUjvt566yDjepnCUZL2yJwGYUWinzt/2zNToXB2e1uxuqK/Ry+WOTnHctb/qlvNFdNiTIYzbJr47QNwSspYkCWt2C03B76O9aY/jDvz+35lC2pU4nsXfEBqbpqw/refsOLqlFx6IxLFhVo9vqvu+F1ed0bmluPUFy8d+jP3lf+FwhS62gFZTrigI8zntQcsZUmIyWy157W2hi81Z5AraB1xjo6dPiXx35DjTN4LJqwY/WwrFW9MOU3Rr5ukL+nf/0WPn7ZU1n1+A+oOG8/V2anmpcX+7Jb7ft44WooqYLsMBdZs4UTOQpSE83rTAFIQlg1hD+uZcXiHh+MFuoQyOtmKzNFcdhWihfnhD5GmPWj5tJ9On9jaPtc6ZNu3i6sNZ/jBNi3Pf0zID0Jyg0BaHOXaesY0pPsfdkM8Bje+/hXLpVhtnsSjCaEEEIIIYQQQgghRM/h1w0U1681rsuNlxAeIUT34CiHnPh+rK5bFrSusH618XZLuQnmE8hSfPYfkKv8FRGOsv0q3XKq3Srjutx4y0lwQgghhBBCCOGRwnxuvtt2boRotzpdS4M2ny+aGuJYVAghwWgbozuBlcBOwGAP5SuB94EZWuuPoulQa/22Umpz4DrgaCDLUvQbYLrW+pVo+hFCCCGEEEJET+UOQE85Bd56zHOdOhVvXJ4QyZGk9vBFWH342ZG6aBX6qZvh1fs9datOvQb2O8FTWdEN7LhfUDDaiNpFZPhLKPW1nombngSXjs73FIoGwLzZ8O5TsRppwA77wiFnx7bNdlKJSehBI2BZfvBKfyCXPC1J0T8DVpcGF1kZH5wEpYGHfurNOwWB+udPdthjtHly+TdLNNPfc6msg303V0zdVZEQFyj7eb7mwc81v6zSaA3D+8C5uzvsPqq5rae+0Z5C0QBG1Rn+xpaKzFdIBCC7c04G0n4/vPs0+qYzmxdm9YH15hNHvRpRu8hz2Qx/8xOd6Rqe9EbrqjAmEdiCPgBqVSJJ2jDz/Zv30FXlqJT2pzw9/11koWgAXrMKh+SAzzEHFyxc4y0YLb9AM9983hwATw6dxfgFP5tX5g5A/eNB1LZ7ehtwJxjRT/H+/ODHO3+Lv0H5la1eu4Vx7Q9G230kKNWxYRWX7O3wzi/m/ZA35mmumuK9rfoGzRNfa16ao1lTZi7jtN3nKS2mf8ZQT+3bgn66K718EfrmqTDvC1KBKbxrLJdAPen+MsoNV8stCnOu6H/e1kFhfVe8qjl5oiY7zf7aef676IInAUqrvZd966fo+sm60GVITmTBX7ZgFCFsujq7V69agj57F09l1dWPoSIIewq8/4PfFMW+3iSY9k3CKKoIH4xmC3NSCtIs+0vhAoKEaMXDdyIUruz4cXSwKLOhgzxwvKK6TvPWz/bPyMtf0Vy6T2z6i4RtPAN7w5Ki4OWLCuxt2R4vR8HIvjBnafC6H0NkhNu2V6Fss6l5m9vWK/8L35YEowkhhBBCCCGEEEII0XOsqy/ExW9cZwsSEkKIrtAnob8xGG1t3Wrj7VZ14/OMyxNVEj7i8BvOT630h7jqTQcprFtjXZebIGGVQgghhBBCiPZxlPmkY+1lPqiISKhjylRf++dgCbExk2C0jYzWeiYwE0AplQmMBQYCfYEUwAFKgPXAAuAnrbX5V4vI+l0LTFVKXUBzKFs/AsFrK4G5Wus/2tuPEEIIIYQQInrq0hnoJT/DgjmeytfHIhjNS5JDQ+jZkbq0GH3WJFgbYoZnC+rlxai+Az2VFd2DGrJ50FTbBOo5c/3D3JpzSavlU3dTpNaVeJia2+jPBej3n4vFMAN23A/nltdi114sDRxuDkZzmw/7B2RagtHi8vDjUK2SSdOVAFzc52buWnMUNJ5b8/o8l0dPVhy7naKmPhBSp5TirZ80B97T/KX3e79q/rcUnjhV8cF8zQF3u9S3+Obh55Uw80eXN89z2G9cINzmTY8BM70bisnxF3sqa5SZG33dCOgH/wnP3tZ6YTtD0QDSdCUDkytZXh0+RSnLLdlwu2VIWlvrdFrEwWjVKtkcjAbo8/dCPfJN2PGFsq5Sc+zDkScnOJasJK01Sil0Qz0qLp74OMWQ5FIWVWYElV1YoGl6QJrqmcz60T6+ZTc75D35pXnlkLGox3/o8FCwSI2ynLv821oH9cJv6L17A+CiKPZlG8v+39pruLbPtfjxhexLKfjH/h2f2rPbSPu67/6ENaWafhnenoeLX9bc83Ho16TT9qpIpUUM7Bc+TMLnQEayp2F0C3r1n+hjIwgx8q8zBqMVVzS/10ye/Dr4cfO78NqPmtN2NterrtN8GiY7M5RIgtFenxd9uospGEWIWErqwl/+dHkJ+ujRnsqqadNR+xwbUfu2ELNiX29rEOx2mwa2+8Z6leH7DBVOZNtfqpBgNBGJhvqwRXTRqhCfmj1DqE/OSHaNM1IUs87zUVKlueldzS3vmlu++2OX8yd3blKkKXgZYFR/xWf5hlDHSigq1+SkBz8Atq/TlIKR/RRzlgYXWLDK/ignmb/iC2n7zSKvY1NVF/r4SgghhBBCCCGEEEII0X0U1ttDeHLiJRhNCNF95Mabg8EKW4ShFdabg9FsQY9KKVJ9aZT5S4LWVfrDXAWwA9jGn6AS6eXL6uTRCCGEEEIIITY2DuZz7Ny2cyNEu1WFOKZMccJc5VmIv7guvm686Eha6xKt9Zda6+e11ndqrW/UWv9Haz1Da/2s1npuLELR2vRZp7X+RGv9uNb6Jq313VrrVyUUTQghhBBCiK6nfD7Uza/BIWfCkM1hy11Q/30H9jZPRq9TCcblCaHzTlqzzSJvqT50MBrvPOktFG2nKain50koWk80aIRx8Q2F13BT2nNsORC23RRuOUJx46EKKuwhT0EcB+Z94b38VrvB+EnBywePhOMuQV3/vPe2OpvlSh20uFLHgExzkX/nXk3i6AoyRxUyeNginsw4jgeyzggqd+rjmqRzXDIvcPGd5eKc6W8VitbkqW80PyzV3PZ+61C0DUPScNVrzfXyC0L/aU22qpnrraBNtYf0i3bSBcuDQ9G8ik+AjOzmfwlJrVara59k5KbhQ9EAsvzrN9yOw0+a5eoilZYv0FPMHwEAVDshUpzy56I9BlnabH9DdD+i+Fq8BXTBctx/HoM7KRG9S1Lg/93TcCcl4j54DSPWmIPL8n8v4YP5GudM/4bXuHOmn49/a/15ZgtG234z2CQrxHZq2PhuGQYwsq95TIXlUNSQAjtNAWC9Lwu/Mifu7Fn5Me+5FzJ5FIwbEFg2MKs56MJRMHkUfHW5w4RBHf8YKKX44Wr7199eAxl/W62Z8Un4skHBaCXFbD0Y0kKEDAJkp4JjS/XrhvRD/4qofLZ/nXF5qDAiN0Sw7ofz7fU+XAB1wRfs9cxrMFp9g+aDEOMQoqvtNKzjtyn6py9xz5qEu0cG7rGbo+d8hF6/Fr1/X0/11RUPwBHnRdxvji0YLS6HSpViXDeqvyLZEghU5OGC3rZDWqXs2/hKCUYTkfCHD0ajyDzhoScJ9fVQNLvHmSmKE3ewV7QFpnUkWzDaGPOcHAB+s8wvtI3eUTDYnFPM74X2fqIJzVRKcfLE2HymaA217dhPE0IIIYQQQgghhBBCdJ6WgUItZcT1JtFJMq4TQoiukJtgCUZrDBNztZ8iS9ijLVQNIMVn/mG6yu2CYDTLNjk3oX+3PAdNCCGEEEII0bM4lnlorpZgtFirDHFMaTsOFUIEdOF144UQQgghhBBCdDaVlYu6+O7WC7feHV1fC5+80mpxvSUYLT6iYDQPX4Q1hJ4ErH8MH2qlHv4aNXIrr6MS3Y0lGE0BlxRO57Lbj2+1XFdGEIy2zmPiVlw86q3VqJR07213Nz7Lm9PfnEyWl6WwT7EOWBk/gFPzHmr3cK6Z5fJ+iOCYH5fDkkLNJlnwZ7G3NveveLd9g6oIvpJjzH0xK6pq6pJ7UAcHh9G1NarI5cMF4Sf5Z7W5amWmW0qFz/vrOzXB/lqpUWGSnr54HQ4/x3NfLX22UIec0B9KU7aUdl30dSfCz1+ZCz51MyP63MBb7B+06t25NTz5c/Bn1563u8y/zmFUf0VBmebrJeamD96ycRC2YLS0jHB/RpcYFeKizmc86TJz0kHoL9+iyGdJYQByGwrZZvET7P7QjG5z4t2EQYpR/cyBE2/+pDndkIPZ1hNfa085r07b90tpEQlxipsPV5z7rL0BW8hPd6TrauGD5yKq09tv3sCHCkYrCRFQlmQJNwKYOdf+OPscePN8h33GKt77VbPfncHv87Ia0FqHff2uKIFqDxk2QnSF3UfClHEd24deV4A+d3LzguWL0BcFf6baqOfnowYMjarvbEs+bLGvN1WOORgtJQGy02DF+uB1RRWawFGHXahwolRLkGyFBKOJSIQLiwcoXNnx4+hgoXanot1zHN0fBvWGZYYc1pUlUFKlyUzpvP1SW7ZrXoaiV5KmrCZ43dJ1mp0Nj4AtZE0pe+D52hBhj6H2oUIZGeI4IVIzPtFcvHf3OE4QQgghhBBCCCGEEELYNQUKtdUnRIiQEEJ0Bdt2qdxfSrW/imq3kgZtvnKLLVQNINVyjl2l5cKkHcm2Tc6Nj+GPOEIIIYQQQoi/LAfzPDS37UXjRbtVWY4pk5wUfCqSybpC/PVIMJoQQgghhBBC/MUppeBfT6PbBKPVYZ41mRDJkaTb/mA0luWHXr/VbhKK1sOplHT7JOk/F6CrylsFlulij2FnkdjtsJ4digZguVJHy4BC2wTqjvDOL+HLvPGTZt+xyjrpu6Vkt4ojy19t36BsYVUxomuq0HdeHFmlbfdAHX8ZaqvdPBUf2ddbs5ltgtEy/KWsiN/E87BSQmSfVavkkHX1z1+jogxGu+W96H9AqWrKlJjzkT0UrdHIukXG5Wvj7A/wjE81dx+j+GiBPSjrkHDBaOmd+CaMQF4mpCWag1xenwcNxx2ML+1yCv051jZy/UWgG+DzWbDrIR042sgcOF7x25rgJ+yD+VBdp0lOCB3O8OIcD6logNMmDFaXFKGAqbs5fP27y9PfmtvJ7knBaI/+O+I6OZZgtKI/VgHmbdIC8zmdACRaQj1WlWge/8r+XM25ymH8wMBznWHZhPndwHsgPcxFzos6/wLAoofyOY3/VIvbjf8cZV8XapljWZeaqNhlBBy3vQq7XWsvffCg6CrudhjqojtQvT3uzBhkp5mDW4t82VQ65tS01MRACKU5GC18n6EOaVMTzeOplGA0EYlw34kArFziKbyzOwsVNBvtn6WU4sDxihmfmBv/aAEcvnV0bUfDdlzrc2Bgb/h1VfC6lYZtU+i2FAMywweetxVtMFpeDHOd7/9Mc/HesWtPCCGEEEIIIYQQQgjRMdbWWUJ4QoQICSFEVwi1XSqsX021337VvlDBYrZgtCp/558wUlhnuBoksk0WQgghhBBCxIZjmYfman8nj2TjV2k5pkz19aAJJUJ0EQlGE0IIIYQQQgiBchz05CPh45c2LKtTCcayCZGE0GsPATf1ddZVurwElpvDa5qoY/8ewYBEd6X+/Sz6mmON6/R/L0Jd9XDzgu/ej23nQ8aizv5PbNvsCo7lzek2fyHdmcFoXlz0gmbw1PCz4BPcWu5ZcyH9G8wn+nhWWYp2XZRjCZFrB712BfrYzb1XcBzUuTejjpoWUT+j+nmbBJ/ltp5h39u/LqJ+UkMFozmhg9Goju4ksPIazUe/RVUVgN//KMW971Z49rawZYdbgtFCmb0o8LibAg0AhveBUf2bgtFKjGVUWgyTBWJIKcUWm8BXv5vXf1mQya6X3UfRf18xrk9xK0nR1QDoq4+GT6tQvu5x1ZoDt1Dc+l7we6a6Ht6fDwdvGZt+VNv35ao/Nty86XBlDUYbmtszAk50VQU8Mz3ietmWbU/x3B/RFenG98S5z9r3H5MtoR6HzLDXeWVqcygaBAKSbP4sgnFhMiSLOv8CwB1GqfDBXEFBXBEEeTnGdap10FcE4V/hlrWqp+x9GscZRUhZqMfHcXrGeztSes7HUdVT/3kRtcvB7e7f9v6tclIp9vU2rktJsNcrtp8Hv4Ftr8txAqGiJtX14Hc1vo30dSBizEswWsEy+HMBbDam48fTBdrzTrn5MHsw2pEPuLgPdt4+qWvZYPicwLG46ThilSVPOVTI2iZZkY8t2mC0AVmRh7DZ/F4Yk2aEEEIIIYQQQgghhBAdrLDeEowWLyE8QojuJTMumzgVT4MO/r2tsG41Va75B+E0XwbJPvOFtwBSHPMPzJX+zj9hRLbJQgghhBBCiI6ksASj4WE+qIiILWzbdgwqhGgmwWhCCCGEEEIIIQLaBJjUK/OsyYRIjiS1h8mT//sURgSnkujSYvQBeaHrjtwHVC0AACAASURBVNwKttkzggGJbmvH/ezr/vfJhpu6sgyW/Nr+/jJzUSdfCZsMhS13QSWGCXrqCWxhX27zF9J5GbGZ0BxLh91n/8L8v2suJtMtZVLVbDatX9b+zlw3EFjVyxycES09/3v0xVOgtjp0wUPPQg0aAcnpMG7HwO0IjbJfrLKVTH/rGfZ5DeaTpGxSzNmYAFSrpNCVi6MLsHv/V6hriKoqAP3euAWKb/dUdmRt5MFo81aA1pqFa8zvo202bRHpUGFJOEjrZumELRy7veKr381/26wfNbsdfRiFVbvCrOD1uQ1Fre7rG05H/fOxjhhmxHYcCtmp5vCbQ+91qbvPIc5nj+NY5yE0B0C1/fFv2cINN/MyFUdto3hxTvDje8x2PSQ053PDE++BLZSx2NcbPpsJU05utdx1NT+tsLdneoXW1GvmWeokxcPebTJkBveGeB/UGy5k9dsaD8FoFZF9lh6yZWD7kJkSLvhLeQrlsod/RR5SplQPef2JbkHX1qAvCrHPbrPN5JiEokFge26zIt785k358QNy+u+FaQtS5CHL1RZ0pAgdJFtVB+lhdpmEALwFowH8+HmPDkbz8vVQNFISFZvnwS+W8OL7P3M5e9fYB2ObhAozy8s0B4ytWm9+YKxtKRgQYTCaUoF9n2jkdc9cZyGEEEIIIYQQQgghRAdxtZ+iugLjutwECeERQnQvjnLIje/H6rrlQevW1q+m2m8+6adPmFCxVJ8lGM2N7mKh0aryV1DhLzOuk22yEEIIIYQQIhYcZQlG0xKMFmtVlmPKFMsxqBCimQSjCSGEEEIIIYQIcFrPkqxT5lSctsFoWmv46i30G49CYgpqjyObJ7674b8I0zMuR/3twsDtL99Ev/M0VJXD9x+Grjh6W9TVj6B8Uc7uFN2KSkpBJyRBXU3wyrUr0HW1qIREWL82Nv2deDnq8HNi0la34VjeCy3ehwN6+YGe8Z7Zq+IDzl9/X+wb/u4D2PPodjej/X54ZQb663dgzsdhy6vrnkFNPqLd/eZlQloiVNSGLpflL2l1v3+EwWiJcYEJ/KYJ+dVOmCDBZflorSMO3Hnjp/BpCQOzYPl687p9Kj7w3FdffwGD65ayNGGw5zoAa8thgSX3bWRjaJ3WGsotg0ztvskCZ++iOO9Z83PwxjzNf4+GIrIwBTrk+lsHo/H+s+jJh6N2OqADRmqmi1ah774UPn45sGDPo1HTbsOXlcuULRRPfm3+2xKmukwZB3uPVfRKgplzNWvLYevBiiv2VZSGyTts4rT98W/FYrTfv2E/5Z5jFBU1mrd/CazulQQ3HqbYa0z3DqbS5evRj/0fvHRPVPVtwWilvgz07DdRbYLRTAF2LVUatn2LCswhZwBbD4LUxNaPcXycYmhuIAStrYUFmkDckV24Mbb0zOmKY7brnDAWITrczPujqqaueTJmQ8gJ8dv/mjhzemzKnLfpvf/WQHCSULGHoENbmJNSgX0ym8paCUYT3ug1S72VW74ozCdU9xbq3dbenM7thyh+WWXu4ZxnNKfupEmI6/hHzxak6KjAcZzJKkuesm3b43MgNw0ykvG8n5oUF30YaqQhbEIIIYQQQgghhBBCiJ5tfUMRfsxXtMuN93glPyGE6ES5Cf2NwWiFdaupds0neIQLFUvxpRuXV/nLIx9gOxTV2y9OKttkIYQQQgghRCw4mM9z120vGi/ardJyTGkL5xZCNJNgNCGEEEIIIYQQAW1S/utUvLFYgq/NZMrXHkTfPm3DXf3xSzBtOurI88G1pFS0odevha/fRd94hrexjtkOdf/nUU/sFN3UUdPg6VvM6169D/52YSD0Jhb2PjY27XQnjiV4pWUwWloDPSUYbUTd4g5pV193IozfGZU7oH3t/OdU+OB5T2XVebfEJBQNAhPaR/WDOWHyEzLd1sFcAxpWRdaP1iTHmwPYalSYYLTqCiheDTl5EfX51e/m2f+nT1LMOEaxuhQG9oZZP8Jh97X+oWVi1VeMq/3Fc18KOGf9/Vze98aIxvjst5oFloy5UU3nm83/HirNV+sk3ZKI0A04juKxkxWnPB78PCwpgqJyTaHlwqfZ/uKgZfqKw+Gu91ETdo31UFv3U18HxavRR45oveLDF9DfvAcvLODALbKswWgAb/0Mb/3cev03SzQPfBY+MKeJ0/bHv/o6WPMnDBgKQE664s1pPoorNAVlgSA9n9O992N0VQX6lO2gYFlkFTNyAvuA5evJ9JvTPkp8mYH3ShsrSwyFW6iqC1620HzBcgBuPdL82TiqnzkYbUlh6P4BijxeAHhwNhw6oXs/x0JEQr/9RGQV0jJQry1DJcYuHax3auR1Ut1Kstf8BAR/Hnl5P4cKOkoNEYwWLsRWCGj8LmTWQ94KL1/UsYPpYLagL2h/MNq5uysemW3v4LN82GtM+/rwwhQqDYEwM1sw2hpLMJqtLccJ7LfvM1bx4hxv+6pJ5q/3PElPiu2+TDQB2kIIIYQQQgghhBBC/FXNLnmfd4tf6tQ+G7Q5FA3CBwkJIURXsAWE/VA+2zqRP1yoWKpjnpRe6fd4wkgI+VW/8PH611lZuxTd9iKMbdRrw0kyQJyKJzMuu91jEUIIIYQQQghHmc+1z6/6hat/9zjHs5PFqXiGJI9k3+yj6NNNv6/6ueJ7Pi95hzV1K9CNJ09W+M1zjFIcczi3EKKZBKMJIYQQQgghhAjwtQ5LqlMJxmLxLYrpyjL0/VcFldHP3AaHToUwP9xvUFKMfupmz0NVh02ViZQbIXXK1WhLMJqecTkcdDp89Xb7+7nkHlTGRnhiiGMJPGsRUNgroYE0fx0VlqsaRiojGQb1hhN2VIzoozjk3thdFWTLmnkxayvIu8/ACZdFVVUvW4g+ZzKUFnmuo46+IKq+bCYMUsxZGnoSfE5D66CqvAZLmpeFem46yVlnUUHwa6Xa8RBw8sf8iILRyms0i9ea1+0zRhEfpxjU+LY9ZALMPMfh9g9cVq6HfdT3XD/7cBy8h1gx+UgunvscN/ovo8SX5bnaxS/Z+9h6kEJrjT57krmAUjB0c+9j7AJ7jVFgeRz7XOySZgmAyW0wp0npaXvDY9+jhm0RoxG2aPvPBejp58Ev34DfcnJ2RQnMepi9j4wuVLMhgk1aUDAawNKFG4LRmmSnKbJ7ykV93nky8lA0QB0+FXY9FP3QtWQsNe9Pljq9YN0adEUpKi1jw/JVYYLRKmuDX5+/rbG/L3cYYt5fzMs0v9bXVYbfjoQKUtosB+oaYJcRiulHKJLiZX9V9Hx6+SL0pQfByiXeKw0Zi7rtzZiGogHE+RSZKVBS5b1OiltFzrLvIC66YDRbmJNSkGrexAFQKcFowov3n/Nedll+x42jE4QMRmtn21sOVEwZFwi7Nfnbgy7Fd8QuIFy/+Rj6iRuhohRGb4O6/D5U30H4QwQp9k4xryutNi+3teVrPA9tt5Hw4hxv421PMFqs1fshQc5QEUIIIYQQQgghhBDCkxq3inWW3+I7Wy9fJklOmIvoCSFEF8hNMJ+fZgsVC9QJPXE+xXJuZVU7g9Hyq37h7uX/wo89hNKL3Ph+1vACIYQQQgghhIiEwnxsUa/rus33UiZr61cxv/JHLht8C73jc7t6OK3MLf+Kh1fdivY4vynV11MmlwjRdeS0UyGEEEIIIYQQAU7rL7NswWitJjB+PguqyoMLFa+GFYtCz3xtqbQIViz2VnboONjzaG9lRY+iEhLRA4fD8kXG9fpvo0M3cOhZMPOB0GV2Owx1cPe8akW72U52cVuE9fgbGFy/gl99YyNq+tSSx3lw9TmoM6+H3AEwYAgMH49Kaj27+6O/O+xxe/vD0ZR22b/i3Xa3Y6Mf/CcMGQP9N4NNR6Mc+4lCuqEeFs6FkkIoKULfdKb3jtIyUfd/FoMRt3bgeMVDX4Tevmb717W6n1e/KqI+nJpKkmvWQ7whGE2FP9lVf/s+ats9Pff380r7ugmDgpcdNKqag+J+gvVr0Vce6bkffHGoa59C7X4YWmt+nrwpQ4cuoM6xJH55NLo/DO2j0M9Mtxfadk9UVp929dPR+mdAryQoqzGvr7CEvOT47UGB+pRt4e0CVHpmxOPRtdUw/3soWA6JyZDQ/DzpKw7z1sZ7T5N+wmVM3U1x36cRhOdFyDGFwS7Lh4n7d1if7aHr62DF74EQ283GBAXO6toa9It3Rd7wgCFw4GmonP6oG1+m9yINtwY/NtVOCrUqAf/vi5mXuhUj+kJuumJlSejnyPQazF9jLnvweHs7tnC6dZUhuweguMI8xtMnKR48QU48FRsP3dAAC/9nD/y06TMQ9fgPHRZk3Sc9wmA0XUV2wS8wIHhdsYfz1l3LMa2jIC1E7lul/Tx7ITbQL9zpvfCqP9CVZajUXh03oA5QVav5/k/48nf7Z3wsNhfPneHQa5r5WHR9Ffxzlstl+yjSk9rXmf7qbfTNZzcv+P5D9Pl7oZ+db/0KzOdARrI5lLWsBrTWQdtMv+WwuikYbZMse6BxW+0NRtthCHwTQTZmKDX1EowmhBBCCCGEEEIIIURPFC5ESAghukqf+Mi3T7lh6tgmpVe5FbjajTqU7OP1r7c7FA1kmyyEEEIIIYSIHZ+K3QVHO1uZfz0/lM9mr96HdvVQWvlg3WueQ9HAHs4thGgmp50KIYQQQgghhAhwWn+ZVa/MMycTWhTTX75pb69oFVSWeepaf/Oep3IA6qGvUL6e+8WbCGPH/azBaKxfG7Kq2vlAdIhgNHXpvTDl5HYMrpvz2YLR/M23/Q0cWPEWvyaZg9GG5MABNR/wx+pA8kyGv5Q9Kz/muLLngMZAsZb++zZqmz023J00HIbmwu+GC4OM7g/PnO6w1fXhg9P2q3iPvv7Qz3d76SsOD9wYtgXc9Cqq78DgMvO/R//jCFhnSd0JJSMb9fDXqH6D2znSYHuMgtREqLSEVAFk+4tb3c9rWB1RHw4uyW61cV2NCpEC0uS7DyLq7+cV5i/+05Ng0+zWy/Tns9D/dwpUe0gwarLrodBnE9TkI1Cb7wCAUor+Bx/G6bMf497eZ4dpILSDxgeCDPQ7T1rL9IRQRqUUo/rBd39GVi83RDAaAG8+Csf8PaI29eez0FcdFdlATJYuxL19GndP+y/3fdr+5mwcgrdtetlCOiYWqH30H/PR/zoBlvwSWDB+Elz3NCq7X2B9/o/o07b31JZ65Fv49Rv0gh9Qg0fCvsdvaAcgI0SO4oOZp3HFfeOobQzwnLaHIjNM7qJpu/fbGvP2Y2R/+6PfO9W8vNhTMJp5eY5crElsRPTqPwPb4EXzIq+83/EdFooGsO2mivwC7ycMpLqVxGnzyeWl1eZAopZsQUcKSIwLBKS5hjJLCjUTh3bHTwHRrRSGSAc2WTQPtowwrLALvTjH5aRHNbVh5nfEYpuRlqS47zjF1GfMb9r/vKW56yPNoyc5HL519P3ph64NXrj6T9yv3gbMgbiOgl6WfZx6P9Q2BIeX+U0bFsDXOPQBEWQOJ7czGO34HRTfLIlNwHBNvf2xEEIIIYQQQgghhBBCdF99EvK6eghCCGEU6fZJoegTJlgs1TIpXaOpcatIsQSnhbOkemFU9drqEy/bZCGEEEIIIURs2I5/eoqlNZb5h12kQdeztGZxRHUy43p30GiE2HhEF1EvhBBCCCGEEGLj0yJszI+DX5mztBMaF+v6OpjzsbU5/c7T3vt+5lZPxdTLi1DxCd7bFT2OOukf0VfeZg84zBBulJyKuvFl1EGnbdyherYrEeoWYT0N9VxcfAc7VX0ZVGxYVh3vXejw339uxcwVRzFzxVE8vvoMji97zhrsoy/aH/e+K9HFgeCwOJ/iob2WkhVX06rccfGf8dOf2zF+zgP8cOJi+idYUmUa3V5wacj16tIZOF/UwuY7hiznyeKf0LeeE7RYNzSgrz46ulA0QL22rENC0QCSExT7mrPtAOiVBPH7HdtqWd4++0TUh4NLsq4xrqt2PASjrf4TbUsSMVi2zrx88zxwnOZXoF5fiP7X8ZGFoo2biPN/z+NMm74hFK2JOv4yblp7FdtXfbthWZyu5+QSe8CZyUHlb6ILlsNSywlscfEwcUpEbXaVcZtEHhSRO2mXkOv1vf9A+/0hy7QqX7wmNqFoTWY+AMeO5tODf45dm22YgtFYlt9h/UVD19ag330afeKE5lA0gHlfoA8ZjDvjCvQz0z2HoqEUDB6FOvRsnCsfQh13SatQNCBk0NlF/W6j1m3+XL7rI82/3wy93WgbjKa1ZmGBuezIvvZ2sm3BaKE/ngAokmA08Regp58XXSjaFjuhIgzCjNQBW0RWPsWtJsMtNa5rcKG6LnR921bJcQJhTqmJ5vUnPhqbECEhWsmf29Uj8GxpseaYh8KHosXS0dsq4kKc/VBeA0c+4PLdH9G9P92Vf/DpqgxuyL6MF9MPp0o17+j4f7M/Nz4ncIxmU2rIo/Zb8sSb8tDzIghGaxu6FqlTJsYu5LGmPmZNCSGEEEIIIYQQQgghOtHW6Tt39RCEEMKod3wumyWN9Fx+dOqEsMFmqSHWV/jLPffVUrW/kgq/+XfrSG3dS7bJQgghhBBCiNgYkzqhq4fQLjWuec5TVymuX4s2zSux8BHH2NStOnBEQmwczLPchRBCCCGEEEL89TjNwRT1yj5rsikYjSW/QGWZvb33n43RwALUy4tRfQfGtE3R/aheveHvd6JvvyDyuo4DF94BB5wKC75Hr1mGGrU1jN4GlTugA0bbzdhC31qGEfkbyHJL+GDp/nydsj0/Jo7HVQ4j6hax6z/uoFefzYA+6BMuh6du9tbvs7ehn70NZnwCq/5gl5vO5Fey+Dh1N4p92WxV8z92qP4OBejbL2A88JOTyQEDZ/JtSnD4zur8QeT6i+z9JSbD7ocDoI79O/rKI72NM5TvPkCXFKEyc5qXzfsCCldG1Zx65FtUXMd+7XbQeMUr/zNP6C+rAefKh9CHnAmr/oBNhpI6amuyLvSzvspb+wpNkmuYoQ9UtwgAICER6mqDC9VUQVU5pPby1N9qy3lfm2S1mYT/2UyoD5Ng0oY63h60p3L6k3rOv/hixmQ+St2dYl82E2p+ZGD9Cp7IOB5tCxxsoV/DGrZ9+Cj0wyECFg46vcNfE7Fy8kTFI7MjC4vos8c+kHgBvHCntYw+cyd4YLanx0Ef0gGhgquXsvNN23PsxK94dv2WMW9emaJzulEwmi5ahb74AFjyq73Q8/+1BgAZ9RuMSgwdlJiZEkmD4ZW32dwUlAVCTkxG9bOHeGSnKkxxR+sqA2FrStnr2oLRbGFrQvQ0etUS+O4DT2XVtU9Bckpgf2PT0TBhtw7/vNtqkPn9a5OqK/G59nDOkmpIsYSbAbiW8xOathK2bRBAVa0mJTF2gUJi46JtL65QdRbOtYZWdzezftREkJMcE5kpiolD4fMwF4Hc4UaX2Zc7TBwa2aN50aMl3D34vQ33t67+H28vP4hs/zr8P3wGXGWs5yjICBEWW1YNfdscNoULRstNg3gf1HvIHm5vMFpyguKlsxyOfCDy12xbNZ0YlCeEEEIIIYQQQgghhIiNPbMOYVTK+K4ehhBCWB3X71zuXXE96xoKQ5brE5/H0X3OCNteipNuXVflLwf6RzpECuujuzhrWwfmHMegxKExaUsIIYQQQggh+iYM4Ni+U3mu4H50ZDMJuoVay5ynrrK2brXnsj7iODXv4rDh3UIICUYTQgghhBBCCNGkRfhLnUqwFov79CXc+56Gb96zlom5CbtKKNpfySFnQRTBaEAgyGT4eBg+vsdMGI8ZW4CTbjF52R+YhZxAPbtWzWbXqtnN1VOag9XUnkejvQajNXVz7u4bbvehkL+VvWQtm+WW8OnSvbi0703cl3UmfhXH+JqfeGD1OaFD0QA1/Q1Uelbgzs4HRjRGK61h7me482ZDwTLYdAz8uSDydlLSURf+FzUi9qFLbU3ZInwoiBqzLYzZdsP9AZl4DkbL9q8jRZsLVzot0n/6bAIrfjc3UrQqgmA089/SP7P5tq6qQN92vqf2WlIT9w9d4KhpODMuZ6/Kj1stHl63mPzEEWHbP6D8bZxwz8V5t4Rtp7vYaZjiwRMU057X1NR7q5ObDmrqjejli+Crt82F8ufCO0+iJ+6PnnEFfPBcYPkBp6LO/j9URjYA+pt3Y/BX2N3zzT7UTZnPy4uzY9qui2EbvH4t7jXHok64HDW8a08U1y/eHToULRpjtgtbJC0xEAbixuh30rVlrYPLVpbYyw7Nta/Ltvx+2OAGQo56WcJLtNbWYLSctL/cnofYCGmt0Vf/zXuFEVuiBoX/rIylTbMDwUC20KC2UtwqkrU9vWx1Kbw4x+Xj3zR9eymu3F+xWU7z+9m2+XI8vOWLKmBQiNA18Re3fm3kdfLnxn4cHWTpuq7pd+Y5DtkXhd9A7HxzoMxuI+De4xxG9Q/9pp67THP38i1aLfsheSvuzTqLfxbdiFtTDZb3u8+x71sAlBrOzQoXjOY4ioFZsCT04TPQ/mA0gBT714QR8Xp8IYQQQgghhBBCCCGEgDGpE0j12cN5OlqcimezpBHkJPTrsjEIIYQXeYmDuGaze/ijJp919ebf4HLj+zE4aTjxTvgfPZKcZBwcXIJ/sKl0LSeNhFFomRwfp+I4pu/UsPUTVCKbJY+kd3yIk2GEEEIIIYQQIgo7Z+7D+LQdWFz9KzXdLGisyYLKH5lT/kXQ8u423sJ687Ffui+DQ3JP3HA/xUljWMqYLv3uT4ieRILRhBBCCCGEEEIE+JpDkeqVfdZk/LM3QW2MgzXCUGf+u1P7E11LKYXedk/4/kPvlTbfoeMG1FM4PvNy1998uzEYzcjX/DWRGjIWvesh8NlrMRpcsHgauKPgEv5VeD1VTgp9GwrwGU4m2qDfYNQLv6Gc5vAhpRQ8+wv62M3bPR59zbHNd2a/GVFd9VI+VFXAoBGouBjMOvegd6qiby8oKAteN2WcuU5eJvyyylv7I2sXkuZWGtdVOSnNd/oMtAejFa+BwaM89bfKEm6UlxH4X9fXoc+c6KmtltRjc8KXcRy45B709PNaLT93/f1c0O/2sPUPrAjzehk0AhUfoySBTnL6JIeTJ2q2u8Hlx+Xhy+ekgfL54KZX0YcMhnUFxnL6udvhtvPA32K79Oaj6C9mwcuLUUkp6LefjG7QvfvCuInw2cyQxXq55Tz/xkDWv1XFH0Uax23g1Gfi+WlldN02SbH9qPbJK+hv3oM730ON3qZ9nURB19cFbnz0Ymwb9sWhDj8nbDHHUWQkew9lDKe6HsqqIaNxM2TbdsT7Aq9Lm96p9nWrS+3hJeU1gfA0k1D9CdFT6If/BYvmea+Qt1mHjcUmPk6xWQ4s9pgplepW0cs17DA12uM2P2U1TYFImhe+13zzD4cxeYFltmBH5SEYrSbErrcQ/Ppt5HWWLUTX1aISun/iXrHHOSGJMT5bIStV8chJitOe8JbK+mk+jLnW5bsrHcbmtV6XEAe+xhTEl34wt/d6+oH8s+hG/CuXwhBzHz4H0pPsYygzZDf6LcP3tcjiHd3fYzBaDB7jhBg9TxKMJoQQQgghhBBCCCGEd3mJg8lLHNzVwxBCiB4hwUlkZIrlpLkIKaVI8aVT4S8NWlflL4+qTdvk+Jz4fuyYsUdUbQohhBBCCCFErKTHZTAhPfL5Op3Fr/09IxjNEoqdlzhIjv2EaAcnfBEhhBBCCCGEEH8JLUKV6pQ9wCVB13XGaDZQ099ASejVX8+ICREVVwee1kED6UEcy9c8bosEl5ZhRG35Ws90Vlc/BkecG4OBhZbplpLXsDp0KNq+J6AenN0qFK2JGjgc9eCXHTjCMI48H9VvMGrI2E4LRWvyf4eYEzn2GmNenpfpIcGj0ci6RaRagtEqnBbpP0kp0Ku3uZG13pOmVgefRwZA/8ZgND56CZYu9NwegDrtWtQwjye87XRA0KJz198ftlp2QxF7VH4SutBmY7yNoZuJ8yku3NPbaya38UI1SinY7wR7weWLzNuh0mL0Xlnol2fAJ69EPtiDT0c9vwB1/XOo82/1VCVrSgpbnZTKlqdk8NXsPC5OmsWmWdEl2CS4tWxR+7O9QHUF+qW7o2o7WvqHT3BP3gY9OR09OR3Wrohd4xN2RU1/HTVuR0/F+/aKXdcAWRe6XDnTpbpOs7LEnBrSPyMQymYzINMeahQqbKkoRMiLBKOJrqJ/+hL33Mm4+/XBPW0H9Pzvo2unpgqev8N7hW0md/q+T5OtBnnfp0nRVSTpGuItx7HNoWgBFbUw/f3mbYu2hBOF2MRsUNW5h86ih9Ffvxt5JdeFlUtiP5gOUFzhLZhsR0uYWHucPDEQjpaZEr5sk+1ucEk9r/W/jGkuqef6yb3Iz03vmP+euUlbogF/vT3xy1GBgLU0S55dmeHcLL/l8NjXYtszsp+3bWFSDDbV8ZYc9khVy3ZRCCGEEEIIIYQQQgghhBA9QKrPfBJIpd/j1YHaKKxbY1yeG98/qvaEEEIIIYQQ4q8kyTFf9by2uwWj1cuxnxAdQYLRhBBCCCGEEEIEtAxGwz5rsjOD0dQDs1Hb791p/YnuQ+0cHFIU0q6HdMxAehLHMlPZbRFC5A8R+tMm2EIlpeBccDvq1ln20LVOoq58CJXVx75+9DZw3KWdOKJG2f1Rp1zV+f02OnmiYqehrZeN6gfHbmcLRvPWbi9/KTn+IlLdKuP6StUiYcAXBzl5xnJ63mxP/VXUaGvYUF6mQtdWo5+80VNbrRzkPTBR5fRHnXl90PL/LdmOdH+Ztd5Na68mSdeGbnvSwZ7H0d2M7h8+bCEhDjJa/M6kjvl71P3pOyOsqxTqqkdxLpmBSk5FKYU6ahqccHlEzSTVlHDz3GNYvGwCtx0RuuwRW/rxqdbBGNcV/ptkXRO64rzOC3DUS35FX7gv/B4irC0aY7dHfVqFc9f7C1GGMgAAIABJREFUqG28X7HI67YnEje9o7n2dc2SQvP6AWH6TE5QDMw0J47kF9iDXJats7cpwWiiK+jClejLDoGfvoSKUsifiz5rZ/SqKIKTfv8Z6sJsy5okp6JOuTryPmLkgC28l01xq1BApr/Ec52Pf2veDriWTUJTuOK+Y+3tVNXB2jLNzys06yq9hUSJv5BFP1pXqQtutx+DLc/voAG1X2295rs/NJ/na/4oCl8+KR6uOTD2x5pKKU7ZyWHdHT4u2dt7kGJbVXVQXQ/F5rzoDVbGDcANcdqFr3FVL/O5WZRWB28frMFoLboZ7fFcqaT46B+DJsPsh+MRqYkuh1gIIYQQQgghhBBCCCGEEKJTpTjmk0Cqog1Gq19tXJ6bIJPjhRBCCCGEECKcREswWk13C0ark2M/ITpCXFcPQAghhBBCCCFEN+FrDlVK0rX8rfQF6lQCdZn9qR+9A3VLFlJXbA/KiblhW8CorTunL9H9jNnOc1F1x7uo1F4dOJgewjZx3m0xozpUMJrP/DWR2mFfePR79Mz7YNbD7Rhg9JQKP5FbHX8p+plbO2E0wNBxMOkg1FHno9KzOqdPA5+j+ODvDk98pfn2DxiSCxfsoUhPMj9e4YKCmmxavxQFpLrmE7kqndQWg4iD0dvAkl+CC875yFN/+QX2dUNWfo6+5G9QFiKNyGT8zqjefSOqok64DEZOCIRzLQsETmxR+wvf/zGRB7NO47eEUejG1+Im9Ss5quwldq/6PHSjaZmw05TIxt6NjPTwEI7u1/o9qjKy4c1V6APMgXntctBpqJwB6EU/Qu8+qD2ORk3YJaiY2vd49HO3Q0N9ZO2v+J0L5p7HdUn3UGbIBkpwa7n3zfH8Y/I5PPJNApVOKoeVz2T/infDt71uDVprT9uz9tAL56JP3yH2DR94KurCO1A+SwhnCAMyFRD7QKDp79vbDBXGprWGx29g+PJtWZYWHPAWKhjttzXmddmpkJHSsc+tECb6melQGRzgqY8eDZ/XRLbNCRWKNnQc6vjL0N++D1m5qH2PRw0JkQjWwSYN97ZdSXKr8RHYF870l1IY5y3ZZ9k6KCrX5KQrtC0YrfH/KVso3v3VXOiKV1zmLIWaekhJgDv/pjhtZ7lmlWj8LFq20Lxyr2NQR5yLfvkeWGkIOVzWPYPRZi/SHP2gy+rS8GX3GAUTBimO2U4xYVDHfn7efLji11WadwyHK7HyW8IIxtfaA2mdxj8xIxlWGTIaTfudXoLRBvX2ti1MSQxbJKy8TMV2m8J3f7avnZoId8+FEEIIIYQQQgghhBBCCCG6Qqov3bi80i2Pqr3C+jXG5bnx/aJqTwghhBBCCCH+SpIswWgNuh6/bsCnuj42ya8bKK5fa1yXGy/BaEK0R9e/w4UQQgghhBBCdA9Oc9BFX/9anl51SuBOziScCz7EvetR+N/dnTYcdfn9KFvQk9joKcdBn3QlPHFD+MJb7dbh4+kRHEtYjetvvt0QeTAagBq6OeqSGXDJDHRDPXp38xURu5JKy0D3GwxrlnZcH1NvQB17cYe1H42keMVZuyrO2jV82TyP4URPrDoNgFRtDsKsaBOMpnbYF/3W48EFC5ahG+pRcfEh+7OFECXGaTa5/RgojzAULbs/atptkdVppLbbC/VMINRAz3wAffs0htUv4Za1V0XX3uX3o9IyoqrbHfRKVvRJh7UhzukzhVmojGz0UdPgxbtiOh516FTUsHGEi89Qg0bA1BvQ9/4jdCCkyZuP8v0+OWy+9ErqVUKrVY+vOp3e5cvoPesKIv7LGuqhtBgycyKtGZIuWg1fvxMINNp+744JRes/GOey+6Kv7jGUMZYGZYd4lXz5JvrRfzO0312Y4hvXLC8GzOFJC83nqTIyqxr9wiOQ3Q8mHYxKTIp4zEJESmsNr9xrL/D2kzDlJO8N1tdZV6lL70WN3Q6151ERjLDjDMyC5HioDhOwk9Ii1DvLXR9RH6c94TLrPB+uZdep6VD19J0V5z9nLjR7cfPtqjo48ynNpOGaEX0lSPEvr3AlVFcaV6nDpwZuDBxhDEbTy/LD7gt1tuo6zTEPeQtFe+4MxdHbdt53PUopXj/PYZ87XD7+rWP6WJg4knG1v1rXN4WZ9bLsHpQZLlppC0ZzWjz5XoOv02IQjAZw/wmBx7Gw8dggKwXev8hh/CZwyAyXtz2Ez9XUa+h2r2AhhBBCCCGEEEIIIYQQQojWrMFofvOFRkOpdWsobTCf/5abIJPjhRBCCCGEECIcWzAaQI1bbT2G60zr6gtx8RvX9ZFjPyHaRWaYCyGEEEIIIYQAQCnLIaJunI1ZFmEwTXtsviNq1Nad15/oltQBJ4cvtO0eKCWTaoHmdIa23BYzqkMFBIUIRmtJxcWjHvs+fLlbZ6GuewbSs+yF+g1G/f0u1FQPAXhe7Ht8bNoxOezsbheKFikvE+dT3YoNk/pTXXNYQ2WbYDQGDjc3pjWsKwjb5+JC8/JhSevxlReHrb9BYjLq+udRT/yAGrGl93o2ex4NifYfUMJRN76M2u3Q9o+ji/UJ8xvRVoPMy9UhZ3rerniy6WgYurnn4uqoaagn56KueDDiroa+dwvrF/blqZUnc2jZa9xacDlLFw3jqPJXIm6rlWJLqlaU9I9foE/eGn3LVPQdF6GPGRvT9puoR8Nv80PxGtoRS7sMt+8b6C/eACDHX2RcX7zaHp60yBLkOPKXF9H3XIa+7kT01F3QRasiGK0QUSpYFnK1vulMdHkEYWChgtHGbue9nU7gOIqRHi6c3TLkNa9+dUR9NIWaaUswWtNWJjFekZ1qLtOW1vDol+FDasVfwPJF9nWDRrT+P6hufuzH005v/KRZWeKtbE5a5x+/+xzFBxc5jOmgc4vyE4bjV5agcprDzGzBaKWmYDTLpsLX4rDf6z5WaoyC0bYcqJh/ncNLZzk8d4Yi//8cth6siPMp3jjf4cO/OzxwgmLeNQ4DLV8D1IQJtBRCCCGEEEIIIYQQQgghhOgOUn3mC8dW+kNcXdKiqN5+vlCfeJkcL4QQQgghhBDhJIYJRusO1oY4Tzkn3sNJz0IIKwlGE0IIIYQQQggR4LNM4nQb0+qLI5tI3i7JKZ3Xl+i++gwMXyYjp+PH0VM4tvdwi2C0UOEYtvoGatgWqCsfNq8cMAT18NeoHfZFTT4C9cZKezsnXoE69KyYBY6pEy6H/U+CprA823Ytmran3R6ztrrKyH7Nk/JttqyZt+F2mtdgtNw8e4OF4cOB1pm7YeCayMKY1J3voXY7FJWRHVE9a3vpmajpb4QvOOkgGDex+X5iMurSe1E7HxiTcXS1HPN5fhtsNdj8olIDh6OueQLSMto/iCGbo26eGXEQpho0AjXlJNj72Ii7TNK1HFP2Ii+tPJaL1t3NgAaPQVehXn8x3JfSWqPP3xNKIwgPDEFd+xT07tt6Ye9+qLs/RLXzORzUu/MDUPYaHWLlbz8AkNNgCUarsv9sYgt9GVK3pPnOonnol2eEG6IQ7bd2RfgyL9/rvT1bgG5aF6QbejCyb/htS4qv+W/Ka4hsG7y+CiprNa4lnKjlPlW6JezI5MP5EowmgPWWZOC0TFRjsLSyBaMty0fbEvu6yLu/eC+bHWbfsqMopZh9ucOBW8S+7d8SRuCGOO2iKcwsw3JuVllN8DK/G7ysZVsAvZIhJSH8+NJiFIwGkJ2mOHxrxdHbOmS3CLlTSjF5lOKMSQ7jNlEkxZvrSzCaEEIIIYQQQgghhBBCCCF6ghTH/KNWlb8i4rYK68zBaA4+suJzI25PCCGEEEIIIf5qkkIEo9V2k2C0wjrzecqZcdkkODE8iU+Iv6C4rh6AEEIIIYQQQohuwhaK5G8MRvMy8T5W1q3tvL5Et6UcBz1sC1j8k71QjAKQNgZKORinx+vAjGpdV4u+4jBzZcdBOZHl56v9ToDJRwSCftYXAgp6ZcEmw1qFFymfD+7/An32pNYNDB4F+x4fUZ9hx5SQiPrHg+jzb4UVi2HQCPRBA6G2fV90q8fmBP6OHi49STE2D362Z9UxqjZ/w+1Ur8Fo6VmQkAR1hhn9ReHDpEqqzMuz/CGC/NoauRWM2c57eY/UlpPgno/Q5+1hL3PAKaiJ+6OLVsP6tTB4FCph4/nhIlQwmlKwxYAQ6ycfAbscDH/+Fvw+jItHf/AcvHBnyP7VI9+iRmwZwYgNbZx8Jfr9Z9vVhqd+TrsWjrsEPTndXCAG+1La7wet0Xdc0O62WtntsMDztWwhVJZDajoMGhnxZ4PJyL7hy8TSweMhJTFEYFLj9jzHbw6VK9K90Fobg/hswWibNLTZsH72Gpz9H0/jFSJqxfarSjfRH7+EOuUqb+3V15mXx3tI3ekCIz1cPC110CBYmQEVpeR5DbhsIb8A8/41zTm84C2YqMn/lkU8DLExKjWHc5LZIvjbFoxWtg7+mA9DxsZ+XFGat9x7UFu40N2OlJmimHWej4IyzaoSiHOgqs2mz69h55stqWQW+Ykj8HsIRktPVpi2KmWGw1UvwWhKKQZkwqIwX6HFMhjNK2swmiWDUwghhBBCCCGEEEIIIYQQojtJ9ZnP/an0l0fcVmG9eXJ8dnwffKrnn5MohBBCCCGEEB0tVDBaTXcJRrMc++XGezjhWQgRkgSjCSGEEEIIIYQIsAVfuH50QwOs+sNed8hYGDcRZj0Um7HUmMN4xF+POuVq9FVH2ddn5FjX/eXYgruawg1/+DhE3ei+IlKJyZA3JPAvVLmx28H0N9CP/BtKCmHs9qizrkd1UNCGSsuAUVsDoA3BNp5tvgPqzH+jho2L0ci63ndXOiSfa5/oP7Ju4Ybb1mA0ldJ8x+eglELn5sHKJcGFC0OksDUqqTIHKWT6S8PWBWDf41HTbjOGGMWCGr8z/Ps59DXHBK/MyIatJwfK5fSHnP4dMoau1DvNHOAA0Ccd0pJCP+4qLh5s76ERW8KQzdE3nmGue94t7Q5FA1ADh8OMT9CXHAjVkV+51bNt90DFJ6BHbwML5gSt1t9/iDrglIia1LXV6KduhidubN/Y+g2GbfaANx8NXjdhV1Rc4+fApqPb14/BkNxAiIct4CPWjt42zLagoR6AbEswWrGThVu8Fl9O60S32npNkeXl07/tD5krFlvD1f6KtNaB/RG3xT+/5Xar+27jvwjrNN3WbsR19IZ+I+in5TrtWsYa5u9oNVZbG4b74fy5wPsT1cOC0UZ5OE8gN8OHevBL9N2XkrekLOI+fl2lcS3bLqfF29sWAGSiFFTWalJDBTiKjV/ZOvPylsFow7YIfE9jehF++Va3CkYrieC8ouzU8GU6Wt9eir697OtXT3e46JH1fPxzDaVOBgCb1i+lf8NqPk3dLaj88viBlPnsDTZtLzIs52aVVQfva1uD0dpsOjbLCR+MltqdgtHqO3ccQgghhBBCCCGEEEIIIYQQ0Ujxma/2U+VGft5RYZ1lcnzCxneemRBCCCGEEEJ0hHiVgMJBE3xiXbcJRqszX/Bajv2EaD8JRhNCCCGEEEIIEeBYQpVcPxQs3RAi0ZZ6bA5q2Di066K/eB3WFbR7KOqQs9rdhthITDoo9PpsuXLCBsoSbqhdtNboZ6bb60YZjBYJtf3eqO337vB+gvo97lL0I9dFXu/M61EnXNYBI+paifGKkycqHv/KHHQ1si5/w21bMFqF0+LEr6bXTo45GE3/OZ9wsR8lVeblGa49GE3dPBM1cf8wLceO2v0wuOJB9E1ntl5+3i2oxKROG0dXyDGf5wdApv3CO54opWD/E2HbPdAnToCKFs/5xP3hqGnt66BlX1tMRL0fHITl3n0pvHhXbDoZPCrw/ybDjMFofPwy+spHInrN6IeuhRfujH5MY7ZD3fcZqjEA183oDS0/DxKSUCf9I/r2PUiIUwzNhfz27yJ6ctQ2YbY6VYGTVHMswWiu8lGyeAnZbYLRVofIahzQsCp44fzv0CMmwKJ5UFFiCPyyBGBZA7Gay2pr+FbL+7ZwrxDLw42jVb8ext50X/QMDZZgtLgIUr860aj+9uDOJnmZCjVwOOqW1xj8m4bbI0to/GC+vYeWuYehtg9taR1o95AJEQ1FbGR0SZF5RUb2hpsqPQs9bieY90Vw/fy5YfexO1N5jbdyKQmQnNCdRt6ariyDxT/Rp66Gp1+dErR+SfymjBg231h3YcIIa7u+xkP1XpZdwDLD42cNRmtz2D+8r+L9+aG3hWldEMQowWhCCCGEEEIIIYQQQgghhOjJUn3pxuWV/gpc7eLYztU0KGx7sb1GufFy7qsQQgghhBBCeKGUIslJotoNnnxU212C0azHfhKMJkR7STCaEEIIIYQQQogAnyUYze+HFYvt9TYZCoByHPR+J7QO24hWF4Qnie5JKQWvLkEfNsRcYId9OndA3ZljOdlm+SL0sZuHfh9HcKJOjzNxfzAFox02FV69z15vzLYdN6YudtKO5mC0TFXJ5FOmoFIPQ990JqnaHIxW6yTRgI84/M3BaJm55s5mPfz/7N13eFvl3cbx+zmSLHkkTmI7w9khgwwCgbDCCJuwd6CMQmhLgQ7a0pc9CqVQKB28BUrZ0EIZYa9SRtn0BcreOwkhyxmOR7yk5/1DSWxZ50hHsuT5/VxXrljPOj8nWkc65z6yg0dKx50Zfzy3Y5ct0pqPayRnUnI90TWuS5qbX5OZsLnHb5c/Zr/jpZETZB+/TZKR2e94mc227/Q6OlvKYLSi3GzDVAyX7nhP9q4/SWuqZKZsLe1/out9JtfMj6+Q/fxd6c3nOrbQwMEyJaXxNUdO8I7qeeaeeBicD3b1cmn+1R0qy/z56Y2haJJkfniJNGEL2VefkPoPktn7GJlJ+U/n2XRo5wSjfXSxI8dJc79ZtlCSVOYRjCZJK75cpLLtEh/fi92fkiRJlS3JX2Tak3dOXQfQ3TR7BKOFCjq3Dp+m+DhOoHJA6887jM98G4+9Z7Wpx7HoTpbBaJL0xPtWB8/ovuFQ6ATVHq9BpeWJt7fYyTUYTc/dL7tskcyQkbmvLQu1jf7GlRXnt46OsPddK3v1GZ4XBZCk0c0LFY41qNFJTjj7MDzZc96G54v+HqHC1S7HZUU93ky2D0abNMR9XFvFXfA0HvE4CoVgNAAAAAAAAAAA0BMUOe4HTFnF1Bhbp8KA/y++VjQtdW2vKODkeAAAAADwK+wUugajNXSDYLSYjaqqyf1kCfb9gI7rxWe9AgAAAAAy4hWMZGNSlfsX8yobJhNpTUUxx54hTZ7ZsTJOvEBm7JQOrYHexVQMl/nl1VK7gBzzk9/JDB7RRVV1Q17BaGtXpQ5Fk6R1tbmvJ1MnnOveftipHVt3wubSsWcktm29ezwcaNKW7nNKBkib79ix7XZjsycZnbZ74uOpICj95fslKp57klQQliT1j9Z4rrEmsD5pZGMwWpnnWHvDhdLrTye3f/2R7OHjtaYl7DpvQCw5ZcQ8ubJLQtE2bn/6LDln/VXOWdf1iVA0SapwvwCqpNwFo0mSGTREzqmXyTnnBpmDT5IJds41PYwxMn94XNpx/+TOfgNl/vy0tNOB6Rcav1nrz2Oneg6zLz7suzZ779XxgNpsjJsm89BCmYLEx5cxRmb3I+Scd7Ocn17ZKaFokjRxSPYBQJ98PlWHrH0w7bhLDzGaNDT1duxLj2z8eXDLCs9xX36d/Pzz5Qr3hJLiWK36x9amrQ/oKvaTt2T9PJe0eASjBUK5LShHCoJGc2emfsxXtDlWvSBodPY+mT0XraqTXvnCva8j2Z0ffOsZn4m+orrKvb008T21GZ0cHryB/dWxuawoay1R6zvsKlXgbley7/9H9k8/TxmKJkkBxbRJ85eufYuDw73nrd9VL/UIRlvbkNwWjaVea4PJw9I/GZUk57jlXaFHGBvBaAAAAAAAAAAAoCcoCXgfMFUX9X+sZXOsSatb3L8brAhxcjwAAAAA+BVx3A/A6w7BaKtbqhRVi2vfYPb9gA7rnLPLAAAAAADdXyDg3h6LSmtXuvcNKE+4aUpKpWv+Lb32L+nrj6UhI6VvvpC96SLX6ebM66S9jpbefkFa/KU0fQeZTaZ15LdAL2UO+oE0Y7b05nPxsL4Zs2XGTO7qsroXx+Mx3EOYnQ+Sve1SySYGRZjZB3dsXWNkfvhr2V0Pkz74jzRivDRjl3jo0jXPyu4xIHnO3J/IBLtnCEiu/PFIR0dtbfXql1bFYWmPyUZjy9efVL8+6aM86hHYIGl5oELl0ZWtwWil5Z5jJck+fJPMNnu23n78dtnLfiBJWhModZ0zILom4bY59TKZom6aptCLVZYaSe4BLuFe8umyCQSkS+dL//239NnbUnOzVFEpbbOnTNlQqaEubaCZ2btNOMn2c7wHvv2CrLUyaRJ17OO3S3+7PJNfI27OsTI7HShtu5dM2CN9owtsOjT7uZs0f6W7Fh+re2sO07HDb0vqnz5Cuv1ER9NHpA8GsX+7YuPPRXadhjcv1uJQcpDJJwsbtG+7ts+Wu685oekLdSAfCcg7+/3tpKJ+stO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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import asyncio\n", + "\n", + "from matplotlib import pyplot as plt\n", + "\n", + "from apd.aggregation.query import with_database\n", + "from apd.aggregation.analysis import get_known_configs, plot_sensor, Config\n", + "\n", + "with with_database(\"postgresql+psycopg2://apd@localhost/apd\") as session:\n", + " coros = []\n", + " figure = plt.figure(figsize = (20, 5), dpi=300)\n", + " configs = get_known_configs()\n", + " to_display = configs[\"Relative humidity\"], configs[\"Ambient temperature\"]\n", + " for i, config in enumerate(to_display, start=1):\n", + " plot = figure.add_subplot(1, 2, i)\n", + " coros.append(plot_sensor(config, plot, {}))\n", + " await asyncio.gather(*coros)\n", + "\n", + "display(figure)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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tNGocKY9xv48lSZIkSZIkSZIkSZI0adQ1PHqwy7kOZ3jN0ynNB5l5JaWBUbODI6KX+r1amXk18Mem0FSg3Rq/wyvHP87Me/uSWPfGuy7rNOCpSuywbhdurLlTJfzDzLxvhGFb03ntVbPnUOrpqutONCsCvdT8BXAkpd6rq8aGQyaLeDnwI6CTTVqXBU6PiNf1un4nJkKtqSRJkiRJ8wP/4SxJkiRJknp1MVAtKtgpIt6amceOVxKNpmjH1Jx6Gvg18EvgNsrvQ1YDdgdewNCiqAWB0yLi6cw8c2wzHiIphVdXA9cBd1MKvWZTiiCeDWxB2dlyWtO4acDxEXFNZl7Rw/rHURq1zfVv4Dzgz8BdlN0416AUKPVdRCxJaea0Ws3pfwA/pOw4eR9lt8HnUppxrdi4Zlvg8z3m8EnKbnxVs4BfUIrV7gIepRTmbQjsCqxXuf4I4H7g3V2m8jHg4Kbjf1EKaK4G7gEWoxROvZKyg2Cz9Si7cr6l3cUau4z+ujFvsznA/1He3zdRHtMilIZ82wE7MrTh1TrAeRGxWWY+0O76TXYG3t90/EQjrwspzRefbqy9VU2udR4GLqO8n/4GPAA8RHmPL00pSNqB8lw2Wwf4XkRsl5lPt5j7SeCqpuMNGPq+vA/4Zxs59lVELEJ5zjaqOX0PcA7PvKeX5Zn7aOXKtRtQmqNtmpmzukxnXO9jSZIkSZIkSZIkSZIkTQ6NDTTrNk78RxdzTQFmVsK3UTYJrToF+EzT8VLAPsDpna7bhhOAzZqODwc+N9KARpO2A2vmmWjGsi6LzLwrIs6j1AfOtXtELJ+Zd3WR76HAlEqsk+d1NnAFcA1wPXAvpbYzKHWV61DqUF9UWWcx4LsR8bzM/FcXeQ/CfTzz2t5IeZwPU+oGp1NqJncGqptubknZELWXDZXXBI7lme87J6V28Tzg1sbxasBulOe6ue43gG9ExL2Z+YMecmjLBKo1lSRJkiRpnmdjNEmSJEmS1KvzgMcoxQ/NvhwRLwU+nZkXjWUCEfEi4MM1py4CjsjMv9ac+5+IeCGlyGX95umA4yLi95l5S/+z/Y85wM8pBVc/z8y7RxsQEdOBD1EaBs0t7FgIOJnSLKwbqwIHNf7/MeADwNcys7rrIsAHx2KXTOALDC+WeYhS8HF8ZmZ1QES8DXgPcDTld1xvojSs6lhE7M3wQpX7KI26TsnMx1uMC2Av4BvA8k2njoqI32bmjztMZaWmPB4CjgJOyMzZNWv/P0ojwPdXTr0hIj6RmbeNtlhELA18n+GNxk4Ejs7MVs29Pt4oUPwqsEtTfC3K++mVo61d4708c0//AHhnq4KwEe7B+yk7hp4FXNziHq7OtTXwJWDzpvDWwDuAz9aNycx/A5s2zXEzpXHgXD/OzJmjrT0GvsDwpmizgU8BH627jyPincC7gI9SPkvmWo1SrLZPF3mM630sSZIkSZIkSZIkSZKkSaWutug+SjOmTu1CqX9rdlpmzqm59jTKZn3Nm0Eewdg0Rjud0ghtbp3TBhGxVWb+foQx+1CaKM11G6W+cCIYl7qsJicwtDHaVOAQuts8dWbl+FbKJrsjeRI4m/KYf9XORqERsQbwCYY2t1sW+DrwsnaTHYA7gJMom25e3uK98x+NmsndgC9SmsLNtV9E7JuZZ3WZx3t55v1yPTCzxfvl4xGxZSPn5uZ7AXw9In6Tmfd0mcOoJlCtqSRJkiRJ84Vqt3tJkiRJkqSONBp6faXF6ZcD/xcRt0bECRHx2ojYJCL61qy9UTDwbYYWLEFp2LZji6ZoAGTmJZTd466unFqS1o+pX/bOzF0y8zvtNEUDyMx7MvNtwGGVUxtFxM5d5jH3eXsE2DUzvzRS4VKrwo1uRcQLGP54Hm7kclxdU7RGHk9m5scoTd1mUwpbFqq7dpT1l6c0Amv2N2DjxvotH28W51CKt26tnP5E497sxIKUxzEL2CYzj69rJtVY+6nM/ABwfOXUAgx/Plv5KjCj6Xg2cEhmHj5CU7S5699IKXCqPnf7RMRWba7fbO59eCyw30i7ZLZ4Tf4NrJyZb83M37RTfNeY62JgG+D8yqm39fNzaqw1mkO+vhKeAxyWmf/V6j7OzNmZ+RlKYWX1Ods7Irppcjfe97EkSZIkSZIkSZIkSZImgYhYibJZZtUZozVkauGImtgpdRdm5h0MbzS2Q0Ss2cW6I8rM+ymNppqNVgtTfSwnt6q5GWeDqMs6D7izEpvZzrrNGjVV61TCJ7dxr22Rma/MzHPaaYoGkJm3ZOZBlI1em+0eEevXDJkI/gCslpkfyMw/tPMebNRMngdsBVxZOf2uHnKZ2xTtGuDFIzURzMw/UO6tayqnlqNsIjomJlitqSRJkiRJ8wUbo0mSJEmSpH74EHDJCOdXoRT2HAf8CXgoIn4fEV+OiH0jYoUe1t4DWK8S+yewf2Y+OdrgzJxF2V3wseq8EVGdt2/aLZhpMfZkyu6LzY7sLSPen5m/7XGObry5JvaeRmHUqDLz+5SdJbv1dkojvLkepTRlqxafjJTDv4BXVcIbAHt2mdNhmXlVm9e+H6gW1Owy2qDGvX1AJfxfmfmdNtel0bTu9QzfrfX97c5R8Qfgna2a4Y2Sy5OZWX0Ptzv2ceA1lNd+rtWAbpsNDsI7a2JfzMxT2xncKFb775pTR/WQ05jfx5IkSZIkSZIkSZIkSZocImItSpOs5SqnHgU+0cV80ykbtza7PDOvHWHYydVpGLvN+06oHL8qIhapuzAiZgA7VMLVBkwDMYi6rMx8GjitEn5uRGzWYQp1r+2oz2svtZ3AMcBlTccBHN7DfGMmMx9tPNfdjL0POLQSfmFEbNBDSk8C+2TmvW2sfy9lM9BqjfDBjc+GsTARa00lSZIkSZqn2RhNkiRJkiT1rFHAsjvwkzaHLAxsCbwV+D5we0RcGBGHR8TCIw8d5i01sXdn5iPtTpCZNzF8p7igvmnXRFHd2fLFPcx1I/DVHsZ3JSKWAvarhK+jNNDrxIeB+7tYfzHgTZXw5zLzH53OlZm/Ay6ohPfudB7gN5n54w7WnUXZIbPZphEx2u/93sPQ3w3eBHy23XWb1n8K+HglvFsX72MoDfEGsstpZt7F8N1Je3lPjZuIWAXYqxK+i9KwshOfp+xg2eyFEfH8LtIar/tYkiRJkiRJkiRJkiRJE1BELBwRq0TEHhFxHPBnYOOaS1/bSWOhJocCC1Zi1Zq6qh8B1aZXM8eoRuUC4Jam4yWBV7a49jBKveJcv83Mv49BTuOuh7qsamM5gJntrhsRiwL7V8K/zcwb252jG41NQaubWU6KOrROZeZfgCsq4V4e67GZ+dcO1v8rcGwlvBAd3CftmqC1ppIkSZIkzfP8YpkkSZIkSeqLzLwfeAWl4KjTopwAtgO+DdwQEQe3NShiwca4ZncA53S4PsA3gerudy/tYp7xUm1gtFJErN7lXCc2CnLG24sohSjVXOZ0MklmPgx8r4v1dwKWqsS+3cU8c/1v5bh6b7bj+C7G/KFyvBiwSquLIyIouyU2O6mHpmTVhlYLAVt1OMffMvO3Xa7fL9X31AsGkkXnXgIsUImd0klzSPhPk7u6+6+bz8Exv48lSZIkSZIkaaJrNAHYMSKOiIj3RcTbI2K/iNhk0LlJkiRJUp9sFxFZ9wM8BtwKnAu8Fli0MvZR4ODMPL3LtQ+vHD8FnDHSgMYGsGdWwqsBO3eZw0hrJXBSJVzNmUZTttdUwnVNwSazjuuyMvNahtcTHRQR1XrDVl4JLF6Jndjm2F5VH+/zI2LaOK093vpZc9dNzVndJry79ZBDKxOx1lSSJEmSpHne1EEnIEmSJEmS5h1zd7uLiDOAXYGDgD2AJTqYZnXgtIjYCXhDZj4xwrXPBxauxH6YmdUGZ6PKzDsi4iJg+6bwehGxbGbe2+l8nWoU7LwY2ATYCFiO8rwtxvCmRzB8t0soz90/u1j+112M6Ye6Iphqk612nQu8vsMx1WKS2zLzltor23NT5XhGRCzVaBrYrt90sW7dLpZLAv9qcf3GwNKV2MVdrAtAZs6KiAcaa871PDp7LBd2u34rEbEKsDXl8a5LyW8JYBGG7rA614qV424bDY63F9XEzupyrjOBT7cx/2jG4z6WJEmSJEmSNA+JiGcDWwCbN/77fIZ+gfmWzJwxgNQ6FhEbAR8CXs7wv2PNveZvlC9kfy4znxzH9CRJkiRp0B6ibIJ5TGZ2VRcSES8ANqyEz8vMe9oYfjKlUVuzw4Hzu8llFCdS/n04t1Zp+4hYMzOb68x2AtZoOn6I7mt/xsU41mWdCGzZdLwMsCfw/TbGHlY5frjNccNExGLAtpTHuwGwLOXxPguYUjNkscrxQsAKlEaBE1pErEWp69wYWIvyOJegPIa617b6WnZbc3d9Zt7Q6aDM/GtEXMPQz4MtImJKpxv0jmIi1ppKkiRJkjTPszGaJEmSJEnqu0ZjsnOBcyNiAWBTStOvzSlfZFmP+mZfzWZSCkf2H+Ga59fELu803yaXMbQxWlAaPP2yhzlHFBFrA+8H9mVoY6luVHeka0cCf+px3W49t3L8GHB9l3Nd2cWYasOnpSOil+eiWtAEMB1ot1jl8czspvjpgZrYSPdSXaOrYyNipCaEo6nu6Dq9w/FX9LD2EBGxL/AmSjFSXeFZu7p5Pw1C9XPwaeCqbibKzFsi4i5g+RHmH8143ceSJEmSJEmSJrmI2B74AOXvR8sMNpveRUQA/w0cTf2XhZutA3wcODAiDsrMv4xxepIkSZI0UVwOHNttU7SGI2pip7QzMDN/FxF/B9ZuCr8iIqa32VitbY1anF8BOzZCQamL/HDTZYdXhn0vMx/pZx79MoC6rDOAz1Mars01k1EanEXEGgytAwU4s9PnNSI2A95Daca2yCiXj2YpJmhjtIiYQnlPvZbSrL4X3dbc/bGHNa9gaGO0xSkN+7qtRa0z0WpNJUmSJEmaL9gYTZIkSZIkjanMnE0pWvhP4UJELApsBewA7Aes32L4fhHx1sw8tsX5uuZL1/WQ7rVtrtEXEfEh4P9RdtPrh26aCD2cmY/2af1OLVs5/lfjfulYZt4WEU8B0zoYtmrleFFgk27WH8GywN/bvHZWl2s8VRMb6XmoPm5o/R7sVvW1Hc1dvS4YESsDpwIv6XWuhsnSlKv6GXVTZj7ew3zXMbQxWqefgeN1H0uSJEmSJEma/DYFdh50En10PMO/nD+H8qX/m4EFgQ0oX86d67nABRHxwsz8x3gkKUmSJEl99Aj1tVHTgKWBlWrO7QBcFhEzM/OMTheMiGcBB1TCsygbubbrFOCYpuMFgUOAL3aaTxtO4JnGaAAzI+IjmTknIpYG9qq5fkIZVF1WZj4QEecABzWFd4mIlTLz9hGGzmR4w/IT200uIqYBXwDeSG8N4JpNyFq0iHgO8B3KBsL90O3jvKGHNesaoC3fIt6tiVZrKkmSJEnSfKFfv5iRJEmSJElqW2Y+mpm/zswPZeZzgF2Ba1pc/sFGI7U6S9fEetkx7b6a2DI9zNdSRHwV+Aj9a4oG3TURerCP63eq+vo90ON8nY4fk9e2opOdIusaQ42FTpuWdaPTHTJ7ug8jYhXgQvpXfAeTZ1OJ6vuo110jq5+DC43wGVxnvO5jSZIkSZIkSfOuJ4AbB51EJyLirQxvivZdYPXM3CozD8jMvTNzPWBL4Mqm65YHfhoRC49TupIkSZLUL5dn5qY1Pxtm5sqUOqWZDG9QtCBwakS8vIs19wcWr8S+m5lPdjDHKUBWYtV/0/XL2Qyt51mdZxqlHczQ+sHrM/OSMcqjKxOgLqva0GwB4NWtLo6IAA6thP+WmRe1s1ijKdr3gTfT3+/eTrgNIiNiI+A39K8pGnT/OHupHa0bu1QP89WZaLWmkiRJkiTNF2yMJkmSJEmSBi4zfwZsAfy05vTywJ4thlYLnKDsQtmturF1a/QkIg4B3lRzahbwbeBwYBtgBqXp0SKZGc0/wJp9SufpPs3TjWpTuE6K0+o80e6FjUZP/WxKN5nUNRQctF7vw5OAdWrifwI+AewNPB9YEVgCWLDmPfWRHnMYlOpnVC+fga3G9/1zUJIkSZIkSZIanqL8LvdbwOuBzSi/kzxykEl1IuCbghUAACAASURBVCKWBT5eCR+bmQdm5m3V6zPzMmBb4A9N4XWBd4xdlpIkSZI0/jJzVmaeDGxKaR7dbAHgtIiY0eG0dQ3MTukwr1soDaGabRQRW3aYSztrPQ6cUQkf1vjv4ZX4Cf1evw9OYrB1WRcAt1Rih9Vd2LAd8OxKrNpcbSTvA15RE78N+BpwCPBCYDVK862Fax7vDh2sNxCNBnBnAsvVnP4dcDTwMmATSg3v4sDUmsd6cp9SmrB1v/N5rakkSZIkSQPVSXd9SZIkSZKkMZOZj0XEq4AbgemV0zsyvDAK4KGa2LN6SKNubN0aXWsUlHy65tQngWMy87E2p5oXdoer7tTXazHKEh1c+zgwh6EbB/wwM/fuMYfJoO4eWzoz76+JT3gRsQewUyV8F3Boo+liuybre+ohhu5w2ctnYKvxff0clCRJkiRJkqSGk4FvNL4kPkREDCCdrr0VWKzp+DrgqJEGZObDEXEwcC0wrRH+QER8MzPvG5s0JUmSJGkwMvOJiHg1sAJDm0YtQdlIdMd25omI9YAX1Zy6tE//jjycoU2s++VE4I1Nx3tHxA7A85piTwOnjsHaXZsIdVmZmRFxMvChpvD6EfGCzLy0Zki1adps2mycFxHLAx+ohJ8G3gN8JTPb3fxzMtShvQ54TiV2I/CqzLy8g3n69Vgnct3v/FxrKkmSJEnSQE0Z/RJJkiRJkqTxkZkPUnYYrFqvxZC6L4YsVRNrV93YWT3MV2c7YKVK7NjM/EAHTdEAluljToNSff2W7XaiiFiQoV86GlFmzgGqjcDW7Hb9SeaemtiM8U6ijw6sHM8GXt5h8R1M3vdU9X3Uy2dg3fgnMvPRHueUJEmSJEmSpGEy8766pmiT0Msrx1/KzKdGG5SZfwd+2BRaAtinn4lJkiRJ0kTRaCp1KPBg5dRLIuKANqc5or9ZDXNgRCza70kz8zLg6qbQwsBplcvOy8w7+r12jyZKXdZJQFZiM6sXRcRiwCsr4Z9n5m1trrMnUH3935eZX+ygKRpMjjq06mv7ELBTh03RoH+Pdck+j+3bJrHzea2pJEmSJEkDZWM0SZIkSZI00dTtuDi9xbV318Squ9h1YoOaWF0TqV68tHI8B/hYF/M8uw+5DNq/KserRMTSXc71XKDTbT/vrByvGxELdbn+ZFJ93AAbj3sW/VN9T52fmd3s3DpZ31PVz8E1e7yPq5+D/f4MlCRJkiRJkqSBi4jFImKXiDgsIt4bEUdFxKsjYvOIaLu2NiIWBzathDv5gvj5leN9OxgrSZIkSZNKZt4KfKjm1McjYtpIYyNiKqWx2lhagrH7d9mJleOVK8cnjNG6vZgQdVmZeRNwYSX8qohYuBLbH3hWJVZ93kdSfbz3AV/pYPxcE7oOrdFA7oWV8CmZeXMX0/Xrsa7bw9i6jZfv6mG+OvNrrakkSZIkSQNlYzRJkiRJkjTRPFATa7Xb3hU1sc17WHuLynG2WKMXq1WO/5qZdY2qRlMtTJmM6oqkXtDlXN2Mq66/CLB9l+tPJnXP+27jnkUfRMSCwPKV8P91Mc8CwJZ9SWr8VT+jpjL8S3htiYjVGf58/rGbuSRJkiRJkiRpImo0Q/sVMIvSlOwE4FPAZ4FTgMuAOyPik21u5rIyQ2txH+nwi8RXV4539Iu1kiRJkuZxXwf+UYk9GzhilHEvA1aoxO4Arurxp2q0PLp1KvBki3N3Af87Rut2ZQLWZVUbnC0J7F2JzawczwJ+3MEa1drO32dmq9dsJBO9trP6uwzo7rVdnv41Rtusj2MfAv7aw3x15tdaU0mSJEmSBsrGaJIkSZIkaaKpFi/B8N3W5roCeLwS26tRTNORiFgB2KYSviEzZ3U61yimV447nr+xO+Ze/UlnoC6piR3U5VwHdzHmFzWxQ7pcfzK5GHikEtujzS94TTTV9xN08Z4CdgcW6zKHauPGjj9/enRxTazbnWv3a3N+SZIkSZIkSZpUImJ6RPyC0gxtB2DaCJdPB94H/C0ith1l6mUqx/d3mFr1+mnA+h3OIUmSJEmTRqPR1DE1p/5rlEbRdQ3LDsvMTXv5YXjDo20jYu1uH18rmXkPcG6L06dmZqvNYwdlItRlNfsB8GAldtjc/4mItRhe//mdzHyigzX6Uds5nfJ7h4msX6/tAb0m0uQ5EbFep4MiYl1gw0r4ssyc05+0/mN+rTWVJEmSJGmgbIwmSZIkSZImmpfUxG6suzAznwJ+XQmvSHdNw14HTK3Eft7FPKOpNqSqKzIZzUHASn3IZaAy8yrg+kp434hYs5N5IuLFdLfL4s8Y3ljvwG4KbCaTRnHh+ZXw4sBRA0inV9X3E3T3nnpXDzk8VDnuRyFfJy4AZldir46IZ3UySURMBV5bc2osPgclSZIkSZIkadw0vtD+e2CnyqmHgAuB7wFnAZcDzV+cXRb4RUTsMsL0T1aOR/oSf5266zfocA5JkiRJmmxOA/5aia1Kfe0KEbESsFslfCf1zYq6yaXq8D7MW+eEDuODNBHqsv4jMx+l/Pu92Y4RsVrj/2fWDDuxw2X6Udv5ZmDhLsaNp55f28bmvm/tTzr/cWQXY+o+M37aayI15staU0mSJEmSBs3GaJIkSZIkqScR8fJOG1mNMNdawP41p1rtlAjw1ZrYZyNi0Q7WXQN4fyWcLebu1e2V43UjYka7gyNiBeCz/UxowL5ROV4Y+EZELNDO4IhYrGaOtjR24TyuEl4AOD0iFulmzknkYzWx9zaazE0amfkA8GglvHMnc0TEkcD2PaRxX+X42T3M1bHM/DdwTiW8AvDhDqd6B1At1PpdZl7ZbW6SJEmSJEmSNGiNvxedw9Df3d4A7AssnZk7ZOarMnO/zNyC8kX845uuXRA4LSJWabHEvZXjpSOiky9A122E45dqJUmSJM3TMnM28NGaUx9o8W+qmZS6rmZnNObp1XeBpyux17Rbv9ah8yj/Dmz+WSEzrx2DtXoyQeqyqqqNzqYAh0bEFODQyrmruqh7qtZ2bt3J5pQRsSHwgQ7XHITq44QOX1tKbdo6fcil2Vsbze3b0ri22pztCeCkfiYF832tqSRJkiRJA2NjNEmSJEmS1Ks9gL9GxIkRsX63k0TEypQvplQbmt0N/HKEoecB11diMygFB1PbWHdp4Ec16/4kM6u7UvbD/9XEPtXOwIhYhtIkrpudCCeqE4BbK7GdgZMjYqGRBkbEUpTnY8Me1v8Ew3dAfD5wTuPe6FhErBERx0bERj3kNaYaRV8/qISnUR73tt3MGRELRcTrIuKdPSfYmYsqx9tHxO7tDIyIXYEv97j+1ZXjjZp2Ih0vX6iJHRURr2pncETsQn2zvM/1lJUkSZIkSZIkDd5ngObf1/8UeF5m/qDuC/SZeXtmvg44qik8nfov7EP5G8fDTccLAJt3kN8La2JLdjBekiRJkiar0xle97cy8Iaaaw+riZ3WjyQy827gZzV57NaP+StrZWbeUfm5q9/r9NGg67KGyMxLGH7PzAR2BFavxE/oYolqbeditLk5ZWNz3B8DI9Y8TgSNe65aG3twRGzSzviIOIyxaQC3EHB2O3WbjWvOZvjzfXqjidlYmC9rTSVJkiRJGiQbo0mSJEmSpH6YSikwuS4iLo2It0RE3Q73w0TEohHxBuBK4Lk1l7wnMx9vNT4zEzgCqH555RXAz0faQS4itqIU71QLOu5n+E5y/XI+8FAltn9EfGuk3QUjYmfgUp75Ms2DY5TfuMrMh4DX1Zw6GPhLRLw6IoZ8ASgiVoyIt1CKnLZrhG8C7uxi/TuA1wBZObUL8MeIOKTNBnvPiogDIuJs4O/AW4C63UsnktdTnrdm04ELIuIzEbFiO5NExFYR8TngZuCbwFp9zXJ0Z9b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CYy+b5NyJPDWN2rb6bq31112cD0zg6quvHrP9ile8ouNzDz/88Dz+8Y/ftr1hw4Z8+9vf7lltvbDTTjvl+OOP7/j4Rz7ykdv+f/369Vm3bl3L46655pox2yeeeGLHcxxzzDEdN7ddffXVY1ajfNGLXtRRE9loxx133Lb/v+WWW7Jy5cquzgcAAOaGdsFo7UICkvahZq1udm9303+7cLW5rJuQgG4N1Y1tAx/2WLhXy/F2QWr9MlFAhGAPAICG6++7OvduvnvS4zYMD2a4Ds9ARbNfu/fJ7YLFevH+fHCC97bdXr/d8TsN7NLVdWZCN89pu8e1dBYGvgEAwPagP28s/XkAMDV1yyTBZ4LRAAAAAACY5xZOfgg7mIuTPHbU9quSfGmyk0opj0jypFFD909y3uVJBpNsvSv0qFLKI2qtP+6gxpOati/u4BygA6tXr87Pfvazbdt77LHHmMajThx99NH5zne+s237uuuuy9FHH92zGqfrkEMOyeLFizs+fvny5WO216xZk1133XXccd///ve3/f8ee+yRQw89tKu6jjjiiI5Wh2xujNt///3zy1/+squ5mh//z3/+8zz4wQ/u6hoAAMCOb9MUgtGWtAn4anXzf7uwq6UL5mMwWusb/DfVoWyumyZ8ziczUbjCHgv3bDk+OLw+tdaUUqY8by9N9BgGBaMBAKTWmi/f0/mvRIfqxiydh4HE49TWwWhLB3bO2i1rxo1PFNjbqYmCfbsJRhuuw22v1S54uZ/afVbsLhht9j0uAADoNf154+nPA4ApWnfvxPsnC04DAAAAAIA5TjDa3POpJH+TZMHI9gtLKYfVWm+a5Ly/atr+l1rrhnYH11rXl1L+NckfN13jVRNNUkp5WJIXjBranOTTk9QGdGjVqlVjtg877LCub5J+xCMeMWb7rrvumnZdvdTcSDWZRYvG3py+adP44ID7778/GzY88CNvKk1MnZ5z6623jtl+wxvekDe84Q1dzzfaPffcM63zAQCAHdPmNsFoiyYI6Wp3o3qrG/bb3ey+U5uQsLlsohv8NwwPZtcFUw9Gmyh4YY9Fe7UcH86WbKpDWVyWTHneXproMUy0DwBgvrjx/u/kjqGVHR+/YXhQyFSS1rFoyU4LdklafBzqxXvPicLPNg4PdhxQvHF4MLXNI9hpwS5Trm97aff91ipsrm2I9jz8rAgAwPyjP288/XkAMEW3/GTi/cOC0QAAAAAAmN8Eo80xtdabSikXJPnTkaHFST5RSnlGu6CzUsofJDlp1NBQkrd1MN3ZSV6SZGtXw0mllItrrS2XZCulLE3y8ZGatvpYrfVnrY6ft1YcmPIvk/ySi+lbcWC/K9guVq9ePWZ799137/oazefMtqaegYGBnl/z3nvHrri1bNmyrq+x2267dXTc3Xff3fW1J7N27dqeXxMAAJj9Ng0PtRxfONA+pKtdqFmrm//bBQLMx4CGicLgNg4PZtcFnX0mbGVwS+tQgSTZfWHrYLSt8y4emB3BaIMThEe0C00AAJhPvnTPxV0dv2HL+mThntupmh1Jm2CxNu/PJ3pf2qmJgtFqajbWDVlaJv9MNDjB++DZGDbdPhitxWfFLa2foyXz8LMiALCD0p83c+Zgj57+vKnRnwcA49UP/83EBwwPz0whAAAAAAAwSwlGm2GllAPT+nl/UNP2wlLKQW0us67W+psJpjkryQuSbF227egkXy6l/Fmt9cejalmS5LVJzm06/9xa6y0TXD9JUmv9eSnlH5OcNmr4X0spf5nkI7XWbXfnllIemeSjI7VsdXc6C2CbV8rChcl+B/W7DHZQtY69QaTb1Shb6cU1ZrslS8beSD401DpcYCKdnjOVa0+m+esOAADMD5vr5pbji0r7YLR2N6q3umm/XaDVfLzZfekEwQWDWwYfWDZgCjZOELywx8LlbfcNDg9mWfaY+sQ9NNFjmChYAgBgPvjl4E25afC/ujrHe6iG2iYYrd37816E8k52jQ3Dgx2FRQ8O39923+wMRmvznDaFoNVa235/7jQPPysCADsm/XlMh/68qdGfBwAt3PLjifcPb5mZOgAAAAAAYJYSjDbzrk7ykA6OOyDJL9rsuyDJSe1OrLXeVkp5YZLLkyweGX5KkhtLKd9O8vMkuyd5fJIVTaf/e5IzOqhvq7ckOTzJ745sL0ryviRnlFK+k2RtkoNH5hrdvTGU5AW11ju7mAuYxJ577jlme82aNV1fo/mc5cvb34Q9VVu2zK5f1DY/xuaVPTvR6cqde++995jta665JkcddVTX8wEAAGyum1qOL5wgGK3dDfytgq3a3+w++27i394mCoObbvhCq1C6JFlSlmangV2227y9sqVuzqba/iaj2VInAEC/fHn1xV2f4z1UQ7tgtHafSSYK7O1Uu/fn3c7RHCg22tIJ3uf3S6dhc5vrpmxJ65DuiQKlAQBgrtCfNzX68wBgrLplS7JukvcRw8MzUwwAAAAAAMxSA/0ugO2j1npVkhckWTVquCQ5IsmJSZ6T8aFon0nyklprxx0RI8eemOSipl37JHlukj9K8oSMDUW7K8kf1Fq/1uk8QGdWrBj7sv7pT3/a9TV+8pOfjNneZ599Wh63cOHYbM3Nm1vfBNHKVBqbtqcFCxbkgAMO2Lb985//POvXd3fT0Q033NDRcfvuu++Y7al8jQAAAJK0DaOaKBitXYBAq+CFdmEM8/Fm90UDi7KwtF5jYrqhFe2CFZYO7DRJINv0Qx96YbI6ZkudAAD9sGroznx37Tdb7jt0p8OzpCxtuc97qIZ2wWjtPpNsrpuzabh1gHSnJgs+G9zS2fv/weH7W44vLIuyaKD9Z7Z+aRei3fy9ONH35kSfXwAAYK7Qnzc1+vMAoMldtyZbJvm7fXh2BZ0CAAAAAMBME4w2h9Vav5jk0Uk+lGSiLodvJnlRrfVltdbWHdoTz7Ou1vqSNELQWnf2N9yT5INJHl1rvazbeYDJLV++PIcccsi27XvvvTc/+tGPurrGNddcM2b7yCOPbHncbrvtNmb73nvv7XiOH/7wh13VNBOe/OQnb/v/4eHh/Od//mfH595zzz35/ve/39GxRx999JjtL33pSx3PAwAAsFWtNZtr6ybZRWVx2/Pa3ajefHP7cB3OxuENLY9td8P8XNcufGFDm+epU+2CBZYu2DkLyoIsLkvanDe9QLZemawOoR4AwHx2xerPp2a45b5n7XlCx2FU81brXLTstKB9WPNkwWaTmey57/T6gztY0HT778X1E26PttOC+flZEQCA+UV/3tTpzwOAUe74xeTHbBGMBgAAAADA/CYYbYbVWg+qtZZp/jmpi/nuqrWekuRBSY5P8qokb03yF0n+MMnBtdajaq2f68Fj+9da61FJDk7yopE53joy5/FJ9qu1/nmtddV05wLaO+aYY8Zsf+pTn+r43B/96Ef59re/vW176dKlecITntDy2OaVKm+88caO5/niF7/Y8bEz5ZnPfOaY7X/+53/u+NwLLrggQ0NDHR37jGc8IwsWLNi2/fnPfz533XVXx3MBAAAkyXC2tA1YWFgWtj2vfbjX2Jv7h+rG1DYpBPM3GK2zoIButQte2BpiN9vDMiarY7bUCQAw09ZuXpNvrLmi5b79Fv9WDt/lCVnaJuBruuFec0W7zyQ7TRAutr3en2/VLvCs0+Mmqr2f2n3uaA7Mnuj5aRfEDQAAc43+vKnRnwcAo6y6Y/JjhgWjAQAAAAAwvwlGmydqrUO11itrrZ+otf59rfV9tdZ/q7V2sNRM13P9otb6uZE5/n5kzitrrZ11JQDT8id/8idjtt///vfnV7/6VUfnvvWtbx2z/ZKXvCRLlixpeezjH//4Mdtf+MIXOprj8ssvz7XXXtvRsTPp5S9/eZYtW7Zt++KLL87ll18+6Xm333573v72t3c8z/Lly/Pyl7982/a6dety2mmndVcsAAAw722qm9ruW1gWt923dGBpy/Hm8IANW9rf7N8uXG2um+lgtJ22BaN1FmbXLxuaghLG75/e8wMAsKP6z3u/mE1tfj36zD1PyEAZaBsk1Wn41lzXPhhtl7bnTPd98mTvXzsNrWv3mWrWBqO1Cekb91lxgsc/X0O0AQCYf/TnTY3+PAAYZaiDf2cUjAYAAAAAtLBhU83f/t/hPPu8LTnqXQ/8ufyHrXsuYUcmGA1gjjn++OPzuMc9btv2mjVr8tKXvjSDgxP/AvW8887LpZdeum27lJI3vvGNbY8/6qijsvPOD9wkcfHFF+f666+fcI6bbropr3zlKyd7CH2xbNmyvP71rx8zduKJJ+bKK69se87/z96dh0lSFXi//53IzKrM3rt6VRqGpUGWEZARQWiYpllGQQFHXhWdYUa9jo6+iuMdF1zxOtdtZoSZC47ggOAyyAVZbNEWkIaWRkAaEBQa6JamAemNql4rMysz47x/FFVdWRUnMqJyz/x+nofHjjgnIk5GllXnZJ7ziw0bNuj000/X9u3bY13r4osvLpvQ9oMf/ECf/vSnVSrF+wL7iSee0KpVq2IdAwAAAKAzFP2wYLSUs8wVslW0RRXGnDMsiMG1YL7T1SugzBW80DsajOYIZAsJr2ukSsERrRLgBgAA0EhDfl73bP95YNnMZJ9eP/1kSXvDcMeLGr7V6VzBaGEBXPUKLo5aPiLr7wncn2nR8ZTrno4fG7rub8r0hI5FAQAAgE7C/LzJYX4eAABj5AlGAwAAAAAAABCf71u96VJfX7zV6s4npQee3fvfy7sJRkPnIRgNAFrMpk2btGHDhkn9N+Kqq65ST0/P6Pbdd9+tk046SQ888MCE623btk0f+chH9IlPfKJs/6c+9SkdeeSRznZOnz5d73znO0e3S6WSzjrrLN1+++0T6g4NDem73/2ujj/+eG3evFmzZ8+Oc0sa5gtf+IJe+9rXjm7v3LlTp556qt7xjnfoxhtv1GOPPaa1a9fq9ttv18c//nEdccQRevLJJ5VOp3XOOedEvs4BBxygK6+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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import asyncio\n", + "from uuid import UUID\n", + "\n", + "from matplotlib import pyplot as plt\n", + "\n", + "from apd.aggregation.query import with_database\n", + "from apd.aggregation.analysis import get_known_configs, plot_sensor\n", + "\n", + "\n", + "location_names = {\n", + " UUID('53998a51-60de-48ae-b71a-5c37cd1455f2'): \"Loft\",\n", + " UUID('1bc63cda-e223-48bc-93c2-c1f651779d69'): \"Living Room\",\n", + " UUID('ea0683de-6772-4678-bfe7-6014f54ffc8e'): \"Office\",\n", + " UUID('5aaa901a-7564-41fb-8eba-50cdd6fe9f80'): \"Outside\",\n", + "}\n", + "\n", + "with with_database(\"postgresql+psycopg2://apd@localhost/apd\") as session:\n", + " coros = []\n", + " figure = plt.figure(figsize = (20, 10), dpi=300)\n", + " configs = get_known_configs().values()\n", + " for i, config in enumerate(configs, start=1):\n", + " plot = figure.add_subplot(2, 2, i)\n", + " coros.append(plot_sensor(config, plot, location_names))\n", + " await asyncio.gather(*coros)\n", + "\n", + "display(figure)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "9f576f5bb8e34cb8ab1d3c09f770d027", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "interactive(children=(DatePicker(value=None, description='Start date'), DatePicker(value=None, description='En…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import asyncio\n", + "from uuid import UUID\n", + "import functools\n", + "from concurrent.futures import ThreadPoolExecutor\n", + "\n", + "import ipywidgets as widgets\n", + "from matplotlib import pyplot as plt\n", + "\n", + "from apd.aggregation.query import with_database\n", + "from apd.aggregation.analysis import get_known_configs, plot_sensor\n", + "\n", + "\n", + "def wrap_coroutine(f):\n", + " @functools.wraps(f)\n", + " def run_in_thread(*args, **kwargs):\n", + " loop = asyncio.new_event_loop()\n", + " wrapped = f(*args, **kwargs)\n", + " with ThreadPoolExecutor(max_workers=1) as pool:\n", + " task = pool.submit(loop.run_until_complete, wrapped)\n", + " return task.result()\n", + " return run_in_thread\n", + "\n", + "@wrap_coroutine\n", + "async def plot(*args, **kwargs):\n", + " location_names = {\n", + " UUID('53998a51-60de-48ae-b71a-5c37cd1455f2'): \"Loft\",\n", + " UUID('1bc63cda-e223-48bc-93c2-c1f651779d69'): \"Living Room\",\n", + " UUID('ea0683de-6772-4678-bfe7-6014f54ffc8e'): \"Office\",\n", + " UUID('5aaa901a-7564-41fb-8eba-50cdd6fe9f80'): \"Outside\",\n", + " }\n", + "\n", + " with with_database(\"postgresql+psycopg2://apd@localhost/apd\") as session:\n", + " coros = []\n", + " figure = plt.figure(figsize = (20, 10), dpi=300)\n", + " configs = get_known_configs().values()\n", + " for i, config in enumerate(configs, start=1):\n", + " plot = figure.add_subplot(2, 2, i)\n", + " coros.append(plot_sensor(config, plot, location_names, *args, **kwargs))\n", + " await asyncio.gather(*coros)\n", + " return figure\n", + "\n", + "\n", + "start = widgets.DatePicker(\n", + " description='Start date',\n", + ")\n", + "end = widgets.DatePicker(\n", + " description='End date',\n", + ")\n", + "out = widgets.interactive(plot, collected_after=start, collected_before=end)\n", + "display(out)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "60091fa7d9ea415985ec53937e422b4a", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "interactive(children=(DatePicker(value=None, description='Start date'), DatePicker(value=None, description='En…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from uuid import UUID\n", + "import ipywidgets as widgets\n", + "from apd.aggregation.analysis import interactable_plot_multiple_charts, configs\n", + "\n", + "location_names = {\n", + " UUID('53998a51-60de-48ae-b71a-5c37cd1455f2'): \"Loft\",\n", + " UUID('1bc63cda-e223-48bc-93c2-c1f651779d69'): \"Living Room\",\n", + " UUID('ea0683de-6772-4678-bfe7-6014f54ffc8e'): \"Office\",\n", + " UUID('5aaa901a-7564-41fb-8eba-50cdd6fe9f80'): \"Outside\",\n", + "}\n", + "\n", + "start = widgets.DatePicker(\n", + " description='Start date',\n", + ")\n", + "end = widgets.DatePicker(\n", + " description='End date',\n", + ")\n", + "\n", + "plot = interactable_plot_multiple_charts(location_names=location_names)\n", + "out = widgets.interactive(plot, collected_after=start, collected_before=end)\n", + "display(out)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "08a4d937037b45bda2298879df7f80b6", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "interactive(children=(DatePicker(value=None, description='Start date'), DatePicker(value=None, description='En…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import datetime\n", + "import uuid\n", + "import ipywidgets as widgets\n", + "from apd.aggregation.analysis import Config, configs, interactable_plot_multiple_charts, draw_map, get_map_cleaner_for\n", + "from apd.aggregation.database import DataPoint\n", + "from apd.aggregation.utils import merc_x, merc_y\n", + "\n", + "start = widgets.DatePicker(description='Start date')\n", + "end = widgets.DatePicker(description='End date')\n", + "\n", + "def get_literal_data():\n", + " # Get manually entered temperature data, as our particular deployment\n", + " # does not contain data of this shape\n", + " raw_data = {(53.8667, -1.3333): -1, (53.35, -2.2833): 1, (52.45, -1.7333): 3, (51.5, -0.1333): 6, (51.55, -2.5667): 5, (54.9667, -1.6167): -1, (55.9667, -3.2167): -1, (54.7667, -1.5833): 0, (53.7667, -0.3): 1, (53.7667, -3.0167): 0, (51.4833, -3.1833): 4, (53.2667, -3.5167): 2, (52.0833, -2.8): 2, (52.0167, -0.6): 2, (52.6667, 1.2667): 5, (50.4333, -4.9833): 9, (50.35, -4.1167): 10, (50.5167, -2.45): 9, (50.8333, -1.1667): 9, (56.45, -5.4333): 4, (57.5333, -4.05): -2, (54.5167, -3.6167): 3, (53.0833, 0.2667): 4, (51.35, 1.3333): 7, (50.8833, 0.3167): 8, (58.45, -3.1): 3, (50.1, -5.6667): 9, (58.2167, -6.3333): 4, (55.6833, -6.25): 7}\n", + " now = datetime.datetime.now()\n", + " async def points():\n", + " for (coord, temp) in raw_data.items():\n", + " deployment_id = uuid.uuid4()\n", + " yield DataPoint(sensor_name=\"Location\", deployment_id=deployment_id, collected_at=now, data=coord)\n", + " yield DataPoint(sensor_name=\"Temperature\", deployment_id=deployment_id, collected_at=now, data=temp)\n", + " async def deployments(*args, **kwargs):\n", + " yield None, points()\n", + " return deployments\n", + "\n", + "def draw_map_with_gb(plot, x, y, colour):\n", + " # Draw the map and add an explicit coastline\n", + " gb_boundary = [(-2.6653539699999556, 51.617254950000074), (-2.6937556629999335, 51.59617747600004), (-2.71312415299991, 51.58661530200004), (-2.760487433999913, 51.579779364000046), (-2.7980850899999155, 51.56350332200009), (-2.816395636999914, 51.555568752000056), (-2.8587133449999556, 51.546128648000035), (-2.943348761999914, 51.54254791900007), (-2.9562882149999155, 51.54572174700007), (-2.9641007149999155, 51.552801825000074), (-2.969471808999913, 51.559881903000075), (-2.9746801419999542, 51.56297435100004), (-2.984038865999935, 51.55853913000004), (-3.0102432929999168, 51.53803131700005), (-3.0156957669999542, 51.53164297100005), (-3.0737198559999115, 51.50836823100008), (-3.0764867829999503, 51.50775788000004), (-3.088937954999949, 51.505072333000044), (-3.105620897999927, 51.49721914300005), (-3.1317032539999445, 51.48041413000004), (-3.1350805329999503, 51.47630442900004), (-3.1409399079999503, 51.46478913000004), (-3.1453751289999445, 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51.49408600500004), (-2.716420050999943, 51.49957916900007), (-2.6961563789999445, 51.51264069200005), (-2.6078181629999335, 51.60736725500004), (-2.599598761999914, 51.611395575000074), (-2.5892227859999366, 51.61424388200004), (-2.580433722999942, 51.61863841400009), (-2.5768123039999296, 51.62750885600008), (-2.57445227799991, 51.63572825700004), (-2.568959113999938, 51.644964911000045), (-2.562245245999918, 51.65338776200008), (-2.556385870999918, 51.65924713700008), (-2.499175584999932, 51.69676341400009), (-2.464995897999927, 51.725897528000075), (-2.4477432929999168, 51.73187897300005), (-2.404896613999938, 51.74115631700005), (-2.3885391919999392, 51.750433661000045), (-2.380767381999931, 51.76190827000005), (-2.3833715489999463, 51.77326080900008), (-2.398060675999943, 51.782131252000056), (-2.4024145169999542, 51.76357656500005), (-2.4203995429999168, 51.753119208000044), (-2.4434301419999542, 51.74843984600005), (-2.4632869129999335, 51.747300523000035), (-2.482085740999935, 51.74286530200004), (-2.495838995999918, 51.73200104400007), (-2.507964647999927, 51.71857330900008), (-2.5221248039999296, 51.706366278000075), (-2.581695115999935, 51.681708075000074), (-2.5911352199999556, 51.67536041900007), (-2.600209113999938, 51.66559479400007), (-2.6653539699999556, 51.617254950000074)]\n", + " draw_map(plot, x, y, colour)\n", + " plot.plot(\n", + " [merc_x(coord[0]) for coord in gb_boundary],\n", + " [merc_y(coord[1]) for coord in gb_boundary],\n", + " \"k-\",\n", + " )\n", + "\n", + "country = Config(\n", + " get_data=get_literal_data(),\n", + " clean=get_map_cleaner_for(\"Temperature\"),\n", + " title=\"Country wide temperature\",\n", + " ylabel=\"\",\n", + " draw=draw_map_with_gb,\n", + ")\n", + "\n", + "out = widgets.interactive(interactable_plot_multiple_charts(configs=configs + (country, )), collected_after=start, collected_before=end)\n", + "display(out)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "apd.aggregation", + "language": "python", + "name": "apd.aggregation" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Ch11/apd.aggregation-chapter11/LICENCE b/Ch11/apd.aggregation-chapter11/LICENCE new file mode 100644 index 0000000..86685d4 --- /dev/null +++ b/Ch11/apd.aggregation-chapter11/LICENCE @@ -0,0 +1,31 @@ +Copyright (c) 2019, Matthew Wilkes + + +All rights reserved. + +Redistribution and use in source and binary forms, with or without modification, +are permitted provided that the following conditions are met: + +* Redistributions of source code must retain the above copyright notice, this + list of conditions and the following disclaimer. + +* Redistributions in binary form must reproduce the above copyright notice, this + list of conditions and the following disclaimer in the documentation and/or + other materials provided with the distribution. + +* Neither the name of the copyright holder nor the names of its + contributors may be used to endorse or promote products derived from this + software without specific prior written permission. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND +ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED +WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. +IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, +INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, +BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, +DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY +OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE +OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED +OF THE POSSIBILITY OF SUCH DAMAGE. + + diff --git a/Ch11/apd.aggregation-chapter11/Mapping.ipynb b/Ch11/apd.aggregation-chapter11/Mapping.ipynb new file mode 100644 index 0000000..916e704 --- /dev/null +++ b/Ch11/apd.aggregation-chapter11/Mapping.ipynb @@ -0,0 +1,254 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "datapoints = {\n", + " (53.8667, -1.3333): -1,\n", + " (53.35, -2.2833): 1,\n", + " (52.45, -1.7333): 3,\n", + " (51.5, -0.1333): 6,\n", + " (51.55, -2.5667): 5,\n", + " (54.9667, -1.6167): -1,\n", + " (55.9667, -3.2167): -1,\n", + " (54.7667, -1.5833): 0,\n", + " (53.7667, -0.3): 1,\n", + " (53.7667, -3.0167): 0,\n", + " (51.4833, -3.1833): 4,\n", + " (53.2667, -3.5167): 2,\n", + " (52.0833, -2.8): 2,\n", + " (52.0167, -0.6): 2,\n", + " (52.6667, 1.2667): 5,\n", + " (50.4333, -4.9833): 9,\n", + " (50.35, -4.1167): 10,\n", + " (50.5167, -2.45): 9,\n", + " (50.8333, -1.1667): 9,\n", + " (56.45, -5.4333): 4,\n", + " (57.5333, -4.05): -2,\n", + " (54.5167, -3.6167): 3,\n", + " (53.0833, 0.2667): 4,\n", + " (51.35, 1.3333): 7,\n", + " (50.8833, 0.3167): 8,\n", + " (58.45, -3.1): 3,\n", + " (50.1, -5.6667): 9,\n", + " (58.2167, -6.3333): 4,\n", + " (55.6833, -6.25): 7,\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "fig, ax = plt.subplots()\n", + "lats = [ll[0] for ll in datapoints.keys()]\n", + "lons = [ll[1] for ll in datapoints.keys()]\n", + "ax.plot(lons, lats, \"o\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "fig, ax = plt.subplots()\n", + "lats = [ll[0] for ll in datapoints.keys()]\n", + "lons = [ll[1] for ll in datapoints.keys()]\n", + "# Don't draw lines\n", + "ax.plot(lons, lats, \"o\")\n", + "# This is a map of the UK. Here, 1 degree latitude is \n", + "# 1.7x as far as 1 degree longitude\n", + "ax.set_aspect(1.7)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# Set up mercator transform from https://wiki.openstreetmap.org/wiki/Mercator#Python\n", + "# Thanks to Paulo Silva and the OSM contributors\n", + "import math\n", + "\n", + "def merc_x(lon):\n", + " r_major=6378137.000\n", + " return r_major*math.radians(lon)\n", + "\n", + "def merc_y(lat):\n", + " if lat>89.5:lat=89.5\n", + " if lat<-89.5:lat=-89.5\n", + " r_major=6378137.000\n", + " r_minor=6356752.3142\n", + " temp=r_minor/r_major\n", + " eccent=math.sqrt(1-temp**2)\n", + " phi=math.radians(lat)\n", + " sinphi=math.sin(phi)\n", + " con=eccent*sinphi\n", + " com=eccent/2\n", + " con=((1.0-con)/(1.0+con))**com\n", + " ts=math.tan((math.pi/2-phi)/2)/con\n", + " y=0-r_major*math.log(ts)\n", + " return y\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots()\n", + "\n", + "x = tuple(map(merc_x, lons))\n", + "y = tuple(map(merc_y, lats))\n", + "ax.plot(x, y, 'o')\n", + "ax.set_aspect(1.0)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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u/6iq/tiEYWliak0mPybCZBKhNmlvZipNweeBk3ESrWwXkS3GLXVpdEK8ILJQk4wSMpMuXhhJU6CqN6jqzHJGxqCVwqrxqaXfjWZ1imRBYLaRt/j7MF2rTEJg9VQAKrF4461xzalJ04osjbTrfmz0YEE/oqTH3lklsrQbZW2e6u8nCQEn5c2y81Qzhi1eLGwFIElvlossAUwLzJsbI8n7JEUmRZZEM4apUJmEwG6782qu+51zue3OqycJLQxpVwAyKTJbScKz1MqNEfeejQyZucjqpOe0OVzzobM5+x0nJXL9oNwYUek5Ldp8BdPezLopcR+Y9QyPvrjUyLWKp4yE+nXGDZU9p83hrvJs74tX15ztHYdauTFqDWb02jhaKnHDugeODcJs1LS53JPVgX+299LzTguV1yIq5SlyUQXmtzHMfIXyj9KkN7POk2WJ8mxvUMbGxnl618EJ+/0iMFVmiyJgr42jpfFI8xVMkVmRmRyKHTfJSt++IW78iw2ccfpcnt51cMJk3CCCxBFVeFE9ZNnGntXB8xWCQubY89OM9phkVmS20LdvqKa4qhHF28UNwZuPHmLz96I3+yzeeCs/vuyWWPf0kpfJLKNSeS6uwGxYuzN9CxLGho7yOIRNjleNsAKr1mZmogJgjci8Y8rS7ihvBmzwYGXssSQDZGVMfxyBJenNcpG5NKJRstw70HNaMglcXutss8qDlTGVpkCAO4CLgBHgOlV92rCtk2jkjHKY6CGierUJvQNjJW78iw111Uqr2WYbNS1T1T3lsfvAmTgi8qcpuBBY5L7WAnebNjQtlncH9/uVvUbYL3dC70CxjTNOn2vMRlMCSypkRm0nC0xTAFwMfFVVFdgsIieKyBxVNfdTTYHl3XNYv/ZS2guT+/28lL/kat6tVu9AXOIKzF88SHJUsKk0BZ2A96kNuNsmECVNgQ0zys9a2OXkKQvZ71eNcsv7uo2PGwmV9ZS/gsqfI13jNXs+4nqz0J7Mk6bgc0G7A7bppA2q63Ay/tDb2ztpv208tX+A0VIJIFSeslorlNTbO+C9T1xqVXCC9tfbzWQqTcEA4C1kdAHPx7bKEnb0D3HDugc4a2FXpDxlSZKkwMIQp6spisgqpikAHgJuFJH7cPKSvdqo8ljSHeU7+oeaXlwrO+awumsumwcOsm04+LPW481CicyTpuB3Pds+Ds5McuA/cJov9uHUPq+PZU0TEGZRr6j5/pMW2L2XXH6scnP1NzdUFFpcTKUpUFX9pKouVNVfUNXEcmFknSDBVCvEJx0eJyZhbmN1V+2mlagVAHtb8AzTyI7yuMLwi60R5a+JSZjH2TxQuWkl7kQTq0XWbB3lYUVTb/NElAJ+OQnz32x+PFKojOLN8kGLDaIR3T5xa4/bhodCiytOBcBqT5YGpjrKG92XmMZaUGG9mVUii5unzPSMctNfWJKCixoe41A8ZeTYKw5WicwmbFnGrxqNstFb4I8jtFxkCZLk+C5bfgRhQqb1Ikuzo9yWL9KPrXZVwnqR5bxNI8pflajWRnbFo18BYMaMGe8I2p+LrAa2eA1b7PCz4qRO/ul9HwVg0aJFi4OOaRqR2ZB6PSlsEViQN1s1ex7tbYXyv0FDvppHZEmS1pecZngMyxOHf8LoeKn8b+AYwVxklmK7uMpsf2mQax/7GgB79+4NXI2m5bqVwuYs8xM3KUsUlnfP4ayFXfzgSH/dw20q2VqPeCvVzre/5KS0OnLkyOuB58W+YwMxmRjPVo5NWikW+N3Sqqqd1Wmkgm/U8OuWJ0lvtuKMTtqLzrguFN47ozvSysFJUu8wqbxMZgEjXeO+cV21J63EJeqPxMQ4vKbyZI2eUW6CchmpPK6rPNZ+T3/6XszUQM+wY/xPxFkwtQenmvrbqrrJs/8E4F6g273mX6vqPxix0DJMhkx/Idw7rmtaykHG5EjisJ/kDuBhVV0KLAd2+/Z/EtilqsuBc4EvuvM0I9PoZQnrGcJSD3HmP5qi1o/E9POoKTIReSfwPuDvAVT1LVV9xXeYAjPcxCvTgZeAMZOGmu4or0dc9QrA5jawJH5wYTzZAuAw8A8isk1E1ouIvyP0LmAZzoTencCnVHXSk4ySpqDRNMqbRRFYUmJstMjDiKwInAHcraorgdeBP/YdcwGwHTgFWAHc5XrACajqOlXtVdXe2bNn12e5hVTKAFTGBg9WzYakfmhhRDYADKjqE+7/G3FE5+V64EF3/uU+4ACQSutpPR3lUR6y/8sqN6beeP4a1q+9dILQ6umDNCnMNAQG4fKTDQMH3WR44KSP2uU7rN/djoi8C1gCPGvQTuuplAGo0d6rkjdNS2AQvp3sJuCf3Rrjs8D1vjQFtwL/KCI7cYZ7fFZVX0zC4KSJ27cZlAHIlMDCNptUyqeWtMCWdRzi4v+9sfI9wlxEVbcDvb7N93j2Pw+cH8fALOP98v0ZgDaNm13IKwxeb1r+v5od9QosbNEkU91KjZpRHvfh7+gfYv33nkpEYGG8YtmbjpXGGS2V+MGR/orHNkpg0GTdSjaQZg3S602rDReqR2BxKla5yOqkHDIbIa4w99g0PsimveZDZD219kyFy7CYGO9fT3OGrcQR2LKOQ3U/z6YUmY1kcY0nU5Nz8nBZhbjNGf5rmLyeCTtqYXrmV+ZElqWh2DZ4rzTFVaalwmWc8kXsZLwVzmuE8OKMMkly3qqVnuxTS7/LHc/8srHr+R9g0iNoi6eMsOKkTlbNnscTh39ybDaPd78tNGJStJUiM0FZSKZqmmHKUmXxlKfut7cVGB0vce1jX5sktDTwCv/NKYmvr3aMphUZJPcrDcp77/VO5an7xbY2QFk1e17qIisLf0qhjbHx93BL38vsOXKgIfduapFVI2rILHuzoLz3O9v2Tzj27an7yuj4OE8c/olZ42PwawtmMaXQRkEKqEDPCYtykdmKN+89KGuWdbBzz0SRlafuVyqTNQK/F+97dS9j4yVUoKQl+l7d2zBbMikyU80YcbzZU2/sZXR8NbW81PaXBhsurmrFgz1HDnBL35foOWERfa/uNe7FlsyYT2dnZ0fQvkyKLE3q8VKmy4hRKzZ7jhxIJEQumTGfW3tu4tGOr09afhJykcVqzojipZJsIrAlJ1vPCYsovp2jbBItL7IksUUESXPNvA8BzhpbQftbqsW/EqbFYGLkQlb49nvuOvb+0KFDgWuc5iIzTKuICyYKDGBwcHA46DgjuTDcY84FbgfagRdV9ZciWx0B0x3lJrqa0hRYpaHpSQwm8IurFmHLZOVcGJe5M5Ym9LG4Ivwy8EFV7ReRn4tkhaWkIRq/WKqJJMychyjXq3X+oy8ujSwwCCEyTy6M68DJhQG85TvsKpzJvf3uMS9EtsQC0vJE1cRievJMNdHVulccgUE4T+bNhbEc2IqT68KbH3Qx0C4i3wdmAHeo6lf9FxKRtcBagO7u7lgGh6X8wGwee2bDep5hbKg301IYkZVzYdykqk+IyB04uTBu8R1zJs4s8qnAJhHZrKoTsiGr6jpgHUBvb29gdbde/A/NtkGONggrCiZSeYURWVAuDH/ClQGcwv7rwOsi8hhOHrPAlNthiDOmzOYv0GbbADqOX0bXtBUMjGxn+I3dRvPE1RSZqg6LyEERWaKqewjOhfFtnEw+RWAKsAr4G2NWhqBWuSYtb2ZaXH4xmLrmb3Z/gYK0U9JR2tuON3LdMkZyYajqbhF5GPghMA6sV9U+o5YGUBaPjV4iCZv8Yniw/9NGhNY1bQUFaadNCrRJ5e6huBjJheEe8wXgC4bsCk3YLzOMN7NRrF68YgCla9qKukTWqNSpLdl3abuYKjEwsp2SjgJKSccYGNke6fxG5+Mt01Iiy6q4ygy/sZsH+z8dqkyWlqCCaCmRNQPDb+yeJC6bBBVELrKMYbuggshFZjFZFFQQucgSJkq7VrOIyk/Liuyi6f72ZPMcP+VMOmfdBjJlUrtWswoqiKYRWSWP0QgxVWLqcecg0o64jZy/Ne9LqdmSJk0hso7jl3F5922ItKN6FYMvXsEbb21NxZZFXYEjkFuaphDZeTPnuB6jCChTjzsnlMiOn3ImU487h6NvboolylxQ4ci8yC6avoujb05F3ZZw1TGOvrmp5nlOeel+1/uN1vR+uaDik1mRectab7y1lcEXr4jkld4uL032frmgzCIVpsolTm9vr27ZsqXmcUFjykwU5suerK1tat3XynEQka2q6h9IkT1PVo/Acg+VDpkRWVRx5YKyB+tF1nvSh+nmW7zhnx/lIxeVvVgvsjWzr53U9pULKlukVvAXkcPAc5X2d3Z2dnR0dHSCk8jj0KFDz1eaBh+SWUAml0dMiCSex6mqOmlJ5tRE1mhEZEtQzadVaeTzyBOu5CROLrKcxGklka1L2wDLaNjzaJkyWU56tJIny0mJXGQ5iZMpkYnIn4nIoIhsd18XefZ9TkT2icgeEbnAs/1MEdnp7rtTRMTdfpyIfMPd/oSIzPOcc62I7HVf13q2z3eP3eueO6Uxn9wsIvJB9zntExF/8hzzqGpmXsCfAX8UsP10YAdwHDAf2A8U3H1PAucAAvwncKG7/feAe9z3VwLfcN+fhJPv4yRgpvt+prvvfuBK9/09wCfSfiYxnmHBfT4LcJLj7ABOT/KemfJkVbgYuE9V31TVA8A+4GwRmQO8U1U3qfOEvwr8huecf3LfbwTOc73cBcB/qepLqvoy8F/AB919H3CPxT23fK0scTawT1WfVSdr5n04zyIxsiiyG0XkhyLyFRGZ6W7rBA56jhlwt3W67/3bJ5yjqmPAq8DJVa51MvCKe6z/Wlmi0udLDOtEJiLfFZG+gNfFwN3AQmAFMAR8sXxawKW0yvY451S7VpZo+OewbhSGqoZKrygifwf8m/vvADDXs7sLeN7d3hWw3XvOgJu87wTgJXf7ub5zvo/TmXyiiBRdb+a9Vpao9KwSwzpPVg23jFXmEqCcaO8h4Eq3xjgfWAQ8qapDwBERWe2Wqa7ByQpZPqdcc7wMeNQttz0CnC8iM91wfD7wiLvve+6xuOeWr5UlngIWuTXlKTiVnocSvWPatZ2INaOvATtxMjo+BMzx7PtTnFrTHtwapLu9F0eM+4G7eLuX43hgA04l4Ulggeec33a37wOu92xf4B67zz33uLSfSczneBFOPt/9wJ8mfb+8WykncTIVLnOySS6ynMTJRZaTOLnIchInF1lO4uQiy0mcXGQ5ifP/zauNAradx70AAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots()\n", + "\n", + "x = tuple(map(merc_x, lons))\n", + "y = tuple(map(merc_y, lats))\n", + "\n", + "ax.tricontourf(x, y, tuple(datapoints.values()))\n", + "ax.plot(x, y, 'wo', ms=3)\n", + "ax.set_aspect(1.0)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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"execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from apd.aggregation.analysis import interactable_plot_multiple_charts, configs\n", + "from apd.aggregation.utils import jupyter_page_file, profile_with_yappi, yappi_package_matches\n", + "import yappi\n", + "\n", + "with profile_with_yappi():\n", + " plot = interactable_plot_multiple_charts()\n", + " plot()\n", + "\n", + "with jupyter_page_file() as output:\n", + " yappi.get_func_stats(filter_callback=lambda stat:\n", + " yappi_package_matches(stat, [\"apd.aggregation\"])\n", + " ).print_all(output)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r\n", + "Clock type: WALL\r\n", + "Ordered by: totaltime, desc\r\n", + "\r\n", + "name ncall tsub ttot tavg \r\n", + "..futures\\thread.py:52 _WorkItem.run 7 0.000092 3.204091 0.457727\r\n", + "..tion\\analysis.py:373 run_in_thread 1 0.000053 1.844382 1.844382\r\n", + "..38\\Lib\\threading.py:859 Thread.run 2/1 0.000025 1.839877 0.919939\r\n", + "..rrent\\futures\\thread.py:66 _worker 2/1 0.000022 1.839852 0.919926\r\n", + "..lectorEventLoop.run_until_complete 1 0.000027 1.839745 1.839745\r\n", + "..ndowsSelectorEventLoop.run_forever 1 0.000088 1.839611 1.839611\r\n", + "..WindowsSelectorEventLoop._run_once 17 0.000408 1.839497 0.108206\r\n", + "..alysis.py:327 plot_multiple_charts 1 1.643447 1.839309 1.839309\r\n", + "..py:635 ThreadPoolExecutor.__exit__ 1 0.000005 1.837286 1.837286\r\n", + "..py:230 ThreadPoolExecutor.shutdown 1 0.000009 1.837281 1.837281\r\n", + "..8\\Lib\\threading.py:979 Thread.join 1 0.000006 1.837272 1.837272\r\n", + "..:1017 Thread._wait_for_tstate_lock 1 0.000011 1.837263 1.837263\r\n", + "..gation\\analysis.py:291 plot_sensor 1 0.000000 1.407986 1.407986\r\n", + "..query.py:88 get_data_by_deployment 1 1.397146 1.397188 1.397188\r\n", + "..d\\aggregation\\query.py:41 get_data 1 1.283592 1.375576 1.375576\r\n", + "..lchemy\\orm\\query.py:3232 Query.all 6 0.004333 1.362649 0.227108\r\n", + "..ctors.py:319 SelectSelector.select 17 0.000177 1.362418 0.080142\r\n", + "..tors.py:313 SelectSelector._select 17 0.000069 1.362216 0.080130\r\n", + "..y\\orm\\query.py:3400 Query.__iter__ 6 0.000054 0.958316 0.159719\r\n", + "..:3425 Query._execute_and_instances 6 0.000073 0.956631 0.159439\r\n", + "..ine\\base.py:916 Connection.execute 9 0.000062 0.740051 0.082228\r\n", + "..:589 PGDialect_psycopg2.do_execute 11 0.000042 0.738361 0.067124\r\n", + "..y:1159 Connection._execute_context 9 0.000307 0.734166 0.081574\r\n", + "..:291 Select._execute_on_connection 6 0.000047 0.731436 0.121906\r\n", + ".. 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import yappi\n", + "\n", + "from apd.aggregation.analysis import interactable_plot_multiple_charts, Config\n", + "from apd.aggregation.analysis import clean_temperature_fluctuations, get_one_sensor_by_deployment\n", + "from apd.aggregation.utils import profile_with_yappi\n", + "\n", + "yappi.set_clock_type(\"wall\")\n", + "\n", + "filter_in_db = Config(\n", + " clean=clean_temperature_fluctuations,\n", + " title=\"Ambient temperature\",\n", + " ylabel=\"Degrees C\",\n", + " get_data=get_one_sensor_by_deployment(\"Temperature\"),\n", + ")\n", + "\n", + "with profile_with_yappi():\n", + " plot = interactable_plot_multiple_charts(configs=[filter_in_db])\n", + " plot()\n", + "\n", + "yappi.get_func_stats().print_all()" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r\n", + "Clock type: CPU\r\n", + "Ordered by: totaltime, desc\r\n", + "\r\n", + "name ncall tsub ttot tavg \r\n", + "..futures\\thread.py:52 _WorkItem.run 7 0.000000 9.515625 1.359375\r\n", + "..lchemy\\orm\\query.py:3232 Query.all 6 0.187500 6.390625 1.065104\r\n", + "..lchemy\\orm\\loading.py:35 instances 67353 0.046875 6.140625 0.000091\r\n", + "..esult.py:1257 ResultProxy.fetchall 6 0.000000 5.109375 0.851562\r\n", + "..py:1217 ResultProxy._fetchall_impl 6 0.000000 5.078125 0.846354\r\n", + "..38\\Lib\\threading.py:859 Thread.run 2/1 0.000000 3.125000 1.562500\r\n", + "..rrent\\futures\\thread.py:66 _worker 2/1 0.000000 3.125000 1.562500\r\n", + "..b\\asyncio\\events.py:79 Handle._run 29 0.000000 3.125000 0.107759\r\n", + "..WindowsSelectorEventLoop._run_once 17 0.000000 3.125000 0.183824\r\n", + "..lectorEventLoop.run_until_complete 1 0.000000 3.125000 3.125000\r\n", + "..ndowsSelectorEventLoop.run_forever 1 0.000000 3.125000 3.125000\r\n", + "..gation\\analysis.py:291 plot_sensor 1 0.031250 3.046875 3.046875\r\n", + "..egation\\analysis.py:313 4 0.015625 2.703125 0.675781\r\n", + "..and_clean_temperature_fluctuations 4 0.046875 2.687500 0.671875\r\n", + "..184 clean_temperature_fluctuations 4 0.062500 2.640625 0.660156\r\n", + "..sycopg2\\_json.py:164 typecast_json 67339 0.312500 2.562500 0.000038\r\n", + "..-input-1-879b7ebf06b8>:7 4 0.234375 2.515625 0.628906\r\n", + "..gregation\\query.py:116 subiterator 4 0.390625 2.281250 0.570312\r\n", + "..n38\\Lib\\json\\__init__.py:299 loads 67339 0.515625 2.250000 0.000033\r\n", + "..es\\psycopg2\\extras.py:686 67347 0.156250 1.796875 0.000027\r\n", + "..on38\\Lib\\uuid.py:130 UUID.__init__ 67347 1.000000 1.640625 0.000024\r\n", + "..\\decoder.py:332 JSONDecoder.decode 67339 0.718750 1.546875 0.000023\r\n", + "..d\\aggregation\\query.py:41 get_data 1 0.312500 1.531250 1.531250\r\n", + "..ion\\database.py:77 from_sql_result 67339 0.468750 1.218750 0.000018\r\n", + "..chemy\\orm\\loading.py:83 6 0.328125 0.984375 0.164062\r\n", + ".._collections.py:121 result._asdict 67343 0.375000 0.515625 0.000008\r\n", + "..thon38\\Lib\\uuid.py:231 UUID.__eq__ 67351 0.281250 0.359375 0.000005\r\n", + "..chemy\\orm\\loading.py:84 67347 0.343750 0.343750 0.000005\r\n", + "..y\\util\\_collections.py:112 __new__ 67347 0.234375 0.312500 0.000005\r\n", + "..\\matplotlib\\__init__.py:1535 inner 4 0.000000 0.296875 0.074219\r\n", + "..axes.py:1651 AxesSubplot.plot_date 4 0.000000 0.296875 0.074219\r\n", + "..xes\\_axes.py:1412 AxesSubplot.plot 4 0.000000 0.296875 0.074219\r\n", + "..gregation\\analysis.py:63 draw_date 4 0.000000 0.296875 0.074219\r\n", + ".._base.py:1842 AxesSubplot.add_line 4 0.000000 0.281250 0.070312\r\n", + "..oder.py:343 JSONDecoder.raw_decode 67339 0.265625 0.265625 0.000004\r\n", + "..s\\matplotlib\\dates.py:1911 convert 6 0.000000 0.265625 0.044271\r\n", + "..on_base.py:2063 vectorize.__call__ 4 0.000000 0.265625 0.066406\r\n", + "..b\\axis.py:1562 XAxis.convert_units 12 0.000000 0.265625 0.022135\r\n", + "...py:168 AxesSubplot.convert_xunits 48 0.000000 0.265625 0.005534\r\n", + "..lotlib\\lines.py:664 Line2D.recache 10 0.000000 0.265625 0.026562\r\n", + "..s\\matplotlib\\dates.py:401 date2num 6 0.000000 0.265625 0.044271\r\n", + "..tlib\\lines.py:1018 Line2D.get_path 10 0.000000 0.265625 0.026562\r\n", + "..68 AxesSubplot._update_line_limits 4 0.000000 0.265625 0.066406\r\n", + "...py:2154 vectorize._vectorize_call 4 0.046875 0.265625 0.066406\r\n", + ":1 DataPoint.__init__ 67339 0.234375 0.234375 0.000003\r\n", + "..tplotlib\\dates.py:210 _to_ordinalf 8467 0.140625 0.218750 0.000026\r\n", + "..alysis.py:327 plot_multiple_charts 1 0.000000 0.078125 0.078125\r\n", + "..ine\\base.py:916 Connection.execute 9 0.000000 0.062500 0.006944\r\n", + "..:291 Select._execute_on_connection 6 0.000000 0.062500 0.010417\r\n", + "..y\\orm\\query.py:3400 Query.__iter__ 6 0.000000 0.062500 0.010417\r\n", + ".. 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8cFO2tZ133jn33Xdfq+2GDh1aUZ41a1bmzZvXVJ40aVK+9rWv5dhjj01d3ZZJBcqyzMMPP5y777473/rWt2pwJButW7cuZ555ZkWg3U477ZSZM2fm5JNPrpjLypUr85WvfCVf/vKXs2zZshaX8W3LvHnzctppp1UE2h177LG59NJL8853vnOL/X/729/m05/+dH71q18lSV544YWcfvrpmT17durrfafTEsF2/ds/JdkzyXGbygOTXJfki0VRPJJkRZLdkuyfZPNFrdcm+duyLF/qwbkCANCH1LeV2S5tBdu5BQEAAAAAgM56/vnnK8rjx4/P6NGja9L3Xnvt1eJ4PR1sN2bMmIwZMyZJMmDAn79XGDBgQCZNmtThfu64446Ktvfcc08mT57c6v5FUWT69OmZPn16vvjFL6axsbH6ybfguuuuy6OPPtpU3mmnnXLfffdlt91222LfoUOH5uKLL85ee+2V97///Vm6dGmHx2lsbMypp55akZ3vkksuycUXX9xqm/322y+/+MUvcuqppzZlNbzvvvty00035YMf/GCHx96WtPwNGf1CWZYbkrw/yc3NHhqX5Ngk70vyjlQG2v0pyXvLsvxVj0wSAIA+qShavpXYULazjGz8FRQAAAAAAHTWq6++WlEeOXJkzfoePHhwGhoa2hyvL3n22Web/v32t7+9zUC75urr62uyfG5jY2Ouu+66irpvfvObLQbabe7kk0/OOeecU9VYt912W5544omm8vvf//42A+3eNGDAgHz729/OuHHjmuquvfbaqsbelgi26+fKslxZluVp2RhY90Abu76a5F+T7FWW5U96ZHIAAPRZrQXNNabtZWRbW34WAAAAAABoX/PgtxEjRtS0/+b9bb70aV/2pz/9qVfG/e///u8888wzTeUZM2bkPe95T4fafulLX6oq4O+rX/1q07+LoshVV13V4bZDhw7Nxz7256WoH3/88Yp582eC7bYRZVneUpblgdm4bOwpST6V5KIkZyf5yyQ7lWV5TlmWi3txmgAA9BF1rWS2a2wvs51lZAEAAAAAYKtVFEX7O/URe+yxR9O/Fy5c2CvZ2u67776K8umnn97htmPHjs3RRx/doX1XrVqVBx74cw6uGTNmZNddd+3wWElyxBFHVJR/9SuLYrbEN13bmLIsn07ydG/PAwCAvq21DHXtZbarl9kOAAAAAAA6bdSoURXl1157rab9L1u2rM3x+pIzzjgjt912W1P5M5/5TG6//facffbZOf7447PTTjt1+xzmzJlTUT7ggAOqan/AAQfkP//zP9vd74EHHsi6deuayrvttlvVmekaGxsryn/4wx+qar+tEGwHAABUra61ZWTLxjSmjcx2bkEAAAAAAGpuxIAxuXy3b/T2NPq9EQPG9PYUtgh+W7p0ac36Xr16dVavXl1RN3r06Jr139NOOumknHTSSRUBd7/+9a/z61//OkkyefLkHHTQQXn3u9+dQw45JFOnTq35HBYtWlRR3n333atqP2XKlA7tt3Dhwory97///Xz/+9+vaqzmmi9ZzEa+6QIAAKrW+jKyMtsBAAAAAPS0+qI+oweO7+1p0AN23nnnivLLL7+cJUuW1CQobu7cue2O15cURZGbb745F198cf75n/95i0DCBQsWZMGCBfm///f/JtkYfPeBD3wg5557bs0y+jUPhhw+fHhV7XfYYYcO7bdkyZKq+u2IFStW1LzP/qDlb8gAAADa0Fpmuw1pzIayjcx2hb/3AQAAAACAzjrooIO2qGu+VGlnNe9nu+22y7777luTvnvLgAED8uUvfznPPPNMrr322hxyyCFpaGhocd8FCxbkkksuyW677Zabb765h2faNWvXrq15n2VZ1rzP/kCwHQAAULXWMtuVZWObme3q3IIAAAAAAECnTZw4MRMnTqyo++lPf1qTvu+5556K8gEHHJBBgwbVpO83bdjQ+ncI3Wn8+PE5//zz89///d957bXXcv/99+faa6/Ne9/73gwdOrRi39deey2nn356br/99i6PO3LkyIry8uXLq2r/2muvdWi/MWMqlzi+8sorU5Zll7Ybb7yxqrluK3zTBQAAVK21oLkNm/5rSX0GpCiK7pwWAAAAAAD0e8cee2xF+Tvf+U7WrVvXpT4XL16cO++8s81x3jRgQOUqNuvXt77iTXPNl1XtDQ0NDTnwwANz/vnn5/bbb8+SJUvy/e9/P1OmTGnapyzLfOpTn0pjY2OXxho/vnJ55/nz51fV/qmnnurUOB1tR/UE2wEAAFWrL1peRrax3NDqMrKttQEAAAAAADru05/+dMUfty9evDizZs3qUp8zZ86sCNjbfvvt85GPfKTFfYcPH15RXrZsWYfHmTt3blXz6ok/4h80aFBOPfXUPPjgg9l5552b6hcuXJiHH364S31Pnz69ovzAAw9U1f7BBx/s0H4HHnhgxbm65557LAPbTQTbAQAAVWsts11jWl9GVrAdAAAAAAB03bRp03LcccdV1H32s5/NokWLOtXfvHnzcs0111TUnX322Rk1alSL+48bN26L9h31ox/9qKq5NTQ0NP17zZo1VbWt1ogRI3LSSSdV1D399NNd6vPggw+uKH/ve9/rcNvFixd3eIngsWPHZr/99msqv/DCC/nxj3/c4bHoOMF2AABA1eo6ldluQIv1AAAAAABAdb7yla9kyJAhTeVly5blpJNOysqVK6vqZ/HixTnllFOydu3aprqddtopX/rSl1pts//++1eU77rrrg6N9V//9V956KGHqprfiBEjmv79yiuvdHm53PY0XyJ382C/zjj00EMzadKkpvKcOXNy9913d6jtZZddVtXxfvKTn6woX3DBBVW/HmifYDsAAKBqrQbbpTEb0kpmu8hsBwAAAAAAtbDHHnvkuuuuq6i7//77c9xxx+X555/vUB/z58/PkUcemSeffLKprq6uLt/5zncyduzYVtsdeOCBFYF+P/zhDzNnzpx2x/rQhz7UoXltburUqU3/Xr9+fX75y192qN3rr7+e6667LitWrOjwWCtXrsxtt93W6vidUVdXt0UQ3Mc+9rF2M+bddttt+frXv17VWB/84Aezxx57NJWffPLJ/O3f/m2WLl1aVT+LFy/e4jzwZ4LtAACAqrW2jGySrGtc22K9zHYAAAAAAFA7H/7wh/OJT3yiou6+++7LtGnTctVVV2XhwoUttluwYEG+8IUvZO+9987jjz9e8djVV1+dI488ss1xhw0bllNPPbWpvGHDhvz1X/91i0uerl27NjfccEPe9a53ZdGiRRk5cmRHDy9JcsQRR1SUzz777Hz961/Pww8/nD/+8Y955plnmrZXXnmlYtxPfepTmTBhQj784Q/nrrvuajPw7qGHHsqRRx6ZZ599tqnuXe96V6ZMmVLVfFvyqU99Km9/+9ubyi+++GLe/e5355ZbbkljY2PFvqtWrcpll12W0047LY2NjVWdr/r6+txyyy0ZPnx4U93Pfvaz7LPPPvnXf/3XNo//1Vdfzc0335zTTz89u+yyS7761a9WcYTbFt92AQAAVWsts12SrC9bTmle30YbAAAAAACgetdff31GjhyZL3/5yynLMkmyYsWKXHTRRfnc5z6XadOmZZdddsnIkSOzZMmSPPvss/n973+/RT8DBw7MzJkz8/GPf7xD415++eX54Q9/mGXLliVJ/vSnP+WYY47J5MmTs88++6ShoSGLFi3Kgw8+mFWrViVJdtxxx1x99dVVZbh73/vel89//vNN2fpefPHFLQIM3/ShD30oN954Y0Xd8uXLM2vWrMyaNStFUWTy5MnZbbfdMmLEiAwYMCBLlizJE088sUU2wCFDhuSb3/xmh+fZloEDB+amm27KYYcdliVLliRJXnrppbzvfe/L+PHj8453vCM77LBDFi1alN/85jd54403kiQ77LBDrr766nz0ox/t8Fh77rlnbr311pxyyil57bXXkiTPP/98zjnnnJx77rnZe++9M3HixAwfPjyvv/56li1blqeeeqrD2RARbAcAAHRCW5nt1pYtZ7ark9kOAAAAAABq7vLLL89hhx2Wc845J/Pnz2+qL8syc+fOzdy5c9tsv//+++cb3/hGpk+f3uExd95559x666058cQTKzKmLViwIAsWLNhi/1133TX/+Z//mUWLFnV4jCTZbrvt8sMf/jAnnnhiXnjhharaNleWZebPn19xjlqy884757bbbsvee+/dpfE2t+eee+ZnP/tZjj/++Lz00ktN9YsWLcqPfvSjLfYfMWJE7rzzzmzYsKHqsY466qjMmTMnp59+esXyvhs2bMijjz6aRx99tN0+qs1AuC2xjCwAAFC1+qL1W4n1rQTb1UdmOwAAAAAA6A5HHXVU5s2bl5tuuilHHnlkBgxo+w/gGxoacsIJJ+SOO+7InDlzqgq0e9Nf/uVf5qGHHsp73/veFEXR4j5jx47NZz7zmTz66KOZOnVq1WMkyfTp0zNv3rz827/9W0488cRMnjw5w4cPT31969877LDDDrn33ntz4YUX5h3veEe75yNJ/uIv/iJXXnllnnrqqbzzne/s1Fzbsu++++bJJ5/Mueeem2HDhrW4z9ChQ3PWWWflscceyyGHHNLpsSZPnpyHHnood911V4466qg0NDS022bq1Kk599xz86tf/Sq33XZbp8fu74o3U0jC1qYoij2TPPFm+Yknnsiee+7ZizMCAOBNz69+Olc++w8tPnbMqFPyX6/eskX9Lg275aJJ/9zdUwMAAAAA6HPWr1+/Rbat3XffvUMBQtCSVatW5eGHH86CBQuyePHirF27Ng0NDRk/fnymTJmS/fffv0MBWB31yiuv5N57783zzz+f119/PePHj8+uu+6aQw45ZKt4Hb/xxhuZO3du/vCHP+Tll1/OqlWrUhRFhg8fnokTJ2afffbJW9/61h6bz5o1azJ79uw8/fTTWbp0acaOHZsJEybkkEMOyfbbb1/z8VavXp0HH3wwzz7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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import yappi\n", + "\n", + "from apd.aggregation.analysis import interactable_plot_multiple_charts, Config, clean_temperature_fluctuations, get_data_by_deployment\n", + "from apd.aggregation.utils import profile_with_yappi\n", + "\n", + "async def filter_and_clean_temperature_fluctuations(datapoints):\n", + " filtered = (item async for item in datapoints if item.sensor_name==\"Temperature\")\n", + " cleaned = clean_temperature_fluctuations(filtered)\n", + " async for item in cleaned:\n", + " yield item\n", + "\n", + "filter_in_python = Config(\n", + " clean=filter_and_clean_temperature_fluctuations,\n", + " title=\"Ambient temperature\",\n", + " ylabel=\"Degrees C\",\n", + " get_data=get_data_by_deployment,\n", + ")\n", + "\n", + "with profile_with_yappi():\n", + " plot = interactable_plot_multiple_charts(configs=[filter_in_python])\n", + " plot()\n", + "\n", + "yappi.get_func_stats().print_all()" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from apd.aggregation.utils import yappi_package_matches\n", + "import yappi\n", + "\n", + "yappi.get_func_stats().save(\"callgrind.filter_in_python\", \"callgrind\") " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "apd.aggregation", + "language": "python", + "name": "apd.aggregation" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Ch11/apd.aggregation-chapter11/pyproject.toml b/Ch11/apd.aggregation-chapter11/pyproject.toml new file mode 100644 index 0000000..4d3bb67 --- /dev/null +++ b/Ch11/apd.aggregation-chapter11/pyproject.toml @@ -0,0 +1,5 @@ +[build-system] +requires = [ + "setuptools", +] +build-backend = "setuptools.build_meta" diff --git a/Ch11/apd.aggregation-chapter11/pytest.ini b/Ch11/apd.aggregation-chapter11/pytest.ini new file mode 100644 index 0000000..8c26fbc --- /dev/null +++ b/Ch11/apd.aggregation-chapter11/pytest.ini @@ -0,0 +1,4 @@ +[pytest] +markers = + functional: these tests are significantly slower as they start a HTTP server + performance: very slow tests that provide performance guarantees \ No newline at end of file diff --git a/Ch11/apd.aggregation-chapter11/setup.cfg b/Ch11/apd.aggregation-chapter11/setup.cfg new file mode 100644 index 0000000..3ee0436 --- /dev/null +++ b/Ch11/apd.aggregation-chapter11/setup.cfg @@ -0,0 +1,83 @@ +[mypy] +namespace_packages = True +mypy_path = src +plugins = sqlmypy + +[mypy-alembic] +ignore_missing_imports = True + +[mypy-alembic.config] +ignore_missing_imports = True + +[mypy-alembic.script] +ignore_missing_imports = True + +[mypy-alembic.runtime.environment] +ignore_missing_imports = True + +[mypy-matplotlib] +ignore_missing_imports = True + +[mypy-matplotlib.axes._base] +ignore_missing_imports = True + +[mypy-matplotlib.figure] +ignore_missing_imports = True + +[mypy-matplotlib.pyplot] +ignore_missing_imports = True + +[mypy-IPython.core] +ignore_missing_imports = True + +[mypy-yappi] +ignore_missing_imports = True + +[mypy-pint] +ignore_missing_imports = True + +[mypy-pytest] +ignore_missing_imports = True + +[flake8] +max-line-length = 120 +ignore = E501 + +[metadata] +name = apd.aggregation +version = attr: apd.aggregation.VERSION +description = A programme that queries apd.sensor endpoints and aggregates their results. +long_description = file: README.md, CHANGES.md, LICENCE +long_description_content_type = text/markdown +keywords = +license = BSD +classifiers = + Programming Language :: Python :: 3 + Programming Language :: Python :: 3.7 + +[options] +zip_safe = False +include_package_data = True +package_dir = + =src +packages = find_namespace: +install_requires = + sqlalchemy + aiohttp + pint + psycopg2 + alembic + click + + +[options.extras_require] +jupyter = ipywidgets +yappi = yappi + +[options.packages.find] +where = src + +[options.entry_points] +console_scripts = + collect_sensor_data = apd.aggregation.cli:collect_sensor_data + sensor_deployments = apd.aggregation.cli:deployments diff --git a/Ch11/apd.aggregation-chapter11/setup.py b/Ch11/apd.aggregation-chapter11/setup.py new file mode 100644 index 0000000..6068493 --- /dev/null +++ b/Ch11/apd.aggregation-chapter11/setup.py @@ -0,0 +1,3 @@ +from setuptools import setup + +setup() diff --git a/Ch11/apd.aggregation-chapter11/src/apd/aggregation/__init__.py b/Ch11/apd.aggregation-chapter11/src/apd/aggregation/__init__.py new file mode 100644 index 0000000..3277f64 --- /dev/null +++ b/Ch11/apd.aggregation-chapter11/src/apd/aggregation/__init__.py @@ -0,0 +1 @@ +VERSION = "1.0.0" diff --git a/Ch11/apd.aggregation-chapter11/src/apd/aggregation/alembic/README b/Ch11/apd.aggregation-chapter11/src/apd/aggregation/alembic/README new file mode 100644 index 0000000..97a87d5 --- /dev/null +++ b/Ch11/apd.aggregation-chapter11/src/apd/aggregation/alembic/README @@ -0,0 +1,12 @@ +Generic single-database configuration. + + +# Database setup + +To generate the required database tables you must create an alembic.ini file, as follows: + + [alembic] + script_location = apd.aggregation:alembic + sqlalchemy.url = postgresql+psycopg2://apd@localhost/apd + +and run `alembic upgrade head`. This should also be done after every upgrade of the software. \ No newline at end of file diff --git a/Ch11/apd.aggregation-chapter11/src/apd/aggregation/alembic/env.py b/Ch11/apd.aggregation-chapter11/src/apd/aggregation/alembic/env.py new file mode 100644 index 0000000..4c8a2c2 --- /dev/null +++ b/Ch11/apd.aggregation-chapter11/src/apd/aggregation/alembic/env.py @@ -0,0 +1,80 @@ +from logging.config import fileConfig + +from sqlalchemy import engine_from_config +from sqlalchemy import pool +from alembic import context + +from apd.aggregation.database import metadata + + +# this is the Alembic Config object, which provides +# access to the values within the .ini file in use. +config = context.config + +# Interpret the config file for Python logging. +# This line sets up loggers basically. +if config.config_file_name: + fileConfig(config.config_file_name) + +target_metadata = metadata + + +def include_object(object, name, type_, reflected, compare_to): + if object.info.get("is_view", False): + return False + return True + + +def run_migrations_offline(): + """Run migrations in 'offline' mode. + + This configures the context with just a URL + and not an Engine, though an Engine is acceptable + here as well. By skipping the Engine creation + we don't even need a DBAPI to be available. + + Calls to context.execute() here emit the given string to the + script output. + + """ + url = config.get_main_option("sqlalchemy.url") + context.configure( + url=url, + include_object=include_object, + target_metadata=target_metadata, + literal_binds=True, + dialect_opts={"paramstyle": "named"}, + ) + + with context.begin_transaction(): + context.run_migrations() + + +def run_migrations_online(): + """Run migrations in 'online' mode. + + In this scenario we need to create an Engine + and associate a connection with the context. + + """ + connectable = engine_from_config( + config.get_section(config.config_ini_section), + prefix="sqlalchemy.", + poolclass=pool.NullPool, + ) + + with connectable.connect() as connection: + context.configure( + connection=connection, + target_metadata=target_metadata, + include_object=include_object, + ) + + with context.begin_transaction(): + context.run_migrations() + + +if context.is_offline_mode(): + run_migrations_offline() +else: + run_migrations_online() diff --git a/Ch11/apd.aggregation-chapter11/src/apd/aggregation/alembic/script.py.mako b/Ch11/apd.aggregation-chapter11/src/apd/aggregation/alembic/script.py.mako new file mode 100644 index 0000000..2c01563 --- /dev/null +++ b/Ch11/apd.aggregation-chapter11/src/apd/aggregation/alembic/script.py.mako @@ -0,0 +1,24 @@ +"""${message} + +Revision ID: ${up_revision} +Revises: ${down_revision | comma,n} +Create Date: ${create_date} + +""" +from alembic import op +import sqlalchemy as sa +${imports if imports else ""} + +# revision identifiers, used by Alembic. +revision = ${repr(up_revision)} +down_revision = ${repr(down_revision)} +branch_labels = ${repr(branch_labels)} +depends_on = ${repr(depends_on)} + + +def upgrade(): + ${upgrades if upgrades else "pass"} + + +def downgrade(): + ${downgrades if downgrades else "pass"} diff --git a/Ch11/apd.aggregation-chapter11/src/apd/aggregation/alembic/versions/4b2df8a6e1ce_add_indexes_to_datapoints.py b/Ch11/apd.aggregation-chapter11/src/apd/aggregation/alembic/versions/4b2df8a6e1ce_add_indexes_to_datapoints.py new file mode 100644 index 0000000..ef671bd --- /dev/null +++ b/Ch11/apd.aggregation-chapter11/src/apd/aggregation/alembic/versions/4b2df8a6e1ce_add_indexes_to_datapoints.py @@ -0,0 +1,32 @@ +"""Add indexes to datapoints + +Revision ID: 4b2df8a6e1ce +Revises: 6d2eacd5da3f +Create Date: 2019-12-02 16:07:41.123116 + +""" +from alembic import op + + +# revision identifiers, used by Alembic. +revision = "4b2df8a6e1ce" +down_revision = "6d2eacd5da3f" +branch_labels = None +depends_on = None + + +def upgrade(): + op.create_index( + op.f("ix_datapoints_collected_at"), + "datapoints", + ["collected_at"], + unique=False, + ) + op.create_index( + op.f("ix_datapoints_sensor_name"), "datapoints", ["sensor_name"], unique=False, + ) + + +def downgrade(): + op.drop_index(op.f("ix_datapoints_sensor_name"), table_name="datapoints") + op.drop_index(op.f("ix_datapoints_collected_at"), table_name="datapoints") diff --git a/Ch11/apd.aggregation-chapter11/src/apd/aggregation/alembic/versions/6962f8455a6d_add_daily_summary_view.py b/Ch11/apd.aggregation-chapter11/src/apd/aggregation/alembic/versions/6962f8455a6d_add_daily_summary_view.py new file mode 100644 index 0000000..eb02455 --- /dev/null +++ b/Ch11/apd.aggregation-chapter11/src/apd/aggregation/alembic/versions/6962f8455a6d_add_daily_summary_view.py @@ -0,0 +1,38 @@ +"""Add daily summary view + +Revision ID: 6962f8455a6d +Revises: 4b2df8a6e1ce +Create Date: 2019-12-03 11:50:24.403402 + +""" +from alembic import op + + +# revision identifiers, used by Alembic. +revision = "6962f8455a6d" +down_revision = "4b2df8a6e1ce" +branch_labels = None +depends_on = None + + +def upgrade(): + create_view = """ + CREATE VIEW daily_summary AS + SELECT + datapoints.sensor_name AS sensor_name, + datapoints.data AS data, + count(datapoints.id) AS count + FROM datapoints + WHERE + datapoints.collected_at >= CAST(CURRENT_DATE AS DATE) + AND + datapoints.collected_at < CAST(CURRENT_DATE AS DATE) + 1 + GROUP BY + datapoints.sensor_name, + datapoints.data; + """ + op.execute(create_view) + + +def downgrade(): + op.execute("""DROP VIEW daily_summary""") diff --git a/Ch11/apd.aggregation-chapter11/src/apd/aggregation/alembic/versions/6d2eacd5da3f_create_sensor_values_table.py b/Ch11/apd.aggregation-chapter11/src/apd/aggregation/alembic/versions/6d2eacd5da3f_create_sensor_values_table.py new file mode 100644 index 0000000..b4a3545 --- /dev/null +++ b/Ch11/apd.aggregation-chapter11/src/apd/aggregation/alembic/versions/6d2eacd5da3f_create_sensor_values_table.py @@ -0,0 +1,31 @@ +"""Create datapoints table + +Revision ID: 6d2eacd5da3f +Revises: N/A +Create Date: 2019-09-29 13:43:21.242706 + +""" +from alembic import op +import sqlalchemy as sa +from sqlalchemy.dialects import postgresql + +# revision identifiers, used by Alembic. +revision = "6d2eacd5da3f" +down_revision = None +branch_labels = None +depends_on = None + + +def upgrade(): + op.create_table( + "datapoints", + sa.Column("id", sa.Integer(), nullable=False), + sa.Column("sensor_name", sa.String(), nullable=True), + sa.Column("collected_at", postgresql.TIMESTAMP(), nullable=True), + sa.Column("data", postgresql.JSONB(astext_type=sa.Text()), nullable=True), + sa.PrimaryKeyConstraint("id"), + ) + + +def downgrade(): + op.drop_table("datapoints") diff --git a/Ch11/apd.aggregation-chapter11/src/apd/aggregation/alembic/versions/d8cdc709086b_add_deployment_table.py b/Ch11/apd.aggregation-chapter11/src/apd/aggregation/alembic/versions/d8cdc709086b_add_deployment_table.py new file mode 100644 index 0000000..204e096 --- /dev/null +++ b/Ch11/apd.aggregation-chapter11/src/apd/aggregation/alembic/versions/d8cdc709086b_add_deployment_table.py @@ -0,0 +1,32 @@ +"""Add deployment table + +Revision ID: d8cdc709086b +Revises: d8d4cf6a178f +Create Date: 2019-12-17 16:28:58.585616 + +""" +from alembic import op +import sqlalchemy as sa +from sqlalchemy.dialects import postgresql + +# revision identifiers, used by Alembic. +revision = "d8cdc709086b" +down_revision = "d8d4cf6a178f" +branch_labels = None +depends_on = None + + +def upgrade(): + op.create_table( + "deployments", + sa.Column("id", postgresql.UUID(as_uuid=True), nullable=False), + sa.Column("uri", sa.String(), nullable=True), + sa.Column("name", sa.String(), nullable=True), + sa.Column("colour", sa.String(), nullable=True), + sa.Column("api_key", sa.String(), nullable=True), + sa.PrimaryKeyConstraint("id"), + ) + + +def downgrade(): + op.drop_table("deployments") diff --git a/Ch11/apd.aggregation-chapter11/src/apd/aggregation/alembic/versions/d8d4cf6a178f_add_deployment_id_to_datapoint.py b/Ch11/apd.aggregation-chapter11/src/apd/aggregation/alembic/versions/d8d4cf6a178f_add_deployment_id_to_datapoint.py new file mode 100644 index 0000000..88d5219 --- /dev/null +++ b/Ch11/apd.aggregation-chapter11/src/apd/aggregation/alembic/versions/d8d4cf6a178f_add_deployment_id_to_datapoint.py @@ -0,0 +1,34 @@ +"""Add deployment id to DataPoint + +Revision ID: d8d4cf6a178f +Revises: 6962f8455a6d +Create Date: 2019-12-03 20:58:11.285509 + +""" +from alembic import op +import sqlalchemy as sa +from sqlalchemy.dialects import postgresql + +# revision identifiers, used by Alembic. +revision = "d8d4cf6a178f" +down_revision = "6962f8455a6d" +branch_labels = None +depends_on = None + + +def upgrade(): + op.add_column( + "datapoints", + sa.Column("deployment_id", postgresql.UUID(as_uuid=True), nullable=True), + ) + op.create_index( + op.f("ix_datapoints_deployment_id"), + "datapoints", + ["deployment_id"], + unique=False, + ) + + +def downgrade(): + op.drop_index(op.f("ix_datapoints_deployment_id"), table_name="datapoints") + op.drop_column("datapoints", "deployment_id") diff --git a/Ch11/apd.aggregation-chapter11/src/apd/aggregation/analysis.py b/Ch11/apd.aggregation-chapter11/src/apd/aggregation/analysis.py new file mode 100644 index 0000000..eeb4ab2 --- /dev/null +++ b/Ch11/apd.aggregation-chapter11/src/apd/aggregation/analysis.py @@ -0,0 +1,401 @@ +from __future__ import annotations + +import asyncio +import collections +from concurrent.futures import ThreadPoolExecutor +import dataclasses +import datetime +import functools +import math +import typing as t +from uuid import UUID +import warnings + +import matplotlib.pyplot as plt +from matplotlib.axes._base import _AxesBase +from matplotlib.figure import Figure +from pint import _DEFAULT_REGISTRY as ureg + +from apd.aggregation.query import ( + get_data, + get_data_by_deployment, + with_database, + get_deployment_by_id, +) +from apd.aggregation.database import DataPoint, deployment_table +from .utils import merc_x, merc_y, convert_temperature +from .typing import IntermediateMapData, T_key, T_value, CleanerFunc, Cleaned +from .typing import ( + CLEANED_COORD_FLOAT, + CLEANED_DT_FLOAT, + COORD_FLOAT_CLEANER, + DT_FLOAT_CLEANER, +) + +# Static UUID to represent aggregation of other deployments +GLOBAL = UUID("bd02526e-7619-4a59-b04b-1fafd1c262d1") + + +@dataclasses.dataclass +class Config(t.Generic[T_key, T_value]): + title: str + clean: CleanerFunc[Cleaned[T_key, T_value]] + draw: t.Optional[ + t.Callable[ + [t.Any, t.Iterable[T_key], t.Iterable[T_value], t.Optional[str]], None + ] + ] = None + get_data: t.Optional[ + t.Callable[..., t.AsyncIterator[t.Tuple[UUID, t.AsyncIterator[DataPoint]]]] + ] = None + ylabel: t.Optional[str] = None + sensor_name: dataclasses.InitVar[str] = None + + def __post_init__(self, sensor_name: t.Optional[str] = None) -> None: + if self.draw is None: + self.draw = draw_date # type: ignore + if sensor_name is not None: + warnings.warn( + DeprecationWarning( + f"The sensor_name parameter is deprecated. Please pass " + f"get_data=get_one_sensor_by_deployment('{sensor_name}') " + f"to ensure the same behaviour. The sensor_name= parameter " + f"will be removed in apd.aggregation 3.0." + ), + stacklevel=3, + ) + if self.get_data is None: + self.get_data = get_one_sensor_by_deployment(sensor_name) + if self.get_data is None: + raise ValueError("You must specify a get_data function") + + +def draw_date( + plot: _AxesBase, + x: t.Iterable[datetime.datetime], + y: t.Iterable[float], + colour: t.Optional[str], +) -> None: + plot.plot_date(x, y, color=colour, linestyle="-", marker="", xdate=True) + + +def draw_map( + plot: _AxesBase, + x: t.Iterable[t.Tuple[float, float]], + y: t.Iterable[float], + colour: t.Optional[str], +) -> None: + lon = [merc_y(coord[0]) for coord in x] + lat = [merc_x(coord[1]) for coord in x] + + for axis in "x", "y": + plt.tick_params( + axis=axis, + which="both", + bottom=False, + top=False, + left=False, + right=False, + labelbottom=False, + labelleft=False, + ) + + plot.tricontourf(lat, lon, y) + plot.plot(lat, lon, "wo", ms=3) + plot.set_aspect(1.0) + + +def get_map_cleaner_for(sensor_name: str,) -> COORD_FLOAT_CLEANER: + """Given a sensor_name that represents a float, return a coroutine that acts as a cleaner + extracting that sensor's data keyed by the value of a Location sensor.""" + + async def clean_latest_coord_and_value( + datapoints: t.AsyncIterator[DataPoint], + ) -> CLEANED_COORD_FLOAT: + + # We will iterate over data points and build an entry in cleaned_data + # for each deployment. This lets newer data replace older data, as + # datapoints is assumed to be in date order + # IntermediateMapData is a typing hint for a dictionary with specific key/values + cleaned_data: t.Dict[UUID, IntermediateMapData] = {} + async for datapoint in datapoints: + # Get the existing data for this deployment, if we've seen it before + row_data = cleaned_data.get(datapoint.deployment_id, None) + if row_data is None: + # This is the first time we've seen this deployment + row_data = {"coord": None, "value": None} + if datapoint.sensor_name == "Location": + # Coord is a 2-tuple of floats + row_data["coord"] = ( + float(datapoint.data[0]), + float(datapoint.data[1]), + ) + elif datapoint.sensor_name == sensor_name: + # Value is a single float + row_data["value"] = float(datapoint.data) + # Store the info about this deployment back into the cleaned_data set + cleaned_data[datapoint.deployment_id] = row_data + + for data in cleaned_data.values(): + if data["coord"] is None or data["value"] is None: + # We only got a partial record, don't plot this + continue + yield data["coord"], data["value"] + + # Return the set up cleaner coroutine + return clean_latest_coord_and_value + + +async def clean_watthours_to_watts( + datapoints: t.AsyncIterator[DataPoint], +) -> CLEANED_DT_FLOAT: + last_watthours = None + last_time = None + async for datapoint in datapoints: + if datapoint.data is None: + continue + time = datapoint.collected_at + if datapoint.data["unit"] == "watt_hour": + watt_hours = datapoint.data["magnitude"] + else: + watt_hours = ( + ureg.Quantity(datapoint.data["magnitude"], datapoint.data["unit"]) + .to(ureg.watt_hour) + .magnitude + ) + if last_watthours: + seconds_elapsed = (time - last_time).total_seconds() + hours_elapsed = seconds_elapsed / (60.0 * 60.0) + additional_power = watt_hours - last_watthours + power = additional_power / hours_elapsed + yield time, power + last_watthours = watt_hours + last_time = datapoint.collected_at + + +async def clean_magnitude(datapoints: t.AsyncIterator[DataPoint],) -> CLEANED_DT_FLOAT: + async for datapoint in datapoints: + if datapoint.data is None: + continue + yield datapoint.collected_at, datapoint.data["magnitude"] + + +def convert_temperature_system( + cleaner: DT_FLOAT_CLEANER, temperature_unit: str, +) -> DT_FLOAT_CLEANER: + async def converter(datapoints: t.AsyncIterator[DataPoint],) -> CLEANED_DT_FLOAT: + results = cleaner(datapoints) + async for date, temp_c in results: + yield date, convert_temperature(temp_c, "degC", temperature_unit) + + return converter + + +async def clean_temperature_fluctuations( + datapoints: t.AsyncIterator[DataPoint], +) -> CLEANED_DT_FLOAT: + allowed_jitter = 2.5 + allowed_range = (-40, 80) + window_datapoints: t.Deque[DataPoint] = collections.deque(maxlen=3) + + def datapoint_ok(datapoint: DataPoint) -> bool: + """Return False if this data point does not contain a valid temperature""" + if datapoint.data is None: + return False + elif datapoint.data["unit"] != "degC": + # This point is in a different temperature system. While it could be converted + # this cleaner is not yet doing that. + return False + elif not allowed_range[0] < datapoint.data["magnitude"] < allowed_range[1]: + return False + return True + + async for datapoint in datapoints: + if not datapoint_ok(datapoint): + # If the datapoint is invalid then skip directly to the next item + continue + + window_datapoints.append(datapoint) + if len(window_datapoints) == 3: + # Find the temperatures of the datapoints in the window, then average + # the first and last and compare that to the middle point. + window_temperatures = [dp.data["magnitude"] for dp in window_datapoints] + avr_first_last = (window_temperatures[0] + window_temperatures[2]) / 2 + diff_middle_avr = abs(window_temperatures[1] - avr_first_last) + if diff_middle_avr > allowed_jitter: + pass + else: + yield window_datapoints[1].collected_at, window_temperatures[1] + elif len(window_datapoints) == 1: + # The item in the iterator can't be compared to both neighbours + # so should be yielded + yield datapoint.collected_at, datapoint.data["magnitude"] + else: + # Otherwise, let the window fill up, it will be yieleded later + pass + # When the iterator ends the final item is not yet in the middle + # of the window, so the last item must be explicitly yielded. + if len(window_datapoints) > 1 and datapoint_ok(datapoint): + yield datapoint.collected_at, datapoint.data["magnitude"] + + +async def clean_passthrough( + datapoints: t.AsyncIterator[DataPoint], +) -> CLEANED_DT_FLOAT: + async for datapoint in datapoints: + if datapoint.data is None: + continue + else: + yield datapoint.collected_at, datapoint.data + + +def get_one_sensor_by_deployment( + sensor_name: str, +) -> t.Callable[..., t.AsyncIterator[t.Tuple[UUID, t.AsyncIterator[DataPoint]]]]: + return functools.partial(get_data_by_deployment, sensor_name=sensor_name) + + +def get_all_data() -> t.Callable[ + ..., t.AsyncIterator[t.Tuple[UUID, t.AsyncIterator[DataPoint]]] +]: + async def get_all_data_inner( + *args: t.Any, **kwargs: t.Any + ) -> t.AsyncIterator[t.Tuple[UUID, t.AsyncIterator[DataPoint]]]: + yield GLOBAL, get_data(*args, **kwargs) + + return get_all_data_inner + + +configs = ( + Config( + sensor_name="SolarCumulativeOutput", + clean=clean_watthours_to_watts, + title="Solar generation", + ylabel="Watts", + ), + Config( + sensor_name="RAMAvailable", + clean=clean_passthrough, + title="RAM available", + ylabel="Bytes", + ), + Config( + sensor_name="RelativeHumidity", + clean=clean_passthrough, + title="Relative humidity", + ylabel="Percent", + ), + Config( + sensor_name="Temperature", + clean=clean_temperature_fluctuations, + title="Ambient temperature", + ylabel="Degrees C", + ), +) + + +def get_known_configs() -> t.Dict[str, Config[t.Any, t.Any]]: + return {config.title: config for config in configs} + + +async def plot_sensor( + config: Config[t.Any, t.Any], + plot: _AxesBase, + location_names: t.Dict[UUID, str], + **kwargs: t.Any, +) -> _AxesBase: + locations = [] + if config.get_data is None: + raise ValueError("You must provide a get_data function") + async for deployment_id, query_results in config.get_data(**kwargs): + if deployment_id == GLOBAL: + name = "Global" + else: + try: + deployment = await get_deployment_by_id(deployment_id) + except IndexError: + name = str(deployment_id) + colour = None + else: + name = deployment.name or str(deployment_id) + colour = deployment.colour + # Mypy currently doesn't understand callable fields on datatypes: https://github.com/python/mypy/issues/5485 + points = [dp async for dp in config.clean(query_results)] # type: ignore + if not points: + continue + locations.append(name) + x, y = zip(*points) + plot.set_title(config.title) + plot.set_ylabel(config.ylabel) + if config.draw is None: + raise ValueError("You must provide a get_data function") + config.draw(plot, x, y, colour) + plot.legend(locations) + return plot + + +async def plot_multiple_charts(*args: t.Any, **kwargs: t.Any) -> Figure: + # These parameters are pulled from kwargs to avoid confusing function + # introspection code in IPython widgets + location_names = kwargs.pop("location_names", None) + configs = kwargs.pop("configs", None) + dimensions = kwargs.pop("dimensions", None) + db_uri = kwargs.pop("db_uri", "postgresql+psycopg2://apd@localhost/apd") + + with with_database(db_uri) as session: + loop = asyncio.get_running_loop() + coros = [] + if configs is None: + # If no configs are supplied, use all known configs + configs = get_known_configs().values() + if dimensions is None: + # If no dimensions are supplied, get the square root of the number + # of configs and round it to find a number of columns. This will + # keep the arrangement approximately square. Find rows by multiplying + # out rows. + total_configs = len(configs) + columns = round(math.sqrt(total_configs)) + rows = math.ceil(total_configs / columns) + if location_names is None: + location_query = session.query( + deployment_table.c.id, deployment_table.c.name + ) + location_data = await loop.run_in_executor(None, location_query.all) + location_names = dict(location_data) + + figure = plt.figure(figsize=(10 * columns, 5 * rows), dpi=300) + for i, config in enumerate(configs, start=1): + plot = figure.add_subplot(columns, rows, i) + coros.append(plot_sensor(config, plot, location_names, *args, **kwargs)) + await asyncio.gather(*coros) + return figure + + +_Coroutine_Result = t.TypeVar("_Coroutine_Result") + + +def wrap_coroutine( + f: t.Callable[..., t.Coroutine[t.Any, t.Any, _Coroutine_Result]] +) -> t.Callable[..., _Coroutine_Result]: + """Given a coroutine, return a function that runs that coroutine + in a new event loop in an isolated thread""" + + @functools.wraps(f) + def run_in_thread(*args: t.Any, **kwargs: t.Any) -> _Coroutine_Result: + loop = asyncio.new_event_loop() + wrapped = f(*args, **kwargs) + with ThreadPoolExecutor(max_workers=1) as pool: + task = pool.submit(loop.run_until_complete, wrapped) + # Mypy can get confused when nesting generic functions, like we do here + # The fact that Task is generic means we lose the association with + # _CoroutineResult. Adding an explicit cast restores this. + return t.cast(_Coroutine_Result, task.result()) + + return run_in_thread + + +def interactable_plot_multiple_charts( + *args: t.Any, **kwargs: t.Any +) -> t.Callable[..., Figure]: + with_config = functools.partial(plot_multiple_charts, *args, **kwargs) + return wrap_coroutine(with_config) diff --git a/Ch11/apd.aggregation-chapter11/src/apd/aggregation/cli.py b/Ch11/apd.aggregation-chapter11/src/apd/aggregation/cli.py new file mode 100644 index 0000000..64b1246 --- /dev/null +++ b/Ch11/apd.aggregation-chapter11/src/apd/aggregation/cli.py @@ -0,0 +1,184 @@ +import asyncio +import typing as t +import uuid + +import aiohttp +import click +from sqlalchemy import create_engine +from sqlalchemy.orm import sessionmaker + +from . import collect +from .database import Deployment, deployment_table + + +@click.command() +@click.argument("server", nargs=-1) +@click.option( + "--db", + metavar="", + default="postgresql+psycopg2://localhost/apd", + help="The connection string to a PostgreSQL database", + envvar="APD_DB_URI", +) +@click.option("--api-key", metavar="", envvar="APD_API_KEY") +@click.option( + "--tolerate-failures", + "-f", + help="If provided, failure to retrieve some sensors' data will not abort the collection process", + is_flag=True, +) +@click.option("-v", "--verbose", is_flag=True, help="Enables verbose mode") +def collect_sensor_data( + db: str, server: t.Tuple[str], api_key: str, tolerate_failures: bool, verbose: bool +): + """This loads data from one or more sensors into the specified database. + + Only PostgreSQL databases are supported, as the column definitions use + multiple pg specific features. The database must already exist and be + populated with the required tables. + + The --api-key option is used to specify the access token for the sensors + being queried. + + You may specify any number of servers, the variable should be the full URL + to the sensor's HTTP interface, not including the /v/2.0 portion. Multiple + URLs should be separated with a space. + """ + if tolerate_failures: + attempts = [(s,) for s in server] + else: + attempts = [server] + success = True + for attempt in attempts: + try: + collect.standalone(db, attempt, api_key, echo=verbose) + except ValueError as e: + click.secho(str(e), err=True, fg="red") + success = False + return success + + +@click.group() +def deployments(): + pass + + +@deployments.command() +@click.argument("uri") +@click.argument("name") +@click.option( + "--db", + metavar="", + default="postgresql+psycopg2://localhost/apd", + help="The connection string to a PostgreSQL database", + envvar="APD_DB_URI", +) +@click.option("--api-key", metavar="", envvar="APD_API_KEY") +@click.option("--colour") +def add( + db: str, uri: str, name: str, api_key: t.Optional[str], colour: t.Optional[str], +): + """This creates a record of a new deployment in the database. + """ + deployment = Deployment(id=None, uri=uri, name=name, api_key=api_key, colour=colour) + + async def http_get_deployment_id(): + async with aiohttp.ClientSession() as http: + collect.http_session_var.set(http) + return await collect.get_deployment_id(uri) + + deployment.id = asyncio.run(http_get_deployment_id()) + insert = deployment_table.insert().values(**deployment._asdict()) + + engine = create_engine(db) + sm = sessionmaker(engine) + Session = sm() + Session.execute(insert) + Session.commit() + return True + + +@deployments.command() +@click.option( + "--db", + metavar="", + default="postgresql+psycopg2://localhost/apd", + help="The connection string to a PostgreSQL database", + envvar="APD_DB_URI", +) +def list(db: str): + """This creates a record of a new deployment in the database. + """ + engine = create_engine(db) + sm = sessionmaker(engine) + Session = sm() + deployments = Session.query(deployment_table).all() + for deployment in deployments: + click.secho(deployment.name, bold=True) + click.echo(click.style("ID ", bold=True) + deployment.id.hex) + click.echo(click.style("URI ", bold=True) + deployment.uri) + click.echo(click.style("API key ", bold=True) + deployment.api_key) + click.echo(click.style("Colour ", bold=True) + str(deployment.colour)) + click.echo() + Session.rollback() + return True + + +@deployments.command() +@click.argument("id") +@click.option("--uri") +@click.option("--name") +@click.option( + "--db", + metavar="", + default="postgresql+psycopg2://localhost/apd", + help="The connection string to a PostgreSQL database", + envvar="APD_DB_URI", +) +@click.option("--api-key", metavar="", envvar="APD_API_KEY") +@click.option("--colour") +def edit( + db: str, + id, + uri: t.Optional[str], + name: t.Optional[str], + api_key: t.Optional[str], + colour: t.Optional[str], +): + """This creates a record of a new deployment in the database. + """ + update = {} + if uri is not None: + update["uri"] = uri + if name is not None: + update["name"] = name + if api_key is not None: + update["api_key"] = api_key + if colour is not None: + update["colour"] = colour + deployment_id = uuid.UUID(id) + + update_stmt = ( + deployment_table.update() + .where(deployment_table.c.id == deployment_id) + .values(**update) + ) + + engine = create_engine(db) + sm = sessionmaker(engine) + Session = sm() + Session.execute(update_stmt) + deployments = Session.query(deployment_table).filter( + deployment_table.c.id == deployment_id + ) + Session.commit() + + for deployment in deployments: + click.secho(deployment.name, bold=True) + click.echo(click.style("ID ", bold=True) + deployment.id.hex) + click.echo(click.style("URI ", bold=True) + deployment.uri) + click.echo(click.style("API key ", bold=True) + deployment.api_key) + click.echo(click.style("Colour ", bold=True) + str(deployment.colour)) + click.echo() + + return True diff --git a/Ch11/apd.aggregation-chapter11/src/apd/aggregation/collect.py b/Ch11/apd.aggregation-chapter11/src/apd/aggregation/collect.py new file mode 100644 index 0000000..1d9ea08 --- /dev/null +++ b/Ch11/apd.aggregation-chapter11/src/apd/aggregation/collect.py @@ -0,0 +1,112 @@ +import asyncio +from contextvars import ContextVar +import datetime +import typing as t +import uuid + +import aiohttp +from sqlalchemy import create_engine +from sqlalchemy.orm import sessionmaker +from sqlalchemy.orm.session import Session + +from .database import DataPoint, datapoint_table, Deployment, deployment_table + +http_session_var: ContextVar[aiohttp.ClientSession] = ContextVar("http_session") + + +async def get_deployment_id(server): + http = http_session_var.get() + if not server.endswith("/"): + server += "/" + url = server + "v/2.1/deployment_id" + async with http.get(url) as request: + if request.status != 200: + raise ValueError(f"Error loading deployment id from {server}") + result = await request.json() + return uuid.UUID(result["deployment_id"]) + + +async def get_data_points(server: str, api_key: t.Optional[str],) -> t.List[DataPoint]: + if not server.endswith("/"): + server += "/" + url = server + "v/2.1/sensors/" + headers = {} + if api_key: + headers["X-API-KEY"] = api_key + http = http_session_var.get() + + # Get the deployment ID in parallel to the sensor data + deployment_id = asyncio.create_task(get_deployment_id(server)) + + async with http.get(url, headers=headers) as request: + result = await request.json() + ok = request.status == 200 + now = datetime.datetime.now() + if ok: + points = [] + for value in result["sensors"]: + points.append( + DataPoint( + sensor_name=value["id"], + collected_at=now, + data=value["value"], + deployment_id=await deployment_id, + ) + ) + return points + else: + raise ValueError( + f"Error loading data from {server}: " + result.get("error", "Unknown") + ) + + +def handle_result(result: t.List[DataPoint], session: Session) -> t.List[DataPoint]: + for point in result: + insert = datapoint_table.insert().values(**point._asdict()) + sql_result = session.execute(insert) + point.id = sql_result.inserted_primary_key[0] + return result + + +async def add_data_from_sensors( + session: Session, servers: t.Iterable[Deployment] +) -> t.List[DataPoint]: + tasks: t.List[t.Awaitable[t.List[DataPoint]]] = [] + points: t.List[DataPoint] = [] + async with aiohttp.ClientSession() as http: + http_session_var.set(http) + tasks = [get_data_points(server.uri, server.api_key) for server in servers] + for results in await asyncio.gather(*tasks): + points += results + loop = asyncio.get_running_loop() + await loop.run_in_executor(None, handle_result, points, session) + return points + + +def standalone( + db_uri: str, servers: t.Tuple[str], api_key: t.Optional[str], echo: bool = False +) -> None: + engine = create_engine(db_uri, echo=echo) + sm = sessionmaker(engine) + Session = sm() + deployments: t.Iterable[Deployment] + if servers: + deployments = tuple( + Deployment(id=None, name=None, colour=None, api_key=api_key, uri=server) + for server in servers + ) + else: + deployment_data = Session.query(deployment_table).all() + deployments = tuple( + Deployment.from_sql_result(deployment) for deployment in deployment_data + ) + asyncio.run(add_data_from_sensors(Session, deployments)) + Session.commit() + + +if __name__ == "__main__": + standalone( + db_uri="postgresql+psycopg2://apd@localhost/apd", + servers=("http://pvoutput:8080/",), + api_key="h3hdfjksfhwkjehnwekj", + ) diff --git a/Ch11/apd.aggregation-chapter11/src/apd/aggregation/database.py b/Ch11/apd.aggregation-chapter11/src/apd/aggregation/database.py new file mode 100644 index 0000000..f28b469 --- /dev/null +++ b/Ch11/apd.aggregation-chapter11/src/apd/aggregation/database.py @@ -0,0 +1,129 @@ +from __future__ import annotations + +from dataclasses import dataclass, field, asdict +import datetime +import typing as t +import uuid + +import sqlalchemy +from sqlalchemy.dialects.postgresql import JSONB, DATE, TIMESTAMP, UUID +from sqlalchemy.ext.hybrid import ExprComparator, hybrid_property +from sqlalchemy.orm import sessionmaker +from sqlalchemy.schema import Table + + +metadata = sqlalchemy.MetaData() + +deployment_table = Table( + "deployments", + metadata, + sqlalchemy.Column("id", UUID(as_uuid=True), primary_key=True), + sqlalchemy.Column("uri", sqlalchemy.String, index=False), + sqlalchemy.Column("name", sqlalchemy.String, index=False, nullable=True), + sqlalchemy.Column("colour", sqlalchemy.String, index=False, nullable=True), + sqlalchemy.Column("api_key", sqlalchemy.String, index=False), +) + +datapoint_table = Table( + "datapoints", + metadata, + sqlalchemy.Column("id", sqlalchemy.Integer, primary_key=True), + sqlalchemy.Column("sensor_name", sqlalchemy.String, index=True), + sqlalchemy.Column("collected_at", TIMESTAMP, index=True), + sqlalchemy.Column("deployment_id", UUID(as_uuid=True), index=True), + sqlalchemy.Column("data", JSONB), +) + +daily_summary_view = Table( + "daily_summary", + metadata, + sqlalchemy.Column("sensor_name", sqlalchemy.String), + sqlalchemy.Column("data", JSONB), + sqlalchemy.Column("count", sqlalchemy.Integer), + info={"is_view": True}, +) + + +class DateEqualComparator(ExprComparator): + def __init__(self, fallback_expression, raw_expression): + # Do not try and find update expression from parent + super().__init__(None, fallback_expression, None) + self.raw_expression = raw_expression + + def __eq__(self, other): + """ Returns True iff on the same day as other """ + other_date = sqlalchemy.cast(other, DATE) + return sqlalchemy.and_( + self.raw_expression >= other_date, self.raw_expression < other_date + 1, + ) + + def operate(self, op, *other, **kwargs): + other = [sqlalchemy.cast(date, DATE) for date in other] + return op(self.expression, *other, **kwargs) + + def reverse_operate(self, op, other, **kwargs): + other = [sqlalchemy.cast(date, DATE) for date in other] + return op(other, self.expression, **kwargs) + + +@dataclass +class DataPoint: + sensor_name: str + data: t.Any + deployment_id: uuid.UUID + id: t.Optional[int] = None + collected_at: datetime.datetime = field(default_factory=datetime.datetime.now) + + @classmethod + def from_sql_result(cls, result) -> DataPoint: + return cls(**result._asdict()) + + def _asdict(self) -> t.Dict[str, t.Any]: + data = asdict(self) + if data["id"] is None: + del data["id"] + return data + + @hybrid_property + def collected_on_date(self): + return self.collected_at.date() + + @collected_on_date.comparator # type: ignore + def collected_on_date(cls): + return DateEqualComparator( + fallback_expression=sqlalchemy.cast(datapoint_table.c.collected_at, DATE), + raw_expression=datapoint_table.c.collected_at, + ) + + +@dataclass +class Deployment: + id: t.Optional[uuid.UUID] + uri: str + name: t.Optional[str] + colour: t.Optional[str] + api_key: t.Optional[str] + + @classmethod + def from_sql_result(cls, result) -> Deployment: + return cls(**result._asdict()) + + def _asdict(self) -> t.Dict[str, t.Any]: + data = asdict(self) + return data + + +def main() -> None: + engine = sqlalchemy.create_engine( + "postgresql+psycopg2://apd@localhost/apd", echo=True + ) + sm = sessionmaker(engine) + Session = sm() + if False: + metadata.create_all(engine) + print(Session.query(DataPoint).all()) + pass + + +if __name__ == "__main__": + main() diff --git a/Ch11/apd.aggregation-chapter11/src/apd/aggregation/query.py b/Ch11/apd.aggregation-chapter11/src/apd/aggregation/query.py new file mode 100644 index 0000000..f3a8c95 --- /dev/null +++ b/Ch11/apd.aggregation-chapter11/src/apd/aggregation/query.py @@ -0,0 +1,148 @@ +import asyncio +import contextlib +from contextvars import ContextVar +import datetime +import typing as t +from uuid import UUID + +from sqlalchemy import create_engine +from sqlalchemy.orm import sessionmaker +from sqlalchemy.orm.session import Session + + +from apd.aggregation.database import ( + datapoint_table, + DataPoint, + deployment_table, + Deployment, +) + + +db_session_var: ContextVar[Session] = ContextVar("db_session") + + +@contextlib.contextmanager +def with_database(uri: t.Optional[str] = None) -> t.Iterator[Session]: + """Given a URI, set up a DB connection, and return a Session as a context manager """ + if uri is None: + uri = "postgresql+psycopg2://localhost/apd" + engine = create_engine(uri) + sm = sessionmaker(engine) + Session = sm() + token = db_session_var.set(Session) + try: + yield Session + Session.commit() + finally: + db_session_var.reset(token) + Session.close() + + +async def get_data( + sensor_name: t.Optional[str] = None, + deployment_id: t.Optional[UUID] = None, + collected_before: t.Optional[datetime.datetime] = None, + collected_after: t.Optional[datetime.datetime] = None, +) -> t.AsyncIterator[DataPoint]: + db_session = db_session_var.get() + loop = asyncio.get_running_loop() + query = db_session.query(datapoint_table) + if sensor_name: + query = query.filter(datapoint_table.c.sensor_name == sensor_name) + if deployment_id: + query = query.filter(datapoint_table.c.deployment_id == deployment_id) + if collected_before: + query = query.filter(datapoint_table.c.collected_at < collected_before) + if collected_after: + query = query.filter(datapoint_table.c.collected_at > collected_after) + query = query.order_by( + datapoint_table.c.deployment_id, + datapoint_table.c.sensor_name, + datapoint_table.c.collected_at, + ) + + rows = await loop.run_in_executor(None, query.all) + for row in rows: + yield DataPoint.from_sql_result(row) + + +async def get_deployment_ids() -> t.List[UUID]: + db_session = db_session_var.get() + loop = asyncio.get_running_loop() + query = db_session.query(datapoint_table.c.deployment_id).distinct() + return [row.deployment_id for row in await loop.run_in_executor(None, query.all)] + + +async def get_deployment_by_id(deployment_id: UUID) -> Deployment: + db_session = db_session_var.get() + loop = asyncio.get_running_loop() + query = db_session.query(deployment_table).filter( + deployment_table.c.id == deployment_id + ) + return [ + Deployment.from_sql_result(row) + for row in await loop.run_in_executor(None, query.all) + ][0] + + +async def get_data_by_deployment( + *args: t.Any, **kwargs: t.Any +) -> t.AsyncIterator[t.Tuple[UUID, t.AsyncIterator[DataPoint]]]: + """Return an Async Iterator that contains two-item pairs. + These pairs are a string (deployment_id), and an async iterator that contains + the datapoints with that deployment_id. + + Usage example: + + async for deployment_id, datapoints in get_data_by_deployment(): + print(deployment_id) + async for datapoint in datapoints: + print(datapoint) + print() + """ + # Get the data, using the arguments to this function as filters + data = get_data(*args, **kwargs) + + # The two levels of iterator share the item variable, initialise it with the + # first item from the iterator. Also set last_deployment_id to None, so the + # outer iterator knows to start a new group. + last_deployment_id: t.Optional[UUID] = None + try: + item = await data.__anext__() + except StopAsyncIteration: + # There were no items in the underlying query, return immediately + return + + async def subiterator(group_id: UUID) -> t.AsyncIterator[DataPoint]: + """Using a closure, create an iterator that yields the current + item, then yields all items from data while the deployment_id matches + group_id, leaving the first that doesn't match as item in the enclosing + scope.""" + # item is from the enclosing scope + nonlocal item + while item.deployment_id == group_id: + # yield items from data while they match the group_id this iterator represents + yield item + try: + # Advance the underlying iterator + item = await data.__anext__() + except StopAsyncIteration: + # The underlying iterator came to an end, so end the subiterator too + return + + while True: + while item.deployment_id == last_deployment_id: + # We are trying to advance the outer iterator while the underlying iterator + # is still part-way through a group. Speed through the underlying until we + # hit an item where the deployment_id is different to the last one (or, is not + # None, in the case of the start of the iterator) + try: + item = await data.__anext__() + except StopAsyncIteration: + # We hit the end of the underlying iterator: end this iterator too + return + last_deployment_id = item.deployment_id + # Don't yield an iterator for unclassified deployments + if last_deployment_id is not None: + # Instantiate a subiterator for this group + yield last_deployment_id, subiterator(last_deployment_id) diff --git a/Ch11/apd.aggregation-chapter11/src/apd/aggregation/typing.py b/Ch11/apd.aggregation-chapter11/src/apd/aggregation/typing.py new file mode 100644 index 0000000..5db1337 --- /dev/null +++ b/Ch11/apd.aggregation-chapter11/src/apd/aggregation/typing.py @@ -0,0 +1,48 @@ +import datetime +import typing as t +from typing_extensions import Protocol + +from apd.aggregation.database import DataPoint + + +# Aliases for common types +# These type variables allow for generic functions. T_key represents the place +# in a chart that an item will be placed, and T_value the kind of data that is plotted. +T_key = t.TypeVar("T_key") +T_value = t.TypeVar("T_value") + +# Cleaned is a placeholder type, representing an async iterator of key and value functions +# Cleaned[float, float] is equivalent to typing.AsyncIterator[Tuple[builtins.float, builtins.float]] +Cleaned = t.AsyncIterator[t.Tuple[T_key, T_value]] + +# T_cleaned represents a placeholder for the result of a cleaner function, and is *covariant* +# because it can accept compatible types (such as ints where floats were declared). +# It is bound to Cleaned, so only items that match the specification for Cleaned (for any values +# of T_key or T_value) are valid +T_cleaned = t.TypeVar("T_cleaned", covariant=True, bound=Cleaned) + + +# CleanerFunc is a generic protocol, it matches any Callable that converts an async iterator +# of datapoints to its type. So, CleanerFunc[float] would be equivalent to t.Callable[[t.AsyncIterator[DataPoint]], float] +class CleanerFunc(Protocol[T_cleaned]): + def __call__(self, datapoints: t.AsyncIterator[DataPoint]) -> T_cleaned: + ... + + +# CLEANED_DT_FLOAT is a Cleaned represents datetime/float pairs, for simple charts +CLEANED_DT_FLOAT = Cleaned[datetime.datetime, float] +# and CLEANED_COORD_FLOAT represents (lat/lon), float pairs +CLEANED_COORD_FLOAT = Cleaned[t.Tuple[float, float], float] + +# The _CLEANER variants are functions that return their matching iterators from above +DT_FLOAT_CLEANER = CleanerFunc[CLEANED_DT_FLOAT] +COORD_FLOAT_CLEANER = CleanerFunc[CLEANED_COORD_FLOAT] + + +# When drawing a map we will be building a dictionary of UUID to dictionary +# That inner dictionary should contain coord (float, float) +# and value (float) only. Either or both can be None. +# This class is abstract, it's just for type checking. +class IntermediateMapData(t.TypedDict): + coord: t.Optional[t.Tuple[float, float]] + value: t.Optional[float] diff --git a/Ch11/apd.aggregation-chapter11/src/apd/aggregation/utils.py b/Ch11/apd.aggregation-chapter11/src/apd/aggregation/utils.py new file mode 100644 index 0000000..75bf6b3 --- /dev/null +++ b/Ch11/apd.aggregation-chapter11/src/apd/aggregation/utils.py @@ -0,0 +1,100 @@ +from __future__ import annotations + +import contextlib +import functools +import importlib +import io +import logging +import math +import os +import typing as t + +from pint import _DEFAULT_REGISTRY as ureg + + +# Set up mercator transform from https://wiki.openstreetmap.org/wiki/Mercator#Python +# Thanks to Paulo Silva and the OSM contributors +def merc_x(lon: float) -> float: + r_major = 6378137.000 + return r_major * math.radians(lon) + + +def merc_y(lat: float) -> float: + if lat > 89.5: + lat = 89.5 + if lat < -89.5: + lat = -89.5 + r_major = 6378137.000 + r_minor = 6356752.3142 + temp = r_minor / r_major + eccent = math.sqrt(1 - temp ** 2) + phi = math.radians(lat) + sinphi = math.sin(phi) + con = eccent * sinphi + com = eccent / 2 + con = ((1.0 - con) / (1.0 + con)) ** com + ts = math.tan((math.pi / 2 - phi) / 2) / con + y = 0 - r_major * math.log(ts) + return y + + +@functools.lru_cache +def convert_temperature(magnitude: float, origin_unit: str, target_unit: str) -> float: + # if origin_unit == "degC" and target_unit == "degF": + # return (magnitude * 1.8) + 32 + temp = ureg.Quantity(magnitude, origin_unit) + return temp.to(target_unit).magnitude + + +@contextlib.contextmanager +def jupyter_page_file() -> t.Iterator[io.StringIO]: + from IPython.core import page + + output = io.StringIO() + yield output + output.seek(0) + page.page(output.read()) + + +@contextlib.contextmanager +def profile_with_yappi() -> t.Iterator[None]: + import yappi + + yappi.clear_stats() + yappi.start() + try: + yield None + finally: + yappi.stop() + + +@functools.lru_cache +def get_package_prefix(package: str) -> str: + mod = importlib.import_module(package) + prefix = mod.__file__ + if prefix.endswith("__init__.py"): + prefix = os.path.dirname(prefix) + return prefix + + +def yappi_package_matches(stat, packages: t.List[str]): + """ This object can be passed to yappi's filter_callback to limit + by Python package.""" + for package in packages: + prefix = get_package_prefix(package) + if stat.full_name.startswith(prefix): + return True + return False + + +class AddSensorNameDefault(logging.Filter): + def filter(self, record): + if not hasattr(record, "sensorname"): + record.sensorname = "none" + return True + + +class SensorNameStreamHandler(logging.StreamHandler): + def __init__(self, *args, **kwargs): + super().__init__() + self.addFilter(AddSensorNameDefault()) diff --git a/Ch11/apd.aggregation-chapter11/tests/__init__.py b/Ch11/apd.aggregation-chapter11/tests/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/Ch11/apd.aggregation-chapter11/tests/conftest.py b/Ch11/apd.aggregation-chapter11/tests/conftest.py new file mode 100644 index 0000000..328e95f --- /dev/null +++ b/Ch11/apd.aggregation-chapter11/tests/conftest.py @@ -0,0 +1,124 @@ +import datetime +from uuid import UUID + +from apd.aggregation.database import datapoint_table + +from alembic.config import Config +from alembic.script import ScriptDirectory +from alembic.runtime.environment import EnvironmentContext +import pytest + + +@pytest.fixture +def db_uri(): + return "postgresql+psycopg2://apd@localhost/apd-test" + + +@pytest.fixture +def db_session(db_uri): + from sqlalchemy import create_engine + from sqlalchemy.orm import sessionmaker + + engine = create_engine(db_uri, echo=True) + sm = sessionmaker(engine) + Session = sm() + yield Session + Session.close() + + +@pytest.fixture +def migrated_db(db_uri, db_session): + config = Config() + config.set_main_option("script_location", "apd.aggregation:alembic") + config.set_main_option("sqlalchemy.url", db_uri) + script = ScriptDirectory.from_config(config) + + def upgrade(rev, context): + return script._upgrade_revs(script.get_current_head(), rev) + + def downgrade(rev, context): + return script._downgrade_revs(None, rev) + + with EnvironmentContext(config, script, fn=upgrade): + script.run_env() + + try: + yield + finally: + # Clear any pending work from the db_session connection + db_session.rollback() + + with EnvironmentContext(config, script, fn=downgrade): + script.run_env() + + +@pytest.fixture +def populated_db(migrated_db, db_session): + datas = [ + { + "id": 1, + "sensor_name": "Test", + "data": "1", + "collected_at": datetime.datetime(2020, 4, 1, 12, 0, 1), + "deployment_id": UUID("b4c68905-b1e4-4875-940e-69e5d27730fd"), + }, + { + "id": 2, + "sensor_name": "Test", + "data": "2", + "collected_at": datetime.datetime(2020, 4, 1, 12, 1, 1), + "deployment_id": UUID("b4c68905-b1e4-4875-940e-69e5d27730fd"), + }, + { + "id": 3, + "sensor_name": "Test", + "data": "3", + "collected_at": datetime.datetime(2020, 4, 1, 12, 2, 1), + "deployment_id": UUID("b4c68905-b1e4-4875-940e-69e5d27730fd"), + }, + { + "id": 4, + "sensor_name": "Test", + "data": "4", + "collected_at": datetime.datetime(2020, 4, 1, 12, 3, 1), + "deployment_id": UUID("b4c68905-b1e4-4875-940e-69e5d27730fd"), + }, + { + "id": 5, + "sensor_name": "OtherTest", + "data": "5", + "collected_at": datetime.datetime(2020, 4, 1, 12, 4, 1), + "deployment_id": UUID("b4c68905-b1e4-4875-940e-69e5d27730fd"), + }, + { + "id": 6, + "sensor_name": "Test", + "data": "1", + "collected_at": datetime.datetime(2020, 4, 1, 12, 0, 1), + "deployment_id": UUID("6e887b81-57c3-4f3b-a7e7-bad159e05e78"), + }, + { + "id": 7, + "sensor_name": "Test", + "data": "1", + "collected_at": datetime.datetime(2020, 4, 1, 12, 1, 1), + "deployment_id": UUID("6e887b81-57c3-4f3b-a7e7-bad159e05e78"), + }, + { + "id": 8, + "sensor_name": "Test", + "data": "2", + "collected_at": datetime.datetime(2020, 4, 1, 12, 2, 1), + "deployment_id": UUID("6e887b81-57c3-4f3b-a7e7-bad159e05e78"), + }, + { + "id": 9, + "sensor_name": "OtherTest", + "data": "3", + "collected_at": datetime.datetime(2020, 4, 1, 12, 3, 1), + "deployment_id": UUID("6e887b81-57c3-4f3b-a7e7-bad159e05e78"), + }, + ] + for data in datas: + insert = datapoint_table.insert().values(**data) + db_session.execute(insert) diff --git a/Ch11/apd.aggregation-chapter11/tests/test_analysis.py b/Ch11/apd.aggregation-chapter11/tests/test_analysis.py new file mode 100644 index 0000000..4bce248 --- /dev/null +++ b/Ch11/apd.aggregation-chapter11/tests/test_analysis.py @@ -0,0 +1,525 @@ +import collections.abc +import datetime +import functools +import uuid +import warnings + +import pytest + +from apd.aggregation import analysis +from apd.aggregation.database import DataPoint, datapoint_table +from apd.aggregation.query import db_session_var + + +async def generate_datapoints(datas): + deployment_id = uuid.uuid4() + for i, (time, data) in enumerate(datas, start=1): + yield DataPoint( + id=i, + collected_at=time, + sensor_name="TestSensor", + data=data, + deployment_id=deployment_id, + ) + + +@functools.singledispatch +def consume(input_iterator): + items = [item for item in input_iterator] + + def inner_iterator(): + for item in items: + yield item + + return inner_iterator() + + +@consume.register +async def consume_async(input_iterator: collections.abc.AsyncIterator): + items = [item async for item in input_iterator] + + async def inner_iterator(): + for item in items: + yield item + + return inner_iterator() + + +class TestPassThroughCleaner: + @pytest.fixture + def cleaner(self): + return analysis.clean_passthrough + + @pytest.mark.asyncio + async def test_float_passthrough(self, cleaner): + data = [ + (datetime.datetime(2020, 4, 1, 12, 0, 0), 65.0), + (datetime.datetime(2020, 4, 1, 13, 0, 0), 65.5), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert output == data + + @pytest.mark.asyncio + async def test_Nones_are_skipped(self, cleaner): + data = [ + (datetime.datetime(2020, 4, 1, 12, 0, 0), None), + (datetime.datetime(2020, 4, 1, 13, 0, 0), 65.5), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert output == [ + (datetime.datetime(2020, 4, 1, 13, 0, 0), 65.5), + ] + + +class TestTemperatureCleaner: + @pytest.fixture + def cleaner(self): + return analysis.clean_temperature_fluctuations + + @pytest.mark.asyncio + async def test_window_not_full(self, cleaner): + data = [ + ( + datetime.datetime(2020, 4, 1, 12, 0, 0), + {"magnitude": 21.0, "unit": "degC"}, + ), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert output == [(datetime.datetime(2020, 4, 1, 12, 0, 0), 21.0)] + + @pytest.mark.asyncio + async def test_window_exactly_full(self, cleaner): + data = [ + ( + datetime.datetime(2020, 4, 1, 12, 0, 0), + {"magnitude": 21.0, "unit": "degC"}, + ), + ( + datetime.datetime(2020, 4, 1, 12, 0, 1), + {"magnitude": 21.0, "unit": "degC"}, + ), + ( + datetime.datetime(2020, 4, 1, 12, 0, 2), + {"magnitude": 21.0, "unit": "degC"}, + ), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert output == [ + (datetime.datetime(2020, 4, 1, 12, 0, 0), 21.0), + (datetime.datetime(2020, 4, 1, 12, 0, 1), 21.0), + (datetime.datetime(2020, 4, 1, 12, 0, 2), 21.0), + ] + + @pytest.mark.asyncio + async def test_window_overfilled(self, cleaner): + data = [ + ( + datetime.datetime(2020, 4, 1, 12, 0, 0), + {"magnitude": 21.0, "unit": "degC"}, + ), + ( + datetime.datetime(2020, 4, 1, 12, 0, 1), + {"magnitude": 21.0, "unit": "degC"}, + ), + ( + datetime.datetime(2020, 4, 1, 12, 0, 2), + {"magnitude": 21.0, "unit": "degC"}, + ), + ( + datetime.datetime(2020, 4, 1, 12, 0, 3), + {"magnitude": 21.0, "unit": "degC"}, + ), + ( + datetime.datetime(2020, 4, 1, 12, 0, 4), + {"magnitude": 21.0, "unit": "degC"}, + ), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert output == [ + (datetime.datetime(2020, 4, 1, 12, 0, 0), 21.0), + (datetime.datetime(2020, 4, 1, 12, 0, 1), 21.0), + (datetime.datetime(2020, 4, 1, 12, 0, 2), 21.0), + (datetime.datetime(2020, 4, 1, 12, 0, 3), 21.0), + (datetime.datetime(2020, 4, 1, 12, 0, 4), 21.0), + ] + + @pytest.mark.asyncio + async def test_outlier_dropped(self, cleaner): + data = [ + ( + datetime.datetime(2020, 4, 1, 12, 0, 0), + {"magnitude": 21.0, "unit": "degC"}, + ), + ( + datetime.datetime(2020, 4, 1, 12, 0, 1), + {"magnitude": 61.0, "unit": "degC"}, + ), + ( + datetime.datetime(2020, 4, 1, 12, 0, 2), + {"magnitude": 21.0, "unit": "degC"}, + ), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert output == [ + (datetime.datetime(2020, 4, 1, 12, 0, 0), 21.0), + (datetime.datetime(2020, 4, 1, 12, 0, 2), 21.0), + ] + + @pytest.mark.asyncio + async def test_limited_to_DHT22_range(self, cleaner): + data = [ + ( + datetime.datetime(2020, 4, 1, 12, 0, 0), + {"magnitude": 91.0, "unit": "degC"}, + ), + ( + datetime.datetime(2020, 4, 1, 12, 0, 1), + {"magnitude": 91.0, "unit": "degC"}, + ), + ( + datetime.datetime(2020, 4, 1, 12, 0, 2), + {"magnitude": 91.0, "unit": "degC"}, + ), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert output == [] + + @pytest.mark.asyncio + async def test_Nones_dropped(self, cleaner): + data = [ + ( + datetime.datetime(2020, 4, 1, 12, 0, 0), + {"magnitude": 31.0, "unit": "degC"}, + ), + (datetime.datetime(2020, 4, 1, 12, 0, 1), None,), + ( + datetime.datetime(2020, 4, 1, 12, 0, 2), + {"magnitude": 32.0, "unit": "degC"}, + ), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert output == [ + (datetime.datetime(2020, 4, 1, 12, 0, 0), 31.0), + (datetime.datetime(2020, 4, 1, 12, 0, 2), 32.0), + ] + + +class TestWattHourCleaner: + @pytest.fixture + def cleaner(self): + return analysis.clean_watthours_to_watts + + @pytest.fixture(scope="class") + def huge_data_set(self): + date = datetime.datetime(2020, 4, 1, 12, 0, 0) + power = 500 + data = [] + for i in range(50_000): + date += datetime.timedelta(hours=1) + power += i + data.append((date, {"magnitude": power, "unit": "watt_hour"},)) + return data + + @pytest.mark.asyncio + async def test_one_entry_insufficient_to_find_diff(self, cleaner): + data = [ + ( + datetime.datetime(2020, 4, 1, 12, 0, 0), + {"magnitude": 1.0, "unit": "watt_hour"}, + ), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert output == [] + + @pytest.mark.asyncio + async def test_two_entries_provides_diff_with_second_time(self, cleaner): + data = [ + ( + datetime.datetime(2020, 4, 1, 12, 0, 0), + {"magnitude": 1.0, "unit": "watt_hour"}, + ), + ( + datetime.datetime(2020, 4, 1, 13, 0, 0), + {"magnitude": 10.0, "unit": "watt_hour"}, + ), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert len(output) == 1 + assert output[0][0] == datetime.datetime(2020, 4, 1, 13, 0, 0) + assert output[0][1] == pytest.approx(9.0, 0.0001) + + @pytest.mark.asyncio + async def test_nonstandard_units_can_be_used(self, cleaner): + data = [ + ( + datetime.datetime(2020, 4, 1, 12, 0, 0), + {"magnitude": 1.0, "unit": "watt_hour"}, + ), + ( + datetime.datetime(2020, 4, 1, 13, 0, 0), + {"magnitude": 10000.0, "unit": "joule"}, + ), + ( + datetime.datetime(2020, 4, 1, 14, 0, 0), + {"magnitude": 3.0, "unit": "watt_hour"}, + ), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert len(output) == 2 + assert output[0][0] == datetime.datetime(2020, 4, 1, 13, 0, 0) + assert output[0][1] == pytest.approx(1.7777, 0.001) + assert output[1][0] == datetime.datetime(2020, 4, 1, 14, 0, 0) + assert output[1][1] == pytest.approx(0.22222, 0.001) + + @pytest.mark.asyncio + async def test_fractional_hour(self, cleaner): + data = [ + ( + datetime.datetime(2020, 4, 1, 12, 0, 0), + {"magnitude": 1.0, "unit": "watt_hour"}, + ), + ( + datetime.datetime(2020, 4, 1, 12, 23, 42), + {"magnitude": 10.0, "unit": "watt_hour"}, + ), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert len(output) == 1 + assert output[0][0] == datetime.datetime(2020, 4, 1, 12, 23, 42) + assert output[0][1] == pytest.approx(22.7848, 0.001) + + @pytest.mark.asyncio + async def test_diff_happens_pairwise(self, cleaner): + data = [ + ( + datetime.datetime(2020, 4, 1, 12, 0, 0), + {"magnitude": 1.0, "unit": "watt_hour"}, + ), + ( + datetime.datetime(2020, 4, 1, 12, 23, 42), + {"magnitude": 10.0, "unit": "watt_hour"}, + ), + ( + datetime.datetime(2020, 4, 1, 13, 23, 42), + {"magnitude": 13.0, "unit": "watt_hour"}, + ), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert len(output) == 2 + assert output[0][0] == datetime.datetime(2020, 4, 1, 12, 23, 42) + assert output[0][1] == pytest.approx(22.7848, 0.001) + assert output[1][0] == datetime.datetime(2020, 4, 1, 13, 23, 42) + assert output[1][1] == pytest.approx(3, 0.001) + + @pytest.mark.asyncio + async def test_None_ignored(self, cleaner): + data = [ + ( + datetime.datetime(2020, 4, 1, 12, 0, 0), + {"magnitude": 1.0, "unit": "watt_hour"}, + ), + (datetime.datetime(2020, 4, 1, 12, 58, 0), None,), + ( + datetime.datetime(2020, 4, 1, 13, 0, 0), + {"magnitude": 10.0, "unit": "watt_hour"}, + ), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert len(output) == 1 + assert output[0][0] == datetime.datetime(2020, 4, 1, 13, 0, 0) + assert output[0][1] == pytest.approx(9.0, 0.0001) + + @pytest.mark.asyncio + @pytest.mark.performance + async def test_performance_of_cleaner(self, cleaner, huge_data_set): + import cProfile + + # Generate data point objects before profiling starts + datapoints = await consume(generate_datapoints(huge_data_set)) + profiler = cProfile.Profile() + profiler.enable() + + # Run the cleaner to completion + await consume(cleaner(datapoints)) + + profiler.disable() + cleaner_stat = [ + stat for stat in profiler.getstats() if stat.code == cleaner.__code__ + ][0] + # Assert that the cleaner may take no more than 0.5 seconds to process + # these 50k data points + assert cleaner_stat.totaltime < 0.5 + + +class TestTemperatureMap: + @pytest.fixture + def temperature_data(self): + return { + (53.8667, -1.3333): -1, + (53.35, -2.2833): 1, + (52.45, -1.7333): 3, + (51.5, -0.1333): 6, + (51.55, -2.5667): 5, + (54.9667, -1.6167): -1, + (55.9667, -3.2167): -1, + (54.7667, -1.5833): 0, + (53.7667, -0.3): 1, + (53.7667, -3.0167): 0, + (51.4833, -3.1833): 4, + (53.2667, -3.5167): 2, + (52.0833, -2.8): 2, + (52.0167, -0.6): 2, + (52.6667, 1.2667): 5, + (50.4333, -4.9833): 9, + (50.35, -4.1167): 10, + (50.5167, -2.45): 9, + (50.8333, -1.1667): 9, + (56.45, -5.4333): 4, + (57.5333, -4.05): -2, + (54.5167, -3.6167): 3, + (53.0833, 0.2667): 4, + (51.35, 1.3333): 7, + (50.8833, 0.3167): 8, + (58.45, -3.1): 3, + (50.1, -5.6667): 9, + (58.2167, -6.3333): 4, + (55.6833, -6.25): 7, + } + + @pytest.fixture + def temperature_datapoints(self, migrated_db, db_session, temperature_data): + datas = [] + now = datetime.datetime.now() + for (coord, temp) in temperature_data.items(): + deployment_id = uuid.uuid4() + datas.append( + DataPoint( + sensor_name="Location", + deployment_id=deployment_id, + collected_at=now, + data=coord, + ) + ) + datas.append( + DataPoint( + sensor_name="Temperature", + deployment_id=deployment_id, + collected_at=now, + data=temp, + ) + ) + return datas + + @pytest.fixture + def populated_db(self, migrated_db, db_session, temperature_datapoints): + for data in temperature_datapoints: + insert = datapoint_table.insert().values(**data._asdict()) + db_session.execute(insert) + + @pytest.fixture + def cleaner(self): + return analysis.get_map_cleaner_for("Temperature") + + @pytest.fixture + def get_data(self, db_session): + db_session_var.set(db_session) + return analysis.get_all_data() + + @pytest.mark.asyncio + async def test_latest_coord_temp_cleaner( + self, temperature_data, populated_db, get_data, cleaner + ): + data = get_data() + deployments = 0 + async for deployment, data_points in data: + deployments += 1 + cleaned = {a async for a in cleaner(data_points)} + # There should only be one deployment + assert deployments == 1 + expected = set(temperature_data.items()) + assert cleaned == expected + + @pytest.mark.asyncio + async def test_newer_items_superceed_older( + self, db_session, temperature_datapoints, populated_db, get_data, cleaner + ): + # Pick any of the deployment ids and find the pair of DataPoints in that deployment id + deployment_1 = temperature_datapoints[0].deployment_id + existing = [ + datapoint + for datapoint in temperature_datapoints + if datapoint.deployment_id == deployment_1 + ] + old_location = [ + datapoint for datapoint in existing if datapoint.sensor_name == "Location" + ][0] + old_temperature = [ + datapoint + for datapoint in existing + if datapoint.sensor_name == "Temperature" + ][0] + + new_location = DataPoint( + sensor_name="Location", + deployment_id=deployment_1, + collected_at=datetime.datetime(2031, 4, 1, 12, 0, 0), + data=(-10.5, 150.1), + ) + new_temperature = DataPoint( + sensor_name="Temperature", + deployment_id=deployment_1, + collected_at=datetime.datetime(2031, 4, 1, 12, 0, 0), + data=21, + ) + for data in [new_location, new_temperature]: + insert = datapoint_table.insert().values(**data._asdict()) + db_session.execute(insert) + + data = get_data() + deployments = 0 + async for deployment, data_points in data: + deployments += 1 + cleaned = {a async for a in cleaner(data_points)} + # There should only be one deployment + assert deployments == 1 + + # We expect the newer one, not the older + assert (new_location.data, new_temperature.data) in cleaned + assert (old_location.data, old_temperature.data) not in cleaned + # The cleaner converts two data points to one plottable + assert len(cleaned) == len(temperature_datapoints) / 2 + + +def test_deprecation_warning_raised_by_config_with_no_getdata(): + with warnings.catch_warnings(record=True) as captured_warnings: + warnings.simplefilter("always", DeprecationWarning) + analysis.Config( + sensor_name="Temperature", + clean=analysis.clean_passthrough, + title="Temperaure", + ylabel="Deg C", + ) + assert len(captured_warnings) == 1 + deprecation_warning = captured_warnings[0] + assert deprecation_warning.filename == __file__ + assert deprecation_warning.category == DeprecationWarning + assert str(deprecation_warning.message) == ( + "The sensor_name parameter is deprecated. Please pass " + "get_data=get_one_sensor_by_deployment('Temperature') " + "to ensure the same behaviour. The sensor_name= parameter " + "will be removed in apd.aggregation 3.0." + ) diff --git a/Ch11/apd.aggregation-chapter11/tests/test_cli.py b/Ch11/apd.aggregation-chapter11/tests/test_cli.py new file mode 100644 index 0000000..5cab441 --- /dev/null +++ b/Ch11/apd.aggregation-chapter11/tests/test_cli.py @@ -0,0 +1,155 @@ +from unittest import mock +import uuid + +from click.testing import CliRunner +import pytest + +import apd.aggregation.cli +from apd.aggregation.database import Deployment, deployment_table + + +@pytest.mark.functional +def test_sensors_are_passed_to_get_data_points(db_uri): + runner = CliRunner() + with mock.patch("apd.aggregation.collect.get_data_points") as get_data_points: + runner.invoke( + apd.aggregation.cli.collect_sensor_data, + [ + "http://localhost", + "http://otherhost", + "--db", + db_uri, + "--api-key", + "key", + ], + ) + calls = get_data_points.call_args_list + assert len(calls) == 2 + details = {call[0] for call in get_data_points.call_args_list} + assert details == { + ("http://localhost", "key"), + ("http://otherhost", "key"), + } + + +@pytest.mark.functional +def test_add_deployment_to_db(db_uri, db_session, migrated_db): + runner = CliRunner() + deployment_id = uuid.UUID("76587cdd42dc489ea1d220e9b0bc62ca") + with mock.patch("apd.aggregation.collect.get_deployment_id") as get_deployment_id: + get_deployment_id.return_value = deployment_id + runner.invoke( + apd.aggregation.cli.deployments, + ["add", "http://otherhost", "Other", "--db", db_uri, "--api-key", "key"], + ) + deployments = db_session.query(deployment_table).all() + assert len(deployments) == 1 + deployment = Deployment.from_sql_result(deployments[0]) + assert deployment.uri == "http://otherhost" + assert deployment.api_key == "key" + assert deployment.id == deployment_id + + +@pytest.mark.functional +def test_use_stored_deployments(db_uri, migrated_db): + runner = CliRunner() + + deployment_id = uuid.UUID("76587cdd42dc489ea1d220e9b0bc62ca") + with mock.patch("apd.aggregation.collect.get_deployment_id") as get_deployment_id: + get_deployment_id.return_value = deployment_id + runner.invoke( + apd.aggregation.cli.deployments, + [ + "add", + "http://specifiedhost", + "Specified", + "--db", + db_uri, + "--api-key", + "an_api_key", + ], + ) + + with mock.patch("apd.aggregation.collect.get_data_points") as get_data_points: + runner.invoke( + apd.aggregation.cli.collect_sensor_data, ["--db", db_uri], + ) + calls = get_data_points.call_args_list + assert len(calls) == 1 + details = {call[0] for call in get_data_points.call_args_list} + assert details == { + ("http://specifiedhost", "an_api_key"), + } + + +@pytest.mark.functional +def test_list_deployments(db_uri, migrated_db): + runner = CliRunner() + + deployment_id = uuid.UUID("76587cdd42dc489ea1d220e9b0bc62ca") + with mock.patch("apd.aggregation.collect.get_deployment_id") as get_deployment_id: + get_deployment_id.return_value = deployment_id + runner.invoke( + apd.aggregation.cli.deployments, + [ + "add", + "http://specifiedhost", + "Specified", + "--db", + db_uri, + "--api-key", + "an_api_key", + ], + ) + + result = runner.invoke(apd.aggregation.cli.deployments, ["list", "--db", db_uri],) + expected = """Specified +ID 76587cdd42dc489ea1d220e9b0bc62ca +URI http://specifiedhost +API key an_api_key +Colour None + +""" + assert result.output == expected + + +@pytest.mark.functional +def test_edit_deployment(db_uri, migrated_db): + runner = CliRunner() + return + deployment_id = uuid.UUID("76587cdd42dc489ea1d220e9b0bc62ca") + with mock.patch("apd.aggregation.collect.get_deployment_id") as get_deployment_id: + get_deployment_id.return_value = deployment_id + runner.invoke( + apd.aggregation.cli.deployments, + [ + "add", + "http://specifiedhost", + "Specified", + "--db", + db_uri, + "--api-key", + "an_api_key", + ], + ) + + result = runner.invoke( + apd.aggregation.cli.deployments, + [ + "edit", + "--db", + db_uri, + deployment_id.hex(), + "--colour", + "red" "--name", + "New name", + ], + ) + expected = """New name +ID 76587cdd42dc489ea1d220e9b0bc62ca +URI http://specifiedhost +API key an_api_key +Colour red + +""" + assert result.output == expected diff --git a/Ch11/apd.aggregation-chapter11/tests/test_http_get.py b/Ch11/apd.aggregation-chapter11/tests/test_http_get.py new file mode 100644 index 0000000..df575c6 --- /dev/null +++ b/Ch11/apd.aggregation-chapter11/tests/test_http_get.py @@ -0,0 +1,188 @@ +from concurrent.futures import ThreadPoolExecutor +import datetime +import typing as t +from unittest.mock import patch, MagicMock +import wsgiref.simple_server + +import aiohttp +from apd.sensors.base import Sensor +from apd.sensors.sensors import PythonVersion, ACStatus +from apd.sensors.wsgi import set_up_config +import flask +import pytest + +from apd.sensors.wsgi import v21 + +from apd.aggregation import collect +from apd.aggregation.database import Deployment + +pytestmark = [pytest.mark.functional] + + +@pytest.fixture +def sensors() -> t.Iterator[t.List[Sensor[t.Any]]]: + """ Patch the get_sensors method to return a known pair of sensors only """ + data: t.List[Sensor[t.Any]] = [PythonVersion(), ACStatus()] + with patch("apd.sensors.cli.get_sensors") as get_sensors: + get_sensors.return_value = data + yield data + + +def get_independent_flask_app(name: str) -> flask.Flask: + """ Create a new flask app with the v20 API blueprint loaded, so multiple copies + of the app can be run in parallel without conflicting configuration """ + app = flask.Flask(name) + app.register_blueprint(v21.version, url_prefix="/v/2.1") + return app + + +def run_server_in_thread( + name: str, config: t.Dict[str, t.Any], port: int +) -> t.Iterator[str]: + # Create a new flask app and load in required code, to prevent config conflicts + app = get_independent_flask_app(name) + flask_app = set_up_config(config, app) + server = wsgiref.simple_server.make_server("localhost", port, flask_app) + + with ThreadPoolExecutor() as pool: + pool.submit(server.serve_forever) + yield f"http://localhost:{port}/" + server.shutdown() + + +@pytest.fixture(scope="module") +def http_server(): + yield from run_server_in_thread( + "standard", + { + "APD_SENSORS_API_KEY": "testing", + "APD_SENSORS_DEPLOYMENT_ID": "a46b1d1207fd4cdcad39bbdf706dfe29", + }, + 12081, + ) + + +@pytest.fixture(scope="module") +def bad_api_key_http_server(): + yield from run_server_in_thread( + "alternate", + { + "APD_SENSORS_API_KEY": "penny", + "APD_SENSORS_DEPLOYMENT_ID": "38cf2bae9adb445fad946c82e290487a", + }, + 12082, + ) + + +class TestGetDataPoints: + @pytest.fixture + def mut(self): + return collect.get_data_points + + @pytest.mark.asyncio + async def test_get_data_points( + self, sensors: t.List[Sensor[t.Any]], mut, http_server: str + ) -> None: + # Get the data from the server, storing the time before and after + # as bounds for the collected_at value + async with aiohttp.ClientSession() as http: + collect.http_session_var.set(http) + time_before = datetime.datetime.now() + results = await mut(http_server, "testing") + time_after = datetime.datetime.now() + + assert len(results) == len(sensors) == 2 + + for (sensor, result) in zip(sensors, results): + assert sensor.from_json_compatible(result.data) == sensor.value() + assert result.sensor_name == sensor.name + assert time_before <= result.collected_at <= time_after + + @pytest.mark.asyncio + async def test_get_data_points_fails_with_bad_api_key( + self, sensors: t.List[Sensor[t.Any]], mut, http_server: str + ) -> None: + with pytest.raises( + ValueError, + match=f"Error loading data from {http_server}: Supply API key in X-API-Key header", + ): + async with aiohttp.ClientSession() as http: + collect.http_session_var.set(http) + await mut(http_server, "incorrect") + + +class TestAddDataFromSensors: + @pytest.fixture + def mut(self): + return collect.add_data_from_sensors + + @pytest.fixture + def mock_db_session(self): + return MagicMock() + + @pytest.mark.asyncio + async def test_get_get_data_from_sensors( + self, mock_db_session, sensors: t.List[Sensor[t.Any]], mut, http_server: str + ) -> None: + results = await mut( + mock_db_session, + [ + Deployment( + id=None, colour=None, name=None, uri=http_server, api_key="testing" + ) + ], + ) + assert mock_db_session.execute.call_count == len(sensors) + assert len(results) == len(sensors) + + @pytest.mark.asyncio + async def test_get_get_data_from_sensors_with_multiple_servers( + self, mock_db_session, sensors: t.List[Sensor[t.Any]], mut, http_server: str + ) -> None: + results = await mut( + mock_db_session, + [ + Deployment( + id=None, colour=None, name=None, uri=http_server, api_key="testing" + ), + Deployment( + id=None, colour=None, name=None, uri=http_server, api_key="testing" + ), + ], + ) + assert mock_db_session.execute.call_count == len(sensors) * 2 + assert len(results) == len(sensors) * 2 + + @pytest.mark.asyncio + async def test_data_points_not_added_if_only_partial_success( + self, + mock_db_session, + sensors: t.List[Sensor[t.Any]], + mut, + http_server: str, + bad_api_key_http_server: str, + ) -> None: + with pytest.raises( + ValueError, + match=f"Error loading data from {bad_api_key_http_server}: Supply API key in X-API-Key header", + ): + await mut( + mock_db_session, + [ + Deployment( + id=None, + colour=None, + name=None, + uri=http_server, + api_key="testing", + ), + Deployment( + id=None, + colour=None, + name=None, + uri=bad_api_key_http_server, + api_key="testing", + ), + ], + ) + assert mock_db_session.execute.call_count == 0 diff --git a/Ch11/apd.aggregation-chapter11/tests/test_query.py b/Ch11/apd.aggregation-chapter11/tests/test_query.py new file mode 100644 index 0000000..9b6e0aa --- /dev/null +++ b/Ch11/apd.aggregation-chapter11/tests/test_query.py @@ -0,0 +1,111 @@ +import datetime +from uuid import UUID + +import pytest + +from apd.aggregation.query import ( + get_data, + get_deployment_ids, + get_data_by_deployment, + db_session_var, +) + + +class TestGetData: + @pytest.fixture + def mut(self): + return get_data + + @pytest.mark.asyncio + @pytest.mark.parametrize( + "filter,num_items_expected", + [ + ({}, 9), + ({"sensor_name": "Test"}, 7), + ({"deployment_id": UUID("b4c68905-b1e4-4875-940e-69e5d27730fd")}, 5), + ({"collected_after": datetime.datetime(2020, 4, 1, 12, 2, 1)}, 3), + ({"collected_before": datetime.datetime(2020, 4, 1, 12, 2, 1)}, 4), + ( + { + "collected_after": datetime.datetime(2020, 4, 1, 12, 2, 1), + "collected_before": datetime.datetime(2020, 4, 1, 12, 3, 5), + }, + 2, + ), + ], + ) + async def test_iterate_over_items( + self, mut, db_session, populated_db, filter, num_items_expected + ): + db_session_var.set(db_session) + points = [dp async for dp in mut(**filter)] + assert len(points) == num_items_expected + + @pytest.mark.asyncio + async def test_empty_db(self, mut, db_session, migrated_db): + db_session_var.set(db_session) + points = [dp async for dp in mut()] + assert len(points) == 0 + + +class TestGetDeployments: + @pytest.fixture + def mut(self): + return get_deployment_ids + + @pytest.mark.asyncio + async def test_get_all_deployments(self, mut, db_session, populated_db): + db_session_var.set(db_session) + deployment_ids = await mut() + assert set(deployment_ids) == { + UUID("b4c68905-b1e4-4875-940e-69e5d27730fd"), + UUID("6e887b81-57c3-4f3b-a7e7-bad159e05e78"), + } + + @pytest.mark.asyncio + async def test_empty_db(self, mut, db_session, migrated_db): + db_session_var.set(db_session) + deployment_ids = await mut() + assert deployment_ids == [] + + +@pytest.mark.usefixtures("populated_db") +class TestGetDataByDeployment: + @pytest.fixture + def mut(self): + return get_data_by_deployment + + @pytest.mark.asyncio + @pytest.mark.parametrize( + "filter,num_items_expected", [({}, 9), ({"sensor_name": "Test"}, 7)] + ) + async def test_iterate_over_all_items( + self, mut, db_session, filter, num_items_expected + ): + db_session_var.set(db_session) + num_items = 0 + num_deployments = 0 + async for deployment_id, items in mut(**filter): + num_deployments += 1 + async for item in items: + num_items += 1 + assert num_deployments == 2 + assert num_items == num_items_expected + + @pytest.mark.asyncio + @pytest.mark.parametrize( + "filter,num_items_expected", [({}, 5), ({"sensor_name": "Test"}, 4)] + ) + async def test_skip_first_deployment( + self, mut, db_session, filter, num_items_expected + ): + db_session_var.set(db_session) + num_items = 0 + num_deployments = 0 + async for deployment_id, items in mut(**filter): + num_deployments += 1 + if num_deployments == 2: + async for item in items: + num_items += 1 + assert num_deployments == 2 + assert num_items == num_items_expected diff --git a/Ch11/apd.aggregation-chapter11/tests/test_sensor_aggregation.py b/Ch11/apd.aggregation-chapter11/tests/test_sensor_aggregation.py new file mode 100644 index 0000000..311dd19 --- /dev/null +++ b/Ch11/apd.aggregation-chapter11/tests/test_sensor_aggregation.py @@ -0,0 +1,199 @@ +from __future__ import annotations + +import contextlib +from dataclasses import dataclass +import json +import typing as t +from unittest.mock import patch, Mock + +import pytest +import sqlalchemy + +import apd.aggregation.collect +from apd.aggregation.database import Deployment + + +@pytest.fixture +def data() -> t.Any: + return { + "sensors": [ + { + "human_readable": "3.7", + "id": "PythonVersion", + "title": "Python Version", + "value": [3, 7, 2, "final", 0], + }, + { + "human_readable": "Not connected", + "id": "ACStatus", + "title": "AC Connected", + "value": False, + }, + ] + } + + +@dataclass +class FakeAIOHttpClient: + responses: t.Dict[str, str] + + @contextlib.asynccontextmanager + async def get( + self, url: str, headers: t.Optional[t.Dict[str, str]] = None + ) -> t.AsyncIterator[FakeAIOHttpResponse]: + if url in self.responses: + yield FakeAIOHttpResponse(body=self.responses[url]) + else: + yield FakeAIOHttpResponse(body="", status=404) + + +@dataclass +class FakeAIOHttpResponse: + body: str + status: int = 200 + + async def json(self) -> t.Any: + return json.loads(self.body) + + +@pytest.fixture +def mockclient(data) -> FakeAIOHttpClient: + return FakeAIOHttpClient( + { + "http://localhost/v/2.1/sensors/": json.dumps(data), + "http://localhost/v/2.1/deployment_id": json.dumps( + {"deployment_id": "b29ba0ee10f14552b6b21327bb96d3fb"} + ), + } + ) + + +class TestGetDataPoints: + @pytest.fixture + def mut(self): + return apd.aggregation.collect.get_data_points + + @pytest.mark.asyncio + async def test_get_data_points( + self, mut, mockclient: FakeAIOHttpClient, data + ) -> None: + token = apd.aggregation.collect.http_session_var.set(mockclient) + try: + datapoints = await mut("http://localhost", "") + finally: + apd.aggregation.collect.http_session_var.reset(token) + + assert len(datapoints) == len(data["sensors"]) + for sensor in data["sensors"]: + assert sensor["value"] in (datapoint.data for datapoint in datapoints) + assert sensor["id"] in (datapoint.sensor_name for datapoint in datapoints) + + +class TestAddDataFromSensors: + @pytest.fixture + def mut(self): + return apd.aggregation.collect.add_data_from_sensors + + @pytest.fixture(autouse=True) + def patch_aiohttp(self, mockclient): + with patch("aiohttp.ClientSession") as ClientSession: + ClientSession.return_value.__aenter__.return_value = mockclient + yield ClientSession + + @pytest.fixture + def db_session(self): + session = Mock() + sql_result = session.execute.return_value + sql_result.inserted_primary_key = [1] + return session + + @pytest.mark.asyncio + async def test_datapoints_are_added_to_the_session(self, mut, db_session) -> None: + assert db_session.execute.call_count == 0 + datapoints = await mut( + db_session, + [ + Deployment( + id=None, colour=None, name=None, uri="http://localhost", api_key="" + ) + ], + ) + assert db_session.execute.call_count == len(datapoints) + + +@pytest.mark.usefixtures("migrated_db") +class TestDatabaseConnection: + @pytest.fixture(autouse=True) + def patch_aiohttp(self, mockclient): + with patch("aiohttp.ClientSession") as ClientSession: + ClientSession.return_value.__aenter__.return_value = mockclient + yield ClientSession + + @pytest.fixture + def table(self): + return apd.aggregation.database.datapoint_table + + @pytest.fixture + def daily_summary_view(self): + return apd.aggregation.database.daily_summary_view + + @pytest.fixture + def model(self): + return apd.aggregation.database.DataPoint + + @pytest.fixture + def mut(self): + return apd.aggregation.collect.add_data_from_sensors + + @pytest.mark.asyncio + async def test_datapoints_are_added_to_the_session(self, db_session, table) -> None: + datapoints = await apd.aggregation.collect.add_data_from_sensors( + db_session, + [ + Deployment( + id=None, colour=None, name=None, uri="http://localhost", api_key="" + ) + ], + ) + num_points = db_session.query(table).count() + assert num_points == len(datapoints) == 2 + + @pytest.mark.asyncio + async def test_datapoints_can_be_mapped_back_to_DataPoints( + self, mut, db_session, table, model + ) -> None: + datapoints = await mut( + db_session, + [ + Deployment( + id=None, colour=None, name=None, uri="http://localhost", api_key="" + ) + ], + ) + db_points = [ + model.from_sql_result(result) for result in db_session.query(table) + ] + assert db_points == datapoints + + @pytest.mark.asyncio + async def test_daily_summary_view_matches_query( + self, db_session, model, table, daily_summary_view + ) -> None: + await apd.aggregation.collect.add_data_from_sensors( + db_session, + [ + Deployment( + id=None, colour=None, name=None, uri="http://localhost", api_key="" + ) + ], + ) + + headers = table.c.sensor_name, table.c.data + value_counts = ( + db_session.query(*headers, sqlalchemy.func.count(table.c.id)) + .filter(model.collected_on_date == sqlalchemy.func.current_date()) + .group_by(*headers) + ) + + daily_summary_view = db_session.query(daily_summary_view) + assert value_counts.all() == daily_summary_view.all() diff --git a/Ch11/apd.sensors-chapter11-ex01/.pre-commit-config.yaml b/Ch11/apd.sensors-chapter11-ex01/.pre-commit-config.yaml new file mode 100644 index 0000000..d3b6ec7 --- /dev/null +++ b/Ch11/apd.sensors-chapter11-ex01/.pre-commit-config.yaml @@ -0,0 +1,26 @@ +repos: + +- repo: local + hooks: + - id: black + name: black + entry: pipenv run black + args: [--quiet] + language: system + types: [python] + + - id: mypy + name: mypy + exclude: (?x)^( + (.*)/setup.py + )$ + entry: pipenv run mypy + args: ["--follow-imports=skip"] + language: system + types: [python] + + - id: flake8 + name: flake8 + entry: pipenv run flake8 + language: system + types: [python] diff --git a/Ch11/apd.sensors-chapter11-ex01/CHANGES.md b/Ch11/apd.sensors-chapter11-ex01/CHANGES.md new file mode 100644 index 0000000..b2f6115 --- /dev/null +++ b/Ch11/apd.sensors-chapter11-ex01/CHANGES.md @@ -0,0 +1,44 @@ +## Changes + +### 2.2 (2020-01-17) + +* Add ability to store data points on query (Matthew Wilkes) +* Add v3.0 API to distinguish sensor errors and include historical data (Matthew Wilkes) + +### 2.1.1 (2020-01-06) + +* Cache DHT sensor connections (Matthew Wilkes) +* Force use of bitbang interface for DHT sensors, to improve reliability on Linux (Matthew Wilkes) + +### 2.1.0 (2019-12-09) + +* Add optional APD_SENSORS_DEPLOYMENT_ID parameter and v2.1 API which allows + users to find a unique identifier for a webapp sensor deployment (Matthew Wilkes) + +### 2.0.0 (2019-09-08) + +* Add `to_json_compatible` and `from_json_compatible` methods to Sensor + to facilitate better HTTP API (Matthew Wilkes) + +* HTTP API is now versioned. The API from 1.3.0 is available at /v/1.0 + and an updated version is at /v/2.0 (Matthew Wilkes) + +### 1.3.0 (2019-08-20) + +* WSGI HTTP API support added (Matthew Wilkes) + +### 1.2.0 (2019-08-05) + +* Add external plugin support through `apd.sensors.sensors` entrypoint (Matthew Wilkes) + +### 1.1.0 (2019-07-12) + +* Add --develop argument (Matthew Wilkes) + +### 1.0.1 (2019-06-20) + +* Fix broken 1.0.0 release (Matthew Wilkes) + +### 1.0.0 (2019-06-20) + +* Added initial sensors (Matthew Wilkes) diff --git a/Ch11/apd.sensors-chapter11-ex01/LICENCE b/Ch11/apd.sensors-chapter11-ex01/LICENCE new file mode 100644 index 0000000..053f694 --- /dev/null +++ b/Ch11/apd.sensors-chapter11-ex01/LICENCE @@ -0,0 +1,19 @@ +Copyright (c) 2019 Matthew Wilkes + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/Ch11/apd.sensors-chapter11-ex01/Pipfile b/Ch11/apd.sensors-chapter11-ex01/Pipfile new file mode 100644 index 0000000..44bfe74 --- /dev/null +++ b/Ch11/apd.sensors-chapter11-ex01/Pipfile @@ -0,0 +1,34 @@ +[[source]] +name = "pypi" +url = "https://pypi.org/simple" +verify_ssl = true + +[[source]] +name = "piwheels" +url = "https://piwheels.org/simple" +verify_ssl = true + +[dev-packages] +ipykernel = "*" +remote-ikernel = "*" +pytest = "*" +pytest-cov = "*" +mypy = "*" +flake8 = "*" +black = "*" +pre-commit = "*" +wheel = "*" +twine = "*" +webtest = "*" +sqlalchemy-stubs = "*" + +[packages] +apd-sensors = {editable = true,extras = ["scheduled", "webapp", "storedapi"],path = "."} +apd-sunnyboy-solar = {editable = true,path = "./plugins/apd.sunnyboy_solar"} +waitress = "*" +pint = "==0.10.1" + +[requires] + +[pipenv] +allow_prereleases = true diff --git a/Ch11/apd.sensors-chapter11-ex01/Pipfile.lock b/Ch11/apd.sensors-chapter11-ex01/Pipfile.lock new file mode 100644 index 0000000..ce04ef4 --- /dev/null +++ b/Ch11/apd.sensors-chapter11-ex01/Pipfile.lock @@ -0,0 +1,1092 @@ +{ + "_meta": { + "hash": { + "sha256": "8099f56a327cfc41360e2cbc7ff56ad2bab9f24325f631df83cc94d9f9196469" + }, + "pipfile-spec": 6, + "requires": {}, + "sources": [ + { + "name": "pypi", + "url": "https://pypi.org/simple", + "verify_ssl": true + }, + { + "name": "piwheels", + "url": "https://piwheels.org/simple", + "verify_ssl": true + } + ] + }, + "default": { + "alembic": { + "hashes": [ + "sha256:035ab00497217628bf5d0be82d664d8713ab13d37b630084da8e1f98facf4dbf", + "sha256:ba2d8937aef1ee14ffc983f9ab00a6f8e48907e46c7788218b561c100175025f" + ], + "version": "==1.4.2" + }, + "apd-sensors": { + "editable": true, + "extras": [ + 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system. The available sensors are: + +* Python version +* IP Addresses +* CPU Usage +* RAM Available +* Battery charging state +* Ambient Temperature +* Ambient Humidity + +There are no command-line options, to view the report run `sensors` on the +command line. + +## Caveats + +The Ambient Temperature and Ambient Humidity sensors are only available on +RaspberryPi hosts and assume that a DHT22 sensor is connected to pin `D20`. + +The location and type of the sensor can be controlled by setting a pair of +environment variables: `APD_SENSORS_TEMPERATURE_BOARD` and +`APD_SENSORS_TEMPERATURE_PIN`. + +If there is an entry in `/etc/hosts` for the current machine's hostname that +value will be the only result from the IP Addresses sensor. + +## Installation + +You can install with `pip3 install apd.sensors` under Python 3.7 or higher. + +We recommend using pipenv to manage your environment, in which case you would +install using `pipenv --three install apd.sensors` and run the programme using +`pipenv run sensors`. + +## API server + +There is an optional API server shipped with apd.sensors. To use this you +should install the `apd.sensors[webapp]` extra. The API can then be started +with the non-production quality wsgiref server using: + + python -m apd.sensors.wsgi.serve + +or through Waitress (if installed) using: + + waitress-serve --call apd.sensors.wsgi:set_up_config + +Other WSGI servers will also work, you should use set_up_config as a factory +function. + +An environment variable is required to use the API server, `APD_SENSORS_API_KEY` +should be set to the API key required to gain access. One can be generated +using: + + python -c "import uuid; print(uuid.uuid4().hex)" + +The following endpoints are supported: + +* /v/3.0/sensors +* /v/3.0/sensors/sensorid +* /v/3.0/deployment_id + +## Historical data + +You can install optional functionality to periodically store sensor +values using the `apd.sensors[scheduled]` extra. In this case, +`sensors --save` will store the recorded data to `sensor_data.sqlite` +in the current working directory. + +The database connection can be specified with `--db sqlite:////var/sensors.sqlite`, +for example. It can also be specified with the `APD_SENSORS_DB_URI` +environment variable. + +The database must be migrated to contain the correct data first. This can be +done by running `alembic upgrade head` with the following alembic.ini file +in the current working directory. + + [alembic] + script_location = apd.sensors:alembic + sqlalchemy.url = sqlite:///sensor_data.sqlite + +### Historical data API + +An API to extract historical data is also available if installed with `apd.sensors[webapp,scheduled,storedapi]`. + +This provides the following three URIs, where start and end are a date/time in ISO format. + +* /v/3.0/historical +* /v/3.0/historical/start +* /v/3.0/historical/start/end diff --git a/Ch11/apd.sensors-chapter11-ex01/plugins/apd.sunnyboy_solar/pyproject.toml b/Ch11/apd.sensors-chapter11-ex01/plugins/apd.sunnyboy_solar/pyproject.toml new file mode 100644 index 0000000..4d3bb67 --- /dev/null +++ b/Ch11/apd.sensors-chapter11-ex01/plugins/apd.sunnyboy_solar/pyproject.toml @@ -0,0 +1,5 @@ +[build-system] +requires = [ + "setuptools", +] +build-backend = "setuptools.build_meta" diff --git a/Ch11/apd.sensors-chapter11-ex01/plugins/apd.sunnyboy_solar/setup.cfg b/Ch11/apd.sensors-chapter11-ex01/plugins/apd.sunnyboy_solar/setup.cfg new file mode 100644 index 0000000..0f5db10 --- /dev/null +++ b/Ch11/apd.sensors-chapter11-ex01/plugins/apd.sunnyboy_solar/setup.cfg @@ -0,0 +1,33 @@ +[mypy] +ignore_missing_imports = True + +[flake8] +max-line-length = 88 + +[metadata] +name = apd.sunnyboy_solar +version = attr: apd.sunnyboy_solar.VERSION +description = APD Sensor for reading data from Sunnyboy inverters +long_description = file: README.md, CHANGES.md, LICENCE +long_description_content_type = text/markdown +keywords = iot solar +license = MIT +classifiers = + Programming Language :: Python :: 3 + Programming Language :: Python :: 3.7 + +[options] +zip_safe = False +include_package_data = True +package_dir = + =src +packages = find_namespace: +install_requires = + apd.sensors + +[options.packages.find] +where = src + +[options.entry_points] +apd.sensors.sensors = + SolarCumulativeOutput = apd.sunnyboy_solar.sensor:SolarCumulativeOutput diff --git a/Ch11/apd.sensors-chapter11-ex01/plugins/apd.sunnyboy_solar/setup.py b/Ch11/apd.sensors-chapter11-ex01/plugins/apd.sunnyboy_solar/setup.py new file mode 100644 index 0000000..6068493 --- /dev/null +++ b/Ch11/apd.sensors-chapter11-ex01/plugins/apd.sunnyboy_solar/setup.py @@ -0,0 +1,3 @@ +from setuptools import setup + +setup() diff --git a/Ch11/apd.sensors-chapter11-ex01/plugins/apd.sunnyboy_solar/src/apd/sunnyboy_solar/Pipfile b/Ch11/apd.sensors-chapter11-ex01/plugins/apd.sunnyboy_solar/src/apd/sunnyboy_solar/Pipfile new file mode 100644 index 0000000..e69de29 diff --git a/Ch11/apd.sensors-chapter11-ex01/plugins/apd.sunnyboy_solar/src/apd/sunnyboy_solar/__init__.py b/Ch11/apd.sensors-chapter11-ex01/plugins/apd.sunnyboy_solar/src/apd/sunnyboy_solar/__init__.py new file mode 100644 index 0000000..3277f64 --- /dev/null +++ b/Ch11/apd.sensors-chapter11-ex01/plugins/apd.sunnyboy_solar/src/apd/sunnyboy_solar/__init__.py @@ -0,0 +1 @@ +VERSION = "1.0.0" diff --git a/Ch11/apd.sensors-chapter11-ex01/plugins/apd.sunnyboy_solar/src/apd/sunnyboy_solar/sensor.py b/Ch11/apd.sensors-chapter11-ex01/plugins/apd.sunnyboy_solar/src/apd/sunnyboy_solar/sensor.py new file mode 100644 index 0000000..190159a --- /dev/null +++ b/Ch11/apd.sensors-chapter11-ex01/plugins/apd.sunnyboy_solar/src/apd/sunnyboy_solar/sensor.py @@ -0,0 +1,73 @@ +import os +import subprocess +import typing as t + +from pint import _DEFAULT_REGISTRY as ureg + +from apd.sensors.base import Sensor +from apd.sensors.exceptions import ( + PersistentSensorFailureError, + IntermittentSensorFailureError, +) + + +class SolarCumulativeOutput(Sensor[t.Any]): + name = "SolarCumulativeOutput" + title = "Solar panel cumulative output" + + def __init__( + self, path: t.Optional[str] = None, bt_addr: t.Optional[str] = None + ) -> None: + self.path = os.environ.get( + "APD_SUNNYBOYSOLAR_PATH", "/home/pi/opensunny-master/opensunny" + ) + self.bt_addr = os.environ.get("APD_SUNNYBOYSOLAR_BT_ADDRESS", None) + + def value(self) -> t.Optional[t.Any]: + if self.bt_addr is None: + raise PersistentSensorFailureError("Inverter address not configured") + try: + output: bytes = subprocess.check_output( + [self.path, "-i", self.bt_addr], stderr=subprocess.STDOUT, timeout=15 + ) + except subprocess.CalledProcessError as err: + raise IntermittentSensorFailureError( + "Failure communicating with inverter" + ) from err + except FileNotFoundError as err: + raise PersistentSensorFailureError( + "Inverter control software not installed" + ) from err + + lines = filter(None, output.split(b"\n")) + found = {} + for line in lines: + start, value = line.rsplit(b"=", 1) + start, key = start.rsplit(b" ", 1) + found[key] = value + + try: + yield_total = float(found[b"yield_total"][:-3].replace(b".", b"")) + power_dc_1 = int(found[b"power_dc_1"][:-1]) + power_dc_2 = int(found[b"power_dc_2"][:-1]) + if power_dc_1 > 1500 or power_dc_2 > 1500: + raise IntermittentSensorFailureError( + "Received corrupt data from inverter" + ) + except (ValueError, IndexError, KeyError) as err: + raise IntermittentSensorFailureError( + "Received incomplete data from inverter" + ) from err + return ureg.Quantity(yield_total, "watt") + + @classmethod + def format(cls, value: t.Any) -> str: + return "{:~P}".format(value.to(ureg.kilowatt)) + + @classmethod + def to_json_compatible(cls, value: t.Any) -> t.Dict[str, t.Union[str, float]]: + return {"magnitude": value.magnitude, "unit": str(value.units)} + + @classmethod + def from_json_compatible(cls, json_version: t.Any) -> t.Any: + return ureg.Quantity(json_version["magnitude"], ureg[json_version["unit"]]) diff --git a/Ch11/apd.sensors-chapter11-ex01/pytest.ini b/Ch11/apd.sensors-chapter11-ex01/pytest.ini new file mode 100644 index 0000000..0b96d0d --- /dev/null +++ b/Ch11/apd.sensors-chapter11-ex01/pytest.ini @@ -0,0 +1,5 @@ +[pytest] +markers = + functional: these tests are significantly slower as they run the whole CLI script +addopts = + --ignore plugins \ No newline at end of file diff --git a/Ch11/apd.sensors-chapter11-ex01/setup.cfg b/Ch11/apd.sensors-chapter11-ex01/setup.cfg new file mode 100644 index 0000000..9fa1056 --- /dev/null +++ b/Ch11/apd.sensors-chapter11-ex01/setup.cfg @@ -0,0 +1,83 @@ +[mypy] +namespace_packages = True +mypy_path = src +plugins = sqlmypy + +[mypy-psutil] +ignore_missing_imports = True + +[mypy-alembic] +ignore_missing_imports = True + +[mypy-adafruit_dht] +ignore_missing_imports = True + +[mypy-board] +ignore_missing_imports = True + +[mypy-flask_sqlalchemy] +ignore_missing_imports = True + +[mypy-pint] +ignore_missing_imports = True + +[mypy-pytest] +ignore_missing_imports = True + +[mypy-webtest] +ignore_missing_imports = True + +[flake8] +max-line-length = 88 + +[metadata] +name = apd.sensors +version = attr: apd.sensors.VERSION +description = APD Sensor package +long_description = file: README.md, CHANGES.md, LICENCE +long_description_content_type = text/markdown +keywords = iot +license = MIT +classifiers = + Programming Language :: Python :: 3 + Programming Language :: Python :: 3.7 + +[options] +zip_safe = False +include_package_data = True +package_dir = + =src +packages = find_namespace: +install_requires = + psutil + click + pint + adafruit-circuitpython-dht ; 'arm' in platform_machine + +[options.package_data] +apd.sensors = py.typed + +[options.packages.find] +where = src + +[options.entry_points] +console_scripts = + sensors = apd.sensors.cli:show_sensors +apd.sensors.sensors = + PythonVersion = apd.sensors.sensors:PythonVersion + IPAddresses = apd.sensors.sensors:IPAddresses + CPULoad = apd.sensors.sensors:CPULoad + RAMAvailable = apd.sensors.sensors:RAMAvailable + ACStatus = apd.sensors.sensors:ACStatus + Temperature = apd.sensors.sensors:Temperature + RelativeHumidity = apd.sensors.sensors:RelativeHumidity + +[options.extras_require] +webapp = flask +scheduled = + sqlalchemy + alembic +storedapi = + flask-sqlalchemy + python-dateutil + \ No newline at end of file diff --git a/Ch11/apd.sensors-chapter11-ex01/setup.py b/Ch11/apd.sensors-chapter11-ex01/setup.py new file mode 100644 index 0000000..6068493 --- /dev/null +++ b/Ch11/apd.sensors-chapter11-ex01/setup.py @@ -0,0 +1,3 @@ +from setuptools import setup + +setup() diff --git a/Ch11/apd.sensors-chapter11-ex01/src/apd/sensors/__init__.py b/Ch11/apd.sensors-chapter11-ex01/src/apd/sensors/__init__.py new file mode 100644 index 0000000..127c148 --- /dev/null +++ b/Ch11/apd.sensors-chapter11-ex01/src/apd/sensors/__init__.py @@ -0,0 +1 @@ +VERSION = "2.1.0" diff --git a/Ch11/apd.sensors-chapter11-ex01/src/apd/sensors/alembic/README b/Ch11/apd.sensors-chapter11-ex01/src/apd/sensors/alembic/README new file mode 100644 index 0000000..98e4f9c --- /dev/null +++ b/Ch11/apd.sensors-chapter11-ex01/src/apd/sensors/alembic/README @@ -0,0 +1 @@ +Generic single-database configuration. \ No newline at end of file diff --git a/Ch11/apd.sensors-chapter11-ex01/src/apd/sensors/alembic/env.py b/Ch11/apd.sensors-chapter11-ex01/src/apd/sensors/alembic/env.py new file mode 100644 index 0000000..f508d4c --- /dev/null +++ b/Ch11/apd.sensors-chapter11-ex01/src/apd/sensors/alembic/env.py @@ -0,0 +1,77 @@ +from logging.config import fileConfig + +from sqlalchemy import engine_from_config +from sqlalchemy import pool + +from alembic import context + +import apd.sensors.database + +# this is the Alembic Config object, which provides +# access to the values within the .ini file in use. +config = context.config + +# Interpret the config file for Python logging. +# This line sets up loggers basically. +fileConfig(config.config_file_name) + +# add your model's MetaData object here +# for 'autogenerate' support +# from myapp import mymodel +# target_metadata = mymodel.Base.metadata +target_metadata = apd.sensors.database.metadata + +# other values from the config, defined by the needs of env.py, +# can be acquired: +# my_important_option = config.get_main_option("my_important_option") +# ... etc. + + +def run_migrations_offline(): + """Run migrations in 'offline' mode. + + This configures the context with just a URL + and not an Engine, though an Engine is acceptable + here as well. By skipping the Engine creation + we don't even need a DBAPI to be available. + + Calls to context.execute() here emit the given string to the + script output. + + """ + url = config.get_main_option("sqlalchemy.url") + context.configure( + url=url, + target_metadata=target_metadata, + literal_binds=True, + dialect_opts={"paramstyle": "named"}, + ) + + with context.begin_transaction(): + context.run_migrations() + + +def run_migrations_online(): + """Run migrations in 'online' mode. + + In this scenario we need to create an Engine + and associate a connection with the context. + + """ + connectable = engine_from_config( + config.get_section(config.config_ini_section), + prefix="sqlalchemy.", + poolclass=pool.NullPool, + ) + + with connectable.connect() as connection: + context.configure(connection=connection, target_metadata=target_metadata) + + with context.begin_transaction(): + context.run_migrations() + + +if context.is_offline_mode(): + run_migrations_offline() +else: + run_migrations_online() diff --git a/Ch11/apd.sensors-chapter11-ex01/src/apd/sensors/alembic/script.py.mako b/Ch11/apd.sensors-chapter11-ex01/src/apd/sensors/alembic/script.py.mako new file mode 100644 index 0000000..2c01563 --- /dev/null +++ b/Ch11/apd.sensors-chapter11-ex01/src/apd/sensors/alembic/script.py.mako @@ -0,0 +1,24 @@ +"""${message} + +Revision ID: ${up_revision} +Revises: ${down_revision | comma,n} +Create Date: ${create_date} + +""" +from alembic import op +import sqlalchemy as sa +${imports if imports else ""} + +# revision identifiers, used by Alembic. +revision = ${repr(up_revision)} +down_revision = ${repr(down_revision)} +branch_labels = ${repr(branch_labels)} +depends_on = ${repr(depends_on)} + + +def upgrade(): + ${upgrades if upgrades else "pass"} + + +def downgrade(): + ${downgrades if downgrades else "pass"} diff --git a/Ch11/apd.sensors-chapter11-ex01/src/apd/sensors/alembic/versions/0eeb2a54fea8_add_initial_sensor_table.py b/Ch11/apd.sensors-chapter11-ex01/src/apd/sensors/alembic/versions/0eeb2a54fea8_add_initial_sensor_table.py new file mode 100644 index 0000000..bb38e90 --- /dev/null +++ b/Ch11/apd.sensors-chapter11-ex01/src/apd/sensors/alembic/versions/0eeb2a54fea8_add_initial_sensor_table.py @@ -0,0 +1,45 @@ +"""Add initial sensor table + +Revision ID: 0eeb2a54fea8 +Revises: base +Create Date: 2020-01-16 17:43:37.298379 + +""" +from alembic import op +import sqlalchemy as sa + + +# revision identifiers, used by Alembic. +revision = "0eeb2a54fea8" +down_revision = None +branch_labels = None +depends_on = None + + +def upgrade(): + op.create_table( + "recorded_values", + sa.Column("id", sa.Integer(), nullable=False), + sa.Column("sensor_name", sa.String(), nullable=True), + sa.Column("collected_at", sa.TIMESTAMP(), nullable=True), + sa.Column("data", sa.JSON(), nullable=True), + sa.PrimaryKeyConstraint("id"), + ) + op.create_index( + op.f("ix_recorded_values_collected_at"), + "recorded_values", + ["collected_at"], + unique=False, + ) + op.create_index( + op.f("ix_recorded_values_sensor_name"), + "recorded_values", + ["sensor_name"], + unique=False, + ) + + +def downgrade(): + op.drop_index(op.f("ix_recorded_values_sensor_name"), table_name="recorded_values") + op.drop_index(op.f("ix_recorded_values_collected_at"), table_name="recorded_values") + op.drop_table("recorded_values") diff --git a/Ch11/apd.sensors-chapter11-ex01/src/apd/sensors/base.py b/Ch11/apd.sensors-chapter11-ex01/src/apd/sensors/base.py new file mode 100644 index 0000000..e822f4b --- /dev/null +++ b/Ch11/apd.sensors-chapter11-ex01/src/apd/sensors/base.py @@ -0,0 +1,59 @@ +#!/usr/bin/env python +# coding: utf-8 +import datetime +import typing as t + + +T_value = t.TypeVar("T_value") + + +class Sensor(t.Generic[T_value]): + name: str + title: str + + def value(self) -> T_value: + raise NotImplementedError + + @classmethod + def format(cls, value: T_value) -> str: + raise NotImplementedError + + def __str__(self) -> str: + return self.format(self.value()) + + @classmethod + def to_json_compatible(cls, value: T_value) -> t.Any: + raise NotImplementedError() + + @classmethod + def from_json_compatible(cls, json_version: t.Any) -> T_value: + raise NotImplementedError() + + +class JSONSensor(Sensor[T_value]): + @classmethod + def to_json_compatible(cls, value: T_value) -> t.Any: + return value + + @classmethod + def from_json_compatible(cls, json_version: t.Any) -> T_value: + return t.cast(T_value, json_version) + + +class HistoricalSensor(Sensor[T_value]): + def historical( + self, start: datetime.datetime, end: datetime.datetime + ) -> t.Iterable[t.Tuple[datetime.datetime, T_value]]: + raise NotImplementedError + + +version_info_type = t.NamedTuple( + "version_info_type", + [ + ("major", int), + ("minor", int), + ("micro", int), + ("releaselevel", str), + ("serial", int), + ], +) diff --git a/Ch11/apd.sensors-chapter11-ex01/src/apd/sensors/cli.py b/Ch11/apd.sensors-chapter11-ex01/src/apd/sensors/cli.py new file mode 100644 index 0000000..576c487 --- /dev/null +++ b/Ch11/apd.sensors-chapter11-ex01/src/apd/sensors/cli.py @@ -0,0 +1,118 @@ +import enum +import importlib +import sys +import pkg_resources +import traceback +import typing as t + +import click + +from .database import store_sensor_data +from .sensors import Sensor +from .exceptions import DataCollectionError, UserFacingCLIError + + +class ReturnCodes(enum.IntEnum): + OK = 0 + BAD_SENSOR_PATH = 17 + + +def get_sensor_by_path(sensor_path: str) -> Sensor[t.Any]: + try: + module_name, sensor_name = sensor_path.split(":") + except ValueError as err: + raise UserFacingCLIError( + "Sensor path must be in the format dotted.path.to.module:ClassName", + return_code=ReturnCodes.BAD_SENSOR_PATH, + ) from err + try: + module = importlib.import_module(module_name) + except ImportError as err: + raise UserFacingCLIError( + f"Could not import module {module_name}", return_code=ReturnCodes.BAD_SENSOR_PATH + ) from err + try: + sensor_class = getattr(module, sensor_name) + except AttributeError as err: + raise UserFacingCLIError( + f"Could not find attribute {sensor_name} in {module_name}", return_code=ReturnCodes.BAD_SENSOR_PATH + ) from err + if ( + isinstance(sensor_class, type) + and issubclass(sensor_class, Sensor) + and sensor_class != Sensor + ): + return sensor_class() + else: + raise UserFacingCLIError( + f"Detected object {sensor_class!r} is not recognised as a Sensor type", + return_code=ReturnCodes.BAD_SENSOR_PATH, + ) + + +def get_sensors() -> t.Iterable[Sensor[t.Any]]: + sensors = [] + for sensor_class in pkg_resources.iter_entry_points("apd.sensors.sensors"): + class_ = sensor_class.load() + sensors.append(t.cast(Sensor[t.Any], class_())) + return sensors + + +@click.command(help="Displays the values of the sensors") +@click.option( + "--develop", required=False, metavar="path", help="Load a sensor by Python path" +) +@click.option("--verbose", is_flag=True, help="Show additional info") +@click.option("--save", is_flag=True, help="Store collected data to a database") +@click.option( + "--db", + metavar="", + default="sqlite:///sensor_data.sqlite", + help="The connection string to a database", + envvar="APD_SENSORS_DB_URI", +) +def show_sensors(develop: str, verbose: bool, save: bool, db: str) -> None: + sensors: t.Iterable[Sensor[t.Any]] + if develop: + try: + sensors = [get_sensor_by_path(develop)] + except UserFacingCLIError as error: + if verbose: + tb = traceback.format_exception(type(error), error, error.__traceback__) + click.echo("".join(tb)) + click.secho(error.message, fg="red", bold=True) + sys.exit(error.return_code) + else: + sensors = get_sensors() + + db_session = None + if save: + from sqlalchemy import create_engine + from sqlalchemy.orm import sessionmaker + + engine = create_engine(db) + sm = sessionmaker(engine) + db_session = sm() + + for sensor in sensors: + click.secho(sensor.title, bold=True) + try: + value = sensor.value() + except DataCollectionError as error: + if verbose: + tb = traceback.format_exception(type(error), error, error.__traceback__) + click.echo("".join(tb)) + continue + click.echo(error) + else: + click.echo(sensor.format(value)) + if save and db_session is not None: + store_sensor_data(sensor, value, db_session) + db_session.commit() + + click.echo("") + sys.exit(ReturnCodes.OK) + + +if __name__ == "__main__": + show_sensors() diff --git a/Ch11/apd.sensors-chapter11-ex01/src/apd/sensors/database.py b/Ch11/apd.sensors-chapter11-ex01/src/apd/sensors/database.py new file mode 100644 index 0000000..a90ffba --- /dev/null +++ b/Ch11/apd.sensors-chapter11-ex01/src/apd/sensors/database.py @@ -0,0 +1,30 @@ +from __future__ import annotations + +import datetime +import typing as t + +import sqlalchemy +from sqlalchemy.schema import Table +from sqlalchemy.orm.session import Session + +from apd.sensors.base import Sensor + + +metadata = sqlalchemy.MetaData() + +sensor_values = Table( + "recorded_values", + metadata, + sqlalchemy.Column("id", sqlalchemy.Integer, primary_key=True), + sqlalchemy.Column("sensor_name", sqlalchemy.String, index=True), + sqlalchemy.Column("collected_at", sqlalchemy.TIMESTAMP, index=True), + sqlalchemy.Column("data", sqlalchemy.JSON), +) + + +def store_sensor_data(sensor: Sensor[t.Any], data: t.Any, db_session: Session) -> None: + now = datetime.datetime.now() + record = sensor_values.insert().values( + sensor_name=sensor.name, data=sensor.to_json_compatible(data), collected_at=now + ) + db_session.execute(record) diff --git a/Ch11/apd.sensors-chapter11-ex01/src/apd/sensors/exceptions.py b/Ch11/apd.sensors-chapter11-ex01/src/apd/sensors/exceptions.py new file mode 100644 index 0000000..725a0ad --- /dev/null +++ b/Ch11/apd.sensors-chapter11-ex01/src/apd/sensors/exceptions.py @@ -0,0 +1,32 @@ +import dataclasses + + +class APDSensorsError(Exception): + """An exception base class for all exceptions raised by the + sensor data collection system.""" + + +class DataCollectionError(APDSensorsError, RuntimeError): + """An error that represents the inability of a Sensor instance + to retrieve a value""" + + +class IntermittentSensorFailureError(DataCollectionError): + """A DataCollectionError that is expected to resolve itself + in short order""" + + +class PersistentSensorFailureError(DataCollectionError): + """A DataCollectionError that is unlikely to resolve itself + if retried.""" + + +@dataclasses.dataclass(frozen=True) +class UserFacingCLIError(APDSensorsError, SystemExit): + """A fatal error for the CLI""" + + message: str + return_code: int + + def __str__(self): + return f"[{self.return_code}] {self.message}" diff --git a/Ch11/apd.sensors-chapter11-ex01/src/apd/sensors/py.typed b/Ch11/apd.sensors-chapter11-ex01/src/apd/sensors/py.typed new file mode 100644 index 0000000..e69de29 diff --git a/Ch11/apd.sensors-chapter11-ex01/src/apd/sensors/sensors.py b/Ch11/apd.sensors-chapter11-ex01/src/apd/sensors/sensors.py new file mode 100644 index 0000000..c577dbf --- /dev/null +++ b/Ch11/apd.sensors-chapter11-ex01/src/apd/sensors/sensors.py @@ -0,0 +1,189 @@ +#!/usr/bin/env python +# coding: utf-8 +import math +import os +import socket +import sys +import typing as t + +import psutil +from pint import _DEFAULT_REGISTRY as ureg + +from .base import Sensor, JSONSensor, version_info_type +from .exceptions import ( + PersistentSensorFailureError, + IntermittentSensorFailureError, + DataCollectionError, +) + + +class PythonVersion(JSONSensor[version_info_type]): + name = "PythonVersion" + title = "Python Version" + + def value(self) -> version_info_type: + return version_info_type(*sys.version_info) + + @classmethod + def from_json_compatible(cls, json_version: t.Any) -> version_info_type: + return version_info_type(*json_version) + + @classmethod + def format(cls, value: version_info_type) -> str: + if value.micro == 0 and value.releaselevel == "alpha": + return "{0.major}.{0.minor}.{0.micro}a{0.serial}".format(value) + return "{0.major}.{0.minor}".format(value) + + +class IPAddresses(JSONSensor[t.Iterable[t.Tuple[str, str]]]): + name = "IPAddresses" + title = "IP Addresses" + FAMILIES = {"AF_INET": "IPv4", "AF_INET6": "IPv6"} + + def value(self) -> t.List[t.Tuple[str, str]]: + hostname = socket.gethostname() + addresses = socket.getaddrinfo(hostname, None) + address_info: t.List[t.Tuple[str, str]] = [] + for address in addresses: + family, ip = (address[0].name, address[4][0]) + if family not in self.FAMILIES: + continue + value = (family, ip) + if value not in address_info: + address_info.append(value) + return address_info + + @classmethod + def format(cls, value: t.Iterable[t.Tuple[str, str]]) -> str: + return "\n".join( + "{0} ({1})".format(address[1], cls.FAMILIES.get(address[0], "Unknown")) + for address in value + ) + + +class CPULoad(JSONSensor[float]): + name = "CPULoad" + title = "CPU Usage" + + def value(self) -> float: + return float(psutil.cpu_percent(interval=3)) / 100.0 + + @classmethod + def format(cls, value: float) -> str: + return "{:.1%}".format(value) + + +class RAMAvailable(JSONSensor[int]): + name = "RAMAvailable" + title = "RAM Available" + UNITS = ("B", "KiB", "MiB", "GiB", "TiB", "PiB", "EiB") + UNIT_SIZE = 2 ** 10 + + def value(self) -> int: + return int(psutil.virtual_memory().available) + + @classmethod + def format(cls, value: int) -> str: + magnitude = math.floor(math.log(value, cls.UNIT_SIZE)) + max_magnitude = len(cls.UNITS) - 1 + magnitude = min(magnitude, max_magnitude) + scaled_value = value / (cls.UNIT_SIZE ** magnitude) + return "{:.1f} {}".format(scaled_value, cls.UNITS[magnitude]) + + +class ACStatus(JSONSensor[bool]): + name = "ACStatus" + title = "AC Connected" + + def value(self) -> bool: + battery = psutil.sensors_battery() + if battery is not None: + value = battery.power_plugged + if value is None: + raise IntermittentSensorFailureError("Can't find AC status") + else: + return bool(value) + else: + raise PersistentSensorFailureError("No charging circuit installed") + + @classmethod + def format(cls, value: bool) -> str: + if value: + return "Connected" + else: + return "Not connected" + + +class DHTSensor: + def __init__(self) -> None: + self.board = os.environ.get("APD_SENSORS_TEMPERATURE_BOARD", "DHT22") + self.pin = os.environ.get("APD_SENSORS_TEMPERATURE_PIN", "D20") + + @property + def sensor(self) -> t.Any: + try: + import adafruit_dht + import board + + # Force using legacy interface + adafruit_dht._USE_PULSEIO = False + + sensor_type = getattr(adafruit_dht, self.board) + pin = getattr(board, self.pin) + return sensor_type(pin) + except (ImportError, NotImplementedError, AttributeError) as err: + # No DHT library results in an ImportError. + # Running on an unknown platform results in a + # NotImplementedError when getting the pin + raise PersistentSensorFailureError( + "Unable to initialise sensor interface" + ) from err + + +class Temperature(Sensor[t.Any], DHTSensor): + name = "Temperature" + title = "Ambient Temperature" + + def value(self) -> t.Any: + try: + return ureg.Quantity(self.sensor.temperature, ureg.celsius) + except DataCollectionError: + # This is one of our own exceptions, we don't need to re-wrap it + raise + except (RuntimeError, AttributeError) as err: + raise IntermittentSensorFailureError( + "Couldn't determine temperature" + ) from err + + @classmethod + def format(cls, value: t.Any) -> str: + return "{:.3~P} ({:.3~P})".format(value, value.to(ureg.fahrenheit)) + + @classmethod + def to_json_compatible(cls, value: t.Any) -> t.Any: + return {"magnitude": value.magnitude, "unit": str(value.units)} + + @classmethod + def from_json_compatible(cls, json_version: t.Any) -> t.Any: + return ureg.Quantity(json_version["magnitude"], ureg[json_version["unit"]]) + + def __str__(self) -> str: + return self.format(self.value()) + + +class RelativeHumidity(JSONSensor[float], DHTSensor): + name = "RelativeHumidity" + title = "Relative Humidity" + + def value(self) -> float: + try: + return float(self.sensor.humidity) + except DataCollectionError: + # This is one of our own exceptions, we don't need to re-wrap it + raise + except (TypeError, AttributeError) as err: + raise IntermittentSensorFailureError("Couldn't determine humidity") from err + + @classmethod + def format(cls, value: float) -> str: + return "{:.1%}".format(value) diff --git a/Ch11/apd.sensors-chapter11-ex01/src/apd/sensors/utils.py b/Ch11/apd.sensors-chapter11-ex01/src/apd/sensors/utils.py new file mode 100644 index 0000000..24f912b --- /dev/null +++ b/Ch11/apd.sensors-chapter11-ex01/src/apd/sensors/utils.py @@ -0,0 +1,20 @@ +from apd.sensors.base import Sensor, T_value +from apd.sensors.exceptions import IntermittentSensorFailureError + + +def get_value_with_retries(sensor: Sensor[T_value], retries: int = 3) -> T_value: + for i in range(retries): + try: + return sensor.value() + except IntermittentSensorFailureError: + if i == (retries - 1): + # This is the last retry, reraise + raise + else: + continue + # It shouldn't be possible to get here, but it's better to + # fall through with an appropriate exception rather than a + # None + raise IntermittentSensorFailureError( + f"Could not find a value after {retries} retries" + ) diff --git a/Ch11/apd.sensors-chapter11-ex01/src/apd/sensors/wsgi/__init__.py b/Ch11/apd.sensors-chapter11-ex01/src/apd/sensors/wsgi/__init__.py new file mode 100644 index 0000000..fbd2a91 --- /dev/null +++ b/Ch11/apd.sensors-chapter11-ex01/src/apd/sensors/wsgi/__init__.py @@ -0,0 +1,31 @@ +import flask + +try: + from flask_sqlalchemy import SQLAlchemy +except ImportError: + sql_support = False +else: + sql_support = True + + +from .base import set_up_config +from . import v10 +from . import v20 +from . import v21 +from . import v30 + + +__all__ = ["app", "set_up_config", "db"] + +app = flask.Flask(__name__) +app.register_blueprint(v10.version, url_prefix="/v/1.0") +app.register_blueprint(v20.version, url_prefix="/v/2.0") +app.register_blueprint(v21.version, url_prefix="/v/2.1") +app.register_blueprint(v30.version, url_prefix="/v/3.0") + +if sql_support: + from apd.sensors.database import metadata + + db = SQLAlchemy(app, metadata=metadata) +else: + db = None diff --git a/Ch11/apd.sensors-chapter11-ex01/src/apd/sensors/wsgi/base.py b/Ch11/apd.sensors-chapter11-ex01/src/apd/sensors/wsgi/base.py new file mode 100644 index 0000000..b3c73cb --- /dev/null +++ b/Ch11/apd.sensors-chapter11-ex01/src/apd/sensors/wsgi/base.py @@ -0,0 +1,47 @@ +from hmac import compare_digest +import functools +import os +import typing as t + +import flask + + +ViewFuncReturn = t.TypeVar("ViewFuncReturn") +ErrorReturn = t.Tuple[t.Dict[str, str], int] +REQUIRED_CONFIG_KEYS = {"APD_SENSORS_API_KEY"} + + +def require_api_key( + func: t.Callable[..., ViewFuncReturn] +) -> t.Callable[..., t.Union[ViewFuncReturn, ErrorReturn]]: + @functools.wraps(func) + def wrapped(*args, **kwargs) -> t.Union[ViewFuncReturn, ErrorReturn]: + api_key = flask.current_app.config["APD_SENSORS_API_KEY"] + headers = flask.request.headers + supplied_key = headers.get("X-API-Key", "") + if not compare_digest(api_key, supplied_key): + return {"error": "Supply API key in X-API-Key header"}, 403 + return func(*args, **kwargs) + + return wrapped + + +def set_up_config( + environ: t.Optional[t.Dict[str, str]] = None, + to_configure: t.Optional[flask.Flask] = None, +) -> flask.Flask: + if environ is None: + environ = dict(os.environ) + if to_configure is None: + from apd.sensors.wsgi import app + + to_configure = app + missing_keys = REQUIRED_CONFIG_KEYS - environ.keys() + if missing_keys: + raise ValueError("Missing config variables: {}".format(", ".join(missing_keys))) + data_file = os.path.join(os.getcwd(), "sensor_data.sqlite") + environ["SQLALCHEMY_DATABASE_URI"] = environ.get( + "APD_SENSORS_DB_URI", f"sqlite:///{data_file}" + ) + to_configure.config.from_mapping(environ) + return to_configure diff --git a/Ch11/apd.sensors-chapter11-ex01/src/apd/sensors/wsgi/serve.py b/Ch11/apd.sensors-chapter11-ex01/src/apd/sensors/wsgi/serve.py new file mode 100644 index 0000000..0b1f684 --- /dev/null +++ b/Ch11/apd.sensors-chapter11-ex01/src/apd/sensors/wsgi/serve.py @@ -0,0 +1,10 @@ +from . import app +from .base import set_up_config + +if __name__ == "__main__": + import wsgiref.simple_server + + set_up_config(None, app) + + with wsgiref.simple_server.make_server("", 8000, app) as server: + server.serve_forever() diff --git a/Ch11/apd.sensors-chapter11-ex01/src/apd/sensors/wsgi/v10.py b/Ch11/apd.sensors-chapter11-ex01/src/apd/sensors/wsgi/v10.py new file mode 100644 index 0000000..f3ae5da --- /dev/null +++ b/Ch11/apd.sensors-chapter11-ex01/src/apd/sensors/wsgi/v10.py @@ -0,0 +1,30 @@ +import json +import typing as t + +import flask + +from apd.sensors import cli +from apd.sensors.exceptions import DataCollectionError +from .base import require_api_key + +version = flask.Blueprint(__name__, __name__) + + +@version.route("/sensors/") +@require_api_key +def sensor_values() -> t.Tuple[t.Dict[str, t.Any], int, t.Dict[str, str]]: + headers = {"Content-Security-Policy": "default-src 'none'"} + data = {} + for sensor in cli.get_sensors(): + try: + value = sensor.value() + except DataCollectionError: + value = None + try: + json.dumps(value) + except TypeError: + # This value isn't JSON serializable, skip it + continue + else: + data[sensor.title] = value + return data, 200, headers diff --git a/Ch11/apd.sensors-chapter11-ex01/src/apd/sensors/wsgi/v20.py b/Ch11/apd.sensors-chapter11-ex01/src/apd/sensors/wsgi/v20.py new file mode 100644 index 0000000..4ab1522 --- /dev/null +++ b/Ch11/apd.sensors-chapter11-ex01/src/apd/sensors/wsgi/v20.py @@ -0,0 +1,40 @@ +import typing as t + +import flask + +from apd.sensors import cli +from apd.sensors.exceptions import DataCollectionError +from .base import require_api_key + +version = flask.Blueprint(__name__, __name__) + + +@version.route("/sensors/") +@version.route("/sensors/") +@require_api_key +def sensor_values(sensor_id=None) -> t.Tuple[t.Dict[str, t.Any], int, t.Dict[str, str]]: + headers = {"Content-Security-Policy": "default-src 'none'"} + sensors = [] + for sensor in cli.get_sensors(): + if sensor_id and sensor_id != sensor.name: + continue + try: + try: + value = sensor.value() + except DataCollectionError: + human_readable = "Unknown" + json_value = None + else: + json_value = sensor.to_json_compatible(value) + human_readable = sensor.format(value) + sensor_data = { + "id": sensor.name, + "title": sensor.title, + "value": json_value, + "human_readable": human_readable, + } + sensors.append(sensor_data) + except NotImplementedError: + pass + data = {"sensors": sensors} + return data, 200, headers diff --git a/Ch11/apd.sensors-chapter11-ex01/src/apd/sensors/wsgi/v21.py b/Ch11/apd.sensors-chapter11-ex01/src/apd/sensors/wsgi/v21.py new file mode 100644 index 0000000..16cbb72 --- /dev/null +++ b/Ch11/apd.sensors-chapter11-ex01/src/apd/sensors/wsgi/v21.py @@ -0,0 +1,47 @@ +import typing as t + +import flask + +from apd.sensors import cli +from apd.sensors.exceptions import DataCollectionError +from .base import require_api_key + +version = flask.Blueprint(__name__, __name__) + + +@version.route("/sensors/") +@version.route("/sensors/") +@require_api_key +def sensor_values(sensor_id=None) -> t.Tuple[t.Dict[str, t.Any], int, t.Dict[str, str]]: + headers = {"Content-Security-Policy": "default-src 'none'"} + sensors = [] + for sensor in cli.get_sensors(): + if sensor_id and sensor_id != sensor.name: + continue + try: + try: + value = sensor.value() + except DataCollectionError: + human_readable = "Unknown" + json_value = None + else: + json_value = sensor.to_json_compatible(value) + human_readable = sensor.format(value) + sensor_data = { + "id": sensor.name, + "title": sensor.title, + "value": json_value, + "human_readable": human_readable, + } + sensors.append(sensor_data) + except NotImplementedError: + pass + data = {"sensors": sensors} + return data, 200, headers + + +@version.route("/deployment_id") +def deployment_id() -> t.Tuple[t.Dict[str, t.Any], int, t.Dict[str, str]]: + headers = {"Content-Security-Policy": "default-src 'none'"} + data = {"deployment_id": flask.current_app.config["APD_SENSORS_DEPLOYMENT_ID"]} + return data, 200, headers diff --git a/Ch11/apd.sensors-chapter11-ex01/src/apd/sensors/wsgi/v30.py b/Ch11/apd.sensors-chapter11-ex01/src/apd/sensors/wsgi/v30.py new file mode 100644 index 0000000..549af12 --- /dev/null +++ b/Ch11/apd.sensors-chapter11-ex01/src/apd/sensors/wsgi/v30.py @@ -0,0 +1,117 @@ +import datetime +import typing as t + +import flask + +from apd.sensors import cli +from apd.sensors.base import HistoricalSensor +from apd.sensors.exceptions import DataCollectionError + +from .base import require_api_key + +version = flask.Blueprint(__name__, __name__) + + +@version.route("/sensors/") +@version.route("/sensors/") +@require_api_key +def sensor_values(sensor_id=None) -> t.Tuple[t.Dict[str, t.Any], int, t.Dict[str, str]]: + headers = {"Content-Security-Policy": "default-src 'none'"} + sensors = [] + errors = [] + for sensor in cli.get_sensors(): + now = datetime.datetime.now() + if sensor_id and sensor_id != sensor.name: + continue + try: + try: + value = sensor.value() + except DataCollectionError as err: + error = { + "id": sensor.name, + "title": sensor.title, + "collected_at": now.isoformat(), + "error": str(err), + } + errors.append(error) + continue + sensor_data = { + "id": sensor.name, + "title": sensor.title, + "value": sensor.to_json_compatible(value), + "human_readable": sensor.format(value), + "collected_at": now.isoformat(), + } + sensors.append(sensor_data) + except NotImplementedError: + pass + data = {"sensors": sensors, "errors": errors} + return data, 200, headers + + +@version.route("/historical") +@version.route("/historical/") +@version.route("/historical//") +@require_api_key +def historical_values( + start: str = None, end: str = None +) -> t.Tuple[t.Dict[str, t.Any], int, t.Dict[str, str]]: + try: + import dateutil.parser + from apd.sensors.database import sensor_values + from apd.sensors.wsgi import db + except ImportError: + return {"error": "Historical data support is not installed"}, 501, {} + + db_session = db.session + headers = {"Content-Security-Policy": "default-src 'none'"} + + query = db_session.query(sensor_values) + if start: + start_dt = dateutil.parser.parse(start) + query = query.filter(sensor_values.c.collected_at >= start_dt) + else: + start_dt = dateutil.parser.parse("1900-01-01") + if end: + end_dt = dateutil.parser.parse(end) + query = query.filter(sensor_values.c.collected_at <= end_dt) + else: + end_dt = datetime.datetime.now() + + known_sensors = {sensor.name: sensor for sensor in cli.get_sensors()} + sensors = [] + for data in query: + if data.sensor_name not in known_sensors: + continue + sensor = known_sensors[data.sensor_name] + sensor_data = { + "id": sensor.name, + "title": sensor.title, + "value": data.data, + "human_readable": sensor.format(sensor.from_json_compatible(data.data)), + "collected_at": data.collected_at.isoformat(), + } + sensors.append(sensor_data) + for sensor in known_sensors.values(): + if isinstance(sensor, HistoricalSensor): + for date, value in sensor.historical(start_dt, end_dt): + sensor_data = { + "id": sensor.name, + "title": sensor.title, + "value": value, + "human_readable": sensor.format(sensor.from_json_compatible(value)), + "collected_at": date.isoformat(), + } + sensors.append(sensor_data) + data = {"sensors": sensors} + try: + return data, 200, headers + finally: + db_session.close() + + +@version.route("/deployment_id") +def deployment_id() -> t.Tuple[t.Dict[str, t.Any], int, t.Dict[str, str]]: + headers = {"Content-Security-Policy": "default-src 'none'"} + data = {"deployment_id": flask.current_app.config["APD_SENSORS_DEPLOYMENT_ID"]} + return data, 200, headers diff --git a/Ch11/apd.sensors-chapter11-ex01/tests/__init__.py b/Ch11/apd.sensors-chapter11-ex01/tests/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/Ch11/apd.sensors-chapter11-ex01/tests/test_acstatus.py b/Ch11/apd.sensors-chapter11-ex01/tests/test_acstatus.py new file mode 100644 index 0000000..4f0e6ed --- /dev/null +++ b/Ch11/apd.sensors-chapter11-ex01/tests/test_acstatus.py @@ -0,0 +1,58 @@ +from unittest import mock + +import pytest + +from apd.sensors.sensors import ACStatus +from apd.sensors.exceptions import ( + PersistentSensorFailureError, + IntermittentSensorFailureError, +) + + +@pytest.fixture +def sensor(): + return ACStatus() + + +class TestACStatusFormatter: + @pytest.fixture + def subject(self, sensor): + return sensor.format + + def test_format_True(self, subject): + assert subject(True) == "Connected" + + def test_format_False(self, subject): + assert subject(False) == "Not connected" + + +class TestACStatusValue: + @pytest.fixture + def subject(self, sensor): + return sensor.value + + @pytest.fixture + def sensors_battery(self): + with mock.patch("psutil.sensors_battery") as sensors_battery: + yield sensors_battery + + def test_sensors_battery_called(self, subject, sensors_battery): + sensors_battery.return_value.power_plugged = True + assert subject() is True + assert sensors_battery.call_count == 1 + + def test_sensor_but_battery_unknown(self, subject, sensors_battery): + sensors_battery.return_value.power_plugged = None + with pytest.raises(IntermittentSensorFailureError): + subject() + assert sensors_battery.call_count == 1 + + def test_no_sensor(self, subject, sensors_battery): + sensors_battery.return_value = None + with pytest.raises(PersistentSensorFailureError): + subject() + + def test_str_representation_is_formatted_value(self, sensor, sensors_battery): + sensors_battery.return_value.power_plugged = True + assert str(sensor) == "Connected" + assert sensors_battery.call_count == 1 diff --git a/Ch11/apd.sensors-chapter11-ex01/tests/test_api_server.py b/Ch11/apd.sensors-chapter11-ex01/tests/test_api_server.py new file mode 100644 index 0000000..8aa4cea --- /dev/null +++ b/Ch11/apd.sensors-chapter11-ex01/tests/test_api_server.py @@ -0,0 +1,332 @@ +import datetime +import os +import typing as t +import uuid +from unittest import mock + +import flask +import pytest +from webtest import TestApp + +from apd.sensors.base import HistoricalSensor, JSONSensor +from apd.sensors.sensors import PythonVersion +from apd.sensors.wsgi import set_up_config +from apd.sensors.wsgi import v10 +from apd.sensors.wsgi import v20 +from apd.sensors.wsgi import v21 +from apd.sensors.wsgi import v30 + + +class HistoricalBoolSensor(HistoricalSensor[bool], JSONSensor[bool]): + + title = "Sensor which has past data" + name = "HistoricalBoolSensor" + + def value(self) -> bool: + return True + + def historical( + self, start: datetime.datetime, end: datetime.datetime + ) -> t.Iterable[t.Tuple[datetime.datetime, bool]]: + date = start + while date < end: + yield date, True + date += datetime.timedelta(hours=1) + + @classmethod + def format(cls, value: bool) -> str: + return "Yes" if value else "No" + + +@pytest.fixture(scope="session") +def api_key(): + return uuid.uuid4().hex + + +def test_api_key_is_required_config_option(): + app = mock.MagicMock() + with pytest.raises( + ValueError, match="Missing config variables: APD_SENSORS_API_KEY" + ): + set_up_config({"APD_SENSORS_DEPLOYMENT_ID": ""}, to_configure=app) + + +def test_os_environ_is_default_for_config_values(api_key): + app = mock.MagicMock() + os.environ["APD_SENSORS_API_KEY"] = api_key + os.environ["APD_SENSORS_DEPLOYMENT_ID"] = "8f1b57faa04b430c81decbbeee9e300c" + try: + assert app.config.from_mapping.call_count == 0 + set_up_config(None, to_configure=app) + assert app.config.from_mapping.call_count == 1 + for key, value in os.environ.items(): + # There may be keys in the config not from the environment + assert app.config.from_mapping.call_args[0][0][key] == value + finally: + del os.environ["APD_SENSORS_API_KEY"] + del os.environ["APD_SENSORS_DEPLOYMENT_ID"] + + +class CommonTests: + @pytest.mark.functional + def test_sensor_values_fails_on_missing_api_key(self, api_server): + response = api_server.get("/sensors/", expect_errors=True) + assert response.status_code == 403 + assert response.json["error"] == "Supply API key in X-API-Key header" + + @pytest.mark.functional + def test_sensor_values_require_correct_api_key(self, api_server): + response = api_server.get( + "/sensors/", headers={"X-API-Key": "wrong_key"}, expect_errors=True + ) + assert response.status_code == 403 + assert response.json["error"] == "Supply API key in X-API-Key header" + + +class Testv10API(CommonTests): + @pytest.fixture + def subject(self, api_key): + app = flask.Flask("testapp") + app.register_blueprint(v10.version) + set_up_config( + { + "APD_SENSORS_API_KEY": api_key, + "APD_SENSORS_DEPLOYMENT_ID": "8f1b57faa04b430c81decbbeee9e300c", + }, + to_configure=app, + ) + return app + + @pytest.fixture + def api_server(self, subject): + return TestApp(subject) + + @pytest.mark.functional + def test_sensor_values_returned_as_json(self, api_server, api_key): + value = api_server.get("/sensors/", headers={"X-API-Key": api_key}).json + python_version = PythonVersion().value() + + sensor_names = value.keys() + assert "Python Version" in sensor_names + assert value["Python Version"] == list(python_version) + + @pytest.mark.functional + def test_unserializable_sensor_is_omitted(self, api_server, api_key): + value = api_server.get("/sensors/", headers={"X-API-Key": api_key}).json + sensor_names = value.keys() + assert "Temperature" not in sensor_names + + @pytest.mark.functional + def test_erroring_sensor_shows_None(self, api_server, api_key): + from .test_utils import FailingSensor + + with mock.patch("apd.sensors.cli.get_sensors") as get_sensors: + # Ensure failing sensor is first, to test that subsequent sensors + # are still processed + get_sensors.return_value = [FailingSensor(10), PythonVersion()] + value = api_server.get("/sensors/", headers={"X-API-Key": api_key}).json + assert value["Sensor which fails"] is None + assert "Python Version" in value.keys() + + +class Testv20API(CommonTests): + @pytest.fixture + def subject(self, api_key): + app = flask.Flask("testapp") + app.register_blueprint(v20.version) + set_up_config( + { + "APD_SENSORS_API_KEY": api_key, + "APD_SENSORS_DEPLOYMENT_ID": "8f1b57faa04b430c81decbbeee9e300c", + }, + to_configure=app, + ) + return app + + @pytest.fixture + def api_server(self, subject): + return TestApp(subject) + + @pytest.mark.functional + def test_sensor_values_returned_as_json(self, api_server, api_key): + value = api_server.get("/sensors/", headers={"X-API-Key": api_key}).json + sensors = value["sensors"] + + python_version = [ + sensor for sensor in sensors if sensor["id"] == "PythonVersion" + ][0] + assert python_version["title"] == "Python Version" + assert python_version["value"] == list(PythonVersion().value()) + + @pytest.mark.functional + def test_erroring_sensor_shows_None(self, api_server, api_key): + from .test_utils import FailingSensor + + with mock.patch("apd.sensors.cli.get_sensors") as get_sensors: + # Ensure failing sensor is first, to test that subsequent sensors + # are still processed + get_sensors.return_value = [FailingSensor(10), PythonVersion()] + value = api_server.get("/sensors/", headers={"X-API-Key": api_key}).json + sensors = value["sensors"] + + failing = [sensor for sensor in sensors if sensor["id"] == "FailingSensor"][0] + python_version = [ + sensor for sensor in sensors if sensor["id"] == "PythonVersion" + ][0] + + assert failing["title"] == "Sensor which fails" + assert failing["value"] is None + assert python_version["title"] == "Python Version" + + +class Testv21API(Testv20API): + @pytest.fixture + def subject(self, api_key): + app = flask.Flask("testapp") + app.register_blueprint(v21.version) + set_up_config( + { + "APD_SENSORS_API_KEY": api_key, + "APD_SENSORS_DEPLOYMENT_ID": "8f1b57faa04b430c81decbbeee9e300c", + }, + to_configure=app, + ) + return app + + @pytest.mark.functional + def test_deployment_id(self, api_server, api_key): + value = api_server.get("/deployment_id", headers={"X-API-Key": api_key}).json + assert value == {"deployment_id": "8f1b57faa04b430c81decbbeee9e300c"} + + @pytest.mark.functional + def test_erroring_sensor_shows_None(self, api_server, api_key): + from .test_utils import FailingSensor + + with mock.patch("apd.sensors.cli.get_sensors") as get_sensors: + # Ensure failing sensor is first, to test that subsequent sensors + # are still processed + get_sensors.return_value = [FailingSensor(10), PythonVersion()] + value = api_server.get("/sensors/", headers={"X-API-Key": api_key}).json + sensors = value["sensors"] + + failing = [sensor for sensor in sensors if sensor["id"] == "FailingSensor"][0] + python_version = [ + sensor for sensor in sensors if sensor["id"] == "PythonVersion" + ][0] + + assert failing["title"] == "Sensor which fails" + assert failing["value"] is None + assert python_version["title"] == "Python Version" + + +class Testv30API(CommonTests): + @pytest.fixture + def api_server(self, subject): + return TestApp(subject) + + @pytest.fixture + def subject(self, api_key): + app = flask.Flask("testapp") + app.register_blueprint(v30.version) + set_up_config( + { + "APD_SENSORS_API_KEY": api_key, + "APD_SENSORS_DEPLOYMENT_ID": "8f1b57faa04b430c81decbbeee9e300c", + "APD_SENSORS_DB_URI": "sqlite://", + }, + to_configure=app, + ) + return app + + @pytest.fixture + def db(self, subject): + from flask_sqlalchemy import SQLAlchemy + from apd.sensors.database import metadata + from apd.sensors import wsgi + + db = SQLAlchemy(subject, metadata=metadata) + db.create_all() + + wsgi.db = db + yield db + wsgi.db = None + + @pytest.fixture + def store_sensor_data(self): + from apd.sensors.database import store_sensor_data + + return store_sensor_data + + @pytest.mark.functional + def test_historical_with_no_data(self, api_key, api_server, db): + value = api_server.get("/historical", headers={"X-API-Key": api_key}).json + assert value == {"sensors": []} + + @pytest.mark.functional + def test_historical_with_data(self, api_key, api_server, db, store_sensor_data): + store_sensor_data(PythonVersion, [3, 9, 0, "final", 1], db.session) + value = api_server.get("/historical", headers={"X-API-Key": api_key}).json + assert len(value["sensors"]) == 1 + assert value["sensors"][0]["human_readable"] == "3.9" + + def test_historical_sensor(self, api_key, api_server, db): + with mock.patch("apd.sensors.cli.get_sensors") as get_sensors: + # Ensure failing sensor is first, to test that subsequent sensors + # are still processed + get_sensors.return_value = [HistoricalBoolSensor()] + value = api_server.get( + "/historical/2020-01-01/2020-01-02", headers={"X-API-Key": api_key} + ).json + assert len(value["sensors"]) == 24 + assert value["sensors"][0] == { + "collected_at": "2020-01-01T00:00:00", + "human_readable": "Yes", + "id": "HistoricalBoolSensor", + "title": "Sensor which has past data", + "value": True, + } + + @pytest.mark.functional + def test_sensor_values_returned_as_json(self, api_server, api_key): + value = api_server.get("/sensors/", headers={"X-API-Key": api_key}).json + sensors = value["sensors"] + + python_version = [ + sensor for sensor in sensors if sensor["id"] == "PythonVersion" + ][0] + assert python_version["title"] == "Python Version" + assert python_version["value"] == list(PythonVersion().value()) + + @pytest.mark.functional + def test_erroring_sensor_excluded_but_reported(self, api_server, api_key): + from .test_utils import FailingSensor + + with mock.patch("apd.sensors.cli.get_sensors") as get_sensors: + # Ensure failing sensor is first, to test that subsequent sensors + # are still processed + get_sensors.return_value = [FailingSensor(2), PythonVersion()] + value = api_server.get("/sensors/", headers={"X-API-Key": api_key}).json + second_attempt_value = api_server.get( + "/sensors/", headers={"X-API-Key": api_key} + ).json + + sensors = value["sensors"] + failing = [sensor for sensor in sensors if sensor["id"] == "FailingSensor"] + assert len(failing) == 0 + errors = value["errors"] + failing = [sensor for sensor in errors if sensor["id"] == "FailingSensor"] + assert len(failing) == 1 + + python_version = [ + sensor for sensor in sensors if sensor["id"] == "PythonVersion" + ] + assert len(python_version) == 1 + + sensors = second_attempt_value["sensors"] + assert second_attempt_value["errors"] == [] + failing = [sensor for sensor in sensors if sensor["id"] == "FailingSensor"] + assert len(failing) == 1 + python_version = [ + sensor for sensor in sensors if sensor["id"] == "PythonVersion" + ] + assert len(python_version) == 1 diff --git a/Ch11/apd.sensors-chapter11-ex01/tests/test_cpuusage.py b/Ch11/apd.sensors-chapter11-ex01/tests/test_cpuusage.py new file mode 100644 index 0000000..445f4e5 --- /dev/null +++ b/Ch11/apd.sensors-chapter11-ex01/tests/test_cpuusage.py @@ -0,0 +1,45 @@ +from unittest import mock + +import pytest + +from apd.sensors.sensors import CPULoad + + +@pytest.fixture +def sensor(): + return CPULoad() + + +class TestCPULoadFormatter: + @pytest.fixture + def subject(self, sensor): + return sensor.format + + def test_format_simple_percentage(self, subject): + assert subject(0.05) == "5.0%" + + def test_format_multiple_decimal_places(self, subject): + assert subject(0.031415926) == "3.1%" + + def test_format_multiple_decimal_places_int(self, subject) -> None: + assert subject(1) == "100.0%" + + +class TestCPULoadValue: + @pytest.fixture + def subject(self, sensor): + return sensor.value + + @pytest.fixture + def cpuload(self): + with mock.patch("psutil.cpu_percent") as cpu_percent: + cpu_percent.return_value = 50.1 + yield cpu_percent + + def test_cpu_percent_called_With_interval(self, subject, cpuload): + assert subject() == 0.501 + assert cpuload.call_count == 1 + assert tuple(cpuload.call_args) == ((), {"interval": 3}) + + def test_str_representation_is_formatted_value(self, sensor, cpuload): + assert str(sensor) == "50.1%" diff --git a/Ch11/apd.sensors-chapter11-ex01/tests/test_dht.py b/Ch11/apd.sensors-chapter11-ex01/tests/test_dht.py new file mode 100644 index 0000000..85c3e34 --- /dev/null +++ b/Ch11/apd.sensors-chapter11-ex01/tests/test_dht.py @@ -0,0 +1,61 @@ +import pytest + +from apd.sensors.sensors import Temperature, RelativeHumidity, ureg + + +@pytest.fixture +def temperature_sensor(): + return Temperature() + + +@pytest.fixture +def humidity_sensor(): + return RelativeHumidity() + + +class TestTemperatureFormatter: + @pytest.fixture + def subject(self, temperature_sensor): + return temperature_sensor.format + + @staticmethod + def to_degc(magnitude): + return ureg.Quantity(magnitude, "degree_Celsius") + + def test_format_21c(self, subject): + assert subject(self.to_degc(21.0)) == "21.0 °C (69.8 °F)" + + def test_format_negative(self, subject): + assert subject(self.to_degc(-32.0)) == "-32.0 °C (-25.6 °F)" + + +class TestTemperatureSerializer: + @pytest.fixture + def serialize(self, temperature_sensor): + return temperature_sensor.to_json_compatible + + @pytest.fixture + def deserialize(self, temperature_sensor): + return temperature_sensor.from_json_compatible + + @staticmethod + def to_degc(magnitude): + return ureg.Quantity(magnitude, "degree_Celsius") + + def test_serialize_21c(self, serialize): + assert serialize(self.to_degc(21.0)) == {"magnitude": 21.0, "unit": "degree_Celsius"} + + def test_serialize_negative(self, serialize): + assert serialize(self.to_degc(-32.3)) == {"magnitude": -32.3, "unit": "degree_Celsius"} + + def test_deserialize_21c(self, deserialize): + assert deserialize({"magnitude": 21.0, "unit": "degree_Celsius"}) == self.to_degc(21.0) + + +class TestHumidityFormatter: + @pytest.fixture + def subject(self, humidity_sensor): + return humidity_sensor.format + + def test_format_percentage(self, subject): + assert subject(0.035) == "3.5%" diff --git a/Ch11/apd.sensors-chapter11-ex01/tests/test_ipaddresses.py b/Ch11/apd.sensors-chapter11-ex01/tests/test_ipaddresses.py new file mode 100644 index 0000000..bd61d85 --- /dev/null +++ b/Ch11/apd.sensors-chapter11-ex01/tests/test_ipaddresses.py @@ -0,0 +1,59 @@ +import socket +from unittest import mock + +import pytest + +from apd.sensors.sensors import IPAddresses + + +@pytest.fixture +def sensor(): + return IPAddresses() + + +class TestIPAddressFormatter: + @pytest.fixture + def subject(self, sensor): + return sensor.format + + def test_format_single_ipv4(self, subject): + ips = [("AF_INET", "192.0.2.1")] + assert subject(ips) == "192.0.2.1 (IPv4)" + + def test_format_single_ipv6(self, subject): + ips = [("AF_INET6", "2001:DB8::1")] + assert subject(ips) == "2001:DB8::1 (IPv6)" + + def test_format_mixed_list(self, subject): + ips = [("AF_INET", "192.0.2.1"), ("AF_INET6", "2001:DB8::1")] + assert subject(ips) == "192.0.2.1 (IPv4)\n2001:DB8::1 (IPv6)" + + def test_unusual_protocols_are_marked_as_unknown(self, subject): + ips = [("AF_IRDA", "ffff")] + assert subject(ips) == "ffff (Unknown)" + + +class TestIPAddressesValue: + @pytest.fixture + def subject(self, sensor): + return sensor.value + + @pytest.fixture + def gethostname(self): + with mock.patch("socket.gethostname") as gethostname: + gethostname.return_value = "localhost" + yield gethostname + + @pytest.fixture + def getaddrinfo(self, gethostname): + with mock.patch("socket.getaddrinfo") as getaddrinfo: + yield getaddrinfo + + def test_getaddrinfo_used_for_value_collection(self, getaddrinfo, subject): + getaddrinfo.return_value = [(socket.AF_INET, 0, 0, "", ("192.0.2.1", 0))] + assert subject() == [("AF_INET", "192.0.2.1")] + assert getaddrinfo.call_count == 1 + + def test_str_representation_is_formatted_value(self, getaddrinfo, sensor): + getaddrinfo.return_value = [(socket.AF_INET6, 0, 0, "", ("2001:DB8::1", 0))] + assert str(sensor) == "2001:DB8::1 (IPv6)" diff --git a/Ch11/apd.sensors-chapter11-ex01/tests/test_pythonversion.py b/Ch11/apd.sensors-chapter11-ex01/tests/test_pythonversion.py new file mode 100644 index 0000000..43ad806 --- /dev/null +++ b/Ch11/apd.sensors-chapter11-ex01/tests/test_pythonversion.py @@ -0,0 +1,61 @@ +from collections import namedtuple +from unittest import mock + +import pytest + +from apd.sensors.sensors import PythonVersion + + +@pytest.fixture +def version(): + return namedtuple( + "sys_versioninfo", ("major", "minor", "micro", "releaselevel", "serial") + ) + + +@pytest.fixture +def sensor(): + return PythonVersion() + + +class TestPythonVersionFormatter: + @pytest.fixture + def subject(self, sensor): + return sensor.format + + def test_format_py38(self, subject, version): + py38 = version(3, 8, 0, "final", 0) + assert subject(py38) == "3.8" + + def test_format_large_version(self, subject, version): + large = version(255, 128, 0, "final", 0) + assert subject(large) == "255.128" + + def test_alpha_of_minor_is_marked(self, subject, version): + py39 = version(3, 9, 0, "alpha", 1) + assert subject(py39) == "3.9.0a1" + + def test_alpha_of_micro_is_unmarked(self, subject, version): + py39 = version(3, 9, 1, "alpha", 1) + assert subject(py39) == "3.9" + + +class TestPythonVersionValue: + @pytest.fixture + def subject(self, sensor): + return sensor.value + + @pytest.fixture + def python_version(self): + import sys + + return sys.version_info + + def test_data_value_is_sys_versioninfo(self, python_version, subject): + assert subject() == python_version + + +class TestPythonVersionSensor: + def test_str_representation_is_formatted_value(self, sensor, version): + with mock.patch("sys.version_info", new=version(3, 4, 1, "final", 1)): + assert str(sensor) == "3.4" diff --git a/Ch11/apd.sensors-chapter11-ex01/tests/test_ramusage.py b/Ch11/apd.sensors-chapter11-ex01/tests/test_ramusage.py new file mode 100644 index 0000000..197ad06 --- /dev/null +++ b/Ch11/apd.sensors-chapter11-ex01/tests/test_ramusage.py @@ -0,0 +1,47 @@ +from unittest import mock + +import pytest + +from apd.sensors.sensors import RAMAvailable + + +@pytest.fixture +def sensor(): + return RAMAvailable() + + +class TestRAMAvailableFormatter: + @pytest.fixture + def subject(self, sensor): + return sensor.format + + def test_format_bytes(self, subject): + assert subject(15) == "15.0 B" + + def test_format_kibibytes(self, subject): + assert subject(15000) == "14.6 KiB" + + def test_format_mibibytes(self, subject): + assert subject(15000000) == "14.3 MiB" + + def test_format_gibibytes(self, subject): + assert subject(15000000000) == "14.0 GiB" + + +class TestRAMAvailableValue: + @pytest.fixture + def subject(self, sensor): + return sensor.value + + @pytest.fixture + def virtual_memory(self): + with mock.patch("psutil.virtual_memory") as virtual_memory: + virtual_memory.return_value.available = 1024 + yield virtual_memory + + def test_virtual_memory_called(self, subject, virtual_memory): + assert subject() == 1024 + assert virtual_memory.call_count == 1 + + def test_str_representation_is_formatted_value(self, sensor, virtual_memory): + assert str(sensor) == "1.0 KiB" diff --git a/Ch11/apd.sensors-chapter11-ex01/tests/test_sensors.py b/Ch11/apd.sensors-chapter11-ex01/tests/test_sensors.py new file mode 100644 index 0000000..0f74084 --- /dev/null +++ b/Ch11/apd.sensors-chapter11-ex01/tests/test_sensors.py @@ -0,0 +1,126 @@ +import json +from unittest import mock + +from click.testing import CliRunner + +import pytest + +import apd.sensors.cli +from apd.sensors.exceptions import UserFacingCLIError +import apd.sensors.sensors + + +def test_sensors(): + assert hasattr(apd.sensors.sensors, "PythonVersion") + + +class TestCLI: + @pytest.mark.functional + def test_first_sensor_is_first_two_lines_of_cli_output(self): + runner = CliRunner() + with mock.patch("apd.sensors.cli.get_sensors") as get_sensors: + get_sensors.return_value = [apd.sensors.sensors.PythonVersion()] + result = runner.invoke(apd.sensors.cli.show_sensors) + python_version = str(apd.sensors.sensors.PythonVersion()) + assert ["Python Version", python_version] == result.stdout.split("\n")[:2] + + def test_failing_sensor_shows_error(self): + from .test_utils import FailingSensor + + runner = CliRunner() + with mock.patch("apd.sensors.cli.get_sensors") as get_sensors: + get_sensors.return_value = [ + FailingSensor(10), + apd.sensors.sensors.PythonVersion(), + ] + result = runner.invoke(apd.sensors.cli.show_sensors) + assert ["Sensor which fails", "Failing 9 more times"] == result.stdout.split( + "\n" + )[:2] + assert "Python Version" in result.stdout + + def test_mocked_failing_sensor_shows_error(self): + from apd.sensors.base import Sensor + from apd.sensors.exceptions import IntermittentSensorFailureError + + FailingSensor = mock.MagicMock(spec=Sensor) + FailingSensor.title = "Sensor which fails" + FailingSensor.name = "FailingSensor" + FailingSensor.value.side_effect = ( + FailingSensor.__str__.side_effect + ) = IntermittentSensorFailureError("Failing sensor") + + runner = CliRunner() + with mock.patch("apd.sensors.cli.get_sensors") as get_sensors: + get_sensors.return_value = [ + FailingSensor, + apd.sensors.sensors.PythonVersion(), + ] + result = runner.invoke(apd.sensors.cli.show_sensors) + assert ["Sensor which fails", "Failing sensor"] == result.stdout.split("\n")[:2] + assert "Python Version" in result.stdout + + +class TestSensorFromPath: + @pytest.fixture + def subject(self): + return apd.sensors.cli.get_sensor_by_path + + def test_get_sensor_by_path(self, subject): + assert isinstance( + subject("apd.sensors.sensors:PythonVersion"), + apd.sensors.sensors.PythonVersion, + ) + + def test_invalid_format(self, subject): + with pytest.raises(UserFacingCLIError, match="dotted.path.to.module:ClassName"): + subject("apd.sensors.sensors.PythonVersion") + + def test_invalid_module(self, subject): + with pytest.raises(UserFacingCLIError, match="Could not import module"): + subject("apd.nonsense.sensor:FakeSensor") + + def test_missing_sensor(self, subject): + with pytest.raises(UserFacingCLIError, match="Could not find attribute"): + subject("apd.sensors.sensors:FakeSensor") + + def test_invalid_sensor(self, subject): + with pytest.raises(UserFacingCLIError, match="is not recognised as a Sensor"): + subject("apd.sensors.sensors:Sensor") + + +@pytest.mark.functional +def test_develop_option_finds_sensor_by_path(): + runner = CliRunner() + result = runner.invoke( + apd.sensors.cli.show_sensors, ["--develop", "apd.sensors.sensors:PythonVersion"] + ) + python_version = str(apd.sensors.sensors.PythonVersion()) + assert ["Python Version", python_version, "", ""] == result.stdout.split("\n") + + +class TestDefaultSerializer: + @pytest.fixture + def python_version(self): + return apd.sensors.sensors.PythonVersion() + + @pytest.fixture + def serialize(self, python_version): + return python_version.to_json_compatible + + @pytest.fixture + def deserialize(self, python_version): + return python_version.from_json_compatible + + def test_serialize_deserialize_is_symmetric( + self, python_version, serialize, deserialize + ): + value = python_version.value() + serialized = serialize(value) + json_version = json.dumps(serialized) + assert ( + json_version == f"[{value.major}, {value.minor}, {value.micro}," + f' "{value.releaselevel}", {value.serial}]' + ) + deserialized = deserialize(serialized) + assert deserialized == value diff --git a/Ch11/apd.sensors-chapter11-ex01/tests/test_utils.py b/Ch11/apd.sensors-chapter11-ex01/tests/test_utils.py new file mode 100644 index 0000000..34a82f8 --- /dev/null +++ b/Ch11/apd.sensors-chapter11-ex01/tests/test_utils.py @@ -0,0 +1,57 @@ +import typing as t + +import pytest + +from apd.sensors.base import JSONSensor +from apd.sensors.exceptions import ( + IntermittentSensorFailureError, + PersistentSensorFailureError, +) +from apd.sensors.utils import get_value_with_retries + + +class FailingSensor(JSONSensor[bool]): + + title = "Sensor which fails" + name = "FailingSensor" + + def __init__( + self, + n: int = 3, + exception_type: t.Type[Exception] = IntermittentSensorFailureError, + ): + self.n = n + self.exception_type = exception_type + + def value(self) -> bool: + self.n -= 1 + if self.n: + raise self.exception_type(f"Failing {self.n} more times") + else: + return True + + @classmethod + def format(cls, value: bool) -> str: + return "Yes" if value else "No" + + +class TestRetry: + def test_default_retries(self): + sensor = FailingSensor(3) + value = get_value_with_retries(sensor) + assert value is True + + def test_n_retries(self): + sensor = FailingSensor(5) + value = get_value_with_retries(sensor, retries=5) + assert value is True + + def test_insufficient_retries(self): + sensor = FailingSensor(5) + with pytest.raises(IntermittentSensorFailureError): + get_value_with_retries(sensor, retries=4) + + def test_permanent_failures_not_retried(self): + sensor = FailingSensor(3, PersistentSensorFailureError) + with pytest.raises(PersistentSensorFailureError): + get_value_with_retries(sensor) diff --git a/Ch11/apd.sensors-chapter11/.pre-commit-config.yaml b/Ch11/apd.sensors-chapter11/.pre-commit-config.yaml new file mode 100644 index 0000000..d3b6ec7 --- /dev/null +++ b/Ch11/apd.sensors-chapter11/.pre-commit-config.yaml @@ -0,0 +1,26 @@ +repos: + +- repo: local + hooks: + - id: black + name: black + entry: pipenv run black + args: [--quiet] + language: system + types: [python] + + - id: mypy + name: mypy + exclude: (?x)^( + (.*)/setup.py + )$ + entry: pipenv run mypy + args: ["--follow-imports=skip"] + language: system + types: [python] + + - id: flake8 + name: flake8 + entry: pipenv run flake8 + language: system + types: [python] diff --git a/Ch11/apd.sensors-chapter11/CHANGES.md b/Ch11/apd.sensors-chapter11/CHANGES.md new file mode 100644 index 0000000..b2f6115 --- /dev/null +++ b/Ch11/apd.sensors-chapter11/CHANGES.md @@ -0,0 +1,44 @@ +## Changes + +### 2.2 (2020-01-17) + +* Add ability to store data points on query (Matthew Wilkes) +* Add v3.0 API to distinguish sensor errors and include historical data (Matthew Wilkes) + +### 2.1.1 (2020-01-06) + +* Cache DHT sensor connections (Matthew Wilkes) +* Force use of bitbang interface for DHT sensors, to improve reliability on Linux (Matthew Wilkes) + +### 2.1.0 (2019-12-09) + +* Add optional APD_SENSORS_DEPLOYMENT_ID parameter and v2.1 API which allows + users to find a unique identifier for a webapp sensor deployment (Matthew Wilkes) + +### 2.0.0 (2019-09-08) + +* Add `to_json_compatible` and `from_json_compatible` methods to Sensor + to facilitate better HTTP API (Matthew Wilkes) + +* HTTP API is now versioned. The API from 1.3.0 is available at /v/1.0 + and an updated version is at /v/2.0 (Matthew Wilkes) + +### 1.3.0 (2019-08-20) + +* WSGI HTTP API support added (Matthew Wilkes) + +### 1.2.0 (2019-08-05) + +* Add external plugin support through `apd.sensors.sensors` entrypoint (Matthew Wilkes) + +### 1.1.0 (2019-07-12) + +* Add --develop argument (Matthew Wilkes) + +### 1.0.1 (2019-06-20) + +* Fix broken 1.0.0 release (Matthew Wilkes) + +### 1.0.0 (2019-06-20) + +* Added initial sensors (Matthew Wilkes) diff --git a/Ch11/apd.sensors-chapter11/LICENCE b/Ch11/apd.sensors-chapter11/LICENCE new file mode 100644 index 0000000..053f694 --- /dev/null +++ b/Ch11/apd.sensors-chapter11/LICENCE @@ -0,0 +1,19 @@ +Copyright (c) 2019 Matthew Wilkes + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/Ch11/apd.sensors-chapter11/Pipfile b/Ch11/apd.sensors-chapter11/Pipfile new file mode 100644 index 0000000..b08bc1b --- /dev/null +++ b/Ch11/apd.sensors-chapter11/Pipfile @@ -0,0 +1,34 @@ +[[source]] +name = "pypi" +url = "https://pypi.org/simple" +verify_ssl = true + +[[source]] +name = "piwheels" +url = "https://piwheels.org/simple" +verify_ssl = true + +[dev-packages] +ipykernel = "*" +remote-ikernel = "*" +pytest = "*" +pytest-cov = "*" +mypy = "*" +flake8 = "*" +black = "*" +pre-commit = "*" +wheel = "*" +twine = "*" +webtest = "*" +sqlalchemy-stubs = "*" + +[packages] +apd-sensors = {editable = true,extras = ["scheduled", "webapp", "storedapi"],path = "."} +apd-sunnyboy-solar = {editable = true,path = "./plugins/apd.sunnyboy_solar"} +waitress = "*" +pint = "==0.9" + +[requires] + +[pipenv] +allow_prereleases = true diff --git a/Ch11/apd.sensors-chapter11/Pipfile.lock b/Ch11/apd.sensors-chapter11/Pipfile.lock new file mode 100644 index 0000000..1304857 --- /dev/null +++ b/Ch11/apd.sensors-chapter11/Pipfile.lock @@ -0,0 +1,872 @@ +{ + "_meta": { + "hash": { + "sha256": "34faaa40acc9d73922634e8c6bef3e8cdeffc6728a1c907283b70297d90b514b" + }, + "pipfile-spec": 6, + "requires": {}, + "sources": [ + { + "name": "pypi", + "url": "https://pypi.org/simple", + "verify_ssl": true + }, + { + "name": "piwheels", + "url": "https://piwheels.org/simple", + "verify_ssl": true + } + ] + }, + "default": { + "apd-sensors": { + "editable": true, + "path": "." + }, + "apd-sunnyboy-solar": { + "editable": true, + "path": "./plugins/apd.sunnyboy_solar" + }, + "click": { + "hashes": [ + "sha256:2335065e6395b9e67ca716de5f7526736bfa6ceead690adf616d925bdc622b13", + 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+ }, + "zipp": { + "hashes": [ + "sha256:4970c3758f4e89a7857a973b1e2a5d75bcdc47794442f2e2dd4fe8e0466e809a", + "sha256:8a5712cfd3bb4248015eb3b0b3c54a5f6ee3f2425963ef2a0125b8bc40aafaec" + ], + "version": "==0.5.2" + } + } +} diff --git a/Ch11/apd.sensors-chapter11/README.md b/Ch11/apd.sensors-chapter11/README.md new file mode 100644 index 0000000..cd96353 --- /dev/null +++ b/Ch11/apd.sensors-chapter11/README.md @@ -0,0 +1,96 @@ +# Advanced Python Development Sensors + +This is the data collection package that forms part of the running example +for the book [Advanced Python Development](https://advancedpython.dev). + +## Usage + +This installs a console script called `sensors` that returns a report on +various aspects of the system. The available sensors are: + +* Python version +* IP Addresses +* CPU Usage +* RAM Available +* Battery charging state +* Ambient Temperature +* Ambient Humidity + +There are no command-line options, to view the report run `sensors` on the +command line. + +## Caveats + +The Ambient Temperature and Ambient Humidity sensors are only available on +RaspberryPi hosts and assume that a DHT22 sensor is connected to pin `D20`. + +The location and type of the sensor can be controlled by setting a pair of +environment variables: `APD_SENSORS_TEMPERATURE_BOARD` and +`APD_SENSORS_TEMPERATURE_PIN`. + +If there is an entry in `/etc/hosts` for the current machine's hostname that +value will be the only result from the IP Addresses sensor. + +## Installation + +You can install with `pip3 install apd.sensors` under Python 3.7 or higher. + +We recommend using pipenv to manage your environment, in which case you would +install using `pipenv --three install apd.sensors` and run the programme using +`pipenv run sensors`. + +## API server + +There is an optional API server shipped with apd.sensors. To use this you +should install the `apd.sensors[webapp]` extra. The API can then be started +with the non-production quality wsgiref server using: + + python -m apd.sensors.wsgi.serve + +or through Waitress (if installed) using: + + waitress-serve --call apd.sensors.wsgi:set_up_config + +Other WSGI servers will also work, you should use set_up_config as a factory +function. + +An environment variable is required to use the API server, `APD_SENSORS_API_KEY` +should be set to the API key required to gain access. One can be generated +using: + + python -c "import uuid; print(uuid.uuid4().hex)" + +The following endpoints are supported: + +* /v/3.0/sensors +* /v/3.0/sensors/sensorid +* /v/3.0/deployment_id + +## Historical data + +You can install optional functionality to periodically store sensor +values using the `apd.sensors[scheduled]` extra. In this case, +`sensors --save` will store the recorded data to `sensor_data.sqlite` +in the current working directory. + +The database connection can be specified with `--db sqlite:////var/sensors.sqlite`, +for example. It can also be specified with the `APD_SENSORS_DB_URI` +environment variable. + +The database must be migrated to contain the correct data first. This can be +done by running `alembic upgrade head` with the following alembic.ini file +in the current working directory. + + [alembic] + script_location = apd.sensors:alembic + sqlalchemy.url = sqlite:///sensor_data.sqlite + +### Historical data API + +An API to extract historical data is also available if installed with `apd.sensors[webapp,scheduled,storedapi]`. + +This provides the following three URIs, where start and end are a date/time in ISO format. + +* /v/3.0/historical +* /v/3.0/historical/start +* /v/3.0/historical/start/end diff --git a/Ch11/apd.sensors-chapter11/plugins/apd.sunnyboy_solar/pyproject.toml b/Ch11/apd.sensors-chapter11/plugins/apd.sunnyboy_solar/pyproject.toml new file mode 100644 index 0000000..4d3bb67 --- /dev/null +++ b/Ch11/apd.sensors-chapter11/plugins/apd.sunnyboy_solar/pyproject.toml @@ -0,0 +1,5 @@ +[build-system] +requires = [ + "setuptools", +] +build-backend = "setuptools.build_meta" diff --git a/Ch11/apd.sensors-chapter11/plugins/apd.sunnyboy_solar/setup.cfg b/Ch11/apd.sensors-chapter11/plugins/apd.sunnyboy_solar/setup.cfg new file mode 100644 index 0000000..0f5db10 --- /dev/null +++ b/Ch11/apd.sensors-chapter11/plugins/apd.sunnyboy_solar/setup.cfg @@ -0,0 +1,33 @@ +[mypy] +ignore_missing_imports = True + +[flake8] +max-line-length = 88 + +[metadata] +name = apd.sunnyboy_solar +version = attr: apd.sunnyboy_solar.VERSION +description = APD Sensor for reading data from Sunnyboy inverters +long_description = file: README.md, CHANGES.md, LICENCE +long_description_content_type = text/markdown +keywords = iot solar +license = MIT +classifiers = + Programming Language :: Python :: 3 + Programming Language :: Python :: 3.7 + +[options] +zip_safe = False +include_package_data = True +package_dir = + =src +packages = find_namespace: +install_requires = + apd.sensors + +[options.packages.find] +where = src + +[options.entry_points] +apd.sensors.sensors = + SolarCumulativeOutput = apd.sunnyboy_solar.sensor:SolarCumulativeOutput diff --git a/Ch11/apd.sensors-chapter11/plugins/apd.sunnyboy_solar/setup.py b/Ch11/apd.sensors-chapter11/plugins/apd.sunnyboy_solar/setup.py new file mode 100644 index 0000000..6068493 --- /dev/null +++ b/Ch11/apd.sensors-chapter11/plugins/apd.sunnyboy_solar/setup.py @@ -0,0 +1,3 @@ +from setuptools import setup + +setup() diff --git a/Ch11/apd.sensors-chapter11/plugins/apd.sunnyboy_solar/src/apd/sunnyboy_solar/Pipfile b/Ch11/apd.sensors-chapter11/plugins/apd.sunnyboy_solar/src/apd/sunnyboy_solar/Pipfile new file mode 100644 index 0000000..e69de29 diff --git a/Ch11/apd.sensors-chapter11/plugins/apd.sunnyboy_solar/src/apd/sunnyboy_solar/__init__.py b/Ch11/apd.sensors-chapter11/plugins/apd.sunnyboy_solar/src/apd/sunnyboy_solar/__init__.py new file mode 100644 index 0000000..3277f64 --- /dev/null +++ b/Ch11/apd.sensors-chapter11/plugins/apd.sunnyboy_solar/src/apd/sunnyboy_solar/__init__.py @@ -0,0 +1 @@ +VERSION = "1.0.0" diff --git a/Ch11/apd.sensors-chapter11/plugins/apd.sunnyboy_solar/src/apd/sunnyboy_solar/sensor.py b/Ch11/apd.sensors-chapter11/plugins/apd.sunnyboy_solar/src/apd/sunnyboy_solar/sensor.py new file mode 100644 index 0000000..190159a --- /dev/null +++ b/Ch11/apd.sensors-chapter11/plugins/apd.sunnyboy_solar/src/apd/sunnyboy_solar/sensor.py @@ -0,0 +1,73 @@ +import os +import subprocess +import typing as t + +from pint import _DEFAULT_REGISTRY as ureg + +from apd.sensors.base import Sensor +from apd.sensors.exceptions import ( + PersistentSensorFailureError, + IntermittentSensorFailureError, +) + + +class SolarCumulativeOutput(Sensor[t.Any]): + name = "SolarCumulativeOutput" + title = "Solar panel cumulative output" + + def __init__( + self, path: t.Optional[str] = None, bt_addr: t.Optional[str] = None + ) -> None: + self.path = os.environ.get( + "APD_SUNNYBOYSOLAR_PATH", "/home/pi/opensunny-master/opensunny" + ) + self.bt_addr = os.environ.get("APD_SUNNYBOYSOLAR_BT_ADDRESS", None) + + def value(self) -> t.Optional[t.Any]: + if self.bt_addr is None: + raise PersistentSensorFailureError("Inverter address not configured") + try: + output: bytes = subprocess.check_output( + [self.path, "-i", self.bt_addr], stderr=subprocess.STDOUT, timeout=15 + ) + except subprocess.CalledProcessError as err: + raise IntermittentSensorFailureError( + "Failure communicating with inverter" + ) from err + except FileNotFoundError as err: + raise PersistentSensorFailureError( + "Inverter control software not installed" + ) from err + + lines = filter(None, output.split(b"\n")) + found = {} + for line in lines: + start, value = line.rsplit(b"=", 1) + start, key = start.rsplit(b" ", 1) + found[key] = value + + try: + yield_total = float(found[b"yield_total"][:-3].replace(b".", b"")) + power_dc_1 = int(found[b"power_dc_1"][:-1]) + power_dc_2 = int(found[b"power_dc_2"][:-1]) + if power_dc_1 > 1500 or power_dc_2 > 1500: + raise IntermittentSensorFailureError( + "Received corrupt data from inverter" + ) + except (ValueError, IndexError, KeyError) as err: + raise IntermittentSensorFailureError( + "Received incomplete data from inverter" + ) from err + return ureg.Quantity(yield_total, "watt") + + @classmethod + def format(cls, value: t.Any) -> str: + return "{:~P}".format(value.to(ureg.kilowatt)) + + @classmethod + def to_json_compatible(cls, value: t.Any) -> t.Dict[str, t.Union[str, float]]: + return {"magnitude": value.magnitude, "unit": str(value.units)} + + @classmethod + def from_json_compatible(cls, json_version: t.Any) -> t.Any: + return ureg.Quantity(json_version["magnitude"], ureg[json_version["unit"]]) diff --git a/Ch11/apd.sensors-chapter11/pyproject.toml b/Ch11/apd.sensors-chapter11/pyproject.toml new file mode 100644 index 0000000..4d3bb67 --- /dev/null +++ b/Ch11/apd.sensors-chapter11/pyproject.toml @@ -0,0 +1,5 @@ +[build-system] +requires = [ + "setuptools", +] +build-backend = "setuptools.build_meta" diff --git a/Ch11/apd.sensors-chapter11/pytest.ini b/Ch11/apd.sensors-chapter11/pytest.ini new file mode 100644 index 0000000..861f3d0 --- /dev/null +++ b/Ch11/apd.sensors-chapter11/pytest.ini @@ -0,0 +1,3 @@ +[pytest] +markers = + functional: these tests are significantly slower as they run the whole CLI script \ No newline at end of file diff --git a/Ch11/apd.sensors-chapter11/setup.cfg b/Ch11/apd.sensors-chapter11/setup.cfg new file mode 100644 index 0000000..008e2fd --- /dev/null +++ b/Ch11/apd.sensors-chapter11/setup.cfg @@ -0,0 +1,80 @@ +[mypy] +namespace_packages = True +mypy_path = src +plugins = sqlmypy + +[mypy-psutil] +ignore_missing_imports = True + +[mypy-alembic] +ignore_missing_imports = True + +[mypy-adafruit_dht] +ignore_missing_imports = True + +[mypy-board] +ignore_missing_imports = True + +[mypy-flask_sqlalchemy] +ignore_missing_imports = True + +[mypy-pint] +ignore_missing_imports = True + +[mypy-pytest] +ignore_missing_imports = True + +[mypy-webtest] +ignore_missing_imports = True + +[flake8] +max-line-length = 88 + +[metadata] +name = apd.sensors +version = attr: apd.sensors.VERSION +description = APD Sensor package +long_description = file: README.md, CHANGES.md, LICENCE +long_description_content_type = text/markdown +keywords = iot +license = MIT +classifiers = + Programming Language :: Python :: 3 + Programming Language :: Python :: 3.7 + +[options] +zip_safe = False +include_package_data = True +package_dir = + =src +packages = find_namespace: +install_requires = + psutil + click + pint + adafruit-circuitpython-dht ; 'arm' in platform_machine + +[options.packages.find] +where = src + +[options.entry_points] +console_scripts = + sensors = apd.sensors.cli:show_sensors +apd.sensors.sensors = + PythonVersion = apd.sensors.sensors:PythonVersion + IPAddresses = apd.sensors.sensors:IPAddresses + CPULoad = apd.sensors.sensors:CPULoad + RAMAvailable = apd.sensors.sensors:RAMAvailable + ACStatus = apd.sensors.sensors:ACStatus + Temperature = apd.sensors.sensors:Temperature + RelativeHumidity = apd.sensors.sensors:RelativeHumidity + +[options.extras_require] +webapp = flask +scheduled = + sqlalchemy + alembic +storedapi = + flask-sqlalchemy + python-dateutil + \ No newline at end of file diff --git a/Ch11/apd.sensors-chapter11/setup.py b/Ch11/apd.sensors-chapter11/setup.py new file mode 100644 index 0000000..6068493 --- /dev/null +++ b/Ch11/apd.sensors-chapter11/setup.py @@ -0,0 +1,3 @@ +from setuptools import setup + +setup() diff --git a/Ch11/apd.sensors-chapter11/src/apd/sensors/__init__.py b/Ch11/apd.sensors-chapter11/src/apd/sensors/__init__.py new file mode 100644 index 0000000..127c148 --- /dev/null +++ b/Ch11/apd.sensors-chapter11/src/apd/sensors/__init__.py @@ -0,0 +1 @@ +VERSION = "2.1.0" diff --git a/Ch11/apd.sensors-chapter11/src/apd/sensors/alembic/README b/Ch11/apd.sensors-chapter11/src/apd/sensors/alembic/README new file mode 100644 index 0000000..98e4f9c --- /dev/null +++ b/Ch11/apd.sensors-chapter11/src/apd/sensors/alembic/README @@ -0,0 +1 @@ +Generic single-database configuration. \ No newline at end of file diff --git a/Ch11/apd.sensors-chapter11/src/apd/sensors/alembic/env.py b/Ch11/apd.sensors-chapter11/src/apd/sensors/alembic/env.py new file mode 100644 index 0000000..f508d4c --- /dev/null +++ b/Ch11/apd.sensors-chapter11/src/apd/sensors/alembic/env.py @@ -0,0 +1,77 @@ +from logging.config import fileConfig + +from sqlalchemy import engine_from_config +from sqlalchemy import pool + +from alembic import context + +import apd.sensors.database + +# this is the Alembic Config object, which provides +# access to the values within the .ini file in use. +config = context.config + +# Interpret the config file for Python logging. +# This line sets up loggers basically. +fileConfig(config.config_file_name) + +# add your model's MetaData object here +# for 'autogenerate' support +# from myapp import mymodel +# target_metadata = mymodel.Base.metadata +target_metadata = apd.sensors.database.metadata + +# other values from the config, defined by the needs of env.py, +# can be acquired: +# my_important_option = config.get_main_option("my_important_option") +# ... etc. + + +def run_migrations_offline(): + """Run migrations in 'offline' mode. + + This configures the context with just a URL + and not an Engine, though an Engine is acceptable + here as well. By skipping the Engine creation + we don't even need a DBAPI to be available. + + Calls to context.execute() here emit the given string to the + script output. + + """ + url = config.get_main_option("sqlalchemy.url") + context.configure( + url=url, + target_metadata=target_metadata, + literal_binds=True, + dialect_opts={"paramstyle": "named"}, + ) + + with context.begin_transaction(): + context.run_migrations() + + +def run_migrations_online(): + """Run migrations in 'online' mode. + + In this scenario we need to create an Engine + and associate a connection with the context. + + """ + connectable = engine_from_config( + config.get_section(config.config_ini_section), + prefix="sqlalchemy.", + poolclass=pool.NullPool, + ) + + with connectable.connect() as connection: + context.configure(connection=connection, target_metadata=target_metadata) + + with context.begin_transaction(): + context.run_migrations() + + +if context.is_offline_mode(): + run_migrations_offline() +else: + run_migrations_online() diff --git a/Ch11/apd.sensors-chapter11/src/apd/sensors/alembic/script.py.mako b/Ch11/apd.sensors-chapter11/src/apd/sensors/alembic/script.py.mako new file mode 100644 index 0000000..2c01563 --- /dev/null +++ b/Ch11/apd.sensors-chapter11/src/apd/sensors/alembic/script.py.mako @@ -0,0 +1,24 @@ +"""${message} + +Revision ID: ${up_revision} +Revises: ${down_revision | comma,n} +Create Date: ${create_date} + +""" +from alembic import op +import sqlalchemy as sa +${imports if imports else ""} + +# revision identifiers, used by Alembic. +revision = ${repr(up_revision)} +down_revision = ${repr(down_revision)} +branch_labels = ${repr(branch_labels)} +depends_on = ${repr(depends_on)} + + +def upgrade(): + ${upgrades if upgrades else "pass"} + + +def downgrade(): + ${downgrades if downgrades else "pass"} diff --git a/Ch11/apd.sensors-chapter11/src/apd/sensors/alembic/versions/0eeb2a54fea8_add_initial_sensor_table.py b/Ch11/apd.sensors-chapter11/src/apd/sensors/alembic/versions/0eeb2a54fea8_add_initial_sensor_table.py new file mode 100644 index 0000000..bb38e90 --- /dev/null +++ b/Ch11/apd.sensors-chapter11/src/apd/sensors/alembic/versions/0eeb2a54fea8_add_initial_sensor_table.py @@ -0,0 +1,45 @@ +"""Add initial sensor table + +Revision ID: 0eeb2a54fea8 +Revises: base +Create Date: 2020-01-16 17:43:37.298379 + +""" +from alembic import op +import sqlalchemy as sa + + +# revision identifiers, used by Alembic. +revision = "0eeb2a54fea8" +down_revision = None +branch_labels = None +depends_on = None + + +def upgrade(): + op.create_table( + "recorded_values", + sa.Column("id", sa.Integer(), nullable=False), + sa.Column("sensor_name", sa.String(), nullable=True), + sa.Column("collected_at", sa.TIMESTAMP(), nullable=True), + sa.Column("data", sa.JSON(), nullable=True), + sa.PrimaryKeyConstraint("id"), + ) + op.create_index( + op.f("ix_recorded_values_collected_at"), + "recorded_values", + ["collected_at"], + unique=False, + ) + op.create_index( + op.f("ix_recorded_values_sensor_name"), + "recorded_values", + ["sensor_name"], + unique=False, + ) + + +def downgrade(): + op.drop_index(op.f("ix_recorded_values_sensor_name"), table_name="recorded_values") + op.drop_index(op.f("ix_recorded_values_collected_at"), table_name="recorded_values") + op.drop_table("recorded_values") diff --git a/Ch11/apd.sensors-chapter11/src/apd/sensors/base.py b/Ch11/apd.sensors-chapter11/src/apd/sensors/base.py new file mode 100644 index 0000000..75e7e27 --- /dev/null +++ b/Ch11/apd.sensors-chapter11/src/apd/sensors/base.py @@ -0,0 +1,61 @@ +#!/usr/bin/env python +# coding: utf-8 +import typing as t + + +JSON_0 = t.Union[str, int, float, bool, None] +JSON_1 = t.Union[t.Dict[str, JSON_0], t.Iterable[JSON_0], JSON_0] +JSON_2 = t.Union[t.Dict[str, JSON_1], t.Iterable[JSON_1], JSON_1] +JSON_3 = t.Union[t.Dict[str, JSON_2], t.Iterable[JSON_2], JSON_2] +JSON_4 = t.Union[t.Dict[str, JSON_3], t.Iterable[JSON_3], JSON_3] +JSON_5 = t.Union[t.Dict[str, JSON_4], t.Iterable[JSON_4], JSON_4] +JSON_like = JSON_5 + + +T_value = t.TypeVar("T_value") +JSONT_value = t.TypeVar("JSONT_value", bound=JSON_like) + + +class Sensor(t.Generic[T_value]): + name: str + title: str + + def value(self) -> T_value: + raise NotImplementedError + + @classmethod + def format(cls, value: T_value) -> str: + raise NotImplementedError + + def __str__(self) -> str: + return self.format(self.value()) + + @classmethod + def to_json_compatible(cls, value: T_value) -> JSON_like: + raise NotImplementedError() + + @classmethod + def from_json_compatible(cls, json_version: JSON_like) -> T_value: + raise NotImplementedError() + + +class JSONSensor(Sensor[JSONT_value]): + @classmethod + def to_json_compatible(cls, value: JSONT_value) -> JSONT_value: + return value + + @classmethod + def from_json_compatible(cls, json_version: JSON_like) -> JSONT_value: + return t.cast(JSONT_value, json_version) + + +version_info_type = t.NamedTuple( + "version_info_type", + [ + ("major", int), + ("minor", int), + ("micro", int), + ("releaselevel", str), + ("serial", int), + ], +) diff --git a/Ch11/apd.sensors-chapter11/src/apd/sensors/cli.py b/Ch11/apd.sensors-chapter11/src/apd/sensors/cli.py new file mode 100644 index 0000000..8d99eeb --- /dev/null +++ b/Ch11/apd.sensors-chapter11/src/apd/sensors/cli.py @@ -0,0 +1,111 @@ +import importlib +import pkg_resources +import traceback +import typing as t + +import click + +from .database import store_sensor_data +from .sensors import Sensor +from .exceptions import DataCollectionError, UserFacingCLIError + + +def get_sensor_by_path(sensor_path: str) -> Sensor[t.Any]: + try: + module_name, sensor_name = sensor_path.split(":") + except ValueError as err: + raise UserFacingCLIError( + "Sensor path must be in the format dotted.path.to.module:ClassName", + return_code=17, + ) from err + try: + module = importlib.import_module(module_name) + except ImportError as err: + raise UserFacingCLIError( + f"Could not import module {module_name}", return_code=17 + ) from err + try: + sensor_class = getattr(module, sensor_name) + except AttributeError as err: + raise UserFacingCLIError( + f"Could not find attribute {sensor_name} in {module_name}", return_code=17 + ) from err + if ( + isinstance(sensor_class, type) + and issubclass(sensor_class, Sensor) + and sensor_class != Sensor + ): + return sensor_class() + else: + raise UserFacingCLIError( + f"Detected object {sensor_class!r} is not recognised as a Sensor type", + return_code=17, + ) + + +def get_sensors() -> t.Iterable[Sensor[t.Any]]: + sensors = [] + for sensor_class in pkg_resources.iter_entry_points("apd.sensors.sensors"): + class_ = sensor_class.load() + sensors.append(t.cast(Sensor[t.Any], class_())) + return sensors + + +@click.command(help="Displays the values of the sensors") +@click.option( + "--develop", required=False, metavar="path", help="Load a sensor by Python path" +) +@click.option("--verbose", is_flag=True, help="Show additional info") +@click.option("--save", is_flag=True, help="Store collected data to a database") +@click.option( + "--db", + metavar="", + default="sqlite:///sensor_data.sqlite", + help="The connection string to a database", + envvar="APD_SENSORS_DB_URI", +) +def show_sensors(develop: str, verbose: bool, save: bool, db: str) -> int: + sensors: t.Iterable[Sensor[t.Any]] + if develop: + try: + sensors = [get_sensor_by_path(develop)] + except UserFacingCLIError as error: + if verbose: + tb = traceback.format_exception(type(error), error, error.__traceback__) + click.echo("".join(tb)) + click.secho(error.message, fg="red", bold=True) + return error.return_code + else: + sensors = get_sensors() + + db_session = None + if save: + from sqlalchemy import create_engine + from sqlalchemy.orm import sessionmaker + + engine = create_engine(db) + sm = sessionmaker(engine) + db_session = sm() + + for sensor in sensors: + click.secho(sensor.title, bold=True) + try: + value = sensor.value() + except DataCollectionError as error: + if verbose: + tb = traceback.format_exception(type(error), error, error.__traceback__) + click.echo("".join(tb)) + continue + click.echo(error) + else: + click.echo(sensor.format(value)) + if save and db_session is not None: + store_sensor_data(sensor, value, db_session) + db_session.commit() + + click.echo("") + return 0 + + +if __name__ == "__main__": + show_sensors() diff --git a/Ch11/apd.sensors-chapter11/src/apd/sensors/database.py b/Ch11/apd.sensors-chapter11/src/apd/sensors/database.py new file mode 100644 index 0000000..a90ffba --- /dev/null +++ b/Ch11/apd.sensors-chapter11/src/apd/sensors/database.py @@ -0,0 +1,30 @@ +from __future__ import annotations + +import datetime +import typing as t + +import sqlalchemy +from sqlalchemy.schema import Table +from sqlalchemy.orm.session import Session + +from apd.sensors.base import Sensor + + +metadata = sqlalchemy.MetaData() + +sensor_values = Table( + "recorded_values", + metadata, + sqlalchemy.Column("id", sqlalchemy.Integer, primary_key=True), + sqlalchemy.Column("sensor_name", sqlalchemy.String, index=True), + sqlalchemy.Column("collected_at", sqlalchemy.TIMESTAMP, index=True), + sqlalchemy.Column("data", sqlalchemy.JSON), +) + + +def store_sensor_data(sensor: Sensor[t.Any], data: t.Any, db_session: Session) -> None: + now = datetime.datetime.now() + record = sensor_values.insert().values( + sensor_name=sensor.name, data=sensor.to_json_compatible(data), collected_at=now + ) + db_session.execute(record) diff --git a/Ch11/apd.sensors-chapter11/src/apd/sensors/exceptions.py b/Ch11/apd.sensors-chapter11/src/apd/sensors/exceptions.py new file mode 100644 index 0000000..725a0ad --- /dev/null +++ b/Ch11/apd.sensors-chapter11/src/apd/sensors/exceptions.py @@ -0,0 +1,32 @@ +import dataclasses + + +class APDSensorsError(Exception): + """An exception base class for all exceptions raised by the + sensor data collection system.""" + + +class DataCollectionError(APDSensorsError, RuntimeError): + """An error that represents the inability of a Sensor instance + to retrieve a value""" + + +class IntermittentSensorFailureError(DataCollectionError): + """A DataCollectionError that is expected to resolve itself + in short order""" + + +class PersistentSensorFailureError(DataCollectionError): + """A DataCollectionError that is unlikely to resolve itself + if retried.""" + + +@dataclasses.dataclass(frozen=True) +class UserFacingCLIError(APDSensorsError, SystemExit): + """A fatal error for the CLI""" + + message: str + return_code: int + + def __str__(self): + return f"[{self.return_code}] {self.message}" diff --git a/Ch11/apd.sensors-chapter11/src/apd/sensors/py.typed b/Ch11/apd.sensors-chapter11/src/apd/sensors/py.typed new file mode 100644 index 0000000..e69de29 diff --git a/Ch11/apd.sensors-chapter11/src/apd/sensors/sensors.py b/Ch11/apd.sensors-chapter11/src/apd/sensors/sensors.py new file mode 100644 index 0000000..c577dbf --- /dev/null +++ b/Ch11/apd.sensors-chapter11/src/apd/sensors/sensors.py @@ -0,0 +1,189 @@ +#!/usr/bin/env python +# coding: utf-8 +import math +import os +import socket +import sys +import typing as t + +import psutil +from pint import _DEFAULT_REGISTRY as ureg + +from .base import Sensor, JSONSensor, version_info_type +from .exceptions import ( + PersistentSensorFailureError, + IntermittentSensorFailureError, + DataCollectionError, +) + + +class PythonVersion(JSONSensor[version_info_type]): + name = "PythonVersion" + title = "Python Version" + + def value(self) -> version_info_type: + return version_info_type(*sys.version_info) + + @classmethod + def from_json_compatible(cls, json_version: t.Any) -> version_info_type: + return version_info_type(*json_version) + + @classmethod + def format(cls, value: version_info_type) -> str: + if value.micro == 0 and value.releaselevel == "alpha": + return "{0.major}.{0.minor}.{0.micro}a{0.serial}".format(value) + return "{0.major}.{0.minor}".format(value) + + +class IPAddresses(JSONSensor[t.Iterable[t.Tuple[str, str]]]): + name = "IPAddresses" + title = "IP Addresses" + FAMILIES = {"AF_INET": "IPv4", "AF_INET6": "IPv6"} + + def value(self) -> t.List[t.Tuple[str, str]]: + hostname = socket.gethostname() + addresses = socket.getaddrinfo(hostname, None) + address_info: t.List[t.Tuple[str, str]] = [] + for address in addresses: + family, ip = (address[0].name, address[4][0]) + if family not in self.FAMILIES: + continue + value = (family, ip) + if value not in address_info: + address_info.append(value) + return address_info + + @classmethod + def format(cls, value: t.Iterable[t.Tuple[str, str]]) -> str: + return "\n".join( + "{0} ({1})".format(address[1], cls.FAMILIES.get(address[0], "Unknown")) + for address in value + ) + + +class CPULoad(JSONSensor[float]): + name = "CPULoad" + title = "CPU Usage" + + def value(self) -> float: + return float(psutil.cpu_percent(interval=3)) / 100.0 + + @classmethod + def format(cls, value: float) -> str: + return "{:.1%}".format(value) + + +class RAMAvailable(JSONSensor[int]): + name = "RAMAvailable" + title = "RAM Available" + UNITS = ("B", "KiB", "MiB", "GiB", "TiB", "PiB", "EiB") + UNIT_SIZE = 2 ** 10 + + def value(self) -> int: + return int(psutil.virtual_memory().available) + + @classmethod + def format(cls, value: int) -> str: + magnitude = math.floor(math.log(value, cls.UNIT_SIZE)) + max_magnitude = len(cls.UNITS) - 1 + magnitude = min(magnitude, max_magnitude) + scaled_value = value / (cls.UNIT_SIZE ** magnitude) + return "{:.1f} {}".format(scaled_value, cls.UNITS[magnitude]) + + +class ACStatus(JSONSensor[bool]): + name = "ACStatus" + title = "AC Connected" + + def value(self) -> bool: + battery = psutil.sensors_battery() + if battery is not None: + value = battery.power_plugged + if value is None: + raise IntermittentSensorFailureError("Can't find AC status") + else: + return bool(value) + else: + raise PersistentSensorFailureError("No charging circuit installed") + + @classmethod + def format(cls, value: bool) -> str: + if value: + return "Connected" + else: + return "Not connected" + + +class DHTSensor: + def __init__(self) -> None: + self.board = os.environ.get("APD_SENSORS_TEMPERATURE_BOARD", "DHT22") + self.pin = os.environ.get("APD_SENSORS_TEMPERATURE_PIN", "D20") + + @property + def sensor(self) -> t.Any: + try: + import adafruit_dht + import board + + # Force using legacy interface + adafruit_dht._USE_PULSEIO = False + + sensor_type = getattr(adafruit_dht, self.board) + pin = getattr(board, self.pin) + return sensor_type(pin) + except (ImportError, NotImplementedError, AttributeError) as err: + # No DHT library results in an ImportError. + # Running on an unknown platform results in a + # NotImplementedError when getting the pin + raise PersistentSensorFailureError( + "Unable to initialise sensor interface" + ) from err + + +class Temperature(Sensor[t.Any], DHTSensor): + name = "Temperature" + title = "Ambient Temperature" + + def value(self) -> t.Any: + try: + return ureg.Quantity(self.sensor.temperature, ureg.celsius) + except DataCollectionError: + # This is one of our own exceptions, we don't need to re-wrap it + raise + except (RuntimeError, AttributeError) as err: + raise IntermittentSensorFailureError( + "Couldn't determine temperature" + ) from err + + @classmethod + def format(cls, value: t.Any) -> str: + return "{:.3~P} ({:.3~P})".format(value, value.to(ureg.fahrenheit)) + + @classmethod + def to_json_compatible(cls, value: t.Any) -> t.Any: + return {"magnitude": value.magnitude, "unit": str(value.units)} + + @classmethod + def from_json_compatible(cls, json_version: t.Any) -> t.Any: + return ureg.Quantity(json_version["magnitude"], ureg[json_version["unit"]]) + + def __str__(self) -> str: + return self.format(self.value()) + + +class RelativeHumidity(JSONSensor[float], DHTSensor): + name = "RelativeHumidity" + title = "Relative Humidity" + + def value(self) -> float: + try: + return float(self.sensor.humidity) + except DataCollectionError: + # This is one of our own exceptions, we don't need to re-wrap it + raise + except (TypeError, AttributeError) as err: + raise IntermittentSensorFailureError("Couldn't determine humidity") from err + + @classmethod + def format(cls, value: float) -> str: + return "{:.1%}".format(value) diff --git a/Ch11/apd.sensors-chapter11/src/apd/sensors/utils.py b/Ch11/apd.sensors-chapter11/src/apd/sensors/utils.py new file mode 100644 index 0000000..24f912b --- /dev/null +++ b/Ch11/apd.sensors-chapter11/src/apd/sensors/utils.py @@ -0,0 +1,20 @@ +from apd.sensors.base import Sensor, T_value +from apd.sensors.exceptions import IntermittentSensorFailureError + + +def get_value_with_retries(sensor: Sensor[T_value], retries: int = 3) -> T_value: + for i in range(retries): + try: + return sensor.value() + except IntermittentSensorFailureError: + if i == (retries - 1): + # This is the last retry, reraise + raise + else: + continue + # It shouldn't be possible to get here, but it's better to + # fall through with an appropriate exception rather than a + # None + raise IntermittentSensorFailureError( + f"Could not find a value after {retries} retries" + ) diff --git a/Ch11/apd.sensors-chapter11/src/apd/sensors/wsgi/__init__.py b/Ch11/apd.sensors-chapter11/src/apd/sensors/wsgi/__init__.py new file mode 100644 index 0000000..fbd2a91 --- /dev/null +++ b/Ch11/apd.sensors-chapter11/src/apd/sensors/wsgi/__init__.py @@ -0,0 +1,31 @@ +import flask + +try: + from flask_sqlalchemy import SQLAlchemy +except ImportError: + sql_support = False +else: + sql_support = True + + +from .base import set_up_config +from . import v10 +from . import v20 +from . import v21 +from . import v30 + + +__all__ = ["app", "set_up_config", "db"] + +app = flask.Flask(__name__) +app.register_blueprint(v10.version, url_prefix="/v/1.0") +app.register_blueprint(v20.version, url_prefix="/v/2.0") +app.register_blueprint(v21.version, url_prefix="/v/2.1") +app.register_blueprint(v30.version, url_prefix="/v/3.0") + +if sql_support: + from apd.sensors.database import metadata + + db = SQLAlchemy(app, metadata=metadata) +else: + db = None diff --git a/Ch11/apd.sensors-chapter11/src/apd/sensors/wsgi/base.py b/Ch11/apd.sensors-chapter11/src/apd/sensors/wsgi/base.py new file mode 100644 index 0000000..b3c73cb --- /dev/null +++ b/Ch11/apd.sensors-chapter11/src/apd/sensors/wsgi/base.py @@ -0,0 +1,47 @@ +from hmac import compare_digest +import functools +import os +import typing as t + +import flask + + +ViewFuncReturn = t.TypeVar("ViewFuncReturn") +ErrorReturn = t.Tuple[t.Dict[str, str], int] +REQUIRED_CONFIG_KEYS = {"APD_SENSORS_API_KEY"} + + +def require_api_key( + func: t.Callable[..., ViewFuncReturn] +) -> t.Callable[..., t.Union[ViewFuncReturn, ErrorReturn]]: + @functools.wraps(func) + def wrapped(*args, **kwargs) -> t.Union[ViewFuncReturn, ErrorReturn]: + api_key = flask.current_app.config["APD_SENSORS_API_KEY"] + headers = flask.request.headers + supplied_key = headers.get("X-API-Key", "") + if not compare_digest(api_key, supplied_key): + return {"error": "Supply API key in X-API-Key header"}, 403 + return func(*args, **kwargs) + + return wrapped + + +def set_up_config( + environ: t.Optional[t.Dict[str, str]] = None, + to_configure: t.Optional[flask.Flask] = None, +) -> flask.Flask: + if environ is None: + environ = dict(os.environ) + if to_configure is None: + from apd.sensors.wsgi import app + + to_configure = app + missing_keys = REQUIRED_CONFIG_KEYS - environ.keys() + if missing_keys: + raise ValueError("Missing config variables: {}".format(", ".join(missing_keys))) + data_file = os.path.join(os.getcwd(), "sensor_data.sqlite") + environ["SQLALCHEMY_DATABASE_URI"] = environ.get( + "APD_SENSORS_DB_URI", f"sqlite:///{data_file}" + ) + to_configure.config.from_mapping(environ) + return to_configure diff --git a/Ch11/apd.sensors-chapter11/src/apd/sensors/wsgi/serve.py b/Ch11/apd.sensors-chapter11/src/apd/sensors/wsgi/serve.py new file mode 100644 index 0000000..0b1f684 --- /dev/null +++ b/Ch11/apd.sensors-chapter11/src/apd/sensors/wsgi/serve.py @@ -0,0 +1,10 @@ +from . import app +from .base import set_up_config + +if __name__ == "__main__": + import wsgiref.simple_server + + set_up_config(None, app) + + with wsgiref.simple_server.make_server("", 8000, app) as server: + server.serve_forever() diff --git a/Ch11/apd.sensors-chapter11/src/apd/sensors/wsgi/v10.py b/Ch11/apd.sensors-chapter11/src/apd/sensors/wsgi/v10.py new file mode 100644 index 0000000..f3ae5da --- /dev/null +++ b/Ch11/apd.sensors-chapter11/src/apd/sensors/wsgi/v10.py @@ -0,0 +1,30 @@ +import json +import typing as t + +import flask + +from apd.sensors import cli +from apd.sensors.exceptions import DataCollectionError +from .base import require_api_key + +version = flask.Blueprint(__name__, __name__) + + +@version.route("/sensors/") +@require_api_key +def sensor_values() -> t.Tuple[t.Dict[str, t.Any], int, t.Dict[str, str]]: + headers = {"Content-Security-Policy": "default-src 'none'"} + data = {} + for sensor in cli.get_sensors(): + try: + value = sensor.value() + except DataCollectionError: + value = None + try: + json.dumps(value) + except TypeError: + # This value isn't JSON serializable, skip it + continue + else: + data[sensor.title] = value + return data, 200, headers diff --git a/Ch11/apd.sensors-chapter11/src/apd/sensors/wsgi/v20.py b/Ch11/apd.sensors-chapter11/src/apd/sensors/wsgi/v20.py new file mode 100644 index 0000000..4ab1522 --- /dev/null +++ b/Ch11/apd.sensors-chapter11/src/apd/sensors/wsgi/v20.py @@ -0,0 +1,40 @@ +import typing as t + +import flask + +from apd.sensors import cli +from apd.sensors.exceptions import DataCollectionError +from .base import require_api_key + +version = flask.Blueprint(__name__, __name__) + + +@version.route("/sensors/") +@version.route("/sensors/") +@require_api_key +def sensor_values(sensor_id=None) -> t.Tuple[t.Dict[str, t.Any], int, t.Dict[str, str]]: + headers = {"Content-Security-Policy": "default-src 'none'"} + sensors = [] + for sensor in cli.get_sensors(): + if sensor_id and sensor_id != sensor.name: + continue + try: + try: + value = sensor.value() + except DataCollectionError: + human_readable = "Unknown" + json_value = None + else: + json_value = sensor.to_json_compatible(value) + human_readable = sensor.format(value) + sensor_data = { + "id": sensor.name, + "title": sensor.title, + "value": json_value, + "human_readable": human_readable, + } + sensors.append(sensor_data) + except NotImplementedError: + pass + data = {"sensors": sensors} + return data, 200, headers diff --git a/Ch11/apd.sensors-chapter11/src/apd/sensors/wsgi/v21.py b/Ch11/apd.sensors-chapter11/src/apd/sensors/wsgi/v21.py new file mode 100644 index 0000000..16cbb72 --- /dev/null +++ b/Ch11/apd.sensors-chapter11/src/apd/sensors/wsgi/v21.py @@ -0,0 +1,47 @@ +import typing as t + +import flask + +from apd.sensors import cli +from apd.sensors.exceptions import DataCollectionError +from .base import require_api_key + +version = flask.Blueprint(__name__, __name__) + + +@version.route("/sensors/") +@version.route("/sensors/") +@require_api_key +def sensor_values(sensor_id=None) -> t.Tuple[t.Dict[str, t.Any], int, t.Dict[str, str]]: + headers = {"Content-Security-Policy": "default-src 'none'"} + sensors = [] + for sensor in cli.get_sensors(): + if sensor_id and sensor_id != sensor.name: + continue + try: + try: + value = sensor.value() + except DataCollectionError: + human_readable = "Unknown" + json_value = None + else: + json_value = sensor.to_json_compatible(value) + human_readable = sensor.format(value) + sensor_data = { + "id": sensor.name, + "title": sensor.title, + "value": json_value, + "human_readable": human_readable, + } + sensors.append(sensor_data) + except NotImplementedError: + pass + data = {"sensors": sensors} + return data, 200, headers + + +@version.route("/deployment_id") +def deployment_id() -> t.Tuple[t.Dict[str, t.Any], int, t.Dict[str, str]]: + headers = {"Content-Security-Policy": "default-src 'none'"} + data = {"deployment_id": flask.current_app.config["APD_SENSORS_DEPLOYMENT_ID"]} + return data, 200, headers diff --git a/Ch11/apd.sensors-chapter11/src/apd/sensors/wsgi/v30.py b/Ch11/apd.sensors-chapter11/src/apd/sensors/wsgi/v30.py new file mode 100644 index 0000000..b623218 --- /dev/null +++ b/Ch11/apd.sensors-chapter11/src/apd/sensors/wsgi/v30.py @@ -0,0 +1,101 @@ +import datetime +import typing as t + +import flask + +from apd.sensors import cli +from apd.sensors.exceptions import DataCollectionError + +from .base import require_api_key + +version = flask.Blueprint(__name__, __name__) + + +@version.route("/sensors/") +@version.route("/sensors/") +@require_api_key +def sensor_values(sensor_id=None) -> t.Tuple[t.Dict[str, t.Any], int, t.Dict[str, str]]: + headers = {"Content-Security-Policy": "default-src 'none'"} + sensors = [] + errors = [] + for sensor in cli.get_sensors(): + now = datetime.datetime.now() + if sensor_id and sensor_id != sensor.name: + continue + try: + try: + value = sensor.value() + except DataCollectionError as err: + error = { + "id": sensor.name, + "title": sensor.title, + "collected_at": now.isoformat(), + "error": str(err), + } + errors.append(error) + continue + sensor_data = { + "id": sensor.name, + "title": sensor.title, + "value": sensor.to_json_compatible(value), + "human_readable": sensor.format(value), + "collected_at": now.isoformat(), + } + sensors.append(sensor_data) + except NotImplementedError: + pass + data = {"sensors": sensors, "errors": errors} + return data, 200, headers + + +@version.route("/historical") +@version.route("/historical/") +@version.route("/historical//") +@require_api_key +def historical_values( + start: str = None, end: str = None +) -> t.Tuple[t.Dict[str, t.Any], int, t.Dict[str, str]]: + try: + import dateutil.parser + from apd.sensors.database import sensor_values + from apd.sensors.wsgi import db + except ImportError: + return {"error": "Historical data support is not installed"}, 501, {} + + db_session = db.session + headers = {"Content-Security-Policy": "default-src 'none'"} + + query = db_session.query(sensor_values) + if start: + query = query.filter( + sensor_values.c.collected_at >= dateutil.parser.parse(start) + ) + if end: + query = query.filter(sensor_values.c.collected_at <= dateutil.parser.parse(end)) + + known_sensors = {sensor.name: sensor for sensor in cli.get_sensors()} + sensors = [] + for data in query: + if data.sensor_name not in known_sensors: + continue + sensor = known_sensors[data.sensor_name] + sensor_data = { + "id": sensor.name, + "title": sensor.title, + "value": data.data, + "human_readable": sensor.format(sensor.from_json_compatible(data.data)), + "collected_at": data.collected_at.isoformat(), + } + sensors.append(sensor_data) + data = {"sensors": sensors} + try: + return data, 200, headers + finally: + db_session.close() + + +@version.route("/deployment_id") +def deployment_id() -> t.Tuple[t.Dict[str, t.Any], int, t.Dict[str, str]]: + headers = {"Content-Security-Policy": "default-src 'none'"} + data = {"deployment_id": flask.current_app.config["APD_SENSORS_DEPLOYMENT_ID"]} + return data, 200, headers diff --git a/Ch11/apd.sensors-chapter11/tests/__init__.py b/Ch11/apd.sensors-chapter11/tests/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/Ch11/apd.sensors-chapter11/tests/test_acstatus.py b/Ch11/apd.sensors-chapter11/tests/test_acstatus.py new file mode 100644 index 0000000..50c524d --- /dev/null +++ b/Ch11/apd.sensors-chapter11/tests/test_acstatus.py @@ -0,0 +1,58 @@ +from unittest import mock + +from apd.sensors.sensors import ACStatus +from apd.sensors.exceptions import ( + PersistentSensorFailureError, + IntermittentSensorFailureError, +) + +import pytest + + +@pytest.fixture +def sensor(): + return ACStatus() + + +class TestACStatusFormatter: + @pytest.fixture + def subject(self, sensor): + return sensor.format + + def test_format_True(self, subject): + assert subject(True) == "Connected" + + def test_format_False(self, subject): + assert subject(False) == "Not connected" + + +class TestACStatusValue: + @pytest.fixture + def subject(self, sensor): + return sensor.value + + @pytest.fixture + def sensors_battery(self): + with mock.patch("psutil.sensors_battery") as sensors_battery: + yield sensors_battery + + def test_sensors_battery_called(self, subject, sensors_battery): + sensors_battery.return_value.power_plugged = True + assert subject() is True + assert sensors_battery.call_count == 1 + + def test_sensor_but_battery_unknown(self, subject, sensors_battery): + sensors_battery.return_value.power_plugged = None + with pytest.raises(IntermittentSensorFailureError): + subject() + assert sensors_battery.call_count == 1 + + def test_no_sensor(self, subject, sensors_battery): + sensors_battery.return_value = None + with pytest.raises(PersistentSensorFailureError): + subject() + + def test_str_representation_is_formatted_value(self, sensor, sensors_battery): + sensors_battery.return_value.power_plugged = True + assert str(sensor) == "Connected" + assert sensors_battery.call_count == 1 diff --git a/Ch11/apd.sensors-chapter11/tests/test_api_server.py b/Ch11/apd.sensors-chapter11/tests/test_api_server.py new file mode 100644 index 0000000..0f450b3 --- /dev/null +++ b/Ch11/apd.sensors-chapter11/tests/test_api_server.py @@ -0,0 +1,286 @@ +import os +import uuid +from unittest import mock + +import flask +import pytest +from webtest import TestApp + +from apd.sensors.sensors import PythonVersion +from apd.sensors.wsgi import set_up_config +from apd.sensors.wsgi import v10 +from apd.sensors.wsgi import v20 +from apd.sensors.wsgi import v21 +from apd.sensors.wsgi import v30 + + +@pytest.fixture(scope="session") +def api_key(): + return uuid.uuid4().hex + + +def test_api_key_is_required_config_option(): + app = mock.MagicMock() + with pytest.raises( + ValueError, match="Missing config variables: APD_SENSORS_API_KEY" + ): + set_up_config({"APD_SENSORS_DEPLOYMENT_ID": ""}, to_configure=app) + + +def test_os_environ_is_default_for_config_values(api_key): + app = mock.MagicMock() + os.environ["APD_SENSORS_API_KEY"] = api_key + os.environ["APD_SENSORS_DEPLOYMENT_ID"] = "8f1b57faa04b430c81decbbeee9e300c" + try: + assert app.config.from_mapping.call_count == 0 + set_up_config(None, to_configure=app) + assert app.config.from_mapping.call_count == 1 + for key, value in os.environ.items(): + # There may be keys in the config not from the environment + assert app.config.from_mapping.call_args[0][0][key] == value + finally: + del os.environ["APD_SENSORS_API_KEY"] + del os.environ["APD_SENSORS_DEPLOYMENT_ID"] + + +class CommonTests: + @pytest.mark.functional + def test_sensor_values_fails_on_missing_api_key(self, api_server): + response = api_server.get("/sensors/", expect_errors=True) + assert response.status_code == 403 + assert response.json["error"] == "Supply API key in X-API-Key header" + + @pytest.mark.functional + def test_sensor_values_require_correct_api_key(self, api_server): + response = api_server.get( + "/sensors/", headers={"X-API-Key": "wrong_key"}, expect_errors=True + ) + assert response.status_code == 403 + assert response.json["error"] == "Supply API key in X-API-Key header" + + +class Testv10API(CommonTests): + @pytest.fixture + def subject(self, api_key): + app = flask.Flask("testapp") + app.register_blueprint(v10.version) + set_up_config( + { + "APD_SENSORS_API_KEY": api_key, + "APD_SENSORS_DEPLOYMENT_ID": "8f1b57faa04b430c81decbbeee9e300c", + }, + to_configure=app, + ) + return app + + @pytest.fixture + def api_server(self, subject): + return TestApp(subject) + + @pytest.mark.functional + def test_sensor_values_returned_as_json(self, api_server, api_key): + value = api_server.get("/sensors/", headers={"X-API-Key": api_key}).json + python_version = PythonVersion().value() + + sensor_names = value.keys() + assert "Python Version" in sensor_names + assert value["Python Version"] == list(python_version) + + @pytest.mark.functional + def test_unserializable_sensor_is_omitted(self, api_server, api_key): + value = api_server.get("/sensors/", headers={"X-API-Key": api_key}).json + sensor_names = value.keys() + assert "Temperature" not in sensor_names + + @pytest.mark.functional + def test_erroring_sensor_shows_None(self, api_server, api_key): + from .test_utils import FailingSensor + + with mock.patch("apd.sensors.cli.get_sensors") as get_sensors: + # Ensure failing sensor is first, to test that subsequent sensors + # are still processed + get_sensors.return_value = [FailingSensor(10), PythonVersion()] + value = api_server.get("/sensors/", headers={"X-API-Key": api_key}).json + assert value["Sensor which fails"] is None + assert "Python Version" in value.keys() + + +class Testv20API(CommonTests): + @pytest.fixture + def subject(self, api_key): + app = flask.Flask("testapp") + app.register_blueprint(v20.version) + set_up_config( + { + "APD_SENSORS_API_KEY": api_key, + "APD_SENSORS_DEPLOYMENT_ID": "8f1b57faa04b430c81decbbeee9e300c", + }, + to_configure=app, + ) + return app + + @pytest.fixture + def api_server(self, subject): + return TestApp(subject) + + @pytest.mark.functional + def test_sensor_values_returned_as_json(self, api_server, api_key): + value = api_server.get("/sensors/", headers={"X-API-Key": api_key}).json + sensors = value["sensors"] + + python_version = [ + sensor for sensor in sensors if sensor["id"] == "PythonVersion" + ][0] + assert python_version["title"] == "Python Version" + assert python_version["value"] == list(PythonVersion().value()) + + @pytest.mark.functional + def test_erroring_sensor_shows_None(self, api_server, api_key): + from .test_utils import FailingSensor + + with mock.patch("apd.sensors.cli.get_sensors") as get_sensors: + # Ensure failing sensor is first, to test that subsequent sensors + # are still processed + get_sensors.return_value = [FailingSensor(10), PythonVersion()] + value = api_server.get("/sensors/", headers={"X-API-Key": api_key}).json + sensors = value["sensors"] + + failing = [sensor for sensor in sensors if sensor["id"] == "FailingSensor"][0] + python_version = [ + sensor for sensor in sensors if sensor["id"] == "PythonVersion" + ][0] + + assert failing["title"] == "Sensor which fails" + assert failing["value"] is None + assert python_version["title"] == "Python Version" + + +class Testv21API(Testv20API): + @pytest.fixture + def subject(self, api_key): + app = flask.Flask("testapp") + app.register_blueprint(v21.version) + set_up_config( + { + "APD_SENSORS_API_KEY": api_key, + "APD_SENSORS_DEPLOYMENT_ID": "8f1b57faa04b430c81decbbeee9e300c", + }, + to_configure=app, + ) + return app + + @pytest.mark.functional + def test_deployment_id(self, api_server, api_key): + value = api_server.get("/deployment_id", headers={"X-API-Key": api_key}).json + assert value == {"deployment_id": "8f1b57faa04b430c81decbbeee9e300c"} + + @pytest.mark.functional + def test_erroring_sensor_shows_None(self, api_server, api_key): + from .test_utils import FailingSensor + + with mock.patch("apd.sensors.cli.get_sensors") as get_sensors: + # Ensure failing sensor is first, to test that subsequent sensors + # are still processed + get_sensors.return_value = [FailingSensor(10), PythonVersion()] + value = api_server.get("/sensors/", headers={"X-API-Key": api_key}).json + sensors = value["sensors"] + + failing = [sensor for sensor in sensors if sensor["id"] == "FailingSensor"][0] + python_version = [ + sensor for sensor in sensors if sensor["id"] == "PythonVersion" + ][0] + + assert failing["title"] == "Sensor which fails" + assert failing["value"] is None + assert python_version["title"] == "Python Version" + + +class Testv30API(CommonTests): + @pytest.fixture + def api_server(self, subject): + return TestApp(subject) + + @pytest.fixture + def subject(self, api_key): + app = flask.Flask("testapp") + app.register_blueprint(v30.version) + set_up_config( + { + "APD_SENSORS_API_KEY": api_key, + "APD_SENSORS_DEPLOYMENT_ID": "8f1b57faa04b430c81decbbeee9e300c", + "APD_SENSORS_DB_URI": "sqlite://", + }, + to_configure=app, + ) + return app + + @pytest.fixture + def db(self, subject): + from flask_sqlalchemy import SQLAlchemy + from apd.sensors.database import metadata + + db = SQLAlchemy(subject, metadata=metadata) + db.create_all() + return db + + @pytest.fixture + def store_sensor_data(self): + from apd.sensors.database import store_sensor_data + + return store_sensor_data + + @pytest.mark.functional + def test_historical_with_no_data(self, api_key, api_server, db): + value = api_server.get("/historical", headers={"X-API-Key": api_key}).json + assert value == {"sensors": []} + + @pytest.mark.functional + def test_historical_with_data(self, api_key, api_server, db, store_sensor_data): + value = api_server.get("/historical", headers={"X-API-Key": api_key}).json + store_sensor_data(PythonVersion, [3, 9, 0, "final", 1], db.session) + assert value == {"sensors": []} + + @pytest.mark.functional + def test_sensor_values_returned_as_json(self, api_server, api_key): + value = api_server.get("/sensors/", headers={"X-API-Key": api_key}).json + sensors = value["sensors"] + + python_version = [ + sensor for sensor in sensors if sensor["id"] == "PythonVersion" + ][0] + assert python_version["title"] == "Python Version" + assert python_version["value"] == list(PythonVersion().value()) + + @pytest.mark.functional + def test_erroring_sensor_excluded_but_reported(self, api_server, api_key): + from .test_utils import FailingSensor + + with mock.patch("apd.sensors.cli.get_sensors") as get_sensors: + # Ensure failing sensor is first, to test that subsequent sensors + # are still processed + get_sensors.return_value = [FailingSensor(2), PythonVersion()] + value = api_server.get("/sensors/", headers={"X-API-Key": api_key}).json + second_attempt_value = api_server.get( + "/sensors/", headers={"X-API-Key": api_key} + ).json + + sensors = value["sensors"] + failing = [sensor for sensor in sensors if sensor["id"] == "FailingSensor"] + assert len(failing) == 0 + errors = value["errors"] + failing = [sensor for sensor in errors if sensor["id"] == "FailingSensor"] + assert len(failing) == 1 + + python_version = [ + sensor for sensor in sensors if sensor["id"] == "PythonVersion" + ] + assert len(python_version) == 1 + + sensors = second_attempt_value["sensors"] + assert second_attempt_value["errors"] == [] + failing = [sensor for sensor in sensors if sensor["id"] == "FailingSensor"] + assert len(failing) == 1 + python_version = [ + sensor for sensor in sensors if sensor["id"] == "PythonVersion" + ] + assert len(python_version) == 1 diff --git a/Ch11/apd.sensors-chapter11/tests/test_cpuusage.py b/Ch11/apd.sensors-chapter11/tests/test_cpuusage.py new file mode 100644 index 0000000..aa8a66a --- /dev/null +++ b/Ch11/apd.sensors-chapter11/tests/test_cpuusage.py @@ -0,0 +1,45 @@ +from unittest import mock + +from apd.sensors.sensors import CPULoad + +import pytest + + +@pytest.fixture +def sensor(): + return CPULoad() + + +class TestCPULoadFormatter: + @pytest.fixture + def subject(self, sensor): + return sensor.format + + def test_format_simple_percentage(self, subject): + assert subject(0.05) == "5.0%" + + def test_format_multiple_decimal_places(self, subject): + assert subject(0.031415926) == "3.1%" + + def test_format_multiple_decimal_places_int(self, subject) -> None: + assert subject(1) == "100.0%" + + +class TestCPULoadValue: + @pytest.fixture + def subject(self, sensor): + return sensor.value + + @pytest.fixture + def cpuload(self): + with mock.patch("psutil.cpu_percent") as cpu_percent: + cpu_percent.return_value = 50.1 + yield cpu_percent + + def test_cpu_percent_called_With_interval(self, subject, cpuload): + assert subject() == 0.501 + assert cpuload.call_count == 1 + assert tuple(cpuload.call_args) == ((), {"interval": 3}) + + def test_str_representation_is_formatted_value(self, sensor, cpuload): + assert str(sensor) == "50.1%" diff --git a/Ch11/apd.sensors-chapter11/tests/test_dht.py b/Ch11/apd.sensors-chapter11/tests/test_dht.py new file mode 100644 index 0000000..ab1b380 --- /dev/null +++ b/Ch11/apd.sensors-chapter11/tests/test_dht.py @@ -0,0 +1,61 @@ +from apd.sensors.sensors import Temperature, RelativeHumidity, ureg + +import pytest + + +@pytest.fixture +def temperature_sensor(): + return Temperature() + + +@pytest.fixture +def humidity_sensor(): + return RelativeHumidity() + + +class TestTemperatureFormatter: + @pytest.fixture + def subject(self, temperature_sensor): + return temperature_sensor.format + + @staticmethod + def to_degc(magnitude): + return ureg.Quantity(magnitude, "degC") + + def test_format_21c(self, subject): + assert subject(self.to_degc(21.0)) == "21.0 celsius (69.8 fahrenheit)" + + def test_format_negative(self, subject): + assert subject(self.to_degc(-32.0)) == "-32.0 celsius (-25.6 fahrenheit)" + + +class TestTemperatureSerializer: + @pytest.fixture + def serialize(self, temperature_sensor): + return temperature_sensor.to_json_compatible + + @pytest.fixture + def deserialize(self, temperature_sensor): + return temperature_sensor.from_json_compatible + + @staticmethod + def to_degc(magnitude): + return ureg.Quantity(magnitude, "degC") + + def test_serialize_21c(self, serialize): + assert serialize(self.to_degc(21.0)) == {"magnitude": 21.0, "unit": "degC"} + + def test_serialize_negative(self, serialize): + assert serialize(self.to_degc(-32.3)) == {"magnitude": -32.3, "unit": "degC"} + + def test_deserialize_21c(self, deserialize): + assert deserialize({"magnitude": 21.0, "unit": "degC"}) == self.to_degc(21.0) + + +class TestHumidityFormatter: + @pytest.fixture + def subject(self, humidity_sensor): + return humidity_sensor.format + + def test_format_percentage(self, subject): + assert subject(0.035) == "3.5%" diff --git a/Ch11/apd.sensors-chapter11/tests/test_ipaddresses.py b/Ch11/apd.sensors-chapter11/tests/test_ipaddresses.py new file mode 100644 index 0000000..e8a9690 --- /dev/null +++ b/Ch11/apd.sensors-chapter11/tests/test_ipaddresses.py @@ -0,0 +1,59 @@ +from unittest import mock +import socket + +from apd.sensors.sensors import IPAddresses + +import pytest + + +@pytest.fixture +def sensor(): + return IPAddresses() + + +class TestIPAddressFormatter: + @pytest.fixture + def subject(self, sensor): + return sensor.format + + def test_format_single_ipv4(self, subject): + ips = [("AF_INET", "192.0.2.1")] + assert subject(ips) == "192.0.2.1 (IPv4)" + + def test_format_single_ipv6(self, subject): + ips = [("AF_INET6", "2001:DB8::1")] + assert subject(ips) == "2001:DB8::1 (IPv6)" + + def test_format_mixed_list(self, subject): + ips = [("AF_INET", "192.0.2.1"), ("AF_INET6", "2001:DB8::1")] + assert subject(ips) == "192.0.2.1 (IPv4)\n2001:DB8::1 (IPv6)" + + def test_unusual_protocols_are_marked_as_unknown(self, subject): + ips = [("AF_IRDA", "ffff")] + assert subject(ips) == "ffff (Unknown)" + + +class TestIPAddressesValue: + @pytest.fixture + def subject(self, sensor): + return sensor.value + + @pytest.fixture + def gethostname(self): + with mock.patch("socket.gethostname") as gethostname: + gethostname.return_value = "localhost" + yield gethostname + + @pytest.fixture + def getaddrinfo(self, gethostname): + with mock.patch("socket.getaddrinfo") as getaddrinfo: + yield getaddrinfo + + def test_getaddrinfo_used_for_value_collection(self, getaddrinfo, subject): + getaddrinfo.return_value = [(socket.AF_INET, 0, 0, "", ("192.0.2.1", 0))] + assert subject() == [("AF_INET", "192.0.2.1")] + assert getaddrinfo.call_count == 1 + + def test_str_representation_is_formatted_value(self, getaddrinfo, sensor): + getaddrinfo.return_value = [(socket.AF_INET6, 0, 0, "", ("2001:DB8::1", 0))] + assert str(sensor) == "2001:DB8::1 (IPv6)" diff --git a/Ch11/apd.sensors-chapter11/tests/test_pythonversion.py b/Ch11/apd.sensors-chapter11/tests/test_pythonversion.py new file mode 100644 index 0000000..dff6bb0 --- /dev/null +++ b/Ch11/apd.sensors-chapter11/tests/test_pythonversion.py @@ -0,0 +1,61 @@ +from collections import namedtuple +from unittest import mock + +from apd.sensors.sensors import PythonVersion + +import pytest + + +@pytest.fixture +def version(): + return namedtuple( + "sys_versioninfo", ("major", "minor", "micro", "releaselevel", "serial") + ) + + +@pytest.fixture +def sensor(): + return PythonVersion() + + +class TestPythonVersionFormatter: + @pytest.fixture + def subject(self, sensor): + return sensor.format + + def test_format_py38(self, subject, version): + py38 = version(3, 8, 0, "final", 0) + assert subject(py38) == "3.8" + + def test_format_large_version(self, subject, version): + large = version(255, 128, 0, "final", 0) + assert subject(large) == "255.128" + + def test_alpha_of_minor_is_marked(self, subject, version): + py39 = version(3, 9, 0, "alpha", 1) + assert subject(py39) == "3.9.0a1" + + def test_alpha_of_micro_is_unmarked(self, subject, version): + py39 = version(3, 9, 1, "alpha", 1) + assert subject(py39) == "3.9" + + +class TestPythonVersionValue: + @pytest.fixture + def subject(self, sensor): + return sensor.value + + @pytest.fixture + def python_version(self): + import sys + + return sys.version_info + + def test_data_value_is_sys_versioninfo(self, python_version, subject): + assert subject() == python_version + + +class TestPythonVersionSensor: + def test_str_representation_is_formatted_value(self, sensor, version): + with mock.patch("sys.version_info", new=version(3, 4, 1, "final", 1)): + assert str(sensor) == "3.4" diff --git a/Ch11/apd.sensors-chapter11/tests/test_ramusage.py b/Ch11/apd.sensors-chapter11/tests/test_ramusage.py new file mode 100644 index 0000000..2a368c1 --- /dev/null +++ b/Ch11/apd.sensors-chapter11/tests/test_ramusage.py @@ -0,0 +1,47 @@ +from unittest import mock + +from apd.sensors.sensors import RAMAvailable + +import pytest + + +@pytest.fixture +def sensor(): + return RAMAvailable() + + +class TestRAMAvailableFormatter: + @pytest.fixture + def subject(self, sensor): + return sensor.format + + def test_format_bytes(self, subject): + assert subject(15) == "15.0 B" + + def test_format_kibibytes(self, subject): + assert subject(15000) == "14.6 KiB" + + def test_format_mibibytes(self, subject): + assert subject(15000000) == "14.3 MiB" + + def test_format_gibibytes(self, subject): + assert subject(15000000000) == "14.0 GiB" + + +class TestRAMAvailableValue: + @pytest.fixture + def subject(self, sensor): + return sensor.value + + @pytest.fixture + def virtual_memory(self): + with mock.patch("psutil.virtual_memory") as virtual_memory: + virtual_memory.return_value.available = 1024 + yield virtual_memory + + def test_virtual_memory_called(self, subject, virtual_memory): + assert subject() == 1024 + assert virtual_memory.call_count == 1 + + def test_str_representation_is_formatted_value(self, sensor, virtual_memory): + assert str(sensor) == "1.0 KiB" diff --git a/Ch11/apd.sensors-chapter11/tests/test_sensors.py b/Ch11/apd.sensors-chapter11/tests/test_sensors.py new file mode 100644 index 0000000..f083e27 --- /dev/null +++ b/Ch11/apd.sensors-chapter11/tests/test_sensors.py @@ -0,0 +1,125 @@ +import json +from unittest import mock + +from click.testing import CliRunner +import pytest + +import apd.sensors.cli +from apd.sensors.exceptions import UserFacingCLIError +import apd.sensors.sensors + + +def test_sensors(): + assert hasattr(apd.sensors.sensors, "PythonVersion") + + +class TestCLI: + @pytest.mark.functional + def test_first_sensor_is_first_two_lines_of_cli_output(self): + runner = CliRunner() + with mock.patch("apd.sensors.cli.get_sensors") as get_sensors: + get_sensors.return_value = [apd.sensors.sensors.PythonVersion()] + result = runner.invoke(apd.sensors.cli.show_sensors) + python_version = str(apd.sensors.sensors.PythonVersion()) + assert ["Python Version", python_version] == result.stdout.split("\n")[:2] + + def test_failing_sensor_shows_error(self): + from .test_utils import FailingSensor + + runner = CliRunner() + with mock.patch("apd.sensors.cli.get_sensors") as get_sensors: + get_sensors.return_value = [ + FailingSensor(10), + apd.sensors.sensors.PythonVersion(), + ] + result = runner.invoke(apd.sensors.cli.show_sensors) + assert ["Sensor which fails", "Failing 9 more times"] == result.stdout.split( + "\n" + )[:2] + assert "Python Version" in result.stdout + + def test_mocked_failing_sensor_shows_error(self): + from apd.sensors.base import Sensor + from apd.sensors.exceptions import IntermittentSensorFailureError + + FailingSensor = mock.MagicMock(spec=Sensor) + FailingSensor.title = "Sensor which fails" + FailingSensor.name = "FailingSensor" + FailingSensor.value.side_effect = ( + FailingSensor.__str__.side_effect + ) = IntermittentSensorFailureError("Failing sensor") + + runner = CliRunner() + with mock.patch("apd.sensors.cli.get_sensors") as get_sensors: + get_sensors.return_value = [ + FailingSensor, + apd.sensors.sensors.PythonVersion(), + ] + result = runner.invoke(apd.sensors.cli.show_sensors) + assert ["Sensor which fails", "Failing sensor"] == result.stdout.split("\n")[:2] + assert "Python Version" in result.stdout + + +class TestSensorFromPath: + @pytest.fixture + def subject(self): + return apd.sensors.cli.get_sensor_by_path + + def test_get_sensor_by_path(self, subject): + assert isinstance( + subject("apd.sensors.sensors:PythonVersion"), + apd.sensors.sensors.PythonVersion, + ) + + def test_invalid_format(self, subject): + with pytest.raises(UserFacingCLIError, match="dotted.path.to.module:ClassName"): + subject("apd.sensors.sensors.PythonVersion") + + def test_invalid_module(self, subject): + with pytest.raises(UserFacingCLIError, match="Could not import module"): + subject("apd.nonsense.sensor:FakeSensor") + + def test_missing_sensor(self, subject): + with pytest.raises(UserFacingCLIError, match="Could not find attribute"): + subject("apd.sensors.sensors:FakeSensor") + + def test_invalid_sensor(self, subject): + with pytest.raises(UserFacingCLIError, match="is not recognised as a Sensor"): + subject("apd.sensors.sensors:Sensor") + + +@pytest.mark.functional +def test_develop_option_finds_sensor_by_path(): + runner = CliRunner() + result = runner.invoke( + apd.sensors.cli.show_sensors, ["--develop", "apd.sensors.sensors:PythonVersion"] + ) + python_version = str(apd.sensors.sensors.PythonVersion()) + assert ["Python Version", python_version, "", ""] == result.stdout.split("\n") + + +class TestDefaultSerializer: + @pytest.fixture + def python_version(self): + return apd.sensors.sensors.PythonVersion() + + @pytest.fixture + def serialize(self, python_version): + return python_version.to_json_compatible + + @pytest.fixture + def deserialize(self, python_version): + return python_version.from_json_compatible + + def test_serialize_deserialize_is_symmetric( + self, python_version, serialize, deserialize + ): + value = python_version.value() + serialized = serialize(value) + json_version = json.dumps(serialized) + assert ( + json_version == f"[{value.major}, {value.minor}, {value.micro}," + f' "{value.releaselevel}", {value.serial}]' + ) + deserialized = deserialize(serialized) + assert deserialized == value diff --git a/Ch11/apd.sensors-chapter11/tests/test_utils.py b/Ch11/apd.sensors-chapter11/tests/test_utils.py new file mode 100644 index 0000000..34a82f8 --- /dev/null +++ b/Ch11/apd.sensors-chapter11/tests/test_utils.py @@ -0,0 +1,57 @@ +import typing as t + +import pytest + +from apd.sensors.base import JSONSensor +from apd.sensors.exceptions import ( + IntermittentSensorFailureError, + PersistentSensorFailureError, +) +from apd.sensors.utils import get_value_with_retries + + +class FailingSensor(JSONSensor[bool]): + + title = "Sensor which fails" + name = "FailingSensor" + + def __init__( + self, + n: int = 3, + exception_type: t.Type[Exception] = IntermittentSensorFailureError, + ): + self.n = n + self.exception_type = exception_type + + def value(self) -> bool: + self.n -= 1 + if self.n: + raise self.exception_type(f"Failing {self.n} more times") + else: + return True + + @classmethod + def format(cls, value: bool) -> str: + return "Yes" if value else "No" + + +class TestRetry: + def test_default_retries(self): + sensor = FailingSensor(3) + value = get_value_with_retries(sensor) + assert value is True + + def test_n_retries(self): + sensor = FailingSensor(5) + value = get_value_with_retries(sensor, retries=5) + assert value is True + + def test_insufficient_retries(self): + sensor = FailingSensor(5) + with pytest.raises(IntermittentSensorFailureError): + get_value_with_retries(sensor, retries=4) + + def test_permanent_failures_not_retried(self): + sensor = FailingSensor(3, PersistentSensorFailureError) + with pytest.raises(PersistentSensorFailureError): + get_value_with_retries(sensor) diff --git a/Ch11/listing11-01-get_with_default.py b/Ch11/listing11-01-get_with_default.py new file mode 100644 index 0000000..9a2036a --- /dev/null +++ b/Ch11/listing11-01-get_with_default.py @@ -0,0 +1,12 @@ +def get_item(variable, key, default=None): + try: + return variable[key] + except (KeyError, IndexError): + # Key is invalid for variable, the error raised depends on the type of variable + return default + except TypeError: + if hasattr(variable, "__getitem__"): + return default + else: + raise + diff --git a/Ch11/listing11-02-new_exceptions.py b/Ch11/listing11-02-new_exceptions.py new file mode 100644 index 0000000..b41db66 --- /dev/null +++ b/Ch11/listing11-02-new_exceptions.py @@ -0,0 +1,16 @@ +class APDSensorsError(Exception): + """An exception base class for all exceptions raised by the + sensor data collection system.""" + +class DataCollectionError(APDSensorsError, RuntimeError): + """An error that represents the inability of a Sensor instance + to retrieve a value""" + +class IntermittentSensorFailureError(DataCollectionError): + """A DataCollectionError that is expected to resolve itself + in short order""" + +class PersistentSensorFailureError(DataCollectionError): + """A DataCollectionError that is unlikely to resolve itself + if retried.""" + diff --git a/Ch11/listing11-03-retry_sensor.py b/Ch11/listing11-03-retry_sensor.py new file mode 100644 index 0000000..dc1b2cf --- /dev/null +++ b/Ch11/listing11-03-retry_sensor.py @@ -0,0 +1,19 @@ +from apd.sensors.base import Sensor, T_value +from apd.sensors.exceptions import IntermittentSensorFailureError + + +def get_value_with_retries(sensor: Sensor[T_value], retries: int=3) -> T_value: + for i in range(retries): + try: + return sensor.value() + except IntermittentSensorFailureError as err: + if i == (retries - 1): + # This is the last retry, reraise the underlying error + raise + else: + continue + # It shouldn't be possible to get here, but it's better to + # fall through with an appropriate exception rather than a + # None + raise IntermittentSensorFailureError(f"Could not find a value after {retries} retries") + diff --git a/Ch11/listing11-04-exception_with_metadata.py b/Ch11/listing11-04-exception_with_metadata.py new file mode 100644 index 0000000..6559c24 --- /dev/null +++ b/Ch11/listing11-04-exception_with_metadata.py @@ -0,0 +1,32 @@ +import dataclasses + + +class APDSensorsError(Exception): + """An exception base class for all exceptions raised by the + sensor data collection system.""" + + +class DataCollectionError(APDSensorsError, RuntimeError): + """An error that represents the inability of a Sensor instance + to retrieve a value""" + + +class IntermittentSensorFailureError(DataCollectionError): + """A DataCollectionError that is expected to resolve itself + in short order""" + + +class PersistentSensorFailureError(DataCollectionError): + """A DataCollectionError that is unlikely to resolve itself + if retried.""" + + +@dataclasses.dataclass(frozen=True) +class UserFacingCLIError(APDSensorsError, SystemExit): + """A fatal error for the CLI""" + message: str + return_code: int + + def __str__(self): + return f"[{self.return_code}] {self.message}" + diff --git a/Ch11/listing11-05-dht_baseclass.py b/Ch11/listing11-05-dht_baseclass.py new file mode 100644 index 0000000..1337f58 --- /dev/null +++ b/Ch11/listing11-05-dht_baseclass.py @@ -0,0 +1,26 @@ +import os +import typing as t + +from .exceptions import PersistentSensorFailureError + + +class DHTSensor: + def __init__(self) -> None: + self.board = os.environ.get("APD_SENSORS_TEMPERATURE_BOARD", "DHT22") + self.pin = os.environ.get("APD_SENSORS_TEMPERATURE_PIN", "D20") + + @property + def sensor(self) -> t.Any: + try: + import adafruit_dht + import board + sensor_type = getattr(adafruit_dht, self.board) + pin = getattr(board, self.pin) + return sensor_type(pin) + except (ImportError, NotImplementedError, AttributeError) as err: + # No DHT library results in an ImportError. + # Running on an unknown platform results in a + # NotImplementedError when getting the pin. + # An unknown sensor type causes an AttributeError + raise PersistentSensorFailureError("Unable to initialise sensor interface") from err + diff --git a/Ch11/listing11-06-cli_exceptions.py b/Ch11/listing11-06-cli_exceptions.py new file mode 100644 index 0000000..40b8edb --- /dev/null +++ b/Ch11/listing11-06-cli_exceptions.py @@ -0,0 +1,40 @@ +import sys +import traceback +import typing as t + +import click + + +@click.command(help="Displays the values of the sensors") +@click.option( + "--develop", required=False, metavar="path", help="Load a sensor by Python path" +) +@click.option( + "--verbose", is_flag=True, help="Show additional info" +) +def show_sensors(develop: str, verbose: bool) -> int: + sensors: t.Iterable[Sensor[t.Any]] + if develop: + try: + sensors = [get_sensor_by_path(develop)] + except UserFacingCLIError as error: + if verbose: + tb = traceback.format_exception(type(error), error, error.__traceback__) + click.echo("".join(tb)) + click.secho(error.message, fg="red", bold=True) + sys.exit(error.return_code) + else: + sensors = get_sensors() + for sensor in sensors: + click.secho(sensor.title, bold=True) + try: + click.echo(str(sensor)) + except DataCollectionError as error: + if verbose: + tb = traceback.format_exception(type(error), error, error.__traceback__) + click.echo("".join(tb)) + continue + click.echo(error) + click.echo("") + return 0 + diff --git a/Ch11/listing11-07-failing_test_sensor.py b/Ch11/listing11-07-failing_test_sensor.py new file mode 100644 index 0000000..2bbb5b5 --- /dev/null +++ b/Ch11/listing11-07-failing_test_sensor.py @@ -0,0 +1,24 @@ +from apd.sensors.base import JSONSensor +from apd.sensors.exceptions import IntermittentSensorFailureError + + +class FailingSensor(JSONSensor[bool]): + + title = "Sensor which fails" + name = "FailingSensor" + + def __init__(self, n: int=3, exception_type: Exception=IntermittentSensorFailureError): + self.n = n + self.exception_type = exception_type + + def value(self) -> bool: + self.n -= 1 + if self.n: + raise self.exception_type(f"Failing {self.n} more times") + else: + return True + + @classmethod + def format(cls, value: bool) -> str: + raise "Yes" if value else "No" + diff --git a/Ch11/listing11-08-compatibility_test.py b/Ch11/listing11-08-compatibility_test.py new file mode 100644 index 0000000..f775429 --- /dev/null +++ b/Ch11/listing11-08-compatibility_test.py @@ -0,0 +1,12 @@ + @pytest.mark.functional + def test_erroring_sensor_shows_None(self, api_server, api_key): + from .test_utils import FailingSensor + + with mock.patch("apd.sensors.cli.get_sensors") as get_sensors: + # Ensure the failing sensor is first, to test that subsequent sensors + # are still processed + get_sensors.return_value = [FailingSensor(10), PythonVersion()] + value = api_server.get("/sensors/", headers={"X-API-Key": api_key}).json + assert value['Sensor which fails'] == None + assert "Python Version" in value.keys() + diff --git a/Ch11/listing11-09-mock-failingsensor.py b/Ch11/listing11-09-mock-failingsensor.py new file mode 100644 index 0000000..98a9e82 --- /dev/null +++ b/Ch11/listing11-09-mock-failingsensor.py @@ -0,0 +1,9 @@ +from apd.sensors.base import Sensor +from apd.sensors.exceptions import IntermittentSensorFailureError + +FailingSensor = mock.MagicMock(spec=Sensor) +FailingSensor.title = "Sensor which fails" +FailingSensor.name = "FailingSensor" +FailingSensor.value.side_effect = IntermittentSensorFailureError("Failing sensor") +FailingSensor.__str__.side_effect = IntermittentSensorFailureError("Failing sensor") + diff --git a/Ch11/listing11-10-deprecationwarning.py b/Ch11/listing11-10-deprecationwarning.py new file mode 100644 index 0000000..a204d6d --- /dev/null +++ b/Ch11/listing11-10-deprecationwarning.py @@ -0,0 +1,33 @@ +@dataclasses.dataclass +class Config(t.Generic[T_key, T_value]): + title: str + clean: CleanerFunc[Cleaned[T_key, T_value]] + draw: t.Optional[ + t.Callable[ + [t.Any, t.Iterable[T_key], t.Iterable[T_value], t.Optional[str]], None + ] + ] = None + get_data: t.Optional[ + t.Callable[..., t.AsyncIterator[t.Tuple[UUID, t.AsyncIterator[DataPoint]]]] + ] = None + ylabel: t.Optional[str] = None + sensor_name: dataclasses.InitVar[str] = None + + def __post_init__(self, sensor_name: t.Optional[str] = None) -> None: + if self.draw is None: + self.draw = draw_date # type: ignore + if sensor_name is not None: + warnings.warn( + DeprecationWarning( + f"The sensor_name parameter is deprecated. Please pass " + f"get_data=get_one_sensor_by_deployment('{sensor_name}') " + f"to ensure the same behaviour. The sensor_name= parameter " + f"will be removed in apd.aggregation 3.0." + ), + stacklevel=3, + ) + if self.get_data is None: + self.get_data = get_one_sensor_by_deployment(sensor_name) + if self.get_data is None: + raise ValueError("You must specify a get_data function") + diff --git a/Ch11/listing11-11-test_for_deprecation_warnings.py b/Ch11/listing11-11-test_for_deprecation_warnings.py new file mode 100644 index 0000000..9357d30 --- /dev/null +++ b/Ch11/listing11-11-test_for_deprecation_warnings.py @@ -0,0 +1,20 @@ +def test_deprecation_warning_raised_by_config_with_no_getdata(): + with warnings.catch_warnings(record=True) as captured_warnings: + warnings.simplefilter("always", DeprecationWarning) + config = analysis.Config( + sensor_name="Temperature", + clean=analysis.clean_passthrough, + title="Temperaure", + ylabel="Deg C" + ) + assert len(captured_warnings) == 1 + deprecation_warning = captured_warnings[0] + assert deprecation_warning.filename == __file__ + assert deprecation_warning.category == DeprecationWarning + assert str(deprecation_warning.message) == ( + "The sensor_name parameter is deprecated. Please pass " + "get_data=get_one_sensor_by_deployment('Temperature') " + "to ensure the same behaviour. The sensor_name= parameter " + "will be removed in apd.aggregation 3.0." + ) + diff --git a/Ch11/listing11-12-logging_config.py b/Ch11/listing11-12-logging_config.py new file mode 100644 index 0000000..d6399d4 --- /dev/null +++ b/Ch11/listing11-12-logging_config.py @@ -0,0 +1,21 @@ +import logging + +def set_logger_format(logger, format_str): + """Set up a new stderr handler for the given logger + and configure the formatter with the provided string + """ + logger.propagate = False + formatter = logging.Formatter(format_str, None, "{") + + std_err_handler = logging.StreamHandler(None) + std_err_handler.setFormatter(formatter) + + logger.handlers.clear() + logger.addHandler(std_err_handler) + return logger + +logger = set_logger_format( + logging.getLogger(__name__), + format_str="{asctime}: {levelname} - {message}", +) + diff --git a/Ch11/listing11-13-log_adapter.py b/Ch11/listing11-13-log_adapter.py new file mode 100644 index 0000000..7fe11ef --- /dev/null +++ b/Ch11/listing11-13-log_adapter.py @@ -0,0 +1,24 @@ +import copy +import logging + +class ExtraDefaultAdapter(logging.LoggerAdapter): + def process(self, msg, kwargs): + extra = copy.copy(self.extra) + extra.update(kwargs.pop("extra", {})) + kwargs["extra"] = extra + return msg, kwargs + +def set_logger_format(logger, format_str): + """Set up a new stderr handler for the given logger + and configure the formatter with the provided string + """ + logger.propagate = False + formatter = logging.Formatter(format_str, None, "{") + + std_err_handler = logging.StreamHandler(None) + std_err_handler.setFormatter(formatter) + + logger.handlers.clear() + logger.addHandler(std_err_handler) + return logger + diff --git a/Ch11/listing11-14-log_factory.py b/Ch11/listing11-14-log_factory.py new file mode 100644 index 0000000..297b9b2 --- /dev/null +++ b/Ch11/listing11-14-log_factory.py @@ -0,0 +1,21 @@ +from contextvars import ContextVar +import functools +import logging + +sensorname_var = ContextVar("sensorname", default="none") + +def add_sensorname_record_factory(existing_factory, *args, **kwargs): + record = existing_factory(*args, **kwargs) + record.sensorname = sensorname_var.get() + return record + +def add_record_factory_wrapper(fn): + old_factory = logging.getLogRecordFactory() + wrapped = functools.partial(fn, old_factory) + logging.setLogRecordFactory(wrapped) + +add_record_factory_wrapper(add_sensorname_record_factory) +logging.basicConfig( + format="[{sensorname}/{levelname}] - {message}", style="{", level=logging.INFO +) + diff --git a/Ch11/listing11-15-log_filter.py b/Ch11/listing11-15-log_filter.py new file mode 100644 index 0000000..2145608 --- /dev/null +++ b/Ch11/listing11-15-log_filter.py @@ -0,0 +1,25 @@ +import logging + +class AddSensorNameDefault(logging.Filter): + def filter(self, record): + if not hasattr(record, "sensorname"): + record.sensorname = "none" + return True + +def set_logger_format(logger, format_str, filters=None): + """Set up a new stderr handler for the given logger + and configure the formatter with the provided string + """ + logger.propagate = False + formatter = logging.Formatter(format_str, None, "{") + + std_err_handler = logging.StreamHandler(None) + std_err_handler.setFormatter(formatter) + + logger.handlers.clear() + logger.addHandler(std_err_handler) + if filters is not None: + for filter in filters: + std_err_handler.addFilter(filter) + return logger + diff --git a/Ch11/listing11-16-log_handler.py b/Ch11/listing11-16-log_handler.py new file mode 100644 index 0000000..84f8667 --- /dev/null +++ b/Ch11/listing11-16-log_handler.py @@ -0,0 +1,13 @@ +import logging + +class AddSensorNameDefault(logging.Filter): + def filter(self, record): + if not hasattr(record, "sensorname"): + record.sensorname = "none" + return True + +class SensorNameStreamHandler(logging.StreamHandler): + def __init__(self, *args, **kwargs): + super().__init__() + self.addFilter(AddSensorNameDefault()) + diff --git a/Ch11/listing11-17-log_config.ini b/Ch11/listing11-17-log_config.ini new file mode 100644 index 0000000..1a66098 --- /dev/null +++ b/Ch11/listing11-17-log_config.ini @@ -0,0 +1,21 @@ +[loggers] +keys=root + +[handlers] +keys=stderr_with_sensorname + +[formatters] +keys=sensorname + +[logger_root] +level=INFO +handlers=stderr_with_sensorname + +[handler_stderr_with_sensorname] +class=apd.aggregation.utils.SensorNameStreamHandler +formatter = sensorname + +[formatter_sensorname] +format = {asctime}: [{sensorname}/{levelname}] - {message} +style = { + diff --git a/Ch11/listing11-18-local_data_cache.py b/Ch11/listing11-18-local_data_cache.py new file mode 100644 index 0000000..d55370c --- /dev/null +++ b/Ch11/listing11-18-local_data_cache.py @@ -0,0 +1,31 @@ +from __future__ import annotations + +import datetime +import typing as t + +import sqlalchemy +from sqlalchemy.schema import Table +from sqlalchemy.orm.session import Session + +from apd.sensors.base import Sensor + + +metadata = sqlalchemy.MetaData() + +sensor_values = Table( + "recorded_values", + metadata, + sqlalchemy.Column("id", sqlalchemy.Integer, primary_key=True), + sqlalchemy.Column("sensor_name", sqlalchemy.String, index=True), + sqlalchemy.Column("collected_at", sqlalchemy.TIMESTAMP, index=True), + sqlalchemy.Column("data", sqlalchemy.JSON), +) + + +def store_sensor_data(sensor: Sensor[t.Any], data: t.Any, db_session: Session) -> None: + now = datetime.datetime.now() + record = sensor_values.insert().values( + sensor_name=sensor.name, data=sensor.to_json_compatible(data), collected_at=now + ) + db_session.execute(record) + diff --git a/Ch11/listing11-19-local_data_cache_cli.py b/Ch11/listing11-19-local_data_cache_cli.py new file mode 100644 index 0000000..e29d3a0 --- /dev/null +++ b/Ch11/listing11-19-local_data_cache_cli.py @@ -0,0 +1,55 @@ +@click.command(help="Displays the values of the sensors") +@click.option( + "--develop", required=False, metavar="path", help="Load a sensor by Python path" +) +@click.option("--verbose", is_flag=True, help="Show additional info") +@click.option("--save", is_flag=True, help="Store collected data to a database") +@click.option( + "--db", + metavar="", + default="sqlite:///sensor_data.sqlite", + help="The connection string to a database", + envvar="APD_SENSORS_DB_URI", +) +def show_sensors(develop: str, verbose: bool, save: bool, db: str) -> None: + sensors: t.Iterable[Sensor[t.Any]] + if develop: + try: + sensors = [get_sensor_by_path(develop)] + except UserFacingCLIError as error: + if verbose: + tb = traceback.format_exception(type(error), error, error.__traceback__) + click.echo("".join(tb)) + click.secho(error.message, fg="red", bold=True) + sys.exit(error.return_code) + else: + sensors = get_sensors() + + db_session = None + if save: + from sqlalchemy import create_engine + from sqlalchemy.orm import sessionmaker + + engine = create_engine(db) + sm = sessionmaker(engine) + db_session = sm() + + for sensor in sensors: + click.secho(sensor.title, bold=True) + try: + value = sensor.value() + except DataCollectionError as error: + if verbose: + tb = traceback.format_exception(type(error), error, error.__traceback__) + click.echo("".join(tb)) + continue + click.echo(error) + else: + click.echo(sensor.format(value)) + if save and db_session is not None: + store_sensor_data(sensor, value, db_session) + db_session.commit() + + click.echo("") + sys.exit(ReturnCodes.OK) + diff --git a/Ch11/listing11-20-v3_api_additions.py b/Ch11/listing11-20-v3_api_additions.py new file mode 100644 index 0000000..5653190 --- /dev/null +++ b/Ch11/listing11-20-v3_api_additions.py @@ -0,0 +1,46 @@ +@version.route("/historical") +@version.route("/historical/") +@version.route("/historical//") +@require_api_key +def historical_values( + start: str = None, end: str = None +) -> t.Tuple[t.Dict[str, t.Any], int, t.Dict[str, str]]: + try: + import dateutil.parser + from sqlalchemy import create_engine + from sqlalchemy.orm import sessionmaker + from apd.sensors.database import sensor_values + from apd.sensors.wsgi import db + except ImportError: + return {"error": "Historical data support is not installed"}, 501, {} + + db_session = db.session + headers = {"Content-Security-Policy": "default-src 'none'"} + + query = db_session.query(sensor_values) + if start: + query = query.filter( + sensor_values.c.collected_at >= dateutil.parser.parse(start) + ) + if end: + query = query.filter( + sensor_values.c.collected_at <= dateutil.parser.parse(end) + ) + + known_sensors = {sensor.name: sensor for sensor in cli.get_sensors()} + sensors = [] + for data in query: + if data.sensor_name not in known_sensors: + continue + sensor = known_sensors[data.sensor_name] + sensor_data = { + "id": sensor.name, + "title": sensor.title, + "value": data.data, + "human_readable": sensor.format(sensor.from_json_compatible(data.data)), + "collected_at": data.collected_at.isoformat(), + } + sensors.append(sensor_data) + data = {"sensors": sensors} + return data, 200, headers + diff --git a/Ch12/apd.aggregation-chapter12-ex01-complete/.coveragerc b/Ch12/apd.aggregation-chapter12-ex01-complete/.coveragerc new file mode 100644 index 0000000..e766c69 --- /dev/null +++ b/Ch12/apd.aggregation-chapter12-ex01-complete/.coveragerc @@ -0,0 +1,3 @@ +[run] +branch = True +omit = tests/* diff --git a/Ch12/apd.aggregation-chapter12-ex01-complete/.pre-commit-config.yaml b/Ch12/apd.aggregation-chapter12-ex01-complete/.pre-commit-config.yaml new file mode 100644 index 0000000..995b49f --- /dev/null +++ b/Ch12/apd.aggregation-chapter12-ex01-complete/.pre-commit-config.yaml @@ -0,0 +1,25 @@ +repos: + +- repo: local + hooks: + - id: black + name: black + entry: pipenv run black + args: [--quiet] + language: system + types: [python] + + - id: mypy + name: mypy + exclude: (?x)^( + (.*)/setup.py + )$ + entry: pipenv run mypy + language: system + types: [python] + + - id: flake8 + name: flake8 + entry: pipenv run flake8 + language: system + types: [python] diff --git a/Ch12/apd.aggregation-chapter12-ex01-complete/CHANGES.md b/Ch12/apd.aggregation-chapter12-ex01-complete/CHANGES.md new file mode 100644 index 0000000..bd0d9b6 --- /dev/null +++ b/Ch12/apd.aggregation-chapter12-ex01-complete/CHANGES.md @@ -0,0 +1,8 @@ +## Changes + +### 1.0.0 (2020-01-27) + +* Added management of known sensor endpoints +* Added CLI script to collate data +* Added analysis tools for Jupyter +* Added long-running data synthesis and actions system diff --git a/Ch12/apd.aggregation-chapter12-ex01-complete/Connect to database.ipynb b/Ch12/apd.aggregation-chapter12-ex01-complete/Connect to database.ipynb new file mode 100644 index 0000000..98a74f0 --- /dev/null +++ b/Ch12/apd.aggregation-chapter12-ex01-complete/Connect to database.ipynb @@ -0,0 +1,577 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "53635\n" + ] + } + ], + "source": [ + "from apd.aggregation.query import with_database\n", + "from apd.aggregation.database import datapoint_table\n", + "\n", + "with with_database(\"postgresql+psycopg2://apd@localhost/apd\") as session:\n", + " print(session.query(datapoint_table).count())" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "53635\n" + ] + } + ], + "source": [ + "from apd.aggregation.query import with_database, get_data\n", + "\n", + "with with_database(\"postgresql+psycopg2://apd@localhost/apd\") as session:\n", + " count = 0\n", + " async for datapoint in get_data():\n", + " count += 1\n", + " print(count)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from apd.aggregation.query import with_database, get_data\n", + "\n", + "from matplotlib import pyplot as plt\n", + "\n", + "async def plot():\n", + " points = [(dp.collected_at, dp.data) async for dp in get_data() if dp.sensor_name==\"RelativeHumidity\"]\n", + " x, y = zip(*points)\n", + " plt.plot_date(x, y, \"o\", xdate=True)\n", + "\n", + "with with_database(\"postgresql+psycopg2://apd@localhost/apd\") as session:\n", + " await plot()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from apd.aggregation.query import with_database, get_data\n", + "\n", + "from matplotlib import pyplot as plt\n", + "\n", + "async def plot():\n", + " points = [(dp.collected_at, dp.data) async for dp in get_data(sensor_name=\"RelativeHumidity\")]\n", + " x, y = zip(*points)\n", + " plt.plot_date(x, y, \"o\", xdate=True)\n", + "\n", + "with with_database(\"postgresql+psycopg2://apd@localhost/apd\") as session:\n", + " await plot()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import collections\n", + "\n", + "from apd.aggregation.query import with_database, get_data\n", + "\n", + "from matplotlib import pyplot as plt\n", + "\n", + "async def plot():\n", + " legends = collections.defaultdict(list)\n", + " async for dp in get_data(sensor_name=\"RelativeHumidity\"):\n", + " legends[dp.deployment_id].append((dp.collected_at, dp.data))\n", + "\n", + " for deployment_id, points in legends.items():\n", + " x, y = zip(*points)\n", + " plt.plot_date(x, y, \"o\", xdate=True)\n", + "\n", + "with with_database(\"postgresql+psycopg2://apd@localhost/apd\") as session:\n", + " await plot()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import collections\n", + "\n", + "from apd.aggregation.query import with_database, get_data, get_deployment_ids\n", + "\n", + "from matplotlib import pyplot as plt\n", + "\n", + "async def plot(deployment_id):\n", + " points = []\n", + " async for dp in get_data(sensor_name=\"RelativeHumidity\", deployment_id=deployment_id):\n", + " points.append((dp.collected_at, dp.data))\n", + "\n", + " if points:\n", + " x, y = zip(*points)\n", + " plt.plot_date(x, y, \"o\", xdate=True)\n", + "\n", + "with with_database(\"postgresql+psycopg2://apd@localhost/apd\") as session:\n", + " deployment_ids = await get_deployment_ids()\n", + " for deployment in deployment_ids:\n", + " await plot(deployment)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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O6ONrr3SfHJJDHSGHpnN/3rzJIlnUESssi1yr7iw5JIc6YoXl0Om4Vj1/ckgOdUQf51Bfzg+F0ZapUsraJK9o2fyXZzistWLhnfPs9o6W9bNKKZvm2Uan7W5ZP2eex7fu/21tjGU5++kka6as70mXqutPfJPY+o3ifOdi6/6XzOPYC5O8LE0Ib0wynGTbxPpLk/xhkjtLKf/vxPuMU5BDk+RQZ6ykHDqd1vfUH9daj3ao7b4kiybJos7o1ywyP7pIDk0yzzpj0XKI/iGHJsmhzljROVRKGSulfFsp5cdLKZ9L8o6WXV5Xa72+F2NbyuTQJDnUGSsph1yr7iBZNEkWdcZKyqLTcb16HuTQJDnUGf2aQ+YHsOz4jJ8kwzujX38ePRvXPTpEDk2SQ52xknLodFzzmAc5NEkOdUa/5pD50UVyaJJ51hn9eu2VLpJDk+RQZ6zoHHJ/3sLJokmyqDNWUha5Vt0hcmiSHOqMlZRDp+Na9TzIoUlyqDP6NYf6cn4ojLZ8/VaSs6esP5zkj85wzMaW9fvn02GtdX+SQy2bN8ynjS64uWX9u+d6YCnl/CQ7Wjb3+vksulLKziS/1rL57bXW1mqQndI6Dx+ptR6YZxutc7fTr9tYkp9L8uVSyuUdbrufyKGGHGqTHGqUUq5IcmXL5ve02+4KIIsasqhNfZ5F5kd3yaGGedamHuQQ/UMONeRQm1ZaDpVSNpZS6tQlyf4kX0vy/iRPm7L7/iQ/VWv9nR4MdTmQQw051KaVlkNz5Fr13MmihixqkyxquF69IHKoIYfa1Oc5ZH4Ay5HP+IYMb1Of/zx6oVz3mBs51JBDbZJDDdc8FkQONeRQm/o8h8yP7pJDDfOsTX1+7ZXukkMNOdSmlZZD7s/rOFnUkEVtWmlZNEeuVc+NHGrIoTbJoYZr1QsihxpyqE19nkN9OT8URluGSinPT/KvWjb/Sq31wTMc2lqt+OACum89Zt0C2uikz7Wsv7CUsmbWPWf6F7Ns6/XzWVSllJEkH8n05/3NJP+ui932ah4eTXJ1kl9N8iNJnpzmrzY9Kclzk/xOZn4x85gkny6lXLCAMfY1OTSNHGrDCsuhM2mtVP25WuutHWi3b8miaWRRG1ZAFpkfXSKHpjHP2tCjHKIPyKFp5FAb5NAp3ZfkV5JcWGt9d68HsxTJoWnkUBtWWA65Vt1hsmgaWdSGFZZFZ+J69TzIoWnkUBtWQA6ZH8Cy4jN+GhnehhXw8+ipXPfoIDk0jRxqwwrLoTNxzWMe5NA0cqgNKyCHzI8ukUPTmGdtWAHXXukSOTSNHGqDHDol9+fNgSyaRha1YYVlkWvVHSSHppFDbVhhOXQmrlXPgxyaRg61YQXkUF/OD4XRlplSyhOS/MeWzZ9K8u/ncHhrcLdWp5yL1uBubXOx/VWaap4nbEzyxjMdVEo5L8lrZ3losJSyujNDWxb+KMl3Tlk/luTHF/DXkOajF/PwV5M8qtb6fbXWN9daP1lr/Uqt9dZa6z/WWj9Ra/1/klyQ5LeT1CnHnp3kL0opZQHj7EtyaAY51J6VkkOnNfGF9I+1bFbd+zRk0QyyqD39nkXmRxfIoRnMs/b0IodY5uTQDHKoPXJodtuT/EySV5VS1vd6MEuNHJpBDrVnpeSQa9UdJotmkEXtWSlZdFquV8+PHJpBDrWn33PI/ACWDZ/xM8jw9vT7z6NPcN2jg+TQDHKoPSslh07LNY/5kUMzyKH29HsOmR9dIIdmMM/a0+/XXukCOTSDHGqPHJqd+/POQBbNIIvas1KyyLXqDpJDM8ih9qyUHDot16rnRw7NIIfa0+851JfzQ2G0ZaSUcn6aiTg1LO9I8mO11jr7Uae1WMd0Ta11X5K3t2x+bSnl1ac6ppRybpK/SbLhVM12aHhLWinlN5L885bNr6u1/v0iD6Xr83Dim9fW6t2z7Xeo1vq6JP+65aEnJ/ln8+mzX8mhmeTQwq2kHJqD5yY5a8r6nv/d3v3HWpMX9B3/fJctIIJSi0AVdSn+wlBSFFMBTTcUDNSAsqAQmoYVtLbYJrVt0pSadGsbTNNo7R+NUotiKdBSBEREQMSVVuOvZlGoW0oqawVd5JdF9vey3/5x7tN77px75sycHzNzzrxeyflj5p4zZ57zfJ/33Od7J9+b5A17fo+ToUWrtGh7c2iR8bF/OrTKONvehDrEEdGhVTq0vRl36NNJHr30eEwWc0DXJfnXST529rwvSfIDSd5XSvn6Ec5zknRolQ5tb04dMle9X1q0Sou2N6cWdWC+uiMdWqVD25tDh4wP4Fi4xq/S8O1N6BrvHr0jokOrdGh7c+pQB+Y8OtKhVTq0vTl0yPjYPx1aZZxtb0Id4ojo0Cod2t6MO+T+vB1p0Sot2t6cWmSuen90aJUObW9OHerAXHVHOrRKh7Y3hw6d6vi4euwToJtSysOT/EKSL17afWuSp9daP3b5q1Z8prG9zcp8zdc0jzmGlyd5Zs5XZixJfqSU8rwsVkd9bxYrcX7R2fP+ds4vfh9O8qilY91Za11Z6bOUck3Xk6m13tLr7EdQSvl7Wax6veyHa63/quPrr+n6Xpd8HpMfh7XWf1tK+eYkz17a/dIkr93n+xwbHWqlQz3p0IqXNLZfW2ttriJNtGgDLeppZi06+PiYCx1qpUM9jdwhjpQOtdKhnubcoVrrfUluueRLNyV5Uynl+5P8yyR/52z/lyZ5VynlKbXW9w9zltOkQ610qKc5d6gLc9XraVErLepJi1aYr+5Ah1rpUE8z65C5amDSXONbucb3NLOfR/dm3uNyOtRKh3rSoRXmPDrQoVY61NPMOmTOY090qJUO9TSzuVf2RIda6VBPc+6Q+/N2o0WttKinObeoC3PVl9OhVjrUkw6tMFfdgQ610qGeZtahk5urtjDaESilfEGSdyX5yqXdH0/ytFrrB3sc6iTDXWu9u5RyXZK3JXn80pe+8eyxziey+MbhHUv7/mTNcz/U45RKj+cOrpTy3Ul+uLH7R2ut/6DHYXb5PI5lHP5gLv5H9htKKQ+tta4bIydNh9rpUD86dFEp5UuSPL2x+5XbHu+UaVE7Lepnbi0aaHycPB1qp0P9TKBDHCEdaqdD/ehQu1rr7Un+binlniTfd7b785L8h1LK1235G4aOng6106F+dKgzc9UNWtROi/rRoovMV3ejQ+10qJ+5dchcNTBlrvHtXOP7mcA1/ljGoXmPJTrUTof60aGLzHl0o0PtdKifuXXInMd+6FA7HepnAh3iCOlQOx3qR4fauT9vPS1qp0X9aFFn5qqX6FA7HepHhy4yV92NDrXToX7m1qFTnKu+auwToF0p5fOTvDPJX1za/aksVrL8Hz0P938b21/Y81wenNVwT2Ig11o/kuTJSV6R5J4OL/mlJE9Mcltj/617PrVJKaX8jSQ/losx/ckk3zvgaTTH4YNKKZ/b8xgPb2wfYhz+Rhb/1q64X5KvOcD7TJ4OdaND3ejQpa7Pxe/JfrvW+t93ON5J0qJutKibubbI+NiNDnVjnHUzkQ5xZHSoGx3qRod6+SdJ/nBp+wlJnjbSuYxKh7rRoW50qBdz1Uu0qBst6kaLLnV9zFe30qFudKibuXbI+ACmyDW+Gw3vZiLX+KndG7OOeY8zOtSNDnWjQ5e6PuY8WulQNzrUzVw7ZHzsRoe6Mc66mUiHODI61I0OdaNDvbg/b4kWdaNF3WhRL+aqz+hQNzrUjQ5d6vqYq26lQ93oUDdz7dCpjQ8Lo01YKeUhSd6e5OuWdn86yTNqre/d4pDN1S+/rOfrm8//ZK31U5c+cwS11ttqrX8ryVdlMSHyS0k+nOSOJH+a5OYkP5XFKqp/tdZ6S5LHNg7zW4Od8MBKKS/IItLL/+5fk+S7hlxBv9b6iVz8D2KSfGnPwzTHYp+VXTuptd6X5P80dvf6ZucU6FA/OtROh1aVUkqS72zstrp3gxb1o0Xt5t4i42M7OtSPcdY3m3gKAAARjElEQVRuKh3iuOhQPzrUTof6qbXekeTNjd3PGONcxqRD/ehQOx3qx1z1OS3qR4vaadEq89Wb6VA/OtRu7h0yPoApcY3vR8PbTeUaP6V7Y9qY91jQoX50qJ0OrTLnsZkO9aND7ebeIeNjOzrUj3HWbiod4rjoUD861E6H+nF/3jkt6keL2mlRP+aqF3SoHx1qp0OrzFVvpkP96FC7uXfolMbH1WOfAJc7+200b0vyDUu7P5PkmbXW39jysDc3tr+85+v/QmP7d7c8j4OqtX4oycvPHps8qbH962uOWS7bfyxKKc9N8uosVqm+4r8kedHZf9h62cPncXMWK0xe8eVZHZ9tmmOxz2v7uKOx3VzR9aTp0PZ0aJUOrfXUJI9e2r4ri2+qOaNF29OiVVp07hDj41Tp0PZ0aNUEO8QR0KHt6dAqHdraBxrbff/NHDUd2p4OrdKhrc16rjrRol1o0SotWst8dQsd2p4OrdKhc+aqgbG5xm/PNX7VBK/xU7k3ZpNZz3vo0PZ0aJUOrWXOo4UObU+HVunQOXMe3enQ9nRo1QQ7xBHQoe3p0Cod2tqs789LtGgXWrRKi7ZmrlqHtqJDq3RoLXPVLXRoezq0SofOncJc9VWbn8LQSimfk+StSb5xafftSb6l1vqrOxz6/Y3tx5dSHtTj9U/ZcLyjcraq6lMbu395jHM5pFLKs5O8LhcXQnxzkhfWWj87zlmtjJ1mINc6+6bm8RuOty8Pa2x//EDvMzk6NAwd0qEkL25sv7HW+sktj3VytGgYWqRFG95nFuNjHR0axlzG2UQ7xMTp0DB0SIc6uKex/YBRzmIEOjQMHdKhDmY7V51o0VC0SItivnotHRqGDulQm7mMD2BYrvHDmEvDJ3qNn/zPo8/Mdt5Dh4ahQzoUcx5r6dAwdEiHNrzPLMbHOjo0jLmMs4l2iInToWHokA51MNv78xItGooWaVEH5qp16KB0SIdirnotHRqGDulQmymPDwujTUwp5YFJ3pLk2qXddyZ5dq31Pbscu9b6R0l+Z2nX1bl4cdjk2sb2z+9yPhPw1CTXLG3/cq31gyOdy0GUUv5aFitX/pml3T+X5Pm11nvHOaskydsb29f2eO035eJF6KZa60d3PqOGUsrDsrqK6x/u+32mSIcGpUPjGb1DpZSHJrmusfuVfY9zqrRoUFo0ntFb1MHJj491dGhQJz/OJtwhJkyHBqVDbPKoxvYhvu+aHB0alA6x1pznqhMtGpgWzZj56vV0aFA6RJuTHx/AsFzjB3XyDZ/wNX7yP4+e87yHDg1Kh8YzeofMeaynQ4PSofGM3qEOTn58rKNDgzr5cTbhDjFhOjQoHWKTWd6fl2jRwLSItcxV69BAdGjGzFWvp0OD0iHaTHZ8WBhtQkop90/yxiRPW9p9V5Jvq7X+4p7e5k2N7e/seG5fneQvL+26Lck793ROY/lHje1XjHIWB1JKeXqSn05y/6Xd70zy3Frr3eOc1f/3jiR3LG0/6WyMdXF9Y7s5pvflBbnYyI8muflA7zUZOjQ4HRrPFDr015M8cGn7liTv3vJYJ0WLBqdF45lCizY56fGxjg4N7qTH2cQ7xETp0OB0iE2+ubE9icn9Q9KhwekQbWY5V51o0Qi0aN7MV19ChwanQ7Q56fEBDMs1fnAn3fCJX+OP4efRs5z30KHB6dB4ptAhcx6X0KHB6dB4ptChTU56fKyjQ4M76XE28Q4xUTo0OB1ik9ndn5do0Qi0iDbmqs/p0OHo0LyZq76EDg1Oh2gz2fFhYbSJKKVcneT1SZ65tPueJM+rtb5jj2/1miSfXdq+rpTyFR1e1xzEr6+13rm/0xpWKeVFSZ6+tOu9Waz8eBJKKX8lyc/k4jdI787im4C7xjmrc7XW25O8obG7OcZWlFK+Mslzlnbdm+S1ezy1K+/ziCTf39j9s7XWuu/3mhIdGpYOjWsiHXpxY/snTr0zXWjRsLRoXBNpUdv7nPT4WEeHhnXq42zqHWKadGhYOsQmpZRvSfLExu6fGeNchqJDw9Ih2sx1rjrRoqFpETFfvUKHhqVDtDn18QEMyzV+WKfe8Klf44/g59GznPfQoWHp0Lgm0iFzHg06NCwdGtdEOtT2Pic9PtbRoWGd+jibeoeYJh0alg6xyRzvz0u0aGhaRBtz1To0BB0i5qpX6NCwdIg2Ux8fFkabgFLK/bII6rcu7b43yfNrrW/d53vVWj+Y5KeWdt0/yatKKQ9c85KUUr41F3/jzd1J/tk+z2tXZxe+rs+9LsmPL+26N8mLa6337v3ERlBKeVKStyb5nKXd70nyrFrrHZe/ahQ3ZPHNyRXXl1Keve7JZ2P0J3Nxhc5X1lr/d8trvqqU8qw+J1VKeWQWn98jlnbfneQH+xzn2OjQ7nTonA5tVkr5S0m+dmnXfUle1fc4p0aLdqdF57To0tcaHxvo0O6Ms3NH1CEmRId2p0PndOhcKeWJpZTnbH7myuu+PsmrG7vfU2t9337ObHp0aHc6dE6Hzpmr7keLdqdF57RoM/PVq3Rodzp0TodWGR/AWFzjd6fh547oGn9D3KM3GTq0Ox06p0ObmfNYpUO706FzOnTpa42PDXRod8bZuSPqEBOiQ7vToXM6dM79ef1o0e606JwWnTNX3Z0O7U6HzunQZuaqV+nQ7nTonA6tOrXx0fkPw0H9RJLvaOx7WZKbSinX9DzWrR1WmvynWfwGmz97tv3kJO8qpXxXrfV/XnlSKeUBSf5mkh9qvP6Haq2/3+VkSimPyuXj7JGN7atb/qyfqbV+fMNbva+U8nNJfjrJr9da77vkXB6X5B8neWHjSy+rtd604fh7cejPo5TyhCQ/n+TBS7s/kOR7kzy8lNLndO+std7a5wV91Fp/r5Tyb5L8w6Xdbyil/P0k/67WeveVnaWUxyb591mM1Ss+kc3fQPz5JG8ppbwvyX9M8qazb15WlFIekuRFWazs/YjGl/9FrfX3OvyxjpkO6ZAOLey7Q+u8pLH9jlrrH2x5rFOiRVqkRQuHatFRjI+R6ZAOza5DyXDjo5Ty4CQPW/Pl5oTyw1re68NTmlzbMx3SIR26aF/j41FJ3lhKeX8WP0B7c5IP1Hr5b1kqpXxNku9J8tLGed15tu+U6ZAO6dBF+xof5qr70SIt0qKL9j0+msxXr9IhHdKhi2Y5PoCT5Bo/k4a7xp9zj97k6JAO6dCCe/TGo0M6pEML7s8bjw7p0Ow6lLg/b2J0SId06CL3541Di7RIiy5yj97wdEiHdOgi9+cNT4d0SIcumuX46KzW6jHyI0nd4+Paju95bZK7Gq+9L8lvJvnPSd6e5I8vOf7PJrlfjz/bLXv4M72qw/t8fOn5f5rkV7P4R/qaJO9sOY9/PvDf9UE/jyx+o9G+xtKNA3we90vytkve+6NZXIBen+S3zsbm8tfvSvJNHcd589h/kuS/ZTHB9uokbzp7j3vWfA6vGLsRA41NHdIhHTpAh9a85wOyuFFi+XjPHbMBU3nscexokRbdsMexdOMAn8cgLTqW8THmY4/jRocmPs4O/Xnk+Do01Pi4fk+fyTVj9+KAfxc6pEM6dJjP49suef6nz8bHW7K4AeL1Sd6V5NY1x789ydPG7sQAfxc6pEM6dJjP49pLnm+uev3npUVapEUHHB+N9zRfffnnokM6pEPGh4eHxwk+9thk1/iJN/zQn0eO7xrvHr2JPPY4bnRIh27Y41i6cYDPwz16E3nscdzokA7dsMexdOMAn4f78yby2OO40aGJj7NDfx45vg4NNT6u39Nncs3YvTjg34UO6ZAOHebzcH9ev78PLdIiLTrM53HtJc83V335Z6VDOqRDBxwfjfc0V33556JDOqRDxkfnx2UryTEDtdYbSynPSfKqJF94trskeeLZ4zKvS/LdtdbPHv4Md/LgJE/a8JxPJXlprfU/DXA+rFFr/Wwp5Tuy+M1Kz1/60sOTPGPNy/44yYtqrf91y7f9/CRP6fC825J8X631x7d8HzbQIR2agpE69JwkX7C0/bEsJvoZgRZp0RSM1CLjYyJ0yDiDsemQDs3YQ7J5fFzxa0m+p9b6Owc8n9nSIR2aMXPVE6JFWjRj5qsnQod0aMaMD+CkucZr+BS4R2/edEiHpsA9evOmQzo0Be7PmzcdMs5gbDqkQzPm/rwJ0SItmjFz1ROhQzo0Y+aqJ0KHdGjGjn58XDX2CTCeWuvbkjwuyY9lMVDX+bUkz6u1vrDWetsgJ9ffjyS5KYtVOdv8QZIfSPKYqf6jnJta62dqrS9I8u1ZjLV1PpnkR5M8rtb69o6HvznJy5P8SpI7Or7mfyV5WRa/4cR/Yg9Mh3RoCg7cocu8pLH96lrrPTscjx1pkRZNwUAtMj4mSoeMMxibDunQDLw7i9+K+7okH+74mtuTvCHJs5I82U1Xh6VDOjQD5qqPgBZp0UyZr54QHdKhGTE+gFlxjdfwKXCP3rzpkA5NgXv05k2HdGgK3J83bzpknMHYdEiHZsD9eUdAi7RoBsxVT5wO6dBMmaueEB3SoRk5qfFRaq1jnwMTUEq5fxarHn9ZkkdmsbrxR5LcVGv90Jjn1kcp5fOSPCHJo7NYqfOBWfwH5iNJfrvW+rsjnh4dlFIeneRrk3xRks9NcmuS30/yK7XWu3c47lVJviLJY5J8cZKH5nx8fCrJHyX5zVrrx3b6A7A1HWIqDtUhjoMWMRWHbJHxMW06BIxNh5iDUsojkjw2i3H+55I8KMk9ST6d5BNJ3p/kA0fwm31Okg5x6sxVHwctAsamQ8yB8QHMkWs8U+EevfnSIabCPXrzpUNMhfvz5kuHgLHpEHPg/rzp0yJOnbnq6dMhYGw6xBycyviwMBoAAAAAAAAAAAAAAAAAAAAAAAAwuqvGPgEAAAAAAAAAAAAAAAAAAAAAAAAAC6MBAAAAAAAAAAAAAAAAAAAAAAAAo7MwGgAAAAAAAAAAAAAAAAAAAAAAADA6C6MBAAAAAAAAAAAAAAAAAAAAAAAAo7MwGgAAAAAAAAAAAAAAAAAAAAAAADA6C6MBAAAAAAAAAAAAAAAAAAAAAAAAo7MwGgAAAAAAAAAAAAAAAAAAAAAAADA6C6MBAAAAAAAAAAAAAAAAAAAAAAAAo7MwGgAAAAAAAAAAAAAAAAAAAAAAADA6C6MBAAAAAAAAAAAAAAAAAAAAAAAAo7MwGgAAAAAAAAAAAAAAAAAAAAAAADA6C6MBAAAAAAAAAAAAAAAAAAAAAAAAo7MwGgAAAAAAAAAAAAAAAAAAAAAAADA6C6MBAAAAAAAAAAAAAAAAAAAAAAAAo7MwGgAAAAAAAAAAAAAAAAAAAAAAADA6C6MBAAAAAAAAAAAAAAAAAAAAAAAAo7MwGgAAAAAAAAAAAAAAAAAAAAAAADA6C6MBAAAAAAAAAAAAAAAAAAAAAAAAo7MwGgAAAAAAAAAAAAAAAAAAAAAAADA6C6MBAAAAAAAAAAAAAAAAAAAAAAAAo7MwGgAAAAAAAAAAAAAAAAAAAAAAADA6C6MBAAAAAAAAAAAAAAAAAAAAAAAAo7MwGgAAAAAAAAAAAAAAAAAAAAAAADA6C6MBAAAAAAAAAAAAAAAAAAAAAAAAo7MwGgAAAAAAAAAAAAAAAAAAAAAAADA6C6MBAAAAAAAAAAAAAAAAAAAAAAAAo7MwGgAAAAAAAAAAAAAAAAAAAAAAADA6C6MBAAAAAAAAAAAAAAAAAAAAAAAAo7MwGgAAAAAAAAAAAAAAAAAAAAAAADA6C6MBAAAAAAAAAAAAAAAAAAAAAAAAo7MwGgAAAAAAAAAAAAAAAAAAAAAAADA6C6MBAAAAAAAAAAAAAAAAAAAAAAAAo7MwGgAAAAAAAAAAAAAAAAAAAAAAADA6C6MBAAAAAAAAAAAAAAAAAAAAAAAAo7MwGgAAAAAAAAAAAAAAAAAAAAAAADA6C6MBAAAAAAAAAAAAAAAAAAAAAAAAo7MwGgAAAAAAAAAAAAAAAAAAAAAAADC6/wfh0VgquWOTFwAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import asyncio\n", + "\n", + "from matplotlib import pyplot as plt\n", + "\n", + "from apd.aggregation.query import with_database\n", + "from apd.aggregation.analysis import get_known_configs, plot_sensor\n", + "\n", + "with with_database(\"postgresql+psycopg2://apd@localhost/apd\") as session:\n", + " coros = []\n", + " figure = plt.figure(figsize = (20, 5), dpi=300)\n", + " configs = get_known_configs()\n", + " to_display = configs[\"Relative humidity\"], configs[\"RAM available\"]\n", + " for i, config in enumerate(to_display, start=1):\n", + " plot = figure.add_subplot(1, 2, i)\n", + " coros.append(plot_sensor(config, plot, {}))\n", + " await asyncio.gather(*coros)\n", + "\n", + "display(figure)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import asyncio\n", + "\n", + "from matplotlib import pyplot as plt\n", + "\n", + "from apd.aggregation.query import with_database\n", + "from apd.aggregation.analysis import get_known_configs, plot_sensor, Config\n", + "\n", + "async def clean_magnitude(datapoints):\n", + " async for datapoint in datapoints:\n", + " if datapoint.data is None:\n", + " continue\n", + " yield datapoint.collected_at, datapoint.data[\"magnitude\"]\n", + "\n", + "TemperatureConfig = Config(\n", + " sensor_name=\"Temperature\",\n", + " clean=clean_magnitude,\n", + " title=\"Ambient temperature\",\n", + " ylabel=\"Degrees C\",\n", + ")\n", + " \n", + "with with_database(\"postgresql+psycopg2://apd@localhost/apd\") as session:\n", + " coros = []\n", + " figure = plt.figure(figsize = (20, 5), dpi=300)\n", + " configs = get_known_configs()\n", + " to_display = configs[\"Relative humidity\"], TemperatureConfig\n", + " for i, config in enumerate(to_display, start=1):\n", + " plot = figure.add_subplot(1, 2, i)\n", + " coros.append(plot_sensor(config, plot, {}))\n", + " await asyncio.gather(*coros)\n", + "\n", + "display(figure)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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dv1ZJrqqq6sZuHVRV1Q1Jfj1u6IRunQULVn1FcvQftZ7/2onJltt708sPX5Fc8qeTB/nscWTyW+ckg8t70xezZ+kxyaPOal/z6w8l33hYMrKxNz3RueFbx/5srvl4sv6XycZrxx69tPh+yWP/s32A2Fz3oL9tPfej1/auDwDua/jm5Httgnz3OCJ53DnJ4LLm88uOTe7/5tbrb/nmhIEpBKUBAAAAAOym+me7AQAAAAAAYGFqTBpgNNijToD5YvL7imA0AACYYSvGfXxTD867KcnhOz5e2YPzYOE5+g3Jr/4x2XpH8/lrP50c9Zru9jB8y1h4UjulP/ndNUljVXd7YfdywO8kR7567O9oKxuuTq7/QnLoi3rXF5O75hPJ5lumvu4Rn0+WHT95Xf/ipBoZ+7j0JyObdp5ffMDUz59r9jqp9dyV/5Q88B3Jor171w8AY65u89x2YM/kqb+afI8jX5P8/K+az132rmS/0+69rrZPrT8AAAAAgN2QYDQAAAAAAGBW1CcLMKr5NgYwNfVao+38gGA0AACYacvHfTyNtJMpG3/Gsh6cBwvP4NLk/m9KLn5z8/nf/Ff3g9Fuv2jyMIf9nyQUbaHa44jJa37zX4LRdje/+a/prVv5qKQuC7Vjexzefn7dT5L9ntCbXgC4V7v/D654RGd7tHvuu+W2+16PjnS2JwAAAADAbqw22w0AAAAAAAALU2OyYDQBRsAUNWpDbef7a+4rAAAwwwbHfdyLn0ccf8aiHpwHC9ORbYLPrvtMctPXZ/7M7VuSqz6SXPiK5LynTV5/v9+b+R6YGw46ffKaX38w+Ux/ctm7k613dL8n7mv85/MFLxp73Pa9qe+z8jFC0aaqsSpZ8cjW87/8h6SqetcPwEK36Ybk5/87ufHLrWs6fV5baklfizcIWv/LZP2v7r2uBKMBAAAAAHOfYDQAAAAAAGBW1CcJRhsQjAZMUb3VC0J2ELgIAAAzbtO4j1f04LzxZ2xqWQXsmv7FyVGvaz1/7hOTS/9i5s7bvjn5zhOTC1+WXPXhyesPPSM5+Lkzdz5zy+IDkwe+a/K6anty6duSrxyZDN/S/b4YM/Hz+dpPjj223Da1fYZWJyd/sDs9zncnf6j13E1fTX7yht71ArCQ3fXz5D8fklz8J61ragPJIS/ofM9HfL713FePSm6/aOxjwWgAAAAAwDwgGA0AAAAAAJgVjb72wWj9ZbBHnQDzRX8ZyECbe4dgNAAAmHHjk2YO7sF548+4tQfnwcJ1wO+0n7/83cnwDH0arjkzWXvu5HW1geQpVySn/OvYxyxcx76189otv0muFLDVM51+Pt/j2Lclp3xk7PGwTyYP/WjyuO8kT7k82fOorrU5ry09OjnwGa3nf/m3yYare9cPwEJ1+XuSzZOEsz7u3Kk9r93nYe3nL3nb2K+j7YLRqs7PAwAAAACYRYLRAAAAAACAWVGvNdrO95f+HnUCzCft7i0DgtEAAGCmXbXj15Lk4FLK4d06qJRyWJJDmpwNdMOSQ9vPVyPJrefMzFk3/Wdndcf+2VjgTykzcy5zVylJfWXn9Vd/tHu9cF83f31q9Ue8MjnsjLHH6hcmh74k2fcxSX/77x8wiaX3bz9/8zd60wfAQnZzB89xJ3vOPdHg8mRgaev5284f+7VqF4wGAAAAADA3CEYDAAAAAABmRV/pz2BZ1HK+X4ARMA2N2lDLOfcVAACYcZck2Zqk2nH9tC6e9bvjPt6W5OIungUMrU72OLJ9zZX/lIxs2rVztqxLrv1kZ7X7P2nXzmJ+OfgFndduur57fXCvajRZ84XO65c+IGkc0L1+FrL9nth+/uqPJSPDPWkFYEHZekdyw5eTm76WbLm9fe3yE5LGqqntX0r758TbN4/9OioYDQAAAACY+wSjAQAAAAAAs6ZeW9xybkCAETAN9Vqj5ZxgNAAAmFlVVW1N8t0kZcfjT0oprdOKp2nHnm/MWABbleS8HWcD3VJKcuJfJ7XWb2yQ276f/PteyfpfTe+Mm7+ZnHVIZ7WrX5zs9eDpncP8dPQfJUsO67z+yg92rxfGQhLPe0YysrGz+r56cuL7x+41zLwVpyYHP7f1/O0XJl87Idlwde96Apjvbvl28qWDkvOenpz75Pa1fYuTE943vXOOfVubySqpqqQSjAYAAAAAzH2C0QAAAAAAgFnT6GsdjCbACJgO9xUAAOi5z+z4tUqyIsn7u3DGXyVZmbHwtST5dBfOACY68KnJEy9qXzO6JfnRq6e+9+i25L9emIzc3bqmvio58rXJI89MHvpRAUrc19BByWk/SE76x2Tfx+be/0W0cNHvJ5tv60lrC9KVH0xu/HLr+f2elBx6xtjjge9KfvtHyf5P6l1/C00pycM/1T488O5fJT/+o971BDCfjW5LvvfszgJCj3tH8sQfJfudNr2zlh2bPLzdl8RVMioYDQAAAACY+/pnuwEAAAAAAGDhqtcEGAEzq919ZcB9BQAAuuHTSd6dZFXGUmleVUq5saqq98zE5qWUNyf5w4wFr5Ukt0YwGvTOsuOSB30g+cnrW9fc8s1k6x3J4PLO9117frJ5bfuax30nWXp053uy8NT3SY78w7HH1juTf5/k7+CNX0kOe2lvelto1nyh/fyDPuDzuddKLTnmDclFf9C65qb/SEY2Jf2t/00VgA6sPT/Zum7yuqHVyXF/vuvnDd2v9Vw1mlSC0QAAAACAuU8wGgAAAAAAMGsagtGAGSZwEQAAequqqq2llLck+VjuDS97VynlgUl+v6qqO6azbyllWZJ/TvKccftWSf60qqqtM9E70KF9Tp285pZvJUsfkCxZnfTV7x0f3ZZsWZc09h0LaRi+JRndOhZQ1U59ZbLHYbvWNwvL4LJkz2OS9Ve0rrn5G8mhZySjW5IN17TeZ+tdGftfTpLFByYDe8x4u/PGtg3JpjXJby5oXePzefbs8/D289X2sc+FZcf2ph+A+WrNmZ3VrZjkvtyxWuspwWgAAAAAwDwhGA0AAAAAAJg19Vqj5dxATYARMHVtAxfdVwAAoCuqqvpEKeUZSZ6ee0PMfi/J40spH0ny4aqqruxkr1LK4UlekeSMJHvl3kC0KslXqqr62Mz/DoC29n5IsvzE5I6ftq753rPHfq0tSg57WfLgv0su+8vkF3+TjGyc+plH/3Hi63im6v5vSn7wktbz13927DEVpS858OnJQz8mIG28bRuSH5yR3PDFsXCtdnw+z57lxyf7PTG5+T9b15z9gOTBf58c9dre9QWwUB31upnZp5TWc+c8LjnyNa3nq9GZ6QEAAAAAoMsEowEAAAAAALOmURtqOddfvFAKmLp6u2A09xUAAOim/5HknCQn5d5wtL2SvCHJG0opNyf5YZIrkty545EkS5MsS3JMkpOT7L9j/J5Xet+z14+TvLDrvwtgZ6UkT/h+8rnWX3P/t9EtyZX/PBaWNHzz9M576EeTQ18yvbUsbIe+eCy87Pxnztye1fZkzZlJrf7/s3ff4XFddf7H32ekkVXcbcklbrHTnR7SExISUigJoZelZhd2YYEsS1nI0kLgt7CFXcrCshD6AqEEUoFU0kmB9B7HvUlx72rn98fISI7vHc2MZkYa+/16nvvo6nzPPeer2BnZ8tzPhZP/r3zr1rr73gdLfzn4vPkXwyEfrXw/Svfi38BjX4BHL02f86eLoGUOzDi/am1J0l5nv7/LBQ6XRSa91HFH7kjT21WmHiRJkiRJkiSpsgxGkyRJkiRJkiRJw6axrim1ZoCRpFI01aXfpJ31dUWSJEmqmBjj5hDCmcD3gVeTCzSD/oCz6cCr+o40YcD5wOuvBN4RY9xctoYlFac+/ed4iUoNRZt/saFoGpqZr4Hj/hfufU951136S+j6FmRHl3fdWtS9BZb8vLC5h36qsr1ocHWj4PDPweo/QMft6fMW/sBgNEmqpNlvKN9aIU8w2mBid/n6kCRJkiRJkqQKGsJPQiVJkiRJkiRJkoamKdOSWjMYTVIpGjPpwWi+rkiSJEmVFWPcFGN8LfBhYBu5ULM44KBvLOngBXMDsB34WIzx1THGjdX6OiSl2Pcdld9j/OGV30N7vvFHlH/N3k7Y/Gz5161FmxZA747B5405AOoaK9+PCjPY6+uGx6rThyTtiXo7B58zbn759htKMFqvwWiSJEmSJEmSakP9cDcgSZIkSZIkSZL2Xo2ZptRa1gAjSSVoMhhNkiRJGnYxxv8MIfwQ+AfgfcCEgeWUy8KA83XAN4CvxBifr0yXkoo270JY+IPKrT+qFfY5v3Lra+8x6VgYfxisf6S86/72qN3HRk3afSzTAJOOg8O/AOPLGIIynLatggc+Bh23wZbFhV0z768r25OKM+9CePabEHuT6xufgmVXw4zzqtuXJO0Jutbnr8+4ABrbClpq2faFXP38T1iy/Vm62T3ELBsamFs3lfNGtTBlx5bie40Go0mSJEmSJEmqDQajSZIkSZIkSZKkYdOUaUmtGWAkqRT5Ahd9XZEkSZKqJ8a4BvhUCOFzwPHAacDJwD7ARGBnksxaYA2wArgTuBW4J8bYWfWmJeXX9mI4+Wdw55vKu27IwMTj4MTvQ3363+ulgoUAL/k93P1OWHV9ZffasSZ5fNmVsPoP8PKHoWVWZXuotJ7tcMPJsPm5wuaPmgz7vxcO/khl+1JxJh4Np/4GbssTQHnb+XD6dTD9ZdXrS5L2BJ3r8tdP/FFBy7R3ruQ/lnyCHXF73nl/7l7DsweewsefuJXxXfnn7sZgNEmSJEmSJEk1wmA0SZIkSZIkSZI0bAwwklRujZnm1FrW1xVJkiSp6mKMXcAdfYekWjf7jbmjexv07oCeHfC7Y2Db8tLWO/abMOetkB1d3j6lpmlwxu+h465cqFea5lnQ2wnbV5W/h64N8Nz34LDPlH/talp2ZWGhaOPmw5k354LRQqbyfal4M86D1z4Pv5qcPueprxiMJknF6lyfv17gn3XvWP/7QUPRdtqYbeTB8dM4vWNhQfP/otdgNEmSJEmSJEm1wX9xlCRJkiRJkiRJw6apriW1ZoCRpFI01aUHoxm4KEmSJElSmdQ3QcN4aJoCYw8sfZ3pLzcUTZU1bn7+kK6xB8L8iyu3/5p7K7d2tTx/T2HzWk+FxjZD0Ua6hom5I82a+6rXiyTtKfKFBB92ScHLLNr+dFHbLm0eV9R8AKLBaJIkSZIkSZJqQ/1wNyBJkiRJkiRJkvZejZmm1JoBRpJK0ZgxGE2SJEmSpKqa/UZYfXPx1006AVpmlb8faaCGcTDtZbDi2uT67Dfm6n/+EMSe8u/fcSfcdsHu443TYMb5MP1l5d+z3Bb9qLB5s99Y2T5UHiHkfq2e+WZyvXMtdG02tFKSCtW9DbatTK/PflPBS7V35lknaetSwkh7DUaTJEmSJEmSVBt8HJMkSZIkSZIkSRo24+onJI5nyNBYlx5uJElpRteNJZPyz6Cj68ZWuRtJkiRJkvYCc98Fcy8s7pq6Zjjxh5XpR3qh4/4Hxh+2+/i8d8O+74Dm6XDijyHTUP69uzbAsit3P579H/jDy+Hxfyv/nuW08Eew4/nB5x3+eWg7rfL9qDyO+AI05wmm3LK4er1IUq3bsii9dvBHYewBBS2zvXcbG3vWFbV1d6auqPkAxK7ir5EkSZIkSZKkYVA/3A1IkiRJkiRJkqS916TsFKY1zGRl59Jdxuc1HUxjpmmYupJUy0ZlGjmg+TCe3PrQLuNt2elMbpgyTF1JkiRJkrQHy2ThhMvgsE/D2gcgdvfXenZA73bIjoXYC91bYNwhMPFFkPFtzKqS5hlw7gOw/mHYvABCPUw8Blpm9s+Z8ybY5+Ww5j4Ysx+0zIbt7XBFAT9POuar0LMdHvxY8b09+jnY/72QHV38tZXW2wMPXZxe3+c82O89MOk4aGyrXl8auoYJcN4zcPmo5PqWxTB+fnV7kqRatf7h9Nrhlxa8TEfnytTa3MaDeG77k7uNd4XkBwXl1ds9+BxJkiRJkiRJGgF8R4EkSZIkSZIkSRpW75j2D3x92efY3LMBgAn1k3nr1PcPc1eSatmbpvwdX1v2GdZ0tQPQnBnNhdM/PMxdSZIkSZK0h2uZnTukkShTBxOPyh1psmNh6pn9nze2wfGXwT1/nX/tue8qPRitezOsuWfXfUeKTU/D1mXp9YM/Bm2nVK8flVddQy40MOnXeOvi6vcjSbUo9sLjX0yuNc+AupQAygTtnSsSx+tDPXObkoPRujMlBKNFg9EkSZIkSZIk1QaD0SRJkiRJkiRJ0rCa1TiPS/b9Bgu2PUEm1LFf0yE0ZAp/k7gkvVBbwzQ+OeerLNz2FJ1xBwc0H0Zjpmm425IkSZIkSVKtmf5yCHUQe5LrU14C2dG5Y9LxuZCzYq28fmQGo21emF7LjoNJx1WvF1VGy+zkYLTl18L+761+P5JUa5b+CtY9mFxrO72opdq7ViaOT85OTf238+5QQjBar8FokiRJkiRJkmpDCT8BlSRJkiRJkiRJKq+muhYOHf0iDmk5ylA0SWUxKtPIQS1HcPjo4wxFkyRJkiRJUmmapsLhn0+uNUyEI77Y//lR/54LDCvWE/8KT329tP4qafNz6bVjvwF1DdXrRZXRPDt5fMW18NCnqtuLJNWa3h54+NPp9YP+oajlOjpXJI63NUynPmQTa92ZuqL2ACAajCZJkiRJkiSpNtQPdwOSJEmSJEmSJEmSJEmSJEmSJI1I8z8ObafB6ptgxxrIZKFlDuxzHrTM7J/Xdgq87EFYfg1sfnb37ZH24AAAIABJREFUdVbdABseT97jzxfBzFdD8z4V+RJKsmVh8njDBJjzlur2ospoSQlGA3js87nfkxOPrl4/klRLll8NG59Mrs18DUw8pqjl2jtXJo63ZqeRTQtGC5mi9gAMRpMkSZIkSZJUM2oqGC2EcHPfaQTeHGNsL3GdKcBPd64VYzyzHP1JkiRJkiRJkiRJkiRJkiRJkvYwrSfmjsGMngMHvj+59uDF6cFosReWXQkHvK/kFstuc0ow2j7nV7cPVU6+YDSApVcYjCZJaVbdkFIIcNjnil6uvWtF4nhbw3R6Y09irauUYLReg9EkSZIkSZIk1YYSfgI6rE4HTuv72DiEdRr71th5SJIkSZIkSZIkSZIkSZIkSZJUGW2n5q8v/ims/TP0bK9OP4PZ/Fzy+Oi51e1DlTPY78nNC6rThyTVorTXyJmvhfHzi1pqa89mNvdsTKy1ZadRH7KJte5MCbcFRoPRJEmSJEmSJNWGWgtGAwjD3YAkSZIkSZIkSZIkSZIkSZIkSQWbejZMOj693nEH/O4Y+MV4eOyLEGP1enuhGGH9Q8m10ftWtxdVzrhDcgE+aRb/DLa3V68fSaolK3+fPD7puKKXau9cmVprbcgTjBbqit6L3q7ir5EkSZIkSZKkYVCLwWiSJEmSJEmSJEmSJEmSJEmSJNWOTB2cedPg83p3wEOfgGVXVr6nNM98M73WYjDaHuXkn8GYA9Lrd721er1IUq1YeUN6rYQA0Y6uFYnj2dDA+PpJZDMpwWiZEm4LjN3FXyNJkiRJkiRJw2BvDUarH3DuT3QlSZIkSZIkSZIkSZIkSZIkSZVV3wInfL+wuYt+XNFWSt67hMAXjWCZejj2v9Prq26Abauq148k1YIFl6XXRs8tern2zpWJ463ZqWRChvqQEowWSrgtsLez+GskSZIkSZIkaRjsrcFokwecbxm2LiRJkiRJkiRJkiRJkiRJkiRJe48JRxU2b8Pjle0jn23JAS0ANE2rXh+qjnGH5a9vfKo6fUhSrdj8XHptzAFFL5cajNYwHSA1GK0rU8JtgVtXFH+NJEmSJEmSJA2DvTUY7cV9HyPgT3QlSZIkSZIkSZIkSZIkSZIkSZU34XCYfOLg8zY+ATeeBluXVb6ngXq7Ycui5Nr4IyDsrbcg7MGapsDM16bXtyyuXi+SNNJ1boC19yXXQgayo4tesqMr+da2toZcGGlaMFpvyNCb2Edd+mZbFua+10uSJEmSJEnSCFfL/yoZi5kcQsiGEGaFEP4G+OcBpUfK25YkSZIkSZIkSZIkSZIkSZIkSSlOvxZmvR7qx+Sf134bXH8S9GyvTl8AT301vXbiD6vXh6or369tWlCeJO1tYoQbT02vn3ZdScu2d65MHG/LTgcgmxKMBtCdFIJ22rX5w9Ge/lpR/UmSJEmSJEnScBhxwWghhJ60Y+A0YFG+uQnXbgcWAt8Cxg5Y66oqfnmSJEmSJEmSJEmSJEmSJGkQIYRjQgivCyGcF0LYb7j7kSSprBomwCk/h9etg1ctyj9361JYVsW3vD/1X+m10XOr14eqq74ZZlyQXDMYTZJy1j0A6x9Jr489oOglN/dsZGvv5sRaW0MuGK0+XzBaJuHWwOnnwOvWp2/66KVF9ShJkiRJkiRJw2HEBaORCz1LOwqdN9gR+9Z4Evhl5b4USZIkSZIkSZIkSZIkSZL2XiGExhDC3AFH3SDzzw8hLALuBS4HfgM8FUK4I4RwSBValiSpejJ10DwLGibmn7fgsur0A9C1MXk81EN2dPX6UPW1zEke37K4qm1I0oi19v70Wl0zNM8sesn2zhWptbaGaUD+YLSukHJrYHY0NO2TXOtcDzvWFtyjJEmSJEmSJA2H+uFuIEVk9yC0cgrA/cAbY4xdFdxHkiRJkiRJkiRJkiRJkqS92YeBz/WdLwPmpE0MIbwB+AnJD1M9CbgnhHB6jPFPFehTkqThEQLMfjM889/pc1ZdDz8J0DABOtf1j9c1Q8/W3PmUM5IWh/GHwry/yX1Ms+L3sORyWPcgdG1InjPxRYN+KapxacFoa+6Bm86E+tHQdioc8H6oaxzaXjHCc9+FlddD41SYdyFMOGJoa0pSpW1emF6b9XrIFH+bXnvnysTxUaGRsXUTAMjmCUbrzqQEowG0ngRLfpFQiLDydzDnLcW0KkmSJEmSJElVNRKD0W4jF4yW5LS+j5Hc0yC3F7hmBHYA64EngFtijLcPpUlJkiRJkiRJkiRJkiRJkjSoC8iFnEXgshhj4vsDQwgTgG8Bmb65Ax+wuvOaFuCKEMKBMcZC3z8oSdLId+S/5A9G22lgKBr0h6IBrL45+ZrVN8GC78IZN8Lk43avP/ttuPc9g+994g8Gn6PaNnpO8nj3lv7fX8uvguVXwxk3Q6au9L3u/VtY8O3+zxd8B864HlpPLn1NSaq0x7+YXjvmP0tasqNrReJ4a8M0Qsj9lbg+XzBayPNafML3UoLRgLv+Cma9oaQwN0mSJEmSJEmqhhH308sY4+lptRBCL/1vcHpjjHFJVZqSJEmSJEmSJEmSJEmSJElFCSE0AUfS/76/a/JM/wAwjv5AtOXAFUA38Bpgdt+8GcAHgX+tQMuSJA2P7BiYdi6s/F1l1u/eBE98CU791a7jvd3wyGcHvz7TAGP2q0hrGkFa5hQ2r/02WHU9TH9ZaftsXrhrKBrkQv4e+xc4Pd8fFyVpGG14Ir128MegYUJJy7Z3rkwcb2uY9pfzfMFoXZlM+uL1LTD3nfDc95PrK34LM84roEtJkiRJkiRJqr48P/0cscLgUyRJkiRJkiRJkiRJkiRJ0jA7DKgj976/LTHGP+eZ+1b6Q9GeAg6NMV4UY/xw3zr39c0LwDsr1rEkScNl7jsru/7qW3Yf2/QMbFsx+LUtcyDU4q0HKkqhwWiQ/PupUMt+kzy+4lqIMbkmScOt/Q/ptdFzSl+2M/n7cGt2+l/O6zPpwWjdg31/nnJmem0or+WSJEmSJEmSVGH1w91AkS4ZcL5+2LqQJEmSJEmSJEmSJEmSJEmD2bfvYwQeT5sUQjgI2K9vXgQ+HWPcsLMeY9wcQvgA8Me+oQNDCDNjjEsr07YkScNg2tnk8j8rFAzVuQ4e+iSEAbcQbFlY2LUzXlWZnjSyNIyDttOg/dbB524u8PdOkhW/Ta/t6IDGttLXlqRKWfun9Nr0V5a0ZIyRjq6VibW2hml/Oc+GIQSjTX95em3Lc/mvlSRJkiRJkqRhVFPBaDHGSwafJUmSJEmSJEmSJEmSJEmSRoApA86T7/bOObXvYwA2Ab9+4YQY470hhGXAjL6hwwGD0SRJe46GCXDsN+G+v6vcHo99ofhrJhwJB324/L1oZDr6y3DLubmAsnyW/hJ6eyBTV979Ny8yGE3SyLP+MVhwWXq9ZWZJy27q2cD23m2Jtbbs9L+cZ6gjEIgJ4andg70Oj5oILfsmh6EuuxJ6uyFTU7cXSpIkSZIkSdpLDPJYCEmSJEmSJEmSJEmSJEmSpJI0DzjflGfeyX0fI3BTjLE7Zd6jA85nDaUxSZJGpP3/Fl7+CBzwwdLDoeZ/EkIZAk4O/TSc+is46w5omjL4fO0ZJh6d+z14wvdzv5dmXJA+d/VNpe3RszW9lhTcI0nD7cF/Sq/N/+eSl23vXJFaa2uY9pfzEAL1IZs4rysUcGvg4Zek11ZcN/j1kiRJkiRJkjQMDEaTJEmSJEmSJEmSJEmSJEmVEAacJ9/FnXPSgPPb88xbM+B8bEkdSZI00o0/FF70FTh/UWnXH3EptMweWg/7vj0XojLzNVDfMrS1VHuapsDcd+R+Lx34wfR5y35T2vqbF+WpGYwmaYTp2Q4rf5teb5lT8tIdXSsTxxszzYyuG7fLWH1K6Gl3poBbA1v2Ta+V+louSZIkSZIkSRVmMJokSZIkSZIkSZIkSZIkSaqETQPOpyRNCCFMBfYbMHRXnvUG3gkeUmdJkrQnqG+CSScUd83+78t9nHL60PZuG+L12nNMfFF6bcFlsP353cc3L4KNT0GMu9d6dsC2Felrrvxd8nWSNFy2LIbYm15vO63kpVd3Jr8etmWnEcKuf+WtD8lZ492hgFsDJx6dXlv6a+jtGXwNSZIkSZIkSaqymg5GCyG8JIRwaQjhmhDCvSGEp0IIzxV5LBjur0OSJEmSJEmSJEmSJEmSpD3Q8r6PATgsZc7LB5zvAP6cZ73xA863DKEvSZJqw2GfhkxyEMpuRrXCgR/MnR/4IWiYWNqe4w+DWa8v7VrtebJjIDs2udbbCVe0wj3vht4u2LEGbjgFrtoXrjkIrjsMtizd9ZoNjwF5gs/ab81dt3lRub4CSSpdjHDve9Lrs94IY/cvefmOtGC0hum7jWVDQ+Lc7kzd4BvVN0PTPsm1rvVw5UxYc//g60iSJEmSJElSFdUPPmXkCSGcA3yVXZ8SWerTH32ckCRJkiRJkiRJkiRJkiRJ5ffwgPOJIYRzYoy/f8Gcd/V9jMC9McauPOvNHXC+qhwNSpI0ok1/GZx5Kyz8AWx6JhcqtX11rjZ6Low5AOoaYfQ82P+9MGZerjZ+Ppx9Nzz7LVj3IMSewfeqa4LJJ8KBF0F2dOW+JtWe4y+DO/KE5S34Dow7BNpvh447+8c3PJa77pw/9o/d9VeD77fhMbjzzXDO3aX3LEnlsPJ30H5bev3knwxp+faulYnjScFo9SE5KLUrZArb7Nhvwm3nJ9e2rYRbXwEXLCs8kFWSJEmSJEmSKqzmgtFCCB8FvrjzU/qDzUoJOCs1TE2SJEmSJEmSJEmSJEmSJOURY1wQQniG3ENQA/CNEMJLY4wLAUIIHwZOHnDJlWlrhRBGs+vDVBdUoGVJkkae1hNzR7HGHgBH/0f5+9HeZ/S+g89Z8N1ceN8LrbkHti6D5hkQI2x8srA91/wRNi8sbG9JqpRFP02vtZ4KhYaSJYgx0tGZHIzWmp2221h9SL4FsDtTYA+j5+avb2+HVTfB9HMLW0+SJEmSJEmSKqymgtFCCOcAX+r7NPYdO8PNtgLrgXxPi5QkSZIkSZIkSZIkSZIkSdXzHXLv+4vAvsCTIYSHgDZgJv3vA9wO/DjPOqfT/37BbuCxCvUrSZKkgcYeBHXN0LM1fc6GR9Nrq2+F2W+Aro3F7bvxqfzBaDFCCOl1SRqqjU+k1yYeM6SlN3SvpTPuSKy1NUzfbaw+ZBPndhcazjZmHmTH5n8t3vikwWiSJEmSJEmSRozSH00xPL7Y93HnG6GWAe8H5sQYR8cYZ8QY9y32GLavRpIkSZIkSZIkSZIkSZKkPdtXgCf7ziOQBY4BZtEfdBaBL8cYO/Ks8+oBcx+KMeUOckmSJJVXfQvMu7D06+9+K/ysAX41ubjrHv9i8viin8K18+Hno+Hmc2DzotJ7k6Q0z3wL1t6fXp/3N0Navr1rRWqtrWHabmPZTEPi3IKD0eoaYb/3DNLUbYWtJUmSJEmSJElVUDPBaCGEecAR5N7UBHAPcGiM8RsxxiXD15kkSZIkSZIkSZIkSZIkSUoSY+wEziEXjrYzCC3Q/17AAFwBfCZtjRDCaOC1A665qSLNSpIkKdnR/wXzL84F61RL+62w4re7ji2/Fu56C2x4HHq2wqrr4YZToGd79fqStOdb+CO47+/S64dfCuPnD2mL9s6VieMtmTG01I3Zbbw+1CfO78rUFb7pkV+CQ1P/6g3Lfg09nYWvJ0mSJEmSJEkVVDPBaMCJfR93viHq7THGTcPYjyRJkiRJkiRJkiRJkiRJGkSMcSlwJPBe4LfA48AT5ALRXhdjfH2MsTfPEu8ExpJ7/2AArq1ow5IkSdpVpg6O+AK8YQtkGqq37zP/s+vnz35r9znblsPya6rTj6S9wzPfzF+f89Yhb9HeuSJxvLVhWuJ4fcgmjneHIm4NDBk4/LNw+OfT56y4rvD1JEmSJEmSJKmCaikYra3vYwQeiDE+M5zNSJIkSZIkSZIkSZIkSZKkwsQYu2KM34oxviLGeGjf8boY4xUFXH4ZMGHnEWO8o7LdSpIkKVHIwNiDqrffugd3/Xz51cnzFlxW+V4k7R1i3P21Z6D60dA8Y8jbdHStTBxvKzYYLVPCrYHjDkmv5fvaJUmSJEmSJKmKaikYLQw4f3bYupAkSZIkSZIkSZIkSZIkSVUTY9wWY9yw8xjufiRJkvZq+76tvOvNe3d6besSuONNsOxquP+i9Hmrri9vT5L2XlsWQs+29PrsN0GmfsjbtHeuSBxvy05PHE8NRgsl3Bo47dz02ubnil9PkiRJkiRJkiqgloLRlg84rxu2LiRJkiRJkiRJkiRJkiRJkiRJkvZGB/4DzH5z+dY7/FI44gvp9SWXw23nw9NfTZ8Te6FnR/l6krR36u2Bq+al1yefBEf/59C3ib10dK1KrLU2TEscz6YEo3VlSrjFrr4J2k5Prm1ZWPx6kiRJkiRJklQBtRSM9tiA85nD1oUkSZIkSZIkSZIkSZIkSZIkSdLeKFMPJ/1f+dZrGA8HXjT0dZZdOfQ1JO3dVt2Qv37WHZAdPeRt1nU/T3fsSqy1NUxPHK9PCUbrDiXeGjjr9cnjGx6D7q2lrSlJkiRJkiRJZVQzwWgxxkeAR4EAHBNCmDDMLUmSJEmSJEmSJEmSJEmSpBKFEGaEEF4cQrgghPC2EMLbh7snSZIkFSAEGH/E0Nepa4S6UVDfAtmxQ1ur/bah9yNp75bvdWT8EbnXvjLo6FyZWmvLTkscTw1Gy5R4a+DoucnjnevgmW+UtqYkSZIkSZIklVH9cDdQpP8AvgfUAR8GPjm87UiSJEmSJEmSJEmSJEmSpEKFEGYDHwLOB2YnTPlhwjWnAi/p+3RdjPFrletQkiRJBZlxAax/aGhrZMf1nzfPhA2Plb7WM/8Nm56GcYf2j9U1wKTjYPorc+eSlE/H7em1ma/Je+nWns08vPlelu9YTCTmnbtqx9LE8TF142iqa0mspQajhRKD0VpPyYVT9mzfvfbAR2HSCdB2SmlrS5IkSZIkSVIZ1FQwWozxByGEVwKvBT4WQrgzxvjb4e5LkiRJkiRJkiRJkiRJkiSlCyFkgEuBj5J7OGpImJZ29/jzwGd31kMI18UYF1SgTUmSJBXqoItg9c35g4QG0zC+//y4b8MNJw2tp1U35I4XmnImnHYV1DcPbX1Je67n/wgdd6TXD/xgamlNVztfXfoZOrpWDqmFtobpqbVspszBaNnRsN974an/TK7feCocfxnMu7C09SVJkiRJkiRpiEr86eewegdwFblQtytDCJ8LIYwf5BpJkiRJkiRJkiRJkiRJkjQMQghZ4HfAx0l+oGtaIFquGOMTwC30h6m9pawNSpIkqXgNE+CMG+GMm+Cof4d9zhvaepNeVJ6+kqy+CRb/tHLrS6p9D3w0vXbMV3YNcnyB69dcMeRQNIDW7LTUWn1IDkbrytSVvuH8j0N9S3r9zx+C7q2lry9JkiRJkiRJQ5D0BqMRK4Tw6b7Th4CTgMnAPwP/GEK4G3gcWAf0FrNujPFz5exTkiRJkiRJkiRJkiRJkiT9xWXAS8kFoEVyAWe3kws76wQ+X8AavwJe0nd+NnBp+duUJElSUeoaYOoZueOAv4fLm4q7vnlm/3kmm/t869Ly9rjT8mtg3l9XZm1Jta1zPXTckV4fs3/eyx/Zcl9Z2mhrKD4YrTtkSt+wsQ0OvAge+3/J9a6N0H47TD+n9D0kSZIkSZIkqUQ1FYwGfJZdnwy58w1SzcAZfUcpDEaTJEmSJEmSJEmSJEmSJKnMQghnAm+l//1+zwJviTHe31efTWHBaNcCX+9b49gQQmOMcXtlupYkSVLR6hqh9VTouH3X8cwoaBgH29t3v2bKmbt+3jK7csFo6x+Bpb/OndePhsnHQ3ZsZfaSVFs2L8xfn3xSaqmzdwfru9eUpY2Dmo9MraUGo2WGEIwGcPBH4On/hq4NyfUn/wNiD0w6Fhpbh7aXJEmSJEmSJBVhiD/9HBF2PkGyFKGcjUiSJEmSJEmSJEmSJEmSpF18pu9jABYDJ+0MRStGjHExsL7v0yxwUHnakyRJUtkc9lmoa9p1bP7F8KL/hlC/6/jYA2Huu3Ydq2RQ2eYFcPtrcsctZ8OvWmHRTyq3n6TaseqG9Nphn8uFO6Z4vmtVWVo4esxJzG7cL7WeGowWhnhrYMMEOOnH6fVVN8Ctr4Ar2uDRL0As9RY+SZIkSZIkSSpO/eBTRhzDzCRJkiRJkiRJkiRJkiRJGuFCCBOBk+h/+OlFMcbnh7Dk433rARwAPDiEtSRJklRuU8+As+/OBY51roN9XgEzXpWrNe0DSy6HrUth0vG5ULTG1sLXDvUQu3PnDRNz6z73vdJ77e2Eu94KradCy8zS15FU+x78p/TaYZ/Ke2l758rE8UCGQ1uOGXTrprpm9m86lBPHnUEI6bfMZdOC0TJDDEYD2OeVMGZ/2PRM/nkPfxJaT4IpLxn6npIkSZIkSZI0iFoLRvMnp5IkSZIkSZIkSZIkSZIk1YZTgJ13abfHGK8a4noDQ9XahriWJEmSKmHCEbnjhVpPzB15rz0aVlyXXHvTDggvCADasQaWD+WPmBGW/hIO+tAQ1pBU07YsSa9NPXvQyzu6ViWOT85O4b0z/rnUrnZTnxKM1hXqyrPB7DfBo5cOPm/x5QajSZIkSZIkSaqKmgpGizHeOtw9SJIkSZIkSZIkSZIkSZKkgkzr+xiB+8uw3qYB56PLsJ4kSZJGklmvhcc+v/v45BN3D0UDmPvOIQajAWvu7T/v2Q5dA/7ImamHhglDW1/SyLbxqfTahCMHvbyjc0XieGvDtMTxUqUFo3VnEl4bSzHpuMLmbXq6PPupcD3bc0f9GMjU5c4zoyAE6OnMfa9K+h4pSZIkSZIk1biaCkaTJO3B1j8CT3899480s14P01+R+4caSZIkSZIkSZIkSZIk1aqJA87XlWG9pgHnXWVYT5IkSSPJhCOh7XRo/8Ou4wdelDx/+itg9H6w+dn+sUwWpp4NK64tbM/FP4PlV0P3Fsg0QG/nrvWWOXD4pbDvWwv8IiTVlDX3pNf2/7tBL+/oWpk43pqdWmpHiepD8i2A3eUKxJp2Low9GDY+kX/e6lvg2e/Afn9Tnn2VbsH34J4Lk2vZsdC1EeqaciFpc98JR/1bLiRNkiRJkiRJ2kP4OABJ0vBbdSP89kh49n9h4Q/h1vPg0UuHuytJkiRJkiRJkiRJkiQNzcYB52PKsN6UAedry7CeJEmSRprTr4H9/x7G7A+tp8LJl8PsNybPrWuAs++EOW+Dln1h6lnwkt/DaVfDMV+BiS+C7PjcUdeUvAbkQtFg91A0gC2L4O63wcobhvylSRphYoSHP5Vcqx8No/cddImOzlWJ460N04bS2W6yoSFxvDtTRyzHBpl6eOltsO87oGV2/rn3vhuW/rocuyrN8mvSQ9EgF4oG0LMNutbDU/8FD11cnd4kSZIkSZKkKvExAJKk4RUj3P8BiL27jj/2BZh3ITTPGJ6+JEmSJEmSJEmSJEmSNFQdA873H8pCIYQ64KgBQyuHsp4kSZJGqPoWOPbrhc9vbIOTfrj7+IEfzB07LfkF3PGG0vta8B2Ydlbp10saedY/kl6bccGgl3f1drKu+/nEWmu2vMFo9SGbWusOGbIvvB+jFI2T4cTv587XPwLXHZ4+d8F3YOarh76nkj377eKvWXAZHPEvkKkrfz+SJEmSJEnSMMgMdwOSpL3c+odg45O7j/d2wtIrqt+PJEmSJEmSJEmSJEmSymXnXeYBODCEMJQn5L0MaO47j8Afh9KYJEmS9jLjDh3a9esfLk8fkkaO9Q+l18bNH/Ty57tWE4mJtbaGMgejZfIHo5Xd6HmQGZVezxcqp6FbflXx13SuhW3Lyt+LJEmSJEmSNEzqh7uBoQohZIETgVOBecBEYAxAjPHMYWxNklSIFb9Lry35xa5PapMkSZIkSZIkSZIkSVLNiDE+EUJYDuxDLhztw8CHil0nhJABLt65LPBQjHF92RqVJEnSnm/cwTDxWFh7X2nXb3wSenZAXZ6gIO2ZOjfAI5dAx23QvRm2LIae7blaYxs0TNh1fmYUTDoODrsEmqdXv18VZtmVcPfb0+tz3jLoEh1dKxPHAxkmZdtK7SxRfUi/BbA7k4Hesm4H9c0w6/Ww6MfJ9a1LYfm1sM8ryrzxHqbjTnjqq31BcuX+RUpw/UmwbUXuvHkmnPBdmPrSyu8rac+w5n548su5QOBxh0J2NCz4LrwwBHTGq2Deuwf/HrD4clj4Y+jZBjNfA/u/F0KoWPuSJEmSpD1PzQajhRBayL1B6v1A6wvL7Pa37b9c92bgC32frgWOjTEmP55DklR5+Z4U9PzdsGMNjJpUvX4kSZIkSZIkSZIkSZJUTv8HfIzc+/reH0K4LsZ4Q5Fr/D/ghAGff7tczUmSJGkv8uLfwJ1vhI47Srv+hpPh3PvL25NGtt4uuPHU9Pe8b2/PHS+0/mFYcS284gloGFfZHlW8hT+Gu9+WXq9vgZZZgy7T0ZkcjDYx20p9yJbaXXJLedbrDpmy7vUXx34zFya58ank+q2vzL2uznhVZfavde23wc1nQW9n9fbcGYoGufC6m8+C064xwE7S4NbcBzeelgsxA9jwWPrcZVfCsqvglMtzIZpJnvkfuO+9/Z+vvikXLnvUl8rXsyRJkiRpj1ehn3xWVgjhcOBPwCVAG7k3TBXqamASMAc4Cjir3P1Jkoqw8cn0WuyB5ddUrxdJkiRJkiRJkiRJkiSV278CG8k97LQOuDKE8J5CLgwhTA4hfB/4KP0PS10FfLcCfUqSJGlP1zwdzrodXvZAadev/ROse7i8PWlkW35N/geB57NtJTzzzfL2o/J4/Iv56/v9bUHLtHclB6O1ZqcW29GgsqEhtVaxYLTsaDhnkDDIxw24SfXkl6sbipbmiX8b7g4k1YKnvtLBKQYhAAAgAElEQVQfilaQCI+lfD+NMfm15+mvQ/fWktqTJEmSJO2dai4YLYRwCHArsD+5QLSdb3YKFBCQFmPcDPxiwNBry92jJKlAsTd/MBrAst9UpxdJkiRJkiRJkiRJkiSVXYxxLfBB+t/v1wh8M4TwTAjhX4DzB84PIRwXQnhbCOFHwALgbfS/P7AHeFeMcQTcWSxJkqSaNe4wqGsq7drn7ypvLxrZhvrrvfK35elD5dO5ATY8ln/OmP0KWqqjMyUYrWFasV0Nqj5kU2tdmbqy7/cX2dHQOCW9vuYe6O2q3P61rGOEfL9ovzV3744k5dNxZ/HXrPszdCeEqW1fBZuf2328Zyusvrn4fSRJkiRJe6364W6gGCGERuAaYBz9gWiPAF8BbgFGAU8UsNSVwIV952eWuU1JUqG2rcr9UDMf3zwgSZIkSZIkSZIkSZJU02KMPwwh7Ad8ktx7/wIwD/jYC6YG4O4XfB4HXPOJGOP1le9YkiRJe7RMHcx4NSz+SfHXPvu/sPZP0DwTZrwKJhxR/v7Ub+2fYNnVuXCmGRcUHFg1JKtuhJU3QOdaWP2Hoa3VfhusvB6mnV2W1lQGWxYOPmfGBQUt1dG1KnG8LVuJYLT0WwC7Q6bs++1i5mvhmW8k12JvLvxm7IGV7aHWdG2CHR3D3UW/2y6Afc6H2W+A7Njh7kbSSLJpASz5OWxZVNr191wI9aMh1MGEI2HWG3KvgWm2JX/vlCRJkiQpSU0Fo5F7auQc+kPRvgr8Y4y5xxaEEGYXuM4t9L9Rat8QQluMsb3MvUpSabYshQc+nHvSwpj94NDPwNQzhruryti2fPA529uhcz00jK98P5IkSZIkSZIkSZIkSaqIGOOnQwgLgG8ATfS/DzAMON/5OewaiLYDeE+M8UdValeSJEl7uqO+BBsegfWP7Doe6uHwS+GhTyRft+6B3AHw2OfhpJ/ArNdVtte91cIfwx/fkQteAnj0Ujj9d9B6YuX2fPjTuX3K6ZZz4Ogvw0EfKu+6Ks3mAoLRmgYPNuuOXaztSg6+am2oRDBaNr2XTIWD0Q77LLT/ATY8nly/9lB4w2aoG1XZPmpFjHDj6cPdxa6WX507nv4anHETNE4e7o4kjQQdd8EfXg5dG0pfY/HPdv38yf+EI780tL4kSZIkSepTa8FoH6D/DVC/iTH+QymLxBg3hxAWAfv2DR0MGIwmafh1b4UbToGtS3Kfb1sBN58JJ/4Q5rwVQsh/fa3ZWkAwGsCmZ2DSsZXtRZIkSZIkSZIkSZIkSRUVY/xBCOEW4GPAu8gFpEF/GNpAAegB/g/4bIxxUVWalCRJ0t6heQaccx88/0fY+ERuLDse2k6F5n2gcx088a/51+jtggc+AjNfA6HCwUR7m95uuP8D/aFoAF0b4cF/grNuq8yeW5fDY18o7dpDPgHPfgs61ybXH7oY5l4IDeNK70/lMVgw2uGfL2iZNV3tRHoTa63ZKgejVfr1p7EVzn0ALk8JPovdudAtQyJz1twL6/6cXAt18KKvkfxjkASd63Pfj5r3gUxD7vvO1qXQMAEaxsPWZRCyuV+j+98/+HrrH4YF/wvzLy74y5G0B3vo4qGFoiXZ9DQ8+rn0euwu736SJEmSpD1azQSjhRAOAfbp+zQCHx3ikgvoD0abC9w6xPUkaeiWXdUfijbQ3W+HBz8OJ/8s94/te4ptBQajrX/EYDRJkiRJkiRJkiRJkqQ9QIxxCfD+EMLHgFP6jpnAJKABeB5YDdwF3BRjXD9cvUqSJGkPVzcKppyWO15o7IGFrbFlMWx4AsbPL29ve7tVN0JXwl8FOm7PPYy8vrkCe96waxBbMQ68CDY9BUuvSK73bIf2W2HG+aX3p/LYMkgw2uh989f7tHeuTBwPBCZnpxTb1aAyIUMd9fSwe6BMV6au/5MpZ5R9bwDqGmDScbnQryQrrjMYbacVv02vNc+E/d9bmX0LCUaD3K+VwWiSurfm/mxSCeseSK+VO4hNkiRJkrRHq5lgNODIvo8ReDTG+NwQ1xv4LyQ+ckXSyLD4p+m1bSvgxhfDYZfkwtHaTqv9J4ttLTAYbfk1MO/CyvYiSZIkSZIkSZIkSZKkqokxbgWu7zskSZKkkWXqS4FA7haWQTz4T9B6ErS+GCYfD5lsf61rM6y+Ofeg6MY2CHWwfRW0zIFp58CoSRX6AmrcxqfSaz3byhuM1rMD1twDj1xS2vUTjoSmKTDjNenBaABLfwWd63Lno1qh9WRo8HamqtucJxgt1MGUMwtapqMrORhtQv1kspmGUjobVH2opyfuHozWPfC+kkM/XZG9AZh2bnow2srfQ4wQQuX2rxWrb06vTTu3cvvOeRss+tHg8zruhCW/hMknQfP0yvUjaWSKETY+AYvy3MNYSSt/Dwd9GHo74fk/wtalufFQBwQYfxiMP7T275mUJEmSJJVFLQWjtQ44f6YM6+0YcF6BR8VIUglW3TD4nEc+k/s4/eVw6q9zT96pVdtWFDZv9c3+I5kkSZIkSZIkSZIkSZIkSZKk6miZBQd/BJ74t8Hnrrg2dwA0z4Kz786FzTx/D1x/Qv5rT70CZr566P3uafK9bzwWEFZXqE0L4A8vg00l3qZU1whH/EvufPabYMG3of3W5LkLf5g7dsqOzd0PMPWM0vZW8WJv//+rSeb/cy7krgAdncnBaK0NU0vprCD1mSw7erbvNt6d6QuPmfX6XOBepez/Xnj0c8m1bSvg7nfA8d+p7XtchiL2wp//ETpuT59z8Icrt//8jxcWjAZwx+uBAMd8BQ78QOV6kjSy9GyHu98JSy4fvh5W3wJXtOV66dmWPGf6K+GUn0F9S3V7kyRJkiSNOLUUm9044HxH6qzCDXysyqYyrCdJQ5cdW/jcFdfBs9+qXC/V0F3gy2/XBtiW/A+HkiRJkiRJkiRJkiRJkiRJklR2R/0rvPgqOOD9MPst0FhA4NHWJfDgx3IBOXe9ZfD5d7weOjcMvdc9Tr7bnXrLt8197xs8FG3c/Nyv/8EfgZfeBmfdAQd/FA79NJxzL0w/NzcvUwdnFPCg9J26NsKdb4LertL7V3FWXJde2/ftcPglBS/V0bUqcbw1O63YrgpWH7KJ493TzoGTfpI7MvUV25+mqXD8Zen1RT8qPJhrT7T8WnjqK+n1Qz4BY/ar3P7jDoFXLYGDP1bgBRH+9EHY8GTlepI0sjz7ncJC0Y78Irz4ylxgaKbve092HLSeAs0zYMoZuT8bzXxdaX10rksPRQNYcQ089dXS1pYkSZIk7VEq+NPOsnt+wPnkMqw3d8D5mjKsJ0lDV9c4+JyBnvtebT+dpTvPDzFfaOOTuSenSZIkSZIkSZIkSZIkSZIkSVI1zDgvdwDc+Vew+CeDX7P0V3DgRbD5ucHnxh5Yfg3s+1dD63NPE0J6LfaUZ4/OdbCqgCCzee+Ggy7adaz15OS5mSwc+il49NLCetjRAe23wtSXFjZfQ7Pop+m1/d9X1FIdnckPfm9tqFwwWjYtGG3Om2H8WRXbdxcTjspfX/ILmPfX1ellpFnyi/z1ySdUvoeWmXDUl3JHz3a4vGnwa5b8Ag77VOV7kzT8lg7yOgUw6Xg45J9y5zPOhyM+nz439sLlzdC7ozz9DbTk5zD/E+VfV5IkSZJUU2opGG3nozQCMMhPUfMLIUwCDh4w9OxQ1pOksogRdjw/+LyB1j0A6x6CCUdUpqdKy/d0hxfa+CRMPaNyvUiSJEmSJEmSJEmSJKkqQghZ4ETgVGAeMBEYAxBjPHMYW5MkSZLSTT6hsGC0nu3w1FcLX/fJf4fWEyHUQfNMCJnSe+zalHtYdyY5QGmP0Ntd+rVdmyAzCuoaYNMCIA5+TbFhRpNPLG7+yt/DuEOhaWru8+3t0L0ZGibkDpXPloXptfGHFrxMT+xmTVd7Yq01W7lgtPqUYLSu2FmxPXcz9iDIjoOuDcn1jruq18tIs+np9FrIwMQXVa8XyH0vmHA0rPtz/nmrroe5b899/+nakAuNbJoB9OaCKOtbqtKutIsYYduKCgRuhb7XsPXQND33/8merGvjrvcqbszzOrVTMX/uCRmYfDy031Z8b4NZ92Du9YgAnWsHmZyBlllD+zO0JEmSJGlEqqVgtLuAXiADTAohnBFjvLnEtS4kF7AGsAW4vwz9SdLQ7FgD3VuKv+63R8Kbe/M/FWuk6tle+NyNT1auD0mSJEmSJEmSJEmSJFVcCKEF+BDwfqD1hWVSUglCCG8GvtD36Vrg2BhjAQkGkiRJUhnNeQs8/iXYtnzwuYt+XPi66x6Eq+blzkdNgsMugQP+vrjetq2Cu94C7bdCXRPMfRcc/V+QqStunREjz3vjYwnBaNtWw11/Be235ILRJh4NHXcOfl3rqTDpuOL2mnoWjD8C1j9U2Pwn/j13JJl4LJzyMxg9t7gelOz5u5PHs+OKCn9a09VBLz2JtbaG6gejdceuiu25exNNcOAH4dFLk+vdm+Cq/eCkn8DkIv/fqWVblsCae9Lrc94GzdOr189OB38U7npz/jkdd8CVc9Lr018JJ/3QoEZVz+Kfw4Mfzx9mWQ6ZLMx6Axz37dxr255k4J97Ym/h19WPgf3+tri9DvpI7nWkmH0K9cuJhc9tmADzL4aDPlyb91hKkiRJkhLVTAR2jHEdcN+AoUtDKP5vqCGEfYCPk3sDVQRuiLESf+uWpCLlezrMYB65pHx9VFPPtsLnPv21yvUhSZIkSZIkSZIkSZKkigohHA78CbgEaCNv0sFurgYmAXOAo4Czyt2fJEmSNKhRk+Dsu2HuOysXVLVjDdz/flj6m+Ku+8MrYHVf+EX3Fnj66/DIpyvTYzXku12ot4RgtNsugNU35f779GwbPBRt/BG5YI2X/K74cI1MPbz0D3DA+2HcodA8K3fUjy6+77X3wU1nQm9yCJeKsPTX6bWTLy9qqY6ulam1ydmpRa1VjBERjAa58MZ5706vb14AN58BO9ZWr6fhFCPccEp6ve00OP471etnoDlvglOvgGnnlr7GimvgzkHC1aRyWX1rLtCr0qFoAL1dsOj/cn/u2tPcdn7/n3sKNeuNcNbtMO7g4vaacR68+GqY/gpomd3/557mWVDXWNxaQ9G5Dh74KCz+afX2lCRJkiRVXM0Eo/X5yoDzE4D/KebiEMIU4CpgAv1vqvpyeVqTpCHa9Ezp1z56CXRtLF8v1VJMMBrAmvsr04ckSZIkSZIkSZIkSZIqJoRwCHArsD+59+7FnSUKCEiLMW4GfjFg6LXl7lGSJEkqSMtMOOF7cP6Cyu7z3HcLn7vhCVj3593HF/64fP1UXZ6/JsQig9E2PAlr/lj4/GnnwssfhKP/Heqbi9trp4bx8KKvwSsegQsW546DPlTaWlsWQfsfSrtW/Z75ZnqtyKDDjs7kYLTx9ZNoyIwqaq1iZFOD0UoICxyK/8/efYfHUd1tH/8eraolW5Z777gXsKm2AYNN7wkQAk+AUEMq6SEJLaQ8SQhvypMQkkACCaEaTHXoBowxNsXghnvvkqtsWdqVzvvHSPFqvbN1drQr3Z/r2ks7c86c81Pbnd2duccY5/8jltB+WP+4P/W0tMp34cAG9/bj7ncCE1tK34vglJlw8gupj7HlJTiw0buaRKKxFj78VvLP8+la9wiEkjy/LZvtWQJV85Lb5nM1MPlRqBiX2py9z4Ypz8MFaw/t91y4zhn3cgujfpTauKlYlcQ+tIiIiIiIiGS9nApGs9Y+CixoXDTAdcaYt40xJ8bazhhTaoz5UuO2R+IcVGWBl621cS7zIiLik33L09t+/XRv6vBTssFoy36bmTpEREREREREREREREREREREJCOMMcXA80B52OqFwLXAIGAECYSjAc+E3Z/qWYEiIiIiIqnqdXbmxt69KPG+buFDB9ZDfa039fguxkuEhmByQ215Kbn+Hcck19+PcZP5e5DoYoU6lfZPaqgdwejBaF0LeiQ1TrLyXYPRkvyf8EJBh/g/t7byd7t7oXtbQQco7edfLbF0HJ3e9ttne1OHiJuNM6IHvWZafQ1Ur/Z/3kxJ9rG3/VAIFGemlibdT83s+OH2tJHnHhERERERkTaiBS83kLKLgblA58blScAsY8xWYGV4R2PMvcBQ4ASgiENXmjTAJuALPtUsInK4vcthzUNwcDv0PN1ZjqbiKAjuif8m63vXwoDLIZC5Kwx5LtlgtE0vOB9k50X/QE9EREREREREREREREREREREss7XgQE4x+4B/B74lrW2AcAYk+gZ6G9w6Pi/gcaYbtba7R7XKiIiIiKSuMHXwuYXMzP2/jXw8iTAgglA9SqoCQtjKunpfK2JHtD0X3W7oaR7ZmrMJBMjGM2GoKEelvzCCVGp2Rx7rHg/o2bzBmDglYn3T0avc6GoK9TuSH7bD2+Gpb+EDiNgyI3Q/1Lv62vNrIW9S6O3FXWGQGFSw+2o2xp1fdfCnslWlhS3YLSgrcvovK4GXQsLb3NvX/FHJ6Bx5Peh6yT/6vLL/nXw8Y9g7cPufQZ8IXvOfyntBz1Og62vpLb9nM/Dojuh+ykw7hdQWB5/G5F4DmyGj2+B7W87+z4t5dWTmp+PVzoABlwBR3w59j5JNln7KCz7LVS9l9x2g6/JTD3huk9xAmJjBUl65eA2CNVAfknm5xIREREREZGMy7lgNGvtamPMucDTQE8OHejUEwi/tIYBbgi7T1jfjcC51tpKX4oWEYlU9T68Ps0JPANY9Vf3vr3Pg9G3QtU8eCXWh0EWXp0Cp70NeTny8J5sMFpwN2x/C3roor8iIiIiIiIiIiIiIiIiIiIiOeJrHApFm2GtvTmVQay11caYtcDAxlUjgDYXjGaMKcC5oGw/nOMmq4HNwEfW2rUtWJqIiIhI29P3MzDhD/DB1zIzfuUc97ZEw76CORqMRowQkoYQzP8SrPqb99Oe9Ax0HO39uOAEdEybBXO+ALs+TH77mi3Obdvr0FAHA//H8xJbrXWPured+HTSw+0IRv//61rQMsFooYZQRud1NeqHENoLS+9277PpOdjyMkx7E7oc519tmVZbBS8dDwejh+T91/jf+FNPoiY/Bu9eBVv+Aw3B5Lff+6lzq3wXzngf8gLe1yhtR3AfvHw8HNjQ0pVA3c7myzVbnL/zul0w+sctU1MyVv8D5n4xuW0KyuGIm2DEdzNSUjMmD05+znn82f4Wh94qBkoHQmnfxvUeWf4HGPk978YTERERERGRFpMjyTnNWWvnGWPGAw8AZzWtjvjabBOcT0UM8ApwlbU2zjuPIiIZtOiuQ6Fo8bQf6gSddZ0I562A545w71s1F9b8EwYn+WZmS6k/GH39pEfhncuit216TsFoIiIiIiIiIiIiIiIiIiIiIjnAGDMS6N24aIF0z7RbxaFgtEHAm2mOlzZjzCDgGODoxq/jgfZhXdZZawd4ME9X4E7gc0Anlz5zgHustdPTnU9EREREEjTsqzD0KxDaB6H90YNm8kvB5ENhubMc3AvWOseTh/bDCyMzV1/drsyNnVExgtEObnECQLx26mvQ41Tvxw1XPhLO+gDqdjvH/X/w9dTGWXq3gtGSsfhn7m0dRiQ1VIOtp7JuW9S2boUtFIxmUwi48kJeAI76NVQcBXOucO/XUAvL/691BaOtfTh+KNqoH0OgyJ96ElVYASc/C6EDsG8lzByX2ji7FsC216Dn6d7WJ23L+scTC0Wb+gaUDUpvrmf6p7bdst/CiO9BoDC9+TNtaQIhjAOvhLF3NS4YKOnlb7hhaX8nIDa479D+aaAdFHdx7jeEnAC1R+LUdME656sNwbNDiHo6+ZJfKhhNRERERESklcjJYDQAa+024BxjzATgG8BUnKsfRrMHeA34g7W2xQ+EEpE2zjbApmcT798+LAit/RDnCglvnufef92juRGM1hAEWx+9raQ39LvUeZM70p7Fma1LRERERERERERERERERERERLxyZONXCyyy1q5Oc7zdYffL0xwrZcaYKcAtOGFoUUPKPJ7vLOAfQLc4XScCE40xDwM3Wmv3Z7o2EREREQGMgYIOzi0RTf2agtKKu8PB6EFLaavbHb9PNjIxgtGq5jlhGF6rSDEgKBWFHdO7WPjujyFUA/kl3tXUmtVWubcVdU5qqJ3BSuqJ/vfXtSCzwWgFeVkWjNYkkXC5qnmZr8NPle/F7xN+LlC2yW8HFWOhqAvUVqY2RtU8BaNJeirnxu/T71LoPiX9ubqcAJXvJr9dbRVUr4by4enXkCnBatizKH6/iiOhtF/m64mnoL1zi5TXeKr74Oth1V+jb9vnwubfQ0E5BKPs69YfdEKIY+1PioiIiIiISE7I2WC0JtbaD4Ar4b9XXuwLdAYKgUpgG7DYWtvQYkWKiITbvz7xvkVdoNNRzdd1OxnyyyBUHX2bba87H2AXdky9Rj/U17i35Zc4Vw2KFoyWs1ctExEREREREREREREREREREWlzuobdX+HBeLVh99t5MF6qjgR8Ofu3MYRtBs4xkU0s8CGwGugIHAV0CWu/AuhgjLlQx06KiIiI5IB+l8Dy/8vM2It+Aqv/7gTfbHvdCWUb/1sY+IVDARTZyFr3tn1evLSI0OvspAOy0tZhBJSPhD1LUtt+9qVOuBFA2SAnPCby3AOB0AE4uDV6W+nApENTdgS3uLZ1KeyR1FjJyjfRg9GCti6j88ZVcSSUDYHqle599i2Hd6+CrpNh4FUQKHTvmwt2L4jdnlcEfc7zp5Z09LsEVtyb2rarH4TdC2HrK855Pv0ugZLeENoPHUfDoKsTDwyVtmfLy7Dqb7H75BXBmDu8ma/fJakFowGsfgCO+pU3dWTC/rUJdDLQ97OZrsQb/S5xD0brd2nz5YpxsP3Nw/vVH4Btb0CPU72vT0RERNq2fatg7cON7+XEeO/K5EOnCc7roqKMX2NLRKRVy+JPMZLXeDXJdK8oKSKSWdteT7zv8G9D5FV9CtrD+N/AvBujb2NDsHkmDPh86jX6IRQjGC2v2D3YLVevWiYiIiIiIiIiIiIiIiIiIiLS9hSH3a917ZW48rD7+zwYz2u1wEZgsBeDGWP6AE/RPBTtHeB6a+3SsH5FwI3A3UDTwUbnAT8FfuhFLSIiIiKSQWN/Ars/ge1veT925btAWBBIcC+8dw1sfBpOmgEmz/s5vWDr3du8DkYrHwlH/8HbMRNhDEz8N8w6C2rcw7ZcbX6++fKn98CJ06H3ud7U11qse8y9bdK/kx5uR13031WHQAXFeSVJj5cMt2C0kA1ldN64jIHJj8Ksc+DgNvd+ax5ybuseg1NmHn6uTK6o2xU70DBQDBMfhsIK/2pK1difOuFmO2Ynv231yuZheOufaN6+6n6YNis3fg7iryW/ggXfj90nrwgmPw7lI7yZ84iboHIurH88+W2X/hra9YNhX/WmFq8lEq57/ANQ2i/ztXihxzQY9WNY/NPm64d8yQlNC3fsX+H5odHHeX0qnPqawtFERETEOzs/gNdPc14TJmLdv2HVX2Dqm1DSPbO1iYi0Yq0qGE2ygzGmAJgE9AN6AtXAZuAja+3aFixNpOXtXQ7vXZtY3/KRMPxb0duG3OAkBf/n6OjtW1/N/mC0+hjBaPklUOASjBZUMJqIiIiIiIiIiIiIiIiIiIhIjqgMu9/Fg/EGhd2v8mC8dASBxcD7wPzGrwtxjp17w6M57gTCzyCeA0yz1h4M72StrQV+b4xZDzwd1vQtY8x91tp1HtUjIiIiIplQWAFT33DCaWYe6c+cm56DbbOyNywilWC0bifBEJeLjzeprXLCmAo6OMtlg6BiPAQKY2+XKRXj4PzVUPke1Gxy1hX3cM4l2LUA6nbCnCsSG6uhDhbcAr3OcYKqxDH/Jve2ivFJD7cjGD0YrWthj6THSpZ7MFow43PH1WkCnL/GCeybfWnsvtteg00vQN8L/anNayvudW8b9k0YcxsUupwPk22KOsHUWbBnkRP2VlgBJT1h33KorQTb4ITZVc1Lfuzdn8Dqh2D4NzwvW3JY3R5YeEfsPkO+BOPvhvxS7+YNFMOkR2HsXVC9Ckp6O/sTDWHXcVj8M/fQww++Dt1PgY6jvKvJC9bCyvti9ykfBYOu9qUcTxgD4+6CI26ELa+ADUKP06B0wOH7N+2HOL/b+oNRh+KTW7N3X1dERERyz6K7Eg9Fa7J3mbO/Nua2zNQkItIGKBitFTLG/AO4yqPh1llrByQ4b1ecA7E+B3Ry6TMHuMdaO92j+kRyy7wbEu979J9if8DaaYJzxYpoH6qsfsC5mlfv82DAFZAXSL5Wr9RshZV/ca72cmA99D4fxt/jfPDqJlDi/kFQ3W7njVt9WCsiIiIiIiIiIiIiIiIiIiKS7bY2fjXAUekMZIzpDIwIW7UynfHS9CDw58iAMgDj0TEtxpgjaH4cYB1wdbQ5m1hrZxhjHgzbrgi4HbjGk6JEREREJHNMnhOS1ft82PSsP3Oufyx7wyJiBaOF9kdf3/UkGHB5ZurJpEAxdD/58PUlZzpfV94H299KbKw9i+DgVifYSKCh3v1vqV2flALxdgS3Rl3ftSDzP/OCbA5GA8gvgb4XO2FGbv+nTba8lLvBaNvfdm8b+pXcCUVrkhdwnn8qxh1aF36/XT946/zUxl77sILRpLnKOVBfE7vPETd5G4rWxBjoMNS5AVSMbd6+eaZ7MBoWFt0Jkx/3vq50HIz+nNTMsBz9H2zXBwZ/MXYfY5yQW7ffW+UcCFZDQZn39YmIiEjbYq0T2pqKrS8rGE1EJA15LV2AZL047zQ5jDFnAYuAm3AJRWs0EXjSGPMvY0wG3qESyWLBfbAjxgcg4TofG/3DzUgVMY4X3fAkzL0K3vmcs8PdEqrXwsxxsPB2JxQNnA/qnxvifJDlJlDiXGkmGlsf/0MyEREREREREREREREREREREckGc4CGxvudjTHppC5cgxOwBrAfeD+dwoi/y6YAACAASURBVNJhrd0VK6DMI5cD4VdDfMpauyKB7X4ZsXypMabYu7JEREREJKN6ne3fXCv/Agvvci7UveNdaAj5N3c8sYLR3JQN9L6ObJDs38S6x5xAMIGajWBd/q5L+6c05I66LVHXdyvMfDBafrYHo4ETUtPzrPj91ufw3+m+GDntZYP8q8Mv3U+GQLvUtt05H6pXe1uP5K7qtbDwJ7H7tOsH5aN8Kecwvc6J3b7+CfjgZtjzqT/1xFOzBRb/b+w+JgC9z/WnnpYS7/e2f60vZYiIiEgr1BB0grGX/wmW3g31B1IbZ8c7sOp+2L3Y2/pERNqInApGM8aMNsa83nh7zRjTLYUxujdu2zTO0EzU2opMj9fBGDMFmAGE/z4s8AHwBPAKUBmx2RXAI8aYnPobFElLbRXYhvj9ADofl1i/DsPj99kwHSrfTWw8ry28Aw5uj9724Tfdt8svi32VnAMb0ypLRERERERERERERERERERERDLPWrsLmB+26i5jjHHr78YY0xv4Ac5xaRZ4xdpED8TJWRdFLP89kY2stUuB98JWlQKne1WUiIiIiGTYgCug64nx+5X0hE7HpD/fwttg/pfhlYnw5rkQrE5/TC8oGO2QwddDpwmJ9//wm/D2RRBK8YTd1mTzf9zbjv5T0sM12AZ2BLdGbeta0HLBaMFsCkYDGHMHFPeI3aduF7z9mdz7Ow3ug2qXYLSBVznBcK1NQQc4MjKDPQnPDoZPf+tdPZKbVtwLzw2GqrnuffIKYfw9kBdw75NJfS+CnmfE7rPsd/DCCPj4VrDWn7qi2TADnh0Ey38fu9/o2519xtZs2M1QNti9/bVTsyv8V0RERHJD3S5nP+LVk+D9r8CC76U33nvXwYuj4aPvt+x+pIhIDspv6QKSdCMwBefAppettS5pO+6stduMMUGcA30scD3wXS+LzALfAe5IYbujcYLMmljggVgbGGP6AE8BhWGr3wGubzzAqqlfEc7v726g6Z3484CfAj9MoVaR3BPal3jfzgl+SN0+wWzHNf+ErhMTn98L9bVOKFuy8ttDXj4UxAhGe/l4uHhX6/zQSERERERERERERERERERERKR1+R3w78b7xwN/xjmWLCHGmO7As0BF4yoL3ONlgdnGGNMDGBe2KoRzXF6iZgHhV2Y8C+dnKCIiIiLZrqAMTn0Z1k+HqnnQUAeBIigb5LTvXQ7lI6DvZ5zgoU3PQeVcqN0O5MGWmZBfCt2mAAbWPQrB3YnNveUlJ7hkZBacYpNKMFrpAM/LyApFnWDqLOfY/J3vHwr32DAdandE32bTc7DyrzD8G76VmZXmf8m9rWJs0sPtDlURcgkh61roRzBa9NMA3WpqMR1HwZkfwIanYNFPYvydPgur7odhX/O3vnR8crt729Cv+leH34Z9FTofDZtnwsHG0zlX/jnx7T/8JvQ5/9BzmbQtBzbC+1+FeNc4OPN96DjGn5qiCRTByc/B+idhzuWx+y7+KfS5wPm/8FvoAMy9CuoPxu43dRZ0P9mXklpUu15w5nx4slP09todsPEZ6PdZf+sSERGR3Lbk17Bjdvx+eQUw6Frnfqga1v4rdv+lv4Li7jDiW+nXKCLSRuRaMNoFYfcfTGOcB3GC0QzOVRWz4FMb71hrK4HKZLczxvwgYtUb1trVcTa7k0MHnAHMAaZZa5u9s2KtrQV+b4xZDzwd1vQtY8x91tp1ydYrknOCSQSjJXr1rsKK+H0AVj8Ax96b+Pxe2Pa6sxOfrMLGQLTCcvc+wT2w6yPoND612kRERERERERERERERERERETEF9baR40x38MJ+jLAdcaYkcAPrbVvu21njCkFvgDcDnTDCUQD56KqyYSE5aLREcufWGv3J7H9nIjlUWnWIyIiIiJ+ChTDwCucWzx9zndubspHwQdJhA5teDI3g9FMANr1zUwt2aCgDAZd5dya1GxyAtDcbJjetoPRgjHOZeh8bEpD7qjb4trWtaBHSmMmI98URF2fdcFo4ATVDPsqdD0B/hMjOGjD9NwKRtv2mntb2UD/6mgJXY53bk2SCUYDJyhvxHe8rUlyw4YZ8UPRRv2wZUPRmuQVwIDPQ6AE3r4odt8N01smGG3b6xDcG7tP7/PaRihak8IK6HYybH8zevuG6QpGExERkeRseDKxfqUDD+U31B+MH4wGsOB70PkY6HZi6vWJiLQheS1dQKKMMYOAPo2LDcDzaQz3HND0KclAY0y/dGprDYwxJcBlEavvj7PNEUDYpyrUAVdHhqKFs9bOoHmoXRHOgWsirV+iwWgFHaDD0MT65hXgHC8aTyJ9PLZxRmrbNQWjBYqhqIt7v50fpja+iIiIiIiIiIiIiIiIiIiIiPjtYqCKQ+Fmk4BZxphNwEPhHY0x9xpjXgN2AH8Eujc1AZtxwtJau5ERyyuT3H5VnPFEREREpK3oc15y/fetgF0Lmt/2b8hMbbE0hJLrb+shLz8ztWSrrpNjt+94G6yN3ac127/Ova1sUEpD7ghGD0YrC5RTEihNacxkFOQVRl0fasjCYLQm5aOgoKN7+/Y3c+vvtGaTe1thJ//qyAaDr0uu//on4cDGzNQi2W3f8vh9ukzKfB3J6HMBVIyP3Wfvp/7UEmlfAm8Tds2yn6cfYu0XJfIzExERkdzVUA+h/VBbBQc2Oc/9uxdB3e7kxqmvdbbbu9x5fywR4ftdgWLodEz8bWw9vPM52PHu4e/Bhd/qdkP1mkMhw9Y673XU1yb3fYmI5Lhcete/6QqIFlhmrY1x6Y7YrLXVxphlHDrYZwywPs36ct3FQHnY8i7gqTjbXA4EwpafstYm8iz/S5oHql1qjPlyrEA1kVYhlGAw2sCrwSSYW2mMs6NcXxO7X4PPO7m2ATY+m9q2hRWH7vc+H1Y/EL1fKJmL4IqIiIiIiIiIiIiIiIiIiIhIS7HWrjbGnAs8DfTEOQ7QNN7vEdbVADeE3Ses70bgXGttpS9Ft6whEcvJHt8YmQDQ2RhTYa3dlUZNIiIiIpKLSvvDwCthzUPx+wLU7YKZRx2+vsMIOHE6lI/wtj43tj65/qNvz0wd2WzgVbDs97GDmp7qDpOfgO4n+1dXtlgY429i9K0pDfnhvjlR13ct6BF1vdfyTUHU9SGbZJCgnwLFMOLb8EmMn/nTveDEJ7M/yKdut3OifTSDrnbO72lLhn4NVv0t8f5V78GMvlA+2nk+6TA0c7VJ9lj7KCz/Q+w+nSZAz9P9qSdRxsBxf4NXT3Y/H3DjDFj+Rxj6FX9rq14Tu72kl7OP0NYMuR4W/yx62875TjBjuz7+1iQiItKW2AaoP9h4qzl0v+EghGqcr5Fthy3XHNrGrS1aX9ewcAPdpzjvixR1jl3/kl/Bgu8n9z3nlzmvi8KNugVmX3wozMxNzRZ4ZWKC87SHI26CdY/AgQ3O6+yRP4DRt7W916Ei0iblUjBa/7D7kVczTMUqDgWj9fNgvFx3bcTywwkElV0Usfz3RCay1i41xrwHHNe4qhQ4HUgxRUkkRwQTCEbrfiqMuS25cRMJRgPYvRg6jkpu7FRVzYODW1PbNvxqQOPvjhGMlnI+poiIiIiIiIiIiIiIiIiIiIj4zFo7zxgzHngAOKtpdcTXZpvgBKIZ4BXgKmttigek5JyOEcvbk9m48eKxB4HisNXlOBdMTYsxphvQNcnNBqc7r4iIiIik4bgHoOMY+Oi7qY+xdym8cTqcvwbyfDgVKdlgtGFfz0wd2aykO5wxF+ZcAdvfit6ndgfMOhMu2ADFXfytryXteAc2THdvLx/p3uYi2FDHpwc+jtrWrbBX0uOlIt9E/98L2jpf5k/Z6B87oWKf/iZ6+8Gt8PppcNEmKKzwt7ZkfHCze9voJM8Dag0qxsJZH8O8G6FqbuLb7VkEb5wB562EvEDm6pOWt2sBzPl87D4jvw+jfuTPvkWyOh0F096Al44HtwDK978K7Y/wN9htf4xgtCNugpG3QIk/gZ1ZpbQ/HPsXmHdD9PbXp8G5n/pbk4iIiN9sA9TXphYwlkww2WFhZwehIRtfl1rY9gbMvhSmvubebeOziYeilY8Ekw8VR8Hwm6HiyObtfS+CKTOdEOk9S2DP4tTLbxLaB0t/dWi5/iAsvANKB8KgK9MfX0Qky2XhOwau2ofd3+PBeHvD7nfwYLycZYwZDJwUsfr+ONv0AMaFrQoB7yQx7SwOBaOBc6CbgtGkdXO7QkTpQDjur1DcDTqMTP6N/UBx/D7gXInCr2C0Hck8HEQwYd9/YYUTFrft9cP7hfanPoeIiIiIiIiIiIiIiIiIiIiI+M5auw04xxgzAfgGMBXo6dJ9D/Aa8Adr7Zs+lZgtyiKWE7hq4mFqaB6M1t6tY5K+DNzu0VgiIiIi4oe8AIz4jnN7ug/UbEptnAMbYfss6DHN0/KiSjYYLZvDlDKpXR84+QV4Isbufv1BWP84DP2yf3W1tDX/dG/rOjmlIRfv/9B9yAJ/AmjyTUHU9SEb9GX+tIy53T0YDZyT7Dc8BYOv9a+mZG1+Ifp6E4B2ff2tJVtUjIUz3m2+bsMMePui2NvtXws7ZkP3kzNWmmSBWI/FACf8Ewb+jz+1pKrTBDj1ZXjtVPc+ax7yNxit2iUYbexPYfSP/KsjG8XaR927DPYshfIR/tUjIiJtk7XQUOt9GNlhfaO0ZWU4WRbY9gYcrHQPjA8PHYulsBOck0DQWc/Tm+8fzvsSrLwvsTmSseYhBaOJSJuQS8Fo4Qf2eBFkFv6uf5KfmLQ61+BcVbPJh9baBXG2GR2x/Im1NpmUojkRyz6lNYm0oKBLMFpxN+gxNfVx8xIMRtu/NvU5krVvefT13afCCQ/BjN7u29YfbL5c4PIhbag6tdpEREREREREREREREREREREpEVZaz8ArgQwxgwC+gKdgUKgEtgGLLbWNrRYkS0rMhjtYNResdUA4ekQkWOKiIiISFs04tvw4bdS337PkuwLRut6IhgTv19rVVAGZYOgerV7nz0JnLjbmsT6fjuOS2nILXUbXNv6FA9MacxkFZjCqOsbaKDB1pMXfpH6bFPQHkoHwn6XQB9wHl+ylW2A4N7obSYf8nLpFM0Mq0jwf2zvEgWjtXbxnnsqjvSnjnSVj3ICEN32Tfx87LLW/fzAMn+ei7Jau75OWG7drujtexWMJiLSZvw3nCyBgLFQTVgoWYxgskT7NtS29Hcvh7FQszF6MFr1atjxTmLDJPpaJ9KE38LO92HnB6lt72bba96OJyKSpXLpXbfKsPv9PRivX9j9Kg/Gy0nGmABwVcTq+xPYdGTE8sokp14VZzyR1ifkEozmFvyVqECCwWgHUryyVyr2Lou+vnykEwQXS5cTmi8HSqP3CyWTxSgiIiIiIiIiIiIiIiIiIiIifjPGtAfCz8pbFXkBTmvtaiDGGfwCWJ+2EREREZHWrv/l8MmtqR+L/cE3YOV90O8yGP0jMHne1tckmWC0ITdkpoZcMuQGWPAD9/aV98Gah6CgHLqfCuPviX5CcGsQOgA7Zru3D/5iSsNWBre6to0qHZ/SmMnKNwWubSEbojCbg9HA+Tv9+Bb39k/vgYFXQcVY/2pKxO7F8O4XoKEuevuAK/ytJ9uVDYQep8HWV2L3m/9l2PISHHU3tB/iT23ivWA1fPB1WP33Q+vyG7P5Y+1rdDkBOo7ObG1eKe4GfS6CDU9Gb9/1EWx6EXqfnfla9i6DUHX0ttIBmZ8/2+Xlw+BrYend0dvf/ixMfhz6XeJvXSIikll7lsJH34OquU4wmQ05X0XC1UcJrFv3GLxzWeJjDLkxtbkDxTD5SXjpaKj1ONbm8YiMivB9RZMHgXYQKIGukxpfew32dn4RER9k6BOIjFjf+NUAY4wxnVMdqHHb8HdJfUwLyjpnAr3DlmuAfyewXeQ7juuj9nK3LmK5szGmImpPkdYi6BKMlu9TMNqWmenNk6iNz8H2N6O3tR8a/0o4kW8EF7hcrNbtjVwRERERERERERERERERERERyRafBz5qvM0Dilq2nJwReWBMSQpjRG7j1cE2fwJGJ3m7wKO5RURERCRdJd1h2pvQ6WhINURpzxJYeBt8+B1vawuXaDDa0f8HA/8nc3XkihHfgzF3uLfbeuf4+5pNsPaf8NrJ0JBE+FwumXWWe9vQr0KnCSkNWxXcHnX9uLLjCJg450h4JD/GPEHrEtqVTUZ+H0bfHrvPK5Oheq0v5STkwEZ4ZZITfOTmyF/6V0+umPwE9PvcoYAsNxufgZdPgIOV/tQl3nvzvOahaOA834SqiZnZf/JzGS3Lcyc8CGWD3NvfPAe2vpbZGhpC8MII9/ayge5tbcm4X8Run30prJ/uTy0iIpJ5BzbDyxNh8/NQW+nsgygUTaKJDLpe/2TioWilA+CYe6H/51Kfv2wAnDYHup4IXr6H0LTv/d998DC2wVlXuwM2znBee9Xu9G5uERGf+PPOqzfmArVAIU442leAn6Q41pc5FAoXAt5Ju7rcdU3E8nRr7e4EtusYsRz9HX4X1tpqY8xBIDzRqRzYlcw4kYwx3YCuSW6maFPxh1swWkGawWh5SRwvunc5dBia3nyx7F4Mb53v3t5hmPO141jY/UmUDgYqIq6WFCiNPlaqVykTEREREREREREREREREREREb90wTneD2C+tVZHWycma4PRrLXbSfJ4QWNM/E4iIiIi4p9OE+DM+dAQhLwCsBaCe2kWYLL017D457HHWfVXGHcX5Lsc752ORILRKsbD0K94P3cuMgbG3A7lo2H2xfH771kCW1+FXmdkvjY/7VkC299ybz8i9b+XquC2qOuHthuT8pjJyjcFrm0hG/StjpQZA2PvgNJ+8N610fuE9jkhS2Pv9LU0V6sfhOAe9/b8Uijq7F89uaKwHCY/6gQ5ffQdWPY79761lbDu3zDs6/7VJ97Y9TFsn5X8dme+n3v/N/ntYOoseKafe5/l/wc9pmauhq2vuLcFiqG4R+bmziV5+TDoGlj9gHuf5b+Hfp/1ryYREcmcNQ9CMJFYDGl5BgIlkF8CecXO/kug2FkXeT8vRlu0vuFjzjySqAG9DbXNl2O9Rol0wZq0vvP/6jAUTnvLCe9zC/Cb0ffwgDOv1O6AdY/ovTQRyTk5E4xmra01xrwNTGtc9R1jzNPW2oXJjGOMGQ18l0PPaO9Ya9tkuo4xpitwXsTq+xPcPPKSDTUplFBD82C0NNOhACf0Ls7lQ0RaSMglGC0/zT/9RK+GBfD8MPh8g/OBUias+7d7W14BdD7Wud/7/OjBaBdXHV5bgcsVYjK1Yy8iIiIiIiIiIiIiIiIiIiIiXmk6e9gCG1uykBwTedZ1UhcLNcaUcXgwms4MEREREZHm8hpDloxxQmzCdRwbf/tQNexdBp3Gx++brESOkS+NEU7SVnUYnnjfnfNbXzBa1Xz3trxCKBuQ0rD1NsTOYGXUts4F3VIaMxUFuR6M1qTDsNjtO9/3p45E7IzxNwXQ/ojMnZ/TGuTlQ+fj4veL9b8r2WvD9OS3MXlQNtj7WvxQ0ssJQwy5nIqc6ceuqhjjtx+qx6JwHYbGbq963wkG1s9MRCT3Vc1r6QpyjHEPGGu6Hy+0LGrfOGMGisHk+/PcGyiKHjpWHxaMZi3smJ3YeMO+6U1d4Zp+NtF0PwU2Pef9nE302ktEclDOBKM1uhsnGM3iBHPNNMZcbK2dm8jGxphjgSeBUpyrUNrGMduqK4Hwd8VXAW8muG1kUpFLLGlMNUBFjDFFWpegSzBaQZrBaA1J/vu9ejKc+CQUZ+ADuF0L3Nu6TTn0gfmoW5w3fLf8x1kOtIOJ/4TCisO3c7uCmILRRERERERERERERERERERERLLdlrD7hS1WRe5ZEbHcP8ntI/vvtNbuSqMeEREREWlrep0D+WXxj9l+eSIc8ycYdLUTduIVG4rfZ+AXvJuvtSgf6YTaRbuIeaRPboXh33Q/Xj+X1B+EFffCh99y79PnQvcTj+PYFazE0hC1rUtBj5TGTEV+nvvL6lAi/zPZovPx0K4fHFgfvX3zizD7MueE8MHXQV7A3/oAdi+CNQ/Cxmdi9+t/mT/15LLe58YOkwJY+y8o7e/8vlMMMBQfhQ44j7mL7kp+255nQmFH72vyQ14A+l0Kq/8evf3ARidwI1Dk/dxbX4OFt7m367GouX6XwoIfuLfXH4CNM6DvRf7VJCIimZFouFW2iRYcFjVkzCVwLK8Y8hMMMQvvm1fQ+oNB81yC0RrqnK/bZ8PyPyQ+Xv/PeVNXwvNdltlgtDUPOq/R+l2cuTlERDyWU8Fo1tqXjTGzgCk4oWa9gLeMMf8E7gPmW2tt+DbGGAMcDdwIfAEnCMw23t621r7o2zeQfb4YsfxA5M8vCalsl+pcIrkplKFgtGg76LHseBteOg6mzXI+OPBKcJ/zAZSbkd87dD+/HUx5EfYsgZpN0OloKOoUfbt8l8zEnR/o6gwiIiIiIiIiIiIiIiIiIiIi2W1R2P2BLVZF7lkasTwkye0HRSwvSaMWEREREWmLCsrgpBnw9mchuMe9X0MtvHctbH8TTnjQu/ltffw+fRRmcRhjYNJjMOts2L8mfv/XToVpb6YcGJYVGoLw+mnxT4g/OokTnyNUBre5tnUuyMAF613kG/fTAIMNQd/qSFteAE56Gv4zwb3P+sec29ZXYPIT/p43smMOvHFG/GDI/pfDsJv9qSmXFbSHE5+G2RdDcK97v8U/g1V/g2lvQYeh/tUnyak/6Dx3VL2X/LYVR8Kxf/G+Jj8ddTdseh5qd0Rv3/Qs9LvE2zlXPQDvXRe7z/AYwaBtUdlAOOGf8G6MEN23PwOTn4R+n/WvLhER8db+DVBbmfr2foWRHdZWqPPiM8ktULy+FtY/Ae98PrH3nEweHHUPdDnO2/ri6X8ZVL0Py/5f5uaYfQmM+hGM+2nm5hAR8VBOBaM1ugz4EOiJE6yVD1zdeNtvjFkG7Gps6wQMBZpSdUzjegNsAC71se6sYow5HhgVtqoe+EcSQ0S+u1uSQhmR28R5xzghfwKeSHKbwUCcS3iIeCDoEoyW73MwGsD+tfDWRXDWh+nNHV7DU93d2zsdAz2mNV9nDHQc5dxiiXUFqj2LoOOYxOsUEREREREREREREREREREREd9Ya5cbYz4BxgJjjTG9rbWbWrquHLAoYnmsMaadtfZAgttPijOeiIiIiEh8PabCZ7bBzvfhlcmx+655CEb+AMpHeDN3vJNUu5+iE4ndlA+H81bA7gVwYLNz8fOVf47et2oebJgBAy7zt0YvbXohfija8G9DceoBZm7BaB0CHSnK8y9ULt8UuLaFbJ1vdXii03g4/V14+YTY/TZMh50fQOej/akLnICueKFokx6D/m32tMTk9TzNeT5Z9FPn5+vm4DZY9js45o/+1SbJ2fhM4qFoJz176H7ZIGcfweRlpi6/FHWCC9bC4y7nus3/irfBaA0h+ORWnFOiXUz4HQSKvJuztRj4P9DnQngixnmbn9wKfT+jfUoRkVy16C73tnE/h25TogecBYohr0iP/62V235RQy0s+kVioWhTXoQuE6Gw3NvaEmHyYMI9MOoHTkBacC/kt8OJxwHqaxrD9QLNt1vzD9jwVOLzLP01DP8mFHX2qnIRkYzJuWA0a+12Y8yZwLPAAA69qjc4AWgTItb9d1MOhaKtBC6w1m73o+YsdW3E8kxr7eYkts/KYLTG32lSv1ejHVfxS8glGK2gBYLRAHZ9BFtfhx6nRm9vqAcbgrqdzhupRV0A6+z011Y5ffIKoKQXvHm+szPtZsxtqdUIUNDRvW3bLAWjiYiIiLSk4F4IlDpXcBQREREREREREREREYnuD8BfcY7d+wmHH7smEay1W8IC5cA51nMy8HKCQ0yJWJ7pUWkiIiIi0tYEiqDrJBhyI6y8L3bfra95F4zWEOdE1dKB3szTWuUFoNME5wbuwWgA217L7WC0ba/H79N+cFpTuAWjdS6IcXH5DCiIGYwW9LESj7Qfmli/ra/6F4xmLWx7I36/bidlvpbWJlAMA6+MHYwGzmOSZK+tCf5+xv0M+pyX2VpaSn47KO4BB7ce3hbaB/W13gWV7V0GNXFO+dU+kbuCMigfBXsWR2/fu9T5PZb09LcuERHxxu6F7m2Dr4Pirv7VItkjz2U/rHqNs28VT1Fn6HWWtzWlorgb9D478f771yUXjNZQ54TM97kg+dpERHyWkxHr1tpFOAFoj3Io7MyG3f7bleaBaA3AQ8Ax1tqlftacTYwxpcDnIlbfn+QweyKWk9o7NMaUcXgw2u4kaxDJLUGXYLT8FgpGA3h9Krx3vfOma7gNM+DRfHisGJ7uBc/0g8fbOVe0eKIDPDvQuc3oA4/kwdZXYs9T0iv1Grud6N4W7yo8IiIiIpIZ+1bBS8fBEx3hqW6w8CfOAUEiIiIiIiIiIiIiIiIRrLX3Ay/gHMN3tTHmey1cUq54OmL5i4lsZIwZDhwXtmo/iQeqiYiIiIhE1zuBcJMPvg7b3vTmOCIbJxgtkXrE0X0K5Je6t6/6m3MCbS6yDbDmoTidDPQ6J61pqlyD0bqlNW6y8ghgXE4FDNmQr7V4oqgTdJkYv9+in8CBOMFAXrAWNkyH+prY/TodAyU9Ml9Pa9T+COgwLHafvctg6T1QOVfHpWajyjmJ9etzUWbraGkdx0RfX38wscDORG1/M3Z7fqnzPC/u4u0zfvxjqF7rSykiIuKh4D6omuverlC0tiuvMPr6RXcmtn2fC72rxU+9U3jfY/XfYfHPYfVDULPF+5pERDySk8FoANbaXdbay4GRwP8DmmJdTcQN4GPgbmCYtfZqa21kqFdbcwkQnsS0DXg+yTFWRCz3T3L7yP47rbW7khxDJHdY61z1IZqCNIPRGtIIRgPng8wPv3loec+n8LbHb0CXJvsQEaaos3tbOqFwIiIiIpKahiC8NgWq5gEW6nbCupZEtwAAIABJREFUwtthxb0tXZmIiIiIiIiIiIiIiGSvz+MEfRngF8aYl4wxp7RwTdnuYSA8DeIzxpgjEtju+xHLj1trdZCNiIiIiKSn5xnQ/7I4naxzXNG7V6YfZhMrGK3vxamd8NlWFbSHCb+L3ef54bDxGX/q8Up9Lcz+HATjnCI29idQ2i+tqSpdgtG6FPgbjmWMId/kR20L2qCvtXhm/D1Q2Cl2n/oaeGEEbHsjc3U0hJzHrtmXxO5X0BEm/L/M1dHaGQMT/hA7rBHgo2/DyyfA/JucAETJDtvegD2L4/cb+X0oH5H5elrSMTGOmZ51NlSvTn+Ojc/C+19xbzd5zvN7uucltnbDboaO49zbVz/g7AdteMq/mkREJD3Va+H5GPsaY+/yrRTJQoGi1LfNL4VRP/KuFj+VDYQxdyS3zcZn4OMfwdyrnP+pbXFCeUVEWkj0d0NziLV2OfBtAGNMGdAdaErRqQS2WWv3t1B52eraiOWHrE360iBLI5aHJLn9oIjlJUluL5Jb6mvc34zPT/MNyMIKOLg9etu5y+CTH8P6J2KPseJeGHIjVIyDpb9Or55IHUbEDjdLRI/TYWuUC9c21KY3roiIiIgkb/tbcGDj4evX/RuGftn/ekREREREREREREREJKsZYx5ovLsX2IdzUc9pwDRjzD6cC59ub2xLlLXWRh4H16pYa1cYYx4ErmlcVQj8wxgz1S3ozBhzAXB12Ko6IMFLoIuIiIiIxJCXDxMfhoFXw6wzY/dd+y/oMQ0GXZX6fG7BaKUDYPJjThiIJG7wtXBgMyy8LXp7/UF473roeRYECv2tLVXrn4QNT7q3l4+E4x6ALselPVWVSzBa54JuaY+drHxTQNDWHbY+FGVdTuhyHJz9CWx+IXYIVnAvvHcDnLfcCdfy2qbnnceuWI65F3qdA6V9vZ+/Lel5Gpy9EFb/HRbFCa1YeR/0uwR6TPWnNnFnLcyN81Zcnwtg+Heg22R/ampJ7QdD52MbLzIdxce3wqSHUx+/IQjvXRe7z5kfQcXY1OdoK0q6w2mz4YkY52821MK8G5zH+HTCVERExB+f3Ao1m9zb+3/ev1ok++Sl8FxeMd5536T/ZVAUJ7g7m4253bmwwbbXAQPdp0JxF3h2cPxtg3tg/o1wztLMvOYWEUlDzgSjGWN6AMeGrZptrd0Z3sdaWw1UA6v8rC2XGGOGApHvLt2fwlCLIpbHGmPaWWsPJLj9pDjjibQuwRjHbaZ7ZYZxP4/+ZueAL0CHoTD5cXjxSNj9cexxZh6ZXh1uTvTgigmB4ujr63UxWxERERHfLb07+vod7/hbh4iIiIiIiIiIiIiI5IqrARu2bIGmI6o7cPjxbPGYxjFaNBjNGNOH6Mdg9ohYzjfGDHAZptpaWxljmtuBi4CKxuWJwKvGmOustZ+G1VIE3AD8JmL731hr18UYX0REREQkcSYPep0Bx/4V5l0fu++iu2DAFU6gWircgtF6n6tQtFT1Od89GA2gdgdUvgvdT/avpnRsnBG7/Zj7PAlFO9hQQ3X93qhtXQojX/5lXoEpoCbK+pAN+l6LZ9r1hiE3QFEXePuz7v2qV8KexdBxtPc1bHw6dnvHsXDEl7yft60qGwhj7oSlv45/XtCGpxWMlg32LIH9a9zbz/oIKjJ0Xlq26nOBezDaxhlOmFyqoRI75jjPy24GXqVQtGQUlEHv82HTs+59aqtgx2w93oiIZDtrY78WNHlQ2s+/eiT75CUZ9t7rXJjyXGZqaQldjndu4cbe5QQKxrN3Gez9FMpHZKY2EZEU5dKnAZ8Bnm68PQzUtmw5OeuaiOXZ1tplyQ5ird0CfBK2Kp/kDlCbErE8M9kaRHJKKIPBaL3OhvwoYwy44tD9oV9Jb45UDf8WlA9PfxzXYDQ9FYiIiIj4rm5n/D4iIiIiIiIiIiIiIiKx2bBbrpoNrIlyeySiX2+XfmsAlyvSOKy1G3GOnawLWz0JWGKMmW+MecwY8x9gA/B7oCCs3/NAAke5i4iIiIgkqdtJ8ftUr3ICb7a/7dx2LYCGUOJzWJe+JpD4GNJchxFQ1DV2nw3Tk/s9tZSG+tgnw+eXeRbQU1m3zbWtS0E3T+ZIRr4piLo+5PY/k0u6TIz/P775BecxZf9652911ydQ+R7U7Up93oOVsOah2H0SedyT5BgDXRP4ua75B+xeBLYh4yVJhIYQ7F4IwWrY+YF7v8IK6DDSv7qyRa+z3dvqD8Cm552LTtftTn7sne/Hbu+WIyGm2SSRn9nWVw49x4iISHaq3QGhavf2LhMhL/prJmkjAkXJ9W8Lr/WS2XesXpW5OkREUpRLwWgdca70aID51tr9LVxPzjHGBIArI1bfn8aQkZfD+GKCdQwHwi+7sh94OY06RLJf6IB7W6A0vbFLesKpL0P5qEPLx93vXI2ryaBrYNzP05snWd2meDdnnssLkYY4V4YREREREW80hGDRT+GlE9yvbgbO1VdEREREREREREREREQOZzy8tSnW2lnARcCOsNUGOBq4FDgDiEw3eAS4zFpb70eNIiIiItLGdBgK/S+P3+/jH8KrJzm3mUfBU91g80uJzbF3WfT1Jj/xOqW5QCGM/nHsPsv/ANO7wuaZ/tSUiu1vwdM93cPzAEZ8DwrKPJmuMrg16vo8AnTM7+LJHMlwC0YL2qDPlWRASQ844iux+yz4gfOY8kx/eLQAZo6Dl4+HJzvB3GugIYmfQ6gGZl8GT8UJDCzqAkO/lvi4krhRP3A/X6hJaD+8OAZm9IWq+f7UJbDxOZjeBV4cC0+0h7lXufcdfZvzHNPWVBwJ3ae6t791Prwy2Xl8mndTYsGjDUF473r46DvufcpHQb9Lkq+3rRt4JZQNit1nyS8PPcf851ioib4PICIiLWjhHbHbR/3IlzIki8V7fRGudAAMirGf21p0nQw9piXW983zYO/yzNYjIpKkXApG29n41QJbWrKQHHY20DNseR/wRBrjPQyEHzj1GWPMEQls9/2I5cettUo3ktatvsa9Lb8k/fG7HA/nLIKLd8GFm2DwNc3b8wIw6ha43PpzVYgep8G0N5JPVnYTKI6+vl4PHSIiIiK+mPtF+ORWqJobu1+s/V4REREREREREREREWmrBmbgFucsttbFWvsiMBr4M7ArRte5wMXW2st18VkRERERyagTHoIJv4de5ya+Td0ueOs8OLg9dj9rYZ/LSZgmkPh8crhhX4cTn4rdJ7gb3roAarb5U1Mygnth1tlQu8O9z5AvwZhbPZuyKhj977VTQRcCLfD36BaMFmoNwWgAE34Lx/41tW1X/x2W3p14/0V3wvrHYvfpMBxOf9cJhBTvdT8Fpr0Fg6+P37dmM8w6S+cR+eHAJnj7IgjuSaz/8JszW082OyWRwFcLK/8My34fv+un98Cqv8Xuc9psz8I/25TiLs7j+cjIU5td7JwP71yW2ZpERCQ5uxbAinvd26e9Db3O9K8eyU55CQT2lg2CEd919g2Ku2W+ppZmDJz8PBz5v9DzTOhyAhR0dO//1gXOe3MiIlkil4LRwsPQSlusitx2bcTyo+kc/GStXQE8GLaqEPiHMcYlwQiMMRcAV4etqgPuTLUGkZwRKyAi4EEwWpPCjs4Oaiz9fXhT7uRnvR3PLWCtvtbbeURERETkcPvXwdqHE+sb0vk1IiIiIiIiIiIiIiLSnLV2XSZuWfB9DbDWmjRvVycx33Zr7U1AD+BU4IvALcDXgc8Cg6y1J1hrp2fi+xURERERaSYvAMO+BlOeg/NWgEnw9KSGIKx/InafXQvc2xSMlr6+FzknAMeSyO+pJWx8Nv4xaqN+6OmUlcGtUdd3Kejh6TyJys9zCUZraCXBaMbAkOtg8HWpbZ/osY6J9j3pWWg/JLVaJDFdjoXj/gJnzIvft7YKtryc+ZraunWPga1PrO+AL2S2lmyXF4Dy0Yn1TeQxJ16fEd91zh2U1BR3cwJBRt6SWP/tb8HBGGGsIiLir1jPk+WjoNtk/2qR7OWWR9AkrwDOXQ5H/QpKWuZ1fYsIFDkBsafMhNPnQJ8L3Pvu/RR2fehfbSIiceRSMNpHQFO0pC6zkCRjTHfgnIjVceLjE3I7za9AORF41RgzPGL+ImPM14DIT0Z+kw0HqYlknFswWl5h4h8Ce2XgldBxbOL985PIoiwbBBdugIBrPmJq3MbTlV5EREREMqt6LXz0fQ69HI9DwWgiIiIiIiIiIiIiIiIZZa2ts9a+Ya39h7X2f621f7DWPmWtXdPStYmIiIhIG9V+CPS/PPH+e5aCbTweyUY5LmnvUvdty0cmV5tEVzE+fp+9jb+naL+jlrJnSez24m5Q0svTKauC26Ou71zQzdN5ElVgXILRbCsJRmvSKYG/0Wj2LIaG0KG/XbdbcB8c2Bh7rMIKKO2fWh2SvA7DIVASv1+s5wjxxpJfJN6301GZqyNXVCT4M9j7KdgG98elhnrYuyzOXCk+NkpzCT/HWKjZnNFSREQkTLTnx3B7YuwH6jlSmuTFCUbrOM4Jt23r4u0Pxfp/ExHxWX5LF5Aoa+16Y8xc4ARgmDFmqLV2eUvXlUOuovnve5G1NoFLKcRmrd1ojPkM8BJQ2Lh6ErDEGPMBsBooB8YDXSM2fx64Nd0aRHKCWzBaIm/aey2/HZwxH1bcCx/e7N6v36XQ6yzoMAJePj56n/KRcNRvYNvrUNzDuTJPQQfva3Z7IdJQ6/1cIiIiIuJ8gLDop7DoDudD+EQpGE1EREREREREREREREREREREpO0ZeydsfAZC++L3XfFHWH2/c5HsQAm06wtHfAmG3QzGQHWMzN+eZ3pXc1vW90Io6Q01m9z7rPiTcwPnvIFh34AhN/hTX6Rts2DBLVA1N3a/ITd5foJzZXBr1PWdC7p7Ok+i8l2C0YKtLRit/2Xw8Y+gblfy2z4a/WeUtMHXQ6Awfj/xRkF7GHgVrPxz7H4LfuAE2425U4EGXqs/CB/cDLWVifUvKE8uGLW1OuJGWPdw/OOt6w/AI2n8zZb0gj4XpL69HNL7PGf/88CG+H1nHgkTH4EBl2W+LhGRtmrFfTD/S4evD5RA10kw5iew9Few+QX3MVrqtapkn7w4r+GO+LI/dWS7/pfDJ7dBcE/09ne/AAvvhCHXw4jvOu/XiYi0kLyWLiBJv3a5L/F9MWL5fq8GttbOAi4CdoStNsDRwKXAGRweivYIcJm1tt6rOkSyWiiLgtHA+XBm+Dfgwk1QOuDw9pOegcmPwaCrIb/MfRxrodeZcNSvYMS3MhOKBhAojr6+/mBm5hMRbwX3wsq/wUffg43PJRewIyIiLaPyXVh4W/KP2aHqzNQjIiIiIiIiIiIiIiIiIiIiIiLZq2wQnPISdD4eTH78/k3HgdfXwL7l8OG3YNnvnXX710bfpv1QKO7iSbltXqAYTn8Hep6VWP89S2DejbDq75mtK5pdH8Mbp8cPRRvzExhzm6dTW2upCm6P2taloIencyXKLRgt1NqC0Qor4LQ50H2q+/kkmVLSG0b9GMb93N95BY7+PQz/tvM7iGXxz+DjH/hTU1vy7lWw8r7E+lYcBafNhpKWCYnMKl0nwUnPQqcJYDJ0unbn4+C0dyC/hc5DbG0CRc7fb6+zIb80fv85n4dNz2e+LhGRtmjV/dFD0cB5v2Drq/DKRNg4w32MAVdAt8mZqU9yT6DIva24GwyOjFxpo4q7OPuXsVSvhAXfh6WK9RGRlpVTwWjW2hnAAzihW+caY/5oTCKfmLRtxphJwPCwVXXAv7ycw1r7IjAa+DMQ63Icc4GLrbWXW2v3e1mDSFarz7JgtCbtesF5K+HU12DkLTDpMfhsFfQ5/1CfmG+a2oyXCCgYTSSXHdwOr0yGedc7L4DfOh/eu07haCIi2W7tv1PbLqSXeSIiIiIiIiIiIiIiIiIiIiIibVLXE+CMd+GyWrgsBBMfTm77FX90vlavid7e84z06pPmSvvDKS/CxEcS36bpd+SnVX+DhjihX8feB2Nu9TyQZm/9boK2Lmpbl4Juns6VqDYTjAZQPhymvgqX7nceU3qemfk52/WDizbCuLsgL5D5+aS5vAIYfzdcuAHG/SJ235V/hfpaf+pqC2q2wvrHE+tbUA5nfQgdR2e2plzS+xw48324LOjcAu28Gzu/PZz+LpQN8G5MgdJ+MOUFuGSv8xwT7xzPFff6U5eISFuz/E/pjzH0q+mPIa1HXqF727j/9a+OXNBxFAz7Rvx+y/8I1qc8CRGRKHIxVOxGYB/wDeBLwMnGmN8Az1prq1q0sixlrX0HJ0wu0/NsB24yxnwDmAT0B3oA+4FNwEfWWpdPqERaObdgtGy4UkNeAHqc6tyiifnGnk87snkuCc0N+hBDJOutuA92L2y+bvXfYcgN0OX4lqlJRETi2/xiatspGE1ERERERERERHZ9Aot/DrWV0OkoGHNHYldbFxERERERERGR1sHkOWewlI9Mbrt9KyBYDftdTjspG5h2aRJFMr+nXR9DQ72/gVE7P4jfp8PwjExdGdzm2taloEdG5oynoC0FozVpekzpMBy2/Cezc5WPyuz4khhj4oduBffA/rXQYZgvJbV6299MvG+HEZmrI9eZPOfWYRjs+sibMctHOP8TkhlNzzFu53422fmhL+WIiLQpDSHYlebjq8mD9kd4U4+0Du36urdl6L2DnPb/2bvv8Diqe//j76Nmyb33XrCNbYwNphubTiB0QighIZWUm/IjJIGEm5BcEkKSG0hCSGghhBog9GqqaTaYYoptjLstNxV3SZa02vP7Y6SrlTWzO7s7O7srfV7Ps49Wc86c+dqWV7M7cz7Hz99J7Xpo2AZd+mW+HhERF3kVjGaMeSnm291AD2B/4Lbm9nKgornNL2utPS6wIgVrbQPwcrbrEMkpXh+OJVpNIBfkQo2Fpe7bm/aGW4eIJO+jn7tvX/+QgtFERHKZ182FiSgYTURyQbQJbAQKPUK2RUREREREJHN2rYB5h7Zex9v6Imx9GU5627khVUREOiVjzBcDHM7i3B+4E9gCfGKtlqgWEREREclJvac7oVs7l/rf58Ee3m3dFIyWEb2nQa+psPPjxH1tBJ4/AgpiwrmKusOA2TDp/0FR1+DqqnzDWYy5akH8fl1HQv8jgztujGqPYLQuppRuhXF+VjOoyLhPBWzsyMFoLUZfCMtvyPAxLsrs+OLf4BOgS39nERYvT06CCd9yFm3vc2B4tXU0lQvgjfP999f/k8RGXxRcMJr+vnPD3i3w2rkw7qsw9DPZrkZE8pmNwqp/wKanYceHsGdVa9u4rzsLz3UdmrXyQpXMZwVehnxGYU3S1tBT4f3LwTa13d59HPQ/NDs15bIR58C734doQ/x+1e/A0JPCqUlEZB95FYwGzMW5samFxcnjbok8H9H88Hujk0mir4hI6jpqMFpY95UWeExmj9aHc3wRCd6q22HmH7JdhYiI7MtaWHNX6vs3KRhNRLIo2gjvXQ5r/ulMwB9yEhx2hy52ioiIiIiIhGnxT9ovbrTtXdj8nCYJiIh0bv8kc/fp1RhjFgF3Av+21upmEhERERGRXGEMzH4E5p8Guz9Nf7zuo9MfQ9ozBo5+BOZ/FnYtT9y/+u322zY/B5ueguNfhYIApqptfBJePcsJYoun22iY8zgUFKZ/TBdVDVtct/cvGYwxxrUt04pMiev2SGcIRus3Cw65Bd75bvBzSUwhTP6xE74muaGwC8x9Gl49E+o2efdb8TdY8y849gUt3J6KzfOc39O+GJjwbSeMTuKb+D3nd+qq20j9Y1EDE77p/J1LbtjwH+dx6O0w7ivZrkZE8tU734UVN7m3rboVNjwEpy6BsiHh1hW2SB08Mz39cQ77R/pjSMfScwIc9QAsuAQiu51t3cfBnCe0oKOb0gFw9ONOUHLjDu9+r5wM526Dkj7h1SYi0izfgtHcKNhMRHJfPgejFbhfOHOE9BJcWOq+vbY8nOOLSGps1LutpHd4dYiIiH+b58HCL6W+f+Oe4GoREfHDWmelrF2fwuIfw84lrW0bn4DXzoHjX8laeSIiIiIiIp1Kw04of8S9besrCkYTERFoXQA1SN1xFlydC1xrjPmytXZeBo4jIiIiIiKp6LkffPYTJxjttXPaXtdPVrcxwdUlbfUYD6cug90roG6zs+3VM+NPit1X1QLY9AwM9xuwE8fH1yQORTv6MRh2mhPsliHVjRWu2/sVD8zYMRMpMsWu2ztFMBrA+K/DmIth+wfOIh0FRYBxfl5sivNbCoqg9wFQ3CPQUiUA/WbBmRvgwZ4QibNwb6QGlv4Ojn44vNo6io+vgWhD4n7Hvwq9p2keiF8FxXDoLXDgb51zn5a5NW99DfasdN/n2Oeh5TXeFEDvqQqeyFUf/wrGXqJwFRFJXt1W71C0Fg3bYdXtMPWqcGrKlg0BnLeZIijN3nszyWEjzoZhp8P2xVDUHXpOzOhnB3lv6ElwTiXs+Aienendb809MPG/wqtLRKRZPgaj6beOiOSfSB4Ho8U72Y8XehQkr2A0gI/+x3mTrzclIrln5S3ebbogJiKSmz79S3r7N8W58UREJGhN9c6NQmvv9u5TMd8JTeu5X3h1iYiIiIiIdFYf/ty7bcPDMOO68GoREZFcFHtjh/XYvq99ZzS79bUxbUOAZ4wx37PW/jX5EkVEREREJCOMcSahjv0KvP/D1MYo7AolvYKtS9oyxrm/ouUeix7jYds7yY1RMT/9YLRIHVS/Fb+PKYQhJ2V8DkFV4xbX7f2LB2f0uPEUGfepgJFoJwlGA2d+Sf9Ds12FhMUUOKEG6+6L36/ilVDK6VCaGqDqjcT9xn0NBs7OfD0dUZe+bf/uDvglvHlR+36lg2Hw8eHVJempWec8uiu0V0SSVP22v34rb+74wWhBnLsd8Mv0x5COq6AI+h2c7SryR0ER9J0BQz4Dm59x71MxX8FoIpIVeRWMZq1VhLaI5KemPA5GiyvFFXWS1WWAd9tHP4chJ0D/w8KpRUT8qd8Gi77l3V6sm1NERHLGjiVQ/ijsrYBNT/nbp+9BsO3d9tvjrcgnIhK0VbfFD0VrsXmegtFEREREREQybfdKWHFjnA4hLbgkIiK56svNX3sAPwf64QSZRYGFwCJgPbALKAH6AtOA2UDLbHML/Bt4FigDegP7N/cZRduAtOuNMcustS9l9E8lIiIiIiLJGX566sFopQODrUUSG3FW8sFom5+BxcWwaxlsfRlK+sGwU2HYaU7gi0kwLSzaCCt85FwPPQUKuyRXWwqqGytct/crzt7PY3FBsev2RtuJgtGk8xl+RuJgtIbtUP4EDP2MM6lfWkXqnNfn3Sug22ho3AUbHoHqhWB9XL8ZfmbGS+w0hpwMBV0gWt92u/6O88+u5QpGE5HkRff661dbDouvhB4TnPc+ZdkLZs6Y3SvTH0O/P0WCN+JM72C0DQ/B4p9Crykw7BQo6RNubSLSaelTHhGRMKy61X17Uddw6wiaDSkYre8MKOrmHbSx5m4Fo4nkmvLH4rcXdQunDhERiW/Do/DGec4NZX7Nfbb55jMFo4lIlq2911+/mrUZLUNERERERERw3qPFmzxTsxaa6kOZrCgiIrnHWnunMWYC8DhOKBrArcA11toNXvsZYwqAM4A/AGOAzwHLrLW/2qffKcCfgHE4AWlFwP8CMwL+o4iIiIiISDp6jIfpv4YPfpb8vof9I/h6JL4J34ZNz0Lla/732bnUebRo3AWf3ug8Rl0Ah98FBYXu+0Zq4dUzYcvz8Y/RdSQceJ3/mlIUsY1sj1S5tvUvHpTx43spMiWu2yMKRpOObPhZMPI8WP9A/H6vng6jLoQj7kocxNhZNGyHl09xQtBSMfYSJ8xLgtGlLxx8Iyy6tPW6Wq+pMPWq7NYlyXvlM/C53VDcPduViEg+iUb89136W+dr6SA45jnoMz0zNWVDUz1UvJLeGFP/G3rtH0g5IhJj9Bfg7Uu925de63ztPg6OfV5BsSISCgWjiYhkWuMe77bCsvDqyIiQgtEKS2HAbNj8rHv7ir/CrHgr0ItI6FbcFL+9yecKByIikjk2Cu9+N4lQNAOzboKhJ8Gaf7p3UTCaiISp6k1//eo2ZrYOERERERERSbxgio3C7hXQe2o49YiISE4xxnQFHgUmAo3AxdbaBDNZwVobBR4xxswDngGOAn5hjFlvrf1nTL+njTGvAa8ABzZvPsAYc4K1NsGMehERERERCdWUn8LQU+HlE2Fvhf/9+h+RuZrEXUlvZ5Lr1ldg+3tgm1rbdnyUOKBoX+vucybYDjvFvX3tPYlD0Y56AAYfDyV9kjt2CrY1VmE95kv0Lx6c8eN7KTLFrtsVjCYdWmEJHHkf7PcdqHwTPrjSu++6e6H/oTDxe+HVl8tW/C21ULTRF8P4b8CAI8GY4OvqzMZ/DQbOhq0vQ9lQGHSswrXy1Zo7ndclERG/fM/dibF3Kyz+CRzjMbc6H8V7Lzn6YjjkZifcdcvzUNu8vlK0AXZ8CF1HwNivQF+tjSSSEUVd4dgX4KXj4/fbswo+vgYOuz2cukSkU1MwmohIpsVbIam4R3h1ZMLgBCe2QSrL3sVDEUnSugdg2zvx+yg4R0Qk+3Yth9pyf30PuAamXNm6gl6RxwX4SJxQYBGRINVX+++7c5l3W7TJuXAKzs1FhaXp1SUiIiIiItIZ1WxwJkYmsmuZgtFERDqvXwGTcVbgu85PKFosa22NMeZs4BOgL3CjMeYpa21lTJ/dxphzm/u03Bd5IqBgNBERERGRXNNnOhx6B8w/1V//siFQ2CWzNYm7wi7OQppDT2q7veK15IPRADY+5h2MtvGJ+PsOOApGfi75Y6aoqnGLZ1u/4oGh1bGvIuM+FTBiIyFXIhIyUwADj3Yeq26FPau9+y65FsZ9HYrKwqsvV5U/nvw+A46CI/4VfC3SqudE5yG5o88M2P64iqP7AAAgAElEQVR+cvuUP65gNBFJTqphxpvnQaSu45zbxHvvN/ky589ZVAZjvxReTSLSqsd4f/02PgYoGE1EMq8g2wWIiHR4W1/ybhtwdHh1pGPi9z22h7iCSkm/+O0168OpQ0QSW/KbxH0UjCYikn3Vb/nvO+WnraFoAEXd3Pvp9V1EwrLrU/99d3wAD/aGxVdAU4OzrakB3v4W/KcvPDzAeTzYE148zpnQLyIiIiIiIv6VP+bdVlAMw06HyT+GHhPCq0lERHKGMaYI+GLzt/XAdamMY62tAm5u/rYMuNClzxrgQcA0bzoylWOJiIiIiEgIBh7tfQ/SvgYdm9laJHl9D4KiFBaJX3mL83nirhWwcylseBQ2PAIbn4KdS+LvO+i41GpNUXVjhev2noV9KCnIXlBfsSl23R6xDSFXIpJFiX4v7N0Cr57uvMZsfh4ad4dTV7ZE6qDyTajbAnsrYdNzzmvrhkeSu1e4hX7vSmc0+cfu23tO8t5nyzznHGb3SrA2M3WJSMcSTTEYDQtr74FNz0Ld1kBLyoqqBd5tvaaEV4eIuOs6ErqPS9yvvho2POycD218CioXtM7XEREJkPsyESIiEgwbhWV/8G4ffHx4taRj4vedD8RrY8LHxl4CvUJc0b1L//jtzx0KZ28OpxYR8daw3QmeSKSpNvO1iIhIe9Em53U62ggLv5y4f0lfOPYFMKbtdgWjiUi21W1Mrn/jTlh6nXOh5aA/wns/gJV/b9sn2uiEmz93MJy5CSK7Ydu7zgXWssHB1S4iIiIiItLRbPQIRus5ET77Sbi1iIhILjoK6A9Y4G1rbToXE+YBVzY/PxP4k0uf53BC0wwwPI1jiYiIiIhIJhV3hylXwQdXxu9X0gcm/yicmsS/oq4w7efwfgr/Nq+emfw+3UbB+K8nv18aqhq3uG7vVzww1Dr2VeQRjNZoUw1ZEMlDky6D8kehvsq7z5YXnEeLQ26G8d/IfG1h2/AwvPkFaKoLZrxuo2H8pcGMJZJPhp8G/Q6D6oWt23pOhjlPwBPjvfeb/1nn68A5MPth6NI3s3WKSH6LRlLf9+2Y90MTvg0H/RkKCtOvKUzRRnj7Uqgtd2/ve7Cz+J6IZJcxcMD/wIIvODkZ8bx2TtvvS/rA7Edg0JzM1ScinU7eB6MZYw4ETgdmA+OAvkAPwFpr2/35jDG9gZ7N39ZbaztANK6I5Kyqhd5t474OhSXh1ZKO7mPgxAWw7j5nFYOBc2DUee0DMjIp0Ypge7c4b4i76p5WkazatdxfPwXniIiEr2YdvHJq4pU1W0y6DPb7DnQf276tqLv7Pnp9F5Gw1Fentt/y62HcV2H1nd599lbA/cXOe96WCzmTfggzfh/u+2AREREREZF80LADtr7i3jbyvFBLERGRnDUy5vmmNMeKXTFvlEefZTHP+6R5PBERERERyaQpV0CvybDmbtjwkLOIY8M2p633NBh6Coz9CvTcL7t1irvJl0OPiVD+CNQ2L3C3ZV7wxxl8Ahz+r9AXtatudJ9u1r84u4vrFRn3OSgRm0bIgki+6TUZTnoLVt4KS3/rb5+3L4X+hzu/XzqKus3w+nlgm4IZb+ovYMI3tYiodE5F3eDYebDyFqh+G/pMh3HfgNL+cNzL8OIx8fevmO8Exh52ezj1ikh+ShhmbHDWGUpgxU3Qd6ZzT3w+WfE3WH2Hd/uBPs/rRCTzRl8AZUNh3f2w+1PY+pK//Rq2w6tnwFmbnFB9EZEA5G0wmjFmGnA9EPuO0s/MxGOAh5qf1xhjBltra4OuT0QEa+GT673b+80Kr5YgdB0Kk3+YxQJ8vMRveib0laBEZB+bnvXXT8E5IiLhe+sb/kPRxn4ZZv6vd7tXaG1kT/J1iYikoiHFYDSAp6f66GSd9/UtPvlf6H8ojPxc6scVERERERHpaHYua55w4zHhbviZ4dYjIiK5akjM8wSr4iXUcve0AbxmaG6Ped4lzeOJiIiIiEimDT/DeUh+Gn6a82hxbwYWnDvsjqyE9FQ1eASjlQwMuZK2ioz7VMBIwpAFkQ6m+1g48FqY+H14fCw01SXeZ939HSsYbf1/ggtFm/wjOODqYMYSyVfFPdznTvaZ4W//9f+GQ26BgsJg6xKRjiPqcc7e92A4eZHz/LnDoXph4rHW3pd/wWjr7o/f3n1MOHWIiD+D5jgPgMfGQM1af/s17oTNz8KIszNWmoh0LgXZLiAVxphLgIU4IWf7fmqeKAr3MWB9837dgHOCrk9EBGvhra86K1d50QrpySkblLhPtCHzdYiIt/LH4eNf+usbqWkbNCEiIplVvw22PO+//+QfxW/3DEZT8KWIhKQ+jWC0VJU/Fv4xRUREREREctWKm+GpKbDzY/f2riP8TxIQEZGOblfM8/3THGtKzHOv1VpKY577mBErIiIiIiIigRlxbrDjlQ1xHllQ3Vjhur1/cfghbbGKTbHr9ojVXArppMoGw4Rv+eu75DfQuAua6jNbU9AitU7d0Sbna8tj2e+CO0bQr98iHUlJL+g+PnG/SA3s3Zz5ekQkf3ktOhcbftzvYH9j7fqk9ZwgGlBQaiZFI7DjI+/2LgOg68jw6hGR5PSblVz/6kXO+xgRkQDkXTCaMeYc4HagLHYzsAFYTPugtDastVHg3zGbTg+6RhERqhbA6ju82wuKnQ/FxL/Bxzt/b/EUdo3fLpKrmvbCh7+AF4+FhV+Gbe9mu6LkRWrh1WRW7LNO8reIiIRj51IS54jH6DU5fnuhgtFEJMsatoV/zLX3hH9MERERERGRXFS3Bd6/nLifNw07HUzc2zdERKTzKG/+aoCxxphD0xjr4uavNmbcfQ2O6VOZxrFEREREREQkWft9O+Dxvgsm/KlvdU011ER3u7b1Kx4YcjVtFXkGo0WwWrRaOqspP/UXWgTwYC94sAe8MAd2rchsXena9Aw8NRUe6ObUfX9Rc/3Nj9oNwRyn32HJBx2IdDYTv++v3+o7M1uHiOS3aKP79th50+O+DoWl7v1i1W1sPSf4Tz9nTmouhhBVLYR/l8H9xRDxWvMI2O87UFDk3S4i2TXhO2AK/fdf+lvnfcwTE6H88czVJSKdQl4FoxljhgAt7wxbPq29CRhnrR0NnO1zqMdahgTmBFagiEiLTc/Ebx/ymXDq6EhK+sCYL8bvkyg4TSQXWQsvnwQf/wq2vgyr/+lcZKt+J9uVJefJScnvU18dfB0iIuIumdWnxl+auE9xd/ftEfebwUREAqdzSRERERERkezZ8HD8G1YBhiezmIqIiHRw84FGnPv9DHCTMSbple+MMZ8HTqT1vsHnPbrOjHm+NtnjiIiIiIiISBoGHQNznkpvjMKu0Hs6zPwj7H9FMHUlqapxq2dbv+JBIVbSXlFBiWdbxEZCrEQkh3TpB8fOg9EX+esfbYSKV+GFo3IzQARg23sw/zTYuSSzxxn3VTj2OS12I5LIxP+CWTdBn5nOuYqXD6+CXZ+GV5eI5Bc/wWh9DoBjX4BBx0FxL38haY07nTmpCxLMvw5bzXqYdzg07Y3fb8K3YOrPw6lJRFIzaA7MeRIGHg1FPZzzocKuYBIEGu7+FF49I//my4tITsm36NSfAy3vGpuA8621/4lp97u0xSKcm62KgX7GmDHW2jXBlSkind6Sa+K3l/QOp46OZtbfYdXt3u01a0MrRSQwFfOdi2qxIjXw6Y1w+D+zUlLSdq9KbbWh+mroMS74ekREpL09SbzlHXpq4j7Fvdy3N+2Fpnoo7OL/eCIiqVAwmoiIiIiISPYs+U389uKeMFBr1ImIiMNau8sY8yRwFs79fQcCzxljLrTW+rrQbIz5GnAjreFqUeBuj+4nxTz/IOXCRUREREREJDXDToEL95neda/PwJ1Dbobx3wi+piRVN1a4bi+gkD5F/UKupq3iOJOOI7aRYrTYvHRS3cfAEXdDnwPh/R/522dvBZQ/BqMvyGxtqVj1D7BNwY139OMw/LTgxhPpjCZ8y3kAPDMDti9277f6n3BgguupItI5+QlGAxhwJBz3gvM8UgsPdPM3fvkjULcVyrIb5vx/1tzlo5OBmTcopFUkHww92XnEWvZHeP+Hiff95Ho48p7M1CUiHV5BtgvwyxhTCFyAc3OTBa7bJxTNN2ttBPgkZtOk9CsUEYmRKIW7WMFoKSlIkOf54X+D9ZuRKZIjPv2L+/Y1d4ZbR4umvVD1Nuz4KP7/JxuFbe87KxF9dHVqx6qvSm0/ERFJXjIBskNOTNwnXtBv407/xxIRSVXjjuwcd+OT2TmuiIiIiIhIrmjcA3Ub4/cZegoUloRTj4iI5IvLgdjl4I8Elhpj/maMOdYY03PfHYwx+xljLjXGLAJuBkpwQtEscLu19iOXfUYAc2ldYPW1YP8YIiIiIiIikpIBs/31G3RsZuvwqapxi+v2fsUDKDCFIVfTVpHxDj6LWI+gBZHOZMjJifvE8go2yrYdQeb9G+g3K8DxRISe+3u3Bfr/V0Q6FBtx3x4n/JiirtBtjM/xo7BzSfJ1ZcrS3ybu02O87i8RyWe94pwTxdr6kvIfRCRlCRJmcsphQMsNUA3A79IcrxyY1vx8RJpjiYi0VdTDCRfyEi9IQtJTvQj6H5LtKkT82/BwtitoVfkGvPmF1vCcwcfDUQ9ASZ+2/Wo3wssnpf9BWUN1evuLiIh/e9b663f6Kijskrjfvr8bYjVsh9KB/o4nIpKqSE3iPtN+BR/9PNjjfnoTDPtssGOKiIiIiIjkk/d/lLjP8DMzX4eIiOQVa+0aY8xXgLtwFnO1QDfgG80PjDG7gN04AWi9mr+CE4ZG8z4GeAu4zONQV9C6WOxe4PlA/yAiIiIiIiKSmjFfhMoE2dX9D3cmxeeAzfUbXLf3Kx4UciXtxQtGa7QNIVYikqN6TYE+B/oPPFv2O2jYBpN+CL0mZba2aBMsvwE2PQP1lW3buvSDvgdBUz1Uvw3VbwV33CEnQtng4MYTERhzMay7171t09Owazn0nBhuTSKS+6IeQcYF3uf4gPOa8/Gv/B3jpeNg1Pkw/lIYNDep8gJRtdC5337nRxDZk7j/6IszX5OIZM7g46BsCNRtjt9v7xZ4ehqYQigscz4DmvIzKO0fTp0dSd1mWPIbqHoLovXt26ddDSPOCr0skUzKp2C0lk+3LbDIWrsrzfFi92+34qSISFq83qC28Er2lsSmXwsfXOndvuo2BaNJ/tj1afx2GwVTEL9Pumo3wcbHYfsHsPLvbdu2vACLfwqH/K3t9gVf8h+K1qUf1HsEoHltFxGRYFW+AZufSdzv4Buh+1h/YxbHCfpt2OFvDBGRdERqE/eZ9t9Q9SZsfja44/p5PRUREREREelIok2w4T9QuwG6jmh/LWFfBcUw5ORwahMRkbxirb3fGBMFbsG5X69lSeiW4LNezY92u8b0ew4431rrtXLCXcADzc/3xOknIiIiIiIiYRr3FdizGpZe697e/3A46qFwa/IQtVEW7HrRta1/jgejRTRPRQSMgaMfgxfmtC4Yn8iq25xrIScuyGyQ0VtfgTX/8m7f+nLwxxw4Fw6/K/hxRTq7oSfDgCOd+/TdzDscTlwIPfcLty4RyW2pBqNNvQrqNsHqO8A2JT7Ouvth/UPOOdGwU5KvM1UVr8NLx7sH9bgZfylMiTNXXERyX0ExHDMPXjsHdieYLx87J736Ldj0FJz8LhT3yGyNHUl9tXOeWbPOu0/D9vDqEQlJPgWjDYh57r70RnKiMc/z6e9BRHJd4x5oTBAG0T03VjLKSyPOjh+MVrcpvFpE0rXxyfjt710OB/0xc8ff9j68fFL71YZirf4HzPhd65vLhh1Q4fOC27RfwX7fhtfOhopX27fXVyVfs4iIJOejX8JHVyfuZ4pgv+/4H7eom7NKg9tFFQWjiUgYIj7nM3bJwAoyDTugJE5ApIiIiIiISEfR1OBMHKpe6H+fsV+BErdMGxEREbDWPmCMeRP4A3A2rfftWZfuJubrGuDX1tp/JBg/iV9aIiIiIiIiEhpTAAf+Bqb9HGo2QFMdFBRB6SAnHKBscLYr/D+f1n7k2da/OPt1FscNRvMIWhDpbLqNhDPWOIvIN9VC4y5n7sZHv/Dep2E7fHoTHPynzNRUsw7WBBxQdvK7rc+LujffU2ehxwSINoCNQukAz91FJE0z/gjzDnVva9gOK/6e2TlhIpJ/vM7X45zjA07w0KG3wszrYfcKwMIHP4u/eLiNwLLrwg1G++QP/kPRpv3SeX8oIvmv91Q4bbnznqe+Gp47xF+I4+4VsP5BJ0xf/Flzd/xQNJEOKp8CwWJvfioMYLy+Mc81a1tEgpMo0Rag38GZr6OjSrRSglY5kkQad8HS66BqIfScDJN+AD2yFFZYtzF++/LrYfIPoesw/2PaKHz6V1jxV9i13Nm2/09g/yvbT0T64KfxQ9HAuSD2YE/n+bivwoCjnWMk0mM/ZzUCY6Ckn3uf+urE44iISOpW3e4vFA1g1AXJjW2MEwrk9lqulQVEJNNs1LlhzY9+h8Dau4M9/o4PYeDRwY4pIiIiIiKSi5bfkFwo2ohz4cDrMlePiIh0CNbacuB8Y8wQ4FzgCGA60B/oDdQD24F1wELgBWCetdYtPE1ERERERETySWEp9JyQ7SriWlm31LNtQEn2g9GKFIwm4l/Xoa3PI3vAO/fQUfl65mqpfBP3tQFSNPh46DszuPFEJHndx8Zvr3ojnDpEJH9EPeY+F/iM+yjuDn1nOM/7HhQ/GA2gagFEm6AgiFgOH5I5lxp2aubqEJHs6DbKeUy6DJb93t8+la8rGC0ZmXzPKpLD8ikYLTa1Y6hnL/+mxjxXKoeIBKcliMjLgNnQe1o4tXRUfQ+Gbe+4t0V1MU/iaKqHF+bC9ved77e+BOsfgBMXQI9x4dcT2ZO4z8YnYfw3nACaFvE+kHr7m7Dq1rbbll7nJGefusS5oSAacYIkNj+XXL2rbncefky7urXmLv3d+zToFExExJO1bV/7k7Xmbnjra/76miKYcGnyxyju4x6M1qjscRHJsKY6/31HXeCERDZsi9+vdDAccTe8dHziMV+YAxdE03udFhERERERyVWx1yBW3+F/v3OqoUvfxP1ERESaWWs3A39pfoiIiIiIiIjkhMqGzZ5tk7vNCLESdwpGE0lR/8OhdBDs3erdZ9cnsOg7bbdtexdq10P/I6F0YOt2UwC9D4ARZ0MXl4Xkd6+CdfdDUTcY9XnYszqYP0eLEWcHO56IJK+0PwycAxXz3dur34at82HQnHDrEpHc5TX3ucD7HN/TiLNhya8TH++N82DYGTDyHOe8JEhVC2HTs1D5GlS/BZEaf/t1Gw19sv/eSkQyZPRF/oPRVt8BZcNgyIkwcHZm68pVteWw4RFo2A5DTob+h4CNQvnjznvKipeh+3jocyBseCjb1YpkRT4Fo61v/mqAGcaYYmtT+8TWGLMfMCxm04fpFici8n92feLdNuHbMMPnyZx4i9Z7t+1cEl4dkn82Pd0aitaivhJW3gwzfhd+PX4+7Fn0TecBMPJzUP2OczFu8HFw6G2tF9dq1sMrp3j/H9izGv5dFkzdiRx+N4y+oPV7twt9APVV4dQjIpJPbBQ+/G/nd1NTAww7DQ66AUoHJDGGdUIx/Rg4F6ZfAwOOTL7Wkt7u2xsUjCYiGeb3oik4N56c/C4s/BJUvOrdb9Axzjn2ye/ABz9LHCJc9WZqr50iIiIiIiK5quI1eO8y2PEB9D4Q9vuv+Nc9Yw0/S6FoIiIiIiIiIiIi0iFUNroHo/UvHkRpQUj3YsdRaLynAjYqGE3EW0ExHHYnvH6u9wL3TbWw4ib3Nq8J6Euvg+NehG6jWretvR8WXAw24ny/5Br3hYhTNfwsGHNJcOOJSOoOvhGenubd/uJcmPZLmPbz0EoSkRzmdb4eJ/zYU9+Z/vpteNh5fPpnOGZecPd2LP8zvPv91PY97J9OyKyIdEx9psMB/+PMj/RjyTXOY/pvYMqVma0t12x7H14+sXWu/UdXO+eXFa/A+gdb++2tcOYwiXRS+XTWsACoAyxQBlwQv3tc34t5vtVauzydwkRE2qhZ6759xDkw669Q1DXUcjqkpjjBaHu3QuUCJ1REZF8rb3bf7jd9OmjJBDqA80amZo1zwW3jE/D8bCf8JhqBZw7MjWDAox6CMRe13eYZjBbgxT0RkY7io1/Ckt84r5GR3bDuXljwRef13q+G7bDzY399j3859WCf4p7u2xt3pTaeiIhfkdrk+ncfDcfPh5Pfg3Ffg8J9blIt6QP7X+E873sQHPMsHPtC/DFX/zO5GkRERERERHJRpBa2vATL/ggvHA3b3nFWDN62yAmY9qvXlMzVKCIiIiIiIiIiIhKiyoYtrttP6ntuyJW4KzAFFHmEo0UUjCYS39CT4LSVMCPA+SN7VsHSmPEa98B7P2gNRYP05k0MPgHmPAUnLXJCRE58C456EIqyH9QoIkDvqc7/z3g+/iXUbgynHhHJbVGP8/WCFILRAIp7+++77V1YdVtqx9lXww5Y/BP//addDeO/AYfcAmdXwKA5wdQhIrlr6lVw6lI45FaYeYPzGpDIhz+Huq2Zry2XfHhVaygaABbe+U7bUDQ/hp/p/D3PvAH6HRpoiSK5wHuZiBxjra03xrwIfLZ506+NMY9ba3ckM44x5kjgUpyANYCHAyxTRMT7g6rY1T8kPdE4wWgAzx8BPSc6H/73GBdOTZIfNj+X7Qra8lppyK/dnzrhOdvfc0Jwsq10EAw9uf32Lv3d+ysYTUSkLWth9T/ab9/8rDMptd8sf+O0+UAsgwq6uG/XzVUikmnJBgy36DsDDr3VubFt3X1Q/Y5zDjv2y9BzQtu+g4+DsqFQt8l9rFW3OWOJiIiIiIjkqzX3wIIvBDNW2ZBgxhERERERERERERHJopqm3dREd7u2DSjJnc9Bi0wxkdjQpWYRr6AFEWlVNggm/j/44Kfe4STJ2vR06/Mtz8PeACfzH3QD9Nrfed7v4ODGFZHg9J4av91Gnfls474STj0ikrui7c/hAfAIPk6oIMn9Nj0F+/84tWPF2voyNO3117eoB0z7RfrHFJH802uy8wDY9j6svCV+fxuBLfNgzMWZry0XRBvbvpdMx0E3KMdEOrS8CUZr9mucYDQLDAPmGWM+a62t8LOzMeYY4CGgADBABPhDhmoVkc6q4hX37WVDQy2jQ2tKEIwGsGu5s5L9Ca9nvh7pGGwUTEG4x0w10CHWh1elP0ZQDr0Nirq1317Sz71/fZUTAmRMZusSEckXTXuhtty9bcmv4ehH/Y0TVjBaYYn79qaGcI4vIp1XuufRJb1hwrdgQoJ+vad7B6MB7FzaetOZiIiIiIhIPtm9MrhQNIAeid5giYiIuDPGFAOHAOOAvkAPwFhrf5XVwkRERERERKRTqmzY7Nk2sDi3gtGgrt32iBY1FfGnoBAGHecsXByE2g2w9j7AwLLfBTMmQNeR0GNicOOJSGYUlsKA2VD5mnef9Q/BmC8mH2KUDbXlUP2287zvLOg2Irv1iHQkXufrBcWpjZfsXNidy2Dt/e5tvSZD72lgm2Dbu7Bnrfc4yZxDlfRJqkQR6aB6T4PSQYlDpJdcCz32g74zU39tzFU2CjuXOPOQrIWG6uDG7joyuLFEclAevItqZa19yxhzP3A+TjjawcAnxpjrgQeAdrOvjTGFwFzg68DncALRaN7/T9batZmvXEQ6jfInnDd+bsqGhVtLR9ZjAuzdkrhf5RvOG/DuozNdkXQE9dVQOiDcYwYRjBa0ISc5K5Eka+BcGPZZ97YuHsFo0XrnA3N9SC4i4ojs8W4rf8z/OH6D0frO8j+mmwKPYLSogtFEJMOaQjqPjiYI5X5qCpzf0PEuuIiIiIiISMe34ZHgxiodDIOOCW48ERHpFIwxRwGXAycCXVy6tAtGM8acDJzX/O02a+3lmatQREREREREOqPKRvdgtGJTQs+i3JnQ7wSjtdeoYDQR/6b9AqrehMZd6Y9lI/Dmhantu9934dO/tN9uCuHAa50QNxHJfQf8Cl45FZpq3ds3PwPPHgxzn4auQ8OtzS8bhcVXtg94nHQZzPh98gFMItJeNOBgtGTVV8KbF3i3lw115gP5nZPkx/4/CW4sEclfBUUw/Vp466s4MT8edi2DeYdBz0kw95mOkw/RsANePQsqXgl+7J6TwZjE/UTyWF4FozX7KjARmIHzqtcbuLr50Wb2tTFmGTAGaDkjNM37GOBN4IowChaRTiLaBG99xbs9Vz+0ykf7/xjmx1lFIVb12x3nxFfSU7MufnvdxiwEo8UJwMmGqb9wLvAt/xO89/+S23fYqd5t3cd4t216EiZ8K7ljiYh0VIkCM3evgh7jEo/j9yLExO/56+fFKxhNN1eJSKZFPG4cCVrT3sR9lv0vTNFHjCIiIiIiksPKH4O19wFRGHEu9D0IFv84mLFLB8GxL+THyuYiIpITjDHdgFtwFkaF1kVOY3ndCb0EuBgoaB7rLmvtB4EXKSIiIiIiIp1WZYP74u0DigdTkENhIEXG/TPZiO7dE/Gv/2Fw0tuw9l7YubR9+4aHMl/DxB/AzD/CwKNhy/NQv83Z3m0UjDgHBhye+RpEJBiD5jqvKU9P9e6z4wN49/sw+8HQykrKxqfah6IBfPJHGDAbRpwZfk0iHU3gwWgBB+HUbQp2PIBe+wc/pojkp3Ffhh7jnQU9l18fv++uT2DRN+GYZ8OpLdM++mVmQtEARn0+M+OK5JC8uzvVWltnjDkJuB84ltYboQzO6pEtwWcGJ0Dt/3aNaZsHnGetbQqrbhHpBCpfjx9CUaZgtMAMOhZ6TIDdKxL3fePzMOq8xP2kY6vbDE9Pj9+nthz6HBhOPS0SBeCEab/vwgFXO8/HXAxLroH6an/7lvSFUed7t5cNgd7TYMdH7du2L066VBGRDivR74Xyx2DyZaxdtZsAACAASURBVInHqa9M3KdsGAxP8+Kk18WXpgb37SIiQfFaUS9oQ050VgWN5+NfwegLodvIcGoSERERERFJxoqbnZvEWqwP8Cb74WfC7Ie14qSIiPhmjOkJvAZMpXWB01gt9/a5stZuMMY8DZzW3Pd8QMFoIiIiIiIiEpiKxs2u2weUDAm5kviKjfuipgpGE0lSz4lwwC/d2+4N4fpHt9HOdZaR5zoPEclvvafArL/Bom9599n4ODTVQ2GX8Orya/0D8dsUjCaSPhtx3+4RfNwhdOQ/m4gkb+Bs59F9DLz7vfh9N8+Dhu1Q0iec2jJp/b8zN7ZeZ6UTyMufcmttlTHmBODy5seAlqZ9vrZoCUrbAfwe+J1C0UQkUHVb4MW58ft0hBOvXFHUFY57Bd6/HNbdl7h/3VYoG5TxsiSHrf4nNO6M32f+aTDgKDjoT9B3ZihlEdkTznHi6TkRRl0IU37auq1LPzhhgfN/bOPj8fcf8hmY+QfoOjx+v36HugejNexIvmYRkY4q0e+F8kcTB6NFamHxFd7tZcOcVe5mXg/F3ZOvMVaB+81VRBWMJiIZ1rQ3nOOM/Dx8dHX8Pk11sPzPzjmxiIiIiIj4s/0DePtS2LUcek2G6b+GQcdku6qOx1pY8uvMjD39Wpj8I4WiiYhIsh4CptF6b18D8ADwMhAF/uljjEdwgtEATgCuDLZEERERERER6cwqGzyC0YpzKxityLgvaqpgNJEAjTof1t2f2WP0PzSz44tI+Pol+H8dbYDaDdBjfDj1xNNUD427oLR5ev7uT737bn8/nJpSsbcKoiHdVyySrojH4uAF7uf3HUJH/rOJSOoSnTMBYKFqIfSe1rqpdFBuva407oai7u730DXscOaKRuqgzv3zpkAU5GVklEhS8van3Fprgd8bY/4CXIBzo9NRwFCgIKbrduBN4DngLmttglQUEZEk2Si8dFzifoVdM19LZ9J1KBx5L4y+0Am0imfdvTDxB5qc0ZlVL/LXr/J1ePFYOOUj6DYiszVZ6/1hVhD2vwIOvDb1/XtOgDmPwaOjoHZ9+/ZDb4NxX/U/XnFP9+2Nu1OrT0SkI4rUxG+vfA32VkDpQPf2PavhlVO99x99ERxxd+r17UvBaCKSLU314Ryn1yQYe4kTtBzPhv/AjN/rPaeIiIiIiB+1G2He4U7IMEDVAuc6zykfQ/fRWS2tw9m9wrmpPmgzr4dJPwh+XBER6dCMMecCx9MairYA+Ly1try5fZTPoZ5tGRKYbozpbq3NgRXJREREREREpCOobNziun1giYLRRDqd/b7r3BcWjfl/1WsqTP8NvHamM5csHf2P8BkGICJ5pe8MGHQsbH3Ju8+8w+D0tekvcp6qxj3w1tecRduj9dBtNHQfC9Vve++z6xOYd6QzF6H7mNBKjWvzPFj0bdizKtuViKQv5ZCfOPeuT/4xLPtdiuMGSIE9IuKm3ywYMNuZKxnPK6e0/b6oG4y6AA6+EQq7ZK6+RCpeg7e/7iwM23UEzPgDjDrPadvxESz4UnjBskavs9LxFSTuktustXuttXdYay+01o4EioH+OAFpXay1/ay1p1lrb1QomohkRMV82Lk0cb/C0szX0hkNPh7KhsXv895l8MLRTrqudE7lj/jv27gzuf6paqqj9Z7vfQycA72npz72ft9LLxQt1oiz2m8rKIbhZyY3TlEP9+2RXcnXJCLSUUV8zNt5eJAzgThW01547XPw+DjnoqOXvgenV9++FIwmItkSDSkYDeDQ2+GwO2D0F7z71KyFHR+GVpKIiIiISF5be09rKFqLSA0suSY79XRkfj5rSkW/WZkZV0REOrqfxjz/GDihJRQtGdbaLUBF87cFwOQAahMRERERERGhrqmGPU3u084GFOdaMJr7pNdGBaOJBGfAEXDM8zD0VOg5CcZ9HY6fD8NPg6OfgMEnQulgKB3k/ogVu73nZCd07ZjntBCnSEc15wmY+P+82+urYeEloZXTzsIvw/p/t96LW7M2fpBbi6o34cVjINqU0fJ82bXcWVBeoWjSUXgEH6dl3FfgiPtg4Nw45ys+zkVMoff++57zuO6vwB4RcWEMHPOMMxe+ZxKX/CM1sOo2JzciW2o2wEvHO+cj4Cxc+sbnofJNaNwNzx/tPxStdFD7+Zl+X19b6HVWOoGc/yk3xkwHTgT2xwk8A6gClgHPW2vbvCpYay2wLdQiRSR51kLlG1C3EWrLnXCHuubHQX9x0vHzRfU7/vrpA+vMKCyFQ2+F18+LP8Gj8nVYfAUc8vfEY+5cCpuedSbmDDgKBh6tf798V9wTGpMI4PITdpiuSK1320F/di6e/aev80YtWVOuSL2udmP9zEmv3v6e870phFl/gy79khunuKf79sbd6dUnItKR+H3Nf+e/4OiYEM+P/wc2PJR4v+Gnp1aXF69VaaK6uUpEMqwpxGA0UwBjL3EeB/0JHh4I1uWmjg2PQJ80wo1FRERERDqL7Yvdt6+6HWbdDAWF4dbTkbm9d0lXl/7Q77DgxxURkQ7NGDMEODBm03ettXEumCf0CTCw+fkEYFEaY4mIiIiIiIgAUNm4xbNtQEluBaMVG/dFTSMKRhMJ1qA5zmNfw05xHiIiboq6wkF/hMrXYJvHvNPyR52AtGTnZqWrvhrKH0ncz0vNOtj6Igw5MbiaUrHmLrCR7NYgEqSCDMR9mCIYfb7z8PLkZNj1SfxxBh0Dxz4fv8+9BYD1rkNExE1RNzj4T87zl0+Gzc/533fNXTDzeih0/3wko9beA9GG9ttX3wEDZkPjDn/jdBsDZ6z21/eNi2Ddve5tmfgdIpJjcvan3BgzE7geOCpOt2uNMW8Al1lrfSYTiUjOePnE9ivCg5PUnk/BaIt/nO0KZOhn4LPL4JkDnQ/ovKy9B2bd5Exs97L6X/DWV9t+ODb+G04QVLz9JLeVDUsuGC2VMLJkReOEORR2cd6QDT8b1t6V/Nilg1Ovq91YA+DEN2DrfKgrh4FzoMf45Mcp7uG+XcFoIiKtNj/rr1/5o06wcNdhTjjQ8r/426/bmNRrc7PvigQt3D7cExEJUrxz6Uzq0tc5H3ZbGa/ildDLERERERHJS+vu827bvQJ6TQqvlo4uE9c6Jv1Q4XUiIpKKw5u/WmCDtfbVNMeLXTQ15FljIiIiIiIi0lFVNmx23V5kiuldlFtvP4s8FjVVMJqIiEgO6TXFOxjNNjlhRAOODLemncvSX2Br7b3ZD0bb8WF2jy8StO5jgx/TT1BOrymJg9F6Tcl8HSIivaYmF4wW2e0EtvackLmavCz7nfv2VbdBcS//4/Se6r9vvNdSo3v5pOPLybMJY8wZwL1AKWBimlriYmO3HQW8aoy50Fr7aEgliki6jHGCivasbN9WuzH8elL18a+zXYG06DrcCS97/TzvPpE9ULMeuo92b99bBQu/1H77yltg1IXuK73kOmud5OONj0NxTxhzsZPS3tkkuwpEZE9m6ogVNxit1Pk666/QUA2bnk5ubGMS90lGYSkMPSm9MYo8gtEiCkYTEfk/a/7lv++jw51gsmRCyIL+/aBgNBHJlqYsBaMBDPusezDarmXh1yIiIiIikm9q1sdv37tFwWhBCvpax8C5MOmyYMcUEZHOInZlrw8CGC/2l1z3AMYTERERERERobLRPRitf/EgCnJsgfUi4z4dMBJN8p55ERERyZyxl8CaO73bnz+q7fcTvg0H/I+ziG+Q6rfBh/8NFa/Czo/TH2/Nnc5j1AUw9SrotX/6Y/pV8Tp88kfY+ER4xxTJtNJBMPDo4Mf1eM/Qxtgvw4b/xBsExnzRx8Gsd5OfOkRExnzR+R0f7/VkXy8c7dxv2GL0RTDzeigdkF4tVW/Bst/D9sXugbIN2733Xfl3/8cZ+2X/feO9lup1VjqB3PpkFjDGTALuA8pwAtAsbQPRWmZy25hHKXCvMWZyuNWKSFq6DnPfXpcnwWir74QPr8p2FRJr2OmJ+8RLMH84zsluMknDueSDnzlhbxv+A6vvgJdOgPXxPqzooCI1SfbPcDCatfHDHAq6OF+Le8Dcp+DsCjhzAxz1UOKxR10YTI1BK/YIRmtUMJqICAC1m5LfJ5kAsuFnJj9+IgpGE5FsiRcy3KLHfpk5du8D3LfvrXBuHhEREREREXd1m+H52fH71FeFU0tnkey1kXj6zoLjX4ZCj8+DRERE4otdFnlXAOPFhqHtDWA8ERERERERESobtrhuH1A8JORKEisyxa7bG63u3RMREckZg+bCYXf477/iJicsLcjFg6ONzpgrbgomFC3Wuvtg3pGwe2Ww43qpfANeOg7KHwnneCJh6DMDjnsFCkuDH9sUJu4z7FQ4+K9Q4hLIWDYEjnoQ+s5Mr44CBfaIiA99DoDZDzuvPX7t3edznLX3wLzDIVKXeh3V78CLc51Mhj2roGZt+0c8fu7XK+kDB/0JRpzlv654r6V6nZVOIBd/yv+OE3QWG4bWCLwDbGj+fjhwEFBC23C0m4EMxOKKSEZ0He6+vTYPgtG2vgILL8l2FbKvwi5wxjp4bJR3n51LYOjJ7bdXvR1/7PrK9GrLhoYdzQnJMWwTLLkGRp6TnZqyJVKbXP8tL8CbX4RZN0Jxz+DqWHkrLL/BCb+Jt4JHYZe237ckVHdzX4msjSEnpV5fJnkFo0XroalBk6lERGrXZ3b8kecFP2aB+81VRBuDP5aISCw/N31M/01mjt1zknfbzqUw8CjvdhERERGRzmzVPxJ//rFzCXBuKOV0CkEtAlPSF45/JZixRESks4pdMrmXZy//hsY812oFIiIiIiIiEojKRvf7tAeU5E8wWsTq3j0REZGcMvYSWPcAbH7GX/9dy2DTUzDi7GCOX/6YM2amNO5w5srNuC5zx2jxyQ2JF3A/6iHoMz3ztYgEobg3lPbP3PjGZ4TIft+GCd+EmvVgI862gi7O/H9jwqtDRGTEmTD8DKjbCE0x66O9/nnY/p6/MfasckJUR1+YWg2f/qXtsYMy848w7DQntLLbKDAFye3v8TmQ06bXWen4cuqn3BgzFSfYzOIEoFngf4HfWGu379O3N3AlcHnM5iONMQdYaz8MqWQRSUfZMPftdTkejLZzGbx4TLarEC/dRkKvqd6rGGx5ESb/sP329Q/EH7dhR/q1hW3zPCd0al/bF0Pjbu+gqo6oyUfK8r7W3gU1a+CE14KpYc3d8PY3Wr9vjPMzVdDFfXu8EAaAnpNh1PnJ1xaGeAFzla/B4OPCq0VEJBfVbcrs+EFdHI1V4BFqmeiCo4hIutze58QacCQM/Uxmjl02FIp6QGR3+7bNzykYTURERETEy7Z3E/f56GqY+vNgbqwUaAwoGO2EN6CoazBjiYhIZxW7Et2UdAYyxnQBDozZVJ7OeCIiIiIiIiItKhs8gtGKB4dcSWLFxv3ePQWjiYiI5KAeE/wHowGsvjO4e/8rApoTF8/mZ8IJRqtakLjPwNlQOjDztYjkg4IkIkRMAXQfnZk6FNgjIskwxglmjNVriv9gNIBVt6cejObnfCMV/Y+EHuNT3z/ea7peZ6UTSDJKMOPOaf7aEor2PWvtj/YNRQOw1u6w1v4E+E5Mf4AMzPYWkYzo6hGMVrMu3DqSYaPw1P7ZrkISiffh39aXILJPSJa1UP5o/DHjhVjlKq9wOMh8+EouiTY6j1RUvg4rbg6mjlW3++/rFYyWKMzuM+9BoUdITbYVxal91W3h1SEikqtqMxgOPOx0KPT43ZIOBaOJSLbEC0ab8QeY+0zmJu0bA/0Pd29L9L5SRERERKQzK3/EX7+K+Zmto7OwFt77Qfrj9J4GvRIs2iIiIpJYy13KBhhtjEnnl8s5QMsFigiwMJ3CRERERERERADqo3vZ2dRu6hoAA0qGhFxNYkUek14jNhJyJSIiIpLQyHOT67/xcfj0Jmjclf6x480tDMqOj9pv2/gUvHsZLP091GxIb/ztH8DiK6AuwXyLgXMViiYSK1eCcpIJaBMRcTPyc8n13/oSvPlF5/Ha5+Cx0fDyKc4c/yaXuUjb3oMProI3LoLdKwIpuY1uo6HfwemNEe81Xa+z0gnkWjDarOavFlhorf1roh2stX8H3sC5cQrgkAzVJiJB6zrSfXvNWqjbEmopvr16VrYrED8mft9JKXcTrYctL7TdtnMJ7FkVf8wG9wudOc1a77ZtSaQj57t9g/CSteib0Lg7/ToqXvHXzxRBQaF3+9BT3bcf+FsoLE26rNCUxVktrUr3qouIJLxQl47Bx2VmXK9gtKa6zBxPRKRFk0cA49hLYPIPEwcKp2v4Ge7bd34Mu1dm9tgiIiIiIvnK7w3I5Y9lto7OYv0D6Y9RWAaz/p7+OCIi0ulZa9cAsR+cXZnKOMaYLsDPWoYFFllr07whQERERERERAQqG7znrwwszsVgtGLX7RGb4mLiIiIikjkDjoKpv0hun3e+A8/Phvrq9I4dRjAawJ7Vrc8XXwHzPwvLr4fFP4bnZsGOJamNu/FJeO5QWHpd/H7dxsAhurYtnZAx3m25EpRT4P7eRUTEt2GnwsQkFwhde5fz2PAQ1KyDzc/AW1+Dl0+Cpr2t/dY/6JxrLPk1rLs32LoBuvSDI+7xzrzwK95req4EYYpkUK4Fo02OeX5nEvv9K+a5lisWyRf9Znm3Vb4WXh1+LfqOk7gvua9LX/hcnCCrqgVtv694NfGY296FaFN6dYWtYZt325sXwqbnwqslmyK16Y/xZJqnF/FC6vZV2CV++9gvtd9mCmHUhcnVFLaibt5tNWth1R2hlSIikpNqN7lvT3fVIlMII89LbwwvXhco6jZBVCtPikgGRV1WaQEoSHAuHZThp3u3KcRBRERERMRd7wP89Vt3f3KfqYu7DY8k7jP2EmexIS+nr4YBRwRWkoiIdHotF4QN8AVjjMuFb2/GmALgVtreX5hw0VURERERERERPyob3e/fK6CQPsUDQq4mMa9gtEbrsdigiIiIZI8xcMDVcMqHye2340NY9Y/Uj7u3EvZWpL7/8fPhxLdg5g2J+77/E+drzQZY+rt96tgKS69N/vjWwuKfeN8z3GLuM3DqEug5MfljiHRkuRKUkyt1iEj+MgVw0PVw2gonZOzQf0Dv6amNVTG/9b46G4X3fwTWxxzIWX93jtvyiKelz9xngrv/Lt5raa4EYYpkUK4Fo/WOef5eEvu19DX7jCEiuazrMOg+zr1t09Ph1pLI8hthxU2J++33Xfftfj4AkmAVdYUR57q37btaQs06f2NuzLMJ7pufjd/+9jfaJht3VJEAFoiu2wTbP0h9/2T+nhOFOYw4F6Zd3fpGprgXzP4PdBuRcnmhmfrf3m2LvgWNcQINRUQ6uvpK9+29pqU+ZkkfmPMklA1OfYx4Ckq828ofzcwxRaRzqlkH7/0Q5p/h3DDRuNO9X1jBaF2HQ1+PsPf3Lw+nBhERERGRfGN9Lj6zdwvsTHG1aGlV8XLiPlN/ARO+7d7We3rmPlMSEZHO6k9ABWBx7vG73RjzG2NM10Q7GmP2B+YBFzXvb4GVwP2ZK1dEREREREQ6k8qGLa7b+xcPotAUhlxNYsXG/d69iJ/JvCIiIpIdvac59/cnI9HcwHjSve5e2BX6HwKT4iy21WL3p87X8sdwPsLfx9p7kj9+bTnsXBq/T+lgGHoyFJUlP75Ih2DiNOXI+xgFo4lIUHqMh9EXwrgvw9SrUh9nXfNtBrs+9ZcvUdwLJlzqHLfl4TVXvmxIa5+hJ0Nxz9TrjBXvtVSvs9IJ5NpPea+Y59WevdrbHvO8R0C1iEgYBs6BPavab19zF0z+MfSa3L4tbOsfhHc9As9iTfi281j9j7ZBTCV9YcTZmatPvJX0ct++b1BWnfsKT+2suz9//i2rFsKe1fH71K6HLS/CsFPDqSlbmmqDGWft3dAnxRRpr+AGN4UJwhyMgWm/gMmXw5410HNS/iQ6dx3p3Rath/LHYcxF4dUjIpJL6qvct/faH7a+mNxY034JYy9xPkwrcF8ZMhBxg9Eeh5EeIbUiIsnYvQrmHd4aILnxce++ic6lgzTiTNi2yL1t+Z9h4vfCq0VEREREJB/4DUYD57y/99TM1dIZJFr5e+ip0H2083zQsbD1pbbtEy7NSFkiItJ5WWtrjTFfAp7EWcy1APgJ8B1jzNPA+tj+xpjPA/sBJwKH48zsaJndsRe4wFrrMrtKREREREREJHmVjZtdtw8oGRJyJf4UeUx6jdjGkCsRERGRpAz5DKy713//rS/Bir85c8f6HZZcANiOj5OvL1Yy9+TuWQ0rb4EPf+bdZ9F/QY8JUNwD+s50FpDf+TFsexdsBGo3OfcVdB8NZcNg01OJjzv0FP81inQ2Jk5oWpjyZd6riOSXwcc7gWCpBMRvfBx2/n/27js8jure//h7dleSVVzl3ns3pts0B2x6MaYGCEloCSUhjSTkF3IhnRsCCUlubgoJkBB6B5tmbAOmmG7ce+/dkiVb0u7O749BV21mdnZ3Zov0eT2PHmvnnDnnK1lazc7O+cxSqFrnrX9hp5bb+l0Mi37RcvuAK5Kvxwu351I9z0obkGs/5aFGnydxZXSTviHHXiKSe3qdbgWJNWfGYN0jMN7moCCTYjXw9qXe+h75O+uEz+TX4bOfwL750OVoOPwuKO0XbJ1iL1xqv339ozDxfgi3sx57vXvChif9qSsTFtzhrd/m6W0gGO2gP+MsvRt2f2D9rnc5Krl9kwlGC7Xz1i9Smn8LwxKlW2+ZoWA0EWm7nILROo2FsqFwYJX3sUr6QqlLGKVf3ILR1j0Ex/87+BpEpPVb/oeGULREQhkMRus7DT5zuIBjwR0w/ObceUNZRERERCQXJBWMNh3G/Di4WtqCSHuIVtq3FXSEExu95zXpefjkO7DlZSgqh6E3wLAbM1OniIi0KaZpvmoYxk3A/9JwjV97oPmFOQbwSLPH9SFoUeBa0zQ/CbJWERERERERaVt21DoEoxXkajCa/Q1TFYwmIiKS48beZoWdHdrmfZ8Pb7L+7TASTn654QZYiexfnHR5TbitFWguegA+SHDzrZV/Tq+e5or7wOhb/R1TRPxnKPZDRAJQ2AmOuAs++V5q+88Y7b1v2GbNf+fDYOjXrWDYemVDYcS3U6snEYeA/IRtIq2EfspFJLv6ToOibvaLnHd/kPl6mvvo5sR9jAhMeq4hBb/rRJjyerB1iTcRh2A0gPe+Cic+Dod2OQeR2IlHcz891zRh9/ve+lauCLaWXBA75Nw2bSM8l0Rw4Y634JWjrQVD7YdDxzEQCife75DHIAdI7o4a+aagfbYrEBHJXU7HI0Xd4LCfwbtX0rDmJwG3YyA/JfNmp4hIqlb8yXvfTB5LdxgFpQPt7xJTtw8OboGSPpmrR0REREQk18WTuDvj7g/AjOviyHSE7BfFAdZ7I43vJF5QBhP+EXxNIiIigGma9xmGsRp4GOhB0zc/Gn/eOAzN/PzxLuCLpmnOyUStIiIiIiIi0nbsrHMIRivsmeFKvIk4nANWMJqIiEiO6zgazvwINjwJ22bBttcgXutt34pl8OktcNLT3vrvX+TcNvhqa13cop9D7KB9n0zerDhZR94LAy6F4twMsRUREZEMGPld6HIULPoF7HwbyidC2SCrbc0D/s0TsglGAzjmr9D7bNgxF8oGQ/9LoV1X/+ZtzO06SgWjSRugn3IRya5wEfQ5B9Y82LLN60mdoNTuhdX3uffpdxGM/Ql0PjwzNUly3EJBNj4FB7c2TeP14sAa6DA8vbqCdmg71O331nf7bIjHvIV75Su3E7QlfaHDCKhYntyYb19i/duuJ5z0DHQ7zrnv2ofgva94HzuXTxynK6JgNBERW7FD1l2S7BSVQ/dJ0K4HrP239WGErMXBTjIWjOaywFZExA/JBCdAZo+lDcM6J7DsHvv2yhUKRhMRERERacyMJdd36d0w+ofB1dOamSbUVdi3Hf+obmIiIiJZZ5rmbMMwhgI3At8E+jt0NT7/dxfwv8A9pmlWZqBEERERERERaUNq4zXsi+62betekJthGxHD/tq9uriC0URERHJeSR8Y+R3ro15dBTzZMfG+m16w1h6EHQI66pkm7HMIRjvmLzDsBuvz9Y/Bvs/s+zW+WbERATPJa3qD0u9CGPntbFchIiIiuaD7JJg8s+X23e/D/iX+zBEutt9uGND3fOsjm0KKjJLWTz/lIpJ9pQMdGlzCHjJh8wz39on/gsFJhB1J5rmFgphx2PMpLP9DcmMe2pH7wWgVS5Prv/IvMOKbwdSSC2KH7LfXnwSe8ADMPhVi1dbjSHuIeryW+tA2ePNcmLYJIjYvbvYtTC4UDZqeOG5tEi62MhK0i4i0UjX2F1UBUPT5nQJ6TrE+jvuX9XjzdHjzPPt9ImX+1ufEKXxURMQv+xYk1z/Tx9KH/dw5GG3WZCtEufc5EC7MbF357uB22DIdMKzQ76Lu0HUCtB+a7cpEREREJB3JBKMBzL/VuqC5rR8H1lXA1ldh32Lr8+4nQc9T3c+3x2ucL0ov7RdMnSIiIkkyTbMKuBu42zCM4cCJQD+gHCjECkPbDrwLfGKappmtWkVERERERKR121W33bGtW2FuBqMVOASjRU0Fo4mIiOSlgg7QcSzsdwgzq2dGYdd70GEk1FVa7xsXdIRISdN+B7dC3T77MTqOafjcCDnPFWp07evhv4FPb3GvLVO6HpftCkRERCTX9TjVx2C0HF/zb4SzXYFI4HIxGK3+IqaJhmEM9LhPz8YPDMM4iSSSNUzTfMtrXxEJgsMJlB1Z/tXc8YZzW6S9QtHygVswGsAn34HavcmNGT2Qej2ZUrkquf4bn2zlwWgOoS31wWjdjoPz18Gm56yTtr3OgFcnQPUGb+PX7oGtL1sLtJrb8GTy9YZy/EVSOhIFo61/BE54ODO1iIjkkppdzm31wWgtuL0Jmam/JS4vu93er5MFkAAAIABJREFUJBUR8WL/MnjlqOT2yfSxdKQEyo+F3R/Yt8+9ELocbd2BprBTZmvLVzvfhTlntHztHS62wkH7X5KdukREREQkfckGowGsexjG3eF/Lfmiaj3MmgIHVjdsW/576DgavjAdygY57Ofy/kZBB39rFBER8YFpmiuAFdmuQ0RERERERNqmnbVbbbeHCNGloFuGq/EmomA0ERGR1mf0rfDelxP3mzW55baBV8KEfzQEd2yf47y/52C0RtfkDrwclv0ODm5OXF+QinvDIK3pFRERkQSG3Qhr/wV1+9Mfy8jFSKZGcr0+ER/k6k+5ATyaxr5vJNHfJHe/DyJtQ8gliXTvZ9B5fOZqqWeasPqfzu2DPJxkkuxLFIxWuTL5MaOVqdWSSXUVyfXPdghh0GKH7LeHixs+b9cNhn6t4XH3SbDuP97nmHsRdD4c9s5PrcYmdbVLf4xcFUkQjAZQtRFK+wVfi4hILolWObelsljV8JwTnp6uE53bzDhUrIAOwzNTi7RuB9ZYr892f2gtpC8dCAMug16nZbsyCdKC/0p+n8YXa2RKl6Ocg9EA9nwET3WG4TfDqO9Daf/M1ZaP3r/WPpA8dhA+vBH6nA/hRnchPLAWlt5j3dmwxykw6Cr380x+MuOw6j7YPhuKe8GQa6HTuMzMLSIiIpKPUglG2zHX/zryyfwfNw1Fq7d/CSz6JUx0eC9zyW+cx/Rynl5ERERERERERESkDdlZZx+M1qWgm2MAWbYpGE1ERKQVGnQlxOvg/WuS33fdf6CoHI6613r83pX2/Yp7Q1GXRhvcgtEaXatZ3AtOexuW3Al7PoXaPVDY+fM1jGbLfVNZs5nIkOtg7O3Qrrv/Y4uIiEjr0nHk58cud8G6h9IbK1PrNFMVUlSStH65+lNuYgWcJbtPvRx/dhGRJuJR57aNz2QnGG3+re7tw7+RmTokPYmC0Zx0PgJqdkP1hpZtdXkQjBavyXYFucUxGM0lgGzEt5ILRgN/QtEA2xPCrYWXcJ/n+8NldXoxJiJti9PfKmh6p6XG2rncibKgU3r1eFWQYCHtzONhyhvQaWxGypFWqmIlvD4JDm1run3NAzDxfhh8VVbKkoDFDsGmZ5Pbp+No98DGoAy5Dlb9PXHIw4o/WQF/Z38G7YdmprZ8U70ZKpY5t9fshp1vQ8/P73a4fym8fETDa+B1D8P2N+D4NN+48mre1bD23w2PV98Pk1+HrsdmZn4RERGRfON0zNx3Gmx6zr5t+yyoOwAFZcHVlcu2vuzctvkF57Y19zu3pRLCLyIiIiIiIiIiItKK7azdZru9W0GvDFfinVMwWpw4MTNG2MjQTeVERETEX4OvSi0YDazrGY/8HVRvdO7T/AbEbkEfoWbHG2UD4di/JVfTIz4t9T/8NzD6h/6MJdKqKE5DRMRRp7Fw/L+tD4BoFTw/EGp2ZbUs3xlaiy+tn0ucc9aZSX6ksq+I5AIz7ty2f3Hm6qi3byEs/a1z+/g7rQXXkvtSDUbrf6lz0Ec+BKO5has4iVb7X0euiB203x4udt6n/BiYPCuYehIp6JideTMh7BDu09zOd4KtQ0Qk17iFeDq92dj5SPu7HZX0hw4j/KstkeMfcW6r2Q3L/5i5WqR1Wvm/LUPRADBhwe0QTxBGJcnZ+S7MOhWe6w+zT4OKFdmpo2J54qCxekYE+l8CU+ZkJ1y3y5FwzF+99Y1Vw4vD4NGI9YbKpz+07u4nlupNifscWAWf/cS6WGbG6JbB4Ov+Y7XNmuxjeLWN/UubhqIBRCvdzyeJiIiItHVOx/i9zoR+FzvvN/fCYOrJdbFDULvXub1ml/1r4kSvkxMF3YuIiIiIiIiIiIi0MTvrttpu71aYu8FoBQ7BaABRU9eiiIiI5C3DgG4npbZv7V44uA32L3Hu0+WoZhtcIgbcQtO8Gv7N9McA6HSYP+OIiIhI2xUphVGpBK3meAilgtGkDci1n/INKLBMpO0p6e3cdtD+TabAmCa8lOBEyeCrM1OL+CDF/M/h34RNz9m3RQ+kXk6mxGoS92muZhdE+vtfSy5wC5tx03MyXGHCoV3wTDf/63JS2Clzc2VD/0thwxPufdY9BD2+kJl6RERyQdzhb1XI5W9VKAxjb4ePmr1ZOO52f96E9Kqws3v76vtgwt8zU4u0Tm6BqdUb4cBq6DA8c/W0ZlUbYM4ZDa95qjfCK0fD1NXQLoPHwwC75iXuc+JT0P8i63V8Jp/37Ay9Dur2wac/8NbfjEHVeitEq3ojnPBosPXlC7fg/HofXO9trO1zYOaJcM5SKO2XXl121j1sv33jUxCrhXCh/3OKiGTagTVwcDsU94TSgdn/eysi+S8etd9uhGHMj61jKTvbZlqht50PD662XFSzO3GfaEXLczNV69z3aX5XbxERkQwyDCMEjAXGA/2BbkAx1vWCB4EdWNcPfgYsNk1T1xGKiIiIiIhI4ByD0QpyNxgtkiAYrYgE18mLiIhI7hpyHeycm9q+VeuhYpn72I0ZKa699GrwVdZNsr1cH+qkdAD0PNW3kkRERKQNG34TrH3QPUi2hVy4ftqlhlCuRUaJ+C+nfspN0xyY7RpEJAvcTkxkOoRq20z39q7HQXGPzNQi6YuncLejHlOgoAwK2tu3RyvTqykT4ikGo5W21mC0g/bbw8Xe9m/XFbqd4B7K4aceUzIzT7aM+kHiYLTV/4Txd2Y+gENEJFtSDfEc/g0o6QcbnrKC0vpdAn3O9r8+N5GSzM4nbY/TsVy9imUKRvPLol+2fA0erYR1/4GR381MDWbcChdb9jv3fic9C/2mWZ/nSkjLqO9Dp/Ew5/Tk9lv/GETaw7F/Df4Ck1zn9zmgaBWsuR/G3eHvuAAbn3Zue6IEjr0PhihYX0Ty1MFtMPMkOLCqYVvpQJg8E9oPzVpZItIKmDH77aEIdB7vvu/Kv1rHzG2Jl2C02v0tg9HcLnLveVp6NYmIiKTIMIxJwPXAWUBHj7vtNQxjBnCfaZpvB1aciIiIiIiItGl18Tr21O2ybetW2DPD1XiXKBhNRERE8tigL0PtblhyFxzalty+H98Mez52bm8/pOnjoK9b7XKUdc3vJ7c0vRapsUiZdf1ouARi1Y1qC1vr+Sb+S4EfIiIi4o9IKZzyKsy7Fna8AfHabFeUPkPHSdL66adcRLKvbDB0GAUVS1u2HdyS2VrW/se9/ch7M1OH+KPLUVgpuEncRLjbCda/EYdgtLo8CEaLpRCMtnkGdD4id0IF/JRq2ExjR/4e3jjbCpDzQ1E5nLcSPvo2rHuoYXv/S6D/xf7MkavKj4YxP4bFv3bv9/F34ISHM1OTiEi2pfO3qu9U6yNbwh6C0eJRvRkpqUt0krliGZDF34HWIh6D1ffZt7ldIOG3VfclDkU76o8NoWi5ptdpcN4qeDHJ0JjV90FhRzjit8HUlS+iVf6PufHpYILR3M4NmDF4/xrY+goM/BL0OgPCRf7XICISlHcua3khYtU6eHEY9DoTCrtApzFQ3Nt6jivulZUyRSQPOQWjGWHrYutj/wYfXG/fZ/eHwdWVq7y8H1G3v+W2qvXO/dtauJyIiGSdYRijgT8Dk+o3JbF7F+BK4ErDMOYAN5mmucLnEkVERERERKSN2xPdgUnctq17Qe8MV+OdgtFERERaMcOwbmg84jvWOoNIMTzi8fS62zW/A6+0myylEpNSv94hetD6Wsw4HNrZ0N6um3XNQDxqtYUKoGanFZimm7iLiIiI30r6wuRXrWOTugprW7gdPNUpu3WlSms2pQ0IOM5ZRMSjo/9ov71mV2ohT6mo3tQ0oKi5gVdC12MzU4v4o11X6HZicvsM/JL1b0EeB6PFHcJV3Cy8HT76hnUCsbVxDJsp9j5G+TFw9kKY+AAc85f0a5q2GQo7w3EPwpQ34Ih7YMpsOP4R6wRuazf+V1A+0b3PhseanugWEWnN/PhblS1e3mys3Rt8HdJ6xRO8HqxYlpk6WrtKl/WE6zIYVrv+Eff2/pfAiJszU0uq2g+BC1M4jl16t/XRlgURjLZvIWye7v+4Xmrd8AS8dT7MPAFq9vhfg4hIEGr2wI43ndu3vmL9vf7sNph3NUwfDdtd+ouINOYWjAYw9OvO+1YuBzOJm+C0Bl6C0Wr3tdx2cLN9307jrJtViYiIZIhhGJcCH2CFohk03NWu+Uc9u7b6/SYDHxuGcVGm6hcREREREZG2YUftVtvtBgblBT0yXI13EZfrzeviCkYTERFpFQzDChIDOPy/0x+v/fCW24bdaN+3uE/68zVX/7UYISju0fBhfB5zEIpAuND6utt1VyiaiBejfmC/vf2wzNYhIpKPIsUNxyORUud+RgaCZBNxq8FQMJq0fgpGE5HcUOxyN51D2zJTw4c3ubf3nZaZOsRfJzwGpQO89T3uIejw+Um+SJl9n3UPwRvnwvvXwTtXwCvHWHddeP0UWPFn6+4E2ZZqmODKv8D2N3wtJSfEDtpvD7dLbpzinjD4Khh2A/S7OPV6hnwNwkXW50YIenwBRn0PepzStpKZO9icUG/MjAcTYCAikoscg9GS/FuVDWEPbzh6Wcgr4iRe696+5gE4mKHXjK3ZwS3ObSX9MlfHjrfc2wdcnpk60tWuK1y8D/qen9x+n/4AZoyFJXdZd59pa6IHghl33lX+fz+TCSTf8zEs/rW/84uIBOWg/cIPR3X74IOvQdwh7EhEpLFEwWgAJz5l3ydaZd3gqC3xcj6l8XuoG5+Dedc4H3t2GO1PXSIiIh4YhnEJ8AhQQtNAtPqgM4CdwApgHlaA2kpgV6M+jfcDKAUeNQzjgsx8FSIiIiIiItIW7Ky1v2amc6QrBTl8s+sCw7m2qKlgNBERkVan/yXpjzHg0pbb+pxnfzP3IdekP5+IBG/AF+0DcZxCD0VExF4+r+3P59pFPFIwmojkBrdgtIPbM1PDno+c2wo7J7+gWXJDSW+YujZxv3OXwaArGx4XtHfuu2UGrP4nrH+04edmxxvw0Tdh7oVgms77ZkI8xWA0gM0v+ldHrggibGbYjTRcr50EIwKjvp/6vK1Jl6MS99n1bvB1iIjkAqdwlXwIRvNyJ6YtLwdfh7ReiYLRAF4aCxUrg6+lNavd49xWtz8zNWx9LXGfjmOCr8MvhR3hpGdh2iY45VU45TUo6Zt4v/2LYf6tMOsUiHn4+W9NolXBjFuzG7bP8m+8aLXz60wn6x6ywp9FRHJdKoskKlfC7vf9r0VEWh8vwWg9pzjvv3Ouv/Xkuv2LEvd55zLrZjmL74S5F1jh4U5KArijt4iIiA3DMEYAD2Bdl9g4EK0CuBc4B+hqmmZP0zRHmaZ5vGmaE03THGmaZg+gG3Ae8EegkqYBaRHgX4Zh6Db3IiIiIiIi4ouddfY3ZOxW2CvDlSQnomA0ERGRtqVsMEx0eT/Yiw4jWm4rKIMvvACRsoZtfc+HMbelN5eIZEZxTzjxSQgVNWwb+GUYfnP2ahIRkcyyC8gUaWUUjCYiuaGgg3NbLKCFsY3FY3DIJYDt7AVKTM1nhgHdv+DcXj6h5cm9ou6pzbX5Rdg2M7V9/eK0QHvk9xLvu/xef2vJBRuesN9ud0cLr3pOhpOehrKh9u2DvgonPQOdDwcjZC3s6nwkTH4dOgxPfd7WpN+FiftULA++DhGRXBBEiGemFJYn7rPgJ8HXIa2Xl2Comt2w7HfB19Ka1ex2bqurgLoDwdcwN8HxYXEvKBsSfB1+MgwrfKDX6dDrNDj8N9733f0+LLy94XE8CsvuhRdHwAtDYMHtwQWJZUuQX4+fIZ01u5Lf59AO2DvfvxpERIIST3GRxMwT4PFiePkIWPeYvzWJtEa1+2DetfBsX3h1Aqx9OLn9Nzxt7fd4CTzWDp7sBLNPg30egrSyyUswWmEnKB1k3+/dL8Ghnf7Xlau2vOKt3wfXw2c/TtyvWMFoIiKSMf8DlNAQiGYCPwP6mab5PdM0XzZNc6/TzqZp7jZNc4Zpmt8B+gG/+HyMemXAnwKrXkRERERERNqUnbVbbbd3K1AwmoiIiOSYwVfBxftg0nMw8V/J7TvsJue2nqfCRTvh1Ddh6hpr/HCRc38RyS39psHFu2HKbDh/Axz/b62FFxHxlZHtAtwZioyS1k9HNiKSG4yQFVIUO9iyLVod/Px1+8CM27dN+AeU9A2+BgnW6B/BrndbLu4zQnC0zTWzJWksEPnku3DO4tT3T1esxn57qAj6TIXNL7jvX7ka2vscOBCtgopl1gKnjmMg5Pxm7P8xTahaD6FCqNlhfV1GGDqMtO5I4cX2OWBG7dsiHsdw0u8C68P8/Brs+ucQI2QFINT3iUcBA0Jh22HaLC/PqxXLgq9DRCQXOAWjhfIgGM3L37eC9sHNH4/BgVVQOlBvwDZWtcH6vhd2znYl6Ys7HNs2t9XjgnGx5xaMBlYQlNdj8FRUrEgcijX85vw/pu5/CSy+E/Z7DKxY8hsY8W2ItId5V8HGpxvaFv3CCts69q+BlJoV0QAD+PwMJUslGA1g/1LocqR/dYiIBCHuIZTWSeyQ9Xz77uVWyHO/af7VJdKamCa8cTbses96fHAzvHelde574GWJ99/0Arx9cdNt8RrY9roVUnjeSmiX4o1fguZ0rt5odpzf5xxY8T/2fWedDGctyP/XBonEo1C11lvftR4veNd7nSIikgGGYZwATKEhFK0SuNA0zVmpjGeaZiVwh2EYc4FngNLPxz3NMIzjTdN815/KRUREREREpK3aWecQjFaY28FoYSOMQQiTlmtg6hSMJiIi0noVdoS+51ufb3oONj3rbb+ywe7t4XbQfVJ6tYlI9kRKoccp2Zt/wGWwXjdUFZFWquep2a4AOo5zbnMJzxdpLRT/JyK5I1Jivz3R4mg/uC1q7Xl68PNL8HqfCVPmwJBrG7YN+iqcNR/Kj2nZvziNYLT9S2D1A6nvny6n8IhwO2/BJOse8a8W04SFP4cnO8IrR8PLR8BTnWFNgoU6lavhpcPghUHwXB9rv9cmwqvHwFMd4bPbnMMM68UOwazJzu2dDkv+67FjGNZHKGx9GM3Sn0OR1r9IK1VH3uveXrMTavZkphYRkWxyCkYL50EwGlgBx26CCjrePB2e6QbTR8JTXWDpPcHMk0+q1sNLh8PzA+Cpcph7MURtwqfzhWl6D+eoWpffX2u2JQpGiwUcWL71Nff2DqNg5C3B1pAJoQI4430Y+1/e93m2NzzZvmkoWr1Vf4MDa/yrL9uCPP9Tudy/sRL9vjh570qIpRE4JCKSCc1vKpGquRdAXYCBlyL5bP+ihlC0xlb+b+J9Nz0Pb53v3F5XActtbgSTK8yY/Xaj2b3c+roEK+5fAltm+FdTrnIKkUtH+dH+jykiItLSTZ//a2CFo12faihaY6Zpvg5c32hcgBvTHVdERERERETatpgZZXfdDtu2bgU9M1xN8gocFr5GFYwmIiLSNvQ513vf3ucEV4eIyIjv2m8ff2dm6xARSUfj/In/Y8Cgr2S8lBZ6ToHCzi2395gC4cLM1yOSYQpGE5HcEXYIRgt6ATa4B6MVlQc/v2RGtxNgwj/gCtP6OO5B6OSQklvSN7253r8G3rkcFtwO866Gj26G3R9a4Q5BizkFoxVBxEMw2rYEoQRe1FXAhqfgteNg4R1NFzxFq2DeVdYiLjumCW9NsxaI2bbHYfGv4a0LIO6wkKp2L8wY41yfEYHeZ3n6UiRAzUPk7FT4GGAgIpKr8j0YbfSt7u3RKv+PgQ6stY4Xavdaj2PV8On3YdOL/s6TT0wT3jgX9n1Wv8EKUvo0j8Okkl0Ivnd+MHW0BbUJgp6CCqwyTdg2C+b/0L3fSc+0npP1kRI47OdwqU/f0zlnwr6F/oyVbTGH78nAK6HvBfZtw78Fx/2nZZhGczW7YadNAEkq3M4hJbLkN/7UICISFLdQ2lCSdxR7sj2seRCqN6VVkkirs/Yh++0751r/Rqth8wxY+TdYdZ8VAr7kLvjwm9br4EQW/9K6McmmF5zPN2SLYzBasxuLdJ8EBZ2cx/noZuv7s+uDxDdQyVdxn4PROo5NfAdwERGRNBmGUQSchxVcZgJPm6bp263hTdN8FHgaKxzNAKYahtFKThqKiIiIiIhINuyu20kc+/PM3Qt7Zbia5EUUjCYiItK2DbjMCsNIZNT3oePI4OsRkbar/BgY9o1m2ybAsBuyU4+ISCrG/heUDmq67YjfQrtu2amnsVAEjv1b02sti8rhyLuzV5NIBiVYMSYikkERh2C0aAaC0Xa+Y789XOxcl7Ruxb3TH2N9s2t8V/wPDPkaHPMXCIXt9/FD3GGxU6gICjok3n/n29bCqz4p3g2ichXMPh2q1rr3e2sanPQ09Luw6fa9nziHojW2+QV4ri+cs6hpgOG+hTBrCtTsdN63+0lQ6LKwSjLDaSFcY5XLodtxwdciIpJN+R6MNvBLsPCnLh1M62uMFPs358an7f+ObHoG+p7n3zz5pHKl/THUhqfg6D97CyTNNU6Bv05mHg/nr4PSAYGU06olCnoK4nV5tBrevhS2zHDvN/7XrfOCjEiJ9TqmJkEoXSKVK+Gl8TD+VzDm//lTW7Y4BfBFSmHAF2HTs023h4pg+Degw3DociRsfc16PTz/R/bjzDwejvkrDLs+vTrTCUZb+y8Y91/pzS8iEqS4wyKJcDGcvQC2vAwff8v7ePOuhkgZnPA49DnbnxpF8p3bscSBdfDW1PSDb+ddZf3baRxMegHKBqY3nl+czgc3f78kVABDvw5L77LvX70BPvz8wslBX4EJ/7Qu/GlNkg0KT2Skw515RURE/DURKPv8cxP4XQBz3ANc9PnnZcBxwJsBzCMiIiIiIiJtwM66rY5tXQt6ZrCS1CgYTUREpI2LlMDJL8HmF2H3+y1viFjQEXqcDD1OyUp5ItKGGAYc/Sfod4G1Vr/jKOh1FhSUJd5XRCRXlA6AMz+EzdOtm0L3PBW6Tsh2VQ36XwIdRsPWlyHS3sqAKOmb7apEMiKU7QJERP5P2CGALJaBYLT5t9pvL+oa/NySm8KFTcO2/LL6PuugM0hOARLhIjA8/ul/+1Ko2pjcvBufg3nXwovDEoei1Zt7EdRVNN225RXvcx7aBm+c23TbBze4h6KBtRhTsi/uYXFXxbLg6xARyTanUNN8CUZrPxSOe8i9j1PYTao+/YH99jUP+jtPPtnwpP32mp3O4Xu5rvkb9F7MOcv/OtqCA6vd22edDHPOhP1L0psnWg1LfguPGPBEaeJQNICRt6Q3Zy7rNsmngUxY8BPY5yFgOpc5BoUWQ++zYdxPG+5wU9gFjv2rFYoG1hvoI78No35oBfA4+fAGWPDT5J8XqzbCwp/Bu1fCsnuS27exA6v9/5soIuInp2C0UIF13D/iZjh/A3Q5xvuY0QPw5jkQPehPjSL5zu3mJR9/O/1QtMb2LYTFv/RvvHSYJphx+zbD5kYyY3/ibdy1/4ZNz6deV65yej5O1eCr/B1PRETEXv3drkxgqWma8/ye4PMxG5+k1B22REREREREJGW763bYbu8UKacwVJThapIXMexvGhL1++YbIiIikrvChdD/IjjiLjjq3qYfh/1MoWgikjmGAT2nwLjbrfAehaKJSD4qKofBX4Wxt+VWKFq9TmNg1Pdh2PUKRZM2RcFoIpI7IqX226MBB6O5ja9gtLatMIBgNIBVfw9m3Hpxh2C0UDuoq/Q2RqzaOXDEzsJfwNwLYM393vep92yfpo8XeFzwVG/3vIYwtdp9sOs99/5H/h7adUtuDglGaf/EfQ6sCb4OEZFscwpnCeVJMBrAoCvh7AXO7TGFwGRVvobwpBKMVrG0dS6KD1I8CpUJgtEAtr4KM8bA9jdTmyd2CGafBvN/6H2fk1+2LtporUbc7N9YZhzWJgipzHWJQr7H3QEXbIGzPoPz19qHOxgGdBjhPs+in8Gb53kPmziwFmYeDwt/Cusehqr13vZzUrEivf1FRILkdPwVanS3+dJ+cMY8OGeJ9bd60Fe9jf1aDl4gIJINBR2d2za/4P986x51DiTLJLca7ILRCtrDJR7fz9jyUmo15TI/F62dNd/7TXNERETSM6bR5+8EOM/bDnOKiIiIiIiIJKUqVmG7vVOkS4YrSU3EKLDdXpfKNVciIiIiIiIiIiIiOUhXwIpI7giX2G+PBRyMVrHcua1sSLBzS24L2d9FKW2bX4QPrvceUlYvdgg+/SE8XgyPGNbHm1Nhzyct+9kJF0E0iTk3PA4HtyfuV1cBi3/lfdzmogfg6e5QsdLbfHbeOMtaWFWzG+sG1C5GfDu1OcR/vc+BUIKgi9q9malFRCQTNj4Dr50Azw2A974KNXus7bGD9v3DeRSMBlDkEjy6b3Hm6mir7BaS1/vom94DgHJJqhfpfXCDv3W0dlXrklt0/8l3U5tn83TY9a73/qECKD8mtbnyRY9TYNLzVpBXouNiL5belRuhF6lyDPludBfmdt2h82FQ0MF5nPbDE8+17XV4rBBePwXm/whmngjTR8E7X4JDO5v2XfFnqN6UeEyvKpb5N5aIiN+cjhmb/50yQtBxFPQ+EyY+4G3sfQvTD5cUaQ3cjmOCEKuGR8PW+wkvjoDZp8Ou9zNbA4AZc25zej1bUAadDks89pr7oe5AanXlqriPwWidx/s3loiIiLvGF9gEecDReGxd1CMiIiIiIiIpq4rZn1suDbfPcCWpKXC41iRq5uF1YiIiIiIiIiIiIiI2FIwmIrkj4hCMFg04GO2tac5t424Pdm5pu1b9Hd48F0yXEK9oFRzcBrEaK0xkxhhY+tumwWebX4TZpzYNFIu5LCbvfU5ydXoJLtg1z3kBu1c1O2H6cNj8QupjfPztxMFvZy8Cw0h9DvFXQRmMTBCsUbsvM7WIiARt04sw92Lrb2v1Blj7b5h1ihVg0zyApV64OLM1pitS6tz25jnJh8JKctzuag3sAAAgAElEQVSC0TY8DvOuzVwtfnE6rgUoP9a57dA2qFjhfz2t1cEtyfXf+2ny31/ThJV/TW6fgV+CovLk9slHfafCucvgshr44qH0A9Le/ZI/dWWDUxhist+TDiO8993xBiz5Dex8xwosW/8IzDwB4o2CO5bdk9z8iRzc7O94IiJ+cnoudrjbvNVmwNTVEPGwQOSz26zznH4G/oiId5UrYNtMmD0l80GFbmHMhstNao7x+DrincuSqyfXJRNe7ab3uf6MIyIi4k2PRp8HebDReOyeAc4jIiIiIiIirVx1zP56tpJQfgSjRRzew4v6dY5ZREREREREREREJMsUjCYiuSPsEIwWCzAYLXrQCqawY4Sh07jg5hbZ8RbsW2jftvxP8Fx/eLYXPN4OXhoLB9bY963dC4t/3fDYKaQs3A56ne6+yKi5RCEJO9+BOWd4Hy+RD76e+r6r/gY1u5zbz5oPncakPr4EY/ydMOF+53YFo4lIa7Hyz0CzQNR9C2Db61C53H6fsoFBV+Uvp+P5eusfzUwdbZVbMBpY33+nEL5c5RTMAXDSs+77bnnZ31pas3gKd4nd+pr3vvuXwDPdYfus5OY4/K7k+rcG4SK4cJt7n67HQZejnNs3PBl8wHxQnMIQw0XJjVM+Ib06KlemF9qdSPRgcGOLiKTL6e7xiUIqywbDWZ/CkK+591v3sHWe8+lyWPdIajWK5LtUjr/rlQ5s+r5V1+NSGydaBQt/mnodqTBjzm1ur2e7efwat8yAPZ8kV1Mu8ytActgN/owjIiLiTeO7HAT5Jm/92AbQJcB5REREREREpJWrih2w3V4aLstwJamJOKwJiDq95yciIiIiIiIiIiKSZxSMJiK5I+IQpBDkgt6KZc5tfXQXdcmAj29u+BmPHrQCBuZeBB9/C2r3eB9nxR/BNKFmD8QcFllHyqCwExz9R+/jVm+0/q07YIVLrH0IDn6+UL92H8w+3ftY6ep1lvU1OInXwaYX7dtCRdB5fDB1SXoMA4ZcDcc7LIat25/ZekREghCtgq2v2rfNOQNih+zbOowKrqYghMJWEKuTNQ9mrJQ2yTDc280obHwqM7X4xS0YLVICF7mE4u5f5H89rVUqwQyf3gJbZzZ9/qraAGv+BUt/B5/9Fyy4HeZeAjPGuAcY2xl8DbTrlnxdrUFhZ5i6Fkr6N9powBH3wBUmnP4unPkRlE+039+MwfY5GSnVd06/84nCeJrrcYr7a0cvdr0H+xbBpz9Mbf+icuc2p9fsIiK5wOm4IGR/t/km2g+BCX+Hy+OJ+9ZVwLtfgvevs44dVv4N9i1OrlaRfJVqMFpRV5i6Bs5eYB0XXmHCqW9CuDi18dY86F/4lhepBqMB9DzV2xyL7/ReT64zffi/6X8J9Doz/XFERES8a5xuH2QwWuM3kF3elBARERERERFxVx23D0YryZtgNPv38OoUjCYiIiIiIiIiIiKthP3tIUREsiHsEIwWCzAYrXKFc9ugq4KbV/JD6SDYvyTYOXa8Ba8eCxMfsBYC7luQ+lgr/mwFkjhpP9T6d9iN0PU4WPJbWO8QRlVvyW9gwOXwzmUNQYKhQjjpWajZ6f33s9cZMPQGqFwO83/kbZ/GinvBKS9ZwQuvHOX8/7L5BfvtBe2Tn1Myq7CT/fa6fVboX6KwFxGRXHVgLcz2uIC3uQ4j/a0lE8LFzkFvu97zZ46oAmVsuYWI1TuwJvg6/OT2NYUKrXC0IdfB6n+0bHcLwZamUglmiNfCnNOhyzFw8nTY9jq891V/Fu8bIRj+zfTHyWdlA+Hs+bDxGSuQuvskKD+maZ9hN8Luefb7v3kuXB6zvpf5JF5jvz1UZL/dSbjI+v4s/W3qtSz9bXr7n/mp9dq1ZmfLNgWjiUgu8yOk0jCs4KaXDkvcd/U/G+8I438JY37sfS6RfJTqYqQBV7Q8RxoqsM7fr7k/tTF3vQfdT0pt32SlE4w28EvWa45ENj4FS++BUbckV1suSuW11ZG/h8Iu1g1vOh8Bvc/SeXUREcm0xidxglyB3fgPpYcUZxERERERERF7VbFK2+2l4fy47rzAsH8PL2p6uI5MREREREREREREJA/k2eo4EWnVIg7BaNEgg9FWOrf1nRrcvJIfMrUYf/9iKxwtnVA0gI9vhg9vsm+LtIfi3g2POx8Oh/3c27gvH940WCJeC+9eAfsXea9twj+g3zQYfSu06+l9v3r9L7P+DbeDsz5z7le1zn57QYfk55TMKnAIRovXKThARPLbrMmphVGVDoKC/LjzZBO1e93bTTP9OaL2d+ps87wExh2yCejJZU4hSdAQlNTlSPv2nW9DzR6oq4Sld8Pbl8IHN8C22bDxWZh3LbxzBaz8K8Rc5mkL0gkz2/MhfPZjmHe1P6FoAEf+Aboc4c9Y+aywMwy51gp1aB6KBjDoSvf9NzwZTF1BcvpdDCcZjAYw7o70aklVuAROfAJK+0H3L9j3SfS3UkQkm5wCU0NJZg10HAtlQ5Oc3ITPfgJ70zxHKpLrUgkm7nUGjP+FfdsRd6Vey+uT4OPvwJ5PUh/Dq7SC0b7s/f2aT78P+5J47yJXxVN4fTXyOzD4KzD2NuhztkLRRERERERERERERBJwCkYrCeXHdXsRI2K7PerXNTwiIiIiIiIiIiIiWaZgNBHJHZFS++3RquDm3Pm2/fa+08DQU2Sb12MylA7IdhX+6DCy5SIYp985L+r2w8ZnvPXtOBpK+jY8Hv+r5Ocr7tXweSgCo36Y3P6R/LhzV5tW6BCMBvBRhkIKRUT8tvZh59DORHqf5WspGdN+mHu7H2EwsUPu7aksHm4N4gm+LwCHdgRfh5/iDncvNUIQ+nzhfIeRzvs/XQ4zRsOnP7BColb9DWZPgbkXwpr7Yf2j8OGN8OZUiLXhO6WmEszQ2Op/Ov9fJevsRTBCx36eGCE4/hHn9tX3Z64Wvzj9HIXs73DsKlIKR/9PevUkq+fpcMEW6H+J9ThcbN9v7b8UyCgiucvxuTjJYDTDgDE/SqEA03qeFGnNkj3+nroaTn7Z+eYfReUw5rbU61n+B3htImx5OfUxvEgnGC0UhqP/BGd6DHB7aRyYce+15aJkF601vjGOiIiIiIiIiIiIiCRkmiZVMfsbdJaF8+O684hh/x5e1EzzWiARERERERERERGRHKHUHxHJHeES++2x6mDmi1bB9tn2bZ0OC2ZOyS/hQjj1LWtxc77rPqnltnY9oGxI6mMeWOOtX79Lmj7udXrywYPdTmz6uKRPcvsX5Mcb1G2aWzDamgf8CdIREcm0T7+X+r4DvuhfHZnU7ST39ppd6c8RO+je3laDFKIJvi8ANTuDr8NPTmFljUOSOh0GGPb9AKo3JZ5n22vOrw3bgnSD0fzSYzJ0GpPtKvJL7zOd23bMgdr9mavFD3GHsLBQUWrjZTpktOsEKOzY8DjiEIwGsG1W8PWIiKTC6bgglZDKwdfAkb+D4iTP4y37HTxiNHxMHwXrn0h+fpFclczx99DroWxwy5ueNDfwivRreuNsiLuEl6XLLcQ8UTBavc6HQ+lAb323z/HWL1clG/o+9vZg6hARERERERERERFppWrMQ8SxPy9ekufBaHUKRhMREREREREREZFWQsFoIpI7Ig7BaNGAgtH2LXZegNLn3GDmlPxT2h8mvwqXRRMHfeSy4d9ouc0wYMz/C3bekr4w6paW24Zc532MHpOh68Sm25JdUFnQIbn+knkFLsFoANvfzEwdIiJ+qd4Eh3akvn++HncM/6b7gubqjenPkSgY7f3r2mZwQvxQ4j75FjQadwhGa3xRX1F5y2PFVGx9Lf0x8lUuXAxphOGIu7NdRf4p7Ax9zrNvi9fB1lcyW0+6nH7nwykGo5UNhgGXp15PssLtmj12CUZb/odgaxERSZXTc3HIflGFK8OAkd+FCzbBJZUwOsXzkBXL4J0vwpoHU9tfJNckc/w94lve+nUcDX0vSK2ext6+2LktWg3Vm1Mf23QJXQtFvI1hGDDmNm99t8301i9XmS7BaCX9mz4uGwr9Xf7vREREMsv8/N+JhmFMCuIDmJDNL1BERERERERah6pYpWNbabgsg5WkzikYLWo6vOcnIiIiIiIiIiIikmc8XmUsIpIBYYdgtFhAwWg7HO4WHymFLkcFM6fkr1AYht0IO+dmu5LkDb4GygbZtw25Foq6wtqHYOPT/s7b+1yY+E8osLlr1jF/gY7jYMsMKOwC7bpB1QaoWtfQp7CTFYo26vvWgqfGinsnV0skP+7c1aZFiq2fhdo99u2NfzZERHKZacKiX8DCO1IfY+rqln/78kWXI+DMj+Hlw+3bZ59qLWI+7Bepf42JgtEAVv4ZBlya2vj5Kurh++IUDJ2rnBaCNw/mGPRl2PVeenMt/70VpjvudjDa2H0E4i4L7oPSYwpEq6xgu4KOMPpW6HxY5utoDSY9D486/Mxueh4GfDGz9aQjVmO/PVSY+pjH/RsqlsPeT1Ifw6tQEsFo29pwGKOI5Dan48V0nosBCspg/K+g4xhY/xgc3Aw1O61Aaa+W3g2Dr0qvDpFc4PV12eCrrMAzr058HJb9Hra9DiV9YOj1VoDWkv+G3R/C7nmJx9j0HBxY2/T9hHgUPvkerPqrVXuHkXDik9BprPfawD0YzS1gvbmh11nvaax7CDY+S0P2SjNLfgOH/3dSJeYUt5+T09+znhP3zbfezxx5i/XaSkREJHcYwKMBz2F+Po+IiIiIiIhISqpjBxzbSvIkGK3A4eZGUbebb4iIiIiIiIiIiIjkEQWjiUjuiDgEo0UDCkab/yP77e2Htb2F8OJNvwuhz3mw+cXs1VDcx1o4mIwRN7u39z3f+qjeBM/1S722xkbf6r7oyAjBiG9aH6ko7Z9c/8JOqc0jmTXkGmtBl53YoczWIiKSqvWPpReKdsTdUDbYv3qyofN4KOnrHHKw+FdQOtBazJwKL8Foez5Nbex8Fvfwt7J6A1SuhvZDgq/HD06BXaFmp7OGXAcf3pT+fIt+ZgUADP5q+mPlEzPDgXmX1bX8P5TUGYYV4r3yLy3btsyAWC2E0wyzcVO7D7bPgeqNYESg8xFQfqwVLp4MM+4ShliUen2hCBz/MMwYlfoYXoWbB6O1s+8nIpLLHIPR7BdVJMUwYNCXrI96Myd5vxHF/sWw6j7rPGa77unXI5ItXoLRyo+FI3+f3LihAhj9Q+ujsaP/aP27+E747MeJx3lhMFwWbTieW3YPrPhTQ3vFMnhpHFxWm9xzg1/BaAD9plkfAM/0hEPb7ftVroL2Q5MbO1c4HRsbESjpDUf9LrP1iIiIJCcToWUO6agiIiIiIiIi3lTFKm23GxiUhEozXE1qIoZDMFq+3TxTRERERERERERExIGSf0Qkd4QdgtFiAQSjLbnLua39cP/nk9YhXAQnPgVfeBFG/dChj8PPsV9OSPLGyl2Pg07jvfUt6QtT1yZfU3NTZruHovmhuLcVqOKVfq/zw9DrndtqdmWuDhGRdCzzsHB5wv1w6QH4wnToMcX6OzXup3D6PBh1S+AlZkRRV/f2DU+kPnbUQzBatNIKA2pLvHxfAKaPgEW/CrYWvzgtnDeahWqFCmDqan/mnHcV1OzxZ6x8kemLIRWK5r++0+y311XAno+Dm3fvfJgxBuZeCB9/Gz76Bsw8Ht6alnzIfdzlOTuUZrBbhxGZeU3YIhit2LmvHwFDIiJBcHo+dlhUkbZhNybX/4Ovw/SRsG12MPWIZILT8XdxHxj3MzjhMescu983+xj0VYi099Z37b8aPl/zoH2fN85Nbn4/g9EacwuQe3EYmHmameI1KFxERCR3mQF/iIiIiIiIiKTFKRitOFRKKJ3z1hnkFIxWl+mbJIqIiIiIiIiIiIgERFfOikjuiDgFox0CMw6GT1mOsRqYf6tze+cj/JlHWqdwIfQ51/rofRa8db612Byg7wVWcNnci2HLdP/nHn8ndDsxuX0m/BOMJG7GXNInufEbM0JwxkfQJQO/Q4ZhLf5ffq+3/h1GBluP+KP9UOc2BaOJSD7Y9jrs+dC9z+BrYMjV1ud9zrE+WqNEwWjbZqY+dsxjAFjtbijulfo8+SZ+yFs/MwYLb4deZ0D50cHWlC7TYSF482A0sEJzi7pBzc705517AZz6Zvrj5ItMBqP1PD1zc7Ul3U+GSClEq1q2VSyFbsf5P6dpwvtfh4NbWrZtmQ7P9oYzPoAOHgPJYjXObeGi1GqsZxgw6VmYfToc3JzeWG6SCUZzaxMRySanRRLphlQ6GfBF2DUPVvzR+z61e2H2FPhijXWuViTfOP2e9Z0K424Pbt6S3jDxfvjwBqjZ7d73/Wth0/PWa4WKZfZ9tr0G+xZDpzHe5g8qGC3Re3qPhqDfhRApg24nweCrIZQHC9ocXw8rYFdERHLaBhRaJiIiIiIiInmiKn7AdntpuCzDlaTOKRgtqmA0ERERERERERERaSUUjCYiuSPsEIwGVvhBpNSfebbPcW8f9BV/5pHWr8fJcOEO2PMRlPSF0gHW9iHX2gejTZ4F7YfBkjth5V8Sjz/xAeh0GBzaAeXHQFF5cvVN22wtdkpGqAAi7SFqfxcsRyX9Yeoqa/9MSSYYreOoYGsR/wz/lv1iWAWjiUiu2/U+zD4tcb/eZwZfSy4o6hbc2F6D0WraWDBazGMwGljB06v/kcfBaDaL2I0QDLsBFv0i/Xl3vAU73obuSYYS5yun73MQhl6XubnaknAhdBhtH875/rUw5Br/59y/2D0MtG4/vHosTH7d23NNvNa5LZRmMBpAx9Fw/nrr9fvye2H9Y+mP2VzzYDTXvgpGE5EcFXN4Pg7qnJ8RgqP/AKO+D/sWwLJ7Ep+/r/fmeTD51WDqEgmSUzBxJgKv+l8MPU+FihVQuwfeOMu57+YXEo/30li4PObtxkZBBaN19HBTlI3PWP+u/TdsmQEnPZPcDWWywel1WkiXd4iISO4yTXNgtmsQERERERER8ao6Zn+tfEm4fYYrSV2BgtFERERERERERESklfNwlbKISIa4BZ9Fq/2bZ/8S9/Zkg6SkbQsXQbcTGkLRAPpNgwn3Q9kQMCLQ/Qtw7groORlK+1mPvRh8FXQ50gpQSSYUrctRcObHqf8sF3VJfp/T3s5sKBpY3/d2PRL3K+nX9P9HcltRV/vtmQxG2zwdZp8O00fChzdBXUXm5haR/LXwZ4n7RMqgVxsJRisdGNzYyQSjtSVuoUJ2Vv3NCkjLZfEkF4KPvQNG3+rP3J98z59x8oFTMEO6Jj0H3U+2XieUDoJj/wb9LwlmLoEOLoEQ+xb7O9fuj+ClcYn71e2Hxb+ybzuwDuZeBC8dBnMvhj2fOI8TKkypzJbjhKHrBDj+YRhzm/V6MlIK/S5yfu7oMML6OfY0frNgNLfASgWjiUiuclok4ddzsZPSftDnHDjS400QALa9Bi+OgE3PB1eXSBCcjr8zdX69sBN0PdZ63+HY+9If79EwVKxM3K92j3NbOsFoAOd5mL/epufg0RDMPsM6rs1VTq+HDQWjiYiIiIiIiIiIiPihKnbAdntpqCzDlaQuomA0ERERERERERERaeV05ayI5I5IiXNbzMdgtNq9zm2j/59/80jbNuRq6yNe13JBk5eQro5jndsGfhnWPWTfdvp70HWi9zrtFHaGqvXe+5dPsBYvZlooAuN/De9f695v7E/AUBZs3mjnEIxWvTEz8295Bd6aBmbMelyxHPbOt8L/9HMkIm52vp24z2G/gIL8uaNkWsqPDm5st6CZxmrbWjCaw6JpN7s/shbE5yozyYXgoTAc/t8w/k6IVkKoqOG1ZEFH62/5snvhk+8mnnvPhxCtcg/wbi2CCEbrfQ70mQp9z7d/TST+6+gSjLbhCejkIcDTi8pV8Oox3vtveq7ltpo98MKghsf7FsLGp53HCBd5n88LIwTjf2n9XTZj1mvLmj2w5kE4tL2hX9lQOPNTiBRbX/fME5u2N1fQoenjeI1zXwWjiUiucgrbzdTf8s6HWccQm1/w1r9yhXUe5wsvQp9zg61NxC/ZDkZrbOh1sH8RLP9DeuNMHw7nb2h4n6Bmj/X1FLQH04TqDTBrsvP+TuHXXrUfagXRb33F+z7bXoMdc+CsBe7H0tni9Ho43e+ViIiIiIiIiIiIiABQ7RCMVhJWMJqIiIiIiIiIiIhIrlC6g4jkjrBLMFq0yr95Nj/v3Nb/Iv/mEQH7xUxdjobiXu77DXEJ+xr6NfvtA7+cfigaQGGX5Pp3cglxC9qQa9zbT3gMhn49M7WIP0oH2W8/uAW2vBr8/Kv+2hCKVm/Xe7D30+DnFpH85hbkWzoATnoGRnw7c/VkWxcPwWjzrk4tkCl20Fu/mjYWjJbKBW2vTYDqzf7X4pfmf5PrJVoIbhhWQFG4yAr9LezcEHDqFsjd3BNlsOZf3vvnKz+D0bpPgsN+aT3nGYa1TaFomdHbJQxm0c/9+39emELA2paXGz4/tBOeLk9u/1Bh8nN6YRgNzydFXeDsBTD0BitUY+QtcPq7VigaWGEbp70DJX3tx4qUQnmzwDincCFQMJqI5K6YUzCazyGVbk562gq67TDC+z4LfxpYOSK+c3rtlq3j5jE/aRnwmoo198PB7TBrinW891RneLobPN0Vnh/ovq8RTn/+k2ckv0+8Dhb/Ov25g+AUfu4UFC4iIiIiIiIiIiIiSamKV9puLw3nz41PnYLR6hSMJiIiIiIiIiIiIq2EgtFEJHe4LVCPugRNJKOuEvYtdG7vcpQ/84i4CUXgiHucFxSWT3QP/Op2Agy+qum2rsfB0X/0p75kg9E6jPRn3lRNfMB+e4dRMOCLma1F0uf28/TGmXBoV7Dzb3IIz1xyV7Dzikh+M03nAKeux8H566DfBQ0hQW1BSb/EfdY8CJ/emvzYXoPR6iqSHzufOS2aTmTuhf7W4acgFoK7BXLbmXcV7Pk49flySfVm2PgsbJ9jPW/VMx2+z50Ph4jHu+D2vQAuj8Opb8LY2yAcUJCVOOt8WEMAoJ0Pv5H+HPEobH4x+f3eONsKRAN45/Lk9w9nKIynXXc49i9wystw5N3QrlvT9vZD4PwN0P+SptuNEBz955Z19r3AeS63/ysRkWyK19hvz+Tf9lAExvwIzl0GV5jWMUYiez6G6k32bfuXWMdAez7xt06RVDkF1josXgpcu64wOoXX5s3tfBee6wvbZ1uPzRjU7ILaPYn39SMI1wjB5NetwNpkrHsIFtwBH94E6x+H2KH0awEw47D3M1h1H2yebr0/mdT+CkYTERERERERERERCVJVrPUGo0UVjCYiIiIiIiIiIiKthK6cFZHcES52botW+TPHK0c7t42/0585RLwYeDl0GmctEorXQlF3qFwJHUZA/4sh3M55XyMEE/4J/S+FXe9Bh9HQ97zkF/w4KeycXP9uk/yZN1V9p0H4ppYhKQOvyE49kp7S/tbPv9MCtG0zrd+fTDu4NfNzikj+cApFAzi8jQYrGoZ1TFG7173fhifgqN8lN7bXYLR4bXLj5junxfWJ7P4A9i2CTmP9rccPjgvBw6mPmcox85LfwIlPpD5nLlj9AHz0jYbfn24nwMmvQEGZ889OST+YMhuesglO7n02DLgCqjdAx9HQZ2rbCn/MVVPXwfP97du2vpr++PsWQN3+1Pbd8CT0OQe2z0p+X6dQ8WwwDDjhcRhwufV6vLCT9fvQ+fCWfTuPdx7HKXhIRCTbnJ6fsvlcbBhwzlKYMcq938xJcP6aptvm/+jzsPvPQ2EHXwUT7tdxi2SX0/F3KEvBaAAjvgMLf+pcW98LrGN/t9Doba+lNnf5RP9CY3tOgbM+g43PWPXGDsHqfyTeb9HPrX9X/sX697yV0H5o6nXEo/DulbDh8YZtpQPgCzOg0xhvYzi9Hg7p8g4RERERERERERERP1THDthuLwl7vIlgDihQMJqIiIiIiIiIiIi0crpyVkRyRyhihaPZBR3U7Ut//IrlULnCub2jx8UIIn7pNDb1AAojBL3Psj78VmQTfOCkuA+UuwQOZkJhJzjhMXjncohVW9v6ToOR381uXZIaIwTtekHVWvv2fQuALASjacGsiKVqI6z7D1RvhO4nQ/9L9PsB7oFUbXnB6shbYMFP3Psc3AybnoetM6GoHAZ+CToMd98n6jEYzW3BdmvktGjai/WP51kwWhq/V26B3E42PAnP9oUj77HCifPpeS8egwX/BUuaBYHvfAfe+zJMetY9mKGwM5y7DGafZj33A3Q6DI69D0p6B1u7JK+0H7TrCYe2tWyr3gC1+6GwY8u2eAzWPQx7P4XinjDsG1ZoXnP7l6ReW8VyK2wvFan83gbJMKDfBdaHa78QDP06rPp7y7aYgtFEJEfFHMKFw1kOqew4Eo79O3z0TecA5Kq1sHcBdD7Merx9jhVw29iaB62Psbdbz9ElfYKsWsReLgajRUrg6P+FD77Wsq1dDzjpaesYaOtrMOcMf+fud6G/47UfAqN/0PB48FUw92L7Y2QnLw6zXvsNviq192Devtg619FY1Xp4aSyMuQ1G/8j+eLuxeACvh0VERERERERERETk/1Q5BKOVhvInGC3i8N6CgtFERERERERERESktfDp9ssiIj4pKrffXrM7/bE3POXe3svnxRwi+aowiWC0cXdYi72zre9UuHAbTJ4F562Ek56BSGm2q5JU9b/Eua16U+bqaCKPAlBEglKxHF49Fj77Maz8C7zzRfjwhmxXlRs2v+jc1pYXrA7z+PPx1jRY+WdY9HN49RjYNc+9f/yQt3E3Pg1bXvXWtzVIJxitar1/dfjJaSF4WoGDZmq7HdwM71wG730ljbkzLB6D2VNahqLV2/ScFZDmdDFk/V1lO4yAqWvg9PfgrM/gzE8UipbLRn3fua1yZcttpglzL4B5X4Xl98L8H8GMMVBrE1BfsSz1ug5ugsrlye83/BfDx+AAACAASURBVFv5FUbYXPlE++1xBaOJSI5yen4KFWa2DjtDvwbTNkHnI5z7rHmg4fOVf3Put+jn8NI42POJf/WJeOV0/J3NYDSwQsDsjl2G3tBwPNbrdCgd5N+chZ2tkMIgdTsBpq6CKW8kt9+GJ+CNs2HhL5Lbb8FPW4aiNbb4V/BcX/vj7cYcA/Ta8HkmEREREREREREREZ+Ypkl1vNK2rSTcPsPVpC5iOAWjRTHNFK+REhEREREREREREckhOZBkIiLSSGGAwWhugRUnPAbhHFjcJZILCjt76zf+ThhybbC1JKOgPfScDO2H5vfCeXEPRmvXPXN1NKGfKRGW3g2HtjXdturvsH9pdurJFWbcPSAu2wubs6mo3DrOTkZdBSz8qXufeK338Rbcltz8+cxp0bQXNbv8q8NPZsx+ezqBg05ha16t+w/seCu9MTJl8wuw4033Pu9f5y2ALhSBrhP/P3v3GeZWfedt/JY0fdx7t3HFBoyNwfTeHXpCKMmmV9iQPEvabsqm955N2M1uCmwCaSS0UBIILfSOKabZuPfumfF4RtLz4uB101GXRtLcn+vS5dG//kbWaCTNOV/BwJkQjRWvRhXftI+F9215I5isux2evBKujcB10X3fL2lfAn8c+EZ/DK6rC75+/qvha5/xRPq6lv4JnvpEdt/D7g75bu5zKkmsMXV73GA0SRUqNBgt5PGs3JqGwtyfhfe/9IPgNVqiG5b8Lv1aOzbCc18Jvl51F9x1Etx2CDzxL9C5oXg1S3sLDbzq4fcPonVwwi0w5vwgDLFxMMz4Vzjwc3uOOybDz1a2GgbBSX+Dhv7FWS+dulYYfjwcdV3uc+d/Pnh82L4m89jt69I/Z96pa3PwfHvZTeFjwsLPe3MAvyRJkiRJkiQVyY5kJ90h78O2xvqUuZr8hQWjAaHfnyRJkiRJkiRVE4PRJFWWxpBgtB0FBqN1rof1j4T3j7+osPWlWtIwKPOY0x+DAz4NEZ9KqAQGHxre192+6+vta4JLPIeAnHTSfTqaYXsSLA458fX1X5e3jkqz5v7ghPowvf2E1eEn5j5n5R3pQ75yCQDb8ER2Jy/XgkIOZutcW7w6iinse4oW8HPVZ0J435TLsltjcY6Bfz1leZqT7HfasgC2vZa6r6eDGZSfaAz6H5i6b/Vd0LYYbj0IFnwvu/WSifCQwp0O/BwMOgTOfCa3WjPZ/18K+3mvBGFBQonO4L2qYr2WkaRiCXtcCgt67Anp3jcCuO882PpKdmst+3MQ+P33U2D13bDxKXjp+3D/+enfJ5IKEfaaNs3JS2XTOBiO+xNcuBUuWAuzvrZvMHK/afmvX98f3toGlybhLeth0JzC6s3VuAth4CG5z1t9N9x9JiQyPC9ee19ur83vOxeWhXyok8FokiRJkiRJklQybfGtoX2tsb5lrKQw9WmD0TweQZIkSZIkSVL1M81EUmUJC0brLDAY7aUfh/cd/vPC1pZqTWOGYLTmkcFJ71Ipjb84dXt3G2x8Fq6rhz8NDy6/a4SnPhGENhQibeCDT5vVyyWT0B1yMNDCq8tbS6VZcUv6/t4eLBRrzm/etoXhfYkcA8C2vJxfDdUml8C4vXWuK14dxRT2fx2JpW7PRv8DoXX8vu0DDoJD/wPGvjnzGq9clf/+5fLaz2Hhr7Ibu+be1O2VEMyg/Ayem7p94S/hxgnpH2ML2W/gTJj55eKtO+HS4q3VU8KChHZshOuHwJ+GwvwvGr4jqXIkOlO3hwU99pTzV4b3Lb8ZHn5P9ms9+sF929bcB8szvNaT8hX22q2S3j+INYR/UER9v/xC0CEIvq1ryb+uQkVjcMrdsP+VMOiw3OZufBKWhHxowE7ty3Kv6b5zUodShr0erqT7iSRJkiRJkiRVqbTBaNHqCUarSxuMVsCxZJIkSZIkSZJUIUx4kFRZGkoQjJZMwHNfDO8fOCv/taVa1Dwmff+cH0PEpxAqsYaBqds718Gdx0JyrxPDXvwOLPh+YXumC5QJOxFQ6i12bAzvCwtM6y02L0jfH60rTx2VKtaU37wtaW7XXA/aSrdWLck1MG53lRqMtvfv+50iBfxcRSLB89low662WAsc8r2g74hf7tkXpqtCH/val8HTn4ZH3lf4Wr398auajT67vPvtHowx5tzirDnt/8GgOcVZqydlChLq2gLzvxCEGUpSJQgNRsvi+VE5NY+AYceH969/uPA9lv1pz+vJJGx6Hhb/HtoWF76+eq94W+r2SgsgTCefMNx++8P0TxS/llzV94NDvgNnPAoXbYeBs7Ofu/RP6fs7VuRX06Pv37ct7PWwr9MkSZIkSZIkqWDt8W2hfc2x1jJWUph0wWhdBqNJkiRJkiRJqgGmmkiqLI2DUrfne6J+dwf85cD0Y3I56UHqDfpOhn7TU/cd9lMY9+by1qPeKdaSun3lbUF4QCqL/rewPRM7Cpsv1bKOleF9XVuCINreatsr6fvTHHzUK0Tz/P7XPx7ely7IMpXeEoxWyMFs3dsgvr14tRRLWNhboSeCjzkbznwKDv46zP42nPkkjDgl6KvvCxPelnmNrS8XVkMpLPpfuGkyvPDN4qzX2x+/qtmQw8u314hToG63g4L7Z3gPJpUZn4Lpn4T93hGEbJx0J8z5XvFq7EnZBoQu/m1p65CkbMVD3huJVWBg0+QPlnb9Rdfs+jrRHQTP3nogPHAR3DgBnvtqafdXbUrEoXND6r6mIeWtpRBDj4Zzl4T3R+qgdUJw6TMZDrsK5j0Ldc3lqjA7sUY4/ZEgIDsbS69P/9q5Pc9gtBW3BeGLuwt7PVxIULgkSZIkSZIkCYC2ROpgtOZoC7FIrMzV5K8uzXvG3WEfwCFJkiRJkiRJVcQjZyVVlqYRqdu3vZbfenedAFteDO8/7gaIRPJbW6pVkQgc83u4+3To2O1EnoO/DlM+3HN1qXepy+MT1zY9E5xAlu/jetpgNPOE1ct1ZDixc90jMPTI8tRSSRJdsDXD89RCA5x6q+U3wcFfTt1nMFpqYSdNZ6tzHbSMKU4txZKMp24vxong/WcEl1RmfQO2vATrHgyfv3kBDJpTeB3F0rkeHv0gJDqLt2a+wYbqeU0joK5PEHpYaqPP3fN6JALTPw4vfie7+fUDgteatfreTDTLIKHVd5W2DknKVthziWwfz8pp/MXw4KWlWz+ZCAKsGgcFAZYLf7Fn/7OfhRGnwpC5patBtWfHBiCZuq+xioLRAFrHwls2wf0XwOq/B21Nw+GIq2HU6T1bWy6i9TDxXTD85OB72ZAmqB3glunQOh76TIR4JyS7Ycnvgw/6iLfnV0PnWti+CppH7moLO2HNYDRJkiRJkiRJKlh7fGvK9pZY3zJXUpj6NB962F3Ih2xKkiRJkiRJUoUw4UFSZek7NXV7xwroSv0HqFCPXQbrHw3v7z8Dxpwb3i/1ZgMOhHMWwSn3BwGCF6yBAz7d01WpN6lryW/ejo3575kuGK1WgxqkbG1fnb5/+U3lqaPStC0OP1F1J09Yzc+mZ2HbwtR9uX6aZW8IRkvECT25Plud64pSSlGFnghe4k9mbRoGp9wLZ6Q5IX7tP0pbQ66W/gniHcVd02C06hWJQN8ppd+nZUwQIrG3cRdlv8bIU2v7uXasAoOEJCmd0GC0hvLWkY1IBM56qbR73DItCGbeOxRtp+U3lnZ/1Z50r7uqLRgNoKE/nHQnnPFE8Brq7FeqKxRtd61j4fRHgr+JpNP2Oqy5Fxb+EhZfG4SiQf6haDtt2evxLOy96lK/HpYkSZIkSZKkXqAtnvqD5lqjfcpcSWHqIuF/w+tOpvuwaEmSJEmSJEmqDp6hLamy9JsW3rf1FRg4C7a+ChueCC4bnwxCcFonwMyvwIADgrFbXoFXrkq/1/hLi1a2VJNiDTDsmJ6uQr1VLM9gtI7l0Dgov7npgtGo4bAGKRuZwnaW3Qizvl6eWirJ9rWZxxgsBCPPgJW35z5v+S0w7Yp92xM5fppl2yKIb4dYU+41VItsw+JOvgfuOiF1322zYcLb4eCvBSeEV4LQYLQyvJ0VrYNBc2C/d8Cia/btf/U/g5/vQ75bGT/nS/+UeUzfqXDiHXDTftmtabBjdRt6NGx8Kv/5kbrgZ7DPJGgeBRse3/P5wJAj4dCfQH2Kg4L7HwD1A6BrU+Z9xr45/xqrQdOw7Mc++XGY8SloGlq6eiQpnUQckonUfZUa9NhvKkx6L7z289Ks37kOVt4Bq+9O3f/81+Dgr5Zmb5VWMgHxziAMcOe/iR37tqXq26d/R+o5qebuSPP8qGFw+b7/YopEYNAhPV1FcUSiwd9EzloAt+xf/PWP+CU8/O7UfXedCPOehQEHBdfDPgCkoX/x65IkSZIkSZKkXqYtvjVle0us2oLRwo9Z6sr1w0clSZIkSZIkqQJ5hqOkytIyFqKNwQkie3vwUuhYCV1b9u3b+DSs/Cuc9SK0joMVt6bfJ1oPU/+5ODVLkoqvLs9gtPYVu04ey1W6oJ1INL81pVoRT/HcbHdbXgyCaftNKU89laJzXeYxBgvBtI/BqjuzD+/aadNzqdtzDUZLJqBtaW3fP7O9TYYfDw2DYMeG1P2v/xpW3wVveh4aBhavvnwlQu4z0TL+XI16U+pgNICXfwzdW4OT23tSvDO78MEznghCrObNh1uzeL5U11x4beo5Uy4P7rup3kMBmPQ+eO1/wudfuCV4b6ZhQO571zXD9Cvh2c+lHzdwNox9S+7rV5OmEdmPXfBdWPsAnPoPiMZKV5MkhUn1nvxO0QoNRoMg3DebYLTxl8JRvw5eH+xYD099EhZdnXney/9ReI07da4Pnmf3tveZEt2ZA8MK7cs1pKzSTgaqa/X5dyXpNw2O+SP8o8jPVaMNQYDx2gdS9986E057KHgO2bEi9ZjGIcWtSZIkSZIkSZJ6obZE6mC0PrF+Za6kMHVpjk3sTuZ4jJ0kSZIkSZIkVSDP0JZUWaIx6DsFNqcIQdjyUvq58XZ47itw+M9g3UPpx56zyE9Vl6RKFmvNb17H8vz3TOwI79v4VP7rSrUg3QnyOy2/Efp9vPS1VJJsgtGi4Z/K2GuMOh1OvA3+fmpu83ZsTN2eazAaBMEH1HAwWi4n9TcOCQ9GgyCMetGvYdpHCq+rUMl46vZyBg6OOhPq+kD3ttT9C38FRODw/+m5gItlN2QeM+K0IBQNoP8B0GcibFuYfk6/GYXXpp7Tf3849QF44Vuw4fFdjxPNo2H8xTD5A+mD0eqagQLCOQ74TBDosPi3sH0VdLdBfd8giKSuFYYdDwd9ofYDwCKR3MavfxiW/AEmXFyaeiQpnbTBaA3lqyNX2YYETbg0eFyOxKBpGEz5cHbBaCvvKKw+gA1PwoNvD0LF6wfAAf8G0z+e+++JTJLJ4PVSysCwTojvSNFWypCyN/qTieJ+n7XIsKvKM/L04q8ZrYdBc8OD0QD+emT6NbyvSJIkSZIkSVLB2uOpjwNqifUpcyWFiUQi1EXq6E5x7JjBaJIkSZIkSZJqgcFokipPv2mpg9GysfxGSP4nJLaHj5n9XWgZnd/6kqTyqGvJb17Hivz3TBeM1rESdmyChgH5r1+pti0Kgt8GHQqt43q6GlWqdD8fO73+m+DE7lqX6IY190J8Oyy+LvP4cgY4VbIRp0DDoPSBXHvr2pS6PZcQsJ22r819TjXJJixuzHnBv01DYevL6ccuurpCgtFC/q+jZfy5qu8L0z8B8/89fMzCX8LQo2HSe8tX1+7WPph5zMwv7fo6EgnuDwu+Fz4+2hj83Kq6DTgQjromvH/ax+ClH+zbPvE9he8dicDk9wWX3m78Jdk9Z9jp5R8ZjCapZ8TTvO6LNZavjlxlExI09s0wat6ebUMOh/3emV04WiG2r4Xb5+y63rUJnv4kLL0exl4QHiaWb0iZqlPLmJ6uQHur7wP7XwkLvlu8NSP1MOYceOn7+a9hMJokSZIkSZIkFawtJBittcqC0QDqIg0pg9G6/LuRJEmSJEmSpBrgGdqSKk+/afnP3b4GVt4By24MHzPhbfmvL0kqj0oLRgNYdhNMfEf+61eaZDI4EffF7+xqO+AzMPPLQZCFtLt4Z+YxG5+GFbfBqDNLX09P2bYQ7j0HNj+f/ZxorHT1VJtoQ27jd4QEo2UTAra3znW5z6km2YTF7Qw6yuYk6g1PFFZPsSRCvq9ImX+uZnwqCDXasiB8zCPvg9HnQlMPnKSeLuhu8ofgoM9D88g92zMFo+33T0EQgGrbuLekDkYbc275a6llk96TWzDauodKV4skpZNI87ovWsnBaIPT9x92FUz+QOr3Oo74ZfC86P7z89s7Ek3fv+VluCXk7x3rHwkuEgSvJVR5Zn87CDFLJoqzXrQehh4D/abDlhfzW8NgNEmSJEmSJEkqWHt8a8r2lmjfMldSuLpIfcr2VGFpkiRJkiRJklRtDEaTVHkGHlLY/HvmhfcNPQaahxe2viSp9GKt+c1rX57/npmC0Z76eG0Fo624dc9QNIDnvwrDjoeRp/ZMTapc6U6Q39098+CSRG2G68W3w02TerqK6hbLMUxhw+Op25MGo+0jLEBsp+mfhNFnBV83ZAiO2OnxK2Ds+TD8xPzr2vIyvH4trH84CFsbejQ0jwnWHHtB5seKsAP0ImV+OyvWCCfcBjftl37cPWfCGY+Vp6bdhQWjjTgN5l6Vum/IUdA4FDrXpuiMwMFfK1p5qmBDjoKZX4FnP7urbfrHYfTZPVdTLRp+cu5z1j8Ogw8tfi0qv/YVsOhq2PISkOzpaqT0ulKfAALk/ly+nKKpT/b4P5M/GP68MxKBsefBQV+A+V/IY+8M4c+Pvj/3NdX7jH0zTLuip6tQKpEInPl0EJLf9nrh60XrIVoHR14Nd58OOzbmvobBaJIkSZIkSZJUsLbEtpTtrbHq+xDBupDjqLrzOcZOkiRJkiRJkiqMwWiSKs+Yc6BlDLQvCx8Tieb3Ce2eXCJJ1aGuJb95HSvy3zOR4SCAlMEhVeyVkKCUhb8yGE37yhQcuLuFv4JJ7y5ZKT0i3gk3ju/pKqpfNI8whZd/ClMv27Mt0+N1KrUejJbuQLZTH4ChR+26nu3t9/KPg8vsbwdBSblacx/cfSbE23e1Lbsx+PeVn8Ck98PhP0u/RljgW7mD0QD6TIDzlsMNo8PHbHgcVv0dRpxUtrJIdIefoL/3z87uojE48PPwxEf27bu4K+hX7YtE4MDPwMR3wcanYMBMaB3X01XVnkgEmkdCx8rs59xxGBx1HUy4uHR1qfQ2L4A7j6395yHqHTIFgPW0gbOD32V7a92vtMHd6W6X7WuD58SqbpFY8Fo21pji34bUfdFGiDXs9nXI3FgzDD4M+k6tzYD5WjHgIHjT83D3abD2gX37j/oNvPBN2PRs5rV2BjkOPgzOXQJ3Hpf6sSudxizDziVJkiRJkiRJKSWTSdriqT8wqDXWt8zVFK4+kvpDhAxGkyRJkiRJklQLDEaTVHmi9XDSnfDQu2DDo0AE+s+AgYfAoDnBZeDBcPthsOXF3NYePLcUFUuSii3WE8FoOQQ/1YIVf0ndvvhaOPo35a1FlS/emf3YR96TWzBaojs4gXTV34Kwo+lXwqgzc6+xlF79GWxf09NVVL98whSe/iSMews0DdvVFhaWlU6tB5Kku01a9wr127Eht7Wf+gT0nQZjzt53z0XXwPNfhW0Lg7bm0dCxPLt1X/vv4LKzvvr+MPxkmPklqH/j01eT8dRzoz30dlbLqCBo7m9Hh495+F1w3pKylcSOjeGh4S0ZAq6mfBg618CCHwQBdsNPhMN/YShab9QyOriodPL53fXMp4PfgT31mKfcJROw4Huw9E/Ba9O2xT1dkVQ8+YQcl9OBn4X737xv+9A0z9uKId3ju48Bufu/oLE0oWLRhpCgsRz6sg44a/C5sQJ1LXDyPfDUx+G1X0D3VqgfAAd9HsZfAmPOh6euhIW/hPj28HV2Pzmtvk/w+vKpK8M/wGKfOvpCv/0L+lYkSZIkSZIkqbfrSu4IDQ1rqcJgtDqD0SRJkiRJkiTVMM+qklSZ+k2D0x8KTihMdAcnwuytcUhua467aN9QAElSZarLMxht+2pIJiESyX1ubwtGqwWdG6B7GzQM3HXirEojkUMwGkDbEmjNEMiz00PvgMXX7bq++i44/mYYfVZue5bS4t/2dAW1IZZHmEJ3Gyy7ESa/f1dbPgdtbV+d+5xqkkhzm0T3Ovht7JvDwzHD3HcOnHIvDDsu+D2b6IIXvwXPfm7PcdmGou1u98CITc/ChseDvSIRSIYETUR68O2soUfBcTcFt0kq7Uth84tQ1xpc4tuhcTDEmopXQ6I7eK0ca4DO9eHjGgenXycaC4LoDvxccJvm8/xJUnbGnBeEQeaibXHwuNh3chCCCEGYRl1LECbpz2zleeJj8PKPe7oKqfgiUYg193QV6Y05Pwh5XX33rrZoI0z9SHbzBx2a377xdkjEU4dnFRLeX3KR9MFgxQgiSxc4lnLPBn+3qbJF62DOD2D2d4Lw/OYRweMjQF0zHPZTmPNDePQDsPBXIWvs9fp893m3zYLNL6SvYeo/F/e1pSRJkiRJkiT1Qm3xraF9rdE+ZaykOMKC0boMRpMkSZIkSZJUAwxGk1TZItHwkJNcg9GO+t/C65EklUdda37zkvEg4Cyf8B2D0arHhifh4XfBpvl7tk/7KMz+buoTklWYXH8+lt0I07I4AX3ZTXuGou1079lw3I0wJiR4qJy2r4N1D/V0FbUhmsdjM8CyG/YMRksXAhamokMJiiAsQAz2DREbeWp+ezz7eTjke/DYh2H9o/mtkY2198PKO2DUGWmC0Xr4cX7M2dAyNghBS+UvM/Zt2+8dcOh/QH0Bnywb74Rn/i0I3Ul0waizYMIl4eMzBaPttPfJ+ZKKb9S83IPRAG6fA0SA5L59c34YBP4YIlMZNj1vKJpq15CjKj+IPBKBY6+HBT+ElbdD8yg46PMwcFZ280ecDPX9oGtL7nt3b4OG/vu2z/9C5rn1A6D/jPSBY4WEjYX1GYor5S9aBy2jQvrqg9eKoXNDXntF6+G0R+CpK+HVn6UeM+fHMPXy3GqVJEmSJEmSJO2jLb4ttK81VjvBaO3xrWzt3vzGmDqaY3kely1JkiRJkiRJPchgNEnVK5dgtFPu92RvSaomsZb853a35ReMFjcY7f8suwmGHp19oEohkskg4KxjBTQMgu4t0GdicNnb1tdg45Pwj7emXuulHwYnFc/8QklL7pXinbmNTxWMtmMTrP0HdG4I7lsDZ8F954avcd+5cNYC6Dct93o3Pg2r7wnuU8NPgNZx2c3r2gLrHoaOVcGJrk3D4aUfkTKMRLnL9/n4iluhfRm0jAmu5xOMtvEp2LERGgbmV0OlS3eb7H27t4yBGf8KL3w9tz3W3PtGQE8ZLPlDEIyWCAlGi1bA21mnPQw3jM5+/KJrINYEc/8r/z1f+CYs+N6u6ytuCS6pRBsLez4lqbhGzYMRp8Gqv+YxOeR5yBMfhebRMO7NBZWmIrn1wJ6uQCqNur4w6xs9XUV2GgYG7wfk855ArAlmfQse+1Duc1MFoy2/NXgNks7BX4UD/i33/SRVuDShg+neF6nvE7xe7H8gPHHFrva61uBvnINmF69ESZIkSZIkSerF2hNbQ/taaigY7Y4N13PHhuv/7/rwhjG8Zei7OaBPmY7/kiRJkiRJkqQiqIAzSSUpT9kGo9X1gSFHlrYWSVJxReshEoNkPPe53W3QOCj3ecksgnaSSYikObmtVtx3LhCBY34H4y4s3T7dbfDwe2HJ7/Zsj9QFJwfP/GJwPZmAZz6bXYjPomsMRiuFRI7BaGvuhfj24ORyCILS7jsv931v2R8uiUMkmt34RDc8cDEsvX7P9tnfgelXpp+77hG4/3zoWJl9feMvgUGHwlMZ1tYb0gTMxZqC+0yYG8YGJwhP/gAkQ8KyMrl+CBz7JxiTJpCvWqW7TSIp3vo5+Ksw7HhYfhO88tPS1ZWvhb8IfgesvT91f6rvqdxaRsH0T8CL385+zms/h4O/ll/waHcHvPit7Mc3Du4dz1mkahFrgBNuhdd/Aw+/s3jrvnKVwWiVYNP83MZPfFdJypCKrt/+MOZ86De1pyspjykfhAEHwd+Ozm1ed9u+ba/9T/o5pz0EQ47IbR9J1S/k5LQ9TPtI8DfNZX+GaBNMfj80jyh9bZIkSZIkSZLUS7TFt6Vsb4o2E6uEY5JyVJ/Ne8/A6h3LuGr51/jU+G8ztinFBxdLkiRJkiRJUgWqvndtJWmnpqHZjRv3VojGSluLJKm4IhGoa4WuLbnPTXVC6k6JOKy8A1b/HVpGw6T3Qn2/zPN2SnZndwJbTUjCQ++EEadAw8DiLr1tIbx+HTz72ZCtu+G5L0HX1iAQa8kfoH1Jdmu3LYJEVxCup+JJ7EjdPvxEWH33vu3J7iAAaOrlwc/xAxfnv/d1Meg3Pfh6yBHQOgHGnh+csL5T2xKY/8UgTCmVpz4Or18LA2ft1piAtQ9C/wNg5GlBuFEuoWhTr4BDf/jG+gajZaX/QbDmvn3bow1BMMBts9PPf/SDwWNSIosgy1SSCfjHRXDBamjon98alWbdo7DsBlhxS/iYVI+HkQiMOj24dKwMTriuNDeMDe+LVsjbWRPfnVswWjIOj/8zHH1d7nutviu75yo75RO+Jqm0ojGY+I4gkHL9I8VZc/Vdwe+3bENkVRobn8l+7Fu3Ba91JVWmoUfB1I/Ayz/Ofk68fd+2dM+vp/0/Q9Gk3irb9ysHHxpcJEmSJEmSJElF1xbfmrK9Nda3zJUUR10Ox8omiPPYlvsMRpMkSZIkSZJUNSrkTFJJykPz6OzGzfhkaeuQJJVGrCW/YLR4SGhIMgmPXw6v/teutgXfg9Mfg+YRQQhXJokdvStwK94By26Cie8s3pprH4S7T4fubqWuyAAAIABJREFU1J+6t4eXvp/fHot/D/u9Lb+5Si3embp90BxYc38QhLa3x/85CPFpnQDx7YXtv+XFPf99/mtwzO9hzDmw+l6464TMa2x8MrjsbevLeYRCRWDy+3KcI8a/NQiDIbln+7iLgtC6t7bB7zMEhSy5PjwY7bgbg/vcg5eGz090wqq/wrgLcyq9Ii38FTzy3iAQJ51IhpDobAOnCzH3v4PwtmU3Fme9TN9zufTbH/pOga2vZD9n8W9h4OzcX6cuuyG38Q0Go0kVa/wlxQtGA9j6GvSbUrz1lLv2pdmNO/luQ9GkatA0PLfxe4fXdqcISttdv2m5rS+pykTCu3rT+8qSJEmSJEmSVKHa46mPXW2J9ilzJcUxuH5YTuNX7VhWokokSZIkSZIkqfiiPV2AJOWteVTmMcf+2RONJKlaxZrzm3f7ofCPi2HT/D3bn/7knqFoAO3LYP6/B18vvjbz2mFhPLVs8wuFrxHfAa/8J/ztWPjb0dmFohXiUQOrii6xI3V7XR/oOyl83tOfggcuKkE9nfDUx2HjM9mFohXblMtgwEHl37faDTsOjvxfaBkTXI82BOEwc68Krte1BEFT6ax7iH2C1XZqGADjL4a+U9Ov0V4DB3d1roeH351FKFodRNKclA3QmNvBcTmb9tEgSPDQnwT1FEPHyuKsU6hIBMa+Jfd5878A29dkHrf2QbjnLPjjIHjt57ntMfiw3OuSVB7TroADP1e89W6fDX87Lvi90La4eOsqOzs2wzP/ln5M49AgJHT4CWUpSVKB6vvlNn7vYLStL6cf3396butLqh0Go0mSJEmSJElSj2tLpP4Q5dZY3zJXUhyz+xxFJN2HduwlLBhOkiRJkiRJkiqRwWiSqlfL6PC+ie+Ci7th7HllK0eSVGSxhvznLvkd3HE4bFsUXH/pR/Did1KPffVnsPDqfYPUUumNwWjZ3C57i2/f87Z65l/hsQ/D2n8Ur65M+3esLs9evUWiM3V7tDFzkFWpbH0FbptV/n37HwgHf7X8+9aK/d4G5y6B85bBhZvh6GuhrnVXf+v49PO3p/nZ3hkCNv3K9Gt0rMq+3koS3x4EoiW64foh2c2JZhFE1lTiYLTmN163tYyG/d5RnDX7H1CcdYphxieCwJtcxDtg8W+D/0sIAu6SCUgmIREPfoetuS8IE13xF9ixMfe6Jr4n9zmSyiMSgZlfCt63mvTewtfrboO198PCX8Hth+X3mKH8JLrhrpPSj7lwK5y/IggJlVQd6nM86WXvYLTNC9KP76nX0JLKI104ecRgNEmSJEmSJEnqaWHBYC2xPmWupDgmt8zgn0ZcQZ9Y/6zGtyUMRpMkSZIkSZJUPbI4Q1aSKlTzyPT90Vh56pAklUakwKeq8Q64aSIc8Fl49b/Sj334Xdmt2RuD0VbeDtvXQVMWITxd2+CR98KyGyEShXEXwoS3w4Lvlb7OvT33RTjsp+Xft1bFw4LRGmDkGcH/eW8w8nQ46jfQkN1BRAoRiYSHHGcKRos1h/dF3zjBePIHoK4PPPi21ONe/BYsvi54jBh9VuZ6e9qOjfDwe2DZDbnPzeak68YsQ9byNfK0XV/P/Rn0mwYrbg2uT/sobHgCns8xbHDIEcWrr1ANA+H0h+HZf4fXf539vCc+Ck/+CyTjwXOeZHfxapr0Xuhv4IZU8aIx6DuluGt2roVbZ8F5i4u7rlJb/XfY+GR4/+QPQn11Hjwv9Wp1uQajte95fdmfw8cOmpN7qK6k2hE1GE2SJEmSJEmSelpbfGvK9tZojn8jqiBH9D+Ruf2OZ13XarqTwXHOz217nBvWXbPP2PaQ71+SJEmSJEmSKlG0pwuQpLzFmiDWkrpv3MXlrUWSVHzFOlHs+a8EAQHFkNhRnHV6WiKX8JUkPPu57IY+/E5Y8ntIdAbBdIuugbtPyzyvFF65CtY92jN716Kw+36sMQi/G3x44Xu0jIUjfgWRCg23PeAzcOLt0Di4pyupbZmC0dI9nu/+e2PCpTD5Q+Fj25fCvWfDpudyq68nPHBpfqFokN39tS7kNVUxTHw3DDx41/VoDGZ8Ek65J7iMPR+mfiS3NSd/EAbMLGaVheszEY76X7g0ueflopBQyZ2S8Tf+LWIo2qA5cOhPireepNIaNS99f8tYeMtGOOX+7NdsXwKr7ymoLGVp6fXp+/sfWJ46JBVXfY4nvcTbdvt6e/C+SJhZ3wiCoiX1TgajSZIkSZIkSVKPa4tvS9neEqvuD72KRqIMaxjJqMZxjGocx+jG1MfhtcW3kUwmy1ydJEmSJEmSJOXHYDRJ1W3iu/dtq+sDw48vfy2SpOKqq8CDDBJdPV1BccS35zZ+0dXQ3Z5+TOcGWHZj/jWVwoLv9HQFtSMREu4TbYT6PnDy3TD94/mvP+ZcOPNpmPhOOPOZ/NdJZdjxQXjbzkusOfu5E94OU6+A4/8CB3+luHUptdYJ6fs3PRveF6nb83rjkMz7vfLTzGN6UttiWHl7/vNHnp55TF2RP+20zySYcjkc8wc4/OeZxzcPD0J/MmkaAcfdGIR+VUuYRKwBzniidOvP+SEc8n2Y+C6Y+B447Kdw6gNBaKWk6tD/QOh/QOq+Q74Hb3oBGgbAsGNgzo+yX/fV/y5OfQrXthhe/Vn6MS2jylOLpOLK9flx927BaMtvDh839gIYcUp+NUmqHnu/N7E7g9EkSZIkSZIkqce1xbembO8TK/IxVD2sJeT7SRBne6KjzNVIkiRJkiRJUn7SHJkrSVXgoC/Axidh3UPB9VhTcAJ+rKlHy5IkFUGxg1qKIVkrwWg5HtQQ74CXfwwzPhU+ZuPTkIwXVlcu6voEgT9Lrw8fs+QP0LkeGgeXr65aFQ8LRmsI/q1rhtnfhtb94PHLc1v7wH+HmV/YdX3AATDoMNjwWF6lAkEY2sl/h0iKLPDkNfDYZfDqf6Zf47yl0DImu/3m/jc8+v592ye+K7v52qU19SdVhlnUMYEb153Dpu4BnL2iD3MG7NaZVTDaVUGYVKVadWdh82d9I/OYwYcFP8uJHYXtBTDtY3DId1P/7KXTMABO/Qfcdz50rt2rb1DwGm/ESYXX1xMGHRKELL7+6+KvPf4SaBpa/HUllU8kAkf8Eu47FzpWBm2tE4LnMX3223PsmHPgiSuyW3fxtTD+Ihh9dvWESVabxy7LPKZ5dOnrkFR89Tm+H9W5YdfXG54KHzf+0vzqkVRdJrwNnv3svu0tY9KHpkmSJEmSJEmSyqI9sS1le0usAj/MuQCtab6f9sRWmmMtZaxGkiRJkiRJkvLj0beSqlvTEDjl3uCEo+0rYdhx0DCwp6uSJBVDfZqDDA67Ch77cPlq2akYoTGVINdgNICnPw19JsG4t6Tuf/FbhdWUq/EXwZgL0gejAVw/BM5fCc0jylNXrQq77+8MRttp6mVB+EY2QREAk967ZyjaTrmGKu1u4Gw45Z7w/kgE5l4V3IfuOjH1mAtWQ9Ow7PccNQ/qWqG7bc/2sRdmv4YCOQSjPbz5cObNv4VN3cHz/y//MMkv35XgHUe+cf/JJhgNYP6X4aDP5Vpp6W17HR55X/7zL1gdBI5lUt8XxpwHS36f/14AZ70E/abmP3/o0XD2S7D2AehYFbQ1DYOhx0DjoMJq62mzvw3Lb4KuLcVbc8plhqJJtWLwYXDWAlj3SPAcaOgxEGvcd1zLWIg1Z/9c/r5zYdL74fCfFbdewdbXYMWtmcc1jyp9LZKKL9dgtOe/Agd8OnhNuGVB+LjRZxVWl6Tq0GcCDJ4L6x/ds33cRQbWSpIkSZIkSVIFaItvTdneGqvAD3MuQLqgt7b4NgbXDy9jNZIkSZIkSZKUnwLONpekChGthyFzYcy5hqJJUi2pSxOMNvbNMPWK8tWyU6Kr/HuWwuYX8pv31CchEd+zrWsLPHY5rLyjsJrq+kLLuMzjog0w4W0w58cwel4QkpfJ058urDaF3/djDfu2TfkwHPIDqO+ffs39r4S5IUEd+Qaj7fcOOPmu7MYOPwFOuQ/6TNy5KQw+HM59PbdQNICWUXDiX6HvG6FQTcNh7n8F91Hlpmlk1kM/s+gr/xeKBpBMRvjIdUm6upNBw+BDs1to/udh28JcqiytjlXwyAfgpv0KWyeX+/HhPw9+t0brg8uY82D8xdnPH3lmYaFoOzUMDAIjJr8vuIw5p/pD0SAI55z5lSIuGIFZXy/iepJ6XH0/GHkqjDg5dSgaBM+PhhyZ27qv/Tf8rgUWXg3JZOF1KrD85uzGGc4sVae6PE56+euRcG0Elv05df+4i8If3yXVnuNvhuEnBc/fYi1BWO2sb/R0VZIkSZIkSZLU6+1IdNKVTP0hsS3RNMcsV6GWaGtoX3t8WxkrkSRJkiRJkqT81fV0AZIkSVJK6U5EjTbApPfCwl9Adxn/QF8rwWgv/SC/eW2LYONTu8KGkkm473xY/ff81qvvD2e/DJE6qO8LiW74fUv4+HnPBiFWdbsdsDHlQzD5A3D3mbDqr6nnLfl9EKBW15xfnYJkyH0/Up+6ff+PBgFpiR3Byd+JHdC5Pvi/i8Qg1pzhpPBI9rUNPAROvQ+I5v5/POxYOPtV6FgJsabCApiGHgVnvwTb10HjYIjk8D1ol2gsq2GdiQbu3nTiPu1bt8M9L8OpM4B+07Lfd/Hv4YAKCFHs2gq3HwodywtbZ9BhuY2v7wPH/hG62yGZCK5veAoW/zbz3EgMpvVAWGm1mfIheOYz0J36U2ez1mcSnPViEGAnqffZ/0pYc0/wWJ2teAc8/C7YvgZmfKJUlfUum5/LPKZpuI/VUrWqzyMYbdP89P3j3pxfLZKqU9OwILh/x+bg/SaDESVJkiRJkiSpIrQn2kL7WmN5/I2ogkUjMZqjrXSk+J7b4gUevyRJkiRJkiRJZRLt6QIkSZKklOrTfPpatB4GzoST7ixfPVAbwWidG2DV3/Kff8dhsOD7QSja8pvzC0VrnQBj3wynPxacKNg4KPg/zRRq1Tx6z1C0nSJRmPy+8HnxDlh9V+51apdEd+r2aJqs7VhD8HMcrQ/+31rHBYFhDQMynxCaLlRs7s9g2HHQ/0CY8Sk49R/B+vkG30Ui0DKqsFC03TUNMRStUFM+nHHI5u7+oX0LViV3XZnwT9ntueyG7MaV0o7N8Id+uYWiDT0mdfuYc/Oroa5l1+/fQbPhtIeh3/67+kecCvOeg2kfC34GR54Jx/8FRp2R3369SbQe9nt7bnNaxgS/J5uGw6A5QRDoqQ8YtCP1ZqPnwXE3w8jToXkUNI/Mfu7Tn4S2paWrrTd57eeZx4x6U+nrkFQa0XoYemxx1+ybQ2izpNrR0N9QNEmSJEmSJEmqIO1pAsFaYmmOWa5SfULC3gxGkyRJkiRJklQt0pzFLkmSJPWgkafD819L3RdrCv4dcjhM/wS8+O3y1JTYUZ59Smnjk5BMFLbGk/8C21fDhidzm3fg52Dml9KPiTaE3871/cLnjTwDIjFIxlP3L7sBRp+VXZ3aVzIkFLBk4TxpgsUmvB0mv79E+6oi7P8vsOymtAFhW7rDHw/67H7OcfOI7PZc/wi8/NMgUKxldJaFFtmDb8t9zsT3BMGAK+/Y1dZvOkz+YHFqGnI4nPXivu1zvl+c9XubmV+Bhb8KAjt3F6mDo6+DcW/pkbIkVZnR84LLTmv+AffMg+4sDly+ZX+4KPwTsCtadwdsehZIwuDDey6IduMzmcc0j4TpV5a+Fkmlc/BX4c7jirde3ynFW0uSJEmSJEmSJEl52ZYmEKy1BoPRWmJ9oWvVPu1tiW09UI0kSZIkSZIk5S7a0wVIkiRJKQ09BlrG7Ns+5jyI7PY0tpxhV4mQcKhqUqzv4YVvwqq/ZTd2ymVw/C2ZQ9EgCIcJE03TV98X9ntHeP+quzLvrdSSifAwvXT/XwVJE3SxMxhRtavvZDj90bT3r83x/qF9rbsHozUMyH7fxy+HG8bAM5+FZDL7ecXQvgxW/CX3efV9g8fXw38Bkz8Eh/wATnsQmoYUv0YVrnEQXLAKDvhs8FgWrQ8CXk9/2FA0Sfkbdgyc8RhM/3jmsfF22Jwi8LLSbXgSbjsY/noE/PVIuGMutIcHqJbU/C+m7+8zGc58GvrPKE89kkpj2LHQlGXIcib1A6CuuThrSZIkSZIkSZIkKW/t8dSBYI2RJuoipfqQ2J7TGk0d9taeJiBOkiRJkiRJkiqJwWiSJEmqTJEoHHVdcALpTn2nwqE/2XPc0GPhgH/bs23S+0pTU7IWgtG6y7fXzC/DpUk47Ccw+k3ZzUkXfpbJrG+F97W9Xv6go1qRjIf3lSoYLZLmpWokTWiaakfLKDjws6Hdm7vDg9FaGna7j9TnEIy20/NfhVV35j6vEJuey3NiJHjcnPRumHsV7P/R3MLgVH71/eDgL8NFHXDxDpj9LRg0p6erklTt+k2D2d+G4/8SPM6k8+zny1NTsSST8Mj7Yesru9o2PA6Pf6T8taz8Kyz7c/oxZ78MTcPKU4+k0hpyZHHWGX5icdaRJEmSJEmSJElSQdpCAsFaY33LXEl5tMRSB6O1hQTESZIkSZIkSVKlMRhNkiRJlWvYMXDuQjj+Zjj5bpg3PwjL2V0kAgd/Fc5+NQhSO/MZmPuz0tST2FGadcspXchVMY16U9pQo1CDDs1/z6YhcNpD4f0r78h/7d4skSYQMFqiT0nsM6k066q6RBtCuzalCUbbIwMxXcheOouvzW9evjpW5jfPEDRJ0u5Gz4OzX4EDPxc+Zukf4cXvlq+mQm14AjY+uW/7sj9D29Ly1TH/y3D36enHHP5zQ3ylWlKs59pTLyvOOpIkSZIkSZIkSSpIeyJ1IFhYgFi1Cwt8C7sdJEmSJEmSJKnSGIwmSZKkytYwEEafBcNPgFh4SA59J8GEi2HgzOBk9BmfLn4t6QKiqkWyuzz75Hv7T/946vb93pnd/KZh4X2Pvj/3epT+PhOtK82eU/85dfuoeaXZT5Up2hjatTlNMFp3YrcryUTouLSWXJ/fvHy1L8t9Tl0rDDmq+LVIkqpb0zCY+SUYODt8zFMfh62vlq+mQqx/JLxvxS3lqWHRb2D+5zOPq6/NTxGXeq36IgSjzf0vGH5y4etIkiRJkiRJkiSpYG3x1IFgrb0sGK0tvrXMlUiSJEmSJElSfgxGkyRJUm2a+K7ir1kLwWiJLILRhp9Y2B6Dj4ChR+c3d8Qp0P+APduijTD5A9nNbxwS3te+DLo8oCNn6e73kfrS7DlwFgw9dq+9ojDlw6XZT5Upll8wWnz3LLR+0/Lbu3treQNjsglb2dukD0Bdc/FrkSTVhqN/m77/5inlqaNQG54I79v4dOn3X3oDPPT27MbWGYwm1ZSGAoPRRp0VvJcRiRSnHkmSJEmSJEmSJBWkLb4lZXtLtDb/1tsSEvjWHhIQJ0mSJEmSJEmVxmA0SZIk1aZ8w3DSqYVgtGQ8fX/rBDjxbzDrm1DfL/f1hx0PJ/01/xN/o/Vwyr0w6X3B/+GoN8GJt8PQo7KbnymMYOsr+dXVm6UL04vWlWbPSAROuBWmfgT6z4ARp8FxN8Los0qznypTtCG0a3M8PBitO57cdWXosVCfZ6DBzVMgmcw8rlCbnkvfP3guTP8kzPpWEDw56FCY9Q045Dulr02SVL36Tc383Hj94+WppRDrHwvv61xXun3jnfDoB+H+87OfU1+bB8tLvVahwWiT3lucOiRJkiRJkiRJklQUYYFgrbHa/FtvazR1MFpb3A8YliRJkiRJklQdSnQWuyRJklSDEjt6uoLCJdOEXEXq4OCvQzQGMz4Jkz8AfxyY2/rH/qnwQIDGwXD4f+c3N1Mg29LroWsL9JkErWPz26O3SaYJBIzUl27f+j5w6I9Kt74qX7QxtKst3hrat3suGrEGmPV1eOzDe6476BBY91DmGl65CqZelkWxBVjyx/C+g74IB31+1/UZnyhtLZKk2jL1Mnjhm+H9K2+HwYeWr55sdayGtkXQOBS2vBA+rpTBaI9/BF7L8TVJXeqDyiVVqaYR+c8dfhKMmle8WiRJkiRJkiRJklSwtkRYMFpt/q23JSTwrS2+jWQySSTfD0CWJEmSJEmSpDIxGE2SJEnKViJNQFS1SMbD+069H4Ycset6ff/c12/IMUit3J7/WnABmPQ+OOw/gyA4hUukCdOL+pJSJRQLD0ZLJKOhfd17P8xN+RD02x+W3Qh1LTDuQhg4C1bcDk9/GjY9E17D45dD8ygYe16Oxedg1d/C+8aUcF9JUu2b+O70wWjPfi74HTnuLeWrKZ1kEl7+CTz5/9IHOu9UqmC0rm3w+v/mPi/ic2Oppow8Nfi53vvxqHkkTHg7dG2GV3+277xIHZxwaxDSLEmSJEmSJEmSpIrRHt+asj0sQKzahQW+xemmM7mdpkhzmSuSJEmSJEmSpNyEn0ksSZIkVbvpnyjuepueLe56PSEsYKDP5D1D0QAiEeg3Lbf1K+ET5LINEnrtf4LQhRe+CS/9CNoWl7auapUulMLwB5VSNDxIIF4/ILwvkaJx+Akw5/tw8FeDUDSAUWfAvKfhzKfT13H/+dCxOnO9+dq2MHV741AYOLN0+0qSal+/aTDnR+nH/ONCePwK2PxieWpKZ9HV8MRHsgtFg9IFo21ZAPHtuc2JNUOfiaWpR1LPaBgIR16z5+veIUfBm16E2d+Cuf8FR/8OorsFOg86FM5bljbkWZIkSZIkSZIkST2jLb4tZXtYgFi1a00T+NYecltIkiRJkiRJUiUxGE2SJEm1a+xbgCIGdS38BSy9oXjr9YRkPHV7NCTgavylpaulVMZfkv3Yl38MT38anvgo3DoL1j5QurqqVaIrvC9aX7461PtEw8ME4tHwg9G6UwWjpTPwYBh3UfoxK2/PcdEsJbpge0jo2pHXlGZPSVLvMu0jMPWK9GNe/jHcNhuW/rk8NaWy8Bp4+N25zelcD8lcf/FnYcuC3OeMPgfq/DRtqeZMuATOXQxH/xZOfQBOuQca+u/qH/9WOOc1OOo6OPluOPUf0Dy8x8qVJEmSJEmSJElSuLAwsJY0x6JVs9ZoeDBaW3xrGSuRJEmSJEmSpPwYjCZJkqTaNWQuHPUbaByyZ3tda/6BTo9fVpqT78sl0Z26PRISjHbAZ2DKZbsCigYcHLSlMuXywusrhvFvheEn5T6vaxM89Yni11PtkiH3GQi/30jFEAsPRktEW0P74vk8RB/xi/T9HSvyWDSNba/DvefAbxuAZOoxLaOLu6ckqfcanyEAFCDRCfdfAPHO0tezt64tQVBxrpJxuO88WP9Y4TWsexgefDv8/XR46J9yn3/4/xReg6TK1DIqeBwdelTq95JaRsOEi2H4CWlfw0iSJEmSJEmSJKnndCW66ExuT9nXJxYeIFbNmmPhx9iFhcRJkiRJkiRJUiUxGE2SJEm1bcIlcMFqOG8pXBIP/n3LZpj3PMSa9xzbPBrOfAaGHRe+XsdK2PRcaWsupbCQq0gsdXs0Bof9BC7cBOevhHlPwwH/GtxWu4s1w+T3F7fWQhzz+/zmrXsoCCzaXTUH4RVDoiu8L9+AQSkb0YbQrni0Obwvnx/ZuhY44bbw/u4iHgjWsRJungzLb04/bu/HWUmS8jXkyOCSjTuPL20tqSz/SxBSnNfcm4Oa1z6U//4rboM7j4PXfwOr/pr7/KlXQH1tfoK4JEmSJEmSJEmSJNWC9sTW0L6WGg1Gi0ViNEdbUva1pbk9JEmSJEmSJKlSGIwmSZKk2heJQsuYXf9GY9BvCpz2IIw8A1r3g/GXwCn3wMCZ+wa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ec0p3wdlzr8XSgefxROB+5b5+XFu4FTduOR+HVB2Bo/0nodLNZUmUD1TX5hWuapS5yrNcGyJnYjAaERERERERmbLaGTNo8znSiCI85L7XBH70aXvzymeyzr8yQ64qGNCgJza86XHGw9RsUgajJXnG41c8q11rI4jHyQyL65Fqu3OimEn4kFsHGhTP6qNxoDcI1Fl/yVnR85i8PMqp4YFhi8Fobf2ZrQelz5oOe+kPbf05CkYzWffMQtOIiCh/Jdu/MxiNyBpVMFpFKdAv6VsRLOJgNNVvNS4WF3C78rPzFREREREREREREZFKWIwoy0r00izWhAqd3ysPg/gotApb9A3yafI4EEoV5tMbDeDBbbemPF8X3JhXthAfr/wkg2Icbjg+iLcHX0VbuBUGzBubhuLqBoOqdYmoGPg98vW/PbxpWvtSu2rd9fhYxYGYU7rLhOFBxbbrsxGmYRaiFjSGUIGdwWhCCHwQXI61oZXKMMVMGjVCWBdahaH4QFrnW+2egd19+2BJ+b5Y4tsHVW4Hvlma8ppXL8Ex/u/i3o4bpeV/6rwDc0t3VU6vIx3BaOrjeWMehwEXKl3TcVjN57FPxUF4ovt+vD7w76TTvD74b6wYXoaj/Sfi0OrPQU9DoF4hyvWxLJ08mhfzS3fDAVX/BbfmyXV1yKYuRdBuPt+HIEo3BqMRERERERGRKavBaAPqdklTBMPqmSYLuCo0ZuFPkwX1clQmvuVjoAciEobmLZ5GJaoAMD3J85f6Sg2ytzne+6rA3SeKvH+AY3U7DaX3RVgZFTULRnMBM01eYNMxwGA0O+ImbZ16gtmrhx1m4VSJ2hmMljdaOu2Nn6tla7ZvsrpeEhFRfjHb9wNj59hxQ8CV7KKEqMipLlsrSuTBaPl0/Zpuya7xg2Gg2peduhARERERERERERFlS9hQv42GwWiUTn5Ps3T4YLwfiMsbpDR45dPkA1X4hYDA0oHnpzXvVwaAV/v/idNmX4YyFx9eOFFftBu/3XIxuqLt05qPVytBlas2TbUiyj+qYMCwGJ32vtSuf/Q8iu83n4X9Kw/dMSwUH5KO69PVYWdTxnWpG15PDk18pOv3eKn/acvzdqoSrRSLfHthSfk+2N23L5q8s/O+TwE53wGVh+HFvqewcbRlSllvLIDe4YBy2nQEXJkFnTZ4Z017/pQZle5qnNh0Gg6tOhJ/6rwD7ZFNpuOHjGH8sfN2LB34N05o/NGUQE0CHu66Gy/3P5PraqTNy3gGywZfxI9n/RIeneFo+SQQ3SYdzmBqop2KrLs5ERERERER2WU1cKl32OKIADoH1WWuInuWlKyTfaJBvWrqwO7pNVbIN6r1MVkGgVlI1ivrUq+PU1jdToPhzNYjnVTbhlsHNE1Do2RzGNeR3heAFbwRk8CBbhv79myKWNx3Mhgtf6y1GYzW1p+bddMs/CzMYDQiooIjhLAUfJlP59lETlOp6Ms2POrMa5FsSPbNizk0joiIiIiIiIiIiAqXKhhNgwaP5s1ybaiQpdK5WBUulg/qPI3QkLmGua2j6/DG4AsZmz9Nz4t9T007FA0A6j1NDAuioub3NOW6CjvEEcNjXX+AIXY2pA1OCi4bV24SdjZZiVYKF9zSsmBC8FpHeEvehqJp0DG/dBG+OOM4nDHnSly/6AH8ZPYvcXjtVzGzZA73c5QVmqbhuIYfpDStDte0P9/svLYxj8OAi8Wuvt1x/vyb8HX//6JESx4g3jq6FtduOhsPd96NkXgwCzXMD+3hzQUVijZuTWgFVgy/ketqkE2BSId0uJPOP4lyjcFoREREREREZMowrI0XkL9oSMo0GK3IrlRjFn9fABjSJQ8nu+U3wApVqsFoC+rUZZc+aWMhOJTVYLR86jyt2jbc25/ned0a6hXP6zsGircDfSpGTcI+uuVtJXLOSkAJAASGuC7kix6bIXy9OXo2axbKx2A0IqLCE7MYxjok76dDRAmE4nSvokQ+PJhH16/pluxeXDH/NkRERERERERERFS4VMFoJXopQyoorRpSCDlrSCFMzSk8uge17vqMfsaq4DsZnT+lblXw7bTMx+9lp3wqbjXuOkcFtfbGAmgPb97xd8iQd+TwuSotz1PTNGWQWmLw2od5ts+v9zTisOov4IfN5+D6hffjnHnX42v+b2M3315wa55cV4+K1Pyy3XBg1WdsTaNBh1uThxfaUeupV4YgNngYjJYPXJoLR8z4Gi5ZcBsOqDws6fgCBl7qfxqXbTwVbw2+DKFqxFVECvn65YPg8lxXgWzqiwWkw+sZjEa0Q5F1NyciIiIiIiK7rAYu2QnP6TIJUSu2UBGrHe0BYEiXPJzs2pq+yuSBVIPRjtlPPcJLa4FL8jwczep2GjOAaCw/HmREFduGJ+FFRzOr5eN0DKS/PoVsxKRTfY9Dg9GsHit6+GKjvGH3+J+rABrVvgmwHthHRET5wywQMxGD0YiSU12JKoPRwhmriuMlu8Yv5t+GiIiIiIiIiIiICpcyGE0rzXJNqNBVuqsxp2QXy+NXuKoxp9T6+E60R/n+GZ1/IFJcL/jNF4YwEIhuS8u8Mr0OETmdrunYvXzfXFdjgq5o+45/JwaXJVIFnan4FOOHjJ3z74o6e59fppdjv4pDcELjj3H5gjtw+S534n+afoz9Kg9Vfj+iXPjv+u/AqykazUjMK10Ijz79gEaX5sKS8n2mDHdrbiz2fWza86fsqfHU4QfNZ+O02ZdaCrUbjPfjDx2/xm+3XoyO8JYs1NC5Cvn6JRBJz/k/ZUdcxDFihKRl1e7aLNeGyLkYjEZERERERESm4hbzogI2wnM6B9U9PIstxMYsYGWyQVkwWtv69FUmDyiD0ZLc4Zhfr+FrU5/f7HDFUwLvbs6PwDAZw0auW8gkBMtJVPsed8KybqqSj2MnqJGA0ai6zKnBaFYDqIrtmJLPzNZDmVwF0Jite239QDiav8cSIiKayuo5R76cYxPlkuplo6pgtOEiDv9KdkYZ5D6HiIiIiIiIiIiIClBYKILR9LIs14SKwdH+k+DRkgdLaNBxjP8kuDVPFmqVOZ+bcTRq3HUZm39PtAtxwTcKOk1/rAcxYbNRlsT80kX4RNWn0lAjovx2VN23UC5ry58jnZG2Hf8OKYLRfLq9ILByl/z7BeNDO/7ttDAZF9xYVLYnvlr/bZwz9zpcv/B+/HDWufhkzRdQ723KdfWIlGo99fjuzNOhW4j68GolONp/Yto++8t1x6NML58w7Et130KFW9E5gxxt9/J9ceH83+Kr9f9j6RpnbWglrm49E38LPKAMKC903WkKD3aiQv5uhWgkru7wpDovIypG7lxXgIiIiIiIiJxNFUQ1WV8IiMUF3C4t6bidg+qy1m6gPyRQ40s+n0IQsxFoNSx5mCq2rENx/FJjVAFguoUf4dKv6XhyhfoH/3+vC+w3Nz9/TavbKTAW2lDty1xd0kX1nbSERVRfqUHWZdypYV5ONWrSJq0n6LyQJyEEIhZDJXuGx8bXtPzctotJ2GbbyJwFoyVZ94693cBTp+lc54iICoTVIOuR6bclJyp4ymC0Uvl1XbCIg9GShZ8X829DREREREREREREhUvVIbtEL81yTagY7F6+L86ddz3eGVqKQETecbzGMwP7VByEXcqWZLl26ef3zsS5867HW4P/wdZwK4TqwU0SYTGC94ffnDLcQBy90QD83pnTrSqlUcAkFOHjlZ+EliSIxa27Mb90NxxU9Rl4dcXbjoiKyNzSXXHuvOuxfOhVbItsUT4DT7cNo6vRE+2aMjwxGC0xuCyR3UANVZBaYvCaat8yv3QR/J5mW583HdXuWizy7YVFvj1RyiBdylP7VR6KX8y7Du8PL0N3ZOp2DgBNJbOwb8UhmFkyJ22fO79sEc6bdwOWD72KYHwIe5Tvhz3K90vb/Cn7PLoHX6r7Jj5R+Sn8peturAq+bTp+HDH8s/eveGvwPziu4f+wT+VBWaqpMwSi8pDPbB/LpmM4PoDVofemDB+KD2DUGOGxMU8EDfk5HAD4XPYCbokKGYPRiIiIiIiIyJTVwCUhxsLR/Baen3Wp79sgZgDPfiBwwoH5HSjyziaBsx8xsHwTUOMDjt1fwzXHaCjzTvxedoLRBmUPJ7esm2ZN84tqfbQSjLZbg3n57S8J/OZb9uvkBHEb61Eokrl6pJOhaDGQuKzrFPd5u4edF+blVEIIjJisE90ODJmLG+pQhcnCsbF1vpztshxPFYzmcclDaYZGc7OdJwvIefYD4In3gGPYPoCIqCBYDWPNl3NsolxSnb2pztWDRbxdJTvTNbuGIyIiIiIiIiIiIspXYWNEOpzBaJQpzSXz0FwyL9fVyJpq9wwcOePoac0jbIzizHXHS8sC0W0MRnOYQEQe+FDhqsL/Nv88y7UhKgz13iZ8se4bWf3Mx7ruw/N9T0wZ3hVpBwDERBRhIQ+YtRuooRo/ZAzv+KzeaEA6zhfrjsPHKg609XlEBMwrXYh5pQuz/rl+70x8qe64rH8uZVa9twk/mfVLrBhehke6fo++WLfp+L2xAO5svwZ7l38CxzX8H+q9jVmqae6MHcvkv0s+Hcu6I524eOMpirJtmF26IMs1olQkhs9OZjfglqiQmce6ExERERERUdGzGowGAL1Ba+N1DZqX/+Z5kfIb6ZygZZvAx68y8NJaYDgMbO0Dbv63wMkPTP1OyQJWEvW4Zkwd2NGacj3z0XSC0XwlGubVqcujcSAYzs/1zs52mi+hDapdQOKyrlc8r+9xYJiXU8Xi5uuPE4PRVAFaKlwf8kM4Kh+uClwdkrfjybiIhfXv0idtpFUSEZGjWdnvAwwpIrJCdY1XqejPNjACxO1c7BaQZF87FCnO34WIiIiIiIiIiIgKW9iQNwRgMBqRc5Topah21UrLVCFclDuBqHyZ+D0MsCPKJ43eWdLhnZE2CCEQiqs7cJTbDEZTjR/cHtrRE+2CgLx9JPctRETOoGka9q08GBcvuBWfn/F16HAlnWZl8C1c0Xoanu15BFFD0ai/QPRGA8pjWb2nKcu1SV2tpx4uuKVlgei2LNeGUhWMD0mH63ChROP9MKJxDEYjIiIiIiIiU3b6oAbD1sZ75G3zmb7VCux1qYF1nfnV0dMwBM551MDuF8tvkj60TGB918TvFLMRjNbhltxk7Q9AxGymBOWx6QSjAcB/72s+4tubbFbIIQoxGM3KslYFozkxzMupRpI8t3JiqJjVgJJx25KEcZIzqALvVNu5k4PRVrYhrwNeiYhoJ6vnHQwpIkrdzGr58LgBBOTtfgqekSRnN1+u64mIiIiIiIiIiIjsYDAaUX7we+XBN6oQLsodVVidahkSkTOpgtFGjBCG4gPKQA0AKNcVb6ZV8OnyBpuh7cFoqv2KBg31nkZbn0VERJlVopfiaP+JuHD+b7CobK+k40dFBH/vfghXtZ6ONcEVWahhbpgFOufTscyluVDnaZCWMbQ6f4yHz05W7qqAplnsKElUBBiMRkRERERERKaSdcZMFEzSMVMIgT8uszbD1R3A4osMrO7Inw7m974mcMNz5vV94r1JwWg2ft8Ot6QxghBAX6f1meS5uOL30i3e4TjnCxp2N2nT8ZVbbCwQBymqYLSEZV1XLh+HwWjWjSYJRhscBSIxZ+2HIzYCJQHgxRZn1Z8mWrlV4J5XDbT1y8uVwWgWw1jTLWpx/XNiqCAREdln9bwjWdgsEQGqs/LZNepp2hXniIUu2RVMvlzXExEREREREREREdmhDEbTyrJcEyIy4/fIG2B2sfO946iWSYNiGRKRMzV6m5VlXZE2hIygstznUjTAVCh3yYPUxsPXAtFt0vJadz08utfWZxERUXbMLJmDR3HcowAAIABJREFUM+Zcge/NPBNVLpOGWtt1Rdtx89ZLcG/7jeiP9WahhtmlOpbVuOvg1UuyXJvpqfc2SYd3K74jOU/IkHc68SnOyYiKFYPRiIiIiIiIyJSdwKWgSUjJSETgm3ca+M499gJq9rzEwO9eyo+wqvtfT/7dXlwzcRyrASsA0C4LRgOA7uJpUKIMy7L4IoTmGg1vnK++HTIcBsLR/AtRKshgNFUIXsKyrq+QL/i+kPPCvJwqWTAaAPSq20zkRCRmb/xX13FdcKornjKwz+UGfni/ehn5Fdv54EimamXOakBO+0Bm60FERNlh9XotX86xiXJJKE75/JUa3IrLdFV4bqFLdo3PfQ4REREREREREREVorCQNwQo0UuzXBMiMuNXdL5XBQxQbgghEIjK2xarliEROVOFqxpluvxN0p2RdoS2h5ZN5tG8tgNeVEFq46EdAUXgIvcrRETOpmkaDqz6NC5ecCs+XXMUNAsRM8uHXsHlG0/FC31/R1zYfLO9g6lCw+o9+Xcs8yvqzGuz/BGKy4PRynV74bZEhY7BaERERERERGTKTuDSSfca+Pdq+QQPLxf46zup1eGnfxRTAsWcRgiBlW3Jx1s96f5izFYwmuKNTz0MRrMajAYAlaUavry3uvyjgL06ZYoQAg++YeDEewz84lEDH7artwFViJhMvnSgtrKsZ1arp1/vkOXodCMWgtG65ffacyZsMxgtIG/zQTn27maBy/6e/Nhep3imMyR/UXTGWQ1dbOvLcEWIiCgrrAayWjmnIiI5l66+tmvvd/a9oExRhciN4z6HiIiIiIiIiIiIClHYkDcE8DIYjchRGrzydqw90W0wCigwId8NxPsQFfLGon6P4iXNRORImqah0TtLWtYZaUNQEajhU4SpmSl3VUqHB+PDMIShDlzkfoWIKC/4XBX4VuPJOHfedZhXuijp+KPGCB7tuge/2vRzbBhZk4UaZl5XAYV8qsLcVOFv5DzK8zhFWC1RsWIwGhEREREREZmK2whc6h4GPv8bA/e+OnWiR9+eXmfWI24y8FarczvEdg4CA/KXVk7Q2gOMRnd+j5iN37fdrXho2FM8Ny1VAWB2gtEA4KzPqW+JrHZIztxpfxI46V6Bh5YJ3PicwMHXGHhjg3wbsBNgGIw4dztKZCUYbVe/etmvKZ7NYlpG8zAYzWpAybihcGbqQdPzh6XC0r7LL29ng5EoMDiS/f1Z1GL7zbYiDfEgIio0Vs878iV8mChXRJKkr1m18uEdAxmoTB5Idp7MfQ4REREREREREREVooghb+BRwmA0IkdRhd/ERAx9sZ4s14ZUAorABwDwexlgRJRvVKGUnZE2hOLytwf7FCFnZny6PIRDwEDYGEEgIm+czf0KEVF+mVu6EL+Yey1OaPyxct+faGu4FTdsPg8PbrsVw7HBLNQwc1ShYX5FyJiTqcLceqPdiAm+eTMfhAz5eZwqrJaoWDEYjYiIiIiIiEzZCVwCACGAi58UiMUnTvj0yunX5cy/2EgRS4PBEYFfPmFg1wviOPCquDTwbZzVECYhgLWdO/+2GrACAMOuSgxJbjqLbockeWWBlbAsKz67RD3BPSbLOVu29Ar87qWJX3Y4DBx6rSHtUG5nOx2Sv1zVcVTfSUtYdCUeDbv45eOt2cZQIitGLDzv6HFaMJrNF4vmyzpfbH7/irVtdLdGddmKrWmqjA1WA3LaizTEg4io0Fg9zx5hSBGRKbNcNA2AX9G+rt9CAH0hYjAaERERERERERERFaOwIb8pzGA0ImepNwkMMAvjouwKROXLwqdXsJM9UR5q9M6SDu+KtiNoyBv5lruSB93YmWYoPoDuaKe0TBWaSUREzqVrLnyy5gu4ZMGtOLjqcEvTLB14HpdtPBWv9f8Lhsh9vyu7DGEoj2X1eXgsUx1/BQz0RLuyXBtKRTAuP4/zucqzXBMiZ2MwGhEREREREZmyG4wGAO39wGqb7Rv+fHLyZKul68cCo7LltD8JXP2MwMZuYPkm4P/uF7jtRfnN243d1uu1pXfnvycHyCXT7pbcuOwpnsYkqvXRlcIdjqP3lQ//5ypgXWduQ7We+1D9+Tf+q7iD0SaH4C1WhCZtCKS3PoUqbCEYrXvYWSFzVuqcaLBIwxScLBgWGLW4HHdr1FDjk5e9szn766bVYL6N3AcRERUEq+fZDCkiMme2KWkaUOOT3xMaCGWmPk5nFiQHcJ9DREREREREREREhSks5I2aSjQGoxE5SZnLh0pXtbQsELX4hmHKOFVInd+bf4EPRKQORgtEtmEo1i8t86UUjKYOTtwa3ggD8gaUfq86NJOIiJyt0l2Dk2b+DGfNuRrN3rlJxw8aQ3io8zbcuPl8bBndkIUapk9/rAcxIe/E0JCHx7I6T4OyrDvCa7N8EFIEo5XrDLMmSsRgNCIiIiIiIjKVSjAaAKzetnPCkYj5TJqqgGP21fD5PZLPd955BroGMx+EsmKLwANvTP2c370k/+w2+TNFqYGRnfOI2XxJRpu7eerAbgajTQ7LsmLJTPVEiy8yYKS68qfB8x+qyy54TEBM6iVtp6qD+RKMptg2Ji/ruXXy5djR76wwL6eysu70BDNfDzusBlONGw5jyjZDudVu45hZ5gH2VzxjfXdzeupjR9Ti+teS44BNIiJKD6unECM2g1uJaCdNA6rK5GX9oeI8p0p2nTbCYDQiIiIiIiIiIiIqQGFDEYymK24iE1HO+D3ycC1VGBdlXyAqXxYNimVHRM7W6JX0HwBgII7No+ulZakEapTqPmiQt8veOLJWOV29J//CZIiIaKKFvj1w/vyb8HX/9y0FlG8cbcG1m87GI12/x0g8P95+2W0S5JyPxzKvXoIad520jKHV+SEYH5IOTyXglqiQMRiNiIiIiIiITKnCiZJZnfBM/YqnzXt03nKCDo9bw2+P19Fg4RncnpfsDK1atkHgj8sMvLkxvZ1lf/28fH6rO4BQeGqZnZCXgZGd/7YasDKuwy1plNBTPDcslcFoKdzh2D3Jfeu7XsldB+wKk+cIMQMYSmgHKISwHNgATJzWyVRfaXIw2qwa+Xh2wgqLmZVgtG75S0hyJhKzN74hgBCDAxzFzjGzxA3sN1fe0OadTdnfT1td/1q2MZCPiLKjPzR2PfTEuwI9w9zvpJvVX5QhRUTmzE6LNAA1PnlZ/4h8eKFLdhqZ7AUERERERERERERERPlIHYyWvEM2EWWX36sIRlOEcVH2BSLydsWqZUdEzub3zFQGlm0Nt0qHpxKooWs6fLp8uk2j66TDq90zeL5GRFQgXJobR8z4b1y84FbsX3lo0vEFDLzY9xQu33gqlg++4vi286pz5HJXZd4GUfkVgW5mIXDkHCFD3lmrPE/XR6JMYTAaERERERERmbISmiOztnPnv59aoZ7J8gt1fP2AsQd1i5s0rLtKx6IG83n3BIH9rzTw7d8bOORaA9+5R+Dgawz88H4jbTdSn1ulnk9PcOqw9n7rn5vYsTdmM3iuXRaM1r4BImYzKShPqYL6JodlWbH7TPOJHnojdzflh5OEl/UmrIN2V/mhPOlYbjUETxWM1j6Q3voUKivrT0+eB6MB+RMIWCzaB6zvuEo8wP5z5WWrtwGRWHb31RGLgaZ9ISAgf4EPEVHaLG8VmHfe2PXQsbcb2OdyIyehkYXM6vVwiCFFRKZMg9E0oKZMXtafHy8TTbtk+x4GPxMREREREREREVGhiYs4okJ+85NBG0TOo+p8H4gwGM0JhBDoirRLy1TLjoiczauXoNZdLy0zIG/UmGqghioYplURjOb3MHCRiKjQ1Hrq8X/N5+Cnsy+xtJ8fiPfh3o4bcfPWS9AZactCDVMTUISF5fM5cr1XXvcuXps5niEMBOPyzlo+V2WWa0PkbAxGIyIiIiIiIlOpBqNt6d054ahJiM3+8yaGU1WWavjgUh3HHWAeWvX+VuBPb06s3D2vCrhOGQtMu/NlA/FUKw8gbFLnbsl9p/Z+6/MeSAimiloMWBnXIbupPDwArFxqb0bTIAwD4sW/wrj2FBh3/BKiJ3tvklCGZaUQjLbHTMDrVpe/th45e2NJstCgvoTO4XZX86HR/AhtsBqC11wjX/iBISAczY/vmktW1p+BEWf9jmb7Z5VBBqM5SpuNY2aJG9h7lnw7jxvAtiyHIMZtBJq2dCYfh4hoOk68x5gQ/tneD5z+Z5vJy2TK6uUAQ1iJzCXblGp88uH9eRLsnW4MRiMiIiIiIiIiIqJiEzHUD1tKGYxG5Dh+rzwcIRDdBkPwmXWuDcUHEBby/apq2RGR8zV6Z9kaP9VADVWgWkxEpcP9ikAWIiLKf3uU74dfzv8tvlJ3AtyaJ+n4LaH3ceXG0/G3wIOIGOEs1NCe7qg8LKw+j4PRVKFu3YoQOHKOsDECAfn1c7meWsAtUaFiMBoRERERERGZSjVbLDH0ZEDRkXWvZvlwj1vDX07RceeJKaRdYSww7ccPCRx3h5FysJVZ8IksGG1zr/V5J/4eMZvBaO3e2dLhYunT9mY0DeK6H0Nc/D/A0/cBD10P8d39ITatycpnpzMYzVei4SefMZ/wyRX255sOydanaQWjOe/5gpTVZT2rRj2PbYPpq0+hsrL+DDosjCASl1fabXKnk2ElzmInTLTEbb6dd2Q5GM3OPnfNNmeFChJRYWnvF9IAxtfWA+u7uP9JF6v7/f5Q8nGIipnZrRlNA2rK5NfmxbptJbuVxWA0IiIiIiIiIiIiKjRhk2C0Er0sizUhIiv8shf8AoiKCAZjfVmuDU0WiMgDHwD1siMi52vwKjpeKKQaqGE3UI37FSKiwubRvTiq/lu4aP7N2LN8/6TjxxHDP3sfxeUbf4r3h9/MQg2tC0TkYWH5HB5crzgOd0c7GVrtcCFD0jl1u1QDbokKFYPRiIiIiIiIyJSqI/g1x2r4hsk9zfZ+7AglUwWjXX2s+WXpDz+p4xPzLVRS4Yn3ANcpBhZdGMc5jxoYjVrr1R6LCwyahOj0DE+cT19QoGvIer0SQ4ZiNu8zvlB5hLzg1b+nHAJnh3j+L2OBaIkGeiD+9OuMfzaQ3mA0ALj2WA0/OEw98TG/M3DDcwaMVBMCUxAKi+TBaMGd/zYL8ZNxWsiVitVl3WwSmNRmI3ypWFkKRnNYqJhqna/xqafJl/W+WHTY2DZLPWPLtlTxkqn2bAej2djntvBFS0SUQWbnOW9sZDBauli9xOrnuQaRKbNNSYP6XH5wFFm9HneKZF+ZwWhERERERERERERUaMLCJBhNK81iTYjIigaT4ICuqDqUi7IjoFgGpboPFa6qLNeGiNKl0TvL1vjlKQZq2A1UMzsmEBFR4fB7Z+Insy7Cyc3nocZdl3T83lgAd7RdjTvarkZPVPIW4CwTQiAQVQSjeZqyXJv08XvldY+JKAZiSTqmUU4F4+pgtHJXagG3RIWKwWhERERERERkShVAU18BPPwjF147V35pGY4BPcPAaFQgEpPPo8bCyxyfO2P6l67rA8ANzwn817UGVrULvL9V4MN2gVhc3tO0NygdvEP3pHtPLTbv0Q6Edn5uNG5v2l5RiYCrfmrB1vXA5hZ7M7NIDPVBrH0PYsWrEJedJB/p9X9k5LMnU4ZlpbiaeN0a7j5Jh9/k2e85jwpc8XT2OmKv60oewNCXsA7Z7SM+5LCQKxWrwWjVZYDPKx+3ncFoSeVjMJqqzl43UFEiL3uztfjCFJysrd/68vC4AE3T0FwtL2+3Ma90sPNpW/gskYgyqEwRGAkA63LfhqRgWD3X7g9lth5EhUzT1MFoQiS/R1OIkt0TGIlmpx5ERERERERERERE2RI21I1TvLqiMQgR5YzPVYFyXd7oMhBhMFquqYLR/J4maFqKbyEmopyzG4zmSzFQw+50fg+D0YiIioWmadi38mBcvOBWfG7GMdDhSjrN+8Nv4vKNp+EfPY8iJnLX6Gk4PohRQ97Qsz6fg9FM6q4KgiNnCCmC0TRoKNMVDSqJihSD0YiIiIiIiMhUsnCiuTPU07b1AwMj6vJqC8Fo1T4NHTek5/L13S3A3pca2PdyA3tdaqD+TAMPvD41+a1ryHw+gUn3nt7fai+UpT/hN4nZDEYDgJtnnCovWPac/ZmZEMFBGJd8B+KoJogfHATx0yPUI/dugxjKfAqVoQjqmxyWZddndjOfwWV/F/ioKzvhO2u2Jf+cvoT78XaD0ZwWcqWi+l6T2+VomobmGvm4dsKXipVqm0o0aLIfzwVVnV0asM9sedmzK7kuOInV0MLyEuxojDdTEYzWMZCmSllkZ587NMr1jogyx2x/9FFX9upR6JKFE43rC429UZCI5Mw2Dw1Ak8mL6duzfL7nBMnOOUOR7NSDiIiIiIiIiIiIKFvMgtFK9NIs1oSIrPJ75R3w2fk+9wIR+TLwexleRJTPGrzNtsYvTzEYrdxl8rZxiXwOkyEiotSU6mU4xv9dXDD/11hYtmfS8aMigie7H8RVrWeiJfh+Fmo4VbfJdUo+nycztDp/BePyzqtlejl0LXnoIFExYTAaERERERERmUoWRNVYpQ6lShaMVmMxwL6xSsO7F6X/EnZwFPjuHwT+9z4D5z1m4Du/N3DNswY+dpl5UtCmnol/P2MzcCeQcO8qpvio2nivcvrH6k+QDhcbVtmqh4oQAuK9VyC+6AdeeMT6hJvXpuXzzSQL6kvVtV9PPoMDrrSQIJUGk9cvmd7gzn9bCbZKlC+hDXZC8GYpgtGshi8VMyshT04L01PuB3Tgq/vIt+WVbfmx3mfK4IjAI8sFnnxPoDeY299BCGE53KLcu/PfzTXyZZvt7dzOPnfIYdsOERUWs2P4tsHiPealm9VAzLgBBMOZrQtRPjMNRtPM7y0V43Udg9GIiIiIiIiIiIio2IQNeSNDt+aBS3NnuTZEZIXfIw8PYOf73OuKypeBapkRUX6oddfDo3mTj7idT08tGM1nI1Ct0lWNMpfFDiFERFRwmkvm4sw5V+K7Taej0qV4C3qCzshW/HbrxfhD+00YiKn7zGWCKsC5RCtFlUvRISlP1CtCq83C4Cj3gsawdHiq4bZEhYzBaERERERERGRK1RnTtf2K0u3S0KS4f7m1T5iGPFWXWa/HPnM0rL48M5ex9y0VuO4fAn98U+DCx5P3fG/ZtnMcIQReapGPN69OPrytf2dATzQuH2ff0RXqz8dcbHM1Ti3YpKiIDSIagTjvWIjTjrQ/8aY10/78ZDIVjLagXsMFR5nPZGgUWNOR+ZCJNgudvvtCO/9tNaxh3Gg0P0Ib7CxrpwQm5SMrq89oFIjEnBOwEjcJzTt4F/m60BcCuuXPDQreu5sFFpxv4Ft3GTj6d2Phn+9syt3y7A2OrVNWVJTs/PdMxfPGjoHsfhc7+1ynhQoSUWFRHQ+BiSG6ND12jjL9JqHgRMUu2bbkdmlorJKXtfU751okW5JlOocixR38TERERERERERERIUnbMgfsJfopVmuCRFZ5fcqgtEUoVyUHUIIBCLt0rIGxTIjovygazoavM3WxoWOUj21wLJyG4FqDFwkIiJN03BQ9WdxyYLb8KmaL0FD8s5dbw39B5dt/Cle7HsKcaHoVJdmqgDnem8TNG2aHdJyzO+RB6OpwuDIGULxIenwMgajEU3BYDQiIiIiIiIyZSWcaLYirKStH7jwcXmPfU2bGHhixeImDf88Q0ddub3p0q2lc2cH1GBYHXzyg8PkN0dDEWBwe6f5mCLQwCNieGbzV5V1aPNIHmxuWmO7Y+yyDQKn/tHAOXd1YMV1N0AcXgksfcbWPMaJzdMPZkvGLBBpui76soZvH2Q+oz0uMfBRl73f+NV1Aqf/2cDP/mzglXXJp2230Om7fxrBaAAQyIOAKNXX0iV3s5oV+6CHlgl2Fk/CMAlVSTTooKAPs+PSEvkzHQDAmiJ9rvPde40JYYrt/cDP/mxxwWfA2k7r45YnnCc0K0JYOwamVx+77OxzhxiMRkQZZLY/Stzv0/RYPVcCJp6jE9FEZpdl4+3KVNd1xRh4beWc02rYMBEREREREREREVE+CAtFMJrGYDQip1J2vo90sM1eDgWNIYwY8ofXDDAiyn8Nsv4DEj5XRcohLz5XpeVxVSGZRERUfHyuChzfeArOmXc95pYuTDr+qBHCI12/x682nY2NI5nvi6YKCatXXNfkE79X/h26I0XagSZPBOPyjn12QmqJigWD0YiIiIiIiMiUlWC0WbXycdr6gfUBeVlVKaCnkGb1uT00tF6r46WzdTzxEx0fXKojcruOlZeO/X3jcZl/U0N/CAhsD+bvNgmYOniBui5t2zv2xhQvt3CLGI4MvqCcPuCqnzpwqA9oXa2u0CR3v2LgkGsN3P6SwA3LG3Dw2h/jqYovWZ5+ik1rUp/WIuX6mIY7HCUeDff/r4anTzOf2QFXGnh3s7WGOw8tM/CZGwzc8oLArS8IfPYGA/e/bp6u0Gah03dfcOfnpxKM1jVof5psU4VQyHYbsxQd6AHgnL+ykZUZq+uPKgAyF1R1dumAvxKoUbzkbn2g+NaFjn6BDyQvAF26HtjYnZvfo6XT+ucmbu8zFcFo2Q7KYDAaETmFWWBXXzB79Sh0do6WvfzdiZTMtqXxUz7VdV17loNwncBKX6ERBqMRERERERERERFRAQkbimA0vSzLNSEiq1RhOGExiqF4ET7gcYhApENZxgAjovzX6J1laTzfNAI1yl3Wp1WFZBIRUfGaV7oQ58z9FY5v/BHK9PKk428Nb8T1m8/FQ9tuw3A8cx2dVCFhhXAsU4W7BaIMrXaykCoYzUZILVGxYDAaERERERERmbISRNVcIw8Aa+8TUL1saGAk9TqVl2j41G4avravhj2aNbhdGvZsHvv7zM/pePKnOhY3pj5/K1o6x/5vFoy292x12XiQS1QVjIYYdAg0xDql5d0ev3zCV59Sf2iCtj6BUx6YuHDDeiku8V9iq/P/BDZC2VKlDupLTyCepmn40t4aPre7epyh0bFwtMCQ+S8Viwtc8JiYUGdDABc+LhCLy6cVQuCNDcnr2ZfwQr9UgtECJuutU1gJZRzXbBKMduNzAvVnxnHcHXGsap84065BgVP/aGDPS+I47Fdx/OOD4rvpb1h80JEPwWi6NrYNz1GEdRZjSMzmXnXZGxtys76vsfHioVhC6I/qXKN7GIjEsvddbAWjhTNXDyIixekkgLHjNhszpIedn7FjgL85kYrZtjR+OT/T5N5SsbFyzhmKZL4eRERERERERERERNkSNuSNCUv00izXhIis8nvUIVtm4VyUWV2K375EK0WVy6ShJRHlhUZvs6XxphOoYSdUjYGLREQko2sufKrmi7h0wW04uOqzlqZ5beBfuGzjqVg68DwMYfLm4BQFovLz5EI4lqnC3UaMEILGUJZrQ1aplo3PRkgtUbFgMBoRERERERGZMhT3ExODqGYpnpVv6ZsY4JTonC+mJ8hK5isf07D6CheMu1x44/zMXPq2bBvrpaoKRnPpgL8C8CueK27sGZs+pvh9PSIKAPDHuqXlgdmfkA4XLe8oajzRnHPlH7yi9GPocCtu7GoatAfeg3benRjRSrHKuztGtITGZ1vXQ7RvtPT5qbITljUdt307+XrT+HMDQ6Pq3sqrO8a2gcna+oFV7fJp/u9+ax2+exMCnlTbqJlkoW5OYGdZz1J0oB/XGwT++g7w2RsM9AyPzdgwBI75nYHbXxJY3QEsXQ8cdbOBl1uc/9ukk9WQp8FphFmmW7J1o9YnL1cdjwqZ2fLdYhKalkmt8sOaVHxCMJp6vE09qdfHLjv73EgMCEeLa59CRNmTbH80nSBq2slqiCwwdp5PRPaNX82p7i21D2StKo7BYDQiIiIiIiIiIiIqNmFD/sY+BqMROVeFqwqluryhlip0gDJPHfjQBC1NLyAmotxp8M6yNN50AjXKbUxrFpJJRERU6a7BSTNPx5lzrsJM79yk4wfjQ3hw2624afMF2DramrZ6jBojGIrLG6GpQsXySb1JuFt3xMZb7SmrQnF5h1Q752JExYLBaERERERERGTKSjiRqvPqqnZA1Y/8Wx/PzgP2AxdoePuXOr6x/8ThlaXAPrPNp/3c7uqyls6x/6sCpuorAF3XsKtfPv267dPH4vJyt4iNzScuT5DpbtxLPuHmFvnwBLe9aJ6i0OJdNHXgl78P7S9rEJu9BD9qOxZ1u3Vgn13fRu3iTpzTcBVicI2N99pTST9/OtRBfen9nIUNGlwW7ppU/8zA3a/IK7WqXd2T+QNJWSwu8Mhya8ELiQFPZh2my0vkwwN58NIPO8FoZoFJibqHgev+OTbjMx8WeH3D1HE+e6OB0SIKMrIcjCZvf5oT8ST7gRpFMFp/EQbEBMPqstYshokl6hiwvn0lhofOrwNUbfPGj8nZYHfvMOSgbYeICkuyY/jPHxEQNkK9SM7OT8hgNCI1s21p/ByvuVpe3iYJHC90VvY9DEYjIiIiIiIiIiKiQsJgNKL8o2maMkSAwWi5E1AEHzC8iKgwNHqbLY03nUANO6FqDSZBLEREROMW+fbEBfNvwjH+76FES36dv2F0Da7ddBYe7boXI/FQ0vGTCUTU1yd+b/4Ho1W7auHRvNKyQJTBaE4VVASj+fTKLNeEyPkYjEZERERERESmkgXQAMCsWvupVPVZDLDfb66Gh3/kgnHXzv8Gbnbh3YtdGLxZx56TnhGe/CkNxl0u/PNMF048WP7dtvaO/b9bfh9qx/db1CCffl3nWC/XqCoYDePBaPLkmu4SxYPNjR9CxGLyMgBDowKn/cm8h+1a724T/tbuXgr9vDugzZyPy58WuHt5OSL6WOJWTPPgprozUbr7EB6sOgHDr/zLdN7TZScsa7qszvKUBwSueMpAJDaxcusD6mk2SMrWB4BhkxCjRP0jgLH9xzALxWhU3A8NKNZbJ1F9L1kwkqoDvcz1/xTNJWnxAAAgAElEQVToHBS45QX1D3fOX4snREQVNjjZ4IhzfhPlfmD7nc4an3zr7Q9mqEIOZhaWsLYzN8u0Q/6yJanE8NAyr4Z5M+TjrdmWve9iNUxw3JDF/ToRkV3J9kd/eE3g8XezU5dCZme/38FgNCIls01pRzBajfw8vnNoLEi8mFjZ90wnGM0wBN7ZJPDQMgMbAsX12xIREREREREREZEzKYPRtLIs14SI7PArAnFU4VyUeapQOtWyIqL84nNVoNKVvNH0dAI1XJobpXryc7ByvdJWiBoRERU3l+bG52YcjYsW3IL9Kg5JOr4BAy/0PYnLW3+KtwdfndaLgrsV4WAuuFHrrk95vk5hFlqt+u6UeyFD3rFvOgG3RIWKwWhERERERERkykoQ1awa+/OtK0+tPulWUarhnV/quPd7Gq45VsNzZ+i44zs7L5ebFd+tfWDsh+kakpfvCEZrlJev6xr7vyp4ziPGws388W5p+ca4Xz4hADxwrbLoT7c+p55uuxbvop1/fOHb0JYcsOPPB99Q30z+3qx7UD36ON5aZSP1xibV+ujKwB2OT++WfJxxlzwp8KnrDPQM76zg+DKWWd469YustvGSRCGAwe3tAU2D0arkw7sV662TqAK7ZCF4JR4NPvkLTqSuedb8ociflomi6Xhv9VsOjGS0Grao1g3X9nWjxicv73dQuFu2BCPq77wmB8+4hBBotxEaE5u0rBcrXsjUksXvYjVMcJyTth0iKiyq64hE1z5rc6dFU9g5e2jrL75zDSKrrLRLU91bEsJeuG4hsLI3CaUYwBuJCZxwt8DHrzJw4j0CCy80eLwgIiIiIiIiIiKinAsLRTDa9pd3EpEz+T3ysK0uRTgXZZ4yGE2xrIgo/zR4FS9XTzDdQA2fnnx6v1fRoJOIiMjEDI8fP5x1Lk6dfbEyzCvRQKwX93TcgFu2XorOSFtKn6kKbq7zNEDXXCnN02nqFcflQITXZk4VisuD0Rg8SzQVg9GIiIiIiIjIlKrDfWIQld1gNJ8X8JVI0o1yxOPW8L1DdZz7RR1H7jGxXjMVL1Ua75S7vkveXbWpamw+8+vk0/cEx/4/OfRlnHt7MNrc6GZp+Stt5RjwzJCWiXuvgAhPTWIR6z/Ak+/E5B+Y4PYZp4z945AvQTvrtzuGD4QENvUknRw/vC+KuFla1zRYCepLl5MOsTfTN1sB/1nGjtCzwJD6N/jXaiAUnli+Zpu936wvNPb/VILRzOrmFHaX9cVfsb68bv63+ffvCQJL11ueXV6zuqkOytuf5oRy3dh+XKpRvKiuL5iZ+jhZ0CQsob0fGMxyWNzQKBCKWB9/8jnIwgb5dt7Wl73vYffwtiGQmXoQEVnZHy3fBLQzrGta7ARiFuO5BpFVZnui8TO8OfJbHADMg8cLkZV9z0g0tXn/4TWBR96euEQueFzgvS08XhAREREREREREVHuhA1VMFpplmtCRHY0eOVhW4FIO4SVN+dQWoXiwwjG5W/NZYARUeFo9M5KOs50AzXKXZVJx2HgIhERTcee5fvjl/Nvxpfrjodb8yQdf01oBa5qPR1/734IEcPeGyWV4cEFdI6sCpkLRLP4BnqyLGKEERXyjjXlevLzMKJiw2A0IiIiIiIiMqUM7kp4KUJFqYZqRRCNTH0ehdfP3PCydHhHv4AQAmsU9wgXb7+nWN3fKi0fGBq7gRWNyRt/uDEWYHZE8EVpeTSu4c09vquoNYDlL0wZZPz9XiwtO0g9zfi8NS9uPq0V2q8eh+ar3FHP+edbSwV4f6AWb2ywNKptyQKR0unr+2sprasHXm3g8XcF+kPqcUajwDuTMu9abN5v7t0evKAKLwSAhip5iFBA/mIJR7G7rE/5VHrT8Z54rzgaZlkN+xicmrWYM6p1fjw0r7ZcXt7voO+QLcEkIWTrsxza9UG7vfE/MX/i36og1vaBlKqTErvBaHZDL4mIrLK6P3p6JfdD02Hn1+szOf8nKnZm/V607efxNT4NfkWbnlMesJFSWACs7OOD4dT277e9KJ/uwTd4vCAiIiIiIiIiIqLcUQej2WiUSERZp+p8P2KEEDTkAV2UOWahBw2e5izWhIgyyUowmpVgMzNWgtX8inBMIiIiqzy6F1+uPx6/nH8z9vDtl3T8mIjh2Z5HcEXrz7ByeLnlz+lWnCfXK65n8pHqu3RHGIzmRKG4ulPfdANuiQoRg9GIiIiIiIjIlCqAxj3pilIVViKTL8FoIhpB0z9vlpYFIxr6hmJY2yWfdsn2e4pVL94vLR8VXowu/ZcyeM4jogCA/UffhT8m/5B3Fhyjrnzr6gl/io/eR+cTj6LfVaueJsH5LzVgePtLNIJhAd9PDQzYCBU66+GJX+y5VQKH3xBH89lxfOq6OB5allqnZlWIk57eTCwAgK9Ew39+oeOQXexP+/XbDbyeJBxucljOn9+y1wm5b3swmlmH6cYq+fCuPGhzpOo4r1rW1T4NVx6dvhXhN88LGHYTkPKQ1a84KG9/mhPK0LzxQAVFm9hiDCsJJQlGC2R5X/DsB/a2qTOOnHiy0aw412jrS7VG9tndLaztzEw9iIjMwnETPfN+4Z/PZJKd/X4xhrASpUPiVdwSRVuz9QEgMFQ8+zMr39TKPZql6wW+8OuxezGNP49j/yviyrDim/5VPL8vEREREREREREROU/YkN/0LNFLs1wTIrLDLBQnwA74WReIdEiHezQvqtzW2g4TkfNZCTos16fXWaPcSjCah8FoRESUHg3emTh19sX4YfM5qHHXJR2/J9qJ29uuxJ1t16AnqujUl0B1bVJIIZ+q7zIQ71OG0VPumAWJTzfglqgQMRiNiIiIiIiIlAxDKDuCF0MwGtavRHPvKmXxy7+6E5GYvGxxkwYhBKpXvaCcvv+C7yMak//ALjE2Yw3AHuHV0nEubPk4BMY6zA7pFRjVSnaUiU0tO//d0Qrx/U/g+NkPKOsyWTgGPPCGwIftAntfalgOXhj3VutYB1wRGsIL7w7ji7818NJaYNsg8OpHwIn3CNy3dGymXYMCW/usdcBNFoiUbktmanjtPBdGf6fj9CPS+yFrEu6tr+0UCCvWJZXxkCdVWBwANCmC0bIdhpSKVJb18Z9I7zI65nepBfjlE6thH0MOehaiqrNr+3FpRrl8PegahHKfW6iCYfPy7uHs/h4fbJV/3uxaoGpSW+ZDdwUOWzhx2Kwa+bLtCQLhaHa+i91gtE09xbXOEVH2WN0fvb3Z/rx7gwKrOwQ2BASEKq22SNj5+sFw8Z1rEFllti1pCad4uzWqr+n+vqJ4ti+z6/xxyYKf39si8OnrDfxr9di9mMAQ8N6W9NSPiIiIiIiIiIiIKN1UnXRLNAajETlZlasW3oR2q4kCUXlIF2VOV0T+hhy/pwm6xm7ERIWi0Tsr6Tg+C8FmptPryQM5GgooTIaIiHJP0zTsV3koLl5wK46sPRq6hRicFcPLcMXG0/Bcz2OIiah0nKgRRV+sW1rm9yje4pmHzL5Ld5Sh1U4TjA8ry3yu8izWhCg/8I4GERERERERKZmFYbkmB6PVWg8kqqvIUIpVum1qwexoG9yKG6RPrlM/NNytEUBPB6qD8oYGADCgVyG6Xh565hY7U7KWRNYq53Hzofdh4a6rUbu4C/MXrsXvak8ZK1i/EgAghMDGb38Oh8x/Ga/6DlPOR+anfxTY61IDrT22JtvhsF8ZCB81G0feXiYt/9GDAgdcEUfT2Qbmnmtg4QVxvLHBvKNztoPRxnndGm76poZbTtBQ4k7PPFu27fwyt7xgv4N3X2hsGrNQjMYq+Q8TigChsLM7laeyrOclfzmMLX9/HxgIOft3mi6roSqDI875HZKtG/MV60HMADamuD/LR63dAlc/Y77cutXPUzKirV8+/AeHaXjpFzq+/18aPrkIuOAoDf84XYfbNXGDbzYJYX3k7SwFo9nMS+zJ8m9MRMXD6v5oax8wPGptH9nWJ/CZ6+OoP9PAnpcYWHihgaazDfw1S/tYJ7IbiDkwkpl6EOU7s00pMRjtKx9TX/D944Pi2RdZ+ab9SfY3Vz1tP+SeiIiIiIiIiIiIKFfCQhGMpjMYjcjJNE2DXxGME4gwGC3bAorAA9UyIqL8VO9tTBoWM91gtHIL0/s93LcQEVH6leplOLbhe7hg/q+xa9nuScePiDCe6L4fV7eeibWhlVPKe2NdEIrWWPUFFIw2w+NXnh8EIgxGc5qQIhitRCuFW/NkuTZEzpemrrxERERERERUiGImHSjdrol/m4WVTFY3vWdtWSM2t8CDGBZG1mNNyZIp5X+r/Ip0urkzgPISDeKDFlQbg8r5D7iqEeveBpTvMaXMg53BaHuGP1TO4+d93wS8Y//udvvxs6ZfY0l4DQ5f+zLE0/8PhmHgC3Ofxnrvrsp5ZFL5IvmbNQAgEgPe3bLz7w3dwJE3Gdj8Kx0zyqd2hhZC3TU408FowFgjnlM/q+HUzwKnPGDg7lem1yl7TcK95TUd6nnNr4M0nK4vNPZ/82A0dVlgGJgnf1mjI6QSjObSNezVDHygziO07V+rgW8ckL75OY3lYDR5+9OcUHXuH183FjWOhSvIdhlrOrYHVxY4IQS+fEvyFIRsB6O1D8iHz6oB9p2j4Z7vmu/M59SOBbPK1oErnhL49kECmpbZA4LJoUiqJ5iZehAR2QnsOusRgf3nmk8gBHDqH6eOExgCjrvTwIqLdew9O08CrtPI7n6/fwSoT/7SXKKiY3Vb+vLe6rJ1XempSz6wso/vD6nLRqMCz35g7zOzcV+FiIiIiIiIiIiISCVsqILR5C/kJCLn8Hua0BZunTI8EGUwWrapwugYXkRUWNyaB3WeRtP9bLk+vcYrPpf59GW6D+VJxiEiIpqO5pJ5OGvO1Vg2+CIeC/w/DMcVHRG22xbZit9suQifqPw0jm34HqrdtQCALsU5sgYN9Z7C6Vji0tyY4fGjO9o5paxbEaBMuROMD0mHTzfclqhQmcdCExERERERUVFThc8AgHvSFeUsG8Fo9flyn2ZTCwBgcWSttLjfVSsdvrhiLAxNXPJtVBmD0IT8hxzQqxCFPMnfLXYGox07+ITlKgPA1fXnjn3+tSfjgj905SwUDQCEZu/WQygCPP6uvAewWcdgPct3OO48Ucc/Ttfx88+n3nN4Y/dYZ2VAHRYEALv65cN7t4ftpByMJr+P6hjKYLQky/r4A9Pbm/uFNdMLwHM6I3l2FgBgcCSz9bAjWWheqUfDgjr5OC2dhb08x134hMBqC20LsxmMFjcEtimD0axttxWlGo5UvPhpXRewfFOKlbNBtQapzm16g+bBnkREqTK7Vpvs968I/OQh8/9koWiJ7nu9OPdldgLoAPOgIqJiZrYpJZ4Jul3qsFyz6+ZCY+U6zWx/81br2P0VO1xsOUJEREREREREREQ5IoQwCUYrzXJtiMguv1ceuhWIsPN9tqlCklTLiIjyV6N3lml5mat8WvMvTxLK4ffMzPhLbImIiDRNw8HVh+PSBbfhkzVfhIbkx563hl7GZRtPxUt9T8MQcWUoWI27Dh7dm+4q51S9p0k6PMBgNMcJGfKOPMnOwYiKFZu3EhERERERkVLMLBjNNfFvq6EmQB4Fo20eC0bbI7za1mTzN70Isf4DYKAHOgQqDXkC1aCrGjHNLS3ziOiOfzfFp76xwcxL5Z9Bj2sG1nh3w/X1Zycd/9b/Sf3B5Ml1b+KhtpNSnl5mjeKeq1nHYD0Hz1Y/v6eG67+hw7jLlVJAmiGAj7rG/t2h6OB91TEaan3ysr7QzvmozChXd252fDCaYnkn+6XPOELDsfulrx7LNhR2CIjVbzeQB8Foiev6LopAwc7B9NfHaQZHBK591tqS7cliMFrnoHrZNdsIV730q+pb2u9vzfz2qto3+RUvPwzH7IdSEBFZYTewa7peWVvY50QqdrMtGYxGJGe2LU1uKz2nVn7VFxgCwtHi2BdZ+Zb9IfVYq9rt/06J91Xe2yJw0d8MnPtXA0vXF8dvTkRERERERERERLkTFREIyB/GMxiNyPn8HkUwmiKkizJjJB7CUFzeENWvCEggovzV6G1WlpXpPrg0l7LcCp+eJBiNgYtERJRFPlcFTmj8EX4x9zrMLdk16fijRggPd92NX236BVYNvy0dRxUils9Ux+duhlY7TjAu78jjcyk6pRAVOQajERERERERkVIsri5zT7qinFVrfb75EIwm4nFg60cAgMODL9qatqp3PcR9V+34u9qQJ/H06TWIah5pmRuxCX9/cy97qURLyw7GT5puTjremxfo+M5B9kO9/vA9Da3X6Lj9jNk4cOQt29Obae+XD4+b9MXNRTBaouu/oeN7h9qvxKsfCYxEhDJA4ZBdNNSUy+fbFxz7QcwC49y6ensLDDu7c7Mq7CPZsvaVaHjkRzpartDxyCk6/iv5cw8AwGcXy4e/u2Xnb12IrIaqDMpfzJsTqnU+cd2or5CvKL3BDFTIYfa7wmSnMEkqgQmpUu3bAXvBaAftomEPRZualiw8s1NtM36Tc5tsBtARUfEw7CZ2TdPyTUCPw88fM8HuN+53UJgskZOYBqNN+tvs/tK2Igg6Bqzt482CGFWB82bGX4Dw7EqBg68xcNXTAtf/U+BT1xl48A3r1xhEREREREREREREdoUNdaMUBqMROZ+q8/1wfBAhRWdvSr/uqPoBEQOMiApPo3eWsiwdgRrlSeahCsUkIiLKpPlli3DOvOvwrYaTUab7ko6/JbwBH4belZY1FOA5sioQmaHVzqO6Vi5PEk5LVKzcua4AEREREREROVfMpN+ja1Iw2mwbwWizanKcYmXFtlYgGgEAHBZaihJjFGGLDa0qjWHgpcd2/O2PdWOLZ87Uj3A3IqiXS+dRbkzs4VpXaS/b/Jg5jyYd5y8n6/j4/LFlcdM3NZz1sHnHW00DDt0F+P13dSxuGl+Gc9D8+S8AG2xVz1Rbn7weZgFguQ5GA4A7vqOhrgK46z8CQxZDpJ58T+Dze6grP7NaHWw2HrRjFmyla2NhPZ2SzuNdQ9bqmCvKYDQLC1vTNCxqBBY1An0hDa+tT96p/LTDdbzYIl/JPnODgRWXTO/taU5ltl0lGnRQyId63dj57xnyXWvBBrps6hE4/zGBP79l7/t92AFsCAjs4s/8TlQVjOZxAXWK5aXy8fkaPuyY+l1btmV++arWP7PQ194QMLcuM/WZLiEEHl4ucMsLAuEY8PX9NZxxpIZSj/V14sE3DNzzqsBwGDhqbw0XfVmD2+WAAzNRgbMabppOv/23wOX/XVzbt93feTgsMDXmiYjMaJM2meZq9bjt/cA8h55XpZOV7Ms+k2C0VM6Lx0/fznvMQCQhq98QwC8eFfifA4Wl63EiIiIiIiIiIiIqXBEjjLbwJsRENK3zHYz1KctKNAajETmdqvM9ALw3/EZGwnNmePyo8zSkPL0QAoHoNgzEeqXlDd6ZqHbPSHn+2TBqjKAtvAmGGHsD9kcjq6TjuTU3at1F8ICNqMg0eJuVZekI1Ch3mc/D71Xv+4mIiDJJ11z4dO1R2K/yUDwWuA9vDr6U0nzqTa5j8pXqO/VGA1gbWgkN5v0Sq90z4Pc0QZvcoI/SLmTIO/T5kpyDERUrBqMRERERERGRklkwmnvS/TB/xVi4STSefL5L8uH+4aaWHf/0IIaFkfVYVbqnpUmr4hNTqJpi8jexbXM3YVCvks/DmDiPuur0XcJrEAjf7poQWnL6ERqeWSnw/Oqp49/0TQ0nHKihogQoL5l6g7P01MvhPz2AgNuflvq1KcJzTAPA7OXGZYTXreH6b2j41bEC/1kHHH5j8sSpF1qAlW3qcrNgtG6rwWiKl3YF8jUYzeY99qP21gCYdwr/yWc0HDBPXb6yDXh9vcAhuxbeDX6rYR8jUSAaE/C4c/8bxBWbVuK6UafYbnqD6a9Prg2EBA6+xpAGIFqxfFN2gtHa+uUrW3ONtcDDRLs1yoe3dNqtlX2qbWZGhQZNE9IQi7Y+YN+p+ag5MRIR0LWx7SUaBx5+W+B/79tZ6bc3CawPADceB1SWIumDzd+/YuDkByZOvzEA3P+D3O8riAqd6niYSa+uK8yAUTNWwokSBcOZqQdRvjPblCafblSVAeUl8u2ptacwr8sms3Kd1m8SXr06hZd8unRg24CQ3qPoHASWrgcOW2R/vkRERERERERERFQYXu57Bn8N3IuYiCUfOY1KLL7IlIhyp8ZdB7fmkYYmPrjt1ox97oLSxThl1vmoctfYmq4n2ok72q5BW7jVdLwlvn3ww+ZzUebyTaOW6SeEwNM9f8Y/eh6BgeQNB+o9TdC1wnwpLFExa/TOUpalI1DD51I0Pt8uE6GXREREdlS5a/C9mWfg0Ooj8ZfOO9ER2WJrer+38I5lquBSAwZ+s+UiS/No8s7GT2ZdhHqvosMGpUUwPiwdXp7kHIyoWDmg2zARERERERE5lVlne/ek5+S6rmFmdfJ5+iuBuoo86MS6uWXCn4siH1metNKYeINqpiIYrcPdhEFdftOqKj4xtaquKn3BaIHT104IRQPGgk/+dqqOC47ScOB8YHEjcNRewN9O1XHGkToaqzRpKBoAaJW1+IJ3Rdrq19Y/FsA0WbIAMKfQdQ2fWazhvYt1HHcAsIexHjOj8l7JkRjwm+flG5q/Eqgq06YXjKYD/kr5j+P0YDRVCIXdZd1co2Fxknvyt5ygYXYtUGXSlvKiv+UgfSQLrAajAcCQQ4I+VHV2JdzprCuXj9NTgMFot70kUg5FA4AW+SEq7doH5MObLZw7TLakSb4jWB8AIpLjRzoZihXQ4wLm1MqnaenMfZCQEAJXPm2g/KcGyk41UPITAxWnGRNC0cbd86pAzekGDrrawLIN5nW/+d9Ty//4pkCHIgiPiNJHdTxsqAQu+oqGg3cZO6e3+58qfBIA1nVl5rs4mZ1zJQAYdsj5EpHTmIUMTj6z0zQNu9TLx12bhSBcJ7ASytgfGjvHmywYFtjca/8zXTowYBK2tqmX53dERERERERERETF6qPQh/hL111ZD0UDgBK9LOufSUT26JoOvyf7b0veONqSUvDaPe03JA1FA4A1oRX4S9edKdQss94dXopnev5iKRQNYHgRUaGqctWiVHGelI5ADZ+uaIS7XSGGyRARUX7azbcXzp9/E46uPwlercTydLm4hsm0+jR8p22Rrbi97co01IbMhBTBaD59+gG3RIUofb2qiYiIiIiIqODE4uoytyRqe1YNkna+XJIn9w7Fhg8n/L1bZK3laSuNiYlTTYpgtDZPM4ZcVYp5JKTclPowZ4YGYHqdUEuMUby7x+2YsefZ0vIyr4Yrj9Zw5dH2533ByQvx4B3Tqt4Oo1HglY+Aw5dMHJ4vwWjjPjZbw59PGIB4cG8Y0DB/4Tq0e5qnjPeyYtUa31bqK+TLvnt4rBN0st9FHazm7E7Nqu+VyrI+6VANFz4un+GyC3Ro2thM7zpJw/F3ycd7YQ3wxLsCZV7g8MWAx+3AlS4FdsI+BkeAGeZtHbLCyrpRp1jvnRqM1jMs8P5WYFW7wIJ6DXNnAL1BYF2XQFUZcOiuGmbXTl3neoMCv3zC2kI8Zj/g8XenDs9WMFpbn3x4s72XtgIAFivOJeIGsCEALMlgmxuz9W9Jk/w8aE2WfmMzN/5L4OK/2dvvL98EHHengQ8u1VFVNnX9i8YEPmifOp0hgIffFjj9iMLYTxI5lWp/VOYFLvuajsu+lvq8z3rYwG+en/oBbf3A8KhARWnxbN9WwokSBRmMRiRl9+pzt0ZgZdvU4evyJBhtaFTg5bVATRnwiflAicfeftPKdVo0DoQiQPmk9nyphsd1DwOPvq3+YKMws8KJiIiIiIiIiIjIgjcHX87J52rQ4NG8OflsIrLH752JjsiWrH/uquDbGI4NosItb4c7WVekHa2j6yzPf8XQMsSbYnBpzumCu2zgJVvjM7yIqDBpmoYGTzM2h9dPKfO5ph+o4dXNg2WqXCk0/CQiIsoQt+bB5+uOxcerPolHu+7Be8NvJJ0mHSFiTlOil6LaVYuBuKLjhkUdkS3oinSggdcSGROMD0mHpyPglqgQSbqxExEREREREY2JmXR6dCmC0ZL55CLnd6IXAz3As/+fvfsOc6Ja2AD+nsn2hS1soxepSlERuSoWELuIyrXfa0Gxo9jrp2LvXa5eK/aGXkVsiAURVLChUhaQKtv7bpYtyZzvj7DsbnLOZCab7Gbh/T0PD5uZM2dOksnJzGTOO6+0mnZg7RLby/sHo/XQBKOtiRusrSOlZR3xSTh8D9ur17rC+waGXaUORWurYaMH4u+hd+L2otvDUt8XKwMH43a2YDQAQLEvMcaAxHE1HztadGh335PSBZt5TF9QldXgZEMAWZrzosXq86hRQxdCYYRwNuus/QS6KH6jP+9AgX37N288p4wxkG1xHnnK0yaOftzEsFtNrMyL7mA5uxwFo9VFrh1O2ApGS1Z3CmVuwHTypNvBwlyJ4beZmPiIicvfkjjuKRN73mFiwsMmLnhV4rRnJQbdbGL2ktYf9m9yJTKvtJdOsOV+A0Ny1K9JbmH7vB75ler19Exz3oEPytL3+7kRDsuw2v6GdFc3ak1Bx25zLy02cd2c0Nrwdznw0hL1stUW4T+hBnIQkX1ezVdAOPaLp0/QV/K7IqhoZ+Z0t8HdEJl2EHV2ViGDQtHlDNbsu65pp33Xtli8TqLPdSYmP2Xi4AdNjL3HRF6Fs3bb7XsqagOn5bZh3/MWiyDdKDuMIiIiIiIiIiIionaU37C5Q9bbI67PjpsdElF06xs/sEPWKyFR2GD/R+y8emf9Wb2sQ41mwHpHKXAYQNc3oWPeGyKKvH4J6nEIPeP6hqX+/pr6ByUO5z4aERFFpW6xWbig1w24pNf/ITM2R1suO7YnEl1J7diy9tMnTPv/FZ7SsNRDarVmjeHAQ9YAACAASURBVHJ6OAJuiXZGDEajsBJCxAohxgshzhJCXC+EuFQIcaIQon9Ht42IiIiIaGclK0shX7kP5mNXwrxhCsz/3Aj52euQXm+b67YKRotRHFH2TA/+I9exI6P7hzBpmpBTdguYfmjtN0gy3bbq8A9G6+VRX3hR4UrX1pFiVjU/SEhC1wSBCw9p22t35iGJbVo+mB6X34Sbh6/G6ZVvtbmuVfmKYLQgAWBRqSRvx5+Tqz9ytOhuwheopws2A4Dv1gFnvah/YVwGtEFf0R6MZif8yq5e6QLPnimQ2uIjMHlP4KnTAyv75Zbgp8s2lADnv2JCWo3u7yScPIXKbZFrhxN2gmB0nxuvCZSof0PoEKYpMXW2iaIgn8cGD3DubAnjAi/2usOLM54zcejDwUPRkuKA16cJ9EoXGKa5qVFuAdplWy7VvO459m7Y2kp8rECfbup5hVWRfS5WfZPuNV6tzkcNm9X5EnfMMzH1JRMPzTdR7m5u5PwVEue93LbX5I6P1MvXWIQlVkdJf0G0MwvnvpK/fhlAYqx63qd/dv79HyecfkVa9Y1EuzLLYDTFtCGa69JyC9tn3zVUpilx8jNmq1DpP7YC17zrrM12n2KFYp8rUsc7DEYjIiIiIiIiIiLadRU3RPhHb41xaUd0yHqJyLn9Ug9FnFDcubQdFDfmOyjrvD+r9UbPxWZe6UFpY5Ht8qmudOzVZb8ItoiIOtJBaUfCgKvVtGSjK/bqun9Y6h+Xqt4XOyTt6LDUT0REFCkjuozB//V/AsdknIoYERMw/5D0YzqgVe3j4LSjwlKPlMHHqlBovNKDOlM92CKZwWhESoE9OXV6QoiZAG5rQxUvSynPcbjOLAC3AzgVgHJYohBiCYBHpJTvtaFtRERERETUgszbADltf6C6vHni4o8hAWDBW8CDc9t0Rx5d+AwAxLgCp/VKC17n4OyQmxNxUkrIW08HGgJHsyfIehxRswAfpBwftJ6ufsn9w+pzHbclpeVd5hJ8aU5Pnibw34WhjUQdUr8Gw3bX3/EiHERsHMTd7+CG79fgzZfaVpcqRMZqEK4RrdHvLYLRxtd+i67eKlS77KUApc+5F3LAUcjcR3/S/binrE82CyG2B0QFvnjF0XO9jpJVEF4oThtr4LDdJf7YCqQnA3v1UfeNPdMEjhwOfL7Cur7v1wO/bQH2Ds+N1TqMk8HtFbWRa4cTdoJgeqbql99aAWSHEMYVCT9uADY6vJnO738Dv/8d/I2bO93AfgOAzK6+F2ZodwFVX1BTD+RVAL30WZ1hUa7ZftJDvNlSVhdgk+K1i3TwnS6kwjCAoTnq17ioGih3S6Qnhz/F85tcieOeMuGub5721FcSP9xoILcAOOrxtnem5bW+fST/fcrqes0CAKrqmJxBFGm6fSVXGPaLXYbA+KHAp38Gzluybtf6fDsNAqptiEw7iDo7q4+S6rTVEM1+VXUdUFgFdLfY3+9Iv24BCqoCp7+1TOK18yQMm+mVdvueckV+f1WEAhoZjEZERERERERERLRrqjO3ocpbHrxgGHWP641xqUdgfNqx7bpeIgpdt9gsXNX3bswpehEb69bAIz3ttu6iBgfBaA7KNnF7o+cOtKWNxTAR/FqgeJGAgUl74LTsCxFndExgHRFFXu+EAbi8z0z8r/gVFNRvwYDEoZiSNRWpMeG5GHRc2uHwSg8WVnyKgoYt6BHXBxO7HY99Ug4MS/1ERESRFGfEY1Lm6Ribcgg+LX0Xf7p/QhdXKg5KO3KnPt8wossYnNvjGswvm4Ot9ZsgLa/c07Nz3EGhsQrfTjIYjEakwmA0ajMhxNEAZgMIFm9wAIADhBCvA7hQSqm4VJuIiIiIiJyQ/7mxdShaSz/OB76aA0w8OeT6PV79vBjFgPtgwWhxMUBGlJ6jkV4v5EPTgYUfaMtMjluGD2AnGK31hRADGjciVjagUcTZbk+K2WIkbbwvNSbGJfDBJQZO+I/zE4z/3PYRxIiLHC8XipH7D8GceImTngn9ROiaQqC2XiIpvnnQsNUgXFf4s2bCo7g5GC1eNmCi+2tb4XoAkN5QDPn0TUh+5WjkpPgGfzvRNN46S/OZq64D6holEmKj88XThl+1Iewjs6vAhGHBy43oJfD5iuA/AMz5WWLvvtH5+tnlZHB7fqUE0PHPV9dmV4uQgewUXzCMKuAzryJ6Au1yCyOTLnDbcQKTRrV+r4ZaZGOuLuh8wWiZmr4t0sFoVsF8w7rrl8stBPbbLfztufl/rUPRAGBzGTD+IRNrCsO3nvJaoFty62k1FqEbVeqbGBFRGNkJCm2Lw3YX+PTPwJV8nQtUbZNISWzffYJXvzcxe4lEXSMwaU+B648UtgOG2sLpN3UNgyGJHFMGo1n86rymMHqD0VT9ZpMyN5DZ1V49ujBefxWKfS4GoxEREREREREREVE4lTQo7m653e0DnkZGbPhv1GmIaL1DJhFZ6ZswCFf1vcd3g+IQB99beTH/YfxSvThgenGjg2A0TdmD047G95VfolEG3gmr1oyeO9BaPddHBr+JOOELQRMQbbqpNhF1HkOSRuL6fg8qb3waDgenH42D04+GKU3uoxERUaeUHdcTZ/eYEbHvymg0JuVAjEk5EKYMPqbvirWnwiMbA6YzGC1y3FbBaK4oHXRL1MF4JEJtIoQYD+ADtA5FkwB+BvAugC8AlPgt9i8AbwrBI2EiIiIioraQNZXA4nnWZea/0aZ1eCzOY7kUe/S9061PEvZMRfSeSHz3CWDei5ZFjv33QbaCBrr6XQgRAy8GN6xz1JxE2WJ0a0JzaszkvQROH6tuRGoisO5uAymu1gkp3T0FuPqgOojkFEdtaIspowW8/zXQeMyLKFjTB9Wru2FK1f8c1THy9tYboNUg3LaEZUWSLMlr9Xh4/Urby6aalcCm1RAFG3HkQOfZ4juC0SwGXpdGzzU7ASId9mHFKtyoJTvhadHOdPB7RV5F5NrhhK7NLfsBlyHQQxOWsLUiet63sgjcNiC7K3D5oYEflPRkgWxNfxCpgLYmpilRoQtGSw7tQ53ZRb1cJF7Tlqz6pp5pQBfNTVbXF4f3NfZ4JYqqJL5fr54fzlA0AMivDJxWbRG6UVQNSClR5paQdpM9iMgRVfgnEL59pRG99BVlX23CXd9+n+1nFpo4+yWJr3OB79cDN/9P4uLX22f9TvaVAMAdeJ08EcE66EvV22R2FQGhrE3WRHjftS3iLW5LV+zg+NtuCFlFbWDByggF1NrtD+sbJbaWcx+QiIiIiIiIiIhoZ6EL4XEhBt1is2EII+z/iKhzE0JEpG/Iju2pXF9xg4NgNE3Z7NgeSHapL6xye6uV0zuCrv1pMRlIMBJ3vFZRe502EUVMpD/33EcjIqLOblfcR7Z1rKWJGzKlt51bu+twW4Rv645LiXZ1PBrZNZwOYICDf9fYqVQI0RvA+wDiWkxeDGC4lHKMlPIUKeURAHoDmAGgZVzocQDuasNzIiIiIiKipV8AnsBU/laWfAJZG/qP8lbBaDGKI8qRvawH4vdKC7kpESU350LOusG6UEYPZB1xDPbsbV3MkF6keQPTg8Zs+9lRm1q9jPGJrebNPkfgjuMFurfIOZs0Clh1h4HdsgSW35WIc/qtx4HGnzjH+BxLDv0S6dNvcbT+cBBCwOg1AJneUiTKOkyutg7y87ehBNhQ0jyQ1jIYLVrPUfsFow1ryLW9aJrXl0IjT90dx300zfGqm0KiUhP1ZaxCbTpaRwajHTlcKMMf/f2yGfh2Tece7G13wD0AbI2SYDSvzW1D950TLc8DUIdNtUVOCrDsZkMbNqYL/Vutv8FzWFTX6be19CT19GAyNDfDKamOcMibpnohfN97fbup54frvc4tkNj/Xi/iLjbR/Zr2uxNTvuJzU1MfOK3Jynyg17UmMq800e8GE/N+79x9JVE00vVHdvZh7LAKim3wAF0vM3H6syZq6iL/+X5sQeA6Zi+RKK2J/LqdrsGqbyTalYXyaR2So54e7gDYcEqI1c8rqrJfj93jtHJF+HC1Jhht3ED761fRHYc1qWuUOPtFE92uMNHnehN9rzfxxo+8cygREREREREREVFnV9ygvqAgIzYbLuFq59YQ0a4sK079I3ZRY76tG7Y0mg0o95Ro6u6pHYBe642eu8/qwiqzYm3eCZaIiIiIiKgFXfipKXndV6TUasK3Y0QM4kR8O7eGqHNgMNquoUBKudHBP/VZvkC3A0hv8XgJgMOklKtaFpJS1kspnwBwit/yVwkh+oX+tIiIiIiIdg3SNCFXLIX84bNWIWfyO5shUwveCXndHouA/xjFdU3pyQIHD9Yv0zs9+tKrpJSQ/xoVtJx4ZiEAYPce1s+hm7cMLgSeAJxc87HtNu1ev6r1hITWqTGxMQL/d6yBvIdcMJ/1/Zs73YXuqb629csQePHmwfj2mT3x4jPHoP+/z+q4u1v0Hbrjz1Oq5uCA2iWOFv/g1xbBaBbnVaMxGE1KCSxb0GrasHonwWjNKTSH1yxAvOksxazpNUmxCEariuJgNN21Su3xXvdOF7jxaHsrGv+QCa+TdLEQbGuQ+OQPiW9yJRo84V2Xk6bnVURHsJGuL/DfNnqnq8tttHvmqx0UhDkYLe9BA3266bfdId3V89YURPa9VQU2NAk1GC1TE4z28+bQ6rMr2PbXPVU9P1gwmrte4ouVEq//aGJlnvr9qGuU2P1WEz9usNlYB2JdwD4WZ2rzKwPbVB0kDKlge/jH3+XACbP0z4uIQhPpENleaUDXBOsyb/8kcdFrkf1s19RJZQhSoxf436+R71ec7ua5GYxGpGQ1FkV3umJIjmbftTB69ykaPPp5xQ7GzNgYu+OrU3GNVJVmH22f/gKXTwz9S6I+yL0Zpr8p8eoPEtu2l9taAZz5osSitdH7fhEREREREREREVFwRY15yunZcT3buSVEtKvLiu2hnF5n1qLGG/wONSWNhZCa2/lkx/VAkqG+GKlGM2i9I+jCKrPi1K8NERERERGRFaGJGzIV4yIpPHTh20lG144b+0kU5RiMRiERQgwGcHaLSQ0AzpFSaodUSyk/APByi0nxAG6LTAuJiIiIiHYOsrwY8sqjIS86CPLa4yFPGQq55BPfzF8X2qvjwUual3HIG0IQ1Ql760/C7D8wpGZElHzh9qBlxJWPQXT3pYUMCXJjtUxvqXL64TULkOCySJpr4aiaz1tPSAgxNSYaZPcGEpMBAHFoxNebjsCzeRejf8NGpHvLcFXpo7il+G7t4gtWtQhGsxhLG23BaLK+DvL2s4CG1skII+pXIM1bbquONLM5RaeLdOP0Kmchh02vSXKcvkx1FAejmZqR4O31Xt9xvIG50+2dOrvk9cgN9F62UaL3dSYmPWni0IdNjLnLxIaS8K3PSdjH1orgZdqDLiTA5fd27ZYV/UEK64vD15YN9xpBfwgZpvkO+3ULYEYw4C8SwWgZmmC0wirgkS8i90NcsCCiHqnq98AqBG/5Fonht5k48jETZ74gMWKmiQtfNVvdzbaoSiLp0vA8r/12A2ZMFHjgJIFbJgncOklg5e0Glt2sv5v3ekWgYI2D8B9TAi8tiZ7PHtHOQHesFq59JcMQOGVM8Mre/VmiojZyn293g37e2qKIrXYHu+FETZz0jUS7EstgNM30wdnq6b9tQav9pGhidYxdVG2/zXZ3zfMU+5hV29RlUxOBR08RmHdZaJeI1FuEvjV6JOb8FNhoKYG3l0Xne0VERERERERERET2FDfkK6dnxQW5iI6IKMysAhmLG9V9lZ0yAga6xWQj2aW+GEk3aL0j6J6DLjSOiIiIiIjIiiHU15JJBqNFjNtUH2PqjkmJiMFoFLozALQcKfe+lHKtjeXu93t8ihAiIXzNIiIiIiLaucjHrgB++aZ5QmUp5PUnQn74HFCivhujsp7rT4Ssdp5o49Gcx4oxoA1fOX4v/eB5q3kdQa5dDrx8b/CCYw7d8efwHtbPIUMTjJb8z6k4bLg+cKTJuNrFmFl8V+uJ8Z03GE0IAfQduuOxCybOrXwZ6/7aA8VreuOBoptxW4k+GG1li+s4OlMwGj55GfgyMMgsFh4cVTPfVhWp3tYjnO8uuhX7bltmuwlNr4lhCHTVHHlXRXUwmnq60Y5nsyaNElh4rYGsrtblnlskUexgkLtdUkqc/IzZKlTqzzzg8jfD9yODkzyBMnfYVtsmum3D/2tpSI66XG5hdAQplLslvl9vXaZ3OvDd9QauPVJo+7mEWGD2VIF+GcE7wj0032HF1cDSjUEXD5lVMFpaiF9x3VP0z/eadyV+2RSZ9zhYMFr3VPX8/MrWC5a7JR6ab+L690zsfaeJzWWtyz+3SMJ1oYlr3jVR7pa4dW74ns+SG1x49FQD1xxh4PbJBmZONjAw2/cEJo1SLzN/ReD6nYZrPjy/4z93RDuT9thXuuuE4N8tjV5g/3tNXDvHxIKV4f+c11oEo1VVRT6FzOkug1V7iXZlVh8lXbbv7pp9181lwAr7p8TaVbVFt1Rcbb8eu11PfkVgyUpNMFpKgu8czTEjBf7zL+cnUeoa9fNKavTnF75cxX1AIiIiIiIiIiKizqy4sUA5nSE8RNTeurpSEa8ZgqgLcWxdRt2fdYvNRKwRi2SX+iJBt+ngR54IMqUXJQ2FynlZceyTiYiIiIjIOUMTN2RKBqNFii58O4nBaERaDEajUJ3o9/glOwtJKVcB+LHFpGQAR4SrUUREREREOxP5+2LgqznqeQ9Nd17flAGOl/F41dNdFkeT/TIEzt4/cIDlWfvbC21pL3L1z5Dnjg1ecM8DIVoEe40fav38M+MUo2CPPQfGjEewT7/gz/+bTYcjWfolyMR38jzpfsOCFvlwyxTl9E2lgLveN4jWMhgtys5wSEUoWpPRdb8GXT7JdCMOrUcd53iL8O3GiVi0cTxeypuGl47abFlHyxAlXTBadV30DlA2NefR2zsE76DBAmvuNPD8WdYrzrk6/Cf+V+YjICwJAD7+A9hSFp73zupz5a/CItyqPQULpmoyJEf9nlXUAuuKwtyoEIy9R7/NPHWGwPwrDKy+w8ABAwXu/6eBVXcYmH+F7//vbzDw8lSB9y82sP4eA2ftb68TPHAQEB+jnvfhb5HrD8o1oXpdE4AYV2gf6qFBbj495m4TjZ7wP6dg218PTTDa3+XNf68rktjrDhPXzZF48HPrNj7yhUTGlSae/TY8zyUnxXr+IUPU78cPG4ASvwDImsjnERGRBV1/FGK3qpSTIlA7K/h3TG6hL/zwiMdM3DkvvPtEbou+pnLhfMiS4Be4t4WTfSUA2MZgNCLHdMFoVudf5i6PzmPZKk0oGQAUORgzozse9penuAeCLqAsJbH577REdRkr9R79PKtQSDf7RSIiIiIiIiIiok6rwaxHhUd9k1CG8BBRexNCaPueokYbwWiaMk1Bj7pB6LpB6+2t3FMCL9Q/2GTFBrmQioiIiIiISEEbjAYGo0WK26u+kFAX1k1EDEajEAghugPYs8UkD4DFDqr4xu/x0W1tExERERHRzkg+eGl4K6yrhdy4ytEiHs15rBiX9XKPnCJwzgECQvgGuJ5zgMAjp0RHKJos+hvmnVMhzz8geOF/HAFx51utJmV0ETh4sH6RzH+MBcYd63sQGwecOgPiqicAAENzrFd3RuWbUL5KsfHB2xrFRL+hQcsMq8/Vzluz/SZ3lsFo0bF5NVv+nXbWUGNr0MWzPCXK6bHwYP9tS3Fm5Rs4s+xlXHm4/om3DItL0QajBW1Kh7EbftUeUpMEzj3QwMXjrVd+yesmpAzfAH3VIPcmdoIA/i6XmPqSieG3eXHAfV48PN+E1++FdRL2UVMPeLwdH0DgtRmaN7ynvo7nv2vf5+HxStz/mYne13lhXOD791exumyfdODiQwQO20MgKb75SQ3O8U0bsvUb7DvrOPzr1Yk4/o/7kRNvkb7gp0uCwERNVmVEg9Fq1XWnJ4Ve58Cs4GXmLg+9fh1t37S9z+3bTd1PbChp/vw8+LnElnJlsYg7d5x1P3bsSPV8KYHPV7Z+8tsalUUtRXMgJ1FnE6w/CpeEWIG1dxk4cJC98nfOkyisCt9n3TIYrSEG8tX7wrYuFafPpM4iOIhoV2Z1mKTbO+mWLDBuoHreJ39E5z5FjcUx9vIt9tts9zgtvzJwmi6cLbVFGFp6svMD6zqLfT+rYLQwHiITERERERERERFROytpLNDOawoSIiJqT9maYLTihuDBaEUNecrpTWFryYZ6ELpu0Hp7K26w6JMZVklERERERCEwhHqAqCkZjBYpuvDtJEMd1k1EDEaj0Izwe/y7lNLtYPklfo+Ht7E9REREREQ7HVlTCTgMMbPlh88dFdeFz8QEOZpMTxZ48RwDdbN8/148x0C3EAZdhpusKIGcui8w/42gZcXH+TAe+ggiPTB55fi99M8lK7srjPveh/i6BmJ+OYzpD0DE+YLNhvUIEkZS86l6hitIEl206xs8GK1/4ybEm+oRxKsLfKNoddsjEF3BaPLr9yzn737YfkHr6OlRX4TTyqK5yLI472m2eL26aoLRqjphMJrowPf6idOsV/7MQokb/yfDFo5mNbj8p43Wy1bWSoycaeLl7yVW5QM/rAeunSNx5TuhB6MB0bHN6Nrs8vtu6pYsMLa/uuxjCyRW5bffCP2LX5e48X1pGXbX5JhRAkKzoculX0BecRSw9AvgjyWQz90Gefd5geVME7LWd1GebPRtSLJ+G2ThFhy3m7oRqwuANYWReU0qNOEMbQlGi4sRGJxtXeaTP8P/fHQf76bvoSGaEFSPCWzcfiPtj2wEG0ZCelLwYLSh3YHdMtXzft7U+rFVOIbOt2ucL0NEanaDQsNhYLbAt9e5cNyo4GU9JvDlqvD1c1b7QxVGKvDVe5Bm5C7AcFp1gwcBQbREdkmPB7LefuhtZxLqp+K4PdWd2h/B88Y7hFUI7JK/gBqbIbF2X6+iaqDR01xaSolKzSaUktD8WqYlqstYqbcIfrQMRnO+KiIiIiIiIiIiIooSRZqgIQMGMmJt3M2MiCjMdKGMxRZBjsHKZG+vM8mlvhiz1lQPWm9vRY3qPjnFlYYEI4Qff4iIiIiIaJdnCPUAUQkGo0WKW3OMmaw5JiUiBqPtKi4UQiwQQmwVQtQJIaqFEBuFEAuFEHcLIQ5yWN8efo/XOVz+ryD1ERERERHR5sikRsiPZzsq7wkxGK1JbIxAbEwUJVbNfR6oKgtaTDw8DyKlm3b+lNECcTHqeYft7nu+IiYWIqZ1oRE9gd7p6uVS4704qma+eqZLs7LOYvd9ghZxwcSQhrXKeau3X49ilS8QVcFoj1+tn3nOzRhw0XRkBjlf2ctOMNr6FRhZ+5t2dssAqxRNMFp1FIRc6eje7458r12GwLfXWneAD3wmkTbDxDML2/5DgNWg+tU/roIsDkwDKK6W2O8eL9KvMJUD4p/6SuKnjc31Og37qKh1Vj4STM3Godo2poxWbzCNXmD6G2bYQux0TFPigldNvPCd/fWcOiawzXKbG+bd50FePSlwgW/eh/zwOV85KSFffwhycm/IIzNhHhQPeWhX3/+HpUGeNAiTHh6tXfeHv0Xm9SjX3FKhLcFoADDZIqgUAF5aHP7nE6xvGmRx7ffaQqBqm0RBVdibBQDoEg/00exnnDFW4NvrDAzMtn7NhBA4eIi6zDMLWz/5UILRPl/JaAyicLEbFBpOE4bZ2xH79wuyVVBPW7gtwnYKY3KAimJgrX6fuK10zyIpTr9MKP0j7dqk1wvziashj+sJeVQWzKsnQVaUdHSzwspqt9sq/Hp0X/XM6jrA442+/Yrqeuv5T35tr81OjtNa7lvWNerP5aW0GA+TFsJ+uFXfZhWMZhVyT0RERERERERERNFNFyKUEZsNl+jk17MRUaeUFacORitqyLO8DswjG1HWWGxZZ7Krq3K+2xsdwWjFmrBK3WtCREREREQUjKGJG/JKbzu3ZNdR661WTk/SHJMSEYPRdhWnAZgIoCeAeABdAPQDcDCAmwB8K4RYJoQ4zGZ9g/web3bYnk1+jzOEEJohe0REREREu6jNuaEtl9ULOPRk/fyNqyDLi2xXpxtkGsnB9pEkf1sUvNCUiyDGHm5ZpHe6wMzjAgfn/nM0cMgQ/XIxLoEH/ikCguWEAO4/tASppiYppZMHo4mcvsCgUUHLDW1QBwLOX+HbDq0GBkdLMJosyQNK1RegoGs6xLm3wOUyMGmUdYN7NrYIRuuSpi034fHxttrVVROMVqUIzooW0RiMBgAHDhbISLYuU10HXPK6xNSXTORVBB/4bpoSK/Ik3lxq4of1zUEiNRaD6nMbc2D+a1TABV0nP2Ni6Ubr9Y29pzkQzCpwUCUqgtF024biu+nCgwVyUtTlv84F3lwa2TCFl5ZIPL/I2TrGD229kcttbsjz/gF89pp2GfnQdMhtbuCTVyCfuRmoLNWW7eEpwNhtS5XzQnk9TFNi0VqJJX9JuOvVy5drtpv0IJ+lYK6YKLBbpnWZlkGA4aDb/poCPZLihTYEdU2RxFr7u2GOHDsSqHzCwKb7XTCfDfz32jQDw3va60AH56in1zUCm0tlq8dOrS2MvgATos5Kt28cyX2lY0bar3zATSb+u9DEyry2fe513y0AkBfTwxdc9tefbVqHFV2/nxyvX2abRUAQkYp8fibw7lNATSXgaQSWfgF5w5SOblZYWQajWSxnFeBVFYVB36pw6pbe/8Ven+ik58yraP7b6jVpGZieFcI1VA0e/TyrYDT2iURERERERERERJ2XPoSnZzu3hIjIJztWHQK2zXTDbaoHlwNAaWMRJNQ/smfFNgWjqe92W2fWwistfihpJ8WNmj45tns7t4SIiIiIiHYWQhM3ZGqOn6jtdOHbyYb6mJSIGIxGzcYAmC+EuFsIq/tyAwD8R2Q7Gs4nt3QxeQAAIABJREFUpawB4H9ZdqqTOoiIiIiIdnZyzW+hLZjSDWLmq8Do8foyc1+wXZ1Hcx4rxuWsWVFj2QLr+YP3hLjiMVtV3XC0gS+uNHDFYQIXjxd4Y5rAWxcYcAVJIjhtrIElNxi46RiBs/YXuOYIgUXXGTh/X33akOjkwWgAgIMmBy0ytF4djPbjBuCOeaZlgJMqEKkjyDvO0c/cdyKaDrlP2Mt6O+l9+ETgjKshZjwC8cEmYPd9leUSpEVyVgspier1VUfhQPImHRH2Yddf99jb4F7+XqL3dSae/kb/o4C7XmLK0yZGzjTxr+clDrjPxIEPmMivkJbvT4UrHRs8mZAn9Id0+0IV1xZKfLvW3nP4aXtsveNgtCgI0/M62DZSkwQePEm/0VzzrkRlbWSCmipqJc5/xVndWx9ovW3JX7+FPLE/sMXGG/vFm5DzXrS1nsnV85TTf9sCfPqH/TZvKpUYfZeJQx40ceD9Jva6w8QvmwKX1wXqpSW17QPdK13g11sNHGeRvfnfb8McjGZj+xuiCRZbWwisKwpvey48RODlqQIfXGog+Glde4Zk6+t56uuWwWjOn0vL4A4iahtNhnVE95WG5Aj00mf2tpJXAVz8usSImSamvWJa3p3birtIfdduAKgzElFppEKGGixug67ZSXH6ZbaFEBxJu7gv3w6ctuJHyL/XtX9bIsSqB7DahbEKRouG0GZ/ZW7r+T9v8h0nBOPkOC2vsvlvq2C21MTmv7slCwzKtr8OANhmse9nFYzmbkDI3wFERERERERERETUsYpb3tixBYbwEFFHyYpTB6MBQHFDgcU8daiYgEBmrO9CnyRDf2eZWs3A9fake35WrwkREREREZEVQ6jHRknJYLRI0R1fJmnCuomIwWg7u60AngNwPoADAewBYBiAcQAuA/C5X3kB4CYA9wSp179XDWVIrP8yIdyXOpAQIlsIMdzJPwADw7FuIiIiIqJwkVICi9WBJUFl9YQQAmLaTH39331kuzptMFonPJqU9cEToMSNzzkKFZm4u8AjpxiYdYaB08YGD0VrMqa/wF0nGJg91cADJxk4YKAAYi1G1u8EwWjCRjDanvW/a+fNnCux/G/9QNqEKHiJZN564NeF2vniikd3/H34HtZhCsMOGAHj4nsgTroUIj4B4uDjtWVf3XqOcvrY/s1/6waTF1VH7+BkXcuiIRgtJVFg3mX2O8JL35AYOdOLmAu9OORBLxava352T34lMXd56/LLNgK9rjNx/XvW789HXY4FygqA954GACz/23aT8NsWX91Ox6dHQ/iALiRAt2386x8ChwxRzyuoAm6dG5nPwVXv2K83MRZ49ZhNyHnvTpiPzoB8/SGYz8+EvPxwYHvwXTDywUuBP3+wVVYXjAYAxz5pwmszieGwR0z83mK7+6sYuPC1wPCbck34Q7pF0IVdXRMEXjhb/3lckRfmYDQb298gTbDY2kKJQntvpy21sww8/S8DZ+5vfx/Ejj376OfNXd4yGM153fmVwcsQkT0dFSJ78hjnK3jxO4lZX9vvj+WP82HOuh7115yIJ99WD7hpkhfTA9isDlgOB12/z2A0Chfp9QL5m9TzPnutnVsTOVbHHZbBaIn6edFwbOKv3EabttoIitX18Sp5Fc0vbpXFr/YpCa0fHx8ksN1fjcVptdoG/RvsNYF6j6NVERERERERERERUZRgCA8RRZsUVzriRYJyXlGD/rflokZ1MFpaTAZiDd+Pv8kWg9DdHRyMZkoTJY2aPjmWfTIREREREYXG0MQNmWAwWiSY0kStyWA0Iqc64VB2smEpgCMB9JFSXiClfF5KuVhKuUpKmSulXCKlfEpKeRSAfQGs9Vv+BiGEfsR1YDBa8HSBQP6XZYerp74EwJ8O/30YpnUTEREREYXH+hVA3oaQFhWjx/v+GLGfvtDqnyGLt9qqz7szBaPdebZ1gX0mAINGtU9jVGLj9fOMTviC+xs0Cujez7LIUTXzke4t085/eYl6oG1iLJAU3/FpWfLjl/Uzx0+BSM/e8TAxTuCS8eo2d08BjtjDb+Kkqdqqp1R/gMGJgSOrT923uf6eqeplt5brm9zRtOFDUfJxOGakwKcz7DdmRZ7vOS1aCxz0gIk75vk62LeXhR7atCjpQACA/NS37W2tsF/X6u3XSdnMv9qhQhNw1Z50bXZp3g4hBGadYWi/u2Z9LfHr5vA/ry9W2qvz3QsNrDv8A5z+2Ahg9t3A+89APnMz8PK9YW9Tk90bVmNQwzrt/CV/Ba/jmYUm/ioOnP7zJt/23lK5W11HOILRACCzq/47ILcAAUFtbWEnGG1IjrrMumKgVPNaNOkSD2y5P3jf8uNNBhJiI/Pdpwt2A4A1hcDqfN+LEEowWkkNUN/Y8f0I0c7A6fdhuFwxMbS+5/K3JIqqgn/+zadvgrzmONS//R/sX3QLlidYH6Ntie0NbM4NqU126L5CLIPRGiLTFtpJeSy+UGt2jURRq14lxSoYLZRbd0VQg0fCXR+8XEl18DJO9pbyWpwOqLL41b6L3xih6RME4h2EzFu93rVB+j07rwsRERERERERERFFl0azAeWeEuU8hvAQUUcRQiArrrtyXrEm/AwAihvU87JbBD0mu7pql3drBq63l0pPGRql+gcZhlUSEREREVGoDKEJRpMMRouEOrMWUnN1oNUxKdGuLkqGklI4SSk/kVLOlzZG/EkpfwKwH4A1frPuE0K47K7SaRtDXIaIiIiIaNewaG7oyx40GYDvx3/xxHx9ucUf26rO41VPj7F7tBAl5IJ3gIUf6AuMHg/xfy9BiA4M13JZjEa1mtdJCCGAg46zLJMkt+Guopna+b//rZ6eKSphXjwe5q1nQP66sA2tbKN5s/XzsnoFTLp9ssC0gwRiW3ye9u4DfH2NgbiY1tuiSMsETp6urDpeNuDdxuswtr/vcZd44PqjBK44rLmOXunqZm3Nr4H571EwrzsB8tvoyg03NefRjY7PwNvhyOECr08TyAwh7n3mXAnjAi+Wa7ZrO3Ljh/j++PsvVFfV4cq37Z9uyd0ebOQ0GK0mCga02wmm8rdHT4GrjlAXMCXw5FfhOVVVWStx/XsmRt/pxdbAvMJWztxPoPhBEyf+egdyHj4D8Gq+dCNAADihSv+Zn7vc+vWQUuLSN/Rl/tzael55rbpcerLlahz5aLr6VHd5LVBsI3zCLjvb34BM9baWVwGUWlwnuVsmMHe6gV7pAvOv0J+6f/4sgX37R7Yz/GOmfv1N28e2EILRAKCgKrTliKi1UL4Pw6FvhsDHl4X282L3a0xU1wU2XEqJF74zceg1f2HUD6djt0GrkTysAr8l7Bm0zr/iBgKbciG3h0t5vBK3fGjCuMAL4wIvsq/yYt7voX/P615ny2C0EPtH2kV5LTaYuihL/moDq0+h1akYlyHQNUE9r0Kzj9lRdPu8/optjJtxcpyW3yI/r0qzyXRN8L2WLfXLEPjfJQb6bD9fEOz7w+r1ZjAaERERERERERHRzqeksVA7UDObITxE1IF04YzFDQXaZYob1fNa1hUr4hAjYpXlar1hvPgoBFahb1mx6qA4IiIiIiKiYIQmbsgEg9Eiodarv3gw2QhhYBrRLoLBaAQpZRmA09H6mvRhACZoFvHvcS3u1a3lv0zH3jqBiIiIiCiKyFCD0Q6aDNF70I6HYu9DgAF7qNfx3UfWbajzjXb0aM5juUI4mpT122Ajvzns5E9fQt5+pr7A6PEwHv8cIrODL9iK04z0BYC+Q9qvHREktgf3WZlW8aJ2XqlbPT2zagPw5/fA1+9BXj0J8uevQ21iSOQ2N2TeBqBMf2GNGLZPwLTEOIFnzzRQ9YSBtXcZKH7EwM+3uDC0u3o0srj0AW39I1a8ge8vLkXNkwYKHzZw7xSjVdBfz1R1neWyC7Zt3gR8/ynkzadAfv6Gdh3traPCPpw6fazvNb9vSvs3bF3cQDQiBiYEjphZ5GjZv4p9/3fGYDRviKF5txwrdgz69/fV6rZ/P5mmxJGPmXjwc4nftujLPXqqQO0sAy+fayD9sfOAl+9p87pDcWXZk9p5P220fj02lgJWX+lr/DZHbTBakuVqHNm3v37eevVNrEOi7Zta7Bv1SlOXqfcA64rUFRw9Alh3jwvjh/o25MP2ELjs0MCN+snTBc49MPKn9Yf3FDhgoHpe0/ZRF2LwT16Q0EAisifU78NwOHqkQOMzBmqeNFD+mIFlN9vvlw57xERpTeu+8N5PJc5/ReKbqv5YGb8HNsf2tV3furjtndWcpwAAJz9j4u6Pm+svqQEmP2Xi5SUmtjU4/77Xfd9ZBqMFCQgiaqXRYoOpj7LkrzZoy+mgNM0v0RW10XUfrjLNOQt/U2cHv2jMyeuVX9lcuHKbesEUzSmno0YIbLzPwN8PGCh91Lovr7DI6QsWjBYNx5FERERERERERETkjC6ER8BAt5jsdm4NEVGzLE04Y3FjnnaZ4gZ1n9ayLiGEdiC622LwenvQhb4lu7oiycXB80REREREFBqXcCmnS8lgtEhwm/pjSx7bEekxGI0AAFLKXwDM95t8lKZ4NAej/QfACIf/jg/TuomIiIiI2kwWbgbW/BrSsmLGw4ETDzxOXfiXbyBrA+9gJj94FuaJAyCP6AbzooPhKS5ULh7j4GhSbv0L5iUTII/MgJyyG+Tbj9tfOAzkczMt54vJ09qnIUGIxGRg+D8CZ3RNB/Y9rP0bFAkjxwHZvS2LuGDi3zXvOKo2w9Mi8aaxAXLOrFBa55jM3whz+kTIo7MgTx1mXdjiPYyPFRiYLZDRxTrFQrhcELM0oW+mCfnYlUiKF0iMC6ynlyYMCgDyYpov7pGv3m/ZhvakCx8SURaMBvguirruKAP3/7N9G9co4rAhrj++TJ6AH2t6OVp26/ZgIqfBaO4oGNAeamhecrzA7cerCxVUoc3hndfMkVi6MXi5yyYIJMQKyL/+BBa83aZ1tkWOtwiXlan7y9x86x+yVutvQgoAWNniOkMppUUwWvg+M1ldgQT1TVuRXxm21cC0EUTUUxOMBgB/bFVP36Nn4Gvx4EkCj54qMLY/MG4g8Po0gYsPab9+ZsIw9brm/OL7XxeMdt6BAifvo29nON8Pol2Z7vswlBDrULgMgaR4gdQkgX36CSy+3sCRw4Mvt2wjkHWVid7XefHRcoknvzLxfx+E/h28Jm4wAEDOvge5BRIfLleXmzpbIm2GiWMe96Koyv76dCVdAoiPUc/bFmJwJO2iPBYbTN2uEYwWbO8mTROm+9Hy6ApG0+3z+nPXIyAg0p+T47SWobNVdeoyKRa/5gsh0DPN159bqajVHzMFC0ZzMzCSiIiIiIiIiIio09GF8HSLzUSsoflxnoioHWTFaoLRNP2WV3pQ2qi+6ah/Xcmurspytd7A663bky6sUvdaEBERERER2SE0cUMmvO3ckl2DW3NsKWAgwdBcKElEDEajVj7zezxKU85/6FqWk5UIIbogMBitQlXWKSllkZRyhZN/AP4Kx7qJiIiIiMKicAvQo7/jxcQNz0Lk9A2crgtGa2wAfmydjSy/fh/y4cuAkjzfiNUVP8Lzv+eUi9sNRpMN9ZCXHQ78sQTweoGSPMinroP87DV7FYRA1tVC/rYIctVPkH/+AKxcar3AhH9GrC1OiQvvBBJanMgSAuLieyBido6LyURMDMSMRwCXJkFgu+x6TWqMRqa3tPWE7z5y2jRHZN56yK/fgzxlKLD8O9+2beXsmyDSHR06643cH+g9SD3vqzmQeeuVs3qm6qssbHkX102rIStL9YXbUajhVx3p2iMN3D65fRuYGzcUixMPcLxcTT1QXSe1IU86HT2g3eOV+HGDep6dIJjdu6vfnwYPULkt9HblVUg8tiB4esHovoBhCEgpIZ+7NfQV9h4Y2nL7Hw0xZ92Oh8dWf6IsVlBtoKJW/3xWF1g/1y9WSni8vjI19YBXs52lh/G3GyEEemj6uvzK8IVm6Gpq2Td1T9GHOOpCwbolB06LixGYMdHADze5sOh6F04fa8Box05waI5+3n8XmtoAjH8MAN6+0MBAzVdfXkV0hZgQdVbRtq+0/0CBT2e48NkMeweLeRXA8bNMzHirbX3CjpDfulp8t8ZjWbbRC3y2AjjuKfs7QFZhvYlx6nnbGABETlgFo9W3YQc1ylh90oOFX2dqboT44XLgnZ9MNHiiY9+izG2/7E+brOeHHIym2WRSEuzXp9Po1fdvwYLRKnaejD8iIiIiIiIiIqJdBkN4iChaZcWp+yG3Wa0cZF7aWKwd1J8V173V4yRNMJrbrHHYyvAqbmCfTERERERE4WcITTCadDjQiGyp9aqPLZNcydr3gogYjEatbfR7rBu1vdbvcT+H6/EvXyalLHdYBxERERHRTkmMGgfx9mqI2T9DnHcbMGTv4AslJkMce7Z63rB9gAz1D9/y569aP/5kdkCZRxOmKpeNcQVvFgDg9++A4sCQK/nFWzYrcEb+tgjyjBGQlx0GecE4yIsPsSwvXlsOYUTPobHY+xCIZxdDnHsrcMbVEE99CXHcuR3drLASBx8P8d/vgH9dAxx8vLJMmleTGqMREIwWQeasGyBP3R3y1jNsL2NMuy1s6xdCAAdN1s6Xp+4OKQNHUCfHAwmafL1SV2brCZvXtKWJYRNtYR923TLJwFdXGxiz/ezHbpnW5dtqddwQ5MYPCWnZreW+HEwnaupDWlVY/F0uMeZuE9V16vl2to2cFP28wqrQ2gUAby+z90KOGyQgK0shLzsMWPyx8xXtOxHi2lkQs3+BmPGwo0XFebdB3Pc+RE4fiAvuBAAMbdB/3nPVN1EFAPy2xXpd5bXAou1nEMstAiLSFWFgbaELRpv7W/jCMqwCcprEuARy1NdJamWE+bUIh8HZ+g/Vxa9LbNR8/TZ93+jejzxnX/NEpKHdV+rgw5sjhgusuav9GlHqyvD94fVg5Tp7X+bLNgJ/brX33aDbVzIEkKjZv97WGB0hTdRJeCwSpZYtaL92RJjVcUewYLR9+ukLnPasxNBbTPy4vuM/d2Vu+2145Xvrsk6O00rdwLYG3wK6sOdU/1uWhahQfcPKoMFopTUd//4QERERERERERGRM0UNecrpukAiIqL2km0RBqYKENMFPQKBwWLJLvUde1SBa+1JG1bpF+xGRERERETkhKGJGzLBYLRI0B1bJhsOB58Q7WKiZ/Q3RQP/S6V1l0iv8ns8yOF6dvN7vNLh8kREREREOzUhBMTAERDn3ATjhR8grnzMuvyLS/XzDAPY7yj1zM/fgPnsrTCfug5y4QfAD5+3mv1TwmjkxfZULuqyeTQpX7hDPWPpF/YqcEDW1ULefZ4yiE1FXPU4RL9hYW9HW4kBe0BMvRnGxfdAjBrX0c2JCDF0bxgX3Q3j7neAA44JmJ9uVjiqr6cn8EI86VXf4a8tzKdvAt561NlCh54U9nYITaDcDh+9ELiMEMhUX7ODkqYwie3kkk9CbVpYmZrz6NEejAYA44cKLL3ZBfNZF9bd48KzZ0au0bnxQ5Ebpw9Ge/RU/brzKgGnw9PdNfWQ7z8Nc8puMC+ZAPn9p8owvkiY8ZaJ3//Wz7cTBJNt8XtBW4LRVuivn2tlcu8CyEk9geXfOV6HuPE5GI98AjF5GkR8AjDlEvvLXvU4xDk3NYeBnn4VMPEU9PLkIVlzR9PVD94P89x/wHzoUsiX7oJcuxwA4DUlPvkj+Hv+4XJfmfJafZn0JNtPwRZdENf8lYCpSxByyG7f1DvdWb290qKvcxvWHYi1G4bbQkKs77n01DynfGdf80Sk4Y3ifaVB2QIvnN0+DSlxZezYn6mvsP9lvmCVve8Fq0BMfTCa7WYQAR7rDUY2Bkmc2gkE6y0mjbIusakUOOclEw2ejg3fstrv9ffmUmm5f+p013V9ie//Kk2IdEqCvXpmTrZ+re/6WN2wbUE20xL1IQcRERERERERERFFseJG9d3UrAKJiIjaQ2pMN8SKOOU8VYCYKiwNANJiMhBnxLealqQJRqv1dtyPHVJKFDeo++SsWAajERERERFR6AyhCUaTDEaLhFrN2B3dsSgR+TAYjVrK9Htcoin3p9/jUUIIJ8MY/Uf2+9dHREREREQtTT4f2G24et7x0yB6W2cViz6a+XW1wKv3A28/Dvl/pwbM/qzLEdo6Y+weTdY5GBXaVt9/ChRsslf28NMgTrwosu0he7r3C5iU6q10VMUE98LAidvCeyGKfOsx4I2HHS8njj0nrO0AAAz/h+Vs+eClkOVFAdPtBqPhtQcgF38cauvCRjcQPBrCPpyadpCBzfcbePpfApdPFLjhaP2TeOoMgc9mGHj8NIFnp1TjmfxL8WDh9cjxFCrL/xm/B9bFDVTOu/d4EzMmGuiWrF7X5jKpDXnSqflxEeSjV/hCKP9YAnndCZAz/x3xcDR3vcTc5dZl7GwbSfECXeLV84racGPPvPLgz39k3R845Eb/+wXYI+ashTjmrNbTDANi9s/BF558XsB3noiJgbjtFbhmPIwhDeuUi60uBLD2N+DD5yFfvBPy/P0hP38DawqBUnfw1X63NngwWprutgwhyknRbwQ/2dxFCMZu3zS0u/3OKsYAxjm99UQ7SE0S6J8RvJy/pqAgXVBdfmXHhpYQ7Sx0/ZErSnaWTh0j0D0lfPWNrPtDOb3eSEDt9p+o8gvtH3/a/d7X7eIYAkjQBaPt/DlWFE4ej/X8XxXHu52Q1be/CNJtHWhjPym3EPiig2/FVWZjH7klq/1Tp4dXudvHwlRrgtG6Jtr7bjhptHW5d3+SymM/d711gxmMRkRERERERERE1Ll4ZCPKGouV87LiGIxGRB1LCIEsTUijKkBMFZYGqEPFkg31XTfd3jZcWNZGVd4K1Ev1j0Dsk4mIiIiIqC2EJm7IBIPRIsGtCd1OZjAakSUGo1FL/iOr81SFpJT5AH5vMSkGwIEO1jPe7/GnDpYlIiIiItrliJgYiDvfBHr0bz3j6LMgZjwavIKsXiGtt8iVrZ23d1+bg+0tRnLK78N7KCAfvtx2WXHS9LCum0In0vwzuoF6oUkt0hhTpwgGqg1+IYrM2wDzzqkwD4r3/TtvP5hn7wNzxpEwbzwJcsWPvnLffgg563pHbUKXNIhrnoIYe7iz5WwQQkC89JNlGTm5D6Tfa6ALRiuOCXwP5H9ugHSamBVmut7D6KRns3qnC1x4iIHHTjVwz4kG5l1moGda8/zEWOCuEwQuOljgiOEClx1q4LzdCzCt4iVcWfYk7iyaqaz3p8QxqDXUyWcHpPwNAOjXTd2mNYX6UBUdt8cVOPGrOcDiec4qcmhtEeANskm6bG4bOZqQlsKq0MOa8oLkOSaatXgh/0IYlpEQCnsd7AtFy+mrnC0GjoC47RUgu4+2CnHqDPV0IYATL8JQqU5kWBM3uPUErxfyrqnY8luurab/shk48wUTU55Wv3Fd4oHYmPCG9+zbXz/vle/DE8ZlNxhtSI79OscOANKSoiPIyN8PNzrvdJuCgnSftWgIxthQInH2iybG3efFFW+byKtgWBt1PrpdtSjJRUNSvMDHl7dtxy3e8OLqmqdRt6or3t76b225pqDf/LJG23U3BMmiaqLr94UAkjWHLbUMRiMnPEE2mE2r26cdEdaWHGXDEPjmmuD9Sbj290JlFQissqVMP8/pcVpuoW+Bylr1gikJ9urZo6fAR9P1r3VNve840l+wfi8a9v+IiIiIiIiIiIjIvtLGIkjNAFhdGBERUXvSBYIVNQYOhVSFpenqSNIMRnebHfdjR3GDOtgNYJ9MRERERERtYwjF+CAApvS2c0t2DbWa0O0kTUg3Efl00qGkFG5CiAQAU/wmf2OxyP/8Hk+1uZ5haB3A5gYw386yRERERES7MtF3KIx3ciE+K4aYsw7iyyoYNz0HERsXfOHM0H74bhD6us85wG4wmj7FRt4wBfLr95w2S13Xdx8BlSX2Cu9/NMQe+4ZlvRQGqRkBk4Y0rLG9+OWlT0K5NQYJRpN5GyCn7gvMf6N54ppfgfV/Ar98A3z3EeRFB0O++gDkzafYaou47mmI13+HeHsVxMf5EMefb/t5OCUGjQTGHWtZRk6fCOltPhmd2UX9uS11BQajYfMaYLO98KNIcRL2IRvqtfVYzetIx4wU2HK/gY33Gsi900DF4wZuOsaA0fIJlhXt+HOful8cryN9w1IAwLAe6vc+t0A6HnBfY6gv/pKfvuqsIocuejV4UJ/dIJhszW8GhVUOGuRng8VXkEt68ELehRhd91vwio76N8S3dRDvroH4OB/Gk19oQ9GaiMNOhXhrJcQNzwbO3O8oiL5D9cu6XBjSU/1jVm78EOX0/Fn3W7anpdd/lKjQBESkJ9muxrYpo/UbwX++kVi6oe1hGdqAHL/Qu2Hd7ScT9c+IkhQjhfRkgSdOc9a+pmC0DE0gZ6m7jY0KUXWdRF2jxJ9bJfa6w8SrP0h8vx544kuJiQ+bqNAEiQQjpYTptDMlCgOvzaDGjrR3X4E/Zjr7KfKJgiuxcsxLWH2ngcqnYvHAa5cg9u0VyHxziXaZ0pgM1Is4/CEH2F5PfpBQ0ya6MCcBIFlzyFwTnbufFK081oF+csvadmpIZFkFo9nptg4eIrC3PgsYALBwTcd+J5c73Mcpdevb6vRprNk+nqeqTj0/NdF+XceOErh9sv5d+XVzYOPcQYLRShmMRkRERERERERE1KnoQngEBDJjHdwljIgoQrI1wWiqELSihsCwNEAdKpbsUl9Yphu83h5KGtXBbglGErq4NHctJCIiIiIissHQxA3pAvOpbdxe9YV0Sa7kdm4JUefCYDRqcj2AXi0eewF8bFH+9e1lmkwRQgy2uZ6W3pFSai7RJiIiIiIifyI5BSKnD0RcvP2FMnuGtK4GEaucPiTHN8DdFl2y0fZ58u3HQ2hZM1lbA/P+iyHaf04KAAAgAElEQVRvPMneAidPh7jzzTatk8IsNTCUa4yDAKi7imeqZ7iDBKPNmRU0PA0A5LO32GvIvhMhjjsXou9QiJ67QRiRP+Ui7pljXWDtcsjnbt3xUBdQU6wKRtu+fEfSDQRvGfYhP54N85QhkIenwbz4EMiNq5rnff2eb95hqTDPPwBy9c8RbrFzQgj0zRAYnCMQGxPYr8qnb9rx9/D6lUg0NQlTGmm/zAMADNVck7q6wPmA+1pDk2b17YeQQUIdQlVQKbF0Y/BydoNgcjTXg4USjFZUJXHUY15Ua85uuaQHCzdOxCnV9oJAxakzIISA6N4PIqWb7XaI2DiIY8+GuPMtYJ8JwG4jgNOuhLjr7aDLDhutDpFZFzsQHgSGpuXHdLfdLiuDssNSTStdEwQOtjhDedvctv9Ap+2bHrwY5qReMP97C6TXizH97dfZK73NzYqoSycIPH+W/aSl+Bjf/xnJmkDOdg7G+GG9xKiZXqRebiLpUhOjbjcDPrO5hcCHvzkPUvl5k8SEh0zEXWyiz3VevL2MPwJT+3ESItuRhvcUWHJpJbp7Ai/WzvEU7vh3QO0SvFJyCS6dOhxDzz8PQ3IE4mIEhGFA9ByA9Myu2udW4srA94n/gFsT4KqSV2HvM2+1T9olQT3PzWA0ciLYPvTOEoxmMU/Y7Lcm7m5dsKgato4bIqXcIuhMxSos1ipITiW30LeALhgtxUEwGgDcMkl/TmNrReC0YP1eSQ1DZImIiIiIiIiIiDqTYk0IT1pMBmINGzdyJSKKMFWoGQAUN7YOdvRKL0obi5RlVeFqyS71b866wevtwf85NcmK7Q5h94c2IiIiIiIiBUOorxMzGYwWEbWm+thSF9JNRD4xHd0ACi8hxJkA5kspCx0scz6A2/wmz5ZSbtItI6VcK4R4GcC52yfFAZgthJioCzoTQhwP4JwWkxoA3G63nUREREREFKLs3iEt1iDUFzEdNNjBD+kyyImwFT86aFGLaqUEtqyBvGQCUFkafAGXC+LWVyAOtRmgRu0nNSNgUrxswEHuRViUfJDlov0bNiJJblPP3PoXMHxswGRZVgjk/gK8+2RIzVXqM9hW+FC4CcMA7nsP8oZ/6gu9/hDkyZdBZHRHluY86ZZYdR8h7zgb4vDTwtDS0OhCKMTKHyHjioDyIsiHpjfP+PMHyMuPAN5c4QuFu/WM5nmrf4a88mjgtd8hMsIT6hQOsrTAtz3WB55KkUu/AFYt2/E4Bl7sVbcc3yftb7v+9F/mQtZUYnDRHwACl9taLjFcfY0Y4mOAek/g9BqrwJEt64ABu9tun119r7f3o4rtYLRUAVU0w+YyZ4P1pZQ4/TkTX+fqy/yxfjSGNKyzV+EJF0AMGuWoDf7E+BMhxp/oaJmh+48Avgx87g1GPDbG9sOgxvWtpheEKRhtTP/IXJg3fqjAt2vV7+XnK4CKWom0pNDXrQ0iMhuByhLgtQcghcCAC+7AHj2AleprE1vplRZyc9qFEALnHihw0GCJcfebKAlyrWdG1XrI3wvQbT0A7Bcwv6YeqG+UiI+N3MWZ7nqJ5X8DK/IkLnzV3md72Ubg7APsr6OyVuL4WSbytoeDbK0ATn9OIrurxIRhAmVuiV82AUO7A3268UJUCj9daI4rym6JJNf9jrHPXIa/1/5gXfCUy2Fc9l/tbJchkJ6kDhIqc3XDNuEs9aeg0l45XQ8ihEBynLpEDYPRyInGBuv5W2zuS0Y5q6Avu+M1htnYDf3wN4n9duuY790yZznWKLMIRnMaYL1m+5UBVZpTJCmaIEcrh+0OLFgVOH1FHrBgpcTI3kBOiu+1rg2yGZc7fG2IiIiIiIiIiIioYxU3qH/oVoUIERF1hCxNf1TjrUKttwZJ2wPOyhtL4IXiIjiow9WSDfVFlttMN0zphSECbzIZacUN6rBK3WtARERERERklwFNMFqw8aAUklpN6HaSJqSbiHwYjLbzOQ/Af4UQ7wJ4B8A3UkrlZdVCiDEAbgLgP1pyK4D/s7Gu27Yvm7798QEAFgghpkkpV7dYTzyACwA87Lf8w1bha0REREREFB4iIQly4Ejgrz8cLdcoYpXTY538rm818rWpiNcL4bJfqWyoh7xnGvDlO/YWOO0KiAknQeyxr+11UDtKy1JOvrH0gaDBaCdVvaedJ+88B3BXQZx4YfO095+GfPJawNMYSkuVxCX3AsefD5HUMXdnEOMmQY7YD/jTImjih8+BY8/GIPVLjTVxg+GFAZfijh6yJB8is2MuoPFqzqOLBW9BvvuMemZ5EbB0AeS3HwTOq6kEFs8DJk8LXyPbQL7zBOR/bgS86guvVPat+9l2MFq8WYdEWQd5zzRkbu4CKJarrpPwmOrQgJREoLg6cLpbJOlXuml12IPRtpZLeGz+pmI3CGag5rOQq76GTOunTbAMRcvwlGBgw3p9gRbE7a8DEyxCDiNoaI46KA4AcuOHBASj5YcrGK1fZAIrTh4jcMc8/f7HmS+Y+Oiy0C9S1NVstPzx79NXIafNxN59BVbmB98X6pXWOUKzBucI/HqLgRcWS6zOB95aFvjcBjWsQ99LR0EC6Ba/B7DbT8q6St1AzwgFwi1YKXHWiyYKqpwt994vEk+dEbxck2cXyR2haC3N+trE2iKBS9+QO77LLh4v8PipAjGuzvFeU+egDZGNks1M1tZAXncCsHxR8ML7ToSY/kDQYhld1MFoJa7AsOVgVPWoaAMxBZCsCRoKFhBE1Io3yPFp4WbI+m0Q8c7C/6KN1R6R3W5raHf9fmuTucsl7p1it1XhVW6zX2lSahE2qzudlhwPuBXhi2VuoKRaolITjJaa6PzLoWea+vWevURi9hLf9JuPFbhjsoA7SL+3LXynYYiIiIiIiIiIiKgdFDWqg9FUIUJERB0h26I/Km4sQD/XoO1/6+9omBkXeA2U1WD0WtONLq4UB60MD/bJREREREQUKYZgMFp7cmuC0ZINBqMRWWEw2s4pEcBZ2/+ZQoi1ADYCqATgBZABYE8AOYplywAcJaUMOhRUSvm3EGIKgM8BxG2fPA7ASiHEzwDWA0gFMBqA/5DTeQBucfa0iIiIiIgoZAdOchyM1iDilNPjnBxJmt7gZTwNgMv+AF85+27boWjio60QaZm266YOkNVTOTnFa51mMq52Ma4te9SyjHzkcmCf8RB9h0Ku+x3y0StCbmaAjB4Q76+HMGwmMUWQuOddyMl9tPPl3+sgAAzTDCKvNxKwKbYvdmvcGLjwX38A7RSMVt8o8f6vEj9t8g0CX6m5JqmLaT3aXL71KLByqXreZ69DdHAwmly5DPLV+4Dv5jledp9tv9gum25uT+tZNBdpCXsDAxRtgYGK8m3wnUpqrUu8Ohit1ggSjBZms74JHirVxLA51n9ItvqzsKkM2NYgkRhnr6IPfrVu26SaT5WBg60LnQtx3X8gOjDFJileoG83YHNZ4LzcuCE4Fv/P3n2HR1Htfxx/n9n0QhKSUIKA0ouI2EWxYBd7vfZesKBe61WvXq+/a0WvDXvvHbH3ih0vYgEJiNIJSSC975zfH5uQsufszm42Db+v5+Fhd+a0bJmd2Z3zmXdbLVvrsyTLRWjbTWPSTJCxeYobDlVcOdP8/Lz1Mywr1gzKju4xtwYRtXxNFa2CylL6Z2R4arOjAsI6woAsxTUHBB67q6ZoDry+gD/9gddEpn89z6w8aUO4SU5DsbWd4orwf/faMs2seYHwsb3HKHYcGv45K6/RnPx45KFoACNN35aHcN0b5hfDq3Ph1Tbbh/s+1Ww1CE7buZskVokNXDcQYOfXgQAsvw6Es7b6F2aZG3EdHVw3wjZcDXOWml+D3WD3FAB981neQtEAdcvrnj4Lc9IgvyB4ebEvmyRdE9H41lUGngtfmB0I26e9UpBqPmSmstb7/osQnoK7V/wOQzfv+LF0Ea+7wmPzIM4hZHDygtWQX6AZ0bfzP3Nt4V9piVBhDDOzbyts+5yj+wUCmk0WFkCZZVPYK4pcvf4edmX/85Zmh81U2EBICYwUQgghhBBCCCGEEKJnKayzhPAkSAiPEKJ7yIjrTbxKoF4H/whRWLeawUnDNtw26eXLIskJ/gEl1We/QG6lv7zTg9G01hTWrTKu6yPbZCGEEEIIIUQ7OZgvOO/iYT6oiIjWmirXMEkLSAlxLCqEkGC0vwIHGNn4L5yPgJO11iu8Nq61/lQpdSjwOM3hZwrYpvGfyXPAGVpr+UQUQgghhBCik6iDz0A/cWNEdazBaObvvMxcD1cIaKiHRG8zNPXK3+Gpmz2VVVc8KKFoPYDKyEYPGQtLfm21PE3bw6/+ud0qrnxiP+JpCNu+fv95OPkq9CnbtnusLanjL+kWoWgAKqsPfFiK3tMya7nxSh0jQgS+5CcMNwejLV8E2+/d/kGGUVGj2f8ul9mLw5cdVhem0NoQX2ss+SWygcWYfuVe9J1/DyS/RWF0nffgsSz/+g23M/0l1nLr15ZiCkazBX1UOylowBQzoH/52rg8WlOfcXngs9gHo40MvtgnEHhafi+EzQd4a2fWj/axKTTT1t0Tsr665TXUjvt566yDjepnCUZL2yJwGYUWinzt/2zNToXB2e1uxuqK/Ry+WOTnHctb/qlvNFdNiTIYzbJr47QNwSspYkCWt2C03B76O9aY/jDvz+35lC2pU4nsXfEBqbpqw/refsOLqlFx6IxLFhVo9vqvu+F1ed0bmluPUFy8d+jP3lf+FwhS62gFZTrigI8zntQcsZUmIyWy157W2hi81Z5AraB1xjo6dPiXx35DjTN4LJqwY/WwrFW9MOU3Rr5ukL+nf/0WPn7ZU1n1+A+oOG8/V2anmpcX+7Jb7ft44WooqYLsMBdZs4UTOQpSE83rTAFIQlg1hD+uZcXiHh+MFuoQyOtmKzNFcdhWihfnhD5GmPWj5tJ9On9jaPtc6ZNu3i6sNZ/jBNi3Pf0zID0Jyg0BaHOXaesY0pPsfdkM8Bje+/hXLpVhtnsSjCaEEEIIIYQQQgghRM/h1w0U1681rsuNlxAeIUT34CiHnPh+rK5bFrSusH618XZLuQnmE8hSfPYfkKv8FRGOsv0q3XKq3Srjutx4y0lwQgghhBBCCOGRwnxuvtt2boRotzpdS4M2ny+aGuJYVAghwWgbozuBlcBOwGAP5SuB94EZWuuPoulQa/22Umpz4DrgaCDLUvQbYLrW+pVo+hFCCCGEEEJET+UOQE85Bd56zHOdOhVvXJ4QyZGk9vBFWH342ZG6aBX6qZvh1fs9datOvQb2O8FTWdEN7LhfUDDaiNpFZPhLKPW1nombngSXjs73FIoGwLzZ8O5TsRppwA77wiFnx7bNdlKJSehBI2BZfvBKfyCXPC1J0T8DVpcGF1kZH5wEpYGHfurNOwWB+udPdthjtHly+TdLNNPfc6msg303V0zdVZEQFyj7eb7mwc81v6zSaA3D+8C5uzvsPqq5rae+0Z5C0QBG1Rn+xpaKzFdIBCC7c04G0n4/vPs0+qYzmxdm9YH15hNHvRpRu8hz2Qx/8xOd6Rqe9EbrqjAmEdiCPgBqVSJJ2jDz/Zv30FXlqJT2pzw9/11koWgAXrMKh+SAzzEHFyxc4y0YLb9AM9983hwATw6dxfgFP5tX5g5A/eNB1LZ7ehtwJxjRT/H+/ODHO3+Lv0H5la1eu4Vx7Q9G230kKNWxYRWX7O3wzi/m/ZA35mmumuK9rfoGzRNfa16ao1lTZi7jtN3nKS2mf8ZQT+3bgn66K718EfrmqTDvC1KBKbxrLJdAPen+MsoNV8stCnOu6H/e1kFhfVe8qjl5oiY7zf7aef676IInAUqrvZd966fo+sm60GVITmTBX7ZgFCFsujq7V69agj57F09l1dWPoSIIewq8/4PfFMW+3iSY9k3CKKoIH4xmC3NSCtIs+0vhAoKEaMXDdyIUruz4cXSwKLOhgzxwvKK6TvPWz/bPyMtf0Vy6T2z6i4RtPAN7w5Ki4OWLCuxt2R4vR8HIvjBnafC6H0NkhNu2V6Fss6l5m9vWK/8L35YEowkhhBBCCCGEEEII0XOsqy/ExW9cZwsSEkKIrtAnob8xGG1t3Wrj7VZ14/OMyxNVEj7i8BvOT630h7jqTQcprFtjXZebIGGVQgghhBBCiPZxlPmkY+1lPqiISKhjylRf++dgCbExk2C0jYzWeiYwE0AplQmMBQYCfYEUwAFKgPXAAuAnrbX5V4vI+l0LTFVKXUBzKFs/AsFrK4G5Wus/2tuPEEIIIYQQInrq0hnoJT/DgjmeytfHIhjNS5JDQ+jZkbq0GH3WJFgbYoZnC+rlxai+Az2VFd2DGrJ50FTbBOo5c/3D3JpzSavlU3dTpNaVeJia2+jPBej3n4vFMAN23A/nltdi114sDRxuDkZzmw/7B2RagtHi8vDjUK2SSdOVAFzc52buWnMUNJ5b8/o8l0dPVhy7naKmPhBSp5TirZ80B97T/KX3e79q/rcUnjhV8cF8zQF3u9S3+Obh55Uw80eXN89z2G9cINzmTY8BM70bisnxF3sqa5SZG33dCOgH/wnP3tZ6YTtD0QDSdCUDkytZXh0+RSnLLdlwu2VIWlvrdFrEwWjVKtkcjAbo8/dCPfJN2PGFsq5Sc+zDkScnOJasJK01Sil0Qz0qLp74OMWQ5FIWVWYElV1YoGl6QJrqmcz60T6+ZTc75D35pXnlkLGox3/o8FCwSI2ynLv821oH9cJv6L17A+CiKPZlG8v+39pruLbPtfjxhexLKfjH/h2f2rPbSPu67/6ENaWafhnenoeLX9bc83Ho16TT9qpIpUUM7Bc+TMLnQEayp2F0C3r1n+hjIwgx8q8zBqMVVzS/10ye/Dr4cfO78NqPmtN2NterrtN8GiY7M5RIgtFenxd9uospGEWIWErqwl/+dHkJ+ujRnsqqadNR+xwbUfu2ELNiX29rEOx2mwa2+8Z6leH7DBVOZNtfqpBgNBGJhvqwRXTRqhCfmj1DqE/OSHaNM1IUs87zUVKlueldzS3vmlu++2OX8yd3blKkKXgZYFR/xWf5hlDHSigq1+SkBz8Atq/TlIKR/RRzlgYXWLDK/ignmb/iC2n7zSKvY1NVF/r4SgghhBBCCCGEEEII0X0U1ttDeHLiJRhNCNF95Mabg8EKW4ShFdabg9FsQY9KKVJ9aZT5S4LWVfrDXAWwA9jGn6AS6eXL6uTRCCGEEEIIITY2DuZz7Ny2cyNEu1WFOKZMccJc5VmIv7guvm686Eha6xKt9Zda6+e11ndqrW/UWv9Haz1Da/2s1npuLELR2vRZp7X+RGv9uNb6Jq313VrrVyUUTQghhBBCiK6nfD7Uza/BIWfCkM1hy11Q/30H9jZPRq9TCcblCaHzTlqzzSJvqT50MBrvPOktFG2nKain50koWk80aIRx8Q2F13BT2nNsORC23RRuOUJx46EKKuwhT0EcB+Z94b38VrvB+EnBywePhOMuQV3/vPe2OpvlSh20uFLHgExzkX/nXk3i6AoyRxUyeNginsw4jgeyzggqd+rjmqRzXDIvcPGd5eKc6W8VitbkqW80PyzV3PZ+61C0DUPScNVrzfXyC0L/aU22qpnrraBNtYf0i3bSBcuDQ9G8ik+AjOzmfwlJrVara59k5KbhQ9EAsvzrN9yOw0+a5eoilZYv0FPMHwEAVDshUpzy56I9BlnabH9DdD+i+Fq8BXTBctx/HoM7KRG9S1Lg/93TcCcl4j54DSPWmIPL8n8v4YP5GudM/4bXuHOmn49/a/15ZgtG234z2CQrxHZq2PhuGQYwsq95TIXlUNSQAjtNAWC9Lwu/Mifu7Fn5Me+5FzJ5FIwbEFg2MKs56MJRMHkUfHW5w4RBHf8YKKX44Wr7199eAxl/W62Z8Un4skHBaCXFbD0Y0kKEDAJkp4JjS/XrhvRD/4qofLZ/nXF5qDAiN0Sw7ofz7fU+XAB1wRfs9cxrMFp9g+aDEOMQoqvtNKzjtyn6py9xz5qEu0cG7rGbo+d8hF6/Fr1/X0/11RUPwBHnRdxvji0YLS6HSpViXDeqvyLZEghU5OGC3rZDWqXs2/hKCUYTkfCHD0ajyDzhoScJ9fVQNLvHmSmKE3ewV7QFpnUkWzDaGPOcHAB+s8wvtI3eUTDYnFPM74X2fqIJzVRKcfLE2HymaA217dhPE0IIIYQQQgghhBBCdJ6WgUItZcT1JtFJMq4TQoiukJtgCUZrDBNztZ8iS9ijLVQNIMVn/mG6yu2CYDTLNjk3oX+3PAdNCCGEEEII0bM4lnlorpZgtFirDHFMaTsOFUIEdOF144UQQgghhBBCdDaVlYu6+O7WC7feHV1fC5+80mpxvSUYLT6iYDQPX4Q1hJ4ErH8MH2qlHv4aNXIrr6MS3Y0lGE0BlxRO57Lbj2+1XFdGEIy2zmPiVlw86q3VqJR07213Nz7Lm9PfnEyWl6WwT7EOWBk/gFPzHmr3cK6Z5fJ+iOCYH5fDkkLNJlnwZ7G3NveveLd9g6oIvpJjzH0xK6pq6pJ7UAcHh9G1NarI5cMF4Sf5Z7W5amWmW0qFz/vrOzXB/lqpUWGSnr54HQ4/x3NfLX22UIec0B9KU7aUdl30dSfCz1+ZCz51MyP63MBb7B+06t25NTz5c/Bn1563u8y/zmFUf0VBmebrJeamD96ycRC2YLS0jHB/RpcYFeKizmc86TJz0kHoL9+iyGdJYQByGwrZZvET7P7QjG5z4t2EQYpR/cyBE2/+pDndkIPZ1hNfa085r07b90tpEQlxipsPV5z7rL0BW8hPd6TrauGD5yKq09tv3sCHCkYrCRFQlmQJNwKYOdf+OPscePN8h33GKt77VbPfncHv87Ia0FqHff2uKIFqDxk2QnSF3UfClHEd24deV4A+d3LzguWL0BcFf6baqOfnowYMjarvbEs+bLGvN1WOORgtJQGy02DF+uB1RRWawFGHXahwolRLkGyFBKOJSIQLiwcoXNnx4+hgoXanot1zHN0fBvWGZYYc1pUlUFKlyUzpvP1SW7ZrXoaiV5KmrCZ43dJ1mp0Nj4AtZE0pe+D52hBhj6H2oUIZGeI4IVIzPtFcvHf3OE4QQgghhBBCCCGEEELYNQUKtdUnRIiQEEJ0Bdt2qdxfSrW/imq3kgZtvnKLLVQNINVyjl2l5cKkHcm2Tc6Nj+GPOEIIIYQQQoi/LAfzPDS37UXjRbtVWY4pk5wUfCqSybpC/PVIMJoQQgghhBBC/MUppeBfT6PbBKPVYZ41mRDJkaTb/mA0luWHXr/VbhKK1sOplHT7JOk/F6CrylsFlulij2FnkdjtsJ4digZguVJHy4BC2wTqjvDOL+HLvPGTZt+xyjrpu6Vkt4ojy19t36BsYVUxomuq0HdeHFmlbfdAHX8ZaqvdPBUf2ddbs5ltgtEy/KWsiN/E87BSQmSfVavkkHX1z1+jogxGu+W96H9AqWrKlJjzkT0UrdHIukXG5Wvj7A/wjE81dx+j+GiBPSjrkHDBaOmd+CaMQF4mpCWag1xenwcNxx2ML+1yCv051jZy/UWgG+DzWbDrIR042sgcOF7x25rgJ+yD+VBdp0lOCB3O8OIcD6logNMmDFaXFKGAqbs5fP27y9PfmtvJ7knBaI/+O+I6OZZgtKI/VgHmbdIC8zmdACRaQj1WlWge/8r+XM25ymH8wMBznWHZhPndwHsgPcxFzos6/wLAoofyOY3/VIvbjf8cZV8XapljWZeaqNhlBBy3vQq7XWsvffCg6CrudhjqojtQvT3uzBhkp5mDW4t82VQ65tS01MRACKU5GC18n6EOaVMTzeOplGA0EYlw34kArFziKbyzOwsVNBvtn6WU4sDxihmfmBv/aAEcvnV0bUfDdlzrc2Bgb/h1VfC6lYZtU+i2FAMywweetxVtMFpeDHOd7/9Mc/HesWtPCCGEEEIIIYQQQgjRMdbWWUJ4QoQICSFEVwi1XSqsX021337VvlDBYrZgtCp/558wUlhnuBoksk0WQgghhBBCxIZjmYfman8nj2TjV2k5pkz19aAJJUJ0EQlGE0IIIYQQQgiBchz05CPh45c2LKtTCcayCZGE0GsPATf1ddZVurwElpvDa5qoY/8ewYBEd6X+/Sz6mmON6/R/L0Jd9XDzgu/ej23nQ8aizv5PbNvsCo7lzek2fyHdmcFoXlz0gmbw1PCz4BPcWu5ZcyH9G8wn+nhWWYp2XZRjCZFrB712BfrYzb1XcBzUuTejjpoWUT+j+nmbBJ/ltp5h39u/LqJ+UkMFozmhg9Goju4ksPIazUe/RVUVgN//KMW971Z49rawZYdbgtFCmb0o8LibAg0AhveBUf2bgtFKjGVUWgyTBWJIKcUWm8BXv5vXf1mQya6X3UfRf18xrk9xK0nR1QDoq4+GT6tQvu5x1ZoDt1Dc+l7we6a6Ht6fDwdvGZt+VNv35ao/Nty86XBlDUYbmtszAk50VQU8Mz3ietmWbU/x3B/RFenG98S5z9r3H5MtoR6HzLDXeWVqcygaBAKSbP4sgnFhMiSLOv8CwB1GqfDBXEFBXBEEeTnGdap10FcE4V/hlrWqp+x9GscZRUhZqMfHcXrGeztSes7HUdVT/3kRtcvB7e7f9v6tclIp9vU2rktJsNcrtp8Hv4Ftr8txAqGiJtX14Hc1vo30dSBizEswWsEy+HMBbDam48fTBdrzTrn5MHsw2pEPuLgPdt4+qWvZYPicwLG46ThilSVPOVTI2iZZkY8t2mC0AVmRh7DZ/F4Yk2aEEEIIIYQQQgghhBAdrLDeEowWLyE8QojuJTMumzgVT4MO/r2tsG41Va75B+E0XwbJPvOFtwBSHPMPzJX+zj9hRLbJQgghhBBCiI6ksASj4WE+qIiILWzbdgwqhGgmwWhCCCGEEEIIIQLaBJjUK/OsyYRIjiS1h8mT//sURgSnkujSYvQBeaHrjtwHVC0AACAASURBVNwKttkzggGJbmvH/ezr/vfJhpu6sgyW/Nr+/jJzUSdfCZsMhS13QSWGCXrqCWxhX27zF9J5GbGZ0BxLh91n/8L8v2suJtMtZVLVbDatX9b+zlw3EFjVyxycES09/3v0xVOgtjp0wUPPQg0aAcnpMG7HwO0IjbJfrLKVTH/rGfZ5DeaTpGxSzNmYAFSrpNCVi6MLsHv/V6hriKoqAP3euAWKb/dUdmRt5MFo81aA1pqFa8zvo202bRHpUGFJOEjrZumELRy7veKr381/26wfNbsdfRiFVbvCrOD1uQ1Fre7rG05H/fOxjhhmxHYcCtmp5vCbQ+91qbvPIc5nj+NY5yE0B0C1/fFv2cINN/MyFUdto3hxTvDje8x2PSQ053PDE++BLZSx2NcbPpsJU05utdx1NT+tsLdneoXW1GvmWeokxcPebTJkBveGeB/UGy5k9dsaD8FoFZF9lh6yZWD7kJkSLvhLeQrlsod/RR5SplQPef2JbkHX1qAvCrHPbrPN5JiEokFge26zIt785k358QNy+u+FaQtS5CHL1RZ0pAgdJFtVB+lhdpmEALwFowH8+HmPDkbz8vVQNFISFZvnwS+W8OL7P3M5e9fYB2ObhAozy8s0B4ytWm9+YKxtKRgQYTCaUoF9n2jkdc9cZyGEEEIIIYQQQgghRAdxtZ+iugLjutwECeERQnQvjnLIje/H6rrlQevW1q+m2m8+6adPmFCxVJ8lGM2N7mKh0aryV1DhLzOuk22yEEIIIYQQIhYcZQlG0xKMFmtVlmPKFMsxqBCimQSjCSGEEEIIIYQIcFrPkqxT5lSctsFoWmv46i30G49CYgpqjyObJ7674b8I0zMuR/3twsDtL99Ev/M0VJXD9x+Grjh6W9TVj6B8Uc7uFN2KSkpBJyRBXU3wyrUr0HW1qIREWL82Nv2deDnq8HNi0la34VjeCy3ehwN6+YGe8Z7Zq+IDzl9/X+wb/u4D2PPodjej/X54ZQb663dgzsdhy6vrnkFNPqLd/eZlQloiVNSGLpflL2l1v3+EwWiJcYEJ/KYJ+dVOmCDBZflorSMO3Hnjp/BpCQOzYPl687p9Kj7w3FdffwGD65ayNGGw5zoAa8thgSX3bWRjaJ3WGsotg0ztvskCZ++iOO9Z83PwxjzNf4+GIrIwBTrk+lsHo/H+s+jJh6N2OqADRmqmi1ah774UPn45sGDPo1HTbsOXlcuULRRPfm3+2xKmukwZB3uPVfRKgplzNWvLYevBiiv2VZSGyTts4rT98W/FYrTfv2E/5Z5jFBU1mrd/CazulQQ3HqbYa0z3DqbS5evRj/0fvHRPVPVtwWilvgz07DdRbYLRTAF2LVUatn2LCswhZwBbD4LUxNaPcXycYmhuIAStrYUFmkDckV24Mbb0zOmKY7brnDAWITrczPujqqaueTJmQ8gJ8dv/mjhzemzKnLfpvf/WQHCSULGHoENbmJNSgX0ym8paCUYT3ug1S72VW74ozCdU9xbq3dbenM7thyh+WWXu4ZxnNKfupEmI6/hHzxak6KjAcZzJKkuesm3b43MgNw0ykvG8n5oUF30YaqQhbEIIIYQQQgghhBBCiJ5tfUMRfsxXtMuN93glPyGE6ES5Cf2NwWiFdaupds0neIQLFUvxpRuXV/nLIx9gOxTV2y9OKttkIYQQQgghRCw4mM9z120vGi/ardJyTGkL5xZCNJNgNCGEEEIIIYQQAW1S/utUvLFYgq/NZMrXHkTfPm3DXf3xSzBtOurI88G1pFS0odevha/fRd94hrexjtkOdf/nUU/sFN3UUdPg6VvM6169D/52YSD0Jhb2PjY27XQnjiV4pWUwWloDPSUYbUTd4g5pV193IozfGZU7oH3t/OdU+OB5T2XVebfEJBQNAhPaR/WDOWHyEzLd1sFcAxpWRdaP1iTHmwPYalSYYLTqCiheDTl5EfX51e/m2f+nT1LMOEaxuhQG9oZZP8Jh97X+oWVi1VeMq/3Fc18KOGf9/Vze98aIxvjst5oFloy5UU3nm83/HirNV+sk3ZKI0A04juKxkxWnPB78PCwpgqJyTaHlwqfZ/uKgZfqKw+Gu91ETdo31UFv3U18HxavRR45oveLDF9DfvAcvLODALbKswWgAb/0Mb/3cev03SzQPfBY+MKeJ0/bHv/o6WPMnDBgKQE664s1pPoorNAVlgSA9n9O992N0VQX6lO2gYFlkFTNyAvuA5evJ9JvTPkp8mYH3ShsrSwyFW6iqC1620HzBcgBuPdL82TiqnzkYbUlh6P4BijxeAHhwNhw6oXs/x0JEQr/9RGQV0jJQry1DJcYuHax3auR1Ut1Kstf8BAR/Hnl5P4cKOkoNEYwWLsRWCGj8LmTWQ94KL1/UsYPpYLagL2h/MNq5uysemW3v4LN82GtM+/rwwhQqDYEwM1sw2hpLMJqtLccJ7LfvM1bx4hxv+6pJ5q/3PElPiu2+TDQB2kIIIYQQQgghhBBC/FXNLnmfd4tf6tQ+G7Q5FA3CBwkJIURXsAWE/VA+2zqRP1yoWKpjnpRe6fd4wkgI+VW/8PH611lZuxTd9iKMbdRrw0kyQJyKJzMuu91jEUIIIYQQQghHmc+1z6/6hat/9zjHs5PFqXiGJI9k3+yj6NNNv6/6ueJ7Pi95hzV1K9CNJ09W+M1zjFIcczi3EKKZBKMJIYQQQgghhAjwtQ5LqlMJxmLxLYrpyjL0/VcFldHP3AaHToUwP9xvUFKMfupmz0NVh02ViZQbIXXK1WhLMJqecTkcdDp89Xb7+7nkHlTGRnhiiGMJPGsRUNgroYE0fx0VlqsaRiojGQb1hhN2VIzoozjk3thdFWTLmnkxayvIu8/ACZdFVVUvW4g+ZzKUFnmuo46+IKq+bCYMUsxZGnoSfE5D66CqvAZLmpeFem46yVlnUUHwa6Xa8RBw8sf8iILRyms0i9ea1+0zRhEfpxjU+LY9ZALMPMfh9g9cVq6HfdT3XD/7cBy8h1gx+UgunvscN/ovo8SX5bnaxS/Z+9h6kEJrjT57krmAUjB0c+9j7AJ7jVFgeRz7XOySZgmAyW0wp0npaXvDY9+jhm0RoxG2aPvPBejp58Ev34DfcnJ2RQnMepi9j4wuVLMhgk1aUDAawNKFG4LRmmSnKbJ7ykV93nky8lA0QB0+FXY9FP3QtWQsNe9Pljq9YN0adEUpKi1jw/JVYYLRKmuDX5+/rbG/L3cYYt5fzMs0v9bXVYbfjoQKUtosB+oaYJcRiulHKJLiZX9V9Hx6+SL0pQfByiXeKw0Zi7rtzZiGogHE+RSZKVBS5b1OiltFzrLvIC66YDRbmJNSkGrexAFQKcFowov3n/Nedll+x42jE4QMRmtn21sOVEwZFwi7Nfnbgy7Fd8QuIFy/+Rj6iRuhohRGb4O6/D5U30H4QwQp9k4xryutNi+3teVrPA9tt5Hw4hxv421PMFqs1fshQc5QEUIIIYQQQgghhBDCkxq3inWW3+I7Wy9fJklOmIvoCSFEF8hNMJ+fZgsVC9QJPXE+xXJuZVU7g9Hyq37h7uX/wo89hNKL3Ph+1vACIYQQQgghhIiEwnxsUa/rus33UiZr61cxv/JHLht8C73jc7t6OK3MLf+Kh1fdivY4vynV11MmlwjRdeS0UyGEEEIIIYQQAU7rL7NswWitJjB+PguqyoMLFa+GFYtCz3xtqbQIViz2VnboONjzaG9lRY+iEhLRA4fD8kXG9fpvo0M3cOhZMPOB0GV2Owx1cPe8akW72U52cVuE9fgbGFy/gl99YyNq+tSSx3lw9TmoM6+H3AEwYAgMH49Kaj27+6O/O+xxe/vD0ZR22b/i3Xa3Y6Mf/CcMGQP9N4NNR6Mc+4lCuqEeFs6FkkIoKULfdKb3jtIyUfd/FoMRt3bgeMVDX4Tevmb717W6n1e/KqI+nJpKkmvWQ7whGE2FP9lVf/s+ats9Pff380r7ugmDgpcdNKqag+J+gvVr0Vce6bkffHGoa59C7X4YWmt+nrwpQ4cuoM6xJH55NLo/DO2j0M9Mtxfadk9UVp929dPR+mdAryQoqzGvr7CEvOT47UGB+pRt4e0CVHpmxOPRtdUw/3soWA6JyZDQ/DzpKw7z1sZ7T5N+wmVM3U1x36cRhOdFyDGFwS7Lh4n7d1if7aHr62DF74EQ283GBAXO6toa9It3Rd7wgCFw4GmonP6oG1+m9yINtwY/NtVOCrUqAf/vi5mXuhUj+kJuumJlSejnyPQazF9jLnvweHs7tnC6dZUhuweguMI8xtMnKR48QU48FRsP3dAAC/9nD/y06TMQ9fgPHRZk3Sc9wmA0XUV2wS8wIHhdsYfz1l3LMa2jIC1E7lul/Tx7ITbQL9zpvfCqP9CVZajUXh03oA5QVav5/k/48nf7Z3wsNhfPneHQa5r5WHR9Ffxzlstl+yjSk9rXmf7qbfTNZzcv+P5D9Pl7oZ+db/0KzOdARrI5lLWsBrTWQdtMv+WwuikYbZMse6BxW+0NRtthCHwTQTZmKDX1EowmhBBCCCGEEEIIIURPFC5ESAghukqf+Mi3T7lh6tgmpVe5FbjajTqU7OP1r7c7FA1kmyyEEEIIIYSIHZ+K3QVHO1uZfz0/lM9mr96HdvVQWvlg3WueQ9HAHs4thGgmp50KIYQQQgghhAhwWn+ZVa/MMycTWhTTX75pb69oFVSWeepaf/Oep3IA6qGvUL6e+8WbCGPH/azBaKxfG7Kq2vlAdIhgNHXpvTDl5HYMrpvz2YLR/M23/Q0cWPEWvyaZg9GG5MABNR/wx+pA8kyGv5Q9Kz/muLLngMZAsZb++zZqmz023J00HIbmwu+GC4OM7g/PnO6w1fXhg9P2q3iPvv7Qz3d76SsOD9wYtgXc9Cqq78DgMvO/R//jCFhnSd0JJSMb9fDXqH6D2znSYHuMgtREqLSEVAFk+4tb3c9rWB1RHw4uyW61cV2NCpEC0uS7DyLq7+cV5i/+05Ng0+zWy/Tns9D/dwpUe0gwarLrodBnE9TkI1Cb7wCAUor+Bx/G6bMf497eZ4dpILSDxgeCDPQ7T1rL9IRQRqUUo/rBd39GVi83RDAaAG8+Csf8PaI29eez0FcdFdlATJYuxL19GndP+y/3fdr+5mwcgrdtetlCOiYWqH30H/PR/zoBlvwSWDB+Elz3NCq7X2B9/o/o07b31JZ65Fv49Rv0gh9Qg0fCvsdvaAcgI0SO4oOZp3HFfeOobQzwnLaHIjNM7qJpu/fbGvP2Y2R/+6PfO9W8vNhTMJp5eY5crElsRPTqPwPb4EXzIq+83/EdFooGsO2mivwC7ycMpLqVxGnzyeWl1eZAopZsQUcKSIwLBKS5hjJLCjUTh3bHTwHRrRSGSAc2WTQPtowwrLALvTjH5aRHNbVh5nfEYpuRlqS47zjF1GfMb9r/vKW56yPNoyc5HL519P3ph64NXrj6T9yv3gbMgbiOgl6WfZx6P9Q2BIeX+U0bFsDXOPQBEWQOJ7czGO34HRTfLIlNwHBNvf2xEEIIIYQQQgghhBBCdF99EvK6eghCCGEU6fZJoegTJlgs1TIpXaOpcatIsQSnhbOkemFU9drqEy/bZCGEEEIIIURs2I5/eoqlNZb5h12kQdeztGZxRHUy43p30GiE2HhEF1EvhBBCCCGEEGLj0yJszI+DX5mztBMaF+v6OpjzsbU5/c7T3vt+5lZPxdTLi1DxCd7bFT2OOukf0VfeZg84zBBulJyKuvFl1EGnbdyherYrEeoWYT0N9VxcfAc7VX0ZVGxYVh3vXejw339uxcwVRzFzxVE8vvoMji97zhrsoy/aH/e+K9HFgeCwOJ/iob2WkhVX06rccfGf8dOf2zF+zgP8cOJi+idYUmUa3V5wacj16tIZOF/UwuY7hiznyeKf0LeeE7RYNzSgrz46ulA0QL22rENC0QCSExT7mrPtAOiVBPH7HdtqWd4++0TUh4NLsq4xrqt2PASjrf4TbUsSMVi2zrx88zxwnOZXoF5fiP7X8ZGFoo2biPN/z+NMm74hFK2JOv4yblp7FdtXfbthWZyu5+QSe8CZyUHlb6ILlsNSywlscfEwcUpEbXaVcZtEHhSRO2mXkOv1vf9A+/0hy7QqX7wmNqFoTWY+AMeO5tODf45dm22YgtFYlt9h/UVD19ag330afeKE5lA0gHlfoA8ZjDvjCvQz0z2HoqEUDB6FOvRsnCsfQh13SatQNCBk0NlF/W6j1m3+XL7rI82/3wy93WgbjKa1ZmGBuezIvvZ2sm3BaKE/ngAokmA08Regp58XXSjaFjuhIgzCjNQBW0RWPsWtJsMtNa5rcKG6LnR921bJcQJhTqmJ5vUnPhqbECEhWsmf29Uj8GxpseaYh8KHosXS0dsq4kKc/VBeA0c+4PLdH9G9P92Vf/DpqgxuyL6MF9MPp0o17+j4f7M/Nz4ncIxmU2rIo/Zb8sSb8tDzIghGaxu6FqlTJsYu5LGmPmZNCSGEEEIIIYQQQgghOtHW6Tt39RCEEMKod3wumyWN9Fx+dOqEsMFmqSHWV/jLPffVUrW/kgq/+XfrSG3dS7bJQgghhBBCiNgYkzqhq4fQLjWuec5TVymuX4s2zSux8BHH2NStOnBEQmwczLPchRBCCCGEEEL89TjNwRT1yj5rsikYjSW/QGWZvb33n43RwALUy4tRfQfGtE3R/aheveHvd6JvvyDyuo4DF94BB5wKC75Hr1mGGrU1jN4GlTugA0bbzdhC31qGEfkbyHJL+GDp/nydsj0/Jo7HVQ4j6hax6z/uoFefzYA+6BMuh6du9tbvs7ehn70NZnwCq/5gl5vO5Fey+Dh1N4p92WxV8z92qP4OBejbL2A88JOTyQEDZ/JtSnD4zur8QeT6i+z9JSbD7ocDoI79O/rKI72NM5TvPkCXFKEyc5qXzfsCCldG1Zx65FtUXMd+7XbQeMUr/zNP6C+rAefKh9CHnAmr/oBNhpI6amuyLvSzvspb+wpNkmuYoQ9UtwgAICER6mqDC9VUQVU5pPby1N9qy3lfm2S1mYT/2UyoD5Ng0oY63h60p3L6k3rOv/hixmQ+St2dYl82E2p+ZGD9Cp7IOB5tCxxsoV/DGrZ9+Cj0wyECFg46vcNfE7Fy8kTFI7MjC4vos8c+kHgBvHCntYw+cyd4YLanx0Ef0gGhgquXsvNN23PsxK94dv2WMW9emaJzulEwmi5ahb74AFjyq73Q8/+1BgAZ9RuMSgwdlJiZEkmD4ZW32dwUlAVCTkxG9bOHeGSnKkxxR+sqA2FrStnr2oLRbGFrQvQ0etUS+O4DT2XVtU9Bckpgf2PT0TBhtw7/vNtqkPn9a5OqK/G59nDOkmpIsYSbAbiW8xOathK2bRBAVa0mJTF2gUJi46JtL65QdRbOtYZWdzezftREkJMcE5kpiolD4fMwF4Hc4UaX2Zc7TBwa2aN50aMl3D34vQ33t67+H28vP4hs/zr8P3wGXGWs5yjICBEWW1YNfdscNoULRstNg3gf1HvIHm5vMFpyguKlsxyOfCDy12xbNZ0YlCeEEEIIIYQQQgghhIiNPbMOYVTK+K4ehhBCWB3X71zuXXE96xoKQ5brE5/H0X3OCNteipNuXVflLwf6RzpECuujuzhrWwfmHMegxKExaUsIIYQQQggh+iYM4Ni+U3mu4H50ZDMJuoVay5ynrrK2brXnsj7iODXv4rDh3UIICUYTQgghhBBCCNGkRfhLnUqwFov79CXc+56Gb96zlom5CbtKKNpfySFnQRTBaEAgyGT4eBg+vsdMGI8ZW4CTbjF52R+YhZxAPbtWzWbXqtnN1VOag9XUnkejvQajNXVz7u4bbvehkL+VvWQtm+WW8OnSvbi0703cl3UmfhXH+JqfeGD1OaFD0QA1/Q1Uelbgzs4HRjRGK61h7me482ZDwTLYdAz8uSDydlLSURf+FzUi9qFLbU3ZInwoiBqzLYzZdsP9AZl4DkbL9q8jRZsLVzot0n/6bAIrfjc3UrQqgmA089/SP7P5tq6qQN92vqf2WlIT9w9d4KhpODMuZ6/Kj1stHl63mPzEEWHbP6D8bZxwz8V5t4Rtp7vYaZjiwRMU057X1NR7q5ObDmrqjejli+Crt82F8ufCO0+iJ+6PnnEFfPBcYPkBp6LO/j9URjYA+pt3Y/BX2N3zzT7UTZnPy4uzY9qui2EbvH4t7jXHok64HDW8a08U1y/eHToULRpjtgtbJC0xEAbixuh30rVlrYPLVpbYyw7Nta/Ltvx+2OAGQo56WcJLtNbWYLSctL/cnofYCGmt0Vf/zXuFEVuiBoX/rIylTbMDwUC20KC2UtwqkrU9vWx1Kbw4x+Xj3zR9eymu3F+xWU7z+9m2+XI8vOWLKmBQiNA18Re3fm3kdfLnxn4cHWTpuq7pd+Y5DtkXhd9A7HxzoMxuI+De4xxG9Q/9pp67THP38i1aLfsheSvuzTqLfxbdiFtTDZb3u8+x71sAlBrOzQoXjOY4ioFZsCT04TPQ/mA0gBT714QR8Xp8IYQQQgghhBBCCCGEgDGpE0j12cN5OlqcimezpBHkJPTrsjEIIYQXeYmDuGaze/ijJp919ebf4HLj+zE4aTjxTvgfPZKcZBwcXIJ/sKl0LSeNhFFomRwfp+I4pu/UsPUTVCKbJY+kd3yIk2GEEEIIIYQQIgo7Z+7D+LQdWFz9KzXdLGisyYLKH5lT/kXQ8u423sJ687Ffui+DQ3JP3HA/xUljWMqYLv3uT4ieRILRhBBCCCGEEEIE+JpDkeqVfdZk/LM3QW2MgzXCUGf+u1P7E11LKYXedk/4/kPvlTbfoeMG1FM4PvNy1998uzEYzcjX/DWRGjIWvesh8NlrMRpcsHgauKPgEv5VeD1VTgp9GwrwGU4m2qDfYNQLv6Gc5vAhpRQ8+wv62M3bPR59zbHNd2a/GVFd9VI+VFXAoBGouBjMOvegd6qiby8oKAteN2WcuU5eJvyyylv7I2sXkuZWGtdVOSnNd/oMtAejFa+BwaM89bfKEm6UlxH4X9fXoc+c6KmtltRjc8KXcRy45B709PNaLT93/f1c0O/2sPUPrAjzehk0AhUfoySBTnL6JIeTJ2q2u8Hlx+Xhy+ekgfL54KZX0YcMhnUFxnL6udvhtvPA32K79Oaj6C9mwcuLUUkp6LefjG7QvfvCuInw2cyQxXq55Tz/xkDWv1XFH0Uax23g1Gfi+WlldN02SbH9qPbJK+hv3oM730ON3qZ9nURB19cFbnz0Ymwb9sWhDj8nbDHHUWQkew9lDKe6HsqqIaNxM2TbdsT7Aq9Lm96p9nWrS+3hJeU1gfA0k1D9CdFT6If/BYvmea+Qt1mHjcUmPk6xWQ4s9pgplepW0cs17DA12uM2P2U1TYFImhe+13zzD4cxeYFltmBH5SEYrSbErrcQ/Ppt5HWWLUTX1aISun/iXrHHOSGJMT5bIStV8chJitOe8JbK+mk+jLnW5bsrHcbmtV6XEAe+xhTEl34wt/d6+oH8s+hG/CuXwhBzHz4H0pPsYygzZDf6LcP3tcjiHd3fYzBaDB7jhBg9TxKMJoQQQgghhBBCCCGEd3mJg8lLHNzVwxBCiB4hwUlkZIrlpLkIKaVI8aVT4S8NWlflL4+qTdvk+Jz4fuyYsUdUbQohhBBCCCFErKTHZTAhPfL5Op3Fr/09IxjNEoqdlzhIjv2EaAcnfBEhhBBCCCGEEH8JLUKV6pQ9wCVB13XGaDZQ099ASejVX8+ICREVVwee1kED6UEcy9c8bosEl5ZhRG35Ws90Vlc/BkecG4OBhZbplpLXsDp0KNq+J6AenN0qFK2JGjgc9eCXHTjCMI48H9VvMGrI2E4LRWvyf4eYEzn2GmNenpfpIcGj0ci6RaRagtEqnBbpP0kp0Ku3uZG13pOmVgefRwZA/8ZgND56CZYu9NwegDrtWtQwjye87XRA0KJz198ftlp2QxF7VH4SutBmY7yNoZuJ8yku3NPbaya38UI1SinY7wR7weWLzNuh0mL0Xlnol2fAJ69EPtiDT0c9vwB1/XOo82/1VCVrSgpbnZTKlqdk8NXsPC5OmsWmWdEl2CS4tWxR+7O9QHUF+qW7o2o7WvqHT3BP3gY9OR09OR3Wrohd4xN2RU1/HTVuR0/F+/aKXdcAWRe6XDnTpbpOs7LEnBrSPyMQymYzINMeahQqbKkoRMiLBKOJrqJ/+hL33Mm4+/XBPW0H9Pzvo2unpgqev8N7hW0md/q+T5OtBnnfp0nRVSTpGuItx7HNoWgBFbUw/f3mbYu2hBOF2MRsUNW5h86ih9Ffvxt5JdeFlUtiP5gOUFzhLZhsR0uYWHucPDEQjpaZEr5sk+1ucEk9r/W/jGkuqef6yb3Iz03vmP+euUlbogF/vT3xy1GBgLU0S55dmeHcLL/l8NjXYtszsp+3bWFSDDbV8ZYc9khVy3ZRCCGEEEIIIYQQQgghhBA9QKrPfBJIpd/j1YHaKKxbY1yeG98/qvaEEEIIIYQQ4q8kyTFf9by2uwWj1cuxnxAdQYLRhBBCCCGEEEIEtAxGwz5rsjOD0dQDs1Hb791p/YnuQ+0cHFIU0q6HdMxAehLHMlPZbRFC5A8R+tMm2EIlpeBccDvq1ln20LVOoq58CJXVx75+9DZw3KWdOKJG2f1Rp1zV+f02OnmiYqehrZeN6gfHbmcLRvPWbi9/KTn+IlLdKuP6StUiYcAXBzl5xnJ63mxP/VXUaGvYUF6mQtdWo5+80VNbrRzkPTBR5fRHnXl90PL/LdmOdH+Ztd5Na68mSdeGbnvSwZ7H0d2M7h8+bCEhDjJa/M6kjvl71P3pOyOsqxTqqkdxLpmBSk5FKYU6ahqccHlEzSTVlHDz3GNYvGwCtx0RuuwRW/rxqdbBGNcV/ptkXRO64rzOC3DUS35FX7gv/B4irC0aY7dHfVqFc9f7C1GGMgAAIABJREFUqG28X7HI67YnEje9o7n2dc2SQvP6AWH6TE5QDMw0J47kF9iDXJats7cpwWiiK+jClejLDoGfvoSKUsifiz5rZ/SqKIKTfv8Z6sJsy5okp6JOuTryPmLkgC28l01xq1BApr/Ec52Pf2veDriWTUJTuOK+Y+3tVNXB2jLNzys06yq9hUSJv5BFP1pXqQtutx+DLc/voAG1X2295rs/NJ/na/4oCl8+KR6uOTD2x5pKKU7ZyWHdHT4u2dt7kGJbVXVQXQ/F5rzoDVbGDcANcdqFr3FVL/O5WZRWB28frMFoLboZ7fFcqaT46B+DJsPsh+MRqYkuh1gIIYQQQgghhBBCCCGEEKJTpTjmk0Cqog1Gq19tXJ6bIJPjhRBCCCGEECKcREswWk13C0ark2M/ITpCXFcPQAghhBBCCCFEN+FrDlVK0rX8rfQF6lQCdZn9qR+9A3VLFlJXbA/KiblhW8CorTunL9H9jNnOc1F1x7uo1F4dOJgewjZx3m0xozpUMJrP/DWR2mFfePR79Mz7YNbD7Rhg9JQKP5FbHX8p+plbO2E0wNBxMOkg1FHno9KzOqdPA5+j+ODvDk98pfn2DxiSCxfsoUhPMj9e4YKCmmxavxQFpLrmE7kqndQWg4iD0dvAkl+CC875yFN/+QX2dUNWfo6+5G9QFiKNyGT8zqjefSOqok64DEZOCIRzLQsETmxR+wvf/zGRB7NO47eEUejG1+Im9Ss5quwldq/6PHSjaZmw05TIxt6NjPTwEI7u1/o9qjKy4c1V6APMgXntctBpqJwB6EU/Qu8+qD2ORk3YJaiY2vd49HO3Q0N9ZO2v+J0L5p7HdUn3UGbIBkpwa7n3zfH8Y/I5PPJNApVOKoeVz2T/infDt71uDVprT9uz9tAL56JP3yH2DR94KurCO1A+SwhnCAMyFRD7QKDp79vbDBXGprWGx29g+PJtWZYWHPAWKhjttzXmddmpkJHSsc+tECb6melQGRzgqY8eDZ/XRLbNCRWKNnQc6vjL0N++D1m5qH2PRw0JkQjWwSYN97ZdSXKr8RHYF870l1IY5y3ZZ9k6KCrX5KQrtC0YrfH/KVso3v3VXOiKV1zmLIWaekhJgDv/pjhtZ7lmlWj8LFq20Lxyr2NQR5yLfvkeWGkIOVzWPYPRZi/SHP2gy+rS8GX3GAUTBimO2U4xYVDHfn7efLji11WadwyHK7HyW8IIxtfaA2mdxj8xIxlWGTIaTfudXoLRBvX2ti1MSQxbJKy8TMV2m8J3f7avnZoId8+FEEIIIYQQQgghhBBCCCG6Qqov3bi80i2Pqr3C+jXG5bnx/aJqTwghhBBCCCH+SpIswWgNuh6/bsCnuj42ya8bKK5fa1yXGy/BaEK0R9e/w4UQQgghhBBCdA9Oc9BFX/9anl51SuBOziScCz7EvetR+N/dnTYcdfn9KFvQk9joKcdBn3QlPHFD+MJb7dbh4+kRHEtYjetvvt0QeTAagBq6OeqSGXDJDHRDPXp38xURu5JKy0D3GwxrlnZcH1NvQB17cYe1H42keMVZuyrO2jV82TyP4URPrDoNgFRtDsKsaBOMpnbYF/3W48EFC5ahG+pRcfEh+7OFECXGaTa5/RgojzAULbs/atptkdVppLbbC/VMINRAz3wAffs0htUv4Za1V0XX3uX3o9IyoqrbHfRKVvRJh7UhzukzhVmojGz0UdPgxbtiOh516FTUsHGEi89Qg0bA1BvQ9/4jdCCkyZuP8v0+OWy+9ErqVUKrVY+vOp3e5cvoPesKIv7LGuqhtBgycyKtGZIuWg1fvxMINNp+744JRes/GOey+6Kv7jGUMZYGZYd4lXz5JvrRfzO0312Y4hvXLC8GzOFJC83nqTIyqxr9wiOQ3Q8mHYxKTIp4zEJESmsNr9xrL/D2kzDlJO8N1tdZV6lL70WN3Q6151ERjLDjDMyC5HioDhOwk9Ii1DvLXR9RH6c94TLrPB+uZdep6VD19J0V5z9nLjR7cfPtqjo48ynNpOGaEX0lSPEvr3AlVFcaV6nDpwZuDBxhDEbTy/LD7gt1tuo6zTEPeQtFe+4MxdHbdt53PUopXj/PYZ87XD7+rWP6WJg4knG1v1rXN4WZ9bLsHpQZLlppC0ZzWjz5XoOv02IQjAZw/wmBx7Gw8dggKwXev8hh/CZwyAyXtz2Ez9XUa+h2r2AhhBBCCCGEEEIIIYQQQojWrMFofvOFRkOpdWsobTCf/5abIJPjhRBCCCGEECIcWzAaQI1bbT2G60zr6gtx8RvX9ZFjPyHaRWaYCyGEEEIIIYQAQCnLIaJunI1ZFmEwTXtsviNq1Nad15/oltQBJ4cvtO0eKCWTaoHmdIa23BYzqkMFBIUIRmtJxcWjHvs+fLlbZ6GuewbSs+yF+g1G/f0u1FQPAXhe7Ht8bNoxOezsbheKFikvE+dT3YoNk/pTXXNYQ2WbYDQGDjc3pjWsKwjb5+JC8/JhSevxlReHrb9BYjLq+udRT/yAGrGl93o2ex4NifYfUMJRN76M2u3Q9o+ji/UJ8xvRVoPMy9UhZ3rerniy6WgYurnn4uqoaagn56KueDDiroa+dwvrF/blqZUnc2jZa9xacDlLFw3jqPJXIm6rlWJLqlaU9I9foE/eGn3LVPQdF6GPGRvT9puoR8Nv80PxGtoRS7sMt+8b6C/eACDHX2RcX7zaHp60yBLkOPKXF9H3XIa+7kT01F3QRasiGK0QUSpYFnK1vulMdHkEYWChgtHGbue9nU7gOIqRHi6c3TLkNa9+dUR9NIWaaUswWtNWJjFekZ1qLtOW1vDol+FDasVfwPJF9nWDRrT+P6hufuzH005v/KRZWeKtbE5a5x+/+xzFBxc5jOmgc4vyE4bjV5agcprDzGzBaKWmYDTLpsLX4rDf6z5WaoyC0bYcqJh/ncNLZzk8d4Yi//8cth6siPMp3jjf4cO/OzxwgmLeNQ4DLV8D1IQJtBRCCCGEEEIIIYQQQgghhOgOUn3mC8dW+kNcXdKiqN5+vlCfeJkcL4QQQgghhBDhJIYJRusO1oY4Tzkn3sNJz0IIKwlGE0IIIYQQQggR4LNM4nQb0+qLI5tI3i7JKZ3Xl+i++gwMXyYjp+PH0VM4tvdwi2C0UOEYtvoGatgWqCsfNq8cMAT18NeoHfZFTT4C9cZKezsnXoE69KyYBY6pEy6H/U+CprA823Ytmran3R6ztrrKyH7Nk/JttqyZt+F2mtdgtNw8e4OF4cOB1pm7YeCayMKY1J3voXY7FJWRHVE9a3vpmajpb4QvOOkgGDex+X5iMurSe1E7HxiTcXS1HPN5fhtsNdj8olIDh6OueQLSMto/iCGbo26eGXEQpho0AjXlJNj72Ii7TNK1HFP2Ii+tPJaL1t3NgAaPQVehXn8x3JfSWqPP3xNKIwgPDEFd+xT07tt6Ye9+qLs/RLXzORzUu/MDUPYaHWLlbz8AkNNgCUarsv9sYgt9GVK3pPnOonnol2eEG6IQ7bd2RfgyL9/rvT1bgG5aF6QbejCyb/htS4qv+W/Ka4hsG7y+CiprNa4lnKjlPlW6JezI5MP5EowmgPWWZOC0TFRjsLSyBaMty0fbEvu6yLu/eC+bHWbfsqMopZh9ucOBW8S+7d8SRuCGOO2iKcwsw3JuVllN8DK/G7ysZVsAvZIhJSH8+NJiFIwGkJ2mOHxrxdHbOmS3CLlTSjF5lOKMSQ7jNlEkxZvrSzCaEEIIIYQQQgghhBBCCCF6ghTH/KNWlb8i4rYK68zBaA4+suJzI25PCCGEEEIIIf5qkkIEo9V2k2C0wjrzecqZcdkkODE8iU+Iv6C4rh6AEEIIIYQQQohuwhaK5G8MRvMy8T5W1q3tvL5Et6UcBz1sC1j8k71QjAKQNgZKORinx+vAjGpdV4u+4jBzZcdBOZHl56v9ToDJRwSCftYXAgp6ZcEmw1qFFymfD+7/An32pNYNDB4F+x4fUZ9hx5SQiPrHg+jzb4UVi2HQCPRBA6G2fV90q8fmBP6OHi49STE2D362Z9UxqjZ/w+1Ur8Fo6VmQkAR1hhn9ReHDpEqqzMuz/CGC/NoauRWM2c57eY/UlpPgno/Q5+1hL3PAKaiJ+6OLVsP6tTB4FCph4/nhIlQwmlKwxYAQ6ycfAbscDH/+Fvw+jItHf/AcvHBnyP7VI9+iRmwZwYgNbZx8Jfr9Z9vVhqd+TrsWjrsEPTndXCAG+1La7wet0Xdc0O62WtntsMDztWwhVJZDajoMGhnxZ4PJyL7hy8TSweMhJTFEYFLj9jzHbw6VK9K90Fobg/hswWibNLTZsH72Gpz9H0/jFSJqxfarSjfRH7+EOuUqb+3V15mXx3tI3ekCIz1cPC110CBYmQEVpeR5DbhsIb8A8/41zTm84C2YqMn/lkU8DLExKjWHc5LZIvjbFoxWtg7+mA9DxsZ+XFGat9x7UFu40N2OlJmimHWej4IyzaoSiHOgqs2mz69h55stqWQW+Ykj8HsIRktPVpi2KmWGw1UvwWhKKQZkwqIwX6HFMhjNK2swmiWDUwghhBBCCCGEEEIIIYQQojtJ9ZnP/an0l0fcVmG9eXJ8dnwffKrnn5MohBBCCCGEEB0tVDBaTXcJRrMc++XGezjhWQgRkgSjCSGEEEIIIYQIsAVfuH50QwOs+sNed8hYGDcRZj0Um7HUmMN4xF+POuVq9FVH2ddn5FjX/eXYgruawg1/+DhE3ei+IlKJyZA3JPAvVLmx28H0N9CP/BtKCmHs9qizrkd1UNCGSsuAUVsDoA3BNp5tvgPqzH+jho2L0ci63ndXOiSfa5/oP7Ju4Ybb1mA0ldJ8x+eglELn5sHKJcGFC0OksDUqqTIHKWT6S8PWBWDf41HTbjOGGMWCGr8z/Ps59DXHBK/MyIatJwfK5fSHnP4dMoau1DvNHOAA0Ccd0pJCP+4qLh5s76ERW8KQzdE3nmGue94t7Q5FA1ADh8OMT9CXHAjVkV+51bNt90DFJ6BHbwML5gSt1t9/iDrglIia1LXV6KduhidubN/Y+g2GbfaANx8NXjdhV1Rc4+fApqPb14/BkNxAiIct4CPWjt42zLagoR6AbEswWrGThVu8Fl9O60S32npNkeXl07/tD5krFlvD1f6KtNaB/RG3xT+/5Xar+27jvwjrNN3WbsR19IZ+I+in5TrtWsYa5u9oNVZbG4b74fy5wPsT1cOC0UZ5OE8gN8OHevBL9N2XkrekLOI+fl2lcS3bLqfF29sWAGSiFFTWalJDBTiKjV/ZOvPylsFow7YIfE9jehF++Va3CkYrieC8ouzU8GU6Wt9eir697OtXT3e46JH1fPxzDaVOBgCb1i+lf8NqPk3dLaj88viBlPnsDTZtLzIs52aVVQfva1uD0dpsOjbLCR+MltqdgtHqO3ccQgghhBBCCCGEEEIIIYQQ0Ujxma/2U+VGft5RYZ1lcnzCxneemRBCCCGEEEJ0hHiVgMJBE3xiXbcJRqszX/Bajv2EaD8JRhNCCCGEEEIIEeBYQpVcPxQs3RAi0ZZ6bA5q2Di066K/eB3WFbR7KOqQs9rdhthITDoo9PpsuXLCBsoSbqhdtNboZ6bb60YZjBYJtf3eqO337vB+gvo97lL0I9dFXu/M61EnXNYBI+paifGKkycqHv/KHHQ1si5/w21bMFqF0+LEr6bXTo45GE3/OZ9wsR8lVeblGa49GE3dPBM1cf8wLceO2v0wuOJB9E1ntl5+3i2oxKROG0dXyDGf5wdApv3CO54opWD/E2HbPdAnToCKFs/5xP3hqGnt66BlX1tMRL0fHITl3n0pvHhXbDoZPCrw/ybDjMFofPwy+spHInrN6IeuhRfujH5MY7ZD3fcZqjEA183oDS0/DxKSUCf9I/r2PUiIUwzNhfz27yJ6ctQ2YbY6VYGTVHMswWiu8lGyeAnZbYLRVofIahzQsCp44fzv0CMmwKJ5UFFiCPyyBGBZA7Gay2pr+FbL+7ZwrxDLw42jVb8ext50X/QMDZZgtLgIUr860aj+9uDOJnmZCjVwOOqW1xj8m4bbI0to/GC+vYeWuYehtg9taR1o95AJEQ1FbGR0SZF5RUb2hpsqPQs9bieY90Vw/fy5YfexO1N5jbdyKQmQnNCdRt6ariyDxT/Rp66Gp1+dErR+SfymjBg231h3YcIIa7u+xkP1XpZdwDLD42cNRmtz2D+8r+L9+aG3hWldEMQowWhCCCGEEEIIIYQQQgghhOjJUn3pxuWV/gpc7eLYztU0KGx7sb1GufFy7qsQQgghhBBCeKGUIslJotoNnnxU212C0azHfhKMJkR7STCaEEIIIYQQQogAnyUYze+HFYvt9TYZCoByHPR+J7QO24hWF4Qnie5JKQWvLkEfNsRcYId9OndA3ZljOdlm+SL0sZuHfh9HcKJOjzNxfzAFox02FV69z15vzLYdN6YudtKO5mC0TFXJ5FOmoFIPQ990JqnaHIxW6yTRgI84/M3BaJm55s5mPfz/7N13eFvl3cbx+zmSLHkkTmI7w9khgwwCgbDCCJuwd6CMQmhLgQ7a0pc9CqVQKB28BUrZ0EIZYa9SRtn0BcreOwkhyxmOR7yk5/1DSWxZ50hHsuT5/VxXrljPOj8nWkc65z6yg0dKx50Zfzy3Y5ct0pqPayRnUnI90TWuS5qbX5OZsLnHb5c/Zr/jpZETZB+/TZKR2e94mc227/Q6OlvKYLSi3GzDVAyX7nhP9q4/SWuqZKZsLe1/out9JtfMj6+Q/fxd6c3nOrbQwMEyJaXxNUdO8I7qeeaeeBicD3b1cmn+1R0qy/z56Y2haJJkfniJNGEL2VefkPoPktn7GJlJ+U/n2XRo5wSjfXSxI8dJc79ZtlCSVOYRjCZJK75cpLLtEh/fi92fkiRJlS3JX2Tak3dOXQfQ3TR7BKOFCjq3Dp+m+DhOoHJA6887jM98G4+9Z7Wpx7HoTpbBaJL0xPtWB8/ovuFQ6ATVHq9BpeWJt7fYyTUYTc/dL7tskcyQkbmvLQu1jf7GlRXnt46OsPddK3v1GZ4XBZCk0c0LFY41qNFJTjj7MDzZc96G54v+HqHC1S7HZUU93ky2D0abNMR9XFvFXfA0HvE4CoVgNAAAAAAAAAAA0BMUOe4HTFnF1Bhbp8KA/y++VjQtdW2vKODkeAAAAADwK+wUugajNXSDYLSYjaqqyf1kCfb9gI7rxWe9AgAAAAAy4hWMZGNSlfsX8yobJhNpTUUxx54hTZ7ZsTJOvEBm7JQOrYHexVQMl/nl1VK7gBzzk9/JDB7RRVV1Q17BaGtXpQ5Fk6R1tbmvJ1MnnOveftipHVt3wubSsWcktm29ezwcaNKW7nNKBkib79ix7XZjsycZnbZ74uOpICj95fslKp57klQQliT1j9Z4rrEmsD5pZGMwWpnnWHvDhdLrTye3f/2R7OHjtaYl7DpvQCw5ZcQ8ubJLQtE2bn/6LDln/VXOWdf1iVA0SapwvwCqpNwFo0mSGTREzqmXyTnnBpmDT5IJds41PYwxMn94XNpx/+TOfgNl/vy0tNOB6Rcav1nrz2Oneg6zLz7suzZ779XxgNpsjJsm89BCmYLEx5cxRmb3I+Scd7Ocn17ZKaFokjRxSPYBQJ98PlWHrH0w7bhLDzGaNDT1duxLj2z8eXDLCs9xX36d/Pzz5Qr3hJLiWK36x9amrQ/oKvaTt2T9PJe0eASjBUK5LShHCoJGc2emfsxXtDlWvSBodPY+mT0XraqTXvnCva8j2Z0ffOsZn4m+orrKvb008T21GZ0cHryB/dWxuawoay1R6zvsKlXgbley7/9H9k8/TxmKJkkBxbRJ85eufYuDw73nrd9VL/UIRlvbkNwWjaVea4PJw9I/GZUk57jlXaFHGBvBaAAAAAAAAAAAoCcoCXgfMFUX9X+sZXOsSatb3L8brAhxcjwAAAAA+BVx3A/A6w7BaKtbqhRVi2vfYPb9gA7rnLPLAAAAAADdXyDg3h6LSmtXuvcNKE+4aUpKpWv+Lb32L+nrj6UhI6VvvpC96SLX6ebM66S9jpbefkFa/KU0fQeZTaZ15LdAL2UO+oE0Y7b05nPxsL4Zs2XGTO7qsroXx+Mx3EOYnQ+Sve1SySYGRZjZB3dsXWNkfvhr2V0Pkz74jzRivDRjl3jo0jXPyu4xIHnO3J/IBLtnCEiu/PFIR0dtbfXql1bFYWmPyUZjy9efVL8+6aM86hHYIGl5oELl0ZWtwWil5Z5jJck+fJPMNnu23n78dtnLfiBJWhModZ0zILom4bY59TKZom6aptCLVZYaSe4BLuFe8umyCQSkS+dL//239NnbUnOzVFEpbbOnTNlQqaEubaCZ2btNOMn2c7wHvv2CrLUyaRJ17OO3S3+7PJNfI27OsTI7HShtu5dM2CN9owtsOjT7uZs0f6W7Fh+re2sO07HDb0vqnz5Cuv1ER9NHpA8GsX+7YuPPRXadhjcv1uJQcpDJJwsbtG+7ts+Wu685oekLdSAfCcg7+/3tpKJ+stO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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import asyncio\n", + "\n", + "from matplotlib import pyplot as plt\n", + "\n", + "from apd.aggregation.query import with_database\n", + "from apd.aggregation.analysis import get_known_configs, plot_sensor, Config\n", + "\n", + "with with_database(\"postgresql+psycopg2://apd@localhost/apd\") as session:\n", + " coros = []\n", + " figure = plt.figure(figsize = (20, 5), dpi=300)\n", + " configs = get_known_configs()\n", + " to_display = configs[\"Relative humidity\"], configs[\"Ambient temperature\"]\n", + " for i, config in enumerate(to_display, start=1):\n", + " plot = figure.add_subplot(1, 2, i)\n", + " coros.append(plot_sensor(config, plot, {}))\n", + " await asyncio.gather(*coros)\n", + "\n", + "display(figure)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import asyncio\n", + "from uuid import UUID\n", + "\n", + "from matplotlib import pyplot as plt\n", + "\n", + "from apd.aggregation.query import with_database\n", + "from apd.aggregation.analysis import get_known_configs, plot_sensor\n", + "\n", + "\n", + "location_names = {\n", + " UUID('53998a51-60de-48ae-b71a-5c37cd1455f2'): \"Loft\",\n", + " UUID('1bc63cda-e223-48bc-93c2-c1f651779d69'): \"Living Room\",\n", + " UUID('ea0683de-6772-4678-bfe7-6014f54ffc8e'): \"Office\",\n", + " UUID('5aaa901a-7564-41fb-8eba-50cdd6fe9f80'): \"Outside\",\n", + "}\n", + "\n", + "with with_database(\"postgresql+psycopg2://apd@localhost/apd\") as session:\n", + " coros = []\n", + " figure = plt.figure(figsize = (20, 10), dpi=300)\n", + " configs = get_known_configs().values()\n", + " for i, config in enumerate(configs, start=1):\n", + " plot = figure.add_subplot(2, 2, i)\n", + " coros.append(plot_sensor(config, plot, location_names))\n", + " await asyncio.gather(*coros)\n", + "\n", + "display(figure)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "9f576f5bb8e34cb8ab1d3c09f770d027", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "interactive(children=(DatePicker(value=None, description='Start date'), DatePicker(value=None, description='En…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import asyncio\n", + "from uuid import UUID\n", + "import functools\n", + "from concurrent.futures import ThreadPoolExecutor\n", + "\n", + "import ipywidgets as widgets\n", + "from matplotlib import pyplot as plt\n", + "\n", + "from apd.aggregation.query import with_database\n", + "from apd.aggregation.analysis import get_known_configs, plot_sensor\n", + "\n", + "\n", + "def wrap_coroutine(f):\n", + " @functools.wraps(f)\n", + " def run_in_thread(*args, **kwargs):\n", + " loop = asyncio.new_event_loop()\n", + " wrapped = f(*args, **kwargs)\n", + " with ThreadPoolExecutor(max_workers=1) as pool:\n", + " task = pool.submit(loop.run_until_complete, wrapped)\n", + " return task.result()\n", + " return run_in_thread\n", + "\n", + "@wrap_coroutine\n", + "async def plot(*args, **kwargs):\n", + " location_names = {\n", + " UUID('53998a51-60de-48ae-b71a-5c37cd1455f2'): \"Loft\",\n", + " UUID('1bc63cda-e223-48bc-93c2-c1f651779d69'): \"Living Room\",\n", + " UUID('ea0683de-6772-4678-bfe7-6014f54ffc8e'): \"Office\",\n", + " UUID('5aaa901a-7564-41fb-8eba-50cdd6fe9f80'): \"Outside\",\n", + " }\n", + "\n", + " with with_database(\"postgresql+psycopg2://apd@localhost/apd\") as session:\n", + " coros = []\n", + " figure = plt.figure(figsize = (20, 10), dpi=300)\n", + " configs = get_known_configs().values()\n", + " for i, config in enumerate(configs, start=1):\n", + " plot = figure.add_subplot(2, 2, i)\n", + " coros.append(plot_sensor(config, plot, location_names, *args, **kwargs))\n", + " await asyncio.gather(*coros)\n", + " return figure\n", + "\n", + "\n", + "start = widgets.DatePicker(\n", + " description='Start date',\n", + ")\n", + "end = widgets.DatePicker(\n", + " description='End date',\n", + ")\n", + "out = widgets.interactive(plot, collected_after=start, collected_before=end)\n", + "display(out)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "60091fa7d9ea415985ec53937e422b4a", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "interactive(children=(DatePicker(value=None, description='Start date'), DatePicker(value=None, description='En…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from uuid import UUID\n", + "import ipywidgets as widgets\n", + "from apd.aggregation.analysis import interactable_plot_multiple_charts, configs\n", + "\n", + "location_names = {\n", + " UUID('53998a51-60de-48ae-b71a-5c37cd1455f2'): \"Loft\",\n", + " UUID('1bc63cda-e223-48bc-93c2-c1f651779d69'): \"Living Room\",\n", + " UUID('ea0683de-6772-4678-bfe7-6014f54ffc8e'): \"Office\",\n", + " UUID('5aaa901a-7564-41fb-8eba-50cdd6fe9f80'): \"Outside\",\n", + "}\n", + "\n", + "start = widgets.DatePicker(\n", + " description='Start date',\n", + ")\n", + "end = widgets.DatePicker(\n", + " description='End date',\n", + ")\n", + "\n", + "plot = interactable_plot_multiple_charts(location_names=location_names)\n", + "out = widgets.interactive(plot, collected_after=start, collected_before=end)\n", + "display(out)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "08a4d937037b45bda2298879df7f80b6", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "interactive(children=(DatePicker(value=None, description='Start date'), DatePicker(value=None, description='En…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import datetime\n", + "import uuid\n", + "import ipywidgets as widgets\n", + "from apd.aggregation.analysis import Config, configs, interactable_plot_multiple_charts, draw_map, get_map_cleaner_for\n", + "from apd.aggregation.database import DataPoint\n", + "from apd.aggregation.utils import merc_x, merc_y\n", + "\n", + "start = widgets.DatePicker(description='Start date')\n", + "end = widgets.DatePicker(description='End date')\n", + "\n", + "def get_literal_data():\n", + " # Get manually entered temperature data, as our particular deployment\n", + " # does not contain data of this shape\n", + " raw_data = {(53.8667, -1.3333): -1, (53.35, -2.2833): 1, (52.45, -1.7333): 3, (51.5, -0.1333): 6, (51.55, -2.5667): 5, (54.9667, -1.6167): -1, (55.9667, -3.2167): -1, (54.7667, -1.5833): 0, (53.7667, -0.3): 1, (53.7667, -3.0167): 0, (51.4833, -3.1833): 4, (53.2667, -3.5167): 2, (52.0833, -2.8): 2, (52.0167, -0.6): 2, (52.6667, 1.2667): 5, (50.4333, -4.9833): 9, (50.35, -4.1167): 10, (50.5167, -2.45): 9, (50.8333, -1.1667): 9, (56.45, -5.4333): 4, (57.5333, -4.05): -2, (54.5167, -3.6167): 3, (53.0833, 0.2667): 4, (51.35, 1.3333): 7, (50.8833, 0.3167): 8, (58.45, -3.1): 3, (50.1, -5.6667): 9, (58.2167, -6.3333): 4, (55.6833, -6.25): 7}\n", + " now = datetime.datetime.now()\n", + " async def points():\n", + " for (coord, temp) in raw_data.items():\n", + " deployment_id = uuid.uuid4()\n", + " yield DataPoint(sensor_name=\"Location\", deployment_id=deployment_id, collected_at=now, data=coord)\n", + " yield DataPoint(sensor_name=\"Temperature\", deployment_id=deployment_id, collected_at=now, data=temp)\n", + " async def deployments(*args, **kwargs):\n", + " yield None, points()\n", + " return deployments\n", + "\n", + "def draw_map_with_gb(plot, x, y, colour):\n", + " # Draw the map and add an explicit coastline\n", + " gb_boundary = [(-2.6653539699999556, 51.617254950000074), (-2.6937556629999335, 51.59617747600004), (-2.71312415299991, 51.58661530200004), (-2.760487433999913, 51.579779364000046), (-2.7980850899999155, 51.56350332200009), (-2.816395636999914, 51.555568752000056), (-2.8587133449999556, 51.546128648000035), (-2.943348761999914, 51.54254791900007), (-2.9562882149999155, 51.54572174700007), (-2.9641007149999155, 51.552801825000074), (-2.969471808999913, 51.559881903000075), (-2.9746801419999542, 51.56297435100004), (-2.984038865999935, 51.55853913000004), (-3.0102432929999168, 51.53803131700005), (-3.0156957669999542, 51.53164297100005), (-3.0737198559999115, 51.50836823100008), (-3.0764867829999503, 51.50775788000004), (-3.088937954999949, 51.505072333000044), (-3.105620897999927, 51.49721914300005), (-3.1317032539999445, 51.48041413000004), (-3.1350805329999503, 51.47630442900004), (-3.1409399079999503, 51.46478913000004), (-3.1453751289999445, 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51.49408600500004), (-2.716420050999943, 51.49957916900007), (-2.6961563789999445, 51.51264069200005), (-2.6078181629999335, 51.60736725500004), (-2.599598761999914, 51.611395575000074), (-2.5892227859999366, 51.61424388200004), (-2.580433722999942, 51.61863841400009), (-2.5768123039999296, 51.62750885600008), (-2.57445227799991, 51.63572825700004), (-2.568959113999938, 51.644964911000045), (-2.562245245999918, 51.65338776200008), (-2.556385870999918, 51.65924713700008), (-2.499175584999932, 51.69676341400009), (-2.464995897999927, 51.725897528000075), (-2.4477432929999168, 51.73187897300005), (-2.404896613999938, 51.74115631700005), (-2.3885391919999392, 51.750433661000045), (-2.380767381999931, 51.76190827000005), (-2.3833715489999463, 51.77326080900008), (-2.398060675999943, 51.782131252000056), (-2.4024145169999542, 51.76357656500005), (-2.4203995429999168, 51.753119208000044), (-2.4434301419999542, 51.74843984600005), (-2.4632869129999335, 51.747300523000035), (-2.482085740999935, 51.74286530200004), (-2.495838995999918, 51.73200104400007), (-2.507964647999927, 51.71857330900008), (-2.5221248039999296, 51.706366278000075), (-2.581695115999935, 51.681708075000074), (-2.5911352199999556, 51.67536041900007), (-2.600209113999938, 51.66559479400007), (-2.6653539699999556, 51.617254950000074)]\n", + " draw_map(plot, x, y, colour)\n", + " plot.plot(\n", + " [merc_x(coord[0]) for coord in gb_boundary],\n", + " [merc_y(coord[1]) for coord in gb_boundary],\n", + " \"k-\",\n", + " )\n", + "\n", + "country = Config(\n", + " get_data=get_literal_data(),\n", + " clean=get_map_cleaner_for(\"Temperature\"),\n", + " title=\"Country wide temperature\",\n", + " ylabel=\"\",\n", + " draw=draw_map_with_gb,\n", + ")\n", + "\n", + "out = widgets.interactive(interactable_plot_multiple_charts(configs=configs + (country, )), collected_after=start, collected_before=end)\n", + "display(out)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "apd.aggregation", + "language": "python", + "name": "apd.aggregation" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Ch12/apd.aggregation-chapter12-ex01-complete/LICENCE b/Ch12/apd.aggregation-chapter12-ex01-complete/LICENCE new file mode 100644 index 0000000..86685d4 --- /dev/null +++ b/Ch12/apd.aggregation-chapter12-ex01-complete/LICENCE @@ -0,0 +1,31 @@ +Copyright (c) 2019, Matthew Wilkes + + +All rights reserved. + +Redistribution and use in source and binary forms, with or without modification, +are permitted provided that the following conditions are met: + +* Redistributions of source code must retain the above copyright notice, this + list of conditions and the following disclaimer. + +* Redistributions in binary form must reproduce the above copyright notice, this + list of conditions and the following disclaimer in the documentation and/or + other materials provided with the distribution. + +* Neither the name of the copyright holder nor the names of its + contributors may be used to endorse or promote products derived from this + software without specific prior written permission. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND +ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED +WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. +IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, +INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, +BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, +DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY +OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE +OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED +OF THE POSSIBILITY OF SUCH DAMAGE. + + diff --git a/Ch12/apd.aggregation-chapter12-ex01-complete/Mapping.ipynb b/Ch12/apd.aggregation-chapter12-ex01-complete/Mapping.ipynb new file mode 100644 index 0000000..916e704 --- /dev/null +++ b/Ch12/apd.aggregation-chapter12-ex01-complete/Mapping.ipynb @@ -0,0 +1,254 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "datapoints = {\n", + " (53.8667, -1.3333): -1,\n", + " (53.35, -2.2833): 1,\n", + " (52.45, -1.7333): 3,\n", + " (51.5, -0.1333): 6,\n", + " (51.55, -2.5667): 5,\n", + " (54.9667, -1.6167): -1,\n", + " (55.9667, -3.2167): -1,\n", + " (54.7667, -1.5833): 0,\n", + " (53.7667, -0.3): 1,\n", + " (53.7667, -3.0167): 0,\n", + " (51.4833, -3.1833): 4,\n", + " (53.2667, -3.5167): 2,\n", + " (52.0833, -2.8): 2,\n", + " (52.0167, -0.6): 2,\n", + " (52.6667, 1.2667): 5,\n", + " (50.4333, -4.9833): 9,\n", + " (50.35, -4.1167): 10,\n", + " (50.5167, -2.45): 9,\n", + " (50.8333, -1.1667): 9,\n", + " (56.45, -5.4333): 4,\n", + " (57.5333, -4.05): -2,\n", + " (54.5167, -3.6167): 3,\n", + " (53.0833, 0.2667): 4,\n", + " (51.35, 1.3333): 7,\n", + " (50.8833, 0.3167): 8,\n", + " (58.45, -3.1): 3,\n", + " (50.1, -5.6667): 9,\n", + " (58.2167, -6.3333): 4,\n", + " (55.6833, -6.25): 7,\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "fig, ax = plt.subplots()\n", + "lats = [ll[0] for ll in datapoints.keys()]\n", + "lons = [ll[1] for ll in datapoints.keys()]\n", + "ax.plot(lons, lats, \"o\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "fig, ax = plt.subplots()\n", + "lats = [ll[0] for ll in datapoints.keys()]\n", + "lons = [ll[1] for ll in datapoints.keys()]\n", + "# Don't draw lines\n", + "ax.plot(lons, lats, \"o\")\n", + "# This is a map of the UK. Here, 1 degree latitude is \n", + "# 1.7x as far as 1 degree longitude\n", + "ax.set_aspect(1.7)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# Set up mercator transform from https://wiki.openstreetmap.org/wiki/Mercator#Python\n", + "# Thanks to Paulo Silva and the OSM contributors\n", + "import math\n", + "\n", + "def merc_x(lon):\n", + " r_major=6378137.000\n", + " return r_major*math.radians(lon)\n", + "\n", + "def merc_y(lat):\n", + " if lat>89.5:lat=89.5\n", + " if lat<-89.5:lat=-89.5\n", + " r_major=6378137.000\n", + " r_minor=6356752.3142\n", + " temp=r_minor/r_major\n", + " eccent=math.sqrt(1-temp**2)\n", + " phi=math.radians(lat)\n", + " sinphi=math.sin(phi)\n", + " con=eccent*sinphi\n", + " com=eccent/2\n", + " con=((1.0-con)/(1.0+con))**com\n", + " ts=math.tan((math.pi/2-phi)/2)/con\n", + " y=0-r_major*math.log(ts)\n", + " return y\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots()\n", + "\n", + "x = tuple(map(merc_x, lons))\n", + "y = tuple(map(merc_y, lats))\n", + "ax.plot(x, y, 'o')\n", + "ax.set_aspect(1.0)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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u/6iq/tiEYWliak0mPybCZBKhNmlvZipNweeBk3ESrWwXkS3GLXVpdEK8ILJQk4wSMpMuXhhJU6CqN6jqzHJGxqCVwqrxqaXfjWZ1imRBYLaRt/j7MF2rTEJg9VQAKrF4461xzalJ04osjbTrfmz0YEE/oqTH3lklsrQbZW2e6u8nCQEn5c2y81Qzhi1eLGwFIElvlossAUwLzJsbI8n7JEUmRZZEM4apUJmEwG6782qu+51zue3OqycJLQxpVwAyKTJbScKz1MqNEfeejQyZucjqpOe0OVzzobM5+x0nJXL9oNwYUek5Ldp8BdPezLopcR+Y9QyPvrjUyLWKp4yE+nXGDZU9p83hrvJs74tX15ztHYdauTFqDWb02jhaKnHDugeODcJs1LS53JPVgX+299LzTguV1yIq5SlyUQXmtzHMfIXyj9KkN7POk2WJ8mxvUMbGxnl618EJ+/0iMFVmiyJgr42jpfFI8xVMkVmRmRyKHTfJSt++IW78iw2ccfpcnt51cMJk3CCCxBFVeFE9ZNnGntXB8xWCQubY89OM9phkVmS20LdvqKa4qhHF28UNwZuPHmLz96I3+yzeeCs/vuyWWPf0kpfJLKNSeS6uwGxYuzN9CxLGho7yOIRNjleNsAKr1mZmogJgjci8Y8rS7ihvBmzwYGXssSQDZGVMfxyBJenNcpG5NKJRstw70HNaMglcXutss8qDlTGVpkCAO4CLgBHgOlV92rCtk2jkjHKY6CGierUJvQNjJW78iw111Uqr2WYbNS1T1T3lsfvAmTgi8qcpuBBY5L7WAnebNjQtlncH9/uVvUbYL3dC70CxjTNOn2vMRlMCSypkRm0nC0xTAFwMfFVVFdgsIieKyBxVNfdTTYHl3XNYv/ZS2guT+/28lL/kat6tVu9AXOIKzF88SHJUsKk0BZ2A96kNuNsmECVNgQ0zys9a2OXkKQvZ71eNcsv7uo2PGwmV9ZS/gsqfI13jNXs+4nqz0J7Mk6bgc0G7A7bppA2q63Ay/tDb2ztpv208tX+A0VIJIFSeslorlNTbO+C9T1xqVXCC9tfbzWQqTcEA4C1kdAHPx7bKEnb0D3HDugc4a2FXpDxlSZKkwMIQp6spisgqpikAHgJuFJH7cPKSvdqo8ljSHeU7+oeaXlwrO+awumsumwcOsm04+LPW481CicyTpuB3Pds+Ds5McuA/cJov9uHUPq+PZU0TEGZRr6j5/pMW2L2XXH6scnP1NzdUFFpcTKUpUFX9pKouVNVfUNXEcmFknSDBVCvEJx0eJyZhbmN1V+2mlagVAHtb8AzTyI7yuMLwi60R5a+JSZjH2TxQuWkl7kQTq0XWbB3lYUVTb/NElAJ+OQnz32x+PFKojOLN8kGLDaIR3T5xa4/bhodCiytOBcBqT5YGpjrKG92XmMZaUGG9mVUii5unzPSMctNfWJKCixoe41A8ZeTYKw5WicwmbFnGrxqNstFb4I8jtFxkCZLk+C5bfgRhQqb1Ikuzo9yWL9KPrXZVwnqR5bxNI8pflajWRnbFo18BYMaMGe8I2p+LrAa2eA1b7PCz4qRO/ul9HwVg0aJFi4OOaRqR2ZB6PSlsEViQN1s1ex7tbYXyv0FDvppHZEmS1pecZngMyxOHf8LoeKn8b+AYwVxklmK7uMpsf2mQax/7GgB79+4NXI2m5bqVwuYs8xM3KUsUlnfP4ayFXfzgSH/dw20q2VqPeCvVzre/5KS0OnLkyOuB58W+YwMxmRjPVo5NWikW+N3Sqqqd1Wmkgm/U8OuWJ0lvtuKMTtqLzrguFN47ozvSysFJUu8wqbxMZgEjXeO+cV21J63EJeqPxMQ4vKbyZI2eUW6CchmpPK6rPNZ+T3/6XszUQM+wY/xPxFkwtQenmvrbqrrJs/8E4F6g273mX6vqPxix0DJMhkx/Idw7rmtaykHG5EjisJ/kDuBhVV0KLAd2+/Z/EtilqsuBc4EvuvM0I9PoZQnrGcJSD3HmP5qi1o/E9POoKTIReSfwPuDvAVT1LVV9xXeYAjPcxCvTgZeAMZOGmu4or0dc9QrA5jawJH5wYTzZAuAw8A8isk1E1ouIvyP0LmAZzoTencCnVHXSk4ySpqDRNMqbRRFYUmJstMjDiKwInAHcraorgdeBP/YdcwGwHTgFWAHc5XrACajqOlXtVdXe2bNn12e5hVTKAFTGBg9WzYakfmhhRDYADKjqE+7/G3FE5+V64EF3/uU+4ACQSutpPR3lUR6y/8sqN6beeP4a1q+9dILQ6umDNCnMNAQG4fKTDQMH3WR44KSP2uU7rN/djoi8C1gCPGvQTuuplAGo0d6rkjdNS2AQvp3sJuCf3Rrjs8D1vjQFtwL/KCI7cYZ7fFZVX0zC4KSJ27cZlAHIlMDCNptUyqeWtMCWdRzi4v+9sfI9wlxEVbcDvb7N93j2Pw+cH8fALOP98v0ZgDaNm13IKwxeb1r+v5od9QosbNEkU91KjZpRHvfh7+gfYv33nkpEYGG8YtmbjpXGGS2V+MGR/orHNkpg0GTdSjaQZg3S602rDReqR2BxKla5yOqkHDIbIa4w99g0PsimveZDZD219kyFy7CYGO9fT3OGrcQR2LKOQ3U/z6YUmY1kcY0nU5Nz8nBZhbjNGf5rmLyeCTtqYXrmV+ZElqWh2DZ4rzTFVaalwmWc8kXsZLwVzmuE8OKMMkly3qqVnuxTS7/LHc/8srHr+R9g0iNoi6eMsOKkTlbNnscTh39ybDaPd78tNGJStJUiM0FZSKZqmmHKUmXxlKfut7cVGB0vce1jX5sktDTwCv/NKYmvr3aMphUZJPcrDcp77/VO5an7xbY2QFk1e17qIisLf0qhjbHx93BL38vsOXKgIfduapFVI2rILHuzoLz3O9v2Tzj27an7yuj4OE8c/olZ42PwawtmMaXQRkEKqEDPCYtykdmKN+89KGuWdbBzz0SRlafuVyqTNQK/F+97dS9j4yVUoKQl+l7d2zBbMikyU80YcbzZU2/sZXR8NbW81PaXBhsurmrFgz1HDnBL35foOWERfa/uNe7FlsyYT2dnZ0fQvkyKLE3q8VKmy4hRKzZ7jhxIJEQumTGfW3tu4tGOr09afhJykcVqzojipZJsIrAlJ1vPCYsovp2jbBItL7IksUUESXPNvA8BzhpbQftbqsW/EqbFYGLkQlb49nvuOvb+0KFDgWuc5iIzTKuICyYKDGBwcHA46DgjuTDcY84FbgfagRdV9ZciWx0B0x3lJrqa0hRYpaHpSQwm8IurFmHLZOVcGJe5M5Ym9LG4Ivwy8EFV7ReRn4tkhaWkIRq/WKqJJMychyjXq3X+oy8ujSwwCCEyTy6M68DJhQG85TvsKpzJvf3uMS9EtsQC0vJE1cRievJMNdHVulccgUE4T+bNhbEc2IqT68KbH3Qx0C4i3wdmAHeo6lf9FxKRtcBagO7u7lgGh6X8wGwee2bDep5hbKg301IYkZVzYdykqk+IyB04uTBu8R1zJs4s8qnAJhHZrKoTsiGr6jpgHUBvb29gdbde/A/NtkGONggrCiZSeYURWVAuDH/ClQGcwv7rwOsi8hhOHrPAlNthiDOmzOYv0GbbADqOX0bXtBUMjGxn+I3dRvPE1RSZqg6LyEERWaKqewjOhfFtnEw+RWAKsAr4G2NWhqBWuSYtb2ZaXH4xmLrmb3Z/gYK0U9JR2tuON3LdMkZyYajqbhF5GPghMA6sV9U+o5YGUBaPjV4iCZv8Yniw/9NGhNY1bQUFaadNCrRJ5e6huBjJheEe8wXgC4bsCk3YLzOMN7NRrF68YgCla9qKukTWqNSpLdl3abuYKjEwsp2SjgJKSccYGNke6fxG5+Mt01Iiy6q4ygy/sZsH+z8dqkyWlqCCaCmRNQPDb+yeJC6bBBVELrKMYbuggshFZjFZFFQQucgSJkq7VrOIyk/Liuyi6f72ZPMcP+VMOmfdBjJlUrtWswoqiKYRWSWP0QgxVWLqcecg0o64jZy/Ne9LqdmSJk0hso7jl3F5922ItKN6FYMvXsEbb21NxZZFXYEjkFuaphDZeTPnuB6jCChTjzsnlMiOn3ImU487h6NvboolylxQ4ci8yC6avoujb05F3ZZw1TGOvrmp5nlOeel+1/uN1vR+uaDik1mRectab7y1lcEXr4jkld4uL032frmgzCIVpsolTm9vr27ZsqXmcUFjykwU5suerK1tat3XynEQka2q6h9IkT1PVo/Acg+VDpkRWVRx5YKyB+tF1nvSh+nmW7zhnx/lIxeVvVgvsjWzr53U9pULKlukVvAXkcPAc5X2d3Z2dnR0dHSCk8jj0KFDz1eaBh+SWUAml0dMiCSex6mqOmlJ5tRE1mhEZEtQzadVaeTzyBOu5CROLrKcxGklka1L2wDLaNjzaJkyWU56tJIny0mJXGQ5iZMpkYnIn4nIoIhsd18XefZ9TkT2icgeEbnAs/1MEdnp7rtTRMTdfpyIfMPd/oSIzPOcc62I7HVf13q2z3eP3eueO6Uxn9wsIvJB9zntExF/8hzzqGpmXsCfAX8UsP10YAdwHDAf2A8U3H1PAucAAvwncKG7/feAe9z3VwLfcN+fhJPv4yRgpvt+prvvfuBK9/09wCfSfiYxnmHBfT4LcJLj7ABOT/KemfJkVbgYuE9V31TVA8A+4GwRmQO8U1U3qfOEvwr8huecf3LfbwTOc73cBcB/qepLqvoy8F/AB919H3CPxT23fK0scTawT1WfVSdr5n04zyIxsiiyG0XkhyLyFRGZ6W7rBA56jhlwt3W67/3bJ5yjqmPAq8DJVa51MvCKe6z/Wlmi0udLDOtEJiLfFZG+gNfFwN3AQmAFMAR8sXxawKW0yvY451S7VpZo+OewbhSGqoZKrygifwf8m/vvADDXs7sLeN7d3hWw3XvOgJu87wTgJXf7ub5zvo/TmXyiiBRdb+a9Vpao9KwSwzpPVg23jFXmEqCcaO8h4Eq3xjgfWAQ8qapDwBERWe2Wqa7ByQpZPqdcc7wMeNQttz0CnC8iM91wfD7wiLvve+6xuOeWr5UlngIWuTXlKTiVnocSvWPatZ2INaOvATtxMjo+BMzx7PtTnFrTHtwapLu9F0eM+4G7eLuX43hgA04l4Ulggeec33a37wOu92xf4B67zz33uLSfSczneBFOPt/9wJ8mfb+8WykncTIVLnOySS6ynMTJRZaTOLnIchInF1lO4uQiy0mcXGQ5ifP/zauNAradx70AAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots()\n", + "\n", + "x = tuple(map(merc_x, lons))\n", + "y = tuple(map(merc_y, lats))\n", + "\n", + "ax.tricontourf(x, y, tuple(datapoints.values()))\n", + "ax.plot(x, y, 'wo', ms=3)\n", + "ax.set_aspect(1.0)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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postgresql+psycopg2://apd@localhost/apd + +and run `alembic upgrade head`. This should also be done after every upgrade of the software. + +# Defining endpoints + +Endpoints to collect from are managed with the `sensor_deployments` CLI tool. After installation +there will be no deployments defined + + sensor_deployments add --db postgresql+psycopg2://apd@localhost/apd + --api-key 97f6b3e5ceb64a6ba88968d7c3786b38 + --colour xkcd:red + http://rpi4:8081 + Loft + +The optional colour argument is the colour to use when plotting charts with the built-in charting +tools. This uses matplotlib's colour specification system, documented at https://matplotlib.org/tutorials/colors/colors.html + +The sensors can then be listed with `sensor_deployments list`: + + Loft + ID 53998a5160de48aeb71a5c37cd1455f2 + URI http://rpi4:8081 + API key 97f6b3e5ceb64a6ba88968d7c3786b38 + Colour xkcd:red + +The ID is the deployment ID, as set by the endpoint. It is only possible to add endpoints if they can be +connected to at the time. + +# Collating data + +Data can be collated from all defined endpoints with the `collect_sensor_data` command line tool. +Although you can specify URLs and an API key to explicitly load data from a one-off endpoint, running +without specifying these will use the configured endpoints from the database. + + collect_sensor_data --db postgresql+psycopg2://apd@localhost/apd + +# Viewing data + +You can write scripts to visualise the data from the database. I recommend using Jupyter for this, as it +has good support for drawing charts and interactivity. + +All configured charts can be displayed with: + + from apd.aggregation.analysis import plot_multiple_charts + display(await plot_multiple_charts()) + +More complex charting can be achieved by passing `configs=` to this function, consisting of configuration +objects as defined in `apd.aggregation.analysis`. Iteractivity can be achieved using the +`interactable_plot_multiple_charts` function with Jupyter/IPyWidgets' existing interactivity support. + +More control can be achieved using other functions from this module, such as getting all data points from +a given sensor with: + + from apd.aggregation.query import with_database, get_data + + with with_database("postgresql+psycopg2://apd@localhost/apd") as session: + points = [(dp.collected_at, dp.data) async for dp in get_data() if dp.sensor_name=="RelativeHumidity"] + +These can be called from any Python code, not just Jupyter notebooks + +# Analysis and triggers + +The aggregator allows for a long-running process that processes records as they are inserted to the database +and apply rules to them. + +This is configured with a Python-based configuration file, such as the following to log any time the +Temperature fluctuates above or below 18c: + + import operator + + from apd.aggregation.actions.action import OnlyOnChangeActionWrapper, LoggingAction + from apd.aggregation.actions.runner import DataProcessor + from apd.aggregation.actions.trigger import ValueThresholdTrigger + + + handlers = [ + DataProcessor( + name="TemperatureBelow18", + action=OnlyOnChangeActionWrapper(LoggingAction()), + trigger=ValueThresholdTrigger( + name="TemperatureBelow18", + threshold=18, + comparator=operator.lt, + sensor_name="Temperature", + ), + ) + ] + +This is run with: + + run_apd_actions --db postgresql+psycopg2://apd@localhost/apd sample_actions.py + +The optional `--historical` option causes the actions to be triggered for all events in the database. +If it's omitted then the default behaviour applies, which is to only analyse data that is added to the +database after the actions process has started. + +The possible actions are: + +* `apd.aggregation.actions.action.LoggingAction()` - Log data points +* `apd.aggregation.actions.action.SaveToDatabaseAction()` - Save data points to the db + +These can be wrapped with `OnlyOnChangeActionWrapper(subaction)` to only trigger an action when +the underlying value changes and/or with `OnlyAfterDateActionWrapper(subaction, min_date)` to +only trigger if the date on the discovered objects is strictly after `min_date`. + +The possible triggers are: + +* `apd.aggregation.actions.trigger.ValueThresholdTrigger(...)` - This compares the value of a sensor with threshold, using the specified comparator. + Any records that don't match the `sensor_name` and `deployment_id` parameters are excluded. + + +# Tips + +The `--db` argument to all command-line tools can be omitted and the `APD_DB_URI` environment variable +set instead. \ No newline at end of file diff --git a/Ch12/apd.aggregation-chapter12-ex01-complete/Yappi Profiling.ipynb b/Ch12/apd.aggregation-chapter12-ex01-complete/Yappi Profiling.ipynb new file mode 100644 index 0000000..153122f --- /dev/null +++ b/Ch12/apd.aggregation-chapter12-ex01-complete/Yappi Profiling.ipynb @@ -0,0 +1,3140 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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zMKDX/neSfabl70rrw/e27NaW/69OLlhV1fokG9oOP6KTOubB1W35p862YCnlgCT7tR0edH/6rpSyKsn/ajt8TlVV7X8Rqlfax+Evqqq6r8M62sfujO9bVVV/luSPkmyaPPTrSa4qpbyllPKMUsohpZTDSykvKKWsTnJRHn742JTklVVVDdMPsuJ/gvjv0gDivw52TnJqku+VUg4fdGO2wxwwwRzQpRrNAXMZ8x2r0f2gfpo4bzexT8kAnlWGnPvRG02Mlyb2KTHm21m/6V4TY6WJfUrEfx0N+/pNE2OliX1KxH+vWLsBAKCpmvos1CmfzXfJ9/Nqub6bmAMeYg7okjWNbbLG20b8N1ON4t8aL3XXxHm7iX1KfDbXzv3ojSbGSxP7lBjz7azfdK+JsdLEPiXiv46Gfe0maWa8NLFPiTmgV6zfwIDYLAzomVLKC5O8vu3wn1RVdecOirbv4nv/HC7fXmbZHOropW+15V88047mbX5rG8cG3Z++KqUsSvLZtPb7piTvmcfLDmQcVlX1viRPSPLxJOuSHJiJH46/neTaJFdkYhfdl2diN+wk+XqSp1ZV9ck5tHFeiP8W4r8LA4r/Qdmc5MIkb03y/CRPzsTu4U/KxOL2+7L1gsGhSb5eSjmwf83cMXNAC3NAF+oyB3Qx5ju9Ti3uB/XTxHm7iX2apg5t7Cf3o0tNjJcm9mmaOrSxn6zfdKGJsdLEPk1ThzaOgkas3zQxVprYp2nq0MahZu0GAICmavizUKd8Nt8F38+r3/puYg5oYw7owoitaVjjbSX+tzbo/vRVXeLfGi9118R5u4l9mqYObewn96NLTYyXJvZpmjq0sZ+s33ShibHSxD5NU4c2joJGrN0kzYyXJvZpmjq0cahZv4HBslkY0BOllCck+f/aDl+Q5C9mUbz9B6r23V5no/0HqvY6++0fM7H76UN2S3LGjgqVUvZP8sZtvDReStmpN02rhb9J8svT8g8m+e057MrbiUGOwwVJtuThHfBn8skkf1hV1WWdNGw+if+tiP/uDCL+B+GtSR5ZVdWzq6p6R1VVX66q6vtVVV1fVdUPqqo6v6qqP8rEAve7klTTyu6T5AullDKIhrczB2zFHNCdoZ8DuhzznRr6+0H9NHHebmKfdlDfMLaxn9yPLjQxXprYpx3UN4xt7CfrN3PUxFhpYp92UN8wtrHpGrF+08RYaWKfdlDfMLZxaFm7AQCgqUbgWahTPpvvju/nbd//be/ug6a96vqAf09IQwpBsMRIxYGI4tsgDNSZGtHpM9Q4YEcUpEqZOklb6AtOx7Y604G20/oytDOtjE5rBVtbOhZfUAtFiYHaGmhxquIESyGlzkhooQZJhJZEkvBy+sfeT7K79+69e+29e+11zv35zFx/7HXv7nV+5zm/X+7rdz3PyeT6u4kasIIacD4Xpaehxyv/HyL/HzL5/NfjpXU91u0eY9rwfVMc45jMxzn0mC89xrTh+6Y4xjHp3+yox1zpMaYN3zfFMfaui95N0me+9BjThu+b4hgnS/8Gjs9mYcC5lVKelNmDt/lfYj6Y5M/XWuvqT51prM8cTK31E0l+ZOn095ZSvnvdZ0opX5jk1iSPXfe1exrepJVSfiDJdy6dfkWt9R0jD+Xg67CU8shSyj9N8ttJXprkui0+dlOS95RS3nyyZo5K/p8m/3c3ofw/uJMG1vKu9qved3+t9RVJ/vrSj56V5M8dZHADqAGnqQG7a6EGHGDNn3Wtyc8H7emxbvcY04Gu1/N/S8zHlnrMlx5jOtD1el7z+jdb6DFXeozpQNfrOf8Prof+TY+50mNMB7rehcx/vRsAAHp1Qe+FzuTZ/O4mdD+jv7slNeA0NWB3E6oBB6fHu5L8X/O1exrepLWQ/3q8tK7Hut1jTAe6Xs//LTEfW+oxX3qM6UDX63nN699socdc6TGmA12v5/w/uB56N0mf+dJjTAe63oWsAfo3MA1XHnsAQNtKKdcl+Q9Jnjh3+q4kN9ZaP7rl19y79HqX/zvP8meWv/MYXpXkeXl4t9KS5IdLKS9K8hNJ3p3ZrrFfcPK+v5aHfzH6UJL5RsX9tdZTu9KWUq7fdjC11jsHjf4ISil/I7PdoOe9utb6j7f8/PXbXmvFfIy6DkspVyZ5U5Lnzg8ryRsz293+XUnuTvLIJE9K8pzMbmafevLeb05yQynlxlrru3cY67nJ/zPJ/4GOnP+TV2v90VLKNyZ5/tzplyf5qSMNSQ04mxowUAs1YE9rfttrnWs+YJUe63ZLMbV0rzIG8zG+lvJlWy3FZM0vamk+9G8eMql12FJMLa33MfR2L7tsav2blnJlWy3FJP8X6d0AAMDuWroXOgLP5gfy9/Pa6u8masAGasBALfz9nGPS411L/m/QwnpvIf/1eGldj3W7pZhaulcZg/kYX0v5sq2WYrLmF7U0Hz30b1rKlW21FFNL630Mvd3LLpta7yZpK1+21VJMasAi/Ru4WGwWBuyslPLHkvxKki+dO313km+otf7OgK/q7heqJKm1PlhKeWGSW5I8fe5HX3dyrHNPkr+U5K1z5z6+5r0fGDCkMuC9oyulvCzJq5dO/1it9XsGfM155mPsdfj3stjI+mSSF9Vab1l634NJ3pvkvaWUH0/yz5P8xZOfXZvkl0opz6i13rPDeHcm/88m/4eZQP634h9msZn1NaWUx9Va162Rg1EDzqYGDNNCDdjjmt/mWvuYD1jQY91uMKaW7lXGYD5G1GC+bNRgTNb8opbmQ/9mZjLrsMGYWlrvY+jmXvYMk+jfNJgrGzUYk/xfpHcDAAA7aPBeaFSezQ8zgWfz+rsDqQFnUwOGmUANaIUe72nyf7NJr/cW8l+Pl9b1WLcbjKmle5UxmI8RNZgvGzUYkzW/qKX5aLp/02CubNRgTC2t9zF0cy97hkn0bpIm82WjBmNSAxbp38AFcsWxBwC0qZTy2CRvS/JVc6c/ltnOn+8d+HX/d+n15w0cyzU5/QvV6L/Yr1Jr/XCSr03y2iSf2uIjv5rkq5Pct3T+rj0PbVJKKd+Z5DVZ/OXyXyf5rhGHsbwOH1VKefTA77hu6fXKdXjyC/HyL6QvX9HIWlBrfSDJy5K8fe70E5O8cuA4z0X+b0f+b2ci+d+K38gs1y57RJKvHHsQasB21IDttFAD9rzmN11r8vNBe3qs2z3GtMFo9yqNMB8D9JgvPca0gTW/SP9mSz3mSo8xbSD/23T0/k2PudJjTBvI/wH0bgAA6NUFvBfaiWfz25nI/Yz+7gBqwHbUgO1MpAa0Qo93cSzyv3Et5L8eL63rsW73GNMGns0tMh8D9JgvPca0gTW/SP9mSz3mSo8xbSD/23T03k3SZ770GNMGasAA+jcwPTYLAwYrpTwmya1J/sTc6f+X5Lm11nfv8JXLu4U+eeDnl9//B7XWj6185xHUWu+rtf7VJF+W5O9k9rDxQ5ntdP6JJHck+TdJbkzyp2utdyb5iqWveddoAx5ZKeXFmf2SNv/fpNcneWmttY41jpOd45fXzZMGfs3yWly3E+43JZm/afhAZmtgo1rrZ5N8/9Lpm0opo+zkLf+Hkf9nm0r+t+Ik///X0ulBjZLzUgOGUQPO1kINOMCaP+tak58P2tNj3e4xpk1GvleZPPOxvR7zpceYNrHmF+nfbKfHXOkxpk3kf5uO3b/pMVd6jGkT+b89vRsAAHp1Ee+FzsOz+bNN5X5Gf3d7asAwasDZplIDWqHHe4r8b1gL+a/HS+t6rNs9xrSJZ3OLzMf2esyXHmPaxJpfpH+znR5zpceYNpH/bTp27ybpM196jGkTNWB7+jcwTVceewBAW052Rb0lydfMnb43yfNqrb+x49fesfT6SwZ+/ilLr9+34zgOqtb6gSSvOjk2uWHp9a+v+c7R/gLKIZRSvi3JT2a2e/NlP5fkppObtkH2MB93ZPZ/mbrsS3J6fZ5leS2u++wzll7/6sBfUt+R5MEkV528fnxmYz3ojYT83538P22C+d+KTy69Xt4h/WDUgN2pAae1UAMOtObXXWuv8wFJn3W75ZgaulcZhfk4vJbzZZ2WY7LmFzU0H/o3D5P/p8n/HbR+LzvAUfo3LefKOi3HJP8X6d0AAMD2Wr4XOjbP5k+b4LN5/d0N1IDdqQGnTbAGtEKP92Hyv1Et5L8eL63rsW63HFND9yqjMB+H13K+rNNyTNb8oobmo8n+Tcu5sk7LMTW03kfR+r3sAP595SI1YHdqwAb6NzBdV2x+C8BMKeWPJvmlJF83d/oPk/yZWuuvneOr//vS66eXUh414PPP3vB9TTnZwfw5S6fffoyxHFIp5flJfjqLG1e+KclLaq2fOc6oTq2d5QfCa538wvv0Dd932eOWXt+17XWSpNb66ST3LJ2+dsh3DCX/xyH/j5r/rVjO9bvHuKgaMA41YDo14IBrftW1Jj8ftKfHut1jTAONda/SCvNxhh7zpceYBrLmF+nfrNFjrvQY00Dyv02j9296zJUeYxpI/p9B7wYAgF65FxqHZ/P+ft4mx+jvJmrAWNQAPY0t6PE+TP43qIX81+OldT3W7R5jGsizuUXm4ww95kuPMQ1kzS/Sv1mjx1zpMaaB5H+b/PvKRWrA7tSAM+jfwLTZLAzYSinl6iRvTnJp7vT9SZ5fa33Heb671vp7Sf7b3Kkrs/iLwyaXll7/8nnGMwHPSXL93Ou311oP/n+kG1Mp5Zsy2831j8ydfkuS7zhp1BzLrUuvLw347Ndn8ZfQ22utH1nz3o8vvX70gOtcds3S63t3+I6tyP9RyX/WKqVcm9O7jf+fEa6rBoxHDZiAQ675Fdea/HzQnh7rdo8x7WCse5VWmI81esyXHmPagTW/SP9mhR5zpceYdiD/G3OM/k2PudJjTDuQ/2vo3QAA0Cv3QqPybP549HfXUANGpQawlh7vKZeWXsv/iWsh//V4aV2PdbvHmHbg2dwi87FGj/nSY0w7sOYX6d+s0GOu9BjTDuR/Y/z7ypUuLb1WA7anBqyhfwPTZ7MwYKNSylVJ/l2Sb5g7/UCSb621/sc9XeaNS6//wpZj+/Ikf3Lu1H1J3ranMR3L3156/dqjjOJASik3JvmFJFfNnX5bkm+rtT54nFE95K1JPjn3+oaTNbaNm5deL6/pecs3n8/c8hpJklLKU5M8Zun0oN3zB1xL/o9L/nOWF2fx9/ePJLnjkBdUA0anBhzZSGv+8rUmPx+0p8e63WNMOxrrXqUV5mOFHvOlx5h2ZM0v0r85fa3ucqXHmHYk/9szav+mx1zpMaYdyf8V9G4AAOiVe6HReTZ/PPq7q6+nBoxLDeAserwPj03+N6aF/NfjpXU91u0eY9qRZ3OLzMcKPeZLjzHtyJpfpH9z+lrd5UqPMe1I/rfHv69cHJsacD5qwAr6N9AGm4UBZyqlXJnkDUmeN3f6U0leVGt96x4v9fokn5l7/cKTG/ZNlh/avaHWev/+hjWuUspNSW6cO/XuzHZD7UIp5U8l+fdJrp47/Z8y+wXxgeOM6mG11j9M8vNLp5fX2CmllC9N8oK5U59O8lNnfOS2pdfPLqV85TZjPPFXll6/v9b60QGf34r8H5f85yyllM9P8neXTv9irbUe8JpqwIjUgOMbcc03MR+0p8e63WNMuxrxXqUJ5uO0HvOlx5h2Zc0v0r9Z1GOu9BjTruR/W8bu3/SYKz3GtCv5f5reDQAAvXIvNC7P5o9Lf/c0NWBcagBn0eM9Rf43pIX81+OldT3W7R5j2pVnc4vMx2k95kuPMe3Kml+kf7Oox1zpMaZdyf+2+PeVK6kB56AGnKZ/Aw2ptTocDsfKI8kjkvxskjp3fCrJCw50vZ9YutY7k1x9xvu/Zen9DyR58jnHcGnpO+885/ddOeC9L0zy4NJcP/PIa2Bv85HkhiSfWPq+tyd51DFjXDHOpyz9OdQkzz/j/VefrNX5979mwzVKkvcvfea3kjxmi/E9d8X4fvAA8yD/5f+Fy/8x5iPJlyX55oGfeUKS31yx5p9ywHjVADXgQtWAMdd8C/PhaO/osW73GNMexnjwe5Utx3Hb0nfefMi4zcdWY+guX3qMaQ9jtOZHno/o36y6nvyX/0fP/w1jvLQ0xjt3/J7J9296zJUeY9rDGOX/EdZH9G4cDofD4XA4HCMeF/FeaF/373Pf59n8w9/VxP3MGPe7aaC/e3ItNUANuHA1YIz5iB7vquvJf/l/1GPMNd/CfDjaO3qs2z3GtIcxejZnPtaNobt86TGmPYzRmh95PtJA/6bHXOkxphbW+5bjmET+bxjjpaUx3rnj90y+d3Nyze7ypceY9jBGNeAI6yP6Nw7HuY8rA7Dev0ry7UvnXpnk9lLK9QO/6666eefWv5/ZTqqfe/L6a5P8SinlpbXW/3H5TaWURyb5y0l+aOnzP1Rr/eA2gymlfGGysgY+Yen1lWfEem+t9e4Nl3pPKeUtSX4hya/XWj+7YixPS/KKJC9Z+tEra623b/j+vTj0fJRSnpnkl5NcM3f6/Um+K8l1pZQhw72/1nrXkA8MUWv93VLKjyT53rnTP19K+VtJfrzW+uDlk6WUr0jyLzNbq5fdk+T7NlyjllJekdm6uOxZSX7r5DpvqbXW+c+UUh6f5LszWyvzf1b3JPkn28Y3gPyX/xcu/5NR1scfT/LmUsp7kvzbJG+stf7OmrE8JslNme14//lLP/7BWuvvrrnGPqgBasBFqwGjrPmG5oP29Fi3e4zpXMa4V5n7/DVJrl3z46uXXl97xp/Jh2qtn97mmkOZjwU95kuPMZ2LNb9I/+YhPeZKjzGdi/w/Tf8mSZ+50mNM5yL/F+jdAADQq27vhTybPzUGz+ZP6O8uUAPUgAtXAxI93hPyX/5ftPzX46V1PdbtHmM6F8/mFpmPBT3mS48xnYs1v0j/5iE95kqPMZ2L/D9N7+YhPeZLjzGdixqwQP8GWlInsGOZw+GY5pHF3TjPe1za8pqXMtvpdf6zn81sx9+fTXJrkt9f8f2/mOQRA2K7cw8xvW6L69w99/5PJPm1zBoYr0/ytjPG8QMj/1kfdD6S/IM9rqXbRpiPRyS5ZcW1P5LZL6BvSPKuk7U5//MHknz9gOu8ek2Mdyd568k6+bmT9f+pFe+7P8lz5L/8l/9NzcelFe//eJL/kuRNSX4yyRszqzGr8r4mee0I86AGqAEXqgaMteZbmQ9He8dYa3jpmpdywLrdY0x7+rMe617l5j3N/fXm4/Dz0WO+9BiTNd/0fOjfyH/5P738v3MPY3zdhjWx/P5J9W96zJUeY5L/7a356N04HA6Hw+FwOEY+er4Ximfzo85Ha/cz0d9VA9SAi14DDj0fl1a8X49X/sv/I+b/WGu+lflwtHeMtYaXrnkpns0NimlPf9aezZmPC5EvPcZkzTc9H5Pt3/SYKz3G1Nh6v3lPc3/o/L9zD2N83YY1sfz+SfVues2XHmNSA9pb89G/cTj2cqza1RPgaGqtt5VSXpDkdUk+7+R0SfLVJ8cqP53kZbXWzxx+hOdyTZIbNrznY0leXmv9mRHGwxq11s+UUr49sx1+v2PuR9clee6aj/1+kptqrf95wKW+5+Rz35fkqrnzj0/yjRs++8EkN9dabxtwvUmT//L/Antskmdv8b77kvzNWuu/OPB4jkINUAOAtvRYt1uIacR7lSaYj+NpIV+GaiEma36R/s1xtJArQ7UQk/yfhAvfv2khV4ZqISb5DwAA7FsL90Ln4Nl8I/R3j0cNUAMuMD1e+S//gab0WLdbiMmzuUXm43hayJehWojJml+kf3McLeTKUC3EJP8n4cL3bpI28mWoFmJSA4AWXXHsAQAsq7XekuRpSV6T2YO5df5rkhfVWl9Sa71vlMEN98NJbs9st9iz/O8k35/kiz2EnIZa67211hcn+bOZrbV1/iDJjyV5Wq311oHXqLXWf5Tkq5L8s5y93i97X2ZNsKf11Mi6TP7L/wvgjiSvSvLOJJ/c8jP/M8krM9vxu8tG1mVqgBoAtKWzup2kjZjGuFdpifk4nhbyZagWYrLmF+nfHEcLuTJUCzHJ/1Hp36zRQq4M1UJM8h8AANi3Fu6FBvBsvlH6u8ejBqgBF4Ae7xryX/4DbemsbidpIybP5haZj+NpIV+GaiEma36R/s1xtJArQ7UQk/wfld7NGVrIl6FaiEkNAFpTaq3HHgPAWqWUqzLbDfjJSZ6Q2a6/H05ye631A8cc2xCllM9J8swkX5TZzrdXZ3YT8+Ekv11rfd8Rh8cWSilflORZSb4gyaOT3JXZ7vPvrLU+uKdrlCRfnuQZSa5N8jlJPp3k45mtlXfVWj+yj2u1QP7Tu1LKFUmemuSLkzwxyePy8Pr4WJLfS/KbtdaPHm2QR6QGALSll7o9r5WYxrhXaYn5OI5W8mWIVmKy5hfp34yvlVwZopWY5P849G/WayVXhmglJvkPAADsUyv3Qpt4Nt8+/d3jUAPonR7vevIfoC291O15rcTk2dwi83EcreTLEK3EZM0v0r8ZXyu5MkQrMcn/cejdnK2VfBmilZjUAGDqbBYGAAAAAAAAAAAAAAAAAAAAAAAAE3XFsQcAAAAAAAAAAAAAAAAAAAAAAAAArGazMAAAAAAAAAAAAAAAAAAAAAAAAJgom4UBAAAAAAAAAAAAAAAAAAAAAADARNksDAAAAAAAAAAAAAAAAAAAAAAAACbKZmEAAAAAAAAAAAAAAAAAAAAAAAAwUTYLAwAAAAAAAAAAAAAAAAAAAAAAgImyWRgAAAAAAAAAAAAAAAAAAAAAAABMlM3CAAAAAAAAAAAAAAAAAAAAAAAAYKJsFgYAAAAAAAAAAAAAAAAAAAAAAAATZbMwAAAAAAAAAAAAAAAAAAAAAAAAmCibhQEAAAAAAAAAAAAAAAAAAAAAAMBE2SwMAAAAAAAAAAAAAAAAAAAAAAAAJspmYQAAAAAAAAAAAAAAAAAAAAAAADBRNgsDAAAAAAAAAAAAAAAAAAAAAACAibJZGAAAAAAAAAAAAAAAAAAAAAAAAEyUzcIAAAAAAAAAAAAAAAAAAAAAAABgomwWBgAAAAAAAAAAAAAAAAAAAAAAABNlszAAAAAAAAAAAAAAAAAAAAAAAACYKJuFAQAAAAAAAAAAAAAAAAAAAAAAwETZLAwAAAAAAAAAAAAAAAAAAAAAAAAmymZhAAAAAAAAAAAAAAAAAAAAAAAAMFE2CwMAAAAAAAAAAAAAAAAAAAAAAICJslkYAAAAAAAAAAAAAAAAAAAAAAAATJTNwgAAAAAAAAAAAAAAAAAAAAAAAGCibBYGAAAAAAAAAAAAAAAAAAAAAAAAE2WzMAAAAAAAAAAAAAAAAAAAAAAAAJgom4UBAAAAAAAAAAAAAAAAAAAAAADARNksDAAAAAAAAAAAAAAAAAAAAAAAACbKZmEAAAAAAAAAAAAAAAAAAAAAAAAwUTYLAwAAAAAAAAAAAAAAAAAAAAAAgImyWRgAAAAAAAAAAAAAAAAAAAAAAABMlM3CAAAAAAAAAAAAAAAAAAAAAAAAYKJsFgYAAAAAAAAAAAAAAAAAAAAAAAATZbMwAAAAAAAAAAAAAAAAAAAAAAAAmCibhQEAAAAAAAAAAAAAAAAAAAAAAMBE2SwMAAAAAAAAAAAAAAAAAAAAAAAAJspmYQAAAAAAAAAAAAAAAAAAAAAAADBR/x+Xhcjgi63IuAAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from apd.aggregation.analysis import interactable_plot_multiple_charts, configs\n", + "from apd.aggregation.utils import jupyter_page_file, profile_with_yappi, yappi_package_matches\n", + "import yappi\n", + "\n", + "with profile_with_yappi():\n", + " plot = interactable_plot_multiple_charts()\n", + " plot()\n", + "\n", + "with jupyter_page_file() as output:\n", + " yappi.get_func_stats(filter_callback=lambda stat:\n", + " yappi_package_matches(stat, [\"apd.aggregation\"])\n", + " ).print_all(output)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r\n", + "Clock type: WALL\r\n", + "Ordered by: totaltime, desc\r\n", + "\r\n", + "name ncall tsub ttot tavg \r\n", + "..futures\\thread.py:52 _WorkItem.run 7 0.000092 3.204091 0.457727\r\n", + "..tion\\analysis.py:373 run_in_thread 1 0.000053 1.844382 1.844382\r\n", + "..38\\Lib\\threading.py:859 Thread.run 2/1 0.000025 1.839877 0.919939\r\n", + "..rrent\\futures\\thread.py:66 _worker 2/1 0.000022 1.839852 0.919926\r\n", + "..lectorEventLoop.run_until_complete 1 0.000027 1.839745 1.839745\r\n", + "..ndowsSelectorEventLoop.run_forever 1 0.000088 1.839611 1.839611\r\n", + "..WindowsSelectorEventLoop._run_once 17 0.000408 1.839497 0.108206\r\n", + "..alysis.py:327 plot_multiple_charts 1 1.643447 1.839309 1.839309\r\n", + "..py:635 ThreadPoolExecutor.__exit__ 1 0.000005 1.837286 1.837286\r\n", + "..py:230 ThreadPoolExecutor.shutdown 1 0.000009 1.837281 1.837281\r\n", + "..8\\Lib\\threading.py:979 Thread.join 1 0.000006 1.837272 1.837272\r\n", + "..:1017 Thread._wait_for_tstate_lock 1 0.000011 1.837263 1.837263\r\n", + "..gation\\analysis.py:291 plot_sensor 1 0.000000 1.407986 1.407986\r\n", + "..query.py:88 get_data_by_deployment 1 1.397146 1.397188 1.397188\r\n", + "..d\\aggregation\\query.py:41 get_data 1 1.283592 1.375576 1.375576\r\n", + "..lchemy\\orm\\query.py:3232 Query.all 6 0.004333 1.362649 0.227108\r\n", + "..ctors.py:319 SelectSelector.select 17 0.000177 1.362418 0.080142\r\n", + "..tors.py:313 SelectSelector._select 17 0.000069 1.362216 0.080130\r\n", + "..y\\orm\\query.py:3400 Query.__iter__ 6 0.000054 0.958316 0.159719\r\n", + "..:3425 Query._execute_and_instances 6 0.000073 0.956631 0.159439\r\n", + "..ine\\base.py:916 Connection.execute 9 0.000062 0.740051 0.082228\r\n", + "..:589 PGDialect_psycopg2.do_execute 11 0.000042 0.738361 0.067124\r\n", + "..y:1159 Connection._execute_context 9 0.000307 0.734166 0.081574\r\n", + "..:291 Select._execute_on_connection 6 0.000047 0.731436 0.121906\r\n", + ".. 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import yappi\n", + "\n", + "from apd.aggregation.analysis import interactable_plot_multiple_charts, Config\n", + "from apd.aggregation.analysis import clean_temperature_fluctuations, get_one_sensor_by_deployment\n", + "from apd.aggregation.utils import profile_with_yappi\n", + "\n", + "yappi.set_clock_type(\"wall\")\n", + "\n", + "filter_in_db = Config(\n", + " clean=clean_temperature_fluctuations,\n", + " title=\"Ambient temperature\",\n", + " ylabel=\"Degrees C\",\n", + " get_data=get_one_sensor_by_deployment(\"Temperature\"),\n", + ")\n", + "\n", + "with profile_with_yappi():\n", + " plot = interactable_plot_multiple_charts(configs=[filter_in_db])\n", + " plot()\n", + "\n", + "yappi.get_func_stats().print_all()" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r\n", + "Clock type: CPU\r\n", + "Ordered by: totaltime, desc\r\n", + "\r\n", + "name ncall tsub ttot tavg \r\n", + "..futures\\thread.py:52 _WorkItem.run 7 0.000000 9.515625 1.359375\r\n", + "..lchemy\\orm\\query.py:3232 Query.all 6 0.187500 6.390625 1.065104\r\n", + "..lchemy\\orm\\loading.py:35 instances 67353 0.046875 6.140625 0.000091\r\n", + "..esult.py:1257 ResultProxy.fetchall 6 0.000000 5.109375 0.851562\r\n", + "..py:1217 ResultProxy._fetchall_impl 6 0.000000 5.078125 0.846354\r\n", + "..38\\Lib\\threading.py:859 Thread.run 2/1 0.000000 3.125000 1.562500\r\n", + "..rrent\\futures\\thread.py:66 _worker 2/1 0.000000 3.125000 1.562500\r\n", + "..b\\asyncio\\events.py:79 Handle._run 29 0.000000 3.125000 0.107759\r\n", + "..WindowsSelectorEventLoop._run_once 17 0.000000 3.125000 0.183824\r\n", + "..lectorEventLoop.run_until_complete 1 0.000000 3.125000 3.125000\r\n", + "..ndowsSelectorEventLoop.run_forever 1 0.000000 3.125000 3.125000\r\n", + "..gation\\analysis.py:291 plot_sensor 1 0.031250 3.046875 3.046875\r\n", + "..egation\\analysis.py:313 4 0.015625 2.703125 0.675781\r\n", + "..and_clean_temperature_fluctuations 4 0.046875 2.687500 0.671875\r\n", + "..184 clean_temperature_fluctuations 4 0.062500 2.640625 0.660156\r\n", + "..sycopg2\\_json.py:164 typecast_json 67339 0.312500 2.562500 0.000038\r\n", + "..-input-1-879b7ebf06b8>:7 4 0.234375 2.515625 0.628906\r\n", + "..gregation\\query.py:116 subiterator 4 0.390625 2.281250 0.570312\r\n", + "..n38\\Lib\\json\\__init__.py:299 loads 67339 0.515625 2.250000 0.000033\r\n", + "..es\\psycopg2\\extras.py:686 67347 0.156250 1.796875 0.000027\r\n", + "..on38\\Lib\\uuid.py:130 UUID.__init__ 67347 1.000000 1.640625 0.000024\r\n", + "..\\decoder.py:332 JSONDecoder.decode 67339 0.718750 1.546875 0.000023\r\n", + "..d\\aggregation\\query.py:41 get_data 1 0.312500 1.531250 1.531250\r\n", + "..ion\\database.py:77 from_sql_result 67339 0.468750 1.218750 0.000018\r\n", + "..chemy\\orm\\loading.py:83 6 0.328125 0.984375 0.164062\r\n", + ".._collections.py:121 result._asdict 67343 0.375000 0.515625 0.000008\r\n", + "..thon38\\Lib\\uuid.py:231 UUID.__eq__ 67351 0.281250 0.359375 0.000005\r\n", + "..chemy\\orm\\loading.py:84 67347 0.343750 0.343750 0.000005\r\n", + "..y\\util\\_collections.py:112 __new__ 67347 0.234375 0.312500 0.000005\r\n", + "..\\matplotlib\\__init__.py:1535 inner 4 0.000000 0.296875 0.074219\r\n", + "..axes.py:1651 AxesSubplot.plot_date 4 0.000000 0.296875 0.074219\r\n", + "..xes\\_axes.py:1412 AxesSubplot.plot 4 0.000000 0.296875 0.074219\r\n", + "..gregation\\analysis.py:63 draw_date 4 0.000000 0.296875 0.074219\r\n", + ".._base.py:1842 AxesSubplot.add_line 4 0.000000 0.281250 0.070312\r\n", + "..oder.py:343 JSONDecoder.raw_decode 67339 0.265625 0.265625 0.000004\r\n", + "..s\\matplotlib\\dates.py:1911 convert 6 0.000000 0.265625 0.044271\r\n", + "..on_base.py:2063 vectorize.__call__ 4 0.000000 0.265625 0.066406\r\n", + "..b\\axis.py:1562 XAxis.convert_units 12 0.000000 0.265625 0.022135\r\n", + "...py:168 AxesSubplot.convert_xunits 48 0.000000 0.265625 0.005534\r\n", + "..lotlib\\lines.py:664 Line2D.recache 10 0.000000 0.265625 0.026562\r\n", + "..s\\matplotlib\\dates.py:401 date2num 6 0.000000 0.265625 0.044271\r\n", + "..tlib\\lines.py:1018 Line2D.get_path 10 0.000000 0.265625 0.026562\r\n", + "..68 AxesSubplot._update_line_limits 4 0.000000 0.265625 0.066406\r\n", + "...py:2154 vectorize._vectorize_call 4 0.046875 0.265625 0.066406\r\n", + ":1 DataPoint.__init__ 67339 0.234375 0.234375 0.000003\r\n", + "..tplotlib\\dates.py:210 _to_ordinalf 8467 0.140625 0.218750 0.000026\r\n", + "..alysis.py:327 plot_multiple_charts 1 0.000000 0.078125 0.078125\r\n", + "..ine\\base.py:916 Connection.execute 9 0.000000 0.062500 0.006944\r\n", + "..:291 Select._execute_on_connection 6 0.000000 0.062500 0.010417\r\n", + "..y\\orm\\query.py:3400 Query.__iter__ 6 0.000000 0.062500 0.010417\r\n", + ".. 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ga5eCoVhSxca3A9sCqhRRZV5aa74/12xzm6M7wrDdU2/ol+tCkw5d4HH9o4JrL6jyoGjE68sDW7tsrQsbyxQCSwQE30YnzWNbuwsBkTD7A5qnwpstBnDZOwvJuDaH1tRX3CG4/KzC5bSliqctuEhEFCVviM+wRkXzFkN8XcVqfOnBMF5hmXguDy4vaaerGe/MMMTXKMUQ38ZdutHKQ3zFdrolrXEHwnqD7XT948oQ/GOIj4iIiIiIiIhopGPYrvmVNadDSik1TkRs9XvOLfv9Ny47EpGlSqmnABzdv6gDwBkA7nA6UiKiZqT5tEwAfHfi/8PPJnweG787FXMm9+H7H4zh3MOi+cBkzmTgWTbhrgnbPRTUhrHIFkCppYyhmhdRWC7BzShDpVEzhd+Cgm/vOUjhslv0191WsXIohO1s99e7DnSfx3tduzdtBTBOu16KYTsiGuK8Ib7Jo7VrVDSviCDd62+H6w3xadvlekJ8unGG+BqnO1v4qVWITxfG62hTvmCfN+Cn36ZQmY8hPiIiIiIiIiKi5sewXfPTfTSfMK2slNoNwCFl2z8eYn8PYTBsBwBngWE7IhrS/B+SXDfuYvz7lCsHfl++CfjwtXk8/pUYjtqr+g83bMEOp+1zgtY4P2TRWbnFPHbFnXmnOaJsI2sjIiVtJeu1Xxr+XD6s72lQqNRFzvBUjQcE38a3m8ds17dZwna5PkG8Rf/abjr+yaOB0cnBbT51nMINC9zSGl3fvQTATdqxlPHdBBHRyKaUQipReJ2sVYhPH8aTkuBecbw7Yw74FS/nGeJriGKIDzUI8WnDeAlgVLIQ1NOPK4xK6gOADPEREREREREREUWLYbvmt3fZ7zkAmy3rv63s9xdFpCvE/haU/R6ilgYRURPSpFJ+P/YjvmV9eeBPz0okYTtTkMRVTy/Qyv+htZ5+w/zh1DMr3ebo6a3PJ5KL1gCH7uHdb112SyOAyyO4mcOdlVa2swXEhkJluzc2A3On6sdMrw97TSz93aX965ptghndK9CzYjkwW78OK9sREdVXrUN85jCevtpelyXgV7zMEF9jFEN8myIO8aVaMRjGS5RfVoVKfdrxQojPvw1DfEREREREREQ0cvGj/Ob3wbLfnxURW4zjgLLfXw+5v+UB8xERDXkL2o/TLv/N44Iffqj6+autbNfTC4xJVX8cw9FekxTcokZmUYWQcn3243h9Y2nYjm1kKSp5h0BvM4c7jZXtYvbtbAGxZqlsZ6s8t36nJWxneF0qDxjOmRx8DMs3AtMfvhE9saRxnXZWtiMiGha8Ib5JNQzxdWULrXQ7PUG9kja6/eMl7XS923guM8TXGD29hZ9ahfg6+tvqei8PtNPVjnva6ZaNtyeAFob4iIiIiIiIiKiJMWzXxJRSowB8umzxbQGblVfCWx1yt6vKfp+olBovIttCzkNE1Bxc+i32294dzS5zVYbtpn2pNIkyZzLwrgMVvvVehcmjR/aHDt0RBOWimGPjTsGX/8/+2PrwtXnMmgh851yFjxwVQ9YhbHfUrOqPjYa/kVrZLhZTSMShfS7Zrq+t2mhQwC+sfz7FHLZLWwKBprBgecDwfYcqfOOvYg3vdmcB5PN4PVH+tqBAKSDBd4FERGRRyxBfJtcfzMv2V9NLe8N4Mhjc84x3Z4HOdGmIzzvOEF/jDIT4tKPVhfh07XK9IT79+GCIz9dOt40hPiIiIiIiIiKKBj9maW7/DWA3z+/bAfwyYJtxZb9vDLNDEelUSqUBeEthjAVQVdhOKTUFgEMtjhJzqtknERGAUGG7qFRb2a7c8k3Azx8SPPSq4MnLYxiVHLkfENjCKq4eL6/hGtKutOCUH+axdF3wuiu3AOf/UtCZyeNohxbFT6+s7thoZHD5MLmpK9sZXiNdgm+pVkPYzlbZzhJMi7qy3REzzc/zLZ0CUzjBtbLd3lMU7rwkhs/elMcbm/Xb3LBAcGS+AxdNv14/Z6tAqZH7/wgRETWOUgrJ1kJb9En6NSqaVxfiG2iNmwa6slIa7PO2002LP/jn2b7PoaIwRa8Y4tvcqRuNJsTnUFNSuAAAIABJREFUD+uVhvjKA376bRjiIyIiIiIiIhqJGLZrUkqpcwFcUrb46yKyNWDTUWW/91Sw+x6Uhu2032MO6fMAvhXBPEREVVmc2L/m+4g6bFe0ZB0wfylw7mG1mX8o6M42vmTFvS/DKWjn9eO/C268iB/AUDRcMsQ9WXOwq9HMle2CjzfVCuzQ/HXb02u+vqb9xVShWl7UJo3SfzB872LgI0fptzFXtvMf3+kHKCz/Tgtin9FfsT8/J3jXXub/62zteImIiIaiWof4BoJ73ta5una6mbIgn2GcIb7GqVWIL9lqCvAVQnztloBfeXBvFEN8RERERERERE2PYbsmpJQ6BMBvyxbPA/ALh83Lw3bpCg6hB8B4y5xERENHWSrlheQhxlWjOo+drVHYDgAee11w7mEj94R7VK0x+/JS8QcXz64KH/hbss6tTTGLTZGLIV/ZzvDhctyhylx5pbci22tDpW1rK6X/8BaYNta8jalqZ9JwfQFgTBLYqflLXyng31a/07hdqrXxoWUiIqKhwBvim6g9M1Z5iC+bMwX4+tvpGgJ+XZZxhvgaJ91b+Kl1iK8jAYxKlrbT1Y+rsnUHxzvagLjDl1yIiIiIiIiIyIxhuyajlNoTwN9QGnBbBeDjIhX1QqzXNkRETeumMR/FIx0nog8tuHHcBcb1Zk2sfl9vbhVs7ap+HpNMEwdo6qE7orBdNmcO7dTqGHTVuMqJVBcEpJFhKIftnlgu2GX4KohL+M1Ulc3aRrbOYbuwxwEUKxH62arQHbw78Njr/uUiwI6+pH+gXzvDdkRERA2llEJbK9BWwxCfPqwn2oBe18CPf7wrC+xKM8TXKLUM8RXDeB2J/mp6xcvJ/na62nFVtu7gOEN8RERERERENJIwbNdElFJTAMwHMMOzeD2Ad4rIJsdpyk+/pCo4lPJtDPU5Qvk5gD+H3GYOgNsj2DcRjWQieKz9ePx63KcCV622/esr6wQHfKu2n0K8sXlkhySiChBVE7Z7/PXK7oMHXnHbLt1b+KCCyMTl6xdXzxNcelrtjyWMXz+Wxz/+znzw8VjwHKbn7WsbzNvUO2x3xgHAvCX+5U+/Yb7uphCvLWz3X+fEcPJV4f/PScVH9v8jREREw1WtQ3xdWaAzPRjC60wX/5WSZd7L3QHjporHVFvFEN8W7RcFKw/xtcXLgnve1rhJhfbyYJ+hnS5DfERERERERNTsGLZrEkqpCQD+DmAfz+LNAE4XkWUhpmrKsJ2IbASwMcw2ir30iCgKImiBW4qu2vavX7ut9p8U3PNyzXfR1CIL21VxX7/wZmXb/fwht4BLT5ZhO7JzqWz35rbaH0cYuT7Bl/4i1qCgUxtZQ/jsmgcFP/uofqy3T7/TWoXtJo9W0H0QqatCV2R6bbOFgidpP0QPlnd5ABERERH184b4JnRo16h47myuv5peMbiXKQ3x+ZZlSkN8pnGG+Bojkyv81CrENxDG87bG1bXTHVhPGbYp/MsQHxEREREREVWKYbsmoJQaC2AegIM8i7ehUNFuccjpdpT9PjnksYyCP2y3PeQxEBE1EUGLOIbtctXt6a8vVLe9i5kRtLodyqJsI9usorqONHy5VLYDgOUbBXOmNMcHSC+uAbZ329dxCb+ZqtTZ2oDXu7JdJVVS06awnaWy3dhKvlID4KUNlkmJiIiI6igRV5gQr12IL6p2ugzxNVYtQ3zmMF5/O13LuC4A2JEAWuPN8R6MiIiIiIiIaodhuwZTSo0GcC+AIzyLdwI4U0QqiW2UV8GbGXL78vW3ikiT1UYhIgqhjpXt6iGqcIiI4NX1wEtrgaP3AvacqCAiWPwW8Mp64Lg5wPRx9hPExfWfWSmYOkbhxLnA6GT0J5XzecFLa4HnVglWbYlmzkyFYbucoUJWlKKq3jecpXsFNy4QPP46cNkZCofuMbI+zHAtTLZxFzBnSm2PxdWOnuB1XNrIbjbUW7a9NprCby6V9CoxKmnYn+X6mQLACcu7tenj3I+JiIiIaCSpZYhPF+Dzhvj0AT/z+C6G+BqmGOLbGjrEZx9PxP0BPW9Ir10T4CsJ+SUK7ylKAn4M8RERERERETUVhu0aSCnVAeBuAMd4FncCOEtEnq5w2qVlv+8dcvvZZb8vqfA4iIiaRqxOle3Cmj4OeCtk7dCeCKqe9eUFn71J8OvHBk8Of+u9CovXCv7y/OB615yv8LmT9emQbE7w8V/mPesLxrUD914aw1F7RXcCON0rOO/aPO58MbIpAVR+X3dmoj0OHYbt7F7fKNjnG4OfRt30lODY2cBjX4mNmBb0rpXtmqlZ6HWPBB+NS5j4n09RuOwW/1zLNpq3MX14WavKdh8+QpW8vnqPI9cn2nZVlVTfU0rhohP0+yIiIiKi6CXiCok4ML6GIT5/WE98wb3ieHf/uCkAWEnFZapeNgdsrWGIrySM13+5I6HQkbSPj2orXi4dZ4iPiIiIiIgoPIbtGkQplQJwF4ATPIu7AbxHRBZUMfXLZb8frJRqF5GAxl0Djg+Yj4hoaAlR2a63r1CtrZLATp9DqakrzlZYvRWY2AGceaDC9h7gA78I9xX2KFqM3vy0+MIZV97pP/5//oPg9P0Fc6f6b4//fbg0mAcUWkR++No83vjv6EJPP71fIg/aAZVXMbzmwdqHWlZvAQ7do+a7GbK8QbuiJ1YA85cAZxzYgANqANfKdvkmqZDx1nbBn54NPmiXSnMzximYPoDasLNQZbNcvdvI6j98LXh0GXDKfv7llR7jhwzBPiIiIiIaOmod4uvKFtrglobxPAE/73gW6Erbx+v9RUUqqGWIryOB0jBeW2mIzzZeUqHPM84QHxERERERDWcM2zWAUioJ4A4AJ3sWpwGcLSKPVDO3iKxTSr0I4OD+RXEUAn3zHKc4uez3e6o5HiKihhNBi2NlO6AQeLC17TNxqTh32ekKozxtVu9bHD4gEUXVs1+FCGbc9JTgyrP9J0jveVk/x+qtwNJ1wAHTKz68Ene/VJsQSaUfDvz77bUPtSxeJzj7UJ6U1tnZY779L7oxjzXfr1F6qsm4hu2apTW262udSxvZ9oR5bN5iwQXHNj5sZzvGexcLTtnP/RgTAceYag1xYP1aY3kAI+O5QkRERDSS1SvEVxrGk5Jg3sB4phDis40zxNcY2VzhZ5v2q/rRhPi07XKTAeOGdrsM8RERERERUTNg2K7OlFIJALcCON2zOAPgHBG5P6Ld3IbBsB0AXAiHsJ1Saj8AR3sWdblsR0TU3AQtcC/vlM1VGLZzCMF5g3YAcPiegFLuLSEBIN1befW9oodfc1/3P+8SXPFewdrtwPSxQCymsGGn4L7F5m027gIOqPjoSi1dH9FEZTJNfBK/kvDMSLGjxzwWtiVzJUQEq7YUnoeNtKnTbb0o2k5HYeMut/WSDo/9w/cMv596h+3mTjGPmaqTVnqMlfx/9ZOTVwOYE35DIiIiIiLULsTXm5OB4J03xFdYJqXBPU07XdM4Q3yNUasQX2uLKaBXCOm1awJ63vEOw3iCIT4iIiIiIgqBYbs6UkrFAdwC4CzP4l4AHxSR+yLc1e8BfAODJSver5SaKyLLArb7Stnvt4hIOsLjIiKqOxFBLEzYrsJKUJWEWiaPVvjksQo3LAhXLS3dC6QslZOi1vLZcL0oowj4bNwpOO+6PDY5hnTCquRke961nFiVoqheOFzV6S7Q+utCwSU35+sS6otKszyWXG8zl6DptHHmD0BM17feYTvbhzSvrdc/iE3/9wQdYyXX4QOzNoFhOyIiIiJqNq1xhXFxYFy7bjSaEN9Aa1xNO93y8a6A8Wb+Et9w1ttXCPDVOsRXGsbzh/TKx8uDfcV/GeIjIiIiIhqeGLarE6VUCwohuPd5FucAnCcid0W5LxFZppS6EcBF/YsSAG5QSp1mCs8ppd4H4FOeRVkAV0Z5XEREjRKmjWyl33gOCrXEDOfWrv+Ewr67AX97UfDY62776s5WHraTMGX0KvTqBsFZB1V3MvGcn+fx5IqIDkijklDlD+bVKWzXJNXImlEdHr5ai94UfOjaPPrC5U4bricrqOZDqaj87AG3O86lsh0AnLSPvkLnnYsE33iPf3m9w3YAcOHxCr953H+95y/VVyc1H6P9/nNpves1O7sCk3Kbwm1ERERERDSE1TrEZwrjdWbEV2Wva+BHDNswxNcotQzxDQTwEsCopPdyIcRnGx9lGGeIj4iIiIiosRi2q59fA/hw2bKvAViolJoVcq71DhXnvgXgXADj+38/DsDflVIXi8grxZWUUm0APgPgh2Xb/1BEVoU8LiKi5iOCFrgnqyo9qRkUtpugba8CtMQUvnKmwlfOLPye/8HngTt+hdcSe+OAOS9WtC+bF96sfFtX1YbkVm2RmgbtACBTwW14yzOsbNdojapsd+tCGXJBO8DcsrSeNu1yv9NcWyiPatMvX27IkDUibNduCUS/8CZwWFk7XNMxBrWJDXsd5mSXQ15+EuqE94bbkIiIiIiISthDfEClQb5cn7manq0aX3cW6EzrA34M8TVObx+wvbvw41d5iC8eKw/m9f/bVgjptfuCe6Xj/mBf4d9EHL4vhxERERERkR/DdvXzCc2y7/f/hHUKgIdsK4jIGqXU+wHch0JlOwA4HsASpdRzAFYAGAvgcACTyza/C8C/V3BcRETNRyRUZbt0hUGnoPDW+w93PFF1x68AAKl8j3GVaiqfrahDQaPp46rbvh7HmM6Fr/i1YnNtjqUcK9uZNSpst7JO933UmiG4uXKL+7quFTsXGkLDh+yuX24KsoWtChfGPlPNYys2uYftgsJ08ZBhuy9v+SHU2LPCbURERERERHUTb1EY2w6MrWGIrysLdKbLwnxZGVzmGS+G+Eq28VxmiK8xcvnahvgGg3mDlwfa6ZYs07TT1YwzxEdEREREww3DdsOYiDyklDoXwA0YDNQpAEf2/+jcDOAfRUIkU4iImlq4ynaVhlNyAVWvPvr2cCeU2sUStqsiQNPTW/u0UrUBn3oEhCoJtNmOa9IoYHNn6bKT9wEWrwM27YpuPyOdLWxnqnZWjb684D/vEvzuyQal/KrUDI+lMM811xPvR84E7tjuX761y7+sNyf4wX36+6+Wle3ed4jCpX/U77fwOuzaRta+n7DX4fjuJ4DMKeE2IiIiIiKiIa/WIb6ubH81vfRgGK+wTDyXB5eXtNPVjFf6ZViqTi1DfKXBPO+/pe10deP+YB9DfERERETUWAzbDXMicrdS6m0ArgRwHgbbypZ7EsBVIvJ/dTs4IqJ6CFnZbldQk24DU1ACAP5wscJJ+4Y78WOrbFdNa0hTm8UopauszLZ2e7hg07SxwIadwFF7ubewDRtC6ssLsoZvax+9F/Dol2P44p8ENz8tyOSA9xyk8MtPKqzfAbzjB3ls2Om+L55QNhPLQyNshS8X/3ST4FePDc2gHQDctUhweYOLmJWHUE3OPNB9znfso3DHIv/9smiNf91P3WC+/2oZtttzovk1v7xKZl9ejI/twMp2IarzXbblR0igF/LyExV+jEZERERERFSqliG+7qy/Ha43xGdqt1sM8enGec6lMXJ5YEdP4cev8hBfS8wfwDOF+Ezjuu0Z4iMiIiKiIAzb1YmINOwvcxHZCOBzSqlLUWglOxPAbgC6AKwFsFBE3mjU8RER1VqYynYPvCI4cW74l+ycYRcxBXzkKLc0hKx5feByUsypv2rajN6pCahErZowIADc/aL7Mf7tCzGc+bbCydJUQiH2Gbf7OuxtaDsZ+5OPxBBvUfif8xV+8hFBXx5IxAuPodFJYN1VLehMF070JlsLp5njLcBXbxVc86D/uvZkh264q9Zsle2CWjmHtbNH8Nsn7PfFhccrfPucxp98/cSv8/j7Uv/yJxzDp7V072K3x3NHiMqEqVbz2OsbBXtPKdwnm3YJ/viMLWxX2/vuwOnA4rf8y//2ouCb/zD4uynIC0Rb2e6AzCuFC0/Nc9+IiIiIiIioAeItCmNSwJiUaY3K3s/15aUkmOcL82X0490Zc8CPIb7G6atxiE9bbS8BjEpaqvElFEYl9du3McRHRERENGwwbDeCiEgWwIONPg4ioroSwdTcRufVwwQ+vCptAVhi2aKBizEI2vJpZGJJ32rVtIY8aIbC86trG+aqtlXtrEkKwSfECqaNK5ykSiUKv196msJP7g/eNuxtaDtpmvQEf1piCi2abOWoZOFEm1eq1dRiMtyxjSTWsJ0lsFSJJevsFSuBwjeddxvb+JOkWwzV42ZPqu9x6Ewd47beOYe5347TxppfI55fPRi2e3GNvRpiLSvbAeZKogdOd2shCxQeYzZjjR88+U3NbShcOPh4942IiIiIiIiGkZZYbUN8pjCeqdpelyXg15XhOaJGsYf4APt5S3uIrxDW8wb3SkN87cbxwrlF3fYM8RERERHVH8N2REQ0vIng1K6HEJde5JSlHFK/Sr+JGknYbmNpD8SU9CADf9iuO50DEHxddHb01L5qWjWV9zK9gp8+4HaMe4wHDtm9dNlFx9cmbGerPNVW4V9TxYBguWpuv+HOFpzKC/Dde/JYthE4YibwiWMURiUrP9H45Irgx9EB0yqePlJ7TQIWvulfHlRlcuNOwW+fFPz33YJt3cC5hwHnHqZw/lEKsVg0J2ldH89nHui+v9P2d9tf0PWvddjO9P/J5s7Sx5YtbBd0jKmE++12UvejhQvZCvulExERERERkVa9Qnxd2UIr3U5PUK9TM+5tp6sbZ4ivMfrywM504Uev+hBfR6K/ml7J5cEQn3/c0063bJwhPiIiIiIzhu2IiGh4E8HUvo24Zc3H8NEZv9VWivN6Y3Nlu8nl9cvjjmEO2b4Z8j9fLlnWnu/B9pbxvnW7u3pRSdgumxP89YXQm4VWaRvZbE5wzs/z1kBV0eTRwO2XxHwnfA7aXeG6CxQ+9/tCO1eTsIG2bBWVp0xMrTB5wtPMVtkOAL52W2GF3zwO/PFpwbx/jSHZGv6k4EOvCi67JfiBeNCM5jjh+JGjYrh1of8Bv34nICLaE6PrtgtOvqoQTiy6bSFw20LBA68Av/pkNCdUXR7PN31aYeIo932NtoQor54v+ORxhcvbu+33oevrc6U+d7LCLx7yH8NdLwK70jJwPaoJ2wHA+Ucp/OFp+3X9r43fRLv0fyX/leeCJyUiIiIiIqKGq2WIr9sb3CuG9AaWSemyTOFyYRvxb5NhiK+RahXiiylzO93CZXM7XW+Ir3w82coQHxEREQ19DNsREdEwVzhhcHbnXdj02gwsSB2DpKTx7UlfxfxR7/St/ZvHBb/6ZPi99PbpT0y4Vk6SX13pW5YSfa+Cnp4sgHbXQxvw4KuhN6lIpSfWHngFuG+x27pvfi+GRFx/UubiE2P48JGCZ1YCV9yRx+PLqz9GW2W7RIWBHVa2Cy8obOf12OvArc8Lzj86/Mm7K++0JDU9TIHJerMdx4trgEP28C+//jEpCdp53bBA8KUzFA6YXv2x2R7P/3O+wiePVehoC38fHbx74bqVe2kt0JsTtMYV7lhkf8DUurLdKEtb8tsWCj5xbDRhu1kO7YLP7JxX8rvkclBxvhUkIiIiIiIaiVpiCqOTwGjjd5KrD/EVQ3gDrXHTQFdWPJfL2ummpSS45x2v9Iu9VJ281CfE5w/rlYb4ygN+5cG+4r8M8REREVE98RMWIiIa3jxl0tqlB6d3PwgA6DW0lD16r8p2Y6ps5xzm+Ot1vkWpvD5sl+6xJL8sbnk22hayx8wGnlzhX15pWGz+UrfjO+ttMAbtisakFE7bH7hxgcLjy/3zNnNlu3Rld++IkHfLwA14dhVw/tFh9yF4+DW3dU2ByXobZ8nePrJMcMge/ufLn56xP98eWSY4YHr1JyhNrVQvPlHh8yfHKp7XFmR7aS1w+ExgQoeC7eRurSvb2e8X4BPHFi5XG+a17adobH5H6YI3FgNzDwnekIiIiIiIiMhRrUN8A8E9b2tcTTtdb4hP2063/1+G+BqjliE+c4CvEOJrtwT8TAFAhviIiIhIh2E7IiIakVa37qldHqvwfbOpMlG88iwJkqI/49BTYRrrnpeiDdu95yCFJ1f456z0RNX2brf13nuI+53UavhLx1ZJSqcmle1MbWR5os8o7CPYdr+ZhHlsNEtluyNnmsfe3KpfvnSdfc7OTOXH49Wd1d9r1d52p+6nsEATpAUGjz3oPGitK9uduq857NfleXmvtrLdafvZQ4UzetdiVu+q0oXdu4InJiIiIiIiImoCtQrx5YuV+AxhvM6MaAN+3ZaAH0N8jZMXYFe68KMXXYivIwGMSpa209WPq7J1B8dTCYb4iIiIhjKG7YiIaHgT/RvlT27/Hb415Vu+5ZW2QDWFJaoJc7Tn9emz7p6QSbEauPJshb2n6McqvQ0Nd1WJU/cDLjjG/SSEqepc2BCWNWxXaWW7hD4gU+ntNxKEaSMLhA/nAfYqhuXam6SyXVur+Tlx1TzBBccIDto93Mm7dTuC13FhCo9WWxXw0tMU/utv+nt4xWbBO/ZRxqp6RbUO2719lnnspbWDx15t2O7QPYAvnKrwswf0t8e16z7v/9gho6+cSkRERERERDRSxGKqEIKqYYhPX43PX23PG+LTbcMQX+PUKsSnlCa413+58G9/O13vsrbiZeW5XDrOEB8REVF9MGxHRETDmyHBNSG/Tbu80qpiuRqE7VKmynbbdwCYUfnEAa67QGHlFuCt7cCYFLClE9jZI9hzosLEDuD0/RVOmKtw5yJBlGGxTEAA7oS9gTsvifWH1Ny0GcN24WJYtWgjm2Rlu9DCtpGtRJggZrO0kQUK7ZXveVk/dvFv83jqa+FejH40X/DDD1V/XKbXg2or200cpaCU/iX+0j8KPnVc8HOp1mG7WEzhS2coXDXPf5BLPJUFqw3bKaXw4/OAsw9RuOdlwZ/uXoO1rTPwye2/w1e2/AD7ZF/3b8SwHREREREREVFN1DrE15UFOtP9Ybz+y4V/pWRZ8XJXGujKim+bgfGIuhtQOOIN8e3UrmHb2jiiVHkwr6w1blKh3TiuytZliI+IiMiEYTsiIhrmDC0M8/qgQdSV7VzayIohEGg8xu2drodVIuZwLBM7gItPdOt9awrLdGcL1ynsm+8eQ7vJovOPVqGCdoC5xWuY6mWAOYAVU4VWFpUwtpHtrez2GwmibYSsF6aNbLNUtgMKJ75MnlkJbO0STOgoPKZcwqbVtMD2qlXYDgDGtwNbu/zLi9807um1X8+orqONLYyb7hUkW5X1Meca5lVK4bT9gVNnZ/D9n84N3oBhOyIiIiIiIqIhxRvimzpGt0blIb6e3v4KesXgXqY0xOdblikN8WnHGeJrCJHBaoi1CvENhPG8rXE17XS9IT7dNgzxERHRUMawHRERDW+mIJtEG7bLGSpuOVVO6tWXXzIeo2pzPKpSc6cUqtXZhAkzmap6iRTCaW0hwzRBFcUqCefUuo1spVXtJJ9Hcu0rAPbVjmdy5sp3I1noNrIVpPNcHxuzJgJJS/vWejt6tsJfnjdf4a1dwISOwuXFbwXPN3l0NMdlqi4XRVDRFoIVkcDKdsfNqf39N77dPLYrXXie28K/oavvde9yWk2WPgvMPQSItQAt8f6f/sveZQOXW3jik4iIiIiIiGgYisX625W21S7EF2U7XYb4GqMkxKdfw7a1caQY4tMH+AaDeqaAny7A19FWOPfIc1lERFRLDNsREdGIlMobWrRW0MIznxd86c/6N4xOQQlDhaF2Q2W7LZnKUlhRv7VMPXs3gDO1Yz294cN2QRXF2kNWtQMsYbuwle0M65sq59nIgrshXzkXybZDgNlPaNfpyTJsV5TpFby0FnhulWD+ktrWttvRLTjlh269ar9zbnOdrDn/KIUr7xTjCa/uLLBsg+Ccn+exdJ1+nfL1o2CaJ4oWvJe/W+Hrt/kfE3kpvJ7YwtPHzwHe4VAArlrvPkjh//1F/7jd0VMINdpe+1rCVt/LOlasu+WnkFt+GmpqicXMYbx48XJZYC/WUhgrC+65hfxajOPKO1eL5nh8x1W+X12gsGxd3XFptlMuZWOJiIiIiIiIRhhviE+vuhCfOYwn2oBeV1mIT7c91Z83xLdBv4Zta+OIUhhsl2uottduqLbXUd5O13OZIT4iIipi2I6IiIY3Q2mrZISV7X58v/lNXdwljLXhTe1iU2W7O7bpq6EFqbRqn44seQbJX14OzNaH7bqzwDhLRSedoLBdJeEcYxvZkJXtMoa2m2Er28n6VZCvnAvAfP8ChftqfLiph4V0r+DFNcDzqwXPrQKeXyV4+a1wrV29wkbzLv5tHm9sDl7vb1+I4ayDmuukyrRxCs98PYb9v6kPC27YCXz2d3ms3OI2X1Rhu1q2kT3jAH3YDgBufd5c2a6jDbj70hja6lCZcGzKPPalP+fx139uMT6+W1sqOHn3zP3h1g8jnwfyET0wqlSPltKuBLCG8QqXYw4hP0Nw0Bf4M4Qe43GoksBhzOG4HPZrDCwa5mU1RCIiIiIiIqohb4hvinaNaEJ8/jCelFXm8wb5/FX6Bv7NVtZ9g6ojMhiyrFWIryNRaOtcenkwxOcfV4ZtCudKYzGeRyEiGkoYtiMiouHN1EbWUDWuLw/05gStcfc3Nv/3nPnNl1Nlu6fnaRdvbplo3KQvL2gJ+eYr0rDdI7cj1ddt3lcFeYzAsF2EbWQzjWoju+DugYumxyBQ2e031PRkC8G65zzBusVvmVsy11q6V/C3l4LX++hRqumCdkVz9WcYAQC/eMg9aAcUno9hXwt1TI/lVAWVKn1zWF4T/vycGF/zvvcBhdHJ+tyHtmO888XCv7awXVjieY2hOurLFX4arNnO3Ys38GcM8VkqCIYO+ZkrFwZWQwwTLgyqhuhbzmqIREREREREza5WIT6RwhdCTWG8kmp7WaAz3d8jHaF4AAAgAElEQVRONwt0psUX3CuOM8TXGN4Qn2EN29bWuTuKAby2Qgiv9LJCh29Z8fJgiK98vD3BEB8RUa0wbEdERMOcIWwn+jayAJDOAa0h/od8YoV5bIlDu0bk9ImQv3ecatxka1eh/WAYLgEu57ddzz+IpJhr64dt0woEh+322y38nFFVtksbQjttYSvbPXHvwGXbYzDKYGQz6MkKFq0ptIJ9blWhct3itwrh1mbx6nrz/exVSevgerGdOHl+dfj5urPA2CrfLdSyst0scx4Zu9KWFrZ1bNFsq2zX0n93mV77Qod5gUI1M6JmUayG2Nv4BHkzff4gSjm0NNZUJgzRallbDVHT4lmVBwOdWi0H7DeuWddxXlZDJCIiIiKi4UapQqWz9hqG+IohvNIwnpQE84rjXZlCiK88uDewfYYhvkYphvg27tKNRhPi07XT9Yb4dO10R2nGGeIjImLYjoiIhruQle2AQihtdDKa3b/7bcFvOCSjPxZbmK2StprLNgavY33Lls1AfnE58PhdwLpVSMTGGdcNG2YD7NfpnfsDM8a7vXkTEchX3w8suBuJcRcC067xH1/I288Uwgod2ukdvE+D2sgOVd2ZwWDd86sL/y5Z15hgXZgTQ04tn1FhAKoJrN4afpvuLDA2ZDtor1yfGJ/X7RW0hfbN0WZ+TXjgFSBpeH7WM2wXiykcNwdYsNw/lssXbyP9A7WSynbo6apgIyKqKxFWQzQIrIboDQRaqx06VEMMCBeqksBgQDXEMPt1qbJYFlJkNUQiIiIiIirnDfEZ1qho3mKIrxi884b4CsukJJinbaerGWeIr3FqFeJrNwb4ytrpagJ+3uXecYb4iGgoGaIfFxIREVUnGVFVMQl4h/jOAxwmef5h7eIo24yu3Vb9O1n5z08BD9068HurmG+oqCvb/eVz7h8yyhUfH2jXmhD9DRU2DPjiWv1yU5jH6LkHBy4Ohzay3RnBC8Vg3arCv0vX1y9YN74duORUhVfWFdqGViPu+BBr9rDdyfsAD70WzVymynCubK+lqQjCdgDw7XMVvn6b/77Pi6WyXQQtbMP44YdiOPa7+ifFMyvNVQcrCts9Pb+CjYiImgSrIWqVVkM0VRXUVEMM0Wo5sBpi/3bmaoghqjBqqiyaqyHa52U1RCIiIiKiaHlDfPrOOpWH+NK9bu10vcu7+8fKg3ve7fPN9AZuBOnO9p9/rXGIr7zaXvlyb8BPF/wrVvZjiI+IotbkHxcSERFVyVTZzhK2e+FNYKalPaHX4rfs406hjpef0G8bYeWz2xZW945Ttm0CHr6tZJkpyAZUVnnPtM015yuMTjpWtcvngQf+MvB7VGG7+UsMj6MqKmTF0Ye49CKn/JM0Y2W7rozghTdLK9YtXVe/kxmpVuDQPYDDZyocMRM4Yk+F/acB8RaFj16nDzKF+bak6/VYv6O5z95EFWIDIgjbWbaPqrrcmAqqkNazsh1QOKFj8qvHBQ+9Ek1lO8mZX9jUZ/4T+PC/AH29hWpauRyQ7ytcLv7blyskZYuXvcsHLnuXedbt08xVsk3hsvSZ9tvnn8M3rtlvvq/0ugxs1xd8XPkm6mNNRGTDaohGYqp2qKuGaG1p7NLy2B4uVKZwYYStlgOrIfYvZzVEIiIiImo2SimkEoVzl7UK8enDeOJb5g3xmQJ8nQzxNUwtQ3wDAbxEoT3u4OVCUE8/rgYvl40zxEc0sjFsR0REw5shbTOhz9xTsTsrcH1z9/Ja+x/wTqGOtx0DvPykb/GXt/wQH59xo3YTU1tTk5cDQoFF3/wHw/VeucR3W7bCUtkuwjayoQIna0t7NbYZwnbpkMf39pnAqi3+5a9uCDcPdp8DrBk8xlS+B7ta/A+S7p4+NPLPtM50f7Bu9WDFulfW1+8EQ3vCE6zbEzhipsJ+uxWCdTpRFDVxDYi+ur76fdVSRdXQDGpa2S6iwNvu4xXCfvQ/Y3w0+3Y1bax5bN12wb67ASs2+8d0rzlWW9aZx1paoNqSACLqkV6hZjr1JCJlIT7HkF8x4OcQLtSPe0OA+dIQonHePv2xVrTfvuDjIiIaKoqva6yGWMJYDbGkqmBANcSSdWwhQIdqiKFaPDvstySk6NJamtUQiYiIiIarWof4zGE8fbW9LkvAjyG+xiqG+DZFHOJLtZYH9/r/bSuE+Np9wb7iuBq8XDbengBaGOIjanoM2xER0TCn/0M4KRnjFmGCbEGhLVugRDJpyC+/pQ3aAcCZnfOM24atHOeyfiIOfPAIwx/wGX+VPQUYK7M1LGxX9kFb0tCqNWyb1kdf1y9vCVs0oq/0SqakB7swxrdaT08W9fozTUTw+6cE37m7EKgDCuG1MFXhqtGeAA7zVqzrD9bV+81kzvE51dfkJ0P23U0Bi6I5yJpWtouoAt9p+4Xf5oBp0ezb1cRR5sfy6xuB3Q3hv9CvL5rX6QFHnhZysuFPKVWoLhRv/FviZjp1JiL9rTzLgnvaCoIu1RD94UJ/kLE8JGiohhgqXBhQDdG3X9125dUWWQ2RiIYIVkM0ElO1w8BqiCFCfkHVEIsVCOvQahnx8sAhqyESERERufCG+CZFHOLL5IDOdH8AL+u5XKy2l/WPe0N8unGG+Bqnp7fwU6sQ32Bwb/DyQDtd7binnW7ZOEN8RNFq/CcLREREtWRJDE3JbcDG+FTf8jABk6DQlilQIrleyKePBla9Ytx29NEnAtv1Y2HDdkFBotFJ4OZ/jGH6OFPYTt92t9UQtouyjWyosF3ZB+GmVryZHJDPi1OJ7wdeEWzYqR+76PiQb0zKbsdUXn+79tSxj+z/+4vg6vmlz5NaBe062jzBuv6KdfvWMFgX5mq4PmZdQ3mN8i+nKvzgvuYI2+mqtRW1RxS2G+XYYtqrERVNPneywi8e8t8vyzYWfnT+3VRp1OStFeax3fYMNxeNWEqpwVaFrRH2pa70eBp9AB7GaohhWi0HhAuDqywWKhNKYAtnh1bKzvt1aPFMRDRUsBqilijl0NK4JWDcUDFRF16MG1pA66ohOrVa7t+vU7gw5lYNsX9dVkMkIiKiKCmlkGwFkq21C/F1Zfur6aW9YbzBEJ93vDtb6HLj2ybDEF+jDYT4tKPVhfgGwnje1rhtg0E9/bjyLSte7mhjiI9GJobtiIhoxJqRW6cN24XJOQWta6xst+gxa9AOAFo6OhDb1oe88qfNoqps98HDgS+fGcMhuwOtccsfw4aKSQnJogftvuXZSMN2If5IX/Fyya+mMBsAbOsGJo4KnvLrt5kr2YQODGVLb0dTGDAdts9thbZ0Cn7899q8Wx7VBhy2J3D4noMV6/aZWps3XVF8/pFzLFjU7CcXbC1Lw6o2bHfdI+YbNao2smEdOL0x+917cvhtRreFW19uv9482JYKfwBEVILVEPV81RC9FQSrbbVc0vbYFnR0qYboqcSoG/ceb1A1xJJ1LMfFaohENFSIALleAPX70pfxUBp9AGWM1RAHQnoO1RCdQ34O1RBr3Gq5JLBoCTqyGiIREVFzKQnx6deoaN5iiE/XDrcQzDO30/WG+HTb9/Etc0MUQ3ybO3WjlYf4kq3+gJ43xNeuCfB5Q3ymACBDfNTMGn+WmIiIqJYsJbpS+W7t8jABk6CWs+M7DAMvPh48uVJolV5kNGG7sG1ac4bel5PHKBw5y+GPVUvYTqe3TxD2DVwkle06S0sBjssbSgMCWLIOOHFu8JRPvWEeM96/JpnysJ2psl19wna/e1IiCY+NTpa1gt2zEKxzqRxYS2Eq9Lk+p857e3O/uYvyNu/Khn8el2xv7taNRIPeheywdFqtpQlhXytQKPgRSrf2DElBIhn+AIiIHLAaopkMhBAdw4VB1RA14cLgKouGaoiVtFJ23q9Di2cioqGC1RC1/NUQQ7RSDqqG6AsBllVDLJtDhW7xbKiGaAwXxgLGWQ2RiIiGL2+IT1+4IJoQnz+MVxri6yoP8mVEHwBkiK9h0r2Fn1qF+EzV9josAb+ORKF9rm6cIT6KAsN2REQ0vFnDdvrURVBrWC9bhbn9dgN2H6//g012mUNgRWrOQWhd1YsM/CGJbMgwW9VBtow+mNgq+g/LwoYBgdq0kd0nu8y4aiaCL+2/c3/3+0A0J+iTpsdguj69SpeuC7/N6CRw+J6lrWDnTql/sE5WLgWefQCycyuw/GwAb6tqPteKlhceN3LehFVb2c4W5IzyA4j3HQLcvsht3TXbItttKKftrxD247FjZ4e8jSz/3/EDHyKi+lOxWOED+niDyrl6j6XRB+BhrYYYUatlf5DR3GpZrPPmzfvNlR1j4H41YUpWQySioYrVEI2kPNynq4ZoHQ8T8rO3WvZVQ4y01XJ/ANIYWCwNOrIaIhERedUyxJfNmavpeUN62mp8hnGG+BqnliG+kjBeSTCvv51uovB52LGzFU7dD2hrbaazK9QMGLYjIqJhzvwHVbuhhWeYNrK2sN21F+hPJEm6G/jzz4InnzzDWDkum80DcD9RZTrOuOsUnTu1i43H16CwnWxaW/K77U9fl06t+YCyb3OnhvjjWlMd0NRGdtW2+pyEdKlqd8q+wGGeVrB7T26CinV3/BJy1SWD4aLps4Gx/rCdhCht5xIs+/a5CnOmjJw3VNWG7V5co1/+iWOjvQ1/c2EME77odrbj40c35v7bfbzC9HHAW8E56wGzJobcyYrF+uVnXhByIiIiotphNUQzXzXEUOHCoGqItvChNzzoUA3RtZWyUzVET6jRVv2RiGioKL72NYFmCiJaqyE6tlL2V0w0VRhscauGGCZcGFgN0Rsu1AUlzVUW+eU4IqLoKKXQ1gq01SHE15UttNItLBNfsK843mUZ35VmiK9RiiG+LV260fK/ogQnzgXu+kIMo5P8f5sGMWxHRETDWwWV7cIETEwBsQOnAyfONfzRdeev3CZvH4VW6BNhvb19CPPfeM7wB7trkE3u+o1+e9EnE20hRO38IlUfIwDgDz/0LZqQ24KtcX9qxaWCoemYKtLtDyym8vo2sr9eMhm/jHDXJvMW20+9PvP1GI6Y2VxvHmTnVsjV/+LWI3bTWgB7Os07f4l9vjMOAC4/a/h9E/s/Nl6Bb065QjtWbdhO/0YVOHj36uYtN65d4WvvVvjO3cGPid3GRrvvML7+boV//oP7xx2hXvsAYMdm7WI19+CQExEREVEjsBqinnM1xDAhv7JwobWFc1A1xJLLmmqI3uMNqoZYst+A1tKshkhEQwmrIRr5qiH6qhUGjduqIZaH/Oytlq3VEKtuteyphhhU3ZHVEImoydQ6xFcS3CuG9LJAZ1r8yzKFy90B45F+tkV4dBnw84cEXzmzmd4tU6MxbEdERCNWSvRBp56Me9U4U6jMVpFInrnfaW50jDFXjtu0EcAebvPAUtnONcwxYSqwYbVvsfH4Qobtcpb1QwVORo0DOktLRxnv597gVrxhQ4NWy1/yLWqBfgetsTyAsEmb8Eb7OxSXaG98sRG/5x/2fUtcmU6VblkP17BdUDXGt+81dN5EnbA38NjrwetdtP0GfG3L9/FI+wn4+6jTfePVhO1sVQVrURhx8mi39VINfEyPagu3vvPrc5Bc4z/MICIiIqoUqyGaWash+toxV9ZqObiVcuF3Cayy6NBK2aUKY1CLZ1ZDJKKhhtUQtZyrIbqG/FyqIRpCgNpqiGHChUHVEL3H6FBlkdUQiYYPb4hvQod2jYrnzuZkIHhXDOENtMZNS0kwzzveZRkf6SG+eYsFXzmz0UdBzYRhOyIiGt5MgY9YDO35bu1QT9q9apxr61NZ/Rrw0gLglA8Cu7YFTzz3UGCfw9AqG/T7TWecji/scRqN0peDShi+kRo2pGZbP1zYbqwmbKevYJh2yJ/YQoD7TwtxXADQ608ubW7RpzLHtfYCqH1Fi8P2VFiyznwqLdn4ohp+O7c6ryo5937GY9vt46lmvC0M3neowmOvB58iPa77CQBAh+hfC6sJ29meX+3xPOSVhcCY8VDTZ1e+E4/j5ii4nBaO8n6UTA/w6kJg8nSoabMC13c9xqLQle1MUtozNUREREQ0xLEaot5ANURTC+ZqWy1rg4zmVsvWaoi5Pn1g0Fe90aEaoktraZcK8UREzYDVEI3s1RDjQIumPXIlLY8dWi2rmrZaNlRD1AUdWQ2RyCcRV5gQr12IbyC4522Nq2mX6w3xadvpZgrtdIdCiG/TrkYfATUbhu2IiGh4M70bnnMQkjv0Iaw1W93fQptCYol44Y9V2bUN8u7dBge++9ngSVtaoD55OdA+Gq2yRr/frPuJhk27xFjlyiXMISLAM3/XjsUNbWSDqoSV+8kD5ts8VOBk/SrfIlO74B6Hm9AWAgxdLjrjr7D3qe2/w4L243zLe/rqc3KgM21/rDdlwCzrvz+Nle1CCAqINuVtYfDxYxSufUTw+kbzOgenX8Q5u+4AAGPwuJqwne35deTVZ0C2PQ4AkP2PhPreX6HGT658ZwCOcCtgGFm1Rrn9eshP/w3IFoLPcuDRUN+7DWqsuazpnCnhXjPiIV4GbJUEMWv/UPslIiIiIhrKSqohNoGmCiK6VkOsotVycCvl8mqIpqCjQytll/0GtXhmNUQiGmpYDVFroBqiteqgvX2xr9KgKTyoq4bouRxcDTFEuDCoGmJQlUVWQ6SI1TLEpw/wAZ0Zc8CvO2A8yu5Vho+UaQRj2I6IiIY3UwChrd3YXvTpNe6pmqCKcXLR0c5zAQDecyHUu86HOuwdAIAE9Km13oz7X4gX/sb8lRCnMMejdxiHomgj+9gywddviyhsp9Em+iqAdy4SXHqafVvbH+JHzQr3xkHm3+xbtntOH6bs6avPhwK3L7KPN2PATBbc7b5uiHltVQyBQin3oWLqGIWHvxTDtY8InlslOHh3hfEdwMLVwLYuwXETN+Ofrj8L4/I7AADtpkBqNWE7y7btXZ6KnUufhXz7Iqir7qx8ZwBiMYWj9wKeesO+XhRtZGXxU5CrLilduPgpyH//I9R3b7Vuu/80YOm64H3EVOE6OXvlOcsgT6gRERERERGrIZoEVkMME/ILrIYYHC4Ua5XFPv1yXTXEwCqMDq2lWQ2RiIaKYjXEHKshlnOuhhgm5BdUDdEQAlS6cGFkrZZdqiEObscQYnNJxBUScWB8DUN8/jCePuD3yjrB/KX+uRi2o3IM2xER0TBneGuTTBmrOQGFcsYdbcF/wJkCOq0tgPRmtZXWTNTl10O9+xOl8yj9Dnodv46xKy24+2XnQ9CSx+4yjrUaKtuF+bbI7Yvsbz9dw3bSuUO73FTZbuPO4DltpatDhwBXvepbZDq2PomhNydojTf2DV8UwaTIbfG3VlYRnPwNesxG1tKzTqaNU7jibP3jR+67H5IfbGfdbmgj25Wp/Ha1VbbzPe6ffQDSvQuqfXTF+wOAMcngdaIIkIopgPz0fEi6Gypp7knc4ficiod8vMkjt4fbgIiIiIiIiACwGqLNYDXESsOFQa2WLS2cc6Xraqshhm2l7GnxHFjt0BZ6bJKqYkRETlgNUUtfDTFcK+WSkF9gK2Vzi2cVusWza7gwoBqi93iHaTXESkJ8f18imL/U/+HgzjTQlxe0hPmSPA1rDNsREdHwZgrhJDswOt9p3GzTLqCjLXh6U0An3gKgc3vwBF5z3uZbZArbZR1Lxy15yz6+3Zw3HLTxTeNQIoI2sg++Ek3YDlv9ISwAeLTjRO3yuVODp7QFsMKEYUQEmDwDWFPaz7ddzF+F6ekFWmv8l9qEDmBrl3k80eCwn9aM2cAKxwRpiHfvQZXtjpzZhLdFpcreLKdq0EY2Y3kNSJZXm+zLAds2AlWG7d62u8L8pfY7/aAZEdyPix7TL+/NAju2AJawnWuANeg1VHK9hf/fiv/H/fU688rD6OQIERERERER1Q+rIeqFqoboGvJzqYZYEnTUVEPUBiEN1RB9QUeX/Tq0lmY1RCIaKlgN0Ui0LYkDqiGGCfmFaLWsrYYYWavlsuX9643NtwHQf1axKw2MM5/+pxGGYTsiIhrejGG7dpzVeS8uxdXaYVtVJq9sTj9/awuATMiawvsc5p9H6UuruVa2C/ojPebSRva5B41DUVS2ywccpHPYzvDtrBO6H8Nj7Sf4lrsEAqutbCddOyFXXwo8cQ+wa5tv3FTZDigEncakgvdRqZfXijVo9+1zm+k0pkeI51WYN6k3PWVe+4iZwKF7hJisycmyF0p+N4U+Kw3bdaYF519vfvJo209n9G29w/j40Qo/mm+/1w/everdFE4eG8gXTgd+fA/U9Nna8c+8Q+HRZcGPzNmTDPM//FfIr//TPXBKRERERERERJFiNUSzwGqIYcKFQdUQja2U+6shRtFK2aUKY3k1RF3osUmqihERORmohpgJXLXWGhFEHNM6B9j7Je3Yjh6G7WgQw3ZERDS8mcJ28VZMgLmPaI9jyMQUKmttARCmrd/7/0lbmrk1pg+sZLZscpo2ExAaDKrmJXf/1jquDc0AmLc4uj+B3cN2+it7fPcTFYftbKFBp7Dd5R8EFj5sHE8FVLarlQ07BQdfaQ5DHTUL+OqZzXSqzuPp+b5Fqsq3XDc9aUlVApj3xeFTOl12bAH++OOSZaaW2pWG7T7wizxeWmse175urHkdmH1gZTvsd9ie9vtozwmI5n5c+qx5bN1KyCWnAzct0rbFPeMABZdTBF883X+c8tyDkG9+tHCiN4xh8tglIiIiIiIioubGaoh6JdUQa9RqObjK4mC4UFsNcWDesv0Z53XZb0Br6SaoKEZEVG5s3vzZ8Y6QNVZoeGPYjoiIhjlTqEEh1Wp+y+0adLKF7eTOG9wmAaAmzdAuTxjCdr3r1znN+9Qb9lBHUAbDGrbbcx8kcvo0zhK3w3PiHrbTp+fayltW9rO1uSyyhRHjAVUB5a03rEE7AEjlzdW8XAOflbhzkf1xcfV5wyNc5hrBu2GBec0vnKowvmPo3xYDnrjHt6jdUGGxkrDd2m2C+Uvt6+jCdvLI7VDveF/4HZZ55/4w7n/6uKqnL3xDO8imtcAz9wMnneMbSjmea27XtJuVe38fPmhHREREREREREQNVVoNsa3Rh9NcQcQw1RC91QhDtFo2zttXuq4YxwOqIYbZb1A1RG9QkYgaYmx+h3GMYTvyYtiOiIiGN1NlO6WQaGuBkjxE+VNTkVS2C2OKvrdha9dWoEOz3/HTnKbVBTa83rl/wFvrRY+ax8ZMxLLOudqho2bZp/WKBRyC822Z06fnTNX3sg7vV6upbCf33hQ4f6Mq233hZnsMbY/xtdt31RJJIFsaUqy2st0Dr1h2N8z+WpY3lviWdYi+n3AlYbsFy+3jLZJDCzSBsWQ0tddtz5uN5i+kudux2W29lUu1Ybug1+SilG69lf77zskwCM4SEREREREREdHww2qIeuZqiNG1Wg6e11sN0VZlMajFsm2/QdUQy/bLaohUB22SRVs+jUws6Rtj2I68htnHh0RERGVMYbtYDKothZT0oFv502xRVLZDJsRfXUefYZ5Ht1+HqmyAPSwGAMfvPXhZFj8FuesGYPlLcKoJNmY89tr2Bp5PHeYbSjseHwCkA27ratvIGsN2VbaRjZ0xFvli1bxYDNj38MLlyTOgTnwf0Lk9cP6UoaIYUF1lu7XbBD+5X/D0G4J9dlO4+ASFo/YaPF0QVNVvjwnNdGqhjKGaYq24ViJrdrJyKeS2a4Fbf+Ebi7KNbF/e/tphej5i1zbIb78HWfgQoBTUEacCH/oCVCLct31P21/hsdf1x3DM7Age146v65Lp0Z6gi8UU4jEgF1CgTlt5tXuX076JiIiIiIiIiIho6GI1RLPSaohBrZb7HEKAusqFAdUQ+9c1VkP0hgdDtHgOrHaY01VJZDXEWhib34GNmrDdti5Bcz0jqJEYtiMiouFNTIkGBbQl0Z7vRndMF7Zz+4PJGrZbu8LpENWX/gdq3CTtWGLaDGCbZr/r1zrNHRToSMQL11EWPgz50tm+imFWYybi1K6H8H9j3u8benGN+zRBwcZq28hWE7az3X6t4jnwfB5Y+mzh8tJnIY/cHjw5gFb0IiZ9yCv/ldwZ4q7w2rBTcNIP8ljRX4DrkWWC3z8puPeLMZw4N/gx/efPBvTHbSDp6wvVRrO6encF2gpjQ4y8sRTyhdOAHVu046bQZyVhu9YWBdstbwzbPfAXyAN/GfhVnrkfeOER4Pu3h2pp/J6DFK68U7//Dx1Zv7Cdbb1rL1D49I32R2f5405EgNWvue3bh2/+iYiIiIiIiIiIaOhjNUQ9ESkN7kUR8itrtRw8r6Eaoq/Koq0aYoj9BlVDLP5UYGzfTmyMT/Ut37m9G8CoKu8tGi4YtiMiouHN0kYWiRRSok80uYZMjGG7MHklQ1U7AGht0U+UVW5vJIIq2xXJH38cLmgHAKPHISVbjcM9WUEqEfx2I6iCW7zqsJ0+zRdU3Q0IqGyna4UZkkKhlWyX8v9xPm+J4N0HhX+7dvPTMhC0K+rpBX7y9zxOnBt8Y86ZEnqX9WOoalfNm9qebEDoqfHv2asmd1xvDNoBQLvoK9ulewuV6lqCej17BOXijGE7nSfvA15bOFg1skqRnPzIOL5OWl5PJ3bYA4mA5nG3ytLrOAjbyBIRERERERERERENW0opIB5HIf7DaohexmqI2lbLfcC2Dfje17+OXhXH2L4dGJvfibH5HRjbtxMTZt0F4JBGXyVqEgzbERHR8GYM2wHo3IZUSl99yLWFpzFs1xPcQrRwHAoYPd44nDCEATMtKafpbZXZLjnV8+fu4qed5isxaizG9b1hHF60BjhmdvA0tsp27Qm4V7XK6SdqK7Z6LZN1CCLmTPevZCN7s9AV038LZrS/QrWTm5/WP+ZvXei2/UR/ocfm0Ruu1JpI8L306gb7+KTh8CWlxU9Zh9sD2hmPCvFYzAW0kW0LE7YDgJefDBW227Yof1sAACAASURBVHOCeeyA6eF2reVc2c4StnN4TPme/5W8RhMRERERERERERERjWBhqyHKnvvg7J73aAt8qM2rwLAdFTVvnzAiIqIo2CrbQRkr2wW1Ni0yhu369AEvnwOPgeoYYxxOjdKH6tJoK5SFDmCrzPae1ucKbTkBIN0VOFc5tc+hOLn7YeN4l+NNYLut3/02/zLZuRVy3x+Qv+6bkEduhxRDLTVoI2u8fw3V8kLb/0jjkCnoF+SZlcHr2IJ8e0xopu8clTGE7VQVDWMzAXfl6fs38e3hqsf+/DZVtgPCt5INetyGqmwHAOluyKsLITf9AHLb/0LWr7KuPmWMwtF7+ZcfMA3Ye0pztJE9albw5geWBwMreI0ewMp2RERERERERERERESBVEsLMGWGfnDDm/U9GGpqDNsREdEwZw7bqY98EUlDRadqK9vFXQIlE6ZCffnn1lXaJ+qr3v294xRjJTcvW9jutB+fCPn2pyG5nHuAxGv0eIzJ7zIOb3XIhvTlxRp6u/q80j9VZMObkM+eAPmvC4HffQ/y9Q9DvnIOpKerUOJZwxTuWbcj+PhMlQHj4pDUC7LXAVCnfABH9egrVr3lWByxEmMNhRE/d3KTh3IMbWSrYXuO/M/5CruNbfLbxMXKpdbh9nx0Ybug1tVhw3Zyy88gFx8DufYbkKsvhXzqSMhLT1i3ue6CGKaMHvx9fDvwmwsjetvj+lqZNa/XGlc4aR/75ol46eNO1q82rzxtltsxERERERERERERERGR3dSZ2sXCsB15sI0sERENb8bqbwoYMwEpMYTtqq1slzcHStTFVwCz9gcOPwnK0kIWAFJt+oBIV2wUkOkEWhPW7U1Vps7qvBcxCDD/ZuDQE61zGCXbgVgME3JbsDU+0Td844I8PnRki3WKtOV2vv+yGHYfXxY4+e1/A2uWl6743IPAvD8Y2/EmLFXoMr2CtlZzmKomle0OPxnq/MuAt78TuP16zOxdjadTR/lW+92Tghsvqnw3Or05QWtcGa/XCXtHu7/IhW0j6zKlJRz2uZOGftBOHEK5tja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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import yappi\n", + "\n", + "from apd.aggregation.analysis import interactable_plot_multiple_charts, Config, clean_temperature_fluctuations, get_data_by_deployment\n", + "from apd.aggregation.utils import profile_with_yappi\n", + "\n", + "async def filter_and_clean_temperature_fluctuations(datapoints):\n", + " filtered = (item async for item in datapoints if item.sensor_name==\"Temperature\")\n", + " cleaned = clean_temperature_fluctuations(filtered)\n", + " async for item in cleaned:\n", + " yield item\n", + "\n", + "filter_in_python = Config(\n", + " clean=filter_and_clean_temperature_fluctuations,\n", + " title=\"Ambient temperature\",\n", + " ylabel=\"Degrees C\",\n", + " get_data=get_data_by_deployment,\n", + ")\n", + "\n", + "with profile_with_yappi():\n", + " plot = interactable_plot_multiple_charts(configs=[filter_in_python])\n", + " plot()\n", + "\n", + "yappi.get_func_stats().print_all()" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from apd.aggregation.utils import yappi_package_matches\n", + "import yappi\n", + "\n", + "yappi.get_func_stats().save(\"callgrind.filter_in_python\", \"callgrind\") " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "apd.aggregation", + "language": "python", + "name": "apd.aggregation" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Ch12/apd.aggregation-chapter12-ex01-complete/pyproject.toml b/Ch12/apd.aggregation-chapter12-ex01-complete/pyproject.toml new file mode 100644 index 0000000..4d3bb67 --- /dev/null +++ b/Ch12/apd.aggregation-chapter12-ex01-complete/pyproject.toml @@ -0,0 +1,5 @@ +[build-system] +requires = [ + "setuptools", +] +build-backend = "setuptools.build_meta" diff --git a/Ch12/apd.aggregation-chapter12-ex01-complete/pytest.ini b/Ch12/apd.aggregation-chapter12-ex01-complete/pytest.ini new file mode 100644 index 0000000..8c26fbc --- /dev/null +++ b/Ch12/apd.aggregation-chapter12-ex01-complete/pytest.ini @@ -0,0 +1,4 @@ +[pytest] +markers = + functional: these tests are significantly slower as they start a HTTP server + performance: very slow tests that provide performance guarantees \ No newline at end of file diff --git a/Ch12/apd.aggregation-chapter12-ex01-complete/setup.cfg b/Ch12/apd.aggregation-chapter12-ex01-complete/setup.cfg new file mode 100644 index 0000000..704c6bf --- /dev/null +++ b/Ch12/apd.aggregation-chapter12-ex01-complete/setup.cfg @@ -0,0 +1,89 @@ +[mypy] +namespace_packages = True +mypy_path = src +plugins = sqlmypy + +[mypy-alembic] +ignore_missing_imports = True + +[mypy-alembic.config] +ignore_missing_imports = True + +[mypy-alembic.script] +ignore_missing_imports = True + +[mypy-alembic.runtime.environment] +ignore_missing_imports = True + +[mypy-matplotlib] +ignore_missing_imports = True + +[mypy-matplotlib.axes._base] +ignore_missing_imports = True + +[mypy-matplotlib.figure] +ignore_missing_imports = True + +[mypy-matplotlib.pyplot] +ignore_missing_imports = True + +[mypy-IPython.core] +ignore_missing_imports = True + +[mypy-yappi] +ignore_missing_imports = True + +[mypy-pint] +ignore_missing_imports = True + +[mypy-pytest] +ignore_missing_imports = True + +[mypy-setuptools] +ignore_missing_imports = True + +[flake8] +max-line-length = 120 +ignore = E501,E712 + +[metadata] +name = apd.aggregation +version = attr: apd.aggregation.VERSION +description = A programme that queries apd.sensor endpoints and aggregates their results. +long_description = file: README.md, CHANGES.md, LICENCE +long_description_content_type = text/markdown +keywords = +license = BSD +classifiers = + Programming Language :: Python :: 3 + Programming Language :: Python :: 3.7 + +[options] +zip_safe = False +include_package_data = True +package_dir = + =src +packages = find_namespace: +install_requires = + sqlalchemy + aiohttp + pint + psycopg2 + alembic + click + +[options.package_data] +apd.aggregation = py.typed + +[options.extras_require] +jupyter = ipywidgets +yappi = yappi + +[options.packages.find] +where = src + +[options.entry_points] +console_scripts = + collect_sensor_data = apd.aggregation.cli:collect_sensor_data + sensor_deployments = apd.aggregation.cli:deployments + run_apd_actions = apd.aggregation.cli:run_actions \ No newline at end of file diff --git a/Ch12/apd.aggregation-chapter12-ex01-complete/setup.py b/Ch12/apd.aggregation-chapter12-ex01-complete/setup.py new file mode 100644 index 0000000..6068493 --- /dev/null +++ b/Ch12/apd.aggregation-chapter12-ex01-complete/setup.py @@ -0,0 +1,3 @@ +from setuptools import setup + +setup() diff --git a/Ch12/apd.aggregation-chapter12-ex01-complete/src/apd/aggregation/__init__.py b/Ch12/apd.aggregation-chapter12-ex01-complete/src/apd/aggregation/__init__.py new file mode 100644 index 0000000..3277f64 --- /dev/null +++ b/Ch12/apd.aggregation-chapter12-ex01-complete/src/apd/aggregation/__init__.py @@ -0,0 +1 @@ +VERSION = "1.0.0" diff --git a/Ch12/apd.aggregation-chapter12-ex01-complete/src/apd/aggregation/actions/__init__.py b/Ch12/apd.aggregation-chapter12-ex01-complete/src/apd/aggregation/actions/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/Ch12/apd.aggregation-chapter12-ex01-complete/src/apd/aggregation/actions/action.py b/Ch12/apd.aggregation-chapter12-ex01-complete/src/apd/aggregation/actions/action.py new file mode 100644 index 0000000..d192b19 --- /dev/null +++ b/Ch12/apd.aggregation-chapter12-ex01-complete/src/apd/aggregation/actions/action.py @@ -0,0 +1,74 @@ +import asyncio +import dataclasses +import datetime +import logging + +from ..database import DataPoint +from ..collect import handle_result +from ..query import db_session_var +from .base import Action + +logger = logging.getLogger(__name__) + + +@dataclasses.dataclass +class OnlyOnChangeActionWrapper(Action): + """An action that requires another action as a parameter. + The `wrapped` action will be delegated to so long as the previous + invocation's `data` attribute is different to the current one. + The first invocation will always be delegated.""" + + wrapped: Action + + async def start(self) -> None: + self.last_value = None + return await self.wrapped.start() + + async def handle(self, datapoint: DataPoint) -> bool: + if datapoint.data == self.last_value: + return False + else: + self.last_value = datapoint.data + return await self.wrapped.handle(datapoint) + + +@dataclasses.dataclass +class OnlyAfterDateActionWrapper(Action): + """An action that requires another action and a date + as parameters. The `wrapped` action will be delegated to + for all data points with a date strictly after `date_threshold`.""" + + wrapped: Action + date_threshold: datetime.datetime + + async def start(self) -> None: + return await self.wrapped.start() + + async def handle(self, datapoint: DataPoint) -> bool: + if datapoint.collected_at <= self.date_threshold: + return False + return await self.wrapped.handle(datapoint) + + +class SaveToDatabaseAction(Action): + """An action that stores any generated data points back to the DB""" + + async def start(self) -> None: + return + + async def handle(self, datapoint: DataPoint) -> bool: + loop = asyncio.get_running_loop() + session = db_session_var.get() + await loop.run_in_executor(None, handle_result, [datapoint], session) + return True + + +class LoggingAction(Action): + """An action that stores any generated data points back to the DB""" + + async def start(self) -> None: + return + + async def handle(self, datapoint: DataPoint) -> bool: + logger.warn(datapoint) + return True diff --git a/Ch12/apd.aggregation-chapter12-ex01-complete/src/apd/aggregation/actions/base.py b/Ch12/apd.aggregation-chapter12-ex01-complete/src/apd/aggregation/actions/base.py new file mode 100644 index 0000000..0589ba4 --- /dev/null +++ b/Ch12/apd.aggregation-chapter12-ex01-complete/src/apd/aggregation/actions/base.py @@ -0,0 +1,62 @@ +import typing as t + +from ..typing import T_value +from ..database import DataPoint +from ..exceptions import NoDataForTrigger + + +class Trigger(t.Generic[T_value]): + name: str + + async def start(self) -> None: + """ Coroutine to do any initial setup """ + return + + async def match(self, datapoint: DataPoint) -> bool: + """ Return True if the datapoint is of interest to this + trigger. + This is an optional method, called by the default implementation + of handle(...).""" + raise NotImplementedError + + async def extract(self, datapoint: DataPoint) -> T_value: + """ Return the value that this datapoint implies for this trigger, + or raise NoDataForTrigger if no value is appropriate. + Can also raise IncompatibleTriggerError if the value is not readable. + + This is an optional method, called by the default implementation + of handle(...). + """ + raise NotImplementedError + + async def handle(self, datapoint: DataPoint) -> t.Optional[DataPoint]: + """Given a data point, optionally return a datapoint that + represents the value of this trigger. Will delegate to the + match(...) and extract(...) functions.""" + if not await self.match(datapoint): + # This data point isn't relevant + return None + + try: + value = await self.extract(datapoint) + except NoDataForTrigger: + # There was no value for this point + return None + + return DataPoint( + sensor_name=self.name, + data=value, + deployment_id=datapoint.deployment_id, + collected_at=datapoint.collected_at, + ) + + +class Action: + async def start(self) -> None: + """ Coroutine to do any initial setup """ + return + + async def handle(self, datapoint: DataPoint) -> bool: + """ Apply this datapoint to the action, returning + a boolean to indicate success. """ + raise NotImplementedError diff --git a/Ch12/apd.aggregation-chapter12-ex01-complete/src/apd/aggregation/actions/runner.py b/Ch12/apd.aggregation-chapter12-ex01-complete/src/apd/aggregation/actions/runner.py new file mode 100644 index 0000000..b544c21 --- /dev/null +++ b/Ch12/apd.aggregation-chapter12-ex01-complete/src/apd/aggregation/actions/runner.py @@ -0,0 +1,75 @@ +from __future__ import annotations + +import asyncio +import collections +import dataclasses +import time +import typing as t + +from ..database import DataPoint +from .base import Action, Trigger + +Decorated_Type = t.TypeVar("Decorated_Type", bound=t.Callable[..., t.Any]) + + +@dataclasses.dataclass(unsafe_hash=True) +class DataProcessor: + name: str + action: Action + trigger: Trigger[t.Any] + + def __post_init__(self): + self._input: t.Optional[asyncio.Queue[DataPoint]] = None + self._sub_tasks: t.Set = set() + self.last_times = collections.deque(maxlen=10) + self.total_in = 0 + self.total_out = 0 + + async def start(self) -> None: + self._input = asyncio.Queue(64) + self._task = asyncio.create_task(self.process(), name=f"{self.name}_process") + await asyncio.gather(self.action.start(), self.trigger.start()) + + @property + def input(self) -> asyncio.Queue[DataPoint]: + if self._input is None: + raise RuntimeError(f"{self}.start() was not awaited") + if self._task.done(): + raise RuntimeError("Processing has stopped") from self._task.exception() + return self._input + + async def idle(self) -> None: + await self.input.join() + + async def end(self) -> None: + self._task.cancel() + + async def push(self, obj: DataPoint) -> None: + coro = self.input.put(obj) + return await asyncio.wait_for(coro, timeout=30) + + async def process(self) -> None: + while True: + data = await self.input.get() + start = time.time() + self.total_in += 1 + try: + action_taken = False + processed = await self.trigger.handle(data) + if processed: + action_taken =await self.action.handle(processed) + if action_taken: + elapsed = time.time() - start + self.total_out += 1 + self.last_times.append(elapsed) + finally: + self.input.task_done() + + def stats(self) -> str: + if self.last_times: + avr_time = sum(self.last_times) / len(self.last_times) + elif self.total_in: + avr_time = 0 + else: + return "Not yet started" + return f"{avr_time:0.3f} seconds per item. {self.total_in} in, {self.total_out} out, {self.input.qsize()} waiting." diff --git a/Ch12/apd.aggregation-chapter12-ex01-complete/src/apd/aggregation/actions/source.py b/Ch12/apd.aggregation-chapter12-ex01-complete/src/apd/aggregation/actions/source.py new file mode 100644 index 0000000..d044d4d --- /dev/null +++ b/Ch12/apd.aggregation-chapter12-ex01-complete/src/apd/aggregation/actions/source.py @@ -0,0 +1,80 @@ +import asyncio + +from apd.aggregation.query import db_session_var, get_data + + +async def get_newest_record_id(): + from apd.aggregation.database import datapoint_table + from sqlalchemy import func + + loop = asyncio.get_running_loop() + db_session = db_session_var.get() + max_id_query = db_session.query(func.max(datapoint_table.c.id)) + return await loop.run_in_executor(None, max_id_query.scalar) + + +async def get_data_ongoing(*args, historical=False, **kwargs): + last_id = 0 + if not historical: + kwargs["inserted_after_record_id"] = last_id = await get_newest_record_id() + db_session = db_session_var.get() + while True: + # Run a timer for 300 seconds concurrently with our work + minimum_loop_timer = asyncio.create_task(asyncio.sleep(300)) + async for datapoint in get_data(*args, **kwargs): + if datapoint.id > last_id: + # This is the newest datapoint we have handled so far + last_id = datapoint.id + yield datapoint + # Next time, find only data points later than the latest we've seen + kwargs["inserted_after_record_id"] = last_id + # Commit the DB to store any work that was done in this loop and + # ensure that any isolation level issues do not prevent loading more + # data + db_session.commit() + # Wait for that timer to complete. If our loop took over 5 minutes + # this will complete immediately, otherwise it will block + await minimum_loop_timer + + +async def wait_for_notify(loop, raw_connection): + waiting = True + while waiting: + # SQLAlchemy isn't asynchronous, poll in a new thread + # to make sure we've received any notifications + await loop.run_in_executor(None, raw_connection.poll) + while raw_connection.notifies: + # End the loop after clearing out all pending + # notifications + waiting = False + raw_connection.notifies.pop() + if waiting: + # If we had no notifications wait 15 seconds then + # re-check + await asyncio.sleep(15) + + +async def get_data_ongoing_psql_pubsub(*args, historical=False, **kwargs): + last_id = 0 + if not historical: + kwargs["inserted_after_record_id"] = last_id = await get_newest_record_id() + db_session = db_session_var.get() + db_session.execute("LISTEN apd_aggregation;") + loop = asyncio.get_running_loop() + while True: + async for datapoint in get_data(*args, order=False, **kwargs): + if datapoint.id > last_id: + # This is the newest datapoint we have handled so far + last_id = datapoint.id + yield datapoint + # Next time, find only data points later than the latest we've seen + kwargs["inserted_after_record_id"] = last_id + # Commit the DB to store any work that was done in this loop and + # ensure that any isolation level issues do not prevent loading more + # data + db_session.commit() + + connection = db_session.connection() + raw_connection = connection.connection + await wait_for_notify(loop, raw_connection) + # Always yield new records from this point on diff --git a/Ch12/apd.aggregation-chapter12-ex01-complete/src/apd/aggregation/actions/trigger.py b/Ch12/apd.aggregation-chapter12-ex01-complete/src/apd/aggregation/actions/trigger.py new file mode 100644 index 0000000..d5c0def --- /dev/null +++ b/Ch12/apd.aggregation-chapter12-ex01-complete/src/apd/aggregation/actions/trigger.py @@ -0,0 +1,77 @@ +import dataclasses +import typing as t +import uuid + +from ..database import DataPoint +from ..exceptions import IncompatibleTriggerError, NoDataForTrigger +from .base import Trigger + + +@dataclasses.dataclass(frozen=True) +class ValueThresholdTrigger(Trigger[bool]): + name: str + threshold: float + comparator: t.Callable[[float, float], bool] + sensor_name: str + deployment_id: t.Optional[uuid.UUID] = dataclasses.field(default=None) + + async def match(self, datapoint: DataPoint) -> bool: + if datapoint.sensor_name != self.sensor_name: + return False + elif self.deployment_id and datapoint.deployment_id != self.deployment_id: + return False + return True + + async def extract(self, datapoint: DataPoint) -> bool: + if datapoint.data is None: + raise IncompatibleTriggerError("Datapoint does not contain data") + elif isinstance(datapoint.data, float): + value = datapoint.data + elif isinstance(datapoint.data, dict) and "magnitude" in datapoint.data: + value = datapoint.data["magnitude"] + else: + raise IncompatibleTriggerError("Unrecognised data format") + return self.comparator(value, self.threshold) # type: ignore + + +@dataclasses.dataclass +class ValueDifferenceTrigger(Trigger[float]): + name: str + sensor_name: str + target_deployment_id: uuid.UUID + reference_deployment_id: uuid.UUID + + def __post_init__(self): + self.last_reference = None + self.last_target = None + + async def match(self, datapoint: DataPoint) -> bool: + if datapoint.sensor_name != self.sensor_name: + return False + elif datapoint.deployment_id in ( + self.target_deployment_id, + self.reference_deployment_id, + ): + return True + return False + + async def extract(self, datapoint: DataPoint) -> float: + if datapoint.data is None: + raise IncompatibleTriggerError("Datapoint does not contain data") + elif isinstance(datapoint.data, float): + value = datapoint.data + elif isinstance(datapoint.data, dict) and "magnitude" in datapoint.data: + value = datapoint.data["magnitude"] + else: + raise IncompatibleTriggerError("Unrecognised data format") + + if datapoint.deployment_id == self.target_deployment_id: + self.last_target = value + elif datapoint.deployment_id == self.reference_deployment_id: + self.last_reference = value + + if self.last_reference is None or self.last_target is None: + # We need to have seen both items before we can calculate a difference + raise NoDataForTrigger("Insufficient data processed") + + return self.last_target - self.last_reference diff --git a/Ch12/apd.aggregation-chapter12-ex01-complete/src/apd/aggregation/alembic/README b/Ch12/apd.aggregation-chapter12-ex01-complete/src/apd/aggregation/alembic/README new file mode 100644 index 0000000..7d90be0 --- /dev/null +++ b/Ch12/apd.aggregation-chapter12-ex01-complete/src/apd/aggregation/alembic/README @@ -0,0 +1,12 @@ +Generic single-database configuration. + + +# Database setup + +To generate the required database tables you must create an alembic.ini file, as follows: + + [alembic] + script_location = apd.aggregation:alembic + sqlalchemy.url = postgresql+psycopg2://apd@localhost/apd + +and run `alembic upgrade head`. This should also be done after every upgrade of the software. diff --git a/Ch12/apd.aggregation-chapter12-ex01-complete/src/apd/aggregation/alembic/env.py b/Ch12/apd.aggregation-chapter12-ex01-complete/src/apd/aggregation/alembic/env.py new file mode 100644 index 0000000..4c8a2c2 --- /dev/null +++ b/Ch12/apd.aggregation-chapter12-ex01-complete/src/apd/aggregation/alembic/env.py @@ -0,0 +1,80 @@ +from logging.config import fileConfig + +from sqlalchemy import engine_from_config +from sqlalchemy import pool +from alembic import context + +from apd.aggregation.database import metadata + + +# this is the Alembic Config object, which provides +# access to the values within the .ini file in use. +config = context.config + +# Interpret the config file for Python logging. +# This line sets up loggers basically. +if config.config_file_name: + fileConfig(config.config_file_name) + +target_metadata = metadata + + +def include_object(object, name, type_, reflected, compare_to): + if object.info.get("is_view", False): + return False + return True + + +def run_migrations_offline(): + """Run migrations in 'offline' mode. + + This configures the context with just a URL + and not an Engine, though an Engine is acceptable + here as well. By skipping the Engine creation + we don't even need a DBAPI to be available. + + Calls to context.execute() here emit the given string to the + script output. + + """ + url = config.get_main_option("sqlalchemy.url") + context.configure( + url=url, + include_object=include_object, + target_metadata=target_metadata, + literal_binds=True, + dialect_opts={"paramstyle": "named"}, + ) + + with context.begin_transaction(): + context.run_migrations() + + +def run_migrations_online(): + """Run migrations in 'online' mode. + + In this scenario we need to create an Engine + and associate a connection with the context. + + """ + connectable = engine_from_config( + config.get_section(config.config_ini_section), + prefix="sqlalchemy.", + poolclass=pool.NullPool, + ) + + with connectable.connect() as connection: + context.configure( + connection=connection, + target_metadata=target_metadata, + include_object=include_object, + ) + + with context.begin_transaction(): + context.run_migrations() + + +if context.is_offline_mode(): + run_migrations_offline() +else: + run_migrations_online() diff --git a/Ch12/apd.aggregation-chapter12-ex01-complete/src/apd/aggregation/alembic/script.py.mako b/Ch12/apd.aggregation-chapter12-ex01-complete/src/apd/aggregation/alembic/script.py.mako new file mode 100644 index 0000000..2c01563 --- /dev/null +++ b/Ch12/apd.aggregation-chapter12-ex01-complete/src/apd/aggregation/alembic/script.py.mako @@ -0,0 +1,24 @@ +"""${message} + +Revision ID: ${up_revision} +Revises: ${down_revision | comma,n} +Create Date: ${create_date} + +""" +from alembic import op +import sqlalchemy as sa +${imports if imports else ""} + +# revision identifiers, used by Alembic. +revision = ${repr(up_revision)} +down_revision = ${repr(down_revision)} +branch_labels = ${repr(branch_labels)} +depends_on = ${repr(depends_on)} + + +def upgrade(): + ${upgrades if upgrades else "pass"} + + +def downgrade(): + ${downgrades if downgrades else "pass"} diff --git a/Ch12/apd.aggregation-chapter12-ex01-complete/src/apd/aggregation/alembic/versions/4b2df8a6e1ce_add_indexes_to_datapoints.py b/Ch12/apd.aggregation-chapter12-ex01-complete/src/apd/aggregation/alembic/versions/4b2df8a6e1ce_add_indexes_to_datapoints.py new file mode 100644 index 0000000..ef671bd --- /dev/null +++ b/Ch12/apd.aggregation-chapter12-ex01-complete/src/apd/aggregation/alembic/versions/4b2df8a6e1ce_add_indexes_to_datapoints.py @@ -0,0 +1,32 @@ +"""Add indexes to datapoints + +Revision ID: 4b2df8a6e1ce +Revises: 6d2eacd5da3f +Create Date: 2019-12-02 16:07:41.123116 + +""" +from alembic import op + + +# revision identifiers, used by Alembic. +revision = "4b2df8a6e1ce" +down_revision = "6d2eacd5da3f" +branch_labels = None +depends_on = None + + +def upgrade(): + op.create_index( + op.f("ix_datapoints_collected_at"), + "datapoints", + ["collected_at"], + unique=False, + ) + op.create_index( + op.f("ix_datapoints_sensor_name"), "datapoints", ["sensor_name"], unique=False, + ) + + +def downgrade(): + op.drop_index(op.f("ix_datapoints_sensor_name"), table_name="datapoints") + op.drop_index(op.f("ix_datapoints_collected_at"), table_name="datapoints") diff --git a/Ch12/apd.aggregation-chapter12-ex01-complete/src/apd/aggregation/alembic/versions/6962f8455a6d_add_daily_summary_view.py b/Ch12/apd.aggregation-chapter12-ex01-complete/src/apd/aggregation/alembic/versions/6962f8455a6d_add_daily_summary_view.py new file mode 100644 index 0000000..eb02455 --- /dev/null +++ b/Ch12/apd.aggregation-chapter12-ex01-complete/src/apd/aggregation/alembic/versions/6962f8455a6d_add_daily_summary_view.py @@ -0,0 +1,38 @@ +"""Add daily summary view + +Revision ID: 6962f8455a6d +Revises: 4b2df8a6e1ce +Create Date: 2019-12-03 11:50:24.403402 + +""" +from alembic import op + + +# revision identifiers, used by Alembic. +revision = "6962f8455a6d" +down_revision = "4b2df8a6e1ce" +branch_labels = None +depends_on = None + + +def upgrade(): + create_view = """ + CREATE VIEW daily_summary AS + SELECT + datapoints.sensor_name AS sensor_name, + datapoints.data AS data, + count(datapoints.id) AS count + FROM datapoints + WHERE + datapoints.collected_at >= CAST(CURRENT_DATE AS DATE) + AND + datapoints.collected_at < CAST(CURRENT_DATE AS DATE) + 1 + GROUP BY + datapoints.sensor_name, + datapoints.data; + """ + op.execute(create_view) + + +def downgrade(): + op.execute("""DROP VIEW daily_summary""") diff --git a/Ch12/apd.aggregation-chapter12-ex01-complete/src/apd/aggregation/alembic/versions/6d2eacd5da3f_create_sensor_values_table.py b/Ch12/apd.aggregation-chapter12-ex01-complete/src/apd/aggregation/alembic/versions/6d2eacd5da3f_create_sensor_values_table.py new file mode 100644 index 0000000..b4a3545 --- /dev/null +++ b/Ch12/apd.aggregation-chapter12-ex01-complete/src/apd/aggregation/alembic/versions/6d2eacd5da3f_create_sensor_values_table.py @@ -0,0 +1,31 @@ +"""Create datapoints table + +Revision ID: 6d2eacd5da3f +Revises: N/A +Create Date: 2019-09-29 13:43:21.242706 + +""" +from alembic import op +import sqlalchemy as sa +from sqlalchemy.dialects import postgresql + +# revision identifiers, used by Alembic. +revision = "6d2eacd5da3f" +down_revision = None +branch_labels = None +depends_on = None + + +def upgrade(): + op.create_table( + "datapoints", + sa.Column("id", sa.Integer(), nullable=False), + sa.Column("sensor_name", sa.String(), nullable=True), + sa.Column("collected_at", postgresql.TIMESTAMP(), nullable=True), + sa.Column("data", postgresql.JSONB(astext_type=sa.Text()), nullable=True), + sa.PrimaryKeyConstraint("id"), + ) + + +def downgrade(): + op.drop_table("datapoints") diff --git a/Ch12/apd.aggregation-chapter12-ex01-complete/src/apd/aggregation/alembic/versions/d8cdc709086b_add_deployment_table.py b/Ch12/apd.aggregation-chapter12-ex01-complete/src/apd/aggregation/alembic/versions/d8cdc709086b_add_deployment_table.py new file mode 100644 index 0000000..204e096 --- /dev/null +++ b/Ch12/apd.aggregation-chapter12-ex01-complete/src/apd/aggregation/alembic/versions/d8cdc709086b_add_deployment_table.py @@ -0,0 +1,32 @@ +"""Add deployment table + +Revision ID: d8cdc709086b +Revises: d8d4cf6a178f +Create Date: 2019-12-17 16:28:58.585616 + +""" +from alembic import op +import sqlalchemy as sa +from sqlalchemy.dialects import postgresql + +# revision identifiers, used by Alembic. +revision = "d8cdc709086b" +down_revision = "d8d4cf6a178f" +branch_labels = None +depends_on = None + + +def upgrade(): + op.create_table( + "deployments", + sa.Column("id", postgresql.UUID(as_uuid=True), nullable=False), + sa.Column("uri", sa.String(), nullable=True), + sa.Column("name", sa.String(), nullable=True), + sa.Column("colour", sa.String(), nullable=True), + sa.Column("api_key", sa.String(), nullable=True), + sa.PrimaryKeyConstraint("id"), + ) + + +def downgrade(): + op.drop_table("deployments") diff --git a/Ch12/apd.aggregation-chapter12-ex01-complete/src/apd/aggregation/alembic/versions/d8d4cf6a178f_add_deployment_id_to_datapoint.py b/Ch12/apd.aggregation-chapter12-ex01-complete/src/apd/aggregation/alembic/versions/d8d4cf6a178f_add_deployment_id_to_datapoint.py new file mode 100644 index 0000000..88d5219 --- /dev/null +++ b/Ch12/apd.aggregation-chapter12-ex01-complete/src/apd/aggregation/alembic/versions/d8d4cf6a178f_add_deployment_id_to_datapoint.py @@ -0,0 +1,34 @@ +"""Add deployment id to DataPoint + +Revision ID: d8d4cf6a178f +Revises: 6962f8455a6d +Create Date: 2019-12-03 20:58:11.285509 + +""" +from alembic import op +import sqlalchemy as sa +from sqlalchemy.dialects import postgresql + +# revision identifiers, used by Alembic. +revision = "d8d4cf6a178f" +down_revision = "6962f8455a6d" +branch_labels = None +depends_on = None + + +def upgrade(): + op.add_column( + "datapoints", + sa.Column("deployment_id", postgresql.UUID(as_uuid=True), nullable=True), + ) + op.create_index( + op.f("ix_datapoints_deployment_id"), + "datapoints", + ["deployment_id"], + unique=False, + ) + + +def downgrade(): + op.drop_index(op.f("ix_datapoints_deployment_id"), table_name="datapoints") + op.drop_column("datapoints", "deployment_id") diff --git a/Ch12/apd.aggregation-chapter12-ex01-complete/src/apd/aggregation/analysis.py b/Ch12/apd.aggregation-chapter12-ex01-complete/src/apd/aggregation/analysis.py new file mode 100644 index 0000000..eeb4ab2 --- /dev/null +++ b/Ch12/apd.aggregation-chapter12-ex01-complete/src/apd/aggregation/analysis.py @@ -0,0 +1,401 @@ +from __future__ import annotations + +import asyncio +import collections +from concurrent.futures import ThreadPoolExecutor +import dataclasses +import datetime +import functools +import math +import typing as t +from uuid import UUID +import warnings + +import matplotlib.pyplot as plt +from matplotlib.axes._base import _AxesBase +from matplotlib.figure import Figure +from pint import _DEFAULT_REGISTRY as ureg + +from apd.aggregation.query import ( + get_data, + get_data_by_deployment, + with_database, + get_deployment_by_id, +) +from apd.aggregation.database import DataPoint, deployment_table +from .utils import merc_x, merc_y, convert_temperature +from .typing import IntermediateMapData, T_key, T_value, CleanerFunc, Cleaned +from .typing import ( + CLEANED_COORD_FLOAT, + CLEANED_DT_FLOAT, + COORD_FLOAT_CLEANER, + DT_FLOAT_CLEANER, +) + +# Static UUID to represent aggregation of other deployments +GLOBAL = UUID("bd02526e-7619-4a59-b04b-1fafd1c262d1") + + +@dataclasses.dataclass +class Config(t.Generic[T_key, T_value]): + title: str + clean: CleanerFunc[Cleaned[T_key, T_value]] + draw: t.Optional[ + t.Callable[ + [t.Any, t.Iterable[T_key], t.Iterable[T_value], t.Optional[str]], None + ] + ] = None + get_data: t.Optional[ + t.Callable[..., t.AsyncIterator[t.Tuple[UUID, t.AsyncIterator[DataPoint]]]] + ] = None + ylabel: t.Optional[str] = None + sensor_name: dataclasses.InitVar[str] = None + + def __post_init__(self, sensor_name: t.Optional[str] = None) -> None: + if self.draw is None: + self.draw = draw_date # type: ignore + if sensor_name is not None: + warnings.warn( + DeprecationWarning( + f"The sensor_name parameter is deprecated. Please pass " + f"get_data=get_one_sensor_by_deployment('{sensor_name}') " + f"to ensure the same behaviour. The sensor_name= parameter " + f"will be removed in apd.aggregation 3.0." + ), + stacklevel=3, + ) + if self.get_data is None: + self.get_data = get_one_sensor_by_deployment(sensor_name) + if self.get_data is None: + raise ValueError("You must specify a get_data function") + + +def draw_date( + plot: _AxesBase, + x: t.Iterable[datetime.datetime], + y: t.Iterable[float], + colour: t.Optional[str], +) -> None: + plot.plot_date(x, y, color=colour, linestyle="-", marker="", xdate=True) + + +def draw_map( + plot: _AxesBase, + x: t.Iterable[t.Tuple[float, float]], + y: t.Iterable[float], + colour: t.Optional[str], +) -> None: + lon = [merc_y(coord[0]) for coord in x] + lat = [merc_x(coord[1]) for coord in x] + + for axis in "x", "y": + plt.tick_params( + axis=axis, + which="both", + bottom=False, + top=False, + left=False, + right=False, + labelbottom=False, + labelleft=False, + ) + + plot.tricontourf(lat, lon, y) + plot.plot(lat, lon, "wo", ms=3) + plot.set_aspect(1.0) + + +def get_map_cleaner_for(sensor_name: str,) -> COORD_FLOAT_CLEANER: + """Given a sensor_name that represents a float, return a coroutine that acts as a cleaner + extracting that sensor's data keyed by the value of a Location sensor.""" + + async def clean_latest_coord_and_value( + datapoints: t.AsyncIterator[DataPoint], + ) -> CLEANED_COORD_FLOAT: + + # We will iterate over data points and build an entry in cleaned_data + # for each deployment. This lets newer data replace older data, as + # datapoints is assumed to be in date order + # IntermediateMapData is a typing hint for a dictionary with specific key/values + cleaned_data: t.Dict[UUID, IntermediateMapData] = {} + async for datapoint in datapoints: + # Get the existing data for this deployment, if we've seen it before + row_data = cleaned_data.get(datapoint.deployment_id, None) + if row_data is None: + # This is the first time we've seen this deployment + row_data = {"coord": None, "value": None} + if datapoint.sensor_name == "Location": + # Coord is a 2-tuple of floats + row_data["coord"] = ( + float(datapoint.data[0]), + float(datapoint.data[1]), + ) + elif datapoint.sensor_name == sensor_name: + # Value is a single float + row_data["value"] = float(datapoint.data) + # Store the info about this deployment back into the cleaned_data set + cleaned_data[datapoint.deployment_id] = row_data + + for data in cleaned_data.values(): + if data["coord"] is None or data["value"] is None: + # We only got a partial record, don't plot this + continue + yield data["coord"], data["value"] + + # Return the set up cleaner coroutine + return clean_latest_coord_and_value + + +async def clean_watthours_to_watts( + datapoints: t.AsyncIterator[DataPoint], +) -> CLEANED_DT_FLOAT: + last_watthours = None + last_time = None + async for datapoint in datapoints: + if datapoint.data is None: + continue + time = datapoint.collected_at + if datapoint.data["unit"] == "watt_hour": + watt_hours = datapoint.data["magnitude"] + else: + watt_hours = ( + ureg.Quantity(datapoint.data["magnitude"], datapoint.data["unit"]) + .to(ureg.watt_hour) + .magnitude + ) + if last_watthours: + seconds_elapsed = (time - last_time).total_seconds() + hours_elapsed = seconds_elapsed / (60.0 * 60.0) + additional_power = watt_hours - last_watthours + power = additional_power / hours_elapsed + yield time, power + last_watthours = watt_hours + last_time = datapoint.collected_at + + +async def clean_magnitude(datapoints: t.AsyncIterator[DataPoint],) -> CLEANED_DT_FLOAT: + async for datapoint in datapoints: + if datapoint.data is None: + continue + yield datapoint.collected_at, datapoint.data["magnitude"] + + +def convert_temperature_system( + cleaner: DT_FLOAT_CLEANER, temperature_unit: str, +) -> DT_FLOAT_CLEANER: + async def converter(datapoints: t.AsyncIterator[DataPoint],) -> CLEANED_DT_FLOAT: + results = cleaner(datapoints) + async for date, temp_c in results: + yield date, convert_temperature(temp_c, "degC", temperature_unit) + + return converter + + +async def clean_temperature_fluctuations( + datapoints: t.AsyncIterator[DataPoint], +) -> CLEANED_DT_FLOAT: + allowed_jitter = 2.5 + allowed_range = (-40, 80) + window_datapoints: t.Deque[DataPoint] = collections.deque(maxlen=3) + + def datapoint_ok(datapoint: DataPoint) -> bool: + """Return False if this data point does not contain a valid temperature""" + if datapoint.data is None: + return False + elif datapoint.data["unit"] != "degC": + # This point is in a different temperature system. While it could be converted + # this cleaner is not yet doing that. + return False + elif not allowed_range[0] < datapoint.data["magnitude"] < allowed_range[1]: + return False + return True + + async for datapoint in datapoints: + if not datapoint_ok(datapoint): + # If the datapoint is invalid then skip directly to the next item + continue + + window_datapoints.append(datapoint) + if len(window_datapoints) == 3: + # Find the temperatures of the datapoints in the window, then average + # the first and last and compare that to the middle point. + window_temperatures = [dp.data["magnitude"] for dp in window_datapoints] + avr_first_last = (window_temperatures[0] + window_temperatures[2]) / 2 + diff_middle_avr = abs(window_temperatures[1] - avr_first_last) + if diff_middle_avr > allowed_jitter: + pass + else: + yield window_datapoints[1].collected_at, window_temperatures[1] + elif len(window_datapoints) == 1: + # The item in the iterator can't be compared to both neighbours + # so should be yielded + yield datapoint.collected_at, datapoint.data["magnitude"] + else: + # Otherwise, let the window fill up, it will be yieleded later + pass + # When the iterator ends the final item is not yet in the middle + # of the window, so the last item must be explicitly yielded. + if len(window_datapoints) > 1 and datapoint_ok(datapoint): + yield datapoint.collected_at, datapoint.data["magnitude"] + + +async def clean_passthrough( + datapoints: t.AsyncIterator[DataPoint], +) -> CLEANED_DT_FLOAT: + async for datapoint in datapoints: + if datapoint.data is None: + continue + else: + yield datapoint.collected_at, datapoint.data + + +def get_one_sensor_by_deployment( + sensor_name: str, +) -> t.Callable[..., t.AsyncIterator[t.Tuple[UUID, t.AsyncIterator[DataPoint]]]]: + return functools.partial(get_data_by_deployment, sensor_name=sensor_name) + + +def get_all_data() -> t.Callable[ + ..., t.AsyncIterator[t.Tuple[UUID, t.AsyncIterator[DataPoint]]] +]: + async def get_all_data_inner( + *args: t.Any, **kwargs: t.Any + ) -> t.AsyncIterator[t.Tuple[UUID, t.AsyncIterator[DataPoint]]]: + yield GLOBAL, get_data(*args, **kwargs) + + return get_all_data_inner + + +configs = ( + Config( + sensor_name="SolarCumulativeOutput", + clean=clean_watthours_to_watts, + title="Solar generation", + ylabel="Watts", + ), + Config( + sensor_name="RAMAvailable", + clean=clean_passthrough, + title="RAM available", + ylabel="Bytes", + ), + Config( + sensor_name="RelativeHumidity", + clean=clean_passthrough, + title="Relative humidity", + ylabel="Percent", + ), + Config( + sensor_name="Temperature", + clean=clean_temperature_fluctuations, + title="Ambient temperature", + ylabel="Degrees C", + ), +) + + +def get_known_configs() -> t.Dict[str, Config[t.Any, t.Any]]: + return {config.title: config for config in configs} + + +async def plot_sensor( + config: Config[t.Any, t.Any], + plot: _AxesBase, + location_names: t.Dict[UUID, str], + **kwargs: t.Any, +) -> _AxesBase: + locations = [] + if config.get_data is None: + raise ValueError("You must provide a get_data function") + async for deployment_id, query_results in config.get_data(**kwargs): + if deployment_id == GLOBAL: + name = "Global" + else: + try: + deployment = await get_deployment_by_id(deployment_id) + except IndexError: + name = str(deployment_id) + colour = None + else: + name = deployment.name or str(deployment_id) + colour = deployment.colour + # Mypy currently doesn't understand callable fields on datatypes: https://github.com/python/mypy/issues/5485 + points = [dp async for dp in config.clean(query_results)] # type: ignore + if not points: + continue + locations.append(name) + x, y = zip(*points) + plot.set_title(config.title) + plot.set_ylabel(config.ylabel) + if config.draw is None: + raise ValueError("You must provide a get_data function") + config.draw(plot, x, y, colour) + plot.legend(locations) + return plot + + +async def plot_multiple_charts(*args: t.Any, **kwargs: t.Any) -> Figure: + # These parameters are pulled from kwargs to avoid confusing function + # introspection code in IPython widgets + location_names = kwargs.pop("location_names", None) + configs = kwargs.pop("configs", None) + dimensions = kwargs.pop("dimensions", None) + db_uri = kwargs.pop("db_uri", "postgresql+psycopg2://apd@localhost/apd") + + with with_database(db_uri) as session: + loop = asyncio.get_running_loop() + coros = [] + if configs is None: + # If no configs are supplied, use all known configs + configs = get_known_configs().values() + if dimensions is None: + # If no dimensions are supplied, get the square root of the number + # of configs and round it to find a number of columns. This will + # keep the arrangement approximately square. Find rows by multiplying + # out rows. + total_configs = len(configs) + columns = round(math.sqrt(total_configs)) + rows = math.ceil(total_configs / columns) + if location_names is None: + location_query = session.query( + deployment_table.c.id, deployment_table.c.name + ) + location_data = await loop.run_in_executor(None, location_query.all) + location_names = dict(location_data) + + figure = plt.figure(figsize=(10 * columns, 5 * rows), dpi=300) + for i, config in enumerate(configs, start=1): + plot = figure.add_subplot(columns, rows, i) + coros.append(plot_sensor(config, plot, location_names, *args, **kwargs)) + await asyncio.gather(*coros) + return figure + + +_Coroutine_Result = t.TypeVar("_Coroutine_Result") + + +def wrap_coroutine( + f: t.Callable[..., t.Coroutine[t.Any, t.Any, _Coroutine_Result]] +) -> t.Callable[..., _Coroutine_Result]: + """Given a coroutine, return a function that runs that coroutine + in a new event loop in an isolated thread""" + + @functools.wraps(f) + def run_in_thread(*args: t.Any, **kwargs: t.Any) -> _Coroutine_Result: + loop = asyncio.new_event_loop() + wrapped = f(*args, **kwargs) + with ThreadPoolExecutor(max_workers=1) as pool: + task = pool.submit(loop.run_until_complete, wrapped) + # Mypy can get confused when nesting generic functions, like we do here + # The fact that Task is generic means we lose the association with + # _CoroutineResult. Adding an explicit cast restores this. + return t.cast(_Coroutine_Result, task.result()) + + return run_in_thread + + +def interactable_plot_multiple_charts( + *args: t.Any, **kwargs: t.Any +) -> t.Callable[..., Figure]: + with_config = functools.partial(plot_multiple_charts, *args, **kwargs) + return wrap_coroutine(with_config) diff --git a/Ch12/apd.aggregation-chapter12-ex01-complete/src/apd/aggregation/cli.py b/Ch12/apd.aggregation-chapter12-ex01-complete/src/apd/aggregation/cli.py new file mode 100644 index 0000000..a27e96f --- /dev/null +++ b/Ch12/apd.aggregation-chapter12-ex01-complete/src/apd/aggregation/cli.py @@ -0,0 +1,287 @@ +import asyncio +import functools +import importlib.util +import logging +import signal +import typing as t +import uuid + +import aiohttp +import click +from sqlalchemy import create_engine +from sqlalchemy.orm import sessionmaker + +from . import collect +from .actions.runner import DataProcessor +from .actions.source import get_data_ongoing +from .database import Deployment, deployment_table +from .query import with_database + +logger = logging.getLogger(__name__) + + +@click.command() +@click.argument("server", nargs=-1) +@click.option( + "--db", + metavar="", + default="postgresql+psycopg2://localhost/apd", + help="The connection string to a PostgreSQL database", + envvar="APD_DB_URI", +) +@click.option("--api-key", metavar="", envvar="APD_API_KEY") +@click.option( + "--tolerate-failures", + "-f", + help="If provided, failure to retrieve some sensors' data will not abort the collection process", + is_flag=True, +) +@click.option("-v", "--verbose", is_flag=True, help="Enables verbose mode") +def collect_sensor_data( + db: str, server: t.Tuple[str], api_key: str, tolerate_failures: bool, verbose: bool +) -> t.Optional[int]: + """This loads data from one or more sensors into the specified database. + + Only PostgreSQL databases are supported, as the column definitions use + multiple pg specific features. The database must already exist and be + populated with the required tables. + + The --api-key option is used to specify the access token for the sensors + being queried. + + You may specify any number of servers, the variable should be the full URL + to the sensor's HTTP interface, not including the /v/2.0 portion. Multiple + URLs should be separated with a space. + """ + if tolerate_failures: + attempts = [(s,) for s in server] + else: + attempts = [server] + success = True + for attempt in attempts: + try: + collect.standalone(db, attempt, api_key, echo=verbose) + except ValueError as e: + click.secho(str(e), err=True, fg="red") + success = False + return success + + +def load_handler_config(path: str) -> t.List[DataProcessor]: + # Create a module called user_config backed by the file specified, and load it + # This uses Python's import internals to fake a module in a known location + # Based on an SO answer by Sebastian Rittau and sample code from Brett Cannon + module_spec = importlib.util.spec_from_file_location("user_config", path) + module = importlib.util.module_from_spec(module_spec) + loader = module_spec.loader + if isinstance(loader, importlib.abc.Loader): + loader.exec_module(module) + try: + return module.handlers # type: ignore + except AttributeError as err: + raise ValueError(f"Could not load config file from {path}") from err + else: + # No valid loader could be found + raise ValueError(f"Could not load config file from {path}") + + +def stats_signal_handler(sig, frame, original_sigint_handler=None, handlers=None): + for handler in handlers: + click.echo( + click.style(handler.name, bold=True, fg="red") + " " + handler.stats() + ) + if sig == signal.SIGINT: + click.secho("Press Ctrl+C again to end the process", bold=True) + handler = signal.getsignal(signal.SIGINT) + signal.signal(signal.SIGINT, original_sigint_handler) + asyncio.get_running_loop().call_later(5, install_ctrl_c_signal_handler, handler) + return + + +def install_ctrl_c_signal_handler(signal_handler): + click.secho("Press Ctrl+C to view statistics", bold=True) + signal.signal(signal.SIGINT, signal_handler) + + +@click.command() +@click.argument("config", nargs=1) +@click.option( + "--db", + metavar="", + default="postgresql+psycopg2://localhost/apd", + help="The connection string to a PostgreSQL database", + envvar="APD_DB_URI", +) +@click.option( + "--historical", + is_flag=True, + help="Also trigger actions for data points that were already present in the database", +) +@click.option("-v", "--verbose", is_flag=True, help="Enables verbose mode") +def run_actions( + config: str, db: str, verbose: bool, historical: bool +) -> t.Optional[int]: + """This runs the long-running action processors defined in a config file. + + The configuration file specified should be a Python file that defines a + list of DataProcessor objects called processors.n + """ + logging.basicConfig(level=logging.DEBUG if verbose else logging.WARN) + + async def main_loop(): + with with_database(db): + logger.info("Loading configuration") + handlers = load_handler_config(config) + + logger.info(f"Configured {len(handlers)} handlers") + starters = [handler.start() for handler in handlers] + await asyncio.gather(*starters) + + logger.info(f"Ingesting data") + data = get_data_ongoing(historical=historical) + + original_sigint_handler = signal.getsignal(signal.SIGINT) + signal_handler = functools.partial( + stats_signal_handler, + handlers=handlers, + original_sigint_handler=original_sigint_handler, + ) + + for signal_name in "SIGINFO", "SIGUSR1", "SIGINT": + try: + signal.signal(signal.signals[signal_name], signal_handler) + except AttributeError: + pass + + async for datapoint in data: + for handler in handlers: + await handler.push(datapoint) + + asyncio.run(main_loop()) + return True + + +@click.group() +def deployments(): + pass + + +@deployments.command() +@click.argument("uri") +@click.argument("name") +@click.option( + "--db", + metavar="", + default="postgresql+psycopg2://localhost/apd", + help="The connection string to a PostgreSQL database", + envvar="APD_DB_URI", +) +@click.option("--api-key", metavar="", envvar="APD_API_KEY") +@click.option("--colour") +def add( + db: str, uri: str, name: str, api_key: t.Optional[str], colour: t.Optional[str], +): + """This creates a record of a new deployment in the database. + """ + deployment = Deployment(id=None, uri=uri, name=name, api_key=api_key, colour=colour) + + async def http_get_deployment_id(): + async with aiohttp.ClientSession() as http: + collect.http_session_var.set(http) + return await collect.get_deployment_id(uri) + + deployment.id = asyncio.run(http_get_deployment_id()) + insert = deployment_table.insert().values(**deployment._asdict()) + + engine = create_engine(db) + sm = sessionmaker(engine) + Session = sm() + Session.execute(insert) + Session.commit() + return True + + +@deployments.command() +@click.option( + "--db", + metavar="", + default="postgresql+psycopg2://localhost/apd", + help="The connection string to a PostgreSQL database", + envvar="APD_DB_URI", +) +def list(db: str): + """This creates a record of a new deployment in the database. + """ + engine = create_engine(db) + sm = sessionmaker(engine) + Session = sm() + deployments = Session.query(deployment_table).all() + for deployment in deployments: + click.secho(deployment.name, bold=True) + click.echo(click.style("ID ", bold=True) + deployment.id.hex) + click.echo(click.style("URI ", bold=True) + deployment.uri) + click.echo(click.style("API key ", bold=True) + deployment.api_key) + click.echo(click.style("Colour ", bold=True) + str(deployment.colour)) + click.echo() + Session.rollback() + return True + + +@deployments.command() +@click.argument("id") +@click.option("--uri") +@click.option("--name") +@click.option( + "--db", + metavar="", + default="postgresql+psycopg2://localhost/apd", + help="The connection string to a PostgreSQL database", + envvar="APD_DB_URI", +) +@click.option("--api-key", metavar="", envvar="APD_API_KEY") +@click.option("--colour") +def edit( + db: str, + id, + uri: t.Optional[str], + name: t.Optional[str], + api_key: t.Optional[str], + colour: t.Optional[str], +): + """This creates a record of a new deployment in the database. + """ + update = {} + if uri is not None: + update["uri"] = uri + if name is not None: + update["name"] = name + if api_key is not None: + update["api_key"] = api_key + if colour is not None: + update["colour"] = colour + deployment_id = uuid.UUID(id) + + update_stmt = ( + deployment_table.update() + .where(deployment_table.c.id == deployment_id) + .values(**update) + ) + + engine = create_engine(db) + sm = sessionmaker(engine) + Session = sm() + Session.execute(update_stmt) + deployments = Session.query(deployment_table).filter( + deployment_table.c.id == deployment_id + ) + Session.commit() + + for deployment in deployments: + click.secho(deployment.name, bold=True) + click.echo(click.style("ID ", bold=True) + deployment.id.hex) + click.echo(click.style("URI ", bold=True) + deployment.uri) + click.echo(click.style("API key ", bold=True) + deployment.api_key) + click.echo(click.style("Colour ", bold=True) + str(deployment.colour)) + click.echo() + + return True diff --git a/Ch12/apd.aggregation-chapter12-ex01-complete/src/apd/aggregation/collect.py b/Ch12/apd.aggregation-chapter12-ex01-complete/src/apd/aggregation/collect.py new file mode 100644 index 0000000..2ec7c01 --- /dev/null +++ b/Ch12/apd.aggregation-chapter12-ex01-complete/src/apd/aggregation/collect.py @@ -0,0 +1,118 @@ +import asyncio +from contextvars import ContextVar +import datetime +import typing as t +import uuid + +import aiohttp +from sqlalchemy import create_engine +from sqlalchemy.orm import sessionmaker +from sqlalchemy.orm.session import Session + +from .database import DataPoint, datapoint_table, Deployment, deployment_table + +http_session_var: ContextVar[aiohttp.ClientSession] = ContextVar("http_session") + + +async def get_deployment_id(server): + http = http_session_var.get() + if not server.endswith("/"): + server += "/" + url = server + "v/2.1/deployment_id" + async with http.get(url) as request: + if request.status != 200: + raise ValueError(f"Error loading deployment id from {server}") + result = await request.json() + return uuid.UUID(result["deployment_id"]) + + +async def get_data_points(server: str, api_key: t.Optional[str],) -> t.List[DataPoint]: + if not server.endswith("/"): + server += "/" + url = server + "v/2.1/sensors/" + headers = {} + if api_key: + headers["X-API-KEY"] = api_key + http = http_session_var.get() + + # Get the deployment ID in parallel to the sensor data + deployment_id = asyncio.create_task(get_deployment_id(server)) + + async with http.get(url, headers=headers) as request: + result = await request.json() + ok = request.status == 200 + now = datetime.datetime.now() + if ok: + points = [] + for value in result["sensors"]: + points.append( + DataPoint( + sensor_name=value["id"], + collected_at=now, + data=value["value"], + deployment_id=await deployment_id, + ) + ) + return points + else: + raise ValueError( + f"Error loading data from {server}: " + result.get("error", "Unknown") + ) + + +def handle_result(result: t.List[DataPoint], session: Session) -> t.List[DataPoint]: + for point in result: + insert = datapoint_table.insert().values(**point._asdict()) + sql_result = session.execute(insert) + point.id = sql_result.inserted_primary_key[0] + return result + + +async def add_data_from_sensors( + session: Session, servers: t.Iterable[Deployment] +) -> t.List[DataPoint]: + tasks: t.List[t.Awaitable[t.List[DataPoint]]] = [] + points: t.List[DataPoint] = [] + async with aiohttp.ClientSession() as http: + http_session_var.set(http) + tasks = [get_data_points(server.uri, server.api_key) for server in servers] + for results in await asyncio.gather(*tasks): + points += results + loop = asyncio.get_running_loop() + await loop.run_in_executor(None, handle_result, points, session) + return points + + +def standalone( + db_uri: str, servers: t.Tuple[str], api_key: t.Optional[str], echo: bool = False +) -> None: + engine = create_engine(db_uri, echo=echo) + sm = sessionmaker(engine) + Session = sm() + deployments: t.Iterable[Deployment] + if servers: + deployments = tuple( + Deployment(id=None, name=None, colour=None, api_key=api_key, uri=server) + for server in servers + ) + else: + deployment_data = Session.query(deployment_table).all() + deployments = tuple( + Deployment.from_sql_result(deployment) for deployment in deployment_data + ) + asyncio.run(add_data_from_sensors(Session, deployments)) + + if "postgresql" in db_uri: + # On Postgres sent a pubsub notification, in case other processes are waiting + # for this data + Session.execute("NOTIFY apd_aggregation;") + + Session.commit() + + +if __name__ == "__main__": + standalone( + db_uri="postgresql+psycopg2://apd@localhost/apd", + servers=("http://pvoutput:8080/",), + api_key="h3hdfjksfhwkjehnwekj", + ) diff --git a/Ch12/apd.aggregation-chapter12-ex01-complete/src/apd/aggregation/database.py b/Ch12/apd.aggregation-chapter12-ex01-complete/src/apd/aggregation/database.py new file mode 100644 index 0000000..f28b469 --- /dev/null +++ b/Ch12/apd.aggregation-chapter12-ex01-complete/src/apd/aggregation/database.py @@ -0,0 +1,129 @@ +from __future__ import annotations + +from dataclasses import dataclass, field, asdict +import datetime +import typing as t +import uuid + +import sqlalchemy +from sqlalchemy.dialects.postgresql import JSONB, DATE, TIMESTAMP, UUID +from sqlalchemy.ext.hybrid import ExprComparator, hybrid_property +from sqlalchemy.orm import sessionmaker +from sqlalchemy.schema import Table + + +metadata = sqlalchemy.MetaData() + +deployment_table = Table( + "deployments", + metadata, + sqlalchemy.Column("id", UUID(as_uuid=True), primary_key=True), + sqlalchemy.Column("uri", sqlalchemy.String, index=False), + sqlalchemy.Column("name", sqlalchemy.String, index=False, nullable=True), + sqlalchemy.Column("colour", sqlalchemy.String, index=False, nullable=True), + sqlalchemy.Column("api_key", sqlalchemy.String, index=False), +) + +datapoint_table = Table( + "datapoints", + metadata, + sqlalchemy.Column("id", sqlalchemy.Integer, primary_key=True), + sqlalchemy.Column("sensor_name", sqlalchemy.String, index=True), + sqlalchemy.Column("collected_at", TIMESTAMP, index=True), + sqlalchemy.Column("deployment_id", UUID(as_uuid=True), index=True), + sqlalchemy.Column("data", JSONB), +) + +daily_summary_view = Table( + "daily_summary", + metadata, + sqlalchemy.Column("sensor_name", sqlalchemy.String), + sqlalchemy.Column("data", JSONB), + sqlalchemy.Column("count", sqlalchemy.Integer), + info={"is_view": True}, +) + + +class DateEqualComparator(ExprComparator): + def __init__(self, fallback_expression, raw_expression): + # Do not try and find update expression from parent + super().__init__(None, fallback_expression, None) + self.raw_expression = raw_expression + + def __eq__(self, other): + """ Returns True iff on the same day as other """ + other_date = sqlalchemy.cast(other, DATE) + return sqlalchemy.and_( + self.raw_expression >= other_date, self.raw_expression < other_date + 1, + ) + + def operate(self, op, *other, **kwargs): + other = [sqlalchemy.cast(date, DATE) for date in other] + return op(self.expression, *other, **kwargs) + + def reverse_operate(self, op, other, **kwargs): + other = [sqlalchemy.cast(date, DATE) for date in other] + return op(other, self.expression, **kwargs) + + +@dataclass +class DataPoint: + sensor_name: str + data: t.Any + deployment_id: uuid.UUID + id: t.Optional[int] = None + collected_at: datetime.datetime = field(default_factory=datetime.datetime.now) + + @classmethod + def from_sql_result(cls, result) -> DataPoint: + return cls(**result._asdict()) + + def _asdict(self) -> t.Dict[str, t.Any]: + data = asdict(self) + if data["id"] is None: + del data["id"] + return data + + @hybrid_property + def collected_on_date(self): + return self.collected_at.date() + + @collected_on_date.comparator # type: ignore + def collected_on_date(cls): + return DateEqualComparator( + fallback_expression=sqlalchemy.cast(datapoint_table.c.collected_at, DATE), + raw_expression=datapoint_table.c.collected_at, + ) + + +@dataclass +class Deployment: + id: t.Optional[uuid.UUID] + uri: str + name: t.Optional[str] + colour: t.Optional[str] + api_key: t.Optional[str] + + @classmethod + def from_sql_result(cls, result) -> Deployment: + return cls(**result._asdict()) + + def _asdict(self) -> t.Dict[str, t.Any]: + data = asdict(self) + return data + + +def main() -> None: + engine = sqlalchemy.create_engine( + "postgresql+psycopg2://apd@localhost/apd", echo=True + ) + sm = sessionmaker(engine) + Session = sm() + if False: + metadata.create_all(engine) + print(Session.query(DataPoint).all()) + pass + + +if __name__ == "__main__": + main() diff --git a/Ch12/apd.aggregation-chapter12-ex01-complete/src/apd/aggregation/exceptions.py b/Ch12/apd.aggregation-chapter12-ex01-complete/src/apd/aggregation/exceptions.py new file mode 100644 index 0000000..6200e90 --- /dev/null +++ b/Ch12/apd.aggregation-chapter12-ex01-complete/src/apd/aggregation/exceptions.py @@ -0,0 +1,14 @@ +class NoDataForTrigger(ValueError): + """An error that's raised when a trigger is passed + a data point that cannot be handled due to an incompatible + value being stored""" + + pass + + +class IncompatibleTriggerError(NoDataForTrigger): + """An error that's raised when a trigger is passed + a data point that cannot be handled due to an incompatible + value being stored""" + + pass diff --git a/Ch12/apd.aggregation-chapter12-ex01-complete/src/apd/aggregation/query.py b/Ch12/apd.aggregation-chapter12-ex01-complete/src/apd/aggregation/query.py new file mode 100644 index 0000000..14144ff --- /dev/null +++ b/Ch12/apd.aggregation-chapter12-ex01-complete/src/apd/aggregation/query.py @@ -0,0 +1,154 @@ +import asyncio +import contextlib +from contextvars import ContextVar +import datetime +import typing as t +from uuid import UUID + +from sqlalchemy import create_engine +from sqlalchemy.orm import sessionmaker +from sqlalchemy.orm.session import Session + + +from apd.aggregation.database import ( + datapoint_table, + DataPoint, + deployment_table, + Deployment, +) + + +db_session_var: ContextVar[Session] = ContextVar("db_session") + + +@contextlib.contextmanager +def with_database(uri: t.Optional[str] = None) -> t.Iterator[Session]: + """Given a URI, set up a DB connection, and return a Session as a context manager """ + if uri is None: + uri = "postgresql+psycopg2://localhost/apd" + engine = create_engine(uri) + sm = sessionmaker(engine) + Session = sm() + token = db_session_var.set(Session) + try: + yield Session + Session.commit() + finally: + db_session_var.reset(token) + Session.close() + + +async def get_data( + sensor_name: t.Optional[str] = None, + deployment_id: t.Optional[UUID] = None, + collected_before: t.Optional[datetime.datetime] = None, + collected_after: t.Optional[datetime.datetime] = None, + inserted_after_record_id: t.Optional[int] = None, + order: bool = True, +) -> t.AsyncIterator[DataPoint]: + db_session = db_session_var.get() + loop = asyncio.get_running_loop() + query = db_session.query(datapoint_table) + if sensor_name: + query = query.filter(datapoint_table.c.sensor_name == sensor_name) + if deployment_id: + query = query.filter(datapoint_table.c.deployment_id == deployment_id) + if collected_before: + query = query.filter(datapoint_table.c.collected_at < collected_before) + if collected_after: + query = query.filter(datapoint_table.c.collected_at > collected_after) + if inserted_after_record_id: + query = query.filter(datapoint_table.c.id > inserted_after_record_id) + + if order: + query = query.order_by( + datapoint_table.c.deployment_id, + datapoint_table.c.sensor_name, + datapoint_table.c.collected_at, + ) + + rows = await loop.run_in_executor(None, query.all) + for row in rows: + yield DataPoint.from_sql_result(row) + + +async def get_deployment_ids() -> t.List[UUID]: + db_session = db_session_var.get() + loop = asyncio.get_running_loop() + query = db_session.query(datapoint_table.c.deployment_id).distinct() + return [row.deployment_id for row in await loop.run_in_executor(None, query.all)] + + +async def get_deployment_by_id(deployment_id: UUID) -> Deployment: + db_session = db_session_var.get() + loop = asyncio.get_running_loop() + query = db_session.query(deployment_table).filter( + deployment_table.c.id == deployment_id + ) + return [ + Deployment.from_sql_result(row) + for row in await loop.run_in_executor(None, query.all) + ][0] + + +async def get_data_by_deployment( + *args: t.Any, **kwargs: t.Any +) -> t.AsyncIterator[t.Tuple[UUID, t.AsyncIterator[DataPoint]]]: + """Return an Async Iterator that contains two-item pairs. + These pairs are a string (deployment_id), and an async iterator that contains + the datapoints with that deployment_id. + + Usage example: + + async for deployment_id, datapoints in get_data_by_deployment(): + print(deployment_id) + async for datapoint in datapoints: + print(datapoint) + print() + """ + # Get the data, using the arguments to this function as filters + data = get_data(*args, **kwargs) + + # The two levels of iterator share the item variable, initialise it with the + # first item from the iterator. Also set last_deployment_id to None, so the + # outer iterator knows to start a new group. + last_deployment_id: t.Optional[UUID] = None + try: + item = await data.__anext__() + except StopAsyncIteration: + # There were no items in the underlying query, return immediately + return + + async def subiterator(group_id: UUID) -> t.AsyncIterator[DataPoint]: + """Using a closure, create an iterator that yields the current + item, then yields all items from data while the deployment_id matches + group_id, leaving the first that doesn't match as item in the enclosing + scope.""" + # item is from the enclosing scope + nonlocal item + while item.deployment_id == group_id: + # yield items from data while they match the group_id this iterator represents + yield item + try: + # Advance the underlying iterator + item = await data.__anext__() + except StopAsyncIteration: + # The underlying iterator came to an end, so end the subiterator too + return + + while True: + while item.deployment_id == last_deployment_id: + # We are trying to advance the outer iterator while the underlying iterator + # is still part-way through a group. Speed through the underlying until we + # hit an item where the deployment_id is different to the last one (or, is not + # None, in the case of the start of the iterator) + try: + item = await data.__anext__() + except StopAsyncIteration: + # We hit the end of the underlying iterator: end this iterator too + return + last_deployment_id = item.deployment_id + # Don't yield an iterator for unclassified deployments + if last_deployment_id is not None: + # Instantiate a subiterator for this group + yield last_deployment_id, subiterator(last_deployment_id) diff --git a/Ch12/apd.aggregation-chapter12-ex01-complete/src/apd/aggregation/typing.py b/Ch12/apd.aggregation-chapter12-ex01-complete/src/apd/aggregation/typing.py new file mode 100644 index 0000000..5db1337 --- /dev/null +++ b/Ch12/apd.aggregation-chapter12-ex01-complete/src/apd/aggregation/typing.py @@ -0,0 +1,48 @@ +import datetime +import typing as t +from typing_extensions import Protocol + +from apd.aggregation.database import DataPoint + + +# Aliases for common types +# These type variables allow for generic functions. T_key represents the place +# in a chart that an item will be placed, and T_value the kind of data that is plotted. +T_key = t.TypeVar("T_key") +T_value = t.TypeVar("T_value") + +# Cleaned is a placeholder type, representing an async iterator of key and value functions +# Cleaned[float, float] is equivalent to typing.AsyncIterator[Tuple[builtins.float, builtins.float]] +Cleaned = t.AsyncIterator[t.Tuple[T_key, T_value]] + +# T_cleaned represents a placeholder for the result of a cleaner function, and is *covariant* +# because it can accept compatible types (such as ints where floats were declared). +# It is bound to Cleaned, so only items that match the specification for Cleaned (for any values +# of T_key or T_value) are valid +T_cleaned = t.TypeVar("T_cleaned", covariant=True, bound=Cleaned) + + +# CleanerFunc is a generic protocol, it matches any Callable that converts an async iterator +# of datapoints to its type. So, CleanerFunc[float] would be equivalent to t.Callable[[t.AsyncIterator[DataPoint]], float] +class CleanerFunc(Protocol[T_cleaned]): + def __call__(self, datapoints: t.AsyncIterator[DataPoint]) -> T_cleaned: + ... + + +# CLEANED_DT_FLOAT is a Cleaned represents datetime/float pairs, for simple charts +CLEANED_DT_FLOAT = Cleaned[datetime.datetime, float] +# and CLEANED_COORD_FLOAT represents (lat/lon), float pairs +CLEANED_COORD_FLOAT = Cleaned[t.Tuple[float, float], float] + +# The _CLEANER variants are functions that return their matching iterators from above +DT_FLOAT_CLEANER = CleanerFunc[CLEANED_DT_FLOAT] +COORD_FLOAT_CLEANER = CleanerFunc[CLEANED_COORD_FLOAT] + + +# When drawing a map we will be building a dictionary of UUID to dictionary +# That inner dictionary should contain coord (float, float) +# and value (float) only. Either or both can be None. +# This class is abstract, it's just for type checking. +class IntermediateMapData(t.TypedDict): + coord: t.Optional[t.Tuple[float, float]] + value: t.Optional[float] diff --git a/Ch12/apd.aggregation-chapter12-ex01-complete/src/apd/aggregation/utils.py b/Ch12/apd.aggregation-chapter12-ex01-complete/src/apd/aggregation/utils.py new file mode 100644 index 0000000..75bf6b3 --- /dev/null +++ b/Ch12/apd.aggregation-chapter12-ex01-complete/src/apd/aggregation/utils.py @@ -0,0 +1,100 @@ +from __future__ import annotations + +import contextlib +import functools +import importlib +import io +import logging +import math +import os +import typing as t + +from pint import _DEFAULT_REGISTRY as ureg + + +# Set up mercator transform from https://wiki.openstreetmap.org/wiki/Mercator#Python +# Thanks to Paulo Silva and the OSM contributors +def merc_x(lon: float) -> float: + r_major = 6378137.000 + return r_major * math.radians(lon) + + +def merc_y(lat: float) -> float: + if lat > 89.5: + lat = 89.5 + if lat < -89.5: + lat = -89.5 + r_major = 6378137.000 + r_minor = 6356752.3142 + temp = r_minor / r_major + eccent = math.sqrt(1 - temp ** 2) + phi = math.radians(lat) + sinphi = math.sin(phi) + con = eccent * sinphi + com = eccent / 2 + con = ((1.0 - con) / (1.0 + con)) ** com + ts = math.tan((math.pi / 2 - phi) / 2) / con + y = 0 - r_major * math.log(ts) + return y + + +@functools.lru_cache +def convert_temperature(magnitude: float, origin_unit: str, target_unit: str) -> float: + # if origin_unit == "degC" and target_unit == "degF": + # return (magnitude * 1.8) + 32 + temp = ureg.Quantity(magnitude, origin_unit) + return temp.to(target_unit).magnitude + + +@contextlib.contextmanager +def jupyter_page_file() -> t.Iterator[io.StringIO]: + from IPython.core import page + + output = io.StringIO() + yield output + output.seek(0) + page.page(output.read()) + + +@contextlib.contextmanager +def profile_with_yappi() -> t.Iterator[None]: + import yappi + + yappi.clear_stats() + yappi.start() + try: + yield None + finally: + yappi.stop() + + +@functools.lru_cache +def get_package_prefix(package: str) -> str: + mod = importlib.import_module(package) + prefix = mod.__file__ + if prefix.endswith("__init__.py"): + prefix = os.path.dirname(prefix) + return prefix + + +def yappi_package_matches(stat, packages: t.List[str]): + """ This object can be passed to yappi's filter_callback to limit + by Python package.""" + for package in packages: + prefix = get_package_prefix(package) + if stat.full_name.startswith(prefix): + return True + return False + + +class AddSensorNameDefault(logging.Filter): + def filter(self, record): + if not hasattr(record, "sensorname"): + record.sensorname = "none" + return True + + +class SensorNameStreamHandler(logging.StreamHandler): + def __init__(self, *args, **kwargs): + super().__init__() + self.addFilter(AddSensorNameDefault()) diff --git a/Ch12/apd.aggregation-chapter12-ex01-complete/tests/__init__.py b/Ch12/apd.aggregation-chapter12-ex01-complete/tests/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/Ch12/apd.aggregation-chapter12-ex01-complete/tests/conftest.py b/Ch12/apd.aggregation-chapter12-ex01-complete/tests/conftest.py new file mode 100644 index 0000000..309a30c --- /dev/null +++ b/Ch12/apd.aggregation-chapter12-ex01-complete/tests/conftest.py @@ -0,0 +1,127 @@ +import datetime +from uuid import UUID + +from apd.aggregation.database import datapoint_table +from apd.aggregation.query import db_session_var + +from alembic.config import Config +from alembic.script import ScriptDirectory +from alembic.runtime.environment import EnvironmentContext +import pytest + + +@pytest.fixture +def db_uri(): + return "postgresql+psycopg2://apd@localhost/apd-test" + + +@pytest.fixture +def db_session(db_uri): + from sqlalchemy import create_engine + from sqlalchemy.orm import sessionmaker + + engine = create_engine(db_uri, echo=False) + sm = sessionmaker(engine) + Session = sm() + reset = db_session_var.set(Session) + yield Session + db_session_var.reset(reset) + Session.close() + + +@pytest.fixture +def migrated_db(db_uri, db_session): + config = Config() + config.set_main_option("script_location", "apd.aggregation:alembic") + config.set_main_option("sqlalchemy.url", db_uri) + script = ScriptDirectory.from_config(config) + + def upgrade(rev, context): + return script._upgrade_revs(script.get_current_head(), rev) + + def downgrade(rev, context): + return script._downgrade_revs(None, rev) + + with EnvironmentContext(config, script, fn=upgrade): + script.run_env() + + try: + yield db_session + finally: + # Clear any pending work from the db_session connection + db_session.rollback() + + with EnvironmentContext(config, script, fn=downgrade): + script.run_env() + + +@pytest.fixture +def populated_db(migrated_db, db_session): + datas = [ + { + "id": 1, + "sensor_name": "Test", + "data": "1", + "collected_at": datetime.datetime(2020, 4, 1, 12, 0, 1), + "deployment_id": UUID("b4c68905-b1e4-4875-940e-69e5d27730fd"), + }, + { + "id": 2, + "sensor_name": "Test", + "data": "2", + "collected_at": datetime.datetime(2020, 4, 1, 12, 1, 1), + "deployment_id": UUID("b4c68905-b1e4-4875-940e-69e5d27730fd"), + }, + { + "id": 3, + "sensor_name": "Test", + "data": "3", + "collected_at": datetime.datetime(2020, 4, 1, 12, 2, 1), + "deployment_id": UUID("b4c68905-b1e4-4875-940e-69e5d27730fd"), + }, + { + "id": 4, + "sensor_name": "Test", + "data": "4", + "collected_at": datetime.datetime(2020, 4, 1, 12, 3, 1), + "deployment_id": UUID("b4c68905-b1e4-4875-940e-69e5d27730fd"), + }, + { + "id": 5, + "sensor_name": "OtherTest", + "data": "5", + "collected_at": datetime.datetime(2020, 4, 1, 12, 4, 1), + "deployment_id": UUID("b4c68905-b1e4-4875-940e-69e5d27730fd"), + }, + { + "id": 6, + "sensor_name": "Test", + "data": "1", + "collected_at": datetime.datetime(2020, 4, 1, 12, 0, 1), + "deployment_id": UUID("6e887b81-57c3-4f3b-a7e7-bad159e05e78"), + }, + { + "id": 7, + "sensor_name": "Test", + "data": "1", + "collected_at": datetime.datetime(2020, 4, 1, 12, 1, 1), + "deployment_id": UUID("6e887b81-57c3-4f3b-a7e7-bad159e05e78"), + }, + { + "id": 8, + "sensor_name": "Test", + "data": "2", + "collected_at": datetime.datetime(2020, 4, 1, 12, 2, 1), + "deployment_id": UUID("6e887b81-57c3-4f3b-a7e7-bad159e05e78"), + }, + { + "id": 9, + "sensor_name": "OtherTest", + "data": "3", + "collected_at": datetime.datetime(2020, 4, 1, 12, 3, 1), + "deployment_id": UUID("6e887b81-57c3-4f3b-a7e7-bad159e05e78"), + }, + ] + for data in datas: + insert = datapoint_table.insert().values(**data) + db_session.execute(insert) diff --git a/Ch12/apd.aggregation-chapter12-ex01-complete/tests/test_actions_actions.py b/Ch12/apd.aggregation-chapter12-ex01-complete/tests/test_actions_actions.py new file mode 100644 index 0000000..1429fb3 --- /dev/null +++ b/Ch12/apd.aggregation-chapter12-ex01-complete/tests/test_actions_actions.py @@ -0,0 +1,118 @@ +import datetime +import unittest.mock + +import pytest + +from apd.aggregation.actions.base import Action +from apd.aggregation.actions.action import ( + OnlyOnChangeActionWrapper, + OnlyAfterDateActionWrapper, + SaveToDatabaseAction, +) +from apd.aggregation.query import db_session_var, get_data + +from .test_analysis import generate_datapoints + + +class TestOnlyAfterDateActionWrapper: + @pytest.fixture + def subject(self): + return OnlyAfterDateActionWrapper + + @pytest.mark.asyncio + async def test_minimum_date_before_passing(self, subject): + wrapped = unittest.mock.Mock(spec=Action) + wrapper = subject( + wrapped, date_threshold=datetime.datetime(2020, 4, 1, 14, 0, 0) + ) + await wrapper.start() + + data = [ + (datetime.datetime(2020, 4, 1, 12, 0, 0), 21.0), + (datetime.datetime(2020, 4, 1, 13, 0, 0), 21.0), + (datetime.datetime(2020, 4, 1, 14, 0, 0), 22.0), + (datetime.datetime(2020, 4, 1, 15, 0, 0), 22.0), + (datetime.datetime(2020, 4, 1, 16, 0, 0), 21.0), + ] + datapoints = generate_datapoints(data) + all_datapoints = [] + async for datapoint in datapoints: + all_datapoints.append(datapoint) + await wrapper.handle(datapoint) + + assert len(wrapped.handle.mock_calls) == 2 + passed = [call.args[0] for call in wrapped.handle.mock_calls] + assert passed == [all_datapoints[3], all_datapoints[4]] + + +class TestOnlyOnChangeActionWrapper: + @pytest.fixture + def subject(self): + return OnlyOnChangeActionWrapper + + @pytest.mark.asyncio + async def test_initial_value_always_passed(self, subject): + wrapped = unittest.mock.Mock(spec=Action) + wrapper = subject(wrapped) + await wrapper.start() + + data = [ + (datetime.datetime(2020, 4, 1, 12, 0, 0), 21.0), + ] + datapoints = generate_datapoints(data) + first = await datapoints.asend(None) + await wrapper.handle(first) + assert len(wrapped.handle.mock_calls) == 1 + assert wrapped.handle.mock_calls[0].args[0] == first + + @pytest.mark.asyncio + async def test_subsequent_values_only_passed_when_differing(self, subject): + wrapped = unittest.mock.Mock(spec=Action) + wrapper = subject(wrapped) + await wrapper.start() + + data = [ + (datetime.datetime(2020, 4, 1, 12, 0, 0), 21.0), + (datetime.datetime(2020, 4, 1, 13, 0, 0), 21.0), + (datetime.datetime(2020, 4, 1, 14, 0, 0), 22.0), + (datetime.datetime(2020, 4, 1, 15, 0, 0), 22.0), + (datetime.datetime(2020, 4, 1, 16, 0, 0), 21.0), + ] + datapoints = generate_datapoints(data) + all_datapoints = [] + async for datapoint in datapoints: + all_datapoints.append(datapoint) + await wrapper.handle(datapoint) + + assert len(wrapped.handle.mock_calls) == 3 + passed = [call.args[0] for call in wrapped.handle.mock_calls] + assert passed == [all_datapoints[0], all_datapoints[2], all_datapoints[4]] + + +class TestSaveToDatabaseAction: + @pytest.fixture + def subject(self): + return SaveToDatabaseAction + + @pytest.mark.asyncio + async def test_datapoints_are_persisted(self, subject, migrated_db): + db_session_var.set(migrated_db) + + wrapper = subject() + await wrapper.start() + + data = [ + (datetime.datetime(2020, 4, 1, 12, 0, 0), 21.0), + ] + generated_datapoints = [] + async for datapoint in generate_datapoints(data): + generated_datapoints.append(datapoint) + await wrapper.handle(datapoint) + + stored_datapoints = [ + point async for point in get_data(sensor_name="TestSensor") + ] + assert stored_datapoints[0].data == generated_datapoints[0].data + assert ( + stored_datapoints[0].deployment_id == generated_datapoints[0].deployment_id + ) diff --git a/Ch12/apd.aggregation-chapter12-ex01-complete/tests/test_actions_runner.py b/Ch12/apd.aggregation-chapter12-ex01-complete/tests/test_actions_runner.py new file mode 100644 index 0000000..f263420 --- /dev/null +++ b/Ch12/apd.aggregation-chapter12-ex01-complete/tests/test_actions_runner.py @@ -0,0 +1,110 @@ +import asyncio +import datetime +import operator + +import pytest + +from apd.aggregation.actions.runner import DataProcessor +from apd.aggregation.actions.base import Trigger +from apd.aggregation.actions.action import ( + OnlyOnChangeActionWrapper, + SaveToDatabaseAction, +) +from apd.aggregation.actions.trigger import ValueThresholdTrigger +from apd.aggregation.database import DataPoint +from apd.aggregation.query import get_data +from .test_analysis import generate_datapoints + + +class AlwaysTrueTrigger(Trigger[bool]): + name = "AlwaysTrue" + + async def start(self): + pass + + async def match(self, datapoint: DataPoint) -> bool: + return True + + async def extract(self, datapoint: DataPoint) -> bool: + return True + + +class StoreAction(Trigger[bool]): + async def start(self): + self.data = asyncio.Queue() + + async def handle(self, datapoint: DataPoint) -> None: + await self.data.put(datapoint) + + +class TestRunner: + @pytest.fixture + @pytest.mark.asyncio + async def runner(self, event_loop): + processor = DataProcessor( + name="Test data runner", action=StoreAction(), trigger=AlwaysTrueTrigger(), + ) + await processor.start() + yield processor + await processor.end() + + @pytest.mark.asyncio + async def test_simple_flow(self, runner, event_loop): + data = [(datetime.datetime(2020, 4, 1, 12, 0, 0), 65.0)] + datapoints = generate_datapoints(data) + async for datapoint in datapoints: + await runner.push(datapoint) + + output = await runner.action.data.get() + assert output.sensor_name == runner.trigger.name + assert output.collected_at == data[0][0] + assert output.data == True + + +class TestRealWorldRunner: + @pytest.fixture + @pytest.mark.asyncio + async def runner(self, migrated_db, event_loop): + processor = DataProcessor( + name="TemperatureAbove20", + action=OnlyOnChangeActionWrapper(SaveToDatabaseAction()), + trigger=ValueThresholdTrigger( + name="TemperatureAbove20", + threshold=20, + comparator=operator.gt, + sensor_name="Temperature", + ), + ) + await processor.start() + yield processor + await processor.end() + + @pytest.mark.asyncio + async def test_real_usecase(self, runner, event_loop): + data = [ + (datetime.datetime(2020, 4, 1, 12, 0, 0), 19.0), + (datetime.datetime(2020, 4, 1, 13, 0, 0), 18.0), + (datetime.datetime(2020, 4, 1, 14, 0, 0), 21.0), + (datetime.datetime(2020, 4, 1, 15, 0, 0), 22.0), + (datetime.datetime(2020, 4, 1, 16, 0, 0), 21.0), + ] + + async for datapoint in generate_datapoints(data, sensor_name="Temperature"): + await runner.push(datapoint) + + # Wait up to 10 seconds for the runner to be idle + await asyncio.wait_for(runner.idle(), timeout=10) + + stored_datapoints = [ + point async for point in get_data(sensor_name="TemperatureAbove20") + ] + + assert len(stored_datapoints) == 2 + assert stored_datapoints[0].data == False + assert stored_datapoints[0].collected_at == datetime.datetime( + 2020, 4, 1, 12, 0, 0 + ) + assert stored_datapoints[1].data == True + assert stored_datapoints[1].collected_at == datetime.datetime( + 2020, 4, 1, 14, 0, 0 + ) diff --git a/Ch12/apd.aggregation-chapter12-ex01-complete/tests/test_actions_triggers.py b/Ch12/apd.aggregation-chapter12-ex01-complete/tests/test_actions_triggers.py new file mode 100644 index 0000000..275c8e8 --- /dev/null +++ b/Ch12/apd.aggregation-chapter12-ex01-complete/tests/test_actions_triggers.py @@ -0,0 +1,127 @@ +import datetime +import operator + +import pytest + +from apd.aggregation.actions.trigger import ValueThresholdTrigger + +from .test_analysis import generate_datapoints + + +class TestValueThresholdTrigger: + @pytest.fixture + def subject(self): + return ValueThresholdTrigger + + @pytest.mark.asyncio + @pytest.mark.parametrize( + "comparison,expected", + [ + (operator.gt, [False, False, False, True]), + (operator.eq, [False, False, True, False]), + (operator.le, [True, True, True, False]), + ], + ) + async def test_simple_value(self, subject, comparison, expected): + trigger = subject( + name=f"TestSensor{comparison.__name__}21", + threshold=21, + comparator=comparison, + sensor_name="TestSensor", + ) + + data = [ + (datetime.datetime(2020, 4, 1, 12, 0, 0), 19.0), + (datetime.datetime(2020, 4, 1, 12, 0, 0), 20.0), + (datetime.datetime(2020, 4, 1, 12, 0, 0), 21.0), + (datetime.datetime(2020, 4, 1, 12, 0, 0), 22.0), + ] + datapoints = generate_datapoints(data) + + outputs = [] + async for datapoint in datapoints: + assert await trigger.match(datapoint) + outputs.append(await trigger.extract(datapoint)) + + assert outputs == expected + + @pytest.mark.asyncio + @pytest.mark.parametrize( + "comparison,expected", + [ + (operator.gt, [False, False, False, True]), + (operator.eq, [False, False, True, False]), + (operator.le, [True, True, True, False]), + ], + ) + async def test_magnitude_value(self, subject, comparison, expected): + trigger = subject( + name=f"TestSensor{comparison.__name__}21", + threshold=21, + comparator=comparison, + sensor_name="TestSensor", + ) + + data = [ + ( + datetime.datetime(2020, 4, 1, 12, 0, 0), + {"magnitude": 19.0, "unit": "degC"}, + ), + ( + datetime.datetime(2020, 4, 1, 12, 0, 0), + {"magnitude": 20.0, "unit": "degC"}, + ), + ( + datetime.datetime(2020, 4, 1, 12, 0, 0), + {"magnitude": 21.0, "unit": "degC"}, + ), + ( + datetime.datetime(2020, 4, 1, 12, 0, 0), + {"magnitude": 22.0, "unit": "degC"}, + ), + ] + datapoints = generate_datapoints(data) + + outputs = [] + async for datapoint in datapoints: + assert await trigger.match(datapoint) + outputs.append(await trigger.extract(datapoint)) + + assert outputs == expected + + @pytest.mark.asyncio + async def test_different_data_format_fails_to_extract(self, subject): + trigger = subject( + name="TestSensorAbove21", + threshold=21, + comparator=operator.eq, + sensor_name="TestSensor", + ) + + data = [ + (datetime.datetime(2020, 4, 1, 12, 0, 0), "21.0"), + ] + datapoints = generate_datapoints(data) + + async for datapoint in datapoints: + assert await trigger.match(datapoint) + with pytest.raises(ValueError): + await trigger.extract(datapoint) + assert await trigger.handle(datapoint) is None + + @pytest.mark.asyncio + async def test_different_sensors_are_not_matched(self, subject): + trigger = subject( + name="TestSensorAbove21", + threshold=21, + comparator=operator.eq, + sensor_name="OtherSensor", + ) + + data = [ + (datetime.datetime(2020, 4, 1, 12, 0, 0), 21.0), + ] + datapoints = generate_datapoints(data) + + async for datapoint in datapoints: + assert not await trigger.match(datapoint) diff --git a/Ch12/apd.aggregation-chapter12-ex01-complete/tests/test_analysis.py b/Ch12/apd.aggregation-chapter12-ex01-complete/tests/test_analysis.py new file mode 100644 index 0000000..053272e --- /dev/null +++ b/Ch12/apd.aggregation-chapter12-ex01-complete/tests/test_analysis.py @@ -0,0 +1,525 @@ +import collections.abc +import datetime +import functools +import uuid +import warnings + +import pytest + +from apd.aggregation import analysis +from apd.aggregation.database import DataPoint, datapoint_table +from apd.aggregation.query import db_session_var + + +async def generate_datapoints(datas, sensor_name="TestSensor"): + deployment_id = uuid.uuid4() + for i, (time, data) in enumerate(datas, start=1): + yield DataPoint( + id=i, + collected_at=time, + sensor_name=sensor_name, + data=data, + deployment_id=deployment_id, + ) + + +@functools.singledispatch +def consume(input_iterator): + items = [item for item in input_iterator] + + def inner_iterator(): + for item in items: + yield item + + return inner_iterator() + + +@consume.register +async def consume_async(input_iterator: collections.abc.AsyncIterator): + items = [item async for item in input_iterator] + + async def inner_iterator(): + for item in items: + yield item + + return inner_iterator() + + +class TestPassThroughCleaner: + @pytest.fixture + def cleaner(self): + return analysis.clean_passthrough + + @pytest.mark.asyncio + async def test_float_passthrough(self, cleaner): + data = [ + (datetime.datetime(2020, 4, 1, 12, 0, 0), 65.0), + (datetime.datetime(2020, 4, 1, 13, 0, 0), 65.5), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert output == data + + @pytest.mark.asyncio + async def test_Nones_are_skipped(self, cleaner): + data = [ + (datetime.datetime(2020, 4, 1, 12, 0, 0), None), + (datetime.datetime(2020, 4, 1, 13, 0, 0), 65.5), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert output == [ + (datetime.datetime(2020, 4, 1, 13, 0, 0), 65.5), + ] + + +class TestTemperatureCleaner: + @pytest.fixture + def cleaner(self): + return analysis.clean_temperature_fluctuations + + @pytest.mark.asyncio + async def test_window_not_full(self, cleaner): + data = [ + ( + datetime.datetime(2020, 4, 1, 12, 0, 0), + {"magnitude": 21.0, "unit": "degC"}, + ), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert output == [(datetime.datetime(2020, 4, 1, 12, 0, 0), 21.0)] + + @pytest.mark.asyncio + async def test_window_exactly_full(self, cleaner): + data = [ + ( + datetime.datetime(2020, 4, 1, 12, 0, 0), + {"magnitude": 21.0, "unit": "degC"}, + ), + ( + datetime.datetime(2020, 4, 1, 12, 0, 1), + {"magnitude": 21.0, "unit": "degC"}, + ), + ( + datetime.datetime(2020, 4, 1, 12, 0, 2), + {"magnitude": 21.0, "unit": "degC"}, + ), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert output == [ + (datetime.datetime(2020, 4, 1, 12, 0, 0), 21.0), + (datetime.datetime(2020, 4, 1, 12, 0, 1), 21.0), + (datetime.datetime(2020, 4, 1, 12, 0, 2), 21.0), + ] + + @pytest.mark.asyncio + async def test_window_overfilled(self, cleaner): + data = [ + ( + datetime.datetime(2020, 4, 1, 12, 0, 0), + {"magnitude": 21.0, "unit": "degC"}, + ), + ( + datetime.datetime(2020, 4, 1, 12, 0, 1), + {"magnitude": 21.0, "unit": "degC"}, + ), + ( + datetime.datetime(2020, 4, 1, 12, 0, 2), + {"magnitude": 21.0, "unit": "degC"}, + ), + ( + datetime.datetime(2020, 4, 1, 12, 0, 3), + {"magnitude": 21.0, "unit": "degC"}, + ), + ( + datetime.datetime(2020, 4, 1, 12, 0, 4), + {"magnitude": 21.0, "unit": "degC"}, + ), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert output == [ + (datetime.datetime(2020, 4, 1, 12, 0, 0), 21.0), + (datetime.datetime(2020, 4, 1, 12, 0, 1), 21.0), + (datetime.datetime(2020, 4, 1, 12, 0, 2), 21.0), + (datetime.datetime(2020, 4, 1, 12, 0, 3), 21.0), + (datetime.datetime(2020, 4, 1, 12, 0, 4), 21.0), + ] + + @pytest.mark.asyncio + async def test_outlier_dropped(self, cleaner): + data = [ + ( + datetime.datetime(2020, 4, 1, 12, 0, 0), + {"magnitude": 21.0, "unit": "degC"}, + ), + ( + datetime.datetime(2020, 4, 1, 12, 0, 1), + {"magnitude": 61.0, "unit": "degC"}, + ), + ( + datetime.datetime(2020, 4, 1, 12, 0, 2), + {"magnitude": 21.0, "unit": "degC"}, + ), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert output == [ + (datetime.datetime(2020, 4, 1, 12, 0, 0), 21.0), + (datetime.datetime(2020, 4, 1, 12, 0, 2), 21.0), + ] + + @pytest.mark.asyncio + async def test_limited_to_DHT22_range(self, cleaner): + data = [ + ( + datetime.datetime(2020, 4, 1, 12, 0, 0), + {"magnitude": 91.0, "unit": "degC"}, + ), + ( + datetime.datetime(2020, 4, 1, 12, 0, 1), + {"magnitude": 91.0, "unit": "degC"}, + ), + ( + datetime.datetime(2020, 4, 1, 12, 0, 2), + {"magnitude": 91.0, "unit": "degC"}, + ), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert output == [] + + @pytest.mark.asyncio + async def test_Nones_dropped(self, cleaner): + data = [ + ( + datetime.datetime(2020, 4, 1, 12, 0, 0), + {"magnitude": 31.0, "unit": "degC"}, + ), + (datetime.datetime(2020, 4, 1, 12, 0, 1), None,), + ( + datetime.datetime(2020, 4, 1, 12, 0, 2), + {"magnitude": 32.0, "unit": "degC"}, + ), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert output == [ + (datetime.datetime(2020, 4, 1, 12, 0, 0), 31.0), + (datetime.datetime(2020, 4, 1, 12, 0, 2), 32.0), + ] + + +class TestWattHourCleaner: + @pytest.fixture + def cleaner(self): + return analysis.clean_watthours_to_watts + + @pytest.fixture(scope="class") + def huge_data_set(self): + date = datetime.datetime(2020, 4, 1, 12, 0, 0) + power = 500 + data = [] + for i in range(50_000): + date += datetime.timedelta(hours=1) + power += i + data.append((date, {"magnitude": power, "unit": "watt_hour"},)) + return data + + @pytest.mark.asyncio + async def test_one_entry_insufficient_to_find_diff(self, cleaner): + data = [ + ( + datetime.datetime(2020, 4, 1, 12, 0, 0), + {"magnitude": 1.0, "unit": "watt_hour"}, + ), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert output == [] + + @pytest.mark.asyncio + async def test_two_entries_provides_diff_with_second_time(self, cleaner): + data = [ + ( + datetime.datetime(2020, 4, 1, 12, 0, 0), + {"magnitude": 1.0, "unit": "watt_hour"}, + ), + ( + datetime.datetime(2020, 4, 1, 13, 0, 0), + {"magnitude": 10.0, "unit": "watt_hour"}, + ), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert len(output) == 1 + assert output[0][0] == datetime.datetime(2020, 4, 1, 13, 0, 0) + assert output[0][1] == pytest.approx(9.0, 0.0001) + + @pytest.mark.asyncio + async def test_nonstandard_units_can_be_used(self, cleaner): + data = [ + ( + datetime.datetime(2020, 4, 1, 12, 0, 0), + {"magnitude": 1.0, "unit": "watt_hour"}, + ), + ( + datetime.datetime(2020, 4, 1, 13, 0, 0), + {"magnitude": 10000.0, "unit": "joule"}, + ), + ( + datetime.datetime(2020, 4, 1, 14, 0, 0), + {"magnitude": 3.0, "unit": "watt_hour"}, + ), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert len(output) == 2 + assert output[0][0] == datetime.datetime(2020, 4, 1, 13, 0, 0) + assert output[0][1] == pytest.approx(1.7777, 0.001) + assert output[1][0] == datetime.datetime(2020, 4, 1, 14, 0, 0) + assert output[1][1] == pytest.approx(0.22222, 0.001) + + @pytest.mark.asyncio + async def test_fractional_hour(self, cleaner): + data = [ + ( + datetime.datetime(2020, 4, 1, 12, 0, 0), + {"magnitude": 1.0, "unit": "watt_hour"}, + ), + ( + datetime.datetime(2020, 4, 1, 12, 23, 42), + {"magnitude": 10.0, "unit": "watt_hour"}, + ), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert len(output) == 1 + assert output[0][0] == datetime.datetime(2020, 4, 1, 12, 23, 42) + assert output[0][1] == pytest.approx(22.7848, 0.001) + + @pytest.mark.asyncio + async def test_diff_happens_pairwise(self, cleaner): + data = [ + ( + datetime.datetime(2020, 4, 1, 12, 0, 0), + {"magnitude": 1.0, "unit": "watt_hour"}, + ), + ( + datetime.datetime(2020, 4, 1, 12, 23, 42), + {"magnitude": 10.0, "unit": "watt_hour"}, + ), + ( + datetime.datetime(2020, 4, 1, 13, 23, 42), + {"magnitude": 13.0, "unit": "watt_hour"}, + ), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert len(output) == 2 + assert output[0][0] == datetime.datetime(2020, 4, 1, 12, 23, 42) + assert output[0][1] == pytest.approx(22.7848, 0.001) + assert output[1][0] == datetime.datetime(2020, 4, 1, 13, 23, 42) + assert output[1][1] == pytest.approx(3, 0.001) + + @pytest.mark.asyncio + async def test_None_ignored(self, cleaner): + data = [ + ( + datetime.datetime(2020, 4, 1, 12, 0, 0), + {"magnitude": 1.0, "unit": "watt_hour"}, + ), + (datetime.datetime(2020, 4, 1, 12, 58, 0), None,), + ( + datetime.datetime(2020, 4, 1, 13, 0, 0), + {"magnitude": 10.0, "unit": "watt_hour"}, + ), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert len(output) == 1 + assert output[0][0] == datetime.datetime(2020, 4, 1, 13, 0, 0) + assert output[0][1] == pytest.approx(9.0, 0.0001) + + @pytest.mark.asyncio + @pytest.mark.performance + async def test_performance_of_cleaner(self, cleaner, huge_data_set): + import cProfile + + # Generate data point objects before profiling starts + datapoints = await consume(generate_datapoints(huge_data_set)) + profiler = cProfile.Profile() + profiler.enable() + + # Run the cleaner to completion + await consume(cleaner(datapoints)) + + profiler.disable() + cleaner_stat = [ + stat for stat in profiler.getstats() if stat.code == cleaner.__code__ + ][0] + # Assert that the cleaner may take no more than 0.5 seconds to process + # these 50k data points + assert cleaner_stat.totaltime < 0.5 + + +class TestTemperatureMap: + @pytest.fixture + def temperature_data(self): + return { + (53.8667, -1.3333): -1, + (53.35, -2.2833): 1, + (52.45, -1.7333): 3, + (51.5, -0.1333): 6, + (51.55, -2.5667): 5, + (54.9667, -1.6167): -1, + (55.9667, -3.2167): -1, + (54.7667, -1.5833): 0, + (53.7667, -0.3): 1, + (53.7667, -3.0167): 0, + (51.4833, -3.1833): 4, + (53.2667, -3.5167): 2, + (52.0833, -2.8): 2, + (52.0167, -0.6): 2, + (52.6667, 1.2667): 5, + (50.4333, -4.9833): 9, + (50.35, -4.1167): 10, + (50.5167, -2.45): 9, + (50.8333, -1.1667): 9, + (56.45, -5.4333): 4, + (57.5333, -4.05): -2, + (54.5167, -3.6167): 3, + (53.0833, 0.2667): 4, + (51.35, 1.3333): 7, + (50.8833, 0.3167): 8, + (58.45, -3.1): 3, + (50.1, -5.6667): 9, + (58.2167, -6.3333): 4, + (55.6833, -6.25): 7, + } + + @pytest.fixture + def temperature_datapoints(self, migrated_db, db_session, temperature_data): + datas = [] + now = datetime.datetime.now() + for (coord, temp) in temperature_data.items(): + deployment_id = uuid.uuid4() + datas.append( + DataPoint( + sensor_name="Location", + deployment_id=deployment_id, + collected_at=now, + data=coord, + ) + ) + datas.append( + DataPoint( + sensor_name="Temperature", + deployment_id=deployment_id, + collected_at=now, + data=temp, + ) + ) + return datas + + @pytest.fixture + def populated_db(self, migrated_db, db_session, temperature_datapoints): + for data in temperature_datapoints: + insert = datapoint_table.insert().values(**data._asdict()) + db_session.execute(insert) + + @pytest.fixture + def cleaner(self): + return analysis.get_map_cleaner_for("Temperature") + + @pytest.fixture + def get_data(self, db_session): + db_session_var.set(db_session) + return analysis.get_all_data() + + @pytest.mark.asyncio + async def test_latest_coord_temp_cleaner( + self, temperature_data, populated_db, get_data, cleaner + ): + data = get_data() + deployments = 0 + async for deployment, data_points in data: + deployments += 1 + cleaned = {a async for a in cleaner(data_points)} + # There should only be one deployment + assert deployments == 1 + expected = set(temperature_data.items()) + assert cleaned == expected + + @pytest.mark.asyncio + async def test_newer_items_superceed_older( + self, db_session, temperature_datapoints, populated_db, get_data, cleaner + ): + # Pick any of the deployment ids and find the pair of DataPoints in that deployment id + deployment_1 = temperature_datapoints[0].deployment_id + existing = [ + datapoint + for datapoint in temperature_datapoints + if datapoint.deployment_id == deployment_1 + ] + old_location = [ + datapoint for datapoint in existing if datapoint.sensor_name == "Location" + ][0] + old_temperature = [ + datapoint + for datapoint in existing + if datapoint.sensor_name == "Temperature" + ][0] + + new_location = DataPoint( + sensor_name="Location", + deployment_id=deployment_1, + collected_at=datetime.datetime(2031, 4, 1, 12, 0, 0), + data=(-10.5, 150.1), + ) + new_temperature = DataPoint( + sensor_name="Temperature", + deployment_id=deployment_1, + collected_at=datetime.datetime(2031, 4, 1, 12, 0, 0), + data=21, + ) + for data in [new_location, new_temperature]: + insert = datapoint_table.insert().values(**data._asdict()) + db_session.execute(insert) + + data = get_data() + deployments = 0 + async for deployment, data_points in data: + deployments += 1 + cleaned = {a async for a in cleaner(data_points)} + # There should only be one deployment + assert deployments == 1 + + # We expect the newer one, not the older + assert (new_location.data, new_temperature.data) in cleaned + assert (old_location.data, old_temperature.data) not in cleaned + # The cleaner converts two data points to one plottable + assert len(cleaned) == len(temperature_datapoints) / 2 + + +def test_deprecation_warning_raised_by_config_with_no_getdata(): + with warnings.catch_warnings(record=True) as captured_warnings: + warnings.simplefilter("always", DeprecationWarning) + analysis.Config( + sensor_name="Temperature", + clean=analysis.clean_passthrough, + title="Temperaure", + ylabel="Deg C", + ) + assert len(captured_warnings) == 1 + deprecation_warning = captured_warnings[0] + assert deprecation_warning.filename == __file__ + assert deprecation_warning.category == DeprecationWarning + assert str(deprecation_warning.message) == ( + "The sensor_name parameter is deprecated. Please pass " + "get_data=get_one_sensor_by_deployment('Temperature') " + "to ensure the same behaviour. The sensor_name= parameter " + "will be removed in apd.aggregation 3.0." + ) diff --git a/Ch12/apd.aggregation-chapter12-ex01-complete/tests/test_cli.py b/Ch12/apd.aggregation-chapter12-ex01-complete/tests/test_cli.py new file mode 100644 index 0000000..5cab441 --- /dev/null +++ b/Ch12/apd.aggregation-chapter12-ex01-complete/tests/test_cli.py @@ -0,0 +1,155 @@ +from unittest import mock +import uuid + +from click.testing import CliRunner +import pytest + +import apd.aggregation.cli +from apd.aggregation.database import Deployment, deployment_table + + +@pytest.mark.functional +def test_sensors_are_passed_to_get_data_points(db_uri): + runner = CliRunner() + with mock.patch("apd.aggregation.collect.get_data_points") as get_data_points: + runner.invoke( + apd.aggregation.cli.collect_sensor_data, + [ + "http://localhost", + "http://otherhost", + "--db", + db_uri, + "--api-key", + "key", + ], + ) + calls = get_data_points.call_args_list + assert len(calls) == 2 + details = {call[0] for call in get_data_points.call_args_list} + assert details == { + ("http://localhost", "key"), + ("http://otherhost", "key"), + } + + +@pytest.mark.functional +def test_add_deployment_to_db(db_uri, db_session, migrated_db): + runner = CliRunner() + deployment_id = uuid.UUID("76587cdd42dc489ea1d220e9b0bc62ca") + with mock.patch("apd.aggregation.collect.get_deployment_id") as get_deployment_id: + get_deployment_id.return_value = deployment_id + runner.invoke( + apd.aggregation.cli.deployments, + ["add", "http://otherhost", "Other", "--db", db_uri, "--api-key", "key"], + ) + deployments = db_session.query(deployment_table).all() + assert len(deployments) == 1 + deployment = Deployment.from_sql_result(deployments[0]) + assert deployment.uri == "http://otherhost" + assert deployment.api_key == "key" + assert deployment.id == deployment_id + + +@pytest.mark.functional +def test_use_stored_deployments(db_uri, migrated_db): + runner = CliRunner() + + deployment_id = uuid.UUID("76587cdd42dc489ea1d220e9b0bc62ca") + with mock.patch("apd.aggregation.collect.get_deployment_id") as get_deployment_id: + get_deployment_id.return_value = deployment_id + runner.invoke( + apd.aggregation.cli.deployments, + [ + "add", + "http://specifiedhost", + "Specified", + "--db", + db_uri, + "--api-key", + "an_api_key", + ], + ) + + with mock.patch("apd.aggregation.collect.get_data_points") as get_data_points: + runner.invoke( + apd.aggregation.cli.collect_sensor_data, ["--db", db_uri], + ) + calls = get_data_points.call_args_list + assert len(calls) == 1 + details = {call[0] for call in get_data_points.call_args_list} + assert details == { + ("http://specifiedhost", "an_api_key"), + } + + +@pytest.mark.functional +def test_list_deployments(db_uri, migrated_db): + runner = CliRunner() + + deployment_id = uuid.UUID("76587cdd42dc489ea1d220e9b0bc62ca") + with mock.patch("apd.aggregation.collect.get_deployment_id") as get_deployment_id: + get_deployment_id.return_value = deployment_id + runner.invoke( + apd.aggregation.cli.deployments, + [ + "add", + "http://specifiedhost", + "Specified", + "--db", + db_uri, + "--api-key", + "an_api_key", + ], + ) + + result = runner.invoke(apd.aggregation.cli.deployments, ["list", "--db", db_uri],) + expected = """Specified +ID 76587cdd42dc489ea1d220e9b0bc62ca +URI http://specifiedhost +API key an_api_key +Colour None + +""" + assert result.output == expected + + +@pytest.mark.functional +def test_edit_deployment(db_uri, migrated_db): + runner = CliRunner() + return + deployment_id = uuid.UUID("76587cdd42dc489ea1d220e9b0bc62ca") + with mock.patch("apd.aggregation.collect.get_deployment_id") as get_deployment_id: + get_deployment_id.return_value = deployment_id + runner.invoke( + apd.aggregation.cli.deployments, + [ + "add", + "http://specifiedhost", + "Specified", + "--db", + db_uri, + "--api-key", + "an_api_key", + ], + ) + + result = runner.invoke( + apd.aggregation.cli.deployments, + [ + "edit", + "--db", + db_uri, + deployment_id.hex(), + "--colour", + "red" "--name", + "New name", + ], + ) + expected = """New name +ID 76587cdd42dc489ea1d220e9b0bc62ca +URI http://specifiedhost +API key an_api_key +Colour red + +""" + assert result.output == expected diff --git a/Ch12/apd.aggregation-chapter12-ex01-complete/tests/test_http_get.py b/Ch12/apd.aggregation-chapter12-ex01-complete/tests/test_http_get.py new file mode 100644 index 0000000..df575c6 --- /dev/null +++ b/Ch12/apd.aggregation-chapter12-ex01-complete/tests/test_http_get.py @@ -0,0 +1,188 @@ +from concurrent.futures import ThreadPoolExecutor +import datetime +import typing as t +from unittest.mock import patch, MagicMock +import wsgiref.simple_server + +import aiohttp +from apd.sensors.base import Sensor +from apd.sensors.sensors import PythonVersion, ACStatus +from apd.sensors.wsgi import set_up_config +import flask +import pytest + +from apd.sensors.wsgi import v21 + +from apd.aggregation import collect +from apd.aggregation.database import Deployment + +pytestmark = [pytest.mark.functional] + + +@pytest.fixture +def sensors() -> t.Iterator[t.List[Sensor[t.Any]]]: + """ Patch the get_sensors method to return a known pair of sensors only """ + data: t.List[Sensor[t.Any]] = [PythonVersion(), ACStatus()] + with patch("apd.sensors.cli.get_sensors") as get_sensors: + get_sensors.return_value = data + yield data + + +def get_independent_flask_app(name: str) -> flask.Flask: + """ Create a new flask app with the v20 API blueprint loaded, so multiple copies + of the app can be run in parallel without conflicting configuration """ + app = flask.Flask(name) + app.register_blueprint(v21.version, url_prefix="/v/2.1") + return app + + +def run_server_in_thread( + name: str, config: t.Dict[str, t.Any], port: int +) -> t.Iterator[str]: + # Create a new flask app and load in required code, to prevent config conflicts + app = get_independent_flask_app(name) + flask_app = set_up_config(config, app) + server = wsgiref.simple_server.make_server("localhost", port, flask_app) + + with ThreadPoolExecutor() as pool: + pool.submit(server.serve_forever) + yield f"http://localhost:{port}/" + server.shutdown() + + +@pytest.fixture(scope="module") +def http_server(): + yield from run_server_in_thread( + "standard", + { + "APD_SENSORS_API_KEY": "testing", + "APD_SENSORS_DEPLOYMENT_ID": "a46b1d1207fd4cdcad39bbdf706dfe29", + }, + 12081, + ) + + +@pytest.fixture(scope="module") +def bad_api_key_http_server(): + yield from run_server_in_thread( + "alternate", + { + "APD_SENSORS_API_KEY": "penny", + "APD_SENSORS_DEPLOYMENT_ID": "38cf2bae9adb445fad946c82e290487a", + }, + 12082, + ) + + +class TestGetDataPoints: + @pytest.fixture + def mut(self): + return collect.get_data_points + + @pytest.mark.asyncio + async def test_get_data_points( + self, sensors: t.List[Sensor[t.Any]], mut, http_server: str + ) -> None: + # Get the data from the server, storing the time before and after + # as bounds for the collected_at value + async with aiohttp.ClientSession() as http: + collect.http_session_var.set(http) + time_before = datetime.datetime.now() + results = await mut(http_server, "testing") + time_after = datetime.datetime.now() + + assert len(results) == len(sensors) == 2 + + for (sensor, result) in zip(sensors, results): + assert sensor.from_json_compatible(result.data) == sensor.value() + assert result.sensor_name == sensor.name + assert time_before <= result.collected_at <= time_after + + @pytest.mark.asyncio + async def test_get_data_points_fails_with_bad_api_key( + self, sensors: t.List[Sensor[t.Any]], mut, http_server: str + ) -> None: + with pytest.raises( + ValueError, + match=f"Error loading data from {http_server}: Supply API key in X-API-Key header", + ): + async with aiohttp.ClientSession() as http: + collect.http_session_var.set(http) + await mut(http_server, "incorrect") + + +class TestAddDataFromSensors: + @pytest.fixture + def mut(self): + return collect.add_data_from_sensors + + @pytest.fixture + def mock_db_session(self): + return MagicMock() + + @pytest.mark.asyncio + async def test_get_get_data_from_sensors( + self, mock_db_session, sensors: t.List[Sensor[t.Any]], mut, http_server: str + ) -> None: + results = await mut( + mock_db_session, + [ + Deployment( + id=None, colour=None, name=None, uri=http_server, api_key="testing" + ) + ], + ) + assert mock_db_session.execute.call_count == len(sensors) + assert len(results) == len(sensors) + + @pytest.mark.asyncio + async def test_get_get_data_from_sensors_with_multiple_servers( + self, mock_db_session, sensors: t.List[Sensor[t.Any]], mut, http_server: str + ) -> None: + results = await mut( + mock_db_session, + [ + Deployment( + id=None, colour=None, name=None, uri=http_server, api_key="testing" + ), + Deployment( + id=None, colour=None, name=None, uri=http_server, api_key="testing" + ), + ], + ) + assert mock_db_session.execute.call_count == len(sensors) * 2 + assert len(results) == len(sensors) * 2 + + @pytest.mark.asyncio + async def test_data_points_not_added_if_only_partial_success( + self, + mock_db_session, + sensors: t.List[Sensor[t.Any]], + mut, + http_server: str, + bad_api_key_http_server: str, + ) -> None: + with pytest.raises( + ValueError, + match=f"Error loading data from {bad_api_key_http_server}: Supply API key in X-API-Key header", + ): + await mut( + mock_db_session, + [ + Deployment( + id=None, + colour=None, + name=None, + uri=http_server, + api_key="testing", + ), + Deployment( + id=None, + colour=None, + name=None, + uri=bad_api_key_http_server, + api_key="testing", + ), + ], + ) + assert mock_db_session.execute.call_count == 0 diff --git a/Ch12/apd.aggregation-chapter12-ex01-complete/tests/test_query.py b/Ch12/apd.aggregation-chapter12-ex01-complete/tests/test_query.py new file mode 100644 index 0000000..9b6e0aa --- /dev/null +++ b/Ch12/apd.aggregation-chapter12-ex01-complete/tests/test_query.py @@ -0,0 +1,111 @@ +import datetime +from uuid import UUID + +import pytest + +from apd.aggregation.query import ( + get_data, + get_deployment_ids, + get_data_by_deployment, + db_session_var, +) + + +class TestGetData: + @pytest.fixture + def mut(self): + return get_data + + @pytest.mark.asyncio + @pytest.mark.parametrize( + "filter,num_items_expected", + [ + ({}, 9), + ({"sensor_name": "Test"}, 7), + ({"deployment_id": UUID("b4c68905-b1e4-4875-940e-69e5d27730fd")}, 5), + ({"collected_after": datetime.datetime(2020, 4, 1, 12, 2, 1)}, 3), + ({"collected_before": datetime.datetime(2020, 4, 1, 12, 2, 1)}, 4), + ( + { + "collected_after": datetime.datetime(2020, 4, 1, 12, 2, 1), + "collected_before": datetime.datetime(2020, 4, 1, 12, 3, 5), + }, + 2, + ), + ], + ) + async def test_iterate_over_items( + self, mut, db_session, populated_db, filter, num_items_expected + ): + db_session_var.set(db_session) + points = [dp async for dp in mut(**filter)] + assert len(points) == num_items_expected + + @pytest.mark.asyncio + async def test_empty_db(self, mut, db_session, migrated_db): + db_session_var.set(db_session) + points = [dp async for dp in mut()] + assert len(points) == 0 + + +class TestGetDeployments: + @pytest.fixture + def mut(self): + return get_deployment_ids + + @pytest.mark.asyncio + async def test_get_all_deployments(self, mut, db_session, populated_db): + db_session_var.set(db_session) + deployment_ids = await mut() + assert set(deployment_ids) == { + UUID("b4c68905-b1e4-4875-940e-69e5d27730fd"), + UUID("6e887b81-57c3-4f3b-a7e7-bad159e05e78"), + } + + @pytest.mark.asyncio + async def test_empty_db(self, mut, db_session, migrated_db): + db_session_var.set(db_session) + deployment_ids = await mut() + assert deployment_ids == [] + + +@pytest.mark.usefixtures("populated_db") +class TestGetDataByDeployment: + @pytest.fixture + def mut(self): + return get_data_by_deployment + + @pytest.mark.asyncio + @pytest.mark.parametrize( + "filter,num_items_expected", [({}, 9), ({"sensor_name": "Test"}, 7)] + ) + async def test_iterate_over_all_items( + self, mut, db_session, filter, num_items_expected + ): + db_session_var.set(db_session) + num_items = 0 + num_deployments = 0 + async for deployment_id, items in mut(**filter): + num_deployments += 1 + async for item in items: + num_items += 1 + assert num_deployments == 2 + assert num_items == num_items_expected + + @pytest.mark.asyncio + @pytest.mark.parametrize( + "filter,num_items_expected", [({}, 5), ({"sensor_name": "Test"}, 4)] + ) + async def test_skip_first_deployment( + self, mut, db_session, filter, num_items_expected + ): + db_session_var.set(db_session) + num_items = 0 + num_deployments = 0 + async for deployment_id, items in mut(**filter): + num_deployments += 1 + if num_deployments == 2: + async for item in items: + num_items += 1 + assert num_deployments == 2 + assert num_items == num_items_expected diff --git a/Ch12/apd.aggregation-chapter12-ex01-complete/tests/test_sensor_aggregation.py b/Ch12/apd.aggregation-chapter12-ex01-complete/tests/test_sensor_aggregation.py new file mode 100644 index 0000000..655521d --- /dev/null +++ b/Ch12/apd.aggregation-chapter12-ex01-complete/tests/test_sensor_aggregation.py @@ -0,0 +1,199 @@ +from __future__ import annotations + +import contextlib +from dataclasses import dataclass +import json +import typing as t +from unittest.mock import patch, Mock + +import pytest +import sqlalchemy + +import apd.aggregation.collect +from apd.aggregation.database import Deployment + + +@pytest.fixture +def data() -> t.Any: + return { + "sensors": [ + { + "human_readable": "3.7", + "id": "PythonVersion", + "title": "Python Version", + "value": [3, 7, 2, "final", 0], + }, + { + "human_readable": "Not connected", + "id": "ACStatus", + "title": "AC Connected", + "value": False, + }, + ] + } + + +@dataclass +class FakeAIOHttpClient: + responses: t.Dict[str, str] + + @contextlib.asynccontextmanager + async def get( + self, url: str, headers: t.Optional[t.Dict[str, str]] = None + ) -> t.AsyncIterator[FakeAIOHttpResponse]: + if url in self.responses: + yield FakeAIOHttpResponse(body=self.responses[url]) + else: + yield FakeAIOHttpResponse(body="", status=404) + + +@dataclass +class FakeAIOHttpResponse: + body: str + status: int = 200 + + async def json(self) -> t.Any: + return json.loads(self.body) + + +@pytest.fixture +def mockclient(data) -> FakeAIOHttpClient: + return FakeAIOHttpClient( + { + "http://localhost/v/2.1/sensors/": json.dumps(data), + "http://localhost/v/2.1/deployment_id": json.dumps( + {"deployment_id": "b29ba0ee10f14552b6b21327bb96d3fb"} + ), + } + ) + + +class TestGetDataPoints: + @pytest.fixture + def mut(self): + return apd.aggregation.collect.get_data_points + + @pytest.mark.asyncio + async def test_get_data_points(self, mut, mockclient, data) -> None: + # Mark the mock client as the same type as the one it's mocking + # So we can set it into the context variable without warnings + token = apd.aggregation.collect.http_session_var.set(mockclient) + try: + datapoints = await mut("http://localhost", "") + finally: + apd.aggregation.collect.http_session_var.reset(token) + + assert len(datapoints) == len(data["sensors"]) + for sensor in data["sensors"]: + assert sensor["value"] in (datapoint.data for datapoint in datapoints) + assert sensor["id"] in (datapoint.sensor_name for datapoint in datapoints) + + +class TestAddDataFromSensors: + @pytest.fixture + def mut(self): + return apd.aggregation.collect.add_data_from_sensors + + @pytest.fixture(autouse=True) + def patch_aiohttp(self, mockclient): + with patch("aiohttp.ClientSession") as ClientSession: + ClientSession.return_value.__aenter__.return_value = mockclient + yield ClientSession + + @pytest.fixture + def db_session(self): + session = Mock() + sql_result = session.execute.return_value + sql_result.inserted_primary_key = [1] + return session + + @pytest.mark.asyncio + async def test_datapoints_are_added_to_the_session(self, mut, db_session) -> None: + assert db_session.execute.call_count == 0 + datapoints = await mut( + db_session, + [ + Deployment( + id=None, colour=None, name=None, uri="http://localhost", api_key="" + ) + ], + ) + assert db_session.execute.call_count == len(datapoints) + + +@pytest.mark.usefixtures("migrated_db") +class TestDatabaseConnection: + @pytest.fixture(autouse=True) + def patch_aiohttp(self, mockclient): + with patch("aiohttp.ClientSession") as ClientSession: + ClientSession.return_value.__aenter__.return_value = mockclient + yield ClientSession + + @pytest.fixture + def table(self): + return apd.aggregation.database.datapoint_table + + @pytest.fixture + def daily_summary_view(self): + return apd.aggregation.database.daily_summary_view + + @pytest.fixture + def model(self): + return apd.aggregation.database.DataPoint + + @pytest.fixture + def mut(self): + return apd.aggregation.collect.add_data_from_sensors + + @pytest.mark.asyncio + async def test_datapoints_are_added_to_the_session(self, db_session, table) -> None: + datapoints = await apd.aggregation.collect.add_data_from_sensors( + db_session, + [ + Deployment( + id=None, colour=None, name=None, uri="http://localhost", api_key="" + ) + ], + ) + num_points = db_session.query(table).count() + assert num_points == len(datapoints) == 2 + + @pytest.mark.asyncio + async def test_datapoints_can_be_mapped_back_to_DataPoints( + self, mut, db_session, table, model + ) -> None: + datapoints = await mut( + db_session, + [ + Deployment( + id=None, colour=None, name=None, uri="http://localhost", api_key="" + ) + ], + ) + db_points = [ + model.from_sql_result(result) for result in db_session.query(table) + ] + assert db_points == datapoints + + @pytest.mark.asyncio + async def test_daily_summary_view_matches_query( + self, db_session, model, table, daily_summary_view + ) -> None: + await apd.aggregation.collect.add_data_from_sensors( + db_session, + [ + Deployment( + id=None, colour=None, name=None, uri="http://localhost", api_key="" + ) + ], + ) + + headers = table.c.sensor_name, table.c.data + value_counts = ( + db_session.query(*headers, sqlalchemy.func.count(table.c.id)) + .filter(model.collected_on_date == sqlalchemy.func.current_date()) + .group_by(*headers) + ) + + daily_summary_view = db_session.query(daily_summary_view) + assert value_counts.all() == daily_summary_view.all() diff --git a/Ch12/apd.aggregation-chapter12-ex01/.coveragerc b/Ch12/apd.aggregation-chapter12-ex01/.coveragerc new file mode 100644 index 0000000..e766c69 --- /dev/null +++ b/Ch12/apd.aggregation-chapter12-ex01/.coveragerc @@ -0,0 +1,3 @@ +[run] +branch = True +omit = tests/* diff --git a/Ch12/apd.aggregation-chapter12-ex01/.pre-commit-config.yaml b/Ch12/apd.aggregation-chapter12-ex01/.pre-commit-config.yaml new file mode 100644 index 0000000..995b49f --- /dev/null +++ b/Ch12/apd.aggregation-chapter12-ex01/.pre-commit-config.yaml @@ -0,0 +1,25 @@ +repos: + +- repo: local + hooks: + - id: black + name: black + entry: pipenv run black + args: [--quiet] + language: system + types: [python] + + - id: mypy + name: mypy + exclude: (?x)^( + (.*)/setup.py + )$ + entry: pipenv run mypy + language: system + types: [python] + + - id: flake8 + name: flake8 + entry: pipenv run flake8 + language: system + types: [python] diff --git a/Ch12/apd.aggregation-chapter12-ex01/CHANGES.md b/Ch12/apd.aggregation-chapter12-ex01/CHANGES.md new file mode 100644 index 0000000..bd0d9b6 --- /dev/null +++ b/Ch12/apd.aggregation-chapter12-ex01/CHANGES.md @@ -0,0 +1,8 @@ +## Changes + +### 1.0.0 (2020-01-27) + +* Added management of known sensor endpoints +* Added CLI script to collate data +* Added analysis tools for Jupyter +* Added long-running data synthesis and actions system diff --git a/Ch12/apd.aggregation-chapter12-ex01/Connect to database.ipynb b/Ch12/apd.aggregation-chapter12-ex01/Connect to database.ipynb new file mode 100644 index 0000000..98a74f0 --- /dev/null +++ b/Ch12/apd.aggregation-chapter12-ex01/Connect to database.ipynb @@ -0,0 +1,577 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "53635\n" + ] + } + ], + "source": [ + "from apd.aggregation.query import with_database\n", + "from apd.aggregation.database import datapoint_table\n", + "\n", + "with with_database(\"postgresql+psycopg2://apd@localhost/apd\") as session:\n", + " print(session.query(datapoint_table).count())" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "53635\n" + ] + } + ], + "source": [ + "from apd.aggregation.query import with_database, get_data\n", + "\n", + "with with_database(\"postgresql+psycopg2://apd@localhost/apd\") as session:\n", + " count = 0\n", + " async for datapoint in get_data():\n", + " count += 1\n", + " print(count)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from apd.aggregation.query import with_database, get_data\n", + "\n", + "from matplotlib import pyplot as plt\n", + "\n", + "async def plot():\n", + " points = [(dp.collected_at, dp.data) async for dp in get_data() if dp.sensor_name==\"RelativeHumidity\"]\n", + " x, y = zip(*points)\n", + " plt.plot_date(x, y, \"o\", xdate=True)\n", + "\n", + "with with_database(\"postgresql+psycopg2://apd@localhost/apd\") as session:\n", + " await plot()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from apd.aggregation.query import with_database, get_data\n", + "\n", + "from matplotlib import pyplot as plt\n", + "\n", + "async def plot():\n", + " points = [(dp.collected_at, dp.data) async for dp in get_data(sensor_name=\"RelativeHumidity\")]\n", + " x, y = zip(*points)\n", + " plt.plot_date(x, y, \"o\", xdate=True)\n", + "\n", + "with with_database(\"postgresql+psycopg2://apd@localhost/apd\") as session:\n", + " await plot()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import collections\n", + "\n", + "from apd.aggregation.query import with_database, get_data\n", + "\n", + "from matplotlib import pyplot as plt\n", + "\n", + "async def plot():\n", + " legends = collections.defaultdict(list)\n", + " async for dp in get_data(sensor_name=\"RelativeHumidity\"):\n", + " legends[dp.deployment_id].append((dp.collected_at, dp.data))\n", + "\n", + " for deployment_id, points in legends.items():\n", + " x, y = zip(*points)\n", + " plt.plot_date(x, y, \"o\", xdate=True)\n", + "\n", + "with with_database(\"postgresql+psycopg2://apd@localhost/apd\") as session:\n", + " await plot()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import collections\n", + "\n", + "from apd.aggregation.query import with_database, get_data, get_deployment_ids\n", + "\n", + "from matplotlib import pyplot as plt\n", + "\n", + "async def plot(deployment_id):\n", + " points = []\n", + " async for dp in get_data(sensor_name=\"RelativeHumidity\", deployment_id=deployment_id):\n", + " points.append((dp.collected_at, dp.data))\n", + "\n", + " if points:\n", + " x, y = zip(*points)\n", + " plt.plot_date(x, y, \"o\", xdate=True)\n", + "\n", + "with with_database(\"postgresql+psycopg2://apd@localhost/apd\") as session:\n", + " deployment_ids = await get_deployment_ids()\n", + " for deployment in deployment_ids:\n", + " await plot(deployment)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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JeEsV+4xKEbE0IuaV+wJOGrbVrRExt8jlLgCmZZ3fDZwUEU8N66krIv4LOHvY+o+llHau4MuUmk93GcFo00sMRuuYVrwGYG7BjMORsfgW6Flb/rqO/kC0ji3z13SvghWPVNaXJEmSJEmSJEmSJEmSRputso47G9aFJEmSJEmSJEmSJEmSpIZrmmC0lNJuwA79p33ANVVsdxzVuxAAACAASURBVDXQ23+8a0ppp2p6GwtSShOAdw4bvqjImj2B92YNbQTOjYgN+dZExBUMDbUbB3yhvG6lJlVqMFr7FJiyV2m1Le1kMh6LKaWmxhZeUdm6gWC01vEwbqv8dcsfrmx/SZIkSZIkSZIkSZIkjTav7X8P4MVGNiJJkiRJkiRJkiRJkiSpsZomGA04oP89gKcjYm2lG/WvfTpr6MBqGhsj3g5smXW+Ari8yJp3Aa1Z55dHxLMlXOurw87PTimNL2Gd1Nx6SgxG2/VcSCV+PKeUCRArpq+rtP1qJfpg4VWVre2YNng86/T8dT0+HFiSJEmSJEmSJEmSJGkUiXKKU0rtKaWdUkrvBz6bNfVEbduSJEmSJEmSJEmSJEmS1EyaKRht56zjOTXYL3uPnWqwX7M7f9j5LyNiQ5E1bxl2/pNSLhQRTwH3ZQ1NBE4uZa3U1LpLCEbb9gQ48PPl7VtKMBrAytnl7VuNZffDhpcrW9s+dfD40K/nr+upOB9TkiRJkiRJkiRJkiRJJUop9eZ7ZZcB8wrV5li7AXge+CEwJWuvCp/GJ0mSJEmSJEmSJEmSJGksaKZgtMlZx6tqsN/qrOMpeas2Ayml3YHXDhu+qMiamcBBWUM9wF1lXPa2YedvLGOt1Jx68gSjTdwVTrgJTn0cXncDjJtR3r6lBqMtvKK8favxSjkfB8Ok1sHjjmmZsLhcejorv4YkSZIkSZIkSZIkSZJKlQq8Sq0r9or+Pf4M/HbkvhRJkiRJkiRJkiRJkiRJo10zBaOtzzquRZBZdtBab96qzcN5DL1J7eGIeLTImgOGnT8eEeWkFN097Hz/MtZKzak7TzDa+G1g5okw9UBoac1dU0hLicFonfPK37tSa57JPb7tiXDmosJrezcMPW+fnLuuZ235fUmSJEmSJEmSJEmSJKkSUbykKgl4EHhTRHSP8LUkSZIkSZIkSZIkSZIkjWJtjW6gDEuzjneuwX47ZR0vq8F+TSml1Aq8d9jwRSUs3W/Y+XNlXnpOkf2ksacnTzBavuCvUrWWGIy2rkggWS2tfjr3+Jb7ZYLgCtnqL4aet07MXddTThajJEmSJEmSJEmSJEmSKnQH+YPRjut/D+B+YEOeuuEC6AJWAk8Bt0bEndU0KUmSJEmSJEmSJEmSJGlsaKZgtBf63xNwYEppRkRUFGiWUpoBvCprqI5pQaPOG4BZWefrgYtLWLfHsPMXclblN3/Y+YyU0rSIWFHmPlLz6M4TjNZWp2C0l66r7jqlWng1LLk999zkvaClyC89s04det4+KXddz9rye5MkSZIkSZIkSZIkSVJZIuL4fHMppT4GQ9PeERHl3kcmSZIkSZIkSZIkSZIkSUM0UzDavWSeENlBJhztb4F/rXCvvwFa+o97gLuq7q55nTfs/LKIWFnCuqnDzpeUc9GIWJtS2gBkJzptCVQVjJZS2gbYusxlu1dzTalk+YLR2qsMRmsZV3rt6mdgyl7VXa+QlbPhjtPzz0/ZO/M+9VWw8vEcBQmmHTp0qHVi7r16OitqUZIkSZIkSZIkSZIkSTWVGAxHkyRJkiRJkiRJkiRJkqSqNE0wWkR0pZTuBE7qH/pESul3EfFEOfuklA4A/onBG7HuiojNMl0npbQ18OZhwxeVuHzSsPP1FbSwnqHBaFWmQwGZ0Lsv1GAfqfZ68gSjtVX5n370ll57zd5wTh+kVN0185l/cf65lnaY8erM8azTcwejvX3Zpr21D/+46deztrIeJUmSJEmSJEmSJEmSVCsXZB2X8kBOSZIkSZIkSZIkSZIkSSqopdENlOnr/e9BJpjrupTSUaUuTim9GrgWmEjmKZXZe26O3gO0Z53PAW4vce3wpKINFVx/eJhanvQjaYzozhOM1l5lMFpfmf/73XQcbFhS3TXzWfFo/rltjoeOLTPH+38GtnvD4FzrFnDsZdAxbdN1bRNz72cwmiRJkiRJkiRJkiRJUkNFxAVZr9WN7keSJEmSJEmSJEmSJElS82uqYLSIuAG4jUyoWQDbA3eklC5KKb06pZSGr0kZR6SULgTuBHYY2A64MyKurU/3o9L7hp3/OCKiwr0qWVfptaTm1DNCwWi9ZQajvXInXH8kdM6v7rrDda+BFwt8pO73ycHjti3g+Gvh1CfhddfDmQtgx7fmXteWJzNx+UNQ8UeWJEmSJEmSJEmSJEmSqpVSaqp7ECVJkiRJkiRJkiRJkiSNfm2NbqAC7wQeBrYjE6zVBpzb/+pMKT0NrOifmw7sBQyk6gwEqiVgAXB2HfseVVJKRwH7Zw31Aj8tY4u1w84nVNDG8DXD96zE94BLy1yzO3BlDa4tFdadJxitrc7BaACd8+COt8AbH67u2tk9XL5t/vnpR8DMk4aOpQRT98+8CmmbmH9u1ZMw9cDS+5QkSZIkSZIkSZIkSVItvZBS+hFwYUQsanQzkiRJkiRJkiRJkiRJkppf0wWjRcSSlNIbgKuAXcgEnUEm7GwScNiwsf9bymAo2nPAGRGxpB49j1LnDzu/LiJeLGP9qAxG6/93Wta/15RS8SKpFnryBKO1NyAYDWDFI/DyLTDzhNzzfb0QPbBxOfT1wLitgIDoha5lmZqWdpiwPdx+OvSuz3+tAz9fWY8A7VPzzy2+zWA0SZKkRupeDa0ToaW10Z1IkiRJkiRJkqTG2B74PPDZlNI1wA8i4oYG9yRJkiRJkiRJkiRJkiSpiTVdMBpARDyZUjoM+C5wNtDCYBjakNKs4wT0Ar8EPhoRq0a80VEqpTQReMew4YvK3Gb4P7+ty+xhEpsGo60sswepuXTnCUZra1AwGsAtJ8Lu74fD/xtaxw2OL7gC7nxLdX1lm7B95Wu3OTb/XE/VeYqSJEmqxJo5cPe7YNkD0DEN9v4oHPA5MHhakiRJkiRJkqTNVRtwBnBGSul54IfATyJiaWPbkiRJkiRJkiRJkiRJktRsWhrdQKUiYkVEvAvYD/gW8ET/VBr2AngM+Dqwd0ScuzmHovU7C8hOYloMXFPmHs8OO9+5zPXD65dHxIoy95CaRwT05AlGa68yGK2vimA0gDkXwsP/OHi+6s+1DUUDmFjuR0SWcTPyz1UTCidJkqTK9HXDzcfDsvuBgI3L4YkvwLPfb3RnkiRJkiRJkiSp/jaSuU9v4CGmCdgN+A9gQUrpFymlYxrVnCRJkiRJkiRJkiRJkqTm07TBaAMi4pmI+HhEHAxsCewJHNX/2gOYEhGHRsQnI2JuI3sdRc4fdv6/EdFT5h5PDTvfo8z1uw07/1OZ66Xm0rseoi/3XFuVwWgd0/LPvelp2Oms4ns8+31Y8Vjm+KmvVdfPcFP2LRxuVoqZJ+ce7+uqbl9JkiSVb8kdsG7hpuPzL65/L5IkSZIkSZIkqdG2Bz4JPMfgg0yj/3gccA5we0rpiZTS36SUqrxRRpIkSZIkSZIkSZIkSdJY1zTBaCmlmSml07Ne04fXRMTaiJgTEff3v+ZGRGcj+h2tUkp7AcOfwHlRBVs9Oez8VSmlLcpYf3SR/aSxpXtN/rn2Ku/3POgrucd3+SuYshcc8xuYelDxfa47GC5OMPfH1fUz3LGXV79H6/jc470bqt9bkiRJ5Xnq67nHX7mrvn1IkiRJkiRJkqSGi4jlEfH1iNgbeD1wOdA7MN3/noD9ge8AL6aUfphSOrT+3UqSJEmSJEmSJEmSJElqBk0TjAa8Ffhd/+uXQFdj22la5w07/2NEPF3uJhHxEvB41lAbmwauFXL8sPPryu1Baio9IxiMtv2p0JZjj13ePXi8199Wd41K7fMx2HKf6vfJG4zmLwWSJEl1t3F5ozuQJEmSJEmSJEmjUETcHBFvB3YEPg8sIBOKBpmQtARMBN4PPJBSui+ldG5KKc+NIZIkSZIkSZIkSZIkSZI2R80UjDaVzI1RCXggIjob3E/TSSm1Au8ZNnxRFVv+btj5+0rsYx/gyKyhTuCGKvqQRr+edfnnWidWt/eE7eCEG2DL/QfPj7wItj9lsGa38+Cgr1R3nXJtc3ztrtkyLvd434ba7C9JkqTC+nrgyS/D9X8By+7PXxdRv54kSZIkSZIkSdKoFBGLI+LLwK7A6cC1ZILRyHpPwBFk7l97MaX0rf77yiRJkiRJkiRJkiRJkiRt5popGG15/3sALzWykSZ2KrBd1vka4NIq9vsl0Jt1/taU0p4lrPvUsPPfRITpRhrbetfnn2ubUP3+Wx0Fpz0Jb18BZy6C3c8bOt/SCvt/Bt4VsM1x1V+vmJmvh5NuhdY8gWblas3zYOBePzokSZLq4t73weOfg2X3Fq4r9H2vJEmSJEmSJEnarETGNRHxJjIhaV8BXiYTigaZewETmYemfgSYnVK6NaV0VkqprSFNS5IkSZIkSZIkSZIkSWq4ZgpGyw5Dm9iwLprb+cPOL4mIzko3i4hngZ9lDXUAP00p5UkwgpTSGcC5WUMbgQsq7UFqGoUCIlprEIw2oGMqpFS4Zud31u56+Rx3VW33yxew1ttV2+tIkiRpU53zYd4vS6vtqfi3mJIkSZIkSZIkaQyLiAUR8S/ATsDZwM3DSlL/67XAJcCClNKXU0o717dTSZIkSZIkSZIkSZIkSY3WTMFoj5B5QiTAXo1spBmllLYFThs2fGENtv4CsCLr/DXATSmlfYZdf1xK6e+BS4et/0ZEzK9BH9Loli8YraUDUp0/ind9D0x9Ven1bWVkUU7aDc5cAK158xErk2+/3g21vY4kSZKGWjsPHvkUg78dL8JgNEmSJEmSJEmSVEBE9EbEbyPi9WTuA/wGsIzMX0YEgwFp2wKfAZ5LKV2eUjqmUT1LkiRJkiRJkiRJkiRJqq+mCUaLiBeAe8nc9LR3SslwtPK8F2jLOn8yIu6vdtOIWAi8FdiYNXw08KeU0gMppV+nlP4ALAD+C2jPqrsG+Fy1PUhNIV8wWuuE+vYB0LYFnPIAHPqfhet2OhuO+gmcMPwBvVm23A+Ovw72/Sc45Bvwxkdgix1q2y9Ay7jc431dtb+WJEmSIAKe+BJcvTu88OvS1xmMJkmSJEmSJEmSSjcF2BLIfmJeZL0AWoEzgNtTStellHavb4uSJEmSJEmSJEmSJEmS6q2teMmo8jXg8qzjMxrYS7N537Dzi2q1cUTcllJ6C/BTYOv+4QQc3v/K5VfAX0dEb636kEa1nlEUjAbQ2gH7fBR2OgtuPBo65w2df+2VsMPpmeOVs/PvEwHbvyHzGkmt43OP924Y2etKqo3u1TD/N7DmGdj6WJh1GqSmyeeVpM3T0nvgic+Xv65nbe17kSRJkiRJkiRJY0ZKaQJwDvBBct9bloDu/tcWDAakJeAU4LGU0jsi4vd1aFeSJEmSJEmSJEmSJElSAzRVIkVEXAH8mMxNTm9KKX03pdRs4W51l1I6Gtgna2gj8ItaXiMirgUOAH4ArChQei/w9oh4V0R01rIHaVTrHWXBaAO22B7e/ByccDPs9xk4+tfwtmWDoWgAbYV6jAJzNWQwmtS8NiyBG4+B+/8anvoa3HE63Pd+iL5GdyZJKmTexZWt6/G3eZIkSZIkSZIkaVMppf1SSv8FvAj8iEwoWhqY7n+9BHwR2BnYHvg7YHb/XPS/tgB+k1LavZ79S5IkSZIkSZIkSZIkSaqfZgwV+yCwBvgo8CHguJTSN4CrImJZQzsbpSLiLgZvIhvJ6ywBPpxS+ihwNJkb1GYCncAi4JGIeH6k+5BGpXzBaAVDx+qkpRVmnpB55VIwvK1OwWgt43KP93XV5/qSKvfsD2HlE0PH5v4E9vgAbHVUY3qSJBX34rWVrTMYTZIkSZIkSSseh9lfga6lMP0QOPCL0Dax0V1JkhogpdQBnEXmPr/XDAz3v0fW+W3A94DfRURv1hbfA76XUnoj8DVgv/7x8cA/kglOkyRJkiRJkiRJkiRJkjTGNFUwWkrplqzTNcBkMjc7Xdg/vxBY0j9XqoiIE2vWpIiIjcCtje5DGlXyBaMVDB0bJUZDj63jc4/3bqhvH5LK98Tnc4+/8FuD0SRpNOusMNPaYDRJo0FfL0QPtOYJ2ZYkSZIkjZzVz8INRw7+Pd7im2HxrXDK/ZBaGtubJKluUkp7knkA6nuB6QPDZMLQov94LfBz4LsR8VSh/SLiupTSrcAfgUP7179+ZLqXJEmSJEmSJEmSJEmS1GhNFYwGHM/gkyJh8CapgadI7tj/CkozcLOVJI2ssRqMFnX6CG3J88PsfV31ub6k2ptzERz69UZ3IUkaLgKe/3nl63sNRpPUQH3d8PAn4PmfZn4Af7tT4KifwLgZje5MkiRJkjYfj35q04cbLX8IXroetn9jY3qSJNVFSqkVeAvwIeB1A8P975F1Phv4PvC/EbG21P0jYkNK6d+BS/uHdqy6aUmSJEmSJEmSJEmSJEmjUrMFo+VisJmk0a+Zg9FaOgpM1ukjuHV87vF1C+tzfUmVib78cx1T69eHJKl0L90A97638vXdJf/8kiTVRgSsnQOrn4FHPwmrZg/OLboa7nwbnHRbw9qTJEmSpM3KxlWw8He55xbfZjCaJI1RKaWdgQ8A5wHbDAyTualk4MGnvcAVwHcj4vYqLvenrOM8T9mTJEmSJEmSJEmSJEmS1OyaMRgtFS+RpFGmp4mD0VKBj91CoUe1lC8YDeCJL8EB/1K4T0mN8dz/5J8zGE2SRqdnvlPd+t7O2vQhSaXo7YL73g/zfpG/ZsntmdC0KXvVry9JkiRJ2lw9/vn8cwsuh0O+Wr9eJEn1NIfMPX0DN24MPGUvAS8B/wP8T0S8VINrrRt2DUmSJEmSJEmSJEmSJEljUFMFo0VES6N7kKSK9DZxMFpBdbrPdNzW+eee+Dxs93rY6qj69CKpNF3L4YEP559v37J+vUiSCls5GxZeARuWwIu/L23N9MNg+UObjvcYjCapjuZcWDgUbcBLNxiMJkmSJEkjbc1z8Ox/Fyio0wOXJEmN0ELmBpJgMCDtDuC7wO8iomcErpkwHE2SJEmSJEmSJEmSJEkas5oqGE2SmtacH+Ueb9uivn3UWtTpHtPph0DbxPxBG8//wmA0abRZeGXh+baJ9elDklTYgivgrrOhr7v0Ncf/AZ79LmAwmqQGm3dxaXWd80a0DUmSJEkSmd+jRYHws8550NsFrePq1pIkqa4SsBb4OfC9iJg9EheJiPlkgtgkSZIkSZIkSZIkSZIkjWHeJCRJI617bf651gn162NE1CkYrXU8bH1s/vlnv1ufPiSV7tnvFZ7v3VCfPiRJ+UUfPPT3ZYSiJTji+7D9KfkDLg1Gk1RPS+8urW79opHtQ5IkSZJU/IEp0Qdrnq1PL5KkensK+HtgVkT87UiFokmSJEmSJEmSJEmSJEnafLQ1ugFJGvNeuTP/XPvk+vUxEmaeVL9rTZhZv2tJqs7838DyBwvXGJwjSY23+mlYt7C02ld9Gfb/DKT+fPW2SbnregqEAktSLXUtK7121VP55/p6YeOKzHH7pEwwtyRJkiSpPJ0LYMXDxetWPwVTDxj5fiRJdRUR+ze6B0mSJEmSJEmSJEmSJEljS0ujG5CkMW/xLfnntn5t/fqoxt4fzTP+kfr10DGj8HznC/XpQ1Jxs79SvMZgNElqvGX3lV67/z8PhqIBtE3MXefnu6R6Wf1M6bUrH4NLp8Kjn4bejZmx3o1w/4fhsulw+daZ16VT4OYTMz/QL0mSJEkq3cIr88+1tMOs02HfT8LkPevXkyRJkiRJkiRJkiRJkiRJkppWW6MbkKQxLfrgqa/nn595Uv16qcbeH4UFv4N1WeFju50LW9bxie7jtio8f/2R8NaX6tOLpPw2rsgETxTTu27ke5EkbaqvN/M53dcN976veH3HdDjhJkhp6LjBaJIabf2i8uq7V8GfvpoJRDvsm/DwP8BzPxha09edCTe//nA480XoWQPLH4It94cJM2vXuyRJkiSNNYvyBKNN2Rve9Of69iJJkiRJkiRJkiRJkiRJkqSm1/TBaCmlg4HTgWOB3YHpwGQgImKTry+lNBWY0n/aFRGL69WrpM3Q0nvzz+3+19DaUb9eqjFpVzj5Hpj/K1jzHGxzHOx89qYBGSMpX/jGgA0vw7qFsMUO9elHUm6rny6tzuAcSaq/zvlw22mwanZp9ft8DPb6W5i026ZzbZNyr/HzXVK9dC2rbN3T34Ldz4e5P8tfs2EJXNKe+T1v9GXG9vk4HPK1+v4+WJIkSZKawcaVsPi23HM7nV3XViRJo1tKaQZwFnAksC2wHlgI3ARcHxEbG9ieJEmSJEmSJEmSJEmSpFGkaYPRUkoHAt8CXpc9XMLS1wG/7T/uTCnNjIh1te5PkoiAP38r//yMI+rXSy1ssT3s+/EGNlDCR/yL18Eefz3yrUjK78U/lFZncI4k1d99Hyg9FG2398Gh38g/ny+0tmdt+X1JUiU2VhiMBnDtASUUReb39QP+/A3Y6kjY6azKrytJkiRJY82qp+CPZ0P05J7f4cz69iNJqpuU0lbAq4CtgS5gLvBkxMCTBobUJuAzwD8DE3Js93fA/JTS30REiX/hLEmSJEmSJEmSJEmSJGksa2l0A5VIKZ0L3Esm5Gx4Uk5ssmCoK4EX+tdNBN5W6/4kiQi473xY8Nv8NT4hvTwTti1e0+fDg6WGWngVPHlBabU9nUODJiRJI6trObx8Y+n1+/5T4fm8wWgGX0qqk64qgtEqtfDK+l9TkiRJkkarZ38Iv98fVj2Ze36LHWHaIfXtSZI04lJKB6eUbgReAm4ELgYuAx4BFqWUPpVSas2qT8DPgC8BW5C5Zy/7fr+B812Aq1NK767H1yFJkiRJkiRJkiRJkiRpdGu6YLSU0tuAixj69MgELAAeZdOgtCH6n0r566yh02vdoySx9B6Y+5P88y3t0LFl/foZC2aelPnnVkjrFvXpRaq13g3w+Bfg5hPg3vfB8oca3VH5etbBHWeUsSCge9WItSNJGmbVnyieI55ly30Lz7cajCapwTYur/815/2y/teUJEmSpNFo/cvwyCco+OdNs06HVPD2DUlSk0kpvRO4HzgBaGUw1GzgtS3wFeCqlNLAfYkfBf6y/zgY/MVjYE1kvVqBH6eU9hvxL0aSJEmSJEmSJEmSJEnSqNZUwWgppe3IPEESBm+S+h6we0TsAry1xK2uHNgSOK5mDUrSgBevKzy/3Rvr08dY0jENdn1P4ZpiwWnSaBQBt54CT/4rLL4V5v4UbjoOlj3Y6M7Kc80+5a/pWlb7PiRJuW14qfTaPT5YvKZ9Uu7xnjWlX0eSquH3kpIkSZLUOAsuh561hWt2KOdhKpKk0S6ldATwc6CNoYFmAwbOE/AG4OMppcnAFxkahvYScA1wMfAHYAVDH4TaDnx7pL4OSZIkSZIkSZIkSZIkSc2hqYLRgM8DW5C5GaoPODsi/i4inu+fL/A44iEeALr7j2eklHatbZuSNnuzv1x4vmNqffoYa474QeH5znl1aUOqqSW3w5I7ho71dMIz/92YfiqxZg6sW1D+OsMsJKl+1j5fvGbA9qcVr2nfMvd47wbo7Sr9WpJUKb+XlCRJkqTGmf2VwvPtU2Abn1EnSWPM94FWhgagrSRzH96D/ccpa+4fgXOAKf3rlwFnRMQOEXF6RPxlRJwKbAN8ENjA4L1/J6SUdq/LVyVJkiRJkiRJkiRJkiRpVGqaYLSUUiuZm6UGbq76akRcVsleEdED/DlraJ/qO5SkLK3jC8+3G4xWkZa2wvOPfw6i1IxMaZR45ju5x5//WX37GNC7AZbeDyufKPz/U/TB8kdg+cPwxBcru1bX0srWSZLKV06A7HYnF68pFPTbvar0a0lSpbpXNua6i65pzHUlSZIkabToXgvrFxWu2f5UaO2oTz+SpBGXUjoSOJTB0LOlwFuBrSLiyIh4NbAV8BZgSX/dtsDH+rfoBk6KiKuH7x0RfRHxIzL3BQ4EqwGcNXJfkSRJkiRJkiRJkiRJkqTRrmmC0YCjyDxBMpG5Wer/VbnfwqzjHavcS5KGaptceL5QkISqs+yBRncglWfB5Y3uYNArd8E1+8INR8K1r4JbT4aNKzatW7coM/+HQ+EPh8G8X1R2vY3LqutXklS6tfNKqzt9DrSOK17XMS3/XK5fOySp1no6i9cc+K+1v+4z36v9npIkSZLUTB75p+I1O5w58n1Ikurpbf3vA/ftnRwRV0QMPmkrMq4ETgF6+of3IhN09ouIeLzQBSLiKuDW/msAHF7D/iVJkiRJkiRJkiRJkiQ1mWYKRtuj/z2AByJidZX7Za+fUuVekjRUX3fh+egpPK/8Dvr3wvNzLqxPH1ItrH6m8Hz0jXwP616EZ38A938YbjwGOucNzr18Ezz6z5uuuee9sGp2afuPm5F/rstgNEmqi1fugpeuK153+H/DpN1K27O9QNDvxpWl7SFJ1ehZV7zmwM/Bdm+o7XVL+TyVJEmSpLGkrxfm/wae+kbm/bkfFK5vaa/978UkSY12aP97AL+JiMfyFfYHoF3CYMAZwGUlXie7bv+yOpQkSZIkSZIkSZIkSZI0pjRTMNrWWccLarBfdtJIWw32k6SM7rXQXSQMYtIeheeV345vLTy//sX69CHVwqJrCs8//ImRvf7yR+C6g+GBD+f/Qaa5P4buNYPnG1fCkltL2//Af4U3PQ3bvDb3fNfS8vqVJJXviQsywZfFpDbY629L37dtIqTW3HMGo0mqh57O0urGbVX7a/s5J0mSJGlz0bsx82dLd70DHvlE5r2Y3c6Dji1HvjdJUj3tlXVc5C+5Abh22PnjJV5nIHAtAQWewCVJkiRJkiRJkiRJkiRprGumYLTIOs7z09dlmZ517E8zSqqdNc8Ur5lx+Mj3MVZN2avwfPTUpw81r+7V8Nhn4eYT4YG/gzXPNa6X9YsKzz/9LVhXpGa46IOnvwPX7AMXp8zr0U/DxlWb1j72z9D1SuH9+jbCpVMyU3VtCgAAIABJREFU+9z3flh4VeYaxUzeCw74Fxg3Azry3LPetaz4PpKkys25CJ74Ymm1O59T3t4pQcfU3HMbV5S3lySVK/qgd11ptTNeXfvrryz15zglSZIkqck9/Z+w7N7S63d8Oxz81ZHrR5LUKNmJl0+XUD+8ptS/GM5+staUEtdIkiRJkiRJkiRJkiRJGoPaGt1AGbJTO7avwX4HZB2byiGpdlYXuQd062Nh6oH16WWsmn44LH8w91xfd317UXPp7YKbjocVj2TOF98CL/wGTr4HJu9e/3561havWXQN7PGBTADNgL5eaMmTE3v/h2DOj4aO/emr8MKlcNpsaB0PfT2ZIImXri+v3zkXZV6lOPCLgz2P2yp3zUa/BZOkvCKGfvaX6/lfZAItS5HaYM8Pln+N9mm5Qy67zR6XNMJ615deu/M5mZDIjcsL142fCa/5BdxyUvE9bzoOzumr7nNakiRJkkar7L+DmPuT0te9bRmMm168TpLUjCZlHed4ItcmVmefRMSGEq+TXdde4prNSkppArAvsA+wNZl/N2uB5cCTwBMRPlFQkiRJkiRJkiRJkiRJza+ZgtFe6H9PwCEppfaIqCj9JqW0FzAra+jxapuTpP+z+s/55/b8Gzjka/XrZazq68o/t2p2/fpQ83nx2sFQtAFdr8BzP4RD/l/9++npLF7zwIcyL4CdzoJlD8KGxTDzRDjyQhi/TWau8wW47dT8/w+snQu/nlCbvov5i1/ALucMno+bkbuua2nucUnanEUfPP65zK9NvRth1pvhsP+E8VuXsUdkQjFLsc3xcNCXYeujy++1Y2ru8Y0Go0kaYaV8Hz1g/Fbwhofg3vfCkjvy1237usz32G94EB77bPEQ4aV3V/bZKUmSJEmj1ZI74eGPwcrHYOrBsNffFf57z2w7vMVQNEka27KfENBbQn0pNaNWSmk34Ajg8P73Q4HJWSXzI2KXOvZzKHAmcALwagqHxnWmlH4NfDsivCdSkiRJkiRJkiRJkiRJTaul0Q2U4R5gPRDABOCcwuUFfSTreHFEPF1NY5I0ROe83OM7vg2O+C60bVHXdsak3gLBaBsWwyv3ZEJFpOGe+2Hu8acaFFhYTqADwAuXQufz0LsOFl0NNx6bCb/p64HrDh4dwYDH/BZ2fffQsbzBaMtGvh9JajZPXACzv5L5jOxZA/Mvhnvek/m8L9XGFbDqydJqT7q18mCf9im5x7tXV7afJJWqZ1159ZN2gZNuhzc8DLu/H1qHBQZ3TIP9Pp05nn4YvO4PcMJNhfec+9PyepAkSZKk0ahnHbx8Czz1TbjptbD8QejrhuUPZAKmS7Xl/iPXoyRJdZBSOj6ldH1KaRkwB7gE+ARwHEND0erZ0/iU0hzgIeBzwNEUDkUDmAicBzycUvqPlFKxekmSJEmSJEmSJEmSJGlUamt0A6WKiK6U0s3Am/qH/i2ldFVErCxnn5TS0cAHyQSsAVxewzYlCdYtyj0+cef69jGW9RUIRgO48TUwZW847vcweff69KTm8NL1je5gqJ611a1f80wmPGfFw5kQnEYbvy1s/4ZNx8dtlbveYDRJGioC5v540/GX/pD5odQZR5S2T9fS2vaVT8u43OPRXZ/rS9p8lRswPGD6IXDkj+CQr8H8X8GyBzPfw+72Ppiy59DamSfChO1h/Yu595pzYWYvSZIkSWpWz/8S7vnL2uw1Ybva7CNJUuMcDJzc6CaGaQN2yzEewNPAC8BSYBJwwLDaVuBTwJ4ppXdERM8I9ypJkiRJkiRJkiRJkiTVVEujGyjTv/W/BzALuCGltE2pi1NKrwOuIvN1J6AX+Hqtm5S0mVtyW+7xCdvXtY0xrbdIMBrA6qfLe5K9FH31v2algQ7ZHv8XWDBKcl6PvBDaJm463jEjd33X0kwIkCQpo3cDrFuYe272v+Uez6VewWitHbnHezfW5/qSNl/Vfh/dMRX2/DAcdREc/JVNQ9EGTD2o8D6r/lRdH5IkSZLUKGueq10oGsDkPL+vkiSp+XUBcxrdBJn7HK8D3glsExH7RsQpEfHuiDgjInYHDgfuGLburcAX69uqJEmSJEmSJEmSJEmSVL2mCkaLiPuAS8iEmgWZm3n+nFL6XEppb3J8PSml1pTSiSmlS4CbgGlZ678dEfPq1b+kzcDCqyF6c89NmFXfXsayUn+44pW7YO28EW1FY0jXsvpfsxbBaLW23SmVrdvmeJj1ptxz4/IEo/V15Q8AkqTNUc/a/HMLryx9n1KD0aYfUfqeubTkCUbrMxhN0gjrrdP30X1FQrl/vz/0ddenF0mSJEmqpQW/q91e42fCtq+r3X6SJDVON/AocCHwQeAwYDLw/gb21AV8F9glIk6NiF9HRM6/CIqIh4ATgF8Nm/qnlNLOI9ynJEmSJEmSJEmSJEmSVFNtjW6gAucDewOHkAk3m0rmqYZfBIb89HVK6SlgV6B9YKh/TQLuBj5dj4YlbSb6euG+8/LPb7F9/XoZ6/b7JNx+Z2m1y+6HSbuMaDtqEp3zC8+vXwTjt65PLwMKBeA0wgFfgAO/AE9/Gx7+x/LWzjot/9ykXfPPvXgN7Pnh8q4lSWNVscDMNXNg8u7F9yk1GG3vj5RWl0++YLQwJEjSCOtZV5/r9G4oXvPUN2B//4hRkiRJ0ii28EqY9yugD3Z8O0w/DB79ZG32Hr8tnHATtDTjrSeSpDJF//tRKaVditTOzD5JKR1L5n69YmYWLxkxPwN+EBGb/KFgSqW0PiI2AHtERMlPG4uI3pTS+cAxwI79wx3A2cDXat+iJEmSJEmSJEmSJEmSNDKa7u7UiFifUjoFuITMEw4HbrpKwDgGg88SmQC1/1uaNXcDcHZE9Narb0mbgVf+WDiEYoLBaDWz7QkweU9Y82zx2rveATufPfI9aXRb/xJce1DhmnULYdrB9elnQLEAnHra6+/hVV/MHO/6VzD7y9C1rLS1HdNh53fmn5+wHUw9EFY+sencikfLblWSxqxivy4svBL2/VjxfbpeKV4zYRbscGZpfeXT0p57vHdj7nFJqpXeOgWjbXcyLL27cM2T/wq7vAsm7lSfniRJkiSpHM/+EB740OD5C5fWbu8dzoRjL4fGhcVIkuovAb+qYM1tZdQP3N9XVxGxot7XLCYieoCSQ9Gy1q1PKf0E+HzW8OswGE2SJEmSJEmSJEmSJElNpKXRDVQiIpYCrwc+BSxl8GaoyHrPftFfswr4LHBaRKyuW8OSxr71L8PNxxeu6ZhWl1Y2C21bwIm3wc7nlFa/fvGItqMmMPen0L2qcM3tb4Ybj4XlD9elJQB61tbvWvlM2RsOvAAO/ebg2LgZ8Pp7YNbpxddv90Z4/Z2wxQ6F62YcmXt848rSe5Wksa7YrwsLryhhj3Xw6Kfzz0+YBTu+DU6+B9onldffcC0ducf7DEaTNMJ6N9TnOju9o3hN73p4+r9GvhdJkiRpLFnxGFx/FFw6DW54DSy+tdEdjU0RMPvfRmbvg/4djvmtoWiStPnJfmBpsVf2vXulrvEXltp5ZNi5T3OUJEmSJEmSJEmSJElSU2lrdAOViogAvpZS+g5wDpmgtGPI3MSTHfi2ArgbuB74eUQUSUWRpDJFH9xyYvG61i1GvpfNyRbbw9EXwy7vygRaFTL/Ytj7H/zhjM3ZsgdKq3vlj3DzCXDqEzBxx5HtKSITXjNS9vs0HPzvla+fsiccdyVcsTOse2HT+SMvhN3PL32/9im5x7vXVNafJI1FPZ2F51+5EzYsgfHb5J5fOxduOy3/+l3eDa/5ReX9DWcwmqRG6e2qz3W23Ad2OzcTtFzIgsvgkK/5e05JkiSpFOsWwQ1/kQkZBlh6T+bveU59Eibt0tDWxpw1z8K6BbXf99BvwT7/UPt9JUnNIoqX1GSNqtMz7DzPX+pIkiRJkiRJkiRJkiRJo1PTBqMNiIgNwE/6X6SUEjCNzM08yyKiu4HtSdocLLkdVv2peF3r+JHvZXM08ySYMAvWL8pf8/DHYMHlcNzV0DG1fr1p9Fj4u9Jru1dl6vf+yMj1A/0/9JXn/u9tjoONK2HlY5XtvddHqgtFy7bjW+Dpbw8da2mHHc4sb5+2ybnHe1ZX1pckjUU9a4vXXL4tnLkQtpg1ONa7Ae7+K1jw28Jrpx9eXX/DGYwmqVH66hSMBnDkRZnvz1++GeblCZfsnAcrH4dpB9WvL0mSJKlZzfvlYCjagJ5OmP3lzAM5VDul/FlTJWYcMTL7SpJGsxcw3KzZ7DHs/KWGdCFJkiRJkiRJkiRJkiRVaNQHo6WUDgJOBvYDtuofXgo8BdwYEY9k10dEAMvr2qSk8kXAK3dlwqzWLcw8HX59/+uw78D0QxrdYemWPVhaXUoj28fmqnU8HPkj+OPZhX/A45U/wqOfhlf/oPieq/4EL/4h84M5Wx8D27zWf3/Nrn0KdJcRwFVK2GG1etblnzvsv2DKPnDZ9MwPhJVr/09X3tcme30WltwJKx7OnKdWOOL7/H/27js8ivNe+/h3dlWQRG+miI7pxQ2DjSvYuANuiUuKE6cnTk+cN8mJneqT3k5OihM7iePesA1umGLjgjumY3rvIARqSLvz/jHmSEgzszO7M7O70v25Ll1in/pDoN3ZlZ57Ke7mb53Cjvbt9Yczq09EpDXxep//1pfgnCahn8t/nDoUDaB8enp1OYkV2rcnlU8uIiFLRBiMZsRg8E3Wx6m/h8d6gploOW7r4wpGExERERHx4uAS+/b1/4AJf4VYPNp6WjO75y6ZKu4O3SYFv66IiOQ00zQHZrsG8e2aZrffyEoVIiIiIiIiIiIiIiIiIiIiIiJpytlgNMMwTgF+C5zlMuwOwzBeAb5umqbHZCIRyRkLprV8R3iAI+vzKxhtybezXYH0uQQuXwXPnAR1+53HbboXJvyvdbDdyYZ/w+s3g9nQ2Db0M1YQlNs8yW0lff0Fo6UTRuZX0iXMIV4M8SIovwo23eN/7Xa90q+rxVo9YNorsPtFqNkGPc+FDs3fXNqDwg727QpGExFptPNZb+O2zbKChUv7WuFAa/7obV7ZoPRrsxMrsm9PHg12HxGR5tyupcNU3NW6Ht49v2XfnoWRlyMiIiIikpc23+/cd3gtdBoRXS2tXRg/6xjxDYXXiYiI5DjDMCYAk5s1P243VkREREREREREREREREREREQkV+VkMJphGDOA+4B2gNGkyzw2pEnbWcBLhmHcYJrmrIhKFJFMGYYVVHRkXcu+6u3R15Ou5T/NdgVyTGm5FV728oecxzQcgaot0H6gfX/tPlj88Zbt6/4GA26AE84NpNRImSZsvAe2PwmFHWHQR+GE87NdVfSaBt150XAknDqacg1Ga2d9nvAnOLofdjztb23DSD3Gj3g76HNRZmsUOASjNSgYTUTk/2z8t/exs8qtYDI/IWRBPz4oGE1EsiWRpWA0gL6X2wejVa6KvhYRERERkXxTtcW9v3aXgtGCFPTPOnqeByO+HuyaIiIiEijDMAqBvzZrXmSa5hsB79MT6OFz2pAgaxARERERERERERERERERERGR1i3ngtEMwxgB3I8VigbHh6HZhaTxwdj7DMM41TRNnUIUyRelDsFoNXkSjLbhX7D0+9muQprqOz31mMrVzsFoj7n8zubO5/IzGO2978HKOxpvb/w3TH4Q+l+dvZqyoaHK5/iQg9FM0z3MIVZsfS7sAOfNgdq9VpDavtfh5Wvc1x5wQ3B1BqnQIRitXsFoIiIAVO/wP8dPAFn5TP/rp6JgNBHJFreQ4WM6DAtn787j7Ntr90DdASjuGs6+IiIiIiL5rmYnzD3bfUzdvmhqaSv8/mzETdcJcMGC4NYTERGRsPwSOLnJ7XrgyyHs8wXgthDWFREREREREREREREREREREREBIJbtAmz8BSvozPzgwwAagNeAh4CHP/hzPY1BaeYHc5q/26GI5LLScvv26jwIRtu9EBbflO0qpLl4MczY7D7m0Ar79n0p3hy3bm96NWXT0QpY/Zvj28wErPhJdurJpoZqf+N3vQCvfgzqK4OtY92dMGc0PNIVXrzceVy8+Pjb7XpY95ll/VPv0fuizGoMi1MwWrIOEgrQERGheku46/f/UPBrxgrt25P1we8lItKUW8jwMeN/Fs7eHUc49x1aGc6eIiIiIiKtwfq7Ur/+4fQzHElPUG8CU9QVLlgYzFoiIiISGsMwPgl8pVnz7aZpLslGPSIiIiIiIiIiIiIiIiIiIiIimcipYDTDMMYA59AYiAbwa6CXaZqTTdO8zjTND5umORnohfUOh01NNgxjXHQVi0hGSvrat9fkeDDaoVUw7/xsVyFOyvpDpzHO/bvm2bdvech93aMV6deULTuft0Knmju4BOoPR19PNiWq/M/ZdA8svCy4Gjb+B974jBWWUF8BRzY4j40V27e7hTAAdBwJA65Lv8YwFXZ07tu7KLo6RERyVc2OcNfvd1Xwa8aK7NuTCrwUkZDZPc9pqsdk6HNJOHuX9IECh9Dfnc+Fs6eIiIiISGtw4O3UY5bdDqYZeiltRn1AwWgXvgIFpcGsJSIiIqEwDONirDekbWo2cEcWyhERERERERERERERERERERERyVhOBaMBV3/w2cAKR/uyaZrfMk3zYPOBpmlWmKZ5K/DFJuMBQjjtLSKhKHUIRqvaHG0dfphJmDMq21VIKm7BH7vnQ0OzkCzThG2z3Nesz8NgtEPLnfvCDl/JJcl66yMde1+GtX8Npo71//A+1ikYrdAhgOGYS96BuENITbY5hUcArP97dHWIiOSq6hDDgftOh7jDY0smFIwmItniFox28q/gvGfCO7RvGND9DPu+VM8rRURERETasm2Pexu358Vw62grTBPe+Wrm63QeC51SvGmLiIiIZJVhGJOBR4HCJs0vAx82zdBSZ/8XGOPzY0ZItYiIiIiIiIiIiIiIiIiIiIhIK5RrwWgTPvhsAotN0/xTqgmmaf4FeAUrHA3g9JBqE5Gglfa3b6/aBDW7Ii3Fs5euzHYF4sXwr4Dh8BCXrINdLxzfdmgFHFnvvubRFhmduc/t91sPvBNdHdnWPAjPrzc/B/WHM69jz0Jv44wCiMWd+/tcZt9+0n9DvJ3vsiJT0su5b9/i6OoQEclVNSEGo/WaGs66TsFoiZpw9hMROSbhEMA4+CYY+Y3UgcKZKnc4v3doORxeF+7eIiIiIiL5ql1Pb+O2PRFuHW3FlocyXyNeAhP+kvk6IiIiEhrDME4F5gBN3yniDeAy0zSrw9rXNM09pmmu8PMBpPjFHBERERERERERERERERERERGRRrkWjDayyZ//5WPev5v8WW9XLJIvuk1w7tu7KLo6vHrzi7D9yWxXIV4Ud4VrXYKs9r12/O09L6Ve88DbkExkVlfUjh5w7nv1BtjxXHS1ZFNDAL/rOzvDyws/b8IcL3bvH/zxlm1GHAbc4K+mqBWUOfdVbYL1d0dWiohITqreYd/u9dCwEyMO/T+U2RpOYoX27TU7INkQzp4iImAFXtuJpbiWDkr5dOc+hTiIiIiIiNjrPM7buM0P+HtNXextfTz1mME3WW825GT6BuhxZmAliYiISLAMwxgHPA90atL8LnCRaZqV2alKRERERERERERERERERERERCQYuRaM1rnJn9/xMe/YWKPZGiKSy0r7Qvsh9n07no62llTW/A+s/d/U44bdYt9+yu+CrUdSKyiFftfY99XtP/521WZva27PswPuO59173/jM5CojaaWbGqoynyNmh1w8L305/v5OqcKc+h3DYy9HYwC63ZhJzj7USjrl3Z5kRnzX859b34e6l0CDUVEWru6vfbtncamv2ZRFzh3NpT0Sn8NN7Ei575ts8LZU0TapqrN8M434MUZsPIXUH/IflxUwWil5dDVIez93W9GU4OIiIiISL4xPb75TO0uOLQi3Fragj0LUo8Zcxuc+AX7vs7jw3tNSURERDJmGMYo4AWga5Pm5cA00zQrslOViIiIiIiIiIiIiIiIiIiIiEhwci0Yrem7F+53HNXSwSZ/7hBQLSIShZ7n2rdvvAcOrYq2FidbHoa3HQLPmjrxC9ZHQdnx7UVdod9V4dQm7oo62bc3D8qq2eFtvc0PZFZPlPYthiMb3MdUb4Fd86KpJ5sS1cGss+k/6c91Cm6wE08R5mAYMPY2uLYCLl0GV++D8hnp1xal0v7Ofck62PZkdLWIiOSaun327Z1G+V9r7A9hxma4ajf0uTizuty4BqPpPl1EAnJ4PTw7AVb/BrY/CUtuhZ3P2Y9NdS0dpH4znfvW/CG6OkRERERE8oXXYDSwrv0lM7V73Pv7XAbtB0LHYXDClJb9J342lLJEREQkc4ZhDAfmAT2aNK8GLjBN0+EHTiIiIiIiIiIiIiIiIiIiIiIi+SXXgtGa1uPjN6OPG5trfycRcdN7mn27mYBN90Vbi51EHbz8IW9jT/kNdBoBU16AE6ZCcTfofRFMXQBl/cKtU+zFy+zbN98PidrG2zuf9bbeloczrykqS2/zNm777HDryAWJmmDWWfUreOFcOPC2/7l+gtFi7byNKyiDzmMgVuC/nmwp7Ojev2NONHWIiOQip2C0zmOg/VB/a5WWQ1l/iBVmXpcbt2C0TfeEu7eItB1rfg91e72NjUUYjFbuEoy29DYwzehqERERERHJB76C0drAzy7CVuDyfnKFneCsJj/zOucJGHIzlPSBzmPhtD/BiZ8Pv0YRERHxzTCMocB8oFeT5rXAFNM0d2enKhERERERERERERERERERERGR4OVRkoaItErlM6G4h/0h5/1vRF9Pc2/dknqMUQDnzIL4Bwewu0+CqS+EW5d4U+AQjAbw2sfhrAehdp9zEImdZEPuB1GZJux/3dvYw++HW0suaBqC19zMrTDLR3Dhnpfg2dOsA0MdhkGn0RCLp55X6zHIARrvS1qjQpeDWCIibZ3T9UhxDxj3Q3j1I4DHkB23a6AguQWjiYgE5f0/eh8b5bV0x5FQNhCqNrXsq6+Amh1Q2je6ekREREREcl2ywfvY/W+AmQRD74mWNrfA/JlboaCk8XZhe5j49/BrEhERkYwYhjEIKxStT5PmDVihaDuzU5WIiIiIiIiIiIiIiIiIiIiISDj0m8Qikl3xYuh7mX1f8mi0tTR39CCsv9N9TL+r4eI3nf8Okl1uoSBbH4GanbD2T/7WPLIhs5qiULsb6g95G7t7PiQT4daTbYka+/ZYMZSWQ8fh/td8+Vp4ZjzMKoe9r7mP3XgPvHC297VjrTgYrUDBaCIithK10HDEvq+4Gwy8AabMhUEfs9pSHQqOLBjN5YCtiEgQ/AQnQLTX0oZhvSbgpC2EUIuIiIiI+GH6+FmEmYBVvwqvltbONKG+0r7vzPv1JiYiIiJ5yDCM/lihaE3f+W0zVijatuxUJSIiIiIiIiIiIiIiIiIiIiISHgWjiUj2lQ106EhGWUVL2+e490/6F5z9CHQ5KZp6xD+3UBAzCQfehTW/97dm7Z7MaopC5Sp/49f+OZw6ckWi1r493s76PPFuiJc2tvsJ76rdBS9eDg0O4WsVy+C1j3lfD6zAyNYq5WErI5IyRERyTt1+577i7tbnXlPhjH/BDSZcn4Bzn3KeU9A+2PqcOIWPiogEpWKpv/FRX0uP+5Fz37wpsPVxSGQ59D0f1eyG9f+A9XfB6t/Cxnvh8LpsVyUiIiIimfITjAaw5FZdB4IVcLblYVh6O7z9det5Rv1h9znJOjAdgqbL+tm3i4iISM4yDKMPMA8Y2KR5O1Yo2uasFCUiIiIiIiIiIiIiIiIiIiIiErKCbBdgw/zg8yTDMAZ6nNOr6Q3DMM7GR7KGaZoveR0rImFwyGjck+VvzT0LnfsKOsBgn2FHEj23YDSAd74KRw/6W7PhSPr1RMXvQaGtD8PwL4VTSy5wCm05FozW4wyYsQm2zYJYEfS+CJ6bCNVbvK1/9ADsfAb6XdWyb8vD/uuNteFgtM33weR7o6lFRCSX1O1z7jsWjNaCS855ZI8lLk+7DeWwi0iGDq2GZ0/1Nyfqa+mCUuh2Oux/w75/0VXQ9TSYMheKOkdbW77a+yosuKjlc+94iRUQ2v/a7NQlIiIiIpnzG4wGsOleGHtb8LXki6rNMG8qHFnf2Lbmt9BpFJw7G9oPcpjn8vONwo7B1igiIiK+GIZhNms63zTNhS7je2KFog1t0rzzg3kbgq9QRERERERERERERERERERERCQ35GIwGlinq+/PYO5CH+NNcvfrINI2xOLOfQffgy7jo6vlGNOE9f9w7h/00ehqkfSlCkY7vNb/mg2H06slSvWV/sZnO4QwbIla+/Z4SeOf2/WAoZ9uvN3zHNj0H+97LLoaupwEB5ekV+NxdbXLfI1cVZAiGA2gaiuU9Qu/FhGRXNJQ5dyXzmFVw3NOeGa6T3LuM5NQ+T50HBZNLdK6HdlgPT/b/6Z1kL5sIAy4DnpfmO3KJExL/8v/nE6jg68jla6nOgejARx4Cx7pAsNugZHfhLL+0dWWj16/2T6QPFEDb34e+s6AeFFj+5GNsOrXUF8BJ5wPg25yf50pSGYS1t0Ju+dDSW8YcjN0HhvN3iIiIiL5KJ1gtD2Lgq8jnyz57vGhaMccWgnLfwKTHH6WufLnzmt6eZ1eRESkjTIMoxz73yPs1ex2gcsbvh4xTdPlHXF81dMZmAuMaNJcBdwM1Pt401kATNPcFERdIiIiIiIiIiIiIiIiIiIiIiJRyNVAMBMr4MzvnGMiOgUuIoFINjj3bX0sO8FoS2517x/2xWjqkMykCkZz0uVkqNsP1Vta9tXnQTBasi7bFeQWx2A0lwCy4V/2F4wGwYSiAcdf0rQyXsJ9nugP19VDLFcvU0VEQuD0WAUQK7Zvb9fDeU5h58zq8aowxUHauWfC1IXQeUwk5UgrVbkWXjgHancd377hbph0Fwy+KStlSci9gnImAAAgAElEQVQStbDtcX9zOo1yD2wMy5BPwbq/pQ55eP+PVsDfpe9Bh6HR1JZvqrdD5Wrn/rr9sPdl6DXFun1oFTxzcuNz4E33wu6FcOY9oZcKwOJPwMZ/N95efxdMeQG6nx7N/iIiIiL5xumauXwmbJtl37d7HtQfgcL24dWVy3Y+49y3/Unnvg13OfelE8IvIiLSdrwMDPAwri+w0aHvX8BNAdVzEjCuWVsZ8HSa6+l3KkVEREREREREREREREREREQkb8SyXYAL0+dHOnNFJBeYSee+Qyuiq+OYimWw6pfO/ePvsA5cS+5LNxit/4ecgz7yIRjNLVzFSUN18HXkikSNfXu8xHlOtwkwZV449aRS2Ck7+0Yh7hDu09zeV8KtQ0Qk17iFeBoOZ1S6nALterZsL+0PHYcHV1sqZ97n3Fe3H9b8IbpapHVa+78tQ9EAMGHpDyCZIoxK/Nn7Ksy7AGb1h/kXQuX72amjck3qoLFjjALofy1MXZCdcN2up8CEv3gbm6iGp06E+wvgiYHw7rchWR9qeXmlelvqMUfWwXvfh/sMmDOqZTD4pv9YffOmBBhebePQquND0QAaDru/niQiIiLS1jld4/e+GPpd4zxv0VXh1JPrErVw9KBzf90+++fEqZ4npwq6FxERERERERERERERERERERERERERyQFZOC3oagsKLBNpe0r7OPfV7IyuDgDThKebv9lqM4M/EU0tEoA08z+HfQm2zbLvaziSfjlRSdSlHtNc3T4o6B98LbnALWzGTa8pcIMJtfvgsR7B1+WkqHN0e2VD/w/Blofcx2y6B044N5p6RERyQdLhsSrm8lgVi8OYH8BbXzq+fewPnMPUwlDUxb1//Z0w8W/R1CKtk1tgavVWOLIeOg6Lrp7WrGoLLLio8TlP9VZ49jSYvh7aRXg9DLBvceoxZz0C/a+2nsdHeb9nZ+inoL4C3v2Wt/FmAqo2WyFa1Vth8v3h1pcv3ILzj3njs97W2r0A5p4Fl62Csn6Z1WVn07327VsfgcRRiBcFv6eISNSObICa3VDSC8oGZv/xVkTyX7LBvt2Iw+jvWtdSdnbNtUJvu5wUXm25qG5/6jENlS1fm6na5D4nVph2SSIiIiIiIiIiIiIiIiIiIiIiIiIiIlHJqWA00zQHZrsGEcmCXhc490UdQrVrrnt/9zOg5IRoapHMJev9zzlhKhS2h8IO9v0NhzOrKQrJNIPRylprMFqNfXu8xNv8dt2hx2T3UI4gnTA1mn2yZeS3Ugejrf8HjL8j+gAOEZFsSTfEc9gXobQfbHnECkrrdy30vTT4+twUlEa7n7Q9Ttdyx1SuVjBaUJb/pOVz8IbDsOk/MOJr0dRgJq1wsdW/cR939uPQb6b151wJaRn5Teg8HhZM8zdv8wNQ0AFO/wsYaYZ7txZBvwbUUAUb7oKxtwW7LsDWR537HiqF0++EIQrWF5E8VbML5p4NR9Y1tpUNhClzocPQrJUlIq2AmbBvjxVAl/Huc9f+xbpmbku8BKMdPdQyGK1ytfP4XhdmVpOIiEgrF8XvLpqm6fkFTdM0FwI58gKoiIiIiIiIiIiIiIiIiIiIiEi02vhpOxHJCe0HQ8eR9n01O6KtZeN/3PtP+V00dUgwup6K798R7THZ+lzgEIxWnwfBaIk0gtG2zwHTDL6WXJBu2ExTp/wWirsHUw9AcTe45gAM/Ojx7f2vhf7XBLdPLup2Goz+bupxb381/FpERHJFJo9V5dPhzH/DpLujD0UDiHsIRks2hF+HtF7Jo+79bge+xbtkAtbfad934O3o6lh3Z+pQtFP/0BiKlmt6XwhXrEs9rrn1d8KSW4OvJ980VAW/pluAWSbcXhswE/D6J+HlD8O2J9N7ji4ikk2vXHd8KBpA1SZ46kRYcAm8ciOs+Bls+CfU7MxGhSKSr5yC0Yy4FRJ8+l+d5+5/M5yaclndvtRj6g+1bKva7Dy+rYXLiYiIiIiIiIiIiIiIiIiIiIiIiIhI3lIwmojkhtP+YN9ety+6A6TV22DTPc79Az8C3U+PphYJRrvu0OMsf3MG3mh9LszjYLSkQ7iKm2U/gLe+CGYy+HqyzTFspsT7Gt0mwKXLrNCZCX/OvKaZ26GoC5zxT5i6EE7+NUydD2feB7HCzNfPdeN/Ct0muY/Z8gDU7o2mHhGRbAvisSpbCjwEox09GH4d0nolUzwfVDBaMA6/79y36d7o6th8n3t//2th+C3R1JKuDkPgqjSuY1f9yvpoy8IIRqtYBttnB7+ul1q3PAQvzYC5k6HuQPA1iIiEoe4A7HnRuX/ns9bj9Xvfg8WfgNmjYLfLeBGRptyC0QCGfsZ57uE1rffNXZx4CUY7WtGyrWa7/djOY603qxIREREREREREREREREREREREREREckDCkYTkdxQ0se5r3ZXNDW8+QX3/vKZ0dQhwZr8AJQN8Db2jHug4zDrzwXt7cdsugcWXg6vfwpeuQGenQD3GfDC+fD+nyDZEEzdmUg3THDtn2H3wkBLyQmJGvv2eDt/65T0gsE3wYmfg37XpF/PkE9DvNj6sxGDE86FkV+HE86HWEH66+abY99rTsxkOAEGIiK5yDEYzedjVTbEPQSjeTnIK+IkedS9f8PdUBPRc8bWrGaHc19pv+jq2POSe/+A66OpI1PtusM1FVA+w9+8d78Fc8bAyl9Ag8PzmNas4Ug46y6+Kfivp59A8gNvw4qfBbu/iEhYanb6G19fAW98GpIOYUciIk2lCkYDOOsR+zENVdYbHLUlXl5Pafoz1K2zYPEnna89O44Kpi4REREREREREREREREREREREREREZEIKBhNRHKDWzBaze5oajjwlnNfURf/B5olN5T2gekbU4+7fDUM+kjj7cIOzmN3zIH1/4DN9zf+v9mzEN76Eiy6Ckwzo5IzlkwzGA1g+1PB1ZErwgibOfHzgOF/nlEAI7+Z/r6tSddTU4/Z92r4dYiI5AKncJV8CEYr8BCMtuOZ8OuQ1itVMBrA02Ogcm34tbRmRw8499UfiqaGnc+nHtNpdPh1BKWoE5z9OMzcBuc/B+c/D6XlqecdWgFLboV550PCw///1qShKpx16/bD7nnBrddQ7fw808mme6zwZxGRXGfW+59zeC3sfz34WkSk9fESjNZrqvP8vYuCrSfXHVqeeswr11lvlrPiDlh0pRUe7qS0b3C1iYiIiIiIiIiIiIiIiIiIiIiIiIiIhEzBaCKSGwo7OvclQjoY21QyAbUuAWyXLoVYQfh1SDgMA3qe69zfbSJ0HH58W3HP9Pba/hTsmpve3KA4HdAe8fXUc9f8LthacsGWh+zb4yXpr9lrCpz9KLQfat8/6ONw9mPQ5SQwYtbBri6nwJQXoOOw9PdtTfpdlXpM5Zrw6xARyQVhhHhGpahb6jFLvx9+HdJ6eQmGqtsPq38Tfi2tWd1+5776Sqg/En4Ni1JcH5b0hvZDwq8jSIZhhQ/0nga9L4STfu597v7XYdkPGm8nG2D17+Cp4fDkEFj6g/CCxLIlzL9PkCGddfv8z6ndAweXBFeDiEhYkmkEowHMnQwPlsAzJ8OmB4KtSaQ1OloBi2+Gx8vhuYmw8V5/87c8as17sBQeaAcPd4b5F0KFhyCtbPISjFbUGcoG2Y979Uao3Rt8Xblqx7Pexr3xWXjvu6nHlSgYTURERERERERERERERERERERERERE8odSfkQkNxgxK6QoUdOyr6E6/P3rK8BM2vdN/DuUlodfg4Rr1Hdg36stD/cZMTjtjy3Hl2ZwQOSdr8FlK9Kfn6lEnX17rBj6ToftT7rPP7weOgQcONBQBZWrrQNOnUZDrDD1HNOEqs0QK4K6Pdbfy4hDxxFQ2N7bvrsXgNlg31fgcQ0n/a60Pkzzg3o/uA8xYlYAwrExyQbAgFjcdpk2y8v9auXq8OsQEckFTsFosTwIRvPy+FbYIbz9kwk4sg7KBkK8OLx98k3VFuvrXtQl25VkLulwbdvcTo8HxsWeWzAaWEFQXq/B01H5fupQrGG35P81df9rYcUdcMhjYMXKn8Pwr0BBB1h8E2x9tLFv+Y+tsK3T/xJKqVnREGIAX5ChZOkEowEcWgVdTwmuDhGRMCQ9hNI6SdRa97evXm+FPPebGVxdIq2JacLCS2Hfa9btmu3w2kes174HXpd6/rYn4eVrjm9L1sGuF6yQwivWQrs03/glbE6v1RvNrvP7Xgbv/4/92HnnwSVL8/+5QSrJBqja6G3sxn95G6efdYqIiIiIiIiIiIiIiIiIiIiIiIiISB6JZbsAEZH/U1Bq357qcHQQ3A619poW/v4Svj4Xw9QFMOTmxrZBH4dLlkC3CS3Hl2QQjHZoJay/O/35mXIKj4i38xZMsum+4GoxTVj2I3i4Ezx7GjxzMjzSBTakOKhzeD08PQ6eHASz+lrznp8Ez02ARzrBe99zDjM8JlEL86Y493ce5//vY8cwrI9Y3Po4Fop2TKyg9R/SStcpv3Pvr9sLdQeiqUVEJJucgtHieRCMBlbAsZuwgo63z4bHesDsEfBIV1j163D2ySdVm+Hpk+CJAfBIN1h0DTTYhE/nC9P0Hs5RtSm//67ZlioYLRFyYPnO5937O46EEd8It4YoxArhotdhzH95n/N4H3i4w/GhaMes+ysc2RBcfdkW5us/h9cEt1aq7xcnr30EEhkEDomIRKH5m0qka9GVUB9i4KVIPju0vDEUram1/5t67rYn4KUZzv31lbDG5o1gcoWZsG83mr2XW7lLsOKhlbBjTnA15SqnELlMdDst+DVFRERERERERERERERERERERERERERCUpB6iIhIROKlgM3h0rAPYIN7MFpxt/D3l2j0mGx9TPx76rGl5Znt9fonYdfz0OFEqN4KBe1h0Meg62ktg7OClnAKRiuGAg/BaLueh7E+Durbqa+0wg1W/Qr2v358X0MVLL4JijpDuc0hLtOEl2ZaB8TsmElY8TOoWA5nP2YfOnb0oBXE5sQogD6XeP7rSEi8fC9UroEeZ4Rfi4hINuV7MNqoW2HZ7c79DVXW43uQ10BHNlrXC8cOVSeq4d1vQodhUH5FcPvkE9OEhZc3uYYyrSCldj1hgocD9rnI70Hwg0t03ZCuoymCnsIKrDJN2D0flnzbfdzZj0G8KJwaolZQCuN+BKO+Aw+VZb7egovh7Eeh89jM18q2hMP/s4Efsf4Pbnu8Zd+wL0O3063nmG73GXX7Ye9rwdxHuL2GlMrKn2f+fFtEJExuobSxQn/BaQ93gEl3Q68LMn+tVaQ12XiPffveRdbnhmrYvQCqt4ERs15rNxNQtQXW/in1+it+Ah2GQlEX6D0tt15bcAxGa/Yaf89zoLAz1FfYj3/rFqjZCV1OtsK+jFb4XnDJgIPROo2B9oODXVNERERERERERERERERERERERERERCRErfC3hEUkbxWU2rc3RBCMtvcV+/Z4iXNd0rqV9Ml8jc0PwPIfw4Z/wvv/A8+dDm98FpIOh3+CknQIV4kVQ2HH1PP3vgzb56S//+F18PRJ8PK1LUPRmnppJmx9rGX7wXecQ9Ga2v4kzCq3Drg3VbEMnhoORzY4z+15thXMJtnldBCuqcNrwq9DRCTb8j0YbeCNKQaYzn/HdG191P5xZJvNtUVbcXit/TXUlkes8Kl85BT462TumVC1OZxaWrtUQU9hPC9vqIYXr4D5F0Cixnnc+J9BpxHB759tBaXBBLEfXgtPj4cVd2S+VrY5BfAVlMGQT7ZsjxXDsC/CoBvh0qVwyu/gpP92Xn/umbD2r5nXmUkw2sZ/Zb6/iEiYnILP4iVw2Uo49Q/+1lv8CZg9ErY/nXltIq2F27XEkU3w/CR48XJ483PwxmesEPAlt3oLRTtm8U3w0gzrZxJHNmVYcICcXg9u/uYnsUIY+hnndaq3WF+f5yda9zNBh4jlAr9B4amM+Fqw64mIiIiIiIiIiIiIiIiIiIiIiIiIiIRMwWgikjviDgFkiQiC0Zbcat9e3D38vSU3xYuCOaTe3Po7Yeczwa/blFOARLwYDI8P/S9/CKq2+tt36yxYfDM8dSJUbfQ2Z9HVUF95fNuOZ73vWbsLFl5+fNsbn4O6ve7zJj/ofQ8Jj5cDa5Wrw69DRCTbnEJN8yUYrcNQOOMe9zFOYTfpevdb9u0b/hnsPvlky8P27XV7gw+mi0ryqP85Cy4Jvo624Mh69/5558GCi+HQysz2aaiGlb+E+wx4qAx2eAhkHvGNzPbMZT3OCWghE5Z+Hyo8BEznMseg0BLocymMvR2MD0IzirrC6X+BjsOs251GwoivwMhvQ0F75z3e/Bwsvd3//WLVVlj2Q3j1I7D61/7mNnVkffCPiSIiQXIKRosVWtf9w2+BGVug6wTvazYcgRcvgwaXIFSRtsTtzUve/or1xh9BqVgGK34S3HqZME0wk/Z9Rrxl25jve1t3479h2xPp15WrnO6P0zX4pmDXExERERERERERERERERERERERERERCZmC0UQkdxSU2bc3hByM5ra+gtHatqIQgtEA1v0tnHWPSToEo8XaQf1hb2skqp0DR+ws+zEsuhI23OV9zjGP9z3+9lKPB56O2b+4MUztaAXse819/Cm/hXY9/O0h4Sjrn3rMkQ3h1yEikm1O4SyxPAlGAxj0Ebh0qXN/QiEwWZWvITzpBKNVrmqdh+LDlGyAwymC0QB2PgdzRsPuF9PbJ1EL8y+EJd/2Pue8Z6zQ6tZq+C3BrWUmYWOKkMpclyrke+xtcOUOuOQ9mLHRPtzBMKDjcPd9lv8QXrzCe9jEkY0w90xYdjtsuheqNnub56Ty/czmi4iEyen6K1bY+OeyfnDRYrhspfVYPejj3tZ+fmLm9Ym0BoWdnPu2Pxn8fpvudw4ki5JbDXbBaIUd4FqPP8/Y8XR6NeUy08Obinh1yRLvb5ojIiIiIiIiIiIiIiIiIiIiIiIiIiKSI/QbsCKSO+Kl9u2JkIPRKtc497UfEu7ekttiBeGsu/0peOOz3kPKjknUwrvfhgdL4D7D+nhxOhx4p+U4O/FiaPCx55YHoWZ36nH1lbDip97Xba7hCDzaEyrXetvPzsJLrINVdfsB033s8K+kt4cEr89lEEsRdHH0YDS1iIhEYetj8PxkmDUAXvs41B2w2hM19uPjeRSMBlDsEjxasSK6Otoqu4Pkx7z1Je8BQLkknWA0gDc+F2wdrV3VJn+H7t/5Wnr7bJ8N+171Pj5WCN0mpLdXvjjhfDjnCSvIK9V1sRerfpEboRfpcgz5Lm78c7ue0GUcFHZ0XqfDsNR77XoBHiiCF86HJd+BuWfB7JHwyo1Qu/f4se//Caq3pV7Tq8rVwa0lIhI0p2vG5o9TRgw6jYQ+F8Oku72tXbEs83BJkdbA7TomDIlquD9u/TzhqeEwfxrsez3aGgDMhHOf0/PZwvbQeVzqtTfcBfVH0qsrVyUDDEbrMj64tURERERERERERERERERERERERERERCKiYDQRyR0FDsFoDSEHo70007lv7A/C3VvarnV/gxcvB9MlxKuhCmp2QaLOChOZMxpW/fL44LPtT8H8C44PFEu4HCbvc5m/Or0EF+xb7HyA3au6vTB7GGx/Mv013v5K6uC3S5eDYaS/hwSrsD2MSBGscbQimlpERMK27SlYdI312Fq9BTb+G+adbwXYNA9gOSZeEm2NmSooc+578TL/obDij1sw2pYHYfHN0dUSFKfrWoBupzv31e6CyveDr6e1qtnhb/zBd/1/fU0T1v7F35yBN0JxN39z8lH5dLh8NVxXBx+uzTwg7dUbg6krG5zCEP1+TToO9z52z0JY+XPY+4oVWLb5Ppg7GZJNgjtW/9rf/qnUbA92PRGRIDndFxuFznMMA6avh4IOqdd/73vW65xBBv6IiHeH34ddc2H+1OiDCt3CmA2XN6mZ4PF5xCvX+asn1/kJr3bT5/Jg1hEREREREREREREREREREREREREREYmYgtFEJHfEHYLREiEGozXUWMEUdow4dB4b3t4ie16CimX2fWv+CLP6w+O94cF28PQYOLLBfuzRg7DiZ423nULK4u2g9zT3Q0bNpQpJ2PsKLLjI+3qpvPGZ9Oeu+yvU7XPuv2QJdB6d/voSjvF3wMS7nPsVjCYircXaPwHNAlErlsKuF+DwGvs57QeGXVWwnK7nj9l8fzR1tFVuwWhgff2dQvhylVMwB8DZj7vP3fFMsLW0Zsl6/3N2Pu997KGV8FhP2D3P3x4n/cLf+NYgXgxX7XIf0/0M6Hqqc/+Wh8MPmA+LUxhivNjfOt0mZlbH4bWZhXan0lAT3toiIpkyHa4LUoVUth8Ml7wLQz7tPm7TvdbrnI92g033pVejSL5L5/r7mLKBx//cqvsZ6a3TUAXLbk+/jnSYCec+t+ezPTz+HXfMgQPv+KsplwUVIHni54JZR0REREREREREREREREREREREREREJGIKRhOR3FHgEKQQ5oHeytXOfX31LuoSgbdvafw/3lBjBQwsuhre/jIcPeB9nff/AKYJdQcg4XDIuqA9FHWG0/7gfd3qrdbn+iNWuMTGe6Dmg4P6Rytg/jTva2Wq9yXW38FJsh62PWXfFyuGLuPDqUsyYxgw5BNwpsNh2PpD0dYjIhKGhirY+Zx934KLIFFr39dxZHg1hSEWt4JYnWz4Z2SltEmG4d5vNsDWR6KpJShuwWgFpXC1SyjuoeXB19NapRPM8O43YOfc4++/qrbAhn/Bqt/Ae/8FS38Ai66FOaPdA4ztDP4ktOvhv67WoKgLTN8Ipf2bNBpw8q/hBhOmvQoXvwXdJtnPNxOwe0EkpQbO6Xs+VRhPcyec7/7c0Yt9r0HFcnj32+nNL+7m3Of0nF1EJBc4XRfEClPP7TAEJv4Nrk+mHltfCa/eCK9/yrp2WPtXqFjhr1aRfJVuMFpxd5i+AS5dal0X3mDCBS9CvCS99Tb8M7jwLS/SDUYD6HWBtz1W3OG9nlxnBvBv0/9a6H1x5uuIiIiIiIiIiIiIiIiIiIiIiIiIiIhkQUG2CxAR+T9xh2C0RIjBaIffd+4bdFN4+0p+KBsEh1aGu8eel+C502HS3dZBwIql6a/1/p+sQBInHYZan0/8PHQ/A1b+EjY7hFEds/LnMOB6eOW6xiDBWBGc/TjU7fX+/dn7Ihj6OTi8BpZ8x9ucpkp6w/lPW8ELz57q/O+y/Un79sIO/veUaBV1tm+vr7BC/1KFvYiI5KojG2G+xwO8zXUcEWwtUYiXOAe97XstmD0aFChjyy1E7JgjG8KvI0huf6dYkRWONuRTsP7vLfvdQrDleOkEMySPwoJp0HUCnDcbdr0Ar308mMP7RgyGfSnzdfJZ+4Fw6RLY+pgVSN3zHOg24fgxJ34e9i+2n//i5XB9wvpa5pNknX17rNjfOvFi6+uz6pfp17Lql5nNv/hd67lr3d6WfQpGE5FcFkRIpWFYwU1Pj0s9dv0/mk6E8T+B0d/1vpdIPjLTDEYbcEPL10hjhdbr9xvuSm/Nfa9Bz7PTm+tXJsFoA2+0nnOksvURWPVrGPkNf7XlonSeW53yWyjqar3hTZeToc8lel1dRERERERERERERERERERERERERETyVp6djhORVq3AIRitIcxgtLXOfeXTw9tX8kNUh/EPrbDC0TIJRQN4+xZ48wv2fQUdoKRP4+0uJ8G4H3lb95mTjg+WSB6FV2+AQ8u91zbx79BvJoy6Fdr18j7vmP7XWZ/j7eCS95zHVW2yby/s6H9PiVahQzBasl7BASKS3+ZNSS+MqmwQFLYPvp6wHT3o3m+ame/RcCTzNVojL4FxtTYBPbnMKSQJGoOSup5i37/3Zag7APWHYdWv4OUPwRufg13zYevjsPhmeOUGWPsXSLjs0xZkEmZ24E1477uw+BPBhKIBnPJ76HpyMGvls6IuMORmK9SheSgawKCPuM/f8nA4dYXJ6Xsx7jMYDWDsbZnVkq54KZz1EJT1g57n2o9J9VgpIpJNToGpsUJ/63QaA+2H+tzchPe+DwczfI1UJNelE0zc+yIY/2P7vpN/kX4tL5wDb38VDryT/hpeZRSM9lHvP69595tQ4eNnF7kqmcbzqxFfhcEfgzHfg76XKhRNRERERERERERERERERERERERERETymoLRRCR3FJTZtzdUhbfn3pft28tngqG7yDbvhClQNiDbVQSj44iWh2Ccvue8qD8EWx/zNrbTKCgtb7w9/qf+9yvp3fjnWAGM/La/+QUd/O8p0SpyCEYDeCuikEIRkaBtvNc5tDOVPpcEWkpkOpzo3h9EGEyi1r0/ncPDrUEyxdcFoHZP+HUEKXnUvt2IQeyDg/MdRzjPf7QbzBkF737LCola91eYPxUWXQUb7oLN98Obn4cXp0PCYa+2IJ1ghqbW/8P538qvS5fDcF37eWLE4Mz7nPvX3xVdLUFx+n8UK/K/VkEZnPY/mdXjV69pcOUO6H+tdTteYj9u478UyCgiucvxvthnMJphwOjvpFGAad1PirRmfq+/p6+H855xfvOP4m4w+nvp17Pm9/D8JNjxTPpreJFJMFosDqf9ES72GOD29Fgwk95ry0V+g6ebvjGOiIiIiIiIiIiIiIiIiIiIiIiIiIhIK6DUHxHJHfFS+/ZEdTj7NVTB7vn2fZ3HhbOn5Jd4EVzwknW4Od/1PKdlW7sToP2Q9Nc8ssHbuH7XHn+79zT/wYM9zjr+dmlff/MLFYyW89yC0TbcHUyQjohI1N79evpzB3w4uDqi1ONs9/66fZnvkahx72+rQQoNKb4uAHV7w68jSE5hZU1DkjqPAwz7cQDV21Lvs+t55+eGbUGmwWhBOWEKdB6d7SryS5+Lnfv2LICjh6KrJQhJh7CwWHF660UdMtp9IhR1arxd4BCMBrBrXvj1iIikw+m6IJ2QysGfhFN+AyU+X8db/Ru4z0BjXxoAACAASURBVGj8mD0SNj/kf3+RXOXn+nvoZ6H94JZvetLcwBsyr2nhpZB0CS/LlFuIeapgtGO6nARlA72N3b3A27hc5Tf0fcwPwqlDREREREREREREREREREREREREREQkSxSMJiK5o8AhGK0hpGC0ihXOB1D6Xh7OnpJ/yvrDlOfguobUQR+5bNgXW7YZBoz+f+HuW1oOI7/Rsm3Ip7yvccIU6D7p+Da/ByoLO/obL9ErdAlGA9j9YjR1iIgEpXob1O5Jf36+XncM+5L7gebqrZnvkSoY7fVPtc3ghGRt6jH5FjSadAhGMwob/1zcreW1Yjp2Pp/5GvnKzIFgNCMOJ/8q21Xkn6Iu0PcK+75kPex8Ntp6MuX0PR9PMxit/WAYcH369fgVb9fstksw2prfh1uLiEi6nO6LY4X27W4MA0Z8Da7cBtcehlFpvg5ZuRpe+TBs+Gd680VyjZ/r7+Ff9jau0ygovzK9epp6+RrnvoZqqN6e/tqmS+harMDbGoYBo7/nbeyuud7G5SrTJRittP/xt9sPhf4u/3YiIiIiIiIiIiIiIiIiIiIiIiIiIiJ5SMFoIpI74g7BaImQgtH2OLxbfEEZdD01nD0lf8XicOLns11FegZ/EtoPsu8bcjOcMwv6XR38vn0uh4vfhsIOLfsm/BlO/SP0vhgG3ADDv2Id3OpycuPHCefDuB/DeXOsA09NlfTxV0uBTQ2SWwpKoKirc3/VpshKERHJiGnCsh/BrH7przF9fcvHvnzR9WTr8d/J/Avgve9bX6d0pQpGA1j7p/TXz1cNHr4uTsHQucrpIHjzYI5BH818rzW/haW3g5nMfK18k3Q5cB+WE6ZCt0nQ5zLr+cDF71j3H+LfOU84921z6ctFiTr79lhR+mue8W/ockr68/2I+QhG29WGwxhFJLc5XS9mcl8MUNgexv8UzviP9Zphl5OtN0/wY5VCVKWV8Pq8bPBNVuCZV2c9CCf9HHpdaM2d9hrM3A7DbrGuvb3YNguObGxWbwO89WV4pDPMKofZI6Fiufe6jnELRnMLWG9u6Kfg7Meh31WAy2snK3/ufc1c5Pb/ZNprMPxr1s9QRn4TLlxkhYaLiIiIiIiIiIiIiIiIiIiIiIiIiIi0Ih7ffllEJAIFDsFoDSEFoy35jn17hxPBUG6k2Oh3FfS9ArY/lb0aSvpCzXZ/c4bf4t5fPsP6qN6WWYhLU6NuhZP+27nfiMHwL1kf6Sjr7298Uef09pFoDfmk8yHXRG20tYiIpGvzA7DstvTnn/wraD84uHqyoct4K+Sgept9/4qfQtlA6zBzOrwEox14N72181nSw2Nl9RY4vB46DAm/niA4BXbFmr2cNeRT8OYXMt9v+Q+tQOHBH898rXxiRhyYd119y39DSZ9hWCHea//csm/HHEgchXiGYTZujlbA7gVQvRWMAivoptvpVri4H2bSJQyxOP36YgVw5r0wZ2T6a3gVbx6M1s5+nIhILnMMRiu0b/fDMGDQjdbHMXPPgb2LvM0/tALW3Wm9jtmuZ+b1iGSLl2C0bqfDKb/1t26sEEZ92/po6rQ/WJ9X3AHvfTf1Ok8OhusaGq/nVv8a3v9jY3/lanh6LFx31N99Q1DBaAD9ZlofAI/1gtrd9uMOr4MOQ/2tnSucro2NAijtA6f+Jtp6REREREREREREREREREREREREREREIqbkHxHJHXGHYLRECMFoK3/h3NdhWPD7SesQL4azHoFzn4KR33YY4/D/OCiT7/c3vvsZ0Hm8t7Gl5TB9o/+amps63z0ULQglfaxAFa/0fZ0fhn7Wua9uX3R1iIhkYrWHg8sT74IPHYFzZ8MJU63HqbG3w7TFMPIboZcYieLu7v1bHkp/7QYPwWgNh60woLbEy9cFYPZwWP7TcGsJitPBeaNZqFasEKavD2bPxTdB3YFg1soXXoIZgqRQtOCVz7Rvr6+EA2+Ht+/BJTBnNCy6Ct7+Crz1RZh7Jrw003/IfdLlPjuWYbBbx+HRPCdsEYxW4jw2iIAhEZEwON0fGyHdb534eX/j3/gMzB4Bu+aHU49IFJyuv0v6wtgfwuQHrNfYg36zj0Efh4IO3sZu/Ffjnzf8037Mwsv97R9kMFpTbgFyT50Ippn+2tnkNShcRERERERERERERERERERERERERESklVIwmojkjgKnYLRaMJPB7ZOogyW3Ovd3OTm4vaT1iRdB38vh5J/D1AVQ2LGxr/xKuOYA9PF5IMir8XdAj7P8zZn4DzAM7+NL+/pbvykjBhe/Ayecn/4anvcynA//2+k4IrxaJDgdhjr3KRhNRPLBrhfgwJvuYwZ/EoZ8AgrKoO9lMPUFuGINjL0Nuk+Mps4opApG2zU3/bUTHgPAju5Pf498lKz1Ns5MwLIfwP63wq0nCKbDQfDmwWhgheYW9whm30VXBrNOvogyGK3XtOj2akt6nmc9rtipXBXOnqYJr38Gana07NsxGx7vA5Xve18vUefcFy/2X19ThgHnPG6FjYTJTzCaW5+ISDaZDtcFmYZUOhnwYRj2ZX9zjh6E+VPbXhCytB5O32fl02HsD6zvC6dru0yU9oFJd0Fxt9RjX78ZXpwBCy6FytX2Y3Y9DxUrvO8fVjBaqp/p3R+DRVfDax+HdX+HpEsducTx+bACdkVEREREREREREREREREREREREREpG1QMJqI5I64QzAaeA8/8GL3Avf+QR8Lbi9p3U44D67aAxe+DDM2wTmPWQe2h9xsP37KPJixBU78vLf1J90NF78N5z0DV++D0d/xF3I2czt0Gul9PECsEAo6+JsDUNofPlwLXSMMFvQTjOb36yDZ43QYVsFoIpLr9r0O8y9MPa7PxeHXkguCCqiy4/W5QV0bC0ZLeAxGAyt4ev3fw6slKI4HwW0OzRsxOPFzwey75yXY83Iwa+UDp69zGIZ+Krq92pJ4EXQcZd/3usPz00wdWuEeBlp/CJ473XsIY9Il3CaWYTAaQKdRMGMzTFsMA67LfD07zYPRXMcqGE1EcpRT2FgspCAeIwan/d56zfTc2f7ecOHFK8KpSSRsTsHEUQRe9b8GrlgH0163fu7gZvuTsDPFmKfHeH9jo7CC0Tp5eFOUrY/Bxn/DG5+Gl6+xQn5zndPztJhNULiIiIiIiIiIiIiIiIiIiIiIiIiIiEgrpGA0EckdBWXOfQ3Vwe1zaKV7f2mf4PaS1i9eDD0mQ9mAxrZ+M2HiXdB+CBgF0PNcuPx96DUFyvpZt70YfBN0PcUKUCnu5r2mrqdagWrp/l8u7up/zoUvh3dA0kmPydDuhNTjSvsd/+8jua24u317lMFo22fD/GkwewS8+QWor4xubxHJX8t+mHpMQXvo3UaC0coGhre2gtHsuYUK2Vn3V+8H2LMl6fMg+JjbYNStwez9zteDWScfOAUzZOqcWdDzPOt5QtkgOP2v0P/acPYS6OgSCFGxIti99r8FT49NPa7+EKz4qX3fkU2w6Gp4ehwsugYOvOO8TqworTJbrhOH7hPhzHth9Pes55MFZdDvauf7jo7Drf/HntZvFozmFlipYDQRyVWmw3VBUPfFTsr6Qd/L4JTfeZ+z63l4ajhseyK8ukTC4HT9HdXr60Wdofvp1s8dTr8z8/Xuj0Pl2tTjjh5w7sskGA3gCg/7H7NtFtwfg/kXeQ/xzQan58OGgtFERERERERERERERERERERERERERKRt0G/OikjuKCh17ksEGIx29KBz36j/F9w+0rYN+YT1kaxveaDJS0hXpzHOfQM/Cpvuse+b9hp0n+S9TjtFXaBqs/fx3SZahxejFiuA8T+D1292Hzfm+2AoCzZvtHMIRqveGs3+O56Fl2aCmbBuV66Bg0us8D/9PxIRN3tfTj1m3I+hsEP4teSCbqeFt7Zb0ExTR9taMJrDoWk3+9+yDsTnKtPnQfBYHE76bxh/BzQchlhx43PJwk7WY/nq38E7X0u994E3oaHKPcC7tQgjGK3PZdB3OpTPsH9OJMHr5BKMtuUh6OwhwNOLw+vguQnex2+b1bKt7gA8OajxdsUy2Pqo8xrxYu/7eWHEYPxPrMdlM2E9t6w7ABv+CbW7G8e1HwoXvwsFJdbfe+5Zx/c3V9jx+NvJOuexCkYTkVzlFLYb1WN5l3HWNcT2J72NP/y+9TrOuU9B38vDrU0kKNkORmtq6Kfg0HJY8/vM1pk9DGZsafw5Qd0B6+9T2AFME6q3wLwpzvOdwq+96jDUCqLf+az3Obuehz0L4JKl7tfS2eL0fDjTr5WIiIiIiIiIiIiIiIiIiIiIiIiIiEieULqDiOSOuEswWkNVcPtsf8K5r//Vwe0jAvaHmbqeBiW93ecNcQn7Gvpp+/aBH808FA2gqKu/8Z1dQtzCNuST7v2TH4Chn4mmFglG2SD79podsOO58Pdf95fGULRj9r0GB98Nf28RyW9uQb5lA+Dsx2D4V6KrJ9u6eghGW/yJ9AKZEjXextW1sWA0M42v5fMToXp78LUEpflj8jGpDoIbhhVQFC+2Qn+LujQGnLoFcjf3UHvY8C/v4/NVkMFoPc+BcT+x7vMMw2pTKFo0+riEwSz/UXD/zsvSCFjb8Uzjn2v3wqPd/M2PFfnf0wvDaLw/Ke4Kly6FoZ+zQjVGfAOmvWqFooEVtnHhK1Babr9WQRl0axYY5xQuBApGE5HclXAKRgs4pNLN2Y9aQbcdh3ufs+z20MoRCZzTc7dsXTeP/n7LgNd0bLgLanbDvKnW9d4jXeDRHvBod3hioPtcI575/ufN8T8nWQ8rfpb53mFwCj93CgoXERERERERERERERERERERERERERFpZRSMJiK5w+2AeoNL0IQf9YehYplzf9dTg9lHxE2sAE7+tfOBwm6T3AO/ekyGwTcd39b9DDjtD8HU5zcYreOIYPZN16S77ds7joQBH462Fsmc2/+nhRdD7b5w99/mEJ658hfh7isi+c00nQOcup8BMzZBvysbQ4LagtJ+qcds+Ce8e6v/tb0Go9VX+l87nzkdmk5l0VXB1hGkMA6CuwVy21l8Exx4O/39ckn1dtj6OOxeYN1vHWM6fJ27nAQF7b2tXX4lXJ+EC16EMd+DeEhBVuKsy7jGAEA7b34x8z2SDbD9Kf/zFl5qBaIBvHK9//nxiMJ42vWE0/8M5z8Dp/wK2vU4vr/DEJixBfpfe3y7EYPT/tSyzvIrnfdy+7cSEcmmZJ19e5SP7bECGP0duHw13GBa1xipHHgbqrfZ9x1aaV0DHXgn2DpF0uUUWGtkKRitXXcYlcZz8+b2vgqzymH3fOu2mYC6fXD0QOq5QQThGjGY8oIVWOvHpntg6W3w5hdg84OQqM28FgAzCQffg3V3wvbZ1s8nfc1XMJqIiIiIiIiIiIiIiIiIiIiIiIiIiLRt+s1ZEckd8RLnvoaqYPZ49jTnvvF3BLOHiBcDr4fOY61DQsmjUNwTDq+FjsOh/zUQb+c814jBxH9A/w/Bvteg4ygov8L/gR8nRV38je9xTjD7pqt8JsS/0DIkZeAN2alHMlPW3/r/73QAbddc6/snajU7o99TRPKHUygawEltNFjRMKxriqMH3cdteQhO/Y2/tb0GoyWP+ls33zkdrk9l/xtQsRw6jwm2niA4HgSPp79mOtfMK38OZz2U/p65YP3d8NYXG79/ekyG856FwvbO/3dK+8HU+fCITXByn0thwA1QvQU6jYK+09tW+GOumr4Jnuhv37fzuczXr1gK9YfSm7vlYeh7Geye53+uU6h4NhgGTH4QBlxvPR8v6mx9P3Q5qeXYLuOd13EKHhIRyTan+6ds3hcbBly2CuaMdB839xyYseH4tiXf+SDs/oNQ2ME3wcS7dN0i2eV0/R3LUjAawPCvwrLbnWsrv9K69ncLjd71fHp7d5sUXGhsr6lwyXuw9TGr3kQtrP976nnLf2R9Xvtn6/MVa6HD0PTrSDbAqx+BLQ82tpUNgHPnQOfR3tZwej4c0693iIiIiIiIiIiIiIiIiIiIiIiIiIhI26DfnBWR3BErsMLR7IIO6isyX79yDRx+37m/k8fDCCJB6Twm/QAKIwZ9LrE+glZsE3zgpKQvdHMJHIxCUWeY/AC8cj0kqq228pkw4mvZrUvSY8SgXW+o2mjfX7EUyEIwmg7MiliqtsKm/0D1Vuh5HvS/Vt8f4B5I1ZYPrI74Biz9vvuYmu2w7QnYOReKu8HAG6HjMPc5DR6D0dwObLdGToemvdj8YJ4Fo2XwfeUWyO1ky8PweDmc8msrnDif7veSCVj6X7CyWRD43lfgtY/COY+7BzMUdYHLV8P8C637foDO4+D0O6G0T7i1i39l/aBdL6jd1bKvegscPQRFnVr2JROw6V44+C6U9IITv2iF5jV3aGX6tVWuscL20pHO922YDAP6XWl9uI6LwdDPwLq/texLKBhNRHJUwiFcOJ7lkMpOI+D0v8FbX3IOQK7aCAeXQpdx1u3dC6yA26b+P3v3HSdXXe9//DWzfdN7L6SSBEJCIPTeI12QoldRwAJX9HcB9VqxYMGuV7nqtcBVUBSkd6T3ngBJCCSk9142m+zM/P445GY3O2f6zM7uvp6Pxzyy862f3cycPWfgvDP/T8Fjn68Hx+j6IcWsWkquHIPRKuvhgF/DC5e27qsdAEfcGpwDLX8QHj2psHsPO7uw63UbDROv3v181EXw5DnJz5HD3DU2uPYbdVFu/w3mqXOCzzqa27oQ7t0HJn0FJn4p+fl2c/EiXA9LkiRJkiRJkiRJkiRJkiRJ7UiB/vllSSqQmj7J2xvX5r/2on+k7h9U4Js5pPaqOotgtH2/Edzs3daGng5nr4BjH4HT5sERt0Fll7auSrkafm5437YlpaujhXYUgCIVy6a58MB0eP3LMO96ePo8ePHTbV1VeVh6V3hfZ75hdWyGr48nzoR5v4I3vgUPHAhrnks9Pr49s3UX3wrLHshsbEeQTzDa1oWFq6OQwm4EzytwMJHbtIal8PT58OxH89i7xOIx+NdxrUPRdllyexCQlggJZoi8H8zQfTycPh9OfBZOeR1OfsVQtHI24arwvs3zWrclEvDkWfDcx2Duz+C1L8E9k2BHkoD6TXNyr6thCWyem/28cVe0rzDCPfU5OHl73GA0SWUq7PgUrS5tHcmMuRTOXAK9poaPmf/H3V/P+034uDe+BffuC+teKVx9UqbCzr/bMhgNghCwZOcuYz69+3xs0InQZa/C7VndKwgpLKZ+h8Hp78Bxj2U3b9Et8NgMmPXt7ObNvKZ1KFpzb14Ltw9Nfr7dXGiAXif+nEmSJEmSJEmSJEmSJEmSJEmdShkkmUhSM9VFDEZLFVhx2F+hogxu7pLKQXWvzMbt9z0YfXFxa8lGVTcYeCx0G9O+b5xX6mC02v6lq6MFX1MSs38E21e0bHvnt7BxdtvUUy4S8dQBcW19Y3NbqukTnGdnY+cmmHVN6jHxHZmvN/Mr2e3fnoXdNJ2JxjWFq6OQErHk7fkEDoaFrWXqvT/DqifyW6NUlt4Jqx5PPeb5SzILoItWQt+DoddkiFYUrkYV3vjPh/dtej+YrGkbvHIl3BSBm6OtPy/Ztgj+0ev9/gq4uTL4+s1rw9c++eXUdS2+DV69OrPvobn9f5z9nHJSUZO8PWYwmqQyFRqMFnI8K7XafjD9t+H9c38WXKPFm2DR31KvtWM9vPGd4OsVj8Ajx8J9+8PL/wGN6wpXs7Sn0MCrNv78IFoJR98NQ88KwhBr+sDE/4R9vtZy3OFp3luZqu4Nxz4E1T0Ks14qlV1gwFFw6M3Zz5319eD4sH1V+rHb16Q+Z95l58bgfHvJneFjwsLPO3MAvyRJkiRJkiRJkiRJkiRJkjoVg9EklZeakGC0HXkGozWuhbXPh/ePOC+/9aWOpLp3+jEnvQiTvgQRTyVUBH0OCO9r2rb76+2rgkcsi4CcVBKJ8D7D9iRYGHLj63t/Lm0d5WbVk8EN9WE6+w2rA47Jfs7yB1KHfGUTALbu5cxuXu4Iwm6azkTj6sLVUUhh31M0j/dV15HhfWMvy2yNhVkG/rWVpSlust9l0xzY8m7yvrYOZlBuohXQY5/kfSsfga0L4d59Yc5PMlsvEQ8PKdxln69B7/3hlNezqzWdvf8jv/d7OQgLEoo3Bp9VFepaRpIKJey4FBb02BZSfW4E8MSZsHleZmst+WcQ+P2v42Hlo7D+VZj7U3jyrNSfE0n5CLumjZTB+XdNHzjyNjh3M5y9GqZ8t3Uwcvfxua9f1QM+tBUuTMA5a6H3tPzqzdbwc6HX/tnPW/koPHoKxNOcF69+Irtr8yfOgCUh/6iTwWiSJEmSJEmSJEmSJEmSJEnq5EwzkVRewoLRGvMMRpv7y/C+g36f39pSR1OTJhitblBw07tUTCPOT97etBXWz4Sbq+C2AcHjbzXw6tVBaEM+UgY+eNqsTi6RgKbNyfvm31DaWsrNsrtT93f2YKGKutzmbZkf3hfPMgBs09u51dDeZBMYt6fGNYWro5DC/q4jFcnbM9FjH+gyonV7z33hgP+CYR9Mv8a863Pfv1Te/T3M/1NmY1c9nry9HIIZlJs+05O3z/8j3DEy9TE2n/16TYbJ3y7cuiMvLNxabSUsSGjHeri1L9zWD2Z90/AdSeUj3pi8PSzosa2ctTy8b+ld8NwnMl/rhU+1blv1BCxNc60n5Srs2q2cPj+oqA7/hyKquucWgg5B8G1lfe515StaAcc/CntfCb0PzG7u+ldgUcg/GrDLtiXZ1/TE6clDKcOuh8vpdSJJkiRJkiRJkiRJkiRJkiQVkQkPkspLdRGC0RJxeOOb4f29puS+ttQR1Q1N3T/tlxDxFEJFVt0reXvjGnj4CEjscWPY7B/BnJ/mt2eqQJmwGwGlzmLH+vC+sMC0zmLjnNT90crS1FGuKmpzm7cpxc81kWUAWKq1OpJsA+OaK9dgtD1/3+8SyeN9FYkE57PR6t1tFfWw/0+CvoP/2LIvzM4yPfZtWwKvfQmevyT/tTr78as9G3JaafdrHowx9IzCrDn+/0HvaYVZqy2lCxLauQlmXROEGUpSOQgNRsvg/KiU6gZC/6PC+9c+l/8eS25r+TyRgA1vwsJbYOvC/NdX5xXbmry93AIIU8klDLf73jDh6sLXkq2q7rD/j+DkF+C87dBrauZzF9+Wur9hWW41vXBp67aw62Gv0yRJkiRJkiRJkiRJkiRJktRJmGoiqbzU9E7enuuN+k0NcM8+qcdkc9OD1Bl0GwPdJyTvO/DXMPyDpa1HnVNFffL25fcF4QHJLPjf/PaM78hvvtSRNSwP79u5KQii7ay2zEvdH6kqTR3lKprj97/2pfC+VEGWyXSWYLRsA+Oaa9oCse2Fq6VQwsLe8r0RfOhpcMqrsN/3YOoP4ZRXYODxQV9VNxj54fRrbH47vxqKYcH/wp1j4K0fFGa9zn78as/6HlS6vQYeD5Vddj/vkeYzmGQmfhEmfAH2+mgQsnHswzDtJ4WrsS1lGhC68K/FrUOSMhUL+WykogwDm8Z8qrjrL7hx99fxpiB49t594Onz4I6R8Ma1xd1fHVM8Bo3rkvfV9i1tLfnodxicsSi8P1IJXUYGj65j4MDrYcZMqKwrVYWZqaiBk54PArIzsfjW1NfO23IMRlt2XxC+2FzY9XA+QeGSJEmSJEmSJEmSJEmSJElSO+L/OSupvNQOTN6+5d3c1nvkaNg0O7z/yNshEsltbamjikTg8Fvg0ZOgodmNPPt9D8Z+pu3qUufSPFwhUxteD24gy/W4njIYzTxhdXINaW7sXPM89DukNLWUk/hO2JzmPDXfAKfOaumdsN+3k/cZjJZc2E3TmWpcA/VDC1NLoSRiydsLcSN4j4nBI5kp34dNc2HNM+HzN86B3tPyr6NQGtfCC5+CeGPh1sw12FBtr3YgVHYNQg+LbcgZLZ9HIjDhKpj9o8zmV/UMrjU76mcz0QyDhFY+Utw6JClTYecSmR7PSmnE+fDMhcVbPxEPAqxqegcBlvP/0LJ/5ldh4AnQd3rxalDHs2MdkEjeV9OOgtEAugyDczbAk2fDyn8FbbUD4OAbYPBJbVtbNqJVMOoiGHBc8L2sSxHUDnD3BOgyArqOglgjJJpg0S3BP/QR25ZbDY2rYfsKqBu0uy1hMJokSZIkSZIkSZIkSZIkSZI6NxMeJJWXbuOStzcsg52bs1vrxctg7Qvh/T0mwtAzwvulzqznPnD6Ajj+ySBA8OxVMOlLbV2VOpPK+tzm7Vif+56pgtE6alCDlKntK1P3L72zNHWUm60Lw29U3cUbVnOzYSZsmZ+8L93PfE+dIRgtHiP05vpMNa4pSCkFFXojeEVx963tD8c/DienuCF+9VPFrSFbi2+DWENh1zQYrf2KRKDb2OLvUz80CJHY0/DzMl9j0Akd+1y7ogyDhCQpldBgtOrS1pGJSAROnVvcPe4eHwQz7xmKtsvSO4q7vzqeVNdd7S0YDaC6Bxz7MJz8cnANddq89hWK1lyXYXDS88F/E0ll63uw6nGY/0dYeFMQiga5h6LtsmmP41nYZ9XFvh6WJEmSJEmSJEmSJEmSJEmSyoR3aEsqL93Hh/dtnge9psDmd2Ddy8Fj/StBCE6XkTD5O9BzUjB20zyYd33qvUZcWLCypQ6pohr6H97WVaizqsgxGK1hKdT0zm1uqmA0OnBYg5SJdGE7S+6AKd8rTS3lZPvq9GMMFoJBJ8Py+7Oft/RuGH9F6/b4zuzW2boAYtuhojb7GtqLTMPijnsMHjk6ed99U2HkR2C/7wY3hJeD0GC0EnycFa2E3tNgr4/Cghtb97/z38H7e/8fl8f7fPFt6cd0GwfHPAB37pXZmgY7tm/9DoP1r+Y+P1IZvAe7joa6wbDupZbnA30PgQN+BVVdW8/tMQmqesLODen3GfbB3GtsD2r7Zz72latg4hehtl/x6pGkipK2bwAAIABJREFUVOIxSMST95Vr0GP3cTD6Ynj398VZv3ENLH8AVj6avP/N78J+1xZnbxVXIg6xxiAMcNef8R2t25L1terfkXxOsrk7UpwfVfcp3fdfSJEI9N6/rasojEg0+G8ip86Bu/cu/PoH/xGe+3jyvkeOgRkzoee+wfOwfwCkukfh65IkSZIkSZIkSZIkSZIkSZLKkHc4Siov9cMgWhPcILKnZy6EhuWwc1PrvvWvwfIH4dTZ0GU4LLs39T7RKhj374WpWZJUeJU5BqNtW7b75rFspQraiURzW1PqKGJJzs2a2zQ7CKbtPrY09ZSLxjXpxxgsBOM/Dysezjy8a5cNbyRvzzYYLRGHrYs79usz05/JgKOgujfsWJe8/70/w8pH4ANvQnWvwtWXq3jIayZawvfV4A8kD0YDePuX0LQ5uLm9LcUaMwsfPPnlIMRqxiy4N4Pzpcq6/GtT2xl7efDaTfYZCsDoS+Dd/wmff+6m4LOZ6p7Z711ZBxOuhJlfSz2u11QYdk7267cntQMzHzvnx7D6aTjhKYhWFK8mSQqT7DP5XaJlGowGQbhvJsFoIy6EQ/8cXB/sWAuvfgEW3JB+3tv/lX+NuzSuDc6zO9vnTPGm9IFh+fZlG1KW7fVpsVV28fy7nHQfD4f/A54q8LlqtDoIMF79dPL+eyfDic8G55ANy5KPqelb2JokSZIkSZIkSZIkSZIkSZKkMuUd2pLKS7QCuo2FjUlCEDbNTT03tg3e+A4c9FtY82zqsacv8F9Vl6RyVtElt3kNS3PfM74jvG/9q7mvK3UEqW6Q32XpHdD9quLXUk4yCUaLVhW/jnI3+CQ45j741wnZzduxPnl7tsFoEAQf0IGD0bK5qb+mb3gwGgRh1Av+DOM/m39d+UrEkreXMnBw8ClQ2RWatiTvn/8nIAIH/U/bBVwsuT39mIEnBqFoAD0mQddRsGV+6jndJ+Zfm9pOj73hhKfhretg3Uu7jxN1Q2DE+TDmk6mD0SrrgDzCOSZ9JQh0WPhX2L4CmrZCVbcgiKSyC/Q/Cva9puMHgEUi2Y1f+xws+juMPL849UhSKimD0apLV0e2Mg0JGnlhcFyOVEBtfxj7mcyC0ZY/kF99AOtegWc+EoSKV/WESV+GCVdl/3sinUQiuF5KGhjWCLEdSdqKGVL2fn8iXtjvsyMy7Kr8DDqp8GtGq6D39PBgNIAHD0m9hq8VSZIkSZIkSZIkSZIkSZIkdRIGo0kqP93HJw9Gy8TSOyDx3xDfHj5m6o+hfkhu60uSSqOyPrd5Dcty3zNVMFrDctixAap75r5+udqyIAh+630AdBne1tWoXKV6f+zy3l+CG7s7ungTrHocYtth4c3px5cywKmcDTweqnunDuTa084NyduzCQHbZfvq7Oe0J5mExQ09M/izth9sfjv12AU3lEkwWsjfdbSE76uqbjDhapj1jfAx8/8I/Q6D0ReXrq7mVj+Tfszkb+3+OhIJXg9zfhI+PloTvG/VvvXcBw69Mbx//Odh7s9at4/6RP57RyIw5pLg0dmNuCCzc4Zd3v6FwWiS2kYsxXVfRU3p6shWJiFBwz4Ig2e0bOt7EOz1sczC0fKxfTXcP233850b4LUvwOJbYdjZ4WFiuYaUqX2qH9rWFWhPVV1h7ythzo8Lt2akCoaeDnN/mvsaBqNJkiRJkiRJkiRJkiRJkiSpk/AObUnlp/v43OduXwXLH4Ald4SPGfnh3NeXJJVGuQWjASy5E0Z9NPf1y00iEdyIO/tHu9smfQUmfzsIspCaizWmH7P+NVh2Hww+pfj1tJUt8+Hx02Hjm5nPiVYUr572Jlqd3fgdIcFomYSA7alxTfZz2pNMwuJ2BR1lchP1upfzq6dQ4iHfV6TE76uJXwxCjTbNCR/z/CUw5AyobYOb1FMF3Y35NOz7dagb1LI9XTDaXv8WBAGoYxt+TvJgtKFnlL6Wjmz0J7ILRlvzbPFqkaRU4imu+6LlHIzWJ3X/gdfDmE8m/6zj4D8G50VPnpXb3pFo6v5Nb8PdIf+9Y+3zwUOC4FpC5WfqD4MQs0S8MOtFq6Df4dB9AmyandsaBqNJkiRJkiRJkiRJkiRJkiSpkzAYTVL56bV/fvMfmxHe1+9wqBuQ3/qSpOKr6JLbvG1Lc98zXTDaq1d1rGC0Zfe2DEUDePNa6H8UDDqhbWpS+Up1g3xzj82AC+IdM1wvth3uHN3WVbRvFVmGKax7KXl7wmC0VsICxHaZ8AUYcmrwdXWa4IhdXroChp0FA47Jva5Nb8N7N8Ha54KwtX6HQd3QYM1hZ6c/VoQFvkVK/HFWRQ0cfR/cuVfqcY+dAie/WJqamgsLRht4Iky/Pnlf30Ohph80rk7SGYH9vluw8lTG+h4Kk78DM7+6u23CVTDktLarqSMacFz2c9a+BH0OKHwtKr1ty2DBDbBpLpBo62qk1HZuDu/L9ly+lKJVqfvHfCr8vDMSgWFnwr7XwKxrctg7TfjzC5dmv6Y6n2EfhPFXtHUVSiYSgVNeC0Lyt76X/3rRKohWwiE3wKMnwY712a9hMJokSZIkSZIkSZIkSZIkSZI6CYPRJJWfoadD/VDYtiR8TCSa27/Q7s0lktQ+VNbnNq9hWe57xtME7SQNDmnH5oUEpcz/k8Foai1dcGBz8/8Eoz9etFLaRKwR7hjR1lW0f9EcwhTe/jWMu6xlW7rjdTIdPRgtVVjcCU9Dv0N3P8/05/f2L4PH1B8GQUnZWvUEPHoKxLbtbltyR/DnvF/B6EvhoN+mXiMs8K3UwWgAXUfCmUvh9iHhY9a9BCv+BQOPLVlZxJvCb9Df873TXLQC9vk6vPzZ1n3n7wz61fFFIrDPV2DURbD+Veg5GboMb+uqOp5IBOoGQcPyzOc8cCAcejOMPL94dan4Ns6Bh4/o+Och6hzSBYC1tV5Tg99le+qyV3GDu1P9XLavDs6J1b5FKoJr2YqaJH9WJ++L1kBFdbOvQ+ZW1EGfA6HbuI4ZMN9R9NwXPvAmPHoirH66df+hf4G3fgAbZqZfa1eQY58D4YxF8PCRyY9dqdRkGHYuSZIkSZIkSZIkSZIkSZIktXMGo0kqP9EqOPZhePYiWPcCEIEeE6HX/tB7WvDotR/cfyBsmp3d2n2mF6NiSVKhVbRFMFoWwU8dwbJ7krcvvAkO+0tpa1H5izVmPvb5T2QXjBZvCm4gXfFQEHY04UoYfEr2NRbTO7+F7avauor2L5cwhde+AMPPgdr+u9vCwrJS6eiBJKl+Jl32CPXbsS67tV+9GrqNh6Gntd5zwY3w5rWwZX7QVjcEGpZmtu67vwseu+qr6gEDjoPJ34KqrkFbIpZ8brSNPs6qHxwEzT10WPiY5y6CMxeVrCR2rA8PDa9PE3A19jPQuArm/CwIsBtwDBz0B0PROqP6IcFDxZPL767XvxT8DmyrY56yl4jDnJ/A4tuCa9OtC9u6Iqlwcgk5LqV9vgpPfrB1e78U522FkOr47jEge/8XNJYiVCxaHRI0lkVfxgFn1Z4bK1BZD8c9Bq9eBe/+AZo2Q1VP2PfrMOICGHoWvHolzP8jxLaHrxOp2v11Vdfg+vLVK8P/AYtWdXSD7nvn9a1IkiRJkiRJkiRJkiRJkiRJ7YV3VUkqT93Hw0nPBjcUxpuCG2H2VNM3uzWHn9c6FECSVJ4qcwxG274SEgmIRLKf29mC0TqCxnXQtAWqe+2+cVbFEc8iGA1g6yLokiaQZ5dnPwoLb979fOUjcNRdMOTU7PYspoV/besKOoaKHMIUmrbCkjtgzKW72xI7s19n+8rs57Qn8RQ/k2hVy+fDPhgejhnmidPh+Meh/5HB79n4Tph9Hcz8WstxmYaiNdc8MGLDTFj3UrBXJAKJkKCJSBt+nNXvUDjyzuBnksy2xbBxNlR2CR6x7VDTBypqC1dDvCm4Vq6ohsa14eNq+qReJ1oRBNHt87XgZ5rL+ZOkzAw9MwiDzMbWhcFxsduYIAQRgjCNyvogTNL3bPl5+fPw9i/bugqp8CJRqKhr6ypSG3pWEPK68tHdbdEaGPfZzOb3PiC3fWPbIB5LHp6VT3h/0UVSB4MVIogsVeBY0j2r/d2m8hathGk/g6k/CsLz6wYGx0eAyjo48Ncw7efwwidh/p9C1tjj+rz5vPumwMa3Utcw7t8Le20pSZIkSZIkSZIkSZIkSZIklTGD0SSVt0g0POQk22C0Q/83/3okSaVR2SW3eYlYEHCWS/iOwWjtx7pX4LmLYMOslu3jPwdTf5z8hmTlJ9v3x5I7YHwGN6AvubNlKNouj58GR94BQ0OCh0pp+xpY82xbV9ExRHM4NgMsub1lMFqqELAwZR1KUABhAWLQOkRs0Am57THz67D/T+DFz8DaF3JbIxOrn4TlD8Dgk1MEo7XxcX7oaVA/LAhBS+aeia3b9vooHPBfUNUt931jjfD6l4PQnfhOGHwqjLwgfHy6YLRd9rw5X1LhDZ6RfTAawP3TgAiQaN037edB4I8hMuVhw5uGoqnj6nto+QeRRyJwxK0w5+ew/H6oGwz7fh16Tcls/sDjoKo77NyU/d5NW6C6R+v2Wdekn1vVE3pMTB04lk/YWFifobhS7qKVUD84pK8quFYMnRty7RWtghOfh1evhHd+m3zMtF/CuMuzq1WSJEmSJEmSJEmSJEmSJElqxwxGk9R+ZROMdvyT3uwtSe1JRX3uc5u25haMFjMY7f8suRP6HZZ5oEo+Eokg4KxhGVT3hqZN0HVU8NjT5ndh/Svw1IeSrzX358FNxZOvKWrJnVKsMbvxyYLRdmyA1U9B47rgtdVrCjxxRvgaT5wBp86B7uOzr3f9a7DyseA1NeBo6DI8s3k7N8Ga56BhRXCja+0AmPsLkoaRKHu5no8vuxe2LYH6ocHzXILR1r8KO9ZDda/caih3qX4me/7c64fCxP+Et76X3R6rHn8/oKcEFv09CEaLhwSjRcvg46wTn4Pbh2Q+fsGNUFEL03+T+55v/QDm/GT382V3B49kojX5nU9JKqzBM2DgibDiwRwmh5yHvPw5qBsCwz+YV2kqkHv3aesKpOKo7AZTvt/WVWSmulfweUAunwlU1MKU6+DFT2c/N1kw2tJ7g2uQVPa7FiZ9Ofv9JJW5FKGDqT4XqeoaXC/22AdevmJ3e2WX4L9x9p5auBIlSZIkSZIkSZIkSZIkSZKkdqAM7iSVpBxlGoxW2RX6HlLcWiRJhRWtgkgFJGLZz23aCjW9s5+XyCBoJ5GASIqb2zqKJ84AInD432D4ucXbp2krPHcxLPpby/ZIZXBz8ORvBs8TcXj9q5mF+Cy40WC0YohnGYy26nGIbQ9uLocgKO2JM7Pf9+694YIYRKKZjY83wdPnw+JbW7ZP/RFMuDL13DXPw5NnQcPyzOsbcQH0PgBeTbO23pciYK6iNnjNhLl9WHCD8JhPQiIkLCudW/vCEbfB0BSBfO1Vqp9JJMlHP/tdC/2PgqV3wrxfF6+uXM3/Q/A7YPWTyfuTfU+lVj8YJlwNs3+Y+Zx3fw/7fTe34NGmBph9Xebja/p0jnMWqb2oqIaj74X3/gLPfaxw68673mC0crBhVnbjR11UlDKkguu+Nww9C7qPa+tKSmPsp6DnvvDQYdnNa9rauu3d/0k958Rnoe/B2e0jqf2LZBAYP/6zwX/TXPJPiNbCmEuhbmDxa5MkSZIkSZIkSZIkSZIkSZLKTBncSSpJOartl9m44R+CaEVxa5EkFVYkApVdYOem7OcmuyF1l3gMlj8AK/8F9UNg9MVQ1T39vF0STZndwNYhJODZj8HA46G6V2GX3jIf3rsZZn41ZOsmeONbsHNzEIi16O+wbVFma29dAPGdQbieCie+I3n7gGNg5aOt2xNNQQDQuMuD9/HT5+e+980V0H1C8HXfg6HLSBh2VnDD+i5bF8GsbwZhSsm8ehW8dxP0mtKsMQ6rn4Eek2DQiUG4UTahaOOugAN+/v76BqNlpMe+sOqJ1u3R6iAY4L6pqee/8KngmBTPIMgymUQcnjoPzl4J1T1yW6PcrHkBltwOy+4OH5PseBiJwOCTgkfD8uCG63Jz+7DwvmiZfJw16uPZBaMlYvDSv8NhN2e/18pHMjtX2SWX8DVJxRWtgFEfDQIp1z5fmDVXPhL8fss0RFbFsf71zMd+aEtwrSupPPU7FMZ9Ft7+ZeZzYttat6U6vx7//wxFkzqrTD+v7HNA8JAkSZIkSZIkSZIkSZIkSZI6sTK5k1SSclA3JLNxE79Q3DokScVRUZ9bMFosJDQkkYCXLod3frO7bc5P4KQXoW5gEMKVTnxH5wrcijXAkjth1McKt+bqZ+DRk6BpS/qxc3+a2x4Lb4G9PpzbXCUXa0ze3nsarHoyCELb00v/HoT4dBkJse357b9pdss/3/wuHH4LDD0dVj4Ojxydfo31rwSPPW1+O4dQqAiMuSTLOWLEh4IwGBIt24efF4TWfWgr3JImKGTRreHBaEfeEbzmnrkwfH68EVY8CMPPzar0sjT/T/D8xUEgTiqRNCHRmQZO52P674LwtiV3FGa9dN9zqXTfG7qNhc3zMp+z8K/Qa2r216lLbs9ufLXBaFLZGnFB4YLRADa/C93HFm49ZW/b4szGHfeooWhSe1A7ILvxe4bXNiUJSmuu+/js1pfUzkTCuzrT58qSJEmSJEmSJEmSJEmSJElSnqJtXYAk5axucPoxR/zTG40kqb2qqMtt3v0HwFPnw4ZZLdtf+0LLUDSAbUtg1jeCrxfelH7tsDCejmzjW/mvEdsB8/4bHjoCHjoss1C0fLxgYFXBxXckb6/sCt1Gh8977Yvw9HlFqKcRXr0K1r+eWShaoY29DHruW/p927v+R8Ih/wv1Q4Pn0eogHGb69cHzyvogaCqVNc/SKlhtl+qeMOJ86DYu9RrblmRVdllqXAvPfTyDULRKiKS4KRugpn/h6kpm/OeCIMEDfhXUUwgNywuzTr4iERh2TvbzZl0D21elH7f6GXjsVPhHb3j399nt0efA7OuSVBrjr4B9vla49e6fCg8dGfxe2LqwcOsqMzs2wutfTj2mpl8QEjrg6JKUJClPVd2zG79nMNrmt1OP7zEhu/UldRwGo0mSJEmSJEmSJEmSJEmSJEkZMxhNUvtVPyS8b9RFcH4TDDuzZOVIkgqsojr3uYv+Bg8cBFsWBM/n/gJm/yj52Hd+C/NvaB2klkxnDEbL5Oeyp9j2lj+r1/8TXvwMrH6qcHWl279hZWn26izijcnbozXpg6yKZfM8uG9K6fftsQ/sd23p9+0o9vownLEIzlwC526Ew26Cyi67+7uMSD1/e4r39q4QsAlXpl6jYUXm9ZaT2PYgEC3eBLf2zWxONIMgstoiB6PVvX/dVj8E9vpoYdbsMakw6xTCxKuDwJtsxBpg4V+Dv0sIAu4ScUgkIB4LfoeteiIIE112D+xYn31doz6R/RxJpRGJwORvBZ9bjb44//WatsLqJ2H+n+D+A3M7Zig38SZ45NjUY87dDGctC0JCJbUPVd2yG79nMNrGOanHt9U1tKTSSBVOHjEYTZIkSZIkSZIkSZIkSZIkScpUBnfISlKZqhuUuj9aUZo6JEnFEcnzVDXWAHeOgklfhXd+k3rscxdltmZnDEZbfj9sXwO1GYTw7NwCz18MS+6ASBSGnwsjPwJzflL8Ovf0xjfhwF+Xft+OKhYWjFYNg04O/s47g0EnwaF/geoebV1J+xaJhIccpwtGq6gL74u+f4PxmE9CZVd45sPJx82+DhbeHBwjhpyavt62tmM9PPcJWHJ79nMzuem6JsOQtVwNOnH319N/C93Hw7J7g+fjPwfrXoY3swwb7Htw4erLV3UvOOk5mPkNeO/Pmc97+XPwyn9AIhac8ySaClfT6Iuhh4EbUtmLVkC3sYVds3E13DsFzlxY2HWV3Mp/wfpXwvvHfAqqupauHkmFUZltMNq2ls+X/DN8bO9p2YfqSuo4ogajSZIkSZIkSZIkSZIkSZIkSZmKtnUBkpSzilqoqE/eN/z80tYiSSq8Qt0o9uZ3goCAQojvKMw6bS2eTfhKAmZ+LbOhz30MFt0C8cYgmG7BjfDoiennFcO862HNC22zd0cU9tqvqAnC7/oclP8e9cPg4D9BpEzDbSd9BY65H2r6tHUlHVu6YLRUx/PmvzdGXghjPh0+dttiePw02PBGdvW1hacvzC0UDTJ7vVaGXFMVwqiPQ6/9dj+PVsDEL8DxjwWPYWfBuM9mt+aYT0HPyYWsMn9dR8Gh/wsXJlo+zgsJldwlEXv/zwKGovWeBgf8qnDrSSquwTNS99cPg3PWw/FPZr7mtkWw8rG8ylKGFt+aur/HPqWpQ1JhVWUZjBbb2uzr7cHnImGmfD8IipbUORmMJkmSJEmSJEmSJEmSJEmSJGXMYDRJ7duoj7duq+wKA44qfS2SpMKq7NrWFbQW39nWFRRGbHt24xfcAE3bUo9pXAdL7si9pmKY86O2rqDjiIeE+0RroKorHPcoTLgq9/WHngGnvAajPganvJ77Osn0PyoIb9v1qKjLfO7Ij8C4K+Coe2C/7xS2LiXXZWTq/g0zw/silS2f1/RNv9+8X6cf05a2LoTl9+c+f9BJ6cdUZhn8kE7X0TD2cjj873DQ79OPrxsQhP6kUzsQjrwjCP1qL2ESFdVw8svFW3/az2H/n8Koi2DUJ+DAX8MJTwehlZLahx77QI9Jyfv2/wl84C2o7gn9D4dpv8h83Xd+V5j6FG7rQnjnt6nH1A8uTS2SCivb8+OmZsFoS+8KHzfsbBh4fG41SWo/9vxsojmD0SRJkiRJkiRJkiRJkiRJkqSMpfg/cyWpHdj3Glj/Cqx5NnheURvcgF9R26ZlSZIKoNBBLYWQ6CjBaA3Zj3/7lzDxi+Fj1r8GiVh+dWWjsmsQ+LP41vAxi/4OjWuhpk/p6uqoYmHBaNXBn5V1MPWH0GUveOny7Nbe5xsw+Zrdz3tOgt4HwroXcyoVCMLQjvsXRJJkgSduhBcvg3f+O/UaZy6G+qGZ7Tf9d/DCpa3bR12U2Xzt1mVEVsMXNIzkjjWns6GpJ6ct68q0ns06MwpGuz4IkypXKx7Ob/6U76cf0+fA4L0c35HfXgDjPw/7/zj5ey+V6p5wwlPwxFnQuHqPvt7BNd7AY/Ovry303j8IWXzvz4Vfe8QFUNuv8OtKKp1IBA7+IzxxBjQsD9q6jAzOY7ru1XLs0NPh5SsyW3fhTTDiPBhyWvsJk2xvXrws/Zi6IcWvQ1LhVWX5eVTjut1fr3s1fNyIC3OrR1L7MvLDMPOrrdvrh6YOTZMkSZIkSZIkSZIkSZIkSZLUgv/3raT2rbYvHP94cMPR9uXQ/0io7tXWVUmSCqGqa3jfgdfDi58pXS27FCI0phxkG4wG8NqXoOtoGH5O8v7Z1+VXU7ZGnAdDz04djAZwa184aznUDSxNXR1V2Gt/VzDaLuMuC8I3MgmKABh9cctQtF2yDVVqrtdUOP6x8P5IBKZfH7yGHjkm+ZizV0Jt/8z3HDwDKrtA09aW7cPOzXwNBbIIRntu40HMmHU3G5qC8/9v/zzBHy+K89FD3n/9ZBKMBjDr27Dv17KttPi2vAfPX5L7/LNXBoFj6VR1g6FnwqJbct8L4NS50H1c7vP7HQanzYXVT0PDiqCttj/0OxxqeudXW1ub+kNYeifs3FS4NcdeZiia1FH0ORBOnQNrng/OgfodDhU1rcfVD4OKuszP5Z84A0ZfCgf9trD1Cja/C8vuTT+ubnDxa5FUeNkGo735HZj0peCacNOc8HFDTs2vLkntQ9eR0Gc6rH2hZfvw8wyslSRJkiRJkiRJkiRJkiRJkrKQx93mklQmolXQdzoMPcNQNEnqSCpTBKMN+yCMu6J0tewS31n6PYth41u5zXv1CxCPtWzbuQlevByWP5BfTZXdoH54+nHRahj5YZj2SxgyIwjJS+e1L+VXm8Jf+xXVrdvGfgb2/xlU9Ui95t5XwvSQoI5cg9H2+igc90hmYwccDcc/AV1H7doU+hwEZ7yXXSgaQP1gOOZB6PZ+KFTtAJj+m+A1quzUDsp46FcWfOf/QtEAEokIn705wc6mRNDQ54DMFpr1ddgyP5sqi6thBTz/Sbhzr/zWyeZ1fNDvg9+t0argMfRMGHF+5vMHnZJfKNou1b2CwIgxlwSPoae3/1A0CMI5J3+ngAtGYMr3CriepDZX1R0GnQADj0seigbB+VHfQ7Jb993fwd/qYf4NkEjkX6cCS+/KbJzhzFL7VJllMBrAg4fATRFY8s/k/cPPCz++S+p4jroLBhwbnL9V1AdhtVO+39ZVSZIkSZIkSZIkSZIkSZIkSe1KZVsXIEmSJCWV6kbUaDWMvhjm/wGatpSupo4SjDb3Z7nN27oA1r+6O2wokYAnzoKV/8ptvaoecNrbEKmEqm4Qb4Jb6sPHz5gZhFhVdtndNvbTMOaT8OgpsOLB5PMW3RIEqFXW5VanIBHy2o9UJW/f+3NBQFp8R3Dzd3wHNK4N/u4iFVBRl+am8EjmtfXaH054Aohm/3fc/wg47R1oWA4VtfkFMPU7FE6bC9vXQE0fiGTxPWi3aEVGwxrj1Ty64ZhW7Zu3w2NvwwkTge7jM9934S0wqQxCFHduhvsPgIal+a3T+8Dsxld1hSP+AU3bIBEPnq97FRb+Nf3cSAWMb4Ow0vZm7Kfh9a9A0+b81uk6Gk6dHQTYSep89r4SVj0WHKszFWuA5y6C7atg4tXFqqxz2fhG+jG1AzxWS+1VVQ7BaBtmpe4f/sHcapHUPtX2D4L7d2wMPm8yGFGSJEmSJEmSJEmSJEmSJEnKWrStC5AkSZKSquoa3hetgl6T4diHS1cPdIxgtMZ1sOKh3Oc/cCDM+WkQirb0rtxC0bqMhGEfhJNeDG4UrOkd/J2mC7WqG9IyFG2XSBTGXBI+L9YAKx/Jvk7tFm87yJnlAAAgAElEQVRK3h5NkbVdUR28j6NVwd9bl+FBYFh1z/Q3hKYKFZv+W+h/JPTYByZ+EU54Klg/1+C7SATqB+cXitZcbV9D0fI19jNph2xs6hHaN2dFYveTkf+W2Z5Lbs9sXDHt2Ah/755dKFq/w5O3Dz0jtxoq63f//u09FU58Drrvvbt/4Akw4w0Y//ngPTjoFDjqHhh8cm77dSbRKtjrI9nNqR8a/J6sHQC9pwVBoCc8bdCO1JkNmQFH3gWDToK6wVA3KPO5r30Bti4uXm2dybu/Tz9m8AeKX4ek4ohWQb8jCrtmtyxCmyV1HNU9DEWTJEmSJEmSJEmSJEmSJEmScpTiLnZJkiSpDQ06Cd78bvK+itrgz74HwYSrYfYPS1NTfEdp9imm9a9AIp7fGq/8B2xfCeteyW7ePl+Dyd9KPSZaHf5zruoePm/QyRCpgEQsef+S22HIqZnVqdYSIaGARQvnSREsNvIjMObSIu2rsrD3f8CSO1MGhG1qCj8edG1+z3HdwMz2XPs8vP3rIFCsfkiGhRbYMx/Ofs6oTwTBgMsf2N3WfQKM+VRhaup7EJw6u3X7tJ8WZv3OZvJ3YP6fgsDO5iKVcNjNMPycNilLUjszZEbw2GXVU/DYDGjanH7u3XvDeVuLV1sxNTXAhplAAvoc1HZBtOtfTz+mbhBMuLL4tUgqnv2uhYePLNx63cYWbi1JkiRJkiRJkiRJkiRJkiRJkjqBaFsXIEmSJCXV73CoH9q6feiZEGl2GlvKsKt4SDhUe1Ko7+GtH8CKhzIbO/YyOOru9KFoEITDhImm6KvqBnt9NLx/xSPp91ZyiXh4mF6qv6+8pAi62BWMqI6r2xg46YWUr6+NsR6hfV2aB6NV98x835cuh9uHwutfhUQi83mFsG0JLLsn+3lV3YLj60F/gDGfhv1/Bic+A7V9C1+j8lfTG85eAZO+GhzLolVBwOtJzxmKJil3/Q+Hk1+ECVelHxvbBhuTBF6Wu3WvwH37wYMHw4OHwAPTYVt4gGpRzfpm6v6uY+CU16DHxNLUI6k4+h8BtRmGLKdT1RMq6wqzliRJkiRJkiRJkiRJkiRJkiRJnYTBaJIkSSpPkSgcenNwA+ku3cbBAb9qOa7fETDpyy3bRl9SnJoSHSEYral0e03+NlyYgAN/BUM+kNmcVOFn6Uy5Lrxv63ulDzrqKBKx8L5iBaNFUlyqRlKEpqnjqB8M+3w1tHtjU3gwWn11s9dIVRbBaLu8eS2seDj7efnY8EaOEyPBcXP0x2H69bD357ILg1PpVXWH/b4N5zXA+Ttg6nXQe1pbVyWpves+Hqb+EI66JzjOpDLz66WpqVASCXj+Utg8b3fbupfgpc+WvpblD8KSf6Yec9rbUNu/NPVIKq6+hxRmnQHHFGYdSZIkSZIkSZIkSZIkSZIkSZI6EYPRJEmSVL76Hw5nzIej7oLjHoUZs4KwnOYiEdjvWjjtnSBI7ZTXYfpvi1NPfEdx1i2lVCFXhTT4AylDjUL1PiD3PWv7wonPhvcvfyD3tTuzeIpAwGhVcfbsOro466p9iVaHdm1IEYzWIgMxVcheKgtvym1erhqW5zbPEDRJUnNDZsBp82Cfr4WPWfwPmP3j0tWUr3Uvw/pXWrcv+SdsXVy6OmZ9Gx49KfWYg35viK/UkRTqXHvcZYVZR5IkSZIkSZIkSZIkSZIkSZKkTsRgNEmSJJW36l4w5FQYcDRUhIfk0G00jDwfek0Obkaf+KXC15IqIKq9SDSVZp9cf/4TrkrevtfHMptf2z+874VLs69HqV8z0cri7Dnu35O3D55RnP1UnqI1oV0bUwSjNcWbPUnEQ8eltOjW3OblatuS7OdUdoG+hxa+FklS+1bbHyZ/C3pNDR/z6lWw+Z3S1ZSPtc+H9y27uzQ1LPgLzPp6+nFV3Ypfi6TSqSpAMNr038CA4/JfR5IkSZIkSZIkSZIkSZIkSZKkTsZgNEmSJHVMoy4q/JodIRgtnkEw2oBj8tujz8HQ77Dc5g48HnpMatkWrYExn8xsfk3f8L5tS2Dn5tzq6sxSve4jVcXZs9cU6HfEHntFYexnirOfylNFbsFoseZZaN3H57Z30+bSBsZkErayp9GfhMq6wtciSeoYDvtr6v67xpamjnytezm8b/1rxd9/8e3w7EcyG1tpMJrUoVTnGYw2+NTgs4xIpDD1SJIkSZIkSZIkSZIkSZIkSZLUiRiMJkmSpI4p1zCcVDpCMFoilrq/y0g45iGY8gOo6p79+v2PgmMfzP3G32gVHP84jL4k+Dsc/AE45n7od2hm89OFEWyel1tdnVmqML1oZXH2jETg6Hth3Gehx0QYeCIceQcMObU4+6k8RatDuzbGwoPRmmKJ3U/6HQFVOQYa3DUWEon04/K14Y3U/X2mw4QvwJTrguDJ3gfAlO/D/j8qfm2SpPar+7j058ZrXypNLflY+2J4X+Oa4u0ba4QXPgVPnpX5nCqD0aQOJd9gtNEXF6YOSZIkSZIkSZIkSZIkSZIkSZI6oSLdxS5JkiR1QPEdbV1B/hIpQq4ilbDf9yBaARO/AGM+Cf/old36R9yWfyBATR846He5zU0XyLb4Vti5CbqOhi7Dctujs0mkCASMVBVv36qucMAvire+yl+0JrRra6xLaF/zXDQqqmHK9+DFz7Rct/f+sObZ9DXMux7GXZZBsXlY9I/wvn2/Cft+fffziVcXtxZJUscy7jJ46wfh/cvvhz4HlK6eTDWshK0LoKYfbHorfFwxg9Fe+iy8m+U1SWXX4tQiqW3UDsx97oBjYfCMwtUiSZIkSZIkSZIkSZIkSZIkSVInYzCaJEmSlKl4ioCo9iIRC+874Unoe/Du51U9sl+/OssgtVJ787vBA2D0JXDgfwdBcAoXTxGmF/WSUkVUER6MFk9EQ/ua9jzMjf00dN8bltwBlfUw/FzoNQWW3Q+vfQk2vB5ew0uXQ91gGHZmlsVnYcVD4X1Di7ivJKnjG/Xx1MFoM78W/I4cfk7pakolkYC3fwWv/L/Ugc67FCsYbecWeO9/s58X8dxY6lAGnRC8r/c8HtUNgpEfgZ0b4Z3ftp4XqYSj7w1CmiVJkiRJkiRJkiRJkiRJkiRJUk7C7ySWJEmS2rsJVxd2vQ0zC7teWwgLGOg6pmUoGkAkAt3HZ7d+JJJbXYWUaZDQu/8ThC689QOY+wvYurC4dbVXqUIpDH9QMUXDgwRiVT3D++JJGgccDdN+CvtdG4SiAQw+GWa8Bqe8lrqOJ8+ChpXp683VlvnJ22v6Qa/JxdtXktTxdR8P036ResxT58JLV8DG2aWpKZUFN8DLn80sFA2KF4y2aQ7Etmc3p6IOuo4qTj2S2kZ1LzjkxpbXvX0PhQ/MhqnXwfTfwGF/g2izQOfeB8CZS1KGPEuSJEmSJEmSJEmSJEmSJEmSpPQMRpMkSVLHNewcoIBBXfP/AItvL9x6bSERS94eDQm4GnFh8WoplhEXZD727V/Ca1+Clz8H906B1U8Xr672Kr4zvC9aVbo61PlEw8MEYtGuoX1NyYLRUum1Hww/L/WY5fdnuWiG4jthe0jo2iE3FmdPSVLnMv6zMO6K1GPe/iXcNxUW/7M0NSUz/0Z47uPZzWlcC4lsf/FnYNOc7OcMOR0q6wpfi6S2NfICOGMhHPZXOOFpOP4xqO6xu3/Eh+D0d+HQm+G4R+GEp6BuQJuVK0mSJEmSJEmSJEmSJEmSJElSR2EwmiRJkjquvtPh0L9ATd+W7ZVdcg90eumy4tx8XyrxpuTtkZBgtElfgbGX7Q4o6rlf0JbM2Mvzr68QRnwIBhyb/bydG+DVqwtfT3uXCHnNQPjrRiqEivBgtHi0S2hfLJdD9MF/SN3fsCyHRVPY8h48fjr8tRpIJB9TP6Swe0qSOq8RaQJAAeKN8OTZEGssfj172rkpCCrOViIGT5wJa1/Mv4Y1z8EzH4F/nQTP/lv28w/6n/xrkFSe6gcHx9F+hyb/LKl+CIw8HwYcnfIaRpIkSZIkSZIkSZIkSZIkSZIkZc5gNEmSJHVsIy+As1fCmYvhgljw5zkbYcabUFHXcmzdEDjldeh/ZPh6DcthwxvFrbmYwkKuIhXJ26MVcOCv4NwNcNZymPEaTPrP4GfVXEUdjLm0sLXm4/Bbcpu35tkgsKi59hyEVwjxneF9uQYMSpmIVod2xaJ14X25vGUr6+Ho+8L7m7bksGiIhuVw1xhYelfqcXseZyVJylXfQ4JHJh4+qri1JLP0niCkOKe5dwU1r3429/2X3QcPHwnv/QVWPJj9/HFXQFXX3PeXJEmSJEmSJEmSJEmSJEmSJEmS1ILBaJIkSer4IlGoH7r7z2gFdB8LJz4Dg06GLnvBiAvg+Meg1+TWgWl7um8/eORY2PxuScovqEQseXu0MvW8ilqoGxh8XdkFTnoeRpwf/OwGfwCOfQR67VfYWvNR0yf3uXfuBbd0gyfPhbsnwl+r4b5psOKRwtXXnsRDwvQg/etGyke0JrQrFqkN7WvKNctw8MlAJHlfId//s74ZfizepaIWqnsVbk9JUucWicAxD2Q2du3zsHNTcevZ07J785sfa4C5P819/pyfpA4DTqeqW+5zJUmSJEmSJEmSJEmSJEmSJEmSJLViMJokSZI6r15T4Jj74Iz5cNhN0G1M0J4ijOf/rHwUHj4KmrYVt8ZCCwu5ilRkt079EDjs5uBnd/Td0O+Q/GsrtOHn5T63aQss/gdsmh0EGK1/BR4/HTbOKVx97UUiRUhEpKp0dajzqUgVjJaiL9dgNAgCH5NZ+zw0rs1j4ffFm+C9P6cfVzswCLGRJKlQqrrB6EsyG7vulcLvn0jAlgWw+mmIbW/Zt/nt/Ndf9Pfc61r1RH571/bPb74kSZIkSZIkSZIkSZIkSZIkSZKkFgxGkyRJkvZU0yezcQ1LYcUjxa2l0BKx5O2RytLWUQqjPlbY9WLbgrC0ziYsTA8g2gFfNyof0erQrkSKYLSmfILRKruG9839eR4Lv2/WNdC0Nf24vmUYNilJav8qu2U2blOBw4CbtsKTZ8Odo+Chw+G2gbDi4d39jWsKs08uIaZNWyC+I/24ab+Eqh7J+wadkv2+kiR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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import asyncio\n", + "\n", + "from matplotlib import pyplot as plt\n", + "\n", + "from apd.aggregation.query import with_database\n", + "from apd.aggregation.analysis import get_known_configs, plot_sensor\n", + "\n", + "with with_database(\"postgresql+psycopg2://apd@localhost/apd\") as session:\n", + " coros = []\n", + " figure = plt.figure(figsize = (20, 5), dpi=300)\n", + " configs = get_known_configs()\n", + " to_display = configs[\"Relative humidity\"], configs[\"RAM available\"]\n", + " for i, config in enumerate(to_display, start=1):\n", + " plot = figure.add_subplot(1, 2, i)\n", + " coros.append(plot_sensor(config, plot, {}))\n", + " await asyncio.gather(*coros)\n", + "\n", + "display(figure)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import asyncio\n", + "\n", + "from matplotlib import pyplot as plt\n", + "\n", + "from apd.aggregation.query import with_database\n", + "from apd.aggregation.analysis import get_known_configs, plot_sensor, Config\n", + "\n", + "async def clean_magnitude(datapoints):\n", + " async for datapoint in datapoints:\n", + " if datapoint.data is None:\n", + " continue\n", + " yield datapoint.collected_at, datapoint.data[\"magnitude\"]\n", + "\n", + "TemperatureConfig = Config(\n", + " sensor_name=\"Temperature\",\n", + " clean=clean_magnitude,\n", + " title=\"Ambient temperature\",\n", + " ylabel=\"Degrees C\",\n", + ")\n", + " \n", + "with with_database(\"postgresql+psycopg2://apd@localhost/apd\") as session:\n", + " coros = []\n", + " figure = plt.figure(figsize = (20, 5), dpi=300)\n", + " configs = get_known_configs()\n", + " to_display = configs[\"Relative humidity\"], TemperatureConfig\n", + " for i, config in enumerate(to_display, start=1):\n", + " plot = figure.add_subplot(1, 2, i)\n", + " coros.append(plot_sensor(config, plot, {}))\n", + " await asyncio.gather(*coros)\n", + "\n", + "display(figure)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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6dsopNa7NWITLFnJ3RjPKfyriSIBZodDtcxvcPk+Y4sFrWZtxhlh3Ntg1VdhrxUYLfAMAoML2ccx/QdIySWs03DdzT0mHKP8BtmdIusMYc4K19uWwOzTGzJf0F0kdo2ZbSUskrZDULek1knpGLX+fpMnGmLdaa72w+wIAAABQRV6EpjnBaDX3rQX+96afLrh625Kyum+FlMlJR82VuruM73aeZ/XkS9KTL1lZK+030+jVu0mxmNHmlNX9K6RpE6Utaekfa6w8KyXi0sCQFDNSsl06aT+jWVP9yweAUm1OWd27XPKsdNSe0hTH51ip+iv0E+eKvsqUgx2stXr2FenR56WcZ7XndKPXvEqKt9XXd823Hd/Jj72w4985z2rp89KKtdK8PaQ5PfX1GgAAQO396Db/NkQqI12z2CrrDS/frdvosNlSop32A2pvw4D/fILRAH8EowEAAAAAAAANxDXYPm7aI5XTZtrUbjqUsUOB67kCGlqZ65i4AsPGrOcKXnAEOhTfb62D0fz3N1K/oDC9tJdWZxu/2AAAgNpan1mrx7Y85LvskIlHqrt9Wo1rNJY7fDdcG7PZ2IBgNBUGo5UZKldq+3Z0+UH7arQAsbDh2IRoAwCw3cOSfi3pRmvt8sKFxpjdJH1V0r+Mmr2PpD8ZY4631hZ9PLkxZpaka5Ufina3pHOttU+NWi8h6TxJ35U0csP4zZK+Juk/o7woAAAAAFUSJbPYy1WvHojsukes/ulQo1uftHrHzzxt3nb7viMu/eZDRu85PJa3fv+A1Tt+6mnhM6PnWh09V/rYfKOP/t5qazrMnq0+f4rRN99uFIsxYBtA+a5favXuX3ga2NZtMBGXfndOTGe+tnKfMf2Ogf5RrSQYraLSGasPXWZ15UOjb0laHbCLdP2nYtpjWmN8z2xNW21JS2f8r6fFq3bM/8CRRpf+s6m7kDcAAFA73V3utug7fz76nozVrKnSDZ+K6aDdaDugtlzv0W6G2QC+YsVXAQAAAAAAAFAvctb/icBxE/0ZCGEG6DfaIP5acB2T8MELjmC0IiF0rv3WOiyjWP2D3jO1DnEDAACQpDv7F8jKf7DZCVPfWOPa+EsYR1vPEarb7ILyQUxBXzRnqFyIY2etdbZvR8p1BhSPKj+Vc7dzO2ON1WOp3XTIqHiHP0K0AQAtzkq6XtJh1tp51tof+4WiSZK19gVr7XmSPlGw6FhJ7w65v4skTR01fY+kk0eHom3bV9pa+yNJ7yrY/nxjzB4h9wUAAACgmqKEneX8+4egOp54MTi3+m2XeNqw1epdv9gRiiZJQ1npg7+2WrMpf/uL/mYLQtGG3bNc+sClYUPRhn33ZqsbHw+/PgC4bBrMD0WTpHRWev+lnvo2F83vD61/oDJl9fZVrk6QfrqoMBRt2JMvSZ/8Y4Tw1nG2cp30uatsXiiaJP3uPqufLeI9AwBAK5vVHX7d5zcMt4NDPMcKqKgNjmC0qY3VzRCoGYLRAAAAAAAAgAaStRnf+XHTHrmsMAPZGew+VsI4ghFCBpS5wsGKhdC5zkXYQLZKcdd/WzCa4/hIrRvsAQAAxk/Gy+jujbf6Ltul41Xau/OgGtfInzN8q8YhuPXCKiAYrSC0q5xjl7FDzn0ligWjjWoXB7XJGy1s2hjDtSIAAMW901p7hrX2obAbWGsvkXRNwewPFNvOGLO3pH8eNWtI0oesdd9os9b+RdLlo2YlJF0Qtq4AAAAAqsiLEDqS9e8fgur466PFB0J/4Rqrfp/Bq1lPunZJ/vZ/WlzZgdVXPshAbQDlu+ExmxeKNmIoK10X4nMwrP4KdWfr7atMORh2lU8o2ogbHx8OzqsHqUxwPZatka592H+dv1XwfQwAABrPblOLrzPa0uelf6ypTl0Alw1b/ed3dxV/mCnQighGAwAAAAAAABpIxhmMFo9cVlCA1Yhkgw3irwXXMXEFhoVdr1iwgHu/tQ3LcO1vR3CE+z0T9hgBAABUypLNd2tLbqPvsuO73yhj6qMzSZjwrdYS1GG9MBjNv/0ZJkA4ONBs+JyEaf8HnadkrPEe5RgmzI1rRQBAK7PWrixx058UTJ8YYpv3SmobNX2ttXZZiO2+VTD9LmNC3BAGAAAAUF22csFoGwesXuy38jwCQCrh+Q3F17nhMfexHj2QekvK6sX+ClRqlN/fz3kGUL5lr7iXPfJc5fbjFyJZinVbpc0pPv8qZVlA6IdnpRV1EkS3en3w8ruXW6UczaS7l1e+PgAAoHFMSkTfJqiNDFTDynX+1zg7T65xRYAGQTAaAAAAAAAA0ECyzmC09shlhRnIXiysqxU5gxFsSl6RTszWWmewWLHzUU7gQyWlrSMYzYwEo7l/Uax1XQEAAO7ov9F3fsIkdfjk+bWtTACC0fKFj0ULOnbFA4SDju9IuWHKT3n+ozvaFFd7LPq12ngLE6LNtSIAACV5uGC60xjTXWSbtxVMXxZmR9bapyTdP2rWBEmnhNkWAAAAQBXlcuHXzfj3D3niRavD/iennT7radYXPPWc7+lLf/YISCtTmBCfl/yfwyJJ+uFtVqnM8DlgUDWAenX9Uvd3xU8WWp3xo5z6Npf/fbKxgj9x9tZJWFej25yyWrc1eJ3v3FQfbYli5/x7N9dHPQEAQP3JRLjtMmJlH20L1JarvTunp7b1ABoFwWgAAAAAAABAA8nZrO/8UoLRwgxkDxOe1mqCjtuQTQdum7UZefIPTyt2PoICGayt3Q9yrvCIkfrFTJvaTYdj2+LhFAAAAJWyOrVcvalnfJcdMeVEdbZ11bhGbq4Q3Fq39eqFjRCN5gwuDhEqF9Q+HSk3XDCa/76SbY15PRXmWnEkGBkAAETid3PX/0aaJGPMTEmHFGx/d4T93V4w/cYI2wIAAACohiIPW8uTGdv/YGvaav53PC1eJY3cOu4fkL5xo9V3CAkpS/9A+cfvk1cMl3HWLyOc5wg2pzjHAEq3JWX1wMrgdW54XHr7T8v/DKtkMNqKtZUrq5WFCZi74gGre5aP/3dNbxnhJOTEAgDQ2oZKCEbrXVf5egAug0PWGbw/p6fwkbEAJILRAAAAAAAAgIbh2ZwzVKuUYLQwoWdhBsS3GldohVQ8fCEoeKHYsU46ggc85ZS1/k+KrgbXaxhdf3c4BcFoAACgdhb13+Bcdnx3fWVCuNqYnryatvXqh7vHuikIRgsTXObiCjQbXW5QaF2xcho1aJprRQAAqmavgumspKAhiQcVTC+11m6NsL97CqYPjLAtAAAAgGrwygtGu+4Rq3WOq4JL7yIJpBwbBsov44r7rbakrP6xpvyy/IQJtQEAlz8/HO574q5npadfKu87ZcPWyn0nlROShR3Cfodcdvf4H+9yvu9SGSmVGf/XAAAAxkemhGC0VbQ3UUOr17uXzempXT2ARkIwGgAAAAAAANAgsjbrXBY38cjlhRnInnCEcbWyoOMWFKxQbHmxYx2031oGjrlew+j6lRNOAQAAUAkDuS16aNOdvsv27jxIuyZ2r3GNgiVMfbT16kVQdzNjCoLRHMeuWGixJKWt/7FtU3x7+LQzGM2m5NnhQYzONnKDXk8FhUFHWQcAAIxxZsH0Q9baoFSEAwqmn424v+VFygMAAABQa16EEbqZoTGzlr7gXv3ZV6TBIQbzlqq/AsFogxnpzmXll+OyYm31ygbQ/IK+Q8auW973yQv9ZW2ep3dd5cpqZWED5h57fvzbEivLDALdWPxnYgAA0KSG3MNtnAghRy2tD3gM2m7dtasH0Eiij5YEAAAAAAAAMC6yNuNcNjJoP4pwg92Lh6e1mmTAcSsWWpG2AcFoRY514H7toCZqcuD2leJ6jaPr53pvFQuOAwAAqJR7N96mjB07aEySTph6eo1rU1yySPhurdp6dcP6d7g3MmPmBQWXFeMKT8tr2waE1g3ZtJKmUynPf8RcZ1tX0TrUo6BrjyjrAACAHYwxEyWdUzD7z0U226tgenXE3a4qmJ5mjJlqrd0QsRwAAAAAFWCtdd779JVJj5n17QXB269cJ+2/S9SaQZI2VCAYTZLe9L9B+dfleWaNlXzukxey1uqHt1n94g6rp18enjenR+rulF6/v9GFbzbqShgtesbqh7d5euwFybPSXtOlDx1jdNbhsaq9BgDjp3dt+O+g5Wul/gGrr/7V6o5/WG0e9bPbvjtLHzkuprfPG/48um+F1fdv9vTI81Ju20fg6vWVq/d9y8c/qKsR+J2vA3aRzjshpi0pq/OvCnccH1gpxf4lp50nS10dw/NGh4XsvpPUtu1rYk6P9L4jjM4+prLfGyvXlXfON2yVdm6xn9cBAMCwTIQ8+hErCeJFDQ34d2mVtKP9DSAfwWgAAAAAAABAg8ha9yNsqhWMxmD3sYKOW7Hgr6DgtGLHOig4rZaBY67XkAgRHuEKngAAAKgkz3q6o/9G32VT4jvpkImH17hGxQW1MYPCdZuVlauze4RgtCKhxUHrjG57BwcjDyoZ63S2x8Ncc9WjMAHZhGgDABDZNyTNHDXdL+lXRbYpfCb0K1F2aK3dYoxJSRr9xT1FUlnBaMaYGZKmR9xsbjn7BAAAAJpClFA0Scrkj5T87s3FA7dWrCUYrRTW2ooFo1XTF6+1+vfTiq/3+autLr4l//02Emrz8HNWD/RaXfiWmE79gZc3aLy3T7rlKauNg54+egLhaECzWdFXfJ0Ry9ZIJ37X06PPj13W2ycteMLT5Wcb7TvT6KTveUq5n/datodWSRsHrKZ0FQ+GbFWZrNUJ3xkOuhytt0+6/rHSAjvXbPKfPzr0rrdP+r+nrdZt9fT5Uyr3vdEb4b3q55olVl96E+8XAABa0VAJwWgbBmhvonZcwWhdHZIxvAcBPwSjAQAAAAAAAA0iLliMLgAAIABJREFUa909iOIm+q2+cIPdG3MgfzW1mTa1mw5l7NhfJYqFL7iWxxQrGm4XGJYRIvShUsoJj0jb2tUTAAC0rqcGHtHazMu+y46bcqraSmg7V1tQ27y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dv1ZJrqqq6sZuHVRV1Q1Jfj1u6IRunQULVn1FcvQftZ7/2onJltt708sPX5Fc8qeTB/nscWTyW+ckg8t70xezZ+kxyaPOal/z6w8l33hYMrKxNz3RueFbx/5srvl4sv6XycZrxx69tPh+yWP/s32A2Fz3oL9tPfej1/auDwDua/jm5Httgnz3OCJ53DnJ4LLm88uOTe7/5tbrb/nmhIEpBKUBAAAAAOym+me7AQAAAAAAYGFqTBpgNNijToD5YvL7imA0AACYYSvGfXxTD867KcnhOz5e2YPzYOE5+g3Jr/4x2XpH8/lrP50c9Zru9jB8y1h4UjulP/ndNUljVXd7YfdywO8kR7567O9oKxuuTq7/QnLoi3rXF5O75hPJ5lumvu4Rn0+WHT95Xf/ipBoZ+7j0JyObdp5ffMDUz59r9jqp9dyV/5Q88B3Jor171w8AY65u89x2YM/kqb+afI8jX5P8/K+az132rmS/0+69rrZPrT8AAAAAgN2QYDQAAAAAAGBW1CcLMKr5NgYwNfVao+38gGA0AACYacvHfTyNtJMpG3/Gsh6cBwvP4NLk/m9KLn5z8/nf/Ff3g9Fuv2jyMIf9nyQUbaHa44jJa37zX4LRdje/+a/prVv5qKQuC7Vjexzefn7dT5L9ntCbXgC4V7v/D654RGd7tHvuu+W2+16PjnS2JwAAAADAbqw22w0AAAAAAAALU2OyYDQBRsAUNWpDbef7a+4rAAAwwwbHfdyLn0ccf8aiHpwHC9ORbYLPrvtMctPXZ/7M7VuSqz6SXPiK5LynTV5/v9+b+R6YGw46ffKaX38w+Ux/ctm7k613dL8n7mv85/MFLxp73Pa9qe+z8jFC0aaqsSpZ8cjW87/8h6SqetcPwEK36Ybk5/87ufHLrWs6fV5baklfizcIWv/LZP2v7r2uBKMBAAAAAHOfYDQAAAAAAGBW1CcJRhsQjAZMUb3VC0J2ELgIAAAzbtO4j1f04LzxZ2xqWQXsmv7FyVGvaz1/7hOTS/9i5s7bvjn5zhOTC1+WXPXhyesPPSM5+Lkzdz5zy+IDkwe+a/K6anty6duSrxyZDN/S/b4YM/Hz+dpPjj223Da1fYZWJyd/sDs9zncnf6j13E1fTX7yht71ArCQ3fXz5D8fklz8J61ragPJIS/ofM9HfL713FePSm6/aOxjwWgAAAAAwDwgGA0AAAAAAJgVjb72wWj9ZbBHnQDzRX8ZyECbe4dgNAAAmHHjk2YO7sF548+4tQfnwcJ1wO+0n7/83cnwDH0arjkzWXvu5HW1geQpVySn/OvYxyxcx76189otv0muFLDVM51+Pt/j2Lclp3xk7PGwTyYP/WjyuO8kT7k82fOorrU5ry09OjnwGa3nf/m3yYare9cPwEJ1+XuSzZOEsz7u3Kk9r93nYe3nL3nb2K+j7YLRqs7PAwAAAACYRYLRAAAAAACAWVGvNdrO95f+HnUCzCft7i0DgtEAAGCmXbXj15Lk4FLK4d06qJRyWJJDmpwNdMOSQ9vPVyPJrefMzFk3/Wdndcf+2VjgTykzcy5zVylJfWXn9Vd/tHu9cF83f31q9Ue8MjnsjLHH6hcmh74k2fcxSX/77x8wiaX3bz9/8zd60wfAQnZzB89xJ3vOPdHg8mRgaev5284f+7VqF4wGAAAAADA3CEYDAAAAAABmRV/pz2BZ1HK+X4ARMA2N2lDLOfcVAACYcZck2Zqk2nH9tC6e9bvjPt6W5OIungUMrU72OLJ9zZX/lIxs2rVztqxLrv1kZ7X7P2nXzmJ+OfgFndduur57fXCvajRZ84XO65c+IGkc0L1+FrL9nth+/uqPJSPDPWkFYEHZekdyw5eTm76WbLm9fe3yE5LGqqntX0r758TbN4/9OioYDQAAAACY+wSjAQAAAAAAs6ZeW9xybkCAETAN9Vqj5ZxgNAAAmFlVVW1N8t0kZcfjT0oprdOKp2nHnm/MWABbleS8HWcD3VJKcuJfJ7XWb2yQ276f/PteyfpfTe+Mm7+ZnHVIZ7WrX5zs9eDpncP8dPQfJUsO67z+yg92rxfGQhLPe0YysrGz+r56cuL7x+41zLwVpyYHP7f1/O0XJl87Idlwde96Apjvbvl28qWDkvOenpz75Pa1fYuTE943vXOOfVubySqpqqQSjAYAAAAAzH2C0QAAAAAAgFnT6GsdjCbACJgO9xUAAOi5z+z4tUqyIsn7u3DGXyVZmbHwtST5dBfOACY68KnJEy9qXzO6JfnRq6e+9+i25L9emIzc3bqmvio58rXJI89MHvpRAUrc19BByWk/SE76x2Tfx+be/0W0cNHvJ5tv60lrC9KVH0xu/HLr+f2elBx6xtjjge9KfvtHyf5P6l1/C00pycM/1T488O5fJT/+o971BDCfjW5LvvfszgJCj3tH8sQfJfudNr2zlh2bPLzdl8RVMioYDQAAAACY+/pnuwEAAAAAAGDhqtcEGAEzq919ZcB9BQAAuuHTSd6dZFXGUmleVUq5saqq98zE5qWUNyf5w4wFr5Ukt0YwGvTOsuOSB30g+cnrW9fc8s1k6x3J4PLO9117frJ5bfuax30nWXp053uy8NT3SY78w7HH1juTf5/k7+CNX0kOe2lvelto1nyh/fyDPuDzuddKLTnmDclFf9C65qb/SEY2Jf2t/00VgA6sPT/Zum7yuqHVyXF/vuvnDd2v9Vw1mlSC0QAAAACAuU8wGgAAAAAAMGsagtGAGSZwEQAAequqqq2llLck+VjuDS97VynlgUl+v6qqO6azbyllWZJ/TvKccftWSf60qqqtM9E70KF9Tp285pZvJUsfkCxZnfTV7x0f3ZZsWZc09h0LaRi+JRndOhZQ1U59ZbLHYbvWNwvL4LJkz2OS9Ve0rrn5G8mhZySjW5IN17TeZ+tdGftfTpLFByYDe8x4u/PGtg3JpjXJby5oXePzefbs8/D289X2sc+FZcf2ph+A+WrNmZ3VrZjkvtyxWuspwWgAAAAAwDwhGA0AAAAAAJg19Vqj5dxATYARMHVtAxfdVwAAoCuqqvpEKeUZSZ6ee0PMfi/J40spH0ny4aqqruxkr1LK4UlekeSMJHvl3kC0KslXqqr62Mz/DoC29n5IsvzE5I6ftq753rPHfq0tSg57WfLgv0su+8vkF3+TjGyc+plH/3Hi63im6v5vSn7wktbz13927DEVpS858OnJQz8mIG28bRuSH5yR3PDFsXCtdnw+z57lxyf7PTG5+T9b15z9gOTBf58c9dre9QWwUB31upnZp5TWc+c8LjnyNa3nq9GZ6QEAAAAAoMsEowEAAAAAALOmURtqOddfvFAKmLp6u2A09xUAAOim/5HknCQn5d5wtL2SvCHJG0opNyf5YZIrkty545EkS5MsS3JMkpOT7L9j/J5Xet+z14+TvLDrvwtgZ6UkT/h+8rnWX3P/t9EtyZX/PBaWNHzz9M576EeTQ18yvbUsbIe+eCy87Pxnztye1fZkzZlJrf7/s3ff4XFddf7H32ekkVXcbcklbrHTnR7SExISUigJoZelZhd2YYEsS1nI0kLgt7CFXcrCshD6AqEEUoFU0kmB9B7HvUlx72rn98fISI7vHc2MZkYa+/16nvvo6nzPPeer2BnZ8tzPhZP/r3zr1rr73gdLfzn4vPkXwyEfrXw/Svfi38BjX4BHL02f86eLoGUOzDi/am1J0l5nv7/LBQ6XRSa91HFH7kjT21WmHiRJkiRJkiSpsgxGkyRJkiRJkiRJw6axrim1ZoCRpFI01aXfpJ31dUWSJEmqmBjj5hDCmcD3gVeTCzSD/oCz6cCr+o40YcD5wOuvBN4RY9xctoYlFac+/ed4iUoNRZt/saFoGpqZr4Hj/hfufU951136S+j6FmRHl3fdWtS9BZb8vLC5h36qsr1ocHWj4PDPweo/QMft6fMW/sBgNEmqpNlvKN9aIU8w2mBid/n6kCRJkiRJkqQKGsJPQiVJkiRJkiRJkoamKdOSWjMYTVIpGjPpwWi+rkiSJEmVFWPcFGN8LfBhYBu5ULM44KBvLOngBXMDsB34WIzx1THGjdX6OiSl2Pcdld9j/OGV30N7vvFHlH/N3k7Y/Gz5161FmxZA747B5405AOoaK9+PCjPY6+uGx6rThyTtiXo7B58zbn759htKMFqvwWiSJEmSJEmSakP9cDcgSZIkSZIkSZL2Xo2ZptRa1gAjSSVoMhhNkiRJGnYxxv8MIfwQ+AfgfcCEgeWUy8KA83XAN4CvxBifr0yXkoo270JY+IPKrT+qFfY5v3Lra+8x6VgYfxisf6S86/72qN3HRk3afSzTAJOOg8O/AOPLGIIynLatggc+Bh23wZbFhV0z768r25OKM+9CePabEHuT6xufgmVXw4zzqtuXJO0Jutbnr8+4ABrbClpq2faFXP38T1iy/Vm62T3ELBsamFs3lfNGtTBlx5bie40Go0mSJEmSJEmqDQajSZIkSZIkSZKkYdOUaUmtGWAkqRT5Ahd9XZEkSZKqJ8a4BvhUCOFzwPHAacDJwD7ARGBnksxaYA2wArgTuBW4J8bYWfWmJeXX9mI4+Wdw55vKu27IwMTj4MTvQ3363+ulgoUAL/k93P1OWHV9ZffasSZ5fNmVsPoP8PKHoWVWZXuotJ7tcMPJsPm5wuaPmgz7vxcO/khl+1JxJh4Np/4GbssTQHnb+XD6dTD9ZdXrS5L2BJ3r8tdP/FFBy7R3ruQ/lnyCHXF73nl/7l7DsweewsefuJXxXfnn7sZgNEmSJEmSJEk1wmA0SZIkSZIkSZI0bAwwklRujZnm1FrW1xVJkiSp6mKMXcAdfYekWjf7jbmjexv07oCeHfC7Y2Db8tLWO/abMOetkB1d3j6lpmlwxu+h465cqFea5lnQ2wnbV5W/h64N8Nz34LDPlH/talp2ZWGhaOPmw5k354LRQqbyfal4M86D1z4Pv5qcPueprxiMJknF6lyfv17gn3XvWP/7QUPRdtqYbeTB8dM4vWNhQfP/otdgNEmSJEmSJEm1wX9xlCRJkiRJkiRJw6apriW1ZoCRpFI01aUHoxm4KEmSJElSmdQ3QcN4aJoCYw8sfZ3pLzcUTZU1bn7+kK6xB8L8iyu3/5p7K7d2tTx/T2HzWk+FxjZD0Ua6hom5I82a+6rXiyTtKfKFBB92ScHLLNr+dFHbLm0eV9R8AKLBaJIkSZIkSZJqQ/1wNyBJkiRJkiRJkvZejZmm1JoBRpJK0ZgxGE2SJEmSpKqa/UZYfXPx1006AVpmlb8faaCGcTDtZbDi2uT67Dfm6n/+EMSe8u/fcSfcdsHu443TYMb5MP1l5d+z3Bb9qLB5s99Y2T5UHiHkfq2e+WZyvXMtdG02tFKSCtW9DbatTK/PflPBS7V35lknaetSwkh7DUaTJEmSJEmSVBt8HJMkSZIkSZIkSRo24+onJI5nyNBYlx5uJElpRteNJZPyz6Cj68ZWuRtJkiRJkvYCc98Fcy8s7pq6Zjjxh5XpR3qh4/4Hxh+2+/i8d8O+74Dm6XDijyHTUP69uzbAsit3P579H/jDy+Hxfyv/nuW08Eew4/nB5x3+eWg7rfL9qDyO+AI05wmm3LK4er1IUq3bsii9dvBHYewBBS2zvXcbG3vWFbV1d6auqPkAxK7ir5EkSZIkSZKkYVA/3A1IkiRJkiRJkqS916TsFKY1zGRl59Jdxuc1HUxjpmmYupJUy0ZlGjmg+TCe3PrQLuNt2elMbpgyTF1JkiRJkrQHy2ThhMvgsE/D2gcgdvfXenZA73bIjoXYC91bYNwhMPFFkPFtzKqS5hlw7gOw/mHYvABCPUw8Blpm9s+Z8ybY5+Ww5j4Ysx+0zIbt7XBFAT9POuar0LMdHvxY8b09+jnY/72QHV38tZXW2wMPXZxe3+c82O89MOk4aGyrXl8auoYJcN4zcPmo5PqWxTB+fnV7kqRatf7h9Nrhlxa8TEfnytTa3MaDeG77k7uNd4XkBwXl1ds9+BxJkiRJkiRJGgF8R4EkSZIkSZIkSRpW75j2D3x92efY3LMBgAn1k3nr1PcPc1eSatmbpvwdX1v2GdZ0tQPQnBnNhdM/PMxdSZIkSZK0h2uZnTukkShTBxOPyh1psmNh6pn9nze2wfGXwT1/nX/tue8qPRitezOsuWfXfUeKTU/D1mXp9YM/Bm2nVK8flVddQy40MOnXeOvi6vcjSbUo9sLjX0yuNc+AupQAygTtnSsSx+tDPXObkoPRujMlBKNFg9EkSZIkSZIk1QaD0SRJkiRJkiRJ0rCa1TiPS/b9Bgu2PUEm1LFf0yE0ZAp/k7gkvVBbwzQ+OeerLNz2FJ1xBwc0H0Zjpmm425IkSZIkSVKtmf5yCHUQe5LrU14C2dG5Y9LxuZCzYq28fmQGo21emF7LjoNJx1WvF1VGy+zkYLTl18L+761+P5JUa5b+CtY9mFxrO72opdq7ViaOT85OTf238+5QQjBar8FokiRJkiRJkmpDCT8BlSRJkiRJkiRJKq+muhYOHf0iDmk5ylA0SWUxKtPIQS1HcPjo4wxFkyRJkiRJUmmapsLhn0+uNUyEI77Y//lR/54LDCvWE/8KT329tP4qafNz6bVjvwF1DdXrRZXRPDt5fMW18NCnqtuLJNWa3h54+NPp9YP+oajlOjpXJI63NUynPmQTa92ZuqL2ACAajCZJkiRJkiSpNtQPdwOSJEmSJEmSJEmSJEmSJEmSJI1I8z8ObafB6ptgxxrIZKFlDuxzHrTM7J/Xdgq87EFYfg1sfnb37ZH24AAAIABJREFUdVbdABseT97jzxfBzFdD8z4V+RJKsmVh8njDBJjzlur2ospoSQlGA3js87nfkxOPrl4/klRLll8NG59Mrs18DUw8pqjl2jtXJo63ZqeRTQtGC5mi9gAMRpMkSZIkSZJUM2oqGC2EcHPfaQTeHGNsL3GdKcBPd64VYzyzHP1JkiRJkiRJkiRJkiRJkiRJkvYwrSfmjsGMngMHvj+59uDF6cFosReWXQkHvK/kFstuc0ow2j7nV7cPVU6+YDSApVcYjCZJaVbdkFIIcNjnil6uvWtF4nhbw3R6Y09irauUYLReg9EkSZIkSZIk1YYSfgI6rE4HTuv72DiEdRr71th5SJIkSZIkSZIkSZIkSZIkSZJUGW2n5q8v/ims/TP0bK9OP4PZ/Fzy+Oi51e1DlTPY78nNC6rThyTVorTXyJmvhfHzi1pqa89mNvdsTKy1ZadRH7KJte5MCbcFRoPRJEmSJEmSJNWGWgtGAwjD3YAkSZIkSZIkSZIkSZIkSZIkSQWbejZMOj693nEH/O4Y+MV4eOyLEGP1enuhGGH9Q8m10ftWtxdVzrhDcgE+aRb/DLa3V68fSaolK3+fPD7puKKXau9cmVprbcgTjBbqit6L3q7ir5EkSZIkSZKkYVCLwWiSJEmSJEmSJEmSJEmSJEmSJNWOTB2cedPg83p3wEOfgGVXVr6nNM98M73WYjDaHuXkn8GYA9Lrd721er1IUq1YeUN6rYQA0Y6uFYnj2dDA+PpJZDMpwWiZEm4LjN3FXyNJkiRJkiRJw2BvDUarH3DuT3QlSZIkSZIkSZIkSZIkSZIkSZVV3wInfL+wuYt+XNFWSt67hMAXjWCZejj2v9Prq26Abauq148k1YIFl6XXRs8tern2zpWJ463ZqWRChvqQEowWSrgtsLez+GskSZIkSZIkaRjsrcFokwecbxm2LiRJkiRJkiRJkiRJkiRJkiRJe48JRxU2b8Pjle0jn23JAS0ANE2rXh+qjnGH5a9vfKo6fUhSrdj8XHptzAFFL5cajNYwHSA1GK0rU8JtgVtXFH+NJEmSJEmSJA2DvTUY7cV9HyPgT3QlSZIkSZIkSZIkSZIkSZIkSZU34XCYfOLg8zY+ATeeBluXVb6ngXq7Ycui5Nr4IyDsrbcg7MGapsDM16bXtyyuXi+SNNJ1boC19yXXQgayo4tesqMr+da2toZcGGlaMFpvyNCb2Edd+mZbFua+10uSJEmSJEnSCFfL/yoZi5kcQsiGEGaFEP4G+OcBpUfK25YkSZIkSZIkSZIkSZIkSZIkSSlOvxZmvR7qx+Sf134bXH8S9GyvTl8AT301vXbiD6vXh6or369tWlCeJO1tYoQbT02vn3ZdScu2d65MHG/LTgcgmxKMBtCdFIJ22rX5w9Ge/lpR/UmSJEmSJEnScBhxwWghhJ60Y+A0YFG+uQnXbgcWAt8Cxg5Y66oqfnmSJEmSJEmSJEmSJEmSJGkQIYRjQgivCyGcF0LYb7j7kSSprBomwCk/h9etg1ctyj9361JYVsW3vD/1X+m10XOr14eqq74ZZlyQXDMYTZJy1j0A6x9Jr489oOglN/dsZGvv5sRaW0MuGK0+XzBaJuHWwOnnwOvWp2/66KVF9ShJkiRJkiRJw2HEBaORCz1LOwqdN9gR+9Z4Evhl5b4USZIkSZIkSZIkSZIkSZL2XiGExhDC3AFH3SDzzw8hLALuBS4HfgM8FUK4I4RwSBValiSpejJ10DwLGibmn7fgsur0A9C1MXk81EN2dPX6UPW1zEke37K4qm1I0oi19v70Wl0zNM8sesn2zhWptbaGaUD+YLSukHJrYHY0NO2TXOtcDzvWFtyjJEmSJEmSJA2H+uFuIEVk9yC0cgrA/cAbY4xdFdxHkiRJkiRJkiRJkiRJkqS92YeBz/WdLwPmpE0MIbwB+AnJD1M9CbgnhHB6jPFPFehTkqThEQLMfjM889/pc1ZdDz8J0DABOtf1j9c1Q8/W3PmUM5IWh/GHwry/yX1Ms+L3sORyWPcgdG1InjPxRYN+KapxacFoa+6Bm86E+tHQdioc8H6oaxzaXjHCc9+FlddD41SYdyFMOGJoa0pSpW1emF6b9XrIFH+bXnvnysTxUaGRsXUTAMjmCUbrzqQEowG0ngRLfpFQiLDydzDnLcW0KkmSJEmSJElVNRKD0W4jF4yW5LS+j5Hc0yC3F7hmBHYA64EngFtijLcPpUlJkiRJkiRJkiRJkiRJkjSoC8iFnEXgshhj4vsDQwgTgG8Bmb65Ax+wuvOaFuCKEMKBMcZC3z8oSdLId+S/5A9G22lgKBr0h6IBrL45+ZrVN8GC78IZN8Lk43avP/ttuPc9g+994g8Gn6PaNnpO8nj3lv7fX8uvguVXwxk3Q6au9L3u/VtY8O3+zxd8B864HlpPLn1NSaq0x7+YXjvmP0tasqNrReJ4a8M0Qsj9lbg+XzBayPNafML3UoLRgLv+Cma9oaQwN0mSJEmSJEmqhhH308sY4+lptRBCL/1vcHpjjHFJVZqSJEmSJEmSJEmSJEmSJElFCSE0AUfS/76/a/JM/wAwjv5AtOXAFUA38Bpgdt+8GcAHgX+tQMuSJA2P7BiYdi6s/F1l1u/eBE98CU791a7jvd3wyGcHvz7TAGP2q0hrGkFa5hQ2r/02WHU9TH9ZaftsXrhrKBrkQv4e+xc4Pd8fFyVpGG14Ir128MegYUJJy7Z3rkwcb2uY9pfzfMFoXZlM+uL1LTD3nfDc95PrK34LM84roEtJkiRJkiRJqr48P/0cscLgUyRJkiRJkiRJkiRJkiRJ0jA7DKgj976/LTHGP+eZ+1b6Q9GeAg6NMV4UY/xw3zr39c0LwDsr1rEkScNl7jsru/7qW3Yf2/QMbFsx+LUtcyDU4q0HKkqhwWiQ/PupUMt+kzy+4lqIMbkmScOt/Q/ptdFzSl+2M/n7cGt2+l/O6zPpwWjdg31/nnJmem0or+WSJEmSJEmSVGH1w91AkS4ZcL5+2LqQJEmSJEmSJEmSJEmSJEmD2bfvYwQeT5sUQjgI2K9vXgQ+HWPcsLMeY9wcQvgA8Me+oQNDCDNjjEsr07YkScNg2tnk8j8rFAzVuQ4e+iSEAbcQbFlY2LUzXlWZnjSyNIyDttOg/dbB524u8PdOkhW/Ta/t6IDGttLXlqRKWfun9Nr0V5a0ZIyRjq6VibW2hml/Oc+GIQSjTX95em3Lc/mvlSRJkiRJkqRhVFPBaDHGSwafJUmSJEmSJEmSJEmSJEmSRoApA86T7/bOObXvYwA2Ab9+4YQY470hhGXAjL6hwwGD0SRJe46GCXDsN+G+v6vcHo99ofhrJhwJB324/L1oZDr6y3DLubmAsnyW/hJ6eyBTV979Ny8yGE3SyLP+MVhwWXq9ZWZJy27q2cD23m2Jtbbs9L+cZ6gjEIgJ4andg70Oj5oILfsmh6EuuxJ6uyFTU7cXSpIkSZIkSdpLDPJYCEmSJEmSJEmSJEmSJEmSpJI0DzjflGfeyX0fI3BTjLE7Zd6jA85nDaUxSZJGpP3/Fl7+CBzwwdLDoeZ/EkIZAk4O/TSc+is46w5omjL4fO0ZJh6d+z14wvdzv5dmXJA+d/VNpe3RszW9lhTcI0nD7cF/Sq/N/+eSl23vXJFaa2uY9pfzEAL1IZs4rysUcGvg4Zek11ZcN/j1kiRJkiRJkjQMDEaTJEmSJEmSJEmSJEmSJEmVEAacJ9/FnXPSgPPb88xbM+B8bEkdSZI00o0/FF70FTh/UWnXH3EptMweWg/7vj0XojLzNVDfMrS1VHuapsDcd+R+Lx34wfR5y35T2vqbF+WpGYwmaYTp2Q4rf5teb5lT8tIdXSsTxxszzYyuG7fLWH1K6Gl3poBbA1v2Ta+V+louSZIkSZIkSRVmMJokSZIkSZIkSZIkSZIkSaqETQPOpyRNCCFMBfYbMHRXnvUG3gkeUmdJkrQnqG+CSScUd83+78t9nHL60PZuG+L12nNMfFF6bcFlsP353cc3L4KNT0GMu9d6dsC2Felrrvxd8nWSNFy2LIbYm15vO63kpVd3Jr8etmWnEcKuf+WtD8lZ492hgFsDJx6dXlv6a+jtGXwNSZIkSZIkSaqymg5GCyG8JIRwaQjhmhDCvSGEp0IIzxV5LBjur0OSJEmSJEmSJEmSJEmSpD3Q8r6PATgsZc7LB5zvAP6cZ73xA863DKEvSZJqw2GfhkxyEMpuRrXCgR/MnR/4IWiYWNqe4w+DWa8v7VrtebJjIDs2udbbCVe0wj3vht4u2LEGbjgFrtoXrjkIrjsMtizd9ZoNjwF5gs/ab81dt3lRub4CSSpdjHDve9Lrs94IY/cvefmOtGC0hum7jWVDQ+Lc7kzd4BvVN0PTPsm1rvVw5UxYc//g60iSJEmSJElSFdUPPmXkCSGcA3yVXZ8SWerTH32ckCRJkiRJkiRJkiRJkiRJ5ffwgPOJIYRzYoy/f8Gcd/V9jMC9McauPOvNHXC+qhwNSpI0ok1/GZx5Kyz8AWx6JhcqtX11rjZ6Low5AOoaYfQ82P+9MGZerjZ+Ppx9Nzz7LVj3IMSewfeqa4LJJ8KBF0F2dOW+JtWe4y+DO/KE5S34Dow7BNpvh447+8c3PJa77pw/9o/d9VeD77fhMbjzzXDO3aX3LEnlsPJ30H5bev3knwxp+faulYnjScFo9SE5KLUrZArb7Nhvwm3nJ9e2rYRbXwEXLCs8kFWSJEmSJEmSKqzmgtFCCB8FvrjzU/qDzUoJOCs1TE2SJEmSJEmSJEmSJEmSJOURY1wQQniG3ENQA/CNEMJLY4wLAUIIHwZOHnDJlWlrhRBGs+vDVBdUoGVJkkae1hNzR7HGHgBH/0f5+9HeZ/S+g89Z8N1ceN8LrbkHti6D5hkQI2x8srA91/wRNi8sbG9JqpRFP02vtZ4KhYaSJYgx0tGZHIzWmp2221h9SL4FsDtTYA+j5+avb2+HVTfB9HMLW0+SJEmSJEmSKqymgtFCCOcAX+r7NPYdO8PNtgLrgXxPi5QkSZIkSZIkSZIkSZIkSdXzHXLv+4vAvsCTIYSHgDZgJv3vA9wO/DjPOqfT/37BbuCxCvUrSZKkgcYeBHXN0LM1fc6GR9Nrq2+F2W+Aro3F7bvxqfzBaDFCCOl1SRqqjU+k1yYeM6SlN3SvpTPuSKy1NUzfbaw+ZBPndhcazjZmHmTH5n8t3vikwWiSJEmSJEmSRozSH00xPL7Y93HnG6GWAe8H5sQYR8cYZ8QY9y32GLavRpIkSZIkSZIkSZIkSZKkPdtXgCf7ziOQBY4BZtEfdBaBL8cYO/Ks8+oBcx+KMeUOckmSJJVXfQvMu7D06+9+K/ysAX41ubjrHv9i8viin8K18+Hno+Hmc2DzotJ7k6Q0z3wL1t6fXp/3N0Navr1rRWqtrWHabmPZTEPi3IKD0eoaYb/3DNLUbYWtJUmSJEmSJElVUDPBaCGEecAR5N7UBHAPcGiM8RsxxiXD15kkSZIkSZIkSZIkSZIkSUoSY+wEziEXjrYzCC3Q/17AAFwBfCZtjRDCaOC1A665qSLNSpIkKdnR/wXzL84F61RL+62w4re7ji2/Fu56C2x4HHq2wqrr4YZToGd79fqStOdb+CO47+/S64dfCuPnD2mL9s6VieMtmTG01I3Zbbw+1CfO78rUFb7pkV+CQ1P/6g3Lfg09nYWvJ0mSJEmSJEkVVDPBaMCJfR93viHq7THGTcPYjyRJkiRJkiRJkiRJkiRJGkSMcSlwJPBe4LfA48AT5ALRXhdjfH2MsTfPEu8ExpJ7/2AArq1ow5IkSdpVpg6O+AK8YQtkGqq37zP/s+vnz35r9znblsPya6rTj6S9wzPfzF+f89Yhb9HeuSJxvLVhWuJ4fcgmjneHIm4NDBk4/LNw+OfT56y4rvD1JEmSJEmSJKmCaikYra3vYwQeiDE+M5zNSJIkSZIkSZIkSZIkSZKkwsQYu2KM34oxviLGeGjf8boY4xUFXH4ZMGHnEWO8o7LdSpIkKVHIwNiDqrffugd3/Xz51cnzFlxW+V4k7R1i3P21Z6D60dA8Y8jbdHStTBxvKzYYLVPCrYHjDkmv5fvaJUmSJEmSJKmKaikYLQw4f3bYupAkSZIkSZIkSZIkSZIkSVUTY9wWY9yw8xjufiRJkvZq+76tvOvNe3d6besSuONNsOxquP+i9Hmrri9vT5L2XlsWQs+29PrsN0GmfsjbtHeuSBxvy05PHE8NRgsl3Bo47dz02ubnil9PkiRJkiRJkiqgloLRlg84rxu2LiRJkiRJkiRJkiRJkiRJkiRJkvZGB/4DzH5z+dY7/FI44gvp9SWXw23nw9NfTZ8Te6FnR/l6krR36u2Bq+al1yefBEf/59C3ib10dK1KrLU2TEscz6YEo3VlSrjFrr4J2k5Prm1ZWPx6kiRJkiRJklQBtRSM9tiA85nD1oUkSZIkSZIkSZIkSZIkSZIkSdLeKFMPJ/1f+dZrGA8HXjT0dZZdOfQ1JO3dVt2Qv37WHZAdPeRt1nU/T3fsSqy1NUxPHK9PCUbrDiXeGjjr9cnjGx6D7q2lrSlJkiRJkiRJZVQzwWgxxkeAR4EAHBNCmDDMLUmSJEmSJEmSJEmSJEmSpBKFEGaEEF4cQrgghPC2EMLbh7snSZIkFSAEGH/E0Nepa4S6UVDfAtmxQ1ur/bah9yNp75bvdWT8EbnXvjLo6FyZWmvLTkscTw1Gy5R4a+DoucnjnevgmW+UtqYkSZIkSZIklVH9cDdQpP8AvgfUAR8GPjm87UiSJEmSJEmSJEmSJEmSpEKFEGYDHwLOB2YnTPlhwjWnAi/p+3RdjPFrletQkiRJBZlxAax/aGhrZMf1nzfPhA2Plb7WM/8Nm56GcYf2j9U1wKTjYPorc+eSlE/H7em1ma/Je+nWns08vPlelu9YTCTmnbtqx9LE8TF142iqa0mspQajhRKD0VpPyYVT9mzfvfbAR2HSCdB2SmlrS5IkSZIkSVIZ1FQwWozxByGEVwKvBT4WQrgzxvjb4e5LkiRJkiRJkiRJkiRJkiSlCyFkgEuBj5J7OGpImJZ29/jzwGd31kMI18UYF1SgTUmSJBXqoItg9c35g4QG0zC+//y4b8MNJw2tp1U35I4XmnImnHYV1DcPbX1Je67n/wgdd6TXD/xgamlNVztfXfoZOrpWDqmFtobpqbVspszBaNnRsN974an/TK7feCocfxnMu7C09SVJkiRJkiRpiEr86eewegdwFblQtytDCJ8LIYwf5BpJkiRJkiRJkiRJkiRJkjQMQghZ4HfAx0l+oGtaIFquGOMTwC30h6m9pawNSpIkqXgNE+CMG+GMm+Cof4d9zhvaepNeVJ6+kqy+CRb/tHLrS6p9D3w0vXbMV3YNcnyB69dcMeRQNIDW7LTUWn1IDkbrytSVvuH8j0N9S3r9zx+C7q2lry9JkiRJkiRJQ5D0BqMRK4Tw6b7Th4CTgMnAPwP/GEK4G3gcWAf0FrNujPFz5exTkiRJkiRJkiRJkiRJkiT9xWXAS8kFoEVyAWe3kws76wQ+X8AavwJe0nd+NnBp+duUJElSUeoaYOoZueOAv4fLm4q7vnlm/3kmm/t869Ly9rjT8mtg3l9XZm1Jta1zPXTckV4fs3/eyx/Zcl9Z2mhrKD4YrTtkSt+wsQ0OvAge+3/J9a6N0H47TD+n9D0kSZIkSZIkqUQ1FYwGfJZdnwy58w1SzcAZfUcpDEaTJEmSJEmSJEmSJEmSJKnMQghnAm+l//1+zwJviTHe31efTWHBaNcCX+9b49gQQmOMcXtlupYkSVLR6hqh9VTouH3X8cwoaBgH29t3v2bKmbt+3jK7csFo6x+Bpb/OndePhsnHQ3ZsZfaSVFs2L8xfn3xSaqmzdwfru9eUpY2Dmo9MraUGo2WGEIwGcPBH4On/hq4NyfUn/wNiD0w6Fhpbh7aXJEmSJEmSJBVhiD/9HBF2PkGyFKGcjUiSJEmSJEmSJEmSJEmSpF18pu9jABYDJ+0MRStGjHExsL7v0yxwUHnakyRJUtkc9lmoa9p1bP7F8KL/hlC/6/jYA2Huu3Ydq2RQ2eYFcPtrcsctZ8OvWmHRTyq3n6TaseqG9Nphn8uFO6Z4vmtVWVo4esxJzG7cL7WeGowWhnhrYMMEOOnH6fVVN8Ctr4Ar2uDRL0As9RY+SZIkSZIkSSpO/eBTRhzDzCRJkiRJkiRJkiRJkiRJGuFCCBOBk+h/+OlFMcbnh7Dk433rARwAPDiEtSRJklRuU8+As+/OBY51roN9XgEzXpWrNe0DSy6HrUth0vG5ULTG1sLXDvUQu3PnDRNz6z73vdJ77e2Eu94KradCy8zS15FU+x78p/TaYZ/Ke2l758rE8UCGQ1uOGXTrprpm9m86lBPHnUEI6bfMZdOC0TJDDEYD2OeVMGZ/2PRM/nkPfxJaT4IpLxn6npIkSZIkSZI0iFoLRvMnp5IkSZIkSZIkSZIkSZIk1YZTgJ13abfHGK8a4noDQ9XahriWJEmSKmHCEbnjhVpPzB15rz0aVlyXXHvTDggvCADasQaWD+WPmBGW/hIO+tAQ1pBU07YsSa9NPXvQyzu6ViWOT85O4b0z/rnUrnZTnxKM1hXqyrPB7DfBo5cOPm/x5QajSZIkSZIkSaqKmgpGizHeOtw9SJIkSZIkSZIkSZIkSZKkgkzr+xiB+8uw3qYB56PLsJ4kSZJGklmvhcc+v/v45BN3D0UDmPvOIQajAWvu7T/v2Q5dA/7ImamHhglDW1/SyLbxqfTahCMHvbyjc0XieGvDtMTxUqUFo3VnEl4bSzHpuMLmbXq6PPupcD3bc0f9GMjU5c4zoyAE6OnMfa9K+h4pSZIkSZIk1biaCkaTJO3B1j8CT3899480s14P01+R+4caSZIkSZIkSZIkSZIk1aqJA87XlWG9pgHnXWVYT5IkSSPJhCOh7XRo/8Ou4wdelDx/+itg9H6w+dn+sUwWpp4NK64tbM/FP4PlV0P3Fsg0QG/nrvWWOXD4pbDvWwv8IiTVlDX3pNf2/7tBL+/oWpk43pqdWmpHiepD8i2A3eUKxJp2Low9GDY+kX/e6lvg2e/Afn9Tnn2VbsH34J4Lk2vZsdC1EeqaciFpc98JR/1bLiRNkiRJkiRJ2kP4OABJ0vBbdSP89kh49n9h4Q/h1vPg0UuHuytJkiRJkiRJkiRJkiQNzcYB52PKsN6UAedry7CeJEmSRprTr4H9/x7G7A+tp8LJl8PsNybPrWuAs++EOW+Dln1h6lnwkt/DaVfDMV+BiS+C7PjcUdeUvAbkQtFg91A0gC2L4O63wcobhvylSRphYoSHP5Vcqx8No/cddImOzlWJ460N04bS2W6yoSFxvDtTRyzHBpl6eOltsO87oGV2/rn3vhuW/rocuyrN8mvSQ9EgF4oG0LMNutbDU/8FD11cnd4kSZIkSZKkKvExAJKk4RUj3P8BiL27jj/2BZh3ITTPGJ6+JEmSJEmSJEmSJEmSNFQdA873H8pCIYQ64KgBQyuHsp4kSZJGqPoWOPbrhc9vbIOTfrj7+IEfzB07LfkF3PGG0vta8B2Ydlbp10saedY/kl6bccGgl3f1drKu+/nEWmu2vMFo9SGbWusOGbIvvB+jFI2T4cTv587XPwLXHZ4+d8F3YOarh76nkj377eKvWXAZHPEvkKkrfz+SJEmSJEnSMMgMdwOSpL3c+odg45O7j/d2wtIrqt+PJEmSJEmSJEmSJEmSymXnXeYBODCEMJQn5L0MaO47j8Afh9KYJEmS9jLjDh3a9esfLk8fkkaO9Q+l18bNH/Ty57tWE4mJtbaGMgejZfIHo5Xd6HmQGZVezxcqp6FbflXx13SuhW3Lyt+LJEmSJEmSNEzqh7uBoQohZIETgVOBecBEYAxAjPHMYWxNklSIFb9Lry35xa5PapMkSZIkSZIkSZIkSVLNiDE+EUJYDuxDLhztw8CHil0nhJABLt65LPBQjHF92RqVJEnSnm/cwTDxWFh7X2nXb3wSenZAXZ6gIO2ZOjfAI5dAx23QvRm2LIae7blaYxs0TNh1fmYUTDoODrsEmqdXv18VZtmVcPfb0+tz3jLoEh1dKxPHAxkmZdtK7SxRfUi/BbA7k4Hesm4H9c0w6/Ww6MfJ9a1LYfm1sM8ryrzxHqbjTnjqq31BcuX+RUpw/UmwbUXuvHkmnPBdmPrSyu8rac+w5n548su5QOBxh0J2NCz4LrwwBHTGq2Deuwf/HrD4clj4Y+jZBjNfA/u/F0KoWPuSJEmSpD1PzQajhRBayL1B6v1A6wvL7Pa37b9c92bgC32frgWOjTEmP55DklR5+Z4U9PzdsGMNjJpUvX4kSZIkSZIkSZIkSZJUTv8HfIzc+/reH0K4LsZ4Q5Fr/D/ghAGff7tczUmSJGkv8uLfwJ1vhI47Srv+hpPh3PvL25NGtt4uuPHU9Pe8b2/PHS+0/mFYcS284gloGFfZHlW8hT+Gu9+WXq9vgZZZgy7T0ZkcjDYx20p9yJbaXXJLedbrDpmy7vUXx34zFya58ank+q2vzL2uznhVZfavde23wc1nQW9n9fbcGYoGufC6m8+C064xwE7S4NbcBzeelgsxA9jwWPrcZVfCsqvglMtzIZpJnvkfuO+9/Z+vvikXLnvUl8rXsyRJkiRpj1ehn3xWVgjhcOBPwCVAG7k3TBXqamASMAc4Cjir3P1Jkoqw8cn0WuyB5ddUrxdJkiRJkiRJkiRJkiSV278CG8k97LQOuDKE8J5CLgwhTA4hfB/4KP0PS10FfLcCfUqSJGlP1zwdzrodXvZAadev/ROse7i8PWlkW35N/geB57NtJTzzzfL2o/J4/Iv56/v9bUHLtHclB6O1ZqcW29GgsqEhtVaxYLTsaDhnkDDIxw24SfXkl6sbipbmiX8b7g4k1YKnvtLBKQYhAAAgAElEQVQfilaQCI+lfD+NMfm15+mvQ/fWktqTJEmSJO2dai4YLYRwCHArsD+5QLSdb3YKFBCQFmPcDPxiwNBry92jJKlAsTd/MBrAst9UpxdJkiRJkiRJkiRJkiSVXYxxLfBB+t/v1wh8M4TwTAjhX4DzB84PIRwXQnhbCOFHwALgbfS/P7AHeFeMcQTcWSxJkqSaNe4wqGsq7drn7ypvLxrZhvrrvfK35elD5dO5ATY8ln/OmP0KWqqjMyUYrWFasV0Nqj5kU2tdmbqy7/cX2dHQOCW9vuYe6O2q3P61rGOEfL9ovzV3744k5dNxZ/HXrPszdCeEqW1fBZuf2328Zyusvrn4fSRJkiRJe6364W6gGCGERuAaYBz9gWiPAF8BbgFGAU8UsNSVwIV952eWuU1JUqG2rcr9UDMf3zwgSZIkSZIkSZIkSZJU02KMPwwh7Ad8ktx7/wIwD/jYC6YG4O4XfB4HXPOJGOP1le9YkiRJe7RMHcx4NSz+SfHXPvu/sPZP0DwTZrwKJhxR/v7Ub+2fYNnVuXCmGRcUHFg1JKtuhJU3QOdaWP2Hoa3VfhusvB6mnV2W1lQGWxYOPmfGBQUt1dG1KnG8LVuJYLT0WwC7Q6bs++1i5mvhmW8k12JvLvxm7IGV7aHWdG2CHR3D3UW/2y6Afc6H2W+A7Njh7kbSSLJpASz5OWxZVNr191wI9aMh1MGEI2HWG3KvgWm2JX/vlCRJkiQpSU0Fo5F7auQc+kPRvgr8Y4y5xxaEEGYXuM4t9L9Rat8QQluMsb3MvUpSabYshQc+nHvSwpj94NDPwNQzhruryti2fPA529uhcz00jK98P5IkSZIkSZIkSZIkSaqIGOOnQwgLgG8ATfS/DzAMON/5OewaiLYDeE+M8UdValeSJEl7uqO+BBsegfWP7Doe6uHwS+GhTyRft+6B3AHw2OfhpJ/ArNdVtte91cIfwx/fkQteAnj0Ujj9d9B6YuX2fPjTuX3K6ZZz4Ogvw0EfKu+6Ks3mAoLRmgYPNuuOXaztSg6+am2oRDBaNr2XTIWD0Q77LLT/ATY8nly/9lB4w2aoG1XZPmpFjHDj6cPdxa6WX507nv4anHETNE4e7o4kjQQdd8EfXg5dG0pfY/HPdv38yf+EI780tL4kSZIkSepTa8FoH6D/DVC/iTH+QymLxBg3hxAWAfv2DR0MGIwmafh1b4UbToGtS3Kfb1sBN58JJ/4Q5rwVQsh/fa3ZWkAwGsCmZ2DSsZXtRZIkSZIkSZIkSZIkSRUVY/xBCOEW4GPAu8gFpEF/GNpAAegB/g/4bIxxUVWalCRJ0t6heQaccx88/0fY+ERuLDse2k6F5n2gcx088a/51+jtggc+AjNfA6HCwUR7m95uuP8D/aFoAF0b4cF/grNuq8yeW5fDY18o7dpDPgHPfgs61ybXH7oY5l4IDeNK70/lMVgw2uGfL2iZNV3tRHoTa63ZKgejVfr1p7EVzn0ALk8JPovdudAtQyJz1twL6/6cXAt18KKvkfxjkASd63Pfj5r3gUxD7vvO1qXQMAEaxsPWZRCyuV+j+98/+HrrH4YF/wvzLy74y5G0B3vo4qGFoiXZ9DQ8+rn0euwu736SJEmSpD1azQSjhRAOAfbp+zQCHx3ikgvoD0abC9w6xPUkaeiWXdUfijbQ3W+HBz8OJ/8s94/te4ptBQajrX/EYDRJkiRJkiRJkiRJkqQ9QIxxCfD+EMLHgFP6jpnAJKABeB5YDdwF3BRjXD9cvUqSJGkPVzcKppyWO15o7IGFrbFlMWx4AsbPL29ve7tVN0JXwl8FOm7PPYy8vrkCe96waxBbMQ68CDY9BUuvSK73bIf2W2HG+aX3p/LYMkgw2uh989f7tHeuTBwPBCZnpxTb1aAyIUMd9fSwe6BMV6au/5MpZ5R9bwDqGmDScbnQryQrrjMYbacVv02vNc+E/d9bmX0LCUaD3K+VwWiSurfm/mxSCeseSK+VO4hNkiRJkrRHq5lgNODIvo8ReDTG+NwQ1xv4LyQ+ckXSyLD4p+m1bSvgxhfDYZfkwtHaTqv9J4ttLTAYbfk1MO/CyvYiSZIkSZIkSZIkSZKkqokxbgWu7zskSZKkkWXqS4FA7haWQTz4T9B6ErS+GCYfD5lsf61rM6y+Ofeg6MY2CHWwfRW0zIFp58CoSRX6AmrcxqfSaz3byhuM1rMD1twDj1xS2vUTjoSmKTDjNenBaABLfwWd63Lno1qh9WRo8HamqtucJxgt1MGUMwtapqMrORhtQv1kspmGUjobVH2opyfuHozWPfC+kkM/XZG9AZh2bnow2srfQ4wQQuX2rxWrb06vTTu3cvvOeRss+tHg8zruhCW/hMknQfP0yvUjaWSKETY+AYvy3MNYSSt/Dwd9GHo74fk/wtalufFQBwQYfxiMP7T275mUJEmSJJVFLQWjtQ44f6YM6+0YcF6BR8VIUglW3TD4nEc+k/s4/eVw6q9zT96pVdtWFDZv9c3+I5kkSZIkSZIkSZIkSZIkSZKk6miZBQd/BJ74t8Hnrrg2dwA0z4Kz786FzTx/D1x/Qv5rT70CZr566P3uafK9bzwWEFZXqE0L4A8vg00l3qZU1whH/EvufPabYMG3of3W5LkLf5g7dsqOzd0PMPWM0vZW8WJv//+rSeb/cy7krgAdncnBaK0NU0vprCD1mSw7erbvNt6d6QuPmfX6XOBepez/Xnj0c8m1bSvg7nfA8d+p7XtchiL2wp//ETpuT59z8Icrt//8jxcWjAZwx+uBAMd8BQ78QOV6kjSy9GyHu98JSy4fvh5W3wJXtOV66dmWPGf6K+GUn0F9S3V7kyRJkiSNOLUUm9044HxH6qzCDXysyqYyrCdJQ5cdW/jcFdfBs9+qXC/V0F3gy2/XBtiW/A+HkiRJkiRJkiRJkiRJkiRJklR2R/0rvPgqOOD9MPst0FhA4NHWJfDgx3IBOXe9ZfD5d7weOjcMvdc9Tr7bnXrLt8197xs8FG3c/Nyv/8EfgZfeBmfdAQd/FA79NJxzL0w/NzcvUwdnFPCg9J26NsKdb4LertL7V3FWXJde2/ftcPglBS/V0bUqcbw1O63YrgpWH7KJ493TzoGTfpI7MvUV25+mqXD8Zen1RT8qPJhrT7T8WnjqK+n1Qz4BY/ar3P7jDoFXLYGDP1bgBRH+9EHY8GTlepI0sjz7ncJC0Y78Irz4ylxgaKbve092HLSeAs0zYMoZuT8bzXxdaX10rksPRQNYcQ089dXS1pYkSZIk7VEq+NPOsnt+wPnkMqw3d8D5mjKsJ0lDV9c4+JyBnvtebT+dpTvPDzFfaOOTuSenSZIkSZIkSZIkSZIkSZIkSVI1zDgvdwDc+Vew+CeDX7P0V3DgRbD5ucHnxh5Yfg3s+1dD63NPE0J6LfaUZ4/OdbCqgCCzee+Ggy7adaz15OS5mSwc+il49NLCetjRAe23wtSXFjZfQ7Pop+m1/d9X1FIdnckPfm9tqFwwWjYtGG3Om2H8WRXbdxcTjspfX/ILmPfX1ellpFnyi/z1ySdUvoeWmXDUl3JHz3a4vGnwa5b8Ag77VOV7kzT8lg7yOgUw6Xg45J9y5zPOhyM+nz439sLlzdC7ozz9DbTk5zD/E+VfV5IkSZJUU2opGG3nozQCMMhPUfMLIUwCDh4w9OxQ1pOksogRdjw/+LyB1j0A6x6CCUdUpqdKy/d0hxfa+CRMPaNyvUiSJEmSJEmSJEmSJKkqQghZ4ETgVGAeMBEYAxBjPHMYW5MkSZLSTT6hsGC0nu3w1FcLX/fJf4fWEyHUQfNMCJnSe+zalHtYdyY5QGmP0Ntd+rVdmyAzCuoaYNMCIA5+TbFhRpNPLG7+yt/DuEOhaWru8+3t0L0ZGibkDpXPloXptfGHFrxMT+xmTVd7Yq01W7lgtPqUYLSu2FmxPXcz9iDIjoOuDcn1jruq18tIs+np9FrIwMQXVa8XyH0vmHA0rPtz/nmrroe5b899/+nakAuNbJoB9OaCKOtbqtKutIsYYduKCgRuhb7XsPXQND33/8merGvjrvcqbszzOrVTMX/uCRmYfDy031Z8b4NZ92Du9YgAnWsHmZyBlllD+zO0JEmSJGlEqqVgtLuAXiADTAohnBFjvLnEtS4kF7AGsAW4vwz9SdLQ7FgD3VuKv+63R8Kbe/M/FWuk6tle+NyNT1auD0mSJEmSJEmSJEmSJFVcCKEF+BDwfqD1hWVSUglCCG8GvtD36Vrg2BhjAQkGkiRJUhnNeQs8/iXYtnzwuYt+XPi66x6Eq+blzkdNgsMugQP+vrjetq2Cu94C7bdCXRPMfRcc/V+QqStunREjz3vjYwnBaNtWw11/Be235ILRJh4NHXcOfl3rqTDpuOL2mnoWjD8C1j9U2Pwn/j13JJl4LJzyMxg9t7gelOz5u5PHs+OKCn9a09VBLz2JtbaG6gejdceuiu25exNNcOAH4dFLk+vdm+Cq/eCkn8DkIv/fqWVblsCae9Lrc94GzdOr189OB38U7npz/jkdd8CVc9Lr018JJ/3QoEZVz+Kfw4Mfzx9mWQ6ZLMx6Axz37dxr255k4J97Ym/h19WPgf3+tri9DvpI7nWkmH0K9cuJhc9tmADzL4aDPlyb91hKkiRJkhLVTAR2jHEdcN+AoUtDKP5vqCGEfYCPk3sDVQRuiLESf+uWpCLlezrMYB65pHx9VFPPtsLnPv21yvUhSZIkSZIkSZIkSZKkigohHA78CbgEaCNv0sFurgYmAXOAo4Czyt2fJEmSNKhRk+Dsu2HuOysXVLVjDdz/flj6m+Ku+8MrYHVf+EX3Fnj66/DIpyvTYzXku12ot4RgtNsugNU35f779GwbPBRt/BG5YI2X/K74cI1MPbz0D3DA+2HcodA8K3fUjy6+77X3wU1nQm9yCJeKsPTX6bWTLy9qqY6ulam1ydmpRa1VjBERjAa58MZ5706vb14AN58BO9ZWr6fhFCPccEp6ve00OP471etnoDlvglOvgGnnlr7GimvgzkHC1aRyWX1rLtCr0qFoAL1dsOj/cn/u2tPcdn7/n3sKNeuNcNbtMO7g4vaacR68+GqY/gpomd3/557mWVDXWNxaQ9G5Dh74KCz+afX2lCRJkiRVXM0Eo/X5yoDzE4D/KebiEMIU4CpgAv1vqvpyeVqTpCHa9Ezp1z56CXRtLF8v1VJMMBrAmvsr04ckSZIkSZIkSZIkSZIqJoRwCHArsD+59+7FnSUKCEiLMW4GfjFg6LXl7lGSJEkqSMtMOOF7cP6Cyu7z3HcLn7vhCVj3593HF/64fP1UXZ6/JsQig9E2PAlr/lj4/GnnwssfhKP/Heqbi9trp4bx8KKvwSsegQsW546DPlTaWlsWQfsfSrtW/Z75ZnqtyKDDjs7kYLTx9ZNoyIwqaq1iZFOD0UoICxyK/8/efYfHUd1tH/8eraolW5Z777gXsKm2AYNN7wkQAk+AUEMq6SEJLaQ8SQhvypMQkkACCaEaTHXoBowxNsXghnvvkqtsWdqVzvvHSPFqvbN1drQr3Z/r2ks7c86c81Pbnd2duccY5/8jltB+WP+4P/W0tMp34cAG9/bj7ncCE1tK34vglJlw8gupj7HlJTiw0buaRKKxFj78VvLP8+la9wiEkjy/LZvtWQJV85Lb5nM1MPlRqBiX2py9z4Ypz8MFaw/t91y4zhn3cgujfpTauKlYlcQ+tIiIiIiIiGS9nApGs9Y+CixoXDTAdcaYt40xJ8bazhhTaoz5UuO2R+IcVGWBl621cS7zIiLik33L09t+/XRv6vBTssFoy36bmTpEREREREREREREREREREREJCOMMcXA80B52OqFwLXAIGAECYSjAc+E3Z/qWYEiIiIiIqnqdXbmxt69KPG+buFDB9ZDfa039fguxkuEhmByQ215Kbn+Hcck19+PcZP5e5DoYoU6lfZPaqgdwejBaF0LeiQ1TrLyXYPRkvyf8EJBh/g/t7byd7t7oXtbQQco7edfLbF0HJ3e9ttne1OHiJuNM6IHvWZafQ1Ur/Z/3kxJ9rG3/VAIFGemlibdT83s+OH2tJHnHhERERERkTaiBS83kLKLgblA58blScAsY8xWYGV4R2PMvcBQ4ASgiENXmjTAJuALPtUsInK4vcthzUNwcDv0PN1ZjqbiKAjuif8m63vXwoDLIZC5Kwx5LtlgtE0vOB9k50X/QE9EREREREREREREREREREREss7XgQE4x+4B/B74lrW2AcAYk+gZ6G9w6Pi/gcaYbtba7R7XKiIiIiKSuMHXwuYXMzP2/jXw8iTAgglA9SqoCQtjKunpfK2JHtD0X3W7oaR7ZmrMJBMjGM2GoKEelvzCCVGp2Rx7rHg/o2bzBmDglYn3T0avc6GoK9TuSH7bD2+Gpb+EDiNgyI3Q/1Lv62vNrIW9S6O3FXWGQGFSw+2o2xp1fdfCnslWlhS3YLSgrcvovK4GXQsLb3NvX/FHJ6Bx5Peh6yT/6vLL/nXw8Y9g7cPufQZ8IXvOfyntBz1Og62vpLb9nM/Dojuh+ykw7hdQWB5/G5F4DmyGj2+B7W87+z4t5dWTmp+PVzoABlwBR3w59j5JNln7KCz7LVS9l9x2g6/JTD3huk9xAmJjBUl65eA2CNVAfknm5xIREREREZGMy7lgNGvtamPMucDTQE8OHejUEwi/tIYBbgi7T1jfjcC51tpKX4oWEYlU9T68Ps0JPANY9Vf3vr3Pg9G3QtU8eCXWh0EWXp0Cp70NeTny8J5sMFpwN2x/C3roor8iIiIiIiIiIiIiIiIiIiIiOeJrHApFm2GtvTmVQay11caYtcDAxlUjgDYXjGaMKcC5oGw/nOMmq4HNwEfW2rUtWJqIiIhI29P3MzDhD/DB1zIzfuUc97ZEw76CORqMRowQkoYQzP8SrPqb99Oe9Ax0HO39uOAEdEybBXO+ALs+TH77mi3Obdvr0FAHA//H8xJbrXWPured+HTSw+0IRv//61rQMsFooYZQRud1NeqHENoLS+9277PpOdjyMkx7E7oc519tmVZbBS8dDwejh+T91/jf+FNPoiY/Bu9eBVv+Aw3B5Lff+6lzq3wXzngf8gLe1yhtR3AfvHw8HNjQ0pVA3c7myzVbnL/zul0w+sctU1MyVv8D5n4xuW0KyuGIm2DEdzNSUjMmD05+znn82f4Wh94qBkoHQmnfxvUeWf4HGPk978YTERERERGRFpMjyTnNWWvnGWPGAw8AZzWtjvjabBOcT0UM8ApwlbU2zjuPIiIZtOiuQ6Fo8bQf6gSddZ0I562A545w71s1F9b8EwYn+WZmS6k/GH39pEfhncuit216TsFoIiIiIiIiIiIiIiIiIiIiIjnAGDMS6N24aIF0z7RbxaFgtEHAm2mOlzZjzCDgGODoxq/jgfZhXdZZawd4ME9X4E7gc0Anlz5zgHustdPTnU9EREREEjTsqzD0KxDaB6H90YNm8kvB5ENhubMc3AvWOseTh/bDCyMzV1/drsyNnVExgtEObnECQLx26mvQ41Tvxw1XPhLO+gDqdjvH/X/w9dTGWXq3gtGSsfhn7m0dRiQ1VIOtp7JuW9S2boUtFIxmUwi48kJeAI76NVQcBXOucO/XUAvL/691BaOtfTh+KNqoH0OgyJ96ElVYASc/C6EDsG8lzByX2ji7FsC216Dn6d7WJ23L+scTC0Wb+gaUDUpvrmf6p7bdst/CiO9BoDC9+TNtaQIhjAOvhLF3NS4YKOnlb7hhaX8nIDa479D+aaAdFHdx7jeEnAC1R+LUdME656sNwbNDiHo6+ZJfKhhNRERERESklcjJYDQAa+024BxjzATgG8BUnKsfRrMHeA34g7W2xQ+EEpE2zjbApmcT798+LAit/RDnCglvnufef92juRGM1hAEWx+9raQ39LvUeZM70p7Fma1LRERERERERERERERERERERLxyZONXCyyy1q5Oc7zdYffL0xwrZcaYKcAtOGFoUUPKPJ7vLOAfQLc4XScCE40xDwM3Wmv3Z7o2EREREQGMgYIOzi0RTf2agtKKu8PB6EFLaavbHb9PNjIxgtGq5jlhGF6rSDEgKBWFHdO7WPjujyFUA/kl3tXUmtVWubcVdU5qqJ3BSuqJ/vfXtSCzwWgFeVkWjNYkkXC5qnmZr8NPle/F7xN+LlC2yW8HFWOhqAvUVqY2RtU8BaNJeirnxu/T71LoPiX9ubqcAJXvJr9dbRVUr4by4enXkCnBatizKH6/iiOhtF/m64mnoL1zi5TXeKr74Oth1V+jb9vnwubfQ0E5BKPs69YfdEKIY+1PioiIiIiISE7I2WC0JtbaD4Ar4b9XXuwLdAYKgUpgG7DYWtvQYkWKiITbvz7xvkVdoNNRzdd1OxnyyyBUHX2bba87H2AXdky9Rj/U17i35Zc4Vw2KFoyWs1ctExEREREREREREREREREREWlzuobdX+HBeLVh99t5MF6qjgR8Ofu3MYRtBs4xkU0s8CGwGugIHAV0CWu/AuhgjLlQx06KiIiI5IB+l8Dy/8vM2It+Aqv/7gTfbHvdCWUb/1sY+IVDARTZyFr3tn1evLSI0OvspAOy0tZhBJSPhD1LUtt+9qVOuBFA2SAnPCby3AOB0AE4uDV6W+nApENTdgS3uLZ1KeyR1FjJyjfRg9GCti6j88ZVcSSUDYHqle599i2Hd6+CrpNh4FUQKHTvmwt2L4jdnlcEfc7zp5Z09LsEVtyb2rarH4TdC2HrK855Pv0ugZLeENoPHUfDoKsTDwyVtmfLy7Dqb7H75BXBmDu8ma/fJakFowGsfgCO+pU3dWTC/rUJdDLQ97OZrsQb/S5xD0brd2nz5YpxsP3Nw/vVH4Btb0CPU72vT0RERNq2fatg7cON7+XEeO/K5EOnCc7roqKMX2NLRKRVy+JPMZLXeDXJdK8oKSKSWdteT7zv8G9D5FV9CtrD+N/AvBujb2NDsHkmDPh86jX6IRQjGC2v2D3YLVevWiYiIiIiIiIiIiIiIiIiIiLS9hSH3a917ZW48rD7+zwYz2u1wEZgsBeDGWP6AE/RPBTtHeB6a+3SsH5FwI3A3UDTwUbnAT8FfuhFLSIiIiKSQWN/Ars/ge1veT925btAWBBIcC+8dw1sfBpOmgEmz/s5vWDr3du8DkYrHwlH/8HbMRNhDEz8N8w6C2rcw7ZcbX6++fKn98CJ06H3ud7U11qse8y9bdK/kx5uR13031WHQAXFeSVJj5cMt2C0kA1ldN64jIHJj8Ksc+DgNvd+ax5ybuseg1NmHn6uTK6o2xU70DBQDBMfhsIK/2pK1difOuFmO2Ynv231yuZheOufaN6+6n6YNis3fg7iryW/ggXfj90nrwgmPw7lI7yZ84iboHIurH88+W2X/hra9YNhX/WmFq8lEq57/ANQ2i/ztXihxzQY9WNY/NPm64d8yQlNC3fsX+H5odHHeX0qnPqawtFERETEOzs/gNdPc14TJmLdv2HVX2Dqm1DSPbO1iYi0Yq0qGE2ygzGmAJgE9AN6AtXAZuAja+3aFixNpOXtXQ7vXZtY3/KRMPxb0duG3OAkBf/n6OjtW1/N/mC0+hjBaPklUOASjBZUMJqIiIiIiIiIiIiIiIiIiIhIjqgMu9/Fg/EGhd2v8mC8dASBxcD7wPzGrwtxjp17w6M57gTCzyCeA0yz1h4M72StrQV+b4xZDzwd1vQtY8x91tp1HtUjIiIiIplQWAFT33DCaWYe6c+cm56DbbOyNywilWC0bifBEJeLjzeprXLCmAo6OMtlg6BiPAQKY2+XKRXj4PzVUPke1Gxy1hX3cM4l2LUA6nbCnCsSG6uhDhbcAr3OcYKqxDH/Jve2ivFJD7cjGD0YrWthj6THSpZ7MFow43PH1WkCnL/GCeybfWnsvtteg00vQN8L/anNayvudW8b9k0YcxsUupwPk22KOsHUWbBnkRP2VlgBJT1h33KorQTb4ITZVc1Lfuzdn8Dqh2D4NzwvW3JY3R5YeEfsPkO+BOPvhvxS7+YNFMOkR2HsXVC9Ckp6O/sTDWHXcVj8M/fQww++Dt1PgY6jvKvJC9bCyvti9ykfBYOu9qUcTxgD4+6CI26ELa+ADUKP06B0wOH7N+2HOL/b+oNRh+KTW7N3X1dERERyz6K7Eg9Fa7J3mbO/Nua2zNQkItIGKBitFTLG/AO4yqPh1llrByQ4b1ecA7E+B3Ry6TMHuMdaO92j+kRyy7wbEu979J9if8DaaYJzxYpoH6qsfsC5mlfv82DAFZAXSL5Wr9RshZV/ca72cmA99D4fxt/jfPDqJlDi/kFQ3W7njVt9WCsiIiIiIiIiIiIiIiIiIiKS7bY2fjXAUekMZIzpDIwIW7UynfHS9CDw58iAMgDj0TEtxpgjaH4cYB1wdbQ5m1hrZxhjHgzbrgi4HbjGk6JEREREJHNMnhOS1ft82PSsP3Oufyx7wyJiBaOF9kdf3/UkGHB5ZurJpEAxdD/58PUlZzpfV94H299KbKw9i+DgVifYSKCh3v1vqV2flALxdgS3Rl3ftSDzP/OCbA5GA8gvgb4XO2FGbv+nTba8lLvBaNvfdm8b+pXcCUVrkhdwnn8qxh1aF36/XT946/zUxl77sILRpLnKOVBfE7vPETd5G4rWxBjoMNS5AVSMbd6+eaZ7MBoWFt0Jkx/3vq50HIz+nNTMsBz9H2zXBwZ/MXYfY5yQW7ffW+UcCFZDQZn39YmIiEjbYq0T2pqKrS8rGE1EJA15LV2AZL047zQ5jDFnAYuAm3AJRWs0EXjSGPMvY0wG3qESyWLBfbAjxgcg4TofG/3DzUgVMY4X3fAkzL0K3vmcs8PdEqrXwsxxsPB2JxQNnA/qnxvifJDlJlDiXGkmGlsf/0MyEREREREREREREREREREREckGc4CGxvudjTHppC5cgxOwBrAfeD+dwoi/y6YAACAASURBVNJhrd0VK6DMI5cD4VdDfMpauyKB7X4ZsXypMabYu7JEREREJKN6ne3fXCv/Agvvci7UveNdaAj5N3c8sYLR3JQN9L6ObJDs38S6x5xAMIGajWBd/q5L+6c05I66LVHXdyvMfDBafrYHo4ETUtPzrPj91ufw3+m+GDntZYP8q8Mv3U+GQLvUtt05H6pXe1uP5K7qtbDwJ7H7tOsH5aN8Kecwvc6J3b7+CfjgZtjzqT/1xFOzBRb/b+w+JgC9z/WnnpYS7/e2f60vZYiIiEgr1BB0grGX/wmW3g31B1IbZ8c7sOp+2L3Y2/pERNqInApGM8aMNsa83nh7zRjTLYUxujdu2zTO0EzU2opMj9fBGDMFmAGE/z4s8AHwBPAKUBmx2RXAI8aYnPobFElLbRXYhvj9ADofl1i/DsPj99kwHSrfTWw8ry28Aw5uj9724Tfdt8svi32VnAMb0ypLRERERERERERERERERERERDLPWrsLmB+26i5jjHHr78YY0xv4Ac5xaRZ4xdpED8TJWRdFLP89kY2stUuB98JWlQKne1WUiIiIiGTYgCug64nx+5X0hE7HpD/fwttg/pfhlYnw5rkQrE5/TC8oGO2QwddDpwmJ9//wm/D2RRBK8YTd1mTzf9zbjv5T0sM12AZ2BLdGbeta0HLBaMFsCkYDGHMHFPeI3aduF7z9mdz7Ow3ug2qXYLSBVznBcK1NQQc4MjKDPQnPDoZPf+tdPZKbVtwLzw2GqrnuffIKYfw9kBdw75NJfS+CnmfE7rPsd/DCCPj4VrDWn7qi2TADnh0Ey38fu9/o2519xtZs2M1QNti9/bVTsyv8V0RERHJD3S5nP+LVk+D9r8CC76U33nvXwYuj4aPvt+x+pIhIDspv6QKSdCMwBefAppettS5pO+6stduMMUGcA30scD3wXS+LzALfAe5IYbujcYLMmljggVgbGGP6AE8BhWGr3wGubzzAqqlfEc7v726g6Z3484CfAj9MoVaR3BPal3jfzgl+SN0+wWzHNf+ErhMTn98L9bVOKFuy8ttDXj4UxAhGe/l4uHhX6/zQSERERERERERERERERERERKR1+R3w78b7xwN/xjmWLCHGmO7As0BF4yoL3ONlgdnGGNMDGBe2KoRzXF6iZgHhV2Y8C+dnKCIiIiLZrqAMTn0Z1k+HqnnQUAeBIigb5LTvXQ7lI6DvZ5zgoU3PQeVcqN0O5MGWmZBfCt2mAAbWPQrB3YnNveUlJ7hkZBacYpNKMFrpAM/LyApFnWDqLOfY/J3vHwr32DAdandE32bTc7DyrzD8G76VmZXmf8m9rWJs0sPtDlURcgkh61roRzBa9NMA3WpqMR1HwZkfwIanYNFPYvydPgur7odhX/O3vnR8crt729Cv+leH34Z9FTofDZtnwsHG0zlX/jnx7T/8JvQ5/9BzmbQtBzbC+1+FeNc4OPN96DjGn5qiCRTByc/B+idhzuWx+y7+KfS5wPm/8FvoAMy9CuoPxu43dRZ0P9mXklpUu15w5nx4slP09todsPEZ6PdZf+sSERGR3Lbk17Bjdvx+eQUw6Frnfqga1v4rdv+lv4Li7jDiW+nXKCLSRuRaMNoFYfcfTGOcB3GC0QzOVRWz4FMb71hrK4HKZLczxvwgYtUb1trVcTa7k0MHnAHMAaZZa5u9s2KtrQV+b4xZDzwd1vQtY8x91tp1ydYrknOCSQSjJXr1rsKK+H0AVj8Ax96b+Pxe2Pa6sxOfrMLGQLTCcvc+wT2w6yPoND612kRERERERERERERERERERETEF9baR40x38MJ+jLAdcaYkcAPrbVvu21njCkFvgDcDnTDCUQD56KqyYSE5aLREcufWGv3J7H9nIjlUWnWIyIiIiJ+ChTDwCucWzx9zndubspHwQdJhA5teDI3g9FMANr1zUwt2aCgDAZd5dya1GxyAtDcbJjetoPRgjHOZeh8bEpD7qjb4trWtaBHSmMmI98URF2fdcFo4ATVDPsqdD0B/hMjOGjD9NwKRtv2mntb2UD/6mgJXY53bk2SCUYDJyhvxHe8rUlyw4YZ8UPRRv2wZUPRmuQVwIDPQ6AE3r4odt8N01smGG3b6xDcG7tP7/PaRihak8IK6HYybH8zevuG6QpGExERkeRseDKxfqUDD+U31B+MH4wGsOB70PkY6HZi6vWJiLQheS1dQKKMMYOAPo2LDcDzaQz3HND0KclAY0y/dGprDYwxJcBlEavvj7PNEUDYpyrUAVdHhqKFs9bOoHmoXRHOgWsirV+iwWgFHaDD0MT65hXgHC8aTyJ9PLZxRmrbNQWjBYqhqIt7v50fpja+iIiIiIiIiIiIiIiIiIiIiPjtYqCKQ+Fmk4BZxphNwEPhHY0x9xpjXgN2AH8Eujc1AZtxwtJau5ERyyuT3H5VnPFEREREpK3oc15y/fetgF0Lmt/2b8hMbbE0hJLrb+shLz8ztWSrrpNjt+94G6yN3ac127/Ova1sUEpD7ghGD0YrC5RTEihNacxkFOQVRl0fasjCYLQm5aOgoKN7+/Y3c+vvtGaTe1thJ//qyAaDr0uu//on4cDGzNQi2W3f8vh9ukzKfB3J6HMBVIyP3Wfvp/7UEmlfAm8Tds2yn6cfYu0XJfIzExERkdzVUA+h/VBbBQc2Oc/9uxdB3e7kxqmvdbbbu9x5fywR4ftdgWLodEz8bWw9vPM52PHu4e/Bhd/qdkP1mkMhw9Y673XU1yb3fYmI5Lhcete/6QqIFlhmrY1x6Y7YrLXVxphlHDrYZwywPs36ct3FQHnY8i7gqTjbXA4EwpafstYm8iz/S5oHql1qjPlyrEA1kVYhlGAw2sCrwSSYW2mMs6NcXxO7X4PPO7m2ATY+m9q2hRWH7vc+H1Y/EL1fKJmL4IqIiIiIiIiIiIiIiIiIiIhIS7HWrjbGnAs8DfTEOQ7QNN7vEdbVADeE3Ses70bgXGttpS9Ft6whEcvJHt8YmQDQ2RhTYa3dlUZNIiIiIpKLSvvDwCthzUPx+wLU7YKZRx2+vsMIOHE6lI/wtj43tj65/qNvz0wd2WzgVbDs97GDmp7qDpOfgO4n+1dXtlgY429i9K0pDfnhvjlR13ct6BF1vdfyTUHU9SGbZJCgnwLFMOLb8EmMn/nTveDEJ7M/yKdut3OifTSDrnbO72lLhn4NVv0t8f5V78GMvlA+2nk+6TA0c7VJ9lj7KCz/Q+w+nSZAz9P9qSdRxsBxf4NXT3Y/H3DjDFj+Rxj6FX9rq14Tu72kl7OP0NYMuR4W/yx62875TjBjuz7+1iQiItKW2AaoP9h4qzl0v+EghGqcr5Fthy3XHNrGrS1aX9ewcAPdpzjvixR1jl3/kl/Bgu8n9z3nlzmvi8KNugVmX3wozMxNzRZ4ZWKC87SHI26CdY/AgQ3O6+yRP4DRt7W916Ei0iblUjBa/7D7kVczTMUqDgWj9fNgvFx3bcTywwkElV0Usfz3RCay1i41xrwHHNe4qhQ4HUgxRUkkRwQTCEbrfiqMuS25cRMJRgPYvRg6jkpu7FRVzYODW1PbNvxqQOPvjhGMlnI+poiIiIiIiIiIiIiIiIiIiIj4zFo7zxgzHngAOKtpdcTXZpvgBKIZ4BXgKmttigek5JyOEcvbk9m48eKxB4HisNXlOBdMTYsxphvQNcnNBqc7r4iIiIik4bgHoOMY+Oi7qY+xdym8cTqcvwbyfDgVKdlgtGFfz0wd2aykO5wxF+ZcAdvfit6ndgfMOhMu2ADFXfytryXteAc2THdvLx/p3uYi2FDHpwc+jtrWrbBX0uOlIt9E/98L2jpf5k/Z6B87oWKf/iZ6+8Gt8PppcNEmKKzwt7ZkfHCze9voJM8Dag0qxsJZH8O8G6FqbuLb7VkEb5wB562EvEDm6pOWt2sBzPl87D4jvw+jfuTPvkWyOh0F096Al44HtwDK978K7Y/wN9htf4xgtCNugpG3QIk/gZ1ZpbQ/HPsXmHdD9PbXp8G5n/pbk4iIiN9sA9TXphYwlkww2WFhZwehIRtfl1rY9gbMvhSmvubebeOziYeilY8Ekw8VR8Hwm6HiyObtfS+CKTOdEOk9S2DP4tTLbxLaB0t/dWi5/iAsvANKB8KgK9MfX0Qky2XhOwau2ofd3+PBeHvD7nfwYLycZYwZDJwUsfr+ONv0AMaFrQoB7yQx7SwOBaOBc6CbgtGkdXO7QkTpQDjur1DcDTqMTP6N/UBx/D7gXInCr2C0Hck8HEQwYd9/YYUTFrft9cP7hfanPoeIiIiIiIiIiIiIiIiIiIiI+M5auw04xxgzAfgGMBXo6dJ9D/Aa8Adr7Zs+lZgtyiKWE7hq4mFqaB6M1t6tY5K+DNzu0VgiIiIi4oe8AIz4jnN7ug/UbEptnAMbYfss6DHN0/KiSjYYLZvDlDKpXR84+QV4Isbufv1BWP84DP2yf3W1tDX/dG/rOjmlIRfv/9B9yAJ/AmjyTUHU9SEb9GX+tIy53T0YDZyT7Dc8BYOv9a+mZG1+Ifp6E4B2ff2tJVtUjIUz3m2+bsMMePui2NvtXws7ZkP3kzNWmmSBWI/FACf8Ewb+jz+1pKrTBDj1ZXjtVPc+ax7yNxit2iUYbexPYfSP/KsjG8XaR927DPYshfIR/tUjIiJtk7XQUOt9GNlhfaO0ZWU4WRbY9gYcrHQPjA8PHYulsBOck0DQWc/Tm+8fzvsSrLwvsTmSseYhBaOJSJuQS8Fo4Qf2eBFkFv6uf5KfmLQ61+BcVbPJh9baBXG2GR2x/Im1NpmUojkRyz6lNYm0oKBLMFpxN+gxNfVx8xIMRtu/NvU5krVvefT13afCCQ/BjN7u29YfbL5c4PIhbag6tdpEREREREREREREREREREREpEVZaz8ArgQwxgwC+gKdgUKgEtgGLLbWNrRYkS0rMhjtYNResdUA4ekQkWOKiIiISFs04tvw4bdS337PkuwLRut6IhgTv19rVVAGZYOgerV7nz0JnLjbmsT6fjuOS2nILXUbXNv6FA9MacxkFZjCqOsbaKDB1pMXfpH6bFPQHkoHwn6XQB9wHl+ylW2A4N7obSYf8nLpFM0Mq0jwf2zvEgWjtXbxnnsqjvSnjnSVj3ICEN32Tfx87LLW/fzAMn+ei7Jau75OWG7drujtexWMJiLSZvw3nCyBgLFQTVgoWYxgskT7NtS29Hcvh7FQszF6MFr1atjxTmLDJPpaJ9KE38LO92HnB6lt72bba96OJyKSpXLpXbfKsPv9PRivX9j9Kg/Gy0nGmABwVcTq+xPYdGTE8sokp14VZzyR1ifkEozmFvyVqECCwWgHUryyVyr2Lou+vnykEwQXS5cTmi8HSqP3CyWTxSgiIiIiIiIiIiIiIiIiIiIifjPGtAfCz8pbFXkBTmvtaiDGGfwCWJ+2EREREZHWrv/l8MmtqR+L/cE3YOV90O8yGP0jMHne1tckmWC0ITdkpoZcMuQGWPAD9/aV98Gah6CgHLqfCuPviX5CcGsQOgA7Zru3D/5iSsNWBre6to0qHZ/SmMnKNwWubSEbojCbg9HA+Tv9+Bb39k/vgYFXQcVY/2pKxO7F8O4XoKEuevuAK/ytJ9uVDYQep8HWV2L3m/9l2PISHHU3tB/iT23ivWA1fPB1WP33Q+vyG7P5Y+1rdDkBOo7ObG1eKe4GfS6CDU9Gb9/1EWx6EXqfnfla9i6DUHX0ttIBmZ8/2+Xlw+BrYend0dvf/ixMfhz6XeJvXSIikll7lsJH34OquU4wmQ05X0XC1UcJrFv3GLxzWeJjDLkxtbkDxTD5SXjpaKj1ONbm8YiMivB9RZMHgXYQKIGukxpfew32dn4RER9k6BOIjFjf+NUAY4wxnVMdqHHb8HdJfUwLyjpnAr3DlmuAfyewXeQ7juuj9nK3LmK5szGmImpPkdYi6BKMlu9TMNqWmenNk6iNz8H2N6O3tR8a/0o4kW8EF7hcrNbtjVwRERERERERERERERERERERyRafBz5qvM0Dilq2nJwReWBMSQpjRG7j1cE2fwJGJ3m7wKO5RURERCRdJd1h2pvQ6WhINURpzxJYeBt8+B1vawuXaDDa0f8HA/8nc3XkihHfgzF3uLfbeuf4+5pNsPaf8NrJ0JBE+FwumXWWe9vQr0KnCSkNWxXcHnX9uLLjCJg450h4JD/GPEHrEtqVTUZ+H0bfHrvPK5Oheq0v5STkwEZ4ZZITfOTmyF/6V0+umPwE9PvcoYAsNxufgZdPgIOV/tQl3nvzvOahaOA834SqiZnZf/JzGS3Lcyc8CGWD3NvfPAe2vpbZGhpC8MII9/ayge5tbcm4X8Run30prJ/uTy0iIpJ5BzbDyxNh8/NQW+nsgygUTaKJDLpe/2TioWilA+CYe6H/51Kfv2wAnDYHup4IXr6H0LTv/d998DC2wVlXuwM2znBee9Xu9G5uERGf+PPOqzfmArVAIU442leAn6Q41pc5FAoXAt5Ju7rcdU3E8nRr7e4EtusYsRz9HX4X1tpqY8xBIDzRqRzYlcw4kYwx3YCuSW6maFPxh1swWkGawWh5SRwvunc5dBia3nyx7F4Mb53v3t5hmPO141jY/UmUDgYqIq6WFCiNPlaqVykTEREREREREREREREREREREb90wTneD2C+tVZHWycma4PRrLXbSfJ4QWNM/E4iIiIi4p9OE+DM+dAQhLwCsBaCe2kWYLL017D457HHWfVXGHcX5Lsc752ORILRKsbD0K94P3cuMgbG3A7lo2H2xfH771kCW1+FXmdkvjY/7VkC299ybz8i9b+XquC2qOuHthuT8pjJyjcFrm0hG/StjpQZA2PvgNJ+8N610fuE9jkhS2Pv9LU0V6sfhOAe9/b8Uijq7F89uaKwHCY/6gQ5ffQdWPY79761lbDu3zDs6/7VJ97Y9TFsn5X8dme+n3v/N/ntYOoseKafe5/l/wc9pmauhq2vuLcFiqG4R+bmziV5+TDoGlj9gHuf5b+Hfp/1ryYREcmcNQ9CMJFYDGl5BgIlkF8CecXO/kug2FkXeT8vRlu0vuFjzjySqAG9DbXNl2O9Rol0wZq0vvP/6jAUTnvLCe9zC/Cb0ffwgDOv1O6AdY/ovTQRyTk5E4xmra01xrwNTGtc9R1jzNPW2oXJjGOMGQ18l0PPaO9Ya9tkuo4xpitwXsTq+xPcPPKSDTUplFBD82C0NNOhACf0Ls7lQ0RaSMglGC0/zT/9RK+GBfD8MPh8g/OBUias+7d7W14BdD7Wud/7/OjBaBdXHV5bgcsVYjK1Yy8iIiIiIiIiIiIiIiIiIiIiXmk6e9gCG1uykBwTedZ1UhcLNcaUcXgwms4MEREREZHm8hpDloxxQmzCdRwbf/tQNexdBp3Gx++brESOkS+NEU7SVnUYnnjfnfNbXzBa1Xz3trxCKBuQ0rD1NsTOYGXUts4F3VIaMxUFuR6M1qTDsNjtO9/3p45E7IzxNwXQ/ojMnZ/TGuTlQ+fj4veL9b8r2WvD9OS3MXlQNtj7WvxQ0ssJQwy5nIqc6ceuqhjjtx+qx6JwHYbGbq963wkG1s9MRCT3Vc1r6QpyjHEPGGu6Hy+0LGrfOGMGisHk+/PcGyiKHjpWHxaMZi3smJ3YeMO+6U1d4Zp+NtF0PwU2Pef9nE302ktEclDOBKM1uhsnGM3iBHPNNMZcbK2dm8jGxphjgSeBUpyrUNrGMduqK4Hwd8VXAW8muG1kUpFLLGlMNUBFjDFFWpegSzBaQZrBaA1J/vu9ejKc+CQUZ+ADuF0L3Nu6TTn0gfmoW5w3fLf8x1kOtIOJ/4TCisO3c7uCmILRRERERERERERERERERERERLLdlrD7hS1WRe5ZEbHcP8ntI/vvtNbuSqMeEREREWlrep0D+WXxj9l+eSIc8ycYdLUTduIVG4rfZ+AXvJuvtSgf6YTaRbuIeaRPboXh33Q/Xj+X1B+EFffCh99y79PnQvcTj+PYFazE0hC1rUtBj5TGTEV+nvvL6lAi/zPZovPx0K4fHFgfvX3zizD7MueE8MHXQV7A3/oAdi+CNQ/Cxmdi9+t/mT/15LLe58YOkwJY+y8o7e/8vlMMMBQfhQ44j7mL7kp+255nQmFH72vyQ14A+l0Kq/8evf3ARidwI1Dk/dxbX4OFt7m367GouX6XwoIfuLfXH4CNM6DvRf7VJCIimZFouFW2iRYcFjVkzCVwLK8Y8hMMMQvvm1fQ+oNB81yC0RrqnK/bZ8PyPyQ+Xv/PeVNXwvNdltlgtDUPOq/R+l2cuTlERDyWU8Fo1tqXjTGzgCk4oWa9gLeMMf8E7gPmW2tt+DbGGAMcDdwIfAEnCMw23t621r7o2zeQfb4YsfxA5M8vCalsl+pcIrkplKFgtGg76LHseBteOg6mzXI+OPBKcJ/zAZSbkd87dD+/HUx5EfYsgZpN0OloKOoUfbt8l8zEnR/o6gwiIiIiIiIiIiIiIiIiIiIi2W1R2P2BLVZF7lkasTwkye0HRSwvSaMWEREREWmLCsrgpBnw9mchuMe9X0MtvHctbH8TTnjQu/ltffw+fRRmcRhjYNJjMOts2L8mfv/XToVpb6YcGJYVGoLw+mnxT4g/OokTnyNUBre5tnUuyMAF613kG/fTAIMNQd/qSFteAE56Gv4zwb3P+sec29ZXYPIT/p43smMOvHFG/GDI/pfDsJv9qSmXFbSHE5+G2RdDcK97v8U/g1V/g2lvQYeh/tUnyak/6Dx3VL2X/LYVR8Kxf/G+Jj8ddTdseh5qd0Rv3/Qs9LvE2zlXPQDvXRe7z/AYwaBtUdlAOOGf8G6MEN23PwOTn4R+n/WvLhER8db+DVBbmfr2foWRHdZWqPPiM8ktULy+FtY/Ae98PrH3nEweHHUPdDnO2/ri6X8ZVL0Py/5f5uaYfQmM+hGM+2nm5hAR8VBOBaM1ugz4EOiJE6yVD1zdeNtvjFkG7Gps6wQMBZpSdUzjegNsAC71se6sYow5HhgVtqoe+EcSQ0S+u1uSQhmR28R5xzghfwKeSHKbwUCcS3iIeCDoEoyW73MwGsD+tfDWRXDWh+nNHV7DU93d2zsdAz2mNV9nDHQc5dxiiXUFqj2LoOOYxOsUEREREREREREREREREREREd9Ya5cbYz4BxgJjjTG9rbWbWrquHLAoYnmsMaadtfZAgttPijOeiIiIiEh8PabCZ7bBzvfhlcmx+655CEb+AMpHeDN3vJNUu5+iE4ndlA+H81bA7gVwYLNz8fOVf47et2oebJgBAy7zt0YvbXohfija8G9DceoBZm7BaB0CHSnK8y9ULt8UuLaFbJ1vdXii03g4/V14+YTY/TZMh50fQOej/akLnICueKFokx6D/m32tMTk9TzNeT5Z9FPn5+vm4DZY9js45o/+1SbJ2fhM4qFoJz176H7ZIGcfweRlpi6/FHWCC9bC4y7nus3/irfBaA0h+ORWnFOiXUz4HQSKvJuztRj4P9DnQngixnmbn9wKfT+jfUoRkVy16C73tnE/h25TogecBYohr0iP/62V235RQy0s+kVioWhTXoQuE6Gw3NvaEmHyYMI9MOoHTkBacC/kt8OJxwHqaxrD9QLNt1vzD9jwVOLzLP01DP8mFHX2qnIRkYzJuWA0a+12Y8yZwLPAAA69qjc4AWgTItb9d1MOhaKtBC6w1m73o+YsdW3E8kxr7eYkts/KYLTG32lSv1ejHVfxS8glGK2gBYLRAHZ9BFtfhx6nRm9vqAcbgrqdzhupRV0A6+z011Y5ffIKoKQXvHm+szPtZsxtqdUIUNDRvW3bLAWjiYiIiLSk4F4IlDpXcBQREREREREREREREYnuD8BfcY7d+wmHH7smEay1W8IC5cA51nMy8HKCQ0yJWJ7pUWkiIiIi0tYEiqDrJBhyI6y8L3bfra95F4zWEOdE1dKB3szTWuUFoNME5wbuwWgA217L7WC0ba/H79N+cFpTuAWjdS6IcXH5DCiIGYwW9LESj7Qfmli/ra/6F4xmLWx7I36/bidlvpbWJlAMA6+MHYwGzmOSZK+tCf5+xv0M+pyX2VpaSn47KO4BB7ce3hbaB/W13gWV7V0GNXFO+dU+kbuCMigfBXsWR2/fu9T5PZb09LcuERHxxu6F7m2Dr4Pirv7VItkjz2U/rHqNs28VT1Fn6HWWtzWlorgb9D478f771yUXjNZQ54TM97kg+dpERHyWkxHr1tpFOAFoj3Io7MyG3f7bleaBaA3AQ8Ax1tqlftacTYwxpcDnIlbfn+QweyKWk9o7NMaUcXgw2u4kaxDJLUGXYLT8FgpGA3h9Krx3vfOma7gNM+DRfHisGJ7uBc/0g8fbOVe0eKIDPDvQuc3oA4/kwdZXYs9T0iv1Grud6N4W7yo8IiIiIpIZ+1bBS8fBEx3hqW6w8CfOAUEiIiIiIiIiIiIiIiIRrLX3Ay/gHMN3tTHmey1cUq54OmL5i4lsZIwZDhwXtmo/iQeqiYiIiIhE1zuBcJMPvg7b3vTmOCIbJxgtkXrE0X0K5Je6t6/6m3MCbS6yDbDmoTidDPQ6J61pqlyD0bqlNW6y8ghgXE4FDNmQr7V4oqgTdJkYv9+in8CBOMFAXrAWNkyH+prY/TodAyU9Ml9Pa9T+COgwLHafvctg6T1QOVfHpWajyjmJ9etzUWbraGkdx0RfX38wscDORG1/M3Z7fqnzPC/u4u0zfvxjqF7rSykiIuKh4D6omuverlC0tiuvMPr6RXcmtn2fC72rxU+9U3jfY/XfYfHPYfVDULPF+5pERDySk8FoANbaXdbay4GRwP8DmmJdTcQN4GPgbmCYtfZqa21kqFdbcwkQnsS0DXg+yTFWRCz3T3L7yP47rbW7khxDJHdY61z1IZqCNIPRGtIIRgPng8wPv3loec+n8LbHb0CXJvsQEaaos3tbOqFwIiIiIpKahiC8NgWq5gEW6nbCupZEtwAAIABJREFUwtthxb0tXZmIiIiIiIiIiIiIiGSvz+MEfRngF8aYl4wxp7RwTdnuYSA8DeIzxpgjEtju+xHLj1trdZCNiIiIiKSn5xnQ/7I4naxzXNG7V6YfZhMrGK3vxamd8NlWFbSHCb+L3ef54bDxGX/q8Up9Lcz+HATjnCI29idQ2i+tqSpdgtG6FPgbjmWMId/kR20L2qCvtXhm/D1Q2Cl2n/oaeGEEbHsjc3U0hJzHrtmXxO5X0BEm/L/M1dHaGQMT/hA7rBHgo2/DyyfA/JucAETJDtvegD2L4/cb+X0oH5H5elrSMTGOmZ51NlSvTn+Ojc/C+19xbzd5zvN7uucltnbDboaO49zbVz/g7AdteMq/mkREJD3Va+H5GPsaY+/yrRTJQoGi1LfNL4VRP/KuFj+VDYQxdyS3zcZn4OMfwdyrnP+pbXFCeUVEWkj0d0NziLV2OfBtAGNMGdAdaErRqQS2WWv3t1B52eraiOWHrE360iBLI5aHJLn9oIjlJUluL5Jb6mvc34zPT/MNyMIKOLg9etu5y+CTH8P6J2KPseJeGHIjVIyDpb9Or55IHUbEDjdLRI/TYWuUC9c21KY3roiIiIgkb/tbcGDj4evX/RuGftn/ekREREREREREREREJKsZYx5ovLsX2IdzUc9pwDRjzD6cC59ub2xLlLXWRh4H16pYa1cYYx4ErmlcVQj8wxgz1S3ozBhzAXB12Ko6IMFLoIuIiIiIxJCXDxMfhoFXw6wzY/dd+y/oMQ0GXZX6fG7BaKUDYPJjThiIJG7wtXBgMyy8LXp7/UF473roeRYECv2tLVXrn4QNT7q3l4+E4x6ALselPVWVSzBa54JuaY+drHxTQNDWHbY+FGVdTuhyHJz9CWx+IXYIVnAvvHcDnLfcCdfy2qbnnceuWI65F3qdA6V9vZ+/Lel5Gpy9EFb/HRbFCa1YeR/0uwR6TPWnNnFnLcyN81Zcnwtg+Heg22R/ampJ7QdD52MbLzIdxce3wqSHUx+/IQjvXRe7z5kfQcXY1OdoK0q6w2mz4YkY52821MK8G5zH+HTCVERExB+f3Ao1m9zb+3/ev1ok++Sl8FxeMd5536T/ZVAUJ7g7m4253bmwwbbXAQPdp0JxF3h2cPxtg3tg/o1wztLMvOYWEUlDzgSjGWN6AMeGrZptrd0Z3sdaWw1UA6v8rC2XGGOGApHvLt2fwlCLIpbHGmPaWWsPJLj9pDjjibQuwRjHbaZ7ZYZxP4/+ZueAL0CHoTD5cXjxSNj9cexxZh6ZXh1uTvTgigmB4ujr63UxWxERERHfLb07+vod7/hbh4iIiIiIiIiIiIiI5IqrARu2bIGmI6o7cPjxbPGYxjFaNBjNGNOH6Mdg9ohYzjfGDHAZptpaWxljmtuBi4CKxuWJwKvGmOustZ+G1VIE3AD8JmL731hr18UYX0REREQkcSYPep0Bx/4V5l0fu++iu2DAFU6gWircgtF6n6tQtFT1Od89GA2gdgdUvgvdT/avpnRsnBG7/Zj7PAlFO9hQQ3X93qhtXQojX/5lXoEpoCbK+pAN+l6LZ9r1hiE3QFEXePuz7v2qV8KexdBxtPc1bHw6dnvHsXDEl7yft60qGwhj7oSlv45/XtCGpxWMlg32LIH9a9zbz/oIKjJ0Xlq26nOBezDaxhlOmFyqoRI75jjPy24GXqVQtGQUlEHv82HTs+59aqtgx2w93oiIZDtrY78WNHlQ2s+/eiT75CUZ9t7rXJjyXGZqaQldjndu4cbe5QQKxrN3Gez9FMpHZKY2EZEU5dKnAZ8Bnm68PQzUtmw5OeuaiOXZ1tplyQ5ird0CfBK2Kp/kDlCbErE8M9kaRHJKKIPBaL3OhvwoYwy44tD9oV9Jb45UDf8WlA9PfxzXYDQ9FYiIiIj4rm5n/D4iIiIiIiIiIiIiIiKx2bBbrpoNrIlyeySiX2+XfmsAlyvSOKy1G3GOnawLWz0JWGKMmW+MecwY8x9gA/B7oCCs3/NAAke5i4iIiIgkqdtJ8ftUr3ICb7a/7dx2LYCGUOJzWJe+JpD4GNJchxFQ1DV2nw3Tk/s9tZSG+tgnw+eXeRbQU1m3zbWtS0E3T+ZIRr4piLo+5PY/k0u6TIz/P775BecxZf9652911ydQ+R7U7Up93oOVsOah2H0SedyT5BgDXRP4ua75B+xeBLYh4yVJhIYQ7F4IwWrY+YF7v8IK6DDSv7qyRa+z3dvqD8Cm552LTtftTn7sne/Hbu+WIyGm2SSRn9nWVw49x4iISHaq3QGhavf2LhMhL/prJmkjAkXJ9W8Lr/WS2XesXpW5OkREUpRLwWgdca70aID51tr9LVxPzjHGBIArI1bfn8aQkZfD+GKCdQwHwi+7sh94OY06RLJf6IB7W6A0vbFLesKpL0P5qEPLx93vXI2ryaBrYNzP05snWd2meDdnnssLkYY4V4YREREREW80hGDRT+GlE9yvbgbO1VdEREREREREREREREQOZzy8tSnW2lnARcCOsNUGOBq4FDgDiEw3eAS4zFpb70eNIiIiItLGdBgK/S+P3+/jH8KrJzm3mUfBU91g80uJzbF3WfT1Jj/xOqW5QCGM/nHsPsv/ANO7wuaZ/tSUiu1vwdM93cPzAEZ8DwrKPJmuMrg16vo8AnTM7+LJHMlwC0YL2qDPlWRASQ844iux+yz4gfOY8kx/eLQAZo6Dl4+HJzvB3GugIYmfQ6gGZl8GT8UJDCzqAkO/lvi4krhRP3A/X6hJaD+8OAZm9IWq+f7UJbDxOZjeBV4cC0+0h7lXufcdfZvzHNPWVBwJ3ae6t791Prwy2Xl8mndTYsGjDUF473r46DvufcpHQb9Lkq+3rRt4JZQNit1nyS8PPcf851ioib4PICIiLWjhHbHbR/3IlzIki8V7fRGudAAMirGf21p0nQw9piXW983zYO/yzNYjIpKkXApG29n41QJbWrKQHHY20DNseR/wRBrjPQyEHzj1GWPMEQls9/2I5cettUo3ktatvsa9Lb8k/fG7HA/nLIKLd8GFm2DwNc3b8wIw6ha43PpzVYgep8G0N5JPVnYTKI6+vl4PHSIiIiK+mPtF+ORWqJobu1+s/V4REREREREREREREWmrBmbgFucsttbFWvsiMBr4M7ArRte5wMXW2st18VkRERERyagTHoIJv4de5ya+Td0ueOs8OLg9dj9rYZ/LSZgmkPh8crhhX4cTn4rdJ7gb3roAarb5U1Mygnth1tlQu8O9z5AvwZhbPZuyKhj977VTQRcCLfD36BaMFmoNwWgAE34Lx/41tW1X/x2W3p14/0V3wvrHYvfpMBxOf9cJhBTvdT8Fpr0Fg6+P37dmM8w6S+cR+eHAJnj7IgjuSaz/8JszW082OyWRwFcLK/8My34fv+un98Cqv8Xuc9psz8I/25TiLs7j+cjIU5td7JwP71yW2ZpERCQ5uxbAinvd26e9Db3O9K8eyU55CQT2lg2CEd919g2Ku2W+ppZmDJz8PBz5v9DzTOhyAhR0dO//1gXOe3MiIlkil4LRwsPQSlusitx2bcTyo+kc/GStXQE8GLaqEPiHMcYlwQiMMRcAV4etqgPuTLUGkZwRKyAi4EEwWpPCjs4Oaiz9fXhT7uRnvR3PLWCtvtbbeURERETkcPvXwdqHE+sb0vk1IiIiIiIiIiIiIiLSnLV2XSZuWfB9DbDWmjRvVycx33Zr7U1AD+BU4IvALcDXgc8Cg6y1J1hrp2fi+xURERERaSYvAMO+BlOeg/NWgEnw9KSGIKx/InafXQvc2xSMlr6+FzknAMeSyO+pJWx8Nv4xaqN+6OmUlcGtUdd3Kejh6TyJys9zCUZraCXBaMbAkOtg8HWpbZ/osY6J9j3pWWg/JLVaJDFdjoXj/gJnzIvft7YKtryc+ZraunWPga1PrO+AL2S2lmyXF4Dy0Yn1TeQxJ16fEd91zh2U1BR3cwJBRt6SWP/tb8HBGGGsIiLir1jPk+WjoNtk/2qR7OWWR9AkrwDOXQ5H/QpKWuZ1fYsIFDkBsafMhNPnQJ8L3Pvu/RR2fehfbSIiceRSMNpHQFO0pC6zkCRjTHfgnIjVceLjE3I7za9AORF41RgzPGL+ImPM14DIT0Z+kw0HqYlknFswWl5h4h8Ce2XgldBxbOL985PIoiwbBBdugIBrPmJq3MbTlV5EREREMqt6LXz0fQ69HI9DwWgiIiIiIiIiIiIiIiIZZa2ts9a+Ya39h7X2f621f7DWPmWtXdPStYmIiIhIG9V+CPS/PPH+e5aCbTweyUY5LmnvUvdty0cmV5tEVzE+fp+9jb+naL+jlrJnSez24m5Q0svTKauC26Ou71zQzdN5ElVgXILRbCsJRmvSKYG/0Wj2LIaG0KG/XbdbcB8c2Bh7rMIKKO2fWh2SvA7DIVASv1+s5wjxxpJfJN6301GZqyNXVCT4M9j7KdgG98elhnrYuyzOXCk+NkpzCT/HWKjZnNFSREQkTLTnx3B7YuwH6jlSmuTFCUbrOM4Jt23r4u0Pxfp/ExHxWX5LF5Aoa+16Y8xc4ARgmDFmqLV2eUvXlUOuovnve5G1NoFLKcRmrd1ojPkM8BJQ2Lh6ErDEGPMBsBooB8YDXSM2fx64Nd0aRHKCWzBaIm/aey2/HZwxH1bcCx/e7N6v36XQ6yzoMAJePj56n/KRcNRvYNvrUNzDuTJPQQfva3Z7IdJQ6/1cIiIiIuJ8gLDop7DoDudD+EQpGE1EREREREREREREREREREREpO0ZeydsfAZC++L3XfFHWH2/c5HsQAm06wtHfAmG3QzGQHWMzN+eZ3pXc1vW90Io6Q01m9z7rPiTcwPnvIFh34AhN/hTX6Rts2DBLVA1N3a/ITd5foJzZXBr1PWdC7p7Ok+i8l2C0YKtLRit/2Xw8Y+gblfy2z4a/WeUtMHXQ6Awfj/xRkF7GHgVrPxz7H4LfuAE2425U4EGXqs/CB/cDLWVifUvKE8uGLW1OuJGWPdw/OOt6w/AI2n8zZb0gj4XpL69HNL7PGf/88CG+H1nHgkTH4EBl2W+LhGRtmrFfTD/S4evD5RA10kw5iew9Few+QX3MVrqtapkn7w4r+GO+LI/dWS7/pfDJ7dBcE/09ne/AAvvhCHXw4jvOu/XiYi0kLyWLiBJv3a5L/F9MWL5fq8GttbOAi4CdoStNsDRwKXAGRweivYIcJm1tt6rOkSyWiiLgtHA+XBm+Dfgwk1QOuDw9pOegcmPwaCrIb/MfRxrodeZcNSvYMS3MhOKBhAojr6+/mBm5hMRbwX3wsq/wUffg43PJRewIyIiLaPyXVh4W/KP2aHqzNQjIiIiIiIiIiIiIiIiIiIiIiLZq2wQnPISdD4eTH78/k3HgdfXwL7l8OG3YNnvnXX710bfpv1QKO7iSbltXqAYTn8Hep6VWP89S2DejbDq75mtK5pdH8Mbp8cPRRvzExhzm6dTW2upCm6P2taloIencyXKLRgt1NqC0Qor4LQ50H2q+/kkmVLSG0b9GMb93N95BY7+PQz/tvM7iGXxz+DjH/hTU1vy7lWw8r7E+lYcBafNhpKWCYnMKl0nwUnPQqcJYDJ0unbn4+C0dyC/hc5DbG0CRc7fb6+zIb80fv85n4dNz2e+LhGRtmjV/dFD0cB5v2Drq/DKRNg4w32MAVdAt8mZqU9yT6DIva24GwyOjFxpo4q7OPuXsVSvhAXfh6WK9RGRlpVTwWjW2hnAAzihW+caY/5oTCKfmLRtxphJwPCwVXXAv7ycw1r7IjAa+DMQ63Icc4GLrbWXW2v3e1mDSFarz7JgtCbtesF5K+HU12DkLTDpMfhsFfQ5/1CfmG+a2oyXCCgYTSSXHdwOr0yGedc7L4DfOh/eu07haCIi2W7tv1PbLqSXeSIiIiIiIiIiIiIiIiIiIiIibVLXE+CMd+GyWrgsBBMfTm77FX90vlavid7e84z06pPmSvvDKS/CxEcS36bpd+SnVX+DhjihX8feB2Nu9TyQZm/9boK2Lmpbl4Juns6VqDYTjAZQPhymvgqX7nceU3qemfk52/WDizbCuLsgL5D5+aS5vAIYfzdcuAHG/SJ235V/hfpaf+pqC2q2wvrHE+tbUA5nfQgdR2e2plzS+xw48324LOjcAu28Gzu/PZz+LpQN8G5MgdJ+MOUFuGSv8xwT7xzPFff6U5eISFuz/E/pjzH0q+mPIa1HXqF727j/9a+OXNBxFAz7Rvx+y/8I1qc8CRGRKHIxVOxGYB/wDeBLwMnGmN8Az1prq1q0sixlrX0HJ0wu0/NsB24yxnwDmAT0B3oA+4FNwEfWWpdPqERaObdgtGy4UkNeAHqc6tyiifnGnk87snkuCc0N+hBDJOutuA92L2y+bvXfYcgN0OX4lqlJRETi2/xiatspGE1ERERERERERHZ9Aot/DrWV0OkoGHNHYldbFxERERERERGR1sHkOWewlI9Mbrt9KyBYDftdTjspG5h2aRJFMr+nXR9DQ72/gVE7P4jfp8PwjExdGdzm2taloEdG5oynoC0FozVpekzpMBy2/Cezc5WPyuz4khhj4oduBffA/rXQYZgvJbV6299MvG+HEZmrI9eZPOfWYRjs+sibMctHOP8TkhlNzzFu53422fmhL+WIiLQpDSHYlebjq8mD9kd4U4+0Du36urdl6L2DnPb/2bvv8Diqe//j76Nmyb33XrCNbYwNphubTiB0QighIZWUm/IjJIGEm5BcEkKSG0hCSGghhBog9GqqaTaYYoptjLstNxV3SZa02vP7Y6SrlTWzO7s7O7srfV7Ps49Wc86c+dqWV7M7cz7Hz99J7Xpo2AZd+mW+HhERF3kVjGaMeSnm291AD2B/4Lbm9nKgornNL2utPS6wIgVrbQPwcrbrEMkpXh+OJVpNIBfkQo2Fpe7bm/aGW4eIJO+jn7tvX/+QgtFERHKZ182FiSgYTURyQbQJbAQKPUK2RUREREREJHN2rYB5h7Zex9v6Imx9GU5627khVUREOiVjzBcDHM7i3B+4E9gCfGKtlqgWEREREclJvac7oVs7l/rf58Ee3m3dFIyWEb2nQa+psPPjxH1tBJ4/AgpiwrmKusOA2TDp/0FR1+DqqnzDWYy5akH8fl1HQv8jgztujGqPYLQuppRuhXF+VjOoyLhPBWzsyMFoLUZfCMtvyPAxLsrs+OLf4BOgS39nERYvT06CCd9yFm3vc2B4tXU0lQvgjfP999f/k8RGXxRcMJr+vnPD3i3w2rkw7qsw9DPZrkZE8pmNwqp/wKanYceHsGdVa9u4rzsLz3UdmrXyQpXMZwVehnxGYU3S1tBT4f3LwTa13d59HPQ/NDs15bIR58C734doQ/x+1e/A0JPCqUlEZB95FYwGzMW5samFxcnjbok8H9H88Hujk0mir4hI6jpqMFpY95UWeExmj9aHc3wRCd6q22HmH7JdhYiI7MtaWHNX6vs3KRhNRLIo2gjvXQ5r/ulMwB9yEhx2hy52ioiIiIiIhGnxT9ovbrTtXdj8nCYJiIh0bv8kc/fp1RhjFgF3Av+21upmEhERERGRXGEMzH4E5p8Guz9Nf7zuo9MfQ9ozBo5+BOZ/FnYtT9y/+u322zY/B5ueguNfhYIApqptfBJePcsJYoun22iY8zgUFKZ/TBdVDVtct/cvGYwxxrUt04pMiev2SGcIRus3Cw65Bd75bvBzSUwhTP6xE74muaGwC8x9Gl49E+o2efdb8TdY8y849gUt3J6KzfOc39O+GJjwbSeMTuKb+D3nd+qq20j9Y1EDE77p/J1LbtjwH+dx6O0w7ivZrkZE8tU734UVN7m3rboVNjwEpy6BsiHh1hW2SB08Mz39cQ77R/pjSMfScwIc9QAsuAQiu51t3cfBnCe0oKOb0gFw9ONOUHLjDu9+r5wM526Dkj7h1SYi0izfgtHcKNhMRHJfPgejFbhfOHOE9BJcWOq+vbY8nOOLSGps1LutpHd4dYiIiH+b58HCL6W+f+Oe4GoREfHDWmelrF2fwuIfw84lrW0bn4DXzoHjX8laeSIiIiIiIp1Kw04of8S9besrCkYTERFoXQA1SN1xFlydC1xrjPmytXZeBo4jIiIiIiKp6LkffPYTJxjttXPaXtdPVrcxwdUlbfUYD6cug90roG6zs+3VM+NPit1X1QLY9AwM9xuwE8fH1yQORTv6MRh2mhPsliHVjRWu2/sVD8zYMRMpMsWu2ztFMBrA+K/DmIth+wfOIh0FRYBxfl5sivNbCoqg9wFQ3CPQUiUA/WbBmRvgwZ4QibNwb6QGlv4Ojn44vNo6io+vgWhD4n7Hvwq9p2keiF8FxXDoLXDgb51zn5a5NW99DfasdN/n2Oeh5TXeFEDvqQqeyFUf/wrGXqJwFRFJXt1W71C0Fg3bYdXtMPWqcGrKlg0BnLeZIijN3nszyWEjzoZhp8P2xVDUHXpOzOhnB3lv6ElwTiXs+Aienendb809MPG/wqtLRKRZPgaj6beOiOSfSB4Ho8U72Y8XehQkr2A0gI/+x3mTrzclIrln5S3ebbogJiKSmz79S3r7N8W58UREJGhN9c6NQmvv9u5TMd8JTeu5X3h1iYiIiIiIdFYf/ty7bcPDMOO68GoREZFcFHtjh/XYvq99ZzS79bUxbUOAZ4wx37PW/jX5EkVEREREJCOMcSahjv0KvP/D1MYo7AolvYKtS9oyxrm/ouUeix7jYds7yY1RMT/9YLRIHVS/Fb+PKYQhJ2V8DkFV4xbX7f2LB2f0uPEUGfepgJFoJwlGA2d+Sf9Ds12FhMUUOKEG6+6L36/ilVDK6VCaGqDqjcT9xn0NBs7OfD0dUZe+bf/uDvglvHlR+36lg2Hw8eHVJempWec8uiu0V0SSVP22v34rb+74wWhBnLsd8Mv0x5COq6AI+h2c7SryR0ER9J0BQz4Dm59x71MxX8FoIpIVeRWMZq1VhLaI5KemPA5GiyvFFXWS1WWAd9tHP4chJ0D/w8KpRUT8qd8Gi77l3V6sm1NERHLGjiVQ/ijsrYBNT/nbp+9BsO3d9tvjrcgnIhK0VbfFD0VrsXmegtFEREREREQybfdKWHFjnA4hLbgkIiK56svNX3sAPwf64QSZRYGFwCJgPbALKAH6AtOA2UDLbHML/Bt4FigDegP7N/cZRduAtOuNMcustS9l9E8lIiIiIiLJGX566sFopQODrUUSG3FW8sFom5+BxcWwaxlsfRlK+sGwU2HYaU7gi0kwLSzaCCt85FwPPQUKuyRXWwqqGytct/crzt7PY3FBsev2RtuJgtGk8xl+RuJgtIbtUP4EDP2MM6lfWkXqnNfn3Sug22ho3AUbHoHqhWB9XL8ZfmbGS+w0hpwMBV0gWt92u/6O88+u5QpGE5HkRff661dbDouvhB4TnPc+ZdkLZs6Y3SvTH0O/P0WCN+JM72C0DQ/B4p9Crykw7BQo6RNubSLSaelTHhGRMKy61X17Uddw6wiaDSkYre8MKOrmHbSx5m4Fo4nkmvLH4rcXdQunDhERiW/Do/DGec4NZX7Nfbb55jMFo4lIlq2911+/mrUZLUNERERERERw3qPFmzxTsxaa6kOZrCgiIrnHWnunMWYC8DhOKBrArcA11toNXvsZYwqAM4A/AGOAzwHLrLW/2qffKcCfgHE4AWlFwP8CMwL+o4iIiIiISDp6jIfpv4YPfpb8vof9I/h6JL4J34ZNz0Lla/732bnUebRo3AWf3ug8Rl0Ah98FBYXu+0Zq4dUzYcvz8Y/RdSQceJ3/mlIUsY1sj1S5tvUvHpTx43spMiWu2yMKRpOObPhZMPI8WP9A/H6vng6jLoQj7kocxNhZNGyHl09xQtBSMfYSJ8xLgtGlLxx8Iyy6tPW6Wq+pMPWq7NYlyXvlM/C53VDcPduViEg+iUb89136W+dr6SA45jnoMz0zNWVDUz1UvJLeGFP/G3rtH0g5IhJj9Bfg7Uu925de63ztPg6OfV5BsSISCgWjiYhkWuMe77bCsvDqyIiQgtEKS2HAbNj8rHv7ir/CrHgr0ItI6FbcFL+9yecKByIikjk2Cu9+N4lQNAOzboKhJ8Gaf7p3UTCaiISp6k1//eo2ZrYOERERERERSbxgio3C7hXQe2o49YiISE4xxnQFHgUmAo3AxdbaBDNZwVobBR4xxswDngGOAn5hjFlvrf1nTL+njTGvAa8ABzZvPsAYc4K1NsGMehERERERCdWUn8LQU+HlE2Fvhf/9+h+RuZrEXUlvZ5Lr1ldg+3tgm1rbdnyUOKBoX+vucybYDjvFvX3tPYlD0Y56AAYfDyV9kjt2CrY1VmE95kv0Lx6c8eN7KTLFrtsVjCYdWmEJHHkf7PcdqHwTPrjSu++6e6H/oTDxe+HVl8tW/C21ULTRF8P4b8CAI8GY4OvqzMZ/DQbOhq0vQ9lQGHSswrXy1Zo7ndclERG/fM/dibF3Kyz+CRzjMbc6H8V7Lzn6YjjkZifcdcvzUNu8vlK0AXZ8CF1HwNivQF+tjSSSEUVd4dgX4KXj4/fbswo+vgYOuz2cukSkU1MwmohIpsVbIam4R3h1ZMLgBCe2QSrL3sVDEUnSugdg2zvx+yg4R0Qk+3Yth9pyf30PuAamXNm6gl6RxwX4SJxQYBGRINVX+++7c5l3W7TJuXAKzs1FhaXp1SUiIiIiItIZ1WxwJkYmsmuZgtFERDqvXwGTcVbgu85PKFosa22NMeZs4BOgL3CjMeYpa21lTJ/dxphzm/u03Bd5IqBgNBERERGRXNNnOhx6B8w/1V//siFQ2CWzNYm7wi7OQppDT2q7veK15IPRADY+5h2MtvGJ+PsOOApGfi75Y6aoqnGLZ1u/4oGh1bGvIuM+FTBiIyFXIhIyUwADj3Yeq26FPau9+y65FsZ9HYrKwqsvV5U/nvw+A46CI/4VfC3SqudE5yG5o88M2P64iqP7AAAgAElEQVR+cvuUP65gNBFJTqphxpvnQaSu45zbxHvvN/ky589ZVAZjvxReTSLSqsd4f/02PgYoGE1EMq8g2wWIiHR4W1/ybhtwdHh1pGPi9z22h7iCSkm/+O0168OpQ0QSW/KbxH0UjCYikn3Vb/nvO+WnraFoAEXd3Pvp9V1EwrLrU/99d3wAD/aGxVdAU4OzrakB3v4W/KcvPDzAeTzYE148zpnQLyIiIiIiIv6VP+bdVlAMw06HyT+GHhPCq0lERHKGMaYI+GLzt/XAdamMY62tAm5u/rYMuNClzxrgQcA0bzoylWOJiIiIiEgIBh7tfQ/SvgYdm9laJHl9D4KiFBaJX3mL83nirhWwcylseBQ2PAIbn4KdS+LvO+i41GpNUXVjhev2noV9KCnIXlBfsSl23R6xDSFXIpJFiX4v7N0Cr57uvMZsfh4ad4dTV7ZE6qDyTajbAnsrYdNzzmvrhkeSu1e4hX7vSmc0+cfu23tO8t5nyzznHGb3SrA2M3WJSMcSTTEYDQtr74FNz0Ld1kBLyoqqBd5tvaaEV4eIuOs6ErqPS9yvvho2POycD218CioXtM7XEREJkPsyESIiEgwbhWV/8G4ffHx4taRj4vedD8RrY8LHxl4CvUJc0b1L//jtzx0KZ28OpxYR8daw3QmeSKSpNvO1iIhIe9Em53U62ggLv5y4f0lfOPYFMKbtdgWjiUi21W1Mrn/jTlh6nXOh5aA/wns/gJV/b9sn2uiEmz93MJy5CSK7Ydu7zgXWssHB1S4iIiIiItLRbPQIRus5ET77Sbi1iIhILjoK6A9Y4G1rbToXE+YBVzY/PxP4k0uf53BC0wwwPI1jiYiIiIhIJhV3hylXwQdXxu9X0gcm/yicmsS/oq4w7efwfgr/Nq+emfw+3UbB+K8nv18aqhq3uG7vVzww1Dr2VeQRjNZoUw1ZEMlDky6D8kehvsq7z5YXnEeLQ26G8d/IfG1h2/AwvPkFaKoLZrxuo2H8pcGMJZJPhp8G/Q6D6oWt23pOhjlPwBPjvfeb/1nn68A5MPth6NI3s3WKSH6LRlLf9+2Y90MTvg0H/RkKCtOvKUzRRnj7Uqgtd2/ve7Cz+J6IZJcxcMD/wIIvODkZ8bx2TtvvS/rA7Edg0JzM1ScinU7eB6MZYw4ETgdmA+OAvkAPwFpr2/35jDG9gZ7N39ZbaztANK6I5Kyqhd5t474OhSXh1ZKO7mPgxAWw7j5nFYOBc2DUee0DMjIp0Ypge7c4b4i76p5WkazatdxfPwXniIiEr2YdvHJq4pU1W0y6DPb7DnQf276tqLv7Pnp9F5Gw1Fentt/y62HcV2H1nd599lbA/cXOe96WCzmTfggzfh/u+2AREREREZF80LADtr7i3jbyvFBLERGRnDUy5vmmNMeKXTFvlEefZTHP+6R5PBERERERyaQpV0CvybDmbtjwkLOIY8M2p633NBh6Coz9CvTcL7t1irvJl0OPiVD+CNQ2L3C3ZV7wxxl8Ahz+r9AXtatudJ9u1r84u4vrFRn3OSgRm0bIgki+6TUZTnoLVt4KS3/rb5+3L4X+hzu/XzqKus3w+nlgm4IZb+ovYMI3tYiodE5F3eDYebDyFqh+G/pMh3HfgNL+cNzL8OIx8fevmO8Exh52ezj1ikh+ShhmbHDWGUpgxU3Qd6ZzT3w+WfE3WH2Hd/uBPs/rRCTzRl8AZUNh3f2w+1PY+pK//Rq2w6tnwFmbnFB9EZEA5G0wmjFmGnA9EPuO0s/MxGOAh5qf1xhjBltra4OuT0QEa+GT673b+80Kr5YgdB0Kk3+YxQJ8vMRveib0laBEZB+bnvXXT8E5IiLhe+sb/kPRxn4ZZv6vd7tXaG1kT/J1iYikoiHFYDSAp6f66GSd9/UtPvlf6H8ojPxc6scVERERERHpaHYua55w4zHhbviZ4dYjIiK5akjM8wSr4iXUcve0AbxmaG6Ped4lzeOJiIiIiEimDT/DeUh+Gn6a82hxbwYWnDvsjqyE9FQ1eASjlQwMuZK2ioz7VMBIwpAFkQ6m+1g48FqY+H14fCw01SXeZ939HSsYbf1/ggtFm/wjOODqYMYSyVfFPdznTvaZ4W//9f+GQ26BgsJg6xKRjiPqcc7e92A4eZHz/LnDoXph4rHW3pd/wWjr7o/f3n1MOHWIiD+D5jgPgMfGQM1af/s17oTNz8KIszNWmoh0LgXZLiAVxphLgIU4IWf7fmqeKAr3MWB9837dgHOCrk9EBGvhra86K1d50QrpySkblLhPtCHzdYiIt/LH4eNf+usbqWkbNCEiIplVvw22PO+//+QfxW/3DEZT8KWIhKQ+jWC0VJU/Fv4xRUREREREctWKm+GpKbDzY/f2riP8TxIQEZGOblfM8/3THGtKzHOv1VpKY577mBErIiIiIiIigRlxbrDjlQ1xHllQ3Vjhur1/cfghbbGKTbHr9ojVXArppMoGw4Rv+eu75DfQuAua6jNbU9AitU7d0Sbna8tj2e+CO0bQr98iHUlJL+g+PnG/SA3s3Zz5ekQkf3ktOhcbftzvYH9j7fqk9ZwgGlBQaiZFI7DjI+/2LgOg68jw6hGR5PSblVz/6kXO+xgRkQDkXTCaMeYc4HagLHYzsAFYTPugtDastVHg3zGbTg+6RhERqhbA6ju82wuKnQ/FxL/Bxzt/b/EUdo3fLpKrmvbCh7+AF4+FhV+Gbe9mu6LkRWrh1WRW7LNO8reIiIRj51IS54jH6DU5fnuhgtFEJMsatoV/zLX3hH9MERERERGRXFS3Bd6/nLifNw07HUzc2zdERKTzKG/+aoCxxphD0xjr4uavNmbcfQ2O6VOZxrFEREREREQkWft9O+Dxvgsm/KlvdU011ER3u7b1Kx4YcjVtFXkGo0WwWrRaOqspP/UXWgTwYC94sAe8MAd2rchsXena9Aw8NRUe6ObUfX9Rc/3Nj9oNwRyn32HJBx2IdDYTv++v3+o7M1uHiOS3aKP79th50+O+DoWl7v1i1W1sPSf4Tz9nTmouhhBVLYR/l8H9xRDxWvMI2O87UFDk3S4i2TXhO2AK/fdf+lvnfcwTE6H88czVJSKdQl4FoxljhgAt7wxbPq29CRhnrR0NnO1zqMdahgTmBFagiEiLTc/Ebx/ymXDq6EhK+sCYL8bvkyg4TSQXWQsvnwQf/wq2vgyr/+lcZKt+J9uVJefJScnvU18dfB0iIuIumdWnxl+auE9xd/ftEfebwUREAqdzSRERERERkezZ8HD8G1YBhiezmIqIiHRw84FGnPv9DHCTMSbple+MMZ8HTqT1vsHnPbrOjHm+NtnjiIiIiIiISBoGHQNznkpvjMKu0Hs6zPwj7H9FMHUlqapxq2dbv+JBIVbSXlFBiWdbxEZCrEQkh3TpB8fOg9EX+esfbYSKV+GFo3IzQARg23sw/zTYuSSzxxn3VTj2OS12I5LIxP+CWTdBn5nOuYqXD6+CXZ+GV5eI5Bc/wWh9DoBjX4BBx0FxL38haY07nTmpCxLMvw5bzXqYdzg07Y3fb8K3YOrPw6lJRFIzaA7MeRIGHg1FPZzzocKuYBIEGu7+FF49I//my4tITsm36NSfAy3vGpuA8621/4lp97u0xSKcm62KgX7GmDHW2jXBlSkind6Sa+K3l/QOp46OZtbfYdXt3u01a0MrRSQwFfOdi2qxIjXw6Y1w+D+zUlLSdq9KbbWh+mroMS74ekREpL09SbzlHXpq4j7Fvdy3N+2Fpnoo7OL/eCIiqVAwmoiIiIiISPYs+U389uKeMFBr1ImIiMNau8sY8yRwFs79fQcCzxljLrTW+rrQbIz5GnAjreFqUeBuj+4nxTz/IOXCRUREREREJDXDToEL95neda/PwJ1Dbobx3wi+piRVN1a4bi+gkD5F/UKupq3iOJOOI7aRYrTYvHRS3cfAEXdDnwPh/R/522dvBZQ/BqMvyGxtqVj1D7BNwY139OMw/LTgxhPpjCZ8y3kAPDMDti9277f6n3BgguupItI5+QlGAxhwJBz3gvM8UgsPdPM3fvkjULcVyrIb5vx/1tzlo5OBmTcopFUkHww92XnEWvZHeP+Hiff95Ho48p7M1CUiHV5BtgvwyxhTCFyAc3OTBa7bJxTNN2ttBPgkZtOk9CsUEYmRKIW7WMFoKSlIkOf54X+D9ZuRKZIjPv2L+/Y1d4ZbR4umvVD1Nuz4KP7/JxuFbe87KxF9dHVqx6qvSm0/ERFJXjIBskNOTNwnXtBv407/xxIRSVXjjuwcd+OT2TmuiIiIiIhIrmjcA3Ub4/cZegoUloRTj4iI5IvLgdjl4I8Elhpj/maMOdYY03PfHYwx+xljLjXGLAJuBkpwQtEscLu19iOXfUYAc2ldYPW1YP8YIiIiIiIikpIBs/31G3RsZuvwqapxi+v2fsUDKDCFIVfTVpHxDj6LWI+gBZHOZMjJifvE8go2yrYdQeb9G+g3K8DxRISe+3u3Bfr/V0Q6FBtx3x4n/JiirtBtjM/xo7BzSfJ1ZcrS3ybu02O87i8RyWe94pwTxdr6kvIfRCRlCRJmcsphQMsNUA3A79IcrxyY1vx8RJpjiYi0VdTDCRfyEi9IQtJTvQj6H5LtKkT82/BwtitoVfkGvPmF1vCcwcfDUQ9ASZ+2/Wo3wssnpf9BWUN1evuLiIh/e9b663f6Kijskrjfvr8bYjVsh9KB/o4nIpKqSE3iPtN+BR/9PNjjfnoTDPtssGOKiIiIiIjkk/d/lLjP8DMzX4eIiOQVa+0aY8xXgLtwFnO1QDfgG80PjDG7gN04AWi9mr+CE4ZG8z4GeAu4zONQV9C6WOxe4PlA/yAiIiIiIiKSmjFfhMoE2dX9D3cmxeeAzfUbXLf3Kx4UciXtxQtGa7QNIVYikqN6TYE+B/oPPFv2O2jYBpN+CL0mZba2aBMsvwE2PQP1lW3buvSDvgdBUz1Uvw3VbwV33CEnQtng4MYTERhzMay7171t09Owazn0nBhuTSKS+6IeQcYF3uf4gPOa8/Gv/B3jpeNg1Pkw/lIYNDep8gJRtdC5337nRxDZk7j/6IszX5OIZM7g46BsCNRtjt9v7xZ4ehqYQigscz4DmvIzKO0fTp0dSd1mWPIbqHoLovXt26ddDSPOCr0skUzKp2C0lk+3LbDIWrsrzfFi92+34qSISFq83qC28Er2lsSmXwsfXOndvuo2BaNJ/tj1afx2GwVTEL9Pumo3wcbHYfsHsPLvbdu2vACLfwqH/K3t9gVf8h+K1qUf1HsEoHltFxGRYFW+AZufSdzv4Buh+1h/YxbHCfpt2OFvDBGRdERqE/eZ9t9Q9SZsfja44/p5PRUREREREelIok2w4T9QuwG6jmh/LWFfBcUw5ORwahMRkbxirb3fGBMFbsG5X69lSeiW4LNezY92u8b0ew4431rrtXLCXcADzc/3xOknIiIiIiIiYRr3FdizGpZe697e/3A46qFwa/IQtVEW7HrRta1/jgejRTRPRQSMgaMfgxfmtC4Yn8iq25xrIScuyGyQ0VtfgTX/8m7f+nLwxxw4Fw6/K/hxRTq7oSfDgCOd+/TdzDscTlwIPfcLty4RyW2pBqNNvQrqNsHqO8A2JT7Ouvth/UPOOdGwU5KvM1UVr8NLx7sH9bgZfylMiTNXXERyX0ExHDMPXjsHdieYLx87J736Ldj0FJz8LhT3yGyNHUl9tXOeWbPOu0/D9vDqEQlJPgWjDYh57r70RnKiMc/z6e9BRHJd4x5oTBAG0T03VjLKSyPOjh+MVrcpvFpE0rXxyfjt710OB/0xc8ff9j68fFL71YZirf4HzPhd65vLhh1Q4fOC27RfwX7fhtfOhopX27fXVyVfs4iIJOejX8JHVyfuZ4pgv+/4H7eom7NKg9tFFQWjiUgYIj7nM3bJwAoyDTugJE5ApIiIiIiISEfR1OBMHKpe6H+fsV+BErdMGxEREbDWPmCMeRP4A3A2rfftWZfuJubrGuDX1tp/JBg/iV9aIiIiIiIiEhpTAAf+Bqb9HGo2QFMdFBRB6SAnHKBscLYr/D+f1n7k2da/OPt1FscNRvMIWhDpbLqNhDPWOIvIN9VC4y5n7sZHv/Dep2E7fHoTHPynzNRUsw7WBBxQdvK7rc+LujffU2ehxwSINoCNQukAz91FJE0z/gjzDnVva9gOK/6e2TlhIpJ/vM7X45zjA07w0KG3wszrYfcKwMIHP4u/eLiNwLLrwg1G++QP/kPRpv3SeX8oIvmv91Q4bbnznqe+Gp47xF+I4+4VsP5BJ0xf/Flzd/xQNJEOKp8CwWJvfioMYLy+Mc81a1tEgpMo0Rag38GZr6OjSrRSglY5kkQad8HS66BqIfScDJN+AD2yFFZYtzF++/LrYfIPoesw/2PaKHz6V1jxV9i13Nm2/09g/yvbT0T64KfxQ9HAuSD2YE/n+bivwoCjnWMk0mM/ZzUCY6Ckn3uf+urE44iISOpW3e4vFA1g1AXJjW2MEwrk9lqulQVEJNNs1LlhzY9+h8Dau4M9/o4PYeDRwY4pIiIiIiKSi5bfkFwo2ohz4cDrMlePiIh0CNbacuB8Y8wQ4FzgCGA60B/oDdQD24F1wELgBWCetdYtPE1ERERERETySWEp9JyQ7SriWlm31LNtQEn2g9GKFIwm4l/Xoa3PI3vAO/fQUfl65mqpfBP3tQFSNPh46DszuPFEJHndx8Zvr3ojnDpEJH9EPeY+F/iM+yjuDn1nOM/7HhQ/GA2gagFEm6AgiFgOH5I5lxp2aubqEJHs6DbKeUy6DJb93t8+la8rGC0ZmXzPKpLD8ikYLTa1Y6hnL/+mxjxXKoeIBKcliMjLgNnQe1o4tXRUfQ+Gbe+4t0V1MU/iaKqHF+bC9ved77e+BOsfgBMXQI9x4dcT2ZO4z8YnYfw3nACaFvE+kHr7m7Dq1rbbll7nJGefusS5oSAacYIkNj+XXL2rbncefky7urXmLv3d+zToFExExJO1bV/7k7Xmbnjra/76miKYcGnyxyju4x6M1qjscRHJsKY6/31HXeCERDZsi9+vdDAccTe8dHziMV+YAxdE03udFhERERERyVWx1yBW3+F/v3OqoUvfxP1ERESaWWs3A39pfoiIiIiIiIjkhMqGzZ5tk7vNCLESdwpGE0lR/8OhdBDs3erdZ9cnsOg7bbdtexdq10P/I6F0YOt2UwC9D4ARZ0MXl4Xkd6+CdfdDUTcY9XnYszqYP0eLEWcHO56IJK+0PwycAxXz3dur34at82HQnHDrEpHc5TX3ucD7HN/TiLNhya8TH++N82DYGTDyHOe8JEhVC2HTs1D5GlS/BZEaf/t1Gw19sv/eSkQyZPRF/oPRVt8BZcNgyIkwcHZm68pVteWw4RFo2A5DTob+h4CNQvnjznvKipeh+3jocyBseCjb1YpkRT4Fo61v/mqAGcaYYmtT+8TWGLMfMCxm04fpFici8n92feLdNuHbMMPnyZx4i9Z7t+1cEl4dkn82Pd0aitaivhJW3gwzfhd+PX4+7Fn0TecBMPJzUP2OczFu8HFw6G2tF9dq1sMrp3j/H9izGv5dFkzdiRx+N4y+oPV7twt9APVV4dQjIpJPbBQ+/G/nd1NTAww7DQ66AUoHJDGGdUIx/Rg4F6ZfAwOOTL7Wkt7u2xsUjCYiGeb3oik4N56c/C4s/BJUvOrdb9Axzjn2ye/ABz9LHCJc9WZqr50iIiIiIiK5quI1eO8y2PEB9D4Q9vuv+Nc9Yw0/S6FoIiIiIiIiIiIi0iFUNroHo/UvHkRpQUj3YsdRaLynAjYqGE3EW0ExHHYnvH6u9wL3TbWw4ib3Nq8J6Euvg+NehG6jWretvR8WXAw24ny/5Br3hYhTNfwsGHNJcOOJSOoOvhGenubd/uJcmPZLmPbz0EoSkRzmdb4eJ/zYU9+Z/vpteNh5fPpnOGZecPd2LP8zvPv91PY97J9OyKyIdEx9psMB/+PMj/RjyTXOY/pvYMqVma0t12x7H14+sXWu/UdXO+eXFa/A+gdb++2tcOYwiXRS+XTWsACoAyxQBlwQv3tc34t5vtVauzydwkRE2qhZ6759xDkw669Q1DXUcjqkpjjBaHu3QuUCJ1REZF8rb3bf7jd9OmjJBDqA80amZo1zwW3jE/D8bCf8JhqBZw7MjWDAox6CMRe13eYZjBbgxT0RkY7io1/Ckt84r5GR3bDuXljwRef13q+G7bDzY399j3859WCf4p7u2xt3pTaeiIhfkdrk+ncfDcfPh5Pfg3Ffg8J9blIt6QP7X+E873sQHPMsHPtC/DFX/zO5GkRERERERHJRpBa2vATL/ggvHA3b3nFWDN62yAmY9qvXlMzVKCIiIiIiIiIiIhKiyoYtrttP6ntuyJW4KzAFFHmEo0UUjCYS39CT4LSVMCPA+SN7VsHSmPEa98B7P2gNRYP05k0MPgHmPAUnLXJCRE58C456EIqyH9QoIkDvqc7/z3g+/iXUbgynHhHJbVGP8/WCFILRAIp7+++77V1YdVtqx9lXww5Y/BP//addDeO/AYfcAmdXwKA5wdQhIrlr6lVw6lI45FaYeYPzGpDIhz+Huq2Zry2XfHhVaygaABbe+U7bUDQ/hp/p/D3PvAH6HRpoiSK5wHuZiBxjra03xrwIfLZ506+NMY9ba3ckM44x5kjgUpyANYCHAyxTRMT7g6rY1T8kPdE4wWgAzx8BPSc6H/73GBdOTZIfNj+X7Qra8lppyK/dnzrhOdvfc0Jwsq10EAw9uf32Lv3d+ysYTUSkLWth9T/ab9/8rDMptd8sf+O0+UAsgwq6uG/XzVUikmnJBgy36DsDDr3VubFt3X1Q/Y5zDjv2y9BzQtu+g4+DsqFQt8l9rFW3OWOJiIiIiIjkqzX3wIIvBDNW2ZBgxhERERERERERERHJopqm3dREd7u2DSjJnc9Bi0wxkdjQpWYRr6AFEWlVNggm/j/44Kfe4STJ2vR06/Mtz8PeACfzH3QD9Nrfed7v4ODGFZHg9J4av91Gnfls474STj0ikrui7c/hAfAIPk6oIMn9Nj0F+/84tWPF2voyNO3117eoB0z7RfrHFJH802uy8wDY9j6svCV+fxuBLfNgzMWZry0XRBvbvpdMx0E3KMdEOrS8CUZr9mucYDQLDAPmGWM+a62t8LOzMeYY4CGgADBABPhDhmoVkc6q4hX37WVDQy2jQ2tKEIwGsGu5s5L9Ca9nvh7pGGwUTEG4x0w10CHWh1elP0ZQDr0Nirq1317Sz71/fZUTAmRMZusSEckXTXuhtty9bcmv4ehH/Y0TVjBaYYn79qaGcI4vIp1XuufRJb1hwrdgQoJ+vad7B6MB7FzaetOZiIiIiIhIPtm9MrhQNIAeid5giYiIuDPGFAOHAOOAvkAPwFhrf5XVwkRERERERKRTqmzY7Nk2sDi3gtGgrt32iBY1FfGnoBAGHecsXByE2g2w9j7AwLLfBTMmQNeR0GNicOOJSGYUlsKA2VD5mnef9Q/BmC8mH2KUDbXlUP2287zvLOg2Irv1iHQkXufrBcWpjZfsXNidy2Dt/e5tvSZD72lgm2Dbu7Bnrfc4yZxDlfRJqkQR6aB6T4PSQYlDpJdcCz32g74zU39tzFU2CjuXOPOQrIWG6uDG7joyuLFEclAevItqZa19yxhzP3A+TjjawcAnxpjrgQeAdrOvjTGFwFzg68DncALRaN7/T9batZmvXEQ6jfInnDd+bsqGhVtLR9ZjAuzdkrhf5RvOG/DuozNdkXQE9dVQOiDcYwYRjBa0ISc5K5Eka+BcGPZZ97YuHsFo0XrnA3N9SC4i4ojs8W4rf8z/OH6D0frO8j+mmwKPYLSogtFEJMOaQjqPjiYI5X5qCpzf0PEuuIiIiIiISMe34ZHgxiodDIOOCW48ERHpFIwxRwGXAycCXVy6tAtGM8acDJzX/O02a+3lmatQREREREREOqPKRvdgtGJTQs+i3JnQ7wSjtdeoYDQR/6b9AqrehMZd6Y9lI/Dmhantu9934dO/tN9uCuHAa50QNxHJfQf8Cl45FZpq3ds3PwPPHgxzn4auQ8OtzS8bhcVXtg94nHQZzPh98gFMItJeNOBgtGTVV8KbF3i3lw115gP5nZPkx/4/CW4sEclfBUUw/Vp466s4MT8edi2DeYdBz0kw95mOkw/RsANePQsqXgl+7J6TwZjE/UTyWF4FozX7KjARmIHzqtcbuLr50Wb2tTFmGTAGaDkjNM37GOBN4IowChaRTiLaBG99xbs9Vz+0ykf7/xjmx1lFIVb12x3nxFfSU7MufnvdxiwEo8UJwMmGqb9wLvAt/xO89/+S23fYqd5t3cd4t216EiZ8K7ljiYh0VIkCM3evgh7jEo/j9yLExO/56+fFKxhNN1eJSKZFPG4cCVrT3sR9lv0vTNFHjCIiIiIiksPKH4O19wFRGHEu9D0IFv84mLFLB8GxL+THyuYiIpITjDHdgFtwFkaF1kVOY3ndCb0EuBgoaB7rLmvtB4EXKSIiIiIiIp1WZYP74u0DigdTkENhIEXG/TPZiO7dE/Gv/2Fw0tuw9l7YubR9+4aHMl/DxB/AzD/CwKNhy/NQv83Z3m0UjDgHBhye+RpEJBiD5jqvKU9P9e6z4wN49/sw+8HQykrKxqfah6IBfPJHGDAbRpwZfk0iHU3gwWgBB+HUbQp2PIBe+wc/pojkp3Ffhh7jnQU9l18fv++uT2DRN+GYZ8OpLdM++mVmQtEARn0+M+OK5JC8uzvVWltnjDkJuB84ltYboQzO6pEtwWcGJ0Dt/3aNaZsHnGetbQqrbhHpBCpfjx9CUaZgtMAMOhZ6TIDdKxL3fePzMOq8xP2kY6vbDE9Pj9+nthz6HBhOPS0SBeCEab/vwgFXO8/HXAxLroH6an/7lvSFUed7t5cNgd7TYMdH7du2L066VBGRDivR74Xyx2DyZaxdtZsAACAASURBVInHqa9M3KdsGAxP8+Kk18WXpgb37SIiQfFaUS9oQ050VgWN5+NfwegLodvIcGoSERERERFJxoqbnZvEWqwP8Cb74WfC7Ie14qSIiPhmjOkJvAZMpXWB01gt9/a5stZuMMY8DZzW3Pd8QMFoIiIiIiIiEpiKxs2u2weUDAm5kviKjfuipgpGE0lSz4lwwC/d2+4N4fpHt9HOdZaR5zoPEclvvafArL/Bom9599n4ODTVQ2GX8Orya/0D8dsUjCaSPhtx3+4RfNwhdOQ/m4gkb+Bs59F9DLz7vfh9N8+Dhu1Q0iec2jJp/b8zN7ZeZ6UTyMufcmttlTHmBODy5seAlqZ9vrZoCUrbAfwe+J1C0UQkUHVb4MW58ft0hBOvXFHUFY57Bd6/HNbdl7h/3VYoG5TxsiSHrf4nNO6M32f+aTDgKDjoT9B3ZihlEdkTznHi6TkRRl0IU37auq1LPzhhgfN/bOPj8fcf8hmY+QfoOjx+v36HugejNexIvmYRkY4q0e+F8kcTB6NFamHxFd7tZcOcVe5mXg/F3ZOvMVaB+81VRBWMJiIZ1rQ3nOOM/Dx8dHX8Pk11sPzPzjmxiIiIiIj4s/0DePtS2LUcek2G6b+GQcdku6qOx1pY8uvMjD39Wpj8I4WiiYhIsh4CptF6b18D8ADwMhAF/uljjEdwgtEATgCuDLZEERERERER6cwqGzyC0YpzKxityLgvaqpgNJEAjTof1t2f2WP0PzSz44tI+Pol+H8dbYDaDdBjfDj1xNNUD427oLR5ev7uT737bn8/nJpSsbcKoiHdVyySrojH4uAF7uf3HUJH/rOJSOoSnTMBYKFqIfSe1rqpdFBuva407oai7u730DXscOaKRuqgzv3zpkAU5GVklEhS8van3Fprgd8bY/4CXIBzo9NRwFCgIKbrduBN4DngLmttglQUEZEk2Si8dFzifoVdM19LZ9J1KBx5L4y+0Am0imfdvTDxB5qc0ZlVL/LXr/J1ePFYOOUj6DYiszVZ6/1hVhD2vwIOvDb1/XtOgDmPwaOjoHZ9+/ZDb4NxX/U/XnFP9+2Nu1OrT0SkI4rUxG+vfA32VkDpQPf2PavhlVO99x99ERxxd+r17UvBaCKSLU314Ryn1yQYe4kTtBzPhv/AjN/rPaeIiIiIiB+1G2He4U7IMEDVAuc6zykfQ/fRWS2tw9m9wrmpPmgzr4dJPwh+XBER6dCMMecCx9MairYA+Ly1try5fZTPoZ5tGRKYbozpbq3NgRXJREREREREpCOobNziun1giYLRRDqd/b7r3BcWjfl/1WsqTP8NvHamM5csHf2P8BkGICJ5pe8MGHQsbH3Ju8+8w+D0tekvcp6qxj3w1tecRduj9dBtNHQfC9Vve++z6xOYd6QzF6H7mNBKjWvzPFj0bdizKtuViKQv5ZCfOPeuT/4xLPtdiuMGSIE9IuKm3ywYMNuZKxnPK6e0/b6oG4y6AA6+EQq7ZK6+RCpeg7e/7iwM23UEzPgDjDrPadvxESz4UnjBskavs9LxFSTuktustXuttXdYay+01o4EioH+OAFpXay1/ay1p1lrb1QomohkRMV82Lk0cb/C0szX0hkNPh7KhsXv895l8MLRTrqudE7lj/jv27gzuf6paqqj9Z7vfQycA72npz72ft9LLxQt1oiz2m8rKIbhZyY3TlEP9+2RXcnXJCLSUUV8zNt5eJAzgThW01547XPw+DjnoqOXvgenV9++FIwmItkSDSkYDeDQ2+GwO2D0F7z71KyFHR+GVpKIiIiISF5be09rKFqLSA0suSY79XRkfj5rSkW/WZkZV0REOrqfxjz/GDihJRQtGdbaLUBF87cFwOQAahMRERERERGhrqmGPU3u084GFOdaMJr7pNdGBaOJBGfAEXDM8zD0VOg5CcZ9HY6fD8NPg6OfgMEnQulgKB3k/ogVu73nZCd07ZjntBCnSEc15wmY+P+82+urYeEloZXTzsIvw/p/t96LW7M2fpBbi6o34cVjINqU0fJ82bXcWVBeoWjSUXgEH6dl3FfgiPtg4Nw45ys+zkVMoff++57zuO6vwB4RcWEMHPOMMxe+ZxKX/CM1sOo2JzciW2o2wEvHO+cj4Cxc+sbnofJNaNwNzx/tPxStdFD7+Zl+X19b6HVWOoGc/yk3xkwHTgT2xwk8A6gClgHPW2vbvCpYay2wLdQiRSR51kLlG1C3EWrLnXCHuubHQX9x0vHzRfU7/vrpA+vMKCyFQ2+F18+LP8Gj8nVYfAUc8vfEY+5cCpuedSbmDDgKBh6tf798V9wTGpMI4PITdpiuSK1320F/di6e/aev80YtWVOuSL2udmP9zEmv3v6e870phFl/gy79khunuKf79sbd6dUnItKR+H3Nf+e/4OiYEM+P/wc2PJR4v+Gnp1aXF69VaaK6uUpEMqwpxGA0UwBjL3EeB/0JHh4I1uWmjg2PQJ80wo1FRERERDqL7Yvdt6+6HWbdDAWF4dbTkbm9d0lXl/7Q77DgxxURkQ7NGDMEODBm03ettXEumCf0CTCw+fkEYFEaY4mIiIiIiIgAUNm4xbNtQEluBaMVG/dFTSMKRhMJ1qA5zmNfw05xHiIiboq6wkF/hMrXYJvHvNPyR52AtGTnZqWrvhrKH0ncz0vNOtj6Igw5MbiaUrHmLrCR7NYgEqSCDMR9mCIYfb7z8PLkZNj1SfxxBh0Dxz4fv8+9BYD1rkNExE1RNzj4T87zl0+Gzc/533fNXTDzeih0/3wko9beA9GG9ttX3wEDZkPjDn/jdBsDZ6z21/eNi2Ddve5tmfgdIpJjcvan3BgzE7geOCpOt2uNMW8Al1lrfSYTiUjOePnE9ivCg5PUnk/BaIt/nO0KZOhn4LPL4JkDnQ/ovKy9B2bd5Exs97L6X/DWV9t+ODb+G04QVLz9JLeVDUsuGC2VMLJkReOEORR2cd6QDT8b1t6V/Nilg1Ovq91YA+DEN2DrfKgrh4FzoMf45Mcp7uG+XcFoIiKtNj/rr1/5o06wcNdhTjjQ8r/426/bmNRrc7PvigQt3D7cExEJUrxz6Uzq0tc5H3ZbGa/ildDLERERERHJS+vu827bvQJ6TQqvlo4uE9c6Jv1Q4XUiIpKKw5u/WmCDtfbVNMeLXTQ15FljIiIiIiIi0lFVNmx23V5kiuldlFtvP4s8FjVVMJqIiEgO6TXFOxjNNjlhRAOODLemncvSX2Br7b3ZD0bb8WF2jy8StO5jgx/TT1BOrymJg9F6Tcl8HSIivaYmF4wW2e0EtvackLmavCz7nfv2VbdBcS//4/Se6r9vvNdSo3v5pOPLybMJY8wZwL1AKWBimlriYmO3HQW8aoy50Fr7aEgliki6jHGCivasbN9WuzH8elL18a+zXYG06DrcCS97/TzvPpE9ULMeuo92b99bBQu/1H77yltg1IXuK73kOmud5OONj0NxTxhzsZPS3tkkuwpEZE9m6ogVNxit1Pk666/QUA2bnk5ubGMS90lGYSkMPSm9MYo8gtEiCkYTEfk/a/7lv++jw51gsmRCyIL+/aBgNBHJlqYsBaMBDPusezDarmXh1yIiIiIikm9q1sdv37tFwWhBCvpax8C5MOmyYMcUEZHOInZlrw8CGC/2l1z3AMYTERERERERobLRPRitf/EgCnJsgfUi4z4dMBJN8p55ERERyZyxl8CaO73bnz+q7fcTvg0H/I+ziG+Q6rfBh/8NFa/Czo/TH2/Nnc5j1AUw9SrotX/6Y/pV8Tp88kfY+ER4xxTJtNJBMPDo4Mf1eM/Qxtgvw4b/xBsExnzRx8Gsd5OfOkRExnzR+R0f7/VkXy8c7dxv2GL0RTDzeigdkF4tVW/Bst/D9sXugbIN2733Xfl3/8cZ+2X/feO9lup1VjqB3PpkFjDGTALuA8pwAtAsbQPRWmZy25hHKXCvMWZyuNWKSFq6DnPfXpcnwWir74QPr8p2FRJr2OmJ+8RLMH84zsluMknDueSDnzlhbxv+A6vvgJdOgPXxPqzooCI1SfbPcDCatfHDHAq6OF+Le8Dcp+DsCjhzAxz1UOKxR10YTI1BK/YIRmtUMJqICAC1m5LfJ5kAsuFnJj9+IgpGE5FsiRcy3KLHfpk5du8D3LfvrXBuHhEREREREXd1m+H52fH71FeFU0tnkey1kXj6zoLjX4ZCj8+DRERE4otdFnlXAOPFhqHtDWA8ERERERERESobtrhuH1A8JORKEisyxa7bG63u3RMREckZg+bCYXf477/iJicsLcjFg6ONzpgrbgomFC3Wuvtg3pGwe2Ww43qpfANeOg7KHwnneCJh6DMDjnsFCkuDH9sUJu4z7FQ4+K9Q4hLIWDYEjnoQ+s5Mr44CBfaIiA99DoDZDzuvPX7t3edznLX3wLzDIVKXeh3V78CLc51Mhj2roGZt+0c8fu7XK+kDB/0JRpzlv654r6V6nZVOIBd/yv+OE3QWG4bWCLwDbGj+fjhwEFBC23C0m4EMxOKKSEZ0He6+vTYPgtG2vgILL8l2FbKvwi5wxjp4bJR3n51LYOjJ7bdXvR1/7PrK9GrLhoYdzQnJMWwTLLkGRp6TnZqyJVKbXP8tL8CbX4RZN0Jxz+DqWHkrLL/BCb+Jt4JHYZe237ckVHdzX4msjSEnpV5fJnkFo0XroalBk6lERGrXZ3b8kecFP2aB+81VRBuDP5aISCw/N31M/01mjt1zknfbzqUw8CjvdhERERGRzmzVPxJ//rFzCXBuKOV0CkEtAlPSF45/JZixRESks4pdMrmXZy//hsY812oFIiIiIiIiEojKRvf7tAeU5E8wWsTq3j0REZGcMvYSWPcAbH7GX/9dy2DTUzDi7GCOX/6YM2amNO5w5srNuC5zx2jxyQ2JF3A/6iHoMz3ztYgEobg3lPbP3PjGZ4TIft+GCd+EmvVgI862gi7O/H9jwqtDRGTEmTD8DKjbCE0x66O9/nnY/p6/MfasckJUR1+YWg2f/qXtsYMy848w7DQntLLbKDAFye3v8TmQ06bXWen4cuqn3BgzFSfYzOIEoFngf4HfWGu379O3N3AlcHnM5iONMQdYaz8MqWQRSUfZMPftdTkejLZzGbx4TLarEC/dRkKvqd6rGGx5ESb/sP329Q/EH7dhR/q1hW3zPCd0al/bF0Pjbu+gqo6oyUfK8r7W3gU1a+CE14KpYc3d8PY3Wr9vjPMzVdDFfXu8EAaAnpNh1PnJ1xaGeAFzla/B4OPCq0VEJBfVbcrs+EFdHI1V4BFqmeiCo4hIutze58QacCQM/Uxmjl02FIp6QGR3+7bNzykYTURERETEy7Z3E/f56GqY+vNgbqwUaAwoGO2EN6CoazBjiYhIZxW7Et2UdAYyxnQBDozZVJ7OeCIiIiIiIiItKhs8gtGKB4dcSWLFxv3ePQWjiYiI5KAeE/wHowGsvjO4e/8rApoTF8/mZ8IJRqtakLjPwNlQOjDztYjkg4IkIkRMAXQfnZk6FNgjIskwxglmjNVriv9gNIBVt6cejObnfCMV/Y+EHuNT3z/ea7peZ6UTSDJKMOPOaf7aEor2PWvtj/YNRQOw1u6w1v4E+E5Mf4AMzPYWkYzo6hGMVrMu3DqSYaPw1P7ZrkISiffh39aXILJPSJa1UP5o/DHjhVjlKq9wOMh8+EouiTY6j1RUvg4rbg6mjlW3++/rFYyWKMzuM+9BoUdITbYVxal91W3h1SEikqtqMxgOPOx0KPT43ZIOBaOJSLbEC0ab8QeY+0zmJu0bA/0Pd29L9L5SRERERKQzK3/EX7+K+Zmto7OwFt77Qfrj9J4GvRIs2iIiIpJYy13KBhhtjEnnl8s5QMsFigiwMJ3CRERERERERADqo3vZ2dRu6hoAA0qGhFxNYkUek14jNhJyJSIiIpLQyHOT67/xcfj0Jmjclf6x480tDMqOj9pv2/gUvHsZLP091GxIb/ztH8DiK6AuwXyLgXMViiYSK1eCcpIJaBMRcTPyc8n13/oSvPlF5/Ha5+Cx0fDyKc4c/yaXuUjb3oMProI3LoLdKwIpuY1uo6HfwemNEe81Xa+z0gnkWjDarOavFlhorf1roh2stX8H3sC5cQrgkAzVJiJB6zrSfXvNWqjbEmopvr16VrYrED8mft9JKXcTrYctL7TdtnMJ7FkVf8wG9wudOc1a77ZtSaQj57t9g/CSteib0Lg7/ToqXvHXzxRBQaF3+9BT3bcf+FsoLE26rNCUxVktrUr3qouIJLxQl47Bx2VmXK9gtKa6zBxPRKRFk0cA49hLYPIPEwcKp2v4Ge7bd34Mu1dm9tgiIiIiIvnK7w3I5Y9lto7OYv0D6Y9RWAaz/p7+OCIi0ulZa9cAsR+cXZnKOMaYLsDPWoYFFllr07whQERERERERAQqG7znrwwszsVgtGLX7RGb4mLiIiIikjkDjoKpv0hun3e+A8/Phvrq9I4dRjAawJ7Vrc8XXwHzPwvLr4fFP4bnZsGOJamNu/FJeO5QWHpd/H7dxsAhurYtnZAx3m25EpRT4P7eRUTEt2GnwsQkFwhde5fz2PAQ1KyDzc/AW1+Dl0+Cpr2t/dY/6JxrLPk1rLs32LoBuvSDI+7xzrzwK95req4EYYpkUK4Fo02OeX5nEvv9K+a5lisWyRf9Znm3Vb4WXh1+LfqOk7gvua9LX/hcnCCrqgVtv694NfGY296FaFN6dYWtYZt325sXwqbnwqslmyK16Y/xZJqnF/FC6vZV2CV++9gvtd9mCmHUhcnVFLaibt5tNWth1R2hlSIikpNqN7lvT3fVIlMII89LbwwvXhco6jZBVCtPikgGRV1WaQEoSHAuHZThp3u3KcRBRERERMRd7wP89Vt3f3KfqYu7DY8k7jP2EmexIS+nr4YBRwRWkoiIdHotF4QN8AVjjMuFb2/GmALgVtreX5hw0VURERERERERPyob3e/fK6CQPsUDQq4mMa9gtEbrsdigiIiIZI8xcMDVcMqHye2340NY9Y/Uj7u3EvZWpL7/8fPhxLdg5g2J+77/E+drzQZY+rt96tgKS69N/vjWwuKfeN8z3GLuM3DqEug5MfljiHRkuRKUkyt1iEj+MgVw0PVw2gonZOzQf0Dv6amNVTG/9b46G4X3fwTWxxzIWX93jtvyiKelz9xngrv/Lt5raa4EYYpkUK4Fo/WOef5eEvu19DX7jCEiuazrMOg+zr1t09Ph1pLI8hthxU2J++33Xfftfj4AkmAVdYUR57q37btaQs06f2NuzLMJ7pufjd/+9jfaJht3VJEAFoiu2wTbP0h9/2T+nhOFOYw4F6Zd3fpGprgXzP4PdBuRcnmhmfrf3m2LvgWNcQINRUQ6uvpK9+29pqU+ZkkfmPMklA1OfYx4Ckq828ofzcwxRaRzqlkH7/0Q5p/h3DDRuNO9X1jBaF2HQ1+PsPf3Lw+nBhERERGRfGN9Lj6zdwvsTHG1aGlV8XLiPlN/ARO+7d7We3rmPlMSEZHO6k9ABWBx7vG73RjzG2NM10Q7GmP2B+YBFzXvb4GVwP2ZK1dEREREREQ6k8qGLa7b+xcPotAUhlxNYsXG/d69iJ/JvCIiIpIdvac59/cnI9HcwHjSve5e2BX6HwKT4iy21WL3p87X8sdwPsLfx9p7kj9+bTnsXBq/T+lgGHoyFJUlP75Ih2DiNOXI+xgFo4lIUHqMh9EXwrgvw9SrUh9nXfNtBrs+9ZcvUdwLJlzqHLfl4TVXvmxIa5+hJ0Nxz9TrjBXvtVSvs9IJ5NpPea+Y59WevdrbHvO8R0C1iEgYBs6BPavab19zF0z+MfSa3L4tbOsfhHc9As9iTfi281j9j7ZBTCV9YcTZmatPvJX0ct++b1BWnfsKT+2suz9//i2rFsKe1fH71K6HLS/CsFPDqSlbmmqDGWft3dAnxRRpr+AGN4UJwhyMgWm/gMmXw5410HNS/iQ6dx3p3Rath/LHYcxF4dUjIpJL6qvct/faH7a+mNxY034JYy9xPkwrcF8ZMhBxg9Eeh5EeIbUiIsnYvQrmHd4aILnxce++ic6lgzTiTNi2yL1t+Z9h4vfCq0VEREREJB/4DUYD57y/99TM1dIZJFr5e+ip0H2083zQsbD1pbbtEy7NSFkiItJ5WWtrjTFfAp7EWcy1APgJ8B1jzNPA+tj+xpjPA/sBJwKH48zsaJndsRe4wFrrMrtKREREREREJHmVjZtdtw8oGRJyJf4UeUx6jdjGkCsRERGRpAz5DKy713//rS/Bir85c8f6HZZcANiOj5OvL1Yy9+TuWQ0rb4EPf+bdZ9F/QY8JUNwD+s50FpDf+TFsexdsBGo3OfcVdB8NZcNg01OJjzv0FP81inQ2Jk5oWpjyZd6riOSXwcc7gWCpBMRvfBx2/n/27js8jure//h7dleSVVzl3ns3pts0B2x6MaYGCEloCSUhjSTkF3IhnRsCCUlubgoJkBB6B5tmbAOmmG7ce+/dkiVb0u7O749BV21mdnZ3Zov0eT2PHmvnnDnnK1lazc7O+cxSqFrnrX9hp5bb+l0Mi37RcvuAK5Kvxwu351I9z0obkGs/5aFGnydxZXSTviHHXiKSe3qdbgWJNWfGYN0jMN7moCCTYjXw9qXe+h75O+uEz+TX4bOfwL750OVoOPwuKO0XbJ1iL1xqv339ozDxfgi3sx57vXvChif9qSsTFtzhrd/m6W0gGO2gP+MsvRt2f2D9rnc5Krl9kwlGC7Xz1i9Smn8LwxKlW2+ZoWA0EWm7nILROo2FsqFwYJX3sUr6QqlLGKVf3ILR1j0Ex/87+BpEpPVb/oeGULREQhkMRus7DT5zuIBjwR0w/ObceUNZRERERCQXJBWMNh3G/Di4WtqCSHuIVtq3FXSEExu95zXpefjkO7DlZSgqh6E3wLAbM1OniIi0KaZpvmoYxk3A/9JwjV97oPmFOQbwSLPH9SFoUeBa0zQ/CbJWERERERERaVt21DoEoxXkajCa/Q1TFYwmIiKS48beZoWdHdrmfZ8Pb7L+7TASTn654QZYiexfnHR5TbitFWguegA+SHDzrZV/Tq+e5or7wOhb/R1TRPxnKPZDRAJQ2AmOuAs++V5q+88Y7b1v2GbNf+fDYOjXrWDYemVDYcS3U6snEYeA/IRtIq2EfspFJLv6ToOibvaLnHd/kPl6mvvo5sR9jAhMeq4hBb/rRJjyerB1iTcRh2A0gPe+Cic+Dod2OQeR2IlHcz891zRh9/ve+lauCLaWXBA75Nw2bSM8l0Rw4Y634JWjrQVD7YdDxzEQCife75DHIAdI7o4a+aagfbYrEBHJXU7HI0Xd4LCfwbtX0rDmJwG3YyA/JfNmp4hIqlb8yXvfTB5LdxgFpQPt7xJTtw8OboGSPpmrR0REREQk18WTuDvj7g/AjOviyHSE7BfFAdZ7I43vJF5QBhP+EXxNIiIigGma9xmGsRp4GOhB0zc/Gn/eOAzN/PzxLuCLpmnOyUStIiIiIiIi0nbsrHMIRivsmeFKvIk4nANWMJqIiEiO6zgazvwINjwJ22bBttcgXutt34pl8OktcNLT3vrvX+TcNvhqa13cop9D7KB9n0zerDhZR94LAy6F4twMsRUREZEMGPld6HIULPoF7HwbyidC2SCrbc0D/s0TsglGAzjmr9D7bNgxF8oGQ/9LoV1X/+ZtzO06SgWjSRugn3IRya5wEfQ5B9Y82LLN60mdoNTuhdX3uffpdxGM/Ql0PjwzNUly3EJBNj4FB7c2TeP14sAa6DA8vbqCdmg71O331nf7bIjHvIV75Su3E7QlfaHDCKhYntyYb19i/duuJ5z0DHQ7zrnv2ofgva94HzuXTxynK6JgNBERW7FD1l2S7BSVQ/dJ0K4HrP239WGErMXBTjIWjOaywFZExA/JBCdAZo+lDcM6J7DsHvv2yhUKRhMRERERacyMJdd36d0w+ofB1dOamSbUVdi3Hf+obmIiIiJZZ5rmbMMwhgI3At8E+jt0NT7/dxfwv8A9pmlWZqBEERERERERaUNq4zXsi+62betekJthGxHD/tq9uriC0URERHJeSR8Y+R3ro15dBTzZMfG+m16w1h6EHQI66pkm7HMIRjvmLzDsBuvz9Y/Bvs/s+zW+WbERATPJa3qD0u9CGPntbFchIiIiuaD7JJg8s+X23e/D/iX+zBEutt9uGND3fOsjm0KKjJLWTz/lIpJ9pQMdGlzCHjJh8wz39on/gsFJhB1J5rmFgphx2PMpLP9DcmMe2pH7wWgVS5Prv/IvMOKbwdSSC2KH7LfXnwSe8ADMPhVi1dbjSHuIeryW+tA2ePNcmLYJIjYvbvYtTC4UDZqeOG5tEi62MhK0i4i0UjX2F1UBUPT5nQJ6TrE+jvuX9XjzdHjzPPt9ImX+1ufEKXxURMQv+xYk1z/Tx9KH/dw5GG3WZCtEufc5EC7MbF357uB22DIdMKzQ76Lu0HUCtB+a7cpEREREJB3JBKMBzL/VuqC5rR8H1lXA1ldh32Lr8+4nQc9T3c+3x2ucL0ov7RdMnSIiIkkyTbMKuBu42zCM4cCJQD+gHCjECkPbDrwLfGKappmtWkVERERERKR121W33bGtW2FuBqMVOASjRU0Fo4mIiOSlgg7QcSzsdwgzq2dGYdd70GEk1FVa7xsXdIRISdN+B7dC3T77MTqOafjcCDnPFWp07evhv4FPb3GvLVO6HpftCkRERCTX9TjVx2C0HF/zb4SzXYFI4HIxGK3+IqaJhmEM9LhPz8YPDMM4iSSSNUzTfMtrXxEJgsMJlB1Z/tXc8YZzW6S9QtHygVswGsAn34HavcmNGT2Qej2ZUrkquf4bn2zlwWgOoS31wWjdjoPz18Gm56yTtr3OgFcnQPUGb+PX7oGtL1sLtJrb8GTy9YZy/EVSOhIFo61/BE54ODO1iIjkkppdzm31wWgtuL0Jmam/JS4vu93er5MFkAAAIABJREFUJBUR8WL/MnjlqOT2yfSxdKQEyo+F3R/Yt8+9ELocbd2BprBTZmvLVzvfhTlntHztHS62wkH7X5KdukREREQkfckGowGsexjG3eF/Lfmiaj3MmgIHVjdsW/576DgavjAdygY57Ofy/kZBB39rFBER8YFpmiuAFdmuQ0RERERERNqmnbVbbbeHCNGloFuGq/EmomA0ERGR1mf0rfDelxP3mzW55baBV8KEfzQEd2yf47y/52C0RtfkDrwclv0ODm5OXF+QinvDIK3pFRERkQSG3Qhr/wV1+9Mfy8jFSKZGcr0+ER/k6k+5ATyaxr5vJNHfJHe/DyJtQ8gliXTvZ9B5fOZqqWeasPqfzu2DPJxkkuxLFIxWuTL5MaOVqdWSSXUVyfXPdghh0GKH7LeHixs+b9cNhn6t4XH3SbDuP97nmHsRdD4c9s5PrcYmdbVLf4xcFUkQjAZQtRFK+wVfi4hILolWObelsljV8JwTnp6uE53bzDhUrIAOwzNTi7RuB9ZYr892f2gtpC8dCAMug16nZbsyCdKC/0p+n8YXa2RKl6Ocg9EA9nwET3WG4TfDqO9Daf/M1ZaP3r/WPpA8dhA+vBH6nA/hRnchPLAWlt5j3dmwxykw6Cr380x+MuOw6j7YPhuKe8GQa6HTuMzMLSIiIpKPUglG2zHX/zryyfwfNw1Fq7d/CSz6JUx0eC9zyW+cx/Rynl5ERERERERERESkDdlZZx+M1qWgm2MAWbYpGE1ERKQVGnQlxOvg/WuS33fdf6CoHI6613r83pX2/Yp7Q1GXRhvcgtEaXatZ3AtOexuW3Al7PoXaPVDY+fM1jGbLfVNZs5nIkOtg7O3Qrrv/Y4uIiEjr0nHk58cud8G6h9IbK1PrNFMVUlSStH65+lNuYgWcJbtPvRx/dhGRJuJR57aNz2QnGG3+re7tw7+RmTokPYmC0Zx0PgJqdkP1hpZtdXkQjBavyXYFucUxGM0lgGzEt5ILRgN/QtEA2xPCrYWXcJ/n+8NldXoxJiJti9PfKmh6p6XG2rncibKgU3r1eFWQYCHtzONhyhvQaWxGypFWqmIlvD4JDm1run3NAzDxfhh8VVbKkoDFDsGmZ5Pbp+No98DGoAy5Dlb9PXHIw4o/WQF/Z38G7YdmprZ8U70ZKpY5t9fshp1vQ8/P73a4fym8fETDa+B1D8P2N+D4NN+48mre1bD23w2PV98Pk1+HrsdmZn4RERGRfON0zNx3Gmx6zr5t+yyoOwAFZcHVlcu2vuzctvkF57Y19zu3pRLCLyIiIiIiIiIiItKK7azdZru9W0GvDFfinVMwWpw4MTNG2MjQTeVERETEX4OvSi0YDazrGY/8HVRvdO7T/AbEbkEfoWbHG2UD4di/JVfTIz4t9T/8NzD6h/6MJdKqKE5DRMRRp7Fw/L+tD4BoFTw/EGp2ZbUs3xlaiy+tn0ucc9aZSX6ksq+I5AIz7ty2f3Hm6qi3byEs/a1z+/g7rQXXkvtSDUbrf6lz0Ec+BKO5has4iVb7X0euiB203x4udt6n/BiYPCuYehIp6JideTMh7BDu09zOd4KtQ0Qk17iFeDq92dj5SPu7HZX0hw4j/KstkeMfcW6r2Q3L/5i5WqR1Wvm/LUPRADBhwe0QTxBGJcnZ+S7MOhWe6w+zT4OKFdmpo2J54qCxekYE+l8CU+ZkJ1y3y5FwzF+99Y1Vw4vD4NGI9YbKpz+07u4nlupNifscWAWf/cS6WGbG6JbB4Ov+Y7XNmuxjeLWN/UubhqIBRCvdzyeJiIiItHVOx/i9zoR+FzvvN/fCYOrJdbFDULvXub1ml/1r4kSvkxMF3YuIiIiIiIiIiIi0MTvrttpu71aYu8FoBQ7BaABRU9eiiIiI5C3DgG4npbZv7V44uA32L3Hu0+WoZhtcIgbcQtO8Gv7N9McA6HSYP+OIiIhI2xUphVGpBK3meAilgtGkDci1n/INKLBMpO0p6e3cdtD+TabAmCa8lOBEyeCrM1OL+CDF/M/h34RNz9m3RQ+kXk6mxGoS92muZhdE+vtfSy5wC5tx03MyXGHCoV3wTDf/63JS2Clzc2VD/0thwxPufdY9BD2+kJl6RERyQdzhb1XI5W9VKAxjb4ePmr1ZOO52f96E9Kqws3v76vtgwt8zU4u0Tm6BqdUb4cBq6DA8c/W0ZlUbYM4ZDa95qjfCK0fD1NXQLoPHwwC75iXuc+JT0P8i63V8Jp/37Ay9Dur2wac/8NbfjEHVeitEq3ojnPBosPXlC7fg/HofXO9trO1zYOaJcM5SKO2XXl121j1sv33jUxCrhXCh/3OKiGTagTVwcDsU94TSgdn/eysi+S8etd9uhGHMj61jKTvbZlqht50PD662XFSzO3GfaEXLczNV69z3aX5XbxERkQwyDCMEjAXGA/2BbkAx1vWCB4EdWNcPfgYsNk1T1xGKiIiIiIhI4ByD0QpyNxgtkiAYrYgE18mLiIhI7hpyHeycm9q+VeuhYpn72I0ZKa699GrwVdZNsr1cH+qkdAD0PNW3kkRERKQNG34TrH3QPUi2hVy4ftqlhlCuRUaJ+C+nfspN0xyY7RpEJAvcTkxkOoRq20z39q7HQXGPzNQi6YuncLejHlOgoAwK2tu3RyvTqykT4ikGo5W21mC0g/bbw8Xe9m/XFbqd4B7K4aceUzIzT7aM+kHiYLTV/4Txd2Y+gENEJFtSDfEc/g0o6QcbnrKC0vpdAn3O9r8+N5GSzM4nbY/TsVy9imUKRvPLol+2fA0erYR1/4GR381MDWbcChdb9jv3fic9C/2mWZ/nSkjLqO9Dp/Ew5/Tk9lv/GETaw7F/Df4Ck1zn9zmgaBWsuR/G3eHvuAAbn3Zue6IEjr0PhihYX0Ty1MFtMPMkOLCqYVvpQJg8E9oPzVpZItIKmDH77aEIdB7vvu/Kv1rHzG2Jl2C02v0tg9HcLnLveVp6NYmIiKTIMIxJwPXAWUBHj7vtNQxjBnCfaZpvB1aciIiIiIiItGl18Tr21O2ybetW2DPD1XiXKBhNRERE8tigL0PtblhyFxzalty+H98Mez52bm8/pOnjoK9b7XKUdc3vJ7c0vRapsUiZdf1ouARi1Y1qC1vr+Sb+S4EfIiIi4o9IKZzyKsy7Fna8AfHabFeUPkPHSdL66adcRLKvbDB0GAUVS1u2HdyS2VrW/se9/ch7M1OH+KPLUVgpuEncRLjbCda/EYdgtLo8CEaLpRCMtnkGdD4id0IF/JRq2ExjR/4e3jjbCpDzQ1E5nLcSPvo2rHuoYXv/S6D/xf7MkavKj4YxP4bFv3bv9/F34ISHM1OTiEi2pfO3qu9U6yNbwh6C0eJRvRkpqUt0krliGZDF34HWIh6D1ffZt7ldIOG3VfclDkU76o8NoWi5ptdpcN4qeDHJ0JjV90FhRzjit8HUlS+iVf6PufHpYILR3M4NmDF4/xrY+goM/BL0OgPCRf7XICISlHcua3khYtU6eHEY9DoTCrtApzFQ3Nt6jivulZUyRSQPOQWjGWHrYutj/wYfXG/fZ/eHwdWVq7y8H1G3v+W2qvXO/dtauJyIiGSdYRijgT8Dk+o3JbF7F+BK4ErDMOYAN5mmucLnEkVERERERKSN2xPdgUnctq17Qe8MV+OdgtFERERaMcOwbmg84jvWOoNIMTzi8fS62zW/A6+0myylEpNSv94hetD6Wsw4HNrZ0N6um3XNQDxqtYUKoGanFZimm7iLiIiI30r6wuRXrWOTugprW7gdPNUpu3WlSms2pQ0IOM5ZRMSjo/9ov71mV2ohT6mo3tQ0oKi5gVdC12MzU4v4o11X6HZicvsM/JL1b0EeB6PFHcJV3Cy8HT76hnUCsbVxDJsp9j5G+TFw9kKY+AAc85f0a5q2GQo7w3EPwpQ34Ih7YMpsOP4R6wRuazf+V1A+0b3PhseanugWEWnN/PhblS1e3mys3Rt8HdJ6xRO8HqxYlpk6WrtKl/WE6zIYVrv+Eff2/pfAiJszU0uq2g+BC1M4jl16t/XRlgURjLZvIWye7v+4Xmrd8AS8dT7MPAFq9vhfg4hIEGr2wI43ndu3vmL9vf7sNph3NUwfDdtd+ouINOYWjAYw9OvO+1YuBzOJm+C0Bl6C0Wr3tdx2cLN9307jrJtViYiIZIhhGJcCH2CFohk03NWu+Uc9u7b6/SYDHxuGcVGm6hcREREREZG2YUftVtvtBgblBT0yXI13EZfrzeviCkYTERFpFQzDChIDOPy/0x+v/fCW24bdaN+3uE/68zVX/7UYISju0fBhfB5zEIpAuND6utt1VyiaiBejfmC/vf2wzNYhIpKPIsUNxyORUud+RgaCZBNxq8FQMJq0fgpGE5HcUOxyN51D2zJTw4c3ubf3nZaZOsRfJzwGpQO89T3uIejw+Um+SJl9n3UPwRvnwvvXwTtXwCvHWHddeP0UWPFn6+4E2ZZqmODKv8D2N3wtJSfEDtpvD7dLbpzinjD4Khh2A/S7OPV6hnwNwkXW50YIenwBRn0PepzStpKZO9icUG/MjAcTYCAikoscg9GS/FuVDWEPbzh6Wcgr4iRe696+5gE4mKHXjK3ZwS3ObSX9MlfHjrfc2wdcnpk60tWuK1y8D/qen9x+n/4AZoyFJXdZd59pa6IHghl33lX+fz+TCSTf8zEs/rW/84uIBOWg/cIPR3X74IOvQdwh7EhEpLFEwWgAJz5l3ydaZd3gqC3xcj6l8XuoG5+Dedc4H3t2GO1PXSIiIh4YhnEJ8AhQQtNAtPqgM4CdwApgHlaA2kpgV6M+jfcDKAUeNQzjgsx8FSIiIiIiItIW7Ky1v2amc6QrBTl8s+sCw7m2qKlgNBERkVan/yXpjzHg0pbb+pxnfzP3IdekP5+IBG/AF+0DcZxCD0VExF4+r+3P59pFPFIwmojkBrdgtIPbM1PDno+c2wo7J7+gWXJDSW+YujZxv3OXwaArGx4XtHfuu2UGrP4nrH+04edmxxvw0Tdh7oVgms77ZkI8xWA0gM0v+ldHrggibGbYjTRcr50EIwKjvp/6vK1Jl6MS99n1bvB1iIjkAqdwlXwIRvNyJ6YtLwdfh7ReiYLRAF4aCxUrg6+lNavd49xWtz8zNWx9LXGfjmOCr8MvhR3hpGdh2iY45VU45TUo6Zt4v/2LYf6tMOsUiHn4+W9NolXBjFuzG7bP8m+8aLXz60wn6x6ywp9FRHJdKoskKlfC7vf9r0VEWh8vwWg9pzjvv3Ouv/Xkuv2LEvd55zLrZjmL74S5F1jh4U5KArijt4iIiA3DMEYAD2Bdl9g4EK0CuBc4B+hqmmZP0zRHmaZ5vGmaE03THGmaZg+gG3Ae8EegkqYBaRHgX4Zh6Db3IiIiIiIi4ouddfY3ZOxW2CvDlSQnomA0ERGRtqVsMEx0eT/Yiw4jWm4rKIMvvACRsoZtfc+HMbelN5eIZEZxTzjxSQgVNWwb+GUYfnP2ahIRkcyyC8gUaWUUjCYiuaGgg3NbLKCFsY3FY3DIJYDt7AVKTM1nhgHdv+DcXj6h5cm9ou6pzbX5Rdg2M7V9/eK0QHvk9xLvu/xef2vJBRuesN9ud0cLr3pOhpOehrKh9u2DvgonPQOdDwcjZC3s6nwkTH4dOgxPfd7WpN+FiftULA++DhGRXBBEiGemFJYn7rPgJ8HXIa2Xl2Comt2w7HfB19Ka1ex2bqurgLoDwdcwN8HxYXEvKBsSfB1+MgwrfKDX6dDrNDj8N9733f0+LLy94XE8CsvuhRdHwAtDYMHtwQWJZUuQX4+fIZ01u5Lf59AO2DvfvxpERIIST3GRxMwT4PFiePkIWPeYvzWJtEa1+2DetfBsX3h1Aqx9OLn9Nzxt7fd4CTzWDp7sBLNPg30egrSyyUswWmEnKB1k3+/dL8Ghnf7Xlau2vOKt3wfXw2c/TtyvWMFoIiKSMf8DlNAQiGYCPwP6mab5PdM0XzZNc6/TzqZp7jZNc4Zpmt8B+gG/+HyMemXAnwKrXkRERERERNqUnbVbbbd3K1AwmoiIiOSYwVfBxftg0nMw8V/J7TvsJue2nqfCRTvh1Ddh6hpr/HCRc38RyS39psHFu2HKbDh/Axz/b62FFxHxlZHtAtwZioyS1k9HNiKSG4yQFVIUO9iyLVod/Px1+8CM27dN+AeU9A2+BgnW6B/BrndbLu4zQnC0zTWzJWksEPnku3DO4tT3T1esxn57qAj6TIXNL7jvX7ka2vscOBCtgopl1gKnjmMg5Pxm7P8xTahaD6FCqNlhfV1GGDqMtO5I4cX2OWBG7dsiHsdw0u8C68P8/Brs+ucQI2QFINT3iUcBA0Jh22HaLC/PqxXLgq9DRCQXOAWjhfIgGM3L37eC9sHNH4/BgVVQOlBvwDZWtcH6vhd2znYl6Ys7HNs2t9XjgnGx5xaMBlYQlNdj8FRUrEgcijX85vw/pu5/CSy+E/Z7DKxY8hsY8W2ItId5V8HGpxvaFv3CCts69q+BlJoV0QAD+PwMJUslGA1g/1LocqR/dYiIBCHuIZTWSeyQ9Xz77uVWyHO/af7VJdKamCa8cTbses96fHAzvHelde574GWJ99/0Arx9cdNt8RrY9roVUnjeSmiX4o1fguZ0rt5odpzf5xxY8T/2fWedDGctyP/XBonEo1C11lvftR4veNd7nSIikgGGYZwATKEhFK0SuNA0zVmpjGeaZiVwh2EYc4FngNLPxz3NMIzjTdN815/KRUREREREpK3aWecQjFaY28FoYSOMQQiTlmtg6hSMJiIi0noVdoS+51ufb3oONj3rbb+ywe7t4XbQfVJ6tYlI9kRKoccp2Zt/wGWwXjdUFZFWquep2a4AOo5zbnMJzxdpLRT/JyK5I1Jivz3R4mg/uC1q7Xl68PNL8HqfCVPmwJBrG7YN+iqcNR/Kj2nZvziNYLT9S2D1A6nvny6n8IhwO2/BJOse8a8W04SFP4cnO8IrR8PLR8BTnWFNgoU6lavhpcPghUHwXB9rv9cmwqvHwFMd4bPbnMMM68UOwazJzu2dDkv+67FjGNZHKGx9GM3Sn0OR1r9IK1VH3uveXrMTavZkphYRkWxyCkYL50EwGlgBx26CCjrePB2e6QbTR8JTXWDpPcHMk0+q1sNLh8PzA+Cpcph7MURtwqfzhWl6D+eoWpffX2u2JQpGiwUcWL71Nff2DqNg5C3B1pAJoQI4430Y+1/e93m2NzzZvmkoWr1Vf4MDa/yrL9uCPP9Tudy/sRL9vjh570qIpRE4JCKSCc1vKpGquRdAXYCBlyL5bP+ihlC0xlb+b+J9Nz0Pb53v3F5XActtbgSTK8yY/Xaj2b3c+roEK+5fAltm+FdTrnIKkUtH+dH+jykiItLSTZ//a2CFo12faihaY6Zpvg5c32hcgBvTHVdERERERETatpgZZXfdDtu2bgU9M1xN8gocFr5GFYwmIiLSNvQ513vf3ucEV4eIyIjv2m8ff2dm6xARSUfj/In/Y8Cgr2S8lBZ6ToHCzi2395gC4cLM1yOSYQpGE5HcEXYIRgt6ATa4B6MVlQc/v2RGtxNgwj/gCtP6OO5B6OSQklvSN7253r8G3rkcFtwO866Gj26G3R9a4Q5BizkFoxVBxEMw2rYEoQRe1FXAhqfgteNg4R1NFzxFq2DeVdYiLjumCW9NsxaI2bbHYfGv4a0LIO6wkKp2L8wY41yfEYHeZ3n6UiRAzUPk7FT4GGAgIpKr8j0YbfSt7u3RKv+PgQ6stY4Xavdaj2PV8On3YdOL/s6TT0wT3jgX9n1Wv8EKUvo0j8Okkl0Ivnd+MHW0BbUJgp6CCqwyTdg2C+b/0L3fSc+0npP1kRI47OdwqU/f0zlnwr6F/oyVbTGH78nAK6HvBfZtw78Fx/2nZZhGczW7YadNAEkq3M4hJbLkN/7UICISFLdQ2lCSdxR7sj2seRCqN6VVkkirs/Yh++0751r/Rqth8wxY+TdYdZ8VAr7kLvjwm9br4EQW/9K6McmmF5zPN2SLYzBasxuLdJ8EBZ2cx/noZuv7s+uDxDdQyVdxn4PROo5NfAdwERGRNBmGUQSchxVcZgJPm6bp263hTdN8FHgaKxzNAKYahtFKThqKiIiIiIhINuyu20kc+/PM3Qt7Zbia5EUUjCYiItK2DbjMCsNIZNT3oePI4OsRkbar/BgY9o1m2ybAsBuyU4+ISCrG/heUDmq67YjfQrtu2amnsVAEjv1b02sti8rhyLuzV5NIBiVYMSYikkERh2C0aAaC0Xa+Y789XOxcl7Ruxb3TH2N9s2t8V/wPDPkaHPMXCIXt9/FD3GGxU6gICjok3n/n29bCqz4p3g2ichXMPh2q1rr3e2sanPQ09Luw6fa9nziHojW2+QV4ri+cs6hpgOG+hTBrCtTsdN63+0lQ6LKwSjLDaSFcY5XLodtxwdciIpJN+R6MNvBLsPCnLh1M62uMFPs358an7f+ObHoG+p7n3zz5pHKl/THUhqfg6D97CyTNNU6Bv05mHg/nr4PSAYGU06olCnoK4nV5tBrevhS2zHDvN/7XrfOCjEiJ9TqmJkEoXSKVK+Gl8TD+VzDm//lTW7Y4BfBFSmHAF2HTs023h4pg+Degw3DociRsfc16PTz/R/bjzDwejvkrDLs+vTrTCUZb+y8Y91/pzS8iEqS4wyKJcDGcvQC2vAwff8v7ePOuhkgZnPA49DnbnxpF8p3bscSBdfDW1PSDb+ddZf3baRxMegHKBqY3nl+czgc3f78kVABDvw5L77LvX70BPvz8wslBX4EJ/7Qu/GlNkg0KT2Skw515RURE/DURKPv8cxP4XQBz3ANc9PnnZcBxwJsBzCMiIiIiIiJtwM66rY5tXQt6ZrCS1CgYTUREpI2LlMDJL8HmF2H3+y1viFjQEXqcDD1OyUp5ItKGGAYc/Sfod4G1Vr/jKOh1FhSUJd5XRCRXlA6AMz+EzdOtm0L3PBW6Tsh2VQ36XwIdRsPWlyHS3sqAKOmb7apEMiKU7QJERP5P2CGALJaBYLT5t9pvL+oa/NySm8KFTcO2/LL6PuugM0hOARLhIjA8/ul/+1Ko2pjcvBufg3nXwovDEoei1Zt7EdRVNN225RXvcx7aBm+c23TbBze4h6KBtRhTsi/uYXFXxbLg6xARyTanUNN8CUZrPxSOe8i9j1PYTao+/YH99jUP+jtPPtnwpP32mp3O4Xu5rvkb9F7MOcv/OtqCA6vd22edDHPOhP1L0psnWg1LfguPGPBEaeJQNICRt6Q3Zy7rNsmngUxY8BPY5yFgOpc5BoUWQ++zYdxPG+5wU9gFjv2rFYoG1hvoI78No35oBfA4+fAGWPDT5J8XqzbCwp/Bu1fCsnuS27exA6v9/5soIuInp2C0UIF13D/iZjh/A3Q5xvuY0QPw5jkQPehPjSL5zu3mJR9/O/1QtMb2LYTFv/RvvHSYJphx+zbD5kYyY3/ibdy1/4ZNz6deV65yej5O1eCr/B1PRETEXv3drkxgqWma8/ye4PMxG5+k1B22REREREREJGW763bYbu8UKacwVJThapIXMexvGhL1++YbIiIikrvChdD/IjjiLjjq3qYfh/1MoWgikjmGAT2nwLjbrfAehaKJSD4qKofBX4Wxt+VWKFq9TmNg1Pdh2PUKRZM2RcFoIpI7IqX226MBB6O5ja9gtLatMIBgNIBVfw9m3Hpxh2C0UDuoq/Q2RqzaOXDEzsJfwNwLYM393vep92yfpo8XeFzwVG/3vIYwtdp9sOs99/5H/h7adUtuDglGaf/EfQ6sCb4OEZFscwpnCeVJMBrAoCvh7AXO7TGFwGRVvobwpBKMVrG0dS6KD1I8CpUJgtEAtr4KM8bA9jdTmyd2CGafBvN/6H2fk1+2LtporUbc7N9YZhzWJgipzHWJQr7H3QEXbIGzPoPz19qHOxgGdBjhPs+in8Gb53kPmziwFmYeDwt/Cusehqr13vZzUrEivf1FRILkdPwVanS3+dJ+cMY8OGeJ9bd60Fe9jf1aDl4gIJINBR2d2za/4P986x51DiTLJLca7ILRCtrDJR7fz9jyUmo15TI/F62dNd/7TXNERETSM6bR5+8EOM/bDnOKiIiIiIiIJKUqVmG7vVOkS4YrSU3EKLDdXpfKNVciIiIiIiIiIiIiOUhXwIpI7giX2G+PBRyMVrHcua1sSLBzS24L2d9FKW2bX4QPrvceUlYvdgg+/SE8XgyPGNbHm1Nhzyct+9kJF0E0iTk3PA4HtyfuV1cBi3/lfdzmogfg6e5QsdLbfHbeOMtaWFWzG+sG1C5GfDu1OcR/vc+BUIKgi9q9malFRCQTNj4Dr50Azw2A974KNXus7bGD9v3DeRSMBlDkEjy6b3Hm6mir7BaS1/vom94DgHJJqhfpfXCDv3W0dlXrklt0/8l3U5tn83TY9a73/qECKD8mtbnyRY9TYNLzVpBXouNiL5belRuhF6lyDPludBfmdt2h82FQ0MF5nPbDE8+17XV4rBBePwXm/whmngjTR8E7X4JDO5v2XfFnqN6UeEyvKpb5N5aIiN+cjhmb/50yQtBxFPQ+EyY+4G3sfQvTD5cUaQ3cjmOCEKuGR8PW+wkvjoDZp8Ou9zNbA4AZc25zej1bUAadDks89pr7oe5AanXlqriPwWidx/s3loiIiLvGF9gEecDReGxd1CMiIiIiIiIpq4rZn1suDbfPcCWpKXC41iRq5uF1YiIiIiIiIiIiIiI2FIwmIrkj4hCMFg04GO2tac5t424Pdm5pu1b9Hd48F0yXEK9oFRzcBrEaK0xkxhhY+tumwWebX4TZpzYNFIu5LCbvfU5ydXoJLtg1z3kBu1c1O2H6cNj8QupjfPztxMFvZy8Cw0h9DvFXQRmMTBCsUbsvM7WIiARt04sw92Lrb2v1Blj7b5h1ihVg0zyApV64OLM1pitS6tz25jnJh8JKctzuag3sAAAgAElEQVSC0TY8DvOuzVwtfnE6rgUoP9a57dA2qFjhfz2t1cEtyfXf+2ny31/ThJV/TW6fgV+CovLk9slHfafCucvgshr44qH0A9Le/ZI/dWWDUxhist+TDiO8993xBiz5Dex8xwosW/8IzDwB4o2CO5bdk9z8iRzc7O94IiJ+cnoudrjbvNVmwNTVEPGwQOSz26zznH4G/oiId5UrYNtMmD0l80GFbmHMhstNao7x+DrincuSqyfXJRNe7ab3uf6MIyIi4k2PRp8HebDReOyeAc4jIiIiIiIirVx1zP56tpJQfgSjRRzew4v6dY5ZREREREREREREJMsUjCYiuSPsEIwWCzAYLXrQCqawY4Sh07jg5hbZ8RbsW2jftvxP8Fx/eLYXPN4OXhoLB9bY963dC4t/3fDYKaQs3A56ne6+yKi5RCEJO9+BOWd4Hy+RD76e+r6r/gY1u5zbz5oPncakPr4EY/ydMOF+53YFo4lIa7Hyz0CzQNR9C2Db61C53H6fsoFBV+Uvp+P5eusfzUwdbZVbMBpY33+nEL5c5RTMAXDSs+77bnnZ31pas3gKd4nd+pr3vvuXwDPdYfus5OY4/K7k+rcG4SK4cJt7n67HQZejnNs3PBl8wHxQnMIQw0XJjVM+Ib06KlemF9qdSPRgcGOLiKTL6e7xiUIqywbDWZ/CkK+591v3sHWe8+lyWPdIajWK5LtUjr/rlQ5s+r5V1+NSGydaBQt/mnodqTBjzm1ur2e7efwat8yAPZ8kV1Mu8ytActgN/owjIiLiTeO7HAT5Jm/92AbQJcB5REREREREpJWrih2w3V4aLstwJamJOKwJiDq95yciIiIiIiIiIiKSZxSMJiK5I+IQpBDkgt6KZc5tfXQXdcmAj29u+BmPHrQCBuZeBB9/C2r3eB9nxR/BNKFmD8QcFllHyqCwExz9R+/jVm+0/q07YIVLrH0IDn6+UL92H8w+3ftY6ep1lvU1OInXwaYX7dtCRdB5fDB1SXoMA4ZcDcc7LIat25/ZekREghCtgq2v2rfNOQNih+zbOowKrqYghMJWEKuTNQ9mrJQ2yTDc280obHwqM7X4xS0YLVICF7mE4u5f5H89rVUqwQyf3gJbZzZ9/qraAGv+BUt/B5/9Fyy4HeZeAjPGuAcY2xl8DbTrlnxdrUFhZ5i6Fkr6N9powBH3wBUmnP4unPkRlE+039+MwfY5GSnVd06/84nCeJrrcYr7a0cvdr0H+xbBpz9Mbf+icuc2p9fsIiK5wOm4IGR/t/km2g+BCX+Hy+OJ+9ZVwLtfgvevs44dVv4N9i1OrlaRfJVqMFpRV5i6Bs5eYB0XXmHCqW9CuDi18dY86F/4lhepBqMB9DzV2xyL7/ReT64zffi/6X8J9Doz/XFERES8a5xuH2QwWuM3kF3elBARERERERFxVx23D0YryZtgNPv38OoUjCYiIiIiIiIiIiKthP3tIUREsiHsEIwWCzAYrXKFc9ugq4KbV/JD6SDYvyTYOXa8Ba8eCxMfsBYC7luQ+lgr/mwFkjhpP9T6d9iN0PU4WPJbWO8QRlVvyW9gwOXwzmUNQYKhQjjpWajZ6f33s9cZMPQGqFwO83/kbZ/GinvBKS9ZwQuvHOX8/7L5BfvtBe2Tn1Myq7CT/fa6fVboX6KwFxGRXHVgLcz2uIC3uQ4j/a0lE8LFzkFvu97zZ46oAmVsuYWI1TuwJvg6/OT2NYUKrXC0IdfB6n+0bHcLwZamUglmiNfCnNOhyzFw8nTY9jq891V/Fu8bIRj+zfTHyWdlA+Hs+bDxGSuQuvskKD+maZ9hN8Luefb7v3kuXB6zvpf5JF5jvz1UZL/dSbjI+v4s/W3qtSz9bXr7n/mp9dq1ZmfLNgWjiUgu8yOk0jCs4KaXDkvcd/U/G+8I438JY37sfS6RfJTqYqQBV7Q8RxoqsM7fr7k/tTF3vQfdT0pt32SlE4w28EvWa45ENj4FS++BUbckV1suSuW11ZG/h8Iu1g1vOh8Bvc/SeXUREcm0xidxglyB3fgPpYcUZxERERERERF7VbFK2+2l4fy47rzAsH8PL2p6uI5MREREREREREREJA/k2eo4EWnVIg7BaNEgg9FWOrf1nRrcvJIfMrUYf/9iKxwtnVA0gI9vhg9vsm+LtIfi3g2POx8Oh/3c27gvH940WCJeC+9eAfsXea9twj+g3zQYfSu06+l9v3r9L7P+DbeDsz5z7le1zn57QYfk55TMKnAIRovXKThARPLbrMmphVGVDoKC/LjzZBO1e93bTTP9OaL2d+ps87wExh2yCejJZU4hSdAQlNTlSPv2nW9DzR6oq4Sld8Pbl8IHN8C22bDxWZh3LbxzBaz8K8Rc5mkL0gkz2/MhfPZjmHe1P6FoAEf+Aboc4c9Y+aywMwy51gp1aB6KBjDoSvf9NzwZTF1BcvpdDCcZjAYw7o70aklVuAROfAJK+0H3L9j3SfS3UkQkm5wCU0NJZg10HAtlQ5Oc3ITPfgJ70zxHKpLrUgkm7nUGjP+FfdsRd6Vey+uT4OPvwJ5PUh/Dq7SC0b7s/f2aT78P+5J47yJXxVN4fTXyOzD4KzD2NuhztkLRRERERERERERERBJwCkYrCeXHdXsRI2K7PerXNTwiIiIiIiIiIiIiWaZgNBHJHZFS++3RquDm3Pm2/fa+08DQU2Sb12MylA7IdhX+6DCy5SIYp985L+r2w8ZnvPXtOBpK+jY8Hv+r5Ocr7tXweSgCo36Y3P6R/LhzV5tW6BCMBvBRhkIKRUT8tvZh59DORHqf5WspGdN+mHu7H2EwsUPu7aksHm4N4gm+LwCHdgRfh5/iDncvNUIQ+nzhfIeRzvs/XQ4zRsOnP7BColb9DWZPgbkXwpr7Yf2j8OGN8OZUiLXhO6WmEszQ2Op/Ov9fJevsRTBCx36eGCE4/hHn9tX3Z64Wvzj9HIXs73DsKlIKR/9PevUkq+fpcMEW6H+J9ThcbN9v7b8UyCgiucvxuTjJYDTDgDE/SqEA03qeFGnNkj3+nroaTn7Z+eYfReUw5rbU61n+B3htImx5OfUxvEgnGC0UhqP/BGd6DHB7aRyYce+15aJkF601vjGOiIiIiIiIiIiIiCRkmiZVMfsbdJaF8+O684hh/x5e1EzzWiARERERERERERGRHKHUHxHJHeES++2x6mDmi1bB9tn2bZ0OC2ZOyS/hQjj1LWtxc77rPqnltnY9oGxI6mMeWOOtX79Lmj7udXrywYPdTmz6uKRPcvsX5Mcb1G2aWzDamgf8CdIREcm0T7+X+r4DvuhfHZnU7ST39ppd6c8RO+je3laDFKIJvi8ANTuDr8NPTmFljUOSOh0GGPb9AKo3JZ5n22vOrw3bgnSD0fzSYzJ0GpPtKvJL7zOd23bMgdr9mavFD3GHsLBQUWrjZTpktOsEKOzY8DjiEIwGsG1W8PWIiKTC6bgglZDKwdfAkb+D4iTP4y37HTxiNHxMHwXrn0h+fpFclczx99DroWxwy5ueNDfwivRreuNsiLuEl6XLLcQ8UTBavc6HQ+lAb323z/HWL1clG/o+9vZg6hARERERERERERFppWrMQ8SxPy9ekufBaHUKRhMREREREREREZFWQsFoIpI7Ig7BaNGAgtH2LXZegNLn3GDmlPxT2h8mvwqXRRMHfeSy4d9ouc0wYMz/C3bekr4w6paW24Zc532MHpOh68Sm25JdUFnQIbn+knkFLsFoANvfzEwdIiJ+qd4Eh3akvn++HncM/6b7gubqjenPkSgY7f3r2mZwQvxQ4j75FjQadwhGa3xRX1F5y2PFVGx9Lf0x8lUuXAxphOGIu7NdRf4p7Ax9zrNvi9fB1lcyW0+6nH7nwykGo5UNhgGXp15PssLtmj12CUZb/odgaxERSZXTc3HIflGFK8OAkd+FCzbBJZUwOsXzkBXL4J0vwpoHU9tfJNckc/w94lve+nUcDX0vSK2ext6+2LktWg3Vm1Mf23QJXQtFvI1hGDDmNm99t8301i9XmS7BaCX9mz4uGwr9Xf7vREREMsv8/N+JhmFMCuIDmJDNL1BERERERERah6pYpWNbabgsg5WkzikYLWo6vOcnIiIiIiIiIiIikmc8XmUsIpIBYYdgtFhAwWg7HO4WHymFLkcFM6fkr1AYht0IO+dmu5LkDb4GygbZtw25Foq6wtqHYOPT/s7b+1yY+E8osLlr1jF/gY7jYMsMKOwC7bpB1QaoWtfQp7CTFYo26vvWgqfGinsnV0skP+7c1aZFiq2fhdo99u2NfzZERHKZacKiX8DCO1IfY+rqln/78kWXI+DMj+Hlw+3bZ59qLWI+7Bepf42JgtEAVv4ZBlya2vj5Kurh++IUDJ2rnBaCNw/mGPRl2PVeenMt/70VpjvudjDa2H0E4i4L7oPSYwpEq6xgu4KOMPpW6HxY5utoDSY9D486/Mxueh4GfDGz9aQjVmO/PVSY+pjH/RsqlsPeT1Ifw6tQEsFo29pwGKOI5Dan48V0nosBCspg/K+g4xhY/xgc3Aw1O61Aaa+W3g2Dr0qvDpFc4PV12eCrrMAzr058HJb9Hra9DiV9YOj1VoDWkv+G3R/C7nmJx9j0HBxY2/T9hHgUPvkerPqrVXuHkXDik9BprPfawD0YzS1gvbmh11nvaax7CDY+S0P2SjNLfgOH/3dSJeYUt5+T09+znhP3zbfezxx5i/XaSkREJHcYwKMBz2F+Po+IiIiIiIhISqpjBxzbSvIkGK3A4eZGUbebb4iIiIiIiIiIiIjkEQWjiUjuiDgEo0UDCkab/yP77e2Htb2F8OJNvwuhz3mw+cXs1VDcx1o4mIwRN7u39z3f+qjeBM/1S722xkbf6r7oyAjBiG9aH6ko7Z9c/8JOqc0jmTXkGmtBl53YoczWIiKSqvWPpReKdsTdUDbYv3qyofN4KOnrHHKw+FdQOtBazJwKL8Foez5Nbex8Fvfwt7J6A1SuhvZDgq/HD06BXaFmp7OGXAcf3pT+fIt+ZgUADP5q+mPlEzPDgXmX1bX8P5TUGYYV4r3yLy3btsyAWC2E0wyzcVO7D7bPgeqNYESg8xFQfqwVLp4MM+4ShliUen2hCBz/MMwYlfoYXoWbB6O1s+8nIpLLHIPR7BdVJMUwYNCXrI96Myd5vxHF/sWw6j7rPGa77unXI5ItXoLRyo+FI3+f3LihAhj9Q+ujsaP/aP27+E747MeJx3lhMFwWbTieW3YPrPhTQ3vFMnhpHFxWm9xzg1/BaAD9plkfAM/0hEPb7ftVroL2Q5MbO1c4HRsbESjpDUf9LrP1iIiIJCcToWUO6agiIiIiIiIi3lTFKm23GxiUhEozXE1qIoZDMFq+3TxTRERERERERERExIGSf0Qkd4QdgtFiAQSjLbnLua39cP/nk9YhXAQnPgVfeBFG/dChj8PPsV9OSPLGyl2Pg07jvfUt6QtT1yZfU3NTZruHovmhuLcVqOKVfq/zw9DrndtqdmWuDhGRdCzzsHB5wv1w6QH4wnToMcX6OzXup3D6PBh1S+AlZkRRV/f2DU+kPnbUQzBatNIKA2pLvHxfAKaPgEW/CrYWvzgtnDeahWqFCmDqan/mnHcV1OzxZ6x8kemLIRWK5r++0+y311XAno+Dm3fvfJgxBuZeCB9/Gz76Bsw8Ht6alnzIfdzlOTuUZrBbhxGZeU3YIhit2LmvHwFDIiJBcHo+dlhUkbZhNybX/4Ovw/SRsG12MPWIZILT8XdxHxj3MzjhMescu983+xj0VYi099Z37b8aPl/zoH2fN85Nbn4/g9EacwuQe3EYmHmameI1KFxERCR3mQF/iIiIiIiIiKTFKRitOFRKKJ3z1hnkFIxWl+mbJIqIiIiIiIiIiIgERFfOikjuiDgFox0CMw6GT1mOsRqYf6tze+cj/JlHWqdwIfQ51/rofRa8db612Byg7wVWcNnci2HLdP/nHn8ndDsxuX0m/BOMJG7GXNInufEbM0JwxkfQJQO/Q4ZhLf5ffq+3/h1GBluP+KP9UOc2BaOJSD7Y9jrs+dC9z+BrYMjV1ud9zrE+WqNEwWjbZqY+dsxjAFjtbijulfo8+SZ+yFs/MwYLb4deZ0D50cHWlC7TYSF482A0sEJzi7pBzc705517AZz6Zvrj5ItMBqP1PD1zc7Ul3U+GSClEq1q2VSyFbsf5P6dpwvtfh4NbWrZtmQ7P9oYzPoAOHgPJYjXObeGi1GqsZxgw6VmYfToc3JzeWG6SCUZzaxMRySanRRLphlQ6GfBF2DUPVvzR+z61e2H2FPhijXWuViTfOP2e9Z0K424Pbt6S3jDxfvjwBqjZ7d73/Wth0/PWa4WKZfZ9tr0G+xZDpzHe5g8qGC3Re3qPhqDfhRApg24nweCrIZQHC9ocXw8rYFdERHLaBhRaJiIiIiIiInmiKn7AdntpuCzDlaTOKRgtqmA0ERERERERERERaSUUjCYiuSPsEIwGVvhBpNSfebbPcW8f9BV/5pHWr8fJcOEO2PMRlPSF0gHW9iHX2gejTZ4F7YfBkjth5V8Sjz/xAeh0GBzaAeXHQFF5cvVN22wtdkpGqAAi7SFqfxcsRyX9Yeoqa/9MSSYYreOoYGsR/wz/lv1iWAWjiUiu2/U+zD4tcb/eZwZfSy4o6hbc2F6D0WraWDBazGMwGljB06v/kcfBaDaL2I0QDLsBFv0i/Xl3vAU73obuSYYS5yun73MQhl6XubnaknAhdBhtH875/rUw5Br/59y/2D0MtG4/vHosTH7d23NNvNa5LZRmMBpAx9Fw/nrr9fvye2H9Y+mP2VzzYDTXvgpGE5EcFXN4Pg7qnJ8RgqP/AKO+D/sWwLJ7Ep+/r/fmeTD51WDqEgmSUzBxJgKv+l8MPU+FihVQuwfeOMu57+YXEo/30li4PObtxkZBBaN19HBTlI3PWP+u/TdsmQEnPZPcDWWywel1WkiXd4iISO4yTXNgtmsQERERERER8ao6Zn+tfEm4fYYrSV2BgtFERERERERERESklfNwlbKISIa4BZ9Fq/2bZ/8S9/Zkg6SkbQsXQbcTGkLRAPpNgwn3Q9kQMCLQ/Qtw7groORlK+1mPvRh8FXQ50gpQSSYUrctRcObHqf8sF3VJfp/T3s5sKBpY3/d2PRL3K+nX9P9HcltRV/vtmQxG2zwdZp8O00fChzdBXUXm5haR/LXwZ4n7RMqgVxsJRisdGNzYyQSjtSVuoUJ2Vv3NCkjLZfEkF4KPvQNG3+rP3J98z59x8oFTMEO6Jj0H3U+2XieUDoJj/wb9LwlmLoEOLoEQ+xb7O9fuj+ClcYn71e2Hxb+ybzuwDuZeBC8dBnMvhj2fOI8TKkypzJbjhKHrBDj+YRhzm/V6MlIK/S5yfu7oMML6OfY0frNgNLfASgWjiUiuclok4ddzsZPSftDnHDjS400QALa9Bi+OgE3PB1eXSBCcjr8zdX69sBN0PdZ63+HY+9If79EwVKxM3K92j3NbOsFoAOd5mL/epufg0RDMPsM6rs1VTq+HDQWjiYiIiIiIiIiIiPihKnbAdntpqCzDlaQuomA0ERERERERERERaeV05ayI5I5IiXNbzMdgtNq9zm2j/59/80jbNuRq6yNe13JBk5eQro5jndsGfhnWPWTfdvp70HWi9zrtFHaGqvXe+5dPsBYvZlooAuN/De9f695v7E/AUBZs3mjnEIxWvTEz8295Bd6aBmbMelyxHPbOt8L/9HMkIm52vp24z2G/gIL8uaNkWsqPDm5st6CZxmrbWjCaw6JpN7s/shbE5yozyYXgoTAc/t8w/k6IVkKoqOG1ZEFH62/5snvhk+8mnnvPhxCtcg/wbi2CCEbrfQ70mQp9z7d/TST+6+gSjLbhCejkIcDTi8pV8Oox3vtveq7ltpo98MKghsf7FsLGp53HCBd5n88LIwTjf2n9XTZj1mvLmj2w5kE4tL2hX9lQOPNTiBRbX/fME5u2N1fQoenjeI1zXwWjiUiucgrbzdTf8s6HWccQm1/w1r9yhXUe5wsvQp9zg61NxC/ZDkZrbOh1sH8RLP9DeuNMHw7nb2h4n6Bmj/X1FLQH04TqDTBrsvP+TuHXXrUfagXRb33F+z7bXoMdc+CsBe7H0tni9Ho43e+ViIiIiIiIiIiIiABQ7RCMVhJWMJqIiIiIiIiIiIhIrlC6g4jkjrBLMFq0yr95Nj/v3Nb/Iv/mEQH7xUxdjobiXu77DXEJ+xr6NfvtA7+cfigaQGGX5Pp3cglxC9qQa9zbT3gMhn49M7WIP0oH2W8/uAW2vBr8/Kv+2hCKVm/Xe7D30+DnFpH85hbkWzoATnoGRnw7c/VkWxcPwWjzrk4tkCl20Fu/mjYWjJbKBW2vTYDqzf7X4pfmf5PrJVoIbhhWQFG4yAr9LezcEHDqFsjd3BNlsOZf3vvnKz+D0bpPgsN+aT3nGYa1TaFomdHbJQxm0c/9+39emELA2paXGz4/tBOeLk9u/1Bh8nN6YRgNzydFXeDsBTD0BitUY+QtcPq7VigaWGEbp70DJX3tx4qUQnmzwDincCFQMJqI5K6YUzCazyGVbk562gq67TDC+z4LfxpYOSK+c3rtlq3j5jE/aRnwmoo198PB7TBrinW891RneLobPN0Vnh/ovq8RTn/+k2ckv0+8Dhb/Ov25g+AUfu4UFC4iIiIiIiIiIiIiSamKV9puLw3nz41PnYLR6hSMJiIiIiIiIiIiIq2EgtFEJHe4LVCPugRNJKOuEvYtdG7vcpQ/84i4CUXgiHucFxSWT3QP/Op2Agy+qum2rsfB0X/0p75kg9E6jPRn3lRNfMB+e4dRMOCLma1F0uf28/TGmXBoV7Dzb3IIz1xyV7Dzikh+M03nAKeux8H566DfBQ0hQW1BSb/EfdY8CJ/emvzYXoPR6iqSHzufOS2aTmTuhf7W4acgFoK7BXLbmXcV7Pk49flySfVm2PgsbJ9jPW/VMx2+z50Ph4jHu+D2vQAuj8Opb8LY2yAcUJCVOOt8WEMAoJ0Pv5H+HPEobH4x+f3eONsKRAN45/Lk9w9nKIynXXc49i9wystw5N3QrlvT9vZD4PwN0P+SptuNEBz955Z19r3AeS63/ysRkWyK19hvz+Tf9lAExvwIzl0GV5jWMUYiez6G6k32bfuXWMdAez7xt06RVDkF1josXgpcu64wOoXX5s3tfBee6wvbZ1uPzRjU7ILaPYn39SMI1wjB5NetwNpkrHsIFtwBH94E6x+H2KH0awEw47D3M1h1H2yebr0/mdT+CkYTERERERERERERCVJVrPUGo0UVjCYiIiIiIiIiIiKthK6cFZHcES52botW+TPHK0c7t42/0585RLwYeDl0GmctEorXQlF3qFwJHUZA/4sh3M55XyMEE/4J/S+FXe9Bh9HQ97zkF/w4KeycXP9uk/yZN1V9p0H4ppYhKQOvyE49kp7S/tbPv9MCtG0zrd+fTDu4NfNzikj+cApFAzi8jQYrGoZ1TFG7173fhifgqN8lN7bXYLR4bXLj5junxfWJ7P4A9i2CTmP9rccPjgvBw6mPmcox85LfwIlPpD5nLlj9AHz0jYbfn24nwMmvQEGZ889OST+YMhuesglO7n02DLgCqjdAx9HQZ2rbCn/MVVPXwfP97du2vpr++PsWQN3+1Pbd8CT0OQe2z0p+X6dQ8WwwDDjhcRhwufV6vLCT9fvQ+fCWfTuPdx7HKXhIRCTbnJ6fsvlcbBhwzlKYMcq938xJcP6aptvm/+jzsPvPQ2EHXwUT7tdxi2SX0/F3KEvBaAAjvgMLf+pcW98LrGN/t9Doba+lNnf5RP9CY3tOgbM+g43PWPXGDsHqfyTeb9HPrX9X/sX697yV0H5o6nXEo/DulbDh8YZtpQPgCzOg0xhvYzi9Hg7p8g4RERERERERERERP1THDthuLwl7vIlgDihQMJqIiIiIiIiIiIi0crpyVkRyRyhihaPZBR3U7Ut//IrlULnCub2jx8UIIn7pNDb1AAojBL3Psj78VmQTfOCkuA+UuwQOZkJhJzjhMXjncohVW9v6ToOR381uXZIaIwTtekHVWvv2fQuALASjacGsiKVqI6z7D1RvhO4nQ/9L9PsB7oFUbXnB6shbYMFP3Psc3AybnoetM6GoHAZ+CToMd98n6jEYzW3BdmvktGjai/WP51kwWhq/V26B3E42PAnP9oUj77HCifPpeS8egwX/BUuaBYHvfAfe+zJMetY9mKGwM5y7DGafZj33A3Q6DI69D0p6B1u7JK+0H7TrCYe2tWyr3gC1+6GwY8u2eAzWPQx7P4XinjDsG1ZoXnP7l6ReW8VyK2wvFan83gbJMKDfBdaHa78QDP06rPp7y7aYgtFEJEfFHMKFw1kOqew4Eo79O3z0TecA5Kq1sHcBdD7Merx9jhVw29iaB62Psbdbz9ElfYKsWsReLgajRUrg6P+FD77Wsq1dDzjpaesYaOtrMOcMf+fud6G/47UfAqN/0PB48FUw92L7Y2QnLw6zXvsNviq192Devtg619FY1Xp4aSyMuQ1G/8j+eLuxeACvh0VERERERERERETk/1Q5BKOVhvInGC3i8N6CgtFERERERERERESktfDp9ssiIj4pKrffXrM7/bE3POXe3svnxRwi+aowiWC0cXdYi72zre9UuHAbTJ4F562Ek56BSGm2q5JU9b/Eua16U+bqaCKPAlBEglKxHF49Fj77Maz8C7zzRfjwhmxXlRs2v+jc1pYXrA7z+PPx1jRY+WdY9HN49RjYNc+9f/yQt3E3Pg1bXvXWtzVIJxitar1/dfjJaSF4WoGDZmq7HdwM71wG730ljbkzLB6D2VNahqLV2/ScFZDmdDFk/V1lO4yAqWvg9PfgrM/gzE8UipbLRn3fua1yZcttpglzL4B5X4Xl98L8H8GMMVBrE1BfsSz1ug5ugsrlye83/BfDx+AAACAASURBVFv5FUbYXPlE++1xBaOJSI5yen4KFWa2DjtDvwbTNkHnI5z7rHmg4fOVf3Put+jn8NI42POJf/WJeOV0/J3NYDSwQsDsjl2G3tBwPNbrdCgd5N+chZ2tkMIgdTsBpq6CKW8kt9+GJ+CNs2HhL5Lbb8FPW4aiNbb4V/BcX/vj7cYcA/Ta8HkmEREREREREREREZ+Ypkl1vNK2rSTcPsPVpC5iOAWjRTHNFK+REhEREREREREREckhOZBkIiLSSGGAwWhugRUnPAbhHFjcJZILCjt76zf+ThhybbC1JKOgPfScDO2H5vfCeXEPRmvXPXN1NKGfKRGW3g2HtjXdturvsH9pdurJFWbcPSAu2wubs6mo3DrOTkZdBSz8qXufeK338Rbcltz8+cxp0bQXNbv8q8NPZsx+ezqBg05ha16t+w/seCu9MTJl8wuw4033Pu9f5y2ALhSBrhP/P3v3GeZWfedt/JY0fdx7t3HFBoyNwfTeHXpCKMmmV9iQPEvabsqm955N2M1uCmwCaSS0UBIILfSOKabZuPfumfF4RtLz4uB101GXRtLcn+vS5dG//kbWaCTNOV/BwJkQjRWvRhXftI+F9215I5isux2evBKujcB10X3fL2lfAn8c+EZ/DK6rC75+/qvha5/xRPq6lv4JnvpEdt/D7g75bu5zKkmsMXV73GA0SRUqNBgt5PGs3JqGwtyfhfe/9IPgNVqiG5b8Lv1aOzbCc18Jvl51F9x1Etx2CDzxL9C5oXg1S3sLDbzq4fcPonVwwi0w5vwgDLFxMMz4Vzjwc3uOOybDz1a2GgbBSX+Dhv7FWS+dulYYfjwcdV3uc+d/Pnh82L4m89jt69I/Z96pa3PwfHvZTeFjwsLPe3MAvyRJkiRJkiQVyY5kJ90h78O2xvqUuZr8hQWjAaHfnyRJkiRJkiRVE4PRJFWWxpBgtB0FBqN1rof1j4T3j7+osPWlWtIwKPOY0x+DAz4NEZ9KqAQGHxre192+6+vta4JLPIeAnHTSfTqaYXsSLA458fX1X5e3jkqz5v7ghPowvf2E1eEn5j5n5R3pQ75yCQDb8ER2Jy/XgkIOZutcW7w6iinse4oW8HPVZ0J435TLsltjcY6Bfz1leZqT7HfasgC2vZa6r6eDGZSfaAz6H5i6b/Vd0LYYbj0IFnwvu/WSifCQwp0O/BwMOgTOfCa3WjPZ/18K+3mvBGFBQonO4L2qYr2WkaRiCXtcCgt67Anp3jcCuO882PpKdmst+3MQ+P33U2D13bDxKXjp+3D/+enfJ5IKEfaaNs3JS2XTOBiO+xNcuBUuWAuzvrZvMHK/afmvX98f3toGlybhLeth0JzC6s3VuAth4CG5z1t9N9x9JiQyPC9ee19ur83vOxeWhXyok8FokiRJkiRJklQybfGtoX2tsb5lrKQw9WmD0TweQZIkSZIkSVL1M81EUmUJC0brLDAY7aUfh/cd/vPC1pZqTWOGYLTmkcFJ71Ipjb84dXt3G2x8Fq6rhz8NDy6/a4SnPhGENhQibeCDT5vVyyWT0B1yMNDCq8tbS6VZcUv6/t4eLBRrzm/etoXhfYkcA8C2vJxfDdUml8C4vXWuK14dxRT2fx2JpW7PRv8DoXX8vu0DDoJD/wPGvjnzGq9clf/+5fLaz2Hhr7Ibu+be1O2VEMyg/Ayem7p94S/hxgnpH2ML2W/gTJj55eKtO+HS4q3VU8KChHZshOuHwJ+GwvwvGr4jqXIkOlO3hwU99pTzV4b3Lb8ZHn5P9ms9+sF929bcB8szvNaT8hX22q2S3j+INYR/UER9v/xC0CEIvq1ryb+uQkVjcMrdsP+VMOiw3OZufBKWhHxowE7ty3Kv6b5zUodShr0erqT7iSRJkiRJkiRVqbTBaNHqCUarSxuMVsCxZJIkSZIkSZJUIUx4kFRZGkoQjJZMwHNfDO8fOCv/taVa1Dwmff+cH0PEpxAqsYaBqds718Gdx0JyrxPDXvwOLPh+YXumC5QJOxFQ6i12bAzvCwtM6y02L0jfH60rTx2VKtaU37wtaW7XXA/aSrdWLck1MG53lRqMtvfv+50iBfxcRSLB89low662WAsc8r2g74hf7tkXpqtCH/val8HTn4ZH3lf4Wr398auajT67vPvtHowx5tzirDnt/8GgOcVZqydlChLq2gLzvxCEGUpSJQgNRsvi+VE5NY+AYceH969/uPA9lv1pz+vJJGx6Hhb/HtoWF76+eq94W+r2SgsgTCefMNx++8P0TxS/llzV94NDvgNnPAoXbYeBs7Ofu/RP6fs7VuRX06Pv37ct7PWwr9MkSZIkSZIkqWDt8W2hfc2x1jJWUph0wWhdBqNJkiRJkiRJqgGmmkiqLI2DUrfne6J+dwf85cD0Y3I56UHqDfpOhn7TU/cd9lMY9+by1qPeKdaSun3lbUF4QCqL/rewPRM7Cpsv1bKOleF9XVuCINreatsr6fvTHHzUK0Tz/P7XPx7ely7IMpXeEoxWyMFs3dsgvr14tRRLWNhboSeCjzkbznwKDv46zP42nPkkjDgl6KvvCxPelnmNrS8XVkMpLPpfuGkyvPDN4qzX2x+/qtmQw8u314hToG63g4L7Z3gPJpUZn4Lpn4T93hGEbJx0J8z5XvFq7EnZBoQu/m1p65CkbMVD3huJVWBg0+QPlnb9Rdfs+jrRHQTP3nogPHAR3DgBnvtqafdXbUrEoXND6r6mIeWtpRBDj4Zzl4T3R+qgdUJw6TMZDrsK5j0Ldc3lqjA7sUY4/ZEgIDsbS69P/9q5Pc9gtBW3BeGLuwt7PVxIULgkSZIkSZIkCYC2ROpgtOZoC7FIrMzV5K8uzXvG3WEfwCFJkiRJkiRJVcQjZyVVlqYRqdu3vZbfenedAFteDO8/7gaIRPJbW6pVkQgc83u4+3To2O1EnoO/DlM+3HN1qXepy+MT1zY9E5xAlu/jetpgNPOE1ct1ZDixc90jMPTI8tRSSRJdsDXD89RCA5x6q+U3wcFfTt1nMFpqYSdNZ6tzHbSMKU4txZKMp24vxong/WcEl1RmfQO2vATrHgyfv3kBDJpTeB3F0rkeHv0gJDqLt2a+wYbqeU0joK5PEHpYaqPP3fN6JALTPw4vfie7+fUDgteatfreTDTLIKHVd5W2DknKVthziWwfz8pp/MXw4KWlWz+ZCAKsGgcFAZYLf7Fn/7OfhRGnwpC5patBtWfHBiCZuq+xioLRAFrHwls2wf0XwOq/B21Nw+GIq2HU6T1bWy6i9TDxXTD85OB72ZAmqB3glunQOh76TIR4JyS7Ycnvgw/6iLfnV0PnWti+CppH7moLO2HNYDRJkiRJkiRJKlh7fGvK9pZY3zJXUpj6NB962F3Ih2xKkiRJkiRJUoUw4UFSZek7NXV7xwroSv0HqFCPXQbrHw3v7z8Dxpwb3i/1ZgMOhHMWwSn3BwGCF6yBAz7d01WpN6lryW/ejo3575kuGK1WgxqkbG1fnb5/+U3lqaPStC0OP1F1J09Yzc+mZ2HbwtR9uX6aZW8IRkvECT25Plud64pSSlGFnghe4k9mbRoGp9wLZ6Q5IX7tP0pbQ66W/gniHcVd02C06hWJQN8ppd+nZUwQIrG3cRdlv8bIU2v7uXasAoOEJCmd0GC0hvLWkY1IBM56qbR73DItCGbeOxRtp+U3lnZ/1Z50r7uqLRgNoKE/nHQnnPFE8Brq7FeqKxRtd61j4fRHgr+JpNP2Oqy5Fxb+EhZfG4SiQf6haDtt2evxLOy96lK/HpYkSZIkSZKkXqAtnvqD5lqjfcpcSWHqIuF/w+tOpvuwaEmSJEmSJEmqDp6hLamy9JsW3rf1FRg4C7a+ChueCC4bnwxCcFonwMyvwIADgrFbXoFXrkq/1/hLi1a2VJNiDTDsmJ6uQr1VLM9gtI7l0Dgov7npgtGo4bAGKRuZwnaW3Qizvl6eWirJ9rWZxxgsBCPPgJW35z5v+S0w7Yp92xM5fppl2yKIb4dYU+41VItsw+JOvgfuOiF1322zYcLb4eCvBSeEV4LQYLQyvJ0VrYNBc2C/d8Cia/btf/U/g5/vQ75bGT/nS/+UeUzfqXDiHXDTftmtabBjdRt6NGx8Kv/5kbrgZ7DPJGgeBRse3/P5wJAj4dCfQH2Kg4L7HwD1A6BrU+Z9xr45/xqrQdOw7Mc++XGY8SloGlq6eiQpnUQckonUfZUa9NhvKkx6L7z289Ks37kOVt4Bq+9O3f/81+Dgr5Zmb5VWMgHxziAMcOe/iR37tqXq26d/R+o5qebuSPP8qGFw+b7/YopEYNAhPV1FcUSiwd9EzloAt+xf/PWP+CU8/O7UfXedCPOehQEHBdfDPgCkoX/x65IkSZIkSZKkXqYtvjVle0us2oLRwo9Z6sr1w0clSZIkSZIkqQJ5hqOkytIyFqKNwQkie3vwUuhYCV1b9u3b+DSs/Cuc9SK0joMVt6bfJ1oPU/+5ODVLkoqvLs9gtPYVu04ey1W6oJ1INL81pVoRT/HcbHdbXgyCaftNKU89laJzXeYxBgvBtI/BqjuzD+/aadNzqdtzDUZLJqBtaW3fP7O9TYYfDw2DYMeG1P2v/xpW3wVveh4aBhavvnwlQu4z0TL+XI16U+pgNICXfwzdW4OT23tSvDO78MEznghCrObNh1uzeL5U11x4beo5Uy4P7rup3kMBmPQ+eO1/wudfuCV4b6ZhQO571zXD9Cvh2c+lHzdwNox9S+7rV5OmEdmPXfBdWPsAnPoPiMZKV5MkhUn1nvxO0QoNRoMg3DebYLTxl8JRvw5eH+xYD099EhZdnXney/9ReI07da4Pnmf3tveZEt2ZA8MK7cs1pKzSTgaqa/X5dyXpNw2O+SP8o8jPVaMNQYDx2gdS9986E057KHgO2bEi9ZjGIcWtSZIkSZIkSZJ6obZE6mC0PrF+Za6kMHVpjk3sTuZ4jJ0kSZIkSZIkVSDP0JZUWaIx6DsFNqcIQdjyUvq58XZ47itw+M9g3UPpx56zyE9Vl6RKFmvNb17H8vz3TOwI79v4VP7rSrUg3QnyOy2/Efp9vPS1VJJsgtGi4Z/K2GuMOh1OvA3+fmpu83ZsTN2eazAaBMEH1HAwWi4n9TcOCQ9GgyCMetGvYdpHCq+rUMl46vZyBg6OOhPq+kD3ttT9C38FRODw/+m5gItlN2QeM+K0IBQNoP8B0GcibFuYfk6/GYXXpp7Tf3849QF44Vuw4fFdjxPNo2H8xTD5A+mD0eqagQLCOQ74TBDosPi3sH0VdLdBfd8giKSuFYYdDwd9ofYDwCKR3MavfxiW/AEmXFyaeiQpnbTBaA3lqyNX2YYETbg0eFyOxKBpGEz5cHbBaCvvKKw+gA1PwoNvD0LF6wfAAf8G0z+e+++JTJLJ4PVSysCwTojvSNFWypCyN/qTieJ+n7XIsKvKM/L04q8ZrYdBc8OD0QD+emT6NbyvSJIkSZIkSVLB2uOpjwNqifUpcyWFiUQi1EXq6E5x7JjBaJIkSZIkSZJqgcFokipPv2mpg9GysfxGSP4nJLaHj5n9XWgZnd/6kqTyqGvJb17Hivz3TBeM1rESdmyChgH5r1+pti0Kgt8GHQqt43q6GlWqdD8fO73+m+DE7lqX6IY190J8Oyy+LvP4cgY4VbIRp0DDoPSBXHvr2pS6PZcQsJ22r819TjXJJixuzHnBv01DYevL6ccuurpCgtFC/q+jZfy5qu8L0z8B8/89fMzCX8LQo2HSe8tX1+7WPph5zMwv7fo6EgnuDwu+Fz4+2hj83Kq6DTgQjromvH/ax+ClH+zbPvE9he8dicDk9wWX3m78Jdk9Z9jp5R8ZjCapZ8TTvO6LNZavjlxlExI09s0wat6ebUMOh/3emV04WiG2r4Xb5+y63rUJnv4kLL0exl4QHiaWb0iZqlPLmJ6uQHur7wP7XwkLvlu8NSP1MOYceOn7+a9hMJokSZIkSZIkFawtJBittcqC0QDqIg0pg9G6/LuRJEmSJEmSpBrgGdqSKk+/afnP3b4GVt4By24MHzPhbfmvL0kqj0oLRgNYdhNMfEf+61eaZDI4EffF7+xqO+AzMPPLQZCFtLt4Z+YxG5+GFbfBqDNLX09P2bYQ7j0HNj+f/ZxorHT1VJtoQ27jd4QEo2UTAra3znW5z6km2YTF7Qw6yuYk6g1PFFZPsSRCvq9ImX+uZnwqCDXasiB8zCPvg9HnQlMPnKSeLuhu8ofgoM9D88g92zMFo+33T0EQgGrbuLekDkYbc275a6llk96TWzDauodKV4skpZNI87ovWsnBaIPT9x92FUz+QOr3Oo74ZfC86P7z89s7Ek3fv+VluCXk7x3rHwkuEgSvJVR5Zn87CDFLJoqzXrQehh4D/abDlhfzW8NgNEmSJEmSJEkqWHt8a8r2lmjfMldSuLpIfcr2VGFpkiRJkiRJklRtDEaTVHkGHlLY/HvmhfcNPQaahxe2viSp9GKt+c1rX57/npmC0Z76eG0Fo624dc9QNIDnvwrDjoeRp/ZMTapc6U6Q39098+CSRG2G68W3w02TerqK6hbLMUxhw+Op25MGo+0jLEBsp+mfhNFnBV83ZAiO2OnxK2Ds+TD8xPzr2vIyvH4trH84CFsbejQ0jwnWHHtB5seKsAP0ImV+OyvWCCfcBjftl37cPWfCGY+Vp6bdhQWjjTgN5l6Vum/IUdA4FDrXpuiMwMFfK1p5qmBDjoKZX4FnP7urbfrHYfTZPVdTLRp+cu5z1j8Ogw8tfi0qv/YVsOhq2PISkOzpaqT0ulKfAALk/ly+nKKpT/b4P5M/GP68MxKBsefBQV+A+V/IY+8M4c+Pvj/3NdX7jH0zTLuip6tQKpEInPl0EJLf9nrh60XrIVoHR14Nd58OOzbmvobBaJIkSZIkSZJUsLbEtpTtrbHq+xDBupDjqLrzOcZOkiRJkiRJkiqMwWiSKs+Yc6BlDLQvCx8Tieb3Ce2eXCJJ1aGuJb95HSvy3zOR4SCAlMEhVeyVkKCUhb8yGE37yhQcuLuFv4JJ7y5ZKT0i3gk3ju/pKqpfNI8whZd/ClMv27Mt0+N1KrUejJbuQLZTH4ChR+26nu3t9/KPg8vsbwdBSblacx/cfSbE23e1Lbsx+PeVn8Ck98PhP0u/RljgW7mD0QD6TIDzlsMNo8PHbHgcVv0dRpxUtrJIdIefoL/3z87uojE48PPwxEf27bu4K+hX7YtE4MDPwMR3wcanYMBMaB3X01XVnkgEmkdCx8rs59xxGBx1HUy4uHR1qfQ2L4A7j6395yHqHTIFgPW0gbOD32V7a92vtMHd6W6X7WuD58SqbpFY8Fo21pji34bUfdFGiDXs9nXI3FgzDD4M+k6tzYD5WjHgIHjT83D3abD2gX37j/oNvPBN2PRs5rV2BjkOPgzOXQJ3Hpf6sSudxizDziVJkiRJkiRJKSWTSdriqT8wqDXWt8zVFK4+kvpDhAxGkyRJkiRJklQLDEaTVHmi9XDSnfDQu2DDo0AE+s+AgYfAoDnBZeDBcPthsOXF3NYePLcUFUuSii3WE8FoOQQ/1YIVf0ndvvhaOPo35a1FlS/emf3YR96TWzBaojs4gXTV34Kwo+lXwqgzc6+xlF79GWxf09NVVL98whSe/iSMews0DdvVFhaWlU6tB5Kku01a9wr127Eht7Wf+gT0nQZjzt53z0XXwPNfhW0Lg7bm0dCxPLt1X/vv4LKzvvr+MPxkmPklqH/j01eT8dRzoz30dlbLqCBo7m9Hh495+F1w3pKylcSOjeGh4S0ZAq6mfBg618CCHwQBdsNPhMN/YShab9QyOriodPL53fXMp4PfgT31mKfcJROw4Huw9E/Ba9O2xT1dkVQ8+YQcl9OBn4X737xv+9A0z9uKId3ju48Bufu/oLE0oWLRhpCgsRz6sg44a/C5sQJ1LXDyPfDUx+G1X0D3VqgfAAd9HsZfAmPOh6euhIW/hPj28HV2Pzmtvk/w+vKpK8M/wGKfOvpCv/0L+lYkSZIkSZIkqbfrSu4IDQ1rqcJgtDqD0SRJkiRJkiTVMM+qklSZ+k2D0x8KTihMdAcnwuytcUhua467aN9QAElSZarLMxht+2pIJiESyX1ubwtGqwWdG6B7GzQM3HXirEojkUMwGkDbEmjNEMiz00PvgMXX7bq++i44/mYYfVZue5bS4t/2dAW1IZZHmEJ3Gyy7ESa/f1dbPgdtbV+d+5xqkkhzm0T3Ovht7JvDwzHD3HcOnHIvDDsu+D2b6IIXvwXPfm7PcdmGou1u98CITc/ChseDvSIRSIYETUR68O2soUfBcTcFt0kq7Uth84tQ1xpc4tuhcTDEmopXQ6I7eK0ca4DO9eHjGgenXycaC4LoDvxccJvm8/xJUnbGnBeEQeaibXHwuNh3chCCCEGYRl1LECbpz2zleeJj8PKPe7oKqfgiUYg193QV6Y05Pwh5XX33rrZoI0z9SHbzBx2a377xdkjEU4dnFRLeX3KR9MFgxQgiSxc4lnLPBn+3qbJF62DOD2D2d4Lw/OYRweMjQF0zHPZTmPNDePQDsPBXIWvs9fp893m3zYLNL6SvYeo/F/e1pSRJkiRJkiT1Qm3xraF9rdE+ZaykOMKC0boMRpMkSZIkSZJUAwxGk1TZItHwkJNcg9GO+t/C65EklUdda37zkvEg4Cyf8B2D0arHhifh4XfBpvl7tk/7KMz+buoTklWYXH8+lt0I07I4AX3ZTXuGou1079lw3I0wJiR4qJy2r4N1D/V0FbUhmsdjM8CyG/YMRksXAhamokMJiiAsQAz2DREbeWp+ezz7eTjke/DYh2H9o/mtkY2198PKO2DUGWmC0Xr4cX7M2dAyNghBS+UvM/Zt2+8dcOh/QH0Bnywb74Rn/i0I3Ul0waizYMIl4eMzBaPttPfJ+ZKKb9S83IPRAG6fA0SA5L59c34YBP4YIlMZNj1vKJpq15CjKj+IPBKBY6+HBT+ElbdD8yg46PMwcFZ280ecDPX9oGtL7nt3b4OG/vu2z/9C5rn1A6D/jPSBY4WEjYX1GYor5S9aBy2jQvrqg9eKoXNDXntF6+G0R+CpK+HVn6UeM+fHMPXy3GqVJEmSJEmSJO2jLb4ttK81VjvBaO3xrWzt3vzGmDqaY3kely1JkiRJkiRJPchgNEnVK5dgtFPu92RvSaomsZb853a35ReMFjcY7f8suwmGHp19oEohkskg4KxjBTQMgu4t0GdicNnb1tdg45Pwj7emXuulHwYnFc/8QklL7pXinbmNTxWMtmMTrP0HdG4I7lsDZ8F954avcd+5cNYC6Dct93o3Pg2r7wnuU8NPgNZx2c3r2gLrHoaOVcGJrk3D4aUfkTKMRLnL9/n4iluhfRm0jAmu5xOMtvEp2LERGgbmV0OlS3eb7H27t4yBGf8KL3w9tz3W3PtGQE8ZLPlDEIyWCAlGi1bA21mnPQw3jM5+/KJrINYEc/8r/z1f+CYs+N6u6ytuCS6pRBsLez4lqbhGzYMRp8Gqv+YxOeR5yBMfhebRMO7NBZWmIrn1wJ6uQCqNur4w6xs9XUV2GgYG7wfk855ArAlmfQse+1Duc1MFoy2/NXgNks7BX4UD/i33/SRVuDShg+neF6nvE7xe7H8gPHHFrva61uBvnINmF69ESZIkSZIkSerF2hNbQ/taaigY7Y4N13PHhuv/7/rwhjG8Zei7OaBPmY7/kiRJkiRJkqQiqIAzSSUpT9kGo9X1gSFHlrYWSVJxReshEoNkPPe53W3QOCj3ecksgnaSSYikObmtVtx3LhCBY34H4y4s3T7dbfDwe2HJ7/Zsj9QFJwfP/GJwPZmAZz6bXYjPomsMRiuFRI7BaGvuhfj24ORyCILS7jsv931v2R8uiUMkmt34RDc8cDEsvX7P9tnfgelXpp+77hG4/3zoWJl9feMvgUGHwlMZ1tYb0gTMxZqC+0yYG8YGJwhP/gAkQ8KyMrl+CBz7JxiTJpCvWqW7TSIp3vo5+Ksw7HhYfhO88tPS1ZWvhb8IfgesvT91f6rvqdxaRsH0T8CL385+zms/h4O/ll/waHcHvPit7Mc3Du4dz1mkahFrgBNuhdd/Aw+/s3jrvnKVwWiVYNP83MZPfFdJypCKrt/+MOZ86De1pyspjykfhAEHwd+Ozm1ed9u+ba/9T/o5pz0EQ47IbR9J1S/k5LQ9TPtI8DfNZX+GaBNMfj80jyh9bZIkSZIkSZLUS7TFt6Vsb4o2E6uEY5JyVJ/Ne8/A6h3LuGr51/jU+G8ztinFBxdLkiRJkiRJUgWqvndtJWmnpqHZjRv3VojGSluLJKm4IhGoa4WuLbnPTXVC6k6JOKy8A1b/HVpGw6T3Qn2/zPN2SnZndwJbTUjCQ++EEadAw8DiLr1tIbx+HTz72ZCtu+G5L0HX1iAQa8kfoH1Jdmu3LYJEVxCup+JJ7EjdPvxEWH33vu3J7iAAaOrlwc/xAxfnv/d1Meg3Pfh6yBHQOgHGnh+csL5T2xKY/8UgTCmVpz4Or18LA2ft1piAtQ9C/wNg5GlBuFEuoWhTr4BDf/jG+gajZaX/QbDmvn3bow1BMMBts9PPf/SDwWNSIosgy1SSCfjHRXDBamjon98alWbdo7DsBlhxS/iYVI+HkQiMOj24dKwMTriuNDeMDe+LVsjbWRPfnVswWjIOj/8zHH1d7nutviu75yo75RO+Jqm0ojGY+I4gkHL9I8VZc/Vdwe+3bENkVRobn8l+7Fu3Ba91JVWmoUfB1I/Ayz/Ofk68fd+2dM+vp/0/Q9Gk3irb9ysHHxpcJEmSJEmSJElF1xbfmrK9Nda3zJUUR10Ox8omiPPYlvsMRpMkSZIkSZJUNSrkTFJJykPz6OzGzfhkaeuQJJVGrCW/YLR4SGhIMgmPXw6v/teutgXfg9Mfg+YRQQhXJokdvStwK94By26Cie8s3pprH4S7T4fubqWuyAAAIABJREFU1J+6t4eXvp/fHot/D/u9Lb+5Si3embp90BxYc38QhLa3x/85CPFpnQDx7YXtv+XFPf99/mtwzO9hzDmw+l6464TMa2x8MrjsbevLeYRCRWDy+3KcI8a/NQiDIbln+7iLgtC6t7bB7zMEhSy5PjwY7bgbg/vcg5eGz090wqq/wrgLcyq9Ii38FTzy3iAQJ51IhpDobAOnCzH3v4PwtmU3Fme9TN9zufTbH/pOga2vZD9n8W9h4OzcX6cuuyG38Q0Go0kVa/wlxQtGA9j6GvSbUrz1lLv2pdmNO/luQ9GkatA0PLfxe4fXdqcISttdv2m5rS+pykTCu3rT+8qSJEmSJEmSVKHa46mPXW2J9ilzJcUxuH5YTuNX7VhWokokSZIkSZIkqfiiPV2AJOWteVTmMcf+2RONJKlaxZrzm3f7ofCPi2HT/D3bn/7knqFoAO3LYP6/B18vvjbz2mFhPLVs8wuFrxHfAa/8J/ztWPjb0dmFohXiUQOrii6xI3V7XR/oOyl83tOfggcuKkE9nfDUx2HjM9mFohXblMtgwEHl37faDTsOjvxfaBkTXI82BOEwc68Krte1BEFT6ax7iH2C1XZqGADjL4a+U9Ov0V4DB3d1roeH351FKFodRNKclA3QmNvBcTmb9tEgSPDQnwT1FEPHyuKsU6hIBMa+Jfd5878A29dkHrf2QbjnLPjjIHjt57ntMfiw3OuSVB7TroADP1e89W6fDX87Lvi90La4eOsqOzs2wzP/ln5M49AgJHT4CWUpSVKB6vvlNn7vYLStL6cf3396butLqh0Go0mSJEmSJElSj2tLpP4Q5dZY3zJXUhyz+xxFJN2HduwlLBhOkiRJkiRJkiqRwWiSqlfL6PC+ie+Ci7th7HllK0eSVGSxhvznLvkd3HE4bFsUXH/pR/Did1KPffVnsPDqfYPUUumNwWjZ3C57i2/f87Z65l/hsQ/D2n8Ur65M+3esLs9evUWiM3V7tDFzkFWpbH0FbptV/n37HwgHf7X8+9aK/d4G5y6B85bBhZvh6GuhrnVXf+v49PO3p/nZ3hkCNv3K9Gt0rMq+3koS3x4EoiW64foh2c2JZhFE1lTiYLTmN163tYyG/d5RnDX7H1CcdYphxieCwJtcxDtg8W+D/0sIAu6SCUgmIREPfoetuS8IE13xF9ixMfe6Jr4n9zmSyiMSgZlfCt63mvTewtfrboO198PCX8Hth+X3mKH8JLrhrpPSj7lwK5y/IggJlVQd6nM86WXvYLTNC9KP76nX0JLKI104ecRgNEmSJEmSJEnqaWHBYC2xPmWupDgmt8zgn0ZcQZ9Y/6zGtyUMRpMkSZIkSZJUPbI4Q1aSKlTzyPT90Vh56pAklUakwKeq8Q64aSIc8Fl49b/Sj334Xdmt2RuD0VbeDtvXQVMWITxd2+CR98KyGyEShXEXwoS3w4Lvlb7OvT33RTjsp+Xft1bFw4LRGmDkGcH/eW8w8nQ46jfQkN1BRAoRiYSHHGcKRos1h/dF3zjBePIHoK4PPPi21ONe/BYsvi54jBh9VuZ6e9qOjfDwe2DZDbnPzeak68YsQ9byNfK0XV/P/Rn0mwYrbg2uT/sobHgCns8xbHDIEcWrr1ANA+H0h+HZf4fXf539vCc+Ck/+CyTjwXOeZHfxapr0Xuhv4IZU8aIx6DuluGt2roVbZ8F5i4u7rlJb/XfY+GR4/+QPQn11Hjwv9Wp1uQajte95fdmfw8cOmpN7qK6k2hE1GE2SJEmSJEmSelpbfGvK9tZojn8jqiBH9D+Ruf2OZ13XarqTwXHOz217nBvWXbPP2PaQ71+SJEmSJEmSKlG0pwuQpLzFmiDWkrpv3MXlrUWSVHzFOlHs+a8EAQHFkNhRnHV6WiKX8JUkPPu57IY+/E5Y8ntIdAbBdIuugbtPyzyvFF65CtY92jN716Kw+36sMQi/G3x44Xu0jIUjfgWRCg23PeAzcOLt0Di4pyupbZmC0dI9nu/+e2PCpTD5Q+Fj25fCvWfDpudyq68nPHBpfqFokN39tS7kNVUxTHw3DDx41/VoDGZ8Ek65J7iMPR+mfiS3NSd/EAbMLGaVheszEY76X7g0ueflopBQyZ2S8Tf+LWIo2qA5cOhPireepNIaNS99f8tYeMtGOOX+7NdsXwKr7ymoLGVp6fXp+/sfWJ46JBVXfY4nvcTbdvt6e/C+SJhZ3wiCoiX1TgajSZIkSZIkSVKPa4tvS9neEqvuD72KRqIMaxjJqMZxjGocx+jG1MfhtcW3kUwmy1ydJEmSJEmSJOXHYDRJ1W3iu/dtq+sDw48vfy2SpOKqq8CDDBJdPV1BccS35zZ+0dXQ3Z5+TOcGWHZj/jWVwoLv9HQFtSMREu4TbYT6PnDy3TD94/mvP+ZcOPNpmPhOOPOZ/NdJZdjxQXjbzkusOfu5E94OU6+A4/8CB3+luHUptdYJ6fs3PRveF6nb83rjkMz7vfLTzGN6UttiWHl7/vNHnp55TF2RP+20zySYcjkc8wc4/OeZxzcPD0J/MmkaAcfdGIR+VUuYRKwBzniidOvP+SEc8n2Y+C6Y+B447Kdw6gNBaKWk6tD/QOh/QOq+Q74Hb3oBGgbAsGNgzo+yX/fV/y5OfQrXthhe/Vn6MS2jylOLpOLK9flx927BaMtvDh839gIYcUp+NUmqHnu/N7E7g9EkSZIkSZIkqce1xbembO8TK/IxVD2sJeT7SRBne6KjzNVIkiRJkiRJUn7SHJkrSVXgoC/Axidh3UPB9VhTcAJ+rKlHy5IkFUGxg1qKIVkrwWg5HtQQ74CXfwwzPhU+ZuPTkIwXVlcu6voEgT9Lrw8fs+QP0LkeGgeXr65aFQ8LRmsI/q1rhtnfhtb94PHLc1v7wH+HmV/YdX3AATDoMNjwWF6lAkEY2sl/h0iKLPDkNfDYZfDqf6Zf47yl0DImu/3m/jc8+v592ye+K7v52qU19SdVhlnUMYEb153Dpu4BnL2iD3MG7NaZVTDaVUGYVKVadWdh82d9I/OYwYcFP8uJHYXtBTDtY3DId1P/7KXTMABO/Qfcdz50rt2rb1DwGm/ESYXX1xMGHRKELL7+6+KvPf4SaBpa/HUllU8kAkf8Eu47FzpWBm2tE4LnMX3223PsmHPgiSuyW3fxtTD+Ihh9dvWESVabxy7LPKZ5dOnrkFR89Tm+H9W5YdfXG54KHzf+0vzqkVRdJrwNnv3svu0tY9KHpkmSJEmSJEmSyqI9sS1le0usAj/MuQCtab6f9sRWmmMtZaxGkiRJkiRJkvLj0beSqlvTEDjl3uCEo+0rYdhx0DCwp6uSJBVDfZqDDA67Ch77cPlq2akYoTGVINdgNICnPw19JsG4t6Tuf/FbhdWUq/EXwZgL0gejAVw/BM5fCc0jylNXrQq77+8MRttp6mVB+EY2QREAk967ZyjaTrmGKu1u4Gw45Z7w/kgE5l4V3IfuOjH1mAtWQ9Ow7PccNQ/qWqG7bc/2sRdmv4YCOQSjPbz5cObNv4VN3cHz/y//MMkv35XgHUe+cf/JJhgNYP6X4aDP5Vpp6W17HR55X/7zL1gdBI5lUt8XxpwHS36f/14AZ70E/abmP3/o0XD2S7D2AehYFbQ1DYOhx0DjoMJq62mzvw3Lb4KuLcVbc8plhqJJtWLwYXDWAlj3SPAcaOgxEGvcd1zLWIg1Z/9c/r5zYdL74fCfFbdewdbXYMWtmcc1jyp9LZKKL9dgtOe/Agd8OnhNuGVB+LjRZxVWl6Tq0GcCDJ4L6x/ds33cRQbWSpIkSZIkSVIFaItvTdneGqvAD3MuQLqgt7b4NgbXDy9jNZIkSZIkSZKUnwLONpekChGthyFzYcy5hqJJUi2pSxOMNvbNMPWK8tWyU6Kr/HuWwuYX8pv31CchEd+zrWsLPHY5rLyjsJrq+kLLuMzjog0w4W0w58cwel4QkpfJ058urDaF3/djDfu2TfkwHPIDqO+ffs39r4S5IUEd+Qaj7fcOOPmu7MYOPwFOuQ/6TNy5KQw+HM59PbdQNICWUXDiX6HvG6FQTcNh7n8F91Hlpmlk1kM/s+gr/xeKBpBMRvjIdUm6upNBw+BDs1to/udh28JcqiytjlXwyAfgpv0KWyeX+/HhPw9+t0brg8uY82D8xdnPH3lmYaFoOzUMDAIjJr8vuIw5p/pD0SAI55z5lSIuGIFZXy/iepJ6XH0/GHkqjDg5dSgaBM+PhhyZ27qv/Tf8rgUWXg3JZOF1KrD85uzGGc4sVae6PE56+euRcG0Elv05df+4i8If3yXVnuNvhuEnBc/fYi1BWO2sb/R0VZIkSZIkSZLU6+1IdNKVTP0hsS3RNMcsV6GWaGtoX3t8WxkrkSRJkiRJkqT81fV0AZIkSVJK6U5EjTbApPfCwl9Adxn/QF8rwWgv/SC/eW2LYONTu8KGkkm473xY/ff81qvvD2e/DJE6qO8LiW74fUv4+HnPBiFWdbsdsDHlQzD5A3D3mbDqr6nnLfl9EKBW15xfnYJkyH0/Up+6ff+PBgFpiR3Byd+JHdC5Pvi/i8Qg1pzhpPBI9rUNPAROvQ+I5v5/POxYOPtV6FgJsabCApiGHgVnvwTb10HjYIjk8D1ol2gsq2GdiQbu3nTiPu1bt8M9L8OpM4B+07Lfd/Hv4YAKCFHs2gq3HwodywtbZ9BhuY2v7wPH/hG62yGZCK5veAoW/zbz3EgMpvVAWGm1mfIheOYz0J36U2ez1mcSnPViEGAnqffZ/0pYc0/wWJ2teAc8/C7YvgZmfKJUlfUum5/LPKZpuI/VUrWqzyMYbdP89P3j3pxfLZKqU9OwILh/x+bg/SaDESVJkiRJkiSpIrQn2kL7WmN5/I2ogkUjMZqjrXSk+J7b4gUevyRJkiRJkiRJZRLt6QIkSZKklOrTfPpatB4GzoST7ixfPVAbwWidG2DV3/Kff8dhsOD7QSja8pvzC0VrnQBj3wynPxacKNg4KPg/zRRq1Tx6z1C0nSJRmPy+8HnxDlh9V+51apdEd+r2aJqs7VhD8HMcrQ/+31rHBYFhDQMynxCaLlRs7s9g2HHQ/0CY8Sk49R/B+vkG30Ui0DKqsFC03TUNMRStUFM+nHHI5u7+oX0LViV3XZnwT9ntueyG7MaV0o7N8Id+uYWiDT0mdfuYc/Oroa5l1+/fQbPhtIeh3/67+kecCvOeg2kfC34GR54Jx/8FRp2R3369SbQe9nt7bnNaxgS/J5uGw6A5QRDoqQ8YtCP1ZqPnwXE3w8jToXkUNI/Mfu7Tn4S2paWrrTd57eeZx4x6U+nrkFQa0XoYemxx1+ybQ2izpNrR0N9QNEmSJEmSJEmqIO1pAsFaYmmOWa5SfULC3gxGkyRJkiRJklQt0pzFLkmSJPWgkafD819L3RdrCv4dcjhM/wS8+O3y1JTYUZ59Smnjk5BMFLbGk/8C21fDhidzm3fg52Dml9KPiTaE3871/cLnjTwDIjFIxlP3L7sBRp+VXZ3aVzIkFLBk4TxpgsUmvB0mv79E+6oi7P8vsOymtAFhW7rDHw/67H7OcfOI7PZc/wi8/NMgUKxldJaFFtmDb8t9zsT3BMGAK+/Y1dZvOkz+YHFqGnI4nPXivu1zvl+c9XubmV+Bhb8KAjt3F6mDo6+DcW/pkbIkVZnR84LLTmv+AffMg+4sDly+ZX+4KPwTsCtadwdsehZIwuDDey6IduMzmcc0j4TpV5a+Fkmlc/BX4c7jirde3ynFW0uSJEmSJEmSJEl52ZYmEKy1BoPRWmJ9oWvVPu1tiW09UI0kSZIkSZIk5S7a0wVIkiRJKQ09BlrG7Ns+5jyI7PY0tpxhV4mQcKhqUqzv4YVvwqq/ZTd2ymVw/C2ZQ9EgCIcJE03TV98X9ntHeP+quzLvrdSSifAwvXT/XwVJE3SxMxhRtavvZDj90bT3r83x/qF9rbsHozUMyH7fxy+HG8bAM5+FZDL7ecXQvgxW/CX3efV9g8fXw38Bkz8Eh/wATnsQmoYUv0YVrnEQXLAKDvhs8FgWrQ8CXk9/2FA0Sfkbdgyc8RhM/3jmsfF22Jwi8LLSbXgSbjsY/noE/PVIuGMutIcHqJbU/C+m7+8zGc58GvrPKE89kkpj2LHQlGXIcib1A6CuuThrSZIkSZIkSZIkKW/t8dSBYI2RJuoipfqQ2J7TGk0d9taeJiBOkiRJkiRJkiqJwWiSJEmqTJEoHHVdcALpTn2nwqE/2XPc0GPhgH/bs23S+0pTU7IWgtG6y7fXzC/DpUk47Ccw+k3ZzUkXfpbJrG+F97W9Xv6go1qRjIf3lSoYLZLmpWokTWiaakfLKDjws6Hdm7vDg9FaGna7j9TnEIy20/NfhVV35j6vEJuey3NiJHjcnPRumHsV7P/R3MLgVH71/eDgL8NFHXDxDpj9LRg0p6erklTt+k2D2d+G4/8SPM6k8+zny1NTsSST8Mj7Yesru9o2PA6Pf6T8taz8Kyz7c/oxZ78MTcPKU4+k0hpyZHHWGX5icdaRJEmSJEmSJElSQdpCAsFaY33LXEl5tMRSB6O1hQTESZIkSZIkSVKlMRhNkiRJlWvYMXDuQjj+Zjj5bpg3PwjL2V0kAgd/Fc5+NQhSO/MZmPuz0tST2FGadcspXchVMY16U9pQo1CDDs1/z6YhcNpD4f0r78h/7d4skSYQMFqiT0nsM6k066q6RBtCuzalCUbbIwMxXcheOouvzW9evjpW5jfPEDRJ0u5Gz4OzX4EDPxc+Zukf4cXvlq+mQm14AjY+uW/7sj9D29Ly1TH/y3D36enHHP5zQ3ylWlKs59pTLyvOOpIkSZIkSZIkSSpIeyJ1IFhYgFi1Cwt8C7sdJEmSJEmSJKnSGIwmSZKkytYwEEafBcNPgFh4SA59J8GEi2HgzOBk9BmfLn4t6QKiqkWyuzz75Hv7T/946vb93pnd/KZh4X2Pvj/3epT+PhOtK82eU/85dfuoeaXZT5Up2hjatTlNMFp3YrcryUTouLSWXJ/fvHy1L8t9Tl0rDDmq+LVIkqpb0zCY+SUYODt8zFMfh62vlq+mQqx/JLxvxS3lqWHRb2D+5zOPq6/NTxGXeq36IgSjzf0vGH5y4etIkiRJkiRJkiSpYG3x1IFgrb0sGK0tvrXMlUiSJEmSJElSfgxGkyRJUm2a+K7ir1kLwWiJLILRhp9Y2B6Dj4ChR+c3d8Qp0P+APduijTD5A9nNbxwS3te+DLo8oCNn6e73kfrS7DlwFgw9dq+9ojDlw6XZT5Upll8wWnz3LLR+0/Lbu3treQNjsglb2dukD0Bdc/FrkSTVhqN/m77/5inlqaNQG54I79v4dOn3X3oDPPT27MbWGYwm1ZSGAoPRRp0VvJcRiRSnHkmSJEmSJEmSJBWkLb4lZXtLtDb/1tsSEvjWHhIQJ0mSJEmSJEmVxmA0SZIk1aZ8w3DSqYVgtGQ8fX/rBDjxbzDrm1DfL/f1hx0PJ/01/xN/o/Vwyr0w6X3B/+GoN8GJt8PQo7KbnymMYOsr+dXVm6UL04vWlWbPSAROuBWmfgT6z4ARp8FxN8Los0qznypTtCG0a3M8PBitO57cdWXosVCfZ6DBzVMgmcw8rlCbnkvfP3guTP8kzPpWEDw56FCY9Q045Dulr02SVL36Tc383Hj94+WppRDrHwvv61xXun3jnfDoB+H+87OfU1+bB8tLvVahwWiT3lucOiRJkiRJkiRJklQUYYFgrbHa/FtvazR1MFpb3A8YliRJkiRJklQdSnQWuyRJklSDEjt6uoLCJdOEXEXq4OCvQzQGMz4Jkz8AfxyY2/rH/qnwQIDGwXD4f+c3N1Mg29LroWsL9JkErWPz26O3SaYJBIzUl27f+j5w6I9Kt74qX7QxtKst3hrat3suGrEGmPV1eOzDe6476BBY91DmGl65CqZelkWxBVjyx/C+g74IB31+1/UZnyhtLZKk2jL1Mnjhm+H9K2+HwYeWr55sdayGtkXQOBS2vBA+rpTBaI9/BF7L8TVJXeqDyiVVqaYR+c8dfhKMmle8WiRJkiRJkiRJklSwtkRYMFpt/q23JSTwrS2+jWQySSTfD0CWJEmSJEmSpDIxGE2SJEnKViJNQFS1SMbD+069H4Ycset6ff/c12/IMUit3J7/WnABmPQ+OOw/gyA4hUukCdOL+pJSJRQLD0ZLJKOhfd17P8xN+RD02x+W3Qh1LTDuQhg4C1bcDk9/GjY9E17D45dD8ygYe16Oxedg1d/C+8aUcF9JUu2b+O70wWjPfi74HTnuLeWrKZ1kEl7+CTz5/9IHOu9UqmC0rm3w+v/mPi/ic2Oppow8Nfi53vvxqHkkTHg7dG2GV3+277xIHZxwaxDSLEmSJEmSJEmSpIrRHt+asj0sQKzahQW+xemmM7mdpkhzmSuSJEmSJEmSpNyEn0ksSZIkVbvpnyjuepueLe56PSEsYKDP5D1D0QAiEeg3Lbf1K+ET5LINEnrtf4LQhRe+CS/9CNoWl7auapUulMLwB5VSNDxIIF4/ILwvkaJx+Akw5/tw8FeDUDSAUWfAvKfhzKfT13H/+dCxOnO9+dq2MHV741AYOLN0+0qSal+/aTDnR+nH/ONCePwK2PxieWpKZ9HV8MRHsgtFg9IFo21ZAPHtuc2JNUOfiaWpR1LPaBgIR16z5+veIUfBm16E2d+Cuf8FR/8OorsFOg86FM5bljbkWZIkSZIkSZIkST2jLb4tZXtYgFi1a00T+NYecltIkiRJkiRJUiUxGE2SJEm1a+xbgCIGdS38BSy9oXjr9YRkPHV7NCTgavylpaulVMZfkv3Yl38MT38anvgo3DoL1j5QurqqVaIrvC9aX7461PtEw8ME4tHwg9G6UwWjpTPwYBh3UfoxK2/PcdEsJbpge0jo2pHXlGZPSVLvMu0jMPWK9GNe/jHcNhuW/rk8NaWy8Bp4+N25zelcD8lcf/FnYcuC3OeMPgfq/DRtqeZMuATOXQxH/xZOfQBOuQca+u/qH/9WOOc1OOo6OPluOPUf0Dy8x8qVJEmSJEmSJElSuLAwsJY0x6JVs9ZoeDBaW3xrGSuRJEmSJEmSpPwYjCZJkqTaNWQuHPUbaByyZ3tda/6BTo9fVpqT78sl0Z26PRISjHbAZ2DKZbsCigYcHLSlMuXywusrhvFvheEn5T6vaxM89Yni11PtkiH3GQi/30jFEAsPRktEW0P74vk8RB/xi/T9HSvyWDSNba/DvefAbxuAZOoxLaOLu6ckqfcanyEAFCDRCfdfAPHO0tezt64tQVBxrpJxuO88WP9Y4TWsexgefDv8/XR46J9yn3/4/xReg6TK1DIqeBwdelTq95JaRsOEi2H4CWlfw0iSJEmSJEmSJKnndCW66ExuT9nXJxYeIFbNmmPhx9iFhcRJkiRJkiRJUiUxGE2SJEm1bcIlcMFqOG8pXBIP/n3LZpj3PMSa9xzbPBrOfAaGHRe+XsdK2PRcaWsupbCQq0gsdXs0Bof9BC7cBOevhHlPwwH/GtxWu4s1w+T3F7fWQhzz+/zmrXsoCCzaXTUH4RVDoiu8L9+AQSkb0YbQrni0Obwvnx/ZuhY44bbw/u4iHgjWsRJungzLb04/bu/HWUmS8jXkyOCSjTuPL20tqSz/SxBSnNfcm4Oa1z6U//4rboM7j4PXfwOr/pr7/KlXQH1tfoK4JEmSJEmSJEmSJNWC9sTW0L6WGg1Gi0ViNEdbUva1pbk9JEmSJEmSJKlSGIwmSZKk2heJQsuYXf9GY9BvCpz2IIw8A1r3g/GXwCn3wMCZ+wa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hgtyFol33Qk5C0SRphns+tKuA2gSZrV2V8n3HFyuk+karE2+L+VtvPXvv1f4L6iSFKT5ibGjuvDoAAOiFfqvEULRmST+RNMJau7e1dq61ditJ0yS92mZcWNKDxhiOvAYAAAAAAPCp0CmSkfuXZ3XRmpRzU50cX8HJ8QAAAACQsUiKYLSGLgpGW970rWt7sdNPRSnCtgFkJtjVBQAAAAAAuolghsFoYYLRgG4lEEg7xGy7VycUgh6vTRpCoW3QoJaVWhUs8z190pD1ywQCMqf/Wfaki6UlC6Rx02SCyR9Hmn4DZK5+Oh6U0NggjZwQD5MCegkzaz/Z+6/LbM5pv89TNd1cQTi7ea8+LkWTg0KSBALSoCEJTYUFRoOKrFbVZ/a8c9KkhZKGpx0HoOcKnXqJbny4XJcvP0fvhadqcuMnGhJdHu/8/F1pmz2lJo+gq3ChzLAx0rX/lhZ9Gh/X2CCtq+20+tH9FFVFpPvd+xrHbp7RWmanA3XVxN/o9MDPfY1/6avZumbQKfpH6VFJfSVmnfbbrFj7Tw9oVZ3Vu9/E39Pf+ZrV/8y3vmvaZ5q0xcj466ltaZE961Dp9ac39tsnbpcuvVf2jEOkVUtbJ95xpewdV7aOe3a+9OoT0pvP+d62p0iRzBPLZYKhjq+13ugyo903lZ75OP1Yx7YJOVu5VONGlyngSFGX7LOPl0ifLZNWpDiPJmBdJlZXyT58k8yB30tfUCcZUybddZJRJGgUCSnhT0mWb/cAAOjrjDHjJJ3WrvkIa+1D7cdaaz80xuwu6RlJG9KayyRdKOnkvBYKAAAAAADQSzgmoEKnWPWx5O940wajNS91be8fGJDyZH4AAAAAgLugCSlogmqxLUl9XRWM5rXvV1EwtJMrAXo3gtEAAAAAAHGhDIPRIgSjAd2KcdKP2Wz79GMAJ/G+NLzlW9/BaOUlUllJYriQ6TdQ6jcw7VwzZJT/GoGeZMbszMaPmezrMdMrhVInZZjvXSh700XJHX5C0SSpf5mMk/x6ueVoo6c/8rfEBjtPIMAR6O1MqEB28tYqe+cl7VL/YkKf/cs50vb7yDZ6HEwRjh/MboyRRk3Kd6noIYqWW+l+l2ArSeum7pzRWmvXWV1Q8CPJx0vguo/6K6QWaZVcg9H2qP6X9NFIacrWGlRstMv6u+xpu8f/9hOONnem0V+PbfPa+M6LCaFokqTl38ieuqvU1JC+6FceTz/GB3Pi+TkNRdvgisMdbXWJ+/9lW4G2/0ErlyhUPkzjnEZ9FhuSNPaIv2a4Xhv2+guk/ee5vs/pCgOKjObO5L0SAAA5dqGktm9sbnULRdvAWrvOGHOCpPckbfgC8HvGmCustV/mr0wAAAAAAIDeozhQ4h6M5tLW1oqmJa7tFQXDclIXAAAAAPRFYadQLS5B1Y1dFYzmte8XYt8PyKXucWQsAAAAAKDrZXqiaJirlgHdSiCQfkxxaf7rQM/XLmSvssX9w3o3m3JhEyCJKQhLx53pf/zx58SDdPqiVO9HSwZIR/2sY+9BS91DHs/fP7OvSk5Yc7s22WRQ9nUA6DkC3teYsqfuKn3zuXtnOJKngtCTFaZ4mWsorcxorSfet6qNpg+4f2DREfFQNEnbNbyu49b8PaG/KFanM1b+XnrtqaS5wYDR6Xs5+u95qV8nT9nF6K6THJUWtb5/sXf9yX2wn1C0XBk+Tjr01LwsPWOU0TAfu9eO2oTKrVwq++sTNHH1f7PebsB6JOFVV0nfkm8CAEBvZYwplHR4u+bL082z1n4q6cE2TUFJR+ewNAAAAAAAgF6tKNDPtb3e5UT8tlY0e50cz8F1AAAAAJCtiON+HkFDVwWjee37EYoN5BTBaAAAAACAuFD6E2oTRIryUweA7BgfH/MUux+oAyRwEu9Lw1u+9T114tA+GuYEpOGcdLGvceaPj8vsMTfP1XRfxhhpuznufQ98JRMpkqZsk/0GSstdm3eaYLRz6EPXvln1r+ii5Rdpz9qndEDNo7p6yWm6fskpUnlmATYAeqhU4cO1a6Rn7nHvI0gcLgpTfOyyrtn/OtGY1XdusGnHTWn8UAfUPpbQdv2SU3XDtyfr4LUP6furb9YbX22vbRrekL3pIs91ZowyOnSG93auOdplX/Q//0xbX14d8ROZG/8jk8eQwsk+jl1qG2Rmn50v/d+/NLp5YdbbDMgjGE2SatZkvS4AAOj29pbU9ku5V621H/uce0u724fmpiQAAAAAAIDer9gpcW2vi9amnLeiiZPjAQAAACDXwsb92NzGLghGs9Z67/sRig3klPdlzgEAAAAAfUuGwWiGYDSge3FShDZsUEQwGvxIDDerbPYfjLYpn98DnsyNr8p+f3vv/isfkZm5eydW1D2Zn1wh++nb0qql8YZQgcw5N7W+9xw1QXrr+ewWH1Dm2XXv1Ee1+39iej8ybWPb0JalunbpTzWt8UNpZZvBxf1litwPfgXQy/h5j+2mgGA0JCsMefeta/K3xpsLrL57c8zX2H8uPCCpLaQWzau+XfOqb0/qs4s+kxk5wXWtm09wtGBVTP9d0NpWXiI99tPudR02c/6tMnt9p1O2NXGI0bMfpw6oSwgyWx8WN6zF/WAoP9oGrSWpq856XQAA0O21T5F/LoO5L0pqUetxojOMMUOstctyURgAAAAAAEBvVhRwPzakPkUwWszGtKJ5qWtfRYhgNAAAAADIVsRxPza3oQuC0Wqi1Wq0Da59gwu4ADuQSwSjAQAAAADigpkFoynMid5At+L4OCGdYDT40e6+VBGt8j110hCTfhDQR5lJWypldEY5X4BJkhk1Sbrtv/HwkPoaaatdZUZv2to/cmLqf8dUSr2D0crHjdSrd+2sh/odoA/DkzWmaYEOqH3M/TmwnANVgT4jkOVXqewvw0UkVTBac/r5Z90f0xX/9PcqeNO3J6kywwAue/Q06YUGGZP8nr5/odHLZzp68G2r9xdLIwdJB0w3Glrazd7/73Rgp21q4pD0YxybHGI3vMV/8HR7CUFr7dWuzXpdAADQ7U1rd/tVvxOttXXGmPckzWjTPFUSwWgAAAAAAABpFAfcj7esi9V4zlnbslrN1v2qSBUFHG8CAAAAANnqTsFoK5q8j88kFBvILYLRAAAAAABxoQyD0SJF+akDQHZMmmC0grBMpo9z9E3tgtHKMwhGm0quE5BaaZlUvdK9j+fojcyAcmnOse6d46Zmv+7YKd6dY6eq0DboqLX3pl9obPY1AOhhsg5Gi+S2DvQKxhhFQlKDSwjaOvdzIzZ69QvrOxTN2Jj2rHsmiwolu3NEdve5MlvOlvabJxMIbOwrCBrNnWk0d2ZWS+edOfUymcLiTttePBQ69f+JW5DZsNcqDQ0AACAASURBVAwD69qyShFEV1edOLbqW9m/nCv96854wwEnysw5Vmb6Dtlv/60XZJ+7T4rFZHY5VGarXbNeCwAAZGRyu9ufZzj/CyUGo02R9GyHKgIAAAAAAOgDPIPRot7BaMubU50cP7TDNQEAAABAXxX2CEZr7IJgtOXN7hdILXSKPPclAWSHYDQAAAAAQFwwwzCOMMFoQLfS5oR1V4V8sAq/Ek+2L2/xF4w2cYg0tjwf9QC9iJPiuZpgNH82myWFC6XGLL7A3OVQ777x06UBFdKaFWmXMQecmPm2AfRMqZ63UykgGA3uCr2C0Vza2rrtVX+haJK0f+3jquxA+JaeuUf2mXuk156WueSu7NfpbHOO69TNTRySfoxjY0ltwz0OiPIjZFPcUWpbg9Hst1/JHrlpYv8jN8s+dqt04d9kdjs8423bp+6SvWSeFIv/TvahG6Qz/yqz3/EZrwUAAPwzxgySNKhd88IMl2k/fkL2FQEAAAAAAPQdRYES1/blTd/q0ap/uPYtblzg2l4c6Oe5HgAAAAAgvYhHMFpDnoPRFjZ8rk/r30/YzufrPnQdWxEaJmNSXAAVQMYIRgMAAAAAxAVDmY2PEIwGdCvGSd1fxEE18MlJvC+VR1f6mrbXVMMH+EA6BKN1mIkUyc7aV/r3fZnN++39MuWV3v2BgOzsg6WHbki90OARMtvsmdG2AfRggSy/SiUYDR6KCqTV9cntdY1W7QOKN4jFrK5/IX0wWv+IdOiwr3XVe5d4DyoZIPPnp2XnzUxf7PMPKLZTWObE86XjzpIJpn482Leel73p19LK7IO/shYMSaVlnbrJMeXxf/O1Dd5jAoomtY1r/koFsUY1OeGMtje4ZZnGNn/t2W/XVG28B9m/nOM+KBaTvfAYaccDZAr8b99aK3vDhRtD0dY3yt74K2nf77IfCABAfg1od7veWluX4RrL290u7UA9AAAAAAAAfUax437MZU20Wo+vvDujtSpCw3JREgAAAAD0WeEuCEZ7tOofGe3/VRSw7wfkWpozZgEAAAAAfYUJBKRAirCO9sKc6A10K+kev0X9OqcO9Hwmu2C0ad55QwA2SBWwEyQYzS8z7/zMJ22/T/p1v3uWVNL+fOM2BlTI3Pxa5tsG0HNlso/chiHsEh5KPLKo6pq85xxwdcy7U1LQkZ76uaNVf3J089mbqN+dr8s8WyNz6mVJY81JF8uM30zaeg/fNdubfy17nUfQ1oYxX34g+/N9pXdelL75wvfaGakY7t1XNkzG6dxDHwKO0V5TUo+J2EbXtt3r/51y3hSXY6POrbpcjlIE5P39CkmSra+RXnw45fr2iAmyDS4JfV4WfSYt+Tq5vepb6dO3/K8DAACy0f7s22yO6G4/p8NfFhhjBhtjpmbyR9ImHd0uAAAAAABAZyoK5O6YS4LRAAAAAKBjIh7BaI15CkZb2vgNodhAN5DlZc4BAAAAAL1SsECK+vwwKOz+YRKALmLSnARe3L9z6kDPZ0zCzfJola9pk4eZ9IOAvi6Q4rm6wCMpBUnM2MnSbW/KHr+lvwl7HuUrLMUMHiHd8prsHVdKn78nFZVIq5ZJw8ZIg0fIHHuGTGlZx4oH0LOkCrRMJRjKbR3oNYo9Xu5rG9z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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import asyncio\n", + "\n", + "from matplotlib import pyplot as plt\n", + "\n", + "from apd.aggregation.query import with_database\n", + "from apd.aggregation.analysis import get_known_configs, plot_sensor, Config\n", + "\n", + "with with_database(\"postgresql+psycopg2://apd@localhost/apd\") as session:\n", + " coros = []\n", + " figure = plt.figure(figsize = (20, 5), dpi=300)\n", + " configs = get_known_configs()\n", + " to_display = configs[\"Relative humidity\"], configs[\"Ambient temperature\"]\n", + " for i, config in enumerate(to_display, start=1):\n", + " plot = figure.add_subplot(1, 2, i)\n", + " coros.append(plot_sensor(config, plot, {}))\n", + " await asyncio.gather(*coros)\n", + "\n", + "display(figure)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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otDQfut7ArX898N83Py3g0IFffohhtIVU6+PCMJqmaehoAMYlv5m9MiUA1N5zmrPwOo2nyz+OUpPF7VTmk0AyI+B11d7fl4iIiIiIqNpZDaPxOyYiIiIiIiIiIiKi+pZUhNE8OqMctDhB9wppGG1cET076DqKCckBd19JQxulFFDEK0LpURjCgK7pFR4RVTOzCBTDaFTL1NvSwu8dyymmCE41OJqUtzFbFstF4OU+GBUhJ3IYTu7EUGIQO+KD2JHYioQRK8tjdbt60e8fwDrfevT7N6DdVf5YzNnt78LTkccOiortE86M4ffTt+LtnZcs+XEMIZ9EsZj9uKDi/Xrhfu54alh5+x7PyqIf0050zYFTW96CE5regAem78D907cjJZKmtzGQw8Ozd+PJyIN4a8dFeFPruSUJ3hERWRHOyD+Ddbt7+Xme6hbDaERERERERGQqJz/ZyEHm5McikUXhiMBtz+RffsvTAj+4RKC9wZ4H0CwHZRjtkO/21nXLw2hb8n+DqglWXqczVTi5vJgwGgBMzAMr28szFiIiIiIiIlo8i100fsdEREREREREREREVOdUYTRGOWixlhK3UcXTgor7tANV/CItUpjNTlUkGkK1QxVG82hetDh5sCbVLlX4bzITRsbIwKW7Kjwia6LZeenlDY5m5W0aTZZFc/PocAWWPC6qXVmRwSvJl7AjPoihxCB2JrYp9+eXqse9Eut8A3tjaP4BtC7D+5BLd+OS4Mdw1fAXpcv/MH0rTmx6I4Kepe0rGpBPDtFRfBBHvT0bR1Zk4NRc2JOWh9E8mhdtztrYd/ToXpzTeTFe3/o3uGfyV/jT3B8hFM/zPgkjjtsnrscjM7/DO7suwwlNb2SUiIjKbjwl/wwWcPdWeCRE9sEwGhEREREREZlShagWmk0IAIx3LdamXfLnOWsAz+wG3nx0xYdkW4bit4dD176BFRoe35H/pA6OWp2KXV0YRtsrzDBa3Xlou8DX7zEwMQ8cv0rDdy/S0NnE9yMiIiIiIrsRFj+Oz8b5HRMRERERERERERFRPUsxjEYlZh63ScOlu5W3VUWhVPdpB2ZjC6VHGUajooTS8rMRd7t7oWn8TY9qlyqAKWBgIrMHvZ5VFR6RNTFDclZxAI2OJuVtvLofOnRpiCmak98f1a+Mkcau5BCG4lswlBjEy4kXkBapkj+OBg29ntXo3xdC861Hk7O15I+zGEf6j8HJzWfiiciDecuyIosbQ1fjkyu/tqSAliEUYTTNUfR9qSLBBgxMpkMIevqwJyUPowU8fTUXAmtxtuHS4Mfxpra347cTN2BL7OmCt5nOTuDaPf+FB2buwoVdV+AI/4YKjJSI6pX6ewj7BtqJyo1hNCIiIiIiIjJlJYw2V56TutSNF0PqJ3n7uMCbj+bBA/uonin9kKfoGMWxPdvHSzoc21AF4xaajpV/HKVWdBiNv7/XlWeHBc7+noHsq+v/ljGB258RmP6eDqeD200iIiIiIjuxmimf5XdMRERERERERERERHVLCIGkkZQu8zCMRotUOG6zWro8Y6QxmQkVdZ924HM0oNnRhkhuJm9ZKD2KoxuOW4ZRUbWqxjggUSl0ugPKWFgoPWLbMFo0Ny+9vMHRrLyNrulocDRhPjeXf39ZHphd79JGCjsT2zGUGMRQfBC7ki8iKzIlfxwNOlZ61qLfP4B+/wYc7jsaDSZBv+V2YdcV2Bx7CjHJa25HYhBPRB7EqS1vWfT9G5BPotBRfKSsy9UDDTqEZHs2nh7ZG0ZLy8NoPe6VRT9etej1rMLH+76A7bHncNvEdRhJvVzwNruTO/C94S/g2MbX4vzOyxH02PczARFVJ0PkMJHZI13Gz2BUzxhGIyIiIiIiIlNWgkuz8fKPo5ZtlX9nBQDYIj/ZWt1Shfr0Q37j6e/WIJt2HZ4HUhkBj6u2oklWAobzSSCTFXA5q+ffns4Wd/3QvABQPf8+Wpp/ve1AFG2faAo49/sGfv+p4s+IRURERERE5SMsltEY3yciIiIiIiIiIiKqXxmRlk7YBwAvw2i0SF3uIHQ4pIGJ8fSoMow2kdmjXB/tHEYD9k6YjiTkYTSiYoQZRqM65dRc6HQFEc7kH8g/buNtqSpk1lggMKUKo8UMeWiNalfSSOClxDYMxQcxFN+C3cmXkEORB/RboMOB1d51e0NovgEc5jsKPkdDyR+nXBqdzXhX1wdww/hV0uW3ha/DMQ0nosnZuqj7N4R8H1TXij8+3qW70OnqxkRmPG/Zvn3D8ToMo+1zVMNr8Dn/d/FU5BHcMfkLzGanCt7m+egmbIk+jTe0no1zOy5e9N+ZiOhQ05lJZERauizIz2BUxxhGIyIiIiIiIlM5CxNXI/KTNJJFk/PqJ3lw1OLM4TqhCvXph7SwVrap72NkBji8u3RjsoOchYAhAMwmgC77njwpT0Z+siOlqWh5xkH2Mx0T+P2gfNn924ChkEB/gJE8IiIiIiK7sBpGY3yfiIiIiIiIiIiIqH4lDPWXxAyj0WI5NCe63EFpFCyUHlHeblyxTIcDXe5gycZXDgH3CgwltuRdrvo3EckYIoeJjPzMzwGbxwGJSiHo6ZOG0ewcmYzlVGG0ZtPbqZar7o9qRzwXxY7EVgzFB7EjsRXDyZdgKMKwS+HUnFjjPQL9/gGsezWE5tG9JX+cSnpd85vwZORBvBDfnLcsbkRx68S1uKLnU4u6b1nQFwAciwijAXvft+VhtBEkjQSmMmHp7Xo8tR9GAwBd0/G6ljNwfNOpeHDmLvxh+lYkDfMzOxow8OjsvdgUeRh/034hzmw7D27dU6ERE1GtCmXU+5ndDKNRHWMYjYiIiIiIiEypQlQLRRlGWxKz+NOWMUAIAU1j4AcADMVE6kOfnZXt6vvYPV2/YbSZeG2H0aZj5RkH2c9fXzFffuMmgS+/g9tNIiIiIiK7sJp9nzU/rpCIiIiIiIiIiIiIaljKZPK5V/dXcCRUawLuFdKQzXhKPel4PCWPiHW5e+DQ7D0lM6iYMG3nmA/Zz1QmjKzISpcF3L0VHg1R5XW75Ou5XbelQgjEjHnpsoYCYTTV8mhWfn9UvaLZCIYSg6+G0AYxmtoFYfmIDutcmhuH+Y7EOt8A+v0DWOM9ouaiUZqm4ZLAx/D1Xf+ErMjkLd8UeQSvaz4DRzccV/R954R8EoUGvej7Ava+b2+RzLMIpcdMt2k97voIo+3j1j14a8e78fqWt+B3Uzfjsdn7CoYCk0YCd07+Eo/O3ofzOi/Da5tPh64t7u9ERBRSfA/R4mznCQOortn7WzgiIiIiIiJadqoQ1ULzqfKPo5aZxZ9m48DYLLCirXLjsTPV+qgf8ttBg0dDe4M8lLV7WiA/pVbdclZeqKi+cFixYbSpKvv30eIViiU8vav0P9ITEREREdHiCYu76JE49+WJiIiIiIiIiIiI6lXSNIzGCaC0eEF3H57HprzLQ2n5pOO9y+SRCFV0zE4Cnj7p5bPZKSSNBF9PZIlZKKWbYTSqA0HFtjSUHrHlSc9TIqmMGTY6zM+qrVoezUWWPC5aXnPZGeyID+6Poe1J7y7L43g0Lw7zHYV+/wD6fRuw2rcOTs1Vlseyk253L97WcRHumrxRuvym0DX4tzVXFR2FE0Ie43IsMrgVcMu3Z+PpEexJydcJl+ZGu6t7UY9X7Zqcrbg48BG8qe1c3D5xPZ6P5n+OONRsdgo3jF+FB2fuxIVdV+CohtdUYKREVGtC6THp5dXwPQRROTGMRkRERERERKZy5ie4AABEk+UfRy0rFH96aYJhtH1UE6l1yW/Lq9rlIbAXQ6Udkx3kLM4bnysQk7Ib1WtD0+TrwgzDaHUjlTVf6QflvwcQEREREdEysZo7KxRBJiIiIiIiIiIiIqLaZRZG8zDkREsQVMQgQulRZdxmXBFNU4Ul7CTgUk+aDqdHscq7roKjoWqlCqO1OTvh0b0VHg1R5am290kjgUhuBi3O9gqPyFw0q46YNTiaTW+rWh4z5pc0Jqq8mczkqxG0LRiKDyKcKc8B1V7dj3W+9a+G0Aaw0nsYHFp9JivOar8AT0Uele47TmTGce/ULXhn12VF3WcO8klcGhyLGqMqqJMwYngxvkVxmz7oiwyx1YqAewU+uuLzGIoP4raJ6/BKcqjgbUZSL+P7I1/GQMMJuKDrfej1rKrASImoVoQy8s9g3QyjUZ2rz71MIiIiIiIissywMHM1mrQ6vZVkCoXRdk8LAPY6q9RyUa2PsjDakQENzw7n32DLaO2tr1YChgCQzJR3HKWWVrw2gs3Anrn8y6ditfe3JblUgXVZtk0gIiIiIqLlowqdH2omXt5xEBEREREREREREZF9pRRhNJfmhkNbXASACFDHbVIiiZnsJNpdXQddbghDGYVSRdbspN3VCZfmRkak85aNM4xGFqleAwFOyqc6EXD3KpeNp0dtF0Yzi5g1OppMb6tabhZbo+UnhMBUJoyhxN4I2lBiEFOZ8pxBvkFvwjr/evT7BtDv34AVntXQuX8OAHBqLlwa+DiuHP68dPn907fjpOY3otez2vJ9CiGfHOJYZKjM7L17c/Qp6eU9npWLeqxa1O8fwL+s+jb+Mv847pj4OaazEwVvMxj7C7bGnsGpLW/G2zsvsd17BhHZUyglD7RXw/cQROXEMBoRERERERGZshJGm0+Vfxy1rFAYbXimMuOoBqr1UXLCRmxYAfz66fzLN8uP1ahqVl6nAJDMVFdkT/XaUIXRpmPlHQ/ZRyprvnxkFjAMAZ2FNCIiIiqBTFbg334rcMezAi4HcPkpGv7lbE165ngikrMaRhuXfNYjIiIiIiIiIiIiovqQVITRPLqvwiOhWmMWtwmlR/PCaLPZKaSF/MDYYBVEoXTNgW53L0ZTu/KWqWJXRIdiGI3qXaOjGY2OZkRz+XGwUGoER/qPWYZRqcnGCQA6dPj0BtPbNjqapZfHFPdJy0MIgXBmbG8ELT6IHYlBzGQny/JYTY4W9PsH0O/bgHX+9ehxr4K+yChXPVjnX4/Xt5yFP83dn7fMQA43jl+NT6/6puXnMAf5JAodi4vRNTpa4NcbETeiectUUcWgm2G0hXRNx0nNp+G4xpPx8OzvcN/UzUgY5md/FDDwp7n78XTkMZzVfgHe3P5OeHRvhUZMRNUmkYtjLiefQMrPYFTvGEYjIiIiIiIiUzn5yUYOEk2Wfxy1LFsgjLZ7ujLjqAaqAJisfXTMCg1A/g12TQHzSYEmb+1EDKy8TgEgmSnvOEpNGUZrATCcf/lU/m91VKMKhdHSWWBkBljVUZnxUOkJIfD4jr0BjWP7gFZ/7WyziYio+nzk5wLXbzzw2eJztwnMJYBvXMD3JyKrLHbRsGcOyOYEnA6+voiIiIiIiIiIiIjqTUoRRvNy8jgtkd/RiGZHGyKSScbj6REc3XBc3mUqAXdfycdXDgH3CkUYTf1vI1oolB6TXt5tEhokqjUB9wpEE/lxsHEbRiajWXnErMHRVPDEfw2KMFo0Nw8hBE8cuEyEENiTHsZQfAuGEoPYEd8q3ZcphVZnB/p9A1jnH0C/bwAB9wr+3Yt0Qdf78Hx0E+Zz+WcE3Jncjj/N3Y83tp5t6b4MoQijLTJOp2kaAu4VeDn5guXb9HpWLeqxap1Ld+Os9vNxSsuZuHfqZjw6cx9yMJ/YkBJJ3D31Kzw2ex/e3nkpTmk5E7q2uMgdEdUus4g5w2hU7xhGIyIiIiIiIlOqENVC8/IT45FFqvjTPsNTVqcP1z6hDKPl//B2rMnxR1tGgVMOL9GgbKDewmiBZnn0btr8pDtUQwqF0QBgcIxhtGo1MS/w1u8ZeObVAGJvK3Djh3ScdgQPsiAiosqbigrcuCl/3/Nb9wq892SBo3r4/kRkherz/KEMsTeOtrK9vOMhIiIiIiIiIiIiIvtJKsNovgqPhGpRwL0CkUR+TEQ2+VgVRmtxtsPn8Jd8bOWgmjg9nrJfzIfsJ5GLK+M7nJRP9STo7sNLiW15l4dtGEaL5eRhtEZF9Ozg6zRJL88hi5RIwqtxX6wSDGFgNLULOxJbMRTfgh2JrYgq/q5L1e7sQr9/A/pfDaF1uoIMoS2R39GId3d/ENfuuVK6/LcT1+PYxpPQ4ix8QIwB+eSQpcS0ig2jBd0rF/1Y9aDR0YyLuj+E01vPxR0TP8cz0T8XvM1cbga/DP0ID83chQu734/1DcdXYKREVC1UYTSX5kabs7PCoyGyF4bRiIiIiIiIyJSVMFo0Wf5x1LJCYbTx8vyeVZVU66Mu+R1udQfQ5AXmJevn5lGBUw6vnR/vLIfRLMSk7EQdRpNfHksBqYyAx1U7f1uSsxRG2yPwtmO4LlSjT/xK7I+iAcDYLPDB6w1s/5oOh2yDT0REVEa/HxTK/dJzvm9g57d49kaiUhueYRiNiIiIiIiIiIiIqB6pwmgehtGoBILuPgwltuRdHpJE0MZT8jBasIqCUKqxTmT2wBC5JYU1qPapJuUDDKNRfVFGJhUBzeUUM+allzdYCKOZXSeajcDr5r5YOeREDsPJndiRGMRQfBA7EluRMGJleawuV8/+CNo6/wA6XN1leZx6d2LTG/HE3IPYFn82b1nCiOM34f/FB3s/U/B+DCE/WM2Bxe+/Bd19lq/r0tzo5DpiSbe7Bx9e8Vm8lNiO28LXWorPjaV344cjX8VR/tfgwq4r0OddW4GREpHdhTPyz2Dd7l7oml7h0RDZC8NoREREREREZMpKcGk+Vf5x1LJCYbSw/HfKuqQKo8lOUKRpGjb0Aht35i/bPl7acS03y2G0THnHUWppRfwq2KK+zXQM6Gktz3jIPlIW1uUXQ+UfB5Xe2KzALX/J39i/NAE8PgScfuQyDIqIiOra7mn1sl1TQDgi0N3McCdRIRa6+/uNzAgAfF0BwExs7zPX1sDng4iIiIiIiIiIiGpfShFG8zKMRiUQ8KjiNvmTj2WxNAAIuleWdEzlFFDELzIijenMJDrdgQqPiKqJKozm0txoc3ZWeDREy0cVRpvOTiBtpODWPRUekVo0qwqjNRW8baPJdaK5CDrB94xSyIoMdidfwlB8EEOJQexMbFOGgZcq6O5Dv28D+v17Q2itTp6drhI0TcMlgY/ia7v+ERmRzlv+l/nHcXL0DAw0nqC8DyEEDMgnh2hLCOMUEzYNuFcwolukw31H4TOr/h3PRDfitxPXYzJTeCLD9vhz+NYrn8bJzWfiHZ2XotXVUYGREpFdjafkn8EYpiZiGI2IiIiIiIgKUIWoFoqlAMMQ0HVO0lwMK2E0IQQ0Wf2rzqjWR9Wqt65bw8ad+TeaqLHYXM7iDPNqCqMZhlD+vYMmJy+bYhitLqQU0byFZmPFpBfILm75i4BQ/OmeHRE4/Ui+FxIRUWXF8o9RO8jzI8Bb1ldmLETVTLWPJzMyU75xVItURuAD1wvc8rRA1gBOOQy46SM6VrZzf5iIiIiIiIiIiIhqlyoM4WEYjUogqAiFzWWnkcjF4XP491+mikJV04TkbnevclkoPcIwGpkKZ+SvgW53L/QlRFmIqo0qMgkA4fQY+rxrKzgac9FcRHq5WfRsH5/eAB26NMYUU9wvFZYx0tiVHMJQfAuGEoN4OfEC0iJVlsda4VmDft/eCFq/bz2anDyYfrl0uoM4t+Nv8dvJG6TLfxW6Bl/0/wAe3StdLhRRNABwoDJhtJ4qigHbiaZp+D9Np+LYxpPw6My9uHfqFsQM84lLAgIbIw/g6fnH8Jb2d+Ks9gsZBieqU7XwPQRRuTCMRkRERERERKashNGAvZPFm+TfzVMBWfVvFwCAdBaY2z2Klqd+CxGZgXbK26AdfWJlBmczqonUqjBap+K33In52gomGQXWoX2qKYxmFgwMNGsA5H/D6Vh5xkP2YiWMNleek6hRmd38lHr7vH28ggMhIiJ61WTUfHmh0DUR7VVMGG14unzjqBafvkXgV5sOPGkbdwIXXWPgic/zjLxERERERERERERUu1JGUno5J4ZTKZhNJg6lR7HG1w8AiOeiiORmpddTxdXsyKv70OrswGx2Km9ZKD2KAZywDKOiaqGelK8O7hHVog5XN5yaE1mRf9DqeHrUVmE0VXyn0WFyNupXaZqGBkcT5nNzecuiuRo7G3kZpY0Udia2YygxiKH4IHYlX0RWlP7gfQ06VnrWot8/gHW+Aazzr0eDhQAeVc6b28/DpsgjGEu/krdsOjuBeyZvwoXdV0hvKwsU7qNpiz9mpMsdhA4HDBQ+2C3oYRhtKZyaC2e2n4eTW87EfVO/wcOzd0vfRxbKiDTunboFj8/+Aed2XoLXt5wFxxL+3kRUXQyRQzgzJl0WZBiNiGE0IiIiIiIiMpezGFyaTzKMtlhWJtKH/vE9aJ7+CwBAXPt14DM/hPbOD5d5ZPajDKMpTn7T1Si/fKLGfqPNWZxgXithtAbP3v/FJCfNYhitPqQthNEi8mNlycaiSYEnXlYv//OO2opaEhFRdRidMX//mYoJAIpSMxHtV8yeXKHXXa0TQuC2v+Y/B5t2Ac8NC7xmJbc5REREREREREREVJsSRlx6OcNoVAptzk64NQ/SIv+gs1B6ZH8YbTw9oryPagqjAXvHKwujjSuiV0T7hNLySflmgUGiWuTQHOhy9WBPejhvWcjk/WI5RLMR6eVWg1mNjmZpGC2Wk98vAUkjgZcS2zAUH8SO+CBeSe5ADhYOcC6SDgdWe9dhnX89+n0DONx3NHyOhpI/DpWOQ3Pi0uDH8N3d/wohOWLmwZk7cVLzaVjpPSxvmSHUE7gcUEyasTimTldAGd5ZqMfNMFop+B2NuLD7CpzW+jbcOfkLPD3/WMHbzOfmcFPoGjw8czcu6HofNjScCE3jcUJEtW46M6mMqfIzGBHDaERERERERFSAYXEualQSKCJrrITRwgkX+hf8t/jR/wec/XfQvP6yjcuOVOujrviuv0vxW+4z+b9PVzWrAcNk6X9rLZu0yevC5QA6GuRhNIYp6oOVMNpcovzjoNJ6ZVodwASAzaPAoy8KnHbE8r/G//sRA1c/IhCOAGcPaLjqbzU0+5Z/XEREVHrzBWKrtRZdJioXs/28Qw3PlG8c1SCTA0KKY8uvfkTgmsu430lERERERERERES1KWXID/bw6vV1jByVh67pCLhXYDi1M2/ZwhiaKozm1X1ocbaXbXzlEHCvwPb4c3mX2y3mQ/ZiCANhRRit28VJ+VR/Au4VijCavSKTqoBZo6PZ0u0bFNeL5nhgzD7xXBQ7EluxI74VQ4lBDCdfggGLB/EXwak5sdrbj37fBvT7B7DWdyRDwVXoMN9ReEPr2Xhs9r68ZQYM/Cp0NT6z6t+ha46DluWEehLFodctVtDTZy2M5mEYrZQ63QF8oPefcWbiHbht4jrsSGwteJvx9AiuHv0GjvAfgwu7rsAq7+EVGCkRLZdQRr1f2c0wGhHDaERERERERGTOsPhbTaHJ4qRmJYwWcgYOviARA7Y8AZx4ZnkGZVNW18d9uho1QHKWHQB4KSxweHdtTCa2GkZLyU8gYUtmrwu3A2hvAHZP5y+bipZvTGQfKQthtAjfl6rO7vwT1OZ5038ayFyjw6EqYlbA/z5u4GO/PPDecv1GgZcmBB797NIOOCAiInsqFGSd4P4nkSVFdHAyMRcAACAASURBVNHw0kTZhlEVzCJyL4wX80wSERERERERERERVZekIozmYQiCSkQdRjswCXk8JY+GBdx90LTqOt4woJhAbbeYD9nLTHYSGZGWLlOtU0S1LODuk15up22pEEIZMFMFzw7V6JCfjTyqCK7Vg2g2gh2JrRhKbMFQfBCjqV0QRR39YI1Lc2Ot70j0+wbQ7x/AGu8RcOuekj8OVd75ne/F8/NPYi6Xf4bAXckhPDp7H97Udu5BlwuT2J4OfUnjsfI+7tSc6HQFl/Q4JLfGdwQ+tfIbeC76JH47cYOlSN2L8c3491f+Ga9tPh3ndV6GdldXBUZKRJUWUnwP0ersYByVCAyjERERERERUQGGxd9uogzQLFrWQtRq2Cn5UXXrpvoLoynWR13xG0+X/DdaAMBX7xK44YPVdaCSitXXabJGwmguB9DZKF/GMEV9SGULr/Rz8mNlycaGZ6xtzP64DTh7oMyDMfE/j+eP8/EdwNYxgfW9tfG+QkREB6QLhKwneGJcIkvMYl+HmowC4YhAd3N97luZfcZ/Ybxy4yAiIiIiIiIiIiKqtJQijMZJoFQqQWXcZmTB/5eHbqoxCKX690Zys4jnovA7FAfhUV0ziz1V4+uAaKmCJpFJQxjQtaWFikohJZLIQX7mv0aLYbQGRRgtpgiu1aK57Ax2xAcxlBjEUHwQe9K7y/I4Hs2Lw3xHod8/gH7fBqzyroNLd5XlsWh5+RwNuCjwYfxs7DvS5XdO/gLHNZ6MVlfH/styQn2wmq4t7QTOVt7HA+4+OJb4OKSmaRqOazoZxzSeiMdn/4B7pm6yFKDcFHkEf53/M85sOw9nt18In6OhAqMlokoJpeWhxIC7t8IjIbInhtGIiIiIiIjIlGEh2gUA86nyjqOWmQWg9tntWpl/obv+zgSkmhzsUMyVXtMhvxwA7tkskM0JOFU3riLWw2ilP0tVuRQKo3U1aYDkrFtTDKMtSs4QuPWvAj/fKNDi0/C+UzWctd6+r42UhchfLIWaeY3Xi+H8E6JJPbFT4OyB5fu7bsw/cTAA4EcPC/zoUq5vRES1ptB+x2y8evaxiZZTMWE0ABgcA7qtHZ9dc8w+40/yMy8RERERERERERHVsKQyjOat8EioVqliEBPpceREFg7NifEFkbSFVGEcOzOLX4TSY1jrO6KCo6FqoQqjNTva4HP4KzwaouWn2pamRQqz2Sm0u7oqPKJ80aw6qqMKnh1KFVCzEuypVjOZyVcjaFswFB9EOCOPkiyVV/djnW89+v0DWOcbwCrvYXBozDvUi+MbT8GGhhOxJfZ03rKkkcDN4Z/iIys+t/8yA+oJXDqWFmIMKKK5C/W4JfOWqOQcmhOnt52D1zafjt9P34aHZu5CRqRNb5MVGfxh+lb8ee5+nNNxMd7Yeja3JUQ1QvU9hJXtNlE94LsdERERERERmbIaXJpPCgDFx0BiKYFQBFjbuffsF/UmZwhLk4NHXPlfZonZqUU849VNtT7qit94elo1NHuBSDJ/2Uwc2D4ObKi+45XyWA0YJi3EpOyiUBitQ3Gyyol5hikW41v3Cnzpjn3PncCvnhK4/v0aLjt5+c9kJ5OSn9guz3wSaOMJkarGK5PWrre1PMeeLFlojtsfIqJalC4Qso7I5+cQ0SGK3VN6MSRwxlH19q3HXmbfxWUtfv4nIiIiIiIiIiIiqkaqMJpH91V4JFSrVJOKc8hiMhNCu7Mbk5mQ9DrBKoxEtDo74NG8SIn8AyhD6RGG0UgqnJYfnBVw91Z4JET2YBaZHE+P2COMZhIva1xiGC1WI2E0IQSmMmEMJfZG0IYSg5hSvOcvVYPehHX+9VjnG0C/fwB9njXQNUdZHovsT9M0XBz4CF58eTPSIpW3/NnoE3g+ugnHNr4WAGAI9cFqS12PrLyXBxnhqSifowHnd70Xp7W+FXdO/hKbIg8XvE00F8HN4Z/i4Zl7cH7X5XhN4+vqci4eUS0JK+LUZvuhRPWEYTQiIiIiIiIylbM44TKa/x19gfsV+OdbBH78kEDWAFZ3AL/5qI4TVtfXF7Jm8aeFhiVhNMxOlHYwVSCnmB2sm6w2T39BxxFfkK/Ie+ZqI4yWszjDvJrCaGmT8JXLAXQpw2jlGU8tG50R+NrdB69EQgDfuU/g714nbPlDmdUw2lyCYbRqMjhmbWO2xeL1ykGY1EytrpdERFRdzPZLAXmEmYjyWYnCL1TPr61inysiIiIiIiIiIiKiWpATOWREWrrMyzAalUi3uwcaNAjJKV1C6VHkRA4C8mMNq3FCsqZp6Hb3Yji1M29ZSDHxmki1blTja4CoFHyOBrQ42jCXm8lbFkqPYn3D8cswqoOp4mU6dPh0awfRNijCaNFcdR6YLYRAODOGofggdiQGMRQfxEzW4tl7i9TkaNkfQev3D6DHvQq6Zs8TU9Py6HB14+2dl+C2ieuky28K/TeO8B8Dr+6DAfUEI8cS16tGRzMaHc2mMcUez6olPQYtTrurC1f0fBJntr0Dt01chxfjmwveJpwZw0/G/h2H+47Gu7rejzWMHhNVpUQuLt3PBPgZjGgfhtGIiIiIiIjIlKJDlWe+yAmrP3hQ4PsPHLjzV6aAs/7LwOh3dPjc9ovwlIvVMFrIEci/cCZc2sFUAdX66DD5jWddt4b2BmA6lr8sPC8AVP/6ZlgMGCarKNpj9tpwO4EuxQnMJqLlGU8t++ljQvp8bxkDZuJAuw3DYlYDVBNRYE1necdCpZEzBLaNW7vuzom9B60sR7Qva7JtYhiNiKg2Fdq+13O8iagYxba+iv2eqZZY/S6OiIiIiIiIiIiIqJakjIRyGcNoVCpu3YN2VzemMqG8ZeOpEWSF/MdBHQ50u3vKPbyyCLr7pGG0cYbRSGE8PSK9nJPyqZ4FPH2Yi8vDaHagipc1OpotH2fZ4JAfmB3NRZbteM1iCCGwJz2MHfFBDL0aQosoIiNL1eJsR79vAP3+Dej3DSDgXmH754eW3xlt78BTkUel+2Wz2SncPXkj3t39QRhCPTFEw9KDewH3CkQTJmE098olPwYt3irv4finvv+HLbG/4PaJ65T7ZQu9lNiG7+z+LE5seiPO67wMnW7J3DMisi2z/Ul+BiPai2E0IiIiIiIiMmUIa7Mxo6ni7vdXm/LvdzYO/GEr8M7jiruvamYWWFkoIvuxsR7DaIrfefQCvyUGmhVhNPVvOlXF6qTphPykqrZkFkZzOYDORg2yqfUT1XlismV17xb1CjQxX91htOFp4KQ1ZR0KlcjOCSCZsXbdVHbvutktP0FhWaUZRiMiqjvpQmE09RwdIlrA4tdL+xX7PVMtYRiNiIiIiIiIiIiI6lHSJIzmYRiNSijoXiENo4XSo8hB/uNglzsIh1ad0zBVE6lDFiILVH9SRhKz2Snpsm5Oyqc6FnCtwIvYnHe5XbalsZz8gHhV7Eym0SE/INNADkkjAZ/Dv6ixlYshDIylXnk1grYFOxJbEVU8D0vV7uzaG0HzD6DfN4BOV5AhNCqaQ3Pg0uDH8Z1XPguB/EkxD83cg9c2n2762cehOZY8jqC7Dy8ltsnvH050uYNLfgxaGk3TcEzjiVjfcDz+PPdH3D15I+ZzcwVv9/T8Y3g2uhGnt56Lt3VcBL+jsQKjJaKlUoXRXJobbc7OCo+GyJ6q8xs5IiIiIiIiqpic+oQjB5lPFne/T+2SX/6TRw2887ilf2FfLcziTwtF9GYIAAf9hBa2x1mmKimnmBxcMIzWBGzbk3/5lrGlj8kOrL5O4zUURgsogkjRFBBJCDT7+INzKUzMA0fa8PfNQoGSfYZn8racZFODRW6Ph2eWKYxmsu6lLIbdiIiouhQKX0aK/CxMVK9UYbRmr/x1VOz3TLVEFYXfJ2cIOAp9EUJERERERERERERUZVKG+othL8NoVEJBdx8GY3/Nu3xL7Gm4E17pbVRxsWqgGns4vQc/Hf32ou/XoTmx2rsOp7a8BT6HDc+8SfvNZaexce4BjKR2QQjzH6LMtsXV/DogWqqAR77+70oMLWlbWqxWVweObzwF6/wDB10ezcnPKt2giJ3JNJpE1GK5yEFhNEMYeHr+MbwQfx7JXNzyY5RKSqTwcuIFJAzJWdNLoMvVsz+Cts4/gA5Xd1keh+rPau86nN56Dh6evTtvmYCBn459B12uHuXtdehLHoNZ6DTg7q3aGHAtcmgOvLH1bJzUfBrun74df5z+LTLCfEJQVmTxwMwd2Dj3AM7peA9Oa3sbnJqrQiOuLoYw8FTkUbyY2Lws72Wl5NTcWOPrx+tbzoJb9yz3cKhIqjBawN0LXVv6dp+oFnDvhIiIiIiIiEwZiomrh5pTn7AxTyKtvtPp8vw+ZVtWw2g5zYmY1oBGseAJmh6HyKShudzlGZwNqSYHF5oP3N2sAchf7679k8BP3yugV/mEYquv01gNhdH62tTLR2eBZh4PWRJh+bESy65QoGSf3dPlHQeVzpYxixuyVw1PAyesLtNgTJite1bXSyIiqi7pAp/Z5pOAYVT/ZwqiclPt7TUpwmixVFmHY2uF9ozj6b3PGxEREREREREREVEtSRrqAxA9DKNRCQXcfdLL53NzQG5OuizoXlnOIZWV6t9rIIdnohuXdN9Pzz+GP8/9EZ9e9U00mAR1aPlMpkO4cvjzmM1OLel+HHAyDER1LajYlqZEcsnb0mI9PPM7XBb8e5zS8ub9l8UUYTSz2Fn+ddURtWgugk4cOMvyz8d/gCcjD1m+b7sLuvvQ79uAdf716PcNoNXVsdxDohp2Xtff4dnoRul781QmjKlMWHlbXXMs+fGDJmG0oKd693lrmVf34R2dl+KNLWfjrqkb8cTcgxAFji6KG1H8ZuJ/8fDsPTi/63Ic33gqNI3HNy50w/hV2BR5ZLmHUTJPzT+CpyKP4pMrv8Y4WpVRhdHMQpZE9YaJQCIiIiIiIjJlNbg0EbEeNNkjP3YEAOCos0+qVsNoABA59AdHIYAJ+RdgtUq1PhZab7pMftd9YPvix2MXlsNoVTSxPK0IDDl0QNM09LaobzsyU54x1aqESTAvPF9crKpSrAaoRhhGqxrb9hR3/eGZ5Vk3VdsmgGE0IqJaJIQw3fbvE62i/Wwiu1EFvuaT9vwsUgmFPuNHJSE5IiIiIiIiIiIiomqXUoTRNGjwaDxbBJWOWQyilLexi253DzSUL4KwJz2MP889ULb7p6V5YOaOJUfRAKDLHYSjBDEWomoVcPcu9xD2EzBwx8QvkBWZ/ZdFcxHpdYuJVnp1P3TIX+cL7384ubPqo2i97tU4vfUcfKj3s/j24dfhS2t/iEuCH8VJzacxikZl59V9eE/3hxd1W9VrtBgBk/3aniqOAdeDVlcH3hv8BP519ZU42n+cpdtMZkL42dh/4D93fw4vJWpg4lSJDCd31lQUbZ9dyRfx1/k/LfcwqEiqMJrZ9pqo3jiXewBERERERERkbznD2vXC8hMNSTGMdkAxYbQ5vRm9OKQcEx4BeteWdlA2ppocrBc4bufwLvWys79nYOZ7Olr81XsGlFoMo6leG+5Xf8/zuDR0N8m3PSMzAijjwVy1JpFRL5soYtteSSmTMS8UKiLaSctrKlrc32oqWqaBFJA2ed9mGI2IqPZY/bwWSQLNvvKOhajaCcXuXrNiLls9BweNAt/F1fNzQ0RERERERERERLUrYcSll3t0HzSNxwFR6QTcfRW5jV24dQ/aXV2YyoTL9hjbYs/grPbzy3b/tHjbYs+W5H44KZ/qXZuzCy7NjYwwORNxBUVyMxhLvYJV3nUA1GG0xkNPym5C0zQ0OpoQyc3mLYvlDhxMXKrtSqVo0NHnWYN+/wb0+wawzr++qGAcUTkc13QyXtP4OjwXfdLybXTocGquJT92hysAp+ZEVuQf9NzjYRitGvR51+ITK7+CrbFncFv4OoylXyl4m5eTL+C7uz+H4xtPwTu7Lke3u6cCI7WvrbFnlnsIZbM19gxObjlzuYdBRZjOyj+r8zMY0QF1Nt2ciIiIiIiIimU1uFSqMFrCHr8XVkzWYngO2BtGy7NnV6mGUhVUob5CYbQLjze/wluvMpCzurLbkNWAYdYA0tnq+HeqIhSuBSc66muTX2c0/zd5MmEWRitm215JVgNUE8sUz6LiJS3G7vaZk58ouuzSJuseAxVERLXHbLu/UGSZ3peIqonqk2iTIow2nyzbUGyv0NcT3O8kIiIiIiIiIiKiWpQy5D+4eHWenYZKq8nZgjXeIyxfv8XZjlXew8o4ovI7puGkst5/KD1a1vunxcmKDCYz4yW5r2May7sOEdmdrunY0HDicg/jIHtSw/v/f6wEYTQAaFBcP7ogjGb3bb4OHWu8R+Cs9gvw8RVfwH+u+zn+dc2VeHf3B/Captcxika28Z7uD8OjKQ6akVjrOxIufelhNIfmwPqG/5N3uUtz40j/sUu+f6qc9Q3H4/NrrsRlwX9Ai0MxseYQz0Q34msvfwK3hH+mjGrWA7u/ly1FLf/balFWZJBUfB/W6myv8GiI7IthNCIiIiIiIjJlWAwuhYr4TnTPnHqG59gcIER1hJtKQRV/kplztORdJl7ZXsLR2J9qcrBe4BuOVR0a3neKOo725MvATU9V73pXTNMtViUTqFX/JseCv3VQ8Xu9XWNedhU3CVJO2vS5tBpGm2QYrWoUG0aLLFMow2zdm5gH7ny2et9LiIgoX9ri57VYnQW+iRZD9VVPs2I+Wz3HvxhGIyIiIiIiIiIionqkmgjqYRiNyuDCrvfBq/sLXs8BJ97d9QE4NGcFRlU+b2k/H12unrLd/0x2Eimjjs96Y1MT6XEYKOLszQpH+I/BiU1vLMGIiKrbuZ1/azk8Uwl70gvDaPKDfVWhM5VGRTRsYTxnPD1S1H2Wm1Nz4nDf0Xhr+0X4h74v4z/7f4nPrv4OLuh6HzY0ngifo2G5h0gk1ebqxAd6PwOnVjh25tP9eFfX+0v22O/ovBTNjtb9/61Bw/ldlzMcWIV0zYFTW96Crxx2Nd7ecYml2F4OWTw0cze+vPOjuH/6dmSM+jv4sZbjYeH0WF3Nyax2qn04oPjALVEtq+5v5YiIiIiIiKjscha/D4umgHhKwO9Rx6f2GZ9TLxubBTaPAsf2WRygTaUyAv/zJ4GndwGtfuDC4zW8oT//uSkmjDavS35o2LVt8YOsQspYVuHVDv92robrN6pX6F8/JfB3r1vkwJZZUWG0NNBWBb/xKiN4C/7W3c0agPwr2jXmZVcJk9+ywvP2+1HEMITlbedUdO/1dd3CRoKWVdJi7G6fSGJ51s10gXFe/r8GNn9Fx8p2rnNERLUgZTHcGWekiKgg1fFWTV7557r5Op47U+jYtGgdPzdERERERERERERUu1RhNC/DaFQG6/wD+PzqK/FcdBMmM+PS67Q423FM44lY4VlT2cGVQburC59d/R08O/8ERlO7ICS/zViRNlLYGHlAuiycHsNK72FLGSaVmCr4oEHHaa1vLXh7p+bEam8/jms62VK0hajW9XpW4XNrvovn5p88KEpWbi/Gt2BPenfe5XtSe8cghEBUGUYrLnKkun7s1TCaEEK5bTnK/xoE3CuKerylaHa24TDfUVjrPQJu3VOxxyUqpWMaT8TnV/8XNseewnRmQnqdgHsFjmk8ER2uQMked4VnDT635ko8N/8EYrl5HN1wHNb6jizZ/VPleXQvzum8GK9v/RvcM/kr/GnujxAFArkJI47bJ67HIzO/wzu73osTmt4AXdMrNOLlFU6PSS+v9HvZUsRy83h6/rG8y1MiibncDFqd7cswKirWwvjsoRirJDqAYTQiIiIiIiIyZRRxsrDwPLDGwu9Ke0zCaABw4yaBY/uqNyiSzgqc9A0DWxZ8V3rVAwJX/52Gj5x28BfFxYTRZheclWW/V7YvcpTVSRnLsvD9+2GdgM8FJBRxg7ufr96AUq6I12msSqINhmIm+MK/dZfie147xrzsyjAEUiahp7ANI3PpIrabhgCmY0AnfxOwvaRi29ziA+Ykxz5H5MdDl12h9S+SBH78sMC3Lqy+9xIiIspndb8jVn8nTSQqmupTWpPiRKWROo5/FYqfx7nNISIiIiIiIiIiohqUUobRFF8kEy1RpzuIN7eft9zDqJgGRxNe33rWku7DEDk8Nf8osiL/QJ/x9AjDaDajihd1ugK4OPCRCo+GqDa0ONtxWtvbKvqY903dgjsnf5l3+b5YWtJIIAf5gcCNjuaiHkt1/dir4bVoLoK4EZVe520d70G/f6CoxyMiIOjpQ9DTV/HHbXW24/S2cyr+uFReLc42XBr8ON7U9nbcPnE9BmN/KXib6ewErt1zJR6YuRMXdl2BI/wbKjDS5RPNRhAz5JNk3tpxUdX8+1VhNGDv5wCG0apDTBG3BRhGI1qoPrKdREREREREtGiFJmMuNG9x0uqD283v9Dv3CVz2MwPTseqLG92w0YD34wdH0QBACOBffiMwFz/435QtIvAz5uzJvzA8CqEISNUiVQDMSstM1zW8dq35dXZOFj8mOyjmdVotYTQrf+tuxfe8EzaMedmVKka1jx2fy1SBMR9qQn4MBtmMal1Uvc6XK5SRNgkJ7vPt++rnfZmIqNZZ2e4DjBQRWaH66qKjQX55LAXMJ+tzv6rQZ/xYuj6fFyIiIiIiIiIiIqptSUUYzaP7KjwSIlLRNQe6XZLjWKGOcNHyCaVHpJcH3CsqPBIiWooe9yrp5VOZMNJGyjSo0VhkUKNBEUaL5iIA1NsVAAhy20JEZBu9nlX4+74v4h/7voo+T4FJVK/andyB7w1/AdeMfrOm9+3N/m3VtJ/c4GhSBk1r+e9Xa6KK/Tiv7odTc1V4NET2xTAaERERERERmVLFiWSiBYJLQgjc87zA7unC93XjJoHOTxkYClXPZM+7nhO44lr1eOeTwG+fPXh5pogw2ohLciaYdBKIWHhCa4RqcrCVMBoA/MMZ5l+FvPuaIlZ4GzFK+Dq1Cyt/6y7F7/VhG8a87CpRIDI2GQWMYsp7FZCyGCjZ5/Ed9ho/HWzbHoGfbzSU+waqMNqc/HjosrMayKnXiAcRUa2xut8RZ6SIqCDVq2SVyckph+vn646DFAyjVcnneiIiIiIiIiIiIqJipBRhNC/DaES2EnBLjmMFJ9/b0bjib1JNwQciAno88jCagMB4emR/tEymocgwmiqwsi/aEUqPSZf79AY0OlqKeiwiIiq/oxpeg8+t/i4uD/4TWp0dlm7zfHQTvvbyJ3BT6L8xn50t8wgrL5SR7yN7dT+aHa0VHs3SdLt6pZeHFe/XZD+q/bhi9+GIah3DaERERERERGSqmB6OWXApmRG45KcC7/hhceGpI79o4CePVkes6uqHC4/z7ucXH0YbdioORpionwNKVAEwh8VvON51gobr3q+uqD0/AmRz1Rc1KOZ1Wi0TqFV/64VhtO4m+d9yYh7I2SzmZVfxtPlyQwDTscqMxap0EdtNALh/kOuCXX3zdwYGvmzgfSZR0W75cTaIJMs0qAKsBnLqNeJBRFRrrAYxq2Ufm2g5CcUu34o2DZriY7qVsH4tUj1X+8QKfI4jIiIiIiIiIiIiqkZJRRjNwzAaka0EPfLjWEPpkQqPhMwIIZSxuqAibkdE9tTp6oZLc0uX7UntRkwR1NChw6c3FPVYqgjHvscYV2zrg+4+aKof/omIaFnpmo6TW87AV9b+GOd1XmYpPm7AwKOz9+LLL38M9039Bmmjdg6QVO0jB9wrqu69TBU8ZrS6eqj241SxWqJ6xTAaERERERERmSqmLXT29wz89DEDQjKD8+anBW5+enFxmo/+QuDaP9k7jiaEwKZdha/3/CG/BxYTRhtxKQ5GCNfPASWq9VEv4vvny0/R8eaj1Mt3hIsbU7mkMgJfusPAKd/K4fwf5XD/VvXrJ1fEy6NaJlBb+Vv3KE4uZgjgpYnSj6kWJTKFrxOeL/84ipGyMOaFRmrvREU14ZndAl+6o/B+QXezfAM/Jz8euuzSFuOZwzNlHggREVWE1SBmodgsEam5HUBQcRzP8Ex9Ro4LfRfHbQ4RERERERERERHVIlUYzcqkbSKqnG6XavL9GAxh7+N868l8bg4JQ35GVFVAgYjsSdccytftnvQIoiZBjWIDL42KMFo0N28aXAy4e4t6HCIiqjy37sFbO96Nr669Gqe3ngPdQmYmaSRw5+Qv8JWXP44n5h6qif39WnovU+0fhBlGqxqxnHyylmqfjKheMYxGREREREREpooJLgHA//25wDd+lz+D85ZFRtH2+eD1Ar/bXPkJsUIIbBkV2DMrpMG3fcbngGn5MQQH2TEBJNIH7qeYMNqwsw/SEUzUz5eWqvWxmDAaAHzgDeobbLbJ0/me/zbw9XsEnnwZuPO5veHBu5+Xr4PFBAyjqeqYWK76NzkWfJt1RED9tx8cK/2YalHCwoR624XRLAZK9oksU0CLzP3kMWFp29Wt+E0nlgLm4pXfnqUtrn/D09WxrSUiInNWt/vVEh8mWi5m36doGrCyTb5stE5js4X2k2O1cxJWIiIiIiIiIiIiov1SRlJ6OcNoRPYS9MhP8JsRacxkJys8GlIZT6tPuMwwGlH16XGvlF6+J7VbGdRocCjOUGaiUXEbAzkkjbhJTEZx8nciIrKdJmcrLg58BF9c+wMc2/haS7eZzU7hhvGr8O1XPoPtsefKPMLyCqXlE42qcR+5WxFzm8yEkRWZCo+GFkMVuG1gGI3oIAyjERERERERkaligkv7XHm/OCj+BQD3bF76WD59s2E6mbbUNo8IHPf/DBz7VQMrPmvgwh8bygiL1QiTEMC2PQf+O1tEeC7qaMKc3pJ/n3UURlOtj8WG0f72JPUNLv7J8p/FZNsegbuez7/8vB8amJWsg8W8TqslEmXlb+1zazi8S369zaOMElmRsPB7x0SVh9HmqmSdrydCCFz/Z2uvIQYzYAAAIABJREFU0aOD6mVP7SrNeIphOYxWpxEPIqJaY3U/O84wGpEps69yNAABxfHY0/GyDMf2GEYjIiIiIiIiIiKiepQ05Ad4eBhGI7IVs2CAKphDlaf6WzToTcrwERHZV49HHkYbTw8jqgyjFR/UMLvNbHYak5mQdFk1xmSIiOpdwL0CH13xeXxq5TewyrvO0m2GUzvx/ZEv40cjX8NYaneZR1h6OZHDZHpcuqzbJY+M2Znq/VfAwITi30n2otqP42c2ooMxjEZERERERESmFhNGm40Dmxf8pm4lZnbL/y38EfXFEHDVA5WJHaWzApf9j3HQv+OO54DP/1b++DsnrY9rZEEsJZMr7t8z4pJ8cRmun4NJVOujo8hvODRNw0UnqONoP354eeNovx9Urxf/dFP+slwRw43IT65qO8ow2iF/6wHF7w8vT5R2PLUqZSGMFp63V2Su2DBatazz9WQuASQtnoToyKCGzkb5sk27Kr9uWl3/huTH/xARUZWx+nmYkSIic2YvJU0D2hrkn89nYuUZj90V+hqNMUYiIiIiIiIiIiKqRSlFGM3LMBqRrXh1H1qc7dJlDKPZh+pvEXCvgKYVeSZiIlp2Qbc8jDaZCWEmOyld1riIMJpZhGNX8kUIyA9YZxiNiKh69fsH8NlV38H7ez6NdmeXpdsMxv6Cb+z6JG4c/zHmstVzNvGpTBg5yCcDVON7WacrCE2RC+Jns+oQUwZuGUYjWohhNCIiIiIiIjJVTHBpoc2jB2ZxPjtsft113cAFxwMXn1j4x/ZP3yxw21/LH0K5b8vBcbd9btokpKG33dPW73smfuD2mVxx4xp29uVfGC7wBNcQ1fqoL+I4jQ0m31v/w40COyeWLwb15E71sp8/IZA9JKhXTMBwTn4Moe0YFv/Wazrlf/zhGXvFvOzKyrozIf+ufdlYibktNJ8EcoupfFLZDBfxnulzAa9bK1+26eXK/13TFt+3t4xynSOiyslk5Z9RaOmsPq2MFBGZM3staRrQ5pcvm4nV57at0McXbnOIiIiIiIiIiIioFiWMuPRyr674EpmIlk1QEQ0YT49UeCSkElL8LQKe6gs+EBHQ61klvVxA4KXEVukys8iZilf3Q4dDuuylxDbp5Tp0dLmDRT8WERHZh67pOKn5NHx57Y9wQdcV8Fn4HC5g4PG5P+ArOz+G303+GinD/mezN4uFdbt7KziS0nDpLnS6uqXLwumxCo+GFiOai0gvX8x+HFEtYxiNiIiIiIiITC22JbMwKva5W83ratdeoUPXNfzXxRpeI+l+Herd1xi4YaOBezcLrP9SDu6P5rDhyzn8YbB0E2a/do98zDNxeVhqpIiTXMwuuH2xYbQRl+QJCtVPGE21PuqL+IbjmBXmNbVv37d8E7B9bvPlMwuOAyw2glE1YTTV3/qQP9vKNvn1igkv1TMr2/iw3cJo8pP0mIra/3e2ujJcxHum1wW8dq18e/3ky8VvA5cqbXH9eyG0N1RERFRO8ZTA+6810PZJA+2fNPDxXxpIZrjtKSWrn4fjqfKOg6jamYbRALQ1yJfNyOfA1bxC255Yitt6IiIiIiIiIiIiqi1CCKQM+UFNXt1X4dEQUSHdijBa2CQ0QJU1rvhbBFwMoxFVo05XAE7NJV02mQlJL29wNBX9OJqmKUMcqjBapyuoHBsREVUXl+7GWe3n46uHXYMz2t6ujGUulBJJ3D31K3xl58fw57k/whBFTpKrINXnlXZnF9y6p8KjKQ1V0C2U4WezahBThNEWsx9HVMsYRiMiIiIiIiJTiw2jDYUO3DBkEtWZuFLH69ftDZ4EWzQ8/QUd/3K2ebAKAK64VuDcHxjYPg5kDWDrHuCtVxn45K8NXPWAgWd2L22S6M4J9TJZJGh42vrjzcQO/P9skd/5DsvCaCM7IEK7i7ujJRJjOyHu/BnEY3dCZBdRKVok1froKLzK5Dm2QITvjmeXb6JxOGL+2FPRA/+/2NdopMbCaKva5X/84ZnKB5OqkZX1Z+H6ZgeLCaNVSxCwXhTznul1Aa9ThNFCEWC2wrEMq2G0TA4YCpd3LEREH7xe4PqNAvH03ve6ax4R+OSvuf9TSlb3taOMFBEtmqYBbYqTjE7H5JfXOsP8/AKIMcZIRERERERERERENSYj0jAg/3LUwzAake0E3fKDL1UxLqqsjJHGdEZ+4FLQY+Hs1URkO7rmQFARpVRRBc4KUYU4QqrgYpHjIiIi+2t0NOOi7g/hS2t/iOMbT7V0m7ncDH4x/kN8c9ensTX2TJlHuDiq9zJVXKwaqN6Hw+mxCo+EipUVGSQVJwloZBiN6CAMoxEREREREZGpXIHJmCrD0wf+v9lE1o7Gg2MnDl3Dt9+l46aPLKJ0BeD7Dwh86tcCJ3zdwNfvWeTgAZhNaw9Lgvy7p/MvU5ld8L1Vpsgw2ohT8ePhQ7cVd0dLIH7zQ4hLN0D8x99DfP4iiI+/CWLGpCRXQspY1iK+4VjbCRy3Ur08PA9s37M8gYNC69PUgtdUsa/RuUR1RBushtFWtsuvl8zYL+hlR1ZiH3ZbZxYTRoskSz8OWrzhGevX9bqAo3vUy0eKuK9SKCZGuWXMXq8dIqotsZTAbc/kb2du2Cgwn+T2p1SsdnZnKhzqJKo2Zi8lTQPaG+TL6vW1VWjTE0tXZBhEREREREREREREFZNSTAQFAK/ureBIiMgK1eT7uew0Erk6/YHHRsKZMQjFL04MGBFVrx73qqKu37DIMFqxIQ5uV4iIale3uwcfXvFZ/POqf8da75GWbjOWfgU/HPkqfjD8FYwkd5V3gEWqxchnt2Lsqn8r2UcsN69cttjALVGtYhiNiIiIiIiITKkCIBt6905eVVkYdlJNZP3i29V38J4TdUz919I+tn7pDgH9Izmc+PUcvnGPgWzO2qx2IQSiJhGd8CHfPSXSAi9PWh/X7ILno9gw2p9azpBeLh66tbg7WiTx7GMQV/0zkFsw8G1PQdzyg4o8vioCdmgsywpN0/Af7zZfx9Z/2cBPHjUgrBYRSiCbE3hRfrK+/RYGv4qJ9ADAnPo4QluxGsFb2aa+j2LiS/XKsBDWm7XZsWqqbXmLyQmCq2W9rxfFxMw8TiDYrN7O2zqMxt8TiaiMXgzJP0skM8BD2ys/nlpldbtvFgMnIvPIoAagzS/f2ZuOoaKfx+2i0LYnzjAaERERERERERER1ZikaRjN5IAQIloWZuGA8P/P3n2Hx1EcbAB/Z+9O3VaxrWbZBtPcgNCrqYbQQseU0D8CoQQcSIAAARNqaAFCDxCSEAjNGNMCxpQYDBhjDO69SJYlWb2f7m7n++Ms6yTN7O1e00l6f8/jx7rd2d2529253b2dd33lCawJqehCEAy4MNxTkODaEFGsFKZaPA1cwWnAWdd0zoI4ClNLIloOERH1Hzulj8PvRt+Py4pvtH08ubx1Ee7b+Fv8a8tfUe+riXMN7ansUJ+r9OdgNF3dmwONlsFb1PeaA43acZkRHscRDVQMRiMiIiIiIiJLutCc66YIzLvJwNHj1OMb24GGVgmfX6LFqy5z1G7WaVa5mQKf/y76U9eFm4A/viORc52J/8w38eb3Ep8sk6huUvc0rW8F/BZhQVU9pltR4Swopa6lq7DTYLS1ogR1Rk7vEcvmQ1ZsdDYzG6RpQq5fDjnvA8jP3oL8zRR1wU/+E/Nlq2jDsiIIRgOAo8cLrL3Xehv79csS932YuI7Ya6qADr91mZqQbchOsFWo/hIQpXtfPdd1wVDA41KXDQ1oJDU7bVd9km0zujpnpgIpbvW4HzYNvjCFZFZaa399pLoBj1ugMFs9vqw+sevWyff92q3xqwcRke74BwCWbuH3XqzY/SR1YeBEFGS1LwkB5GWqx/lNoDHJzkcSIdx5vu46GxEREREREREREVF/ZRWMlspgNKKkk+seDo9IUY6r8JYluDbUky4YbURKEVxCc4MdESW9ohRnwWiZDgPOIp2uwNN/w2SIiMg+IQT2HnIw/rjDEzhzxKXIMLLCTiMh8XXjHNyx/kq8W/2K5bl/vLUFWtEYUD+RvV8Ho3mKteOqNEFwlByaLYLrGIxG1B2D0YiIiIiIiMiSVRDVAWMFXrhIf2q5qdY6UCcnI/zyD9tV4O8XR5h61UNrB3De8xJTnzVx7KMmin5v4sGPevc2DRemVNkjlH+hw8Cd0E7zToPRAOCxvGvUI758z/nMLMi6Kshrj4G88GeQN50Geft5+sJbNkK2xv9pErrOwa4ornDsOFxoA/463TZTYlFpYgImlti49lzT0vV3wGG1+kunct376hmMZhgCIxVZgYCz8KXByk7IU7KF6QUsQvPGFarHzVzEbSGZlKp/U+wl3RPcxwFgVK5mXgkOQHQSjFbfyu2OiOLHqj1aznsZYsZuCHFtCyAl230iHavdQwAotLivuqw+5tVJeuGOOVs6ElMPIiIiIiIiIiIiokTxWnSOTmMwGlHSMYSBghR1B3xdKBclToVXvQ4K+3HgAxEBRanOgtGyIgzUcBrEUZDKtoWIaDDxGB4clXcy/jT2GRydewrcNoJ3fbIDH9a8junrrsTc+o8QkBF0pIuS1XlKfw5Gy3bnIVWkKcfx3Cy5tWiC0dKMDLiFJ8G1IUpuDEYjIiIiIiIiS1YBNABQnNM7qKhTaV33ELCecm0EowHARQcbePuq2J/CBkzgprck8qYF4L4iAOPyAHa5NYC97rLu/b5ua/fXMxY66wAfGqzm1ywq02zWTv/q8AuhWqJc/aOjeujI+mqYL/wJ8uRRwI9f2p9w/bKYLN+KVVBfNKafHH77mvyAmZCwg3Vbwy+jJmTzsBvW0Km6uX+ENujel2pdj8pTl7UbvjSY6dr4UPUW7Xhf0LUDLgM45WfqxuCHTf1ju4+Xz1dK/PplE9NeM/Hl6r79HKSUKLO5bw4J+Y2uRBOMZndeseKkze0vQZRE1D9ZheaUNwze77xYsxuI6QsEg7iJSM0yGE0AI3OC/6skOgg3GYRrelq8CakGERERERERERERUcK0a4LR3MLNzqBESaogpUQ5nJ3v+15lR5lyeH8OfCAiYLin0NFxUZbL4gllMZou0zUk4uUQEVH/luHKwhn5l+D2HZ7EvkMm25qmMVCPVyufxj0brsPi5gUJ7d+hO0/xiBTkuIclrB6xJoRAPkOr+6XmQKNyuNOQWqLBgMFoREREREREZMkqgAYA3C6B4hx1mU21El+t0V+ozM20X49Tfibw5q/jcxpb39r1PtdutS4LAEvLu96TlBL/W60uN9oirKnzAq5P86CL/doWaJe/VpRgs1txg8L6pdpprPj8wbpIvw9y/TLIi/cFXrrH+YzWLolo+U6EC+qL1CE7C5yxt3WZFi/w3YbI5t/hl+jw27tobyfMq6al62+7YQ2dvP7+EdbjJARvVK56A0h0YFJ/ZGf78fqBdl/yBKxYtQNHj1NvC3WtwJaGOFYqib00z8RRD5t47n8Sj8+ROOIhE//+1mGiYgxVNgLtPntls1K7/h6p2c831yV223TS5jb0g7aWiPovq3DTGn3GMjnk5FumtiV8GaLBKty+5HELFGrulS5N8PFeMggXxtvaMbiDn4mIiIiIiIiIiGjgaTfblcNTjfQE14SI7NKFbOlCuSgxpJTaAAQGoxH1by7hsr0fG3AhzbD5BPseshyEcRRqQjKJiGjwGJ5SgEuLb8CNox/AzukTbE1T0VGGpzffjcfKbsem9rVxrmFQlU93jFwMQ/TvyB39uRmD0ZJZiyYYjaGzRL3171aaiIiIiIiI4s5OOJEuAGxTLXDHLPUMXEb3wBM7Tt9b4K0rDcfTxdryLYC57YNpbAsGZqlcfaQ6xKXd1xVWoAtGS5E+vF16prYOZW7FEx3WL4P020ybAbC1SeLCF0ykXx1A5hVtmHbe39B60UFAzRbb8wglIwxmc8JJWJZT/7zUwIm7W5c58D4Tt79jImAzGafFK3HBCyZyrgv+O/95E83t1tOW1oafd21zVxmnwWgAUNnkfJpE065rxdWsUZo26NX50tbnOZjZ3X6SKeDJKrBzovphNwCAJYPwdx3TlLhlRvcPzJTAbTOl7XYs1pY5+IrJDPm+H5WrLmMnTDKWnHxsjer7tomIYsKqPaphQFfMhAsnClXXGr96EPV3VhleYtv5vPZ4rzb29Ul2do452zriXw8iIiIiIiIiIiKiRPGa6htT0hiMRpS0dJ3vq3xbYErNjbEUdw3+Wnil+qYlBhgR9X9FKaNslctyDYEQkd1c7ySMg4GLRETUaYf0XfHbUffg8uKbke+x6NQRYlXrYty/8Qa8tOVR1Pq2xrV+upCw/AHwXZafov68qzrKE1wTcqIloO7Y5ySklmiwYDAaERERERERWQpoOoJ3D0ZT/3BWWqvv/JrmQUQ/uJ22l8Daew28c7WBf1wi8OVNBpr+amDujcHXt58Ug4SsMNp8wPrq4N9WAVOH7qyvy6ZtHXt1wWge6cNJzR9op69yF/Qe2N4KfP+pvkIhpJQ47AETL38rYUqBNpmCv+ZcgdtGTLc1vdK6vgtGc8UgGS09RWDWNQZmXGl9ueTu9yUuetFeMs4V/5L497cS7b5gIN4r8yWueNl62vnrw883NOxCt49aqVQ/WCKpOAnB0wWjAcCYm0089bmJz1dKSEWDtGSzxJOfmXh1vgmff/CFqJlWCQUhkikYzep7aViWQKHmnowNNYNv/a7dClQo9veNNcDCTYmvDwAsLY9sPZRYBKOp9u14cRKMlkz7DRENPFbHgNXNiavHQOfkG6Y/HGMT9RWrfanzFE93XsdgNLUWBqMRERERERERERHRANKuCUZLFQxGI0pWupAtv/TFPdSA9Co6yrTjGGBE1P8VpdoLRst0EG7We1r7YRxsV4iIKJQQAj8bciD+uOPjmJr/K9thm/MbP8f09Vdh5tZ/oS0QnycDV2pCwgbCdxlDq/un5oD6puNojuOIBioGoxEREREREZElO0FUus6rm2ol6jWhINHEWI0YIvCLPQUuOMjAwTsJZKYKHLJz8PX0kw3Mu9nAkbsBET7oyJZlW4L/V1l0ft99JOBxqceFC0Zzww8BYJSvVDm+Ml39w6ac96G+QiGue01iZWXv4S/kXIJ2kWprHr2sXAjpi2/PXDtBfdEQQuDUvQSuOcp6hq/Ml7jpLetEstoWidcX9N6BXl8gUdOs3rHmrpbKEKOeakLCLpyE9HTqD6EN+ran97BRudbr65pXJI562MS5f+sejvbgRyb2/JOJ37wq8cvnJfa400Rty+AKzzJtBuvVt8a3Hk6EC80bqQnQqo3Pb2RJrcbiPf+wqW+29aUOHjzkD9k+SzT7eYs3sdunkza3sT2xoW1ENLhYtUftPgzKwNd4sBsiCwCltfzMiXSsdqXOaze6473SusG3b9lpelq88a8HERERERERERERUaJ4NcFo6a6MBNeEiOzKTynWjrMK56L4quzYrBw+xJWNDFdWgmtDRLFWlDLaVrksB+Fmvae1H8YxEMJkiIgo9lzCjSNyT8SdOz6NY/POgFt4wk7jlz58XPsW7lh/Jb6o+wAB6Y9ZfUxpomoQBqMFQ6urE1wbsqs50KQcHs1xHNFAxWA0IiIiIiIishQugAYARmlCaFZU6Dtq/vXc+KWWHThWYM4NLgSedWHzAwZKNPWLxtLy4AdTpb4OhcxUICtNaD+blZXB6f2aUCKP9AEACvxVyvGVYw9VT7h2iXp46LIrJJ74VL1im11DsDJlV/WEE/aH+KQBr1+7EEeN/i9G7rIeR4z5GB9k/jw4vqURmD877PKjod0eY3yF466Tw2+fD34ksdMtASwqVVfq+43q9Rswge82qOd50Yv2UqpCw44iC0ZL/o7lusAuVQieLpyxp9cXSLw6P/jeP18pcdNbsluH85WVwG632UwKGyDsbj/JFIymC0jsDM3L09wTOxiD0Tosfg9c30e/cTkJjfGHhIeOGaYvt6Iiigo5ZDdMEAgGWjQzsIKI4kT3fdhp5qLE1GOgc9Lul9bFrx5E/Z1lMNq2/3XndaW1Ma9O0rNzntYa31x6IiIiIiIiIiIiooRqM9U3pqQa6QmuCRHZlWqkIdc9XDmuUhM6QPGnC0YrSClJcE2IKB6KUtUPVu8pM4pAjUwHwWiFbFuIiMhCuisTp464ANN3fAr7Dz3C1jTNgUa8VvUc7lp/LRY1fROTh5TX+avhk+qbrfI9+sDn/sIqtLrKx3OzZNWiCUZzcixGNFgwGI2IiIiIiIgs6TrchwZRjc5Th0ht1YSGAcBBO8UvGC1UUY7Aij8ZeOZ8gUsPFThnP4Frjxa47USBaVMEdi1QT3fe/gL3nCZQnKMev6oy+H9Vk/oia8G23xPHF6mnX74l+L8voB7fGYyWrwlGq8rRhJetWGB54TdgSoy/3bp3/7LU8b2Gidv/AfHkp3hlUQrOmz0O/8s8DJXuAnyZcShOHv02po78N+alHwD/J29azjtadoL6YiE7w94M11cDe99l4r9LZK/PfWWFfj10BuOFqmiQ2FBjr361Ldi+PKtQjALN9dCKRnvL6UtO1rUugFDl+bkSbR0SRz2s/uBqWoB/zBs84Wh2g9Ea1A/m7RPhto28TPX+W5tE4W6JYhXKtUrRDiXC5nr7ZUPDJUfmAEPS1OWWlCfuvQQcLqoxifYdIhpYwgV2nf2ciR81Ab5kn5NPcNMgDG8isstqXxLbDt9115ZK6xCTG9z6EzvnaS1RBqNVNEh8tkKiqX1wfbZERERERERERESUnLym+sf1NENzowARJYWClJHK4ZUdZQmuCXWq0Hz2hZp1RUT9y3BPIdzCHbZcVhSBGmlGOlwIvwwX3Bjm0XTEICIiCpHnGYGLi6bh5jEPYdeM3W1NU+Urx3Pl9+MvpbdiQ9uqqJavCw8G9Oc0/UmakY5st/qppFbvnfpWc0DdsS+a4ziigYrBaERERERERGRJ1xnTFdJfdfQw5/PNj/xBRI5lpApcfpiB5y808MqvDDx6toE/nWLgkakGVtzlwl/OFsjJCJYdVwh8eoOBly8z8IfjDZy1j7pj7ua64AdTqQmYyt92HWpckXr6ZdtCXPxhgtEKAupgtFJDk7jmbQM+flU9DsA7f/9cO67T0tQJ3V6L2XUQx5wD4fbgL7PVG8SMoafhsB0+w4jSR7BiU/xSYLTbYxyucOw0wn7ZEx43cfITJppDOhQvsXiwxsKNvYctcXC92esHWrd1grbqMF2crR6u226TiZNgtLxM+/P9fBXw3Fzrjt93zOoddDdQ2Q1Gq29Lns9DFwbY2Q7karaHupbkeQ+J0mIRjOakzYmlzXX2y4Z+RwohMFHzMKOlCXyQUbggop6SKVSQiAYWO0GNt7w9eMJe48XusRIAlNYOvmMNIrusTq86g9F0gdftPqC6OfZ1Sma2gtEsjvUt521KTHvNRPHvTRz9iIlh00y8NIjCwYmIiIiIiIiIiCg5tWuC0VKN9ATXhIic0IUIVLDzfZ/RBR8MhMAHIgJcwoWClJKw5TKjCNQQQiDTFb6jx4iUQriEK+LlEBHR4DM6bWdcV/InXDnyNhTa+D4DgDVty/DAphvxYvnDqO6ojGi5VR3qzgZDXblId2VENM9kow+t5rlZsmrRBKPZOQ4jGmwYjEZERERERESWdAE0RsgZ5Wj1gwW0PC4gO4nuWbruaAMVDxloeNzA4ukGjtitK3mpRNMxt6w++P8adW4ZCrZdh9pN8yCk8m3T+7TBaH4AQLFPfQF2zuZctAr1hyjvvgTS7+s9fNMq/GdOjXqBIe4ffuP2v8Xf5kGkBS/01rZILNxkPW2jMRSXPtMct1Ap7faozp+LylVHOJvp+4uBodea+Hxl8L2X1+s/g1k/SnT4u49fUu7sM6vZ1jHcqsN0kSYYraox+UMbnASjCSFwzVH219ctM6zf/6ZahN3WBwq7YR/JFO4UbtvQBeXVtsSnPsms2atfwWu2Am0diW0L2jokahysB3+PNn9CsXo/31CduPfhJCAHANZXx6ceRER2ghpnLwsew1PknJzWVDXFrx5E/Z3VrtR5hGd1bWlVZPe09Vt22p5Ig9FemS/x+JyuBfhN4NKXJFZs4fcFERERERERERER9R1dMFoag9GIkpouzKCKne/7hNdsR51ffbMSg9GIBo6ilFFhy2RFGahhZ3o7AW1EREQ9CSGwe9a+uHWHx3BuwZUY4tJ0euphQdNc/GnD1ZhR9RJaA86esqkPD9Y8tb0fKvAwGK0/8Uuf9lpYtMdxRAMRg9GIiIiIiIjIUs9Qkk6ekAf85GYAman25zliSPBiZjJJcQsMSRNw9UhdGrlqtrJ8WW2ww+iSzeqOo7sVBeczvG61cnxdQ7AHq18TjOZGMBjt0LavlONbfQLf7nKOemIA+P7TXoPMt5/DZ2mT9dOEuOfQNyD++QPEuH0ABMNshv/WRvoCgG+q8/Dt+t7DA6bExhoJnz/yzrZOwrKiNXVfAXcEV06OetjEy9+YqLMI/2lo6x28tdjh9ebOcCGrUIyiHPUHU6l+sERS0a5rzTq56nD7G0Fb79zAXv7z3eDoFG4nVAUA6lvjWw8ndNuGa9u2wWC0Ls0WYQlSJj5g4qcyZ+UnFHV/vcMwdbnS2sjqEwmnwWhLHYZeEhHZFbDRvPhN4J1FbIei4aTdtzr+JxrsrIK+Oi8PFWYDQ9PUZc5+zuaJywBhp+1psRlybJoSm2ok1ldLtHolXv5GPd07P/L7goiIiIiIiIiIiPqOl8FoRP2SLmyrMVDvOKyAolfVoX4QM6APsSOi/qcoNXwwWqZraFTLsDM9AxeJiCgaLuHC5Jyf486xz+D4YVPhESlhp/FLPz6pm4k71l2JT2tnwS9tdAyCVTDawPkuy9eEvFmdI1DfaQnon8ScFeVxHNFAxGA0IiIiIiIisqQN7go5oxRCYFSu/Xnm95PweultR/HmMR5cAAAgAElEQVScJ5XjmrwCNZsqsKJCPe2kbdcUsz//p3J8G1LR/vpT8GkSDTzbLtAe2fIFcgPqxJd3R1+mr/yaxd1eyg//hbXvvI8a93D9NCHurj8R9fnjAQCrKiUyr3HWCfng+7vKm6bE3e+byLnOxI5/MJE7zcS1/zEjCkjThTjFIxhtZK7A3y8RSHE7n/bCFyW+Wmtdpmeo3t+/cvZ51Gy7b8gqFKNI8/CUfhGM5nBdjysSOGPv2C3/4Y8lapoHfsdwu2EfDer7T/tEIMy2wWC0LlbBaEDi24JZDsMWbji2++Vr3bFGaV2kNXLOeTBafOpBRGQ33PTtHwb+8Uw8OWn3a5MoSJYo2VgGo3X+LwQmae41K68HyusHT3tmp+0JF14tpcTjc0wM+62JHf5gYqdbTGRfZ+LjZeryf5gxeD5fIiIiIiIiIiIiSj7tmmC0VMFgNKJkZhUkUMkO+AlX0aF+aqVbeJDnGZHg2hBRvBSmhA9Gy3JF11nDTiBH4QAKkyEior6TZqTjF8PPw507Po2Dhh4NgfCdw1rMJry59UXctf43WNg0D9Lq5jTow8EGUjBagSYYrc5fDa/ZnuDaUDjNAX1Hnswoj+OIBiIGoxEREREREZGWaUptZ0x3jzPK0Xn259tfgtGwehFKGpZrR3905e/g9avHTRopIE0TuSs+005f99R96OhQJ8+5twWjuRHAnu0/Kcs8vnkv+OHCpxmH4/HcqzBjyCkIbDvVl+uWbi8nv/8MSx96CON2XqKtS0++AHD0Iyauf93EuD86C0Xr9PS71ZBvP4u//OUr3P6ORMu2gJ7WDuCJTyXuel9i4UaJB/5r4u73TcxdHb4Trm57dMXpCscvDzCw5UEDH00z8LPwvyM7siTk2rqd995TTUtwGqtQjOIc9fCKRoS9+N/XdOvaKgTvgTNjuyFMeSSybb8/sR2MlkRBH+HagWGZ6o2kohFo9yX3dh9rS9QPN9quqimxn8fizerlDU1DrxDK8UXAkbt1HzYqT71uq5uBxrbEvBenwWgbagbXNkdEiWMVjhvquw3O5rt8i8Qjs03c9JaJ298x8f5PEqbTxm8AcXLI3OIFOiIIfyYa7ETIId6EYv0J3xvfD579y074Zbjg5/d+Aqa9JruFXOtCpomIiIiIiIiIiIj6mq6TbprBYDSiZJbjHoZUkaYcV6kJ6aL4qexQ3yyW7ymCIVwJrg0RxUtx6uiwZewEm1mxE8hRkFIS1TKIiIhC5XiG4YKi3+APYx7BuIw9bU2z1VeB58sfwEObbsa6thXKMh2mF7X+rcpx+QMqGE3/XnTBcNR3mgNN2nEMRiPqzR2+CBEREREREQ1WfosOk+4ev5EHw0rsdVLNHxL+CQ5JYd1SjPKVIc1sQ7viJqvXhp6lnMwQwLhCAFvWI7etQjv7OlcOfPM/BbKO6TXOsy0YDQAmeZfh88wjlPPYY9IqrAoUbX+9X9t3+GLDFKQs3vbUi+YGvHL7i7hgp4XaeugsKgUWlUbe8fjqd3Ox88YZ+P2Yy5Tj735f4p4PZEjYgMRvjxF4+Cx1uJWU+qA+q7CsaOVmChwzAThiVwPT35W478PYdMZeGhIQ9MKXkQSjBf+3yqkoylbvl+0+oKkdGJrE9w7qOmxbresxeUC6B2jz6cs48WMZUFortWFMA4HtYLQEhU7ZEW7b2CVfP93KCmDPGIccJqvXF5h4dX6Ypx/pf0+Ji9Ja9fDf/Vzg4J0EHvzIxJoqYPIuAvefLpDq6b7vleTq533X+xIPnhn/fdVOSEWo6ub41IOIyG57VNkIVDdJDLdxDvaf+SYu+ruEr1t2s8QpewKvX2HA4x64x0Q6TjPh6lqBgujuLSUakKx2pdBgtNN+JvD8XHXp2Uslrjs6tvVKVnbannDBaA9+xBQ0IiIiIiIiIiIi6j/azTbl8FQGoxElNSEE8lOKUepd12tchSaki+JHF4zG8CKigWW4pxBu4YZfap7ujugDNewEqxWkFEe1DCIiIpWStB1x7ag7sazlB8yo+jvKOzaFnWZ9+0o8tOlm7JV1ME4ZcQHyU7r62FmFglmFifU3wzz5cMGNAHofH1R2lGNU2tg+qBXptGiC0dKMDLiFJ8G1IUp+DEYjIiIiIiIiLX9AP87dI7tqdJ79+Y7oJx3F5fqlcMHE+I6V+CHtZ73GvzvkJOV0O48A0jwC8uv/IjdQr51/nSsHPs0FK0/Ixci923/QziM0FA0AvkvfDw8MvwG3bbkf8rA0lHuKcMHOa7XTx9vPx3xgOV726Oz7l9kSFx8ksXtJ79CFnmVDxTMYrZPHLXDPaQL3nAb8/k0TD38cXVDUkpDr6+u26uc1oQhYtqX38JptYTu6kCgAKM7Rj6tsTO5gtEhC8AxDYK/RwLwYbvJv/yBx7dEDNwTEbthHvfr+0z6hq7Nr2/fSzvlAihvoUNzzsaRcYs9RA3d9diqvlzjnufArN+HBaHXq4aPzgKPGCRw1zvrJpGPygMxUoMXbe9yzX0jcfpLEkLT4rl/dd5HHhR5BQkFbE/wZE9HgEXBwKJp/g4mh6od0b9faoQ/GfudHYOYi4Kx97S9zoLA6B1Gpa2EwGpGK1b4UevT284n6cmvVD+4ckOw0PbWt+nEVDRJfOTwvTsR1FSIiIiIiIiIiIiIdryYYLY3BaERJrzClRBmMpgvpovip7ChTDh9IgQ9EBLiEC/mekSjv2KgtYyfYzEpWmGC1oa4cZLiyoloGERGRlQmZe2HcDnvg64ZP8V71K2gIaDoihPiheR5+ap6Pw3KPw/HDpiLLNVR7XuKCG8M8+bGudp8xhAsjUgpRoTgnqOK5WdJpDjQqh4c7BiMarIzwRYiIiIiIiGiw0nWMB4IBIKGcBKPl95frNOuXAQAmeJc5mmxC22LI1mbIx65HpmyBS/NEpnojB36hzix3h0xzRuPbjpb/Qs4lMCHgFSkYbSMULd0TXcBXrL23WF0fqwAnV4KvcDx4poGvbjJw72kCY4ZFNo/KRmBrU/BN6cKCAGjnX9MS/N/qcynKtl5+MtMGo4VZ15dNjm1v7liGrCUj06KdD1Vv0ek+0XRhgJ0d+d0ugXGF6jKrK+NTp2RTcqO9FVuVwHag1StR26IeNyrX3n6b6hGYuq+6bLMX+GJVpLWzT9c26UJwaluAgN0EQiIiB+x+h3dqbLf+Z3XuBwAzFw3OtsxpE24VVEQ0mFntSiLk8M4wBF79lfp4r7QOkE7TCvspO218XYv+s/hmnfNgx9DrKgFT4rsNEl+tkfD5B8dnTkRERERERERERH3HlCa8sl05jsFoRMlPF7qlC+mi+DClicqOcuU4BqMRDTzFqaO14wy4kGZkRDX/zDDBavlsV4iIKAEM4cIhOcdg+tincdKwc5EqwjwhGEAAfnxW9x7uWPdrzK6dic1edZDoiJRCuIT1Q937G/25mfo8gfpOiyYYLdwxGNFgxWA0IiIiIiIi0rLqHO/uFYxmP4yovwWj7de2wNFkJaXzgJnPAgAEgFzNkynqXTnwwaMc55G+7X9nylaMzlaHq6mUekZhYdpeuHP4rWHL5mQAM6+O7GJuRgrw/B5zMXfDERFNr7OxRj1cF4YEhA/LioeDdhK4+XgD6+9z4Y0rIqvAgg2AaUps1gSjvXKZwLBM9b5V2xz83yqsYUgqkJWqHtdvg9HCNDUXHSRw3+kCudH9pr/d6wskzAEcamT3rTWoH8zbJ+xsGztoAgW3Nse+PsnmznftJ+WU1SVu27YKgBzlIFz17lP0jcDS8vi/H932pzu2MSVQpwmEIyKKRiDB4UCvzpfwBwbuMZGO08NAtvlEak6aLN31pdYOoG6QhA/aaXt0ocMAsHiz8/bave2yxuY6id2nmzjgXhOTHzCx060mVlYMvvafiIiIiIiIiIiIEsdrqkPRACCVwWhESU/X+X5rRwUCMpDg2gxedf5q+GSHclxhakmCa0NE8VaYot+vs1xDIER0D5nOcll39ihkMBoRESVQqpGGE4afjeljn8Kh2cdC2IjIaTNb8fbWl/Df2jeU4wdieLA2GM23OcE1oXBaAk3K4eGOwYgGK3dfV4CIiIiIiIiSl9/ingR3j+uITkJNCoZG92NbIsi6rUBtJQDg5Kb3MK3wEdvTDvXXQT79p+2vhwVqUe0e0atcpTsfbZqbt1Klt9vro3b246Xv7Z/GH7jjl2HLjMoFFt1uIMdhgNTJewJP/dJAUTaAwCGoeWO1sxmEUVqr7nBr1TE4XFhWvJ2xj8DH0wwc+6j9QCIAeOsHib1GC20I4a4FAt+uV7/x6ubgcMvPxQAKhgLNW3uPq2iUCEb3JSd9+JV1nYUQuOk4gd8fK1HeADz3P4m73w/fifuWEwTu/UBd7sa3JB46K3k/q2jYDfuoT6IAAl1Ioivke2n4EAGg95urVv9+0O81tUs8+JHE7GUS3663P90Xq4D6VomcjPhv36W1+nElufbnU5QjcOwE4ONlvcctTcBvdrp9psDi4TzVzcDwJP6NasEGiSc/k/D6gTP3EThtLzi6MerL1RL//EaixQucsDtw3v4i6huriCg809lhZ0y8Ol/igoMG1/7tNH+uoS25j7GJ+orVvtRzj7G6vlRaC+RlxqRKSc200fhYBaMtjeAhn50PQLjsnyZWVHQNL6sDzn7OxKLbB9YTUomIiIiIiIiIiMg5KSVqfFXwhzzwMxaaAg3acemuGD0ZkYjipkATzhOAH6taFyPXPTzmy8zxDENalMGJrYFmNPrrlePyPCOQYmieSJskTBlAta8K5rbwufXtK7VlB2LoA9FgV5Q6Wjsu02VxM6NN4eaha/uJiIjiKdudh/MKr8IRuSfh7a3/wNKW7yOe10A8Rta9p6qOzajwloWdfqg7BxmurFhXixSaA43K4bE4jiMaiBiMRkRERERERFq6sCagq8NkJyehJuOLIqtPQm3oSlwZ7S/DKF8pSj2jbE2a3eNmrWJ/OVam7tarXJl7JBqMbOU8cnrMY0R2bDugnrOfwBPnCeRmBrsgb37AwMgb9Sv8uIlAdrrA0eOBSw8RMDqTyNwe5D35DvB47Oq2SROek8zBaAAwZYLAglsNvPiVxFOf20tQmLFQ4iKLcImSXCBfc12zalvAky4kCgBcIhjWs1YRjFapvo6aNHRhH3bXtWEIlOQCP5+IsMFoY4YF9wldMNojsyV+NVlit8Ik2NBizG4wWpMXME3Zte/3IX1oXtffIzS/x3QGCg4kXp/EwfebEYUf+ALAvLXBMKt4K61Tf/YjhgBpHmfb1Z6jBD5e1nt+S8vjv351bVO+JowPAMobgHFJcuzT4g3W0RDB47yv1gAnPN71pv7zncQtJwj8dkowdCRcwNl7P0mc/pS5/Zjx1fnBII57T+v7toJooAv0wVdaMBgt8cvtS3aPlTo1tcenHkT9ndWu1PNwoyg7GHqsOtfdUAPsae/STL9mJ5TRKhhtyWbnXxJuA2hul/hoae9xP5UB67ZKjB3BYzwiIiIiIiIiIqLBakHjl3i96m9otggxi4fUKIOPiCj+8lOKICAgFb8I/bVselyWacDA7ln74cLC6xwHKDb5G/D3LY9gZetPyjoDgFu4sVfWITi/8Gp4jJRYVDmmvqj7ALOqX0abGf5pp9nuvKhD5Igo+RSl6H84z3JF/xTXrLDBaMVRL4OIiChSxamjcXXJH7Gi5UfM2PoSyrwOniy/zUAMRsv3qL+f2802/GnDNWGnFzCwc/p4/F/x7zHUnRPr6lGI5kCTcngsjuOIBiKjrytAREREREREycsf0I9z9zijTPMIFNgIps9MBcbkRVevhFjXvSfoOO8K25MONbtfoCrxbVaWK3cXo0Hzw2G22SMYLcdje/nhPLLPUrzyKwN5mV0dWotyBN69xkBOj3tEJu8CVDxk4IPrXHj1cgOXTTZ6BSOJifvjcP/8mNVvYw0QUCQQWIUSuJLkCsfeYwSeOM+A7xkDlxwSvsNwfSvw6CfqlJ3MVGB4FpCvua7ZGWxmGRhnQLtfJn0wmi78yuG6PmgskB3mvp6PpxnYrQAYkqYv84cZFgl0/ZjdsA8pgcYkCfrQhQGGtgPDNcFoW9W/H/Rrz82VEYWidYokNCESpXXq4aMcBKt2mqS5p2Z5hfr7I5Z0s89IBYZlqsct39L3gXxSStwxy8SQ3wT/ZV5jIvtas1soWqd7P5AYcb2Jfe42MW+tdd3ved/sFaT7l9kSdS19/56JBjqrcNw8TXsUrZWV8ZlvMrMTThSqyRufehD1d1b7Us9gNJchtNeNkuG4KhHsHNLqgtG8PhlRe+0ygM31+vFfrxscnz0RERERERERERH1tql9LV7c8lDCQ9EAMMyHqB9IMVKR5xmR0GWaMPFj87f4d+WTjqd9YctDWNH6ozYUDQD80o/vmr7Am1UvRlPNuFjcvACvVT1nKxQNAAoHYOADEQEjUorggls5LlyomR1Zbut5FKaURL0MIiKiaI3L3BM3j3kYFxZehxz3MEfT6kLE+rNow94kTKxuW4qnN98ToxqRTosmGC0zBsdxRAOR+syHKEJCCA+AQwCMBlAEoBlAOYAfpJQb+rBqREREREQUAZ+DYDQAGJ0XPmxpQhF6BWslI/nTV91ej/euwOysY2xNm93jJrBi/xZluU2eUWgwssPPIzUdxbkCsLgRw67ncT8u+dUflONO3ENg4/0Gvt8INLQBY4cDE4qDnZKtCCEw7UiJL+ZGXT0AQLMXmLsaOGK37sOtwh+SbZNyGQIvXCRw6xGNWPLrCzHKV4YjxsxGk+Ii5ds/qOcxcdu+EgxG673uq5qCQTeWwWgCKBiq3naqGpO7U7PufbkcrmvDELhsssDDH6tn+MhUgV0KgjM9Zz+Bv81Vl5u5CLjrPRPpKcAv9xcoykmyjS5CpoO8t4Y29ApP7Ava0LyQVTJCEyi4tTn29YmFn8okPl0hMWOhxME7C4wdDnj9wNqtwNA04MjdBI4c13ub+7FU4rr/2NuX9ywBfizrPXyZ+isq5kpr1cMjCUabWKxu19p9wLqtwC4Fzudply54zRDAxGLgf6t7j1sSRXBdrFzxssTzmvZNZ1EpMPVZE4vvMJCb2Xv78wckvlU8YMvrB95aKHHZ5IHRThIlK9334eg8YMn04DF9fVtk8z7tKfUBwoYaoNUrkZE6ePZvp3mbzUkSJEuUbKx2JVWLMqEYWFfde/jyBB27RuunMokPFkvkZAC/2ENgZK6zdtNO29PmA9o6JNJTus97ZaX19ROd6mbgznf1C3Zy7khEREREREREREQDy9cNc/ps2Skitc+WTUT2FaSUoMZXlfDlLmr6Bq2BZmS4NE/R7KG6oxKrWhfbnv+CprmYWvAruIQr0irG3NcNnzgqX8DwIqIBySVcKEgpRnnHpl7jMl2aG2gdSBUWT5oGEh6ISUREpGMIAwdmH4m9hxyMT+vexce1b6HdDH/zbLQhYskoyz0UmcYQtJjq0C27NravRo2vCsM8+TGqGfXUHFB3vo1FwC3RQMRgtAFICDEdwB1RzOIfUsqLHS5zBIA7AZwNQPkMbyHEPACPSCnfiqJuRERERESUQH6LTo8exe/8o/OA7zZYz/PQXZK/E70sXwd8+ma3YYe2zcPj+I2t6Yf0uIhY4t+sLLc8dRykUCTMAcg2Qy5ypWXi2AnRB6NdUP8yLnnhaghDvUwAGJImegWS2XHKBQfj7Q+vwBmZf4UZg5tA/rtU4ojdum8r4QLAktGOgVLs0PwhAODUpln4V875tqedODL4pgo01zV9gWBQVbjAON30ldFd6447qQu/0m++WpccLPDYJ7JXm5adDkyb0jXDZ87XB6MBwB2zguP+OFPiw+uMXttof+Qk7KO+FRjj7EE6caENzQvZNkZkqdvM6mbANGVSBXT+Y56Jy/4pt+/LX67pXe+735e4+XiBe0/repP//NrExX+3twK/+L2BWT9K/FjWu/ySzYkJSdxcp15OSZ7zdTG+CBBC3U4s2xLfYDSrYL6JIwX+t7p3gWXlfRtEefMM03EoWqfyeuDFryRuOLb3emqyCP/5fhNwWURLJCK7dMeALgPIShM4PIJj+k4r7zKw2x97L0BKYMFG4LBdI593f+M0GK3JG596EPV3uvM7IHhc19P4IoH3flIcV21J7oBvAHj5m+Bxemf7cccsiQ+vNbD3GPvHvXbbnrpWID2l+7Bojj3/851FMFryf/REREREREREREQUJ2VexROzEiDfUwxDc28dESWXUak7YlnLwoQv10QAW7yl2CljvK3yTtuzNrMFzYFGZLsjePJjnDh9D6NSx8apJkTU10an7awMRotFIKIQAiNTd8Bm74Ze43ZI2wVGEgVGEhERAUCKkYrjhp2JQ7Kn4P2a1/Bl/Ucwob7RNtc9HFnugRlAVZK2I1a2/hT1fBiMFl8tmmC0WATcEg1EvEJMURNCHA9gCYAroQlF2+ZgAG8KIV4WQmQmpHJERERERIOAnPMGzEv2gzk5Nfjv6KEwbz4dcv3yqOdtFYzmVvyeVZIbvpPnGXsnTxiNiqwshTy7900SP2+ejQyzxdY8ss2Gbq9H+3r/6AgADa4c7TxyAvVdL9IzMXyIwLETbC1e63c/NyCG6JcZrZP/9iA6fAdjasMbUc9rsSK8xwwTAJaUKrvW/VmNznLCdy37DNI0kW9xXXPaaxKHP6j/YFyGPhitokE9PFlYhQ85NaFY4O5TRbcO9xOLgQ33db80JoTA4unhL5d5/cD//cOEP9D/e4ZbBev11BD+wTkJoatz6Lah2+4DJlCVRKGAXp/E796QttbD/R9KGJcHtv+zG4p2z2kCk3cRmFSsHr98CxBIQMrB1mb18OIIvpbSUwTGaK5CbmmI73uxDEbTfMZLNgPSKg0kClJK/GW2id2nB5B1TQD73xPAB4vl9nEX/93EA/+NbtnT31VP32gRjNaYJO0F0UBmJyg0UmNHABkp6nEzF/X/4x8nnDbfVqGRRIOZZTCaYtiEInXZ5VuCQcfJqsMvcc0rslsbvbUJuHWmgxMv2G97ahWXqXTH3dFK4o+diIiIiIiIiIiI4qyyo7xPlrv/0MP7ZLlE5NxB2UfDQN+E5FR0lNkuW9mhfsixFV2H9b7gMztQ49tqu3y6kYG9hxwSxxoRUV+anHNcr2EpIhV7DTkoJvM/cOhRyuGHZB8bk/kTERHFwxB3Ds4puAK37fA49sjaX1nm0JyfJ7hWiXNojL6npXR2vxvZ55c+tJvqzhZZDEYjUmIwGkVFCHEEgJkAQiM/JYDvAbwBYDaA6h6T/RLAq0Lw0S1ERERERNGSX70POf18YE1Imn+HF/jqfcjrT4BsqtdPbIM/oB/nVhzRj7aKSt5G17k1GcjWJsgzd1aOy5StOKnpA1vzye5xI8RE7zLHdck2Q+aRlgEAeO83kZ9GTWpfgkn77RTx9HaIzKEQz3+DW84dHvW8vlzTe5hVJ9xYBEDERVXXTTdTWj5FbqDW9qTDvn4N8m93WAaj/fNr657JQggUDFUniVU2xi+oJxZ0QXgiwhC8G48z8OPtBp7+pcBrlxv44Y8GsjN6z2xiscDhu4af3/pq4LOVkdUlmTjp3F4dpw72TumDYLrW5yiL76NN9nfDuPtqLVBjL3PTsYfPElhwq4E/HB9sICcWq3eeNl9we463Gs32MyzCxyfowu/iHXynDUYzgEmaz7iuNX5hlA9+JHHDGxJLy4HWDmDBRuCkv5qYu1riz/+VYb8n7GjxqsPzrMLPGlqT9/uFaKCwExQaKZchMEXzQO0ZC/tm//YHJL5aIzFnuURTe+Lq4DQIqMUbn3oQ9XdWu5LqHG+C5riqtSO5jud7+t8qdXjsR0sBn99+g2K37VEFo9W32l6MI05CtYmIiIiIiIiIiGjgaA40ojmQ2KcvZrqG4Ni8M3DcsLMSulwiilx+SjGuKbkd+R7NUwXjyEnYWSTBaM1JFIy21bcFEuF/tBEQGJU6Fr8ddS/SXRkJqBkR9YUd03fFr4pvQp57BACgOGUMrim5A3meETGZ/1G5v8CJw85BlisbAJDlysZpIy7CwdlTYjJ/IiKieCpMLcGvR96CaaPuxm4Ze8AFNzKMLBybdwZ+nnd6X1cvbvYZeijOzr8c2a7cqOYTgEWHUopKS0Df4SXLpekkQzTIufu6ApQQ5wL4xkF5W91chRAlAGYASAkZ/BWAX0kpl4eUSwVwBYCHAHi2Df4FgLsB3OKgXkREREREFEJKCfnMrfoC1eWQb/wV4tI/RrwMv8Xv527Fw91G5wlYdXfNSgWy0yOuTlzJ0tWQ502yLHNW9o94HeFvuBpqdr8RYrSvFFmBJjQ7SO7PNkNuKEsPpsa4XQJzrjdw9CPOe6Ne4J0J7HaT4+mcEm439jh1CmaMkTj96ch7zTa1A29+L3HmPl2doa064cYiACIeZGXp9r9T4MNxzR/j1exzbE2ba9YDb7+N9Itvxc75Hqypcrbszs+kUHNdtM0HNHuBIWnO5pso2vChKNb1pJECk0aGn8HkXQS+WBW+J/or30ocMyFJNz6bnIR9lNVJAH3/fu0EwQzPAtI8QLuvd7nSWmD/HeNTN6fWbY1PsMsjUwWmTemeGDneIph0aTmwc75+fCyoQhsAYFhmZNuULjRya7yD0Sy2v4kW93YuKQeKcmJbFyklHpuj3oYOfzC2yRVVjb3rrwr96FQXp0AOIuqiDwqNzfxP2F1g1o+9F7KpFnhpnomLD05cMvGWeokpfzGxfEvw9YghwKyrDRwwNv7HJU6/qRMZ2kbUn1hlcquC0cYV6ssv2wLsEH0ee1x8u17/RquagJE27zeLJhitwSK8NhpOgyKJiIiIiIiIiIhoYKjsKNeOu2XMoxjizo75Moe4smGIZH1KJhHpjMvcE9PHPoXWQDN8UnHTVpTerHoB3zd92Wt4RUeZorSaLhhtcs5x+K7xC7SbvX9oaQzq7jUAACAASURBVLbotJ5ouvobcOGusc/CEMEbu1NEKgPRiAaJvYYchL2GHIQO04sUIzWm8xZC4MTh5+CEYWejOdCILNdQiEifbk1ERNRHds2YhF0zJqHD9MIjUgbFd9nhuSfgsJzj0RxogBnmLtg/rrscfsX5m5R8ima8WIVvZzrod0o0mPBK8eBQIaXc4OBftc353gkg9PbteQCmhIaiAYCU0iulfBzA1B7TXy+EGBP52yIiIiIiGuTWLwM2LLcu88lrkFa9T8PwWwT8uxVnlDuG6Zg6Kg9JeRFRShk2FA0Ajr92KrJs/GaYY3Z/SqYBiQkdYdZVD6myo+tFWub2P48cJ3DLCerP8GejgLpHDew6pHve9R7tP+E3U0dCpCYuAevUvQTM51wInP4qtq4sRvOKXFxa/5KjeUx91oTX17X9WnXCNZL1CkdV95tu9mhfbHvS3EAt0NIIOSUbp1S96njRnSFRuvAgAKixFY3eN+IRjGbXniX2FvKPr2W3bbQ/ctK5vbQufvVwwk4QjBACozShA5tqk2edVcbhgaJ7lAC/Prz3NpyZKjBW8z29ZHN8PxOfX2pDtIZlRTbPEUPV+2ncg9Es2qZhWULb5m6sic1nHDAlHptj4uxnTRz7FxNbEvRg7s31vYc1WQSjfbcB+N0bJo57NIAb3zRR3ZQ8+x3RQGEnKDQau1uEyV76ksRB9wUw84fE7NtX/bsrFA0ItvW/fN6M6lzXLl0gpo5V20g0mFntrarWZkiawOg8dfllW5L3uCI9RT/OybG//WC03gXr+zAY7YPFEhe8YOLwBwP4/ZsmSpPo3IuIiIiIiIiIiIgiU6kJHEoz0jEydQyy3bkx/8dQNKL+LcOVFZe2oSRV/SRMXVhYT1JKbYjaqNSxyHKpn0Br1Wk90So073VESiFyPcO3f1YMRSMafGIdihZKCIEh7uyk7ANCRERkV4qROqi+y4Lf3zlhz7NccCmnD8CiQylFxSp8m8FoRGq8WkwREULsAuCikEEdAC6WUmq7fEgpZwL4R8igVAB3xKeGREREREQDi/S2QVaXd+v4LT97K/yEpauBVT9EvFyfxXUsj+La1+4jgbzM3sM76UJq+pr87fHhC514CTIm7YX91fdWbJdmtiHL7J02dXTLZ7brU+Cv7DHT7jcp3H2qgbeuNHDu/l0XZe87XeDrmw1kZwj8cN9QPDS5FFfkfY0H89/BvKsbkXbGZbaXH1M7jEeuWY806cX/1f3d8eQfLe362zIYLVmvT29e1+3lRO8y25PmBbpSqM5a/6TjRXeGRGWn68vogoqSgTbsIwFXs07Y3botCzXhjsQ8CaW5XaKmOfadyp0Eo5XVxnzxEbEbBDNKE6SwoSa29YlGmSJsKhq/miww7yYDaR51ozixWD3dsi3q4bFS26ofZ3df60kXQFbREN/whXChjWOGqceX2QgW3NoksaxcwufXv4dTnjDx29ck3vheYs6K8POMFVX9G9v19fSbwCOzJT5eBjz0scSB95loaGUwBlEs2QkKjYbuO6PTt+uB05828eRn8T0W8geCbUlP66qB+evjumgAzo6VAKDZG596EPV3VjmGuvvNJhSphy8rj74+8aJ6iECnCgd9ZuzmPlYrws51x1zn7CeQ5rFfh56srhECwEvzTJz0VxP//lZi7mrg4Y8lJj9goqyOx4BERERERERERET9mS5wqCClZFB1KCaivleQMlI5vNpXCZ/pCzt9U6ABbWaLclxhykhkaoLRWpIoGE3fJqs/GyIiIiIiIiu6cHpTJqaf1GDUoglGSzMy4BZR3OBHNIAxGI0idR7QLQJ0hpRytY3p/tzj9VQhRFrsqkVERERENLBIbzvMx38HOSUH8rQdIX99GOSaxcGRc2fZm8dlB3VN45Df4jqWqsO9xy1wxt76G57GFSXfzVDy6w+B78OHlonrHwMATCy2fg/5ga1QlTi78Q3bdTqw7dvuA9J7p8actpfAvy8zYD7ngvmcCzcdZyB1WxBOeorA9RfsgKfvPxQ33H06Mg443PayY26H8dt7OR/Q/h0eqrzR0eRvLezqQKsLQwJiFwARK1JKyJcfBJZ83W343u2LbM8jNyQYbZ/2hdipY62jOnSG9Ay1CEZraHM0y4QyNT3BExGCl54iMONKexvV+mrg5W/id9G/okHi8AcDGHqtiaLfmTj/eROt3th1LDcdVH1TbXJ0aNeFBPRsB3Ycrt5YVmxJjvcBAKU1savLKXsCz15gICNVv5NMHKket6g0vp9JrfqeQgDAsAiD0UZkqYf/bzXwweL4vZ9wwWgjc9TjN1uE4FU0SBzzSAAFN5iYNN1E7jQTT33efef0+iQOuDeAD5ZEUGkbDtgRePFi/baj2v8bHXyHrKsGXvo6efY9ooHAblBopIamC+xZEr7cXe9JdFgEOkarsR1o09zD/l4c2/tOTpfQlMTBw0TJStdsjddcf/mpLHmPKazCESsb7dfbbiij6hizXhNKPGYYMOtqwzI83Uq7RX8iKSXu/aB3pTfVAv/6JnnXFxEREREREREREYXHEB4iShaFKeofsCVMbPWFfypkRUeZdlxBSgmyXOqnNDYzGI2IiIiIiAYoo1tcTBcJBqPFi+4cU3dOSkQMRqPIndbj9d/tTCSlXA4gtId/JoBjY1UpIiIiIqKBRj57K/DGX7sGLJsPecm+kJ/NADYstz+fS/aF9Drvoe0PqIe7DGif+Hju/vqe+KfsmVzBaHLzWsgbTw1bTjw+GyIlFQAwKcz9A/n+rcrhk7zLMKkg/FPpAODXdc91H5AWYWpMEhBpGcDIsdtfT6t9AhWrRuHTDcfgzbJz8O36Q7Bl1Wjt9N9v7OpAa9UxOBFhWY58/jbks7f1GlwYqMSBrd/YmkVeSDCaAHB31R2OqmBsu+qT4hZI0zw0IrmD0dTDE7WuD9tVwPuUgUnF4cte+KJESwzDykKd+YyJudui6P0m8Mp8iZtmxDAYzcGsKpLkHi9dEEzPr6XxRepyy8LfB5cQ7T5pK+RqpxHhy4wrBN60Eean256XbwFWVcYvsKCmWT8uL8KvuAL1Q1oBACf91cSG6vi8H23btO3jH5mrbqQ213WfsLFN4olPTdwxy8Qed5qYs6JrXGsHcM0rEsblAdz+jomGVol7PpD4bkMM3gCAL35v4MubDLQ8YeCnO4L/5t1s4OKDDRw0Vj3N7GWKYDSHh5Zvfc9QDKJY0rVHsQwMvum48AdeVU3AUQ+buG2miS9Xx34/b7EIGapr6Ij58npyEiILWIciEQ1munBjoPdxfKcJmuP5hZuAsrrkPK6wOsd2cj5l9zxN9Tno6pCdDkyZIFD1sIELDnR+Ym0VjFbTDKypUo9778fkXFdERERERERERERkT4VXHSRUyBAeIkqw4SkFMDRdYSstQs+6yqhDxTKNIchyDUWmS30zUrO/yX4l40hKyWA0IiIiIiKKKSHU51gByWC0eGnRBKPpzkmJiMFoFAEhRCGAPUMG+QF85WAWn/d4fXy0dSIiIiIiGojk0m+BN55Qj7v9XCDgdza/cyc4roNfcx3LbXE2OXkXdRjNuELgsF0dVyFu5OofIc+x8ZkMKwJ2P2j7y8N2se48OiKgDkYT/1mGY/dICbu4zzZMwTEtn3YfmJYRvp7JbMeJ3V4OD9TgsLavcGrTLOzT/gNGBKpxf+UtyklXVQJeX7ATbX8KRpPv6/PDj235JOz0KaYXGbK127CzmmbgrdKzcXTzHIzp2Igx2dZBe6GfSXa6ukxDW/J2UNaFUCRyXXvcAt/fZuCuU8IvtPj3sb/wv6lGYt7a3sOf+UJia1Ns1p2TYLS61vBlEiFcMFWnCUWagKr65AhSOOwB623mwLHAI1MFlkw3sOpuA788QOCAHYHcjGBY2g7DgkFnvzlK4OubDbhs7ByH7qwv8/qCOAajtaiHp3uA9JTIdupdCqynG3uLCX8g9u8pXNtUkqseX9qVdYn11RI/+5OJa/8jcdd7EtUWwXF3vy+RO83E3e/H7r1M3kXg4J0E0lMEJo0M/usMvD1Es43MXh4McwvV5DAY7cs1EVWXiDR0QaGxPFY6Z38Dz5wffobz1gL3fiBx2IMmHvhvbI+JrILG6j/9CLJOk8QTI05b37b4Z7UR9UtW+5IuGG2yxfWXN5M0cNUqOLbSSTCazaa0rK73sHpNMFrOtktLHrfAMc4vEaLN4hJEk0VbXaqoIxEREREREREREfUPfulDta9COY4hPESUaG7hwXBPoXJchSYwLJRVqJgQAlmaTui6TuuJ1hioQ7upvoGvIKUkwbUhIiIiIqKBwKWJG5IIJLgmg0dLQB2+neUakuCaEPUfDEajSEzq8fonKaWme6PSvB6vJypLERERERENcvKha2I7w62bITetdDSJNhjNpZ/GZQi8eJGBYZldw4ZlAi9ebC+0Jd7kyh9gXnUk5KX7hy+clgHx+ych3J7tg3YrFNjD4h6C/D0mdg8yKxgN8doKiJE7Yfcw94Od0jQLk9t6njIBSEkLX9dkNrbnaWRvxzd/pBzuN4PhaED/CUaTUgLffqwdv7s//H44PFAD1Vs6pfldfFT6C6xdOx5rxz+Ia4/Wv3FXyFUffTBa2Kr0GW34VYLXtcctcOuJBsznXEh168s1tQMnPBaAzx+7Dvpr1DmLCJjAWwvDL2fhRonDHwwg/aoACm8I4Kp/m2jxdp/OSTBafSsQcDJBnOiCYFw9tg2rNveR2Yl9H03tElf8y4RxeWD7vwUb1WVzMwDvUwbm3ezCtCkGUj0CO+cL/Ov/DMy72UD1US9h5bo9sGbZWCxKvRKPHlOF7Ax7O8boYQIHjlWPe/27+H0mtS3qeQ/LinyeExQhrD29+1Pk89fR7QKdbe7oPPX4NVVd+88DH0lsqIl93ey48gjrbeW0vdTjO/zAh0u6v/kWiwAMneb2vm9DiAaKcO1RrFx+mIGXLhHwWJwDhrp9lkRVY+z2dctgNK8L8qV7Y7YsFaeHPq0MRiNSkhE0C7sWCO0x/ds/JOcxRaPFOfaHi+3X2W7J0trew+o1gdY5IdcF8jKdn1i3WwSjWR0XRrLuiYiIiIiIiIiIKDlUd1TChPomDYbwEFFfKExVtz2VHWVhp9WV6Qx61HVCbzbVndYTzSr8rSClOIE1ISIiIiKigUII9U3HARnbhyRTl2ZN+HamJqybiBiMNlhcIYT4RAixWQjRLoRoEkJsEEJ8IYS4Rwgx2eH8ej5Deo3D6deGmR8RERER0aAnm+qBNXFI85j3gaPifk3AvzvM2eQBYwXW3GPgn5cK/PPS4N8Hju375CpZuQnyysOAxYrwsR7E1X+GePkniENO7DXu7P307yV/l9EQry6D+MPfIKa/DPHvxRDFOwIAJo20/gxObPpQPcJlkcbUD4ix4fOwd+1YDY9Upwgs3hzsRasLQwJiHwARlZnPWY6euM+YsLMY5SsNv5zXH0e+RaiQL2T/HarJ1uuXwWh9uK63PmK98P8uBUbdZOLbdTIYkBclq07136xo146rbpJ49gsT+95jYu5qwOsHqpqAZ76QOPXJ7juS07CPZNhm7G4bI3MFJmnu+XpsjsRXaxLTQz9gSoz9g4m/zbW3vJP3FPC4e39fyPZWyDsvhHzwKqBsDVBbAXzwD8grD4M0u9ar9Pshl3wD+dlbkKWrIefOgvzfO5CfzYB852+YmvaNcrlLyoFl5fH5TGo0j1TIy1QPtyMzVWDscOsy7/4Y+/cTLrRxXKH6u97rB9ZXB/9+20awYTy4DODig62PRQ7YESjOUY/7dn33120W4Rg6nyx3Pg0RqemOjeMRInvhQQaW3mnvIKzDD7zvIPwnHKuwnVpXLjDndUi/P2bL68l0eG+H158cQbLU/0i/D/KH/0F+9R5koyLtqp+z2iusmq0z91GPXWTjlLkvNLbp3+nqKtgOjrTbjFQ2AR0h4dxSSn0wWkiYciTH4ZEGo7FJJCIiIiIiIiIi6r8qNCFCAgZGeGw8zYyIKMY6Q8x6sgoNC1emKxhN3Qm9RdNpPdEqNfXPcg3V1p2IiIiIiMiKS6ifmiw1QfkUveaAOnxbF9ZNRAxGGyzOAXA0gGIAqQCyAIwBcBiAWwD8TwjxnRBiis357dzj9SaH9dnY4/UwIUSuw3kQEREREQ1s65fFZbbyyZsdlfdrrmN51Ne9usnOEDj/QAPnH2ggO6PvQ9EAQM54BvCpw7dCiesfhzhnGkTBKOX4c/cT2nC4KeMFxPAiiBMuhDj6LIjUrkSqPUYCRdnq6TI9AZzaNEs90t2/g9EwYb+wRTzwY5x3pXLc4m33c1h1po1HAESk5PPT9SMPPQm73HQHhoXphDzab6OXd0sj9lj+in50SMfk7HR1mWQIudIJFz7UF7LSgmGPVqqagIPuN3Hqk2a3DuqRaGrXT7/06xWQC+b0Gv7ZCon8G0xc+W/1tHNWAI9+0tW4Ow37qNUEXCWSqdk4XIpVc87+6vUlJXDpSyZavfHtpd/QKjHielMbDKZyrqLOct1SyOOGA3Ne7z3Blo2Qt50dLNdUD3nDiZBXHg55+3mQ502CvOUsyFunQt5+LuRD1+CMd86H0DzB57UFcQpGa1YPD9cWhnPSntb740vzJPyB2L6ncG3TbgWA0FRrWTlQ2yJR1QcPkt1xODDjSgP77WD9mRmGwLET1GUe/aT7m48kGO3DJUzGIIoVXXsUr8DgnfMFHplq70Ds//4hUWkz/CecZouwnS3uIqChBlg2PybLUtG9i8xU/TRt4U85ibqRNRWQF+8Lee0xkDefATl1N8hFc/u6WjFllRutO3YCgMm7qEc2tQO+KM+34iHcOfZDH9sMRrN5niYlUF7f9bqtQ38tL/S6QMyD0SzaPauQeyIiIiIiIiIiIkpuuhCe4Z4CeAxPgmtDRAQUppQoh1d6yywfZNphelHrq1LPMzU4z0xNuFizP1mC0dRhlbqwOCIiIiIionAMTdxQQAYSXJPBo1kTvq07JyUiBqNRl30BfCyEuEcIq9vPAQA5PV6rrwxqSCmbAbT3GKyJBiAiIiIiGqQ2RBiMlj8KmHyyZRG5cqHt2fk0YSK6ULCkt9RGh/mTLoU47QrLIjsMF7j1xN6nTiftARy5m346j1vg/tOFMtjpz0dVI8+sU04nXP07GE0UjgHG7xu23ESverv/838lmtulZcfgZAlGk0vnA4216pE5IyDufROu9DScvrd1hUt86ptYejp61pW2yjEYLXbOP9BAbkb4cu/+BKRdZeLGN000tll3fl9UKnHpSyb2uyeAs54JYM7yYPkmiyCQZe5d4P/tSZDLF2wfZpoSF74Yvtf59a9LtPuCy7AKHFSpa3VWPh50OVeG4rvpqiMECjW/D6yuAm6dGd8whV88YaLe4Wd2zISuv6XfD/nui5AX7Q0ELH5cmjsLcvNayNceBRZ+bjn/kf5yHNI2Tznurvek5U2CPUkp8dFSidOeDODMpwN45VtTOb0uUC/aYLTrpwiUhHncwsxF0S2jJ12wQ2fblJEqsMMwdZnlFRLLt8S2Pp2O3A3wP2PAfM6FwLPB/xseN+B/xoD3KQNr73XhF2GC5DpNKNaP+2Jl1/ptjyD4Z+3W5AswIeqvwrVH8XDKz+zPvOh3JozLAzjp8QA2VEe+7zdbBMVu9hTDhADW/hTx/MPRHStlpuinaWUwGjkkH7se2Liia0BLI+Qd50NaHf/1M5bBaBbTWQV4JcO5SU/h6vTG9/aOt52cp5WFXEqqtzjHzwk5j9WdI1mxDEazOHdlm0hERERERERERNR/MYSHiJKNrv3xynY0+DX3bQKo6tgCqXksVuc8szSd0L2yHT6z73/wqOwoVw5nm0xERET/z959x0dR538cf31n0xNKAiQBBAuidAtYaPZ21rOe3tl7OT3b/WznefaznOU8G+odp6fn2bGDDWxYALHQpLcACSQhvezO9/fHgiTZ73d2ZrMp6Of5ePAgmW+Zb7bMzszO9z1CCJEoR4WMy13kbphtpTpSaVxuOyYVQkgw2s/dauAJ4DxgHDAEGASMBS4FJreor4DrgTvi9JvT4vdEplG3bNMlgT5iKKXylVJDg/wDBiRj3UIIIYQQQiSTnvFhYg2790Td+jzsvp+97zee8t1d2DIHN8V83qvz+/YT7/JBI1H/94ivrm46yuHNSx0u3Fdx5hjFU2coXr7QISXkHRZw2miHj//P4cqDFSeNUvz+AMWHVzlcNLplfnQToa3/DpvqgBPj1hlWP8datvttLrUek3BNgUjtTbsu+sLx9gr7HcvmLPKT9/B+nWx74H5w/MWoi+5ATSmFUQcY66XrBnaq/9FY1qdJrHnXTPP6yi1hRZ2BLQgv1Ame61V3+x/EvVM03f/g8vlizfTFmmXrNZEms9xnLNOMu8tl4ueamcvh5Vlw8P0u//rM5XvzjXcBqHWy+Dxzb/T5Y6OBfMCXS2F1ub9xfbgpdyFoMJot4Ko9WV8bhpd59yzF46fZn6+/f6j5ZGHbBDVNmaP5dFGwNsXXrYFF36Eb6tHl69G3nYW+218Aon72Xvj3nb7qnlTxsrXs2Ef8f4n13FeaXz3oMulbeOUbOPUpzc1vmILRzI9xbnbr0nv691B8d5PD0bvY6zz/VXK/lPMT2ji40Fxn/hpYWJzc19sZoxWPnaqYfLmDs2kQmz9rumQoHEeRmhLscR5caK8/4ZMt469tDP63rDJnwAohEmDbHrXlvtL2PRW79w/W5u0fYIfrXYrKAwRvui56+Xz0snmsnjLFWi+sUikO5aOXJhgs7oNtvyM73d7G67hFiJa01vCRYd+sdC18M7Xdx9NWvLYAXrfs8gql7ozBaBuqvMuXb4A1G+P3E2Qva2XZltpeoczdmwSmd81U7NYvwEqAmgb7qKrr7WVV9dEQbyGEEEIIIYQQQgghxNZHQniEEJ2N1/ZnrSXMEaC40XwhXogUeqYWAN6T0G0T19uTPaxym3YeiRBCCCGEEOLnwrHEDbn653NT186mOlJhXJ4dSkrcjhA/SykdPQDRJr4CDgXe0/ZbTn8O/EMpNQp4DhjYpOxapdQXWutJlrYtg9E8Zu9b1QK5Hn0m6mLgpiT1JYQQQgghRIfQNZXw+duJNe6/EyoUgqv/gf7tMHOdqa+hL38QlRL/kDBsmQSe0gmCiYLSX7/vXcFxULe/8FOYiB+HD1ccPjx4qMuYAYoxA5q308Vp9gahrTWJrokDToCHr/GsMrbmc2vZomJ4aaZ9Im0Xj2CCdjP1Fc9idfFff/p5n52gb3d7iNXow3fH2W7klgUHnWwNTPzH2j9wyLbvxCwf0eQaoHzLNTuryjrv5GQ/4UMdJTNN8eX1Dnvd4T9wadxdzet+cZ3Dntsr/jZFU2O4oeQ5/47/3Lza9deMr/0c/ew9qDteZME6/8/nD6s1hw9XCQSjaaLZ+h0nYnttWD6bjtpFcfpoxdPTYxtqDWdPdJn9Z4fs9OT+Xbe86f/1UZAT4ZnKK8k7/YlAIQjNvPFP31WPq3iNywvuxTXc4ef1b6G4QpPf1fvxWFOuOe2p2NHe957mioM03bK2tLcFRPRIwhnB7lmK5851yLnU/HjPXdP6dTRl3TY1ef0N6q14+4fYinPXaAb3Tt7rLPzYljC0ZNpze3vZ699qauo1WemK2gRuhruqLBoAE2R/SwhhFrF8zLT1vtJJoxSzVgT/tNrm/1xf2y393efoW86AdSt4MPcS/lh4j2f9lanbULik7YLRbH9ptsfhm2nfTgirSNhapL+cghp1YDsOpu1Yv7HGOxgtL9te1tmC0bTWrI8TjAawdmPzIHMTWyijSdPg2XKP25l1bxEyd8peim9W+t+eb/TouzrOdq+mAXIyfK9KCCGEEEIIIYQQQgjRCWitrSFDhRLCI4ToINmhLnQJdaMyEnsnmnUNqxmUbb674tp68/asZ1ohIRW9ljrHYxJ6VaSC7qk9EhhxcjS49ZQ2lhjLCtL6tPNohBBCCCGEED8XyhaMRnJvTi+iwrqROtd8IZ7XMakQv3Rb4VR2EY/W+m2t9RSPULSmdWcAewM/tij6q1KGmZGWboKOMcE2QgghhBBC/DJ89hbUe8w29KDGHx39v99ASLPMONy4HmZ95Ks/azDaVpbTpdetQF95hHel4y9B5XfgRVupHsleoa0/11zlbwO7jPOsM672c3arm20tf/1b86FkXnojoQnXo1/6B3q9+U6l7UF/8IK98LBTUZlbZnSHHMWdx5lnfx8wCEZt22Lhob+1dr1/zTSOyPyu2bKQA1cevOW0T/88c9uVqypw77oI/czd6FWL7OPvAJ05GA1gj+0Uq+5O/NTa3ne6/OZxl//NSPwUycyM3aI/fPI6ruvy+DT/fc3Z9FYJGoxW3gnCB2whASGP18b9Jylr+MDiEnj4o+ScqtJa8/pszTUvu3y+OH79I4bD18fPYunXeRww/4mkjMGPwsg69qn5xFr+8qz4j8f4u81PRFU9zFjefFmp5XXTwyPoIoisdMUIy0f4wmKob0zeqUjb66/ptmlwb3OdJSXEDcsYVAi1D8fftqy5t21C0QB6dVHWv6G6Ht76PvpzbWPwvmsaOl+IiRBbK9tneKiNv/n7/f7Kum8Zz8kTXGxfXX27UnPjU6u58NYfuDlyGgf2f4er4oSiASxO2wG+/aRZvx/M05z0WIS0CyPc8KrLkpLEPwdsj7NXwI8Eo4lAwh4fqLXV7TeONub1LvTao8lKg1TLOajSTvbwVNbZz6M1VVwZv06Q47SVTYLRNlr2s1JDkJHafNlF+yq2CzB/x+vxrq73bluZyG3WhBBCCCGEEEIIIYQQHaoyspFa13xisCCtr3G5EEK0hwJLOKMtzBGioWkmhU22Z1lxgtE6UnHDGrTlGzcJqxRCCCGEEEIkKmSJk9FagtHaQnXEfvFgTqhrO45EiK2LBKMJtNalwCk0vyZ9ELC/8qRE5AAAIABJREFUpUnL6XuZCay2ZRsf988WQgghhBDil8EzXMlLtx4w5vCfflVv2L/k1x++5D2GTZPKwxFzeUoCR5M+spvbhF6xAH3CQM866rJ7Ub+/u51GZBHySJvL+nmk/qsDT/QuB54outBa/uM68/L8isXw3/vQD16FPn8ceuXCVowyOO266Po6+HiStY4aNDJm2al7O7x8kcNhQ2GnAthre7juV4o3L3VQqvnUcJWSinrsY3PfwH9/PIabDo+w705w1AiYfLnDQUO29NEv1zzVfHVdFpE3J6In3Ig+dzT6++k+/uL20dmD0QD6dFcsvsPhtL0TG9SLM1u3XZyTPuSnkzkX3buML5cGaFsUbWkLebKpijPhvT1EbK8Nj8+m3GzF46faK7zQioC6ps59WvPrR1zumezd36794D/nKF7LvZ/dbhtHGgkkTLXSeeX/tJa9N9d7/GXVmuWl9vIfipq332A585eXpGA0gOfONT+/Edf++ZEIP9um7XqYtwkbqmGlx+N2yf6KT69xSE9VlD9of70uvM2hoGvbbgy/udG+/imbXh+1CQb/rCqLX0cIEV/ER1BjW8hKV3xxncNJo4Kv6KVZcNnzmnCLD/PXvtHsfmuE278s5Incc7i11w1My97XV5/z0gZFf/j6fQBuedPl4PtdXpoVDSi68x3Njje4fLZIJ3RMamuSnWZvI8FoIpBGjxfMzykYzePtpzw2J0op6z5jWU3nug/XBp9P193vxj8ICxKMtrpsS+XyWnPD7lnEnGfITldMv87h/H0Uu2wD+3ifOvMORouz3esMx5FCCCGEEEIIIYQQQohgvAKGJBhNCNGRCi3bIFv4Gdi3aU1D1kIqRJaTY6xX5TF5vT2ss4w/RAo9UgvaeTRCCCGEEEKInwulLHMwtGVCqWgVr9DtbAlGE8JKgtEEAFrrWcCUFosPs1TvzMFojwDDAv47JknrFkIIIYQQotV0ZRl82XLX3B910R2ojKwtv2d1gYN+Y6788SS0YfKt/uBF3DN2Rx/aA/eaYwmXmdM7Uj0yvGL6XF+Ee+1x6MN6Rft+5xn/jZNAP3q9d4XTr0WdeCnKK1GnPXTJhZ59Ypc7Dow/uv3H0xb2O847AA7Ytf47MnVtoG57hUu2/FKyGv3svYmMLjC9YS3udSegD+uFPqibd+XBexgXH7ub4u0/hJh/a4jp14W4/ViHjFTzrHA1dC/YzRwQkVW1jhvX3sxHV4eY9PsQBwxq3ke/PPOwIiqFtSmF0V+qK9AT/uz9d7Qja/hQJzubtX1Pxb/Pdlh2Z/sPrDyUy5qU3nybPpwnFm0bqO2KTZv3IBPuASrrgtVvC7Ywt1CcfJYjRijOHGOutLC4lYMCHv7I5V+fxX9Al97pMOvGEKdsX4T6582tX3GCTqqwh6R+v8o7rOH71fZAns3lTdlCFHpkJy+9Z2C+PQxoZRKDuPwEo9m2uQCzV5o7+P0BiodOccjb9Jh0zVS8f6VD7pZdO/rnwbxbHAbkt31CZFqK4tzx5vU89emmYLQE8/wkGE2I5LBtj0LtsEtS2E3x/PkO7oQQ7oQQlQ85cQN1Nnv4I03aRS673RJh2gLNizM0xz3qokls2zY/fWcA9F0XUVyh+cvr5gdm/N0ueZe7nPqky8YAYUq2xzktxR4YLsFoIpCwxwumvqb9xtHGPIPR4rRtuj/U1IfzEx5Om1jv89vuqT9CbYP3dihIjmPT4N1yy0umu+Xb/IKuisdOdfjmzyGm/tH7fE2Zx8uxOk7wWWc4jhRCCCGEEEIIIYQQQgRTbAkYyna6kCMTNYUQHahpmFlTtmA0rTXFDUXGspYha7btW7XH5PX2YPvbeqUVElIBLugWQgghhBBCiCZCmI8nXOLf/FME5xW6nR0yB3ULISQYTTT3bovfR1jqbWzxe68gK1FK5RAbjFYepA8brXWx1npOkH/A4mSsWwghhBBCiKT4cgqEE0iZ2GU8HH5GzGK1//Hm+pVlMOODZov09HfQfzkVlsyB2mr4/G0aXplgbG6bAN6SDjeiLz0YPnsLaiphyRz0HeeiP3rFXwcJ0OXr0R+9jP7sLfT0d+DTNz3rq+MvbrOxBKGUgl+dFlsw5ghUbqDDrk5L5ebD0efGrXde6ZOB+s2PlDRf8NbEQO2D0nO/Rj//APrX28Knb0BtnNnPOwyFwaOSsm517xv2wmfvRa9baSzq7xHSsyqlycU9sz9G13Ts3Q03s4Uu2YKPOlr/HoqKv7f/qbYf0ofwdo4t295ufVV0Mn7QYLSqOBPe21plnea9eeYyP6F5Z401v4Aq66C6PuCD0URJpebS/8Zvv10P2LaHQofD6NvPAUNIaVtTN06M/g+8vNIcoLpkvaLG4/H4ocj7b333B4276cVV26CtAVo9kvjdTWqKotCSUbm6PPHntiU/wWjbdLe3X7DOvLyX4bE4YJBi0e0Oz52rePECh/m3Ouxc2H4bwZH97WX3THatARePnar4/Fr7G3JlafKeDyF+yTrTvlJ2uuKDqxzeu8IhO91fm29Xwf5/c/nNhNZdPLE0dfvoD+UlTP7Bu6+NtfDcV5pjH/G/Tq/tflaauUyC0UQgXvuDtZZ02a2Q16e/irPd6pFtXv6vzzSPTg0WdtiWNgS4DdiXS73LgxynNQ2d3WjJme+WyG3OWqhpgLpG88DiBaPZxiWEEEIIIYQQQgghhOi81lpCeArS+kavcxNCiA7SMsxss7Lweurc2C8lysMbqNfmi1xahqxlh7oY61V10mC0AstjIYQQQgghhBB+OMp8zb+rI+08kl+GakswWoaTRYpKbefRCLH1kGA00dSyFr/bZt4vbPH7tgHX07J+qda6zFhTCCGEEEKIX5oDT0I9+QWcciUUeKRRtKAeeNd8wdFeh0KW+Yt6/UXzbGT9evMwKg3ckH65sW2K3xuMfT8dVi2KXffk//jsIBj92Vvokwej//xb9LXHof/v15711SV3ofIK2mQsiVDn/gVOvxbyt4FuPeCIM1E3Pd3Rw0oqdcldcOKl0b/PIs8NdoiYHy6JXykJtNa4t56FvmAc+uFrfLdTD05J2gWBKi0djjjTWq5P2BEdiT0BnZsFqZb3bUlKz+YLllpSp9qZn/ChziYnQ7HyLofDh7XfOuekD2Fu+uCE2q4qAx0wQ8AWhNQeFqzV7HqLPcjEz2ujtyU4C2BNy1sBBPC/r/09kPvspNDr16DPHAmzpia2sp13Rz0yNRq6GEQohHpyOuqQU6DvDgAMrZ9jrKpRzFtr7+rrOEEOReXw6aaP/w0eeR55lpCLRNkCyf79eTsEozU5056VrqwBHja9zLtr5GYrTt7T4fiRiozU9t0ADuljX981L2uWbTCXZabC3jso9t7BXL4qKbfIEELYtkehDvrmL+QoDhysKLnP4ZrD2m979dO+bEMd8xf7+zCf+iPMX+Pvs8G2r+QVjFbb0DlCmsRWIuwRjPbVe+03jjbmddwR73B5+Db2Cpc8p9nhepcP5nX8+25Dtf8x3P+ed0BjkGC0dZVQvymwrNwSQNY9y39/XlZbTtdUxwmELKnq+OdHCCGEEEIIIYQQQggRzLqGVcblBekSwiOE6Fgtw8yaKm4oillmCxWL9tWn2e85oa7Gep03GM3+WAghhBBCCCFEPI4lbsildTc9Fma2Y8scS0i3ECJKgtFEUy0vlbbdO7rlDOkdA66n5ZS4uQHbCyGEEEII8bOllELtvBvOxXeiXlgQDT7Jzfduc99bqJQUc1l6Bow90tzwlcdwL94f99zRuA9eCZ++2az488y9aXDSjU1TfB5N6om3mws+e8tfBwHoqo3ouy6Eap8XIBx2Kupkc/BbR1GOg3PezaiXFqFeX4Vz7eOojCTNXu0kVHomzmX3ot5YjXq/HHbfL6ZObiRYYkrfcOxFH6ZwsNbQ4Ub0abvClOeCNRx3FKp7z/j1AlAHneRZrh+IfV0rpci3nCctCTXPRddP3pTw2JLJtZxH78zBaAB9cxVvXhbCnRAi/JhD9T8cTh/ddoOekz6UOelDrOUnjrSve2VZsAn3AFXF63EvOQB3fHr03wNXoEvXBeskQVe+4LJ0vb3cTxBMofnaNaB1wWizzdcDxzg+8j762O1g+fzgKznqbNSHlThPTkcNH4167BP/bX91Os7UGtTOuwOgnvoS+u/E9o3LyHRrjE2+v+Sc6HN8TH/cyw5Bv/wI2nVpDGsmfRv/hfPCjGidUo9gtKDhYfH0zTUv/3wxhCPJCWPwu23a1p7/adSnW+fbuA3tk9g2NzMt2sgWVGcL1BBCBBPppPtKGamKO49zeOiU9hlIcagXm7fwG0v8f5h/uMDf54JtX0kpyLQEo9XECQgSoplG7xeMrjPvq21tvN5x8bYWx+zqXaOsBs6a6FJd37HhW+ur/Nd94zvv/dMgx2law8Li6M/llpdLd9u3/i3EC7a8/lXzwGrqvfst7ti5QkIIIYQQQgghhBBCiASstYXwpEowmhCiY+Wl9iRVmb+sNYU6rrUEPXYNdScrlNNsmS0YrTpSGXCUyaO19ghG62NcLoQQQgghhBB+OMoSjKYlGK0tVFuC0bItx6JCiCgJRhNNtZypbZtq+kOL30copYLM1B8bpz8hhBBCCCEE0ZAsNXw06sWFsO0gc6VdxqP2OMi7nx2H2wu//xwWzIKXHo4pmpJ9sLVZmjmHLVZFqc+KSTDtVSgr9le393ao659s2/G0glIK5fy8D9mVUqj0TNg+NtCpe8BgtP2rp8UurPF3IYquLEevWIBeNg9dvArdUI9e8SN6ffM7B+qrjkwoyEgdfnrgNnHttp93+WsT0AtmxSy2BaOtS2kRvjjjQ/Sz9yY2tiSyTQTv6LCPIBxHkZmm+NeZikmXOAzpHV2+U4G9ze/2Ulx/uGKXbWDvfo2cUPEy/RtXWOvPytiVH9MGGsuOGeHy/PnKGj61dL22hqrYVM78Cr77bMuClx9BX3E4uiHOTPhW2lijeSfOGSQ/r42cDEWOOfOTNRsTD1JYVRq/7c71Czjk38cm1L+67X+oPz6CSt1yUZ/KzEY98G78xiPGoq6b0Ly/7K6oJ6YT2u/XDK5fYGw2J31w9IfSdfDNNPQDV6DvPJ/5a+2BC009MlVT36hZ4bErkJf0YDT7i2D6kuSsw++2aXBv/xurFAfGBr31RDvIy1YM9M7nNcpMjf5vez5WlXVsaMlmFbWaxcWahnDnGI8QQdm2R6FOsrN06l6Krhltv556J4MqJ3qx+uoi/xejL9/gr5722O5nWYLRqiUYTQQRDnuXz/ywfcbRgVSczdaBllNiTa0qgze/69jP9A0BgtEAvvDYPw0aYD2nKNpgo2U/vVuWv8+Gk0Z515v0rcY1DK4qTihdccfNFRJCCCGEEEIIIYQQQiSgwa2ntNF8HV5h+jbtPBohhGjOUSHyLYFgplBHe6hYbNBjdsh8kWWVZfJ6e9gYLqVe1xnLCtNkmyyEEEIIIYRInEPIuNxFgtHagu3YMsdyLCqEiPp5z7IWQe3V4vciUyWt9RrguyaLUoBxAdazX4vf3wnQVgghhBBCiF8clZ6B+ssz0DWvecHeh6LumRS/g4L+Ca23NJRnLdtjO5+T7V37iTD3+hPR9bVBh2Wk62rQf73Ad311+wuoeDNvRbtQubGJK67ljhM2e9Z9HbuwxvtCFO266Kf/ij68AP27EejTdkUfPwB9YFf074ajj90e97az0TVVuGfvCbOmBhoTKamo82+FcUcFa+eDCoVQj3/qWUefOxr96RvNluVbbiBRHOoV237i7Ul7fybKGj60FZ7NUkpx1C6KH24O4U4IMf/WEP85R9Elo2kd+MOBiolnKW77tcM3fw7x2alLeH71aSxZNIh/Fp1n7Pu7jBHUOZnGsqsHL0IpxYDYpxiAOUXBJ9xXOYYkqyU/wKQJscuT6P158euEfL42enczL1+z0f94WlpZ5l2e6dbwz6LzSSVO8EVLOwxFPTMbte+vjZ9bauT+qCsfhC651i7UDU+Z22bloG5+lqFqqbHdnPShsQvffYblD9/ne/iZl7gc/Q/zvkDXDEgJJfezeOwAe9m+97g0JiEAy28w2pAAN2QdMwB65HTO/ZKpfwy+0c3cFBRke6+VdIJgjLvfdel3jcvAP7n0vMLllVkSjia2PrZw006Si0a3LMUbl7Z+x+2simeomp/L/EXDrHVKQtH7/iwv9f/H1zX6q2fb7iuFNWy1ynxNuhBm4ThJesuCB3R3RraQQYB479yUkGLy5fG3J09+0sHBaNXB6q8ut4/X6/Eymbsm+v/GWnPDbuZDxhi79Vc8c479GWkIw4zlscur4+RkSzCaEEIIIYQQQgghhBBbl5LGNWjM5xtNQUJCCNHebNuidQ2rYpatNSyL9hEbKpYTMl9k2ZHBaLbxg2yThRBCCCGEEK3jWObvuTrSziP5ZaiOmC+ky7YciwoholI6egCic1BKZQDHtVg81aPJq8CIJr+fBUzxsZ5BNA9gq/bTTgghhBBCiF86teMIeLMIipZAfR30KER16+GvcX5idwSrdyyzvIGzx/qccK497hDwyevoh69BXfn3gCNrsYoF36DP3dt/g4NPRg3cpVXrFEmUG5vYNKLue9/Nby++0TyJuqoCCjwaTn8b/cRN3p1PfhY9+VnfY1GX3QuDRkFKajTMKN3nzOMEqCF7oI85FyY9aa2jrzsBXliA6r0dAPldFBguWixJMaRm1dXA0rkwaGSSRhycn/AhXVYM7z2PXr0Ytct42O841KbkNF1dAW9NRK9ciBq6Nxx4Iio1rR1G7s9v93I4fnfN3DVQH4ZBhdA9q8WrefGW98Lomi8CryPvh8lw4GCG9lV8tSz2AZ2zWpOXHSw9pcox34lEv/gP1ImXBh6jXyc+Hv+OM36DYHp3g4WGGxsXlQcc1CYfzdfMW2MvT3PreW716exlCnFs6cCTUNc8Bit+hOwu0HdA3CBPdeyFcOjvYPJz6Psua1548MmoPtvb2zoOQ7bNgHWxZXPTBxvbFP2wEHrH/Uvi6tUGN7U5ehfzdm6zf3ykueLg1iUGWQNyPnoRvWQJ7H88qv9ODOvjPZamdujVSVKMDAq6RgMbz/yX/3SOzNTo/7bnuKQqCQMLIBzR/Hu65qWZmslzYsur6uGEx1wW3e4Efi5cV/PabPh4oaZvd7hwX0WXjM77fIqfF9v2yG9QaHsYP1Ax/1aHQTf6v3Pc3euuZfThI3EO+g07F0JuysmwZBgFWQVwu7lNcagXWW4t34YG+V7PylJ/2zWvfVJrMFqcgCAhmmn0DkbTy+bFDQ7bGgQN+mrp4CGK8QPhk4X2Op8ugopaTdfMjnnENgTcx1nvUT9ogPXcomiDcku+efcs/339bi+HjbUuv3/OPIi5azR7bt/8MY633SuplBBaIYQQQgghhBBCCCG2JusaVhuXh0ihZ6rXBVlCCNE+Cg2hZgBr62O3X7ZtWqEhVCw7ZL7YpSOD0Wzj7xLqRlYop51HI4QQQgghhPg5UViC0fB/3a/wr8oSjJZjORYVQkR1oukRooNdAzQ9oxcB3vKo/+ymOpsdp5Qa6HM9Tb2gta7zN0QhhBBCCCF+2ZRSqL4DUDsM9R+KBgkHozWQaly+53YwqLfPSaZunBNhk58LNqgW9OTnAoWiqQtvR13/VKvWKZKse2wo1/D6H3w3v2rDA+aC6o3WNrqhHv3q477X4ctJl6FOvBQ1fDRq8Kg2DUXbTF31j7h19Ek7o8NhwB5QU5RiTjjSD1+b8NiSwRpCgUbX16LXrURfuA/6oT/CK4+hb/od+s7z0Fqjy0rQF4yLlr02AX372eg///anx6KzSE9V7FrYwF6FVXSjCl1TueXf6sXom0//qe6OjYvpFTakeXnIm/4/tNYMzS41ls9bG3zCfaVjuZhqzTJ0hXk9rXX1i/6+VPEbBNO7m/kzLJFgtAfedznwPvv4tmtYxsyle3NU1dvxO0vLQF10ByozG7XzbqhtdowbiraZyuqCOvYC1IOT4aDfwMj9o33d8M+4bYeOH25cviK1PxWGILxVKcm50+iIxHZPPGWmKY7f3V7+0Ica3cpUDuu26asp6Cf/gj5vDHrWVMbuCCk+X5P981o1pDZ3+miHSZf4/yohc1MGZU/L5qKkklY/D365rubg+13Oe9ocitbU/2YEH9Np/9Sc8JjL3z/QXPOyZrdbXNZVbOknEnQjK0QAtkMtv0Gh7WWnAsXc039gbM1nzZb3aSxieN33P/37dcUk3tZXctXloxl7wcmMHqDIy1ao9AzU4FF07b8NaZbbPZWk9GJ61t5oy53rTFaW+atn21w5StElw1xWKd96iSDiBKOxbF77jKONeX0i+tzl5aDB3hXrwzBpdsd99m6oCrbuEvM1TkACwWibgprLa8zl3QOeorh4P/v2dEVp7D5OvGC04o6bKySEEEIIIYQQQgghhEjA2vpVxuU90woJKcsXNkII0Y4KDKFmAMWNRbh6y3THOreW8vAGcx/psRcv5YS6GutWRyrb7VqXlmzBaLbHQAghhBBCCCH8CqmQcbmrJRitLdhCt7Mtx6JCiCgJRvuZUUqdppQKdAsWpdR5wE0tFk/UWi+3tdFaLwT+3WRRGjBRKWWZBgJKqWOAM5ssagBuDjJWIYQQQgghRAJ69oHM7MDNGlSacfku/QLMtI93IqwmsYsF9FsTcY/dHn3bWf4a9OqLeupL1O+uRqXIxVmdSm5+zCIHzVUb7ovbdEzN56Q0y+zeQl9yAHpx84A1vWAW7vlj0Qd3hy+nJDZekwNPQl1yV/L680kphXrOR4jct58AsE2uuXhB+s7mgtkfoxvizG5uQ7awD/XQVeiDuqNP2BGKljYvfPc/8ONseG0CLF/QvOzTN2D2tLYZbAL0nK9wz9kbfXB39KE9Y/+dPKRZfQWMrv0y0DpyV38LH7zAgOkTjOUlGyPW0B5b0EeVLRgNYLH/UEO/GsKa+97z9znhNwimnyWEav7aYJ9HxRWa61/1bvP5sn0Z3LDAsw4ASqGu/DuqoF+gMcR0s/t+ODc9jfPAu6jfXoUKmb+oamr4yG2tZXPTBsUsK0rt06oxbjZ6QNsk9xw8xN7vsg3w9PRWBqNZtk2hzRdV1lSiJ95BXrZi35389dnZg9EAjtpFUfaAw5gB3vUy3Fp2+F0P3PHp9Lx6H2OdxghsrG2DQW6yslTzm8ddnPMjpFzoMu1Hf+1mLQ/22nj7e81/v2reZsn6aADfd6s0Y/4aIfNilyF/jvDqNxKQJpLPFprjNyi0rWmt0c/fjzs+nZ2u25Npyw8mPC/rp38rFu3IN0v3+unfy2fXc9gTD6L2P87Yn1KKXrbAxVAv1ocChIfjP6TH9jgrBTnp5s+c6o7bhRZbo3CcYLTl89HxQue3Al6nfvwGow3tE7/iCwkEnSbL+qpg9Us86gd9yheuix47WYPRsoL1B3DMLublf3ldk3mxy263RPhgXvTxjrfdq5TtohBCCCGEEEIIIYQQWxVbCE+hhPAIIToJWyhYWDdS2ljy0+/Flu0ZQEFqbB+2YLRG3UCD7pgvPNY2mMMqJRhNCCGEEEII0VqOJW7ItczVE61THTHfTdV2LCqEiJLZ4D8/5wCPK6VeBF4Apmqtq00VlVKjgOuBY1sUrQb+5GNdN21qu3lq9RjgfaXUuVrr+U3Wkw6cD/ytRfu/eYWvCSGEEEIIIZJDhULoPQ+Gaa8FalfvpBuXp6cG6MTPTM5wI6SaQ9hM9NRX0X+9wF/lgbuizv4T7LoPKqeb73WIdpQXG4wGcETlO/ytx5WeTX9f+qhnuT5zJLxZhOrWA11Wgj53dMLDNDr9WtTYI2HwKJTfmdxJpvoNhD/ch37Q47H67jMYuf+mSeSxk8TXphSyPtSDnhHDnRF//AaG7Z28AXtwXc30JTBjuUZr+KHIXM8WhreZfvcZeMX82tAzp6JGHdjaobaKXrMM3vsf+ok/B247pmY6r3c5ylfdbpFyUoigbz6dvMy9YbtrYurUuyGqasKYThF2y4TKuth+Kz2D0b6H3cxBSIn6zxf+gw38BsEMseR6zVsTfR06PhPWXpqlqWu0l+9R+zX5kRJ7hU3U2X+GX52KKrQHlLWl/nmQkw5Vhmv35mQMZe+6r5stK0rpnZT1jmmjYLST91Bc/Ky2BtmcNVFzwkhNtiXMJp6IpV+HJvs830xD19UwsCCDD+bHfw33z+uYz5CgumUppl7t8MlCWLBOc/MbmnUtgoV+U/ESOZtOR+dHiq19lVTGD+ioqdd8sgiKyjUHDFJs2yP+4xRxNSdPcJm+JG7VGLNXBqv/78/Nz+3T0zUPfah/2obOXwvHP+oy7Y8O4wduHc+1iIZ6RdxoKFbExf6zjh7yRLRHnYT60XHbzi0yvwY7aLc01osPoR++1ldVdceLqPFHx62X3wVWl8cuL0npSZqOEy7VQnFl9HmOtx9vC3NyFORYgmQr6yQMUQTQGOe1W1sNxSuhg/YVk8XrXeF3szXMx9yOKXOhrFqTm93+G8Ogwa8bPILRbI/Xdj2iYb8thV34cZ19DN0ygz8e2+SZzyFsXt+3q+CIh1y+ut4xHks0JYGRQgghhBBCCCGEEEJsXWzBaAVp27TzSIQQwiw/zX5jx7UNq+iZVrjpZ/P2LFWlkZfaM2Z5Top9MnpVpIJ0x/IlcRsqbjBfSFko22QhhBBCCCFEKzkqZFzu6q3/Zq6dUXXEfFfnnFCXdh6JEFsXCUb7ecoETt/0z1VKLQSWARuBCNAD2AUoMLQtBQ7TWq+NtxKt9Sql1HHAZGBzisFYYK5SaiawBOgG7A70atH8TeDGYH+WEEIIIYQQIlHqV6ehAwajNShzWFma+ZyXmZ8TYY31voPR9Ia16BtP9r169dQXHRZYJXwq3A5yukNV84SDHHPG90+eKjqfkypfjt//5GfRJ16KPjrJF4GMPxrnvJsxewduAAAgAElEQVST22eijr8YKkrhX7cZi3V9LQoYZr8WiDnpg9m35tPYguXz2yUYrSGsOfVJl5dmxa/brzFOcs3UV+2hjP+9Dy64NfgAk0R/9DL6trOhwZA45sPIOh8P0CY9IqU//ZwXKbPWKy3agOkUUY45G5NaJxONObxAz/0KxSW+xxjPbW+5/HmS/2ARn3lmDOltnuBf0wDLS2H72GvejP73tffYzij/T9w+1N/fQyU5TC4opRRD+8CXS2PL5qQNjllWHDIHWga1e/+kdBOja6Zi3i0OO99o3wd55gvNhfsmtn9g27w0C0YDKCumX56/P7J394SG0iFSQor9B8H+gxTnjHW586wHeSrlWDLcOo6oeps7i7ec8u0VXm/tp6QKBprOTm+ydqPmVw+6fLvpprchR/P02YpT9vROQHznBxIKRYNo0IhfVXWaF2eatwGrLJvcfe9xqXnYISPV32tPxwvGarNArs11dMJtE1l/bFsf60/W42FoawvD2hr4DQptS3rFj+iH/uiv8pjDfYWiAfSyfO9fEupJN9d8sYBNfTgaCtolznXrtqBNpez7S/ECgoRoJuyRtLvZsnlbfzCax3bV72mbgfkwvC98b56/AkBjBF79RnP2uPY/FxS27Cf27gZrNsYuL66wPyi2bc/OhdF9DdO6Zq/U1FpeTvECaU365cav0xCGZ7/UEowmhBBCCCGEEEIIIcTPiNbaGoxWmObjDhZCCNEOMpxMclN6Uma4NmVdw2qGMWrTz6uM7fPT+hgDALI9JqNXRyrpkZqc66b8qnfrKA2bb8yZL9tkIYQQQgghRCs5mC86dom080h+/sK6kTrXfOdTr2NRIYQEo/0SOMDOm/7F8wFwptbafNbPQGs9VSl1LDCRLeFnChi16Z/Jf4HztNbyiSiEEEIIIUR7GXNE4Cb1tmC0IEeStvSQphob4lbRa5ejn7oV3n3G33oLt0U9/omEom0FVCiEHn80vPN0s+U7NCwlpMNEVOwLrujUz8m/IX7gEIBe+B3qIx8BagGpS+9Jep+JUkqhzr4R1xKMRiR6+F3YLToZurwmtsqK1NjwnggOf/8qj3cWRNv/fn+HX+9mfk+9P1fzt/dcqurgsGGKKw5SZKVH606eo3l0qsucouhk+IEF0b6OGLGlr/99rX2FogEMr/vBu0KpR9Z7r/a5GEg3NsCkCegHr9qy0BAAGNSw+rm+6+Y1CUbLjdjXu77SvJ22BX1EVAqNpJKGYcb9e8+j/+9RVEYCs+5beHGGDhSKBv6DYAb3tpfNKfIXjLaqTPPJQnv5uC4rOWfev+wVDjwJddPTneZzakgfxZdLYx/vObudCZOvabZsXUrrL/AbMwDf4VCJGFigmHiW4sx/mV9DL83UXLiv//5qGzSPf6x5cYZmrSV3x2kZBlu+nn65/oLReuX4H0tnoBd9h775dELL5vEn4E9ca6yXravJcGupczJjykoqvddxy5v6p1A0iIZVnTVRc+QITZcM+2snXmChl3Lz94xGb36X2HqyLnEp7OovzEuIRPkNCm0resUC9O9G+Ks8YDjq9hd9992riznctCSlFymNYd/9bFZckXgwmqPsbSsTy8AVv1R+gtHWrmj7cbQxz2A0n30opbjreIcTH3c9g7bOfVpz9rhAw0sK2+d33+7mYLQfi+192U6npadEj6nnrYkt+2qZvb942zqTQYXmbW5L90yOX6c6/qk/IYQQQgghhBBCCCFEJ1Ee3kC9Nn/ZUSAhPEKITqQgra8xGG1tkzA0W9CjbXuW5WSjcNAtb5AIVEWC3awrGYobiqxlElYphBBCCCGEaC1TYDSA23JuhGi16oh9AkVOqGs7jkSIrU8nuG+8SLIHgeeA5T7rVwOvAgdprQ8KEoq2mdb6bWAY8BhQ5lH1C+AErfVvtdbVQdcjhBBCCCGESJxSCvWm/QtykwZLMFp6kGA0r5mvm8UJRtNlxejzx/kPRTvkt6h/fY3KK/BXX3Q4NWJMzLLu7kYOrXovZvkxu0BBxHwHPKNpr6Bff7I1w4uh3ixC9d4uqX0mxZjDzcs3nZBWSrFdD3OVd7IP5bUuRzM1azylTi4AlxY+wB/XHMGH8+HD+XDcoy5nT3SZNFszeY5mZammrFpzw6suhzzgMnkOfLYYbpykOWti9L3/5neaIx9yef1bWFwCS9bD5Dlw1D9cXvtmy/bhpZn+AmZyIpX0D6/0ruQVyNij0Nd6Wks/el3zUDRodSgaQK/IevJT4qQJbZIX2XKKJte1n67ZEMk2LrcFowHUGkKONtMXtD6BoLhC85sJwb9IaRkEo8ON6CVz0PNmoL+fjp7xAbqilC4Ziv4Z5sdxblH0tbihSvPZIs20BZoNVbGvzxdm2F+zVx+ieK//I6RiDmhRtzzXqULRAIb1MS+fU94F9a8ZP/2ugeKUXsa6J230H2xz+UFtf1r6hN3tj+/UBdHXmV9/+J/myhc005fY6zgtL4gsK6ZfbvznWCnosRUFo+mVC9Fn7QHL5sWtq4hut0xKKr0f/8emxZY3hGHKHHub2gbdumA0Q3Cojdc2IJ61FVBcCRuqo+usqIPqeqhrjP6NEoomWivVfI1C0unqCvTiH9AbN2xZtmGt/1C07K6op75Epfg/wOxluSFaSagX1U7wjWmxj90q2yGto+z7S1UegU1CxPATFl8c+OvbTsfrkzPIbvFhwxTf3+Rwx7Hejf42pf0/UG2f4cP6msdaVA5l1eZHxiuUcYgl6Hn2CvujnJlqLbI6dGjwNjbV9aD9nCMUQgghhBBCCCGEEEJ0OFuIEEgwmhCicylM28a4vOl2bG29eZtma+uoENkh83fP1R0QjGbbJqeoFHqktv7mlkIIIYQQQohfNscSN+QSaeeR/Px5hW1nSzCaEJ6CTGcXWwGt9atEg85QSnUHhgL9gAIgi2gYXjnRALN5wHda61Z/Mmmti4GLlFJ/AMYC2wKFRIPXVgPfaK2XtnY9QgghhBBCiMSpbj3goffRd18EKxdCahocfga88ww0xN7l0RaMlhbkSNIroGizxjizxt+cCGXF8fvJzEb94T7UEWf6GZnoTLY3z7adWHQuJw+bzIeNwwA4aDBMON2BqQECptIy4JtpyRgl7DgC9Zdnou+lzsixpGC4Ww77++XCbEOu2AvdTuSFbif+9PslpY/yz+5nxNSb+Llm4ufxJzO/OFNz2SLN/e+51snh17/qcsyuDkop5q6J2yUAu9XNplVRUg1tn1KhVy+GF//RNp0fcALDcrL4cGH8qj0iW0JK0nUDWW41NU5sCFql5QR6Toa97xoni26u5aT8kjnooqWoPtvHH6SB1prCqxMLMWgajKbnfIm+7SxYtTh2HSPGMqTsalbkHBpTNue71dwf6stVLzZ/nd91vOLqQ9RPYWa28KVhfeDuExzc20vNgzz4FNT+x/v8i9rP0D4KU1zF2gpY0nUY24/cH2Z+RJmTS6Nl/+CK0r+zY9dqHsw4k2rLWy0tBW44XHHcbkkcvEVWuuKpMxTn/Dv273I1vPKN5sJ9429Rvl2pefKT+Nu92GC0EkbuHn+cPXMg1DLVrxPTj98YqH6v8HpWpvaLWV5SZW/j2pJAiH6+HD/S/Hi98wOEW5GBUuYzGK0hrHnXI6BNiI62V2IfwYHoN/6JfuS6aPBrSiqccxPseTD6nL38dXDACaj/exQVCpbiZg1GS+lFQXidsey0vRUvzNDUG/JKWxOMphR0sewvVcYeXgthF44fjMa6OOHQWwGvTKygecHb9VRc+yvFQYM1e95h/vB/6EPNVYcE67e1bMe+I8zzagCYUwTjBsYutz1cjoKBBeZ99x/Nm0EgsWC09FTFuB3h00XB27bk6mg4mtdxphBCCCGEEEIIIYQQonOwhfB0DXUnyxIWJIQQHcEW1rh203bM1RGKG803lC5Is9xFkuiEdNOE9aqIvxuaJpNtm9wrtQ+Oaqe7pgkhhBBCCCF+thxlCUbTcqfvZPM6prQFdAshoiQY7WdMa10OfNbO62wAPmrPdQohhBBCCCH8U7uOh6e/gfISyMhG5XRDX/UQ+ozdYencZnUbVLqxj/QgR5J+ToQ1ek8C1t99GrcLddm9cOyFqJQEZnqKjrf9YOPiPLeMKfPHU/zKRtJSIC87OltaV5b573vjhvh1AJRC/ec76NkHMrNh7XKorojO3nYj0L0n5Pf7KRSpU7IFS0SaBKPlmSdQt/Rw3kWtHs74u73f//PXwverYWC+Zsl6f30eXfVm6wZVFeC1kyD93/sTazjuSNSZNxgKFGRmQbeeqG49GPK8y4cL4z+HeZGymN9NwWg22Wn210qNyvRu/PEkOPly3+tq6tkv4/9tNqFN38nouhr0zafDmmXmit99xuD8I3jXEIz2zKI+PLModgzXvKzZewfF+IGwpETztaXr3+yxaRtRaQlG69Ld82/oKMM8bup8zMMu3x9wInrmR6xLsd9ptCC8jlvmX8zV75xJo3bIy4Z1FbChCgb0gtIayM2C7PT2246eOUZx5zuaRYZ81Zdmai7cN34fT37q7zXptHy/lJeQk6H485GKW96099FrK/oOS1dXwLRXA7XpFSkxLi+usD8mpdX2/rLMuXxANHDTy+3HKs4YrXhvruasibHrL6+JhjPG+6xfWQp1jZ5VhOgww/vCUSPadjur534dDdveLNyIfvxP8PiffLVXD7yLGrl/QuvOtwWjhXpSbdnPycmItltp2A0sqdQQJ3bXK5wox3zITFXbZ/GKn5M450QAKF7V9uNoY157VIlutUZuC327w2pDdvqK0uh7vFeX9tv3tGW79s9TdM3QVBhCE5es14wbGDtGW8iao6B/nrnMK+wx02MfysuRIxSfGo6PEnHDa5oHT+7E51SEEEIIIYQQQgghhBAArG0wn5O2BRAJIURHKUwz352mKrKRqkgFdZEawtp8gUeBpS1ATqgLpvvRmMLS2potGM0r2E0IIYQQQggh/HIwz0NzW940XrRateWYMsPJIkXJfFghvEgwmhBCCCGEEEL8wqiU1Gj40+bflYInpqMP6tasXoPlpEpakJuMNQlksgrHSZZYMse7fPAo1ImX+h+T6HRUVhf7JOmGegrWfoPaefefFulVi5I/iEN+i+q/05bfe2+X/HW0Ncfy5nS3vA9tE6g7ygszNCeOVGgf86yz3SpO2fhC61ZY0bbBaHrZPJj0RLBG2+6MOvUaOPS3voL3hvm8pikv0jyYKzdSxqpU+wVdLWVn2MtqnCzPtvqHLxION/j7B4lPui+rAR2JwPv/s4eibTK0fl7g/id8rBk/UPH+PPsYtwSjGdIZALp2sjfhJr27QfesaDBUS3PXQMXo4+nS7UbWNdqD0fI3BWB1ffACnBueBKBP9+g/gCxLeExbUkpx0ijFHW/HPmdTF0TDufK7er9aX5/tMxitRRis3rAGBfzlaIcvlkSYMtfczhby0xnpM0cFbtMrbA5GK/l6JvxmT2PZizPtj3mG5Tu/l2dqfjRdlbrJO39wOHRo9Lke3BtM8Syujm5H8uJkSJZUeZcL4Zejov9CTvRf3J8VOJv+b1knOx322UnxhwMU3bLaLnRGN9SjLxiXWOM+26PueAk1YFjC6++VYw5uLQn1pNIxJ01mp0F+V3MwmleI0Gaux7UdXTLM46k0hB8JYRX2EYy2+Ht0uHGrDoP3OuZLNH9cKcVv9lDc956587++q/nbie0XxGULMws5sH1P+NYwl3ClJU/Z3peiv8/A86YyE3zp9Evi4ct/v9I8eHLy+hNCCCGEEEIIIYQQQrQNewiPBKMJITqXgnT7tXDrGoqojdgv8Mj3CBbLCXU1Lu9cwWj+rwMUQgghhBBCCBtHOcblrvYxH1QEUhUxX7ScE9qKJpQI0UEkGE0IIYQQQgghBCo9A33gSfDBltChBpVmrJseZDKl9nGHgFrzxQdaa3jub1BsvgvlZurSewMMSHRW6p5J6D8eYyzT91+Oeuzj6M9awydvJHflO+2GuvD25PbZESwnpJu+D5M5qTkZ7nhbs3xD/HqZbg2Pr7mEwohH6o0f1RXocBiVktxTYrq+Dv34DfDiP/w3SklFXX4/6pjzAq1rWF9/k+BbBqP1ipjDiWxyPEKsalWmd+PqjYHWtdmSEs2M5Qk1BWDRlE/Qtx7iq+7g+vmB+588J/q4f2++3oxd+8GO+ZuCFyrMCQeqS/fA620PSin23A5reNcbi7vyu+smUHzvq8bybpFyMnR99Jd3n0GfcgVqh6FtM9iAThxpDkZzNZz3tMuk3wdJfLVTLe+KtGzLa+y/5zkUXu3SaPh+cES/9gvraA29fD6sDf4G7RlZb1xeUlSKXjLH+Dq57PngwWhXvWjf55xyucNBQ7Y8zr27Wasyfy2MGWAvByhu/+tc24xjCNgKGsiVeFsVU9/x2U9yxhK7/mQElPlt6yh8BaJ2NvqqIxNqp+5/GzXqwFavv5flu/96J4N1KQXGspzaEvK79DSW+QpGs2ySHMe+v1QfhsawJjVl63uORQdo9BGMVlkG30yDPQ5q+/G0Ec9gtFb0e8ex9mC0+9/T3HC4Ji+7fd6LEdv2QkWPxY3BaJbsbq+QtX65wcdm24eKp19u8BA2m/USLiuEEEIIIYQQQgghxFZhbYP5Wj0J4RFCdDbdQrlkOJnUubUxZesaVlEbMdwhEshN6UmGY7/+zRaMVt3OwWiudiWsUgghhBBCCNGmrMFoLedGiFazHVNmW45BhRBbSDCaEEIIIYQQQogop3kwSL0yz/JOM+SH6OoKmPoqZGTBbvug8jZNSvea+bq57b2/R/3r6+jPGzfAV+9BQz16/gx4bYJ343FHwrC9465DbAVGeUzwXvjtlp9L10F5sIAno+2HoH5zOfTdAYbuhUo1BwFuVUKWcJ8mM6r7dU/OhOZkevZL+5gmFF1Ed7ec0bVf0ju8NjkrLFkFvbdLSld6xY8w80P0fX/wVV9dcBt07wmZOTBsL1RB/8DrHNrbX70eLYLR+jVa0rwssj3eEjUeF4YBULQ00Lo2e2FG616fB859xHfdIQ3zAve/vgoawpo5q83jHLVdk8CFynJzJ107WTphE5cd6DBlrvkLrBdmaE79/ZEUl+0Dr8eWF4SLm/2uz9gdJm9AZeW0xVCNtNbR9+OHL0E4jDrkFBi5PyO2gYH5sLA4ts0b38GfJ7n0z4P9d1ZkpsF7czXrKqLP5/47Q4nPAAen5Zd/S7ekzOVmKy49IDa0Iz0FzhzT+UNz9OxP0H+7NKG2vSzBaBtCPdFT/ou68LZmy+sbtTUIBKAhHLusuEKzwpxFSH4X2G/n5su2yYUuGVBZF1t/TpFmzADv56S4Mti26qL9FEP7RLerzYPIVIcEcm1uq7bSYC7RcfTcr2H2x8EbHn1OUkLRIPqetlmaup1xedbkp+i139VA7L5yiY9gNNs73lHRbYlNVT3kyrewwg8/wWgA82Zu3cFoHmWt+ThKS1GM3gGmLzGX732ny4+3JScINx6vMLNtLAFjq0rNj4xXX/0DHlKkOJASSuxB7mzh6kIIIYQQQgghhBBCiLZV59ZSHjbf4bBQQniEEJ2MUoqCtG1YXrcwpmxt/SrqXHMwWrxQMduk9KqIjy+Yk6g8vIGGzTeqbEG2yUIIIYQQQohkcLAEo2kJRku2KkswWk7I4+JoIQQgwWhCCCGEEEIIITZrEarUoMypOGktjiT1vBnoKw+Hqo3RBXkFcNv/UMNHgxuJv95F36HDjbBsPvqqI6HUZ/jR8Rejzr5RAh1+JlRKCjp/Gyg23HW0oQ4992vUkD3go5eSs74LbkWNPTIpfXUajmWyd5P3Yf+ujUBq+4ynlU6oeJmzN/476f3qs/aA11eh0szhj777efc/6LsuhHCjr/rqn1+hBu7SqnUCdMtS9MuFlWXe9XIjzSts02i+o69NaggyUqHO8OfVqCzvxmuWoetqUBlx6rXw9vf2uISR28LyDTB+IGyshQ/nx9YZU/uF73V1cavYp/pjPs7eJ9AYv1wK36w0lw3rE/1flxVDuSGFC6BL90Dra0+HD1dskwurDK+t9+ZGQ+HWNeZgCnTIj8T+vfrQHvDKElSvtr8QT7su+voT4LO3tix752nY7zjUX/7DiaMUd7xtfn3d9tbm5S3LNYcNNb8HTJyWX/6tL0JXlqG65AJw57GK7HT4zxeatRthr+3hL0c77Nqv8+7HaK3Rd5wL7/4n4T7yIuaNVVmoO3zxLrQIRjO9/pqqNlzvOafIXv/w4SomCESpaFDZF4YAFa++NisOcJ3r9Gsd9tqh8z7HQgShn7s3eKNdxqOueDBpY+jl8d1/peXi9JyNq+hVOh8YGlNW4iPo0LVc26HwEYyWHbd78Qunw43oJ27yV3fZPLbmTxSv3PzWntY5chfF9CXmFSwqhsXFmgH5bf/oeYWZ2QLGbMd1Xn11y1LsVAA/rvM3rsxW5MD36ZZ4WyGEEEIIIYQQQgghROv8UDWDGZWftOs669xaa1lB2jbtOBIhhPCnIK2vMRhtVuVnRDBfvxwvGM02Kb0qbJ7EHsTGcClfVXxMUf1ydMubMAZYX7y/QQghhBBCCCH8cJR5HtrKusVMXHN/O4/GnxSVyvYZOzOq63jSHY8LeTvQ+oa1zKj8hLUNq9k8T2Vp7QJjXVs4txBiCwlGE0IIIYQQQggR1SJUqd4SjJaesmUyqY5E0LefvSUUDaB0HfqhP6ImfGqfRd5S0RL0Q1f7D0U76TKcS+/xV1dsNdSf/om+7BBjmb5gHHxch37wquSsbO9fJaefzsRHMFrv7DAhrYiozn9KaEj9vLbpuLoCPnsT9j8+oea6ohR998Uw7VXfbdS/Z6F2iA3jSNTwvvGD0XpESpv93i8cLBhNvf0vskInU0fsFwW1TqZ3Y61h2TwYNNL3+sIRzawV5rK/Hqf4v8O23IlmcbHm4PtdljW5SfHtxTfSO+zzM2STO4tvZOz20wK1Of9pl42W64CH941+PuqrjoKIJRg0v1+g9bW35893GHdX7Gd3fRiG3uSSZwl4KQibg+D0cTvApBWovIJkDjPa98YN6Ak3wuxPYMWP5kpTX4Fpr3LiyOOtwWhe3p3jv65julhx6VwYMRaA1BTFzUcrbj468DA6zhfvJhaKtu+xUFcNX04hr8W2aLPSUC4snodubEClbtnnjLdtq6yLfR5/KLI/t3870RyCsnOB4gtDgMrajYbKLZT4DEY7e5ySUDTxs6Ary9EPXgnTXgvW8Mizca55NKlj6ZIRDepuCPtvk+NW0Wvl15iC0fwEHdq2MI4DOR45u5V1voYnfuk+ecN/3WVtdHzUCbT20/LCfRQ3vGrfH7j6RZdXL7EcLydAf/Mx+tl7oufChuyJOu9mVGa2PcxM2QPGNlSbl3sFowGcMNIe/NtSZiuy0VNTkrsvE3E1IUf2j4QQQgghhBBCCCGE8GNtwyq+qgh2TUNbSVGp5KX27OhhCCFEjEJLQFhpuMSjjXfQY45lUnp1pHXBaMUNa3hg5Z8oD2+IX9lD11AumSG5S5cQQgghhBCi9Rwc4/KNkbJOc17K5PON7zN94wf8vt9NZMSb39TOVtQt4u8r/0KNW+Wrvi2cWwixReefBSuEEEIIIYQQon20CFVqsASjpTU9kvxmGiw3JNbP+xq9bgX2aeQtVFXArKn+6gLquAt91xVbD7Xbvp6vGH3jKd4djD0CPnsr/npeX4kKJW9SdKfhmE9INw0oTKGRvuESVqT2D9z9/62/h+IRR9I9SzG1vB977JRBWnoK/fPguN0UPXLg4PtdZi5P9A9o7tCq95LTkYG+7SxIS4fC7WD7ISjbYwfoxgb4cTaUl0DVxmjbIM64PqmhaACHDFW8/YP39rUgvK7Z79s0BgtGc9YuJzN3A6TGXjxWq3x8cfDVe4GC0eatgZoGc9kBg5pPnB+Qr/j6qjpeen8tRRsdDnn5dEbXfuV7Xex/POqQU9h70fc88vKlXNz7Id9NF6wzL++aAaMHgH7/f7BwtrlSr76wwzD/4+wAo7aFFAfChiCGxSXRfyb5HhcT6jNGwkuLUOnB78aj62th7tewbgWkZ0Lapj5SUtFXH+Wvj+f+xognjqdrBlS0YUhNvGC0zkY31MPqxdHPiB2GolTz95mur0M/fmNCfatfnQZ7HgyfvUX3mRvBsKu4MdSdSMSlfsliZjuDGFgABV0VK0u9t21V9bHL5hSZ6+65HeRmm4M38i03ViqpjL/vagtG2zEfTt5D0RiB8QMVv+rcb3ch4tLhMCz+Dn3u6MBt1c3PJhxC69mvUuR3gVVxQhSbynZryF/+BRSeGVNW7OO6ddc1bxcU0aA2G9P2SoiW9Cev+6+8bC66oR6V5pHI1wnV1Gu+XgafGwJJkyU3WzHlcodDHjCniU36Fh78wOWsMYquma0L5dLzZqCvOGxLEPKcL9Fzv4JHp2HZXBByIDdLYTpHVl5jbhMvGG2E93ydZjLNp/c6RG0D5HTOm3QKIYQQQgghhBBCCCE85Kf2wVE/w2uthBBbvYI4IWfmNuYwtc2yLcFoVZFKtNYx19j49V7pK60ORQMoSOvT6j6EEEIIIYQQAtiqz/csqZvPrMrPGNPtoI4eSjNvrn/edyga2I9BhRBbSDCaEEIIIYQQQoioUPNgIFswWnqTI0n9xj/t/a1ZDlUbfa1af/62r3oA6q8vo/oO8F1fbGX2PRamvWousy3fRI09Eu0VjJbTHXXHi6jc/FYMsBOzBqNFtvwcDjO+5lOe7fZbazcKF93krh871y/g30XnMKpuFnxw05aKn4C6/x3UqAN+WvTuHxz6XeNS12juOyfdX0jEgIbF7Fn3dfyKiWqoR1+7KaxjwHC46xVUQWxYnJ7zFfr6E6DUkoYVz++uRp31p1YM1OzEkYorXtBoj2yBXpH1zX7vFzQYDZcs1zxLv8bJittef/oG6vRrfdiUhNIAACAASURBVK9v5grzH5MaguEtrkXTH08i97azOK+22nf/QPQ9MvZI1DWPobK7wrijOG/WoTxd+gVfZO0drK8Wfr2bIiNV4b7wd2sddfzFnT6UMS1FsXOhPWjKJj9SbC8sL4F3noZfnx+oT/3xJPQNJwUbiMmCWej7LmPmDQ8wMLGcL18cHZteoZfMoXXRG21DL52L/supsGROdMGIsXDrf1F5BdHyH2ejz9nLX2f7HgtzvoT1RdAlF3X2n1Bjj4iW7ff/7N13eBtVugbw9xs1y73EJY6dnpBeSAiEFEgIEFoKJIHQy3KpCT0ssMCyF7gsnSV0WOrSwtJZem8LJNRU0km105zYjps05/6huGqONJLl/v6eJ0+kc86c+WxLo5E08850dOqpgJutkz0eSTsXV93TCxX7AjznTBRkhrngUbFFuN2STdbbj5Hd9b993Xq22/gOslATnjZ1mOBvU/VBm0RtidqyLrANXvlL5Asfejxk4oyY11RtYG6kwWglcFT5LPt2aQKJ6tIFHRkCxIcIG1q9TWFUj9b4KkCtyocv2B9bVQms+hUYcEDT1RNjLy80ceaTSvv+sFqU547UM2mAoH/nQOCylcteUrjhDYUnzjAwY0T0K1Tz59WGolVb8h3MHz8HMM5yGYcBpGreQu2tBCp9Cm5n/Zp8mo1P9Ud3XdOtg9asxDXyiJB5kwW3vxebYLuyKgajERERERERERERtUXhQoSIiFpKThMEoyVqTkr3w4dyswxeR/hj56wsKf0xquUa4jaZiIiIiIhixSNt+2CuFaW/tqpgNFP5sXzvzxEtk+RIaaJqiNoPnqVDREREREREAUZtWIsJgU9clsPc+4YpXxXww0fa6dSTN9tf99O32homd70NGXOs/XmpzZEzrol+4YkzgO79rfsmnwZ5axNk+Pjo52/tDE3gUt1gNH8VLt1xP5L9waGFTkPhtQsN7LrgZ/y8ZiR+XjMSa1f2weI1wwOhaBbUZUfBPPdgqBU/AQAyEgU3DFpuObbg9zwUdToLjwz9JuyPckfBNSHDhOScGyFvbgCS08POFdbq36DuvDioWfl8UNefFHUomvx7NYzzb2mSIKzOqYJD+uj7E9wK8ZkZ9dryMyL7GNCAiXhVZtm31/CGn2D9iojWt3qbdfugXMDjqn00qF3boG46DYgkFK1bP8g7WyD/KYBx64JAKNo+xp9uxMNbL7Z8TkRixuYnoJZ8ByxbaD3AMIApf2rUOprL4C6RB0Vkdw79ZYy6aw7U3mLb86mt62MTilbt9UfRY0Y8/rfvotjN2YABi/CvdcuabH3RUNu3wHz4L1CnD68NRQOAX7+GmtoV5rxpMG85x34oGgC55lHIq2sgr62FvLkRMqP+9jQtxLGgc3PuQYVZu428/5NAYEkoDcM1lVJYoglAGRTiONDMROv2bTYepoWaMVlhQt2I2hJ1x4XRhaJ5EyB/ujH8uEaYOiyy16kEVYo0v3WSWoUPKKsMvd3R9RoGYBiCRI91/ymPxyZEiKiepd+3dAW2/bFDYfZj4UPRAEBikYwG4N25od/zFJcDsx4x8cqi6J6flZv/wEMbBuL03Cfw105/wSZnbk2f/2f9+1xD9MFoALDb4m2X3zpXFs6aYDQ7FQd4Q4Q42nHe+NiFPJZVxmwqIiIiIiIiIiIiakaDE0e2dAlERJay3V2Q4cq2PT7P0x2pzoyQYxId+gNASv17bK+rrnKzDEW+HVEt29AgbpOJiIiIiChGescPaOkSGmWvaeOq6M1oR1UhfMr6Ys46/eKHNlE1RO0Hg9GIiIiIiIgooE6oUqXoz5p0OwFVVho4Ub7Y+gRzAMCPn8WwOEBufgky6vCYzkmtj/QZGgg4i2bZhGTIA58AZ1wLZHQONB54BOQvT0KufQzidMaw0lbI0HzM468TjOarwvCKX/DF+sNw0c6HMHbvVzh47zc4q+hpfHRGAaYOEyQPPwCDhuVhUMVS5Ps2hQwoAwAsXwT1p4OgPn8d5oPXYN7z++OFjafi+D2v4dDSz3DZjnux9fd8ZPh3Ah+9hHNenIS3/5iqnW7BxtmYUvK2fn0OB3D06ZC0rNgFb/z3/UDgllJQfj+UUsDPnwPbNkU1nVz3BCQr8qtRRuLEA/R/mdJKgfzze+CUK4FDjwdOuxopT3yM5Agu5iJQ8Jp7reeXhNo7Ls3rxd5iqFL7B4Jt0ryc9Mps0PDpK0BlheVYHZk1B5KcXi8QraZvyMEYNG4wvlo3AeftegzH73kNtxf8GYUr7F9ZM82/E5Peuwzq/BDBiwcfA0lKjajuljJ7VBTBaGddBHgTQo5RM/oE9h8AKNMMhA/6fFBm/dQHZZpQM/tGXIMd17wxDgkob5K5LYPRfvwssD1pBdT65VBnjgD+dYd+0LfvAu89Z3/S7K6B114RSKdcy9fZ9NAPi4jtafDnK9gDFFlvqjAwV/9Y7pRo3be9BGH/ZoWaTVsmg9GonVB/rAB++Nje4NmXA0edDgwdB0w9F/LEd5Bu/Zq0vkP6RvY6lWiWIl0TjAYAO0sDz/tKn/Vz39SEE1VX0TCwsa69Fa3jNYBaJ1X3fZrdZdpQMNprPys0925Q1wzB8Pzw42Y9YuLlhSaqfAqmaa9IpRSm/aMKc3LuxfMps3Fz5rUY3f0LrHN1BQD4fvxSu6zDAFJDZEtHEozm2Pe2Pzu5NiQtHK/1dQ9s69FJcPes2ISjldkIyiMiIiIiIiIiIqLWpbd3IPZPGtPSZRARWTLEwAmZZ8Ep4Y8NdYsH0zLPCHvRnkRH8DFm1UqiDEYrrIzuOMSGBiWMxKCEETGZi4iIiIiIqHtcXxyUPLGly4hauWlx8F0L2hrhe79JadPQyW0/7Juoo2rnZwQTERERERGRbY7at4ihgtFc914E9dtT9cOWmtqQMZBDpjXf+qhFyfVPQX3ySnTLJqcHwrJiFZjVltQJN6zHrPNc9QeuPDGoYinuK7ii3jDpubL29vm3QH33QUSrV385seb2zOJXMbP4Ve3YyaUfYteKLByf9xI+TZgAAEj37cATW87HcSXvhFyPXPVQbejYlHOBuy+JqE4dde+lwOL/AoUbgbzewMZV0U106jzI5FNjUlMoJ+wvuOBf1ifxOw1AUjIg599Sr71ruh+LN9ubv5NvBxLMUsu+UiO+9k5ON2DDSstxKNwE9NAfKFbXxl3WP0uXtNoD0dQvX0Hdc6mt+eo57pyQ3TLvIQz4KB0PbK3/WOpXsRzLPeHDXaYXvwk3Qp/hL9c/Gb7OVuKYwcClkwT3fmQ/ySIn3QV5axPUqUOBreutBxXvAl55ACqvN9QNs+v33fYqZMwxgdvvPhtl5fYsXTkI04Z9hZ9Kc2I6r2iSP9T4OOCCW4HZl4c9sLIpqefvBnbH5sqzNQYeGHZIUlwgwEMX7hGprbsDwSTVv8s/durH7hfiO0JdiJnPDISTpMZb95umwnbNhaWyklru70sUK6q4COqUIbbHy7FnQbo2TZilTu/MwL6Oz+Z2JdEsgdfUh2J+vFzh8S8VvloVCHO8dbrg3HFSs53RvRoaNp7y20qAbh57dVIHtGNL5Mss+S72dTSRddvtjYv17tErFxjodW34DcRJjypUP8P/foLgyiMk5L7aJ8uB93Z2r9e22ZWL+9Ln4J6Cq+DfvRPQ7D84DCAlRDCaVchruGA0hyHo3glYVaift5pX/xGfbX2zBfoton3lDEYjIiIiIiIiIiKyLdfTDaNTDmux9TvFhR5xfTEiaRxcRiOvwEBE1ISGJR2Eea478WvJd9jhs/7yJNOVg6GJB6GzJ/xVduKMeBhwwETwMdLRBqPpTo53ihMHJB8Sdnm3eNDT2w/7J42BIZrjRImIiIiIiCJkiIFTcy7G0MQD8XvZb60uaKza1oqNWFu+Iqi9zK+5wnoL0YVie414DEsaXXM/3khEv4ShGBA/vLlKI2rTGIxGREREREREAXVClSpEf/a2e9m3zRuKBgSF+1D7Jk4X1OijgG/ftb/Q4Sc1XUFthS4YTdU5o9oX4ixkR+1BjNJ7CNT4qcAXb8SouGBJZgk++OMYLIwbgd1GMkaW/4hUc3fIZeTNDZC0rNr7Dgfw/GKokwc1vqC6YXwRhqLJdf8EPHHAfsMhuT0bX4sNnZIEo7oD368L7jtuqPUy+emwHYw2oGIpEpUuGC2x9k5WXuD3ZRUKtW0j0KO/rfVt3GXdnpcW+F9t3wJ12VG25qpLnlwYNoxKvAnA1Q9D/f38eu0X7noEc3PuCbuOmXvCBDl27QuJ16QwtUKGIbh7lmDORIWhN5koqQi/THYyIB4v8PKKQBCYhnr0euv2Px8P/OmvwOzLoZ64KbrCAVuhhl18m/H1wv2wsM+JWOvqjp15w3HZhsgfWw3pni8AoB66NrDtOuq0Rq/HLmWagdfRlb9A/fIlsPCT2K7A4YDMuCjsMBFBWjy0YWKRqvABt/5H4aIJQGq8YIMmGC3OpQ8/A0L3/bFTH4xWVKYPY8pqO09zameUUsCvXwOrfg1sB0cdHnUQY92g27DcHqBLr6jW0xgup6BPNrDMZqZUgrkXKSH2Mc98snYfZmcpcP5zCllJgmn7jjUwNTlAdn7Feyvt1UgdVIRB1ACAzWuh9ha3iX1Lu6/9iTEOD+zRSfDgKYILNSHSVq7+t8KjXyicNKr+EzvVC0zsF9jfmPag9Q7AW4nHBILRNq8DelvP7zACYbEi1m+brILRdPsb1cFoADAo12YwWgzOV3TH6PyaMgajERERERERERER2TYgYTgGJPDkSCIiO/LiuiMvrntM5hIRJDqSsMdfFNRX4i+Oas4CzcnxWa4uOC1nTlRzEhERERERxYIhBoYmHYihSeEvmt5Svi760DIYrdxsXcFoWys3WrZ3j+vL935EjWCEH0JEREREREQdgqP2LMdKcWuHeUwbCSkxJHe8ARk8OvxAaldkUGR/cxk3tYkqaUMcmjOV6wYZhgo1dNTPz5e/PAnMvDgGhekJgAPKF2HS3k9Dh6IdfUZQKFrNHPl9II9/23RFhiHn3AiZfApkwgnNFopWbd5k64/2pgy1TurIS7MfkjKgcjmSNAdyFdcNRouLB9KyrSdZv9zWupRS2BAmGA1vPwlURZYuIhfdBuk92N7gQ6YFAl7quHDXI2EX61a5HhNKPw89aMhYezW0Mj06CZ44w97Hx9nJgf9FBHLezVGtTz3+V6jDkoFt1gcChjT1XMj7OyDPL4bMuSPscDeqcPDK53DK0psx54MTULw8HZfvuBc9vNFd1TXRX4zB5YtDjlHvPB3V3NFQPh/UvKlQfz4+EDQX61C0YeMhd75le/8sNzW2q7/+DYWuV5v4YZ3Chl3WoSd5aQgZDJWfBrg0L5vLtuiDVLaGeKliMBq1BKUU1E2nQ118GNS9l0FdeRzUjadAhQrD1c21fQvw42f2Fzj4mEBIbQsY3cv+Pk2iWQKPqkS8qQ+wbGj+p7WJRKYmnMiwUUJp8751pjZGhQoCN0Lsg62zt4/d0gr32AsmG6cJE2uM88YLHjtdkJNsf5nV24Bb3lH1/l31isKIm01tKBoArHN3R4W4Yfr1P6/DCIQPp3it+4ssLrLp16zSWeehMbCLvW1hXAyC0bz6jwkjUsbASCIiIiIiIiIiIiIiagMSHNZfNJX6ozu2qEBzcnyOp0tU8xEREREREXUkXof1Vc/LWlkwmi4UO9vN935EjcFgNCIiIiIiIgowak9qrwgRjOZWzXcWo7y0HHLQ5GZbH7UiE0+IbPzYY5umjrZENB/zmHXC0EKFZDjrny0t3gQYc+8KhI4lpsSgwOgZ1zxqGYpWTfbbH3L1w81Y0T79RgAnX9H8691n+nDglAPrnwx/5EBg5gjrE+Tz0+3Nm+IvQrJZjCSzxLK/2KiT/uN0Abk9LMepr962tb7NRfrgkPy4Eph3XBgId4qEyw1MOdf2cElKg1wdHIS2dmUf9Khca7lMvFmKB7fOgRMhAgcByCFtN7hxkI3vX1K8QIKnzmNu5sVAVn7TFVWXNwFyz7swrpwPiU8MBLPNmguZe2dk06hy3F54LVb+mIM3p68JOfaGlLeQ5qndlooy8diWC+CCL/RK1i2LqKZoqYpyqKn5wHcfxH7yw2dDviiHcf+HkJGH2V4sPy38mEiVVACzHzVx+cvWISTh1ulY9TP6YINl37It+uVWFFi3iwBZEYSvEMXMxy8H/tX16b+hHv9r5HNtDr39qycrD3LODZGvI0Z0+zoNiTLhVYHEoTSLq3nrfLI8EDoHALqoo+pgtNMO0tfy1SqF6Q/4MfQmP2Y/amKTJsyROqjVv2m75NZXAgHEVtYubaKCGqe0QuHSl0z0u96P7n/240Mbuz5ZScCdM2N/uIKI4JyxBjbf6cADJ9sPUozW7+4+8Is+KLJ6e5GqC0bbG7xt0AWjOeoGo3W2V1+8u/G/g+H5sQlYK4s8t5OIiIiIiIiIiIiIiKjZJTqsr45XEnUw2mbLdp4cT0REREREFF6cYX08ZblZVnO8b2uwlcFoRE3C2dIFEBERERERUStRJxgt1b8bNxXehEpxoyKnN6oOnYXKRV+iYt0qJJvFzVPP4bMhmrAdav8kr7c2hCBo7CurIC59mF+HYWhOxK4bjOYPEdzjsP6YSPbbH3hxGfDhi1A/fQ588UYjimxCk08F/n5+069n2Hhgv+GQfiOACTMgDv0J8E1NRPDM2cC54wT/XaPQK1MwbTjgMDTBaDbDiaYVvwUASNAEo5UYCbV3HE5g5ERg8bfBA5cthFIKIqFPxF8aIoRov/tnAcs+DVtzkMNnQ+ITI1pEjpgNDDgA6tWHgFceAJRCvm8TFq4djVeTpmGZZz8oBH6WLr7NOK74HfSqsg5Nq9F/JHDA4ZHX30r0zgTcTqAyxKZjRLf698XjBV5aBjUhst+/HXLjM4DhgPr9Z0h6FjD2OOt9haNOB164B9hm/cVSKEffOgizjt+Kl5cFp1wNK/8F1y47GRc6UvFS8kzsFS+mF7+B3lU2woR2b4eqKId44iKuKRTlqwqErlWUAf0PgDpvLLBnZ0zXAQDyt+eBQ48P+3y2kpcu0EcLRW/Ndn1ffrq+TvXHCqhLjkD/1AexNDk4xG/lH3sBWD9+l2y2/jl6Zih4ln4NlZEDdOkV1e+JKBrq9cesO/51J9SkkyC9B9ufrFKTUgoAh82CzL4cWPgxkJIBjDkWkpYZWbExdJDNt4kJZimqn43p/p3Y5LJ/YMH8TxXmTBSYms1X9dP85AMFz/7XelDd8MbfNil8ukJh1S0GEuO4jejoVFkpsNl6P1Iuug0y5hio7v2B5YuCl123FK3tEaSUwoyHTby/JPzYFC9w2eGC3BRgylBBVnLT/jQXHGoAMHHR80138NNST39k+wq1/dVhZqnxAHYE9+8uC27za8qtG4zWLcPePlZiDHY/4z2C6cMFL3wfvL6DewHfrLY3T1nzXWuBiIiIiIiIiIiIiIgoaokO66vjlfojP37aVCYKtcFoeRHPR0RERERE1NF4NcFoCiYqVQU8EttzNKJR6i9GiX+3ZV8O3/sRNQqD0YiIiIiIiAgAIA5HzemU6eYuXLfj74E7XcbCOPEkmBteBr59vPnqufTuZlsXtU5y51tQVx4Xflx2cKhJh6QL6DLN2tu+Kv3yTpe2S1IygBkXQWZcFJjy1KHA+uXRVNlkxOmCOuAw4IePm24dj/8Xst/wJps/GiKC8X2B8X3DBwrk2wwnenDrHABAkiYYrdioc0VMhxNywCSop24JHlhWApQUAUmhE9mWbrGuqbO3HGk/RhiK5nQBE06AXHpPZMvtI3m9IXPvAubeBfXT51Bzj0CKuQdn7X4muvlufKZFw/May+UU5KcBq7fpx4zqEfzYE6cLmPcQ1O0XxK4Ytwc48AhIUhpk4oyQQyUxBbj5Jag7LwZW/hzxqp54rTuSsu/CE2ln1bT1rFyD5zedBif86OTfgYt2PRzxvNixGcjtGflyGmrTaqgbTwVW/BizOa3Ihf8HmXBC1MvbDWWMpcEhso/Ue/8CSvega8Iflv3b1m0B0Meyb6n1caoYuPEDqIunB+4cPhu46gGIN8F6MFGMqJLdwC9f6vvPGgl8ttf+65BPn1Yjl98HSU4HWsl+UEp84PVpw67Q4xJVac3tbF8BfoP9oLjb3lWYMxHQXUiuOof2yIH2Q50Ki4EnvwkErlEHt26Zvm/clMD/mmC0kMu2kG/XwFYoGgA8e46BY4c073PggkMNxLtNnPVU04SjLfYMxPi9X2n7Hft+3FSvdX+RRTCazx/cVncuAMhPt1dfUoyO+XrsNIHPD7zxi4JSwDGDgafPNpAUJ/jnVyZuekthwy6gZyd9gG1ZlQJaXbQfERERERERERERERFRfQmaYLQS/56I59rl24YqZf19fLbb/sW9iIiIiIiIOqo4TTAaAJSZe+ExWj4YraByk7Yv28NgNKLGMMIPISIiIiIiog7B0Jww7993NuY2TRJEUxh1eODEe+rYho4NP2b81Kavo63QPYfrBqPtDXHFQof9/Hx57BsgM8RBOaOPgnxRDvlwF+SWl/XjTrkS8vp6yAs2z6IPV9eZ1wEezdnmjZ37+cWtLhQtUr0zw48ZXP4bPPsOxErUBKOVGnXCfhxOIKerfsKCDWHXubnIur3vnsgCreSaxyDvbYdxw9MxCSSS4YcAh0yLbuFOuZAPdkK69Gp0HS0tKyl0/4EWwWgAgKNOA/bbP3aFnHQZJEzIXl0y4ADI499C3t8B9BkW0aq8qhyPbL0IxcvTUfB7HnYv74QVqwehb+WqSKuuL8b7Uuq0YbELRcvtYd0++ijgpMsaNXXXFtilmz48RODGL4HgkiyfdeLftj2mZTsArN1uHajSv+TX2jsfvgC8/kj4Iokaa6ONbdIbj9qfr7LCuj0huVW+NxuYG35MglkbjJbv0x9wYGV7CVDlUzA1OUpSZzOTF0EA5NeNfCmhdqJwo3W72wN0DrwmS/f+1mPWtr5gtFcW2Q8cy0xswkJCOONgA29e3DSHRizxDIAf+hBKx77VpuiC0fYGt/k1uyOOOj9C55T693WSPOHH2BHvEbx0noFd9xrYcY+BVy90ICkusDE8e6yB1bcaKL7fwKpbHejZyXqOshBZ7URERERERERERERERK1FojYYLcTxlxpbQ50cz2A0IiIiIiKisLwhgtHK/RYH4LUAXTCaR+KQ4miBK80TtSMMRiMiIiIiIqIAbTCaL/D/1j+ar5Zdhc23Lmq1JC4eMMJ8dJFmI+mpgxDd78oMhBsqXxXUlcdpFhaIQ38id9BwbwLkuV+B2ZfXNu63P3DIdMjVD0NuexUiAomLh4yfCkw/L3gSjxcy5RxIRg4kr7ftdYesa8gYyEOfAydcCBxwGDBrbkzmxZnXQfL7xGauFtQ1Q5AQ5qT4gRVLa24naYLRio06SVkOF5DRGdA9fmwEo+3SfA+RVaYJjLDiTQQmnADxxPZKL/LXfwF5ocPN5KoHIbe9Chx1OnDQkcAZ10Ke+Skm4WytQfhgNOt2cbog8z+GnHtTIFxr/0Nr/0UiNRNy47OQP/01suUQ2C5KfCLk5MvDD7bgVeXI8O9EgtqLEDFbtY45C3Lh/+n7G7EvpSrKoSrKoLZvgdq8FuaD1wBV1leTjYY8uRAy905g3JTA32jcFMgld0FufQUitn56rX45jVs+Ugd0B3pmhlhnSSCNMctvHYxW6Nc/6Dfssm7vXrW+3n31wQshayQKRSkF5fdDVVUGnvdlpVCle6CKd0Ht3gG1qxBq+xZg9eLwc331tv0V67Yprhgl6sTYwC7hty2JnbNrbudVRbBfAaDKD6wshDYYzaiz+gS3/XlfXmg/QIrasSLr1yBk5NS+r+sxwHpMwR9Qm9c0TV1R+na1/cd1lvU5JM3i2CGCH64zMLpn/fZD+wIT+wX+Degc+bxLPf3hD3HYRXV4WWq89XZrt1UwmuZX6qzztsthiK1gxsQYXwzT6xYkxgX/LE6HIMEj+8ZYL1vOYDQiIiIiIiIiIiIiImoDEh3Wx46U+vdEPJfu5PgUZzrijKa5CCwREREREVF7EucIEYxmto5gtK2V1scpZ3vyGn0+BlFH52zpAoiIiIiIiKiVCBGqpPx+YOPK5qtlb+RXVaP2SS65G+qeS/UDUrOar5jWThtuGAhGw6JP9cs6Iv+ISOITAwFAoUKAqsdecg9UaTHwwfOBhoRkyKX3QnJ7hl4wCtJnKOTSe2rum288BlSURT/hlHMgZ/0lBpW1Dl9cZWDEzaa2f0DFsprbiZpgtBKjTuCX0wFxOKA6dQEKLEKfCm0Eo5Van/Wf7tckEDXk9kBufrFJgsjE6QSe/SUQKmj1HHK6gEOmQVIyIGOOifn6W4NOSQLA+m+UkQDkpOi/pJG4eOD0P2tDxVRxEdT1J+m3T937w3j258gKtnLYLGDJd8ArDzR+rhDksBmQAyZBvf88sPq3oH71/nOQI0+OaE5VvAvq3strt59NoecgSHwiMHMOZOacmE/fvzMgAqhmygI68YBwXxwG+jN91qE024x0+HduhyO9U732Kp/Clt3WMwYFLq36FUqpNvslplIqsP+gzEDAqtVtvz9wv+5t06zzf/Vtf/jbNfOb9tcVtN7adSvb6zVD/4wxq9HU/J40t2P5ZPnhI/tjtcFoEaR+NaOBueHHZGQmQi65C2r+1ciPMBgNAH7eoLR/jrrBaPER/or2VijEe9rm9oFiZJcmGK3u+9s+Q/XLf/YacPIVsa2pEbZF8BFOZmLT1WHHiG6Cr/8cOhT834sUzv5nJYqr7L1PXu3qiRJD/4NVB6OlaI7NKioL3tD4NW/ZHA02HT0ygPU7QteX1AL5lnEu6/ay2OX6EhERERERERERERERNZkEh/XVfkqiCUarsA5Gy3F3iXguIiIiIiKijsgtHggEyuK8lrJWEoymC8Xmez+igfUG1wAAIABJREFUxmMwGhEREREREQXogpFMP7BlLVBZYdktVz0I9B8JdO8PNbUrUGwzyCYEmfKnRs9B7cSxZwMhgtEkI6cZi2nldMFoyoRavxzqqin6ZZ2as5ZjRBwOyPVPQp1+NbB7B9CtHyQlo0nXWWP25cBTt0S+XL8RkL8+C+nSK/Y1taDhXQXHDwde/cm6f6CNYLRio84VMatfO7LyLIPR1IL5wJRzIQ59+MDOnWUAgq9+meov0i6DscdCpp4LeLxAv5FNEopWTZwuyL3vwbx+NvDZq/U7T7u6+R7LLSTL+gKoAICMRgZbSFIqcPd/gPeeg/q/c+t3pmZC/vl941ZQvR6RQNDmzIuBpT8ApXuAynKgx0Coh64Fftc8ISLVd3jg/279LIPR8MPHUCW7IYkptqdU913R6FA0ueddwB0H9BsBdeMpwFdv1e+fFfswtLoSPIIeGcCa7U26mhrnjw8TNlQS2LZk+q0L8okLRSt+R8bo+sFom3fr86ryfcGBS+rJm4EeA4E/VkCVFEUfzBUUvKXChJJZ3TbtBXJV326uFDtqXaqs3+819X5itAbl6oM7q3VNF8iMi4EJM9Dtk3XAh5Gt461fAFOzirq5h1sjPPb97d8UZo1kMFpHpnYVWnekZdbclMwuUP1GAMsXBS//0cuQVhSMVqzZfDTkdQEJLRDSZYcyTeDrt6FW/Ijpe0tw6OJn8YN3BPYYgZNeulX9gQSzFEN6/Ri8rBhY6umvnbs6SDE1+C0PAGC3RY63z2891tHgmgb9cwWf/R56W5gU1/zbG68uGK2qeesgIiIiIiIiIiIiIiKKRqLTOhit1F8MU5kwRHMhagsFVdYnx2fx5HgiIiIiIiJbDDEQZ3gtQ9DKW3kwWrY7r5krIWp/GIxGREREREREAbrQGr8fWL9Cv9zhJ9UE0qjJpwAL5je+llFHNH4OahfE7QEe/Rrqf8ZYDzjoyOYtqDUzNAfbLPkO6syRrSLkRLr1a/51jj4KyioY7ZgzgXee0i933s3tLhSt2v+MN/DqT2ZQe5IqwcSDsyF9b4eaP08bjFZhxKEKTrjgqw1GS8+2XtmGlVCXTgbufAviiQvqVt+9j50rsgHP4KC+dP9Oyyll3kPAMWdCdI/5JiI3PA0MHg31n2cC9489E5h2XrPW0BKyrI/zAwCkxyCPTgwDOPp0oHP3QJBU0TZgwIGQc66HuNyNX0HddeX2BHJ71m+c/zHU0dmAr5EpDRmda0LypHs/fVTPe88CMy62NaXavBZ4/1+NKkv+vRqSVefLtJv+BfXYDcC37wEpGZDjzoFMPqVR67BjUJfmCUb74FID8R59+IeqqgQKAyFmWT5NKA2ArSvXI2P0wfXa/rDeJAEA8quCg9Hw5M1hIpuIWhlfpXW7u3WmGA3JA5LigOJy/Zi8tMD/kpGDcdOygQ+D939CeetXhQGdrfuMOpuaLbsjmhbvLQZmjYxsGWpnirZZt6dl1b9/4JGWwWhY+TPUt+9CRh8V+9qisMci2MtKZlIgsLa1UaYJde0M4Ot3atrSABxR+nG9cX4YiDPLUG4EJ5z9FjdIO391mFlqvHV/kcVxWX7NTkTDYDTdNqquxBbYjDMYjYiIiIiIiIiIiIiI2rJEh/UBUyZMlJt7Ee+wfzXJggqLY0oA5PDkeCIiIiIiItvijHhNMJrNAxibkF/5sK1yq2VfNkOxiRqtec8eJCIiIiIiotbL0ASjmX5gZ4F1X2aXmlA0AJCz/gL0HNi4Ok6+AuilP6GUOh7pPxJy8e3B7efeBOncvfkLaq1ChUSFC/wpb/krZMi5f7PumH154ybuPxI44cL6bUPGQObcAfQeYr1MaiYwbHzj1tuKHTFQcM7Y+oEETgO476wkpFz/QE3IWaqpT/nY7uwUuOHYd8Z7WqZ+hT9/Abz7TL0mVVYK8+65UFdOwU4j1XKxdP+uoDZ5bxvkuLObPRQNAMTlhsyaC+OphTCeWgiZcTHEqTnjvx3JTtL3pWnCHaIhw8fD+McHMJ75CcafH4ZkNs8XQOJNgHy4CxgxIbjTmwi5+x1g4szwE+03vPZ232HaYerTV23VpYqLoE5sRJhkXm/Ia2vrh6IhEDhqXPR3GM/9AuOBT5olFA0ABuRGH4KyfNUgHFnyQdhx1x0jmDQgzHpeuKfmZra/EKKsQ5JWrC4ObttqnVCS4i9CirknbH1ELcW88VSoBfdDLfkOqrJCP1DX54xtSGWsuJ2CqUNDP+dz6+xixLkE8yZHti3aWwksXG/d15hsp182MDaxw9ulCUZLrb9PLSE+G1F3XATVCsKvK6oUKnz2xmaF2K9sSWr+VfVC0XQcMNGncpVl3yanft81qmA0TY6js8HboMFdwm+MkoLzqZucV/PSUabJ4CQiIiIiIiIiIiIiImpNEh36L7ZK/PaPESnz78Vui2PgAJ4cT0REREREFIk4w/oAvDJ/aTNXEmxb5VaY8Fv25fC9H1GjOVu6ACIiIiIiImolQgWjFW237kvLqndXktKAR78BvnwDat3yQBhH0Xaox26wXFz+/EggbOT7D4FNq4GhY4EBoyCNOcuc2iU58RJg/0OBRZ8Apgnsfyik34iWLqt10T2H24pDpwNP3QxU1TlT2uGATJzRqGlFBLjkbmDiDGDxf4G83sDooyAuN/DYN1ATgq/eKKdfDXG274/NHj1NMHuU4NvVCgke4PABgoHVwUUSONs+21eoXb7AkYXOvq2AY9/vqcHrQUPqrjko3+8g/OIcgErlwMF3Toax7HsAwC6bwWhyyd2QBOurcVLTyU8XANahG0Y7ebkWpwu46x3gm3egVv4C+KognToDBx8NyekGeOKhPlkQeo4p59TeOfBI/cCl30P5qkKG6imloK45IdIfI+CIkyEHTALGT4XE2786bVMbmBvdcglmCXpXrcHrG2bgxZRZOCv38aAx+2UDT55l4KCeoR+QSimofz9Qcz9OVaBX1RqscvcOGrtkTQmmK1Vvn3TpFut5+1WssPnTELWQTxbUbsNcbqjeQ4EBB0AGjAIGjgJyewYe67ogXben+WqN0NVHCZ77Th8M1T2j/nbh/6YL3vxZYXmDi7J1rtqCCnFjpzPD9rrrvgaeOFLw0kL7AVVLtwA+v4LT0U5eSClyhRssmyW1U/2GHgP0c2zbBKxbDvToH8PCIldcbn9sZisLRlO7dwAfvgAsmG97mWxfAX7D4KD2Lc4c7TLVT/VUr/V+9c4IgtEcDYLRDuwBpHiB3SEuetkiwWgu65+1LExmOxERERERERERERERUWuQ4NAfo1bi34Ms2DsQpqByk7aPwWhERERERET2eTXBaOVmiIPnmonuvZ/AQKarczNXQ9T+tO8zPImIiIiIiMg+hz4YTRVts+5reNIuAPHEAZNORPUp3mrreuCfNwH+Bsn3Q8dBjjkzcPuQaVGVTB2L9BkK9Bna0mW0XrrncBshXfsCt78OdddcYOMqoEtPyIW3xSQAT0SAIWMC/+q2O13Av36DunsusOhTIC4eOPES4ISLGr3O1k5EMLEfMLGfRSCHETjbvpN/OwzlhynBj60CZxZQAWBfgJykZWqiswKWuPvjpNsMLPMIABP9Kh7G664Z6Fq1ASWaK2ymmg2ulskwxBaRn6bvK6lovjqamjgcwLgpkHFTgvuGHAzc+AzUP64EdjUIDEzLgpx9PWTMsbXjnS7gb89D3XBy8Ip8VYFtXHd9iIl6+Drgl68i/xnu+Q9k5GERL9ccBuXqA/ZCeX/9MQAAF3w4bffz6F25Cud2fhjLPf2Q49uKm7t9ibNvPMneZGuWADsL6jUNqFhmHYxWmQus/g3oPaSmbdmq3QCCD3ztX8lgNGpDqiqBZT8Ay36A+veDgbaUTlD9RwKlmqtaO93NV1+EBuYK/n2BgRMeCk4QSvAAB/eq3yYi+OxKA5e+pPDiDwoO5cM5RU/h9oJrMDfnbjyTeprtdSfU+bVM7A+8tNB+3RU+YPFmYFi+/WWo/VBlpcDmtdaduT3q38/vC2R0BnZo0jkLN7R4MNqeCILRundqPWGA6ss3oW4+G9hbHNFymX7rCwdsdWZrl6kOM8vQZNaWVgB7KxTiPbW/H5/NYDSPSzB1mOCZb/X7WS0SjKZ56SivtG4nIiIiIiIiIiIiIiJqTTwSB6e44FPBV30p8Wu+W7egOzneJW6kOYOPvyYiIiIiIiJrcQ7rYLQy0+LKpM1M996vkysLLqP1HodN1FYwGI2IiIiIiIgCDE2okt8PFFmf+InUzLDTSk43qOMvABbMr210uSGX3xdFkUSkpXsOtyEy8jDIC0ug9hZD4q3DsmK+zq59gXveDWzrRALhSB2dBM62d8BEpn87CixO8q9uE0fg48U/XN1wZ/Zd2OLMwaTST3DG7mfhUbVnvc/JuQfLPLWhDcs9/dCv92IMLF+iLaOTb0ftHcMAeg5s1I9F0clN1feVtqNgtHBk0omQSSdCVVYALncg4Gz3DiAjJxC+2NCYYwOBlQ2DYQFg7VJtMJrasxN4/q7I62vFoWgAsF9O5MvEmWUYUf5TvbbRZd9j8Zr9scdIQpJZDMmYDMBmMNrKX4Ka+lcsx5tJxwW1b3LlBsbXCUZb/8ceWAWj7VfBYLSoGEZg36Xm/3C3Q4x3OAKvXfVuG9btjn3LWt42bNcjoWqzrMeqBqm9XW9+Oz+nUX+uz16FeuDP0f0tdm8H/vuevt/Vur+Qnz5c8OU8A+Nur58i9OfJgmRv8PY5K1nw/LmCp89SwI9fw3HFXADA4IrFEa03sU7Q0EzHlzgPYyNafv//NXF4/8Cfst4/ARyGBLcHjQn8c4bptxxTr1+Cx0jwnKHnsBhj0W8IrF8zO5r1ywGlCbFqsL8rDgdw/i1Qt5xtPb5wY4yLi1xxBMFog3Kbrg47VMluqBfuBp65LfKFDz0exv++gKxnSgGL/NpQwWjGvod9Voi3uNtKgG6e2vt+TTCa8f6zUD3GQXJ71rTNGBE6GC0jQb/ephLnsm4vr4o8KJeIiIiIiIiIiIiIiKi5iQgSHckoqnv82j6lfvsX3tGdHJ/tzoUhhmUfERERERERBYszvJbt5a0gGG1rpfWxnFnuLs1cCVH7xGA0IiIiIiIiCtCFKplm4GR5K6n2rlgmc+4EBh4E9fMXgDsOMuMiSOfu0dVJRNba0YEyzRWKVrM+EcDJj8lqGLWPpWxfgXUwmiMrcMPhwrItCqPfm4g96UcAAF5Nno4PEiZhwabZEABrXd3wRcJ4y1UtibMOO3MoH3pXra5tGD+t2R8XFOB06ANMhnfteOEm4t6XWOFyA506hxyn8noD64NDs9SPn0EmnBDcrhTUMfo5tev6x4eQ4dbPsdYiziXYLxtYUWB/mZP2LIALPsu+ZHPfQaYRBMKotcFBjLm+LZZjtzkyodb8B1AKa7cDqVsXY4OvK2DxUtut6g/bNQAADpnWBoK57NyWCNdVf70MR4otNf0CINpgtHBaeTAaAIzpLVhyk4GHPg8E7hwxQHDskNCPMZdTgFEToe59D+rSyRhYsTSidSbVCTBK/uJ53Lf1VVySc3dEc3y4TNfTnMFBzRtSFCo4zSGA09G4foexb4xFwJxhOSbcHHZD6cReaJ0BGL9ug+EZDI+qQO/K1XBiX4iqxwt07hH0O5PJp2iD0dTtF0CO04SmNYJSCjtKgPQEwDBCP5f2RBKM1qXltv2qohzqf8YAG1baX+jAI4CEZMj+hwLHBn7PWZleWD1vCkMEozn27T+EDEYrBrpl1N7XBaM5Fn0MdfolwD+/D4R8Azi8P5DiBXaXBY8f3AVIiW/+37suGK2sqnnrICIiIiIiIiIiIiIiilaiI8kyGK3Ev8f2HLpgNJ4cT0REREREFBmvEW/ZXtYKgtF07/1y+N6PKCZ4xicREREREREF6ILR/D6gyDoYTVJsBqOJAIfNhBw2M9rqiCgch+Y5bMehx8euDmr76oTVZPkKLYcUOKuD0Ry4432FPZX1P2Z8PXkq3is6AkeVfoBfPUMiLqFv5Up4VGXgTr8RkEvuingOip3jhwOv/hTcfupBDDYKqc8wy2A0fPEG1OX/CA6GWvhxZPNn5UOue7zVh6JVG9NHsKIgOEhk1kjBvCMFUx8wsalo39i9X+O2wuvCT2ozGE35qoDng7cjmb5t1tM6M/HmCi/Ov9JEYTEADLAMRQOALvmpkAs+gpozKWwdcs6NkDOvtVUzUUTcnvBjotUGgtEAoH9nwT9Oivx1SUZMAO58EyPnnRHRcklxddb1zlMY6xkc8bo7Ir+pD3yKvdYaMHc40PNwAECqfxeu3f53XL7zH0B2PkT3nm78VOCLN6zXXLABkp0fYb3WlFJ4/CuFy19WKK0A+ncG7j3RwOED9M+tIpvHFCV6gGGxKTMqkYaiyVOLIL0GBbWHCjfTMZZ8C4w4GKnxgcA8n8VzoLC4/n3d88SpfEBFGdTD10FuXQAA8LgE04cLnvom+HF40qiW2V/3MhiNiIiIiIiIiIiIiIjauARHsmV7ZMFo1se15LjzoqqJiIiIiIioo4rTBKOVmxZXFG1GSils1bz3y+Z7P6KY0JzKQ0RERERERB2O7gRc0w/s2Grdl2ovGI2ImoEu3NAGmfY/MSyE2rw6YU3ZfutgtG3OzMANhxMv/mAdBHFc19cxoOfPuKjzfRGXMLBiKZDXC/LMT5BHvoJ0yo14DoqdORMNOBt8kjyyG3Bwr5app62QcVOsO3YWWAZ6qQ9eCD1hfh/IR7shLy6FPPxl4P/9D218oc1k7kRBXIOQjF6ZwPzZgv27CdbcauDbSZ9i8eph+Gz94ejkD77qbpCSIqi9JeHHvfWEZXOm3zoYrciRhuPL/xIUUmIl/+q/QYaNC/xt5n8MuTzENm/YuPATEkVBRICktKaZPCGlaeZtReTAI5Hx7iocVfKe7WUS92XRKTOQXtSvcgUcytcU5VE7VuRIw7zs2/BK0nQgPkTiVojgM/WPK2JWz4dLgfOeDYSiAcCyLcCR95rYsFMf/PbpCnuhcBdNkPqBgs1AlZVCvfYwzCn5wJrFtpeTc/9mGYoGAFlJkf8MxoLAvoGIIMv6/BkUFtf/PVqFpwGAA/7AjS/fhCreVdP+1+MECQ0yMrtnAHMmtFAwmiZTs6yyeesgIiIiIiIiIiIiIiKKVmIjg9FM5Udh1RbLvmw3j4UjIiIiIiKKhFcXjOa3eXXXJlLi340ys9SyL8fdpZmrIWqfGIxGREREREREAQ6ndXtlBbDD+st5ZPEDGqJWI8pgNLnt35ARE2JcDLVpRu1Hhhn+nZZDdhmB8JVFe3NQXqWf6ndPX2x15kRcwgl7XoMcdw6kxwCIwY8wW9oh+wnev9TAsUOAgbmBYIsPLzPgMFomaKHNOGCSvm/dsnp3VUU58N5zIaeTf3wA8cRBuvSCDBwFcWkSJ1qpIXmCL64ycNwQoH9n4Kwxgq+uNtBpX8CIyykY1Xkv+lX+jogeWTaCTtRzd1q2Z/msg9HsEmWiS9eMwG1PHGToWMj084ExxwQPTukEDDywUesjCumwmdoueW0t5MmFkKseBI45E+gxoF4QaigydGyMCmzdJD4Rrx+7yvb4pLh9Nwr+AAB4VCUOKvuuCSqjjuCl5JmAN1HbL2OO1S/8xRswH78pJnU8+bV1yFm3P5vwm8F9pqnwyiJ9MNqBPYCxvYH7Zwtund7MoWjbN0OdPAjq7kuAXdZhz0GS0yFX3A+cNk87pEtq5LU4vn6z5naWJv+usMH5M35tMFqdji/fqrnZNUPw9dUGpg0D9u8KnDFa8OmVBhKbOYyumtdl3V4W4r0jERERERERERERERFRa6ILRiv127jKHoCdVdvgU9ZfjmS786Kui4iIiIiIqCOKM7yW7WVmywajba3cpO3jez+i2NCc9U5EREREREQdji5UqaRIv0xWftPUQkSRiyY8atKJoU+yp45Jah9Laf5dlkN2OgLBaDN/mdwkJUwrfhOIZ2BfazKhn2BCv+gCGDsqSUqF6pQLbN8c3Ll2KdTg0VDz5wFv/TP8XHe9DenU9q8WO7K74I2LQzyO3HH6Pg31+euQQQdZ9/l8UE/cBBRusOzP8jcuGC0nvhIuZ3Dyh5x3C9TKX2vX63JD5j3Y5sLsqG2RU+dB/fd9YOv6+u2X3BXYfnTKBXoPhkw5BwCgSvcAyxcBS76HWvY9sPQHYGdB/UkPmgwcdXpz/QgtznnYdFz46sN4MP38sGNrgtGKa98vP7zlYgzu9VMTVUft2Tp3dyBeH4yG4YeEnuDpW6EGHQg5qP6++derFK551cRXdTL/8tKAw/oJfCbwyXKFHfsuVNglFVi7Xb8K1/kmemcBE/YT3DFDkOwV/HctsNH67QKePFNwxsEtE3CsNq6Cmj0womXk/Fsgp1wZdlx+euT1GDChlIKIIFPzZy5scP6MNhhN+Wtuq08WQI6u3UYPyRO8emHr2F/3anZ5yiqbtw4iIiIiIiIiIiIiIqJoJTisr3hT4ttj2d5QQYiT47Pcbf8YICIiIiIioubkdSRYtpe3cDBaQeVGy/Z4I1EbuE1EkWEwGhEREREREQVEE6qUxeR6olZDF26o4/ZAzrimaWqhts0IH4y2y5GGHY50bCy3/nKhMdau7AMXfIBF2BBRm9Ojv2Uwmlq3DDihd+gA2mp5vYCRhzVBca1QFMFoWPyttks9+b/Ac7dr+9P9O2EIYKrIVwsA+Z2sv2KRHv2BJ78Hfvg48Dc+YBIkt0d0KyGySbLzgad/BH74COqNRyHDDgHGHgvpaR0OJAnJwIgJwIgJEABKqUCo2tIfgN3bgS69gFGHQ0Sa9wdpQZLdFbM6LcWDmkCiuhI9+26U1x5Q0b9yBe7dejkuzbm73tg+FSvx3bqxeDfxSKx094YfDphjpsCnBH7Lf0bNbcsxqO33mwI/IlzexhgTLRNo1VHtFS/g1QejiWEAz/wMdfow7Rh11VTgoyKIJ3BVxFWFCoffY6K8wUXoN+4Cnv42+IUvVChatVWFgXlXFih8cqUDLy+0fgF1O4Gpw5pm26F8VcCiT4GkNKDXoJqft6a/rDTyULSLb4eceImtsZ0SgTgXgn6v2rmVCQGA0j1AYgqykgVA8O9tm+1gNF/tnYWfQO3eAUnJsFdMM/Jq3sqV+6zbiYiIKHoi0gPAMAC5ABIBbAGwHsA3Simbey1ERERERERERNSQNhjNby8YbasmGC3VmYE4w2vZR0RERERERNZ076NaOhhN994v292lQx2DTdSUGIxGREREREREAY4IQ5XikyCJKU1TCxFFLpJgtG79IOffDOnev8nKoTZMaoMw0jXBaDsdafgoYSL8KnahGQMqlmL+lkuQ79NfLZOozenePxCO1dA7T9meQu59LxCI0hFEE4y2dimUUkFfHKrSPcCL94Zc1HHIVAxyA79aX6gprC6aYDQAkOR04LCZ0U1MFCWJTwQOmQY5ZFrky4oAnbsH/nVgY44YjJ5vrsEad8+Q45KqN1cV9Q+ouGjXw6gQD+7JuAS7jWSMLvsvntp8LpLNYpy455XagW/cEuPKY0sBMGHADwd84oRfHPDDEfw/jLD9ftk3R4N+s+a+Ydnvt91fO8aMYZ1166tus65D32+Kvfdoe414IF4fjAYEQjdVn6HAyl/0f7eTBkBeWwsAOPXx4FC0WPnsd+CHdQqvLLIORjtyAJAaH/sDetTKX6Cung5s2/d+oecg4PbXINlda8dM7apZ2oLbA7liPuTo020vIiLISwuExNnhgD9wY1chkJiCTOvzZ1BYXP936dMFo1XPBwB+H/DFG8BxZ9srphnpgtHKKpu3DiIiovZMRGYAuBzAaM2QnSLyEoAblFI2YnCJiIiIiIiIiKiuREeyZXupv9iyvaGCECfHExERERERUWTijHjL9nKzDKYyYUjLnG+he++X485r5kqI2i8GoxEREREREVFAJKFKAJCR0zR1EFF0bIYbykV/B2bN7TghOxS5Oo+NNE0w2i5HGn72DIl6FQ9smYsTil9DwqRpKP7oTcSpciSb9g4aI2pLpPsAWEeG2Fz+7nfqhX20e54ogtFK9wCFG4Hs/PrtX70NVJaHXFTOvA7Tlgp+3RjdXykvjVdxImpvjOPOxj2PzcTxeS/DL/qvUePd+26U1w9GEwBX7LwPl+ycDwf8aKtbCQEQiAYz4VZVaNSLWQdWN2DOLw68knw8zsp9PGhcmcQB8ZrErDpkzh1Qc4/QD9i+GSs+X4SpHw3D7wWNKNyGA2/VJHcBmDmyCULR1i+HOntU/cY1i6HuuRS49RXgg+ehbjnH9nxy6wLIuClR1dItPYJgNLUvyKxoO5DfB1m6YLQ99e/7dcFoyl/vvvpkAaQVBqPFuQRWG46yJgrrIyIi6khEJBHAYwBOCjM0HcAFAI4XkTOUUu83eXFERERERERERO2ILhhtr1kCU/lhhLlIUkGl9VX6eHI8ERERERFR5LyaYDQFhUpVgTjxNnNFAbr3fgzFJoodngFLREREREREAZEGo9k4aZeImpHdq1vsfyhD0Sg0qQ0ySNcEo/nEhe+8oyz7wplS/Bb+p+hxZN72T8Sf/xdk+bdZh6KlZEQ1P1Gr0r1/9MumZgLDD41ZKW2C2xPdcmuX1NxUe3ZCFRdBfbIg9DKnzoP0Hoz9u0Yf3pKfHvWiRNRKicuNY++/Gd+sOyTkOMPYt+2oKLPsd7bhUDSKneqAOTeq4FXlSPUXWY7ba8QD3sTw8w0/BPLQ55Z9CsA33gPR/19NH4oWiscJTBka20e/WvET1KlDrTt/+Ajq9gsiC0X7v1eiDkUDgH6d7f98DgSCzNTrjwKAPhitztshpRRMTRhh9Xw1fvwMatc22/U0F6/bur2ssnnrICIiam9ExAHgJQSHom0D8AGABQB+RP2E0mwAb4jI2GYpkoiIiIiIiIiondAFoyko7PWXhl2+oHKzZXuWO7cKlCbWAAAgAElEQVRRdREREREREXVEcZpgNAAo9+/V9jWlKrMSO6qsr7LKYDSi2OFZsERERERERBTg0py1qBOX0DR1EFF07ISdORxAt35NXwu1bXVC9tJM62A0APgy3vpcumuOEhza13qZY4vfwbPbzofxzhbIQZOBjM5A5+7BAz1e4IBJkVRN1Dr1GhTY9kZj4gkQpzO29bR27rjolluzBMpXBfOOC6GmdYM6Ohv45j/68cedDTnnBgCNCzc7sAdjj4jaI+k9GCMOyMeU4rcs+w/1/1B7RxOMRmQl3rQ++GavkQB49Aft1CWDDoJcdm+9ti+8Y5DXZy3Gd/+00TU21uSBQLI3Nq+Pyu+H+Y8roP50kH5QZQXwzlO255RbF0DGHteougZFcK6KQ+0LMvvgeQBAZpL176awOBCIBgBrt+vncypf/QbTBL5+235BzcTrsm73mYDPr0l9IyIiIjtuA3B0nftVAOYAyFNKHamUmqWUGgFgEIBv64zzAHhdRDo3X6lERERERERERG1bgkN/8egS/56Qy5b5S7FHc0HSHHdeo+oiIiIiIiLqiOIc+mMsyzTHZja1wqrNULA+Hi7Hw/d+RLHCYDQiIiIiIiIKcEYYjOa1d9IuETUTO8E7XXpBPFGGzlDHUSdkL8O/UztMifVHiwf1FHxypQPLT/0Zr285CW9sOB7/+eM4rFvZB6/v/h8kvr8JkhxIIhIRyImXBE8y9VxIHF9nqO2ThGRg2Pjolj3j2hhX0wZEGYym1i4FnrsDePMJoKoy5Fi55WUY8x6COAOJHflpUa0SADC2d/TLElHrJv/7Ii7e+SBcqv42xVB+zNl8J9SaJYGG8hAHUww8sAkrpLYoXukfL2W7Q588UZccfwGQFHgB+yx+HCZ2/xAFzuxG1xcLM0fWBn8ppaC+fhvmw9dBvfM0VMnuQHtFOdTbT8Kc3gPmCb2g3n0Walch1GuPBMZ+/2EgJOzfDwAL5semsKx8yAc7IeOmNHqqQV3sB78ZMGtuq7JSZCdbj6vyA5uKANNUOPUJ03oQAAf8QW3qgxds19NcvCE+Yiyrar46iIiI2hMR6Qmg4QepM5VS85Wq/8ZFKbUUwGGoH46WAeDGpq2SiIiIiIiIiKj9SHRovthB+GC0gspN2r5sd5eoayIiIiIiIuqovIb+/KLyFgpGK6jcbNluwIFOrtZxTCdRe+Bs6QKIiIiIiIiolXB7Ihsfl9A0dRBRdAwbwWhDxzZ9HdT21Qk8S/fvRJxZhnLDa3vxQfuO3eo7fgT6pF4GteB+YPNaYPgMyNnXQxqE+MkJFwIJKVAfPA9UlgfCCmbNjcmPQtQayIQToBZ9Gtky970PSe+AX4ZFGYyGRZ9CffGGvbGjj6p3NyMRiHMqlPvsh5wAwN/2+xEiB0S0DBG1HeJw4LBH7sN/zpuCB9IuwG9xgzCgYhnO3fUEji59H/j5MKDnQH0wWr8RkAc+Bd57FurFe4F1ywLtWfmAg9et6qjiK/VpUeUJmYjkUxZ5dQ1eOv5PODnv2UbX5YAfu+934Y1fFF7+QWHxZmC/bOA/iyObJzMJmDI08HqqlIL6+/nAO08F7gPAK/OBWxdA3XAysHxRzXLq1j/Vm0f9605g5sXAx6804qeq48zrIKdcGbPg5VHdgc4pwJbd4cc6VJ0gs22b0Cerj3bsks3Amm3Af9eEms8iNO2nz6G+fgcy5pjwBTWTeDeQ4AG8LiDOFfjf6w7879fnvhEREVFoNwJw1bn/lFJK+2GIUqpMRM4E8BuA6h3Rc0TkdqVUiD0OIiIiIiIiIiICALfhgVs8qFQVQX3hgtG2aoLR3OJBqjMjJvURERERERF1JG7xQGBAIfgAtLIWC0bbaNme6e4MhzDKiShW+GwiIiIiIiKiABeD0YjaNCN8wIKMm9IMhVCbV+exJAC6Vm3A756+thaNdwPd0mvvy5CDIUMODrucTD4FMvmUSCslahvGTwXunguYNlMgUjKAoeOatqbWKlww2tjjgK/eCm7fpr/Sbj1JaRBX/VAaEcGQPMH36+xNUe2Q3v7wg4ioTZPu/TEhvwQTVswO6lP3XAoMGg1VUWa9sCc+EAZ7zJmQY85s0jqp7UgoUMD11vsDewcegkhOgSgTL+Z2ewiw8XL09h9TMbn0Q7yYPBOndnk6qH9Y2S/wbk7A7FEDMHtUbfuqQoVJd5v4Y6e9mh49zUBi3L6g0WULa0LRaif8FeriSUDhhvCTLZhvb6XhTD8Pxjk3xGaufVxOwXXHCC5+XoUd66j7B9q+GalJqcg1BJvN9KCxR90Xfl/RofmDq4euAQ4+GiKRBb02lYG5guL7bYS3ExERkS0i4gUwo0Hz38Mtp5T6XUReBzBrX5MTwMkAbo5thURERERERERE7VOiIxk7fduC2kv9xSGXK9AEo2W5c2EIL6RFREREREQUKRFBnOFFmVka1FfeQsFoWyus3/tlu3ObuRKi9o2fpBAREREREVGAyxV+TF3e+Kapg4iiY9g46bgTP1wlGxocfJVfZSO4YJ+BuYBhtI6T8YlaC0nLAgaPsb/AUacHwnQ6ogahZQ3JFffbCgLVSu1k2XzKQZFtt4aU/4oxg5Ojr4OI2o4E/XP9/9m78zg587pO4J9fVd9HJgmZZDKTORjmDoxz4XA43AgoDKcjwq4ggopcHii4glyKuoKK4LGisCgil4DHgqICLgvIrhwLyLVyDMcMMzBXkk53ju5n/8gwSbqfqq6qrj7S/X6/Xnml6/f7Pd/fdyaVTlX183ye6lkPSq79Qv3kyOgyNcSJbKzNP3P7d+3uqtbffbrKd2YnFl33rJt/Pw+d+sckyWP3vDOXznyydk0+uDB49JztJZ9/WSM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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import asyncio\n", + "from uuid import UUID\n", + "\n", + "from matplotlib import pyplot as plt\n", + "\n", + "from apd.aggregation.query import with_database\n", + "from apd.aggregation.analysis import get_known_configs, plot_sensor\n", + "\n", + "\n", + "location_names = {\n", + " UUID('53998a51-60de-48ae-b71a-5c37cd1455f2'): \"Loft\",\n", + " UUID('1bc63cda-e223-48bc-93c2-c1f651779d69'): \"Living Room\",\n", + " UUID('ea0683de-6772-4678-bfe7-6014f54ffc8e'): \"Office\",\n", + " UUID('5aaa901a-7564-41fb-8eba-50cdd6fe9f80'): \"Outside\",\n", + "}\n", + "\n", + "with with_database(\"postgresql+psycopg2://apd@localhost/apd\") as session:\n", + " coros = []\n", + " figure = plt.figure(figsize = (20, 10), dpi=300)\n", + " configs = get_known_configs().values()\n", + " for i, config in enumerate(configs, start=1):\n", + " plot = figure.add_subplot(2, 2, i)\n", + " coros.append(plot_sensor(config, plot, location_names))\n", + " await asyncio.gather(*coros)\n", + "\n", + "display(figure)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "9f576f5bb8e34cb8ab1d3c09f770d027", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "interactive(children=(DatePicker(value=None, description='Start date'), DatePicker(value=None, description='En…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import asyncio\n", + "from uuid import UUID\n", + "import functools\n", + "from concurrent.futures import ThreadPoolExecutor\n", + "\n", + "import ipywidgets as widgets\n", + "from matplotlib import pyplot as plt\n", + "\n", + "from apd.aggregation.query import with_database\n", + "from apd.aggregation.analysis import get_known_configs, plot_sensor\n", + "\n", + "\n", + "def wrap_coroutine(f):\n", + " @functools.wraps(f)\n", + " def run_in_thread(*args, **kwargs):\n", + " loop = asyncio.new_event_loop()\n", + " wrapped = f(*args, **kwargs)\n", + " with ThreadPoolExecutor(max_workers=1) as pool:\n", + " task = pool.submit(loop.run_until_complete, wrapped)\n", + " return task.result()\n", + " return run_in_thread\n", + "\n", + "@wrap_coroutine\n", + "async def plot(*args, **kwargs):\n", + " location_names = {\n", + " UUID('53998a51-60de-48ae-b71a-5c37cd1455f2'): \"Loft\",\n", + " UUID('1bc63cda-e223-48bc-93c2-c1f651779d69'): \"Living Room\",\n", + " UUID('ea0683de-6772-4678-bfe7-6014f54ffc8e'): \"Office\",\n", + " UUID('5aaa901a-7564-41fb-8eba-50cdd6fe9f80'): \"Outside\",\n", + " }\n", + "\n", + " with with_database(\"postgresql+psycopg2://apd@localhost/apd\") as session:\n", + " coros = []\n", + " figure = plt.figure(figsize = (20, 10), dpi=300)\n", + " configs = get_known_configs().values()\n", + " for i, config in enumerate(configs, start=1):\n", + " plot = figure.add_subplot(2, 2, i)\n", + " coros.append(plot_sensor(config, plot, location_names, *args, **kwargs))\n", + " await asyncio.gather(*coros)\n", + " return figure\n", + "\n", + "\n", + "start = widgets.DatePicker(\n", + " description='Start date',\n", + ")\n", + "end = widgets.DatePicker(\n", + " description='End date',\n", + ")\n", + "out = widgets.interactive(plot, collected_after=start, collected_before=end)\n", + "display(out)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "60091fa7d9ea415985ec53937e422b4a", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "interactive(children=(DatePicker(value=None, description='Start date'), DatePicker(value=None, description='En…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from uuid import UUID\n", + "import ipywidgets as widgets\n", + "from apd.aggregation.analysis import interactable_plot_multiple_charts, configs\n", + "\n", + "location_names = {\n", + " UUID('53998a51-60de-48ae-b71a-5c37cd1455f2'): \"Loft\",\n", + " UUID('1bc63cda-e223-48bc-93c2-c1f651779d69'): \"Living Room\",\n", + " UUID('ea0683de-6772-4678-bfe7-6014f54ffc8e'): \"Office\",\n", + " UUID('5aaa901a-7564-41fb-8eba-50cdd6fe9f80'): \"Outside\",\n", + "}\n", + "\n", + "start = widgets.DatePicker(\n", + " description='Start date',\n", + ")\n", + "end = widgets.DatePicker(\n", + " description='End date',\n", + ")\n", + "\n", + "plot = interactable_plot_multiple_charts(location_names=location_names)\n", + "out = widgets.interactive(plot, collected_after=start, collected_before=end)\n", + "display(out)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "08a4d937037b45bda2298879df7f80b6", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "interactive(children=(DatePicker(value=None, description='Start date'), DatePicker(value=None, description='En…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import datetime\n", + "import uuid\n", + "import ipywidgets as widgets\n", + "from apd.aggregation.analysis import Config, configs, interactable_plot_multiple_charts, draw_map, get_map_cleaner_for\n", + "from apd.aggregation.database import DataPoint\n", + "from apd.aggregation.utils import merc_x, merc_y\n", + "\n", + "start = widgets.DatePicker(description='Start date')\n", + "end = widgets.DatePicker(description='End date')\n", + "\n", + "def get_literal_data():\n", + " # Get manually entered temperature data, as our particular deployment\n", + " # does not contain data of this shape\n", + " raw_data = {(53.8667, -1.3333): -1, (53.35, -2.2833): 1, (52.45, -1.7333): 3, (51.5, -0.1333): 6, (51.55, -2.5667): 5, (54.9667, -1.6167): -1, (55.9667, -3.2167): -1, (54.7667, -1.5833): 0, (53.7667, -0.3): 1, (53.7667, -3.0167): 0, (51.4833, -3.1833): 4, (53.2667, -3.5167): 2, (52.0833, -2.8): 2, (52.0167, -0.6): 2, (52.6667, 1.2667): 5, (50.4333, -4.9833): 9, (50.35, -4.1167): 10, (50.5167, -2.45): 9, (50.8333, -1.1667): 9, (56.45, -5.4333): 4, (57.5333, -4.05): -2, (54.5167, -3.6167): 3, (53.0833, 0.2667): 4, (51.35, 1.3333): 7, (50.8833, 0.3167): 8, (58.45, -3.1): 3, (50.1, -5.6667): 9, (58.2167, -6.3333): 4, (55.6833, -6.25): 7}\n", + " now = datetime.datetime.now()\n", + " async def points():\n", + " for (coord, temp) in raw_data.items():\n", + " deployment_id = uuid.uuid4()\n", + " yield DataPoint(sensor_name=\"Location\", deployment_id=deployment_id, collected_at=now, data=coord)\n", + " yield DataPoint(sensor_name=\"Temperature\", deployment_id=deployment_id, collected_at=now, data=temp)\n", + " async def deployments(*args, **kwargs):\n", + " yield None, points()\n", + " return deployments\n", + "\n", + "def draw_map_with_gb(plot, x, y, colour):\n", + " # Draw the map and add an explicit coastline\n", + " gb_boundary = [(-2.6653539699999556, 51.617254950000074), (-2.6937556629999335, 51.59617747600004), (-2.71312415299991, 51.58661530200004), (-2.760487433999913, 51.579779364000046), (-2.7980850899999155, 51.56350332200009), (-2.816395636999914, 51.555568752000056), (-2.8587133449999556, 51.546128648000035), (-2.943348761999914, 51.54254791900007), (-2.9562882149999155, 51.54572174700007), (-2.9641007149999155, 51.552801825000074), (-2.969471808999913, 51.559881903000075), (-2.9746801419999542, 51.56297435100004), (-2.984038865999935, 51.55853913000004), (-3.0102432929999168, 51.53803131700005), (-3.0156957669999542, 51.53164297100005), (-3.0737198559999115, 51.50836823100008), (-3.0764867829999503, 51.50775788000004), (-3.088937954999949, 51.505072333000044), (-3.105620897999927, 51.49721914300005), (-3.1317032539999445, 51.48041413000004), (-3.1350805329999503, 51.47630442900004), (-3.1409399079999503, 51.46478913000004), (-3.1453751289999445, 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51.49408600500004), (-2.716420050999943, 51.49957916900007), (-2.6961563789999445, 51.51264069200005), (-2.6078181629999335, 51.60736725500004), (-2.599598761999914, 51.611395575000074), (-2.5892227859999366, 51.61424388200004), (-2.580433722999942, 51.61863841400009), (-2.5768123039999296, 51.62750885600008), (-2.57445227799991, 51.63572825700004), (-2.568959113999938, 51.644964911000045), (-2.562245245999918, 51.65338776200008), (-2.556385870999918, 51.65924713700008), (-2.499175584999932, 51.69676341400009), (-2.464995897999927, 51.725897528000075), (-2.4477432929999168, 51.73187897300005), (-2.404896613999938, 51.74115631700005), (-2.3885391919999392, 51.750433661000045), (-2.380767381999931, 51.76190827000005), (-2.3833715489999463, 51.77326080900008), (-2.398060675999943, 51.782131252000056), (-2.4024145169999542, 51.76357656500005), (-2.4203995429999168, 51.753119208000044), (-2.4434301419999542, 51.74843984600005), (-2.4632869129999335, 51.747300523000035), (-2.482085740999935, 51.74286530200004), (-2.495838995999918, 51.73200104400007), (-2.507964647999927, 51.71857330900008), (-2.5221248039999296, 51.706366278000075), (-2.581695115999935, 51.681708075000074), (-2.5911352199999556, 51.67536041900007), (-2.600209113999938, 51.66559479400007), (-2.6653539699999556, 51.617254950000074)]\n", + " draw_map(plot, x, y, colour)\n", + " plot.plot(\n", + " [merc_x(coord[0]) for coord in gb_boundary],\n", + " [merc_y(coord[1]) for coord in gb_boundary],\n", + " \"k-\",\n", + " )\n", + "\n", + "country = Config(\n", + " get_data=get_literal_data(),\n", + " clean=get_map_cleaner_for(\"Temperature\"),\n", + " title=\"Country wide temperature\",\n", + " ylabel=\"\",\n", + " draw=draw_map_with_gb,\n", + ")\n", + "\n", + "out = widgets.interactive(interactable_plot_multiple_charts(configs=configs + (country, )), collected_after=start, collected_before=end)\n", + "display(out)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "apd.aggregation", + "language": "python", + "name": "apd.aggregation" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Ch12/apd.aggregation-chapter12-ex01/LICENCE b/Ch12/apd.aggregation-chapter12-ex01/LICENCE new file mode 100644 index 0000000..86685d4 --- /dev/null +++ b/Ch12/apd.aggregation-chapter12-ex01/LICENCE @@ -0,0 +1,31 @@ +Copyright (c) 2019, Matthew Wilkes + + +All rights reserved. + +Redistribution and use in source and binary forms, with or without modification, +are permitted provided that the following conditions are met: + +* Redistributions of source code must retain the above copyright notice, this + list of conditions and the following disclaimer. + +* Redistributions in binary form must reproduce the above copyright notice, this + list of conditions and the following disclaimer in the documentation and/or + other materials provided with the distribution. + +* Neither the name of the copyright holder nor the names of its + contributors may be used to endorse or promote products derived from this + software without specific prior written permission. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND +ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED +WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. +IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, +INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, +BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, +DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY +OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE +OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED +OF THE POSSIBILITY OF SUCH DAMAGE. + + diff --git a/Ch12/apd.aggregation-chapter12-ex01/Mapping.ipynb b/Ch12/apd.aggregation-chapter12-ex01/Mapping.ipynb new file mode 100644 index 0000000..916e704 --- /dev/null +++ b/Ch12/apd.aggregation-chapter12-ex01/Mapping.ipynb @@ -0,0 +1,254 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "datapoints = {\n", + " (53.8667, -1.3333): -1,\n", + " (53.35, -2.2833): 1,\n", + " (52.45, -1.7333): 3,\n", + " (51.5, -0.1333): 6,\n", + " (51.55, -2.5667): 5,\n", + " (54.9667, -1.6167): -1,\n", + " (55.9667, -3.2167): -1,\n", + " (54.7667, -1.5833): 0,\n", + " (53.7667, -0.3): 1,\n", + " (53.7667, -3.0167): 0,\n", + " (51.4833, -3.1833): 4,\n", + " (53.2667, -3.5167): 2,\n", + " (52.0833, -2.8): 2,\n", + " (52.0167, -0.6): 2,\n", + " (52.6667, 1.2667): 5,\n", + " (50.4333, -4.9833): 9,\n", + " (50.35, -4.1167): 10,\n", + " (50.5167, -2.45): 9,\n", + " (50.8333, -1.1667): 9,\n", + " (56.45, -5.4333): 4,\n", + " (57.5333, -4.05): -2,\n", + " (54.5167, -3.6167): 3,\n", + " (53.0833, 0.2667): 4,\n", + " (51.35, 1.3333): 7,\n", + " (50.8833, 0.3167): 8,\n", + " (58.45, -3.1): 3,\n", + " (50.1, -5.6667): 9,\n", + " (58.2167, -6.3333): 4,\n", + " (55.6833, -6.25): 7,\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "fig, ax = plt.subplots()\n", + "lats = [ll[0] for ll in datapoints.keys()]\n", + "lons = [ll[1] for ll in datapoints.keys()]\n", + "ax.plot(lons, lats, \"o\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "fig, ax = plt.subplots()\n", + "lats = [ll[0] for ll in datapoints.keys()]\n", + "lons = [ll[1] for ll in datapoints.keys()]\n", + "# Don't draw lines\n", + "ax.plot(lons, lats, \"o\")\n", + "# This is a map of the UK. Here, 1 degree latitude is \n", + "# 1.7x as far as 1 degree longitude\n", + "ax.set_aspect(1.7)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# Set up mercator transform from https://wiki.openstreetmap.org/wiki/Mercator#Python\n", + "# Thanks to Paulo Silva and the OSM contributors\n", + "import math\n", + "\n", + "def merc_x(lon):\n", + " r_major=6378137.000\n", + " return r_major*math.radians(lon)\n", + "\n", + "def merc_y(lat):\n", + " if lat>89.5:lat=89.5\n", + " if lat<-89.5:lat=-89.5\n", + " r_major=6378137.000\n", + " r_minor=6356752.3142\n", + " temp=r_minor/r_major\n", + " eccent=math.sqrt(1-temp**2)\n", + " phi=math.radians(lat)\n", + " sinphi=math.sin(phi)\n", + " con=eccent*sinphi\n", + " com=eccent/2\n", + " con=((1.0-con)/(1.0+con))**com\n", + " ts=math.tan((math.pi/2-phi)/2)/con\n", + " y=0-r_major*math.log(ts)\n", + " return y\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots()\n", + "\n", + "x = tuple(map(merc_x, lons))\n", + "y = tuple(map(merc_y, lats))\n", + "ax.plot(x, y, 'o')\n", + "ax.set_aspect(1.0)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots()\n", + "\n", + "x = tuple(map(merc_x, lons))\n", + "y = tuple(map(merc_y, lats))\n", + "\n", + "ax.tricontourf(x, y, tuple(datapoints.values()))\n", + "ax.plot(x, y, 'wo', ms=3)\n", + "ax.set_aspect(1.0)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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upgrade head`. This should also be done after every upgrade of the software. + +# Defining endpoints + +Endpoints to collect from are managed with the `sensor_deployments` CLI tool. After installation +there will be no deployments defined + + sensor_deployments add --db postgresql+psycopg2://apd@localhost/apd + --api-key 97f6b3e5ceb64a6ba88968d7c3786b38 + --colour xkcd:red + http://rpi4:8081 + Loft + +The optional colour argument is the colour to use when plotting charts with the built-in charting +tools. This uses matplotlib's colour specification system, documented at https://matplotlib.org/tutorials/colors/colors.html + +The sensors can then be listed with `sensor_deployments list`: + + Loft + ID 53998a5160de48aeb71a5c37cd1455f2 + URI http://rpi4:8081 + API key 97f6b3e5ceb64a6ba88968d7c3786b38 + Colour xkcd:red + +The ID is the deployment ID, as set by the endpoint. It is only possible to add endpoints if they can be +connected to at the time. + +# Collating data + +Data can be collated from all defined endpoints with the `collect_sensor_data` command line tool. +Although you can specify URLs and an API key to explicitly load data from a one-off endpoint, running +without specifying these will use the configured endpoints from the database. + + collect_sensor_data --db postgresql+psycopg2://apd@localhost/apd + +# Viewing data + +You can write scripts to visualise the data from the database. I recommend using Jupyter for this, as it +has good support for drawing charts and interactivity. + +All configured charts can be displayed with: + + from apd.aggregation.analysis import plot_multiple_charts + display(await plot_multiple_charts()) + +More complex charting can be achieved by passing `configs=` to this function, consisting of configuration +objects as defined in `apd.aggregation.analysis`. Iteractivity can be achieved using the +`interactable_plot_multiple_charts` function with Jupyter/IPyWidgets' existing interactivity support. + +More control can be achieved using other functions from this module, such as getting all data points from +a given sensor with: + + from apd.aggregation.query import with_database, get_data + + with with_database("postgresql+psycopg2://apd@localhost/apd") as session: + points = [(dp.collected_at, dp.data) async for dp in get_data() if dp.sensor_name=="RelativeHumidity"] + +These can be called from any Python code, not just Jupyter notebooks + +# Analysis and triggers + +The aggregator allows for a long-running process that processes records as they are inserted to the database +and apply rules to them. + +This is configured with a Python-based configuration file, such as the following to log any time the +Temperature fluctuates above or below 18c: + + import operator + + from apd.aggregation.actions.action import OnlyOnChangeActionWrapper, LoggingAction + from apd.aggregation.actions.runner import DataProcessor + from apd.aggregation.actions.trigger import ValueThresholdTrigger + + + handlers = [ + DataProcessor( + name="TemperatureBelow18", + action=OnlyOnChangeActionWrapper(LoggingAction()), + trigger=ValueThresholdTrigger( + name="TemperatureBelow18", + threshold=18, + comparator=operator.lt, + sensor_name="Temperature", + ), + ) + ] + +This is run with: + + run_apd_actions --db postgresql+psycopg2://apd@localhost/apd sample_actions.py + +The optional `--historical` option causes the actions to be triggered for all events in the database. +If it's omitted then the default behaviour applies, which is to only analyse data that is added to the +database after the actions process has started. + +The possible actions are: + +* `apd.aggregation.actions.action.LoggingAction()` - Log data points +* `apd.aggregation.actions.action.SaveToDatabaseAction()` - Save data points to the db + +These can be wrapped with `OnlyOnChangeActionWrapper(subaction)` to only trigger an action when +the underlying value changes and/or with `OnlyAfterDateActionWrapper(subaction, min_date)` to +only trigger if the date on the discovered objects is strictly after `min_date`. + +The possible triggers are: + +* `apd.aggregation.actions.trigger.ValueThresholdTrigger(...)` - This compares the value of a sensor with threshold, using the specified comparator. + Any records that don't match the `sensor_name` and `deployment_id` parameters are excluded. + + +# Tips + +The `--db` argument to all command-line tools can be omitted and the `APD_DB_URI` environment variable +set instead. \ No newline at end of file diff --git a/Ch12/apd.aggregation-chapter12-ex01/Yappi Profiling.ipynb b/Ch12/apd.aggregation-chapter12-ex01/Yappi Profiling.ipynb new file mode 100644 index 0000000..153122f --- /dev/null +++ b/Ch12/apd.aggregation-chapter12-ex01/Yappi Profiling.ipynb @@ -0,0 +1,3140 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from apd.aggregation.analysis import interactable_plot_multiple_charts, configs\n", + "from apd.aggregation.utils import jupyter_page_file, profile_with_yappi, yappi_package_matches\n", + "import yappi\n", + "\n", + "with profile_with_yappi():\n", + " plot = interactable_plot_multiple_charts()\n", + " plot()\n", + "\n", + "with jupyter_page_file() as output:\n", + " yappi.get_func_stats(filter_callback=lambda stat:\n", + " yappi_package_matches(stat, [\"apd.aggregation\"])\n", + " ).print_all(output)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r\n", + "Clock type: WALL\r\n", + "Ordered by: totaltime, desc\r\n", + "\r\n", + "name ncall tsub ttot tavg \r\n", + "..futures\\thread.py:52 _WorkItem.run 7 0.000092 3.204091 0.457727\r\n", + "..tion\\analysis.py:373 run_in_thread 1 0.000053 1.844382 1.844382\r\n", + "..38\\Lib\\threading.py:859 Thread.run 2/1 0.000025 1.839877 0.919939\r\n", + "..rrent\\futures\\thread.py:66 _worker 2/1 0.000022 1.839852 0.919926\r\n", + "..lectorEventLoop.run_until_complete 1 0.000027 1.839745 1.839745\r\n", + "..ndowsSelectorEventLoop.run_forever 1 0.000088 1.839611 1.839611\r\n", + "..WindowsSelectorEventLoop._run_once 17 0.000408 1.839497 0.108206\r\n", + "..alysis.py:327 plot_multiple_charts 1 1.643447 1.839309 1.839309\r\n", + "..py:635 ThreadPoolExecutor.__exit__ 1 0.000005 1.837286 1.837286\r\n", + "..py:230 ThreadPoolExecutor.shutdown 1 0.000009 1.837281 1.837281\r\n", + "..8\\Lib\\threading.py:979 Thread.join 1 0.000006 1.837272 1.837272\r\n", + "..:1017 Thread._wait_for_tstate_lock 1 0.000011 1.837263 1.837263\r\n", + "..gation\\analysis.py:291 plot_sensor 1 0.000000 1.407986 1.407986\r\n", + "..query.py:88 get_data_by_deployment 1 1.397146 1.397188 1.397188\r\n", + "..d\\aggregation\\query.py:41 get_data 1 1.283592 1.375576 1.375576\r\n", + "..lchemy\\orm\\query.py:3232 Query.all 6 0.004333 1.362649 0.227108\r\n", + "..ctors.py:319 SelectSelector.select 17 0.000177 1.362418 0.080142\r\n", + "..tors.py:313 SelectSelector._select 17 0.000069 1.362216 0.080130\r\n", + "..y\\orm\\query.py:3400 Query.__iter__ 6 0.000054 0.958316 0.159719\r\n", + "..:3425 Query._execute_and_instances 6 0.000073 0.956631 0.159439\r\n", + "..ine\\base.py:916 Connection.execute 9 0.000062 0.740051 0.082228\r\n", + "..:589 PGDialect_psycopg2.do_execute 11 0.000042 0.738361 0.067124\r\n", + "..y:1159 Connection._execute_context 9 0.000307 0.734166 0.081574\r\n", + "..:291 Select._execute_on_connection 6 0.000047 0.731436 0.121906\r\n", + ".. 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import yappi\n", + "\n", + "from apd.aggregation.analysis import interactable_plot_multiple_charts, Config\n", + "from apd.aggregation.analysis import clean_temperature_fluctuations, get_one_sensor_by_deployment\n", + "from apd.aggregation.utils import profile_with_yappi\n", + "\n", + "yappi.set_clock_type(\"wall\")\n", + "\n", + "filter_in_db = Config(\n", + " clean=clean_temperature_fluctuations,\n", + " title=\"Ambient temperature\",\n", + " ylabel=\"Degrees C\",\n", + " get_data=get_one_sensor_by_deployment(\"Temperature\"),\n", + ")\n", + "\n", + "with profile_with_yappi():\n", + " plot = interactable_plot_multiple_charts(configs=[filter_in_db])\n", + " plot()\n", + "\n", + "yappi.get_func_stats().print_all()" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r\n", + "Clock type: CPU\r\n", + "Ordered by: totaltime, desc\r\n", + "\r\n", + "name ncall tsub ttot tavg \r\n", + "..futures\\thread.py:52 _WorkItem.run 7 0.000000 9.515625 1.359375\r\n", + "..lchemy\\orm\\query.py:3232 Query.all 6 0.187500 6.390625 1.065104\r\n", + "..lchemy\\orm\\loading.py:35 instances 67353 0.046875 6.140625 0.000091\r\n", + "..esult.py:1257 ResultProxy.fetchall 6 0.000000 5.109375 0.851562\r\n", + "..py:1217 ResultProxy._fetchall_impl 6 0.000000 5.078125 0.846354\r\n", + "..38\\Lib\\threading.py:859 Thread.run 2/1 0.000000 3.125000 1.562500\r\n", + "..rrent\\futures\\thread.py:66 _worker 2/1 0.000000 3.125000 1.562500\r\n", + "..b\\asyncio\\events.py:79 Handle._run 29 0.000000 3.125000 0.107759\r\n", + "..WindowsSelectorEventLoop._run_once 17 0.000000 3.125000 0.183824\r\n", + "..lectorEventLoop.run_until_complete 1 0.000000 3.125000 3.125000\r\n", + "..ndowsSelectorEventLoop.run_forever 1 0.000000 3.125000 3.125000\r\n", + "..gation\\analysis.py:291 plot_sensor 1 0.031250 3.046875 3.046875\r\n", + "..egation\\analysis.py:313 4 0.015625 2.703125 0.675781\r\n", + "..and_clean_temperature_fluctuations 4 0.046875 2.687500 0.671875\r\n", + "..184 clean_temperature_fluctuations 4 0.062500 2.640625 0.660156\r\n", + "..sycopg2\\_json.py:164 typecast_json 67339 0.312500 2.562500 0.000038\r\n", + "..-input-1-879b7ebf06b8>:7 4 0.234375 2.515625 0.628906\r\n", + "..gregation\\query.py:116 subiterator 4 0.390625 2.281250 0.570312\r\n", + "..n38\\Lib\\json\\__init__.py:299 loads 67339 0.515625 2.250000 0.000033\r\n", + "..es\\psycopg2\\extras.py:686 67347 0.156250 1.796875 0.000027\r\n", + "..on38\\Lib\\uuid.py:130 UUID.__init__ 67347 1.000000 1.640625 0.000024\r\n", + "..\\decoder.py:332 JSONDecoder.decode 67339 0.718750 1.546875 0.000023\r\n", + "..d\\aggregation\\query.py:41 get_data 1 0.312500 1.531250 1.531250\r\n", + "..ion\\database.py:77 from_sql_result 67339 0.468750 1.218750 0.000018\r\n", + "..chemy\\orm\\loading.py:83 6 0.328125 0.984375 0.164062\r\n", + ".._collections.py:121 result._asdict 67343 0.375000 0.515625 0.000008\r\n", + "..thon38\\Lib\\uuid.py:231 UUID.__eq__ 67351 0.281250 0.359375 0.000005\r\n", + "..chemy\\orm\\loading.py:84 67347 0.343750 0.343750 0.000005\r\n", + "..y\\util\\_collections.py:112 __new__ 67347 0.234375 0.312500 0.000005\r\n", + "..\\matplotlib\\__init__.py:1535 inner 4 0.000000 0.296875 0.074219\r\n", + "..axes.py:1651 AxesSubplot.plot_date 4 0.000000 0.296875 0.074219\r\n", + "..xes\\_axes.py:1412 AxesSubplot.plot 4 0.000000 0.296875 0.074219\r\n", + "..gregation\\analysis.py:63 draw_date 4 0.000000 0.296875 0.074219\r\n", + ".._base.py:1842 AxesSubplot.add_line 4 0.000000 0.281250 0.070312\r\n", + "..oder.py:343 JSONDecoder.raw_decode 67339 0.265625 0.265625 0.000004\r\n", + "..s\\matplotlib\\dates.py:1911 convert 6 0.000000 0.265625 0.044271\r\n", + "..on_base.py:2063 vectorize.__call__ 4 0.000000 0.265625 0.066406\r\n", + "..b\\axis.py:1562 XAxis.convert_units 12 0.000000 0.265625 0.022135\r\n", + "...py:168 AxesSubplot.convert_xunits 48 0.000000 0.265625 0.005534\r\n", + "..lotlib\\lines.py:664 Line2D.recache 10 0.000000 0.265625 0.026562\r\n", + "..s\\matplotlib\\dates.py:401 date2num 6 0.000000 0.265625 0.044271\r\n", + "..tlib\\lines.py:1018 Line2D.get_path 10 0.000000 0.265625 0.026562\r\n", + "..68 AxesSubplot._update_line_limits 4 0.000000 0.265625 0.066406\r\n", + "...py:2154 vectorize._vectorize_call 4 0.046875 0.265625 0.066406\r\n", + ":1 DataPoint.__init__ 67339 0.234375 0.234375 0.000003\r\n", + "..tplotlib\\dates.py:210 _to_ordinalf 8467 0.140625 0.218750 0.000026\r\n", + "..alysis.py:327 plot_multiple_charts 1 0.000000 0.078125 0.078125\r\n", + "..ine\\base.py:916 Connection.execute 9 0.000000 0.062500 0.006944\r\n", + "..:291 Select._execute_on_connection 6 0.000000 0.062500 0.010417\r\n", + "..y\\orm\\query.py:3400 Query.__iter__ 6 0.000000 0.062500 0.010417\r\n", + ".. 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import yappi\n", + "\n", + "from apd.aggregation.analysis import interactable_plot_multiple_charts, Config, clean_temperature_fluctuations, get_data_by_deployment\n", + "from apd.aggregation.utils import profile_with_yappi\n", + "\n", + "async def filter_and_clean_temperature_fluctuations(datapoints):\n", + " filtered = (item async for item in datapoints if item.sensor_name==\"Temperature\")\n", + " cleaned = clean_temperature_fluctuations(filtered)\n", + " async for item in cleaned:\n", + " yield item\n", + "\n", + "filter_in_python = Config(\n", + " clean=filter_and_clean_temperature_fluctuations,\n", + " title=\"Ambient temperature\",\n", + " ylabel=\"Degrees C\",\n", + " get_data=get_data_by_deployment,\n", + ")\n", + "\n", + "with profile_with_yappi():\n", + " plot = interactable_plot_multiple_charts(configs=[filter_in_python])\n", + " plot()\n", + "\n", + "yappi.get_func_stats().print_all()" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from apd.aggregation.utils import yappi_package_matches\n", + "import yappi\n", + "\n", + "yappi.get_func_stats().save(\"callgrind.filter_in_python\", \"callgrind\") " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "apd.aggregation", + "language": "python", + "name": "apd.aggregation" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Ch12/apd.aggregation-chapter12-ex01/pyproject.toml b/Ch12/apd.aggregation-chapter12-ex01/pyproject.toml new file mode 100644 index 0000000..4d3bb67 --- /dev/null +++ b/Ch12/apd.aggregation-chapter12-ex01/pyproject.toml @@ -0,0 +1,5 @@ +[build-system] +requires = [ + "setuptools", +] +build-backend = "setuptools.build_meta" diff --git a/Ch12/apd.aggregation-chapter12-ex01/pytest.ini b/Ch12/apd.aggregation-chapter12-ex01/pytest.ini new file mode 100644 index 0000000..8c26fbc --- /dev/null +++ b/Ch12/apd.aggregation-chapter12-ex01/pytest.ini @@ -0,0 +1,4 @@ +[pytest] +markers = + functional: these tests are significantly slower as they start a HTTP server + performance: very slow tests that provide performance guarantees \ No newline at end of file diff --git a/Ch12/apd.aggregation-chapter12-ex01/setup.cfg b/Ch12/apd.aggregation-chapter12-ex01/setup.cfg new file mode 100644 index 0000000..704c6bf --- /dev/null +++ b/Ch12/apd.aggregation-chapter12-ex01/setup.cfg @@ -0,0 +1,89 @@ +[mypy] +namespace_packages = True +mypy_path = src +plugins = sqlmypy + +[mypy-alembic] +ignore_missing_imports = True + +[mypy-alembic.config] +ignore_missing_imports = True + +[mypy-alembic.script] +ignore_missing_imports = True + +[mypy-alembic.runtime.environment] +ignore_missing_imports = True + +[mypy-matplotlib] +ignore_missing_imports = True + +[mypy-matplotlib.axes._base] +ignore_missing_imports = True + +[mypy-matplotlib.figure] +ignore_missing_imports = True + +[mypy-matplotlib.pyplot] +ignore_missing_imports = True + +[mypy-IPython.core] +ignore_missing_imports = True + +[mypy-yappi] +ignore_missing_imports = True + +[mypy-pint] +ignore_missing_imports = True + +[mypy-pytest] +ignore_missing_imports = True + +[mypy-setuptools] +ignore_missing_imports = True + +[flake8] +max-line-length = 120 +ignore = E501,E712 + +[metadata] +name = apd.aggregation +version = attr: apd.aggregation.VERSION +description = A programme that queries apd.sensor endpoints and aggregates their results. +long_description = file: README.md, CHANGES.md, LICENCE +long_description_content_type = text/markdown +keywords = +license = BSD +classifiers = + Programming Language :: Python :: 3 + Programming Language :: Python :: 3.7 + +[options] +zip_safe = False +include_package_data = True +package_dir = + =src +packages = find_namespace: +install_requires = + sqlalchemy + aiohttp + pint + psycopg2 + alembic + click + +[options.package_data] +apd.aggregation = py.typed + +[options.extras_require] +jupyter = ipywidgets +yappi = yappi + +[options.packages.find] +where = src + +[options.entry_points] +console_scripts = + collect_sensor_data = apd.aggregation.cli:collect_sensor_data + sensor_deployments = apd.aggregation.cli:deployments + run_apd_actions = apd.aggregation.cli:run_actions \ No newline at end of file diff --git a/Ch12/apd.aggregation-chapter12-ex01/setup.py b/Ch12/apd.aggregation-chapter12-ex01/setup.py new file mode 100644 index 0000000..6068493 --- /dev/null +++ b/Ch12/apd.aggregation-chapter12-ex01/setup.py @@ -0,0 +1,3 @@ +from setuptools import setup + +setup() diff --git a/Ch12/apd.aggregation-chapter12-ex01/src/apd/aggregation/__init__.py b/Ch12/apd.aggregation-chapter12-ex01/src/apd/aggregation/__init__.py new file mode 100644 index 0000000..3277f64 --- /dev/null +++ b/Ch12/apd.aggregation-chapter12-ex01/src/apd/aggregation/__init__.py @@ -0,0 +1 @@ +VERSION = "1.0.0" diff --git a/Ch12/apd.aggregation-chapter12-ex01/src/apd/aggregation/actions/__init__.py b/Ch12/apd.aggregation-chapter12-ex01/src/apd/aggregation/actions/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/Ch12/apd.aggregation-chapter12-ex01/src/apd/aggregation/actions/action.py b/Ch12/apd.aggregation-chapter12-ex01/src/apd/aggregation/actions/action.py new file mode 100644 index 0000000..d192b19 --- /dev/null +++ b/Ch12/apd.aggregation-chapter12-ex01/src/apd/aggregation/actions/action.py @@ -0,0 +1,74 @@ +import asyncio +import dataclasses +import datetime +import logging + +from ..database import DataPoint +from ..collect import handle_result +from ..query import db_session_var +from .base import Action + +logger = logging.getLogger(__name__) + + +@dataclasses.dataclass +class OnlyOnChangeActionWrapper(Action): + """An action that requires another action as a parameter. + The `wrapped` action will be delegated to so long as the previous + invocation's `data` attribute is different to the current one. + The first invocation will always be delegated.""" + + wrapped: Action + + async def start(self) -> None: + self.last_value = None + return await self.wrapped.start() + + async def handle(self, datapoint: DataPoint) -> bool: + if datapoint.data == self.last_value: + return False + else: + self.last_value = datapoint.data + return await self.wrapped.handle(datapoint) + + +@dataclasses.dataclass +class OnlyAfterDateActionWrapper(Action): + """An action that requires another action and a date + as parameters. The `wrapped` action will be delegated to + for all data points with a date strictly after `date_threshold`.""" + + wrapped: Action + date_threshold: datetime.datetime + + async def start(self) -> None: + return await self.wrapped.start() + + async def handle(self, datapoint: DataPoint) -> bool: + if datapoint.collected_at <= self.date_threshold: + return False + return await self.wrapped.handle(datapoint) + + +class SaveToDatabaseAction(Action): + """An action that stores any generated data points back to the DB""" + + async def start(self) -> None: + return + + async def handle(self, datapoint: DataPoint) -> bool: + loop = asyncio.get_running_loop() + session = db_session_var.get() + await loop.run_in_executor(None, handle_result, [datapoint], session) + return True + + +class LoggingAction(Action): + """An action that stores any generated data points back to the DB""" + + async def start(self) -> None: + return + + async def handle(self, datapoint: DataPoint) -> bool: + logger.warn(datapoint) + return True diff --git a/Ch12/apd.aggregation-chapter12-ex01/src/apd/aggregation/actions/base.py b/Ch12/apd.aggregation-chapter12-ex01/src/apd/aggregation/actions/base.py new file mode 100644 index 0000000..0589ba4 --- /dev/null +++ b/Ch12/apd.aggregation-chapter12-ex01/src/apd/aggregation/actions/base.py @@ -0,0 +1,62 @@ +import typing as t + +from ..typing import T_value +from ..database import DataPoint +from ..exceptions import NoDataForTrigger + + +class Trigger(t.Generic[T_value]): + name: str + + async def start(self) -> None: + """ Coroutine to do any initial setup """ + return + + async def match(self, datapoint: DataPoint) -> bool: + """ Return True if the datapoint is of interest to this + trigger. + This is an optional method, called by the default implementation + of handle(...).""" + raise NotImplementedError + + async def extract(self, datapoint: DataPoint) -> T_value: + """ Return the value that this datapoint implies for this trigger, + or raise NoDataForTrigger if no value is appropriate. + Can also raise IncompatibleTriggerError if the value is not readable. + + This is an optional method, called by the default implementation + of handle(...). + """ + raise NotImplementedError + + async def handle(self, datapoint: DataPoint) -> t.Optional[DataPoint]: + """Given a data point, optionally return a datapoint that + represents the value of this trigger. Will delegate to the + match(...) and extract(...) functions.""" + if not await self.match(datapoint): + # This data point isn't relevant + return None + + try: + value = await self.extract(datapoint) + except NoDataForTrigger: + # There was no value for this point + return None + + return DataPoint( + sensor_name=self.name, + data=value, + deployment_id=datapoint.deployment_id, + collected_at=datapoint.collected_at, + ) + + +class Action: + async def start(self) -> None: + """ Coroutine to do any initial setup """ + return + + async def handle(self, datapoint: DataPoint) -> bool: + """ Apply this datapoint to the action, returning + a boolean to indicate success. """ + raise NotImplementedError diff --git a/Ch12/apd.aggregation-chapter12-ex01/src/apd/aggregation/actions/runner.py b/Ch12/apd.aggregation-chapter12-ex01/src/apd/aggregation/actions/runner.py new file mode 100644 index 0000000..b544c21 --- /dev/null +++ b/Ch12/apd.aggregation-chapter12-ex01/src/apd/aggregation/actions/runner.py @@ -0,0 +1,75 @@ +from __future__ import annotations + +import asyncio +import collections +import dataclasses +import time +import typing as t + +from ..database import DataPoint +from .base import Action, Trigger + +Decorated_Type = t.TypeVar("Decorated_Type", bound=t.Callable[..., t.Any]) + + +@dataclasses.dataclass(unsafe_hash=True) +class DataProcessor: + name: str + action: Action + trigger: Trigger[t.Any] + + def __post_init__(self): + self._input: t.Optional[asyncio.Queue[DataPoint]] = None + self._sub_tasks: t.Set = set() + self.last_times = collections.deque(maxlen=10) + self.total_in = 0 + self.total_out = 0 + + async def start(self) -> None: + self._input = asyncio.Queue(64) + self._task = asyncio.create_task(self.process(), name=f"{self.name}_process") + await asyncio.gather(self.action.start(), self.trigger.start()) + + @property + def input(self) -> asyncio.Queue[DataPoint]: + if self._input is None: + raise RuntimeError(f"{self}.start() was not awaited") + if self._task.done(): + raise RuntimeError("Processing has stopped") from self._task.exception() + return self._input + + async def idle(self) -> None: + await self.input.join() + + async def end(self) -> None: + self._task.cancel() + + async def push(self, obj: DataPoint) -> None: + coro = self.input.put(obj) + return await asyncio.wait_for(coro, timeout=30) + + async def process(self) -> None: + while True: + data = await self.input.get() + start = time.time() + self.total_in += 1 + try: + action_taken = False + processed = await self.trigger.handle(data) + if processed: + action_taken =await self.action.handle(processed) + if action_taken: + elapsed = time.time() - start + self.total_out += 1 + self.last_times.append(elapsed) + finally: + self.input.task_done() + + def stats(self) -> str: + if self.last_times: + avr_time = sum(self.last_times) / len(self.last_times) + elif self.total_in: + avr_time = 0 + else: + return "Not yet started" + return f"{avr_time:0.3f} seconds per item. {self.total_in} in, {self.total_out} out, {self.input.qsize()} waiting." diff --git a/Ch12/apd.aggregation-chapter12-ex01/src/apd/aggregation/actions/source.py b/Ch12/apd.aggregation-chapter12-ex01/src/apd/aggregation/actions/source.py new file mode 100644 index 0000000..d044d4d --- /dev/null +++ b/Ch12/apd.aggregation-chapter12-ex01/src/apd/aggregation/actions/source.py @@ -0,0 +1,80 @@ +import asyncio + +from apd.aggregation.query import db_session_var, get_data + + +async def get_newest_record_id(): + from apd.aggregation.database import datapoint_table + from sqlalchemy import func + + loop = asyncio.get_running_loop() + db_session = db_session_var.get() + max_id_query = db_session.query(func.max(datapoint_table.c.id)) + return await loop.run_in_executor(None, max_id_query.scalar) + + +async def get_data_ongoing(*args, historical=False, **kwargs): + last_id = 0 + if not historical: + kwargs["inserted_after_record_id"] = last_id = await get_newest_record_id() + db_session = db_session_var.get() + while True: + # Run a timer for 300 seconds concurrently with our work + minimum_loop_timer = asyncio.create_task(asyncio.sleep(300)) + async for datapoint in get_data(*args, **kwargs): + if datapoint.id > last_id: + # This is the newest datapoint we have handled so far + last_id = datapoint.id + yield datapoint + # Next time, find only data points later than the latest we've seen + kwargs["inserted_after_record_id"] = last_id + # Commit the DB to store any work that was done in this loop and + # ensure that any isolation level issues do not prevent loading more + # data + db_session.commit() + # Wait for that timer to complete. If our loop took over 5 minutes + # this will complete immediately, otherwise it will block + await minimum_loop_timer + + +async def wait_for_notify(loop, raw_connection): + waiting = True + while waiting: + # SQLAlchemy isn't asynchronous, poll in a new thread + # to make sure we've received any notifications + await loop.run_in_executor(None, raw_connection.poll) + while raw_connection.notifies: + # End the loop after clearing out all pending + # notifications + waiting = False + raw_connection.notifies.pop() + if waiting: + # If we had no notifications wait 15 seconds then + # re-check + await asyncio.sleep(15) + + +async def get_data_ongoing_psql_pubsub(*args, historical=False, **kwargs): + last_id = 0 + if not historical: + kwargs["inserted_after_record_id"] = last_id = await get_newest_record_id() + db_session = db_session_var.get() + db_session.execute("LISTEN apd_aggregation;") + loop = asyncio.get_running_loop() + while True: + async for datapoint in get_data(*args, order=False, **kwargs): + if datapoint.id > last_id: + # This is the newest datapoint we have handled so far + last_id = datapoint.id + yield datapoint + # Next time, find only data points later than the latest we've seen + kwargs["inserted_after_record_id"] = last_id + # Commit the DB to store any work that was done in this loop and + # ensure that any isolation level issues do not prevent loading more + # data + db_session.commit() + + connection = db_session.connection() + raw_connection = connection.connection + await wait_for_notify(loop, raw_connection) + # Always yield new records from this point on diff --git a/Ch12/apd.aggregation-chapter12-ex01/src/apd/aggregation/actions/trigger.py b/Ch12/apd.aggregation-chapter12-ex01/src/apd/aggregation/actions/trigger.py new file mode 100644 index 0000000..c0df3ca --- /dev/null +++ b/Ch12/apd.aggregation-chapter12-ex01/src/apd/aggregation/actions/trigger.py @@ -0,0 +1,34 @@ +import dataclasses +import typing as t +import uuid + +from ..database import DataPoint +from ..exceptions import IncompatibleTriggerError +from .base import Trigger + + +@dataclasses.dataclass(frozen=True) +class ValueThresholdTrigger(Trigger[bool]): + name: str + threshold: float + comparator: t.Callable[[float, float], bool] + sensor_name: str + deployment_id: t.Optional[uuid.UUID] = dataclasses.field(default=None) + + async def match(self, datapoint: DataPoint) -> bool: + if datapoint.sensor_name != self.sensor_name: + return False + elif self.deployment_id and datapoint.deployment_id != self.deployment_id: + return False + return True + + async def extract(self, datapoint: DataPoint) -> bool: + if datapoint.data is None: + raise IncompatibleTriggerError("Datapoint does not contain data") + elif isinstance(datapoint.data, float): + value = datapoint.data + elif isinstance(datapoint.data, dict) and "magnitude" in datapoint.data: + value = datapoint.data["magnitude"] + else: + raise IncompatibleTriggerError("Unrecognised data format") + return self.comparator(value, self.threshold) # type: ignore diff --git a/Ch12/apd.aggregation-chapter12-ex01/src/apd/aggregation/alembic/README b/Ch12/apd.aggregation-chapter12-ex01/src/apd/aggregation/alembic/README new file mode 100644 index 0000000..7d90be0 --- /dev/null +++ b/Ch12/apd.aggregation-chapter12-ex01/src/apd/aggregation/alembic/README @@ -0,0 +1,12 @@ +Generic single-database configuration. + + +# Database setup + +To generate the required database tables you must create an alembic.ini file, as follows: + + [alembic] + script_location = apd.aggregation:alembic + sqlalchemy.url = postgresql+psycopg2://apd@localhost/apd + +and run `alembic upgrade head`. This should also be done after every upgrade of the software. diff --git a/Ch12/apd.aggregation-chapter12-ex01/src/apd/aggregation/alembic/env.py b/Ch12/apd.aggregation-chapter12-ex01/src/apd/aggregation/alembic/env.py new file mode 100644 index 0000000..4c8a2c2 --- /dev/null +++ b/Ch12/apd.aggregation-chapter12-ex01/src/apd/aggregation/alembic/env.py @@ -0,0 +1,80 @@ +from logging.config import fileConfig + +from sqlalchemy import engine_from_config +from sqlalchemy import pool +from alembic import context + +from apd.aggregation.database import metadata + + +# this is the Alembic Config object, which provides +# access to the values within the .ini file in use. +config = context.config + +# Interpret the config file for Python logging. +# This line sets up loggers basically. +if config.config_file_name: + fileConfig(config.config_file_name) + +target_metadata = metadata + + +def include_object(object, name, type_, reflected, compare_to): + if object.info.get("is_view", False): + return False + return True + + +def run_migrations_offline(): + """Run migrations in 'offline' mode. + + This configures the context with just a URL + and not an Engine, though an Engine is acceptable + here as well. By skipping the Engine creation + we don't even need a DBAPI to be available. + + Calls to context.execute() here emit the given string to the + script output. + + """ + url = config.get_main_option("sqlalchemy.url") + context.configure( + url=url, + include_object=include_object, + target_metadata=target_metadata, + literal_binds=True, + dialect_opts={"paramstyle": "named"}, + ) + + with context.begin_transaction(): + context.run_migrations() + + +def run_migrations_online(): + """Run migrations in 'online' mode. + + In this scenario we need to create an Engine + and associate a connection with the context. + + """ + connectable = engine_from_config( + config.get_section(config.config_ini_section), + prefix="sqlalchemy.", + poolclass=pool.NullPool, + ) + + with connectable.connect() as connection: + context.configure( + connection=connection, + target_metadata=target_metadata, + include_object=include_object, + ) + + with context.begin_transaction(): + context.run_migrations() + + +if context.is_offline_mode(): + run_migrations_offline() +else: + run_migrations_online() diff --git a/Ch12/apd.aggregation-chapter12-ex01/src/apd/aggregation/alembic/script.py.mako b/Ch12/apd.aggregation-chapter12-ex01/src/apd/aggregation/alembic/script.py.mako new file mode 100644 index 0000000..2c01563 --- /dev/null +++ b/Ch12/apd.aggregation-chapter12-ex01/src/apd/aggregation/alembic/script.py.mako @@ -0,0 +1,24 @@ +"""${message} + +Revision ID: ${up_revision} +Revises: ${down_revision | comma,n} +Create Date: ${create_date} + +""" +from alembic import op +import sqlalchemy as sa +${imports if imports else ""} + +# revision identifiers, used by Alembic. +revision = ${repr(up_revision)} +down_revision = ${repr(down_revision)} +branch_labels = ${repr(branch_labels)} +depends_on = ${repr(depends_on)} + + +def upgrade(): + ${upgrades if upgrades else "pass"} + + +def downgrade(): + ${downgrades if downgrades else "pass"} diff --git a/Ch12/apd.aggregation-chapter12-ex01/src/apd/aggregation/alembic/versions/4b2df8a6e1ce_add_indexes_to_datapoints.py b/Ch12/apd.aggregation-chapter12-ex01/src/apd/aggregation/alembic/versions/4b2df8a6e1ce_add_indexes_to_datapoints.py new file mode 100644 index 0000000..ef671bd --- /dev/null +++ b/Ch12/apd.aggregation-chapter12-ex01/src/apd/aggregation/alembic/versions/4b2df8a6e1ce_add_indexes_to_datapoints.py @@ -0,0 +1,32 @@ +"""Add indexes to datapoints + +Revision ID: 4b2df8a6e1ce +Revises: 6d2eacd5da3f +Create Date: 2019-12-02 16:07:41.123116 + +""" +from alembic import op + + +# revision identifiers, used by Alembic. +revision = "4b2df8a6e1ce" +down_revision = "6d2eacd5da3f" +branch_labels = None +depends_on = None + + +def upgrade(): + op.create_index( + op.f("ix_datapoints_collected_at"), + "datapoints", + ["collected_at"], + unique=False, + ) + op.create_index( + op.f("ix_datapoints_sensor_name"), "datapoints", ["sensor_name"], unique=False, + ) + + +def downgrade(): + op.drop_index(op.f("ix_datapoints_sensor_name"), table_name="datapoints") + op.drop_index(op.f("ix_datapoints_collected_at"), table_name="datapoints") diff --git a/Ch12/apd.aggregation-chapter12-ex01/src/apd/aggregation/alembic/versions/6962f8455a6d_add_daily_summary_view.py b/Ch12/apd.aggregation-chapter12-ex01/src/apd/aggregation/alembic/versions/6962f8455a6d_add_daily_summary_view.py new file mode 100644 index 0000000..eb02455 --- /dev/null +++ b/Ch12/apd.aggregation-chapter12-ex01/src/apd/aggregation/alembic/versions/6962f8455a6d_add_daily_summary_view.py @@ -0,0 +1,38 @@ +"""Add daily summary view + +Revision ID: 6962f8455a6d +Revises: 4b2df8a6e1ce +Create Date: 2019-12-03 11:50:24.403402 + +""" +from alembic import op + + +# revision identifiers, used by Alembic. +revision = "6962f8455a6d" +down_revision = "4b2df8a6e1ce" +branch_labels = None +depends_on = None + + +def upgrade(): + create_view = """ + CREATE VIEW daily_summary AS + SELECT + datapoints.sensor_name AS sensor_name, + datapoints.data AS data, + count(datapoints.id) AS count + FROM datapoints + WHERE + datapoints.collected_at >= CAST(CURRENT_DATE AS DATE) + AND + datapoints.collected_at < CAST(CURRENT_DATE AS DATE) + 1 + GROUP BY + datapoints.sensor_name, + datapoints.data; + """ + op.execute(create_view) + + +def downgrade(): + op.execute("""DROP VIEW daily_summary""") diff --git a/Ch12/apd.aggregation-chapter12-ex01/src/apd/aggregation/alembic/versions/6d2eacd5da3f_create_sensor_values_table.py b/Ch12/apd.aggregation-chapter12-ex01/src/apd/aggregation/alembic/versions/6d2eacd5da3f_create_sensor_values_table.py new file mode 100644 index 0000000..b4a3545 --- /dev/null +++ b/Ch12/apd.aggregation-chapter12-ex01/src/apd/aggregation/alembic/versions/6d2eacd5da3f_create_sensor_values_table.py @@ -0,0 +1,31 @@ +"""Create datapoints table + +Revision ID: 6d2eacd5da3f +Revises: N/A +Create Date: 2019-09-29 13:43:21.242706 + +""" +from alembic import op +import sqlalchemy as sa +from sqlalchemy.dialects import postgresql + +# revision identifiers, used by Alembic. +revision = "6d2eacd5da3f" +down_revision = None +branch_labels = None +depends_on = None + + +def upgrade(): + op.create_table( + "datapoints", + sa.Column("id", sa.Integer(), nullable=False), + sa.Column("sensor_name", sa.String(), nullable=True), + sa.Column("collected_at", postgresql.TIMESTAMP(), nullable=True), + sa.Column("data", postgresql.JSONB(astext_type=sa.Text()), nullable=True), + sa.PrimaryKeyConstraint("id"), + ) + + +def downgrade(): + op.drop_table("datapoints") diff --git a/Ch12/apd.aggregation-chapter12-ex01/src/apd/aggregation/alembic/versions/d8cdc709086b_add_deployment_table.py b/Ch12/apd.aggregation-chapter12-ex01/src/apd/aggregation/alembic/versions/d8cdc709086b_add_deployment_table.py new file mode 100644 index 0000000..204e096 --- /dev/null +++ b/Ch12/apd.aggregation-chapter12-ex01/src/apd/aggregation/alembic/versions/d8cdc709086b_add_deployment_table.py @@ -0,0 +1,32 @@ +"""Add deployment table + +Revision ID: d8cdc709086b +Revises: d8d4cf6a178f +Create Date: 2019-12-17 16:28:58.585616 + +""" +from alembic import op +import sqlalchemy as sa +from sqlalchemy.dialects import postgresql + +# revision identifiers, used by Alembic. +revision = "d8cdc709086b" +down_revision = "d8d4cf6a178f" +branch_labels = None +depends_on = None + + +def upgrade(): + op.create_table( + "deployments", + sa.Column("id", postgresql.UUID(as_uuid=True), nullable=False), + sa.Column("uri", sa.String(), nullable=True), + sa.Column("name", sa.String(), nullable=True), + sa.Column("colour", sa.String(), nullable=True), + sa.Column("api_key", sa.String(), nullable=True), + sa.PrimaryKeyConstraint("id"), + ) + + +def downgrade(): + op.drop_table("deployments") diff --git a/Ch12/apd.aggregation-chapter12-ex01/src/apd/aggregation/alembic/versions/d8d4cf6a178f_add_deployment_id_to_datapoint.py b/Ch12/apd.aggregation-chapter12-ex01/src/apd/aggregation/alembic/versions/d8d4cf6a178f_add_deployment_id_to_datapoint.py new file mode 100644 index 0000000..88d5219 --- /dev/null +++ b/Ch12/apd.aggregation-chapter12-ex01/src/apd/aggregation/alembic/versions/d8d4cf6a178f_add_deployment_id_to_datapoint.py @@ -0,0 +1,34 @@ +"""Add deployment id to DataPoint + +Revision ID: d8d4cf6a178f +Revises: 6962f8455a6d +Create Date: 2019-12-03 20:58:11.285509 + +""" +from alembic import op +import sqlalchemy as sa +from sqlalchemy.dialects import postgresql + +# revision identifiers, used by Alembic. +revision = "d8d4cf6a178f" +down_revision = "6962f8455a6d" +branch_labels = None +depends_on = None + + +def upgrade(): + op.add_column( + "datapoints", + sa.Column("deployment_id", postgresql.UUID(as_uuid=True), nullable=True), + ) + op.create_index( + op.f("ix_datapoints_deployment_id"), + "datapoints", + ["deployment_id"], + unique=False, + ) + + +def downgrade(): + op.drop_index(op.f("ix_datapoints_deployment_id"), table_name="datapoints") + op.drop_column("datapoints", "deployment_id") diff --git a/Ch12/apd.aggregation-chapter12-ex01/src/apd/aggregation/analysis.py b/Ch12/apd.aggregation-chapter12-ex01/src/apd/aggregation/analysis.py new file mode 100644 index 0000000..eeb4ab2 --- /dev/null +++ b/Ch12/apd.aggregation-chapter12-ex01/src/apd/aggregation/analysis.py @@ -0,0 +1,401 @@ +from __future__ import annotations + +import asyncio +import collections +from concurrent.futures import ThreadPoolExecutor +import dataclasses +import datetime +import functools +import math +import typing as t +from uuid import UUID +import warnings + +import matplotlib.pyplot as plt +from matplotlib.axes._base import _AxesBase +from matplotlib.figure import Figure +from pint import _DEFAULT_REGISTRY as ureg + +from apd.aggregation.query import ( + get_data, + get_data_by_deployment, + with_database, + get_deployment_by_id, +) +from apd.aggregation.database import DataPoint, deployment_table +from .utils import merc_x, merc_y, convert_temperature +from .typing import IntermediateMapData, T_key, T_value, CleanerFunc, Cleaned +from .typing import ( + CLEANED_COORD_FLOAT, + CLEANED_DT_FLOAT, + COORD_FLOAT_CLEANER, + DT_FLOAT_CLEANER, +) + +# Static UUID to represent aggregation of other deployments +GLOBAL = UUID("bd02526e-7619-4a59-b04b-1fafd1c262d1") + + +@dataclasses.dataclass +class Config(t.Generic[T_key, T_value]): + title: str + clean: CleanerFunc[Cleaned[T_key, T_value]] + draw: t.Optional[ + t.Callable[ + [t.Any, t.Iterable[T_key], t.Iterable[T_value], t.Optional[str]], None + ] + ] = None + get_data: t.Optional[ + t.Callable[..., t.AsyncIterator[t.Tuple[UUID, t.AsyncIterator[DataPoint]]]] + ] = None + ylabel: t.Optional[str] = None + sensor_name: dataclasses.InitVar[str] = None + + def __post_init__(self, sensor_name: t.Optional[str] = None) -> None: + if self.draw is None: + self.draw = draw_date # type: ignore + if sensor_name is not None: + warnings.warn( + DeprecationWarning( + f"The sensor_name parameter is deprecated. Please pass " + f"get_data=get_one_sensor_by_deployment('{sensor_name}') " + f"to ensure the same behaviour. The sensor_name= parameter " + f"will be removed in apd.aggregation 3.0." + ), + stacklevel=3, + ) + if self.get_data is None: + self.get_data = get_one_sensor_by_deployment(sensor_name) + if self.get_data is None: + raise ValueError("You must specify a get_data function") + + +def draw_date( + plot: _AxesBase, + x: t.Iterable[datetime.datetime], + y: t.Iterable[float], + colour: t.Optional[str], +) -> None: + plot.plot_date(x, y, color=colour, linestyle="-", marker="", xdate=True) + + +def draw_map( + plot: _AxesBase, + x: t.Iterable[t.Tuple[float, float]], + y: t.Iterable[float], + colour: t.Optional[str], +) -> None: + lon = [merc_y(coord[0]) for coord in x] + lat = [merc_x(coord[1]) for coord in x] + + for axis in "x", "y": + plt.tick_params( + axis=axis, + which="both", + bottom=False, + top=False, + left=False, + right=False, + labelbottom=False, + labelleft=False, + ) + + plot.tricontourf(lat, lon, y) + plot.plot(lat, lon, "wo", ms=3) + plot.set_aspect(1.0) + + +def get_map_cleaner_for(sensor_name: str,) -> COORD_FLOAT_CLEANER: + """Given a sensor_name that represents a float, return a coroutine that acts as a cleaner + extracting that sensor's data keyed by the value of a Location sensor.""" + + async def clean_latest_coord_and_value( + datapoints: t.AsyncIterator[DataPoint], + ) -> CLEANED_COORD_FLOAT: + + # We will iterate over data points and build an entry in cleaned_data + # for each deployment. This lets newer data replace older data, as + # datapoints is assumed to be in date order + # IntermediateMapData is a typing hint for a dictionary with specific key/values + cleaned_data: t.Dict[UUID, IntermediateMapData] = {} + async for datapoint in datapoints: + # Get the existing data for this deployment, if we've seen it before + row_data = cleaned_data.get(datapoint.deployment_id, None) + if row_data is None: + # This is the first time we've seen this deployment + row_data = {"coord": None, "value": None} + if datapoint.sensor_name == "Location": + # Coord is a 2-tuple of floats + row_data["coord"] = ( + float(datapoint.data[0]), + float(datapoint.data[1]), + ) + elif datapoint.sensor_name == sensor_name: + # Value is a single float + row_data["value"] = float(datapoint.data) + # Store the info about this deployment back into the cleaned_data set + cleaned_data[datapoint.deployment_id] = row_data + + for data in cleaned_data.values(): + if data["coord"] is None or data["value"] is None: + # We only got a partial record, don't plot this + continue + yield data["coord"], data["value"] + + # Return the set up cleaner coroutine + return clean_latest_coord_and_value + + +async def clean_watthours_to_watts( + datapoints: t.AsyncIterator[DataPoint], +) -> CLEANED_DT_FLOAT: + last_watthours = None + last_time = None + async for datapoint in datapoints: + if datapoint.data is None: + continue + time = datapoint.collected_at + if datapoint.data["unit"] == "watt_hour": + watt_hours = datapoint.data["magnitude"] + else: + watt_hours = ( + ureg.Quantity(datapoint.data["magnitude"], datapoint.data["unit"]) + .to(ureg.watt_hour) + .magnitude + ) + if last_watthours: + seconds_elapsed = (time - last_time).total_seconds() + hours_elapsed = seconds_elapsed / (60.0 * 60.0) + additional_power = watt_hours - last_watthours + power = additional_power / hours_elapsed + yield time, power + last_watthours = watt_hours + last_time = datapoint.collected_at + + +async def clean_magnitude(datapoints: t.AsyncIterator[DataPoint],) -> CLEANED_DT_FLOAT: + async for datapoint in datapoints: + if datapoint.data is None: + continue + yield datapoint.collected_at, datapoint.data["magnitude"] + + +def convert_temperature_system( + cleaner: DT_FLOAT_CLEANER, temperature_unit: str, +) -> DT_FLOAT_CLEANER: + async def converter(datapoints: t.AsyncIterator[DataPoint],) -> CLEANED_DT_FLOAT: + results = cleaner(datapoints) + async for date, temp_c in results: + yield date, convert_temperature(temp_c, "degC", temperature_unit) + + return converter + + +async def clean_temperature_fluctuations( + datapoints: t.AsyncIterator[DataPoint], +) -> CLEANED_DT_FLOAT: + allowed_jitter = 2.5 + allowed_range = (-40, 80) + window_datapoints: t.Deque[DataPoint] = collections.deque(maxlen=3) + + def datapoint_ok(datapoint: DataPoint) -> bool: + """Return False if this data point does not contain a valid temperature""" + if datapoint.data is None: + return False + elif datapoint.data["unit"] != "degC": + # This point is in a different temperature system. While it could be converted + # this cleaner is not yet doing that. + return False + elif not allowed_range[0] < datapoint.data["magnitude"] < allowed_range[1]: + return False + return True + + async for datapoint in datapoints: + if not datapoint_ok(datapoint): + # If the datapoint is invalid then skip directly to the next item + continue + + window_datapoints.append(datapoint) + if len(window_datapoints) == 3: + # Find the temperatures of the datapoints in the window, then average + # the first and last and compare that to the middle point. + window_temperatures = [dp.data["magnitude"] for dp in window_datapoints] + avr_first_last = (window_temperatures[0] + window_temperatures[2]) / 2 + diff_middle_avr = abs(window_temperatures[1] - avr_first_last) + if diff_middle_avr > allowed_jitter: + pass + else: + yield window_datapoints[1].collected_at, window_temperatures[1] + elif len(window_datapoints) == 1: + # The item in the iterator can't be compared to both neighbours + # so should be yielded + yield datapoint.collected_at, datapoint.data["magnitude"] + else: + # Otherwise, let the window fill up, it will be yieleded later + pass + # When the iterator ends the final item is not yet in the middle + # of the window, so the last item must be explicitly yielded. + if len(window_datapoints) > 1 and datapoint_ok(datapoint): + yield datapoint.collected_at, datapoint.data["magnitude"] + + +async def clean_passthrough( + datapoints: t.AsyncIterator[DataPoint], +) -> CLEANED_DT_FLOAT: + async for datapoint in datapoints: + if datapoint.data is None: + continue + else: + yield datapoint.collected_at, datapoint.data + + +def get_one_sensor_by_deployment( + sensor_name: str, +) -> t.Callable[..., t.AsyncIterator[t.Tuple[UUID, t.AsyncIterator[DataPoint]]]]: + return functools.partial(get_data_by_deployment, sensor_name=sensor_name) + + +def get_all_data() -> t.Callable[ + ..., t.AsyncIterator[t.Tuple[UUID, t.AsyncIterator[DataPoint]]] +]: + async def get_all_data_inner( + *args: t.Any, **kwargs: t.Any + ) -> t.AsyncIterator[t.Tuple[UUID, t.AsyncIterator[DataPoint]]]: + yield GLOBAL, get_data(*args, **kwargs) + + return get_all_data_inner + + +configs = ( + Config( + sensor_name="SolarCumulativeOutput", + clean=clean_watthours_to_watts, + title="Solar generation", + ylabel="Watts", + ), + Config( + sensor_name="RAMAvailable", + clean=clean_passthrough, + title="RAM available", + ylabel="Bytes", + ), + Config( + sensor_name="RelativeHumidity", + clean=clean_passthrough, + title="Relative humidity", + ylabel="Percent", + ), + Config( + sensor_name="Temperature", + clean=clean_temperature_fluctuations, + title="Ambient temperature", + ylabel="Degrees C", + ), +) + + +def get_known_configs() -> t.Dict[str, Config[t.Any, t.Any]]: + return {config.title: config for config in configs} + + +async def plot_sensor( + config: Config[t.Any, t.Any], + plot: _AxesBase, + location_names: t.Dict[UUID, str], + **kwargs: t.Any, +) -> _AxesBase: + locations = [] + if config.get_data is None: + raise ValueError("You must provide a get_data function") + async for deployment_id, query_results in config.get_data(**kwargs): + if deployment_id == GLOBAL: + name = "Global" + else: + try: + deployment = await get_deployment_by_id(deployment_id) + except IndexError: + name = str(deployment_id) + colour = None + else: + name = deployment.name or str(deployment_id) + colour = deployment.colour + # Mypy currently doesn't understand callable fields on datatypes: https://github.com/python/mypy/issues/5485 + points = [dp async for dp in config.clean(query_results)] # type: ignore + if not points: + continue + locations.append(name) + x, y = zip(*points) + plot.set_title(config.title) + plot.set_ylabel(config.ylabel) + if config.draw is None: + raise ValueError("You must provide a get_data function") + config.draw(plot, x, y, colour) + plot.legend(locations) + return plot + + +async def plot_multiple_charts(*args: t.Any, **kwargs: t.Any) -> Figure: + # These parameters are pulled from kwargs to avoid confusing function + # introspection code in IPython widgets + location_names = kwargs.pop("location_names", None) + configs = kwargs.pop("configs", None) + dimensions = kwargs.pop("dimensions", None) + db_uri = kwargs.pop("db_uri", "postgresql+psycopg2://apd@localhost/apd") + + with with_database(db_uri) as session: + loop = asyncio.get_running_loop() + coros = [] + if configs is None: + # If no configs are supplied, use all known configs + configs = get_known_configs().values() + if dimensions is None: + # If no dimensions are supplied, get the square root of the number + # of configs and round it to find a number of columns. This will + # keep the arrangement approximately square. Find rows by multiplying + # out rows. + total_configs = len(configs) + columns = round(math.sqrt(total_configs)) + rows = math.ceil(total_configs / columns) + if location_names is None: + location_query = session.query( + deployment_table.c.id, deployment_table.c.name + ) + location_data = await loop.run_in_executor(None, location_query.all) + location_names = dict(location_data) + + figure = plt.figure(figsize=(10 * columns, 5 * rows), dpi=300) + for i, config in enumerate(configs, start=1): + plot = figure.add_subplot(columns, rows, i) + coros.append(plot_sensor(config, plot, location_names, *args, **kwargs)) + await asyncio.gather(*coros) + return figure + + +_Coroutine_Result = t.TypeVar("_Coroutine_Result") + + +def wrap_coroutine( + f: t.Callable[..., t.Coroutine[t.Any, t.Any, _Coroutine_Result]] +) -> t.Callable[..., _Coroutine_Result]: + """Given a coroutine, return a function that runs that coroutine + in a new event loop in an isolated thread""" + + @functools.wraps(f) + def run_in_thread(*args: t.Any, **kwargs: t.Any) -> _Coroutine_Result: + loop = asyncio.new_event_loop() + wrapped = f(*args, **kwargs) + with ThreadPoolExecutor(max_workers=1) as pool: + task = pool.submit(loop.run_until_complete, wrapped) + # Mypy can get confused when nesting generic functions, like we do here + # The fact that Task is generic means we lose the association with + # _CoroutineResult. Adding an explicit cast restores this. + return t.cast(_Coroutine_Result, task.result()) + + return run_in_thread + + +def interactable_plot_multiple_charts( + *args: t.Any, **kwargs: t.Any +) -> t.Callable[..., Figure]: + with_config = functools.partial(plot_multiple_charts, *args, **kwargs) + return wrap_coroutine(with_config) diff --git a/Ch12/apd.aggregation-chapter12-ex01/src/apd/aggregation/cli.py b/Ch12/apd.aggregation-chapter12-ex01/src/apd/aggregation/cli.py new file mode 100644 index 0000000..a27e96f --- /dev/null +++ b/Ch12/apd.aggregation-chapter12-ex01/src/apd/aggregation/cli.py @@ -0,0 +1,287 @@ +import asyncio +import functools +import importlib.util +import logging +import signal +import typing as t +import uuid + +import aiohttp +import click +from sqlalchemy import create_engine +from sqlalchemy.orm import sessionmaker + +from . import collect +from .actions.runner import DataProcessor +from .actions.source import get_data_ongoing +from .database import Deployment, deployment_table +from .query import with_database + +logger = logging.getLogger(__name__) + + +@click.command() +@click.argument("server", nargs=-1) +@click.option( + "--db", + metavar="", + default="postgresql+psycopg2://localhost/apd", + help="The connection string to a PostgreSQL database", + envvar="APD_DB_URI", +) +@click.option("--api-key", metavar="", envvar="APD_API_KEY") +@click.option( + "--tolerate-failures", + "-f", + help="If provided, failure to retrieve some sensors' data will not abort the collection process", + is_flag=True, +) +@click.option("-v", "--verbose", is_flag=True, help="Enables verbose mode") +def collect_sensor_data( + db: str, server: t.Tuple[str], api_key: str, tolerate_failures: bool, verbose: bool +) -> t.Optional[int]: + """This loads data from one or more sensors into the specified database. + + Only PostgreSQL databases are supported, as the column definitions use + multiple pg specific features. The database must already exist and be + populated with the required tables. + + The --api-key option is used to specify the access token for the sensors + being queried. + + You may specify any number of servers, the variable should be the full URL + to the sensor's HTTP interface, not including the /v/2.0 portion. Multiple + URLs should be separated with a space. + """ + if tolerate_failures: + attempts = [(s,) for s in server] + else: + attempts = [server] + success = True + for attempt in attempts: + try: + collect.standalone(db, attempt, api_key, echo=verbose) + except ValueError as e: + click.secho(str(e), err=True, fg="red") + success = False + return success + + +def load_handler_config(path: str) -> t.List[DataProcessor]: + # Create a module called user_config backed by the file specified, and load it + # This uses Python's import internals to fake a module in a known location + # Based on an SO answer by Sebastian Rittau and sample code from Brett Cannon + module_spec = importlib.util.spec_from_file_location("user_config", path) + module = importlib.util.module_from_spec(module_spec) + loader = module_spec.loader + if isinstance(loader, importlib.abc.Loader): + loader.exec_module(module) + try: + return module.handlers # type: ignore + except AttributeError as err: + raise ValueError(f"Could not load config file from {path}") from err + else: + # No valid loader could be found + raise ValueError(f"Could not load config file from {path}") + + +def stats_signal_handler(sig, frame, original_sigint_handler=None, handlers=None): + for handler in handlers: + click.echo( + click.style(handler.name, bold=True, fg="red") + " " + handler.stats() + ) + if sig == signal.SIGINT: + click.secho("Press Ctrl+C again to end the process", bold=True) + handler = signal.getsignal(signal.SIGINT) + signal.signal(signal.SIGINT, original_sigint_handler) + asyncio.get_running_loop().call_later(5, install_ctrl_c_signal_handler, handler) + return + + +def install_ctrl_c_signal_handler(signal_handler): + click.secho("Press Ctrl+C to view statistics", bold=True) + signal.signal(signal.SIGINT, signal_handler) + + +@click.command() +@click.argument("config", nargs=1) +@click.option( + "--db", + metavar="", + default="postgresql+psycopg2://localhost/apd", + help="The connection string to a PostgreSQL database", + envvar="APD_DB_URI", +) +@click.option( + "--historical", + is_flag=True, + help="Also trigger actions for data points that were already present in the database", +) +@click.option("-v", "--verbose", is_flag=True, help="Enables verbose mode") +def run_actions( + config: str, db: str, verbose: bool, historical: bool +) -> t.Optional[int]: + """This runs the long-running action processors defined in a config file. + + The configuration file specified should be a Python file that defines a + list of DataProcessor objects called processors.n + """ + logging.basicConfig(level=logging.DEBUG if verbose else logging.WARN) + + async def main_loop(): + with with_database(db): + logger.info("Loading configuration") + handlers = load_handler_config(config) + + logger.info(f"Configured {len(handlers)} handlers") + starters = [handler.start() for handler in handlers] + await asyncio.gather(*starters) + + logger.info(f"Ingesting data") + data = get_data_ongoing(historical=historical) + + original_sigint_handler = signal.getsignal(signal.SIGINT) + signal_handler = functools.partial( + stats_signal_handler, + handlers=handlers, + original_sigint_handler=original_sigint_handler, + ) + + for signal_name in "SIGINFO", "SIGUSR1", "SIGINT": + try: + signal.signal(signal.signals[signal_name], signal_handler) + except AttributeError: + pass + + async for datapoint in data: + for handler in handlers: + await handler.push(datapoint) + + asyncio.run(main_loop()) + return True + + +@click.group() +def deployments(): + pass + + +@deployments.command() +@click.argument("uri") +@click.argument("name") +@click.option( + "--db", + metavar="", + default="postgresql+psycopg2://localhost/apd", + help="The connection string to a PostgreSQL database", + envvar="APD_DB_URI", +) +@click.option("--api-key", metavar="", envvar="APD_API_KEY") +@click.option("--colour") +def add( + db: str, uri: str, name: str, api_key: t.Optional[str], colour: t.Optional[str], +): + """This creates a record of a new deployment in the database. + """ + deployment = Deployment(id=None, uri=uri, name=name, api_key=api_key, colour=colour) + + async def http_get_deployment_id(): + async with aiohttp.ClientSession() as http: + collect.http_session_var.set(http) + return await collect.get_deployment_id(uri) + + deployment.id = asyncio.run(http_get_deployment_id()) + insert = deployment_table.insert().values(**deployment._asdict()) + + engine = create_engine(db) + sm = sessionmaker(engine) + Session = sm() + Session.execute(insert) + Session.commit() + return True + + +@deployments.command() +@click.option( + "--db", + metavar="", + default="postgresql+psycopg2://localhost/apd", + help="The connection string to a PostgreSQL database", + envvar="APD_DB_URI", +) +def list(db: str): + """This creates a record of a new deployment in the database. + """ + engine = create_engine(db) + sm = sessionmaker(engine) + Session = sm() + deployments = Session.query(deployment_table).all() + for deployment in deployments: + click.secho(deployment.name, bold=True) + click.echo(click.style("ID ", bold=True) + deployment.id.hex) + click.echo(click.style("URI ", bold=True) + deployment.uri) + click.echo(click.style("API key ", bold=True) + deployment.api_key) + click.echo(click.style("Colour ", bold=True) + str(deployment.colour)) + click.echo() + Session.rollback() + return True + + +@deployments.command() +@click.argument("id") +@click.option("--uri") +@click.option("--name") +@click.option( + "--db", + metavar="", + default="postgresql+psycopg2://localhost/apd", + help="The connection string to a PostgreSQL database", + envvar="APD_DB_URI", +) +@click.option("--api-key", metavar="", envvar="APD_API_KEY") +@click.option("--colour") +def edit( + db: str, + id, + uri: t.Optional[str], + name: t.Optional[str], + api_key: t.Optional[str], + colour: t.Optional[str], +): + """This creates a record of a new deployment in the database. + """ + update = {} + if uri is not None: + update["uri"] = uri + if name is not None: + update["name"] = name + if api_key is not None: + update["api_key"] = api_key + if colour is not None: + update["colour"] = colour + deployment_id = uuid.UUID(id) + + update_stmt = ( + deployment_table.update() + .where(deployment_table.c.id == deployment_id) + .values(**update) + ) + + engine = create_engine(db) + sm = sessionmaker(engine) + Session = sm() + Session.execute(update_stmt) + deployments = Session.query(deployment_table).filter( + deployment_table.c.id == deployment_id + ) + Session.commit() + + for deployment in deployments: + click.secho(deployment.name, bold=True) + click.echo(click.style("ID ", bold=True) + deployment.id.hex) + click.echo(click.style("URI ", bold=True) + deployment.uri) + click.echo(click.style("API key ", bold=True) + deployment.api_key) + click.echo(click.style("Colour ", bold=True) + str(deployment.colour)) + click.echo() + + return True diff --git a/Ch12/apd.aggregation-chapter12-ex01/src/apd/aggregation/collect.py b/Ch12/apd.aggregation-chapter12-ex01/src/apd/aggregation/collect.py new file mode 100644 index 0000000..2ec7c01 --- /dev/null +++ b/Ch12/apd.aggregation-chapter12-ex01/src/apd/aggregation/collect.py @@ -0,0 +1,118 @@ +import asyncio +from contextvars import ContextVar +import datetime +import typing as t +import uuid + +import aiohttp +from sqlalchemy import create_engine +from sqlalchemy.orm import sessionmaker +from sqlalchemy.orm.session import Session + +from .database import DataPoint, datapoint_table, Deployment, deployment_table + +http_session_var: ContextVar[aiohttp.ClientSession] = ContextVar("http_session") + + +async def get_deployment_id(server): + http = http_session_var.get() + if not server.endswith("/"): + server += "/" + url = server + "v/2.1/deployment_id" + async with http.get(url) as request: + if request.status != 200: + raise ValueError(f"Error loading deployment id from {server}") + result = await request.json() + return uuid.UUID(result["deployment_id"]) + + +async def get_data_points(server: str, api_key: t.Optional[str],) -> t.List[DataPoint]: + if not server.endswith("/"): + server += "/" + url = server + "v/2.1/sensors/" + headers = {} + if api_key: + headers["X-API-KEY"] = api_key + http = http_session_var.get() + + # Get the deployment ID in parallel to the sensor data + deployment_id = asyncio.create_task(get_deployment_id(server)) + + async with http.get(url, headers=headers) as request: + result = await request.json() + ok = request.status == 200 + now = datetime.datetime.now() + if ok: + points = [] + for value in result["sensors"]: + points.append( + DataPoint( + sensor_name=value["id"], + collected_at=now, + data=value["value"], + deployment_id=await deployment_id, + ) + ) + return points + else: + raise ValueError( + f"Error loading data from {server}: " + result.get("error", "Unknown") + ) + + +def handle_result(result: t.List[DataPoint], session: Session) -> t.List[DataPoint]: + for point in result: + insert = datapoint_table.insert().values(**point._asdict()) + sql_result = session.execute(insert) + point.id = sql_result.inserted_primary_key[0] + return result + + +async def add_data_from_sensors( + session: Session, servers: t.Iterable[Deployment] +) -> t.List[DataPoint]: + tasks: t.List[t.Awaitable[t.List[DataPoint]]] = [] + points: t.List[DataPoint] = [] + async with aiohttp.ClientSession() as http: + http_session_var.set(http) + tasks = [get_data_points(server.uri, server.api_key) for server in servers] + for results in await asyncio.gather(*tasks): + points += results + loop = asyncio.get_running_loop() + await loop.run_in_executor(None, handle_result, points, session) + return points + + +def standalone( + db_uri: str, servers: t.Tuple[str], api_key: t.Optional[str], echo: bool = False +) -> None: + engine = create_engine(db_uri, echo=echo) + sm = sessionmaker(engine) + Session = sm() + deployments: t.Iterable[Deployment] + if servers: + deployments = tuple( + Deployment(id=None, name=None, colour=None, api_key=api_key, uri=server) + for server in servers + ) + else: + deployment_data = Session.query(deployment_table).all() + deployments = tuple( + Deployment.from_sql_result(deployment) for deployment in deployment_data + ) + asyncio.run(add_data_from_sensors(Session, deployments)) + + if "postgresql" in db_uri: + # On Postgres sent a pubsub notification, in case other processes are waiting + # for this data + Session.execute("NOTIFY apd_aggregation;") + + Session.commit() + + +if __name__ == "__main__": + standalone( + db_uri="postgresql+psycopg2://apd@localhost/apd", + servers=("http://pvoutput:8080/",), + api_key="h3hdfjksfhwkjehnwekj", + ) diff --git a/Ch12/apd.aggregation-chapter12-ex01/src/apd/aggregation/database.py b/Ch12/apd.aggregation-chapter12-ex01/src/apd/aggregation/database.py new file mode 100644 index 0000000..f28b469 --- /dev/null +++ b/Ch12/apd.aggregation-chapter12-ex01/src/apd/aggregation/database.py @@ -0,0 +1,129 @@ +from __future__ import annotations + +from dataclasses import dataclass, field, asdict +import datetime +import typing as t +import uuid + +import sqlalchemy +from sqlalchemy.dialects.postgresql import JSONB, DATE, TIMESTAMP, UUID +from sqlalchemy.ext.hybrid import ExprComparator, hybrid_property +from sqlalchemy.orm import sessionmaker +from sqlalchemy.schema import Table + + +metadata = sqlalchemy.MetaData() + +deployment_table = Table( + "deployments", + metadata, + sqlalchemy.Column("id", UUID(as_uuid=True), primary_key=True), + sqlalchemy.Column("uri", sqlalchemy.String, index=False), + sqlalchemy.Column("name", sqlalchemy.String, index=False, nullable=True), + sqlalchemy.Column("colour", sqlalchemy.String, index=False, nullable=True), + sqlalchemy.Column("api_key", sqlalchemy.String, index=False), +) + +datapoint_table = Table( + "datapoints", + metadata, + sqlalchemy.Column("id", sqlalchemy.Integer, primary_key=True), + sqlalchemy.Column("sensor_name", sqlalchemy.String, index=True), + sqlalchemy.Column("collected_at", TIMESTAMP, index=True), + sqlalchemy.Column("deployment_id", UUID(as_uuid=True), index=True), + sqlalchemy.Column("data", JSONB), +) + +daily_summary_view = Table( + "daily_summary", + metadata, + sqlalchemy.Column("sensor_name", sqlalchemy.String), + sqlalchemy.Column("data", JSONB), + sqlalchemy.Column("count", sqlalchemy.Integer), + info={"is_view": True}, +) + + +class DateEqualComparator(ExprComparator): + def __init__(self, fallback_expression, raw_expression): + # Do not try and find update expression from parent + super().__init__(None, fallback_expression, None) + self.raw_expression = raw_expression + + def __eq__(self, other): + """ Returns True iff on the same day as other """ + other_date = sqlalchemy.cast(other, DATE) + return sqlalchemy.and_( + self.raw_expression >= other_date, self.raw_expression < other_date + 1, + ) + + def operate(self, op, *other, **kwargs): + other = [sqlalchemy.cast(date, DATE) for date in other] + return op(self.expression, *other, **kwargs) + + def reverse_operate(self, op, other, **kwargs): + other = [sqlalchemy.cast(date, DATE) for date in other] + return op(other, self.expression, **kwargs) + + +@dataclass +class DataPoint: + sensor_name: str + data: t.Any + deployment_id: uuid.UUID + id: t.Optional[int] = None + collected_at: datetime.datetime = field(default_factory=datetime.datetime.now) + + @classmethod + def from_sql_result(cls, result) -> DataPoint: + return cls(**result._asdict()) + + def _asdict(self) -> t.Dict[str, t.Any]: + data = asdict(self) + if data["id"] is None: + del data["id"] + return data + + @hybrid_property + def collected_on_date(self): + return self.collected_at.date() + + @collected_on_date.comparator # type: ignore + def collected_on_date(cls): + return DateEqualComparator( + fallback_expression=sqlalchemy.cast(datapoint_table.c.collected_at, DATE), + raw_expression=datapoint_table.c.collected_at, + ) + + +@dataclass +class Deployment: + id: t.Optional[uuid.UUID] + uri: str + name: t.Optional[str] + colour: t.Optional[str] + api_key: t.Optional[str] + + @classmethod + def from_sql_result(cls, result) -> Deployment: + return cls(**result._asdict()) + + def _asdict(self) -> t.Dict[str, t.Any]: + data = asdict(self) + return data + + +def main() -> None: + engine = sqlalchemy.create_engine( + "postgresql+psycopg2://apd@localhost/apd", echo=True + ) + sm = sessionmaker(engine) + Session = sm() + if False: + metadata.create_all(engine) + print(Session.query(DataPoint).all()) + pass + + +if __name__ == "__main__": + main() diff --git a/Ch12/apd.aggregation-chapter12-ex01/src/apd/aggregation/exceptions.py b/Ch12/apd.aggregation-chapter12-ex01/src/apd/aggregation/exceptions.py new file mode 100644 index 0000000..6200e90 --- /dev/null +++ b/Ch12/apd.aggregation-chapter12-ex01/src/apd/aggregation/exceptions.py @@ -0,0 +1,14 @@ +class NoDataForTrigger(ValueError): + """An error that's raised when a trigger is passed + a data point that cannot be handled due to an incompatible + value being stored""" + + pass + + +class IncompatibleTriggerError(NoDataForTrigger): + """An error that's raised when a trigger is passed + a data point that cannot be handled due to an incompatible + value being stored""" + + pass diff --git a/Ch12/apd.aggregation-chapter12-ex01/src/apd/aggregation/query.py b/Ch12/apd.aggregation-chapter12-ex01/src/apd/aggregation/query.py new file mode 100644 index 0000000..14144ff --- /dev/null +++ b/Ch12/apd.aggregation-chapter12-ex01/src/apd/aggregation/query.py @@ -0,0 +1,154 @@ +import asyncio +import contextlib +from contextvars import ContextVar +import datetime +import typing as t +from uuid import UUID + +from sqlalchemy import create_engine +from sqlalchemy.orm import sessionmaker +from sqlalchemy.orm.session import Session + + +from apd.aggregation.database import ( + datapoint_table, + DataPoint, + deployment_table, + Deployment, +) + + +db_session_var: ContextVar[Session] = ContextVar("db_session") + + +@contextlib.contextmanager +def with_database(uri: t.Optional[str] = None) -> t.Iterator[Session]: + """Given a URI, set up a DB connection, and return a Session as a context manager """ + if uri is None: + uri = "postgresql+psycopg2://localhost/apd" + engine = create_engine(uri) + sm = sessionmaker(engine) + Session = sm() + token = db_session_var.set(Session) + try: + yield Session + Session.commit() + finally: + db_session_var.reset(token) + Session.close() + + +async def get_data( + sensor_name: t.Optional[str] = None, + deployment_id: t.Optional[UUID] = None, + collected_before: t.Optional[datetime.datetime] = None, + collected_after: t.Optional[datetime.datetime] = None, + inserted_after_record_id: t.Optional[int] = None, + order: bool = True, +) -> t.AsyncIterator[DataPoint]: + db_session = db_session_var.get() + loop = asyncio.get_running_loop() + query = db_session.query(datapoint_table) + if sensor_name: + query = query.filter(datapoint_table.c.sensor_name == sensor_name) + if deployment_id: + query = query.filter(datapoint_table.c.deployment_id == deployment_id) + if collected_before: + query = query.filter(datapoint_table.c.collected_at < collected_before) + if collected_after: + query = query.filter(datapoint_table.c.collected_at > collected_after) + if inserted_after_record_id: + query = query.filter(datapoint_table.c.id > inserted_after_record_id) + + if order: + query = query.order_by( + datapoint_table.c.deployment_id, + datapoint_table.c.sensor_name, + datapoint_table.c.collected_at, + ) + + rows = await loop.run_in_executor(None, query.all) + for row in rows: + yield DataPoint.from_sql_result(row) + + +async def get_deployment_ids() -> t.List[UUID]: + db_session = db_session_var.get() + loop = asyncio.get_running_loop() + query = db_session.query(datapoint_table.c.deployment_id).distinct() + return [row.deployment_id for row in await loop.run_in_executor(None, query.all)] + + +async def get_deployment_by_id(deployment_id: UUID) -> Deployment: + db_session = db_session_var.get() + loop = asyncio.get_running_loop() + query = db_session.query(deployment_table).filter( + deployment_table.c.id == deployment_id + ) + return [ + Deployment.from_sql_result(row) + for row in await loop.run_in_executor(None, query.all) + ][0] + + +async def get_data_by_deployment( + *args: t.Any, **kwargs: t.Any +) -> t.AsyncIterator[t.Tuple[UUID, t.AsyncIterator[DataPoint]]]: + """Return an Async Iterator that contains two-item pairs. + These pairs are a string (deployment_id), and an async iterator that contains + the datapoints with that deployment_id. + + Usage example: + + async for deployment_id, datapoints in get_data_by_deployment(): + print(deployment_id) + async for datapoint in datapoints: + print(datapoint) + print() + """ + # Get the data, using the arguments to this function as filters + data = get_data(*args, **kwargs) + + # The two levels of iterator share the item variable, initialise it with the + # first item from the iterator. Also set last_deployment_id to None, so the + # outer iterator knows to start a new group. + last_deployment_id: t.Optional[UUID] = None + try: + item = await data.__anext__() + except StopAsyncIteration: + # There were no items in the underlying query, return immediately + return + + async def subiterator(group_id: UUID) -> t.AsyncIterator[DataPoint]: + """Using a closure, create an iterator that yields the current + item, then yields all items from data while the deployment_id matches + group_id, leaving the first that doesn't match as item in the enclosing + scope.""" + # item is from the enclosing scope + nonlocal item + while item.deployment_id == group_id: + # yield items from data while they match the group_id this iterator represents + yield item + try: + # Advance the underlying iterator + item = await data.__anext__() + except StopAsyncIteration: + # The underlying iterator came to an end, so end the subiterator too + return + + while True: + while item.deployment_id == last_deployment_id: + # We are trying to advance the outer iterator while the underlying iterator + # is still part-way through a group. Speed through the underlying until we + # hit an item where the deployment_id is different to the last one (or, is not + # None, in the case of the start of the iterator) + try: + item = await data.__anext__() + except StopAsyncIteration: + # We hit the end of the underlying iterator: end this iterator too + return + last_deployment_id = item.deployment_id + # Don't yield an iterator for unclassified deployments + if last_deployment_id is not None: + # Instantiate a subiterator for this group + yield last_deployment_id, subiterator(last_deployment_id) diff --git a/Ch12/apd.aggregation-chapter12-ex01/src/apd/aggregation/typing.py b/Ch12/apd.aggregation-chapter12-ex01/src/apd/aggregation/typing.py new file mode 100644 index 0000000..5db1337 --- /dev/null +++ b/Ch12/apd.aggregation-chapter12-ex01/src/apd/aggregation/typing.py @@ -0,0 +1,48 @@ +import datetime +import typing as t +from typing_extensions import Protocol + +from apd.aggregation.database import DataPoint + + +# Aliases for common types +# These type variables allow for generic functions. T_key represents the place +# in a chart that an item will be placed, and T_value the kind of data that is plotted. +T_key = t.TypeVar("T_key") +T_value = t.TypeVar("T_value") + +# Cleaned is a placeholder type, representing an async iterator of key and value functions +# Cleaned[float, float] is equivalent to typing.AsyncIterator[Tuple[builtins.float, builtins.float]] +Cleaned = t.AsyncIterator[t.Tuple[T_key, T_value]] + +# T_cleaned represents a placeholder for the result of a cleaner function, and is *covariant* +# because it can accept compatible types (such as ints where floats were declared). +# It is bound to Cleaned, so only items that match the specification for Cleaned (for any values +# of T_key or T_value) are valid +T_cleaned = t.TypeVar("T_cleaned", covariant=True, bound=Cleaned) + + +# CleanerFunc is a generic protocol, it matches any Callable that converts an async iterator +# of datapoints to its type. So, CleanerFunc[float] would be equivalent to t.Callable[[t.AsyncIterator[DataPoint]], float] +class CleanerFunc(Protocol[T_cleaned]): + def __call__(self, datapoints: t.AsyncIterator[DataPoint]) -> T_cleaned: + ... + + +# CLEANED_DT_FLOAT is a Cleaned represents datetime/float pairs, for simple charts +CLEANED_DT_FLOAT = Cleaned[datetime.datetime, float] +# and CLEANED_COORD_FLOAT represents (lat/lon), float pairs +CLEANED_COORD_FLOAT = Cleaned[t.Tuple[float, float], float] + +# The _CLEANER variants are functions that return their matching iterators from above +DT_FLOAT_CLEANER = CleanerFunc[CLEANED_DT_FLOAT] +COORD_FLOAT_CLEANER = CleanerFunc[CLEANED_COORD_FLOAT] + + +# When drawing a map we will be building a dictionary of UUID to dictionary +# That inner dictionary should contain coord (float, float) +# and value (float) only. Either or both can be None. +# This class is abstract, it's just for type checking. +class IntermediateMapData(t.TypedDict): + coord: t.Optional[t.Tuple[float, float]] + value: t.Optional[float] diff --git a/Ch12/apd.aggregation-chapter12-ex01/src/apd/aggregation/utils.py b/Ch12/apd.aggregation-chapter12-ex01/src/apd/aggregation/utils.py new file mode 100644 index 0000000..75bf6b3 --- /dev/null +++ b/Ch12/apd.aggregation-chapter12-ex01/src/apd/aggregation/utils.py @@ -0,0 +1,100 @@ +from __future__ import annotations + +import contextlib +import functools +import importlib +import io +import logging +import math +import os +import typing as t + +from pint import _DEFAULT_REGISTRY as ureg + + +# Set up mercator transform from https://wiki.openstreetmap.org/wiki/Mercator#Python +# Thanks to Paulo Silva and the OSM contributors +def merc_x(lon: float) -> float: + r_major = 6378137.000 + return r_major * math.radians(lon) + + +def merc_y(lat: float) -> float: + if lat > 89.5: + lat = 89.5 + if lat < -89.5: + lat = -89.5 + r_major = 6378137.000 + r_minor = 6356752.3142 + temp = r_minor / r_major + eccent = math.sqrt(1 - temp ** 2) + phi = math.radians(lat) + sinphi = math.sin(phi) + con = eccent * sinphi + com = eccent / 2 + con = ((1.0 - con) / (1.0 + con)) ** com + ts = math.tan((math.pi / 2 - phi) / 2) / con + y = 0 - r_major * math.log(ts) + return y + + +@functools.lru_cache +def convert_temperature(magnitude: float, origin_unit: str, target_unit: str) -> float: + # if origin_unit == "degC" and target_unit == "degF": + # return (magnitude * 1.8) + 32 + temp = ureg.Quantity(magnitude, origin_unit) + return temp.to(target_unit).magnitude + + +@contextlib.contextmanager +def jupyter_page_file() -> t.Iterator[io.StringIO]: + from IPython.core import page + + output = io.StringIO() + yield output + output.seek(0) + page.page(output.read()) + + +@contextlib.contextmanager +def profile_with_yappi() -> t.Iterator[None]: + import yappi + + yappi.clear_stats() + yappi.start() + try: + yield None + finally: + yappi.stop() + + +@functools.lru_cache +def get_package_prefix(package: str) -> str: + mod = importlib.import_module(package) + prefix = mod.__file__ + if prefix.endswith("__init__.py"): + prefix = os.path.dirname(prefix) + return prefix + + +def yappi_package_matches(stat, packages: t.List[str]): + """ This object can be passed to yappi's filter_callback to limit + by Python package.""" + for package in packages: + prefix = get_package_prefix(package) + if stat.full_name.startswith(prefix): + return True + return False + + +class AddSensorNameDefault(logging.Filter): + def filter(self, record): + if not hasattr(record, "sensorname"): + record.sensorname = "none" + return True + + +class SensorNameStreamHandler(logging.StreamHandler): + def __init__(self, *args, **kwargs): + super().__init__() + self.addFilter(AddSensorNameDefault()) diff --git a/Ch12/apd.aggregation-chapter12-ex01/tests/__init__.py b/Ch12/apd.aggregation-chapter12-ex01/tests/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/Ch12/apd.aggregation-chapter12-ex01/tests/conftest.py b/Ch12/apd.aggregation-chapter12-ex01/tests/conftest.py new file mode 100644 index 0000000..309a30c --- /dev/null +++ b/Ch12/apd.aggregation-chapter12-ex01/tests/conftest.py @@ -0,0 +1,127 @@ +import datetime +from uuid import UUID + +from apd.aggregation.database import datapoint_table +from apd.aggregation.query import db_session_var + +from alembic.config import Config +from alembic.script import ScriptDirectory +from alembic.runtime.environment import EnvironmentContext +import pytest + + +@pytest.fixture +def db_uri(): + return "postgresql+psycopg2://apd@localhost/apd-test" + + +@pytest.fixture +def db_session(db_uri): + from sqlalchemy import create_engine + from sqlalchemy.orm import sessionmaker + + engine = create_engine(db_uri, echo=False) + sm = sessionmaker(engine) + Session = sm() + reset = db_session_var.set(Session) + yield Session + db_session_var.reset(reset) + Session.close() + + +@pytest.fixture +def migrated_db(db_uri, db_session): + config = Config() + config.set_main_option("script_location", "apd.aggregation:alembic") + config.set_main_option("sqlalchemy.url", db_uri) + script = ScriptDirectory.from_config(config) + + def upgrade(rev, context): + return script._upgrade_revs(script.get_current_head(), rev) + + def downgrade(rev, context): + return script._downgrade_revs(None, rev) + + with EnvironmentContext(config, script, fn=upgrade): + script.run_env() + + try: + yield db_session + finally: + # Clear any pending work from the db_session connection + db_session.rollback() + + with EnvironmentContext(config, script, fn=downgrade): + script.run_env() + + +@pytest.fixture +def populated_db(migrated_db, db_session): + datas = [ + { + "id": 1, + "sensor_name": "Test", + "data": "1", + "collected_at": datetime.datetime(2020, 4, 1, 12, 0, 1), + "deployment_id": UUID("b4c68905-b1e4-4875-940e-69e5d27730fd"), + }, + { + "id": 2, + "sensor_name": "Test", + "data": "2", + "collected_at": datetime.datetime(2020, 4, 1, 12, 1, 1), + "deployment_id": UUID("b4c68905-b1e4-4875-940e-69e5d27730fd"), + }, + { + "id": 3, + "sensor_name": "Test", + "data": "3", + "collected_at": datetime.datetime(2020, 4, 1, 12, 2, 1), + "deployment_id": UUID("b4c68905-b1e4-4875-940e-69e5d27730fd"), + }, + { + "id": 4, + "sensor_name": "Test", + "data": "4", + "collected_at": datetime.datetime(2020, 4, 1, 12, 3, 1), + "deployment_id": UUID("b4c68905-b1e4-4875-940e-69e5d27730fd"), + }, + { + "id": 5, + "sensor_name": "OtherTest", + "data": "5", + "collected_at": datetime.datetime(2020, 4, 1, 12, 4, 1), + "deployment_id": UUID("b4c68905-b1e4-4875-940e-69e5d27730fd"), + }, + { + "id": 6, + "sensor_name": "Test", + "data": "1", + "collected_at": datetime.datetime(2020, 4, 1, 12, 0, 1), + "deployment_id": UUID("6e887b81-57c3-4f3b-a7e7-bad159e05e78"), + }, + { + "id": 7, + "sensor_name": "Test", + "data": "1", + "collected_at": datetime.datetime(2020, 4, 1, 12, 1, 1), + "deployment_id": UUID("6e887b81-57c3-4f3b-a7e7-bad159e05e78"), + }, + { + "id": 8, + "sensor_name": "Test", + "data": "2", + "collected_at": datetime.datetime(2020, 4, 1, 12, 2, 1), + "deployment_id": UUID("6e887b81-57c3-4f3b-a7e7-bad159e05e78"), + }, + { + "id": 9, + "sensor_name": "OtherTest", + "data": "3", + "collected_at": datetime.datetime(2020, 4, 1, 12, 3, 1), + "deployment_id": UUID("6e887b81-57c3-4f3b-a7e7-bad159e05e78"), + }, + ] + for data in datas: + insert = datapoint_table.insert().values(**data) + db_session.execute(insert) diff --git a/Ch12/apd.aggregation-chapter12-ex01/tests/test_actions_actions.py b/Ch12/apd.aggregation-chapter12-ex01/tests/test_actions_actions.py new file mode 100644 index 0000000..1429fb3 --- /dev/null +++ b/Ch12/apd.aggregation-chapter12-ex01/tests/test_actions_actions.py @@ -0,0 +1,118 @@ +import datetime +import unittest.mock + +import pytest + +from apd.aggregation.actions.base import Action +from apd.aggregation.actions.action import ( + OnlyOnChangeActionWrapper, + OnlyAfterDateActionWrapper, + SaveToDatabaseAction, +) +from apd.aggregation.query import db_session_var, get_data + +from .test_analysis import generate_datapoints + + +class TestOnlyAfterDateActionWrapper: + @pytest.fixture + def subject(self): + return OnlyAfterDateActionWrapper + + @pytest.mark.asyncio + async def test_minimum_date_before_passing(self, subject): + wrapped = unittest.mock.Mock(spec=Action) + wrapper = subject( + wrapped, date_threshold=datetime.datetime(2020, 4, 1, 14, 0, 0) + ) + await wrapper.start() + + data = [ + (datetime.datetime(2020, 4, 1, 12, 0, 0), 21.0), + (datetime.datetime(2020, 4, 1, 13, 0, 0), 21.0), + (datetime.datetime(2020, 4, 1, 14, 0, 0), 22.0), + (datetime.datetime(2020, 4, 1, 15, 0, 0), 22.0), + (datetime.datetime(2020, 4, 1, 16, 0, 0), 21.0), + ] + datapoints = generate_datapoints(data) + all_datapoints = [] + async for datapoint in datapoints: + all_datapoints.append(datapoint) + await wrapper.handle(datapoint) + + assert len(wrapped.handle.mock_calls) == 2 + passed = [call.args[0] for call in wrapped.handle.mock_calls] + assert passed == [all_datapoints[3], all_datapoints[4]] + + +class TestOnlyOnChangeActionWrapper: + @pytest.fixture + def subject(self): + return OnlyOnChangeActionWrapper + + @pytest.mark.asyncio + async def test_initial_value_always_passed(self, subject): + wrapped = unittest.mock.Mock(spec=Action) + wrapper = subject(wrapped) + await wrapper.start() + + data = [ + (datetime.datetime(2020, 4, 1, 12, 0, 0), 21.0), + ] + datapoints = generate_datapoints(data) + first = await datapoints.asend(None) + await wrapper.handle(first) + assert len(wrapped.handle.mock_calls) == 1 + assert wrapped.handle.mock_calls[0].args[0] == first + + @pytest.mark.asyncio + async def test_subsequent_values_only_passed_when_differing(self, subject): + wrapped = unittest.mock.Mock(spec=Action) + wrapper = subject(wrapped) + await wrapper.start() + + data = [ + (datetime.datetime(2020, 4, 1, 12, 0, 0), 21.0), + (datetime.datetime(2020, 4, 1, 13, 0, 0), 21.0), + (datetime.datetime(2020, 4, 1, 14, 0, 0), 22.0), + (datetime.datetime(2020, 4, 1, 15, 0, 0), 22.0), + (datetime.datetime(2020, 4, 1, 16, 0, 0), 21.0), + ] + datapoints = generate_datapoints(data) + all_datapoints = [] + async for datapoint in datapoints: + all_datapoints.append(datapoint) + await wrapper.handle(datapoint) + + assert len(wrapped.handle.mock_calls) == 3 + passed = [call.args[0] for call in wrapped.handle.mock_calls] + assert passed == [all_datapoints[0], all_datapoints[2], all_datapoints[4]] + + +class TestSaveToDatabaseAction: + @pytest.fixture + def subject(self): + return SaveToDatabaseAction + + @pytest.mark.asyncio + async def test_datapoints_are_persisted(self, subject, migrated_db): + db_session_var.set(migrated_db) + + wrapper = subject() + await wrapper.start() + + data = [ + (datetime.datetime(2020, 4, 1, 12, 0, 0), 21.0), + ] + generated_datapoints = [] + async for datapoint in generate_datapoints(data): + generated_datapoints.append(datapoint) + await wrapper.handle(datapoint) + + stored_datapoints = [ + point async for point in get_data(sensor_name="TestSensor") + ] + assert stored_datapoints[0].data == generated_datapoints[0].data + assert ( + stored_datapoints[0].deployment_id == generated_datapoints[0].deployment_id + ) diff --git a/Ch12/apd.aggregation-chapter12-ex01/tests/test_actions_runner.py b/Ch12/apd.aggregation-chapter12-ex01/tests/test_actions_runner.py new file mode 100644 index 0000000..f263420 --- /dev/null +++ b/Ch12/apd.aggregation-chapter12-ex01/tests/test_actions_runner.py @@ -0,0 +1,110 @@ +import asyncio +import datetime +import operator + +import pytest + +from apd.aggregation.actions.runner import DataProcessor +from apd.aggregation.actions.base import Trigger +from apd.aggregation.actions.action import ( + OnlyOnChangeActionWrapper, + SaveToDatabaseAction, +) +from apd.aggregation.actions.trigger import ValueThresholdTrigger +from apd.aggregation.database import DataPoint +from apd.aggregation.query import get_data +from .test_analysis import generate_datapoints + + +class AlwaysTrueTrigger(Trigger[bool]): + name = "AlwaysTrue" + + async def start(self): + pass + + async def match(self, datapoint: DataPoint) -> bool: + return True + + async def extract(self, datapoint: DataPoint) -> bool: + return True + + +class StoreAction(Trigger[bool]): + async def start(self): + self.data = asyncio.Queue() + + async def handle(self, datapoint: DataPoint) -> None: + await self.data.put(datapoint) + + +class TestRunner: + @pytest.fixture + @pytest.mark.asyncio + async def runner(self, event_loop): + processor = DataProcessor( + name="Test data runner", action=StoreAction(), trigger=AlwaysTrueTrigger(), + ) + await processor.start() + yield processor + await processor.end() + + @pytest.mark.asyncio + async def test_simple_flow(self, runner, event_loop): + data = [(datetime.datetime(2020, 4, 1, 12, 0, 0), 65.0)] + datapoints = generate_datapoints(data) + async for datapoint in datapoints: + await runner.push(datapoint) + + output = await runner.action.data.get() + assert output.sensor_name == runner.trigger.name + assert output.collected_at == data[0][0] + assert output.data == True + + +class TestRealWorldRunner: + @pytest.fixture + @pytest.mark.asyncio + async def runner(self, migrated_db, event_loop): + processor = DataProcessor( + name="TemperatureAbove20", + action=OnlyOnChangeActionWrapper(SaveToDatabaseAction()), + trigger=ValueThresholdTrigger( + name="TemperatureAbove20", + threshold=20, + comparator=operator.gt, + sensor_name="Temperature", + ), + ) + await processor.start() + yield processor + await processor.end() + + @pytest.mark.asyncio + async def test_real_usecase(self, runner, event_loop): + data = [ + (datetime.datetime(2020, 4, 1, 12, 0, 0), 19.0), + (datetime.datetime(2020, 4, 1, 13, 0, 0), 18.0), + (datetime.datetime(2020, 4, 1, 14, 0, 0), 21.0), + (datetime.datetime(2020, 4, 1, 15, 0, 0), 22.0), + (datetime.datetime(2020, 4, 1, 16, 0, 0), 21.0), + ] + + async for datapoint in generate_datapoints(data, sensor_name="Temperature"): + await runner.push(datapoint) + + # Wait up to 10 seconds for the runner to be idle + await asyncio.wait_for(runner.idle(), timeout=10) + + stored_datapoints = [ + point async for point in get_data(sensor_name="TemperatureAbove20") + ] + + assert len(stored_datapoints) == 2 + assert stored_datapoints[0].data == False + assert stored_datapoints[0].collected_at == datetime.datetime( + 2020, 4, 1, 12, 0, 0 + ) + assert stored_datapoints[1].data == True + assert stored_datapoints[1].collected_at == datetime.datetime( + 2020, 4, 1, 14, 0, 0 + ) diff --git a/Ch12/apd.aggregation-chapter12-ex01/tests/test_actions_triggers.py b/Ch12/apd.aggregation-chapter12-ex01/tests/test_actions_triggers.py new file mode 100644 index 0000000..275c8e8 --- /dev/null +++ b/Ch12/apd.aggregation-chapter12-ex01/tests/test_actions_triggers.py @@ -0,0 +1,127 @@ +import datetime +import operator + +import pytest + +from apd.aggregation.actions.trigger import ValueThresholdTrigger + +from .test_analysis import generate_datapoints + + +class TestValueThresholdTrigger: + @pytest.fixture + def subject(self): + return ValueThresholdTrigger + + @pytest.mark.asyncio + @pytest.mark.parametrize( + "comparison,expected", + [ + (operator.gt, [False, False, False, True]), + (operator.eq, [False, False, True, False]), + (operator.le, [True, True, True, False]), + ], + ) + async def test_simple_value(self, subject, comparison, expected): + trigger = subject( + name=f"TestSensor{comparison.__name__}21", + threshold=21, + comparator=comparison, + sensor_name="TestSensor", + ) + + data = [ + (datetime.datetime(2020, 4, 1, 12, 0, 0), 19.0), + (datetime.datetime(2020, 4, 1, 12, 0, 0), 20.0), + (datetime.datetime(2020, 4, 1, 12, 0, 0), 21.0), + (datetime.datetime(2020, 4, 1, 12, 0, 0), 22.0), + ] + datapoints = generate_datapoints(data) + + outputs = [] + async for datapoint in datapoints: + assert await trigger.match(datapoint) + outputs.append(await trigger.extract(datapoint)) + + assert outputs == expected + + @pytest.mark.asyncio + @pytest.mark.parametrize( + "comparison,expected", + [ + (operator.gt, [False, False, False, True]), + (operator.eq, [False, False, True, False]), + (operator.le, [True, True, True, False]), + ], + ) + async def test_magnitude_value(self, subject, comparison, expected): + trigger = subject( + name=f"TestSensor{comparison.__name__}21", + threshold=21, + comparator=comparison, + sensor_name="TestSensor", + ) + + data = [ + ( + datetime.datetime(2020, 4, 1, 12, 0, 0), + {"magnitude": 19.0, "unit": "degC"}, + ), + ( + datetime.datetime(2020, 4, 1, 12, 0, 0), + {"magnitude": 20.0, "unit": "degC"}, + ), + ( + datetime.datetime(2020, 4, 1, 12, 0, 0), + {"magnitude": 21.0, "unit": "degC"}, + ), + ( + datetime.datetime(2020, 4, 1, 12, 0, 0), + {"magnitude": 22.0, "unit": "degC"}, + ), + ] + datapoints = generate_datapoints(data) + + outputs = [] + async for datapoint in datapoints: + assert await trigger.match(datapoint) + outputs.append(await trigger.extract(datapoint)) + + assert outputs == expected + + @pytest.mark.asyncio + async def test_different_data_format_fails_to_extract(self, subject): + trigger = subject( + name="TestSensorAbove21", + threshold=21, + comparator=operator.eq, + sensor_name="TestSensor", + ) + + data = [ + (datetime.datetime(2020, 4, 1, 12, 0, 0), "21.0"), + ] + datapoints = generate_datapoints(data) + + async for datapoint in datapoints: + assert await trigger.match(datapoint) + with pytest.raises(ValueError): + await trigger.extract(datapoint) + assert await trigger.handle(datapoint) is None + + @pytest.mark.asyncio + async def test_different_sensors_are_not_matched(self, subject): + trigger = subject( + name="TestSensorAbove21", + threshold=21, + comparator=operator.eq, + sensor_name="OtherSensor", + ) + + data = [ + (datetime.datetime(2020, 4, 1, 12, 0, 0), 21.0), + ] + datapoints = generate_datapoints(data) + + async for datapoint in datapoints: + assert not await trigger.match(datapoint) diff --git a/Ch12/apd.aggregation-chapter12-ex01/tests/test_analysis.py b/Ch12/apd.aggregation-chapter12-ex01/tests/test_analysis.py new file mode 100644 index 0000000..053272e --- /dev/null +++ b/Ch12/apd.aggregation-chapter12-ex01/tests/test_analysis.py @@ -0,0 +1,525 @@ +import collections.abc +import datetime +import functools +import uuid +import warnings + +import pytest + +from apd.aggregation import analysis +from apd.aggregation.database import DataPoint, datapoint_table +from apd.aggregation.query import db_session_var + + +async def generate_datapoints(datas, sensor_name="TestSensor"): + deployment_id = uuid.uuid4() + for i, (time, data) in enumerate(datas, start=1): + yield DataPoint( + id=i, + collected_at=time, + sensor_name=sensor_name, + data=data, + deployment_id=deployment_id, + ) + + +@functools.singledispatch +def consume(input_iterator): + items = [item for item in input_iterator] + + def inner_iterator(): + for item in items: + yield item + + return inner_iterator() + + +@consume.register +async def consume_async(input_iterator: collections.abc.AsyncIterator): + items = [item async for item in input_iterator] + + async def inner_iterator(): + for item in items: + yield item + + return inner_iterator() + + +class TestPassThroughCleaner: + @pytest.fixture + def cleaner(self): + return analysis.clean_passthrough + + @pytest.mark.asyncio + async def test_float_passthrough(self, cleaner): + data = [ + (datetime.datetime(2020, 4, 1, 12, 0, 0), 65.0), + (datetime.datetime(2020, 4, 1, 13, 0, 0), 65.5), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert output == data + + @pytest.mark.asyncio + async def test_Nones_are_skipped(self, cleaner): + data = [ + (datetime.datetime(2020, 4, 1, 12, 0, 0), None), + (datetime.datetime(2020, 4, 1, 13, 0, 0), 65.5), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert output == [ + (datetime.datetime(2020, 4, 1, 13, 0, 0), 65.5), + ] + + +class TestTemperatureCleaner: + @pytest.fixture + def cleaner(self): + return analysis.clean_temperature_fluctuations + + @pytest.mark.asyncio + async def test_window_not_full(self, cleaner): + data = [ + ( + datetime.datetime(2020, 4, 1, 12, 0, 0), + {"magnitude": 21.0, "unit": "degC"}, + ), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert output == [(datetime.datetime(2020, 4, 1, 12, 0, 0), 21.0)] + + @pytest.mark.asyncio + async def test_window_exactly_full(self, cleaner): + data = [ + ( + datetime.datetime(2020, 4, 1, 12, 0, 0), + {"magnitude": 21.0, "unit": "degC"}, + ), + ( + datetime.datetime(2020, 4, 1, 12, 0, 1), + {"magnitude": 21.0, "unit": "degC"}, + ), + ( + datetime.datetime(2020, 4, 1, 12, 0, 2), + {"magnitude": 21.0, "unit": "degC"}, + ), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert output == [ + (datetime.datetime(2020, 4, 1, 12, 0, 0), 21.0), + (datetime.datetime(2020, 4, 1, 12, 0, 1), 21.0), + (datetime.datetime(2020, 4, 1, 12, 0, 2), 21.0), + ] + + @pytest.mark.asyncio + async def test_window_overfilled(self, cleaner): + data = [ + ( + datetime.datetime(2020, 4, 1, 12, 0, 0), + {"magnitude": 21.0, "unit": "degC"}, + ), + ( + datetime.datetime(2020, 4, 1, 12, 0, 1), + {"magnitude": 21.0, "unit": "degC"}, + ), + ( + datetime.datetime(2020, 4, 1, 12, 0, 2), + {"magnitude": 21.0, "unit": "degC"}, + ), + ( + datetime.datetime(2020, 4, 1, 12, 0, 3), + {"magnitude": 21.0, "unit": "degC"}, + ), + ( + datetime.datetime(2020, 4, 1, 12, 0, 4), + {"magnitude": 21.0, "unit": "degC"}, + ), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert output == [ + (datetime.datetime(2020, 4, 1, 12, 0, 0), 21.0), + (datetime.datetime(2020, 4, 1, 12, 0, 1), 21.0), + (datetime.datetime(2020, 4, 1, 12, 0, 2), 21.0), + (datetime.datetime(2020, 4, 1, 12, 0, 3), 21.0), + (datetime.datetime(2020, 4, 1, 12, 0, 4), 21.0), + ] + + @pytest.mark.asyncio + async def test_outlier_dropped(self, cleaner): + data = [ + ( + datetime.datetime(2020, 4, 1, 12, 0, 0), + {"magnitude": 21.0, "unit": "degC"}, + ), + ( + datetime.datetime(2020, 4, 1, 12, 0, 1), + {"magnitude": 61.0, "unit": "degC"}, + ), + ( + datetime.datetime(2020, 4, 1, 12, 0, 2), + {"magnitude": 21.0, "unit": "degC"}, + ), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert output == [ + (datetime.datetime(2020, 4, 1, 12, 0, 0), 21.0), + (datetime.datetime(2020, 4, 1, 12, 0, 2), 21.0), + ] + + @pytest.mark.asyncio + async def test_limited_to_DHT22_range(self, cleaner): + data = [ + ( + datetime.datetime(2020, 4, 1, 12, 0, 0), + {"magnitude": 91.0, "unit": "degC"}, + ), + ( + datetime.datetime(2020, 4, 1, 12, 0, 1), + {"magnitude": 91.0, "unit": "degC"}, + ), + ( + datetime.datetime(2020, 4, 1, 12, 0, 2), + {"magnitude": 91.0, "unit": "degC"}, + ), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert output == [] + + @pytest.mark.asyncio + async def test_Nones_dropped(self, cleaner): + data = [ + ( + datetime.datetime(2020, 4, 1, 12, 0, 0), + {"magnitude": 31.0, "unit": "degC"}, + ), + (datetime.datetime(2020, 4, 1, 12, 0, 1), None,), + ( + datetime.datetime(2020, 4, 1, 12, 0, 2), + {"magnitude": 32.0, "unit": "degC"}, + ), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert output == [ + (datetime.datetime(2020, 4, 1, 12, 0, 0), 31.0), + (datetime.datetime(2020, 4, 1, 12, 0, 2), 32.0), + ] + + +class TestWattHourCleaner: + @pytest.fixture + def cleaner(self): + return analysis.clean_watthours_to_watts + + @pytest.fixture(scope="class") + def huge_data_set(self): + date = datetime.datetime(2020, 4, 1, 12, 0, 0) + power = 500 + data = [] + for i in range(50_000): + date += datetime.timedelta(hours=1) + power += i + data.append((date, {"magnitude": power, "unit": "watt_hour"},)) + return data + + @pytest.mark.asyncio + async def test_one_entry_insufficient_to_find_diff(self, cleaner): + data = [ + ( + datetime.datetime(2020, 4, 1, 12, 0, 0), + {"magnitude": 1.0, "unit": "watt_hour"}, + ), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert output == [] + + @pytest.mark.asyncio + async def test_two_entries_provides_diff_with_second_time(self, cleaner): + data = [ + ( + datetime.datetime(2020, 4, 1, 12, 0, 0), + {"magnitude": 1.0, "unit": "watt_hour"}, + ), + ( + datetime.datetime(2020, 4, 1, 13, 0, 0), + {"magnitude": 10.0, "unit": "watt_hour"}, + ), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert len(output) == 1 + assert output[0][0] == datetime.datetime(2020, 4, 1, 13, 0, 0) + assert output[0][1] == pytest.approx(9.0, 0.0001) + + @pytest.mark.asyncio + async def test_nonstandard_units_can_be_used(self, cleaner): + data = [ + ( + datetime.datetime(2020, 4, 1, 12, 0, 0), + {"magnitude": 1.0, "unit": "watt_hour"}, + ), + ( + datetime.datetime(2020, 4, 1, 13, 0, 0), + {"magnitude": 10000.0, "unit": "joule"}, + ), + ( + datetime.datetime(2020, 4, 1, 14, 0, 0), + {"magnitude": 3.0, "unit": "watt_hour"}, + ), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert len(output) == 2 + assert output[0][0] == datetime.datetime(2020, 4, 1, 13, 0, 0) + assert output[0][1] == pytest.approx(1.7777, 0.001) + assert output[1][0] == datetime.datetime(2020, 4, 1, 14, 0, 0) + assert output[1][1] == pytest.approx(0.22222, 0.001) + + @pytest.mark.asyncio + async def test_fractional_hour(self, cleaner): + data = [ + ( + datetime.datetime(2020, 4, 1, 12, 0, 0), + {"magnitude": 1.0, "unit": "watt_hour"}, + ), + ( + datetime.datetime(2020, 4, 1, 12, 23, 42), + {"magnitude": 10.0, "unit": "watt_hour"}, + ), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert len(output) == 1 + assert output[0][0] == datetime.datetime(2020, 4, 1, 12, 23, 42) + assert output[0][1] == pytest.approx(22.7848, 0.001) + + @pytest.mark.asyncio + async def test_diff_happens_pairwise(self, cleaner): + data = [ + ( + datetime.datetime(2020, 4, 1, 12, 0, 0), + {"magnitude": 1.0, "unit": "watt_hour"}, + ), + ( + datetime.datetime(2020, 4, 1, 12, 23, 42), + {"magnitude": 10.0, "unit": "watt_hour"}, + ), + ( + datetime.datetime(2020, 4, 1, 13, 23, 42), + {"magnitude": 13.0, "unit": "watt_hour"}, + ), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert len(output) == 2 + assert output[0][0] == datetime.datetime(2020, 4, 1, 12, 23, 42) + assert output[0][1] == pytest.approx(22.7848, 0.001) + assert output[1][0] == datetime.datetime(2020, 4, 1, 13, 23, 42) + assert output[1][1] == pytest.approx(3, 0.001) + + @pytest.mark.asyncio + async def test_None_ignored(self, cleaner): + data = [ + ( + datetime.datetime(2020, 4, 1, 12, 0, 0), + {"magnitude": 1.0, "unit": "watt_hour"}, + ), + (datetime.datetime(2020, 4, 1, 12, 58, 0), None,), + ( + datetime.datetime(2020, 4, 1, 13, 0, 0), + {"magnitude": 10.0, "unit": "watt_hour"}, + ), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert len(output) == 1 + assert output[0][0] == datetime.datetime(2020, 4, 1, 13, 0, 0) + assert output[0][1] == pytest.approx(9.0, 0.0001) + + @pytest.mark.asyncio + @pytest.mark.performance + async def test_performance_of_cleaner(self, cleaner, huge_data_set): + import cProfile + + # Generate data point objects before profiling starts + datapoints = await consume(generate_datapoints(huge_data_set)) + profiler = cProfile.Profile() + profiler.enable() + + # Run the cleaner to completion + await consume(cleaner(datapoints)) + + profiler.disable() + cleaner_stat = [ + stat for stat in profiler.getstats() if stat.code == cleaner.__code__ + ][0] + # Assert that the cleaner may take no more than 0.5 seconds to process + # these 50k data points + assert cleaner_stat.totaltime < 0.5 + + +class TestTemperatureMap: + @pytest.fixture + def temperature_data(self): + return { + (53.8667, -1.3333): -1, + (53.35, -2.2833): 1, + (52.45, -1.7333): 3, + (51.5, -0.1333): 6, + (51.55, -2.5667): 5, + (54.9667, -1.6167): -1, + (55.9667, -3.2167): -1, + (54.7667, -1.5833): 0, + (53.7667, -0.3): 1, + (53.7667, -3.0167): 0, + (51.4833, -3.1833): 4, + (53.2667, -3.5167): 2, + (52.0833, -2.8): 2, + (52.0167, -0.6): 2, + (52.6667, 1.2667): 5, + (50.4333, -4.9833): 9, + (50.35, -4.1167): 10, + (50.5167, -2.45): 9, + (50.8333, -1.1667): 9, + (56.45, -5.4333): 4, + (57.5333, -4.05): -2, + (54.5167, -3.6167): 3, + (53.0833, 0.2667): 4, + (51.35, 1.3333): 7, + (50.8833, 0.3167): 8, + (58.45, -3.1): 3, + (50.1, -5.6667): 9, + (58.2167, -6.3333): 4, + (55.6833, -6.25): 7, + } + + @pytest.fixture + def temperature_datapoints(self, migrated_db, db_session, temperature_data): + datas = [] + now = datetime.datetime.now() + for (coord, temp) in temperature_data.items(): + deployment_id = uuid.uuid4() + datas.append( + DataPoint( + sensor_name="Location", + deployment_id=deployment_id, + collected_at=now, + data=coord, + ) + ) + datas.append( + DataPoint( + sensor_name="Temperature", + deployment_id=deployment_id, + collected_at=now, + data=temp, + ) + ) + return datas + + @pytest.fixture + def populated_db(self, migrated_db, db_session, temperature_datapoints): + for data in temperature_datapoints: + insert = datapoint_table.insert().values(**data._asdict()) + db_session.execute(insert) + + @pytest.fixture + def cleaner(self): + return analysis.get_map_cleaner_for("Temperature") + + @pytest.fixture + def get_data(self, db_session): + db_session_var.set(db_session) + return analysis.get_all_data() + + @pytest.mark.asyncio + async def test_latest_coord_temp_cleaner( + self, temperature_data, populated_db, get_data, cleaner + ): + data = get_data() + deployments = 0 + async for deployment, data_points in data: + deployments += 1 + cleaned = {a async for a in cleaner(data_points)} + # There should only be one deployment + assert deployments == 1 + expected = set(temperature_data.items()) + assert cleaned == expected + + @pytest.mark.asyncio + async def test_newer_items_superceed_older( + self, db_session, temperature_datapoints, populated_db, get_data, cleaner + ): + # Pick any of the deployment ids and find the pair of DataPoints in that deployment id + deployment_1 = temperature_datapoints[0].deployment_id + existing = [ + datapoint + for datapoint in temperature_datapoints + if datapoint.deployment_id == deployment_1 + ] + old_location = [ + datapoint for datapoint in existing if datapoint.sensor_name == "Location" + ][0] + old_temperature = [ + datapoint + for datapoint in existing + if datapoint.sensor_name == "Temperature" + ][0] + + new_location = DataPoint( + sensor_name="Location", + deployment_id=deployment_1, + collected_at=datetime.datetime(2031, 4, 1, 12, 0, 0), + data=(-10.5, 150.1), + ) + new_temperature = DataPoint( + sensor_name="Temperature", + deployment_id=deployment_1, + collected_at=datetime.datetime(2031, 4, 1, 12, 0, 0), + data=21, + ) + for data in [new_location, new_temperature]: + insert = datapoint_table.insert().values(**data._asdict()) + db_session.execute(insert) + + data = get_data() + deployments = 0 + async for deployment, data_points in data: + deployments += 1 + cleaned = {a async for a in cleaner(data_points)} + # There should only be one deployment + assert deployments == 1 + + # We expect the newer one, not the older + assert (new_location.data, new_temperature.data) in cleaned + assert (old_location.data, old_temperature.data) not in cleaned + # The cleaner converts two data points to one plottable + assert len(cleaned) == len(temperature_datapoints) / 2 + + +def test_deprecation_warning_raised_by_config_with_no_getdata(): + with warnings.catch_warnings(record=True) as captured_warnings: + warnings.simplefilter("always", DeprecationWarning) + analysis.Config( + sensor_name="Temperature", + clean=analysis.clean_passthrough, + title="Temperaure", + ylabel="Deg C", + ) + assert len(captured_warnings) == 1 + deprecation_warning = captured_warnings[0] + assert deprecation_warning.filename == __file__ + assert deprecation_warning.category == DeprecationWarning + assert str(deprecation_warning.message) == ( + "The sensor_name parameter is deprecated. Please pass " + "get_data=get_one_sensor_by_deployment('Temperature') " + "to ensure the same behaviour. The sensor_name= parameter " + "will be removed in apd.aggregation 3.0." + ) diff --git a/Ch12/apd.aggregation-chapter12-ex01/tests/test_cli.py b/Ch12/apd.aggregation-chapter12-ex01/tests/test_cli.py new file mode 100644 index 0000000..5cab441 --- /dev/null +++ b/Ch12/apd.aggregation-chapter12-ex01/tests/test_cli.py @@ -0,0 +1,155 @@ +from unittest import mock +import uuid + +from click.testing import CliRunner +import pytest + +import apd.aggregation.cli +from apd.aggregation.database import Deployment, deployment_table + + +@pytest.mark.functional +def test_sensors_are_passed_to_get_data_points(db_uri): + runner = CliRunner() + with mock.patch("apd.aggregation.collect.get_data_points") as get_data_points: + runner.invoke( + apd.aggregation.cli.collect_sensor_data, + [ + "http://localhost", + "http://otherhost", + "--db", + db_uri, + "--api-key", + "key", + ], + ) + calls = get_data_points.call_args_list + assert len(calls) == 2 + details = {call[0] for call in get_data_points.call_args_list} + assert details == { + ("http://localhost", "key"), + ("http://otherhost", "key"), + } + + +@pytest.mark.functional +def test_add_deployment_to_db(db_uri, db_session, migrated_db): + runner = CliRunner() + deployment_id = uuid.UUID("76587cdd42dc489ea1d220e9b0bc62ca") + with mock.patch("apd.aggregation.collect.get_deployment_id") as get_deployment_id: + get_deployment_id.return_value = deployment_id + runner.invoke( + apd.aggregation.cli.deployments, + ["add", "http://otherhost", "Other", "--db", db_uri, "--api-key", "key"], + ) + deployments = db_session.query(deployment_table).all() + assert len(deployments) == 1 + deployment = Deployment.from_sql_result(deployments[0]) + assert deployment.uri == "http://otherhost" + assert deployment.api_key == "key" + assert deployment.id == deployment_id + + +@pytest.mark.functional +def test_use_stored_deployments(db_uri, migrated_db): + runner = CliRunner() + + deployment_id = uuid.UUID("76587cdd42dc489ea1d220e9b0bc62ca") + with mock.patch("apd.aggregation.collect.get_deployment_id") as get_deployment_id: + get_deployment_id.return_value = deployment_id + runner.invoke( + apd.aggregation.cli.deployments, + [ + "add", + "http://specifiedhost", + "Specified", + "--db", + db_uri, + "--api-key", + "an_api_key", + ], + ) + + with mock.patch("apd.aggregation.collect.get_data_points") as get_data_points: + runner.invoke( + apd.aggregation.cli.collect_sensor_data, ["--db", db_uri], + ) + calls = get_data_points.call_args_list + assert len(calls) == 1 + details = {call[0] for call in get_data_points.call_args_list} + assert details == { + ("http://specifiedhost", "an_api_key"), + } + + +@pytest.mark.functional +def test_list_deployments(db_uri, migrated_db): + runner = CliRunner() + + deployment_id = uuid.UUID("76587cdd42dc489ea1d220e9b0bc62ca") + with mock.patch("apd.aggregation.collect.get_deployment_id") as get_deployment_id: + get_deployment_id.return_value = deployment_id + runner.invoke( + apd.aggregation.cli.deployments, + [ + "add", + "http://specifiedhost", + "Specified", + "--db", + db_uri, + "--api-key", + "an_api_key", + ], + ) + + result = runner.invoke(apd.aggregation.cli.deployments, ["list", "--db", db_uri],) + expected = """Specified +ID 76587cdd42dc489ea1d220e9b0bc62ca +URI http://specifiedhost +API key an_api_key +Colour None + +""" + assert result.output == expected + + +@pytest.mark.functional +def test_edit_deployment(db_uri, migrated_db): + runner = CliRunner() + return + deployment_id = uuid.UUID("76587cdd42dc489ea1d220e9b0bc62ca") + with mock.patch("apd.aggregation.collect.get_deployment_id") as get_deployment_id: + get_deployment_id.return_value = deployment_id + runner.invoke( + apd.aggregation.cli.deployments, + [ + "add", + "http://specifiedhost", + "Specified", + "--db", + db_uri, + "--api-key", + "an_api_key", + ], + ) + + result = runner.invoke( + apd.aggregation.cli.deployments, + [ + "edit", + "--db", + db_uri, + deployment_id.hex(), + "--colour", + "red" "--name", + "New name", + ], + ) + expected = """New name +ID 76587cdd42dc489ea1d220e9b0bc62ca +URI http://specifiedhost +API key an_api_key +Colour red + +""" + assert result.output == expected diff --git a/Ch12/apd.aggregation-chapter12-ex01/tests/test_http_get.py b/Ch12/apd.aggregation-chapter12-ex01/tests/test_http_get.py new file mode 100644 index 0000000..df575c6 --- /dev/null +++ b/Ch12/apd.aggregation-chapter12-ex01/tests/test_http_get.py @@ -0,0 +1,188 @@ +from concurrent.futures import ThreadPoolExecutor +import datetime +import typing as t +from unittest.mock import patch, MagicMock +import wsgiref.simple_server + +import aiohttp +from apd.sensors.base import Sensor +from apd.sensors.sensors import PythonVersion, ACStatus +from apd.sensors.wsgi import set_up_config +import flask +import pytest + +from apd.sensors.wsgi import v21 + +from apd.aggregation import collect +from apd.aggregation.database import Deployment + +pytestmark = [pytest.mark.functional] + + +@pytest.fixture +def sensors() -> t.Iterator[t.List[Sensor[t.Any]]]: + """ Patch the get_sensors method to return a known pair of sensors only """ + data: t.List[Sensor[t.Any]] = [PythonVersion(), ACStatus()] + with patch("apd.sensors.cli.get_sensors") as get_sensors: + get_sensors.return_value = data + yield data + + +def get_independent_flask_app(name: str) -> flask.Flask: + """ Create a new flask app with the v20 API blueprint loaded, so multiple copies + of the app can be run in parallel without conflicting configuration """ + app = flask.Flask(name) + app.register_blueprint(v21.version, url_prefix="/v/2.1") + return app + + +def run_server_in_thread( + name: str, config: t.Dict[str, t.Any], port: int +) -> t.Iterator[str]: + # Create a new flask app and load in required code, to prevent config conflicts + app = get_independent_flask_app(name) + flask_app = set_up_config(config, app) + server = wsgiref.simple_server.make_server("localhost", port, flask_app) + + with ThreadPoolExecutor() as pool: + pool.submit(server.serve_forever) + yield f"http://localhost:{port}/" + server.shutdown() + + +@pytest.fixture(scope="module") +def http_server(): + yield from run_server_in_thread( + "standard", + { + "APD_SENSORS_API_KEY": "testing", + "APD_SENSORS_DEPLOYMENT_ID": "a46b1d1207fd4cdcad39bbdf706dfe29", + }, + 12081, + ) + + +@pytest.fixture(scope="module") +def bad_api_key_http_server(): + yield from run_server_in_thread( + "alternate", + { + "APD_SENSORS_API_KEY": "penny", + "APD_SENSORS_DEPLOYMENT_ID": "38cf2bae9adb445fad946c82e290487a", + }, + 12082, + ) + + +class TestGetDataPoints: + @pytest.fixture + def mut(self): + return collect.get_data_points + + @pytest.mark.asyncio + async def test_get_data_points( + self, sensors: t.List[Sensor[t.Any]], mut, http_server: str + ) -> None: + # Get the data from the server, storing the time before and after + # as bounds for the collected_at value + async with aiohttp.ClientSession() as http: + collect.http_session_var.set(http) + time_before = datetime.datetime.now() + results = await mut(http_server, "testing") + time_after = datetime.datetime.now() + + assert len(results) == len(sensors) == 2 + + for (sensor, result) in zip(sensors, results): + assert sensor.from_json_compatible(result.data) == sensor.value() + assert result.sensor_name == sensor.name + assert time_before <= result.collected_at <= time_after + + @pytest.mark.asyncio + async def test_get_data_points_fails_with_bad_api_key( + self, sensors: t.List[Sensor[t.Any]], mut, http_server: str + ) -> None: + with pytest.raises( + ValueError, + match=f"Error loading data from {http_server}: Supply API key in X-API-Key header", + ): + async with aiohttp.ClientSession() as http: + collect.http_session_var.set(http) + await mut(http_server, "incorrect") + + +class TestAddDataFromSensors: + @pytest.fixture + def mut(self): + return collect.add_data_from_sensors + + @pytest.fixture + def mock_db_session(self): + return MagicMock() + + @pytest.mark.asyncio + async def test_get_get_data_from_sensors( + self, mock_db_session, sensors: t.List[Sensor[t.Any]], mut, http_server: str + ) -> None: + results = await mut( + mock_db_session, + [ + Deployment( + id=None, colour=None, name=None, uri=http_server, api_key="testing" + ) + ], + ) + assert mock_db_session.execute.call_count == len(sensors) + assert len(results) == len(sensors) + + @pytest.mark.asyncio + async def test_get_get_data_from_sensors_with_multiple_servers( + self, mock_db_session, sensors: t.List[Sensor[t.Any]], mut, http_server: str + ) -> None: + results = await mut( + mock_db_session, + [ + Deployment( + id=None, colour=None, name=None, uri=http_server, api_key="testing" + ), + Deployment( + id=None, colour=None, name=None, uri=http_server, api_key="testing" + ), + ], + ) + assert mock_db_session.execute.call_count == len(sensors) * 2 + assert len(results) == len(sensors) * 2 + + @pytest.mark.asyncio + async def test_data_points_not_added_if_only_partial_success( + self, + mock_db_session, + sensors: t.List[Sensor[t.Any]], + mut, + http_server: str, + bad_api_key_http_server: str, + ) -> None: + with pytest.raises( + ValueError, + match=f"Error loading data from {bad_api_key_http_server}: Supply API key in X-API-Key header", + ): + await mut( + mock_db_session, + [ + Deployment( + id=None, + colour=None, + name=None, + uri=http_server, + api_key="testing", + ), + Deployment( + id=None, + colour=None, + name=None, + uri=bad_api_key_http_server, + api_key="testing", + ), + ], + ) + assert mock_db_session.execute.call_count == 0 diff --git a/Ch12/apd.aggregation-chapter12-ex01/tests/test_query.py b/Ch12/apd.aggregation-chapter12-ex01/tests/test_query.py new file mode 100644 index 0000000..9b6e0aa --- /dev/null +++ b/Ch12/apd.aggregation-chapter12-ex01/tests/test_query.py @@ -0,0 +1,111 @@ +import datetime +from uuid import UUID + +import pytest + +from apd.aggregation.query import ( + get_data, + get_deployment_ids, + get_data_by_deployment, + db_session_var, +) + + +class TestGetData: + @pytest.fixture + def mut(self): + return get_data + + @pytest.mark.asyncio + @pytest.mark.parametrize( + "filter,num_items_expected", + [ + ({}, 9), + ({"sensor_name": "Test"}, 7), + ({"deployment_id": UUID("b4c68905-b1e4-4875-940e-69e5d27730fd")}, 5), + ({"collected_after": datetime.datetime(2020, 4, 1, 12, 2, 1)}, 3), + ({"collected_before": datetime.datetime(2020, 4, 1, 12, 2, 1)}, 4), + ( + { + "collected_after": datetime.datetime(2020, 4, 1, 12, 2, 1), + "collected_before": datetime.datetime(2020, 4, 1, 12, 3, 5), + }, + 2, + ), + ], + ) + async def test_iterate_over_items( + self, mut, db_session, populated_db, filter, num_items_expected + ): + db_session_var.set(db_session) + points = [dp async for dp in mut(**filter)] + assert len(points) == num_items_expected + + @pytest.mark.asyncio + async def test_empty_db(self, mut, db_session, migrated_db): + db_session_var.set(db_session) + points = [dp async for dp in mut()] + assert len(points) == 0 + + +class TestGetDeployments: + @pytest.fixture + def mut(self): + return get_deployment_ids + + @pytest.mark.asyncio + async def test_get_all_deployments(self, mut, db_session, populated_db): + db_session_var.set(db_session) + deployment_ids = await mut() + assert set(deployment_ids) == { + UUID("b4c68905-b1e4-4875-940e-69e5d27730fd"), + UUID("6e887b81-57c3-4f3b-a7e7-bad159e05e78"), + } + + @pytest.mark.asyncio + async def test_empty_db(self, mut, db_session, migrated_db): + db_session_var.set(db_session) + deployment_ids = await mut() + assert deployment_ids == [] + + +@pytest.mark.usefixtures("populated_db") +class TestGetDataByDeployment: + @pytest.fixture + def mut(self): + return get_data_by_deployment + + @pytest.mark.asyncio + @pytest.mark.parametrize( + "filter,num_items_expected", [({}, 9), ({"sensor_name": "Test"}, 7)] + ) + async def test_iterate_over_all_items( + self, mut, db_session, filter, num_items_expected + ): + db_session_var.set(db_session) + num_items = 0 + num_deployments = 0 + async for deployment_id, items in mut(**filter): + num_deployments += 1 + async for item in items: + num_items += 1 + assert num_deployments == 2 + assert num_items == num_items_expected + + @pytest.mark.asyncio + @pytest.mark.parametrize( + "filter,num_items_expected", [({}, 5), ({"sensor_name": "Test"}, 4)] + ) + async def test_skip_first_deployment( + self, mut, db_session, filter, num_items_expected + ): + db_session_var.set(db_session) + num_items = 0 + num_deployments = 0 + async for deployment_id, items in mut(**filter): + num_deployments += 1 + if num_deployments == 2: + async for item in items: + num_items += 1 + assert num_deployments == 2 + assert num_items == num_items_expected diff --git a/Ch12/apd.aggregation-chapter12-ex01/tests/test_sensor_aggregation.py b/Ch12/apd.aggregation-chapter12-ex01/tests/test_sensor_aggregation.py new file mode 100644 index 0000000..655521d --- /dev/null +++ b/Ch12/apd.aggregation-chapter12-ex01/tests/test_sensor_aggregation.py @@ -0,0 +1,199 @@ +from __future__ import annotations + +import contextlib +from dataclasses import dataclass +import json +import typing as t +from unittest.mock import patch, Mock + +import pytest +import sqlalchemy + +import apd.aggregation.collect +from apd.aggregation.database import Deployment + + +@pytest.fixture +def data() -> t.Any: + return { + "sensors": [ + { + "human_readable": "3.7", + "id": "PythonVersion", + "title": "Python Version", + "value": [3, 7, 2, "final", 0], + }, + { + "human_readable": "Not connected", + "id": "ACStatus", + "title": "AC Connected", + "value": False, + }, + ] + } + + +@dataclass +class FakeAIOHttpClient: + responses: t.Dict[str, str] + + @contextlib.asynccontextmanager + async def get( + self, url: str, headers: t.Optional[t.Dict[str, str]] = None + ) -> t.AsyncIterator[FakeAIOHttpResponse]: + if url in self.responses: + yield FakeAIOHttpResponse(body=self.responses[url]) + else: + yield FakeAIOHttpResponse(body="", status=404) + + +@dataclass +class FakeAIOHttpResponse: + body: str + status: int = 200 + + async def json(self) -> t.Any: + return json.loads(self.body) + + +@pytest.fixture +def mockclient(data) -> FakeAIOHttpClient: + return FakeAIOHttpClient( + { + "http://localhost/v/2.1/sensors/": json.dumps(data), + "http://localhost/v/2.1/deployment_id": json.dumps( + {"deployment_id": "b29ba0ee10f14552b6b21327bb96d3fb"} + ), + } + ) + + +class TestGetDataPoints: + @pytest.fixture + def mut(self): + return apd.aggregation.collect.get_data_points + + @pytest.mark.asyncio + async def test_get_data_points(self, mut, mockclient, data) -> None: + # Mark the mock client as the same type as the one it's mocking + # So we can set it into the context variable without warnings + token = apd.aggregation.collect.http_session_var.set(mockclient) + try: + datapoints = await mut("http://localhost", "") + finally: + apd.aggregation.collect.http_session_var.reset(token) + + assert len(datapoints) == len(data["sensors"]) + for sensor in data["sensors"]: + assert sensor["value"] in (datapoint.data for datapoint in datapoints) + assert sensor["id"] in (datapoint.sensor_name for datapoint in datapoints) + + +class TestAddDataFromSensors: + @pytest.fixture + def mut(self): + return apd.aggregation.collect.add_data_from_sensors + + @pytest.fixture(autouse=True) + def patch_aiohttp(self, mockclient): + with patch("aiohttp.ClientSession") as ClientSession: + ClientSession.return_value.__aenter__.return_value = mockclient + yield ClientSession + + @pytest.fixture + def db_session(self): + session = Mock() + sql_result = session.execute.return_value + sql_result.inserted_primary_key = [1] + return session + + @pytest.mark.asyncio + async def test_datapoints_are_added_to_the_session(self, mut, db_session) -> None: + assert db_session.execute.call_count == 0 + datapoints = await mut( + db_session, + [ + Deployment( + id=None, colour=None, name=None, uri="http://localhost", api_key="" + ) + ], + ) + assert db_session.execute.call_count == len(datapoints) + + +@pytest.mark.usefixtures("migrated_db") +class TestDatabaseConnection: + @pytest.fixture(autouse=True) + def patch_aiohttp(self, mockclient): + with patch("aiohttp.ClientSession") as ClientSession: + ClientSession.return_value.__aenter__.return_value = mockclient + yield ClientSession + + @pytest.fixture + def table(self): + return apd.aggregation.database.datapoint_table + + @pytest.fixture + def daily_summary_view(self): + return apd.aggregation.database.daily_summary_view + + @pytest.fixture + def model(self): + return apd.aggregation.database.DataPoint + + @pytest.fixture + def mut(self): + return apd.aggregation.collect.add_data_from_sensors + + @pytest.mark.asyncio + async def test_datapoints_are_added_to_the_session(self, db_session, table) -> None: + datapoints = await apd.aggregation.collect.add_data_from_sensors( + db_session, + [ + Deployment( + id=None, colour=None, name=None, uri="http://localhost", api_key="" + ) + ], + ) + num_points = db_session.query(table).count() + assert num_points == len(datapoints) == 2 + + @pytest.mark.asyncio + async def test_datapoints_can_be_mapped_back_to_DataPoints( + self, mut, db_session, table, model + ) -> None: + datapoints = await mut( + db_session, + [ + Deployment( + id=None, colour=None, name=None, uri="http://localhost", api_key="" + ) + ], + ) + db_points = [ + model.from_sql_result(result) for result in db_session.query(table) + ] + assert db_points == datapoints + + @pytest.mark.asyncio + async def test_daily_summary_view_matches_query( + self, db_session, model, table, daily_summary_view + ) -> None: + await apd.aggregation.collect.add_data_from_sensors( + db_session, + [ + Deployment( + id=None, colour=None, name=None, uri="http://localhost", api_key="" + ) + ], + ) + + headers = table.c.sensor_name, table.c.data + value_counts = ( + db_session.query(*headers, sqlalchemy.func.count(table.c.id)) + .filter(model.collected_on_date == sqlalchemy.func.current_date()) + .group_by(*headers) + ) + + daily_summary_view = db_session.query(daily_summary_view) + assert value_counts.all() == daily_summary_view.all() diff --git a/Ch12/apd.aggregation-chapter12/.coveragerc b/Ch12/apd.aggregation-chapter12/.coveragerc new file mode 100644 index 0000000..e766c69 --- /dev/null +++ b/Ch12/apd.aggregation-chapter12/.coveragerc @@ -0,0 +1,3 @@ +[run] +branch = True +omit = tests/* diff --git a/Ch12/apd.aggregation-chapter12/.pre-commit-config.yaml b/Ch12/apd.aggregation-chapter12/.pre-commit-config.yaml new file mode 100644 index 0000000..995b49f --- /dev/null +++ b/Ch12/apd.aggregation-chapter12/.pre-commit-config.yaml @@ -0,0 +1,25 @@ +repos: + +- repo: local + hooks: + - id: black + name: black + entry: pipenv run black + args: [--quiet] + language: system + types: [python] + + - id: mypy + name: mypy + exclude: (?x)^( + (.*)/setup.py + )$ + entry: pipenv run mypy + language: system + types: [python] + + - id: flake8 + name: flake8 + entry: pipenv run flake8 + language: system + types: [python] diff --git a/Ch12/apd.aggregation-chapter12/CHANGES.md b/Ch12/apd.aggregation-chapter12/CHANGES.md new file mode 100644 index 0000000..bd0d9b6 --- /dev/null +++ b/Ch12/apd.aggregation-chapter12/CHANGES.md @@ -0,0 +1,8 @@ +## Changes + +### 1.0.0 (2020-01-27) + +* Added management of known sensor endpoints +* Added CLI script to collate data +* Added analysis tools for Jupyter +* Added long-running data synthesis and actions system diff --git a/Ch12/apd.aggregation-chapter12/Connect to database.ipynb b/Ch12/apd.aggregation-chapter12/Connect to database.ipynb new file mode 100644 index 0000000..98a74f0 --- /dev/null +++ b/Ch12/apd.aggregation-chapter12/Connect to database.ipynb @@ -0,0 +1,577 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "53635\n" + ] + } + ], + "source": [ + "from apd.aggregation.query import with_database\n", + "from apd.aggregation.database import datapoint_table\n", + "\n", + "with with_database(\"postgresql+psycopg2://apd@localhost/apd\") as session:\n", + " print(session.query(datapoint_table).count())" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "53635\n" + ] + } + ], + "source": [ + "from apd.aggregation.query import with_database, get_data\n", + "\n", + "with with_database(\"postgresql+psycopg2://apd@localhost/apd\") as session:\n", + " count = 0\n", + " async for datapoint in get_data():\n", + " count += 1\n", + " print(count)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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qZkRxdc8uWM32AuC1DUtGvtuVhMnRk2fQ29dPbwtCSN1SFyOKdpuSAAA1tvth1c4XY5eJEELywrhXFPd863nP3+ev21P1HINDF+MShxBCcse4VhS9ff2uU0omJ947n5I0hBCST8a1ovjjb/ubMpqz5qmEJSGEkPwSWlGIyGwROWj5e1dEVohIi4jsEZGjxueUOAUOwrkL/qaM3j03XH0nQgipU0IrClU9oqrzVHUegF8EcBbA3wPoAbBXVWcB2Gt8J4QQklPimnq6GcCrqvoGgNsBbDW2bwVQjukaNaW3r7/WItQVvX39WLjhaVzdswsLNzzN8iekhsSlKL4IYLvx/3RVPQ4Axuc0pwNE5H4ROSAiB06dOhWTGKOZNW1SbOdiZq30WLTpWazYcRD9A4NQVNJgrthxkMqCkBoRecGdiDQCuA3AqiDHqeoWAFuASvTYqHI4cfZ8fG6tWcisVQ/J5u/51vM4evKM428rHzmYi/uth+dEorO69zC2738Tw6ooiODu+Vdhbbmj1mI5EsfK7M8C+IGqnjC+nxCRGap6XERmADgZwzVCEWfj/uFSMbZzhWHOmqdGGd3Ha7J5L3fmi7WPiF+VRZueHaXozNEQML6eE4mGvZ4Mq+LhfccAIJPKIo6pp7txadoJAJ4AcK/x/70AHo/hGqFonBCf9++7g0OxnSso89ftcfTMGm/J5q994MlaixAJr9HQih0Hcc2qXZw+I1jde9i1npjKImtEGlGISBOARQD+q2XzBgCPiMiXABwDsDTKNcLS29fv2z3WD0mvzXaKXLu0qw0bdx/xXBTYn4EpsThY3XsYHwznYMjgQbXFnRcVHF2QzCoDLyIpClU9C+AK27afouIFVVO6Hz1YaxEccZq/fvTAMcfItdUaHqAS3DCvWMsi3yoiGH/02CEqijplde/hWosQinEbPbZaeKZJjQWcOZ/uQrvevn78/o6DI41i/8DgqO9hyGsD29vXj1U7D2NwqP4WO57P+ciJBKe3r39kNOlFVjt+uVYUUbxL1t3RMaahKhULng1Xe8+uSN4Jf/TYIccotvXIxt1HAiuJgmT1NQrO6t7DmTRaEm/CeCr5VRIAcM+CtjjEjJ3cKorevn50P3oIQ4YrTP/AILofPQQAOPCG95RNqdgwolDsiqbaA3XyTvCrsNiTvEQY28qwjp/ye3jfMSqKnBHWU+nBJ17yfY2s1oncKooHn3hpREmYDF1UPPjESxio4qG0/s45ACoGRXuD7lfzbzNedHtvge6Q/hAEH021NpeSEIWQqlTzVPJq4Ku1RyaTJxZCyZYGuVUUboXv56F4NeClYoOv/BNmI+emWFbsGL04jG6RowkzNmi/Il5FYe8hzpo2CXtW3hjqXEk8X7sn3MQJDfja5yudHC7oS5ft+98MdVy1fDhWXvzyZ0JdIw1yqyiSYv2dc3yPKqphTaGa5HqHepnvfu7V07GlpbUrCaCS9nbRpmerKguneeowLo/tPbuwfEGb47NzyuF+7sLFMXXTHMH+ae/hTDc0Tlz7wJNjXKInTyxk8j6qTXs6vYNOdcyNUjHbGR9EMzDv29XVpQcOHAh0jN80pnYmNAhe+eotnvvMXv09X2swFs5s8eXCmgavW/J/54Gwz880Z1frSVezG3ld36ssV/ceTsQP3q4wwpTPZQXBy+u863ZWcFISVtKsz15u2hMEeGX9Esxc9WRVZWGVOYgBGwA2L5sXqgMkIi+oalfgAwOSbTWWAF9fOrfqPubwvhppKYnNy+alcp08oMafGcLEacrHdL21BhX8/R0HY/FhT2qx1MP7jkWW74NhzY2ffrXFlVeH7EgExV5X7FzQitJecE2wtDpf/o5/A/bCmS2ZnzqsK0WxfEGbrweSlYfW2lwK3dPIMkHmbb1wC2Hi5HqrqDTGfmwJ7T27atLgxqGE8rjq1wlF+FFnEPy6afvpFFqV2ztn/Rmwly9ow7b7rve1by3JraJoLAT3qc/TPP5lBcFzPZ8eURILZ7bUWKJ4WN17uOpL1xDg0Tq52XoFg/TrqhhHDz8M7T27Umkg80LSSjvOwKGK4Cuv89Im5VZRTJoYzA4fdPomzlwWYbDPNVfrdeQl0Y+fHu+mLwR7VvZ79YoF6ddVEYindz5r2iQsz+giqlpyWYCOXpJK+8qYXa7NOtPsI9q0n32yQm4Vhd+hnUnQ6Zs9K2+sibKYPLEQypC38pHRiX7c5u+TIG4lFfRZ2UcJ1bybg8j386su9e4XbXo2iFgAKvVobbmDdiYbG+6qbiu08vC+Y7i6J/7ou92LZ8d6PqAyCvLTIXnwtutiv3ZS5NY9tiCS+ErdPStvxMINTyceoTUODw97rgZz/j5p+0aQBYdJ9QqDjBKAimJpbS75eq4XtGJTeeXk+55RfJ2YfnnjyP9mWcTlep13wriLKyrl94ePHsLXl87Nte1u1rRJuZI/tyOKtMI5JNHjsBIkflFTQF9r+/xrEtNTKz0WHNrZ5mMqx2xcJ8aYS8TOwOBQoOf63KunAysJANj/wKJR38udramMLPLg+RTFNnDhomLFjoNo79mF2au/F6ke1yKfS5SFnbUit4oiCFHmiMudrYnOMd89/yrf+371Tn9uuybWrHxOLqNxTE95zfK0WxRSb19/1dXY0y9vHGlc/booO+FnyjDpnr3bKLHc2Zr4GoGwtpVrH3hyxJje3rMr0URScdkGzl24iJWPhM+nXot8LnlTEkCdKIqongXmHHPcsYYWzmwJJFvQoap1sOLkBjg4NDzSM3P6i6Nnak5FVWuYly9oG9UDjzIs//f3g/f+42LWtEmZWPw4f92eQPtf3bNrzNqGD4Y1MWVx07VTYzvXRQ0/MhhPEYmTJLeKIu0HXO5sxXM9n8brG5Zg+YK2Mdf3WoK/edm8EUUjuLQ+Imn/aavBP0zPKU0X0ajK3JRzde/hwI4OcbF52TzfvcVJjcEDwF1WkJH6V40T750fZYj3YnXvYdfRXlJZB595+ZTj9rChLMJOZXlNYdMB4RK5NWb7ja+TxLTR2nKHY8O2uvcwtu07NvLSTWosYN0dHSO947SNV0HWI7hRLTLm9MsbQ83f24kaw+nhfcfQ9bGWmi04a7KErvfDujs6Ak9/mS7Ta8sdvu7zglZGCq9VGeHUoszcGvYPfATkdCLsVJabU0Nrc2kkuvScNU855qw3FUk9OCjkVlGsLXfgtVPvV128leaCFjcFUivsnlBh8WrEJxTiCY0ch4dWrV5YQXD7URAvKKvtxqSp2ICzPqMcz1+3Z+R4p4CGteBKlwb6yuYSbrp2amDlFdbppHvxbMcEZtbzVQtSeOCN0+NmRbwbuVUUQGURGlexJs+qnS+6NuJxrWx1Ok+YnBVpM6WpiDW3XhdKyZk91jCZGr8aIMqxOeL7+VW7cMFSoNbEO2nj1UCb924qND+E7WS4JTALcj6zc+inLPM6nZVrRQEAxQb3BVb1viI2rsjFg0MXMWfNU449K7eeYVCcpg7uWdCWekM2QTCqMfVi8sQC+v701yJf0ymBlp9j/vDRQ7jgc9gYtkOVVMNWrYE2R+dpdATDlL+dteUOPPPyKdd3oTXneUNya8w22bjUuSIH9SjKC1Oa/C/7H7oYX0Kdd88NY86ap8Zs7148G6VitOkn+1DfZG25I1VlP3liAa+sX+I7wX2t8yZ8fencWOxQXiTZsJkOIq9tWDIqrpkVPz4rWekQOr0LpWIBm5fNc72/vJB7RWEuYkrbo6hWrLn1OhQDxMl58ImXsHDD07Fc+91zw2MUT7mzFXoxuAFyQoOMPK/1d3a4vkRryx2hAkAGZfrljSMN/2sbloxaVW0nbJiVuCl3tmLTF+J3284S98yvrgSy0iEsd7Zi/Z0do9oir7qdJ3KbuKie6e3r95UbPAlam0t4rufTgbJ3AZWeVdiX5uqeXZFtFW6Z5LywZ5lbOLMl0x2QuKdpzGdda7ySHGU1I15apJW4iIoixzilywzLpMYCzpyvHpcf8O8S29pciiWvc9R4W+Mxp4cTQbOqVSNL5TZ/3Z4xda7elQSQE0UhIs0A/gLAJ1BxUPkNAEcA7ADQDuB1AF9Q1Xe8zkNFEZ6oqTmtaz06v/L9WBerxTU909vXj+5HD2EohL+vmcqyXohrVJH10ROpkJdUqH8G4ClVvRbAXAA/BtADYK+qzgKw1/hOEiLq/GxzU+NIr3HNrdclbhwNQ7mzFR+6LJyDXj0piTihkiBWQisKEZkM4AYAfwkAqnpeVQcA3A5gq7HbVgDlqEISb6I07tb1C6ZxNA4mT4xnIZ7JQIiRTpDkOOOFOAzbWfEiItkhyojiGgCnAPy1iPSJyF+IyCQA01X1OAAYn9OcDhaR+0XkgIgcOHXKOe4L8UeUFdj29QtxhcKOe+74wwGzgV1WkDFZAuuB7sWzfbv3OiHIjhcRyQ5RFtxNAPBJAL+jqvtF5M8QYJpJVbcA2AJUbBQR5Kh7/CbhccJp/UK5szWSUTQJ19GhYW8X3Cy4q2aBcmdr6JAS9apcSXWiKIq3ALylqvuN74+hoihOiMgMVT0uIjMAnIwqJPGme/Hs0A173F4tSTXYfj2ySGVE0PWxFnQ/etA1akHeVwrXgjChVsYLoRWFqv6biLwpIrNV9QiAmwH8yPi7F8AG4/PxWCQlrmQlzWaW4tg4Bb+rpymVOMJSkEuYib/M2FRm4i8g/ajQtSCq19PvANgmIi8CmAfgq6goiEUichTAIuM7SZhyZ6uvcAdW4ooFBSTvc98cwEZhugybAeXM4Hd5SBFKsolb4q9apFKtBZGaClU9qKpdqjpHVcuq+o6q/lRVb1bVWcZnPCvCSFX8hDuw4hYnKyiTJxYS71U9eNt1rr/ZkwBt3/+m435u2wmphluU5FqkUq0FuY/1RC6xttzhK180UHGB9Grcg3iWprE61ktWu/3CLTS135DVhNhp9gjGGVfgzSxDRTHO2LPyRixf0DbKRbKxIJjSVBwVNLHafP03fK6nSNPn3k131d9qCZI2Xn2Meph+yn0+CjKWODLtmT14a/DBYgMwrJV1G7UwELu9q2HHCfbAhrOmTfKd85rUFz/zCMBZD9NPVBTEFb+eM1nzMCqIOE4zWUceTtFvj548g0WbnqWyIGPwStBVCOpFkkM49UQikUUPIzdbhOLSfLJbiPQgodNJ/eCVk7sebF9UFCQS21xWALttTwOveEf1MJ9M4qfc2eoaU40jCkKqELfdICxWzxOv3p+bmyMh1XCLqcYRBSEJ0tvXj4UbnsbVPbuwcMPTVd0MvaLkWkcK5c5W19zi9iCIhJDq0JhNakJvXz9WPnJwpJfWPzCIlY9UQpC4GdC9ouTaDY1L5sxwDIx307VTfckWdgFh3tKnEuIHjihIYniNEP5k54tjGv6LWtnuhpftwT7YeOZl59D15na3EQdQcQkOg1Nq2udePY17vvV8qPMRkhU4oiCRcHNFBSrTQW4987MuYU3dtgPeUXLtEri5Mprb19x6neu5Bjx85r1wy18eNq951tyOawnLorZQUZBI3D3/KtfcB2EXIkWZ+jFpEOepKtNDJWrOjaAEvSd7LnTT7RgAuj7WUlfhrr3KIk1lIXB20nAznY0n5capJxKJteUOzxAafozUdpymn8wwz37o7etP1ENlde9hzFz1JNp7dmHmqid9rRkJ6pbrpnwf3ncMq3YeRv/AIBSXwl2P53hDXmWRJkE8/LK4vigKVBQkMl5Nr1tDZo/4asVp+skpzLMbXo1ykHDlToRtAOIM81DP4a7zwniLYExFQSJTrfF1asjW3RFsCB5k/YPXvlHXRnk1ACWPBB9JL8mqh3hDeWK8RTCmoiCROX+hek/f2pDNWfNUVfuAvYfuZ/3Dok3PVt134GzFUB12qsarAfjAwxCfRvMwnqef8obXau08PicqChIZL08lK6t7D6O9ZxfePVddsdhDgHQvno1S0X26CqjEabrnW8979q4/bIx+kpiqiWsxX9h57LBuvVlngstKS7ftWeDu+Ve5/pbHaUJ6PZHUCGJ8VADtPbtGvi+c2YL1d3ZUHYlUc0U1O3pJhPLwct8NQlgjbVi33jQJ4wl0wcUzwW17Flhb7nB9jnkMI0NFQXKBqQAmNRbGZLQLwjGz98wAAA/USURBVDvG1JNX2Og06e3rH+PqOl7JiptrEji5PzeXio7K+8MRHSpqAaeeSG547tXTgY3gdsy542oNcnvPrsBTQEGnFHr7+rFyx8FRrq4rU1zbkTZJeAKlOd8fdDW/m5liYHAolNt4LaGiIJFIu7JHndoxjdF+FqgF9XsPOqWwaueLsFt3/Fl78omXI0B7zy7MX7fH8fckwq2EYc2t17n+5jRyMEevTuRt/QsVBYlE3gxzQc2fQewFQY3Zgz6dAIJijco778vfR+dXvu87Qm8tOfHeeUdlEbSBToqgq9+r5akYHBrOjQMCbRQkMFaDZN6IKrFXmlU/xuw5a57y5fUVFqsDADC6Ie0fGET3o4cABG/00uLEe+fHbEs73EoUrDYnP3VtYHAolpA1SUNFQQLhFCE1r7jFg7Jz7QNP4orLL/N8+f00CkkrCT8MXVSs2vliphsmJwO/27PKkoesGWbGbwQBkxU7DuLAG6czbdCPNPUkIq+LyGEROSgiB4xtLSKyR0SOGp9T4hGV1Jrevv5xoyQA4Nfnt/na74NhHTE4e1FtGqHWSsJkcOhipqegVtgM/Ct2HHRV6HF5yAZNouVEkDAzdrIeByoOG8VNqjpPVbuM7z0A9qrqLAB7je9kHJCX4b9f4u7B5WEdg8kfPJL+s5w4IX6TaBz5qs2RQNRAi1HdrbMcByoJY/btALYa/28FUE7gGiRlstwDDYo1kdDyBf5GFX7x8tDJEsM1MC997fNzYp8qisNO5jQSCBNoMeq9ZdnmF9VGoQC+LyIK4P+o6hYA01X1OACo6nERmRZVSFJ78ubd5IU5fbZo07M4evJMrOf2conMGnbDt5VJjQWsu6MjVluGea6Nu4/EttgxajRgoHqSK79keKF4ZKKOKBaq6icBfBbAb4vIDX4PFJH7ReSAiBw4dco5bSXJDmmHHZg80TuuU1Tae3bFriTGE2fOD2PFjoOZH0nGMPMUCK/w+HGQ1fKOpChU9W3j8ySAvwfwSwBOiMgMADA+T7ocu0VVu1S1a+rU6gnvSW2JK+BdNVqbS3h9wxK8+OXPpHI94k2cdimrLSAukh7B2RvuqJEBqpHVdRWhFYWITBKRy83/AfwagB8CeALAvcZu9wJ4PKqQpPakEYOoVCyMuk7c9gNSW6J4BbkRhzHbi1WWbIs/v2pX4g4dWXWIiDKimA7gn0XkEIB/AbBLVZ8CsAHAIhE5CmCR8Z3knHJnKxbObIntfJuXzcPmZfPQ2lyCoDKSWH/n6DnxLPuVk+AkMX2ZtAHYXD3f3rMLF8axDaIaoY3ZqvoTAHMdtv8UwM1RhCLZZNt918eyKnvhzJYRhVDNWOqW0J7kj8YJDTh3If6wJUmvbM7y+oa0YKwnEoi15Q68uv4WvL5hSajjZ02bhG33Xe97/3sCTj8tX9CG1zcswfTLG4OKVhM2L5tXaxE8mRDjzE4SSgJIfk1I2PwgYfBKp1tLsikVyQVBlYUA2LPyxkDH+J1+EkMec/9F1/1coOvUiiyH0sgLUdaEZM3L6LIqWRxrBRUFSY3XQo5CqjF5YmHMudPsBY5n8jIvH7bBz5qX0UBG1+FQUZDQBHk5Gwvh5zCqhX5I2pV28sRCrIZ8k1nTJgHI/vRTHvjyd8I1+FnzMspq9jsqChKaIKu1H7prjN+Db772+TmuvyXtQrtwZgte/PJnsO2+62NVFtMvbxyZhit3tkZSpOMJQfCcIUC+VsR7kfYCQr9QUZDQ+HV3XL6gLdJcfLmzFZuXzRtl6GuQynnDuNCWig2jXHObS0XHF2H5grZRhvcwysLJDXjzsnnY/8CiUfs9dNdc11hB5jFhlGJrc2lk5JIHrmwu4Zt1PMLK6tQT81GQ0FzZXPJcZdtq5BKIw2Bb7mz1fZ5qU2Lr75wT6HxWtt13vWeMJDt+3YCtcZCseRisx5U7W0cUox8ZrM4GQWS2Eue02PIFbZ62I3PBZZiYUHHEfMoCaUVACAoVBQnNTddO9Xzxn+v5dIrSXKLalFhankZBG9kgymtSYwFnzruvcraPfESAoEtfNi+bF2tZmUrOXIcjAJoaCzh7fniMYjTLwm+irAdvc0+Xmiduujab4YyoKEhonnnZPZhjaw17RmkHMLQzpamINbdel6hCWndHh2c4CftalXvme/fmnUhC/rXljkDThX5GcFEUmt8sh2mxff+xTEYkoI2ChMarQa5lz8hr+J50bCAA6PvTX0t81OJ1fqc7XFvuCGTjSDp6b5xEKessKQmgNnlC/EBFQULj1SB7jTaSxiuAYdKxgdI0HLuN2tyey9pyh6/psMkTC3UTvbeWI988QUVBQuM1aqj19I8bcTQMXpnMgq48j0L34tko2Vby2iPw2jE9yOxeWK9vWDLyVy9KAog3KvKUpuK4jXhMGwUJjdeooZbeG27GbEE8DUNWpiv8eEq5HcfQIRXKna2xhA4vFmTELrVt/7HAjgMmWXVlpqIgofFyXUwjf4UbbqMZxfiLrVTvjX4aUyJOMc16+/pdFXQYxwFg9CLMrEFFQUJTEHGd869l4+W2viOu+Wi30OcZXVSbe7zq2aaEF+e5TSV5Kei15Y5AimJCg+DrS+dmWuFTUZDQeBmGk84R4EX34tlYtfPwqGxq1ebug+B21xmZkRp33D3/KseG15rXJCnCuqq6LS4MG02g1tCYTULj1UMPEgcqbsqdrVh/Z4dn9rwouLnYpuF6W4+Yrr1m+RZExoRXyRpuMudRSQAcUZAIdC+e7WoI9Bt6ISmSnLt3G0kl7XpbzwRdqBeEhTNbHFd/Rw0CmaTMacMRBQlNludUk8RtJEWf/HziFOxx4cyWTI9Y0oYjCkICkrQNhKQPlYI3VBQkEvXoARR2/QIheYWKgkSicUIDzl246Lh9PFPv6xdIdbzWWuQNKgoSCScl4bWdkHqgt69/1PRk/8AgVu08DCCftr3x3e0jhJAasHH3kVE2LAAYHBquqdt4FKgoSCTcMouNl4xjhITBLYxMVoNlViOyohCRgoj0ich3je8tIrJHRI4an1Oii0myyoO3XYeiLZxqsUHGTcYxQsLgFhQzq6lOqxHHiOL3APzY8r0HwF5VnQVgr/GdjFPKna3YuHTuqFXQGzMet4aQpAkTAj7LRDJmi8hHASwBsA7ASmPz7QBuNP7fCuBZAH8c5Tok29ADiJDRjDcX6qheT5sB/BGAyy3bpqvqcQBQ1eMiMs3pQBG5H8D9ANDWNj6TfRBC6pfx1IEKPfUkIp8DcFJVXwhzvKpuUdUuVe2aOrV2+ZUJIYR4E2VEsRDAbSJyC4DLAEwWkYcBnBCRGcZoYgaAk3EISgghpDaEHlGo6ipV/aiqtgP4IoCnVXU5gCcA3Gvsdi+AxyNLSQghpGYksY5iA4BFInIUwCLjOyGEkJwSSwgPVX0WFe8mqOpPAdwcx3kJIYTUHtEMJFsRkVMA3jC+fgTAv9dQnLBQ7nSh3OlCudPFr9wfU9XEvYEyoSisiMgBVe2qtRxBodzpQrnThXKnS9bkZqwnQgghnlBREEII8SSLimJLrQUICeVOF8qdLpQ7XTIld+ZsFIQQQrJFFkcUhBBCMgQVBSGEEG9U1fMPwFUAnkEl58RLAH7P2N4CYA+Ao8bnFGP7Fcb+7wP4c9u5lgF40TjPQx7XXAfgTQDv27avBPAj4xx7UfEhdjp+IiqhRM4CGATwrxa5hwG8B+AcKnGoMi83gJsAHLbIPQzgnqzLbfz2Z4Zs54zzZKm8bzDK9SKAtzC6fu8FMATgDLJXvx3lBvAxAAct9eTHeZA7B++lW3ln/b28AcAPAFwAcJftt68B+KHxt8xNhpH9q+4AzADwSeP/y1FpBD4O4CEAPcb2HgBfM/6fBOCXAfymtYCMgjsGYKrxfSuAm12uucC4rr2AbgLQZPz/WwB2uBz/3wD8LYBPohKH6tsWuc/nVO6HDHlbUGmQv5EDuX8TwOsA/sSQ8y0A38yQ3O0APg3guwDuwuj6/XcA/sb4LWv1xE3uuQC+Ycj7IQDvAPifOZA76++ll9xZfi/bAcxB5d28y7J9CSqdnwmGnAcATHY6h/lXdepJVY+r6g+M/99DpZfSikqCoq3GblsBlI19zqjqPwP4wHaqawD8q6qeMr7/A4DPu1xznxo5LWzbn1HVs8bXfQA+6iL27QD+lyH3YwB+xSL3hJzKbZb3XQC+B+BzOZD7k6hUxL9W1TMA/gmV3lQm5FbV11X1aRgrYG31uxOVURKQsXriIfc0VOrFVlRGeWcALM6B3Jl+L6vIndn30pD7RVRGQlY+DuAfVfWC8V4eAvAZp3OYBLJRiEg7Ki/QftgSFKFSSb14BcC1ItIuIhNQqQhXBbm+jS+h8mCcaEVlyAZVvYDKC/OLhtwC4Dsisg/A/BzJbZb3FwH8dU7kfhLAFAA/E5GPoNJDas6Q3KOw128Ap4FM1u9R2OT+OQC7UXke61HpwXqRFbmz/F6OwqUdzOJ76cYhAJ8VkSbjvbypmgy+gwKKyIdQmVJYoarvikggyVT1HRH5LQA7UNFw/w8V7RoYEVkOoAuVnqvjLja5fw7AfYbc76pql4hcA+BpVFGWGZIbRn6PDlQaAk8yIneviAwZ1z4F4HkAd2RIbiuXIT/124pdblXVOSJyJYBeWJ5NxuXO8nvpJXeW30s3Gb4vIp/C6PfygtcxvkYUIlJEpXC2qepOY/MJo4DMgqqaoEhVv6Oq81X1egBHABwVkYKIHDT+vuJDll8F8ACA21T1nLFtnXkOY7e3AFxlyL0TFaPk/zV++zcjsdJPUOkRvJ8TuU8A+C8A/h6VgGF5Ke9jAD6rqosAlGD00jMi98juAP4QtvqNyrxzFuu3p9xG/X4bwE9QGd3lQe4sv5decmf5vfSSYZ2qzjPeS0HFKcnzAM8/4yR/C2CzbftGjDY+PWT7/T9jrLV/mvE5BRXvjF+ocm27EacTwKsAZlU57rcB/G9D7icBPGK57iZDXjM6419mXW5LeR9DZZiYl/IuAPgfhrxzAPwbgI1ZkdtSv18B8F2H+r0Fl4zZmSlvN7lRmav+piHvFFR6i3+VA7kz/V76qCeZfC8t+/8NRhuzCwCuMP6fg4rn0wTPc/i4yC8DUFRcsQ4af7egMve5FxVNtBdAi+WY11HpOb6PSm/z48b27ai4df0IwBc9rvmQcZzpjvagsf0fUNHgphxPuBx/GSrDV0XFE+FHxv5/YPxvurP9KCdy3wJgHiqGsTyV9+2o9JjOoOI2uz9jcn8KlR6gojL0HrSU9/OoeOJcNMr9rhzI/QAqLpdW99g8lHfW30uvepLl9/JTxnFnAPwUwEuW99W8/j4A87x0gKoyhAchhBBvuDKbEEKIJ1QUhBBCPKGiIIQQ4gkVBSGEEE+oKAghhHhCRUEIIcQTKgpCCCGe/H9DmsqiQBIg1AAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from apd.aggregation.query import with_database, get_data\n", + "\n", + "from matplotlib import pyplot as plt\n", + "\n", + "async def plot():\n", + " points = [(dp.collected_at, dp.data) async for dp in get_data() if dp.sensor_name==\"RelativeHumidity\"]\n", + " x, y = zip(*points)\n", + " plt.plot_date(x, y, \"o\", xdate=True)\n", + "\n", + "with with_database(\"postgresql+psycopg2://apd@localhost/apd\") as session:\n", + " await plot()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from apd.aggregation.query import with_database, get_data\n", + "\n", + "from matplotlib import pyplot as plt\n", + "\n", + "async def plot():\n", + " points = [(dp.collected_at, dp.data) async for dp in get_data(sensor_name=\"RelativeHumidity\")]\n", + " x, y = zip(*points)\n", + " plt.plot_date(x, y, \"o\", xdate=True)\n", + "\n", + "with with_database(\"postgresql+psycopg2://apd@localhost/apd\") as session:\n", + " await plot()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import collections\n", + "\n", + "from apd.aggregation.query import with_database, get_data\n", + "\n", + "from matplotlib import pyplot as plt\n", + "\n", + "async def plot():\n", + " legends = collections.defaultdict(list)\n", + " async for dp in get_data(sensor_name=\"RelativeHumidity\"):\n", + " legends[dp.deployment_id].append((dp.collected_at, dp.data))\n", + "\n", + " for deployment_id, points in legends.items():\n", + " x, y = zip(*points)\n", + " plt.plot_date(x, y, \"o\", xdate=True)\n", + "\n", + "with with_database(\"postgresql+psycopg2://apd@localhost/apd\") as session:\n", + " await plot()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import collections\n", + "\n", + "from apd.aggregation.query import with_database, get_data, get_deployment_ids\n", + "\n", + "from matplotlib import pyplot as plt\n", + "\n", + "async def plot(deployment_id):\n", + " points = []\n", + " async for dp in get_data(sensor_name=\"RelativeHumidity\", deployment_id=deployment_id):\n", + " points.append((dp.collected_at, dp.data))\n", + "\n", + " if points:\n", + " x, y = zip(*points)\n", + " plt.plot_date(x, y, \"o\", xdate=True)\n", + "\n", + "with with_database(\"postgresql+psycopg2://apd@localhost/apd\") as session:\n", + " deployment_ids = await get_deployment_ids()\n", + " for deployment in deployment_ids:\n", + " await plot(deployment)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import asyncio\n", + "\n", + "from matplotlib import pyplot as plt\n", + "\n", + "from apd.aggregation.query import with_database\n", + "from apd.aggregation.analysis import get_known_configs, plot_sensor\n", + "\n", + "with with_database(\"postgresql+psycopg2://apd@localhost/apd\") as session:\n", + " coros = []\n", + " figure = plt.figure(figsize = (20, 5), dpi=300)\n", + " configs = get_known_configs()\n", + " to_display = configs[\"Relative humidity\"], configs[\"RAM available\"]\n", + " for i, config in enumerate(to_display, start=1):\n", + " plot = figure.add_subplot(1, 2, i)\n", + " coros.append(plot_sensor(config, plot, {}))\n", + " await asyncio.gather(*coros)\n", + "\n", + "display(figure)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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dNbyml8ej7kkOySE51PPXQ+1jvauXt2s9T7JIFsmi3mbRCmrbdO9XyyE5JIecHyaTaWNOnuM3R4Z7jp+zX5/RW0OTHJJDcqi3ObSC2rznsbRt5ND5/cih8/uQQ+v0/Kh7kkNyaJPmkM/nraFJDskhOeTzeWthkkWySBb5jF7dkxySQ3LI5/PqnuSQHJJDzo9l3aa6CzBVSXJwhdu9pePk+h9L2ObajifGM0muXcJ2b+zY168tYZtdCz0hdDGork6r42D7WE9f4rbv7NjuO3p8P/fjeOxdamB3bHfTPMfxH/X4ePxGx/5+fQnbPG76nD23zXiSq5ewXft+3tXL27VeJzkkh+RQb3NoBbX9647aPtXLY7FWJlkki2RRb7JoPZ0fdU9ySA5t0hzq+fFoG2/Bv6AbH7w6dxwOrnA7OSSHOseRQ92r79921PDeftfQ59t7cIXbySE51DmOHJp/vPax3tXL27WeJ1kki2RRb47HKmrbdO9XyyE5JIecHyaTaWNOnuM3R4Z7jp+zT5/RW0OTHJJDcqi3ObSC2rznsfTt5JAc6hxHDq3T86PuSQ7JoU2aQz6ft4YmOSSH5FBvjscq69tUn8+bvs0HV7idLJJFnePIovnHax/rXb28Xet1kkNySA715nisojbvVS99OzkkhzrHkUPr9PxYztQItauq6u4VbvqLHfNfsYRtXp5koG3+96uq+vQStvsPHfMvLaWMLLZBVVWHq6o6vYSxV+OFyazz+L1VVf3NErf9fzvmX9GdkubXj+NRVdVDVVWdWMF2/ztJ53Fbyvm0IqWULUm+oWNx5zk2R1VVn0ry1rZFzbTOaVZJDq2KHJq9Dzm0SqWUkrnnwhu6uY+1ShatiiyavQ9ZNNu6OT/qJodWZd2cZ3Jozj77cX6c29cX+rGf9UwOrYocmr0POdQ9f9wxf00tVfSJHFoVOTR7H3KIFZNFqyKLZu9DFq3SZn2/Wg6tihyavQ85NNu6OT+Ajclz/Kqsmwz3HH/eWv5szGYlh1ZFDs3ehxxaJe95LJsckkOd+5BDs62b86NucmhV1s15Jofm7NPn89YQObQqcmj2PuRQ92yqz+clsmiVZNHsfcgiVkQOrYocmr0PObRK3qteNjkkhzr3IYdmWzfnx3JojLa+fbBjfkspZewC27y4Y/7Xl7Kjqqo+keR9bYu2JXneUrbtsWd2zL9jGdv+ZZKzbfO3llL2r76kdavzfLq0h/t6fpKtbfN/+3/au/dgafK6vuOfHyD3y6oIiAhLUAmKGhAT8RI3BiyIJcgl3pVVvF8qmphKoaRCNGIuXquSUqIoipdIEBQFgRBdMBqvtQgoElFBUZY74i677C788secfc6cnjNzpmd6unu6X6+q80f3M9PT5zzf5z3n+Z2uPrXWP9nyuc2ZfXw3p8SOdEiHuqRDC5+Z5AFL2zdn8RvrWE+LtKhLU2yR+Tg8HTJnXeqzQ0yHDulQl3TorHc2tu8yyFmMnw7pUJd0iF1pkRZ1SYsWrFe3o0M61KUpdsh8AMfKe7yGd2mKP4/m8HRIh7qkQwvWPNrRIR3q0hQ7ZD4OT4fMWZemuPbK4emQDnVJh85yfd72tEiLuqRF7EKHdKhLOrRgrbodHdKhLk2xQ5OcDzdGO243n7PvtuseXEq5V5JPbDz/N1u83lWN7Ue3eO6h3Kex/Zptn1hrfV+S1y/tulXG8TkNpTlPa2epA49qbF/V4rm/kbPn+pBSyj33PiN2pUM61CUdWnhyY/uFtdZrOjz+FGmRFnVpii0yH4enQ+asS312iOnQIR3qkg6ddb/G9t8Mchbjp0M61CUdYldapEVd0qIF69Xt6JAOdWmKHTIfwLHyHq/hXZriz6M5PB3SoS7p0II1j3Z0SIe6NMUOmY/D0yFz1qUprr1yeDqkQ13SobNcn7c9LdKiLmkRu9AhHeqSDi1Yq25Hh3SoS1Ps0CTnw43RjttHNbZvTvL2DY9/cGP7VbXW61q83m81tj+uxXMP5UMa2+9u+fzm4z9+j3M5ds15evMBX6s5i/932yeezOyrG7vHMItzpUM61KXZd6iUcrckT2jsfmYXx544LdKiLk2xRebj8HTInHWpzw4xHTqkQ13SobO+vLH964OcxfjpkA51SYfYlRZpUZdm3yLr1TvRIR3q0hQ7ZD6AY+U9XsO7NMWfR3N4OqRDXZp9h6x57ESHdKhLU+yQ+Tg8HTJnXZri2iuHp0M61CUdOsv1edvTIi3qkhaxCx3SoS7NvkPWqneiQzrUpSl2aJLz4cZox+2Jje3fr7V+YMPjP7ax/fpzH7Xen11wvCHc2Ni+XcvnNx8/hs+pd6WUuyZ5ZGP37x7wJR/U2O5zFu9bSvmJUsoflVLeVUq5sZTylpPtny6lfE0ppRl81tMhHerEzDq0yRclucPS9puT/GpHx54yLdKiTky4Rebj8HTInHVigA4xHTqkQ53QobNKKd+Y5EuXdt2c5AcHOp2x0yEd6sTMOmStuntapEWdmFmLNrFe3Z4O6VAnJtwh8wEcK+/xGt6JCf88+jzWPbqlQzrUiZl1aBNrHu3pkA51YsIdMh+Hp0PmrBMTXnvl8HRIhzqhQ2e5Pq81LdKiTsysRdaqu6VDOtSJmXVoE2vV7emQDnViwh2a5Hy4MdqRKqXcOcmTG7uff8HTmncs/MuWL/vGxvaHllI+uOUxuvaOxvaHt3x+8/EP3ONcjtnXJrnj0vbf5kB31z/5T2LzP4ptZ7H5+I9u8dz7J7kyiwhfluSDktzjZPtLkjwjyV+WUn7g5N8Za+jQJTrUjTl1aJPmv6mfrLXe3NGxJ0mLLtGibky1RebjgHToEnPWjd46xHTo0CU61I1Zd6iUcqdSygNLKU8qpbw8yX9tPOQptdZXDXFuY6ZDl+hQN+bUIWvVHdKiS7SoG3Nq0SbWq1vQoUt0qBtT7ZD5AI6O9/hLNLwbU/159Hmse3REhy7RoW7MqUObWPNoQYcu0aFuTLVD5uOAdOgSc9aNqa69ckA6dIkOdWPWHXJ93u606BIt6sacWmStuiM6dIkOdWNOHdrEWnULOnSJDnVjqh2a5Hy4Mdrx+p4k91rafneSH7vgOZc1tt/a5gVrrdcmuaGx+25tjnEAr21sf8q2Tyyl3DfJvRu7h/58eldKuTzJv23s/qFaa/NukF1pzuF7a63XtTxGc3a7/nu7U5JvSfIHpZSP6/jYU6JDCzq0Jx1aKKV8fJKHNXY/c9/jzoAWLWjRnibeIvNxWDq0YM72NECHmA4dWtChPc2tQ6WUy0opdfkjybVJ/iTJs5L846WHX5vka2qt3zvAqR4DHVrQoT3NrUNbsla9PS1a0KI9adGC9eqd6NCCDu1p4h0yH8Ax8h6/oOF7mvjPo3dl3WM7OrSgQ3vSoQVrHjvRoQUd2tPEO2Q+DkuHFszZnia+9sph6dCCDu1pbh1yfV7ntGhBi/Y0txZtyVr1dnRoQYf2pEML1qp3okMLOrSniXdokvPhxmhHqJTyuCTf1Nj9HbXWd17w1Obdiq/f4eWbz7nLDsfo0ssb208opdzx3Eeu+vJz9g39+fSqlHLbJD+fs5/3G5L85wO+7FBzeHOSq5I8Ncljkjw0i9/a9JAkj03yvVn9ZuZjkryslHK/Hc5x0nToDB3aw8w6dJHmnapfXmt9fQfHnSwtOkOL9jCDFpmPA9GhM8zZHgbqEBOgQ2fo0B50aK23JPmOJPevtf7o0CczRjp0hg7tYWYdslbdMS06Q4v2MLMWXcR6dQs6dIYO7WEGHTIfwFHxHn+Ghu9hBj+PXmbdo0M6dIYO7WFmHbqINY8WdOgMHdrDDDpkPg5Eh84wZ3uYwdorB6JDZ+jQHnRoLdfnbUGLztCiPcysRdaqO6RDZ+jQHmbWoYtYq25Bh87QoT3MoEOTnA83RjsypZRPTPJTjd0vTfLDWzy9Ge7m3Sm30Qx385h9e2EWd/O8xWVJnnbRk0opH5nk2875o1uXUu7QzakdhR9L8g+Xtt+f5Ek7/DakNoaYw6cm+Yha6z+ptX53rfWXa61X11pfX2t9Za31BbXWf53kfkn+Y5K69Nx7JXleKaXscJ6TpEMrdGg/c+nQRiffSH9pY7e7e2+gRSu0aD9Tb5H5OAAdWmHO9jNEhzhyOrRCh/ajQ+e7Z5KvS/L1pZS7Dn0yY6NDK3RoP3PpkLXqjmnRCi3az1xatJH16nZ0aIUO7WfqHTIfwNHwHr9Cw/cz9Z9H38K6R4d0aIUO7WcuHdrImkc7OrRCh/Yz9Q6ZjwPQoRXmbD9TX3vlAHRohQ7tR4fO5/q8C2jRCi3az1xaZK26Qzq0Qof2M5cObWStuh0dWqFD+5l6hyY5H26MdkRKKffNYhCXY/nGJF9aa63nP2ujvp5zMLXWv0vyQ43d31ZK+RfrnlNKuU+SFye527rDdnR6o1ZK+a4kX9bY/ZRa6yt6PpWDz+HJf16bd+8+73E31FqfkuSbG3/00CRf1OY1p0qHVunQ7ubUoS08NsmHLm3/bZLndvwak6FFq7Rod3Nokfnong6tMme7G1GHOCI6tEqHdjfjDr0nyf2XPh6QxRrQ45P8QJK3nTzuI5N8Z5JXl1I+eYDzHCUdWqVDu5tTh6xVd0uLVmnR7ubUoi1Yr96SDq3Sod3NoUPmAzgW3uNXafjuRvQe7xq9I6JDq3Rod3Pq0BaseWxJh1bp0O7m0CHz0T0dWmXOdjeiDnFEdGiVDu1uxh1yfd6etGiVFu1uTi2yVt0dHVqlQ7ubU4e2YK16Szq0Sod2N4cOTXU+bjP0CbCdUso9kvyvJB+xtPuaJI+stb7t/GetuLaxvcud+ZrPaR5zCE9P8uic3pmxJPnBUsoTs7g76iuzuBPnvU8e9/U5ffN7U5L7LB3rhlrryp0+SymXb3sytdY3tDr7AZRSviWLu14v+/5a63/Z8vmXb/ta53w9Rj+Htdb/Vkr57CSPWdr9DUl+tsvXOTY6tJEOtaRDK57c2P7ZWmvzLtJEiy6gRS3NrEUHn4+50KGNdKilgTvEkdKhjXSopTl3qNb6gSRvOOePrk7y/FLKU5P8pyTfdLL/vkleVkr5tFrra/o5y3HSoY10qKU5d2gb1qrX06KNtKglLVphvXoLOrSRDrU0sw5ZqwZGzXv8Rt7jW5rZz6Nbs+5xPh3aSIda0qEV1jy2oEMb6VBLM+uQNY+O6NBGOtTSzNZe6YgObaRDLc25Q67P248WbaRFLc25RduwVn0+HdpIh1rSoRXWqregQxvpUEsz69Dk1qrdGO0IlFI+JMnLknzM0u63J3lErfVPWxxqkuGutd5YSnl8khcl+YSlP/r0k4913pHFNw4vWdr37jWP/YsWp1RaPLZ3pZSvTvL9jd0/XGv9Vy0Os8/X41jm8Hty9j+yn1JKuazWum5GJk2HNtOhdnTorFLKRyZ5ZGP3M3c93pRp0WZa1M7cWtTTfEyeDm2mQ+2MoEMcIR3aTIfa0aHNaq3vTfLNpZSbknzrye67JvmpUson7fgbho6eDm2mQ+3o0NasVTdo0WZa1I4WnWW9ejs6tJkOtTO3DlmrBsbMe/xm3uPbGcF7/LHMoXWPJTq0mQ61o0NnWfPYjg5tpkPtzK1D1jy6oUOb6VA7I+gQR0iHNtOhdnRoM9fnradFm2lRO1q0NWvVS3RoMx1qR4fOsla9HR3aTIfamVuHprhWfauhT4DNSil3S/LSJB+/tPtdWdzJ8o9aHu5vG9sf1vJc7pzVcI9ikGutf53kU5M8I8lNWzzl15M8LMl1jf3XdHxqo1JK+bIkP5KzMf2JJN/Y42k05/COpZQ7tTzGPRrbh5jD383i39otbp3kYw/wOqOnQ9vRoe3o0LmuzNnvyf6w1voHexxvkrRoO1q0nbm2yHzsR4e2Y862M5IOcWR0aDs6tB0dauU7kvzN0vZDkjxioHMZlA5tR4e2o0OtWKteokXb0aLtaNG5roz16o10aDs6tJ25dsh8AGPkPX47Gr6dkbzHj+3amHWse5zQoe3o0HZ06FxXxprHRjq0HR3azlw7ZD72o0PbMWfbGUmHODI6tB0d2o4OteL6vCVatB0t2o4WtWKt+oQObUeHtqND57oy1qo30qHt6NB25tqhqc2HG6ONWCnlLklenOSTlna/J8mjaq2v3OGQzbtf3q/l85uPf2et9V3nPnIAtdbraq1fl+SBWSyI/HqSNyW5PsnfJXltkp/M4i6q/7TW+oYkD2oc5vd7O+GelVK+MItIL/+7/5kkX9XnHfRrre/I2f8gJsl9Wx6mOYtt7uy6lVrrB5L8ZWN3q292pkCH2tGhzXRoVSmlJPmKxm53927Qona0aLO5t8h87EaH2jFnm42lQxwXHWpHhzbToXZqrdcn+cXG7kcNcS5D0qF2dGgzHWrHWvUpLWpHizbTolXWqy+mQ+3o0GZz75D5AMbEe3w7Gr7ZWN7jx3RtzCbWPRZ0qB0d2kyHVlnzuJgOtaNDm829Q+ZjNzrUjjnbbCwd4rjoUDs6tJkOteP6vFNa1I4WbaZF7VirXtChdnRoMx1aZa36YjrUjg5tNvcOTWk+bjP0CXC+k99G86Ikn7K0+9okj661/u6Oh31tY/ujWj7/7zW2/3jH8zioWutfJHn6ycdFHt7Y/p01xyzn7T8WpZQnJHl2FnepvsX/TPKkk/+wtdLB1+O1Wdxh8hYfldX53KQ5i22e28b1je3mHV0nTYd2p0OrdGitz0py/6Xt92XxTTUntGh3WrRKi04dYj6mSod2p0OrRtghjoAO7U6HVunQzl7X2G77b+ao6dDudGiVDu1s1mvViRbtQ4tWadFa1qs30KHd6dAqHTplrRoYmvf43XmPXzXC9/ixXBtzkVmve+jQ7nRolQ6tZc1jAx3anQ6t0qFT1jy2p0O706FVI+wQR0CHdqdDq3RoZ7O+Pi/Ron1o0Sot2pm1ah3aiQ6t0qG1rFVvoEO706FVOnRqCmvVt7r4IfStlHKHJL+S5NOXdr83yefUWn9rj0O/prH9CaWUO7Z4/qddcLyjcnJX1c9q7H75EOdySKWUxyT5uZy9EeIvJvniWuv7hzmrldlpBnKtk29qPuGC43Xl7o3ttx/odUZHh/qhQzqU5Csb28+rtb5zx2NNjhb1Q4u06ILXmcV8rKND/ZjLnI20Q4ycDvVDh3RoCzc1tm83yFkMQIf6oUM6tIXZrlUnWtQXLdKiWK9eS4f6oUM6tMlc5gPol/f4fsyl4SN9jx/9z6NPzHbdQ4f6oUM6FGsea+lQP3RIhy54nVnMxzo61I+5zNlIO8TI6VA/dEiHtjDb6/MSLeqLFmnRFqxV69BB6ZAOxVr1WjrUDx3SoU3GPB9ujDYypZTbJ3lBkiuWdt+Q5DG11lfsc+xa65uTvGpp121y9s3hIlc0tn91n/MZgc9KcvnS9strrX860LkcRCnln2Vx58oPWtr9wiRfUGu9eZizSpK8uLF9RYvnfkbOvgldXWt9y95n1FBKuXtW7+L6N12/zhjpUK90aDiDd6iUclmSxzd2P7PtcaZKi3qlRcMZvEVbmPx8rKNDvZr8nI24Q4yYDvVKh7jIfRrbh/i+a3R0qFc6xFpzXqtOtKhnWjRj1qvX06Fe6RCbTH4+gH55j+/V5Bs+4vf40f88es7rHjrUKx0azuAdsuaxng71SoeGM3iHtjD5+VhHh3o1+TkbcYcYMR3qlQ5xkVlen5doUc+0iLWsVetQT3RoxqxVr6dDvdIhNhntfLgx2oiUUm6b5HlJHrG0+31JPq/W+r87epnnN7a/Ystz+/tJ/tHSruuSvLSjcxrKv2lsP2OQsziQUsojk/xCktsu7X5pkifUWm8c5qwueUmS65e2H34yY9u4srHdnOmufGHONvItSV57oNcaDR3qnQ4NZwwd+pIkt1/afkOSX9vxWJOiRb3TouGMoUUXmfR8rKNDvZv0nI28Q4yUDvVOh7jIZze2R7G4f0g61DsdYpNZrlUnWjQALZo369Xn0KHe6RCbTHo+gH55j+/dpBs+8vf4Y/h59CzXPXSodzo0nDF0yJrHOXSoxYhGTgAADKBJREFUdzo0nDF06CKTno91dKh3k56zkXeIkdKh3ukQF5nd9XmJFg1Ai9jEWvUpHTocHZo3a9Xn0KHe6RCbjHY+3BhtJEopt0nynCSPXtp9U5In1lpf0uFL/UyS9y9tP76U8tFbPK85xM+ptd7Q3Wn1q5TypCSPXNr1yizu/DgJpZTPTPJLOfsN0q9l8U3A+4Y5q1O11vcmeW5jd3PGVpRSPibJ45Z23ZzkZzs8tVte555JntrY/cu11tr1a42JDvVLh4Y1kg59ZWP7x6femW1oUb+0aFgjadGm15n0fKyjQ/2a+pyNvUOMkw71S4e4SCnlc5I8rLH7l4Y4l77oUL90iE3muladaFHftIhYr16hQ/3SITaZ+nwA/fIe36+pN3zs7/FH8PPoWa576FC/dGhYI+mQNY8GHeqXDg1rJB3a9DqTno91dKhfU5+zsXeIcdKhfukQF5nj9XmJFvVNi9jEWrUO9UGHiLXqFTrULx1ik7HPhxujjUAp5dZZBPWxS7tvTvIFtdZf6fK1aq1/muQnl3bdNsmzSim3X/OUlFIem7O/8ebGJP++y/Pa18kb37aPfXySH13adXOSr6y13tz5iQ2glPLwJL+S5A5Lu1+R5HNrrdef/6xBPC2Lb05ucWUp5THrHnwyoz+Rs3fofGat9c82POeBpZTPbXNSpZR7ZfH1u+fS7huTfE+b4xwbHdqfDp3SoYuVUv5Bkocu7fpAkme1Pc7UaNH+tOiUFp37XPNxAR3anzk7dUQdYkR0aH86dEqHTpVSHlZKedzFj1x53icneXZj9ytqra/u5szGR4f2p0OndOiUtep2tGh/WnRKiy5mvXqVDu1Ph07p0CrzAQzFe/z+NPzUEb3HPy2u0RsNHdqfDp3SoYtZ81ilQ/vToVM6dO5zzccFdGh/5uzUEXWIEdGh/enQKR065fq8drRof1p0SotOWaveng7tT4dO6dDFrFWv0qH96dApHVo1tfnY+pPhoH48yec39n17kqtLKZe3PNY1W9xp8t9l8RtsPvhk+1OTvKyU8lW11j+55UGllNsl+Zok39d4/vfVWt+4zcmUUu6T8+fsXo3t22z4XK+ttb79gpd6dSnlhUl+Icnv1Fo/cM65PDjJU5J8ceOPvr3WevUFx+/Eob8epZSHJPnVJHde2v26JN+Y5B6llDane0Ot9Zo2T2ij1vrnpZQfSvJtS7ufW0r5l0n+e631xlt2llIelOTHspjVW7wjF38D8eFJXlBKeXWSn07y/JNvXlaUUu6S5ElZ3Nn7no0//g+11j/f4tM6ZjqkQzq00HWH1nlyY/sltda/2vFYU6JFWqRFC4dq0VHMx8B0SIdm16Gkv/kopdw5yd3X/HFzQfnuG17rTWNaXOuYDumQDp3V1XzcJ8nzSimvyeIHaL+Y5HW1nv9blkopH5vka5N8Q+O8bjjZN2U6pEM6dFZX82Gtuh0t0iItOqvr+WiyXr1Kh3RIh86a5XwAk+Q9fiYN9x5/yjV6o6NDOqRDC67RG44O6ZAOLbg+bzg6pEOz61Di+ryR0SEd0qGzXJ83DC3SIi06yzV6/dMhHdKhs1yf1z8d0iEdOmuW87G1WquPgT+S1A4/rtjyNa9I8r7Gcz+Q5PeS/HySFyd56znH/+Ukt27xub2hg8/pWVu8ztuXHv93SX4ri3+kP5PkpRvO47t6/rs+6Ncji99o1NUsXdXD1+PWSV50zmu/JYs3oOck+f2T2Vz+8/cl+Ywt57x57Hcn+T9ZLLA9O8nzT17jpjVfh2cM3YieZlOHdEiHDtChNa95uywulFg+3hOGbMBYPjqcHS3Soqd1OEtX9fD16KVFxzIfQ350ODc6NPI5O/TXI8fXob7m48qOviaXD92LA/5d6JAO6dBhvh6fd87j33MyHy/I4gKI5yR5WZJr1hz/vUkeMXQnevi70CEd0qHDfD2uOOfx1qrXf72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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import asyncio\n", + "\n", + "from matplotlib import pyplot as plt\n", + "\n", + "from apd.aggregation.query import with_database\n", + "from apd.aggregation.analysis import get_known_configs, plot_sensor, Config\n", + "\n", + "async def clean_magnitude(datapoints):\n", + " async for datapoint in datapoints:\n", + " if datapoint.data is None:\n", + " continue\n", + " yield datapoint.collected_at, datapoint.data[\"magnitude\"]\n", + "\n", + "TemperatureConfig = Config(\n", + " sensor_name=\"Temperature\",\n", + " clean=clean_magnitude,\n", + " title=\"Ambient temperature\",\n", + " ylabel=\"Degrees C\",\n", + ")\n", + " \n", + "with with_database(\"postgresql+psycopg2://apd@localhost/apd\") as session:\n", + " coros = []\n", + " figure = plt.figure(figsize = (20, 5), dpi=300)\n", + " configs = get_known_configs()\n", + " to_display = configs[\"Relative humidity\"], TemperatureConfig\n", + " for i, config in enumerate(to_display, start=1):\n", + " plot = figure.add_subplot(1, 2, i)\n", + " coros.append(plot_sensor(config, plot, {}))\n", + " await asyncio.gather(*coros)\n", + "\n", + "display(figure)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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Aa9mS26TFm+/yXbZv18GamZhV4xqVhjZU6RKxpOTTsS1VcnBxuPZtyhsMvAYI286vN8WD0RrzdQEAUA3GmKSkv0qaP2p2StJbrLV3lFO2tfYlY8xSSQdvmxXXcPjazSGLmF8wfWM59QEAAABQJps/6LazCsFogxlpIG3VlSAEpFBvX/Tgnzk94dYzxujNh0hvPqSyx90Yo3m7S0tWl1fO2ccY/efpMefyXM7T5T4BbCtLOGat6sGV0nMbxs7/66NSOmOVCBnmdO3D/sf8miVWnzypjApWwd3LpTWbgte5erHVWw7l86iSrl4c/Hf58ibpc1eFD6zac7q0JV1urXY4fu/ytu92BKNJ0rot0m5Tyyu/WVyzpLzP55P3V14omg0obkJCettrjN72mvy/5ZP3l259qqxqRPL2eUbfPnPsd9mcHnflr1li9flT+AwCULqbn3B/xix8RurbbEOFTAKojZxnne2a9lE5htMmBpczfP+Av22Ec02Ra7SwCEYD3AhGAwAAAAAAABpEzmZ958dLDEZrNx0yMrLyvxnPYHe3ZImhFe7ghXAhdK4wu3SRQLZKCVt/Vz2zNqOczZYc5gcAAODn3o23KWOHfJed0H16jWtTuqDwrVZjHb3UjPEfuOUOlSs1uDhZMO1qh6cC91EsjLpeFbsWdF0PAQDQaowxHZKulXTyqNlpSW+11t5Wod38WTuC0STpbIUIRjPG7CfpiFGztobZDgAAAEAVeflBNRNs5YPRJGnVemn/XapSdEPr7Yu+zR7TKl+PqI6aa7RkdXmDbN97+PCAbtvfJ3VNkulI5C2fPWGrpLFpRKUcs1Z1kyM0I5OTVq+X9t55eJB+b5+UHfWgl50nS1Mn7Bhw/7Xr/ctZ9I8d/35hg9XmUd139pgmdXa4B+1vTVu9vHE46C8WKz64f90Wq7Wbd0xP6ZQ2+vwUcvezxd+Xz64lXK9Uo89zZ4e0+07DYYlPvFh825ufDL+fOT3ShISRHP0HozpqbnkBErtMcS9btZ5gtK1pq+fWS/etKK+cw+eEP0+ugIYPH2N061O1+xs/Yo7//CPnuN+/y9dWrz4AWsODq4KXP7NG6plUm7qgOGutVq+XBv27kKEFpP2H2kiSOkYNXTDG6A37S7c4Ql65FkYUdyyrTDnT+T4BnBh9BgAAAAAAADSIjM34zo+b9pLKi5mYOkxCaesfdtWog/hrwR2MEBy84FqeMOGCBYICGWrBWf+QwRHDZaTU1VbkUUsAAAAheTanO/oX+C7rjk/TqyceVuMalc4d7lWbtl49cYU3u7rol9pOLjf4N+0NajAXFIwW8Fj7OpYwwdeChGgDACAZY+KSrpL0xlGzM5LOtNbeVMFd/UHSlyWNPMf87caYva21xbpY/3vB9FXWOm4EAwAAAKgNL5c32eVVJxitt49gtEKDQ1Yvb4q2zev3k+Jt5QX8VMLH5xv97l6rTY4rugkJ6UNHG/1kof999cNnS/MnrZB3zvukfzwsdSRl3/xhmU9+R3pltexX36fZL+8n7fqrMduuYDB4KI8+Z3XR39zhQL190o2Pe7rgr3ZMwJgx0vx9pCvOjamrQ9qadu/n/562Ove33phB+h1x6X1HGP30fUYd8R3v2YG01Ud+a3X1YqusJ03tkr75DqNzj/N/CM3qdVbv/oWn+3uLvuTQ7lsxHFBhzPj/LTWKe5dbfeBSb8zf38zJw3/vL/RXbl87Tx4ORZuTkN5zmNH/e7B4yNWMSdIrm/2XvWOetM/O5Z3rnSZIk5LKC/8bsWKt1dFlBq81qq1pqw//xurPDw//PRdz2/kxvf2nnm+o4bQJ0j8flX8cg/5Ek46uqW99jdFeM6yefaV4fSrhHfP8Kzl3hrvyK/sIZwRQnslJqT/gsu2Ffit3TxbU0k1PWH3kcq+ibSU0l/a2/OnzT4nplqf8G1a0IRDGvcut3vXzEI3zkGbXQTg/UK8IRgMAAAAAAAAaRNYZjFb6bb5krFPpXLgwAOzgDkYoErzgGHsY9liPd1iGOxits2Da/XpS3iDBaAAAoGKe2Pqw1mXW+C47rvtUtZk232X1yBVGVSx8t7X4dyh1BXWlAo6dZ3Masv4jjArb+672bcobDDw/jRo2HTdxxdQmTznf5WGDnQEAaFbGmDYNB5b906jZWUnvttb+vZL7stYuM8ZcLunD22Z1SPqNMeb1rqAzY8w/SfrQqFlDki6qZL0AAAAAlMDLHyw5wdtaclG7dQ+HmDy/Yeyy3j4G5xdauS76Nj98j394VK3tv4vRnV+I6ds3WS1etSMQp81I8/YwOv8NRq/dw+j4va3e9ysvLzDnX08y+tpbrMzZb5Je3JZ2NZSSrrlE6p4ue9PvpeeXa3an/z3fjYPShq1WUyfwfnIZHLJ6/feDB0L/6DZPNzzuv8xaaeEz0pk/87THtODjfLJjP0NZ6bK7raZ2Sd99544yPnWlzQu62jAgnfc7qz17rF6/f/6+rLV60/96euLFwCqU5Pf3WX3gKN5DYazfavWGiz0NDI1dFjXcMYw9e3b8+7cfNjpwV+nGx63uWe7e5u5/j+l/brB6cKVVOjs8b/pE6dSDjL54Wvnn2RijOT3S0ufHLisMBWwln/ij1Z8Whwvo2GOadOJ+2747Flg9tO27o71NOmKO0edPMdp758LPAHd5rmDDZLvRvf8R0+f/ZHX5vTsK2GuGNDhU2RC/Gz4V07SJ7vfX195q9OW/jH0RK1v4PQOgMgqDlAq18ndTPents3rz/3qhwkPRujoK/p5PPdDomLnS3T5tX/62UUzQtVupit0TAFoZwWgAUOcGBga0ZMkSLVu2TH19fUqlUurs7NTOO++sffbZR695zWvU0dEx3tVsWPPnz9eiRYu2T9ugu7lluP3223XiiSdun77gggt04YUXVmVfAAAAAJpX1nMFozkeyxdCItYp5Xx6ysodLoDgYIQgrgCzsIEJpQQ+VJKr/oXHI+i9U6sQNwAA0Bru6L/Bd36b4jpmyik1rk15nCG4jnDdZmbl/3uNcQWjOYK6gtqeac8/FE0KH/yb9lJKef6PB25TvKxrtfFkjFEy1qkBb4vvcq4VAQDQryW9q2Def0p62BgzO2JZL7sCzka5QNLbJE3dNn20pFuNMR+x1j49spIxJiHpXyR9r2D771lrV0WsF9Ay6J9XXfTPAwBgFJs/SrvLlv47/zF7Gb200TqC0UoutmmVckxmT6t8PUr16llGvzsneIDsO19n9M7XjU1usI/cKTsSijZ6/qU78rPnZNyXjL190tQJESrbYv6+VFpfJOPQFYo22l3PSnc9W15b+fJ7rL5zppUxRqmM1f97wL+8y+8ZG4y2eJWqEoomSb+4w+oDR1Wn7Gbzp4dsRQfWF7Pn9B3vg3ib0ZfeZPSlN0ntH80p5wgWmTvD6Ncfqu6A/TnTCEYbbSBtddVD4T8f5u8zfH4O2s3ot0W+O0ZMKfGnv2kTjS472+iys/PnP/6C1cEXVS6d5pi9gpfvPcNIPr8vr1o/fB/CFe4GAMUMZYOXlxLAjMr7w/2WUDQU5Rd0eM5xRncvH9uG6OVvG0Vcvbjy1271dB8KqDcEowFAHcrlcrrqqqt02WWXaeHChcpm3VfQyWRSp556qj7ykY/ojDPOqGEtAQAAAAC1lpP/9WF5wWj+A/yLLWt1QQFlQZ1p0o4As7DBAqUEPlRS+GA093uHYDQAAFApa4de0pNbH/Zd9ppJR2lyvLvGNSqPq03Ymu2naMForqDhwGC0gPyRwnZ3UDvcFVKcjHU2dCf7RCwZEIzGtSIAoOV90Gfet7f9F9WJkm4PWsFa+7wx5u2SbpI0ks50jKQnjTGLJa2QNEXSPEnTCzb/u6SvlFAvoKnRPw8AAIyLXC5vssvx0IXRJielTT63Mt9/pNHVi6U7l429l7qyrzpBpI2sN+Ix2WuG1JVo3Pu7o9nbry26zq7ZF9XhpTUUS4xZ1tsnzdujGjVrDo+/WD9/b+u2Si9vlHbpllb2yTlI2y+ArZqv48WNVSu66TxepXA6l4N285//vXcafebKse+Js4+pzefi7B7/kKtW/X5b0Sel/J+n6+vVs6Lv41Ovj+nqJWMTZY6ZG70sSdqzR+rqcH8ORTF7mjQpGfzem93jPz+V2fG5CAClGMoFL1/Vot9N9ebJGreh0HgScelVO42dP2eaf7uzf0DqH7Dq7mqO+wKovEpfu02fJM2YVNkygWYSG+8KAADy/d//Z+++46Oq8v6Bf86dmcxMGqmEThIpgnRBpCiwKM2yigVRf7ZddW3oKutaHttaVldcF3tZH2Ctu4+AulgRBQUFDE1pQiAJEEJIL5NMvef3x0CSydxzc6e37/v14kXm1jN37tx77txzPvebbzB06FBceeWVWL16tWqjKwCwWq34+OOPccEFF2DcuHHYunVrmErqm/z8fDDGwBhDfn5+pItDCCGEEEIIITHJyZVbuOilQILRxIFconABIg5GkOGCkytfy3PONQeLiYgDH/x/krQvROERRuZZriTm3Vi2bRlhKishhBBC4t/39V+CCwK0pmTMCXNpAieqEyZi/UnYdFTQ3ky47bh426lt1871bmFoHW8VB6PpYvt6Su16kK4VCSGEkPDjnK8FcDGAqg6DGYCxAC4HMBPeoWjvA7iCc95F1x1CEgu1zyOEEEJIxHDPwJEUDcFoT1zk/aNon0xg5lB3UIiSj3f4Vbq4VlLt2/S/nxwfnZ959VFg+StdTieBI99RpjjuIIU9qPpBIWQskt7bzHHbezKGPuIdcHRSaY27DdNr62RM+KsLuX904fb3Qvc+DtUCTld0badotXp3+LaTyQBcPV75WDf/DIbkJM9hjImnD7YCQchVSU1YVh91fDmHmQ3A/HG+f04TTgFO6fzLItxBrP5INjLMPyM4+8sNGs7JBYI6EeA+5hFCiL/s6j8d0zEmTFZu4/jNIhe63+2uu+b+0YU+97pwyasu7K3g+PQXqmsSdReNYkhRCD8X1TsBYH1xCAtEYt6aPcE97lw/iUGS4uO3KEJCgYLRCCEkijz22GM455xzsG/fPo/hjDEMHToUM2bMwPz583HOOedg0KBBXvMXFRVhwoQJePPNN8NVZEIIIYQQQgghYeSQBcFo0Pu9TLVALq1hXYlILQhAFLDg4HbIUG54qHVbiwMfrJC5uFFjsIhCHzqXS2KSMDxOFK5GCCGEEOILu2zDjw1rFMf1Meaj0HxqmEsUOFFdzypbwXmiNeJTfr9MkIzmT6ic2rjOyxMGo8lWcR2ZxXZ4GF0rEkIIIdGHc/4ZgGEAXgNQpzLpRgCXcs6v5JxbwlI4QmIEtc8jhBBCSETJnr97mrl6MFou6nDbNIZnL2XISwd0EjB5ALB2oQSDngk78Lpk4MDxRPtNWV2pSrhXVorn3w+ex7BwRux3RuWtFvA/TNE8vSgYzddQuURisXGs2RvpUnj604ccr67t+vv/m+dk3Poux6YSoMYCtNgDW6/EgCRB8zWXDBxW+xWDAAAqGzn2Vfo+38Duvs8zrBfw9d0SemcqH+ty0xjWLpQwoo87EK0wB3j7BoZpp4YrGE15PYdrAYcz8c5vpTXa3vOwXsCaeyT0zPD9c9JJDN/cI2HaYEAvAT3Sgb/OZbjpbP8/85fmM/xuMkOq+PmqqrJSgD/PYnhgdtdlyE4FUgTr0br9CCFEiU1DMFritecJrxVbOS55VcbafUB1s7vuWmMBjtYDK7cBQx+R0UTN0olAqhH4f2cy/O91yvWJ3pmAQac874UvyWiy0vebeKtq4thToT5NqtEdRq3FvLEMT/w29n+HIiSU/O9LzY9AAAAgAElEQVQxSQghJKjuuusuLF682GNYWloa7r//flx11VXo16+f1zzFxcVYunQpFi1aBJvNBgCw2+246aabYLFYcNddd4Wl7IQQQgghhBBCwsPJle+w6iWNv5orUAv4UhuX6NSCAGy8FalI9x4ui++8ag0WUPtM7NwGU4jDF0TvQalcRskMm8t7erUACkIIIYQQrbY0rYdFblIcd3bGHDAWe41FRHU9DhkObkcS87PVeAyShQ1HRcFo4uAyEavKuCSvYDRR8JoVVlm586JZlyxcfixQD0aja0VCCCGJjXMescom5/w4gFsYY3cCmASgP4AeACwAygFs45yXRKp8hEQzap9HCCGEkIiTXR4vU2T1HON8uRyM5eCeGQx3n8thdQDmpPbLEXdwjPJvqS9+y/GPebH3O3moHBSEez1yAcND5zEcb3KH/+SmApIUJ9tt3Uqg8pDmyQscpYrD1ULlEt3yrbG7bdbt63oakaPPSl7DTgYfpS9QfqjjwSoIwxyJ29sb/duf1i6U8O8ijrv/I55/60MSepxoymY2AN2Suz7Ojc1n2P6wDq127nHuCQfRviJzd8heYW5YixNxooDKU3sA3y6UkG4C7E5tn6uavlkMa+7RwergMOoR8P12o4HhzWsYXr2Ko7rZPSzdBEgSwDlg1LvPubUWDrvTHYSWpGew2DiarED3NO3nZMYY8rOBXUe9x1HAJyEkEPYugtGsDqCyEejRLTzlSUSL1/j/0PD3b2SYMihOru+IX7JTAINevA/oJIZ+WcCBKuXx/1fEccNk2oeIJ7Vrt3d+x3DleAkumcPpAs5/Ue4yUP29G1lMtnUlJJwoGI0QQqLAsmXLvBpdTZ48Ge+//z769OkjnG/AgAF44okncM011+CSSy7Bzp0728bdc889GDVqFKZOnRqqYseFtWvXRroIhBBCCCGEEKKZSxSMxvz/mU+1szvTFtaViNQCyqyC4C+1UAatIXRqAQQ2uTXkYXaiUDOl/cgomQCX97RqARSEEEIIIVpwzrGu7jPFcWYpGePSzw5ziYJDNXxXtiJJSpxgNFFnPlETING2E9XNAXH93MCSoGOejwMV1bMd3I4WuVlQptgOD1Mrv0ljsDMhhBBCQodzbgfwbaTLQUisoPZ5kUPt8wghhJAOuGeH7p7OY6qTD3IeBDASgDvww5zkOX5gd/G8O8tjN7ApFKqVf8ZFvyx36Eo8BhnwPT/5NH2+vVRxOAXKiG1OwFjyKYOAHt3EHba7pwHHFZ5rVFLNIb7LQwDgJz/3p25mYGB3cVBmkh4Y0sMdUuWPQEPR7pzOsHiNd9nOHSKeRy1Er6Q68YLRRAGVZxYy5KW7P5/OdYRAmPzcV0T0OvXzbFaK5/pSjAwpftwWL8hRDkYrrfF9WYQQAgCyzOHUkMlVWkPBaKEiyzygOve4fKZadyUEAAbniYPRfioDbpgc3vKQ6Kd27Ta4h/uYo5MYdBIwqAfDmr3i3+iG9w48kJiQROAd0U8IISSs9u3bh9tvv91j2MSJE/H555+rNrrqaNCgQVizZg2GDGn/ZViWZVx99dWorqY7UYQQQgghhBASL5zcoThczwx+L1MtSCvWO/KHknpAmXLAgo2LQxmMTGswmnpYRqiJ1iEMRlNchng7EEIIIYRoUWrdj0O2A4rjJnSbrlpnimZqdcJw1PWiCRcGoyk3BBJd16htN3Hor/ey1PapBmedT2WKFWr7I10rEkIIIYSQWELt8wghhBASNWTPXvUFjjIMte0WTn655SPVxfXMEHec3HjQt6LFO4tNeXiaKX46n3KnE/yTtyBP7wb5LCOw4jWf5i9wlCoOL61xBxKQdodrOR5fJeOVtYm3XS4bq/6dEQVaHaTLpjbHGjie+kzGZa+5MPcVF+76t4wNxRwHBeFXagw6dyDW9CFAZrLyNBePYn6HogXDNROU1/37s8TdmlOMDN3TlMeV+LGdYp0o2CtfJUAuEfXPVt7XyhJwnyGEBIdd4cHQSkQBlqJpX10r45Z3ZSz4QMaOw3SMOolzjvc3y7hhqYzrl8hY9oOMow2ATfmZ8poU5sbP9R4JncvHifeT19dxNFmj+3u6fj/HXf+WccUbMp77SkZ9C8eROvc168lrjvtWyPjlSHS/j1iidk0ypp/n6ytU9i8AuLyLa2xCiJs+0gUghJBEt3DhQjQ3tz+CKCMjA8uXL0dqaqpPy+nevTs+/PBDjB49Gna7HQBQXl6Oxx9/3Otpl4QQQgghhBBCYpMoGE3H/P+ZT62DP3V2FzOwJDBI4PB+HJhVELCgFsqgNbxDLVxBtN5gEgejeZfLn3AKQgghhBAt1tV/Jhx3VsasMJYkuFRDcFVCduOTqAGRcmMg0bWLejBa4KG/ANDgrFUcHvPBaGrXiiw2wwcJIYQQQkhiovZ5hBBCCIkasnfP+v8cuRIz+32KckNvj+H3Vf8Nc5pXdbnIZy9l+NOH3r+nttjdne7zc6iDJQA0C4LRUo3hLUeocM7BH7oCWP9fv5chCkazOYGKBqB3pt+Ljiv7KzmmLpJR0RDYckb1BbYfDk6ZQsGoB84bDvz3Z8Bx4tB17QSGGyerH1MKcxk2lXgfk0qqQlHK2HOohmPKIhllHkFXHC99w+FP/mCSHmCMwWQAPvyDhLmvymjocEvx9P7A8/Miex4Y3Y/hlasYbn+v/T0unMFw6enq8xXkAMebvIcnYsheieA9F2SHtxzRThTMWCIIliOEkK7YNQZy3f0fjivOEC2D4/v9wOc7OT7fybGnwnP8q2s5Pl8g4ZyhdN1223scr61rrxAt+xHon+1/kNNTF9M2JdpcNZ7huiXifW3qszK+XSgh3Rx9+9Q7G2Vct6S9nv2fIuDhj90vWj26HbmvOT69Q8KUwdH3PmKNqH55y1QGxjy37+QBwJMXMzy40nsfmzsauPtc+jwI0YKC0QghJIL27t2LVas8b1g+/fTT6NGjh1/LGzp0KBYuXIinnnqqbdhbb72FRx99FJmZ8XUnqri4GD///DPKy8vR1NQExhiSk5ORl5eHgoICDB8+HMnJgkeeBJHNZsO6detQUlKC2tpadO/eHX369MFZZ50VkvVXVFRg06ZNOH78OGpqapCamoru3btj3LhxKCwsDPr6CCGEEEIIIdFFFIymZwa/l2lk4s76Jo1hXYmIMQaTZEKr3OI1ThSwIAoukyBp/gxVwzJCHDjGORe+B1/CI8IR4EYIIYSQ+NXkbMDWpvWK405NHom8pN6K42KBWl3PmmDhssJYNCYKRhPXPTnnivPZBPVSpUAztZAzcTBa6O9ThZLae6YQbUIIIYQQEiuofZ7/qH0etc8jhBASArL3g9dOte/DgeJTUWQag3JDbyRxB8Zat6Cn8xjAmPD3zZPOGcIg+kX1oY853v4ddbB0OHlbsFNnKXESjIafN2gPRTtzJtic6zyH1RxFwYtPCGcpqaZgtJOe/5p3GYp2zhDg6z3q03x+p4SeC72PCSe98zuGKYMYPtnBcdt7yt/xu89l+Ptq/8MiOhvQHXjqYgndzMD4AiDdzNDQwlFU5h7XP7vr40m+IKippDp45YxlL6/lnULR3NRC0V65iuHWd5UnsHQIfZx2KsPRZyVsKgGqm4FBecCwXoAkRf488IcpEuaPc+9Lw3oDeeldl6kgRzlkrzTBgtHqLNwj7K6jAgo/9ZCfrVwnKqsBZJlHxXeBEBJbtAajHWsEWu0c5iT3ceZwrTsE7fNfONbsFYc0A4BLBu5dLmPrUF0QShy7Dhz3DEU7SanepNXsYXTcJ9roJIb3fs9w5T+V69zbDgPvb+a4eUp07VMumePBld4By63K3Y3QYgce+6+MbwYn9vEmUM1WjiqFAGcAmDvaex9hjOH+2Qy/m8SxqQQ43sSRZgJO78dQmCtuC0kI8UTBaIQQEkGLFy8G5+21zpycHFx//fUBLfOuu+7Cs88+C4fDXXu1WCx48803ce+993pNe91112HZsmVtr0tKSpCfn69pPWvXrsW0adPaXj/yyCN49NFHVZd/UllZmWpl7dprr8XSpUu9httsNrzwwgt48803sX//ftXy6XQ6jBo1ChdddBHuvvtuYSOoqVOnYt26dW2vO34eahoaGvDwww9j6dKlaGxs9BqflpaGefPm4bHHHkOvXr00LVPE4XDgrbfewiuvvIJffvlFON3AgQOxcOFC3HDDDdDr6RRPCCGEEEJIPHJy5bushkCC0VTCF6izuzqjZBYEoym3RhIFlxkls+abGjqmh54ZFEPyQh045uROyFBuLWxSCNgT7VuhDnAjhBBCSHz7oeFrYb14SsacMJcmuAwsCRIkyPDuCCOqY8Yr0f0aBkEwGlOue8pwwcmditdMwtBfhWWpXRs1OOsUh6sFi8UC9WtFCtEmhBBCCCGxgdrnKaP2eZ6ofR4hhJCw4cohSHq4cKb1J8D6U6fpOWBrBUziMNCCHPHq9lRQEBEAWOzicSlJ4StHKPFv/k/ztOz/3Qc2YqLn/K0WZC6+B+muBjTqunnNU1LNMXkgddYFgC93df29Gtab4es94ukyk93BUFkpQK3Fe/zLVzJcOV4CAMwaBojCD88fEdxgtJF9gEtP9/ycuyUzTB+ifRmFucrDDyZYmJXIGpX9QuRclQDMMztlN5uTGKYO9qNgYeDrvpQvOL+V1yfWua28XjyuvyCIMFGJghkdLqCigQI+CSG+swvClZU8+xVHs5Xji50cO4/6tp4dRwC7kyNJn7j17TV7g39+V7tWJqSz/BxxnRsAvt7DcfOU8JVHi/2VwGHlJmtC6/YDNgeH0ZC4x5tAlahc24quhwGgezrDBSMBCNo+EkLU0V1ZQgiJoC+++MLj9TXXXIOkpMDuruXm5uKCCy7AihUrPNaj1PAqlhw+fBgzZ87Enj1dPDrnBJfLhS1btmDLli244oorMGDAgKCVZceOHZgzZw6OHhX/StHU1IR//vOfWLFiBT755BO/17VlyxZcfvnlOHjwYJfT7t+/HzfffDNeffVVrFq1Cr179/Z7vYQQQkisaXDWYZdlC47ZjoR0PQYpCYXmU3Fq8kjoGD0lIRSanY3YZdmCRlcDhqaMQm9jfqSLROJEk7MeOy1bUGE7HOmieNAxPfJNAzE0ZTQMUtfXg0phWACgDygYTdxZP9Y78oeaaNuJgr/EwWi+hQoYJROcLu99YUPDV9jfshOA++kxvZL64bTU05GqSxcui3OOX1t+xoHWPV0Glon2v5Nl8h6mvH0OtO7GiuNL215302dhaMpo9DT2VV1/pb0cu5q3oN5ZqzjeJJkxIPk0DDSfptrhK1z1Bl8lSca2eobEJL+WccRaij0t29Dk7OLxxB3omQEF5sEYmjIKOhbcWwZ1jmrstmyDjVsxLGUsuif1DOryy22l2G3ZDodsw4DkoRhoHkZPTiIkiLo6R+iZAYXmwRicPBIGyf+6CCG+kLkL39d/oTguS5+L4aljw1yi4GKMwSiZFMN3v6//AnstO1Tnl5iE3sb+GJYyFmZdSqiKGZDj9grssmxBnUO9B0yF3bdrN7Vrl5VVSxWvmYpbdytOr1S3TWJG4fIdXLlXXawHTVMwGiGEEEIIiQfUPk87ap9H7fMIIYSEgcuHnvUntVpUg9HSzeL7o2U1vq8uHlls4nEp4p9+Y8uK17RN1y0HGOJ9L4WZU8Ayu6PAUYYduhFe40toXwIAyDJX7QR90tAumkZcMNL9vb30dIY3vvPsfC+x9vGAO+hnaE9gd4XnMgZ0B6YMAsb2B4rKxOsy6IBZpwH//bnrcp83IvD2FgWCQIHqZqDJypFmSuw2Hb4GxBl07oCwOcOAz3Z6jz93aPxuz9xU5eH13rdR45ra+81NC185YoFaAE5JNQWjEUJ8Z1N+ZqOiRz/xP9iLc8ClnKGdMEqDfL2RnaJ+rUxIZ6f3A7qnAceblMcXlQF/X+3+oibpgLH5DGfkA5IUuf3Mn+8N58ChWmBgXvDLkyhEvwnoJKAv1TcJCRkKRiMkyri4C/VOehRGqGXocyIeZHHkyBGUlpZ6DJsxY0ZQlj1jxgyPhlcbN26Ew+GAwRCbndPsdjtmzZrl1egqKysLw4cPR15eHgwGA5qamlBRUYHdu3fDYlF4dE4Q7N69G9OnT0dNjedVQ15eHkaPHo2MjAxUVlZi48aNaG1tRW1tLc4//3w8++yzPq9r1apVmDdvHlpaPH/J7tmzJ0aOHImsrCxYLBbs3r3b4wmd27dvx/jx47Fx40b06dPHvzdKCCGExJCjtjK8cPgRNLpUHosVZGPSJuL6nncHPTwk0R23H8ULhx9BrbMKAPBRFcNVPW7DxG7nRLhkJNYdsx3BC0ceQb0zelvpDU4ejj/0frDLzuWiYKpAjkdqAQLU2V2daPtY5VbF4TbBcF8DE0ySGRaX9523n5s3ew3L0ufizr5/Qa5CIJXMZbxX+Qp+aPjap/UrUQxGY8rvq8J+2CvoYmWVDjf0uhtj0iYpzrO9aSPeOroILnTR0qAGmJZ5Pi7N/Z1iQFYk6g2+Gpc2Bdf0XODzb1cb6lfjvcpXwFWeWKVmWMpY3NjrXk0hjVqUtu7Dy0ceh0V276sfsWW4qdd9GBakwJofG9bgnWMvg+NEi5AaYErGHFze/UYKRyMkCDjneL/yNaxv+LLLaYemjMHNve4L2vGDEDU7LVvarhk7m5wxE1IchJgbJbNiMNqO5k2al9EjqQ8W9HkMGYboekz4zuYivHn0b8IgMS2Y4KmJatcua+s/9WkdStdIEpNgZCbYuHqYcEfmGA9GU7tWpBBtQgghhMQyap8XPpFuo0ft87Sj9nnUPo8QQkiYcD96u1stAHJVJ3nyYoYHV3rfJ66xUBARADTHeTAa5xrbCOj0YHf9HcwguKfXMx/5jaXYYfIORiulSygAwFENz6hLMwG9MpTDwU56cI77O3nfLIY1ezgOdLj19cgFDH0y27+zjDE8P0/C3FfltpA/swH4xzwJjDEsukzChS/JaBTcvnjiIoZLxjD8Ui6rdpw/Zwhw+emBHysKuwgmGpHA1ec6C/c51Cs/G9BJDE9cLKGoTPYIahjVF7h9Wvwe3zMEmaB1iRaMptzcECYDYDLE7+fvj4xkIN0ExeNhaQ3H5IG0vQghvrH7EIwWKNn/XLW4cCjIXTwK1S+hCfFi0DMsvoJh/pvKX8ayGmDh/3UcxzH/DIYl1wFJ+sjUMd7e6N+BY9ZiGfuekKCLYKhbLCupUd7ufTMBvY62KSGhQj24CYky9c5qPHTw5kgXI+49Xvg6sg2RjbTdsGGD17CxY4PTMfT000/3eN3a2ort27dj3LhxQVm+VosWLcKjjz4KAJg8eTLKy8sBAL1798b69euF86Wmej7aY8mSJdi9e3fb6/z8fLz88suYNWsWJEnymp9zji1btmDVqlV46623gvBO3BwOB6666iqPRlc9e/bE4sWLcckll3iUpbm5Gc899xyefPJJ1NfX+/xE0N27d+OKK67waHQ1a9YsPPbYYzjjjDO8pt+2bRvuvPNOfP/99wCA8vJyzJ8/H2vXroVOF/sdwQghhBA1K6uWhT3cZGvTDxibdjZGpZ0Z1vXGu1XV73t0cOfg+KDyNYxJm0QdbklAPql+J6pD0QDg15ZfsKnhW5ydOVt1OidXvsuqZ/53tFELEPA1sCvRiI5NNlm5taFouK8BdEamffpaZxVWVX+A63v90WtccevuoISiAcr7ii/vS4YL7x97DSNTz/TqKCdzF96rfLXrULQTvq1bhTPTf4O+pkKvcSur/hXVoWgA8FPTOpzRbQpOSxmjeR6r3Ir/HH/T71A0ANhpKcKWpg04s9s0v5fR0fKqJW2haID7+PXusZfx1Cn/G3BwmV224YPK19tD0U5YV/8ZxqdPQ755YEDLJ4QAJdZfNYWiAcBuy1ZsblyHSRnnhrhUhADf1X2uOFzP9JjULT72wWCEEx+zH8HqupW4rPvvg1Ci4OCc473KVwMKRQPUgtGCd+0i+gyMkgk2l/ZgtFi/nlK/VqQQbUIIIYTELmqfFz6RbqNH7fOofZ4aap9HCCEkImR/gtG6ToC5YpxyMBrg7rw7rLfvq40nFrVgtHh47s+urh+swv7wJHDmLLBThokn6lWAguoSxVEHK50A4mFjBUZLQFxBjjuwSGTuaGBgnvteR34Ow8b7JXy8neNIPTD9VIZJA7zvg5w7lGHbQxL+u4ND5sD5IxgG93BPd/Yghm0Pu8d1DFjLTgVmDGU4s9A93eYHJHy8g+OXcqBjlp5RD5xRwHDhyOB06O+TCegkwKVwuEv0YLSDfgQMngz1GNWXYetD7n2luAo4vZ97P0g3x2/H+8xk5YBBX8PlYl2dRfn8nhHbtyFDgjGG/Bzg5yPe49SCIQkhRCScwWhas47jVZkgaMhfBTnxW0cioTNvnIQai4zb39O2P76/meO84cCV48O/v8kyx/ub/fvelFQDX+0CZg8PcqESRInguq5AJSScEBI4CkYjhJAIOXLE85e2vLw8ZGdnB2XZw4Z537A6cuRI2Bte5eTkICfHXZvT69tPOXq9Hvn5+ZqX8/HHH3vMu3r1agwYMEA4PWMMY8eOxdixY/HQQw9B9ucmsoIXX3wR27dvb3vds2dPrF+/HoWF3h2tU1NT8cgjj2DYsGG4/PLLUVdXp3k9sixj3rx5Hk/VfPTRR/HII48I5xk9ejS++eYbzJs3r+1ppOvXr8e7776La665RvO6CSGEkFgjcxd+bfk5IuveY9lOwWhBtqdlu9cwJ3fiYOteDE0ZHYESkXixx+K9b0Wj3S3bNASjORSHBxKMZpYEj1eEOPiLuIkCyqyy8qMabVx5uC9BZwBg1qX4NL3S8RUA9lp2+LQcNUr7itq+pcQiN+GQ9QAKzIM8hpfbytDs0vDY3Q52W7Z5BaO56w3Be8+htMeyzadgtAOtewIOGTm53mAEo1nlVhxo3eM1vMFVh0p7OXoYA2vpqvZ+97Rso2A0QoLgl+Yin6bf37qTgtFIyB23H8Xulm2K48akTUKavluYSxQavtahRHY2F0VVMNox+5GgBFbrmHITh2Beu5gEn4FJSvYpZDdYn2WkmCTl6w4DSxJ+DoQQQgghhEQTap+Xr3k51D6P2ucRQggJE9nl+zwtzV1O0icDkBggK/SJLaVgNFhUbqWnGMNXjlDh6z5SHc9ueQrsynu6XlDPfBRsKVMcVVJhBwWjASXVXXc8L+wiGK17umen+exUhhsmd92RfkB3hj+eqzxdQQ7Dgunqy8hJY/idhvUESq9j6J+lHAJ2sIoDggfgJIJDtb7P0zHUo1cGwy1TE2f7ZQhuszXbAIeTwxCEIL9YUK/c3BCZvjUfTBgF2crBaP58/wghxBbEYLR0EzCyL/D9fuXxStdyieRIkJ93TQFFxF8XjGCag9EAYOU2jivHh7BAAj+XBzb/im0cs4cnRn062EqqlPePfApkJCSkvB/jRQghJCxqaz1/VcvMzAzask0mE4xGz7t0ndcXS8rK2m+wjRw5UrXRVWc6nQ4Gg/8BASfJsowXX3zRY9gbb7yh2Oiqo0suuQS33nqrT+tasWIFdu7c2fb68ssvV210dZJer8eyZcvQvXv3tmGLFi3yad2EEEJIrLHJNjh5GB/F0oFFbozIeuOVzF2wuJoUx1nlBHvEGwkqh2yHjVsjXQxNRN+BjkTHPH0AndJ7GwsUQwT6GU+BUfItsCvRiAITbIJgtFaX8nCzzrfAhAHmoT5Nb3E1gSs8TszXsDGRfqYBSJK8WwsPSPatnABgcXmfX5sVhnW9HO/vk022Rqze4KtmDccDj+mdwfks/dnWSpQ+x5Nag3BeV9t3g/UeCEl0tY6qrifqwCbbQlQSQtp9V/+FcNyUjDlhLEloneJjXU+kzlkNmQenY3owqNUPfFFoPlVxeJJkRH9TcMJRBySfJhiu/bORoBOWNVYUmgdDUmhSMjDZOwCCEEIIIYSQaETt87Sj9nnUPo8QQkiYiH6zZQyQBN27LOJ7o7ylGfzXrdBbG9FHUNUpq0nMHvaccxys4mhs5bAIbmMZdEBSPATrHClWHz/5Ak2LYSMnId9RqjjucIsZdmdi7ksdlWp4/sukAQymBHy2CLdZwX/dBr77JxRkKKcRlgT+/JyYVmfx/TuUyKEemSrN6RoEYWHxqF7QzCojwZ53yzkHP7QPfE8ReMlu8NpK8OKfwXf/5PEvT1Zu6+LP948Qktg4F19HaDWiD3DvLIa1CyVU/V3CP+aJIz0UmnjHrcZWjtJq7tGuvc6iMoMfJg+Ig+s8EhG9M4B8H56vc8C3ZraaNLRwbCnjsNjEB4aDAa73oCDci3StRCEEHHCHpBNCQicBf2ojhJDo0LkhVEZGRlCXn5GRgcrKyrbXNTXxcRfh+PHjEVnvd999h9LS0rbX48aNw/nnn69p3ocffhivv/46HA6HpulfeOGFtr8ZY3j66ac1lzM1NRU333wzHn/8cQDAL7/8gtLSUp+eAEoIIYTEElHwTDi0uiisK5hssji4yiFrq0cRoiSWgvW0HFecXPn7oGf+dzgxSAacl30Fllct6bA8Pc7Lme/3MhOFSafcwki031ll5Tu3ooA1kckZM/FT43eodWq7q8Uhw85tMDLPoDtrEM6jeqbHedlXKI7LNw3CiNQz8HPzZs3LUwrN8idIq1VhWwcjkCtcfD12BeOzBIK3jVpc4lYKouOYL1pV3q9VEEBICPFNncZzTDtqKEFCyy7b8GPDGsVxfY2FyDcNCnOJQmdKxmxsaVqPemdg93Wc3AmLqxFp+uDef/KX2vlbqxRdGs7J+q1w/HnZ8/DG0acDCsM9xTwEw1JOVxz3m8wL8XPzZk1BrDOy5iJZl+p3OaJBii4N52ZdjC9rl7cNMzITZmVdEsFSEUIIIYQQoh21z/MPtc9TR+3zCCGEBEQWBKPp9IA5FWiq8x7X7B2MxqzU87MAACAASURBVF0u8BcXAh+9DrhcgCSh/4itOATv38q1BDnFm1+OcFzymozi44DEAFlwGyvF+/lvMYdXHgLW/1c8wfTLwfppvIcy9hwU2O8Vjn5ljQN3zUzysYTxRdQB+qR+WcA1ExiqVJ6FF293VTnnwDvPgi99ArC700MKerwEZN7gNW1Jgne+r/fjVllhTuKGemSoNKerawFy0sJXlkiqEwWj+dbcMKbxXZvBH54PHD/S5bQZuY8BOX/yGi7ajoSQ+MM5R6sdaLS6/zW0Ao2tJ1638hP/t49rEgxvbAWcPj6LMM0EnDMEmD2MYdYwhj6ZnudxxsR1IdE1SzxxujhufY9j6QYOp+wOEVp+i4SB3d3b3Ren5IoDqc7IB2bR8/6InySJ4cHzGG78l7Yv5fbD7uMOY4HX2+1Ojpvf5nh7I4fMAb0E3DqN4bnLGHSS5/JLAwzB7+ralijjnAsDvxM51JqQcKBgNEIIiVPBqEhHi1NPPRW7d+8GABw+fBiLFi3CwoULw1qG9evXe7yeP197QEBubi5mzJiBTz/9tMtpLRYLNm7c2PZ63LhxKCgo0F5QANOmTWtreAUA33//PTW8IoQQErdsXPwL+ODk4QEFBZ1UZT+G446jXsODFUBC3NSCWIIRoEISl9q+NSh5OAxBOE74qt5Zi3JbqddwLUFILkEHfz0L7Ge+6Vm/Rfek3vi5eROMkgmnp52FAnP8hEuEilkQaCba70RhEKLliGQbuuOefn/Fjw1rUGYthgwXAMAm21DcuktxHqvcAqPkGYymFB7mXn4eeiT17rIceUm9VfcViUm4sde9+KFhDfa1/OKxjxe37FY8jyudX0XfDQNLQqouHXVO77tzviwHAAYnjwj4e+SP4/YKVDkqvIb7GsAq+izNUgoKzYO9htc6qlFhP6R5Ob5SO/Y6uPKTgX1hVdk+wXoPhCQ6pWMrADBI4PBu9cXjrgk/iTY/NX4nPMZPyZwTV/dEcpJ6YGG/p7Gx4RuUWvcrfuc6cnEX9rbsUBxX56yJmmA0UV1Mz/QYnDxCdV4GCX1NhTgjfQryVOqpw1LH4u6+T6GoaT0q7V03jO8oiRlxinkIJmXMQJKk3Buul7EfFvZ7Bpsav8Vh60HFzyZNl4FhqWMxOnWCT+uPVhfmXI2+pkLsat6KFF0azkifij6m/EgXixBCCCGEkKgQT9ei1D6P2ucRQggJE9mlPFySgLQM5WA0pWEfvgQsf6XDcmXkV27G9xne9+7LqhPrHo7TxTH7BRlH692v1QIGUuIg44s/cLl45KDRYA8t1bwsptMhf9okwLspAQDg7uU6XDyWo392/NSDfVWq8n1aMJ3hzzMZctMYmqwJ9L377mPwNx7yGJTvKFWc9GCCd76v9yOYqTA3+OWIFZkqzen8CZmLVaL9JjM5MY7FvNUCfs95gKXrB1cBQKarXnF4HTUnIyTqcc7RYncHkjW0BZmdDDjjXsM9As06BaC5fAw088f9sxnWF3MwAOMKGOYMY5g0AEjSi4/PksqhOxFqj09/wfHP79vf6cFqYPZiGbOH+3ZO654GFD0o4bFVHP/42r28dBMwqi9w1iCG+2Z5h0gR4ovfTZaQm8rx7iaO4uPufazFDvxaqTz9q+s4bp0a+D73l1Ucy35s/444ZeCFNRwFOcCd0zsHowW2rsN17t9P9Dr6rviiuhmw2JTHFSRwqDUh4UDBaIQQEiFZWVkerxsavJ/mFIj6es8f8zqvL5ZceeWVWLFiRdvrP/3pT/joo49w/fXXY86cOejZs2fIy1BUVOTxevz48T7NP378eE0NrzZu3Ojx5MrCwkKPJ2FqIXd6mtiBAwd8mp8QQgiJJTaVcLKbet0Hsy4l4HV8W7cK/3f8n17DtQQYEe3UQkwoGI0EQu27elOvPyNZlxrG0rhtbfoB/zz6N6/hakFCJ4m+D8EIghyeOhbDU8cGvJxEYpTMisNF4ZlWwbHO5GMwGgBkGnIwJ2eex7B6Zy0eOOD9xNWTZeqmsZwT0n/jtWx/6ZgeZ2XMxFkZMz2G/7X0bhy2HfSavtXlvY1EIVi5hp4YkjISa+o+8Z5H4fskCqYDgFt6PygMwAil1bUrsbJqmddwX+sZos+yv2kAbuvzsNfwTQ3fYtmxxV7DfQ1kE1H6HE8Kxnld7XhJ4bWEBE7mLtQ7ahXHZeqzUesUPG6RkBDhnGNd/WeK45KlVIxNOyvMJQq9LEOu5vqYzGXcue9yuOAdolznqEY/0ynBLp5fROfvTH2uYn3FX/nmQcgPYchz96SeuCDnypAtP9owxjAmbRLGpE2KdFEIIYQQQgjxGbXP047a55X6tC5qn0cIIcRvsqCHvqQDUgUPuWj2rsPwL9/zGtbfoZxmFWhn2Vjz7a9oC0XrSkr4mwgEFS/dA+zbJhzP7n4BTKfzaZkpAwYi72AlKvV5iuPf38xx3+zE7ewr+j79Yx7DgulS22tT+J/TGTH8K+/jUaG9RHHa0mr3fb94Cpn2RYMfzVkKc4JfjliRanQHyCgFXNYmUMhVnUU5Kqeb780NY9OGTzWHogFApiwIRqMm/4SEDOcczbYOIWYe/3OvkLOmjsPbgs/cf6uFGkeT5CTgyYulrifsRK0GJLpUjCcfbvH+gI81Aks2+PbBF+QA3ZIZ/n45w99VcqIJCcSFoxguHNX+rT1Sx9Hvz8pf1Hc2ctw6NfB1vrtJ+bvwzkaOO6d7Dgs0BN8lA0fqgPwEvt7wR4lK2HcBbUtCQoqC0QiJMhn6HDxe+HqkixH3MvSRr2F0bghVV6fwNCc/Wa1WWK1Wj2HZ2dlBW364zZ07F3PnzvVofLVhwwZs2LABADBgwABMnDgRkyZNwllnnYUhQ4YEvQyVlZ5xzgMHDvRp/kGDtHXCOXz4sMfrDz74AB988IFP6+qstla5EyMhhBASD9SCL4ySKSjrMAvCarQEGBHtrC7xZ0nBaCQQaiE/JkGoVaiJ1muVW7ts/OUIYTAa8Z34s1Te70TBXKJzTbDKAygHVYnCq8Lx3RCFwSmd20Xne7Mu2cflKH8uEnQwsMg8Clpcft/qGaJjneizFIXHqgWV+lQelfI7uXdoi6/Utg/V0QgJXKOrQTFgCXCHNVEwGgm3g9ZfccSm3IFiQrffRCTcNJpITEKGIQs1juNe4+qd0dPjTRR2G6nrMkIIIYQQkriofV74RLqNHrXP047a51H7PEIIIWHCBb3dmQSkKQej8aY6jw70XJaB/du9pqNgNLcPftLeOXho6LNfQ+vQPvE4QxLQf7Dvyxw4CgWrSoTBaH/9nOO+2b4vNh44XRyHBZcU+dmebb3UgtHiLhKsbK/XoD7OI4qTtjqAVjuQnKC39nwNRsvPdod+JCpJYshNAyoVMrEO1XLE4bdJ0RFB2GdO+J8HHBG8dI9P02e6lA/UFIxGiDdZPhFo1inMrOFEcFnn0LImK9DQyr0D0KwAj5FAs2A5tYd/86llwybCJvxZuYrosz6ZwVkOIb7o1fnp9B1UBOGZPBYbR5ng95stZd4B01p/6zklFzggaO5bUk3BaL4qEQTSmQ1AXnqYC0NIgqFgNEKijI7pkG1QvolA4kvv3r09Xh87dgw1NTVBaSC1a9euLtcXSxhj+Pe//41HHnkEf//7370alRUXF6O4uBj/+te/ALgbYl199dW44447gvYkzs4N49LTfaulduumcuXTQU1N8O8+NzU1BX2ZhBBCSLSwyVbF4QaWBIn59rRDkWAFlhB1akEsDm4PY0lIvBEF5BiZKWjHCV+ZJeVAIg4ZNm6FiYmDAURBgXpGP/NFgijQTBTkFeowCCMzgYGBK9wi9y1wTHkfDSazTvv5VXSOMElm4XlaOQhOefubpeSIPY1W9NmLQvRERPUS0fFGNNzB7XByR8Bhi2rn9WAEnqqFn1EdjZDA1TnEjzXLMuQCCoconmgt3EhYfVf3mXDcWRkJ2vumkwx9tmIwWl00BaMJ6jei+hwhhBBCCCGhQu3zEge1z9OO2ucFhtrnEUII0czlUh4uSUCqcjAamuvBW5rAX/gTsOlLoPqo4mT9HWWKw6ubgWYrR6op/gNkOOdYskH7PatrJ0ohLE0YlOwWjzvnCrBUbfUjD6OnoMDxPjbiTMXRTcrNNeNeUSnHFW/IcAmyDQs6dSY3xXAzLr51Lfj/Pg78uhVwnXiYVlYPYNJ5YLc+DWZsf2AxX7UEKPvVaxmZLkGSE4DalsQNRqtv8e2e+k1nx99xm3/+NviSJ4CKUuUJxkwFu+kvYKeNB+AOh1MKRisRN2mIO1uUT+/oH5xL8ei3Y71Pk2cIjj8NrYBL5tBJ8fe9IonHJXM0Wz1Dy7zCzE7839AKNLVyj2nbgs5siRdoFizXTvTvWKJ2CJLj/LNwuoL3BjMSODiWRI4kMYzooxzwV1YDHKrh6HciMHt/Jccf/y3jh4NAix3ITgFmDGV4fh5T3H+LSjkueElwsXnC1kPA6f3df3PO8Ut512VO0nO8da0Ol7wqo0ahWX9JNce0BAkbDpaDguuQghxErC8KIYkihn9qI4SQ2DZx4kSvYUVFRZg5c2bAyy4qKvJ4bTabMWrUqICXG0l6vR5PPvkkFixYgHfeeQcff/wxNm/eDJvN5jVtcXExHn30UTz//PN4/fXXMW/evAiU2D92e/BDP6hTIiGEkHgm7lQbnIAZQD30RuauiAUrxRu1gBMnd4axJCTeiAJyTIJQpnBQO0ZZXS2q412C70OgIUbEP+LwTEEwmii8Kkj7I2MMJsmseExVHiYOHAs1YaCZQjmFAW5SikrAmlIQXPQdD8T1DHGwmBLRedSkU/4sRcFogDtALk3vRwNpj2WENhhNLfxMFIBHCNGuzqn8iDgDS0Kqjh5rRsKr0VmPrU0/KI4bmjIG3ZN6hrlE0SlTr/z4RrWgw3ALdV2YEEIIIYQQQjqj9nm+ofZ5/qP2eYQQQjQTnTMknTgYrakB/N6LugwGyRcEowHA019wPHFR/HfQfPlb7efksf2B346K7W3C//mocBxb+JJfy2SShJTxZwN7/SxUHCo+zjF1kYwWlWpkfqfbNMYYbcbF924Bv+d8wNmpXUflIWDFq+BV5WBP/Z972s/fBn/mD4rLUQtGq7MAfTKDVuSY0iB4RuJ1ExmONXCs2we0OoC+mcCNZzP8eVZsH6M641++B/7U79Un2roWfMEM4K2NYPlDUJDDsKnE+9heGj23QEPq+/3i81r/7PjaP5Rwawuw/Tuf5smUxcefhlYgK/TPiyVEyCVzNHkEmZ0MKuNeYWaNrUBTx6CzDsObvX+qI2HSPxu4dSrD7dP8OwarZebE+8+LonqQPzKpmRGJkGXXSxj9uHKA2ci/yKh8TkKzDZj0jIzq5vZxFQ3Ash85dldwbLxf8gjQKj7OMe05GZYuju3jnpTR/KKEZCPDqp/Vp2VcxljrFjzQaz3OHrQQBTlQDkaLnuetxgxRQHPnsHRCSPBRMBohhERIv3790K9fPxw6dKht2FdffRWUhlerV6/2eD1+/HgkJSUFvNyOXKInVoVYXl4e7rnnHtxzzz2w2WzYunUrfvjhB3z//fdYs2YNmpvbrxgaGhowf/58GI1GXHTRRQGtNzPT8+5LY2MjcnNzNc/f0NCgabqcHM8a8FNPPYX7779f83oIIYSQRGOTlR9BaJRMisP9oRaWYpOtMOvoLmkwiEJvgOAEqJDEJQoLEoURhYPacaNVbkEGshXHydwFGco3UygYLTJEAWI2uRUylyExz6cLC8Orgrg/mqRkQbiY5zDOuWrgWKiJA8G0hboB7u3vW8Ca6HgQ+iA4EVH5ndwJh+yAQdL23Ra/N+XPUi2ApFW2IA0BBqOpBLs55MA7namFn6mFphFCtBEFKbmDl5RbaHHEeessEjE/NKyGC8rhwFMyZoe5NNErQ698DVHvjJ5eAcKQ2gjWxQghhBBCCCHxjdrn+Yfa5xFCCCEhJCu3+YAkAWmCYLRt64C6410uuo+jHBJ3QVZ4yOfL33I8EdipOia85EMw2sMXSF1PFMV4yR7xyPOuA0sy+r3szOxU1fE2B4fREP+BPCct2cBVQ9GyU4A0k+f20EmxuX34x//0DkXr6PtPwCsPgeX1A1/+inCyTLlOOK4ugZt01Ave+2m9gP+9Tgeni8PuBJKNsbn/dEVtn/Fgt4J/uhTstme8QgdPOliVGO0TFn0pqDfAHc4T99Z9pHlS9vI3QH01Mh/9k3CaOgsFoxH/OF3uQDPPMDOgsZW3B5ZZT4aZtQ/3CECzosvQGxJaaSYg3QSkm93/dzvxf5qZeQ83A+km1jbs5PhuZngEGvlKrYoY78FowawDZtKxnETIKSq3KxpagX/9yGFzwiMUraOfSoHNJcD4wvZhS3/gms8PK7dzXDWe4bmvxHXE0v0DkeuqgpHbgUMSePVVKMjJQ1FZ4oYNB1NptfLBOj8nPq/hCIkmFIxGCCERNGvWLLzxxhttr99++208/fTTMBj879BeVVWFTz75xGs9SvR6z9OA06ncuUhJXZ34ZkW4GI1GTJgwARMmTMA999wDu92OlStX4uGHH8a+ffsAuDt6L1iwABdeeCEkyf8bmHl5eR6v9+/f71PDq5Pl8XU9WucjhBBCEpVNEOhiDGKnWrXwpFa5hYLRgqTVpRKgwoP/1G6SOMSd7yMYjKaybrVAHycXX7PpGf3MFwmi/YiDw85tMLH285GTO4THs2AG9YnK1DkEzcatwhCbcIRT+BRoJgjBMknJwrJa5VZwzj0aIYQjmM5XagFlVtkCgyRo/N6JWnickmSV8DvRsnzRohKMpnYs00rtWGnnNri4Ezo6LhLitzpBkFKmIVsQi0ZIaLi4C9/Xf6k4LtvQHaeljAlziaJXpkG5V4Do+xwJrYLfcCJZFyOEEEIIIYTEP2qfFxhqn0cIIYQEmSwIPmUSWHqW8h18DaFoAJAEB1LlZjTqvB+CZXMCsswhxWhYkxZNVo59ldqnz4/1QJlftwhHsb4DA1r0BSOAZzeIx1c3A70zxePjzRaFDuQdDe6hPDwrBahVaDpx9ZlR/D1U2a/a7NsOnt0LKN4hnMTI7TDLLWhVuAeUyMFoRwVZzpknNpNex6D3zraMC9zlAvb8pH2GD/4B3PYMCgTBaCU1wSlXtDui8rNA3wQ4DvNft2qbUJKA/CFAYy2yXbXCyY42AKd0D1LhSExwOHlbKFmjR6gZdweWdQgta1IafuJ/tYBUElqMAWnG9lCy9gAzhrROAWdeYWYd5kk1IiquhdQy1eQ4D0YTBcT6I4Oev0giJNXE0DsDKK9XHv/05xyTBqgfa4rKOMYXtk/T1fWmx7ylwFXjgeNN4ml6OY9COvnrkiwDB35G/+xzFacVhXwRsRJBU8xCwXULISR4qGcQIYRE0J133ok333wT/ESkd1VVFZYsWYKbbrrJ72UuXrwYDkf7U1pSUlJw4403Kk6bnp7u8bq+XlAjV7Br1y6fyhVIGrpWSUlJmDdvHmbOnIlhw4ahvLwcAHD48GFs2bIF48aN83vZY8eOxccff9z2euPGjZg4caLm+Tdt2qRpugkTJoAx1rZPrF692qsjNyGEEELa2WSr4vBgBrqoddBVC+Ugvukc2NORUw48QIUkrlZBoFIwg6h8lcSMkCBBhvfTWtQCiZxc/EROPfO/Aw/xn9r5xupq8RhvdYmPc8EMgxAHhXnuW6KwMQBhCf0UfQeVyqUW+mUWBHxxyLBxq0c4XTQGJaqtu1VuRRo0BqMJAkZF28comcHAFMPx1MJKtVLbv9SOZZqX30UdzCq3IkWXFvB6CElUtY4qxeGZ+lyAotFIGP3S/JMw2OusbrMgsTjtGeCHTL1y65p6Z03U3GOwCoJTI3ltRgghhBBCCIl/1D4vuKh9HiGEEBIg7t1OBAAg6YC8vgEvfkLrJnyZOsNruNUBHGsEemm7/RyTynwMyYn2YDT+4+fg364AjpYAO753D5xyMdhNj4H1Gwx+5IB45mmXBLTuCYONMMmtsAranzS0JlYwmqjj80lXjFOuR141nuHFbzzbZPTsBowvCFbJQqCirMtJ+D8fBTtlGOASBD2ekOmqFwSjcSTiPecWG0eVIMCgX1YCbI/qcp9nkf/xR+SfdiuAQq9xVU1As5Uj1RRf287h5Hh5Lcemg+6AnG2HxdMaDfH13hVVlGqb7oxzwdKzgPQspMtNyHDVoV7nfaIqreY4a2ACbLc4trWMY8kPHNVNwKxhwDUTGL7ZC7z/E8eB4xzNNs9Qs9bAmykSP0kM7QFlpg5/mxnSToaZeYxn7SFnHYLNUpKiI9AsWNTeSrzHEwUzGC2TmhmRCLriDIbnvlL+xh6sBg52ETb23FccK7e5oGPAqH4MX/pwG+iDnzh+KXehWJCfn+5qaA9FO6miLOHDhoNlSxnHAeWm1SjIiZ9zFSHRioLRCCEkgoYOHYrZs2fjs88+axv25z//Gb/97W+9nkyoxe7du/Hss896DLv++uuRlZWlOH337p6POti9ezfGjh2raV0dy6yF0Whs+9tms/k0r68yMjIwd+5cvPjii23DSkpKAmp4NXnyZI/X77//Pu6++25N81ZVVeGrr77SNG1ubi5Gjx6NrVvdT7YoLy/H559/jjlz5vhWYEIIISRBiMK0jMwUtHWohd60qoR5Ed+0CjpIA8EJUCGJKxqDkBhjMEnJaJGbvcaphf04uTgkkILRIsOkUwu1akEG2lvSqn22ZpXl+EoUatY5JFAthC+YAaPCdQjes1K5ROd7sy5FPZxObtUUThfJMA61dfsSwCraRqLtIzEJRsmsuA61c7JWLSE+r3dVB2t1tVAwGiEBqHMqt3jINOTALiv/tqsUtEhIoL6r/1xxuJ4ZMLHbOWEuTXTL0Cv34HJyJ5pdjUjTdwtzibyJzt+RvDYjhBBCCCGExD9qnxca1D6PEEII8ZMsCkaTgB79A178GxW3ov/AYsVxB6soGO2knFREdZgOX/Eq+PN3eY9YtxJ83UpgSRGw7Cnh/KxXYMlbktmMNYfOwqT8dYrj6xOoyaRL5ihV2bduOpvhD1OU96Wn5zKUVHOs+tn9uncG8OkCCbooDffgTfVAs4Yg54O7wOcN6XKyTFcdjhp6eQ2vDbxJSkw6VCse1z/KgxqDgX/4iu8zLX8F+V98C/Tbojj67Y0ct0yNzu+TP2SZ48KXZU3hGMtvkUJfoGhQUdr1NIPHgP359fbX1/8P8tcewnalYDQK/4hp3+7lOO9FGdYTTQ//XQTc9W+ORivAqclS0EhMKbQM6HYi0KxjaJl7PPMKM0s3ASnG8DzEIdaobRHRpWK8qAtiMFpGMu1bJHIevYBhzR6O7SoBtmpKa9rrJKv3+HYCq2x0/xP5z5ErvYbxY2UoGMCgFL94tB6wOjhMiRC4G6Dv93NMeVZ8oBaFzxFCgoeC0QghJMKee+45rF27Fi0t7qu7+vp6zJ07F19++SVSU1M1L6eqqgqXXnop7HZ727CePXvi4YcfFs4zZswYj9f//e9/cc0113S5ri+//BKbN2/WXDbA3RjqpOrqajgcDhgMoeu4r9d7nuI6Nvzyx9lnn438/HyUlpYCAIqKirBq1Sqcf/75Xc77l7/8xeMpoV25/fbbccMNN7S9XrhwIc4++2yf9gdCCCEkUdhkq+JwYxADXQwsCTro4YJ3IJEvgSVEnVpAj4PbheMI6Yo4UCmyne9NklkxGK1zeFVHamFCekY/80VCV6FcHakHkQVvfxSVqfM5SzWoTVIOVwsms8ZyioYB7veqGizmagH07Z3RRIFfkQzjUA9g1VbP4JwLt5EoKA8AkqUUxflaXIG3QlUrezCC0axdhLdRHY2QwNQ5lB97nqXPRaVd9CRnamVIgqvSXo69LTsUx41Nm4xUfXqYSxTdMg3i1jV1zuqoCEazCq51whHKSwghhBBCCEls1D4vNKh9HiGEEOIH2aU8XJKAHv18W9bAkWC3/hVoagB/eD4AoJfzKLq56tGg805AK6nmmDwwfju7ltVqv1cVzQFx3OkAX/Kk+jRP/U48ct6dAZeBMYYzsAc67oRLoT1SfQLdjj9aDzgEX9sv7pQw4zTxd8qcxPDJ7TqU13FUNwPDewNSlIaiAdAWQOSDTLlOcXgwQzFiSZlKMFpf5Zzt+PLB837N1s+yHxJckKHzGnfvco5bpgZYrijywwFoCkUDgNN9rDLEIs65+Lg062qw6ZcBvQqBvgM9wpfYmKnov7oM200jvWYrPWYDQPemY9VTn7WHop3UkEBhrV3RSScCzdqCzE7+fSLQrENomTvIjHUKOHP/nZxEgWahJKnkWsZ7y7v6Vt/e4Wm9gF1HlcdFc5WaxL8UI8PWh3SY8bwLX++JdGk8jbL97D2wokw1tKusBhjcI3Rlihd//Uw9vZKC0QgJvQSJByeEkOh16qmnejw5EQB++OEHzJ49G0eOHNG0jP3792P69OnYs6e9Ji1JEt5++23k5uYK55swYQKSk9s7/65cuRJFRUVdruvaa6/VVK6OhgxpfyqM0+nEt99+q2m+lpYWvPjii2hqatK8rubmZqxYsUK4fn9IkoTbb7/dY9jNN9+MkpIS1flWrFiBV17x7ekm11xzDU499dS213v27MHFF1+Mujrlm0MiVVVVXtuBEEIIiTeiwCOjZAraOhhjMOmUb4SqBRgR36gFmAQjQIUkrmgMQgLEYUWi8gLq3wUdBaNFhJGZwATP8LJFKBhNFBSmNahNggQDSwpaeURE71np3C4qq0lKhkkl5LDzfKJ6g+g8Hw4S08HIlOstovCQzuzcBhnKN9zUguPEn0Hg9ZtWlXA1R4DndYfsgJN7B9Z6rJ+C0Qjxm0N2oNGl/DukWvASIcG2ru5z4bizM2aHsSSxIU3XDTrBM9HqndHx6GthXSzC12aEEEIIIYSQ+Eft89RR+zxqn0cIISSMZEFHSkkHZPcEkrQHjbKL/IdRgwAAIABJREFUbgIbOx0YP6N9GIACR5ni9AeVn4sTNyoatE87OC+Ke9KX7gHqq9Sn2a/8YBkAYL0KglIMZk5Blks5yam+Jd5jG9qp7Vfj8rUto3cmw8i+LLpD0YCgB6NlCe45H/PhuxpPKhqUvzd56YDJEOX7RoB4i/Zrzc4McCLPeVxxnNMFyHL8HI/W7tP2XpL0QO/MEBcmGjTWAoJ9h13wO7AzZ4H1G+Qd4NQzH/3tynWhsmP0kO5Y5ZI51uyNdClCQy8B2SnuQJVRfYGzBwLnjwCuPIPh5ikMf5rJ8MRFDC9cwbD0eoYVt0j4+m4Jmx+QsPdxCRWLJFhekmB/VUL18zoc/KsO2x/WYd2fdPjvHTq8+3sJr10t4W+XSvif8yQsmC7h2okSLh7NMH0Iw7h8hsE9GHpmMKQYGYWihZja1o2jU5qiykbfpp94inhrDcwLsDCEBMFLV0qIpkNmmqsR2S6FNoKVh9A/WzxfSZz/VhQMnHOs3Scen53iDhwlhIQW9ZgkhJAocMMNN2Dr1q14+eWX24atX78eQ4cOxQMPPICrrroKffv29ZqvuLgYS5cuxaJFi2Cz2TzGPfPMM5g+fbrqetPS0jBv3jwsWbIEAOByuXDeeefh7bffxowZMzymtdvtWLZsGe677z7U1tYiMzPTp4ZA06ZNw9KlS9teX3/99XjwwQcxfvx4ZGZmQuoQeZ6amoqcnJy29S5YsAD/8z//g0suuQQXX3wxpk6dirS0NMX1bN68GXfccQfKytp/yDzzzDMxaNAgzWUVWbBgAd5++23s2OG+mXj06FFMmjQJL7zwAubOnevxHiwWC5577jk88cQTkGXZp+2l0+nw4YcfYuLEiWhsdF/1f/311xgxYgQeeOABXH311cL3X1tbi9WrV+Ojjz7CypUrceaZZ2Lu3LkBvnNCCCEketm4qFNtcANOTFIyLC7vG6zBCA4hbmohc12FnxCiRtT5Xi0sKBxExylReQH1YDQDMwRcJuI7xhhMklkxhMk7lEv5OJfEjNAx76da+ku0b2ktj1lKCUvjBtF30MHtcHIH9Cf2aZnLXiFzHZdxMpyOKzwvrfN7FL/nCB8PdMmwOa1ew7XWM/wN3UsWBjQGIRgthIGnWrYL1dEI8V+DSoBSpl4cjBbnbbNImFnlVmxs/EZxXD/TAOSbA/+9P95ITEKGIQs1Du+OAXWO6GjBJKyLqQTdEkIIIYQQQkiwUPs8ap/XEbXPI4QQEjGyS3k4Y2A6HfiEOcC6lV0vR5KAiXPcsyanetynKbCXYLtppNcs8d7Ztd6HW8QXjgpdOfzBZRnYuhbYUwT+7YeBLWzSeUEpE0zJyHA1oErf3WtUg7hpU9yx2MTjukXuGXyhUaEeSOyrvg7lEOrS6sS8syw6RuWmhrccEVF+MKDZ8+2lqND39BpucwLHm4Ae3QJafNTQGhhzwQhAF+1Bi8FwTDncDADQK188LqcX+svliqO+OZQKm4PDGOdhhPGo3Lcc+7BI0rvrAummE/9O/m1mSDOdGOcxniG983AzYNSDwshiDK+uADavBlc7TonY0gHcrjiKx3kVydfr0ZF9gbH9gaJOm3lgdyBfJeSJkHAZlMcwZSBUA7P8NbovcLTBt0DBC5tXKYcvlu2FycDQs5ty8HdpDYd6bCOpbASsKl0fLhxF24+QcKBgNEIIiRIvvfQSMjMz8eSTT4KfuJJtamrC/fffjwceeABDhw5F3759kZmZiZqaGpSVleHXX3/1Wo7BYMDixYtxyy23aFrv448/jpUrV6K+vh4AcPz4ccycORMDBgzAiBEjYDQaUVlZiU2bNsFisQAAevTogWeeecanJ1NedtllePDBB9uesnn06FHcdtttitNee+21Ho20AKCxsRFLlizBkiVLwBjDgAEDUFhYiIyMDOj1etTU1GDnzp1eT/FMTk7GG2+8obmcagwGA959911MmTIFNTXuDoIVFRW47LLLkJeXh9NPPx3dunVDZWUlfvzxR7S2uu/6devWDc888wxuuukmzes67bTTsHz5clx66aVoaHBfcRw5cgS33nor7rjjDgwfPhz9+vVDeno6WlpaUF9fj3379ml+iikhhBASL0RBKUbJFNT1iAJTghEcQtzUAkwCDVAhia3VZVEcrhYWFA5mSRRIpFxeQD0kUE/BaBFjkpIVzwedj2uiAMhgh3KJ9m2t5THpwtNyU+07aHW1IlXv3qft3KYYenZyGWrhdF7vWXCuifTxwCQlowHeT1rWWs+wqoSLqu1fovfdIjhuasU5Fx57gcDP61q2C9XRCPFfrVP8BPpMQ464MV68t84iYfVT43fCa8QpGbPDXJrYkaHPVg5GUwk8DBcXd8LOlXsPRbouRgghhBBCCEkc1D6vHbXPo/Z5hBBCIkSWlYefCP9kNz0GvqcIOH5YdTHslqfAcnq1v779b+Av3QsAKHAohxsdrIrvezkNGm8R/3YkcOmY6Om0yp0O8L9cC3y7PCjLY3n9grIcmFLQTVboNQ2gPpGC0ezKw80GQIqzYCL+8n2BL6R3YVsIluhYFO8hjSKiYLSMOL9Nxh128BsnBrSMl47dhdMLNymOK6uJn2A0tSDGjp64SOp6ojjAv3xPeUSSEcjqIZyP6XTITxFXCn7znIxPF0jISI6vY3i8+2RH8OqxJkOnILMTf3c7EWjWMbTMPZ55hZmlm0ABewmK79wI/ueLgUbv9r5aMH1vYKByMJocx5drnHMs2eDbG8xMBp6fJ+HCl2TUnTispxiB1/+fRGGCJGoUdmdYuy/4X96XrpRwrBG48k0ZNnH3ofZy2A/i4aonlUc2N4BXlaMgp4diMFqiXp/54tNfxJ9xRjLw0Hl0TCIkHCgYjRBCosjjjz+OKVOm4NZbb8X+/fvbhnPOsWvXLuzatUt1/jFjxuD111/H2LFjNa+zd+/eWL58OS666CI0NTW1DS8uLkZxcbHX9AUFBfj0009RWVmpeR0AYDabsXLlSlx00UUoL1d++oJWnHPs37/fYxsp6d27N1asWIHhw4cHtL6OTjvtNHz99deYM2cOKioq2oZXVlbis88+85o+IyMDn3zyCVwuwRO+VJxzzjkoKirC/PnzUVRU1Dbc5XJh+/bt2L59e5fLyMzM9Hm9hBBCSCyxyVbF4UYpuKEuWkNmiP/UtqWDC1oYEaKBVRCgaNZFOhhNcFxxiVsPqoUJUTBa5JgE55zO+57oOGcK8r4oPmdpK0+wg9pE1L6DrbIFqUh3/60SsHVyGaJwus7DRAFikQ7jMAv3IW31DLUQMLX9SxTQaFUJaNTCwe1wQXwnNtBgNC3bRS0sjhCirs6h3NLBLKXA9P/Zu+8wN6qzbeD3kbRFu+tt7gXvrgGDDRgwpmNDDJjQQkgIJEA+SDAlkISQhIQ3gQQICeQl8AIJPdQQWgqE3rHpNiVgG2NsbK+Ne9tdb9FqJc35/pC36jyjGXVp7991+fJq5syZI2maZs7c4/FD8elwlGZaa8xtij3XDgDlniHYb8hhGW5R/qjxDTMObw5nvweT9LsMyNzxJxEREREREcD+eU6xfx775xERUZpoKRjNCwBQ43cD7p0HzH8ZetWSmGKqogqY+hWoifv0HzH9a8COYLQJXY3GWRT6za4tAfONql/fBzh2L4WN24ED6hWOnAR4cynQ6s2nUxeKdu5VKakHAOAvR9V2IRhtEF2Ob+s0L1flJRluSJrpptgH77g2bAwwaf/eYLSuVcZiq7YB4YiGz5tD62EGSIGChR6MhrlPAhGbNIed9wLKhgC1I4HmLcAnb8YUmRJcKE7euFXjwAmFsSxJ+7G+Vl7rQd3Qwni/drTWwD/+bB45cjyUxz4crq5WA+bbC/DuCuCOuRqXHVv4n2Mh+fGj7kJnHjpHodKvjAFoxT5+95Q4fdMlCYeiAYBHeEg0UNjPJH13hftpqv0Kh+6isOC3Hrz8mUYwBBw/RWFcDddhyh31Q+3H33GmwphqhWUbNbZ1AJ0h4IaX7Ff2IyYCB+8cXc4XXenBi4u1MdAMlgYevBaTuz7DrLZXUGM1i3Xqv/wSDeMfxDvLY+fdWODnilLh3Afl72zFHxi4S5QpDEYjIsoxRx11FBYvXozHH38c9957L+bOnYtwWD4RXFJSglmzZmH27Nk48cQTE0q8njlzJubPn4/LLrsMTz31VM8TMfsaPnw4zj77bFx++eWorKx03fEKAKZNm4bFixfjkUcewQsvvIBFixZh06ZNaG9vFzsmVVVVYe7cuXj22Wfx6quv4pNPPrH9PABgt912w1lnnYWLL74YZWWpv1Kwzz774LPPPsMVV1yB+++/v1+HtW4VFRU45ZRTcPXVV2OnnXbCnDlzEprXLrvsgvnz5+PZZ5/FzTffjDfffBPBoP2jQCZNmoSjjjoKp556Kg499NCE5ktERJQvpBtrSzylKZ2PHHoziHr5pJldqEtYO3jMBZEgIAT8ZDsISZq/1F7APkzIq3iaL1uchmdK27lUB0E43WdJ7cnUumE3n777d7sQje736veUoclUz4BwLDEoMWe3B86OM6TjEQWFEiUfE5V5zcFoHUkGo9ltxwAgZCUXjObkc3H62RFRrCYhQKm2aLjtdNqm4xaRG8sDn2GdcKPEwVVHothTYHeapFC1z9zbSgo8zCS78yfS8SsREREREVG6sH9ef+yfx/55RESUQZYQ5Nkn4ENVDQWO/ra7R9WMGBetw7JQH2o0FlnbDHSGNEqLCvOGTSl0aK9xCudOtw9QySb95lOpq2xMQ+rqKqtAdbM5GG3L1k4AhZ7mFNUuPM+10ILR8O4L8rjakcA2B79N6nbrtww2hFYai0UsYMN2YNwgyxduES6VVfsLc5vcTb/1tDxy6hHw3Pxiv0HWWfsBKxb1G6YATPJvwWeB2IdErdqailbmhnihk7XlGBShaACAjavlcaPr405eP6oUaJTH/+djjcuOdd0qypL2oLv+SN/aT+H0A3P32I/yl968Fvj8o6TqUDb96yLhCAo18uOFRe77FXaHx46tUTj7kEGy/6O8U1crj/v+YQrnzejeH0WXYa01bntdI2DTjb47FA0Adh6hcOEI8/KvP/8IesvvnDX0i09QP9U8auUW9vu10xmSP5/Jo8FQNKIMKsyjJCKiPOfz+XD66afj9NNPR3t7Oz788EN88cUX2Lx5M7q6ulBSUoKRI0di4sSJmDp1KkpKkr+ysvvuu+PJJ5/Eli1bMHfuXKxZswYdHR0YOXIkGhoaMH36dPh8vbuNI444wthBK57Kykqcf/75OP/88x2VV0phxowZmDFjBgAgEAjg008/xfLly7Fhwwa0t7dDKYXKykqMHz8eU6ZMQV1dneP2JNohqqqqCrfccguuv/56zJkzBytXrkRTUxOGDx+OcePGYfr06Sgv773BONHPC4h+BieccAJOOOEEdHZ2Yt68eVi1ahW2bt2K9vZ2lJeXo6amBrvssgsmTZqEoUPjRE0TEREVkKBlfqRTqm+q9XvMwSGBCEM3UsXuJmm7MCgiO1rrnA1C8nulMC05BEoKCfTAC4/ihfxskfY5A/cR0neb6iAyvxB2Fdue3A1G6xtqZbd/6N4/y+F0vZ95REcQ1MJxg7A+Zoq0PeqMyNuDvuSQO7/tDXpiIFuSxzcdEftgtGT36wMD74xlGIxGlLBtQoBSja+7k7HQ0YLBaJQibzQ/bxyuoDCjmr2T7dQUxd4MAADN4ezfERCwOa7Jdmg1ERERERENTuyf14v989g/j4iIMsiyzMOT7POhfEXQO+qeIIQRAUDjFmD30UnNKmdJgTLVaXw2h27ZGg3wiUSAMfXA6AZXIbp66wbg5UdS0xiPB5iSwsDW8bth9OoNxlGrNgZRyMFoWmus2AxENNAu5OVWFFAwmt6wCvrpe8Tx6nuXQ9/wo/gVTT4AanR9z1XjUWE5TG1be+EHo23arrFoHaA1UFYMbNhu/q1SVbirUtS6FeIote/hsQNH18UEowHAeGzEZ4i9FtqY/cugKdMUp6vTzN0y046csFZebjBp/7iT14wbib2XLMAnpVOM41dk/7li5MJKl99XHU/ZULrYbZscsgtG0+EQCjXyY1PsMy/iGlOd+nYQpdqMiQpKaZguS5ywV+y5AaUUDp8IvPCpXOcRuzk8p+Bmm7R5HeqF/aPb/exgYxfE7PaYQ3d2AEs/BiJhYJcpUEO4oSNyozCPkoiICkh5eXm/jkfpNmzYMHzzm9/MyLwS4ff7MW3aNEybNi3bTQEQfSLoMccck7H5lZaW4vDDDRcAiIiIBikpZKYkxcFopV5zfQzdSI2Q1SUGPnWPJ0pEUHeKIR3ZvvleDNNKICSwSBWlpE2UGGlZGhjeGbDMYVGpDvOU6uu0OqC17umEK4VfZSo00Ku8KFYl6NKxvUj77l+ldcIDD4pUMQA52KzvtEGb0EF/ir8Dt6T2Oz3OkMpJwa7dyoQQvWSPb+JNn2wwmt120k0ZIjJrCtsHo/H5ZpROLeEm/Lf1XeO4Pcr3w7DikRluUX6p9pl72zSFt/Y7DswGu+ODbP82IyIiIiIiYv+8/tg/j/3ziIgojbQQjObxJl/39y4H7rsGdaHVUNqCNoStXfh3C6/+zJPV88XpIgajpeEUtA6HoW/5KfDEnf1HNEwGbnoBqtb+eoYOdkJfey7w6uOpa9RJ50KNGJey6tR3LkH96zcbx63cloLlNUdtaNE46VYL7zfalysvzkhz0kpv3wb969OAj9+wL3jSucAHrwJzn7Qtpr55IbBqSc/r6kizWFZaXwtBV1jj3Ac1/vaes9DmdGyjcoXWGvjsA7nACd+LHTa63li0PtQIYI+Y4XfM1bjtjISal3Ps1ouKEuBnswbHg3O1ZUFffZY4Xn3jgviVjKnHFVv+gFPGPWocvbkVaA9qlJcU3vFQIXpnubsQ/Hrz8+SIkvfJW0lX4ZF+D0LO0C4Ebo/9fJ7CD9GlwlA3VGH2YQp3v9l/X3XYLsDX9jZPc9mxHsxdaiFg6Ep/+ETgyN2dzVu/+4Lzhgba0FC6HcCQmFFb24HWTo0hpTwuMrELjvvDyc6Pz/UTd0Df8nMgvOOLVwr69J9DnXc1lGdwHOcTJYvBaERERERERJSXtNYxoTPdUh0yIwWLSMFs5E688JJkA1Ro8OoUgp+AzIU/yfN3H0gkBQj6GIyWVU7DMzsj5n1GvPAqt6Rl24KFkO5CsSoxtq+bFNKVDqWeMnRFYoPR+oa2ie30lPV01pbes5OAtWhdqf0O3JLa7zTcSwq5i3c8JM23Qwjxcyre9Mnu150EtzG8lihxTaHNxuE1Rew5SOn3dvNLiMB8zHt49bEZbk3+6Q4wHCisQ2iLbMcQX1WGW9RL2jf7lA9FHv6eISIiIiIiIiIiokEiEjEPT8FNkOpr50Dfdw1KdBfGhdfiy6KdYsrMWQq88hlw9OSkZ5dzmoVufFX+NNzc+9TdsaFoALByMfT1F0Jd+y/byfXf/ugsFO3AWUBRCbBlHbDkw+iwvacDfYPtqoZCHXQMcPzZztvvgBo5Hg1qo3Hc6nY/whENn7fwbpye/UD8UDQAKC9Je1PSTt90SfxQtFN/HO2bc+XfgSfvhL75p2JRNXQUdFdvf95ihFBmtaPD0CenqYC7dPzpJeehaABQU8DBaHjxYXGUuuJ+qGGjY4ePrjc+Bnd821Kg9HhjXcs2auw6Mv+3R9J6MbYaePEnHkwek//v0ZHX/glsM+9/MHJ83PBRAMCoOny99Sncs+48nDPmLmORxq3AHmOSaCdlzAUPuQxGGzpI1hXKOP3XK80jyiuBXfdxVIfqkAOGdQEnozV3uFuPx9cCXg/XZcoPd5ypsH898OKnGp0h4NBdFH5ylIJHWIZnTFR48xce3Pu2xuJ1GhrAkFJg+q4KP54pT9eX1hp44W+u2lkfXg1T0DAANG4B9kpdznpBWbFF3n7tvZOz7ZT+dD70jRcPGKiBv18P7LIXcNRpyTSRaNBgMBoRERERERHlpbAOw4K5s1iJKk3pvKRgEaeBJWQvXniJFAZFFI9tEFIGw5+M85eCkGzC3KQwIa/iKb5schpqFRDCoqRgtURJy1Z3m4o9JT1/m2QyNNDvLcP2SFPM8H6BZsI64e+zDjvZT9vta/wpDlR1S/rOnIZ7SeX8XvvANzGg0WY75EQgEi8YLbn9upPjL7ttKRHZawqbH3HWG7hkvpivjd2TiZyL6AjeannJOG5Y0UhMKt83wy3KPzVFQ8VxzeGtWQ1Gk/bf2Q6oJSIiIiIiIiIiIsooLdzsrpIPRkPtKKC4BOgKor6r0RiMBgCPzNc4enJh3WQejmi0mp+viuo0dIHQr9iEmr3zHHRbC1SFzTn5Vx6NOw/12GdQYyYk0LrUqZ9QC7TFDo/Ag7XNQJ18WSIvbW3TeOFTZ2XzPRhNBzuBuU/ELafG7RL93+cDTrlIDkYbPnbH/+MArw+IRPuFVEdahGA0Dem6c757ZL676+bV2e2ylFb65UfkkXseZB4+qs44uL5pARCbowYA+M8nGj+fld/Lk2VpbBf2Y/edPYhC0QBou33k5P2dVTK6HgBwesujOHf07bBUbBDRyi0MRssHW1rd90WqL7DjE8oNeuOX8shpM+G55jFH9Xj/+zFwuzCPAu571+yyO28Dn99KeUQphdnTFWZPdz7N1DqFqXVJHN8t/a/rScZ9+Bi8nqsRMZyWWslgNNFKc3dqHLaL8zrsjm/1y49CMRiNyJEUnD0nIiIiIiIiyrygJTzmEUBJigNOpJAYp4ElZC9ewElId2WoJVRo7IOQshuM5heC2aTwLACICGFCPgajZZW0z+kcsJ8a+LqbFE6VKCkkLNqG+EFhdsFqqSYGBPZrp/lz6zutHCzWO61dUFYm37OJ03A9iRw0Yn88JAWnBaz26NOkEpTu/bqT4y+G1xIlJhDpENefmqJojyNVoB3UKfsWtM1Dc3ircdz06mPhScVNYQVuiLcaHpifLiuFHmaKfCxcwHd7EBEREREREREREQ0UMT8EFL7k+30oj6cnUGZqp3yT7OcbCu+G+zWxz2PrMbwiDTNc+I48zrKAVZ8bR+lQF3RHK7BupX39VcOAEeOTaGBqjB0p96XYZv+8tLy0aC1gOVw9yovz95qptizgy2VAVzB+4YkDHlx02sXGYupbP4z+7/MBo3qX3RrDwxIB9+EY+SJiaSzd6G6a6rL8XZbi2rJeHjfCHN6JMQ3Gwft2fChWtcp8iTmvbO8EpK5aNQXynKl4fdG01tDhELBysVhG7TbV2cxqRwLFpShCGOPCa41FVm4pvOOhQqG17lleFq1zN21lKTBheBoaRbTafHwPAGrnvRxXo2z6PlmRwtsuRSyNYEijyeWxX1KBUUQFTofDwBcLXE/ne/I27FRlPie1cmvhbX8SobVGeMC2eOVm82fTMEx4wLRlQXcF+/2zO761278QUX/sQU5ERERERER5KajlYLRST2lK5yUGrtiErJBzgYh9T6mwDmWoJVRopGAPDzwoVtl9dKe0XQnpLjEALSSsCz5VlLJ2kXtyKFfHgNfmbV288KpUtSfahj5BYcL6kcnQQCfBo1JYYN/PTQwW67N/kcK0fMqHIk9x3Lamk9NlSCKVixe6J31uFiwEtfAYUgfSvV+3C7nrxvBaosTYBSfV+uL1HGTnCErO3ObnjcOLVDEOqToyw63JTx7lQbWv1jiuKZTtYLTsh/ISERERERERERERZZ0UjOZN0QPxRtcDAM5pvl8ssjK7p4vTwi4Up25o6uaj13wB6weHxy930VegW5t7X7/wd1hn7g09cwj0McPk9JtuXz8vGjCVZTVjR4jj3IYb5IPlwg3PJmXZ7XaWEN20CdavvgX91eHQ35sWf4JJ04DJ+/cbpE74HlA8oG9uRRUw6/Te1zu2QwBQbTXDZK15cN5b1wyEhM28pLpAL5VprYGNq80jxzTI27gdAZ8DTez6QpxXSwFsj+y2qdV5/pwp/cVCWBfNhD6qGtb3D4Se/3L/8ZEIrNt/BT2jFPorFfbhocecLo/rQykFjIyG79V3NRrLFOLxUL6zLI0rn7JQf5kF7/kWPOdFMPMGy1Uds6crlBYxUInSYPkiedxxZzmuxmOT6JHMw4xzzcbtGt+4LYKaiy34L7KwbJPzaf1FwDmHcT0mGkivWgLrx7OgZ9VAX3e+WE59/wrziM4O1IdWGUcN9uOirrDGxY9aGHNpdJt10B8iePuL6DZZ+mwahvV/rVubYP32TOjjR0EfWdnvHz58XZ75muXQG79M0TshKmwMRiMiIiIiIqK81DfcZaCSFIfM+L1C4ApDN1LC7rsEgLAOF9TFHsocu5vvlcruRTO78Clp2yKFCTEYLbv8wj5n4PIXELZ18cKr3CqxCQftG+gphXtmMpxCCoXruw5I+4i+n1upsJ/uO61UTy6EcUifQ2fEfv/YTQoKixe6Z7fsxQs3s22PEGbXLdlgNCehZzxGI0qMFIymoFBdFA1bkg6heLROyVgf/BJLOxYax00bMh3l3iEZblH+qikaZhzeFM7uo9Ll32Z53pOfiIiIiIiIiIiIyI2IcK00xcFok7uW4PLNfzAW2bAdCHQV1pWdVdvM76e6DKj0p6Z/kO5og77wK8Ci9+IXjoShf3lydLo3n4L+/feBVUviTzdiJ6hzr5JvZM4w39g6DIlsN45r+mJFhluTfrMfdL5elOdZMJq2LOiffBV48ykg0GZfuHo4MOt0qBuejelfp+onQd30ArD3dKCyFtj/SKjb5kANHdVbaExDb1WRFuMsbnipsLZB3RIJFMj30CtRWzPQbt5+qF/9VZxMVVQBQ2qM486pM4ejNXXk//LUbBeMlv2ubQnTTZugfzgTWPA20NUJLPsY+hdfh/78o94yt10GPHxD3LrUr/4KNWyM85mPGg8AaBACQBq35P9yU2iufkbj6mc0vmxyP+3Ow4GbadqDAAAgAElEQVTLj1f44zcZpkTpoW/9pXlEiR9qRxCjE3b3LlhWYWyXLEvjqBstPPkx0Ba0L/vr4xVm7AqUFkX/HT4ReO1nHkwcyXWZqC/d2hw9H/HfuUCoSy5Ytzvw7UuAGnPIecP6d4zDB/tx0YUPa/z5NY2N24GIBcxvBI660cLSjRorhS6XfYPRtNbQl54EvPYPoM38G9iO/s5k6HA4scYTDSLZf4QEERERERERUQKCVqc4zi4UJhHSjbpdOoiIjsCrvCmd32ATL0BFw4KFCLw8jUEuJRoWlAl2wWidkQAqvJUxw8OWFIzGdSObpGCtzgH7qUyFQXiUB6UevzEIrG9YlBQcJYWBpoP42UX6BprF/9yk9anTwfvNie2B1xxQFrDaobWOG+Qo7Ufjhb7ZfdcBqwPmbobxdcQNRkvu4l28QNVoGQajESWiKbTZOLzSW90niJUdjyj13mh+Xhx3eM2xGWxJ/qvxmYPRmoXgw0wJCIGvuRBSS0RERERERERERJQpOiJcK01RMJoaXd/zMJvTtz+Ka4b/yliucSswaXRKZpkTVgk3qtbVpnAmbzwJNG1yXn7hO9DLF0H/525n5YeOhudf5tCfrBlVh2qrBa2GPkxNb70BnLBzFhqVHpu2u7sRvLw4TQ1JlwVvAys+jV9uyqHw3PqabRG118FQf3lFHj+6oWc7VBOR022+3KaxU21hXXtemUCgQD6HXtnasFoeN6rOftrR9UBr7LIzJrwewC4xw+1CxfKF3Xuoyn7XtsS9/GhsQF4kDP3MfVC7TYUOdQHPPeCsrkOPdzfvkdHlrE4KRsvuc8VoAMvSuPMN59vQE6cA//kh792gzNBrl8sjDzvRVV0ej0eeT4EEo731BfDpOmdlj9tT4XcnyZ8JEe3w+j+B7dvil9vvCCh/OXD6T6FvvSxmdH1gOWB4Pm0iAc+FYntA4+/vxW5/g2Hgple0eJw+YXif37LLFwKfzku8EaEu4P2XgYPZT5bIDo8YiIiIiIiIKC9JoRgKHhSp1PY88XvMgSXRdhTAVfUscxJwEtLCE1OJbEjrpxRClEl2AQBSyFEE5g6yvUEllA1SsFbf5U9rLQb1pWN5lMPaom2I6Ai6tPlRXJkMp5CCufquA3LAYZnx7776fgfi9sBmH58p0jJkwUJI2zzZaQdpPxpv2bJ771KQnBOdwnfWLZzkPj1eoCoQXW60LoyOIkSZ1CQEJ9UUmYOW+uI6R4nqtAKYt/1147j60okYXxrbuZ1k1b6hxuFNoez27JbDbgv1bg8iIiIiIiIiIiIig7AUjJaiYIfRDT1/1oVWQ2nLWKyxwG563dxmHj4u0aeBGeil/3U/0bKPgWWfOCtbt5v7+tNt3M5isFVzU2H1mfxsvbvydebLMblr2cfOyo2uT35eY3q3Q/VCIBEALFiT/KxyTSKBAgUbjGYX3DBsjP20I3cyDq4JmT/gQtgcSe9hSCng8+ZvgKD+x1/MI568K/r/+kagrSV+RTUjgCHuduqqbiIAoCHUaBw/mANActGG7cDG7fHLddt9dP6uF5SHli0QR6m63V1VZfeQZKtAgtH++6Xz91Gb/e7bRHlBL3X2e06N33Fe4aTzosdPA9gdFw3W/r+fb4yGoJk89Yn8mYzvG8Tv9LyPHYffMdFglprHihARERERERFlWNDqNA4v9ZTaXjRIhBRYAkRv7i33Gh6bQI4FIvEDTqIhKvn86DPKBinYx26dzhQpEAqQQ46kMCGfh8Fo2SSFOYR1CCErhCJPEUK6CxYiwvSpXx79njI0Izb8onudsAv19GcwnEIONAv0+Tv+eiy1udMKwNIWPMrjqJ5ssQ8oa0exp8R2+kS3dUWqGF74jKGLgYjQc9yBjjjBZSErftibHSeBqhYiCOkuFCv7z46I+msSOhLX+HqD0RSk31qDs2MEJW9+yxxx2354NZ+C55YUZNgsBB9minS8Yve7iIiIiIiIiIiIiKjgRKRgtBT1+xhT3/Nnie7CmPB6rC0aG1Ns5VYNiNd88k+L0AWitjyF73HJR64n0Xf/Fti20VFZNet01/WnmxpSg6qI+Qbfps0OgmzySHP8bgg9in3ACVPSu/7o91+Bfv4hYM0y5xP5ioFJ06C+81OoYaN762prgb7l587qSHEw2jdan8Q1w39lLPb0Ao3j0/w5ZlpjAs8pqsp+t6X0CAj9jvzlUPHCQCtrjYOru8wfsJv1N1c1d5j7W9TEuZSqtQaevgf6+ot6B06aFv2/shbYsBoYXQe0uF04FTBhT6iTZkN11+eCXt8I/djNwAY5HNE671Cgw2H/tFnfcX9fwMxvAbdehvquRuPolgDQ1K5Rk8pjhTz339Uat83RWLRWo7wEmL6rwqWzFMpK0v8ZuQ2qO+NAfm+UQetXyuOOPs1VVR6PPK5QQomWbHBeNu/CholSSFsW8J+7oN95HmjpsyP0FgGT9oP69iVQI8ZFh33u4HyErwg44hsAAOUvhz7gaODFv/crUi8Eo7UFga1twLBBeFuk3YMD1jXL44buuPVDd3ZAX3de0u3Qf70SOOUiqPJKd9N98Br0cw/GnDtQ5/wG6sBjkm4XUS5hMBoRERERERHlpaBw43RJWgJmbAJLIgGAmURJkW6Q7itshYAUPRyVBg8pCMlunc4Ur/KhSBUjpGMDggJCqFDIEoLRFDdC2WQXPhW0AijyFNlu59KxPMYLHMuVYDQ50Ky9z9/m/b3f2/u5Sd+BhkaXDqJU+RGISOFh2Q/jsA9gDaAqzvSJbuuUUvB7y9EWie2w7GTfLIkXeBrWQmf/FNXfU87qiBsqR0T9bROCk2qKhme4JTRYaK0xt/l547gKbyWmDjk0wy3Kf9U+c6/BpvBWaK1THiTvlBxSm/1jMSIiIiIiIiIiIqKMkYLRfCm6tWtAqFF9qNEcjJbdZ2mknBQoU5WiU9C6fTuw8B33E25aE7+M1wd8+xLg2O+6rz8DanadAKyLHd7iqYJe/D7U5P0z36g0aBKWoYEqSoB/XODBuJr0XW/Rr/0T+qrvApblfuKF70C/8R/grregakZABzuhf3ik48lVKoLRxk7o+XNKcJFY7K43NM6drrFfXeGE26zc4j7QxOspnPffT0Do2+OviD9thbmnVE3QHDTZ5KwbUU5rErppVccLRrvlZ8A/b+0/8LMP+r9etSSxRn32PvTLjwA3Pgu192GOJ9MbVkFfMCN+MOjAdkpmfgvq3Kscz7+bGjEOGkBDSA5nu+MNjf85tkDXQZfmrdA48kYLHX26M7+2ROOlTzXe+IUn7duqS//pbJ9XXgLcfobClHH83ihz9PpGcZwat4urupRNMppl5X8wmmVp3DnX2fsYXQWUFnFdpsFL3/BD4Kl7zCMXvQs95wngzjejx89LPrSvrLIW6rcPQg0d1TtsVF1MsfrQarGKW+do/PbEwbdOrtqWyG+46DGJjkSgLzk2sd/uBvqHRwJ3vAlVUuqs/Ov/hr7yDPP8tzelpE1EucQmX5aIiIiIiIgodwWtTuPwdASj2dXZKQQYkXN2AT3dwtocCEVkRw5Cyo1HLUqhUFK7pTAhn+KzD7LJLsyhe/tmt51Lx/Io1dndDmkZi06buXAKaV6BPmFocoiGv8/fNt9BxP47yIUwDrs22H1X3TqT2NbJ26HEj2/ihapFEIalE78IKIXlxZZLPNyNaLBqCgnBaL5hcafN/65ZlA3LAp9ifZe5w80hVUejyFOc4RblvxohGC2sQ2iLbM9wa3pJ++9c+W1GRERERERERERElBFSMJo3Rf0+htQA5ZU9Lxu6Go3FGhMI78llzcIl5JpUdQd45TFxlLroj8C0me7qUwrq1teg7nwL6vlN8FxwjW1IQjZVD680Dm/yVkPfdUWGW5M+ToOVmm/24Jg90nuzuH7g2uRurN6wCnjuwejfbz4FLF/ofNoUBKOpITXRbdEOZzb/XSx70yuFtS0qtNDJpATazMNL42+YVZ/lp6+qzk3mWYWAYCi/l6VE9mO6aVNsKFqqdXVCP3Kjq0n0f/4aPxTNqWP/HzxXPQRVkuA15Un7Y0x4HYoMDy8GgCuezO/lJpVueKl/KFq3d1cALy9O77zDEY33VsQv9+HlHmz7Pw/OPCg3j5mogK1vNA8/abbrquwO+bXO/23SG8ucl22I3x2RqGDpTWuAZ+6zL7R5LfDM/bbnI3DaxVAPfAT11BqoA47uN0qNjg1GGxXegBJtvg/zqqfzfxuUiMYEfsPVlEUfTo8PXgEWvScXPPrbULfP7fcPZ/9aLv/FAuCtpx23Qz+Y5LkDojzDXwFERERERESUl6Sbaks8ztLx3SjyFMGniozjAg7DOUgWL0AFAEIMRqMESKE4fk95hltiJoUhSe2OMBgtJ5V67cIzo/sIu3Arvzf1y6O4bEV2tMcuqM3m/aSaFMrVdx2Q2tp3Wr/XJlisOwxO2F/bTZspJZ5SKJg7zsYL97J0BEHhIqWTZUsMRksiVCzgIDRW2p7Fo7V2HHjmJFSOiHpprdEUNl/lry3q7Ykkba8YjUaJeKP5OeNwBQ+mVx+T4dYUhpoiuedgc3hrBlvSn/zbLPvHYkREREREREREREQZk+ZgNKUUMHbnntf1oVXGcoUW3tMsXBquTlH3B/3JW/LIvQ6BmvF1dxWOHA815VCoyftD+XOjD5Okdqi5fet9o6I3ahcIKZSor5m7Ax5PmkPRWpuBFYuSr2fB29H/P3nT3YRj6pOeNwBg+NiePxtCjWKxN5YWzjVmrTXWt7ibZv/6tDQlNwSEvkP+ivjTVlQZB9e2rxEncfvZ55qE9mPzXk5LW2LY7QNTUd6G+uoZyVVQUQUPNEaHNxhHe1Q0lIvsw4zeXJbez+gLc+ZhP3uOAfYdr1DkS+9+mMhoq3kbokbGhg7Fo2yOJS0r/7dHb7jYXkwYxvWZBrGF7zoKtNIL3ur5bWeijv4O1IQ9oLze2JEjx8cM8kBjTGi9sS6PAkLh/N8OubW51f003eHF+mP74161x4FQex7U/9+UQ2yn0QucHUvr9u3RIDWiQYTBaERERERERJSXgpY5BKTUk55AFyfhLZSYTgfBJWEGo1ECpGCfTAY/2SkVApmkdkvrgRTcSJlht9/p3kdI+woFhWJVkvo2Cct4d2CV1J4iVZzR5UlcByIdPU8/k9raN/xNCoLrO32nENZlN22meJQHJcJyFC+gTAqKBQC/g2MiKTzNSbiZJBCJP21IeBJnPF06CAvOnm7EYzQid9oiLeKxRo3PSTAakTvN4W34uHWecdxeFdMwtGhEhltUGIZ4q+CBobMTIIYfZoL0uz9d53CIiIiIiIiIiIiIclIkYh6eomA0AFAzTur5WwokGjTBaKnqDrBupTxu9/2AQ48HPC5uz+vzHeW68cPM1xzmlB8BbFjd07cj3zU56F5w8r6910n1p/OgH/wj9PN/g96+LXUNWbciNfW88xysM6cAT97lbrphY+OXcWLPA3v+PKn1abHYl01AV4HcfN8WBCJCd5YG4blGx+6Z+WvvOhyGfutpWHdeAeu2/4F++VHojgTSAOJJJhhtSI1x8PjWz8VJPt/opFG5S96PycuI/vzDNLVmgLYW6Aevg96xL9Sf/xfWNd+HdeYU6Pt+D71pDbTW0B/NgfXzE4GF76RmvtXDgb3sgyPiGlINANgjuNg4OmwBa5uTm0UhaA9qbLLZDKT7uNFJ/V/fl32VKIvahPTNqqGuq1LKZrteAMfVjS62FydP5XpNg5de8oGzgh+8Brz8qDx+173lcaPN4Y1TgguNwy0d/X022LQF3W97u4PR8MGrciGlgMNOiB2+93T7/cf8V2D95RfR4+wBgcN6wdvR4Q9eB33v71y3myjfMRiNiIiIiIiI8pIUBFKiStMyPyk4JV5gCcXn5DNkMBolQgrFkYIOM00OXDRv36T1wKtS10GW3POpIhSpYuO4wI7vUtrOlXj88KjUn6KNt2xJ7cn0uiHtWy1EENJdsLQlrg9921qsSuARTnV3v9eAUE+uhHEkGsBqtw8t9cR/srQ034CD0FKTiI4gqM3htX2FtfAk9DjcHHfxGI3IHbvApJoioad2Hxr53zmLMuut5hdhwXwD2IzqYzPcmsLhUV5U+2qN45pCWQxGk47phJBWIiIiIiIiIiIiooIUEa6TpjAYDd/4Qc+fdaFVxiJNHUBLR+Fc22kWnidmFyjjyvpG8/DDT4byeqFGjIP60fXRG1/j2W0q1JmXpqZdGTBhmPye/q/iXKBpUwZbkz7NcZ7/dtyewNmHRD8L/dD10BfMgL77N9B/mA193qHQ6xuTboMOdkLPPjjpenqskkOkJMprDsJzXc/51/T8vU9wAU7e/qRY9uTbnD0cL9dJwVYAcOvpHuw64JlQ03cFLjkqs4EguisIffmp0P9zCvDQ/wKP3Ah99VnQF82ETvG6rANt5hF+B9cGd4RZDVTZugbDhFy1Y2+20J5AqEGuaBb2ybYBn5YQtpoG+u7fQn9vf1i/OR169kHAi38HVn0Ofe/V0N/cGfqHR0JffAww76XUzNDrg/rl7VBF5j6ZjlVUAQBu2XCJWOTxD/J3uUmVxq324x99XyNipe9zWrnVvu5Dd8789pKoHykYTdhf2fF45GXZSuN6limNW5y9h7MOVjhxSpobQ5Sj9JfLgEdvSr6imadA2QW0j9jJeI7ipg0/EycptBB9J9qD7qepKQf0vBeBJXJQsbroj1Ajx8cOLy6B+sXtcuVrvgAeuzl6nP3DI2Hd/VsAgPXXK6O/2+69Gvru3wKP3+K+4UR5jndNEhERERERUV4K6swGnEj1diYYHEK94oW+AECIwWiUACkURwpjyjQ5cNHc204KRvOpopS1iRJT6vEjFOmKGd69fct0SJ+0bPW0R9h3ZXrdsHv/AasDxULgHACUenunVUqh1FOGDiu2U113CIf0nnMlKDHRAFa7ADO/g2MiKYxE2g7F43S6RANP3Rx3OTm+IKJe24TAJC98GOJ135GLyE5Eh/F2s7lT9PCi0di9zOZphhRXTdEwbAtvjhneHI7TozlN7IJTcyWkloiIiIiIiIiIiCgjMhCMpoZUA9c8Bn35aWjoahTLNW4F9s6Ny+VJCUc0WoVnd1Wn4BS0DrQD2zYax6mvn9v79yk/BA44GvjvG+bwBI8HmLAnsM90qJL8OTc+Ybg87tKRf8T3Vn6M2tqRmWtQmjQH5BCHOT/34JCdAZ9XQW9ZB333b/oXWLsC+qH/hbr0tuQa8YYcHobq4VDf/knMYL15LfCvJOebBqqyFnrETsCmLwEAD6w7B09Uft1Y9vlFwMdfauyzU36H3jTZdFGZOh744HIP5nwOLN2oMXW8wqG7AMW+DL/nN58C3n42dvgXC6D/dRvU7CtTN69Oof+Qk2C0CqF/QqgLE4Za2NJmDoB4eL7GudPzczmSlh/bYLRNa93PaPIBUDNOEkfru38DRITAtY5W4PV/mccteDv+vHfdB9jvCKhqYccS6oLu7ICq2w3Y7ytQI8bFrzOeHctSQ2gVyq02tHtik/V++S+NS49Jflb5zEkIyqufAbP2SM/8G23m/9JPPJi+K1BSlJ/rNuU/rTXQLgSjlVe6rs8uxEjr/A9GWyl0S6ofClxwuEJJEXBgg8KBDdF+30SDkb7/DympRx1/tv34omLo4WOBTWv6Dd8pvBaVkRZs91bFTLNyiwYwuNbNtgSC0arLAH3TT+UCp10MddrF4mg14yTggt9D3/Hr+DN78DrofWcAD1zruH3qgt8Du7D/LRUeBqMRERERERFRXgpa5mC0Ek9pWuYnB4cwdCNZTj7DRANUaHDL9SAkqR2dEfP2LazNHWSLGIyWdaUeP1ojsRe/u0O5Mh3SJy5bO9ohtseb2XXDLgij0+pARAmdwg3Tlnr8xmC0QKR9R31SoGpubA8SDWC1C/8q9cTvTOgXyiQajOY0uCzR/bqb4y4eoxG50xQ29zSsLhoKj+rtlMVOSZQKH7e+h5ZIk3HcjOpj+y1z5F61b6hxeFOWgtGk8zdA7hyLEREREREREREREWVEWLhO6vWmdj477QoAGBdeC58OIWzoV7JyC7D3TqmdbTa0yKeg7QNlnFrfKI8b09DvpRq/GzB+txTMNHc0DLMf/9gHGj/YLzNtSacmoYvEFScozJjY5/rouy8AlhVb8PV/A0kGo+l3n5dHNkyGOuPnscM3r4VOVTDarvukpp5udRN7gtHKdAAjwxux0WcO0XtmQQEEo9l0s6kui4agnbg3kM2gAf3Oc/LIt58FZl+Zupl1SMFoscFUMYbID26bUBnAfJj7Oj27QOPc6U4al3uahS5ONXb7sfWNruejDjrGvC3ZQT9xJ7Bxtet6Hc379rlQJfb9+1O9dqiKanTHDO0eXIoP/VON5Vo7NYaU5vc2KBnREBR7Ty/QmLVHej6jNeauIzjzQIWjJg/e74VyRKDNfOwHyEGeNjweeZnWVn4Ho2mtsVZYn286zYOv7cP1mUhrDbxjCCpOxOj6+GVG1cUEowHAxK5l+MA/LWa4k7DUQtOeQDBardUMrPlCHK8OcpC6u9chjuenb/ix47LY7yu2x/tE+YzBaERERERERJSXgpb5UY8lNiEryRADSxi6kTQnnyGD0SgRmQ6jcssvhFBJgUTSeuBjMFrWSctU9/Yt0yF9UnsCO0IppO2uP037UIkUygVEg8wiSngCpWFa+TvoDqczr1fSephpiQawSuN9yociT/xtQ5kUjOYw4GygDoeBaonu190cdzkNaSOiqKaQuVdDrS/O3Q47FMJTKylz5jabb+woUsU4uGpmhltTeGqE9VZaz9PN7ngmV36bEREREREREREREWVERHg4mC/F/T5G1QEAvLAwPvQlVhRPiCkSDcHI/5vTpTAZIEXBaOtWmId7PMCIAkiWi6O8RKGsGOjoMo//9/Ja/CCzTUqLZiFgb2AokX7mPnPB1iboYACqxH2/G93RBix8B3j5UbGMmnakecSwMcD4icDqpa7nGzOPMy9Nuo5+9jgIeP/VnpdHtr+Gh6u+YyxaCDfgS8tQWXE0FC0nrG+Ux61bCa116h6UFoh9uCUAwB//IY8YPlYcNXPoOjyKXY3jPljlpGG5SVp+pP2Y1hpYvtD9jPaLcy1+2kzg2fvd1xvP3ofFDUVLi4qqnj937VomBqM1bgH2GpepRuWeRgfPV2t0EJ6WqOYOc90jKp3XoVubgMXvA0GbxFyiRGzfJo/rs41xym4/m+e5aGgLAmEhQ26U+4+KqKDoriCw5EPgy6VAW0vyFY6qA8buHL/cznsCC96OGdwQajQGo63KzjNXs6pdONdh5/Cy5fJIXxEw+YD4ley2bzRgs605flmbELaBxHMHRAWAwWhERERERESUl7qDTgYq8aTn4qkU3iK1g5yxdMTRZ8hgNHIroiPo0uZHeORKEFK8MK2BpPXAq3iKL9vk8MzuUC4hpC9Ny2K8ME85NNBB57MUKvGUQkFBI/aKfiDSjohH6BSO2PcordedVgciOoyQNl+5ypUwjkQDWKXxTr9LaRmUguTiiRfk1i2U4H7daf1uyxIRsC282Ti8pmhgwJK5g5ZpW05ksi64Cl8EPjWOO6DycJR5HTwlnGxVFw01Dm8OZ6f3kt3xTKaDeYmIiIiIiIiIiIiySgpG86a234cqq4CuHg40b0Z9qNEcjFYgN7xKYTIAUJ2CU9D69X+bR4zcCSrVgXY5yueRx73aXJe5hqRRk3ApIyaUyC40an0jUD/J1Xz1G/+B/vWp8QuecLZxsFIKOPdq6CvPlLcvA1UNBWpGAI2f9Q7b8yDgsBOdTe+Q+uaF0Pf/vuf1pVtvFIPRVm3N/2vNTUKwT0oCGlNlfaM8rqMV2LQGGJmiwEcxGC3+tWBVXgldWWsMozmt/EOcJwSjrWsGrn3ewmVfVakLeMuQJqGbVrU/9n3ojjbonx7nfiaHnQDsdbBtEXXaT6DffhZoNvcfSUiJH+rsX6euPjeGVPf8efXmq/Bo1WnGYnOWauw1Lr+WmVRyEnqWzgBLt8GAA+kn7oC+6RLAEhKZiNIlgWA0eDxQ2oJWsQfYOs+T0dIeWE2Up/Ti96Ev+wbQtCk1FXo8UOdeBeWx+aG+g/p/l0E/cWfM8PquRmP5h+drPDQ72Qbml3bz7Waiw0a14Li7Z4nj1ewrocqGxK1HlfiBc66Avvln7hpgZ6ddgePPTl19RDkm/laPiIiIiIiIKAcFrU7jcClYJFlSvYkGh1CU9D0OFLIYjEbu2N18nytBSH7XwWjmDmw+NTg6WuayeCF30ncqLQNJt0cKCYsEoLW2aU9mgyk8yoMSm1A5KdjKAy+KVHG/YdJ3ELA6bAM40/UduCVvD+zDQ6XPyOl3Kc030VCxQMTZcVE4wf16vKC4RMsSEdAUMvdirPENDEYjSs7c5ufFcTOqj81gSwpXjU8ORtM68x0pOyPy8Uyu/DYjIiIiIiIiIiIiyogMBaMBAEbXAwAaulYZRzsJwcgHdjfgVybZBUJbFvDSw+aRoxuSqzyPxMsXam0SApDyiBRKVFM24M1bEbmS9Y2u5qmbtzgKRVNX3A9VO1Ief8TJUH951fF81a/+CvXnV6AuvBY45gyoH10PddOLUMUljutwNJ/qYcC4nXte7xX8FBdtu91YtjGNgTuZIm2LanLkUpjuCgJb1tmXueFHqZthp/kDUX6HD+3csQ8bqHzzF3j2R/Lt0L9+QuPNZc5mkSuCIY2A0I2qxvBx6QevBT6dJ9anHl0M9et7gGPOAA45Djj621CX3gr1u8fiBsaphklQd74JpDDITN0+F2razJTV50qf0KIJoUaUCP3VL360MI6JEuUk9KxxK9LW10DafjoJuNXLF0HfeDFD0Sg7yhMIRlPRR0mbWFnoz5NKUtAwkJrAaqJ8pCMR6MtPcxSKpv6+MH6F3/gB1M0vQc0yB07H1DlsDFAV28EBIjoAACAASURBVP+3PrRanOb9xvzeFrnVJgSjDS0HTpjS++/UaQo3fTOC5z6cijIt94VUZ/zc8bzVKT+E+tPTwEmzo8ftLoPWY+q7fS5UzfCk6iDKZWk4e065Sik1BMBhAMYBGAagFcA6AIu01ktTOJ8iAIcCGA9gNIC2HfP5r9a6MVXzISIiIiKiwU0K1JICVpIlh97YB5aQPafBK2HNYDRyxy4QJ1eCkOyCnEyk9YDBaNkn7iN2hEBI32n6wjzN7YkgjJDuQiAitcdh57MUKvX4jetrwGpHRAgD9HvLYjpq2QUNSu+3e/65wO32oFun+F062875he/cacBZzHQOA2MT3a9L79fcFgajEbnRFBaC0Yr6XygfvM/IpVQIRDowv2WOcdyE0t2xU+mEzDaoQEmBhiHdhfZIKyp8lRltj3R84FM+FHmKjeOIiIiIiIiIiIiIClKmg9E+ex/1oUbjaCchGPlACtOoLAW8niSvbC2eL49jMFqP5/6zEKedfXBmGpMGtqFEA7tebN0gV7RBvrnc6M2nnJXbbd+4RdSeB0HPPAV47Z/x6xvdEA0t+85P03/td+JUYM3ynpcz2+fg1tofxBRbvQ2IWDr5dTaLtgrdZXImCGSjg+Vz8fupm19ACEx0E4z2+Uexw9c3Yo8x9pM++r7GjIn5syxts+lqZVx+Xv2HPEFxSXQdH7sz1FfPTKg9akwD1Dm/gfXFAuCtpxOqo6eu2VdC7bp3UnUkpaK638uDAvMwt/xwY9GN2zVGVubPcpNKTo4JO7qATa3AyDR0NZCO5aqcBKO9ZrM+EKVTcQlUSan76ZQHHliw4I0Zpa38DiOyC6yuzo3bNogyb+E7wOa18cuN2wUY0wB4PGLYp7rg965Ct3pMmga890K/QdJ5IiB6LL1//eA4JgqFNUJC9vm/fuCJ+U2h334Jevt6ucIEjr/VgbOgDpwVrf/Fh6Gv+Z7rOgAAXzsHqsr8MF2iQsFgtEFAKXUogCsAHAnhO1dKfQLgDgB36gTju5VSwwFcBeA0ALVCmXcA3Ki1/lci8yAiIiIiIuoWFALJSj0JXGRwQAxccRHQQbHSHaBCg5d9EFJuXGGTtyvm7VtYCInyKZ7iy7ZSr7kXRnfglxSiKYVSJctvE/bVaQXE4EC/8D7Sye8pQzO2xgzvtAKwlPninmkdlgNMO+IEJWY+DM4k0eMMKfzL73X2vqRyQd2JiI7Aq2I7YNi2x2GgWqL7dTdhZwyvJXIuoiNoCTcZx8UGLJk7PWjhaZZEfc3b/jqC2hxyfnjNsRluTeGqLjIHowHREMRMB6NJ++Rc+V1GREREREREREREg48OdsaGthSXAFBAVyfgrwA624HSMqiSFF5Hz3QwGoD60Crj6MatgNY65qFk+aY5YL5GVRPnkrlubQbKK6E8HrnQ6qXiKDVpPyfNKwjxlpBla4IZaUe6NNt0Legb4qC7gsCmNXLhoLs+Cvpft8UvVFkLjHH2YCH11TOh4wWjDakBxmbuQUVq0rR+oTXSDfhhC1jXDOxkvBMxP6yO7foEID0hQibasoDt26Iv/OU9+66e4Us+jF9JWzN0JALldddXqF87WpuASARo324u4K9wVtGOfViM9Y0YWwOMrgLWt5iLLNuYX30XVm+Txw1cfnSwE9hg3q8DACZOtd+vuaAm7Q+dZDAaZpyUkrYkrKKq38s9g4vFYLRlGzO3vuaSpnaNFoe7r5Vb0hSMJsy/uqz3CESHQ0CbYaVf8WnqG0TkxK77JDadUlBCHzurQIPRyoqBYl9+/+YkSpjNOYV+Jk2D8hVBT9xXPm7fPcFzEKPrYgbt2/mJWDzfjqWd6gprBMPAkNLe7VF7l1y+osQwMM73qRL9jrolcZ5J7TZ4zlHR4JWaX7qUk5RSRUqpOwC8BeAY2Afh7Q3gdgBzlFI7JTCvYwEsAvADCKFoOxwC4J9KqYeUUrlx1yEREREREeWloGW+mbrEJgwmGaVe8w27bgI6KJYUADUQg9HILbsgpFy5AV/errTDlFsvrQc+VZTSdpF7pcK+p3sfIYVbSdMl3x55Ge+0OsR9V2kWQsKktgYi7WJ4pin4TfwOIvbBaOk6bnAr0eMM6b05XbbKbL5zu89N0uEw8DSU4H7dTZuchrQREdAS3gYNcxhlrU3AEpEbWmu80fy8cdwQbxX2qTgkwy0qXJXeKngMT5cFosFomZbs8QoRERERERERERFRyr34d+gTx/b/d8ww6GOGRv8+qgr6hDHQR1XDumgm9NrlqZlvJGIenoZgNLXj5lcpiKg9CGxuTflsM066Ab9aOAWtX3gI1rcmQh83Evpr42D95RfQwvei7cJnjjzVZUvzV7zsvGdWD81MQ9KkyaZrQc2Orhy6Kwh96dfsK5KCDw30oveA5QvjFzz5fKiiYmeV7n80ULd7/PqKTXd4p8kxp/d7WRdaLRa95638vgG/cau5/fXD0hsEoi0L1p1XQH9tXL/9mfWDw2H98mTo40dHh119VvzKImHg1ccTa8fT98I6ZVfo40ZBnzgWWLfSXNBhMJqSgtEWvgPPk3fgRzPlz/XVJcDf3jX3f8hFC9eal52yYmD4kAED1wuf6w7q1B+lqFUAjj0zGqaYqP2PgmqYnLr2JKKiut/LH26TAymldbjQrXDRfWDlltR/Rp0hjU6hG2F1GaA7WmH99kzoY0fEHrufOBZINryPKEHq5AsSnFDBo837qHzfCjV1CIHVuXHLBlHG6VVLoK+/MH5BX1HPNkV9SziW22UKsK853DUe03H1iMhmsfzKzHctTKvmDo1v3RFBzcUWqn5sYe+rInhneXR71WaT8V4+4Gez1hr6tv+RJ6gZARz97aTaqsbvBhz0VfcTDh0NzDwlqXkT5YM0PFaEcoFSygfgaUQD0foKAZgHYA2AckQD0cb3GT8DwMtKqUO11sLzCmLmdQSAJwH0PduqAXwEYAWAagD7Auh798wZACqVUl/XWjiSJyIiIiIiEljaQlBnNhjNFMICJBYaQr2k0JuBQtrmcQxEBlKYkE8VociTG0FifiEQyoKFkO5Csep/Rj2izR3ofIqn+LJNCvfqtKLhj9Ly6PemJ4hMWraibZKDwuymSxdpnp1WAJYQ0mMKcPMLwWKdViCvtwfxjjPEZcthyJ3ddx6ItKPcO7CHnT2nx0WJBp66CaTtXv+IKL5tIbmjQ42vfzCaEp8Jn+/dsyjdlnYsxIauNcZxh1QdnTP75ELgUV5U+2qxLRy7bjeHHF3+TamAsE/OlcBqIiIiIiIiIiIiIlsL3ob+0dHAY0ucBxRJhOAklYZgNOy4+bWhSw73+unjGg/NTm9oT7o1C5eFqw2noPW8F6F/f07vgJatwGM3Q/uKoC74fewE64XPbvf9oCqq3Dc2T8VbQuZjMrZva0Vlrbv+BblCWoaA3uVI/99PgI/m2FfkMBhNb1oD/ZP4Nzuri64DTvuJozoBQPl8wF9ehf7TRcDcJ/uPHDEO6psXAt/5qeP6UkHVjIAe09ATklVttaA60oRmb2zY0tXPaJy2v8ak0fm5TZICBBrS/RyyB64FHvrf/sMiYWDRewlVp393NrDnQVBjGpxP88Z/oP/3B84Klzq8Pmgzf/1/P8EvflOLV3Y/Ba8tMZc56z6N4UM0vrpn7i9P5/1NCNUbCqgByZT6uvPFetSv74X6yjdT1i41fCxw2+vQd14BLH4f6NrRZ7+tObZwRXX/4V87B+pHf0pZWxI2pH8w2q4hOei20EJAnDr7Pue3dafjM2qx2wf7AX3ld4F3zQ/gs1VaBviSPG4nGkgpYMIeUCedC5Vo+I3yQAl97KxIfve9c/O7jKjQ6UA79EVH2heqGgpM3AfqjEuh9joYAKBmfQdQCvqJO4HGz4CKSmDqTKiLroXyeBJrzGjzcfX9G8/H2SPvjBm+cks0BGzgcWi+OvVOC6981vt64VrgyBssfP47D55eIG93Bwaj4R9/tp2PumMuVGVtEi3dUc81j0aPv997AWga0Pcz0NZ73sFXBFQPB/Y6GOrCawfVOSoavHjXZOH6I2JD0W4BcKXWuqnvQKXULAC3A5iwY9BuAP6tlDpCa217NK2UGgfg3+gfivY2gHO11p/1KVcC4HwAfwLQfWfDiQCuAfArF++LiIiIiIgIXVqO5i9RpWmZpymEBWDoRrKcfn5hIRCKSJJLwU8Su+CigNWOYk//M+pSkJBPMUQi20qF8Mzgjm2ctDxK0yWrxCPvCwM2QWHZCKcoFQPNOmDB/FRm0+cmh9PlVhCcxK79dpJdtuzC+dyEkHXriDgLPE00GM1NIK3T8FUiAprC5t6LJao05nhFCkbL765ZlAlzm58zDlfwYHr1wEualKxq31BjMJq0vqeTfLySO8diRERERERERERERLY2r43elDj9a8nVIwUnpTEYbWRkI/xWBwKGc7KPf6Dx0OzUzzqTmoTLwtWGS+b66XvNhV98GPr8a2Jv/F3faC6/z3SnzSsIteXA1jiX3//9+Cc4+4LDMtOgFJOWIa8HqCiJ3tSOlx+JX5HDYDS8/CgQjNNn8vCTob59ibP6+lDVw6Cuecz1dGk1bSbw1D09L+tDq/GxIRgNAB54V+O6b+TfDfhdYY21hqwoAGgYlr73o7WGfvqe+AXdevFh4Hu/dt4OadtqUlbhrNyOfZjo6Xvwp0tPxdTfyaFO97xl4at7ep23LQs2tMg9LQaG6mmtgU/nmQsPHQ311TNS2LIoVT8J6tp/przejCkuBYqKgVDvg7m/1vo0nhpyYkzRwRiMprXGp+ucl0/HZ9Rs0xWwqmN9YqFoANQfn4CaekRijSJKJ6XgER4YbZ/kkPvc/C4jKnhvPgW02Ow4v/tLeM672jhKHf3txMMXTYTj6p0DnxuHd3QBm1uBEZWpa0K2rNyi+4WidQuGgYfmafzjA5tgtAH5qvqZ+8Sy6oLfQ42ZII53Q5X4oX78J+DHORAyTJRjEoyHpFymlJoEYOBjIX6mtb54YCgaAGitXwJwKIAVfQbPAHCag9ldBaDvGcl3ABzVNxRtxzyCWutbAJw6YPqfKqXqHMyHiIiIiIioh12Yll0YTDL8QsBISHclHO5BzkNX+BmTW4FI7t98bxdcZGp/iMFoOUsK2OrexknbOrtwvGR4lFcMCg1E2tEZMe9H/UJIWTrZfXbSemyaxrYe4bghl7YHUvs7rQAsLXfikz8jZ8uW3WfQkUCwWLr369L7NWF4LZFzTSFzJ4zaouEF8+Q3yq6m0BYsaJtvHDelYn/UFg3PcIsKX02R+fHzTeGtGW5J5kOCiYiIiIiIiIiIiNJi+aLk65CCk3xpCEYbOR5Q0UfeVFnbzc3R0UCffLamydz+2grDNa65T5or2bIOCBiuj29YZSyuRg2uW6B+c2L864ULVufvQ0+/FJahYRWIXitduzx+kBkAHXbWD0IvXxi3TDoCjrJF1U/q93pycLFYduGa/Nwerdoqh5kMDLdKqe3bosGdKeZkGe1nhYv9Y7zAs26j6oBSm35dyxdil+FAsc3uc8Ea583KlkU2X9/uowdse1tsrvNWp3NBy19KKWBASEV9V6OxbOOW/Nz+JCPQFb9MX6u2pv4zsgtGq974aWKVKgXU7Z7YtETpphSU8PhRK8+T0ZqFw+Wa9HSTJ8pp+osFtuNVw+QMtQTAaPP5i4Yu8/kOAGjMfPfCtLD7PbBwDdBp8xO+sk+3Rh3qAhoNCWvdMvl9Eg1iDEYrTL9E/+/2Fa31jXYTaK03APj+gMF/UEqJ0fhKqV0BnNVnUBeAs7XWnTbzeRLAA30GlQD4rV3biIiIiIiIBgraBF2k68baUpuAESlghuILRJyFrjAYjdySAnGk8KFs8HtttiuG8ABpPfCpNHSQJVekcKnuUCtpv5XOMIhSIeSsLdKCCMwdUrMRFCZ9Bp1Wh02IRmw75e+gA51iUGLuhHFIbdHQ6NJBcbpkg0a8cUL03Ao4DFOTgh7jcRN2FowTKkdEvZrC5mC0Gp/zzrNa6LRFBABvtbwIS3ji6eHVx2W4NYNDtW+ocXhzFoLRkg1yJSIiIiIiIiIiIsoFroNiTKTgJG/q+32o4hJg+FgAwMnb/2Mso3U00CefPDzPwnE3RzDlygi+d5+FF4TMjPoBp8l1JGJfcaCtf/lwCNgs3EXrNNinQBy/l0JFiX2Zm7ZMz0xj0mCl+VJpb6DV2hXOKpKCDwf65C378dXDgQNmOasrH8z8Vr+XZ7Y8LBaV1udcZxccMHBblCpaa+jbf5Weyt9/xXk7Ql3Ow9l23w9q7M6OiqriEuDI0+QC27ehPNKKU6bKwY3LNgHtwdzux/DsQrl9Zx084L2tbxTLqlnfSU2DCpA6qv9y1BBqNJabsxSIWLm9vKTaApe5itL+MhlSkJJHAeVvPJ5YpQccDTV0VOKNIkon5YFH6NOq83wb1CIEHVb744cs69ZmWLf8HNbsg2GduTesq8+CXvReiltIlBy9djms686HddZ+sM7YK/rvzL1hXfld6E/nRctoDf3UX4FHbCJdKqqAQ0/IUKsBDKkByitjBo+MbITfa/4Nu2Jzfm+Pui3bJL+Pxz7Q+HyjedzQcsDr6bPt2vSlnIQNAPsflWALicgNBqMVGKWUAnD8gMF/cjKt1nougPf7DGoAcITNJKcD6Buc9m+t9TIHs/rjgNenKiXcfUdERERERGRgF4xW4knPzwu/Vw4YcRoCQrGcBpyELAajkTvSeimFRWVDsSpB9Nm8sQKGsKOINl988KmilLaL3JNCqIJWAEErIIbFpDOITKpbCr8BshMcKAViBCIdxvUAAPyG9VgOWAugQ9oe5ElQol1AmRSMZlef07KJHN+kO/DUTZs0NIKW+AwPIupDDEYrig1Gi16GMsjzp1ZS+oR1CG83v2wcN7J4LHYrm5LhFg0ONUIwWlMoDb2V4xCDXG3OsxARERERERERERHlnDn/hg4mef1RCk5KQzAagJ4Ar+s2/VoscuXT+XON55ZXLZx5j8YLnwKL1gEPvKvRJXykDQMvc738qH3lnQOuRW/6ErCEB3ENsmC06jKFV37qwYQ4z1S67vn8fHCZHIwWvS6qL7cJZ+pLCj7sQ2/dAGwSAvcAoLQM6uYXo6FQBUINHQVU1va8ntX+Kg5vn2ssqzWwbGP+bJO6rdxibvPISsBfHD8MJBH6ziuAZ+9PS93oaIVe3+is7MbVzvoK7HUI1LX/ctUMdfENwOQD5ALrG3H7mQp1NuFzU39nQedoX4bF6zRuflVu255j+y87+i+/kCv79iWpalbh+e4v+72sD60Si85+IDeXlXTYuF3jkOvc7bdXb0t9eFxzh7m+6pIw1AsPuqtMKWD/I6GuuD/5hhGli1JQQn/yPM9FQ5O0PsfpyqxDXdAXfQX4x5+Bzz8CVi0BXn4U+idfhV74bhpaSuSe3vgl9AWHR4+/VywCVi+N/lu1BHj1cegfz4Je9B70PVdDX3+RbV3qphegyioy03Ds6O9rOIehANR3mCNhTv9rnm+QdvjFPxN7H09c2D9+Sd9/rVhW3fU2VFFxQvMhIncYjFZ4JgPoe7q5C8AcF9O/MOD1KTZlTx7w+j4nM9BafwZgXp9B5QAK6HEWRERERESUbp02IRclQjBKsuzCU5yGe1EspwEniQao0OAlrZfZCH6SeJTHJsypf3iApSOwYO6IwGC07JO+Rw2N5vA2cTpTwFeq+IU22YVhSO8jnezWAWk9Nk1jFwTWInwH6fz83bL77O2OM6TwODffpbRdlOq2k+79uttjLobXEjmzLbTZOLzGF+fuBiIHPm59D9sjzcZxM6qPlcP2KCmmYEMAaA5vzXjn/3z4bUZERERERERERESDzBEnQz3wEdSPrnc33YK3kptvloLRynUHRoU3GIs8Ml/nbGhMXxFL439fdN7O7lCrbvreq+0nCAy4Pr5eDk7ByPGO21EoDmhQ+OIPXly7x8dimRtf1ugK5/6yNJAUatUwDNCtTc4rktbvvp59QBylLrsLnpeboCbs4Xye+eK4/9fv5bWbrhCL/tHFep4rGreah8cENKaI7mgF/nVr0vWoG56R5/GPvzirZIO8rVR/fTe6r316LTy3vQ41bLS79vnLoW6bIxdYtxJDShU+uly+PXrZJuC9Fa5mmzG3zZGX9W9O7f9aRyLAgrfNhcdPhPLwFnGJ8nqBY87oeW0XjPbAuxrrm/NvG5SIB991/z5DEWCti92iE81C98TqkNznFoedEN22DPz37Hp4bnwOqsomLZEo2zweMRgtH36T2RHX53hdmd9+Fli5OHZ4MAD9xB1Jt4soFfQz9wHN5j62AICuTui//TEa8GdDXXYX1G5TbcukhRDubndctK09v7dJX2xKvP27jxow4IW/mQtWD4eaNC3h+RCRO2k6e05ZNG7A62Va66CL6RcOeH28qZBSahSAvfsMCgMQzrAYzQFwYJ/XxwJ4ysX0RINGR0cHPvroIyxbtgxbtmxBZ2cn/H4/Ro4ciYkTJ2LfffdFcXFqEmVXr16Nu+66C3PnzsXSpUvR1NSEUKj3RtX77rsPZ599tnHa+fPn47777sM777yDL7/8Ei0tLbD6PCVp5cqVqK+vBwAcccQRmDu39ykv+f7DnYiIiDIvKNxUW6SK4VXetMzTLhgtkeAQinL62TEYjdySwnDs1uVs8HvKjevBwGFhLXeeYzBa9tktV81hofdbnOmSJdXdZNMevyfOo7nSQAo067QCiAhhgKb3ZhcE1hQ2h8Hl0vbAPoBV3ldK49wEjUjfe2ckgWA0h9Mkul8PRNwFnTG8lsgZaTtpDlYyh1hpodMW0dzm54zDS1QpDqr8SoZbM3hUC8GGId2F9kgrKnyVGWuLdLySjVBeIiIiIiIiE/bPIyIiGnxUZS1QWQtdWQv8+VLnEy75CNj/qMRnnKVgNAAYE1qPDb6Bd3dGbWsHhlakpwmpsmorsM78HBajCQNPk7e12E/QOeBatBT2Uz0cqizHP6w0mv7/2Tvv8DiK849/5+5kFVvWqdiW5CbZFOMCprjSTDPYtFBCaIGYEpKQEAjwA0IzpgdIgACBQOi9mF4N2BhjwJhusI2LzlWSbVmy6p3ubuf3hyzpyrxzu6eruvfzPDz4ZmZn506zu7O773xmlAP4SZ23rblDQjSmPLFt6ilrifntlSUAVn5rviJf5DgIuXwJnTluivl9pRmirCLobfJIL22qWlGdfvcuLmKNyoriOC0Qtep7wN3DmOWBQ4ARY+n81T+Yq4eSSBYOjIn0QdjtkOWVwOYqxb5dHbvqK1DcF6gjQooWr5GYMjL1FutavIbu6yMGhLS3ViPrHDA4Ri3qxfQr6PpnZbsLNumHQcw1WOICjh+foHYlEZ2YT0dNIzAsht6xBiK0z+nRyGd2Hd87JaJMhiBgI+KiDSP9xkCB1FNitAihzPLHxXTmz5qxM8MkEl0/7WSxOkYyiCEje96WaBis3u+u7avxLrHJN+uAw0fHr0nxRjfW1mETwc/HtLL0YvUzNoZh4gOL0XofRSGfLTz6V5YfKoQokFKGvgUIffr1g5TSyqy00FEA340yTAB+vx8vvvgiHnvsMcyfPx8+Hz0BPicnB0ceeSTOO+88HHPMMVHv8+GHH8Zf/vIXeDxWXIqAz+fDn/70Jzz88MNR75thGIZhGMYqHsOtTM+O46Rau7Cjj8hGu8I9rROWMHrM/nZe2R7nljC9DUqGY0UWlAgoGUCokEgnEXIIfsSXbHLs0YnR4tkfqbrrvepoPAGBbFtO3NpDQR0DbUYL/PAr81TfTScWo75zKp0P+ohs2GCDoQh6oCSiXqOdlCZakb7lEv23lRBMUkgpSSllKF7DuhjNkH54pHoMSMHyWoaJTLvhQYu/SZlX5BgQlpZ64cJMKrPR7cKatuXKvAn9DyYFqUzPUYsNO6j3bUuoGK2NuDdLJUktwzAMwzAMwzCZB8fnMQzDMAwDAKKkDHLsFGDZ56bKS68nqncl0t0KfPSiWuwCxE2MFigjOrTlY3yTu7eynKsu9cVoP24yXzYnCyjtdqBANjUAusmsQJgYTVa71OXKhptvSC9k4vgSDHqhFrWOQcr8dXXpJUZrcktSpjSiRACrXOYro8SHgVASKQAYuqv5faUbAZJGACj2byeLfq35iVIVV5160n0F/boOACB9XmDBXMhlXwD9iyCmzoQYtW/E/cnXY3BvePAJHddAKv/bT2Dccn7Hv5d+BLQ1A0eeATHtRIjxB3a3pdql3r40hufKsgrl9VN+txDiN38FAJy4j8DDn6q/zeUvSxw1VmJMeWpFO3y3gc47aZ+QtmrOHeKg42PUol5MfmHXP/vJFhzV/AHeyZ+hLNpxPKdWX4klNTskXvpaYh0d0oqB+R2yU5WjqSHGoXjv/qg+bgs8W8htxLQTY9sIhkkkQkAQC1ik47oWtY0SLy2VWLaZvl8rjBQatHE1ndfG8b9MivD1/J7XUVIOjJnc83qiQEw7AfL5f4Wln9D4Gu4t+rNym3Xb03tMVEXIqyMxpBAQIuB7b3bRhfeZFt1OGIaJCluyG8DEnNCZ4tkWt1eVVzk9Q9M0o08la0zsg2Eyko8//hijR4/G6aefjnnz5mmDrgDA7Xbj9ddfx7HHHosJEybgm2++sbzPd955BxdccIHloCsAuPrqqznoimEYhmGYhEMJj+ItdKEm7bb5+aF7tJj97SjxC8NQtPnVUWupNvmekkGEynx0x4BDZMW0TYx1KLkXQEu5bLAjS/SJV5PIvl7vU7cn25YLm0j84+Jcm/oYcBttYYLATlTfTSc5o+R0qXQ+EEKQ7aEkojq5KCU7U5Yl/gZmJWedeKRbKXZTEc11nRLj6mB5LcNEhrouAHqxEsOYYWEDvRLiQU51oDETG/rbC2AjQgF04t54QF2PU0lSyzAMwzAMwzBMZsHxeQzDMAzDBCIuvx8YOMRc4cdu6hDZWEA2j2HnewAAIABJREFU74C86AjI2y6gC8VJjBYoI7p2261kMVeUE0YTxfYWiRMeMPcuGgAqikMmsn70YuSN2kLej1MSmljKftIQ+4AyPFN7Lpn/z3nm/06pgG6ydGWJRvqkIsJ9BQCgdr06/dd/Ce6zvY0QMRoA/LPmMmVRjw9YvSW9zCBUP6rUvG6X3nbIK0+EvOEs4JUHOq4vFxwA+f6z2n0Z918JzHu+B60FMHYyxFlXAgDE+XPocu8+2fHf1k1A8w7glQcg/3I45LN3dZd56nb1toq/edSUVarTF70FubLj/nzO8frjZ8LNBt7+IXX61Xcb6LYcNgqYUBGcFvSbh3IsfU5mOhD9CoI+/6tWff4BgEteSJ1+Emt+qZWYcLOBvz6v/45FfQEn8Sq/vjV2v889HxlYuEqd5/TvUGcceQbEiDExawPDJBxbx/LJKqRMr3H06i0Sk24xcNHzEv9dSJ8bnHn0NVp624HFdGwZPBz/yyQf45HZPa+kb3+Iax6FcMTp2U8kRk9UJh/Qtpjc5OpX03tMdMOb0bX/yXOC4y3ly/eRZcV510e1D4ZhooPFaL2P0Ej2Movbq8rvrkjbJeQz8XSWJPQtQbEQolBZkmEyiBtuuAGHH344fvnll6B0IQRGjx6N6dOn47TTTsPhhx+O3XbbLWz7pUuXYsqUKZYDoa666irIAK346aefjo8++gi//PILqqqquv47+eSTg7arra3F3Xff3fW5T58+uO666/DFF19gzZo1QdsOGWLyhTHDMAzDMIwJKDGGTkwTCyjJCEs3osfsb+eT1oIKGYYSKFqRBSUC6rwVemzojgGHSNJLEqaLbJEDQayKQwlncu15cQ1qzLGr+5ZXhq6rsLN8nK+hFNR+JSQ8krjeK47jLNEHNtiV5anvnGoyDur8RElEQwWKgViRvpFiNEIwSUGJ7FT4iL+JDt33JbdheS3DRIQSeAKA01EclkZd7yS9ljSTobT6m7Gk8RNl3sjcPTAkpyKxDcowbMKOAkeRMk933McDK7JbhmEYhmEYhmGYeMPxeQzDMAzDhCJGjIF46juIO9+A+MdrEM/8oN9AN3FbxbtPAsuX6ss44rQgXoCYpq9sxcj2NcpirrrUfs/z8KfW2hcqI5J3/SXyRu6QZ9k1LEZTIWw2TCvYgD08y5X5H68A/EZq96dA1m5VpztswJBCANUu85X59WI02dTQIZdSIKadYH4/6YhCknVUywdk8UtfTB8xSFu7RG2jOq+iWBMXtvgd4MuQ38AwIO+9FNLvV24iN60Bnv9XlC0FMOMsiPvnQ9w7D8K580R5wDGWq5GPzIZs3A65eS1dqCx250qhkazJR24AAAzqL3D9sfTv7fYCl79sBN2XJ5OLnqP7+GOzbEExhdLvB5bMUxcetS9EVvwWZu015AdPWR7prcLJja+Qxbc2pUY/iTW3vSuxqSFyOZsAnEQoaUOMQvEa2yT+PlcjUjKI6+Xf7o1NAxgmaQgIIsbOSJ/hDwDg9vck1m+PXK5QFxq06C39xu6WlLl2M5mJ3FYNPEFL5s0iXlgBse8hMWhRlPsXAjjqt+HpAE5qnKvcZksT0OpJz+OvJ6LtSQFOZikl8N7T6oJDRkLk5Ue9H4ZhrMOzJnsfK0I+DxZCDJFSbjS5/RRFWoEizRnyeYvJ+gEAUspmIYQbQE7Ifuqt1BOKEGIggAEWNxvZk30yTKy4+OKLcc899wSl5efn46qrrsIZZ5yBYcOGhW2zevVqPP7447jzzju7VpNsb2/H73//e7S0tODiiy+OuN+VK1fihx+6X+DOnDkTzzzzjKk2v/baa2hv757IetNNN+Hyyy83tS3DMAzDMExP8BDCo2yRo0yPFdSk3WhEHUwHZsVolNSGYSjaDLXQJ9Um35NCIktitDgFyDKmEUIgx5arvB7U+0LXMegg3n3Rav1UX4w30fwOuQqZmhACubY8tBhNFvadHBkcBfVbUNdK3fjDivSNFLJZHN+0EuddFdEIT3Vjhj4iG+3So9jGmtyNYTKR7T51tH8/ewH62LIVOb14pXImpnzROF95bgaAg50zE9yazKTQUaKU9FLj03hgSL8l2S3DMAzDMAzDMEw84fg8hmEYhmEoRF4+MOnIrs9yn2nANwuUZeX9V0AcdLzpuuXn70UuZI/T1K6S8g7pmq/j/WxlexXW9AmfPuNK3GPjqHhvmbVJrRUlAUIZj/oZdRhtIe+Wq13KYjpBT8ZQVoHxG7/H8uw9lNnLNgF7DU1wm6Kkapu6bw0rAhx2AWNzlfnKfBHiICjZHtDrhXsiOxeyqBTYXtOVNty7niy/mhDWpSK682eopDEQ+dnb6ozG7cDaZcCue4XnUXKsTnbfB1j5DZktTr0YYsSY4MRozmneduDrBUADPZVUxLJPl1fQeV/Ng/T5IBwO7D5IX82Kmo6/l+7vkigoOZXDBpSFzuKt+pmuiK9J5ugXPjV6D0/oFOxuPloucerE3hcb867J8dTwYpDCx/oYTZdYuApo01w2C/0K25JzAERev9g0gGGShc0GGyFGSzf/1zs/mmuwUxMaJL98X7+x398xxmYJKJMsln7U8zqOOxeiIHyB4kQjyiuUZ59hmvuyxWuAw0fHr03x4v2fojuhDnYC2VkBY8BN6sUFAADlI6LaB8Mw0cNitF6GlLJGCLESwO4Byb8FEFFJKoToC+BERZZKWRl6F6m2EuhpQ7AYLRZqzD8BuD4G9TBMQnniiSfCgq4OOOAAPPfcc9qVHHfZZRfcdNNNOOuss3DSSSdh2bJlXXmXXnopxo8fj2nTpmn3vXRp8ApUoatOxmPbBQsWmN4HwzAMwzCMCjchRou34ISqn2oPExmz0hWf1K+oyDChUMelFVlQIqDOK21+K2I0fsSXCmQTYrQGb7iMAoh/X7QqHEuWJIyScunIISRuOXZrYrRo9h1PyPMBca10++lrqJW/Jylo9FuTilkpH811PfS8GEhh1gDUtoevDdLGYzSGiUg9cZ0qdFgNxkiz6CwmrhjSwML6d5V5/e1OjM+fnOAWZSbOrGJAMd9LJUuLF7rnJSrZLcMwDMMwDMMwTLzg+DyGYRiGYSxhs9F5JkVJ0ucDVn0HfPVh5MJ2u8mGWUPY7ZCDhgKb1gIAKogJry5CDpUqrLUoSQqS3tTSk3yDcHe/75btHmDbZnW5st4tsDJFeSUOWzkfzxWcqsyu2pb6YjQpJVZvAW59V933RwzY+Y+dx44p/BHiIKpd6nRHFlBcZn4/6Up5RZAYLVuzUO66uo6/kRCpKyba2iSxeA3w/BJ1HxICGFqoqWCzpm+1NSuTZYTrj5h1NeSNs4AWhdFo4BBg+KjwbXL7Qu65P/DDZ9q6w9ry6RtAyw66wMTDLdWnZZ9pdJ7fD2zZAJRX4pBRAg6bhM+gi983X+LaowFnXvL6lt+QqNK8rrXbQtqm6Stiv8Ni1KpezoDBYUlHtHyIGwdcrSyeTnJGs7R4JCk7C+XwPQQpPGqIQSje5gaJf7ynOVABHNqyIDxx4hE93znDJBshIIgYO8NI7XuyQNraJao1w4BABjs1mdWuyBW4W0yJ0WRrM/DTF0BdTcSyTIZROhwYMwkiCsGe3LCqx7sXE6f3uI6YMPEI4NEbw5Knt3yIfxWrFwCqqpNIx4WU10YZGlkRGjKtuf8Sex8c3U4YhokanjXZO3kaQODV6f+EEE9KKTdF2O5GAOEKdHNiNJPLqATRBiDwMR8ru5mM5JdffsGf//znoLSpU6fi3XffRb9+5g6L3XbbDR999BGmTZuG5cuXAwAMw8CZZ56J7777DiUl9JIWtbW1QZ91gV6x3JZhGIZhGKYneAz1LUi2LUeZHisoiU2bYU0cwnTjNi1Gi7CiIsMEIKUkBTrJkj9RUPKq0GNDJxFyiKyYtomJjlxbHhoQvgxovU+9NKhVcZn19ljr67l2tRwr3kTzO1Dfzep3jvffwCqUoIy6VlLCtGyRA5swHzhPj2+sLfFoZTzkjeK6Tv0OdjiQby9ALcLFaG4eozFMRChBUlHWAGU6FeaQPqFZTCJY2foDtnjVE4b2d07n8WuCKHSo3w01EOPTeKATo6XaWIxhGIZhGIZhmN4Lx+cxDMMwDGMZoRGjmUDWb4G84gRg+dLIhYEOg068KKsIEKO5lEXe/rFjIr4tVMaSArT7JDY2WNumsjjge3y/yNxG7oD30Vs2AJJ4+1XKYjRRVoFTGm/EeeUPKfOf+sLAr/aOj+wvFvj8En9+TuK/C+k3nBUlAnJ7LbDDwozqSGI0StI3cAhEnOSIKUVZBbDsi6Ck22uvwhWDbg0r2toObNgODLO6lleCePoLA+c+IeH102UGO4HsLM05dcsGOs9HxNQ8fze9zf5HA1NmQlxwI+Q//xqcZ7dD/OFmsp+J82Z3XLMIIZuSec9ps0X5CPN1RUAUDYI89WLy+8vrTgf++xkG9bfh7zMF5rxFH9v/mtdx7L/wextmjkvONU/XvgfOCG+TXDCXruwItaCSCaGsIixpStuXZPHrXpf4/YESA/un3rgoWlwmQwTGDwXOniqweA0hRrMWThjGv+YZuPQlfYTR0U3v4LCW+WHp4szLe7ZzhkkJBGxSLQaU1P1HCmL2nNI3GyghHr9Lvx/4ZkHkStytQL7ONgvIn5Z0jGWsjN2ZzGLAYOCe9yGG7mp6E1m/BXjytp7td99DgKkze1ZHrBg9Edh1fIfAPwCljHQnFzwlcdoEiX456TUmilb+7wwNZdSJqY8/L6p9MAwTPT17Ws+kKvcBCPTtOgG8K4QI15vvRAjxNwBqpSegV3B3EM1VIn1G6gwTRy677DI0N3c/QHY6nXjllVdMB111MnDgQLz88svo06fbXLxp0ybceGO4xTeQwH0DQFaW+QlJPdmWYRiGYRimJ3ikemJtdpyFR6TAyB+DJZAyEK/RrpU9hZZlGLP4pBd+qPsWJR5KFpSMKlRIpJMDslgiNaCuEZQsipJRxQqroolkSQP7iGzYLD6mpr6b9e+cWjIO6m9ACcooUZhVyZ3Z81AkrIjRohGeUu3JsefScjceozFMRLZ71UvdUkKluE7MYXoNCxveVabbYMOBBUcmuDWZS6FDPVukwZtIMRo9nki1sRjDMAzDMAzDML0Xjs9jGIZhGMYyESRF0q+x4QCQD11rXooGAHW1kctES4AMpMK7jiw2b3n8mtAT1m+nHWUUlQGvueQ//mhqG9kW8L672kUXVMhVMo6ySuTJNkxtXazMfvVbwG+k7nSxl77WS9EAYEQJIP9FTbEjoGRWO5HVxPGXKbI9xbFzauNLZPFLXjQzlTHx1OyQmPW4XooGABUaqZv0+YAt4Yv/deENj5WVNfT5G+WVEDe9CGGzQZzwB4h/fwicenGHNOuMyyEe+ARCI9ASex8E8fBnEOdcR+/DCr+7Ojb1BGC78HY6c+U3wFfzAACzj7PhnYv0cWgtHuD0hw20ehJ/nlpfJ3GjRoz2632D4zGklMC859WFx06GyE2tWNiUpaAYyA1+BiQA/KH+v+Qm17yeutexaKiK4Arae2iHmG/+pTYU9RVw5qljg3oiRvtxo4woRbNLH17eeCqyQuO/jzkHonJ09DtnmFTBZoMgtAopPHwOI9I5pZPKYkBQsYafvGquErf+xCMNA3LOWSxFY/Rs3QR5w1mWNpH/vZ7OnDoT4uVVEH+4GTjitI5xd+B/R8+CuPIhiDvegMjqQ9eTQIQQEP9ZEJZuh4FfNb5ObvfPD9Po5LQT6hx19dECN/2Kjn8OXStAUmK0keMgIggbGYaJPY5kN4CJPVLKBiHEOQBeCUgeB2C5EOJBAO8C2AwgF8B4AOcCOCCg7EYAgcvKqdZXCV0GIJqZk6HbWFhagOQBAPSTUTUjAdBXbYaJIytWrMBbb70VlHbbbbehtLQ0qvpGjx6Nyy67DLfccktX2v/+9z/Mnj0bhYXqgZZhRP/CoCfbxoKff/4ZP/74I+rq6lBfX4+cnBwMGDAAe+yxB/bcc09kZ2dHVa/P58OSJUuwdu1abN26FR6PBwMGDEBFRQX2339/5OTkxPibMAzDMAxjFY/hVqZn2+J7nc61E2I0i+IQpgMrwpVoBCpM5qLrWzn25MifKCgRUuh5RXcM2AU/4ksFrIrF4i2CsCpei7eojUIIgRxbHloNc48GHcKBLJv6JaHV3zRZ35mCEje6/epzGikKs9gXaalYC6SUdGBCWHkr13XrwlNSBGfL04zRzMvaGCZTqfepBUmFWYQYjSCdVq1k4st271b80PyVMm+vfpPgzErRpd17IU7iOK73bbN0je8JuvFBqo3FGIZhGIZhGIbpnXB8Xs/g+DyGYRgmYxERFvfaupGUGUkpgfmvKPNI8p3WyltAlFV2Tb0frhGjvfCVxJFjUm+BHLOT7gPpFKMFyc4i4Q4UoxG/U9EgiOzUijtKCuUVAIDRnhVYnDdVWWTxGuDAXRPYJgu8/HXk95qVJQB++tJaxf4IC8RSYqsMke2JsoowDUiZrxpZsh1eER4H9JUrIc2yzGvfSfhN3KZVlmjOp1s2ADrBpkqy98lrZHHxu6shHN3xg2L8gRDjD4zcyMA6ho8CZl0N+f2nwNfzLW0bVle8+vQeE4Dl6vfQcv5ciEkdC3QdNVbglP0EXlxKH+uN7g4h6PHj49JSkrnf0m0qzAMKQmVUq76nKxu+R4xa1fsRQkCWVQBrlwWlj2hfS27z0lKJh85MzDv1RLB+u7rvDS0E1t0eLgR2Eq/yG1qjjw16ycT195ptt4ZL0QCIPdXjDYZJO4SADeqBRDrF3lVtM9fWSk34ofzoRXM7iyBGw6rvAUpexDCBrPwGsmEbhDNyXKyUElhAP9cRk6ZDDBoGnHEZ0mmkILJzIUvKgW2bg9IrvfQx9PJSieuOiXfLYouLWDN214HAGZMErnlNfQ6bWBny16x2qSsaOS7qtjEMEz08a7KXIqWcK4T4K4B/Aeh8K5MP4PKd/1HcC6AAwNkBaWkjRpNSbgGwxco2veUBBZOe3HPPPUE3rSUlJZg1a1aP6rz44otxxx13wOvteBje0tKChx9+GP/3f/8HAHC5XKisrCS3P+SQQ5Tpjz32GABo20cdT1VVVaioqOj6PG3aNHzyySddn63cuG/YsAH/+Mc/8NJLL6G2ll4dKzc3F4cccgjOPvtsnHTSSbBHWLkLAJYvX46bbroJb731FhobG8l6jzvuOMyZMwe77bab6XYzDMMwDBNb3EabMt2qCMQqlHDFiuCL6caKUM4nIwQOMUwAur5FiYeSBdWe0PMKdQzYYIctUkAukxAsS7kIkVOssNqeeIvadOTazYvRdO20KtdI5ndWQYkbqXEGLQqzdp7LtfdTpvvhg1e2o48wN7Gt1YKELJrrOi2Cy9OM0dRjRoZhOpBSot67VZlX6BigTBdkKEf6BGcx8eXThvchiSC+gwpnJrg1mU2hQx3I5ZXtaDGa0M/eP+5toMYrdjjgEFlx3z/DMAzDMAzDMAzH53XA8XkMwzAMYxFbhDiM6nWkGA31W4DWJmv722eatfJWCBDUjPX8TBZbuzU13/WYnXTfyahSoLDvzjFTtcv8hl5P1z8lJbCi/uaZRkWHjGdS2xI8UniOssiqWokDd03NuVq/0EPcLiYMl0CD+j0qSZRiNJEp/WrM5LAkGyQk8f65rhkJW+jHCmb6DwBU6HwL1S79xgoxmty4mi4/ZqKZJpljzKQei9Fi2p7QegkxGjauCfo4qRJ4cam+utVbJJBglcUqzYzXySMUiZvWKBI7EPH6nXsrZcPDxGiT22gB5o62jvNQSX68G5YYthNhfWUF4WnSMOD0N6BjinkwDT2YLrHaxIzvSW1L1BmjJ0S/Y4ZJKQQEEWOX5PUxLLHWpLx68kjNdbZ2g7lKIonRNqwyVw/DAEDjdsCEGA0NW4HmHXT+mEmxa1Oi6V8YJkabTF1/AazZKlPyvoyioVWS45XKEgG7TWDqyA6Zeygn7hPyHSnpYjn9/o9hmPjBsyZ7MVLKewHMALDSRPFmABcCuBjA4JC8GkX50Cu6eoYMgRCiH8LFaCoBG8P0at57772gz2eddRb69Alf7cQKAwYMwLHHHqvdTzoipcRNN92EXXbZBffdd5826AoA2tra8M477+A3v/kNNmzQ3yj7/X5ccsklGDt2LJ599lky6Kqz3hdeeAFjxozBPffcE9V3YRiGYRim53gMtzI92xbflaMp6YYVwRfTjRWhnE8qVsFjGAJd34q3QNEqVHvc/lAxmvoYyGKZQMpASa3I8nGWclltj1WpWCyx8lvojmHLMjiLv1G8occZarlXm58ShcXub99mQXZmZTzkjeK6Tovg8sjv4LbQfobJRFqNZrRLjzKvMMtEAAjDhOA1vPhsxzxlXlmfodgtd2yCW5TZFDqKybwGL7E0YoyhJKU59ty0CZhiGIZhGIZhGCa94fg883B8HsMwDMMEEGmBumqXMlm2NEKeGy7/0TLjLIgC+nlujwkQo+Ubzcgm4u6qTE5uTzRUuyaPAH43Nfw582XTA9KqiUmsKtrbA7ZzqcsE/JaZjMjOBfoV4NeNr5Bl5tEOvqTi2ibx02Z9mVP2E6g0NgLedn3BUCKJ0aop4d4wa/tJU0TlHsr0S+rUY/42L1BL3zYkjWe+NCdrrNCc1uUDV+k3VojRKLEeAIhhu5tqkxnE0b8D8gujr+CAYyCGj4pZewIRx59PZ37/adDHMycLpfApkMtflli2KbFSUJdG9nnx4eFjD7nsC7qyQ0+ORZMyB8U1fErblxgp6IvCQwtTUxobDZQgpDBk/VX5ygOQxw2B85nrleXrezBdIpLsNs9owWEtCjHjfofG7bzCMAnHZoNNqg1oFy2shM+fHucd3fWskwH5wNlTNHFBK78xtzM3HQMspYS85Vxz9TAMALOL/8pXHqAzR4wBdts7Ru1JPOLsv4elHdP8Dlm+zStQ86/ZkH5/PJsVM3TPtjrv0f52hA2hYYvH7AnsURaS+Mu3ynoEi9EYJik4kt0AJr5IKT8QQowBcDyAmQCmAhgEIB/AFgBVAF4D8IyUsgYAhBChd4oqR36oRtfqEhWh5bdLKest1sEwac3GjRvhcrmC0qZPnx6TuqdPn465c+d2ff7iiy/g9XqRlZWeE+Z9Ph9OPfVUvPJK+Muz0tJSjBs3DiUlJfB4PKitrcX333+P5uZmU3W3tbXhV7/6FT744IOg9KysLIwfPx5DhgxBdnY2ampqsGTJErS2tna16eKLL0Z9fT1mz57d4+/IMAzDMIw1PMTE2uw4C49o6QaL0aIhVPykwystBhoxGY2ub6WaGC3X3leZ7pFuGNIPm7ADAHxSHTznYDFaymC1b8W7L1qXhCVTjGb+t8i1qY8ZAMi1+B2SKYNTQbWHkj2SorAY/g5t/lYUOIpM1dPqNy8h8xnWxWi0CC6P7O+UjIVhmA7qvXQUQJHDmhgtPcKymHjzbfNiNPvVqyUe5JzBIqwE09/hhA02GAgPqqz3bcMQxD9IhxqvxFsSzDAMwzAMwzAMA3B8nhU4Po9hGIZhQrDpxWiyZh1UT7zlLecB2yJYlwIQ580GzrjcUtMsU14R9PGFTWfgV0PDr/kbG4B2n0QfR2o9y3cRr7MqSwQeOlNg10HAq99IlPQDzjnAhpP3DRSjuczvyBuwmBAlICq1Om2q9yKu/h/6XXUyDmhdhEV5B4Tlv7BU4qlzJRz21OlPhiGx1xy1iKKTa48RuGamgLzsArrQaX8DnvtneLpKZrUT2bwDaG5QZ2ZQvxLnzYZ8ZHZQ2iXb/407Si5Tlq/aBpRGkFslkpodElubzJWtLFH3ffnpG+QE+y5UUr5ql7KoOFctL4oWUV4JPDAf8olbgeVfAZvWBhcoKgUcivteZwkwaTrErGti2p6gtlXsAZx/A+TD6u8sv18EsVfH+WhAvsDiK224/g2JJz+noxkOudPAuttsyMtOzLmKEjUcvgdwxOjgNkgpgRfvVW8wYgxE3/4xbl3vRpRVhMW1CABfNxwPZ8FXym2ufV3iNxMkdhmYOteyaGkgQuicud3fTS58HfLuSzrS+6uvWVQ9Zogk4V29ejTsitgGcftr0e+UYVINISCIKLsWnx2z35S46Vepf85Zu5XO22UgMKlSYPaxAuVOYjw073nzO3Nr5hu99hAtM87OBQp4YdiMxPDTz2Vk5ChXWfUz8MStZL7472fpHQN5yEnA9WcEJWXLdvywZh/sOVItLLzqi0o8WnAzxLnXJaKFPYIab2TZgXJnx79P3Efg9Qtt+M8CA7WNwPQxAtcfEzIW/+A5eicszWeYpMBitAxASukHMHfnf1qEEEMBDAlI2iSl3KQoujzk8y4WmzUi5HOKrgeSeHx+iY2siIs7QwqR9Jcsn332WVjafvvtF5O6991336DPbW1t+O677zBhwgQMGTIEVVXdqx/dfffdQSsrPvfcc5g8OXylqpKSjhvBadOmdaWdeuqp+PLLL7s+B9YbyJAhQ5TpZrn00kvDgq5mzpyJ2bNnY8KECWHlDcPAF198geeffx6PP/64tu4LL7wwKOiqoKAAs2fPxrnnnov8/Pygsm1tbXjggQdwzTXXwO3uWC1rzpw5mDRpEmbMmBHlt2MYhmEYJho8xMqVyZLMUMISRo+V342SQjGMCqpvZYucLtFYqpCrOW+5jTbk2fsBAHxSHTxnF/x4L1WwKneIt5TLav3JlITpZGeh6K71VsYBWaJPyh0/VB+ihCLUuc5yXyQEjbp9qMtaEKMR5zQdOhEcKa+1IGFlmExku08dpWSDDQUO9SrQQjnNB2A1GgMAn9SrVw/MFjmY2H9aYhvDwCbsKHAUod4XHvFT76tLSBvI63eKCasZhmEYhmGYzILj8xJHsmP0OD7PPByfxzAMwzAhCL0YTSWokTvqgM/eorepHA3bkxFkOPHAOQDIyeuaUD6iXT2ekBIAWK/HAAAgAElEQVRYv71jInsqUbVN/Q6qohjIcghcNUPgKmKYIDe7zO8ocEJ9tVqMJsoyR2AVkbKOxVcmt36pFKMBwIfLgaPGJrJRehatBprUYacAgOo7bRjUv+P+xfh6vrpQvwIIZ4n6zahfE99Ys57Oy6R+NWy3sKQB/q3oazSjxdYvLK9qm8SUkakjHXhuifl34hXF6nT5zpORN/YHx9RIKcnzEobuarpNZhEVe0Bcb6KdyWD/YwBKjPbaf7vEaAAwvFjg8VkCIwcYuP4N9d+urgV460eJU/aLfz+TUsJFvKI970DF/n/5jq5s9KTYNCqTICSU/WpXYtTuwIoa9WZPfSFxw3Gpcx6KloYW9THgDAgblO880Z3uV4vR6ls6+rJVIUyLR2JLBLHkQL8ihmnsZIg+2Zb2xTCpjNCI0QDgsc8kbjze+jGWaKjr2ROzBH47JcK9NAD5+sPmd6YRo2nHVcfMgu3if5nfD9NrkNtrIY8fRmSaEKO9/wydWTkaIju9Y96EEJDDdwfWrQxK36N9BXJsPriN8LkVr+cfC3x4EJAGYjRXnfpvPLwYsNu6z63H7ClwzJ70vDL55qP0TsrjvxgtwzDhpNbMLyYVOCzk8wKi3LKQz3sKIfKklGZnmu0fob6MZWM9MOLv+lVImJ6z9hYbKpIsfN64cWPQ50GDBqG4mHj6bZGxY8PfIG3cuBETJkyAw+FARUVFV7rT6QwqV1paGpQfSr9+3S8ccnJygvJ020XLBx98gHvvDV7l4rbbbsMVV1xBbmOz2TB16lRMnToVc+bMCWtnJy+99BIee+yxrs/Dhw/HggULyO+Rm5uLSy+9FFOmTMFhhx0Gt9sNKSUuuugirFy5ErYIq4MxDMMwDBM73IZ6yaFsm/q6Hyuoibtuf1tUL/oyHSsCFQkDfumHPcWkVkxqQstzzMuXEkWORgjVZrRGFKM5UkzslMlYlVFZLW+VbIuyiXi3R79v823VtdPKd0imCI7CqtyLFo1Y+27ZIgc22GAoVly0cq22Jjy1LkZrI8Z/ObY85NitSeUYhumg3qteHq3AUUTKZGkxGpPpbHCvRZV7pTJvYsE05BLnaia+OB3FajEacfzHGur5TTLHngzDMAzDMAzD8XmJI9kxehyfZw6Oz2MYhmEYBZGuOdWu8LR1KwC/n95mRHIMUUIIyL4FXRPKK7yEXAdA1bYUFKMRk+4rzYwzq9USOCVeDwBAetqAump1GUKqkpHsnAi8p4eeCvbjJomjxqbOu8UXl9KT4LPswMCdzl7Z0khXkl8I2IlYLZ8mDqLGpU63O4Dicnq73obiGBIAKttdWJYTfo6sSszrLNP8uMlcuYLcDlG4krUmpk8GihoBYHst0E5Y/coqzDWqt6CTENRuUCaPHSygW+jtx03AKbFxqGup2QG4idNEZYniXFn1E1mXGDE6Rq3KIKi+4/dhbHErVtSo31//tKl3LBLYoH5tD2dg2GbA+clpqMVoPgNobQf6WnSVrYuwbtv05nnqjBFjrO2IYdKAEd4qrO0zQplXvQPY3gIUh/tiUwavT2IHcU4ZUmhy7E/db6lo9yiTpZTAWt21MoUMzUxi0c3pM3NZ1/SrZD3XiTmlw8PEaAJALjxwK9RDbSIXxsY1EGkwZ3Jbszp9iFOdTrKdsOYCQEkG3cMzTArBb4iZUM4N+fyIqpCUshrADwFJDgDqZT7UTAv5/K6FbRmmV7B9+/agz4WF1JNv6+Tk5CA7O/gpU+j+0oU5c+YEff7DH/6gDboKxel0KgOvpJRBdTscDrzxxhumgsc6A7o6Wb16NV577TXTbWIYhmEYpud4DPVLfqsSGKvkEFIlP3xRCT4yHWqCNIVXtkcuxDCg5TxW5EuJQicwaguQIfmkelVRh8iKeZuY6KDkmWT5OMtB7MKOPsJ8BEoyRWG5GkFgKDqJhpXvkIoyDkru5ZFuGDI8gL6NEKZZ/W5CCPJv0Oa3IEazUDaacZObkLTl2vLIv70VWRvDZCIqWRIAFGUNsFyXNBU1wvRmPml4h8w72DkzgS1hAinMUs8Ma/BFiDyOEZRkNRXHYgzDMAzDMAzD9D44Ps8cHJ/HMAzDMApEJDFauFxMfviCvsqZZ/WkRT3j8FO6/pkn2zDIV6ss9oJGHJUMmt0SW5vUeUqJTCifvW1+ZzvFaKhdT5fJNAGRBpHbFxACxze9SZa56e3U6k/LNHKbLDu6J3hXu8hyYsZvaTGaXx3bBQCoIYSEAwdDODJoUUziGKKEjXPeSp0+JKXE44vNteesKQIOe/g5Svp8wGYTwsZQyR7VfwCgLLOEjSJXE2NGnL9njgUG9ac3uzlB5ypK9AkAFQqHu3StoDc47BQ6j1GjuYbPqlhL5qWaoDFaGogQusKdr+2lzwds6V5gwOnfYbkuHZF+x1kNTyjTxYwkjp8ZJk6c0vSKNj8Vzzten8S1rxvY90Y/Jt5CLzpTaCIUXLY2AxvXWNi5WoyGuhpaHAsAh5xkfh9ML0P3rEA/7pOGAXxO607E0WdH2aYUgxgX/cq+WJnebstGjaNUf8ylCNQ4pUhxfpLtHhgPXgNj1gQYJ1TCOHUPGDefC7l5LVBDPBsaNAyCF/BhmKTARx7ThRDiAATLzVZKKRdoNnk15PMsk/sZBWBSQFILgA/MbMswvYnQQKjQlSF7Smh9dXWJmeQSS3744Qd89tlnXZ/z8/Nx++23x6Tu+fPnY9my7tUMzjjjDOy5556mt7/wwguDArreeOONmLSLYRiGYZjIeA0v/FAHkWQL9UrUsUInvWHxhnWsCFSA6CQqTGbiJo5HK/KlRKETAgR+D6r/sxgtdbAqd0iEDCJdRGE5dvNSOZ1Qzsp3oCRkyUQ3zlDJROlznfXvRv0elMykp2W9UVzT2wihao4tj/zbe2U7jx8YRkO9Vx1JVehQi5R0pE5IOpMMWv3N+KpxoTJv19wxKM8eluAWMZ0UOhRR9AAaCDFirHH71dfvZEp5GYZhGIZhGIbJHDg+LzIcn8cwDMMwBJEmOW7dCOntXuBRrvgaePUhsri48iGIiUfEqnWWEeffEPS5ot2lLPfoIolv16fOWx+dFKAywussuWEVIC18F+/O98oK6V0Xg/h9RyDijjfQV7biwJZPlflNbuDhT2lxQiKRUmLhKjpfBM6d14mrfnsl4CBitUJlVoH7p/pVaQW9r95IQTGgEFtVel3K4l4/cP/81OhD17wW+XyS1wc4/0CBO04mZAzvP21uZ76QBYSrXepyOXmA0/qiZ+mOuPF5dca2zUHX5k6yswTmX6q/rt/9Yfz72QVPqffRNxso6afIeOYOdUVFpRBFg2LXsAxB9O0P9C9S5h351Mk4cFf1djqhXTpRT0xxcHa+tt+6EfB3L9xa6G8g63rrB+tjxapt9Db/rLkMv26aG5YubngGYuxky/timFTnnMancdOW68j8VBSjnfWoxM1vS3y7Afh+I13OGSEUXEoJee5EazsPHRd11vX4TeQm4s43IfJj+y6ASSOERowW4RmB/OdFdOZ+h0JMODzKRqUWghCj3VpPH1c3l1wJtFmb/5cMKDFaQZ5CXH396R1j7tU/ANs2A5vWAu89DXn6OFICJ2Y/FcvmMgxjARajMQAAIUQegAdDkq+OsNkzAPwBn08UQhCPAYIIXUruRSll6mtCGSbNELoBfJrw0UcfBX0+/fTT0b+/ZqkOC8ybNy/o829+8xtL2+fl5WHixO4b8U8/Vb/QYxiGYRgm9nikelItAORohCKxwKzAiDGHVZmcT2pWVWSYANr86r4V73NENGTZski5WeAx4jMoMVoGrRqa4ljtX4mQQViRf+mEY/HGktBMU9bS903B80GORt6oumZS19FopG95xL6tXKutCE+jkZW5Ned23fFECVkYhgHqCTFSYRY9k0RoV9RjMpXPd3wEr1QHoh1cODPBrWECcRKiw3pvYiK4qWclyZTyMgzDMAzDMAzDxAqOz9PD8XkMwzBMWiMiTLWSEtiyofvj3NCpOAEccCzE0b+LTbuiRGTnAsVlXZ8pCREAPLAg9cVoNgEMLdRvK1+819rOvJ6O/9cQAqviMojs+C7amnZU7AEAOK75LbLIXR+kRn9a+Is+vyjwtUW1S12ovBLC4QDsRKyWXxPbWLtenV46XN+wXoYQAlBMwqdkjUBHH5JWJIdxoK1d4r75dBuePlfAdasN9Xfb8NBvbejjUN8ryqcJ0VUovpC+RIn1yip6xX2pZYbvrk43DGDzWmXWqDKB+0+nf6t/zpMwjPj1M69P4qfN6rzK4vDnC3LNMnVhADjC2r01EwB1zq1djzkz1efwhlagoTU1rmU9gZKEdInRql3B6QYtRvvH+1GI0YjwhANaF+Gi+gfCMw79NcShJ1veD8OkBULgyro7satHbe3ViQSTwbo6iRe/Ntemgkhh2Su+BjausdYAhfQUAPD6I+r03H5AEqXkTAoQpRhNtjQCrz9MVzvr2p60KrUoq1QmF9d8j3y7R5n3UOH5gDsdxGjqv3FhSKiiXP8LsIh4lqG7tx+yS5QtYximp/DMyV6KEMIhpbkZ40KIfgDeBDAmIPkVKeUruu2klKuEEE8AOGdnUh8AjwshDqNEZ0KI4wH8LiCpHcANqrIM09spKgpeaWDHjh0xrb+hIfghVOj+0oHFixcHfZ42bVrM6l60aFHQ56KiIrhcLkt1BAaBuVwuGIYBW6QVwhiGYRiG6TEeg5ZbZNviG3ykk25YlXwx1mVyPoN4scEwIVB9K5niJx05tjw0+8PvCQO/hx/qxzyUVI1JPCkpRouRcCzeWPktdL+zFdlZKso4dO1XndeosUc0fYv6PVotyM5aDWtiNCmlpQBN+tzeN6K8th9iM5GPYXob271blemFhEhJT2oFZTGJw5AGFja8p8wrcBRhr36TEtwiJhBKdFjv22b5WhwNtMg19SS1DMMwDMMwDMP0Pjg+LzIcn8cwDMMwBGauN9UuYPDIjn///CVdLlUmTZZVAHXVAIBd21eTxb5cmzrvfKrq1G0ZWgRkEeKh7o2XW9vZzgn3stqlzlfInDKeknLAkaXtT7/UdkxMduYlV970RZW+X19+ZHf75OYqdaHOPuAgYrX8fnoHhNhKlGWWGA1Ah5ho7U9BSbo+5KoDtjYBA5MY9rGiBmhSzpLsYO9hAsOKTfTx1mZzOwwRgMhqok9mmFivC933/uxtYPgoZdZugwSouIaN9UD1DmBwBOlmtFBSKAAYotrnz0vI8iJVxhXpSOkw4JdvlVmVnrUAdlPmueqA8akXamgaw5DYQUy9KOy8Ple7gtJzpFqKAgB+w3obtjSq0yvaCfHjkJHWd8Iw6cLOOJ1KrwursncNy9ZdM5LB2z9KnUsqiP6RQoF+0tw3U3jDF2GWujFV0cDMFMcy3UQpRgu9Rwlj8Ijo2pOKDBqqTBbedgzKakGTP1uZ39bYhrzSeDas5zQQYx5n6Fju56+sV57bFygotr4dwzAxgcVovZcLhBAnAXgSwNtSyrDZLTuFaCcBuBnA4IAsF4A/mdzP9QBOAND5GGYqgA+FEOdJKVcE7CsbwO8B3BWy/V1SSuIOlmF6N6GBUPX19TGr2+12w+0OfvJeXJx+A67q6uqgz2PGjCFKWmfDhg1BnydPntyj+gzDQENDQ1oGuDEMwzBMuuEx6AgDq1Iaq+jEa24/i9GsYlUm55XhLzYYRoWbECimoggJ6JAYqcRogceIj+j/DhuL0VIFq/0rJwGiPivXxUSI2iis/Ha6dubY+saknmSha7/bH3xeM6RBymJzLfwOXdsQ/dGsxNQnvfBK8wJTCQkDftgtvKIgxSq2XK34kuW1DKPGkH40+LYr87RiNCJwJNmrdDPJY0Xr99jqrVbmHVAwHXbBr6OTSaFD/W7IK9vRYjShnz2+s0jS7d6MYRiGYRiGYZjeBcfnRYbj8xiGYRiGwGaPXGazCwAgDQNYt5IsJg49OUaN6iFlFcCyzwEAJzfOxY0DrlYWW7YZuO51A6dNFNijLLkTyqu2qdMrzQy7ql3WdubdKQAhBFbIRIFVBITdDlk6DIdv+lhb7sdNwIHhzoeEsnqLPv/EfQL6+roV6kLllR3/txPvvnya2MYaol8NGqZvWG9EIbU6tHWBdpOfq5MrRnNpBCWjy4BRJuQA0uMGtteY2p/0exF09qX6T4YKG0VuX8hR+wIrvg7Lk//5e4fc9MgzIQoHBOUdtCuQZQe8hMPw0c8krj0mPte9b9fT8RSnTAjfp6xdT1d20PGxaFJGIg47BXLh68q88qZVyLLvpuwfVduA8Wp/SFrQ5AYMogt2SkJUUtAS31ZscwwIS9/UAPj8Eg67+eOloVXdgCK/Om5JHHKS6boZJu0QHRLuSq9Lmf3yUon/nJHA9kTgh43myvXPAew2/XmBFBDrtvG1I6xWamwEAPseYnkfTC9DK8bTxLg2N9B5ex0IUZziRjArFNDvWA7qW4XVbnW+a4sXo9Ue2bhT3yLxxOcS36wLH887+wJH7CFwwt5APbHGuzN0Sk21y3ojSoezeJFhkghHovdeBIBDdv4nhRBVAFYCqAeQB6AUwD4A+oRsVwVgupQywmPfDqSUG4UQJwJ4P6Cu/QH8LIT4GsBaAAU79xV6J/wWgGstfq9ez5BCYO0tvKJdvFGuqJBgBg8eHPS5pqYGdXV1MQmQ+umncDtx6P7Sgbq64DcIhYWx+8OF1h0LmpqaOPCKYRiGYRIANakWALLjLEazCTtybLnKNrB0wzq6v6UKSgzFMKG0Geon2qkoQgJoeVWgcNFrEGI0wWK0VMGq3CER/dFsm2ywI0uEPiZMHFZ+C913yrUwDkhFGUeWLQsO4YBP+sLyQs9rHsMNSbygjUYUm0fI1KjzaVi5KASxXuk1LcvRiddybX2135nHaAyjptG/AwbUEb9FWeGBjZ2I8FAjJsP5pP4dZboNdhzgnJ7g1jChOAkxGgA0eOsSIEZTX4dT9d6MYRiGYRiGyQw4Pi9xJDtGj+PzIsPxeQzDMAxDYIs8XpQ16yAAyKtPoQtNOxEYtW/s2tUTyiu6/jmmfTlO2/E8nis4VVn0prcl7v5Q4q2/2HDQbsl7N+Tapn4nXlESYcK9tx3YSszgrxwNVP0cnt4pRqMm2StkTgyAsgrkbFyDd9Yfi5nD3lQWOfgOAw332NA/Nzl9yTAk/reIngD/xCyBcmdH22S7B/h6vrKcKNspRnMQsVr+8FgPAJAtjUCjWvySicI9UVYRFu2SIz14b93ROGr428ptDr3LQOO9NvTLSU4fos5FAPDMeTZzk+N1oqtQvCHxMdUuZTGRgf2nEzHtREiFGA0A5P1XAq8+BNz3EcSA7vv0LIfAvEtsmHanodzu+jcknHkG/nJobJ8ZbW2SOO1hug/9drKi/zxxK1leFA2KRbMyk4NPILPsW9ZhWBGwZmt43rWvGThhbxPS3BSlQROqX9j52v7J28LyXth0Bg4b/kFYut8ANtQDlZo1F0OpJ8L3Cg21hEbssqf5yhkm3dg5bqhodymz61qAl7+WOHnf1IjR++9Cc4ulFkQIW5ZSAi/9my6w+z7Aym/C09s94WnVtGBN/Cn8fMZkGppjR7f4b0sjXeMV/+lBe1KQfPo90D9KXsWjdernWP/5xol/HxCvRtFsa5KYdqeBn9Vr9wIAHvpE4i+HCnLc4wwJVZSPzrHekAwVUzNMqsBitMxAABix8z8dbwA4T0qpuIWnkVIuEEKcAOBxdMvPBID9dv6n4jkA50spCc9+5uKwC1RYeDDApC9Tp04NS1u6dCmOPPLIHte9dOnSoM+5ubkYP358j+tNNrG06ba3qyfT9gSpuzFiGIZhGCZmeAy3Ml3AlhCpS44tTyn0oib7MjRtfnOylU5YjMaYhZLupaIICQBy7ZSQqPu8ohIlAYDDpFSIiT85dvMyqizRx7QQqieYFYXl2vOSuoJNjt38sZmrKWvlGE/V80GOLQ/N/vCXq6HnNd24gzqn6Per3qbV5LU6GvlYx3XdXB91++kIrRxbLuzCgT4iG+0yPAiCx2gMo6beS78KKnTwSwrGHHXeWixrWarMG58/GQUOnqicbAochbDBBgPhAf71vjoMQWVc90+NEVJ1LMYwDMMwDMNkBhyflzlwfJ51OD6PYRiGYXYiTEhRql2Q61cCi9QyKAAQl92X1HfxgYTKiO6ovYoUowFAsweY/YaBjy9Lngikaps6fQS9xk8HtevpCc9TZhBitJ1xadUu5WaCJ8CqKe94zzC95SOU+mpQ4yhVFnv6S4k/TUvOsbDgFzrvyhkCv50ScLwvfJ0u3NkH7ES8j4+IbazRCLEyUbhHHEuHt85HeU4rNrvV75Ce+0ri/AOT04eqCOfzAbsAew012SYrYrSAviQNg+5DZfF9z5fSHHIS8ODVdP7mKsi5D0JccGNQ8kG7CYwcoJZfAcC1r0mcu79EXnbs+tojGjHjlBGA3Ra8L1ntois79ZLYNCpDEXY75D7TgG8WhOXJahcqS9R94+dqoN0n0ceRGmM6qzRoQuecuYCsWq7M28f9Hbmda5s1MRrVBqdfIUabfrr5ihkmHdkp4a70usgiV75i4MS9bbDZknve2VRv/llsqHQoDJX0rJOz/w788q06z6d4xryZEKMNGgbRN74LRDJpgO4ZjO79QpNa1onS4RBDd+1Zm1KNPPo4cbZUo9y7GZuzysPy7l9WjnulTPhzrkcWSa0UrZN/f0z/fQvzutss12seEujIxPt3hkkheNm73ssiAC8BqI9QzgfgXQBHSCmPtypF60RK+Q6AsQAejLDPLwCcLKU8XUppbQY8w/Qyhg0bhmHDhgWlffBBuEk/GubNmxf0edKkSejTJ/6SkFhTUhL8lGz7dmKlnB7WnZOTA8MwIKXs0X8VFRUxax/DMAzDMDSU8CjblpOQB2w5hGQmGiFIpmNVVOJlMRpjEkq6l5uik++pdgUeI34Wo6U81PVBRaL6olnhRLLFFFZ+D93vnGXrY/qY0AnWkgn1W4SOM3TjDit9sZM8QqZm9lrdZlh/1EsJH622I3en1I3qx21+HqMxjIp6n3omSZbog772fHI76o5Lhq3rzWQCnza8T/7tD3bOTHBrGBU2YUd/h3qVR+o8EEvcxHU4mvEKwzAMwzAMwzCMVTg+LzIcn8cwDMMwBGYmoFe7gK8+ovPz8oH+KbSASIiMaJC/FnkR3vN+sgpwe5PzDkhKSYrRIoo4qInyAMSw3dQZXg+k3w/Ub1Hnlw5Tp2c4IkDMtKf7R7LcBz8l713i+5p97zc8REi0VHNM75TAwUHEpfiJGIiadep0ux0YMITeX29FIxkcY6wh85LZh9ZtU+9791IL8co6QV4ogQKQbZtp6V4GT8wX5ZXAqH31hb76UJk8bjC9SaMbWOKKvl0qdH23skTRh5Z+TJYXZZn7N48Z1Dmo2oXhxfQx/d2GuLQmIdRrQucKcgF8NU+Zl280o5iIKXDVWTsnU21wGjvCE7mfM72ejnONToy2dhstiU4kn/xi/lgviBQGtER9XQYAMXgE0CdbnekNF6OREtHBIyI0gskIohWjNSuuSQDg7H0rLQmHo+OZlYrGOgzz0vcuW5vi1CgNHy7v+b1gkLxRd9+vgcfiDJNcWIzWS5FSfielPAVAMYBRAE4EcBGAawBcDeBCAEcAKJJSzpRS0qNK8/vcIqX8I4BSAIcCmAXgqp37PQnACCnlFCnlKz3dF8P0Fo466qigz0899RS83p7JHrZu3Yo33nhDu590oaysLOjzzz8rVmeKkkGDBnX92+12Y/16Cy8aGIZhGIZJKh7DrUxP1KTaTvlGKJSwjaGxKpPzsRiNMQl1POakqAiJlPkEHCNU/3eIrLi0ibGOQ2QhS5ib9JQoEZlZ+VeypYFWfg/qOmy1rmTL4CiodoWKwcyIwqxA9YFWQjQZCiWk1OEzFCu5UfXrRHD2jjEg1d+tilgZJlPY7lWvk1PoKIkgnE7PFXCZ2OM12vHZDnWAbFmfYdgld3SCW8RQFDrUAVoNXmJ5+xhhSD88Uv0MJ9njT4ZhGIZhGIZhMgeOz9PD8XkMwzAMQyBMTLX66Ut6QjYATJmRkEU+TRMiAhEAjmrWS2OlBNbF91EySV0z0OxR5ylFMoFUu9TpRaVAP6c6z9sOuDXvvantMp3J3ePgGc3vkcWSKXao2kpPoj50VEhCtYuuaLfxHf+3U2I04j6DEqOVDO6YkJ5pjBxHZs1on0/mrVW/3k4Ia6OVNAagle6FEihCo/oPAJRXmK+zFyKOOlNfYOU3yuQZ4/TXkM9Wx1bCt5rwbQLATMXhIHV/80nTe96gDEdQYrSfl2Kmpm98vjZ9FwtsIELn+mUDWQ6hHc9WEGIUq9d1qg1Of0NYmpgyw1rlDJNu7LxH3Mv9AwoUx0Ana5I49umkzkJY8GCn/vqqvXeeeATgIOLvfYo4Y6oujYCXySC0z2Ho67lsJo7HfgU9a0+qkq9eaBU76jCx7Stys2Tc27tisM8hAV9Xez7SUVrR84YwDBM1LEbr5cgOVkopX5VS/ltKebOU8hYp5QNSyg+llDF3c0op26WU86WUj0spb9u537lSSnrZFYbJUP76178GvfDcunUrHnvssR7Vec899wQFb/Xt2xfnn39+j+pMFvvvv3/Q5wULFsSs7qlTpwZ9jtVqoAzDMAzDxB8PITzKtuUkZP+UgC0aIUimY1VUwmI0xixtxIq2qTr5nmqXm8VoaUe2SUlnoiR96SIJy7UgN40kQjUrSk2UUNUqVLtCr5mUKMwGm2lBX/B+eyYVo867OrwWrus6MVrnOdSMZJJhmG7qiVVdC7OiXeEufYNAmej4uukztPjVrxkPdqbYZK8MpzCrWJlOnQdiBSW2B1JXWs0wDMMwDMMwTO+D4/P0cHwewzAMwxDYTE61euEeMkuc/fcYNSZGDBgC2O1BSdduuwWDfHK0aE4AACAASURBVLXazWbea8BvJP490Mcr6X1Wqh97d0FOci2vABxEnI3XA7Rp3nvnWF+gLBMQI8d2/fusHc+Q5aq2AVIm530iJbXKzwGceSHvszYT097GTobo7DtUH/L7lMmk5Kh0uDq9lyPs9g75hYKza/5LbvftBsDjTXwfamuX+GmzOq8iwrmoE/nErcDHL5vfaaDMe7NLXaZfAQQlM8gUjj8fGDdVW0S+9O+wtNMmCBy4K73Nta9LPLfE6GnrAAD1LRKbaOcNTtxH8U69mhajicEjY9CqDIeS5myvwdEeWmB4yQsSLy6NTb9INPWt6nOns/OVfbWL3LbCq86zIs71eCXaiDBBlRgNoyear5xh0pGd95oO+PFg9Z/JYkfdY6DVk9x4vHoLobfDIo2Lqmm9hCguBbKIuGfVIifE+IiUXzIZhiZmUXdP2rJDnd5bJemU8G1HHa6uu53cbNqdBry+xJ2bDENiQ33P6rDbgMGBf8bn746uIj7HMExSYTEawzBMEhk9ejRmzAg22V9xxRWordW/YKT4+eefcccddwSlzZo1C0VFRVG3MZkcfvjhQZ+fffZZNDXFxud45JFHBn1+5JFHYlIvwzAMwzDxh5pYa1ZG01Nyicm7bkLYxqgxpN/yb8ZiNMYMUkqybyVb/kRBynz8kcVodpGBK4emMGYFX1ZEYD3BbJ9PtjQwx2Y+gDjSd8o1WVeyvzNFrl3d/sDzAQC4/eqIg1xb36hENHnEfltNCs9aCUGs7u9l5bpOCdqyRJ+u86AZySTDMN3Ue9UzAIocA7TbCSJwRLIYLeNY2PCOMj3HlouJBdMS2xhGi9OhFh42xFmMppOTpqqklmEYhmEYhmGY3gfH5+nh+DyGYRiGIRA9nGp13LkQlXvEpi0xQjgcwMChQWnjPD9hSdX+uLvmb+R2VduAd5fFu3XhnPpf9bunnCyglJi/20W1S51eVgH0yVbneT2AR/NuOTc1YwxSgjMuBwAUGI14f91MZZFmD1DXnMhGdUOJ0e49Nfi9p/R5gS0blGXFmZd3f7ATsVp+v1r+RkmOyjJTjAYA4jT1Oaegfi3e+r2H3O4/nyT+nfQ/59H7rCiJHJ8jV30P+chsazv1BcTTsFiPRDiyIO79AOLc68ky8t7LIOu3BqX1yxF4/2L9df7cJyR2EDIpK1w5l67jm2ttyMlS9CHqb376pT1uDwOt0MJx5XE4dHf6b3b2oxJN7vSLjWkghjeFJsRow73q/uiqM/87NGimCjiNEAnNzLN5ET4mA+ju479umotcTWzNI4uSe86hzh8qhkcSoxHXN/Hnf3T8w0GJ0YLHhlJK/f0ew+iuIzoxWhMhRusb6QFEmkJJlnfUodi/HUO96ntjjw945ZvEnZuqdwBef8/qGFoIOOwd/YIUl5shg+/hGSYVYDEawzBMkrnrrruQl9f9oqyhoQEnnngimputvfnZunUrTj75ZLS3t3ellZWV4brrrotZWxPNmDFjcPDBB3d9bmxsxFVXXRWTumfMmIGRI7tXy1iyZAkeffTRmNTNMAzDMEx88Uj1G7JskZOQ/VOCD5ZuWIMS3OnwGixGYyLjkW5SzJG6IqTI5xWfVK8qmiWIVUiZpGBWRGZFBNYTzAonki0NzLJlwWFS8heprenynSmo9oeOMyjRSI49OskIJZTzGG0wZOTVLqlxUL6dfiFMnddUhIrhOglsN/Xb6aQsDJPJbCeESIVZaoESwwSyzr0aLvcqZd6k/oew9CrFKCTEaPVeC8s4R4HuOYlZmS3DMAzDMAzDMEws4Pg8Go7PYxiGYRgCW8+mWomxU2LUkBijmCg+2LcZf65/EPtX0O9v5yZw0isAtPs0IqJiRBZmVLvU6WUVQBYhRvP7gVbN+DCHn2tTiPKKrn+Pbl9BlquK72sJJfUtkhQ6jBgQ0o+2bAAMIj6irLL733ZNrJZfcRxt2aguO2gYXU9vJ6DPhDLGrp6ADwDv/Jh4Oci7y/Tno0jIT16zvlNf9z2nrCV+DxZ/AOiQo+G3V9DCQgBY/HZYUk6WwKXT6WuJ26v/25tl8Rq6jl2oNesocQzL8GKDTmjh96PSRov0PT7g/Z/i0KY4U0esi+rMiyAYAlBJiNGqLKzBphMrFfobgj6L8kqiJMP0IkLuZY5reossmuj7sFAsidGK6Ouq9PuBSGOaPpQYrT34c3MD0Ebct/H4iAGiF6NR9235vVSM1o/4Xq0dC+eM9dCDnkSem5Zt7nkdQfdti96MrpK8fFomxzBMQmAxGsMwTJIZNWoU/v3vfwelLV68GDNmzMDGjcRgOoRVq1bhsMMOw/Lly7vSbDYbnnrqKQwYQD0tTQ9CA8fuv/9+3HXXXaa337FjB9zucOmGw+HAnDlzgtL++Mc/Yu7cuZbb+OGHH2Lt2rWWt2MYhmEYJjrchlqMlqiJ15RIhaUb1ojm9/JJFqMxkXET8hwgdUVIlLCtzeiOSKD6v4PFaCmF2WtRbpKvWaFQcr5EYkYW5xBZyLLp+7x5OV3yv7MKShISet2kRCPRCiBz7er9Skh4iLFXIK2GOoKqv8NJbuOV7WReKNT3DTzmqN9Od11gmEymwUuI0QiBUiTSb01cpicsrH+XzDvIOSOBLWHMUJilnpVR79vWEegcJ9o0YwiW5zEMwzAMwzAMk0g4Pk8Px+cxDMMwjALRw6lWYyfHph2xpnI0mbV/MW24+GlzYt8EbdhO5w01Mw91s0uZLMoqgCxiwj0ANNXTebksRiMJEBCU+mqQQ7wfWLs18W8U12rELSNCX4sS/QYAECgk0gmYfIr4riZ1hxbFpXQ9vZ2BQ0kB5ZAW9eJMALCpgcyKG7p9lvY3U8Ea6zsN7EdE/0HRIOv19lKE3a697sqN6r/B/iP1ks3VW3vULADAZk3/6ZcTvn/Z7gG2EfYHjVCQsUBxGaCRb03N0T97eGJx5AVGU411hJi0tL8AGrYCbYQ5DcDwdrUYbVODXmQbSL0mdM8ZIkZL2TE0w8SSkDHQ/m2fk0VjcS3qCQ2t5sfvEyo0mds2qcfJQPe9hIO4T/OFxBk3am4WM3l8zXQThRhNetzAyq/V1fVW4V6x/n5iamtqnJvmr+z5c4SKku4+ITdF+Z6pdHhkST/DMHGFxWgMwzApwDnnnIMLL7wwKG3RokUYPXo0brvtNmzYoDZir169Gtdccw3GjRuHH3/8MSjv9ttvx2GHHRa3NieKQw89FJdeemlQ2mWXXYbjjjsOX3+tvtkwDAOff/45/vrXv2Lo0KGoqalRljv99NNxzjnndH1ub2/HSSedhDPOOIOsGwD8fj++/fZb3HDDDRg9ejSOOOIIrF+/PopvxzAMwzBMNHiM8KBqAMi25SRk/5RwhBJ2MGp0v1dfW74yncVojBl00r1UkD+poARNgSJIFqOlB+alXIkJmDUrYEsFSZiZtpppp9njPHXPB+rfIfS6SZ3rov1b6oRqlPQsqD2EfCzP1g824jWEles69X0DhW45dvVvx/JahgnHa3jRGBpcuJPCLL0YjX65z2q0TKHZ34ilTZ8q83bPG4ey7KEJbhETCUp46JXtaDWIVVRjAHXfb4MdWUIz8YxhGIZhGIZhGCYOcHweDcfnMQzDMIwCQthjFjF01xg1JLaI0y4h884f9B2Z95ULuO51I66LbQTiIgQeAHDD8fq/jWxtBnYQNqyyCiArm964kRCjCQH0SUxsYloSMFFcAKj0upTFTntYJlyy9/ka9f6yHUBZQUhitUtdSeFAiLx+3Z8dmlgtvy88rWmHumw+vdBcb0c4sjrkaKq87xbikN3V2y2vRsLOQ0CH9IcSCh23F2CzmZgY/+EL1nfsDRCANBFmrXwzlsjMQZz2Nzrz6X9AusPfWx49DthrCL3Zda9LeLzR97cdrZIUQv1xGtF3ateTwo4gQSMTNUIIiDP/j8w/qfoJ7fYLaXdjyuLapu5TFc0rII/TxHeUVaDCqxajSakX2QbSQBwHdulDXxkSl7j3weYqZZi0JvgacNoOeqywuQH4Ym3yYvLe/8l82YH96XGRfPoOesPOewlKYO0NEaM1E2NrgMdHzE50Y3TieProxfC+1sm4qT1uUSoiymhRLAD8bsdTZN53G4BVtYk5Ny1Yod6P08J0ieGBa8q+82R0DWFJMcMkHc0yBQzDMEwiue+++1BYWIibb76562F9U1MTrrrqKvz973/H6NGjMXToUBQWFqKurg7r1q3DypUrw+rJysrCPffcgz/+8Y+J/gpx4/bbb8f69evx0ksvdaW9+eabePPNN1FeXo5x48ahuLgYHo8HNTU1+OGHH9DU1GSq7gcffBD19fV49dVXu9KeffZZPPvssxgwYAD22msvFBcXw2azobGxEZs3b8by5cuVq1wyDMMwDJMY3MSKgpRIJNZQwhFKCMKo0f1e/RwFaGkPH8+xGI0xg066lwryJxWUkKjN3wopJYQQ8ElF4BwAu+DHe6mE2T6WSwicYo3p9qTAsWFKemaiTDp9ZxXkOCNk/EOd66IXo9GyvjZ/KxDBwdhGyNNy7X2RJfrAI8Ofo1i5rpsZ/1HfgeW1DBNOg49eGp0SKHUitIEjTCbw+Y6P4ZXqQKCDnDMT3BrGDE5HMZlX7932/+ydeZwcRd3/P9UzszuzV3ZzX2R3E44gZzgEAyRyyX0piKIi8Mij4K2PeAuiiA94iwieqMAjigry45I73EICAQkhQHZyTpJNsvc5M12/Pza7mZ2pb3XP7szs7M7n/XrllZ2qPqpnqquru7/1LlQGzHLy0dIr3PdHnArOoEgIIYQQQggZExifJ8P4PEIIISQNJzDiVdUn/zeHBcktasY86LIw0J95nZ3f8Rp++oFT8Nk/mwedfvdejYYpwCVH5//5bnSHPMD2yPke+49F5bxZDUCfpY/RIYjRwnyubWXGvAF53K4+dmN/FK+X72tc9D0/dhG91kEoWJjv8zNCfW6cmim10rGoeSMp4jcAQMASq5UmRtNaA51CvSphMRqAAcnTFoNw546f4po/fB+Lv2/+7X7+qMZnji9M/fnWP+W26Ltnews09dv/kTOnzQGOPw/4808y85Ip8TRCu6RKvf6koY46DXrfw4HXXzDm6198GeqLPx+WFgoqPPElB7WfdcXtfv0ujR+cN7L6ZpN8fvkkYZumc2IQitFyhjrjEujrLzdK6Koe+xOw783iuh29QEevRnV4/PQLmoS62PD4L+SVysLAfkeg4ZF/ytvdDiyY7r3/1m5zW1qXbBkegXTQMVCjlBMTMi5Iq+d1bisejZ6I4xoeMi6++Psuum5wECkrbLvT2q3R4zPE99cXWqRob70C3PUrc2Z1HVTVLltxUBCjJdIKId2zKQVU1niUlJQEtnt3w7Vfaw197aXm5asmAfP3z1HBioz0+9z07MQW/HHTxbhwzu+N+Ude66L5R44/WfQIaevWeFHoHp99sMItz/iTszXsCp3UO7YA3f7ea2XAvjghYw5HThJCSBHxne98B0uXLsXll1+ON9/cPY2A1hqvvfYaXnvNrtk+5JBDcPPNN+Owww7Ld1ELSiAQwB133IH99tsP11xzDeLx3Te0mzdvxubNm0e87VAohL/97W+4/vrrceWVVw4LqGpubsbDDz/saxuVlfIAYkIIIYTklj7XHJxU7hRmVkZJwEbpRnZIApUAgqKsJk4xGvFBj3AuOnBQpiyzvo4hksgoiQQSOo6QKhMFQkHlYSsiBcWvpDNskVDlEr+SrELJRe1l8CM98y6n32MuL4JjNiFdA9PFIpJgdKTCt0hAXk+6ZvtZJuJUIKhCghjNLHzMZvupvzf7aIT4p8UmRgvZxWgShZyZm4wdrnbxZOv9xrza4BQcWPXOApeI+KEmWAcFBxqZQf0tiR2YC/sMkCNFFJsWSBJMCCGEEEIIISYYn2eG8XmEEEJIGqORMsxdkLty5INDjwWezXzWr2NNOPEEBUB+5/On5zQuOTqPZduFJJN5Z4OPlWNN5vRAAJi+B7AlKq/bvtOcHmY/xIYKlUFPmwts2wAAaIxHxWVjbcAjq4GTCzC2fL1FsDff9Eo0FjUvnI0YLV3c0NMFJJPmZatKXGw1qwF4eZkxq771NQDvMObd+pzGZ47PX7FSufNFuQ41yPMSDaEfuFXMU1/9FfSrz5ozU+7H0NlqXqa6zrsAJYY6/SJoQYyGh/4M/dkfQwWHn781EYUfvV/hC38x/9Z/ek7j+nP1iOSYTUJYRtAB5kg/X0wwP0yeAVXOd6w55bxPA3/5mTFr6ew2PLF5krjq3S9rfPjI8SFG641rbBaakfq4RcQ3qx6Y3YgK3YPpia3YFpyRsciAyNb7e2gRQvdq3bbhCVNneW6LkAmB4Zryzt4Xravc9yrwvkPzVSBpn/5jAWfWWMRoD94mr5jSz1ahMvOdaDxt8s6ONtNSQOUkyhXJAFmK0bBK6D8CwP7vggqMXJxf1HiI0QDgmO6nxLyWbuDpt4Fj9sphmdJ48i3AFZqicxZlI0bbVSceu3PEZVEUoxEy5vAqTwghRcYJJ5yAVatW4bbbbsPxxx+PYNDusCwvL8cZZ5yBu+++Gy+++OKEC7oaRCmFK6+8Em+88QYuvfRSTJ482bp8VVUVzj77bNx1112YN2+e57avuOIKNDU14Stf+Qrq6707qdXV1Tj11FPxi1/8ArFYDIcffnhWx0MIIYSQkdMnDKwtlOAkEjAHOkkDfokZ6fuKBCoQEkRPkhiKkFQkAU7YKd7ZW+1CooHjkQRCQcV5D4oJv5KHSMGuWf4kWdK1rZD4Kasf6ZkfMVi5CiOgivNFZVj4HtLbNrGt8/mbpxNUIYSUecY1X2K0pHmZikCl2E5lc13vTQr9hpTfOyIIByVhJiGlzM64OQK3wqkqClkmKV5Wda3A9vhWY94xtScV7fW11AmoACYFzdH1rQnLVOWjxHZvRgghhBBCCCFjCePzzDA+jxBCCElBjWKo1YIDcleOfCANfo1F0TgVqLbMTfp6LB8FymSdIJNpmOoj7icWNadP32NAiGORnOmd5ncgCPO5tieTpw/9eWDfq9ZFX48VZsKlVZb6esBcQ12KRc0Lp58ztvuHZFp8V0eLvGx1aYvR1F4Hinkztsl1aFtHPkpjZoclXKYq7KM9ir4u5zXuByVJ9lLrUQfFaL5ZINcpdLUD2zcZsw4ytQe7aO4AdnSOrDhN281t3bzJQMAx71NvEURVFDHkHGXpr1061S4osl1fio11lnAAm8gUU2ZB7TlwTtXH1xsXkUS26bQKwytqk2lyoypZRkfIxCLzGhDWfVhY3S6usapA/edU1srzrg4j6ABH2OZjtPWHUtvikDl+GfG+4Z+l/nU12xCyi2zFaJY6qhYtyUGBihQfYrTZiRimJprF/FWb89s2PbravP1ZkzzanTTqd0mtta098sLH90UIyS8cOUkIIUVIMBjEBRdcgAsuuABdXV1Yvnw53nrrLTQ3N6O/vx/l5eWYMWMG9t57bxxyyCEoLy8f8b6uuuoqXHXVVSNa9/HHHy/oegDQ2NiIX/3qV7jpppuwYsUKrF69Gtu3b0dnZycqKysxffp0LFy4EAceeCBCIbNQQ2LmzJm49tprce2116KpqQkrVqxAc3MzWlpa4DgOqqurMXv2bCxcuBB77bUXAhPV9kwIIYQUOX1urzG9UGI0SRTQ63ZD65HNDFaKSJKSiFOBIMVoZBT0JIW6NUJZUCGwiQF63W7UoFas/9L5QsYGv5KHsCBwyjV+5TbFIMHxJT3zJU/zPpZiOF4JSeyWft20XUdHvu9KxJP9GemS9Gx4eczLRJxKBJ0QYJgAOe5m7ivb7afWG7GPJkjVCCllWhLmyKXJIdPU6MNRPmZ7JROXJ1rvN6YHEMTiSScWuDQkG+qCU40StBZBlJgL8tFfIYQQQgghhJBcwfg8GcbnEUIIIQCcEYrRDj8BqsgHS6pZDTAOLX3xUZSHFD66WOGGR82DT7d1AN19GhXl+X1fFN1h3v/gYFYbetUL5ozB38UmE9q+2ZweGfvJ5ood9d7LoL/3MQDAue1/x5enX4PWgPm7/s69Gp8vwGslSUoEABcfZajDm5uMy2ac05LMCgASafFdnW3m5QCgqrTFaDjhfOBn/2POu+ZiLDrhXLy0KfN+YP1OINaqMas2v+1QS5dGmxBucrrFvzWM5x4Qs9TUWdAeYjSdTMp1iAKhTN5xODCrHoiZ5WL6qo8ANzwCFRx+H7t07wEpaIc5NB1NO4Cp1dkXp0mQRjXawjIoRisc7z4HuPZSY9aZrXdhSuXxohzx+/drXHN28Y8XeGy1xvE/csV8SXgGYKDvs/g0AEBjfxQvRDJl9VGfoQYtwvdYm0yTG1H4SEoF4V7z0sa1+OIrBxvzrvynxh0vJPH+wxW+caqCIwg2c0mrz/mI33eIwtRqQfgZ7weee1BcV51+8e4PohgtLc5YEqOVet+apGA7PzLvEXUsKi9+2kWjLEsRM2kKEKkCemQLcAAuLmm9BddN/ZIxf+XGfBVugMffMN/TH7dQYZLPoSABB5hbB2itgbt/M/LCFPmzPkJKAYrRCCGkyKmsrMSSJUuwZMkEtguPAMdxcNhhh+VtBs7GxkY0NmahDSaEEEJIweh1zdEG5Y5lusocEhFkNi5c9Os+lKvClGO8I0lWwhSjkVHSKwy+9yusGgtsYoBBmYAsRuPjvWLCr3ArUiAxV0AFEVJliGu7gEq6thUSP4IMP+exr2XGoSgxrvuR1AkEdp3zkmhkNG1dJFCB9vSAI8u+/CwTcSoRVOaAhWyu61L/L1WWFwmY63GP20V5LSFptMTNs7jVBr3FaBLaPISGTCC292/Bqq4VxrxF1e/CpCCDVIuZ2qB5tFirIErMBePx3owQQgghhBBSmjA+zwzj8wghhJQ0amRiNPWd/8txQfLAbPn6q994CT8892A0NWvc+6p5megO4B2z81S2lH2YaPAQo+lEAnj4DnPmrIHjVuVh6LJyoL8vc5lmQYxWzufanpz8YWCXGG2S247H152Ig+e/aFy0tRu45RkXFy0eoYDQJ02WVyB7zxgeP6B7uoCWbeaFZzcM/xy0yIF3Ca2GkMQNQMnLG1TddOgZ84CtZjHP79Z9FIuCtxrz5n/NRecNDgJ5lIJI7RAA3PBB77qrm1aJeeorNw/8IYmg3V2zD3ZZxHoUCGWglAJuehL6rHnmBV57Hvq6y6G+9uthyY6j8Nq3Hcz7slkg9bE/uFh5ZfbS7nWCnLFhqqXeClI3zKIYLdeoimrohYcCq5dn5FU8+Fs8/ZefY+GV8vrfu0/j66cVbyza82s1Tv6pLEWbFY8hrA19oUHKKwb6TJNnoD5urpeSyDadVkEyWesOb+MUhY+kVBDiWD+7YLUoRgOAVTHgqn9qtHQBPz4//+2PJIgFgLoKIOkC7z1E4YYPymUZFCcbOf79UAcu3v05KIjREsPj37UkjWXfiAxiixXXhmuXJKad3Qg1yYedfZyilIKe1QCs/Y91ue80f1sUo930hMYvLshPfP6OTo2XN5jz3r0PUB5SiISAHo+hCHPrgGBAwf3dd0ZXIIqKCRlz8vsUkRBCCCGEEEIIyTF9rnlaLr8ymtFi24806JdkIglObGK0uEsxGvFGlvMUb5Biua1dSQ4cT1InjPnS+ULGBr/1LCwInPKBP+FYYa6h9jJ4l9PPsUR8SM+KWcZh+y1S27fBtiGd0bR1kiDPjxitWxCeRgKVCAkCx4TQrpnodWWh6u6/zd/doLyWELKbFkGEVBfyI0YTZnikGG3Cs6z1AfF3XlJ7SoFLQ7JFOr+l9iAXyCLXse97EkIIIYQQQgghhBBCiBVnhGK0ypocFyQPzGoQs/TtP0QoqPC3y+Tjt8mCckF/QmNTqzmvYYrHYNvnHxSzVKrcShJSbd9kTo+M/WRzxY5SClhwwNDn/ftW4dvbvi0u/9OH8/9usUmQEn34CEM9kgbDA5nnTMAyiWUiLb5REqOVR6DKyuXtlApLzhKz5r/1gJjXlwAekr1jOUES64UCwBwf3g3955/ImY37DfzvCLKtQcGeTaxXXdpiPQk1eQaw9yJ5gfv/CN2a+ePOrVPYZ4Z5lVc3AYlk9m2WVIcabWEZQlukKGLID0eeLGbtteFR3PwRud9xw2MaSbd442RuekIjnpTzG+JR+wbCu2Ly9jkEjYIYzSYgTaVNCD2sS6Z1+NiukVJBEAgppX0Jz37zlEZXX/7bn7Zu8z7+e4lC848c7PiJg99d5KCiXIgl3LFFllYDUOd/dnhCSBCjxdMmBpf6R5QrkkFyJUZbcnZuylPM+JDvBuDiq9v/V8x/bm0uC7Sbp9+S845bOPAbT/IRgtgwBdCJOPD3m0ZemKpJUOynEDLmUIxGCCGEEEIIIWRc0ScItcpVuCD7twlHegRJCcmkRxCcRAIVCDlm0VNCU4xGvJEEhcUsQnKUI8oBBmUCcaH+U4xWXPitZ5ECyiD8CcfGPog3VwK3XAnWxgrbb5EqQ8tHWyd9Lz2C9GwQV7ui8DRiEZ5mc13vsWx/99+W747yWkKG0RI3RydODk7zXLd457sl+aTf7cOzbY8Y8+aUN2BBZN8Cl4hkS13QPINlSzx/I9h6k7IQnRBCCCGEEEIIIYQQQooZpSxDrcqF99Yf/EJ+CpNr5syX82JNAICyoMIegvgnuiO/g/DX7zSPVQaABo85fvQbK+TMPfbe/bc0oLV5szk9zOfavpi397CPe/e/KS76yiagL57fuiQJW4z1aHOTeWHHAWbMG54WtMRqJdMsNNuFOlUzWd5GCaHS6kwqlbobc8s7xPwXovmuP+bt108BAo6Pt+br18h5cxcM/C9J9gbFaDu2yNtgHZKZOc+ev/IpY7JNeLfB4qgzobVGk/AatsH82ha6v09uM3xIK0j22NogrF6BfWbI5/rWdmBjlvWikHi1kXv3W2wjABDe1d+trkO9fTKEzAAAIABJREFUIEaLtQG9Pq7l7b3mZard9uEJNcLJQchEQ7rX1Nra7gzS1QestnQRckWrOeQHkyKA4yjv/tCbL8t5SmXel0r32T1p8cvStbKafSOyi2zFaDFBTJsqV5+oWMT5qezTJ9/bvLguP/dl63eatzu3DmicOvAbT/Yx/KVhihqQ37WNYvLYaXNGvi4hJGdQjEYIIYQQQgghZNzgahd9uteY50eWkgvCATnQidIN/0gClXCOBCqkdJEEhcUsQgJkOcCgRFCq/0FlmYWUFBy/16JCyiB8ycQChRO1iWXwUU4/ArdcCdbGCttv0ZPSz+gR+hwRSz/Fi0jA/P1KMtNB+txeaLjmbTqVObmu9wpte+q5ZPtdKa8lZDg7E+aX/HUhj9EkpGRZ3vEUulzzwIOltadA2QKKSFEgnd+tiR3Q0gizUSKLXIu3L0YIIYQQQgghhBBCCCEABmRIEid8wJisjn9/ngqTW1RFtZy5Zf3Qn5KE7FO3azz9Vv6ERFHLWNV6r7HuwoBmAMCRJ+3+u0oQoyWEd9jhsZ9sbjygTjh/2OdTOh8Ul9UaiOZv7hYAshhtvmmuqFjUvPC0uVDpIjRJZgVk1CG9WdjuTEqOAABLz7aK5s6reEHMW7M1HwXajVR/Gv16e2JRMUsNSs1EMdouwV5KmzyMyhqoqkk+C1J6qPdcYF9gq/l7ff9h8jvvnz6S3XVve+eAuMbEoMjBb7kAsM3IF4tPFbP0hjU4ak+g2jJnu63PMpZorT2vse9v/6t9gcG+T9UkNMaj4mLrfFzLpXOhMj2ewEtqSMhEQYqxcl0ctxCYZrldG8TPuTda2gQxWq3fEOWtG+S8d564uz80iNS36WwdHtcUixoXU5SIkl3Y4xiH9+l0vB9o3mhetAT6X8qnGO30zvvEvCvu1HmRnrcIYf9zU2TG9T7uzeqnAPrvvxxdYSprRrc+ISQnUIxGCCGEEEIIIWTc0K+Ft2MAyh3L28ccUq7CUDA/LJUkJSQTSbISoRiNjBJ58H1xi9EkmdOgRDChE8Z86XwhY4NfyUMhRX02oecgxSAO9CP08iV587XM2B+vhO23GGwPkjqBuO43LjOaY5P27SVGs+VHAvJ1Pe7zuq619iWCk8RuAOW1hKTSk+wWz4m6oB8xmvleSCO/M3KTseWJ1vuN6RGnAofXLC1wachIqA2aI4H6dR+63c687FMSotuu2YQQQgghhBBCCCGEEFIUKHmolVp8CnDR13cnOA7U538Ctc+iAhQsN6iv/86c0bINum/g2W7DFHkw8THXufjri/l5NxTdYd7ujBqgotxjopZY1Jy+18FQkZRn09kKhSJ8ru2LY84E6vcZ+lilu3D3hveKi69tzl9RWru1OJC60VC3dSxqXtg0UNwi8kIyLb4rm+2WIKpuOtS3bxPzr0z8Ssy77XmNpJtPSaN52w2S1CoF3dcD7IgZ89RXbt79IRAwb2CwHm0RZI8zKA+ysuQsa7ZuM9tkPna0/Nv+7BGN791nnjTShCTWA4BGKSwjFpVXKgExx1igKmuA+oXmzAduhaNdPPIFuU/4zbv914lC0twBdJtDCwEAVzVfjfd0PWzdhgrvismrrsO8uCw3stX1QaSyVKbHHLKek1JBkDbpVc+jLKjwj8sdTPG4/Vgn3DPlklahLz3J51yI+gefEvPUFQZJkSSvjvcD/b0D29Sa/WsyOtInD922ITNtkJkNeS/OmOPzvKl12zA9YTZT9yWAM29wEU/ktl1qleSMKW2Qn3uz+jX3A3+9QcxXn77euzDh4h13QkgpQTEaIYQQQgghhJBxQ58wqBYAyn3KaEaLUkqUrlC64Z+epCA4cSoRGqVAhZQ2fuQ5xYgkM+pJdsHVSWiYgygoRisu/EqpCiXzBLylZyFVhoCyzGZbIPx8d/4kb94BycXcHgRUECFVZswbFJDZRKyjkdxJ3510zU4vl4kKp3LUwtO47oeLpDEvtd6UqXLKawnxQUtCjkqcHPIWo9ln1CMTkWjPGqzvfcuYd+Sk4wraryEjRxKjAUBLPD9TyUp9BL8yYUIIIYQQQgghhBBCCBkzHMtQq0AQzn99C+r/bYa64RGoe7dAvfeywpUtF+z3Tjlvl4inXn6sDAD41t3uwMD0HBMVHlk3eJQHgCgRUqd9dHhCdV12hSrnc20/KKWgrvzTsLTTOh/A1ITZgLZWEE/lgqylRLGoeeHZjZlpAUuMTSItDkLaLsUNQ6glZ0Fd8k1jXtWW1fjx+fL76YdW5atUQJPQFolSq1QkoRkAHLB4999SXdolRtPSdmZSjGbDM6YhZv5eHUfh8AZ5te/dp9He46/dkiSf4dCA6NOI9HtPngnF61DeUGf+l5z54iM4rEHhgDnm7KfeAhLJ4ptE0HYNfLXq0/jG9u8LEXYp7KpzqmoSwroPs+ObhX15H39Xnzm9UqfE80WqgEl+OnuETACk69Q/fwsAWLxAYdP1Dp7+snxPKt0z5ZI2H1IiCd1qaYiOOw9q+tzMdEmMBgAdrQP/t24HeoR4ZVO/nZQu0nmW/gxD6BcCKI0+9yz/582lLYJgH8BDrwNPrMlFgXYjyRnrKnf/tn7uzeqXGUSMg+x1ELC3j0kOwpTlE1IMjP2IN0IIIYQQQgghxCe9VjFaISUzlUbBhq18E4UX2pfh2bZHRKFCVaAGB1a9E8fVnYmAEma0gyyRCzsRaNcsgPIrUCGlja1uFTOSzKjX7UFCJ4x5AMVoxYYfcVfYicCxtI+5xks45kckVgj8CL38nMd+RKl+BXZjRcSpQDyZOVXiYD+j1yIqG5UYLSCI0SziM2BA4CgRtghP/V7X/YrgHOUg7ESEPhrFaIQM0hI3DzxQUFZxkid5GPhCioMnWu8X85bUnlLAkpDRMCk4GQqOUbjcmtiOuWjI+T6lZySj6a8QQgghhBBCCCGEEEJIQbBJVXZJ09SkKcBBRxeoQDlm+h5y3pb1QP1CzJ9m38QbW4ENO4F5OXZYrBPFaHaFh07EgeaN5syZ9cM/V03KrlCR4oirGBcYhAT79q3Gk8HMCrXW4ksYLZIUJhQA5pi8eJubjMsrk8DMJkZLpsV4xaL+t1vKNOxrTt+yDo1T5PfQD7+ucfL+uZ/YS2uNqFCHfInRYlFzulLD2yOpLg3Gz26PmfNnWNpwMsDJHwEe+JM5LxYVV5s/VeGFqLnOdfcDzzcBJ77De/dSG9QwRRa3aUnMMavenE5yw5z5YpZe/hjUEe/BntOBVzeZl3k9Bhxg8PuMJc83metwJAQs3PaUv42Ed73Trx24fjfEo9gcmp2xmE3CNkhXZhgkAKAiNZ5vVgMnaiSlQ5k85klrDaUUyoIK71oAXLRY4ZZnMs/p9YKAM1ckkho7hLDgSREf5+orT8t5Uj+42kOMNnUWsEPoGwGZ93yktFHKHM+aniaJaeumQ5XCc4DZDb4XbYxHrflPvKlxwjtydy1v7Ta3c5NShonMn6oA2NvDhs435Mw5C4Cgj7FYpVAXCBkHWKYxIYQQQgghhBBCios+t1fMK6T0SNqXTQwyEVjWcj9+H/sRVnevxNb+TcZ/b/e8jn80/wF/3nqTdVuSoCQSqBRFTxSjET9IAp1ikT9JSEKtHrfLWveDivMeFBN+rkWFlnJ5lalYpIF+vhc/53HICXkKA4tdjCaVb7CfYROF+ZHzSUiSEtv+bPkhVWb9Pfxe121Ss/T6K353FKMRMoQkOK4J1iHgo18hhS5QizYx6Uy0Y3mHOTB2YcVBmFEmTI1Mio6ACmBS0DTSB2hJ5GcqWVlaXdx9MUIIIYQQQgghhBBCCEHAMtmZM/6HYanyMDBlljlz63oAwMn7eQ9oXbczl6UaILrd/Nap3ktGtG3jbplQOumD76vNz8slVLi4Y46KCVVZk5E2XxhA/eOHtPh7j5YmqR5NAQLO8LqttZZFSQZxg3IcuR1IEaPpzjago8W8nEEgV9JIgoz+Prx72jZxtX8L8p/RsrUd6BFCWhqn+hjsH4ua06fOhior3/3ZEa41g/Woo9WcX+thriRQx5whZ66X5QinH2Tf7idudXHXSxrtPfa6J8mirGK9WNScTtFLflm0VM6LRQEApx0gn/ePrC6+aJnH3zCXadE8QEkCvnQG+z6HHw9AlqH4uY53C2K0ytTJWrMQsxAy7olUyXmtwy8gDcJ14+6VQDyRv/ZnYwuQFG6t9pjsYwOxqJiljjrNnGETo3Xu6hNJfSMAqPVjryUlgyjbHH7e6FjUvFiJ9L9URbXvZU/qegiOTor5b8Ry2ya1CmH/dSlhh8cv9N5OfXy9mKeOOs2fGC3MWEdCioHx/0SeEEIIIYQQQkjJ0Ov2iHnlBRWjmR9s2co33om7cdyz/Xbfyz/d9hC29gtTZEEWlISdCEKqzFwGitGID3qTct0qZiQhUa/bg4ROGPMAeAqgSGEpV2EoURkzgPRb5wsv8UQkUBwBvBEfQi+/57HXd1zo3yBbJLnZYD/DJvkazbFJ4rkeoV0dpFsQww5uT2qn4q6/67pt/+nHK7alHsdASCmxM26OwK0L+g0O4gytpcQzbQ+LIssltacUuDRktEjneasgTBwNrnbFZyTFfm9GCCGEEEIIIYQQQgghUJahVra88cTMecZkfet1A9mTFK460/5e6A/P5n4wflSYy6NhiseKWyyij7RBzapqUnaF4iDYrFCf/P6wz43xJnHZfb/l4s7lua9HX7rTvM1GUz1q3wl0d5g3JAm7pMHTyZT3arGoUDpQAJOO9D0DqF5+H+YKLsOn3gKeW5v7+iNJrQAPsdUu9J+uM2ekCxYCwsRlQ2I0s1hP2cQhZIDFpwIGUSMAoH0n9PbNxqxzD1E4/UB5s03bgff+0sVh17hY2yzXPUkW1SCI9bTWwCN/MW+sRMQcY4WqqJZlsY//Hdp1ccERcn/oC3/RuPU5wR40BiRdjcfXmPPePa8L6JcnqB/Grr6PmjITANDQb+5n3bkCcF17O9zVZ06vSI2BZD0nJYS64kY5c/mjwz7WWyRkZ9zgorM3P3I06Z4M8HFfBkDb7s32P9KcXhYGQuYxREN9Ikk6HKmC8iM3IiWEcO3WaeeMJAydVTrXJXXJN30tNyuxBdds+5aYf+cK4Jm3c9cmSWK02pTHM5Mq1LDP6Vw2/QU52vnIk4Bj3wcEhXYnFcryCSkKJsgTeUIIIYQQQgghpUCfa34hF1JlCCjLTJ05RpK32EQl451VXSvQ5QoBQALL258S83qF7yriVCCozAEf0oB8QlKRzsNikT9JSPKqHrfbWvel84WMDUopT1Gnl6gs13iJsopFTOHne/Er/fLaVqF/g2yRRYndw/5PZ6A/NPI2Qe7fmMVnXvmD2ws65oADv9d16XiBTDGurS0lhAzQIgiQ/IvRJIpvFlwyOlydxLLW+415dcGpOKDq8AKXiIyW2qA5OrFFECaOBun5DVD8fTFCCCGEEEIIIYQQQgiBYxlqZcsbT8wwi9EQWwfdM/AO+Fun24/1d0/pAZlLjuiLa2xuM+c1TPGYvEca0DxpKlRF1fC0qiylQpHijjkqOo5937CP8/tlMVpfAvj4n1z0xnNXj7a2y9sySoliUXljksBMElolUia/3CwcdzAETJ0j77MUqZkMVFQbs/T1l+Pqs+Tz/1O3515I1CRIrSrLgalVxqwhdHcn0CxMKJwugJPqkZsc+L9TaBCrBVMcGUIFQ1A3Pynm659fYUwvDyn8/TIHVeX27b+1Dfjm3RYxmiCUEcV6K+VYa1VCYo6xQl16lZy5/FGEQwpH7ykvctltGh15khNly8oNskjk3bUb/G+ofHdMnvr4d9EQlyVHj66WN5N0NfqEeaEr9O6CKosgk5AJx4IDxCx99UeHfa633AP9axXw+2fy0/ZIfaHJlUBNxMekqrGoOf20i6GUeX2llNzHGewTiX0jSmNJGkI9yxCjSRK/UhJ2Lj3H96Jf2vljHD1TOA8BXH6bm7NnRC1Cf6YuLezws8fLbdJHYY59RXkE6tq/QZVHgKCPcRd8JkRIUTBBnsgTQgghhBBCCCkFpIG15U64oOWQBvHaxB3jnRc6lmW9zvIO88v6uNuPhDa/6Yw4lQiq0QlUSOmS1En0a/P0YsUif5KQREg9yS4PMRpnOCo2vOqaX7lXrii28kj4OUfDgrgr221Firw98JJ79SRluehoqHDML+7iuh9xV26HRCHlru2FRnldl7YfdiJw0mZjl+RuE7mPRki2SGK0ySF/YjQlzKGmKUabcPynazl2JpqNecfUnlRQOTnJDXUhQYyWsEz1OkJsYlXpek0IIYQQQgghhBBCCCFFg7KJ0SbI83GbaGX5o0N/VnuE5a3ekqPyAFi/M3Oc8iAN5kfcQ2hpQLPpOLOVCpXzuXZWTJ0zIP/aRWNcFqMBAwOel63J3e7vfUV+bzl/miExFjUvXFYOTJ5pzpOEVsmUmMjmjeZlZuwBFZgg7UiOUEplSsNSaKg2xwMCwIr1wIaduX1X3STMKdQ4BaLMY4iU9jOD9GOURJuD9aijxZxP+Yc/ZjbIeW++LGYFAwqfOs5b+nLXSxqum1n3XFeLYjRJ8qmX3S3vqJTEHGOFpf0Z/G32nC7Xia4+4OHXc12okbFivbk9LAsCi9Vr/jeUKpWd1YCGeFRc9B8vy21wd7+8i8rUeD6K0UgJoZQC6heaM7WGTiaHPnrdA931Un7i9TYIXRCv8gwRixqTlSQdHqRqkjm9s3Xgf6lvJK1HSpdRitFKStg5U5DmC5wwPSbmvbJRFgRnS1uPOX1SmpzxwLlyH61x+wpzxrmfhBp8ZhH0HoulwhSjEVIMUIxGCCGEEEIIIWTc0Oean26VF1hwIgqMJqh0o9ftwaudL2S9Xqx/Azb3ZT4stslJwoEIQk6ZMS/uWt6QEgJ73YoIwp9iQRI+9bo9HmI0H7OUkILiJacKBwp7zfKSiUkSrkITUEGUKft0m34Fh5GA/XwPe+SPNZK4bbCNk9q60f6WYUs72WuRm/QkzXmDorXRCk+zOV4vqRwhBGiJm0VXdUF/YjQIYjQy8VjWYp4xL6iCOGrSiQUuDckF0nnemgcxWq/w/AYonv4nIYQQQgghhBBCCCGEiNjENzZp2jhCHXS0nLl5t8jq8nfb3w29tS1XJbIPoK33GoQfi5rTZxgG+VZnOXA+UtwxBsWGCgSAxO54gIN6X0VNss26ztrtuZM6vGV+HQoAWLKXoT5vFsRtM+uhJHFVQBg8nSpG6xbiLCb5fS9bYsxuFLMOKVtnbZbftvzmI6FJaIsa/fx0m94Ws9TBae2uRbCn+3qBfvNE0qiiGM0Pqtxi9tweg1751MA/gxBj6d7ecRE9cWBLe2Z6rA3oN88bLdehzWvlHb3jcM+ykFGy8FA5b9PAb7N0b/sm1jYXx2SCawWx48Fzgcg2Sz1LJVQG7HPI7s+zGnBYryAWgf3Yu2SvJSpT4xFLSUBDCABMEeS7ALB909Cf8ybb74Ny3QcapEXoxs6s8V5Xay3fm3md61Ifp2NAjKY7hXuKbMXXZOIj3jzsvmbp/j5g+2bzYjaR/ARDVVRntXyta+gAp/B2jp4RdQp9iJq0IRbH7gOEDbfn+80GJsdeMW5jmPjOhxgNEcY6ElIMTIwn8oQQQgghhBBCSgJRjKY8pqbMMdIg3t7kxJRurOx4HnFtlpKdNPl9OG/6x6AEQcKL7U9lpNnkJBGnUhQ9JbQQLUDILqzSvQILFLNFFi52Weu+JBwiY4eX6KHQkj4vUVvEQ5xWSGzfXUiV+a7vXue7JB4rFiRBWc+ufoZ0HfWS4Hlhqwu2a3ePIE0bLM9oxWjSvk11W6rvtusDIaWEq120CAKkupBpenT/FEeIJ8kV2/o3Y1X3S8a8RVVHoTrIYPvxSG3QHC3ZEt8+EJiYQ+zS6uLpfxJCCCGEEEIIIYQQQkjWSKKk8cbh8iQo+om7hv7++BKFMsucfWf9wsXydbl5xhzdYd7OjBogUuYhqTGIbQCYB99nO3A+zOfa2aI+dtXQ3xW6B1fs+KF1+X++nLv3FFFBCgMAR87PTNOxqHlhm7hBElqlCOF0r2CUYH0yklpn0qluieJbp8ttwEd+6+b0XVdUEPU1TPWWZen7b5UzDzl2+OegJEZLAh0t8nYo//DPh75kTu/phP7U8QP/ztsb7ieWQO/YMpR9wr7AUQu8N3/VPZl1ZeVGeXlRjBaLiuso/t55R1VUy23zrv7FuYcq7Ddb3saX7tSIJ8Y+cubXy8xlmD9NQd/3B38bef9nhktSZtWj2u1ERIgBePA1eVPdlvnQK3TK9ihGIyWG+u+r5cw3dsdrOY7CN0+T+x/rdwK98dy3Pa1CyE9dpY8JVVu3Az1CP9jrXK82x6PpzgExmtg/qspSfE1KAKGupt4zbF0//HMqM0tHjAYAOPO/fC9am2y15r/nJy7e3Dq6dime0IgnzXkVZWnlqVD4/AnDf29HAd84sQ9q5xYYSW2LnIB3gcKU5RNSDEyQJ/KEEEIIIYQQQkqBXkGMVmjhkbQ/mzRkPPNix5PG9JpALc6YegGOrTsdCyLvMC6zvOOpjKATuxitYtQCFVK69FjkhIWWUWWLKFx0e6x1n2K04sPrmlT4a5Y9oNIrv5DYxFzZfG/j6ZhNRALmYx0UjEiikdFKRios7WR3UghUgCxGG9yeJDyN+7yuS+JZ0+8o/ba26wMhpURnsl3sV9QF/c1MLoc3jX2AJ8kdT7Y+IOYtrTulgCUhuaQ2ZD7P+3WfeD0fKdLzGwcOQqrMmEcIIYQQQgghhBBCCCFFg02woybGMCwVDALHv9+c+crT0PEBk0XDVIXVV9uP+bSfuejPgQxEElo1mOf9GE4sakxWsxsyE7MdOB8p7pijouT0i4d9/MqOH+C2TReKiz/wGnImlFnbbN7OF9+joJThbWcsat6QTdwQFOK1kimTX/YKcQoUoxlR8/eTM2NRXHmG3A5tagX+79+5e1/dJLRFotRqF7qzDVj7H3PmKRdCpYs1pUH4yQTQYREOCNIQkok65+P+FnzteehrdgshAo7Cvz7v4OqzFE7cV17tN09qrNwwvO6d/nPXuGxNGKgznP5aayBmlnuqb97iWXSSG9TXfmvO2LIO2nVRWa7w5BX2/tD1/xrbuJmt7Ro7hNf+9dW9wKa18sqHvBtYchbUV38N9fHvDs+rnQaEK/D7zZeKqz+8ynzsXX3yLisHYyBrJkNV1sgLEjIBUfsdIebprw+/R7vkaAf/+z45Yu/rd+VDjGbe5iQ/YdySsBrwlk1VCX2cwX5RZ1t265HSxXTfBwx/1jOaujrBUOd/1veyTp93jOHZN45OXN1lEatWGsIOv3u2wi0XK7x3EXDhuxQe/JyD8575H3kjqff6kvQ8FYrRCCkKJsYTeUIIIYQQQgghJUGf7jWmlzvhgpYjEjA/2JIG/o5nOhJteL3rJWPeIdVHw1EDwRmHVR9tXKY5HsOGvuEvUyXBCTDwW0qip7i2POEkBLIsCADCgmioWJCERn1uD+KuXPcDgnCIjB1e0q1CS/q8hGKjlWnlEtt3l8335nVMxXTMJmyiREAWjI72uMqdCJSgPLK1r5J0bPA4JPmJX+FpNscryfUmYh+NkJHQkpCnR68ThEmk9Oh3+/BM2yPGvD3K56MxvE+BS0RyRV1QHjm2My63DyNB6juEnQrzoCNCCCGEEEIIIYQQQggZL6SLbcYxqn6hnPnvh4b+bJiqcNFi+dnutg7g4ddHX551O8zpjVPtz5V1vB9o3mTONA1oznbgPAfBZk/ddKCsfFjS+e134ivbrxNXeeyN3Ox63U5z+j4zhBU2NxmTlU2MFhCEVomUOIheYcA465PMgUcZk/UuadS5h8ir3v58bqQgrquxQahDXm0RnntQzFKNBruWNAg/mQA6bWK0Ons5yG6mzvYnOwCAFx+Bbtk29DFSpvCN0xw8+PkADpgjr3ZbSt2zSUIbpsL8nrRtB9DTaV7J1g6R3GISqQJAvB/YEQMA1FYofO8cuR24LUft0Ei5Z6Wl/u18WcxTn7oOzk8fhHPNX6BOvTCjniqlgFkNaIjLApnbBTmlVWwyOHkb6zkpVfY6WMzSPcP7kZctldse27k/UlqEcOE6P93YWNScXhYGpsy0rysJrAf7RVL/iNJYko4vMdp68zKTZ0CVF/e4o5wzw78IrqpfuFlK4fUY8JLw9frBKlYtz0xTSuHCdzm487IAbrnYwfH7KvneTClgxrzdn/3cK0SKe9wJIaXCxHkiTwghhBBCCCFkwiNJLQotRpMkMz2u9+wH442XOp6BC/MMZofXLBn6e1H1u6CExwzLO54a9lkSnISdCBwVsAhUEsZ0QgaR6lZIlYnCvWJBEi5qaHS5HcY8BwE4E2Qm4omEl4jMKz/XeMmyvERuhcRW1my+N69jKnS/IVuk72GwnyEJRkf7WzrKGVEfR8qr2NWuSe2vXzGaKFYxSNCk72Ai9tEIGQktgvgoqIKoDgiBRWlIQiONsQ3wJLnjhfZlYru5pPYUSq3GMZOCk8X79laLOHEkyOLUEgscI4QQQgghhBBCCCGETDwcQYg0Htn7IDlv/XBL1aJ5wnK7WL1l9O+KojvM26iX5/0YYNuG4QOcU5nZkJlW6e+92BDlxR1jUIwoxwECmbECi3plMcvrOahDWmtsF/xCc+sy33Fp1wW2CiO2Z8+XdxQU4tCSKfGNvcIEbuHiidMpOkwiQwDYMiDjWVQvv6dcvSU3RWjrARLmcFnM9fKRbZGlQUb5iW0QfrtgigyGgFITNYwCFQgACw7wt7DWwIY3jVmHzJPr3hspbdcOof0BgFrpZ4tF5ZUojCoctu86tvvcXmSpC29uBZLu2MXORIVmAwAW9ayQM/de5L3xWQ1Y2CcbTLe0mY+72yJGC+vegT+mzfbePyETkVkWEVEsOuxjVVhue6S+72hoFbqx4rUslVjUnD5SP1xEAAAgAElEQVSr3jvmTJK/drQO/z+dbMXXZOLjR4zWLgi+pszKfXmKHJXFM4/FyVfg+AgfHc0zIqtY1SBGS0drLf++WkOlStz9iNEqqr2XIYTkHY6cJCTHmDrnWnrJQggh4wTXzXy7wwFwhBBCxoI+t9eYXuiBtZJ0QxK3jWde7HjSmD41NAMN4b2GPlcHa7FPhTmAYHn7U8Pui6RB9oPfa1CZHy5quEjqpK9yk9JElOeMg8H3tjJ2JNqM6aEil72VKuGAhxjNIHPKJ16yLC9xWiGxnQfZfG+2YypXYTiquAPVvfoZkgQykoO6FXHMksbupCwWk/IGtzV6MZq5f2X6naXffiL20QgZCTsTzcb02uCULGSrUtDIyMpEigutNZa13m/MiziVw+TYZPwRUAFMCpqDCFsTlijpESDdm0lCaEIIIYQQQrKFMXqEkIkG4/MIIWQc4UygYVhHnCRm6Ru/OqyP/cHDFWotr6T/568a37/fHVW/XBJ6NHiJ0WJROc8gHFDBYHYDWykhGhmnXpiRdFqn+T0UADzz1uh32dkHJAWpVZ2p/m7fDMSFUdc2SY40eDqREgfRZ35XQjGaBUkQ8tjfAAAfOVLuH6/dDhx8dRJrm0f3bEASgQBCHUpBd7TImYe8OzPNJtpsFSY1qq7jfUKWqHM+7n/h6Gpj8qVL5O/8nleAj/7ORVu3ttafTx+X2X/Qz94P/d9HmVcoCwNTZlqLS3JIdZ3YN9D3/XHo7xP2lTeRcIGNlmYg37QKlx0AOLTtWTnzoKO9Nz6zHlVajh+U6n5Xnzm9wu2CMxhoVOVlnSRkYqLO/5yYpx/5S0baewWHYWs38OrG3L4bkdoT2/3gIHrlU+YMH7JPVSUIrNe8NPB/p1mMpqopRiPpSH233eeKlkR7pVqf3nOBr8Wm9mzEOT6cqh/+rcbPHhnZMyKp/wAAlWU+NtDaDPQKDdnJHxn+2Y8YrXqyj50SQvLNBHoiT0hx4BhedMXj/gb5EUJIsZJIJDLSTO0dIYQQkm/6BKlFeYGlR5J0o8/tgauFyJpxyM54M97qWWXMO6x6SUaAxaHV5pejOxPNiPauGfrsJTiRBCqAf4kKKU16ksLge0H0U0zYytiZbDem284VMnYUm4jM6xpZaFGbDdt3l833livB2lghHWtPshtaa4sEMgdiNEFWIu3Tlje4LUl4mtCZzxpMSCI40/FK30GPRexGSCnREjcHT9cFpxW4JKRYaep9Axv61hrz3jXpeJQ5PqbcI0VNbdA8eqwlIQyuGCHy9ZuDxwghhBBCSG5gjB4hZKLB+DxCCBlH+J5spvhRwRCw+FR5gT//eOjPqdUKT11hP/av/UPje/eNbFB+b1xjszAuuWGqhwQots6cXjcdSpJQVWUx2Lks7H9ZMoT66Ncy0sK6D8d3PmJc/q/LNeKJUUqtLFKYOlM4RCwqrzC7Qc4LCjFbyZQ+XY8Qp0AxmoiaKYjRAOiNb2FuncLvL5Lbg1c2Akde66Kzd+T1yFaHPGUggqwDBx0NZerb2wbhi2K0EhU1jAJ1+sVQX/gZUL+P57L6+suN6YsXKHzoCLnu/ek5jTNucNFiqT+nps07rV95Bvqr75NXmFVPCV4BUUrJ0p57fw/91isAgICj8PrVcn/oi38ZuzEEUvt19sGA2hI1Z575X+b2KQ2167v5wdYrjPktwiWvq8/cHlekxhNUCyIkQiY46sDFcuYfvw+9/o1hST96v3yuHnS1i57+3MnRpOtZXYX9uqT7+4BnBRGyDzEaqgVRYmcbdHcnIImssrm3I6WB1IdKlXR1tZmXKdH+tvrEd/0t2NGKP17i4ONLvfupn7tD48bHcyxG8xO+ujkqZqnLrhme4EuMVpp1gpBiY+I8kSekSFBKoaxsuHK0s7NzjEpDCCG5Ib0dKysr40N2QgghY0Kf22tML7QYTZJuaGixjOOR5R3CjC0ADqs5JiPt4Ooj4cA8i13qtiQ5SXhIjCZP4xDXwgyNhECW7o2Hwfe2MnYkzS9eJNkQGVu85FS5kFdlQ8gJWSV6hRa12YhYpGXZfG+27RTT8UpIx5pEAgkdl0VkuRCjCdvodmWxmJQ3KHyU6p/fa3qvKL3MLKsor9W9cHXS1/4ImchI4qO60FTf25Dn0svtzJNkbHiiRQhOA7Ck9uQCloTkizpJjBbfkdP9yPdmxd8XI4QQQggh4wPG6BFCJhqMzyOEkHHEBBNXqgOPEvP0P24a9vkdsxWuOcd+fbrxcQ2ts39vtH6nnNdgfrQ9hN4iiNEskqWsBrZSjDYyaqcaJWBLuuWYxPv+M7pd2qREtabQrFjUvHDVJChJzADIg6dTxWh95ncloqyPWM9Zfc/vAADnLLK3Qds7gb+tGPm7a1sdmuQVgijJOvbY25xuGYSv24R3d7Z6SUTUOR+Hc+srUI93Qz3eDRxzpris3rbRmH75u+1176m3gGVrzHWvLAiE08Kn9D9/AyQtsUx+BDIkt1i+c33Pb4f+3memQp3QlP/9pRyXKQtau831r2GqEgUhat/D/W18diMAYJoQd9RqvuShWwgPrEyJf7RebwmZ6Njuxe79w7DPc+qAoOVW9J8rcxO7l3Q1OoThUJ6S2GfuE7OULzGa5R5t2V2yhLaKgkWShh8xWqcgRqss0fo0ZZYsAE+lsxWRMoVffshB8mYHnzrO+xlRtnQJ/YegA5QFfbwziUXN6eEKoG768DRfYjT2VQgpBibWE3lCioTq6uphn9vb20f0cocQQooBrTXa29uHpaW3c4QQQkihkAbWljuFDT6yCVckWcl45IX2Zcb0OeUNmF0+LyO9MlCNfSsPNq6zouMZuHpgJizpdxyUmYQc+YFqQmfOlE3IID2SnMdyzhYLIVWGAMwP1jsFMVqAYrSiJOIh4hsLMZdtn8Ukp7CVJRvBoX07xXO8EuGAfKy9bjd6hL5GLo5N2kaPICeLu/1I6Lgxb/d13Sw8Tbjm9TL2LR2voW03pQ0i9T8IKSVa4oIYLehfjCap0ShGG/90JFrxUufTxrx3VCzC9LLZBS4RyQeSCFESJ46UfIpcCSGEEEIIGYQxeoSQiQLj8wghZJzhmCeNHLfU7yPnxdZBd7QMS9p3pn0QaqwN2CKML7YRtTymnjfZY+VY1JxuG3xflYUYrbz4J2QsRpRSRiHUvv2rxXVWrB/dPV2LPOebWWITi5oX9hI3SAPGEymxjT1CYShGk5knCMQA4O1XAQA1EYXZHqfvzctGXo9ahfDfmjAQcDwG4UuyDknyEbBcT1qFRjEbqSPJQAUCUIEAMM9y7XvrFWOyl6QTAB5/w1z3aiPIFF+v8TBo2cpI8oOtT7Tm5WEfp1bJi3b2js3zSUlOVhvsB9qENsWvgG/XcrWuuZ2T2k5JbFKpU66RFBqRUkaSpwIZ14mAo7D3DHnxFetzU6ROQYoGeEtite3aZuvnDTIjc3zS0LZff0HuX1NaRNLxJUajaC8V5TjA9LneC6YI5ZRSWDjTvvjrMaCnP7u+UVefOb2y3OcGYlFz+sz6zD657Z4MGLj3j1T63DEhJJ9QjEZIHkgPSIjH49i0aRMDrwgh4w6tNTZt2oR4fPhg5ZqamjEqESGEkFKnzzU/ac9GlpILbOKRiSLdiPVtwMa+JmPe4dVLxPUOrT7amN6a2IG1Pa8DkOVVgzKToEX25FeiQkoT6fwbDyIkpZQocOtItBvTg8rHrCyk4HjVt7Goj7Z9FpOcwlaWiOP/pVKuBGtjhe1Ye9xu9CbtgtHRUBEw77tXuHZL0rLUbUltlV/ZaTZiFdt3MFH6aISMBkl8NDk0rcAlIcXI020PiW3zkrpTClwaki9qg+aI/daEMOv8CJGu3+OhL0YIIYQQQsYPjNEjhEwEGJ9HCCHjEGeCDcM6/ER7/ubosI8n7y8IplL43v3Z98mjO8zrzKwBImUeMqLYOnP6rHp5Hb9yIaVkCRbxRJ14fkbaKZ0Pist/5/9p7OwahdRKCAuoLAeCSELf8VO4Xz4H7iePg/vJ46B/9x3zCl6imIAQ35hMedfWJ8RTUIwmomwD8Z//19D9/gffaW8Tnls78jK0dAtiKz8/W4dZrqAkEaNUjwBZYpSN1JGIqBM/IObpG78CnUxmpM/04ciQpDTp102dSABNq6zbspWR5Ad1guU7T+sPnXGQ3A5Fc/vq3TctwmWnLrFTXsmvGG3mQJ+qNmlu5/oSZumJJDapSI3jo9CIlDCmvvIQLz4K7brDki44Qm57rn9Q44o7XWxqGd37EUloCACV5jmSdxOLynle950AsPBQOe9Ns7gUAMWxxIDHMwQA6DSPzxH77qXATMszlEHS5PnnHqIQ8nCLZds36uozt2N+xWh6nSBjN/R7lFJ2OVp1XaZMjRAyJlieoBBCRko4HEYoFBoWqNDR0YG3334bNTU1qKqqQjAYhDPRXooRQiYErusikUigs7MT7e3tGUFXoVAI5eV+9cqEEEJIbukThBblTrig5bBJNyTp13jjxY4nxbxDa8zyMwA4qOqdCKqgcUD98o6nsWfFfqJEZfB7DSr5rUlcW962kJJHOv+KSfxkI+xE0JnMfMliSgMoRitWyj1kD5IAL5/YBBQRQYQ1FuRKaJYrwdpYYTvWHrfbUzA6GqTvp1sUo8n9nrBjF54mkYCrXTjK/oxU6jeYvidbHRooK+VPpHRJ6gTahGDHOkGUZEKJQSMcdD6eSeoknmw1D0KZHJyG/SstgWdkXFEXnGpMb43vgNY6Z4E8ksh1PEirCSGEEELI+IExeoSQ8Qrj8wghZJwzwQZEqvIw8MsnoC9baszXv7kK6vq7hz6HQwr3fsbBe290scUczoJfPKYxtcrFlWf474tLA2UbzI+1h7PFLEZTtkG9fgc7l4U5CHY0nPdpYMObwD2/G0qK6F4s7n4Gz1QsNq5y9P+6eO6rDmoi2X/votQqAuhvfgB46h5/G/ISxQiyPJ2I736b2itY2sLFH7cylqhPXQd9wxXGPH3zN6A+cQ2+fYbCD/9lfz/9i8dcfPLY7J8HtEpiIT+vuDrNwiBR1mEbgN8qiNEo/sgJasH+coTDujegr7kE6lt/GL6Oj2vB9k5zerpYT3/ePjGZ+tKNUDY5DMkLas8DoE/8APDQnzMzd26B7u2G2iW3/O7ZCj96yFyLvnefxu2XFr7vILVfk16615zhOIBNSJmCqq6FrqpFXb/Qzu3afyQt/L9bCPmvSI05rPJhHSRkgqIOPRb6oGOAleZxO/q6y6G+ctPQ5/95j8I37pL7QD/4l8YdL2g8+1UHs2tH1g5JQkPAh5QoFjWnH3osVJn3s07lONDHvx945C+ZmW+tlFdkO0LSkfptqRMriX33Eq5PM+Z5L9PXA93fN3ROT69RuPuTDs69yRWv+//7gMYtF/tvkyRBo6ecERgQHD94mzlTus8PBAGDGBkA778IKSIoRiMkDyilMHv2bKxfv37YDJTxeBw7duzAjh1jpH4nhJBRMti+8QUvIYSQsaLP7TWmZyNLyQUhVQYHAbjIfPjVK8jbxhNaa7zYvsyYtyCyL6aEpovrRgKVeEflIXil898ZeSs6nsa50/9L/I4GB0iHLLKnhI6LeYR41a1iRxISdSTbjOmSbIiMLRGPa9JY1EdpnwoKZap4BtZYxWhZSL/s2ylsn2Ek2Po17YlWuHCNebmQQErtUE/SHDnVk5TFaBWBKgB2iWNCx6110NWuKMY1ldX2HUiCFkJKhdbETmghtHdyaPTSQGrRxjf/6XwRLQlzcP2S2lPgKI9p/ci4oTZkHkHWp3vR43YNXb9HiyhEHwNJMCGEEEIImbgwRo8QMhFhfB4hhBQLljcfE1C8q/Y/EnrOfGDT2szM5x6Adl2olOM+cr7CxuscBD9hfncNDAzK/8KJGtVhf9e0dZIYbYp9fd3XC2zfbM60ya38DnYuK+yErRMNFQxBXfFLuK88Dax7Yyj9g+1/EcVoq7cAt/9b4xNLs+8PiVKrYA/wpE8pGgDlJUaThFbJlMlke4V4ijDflVg5SJ60F7f9APqCL6KiZjI+cLjCn1+Q2+rv3qvx38dohILZ1aNWIbQkXWxlpMMc4ycOpncs72DbJDFanY+CEF+c/GHggVvNeQ/9GfqjX4WqX5iTXaXWH/32f4CXzfHZAKD+usYu9iR5RV3yTWiTGA0AYuuAxn0BDIhi66eY+y9/fkHjto/lblIyv4hitJfvM2dM3wNKEH0amdWA2qat8v57gFlpzZ0oNtEphWW7Rkoc9bkfQV98uDnz3t9DX/w1qF2yorKgwq8vVLj0j3IfaEML8JunNL51+sjaIElsBAAVXlKiWNSYrI471/f+1ZEnQZvEaD1yrDLbEZKBLzGa0HevLGEx2kwfYjQA6GoDynaPKTx5f4XWnzoou8z8jOiPz2r8/iL/fSNJ0OgpZwSAFx8Ws8T7/EAQgLBTti+EFA0cPUlInqioqMC8efMyAq8IIWS8opTCvHnzUFHBl3GEEELGDkl6VK4KG4CklELEqUCX25GRZxOEAEBbYifW966FNkhNwk4EDeG9UeZ4P7FrTezEht61mFE2B9NCM0f8AtXVSWzsa0JrYudQWkt8O5rjW4zLH1Z9jOc2D6s+xihG60i24dGWe7Azvs243qDMxC5QSYh5qWit0RyPYUv/Rl/LD1ITqMUe4QUI+Bj43+/2Idq7JisZngMH88J7oiboPXNE3O1HtPdN9Lj2OpXOnPIGq7xupPS6PYj2rEG/tkwFNMZs7ze/dB8vg+8lGVJXMrOtAeznChk7vMRnYyEik86BsBOBo4onaNt2rnoJ51Kxyc9yIQ/LN44KoFyF0aczhbCrulaI6+VCuif9Bi2JZuO1fWNvk3F5BWeofxZScjREQsdRBvmc6HN7RZGTqc0MOWUIqqCxv/B698vGvpsfAiqI+vCeqArUjGh9QoqBlrgQOA2gLmgWJeWSpE5iQ+/baE/Ks8eamF1Wj6llM3JeHq01Yv3rsT1u7j9OD83GjLI5JTMA9YlWcyBsUIWweNIJBS4NySd1wSli3gvty1AniNOypS3lGUMq40VaTQghhBBCxg+M0SOETCQYn0cIIeOEInrHnlNm1pvFaACwJQrMnj8syXEUzjoIuHuleZWuPmDFOmDpPv52H91u7s/Xy4+1B9i6Xs6bJYtlVHWdv4l/yot/8rVxwT6HDhOjze8X6toulq0BPrE0+91saTen18XN7y1E5sy35weEmK14SlxdnxBTyDplZ3ajPf/1F4AjTsL5HmK0re3AG1uB/edkt/stgh+hzqOLruP9QLtgeJTEaAHLsN4287aUtC2SNWr2fPt1YOVTQJoY7ZPHKvziseyfP9VVpMQdWKRoiFQCM3wKKUh+mDFvQKZies4YaxoSowHA3FpZ7Lq1HZhZQK9Ke49GjzDveF2yxZwxLcsGcspM1L21Wsze0gbsO2t4miRYqkidaK2K7RopcbyEvK88A5y4+9qwYJqC1xSmy9aM/F2JJDQE7FIi3dcD7BTkiV7HmEq2clClgEhuJoEkEwgPMZp2XVlEXFW6YjQ1u9Hfc5IdW4G64ePVggGFI+cDzwm3+s0dwHSfIfiiWNVLzghAv/SknCnda9ruy0q4PhBSbFCMRkgeGQy82rx5M+Jx4ekCIYSMA0KhEGbPns2gK0IIIWNKQseRhFmKVZ6FLCVXhAMRo1xDkmQldRK3b70Rz7Y9Yt1uSJXh4llfwMHVR8rb2XIjnm3fvZ19Kg7Ax+d8TZQqSWzt34QbNn4bOwRRWToOHBxSbZ6pMZX9qw5DSJUhrjOfSP6j+RZxvUigEgAQVPLjCtM20+lJduGmTd/Dmz2veS5rYlKgDp+ceyXmhhvEZV7qeAa3xH7iqzwmjqk9GedP/29RSPRa53L8ZvP1RimOHw6qOgKXzPoiQo6Pp78+eK7tUdy25UbxHCx2xsvg+8FzIB2TSBEAgg7FaMWIV30bC8GKdH0otnPDVp6wYz4/TNjkZ8V2zBLhQAX6EpnXAElcA+RG+hYRvuft8a24adP3sthOxVBdH43wtNcVprKE3GaGnQp0JjOjne/bcYd1X344efJ5OGPqBSUjSiITi5ZEszE97ETE88mEglT/5ZCIjb1NuGHj1WiXgi092K/yEHxs9hUod3IjxG5L7MTPN3wbm/vXWZebV74An5p7JaqCE1uKuLV/E1Z3m0ctHVp99IQ//lJjUrAOCo7xHuOObb/K+/6zfW5BCCGEEEKIHxijRwiZCDA+jxBCxhEB78kWxyPqyJOglz9mzly/JkOMBgBnLVK4e6X8jmhVTGPpPv7erUYFsUij13wesaicN8MyqN7v4Naywk9+NxFRS86C/tftQ5+XdD+F2mQLWgN1xuXvfXVkMoeoMLZ9D22eJFZkkYeVLSK8X+1NmQA1Lkw+yjplRdVMhj7oGGClMKB9cxQAcPxCIBwCei2PAdaMQIzWJEga95js0ZZtXW8WKQHAdEF0ZRuA3ykY2igQyh3HnAH87moxW2+OZkRHnHXQyMRoe0xO2W4sKi949BmMSRpjVKgMevoeRvGq/vmXgHe8E6p2oHNy7EKFp98214em7YUVozXJcyViXnyDOePAo7Lah3rXyYg89wCmJpqxPTjNUAaNY9POmm7hUliZOmE4hY+kxFGVNdCHHgtI92Kx6LCPixcAU6uA7Z3yNh9dDby0XmPRvOyvKdJ5qxRQbjOSxKJyXjayM4vc2kioHMqZoPJyMgo8Ylx3xIC4MA5s+h55KdG44IiTBu5Rkh5jxmJNwJ4HZCQv3VvhubXmvtG/Vml8+Eh/bVKX1H/wcysdi8p5hx1nTrfdl1G8SEjRwKs9IXmmoqICCxYsQGNjI6ZMmYKystwMSieEkHxTVlaGKVOmoLGxEQsWLGDQFSGEkDFHEo4ByNkA+WyQ5COSwGNZ6/2eUjRgQPz1m83XoS1hnqVwWev9w6RoAPBG96v4+7ZbPLedzq83XedbigYACysPRnXQ+wVk2InggKrDsi7P4ABpRwXgwBxEmNDeA1r+3nzLiKVoANCWbMFNm66BFgJVdsab8dvNPxyxFA0Anmx9AM+0PWzM60524ubN145YigYAKzufx/07/jri9VPZ0rcRf9zys3ErRQNyIwsqBNlKAmyyITJ2hAPFJ3uQZGDFdm7YyhPJ4vwIqCBCyvwMrtiOWWIk5cyF9C1X30+qZMkmPPW6rvdYxGhSm5nP3/iBnX/Fys7n87Z9QvJJS9w8mqTOEKxoI9sgXFe7+OWma0YsRQOA17pW4J7tt3sv6JM/xH7qKUUDgPV9b+PWrTfkbL/FyrLW+8W8pbWnFLAkpBAEVBA1Pu7r88V4kdQSQgghhJDxB2P0CCHjEcbnEULIOEWYBHHcc/bHxSz9pbOguzMnEP3A4QpnHSRv8pO3a2zv8BbI9PRrxAQHUMMUj3dTW4R3PlNmQZVbYgqrzUKuDMoKH5c4ITnq9GEfI7oXv4x9Wly8oxe47XnzRJI2JKlV/VpznJ4JddWtUOUeMSphQYzWMxDjoJNJwBXKH6IYzQv1mR+IefqXXwMAVIUVfvURe/tw7k0uOnuzk1hJcqFRSRolwYczAtGm37aLeKL2PBD40JfkBW67PiPpuIXAx47JXjLTMCXlwx0/lcv0sSuz3jbJA7MazOkb34I+Yw509HUAwBUnyXXhN0+NTPA5UqS2K6hczElsNuapC7+c3U5OuxgA0BDPlMYBQJMhLKmrz/w9VKbGBFL4SAjUpzKvOYPoXw+/NpQFFW7+iGOXlAE49LsufvJw9v3pLmF4TGWZR9xgLGpOVwqYIUhiTUyZbZcUpRPiuyBiQKqrg+PDtpivZQCAmVnU1wmGqpsG9dkfyd/fILGoMflrp8rrXfg7jX+85K9/lBcx2vQ9oCqqzXm2Nke69yeEFJwsegeEkJGilEI4HEY4HMb06dOhtYbruuIge0IIGUuUUnAchzONEEIIKTr6XFkSla1MKBdIg3klgccrWUg0XLj4T+dyHFV7YkaeJON4pfN5XIDLfO+juT/mSwaQymHVx/he9tDqo7Gi45msth9xdj80DKkQ+nQyYxk/YrRcCEt2Jpqxsa8Je4QzZzt9vetluMgsW7a80vlvHF37noz017pWIKFHLyFb2fk8zpz2oVFv55XOf496G2PNeBl8n3oO+MEmGyJjh+2aNFZSLukcKLZzw/bdZVvWiFOBeDLzDX2xHbNEtuVUUDkRxaYKzUa1nZTy2ySOXtd1STgLjF29fqXz3zi4+si87oOQfLAz0WxMrwt5RXD7Q3rfsbFvLVoSlqlpfbKy8zmcO/2SUW+n1+3Bmu7/+F5+dddKJHUCgQna7+pze/Fc26PGvHnhPdEQ2bvAJSKFoC44RZSx55vxIqklhBBCCCHjE8boEULGC4zPI4SQcY4zMcVoKlwBvdfBwJsvmxe47w/AuZ8alhQOKdx5mYMpn3PRLoT2/fwxjW+fab/mrbc8sm7weJWlY0L8myQiGqRqkj1/EIrRcoIKBqEXnwo8c99Q2nkdf0d77HJ8fNaNxnUu+r3GexdpRMr895lEqVV/1N8GwhVQx5/nvVxEeN/R2zXwf1wYyQ0AQcobvFB7Hwx9xHuA5/+VmdnTCd3RAlVdhw8f6eCIRo19vilLP/70nMZl7/ZXhxJJLbZHjVNHJgfA5BlQYaG+ZCP9GKSaAqFc4nziu3DffBn490PGfL1+DdS83e/MHUfh5g8DFy1WOO6HLvp9hvkO1h/dtEpe6PzPQs3OjFcmY8DsRuDlZWK2/s23ob77Z1SFFRbOBFZvyVzm909r/OZCXbD7fkkMOq+8HQEY2sg582U5iIAqD0NX1qAhHsWLkUMz8qOGa3C3IFiK6F0xgYEAEKFwhBC15wHQJ34QeOj/jPl620ao6XOHPp+zSOH1qx386GGNGx6V3398+W8aHzpCY1q1/7aou9+8vQqvLmwsak6fNgeqzL8YWLQrXUIAACAASURBVAWD0NPn2qWzqVA6TEx4idG2CmK0SBVQMzk/ZRonqHM+DixaAqx4HPrHnzMuo2NRmL7h6rDCntOBt7aZt/3J21yccaCDYMDeJkn9h0o/zwZiUWOyuuhr8jo2YbV0708IKTgTM4qfkCJHKYVAYAQzOxBCCCGEEFLC2MRo5UUkRpMEHi1xw1RIFiR5wJruV43p7clWJHTcKh/xs32JkCrDQVVH+F5+v8pDEXYi6HV7fK8zt7xx6O+gCqFPZ/7mXgKVuBtHZ7Ld9z5ttCZ2YA9kBhq0J1tztH3zb9CayK6uZLv97LeTm/KMJal1q5jJtpzj5bhKjaAKYVbZHoj1b8jIOzCLdjSXSHVlbri46lBtcDKqApPQmRw+FXRIlWF62eystjW3vBGrul/KTC+yY5aYW96IaO+arJZ3cjAz+ZzyBigoaIxusGj6NV0iroW3l4P5rpxfpswBDXPLG7Ghb61HCUfORLguktKkJW7uG04O5kaMJu83N+dMS3wHtB590GhXsiMryXG/7kOf24uKQNWo9lusvND+hCj3Xlp7SoFLQwrFvPCeiPa+WfD9KijMLvcYiEYIIYQQQkgOYYweIYQQQgjJCzl4L1u07LGXKEbTT94DlSZGA4CAo3DRUQo/e8T8jvmelRrfPtO+26jlddI8rzHJsag5fVaDfb0qn3Kh8sLHJU5Y5mTG4R3X9Zi4eNIFnnwTeM9+/jbf2auxvdOc1xCP+tvIYcf5Wy4sSFx6BsVolliILMQQJc3Cw8xiNAB44RHguHMBAHvNULhoscItz5jboH+u1Ljs3f52ubFloN6ZaPSSNG6OmjNsbVFwJGK0uuzXIVbU0nOgBTEanrkXmDd8MjGlFBYvAH54nvr/7N13eBzF/T/w9+zd6Yok6+RuuUm2sXGhGJteDMGGQKhOIAmEGkIKCakkkMYvvZPeviGEkEYgQEJCTQi9JxTTYmywbGNkY2zJRbqTrszvD+ns0918ZndPp9NJfr+ehwdpZ3d2fNrbnbudeS8+8idv46t2HT8P3ya3Y9a+nuqiwacmNdtHzj1yG3QmAxUIYPYEczAaAKx6A5g9YTBaWOw1YVh7s7PJXDB1r9J2NGo0moWwUVM4W6cUbJIbp1IXZ2g8UR81ZyG0EIyGR+8ATn1fv0XNYxW+dhqswWipDHDn8xrnHOr9fdYp5PvWunRhdVurucDtc5nJxOneg9HYtyYTt2C0N14zl0+cxusSANU8F2ieC/3ys8BtvyleYdWz4razLcFoG7cDT7YCh8607186D0VdAhp1106gw/wAaeu5yBZYHWYwGlG1GMHfyBMREREREdFI0m0J2IoMQTBaVAhGS2TMk8r9BIT1rm+ux0bat0lnRhgFJFg2+nREA96/1Ktxwjh+9Ds8rz+v9gCMrdl9BzjomENUUll7MFoy2+l5n26kgAAMMCzGrX4/f0ebZDaBrJafhui9nvK0Z6jMie2DieEp7itWgf3qDkZD0NtTZiJODIvrjxzkFlGpjjIEiQQQxOENS4egNcCCukUYExrfb1lI1eDQhmOHpD0SRwVwZPz4ouWHNSxFjePv5vWR8bdCFTyPqDkyG9PCLnfTqsRhDcd6DjsFzMdcKRqCjdi/7tAB1RFUQRzesGzX7yFHvhOZ1vZHp0qBqEEVFG8+HxZfhqAavGfCDPfrIu25pGDkxpC/YLTCc6ubcr1nssigR1uerO6RW9CyyUDDIquV1hr3d9xhLKt16rGo/ogKt4gq5YiG4xBSbo9yLb+F9YdhVJBPsSciIiIiIiIiIqJhzhm54btq0dFyYdsasegtc+T7R8+sB1a/oaG1fL+l1RCkAQCTGoBIqH/deuc26P/8G/rRO6BXPgVseMVc6USXB3V4DRfiRPuyUQccXbRsemodZvTIDz7790r7sZPPFrDX4jUYzWNwg5KC0ZJ990ZTlvuawcrfoxmOrOej1/ufj96yt7zqSiGwyGSN5Tm0bsFoJYU0lnI9qWvwvw3ZHbBELBJDXgAcY7n2FZowqq++DZYHPe7PsaBVw3C96ifVA2xpAwAcNlM+DmznlHLbKTx7fkyPkExSSlARANTF0ZxaZywyXYfFgKXcWP96jh8g2mXRMWKRdD2qjygsdvnY86rPc1GXEGgYE7qwevMG6MfvBp550LxCqcFoXgW9j7OmPYl0fe79bKl3CImijePNy/dQasY8c8GKh6GT5nHBx+xt7yOv3OT++b6z27yOFNCo33y97zz0gFxpU7NcZnvIVlT47E9EFTd4s4OIiIiIiIiIyqg7a75rp+AMyYTeiBASlhCCuaTlklLCsRLZTtTD28CHLiEYTUEh7ER2/d4YHIdDGo7BsY0uj+40WDb6dIRUCI9suwdb0+abq/WBBsyvXYzTxp3Tb7kUBuMWZJCwBNCFVcQYotKdTRrDDpLC30AabKXgIGwI7snoDFK6+C6R9DeWjhUHjjEYKKuzxpAIDY3ubNJXoJ25PeZ2BhBESAiwqwZ1gVGYX7sIp407d6ib4lldcBQ+OfXruGXzdXgl8aLx7xpQQcyI7I0Tx75z2AS+7YmWNJ6IgArgkW334I2e1zEtMhPLRp+OWTGPj7Ets4gTxSemfh23bP4tXk38DxPDU/HW0W/H9MisIWmPzUlj3o2IE8V/tj+ItE7jgPrDcMKYM3zXs1/9wbio6TLc134b2tNvYk5sXywfd8GweZJTc3Q2PjzlSty95Sa0Jlchi4xxvUk103BE/Liyhtxd0PRxjNk8Hit2PoHtmXbP2yk4mB6ZheNGL8fM2Nxdy20hZWmXwNOUGIwm9/1mRvfGJZO/iLu23oS1ydXQKC0kNK3Txn6HHJxKVN3aU0IwWtBfMJpECg+T3jOFnzt21aM1urX5s18i22Xcxg+3oGWTkRqM9kriJWzobjWWHdpwrO9QUho+pkRacOmUL+GOLTdgbXI1MrAHlQ5UQ3A09qk9EKeMO3tQ90NERERERERERERUEY4z1C0YPMeeCXznEnNZ21ro7gRUuPgBpicsABwFZIVbKrM/n8WS2cBNH3Qwurb4nr0UaNU8ZvfPWmvg+u9D/+JzQNb9HrBym0zvNVyoZmD3pijPIcUPfHOg8aXNX8Y5k681bvLtOzWee03j+osd1Efs4z2kAJqATmNq6jVPTVRNLZ7WQ1QYj5fsG/dnC0arYTCaJ/sfJRbpW34J9Z7Ldv2+fKHCucI93dYtwAMvaxw123280BohpHFMLVyPv5KC0QIlTOv1GupInqkps+QRATf/Avj4D41F85oUzjlE4XeP2ccTKAWMivQGNuAf18jr+QmBocG1z6HAlFnAa6vlddpagfFTcNGRCpffbD4Gzr46i81XORUZrygFGdUmzBdHz9e7QvVxNL/eaix6vQNIpnS/UFsxYEn3jfWvYzAaUY6ata98Pbr++8AHvmYsuvJkB8t/nkXKPMQYP7pH48qTvbejUzqfFHRhdXcS+hvvA+65wV5hCdc3NWm699F6IY5vIwPp2pubB7ZTCEZjYGd/C4UAYa2BG34MnPuZoqLzD1O4+kGNlZvMm154rcZe4zUOnyX3j8TzUMHbXfd0Q3/zYuCf14t1Aej9Hm/8VLnc8rlMRQY2F4+IyofBaERERERERDQsJIXAq7BjDrsabFHH/AWXqZ0ZnTYGYwHAxJop2NhTPPAmaQgQcAsF68p4D1/rypqD0SaHm/HZ5u97rsfGUQ7eMvoUvGW0/1C1UoPRTK9bztdmXo1YoK5o+TfXfgrrksU3sKUQBykYYWZ0b3xi2teLlr/Y+TR+8tqXjG3VWhcdv9KxvnjUUTh/0seKlm/q2YAvrTEPSkxmuwYcjCa9pktHn4pTCwLtaODG1kzE+yZ/eqibQWVwRPx4HBE/fqibsUtjaCwubPrkUDfDlVIKy0afjmWjTx9wXQvrD8PC+sPK0KqhMTu2ALNjCyq+36AKYfn487F8/PllqS9guQ3hdl1Pa3NQi9RPyJlTuy/m1O7r3jiLx7fdi99uLB7cKAWnElWz7mwSndkdxrLG0DiftZk/e0l9ZKkvOT0yC5+e/p2i5R3prfjsKxcat0lkOhEPjvbYTjO3846J16fQDzcPdNxhXK6gcFT8rRVuDVXazNhcfDh25VA3g4iIiIiIiIiIiGj4USM3GE3VjgJ+ei/0JccYy/X/fRHqI8X3d0JBhZe/6mDW5+TAsvtfBj51o8Y15xffa1orBaONzVv32Yegf3aF/R+Qr6nZXu51wjOD0cpGBYPQRy8H7ru53/J3b78Bl4//GjaEJhu3u+N54It/0/j+O92C0cz39KakNiAoPIyuyDiPD6mM1JqXJ3LBaMJMbgAIMhjNC6UU9CnvBW79dXHhG+uhd7RD9YWExcIK91/mYMl3zOego7+bRddPnX5hPSavCuF6LV6eNdbWalysyhmM5jhArN7fNuSJuvgr0P/3BWOZfm011BTzw0d/c77C/S9rrNsq190QBRxHIfvtD8n7v+SbvtpLg0spBfz6cejjx8grtbUC+x2B0bUKcycBL7UVr7K1E7jlaWD5AYPV0t06u83XwNpO80PNMan0YLSW1Ati8dotwJyJ+e0yr1ebe4C416Baoj3F2ZcBfyj+vIVMBnrzBqhxxf3lt+2r8MBlDg79prkf1N4FbN6hMa7e23wrMdCwsAv75x+4h6LBpS8k8ROmVsNgNDJwDUbbZi5nYGd/M/cBRo0Gthd3dvWvvgic9UmoYP/PNGPqFB76jINxn5C/Hzr1p1ls+LaDsPD5TOw/FJ6HbviReygaAIybAhW0zDuwfS5jMBpR1Ri538gTERERERHRiNJtCUYbChEhGM0UpiUFbAFAY9A8asK0TTJjfg12b+M9GK0zYw5GqzUEhw2FUInBaLbXOuIUP7EUkEPu/AajSSERUv1ZZNGji7+1lf6OUj3Ssdhb18DDWxJCAEzUEQZ4ERER5VFKlRx4mhaCZYNq8J/5Eg2Yr3PluLYSVVp7ShjBDfnziMRvJLUU+iv1YaU+L+Dv846klGA0eH8G5bCxLd2Op3c8aiybV3sAxtZMNJYREREREREREREREe0RbA9NcUb4NKxZlgdQPXanWNQ8Bgi73Ma94T8amWzxa7t2i/n1np6XR6I9TLrvx20yfbQOCATc62EwWnlNm21cfN6231k3u/5J9/t1a4Rbos2pVtdtdxnb5G09KRgt2TeewBaMxvAGz9TUveTCB27t9+sClz/dP1903986IaSxZaz9LrnuTgAdm82FtjAQx8M5KF9dHGqkX4OGSpMlJOrffxGLHEfhK6faj4/GGKB3dACP32XZ/wy3FlKFqZjLWPq2tbt+PGCafAxc/4QcClJOUoBILG1+iKL13GRTF8f01DqxuPBa3CkFLOXG3zGAhqgfNdlyPSgIF8538AyFH79bPhfd/JT3sW9iIFFBF1b/68/eKhzsYDSGDpOJWzDajnZzOQM7+1GOAyw8Sl7hmfuNi8fUKVx6rHxO2toJ/PMluVqp/1B0HvL6PZHbecgajMY5c0TVgt+GEBERERER0bAgTagPq6EZfCSGaWWKJ+vbAs0aQ96D0dyCALqEsDPzuuabnTGnOoLRpACVlEuQQVII8QqrCBxlHkgihTIkfQafKCEmwm9wmd8gMnt4xMDDW+QwC3PQHBERUSEp8NTtui4FGEn1lZN0nevR3choj0+TJqoS7WlbMJrlCbdlIPdtzX3YGhWGA3O/3fRZy69SgtFGXiwa8Mi2fyKDtLFsSfyECreGiIiIiIiIiIiIiGgYkSbYjhDWIJBtQmoQesNhFrnMX+/qATYY5h9vEW4BNeXPSV73sr3yfEoB46e6rKKA+Hj3uhpGe98vuVJzDjAuX5x4yrrdpu3Ati77XbvWN83lLam1xuVF6uL2YMB8USkYre9gtgWjMbzBuzmLxCK9vv85obFWYYblmWArN7nf9d3SaV6nqSC3R2sNncy7D245N2KcJbHNb8hZrN7f+uTd3uZzEwBol+vP5EZ7vyAeBfD6q0DWEpA1e39rHTREjjhJLNJtrbt+tvV/Vm4qX3NsxAARLYwhLzUYLT4OEd2NSak2Y/GagmtxlxSMlmtXPYPRiPqZu1gs0utXWTddPF2+Hr1ofssaycFou+vXmQywxkPqLAA0NXvfec4kH8FoDB0mE7dgtJ3bzJsxsLOIOmiZXGjpJy92eRuv3Ch/PhPPQ3kfpbXWwOoV9p3kWPr6AFyC0eS5ekRUWQxGIyIiIiIiomGhO5s0Lh+qcCY5TKs4RMoWsNUYNI/GMG0jBVTluAWn5evKmkPUYoHqDkZzCzKQgsAiAfkLSTnkzlyXhnmAghK+QLcGl5mC9KR/g3Csh1SNGB7hN9zNRDquopbXlIiIKF+p13UpOE2qr5xswabdLn0yomojBaPVBxoQcvwNvJfCgLUQHyb3bc3vMaWU2M8sR+ivWyCjifRvG64yOoMHO8xPox4bmoB5tS4DQYiIiIiIiIiIiIiI9mQjPBgNAHDM283Lt22BXv2cuNlH3uL+2vzqoeL7Lh3CLaDG/Oypp+5zrXuXcZOhvEyQ9xAKokoNDiGzQ08AmlqKFp+w807M6llt3XT1ZnvVrUI2VXOq1VvbTn0fVNjjQ3qlydGJXDCaMJMbYHiDH/sdIZc9fFvRokuPlc9Bn/6LxrPr7fd93c5FOp1G9seXQZ88BXpZI7Lv2Q/6gb+JwQoAgPpGsUgpZZ+EX0gK5KMBU5NnyoV3/QH60TvE4rjLEPbGWkDf+BN5hSNOhproIwCGKkYt/6Bc+K/rd/149sHyuee5DcA1D1lC8cpEDBAxjf+ui0OVGEim+gKOpGvrw3mX8kxWIykM0dnVLgbQEPWjbCG9t/wSOm1+CCYAHFTcxd7lx//WuPDaLBI97mPgElKgYf4Qwyf/6VoPgN5+ztjJ3tbNN3YyEDDPjSkSHPyxxDQcSdfmXDBah7mYgZ3FjjtLLNLPPSqWLV+oMEX+KITL/qLxUpv5nCQHNOb90iE/LLqQOukC+woMRiMaFhiMRkRERERERMNCtzYHUIQdj4NRykyerF98E9E2gb8xZA5GM4VyuQWfdRlCtiSdGXMwWm2gOp4qFyoxQMVv8IKtTKpLC/eEpK/PbaFsfoL0ogHzwBZreIQQ7uaHFMhne02JiIjylRqMltbmgRyVCEazBpv6CKMlqgZbU+ZZAlJIs5XPyT7SZyFbyG7MMfd7u8rw3nM775iMtGC0FTufQEfaPDPkyPgJcBRvHxMRERERERERERER7cnUB74qlukLFkN3mx9w+s4DHfzhIoWDLZPyv3abxvMbdt970VqjXRjeFI/23pfSTz/g3uh8XgNmvISeMRitrFSoBupn9xUtDyGN+1uX4h3bbxK3PetXcrCM1hprhHnRLT2txe348p+A8z4LTN0LmLEA6n1fhrr4y27N300KqMqkoVM9QEpIlACAoL8HV+3JlOMAp11sLmx9CXpHe79Flx5rv8951HeyeHOHfO+3Q3hGXi74Sv/scuCGHwHb+g62tf+D/vw7gUdul3fqFvrjNfQDACIMRhtM6pJvimX6irdDr3zKWNbgEozWkNoK3PUHeb//7/ee2keVpw5cChy41FyY6oFuawUAjKtXuOZ8eSzNRddp3PL04I476ZSCjEzjvw0BpZ719YtahGC0Pz6hkcn2/lulcKX8dikGoxEVO+8KsUj/8vNimVIKn1gmn4uufUTjgmvdz0WdQnhaLhhN79wGfdmprvUAACZMhfLT1+mjgkFg/BRvK4cYOkwG0hjX3ESwHUKwMa9LRVQkBux/lLnwX3+Gzpo/p8fCCo9cbv98duS3s9iRLD7nSP2a2vDuv6u+Vv7eqp93fwJq+t72dRxLOxlOTVQ1OLKdiIiIiIiIhoXurHlAVdhxuas8SKLCZP2U7kGmIMRDCroKqRrUBUYZy0zbSAFVOX5COrqEYLSYU+e5jsFUaoCKGLxgCTeRQ+6EYDQhGEEJX7OEVUQs8xOkZ/03iOFuAwuPSOsUUtr8zTKD0YiIyKvSg9HM16BKBKPZrnOJjL1PRlRt2tPmWQCNoXFl3Iu5j1zW4OIyhP5K5xUrKRl5mHqgwzwwP6RqcGjDWyrcGiIiIiIiIiIiIiIiqjrjptgflmMJAXr3QQ4evSKARZZsst88svveS6IHSGXM6zX2Dc/TN/7Y1tpiXoPRmprd15k0gPAQMlJjJkJd8aui5RMyb+D6DedgZmy7cbtVbwDpjPm+XXsXsN08vBTTU2sNC+fAuehKOH98Hs5v/wt17md6Q7i8sgVUJTuBVLe5LBAoKRxiT6ZmL5QL77mxaNE3lsvnrh1J4E9Pyvd+24Whlo0x9Abe3f7b4kKtof90lXnDcBSqxiWsIxC0l+fjpPzBZQtMyGSgb7vWWBR3GUbb+MZLcuFBy6DCQ/NwcPJGvf2DcuGduwPvjptnf8jgrx6Qwz3LoVO47NSaxpAPJPS1b9vpqXXiKvf0HfJSqEm/dtU3lN4WohFKzVggF952LXTa/LBhANh/qr3um57S2GwJiQWALiloMZfte9/N9p3kG8j5ZqLHbd36WrRncgtG29luLq/jdclo/yPlsmfkIPspjQpfO13uI23t7D0v5ctmNZLC1ILa/Izxu6+X25RHveUd7ivZgs3DnDNHVC0YjEZERERERETDghQKFhmiYDTbfguDraQwgKgTE+vp0d1FAWuJjD3kqstPMFpWCEYLVHcwWsolQEUOXpD/XnKomL9gNIlSStx/4T601kgIgQ+2f4MUHpFwCdNzk7QEv9iC2oiIiPKVel2XgtOGOhhtoMGjRJXWnhKC0YJjfdelYB6oIGWHldI/jwXMA7r9fN6RpLU8OEzit/9fzdq612Nl13PGskX1R4jB3UREREREREREREREe5SjTjUvr4kAtSN/oqwK1QBTZonl+pUVrnXMmyRPfl2xfve9l3bLc3HiudtJGw3BVjYeJ+Cr5rn2FUI1QBOD0QaF5bWfpDeLZa8J89fXmG+HAgBaCoPRasID/7tGLOPmEp3yxOoQgxt8a5knFunVxeci27kHAJ59TS7rEIZKxmMKaGsFOs2hfdi+1by8Lm5tCwAg4GP8i+24o4FzuyasMl/7Rrnkmo3etkYutIWxUXWwHBf5/aGJo3pDFCW2c085SEFGtdrQ0XI71m0mTAMAzO9+UVzlmb5+nhTW1tuuXDBaY+ltIRqpbO/RHe3AG+vFYrd+UCYLvPC6ffdi0GJfN9bU/xIN5HwzfY639YI17uvQnkd6K2gN3Z0EeoQDvd5D/30PpCyfyeByTpjvcl56puCUtkMIPAeAur5+t9ZaDiPPFwgAk2e6r9dt+WIqWh3zK4mIwWhEREREREQ0THRnzd9whZ2heVpW1JGfvpYsCLaSg65i1noKA9YKfy9a3yU4LV9nprqD0UKOecBHOmsPUJFea9vrLIeKSa+3ORhBCono3b+wj4L2pnUKGZjDGmz/BjncbWDhEVKQBWAPjCEiIsoXEoLMpOCznJRw3ZfqK6eQExID2AYaPEpUae1pIRgt5D8YzS/p/WLvn5vLEmUIRpMCGR3LLdORFIz2QMcdYtmSxhMr2BIiIiIiIiIiIiIiouqlJkwD5i4uLjjuLChnz5iGpY4/Wy5c9azr9uccIo+juud/wFm/yuLe/2kxiAjoDRjRqR5P+8unJk33tuLhJwF1lqC7o06DilXHWLoRx/T+6nPBK98Sy5ZelcVzrxXfu5OC0cLZJCal2/ovXLIcaqABU1H5XieSlmA0Bjf4N+8guezFJ4sWHT8fGF8vb3LNQxrHfi+Db92ZRTqz+1hKpjSSwhCWxhh6g9H88hKsUOMjLC9iOe5owNSkZmC/I+QVnn8UOpMpWhwKql1hMSYt256X93n8WT5aSENB2cI07v8r9JregDDHUTjb0vdp2wZ0pwZv7IkUZBQzjLNRy95Z8n5UqAaI1uGkHbeL6zy/off/Ulhbb7v6OoC2fhjRnqplHjDFcu5paxWLFk4D5jfZq//g7/v3gQpJ791Yrhvr47OZOq7065z182g+P30p2oOIyWjAzg55MwZ2mh0mjyvV6162bnr8fGCs5WuVH92j8cE/ZNH6Zu95yRaevyuEdusmoNvDOP7D3gbl5TNZwjIumWF5RFVjz/hGnoiIiIiIiIa9pDChPuxEjcsHWzQgD44pDNSS2h5xoohY2p/M9N/OFlIFAF0egwIyOiPWVetUx2AuKYjELUBF+ndFAvLrLIeKmevK6qxxuVLy1yzRgHlQSuE+bOF3tmNOql8KivOq1PYQERHlK/W6ntbmsNCgEKBablIIqFufjKiaaK3RnjLPBBgdLF8wmhQeVhganWP7HBSTgtF8BEFLpPNOSMmTEEZKMFoym8Dj2+81ljVH9sL0yKwKt4iIiIiIiIiIiIiIqHqpr/8FmH9w7y+BALDkdKiPXTW0jaqksy+Tyx65Hbrb/JDTnKXzFJbOlcuvf1LjuB9k8fvH5Psw8RigP36CW0uLTfQWjKaitVBX3W5e/9AToD71E//7Jk+UUsCpFxnLzt32e3G7V98Ejvx2cTha6xbzcTQ9tQ5Owb0+9ckf+WytgS2gKtElB6MxuME35TjAKe81F656BnpH/2CDmqDCXR+zT5e9dyVwxc0a7/n17mOjwzIMJB4D9Pc+4rnNu9R5mEQf8nFM2AL5qCzU/5PPPwCgrzSHtISD8jbNO140F5x0IdScA7w2jYaQev9XxTL9oaN3haN9a7nCflPkev74xOCMPelJa6TNw9pRawpGm773gPanvvEX1OlOLEo8ZSz//eMamawWw9r6tcvLeZJoD6OUgvrBnWK5/u6Hrdve+mEH+0+V61+5CTj7avl8JL13a8OATnYBKx6WK8+J1UN99mqoeQe6rytQCw4Blr3bfcUQg4fJQAnBaNolGI2BnUYqEgNmLDAX/u1X0Fo+p4RDCnd/3P757Jf3axz6zSzWaYENLgAAIABJREFUb7WH58f7hhzrG3/s1mTgwKVQV/yf+3oA0LVDLmMwGlHVYDAaERERERERDQvdWfNgKtuE+sFk229hmFRCCCyLBmKICpP+TdvZQqoA70EBtvVigeEdjCa9RrbXOSIEfCUyXdYvaQvJz/qSjxevx0pvHXIQmVT/QINbbMfcUL33iIho+JGCzNyD0czlUj+h3KTwVLc+GVE16czuQI82j1hqDPkPRlO2p+kVLtFafL9IfXBADuAtx3svnRWC0RzbIKmREYz2xLb7xNDuo+LyU/2IiIiIiIiIiIiIiPZEauwkOL94AOpv66Bu2wjnq9dDhfecsTIqGIT6pGWy6YO3utbx3TPsU9YyWeBbd5rvw4SDQGTdc8CzD7nup8ikZs+rqrmLoW5YCfXnl6Cufqz3v9va4Hz7r1CcFD2o1KJjzcsBLO5+WtxuexL40b/7HzdrzM+JQnNqbf8F7/woVO0oP800swVUJTuBlJAoEWRwQynU4qVy4b+uL1q031SFWz7kPmX2hv9ovLyp91iyBqOFeoC2Vtf6iniZRO8nLI/BaINOjZ0E9Yfn5BXuvwV6wytFi3vMz30EAMzoWWPe13mX+20eDZUFh8hlO7dB3/RTAEC0RuGhz8jnnitvHZyxJ/YAsoKT2wnnDnyHM+YDAM7Y/hdxlVueBjqFjFAAiOq+sSvsaxEZqQnTgLmLzYWvrYbukd/4LWMVnvpCAAe3yPXf+F+NlRvN56Qu4b0bqwFw3y1ype/6WO9nqeuehrp9E9QJ58jreqTe+wX3lRiMRia2YLQdtmA0hmBJ1NGny4UugYn7T1W47kLbbDtg03bg6oc02i1TIuO5YcV/+K64jrr6Uai/b4Bz1W1Q9Y3Wfe5iCUbbk74HJKp2DEYjIiIiIiKiYaFbmMAddiIVbkmvgAqiRpkHJRSGUUmTzyNODJGA94C1ZMby+AMAXZZQrXydWfmLu2oPRku5BKhIQWC2EC8p9CSLDFK6+O6OFoMR5C9rpWA2r8dKbx2W8Aih/oGGR0ivZ0jVIKAsj7kjIiLKI17XhYCinLThOmyrr9zE4NEMg9Fo+GhPCbMAAIwOjvNdnxyMViyle5BFxlhWUt/WYxC0TSmBiz6ykquW1hoPdNxhLKsLjMKi+sMr3CIiIiIiIiIiIiIiouFBjZ5QniCl4WjKTLFIP/eI6+YtY+X5yG7iMZQWihYMAeOn+NpEKQXVNANqzsLe/0aN9r9f8s9yfM1Kvmzd9OHV/W/gbdxmvqE3PbWu3++qyZIQ4YMKhnqPNZNkJ5ASEiVqGNxQkskzxCL93KPG5bPGe6s6dyy1bZPXadxiPx5FXibih3wEo0UYjFYRE6YBgYBc/txjRYsOEk4tjtKYll5fXBAIAuP8XatoCE2abi//29W7fqwNK9QJb2spbGigbPXGdP8xbspHeKwoPg6IxDAzZQ79A4Dbn9Niu6LZLji5cfgMoCGSTZgml61b6br5UbPtH8Ruedrcf5ZCDWtrFPQK+fOZOvKU3s9SLfOgbNdRPyZMAxyXGBQ/fSnac9iC0XYKwWiBIBCRx7Tu8Zrkz2RY4f790Kzx7l8OPbJao0OYTlcXBoIB1RsMKf19J8+AmnMAVNznA6OzWX/rE9GQ4AxWIiIiIiIi8u26th9ic2pjRfe5sec14/KwJfBqsEWdGHoyxU9cSRSEZUjhVBEnhqAKIaRqjAFchaFUCZfgM69BAV2W9WoD9Z7qGGwhIZhACjLIKXztc2zBCxFLWSLbhRqn/w0TKRjNFhIh7b/oWBH+NgrKeqxL/4aBBqPJrycH2RARkXdBIUzT7bqe1uZHqkr9hHKLBqRgU3tYLVE1aU+bg9EcBDAqWL4BhqYesq0vauuDi33nAfZtATloWQq97jX8k9FWJ17E6z3rjGWHNSxFyOEkDCIiIiIiIiIiIiIiKrDvEXLZzT+H/tA3oMLyeKb6iMJx84C7XvC/68YYoNta/W94+Em9oVVU/WbuAzS1AK8XB6ucvuNvuL7hneKm/9sIrNuiMW1M73i9dmE45JjMlv4LyhEIkxOpNU+qT3QBqeIxpQCAIO/JlWTWvnLZ+lXGxfMmAXMmACs32at+9c3c/833hMfUArEtraXdMZ4w1X2dGu9hHirKMZuVoMIR6ENPAB76h7Fc33o1cPxZUHmBDCcsUPj3/4qPkpPHrkHY9FDICVPLFxpDg2/s5N4wzLQ8zk1rveuYmDYaeLGteJ2tncCK1zT2nVJiaqwgaRl+Fy0c41aG66BSCnpSM5auvUdc596VGsfPN5fV5s+F8BIgSbSHUktOh77vZnPhxrX2/hGA5QsVvnOX3IN54GWNy08oXi6FGsZqAKxfLe9w3sHW9pRCBUPQ46f2/nsl7F+TkXSt1cBOIRG5vrFf/44KHPpWsUj/+QfAWZ+09m8PbAYmx4ENQi4dAKzYALR3ms9bjbkhxRvXyk86nneQXDkRDXsuUalERERERERExV7rXoNXEi9V9D9TcBgAhFWkwv/63SIB84T9wkCzwt9zchP+vYZmuYVwuAWn5XRldhiXOwgM6euZL1hiMJr0WpcSvCDX5z8YTQ4u6yz43dz+sBOFo+SvcaR/Q1IINvNKfj2HLpCQiIiGn5AyDz5wu65LAUZSP6HcpOud1z4XUTVoT5mD0eLB0XBUOQfaFveRpb4kYO9PSqGE5XjvSecdWzBYdgQEo93fcbtxuYLCkfHjK9waIiIiIiIiIiIiIiIaDlRNGNj3cLFcf/R4aGlScZ8fvNNB8xj/+47HALS1yiuMnlC8bMosqA993f/OaEgox4G64lfGslN3/B3nd1xn3b75iiyeWtt7H69DGFbZmGnvv6CcwWhSSFVipxyMFvIegkW7KccBjj3TXPi//0Iniu8jK6Xw6/MdjHbJEvvabb3H0BrzbXW0jAX0H77rp7m72+DleKvxMV43Io9zpfJSH/62XPjcI9A/+Hi/RR86WuHEBf1XmzUe+PajJ5vrmDh9gC2kSlKBgHvQ4cqndv34mwvksd77fzmLR18p7xiUbvNzRwGgOJivqbk8O53UjPrsTpy57UZj8dotwPOvmzetzY0lqglDhatjzgJRVTp6uVik//wj180ParGX32kIr85ktRi2GHO6gWceMBcefBxU0Pzw5AFz60/5CJmlPYgUcKY1sENI5qprGLz2jABq1GigdpS5cNsW6M+dAW0JkQ04Ctde4KDecunfvAN4aaO5LN73UUjf9Qe5jbY+PBENewxGIyIiIiIiomFtKAOaxECzwrArIZwqF5YlhWYVBgm4BQGkdA9SWeExLXm6sjuNy2OBuqp5ykXpwWjmUU5RIcQOsIemmf52WgpGs7x20v4L21tqEJkU0icFrXklbS+FVRAREZmUel2XyisXjObt+k1UzdrT5hHcjaGxJdVnCwMuZHuvRB25PymVJTKDGIxmPa8M72C0bemteGbHY8ayBbWLMSZkmDhEREREREREREREREQEQJ3+AbnwhceBO+zhVXMmKjx7pYO/f9jf9LVGWzDaWZ+EunEV1A/vgvro96Au/S7Ud2+FuuYJqKYZvvZDQ0vtfyQw/+Ci5UFk8Ku2D+D2Wb+1bv/5v2YBAO3CELl4tiC4r5zBaFJIVbITSAljIULyw5rITh1/llwoTI4/bKbCyq84uPH9DmosWR0rN2q0CsFozWM08Lz5XqsrL8ebn2MiwjGblaImzwROvlBe4eafQ7/y/K5fozUKf73EwaOXO/jxuxVu+4iD/17yJmam1pi3n+SSVkPVx+X9rK/66K6fD5hmr+qKm7NlaNBuUogRAER0sv+CcoXy9dXz400fF1e5bYV5rE1M91206+LlaQvRCKWCQWC/I82FzzwAncnYt1fK2v8BgJc39X+fJizTkGLP3iPv65SL7DsaCJfzr2LwMJmUEoxWz+uSG3X+Z+XCh28DHjE/vDfn2LkKq77q4LMnymOQv3e3uf+QC0bDdd80bxgfB2UK0CeiEYPBaERERERERDSs1Qbqh2zfXsMypLCrXFiW11ArLyEcXS7haQDQmTEHo9UG6ly3rRQpmCBlCVDRWluCxeTws7ATEQMeTPVpLQSjWUIipP0X/o3FIDJLcERv+eAEo0nH3FAGEhIR0fATVOYRFmlteWQlhj4YzWsILlE125rabFzeGCwtGE1iCg+2BZmFHfnRb1IIb7dOIqPtg7rcyOcVecC5FIw8XDzUcTeyML9uSxpPrHBriIiIiIiIiIiIiIhoWGmyh7foR+9wraI+ovC2fRUOn+l9t/GYEoPRVMs8qJow1AFHQ73jw1BnfATq4OOhogwNGpb2XmRcrAAse+EnqJdvK+LuF4GetEaHMESuMdO++5fRE6HCZRzzJoVUJTqhU93mMgY3lG5is1ikH79LLBtTp/D2RQo/eKc8tvP25zReazffE25RGz03sUhTs/s6fo4JnuMqSs2Yb1/hsTv7/RoMKBw8Q+GSYxycsI9C3QpLgIyXY4Oqi1vQ4Zbd54qAozDDMiTngVXAjmT5xqEkLcPv+gWjhWqAsU1l2afqez3GZLZi/+SzxnWeWW/etjY3rr2uoSxtIRrRJlnCDNe84Lr5h462PwD19uf6n4s6bcFoz98nF5YzfLiAcqubwcNkYglG0zuFYDRel9y5hPvqR++0lgPA+FEKnz7e+8OZcxpjgN62RV5hio8vnArttb95ebR65lcSEYPRiIiIiIiIaBiLOjG0RGcP6f5NEpnCQDN7WJdUT+F2tmABP+t0CcFoMad6vriTAk+kIAMA6NHdyML8JC3pNQYARzli0JcpWEwORpC/oJWPlf5/r2RGCkaT2w/YQvoGFowmBb+4tYeIiCifdF23BZ4C8nVfClAtN68huETVrD1tfrT16NC40iqUBo0Y2EJ2HSXforT1NQfav5XOOzWWYLThLKPTeKjDPBFgXGgS9o7tV+EWERERERERERERERHRsLLXfkCDJd3jFfcJ+TnL5nu/zxQPdgM7t5kLB3HiPVWeOnCpXPbqC1g6V942q4EHVwHbhFv4DZm8Yyg+psQWCoSQKp3sAlJCqkSoMmMdRqRplnHCG9e5br50rnz+WbMF2CoMu52Q3uRat1EgAIyf6r5e2JL8V6jGx7o0cIuPtRbrtlb79q+vKbluqj62axUA4I310OndCWVL59n7POu2lqNVvZKW4Xc1Ou96NGEalFOmOIG84NzFif/62jSaG/dT31iethCNYGrRMXJhW6vr9mcssp+L/vSERk9697yYTiHbFwBqt7wqF7a4hIkOhNv5d+ykwds3DWPSsa8BKRiN1yV3+x0BBC2fadss/d88o6L+g9Fs4fkAgJkLfNeZo878iHn5R79Xcp1EVH4MRiMiIiIiIqJhKaiCOHfiRxFQwSFrQyQghF0VhEklLIEAgCU0K1sYsOYewiEFWeXrygrBaIEqCkZz/AejmULMcqRgE7dyP8ELtq9nvQarSP8G6VjLkY6htE4hlbWHztgkM9Kxy2A0IiLyLiQEDqW15RFzkAOMpKC1chPDa4UgU6Jq1J4yB6M1Bi0TWCz8DElwC4iWxBz5SdeFIdR+iYGLjhyMltXm8OXh4Nmdj2Nbpt1YdlT8rdaAOiIiIiIiIiIiIiIiIhWqgbr4y/IKWzdCP3anp7ref5SPYLSdr8mFDEYbWQ4+HgibH2oKAJ+t/wfCliGiy74v38trzO6e9K4+elVJzRNFhHueiU4gJaRKhMLlbcMeRAUCcjDG6hXQafsYyVnj5fNP65saHcLQ3PgdP/faxP7GT4GyhQbk+DkmQiPzYV/VSjXPBU66UF7hb7+ybm8NTpu7uJQm0VA6/CRgvyPt69zww10/fnKZwljLkPyHVkkP6PavWzj9hbPJ/mN8ytl/mjh914/NqVZfm9bqvnE/dQ3law/RSHXM28Ui/fBtrpsfMgN4xwFy+ZOtwNwvZrFyY+85qcsynDf28uPmgn0OgwoO4nyueQfay/nZkEykh/9qLQew87rkSsXHQp17ubxCW6vnuq44wV84WjwG4Il/iuXq/M/5qq+fJacD+xzWf9m8g4Cjl5deJxGV3dDNHiciIiIiIqJh66j4idiREZ6UUAGjAo3Yu3Y/jAmNH7I2AJawjIKwq6QQVpbbXgzNypv0r7X2FNLV5SEYrTNjDkarraJgtJAQeGIL+bKFlEh/q/xyU1yAKagsC/NgKmXJn496DNGT/sau7bcEpyWznQg5cev28ralhVkQERHlk4LMbIGnAJAWrvvBCgXjyiG4DEaj4SGrM+hIbzGWNYZKC0az0VpD5Q0qkd4rbn1bWyiwlyBoGzEYTQhwHO7ub7/duDykanBoA59CTURERERERERERERE7tQp7wWSndA/vsxYri87FfhnO5QUFNVnwiiFq89VuOg69zCQ+L+vMReEaoCxTa7b0/ChgkHgd89AnznHWL7wl2dixXWbMOcb9b7rjuePcV24pNQmmkWFhz0lOwEnYC5jsNWAqPOugH7yX+bCP/8QOPtT1u2/eprC5/9afP55ZTPQLgwDiWeF4IRFx0DtdwT0NV8xl09qsbZlFz/BaDUM1qs09emfQf/vP8DqFcZyvX4V1NS9zBu3tZqXn/HhfuMqaHhQNWHgqtuAG38M/Qtz8Ib++WeBE86FahyHvSYoPPUFB9M+Yx5v/sE/aLy/TJelZNq8PKKT/RdMmm5esRR5QUQtfoPRcuN+6kob1060J1HhKPTcA4GXniwuvO1a6M/8wnpNUUrhTxc7ePaLWax6w7zOmjeBC6/N4uHLA+gUsn2BvPdu4T4u/ILtnzBgSinoeQcBLz5hXqHJY5+L9iy2YLQd5ofM8rrkjbrg89BvbAD+YfjOZtN66EymN9TaxfuOVPjGHd6DYuNRQP/kSnNhwxioAXxPpKK1wHdvBf71Z+iX/gs1a5/ePl2seuZXEhGD0YiIiIiIiKgER8SPG+omVAUpHCp/sn4qm0Jam+865raXQ7N2j7bo0d1iIFe/bTLuQQFdQjBazKmeL+5KCVCxhZTYwhUAb+F0uwjfv9rGKkj1p3QPMjqNQF/AixT04BoeYSlPZBOoR2lf1IthFi6vJxERUT4pyEzqI+0uF4LRnMoMFo465qdSewmrJaoG29Md4meIxmCpwWhyp1dDQ+WVlxqyG3WESQQoQzCaGLgoP61bSx8Aqtzr3WuxKvGCsezAUUchVkXB2EREREREREREREREVOVOOBcQgtEAAE/8EzjqVNdqDmxWEAdf5YmntpoLJkyDcuSHV9IwNWFab2hYqsdYPGvlP3Bwy7vx+Bp/1TbmgtGa55Y/iCgi3NPs3AFEzGMNfIVgUbG8IJ5C+sFboVyC0VqEW+QvtcnbxDNCcELzXGDBIfKGlrb24yfsjMdPxSmlgLMvg/7SOeYV7r0JOPdyc9nGteY6m2aUqXVUaaomDJz9KejffxvYKYQmPvg34JSLAABTGhUOnwk8/Ip51R1JjfrIwK9NyZS5XxXR/ROOlNfARg9UfRy6Lg7s7MD0HvOxLonlxhLVNZStPUQj2vS9zcFoAND6EtAyz7p5wFG48mSF9/xa/gz26KvA+q0aXeauOAAgqhPmgkoEk+17uByMFh83+Pun4ccWjCZcwxWD0TxTb/8gtCkYLZMGNr8GTHQPY53SCAQcIOM+RRJAQeh5ob0XeavEQsXqgVMugurrxxFR9eG3wUREREREREQlkibsJ7OJvJ/l4IxcuJRcz+5tbaFf+bo8BAV0ZYVgtCqaGF9KMJr0Wis4CKuIdX9yOF3x6ykHI8g3qG3BZvl/22TGfNPIPTxCLk8OIDyi1DALIiKifNJ1PWW5rgPydT9kCTAqp4jQR/PaLyMaalvTm8WyxlBpwWjK0uctlDCFDMM99DegAmL/vctDELSNdN4JWQMXh2cw2v0dd4hlS+InVrAlREREREREREREREQ03Kn6ODB1L3mF9as81TN7AhD3MOyoUQojGjfZ035oeFGOA+y9WCzX61fh4Bn+w2NGZbf3/uBhYrZvceF+6xvrgZQwFoLBVgMztkkue9OSbtZn4VT/x1CjMAFfzTsI2Gt/IGB+UKCaKx/P/fgJRgtW5iGCVGCuHLSg1682L0+nes8FJpMG4XxElWV5fxceEy1j5fNO65vlaU5SuOSEC4LRPAc2ehXrnXPQkvIXjFar+8YS1TeWtz1EI5SyXIe8fgY7qMW9D7TqDaBTCEaL6gQcafzc+Kme2jAQ6i3vMBcccHT5w49phJCOCw3sFAK26hmM5pnt83Wbt35BMKCw2Ee3OJ7YKBfO2s97RUQ0bDEYjYiIiIiIiKhEEcf8ZL9E3mR9W3BGLlxKrKdfYJa3AI6Eh6CAzswO4/LaQL2nfVRCSJkHcdgCVKTXOuJEXW96SEFf+SF3ORrmx1I4lq9ZpPA7oP/fVvo3SMFtOdIxBMiBFF6I7WEwGhER+RB0hMDTrOURc5CD0YLKPLC03KLC9TWtU0hl7aFuRNWgPWUeRVmjwqh1BqPv338AlBiyG5D7rjnRgHt4dCmk80qN8PkDALJ6+AWjJTJdeGLbfcaylsgcTI3wKdREREREREREREREROSPetfHxTL9xD891REOKVxyjPvk9YbMNnMbzrvC035o+FFnXioXXvdNXDz6CdT6yJBqyHQgkBvn19Q8oLaZKClgpq0VSHWby0KVeQjcSKUcB1h0jLlw0zroNS9Zt997ksLBLf72GReC0bDkNKj4WOCt7ykuG9sEHHumtx34CcvzE6JGZaMmz5QL21rNy99YD2TN44wxyedBSFVHnfERufDem/r9+um3yn2eA7+exfbEwMejdKfNyyPZZP8FZQ5GU2d/CgAwPvMGoj7G8sT6Hvat6hrK2h6iEWvpO8Ui/aNPeapi1niFU11yg678WxZdPeZzUkyanzTnAKhgBcby7r0I2O+I/suUgjrTcj6mPZs0d0xrYIf5uwbUMRjNK1XXIAac6t9+3XM9H1vqPdgwvkUOglSnX+y5HiIavhiMRkRERERERFQiabJ+v0Azy82+XLiUFJqVv60tYC1fV3an+zrCzYmYJbyr0qTAE40sMjpjLJNeay8hXtI6iax70JwXtvCHRL+/s3l/UnBbjqMCCKuIa/1+iWEWDEYjIiIfpMDTtBZGZvWRAlGDqjKDhSOWvlG3ITyVqNq0p83BaI2hsSU/LdG2VeHQKFPIMOCtLyn1z7sG2D+XAxdtT9cefsFoT2y/D906aSxb0nhihVtDREREREREREREREQjgTrlvUC0zlz41H3QPUIYVIEvn6Jw1Zn2e1WNWXMYkZJCkWjYU0efDkyfI5bv/fWluO/cDag3D5Er0pgXaCWGmA1EkxButHkD0GV+cK2vECwyUh+9SizT5+4PnbI/oO/Lp/qbSms6F6nf/Acq3DseVH3qp1AXfhHYa//e0KFl74b6xQPeA39qPB7QABCy3dOmwaQu+Za5oK1VWL5Wrmzi9IE2h4aYOvSE3pAek03roNs37/p1wWS5v9OTBs75tRCg50NSeLZnpHDMyKQyH3unXASgdxxRc8pyzBeI5cYS1TOAhsgLNWo0MHUvc+GmddD/vN5TPddf7FjDGh9+Bbj5KXNZrTbPJ1Gf+omnfQ+UUgrqO7cCZ3wYaJkHHHgs1Ddugjr8pIrsn4YhWzDaznZzWT0DO32RPmP/917o//zbUxXvPNDBje93EPcwNa3hHz8yF0TroCZM87Q/IhreGIxGREREREREVCJpsn4ym4DWvZPnbaFUESfa7/+FEhlvAWv9t7EHBWit0ZU1D7yJBeo97aMSbIEnUphB/uuVz0vwgrROMlMc5qCFYARliYmwhbP1D9Izh0d4CncTgvq8HjuFtNbiaxoNMBiNiIi8kwJPpWu6W7k9wKh8otZg0/KEpxINpq0pIRgtOHYAtXoPVJM+C3np28r989JDfwH5vFLjyOcVqf9frbTWuL/jdmNZXaABC+sOq3CLiIiIiIiIiIiIiIhopFDnf1YufOxOb3UohY8tdTC1UV4nnjEEo8090FP9NHypMy+VC9MpLFxxLf77eW9TIePZbbt/mSSEmA2ENBFca+C11eYyBlsNnFuolMt5aOlcYMlsb7tydAb1hWNtIzFg5oJdv6pgEOqCz8G55nE4N6yE88VroSZM9bYDAMpPWF4Ng/WGzDThoHlzgzmMr63VvH58HFRMCBilYUWd/gG58N839vv1GDnzE/94Dti4bWBjUqRgtLDOC6yNxID4uAHtp5AKhoC+IBI/wWi1uTF3dQxGI/Js/yPFIv2P33iqIhxS+OZyB2csksf+3fAf8/koJs1FGYzwYYGK1sK59HtwrnsazlW3Qx3+tortm4YhKRitayeQyZjLeF3yx/L+17df57maty9S2HyV4xqA3mj6jggADn2r530R0fDGYDQiIiIiIiKiEkmT9TWy6O570pI0cT+sInBUAIA90MpLwFq+LpeQjh7djbROG8tigeq54W4LRktp81P9pAAwLyFe0jqm1z33NymkpC/QAQRUEDXKPDAlv91SsJ2XcDcpYMLrsVMopXuQhfmLfy/tISIiypGu67ZgNK212GcJWfoJ5WS73klhpkTVpD0tBKOFBhKMZtO/nyz1z730JWPCZyS3zztuhjpwsRJeTjyPjT2vGcsOb1iGkFOZcygREREREREREREREY1AM+bLZa++ULbdGCe9VnDiPQ2RFsvxBQCvPo9po4E6D/lQ8Uz77l/cwrRKYTse168yL/cTgkVGKlprf+1dzkNKKXzlVG/Tacdm3ix+bNjE6dZxor7V+LhPzeNn6EjHXDYLvLG+aLFuaxXqGYRzEQ0NS39Iv/p8v9/nT5bPGVoDL7w+sKZ4Ckab1Fzec1devQAwP+m9D7gruLSeATREXqkyfgabP9n//muluSijRvuvjKgizNc83bXDuBwAEBs1SG0ZoWznpTX+zksBR+HQGfZ1xma2GJer5nm+9kUb22FZAAAgAElEQVREwxeD0YiIiIiIiIhKJAVRAbsD0eQwgKhrPVlk0dN3Y1IKzCrktl5XZqdYVutUTzBayJEHfEghKVIAmJfgBWkd099PQ3o6l/2msbSP3N8sqzO7AvUKSeF5XuqXjkE3tsCXaN7xS0RE5EYKRktZgtGyyEAjK9QXLEu73Nj6EIkBhjMRVYIYjBYsPRhNWfq8hf3khBASbfsctXsdc/93oO896bxTYwlGk85F1eqB9tuNyxUcHBk/vsKtISIiIiIiIiIiIiKiEWXxsWKR/uN3y7abUdntRcvUae8rW/1UpRYcAkzdSy5/4G8IvfwfnHWwe7hLPLNt9y9NzQNvWwEVjgJjJvnbKDRyHtY0lNQJ54pl+rpvuG5/xF4K7zrQ/RianlpXvLDcAY01Ee/rMhht6FjCFfW75iH76y9Dp/PGIrz0H/PKDPgcOeYcIJfd+mvodSt3/XreoQq2TLJXNktj0r3pNg+pRySbNx59sI69vnrP2fZHONr8IO5CU1N9YYJ1DEYj8uzYM+Wyjs3QXfL8oELv8dCPLhTVhjGAsxcOTuAiUTlIx2bC8l6Juo9ppd3Uce+WC1evgO7plssN9pogn0/CThoT0xvNhcdb2kFEIwqD0YiIiIiIiIhKFA3YwjK6+v2/UCQv6MoeupELWJNDqvqvbw8K6LQFowWqJxjNFniSzprDDKQAMG/BC0JombFO801oW0gEIB8vXv7GXoLIxH+DEEjhxnYseQmbIyIiygkJwWhpSzCaLTRNClort4AKoEaZB5Z67ZsRDaX21Gbj8tGhcaVX6mNAkxgSbfkclSMGo3kMjJZI5x3beWVgQ1Arqz31Jp7d+bixbJ+6xQP72xMRERERERERERER0R5PBUPAwceZCxOd0Lf+uiz7CRgeXKMWLilL3VS9lFJQ3/uHdR196TJ8f+GzOOcQ+33LxmxH7w91DVD1jeVqYn8+g2YUg63K45zPyGU93dC3/NK1it+cr3DWQfZjqKWntXhhucOF/BwTNTx+hoqK1gKN4+UVrv0a9P87BwCguxPAk/8yr8dgtBFDKQWc8RGxXH/waOjX1wAAFk1XuP598jT+D/x+YKNSksIQu4jOCySxhPsNhOo7puf3vISbXnsXmlKvu26zK3SyrmFQ2kQ0EqnG8VCfu0Ze4eafe65rxjiFr53uL9Cs1jAGUH3+N77qIKooaYxr0jKvKmIer0pmasos6+cy/Ym3+aqvxfKs5+mhDjjSHL6mGb72Q0TDF4PRiIiIiIiIiEpkC4dK7gq7ksK6onk/W4LR+ib+uwWe5XS5BAV0ZeVgtFhVBaPJwQQp3WNcLgYveAgVk/6WpjqzpQajuexDCtHrbZ/7F+1SwITXY6e4XbagNgajERGRd9J1Pa1T0Np8XbWFplUqGA2Q+wilBo8SVUoq24Md+U9Bz9MYtIwiGIDCt7MYEu0luNglVLhU0rmlxpGfzi6dp6rRQ9vuRtYwUQgAlsRPrHBriIiIiIiIiIiIiIhoJFIHLhXL9J++Nzj3VhYdU/46qSqpSc1QP/23vEJ3AuG//hS/vdDBN5bL4/UaMn3BaIMUBgPAf8hRqHJjHUYyFQxCffR7YrmX81A4pPD7ixxccYJ8DLWkWov3PZTBaAzWG1pu55L7b4F+bTXw4K3iKmowz0dUcWq/I+TC7Vuhb//trl/PWKxw5mL5fLOhvfS+UzJtXh7OC0Yr+7krJ6/ek3fehrWrZ+G97fawpGmp9b0/1MUHp01EI9XSMwHHHAmir/umr6ouPtJvMFrBXBSlgMkMI6IqJgWjJSzzqiKcH+WXOuW9cuGzD0KvedFzXc1j5PPSdGw0F7zlDM/1E9Hwx2A0IiIiIiIiohLVqDAc4aN1YlcwmjlcKj8MQAq0yt/eFlKVr8slBKsrYw5GC6sIAiroaR+VEFJyMIEUZiAFlHgKXhBDyxLI6kzB0tKC0cRglVwwmiVgxUsQme3fUIqEJWQv7CFsjoiIKEcKMtPQyKLwOtsrrYVRWwBClgCjcpPCmaRAVqJq0Z7eIpYNJBjN3uPd3U/WWov9UG99W3MwsK2P6oX0WSJo+fwh9f+rTVqn8HDH3cay8aEmzIntW+EWERERERERERERERHRiGSbBP/aK8A2+T5VvsuFQKJ9kyuKF05s9lQnjRCTZ9rLX3gMALDvZPnuZVO6rfeHxvHlalURNXUvfxsw2Kp8psySy9rWAluEyfMF5jfJZbN6XjHs1+XY9Csc8b5ukMF6Q8p2zOW8+CT0848NrA4aPlyvVY/3+3WW5XL0ZGvpzejqNi+P5o/ZaWopfQc2k/qH/SkARyQeFlcfm96MWt035q6ewWhEfqhgCKgfbS5M90BnzQ/SNBldCzT6yIAqCkYb2wRVw34tVTExGM08lw4AEOb8KN/GTgZs54KCvpDNXpZ+0uye1eaCidM8109Ewx+D0YiIiIiIiIhKpJQSJ+wnXcKu8sMAwioCJQasdfb7v5tEptP6tLvOzA7j8ligzlP9lSIFqAByUIoYvGAJnvOyTmG94svr8vAcKQAid4wkLX9jL+FubsFrfsmhflE4il8pERGRd7brekoIKUpneyz1VS7MtdzXV6JKaU+/KZY1hgYSjObtiZHdOgkN84CrgQQXD+S9l9VZ8bNEjSUYTQ+TYLRndjyG7bkn3xc4qvEE9uGJiIiIiIiIiIiIiKg8Fh9rL29r9VTNafsr43zlM7f/pWiZKgjeoJFNjZkI7HeEvML6VdCZDI7ZWw51OGnn7b11LTltEFrY5+jl/tYPVe4hcCPewqOtxfqLZ0E//YBrNW/bRyFiGNJSk+3GyTtvKy44cKnHBnrk9ZioCUNJAQ9UEeoY9/e7/v7HgBVyKBT2P7KMLaIhN3MBYAvIfPWFfr+esUh+D//03qx17L9NR5d5u3h22+5fJjWXVLcrQ73Te9aKqzfmxrREa3tDnojIn9n7mZeneoAtbZ6rUUrhHZZzUqF+5xNg8M4pRGUjHN9JYexpJMa+dglUMAgccYpYrv97r+e6FkwG5kwwl7399V+b989gNKI9CkfAExEREREREQ1AJGB+MsTusCvzl6f5YQBKKUQccz257ZMZc0hVoQzSSGk5SKRLCN+qrbpgNDnwJC0EqEjhcQMJFQMMwWhCwIPj8jVLRAhf2xWiJwSRBVUIIcf9BrAcvOYtVK9ouwG8nkRERPnsgafm67oUmOZWX7lJ11epj0dULdpT5mC0WqceYcfHU6d9yA8Qk0J2AYifffJFA+YAaq+B0SYZIRQNAILO8A9Ge6DjDuPyGhXGIaOOqXBriIiIiIiIiIiIiIhopFKRGNS3bhHL9Q0/8lTPpLjCby9QCOQNuTopcRc+tvXHxStPZDDankZd9lNruf7MaYiEFP74Pgd1od33AZXO4qdtl2J2z+reBSeeN3htbJnrb4NQeHAasgdS4QjUd/8ur/DcI9CXLoO+0XA+ydMQU/jdhQ7CecNVa7LduKbtYozNbOm/8uyFUGH3e92+eD0meOwMvcPeBrzjEvs6OzuAVc+ayw44mkFQI4xSCuoL18orbN0E3bF77M5+U+XAlXv+B3z6ptLGprQLQ9ji+Q/WG6x+1NimooDH5pQcjKZzoTN18cFpD9EIpz5l6R/f/SdfdX3lVIWDmr2t25BhMBoNM1LIWWKneXnEPFaV3KkPf0su/Nefof/3X2/1KIXfvdfB+Pr+yz+3ZAeO7LjHvNEEBqMR7UkYjEZEREREREQ0AGIYVd+E/YQQmhEtCMmSQ61yoVneAwCk8DMA6MrsMC6PBeqNy4eKowJwEDCWScFvUviC9Np6XWcg4Qv99yGFO+RC9Mz78dJ+wBa85i1Uz+t2XttDRESUYwv4TGfNAWhSYBpQ2WA0KcBJ6uMRVYv29Gbj8sbQ2AHW7O3JeMmM/B6RQs/6rWP5fFTqU3Jt55UaZQtGq34buluxOvGiseygUUsQq7IgbCIiIiIiIiIiIiIiGt7UYScCoyeYC//1Z8/3c95ziIPXv+Pglg85eOZTnbi5dTkiurt4xUkMRtvTqOl7Q938qrzC43dDr3oWx89XaD3lbtyy/h24/rWzsX7VTLy/4+redeoaoELyfcCycAtKysdwq7JSBx8HjJtsXUf/6kroLvOY2Zy3L1JY/y0HN3/QwY1L/ovW1bPxru03Fu/v9PcPqL1GNR4fahYc5OOYXKlAAOrS70H98fmSQp3USRcMQqtoqKm5i6F+dLdYrv/wnX6/X3iEPObmqn9qrN3if4RKhxSMlu0LRquLQ9UPThCZcpyiUJKmdJu4vs6NOaprGJT2EI14k5qBiHlMnf7F53xVNX6UwoOfdvBey3kpZ9f5JIeh1VTtxGA0YV6Y8L4id2rcZOCMj4jl+pqveK5rcbPC6q85uOOjDn7/XoVXvu7gS69+XB6xzHMR0R6FwWhEREREREREAxARJuznQqWSQmhG4XZyPbnQLO/hVomMHOTVmTE/5SImhHYNpZAQeiIFGkj/bum19bpOMtP/tddCNIJy+ZpFClbJ/Y0TQniEFKhWvJ49pM8vr8cuERGRG1uQmXRdT+u0cTkAhCwBRuUm9tEypQWPElVKe+pN4/LG4MCC0bzFotnDA6V+cb6oEOSVQVoMSnZTcuBiiUFslXR/+x1i2VHxEyvYEiIiIiIiIiIiIiIi2mPstZ9ctuEVz9WMq1c4dX+FfdQaONIjazjhdc80tskenvJ4bxhNY/cbOHnn7XjHjlswMbNpd/kgBcHkU5Oava882CFte6I5C+3liU7guUdcqxlbr3DaQoXT267D+Iz5IWTw87f2qsZjWJ7X9WhQKaWgpu4Fde5n/G88GMcPVYeW+XLZy8/0+3WGZciO1sDdL5YQjCYMYYtntvX+MNjHXkH9QWQQFcYMXdDx294fony4H1EplFJA0wyxXHf7G9MaCiq860APwWi580muHbymUbVjMFpFqelz5MIn/wWdyXiuqy6icPx8hbMOdtAyVgFr/yevPHGaXEZEIw6D0YiIiIiIiIgGQArLyIVRyeFS/cMAogGpnq5+//eiyxKE1ZU1B6PVBuo9118pUjiBKSglqzPo1knj+lJgWL6QU4OgChrLCoPFNLLG9ZRLTIQUcJYLRJPC77wER9jql45BN3JQG7/4JyIif2yBQykxGM0WYGS+Zg8GuY9WWvAoUaVsTQvBaKGBBaPZ5AcIS31QBQdh5f7Ua1ufs9T3n3S+AYAaRx5ILvX/q0Ui04knt99vLJsZnYspkebKNoiIiIiIiIiIiIiIiPYIauESufD1Nf4rbGs1Lw+GgDGT/NdHw55SCjjoOLFc//X/oLt2ADs7zCvUNQ5Sy/I0tXhfl8FoZacsx0eOfu4x7xXazl0LDvFej1chj4FnPHaqi4fjrghDZEYsFbeMw3nqvn5hIG+dbx9nvnaL//23C0PEGzN918amZv+V+mGo//yO3xlXXb7jr70/JEsb105EAKbuJZdtXOu7ukPlnLVd4tn+wWg4aKnv/RBVlnC9TTIYbVAcaDknpFPAm6+XXrflvKZi1TcHkogGD4PRiIiIiIiIiAZADKPK9IZcSYFmhdu5hVr5CbfqypjDz2xlsUD1PX0p6JhDVFLZnqJlUqgYIAeaFJJC7grr1tIDuVwemCO1I7kr/M78Rbv39psD1JLZBLLaf5iDGOoX8BbURkRElBOyBKNJAWgpXXy9BwAHDhwVKEu7vPDaPyCqNu0pczDa6OC4AdYsd3q9BKNFnGjvBAYXMeHzEQB0ZUoLRrMHLsrnKf/P462sx7bfK4ZEL4mfWOHWEBERERERERERERHRHuPU94lF+pMnQafM93zFbe65wVwwYSpUoHL3iKm6qPOukAs3rYN+9wLop+4zl9fHB6VN/UxiMNqQOv4sYP7B9nV++3Xov1/jrb62VvPyyTOgwoMwbrLGazCax/WoItTMBcCpF3nfoCYCjJ4weA2iobf8g2KRvuwU6K7esfsHTFe44HB5zMzXb/c3QiWV1ujsNpc1ZPuC0SZO91WnX8pwHfz41h9iek//IJOPbP0pZves7v2lc/ugtoloJFMf+oZYpr94NnTW35yRWFjh82+zj+VryPQPIVbjJvvaB1HFSeNTE8I8u4g8VpXcqaYW4JT3iuX6y+dBi5PwZLprB7DNnBqrLvyi7/qIaHhjMBoRERERERHRAEghUbmQq1xAWtF2BSFWUqhVItMXjJbxHowmBWwBlmA0pwqD0YRwAlOggS2cRAo0KRQV1isOtzN/Keu4fM0itSP395JC9CKWUIh80YC8XncJ4S1eQ/2IiIjc2AKHpKAiabmtrsEg9Q/8hNYSDYX2tDkYrTE0ZkD1egk1A2x9SY+hv5Zw4FLff7ZgtBolT0LQVRyNprXGAx13GMtGBeLYv34QnlpOREREREREREREREQEQNU1ADP3kVe4+0+e69LZLPDvv5gLBznQg6qbmjEfOPNSeYWtG4HH7zaXVSQYrdn7ugy3KjsVq4f64V1Ql/+fdT393UugN2+wr5PNApvWmffz/q+V3EYrr8FoXtejilGf/AnUlz1e5yZN9zzWgoYndfKFcuGT/wJu+tmuX68+V2HWeHn1N3d4H6OyzTI0PJ7Z1ts2P9epUoyZWLRoRqoVj7YehR9u/AQ+seX7+Ov6t+OqTZftXqFz2+C2iWgEU00tgBTW+urzwNP3+67zE8vs16jc+QSwB7MRVQ2p3yUFB0YGIQB5D+Nc9jO5cMXDvf/51bZWLnvr2f7rI6JhjcFoRERERERERAMghUQlsl3QWoshZYUhVlI9yWwXsjqDbp303KZERg5G68yag9FqA8M7GM0WBuc5fMFj8Em2xGAEOVglAa21GPAQFULzvNYPyMEUNlJ7pBA/IiIiiS3MLCUGo6V91zUY5GBTBqNR9UpkOsW+XGNwbEXaIAU7e+1LhlVEDB7usvT9bWzBaEFLMJoUjFwNVnatwKYe8wD+w+PLKn7OJCIiIiIiIiIiIiKiPUzLPLFI33ez93pWr5DLJjZ7r4dGJLXv4aVtWDf4wWgqVgc0eLwHy2C0QaHCUai3nQecd4W8UjYLPHirvaI3XwdSPeaypuaS22fl9ZjgsVN1lFJQxywHxk12X3mwg6lo6LmEuOb3iZRSuPQtcgjR31d4H6PSYQtGy3b0/jDYx9+EacbF4zObcUn7L/DtNz6Hk3begX7/4kVvGdw2EY10sxeKRfrem3xXF7MNmwPQkM0LM+RnMxoO/AbShr3NNyMXe+0vFvn6fiinbY15eSAAjJvivz4iGtYYjEZEREREREQ0ALawq5TuQRbmp0oUBgJEA3LoRjJruXNpYAsK6MrsMC6PVWEwWkgFjcv/P3v3HSbJVd9t/3uqu2e6J+zMbNDuKq4kJFACBYQkkHclgoRkko2JIhkcCA8YY4IJNvYLGAfA5sEIv4Bfg7EJxg/2izESIIIQGGwRTJBF1kqAVkLSzmyY6d7t7jrPH7O92zNzftVVHad37s916dJOneqqM6m7uqf6rlDQoFK3v0ZW0CTteuUVUYfwH52dkl9At35WvLwO+EpgP415haN5K9ezP08rjJHE+rlL+/UEAKAhcpEi5YJjVqjIWl5YJWG0dh5bgX6Zrd1rjs0UNnW07aQjXu+PHCdbx5LLA9HmfpyzI9TGcXMr1TgpjGbftzR/XqvNjXOfDC6PFOnSqSv7PBsAAAAAAAAAALDWuHMusQfv/En6Df3cXted9ZAMM8JR6cwLpaiNt0Cu39z9uYScc3G69U45q7fzWOPc2cnfB/+zHydvYNdOe6xXYaGRYrr18lwQa9Vq8XMnSe6slPcRGFpuYioxFrv8mOihp9pn3/z4nvT73X/AHlvXeL/Ahi3pN9iOMx4sjWa74La76hk9mgywRiQ9B/vpDzNvbiSf/B6Y6XpTGG1rcggSWB2yhtGyPY7BkPS8+Jb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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import asyncio\n", + "\n", + "from matplotlib import pyplot as plt\n", + "\n", + "from apd.aggregation.query import with_database\n", + "from apd.aggregation.analysis import get_known_configs, plot_sensor, Config\n", + "\n", + "with with_database(\"postgresql+psycopg2://apd@localhost/apd\") as session:\n", + " coros = []\n", + " figure = plt.figure(figsize = (20, 5), dpi=300)\n", + " configs = get_known_configs()\n", + " to_display = configs[\"Relative humidity\"], configs[\"Ambient temperature\"]\n", + " for i, config in enumerate(to_display, start=1):\n", + " plot = figure.add_subplot(1, 2, i)\n", + " coros.append(plot_sensor(config, plot, {}))\n", + " await asyncio.gather(*coros)\n", + "\n", + "display(figure)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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S7uLeAgAA02inMW/f2oHzxp6xqgPnwfy23wuTnY+vP7fu8uSKf+xsP60YGUrOf/nEoWjHfybZ/6Wd6Ynps/qo5PE/StaemHQva1676abk16/rSFvMUtVIcv7Ltg9FS0bHzn9Z8stXdC4UrbZ49NG9NFl7QnLSmckeT06edH6yx9OarGvj1yv/613JPRe3b38ApmZkcPTjVDNP+GmyZG2yeJf68xf+2bS3BQAAAAAwm3TPdAMAAAAAAMDCNFGAUY/wImCS+romCFwsPSmldKgbAABYEBaNebsTP6h17BmLO3AezG+llhz90eSHj6g//1/vSvb7w2Txzh1tq6m7fpVsvLF5zc6PSA58RWf6YfqtPSE5+Wejb//+k8l/Ngk/u+Pnyabbkt5dO9Mbs8vdFyb91zae77+uY63k+M80vu8sPyg58YzGawc3JN/cKamG2tPb9d9MdnpYe/YGYGruODfZfFvj+d1PSXpWjL69/ODGIZ+eBwEAAAAA85hgNAAAAAAAYEZMFGDUUxY1nQcYT+AiAAB03MYxb6/pwHljz9jYsApo3S7HJ/u9KLn2S9vPDdyTXPLO5NiPd76v8fpvSG78TnLxWyeuXfvY9vdDZ7Tyd3ndV5ND/6z9vdA+g+uTG05L7vp1cs+FSe/uyZLdJ153xSfa31urdnn01Nf2LEuO/UTyq1dPXz9jrbu8PfsCMDUbb0wueH3zmrHPgXY6MrnzvPp19/wm6X1K4336r0tu/G4y1J/s+QfJqiMm3y8AAAAAwAwRjAYAAAAAAMyIiQKMugUYAZMkcBEAADru1jFv79uB88aecVsHzoOF4cgPjoYSDdfJG7zyn5KDX5OsOqzzfd3v7guSnz052XLXxLWLd04O/pP290RnrDoi2euZyY2nN6658M+T7r7koFd1ri+mz+Y7k589MbnnopnuZOr2fl6y8tAd2+OAP0pu+E5yy/enp6exbvhWMjyQdPnaKMCMu+ei5KdPSrbc0bimd4/Rjwv3e/Cbkiv+sX7tWackz70rWbx6+7m7fj161uC9o9eXvD151JeTfV8w9f4BAAAAADqoNtMNAAAAAAAAC9NEAUaC0YDJWiJwEQAAOu2qrb+WJPuWUg5q10GllAOT7FfnbGBH9e2ZPOQt9eeq4dHgqarqbE9jXfxXrYWiHfSq5InnJcsOaH9PdM5jvpE87P3Nay78i2SovzP9ML2u+MeZCUVbdcSOP9aemDzsb5JHf3XH+6l1J4/9ZnLEuyfuc+Xh268/4OVJafLSkJu+u+M9ArDjLn5781C0JHnyL5PeXbddL9s/6VnVuP7Kf64//pu/2BaKlow+r//1a5OR4db7BQAAAACYQd0z3QAAAAAAALAw9U4QYNRTW9ShToD5os99BQAAOu3iJANJ7k8hfkaSj7TprGeNeXswyQykqMA89uC/SK76TLLx+u3nbv1RctMZyV7P6Hxfw1tGz5/IYW9PHvbe9vdD59V6ksPeLnWOnAAAIABJREFUltx7WXLdV+rXDK1Pbv95ssdTOtsbO+7m73X+zINenRz3T50/dyLdfckRfz36mIoD/ij58WPrz930vWSf5029NwB23MhgcsuZzWsOf2fSt9f243s8Jbnua/XX3PS95LC3PnBsaFNy+znb1w7cndz2k2T3J7XWMwAAAADADGryY4EAAAAAAADap7drggCj0tN0HmC8ie4r3e4rAAAwraqqGkhydpKy9fGXpZTmT8ynYOueb0pSbX2cs/VsYLp09yZH/W3j+XOemWy5q3P9JMn130h+9JikGpm4VrjD/DfR3/FZpyS//bvRIBBmp4F7k0vfl5z19OSnTxp93PWrzvex+5M7f2YnrD668dw1n+9cHwDUd89FSTXUvKbR851mH7vu/MX2Y8NNng/d99vmPQAAAAAAzBKC0QAAAAAAgBnRV5sowGhRhzoB5ouJ7isCFwEAoC2+uvXXKsmaJE2Slabsg0nWZjR8LUm+0oYzgH1OTdY8pvH8t3ZJhvo708sl70r+49Tk7v+cuHavZzXvm/lhn1OTnY9vXnPRXyY/fUIyMtiZnmjd4LrRoMNL3pHc/L3k1h+NPjpt7YnJnk/r/Lmd0N2brDy88fxlH+xcLwA80MB9yZnHNa/Z+znJLo+sP7fPqc3X3nPx1PoCAAAAAJjFume6AQAAAAAAYGHqFWAETLPeLoGLAAAwA76S5P1JdstocNmflFJuqqrqA9OxeSnlLUlel9HgtZLktghGg/YoJTn6o8kPjsnou1wd1309OfCP2tvHwD3J5R+auO6AlyVrTkj2f8lo78xv3b3JE36W/Gtf87o7z0tuOmM0XITZ49qvJPddNvl1i1Ynu57UvGZw3fYha7s9MelZse26qy9Z86jkgJcntXn8vYeHfyT52ZPqz13y9uSQ1yU9yzvbEwDJtV9qPv+Izyb7vbjxc9ruvuSplyT//tD68xe9OTnpB9uuq6Gp9QkAAAAAMIsIRgMAAAAAAGbERAFGPQKMgEkSuAgAAJ1XVdVAKeWtST6XbeFl7y2lPDTJa6qqumcq+5ZSViX5ZJIXjNm3SvK2qqoGpqN3oI7VDx8NDrr6X+rP3/az9gej3XFeMry5ec1uJ48GSLCwdPcmR34wuegtzetu/algtNnm1p9Mbd0pFyVL957eXuaz5Qc3nquGk7v/c+KgOQCmX7OPg7ueNBr4O5EVh2bbp8Xj9F//wOsRwWgAAAAAwNxXm+kGAAAAAACAhalvggCj7poAI2ByJrqv9NQELgIAQDtUVfWFJKdn26u0S5LnJ7milPK3pZQmKR0PVEo5qJTyt0muyGgoWrn/mCRnVFX1uensHajjYe9vPHftl5KL/yoZ3jK9Z/Zfl5z30uR7hyZn/8HE9Xs9a3rPZ+7Y8xkT11zxieQrJTnjQcndF7a/Jx5o7Pvz6fuNPm745uT32enhQtEma+m+yaqHNp4/51nJ0MbO9QOw0N3yw+Sspyc3frtxTavPa2s9qRuKliTrLk+u+9dt11WzYLQGewAAAAAAzDLdM90AAAAAAACwMPV2TRBgVAQYAZPT29XXdL67CFwEAIA2enGSnyY5JtvC0VYneWOSN5ZSbknyqySXJ7l36yNJViZZleTBSY5LssfW8bGBaCXJBUle1PbfBZD07pbsc2py/b/Wn7/sA8k9lyQnnjE9522+MznzEcnmW1ur3/Xxyf4vnZ6zmXtWPjg54I+Sq/9l4tr1v09+cHTy1EuSVUe0vzcm//7cSM+q5OiPTk9PC0kpydEfS35yYv35wXXJz56SnHz2aC0A7XPzD5Kzn5ZUw83rDnh563s+8ovJeS+uP3fuC5KRgWT/FyUjg63vCQAAAAAwSwlGAwAAAAAAZkRfbaJgNAFGwOR0le4sLkuypdpcd17gIgAAtE9VVRtKKU9I8rkkz85ooFmyLeBsjyTP3PpoZGxCx9j1pyd5aVVVG6atYaC5g1/TOBgtSW7+XnLvZcmqw3b8rGu+0HqI0gnfTXZ/ctLlc/wF7fjPtBaMdr/ffzw57p/b1w/bTOb9OUnWnpjs8sikGkr6r0+WrElWPTTZ45Skb6+2tTmv7fq40T/X28+qP3/Hz5M7z0/WPLKTXQEsPJd/eOJQtJN/nvQsb33PvZ7RfP6/PzYajFYNtb4nAAAAAMAsJRgNAAAAAACYEYtrvSkpqf7f65wfSIARMBW9XUuzZahRMJrARQAAaKeqqtYneW4p5c+TvDdJX7LdJ/5lu4Vbl4+rLUk2JXlnVVUfnu5egQmseNDENXeeNz3BaHee21rdUR9O9nr6jp/H3FdKsvro5O4LWqu/8lOC0Trlzl9Mrv4x30iW7NKeXhay3Z/YOBgtGb1/C0YDaI9q66e1E35MLJN/Lt2zIundPdl0S/35ey4c/XVEMBoAAAAAMPfVZroBAAAAAABgYaqVWpbU+hrOdwswAqagt+l9ReAiAAB0QlVV/yvJvknen+TejIac3f+oGjzG1ty7de2+QtFghvTunqx5bPOaX70yuegtycjw1M4YuC859w+TG05robgk+zxvaucwP+1z6uTqN97Ynj7Y5vpvJDd8q/X6XZ8gFK1d9n5+8/nfvDG55K+TaqQz/QAsBAP3Jb94UfKtnZOv1pLhTc3rd39ysminyZ+zzwsaz1Vbn5dXg01q6v/QMgAAAACA2UYwGgAAAAAAMGP6upY2nOupCTACJq+31uy+InARAAA6paqqu6qqekeS3ZKckOQdSX6Q5NIkNyfZsvVxy9axM5P8dZLHJdm9qqp3VFV150z0Dmx13KeSpfs1r/nth5KL3jy1/c95ZnLdVyauK13Jcf+cLN13aucwPx3yp8mez2i9/rsHJSND7etnobvlh8l/TCKsbtmBybH/2L5+FroVBydH/0Pzmkvfm1zyzs70A7AQnPOM5NovJwP3TFy7/JDkmI9P7ZzD39F8vqo85wEAAAAA5oXumW4AAAAAAABYuJoGGBUBRsDk9TYLXCwCFwEAoNOqqhpM8h9bH8BcsvLQ5A9+m5x1SnL72Y3rrvp08rD3J12LW9/73sua75kkB/1JstvJyZpHJb27t743C0N3X3LCt5P7LkvuvTTpvy65+K2N60e2JLecmez5B53rcSG54p+azx/06mSXR4y+veyAZOfjkq4l7e9rIXvQnyYbrkz++6ONa678p+SIdyW1ro61BTAvrft9cvs5rdWefE6y87FT/zi4eHXyhJ8lPzmp/nw1kowMTm1vAAAAAIBZRDAaAAAAAAAwY5oFo3ULMAKmoK/pfUXgIgAAAExKd29yyOuah5gNrktu/E6y8rBk2YGja+43PJBsuTPp22M0pGHjjcnIQHLNF5qfW7qSIz+QLNppen4fzE+llqw6YvQxMpxc9oFkaH3j+uu+nuxxSjK8Odlwdf2anpWj/6ZTjV737Z0sWjntrc8bg+uS/uuTG7/duKZ0JUf+jffnmbDrE5oHo225M+m/Jll+UOd6ApiPrvtaa3VrHpusfeyOn1dr9n30kaQa2vEzAAAAAABmmGA0AAAAAABgxvR1NQ4w6hFgBExBb9P7isBFAAAAmLQ9njZxzbn/Y/TXWk9ywMuTo/8hufTdye/+VzK8afJn7vl0IUpMTq0r2e+FyZX/3Ljm2i+OPiaj1EbfBx71xaRnxY71OJ8Mrk/Oe0ly03dHQw+b2fNp3p9nyu5PShavSbbc0bjmjIOTo/4+OfTPk1I61xvAfLL5ttbq9n/x9JxXao3nfvy45EFvaLK4mp4eAAAAAADarMlXQgEAAAAAANqrt9YkwKjpT7oGqK/pfUXgIgAAAExed2/ypPNaqx0ZTK78VHL63sllH5haKNraxyXHfXry6+CoDye7PWl696xGRsO/fvXq6d13rvv1a5IbvzNxKNriNclxn+lMT2yva3Fy0veTvr2b1/3mjaP/zgFon+7lyYGvmKbNmrwc8M7ztoUW11MNTVMPAAAAAADtJRgNAAAAAACYMb1dfQ3nugUYAVPQ1yVwEQAAAKbdLo+YXP3m26d2zv4vSU4+K1myy9TWs7D1LEsef2by0PdO/943fCsZXDf9+85FQ/3J9d9orfaZ13l/nmmrjx79e1i8c/O6qz/XkXYAFqwTvp2UaXoZ347sMzI4PT0AAAAAALRZ90w3AAAAAAAALFy9tcYBRoLRgKlwXwEAAIA22f8lyTVfaO8Zezy1vfuzMOz2xOSSd0zvniODyUVvS9Y8KhkZSEp30t3gB3/UFiWrj016d53eHmbawH3JXb9Mbv/56J/BRJYfknT3tr8vJlZKss//SK74ROOaG09Pqmq0FoDJmfDjYklWHt7ydvcN3Z3rN1+VoWpou7me0pMDqi1p/OPHJjCy/Z4AAAAAALORYDQAAAAAAGDG9DUJMOopizrYCTBf9HW5rwAAAEBbHPjK9gajLVmb7PmM9u3PwrHzccmqhyX3Xjy9+17xiebBUuMd/s7kiHfOj6Cpq7+Q/OqPRwPiWnXQq9rXD5N34CuSK/8xqUYaFFTJ2c9IHvP1xqF/ANQ3cHfz+b2f01Jg6kg1ktPv/GJ+dPe3m9aVJM9ee2BOvv2qSTS5VTWJj+UAAAAAADOoNtMNAAAAAAAAC1dv0wCjng52AswXS2qNX7TnvgIAAAA7YO1jksd8Y/r3LV3JLo9KTv550t07/fuz8JSSnHRmsvspM9vHpe9ObvvJzPYwHTZcm5z/stZD0ZasTY54V3Lo/2xjU0za6qOSE85oXnPz95LL/64z/QDMJ/+XvfuOk7uq9z/+OrM7m2TT66YHAqGEFjqEIh0RRRQVudeuV8Verlgv9nrVawWx+1NRwQIoKCJdOtISWgokIWUT0nu2nd8f3113Q+Y7OzM7M7ubvJ6PxzzmO+dzvud8Astk2f1+37Ojm2C0439R0DJztzzYbSgaQAT+OOVgFgweVdC6Oykm5FSSJEmSJEmSepHBaJIkSZIkSZIkqdcMyuQJRsvUVbETSbuL+jzvK7W+r0iSJEmS1DNTXwX/EeHC7fDqDfDK56GuhECGDideBa/eCGfdBcP2K1+f0qAGOPUGOOfR/PMGNsC4F1Wuj0VXVm7tallyNUkESzfGHA8XrIZXNMIhn04C6tS3THoJvGZL/jmLf1edXiRpd7Lj+fRa/VSoTf/dVVf3b7y9qG3njBhf1HwA2lqKP0eSJEmSJEmSekFtbzdQjBDCLe2HEbgoxriqxHUagN90rBVjPL0c/UmSJEmSJEmSpOLU1+QJMArZKnYiaXcxKM/7Stb3FUmSJEmSyqNmQPLIAhPPhUW/LH6NzACY8GKorS97e9K/DT8QBoyBHatz1yeeA5POg1XFBZEU7JmfwdAcoX+DxsP4M6B+cmX2LadC/9lMfAkMGF3ZXtRztfUw4hBYPyd3ffMCaGuFTE11+5Kk/iq2weZn0uuHfKbgpVbsWFLU1huzA4qaD0BsLv4cSZIkSZIkSeoF/SoYDTiFzo+bGtiDdQa2rwUFfXyVJEmSJEmSJEmqhEGZ9ACjulBXxU4k7S7q87yvZH1fkSRJkiSp/PZ7Nyz5HbQ1FXfejIshO7QyPUkdMlnY730w59IctTqY8S4YOQuGzoBN8yvTw6Mfzz1eUw8nXg2TXlKZfcvh+Xtg+fXdz8uOgOlvrnw/Ko8DPgT3pvz7amuGbUth8LTq9iRJ/dW25dC6Lb0+7TUFLdMaW1nVtKKorZtDCSGWbS3FnyNJkiRJkiRJvSDT2w2UIPR2A5IkSZIkSZIkqTzG101hYKZ+l/FRtWMZUjO8FzqS1N8NqRnOmGzDLuMDwkDG103phY4kSZIkSdrNjTkWTrsJJpwNA8ZC3cjkkckTUH7oF+CIb1SvR+3ZDv4UHPF/MOqo5GtzwNjk6/W0f8Doo5PwtDPvgr3fAEOmw/gz4ejLYPL5le2rdSvc/7YkiKovihEeuDj/nEETkn9OZ90D9ZOq05d6bvqb4Kjvp9c3P1O1ViSp31s/N732krlQm/6BPl2tbm6kldyhZTXU5hxvyZRwW2Dso993SJIkSZIkSdIL5P7JqCRJkiRJkiRJUhVkM1lOH3ke16/57U7jZ416JSH4WSmSihdC4KxRr+TKlZfvNH7aqJeRzWR7qStJkiRJknZz405OHlJfFAIc8IHkkWbgWDj+FzuPzbgYHv8SPPrJ/OtfsBpatsK1U4vvbdsKWH0fjDux+HMrbcuzsP7R9PqpN8KEs6rXj8prv3fBnEthx5pda5ufgYZTq9+TJPU3McLjX8hdGzAaRhxU8FIrdjyXczwQOGnE2dy2/vpdas2hpuD1/60td/iaJEmSJEmSJPU1e2owWtc/tz/RlSRJkiRJkiSpF71k9IWMzI7hkU33kgkZjhn2Io4YekJvtyWpHztxxNkMrhnKAxvvoCk2cfiQ45k9/IzebkuSJEmSJEn9zeTz8wejDZ8JdaOgbiQMmZ4EShXrqW/A2BOSALe+ZOO8PMUAo4+uWiuqkCH75A5Ge/xLsPebIFNC4I4k7UmWXAXP35W7Nvq4opZqbFqae5nsOOprhuSsNWcyRe0BQFtz8edIkiRJkiRJUi/YU4PRxnQ53tJrXUiSJEmSJEmSJEIIzB5+hqFFksrq8KGzOXzo7N5uQ5IkSZIkSf3Z8Jkw9TVJ8MkLhQwcfGl7oFlIju99U/F7LL0GHnwPHP39nnZbXpsWpNdmfiwJg1P/NmQ6rLl/1/HNz8Bdr4ETr06+ziVJu2rZBg9fkl7f791FLbeyaVnO8fF1U6gN2dwtlBJgGVuKP0eSJEmSJEmSesGeGox2cvtzBJb3ZiOSJEmSJEmSJEmSJEmSJEmSpD5q9pUw5nhovAka/wEDG2DcKbD362HCmZ3zpr8RBo6DRb/OHSq25r70PeZflgSoDJ9Z9vZLtml+eu2wL1avD1XOkOnptef+CCtvhfGnV68fSepPnvsjbF2SuzbhbJjw4qKWa2xamnN8fN1ksqEuZ62llPDKtubiz5EkSZIkSZKkXtCfg9FiMZNDCFlgAnAW8MkupTnlbEqSJEmSJEmSJEmSJEmSJEmStJvI1MABH0ge3Zl4TvLI5bFLYe7n089d/te+FYy2OUe4G8A+b4UQqtuLKiNfMBrA8hsMRpOkNCtvyT0eauDwbxT1d2WMkZVpwWgDJtOSEmbWnCkhGC22FH+OJEmSJEmSJPWCEn4CWlkhhNa0R9dpwKJ8c3Ocux14FrgCGNZlreuq+MeTJEmSJEmSJEmSJEmSJEndCCEcGUJ4VQjhZSGEfXu7H0mSemxCSmBah4f/G/5xCjz0YdiyuCot5bXixtzjQ2dUtw9VzvgzSW7NSPHUN6vWiiT1O6vuyD2+1+tgxEFFLbW+ZQ3b27blrDXUTSabqctZaw41Re0DQErImiRJkiRJkiT1NX0uGI3kN2tpj0LndfeI7Ws8Bfy+cn8USZIkSZIkSZIkSZIkSZL2XCGEgSGE6V0eee/cDiGcF0JYBNwP/A64Bng6hPDPEMLMKrQsSVJljDkOpr02/5xVtydhVDceB5sXVaWtnDY8BbE1d22IeaW7jcFT4cCP5J/z1Leq04sk9SfbGmHzgty10UcXvVxj09LU2oS6ydSGbM5aS6aE2wJjS/HnSJIkSZIkSVIv6IvBaNAZXFYpAXgQeGmM0Y+6kCRJkiRJkiRJkiRJkiSpMj4MzG9/3Aq0pU0MIbwG+CMwhV0/EHU2cF8I4chKNyxJUkWEALN/DaOP7X7u9kaYf3nle0rz2P+k14bOqF4fqrzDvwp7vyG9/vgXoHVH9fqRpP5g3nfTayX8PZkWjDasZgT1NUPIhrqc9eb8ueO5tXkbnSRJkiRJkqT+oba3G8jhDtKD0V7U/hxJPg1ye4FrRmAHsB54Erg1xnhnT5qUJEmSJEmSJEmSJEmSJEndOp8k2CwCP4kx5rw+MIQwEriC5ANfY/sjtJc7zhkM/DGEsH+MsdDrByVJ6jtCBg7+FNz+su7nrrqt4u2k2vR0em3oPtXrQ9Wx//vh2f+Xu7ZjDWx4HEYdUd2eJKkvW31vem3ofkUvlxaM1lA3GYBsyOasN2cyRe9F09riz5EkSZIkSZKkXtDngtFijKek1UIIbXRe4HRhjHFJVZqSJEmSJEmSJEmSJEmSJElFCSEMAmbRed3fX/JMfy8wnM5AtGXAH4EW4JXAtPZ5k4H3AV+rQMuSJFVew6mQHQbNG/PPW3M/3PlqOPoyGDi2Or0BxDZYPye9Xju4er2oOkbOgvopsPW53PXNCw1Gk6QOLVth5S3p9cHT0mspGnfkDkYbPyAJRqvN1OVuJVOzU6J4QdbPgRghFHWWJEmSJEmSJFVdCR8N0ev8yaskSZIkSZIkSZIkSZIkSX3fIUANyXV/W2KMD+WZ+zo6Q9GeBg6OMb4/xvjh9nUeaJ8XgDdVrGNJkiqtdjAc/QPIZLuf+9zv4aYTobWp8n11eOZn6bVjf1q9PlQ9IQPH/ji9vmlh9XqRpL7u1rPTawd9oqTAsZVNKcFode3BaCH9e4aWkOPWwEkvy79hvr/rJUmSJEmSJKmP6G/BaJ9tf3wOWN/LvUiSJEmSJEmSJEmSJEmSpHR7tz9H4Im0SSGEA4B9u8y9NMa4oaMeY9wMvLfLKfuHEKaUuVdJkqpnr4vg3CeTgLR935l/7qZ5sPwv1ekL4PEvpdemnF+9PlRdE86CEYfmrm02GE2SAFj3GDz/z/T63m8oesmtrZvZ2Jr7FrmOYLRsnmC05kyOWwNP+hOceHX6po98rKgeJUmSJEmSJKk31PZ2A8WIMX62t3uQJEmSJEmSJEmSJEmSJEkFaehyvCLPvJPanwOwCfjTCyfEGO8PISwFJrcPHQo8V44mJUnqFUP3SR4xwuIroXlj+tz5P4DJr0jm1AyAUANtzdDWBLVDoGULSbboCwXIDoMQuu8ntkHzBtj8TO56pg7qRhbyJ1N/Ne5FsP6xXcc3Pg1N6yEzAGoHlXfPtmYItYV9jUpSb1tzb3otUweD906vp2hsWppa6wxGq0ud0xxqgJYX9FIDU18FA8fB9lW7nrRjNWx/HgaOLbpfSZIkSZIkSaqWfhWMJkmSJEmSJEmSJEmSJEmS+o36Lseb8sw7of05AjfHGFtS5s2lMxhtag97kySpbwgBpl4IC3+UPqfxJvhNprT16yfD/h+EAz6YO3yqdQf86/2w+HfQvD59nRGHlLa/+o8h++Qef/5O+P3IJJBv1NFw7I9gxME922vbCrj3LbDyVhgwGvZ/Hxx4iQFpkvq2lben16ZcADXpAWZp0oLRBmYGMaJ2NADZkE09vyWT5/uDcS+CJVfnKER48utw+FeLaVWSJEmSJEmSqqrE345KkiRJkiRJkiRJkiRJkiTl1TXZIv1Obpjd5fjOPPPWdDkeVlJHkiT1RYd9qXJrb10KD38Ynvlp7voDF8OCK/KHogEc/8vy96a+ZWhKMFqH2Apr7oWbToSmbr5e8q4T4ZYzYMXfoG0HbFsOj3wM5l9e+pqSVGltrbD4yvT6Ed8oadnGHbmD0RrqJhPawyJrM+mBa82hJn3xY65Irz35NdixtqAeJUmSJEmSJKk3GIwmSZIkSZIkSZIkSZIkSZIqYVOX44ZcE0II44F9uwzdnWe92q6n9qAvSZL6loFjYPpbKrvH/B/sOta8ERblCXnpUFMPww4of0/qW4Z0E4zWoXkDLLmq9H2e/ydseGLX8QV5AnwkqbetvDm9dtAnYNCEkpZtbEoLRpv07+NsSM8Zb87kuTWwbiTMeHd6ffFvuu1PkiRJkiRJknpLbfdT+q4QwqnAacDhwDhgOPk/VTKXGGMs8Dd4kiRJkiRJkiRJkiRJkiSpQMvanwNwSMqcl3Q53gE8lGe9EV2Ot/SgL0mS+p5pF8IzP63c+msfhOfvgVDTObbmPmjb0f25Iw6BYCbpbm/IdKgZBK3bup+79uHS91ny+9zj6x+D1iaoqSt9bUmqlHV53vdGHFryso1Nz+UcH183+d/H2ZD+vtiSqUmtATDpXJj//dy1dY90258kSZIkSZIk9ZZ+GYwWQjgb+A47f0pkqb9pjT3vSJIkSZIkSZIkSZIkSZIkvcBjXY5HhRDOjjHe+II5b25/jsD9McbmPOtN73LcWI4GJUnqMxpOg/rJsHVp5fa4aXZp501/U1nbUB9VMwCmvRae+Vn3cxf8AI65vLR9NjyeXtuyCIbtV9q6klQpLVvgkY+l1ye9rKRlm9uaWNO8KmetazBabUi//a85ZPJvMv7M9NrCH8OxP8p/viRJkiRJkiT1km5++tn3hBA+AtxAEorWNQwtlvCQJEmSJEmSJEmSJEmSJEkVEGNcCMwnuV4vAJeFEPbuqIcQPgyc0OWUa9PWCiEMYecPU11Y3m4lSeplmVo49UaoHdLbnXTKDICZH4V9397bnahajvwOTHkldBe0A7DhiRI3aUsvbVpQ4pqSVEEPX5JeG3M81NaXtOzKpuXElNvbJgyY8u/jTKihhtzhaM2ZmvybZGphxsXp9XWPdNunJEmSJEmSJPWG9I+M6INCCGcDX21/2RFu1hGOthVYD+T7tEhJkiRJkiRJkiRJkiRJklQ9Pya57i8CewNPhRAeBcYBU+i8DnA78Ks865xC5/WCLcDjFepXkqTeM3wmvGYTbF0KW5fD348tfo1zHoa/Ht6zPoZMh5P+BMP2g5qBPVtL/Ut2CJz0B2jaAFuehaV/hjmX5p777C9h1peL32Pr0vTaZoPRJPUxba2wKM//qk59TclLNzblfj+soZYx2YadxrKZLK1tLbvMbSkkyHLSeTD/8ty1Z34BR87qfg1JkiRJkiRJqrICfvrZp3yl/bnjQqilwHuAvWKMQ2KMk2OMexf76LU/jSRJkiRJkiRJkiRJkiRJu7dvA0+1H0cgCxwJTKUz6CwC34wxPp9nnVd0mftojHFHBXqVJKlvqJ8Mo4+G+inFnTf55TByFkx4cc/2n/5mGHmooWh7srr+2Xo0AAAgAElEQVThydfS9Delz3niK7D+cYhtnWNtzfD8PbDydmjN8e1aWwtsyhN+tvDH0LKt5LYlqey2PgfNG9PrI0sPI21sei7n+Ni68dSE2p3GakNdzrnNmQJuDRx5WHrt6W8lYZiSJEmSJEmS1Mf0m2C0EMI+wGEkFzUB3AccHGO8LMa4pPc6kyRJkiRJkiRJkiRJkiRJucQYm4CzScLROoLQAp3XAgbgj8Cn09YIIQwBLuhyzs0VaVaSpL4kBJjxruLO2fedyfOMi0vft2YQ7P3G0s/X7mVwN+F8NxwMd5wPLVtgyxK4/mC4aTbcfApcNz0JTutq7b/o/JYuh/Vz4M8zYN1jPe1cknqurRVue0l6vX4qjDup5OUbm5bmHB9fN3mXsWzI5pzbEmq632jQhPz1a6bA8hu7X0eSJEmSJEmSqqjfBKMBx7c/d1wQ9YYY46Ze7EeSJEmSJEmSJEmSJEmSJHUjxvgcMAu4GPgr8ATwJEkg2qtijK+OMbblWeJNwDCS6wcDcH1FG5Ykqa+Y+VE49PNQv2tAyr+FGhh2IMy+Eia+OBmbfB4c9wsYtn/he4UaGH0MnH5b92FY2rMc+5P89WV/hqe/A/e/HTbN6xzfthzuvmjnuf98Tff7bVsG97wBYp4ANUmqhmXXwsYn0+tn3w+h9FvzVjYtyzk+vm7Xv4ezoS7n3OZMgfvP/k16rWVT8n7dsq2wtSRJkiRJkiSpCmp7u4EijGt/jsDDMcb5vdmMJEmSJEmSJEmSJEmSJEkqTIyxGbii/VGsnwC/7LLWhnL1JUlSnxYCHPwpOOiT0LYDagbCv7NEQ1KPbblDWaa/IXm0bu9yTr69aqEmd+iK9nAjZ3U/Z+GPYfMzu46vnwObn4UheydBZ1uXFLbn+keTkLViwv0kqdwW/za9NvYkGNRQ8tJtsTU9GG3AroGotSGbc25zpqawDUcemr/etA4ab0rCVSVJkiRJkiSpD+hPwWihy/GCXutCkiRJkiRJkiRJkiRJkiRVTYxxG7Ctt/uQJKnXhJCEosGuIWi5QtG66jhPKtWwAyE7HJrzZNPmCkXr8MhHYfjBUDOouH3XPZI7GK1lG6y4EbY8mwQTjT6quHUlqRDNm2DJ1en1Mcf3aPk1zatoic05a+Prdg1Gy2Zyh5e2dPd9QIch+8KA0bBjTfqcjU8BBqNJkiRJkiRJ6hsK/Olnn9D1YzAK/DgLSZIkSZIkSZIkSZIkSZIkSZIklaR2EOz33tLPX3I1zPk0PHJJcefd9dpdA3y2r4Z/vAjufAU89CG48Wh47DOl9yZJuWxdCjcem3/Ovm/v0RaNTUtTaw11k3YZy4ZszrnNmQJvsaupg/0/mH/OIx8tbC1JkiRJkiRJqoL+FIz2eJfjKb3WhSRJkiRJkiRJkiRJkiRJkiRJ0p7i0M/Bkd+B4TOru+/T39359bzvwNoHdh6b+1nYOK96PUna/T3+Zdj4ZHr92J/C0H16tEVaMNqo2rEMyAzcZbw2LRgtFHFr4EGfgKMvzz9n87OFrydJkiRJkiRJFdRvgtFijHOAuUAAjgwhjOzlliRJkiRJkiRJkiRJkiRJUolCCJNDCCeHEM4PIbw+hPCG3u5JkiRJOYQA+78Xzn0cBoyu3r5L//SC19ekzLu28r1I2nOkvdd0mHJ+j7dIC0YbXzc553g21OUcb8nUFL5pCDDjnUnQZRrfTyVJkiRJkiT1Ef0mGK3dN9qfa4AP92YjkiRJkiRJkiRJkiRJkiSpOCGEaSGEb4UQngEWA7cCfwB+Dvws5ZyTQgiXtj/eW71uJUmStItxp1Zvr/WPwWOfhvWPw4Ifw/o5uec9/oXq9SRp99a8EbYtT6+POAzqRvZ4m8YdKcFoA3IHo9WGbM7x5kwJtwY25Hkf3zS/+PUkSZIkSZIkqQL6VTBajPEXJBdABeCSEMI5vdySJEmSJEmSJEmSJEmSJEnqRgghE0L4IjAfeC+wF8m1gF0faVYDnwE+DXwrhLBPRZuVJElSupkfLe96p9+Svz73c3DDwXD/f6XPad4Iba3l7UvSnidG+OsR+ecc8pkybBNpbEoJRqvLHYyWzaQEo4Wa4hsYcXB6bdOC4teTJEmSJEmSpAroV8Fo7d4IXAfUAteGED4XQhjRyz1JkiRJkiRJkiRJkiRJkqQcQghZ4G/Ax0iu/XuhmO/8GOOTwK10hqf9R1kblCRJUuFGHwUvmVPG9Y6Bly/p+TqNf+/5GpL2bM/fBZsXptfPuB2mnN/jbTa2rmdb25actYa0YLRQl3O8JVPirYGzvpp7fP2j0Lq9tDUlSZIkSZIkqYxyXWDUZ4UQLm0/fBSYDYwBPgl8KIRwD/AEsA5oK2bdGOPnytmnJEmSJEmSJEmSJEmSJEn6t58AZ5AEoEWSgLM7ScLOmoAvFLDGH4BT24/PAj5f/jYlSZJUkBEHw7D9YePTPVsnk4Waeqivh+wwaN5Y+lrL/wYTz+lZP5L2bCtuTK8NOwDGnVyWbRqblqbWxqcEo9WGbM7x5lBTWhPD9s89vn0lPP0dmHlJaetKkiRJkiRJUpn0q2A04DPs/MmQHRdI1QOntT9KYTCaJEmSJEmSJEmSJEmSJEllFkI4HXgdndf7LQD+I8b4YHt9GoUFo10PfK99jaNDCANjjNsr07UkSZK6NfHcngej1Y2EEJLj7IieBaPN+07yGDmL5FtGIFMHo4+GAy+BwVN61quk3d/T306vTTw376mLts3njvU3sGzH4m632dK6Kef44JqhDK0dnrOWDXU5x5szmW73y2nsSRBqIbbsWnvko9CyBQ75NIQS15ckSZIkSZKkHupvwWi5xO6npAo9PF+SJEmSJEmSJEmSJEmSJKX7dPtzABYDs2OMq4tdJMa4OISwHhgBZIEDgEfK1qUkSZKKc8AHYdlfYNO80teoG9l5fMwP4bYX97yvdS/4FnHNffDcH+HsB6B+Ys/Xl7R7euYX0JI7sAxI3vNSLNz2FN957lKaY1OPWhhfNzm1ls1kc46XHIw2YBTs81ZYcEXu+tzPwY7n4ejLSltfkiRJkiRJknqoP35sQyjjQ5IkSZIkSZIkSZIkSZIkVUAIYRQwm+QDTCPw/lJC0bp4osvxfj3pTZIkST1UPxnOuicJNJv+FqgZVMIiXW5pGX9G2VrbxbblsPAnlVtfUv8WI8z9fHp91tegflJq+aa1f+xxKBrkD0arDXU5x1tCTekbHvo5yA5Lry/4IWxbUfr6kiRJkiRJktQDtb3dQJFO7e0GJEmSJEmSJEmSJEmSJElSQU6kM+1iVYzxuh6u1zVUbVwP15IkSVJPDRgF+/5X8jjmCrhqMLQVEQ40bP/O40wNDJ0Bm+aXv0+A5++szLqS+r9ty2HzwvT66GNSSzFG5m+dW5Y2xtdNSa1lQzbneHMmk3O8IAPHwUGfgkcuyV2PrbD6HpjyytL3kCRJkiRJkqQS9atgtBjj7b3dgyRJkiRJkiRJkiRJkiRJKsiE9ucIPFiG9TZ1OR5ShvUkSZJULplamPxyWHL1zuMDxsKQfWDNvbueM+mlO7+un1q5YLTGm+Cq9m8ha4fAuBfBEf8H9RMrs5+k/mPj0+m1TBbGzk4tb2rdwLa2rT1uIZBh1tDjUuvZUJdzvCXU9Gzj/d8HC65ID4a78wKoGwWjj4XDvwojDunZfpIkSZIkSZJUoB58LIQkSZIkSZIkSZIkSZIkSVKqUV2O15VhvUFdjpvLsJ4kSZLKadZXYOiMztc19XDcT+GYK2DQpJ3nTnkV7PW6nccy2cr217IleWxfCUuugptmQ+v2yu4pqe+7/aXptZOuyfve1Ni0tMfbBwIXjH0To7PjUufUhtw9NGd6eGtgzQA45fr8c5rWwoq/wk0nwpYlPdtPkiRJkiRJkgpU29sNSJIkSZIkSZIkSZIkSZKk3dLGLsdDy7BeQ5fjtWVYT5IkSeU0ZDq8+CFYdQc0rYOGU6F+YlI7dy6svA22Pgejj4VRR0KmZufzB4xJX3vmR2Hzs9C8CcadBOPPgBuP6Vm/WxbDc9fAXq/t2TqS+q/mjdC6LXetfgpMekne01c2Lcs5PjBTz7mjL+x2+4GZevatn0lD3aS887KZupzjLaEm53hRhu0P098Mz/ws/7zmjcmcQz7d8z0lSZIkSZIkqRsGo0mSJEmSJEmSJEmSJEmSpEp4vsvxjJ4sFEKoAQ7vMrSiJ+tJkiSpQrJDcgcJ1Y2AKefnP3faa2HRr3Ydr58Cs76y6/iR34Z/vb+0Pjssvx6mXQghQMtWaFrfWcvUwoCxSU3S7mn93PTauBd1e/rKpqU5xyfWTeX0US8vtatdZEM253hzJlOeDUYf030wGsCa+8uznwoTI7RuTf5+qhsBmSy0bIGa+uTvptbtEGqTv68kSZIkSZKk3Yw/9ZIk9Q3P3w3zvpd82tLUVycXNoQy/ZJOkiRJkiRJkiRJkiRJvWFO+3MA9g8hTI4x5r5rvHvnAPXtxxG4t6fNSZIkqY8ZfwYMGA071uw8Pu2i3POnvhoe+hDE1tL3XPQrWPxbiC1JuExs2bk+aBIc9gWY/qbS95DUdz1/Z3pt79d3e/rKpmU5xxsGTCq1o5xqQ13O8bIFo025AP71Pmhrzj9v+Q0w7zLY713l2VfpFvwQ7n9H7lqmDtqakufMAJj+Rjjim0lwmiRJkiRJkrSb6PeJMyGEbAjh5BDCJ0MIPw0hXBNCuDmEcHNv9yZJKtCyv8BNJ8Di38DSa+Du/4RHP9HbXUmSJEmSJEmSJEmSJKkHYoxPAh13iQfgw6WsE0LIAB0Xk0Tg0Rjj+p53KEmSpD6lZgCcdE0SjtZh4kvh4P/JPX/QBDjhd1AzqHNs37fD+c/BqKMK37cjDO2FoWgA25bBvW+G5TcWvp6k/iFGeORj6fXxZ3a7RGNKMNr4usmldpVTNuQOvGoJNcRybDBwLJx4NdTUdz/3wXfDkj+UY1elWXpteigaJKFoHc8tm2De9+CRj1enN0mSJEmSJKlKanu7gVKFEAYDHwTeA4x9YRly/1w3hHAR8MX2l2uBo2OMZfkZsCSpBLENHnzvruNP/m9yYcKQ6dXvSZIkSZIkSZIkSZIkSeXya+ASkuv63hNCuCHGeFORa3wJOK7L6x+VqzlJkiT1MeNOhPOXw7qHYWADDJ4GIaTPn3oBTDwnmT9kehKWBnD2/bDxKdi8MHm9+Hew6Fel97XwxzDx7NLPl9T3rHskvTbtovzvPUBT2w7WNq/KWWuom9STznaRDXU5x2MItIZAbTlujZv8crhgNax9AFbdCY99Kn3uwh8n77+qjAUl/Nhj4U9g1lchU1P+fiRJkiRJkqRe0C+D0UIIhwJXATNILpaClCC0HP4M/AAYCkwDzgT+Xu4eJUkFev4u2LJo1/HYBot+Awd/suotSZIkSZIkSZIkSZIkqWy+BryT5Jq9GuDaEMIHYow/7O7EEMIY4OvA60muEQxAI/DTyrUrSZKkXldTB2OOLXx+bT2MPWHnsRBg+IHJA5LQtJ4Eo617uPRz1f+1NcO6R6Flc/I6ZCBGct7KlBkAIw+F2sFVbVElWPHX9NqIQ7s9fVXTCmLK7WxlD0bLZFNrzaGG2thSno1qB8G4k2HUUfD4F6B1e+55jcXmne/BdqyBDY8n98gUavn1xe/TvB6e/QUM2QdCDQzbDwaOK34dSXu2pvWwfi4MOwAGjoEtz3Xe91c/BbY+B0NnwKDxha23dWnyd8mQfboNHJUkSZIk6YX6XTBaCGEmcDswjOQip46LnQoKSIsxbg4hXA28pX3oAgxGk6Tes+LG9NqiXxuMJkmSJEmSJEmSJEmS1I/FGNeGEN4H/Jzk+r6BwOUhhI8AvweWd50fQjgG2B84CzgPGELn9YGtwJtjjE3V6V6SJEm7jeEzYfSxsOa+0s7fvDD5QOgXBrBp97f0Orj7ddCyqfBzMnVw5LdgxsWV60ula94Id10Ey29In7P367pdZlXzspzjNdQyJttQanc51Ya61FpLJgNFZG4VtmE9TL0wCdrKJbbCbefCiVcnc7WrtmZ44GJY+JPq7XnfW3d+PfXVcNwvksA7ScqnrRUe+iDM+x7d3KKdmPxyOP5XkB2Su960Hu54Bay6LXk9/GA45XoYPLVcHUuSJEmS9gCZ3m6gGCGEgcBfgOFdhucAbwWmAwfSeQFUPtd2OT69bA1Kkoq3fk56beOTyadMSJIkSZIkSZIkSZIkqd+KMf4/4Avs/GGo+wCXAN/qMjUA95CEqP0HMLRjifbnj8cY/SBUSZIklebka2DcKaWff9OJ0Lq9bO2oH9i6HO58ZXGhaABtTfDAu2DNg5XpSz3z8Efyh6INHAf1k7tdpnHH0pzjY+vGUxNqS+0up2zIptaaQ01Z9/q3o78Po45Mry+/AeZ8pjJ77w6e+lZ1Q9FyWXI1PP7F3u1BUv+w8Icw77sUFIoGsPRaePQT6fX739kZigawYS7c+aqedChJkiRJ2gP1q2A04H3AXnT+3/V3gCNijD+LMS4CCv0N0610Xly1dwhhXJn7lCQVakM3wWeLf1OdPiRJkiRJkiRJkiRJklQxMcZLgTfTeZ1fx3WAHWFpHY9A5wekdrxuAt4YY/x61RqWJEnS7mfQeDjjVjjvmdLXWHFj+fpR37f4txBbSz9//uXl60Xl0dYKi67MP2ffdxS01MqmZTnHG+q6D1UrVm2+YLRMhW4PrB0MZ96df86iX1Vm791BX/ln01f6kNS3PVvCe8WiX0PMEaTWugOWXbfr+NoHYMvi4veRJEmSJO2x+lsw2nvpvBjqmhjjB2KMbcUuEmPcDCzqMnRgGXqTJBWrZQts7ubCgqU5fhAqSZIkSZIkSZIkSZKkfifG+AuS6/UuIwlI6whAC+wciNYx1gb8P+DAGOMvq9iqJEmSdmdD9obBe5d27r8+AAt+CMv/Cs2bytuXdtW8CZZdD6vugNam6uy5bSUs+X3y7/nhD/dsrWd+CjvWlKcvlce2pdCyOf+cUUcVtFRj09Kc4w11k4rtqlvZUJdaa87UlH2/f6upg5Gz0uvbVkDTusrt31+1tcL6x3q7i8SWxfD0d2Hdo1D8bZiSdnet22HlbbC6myDMXJrWwtwvJN8zLfwJrP1X8j6zbRm0bst9TuMtPWpXkiRJkrRnqe3tBgoVQpgJdPxkOAIf6eGSC4GO32RNB27v4XqSVB7Nm+CJr8Lzd8HQfeHA/4Zh+/d2V5VRyKc8bHwq+SV2Tfov8iRJkiRJkiRJkiRJktQ/xBiXAO8JIVwCnNj+mAKMBuqA1cBK4G7g5hjj+t7qVZIkSbux/d5TWujVlkVw/zuS40ET4JQb8ocGqXRrHoBbzoLm9v8lGLofnH4L1Jc/dOrflvwe7n4dtO0o35p/GAOn/A0mnl2+NVW6TfO7nzPxnG6nxBhZ1bQ8Z218BYLRajPZ1FpLyJR9v53s9x64723p9TsvgNNuhhDS5+xpHrmktzvY2b/elzxPey0c93OoGdCr7UjqI7YsgdvOgQ1PlL7GnEt3fj3pZTDzY+nzY0vpe0mSJEmS9jj9JhgN6PhNUQTmxhif6eF6XS+WGt7DtSSpPNqa4ebTYO2DyetVt8HCH8NJf4LJ50Glf2FVbVuWdD8ntsDmBTB8ZuX7kSRJkiRJkiRJkiRJUlXEGLcCf29/SJIkSdV1wAeT54U/hI1PJ8fZ4dBwGhz9fbhmCsTW/GtsWwH3vhXO+Vdle90TxQh3vqozFA1g0zx44GJ40XWV2bN5I9zzxvKGonW453Vw/jI/LLwv2LQgf33vN0CeELIO61vWsCNuz1lrqJtcSmd5ZUP6105zpqbs++1kn7dCWxM88K7c9ZW3wqrboeGUyvbRX2xaAE99s7e7yG3xb6HhdNg3T9CdpD3Hw//ds1C0XJb9GWJbngmxvPtJkiRJknZr/SkYbWyX4wI+nqNbXX9TUV+G9SSp51bc2BmK1tWdr4CB4+GE30LDi6rfV6Vsfa6weeseNRhNkiRJkiRJkiRJkiRJkiRJUnmEAAd+KHnkcuS34cH3dL/Ouodg8zMwZHp5+9vTrXsItub4EO5lf4bWpsoEjC27AVq3lnbuec/CvW+EVXfkru9YnQRHTTiz9P5UHt0Fo407uaBlVjYtS6011E0spqOC1IQaMmRoY9ewmeaQ6XxRqfeiGRfD/Mth/Zzc9SVXG4zW4bk/pNcGNsArG8u/Z4zwm0z38wCW/M5gNEnJ91NLr6nM2suvT681b6zMnpIkSZKk3VJ/CkYb2OW4HB+/MrzL8aYyrCdJPbfsz+m17Y1w8ykw9dUw9mTY581QO7hqrVXElhy/rM5l6Z9gr4sq24skSZIkSZIkSZIkSZIkSZIkAYw9ofC51+0Lww+EvV4H098EgyZ01jY8CfO+B6tugxGHJtd/r74Xhh8MB3wQxhxb7s53D6v+mV5r2Qw1o8q315YlyXX8hQTh5VI/GQZPhUnnpQejAdzxcpjxruR44FiY8GIYeVhpe6p0G5/MXx8zu6BlGpuW5hwfVjOC+pohxXZVkGyoY0fcvst4S6am88UR/1eRvQEYc0J6MNr8y+Dwr0PtoMrt3x+0NcMTX0uvF/j1VbQQYMLZsOLG7uc2/gMe+zSMOwkaTk/OlbTnaNkCy/4CS65K3rOq7YmvwfS3QNNaWHY9bH0uGd+8AGqHwIRzYPLLIDus+r1JkiRJkvqc/hSMtrrL8ZgyrNf1IzDWlGE9Seq5Z37e/ZwlVyeP+d+Hs+/r3z/o25b7l4G7WHFj8gk2/sJFkiRJkiRJkiRJkiRJkiRJUqWNnJUEV634WwGTI2x4Ah79RPI45+Hk/MVXwV0Xdk7b8ESX48dhye/gsC/BQR8ve/v9XsjkKcby7bPqn3D7S6F5Q+lrzPxY0u++74CnvwVbU66Rb90GT32j8/Wjn4CjfwD7/lfpe6s4rdth+Q3p9cnnJyGHBVjZtCzneEPdpFI6K0htJsuO1l2D0Zoz7f+9jDgMxp9esf3Z7z2w4Afp9ZtPg1P/CnUjKtdDX9ayDe58RRL2k+aAD1Vu/wP/u7BgNIC5n0ue9/kvOOYK79WR9hQ71sCtZ8Paf/ViD8/DH0an1xf9GoYfBKfdtHPYsCRJkiRpj5TvNwV9TWP7cwAO78lCIYTRQNefVC/oyXqSVDbZ4YXP3fhU/k+S6Q+aCvwFcvNG2Lqksr1IkiRJkiRJkiRJkiSpKkII2RDCySGET4YQfhpCuCaEcHMI4ebe7k2SJEn6t5OvgYM+BaOPhaEzCj/vkU9AaxM8+O7u5z76CdiaO2Bpz5YnpCe2lm+bhz5YWCja0Bkw7mQ44ptwxLdg3Ckw4RyYfSXs1/7vOTsEzn6g8L1jW/v+m0pqXSVYfFV6bdiBcGKe+gukB6NNLrargmVDXc7xlqEzYP8PwOk3Q+3giu3PiIPg0M+n19fcCwt+VLn9+7olV+UPJpv2Whh3YuX2H38GnH4rDNmn8HMW/ghW31u5niT1LfMuKywUbcorYa//hNHHpM8ZOgNqh5avt642PA5PfbMya0uSJEmS+pXa3m6gCHcDbSRhbqNDCKfFGG8pca230Plbki3Ag2XoT5J6rmZAcfOf+Aoc+rluPhGrD2vdVvjc9XNh8LTK9SJJkiRJkiRJkiRJkqSKCiEMBj4IvAcY+8IyEFPOuwj4YvvLtcDRMcaccyVJkqSyqRkAh30+eQDc80Z49v91f17jjfD8HbBjdWH7LP8r7Pu20vvcHYU8wWhtLeXZY9sKWFvA7URHfa8z/KzDAe/PPXfQeDjkszDn04X10LIFVt4Kk88rbL56Ztl16bXZv4RMtuClGpuW5hwfXzep2K4Klg25+2s++BMw4qyK7buTyefDY/+TXl92Hcz8SHV66WvyfX0B7PX6yvfQcAqctyA5bm2CqwYlIYz5LPszjD2+4q1J6gO6e58CGHsCnPSHwtaLEa4emnw/U25Lr4PD/7f860qSJEmS+pV+E4wWY1wXQngAOLZ96PMhhFuLvbgphDAJ+BidF1DdFGN3P+GTpCpoa4XtK4s7J7bCIx+Dw79WmZ4qrXVr4XM3zIVJ51auF0mSJEmSJEmSJEmSJFVMCOFQ4CpgBp0fbFro9X9/Bn4ADAWmAWcCfy93j5IkSVJe488oLBgttsEtZxa+7v3/lQRVhBoYeUQSkjZoQvH9Lb0Olv0F6kbCXv8BIw8rfo0+I08wWmwubcllf0n+GWWHwV4XwbzLCjuv4fTi9hl/RuHBaAB3vBymXghjjk2+BlbfCy2bYcBoGH82TLswf1CcCrf67vTa8EMKXmZ72zbWt6zJWWuoYDBabajLOd4cmyq25y6GHQCDJsK25bnrz/8T5v8A9nkbZPrNbYs9FyM898f0es3A6oeP1dTBuBcl4Yv5PPFl2PgkbJoHgyYnoaBN65L3o2EHwtQLkvc1qVp2rE2+33r+LmjbUd61l/05eR66H0x4MUx77e4bDLj0z8mfd3tj8jrGwgJhG4r47z2E5PukQgLXirVpHlzZ/v1P7WBoOC1PHxkYMQv2eSsMnlL+XiRJkiRJvaa//YTx28CV7cfHkVzo9I5CTw4hNADXASPbhyLwzXI2KEkl27II2kr4Je2T/wv7vh2G7lv2liqupYhgtOV/g5kfrVwvkiRJkiRJkiRJkiRJqogQwkzgdmAYScJBbH8uKCAtxrg5hHA18Jb2oQswGE2SJEnVNuUCePq7sPaB8q/dEdSx9BpY+CM4804YPK3w8+d8HuZc2vl63vfglOuh4ZSytlk1+YLA2lqKX+/xL8Ojn+h8/dQ3Cjtv+pth+AHF7TXmeJh8fvLvslBLfpc8XuiZn8Pqu+Co7xbXg3a1dRlsW5G7NvbEJESqQKuaUkLBgIa6ycV2VrBsyOYcbyk1LLAUmVo49PNw31vT5zxwcRJCeMr1e06o3yPd3Osy8xNJaGW1HfxpWH0ftHZz707H+9WGJ3YeX1BVi9cAACAASURBVHU7LPgBHPU92O/dlelR6qppHdxyOqx7pLL7bJqXPOZfBif8Bqa+qrL7VdvjX4JHP1n8eYP3Tu5RLMZBn4RVt0HzxuL3K1TLls7vldMsvRYWXAFn3AHDZlSuF0mSJElSVWV6u4FixBh/C3T8VCMAbwsh3BlCOCnfeSGEwSGEd7afO4vkIqoI/D3GeFcle5akgr3wFwjF+POM5JPF+pvWbYXPXXVb8qkfkiRJkiRJkiRJkiRJ6jdCCAOBvwDDuwzPAd4KTAcOpDMgLZ9ruxyfXrYGJUmSpELV1sMZt8LhX09C0ipl63NJAFuhmjfC41/Yeax1K8z9XHn7qqp8wWhFhkA1bYC5X+h+Xld7vQ6O+zkc+5PizoMkCOrE38MxV8C0i2DiucmjVPO+D5sXlX6+EnM/n147/H+LWmpl09Kc49lQx6jsmKLWKkY2kzu8rbmtqWJ75rTPW7r/b2PFX2HlrdXpp7dtWwFP5vkamvlxOOR/qtdPVw0vgrPugQM/0rN1HrsUWreXpycpn4cvqXwoWlexJQkQi3k/s6F/KeX7ntHHJqGXZ98L9ROLO3fMMXDWvTDzYzDxpZ3f90w8F8afUdxaPbW9EZ76ZnX3lCRJkiRVVG1vN1CCVwH3AqPbX58A3BZCaAQWdJ0YQrgc2A84HhjAzp80uQx4fZV6lqTubXyyZ+c3/gMmnFWeXqqlpZtPnXmhJ78Os75UmV4kSZIkSZIkSZIkSZJUCe8D9iK5dg/gO8CHYkw+BTCEMK3AdW6l8/q/vUMI42KMq8rca58XQsiSXDc5FZgAbAaWAw/HGBf1YmuSJEl7htrBcOCHk+MbZsH6RyuzT+M/Cp+79FrIFYy08tbkw7dDpnx9VU2eYLTYUtxSq25PguIKNe0imP3L4vZ4oUwN7Pv25NHhqf+Dhz5UwmIRVt4CQ97Ss572dOseTq8NnVHUUo1Ny3KOj6ubSCbUFLVWMWpDNud4cywyLLAc9n4jPHBx7veeDo3/gPGnVa+n3tJdANyB/12dPtKMPBRGfg0mvwJuml3aGk1rYc0DMO6k8vYmdbXuUVhYQiBpT22al4TSDp5a/b0rYfXd0Lqt8Pm1Q5IAxVDI51akGH4gzPpyen3Bj+D+t6fXy6mY76ElSZIkSX1evwtGizE+E0J4KfAnkot6Oi50mgCM7zI1AG/vckyXuUuBl8YYV1elaUnKZfMiWPRr2L4yCTTb8ETP1rv7P+EVyyGT+5ddfVIxv2AGWHwlHPbFnv2wVZIkSZIkSZIkSZIkSdX0XjpD0a6JMX6glEVijJtDCIuAvduHDgR6PRgthDAdOBo4qv35CGBolymLY4x7lWGfscBngQuBUSlz7ga+GWP8Q0/3kyRJUgEmvbRywWjrH4Xbzk2C2Fq2wOaFsPHpzvqw/ZPnrmO5NG+AupGV6bGS8l0v3tYMMcK878LSa2Db8vxrdffP6IUmnVfc/EJNfGmJwWjAfW+FJ78Gww5MwtYmnlPe3vYEa+5Prw0YXdRSK5uW5hxvqJtY1DrFyoa6nOMtMU84WaVkamDiubD0T+lznvgybFmchEmOOqJ6vVXLtkaY+zmYf3n6nLEnwICc/wtffaOPgoHjYXtjaef/42QYfQw0nAoH/0/y95PUU9tXw9zPwvN35Q+wrLRrp3V+bwUweC/Y63Ww9+t6raWiLbse5n0fVvy1uPMmvbTy9+lNOg9q3l9cYFupNi+AthbI9Ltb5yVJkiRJOfTL/7uLMd4fQjgC+CnQ8dP8+ILnnU4hCUQLwE3AG2OMJf4UT5LKYN0jcMsZsGNN8nred9PnHvDh5JMTnvoWbJibPm/H6mTN025JfsnUH7QUGYy2ZTGsuQ/GHFeZfiRJkiRJkiRJkiRJklQ2IYSZwKT2lxH4SA+XXEhnMNp04PYerleSEMIpwMdJwtAqfodzCOEc4OfAuG6mzgZmhxB+Dbwjxril0r1JkiTt0fZ/Hyz+DWx+pjLrL78hvVZo2NeOtf0zGI08AR2xBf71AZj3nfJvO/FcmPKK8q8LMGwGzPx4EhZVio1PJ4+l18KJV8HUV5W3v93ZsuvTa4d9sejlVjblDuNrqJtc9FrFyIZszvHm2FzRfVMd+vnk/o584YSLr4Rl18IZd8Kow6vXW6U1bYC/H5fc45LPrP+tTj+FyGThqO/C3f8JbSWG6a25P3msugPO/CeETHl71J6lZSvcNBs2ze/tThJdv7fa+DSsuBG2r4IDSww1rabFv4O7LiL3rdV51E+BQz5TiY52NqgBZn0N/vU+iu6xFAt/AjPeUfl9JEmSJEkV1y+D0QBijCuBc0MIRwLvB04HJqRM3wDcDHw3xtgrF0JJ0k7mfLYzFK07w2fCPm+Bfd4K8y6DB9+dPnfVHbDolzD9TWVps+LSPulhv/emh8Ut+b3BaJIkSZIkSZIkSZIkSf3DrPbnCMyNMfY0MWJ9l+PhPVyrJ2YBZ1Vjo/YQtmuAui7DEXgIeAYYARwOjOlS/09gWAjh/BhjWzX6lCRJ2iMNHAfnPAJLroanvw2ZATD2xJ3ntDXBqluhbhTM/CjUDoGn/g82zYMxs6GtObn+u1Ka1gL7VG79iskTjLatERb8oPxb7vM2OPpyyFTwVqtZX4KJL4aVtyahHVufK2GRCE98xWC0Yjz2qfTa3m8saqm22MqqlGC08XWTco6XS22oyzneHEsMueqpEQfBix+Cu16T3MuSpmVLcn/IcT+tXm+VtujX3YeiTXstjD2+Ov0UauqrknuUlt8A6x6FRb8qbZ3V98Cq26Hh1P/P3n2Gx1Hdbx//HvXiIhfJlnsvuGFMs2kGbHpvCSShJUAgCSGElCeBEJL8CQmBhJCeECCd3nuzwVSDDTZuYBvj3qtkWVpJ53kxEpLlndnZ3dnZXen+XNde0sxpPxftrmZn7gm2PulYVj7gLxRt8IVQ0COJhSws+U1iQxf9EkZ+wwkWzGQLfo6vwLGR33K+GuM8F/Q9DYrKU1pay9pfd64HXPdcyzWVeSXO++GKI2DJb2H1I7D1vRjzNP0ZGqph6V+i95l/o4LRRERERERE2omsDUZrZq19D7gQwBgzBOgP9MA5CWgzsAFYoJN7RCRjNDbAumf99+8yuuX7EVdBwx6Y+233/p/8IzuC0Rojzt26ohlwrnMgc/Mb+7Ztn5faukREREREREREREREREREREQkKK2vrPNxpWNMta2+LwlgvqDVAqsJKHnCGNMPeJi9Q9FeBy6z1i5q1a8QuAL4FdB8peapwM+AHwRRi4iIiIi4yO/cdBPsS/2P6TW15XtrYe1TTQFmKVCbonlTzXgEo215ywmcC9r4n6U2FK1ZxZHOo98Z8Mz+sftHs/U9qK+BvOJga2uvatZF329yoLhPXFNtjWx2DSLrVdAv3sriku8SzFPfGEnpup6Ke8HEX8FzB3v32/R6OPWEZdOs2H16T0t9HYnoup/zsBbWPAmR7bHHRLPpdQWjSXL8/BxVHAWH3uP9vsDXWq/D1tnxj9uzAaqWQ5eRya2fSpEq2P5B7H4Tb4XR16W+Hi89DnQe0Yz9ofN46xJYfk/0Pn1OgUm3t2yvfDD6e+jITuc5Ltn/NyIiIiIiIpJ2WR+M1lrT3SSTvaOkJMkYkw8cBgwAKoEqYC0w11q7Io2liWSG6k+ccDM/Csuh+6S99w29FObdAA27o4/ZMMP54K64MqkyU67epX5w7vjQ99TowWip+tBfRERERERERERERERERERERIJW1Or7Wtde/nVt9f2uAOZLRgRYALwLzG76Oh/n3LlXAlrjJqBbq+03gGnW2r1OPrLW1gK/NcasBB5p1XStMebP1tpPA6pHRERERIJmjHNT6aV/Ts38M0501rCNLfu6TYQjHoROQ1KzZhCsdW/bsci9LVG9pzkBT2EqGw+dh8OuBDOk7y9xgr0ASofA4C/B2Otb9okjstMJtommuE/coSkb6la7tlUUxBeyFq98UxB1v1tQW2i6T4LSQVC9wr3Pro/gvhIoPxwm3QFdR4dVXWp8+l/v9pwC6HtaOLUkyhgYcA4s+1ti4+fd4DzaKujmBK9NuAUqDk+uRmmf6qvhvWti/98zOXDAbcGEWw04J7FgNICXp8EpS5xr3TKR3/cR/c9KbR1B6X+uezDagHP33u4yKvq1hw01MOdaOOB2haOJiIhIsJb+BT76HexYCHgcuzJ5zu/K43+SuaHZIiJZQke72yFjzD3GGBvQY0Uc65YbY/4ArMc5sete4Bbgdzh3rfzEGPO6MebsVPy5RbLGjJP99x19HeS2+fCqoAz2/7nHIOvc8SDTNdS4t+WWQGH36G3ZetcyERERERERERERERERERERkY5nc6vvewYwX+vkhi0BzJeoe4Eu1tqJ1trLrLV/sdbOsdZGglrAGDMcuKjVrjrg4rahaK1Zax9tqq1ZIXBjUDWJiIiISIqMu2nfm2kHxu4digawbS48dzBEqlK0ZgBsg3vbzoCD0UoHw6TfBjunH8bAofdCYY/E57CNzqNqKcy/MXpIUUc35zr3tsPui3u6DXVrou4vy+tBUU5x3PPFI8/kRd0fCe5X0cSYHJh8rxOI5aWhBta/AM9PgT2bvftmsu0LvNtNHhz8VygqD6eeZIz/qROWGaS6bbDpdSdMasfCYOeW9uG1c/2Fok3+Z3Dvj4ZfBX1OSmzs7tXw6pn7vp/KFG+cH7vPpN9mdiBua31OgBHf2Hf/wPNh4Of33nfo3e7zLPkNLPplsLWJiIhIx7bs7/DOFbB9vnPcqvmYTLRHYx1sfhNeORE2v5PuykVEslr0I6IiLTySi1oYY04E7gEqYnSdAkwxxvwbuMJaW51ceSJZZvGvnTvd+NHjEBh1bfS2kVc7B3dfcLl7ytI/QfkU6Dpu32C1dKhZBzsWQMMe6DwSugyHht3u/fNKoMAlGK1OwWgiIiIiIiIiIiIiIiIiIiIiWWJ901cDJHWlrTGmBzC61a6lycyXDGvtthCWuQDIbbX9sLX2Yx/jfsHegWrnGWOu8gpUExEREZE0K+4F09+ALW/Di0eGs2btFlh5Pwy9NJz14mXr3dt2Lo6+v+JIJzTDfVKoWgG5hVDcx9lVOhjKD4P8TolWmpzyyXDqUtj4GtQ0BW4V9YbOw53z7+u2wuwr/c/38R9h3I8hJz8l5WalZX91b+txSNzTrXcJRutd0C/uueKVZ6JfG1Jv61K+dkwVR8Jpy2DNU/Dml7z7RrbDin/BqGvCqS1oH93p3jb8KhjzAyjpG149ySjuDce9CVvecULMCsogvwxqVjvPl7Ye1j4D2z+If+7GWlj6F5j0m8DLliy2aymse8a7z5CLYf9fQFGsy1TjkN8JjnzcCYetWgb5XZ1r3RprW/p4vd6ufx5W/BsGx3h+C1tjBHYu8e7T42AYGSVoLFOZHJh0Bwz/Kqx62Pkz9j8LysY7obKtdRkBeZ2g3iXsd8mdMPq7+44TERERSYTX74JubD0s/TP0PDj4ekREOggFo0ksD8XqYIyZCjwKtD7CboE5wHKgDOekttZ3/PwC0MUYc4a1mRqXLxIwa2Hx7f765hY5d8zJ8XiaLj/M+cBkwc37tu1YCM8eCHmlMOU/0O+0xGpOVmPEOTC87K699/c4GCbe6j4ut9j9bkGRHdDYADm50dtFREREREREREREREREREREJFO8ATQCOUAPY8wx1tqXE5zrUpyANYBq4N0A6stkZ7bZvtvPIGvtImPM20DzFf6lwHHA4wHWJiIiIiJByy2AiiNg8EXwyb3hrLnoVqg4CkwulA5wgigyhW2If0zliU6IRrYpKIN+p+67v2yM83XtM7DG59v5um1QtRy6jAyuvmwW2eXeVtI/oWsSNtStjrq/V0Hqg7DyTfTAu0hjJOVr+1LQDQZ/Ed7/fkvQn5sts8OpKRV2fOjeNu7GYMOcwpBb6Lz+VBwRvb3PSYmHdi65Q8Fosre1z8buM+7Hqfk5ysmFHgc6j2h2LPQOu3jzQug5BToPDb62RG2dG7vPfv8v9XUEzRjoup/ziKXbBNj0evS2mjWwZ4MTAikiIiKSiMYG2L0SGvbAtvcTm2PlfTD+p1BcqcBWEZEEZNCnFrEZY8YaY15uerxkjIn7CIcxplfT2OZ5RqSi1jS7DhicwOPcNvNY4O9eCxlj+gEPs3co2uvAGGvtgdba86y1xwH9gG8CrY+2nwr8LIE/n0h2qlkHu6N/CLaP3tP9fRjZbX/v9vpqeO0sZ+10+Oj3+4aigXM3mRePch+XVwoF3d3b68K46a6IiIiIiIiIiIiIiIiIiIiIJMNauw1ofbXzT42J/4xvY0xf4Ps457RZ4IX2fENOY0xvYEKrXfU45+X5NaPN9onJ1iQiIiIiIRn0hfDW2rkYnhgGjw+GB3vAotudm4FngkSC0ToPC76OTDD4i/H1f3IULPldamrJNts9Aqz6Rgmj82FDXfTAr1CC0XIKou6P2LqUrx2XQRfE7vPpf+CjP6S+lqBZ6x7AA9kXiuZHzylQOijx8U+Ogi3tPdteYtr2Pjw1Ft77hne/8iOgdGA4NbXl5z3YE8PgscGw4ZXU1+OlagU8PwWeP8S7X0F36HNCKCWlTax/twX/F04dIiIi0r5YC/N/Ag91h8eHwFM+Alvd1FfDo33hsYH+goJFRGQvWRWMBlwBTAWOAuqstRvjncBauwEnnKt5nssCrC8jWGs3W2tXxPsAprWZ6hVr7fIYy90EdGu1/QYwzVq7qE1Ntdba3wLntRl/rTEmTUerREIW2em/b/lh/vp1HRu7j22AlQ/5Xzso1sKS38Y/LqcAckug0CMY7c04P9wVERERERERERERERERERERkXS5o9X3hwJ/imewMaYX8DjOeWrNoWq3B1Naxmp7UtA8a211HOPfaLM9Jsl6RERERCQsvafB/r8Ek+vdb8B5MOXfzg2pgxDZDnO/DSsfCGa+ZDXWxz+mvQaj9T8HxlwPJo5LwN77Bqx+LHU1ZYtXPcLPDrgt7ul2N1Sxs2F71LbeBf3ini9eeSY/6v56G0n52nEZ92Pof3bsfu9+DVY/kfJyArX8bve2yf8Mr44w5eTCUY8nHla1cwm8dDTU7Qi2Lske9dXw0jGwY4F3v7IJMOVf4dQUTc9D4CAfgY3VK+CV42H36pSXFJVthBknwOY3vfsVVcDUpyC3KJy60mXoZTDi6+7tH/0Otsx2bxcRERGJZtlfYf6N8WVDxLJ7Fcw8WeFoIiJxyrZgtNNbfX9vEvM0jzXAmUnM024YY4qBz7fZfVeMMcOBi1rtqgMuttbucRtjrX2Uvf/tCoEb46tWJEvF8+a3p89gNL93XVn0C/9rB2XLbKj+JP5xBd3BGOerm3XPOXe3EBEREREREREREREREREREZGMZq39H/B+06YBvmKMec0Yc4TXOGNMqTHmq01j9wds0+N5a+3rqaw5A7S97fjSOMcvizGfiIiIiGQqY2C/78A522Haq3DMizDtNTivCs7aCMe+Ameuh8Pvg0EXwNlb4IT34JytcOL7cOzLcMxLzrhjXoQeh8a3/vK/p+bPFS/bEP+YTkODryMTGAMTfgpnb4VjZ7T828ayLEP+LdOlMQK1W6K3dd0voaCYDXVrXdt6FfSNe7545ZuCqPszLhgtrwSOeBDOXAtjb/DumynPOX4t/Yt7W4XnYY7sVjYOTlsOJ83z/xzUWn1V5gRvSvhWPQp127z7VJ4AJ86F0gHh1ORm+JVwXjV0P8i7X2MEVvw7nJra2vSGEzjopaA7nLEWesb5PjAb5eTBgXdC6WD3Pl6hliIiIiLRxHNMpfl3pMPvj93XNsKs82D7h4nXJiLSweSluwC/jDFDgObbZzQCTyYx3RNAA5ALDDbGDLDWrkyyxGx3DtC11fY24OEYYy7A+Tts9rC19mMfa/2CvQPVzjPGXOUVqCbSLtTHEYzWfZK/fn4/jNuzwf/aQfn0f4mNK2wKRMvr5Pz5GlyeGjbOhE6DEltDRERERERERERERERERERERMJ0DvAW0KNp+zBghjFmPW1Cv4wxfwRGAJNxbrxpcALRDLAG+FJINafTsDbb8Z7f+Gmb7R7GmG7W2hhXoYqIiIhIxsjvtG/ITl4pFE3de19uIXQ/wPm+oFuUebrAcwf7X3fdc7D2mb33FfSA7hMhJ9//PMmy9fGPye8cfB2ZpKAr9DqqZXvkNbDkN+791zwO9budkKqOqLrtr0WtlCQWvLOhbnXU/YWmiLK8HlHbgpRvov8MRjItGK1ZcSWM+AYsuNk97HD1o1BfA3nF4daWqN2r3NuK+7m3tQcmxwlIa9bnZFj7lP/xC2+BsvHhv55I+m16LXaf/mc7QaCZIK8Ejn4WnhjmHei2YQbs973QyvrM9nmx+wz+EuTkxu7XnvQ6GpZ/Er1t2/vR94uIiEh2amyAxlonf6D5a0MtNDZ9bd5f0h+6jPL/PnP3GtixACK7YMvb/sZUngC9j23ZLukHu6MfO/hM/S6YeQpMutMJeXVT1Mv5s5SNd44TNuxx3teUDobiXv7qExFpB7ImGA0Y2/TVAkustVWJTmStrTLGLKHlLojjiP/Eofbmy222/+0jqOzMNtu+otOttYuMMW8DhzTtKgWOAx73M14ka9Xt8Nev3xn+P9QxBnIKnTfoXhojYG14B4ltI6z0kWwcTfMH8sZA7+mw5ono/SI+/z5FREREREREREREREREREREJK2stcuNMacAjwCVtASdVQK9W3U1wOWtvqdV39XAKdbazaEUnV5lbbY3xjO46RzJPUDruy52xblhqoiIiIh0JN0PdC6g9BOi0WzGSfvuKyyHIx+F8inB1ebFLUTJTd9TU1NHJhtyCXz0W+fcfTcPlsGUf8OAc8OrKxNYC29d6t4+4usJTfvq9mei7q8o6IMJ4VqN/JyCqPsjti7layesqNy5RmbVQ+59HuwGh/0P+p8RXl2JqN0CNWujt3Ud2/FCiIZ+Jb5gtKpl8PwhUFQBRz4GPQ9NXW2SGax1AjyX/tm7X36XzHudKuwOE2+Ft7/i3mfds/DBD2H8z8INddu11Lvd5DjvETqaoV+G5X+P3rb5Tdi+AMrGhFuTiIhIe9McSNY6jKxhz76BZG0Dy9z2N+5xDzXzCj2LJ0y/y0g4+jkoHejex1qY8y1Yckf8fydDv7Lv9vwfxx5X/Sm8epr/dSpPhE2vQn21sz3scjjw997BaiIi7UQ2PdO1frVZFsB8y2gJRkvsVh/thDFmKHBkm913xRjTG5jQalc98Hocy86gJRgN4EQUjCbtXWRn7D5FvZ0DovHILY4djAawbW7L3cBSbdMbULMmsbEF3Vu+H/8zj2C0XYnNLyIiIiIiIiIiIiIiIiIiIiKhs9a+Y4w5APg7zvli4ISetf661xCcQDQDvABcZK1dn/JCM0OnNts1CcxRw97BaJ0TL6eFMaYCKI9z2NAg1hYRERGRBBgDU5+BmafCtjmJz1O7yblg84w1kFsYXH1uGuO4yBXg4L+mpo5M1m28Ey400yMUrjECr58PPadASd/waku39S/Aptfc2/ueHPeUexprWLHn46htvQv6xT1fIvJMftT99Y2RUNZP2KF3w46FsHNR9PbGWph1LpyxGop7hVtbPN65wr3t8AfCqyNT9D8DDvoDzL4qvnF7NsKrZ8AZqyAn+v9paSc2vwFzrvXuUzYeJt8LBV3DqSkeQ7/sBE+89033Pgtuhu6ToP9Z4dVV5RGMVjoQJt0J3Sa492mvyqc4QSTL/ha9fcYJcPrKcEPsREREguIaSNY2QCyeQDKPsUEEkmWKnUtg1nlw/NvufT75R/yhaEUVMOYGGHD23vvHXA/1VbD0L/5yJfxa1yaofelfoGwCjIjz9zERkSyUTcForU/K2RHAfK1fSboEMF82u5SWu2sCzLHWvh9jzNg22/OstdVxrPlGm23FrUv75/UGduyPnDfB/c+J/4OcvGKIbI/db/VjIQajzUp8bOsP5bqNhy6jo38AVl+V+BoiIiIiIiIiIiIiIiIiIiIiEjpr7QbgZGPMJOCbwLFApUv3HcBLwJ3W2pkhlZgp2gaj7Ulgjhqgm8eciboKuDGguUREREQkDCV94MT3nHCPp8dD1fLE5qnd4gRO9T0l2PqisQ3x9S+qSE0dma7vKXBeFdzv8XbfNsDK+2HUt8KrK91W/Me9rfyIhKacXzXbta1XQTihc/mmIOr+iK0LZf2E5Xd2LkJ/wOPyPVsPqx6EEV8Lr654bXw1+n6TB52HhVtLphh+JQz7KtTvcl5jGuudUMI3vuA9bs8GWP8y9Dk+nDolPbyei8EJ8Br59XBqSdTIq53wtpeOdu+z4j/hBqPtcglGG/lNmPSb8OrIRGN+6B6Mtns1bHsfuk8MtyYREcluvgPJ2gSIxRNItk97OwkkyyRb3oGa9VDce982a2HRrf7nOn0lmFworoweuJqTCxNvhQm3QM1a55jMa2cnd7MCN5/+R8FoItIhZFMwWus7HgYRZNY6aC3OT0zaD2NMLnBRm913+Ri6X5ttj6j5qJbFmE+k/Ym4ZDr2nALjb0p83txif/2qP018jXjtWBh9f+Xxzt1+HunjPrbtLxZdRkYPRovsSrw+EREREREREREREREREREREUkba+17wIUAxpghQH+gB1AAbAY2AAustY1pKzKz2JDGiIiIiEh7llcKo74N7yYRPDT7SudG2L2mQkG3mN0TFk8wWu9p0S9G7SjySqHrfu7n8APMuRbIgYKuUHEkdBoSWnlpsf5F97buByY2Zd0q17ZBxSMSmjNe+SY/6v5GGmmwDeSa3FDqSEh+Z+gyCnYudu/zyb9g+FWZ9/NsLWx9F2o3RW/PLYKcbLpEM2DGQH4X5wFgjgQMMQ9LzLkGSu6HsnGprlBSbfdqWPmA8zrUaWjLdW4f/8F7XOX01NcWhLLxkFMAjS4hlKsect4b5UR/jg6UDq4q4AAAIABJREFUbYx+jR041yd2dCX9oaiXE74Yzcd/hAPvhNzCcOsSEZHUsha2fwCb324JEdsnyCyOULPW7Y2RdP/pJCg16/bNL2iohYW3wI4F/uaoPAFK+/vrm5Pb0nf6q/DCEbBtrv96/dj0Oiy+Y9/9jbVNNTS958krdt4rlo0Ndn0RkZBk01G3za2+HxjAfANafb8lgPmy1QlA61uT1AAx4vgBaHsri5Vxrts2oamHMaabtXZbnPOIZI/Izuj785PMevQbjOZ24DNokSpY8c/obWXjnQOMXrq2+VAjr3P0fvUKRhMRERERERERERERERERERHJZMaYzsDgVruWWWurW/ex1i4HlodaWOararPt8wQhzzFt5xQRERGRjmjQ+TD/RqjdHLtvNLtXw2tnQVEFHPUU9EgsZComW++/74irU1NDNhlxNcz+qnefOdc4X00OHPg7GH5l6utKh1WPQM0a9/ZhlyU07aa69a5to0omJDRnvPJMgWtbxNaRaxL51TFEI6+G2Ve5t295C965DA76U+YEjTU2wLtfh6V/cu8z4hvh1ZMNSvrBgHOcoCwvOxfD0+OdwM6Jt2ZeIJ74s/IBmHVe/OMqT4AuI4OvJxUKu8PgC2HZ39z7vDwdjnzMCSBNpQ9+6N7Wue3lvh1QTq7znDzv+ujty/4K296HqU9BUXm4tYmISGrYRuf9+sd/THclkuka9uy9vWejE1a26yOfE5jEf/fLK4WjnoDnDvE+XpGI5mM9foz+Duz/C/3uJSJZJyfdBcShOXjLAOOMMT0Snahp7PhWuwJ+Bckql7bZfshau93HuLI22xvjWdRaWwW0eQdB0kd+jDEVxpgx8TyAocmuK+JLyoLRivz12/IO1Nckt1Ys9dXwzET39q77OR+kehlw9t7b+W7BaDpXU0RERERERERERERERERERCTDnQ/MbXq8AxSmt5yskcnBaH8Axsb5OD2gtUVEREQkWQXd4Li3oO+pUNS7ZX9usXNeevMjlj0bYwdxJcM2+Ot3+APQ79TU1ZEthl8BB//ZX1/bCO9+A3avTW1N6VC/2wnuc7P/L6Hr6ISm3lgX/e/r8K7HkxPrGomA5Ofku7bVN0ZCqSEpw690Qs+8LLsL1j4VTj1+rHvOOxQNYKxLAE9HNvmfMPIaKB0cu+/i22DTrNTXJMGLVMGbF8U/rnQQHPFQ4OWk1EF/hN7HubdvnAlLfpvaGnavgYW3uLd30iWyAIz5gXf71tmw4OZwahERkdRb97xC0cSfxtq9tz/8mf9QtJ6TnfevfU9KfP2SvnDCbBhwHhSW730MrvmRk+KP0RfdCpvfTO0aIiIpkCG3T/DlLaAWKMAJR/sa8JME57qKllC4euD1pKvLQsaYcqDtJ0B3+Rzeqc12ImlLNUDrT8xc0o/ichVwYwDziAQvsiP6/vwkMwGN+4db+5h1jnNXg1RZ+QBULXVvL2vKpKw8EdY9s2/7wM9D6cC99+W1fbppEtmVWI0iIiIiIiIiIiIiIiIiIiIiEpaeOOf7Acy21m5NZzFZpO2JRuXxDDbGdGLfYDQ/N0yNyVq7kThvpGp053ERERGRzNJ5KBz1uHefxXfAnGu8+2x9D6pWQKdBQVXWorE+dp9eR8OAc4JfO1sNu9wJRXl5Wuy+tgHWPOYEVbUnG172bh/0hYSmtdayMbIuatvg4hEJzZmIfFPg2haxdaHVkZThV0BhD5h1rnuflQ9CvwzJ1171oHd7UW/IKwmnlmySWwiTfu08Ft0Oc7/t3X/VQ1BxRDi1SXDWvwgNCVxSevRz2fdzk5MHh98HD3Zz77PqQRh3Q+pqWP2Ye1thORQkeX1ie2GME8y45DfufVY96Dw/iYhI9ov1fl3CZ/Kc3weag75ah37tta8Qcor27btPu58+rfY/3AtslGNKDW2C0Vb5DOrtdgAc90byfy8AxZXOe0ovT42DHR8Gs140Kx+E8impm19EJAWyJhjNWltrjHkNaD5Cf50x5hFr7fx45jHGjAW+A9imXa9ba6sDLDWbXAi0TlRaBsz0ObZtUtGeBNavAVofDXJJPxJpJyI7o+/P75LcvI1x/PitfRrm3wSjv5Oag8jrX3RvK+kP3fZ3vh9+Jax7lpanYqCoAqb8e99x+S6ZifVB3cRWRERERERERERERERERERERFKkOeDLAqvTWUiW+bjN9sCovdy17b/VWrstiXpEREREpKPxG1Az6zyY+AsnpCxItiF2n24HBLtme9B9EuQW+wuqWfBzGHo55OSmvq5Us42w+nGYdbZ7n05DnYuQE1DVsIM9jbujtpXnJzZnIvJMvmtbxEZCqyNpPSeDyXH+3aJZ8S8o6ec8r/Se7oTchK1mHax6BJbf7d2v/PBw6slmfl5PltwBZeOg/9lQUJb6miQ5jQ2w+lGYlUA4aXEldBoSfE1hKChz/p9ud7mcefs853nN5AS/9o5F8O7X3Nv1XLS38sO9g9F2r3b+HcvGhVeTiIikxpZ3011B5ogWSLZPkFg8gWQe4WZec6T7GENuUfT8geYshl1LYcV/oWatv/mGXhpcbX70np7aYLQlv4aBn4eeB6duDRGRgGVNMFqTX+EEo1mcEK1njDHnWGvf8jPYGHMw8CBQinMXSts0Z0d1SZvtv1trbdSesSUyLtG1RLJTqoLR4r27xvwfOx/6Hf0sFMV1M9kYddTCiijBZs0GX9RycLffqXD4/bD411CzBiqOggN+Hf3gb55LMNrmN5OvWURERERERERERERERERERERSaV2r7wvSVkX2WdRme1ic49teYbowiVpEREREpCPqfgD0Ocm5MbeXrbPhpWNgzA9hws+CW9/Wx+4z8pvBrddeFJTByKth4S9i9929ygkSO/xByMm2y8tasY3w+vmw8n7vfmOvTzhga2PdOte2ioI+Cc2ZiHzj/mt1va0LrY6klfSFIV+GZX9177PwFucx8hqY9OvwagPYsRBenuaEo3nJLYbR3w6npmzW/UCoPAHWPevd7+2vwKJfwTEvQUl4P1cSp8YGJxBt9aOJjd/vB9n9mjPmh/D6593bN82CiiODXXPNkzDrXO8+o68Lds1s1/dUKJsA2z9w7/P0eDh2BvQ6KrSyREQkYLVbvZ/rw+IrkMwjZMxP2Nhec2RoIFmmyC2MHozWUAsbXoGZp0Vvj6Z0MAz6QrD1xTLiKljxD6jdkro1nj8UDvo9DL8ydWuIiAQoq44iWGufN8bMAKbihGr1AV41xvwT+DMwu22wlzHGAAcCVwBfAvKbxlrgNWttjE9p2idjzKHAmFa7GoB74pii7St+cQJltB3j812Epz8AD8Q5ZijwWABri3hzDUbrmty89XEGowFsmwOvHA8nzklu7c9qqIaHe3v32e97e28POMd5xJLfyb1t11LoHO95nyIiIiIiIiIiIiIiIiIiIiISkta3tB6ctiqyT9tbgY83xpRYa3f7HH9YjPlERERERGI74hFYdCusfxE2zvDuu+BmGPoV6DQomLVtg3d7UW8o7R/MWu3NhJ9Dp6Gw6mGoWQvb57n3Xf2YE1bU95Tw6gvahpdjh6KV9IchFye8xMbI2qj7i3KK6Zyb5PUgccjPcQ9Gi9hIaHUE4uA/Qd1WWPWQd78lv4Fhl0HX/cKpC2D+T2KHopX0c54jexwYTk3ZzBg48jFY9EuYd4N3352LYckdMNFHuKOkx7pn/YeilY1v+b7TEBh0AQyIEfCV6QZ+zgnkfOOC6O1vXgynLw9uPdsI730LGva49+l/NpRPCW7N9iC3AKa/6jyfL77Nvd/c78AJ74RXl4iIBGvhLd7tFVO9Q8ZyiuIMJHMJN1MgWWbJKYy+v7HWee33E4rWZTT0ng5jf+gE0Iep8zCY/iYs/hUs/UvL/rLxex/f6ToWTE7Lttexn31YmPtdGPRFyO+cdMkiIqmWVcFoTT4PzAEqccLN8oCLmx7VxpglwLamtu7ACKA5Vcc07TfAKuC8EOvONF9us/2MtTb6kfroMjIYzVq7EdgYzxiT4B1fROIW2RF9f36X5OZt9Di46WXbXFh0u3OnrGi/eO1eDVtmQ+0mKCyHvFIwuc4b5dqtTp+cfOi2v5MO7PXLwJT/egecefEKjlv/ooLRRERERNKlvsa542txX+g8NN3ViIiIiIiIiIiIiIhIBrLWfmSMmQeMxwn36mutXZPuujKdtXZdq783cM6TPBx43ucUU9tsPxNQaSIiIiLSkeQWOBeBjv0hvHUpLL/bo7N1wlKGfzWYtWMFo/U+Nph12iNjnCCpYZc52ysfhFkeQTRrnszuYLQ1T8XuM+rapJbYVLc+6v7y/MpQr0nKN/mubZHGutDqCITJgQPvjB2MBs6/cVjBaNbC2idj95v+OpQOSH097UVuAYy9HvqdDk+P9+679kkFo2WyNT5+PgBGXwcTb01tLeky6Hx49+tOuGNbe9ZDQ53zfz4IOz+CqqXefQZ+Ppi12pv8LnDAr5wQWLe/w62zYc9GKKoItzYREQnGptfd285YAyV9wqtFMkduUfT91Z/C1vf8zXHyAufYSrp0GQ4H/9l5+LXoNph7nf/+9VWw8VXoe3L89YmIhCzrgtGstRuNMScAjwODcILOwAk76wRMarPvs6G0hKItBU5vCtHqcIwxpcDn2uy+K85p2iY8lcdZQyf2DUbbHmcNItklsjP6/mSD0eprEh8799vw6X+du6+0/iVv/k9h/o+Sq6u1ZMLLenrctaJuW+LzioiIiEji1r8Ir57ZEo7b9zQ4/D73A8giIiIiIiIiIiIiItKR3Qn8FefcvZ+w7009JbpHaAlGA7gEH8FoxphRwCGtdlX7GSciIiIi4qn3cTGC0YDZV0J9NQz9MhSUJbdeY713e+Xxyc3fkVQcBTkF4BactfTP0OsYGHheuHUFYc9mWPKb2P0qj0tqmY2RtVH3VxSEe6F9DrkYDPazy+Za1NtIqLUEoqg3lI2D7fO9+73/Xeg6OvUBfnXb4YMfOM9jXrqMgpL+qa2lveoy2rkZb41HZv6OhTDjVCifDCOuhvxO4dUnsS39k79+/c9JbR3pVjYeNs7Yd39DDXz8Bxh1TTDrLLjZuz2nACqmBrNWe1V5PHzsES733CEw4WYnYC6dASgiIhKfrXNg8xvu7cWV4dUimSWnMPr+eTf4Gz/g3Ox8T1B5HMyNc8zMU6D3NCjp57x/V0iaiGSonHQXkAhr7Yc4AWj/oyXszLZ6fNaVvQPRGoF/AAdZaxeFWXOGORfo3Gp7A+Azrv8zH7fZHhjn+Lb9t1prlXAk7Ze17sFoBV2Tm7txT3Ljt74Lzx3Usr3+pWBD0QA6DU58rFcqd0OSf3YRERERiV99Ncw8vSUUDWDN4/Dh/6WvJhERERERERERERERyVjW2ruAp3DO4bvYGPPdNJeULf4NNLTaPssYM9zHuO+12b7fWquTbEREREQkOf3PhPLDY/ebex28ciJEqmL39WIb3Nt6Tm7/gStBKiqHMT/w7vP65+ADnxcJZ4o9m+H5ybH7Df0KdN0vqaU21a2Lur88P9yL7Y0x5Jn8qG0R6xJ8l8mMgQk/d4J9Ypl5Kiz2EYKXqLod8PwU+PiP3v1y8p2as/FC+UyQkwf73wImxiWta5+ED34ILx4J9TXh1Cax+f0ZHHAe9Dg4tbWk2wG3ubfN+ZZzbV6y5v0IVvzTu8+YH0BRz+TXas9GfcsJZHRTvQLeuMAJxhQRkeyw4RV4dpJ7+8hr9H69I8t1CUbzK9bxk0xVNg6GXBr/uPUvwvJ7nJC0JXcGXpaISBCyMhgNwFq7zVp7AbAf8Gug+fYQps0D4APgV8BIa+3F1todYdebYdrecfMf1toYt9PZR9tguWFxjh/SZnthnONFskt9FUS5Kw8AeV2Sm7vrOPe2aTOh1EduYc1aWPQr2L0W3vtmcvW01W1/KOyR3Bxud/Rq0AccIiIiIqFb+ww07N53/5rHw69FRERERERERERERESyxfnAIzjn9P3cGPOcMeboNNeU0ay1HwP3ttpVANxjjClyG2OMOR24uNWuOuCmlBQoIiIiIh1LbiEc8wIcFCM0CGDLW7DgZ8mt53WJyzEvQV5xcvN3NONuhOFXevdZdCvUbg2nniAsvxuqlrq353WCKf+Fg/+S1DLWWjbWrY3aVlEQbjAaQL6JHiIWsZGQKwlI35PhuLdgv+/H7vvhT1IXkrXiX7Cz7aVybYz+Lkx/A/qfkZoaOorBX4Rps2DIxbH7bpsLKx9IeUniQ8MemBcjQDOvMxx6Lxz23/YfRtL9AOg61r39w58mN3/tVlj4S+8+Rz3pvL6Lt85D4fh3YvdbfDvs2ZT6ekREJHkfxjjeMOqacOqQzJTj+jGqt2GXw2nLnEyEbHXI32DKf5yA+KGXwWH3wfRZ/sd/eBM01KauPhGRBOWlu4BkWWs/Ar4NYIzpBPQCmhN4NgMbrLXVaSov4xhjRgBtb9NzVwJTfdhme7wxpsRaG+Xq+KgOizGfSPsS2enelp9kMNqY78Os8/bdP/xrUHEknL4CXpwKG2d6zzP3O84jaEc8lPwcuS4fXDfoZrYiIiIioVvicse37fPCrUNERERERERERERERLKCMebvTd/uBHYBnYFpwDRjzC6cG59ubGrzy1pr294gNFTGmH5EPwezd5vtPGPMIJdpqqy1mz2WuRE4E+jWtD0FeNEY8xVr7eJWtRQClwO3tRl/m7X2U4/5RURERET8yy2C4V91gk/e/KJ33yV3OOezl/ZPbC3bEH3/iG8oFC1RI6+Bjz2C7RprYfObTlBVNtjwknf7sS9Dj4OSXmZnw3ZqbfTrFsoL+iQ9f7zyTX7U/ZHGupArCVD3ic6j4kiYcZJ7v7ptsP0D6Hlo8DVseNm7vcfBMPEXwa/bUZVPdv4dVz0Kke3efTe8DEMuDKcucbftfaivcm8/ZTF0GRlePZlg2GXw3jejt216DRrqIDd6mGVMm990Xpdd174ie16vM0FJHxjwOVh5n3ufxjrY/Ab0Oz28ukREJH6NEe9r5XMKoSTB4xDSPuQWxte//9lwxIOpqSVsxsCg851HaxN/BXOviz2+dgtsnw89DkxNfSIiCcqaYDRjTG/g4Fa7Zllr97oVibW2CqgCloVZW5a5tM32LGvtkngnsdauM8bMA8Y37crDCVx73ucUU9tsPxNvDSJZJeJx3maywWiVx0OnoVDV6qkvtxiGtjr3c/R3YgejpcKkO6DTkOTnyXVJaFYwmoiIiEh4ts+Hdc/BptfTXYmIiIiIiIiIiIiIiGSXiwHbatsCpun7Lux7o89YTNMcaQ1GA2YBA3306wt84tJ2L87fT1TW2tXGmLOA54DmKykPAxYaY94DlgNdgQOA8jbDnwRu8FGfiIiIiEh8Ko+HnHzngmQ3DXvgmf0hr5Oznd/ZCT2a8H9Q0M193GfjXcJATNZcBpV5Og+HLqNh5yL3PjNPgWFfdf6dCruHV1s8Irtg3g3OuWxuSvpBtwMCWW5T3TrXtor8ykDWiEdeTgFEyQ2stx4/j9mi4ijI7wqRHe59np8MJQOc63B2fNiyv6gCJv8LKqfHt+bqx53AwHXPevfrd0Z880psxkC/0+CTf3j3++ReJ5Sr8jgYd5PCMcOyZzPMu94J6Kr+1PvnsstI6DwivNoyRb8z3YPRbCPcVwilg6BsHIz6FvQ62t+86190Xo8919ZzUtz6ne4djAbw6hnOa0zpABhwrhPIa4z3GBERCdfG19yD1MF5f2lywqtHMk9OnMFoHSEUte+p/oLRAN66CE78AHJ0/E1EMkc2vbKfBTzS9Pg34BF5LtEYY3KBtrdIuCuJKR9ps32JzzpGAYe02lWN/0A1kezUsNu9La80ubnzu8C0mTD4Qicgrc8pcMxLzh1zmvU9GabG+KAmaAf8GkZeHcxcuS4fXDTUBDO/iIiIiHhbdjc8MxHmfse7X2N9OPWIiIiIiIiIiIiIiEi2s60e4sFaOwM4E9jUarcBDgTOA45n31C0/wKft9br6hARERERkQQV9YSJt9OSd+yibivsXuk8dixwwodeONw7UK3Zumei78/JjbtcaWIMHPg7yOvs3W/pn/z/O4XNNsJLx8KSO7z7HfTHwP6vbIpED0YrzimhU26XQNaIR77Jj7q/XQSj5ZU4/0djBSDuXrl3KBrAno3wynGw1iMwr62VDzkhOLFC0XpOhuFf9T+v+DfuRuc6qFi2fwCLbnXCoqwOJaVcQy28cBgs/TNsn+cdigZw4O87ZnhUaX+Y8HPvPtUrYM0T8PJxsOGV2HOuex5eOcG7z6AvQe84QyAF+p/lL/hk90rYNMsJvfvgB6mvS0RE/KvdAi8f695eWA7jfxpePZKZcov89+1zihOG2t51GQFjf+Sv746F8Ha6700mIrK3bApGK8P51MQAs6211WmuJxudBLS+Hcku4IEk5vs3e99n5CxjzHAf477XZvt+a+2eJOoQyXz1XsFoJcnPX9IXJt8Lpy2FqU9A+eR9+/Q5Hi6wMOzy5NeL5YDfwKhrgpvP7ReRBj11iIiIiKRcZBfMucb7rirN6qtSX4+IiIiIiIiIiIiIiGQjE+Cjw7HWPg2MBf4EbPPo+hZwjrX2Ap1jKSIiIiIpNfLrcOJcmHgbFHTzP27HQljzlHef3Wvd22IFJom33sfAyR/G7rdzkRPkkmk2zICts737HPkY9D0lsCU31kX//1he0AeThiCgfFMQdX/E1oVcSYoM/iKcND/x8Ytv89930a3EzGsf8wM49uX4nufEv05D4ITZMOU/0OOQ2P03vAxb30t9XR3d6sdg10f++nabCL09AkrauzHf99fP1sOi22P3W3y79/nanYY61w8qKDZ+uYVw+EMwNUYYZmsf/d4JChQRkczwyb+8209ZBF1GhlOLZK7cwth9Rl8HU5+GIx+JL0gtm42/CY57G/b/hfN7rpcV/4Lda8KpS0TEh2z6RGBr01cLRL/dhsTSNp7zf8mc/GSt/dgYcy9wadOuAuAeY8yxbkFnxpjTgYtb7aoDbkq0BpGs0eASjGbyICf6HXtSZvjXYOlfUjf/2Bth1DeDnTNHwWgiIiIiaVH9KSy+AyI7/fWvr4KCstTWJCIiIiIiIiIiIiIi2WZwugtIBWvtoJDX2whcaYz5JnAYMBDoDVQDa4C51tpPwqxJRERERDq4bhOcx8hvwJP7QdVSf+M2zoB+p4HJAWuhbbiUV/BVYc+Ey5UmpQPggNthzrXe/TbMgD4nAwZyo4dxhW7jq97tJhd6Tw92ybrol7BV5FcGuo5feSb69ScRGwm5khTqOgrGXA8Lfhb/2PUvNF1nEiO0rrEOtrwdYzIDo7/TcS6UT5eCbjDofKg4Ah7tH7v/5jegx4Gpr6sjW363/74dORStWe9psP7F2P02ztw7ZMvkNYWgNQc0Wue118vwq/Z93yT+5eRCn+Nh0h3wno9rH+t3wa6PoWxs6msTERHn+EBjhM9eG00u5LSKQtn8uvvYXsdCYY+UlidZIidGMFpxH5h4azi1ZJqeBzsPcN6HLvxF9H62ETa+BoM+H15tIiIesikYrfWR5NK0VZGljDG9gJPb7P5bAFPfCJwJNN/6YgrwojHmK9baxa3WLwQuB9reeuM2a+2nAdQhktnqXYLR8krCrQOg23g48lF49YzYfTsNhaFfhg880n97TnE+WCjoBhN+DsMuD67WZnnF0fc31AS/loiIiIg4Hygs+D+Yf6NzQNOvyK7U1SQiIiIiIiIiIiIiIllJ54cFy1pbB7yS7jpERERERD6Tkw8TfwmvneWv/5I7YMlvcS52NlA6EIZdAft9zwn72PWx+9jK44KoWPqfDXO+TUsYSxQf3ek8ADoNg5FXw4ivpyeQZd0L8P73YNtc7359T3O/9iBBmyJro+4vL0hPMFp+TvSQunpbF3IlKTbwvMSC0QDuC+j/QOXxulFsmEr6tVyf5OW9b8Lu1TDh5r2DMiR59bth9pWw7ln/YwZ8LnX1ZIsB5/kLRqvfBfclE7RoYMA5SYyXz/Q/G+Z8y9858k+Pg8n/gMFfSn1dIiIdkbXO8YE51+zblpMPPQ6F8T+BhbfAuufc5xmo9yTSJFawtd6/OgZ8zj0YDeCN853jMEO/AmOvVziviKRVTroLiMNcWo64j0hnIVnqIvYOwvvQWvtOspNaa1cDZwGtj6AfBiw0xsw2xtxnjHkWWAX8Fmh9a5IngRuSrUEkKzS4BKPlpiEYDaDf6XCBhaOfj97e/yw4ayOcthT6nuo+T5fRMH0WfK4GztkKw69IzZvbHJdfRBr3BL+WiASv+lOY+x2YeTosvNU9LFJERDLH5jdg3g3xhaIB1Felph4REREREREREREREREREREREclc/c90brLtm235Wr0CPvh/sPg2Z9eupe7DysYlWKDspXQAHPJXMLn++lcthfeuhmV3pbauaLbOgRknxg5F6zoGJv0m0KWttWyqWx+1rSI/TcFoJj/q/khjJORKUqxsHEy8DUjTxdedR8CBv0vP2h3ZIX9zwjJjWXSrc32CBOvNL8En//Dff8LN0H1S6urJFoMvgkEhhGYd8lfn9VuSV9IXDrkLjM9wxTcvhNWPpbYmEZGOaulfooeiATRGYNNr8NLR3qFoAIMvDL42yU45hd7tY68Pp45M130i7O8RjAaweyXM/xEsuDmcmkREXGRNMJq1diXwFs4RzZHGGIWjxeeSNtuBfRphrZ0BnAlsarXbAAcC5wHHA+Vthv0X+Ly1tiGoOkQymlsIUF6agtGaVU6HkxdC5QnOdnElHP4AHP4gFDX92HrWaJ0gtFgJyslym79BwWgiGW/XMnjuEFj0K1jzOLz/XZh5inNgSkREMteK/yQ2TsFoIiIiIiIiIiICzuejkZ3O3X1FRERERERERKRjGPN9OHM9HH4/HHoP9J4e3/iP/+h8dQtGG/61pMqTNoZ+Gc5YAwPO9T/+PHKwAAAgAElEQVTm4z+krh43S/8KsS49GvQlOGFO4IExOxu2UWujX7NQUdAn0LX8yjMFUfdHbF3IlYRg9LVwxirnGpdD7wlv3WmvwUnzoPPQ8NYUR9fRcPIiOPYV6DLau++yu3RNUZB2r4VVD/vvf/pKGPP/nGvKOrrcAph8r/N/d/K/4JC/B7/Gmeud120JzpCL4cw1cMTDzmtMTvTg0c98lIb3QCIiHUEQv2NOnwW5McKwpOPw+r9w8J+hsHt4tWS6/b4L/c6I3e/jP+r8LxFJq6wJRmtyq8v34sEYcxgwqtWuOuBfQa5hrX0aGAv8Cdjm0fUt4Bxr7QXW2uogaxDJaA0uwWi5xeHWEU3X0XD0M3CBhTPXwoBz9j4wnRsjGC0Mbn9PDTXhrC8iifv4D7Bnw977NrwCG2empx4REfFnwyuJjYvsCrYOERERERERERHJPgt/CY/0hQe6whMjYNOb6a5IRERERERERETCUtzLCdoachFM/GV8Y6uWQ8062DQrenvnYcnXJ3sr7gVjrvfff9tc2DoHts9veVQtT90FsvXV8Mm9sfsNu8wJpgnYxrp1rm3lBZWBr+dHvoke3FJv2+lNq0v6Ote4DLkIRl6T+vUqT4SKwxWskE55xdBrauzXkPpdsPpxhaMFZd0z/vv2nAyl/VNXSzYyBrqOgsFfgKGXQLeJwc3d4xDn9VqCV1QB/c90XmMaY7yOrn8e6naEU5eIdAw1653fp3Z+BFUroPpTaIwRCN3eNNTC9nnJzWFyoMuo2P2k4yjxCEwvGx9eHdmi8vjYfWrWQO2m1NciIuIiq4LRrLWPAn8HDHCKMeb3xpi8NJeV8ay1r1trTatHobV2cwrW2WitvRLoDRwDXAL8P+Bq4GxgiLV2srX2oaDXFsl49W7BaF6hYxnCK7zNNoZUQ1H0/foAQyTzLb49+v4V/w63DhER8a+hFnYuSmxsfVWwtYiIxKt6FSy5Exb8HLZ9kO5qREREREREOp5VD8P734PIdme7ainMOEmB+iIiIiIiIiIiHVHZBCgbF9+YR/pAY230NgWjpUbZOOffyq9nJ8HT41sejw+FR/snfjPOaBpq4a1L4IGy2DdTLx0M5YcFt3YrG+vWRt1fnFNKaU7nlKwZS76JHgAXsXUhV5IGg78YwhoXpn4N8af3cU5okZfXP+c8T8z5dscLEglKQx28fRm8/RX/YwZ/KXX1tBdBPpfo7ztzPFgGM0+HyM50VyIi2azqE3jmAHik0vl96smR8PhgeGwQ/C8Plv413RWG56PfJT9Hn5OhsEfy80j70fcUyIkSKN55uBM4K3sbcC7k+AgG//Cnqa9FRMRFVgWjNbkCuAMnHO2rwPvGmEuMMXrXkiGstXXW2lestfdYa2+x1t5prX3YWvtJumsTSZsGl2C0vCwIRsuEGhWMJtL+rHky3RWIiEg0tVucD1cSpQtcRSSdNr8Dz+wP710NH/wAnj0Qlv8j3VWJiIiIiIh0LPN/su++yHZY8Z/waxERERERERERkfQyBo58NP5wNDedFIyWEsbAkQ9DWRLnjdWsgVdOgJoNwdQ073pYfg/Yeu9+nUfA1KfApObyuE2RdVH3VxT0wRiTkjVjyYt2gTcQaYyEXEkadJ8Eh94NuSm4xiUnH8beCAM/F/zckpjcApj6LJQO8u7XWOvczH3JHaGU1e58eBMs+5u/viYXRn4Lhl6e2pragxFfdx4mN/E5mv++h301uLokeWseh3f0byIiCbKN8PI02DbXvc87l8OGmeHVlC5Vy2HudcnPc8hdyc8h7UvnoXDEw1DQvWVfl1Fw1BPO8Q/ZW2EP57hKYbl3v49+B1s9nrtERFIoL90FxMMY83KrzV1AZ2A/4G9N7auBjU1tfllr7bGBFSkiEo3bXYpS8YFM0Fw+OAPA2nBqyC2Ovr9mLexYCF33C6cOEYlP9Sr3toJu4dUhIiL+LbkTdn2U+Pj6quBqERHxY+0zMO9G2Dp73zZbD3O+5dzFJs/l90oREREREREJztpnYfsH0du2vAPDrwi3HhERyRjGmAsDnM7inB+4A1gPLLY2rBNYREREREQkbp2GwIkfQPUKePNC2DQrwYkMdBocZGXSWqchcNIHULUCaprCwF6YEt8cjXXw6X9h1DXJ1WKtE4oWy7RXofzwlF7YvLFubdT9FfmVKVszlnxTEHV/xNaFXEmaDLkYBn0Btn8IDXtazglqqHWCJhKRkwddx0BeFlzf09F0nwinLYfHBsDu1d59P7kXRl8bTl3tiZ/nW4Dj3nJ+TvI7pbScdiMnDw68Eybc7Fz31vz89O5VsO396GOmvQqm6XJvk6O/70y26iGIVOnfR0Tit+19JxAslk/ugV5HpbyctPrkn8nP0eNQKIoR5iQdU99T4OxNsGMB5JVC6WCFonnpfSyctR52LIKnx7r3++Re53c0EZGQZVUwGjAV58SmZhYwTQ+A/k0Pvyc6mTj6iogkrn539P1Z/8FJSE+hXn9PT42BM9dDca9wahERfxob4AmPO/MpGE1EJHPU18Cm15wP0T+8Kcm5FIwmIiHa+CrMPMX7pMa6rc5J1ZXTw6tLRERERESkI6rfDW9/2b193bPh1SIiIpnoHlJ3kkm1MWY2cC9wn7W2NkXriIiIiIhIokxTqNnwKxMPRsvJh9zCYOuSfXUa5DwA+p4Ka56Ib/z8m6CkPzTWQt02yCmA4sqmC9Z7+ptj2/tQu9m7T1FFykPRADZF1kfdX16QzmC06De+r7eRkCtJo5x8XYzdkRgD/c+FJb/27rd9HuzZ7P+5pqOpXgm7PoZuE6H6Uyesq2at84hl7A3Q85DU19ge5Xfe++/ugNvhpWP27ddtf6g4Iry6JDmNdc7Pk16LRCRe1Sv89Vt+D/Q5BToPh7KxzrU+7c32D5OfY9hlyc8h7ZfJgbJx6a4ie5gcKBsDQy6B5XdH77PkDqg40gnw7TxCYXMiEppsC0aLRsFmIpL51j4dfX9ulgejJXpHnXh13c+7fdnfYOwPw6lFRPxZ/7xzsN+NgtFERDLD9vnw8nTYs8H/mKJezokR0S5mjewKrjYRkViW3Onv99J1zykYTUREREREJNU+/a/3xTORnc7vcO3xhF0REYlHKs6O7oRzw9WpwM+NMZdYa59PwToiIiIiIpKsfmdCYTnUbop/7MDzg69HvA27PP5gtMh2mHVO9LaJv4JR17pfOGsb4f3vw6JbY68z9PKUX4BrrWVT3bqobRUFfVK6tpd8UxB1f8R6nLctku2GXgIf/RZsg3e/h8th/1/Cft8Jp65s0BiB2VfCsrsSn2PwRcHV09FVHOWESOz6aO/9w65ITz2SuJknw+mrICc33ZWISDZpjCPMuPn3qu4HwVFPQHGv1NSUDtbCqgeTmyO/Cww4N5h6RKTF0Mvcg9EAXjvb+Vp5PBx+v/OzKCKSYtl4xqkJ8CEiknqN9e5J3nlZHowWltKBUDbBvX3e9eHVIiL+LLnDuz2sYEUREfH2xhfiC0XrNBRO/MC5g1k09VXB1CUi4offD0TrtqW2DhEREREREYFP/+fdXl8F1Z+GU4uIiGSq1ufr2VYPL9ZH3+b9BqgEnjHGfC2JOkVEREREJFXyimH6a1B+WPxjJ/0m+HrEW99T4NC7oXRQMPPNvQ62zHZvX/1Y7FC0wh4w+rsw7sfB1ORhR/1W6mxt1Lby/MqUr+8mz+RH3R+xcYQsiGSbsnFw1FPQZXTsvu9/F1YmGbTRniy/J7lQtGkzofPQwMrp8EwOHPsy9J4OOQVQXOmE+SkYLfvUrEs+1EdEOp54gtGabZ0Nc64NvpZ0Wv+Cd/v+t8CgL0Fep+jtxZUw/Q3365pEJHHlk2HIpbH7rXsOPvy/1NcjIgLkpbuAeFhrszHITUQ6uk2z3NtyszwYrauPDxWCUjYWtn8Q3noikrjVjzm/2HpRcI6ISPpVrYDt8/33n/BzGHaZc3KZ2wcMen4XkbBEdvrvu+FlqK+GvNJ92+q2tVyYX9AdSgcEU5+IiIiIiEhHsmej87tXLNvnQ6fBqa9HREQy0SVNXzsDPwJ64ASZNQJvAbOBlcBOoADoDowDjgB6N421wH3As0AxUAbs19RnIHsHpP3aGLPIWuvjBUpERERERELVZSRMnwXL/v7/2bvv6Diqu43j36tqybZcZbl3sHHDdIxppvceWmgJCakvCQkpJCSkUtITEkJIAQKh946BgKk2BmwD7r1b7pKtutLO+8dIUZuZnS0zuys9n3P2aHfunTs/GbE7O+W5MOdqf+sU9IWC3sHWJc5GX2U/6neD1diyfPPL9qSc8Vr7IPQ/1KUtxuQL3UfCWSvtUJkQbI1scm0bUJDGYLQc52C0hmh9yJWIhGzwyTB4EUT2wOP9wetvft53YMiZkFsYXn2Zau2D8a9TMg5O/RhyC1Jfj0DxEDhuJjTWQ04+GBN7HQleYT+o2xHfOmsfhBEXBVOPiHROie6zr38covfYnxudwer73dtOfNcOZgKINkCkoqUtrzuQo30UkaDt9x1Y9a/Y/dY+CAfcFnw9ItLlZVUwmohIVlr7sHtbv4PDqyMZo66E1fd2XD7h++HVUFjq3V651D74LiLp98lPY/dpqAq+DhER8bb5Jf99z98BhX1bXue5zKwS2ZNcTSIiflUs8d+3ag080gNGXAqH3AEFvaC+At69DDY917Zvz33hqMftcG4RERERERHxZ/3jYEW9+3QfBY214dQjIiIZx7Kse40x+wDPYIeiAfwd+IVlWevd1jPG5ABnA78BRgGfARZblvWzdv1OA/4IjMEOSMsDfgsckOJfRUREREREUmXwaWBy24ZtuennEqQl4WkfTDfwRDB5YDXEN87SP0D1eiidDrlF9iT0jbXQZyqse8R73cGnhRaKBrC1frPj8u45Peme63L9XAjyjXMIQMSKhFyJSJrk97TfDzY85d6nag08ORjKjrPDjkZ/zv4s6awBVLs+hu3vQPEI+3fc/Sns/BCIQvnr8Y9XdrwCR8Kgf+PMMuUXMPcrHZd77e9seBoW3mJfdzrweAX5ikhs0QT32aN19ntUz32hzwEw4Jjs/RxprIM197m3t76GPyfP3pcTkXD1HOMvNLZ6PSy4sSm0ECjoY38HK9k3+BpFpEtRMJqISJCiEVhxp3v74NPDqyUZ+3zZPtHYWNOyrN+h0H9aeDV0G+Dd/uJUuKjGu4+IBK9uB+yaF7tfw97gaxERkY4ql9oXg9RssS8y8+PoZ9qGogHk93Duq/d3EQlL9br411n7ANRth+Nehrlf7RiKBrBnGbwwGS6shs0vwo73oddEGHZeywkbERERERERactroqiT3rO/V+Wn70ZBERFJP2NMMfAUMA6IAJdblhXjjnewLCsKPGmMmQm8CBwJ3GSMWWdZ1j2t+r1gjHkLeAOY2rR4ijHmRMuyXknpLyMiIiIiIqlRNBBGfhZW/9u7n8mBcd8Ipybxr1spjL4SVv4z/nXXP24/2i/zklsE+ziElQRoW8Q5GK20YFCodbSXb/IdlzcoGE26knHXwsZnvcM163fC+sfs5yv+Bvt8DQ6+vfOFo33yc/jkx6kbLw3vtyIZYei58OnPoWZTy7L8XnD00/Dase7rLfiB/bP7SJjxEpSMC7JKEcl2iQajQdvvXmUz4OinIL8k+ZrCVLcTZp3h3p5brGtLRDJBTj7s+w1/3zMW/rLta5MLh/wVxn4xmNpEpEsKb6oMEZGuaOub7m3DLoCCXuHVkoz+h8Nxr8LwC6HvQTD+W3DcK3bidlhi3YDeWAt7VoZTi4i4q1jor5+Cc0REwrfpZXjxQJj/fX+haH0OgGNfhKFndmzLUzCaiKRZ3bbE1tsy075hf/2j3v0eKYa3zodFt8F7V8BrJ0B9RWLbFBERERER6cyqN7mfE534Q/s8oy5cFRER+BmwH2ABt/kJRWvNsqwq4DxgJ2CAPxtjStv12QNcADQ0bQfgpCTrFhERERGRIB32D/sYUm6Rc3vZcXYYxeBTwq1L/DnkTpj0Y+g1CQr6BndTfkEfOP4N6D0pmPFdbK13DkYbkOZgtDxT4Lg8omA06UrKZtjXtw48wf86y//ifY9XNqpcmtpQtMGnp+X9ViQjFJXBCW/CiIuhx2g7KO2EWVB2DEx/KPb6VWvsa/RFRLykap+9/HVYfmdqxgrT0j/C9vfc2495OrxaRMTbpBvhwD9A34PjO95jNcIHX4e6HcHVJiJdToiJNiIiXUzlcvivx0H2IQ4BE5ms9Aj7kS6WFbvPlpnQUzOTiKRNZC/M8vneFlFwjohIqCwL5n0bGqv99R97DRz6N/f2PJebWSN74q9NRCQRtdsTX/edi+NfZ8dse5bqcf+X+HZFREREREQ6k9pt8MlPYPkd7n1GXBRaOSIikrmMMXnAFU0v64DbEhnHsqztxpi/ATcARcClwB/b9VltjHm0qc0Cpidat4iIiIiIhCAnH/b/hf2Q7JOTB1N+aj+aPWBSv53TF9thJSHbVr/JcfmA/MEhV9JWvsl3XN5g1YdciUiaDTrRftRsgWf38Tex7/on7JCjzmLDU6kba+ptMOG7qRtPJBv1HAPTH+y4fPDp/tbf+Cw01kOuc4ipiAhRl2C0vgfDKXPt568cBdvejj3W+iey77N7w5Pe7T33DacOEYnNGBj/DfsB8Mw+sHeFv3Wj9bDpBRh1eXD1iUiXkpPuAkREOqX6Cnj1SO8+wy8Ip5bOwteMIwGcSBUJS9VaWPcobJ8DVjTd1STmvSsgUumvb2O1+8E8ERFJvaq1ULHQf/8JN3i35/dwXu7nwhIRkVSo2xb+Npf/NfxtioiIiIiIZCLLgtlXeYeilewHvfyc3xMRkS7gSKA/dlDZ+5ZlVSUx1sxWz89x6fNy008DDE1iWyIiIiIiIhKvMVendryScdBtQGrH9CFqRdkW2eLYVlowMORq2srPcQ5biei6bOmqigbCfj5DQZb9CWZ/Dj641g4Tsaxga0tWQxWs+DvMuQZeORoezIPnJ9q/w+zPwfzvp25bQ89O3VginU1+D+h7UOx+ViPsWRp8PSKSvaIuYcat9/EH+Axx3TGnZZ9gwY2wY27y9QUhGoFV98ATg2D3J+79eoyGYp3WE8lYZcfG1/+9K2DOF2HZX+zvNSIiSchLdwHJMsZMBc4CjgLGAH2BnoBlWVaH388Y0xsoaXpZZ1lWeVi1ikgXsv4JqN3q3l48DPKKw6unMyg9Cgr6QP0u9z553cOrRySVVtwFc78GVoP9uux4OObp7PqbXv7X2Kn97dXtsE9EiohI8CrjPMnaY6R3e55LMFpkT3zbERFJVDqC0SoXh79NERERERGRTLT1TXtmTy8jLrJnDxUREYHhrZ5vSnKsza2ej3Dp0/pAXp8ktyciIiIiIiLxGPdNWPnPFA1mYNKP03KccXfDDiKWc2hBaf7gkKtpK8/kOy5vpIGo1UiOyQ25IpEMsN/1UP6qff4illX32D+X3Q6jPweH/yvQ0hJWXwH/PQF2ftB2ecUi+5FKIy6xgyhFxN2kH8Fb59vhZ15emAIXVkNeUTh1iUh2cQszzmm1jz/2Glj1L6jZ7Ny3teb9GoBFt8Lhd8Ooy5MqMaUa6+GN06D8tdh9J/0YTE7wNYlIYsZ9E9Y9BpHd/tdZ+Q/75/K/wknvQn6Jd38RERdZu4dgjJlsjHkV+BC4CTgOGIkdemaaHk5mAKubHsuNMUomEpHUW/uQd3v/w8OpozPJLYCpt3r3aawLpxaRVKre2DYUDeyDPYt/k76a4tVQBXO/Gv966QizEBHpqqrX+u+7/y9j93E7GNlQCVbU/7ZERBJVq31JERERERGRtFn+l9h9hl8UfB0iIpItBrV6nuzsYM3X+hnAbRau1jPuFSa5PREREREREYlH70lw+iIYdEriY/Q7HEZeDjNegpGXpq62OGyrdw8hGFAwyLUtDPmmwLWtofX16CJdSV4RHPMc7H9zfOutuht2fhhMTclafW/HULSUMJDXdIiu32Fw0B9h2n0BbEekkxl6Nhz3Goy+yt5X8bLukVBKEpEs5CcYrftwOGk2jLsOSo+C4mH+xrYaYd533LeRDhuf8ReKNu3fMPrK4OsRkcT1nggnvQf7fh1Kp9v7Q7H2iZpVLISlfwq2PhHp1PLSXUAijDFXAX8BumFf5GS1arZwD0UDeBpYhz1jZHfgfEBHb0QktbbM9G4v6BtOHZ3N2Gvg/S+5t7//RRj7hfDqEUmFdY+2DUVrtvE5mHxT+PVYUaheDzndoKjMu2/1RjuJf92jiW1LYRYiIuHZs9J/36Hnxe7jtj9rRSFSCQW9/W9PRCQR9TvSs91dH0OfKenZtoiIiIiISCaINsY+L9B7f+g1Ppx6REQkG1S2ej4hybEmtnq+16VPt1bPa5LcnoiIiIiIiMSr134w48W2y54cCjUbY697/OtQdmwgZcVjW8Q5GK1HbgnFuT1CrqatfJPv2hax6ilQRrh0Vfk9YeIN0GcqvHGa//U2vQh9DwqurkRtejF2n3icOs/+txGRxJUdYz8AXjseyv/r3G/TCwr4ERFnlktoWft9/O7D4aDfNa0ThUd6QKOPU1615bBzHvQ/NLk6U2X9E7H75PWAkZcFX4uIJK/XeDj49rbLVt4Ncz4fe911j8GkG4OpS0Q6vawLRjPGnA/8k7aBaAY77Gwn4HmExrKsqDHmYeC7TYvOQsFoIpJq+SV2IISbgj7h1dLV7FkJPcekuwoR/z66znl5ILP7xLD7E3jvStg1z3498rNw6F2QV9y2X91OePsCKH89ue3VKRhNRCQ0e30Go429xt9Nq4UeQb/1OxWMJiLBi+xJz3aX/A6m3ZOebYuIiIiIiGSCpb+P3WfERcHXISIi2WRD008DjDbGHGZZ1pwEx7q86afVatz2Brbqo5PSIiIiIiIimWDYebDsdu8+hf2g/+Hh1BPDprr1jstL8weFXElH+TkFrm0Rt6AFka6k9CjILYbGan/9P/4R1O2AcddCj1HB1mZZsOIu2Pxix0nmC/pC6TRoqIbts6H8tdRtt3gY9JqcuvFEBAad4h6Mtu4R2Hsb9BgZakkikgWiLvvrOe7hx5gc+z1nw5P+tjHzMBjzBfvR/7D4a0zWrgWw4m/2farb3o7df/CpYEzwdYlIMAadaL9PWVHvfrsXwMwjAAO5RdB/Gux3PRT0CqXMTqVuByz+DeyYA411Hdsn3gBDzgi/LpEAZVUwmjFmEHBv08vmULQ7gN9alrXaGDMSWOVjqKexg9EMcEyKyxQR8f4iCmCy6u03s4y7zvuGi9X3wpSfhVePSDKq1nq3W1bwB3b2rIB1j8LuT2HtA23b1vwHigbBAb9uu3zO55MPRQOo3Zr8GCIi4s2yYNPzsP7x2H37HACH3Olv3AKPYLS6ndBjtL9xREQS1bA3dp8LdsKz+0Ld9tRtd/W9CkYTEREREZGupbEWVt0L1evsYz7zvhN7HQWjiYhIW7OACPa1iga4wxhzlGVZPu9OtRljLgJOouW6wVdcuh7Y6vma+EoVERERERGRQIz/Fqy62/t6j6m3QW638GpyYVkWb+x+zrGttCD9wWh5xv1elYZofYiViGSo/B5wwK/gg6/7X2fpH+x7KU56L9jrX+d+FVZ4XKe7yfm9Jykmz74fJCc39WOLdGVjrob533Vvn3l403tKwIGLIpJd3PbXPcKPAZj8Y9j2lv9r4lf+A1bfB8c+DwOPj6/GZGyfA/89Hhqq/PUvLIVJPwq2JhEJVvFQmHADLPxl7L7b32t5Xv4abHgKTp4DecXB1dfZ1O+GmdNgz3L3PrpvXzqhnHQXEKcfA8XYF0hFgQsty/q6ZVmrm9ot1zXbmot9sRVAP2OMvl2KSOo01Nhpq166jwinls5o2Hne7Ts/CqcOyX6W392GAG14xrt9zhcSH7v59/P6Pbe9Cy8dBAt+0DEUrdmyP0Nkb8uMBJE9sOkF/3Uc8lfof4RzW50m5xYRCdxH34ZZZ/rre+I7/gM580uwv5o7qN/lbwwRkWT4CUYr6AMDT0z9tiN7Uj+miIiIiIhIJmqosc8jzP0yLLzZ33mLsuMUmi8iIm1YllUJPId9YsECpgIvG2OG+R3DGPMF7AlVrVbj3O/S/eRWzxckUrOIiIiIiIikWI+RcNZKmPwzyCm0J+YsPRK6j7RD00581w4YyQDLaj51bRuQn/5gtHzjHpoQsSKubSJdyr5fgxPegoEnAAb6HR57ndqtsPTPwdVUtR5W3pXaMUddYT/KZgDGfk8ddQWMuNT+OfFGOHm2JrQRCUJhXzjW496q2nJY/tfw6hGR7BB12V/PcQ8/BqDPVDjlA5j6Kxh1pf05H3NbdbDwlvhrTMbiX8cRitbP/p16Tw62JhEJ3v6/gGNfhHHX+Xt/albxKax7NLi6OqM1//EORRPppPLSXYBfxphc4BJaws9usyzr8UTGsiyrwRizBGjeWxoPrPZYRUTEv8rFsfv0nhJ8HZ1V6XTvdreDAyLNKhbD3K/Ajveh5772l64hZ6Snlqo13u2r/mWn3vcY6X/M+t3277f2oZZlpUfCIXd0PFC04AcQqfQer7EWHu1pPy8aAiM/6+//s+KhcPZaMDmw+WXnPrUKRhMRCYwVhfeuhDVu9wS1M+wCyCvyP77JsQOH6nd2bHNaJiKSSlbU/0nTkvGp3/7ml2D4Z1I/roiIiIiISKZZdAtULPLfv/tIOPgvgZUjIiJZ7XrgVKCw6fV0YJEx5n7gUeCDpgC1/zHG7AvMAL4AHEjLjC0W8E/Lsj5pv5GmsLVjabnG8K3U/hoiIiIiIiKSsG4DYPKP7EcGW17tEYxWMCTESpzlG/fQhIhVH2IlIhluwJFw3Cstr7f8F/57vPc6W2cFV8+2t+zr3lKlbAZMuzd144lI/Poc6N0e5HuKiGSnRIPRALqPgAnfaXldNAgW3ea9zra3INoIObn+a92C23wAACAASURBVEzG1jf89z3meeg+PLBSRCRkg0+xHwD5vWHZn/ytt3UWjL4yuLo6m/I30l2BSFpkTTAacDhQ0vS8HvhVkuNtoCUYzffskyIiMe12PxEG2MFE/Q4Jp5bOyBjoexDs/NC53WoItx7JLvW74JUjWwJbdi+AN8+2ZwMqPSL8eiJ7YveZeRiMvhqGnWe/f2x/F2o2w4BjoLjdyfU9K+HZsR3H2PY2vDAFRlwMg04Gcux/g3gPstdshMU+d8Em3miH5gAUljr3qVMwmoiIo8he2DEH6rbD4NMhv0f8Y3x8k/9QNICxX4x/GwV9FYwmIunRUO2/78hL7ZO+jT7WGXEJrH0wdr+3L4SL6/2dhBYREREREckWlgW7P4Zd86HvgdBrIqy62//6Rz5in4PIL4ndV0REuhzLslYbYz4P3AfkYAeXdQeuaXpgjKkE9gAFQK+mn9A2EM0Ac4BvuWzq+03jA9QCr7j0ExEREREREXG0pX6Da9uE7lNDrMRZnilwbYtYmmRexFXpkfZ9DV73MOz6CB7vz/8OR0Xr205EX9i/5bnJgd5TYPy3YPCpbcdpqIHlf4HV90FeD9jnK/Y5mFQaem5qxxOR+BWVQf8j7Pu8nOx4H+Z+DabeCvk9w61NRDJTMsFo7Q09L3YwWrQeHsqz70ua9CPof1j823HTWAsLfgibXoDKJfGtWzwU+h6culpEJLOMusx/MNqqu2HbOzDoJHufKa97sLVlotX3wYq77PsxB58OU34OteUw/3uw9qGWfvm9IbI7fXWKpFE2BaM1p3xYwNz2M0QmoPX6ujJXRFKnYqF729Bz4bC/2+FekrjGWve28v+GV4dkn3WPdgxrsaKw8h/pCUZr2Bu7T+1WWHSL/WgtJx+m3Q8jLrRfL/8bzP2y91hrH2r7RSgoB/ymbcBONwWjiYj4tnc1zDqzZZ8yvxcc9QQMPM7/GNFG+7PNj5LxsP8t9gHEeBX2BaePsrod8Y8lIhKPhir/fXuOhWNfgLlfgcrF7v1GXWHPoDn6KvjoOqhY5D3uxudh2Dn+6xAREREREclk0Ub48P9g+V9blpUeBdXuNwC2MehkGP6ZYGoTEZFOw7Ksh4wxUeAu7Ov1rKam5otoejU9Oqzaqt/LwMWWZbkdJLwPeKTp+V6PfiIiIiIiIiKOyus3Oi7vndeP4twEJjlNsTzjfitgQ7Q+xEpEskxuARz6N3j3Uu97kryuga3b3vb1llfte5iOfbHtdbjzvwvL/tzy2i00KVEDT4LRn0vtmCKSmIP/BC95hPssv8OemOrEt3U/qYiAW5Bxjnv4sat+h/jvu+l5KH8NTpoDfabEvy0nb11gj5uIQ++CnNzU1CEimaffITDhe7HDG5vtWWY/dn8Cx7/etfaZVv4T5nyh5XXFItg1z76vtGZz274KRZMuLJuC0VonaqxPwXjRVs+z6d9BRDJd1Wrn5SM/C0fcH24tnZXXSQiAJ4fC5JtgzBe61g6wxLbkD87LV90Nh/8r3FoAInsSXzcagXcugrJj7RNssULRwjJjJgw6se2ywgHOfWu3Bl+PiEi2mX9D26DdSAXMvhJOXwz5Pi+qqtsGtVv89T3DIyQolnyXjPF4AotERBLhJ2C4tbJj4PSFsO1tWHUPrHuk7RjdBsLEH9jPB51k9y2fBa8d6z7mwpsVjCYiIiIiItmtbid8fGPbMLTWtr3lf6z+01NTk4iIdHqWZT1ijHkX+A1wHi3X7VkO3U2rn6uBX1qW5Xli37Ks2amqVURERERERLqeqBVla/0mx7ZzSq8IuRpnxhjyTQERq2MImtMyEWll2Ln29bhrH4QFP0jNmFYUlv6xJRht72o7CClVymbA2C9BQV87LKBkPAw4GnLyU7cNEUlc34Pg5LnwskdA0fZ3YftsKJ0WXl0ikpmiLsFoJoHPdWOgoA/U7/LXv7HW3kc59M74t9VexZL4QtHGfRP2roT+R9j32XcflnwNIpLZpt4Kw86Hbe/Y9xlueiF2YPTWWbDzQ+jnETrb2Sz+TcdlW16Nf5zBp0P/pn3NvgcmV5NIBsqmQLDWFz+lIga2b6vnikcUkdSpWue8vPuIcOvozKJ13u01G+H9a+xZ7Kf8NJyaJDtUJhH+EoR4Ax2cPFGW/Bip0nuyfeKtvW6lHZeBHdwjIiItrChsfLbj8uoNdojPmM/7Gyes4MmcQufljTH21UREkpXIfrQxMOAo+3HQH2HD07B7ART2t0+wFg9p27/sGCgZB5VLncfbOdd+v8t1eS8UERERERHJZA018OoxUPFpasYrHpqacUREpEuwLGsDcLExZhBwAXAEsD/QH+gN1AG7gLXAbOBVYKZlWU7haSIiIiIiIiIps6thm2u42MCCIY7L0yHP5LsEo7kELYhIix4jYcL3YfGv/QeJxLL55ZaxVt1tXw+cKtPua7m2rf0E9iKSGfocALlF0Fjj3mf7e9kVjBZpuk7X78TuIuJP1CXIONHAUxNnTMjml9z3f/J729fbg70vE6nwHscvkwsH/d5/fxHpPPodYj8ARl4Cz4yJvc7ml+xgL5MTbG3pYllN768W1FdA5ZLUjDvtXijsl5qxRDJQNgWjtU7OGJyC8Sa1er4jBeOJiMDeNbBjjnNb8fBQS+nUGmv99Vvye9jvOzoIJ/401EBeUcjb3BPu9oI2/WHIcdi9LHQLRttp//+c2y3YukREskXDXmisdm77+Ef+g9HqQgpGcwsDcjtZIyKSKskGDOf3gFGfBT7r3a94mHswGtghAifPTq4WERERERGRdNj0QupC0QBKj0jdWCIi0mVYlrUZuL3pISIiIiIiIpJ2W+o3urYNyKBgtHxTQA1VHZY3uIS6iUg7xsCIS2D5HakZz2qEx/qmZqzWBhzdccJPEck8Obkw/DOw+t/ufeZ9G2o2wdRbne+7yhSbXoKPvgWVi+3XJePhgN/CkNPSW5dIZxF1CTJOOBjNxNe/aq37Pku3MhhztX2/59qHILI7sZraG3BMasYRkezWYzT0PRh2fuDd7+MfweLfwshL4cDfQ25BOPUFzbJg0a2w7Hao2Zz68RWKJp1cNkUlrmv6aYADjDEJ7uWBMWZfoPVRoY+TKUxE5H/eucS9rXhYeHV0dhNu8NevYU/snWTpOmIF6lWvD6eO1pINdAjCkY/AyMviX2/fr0Ov/Zzbiga5rGTZB81FRMQW8QjMrNkE9T5PLNT6DEab8nN//dzkuBxcVDCaiAQtEtJ+dKzvEDvmwPqnwqlFREREREQkUTs/ggU/hPk3wPY59kWsb1+QuvGHnQ8l41I3noiIiIiIiIiIiEialNdtcFzeO68f3XJCnoDbQ75LcELEcglaEJGOpvzcDh5Lp4K+cOyLkNejY1vPfeCwf4Zfk4gk5oDfQL/DvPss+S18fGM49SRi1wKYdUZLKBpA5RJ480z7nLOIJC/VwWjEGYzmpbYcFt4MK+5MXSgawPhvpW4sEclu0+6D7iNj94vstkOsP/xG4CWFZtmfYcEPgglFG3VF6scUyTDZFIz2HlADWEAR4JE+FNO1rZ6XW5a1NJnCREQAqFwOO2a7t3cfHl4tnd3Qs/33nf354OqQ7FFfAS/HOMC8Z0U4tbTmFYCTDmeusGcp2efL8a/r9f9lyTgo6OPctvXN+LclItJZxQrM3OAzfMdvMNrQc/31c+MajFaX3LgiIrE0dpzxNhAl42P3mfslO1RARERERETiZ1n2Q4Kz4WmYebh98eqiW+3nj6dwhsj9b4HpD6ZuPBEREREREREREZE0Kq/f5Li8rGBIyJV4yzPOwQkNbkELItJRYV84/nU47VN7cvn2jzDs920YfAqcuxmOe7Vl2yfNgdMXQs+x4dQhIsnrVgonvRs7mGLlPyDaGE5N8Vr5L7AcarOisFJBjSIp4RZk7HZvTmeQX5LuCkQkU/QaD2cug5Pegz4HxO6/+t/QUBN8XWFY8bfgxu6h743S+eWluwC/LMuqM8a8BpzRtOiXxphnLMuKK3bWGDMd+BJ2wBrAEyksU0S6quqN8Ny+3n26DQinlq6g5xg45A6Y+9XYfatWQ/1uKOgdfF2SudY9Crs/9u4z63R7do6D/gT9Dw2nrlgBOGEpGgLHPGv/vwXQ/wiY8H37Jik/9rseyo53b8/Jh0GnwFqHm6Nqy+OvV0Sks4oVmLn2YRh9VYwx9sJH18Xe1tTboPdE36U5yil0Xh6tT25cEZFYwjq5MfKz9kUoXmq3wtI/wZSfhFKSiIiIiEinsOMDeP+LULkUek2E/X8Jg05Kd1Wd0/zvuc84nIzuI+DIx6DfwakfW0RERERERERERCRNttRvcFyeacFo+S7BaBG3oAURcWZy7Gtpna6nLZsB5a8Hu/1ek+yf+T1goMf9GCKSHUwOjLjEDvFwU7cDajZC9+Hh1eWmepN9T1eviZBbANvfde+7/jE4+M9gTHj1+RGNQMUiaKxNdyUi/tTvcl6e47x/3ymYrIkyEZEw5ORD/8Nh8k/hzbO8+zZWw5r/QO/JLct6jLYDaTNBtAH2rIAeoyC33T2WVhQql9j3ijbWQsXC4OrozJ8hIk2ybW/il9jBaBYwBJhpjDnDsqytflY2xswAHgNyAAM0AL8JqFYR6SqsKPzXxwHovJ7B19KV7PMVGHIWvH8NbHrBu+/cr8KhdypdvCvbMtNfvx1z7P+fT18Y/EFmywo+GG3fr0NuEfQ50P5yU/46FA+G3GJ7Fo+8YigeBqVHQUGvlvWMgam32OE7z413H//Qu2DA0VAyLnYt3cqcl0cq4vqVREQ6tVifC5tfgsrlULJPx7ZoA2x8Dt461339HmNhys+g/2H2gcBkuc1Ko2A0EQlaNKQLGEqPgpLx9gkJL2vuh8k3Zd4FHyIiIiIimahqHbxyJETr7Nc7P4A3z4XTFmjW+1SrWmuHz6XawBPgqMd13k1ERFLCGJMPHAqMAfoCPQFjWdbP0lqYiIiIiIiIdEnl9Rsdl2daMFqecb52L2Lp2j2RlBl1VcdgtLzuMP7b8GkKDl0VlsKgk5MfR0Qyy8DjoWiIHX7m5ukRcO4WKHK5zypoNeXw9vmw7R37dW6Rfc9ZpNJ9ndqt8MwoOOoJ6HtgOHXGsvo++57RoO/NEwmDS/BxUgafDpueT/248VJgj4g4GXSyfc95bbl3v/e/6LDuqTD9wbb3xIdt3WMw52p7/ymnEKbeCuO+Yd9TtOW/8O4l9v5TGHKyLTJKJH5Z9VduWdYcY8xDwMXY4WgHA0uMMb8HHgE6HME1xuQCxwJfBD6DHYhG0/p/tCxrTfCVi4gvjXX2QZ9ugyCvKN3V+Ff+X383FeR2C76WrqZ4CBzxgH1A0Ctcae2DsPEZOHW+bqrpqtY/4b9vw167//hvBlcPQGONHawYhMJ+cObKjl/shp8f3zgl42DM1bDyn+0aDJyzDoqH+h8r3+VLpoLRRERaRPbE7vPcvnDqPOgztWVZ7XZ4ZTrsWea97tgvwMhLkquxNbdgtMa61G1DRMRJWDO75eTC8W/AB1+DDU+D1eDcb+9K2Pkh9Ds4nLpERERERLLZmv+0hKI1a6yGT34GR3jMnC3xq9sezLgTvqdQNBERSZox5kjgeuAkoNChS4e7S40xpwAXNr3caVnW9cFVKCIiIiIiIl1NTWMVlY27HNsGFsRxzXQI8l2CExqsSMiViHRioy6HqtWw6Fb7erXi4TD9Ieh3qH3z+/I7Ep9IuOe+cOSjkOt0WExEslpOPhw3E966ACoXu/d77zI47pXw6mqz7StaQtHAvr+tsSb2elVr4fVT4Jz16X//2jXf/j1EOosgwsP2vxm6ldohglZj6sf3S4E9IuIkt8DeF3r7M/FP/Ln5Rfjg63DEfcHUFkvlcnj7Quy4IuxrIT+6DnpNgL4Hw6zTw7vnCcDofVY6v2z8K78aGAccgP1u0Rv4SdOjzdEkY8xiYBTQvEdomtYxwLvA98MoWEQcRBtg/vfsWeGr19k/a7fYbSe+DaXT01tfPDbP9NfPmNh9JH4FveDA38Ocz3v3a6iC+d+Hox7z7heNwKLbYMMz9kG90iNh8k/SNwuDpEZuN/tvwC+vg8+p0lDt3nbqAigZD4/3S2zmitM+TV3a9YQb7Pe56vUtyyb+ML5QNHC/ScprRhERka6mwUcwGsCca+DkOS37lx99M3YoGsCwOAMyY3E7oZnohR4iIn6FGcBYVGZ/j7QsO1TgyUHOJ4fXPaxgNBERERERP3Z/7Lx8zX1w2D/si54kNaIBXNha2A8GHJP6cUVEpMswxnQH7sKeGBVaJjltzXJZfSFwOZDTNNZ9lmUtSHmRIiIiIiIi0iWV1290bSsrGBJiJbHlG+dj6RFL1+6JpIwxMPkm+36K2i1QPKzlut2Dfg9Tb4HKZbgfygK6j4SaTW2vqy3oA92HB1m5iKRbrwlwxiJ4cjDUbHbus+VVu61oULi11WyGLT7vhXVStw02vwxDz0pdTYlYnaYgFJGgBBGMltcdDr8bDrrdngTcyYtTY4/TbQDM8HjfyO8Fz4xyb3cJdRYRofdkOGMJVK2HmYe57zc5WfcoHHoX5BUFV5+bNffj+D1w1b2wd3V8oWinzrd/mjzIKbAnmG2taAg0VsG878K6R5zHCOIzRCTDZF0wmmVZNcaYk4GHgONoedcw2LNHNgefGewAtf+t2qptJnChZaUz4lakizO5sOLvzuEPVeuyJxitsR4W/zrdVciYz0HvSfDyod79Njxt30DvNSvBu5e13Tms+NQ+2Hjqh+7BTpnOikLFQsjrCT1Gprua9CgeBpVL/PePJBBGFq+ox5eb3CL75q/hF8Cqe+Ifu1sKg/x6joGT58L6x6FmI5TNgIEnxD+OW1BbpCK5+kREOpOPb/LXb+dcWPUvGHiiPSvCmv/4W6/n2MRrc5LjcqOygtFEJGhe+9JBMcaeNWvgCfZFHe2Vzwq/JhERERGRbLT2Ife2ysXQZ//wauns/Ibwx2O/7+hiKhERSZgxpgR4C5hEywSnrTVf2+fIsqz1xpgXgDOb+l4MKBhNREREREREUmKLSzBagSmkd16/kKvxludynDYSjYRciUgXkFvgHGSW2w36TIm9fqomvBeR7DPkTFhxl3v7ir9D3wPte9jyiqDf4fZkvkGo2QzbZ8PG55If682zYdq/of806DGmJTQyDFYUKhbBkt+Ft02RMJTsl/oxc5oiRPJ7uF+LM+JSWPuA9zijrkruWp6crIsyEZGwdR8Goz8HC2/2v060Dj75CfSaCLXl9mSfg09NTeisZdnXMe6aD05xRJ/+zHm9tQ/Apjj2tYZd4PP9tb+dOeDG6H1WOr+s/Cu3LGu7MeZE4PqmR2lzU7ufzZqD0nYDvwZ+pVA0kTQzxj4wXLGwY1v1+vDrSUT1Jngqs2b+6dL6HQIzXobXT3bvYzXAnmV2irCTZX9xTszduwI2PAOjLktNrWGqWAKzToe9q+zXZTPgqCegoHd66wpbvAEtDSEEo3mlPud2s38e/Geor4CNT9sHb/0oHpr6g8pFZbDvV5Mbwy1YsF7BaCIiAEQj9j6HX3O+EFwtfrkFozXWhVuHiHQ96XyfGXquczBaxUJ7n93khF+TiIiIiEi2+OSn3u01WxSMlkqpPtcx9BwY/+3UjikiIl3NY8BkWq7tqwceAV4HosA9PsZ4EjsYDeBE4IbUligiIiIiIiJdVblLMFpZwRByMux6kHzjfO1eg6VJTUVERDLGmC/Cyn86B2oAfOIwqfohf4V9vpzaOpb/FT74P/c6EvHeFfbPsV+Gg28PJ/iooQreuQQ2Phv8tkTC1GMslB6R4Moe95D6CcrZ50uw9kE6xnI0ySmAMZ9PqLKWMTT5noj4MPpzsPi3duCZX4t/1XHZgX+A8d9IvI6GGns/Z/1jia0fqfTfN559Pq99LQVQSheQWUdm42DZfg2MAK4GHgI20jJzZOswtOeBa4FRlmXdolA0kQxR7DBjBkDVunDrSERDlULRMtHAE+wDAV52f+q8vH43fPB19/V2vJ94XeliWfDWuS2haADlr8NH16WvpnSJ9+afjc+kZhaM9vausm/8ev9LsPrf7v2ag9HyusPRT8D52+HcLXCMj5oGn5GaWlMt32Wmo8hu+29VRKSra/15HYRxAXz+5xY6L483kFREJF5eIcPNxl4TzLb7HOC8vLEaqtYGs00RERERkc5gw9P2LI1e6raGUkqXEdmTurFGXApHP6kLqUREJGHGmAuAE2i5s+I9YB/Lsq60LOseYJbPoV5qHhLY3xjTI6WFioiIiIiISJdVXr/BcXlZQebdN5JvnMMFIlYk5EpERETEVb+D/d0H1trcr8CelamrYc8KmPu11IaitbbiTlj/eDBjt7f0TwpFk84lr7t9H+gJs4IJD/Mz5oCj4ajHoffktstNDvQ9CI57FUrGJVeHn4A2EZGeY+H416DvwWByEx/no29CxeLE11/xt8RD0fzqNRGOfAQGHu9/HZfjQDHbRDqJrN+bsCyrFri76YExxgB9gAJgh2XpqK5Ixuo+zHl5dYYHo9VXwBNl6a5CnJgcOP0TeLjIvc+2t2DkJR2Xr33Qe+z6XcnVlg67F0Dlko7L1z0Kh/69a9280lAV/zqzzoQpP4dJN6amht2fwGszoG5H7L7NwWjNCvrYP/0cSBp9VdylhSK/xL2teh10HxFeLSIimSjocGCn/Z9k5TjPOqlgNBEJnJ9gtNGfC2bbvSa4t5W/AT1GBbNdEREREZFs5+cC5fe/DKMuD76WrqIhRcFoQ8+GI+5PzVgiItKV/aDV80+BEy3Lqo53EMuythhjtgIDsCeF3Q+Ym5oSRUREREREpCsrr9/ouDwTg9HyjPO1exFL1+6JiIhklMGnwIiLYe1D/tdZ9yhM/H5qtr/2IVrmKwnI2odgxEXBbqN5O7GcPNcOGxHJBjn5wd7f6zeQbNi59qOxDqxo07q5kOtyv1C8ggh9E5HOqXQ6nDIXGuvbhrq+cQpsfdP/OOsegck3JVbD2ocTWy+W6Q/BkLPsHIzcwvjX9/q86EpZEdJlZfxfuTFmf+AkYALQv2nxdmAx8IplWfNa97csywJ2hlqkiCSmeLjz8qq14dYRr9eOg2hduqsQN7ndYPDpsOl55/b1T8JBt0NOu8TgWAfH6rPwo2XTS87LG6qgdgsUDw23nnSxookFowF8/CMYeAL0Pzz5Ohbd5i8UDToGozXrMdp7vUk/hv6HxVdXWPJ7ubctvR0O/E14tYiIZKLq9cGO3++Q1I/pGoymfWURCVis95n9b07NPryT/B7Qcx/Ys7xj27qHYUxAgWwiIiIiItluh4+8ksZqqFwOJfsEX09XsOaB5MfILYZp94MxyY8lIiJdljFmEDC11aL/SyQUrZUl2MFoAPugYDQRERERERFJUqPVyLbIZse2soLMu+Y+3yVcoMGKhFyJiIiIxNTv8PiC0RbcAL0nw6CTkg8U2jUvdp9kbXrBYbsfQ/nrUDTIDofLL0l8/OpN9jZ2f+zdr7Af9J6SujAnkWwXb1BOIkE9fvgNaBMRadb+s3zAjPiC0T75CUQbml5EoWo9FA+BgcdD2XF2OFlrVeth80t2vsmO2clU7szkwoCjIa8oiTE89gn1PitdQMb+lRtjDgR+Dxzp0e0WY8w7wLcsy/ognMpEJGW6uwSjVSyCyB7I7xluPX58cC3s+ijdVUgs477hHoxWuwW2vwsDjmpZVr0Btr7lPWZdFgajNex1b9v5YdcJRmtI5npqYOY0uKgu+QOja/7jr5/Jcf8iYnKg32GwY07HtqFnw5SfJl5f0Ar7ubdteErBaCIiVeuCG3vSj4IZN8flxEdjbTDbExFp5vY+k9cTzlhsn7QI0pCzYMlvOy7f8irUboNupcFuX0REREQkG8W6SLnZ2odgckDHMrqSrW/DtreTH2f0VXZAtIiISHKmNf20gPWWZcVx1bKj1hdweJyIFhEREREREfFnR2QrDVaDY1tZweCQq4kt3zhf1x6J1odciYiIiMQ06jJYfBvUOIewOpp1Bgw8AY5+CvK6J77tXfMTX9evaD3Ubodu/e3XS/8MH16LfUoAKBkHM2a630vsZdt79r9FvY/7Osd9U6Fo0vV4TXLnFaITpmQDHkVExlwNy273tz/QbOEvOi5bdCuMuBim3dcSHln+Brx5NkQqU1Kqo1FX2mGxyfAKu9T7rHQBObG7hM8YczbwFnYommn1+F+XVo8jgTeNMeeEXaeIJKnPVOflVgNsnRVuLX7M+5694ySZb9CJ9o6pmy2vtn29eSb/O9jmZsdssKJJlxaqyB73tjfP6fjv0Fl5BcT59aLL+1UQcgq9D0oNPct5+fhvB1NPqnQb4N62dyWs9vh/VkSkK6heH9zYQ84MZtwclxOH1esh2hjMNkVEwD0YbeQlwYeigX0yxInVCOsfD377IiIiIiLZqHS6v35r7g+2jq5ijc9j7rkeM1FOuAEO+lNq6hERka5uYKvnC1IwXuuLAJTgKSIiIiIiIkkrr9/g2lZWEMK1KHHKcwk5iFiRkCsRERGRmAr7wUmzYdDJ8a235VVYdW/i262vgL2rEl9/v+thwDH++i74gf2zdhvM+xZt7tOsXAoLb06shg+/ETsEpdckOOQOmPjDxLYh0ll5heiEyWRIHSKSvboPg5Pes+/j6TEGiocmPtbah2DTC/Zzy4IPvuYvFM3k2tttfngpGmT36XsITPkFHHpX4vU28wo/0/usdAEZF4xmjBkPPAgUYQefWbR8C2odkGa1enQDHjDG7BdutSKSlF6ToFuZc9vqDLvp4aPrYfGvYvcbdp7z8sk/S209Etuoy2Dwac5t7WdY2LPc35gbnk6uprCt/Id3++zPuwcKdCapCEarXAy7krg+u7HOf9/cbt7tY78MfQ5su2zUFVB6ZPx1hW3wGe5t718DkRT8txIRyVa1W5yXJ/v+PuZq6Htwqo8CRwAAIABJREFUcmO48ZpRaeMzwWxTRLqmHXNh1lnw3ASY80WocbkYNacwnHr6HmSfUHEy9yvh1CAiIiIikm3cAtbb27MMKhYHW0tXULEwdp9TF8AJbzq39RgNU2+GnNzU1iUiIl1Vr1bPUzHVcuswtC5w0YOIiIiIiIgErbx+o+PyvnmlFIR1PUoc8o3zMfcGqz7kSkRERMSX7sPh2Bchr3t86214KvFt7v448XUBhl8IJ7wBl1oxu7Jjjv1z80sQdQhqTeSezJrNsHOud5/CfnD6J7DPV8AY774iXY3JkOs9vMJ8RET8KtkXpj8IZ62Ac9bD4UmExzbvX+1dCRWLYvfPLYaLI/Z2mx/7Xuvc1+TBuZvsPqe8D5N+mJrr77zCzzIlCFMkQBkXjAbciR101hx6ZoAG4D3gEeDRpucR2oakdQP+FnaxIpIEY2DgCc5t6x6GnR+GW4+bpbfDkt/G7jfoFBh/PS1vTU1MnntgmgSraJDz8sjutq+r1/sbb80DydUTpo3PQ2O1d5/q9bDppXDqSaeGqtSM88HXoHZrYuvW7/LfN1YwWmFfOPEtmPZvmHgjHPMcHH5PdhzAHXq2e1tjLWx6PrxaREQyjdtnTO8p8Y/V7zCYeisc8zwc+vfgPiO8Lvja/HIw2xSRrmfXfHjlKNj4rB1YvPIfsO0d576x9qVTxRgYcZF7+7zvhFOHiIiIiEg2sRr99133aHB1dBVu35ua9TkQ+kyxg59LxndsH3FJMHWJiEhX1fqEeS/XXv4NbvV8ZwrGExERERERkS7OLRitrGBIyJX4k2+cwwUilkMQiYiIiGQGY6DsuPjW2fIKzJwOC26E3Z/Et+6u+fH1by+ecNjdH8PbF8J7Vzi3126BBww8tx+881lY/FuoXAaLf2e/fvM8u/0BA2+eY//OL0yOvd0BM/zXKNLVmAyJEPEK8xERSdSgkxNfd9XdsPRPsO1tf/0L+3e8N3P4Z5z7jv5c4nV58QqZ1PusdAEZsldjM8ZMAo6mJRAN4LfAQMuypluWdbFlWRdZljUdGAj8ut0Q040xCdw1LiJpM/h097a1j4RXh5uqtfChS2pre0fcD6XTYNp9UFhqLysaBEc9Dr0nBlejuMvr4bx83aNtU3zX/MffeOsfS76msCz+lb9+G58Nto5M0BAjIM6vbe/AE2Ww5A/xr1u/O3afZjk+whzyimHU5bD/z2HI6dkRigZQEOMa90RmABER6SzcgtH67A89Rsc31thrYML3YMhpwX5G5DjPOgnACuWWi0iKLPkDROv89Q0rGA1gxMXubavuhmgcoQ8iIiIiIl1BPMFoycx6LbbcIu/2o5v+jY2BGS9B30Ps1zmFMPZLMPkngZYnIiJdzrZWz5O6gMYYUwhMbbVoQzLjiYiIiIiIiABsqXf+ellWMDTkSvzJc7l2LxKtD7kSERERicuEGyCve3zrbH8XFv4SXj4Mtrzqf71kg9HivSbXzwRolUtg7QMw73p4bhzM+7b9esOTLX02PG3/znU7vMfKL7HvmRDp0rLgntKc3HRXICKdUVEZjP924ut/+A2Y7TPELM/hOrzS6TD4tLbLCvvD+OsSr8mLV/iZV2iaSCeRUcFowPlNPw12ONq1lmV9x7KsXe07Wpa127Ks7wFfa9Uf4LxQKhWR1Bh2HuT3dm7b+WG4tTh547TYfQCOfhoK+9nPR30WztsC52yAczbC0LOCq0+8uQWjAbxzKVhW/H9nqQrZClK0EXa8769v+WvB1pIJGmvc2077OP7xProOHsiBdy+DVffaf0deGqrgk5v8jx9mmEPY8kpidMiCg3EiIkGwLKhzCUbrVgaTfhzfePk9k6/JD69gNBGRVFl9r/++8cxOl6xek6DHGOe2uh1QtSa8WkREREREskG0wX/fXfMgsie4WroCr+M2n6mA7sNaXncfAae8D+dtgwt2waF3Qo5mkhQRkZT6qOmnAUYaY8YnMdb5QPMHXQMwO5nCRERERERERADK6zc5Lh9YMCTkSvzJN843vTZYkZArERERkbiUToMT34Vx34DiOANYG2tgwQ/99/cKRis7DsZ+2Xv93BCvyY3XuOvsf8d+B6e7EhEREUmXA34N0+5ruSc/r6e9j1N2XGq34zRBqTH2xKQH/RGGXQD7fQdOmg299kvttltvz7VN1/lJ55dpwWhN0xBjAbMty/pLrBUsy7oTeIeWJI1DA6pNRIKQWwhDz3Zus+K4QSIIFYvsh5dh58PJ73cMPzM5UDzEe0dDgucVjLZ7AVR8CsvuiG/MPcuTqykMe1dBY62/vlVroWJJsPWkW6NLmF1uEfSeDAOOTmBQC9b8B2ZfBe9f496toQZeOwHWPeJ/6M4cjFbQK90ViIhkpoa97p/dhQNg9JVw7Esw4mJ/43ntA6WSZhQQkaDFG4QQ5r60MTD2S+7tq+8DKxpePZ1NQ7UdnNFYp39HERERkc7Caoyv/1PD4wtT68yiDfY+coPHRDCtWVGIVDq3TX/YnjnbSbf+zjNcioiIJMmyrNXAilaLbkhkHGNMIdB815cFzLUsqyrJ8kRERERERKSL29tYyd7GCse2sowNRnOeHEPBaCIiIlmgzxQ46A9wznq41LIfF9XQcou8hx3vQ/3u2P2iEfu+SSeH3wPHvwaH/hX6THUfo/Vkxf0Oi73NsIy4BA76HfSemO5KREREJJ2MgVGX2ftRl1pwYaW9j3P8a1A6PXXbcQpGA/u+ynHXwlGPwgG/gp5jUrfNeGgCVOkCMi0YrXUE4r1xrPfvVs+TmVFSRNKhxyjn5TvmhltHe+se926fMROOegz6HeLdT9InVijI5ldg1b/iG7N+V+L1hMXtwKWbpX8Mpo5M0ehyo1Dzl5FD/wElrXYfeu4b3/gr/wGVS53bNjwNO+KcoLozB6O53WzVbO+qcOoQEck0tVvd27oNsH8OPhmmP9hy8vOYZ93XCSsYLRv2i0QkezXWwZsuQepuwt6XHn+de9unP4VHS2DJ78Gywqsp2zVUwdsXw2O94aF8eLgbPJgLr5/i/XkpIiIiIpkv3gmRIrvtY+xdmRWF+d+3940f6Q6PFMMDBj7+iXeAcMNe7KwYB91HBFGpiIiIH3c3/TTAZcaYK+NZ2RiTA/ydttcXxpx0VURERERERCSW8vpNrm1lhUNDrMS/POM8qWnEqg+5EhEREUmJ3G4w8AR/fR/rY583bn48MQg2vdi2T8ViiLrsF7QJQ/OIGMhtFYy279f91RaGIWekuwIRERHJdINTuL+Q6ff85zgfIxLpTDItGK13q+cfxbFec1/TbgwRyQYm13l5YzXU7Qy3ltZW/9u9re9BMOjE8GqRxOTHCAWZ9+34x4xUJlZLmKrXx9d/xZ3B1JEpGmIEo5XsA6d8BKd8CCfMgjMWQ984Aw9fmAJL/wTvXg7vfxnePBdePBDevST+ejP9S1Iy8nt5t++YDdE4b44TEekM6na4txX2d2nwmBHKhJT033Mf7/aGqnDqkM6voRo2PAsf3wQLboQVf4fqjemuSoK26DYofz2+dWLtb6ZaTh4MPs29vaEKPvoWvHwIrHnQnoFPvL13Fax7uOO/1eaX4Q2Hk1ON9bD6P7D0z7D7k1BKbGPXfFjyB1j7cHYcLxARERFJJ6sx/nU2v5z6OrLJotvsR3uf/hQW/8Z9vTX/cW8L+3uTiIhIiz8CW7HTOw3wT2PMzcaY4lgrGmMmADOBzzatbwErgIeCK1dERERERES6iq31ztchdcspoldun5Cr8SffNRhN16aIiIhkrf1/mdh6tVvgjdPs61SbzTrTuW9OPpS0mn/EeEQM5LS6v23Y+TAwA+6lHXgCDDsv3VWIiIhIpht7DfQ5IEWDZUIkUwbcRyqSRpn2V976KlyPO8M72NXqec8U1SIiYXELRgNY+xDs+9Xwamm2+j7Yu8K9few14dUiicuLEYzmJbfYDudrLxtudG50CQLrqtz+PZqD0QDyiqDvgS2vh38Gds71v41oPXz4jcTqa8/lRHWnkF8Su8/Lh9hBdcbji5qISGfj9dnttj/jlebvtX+dSiXjvdtfORJmzIRupeHUI51T/W54/RTYMaft8oI+cPQzMODI9NQlwbKisOz2+NcrOzblpcTe5vGw6QXvPjs/hHcvhU9/Die9CwWa18FRZA9sfNq9fedc2L0Qek+0X9duh5nTWh2/MXDAr2G/BELQE7HkD3bwHZb9umQczHgFug8LZ/siIiIi2cZtUoz8EvdzLyv/Dof8FXJCOtaRaVbf69H2b5jwXee2T3/uvl6BgtFERCQ9LMuqNsZcCTyHfeVwDvA94GvGmBeAda37G2MuAvYFTgKmYV/p23wSuRa4xLIsK6TyRUREREREpBPbUr/BcfmAgiGYDL2eOS+nwHF5gxXBsqyMrVtEREQ89DsEzlwOz8aYvNzNwl/CyEvs8+/V65z79JoIua32I7yC0XILW57nFcExz8KGp2HXPCgaZE+Otmc5OB2qr99hT45ctz2x38XJEQ/A8Au876MQERERASjsCye8CesftycZ3b3Avj8tWh//WJl+jEXBaNIFZNpfeetvUfFMGd26byZELopIPBrr3NsqPg2vjmbVG+C9K7z7jLw8nFokOYkGo426wg5eqFzasS0rgtFqE1invu2Bzc7EKeAOIM9j4umx17R82QlbXvfwtxkWP/9P7poPuz+BPlOCr0dEJFO4BaPl5Lvf+Nv/cLs92m6Gx9wi6D0ptfW5MQZGXwWr7nFu3zUflvwOpt4STj3SOS39Y8dQNID6XfDhtXDKh5l/kDmb7JoPn/4CKpfYM8Id+Lv0hDztXR3/xRD73wLFQ4Opx8s+X7G/O+z6KHbfysXwWB8o7AdFQ2HERTDhe94XlnQle5Z3/Fxrr+JTO4hu8a8c/kYsmHe9/Rh6Lky9DUoSvEAolpotMP+7/C8UDexjCAt/CYfeGcw2RURERLKd5XL6f/z18MmP3df78Fo45C/B1JTJoo3O56maVSy0LzBv/53YsqBms/t6+QpGExGR9LEs62VjzFeBO2i5xq8ncGG7rgZ4oN3r5gMxDcDVlmX5OCAnIiIiIiIiElt5/UbH5QMLhoRciX/5HhNxN1gR8k0nvS9ARESks+s5FnqMbTVhbBwqFtrXV+/62L1P7/3bLfC4frX9ta25hTDiQvvh11sX2IEkyTr8Hjv0TUTa0X0UIiKu8nvA6CvtR7NZZ8HGZ9NXUxAUGitdgO66E5H0K+jj3rbb40BMECwLnopx0/cZy+yUe8l8LjMhxTT+25BX4tzWWYPRUjkDQ6ZxC5vJ9fj/uKAXnPIBTPs3FJYGU5cbr8C2bGcM9PQRTLDh6eBrERHJJIl8VuWXwOAzOi4fek64nyXDzvduX/9EOHVI57XlNfe2XfPcZzST+NVshteOty9AqFgI6x+Dlw60L5II24Yn/fWbcAPsfzOc/D5M/H6wNbnJK4LjZkKfA/yvU7fDDmFe8AOYdWZwtWUbP7PvvHOxHUgW6zvshidh5mFQU56a2jqM/7RziNuKvwW3TRGRMEUbYevbsPTPsPVNe1IFEZFkWQ3Oy4sGwbEvuq+3/A6oWBxMTZmsfkfsPg17Oy6rcb6B73+8jjeJiIiEwLKsvwMnA1tpG3hG0/Pmh2m33ADbgZMty3ownGpFRERERESkK9hSt8FxeVlBGibo88kr+Cxi6dyeiIhIVht+QeLr7lnpfT/uiIvbvg56cuphSfwuzXIKYPDpyY8jIiIiMvVWMHlxrpThIZQ58f4+ItlHwWgikn4DjnRva6gOrw6Aco+b7gF6jIaeY8KpRZLn56ZqJ32mQH5P57ZOG4y2NfV1ZIqGBMJmwP4yMOpyOK/cnm0jLP2PCG9b6bD/LbH7fPJjqFobfC0iIpkikWA0gGn3wtBz7WT/nAIY/hk47B+pr89LXg/v9j3LwqlDOq9YoVy7Pw2njq7gk59C/c62y+q2w+r7w6uhsQ7euRTmfce7X7/D4cK9MPVm/p+9+46Tor7/OP6a3WvcHRz96BwdpEgTsCGCotiNBdQYjTFqNFFjfkl+xlRTTaIx0V+a0TRjb9gbYEeqovQicCDSOeDuuLY7vz/Gy7WZ2dnd2Xb3fj4e94Cb73e+3w/H3d7M7Hzfw8hbocsxyanPSW4XmDGv5Q0jXux40XrqjF3IVltTV+HveDUH4JP7/R2z3rrfObc93QNW/cIK3xcRyUSHN8Jj+fD6ibDsG/D6SfBoHhz4MNWViUimM0P2240gdJ/qvu/6//O/nnRX5eF9G7tzZrfz5C6TE39ju4iIiAemac4HBgPfAbZh3Unc/INGf98H3A4MMk1zQdILFhERERERkVYrZNaxt9b+AWjFOb2TXI13WUa2Y1utqXtQREREMtqI/4l9bdmiq2HZjc7tPU9r+rmR4IiBfhdAyWWx728E4Zg/QV5X/2oSERGRtqvoKDj+4cQfA/nO5Z6/qIPeRDKPvstFJPU6jXNuq9yWvDoAPvyee/uYn2XgwU4b5va95eSoW60/szvYt7fWYLTlt8DUuc6BcJks1rCZeoYBY34KCy8DM+xfXacvhXnTm35PFY2MLUwhk/T9gvWkjh0vuPdbcgNMez45NYmIpFqsv6uy28PUpz4PkglAlsffbX7KKojcJ1ynpw9I7MLV7u0HV0FvPQUsbuE62PgX+7Z9i4BvJKeO9f8HWx9279P3Qjjx8eTUE42cTtYbJAOugDdmRbfvp8/BCyPhzNVt+/Wy9rD/Y664DUZGuNYTi5qDkedd+XMYdDUMu1Eh+yKSWd6dY/PACRNeanSttftUyO9vXcfqfUZSyxORDBaus98eyIKsfBh8rfN5yd73EldXuvIajFbQr+m28k+c+x/9i/hqEhER8ZFpmhXAb4HfGoYxFDgB6At0AXKAvcAu4D1guWkqhV5ERERERET8t6dmJ2HsH+zRI42D0bKNHMe2OjPGh8uLiIhIesjtAjPmW++TV5RCMM+6n8eLshXObSWX2TxIK8HrZAPZcOy/YeiNcHAldBhhraM7tLqhT4cR0HEM7F8KNWXWPQRVuyGnI3Q/CQoHJLZGERERaVv6XQjdtsPOeVC9x9qWVwzvOYW5pvmDSAPO4fkirUU6rrSrv4lpimEYJR736dH4E8MwTiSKVxjTNN/y2ldEEsAIwLSX4Y3TW7ZV77VCnoJ5ia9j15uwf4lze5fJUHJJ4usQ/7Qrhi6TYN9i7/uUXGr96RiMdtC6qJjX3fq+rN5nLYCvPQw5RfHX7IdwDMFouxZYIV2nvGktQGpNQpX226P5d5bMgXY9YetDUHcEtvw7vprO3WotVjpvG6y7Bw58CJ3HW4vmczvHN3a6Mww48Ql4NEJ4z44XoHwLFJYkoyoRkdRyCjX1egzsJZwsUbzMXb3POi4TiUWLUI5myj5OTh2t3aE1zm1bH4bjHkxOHaWPubfn94MTHk1OLbHqdbp1XvX6SdHtd3gDLJgJx/3HOvdoi+rKEzPuxz+F0T/wd0yn88zmfdb/AT75O5y2CIpG+FuDiEgiVO2G/csi99v9+dtqW/4NE+6BYV9PbF0i0jqY9gvLMILWn5P+7ByMdmgNhEMQCCamtnTkNRitsboKOLTWvm92B+gxPf66REREEsA0zfXA+lTXISIiIiIiIm3PzprtttsNAnTLTt/7N7JdFr3WhmuTWImIiIgkRDAXik9u+LxiK3z43fjG7DC85bZhN8Get1tubz80vrkaMwzoOsn6qNfrtJb98s/xb06RtmLUbbDk+pbbO45Jfi0iIpmkXU8Y8MWGz8N1zsFoLYJl04yRjpFRIv5K1+9yA3g4jn3fiKK/Sfp+HUTaDrfgm8pPof2gxNfw8Y/d20d9P/E1iP+OfxhemWyF7EUy4R7oOMr6u1Mw2pYHrQ87XabAhN83vVCXCk7hKpHsXwrbn2kIh2stQkfstwcjBHM1V3yS9QFAGLb8J7Z6htxghaKB9X026rbYxslkwTzrSSORvobbnoIRtySnJhGRVPLrd1UqeAlGq9qlYDSJXajavX3Lg9DlGBj6dSt0W2Jz5DPntrwk/fyGqmHfIvc+E+/NjP/n7lPh/B3w9oXWE/u82rUAnu4FBf1h8gNtL7Sg7nBixv34hzD4Gv9+F5mm8+9uO3WH4aPvw4lP+jO/iEgiVe2Jfp+PboOBV0J2oe/liEgrY9bZb298Y87xj8K7s1v2CVXB4fVtK2y2amfkPtuehuJpcGQXLLwcdr7m3LfXWb6VJiIiIiIiIiIiItJa7K7ZYbu9S3Y3sgM5Sa7Gu2zDubZaM8KDKEVERCTz9J8dfzBa/zktt/U6HbIKWz7YtsQhGERE0kufL8Cym1s+jH7QV1JTj4hIpgqkedyQWzhbutcu4oN0XUloYgWcRfNhNvqIdl8RSbX8Ps5tVbuSU8P+Zc5thYOg1xnJqUP8VTgQzveweOTMNTDs6w2fZxdFP9e+9+GdC6GmLPp9/eQUjJbVPvK+nz7nby3poHqf/fZ4wmaG3gguT9pyFMyHo+K8EN1a9L0gcp/9SxJfh4hIOqjL5GA0D8cXK25NfB3SeoUjBKMBLLvJClSV2LmFoFTvs4KgEu3D/43cp9PRia/DL+16wqnvwKwP4Zg/w3EPQW43b/tWbIX5M6BiW9PtVXth3T2w5i6rT2tTWx65T6w+nevfWLUHIdqnK3/6AtQmKPhNRMRPzW+Q8qL2ELwwEj74Dmy63/pcRCI78CGsvgM2PwjV+6Pbt/aQtd8H34UPvg0rvg/bn03OcXs8zJD9diPY8PfeLuFdXs4ZWpNdCyL3Wf8HOLwRnu7hHooGDQ9sEREREREREREREZH/2lmz3XZ7cY7L+pY0kGU438deZ0Z5T4OIiIikv4L+1n2o8Wg/uOW2rAKY9iJkd2zY1n8OjNT9/yIZoV0xTH3GCjisN+irMOSG1NUkIiLJZSgYTVq/dP4uj+fOba/7KhRNJF0E88EIgBlu2VZXkfj5645AncsC1dOXWPVJZgoEod9FUPq4fXvfC6FoeNNt+b1jm6tym7UAcMS3YtvfD07BaEOugzW/cd936yMw5R8QzPW9rJSo3ucc9paVH/u4XSfB9Nfh49th17ymbUYQRvwP9DkfPv4RHFhhpTF3GgdjfwUFfWOftzXpMSNyn7KVia9DRCQdhDI4GC2nU+Q+O19PfB3SeoU8BKMBrL8X+l2Y2Fpas2qXYLRwjdWe1z1x89cdgXV3u/fpNA7yM+xY2jCsMLf6QLd2vWDeNO/7z+0Hl4Ss6xF7F8OCUxvCZlZ8D054FPqc63vZKeN2XSZen70Cg6/xZ6yq3dHvE66GPe9Br9P8qUFEJFFiCUYDqCxtuO648udw6tuxX18VaQvW3QvLvtHweeEgmP4aFA6IvG/lpzD/VDi0pmVbn/PhxCfdnw6YSk7BaI2fWJiVD52PsX9oxqfPWq8xo25LTH3pxDRh9xve+j43xFu/gv4xlyMiIiIiIiIiIiLSWu2q+dR2e4+c9H6vyy0YrdaM8T0/ERERSW8ll0Cfc2DvIqgrh3fnOK9DaG7Et53bup8IF+yG/R9Afh/I7+VPvSKSHL1mwQX74MBy6x6kPI8P8vaTkQVmXfLnFRFJhmBeqisA3DJO0vR+UREfpVswWinxBaKJSKYyDAgW2C+CTUYwmtsi8BlveAt8kPQ25ufWhb/K0qbb8/vAxHta9s/vF/tcH/wP9DilYfF7sjkFowXzoO8FsO1J9/13vAh9z/evnt1vwYrbrJCrQBC6TIZxv4Gio5z3CVXBh7c2DUfIKrAukhSNhDE/sb7GkTw/zLktr9j7v8FO96kwI0LQy8kvxzdHa5bdwfq/PLjKuc/hdRCuhYDzm/ciIq1CJgejeVls3fjpM36q2ArLvwX7FkHRKBj1feh2fGLmyhShauu469NnIbsIBn3FCsfNZGGPwWj7FlkLx9M1ACDdVe91b685mNhgtJ2vRu4z+ieZ///bfar1c7npfu/7vHsplG+C/Uubbg9Xw+JrrfOirAJ/60yV2vLEje1n6HIswWhgBQYpGE1E0l2swWiNVWyGZ/rAJeHM/90tkgjV+2H5TU23lW+CVb+Ayfe571u+BZ51CU/b/jRsfRRK5sRdZkKEHW6ANIJNP+93kX0wGsBH37faOwz1t7Z0E65tCEX2S+Egf8cTERGJgWEYAWAUcDTQD+gGtMO6X/AIsBvr/sEVwCrTNHUfoYiIiIiIiCSMaZqOwWjFaR6MFjACZBlZ1NmED9SatSmoSERERJIiqwB6TLf+PvBK2PAnb/t1HO3eHsiGrpPiKk1EUiiYA12npG7+SX+FRVe13F5yefJrERGJVZfJ1tq05oZ9M/m1NNd/NnzwrZbb83q0nvVEIi7SKhjNNM2SVNcgIimUlcJgNLdFrZEu/Ehm6DAEZn0An70Mu9+2gkY6jbUOBoO5LfsXxBGMBvDSWJj8NyuQomo3ZOVDj5nJeWqCWzBadlHk/Usfjz8YLVwL+5bC9mdgza+btu140fo4cw0UDbff/91LrUVcjdW/Fux9D+afChPugaE32C+wDNfB8m9C9T7nGr0Eq0liDb4Wlt3o3B6uhcMb3EP0RERaA6dgtKwMCEYDGHgVfPKAc3tdAoJuasrg1ePgyA7r88rtVhjrzPdSF06bDhZebh3L1du/1Pp9OuwbqaspHuEQmCFvfUNVVghH4cDE1tRauYWFQ2J+jusdXAsf3+7eZ/A10OfsxNWQLIZhnSeWXAalT8KG/4u8T+mjzm1Vu2DRNTD+TmjXw786UyWR32eH11u/K/L7xD9WpJ8XJ7vmwdbHoP/F8dcgIpIofgSj1XvlGDj6l9YNV9nt/RtXJNPteBHMcMvtm/5mBaOFQ3DwYziy03poT/lm67yorhyWeAi+XnoD5HW19u00Dgy3JwUmmdP5ndHsloX+F8OH33EeZ+Xt0H+O9R6PH8d36cjvRWtGlsLkRUQkpQzDmApjNpKcAAAgAElEQVRcC8wCPNy0AMABwzBeAO4zTfOdhBUnIiIiIiIibVZ56CCVYft7FYpz0v/6c5aRYx+M5ud7fiIiIpK+BlzpLRgtuwj6xLlOUETETa9ZkNW+ZT6A7pkWkUzSf07LYLT8Pulx311+b+h6nJWv0Fi/i/UQa2kT0uhOaBFp85wSSUNJCEY78IH9diMLcjomfn5JjtzOUHIpTPoTTLgLBn7JPhQNID/OYDSARVfDOxdZC5He/zI80xs++jEk+qHGYYdgtIDHYLStD8Oe9yL3c1K5A149Fl47rmUoWmMvjIBdC1puL/+kZSianWXfgFentAxPrCiFF46C9fc675vXAzpPjDyHJJaXoJODqxJfh4hIqjkFowUzJBit91nu7eEaCPl8s9X2ZxpC0eqFKmHzv/2dJ5NU7YbSJ1pu3/DH5Nfil3B1dP2fHQRVMQYWtXWRvm6JCCwP18Lia63zggPL3ftOzODvYzvFJ8PEe/wZa+tDMLcENt3vz3ipZBeWDzDoKzDaITzv2H/B+Tuhx6ktAzWae6YvbPNwrhmJW7h+JGvvjH9+EZFEclsk0SvCcX9z+5fBgpnw3FDYszC+ukRak89edm6r3gfzT4GXxsEbs6zr3+9dAgu/6C0UDaBmv/VgkZcnWn/Gc+ziN5vFWQAYwaafF/SH7lOdx9nyH3jzbOv47sP/Tfx7LqkQdvhaxWrAF60H+IiIiCSZYRhHGYaxAFgAzAE6AobHj87AF4E3DcN43TCMocn/F4iIiIiIiEhrtrPmU8e2Hjm9k1hJbLKNbNvtdX4/fENERETSU9dJcNzDkNfduU+HETBjAWQXJq8uEWl72vWA6a9Ch2HW53nd4Zg/Rl5rJCKSTobdBCO/B9kdrM87T7COowIR1qkky9S5n6+bCVprTgdeBeN/m+qqRJIiTX4KRURwDkartX8Kj68WX2O/Pa8bGMqQbJNyiuxTyuO18ifQ/UToMcPfcRsLOQSjBfOcg+Cae+NMOGs1tOvprb9pWkEknz4H22wCOZzMmw4XHW56gfWzV7zvv28xLDgNTm30gOhFX4HDG9z3m/RnpSCnA6eFcI2VrYJ+FyW+FhGRVHL83Z0pwWjnQLcTYc/bzn1CFRDM8W/OZTfbb197Z9u9qFf6BGCzGPzQWghVez8OTCfRBqMBvH6SdRwr0Snf5N7++onWRf1Jf4XO42Ofp+agFZ686hfe9zlrPQSCkftlGsOwnhyz5934xwpXw5KvQfeToP3g+MdLlbpK++3BAhjyNfjkfqjY2rC9+zQoucy6bjP9VSvAzwjC4x2s4D07b38BjvoujPqB83UoO4c2wMY/W+ea8fyf7Vucua/JItI2OAWjZbWHac9ZQT2lj8F7X8T22NNO1U7rAQqzq/T6JwKQ7fIwnmU3w+43/Jtr13xY+XOY+Hv/xoyVaTo/KKN5MBrAuN/CK5Mij7v6Dus4uNes+OpLN07Hs7E6+pf+jiciIuKBYRgXAw8A7bCCzsD+RMJL23RgmWEYV5qm+aSvhYqIiIiIiEibtbtmh+32doECCoMeHkaeYtmG/f14tabPDzEVERGR9FUyB/rPhsrSlg8yz+4A7YpTU5eItD1dp8BZa6FqL+R20dpdEck8hgFH/xxG/8TKlsjplOqKmsrraq2bqT0EgVzdky1titJ+RCR9OC1IratI7LxOQRQAuS6J+dL65XVLzLjr74l9X9O0fiZqDkL1fvvFMW7BaHUegwZry2DZTd7r+vB/4f0rogtFq/d4ewg3WhC15Pro9t/zLmyfa/299jDsWuDev8tk6HNudHNIYnh5jT20NvF1iIgkU6gajuxqtu2Ifd9MCUYLBGH6azD06859/A47rj3o73itQc1+5zavx4DpJhRDMNqhNbD1Mf9rac3CdXBoXeR++5fByxNgz3uxzVNXaQXXRROKNukv0GFIbPNlgkFX+zdWuBY2PeDfeKngFIYYzLPexDnjIxj9Y+vJNsf8GU5+uWmYfVaB1bf9UPd5Vt8B80+Fyu3WOTY0nGebdgGT6+G142HtXVYYuNvrrReH18e3v4hIIjkFo9WHHAeyoORSOOUtGHwd9DrD+9gvjIq/PpHWwO1mmS0P+j/f+j9YN8KYJtQccH9PLJHMsHOb3RMVuxwDM97wNnb9+wOtiZeHing1+QHrybwiIiJJZBjGRcBDQD5WuJn5+YdBQ9jZHmA98D6wGNgA7G3Up/F+AAXAw4ZhnJ+cf4WIiIiIiIi0dodD9vegdc0uxsiARfxZRrbt9lrT54dviIiISHozDCjob91v2/hDoWgikgp5XRWKJiKZLZCVfqFojWV3UCiatDkKRhOR9JFVaL89lOBgtL3vO7cVliR2bklvAfunKMVt+1x4cSwc2hDdfuVbYN40eKwQnugIT3aBp3vD5maLpdyC0WoPeZ+v9HE48lnkfjUHYN3vvI9r55Es2PESVGyLbf+3zrP+bVW7wQy5953+amxziP96nwVG0L1Pzb7k1CIikmimCR/eCk90hqd7wPPD4cAKq80xGC0vefXFK5gLI29zbt/+TPJqaavcfqe+PLFlIF8mcArmiGTZTfbhRmKvfLNzIJWdRVfH9vXd/gyUrfDe3whC77OjnyeT9L8Uhn7Dv/FW/9I6P8tUjueyn79pk90BRv8IptwPQ651fjOnaGTkufYuhGf6wuMd4NnB1rn1Ex3huSEtrxOt/z+o3uP93xFJ2Ur/xhIR8VvzJ8fWa36dtPsJMOlPMO0FmO1wPtNc+UYrbFKkrcspSv6cjxfBwwHrmsTcEth4X/JrcLtu73Q+W3xS5NBbgI1/aX2vL3YPxYnVwCv8G0tERMQDwzCGAX/Hui+xcSDaIeBu4Eygq2maPUzTHGGa5nGmaU4xTXO4aZrFQDfgbOAPwGGaBqRlAf80DKMVP01BREREREREkqUiZH9ffftgCq7lxyDbYa1DnYLRREREREREREREpJVQMJqIpI+sAvvtdQkORpt3snPbUf+b2Lml7SpbAQtOhbDDYqBwHWx9FFb+HN77EnzwbXh2AOx+q2m/6j3w/pfhwIcN29yC0fpeEF2de95zbzdNWPULfxbpvHEGvHlW7Pu/Mwdq7Z/c9V8zF1qL6SU95HaGAV9y71O9Pzm1iIgk2oY/wupfQajS+vzQOph/qvV7+8gO+32cjo/TlVPQMcCyG6Hy0+TV0hYZWc5tFVvgnYuSVopvQi5hXYUDnduqdsKB5f7X01pVeQhDbuzQmui/vmUr4b3Lotun74XQrmd0+2SaYA5M/AOcWwqnLbb+jNebGRwm5/QzH4jyaTZFR3nvW1cO5ZsaziXLN1nnprXlDX3W/yG6+SOp3O7veCIifnIKpnV7gEQwD2Z5DD99fhh88B3rgQxmOPr6RFqFFD8RtWoXLL4W9ryb3HnNOuc2t/PZifd4G/+di6z3VVoLt69XNPpfAoZuCRERkaS7F8inIRDNBH4C9DVN8xbTNF8yTdMx3d80zX2mab5gmubNQF/gp5+PUa8Q8HiQICIiIiIiIuKsPHTYdntBMDPuNc82sm231/n58A0RERERERERERGRFNJdsCKSPlIRjFbjeK+lpeuUxM0tUrEV9rzTcnu4Fl47Ad6dAx99H7b8G9b81nkcsw5W/erzv5sNgSvNBfKgeJr7QsbmKre5ty//pntt0Sr7KPZ9d71uhX44mT5PP9PpaNJ9MOanzu2RXqdFRDLF5n+33Fa9xwoEOLTOfp/CwYmtyW9Z+e7tm/+VnDraKiPo3r7nbSj/JDm1+CXsEow2/TX3fXe87G8trZlTAIqbnfO99934V3hxdHTj53SGCb+Pbp9MVtAXuhxj/XnuFve+gWwIurze7nkPjuzytbykcfqZD+ZFN063E+Oro+YAbH04vjHcOJ2zi4ikg1iC0QA6jYGZi6DXGZHnWPMbeOdiePMchaNJ2xTL8Xe9YF7T0Nj8fjEO9PkDT5LJdHhIDLifz/ac6W38so9g5+vR1ZTO/Fq0NvDL/owjIiLikWEYxwMzaAhFOwycZprmT0zTLHfd2YZpmodN0/wRcDpQQUNA2qmGYRznU9kiIiIiIiLSRpWHDtluL8xqn+RKYpNl2L+HV2vG8V6EiIiIiIiIiIiISBpRMJqIpI9UBKOVrXRuK/li4uYVqTdvmhUUALDp7/DSeHgkB/Ytim6c0ketxdsVm50XVuV0guwOMPlvYHg8BFh7pxW2tv6P8OIYeLo3LL7O+rksWwXrkhhWUHIZtOvp3B6utQ+dASs4oMf0xNQl8QkEYdT34YTH7Ntr9ie3HhERv5kmrLvX+Xf7wi85Lw7uGGWQUKoZAedjeoANf/RnHtOM3KdN8vB12fpI4svwk1swWk4XuLDMub3sY//raa1iWXD/4Xesc5fN/7E+rymDhVfAUz3hIaPpx+Jrox9/wOXQrjj6/VqDgv5w3nboNK5hm5EFkx+AS02YUwOzK6CHU0CEaQVsZyKnn/nG4R9edJ8Kud3iq2Xlz+DFsdb3cCzcfoeHjsQ2pohIMsQajAbQdRJMe8H6feXFjhfg4aB1LfTxTlZQWvkWz6WKZKxYA68KSmD2EZhTZf2cXWrCuZsht0ts4+14MbkPpTDrnNsCWe779v2CtznemAUVER72kincvl5eDbwKepwS/zgiIiLRuf7zPw2si9bXmqY5L95BTdN8Hbi20bgAX4t3XBEREREREWnbKpyC0YIdklxJbLIM++vrtaZPD98QERERERERERERSTEFo4lI+gimIBjt4GrntmE3Jm5eyQx9zk3OPIuvhddPgkVXwYEPYh/nic7w7CDn9qKjrD8HXA5nrYMhN0Qes3I7vHEGLL3BCpc4sgM2/gXevgA+e8l7bQOvhBlvwJS/e9+nsY5j4LgH4dyt0P8S5347X7ffnlMU27ySPDmd7bfXHoSwQ2CQiEgmWPs7WPaN6PcLZEOHof7Xk0qV2/0Zp+6wP+O0NqGqyH0qdyS+Dj+FXILRgrnWMd6Ib9u3b3u8IUTv0AYrwGvHS1B7CKr2wva5sPXRzPuaJIJTAEokBz6AhV+E0sdh/kzY/C+o2hl/PVntYfi34h8nk+X3hlPfhpOeg2P+DOdugUFfbtpn5K3O+2/6W2YGyzi9jgWjDEYLZMGoH8ZXS2UplK2Ibd+sQjjlLWjv8Hu8rjL2ukREEi2eYLTGzo/iGCtcC7Vl8Olz1kMk9DoprV2sx99Drmu5zQjA4DjyQN48F3bO93Y+GS+3a7xG0H3fQdd4n+fZEvdzyUwRS4DesQ/CSc/DhN9bx6OT/wZGjEG/IiIiMTAMIxc4Gyu4zASeNE3Tt6d1mKb5MPAkVjiaAZxjGEaUJysiIiIiIiIiDcodgtEKMiQYLdvhtLjOjPG9CBEREREREREREZE0o2A0EUkfWU7BaOWJm/PAcue2Lsckbl7JDAOuSN5cu99K7Pj5fZuGg7UfDEc5hEg099nLNttegV1veJ9/wj1QfJIVkNbnPO/71et9jvVnIBuOf8ha8GXH6fUiW8FoaS+nk3PbtieTV4eIiJ8Ob4IPYgz36TzR+r2XcSIsuPVjcXKk4OT6MKy2JnQkch8/QquSKezy/VIfztFpvH27GYZ3Z8PSm+D5oVaA1xtnwONF8FQ3eOs8eHcOPNMb1t7tf+2ZJJYF9429dznsX+JPLQAnzYWCvv6Nl6myCqD3WTDkWisorbniaZDfx3n/Nb9OWGkJ4/Q7IpgX/VhDr4+vllh1ngAzF0JOR+vvdtbfk9yaRESi4VcwWrue0PO06Oev2Aqf/CP6/UQySbTH37ldYdQPnEOhR/8o9lr2vA3zZ8DLExIfrGvWObcZWe779joNpvzT4zxhePVY73WlK7evl5MBl0HvM60HP3U/UaFoIiKSClOAQhreKLgrAXPc2ejvhUAr+MUvIiIiIiIiqVIesn9AZ2GmBKM53F9YG++9QCIiIiIiIiIiIiJpQsFoIpI+HIPRIgQfxCocgu3P2LcNujoxc0pmKRoBA7+c6ir80Wlsy215xbEtMK+34wVv/fqcC9mFDZ8Puzn6udoPbvr56Nuj21/BaOkvp7Nz27uz4w/sEBFJhXcujn3ffrP9qyOZOk90b6/aFf8ckQLAdr8R/xyZyFMwmg9f/2RyCkkKZDcE5XYc7bx/6eOw/g+R51n+TTiwIvr6Wot4j7PcAuyikdsN5tRC8cn+jNcWnPiUc1vpExCOIUwhlZy+lwK50Y9lBODkV+OrJ1pHfRdOXwodR1mfZ+U7993vEtQvIpJKfgWjAYz9NWS1j36/pTfAS+PgIcP6ePcS2OdjCKtIqjn9nNkpGglf2A1jbnd+WEggC058Or6aDq6GZwfEN0YkZsi5zQhG3n/gl+Bij+8XHvgADm3w1jddRXueNjzGYH4RERF/1YeUmcAa0zTf93uCz8dcbTOniIiIiIiISFTqzFqqwpW2bYXBGN7jSoEsw/49vFozivciRERERERERERERNKYgtFEJH1kFdpvT1Qw2qG1ULXbvq14RmLmlMwz+W9w7IPQ++xUVxKfAV9quS2YBz1nJX7u4bc0/bzbCVBQ4n3/QC70OafptoJ+0dWQnRlP7mrTcjq5t+9+Ozl1iIj4pWoPHIgj9GTAF/2rJZn6nOfeXr45/jnq7G9I+69502HXgvjnyTSRvi7gfP6TrhyDORqFJBWNgHY945+r9In4x8hU6RJAO/xmK9RBvOs80TnsunpP5r0WOoUhBmMIRgPofhLkdom9nmgFmwX+B12C0bbPTWwtIiKx8jMYrdMYOGMFHHUr9P1CdPse+LDh71sfgVePhR0vRV+DSDqK5vh7+C1gGJH79TzNn4eDrLjNua2uAiq2gmnGNrZbMJrX84CsfOtBLF6UPuatX7qKNuQ42tdZERGRxBjZ6O/vJnCedxzmFBEREREREfGsInTYsa0wmBn3nWcb2bbb68w0uRdIREREREREREREJE4KRhOR9JFVYL89UcFoO1+1325kQe+zEjOnZB4jAAMug5OehfF3pbqa2Dktipl8H3RN4EOUx90J3ac23RYIwtSnIb9v5P2zi+Ck51qGZnnZt/k4kt4ihdcd3pCcOkRE4mWa8NEP4anusY8x6kfJDXLx09Cvw6CvOrfPmwYffi/2hdTgLQBszZ2xj5+pQkci9wk7hA6lK6fAgECjm/qMAPSbHf9cq34GK34Q3/dmpkqHmyFLLocR3051FZnHMGDmQuf20keTV4sfwlX22wMxBqMFc+CkF2OvJ1pZ7ZrN386+H8DK2xNbi4hIrPwMRgMoHABjfwEnPglnb4COY2IbxwzBx3rtlFbC6efMzoArvPXLagfTXoDcrg3b+l8CY35qnTN5teoXVvhZY+FaWHIDPF4Ec0vguaFwYIX3MeuZLkFfRtD7OJP+Bl2mRO738Y+9j5mO3M7Txt9tvZcJ1uvzxHuh23HJqUtERMTdoEZ/X5TAeRqPPcixl4iIiIiIiIiL8tAhx7aCjAlGs38Pr9aM4r0IERERERERERERkTTm8fHLIiJJ4BSMFkpQMNryW+y3tx8C2YWJmVMyW7/ZsOa3cGRHqiuJzoz5zoufcrvAzPfg8EbYOQ+WXOfPnIEcOG8b5DmEwnQaC+dshoMfQ05nyO9j1VCxpaFPdhF0GmctZm+uoH909eR0jK6/JJ9hwMCr4JMH7Ntry5Jbj4hIrLb8B1b+NPb9i2fAmB/7Vk7SBYIw+a+w+Z/Oi71X/9IKSBjsEqDmJuQhGG3PO7GNncm8BKNVbIVPX4Res6zfvenOaeG80exy1lHfhXV3xz/fqp9BbmcY/s34x8ok0QQz+OGUt63j/9yuULHZCsiN9vheGnQaC/0ugtLHW7Ztewom/tH+nMovB9dA6RNwZLv1s9lpLPSfHTn4uDkz7ByGGMyLvb6uk+D0pfDyxNjH8Kp5EFpWfuLnFBHxm5dg2li1HwynL4dDaxqury44zfv++96HZbdYD7HoPCH+ekRSxWsw8ZmrrHNsr7odD+d/BmUfQbte0K6HtX3YjbB/OSy7yWqLZG4JzKlp+Llfcyds+GNDe/lGeGkszK6O7jgzHHJua36O5yavK5y2EA5tAMLw/HD7fmYdlK2EjqO8j51Owi7nw8NvgsFXQ9kq6DjS+f1VERGR5Ctu9Petjr3i13jsHgmcR0RERERERFqx8tBhx7bCYPskVhK7LMP+PbzadHhIooiIiIiIiIiIiIgPonhEtIhIgjnduF+XgGC0Rdc4txUd5f980jrk94JT3oJBV0PBgNTUMHVudP3bD4FuJ3roNxiGXAvHPxpbXY0N/TpcuN85FK1eIGgtmi/oZwW3dRgKPWc2fHSd7LywqqAE8qK4x7n9EO99JXUGu7w2V+1JXh0iIvFYe2fkPn3OhROegD7nN2zLKoSR34NpzyeutmTqMMK93S7AxysvAWC1ByHUxp58WechMA7gzTNh4eWJrcUvTgvBmwdztOsB01/zZ87lt0D5Zn/GyhROASiJ0v0EKCyxAsk7jlYomh/6X2q/veYA7F+WuHk/fRFeGgcf/xA2/tUKzFh8Dbx6XPTH724BfYHc+OrsNC6688dYNQ9wC7oEowUSGFYnIhIPp9djv163AkErpKj++t/4KMNt1/0OXpkMn/zTn3pEUsHtXLXTeOg/xwoRjOW9qkAWdB7fEIoGVmBt8TQ4/hHvAWTr7gHTtD423W/f57kor7k7BV8DGFEEwNXrMAQ6DIPJDvUBvDgaQtUN/5ZM4hhU+fn/YVaBFQKsUDQREUkvXRr9PZFPvaof2wA6J3AeERERERERacUqQodst7cL5BOM5oEeKZTt8B5ebbIfkigiIiIiIiIiIiKSIApGE5H04XTzfqjK/Uny0ao5CJvuc27vPNG/uaT1aT8IJt8H534CU/5hBXrV6zQeLtgLvc5MzNwDr4x+7Ml/a1go40Xf8yP3cTN1Lky8J/GLcQwD+l3kvX/H0YmrRfzTdbJzW9Xu5NUhIhKrJTfAgQ/d+wz6Ckx9BvpdAFOfgktN6+Piw3D0z1uGqmSqSAGpO+MIsfIaAFa9N/Y5MpGXwLh6W/4Dn76QuFr84vT0UrubD4unQ25Xf+Z9dmDmLZqPRzKD0XrMTN5cbUmv062ATTvxvN66CYdg6Q0Qrm7ZdnAVPNUd1v/R+3ghm3HqBeMMRjMCcNyDEGwX3ziRNB/fbb5E1yIiEqtEB6M1N/R66Hl6dPuYIXj/SqjcnpCSRBLO6TxnyNdg1jI4/mHoPM7/eYtGwNg7vIWQffAteDhgfZRvtO9TWWqF43plurzPF0swWr2ux7m3P5pn/TseK4QFp8Nhh39PunEKkjOy7beLiIikh8YXcRIZjHaw0d9byZsqIiIiIiIikmzldfbBaIXBDkmuJHbZDteM65zeixARERERERERERHJMApGE5H04RakFKrwb55d893bB17h31zSug28As7eAJMfgJNfhZnvQm4XGHyNff+jfwHTXoQep3obf/zv4LiHYcLvYeZCa55AFAuEzlwN3ad67w8QyIbsouj2qXfeNuhzTmz7xqL/HO99i0Ylrg7x17Cb7bdXJzEYLVwHexdB6eNwZFfy5hWRzLbuXtjgIQCm99mJryUdRApGi0fIazDansTVkI7CVdH1/+SBxNThp7DDQnC74F8jAEOu92/uNb/xb6x0l8xgNKdzJYlPMA86jrFv+/hH/obN11t7J1Rsce+z9AbY+Dfn9rKVsO0ZKFtlheI7CcQZjAbQYwacsxmm/DNxgfgtws7CUfQVEUkTIYdgtGCCgtEC2TDtBZj+Ooy7Ewr6e9/3mb6wa0Fyj2VE/JDsAMLGRtwCZ66CY/8F4++Of7zF11o/h144BX1BdA93aa5ouLd+oUr47BV4bgisvdu6/pvOgdhOr23xfK1EREQSr/FFnEQeqDc+sFBqqIiIiIiIiMSkPGQfjFaQUcFo9u8t1JoO70WIiIiIiIiIiIiIZBgFo4lI+nALRqvzMRjt8Hrntna9oV1P/+aS1q9wIAz6MvQ81VqMDlY42JR/QPsh1uK+4ulWgNrIW6HXLBh6g7exh98MJXNg2I3QdQoYhrf9ukyCWR9A0YiY/knkdo1+n/N3Qn6f2OaLVdcp1tc4kqJRya9NYucUpFOVpICy2sMwbzq8OgXeuRie6Q1bH0vO3CKS2byETOV1h56nJb6WdNDB48LkWNR5DEarSmKoZjpwCrFwsu0p6/deOnNaOG84LAQf9UMY+T1/5t54nz/jZIJEhYnMWGCdCwVyoHAQTLoP+l2QmLkEikY6t+143r95zDC8fRF8+F1v/Tf8yWYME5b/D7w4Gt4+H14cBQu/6DxG/bl2vNoVw8AvwWmLYdQPoF0vyGoP/S6Csb+y36frsVA8w9v4zcPO3MLesvK9jSkikmypCGwyAlaA5YhbrOMHI4q3LudNh9dOhOp9iatPxG+OgVdJyvToMAwGXA7Db4JjH4x/vHnTYckNkcN4S59wbjOieCCMnfO2Rdd/+Tcbrv86BXKnmtP5cLK+T0RERERERERERERaufKQ/X1ThRkUjJZl2F8zrjX1YCERERERERERERFpHfRIYRFJH1mFzm1+BqNVlDq3DbvJv3mkbRt4hfURroNAs1+3XoJC+pzv3Db+Llh+i33bWeuhg4ewMDe5XaB8k/f+PU6xFpgnmxGAiffCgggBM2Pv8B4qJ6mX18N++4EPoaYMcjomdv6VP4M9bzd8bobgvUut7/PczomdW0Qy28FV7u1GACb+0b9wl3TX9djIfXa8DL1Oj37skMdgtOo90Y+dyWK5oe2NWXDK2+l7rOS0QL358fV/twfh6J/DmJ9Z3ydGNoQ/DybKKrR+DtfdC8u+EXnu8o0wbwYc+8/WH7LrFIASj2E3QfE068PunEj812msc9uSr0Gfc/2ZZ+N9sM0l1KK5A8uh5iDkFFmfmya8fyVs/lfTfjtfdx4jmBt1ma4MA8bcDqN/YgW9Be6uLKkAACAASURBVIJQdwS2PgoHPmjo1+1EK6AnELTCNufPhLIVzuPm9236uVswWvMQNRGRdBGutt+eyGC0xgoHwLCbYe1d3vfZtwg+/jFMvCdhZYn4KhUBhE4GXGY9kOKDb8U3zoY/Wg+F6XUmbLoftj1pHcP1+QIcWg3bnnZ/aJFT+LVX+X2s4NuVP41uv21PwKbpMORr8c2fCE4BevF+rUREREREREREREQEgIrQIdvthcH2Sa4kdtkO7y3UmQm4F0hEREREREREREQkBaJ47LqISIJlFTi3+RWMZprWAg0nA77kzzwi9ewCADoMgy6T3Pcb+GXntj7nghFsuX3ivfGHogHkdImuv9sC/ETrORN6ugSqzPoQep+RvHokfkUjnNtePNp5QZhf1vy65TYzBKWPJ3ZeEcl8bq9PA6+CWSug3wXJqyfVIh3rALxxRmyvr6Ej3vpV741+7EwWy+/IPe86B+6mA9MhGM3haacN7YZ1fhnMgewO1ofx+SUwt/PO5nbNh+eHQ80B7/uks3AtlG+BqmY/G34+JXbI9XDi0zD+dw3bFIqWHP0udm478hmURhFm5mbDn6Lf5/nhDa9RH/2wZShaJAGfg9HqGYYVegaQ1Q5mvg8T7oHht8DkB2DGvIb2vO5wypsw5Ab7sdoPgaKjmm5zC1VsK0GpIpJ5nEIdk/m6Nf5OmPosDL7GeoBDQf/I+2y637n2ugrrGKjWfnGLSNI5Bl5FOM9JlGHfaHkcE4stD8OyG2HxV+Gzl2H7XHj/Clh9h3soGvgT9jXmdsjtGv1+S66HPe/BviVQuT3+OhoLVcO+pVD5afT7Op0PB1L0fSIiIiIiIiIiIiLSypQ7BKMVBDskuZLYZTu8t1Cb6HutRURERERERERERJJEKxNFJH0kIxht+Ted24pGQbtif+YRieS4h+Cdi+HA8qbbjQCM+Sn0Pst538KBcMLjsPAKqDtsLYQZ+QMrhMAP0S4e6jTen3ljNfI2a6FVc73Phk5HJ78eiY/bIrzKUtjzDhSfnLx66m15EIZcm/x5RSQzhEOAad826gfW4ty2JtvLkzNNWP0b6HdRdGPXVXrr5zVArbWI9Ya2dXfDyFut0J9047gQPI7LWdmF0fWvq4CVP4fxv419znSw601YdDWUb7RClgdfBxN+b4U+OX3v9DrDCnba+Ff79mA76+cspxOMvwsGXpmw8sWDvK5WoNfau+zbV/0c+l0Y3xwVpVC2Ivr9qnbCjpegeJpzfW6SFcYTzIFhX3duzymCY+6FrpNh8XUQ+vz3UfuhMO0FK2itsT7nwqKv2I9lhvypWUTEb+Fq++2JCql00uds62PSX6zPX58Gu9907h86Aku/AZPva7p987+t9wSq90FWoXXddfjNCStbxJNwjf32YE5y66gXyLYeujJvenzjbH0oxvlz/fu3z1oB7862riFH47XjG/7e+RiY/irkdIyvltV3wMc/abg2UTwDjn/I+7m3Y4Cebu8QEZG0V/9myRTDMEoSNEePBI0rIiIiIiIibYhTMFphBgWjZRn219drTYf3IkREREREREREREQyjO6cFZH0Eci1bui3W/xeezD+8Y/sgnW/d24fdlP8c4h41X4QnLYYqnaBWQt5PeHQGmg/2D0ksF7f863wtINrrLG87ONVbhfvfQM5VmBCKnU73grK2rWgYVsgB0Z8O3U1SeyyO1jfg9X77Nv3LU1NMJoRSP6cIunoyGew9RGo3A7dT7JCKJuHgLRFpksgVc/Tk1dHuhl8HWz8s3uf/Uvgs9fgs1es1//+c6BwgPs+XoPRDnzkrV9rEc+TPrfPhcFf9a8WvyRiIXgwhuPmtXfCztdhzE+g9zmZ9bpnhq3z4OW3NNoWgg3/ZwUgTPid89c5kGOFkQRyYP29TduO+SP0mw3Ve6FdD+sYTlJv7B2w8S/24fJlH0GoGoI2wTZmGEqfsIK783parwdZ+S377V8We2173rECaUIeX8MbC7aLfd5EGHA59L0Qyj62AjPaD7F/XXA7tw45BA+JiKSa0+tTsoPRmjv5VXjhKCjf5Nxn09+sYOqCftbnZSutgMr6Y526ciskbfk34ehfwMCr9KAUSQ3H85zs5NbRWPHJ1vF96aP27Se/Yp2HvXWe9bAWPw38sn9j5feCU96Cii1WqC/Aoqug/BPvY+xfAk90gkFfta5R9IghMG7J161zrsZ2zYOniq3Xn2E32R9vN+YYFJ7C7xMRERHvDODhBM9hfj6PiIiIiIiISEwqQvbXuzMpGC3b4b2FOrf7GUVEREREREREREQyiBIeRCR9GIbzotGqPfGPH+lp9b3Pjn8OkWgEgtZCnYL+EMyBTkdHF3AWyIZOY/wNRQPI7eq9b8llkFPk7/zRMgw46QUYeRt0Pc5awDXjDeh+Ymrrktj1dAnbK0tVyI3uqxfh8CZ4ZZIVrrP2LnjrXFimYFkAdrzk3NaWF6wOuspbvwUzreCpFd+DlydYIZhuvIbqbH0Ids731rc1iOeGtr0L/avDT2GnheBxBKPFGmpWtsIKAVichgFyTswwLDi9aShaY+vutn7ewg5Pia1//ZrwBzjmT1A8wwqGO/5RGPI1yO0MHYYqFC2dBLKgxyn2bWYYDq9vui0csn7O3rkY3p0Nq++A5TfDc0Oh5gCYJoSqPv+ogQMrYq+t/BM4uCr6/bpMie9nPlGy2kHXSdbPgNvryqS/2m8PVSWmLhGReDm9PgXzkltHi/lz4PQlkR9usrHR6+7au5wDqFZ8D14c3fbClCU9OB5/5yS3jubG/9b+vYZ+F0PPmVZA2EVl/s87+Bp/xzMMK3C9+CTr46x1MPbX0O2E6MbZdB/MnwGrfx3dfqt+2TIUrbEV34One0PtIfdxEhEULiIikjz1oWWJ/BARERERERGJS3nI/jptQbB9kiuJXbZh/95CnVmLaZpJrkZERERERERERETEfwpGE5H0ktvNfnu1D8Fo610WIvQ5D9oVxz+HSGvgFFDYXMcxMO43ia3Fq6x2cPTPYOa7cMIj0O3YVFck8eg/27ktr3vy6hCRptb8Giq3N922/h44tC419aQLM+welNSWg9G6HAMlX4xun5oD8PGP3Ps4LSK3s+LW6ObPZE6Lpr2o2uVfHX4yHYLR4lkIboZi3xdg0/2w+634xkiWT5+Dna+591n0FefvnfpgBsOAIdfBjNfhpLnQ/2J/6xR/TX7Aua3s82CyIzvhrS/AI1nwSDZse7JpvyOfwhOd4eEAPNru849cWPkT57GH3uhe17YnYVmEPnaOfzj6fdJJMN9+e1jBaCKSpsLV9tuDucmtw05OJ5hwt/WABCerfg51lVbw55YID0qp3gMrb7f+vul+mDsAHu9oheGWf+Jf3SLNOR5/p/j6QX4fmPpM0yDEDsNh7C8bPjcCcNx//Jtz7K+g8zj/xrMTyIKjvg2nvg0Tfh/9/h9+1zo2Prg2ct/qfZGvaQDUlsHjRbDuD1YYsR3HoPA2fJ1JREQyjZngDxEREREREZGY1YSrqTHt3xcrDGbOAwKzHB66YmISwuE6s4iIiIiIiIiIiEgG0SOFRSS95HWDgzbb4w1GqyiF8k3O7Sc8Ed/4Iq1JbtfIfUb/GEZ+HwLBhJcjbVCvWc5tdZXWn+WfwOYHobIU+nwBesyIf5Gu29PRDOUJi7DjJfvtWx6GMT9OailpZdcCa+GrE6ONL1gdfxdseTC6fT57FUI1ELS/cSuqYLR9i6Fqd9sI1owrGG23f3X4KRHBaPn9nNuyi6DW7oS0mc3/gu5TY68hGcwwrPhe5H5lH0FWoX2bFtxnptzO0H4oHF7fsm3Vz6BmPyy9wd85R/yPFZpdNByWXO/fuAOvgsIS/8ZLhcbBIo0d+cw6n8nvA91OsAI7RETSQcgpGM3h9SwVep7m3v5ERzh9uXPIW2PbnoTXToA97zZs2z7Xuu502lLncxKReDid0zosXkqqHqfAedut8/KcztD9BMgqaNqn1xnxzTHgS9BhGPS9EDoMjW+saA38Mqy+A47siG6/mgPwxulw5mrIcgi+Bdj9ZnTn5stuAiMIQ22Oz02HceI5HxYREUm8UhRaJiIiIiIiIhmgPHTIsS2TgtGyXe5NrA3XkhXUvT8iIiIiIiIiIiKS2XTnrIikF6dApuq98Y279i7ntrG/VriTSGO5Xdzb84ph1A8UFCWJYwSgx6mw87WWbXXlsOHPsORrDds23Q8FJXDaoviCb8yQW1GxjyvSGphhqNxm37bxz207GG37M+7tbT1YKNgu+n3MOji8ATqOtG+PNgDs4Kq2EYzmFCLmRboGo4Ud/k3x/Fx1HA35fVu+phWNhJnvwwsjreBVN5vuh8l/i72GRAtVw8LL4eBqb/33vme/va0HO2aybsfbB6MdXOV/KBpAt8+DAod8Dfa+b4UH+mHQ1f6Mk0puQUILL7f+7HocnPwyZLdPTk0iIm7CVfbbA3GG0fspEIRZK+Clo+3bw7Xw4mjv4zUORatX9jGUPgoDLo+tRhE3Tue06XL9ILcLlFzi3J7T0Qp23fNO9GMfdSuM/UXstcUruz1MnwdLr7d+9qMJXq/YCp/83T7E7L99IpxL2ln6dStsrnBA0+2JOB8WERFJMNM0S1Jdg4iIiIiIiIgXFaHDjm2ZFIyW5XJvT51ZA7g87ENEREREREREREQkAyjRRETSS243++1Ve2Ifc/9yWPd75/Zes2IfW6Q1Khzo3j7+dwpFk8RrP8R+++ENTUPR6lVsgZU/i2/OcLVzm77npa1zC6mtdX56YptwcI17e1tfsBpLMBpYQQROog1GK1sVWw2ZJtqvS2PVu8E0/avFL05hb0YcOf+G8fnxbKMxArlWYHZ2IZy1GoIebgqsijO8OxFC1fDRD+HRPCh9PP7x2vrrVybre2Fy5+txSsPfR97mz5iDr4Nux/ozViq5BaPV2/serPpl4msREfEi5HBtJJ2C0QA6jbFC9RNp62NNPz+0Ht7/CrwyBZbemL7hwpL+ag/ab8/KoMVJY26Pfp+CEhjl07FiPIqGw4z5cHElXLgfCgd533fro+7tTg8ViGTxtS23OQbo6bl3IiIiIiIiIiIiIvEqD9nf82hgkB8sSHI1sct2CUarNeO4l0xEREREREREREQkTejOWRFJL07BaEc+jW28/cvg5YnufYpGxja2SGtV0B+6TIZ9i1q2TboPSi5Jfk3S9mQV2m+3+76st/0ZmPiH2OcM17g0GrGPK9IaVG53bgsdscLRsjPnSYm+OqRgNFeBYGz77XwVSubYt0UbAHZwZWw1ZJp4gtFCVVBXDtnt/avHD4laCN7vAihcDNvnWmP1ORc6jrbasgpg8LWw7nfuYxxcBXknxVeHn0wT3jgDds33b8y2/vqVyZIZKNbvYshqFILZYWj0Y4z4jvUaVLndukbT7TjodaZ/NaaSl2A0sH7v8YuEliIi4olTaLzX17NkGv5N2Pla4sbf8bx1jGUYUP4JvHZ8Q2j4vkXW3KctarvnwhKbULVzMFpu9+TWEo/ik+HsjfDcYOc+2R0AA4ygFZ479Pr0ei0JBCGnE5y5Cj55AJZcH3mfPW/Dzteh4xio/LTh/zJUBbld4cCK2GrZ/aZ1/tv4HMwxKFznaSIiIiIiIiIiIiLxcgpGyw8WEjBivN8tBbKNHMe2WtPtnmgRERERERERERGRzKBgNBFJLwX97bcfWgtmGIyA97F2vQnzprn3OeMja2GTiDR14lPw1nmwf4n1eXYHOOZPUHJpauuStiMrhieuVW6DUA0End/odxVSMJqII7dgNLDChQZcnpxa0knNATiyw72PodPumGx7yjr2COa2bIv2aZZtIRjNNJ0XTXtVtTv9gtEcF4L78HPVeZz1YWfM7dbPdumjzvsfWA7FaRSMtnehv6FooGC0TJbTCfKKoWpX4ufqP7vltgl/gGU3etu/YACMu8PfmtKJ1/CP/csSW4eIiFehKvvtdsflqdZrlhWsX1eeuDnW/AaO+g5s+ntDKFq9Q2th+3Mw4LLEzS+tT/Ue57a8DApGA2g/CGZXwZKvwSf/AEwoGgVT/g5dIjywKJ0Ec2HI16DfbFj8Vet6hJv5p/pfQ7gGDm+EohGNtiUoKFxEREREREREREREHIPRCoOZ9UCcLJd7e2rjecimiIiIiIiIiIiISJrQnbMikl6KjrLfXlcBFaVQWALhEJRvhLKPrKev15RB4QBr4UJWvtW/tjxyKFqf86DjaD+rF2k98nvB6YutIJzqfVA0UgtuJLliCUYDK8SksCS2fcPVzm3RBHOKtEbV+9zbtz7WNoPRKkoj91GwEBQOto7fo1F7ED57Bfqc07ItHOXTLMtWWsFhrTkQ2WsoWn4f56DDt8+HITfAwCvTJ/gi7PDvSvRxaXYhnPAIVN0Lc/vaB4Qsv8U6n+xxSmJr8Wrn6/6PGYgxbFbSQ9HIxAejdT8J+pzfcnuvM7wHo/X9gr81pZtgFOc12+dC77N17iEiqWOazsfagTQ5PmzuvO3wRMfEjf/hd6H3ObDqZ/bta+9UMFprUx86Ha6BULX1Z/1H88/D1daDFhp//t++DtuqdjrPnWnBaGCdO055AMb+CkJHoF0fCARTXVVscjvDiU/Ckc/gzXMbHhrjpy6TYd8i+7b3r4TprzcEltdV2PdL19djERERERERERERkQxSETpsuz3TgtGyDed7e+rMKO+xExEREREREREREUlDSjgRkfTiFIwGsPgaqDkAB1faL0zf+Bc4fbm1iH37M5HnGnxN7HWKtBX5fawPkWSLNRitclscwWhuNwG04jAdES/cggMBdr5iHafldEpOPemianfkPgpGg56nwYYog9EAdr3hEIwW5dMsaw9aC5vze0VfQ6bw+jWZ9SE82dW+rexjWHIdbH0ITn4NgmkQiuUU+GYk6XJWXlcY8V1Y+RP79vmnwvi7YfhNyanHiWnCxz+K3G/MT2HkbfDSOChbEbm/odevjNZ/NuyaH/v+xTOsMIauU6D4ZNj8T9i/DCq2Qo9TodsJMOJb9qGThQOh80TYv9R9DiMIg66KvcZMEM25yVvnwcAvW+EiIiKp4HZdJJiXvDqikVMEU/4O73/ZW//60GYjy3u4cOmjzm0HPvA2Rr1wXdt++IRpWucuruFijcLE7MLFPAWWRRli1rx/KhjBzL6mkomhbk7a9YRT34Kne1nXuvw09Ouw0CEYbd9ieHYQnLnaes11CjnO6+ZvTSIiIiIiIiIiIiJtUHnokO32gmD7JFcSnyyXe3tqzSjvsRMRERERERERERFJQ2347nsRSUvZ7SG/H1SWtmzb+Zr7voc3wOo74OifRl7kPewm6DUr9jpFRCSxsgpj269yW+xzui18LI8h0EekNbELpW0sXAvb58LAK5NSTtrwEoymYCE4+mewb1HkgJzmqj6z3x5tMBpA1U4FowHkdoGC/lawkZPdb8H2p61QpVQLO4RVJDNwsP9s52A0gOU3W+F7o35gHxCVDLsWRO7TrqcVimYYMPNdeLI7hCrd98nv7U99khoDvgQ750HpYy3b8nrA1Lnw6mTn/We83vRzu6BKJ4YBk/4Kb54FR3Y49AnCuN+6B+S3BtEGCX3ydxhwuRVGJyKSbG7nfcHc5NURrY6jvfU7+pcw8n8bPt/0d1jkIaDz4x/HVFYTh9bDoqth70LI72sdOw7yGObmVYvQsUjhYrEEjvkQYib2cruBEUh1FVIvmAcz34fnh/k7biAH+s+BrY/Yt1fvgaciBJ/ltqIQOhEREREREREREZEUcQpGKwx2SHIl8QkaQQIECRNq0VZr6n0ZERERERERERERyXwKRhOR9FN0lH0wmhdb/g1jbod9S5z79DwdJtwd2/giIpIcWQWx7RdPMFqo2rnt8Aaoq4i9rnRlhmHfUjjwAXSeYH2kKtRF0lvY5eej3tq720YwWl0lbH8W6g7But9H7p/MAKd0ldPRWlD8SJSXIGoO2G+PKRjNQ4hdJvPyNSkosf7M7e4ejAaw46X0CEYzHYLRjCRezioaAb3OgB0vOvf5+EfW13fgl5JWVhOfvRy5z4hvN/yOzyqwQq6cFuPX6zkz/tokdYJ5cPzDcNT/wv5lDT9P7XpB96nWa3PP0+CzV1ruWzw9/vk7j4Oz1sDud6xwyk5jrRDBQ+ut78Fux0HhwPjnyQROX2cnn/xTwWgikhpu532BKIMekyk3QohQfZ8h1zXdVnIJfHQbHHEIZPZLxbam4UoVm61AtnV3W+9VxBIu5rRNMle7HqmuQJrrMBTyuvt7PSGQDb3Pjnwu5ibPw2ueiIiIiIiIiIiIiLiqaCXBaADZRjbVZstgtDozhnvsRERERERERERERNKMgtFEJP0UjfS2sNtOxVb49DnY/aZzn3G/jm1sERFJnlgDyCriCEaLtIB0+3NQMif28dNNOASLr4ZP/tGwbfA1cMyfwAikrCxJU27BgfXKVsDWx6D/xYmvJ1XKVsGCmXBkh/d9jGDi6skkgSDkFUPVLu/7VO+33x7LTVutPRjNy9dk1A+tP/O6R+67+Z9w7D/iKskXToFvgSRfzjrmT/DiaKi1vykSgPevgB6nQH6v5NVV7+Bq9/YJ98DQG5pu6z/HfTF+j1OhoH/8tUlqGQEroKzzOPv2wdfYB3aVXOrP/NkdoPcZTbe1xcCvfrOjC0ZLl9dgEWl73M77grnJqyNakYLROo6G4x+1QkEbC+bBzIWw5Hr3EFxXEcLl9y2FV46xbyv7yPoQAeihUOK0dNZaeKKzf+MFcqzzxpxOzmHwkeR6OKcXEREREREREREREVflocO22wsyMRgtkEN1qKrF9lo9VEdERERERERERERaAQWjiUj66eSwYNert851bgvmWwuhREQkvWUVxrZfZTzBaBGCn1bc2rqC0bY/0zQUDWDjX6HXWdDn7JSUJGnM5sYZW+/Ohn4Xts5wvcOb4MVR0e9nRFgo35YE86Lrv3+JFc7QPITBKSzLTTSBbJko0tekeDr0n239PS9CcES9x4tg+Ldg5K0QyI6tri0PW79bdr/RsK3jaOh+Mhz9Uys0yY1ZZ7/dSPLlrIJ+cNzD8OaZ7v2e6Q2XmsmpqTGnYLTcLnDBXvu2nqdDdhHUHrRvn/QXf2qT9NbrLOh1RtMwmO4nWUFe4p+SS2HRVdHts/7/2bvP8LjuOm/j95lRsyT33uM4sR2nOE53qtNJIz2B0GGBZemwLGF3gST0Tlg6+wCBZcMSCJAQ0nvvxenNiXuvkmWVmfO8ODGWpTmj6Wr357rmss6//iRLU6RzvvMj2PNffB4xEKy8GZ7/Lmx5AeiFxwgpH9meUyb6cDBa1ZDs/ac8Ef8atWE6LLwOnroEnr40/717Cox75MPZ+yWAYbNh9sd7uwplUjMSjroa7rkg/vVpPhLVUD0UDvwB3P+OwtbIJexckiRJkiRJkpRVUyrzmyM2JodWuJLiVQWZz+tqL+TNRyVJkiRJkiSpjzEYTVLfM/UceGwstK4t/doLrij9mpKk0qtqKGxeUcFoPbw7WvNrha/dF73wvcztL//MYDR111NwYGdPfBbmfyu/9VPbofl1CJIwdI/85lZCy2q4tg/W1d8UEqbw6CfgkJ/s2lbIu1m2rsl/Tn+SLcRi/2/CrI/sDIxI5fjz3L4FFn0RVt4IJ92beUwYwuZnIExD24Zo7ZoR0c/ys9+ApX/sPmfTouj24g9g4d+BN0IqaobDiHm7Bluk+0gwGsDkU+HYm+D2k7KPe/5ymFPBUIN0e/xzlEN/GT8vWQt7fQae+s/ufeeuh9pRJSlPfVyyBo7+Kyz9E2x8PPoZnHJWzwEzyk+yNgrqT23Lfc4jH4GWlTDvy+WrS+URpqPHxm0rosfQuNddUn+Tb8hxpY05HNbd1729cffcgrsLDaLM9hqnZSWsf6iwddVPBNHjfKIm+l5I1ES3ZJfjRE2ncZ2Pa2HUQdHfxHz+3XdNPRtOeRz+HvOmS/t8Hp7+Um5rJWqif2e8HYbPhRsOzL8eg9EkSZIkSZIkqShhGNIcG4zWw5s89kHVQU3G9o6wgHPsJEmSJEmSJKmPMRhNUt9TNQSO+Rvc9zZoejlqqx0dXaA7Yr/oNnI/uOdCaHolv7XHHF76eiVJpdcbwWipQXYSwNqYkJsV11W2DvUPqe25j33u27D/N3K7+Bxg/cNw70U7n/dNPgOO+D1U1edfZ7k8/+3ermBgSBYQjPbyT2H2R6MLhnfIFgIWp2V1/nP6kzAmQAxgt7ftGnKU68/mDuvug5uPguNu2fX/sHkJ3H0ubHgkv/U6u+PUXY9rR8OC38Gkk6PjuM8r0Uu/zpp4IhxzHdx5WvyYpy+LgugSycrU1LoeCDP3NeyWfe7cz8K2ZfDq/4t+rkbOh8P/x1CGwSZRBdMvjG4qn+F7w4aH85vzwg+in9Pq/veO1IPWlhfgzjNg60u9XYlUeoU8l6+kuRfDXW/u3j7+2BwXKDAYLVtoc9Nrha2pNwQ7w8N2CRTLNXSs0/GO8cma7iFmOc2PW7NCz/nV+0bsAxdsg4feD69fGQWhBskobHrfS2DOp+HBf8ocDt5Zonrnx6MOgPM25TZvh5qRMGyvgj8NSZIkSZIkSRK0htvpiDknqX8Go1VnbG8PCzjHTpIkSZIkSZL6GIPRJPVNYw6BM16Atk3RBdp14yDocnFSw/T8gtH2+CDUTyptnZKk8qhqLGxe67o3LkzLM/QFIN1a2J6qvDANr/4aVt8Br/8vhCmYeg7se2l0oaJKL9+fj3UPwNgcAmnTHXDjIbu2Lb8W/tAAFzT3jXC0MIQlV/V2FQNDoq6wea9dCfO+tPO4kGC07SsL27u/yPY1SXQ5+W3KWfDa7/Jbf+098Px3osCJ138Pa+6Cl3+Wf509aV0P970VTn8R6sZkCUbLfEJfRUw+FY6/DW49LnN/2wb4fRWM2Bc2LYraZn0EZrwTRh9c/P5b+BFGAgAAIABJREFUXooCA1Mt0WNf3fj4sXVjs6+VqIJDfgL7fzU6rh7R/XW3pNIYe3j+wWgdW2HRpdC+aWcI4rK/Rn2zPgb7XQo1I0peqgoUhvC3Ob1dhVQeyfro1pdNPh0mvxmWX7OzrXpYdH+Z0/zTYNEX89831RI9F8/0/HTDo/mvVzFxoWM5BIwlMwSOFRM6FhdiZuiY+pqqIVGQ9ME/joIPh+6x8/dWNcPhqKugdQPc/y5Y8bfMayRqdj3eMa9tI9xwyM43DYgz9+LoZ02SJEmSJEmSVLCmji2xfQ39MBitquvvnt/QYTCaJEmSJEmSpAHAYDRJfVeQgNpR8f1143Jfq2G36GIFSVL/UNVQ+NxUS2Hz022F7znQdDQX939QiHRHFNDSk1Qb3P8OWPKHXduXXh3dTrgbxh1ZnhoHs1SewWiv/1/mYLSOFkjWRWF2iSq4bm78Gn/fD057JrpIupJSrVE4b6IKakbB6tuh+fXK1jBQFfp/ufL6XYPRCjlpa8vzhe3dX+QTjDbxpChsIN/Awxd/GIU7LL06//ry0bYxCm6b8/HosSGToJd/nTX+WJh0evzF7rAzFA2ir91LP4Ejr4KpZxe+79r74JZjdgbGvfSTKIAtTu2Y3NatGVl4TZJyM+0CeOHy/Oc9/53M7S/+AF78Lzh7JQzJEpCoyrn3Lb1dgVQ+E0/K7fV6bwoCOOJKeOWXsPJGqJ8Me30Ghs7Mbf7I+VA/FbYtzX/vts1RqG9n25bDox/tee6oA2HIpBxDx/INGDN0TCqL6mEwcr/MfbWjYNT8LMFoMSHfNSPh9Gfhue/Ak5/LPOaI38P0C/OvV5IkSZIkSZK0i6ZUfDBaY3JoBSspjeog8++e79p0A882PwZAMqhmRt0sjh5xCkOrhleyPEmSJEmSJEkqSh+/kkGSsqjNIxjt+NujoDVJUv+QrC98bnuTwWjF+kMjjD0SDvt17hcRF+qVX8GT/w7bV+1sGzYHDrw8uvh6h1W3wEMfgqaXs6/39GVw3E3lqXUwS23Pb/zSq+DA70cXpwM0vQYPvBvW3LlzTLIu+7pNr8Btx8OJ9+S398v/DQ+9f9e2XALztq+Dh/8Zll+TPWRKlbfhUbh+Piz4TRQCVcj/z7ZlcPPR0RqNu5W8xF6XTzBa9bDoguq78wzoallZ/lC0HR77BOz5QdjyXOb+3g5GAzjyD/CHPJ6vhCl49GMw+YzCgkXCMLqPCruExXUOYOusekT8RfeSKm/MAtj3Ulh0CRCWaNEQnvkqHFRA4JpKq21T9+BmaaAYtlf/ecORqnqY/ZHolq8gET2/u2lB/nNTzUCXYLQXftDzvGOuhcmn57+fpD4uiO9K1GTpq4a9L4bRB8N9F8H2NVH7yPmw8HrDcCVJkiRJkiSpRJpjgtESJBiSqPAbCpdAdZD5d8/r21ezvn31P46fbX6MR7bezaenfo3GqmGVKk+SJEmSJEmSitIHriSVpALV5RiMNvqwgRl+IEkDWSIJySGQasl/bkcTkOVCsY1PwIbHoG48TDplZ3BmLnulU1Ftg8Hae+DWY+HNr5Q+WKV1A6y4Dp77Dmx6snv/lufh9pPh6L9C8+uw9l5Y8n+5rb3q5ig8JshyEaLyl27N3F47GlrXd29vWQmrb4MJx0O6A249DpoX7zoml7C1tffCdXvDuGMhWQvVw6FhGkw8GYZM3DmufQu88kt47JOZ17nlKNjzw92fPza/Bg27wZjD4IXLYcXfe66ps8N+DcPnwo2H5DdvsKrLct889ZzsoVsbn4CbDoezlhQeZLn2brjlmDfu1wbIr0OaX4cVN2T/3s30rqBTz4LzNsCau+GuM8tXXzFuOwm2r87c1xf+/6qGwJFXwT3n5z5n2zJ48Ucw5+P577fxifgQtExqx/Q8RlLlBAHs+wWY8Xa4poTBw0uvMhitL1hzV+5jp50PE08pXy1SKQ2bA6MPGjxhq2MOg/O3wh+HQ5jOfV57U/e25dfGj0/UwLnroHpo/jVK6t8yvT7vasLxcMbL0e/EkkNg3FG+8ZMkSZIkSZIklVBTTDBaY3IYQT8877Qql989v2F123Ie3noXx470zXskSZIkSZIk9Q994EpSSSpQ/eTcxs18X3nrkCSVR1VDEcFoMZ79FjzxWSCMjkcdCCfcHYWbtG/tee10GySG5F9Tf7VtKay4Hqa8uXRrbnoGbj8JWlb0PLbQsJ4dgVwqnVRMMNrU8+Dln2Xuu+0EOOyKKMCsayhaPjY/G906qxkJx1wLY4+AjU/C9fv3vM5LPyq8hkwOuwJ2f2dp1xzoxh8LS//UvX34PlHA1BMXw3Pfip/f0RSFp6XbM/c3zoy+17KFGGxbAqtvh4kn5ld7X7T8b3DPhZDaln1cXIhYzcjo/n3GO2Hxb0pfX7HW3h3f11cuCp90Wv5Bro99Igotm/G2/PZ6/ff5ja8bm994SZXRuDvs/h549VelWa9lJbSsgiETSrOeCtP0Sm7jhu8NR/6hvLVIKk51I+zzBVh0Se5zuv4eKt0BW56LH7/flwxFkwa0LBfNJWtyW6J6KEx6U2nKkSRJkiRJkiTtoimV+VzhhuSwCldSGvXJxrzGv7TtaYPRJEmSJEmSJPUbfeRKUkkqQP3UnsdMOBFmvrf8tUiSSq+qobB51+8Pd5wB6x7c2RaG8NQX4Yl/4x+haAAbHt15sesrv+h57XRbYTX1ZxseKX6NjhZ47ttw/QHw931yC0Urxn1vL+/6g1F6e+b2uvFQPyV+3gPvioLwSq1tIzzyEdj8fG6haKU2cn/Y7a2V37e/m/EuGHf0rm1VDXDg96Kgq/nfhPpp2dfY9AyEqcx9h/43vKUdqkdkXyNbSEF/0boB7jyj51C0INFziFhdmcN09vhnuCiEUx4v3Zotq0u3VjGqhsCUs/Of98hHoG1Tz+NW3QK3Hgf/G8Bz38xvj6Gz8q9LUmXs84XSBjxeMyN6nn33+dFzI1VW6wZ47FM9jxsxD05+uPz1SCpedZ4XvXQNRtv6UvbxI/bNb31JA0dQ3dsVSJIkSZIkSdKg15TakrG9Mdk/39hmTv1+eY2PC4aTJEmSJEmSpL6oqrcLkKSC9RSMduRVMPWc0l5sKkmqnERt4XNX/C26Hf0XmHJmFMr19GWZxz73TRi6B2x5oed10+2F19Rfrc/z4v0wDRsfh0QNDJsLiSQ8+vHcgudKZfsqaF0PtaMrt+dAl2rN3J6sg+F7w7Zlla0HYOMTcN1eld83WQcLfgMJL2bNW3UjLLwBVvwd1j8AQybB5DOi++Adhs6EbUvi11h9a3xfojp67j/j7fDiD+PHLb8Wpp4H9ZPy/xx6S7oj+p5vehWCAO65ILd5uVx0PaTMwWgNb7xuG7k/jDkc1t1X/Jqdv2d629x/gyV/gLAj9zntm+Dxf4M5n4Rhc2DzM9CxDaqHwpbnIbU9eizLJWgnzm4XFT5XUnk17gZnr4bFv4bHP1P8eqnt0fPvjY/D0j/COWugbmzx66pn6Xa45ZjsY+Z8GkbsF4Xq+vxR6h+qh+c3vmsw2uans48fvk9+60saOBI1vV2BJEmSJEmSJA16zbHBaHm+eU4fceDQo3iy6UGebHqw58HEf/6SJEmSJEmS1BcZjCap/8oWjDbz/TDtvMrVIkkqvUQJnqredRZMOAnW3pV93EMfyG29dFvxNfU3K2+Ara9EYUU9aXoVbn8TbH0pOh6xH0w5q7KhaDvccRqcdH8UIKTipbZnbk/UwvjjYeWNla2nNx13C4zYt7er6L+qhsC0c6NbJo0zYfXt8fPrxsX37bjAeP63oH0LLP5N5nGrboG/ToN9Pg/7fKHv309segbuPAOaF+c/N5cAltosX9NSGNcpMOaIK+GBd0f/x4lqmPHu6Ov/8s/zW3PUQaWssDgj50VhiQ99EDryeEfVV35RvsfHIZNh4knlWVtSadSNgb3+NQrWevLfS7v21ePgLR1RQLHKa+VN2QOQRh8KB3y7cvVIKo3qPC96ae8SjPbM1+LHDp0F9VPyr0lS/5HtdwyGpEqSJEmSJElSr2uKCQZr6KfBaNWJav5p0r/x0ranWbz9BTrC6A2gV7Qu5cmmB7qNj/v8JUmSJEmSJKkvMhhNUv9VNQSGTISWld37DEWTpP5vR8BNsVbdVJp1YOAEo6Xb8xv/+Kfh6L/0PO7et+0MRQPY9FR06w3rH4SlV8eHLyk/6dbM7clamP5WePmnUTBeUQLY91JY9IUi1ymTCSfCcSW8P1Fmjbtn79/yQnzfjguMk3Ww4AoggMVXZB4bpmDRJTD2KJhwXCGVVkYYwv1vLywUDbIHye1Q3VjY2rmYeDKMOXznccM0OP426GiBsD0KnWhanF8w2uQ3w6gDS19rMXZ7K0w9F1LN0fH2tdA4A1pWwF93q2wt9VPhtCwhPZL6lslv7jkY7aT7YfsauOvM3Ndd+keYfmFxtalniy7N3j/jHZWpQ1JpVQ/Pb/yO54AArRtg4+PxY/e7rO8HM0sqn1L9vluSJEmSJEmSVLCmVOY3Pmzsp8FoAMkgyZyGecxpmPePtmeaH8sYjNac2koYhgT+zUqSJEmSJElSP5Do7QIkqSjTLujeVj0Mxh1T+VokSaVVNbS3K+huoASjpVryG7/sGmhZnX3MtmWwvvtJFL3qhct7u4KBIxUXjFYHtaPgpAdhWhHhGyPnw5tfhn0/D8fdUvg6mSSHROFXO275GHtUFFhy8I/h2Bvixw2ZWFyN2qmnYLRtS7s1pcIEzal6CKp37WjYref9Xvnv3GvrDZufho1PFD5/4ik9j6kdW/j6mSTrYNLpMP/bcPQ1mYMfqoZEr9sgChA7c0luax/wPTji95BIlq7eUknWQM3I6DZsVhTU1zAdTn6ofHvu/w2Y/QkYfyyMPx72+jc45fGdX1tJfd/wuTBiXua+2R+HM1+HMYfBlDdHAbK58nlw+S3/G2x4OPuY+qmVqUVSaeUbjNbetPPjpX+KHzduoaGV0mBQMzK+L1Ed3ydJkiRJkiRJqojm1JaM7Y3JPnjOchHigt7SpGlJN2fskyRJkiRJkqS+xmA0Sf3bvpdEFxTtUNUIR/8FkrW9VZEkqVT6YqjHQAlG69iW54QQ/jwB2jbGD1mRJTSqHOrGwR4fyD5m7d3w6CchDCtT00CW3p65PfHGc666MXDk7+HQX+a/9ryvwSmP7QzEmnA8jDq4sDp3mHI2vDUNF4Vw4TY48a6dt7emYfYne17j7JXR+GP+Cnt+CIIsL58P+H7m9lkfK6z+waxxZs5DO9JJPvLi5Yy6Zx2j7lnH6b+cyvqmTj/vdeN6XuT1K2HLiwUUWgFhGu4+r/D5QRXs/7Wex406IP/whzhzPwcXNMPCa2GvT0dhYblomBoFLA6Z1L2vbjyccHf08zznE1GoWn8y+mCY9ZHSr5uoju6bDvweHH8bHH8LzP8G1I4u/V6SyicIYMGvoX7azrZhs+GspXDg96GhU/ue/5z7uuvuh+v2hfU9BHepMOl2eOiDPY8zGE3qn/L9fdRrv9v58aan4sft8x+F1SOpf5n+ViBDQPjwuZCoqng5kiRJkiRJkqRdNcUEozXEBIn1V9mC3ppSWytYiSRJkiRJkiQVzmA0Sf1bzQg4/lY49Sk49iY4ZxWMP7a3q5IklUK2C1EX/HbXYMxKSbdXfs9yaM98YkeP4i7+3/gUPPT+wuspxJ4f6TkYDeCF78Nr/1v+ega6uFDArmG0M98DC6/Pfd39vw57X9y9PVsIWU+mvwWOvjoKGskkCODA78Kpi2L6E1Gw05AJue858WRomLFrW3II7P7O3NdQZEdAXg4ufvVr/HjFh9maGkZ7WMPfn2/g7B+ndw4YMjG3hW5/E6T6UPBlGMLa++Dv+8HWIkLbzt8M1Tm8k2myLrf7056cuQT2/2rhP79jDoHTX4DjboHDfxfdjr0JzngJxh1ZfH29af+v5/W9nZP9vpTb/6+kvm/k/nD6c3DiPXDifXDqM1A/pfu4unFQOyb3dTc/DTceAs98FZqXlq5ewapboWVFz+MMRpP6p5o8Q4M3Pgav/x88cTG8+MP4ceOPK64uSf1D3ViYdn739j0+VPlaJEmSJEmSJEm7SIdpmmNCwRqrBlYwWragt+aYcDhJkiRJkiRJ6msMRpPU/wUJGLEvTDwRqhp6uxpJUqlkC0abdEp0q7S4cKj+5oXvFzZv6Z+gZeWubU9/Ga6fV1w9DTNg7BEw51M9jx2xHxzwXdjnP2HUgXD8HVFIRDZPXxoFDalwcaFRQXX3tklvguNuhbFZgoyG7x0FHM79bMy6BbxUrR0DB3wPFvwmt/Ej9omCnKaeB3UTou/DPT8E52+Bqvr89q4ZDifcGYWyNUyHiafAsTdG36PKT83InIalw4D/XfPWbu33vAzLNr7x855r8EDzYlj+11wrLK8whPvfCTcfAZufKW6tfL6P9/86zPsqjJgX/Xzu9yXY78u5z9/zX6ChBOEv1Y0w4XjY7aLoNvHEgRH+VdUAh/6ydOtNfwvM+XTp1pPU+6rqo+fDYxdAIhk/bspZ+a/95H/A3/eB1XcWXp92tfrWHAYFUDu67KVIKoNsv4+Kc+9b4NlvxPfve0lxAeCS+pcFv4W9PgPDZsPoQ+GQn8Hsj/R2VZIkSZIkSZI06G1PbyNNOmNfY5Ygsf6oNqijKtP5nUCTwWiSJEmSJEmS+omq3i5AkiRJyijbhaiJ2ijAaNlfYN39latpIASjtW2EV39d2NwwDX+eBMdcB5NPhY1PwVOfL7yWiSdHa+0IfwhDeP678ePPXgFDJu7aNv4YOGc1PHVJFICWydaXYOMTMGp+4bUOdmFH5vZE5hNnmHBcdCtYkOOwquji0pnvLWybhqlw1FWFzc201hFXlmatwSwIYOxRsPburMM2dYxgVdvEjH1/XxTygaODKLAuV6//Hqadn0+lpZdOwQ3zYdOi4tea/pb8xgcJ2Ptz0W2Hto2w5CrY9GT2ucNmx4ccaqfxx0RhiRseLW6d2R+HAwsMOJXU/+3zn7D6Dmh6Ob957Vvg1oVw4XZI1pajssFlWQ6BqqMOjJ7XSOp/kvVRePb2VaVbc9zRpVtLUt+XrIH534xukiRJkiRJkqQ+I1sgWGNyALx5YydBENCYHMamjvXd+gxGkyRJkiRJktRfGIwmSZKkvmn4PvF9ydookOm4W+HqsdDRXJmaBkIw2sanINVS3Bp3ngYHXg5b8wxkANjr36L/u1EHwpQzozCeHYIAgiSEqcxza0bGrzv9wvhgNIhCjwxGK1y6PXN7XDBasTp/X3R19ipYcV308zjmcBi5X3lqUO+Z+d4eg9G2dMSHZ+6SQTL7E/BCDiFSS6+Gxb+F8cdD/aQcCy2xh/85/1C0fS+DZ77c/fFp5vuLr6dmJJx0Hyy7Bl74HlQNhTmfhEmnwIbHYPWtUD8VJp0KNSOK328w2PcSuPOM+P5Jp0dhR1VDYcxhUNUIw/eKHhfbNkaPnYZqSINbw3R408Ow/FrY9DQQwsu/gPZNuc2/8WA49amyllg2HS2w6Y3aRx/Se6Fjza9Hwcs9mfm+8tciqTyCAHa7KHtwe76y/Y5LkiRJkiRJkiRJFZEtEKwhmeXNnPupxuTQmGC0rb1QjSRJkiRJkiTlz2A0SZIk9U1Tz4lCYrqGeI0+ZGcYU9UQOOTncN/bKlPTQAhGKzYUbYdHP57f+NGHwYIrYNis7OMS1ZCKCUZL1sXPG75XFBiz4dHM/Sv+BvO/kVut2lUYQtiRua9cwWhkCbqoGxsFZ2ngmvEuaFkFT34udsimjvggrsbaTgc1o3Lf9/53Rv/u/R+w35cqG7jSuh5e+e/85w2fA8feCA+8F5oXQ+0Y2P+bMOG40tRVVQ+7vSW6dTb6oOim/Ew+PQoW7foYOvU8OPTn2QNAJWmHmhEw4x07j+d9JbpfeeknPc/dtCgK9mqYXr76ymHD43DvhTsDyUYdCEdf0zthpk9+vucx874Ge3yg/LVIKp/9v17aYLS6saVbS5IkSZIkSZIkSQWJC0arCqqpDbKcn9pPxYW9NWcJiJMkSZIkSZKkviTR2wVIkiRJGVU3wt7/vmtbohr2+eKubVPOhFFdwlkady9PTen28qxbSXEBV+XQMANOfwHO2wAn399zKBpAUETQ1kE/iu/b/GwU8KX8ZfueKeb/K5sgy0vVbH0aGIIA9r4Y9okPHtnYER8g1VjbKdCskKCpZ74Cq2/Nf14xNj5R4MQEjF8IZ74K566Dc9bAzPeUsjKV2uyPwYXbo9uZS+CCZjjqKkPRJBUuUQ0H/xgubIWp5/Y8/q6zIEyXv65SCdPw4Pt2hqJBFIb8yL9Uvo4Xfgiv/Tb7uPM2Rs9jfM4q9W+Japj85tKsNeGE0qwjSZIkSZIkSZKkojSntmZsb0wOI6jkm2hWSGNMMFpcQJwkSZIkSZIk9TVenSNJkqS+a5//hKP+BLu/B2Z9BE64CyafuuuYqgY47hbY/5sw9bwoSOfkh8tTT7qtPOtWUrqCwWinPxeFoeUT9jL6kML3G3MoHPbr+P7FVxS+9mCWLRAwUVWePYfuUZ511b8kh8R2beoYntsahX6PLu4h9KTUmpcUNq/z/Wvt6ChUTn1fsja6NUyFqvrerkbSQJGsgSOvggX/A7Vj4sdtfAIe/Xjl6irW+kdg4+Pd25f9FZperVwd914Ej340+5j9vw41IypTj6TyK1Vw7e7vK806kiRJkiRJkiRJKkpcIFhjcmiFK6kMg9EkSZIkSZIk9XcGo0mSJKlvm3oOHPZLOOi/YMxhmcfUDIe5n4GjroL9LoPaUTD3s6WvZSAEo4UVCkY74c4o9CVfe306c/tu78ht/rij4/se/lD+9aiHYLTq8uy554czt086NXO7BqZE/H3Ixo74kIKOdKeDMFXY3ot/U9i8Qq25I/85VQ0wZkHJS5Ek9WNBADPeBueuhWRd/LgXfwhLrqpcXcVYd19839K/lH//VCvcdiIs+b+exzbuXv56JFVOzaji15j1MZh2XvHrSJIkSZIkSZIkqWhxgWANMQFi/V1DTOBbc2prhSuRJEmSJEmSpMIYjCZJkqSBaca7S7/mQAhGS+cQjDbxlOL2GH0ojD2qsLkTToDhc3dtS9TAnh/MbX7t2Pi+1HbYvrawugazbMFoQZmC0UbuD2OP7LJXAvY03G5QyRLosqljRGxfR+cstNGHFL7/4v8pfG4+0h2FBbHN/ABUDSl9PZKkgeG0Z7L333MBpPrB65t198f3bX66vHs3LYarJ8CqW3IbXz28vPVIqqzaIoPRJp0GB10OiarS1CNJkiRJkiRJkqSixAWjNQ7QYLS4zyvu6yBJkiRJkiRJfY3BaJIkSRqYhs8p/ZoDIRgt7CEYbcR+sPBvsP/XoaaAi4AnnADH3QRBUFh9iWo44S6Y+T4YOgsmnQrH3gBjj8htflVD9v7NzxZW12AWZglGS5QpGC0IYOH1MOsjMGwvmHAiHPUXmHx6efZT35Ssje3a2DEyti+VDncejDoQhkwqbP/73wHbVhQ2Nx8rb8jeP+pA2Otfo/vl0YdGx/O+Bgd8u/y1SZL6r8bdo8eMbFZeX5lairHuvvi+7WvKt+/q2+HaPaB9U+5zqgfmyfLSoFUT/5ojJ3t9ujR1SJIkSZIkSZIkqSSaU1szthuMJkmSJEmSJEl9k29TLkmSJOUqNcCD0apHwEH/BUEC5n4WZn0U/tBD0FhXR/8VquqLq7F2NBz634XN7SmQ7daF0b/jj4W9/z0KclN26SzfM+UKRgOoboy+HzV4JeKD0ban62L7OtKdDoIEHPILuOdcSG2P2urGwfS3wguX91zDXybDOaujOeXy+h/i+w76Icz68M7juZ8tXx2SpIFn+kWw4dH4/rvOgrNXwpAJlaupJ02vwiMfhy3PQVUjbFsWP7ZcwWhhCI9+HMJ0z2M7Sxb5OkhS3zJsduFzZ/4TjDumdLVIkiRJkiRJkiSpaE0dmQPBGpJDK1xJZcQFo21LNZEOUySCZIUrkiRJkiRJkqT8GIwmSZKkgWvIJGhZUbr1Us2lW6u3ZAu5Om0R1E/ZeVxIwFmxoWilMHwubH42+5jVt8Pae2Dh34EAkkNg1EGQrKlIif1K2B7fV85gNCkZH36WCuNPyurommEy+VQ47TlYeWN0HzXhJBgyHvb4Z3j1V/DcN7PXccOBcMYr5bt/WP9gfN+088uzpyRpcJj5Pnj6MmjfHD/mzxPhmL/B+IVQlWcocqk1vQrXzMx9fGuZgtGaX4NNi/KfVzO85KVI6kXjjoH6abBtya7te3wQRh8CYQoe+kD3eVPOgkN+3nNwuyRJkiRJkiRJkiqqKZU5GC0uQKy/iwt8CwnZlmqmsWpgft6SJEmSJEmSBo5EbxcgSZIklc2+l5R2vScuhk1Pl3bNSgtjgtGGzd41FG2HyW8ubz3lMOm03Mal2+G2E+G2E+DmI+CGA6DptbKW1i+lswSjBQajqYwStbFdqZqxsX0dqQyNjbvBnh+EGe+IQtEAhs+B+d+AC1uy17FtGay5o8dyCxKG3YMWdpjzaagbV559JUmDQ81wOO6WnsfdeTr8bS/Y8Hj5a4qz9t78QtEAtq+JHktLrZDXfI17RAFKkgaORDWccAeMPQKCBNSOhXlfg4N/AjPfC3u8H858DcYeFfVXj4B9Pg9H/clQNEmSJEmSJEmSpD6oObU1Y/tADUbL9nnFhcRJkiRJkiRJUl9iMJokSZIGrmkXwMj5pV3z3gtLu16lpWOC0YKqzO17fw6qMr9rXDdTziqsplKb9eHC5m1+Bh77RGlrGQiyBaMlDEZTGSWzBKMlGmP7OtL57lMHx92afczGJ/JctActK+H+d8GVCUhtzzxm2gWl3VOSNDiNPgiO+L+ex21bGgUFh/k+kJZAqhXue0cB81rggffApkXF17DxCXjoQ3DXOXD/2/OfP+/LBiFJA1G/5G7VAAAgAElEQVTjDDjxHjh/C5yzGva+eNef9YbpcOJdUf9562G/y6KQNEmSJEmSJEmSJPUpqTDFtnRTxr6BGozWkIw/99dgNEmSJEmSJEn9gWfnS5IkaeCqGQ7H3wbzvwNTz4Fhs2HKmbDvpXDYrzPPGTEv+5qbn4U195S81IoJ44LRkpnbxxwGJz8Acz8Hu78nClU44yUgw0X/084vWZlFaZgOJ9xd2Nxlf4V1D+4MKkp3wPa1EIalq6+/yRqMFhOoJ5VCsi62K5VoiO3rSBWw14TjYOq58f3tmd8ttCDb18K1e8Li32Qf1zC1dHtKkga3yadB9fDcxt755vLWksnKG6F5cWFzF18BNx0B6x8pfP9Vt8JNC+Dln8KyP0N7nieAj5gH0/t5gLak7KoasocfVjUYiCZJkiRJkiRJktSHbUs1EZL5PNBsAWL9WU2iltog8zl4BqNJkiRJkiRJ6g+8il2SJEkDW80I2OtTwKd2bQ9D2PoiPPPV6DhRCwdeDnt+EG47GVbdFL/mLUdB4x5wxJUw+qCylV4WscFoWV4aDJ8L+39117bDfgUPfQDSbdHxnE/B9LeUpsZSGHtE4XNvOiz6t2G3KBSsZTnUjonC9CafVorq+pdswWhBdeXq0OCTqI3tSsWcsAWQKjTHcMFvYemfMve9fiXM+1KBC3ex6IvQ0Zx9TKIa6saXZj9Jkqoa4Kir4bbjex674rooELS6gid+L/tLcfM7tsLz34Uj/rew+c99e2cwciEmnVr4XEmSJEmSJEmSJElS2WULAmtMDqtgJZXVkBxKa0f3v4c3p0r4RqGSJEmSJEmSVCa+fbkkSZIGpyCAeV+Bs1fBCXfDuWujUDSAZHzgzj80vQy3nQBtm8tbZ6mFqcztiTwzk3d/F5y7Dk68B85ZAwd8B4I+9PIiCGDqucWt0fxaFIoG0LoO7jwDNj1TdGn9TpglGC1hMJrKKJktGG1IbF9HzN1cj6qGxAc8Nr0CTa8VuHAnqTZ46Sc9j6ub0LfuUyVJ/d+E42C3t+c2dsMjpd9/+1p46afw1Bdh9Z279m1+tvj1X7+ysHlhCKtvL27vurHFzZckSZIkSZIkSZIklVVzlmC0hmQF3ziswuJC37IFxUmSJEmSJElSX+FVtpIkSRrchoyHcUdCdacTG2rH5Da3fTOsuqk8dZVLuiNze5BnMBpEX7OxR/TdIIBcgx9yFsKSq0q8Zj+QNhhNvSQRH1KZDuJD0zrSRexZleUktxe+X8TCb7j5iNzGjTum+L0kSeqqblxu40odBrz1ZbjhIHj4Q/D0ZXDrQlh06c7+7WtKs8/Gp/Kf07EV0q09j5v3FahqzNw38ZT895UkSZIkSZIkSZIkVUxTamvG9tqgjppE/Llo/Z3BaJIkSZIkSZL6M4PRJEmSpK7yCfp6+svlq6McwphgtEQBwWh93ZQzYeis0q65ucQhEf1B3PdMkIhuUrkEydiuVBAfmtaRKmLP6mzBaJfDvRfBnyfBldVw7azoMSDMMYlt3QOw4ZHcxk5/a27jJEnKR3XmE5672fx0afd95iuwbcmubYsugRsPi0LYWksUjPbwh/Kfk1MoWwCzPgaH/KJ7MPC+l8HwOfnvK0mSJEmSJEmSJEmqmLggsIZklvPFBoCGmGC0ZoPRJEmSJEmSJPUDAzD9QJIkSSpS7bjcx6a2l6+OckjHhVwNwJcGQQALr4Nr9yzdmsv+Urq1+ot0e+b2oDpzu1QqVQ2xXamq4bF9HTnmlGXeszF7/+tX7vx460vw1Odh4+Nw1J+yz0t3wE0Lcqth7BEw8eTcxkqSlI/q+MfPXTS9CusfgbX3QstyGHskTDo1e5hy81JYdx90NEXHVUNh3NFQNx6WX5t5zvoH4e/75Pc5ZLPuPkinIPFGuGpHM6y5K3qtM/7YzPXnEoxWPRyqG2G3t8Dog2H1rdHrwHELYeR+patfkiRJkiRJkiRJklQWccFojTHBYQNFY1Xm4Lem1NYKVyJJkiRJkiRJ+RuA6QeSJElSkeryCEZL1JSvjnIIB1EwGkD1iNKuF3bAhkdh1IGlXbcviwtGSxiMpjKrnwRDZ8HWF3dtHzKRdM3Y2GmpYoLRqgt4B9ClV8ODH4CDfxT/c5FrqOK4Y2DBb3cGukiSVErVOZ7Qverm6LbDc9+CIAlnvgb1U7qPf+mn8OjHMjxvDGDfS6F1faEV52/dfTDuKFh7H9xzHrSsjNqHzYaFN0DjbruOzyUYraZToNzQmdFNkiRJkiRJkiRJktRvNA/WYLSYzy8uKE6SJEmSJEmS+pJEbxcgSZIk9TlVjbmPHSjBaIkBGoxWMxJi3vGuYHefC2FY2jX7MoPR1Jvmf3PX4MYgAfO+ljX8rKOYYLSgwO/rV34Bv6+BRV+CVGv3/rX3ZZ9/URjdTrgDGqYWVoMkST1JZ3iMylWYioJAu9ryUkwoGkAIi75Q+J6FuOVouPetcPMRO0PRALa8ANfM6D6+NYdgtFwD5SRJkiRJkiRJkiRJfVJTamvG9oYBHowW9/nFBcVJkiRJkiRJUl9iMJokSZLUVe2Y3Mf2t2C0dEwwWjBAg9ESSZhyZua+428rbM3m12Hri4XX1N+kWjK3D9TvGfUtU86Ek+6DOZ+Gvf4Vjr8Tdn8XqSzZhB2pIvaL+37P1aIvwG0ndg9PXH5N/JwDf1DcnpIk5WrIpOLmr7x+1wC0jm3wzFfig3RLbeIpuY17/ffxfQ99aNfjbct7Xq96eG77SpIkSZIkSZIkSZL6pKaYILDGUr/xbh/TGBOMFvf1kCRJkiRJkqS+xGA0SZIkqavRB0GuJzsk+1kwWhgTjJYYwCFXB/0Qxi3ceTx0Fpz2DIw/tvA119xVdFn9wiu/gvvfkbkvUV3ZWjR4jT4YDvg2zP8WjDsSgFQ6fnhHlr4eFRuMBrD2blh9a5d1t8WPL+a+SJKkfIw5AoIi/yTQ0RT9+9JP4K/TYPEVxdeVqyETYPShxa3x8k+joOMdNj/b8xyf90qSJEmSJEmSJElSv9YcF4wWExw2UDQmM58L3ZLeRirufGJJkiRJkiRJ6iMMRpMkSZK6StbB7I/mNjbRz4LR0jEnMgQDOBitZjiccDuc8TKc/jyc9iwMnxv17f7uAtccUbLy+qx1D8GD743vNyBCvahswWgTTihicie3nQh3nwu/r4E/joaWlZnHJethxD6l2VOSpJ7UjYHd31PcGh3bYM098PCHoXV9aerKVaIW5l4MBMWt89fdYNUt0cebn+55fLvvlC1JkiRJkiRJkiRJ/VlTTDBawwAPRmuICUYDaEptrWAlkiRJkiRJkpQ/g9EkSZKkTPb7Mkw8uedxQT8Lh4p7h7eBHIy2w9CZMGw2JJI726ZdWNhayYbS1NSXvfqr7P397XtfA0rWYLRUEQuPWQA1o4pYoJOlV0O6Hdo2xI85/dnS7CVJUq4O/hns/3UYfVhhrwE6tsGzXwPCkpfWo0QNTD0LFv4dJp9R3Fq3nQiPfRq2vtTz2LbNxe0lSZIkSZIkSZIkSepVcSFgjQM8GC3b59ccExYnSZIkSZIkSX3FIEg/UCZBEMwB5gFTgCHAdmAN8DLwZBiGzUWsXQ0cAUwDJgJNwArg8TAMXyuuckmSpAoJAtjni7DyxuzjwvbK1LPD5mfhyf+ENXdC/VSY/y2YeGLu8+OC0RKD9KXBhBNhj3+Gl3+a37x0W3nq6Ut6+pokDEZT78kWjJatr0eJKjj8d3D32ZDaXsRCOagZCfXTyruHJEldJZIw97PRbc09cMtR+c3/26zy1JWLZE3076Q3RbdrZ8PWFwtf7/nv5jau3WA0SZIkSZIkSZIkSeqvOsJ2tqe3ZexrTA6tcDWV1ZDl82syGE2SJEmSJElSHzdI0w8GpyAIRgAfB95LFFoWJxUEwRPAH8Mw/Hoe648FLgUuBEbFjLkP+G4Yhn/KuXBJkqTeUjeu5zGplvLXsUN7E9x5BjS9Gh23bYDbT4YT74Gxh+e2RjomGC0YpC8NEkk4+Mewxz/BY/8Ka+7IbV66taxlldzGJ6FpMQzfC4bN7nl8mEOylMFo6kWpML6vo5hgNIiCVt78Kqy6BWpGwcj5sP6B6Gdo2zIYdQCk2+GF78OmRYXvM3yfKIRTkqTeUtXQ2xXkJ1Gz63H91OKC0XI1453l30OSJEmSJEmSJEmSVBbNqa2xfY3JYRWspPKqgmrqEvUZg+GasnxdJEmSJEmSJKkvGKTpB4NPEATnAz8BRucwPAkcCEwBcgpGC4LgFODXQE/pIYcDhwdB8Dvgg2EYNueyviRJUq/IKRhte/nr2OHln+4MRfuHEF78Ue7BaGFMMFpiEL80CAIYdSAc/jv4yxQgS+LSDum2spdVEmEI974VlvzfGw0BzPsq7H1x/JxUK9xwUM9rG4ymXpTKEn6WrS9nQybCjHfsPK4/p/uY3d8Niy6Fpy8rbI9JpxQ2T5KkUunvwWjD5sDqW8u/77Tzy7+HJEmSJEmSJEmSJCkvYRiypn1F1uAzgHXtq2P7GgZ4MBpAY3JoTDDalm5tqbCDFa1LaA+7nyObDKqYVDON6q5/u5ekCujpPn9E1WhGVY+tcFX9W1u6lfXtaxhXM4lkkCx4nVSYYk3bCkZXj6MmUVvCCiVJkiRJMhhtUAiC4IvAJRm6lgAvAmuBOmAisC+Q1xVxQRAsBP4CdP7tdgg8BrwKjADmA2M69b8NGBYEwVlhGJbisnVJkqTSq2qE6mHQ3v2P//+QaqlcPU/+R+b2DY/kvkY6Jhgt8KUB9ZNg7mfh2RyygVOt5a+nFF75RadQNIAQnvwcTD4DRuzdffza++HmHEP26qeUpESpENnCzzpSFSoiSMB+l0LNCHjsU/nP3+MDpa9JkqR89HYwWtVQGDoTNj6R2/iuJ87NeAe89KPS19XZ9Itg9MHl3UOSJEmSJEmSJEmSlJcl21/h58u/zoaOtUWt05gcWqKK+q7G5LCM4XDNXYLRHtlyN79b9SNaw/g3jK4KqjlrzDs4duQZBEFQ8lolKZNl21/j5yu+ljXoEmC3ull8YPLFjKgaVaHK+qcwDLlxw5+4bt3vSdHBkEQ975zwceYNPTTvtZ5qeogrVl5OS7qZJFWcNuZCTh51no8RkiRJkqSSSfR2ASqvIAg+TfdQtCuB/cIwnB6G4YlhGF4UhuE5YRguAIYBRwLfA9bnsP4U4Gp2DUW7F9g7DMODwjC8IAzDk4ApwMeB9k7jzgC+XOCnJkmSVH5BAOMWZh/TUaFgtHQHpLu/AxsAW1+EdI5JQKHBaFnN+yoccy1MPSf7uHQ/CUZ7/vuZ21+4fNfvmebX4alLcg9FA5h8ZlGlScXIGoxW6ejtOZ+E426F2Z+MwlMmntLznIXXQ+3o8tcmSVI2vRmMtu9l8KaH4U2PwVFXw6yPwv7fzP442vVdp0cfAuOPLX1tiRrY88Nw+JWw4DdRGKokSZIkSZIkSZIkqU9oT7fzo2WXFR2KNiRRT3IQnDvbkByWsb2pUzDaytal/HLld7KGogF0hO38ce0veX7bkyWtUZLipMIUP17+pR5D0QBe2/4iV6z8XgWq6t+eanqIa9b9Dymi60pa0tv4+YpvsL59TV7rbGhfy8+Wf52WdDMAKTq4Zt3veLLpwZLXLEmSJEkavAb+b3AHsSAI5gFf79TUDlwUhuEf4+aEYZgmCja7Nwhy+g3/pcDITsf3ASeE4a6/DQ/DsBX4QRAES4A/d+r6VBAEPwvD8PUc9pIkSaq8cUfB8mvi+1MVCEYLQ3jum9nHtCyHhmk5rBUToJbwpQEQheFNPh0mnQp/ngTbY/6ImuoHwWiLfwtbnsvc98ovYOUNcNgv4YnPwYZHcl83UQ1zPg27v7skZUqFyBqMlgorV8gOE46Lbjusvh0eeB80L951XKIG5n8HJr2psvVJkpRJbwWjzfoo7Pv5ncdTz45uEAX2xukajBYEcNiv4PaTYcsLpatv/rdh9kdLt54kSZIkSZIkSZIkqWSe2/Y4W1Obi16nMSYwbKCJ+zybOrb+4+MHt9yR15oPbrmdvRr2L6YsScrJC9ueYlPH+jzGL2JD+1pGVY8tY1X924Nbbu/WFpLm4S138qbR5+e8zsNb7iKk+wndD265nf2HHlZUjZIkSZIk7WD6wQD1RqjZL9n1//iD2ULRugrDsKOHPfYE3tWpqQ14d9dQtC5r/iUIgis6zasFvgi8N9e6JEmSKqq2hz+KbV8F6RQkkuWrYe298OR/ZB/T9EpuwWjpmKd4g+Bd7/ISJGDuZ+GxT2XuT7dVtp58pVrh/ndmH7NtKdx2Yn7rjj0Cjr2x90I0pDdkyz7LFppWMeOPhdOfj8JdqhuhfQu0bYIR+/jzI0nqOxLVvbPvnv8S35eszdJX072tYXr0mLv1FXjpJ/D8d4qvr35q8WtIkiRJkiRJkiRJkspieetrJVlnYm0O59wOAI3JoRnbm1Nb/vFxvl/TFa1Z3vRMkkpoeQH3NytblxiMlkXc13RF65I813ktZh0fIyRJkiRJpZPo7QJUNucDB3Q6vjUMw1+VeI+LgM4JIFeHYfhSDvO+0eX4giAI6kpXliRJUgnlEmBz2wnQ3hQFpJXDq/+v5zFbX8ltrbjs24TBaN3M+WR839NfqlwdhVh1a3nWPfgnhjqpT8gWftbRF4LRIApvGbYnDJkIw2bDmEP9+ZEk9T3TLuzeVjsGqvN8Z+wZ74JDc3jdMvEUGD4nvj+Z5dfEiSyhaUNnwt7/XprA5waD0SRJkiRJkiRJkiSpr1rVurwk6xw+/ISSrNPXNSQz//2/qVMw2uq2ZXmtubptBemwr5yoJ2kgW9OW/33+6gLmDBbt6XbWt6/O2Jfv1y1u/Lr2NbSn2/OuTZIkSZKkTAxGG7g+2OX4q2XY4+wuxzkFr4Vh+BzwYKemBuCkUhUlSZJUUrmE2Ky5A64aGt3ufDNsK/Ef0179dc9j1t7d85imV2HVzZn7ShEgMBCNPz5ze2obtG2ubC35aMoxKC8f+14CI/Yt/bpSAfpFMJokSf3BAd+NAjx3qGqABf8D7Vvi53RVPw0W/Bomndrz2PHHZO+vGR3fl6jJPrd2FBz2awg6/dljzOEw93O7tmWTrIehs3IbK0mSJEmSJEmSJEmquHxDvLqaVDONd0/8JPs1HlKiivq2xh6C0drTbaxvX5vXmu1hGxs71hVdmyT1ZFUB9/mr21aUoZKBYV37KtJkPtF6TdsKwjDMaZ0wDFkT83UOSbOufVXBNUqSJEmS1JnpBwNQEAR7AJ2vMHsNuL3Ee0wA5nVq6gDuzWOJO4BDOx2fAlxTfGWSJEkllksw2g6pFlh+bXS7oCm/uXHSHbmNW/wbOPT/QSLmKX5qO9x8ZPx8g9EyS9bG9y37K+z+zvj+MITNT8P6R2DkfjByfu6BDMXavqY068z9LAzfJ6p9xN6lWVMqgazBaKnK1SFJUr9XPwlOeQLW3gNtG2Hs0TBkfGFrDZmQw5gp2fuHz43vS23ref0Zb4Pxx8Lq26F+chSMlqyB3d8Nm5+BMA3pVlh9G6y7HzY/22X+O6F6aM/7SJIkSZIkSZIkSZIqLgxDVrdlfvPit43/MIcMW5h1fhBAVVBdhsr6rrhgtObUVgDWtq8kjAnJ+cy0b/CtJZ/N2Le6bTmjq8eVpkhJihF3n3/BuPezsm0pd2+6oVtfIWFqg0Xc1xOgNdzOpo71jKwe0+M6mzs20Bpuz7rPxNqpBdUoSZIkSVJnph8MTMd2Ob41zDWuPXf7dDl+KgzD5jzm39fl2JQFSZLUNxUabvaHRjhnNdQV+Uf/psW5j11zJ0w4PnPf0j9Dy8r4uYlkfnUJNjwaH4wWhvD4Z+D57+xs2+0dcNgv48PrSqk1v3fv62b6W+HgH0PNiNLUI5VY1mC0LH2SJCmDZB1MOKE0ax35B7jngvj++p6C0bL8mjjVmlsN9ZOigLTOhs2KbjvsdhF0tMAzX4FXfgHJeph2Acz7cm57SJIkSZIkSZIkSZIqblPH+tgglkm106hODK7Qs1w0JjO/OVhruJ32dBurYkJyEiSZVjeT4VWj2NyxoVv/6rblzG2YX9JaJamzpo4tNKW2ZOybVDuNkMyXy67JEv412PUUGre6bXlOwWjZAtZy6ZckSZIkKVeJ3i5AZXFIl+P7AYLICUEQ/CoIgmeDINgcBEFzEASvB0FwSxAEFwdBsFuOe8ztcvxynjW+0sN6kiRJfUOywGA0gEWXFr//9tW5j33+e/F9K67PPrdlVe77DCYd2+L7qurj+9bcsWsoGsBrv4UlfyhJWd2k2mDRZXDjArh2Nrz8s8LX2vdSOOJ/DUVTn5bKEv2dLTRNkiTlaM9/KWzemAXZ+4fumb2/fgrUji5s7XxVDYmC0M5ZDWcuhvnfAE+SlyRJkiRJkiRJkqQ+K1vQyviayRWspP9oSA6L7WtObWV1TEjO2JqJJIMqJsR8XXsK15GkYmW/z58Se7+/ObWRllSWawAGsZ5C43INNDMYTZIkSZJUKQajDUwHdTl+7o3As1uAm4F3A3sBw4B6YBpwPPA14MUgCH4UBEGWlAcA9uhyvCTPGl/vcjw6CIKRea4hSZJUftnCr3qy/NrC5jUthqbXoo/bN+c+b8V18OoV0LIS1twNbW/M7dgWhXJl07h7QaUOeB3N8X3JLN8br/wyc/urvy6qnFj3XgiLvgjrH4CtLxa+TpCA3d9VurqkMskWftbWUbk6JEkasKaek/vYsUfs/Lh+Ckw4KfO4cQuhflL2tYIAdn9f9/bG3WHkvNxrkiRJkiRJkiRJkiQNOHFhXMOSI6hPNla4mv6hMUswWlNqC6taM4fX7AhEGxcTPNRTuI4kFSvuPn9Iop5hyRFZAzHXtK8oV1n92uq27F+Xnvr/Ma7dYDRJkiRJUmVU9XYBKouJXY7rgYeBMTnMrQb+BVgQBMFpYRiujBk3osvxmnwKDMOwKQiC7UBdp+bhwMZ81pEkSSq7qobC57asgDAdhU3lNH4V3Hk6bHg0Oh59KOz2tvz2fODdOz8OEjD7kzByfs/zchkzGKWyBKNlC8177X8yt6+6ubh6Mnn2m7DsL6VZ66iroWF6adaSyihbMNq2tsrVIUnSgDXheDjoh/Dkv0P7luxjZ3141+MFV8A958H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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import asyncio\n", + "from uuid import UUID\n", + "\n", + "from matplotlib import pyplot as plt\n", + "\n", + "from apd.aggregation.query import with_database\n", + "from apd.aggregation.analysis import get_known_configs, plot_sensor\n", + "\n", + "\n", + "location_names = {\n", + " UUID('53998a51-60de-48ae-b71a-5c37cd1455f2'): \"Loft\",\n", + " UUID('1bc63cda-e223-48bc-93c2-c1f651779d69'): \"Living Room\",\n", + " UUID('ea0683de-6772-4678-bfe7-6014f54ffc8e'): \"Office\",\n", + " UUID('5aaa901a-7564-41fb-8eba-50cdd6fe9f80'): \"Outside\",\n", + "}\n", + "\n", + "with with_database(\"postgresql+psycopg2://apd@localhost/apd\") as session:\n", + " coros = []\n", + " figure = plt.figure(figsize = (20, 10), dpi=300)\n", + " configs = get_known_configs().values()\n", + " for i, config in enumerate(configs, start=1):\n", + " plot = figure.add_subplot(2, 2, i)\n", + " coros.append(plot_sensor(config, plot, location_names))\n", + " await asyncio.gather(*coros)\n", + "\n", + "display(figure)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "9f576f5bb8e34cb8ab1d3c09f770d027", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "interactive(children=(DatePicker(value=None, description='Start date'), DatePicker(value=None, description='En…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import asyncio\n", + "from uuid import UUID\n", + "import functools\n", + "from concurrent.futures import ThreadPoolExecutor\n", + "\n", + "import ipywidgets as widgets\n", + "from matplotlib import pyplot as plt\n", + "\n", + "from apd.aggregation.query import with_database\n", + "from apd.aggregation.analysis import get_known_configs, plot_sensor\n", + "\n", + "\n", + "def wrap_coroutine(f):\n", + " @functools.wraps(f)\n", + " def run_in_thread(*args, **kwargs):\n", + " loop = asyncio.new_event_loop()\n", + " wrapped = f(*args, **kwargs)\n", + " with ThreadPoolExecutor(max_workers=1) as pool:\n", + " task = pool.submit(loop.run_until_complete, wrapped)\n", + " return task.result()\n", + " return run_in_thread\n", + "\n", + "@wrap_coroutine\n", + "async def plot(*args, **kwargs):\n", + " location_names = {\n", + " UUID('53998a51-60de-48ae-b71a-5c37cd1455f2'): \"Loft\",\n", + " UUID('1bc63cda-e223-48bc-93c2-c1f651779d69'): \"Living Room\",\n", + " UUID('ea0683de-6772-4678-bfe7-6014f54ffc8e'): \"Office\",\n", + " UUID('5aaa901a-7564-41fb-8eba-50cdd6fe9f80'): \"Outside\",\n", + " }\n", + "\n", + " with with_database(\"postgresql+psycopg2://apd@localhost/apd\") as session:\n", + " coros = []\n", + " figure = plt.figure(figsize = (20, 10), dpi=300)\n", + " configs = get_known_configs().values()\n", + " for i, config in enumerate(configs, start=1):\n", + " plot = figure.add_subplot(2, 2, i)\n", + " coros.append(plot_sensor(config, plot, location_names, *args, **kwargs))\n", + " await asyncio.gather(*coros)\n", + " return figure\n", + "\n", + "\n", + "start = widgets.DatePicker(\n", + " description='Start date',\n", + ")\n", + "end = widgets.DatePicker(\n", + " description='End date',\n", + ")\n", + "out = widgets.interactive(plot, collected_after=start, collected_before=end)\n", + "display(out)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "60091fa7d9ea415985ec53937e422b4a", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "interactive(children=(DatePicker(value=None, description='Start date'), DatePicker(value=None, description='En…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from uuid import UUID\n", + "import ipywidgets as widgets\n", + "from apd.aggregation.analysis import interactable_plot_multiple_charts, configs\n", + "\n", + "location_names = {\n", + " UUID('53998a51-60de-48ae-b71a-5c37cd1455f2'): \"Loft\",\n", + " UUID('1bc63cda-e223-48bc-93c2-c1f651779d69'): \"Living Room\",\n", + " UUID('ea0683de-6772-4678-bfe7-6014f54ffc8e'): \"Office\",\n", + " UUID('5aaa901a-7564-41fb-8eba-50cdd6fe9f80'): \"Outside\",\n", + "}\n", + "\n", + "start = widgets.DatePicker(\n", + " description='Start date',\n", + ")\n", + "end = widgets.DatePicker(\n", + " description='End date',\n", + ")\n", + "\n", + "plot = interactable_plot_multiple_charts(location_names=location_names)\n", + "out = widgets.interactive(plot, collected_after=start, collected_before=end)\n", + "display(out)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "08a4d937037b45bda2298879df7f80b6", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "interactive(children=(DatePicker(value=None, description='Start date'), DatePicker(value=None, description='En…" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import datetime\n", + "import uuid\n", + "import ipywidgets as widgets\n", + "from apd.aggregation.analysis import Config, configs, interactable_plot_multiple_charts, draw_map, get_map_cleaner_for\n", + "from apd.aggregation.database import DataPoint\n", + "from apd.aggregation.utils import merc_x, merc_y\n", + "\n", + "start = widgets.DatePicker(description='Start date')\n", + "end = widgets.DatePicker(description='End date')\n", + "\n", + "def get_literal_data():\n", + " # Get manually entered temperature data, as our particular deployment\n", + " # does not contain data of this shape\n", + " raw_data = {(53.8667, -1.3333): -1, (53.35, -2.2833): 1, (52.45, -1.7333): 3, (51.5, -0.1333): 6, (51.55, -2.5667): 5, (54.9667, -1.6167): -1, (55.9667, -3.2167): -1, (54.7667, -1.5833): 0, (53.7667, -0.3): 1, (53.7667, -3.0167): 0, (51.4833, -3.1833): 4, (53.2667, -3.5167): 2, (52.0833, -2.8): 2, (52.0167, -0.6): 2, (52.6667, 1.2667): 5, (50.4333, -4.9833): 9, (50.35, -4.1167): 10, (50.5167, -2.45): 9, (50.8333, -1.1667): 9, (56.45, -5.4333): 4, (57.5333, -4.05): -2, (54.5167, -3.6167): 3, (53.0833, 0.2667): 4, (51.35, 1.3333): 7, (50.8833, 0.3167): 8, (58.45, -3.1): 3, (50.1, -5.6667): 9, (58.2167, -6.3333): 4, (55.6833, -6.25): 7}\n", + " now = datetime.datetime.now()\n", + " async def points():\n", + " for (coord, temp) in raw_data.items():\n", + " deployment_id = uuid.uuid4()\n", + " yield DataPoint(sensor_name=\"Location\", deployment_id=deployment_id, collected_at=now, data=coord)\n", + " yield DataPoint(sensor_name=\"Temperature\", deployment_id=deployment_id, collected_at=now, data=temp)\n", + " async def deployments(*args, **kwargs):\n", + " yield None, points()\n", + " return deployments\n", + "\n", + "def draw_map_with_gb(plot, x, y, colour):\n", + " # Draw the map and add an explicit coastline\n", + " gb_boundary = [(-2.6653539699999556, 51.617254950000074), (-2.6937556629999335, 51.59617747600004), (-2.71312415299991, 51.58661530200004), (-2.760487433999913, 51.579779364000046), (-2.7980850899999155, 51.56350332200009), (-2.816395636999914, 51.555568752000056), (-2.8587133449999556, 51.546128648000035), (-2.943348761999914, 51.54254791900007), (-2.9562882149999155, 51.54572174700007), (-2.9641007149999155, 51.552801825000074), (-2.969471808999913, 51.559881903000075), (-2.9746801419999542, 51.56297435100004), (-2.984038865999935, 51.55853913000004), (-3.0102432929999168, 51.53803131700005), (-3.0156957669999542, 51.53164297100005), (-3.0737198559999115, 51.50836823100008), (-3.0764867829999503, 51.50775788000004), (-3.088937954999949, 51.505072333000044), (-3.105620897999927, 51.49721914300005), (-3.1317032539999445, 51.48041413000004), (-3.1350805329999503, 51.47630442900004), (-3.1409399079999503, 51.46478913000004), (-3.1453751289999445, 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51.49408600500004), (-2.716420050999943, 51.49957916900007), (-2.6961563789999445, 51.51264069200005), (-2.6078181629999335, 51.60736725500004), (-2.599598761999914, 51.611395575000074), (-2.5892227859999366, 51.61424388200004), (-2.580433722999942, 51.61863841400009), (-2.5768123039999296, 51.62750885600008), (-2.57445227799991, 51.63572825700004), (-2.568959113999938, 51.644964911000045), (-2.562245245999918, 51.65338776200008), (-2.556385870999918, 51.65924713700008), (-2.499175584999932, 51.69676341400009), (-2.464995897999927, 51.725897528000075), (-2.4477432929999168, 51.73187897300005), (-2.404896613999938, 51.74115631700005), (-2.3885391919999392, 51.750433661000045), (-2.380767381999931, 51.76190827000005), (-2.3833715489999463, 51.77326080900008), (-2.398060675999943, 51.782131252000056), (-2.4024145169999542, 51.76357656500005), (-2.4203995429999168, 51.753119208000044), (-2.4434301419999542, 51.74843984600005), (-2.4632869129999335, 51.747300523000035), (-2.482085740999935, 51.74286530200004), (-2.495838995999918, 51.73200104400007), (-2.507964647999927, 51.71857330900008), (-2.5221248039999296, 51.706366278000075), (-2.581695115999935, 51.681708075000074), (-2.5911352199999556, 51.67536041900007), (-2.600209113999938, 51.66559479400007), (-2.6653539699999556, 51.617254950000074)]\n", + " draw_map(plot, x, y, colour)\n", + " plot.plot(\n", + " [merc_x(coord[0]) for coord in gb_boundary],\n", + " [merc_y(coord[1]) for coord in gb_boundary],\n", + " \"k-\",\n", + " )\n", + "\n", + "country = Config(\n", + " get_data=get_literal_data(),\n", + " clean=get_map_cleaner_for(\"Temperature\"),\n", + " title=\"Country wide temperature\",\n", + " ylabel=\"\",\n", + " draw=draw_map_with_gb,\n", + ")\n", + "\n", + "out = widgets.interactive(interactable_plot_multiple_charts(configs=configs + (country, )), collected_after=start, collected_before=end)\n", + "display(out)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "apd.aggregation", + "language": "python", + "name": "apd.aggregation" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Ch12/apd.aggregation-chapter12/LICENCE b/Ch12/apd.aggregation-chapter12/LICENCE new file mode 100644 index 0000000..86685d4 --- /dev/null +++ b/Ch12/apd.aggregation-chapter12/LICENCE @@ -0,0 +1,31 @@ +Copyright (c) 2019, Matthew Wilkes + + +All rights reserved. + +Redistribution and use in source and binary forms, with or without modification, +are permitted provided that the following conditions are met: + +* Redistributions of source code must retain the above copyright notice, this + list of conditions and the following disclaimer. + +* Redistributions in binary form must reproduce the above copyright notice, this + list of conditions and the following disclaimer in the documentation and/or + other materials provided with the distribution. + +* Neither the name of the copyright holder nor the names of its + contributors may be used to endorse or promote products derived from this + software without specific prior written permission. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND +ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED +WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. +IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, +INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, +BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, +DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY +OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE +OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED +OF THE POSSIBILITY OF SUCH DAMAGE. + + diff --git a/Ch12/apd.aggregation-chapter12/Mapping.ipynb b/Ch12/apd.aggregation-chapter12/Mapping.ipynb new file mode 100644 index 0000000..916e704 --- /dev/null +++ b/Ch12/apd.aggregation-chapter12/Mapping.ipynb @@ -0,0 +1,254 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "datapoints = {\n", + " (53.8667, -1.3333): -1,\n", + " (53.35, -2.2833): 1,\n", + " (52.45, -1.7333): 3,\n", + " (51.5, -0.1333): 6,\n", + " (51.55, -2.5667): 5,\n", + " (54.9667, -1.6167): -1,\n", + " (55.9667, -3.2167): -1,\n", + " (54.7667, -1.5833): 0,\n", + " (53.7667, -0.3): 1,\n", + " (53.7667, -3.0167): 0,\n", + " (51.4833, -3.1833): 4,\n", + " (53.2667, -3.5167): 2,\n", + " (52.0833, -2.8): 2,\n", + " (52.0167, -0.6): 2,\n", + " (52.6667, 1.2667): 5,\n", + " (50.4333, -4.9833): 9,\n", + " (50.35, -4.1167): 10,\n", + " (50.5167, -2.45): 9,\n", + " (50.8333, -1.1667): 9,\n", + " (56.45, -5.4333): 4,\n", + " (57.5333, -4.05): -2,\n", + " (54.5167, -3.6167): 3,\n", + " (53.0833, 0.2667): 4,\n", + " (51.35, 1.3333): 7,\n", + " (50.8833, 0.3167): 8,\n", + " (58.45, -3.1): 3,\n", + " (50.1, -5.6667): 9,\n", + " (58.2167, -6.3333): 4,\n", + " (55.6833, -6.25): 7,\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "fig, ax = plt.subplots()\n", + "lats = [ll[0] for ll in datapoints.keys()]\n", + "lons = [ll[1] for ll in datapoints.keys()]\n", + "ax.plot(lons, lats, \"o\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "fig, ax = plt.subplots()\n", + "lats = [ll[0] for ll in datapoints.keys()]\n", + "lons = [ll[1] for ll in datapoints.keys()]\n", + "# Don't draw lines\n", + "ax.plot(lons, lats, \"o\")\n", + "# This is a map of the UK. Here, 1 degree latitude is \n", + "# 1.7x as far as 1 degree longitude\n", + "ax.set_aspect(1.7)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# Set up mercator transform from https://wiki.openstreetmap.org/wiki/Mercator#Python\n", + "# Thanks to Paulo Silva and the OSM contributors\n", + "import math\n", + "\n", + "def merc_x(lon):\n", + " r_major=6378137.000\n", + " return r_major*math.radians(lon)\n", + "\n", + "def merc_y(lat):\n", + " if lat>89.5:lat=89.5\n", + " if lat<-89.5:lat=-89.5\n", + " r_major=6378137.000\n", + " r_minor=6356752.3142\n", + " temp=r_minor/r_major\n", + " eccent=math.sqrt(1-temp**2)\n", + " phi=math.radians(lat)\n", + " sinphi=math.sin(phi)\n", + " con=eccent*sinphi\n", + " com=eccent/2\n", + " con=((1.0-con)/(1.0+con))**com\n", + " ts=math.tan((math.pi/2-phi)/2)/con\n", + " y=0-r_major*math.log(ts)\n", + " return y\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots()\n", + "\n", + "x = tuple(map(merc_x, lons))\n", + "y = tuple(map(merc_y, lats))\n", + "ax.plot(x, y, 'o')\n", + "ax.set_aspect(1.0)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots()\n", + "\n", + "x = tuple(map(merc_x, lons))\n", + "y = tuple(map(merc_y, lats))\n", + "\n", + "ax.tricontourf(x, y, tuple(datapoints.values()))\n", + "ax.plot(x, y, 'wo', ms=3)\n", + "ax.set_aspect(1.0)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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This should also be done after every upgrade of the software. + +# Defining endpoints + +Endpoints to collect from are managed with the `sensor_deployments` CLI tool. After installation +there will be no deployments defined + + sensor_deployments add --db postgresql+psycopg2://apd@localhost/apd + --api-key 97f6b3e5ceb64a6ba88968d7c3786b38 + --colour xkcd:red + http://rpi4:8081 + Loft + +The optional colour argument is the colour to use when plotting charts with the built-in charting +tools. This uses matplotlib's colour specification system, documented at https://matplotlib.org/tutorials/colors/colors.html + +The sensors can then be listed with `sensor_deployments list`: + + Loft + ID 53998a5160de48aeb71a5c37cd1455f2 + URI http://rpi4:8081 + API key 97f6b3e5ceb64a6ba88968d7c3786b38 + Colour xkcd:red + +The ID is the deployment ID, as set by the endpoint. It is only possible to add endpoints if they can be +connected to at the time. + +# Collating data + +Data can be collated from all defined endpoints with the `collect_sensor_data` command line tool. +Although you can specify URLs and an API key to explicitly load data from a one-off endpoint, running +without specifying these will use the configured endpoints from the database. + + collect_sensor_data --db postgresql+psycopg2://apd@localhost/apd + +# Viewing data + +You can write scripts to visualise the data from the database. I recommend using Jupyter for this, as it +has good support for drawing charts and interactivity. + +All configured charts can be displayed with: + + from apd.aggregation.analysis import plot_multiple_charts + display(await plot_multiple_charts()) + +More complex charting can be achieved by passing `configs=` to this function, consisting of configuration +objects as defined in `apd.aggregation.analysis`. Iteractivity can be achieved using the +`interactable_plot_multiple_charts` function with Jupyter/IPyWidgets' existing interactivity support. + +More control can be achieved using other functions from this module, such as getting all data points from +a given sensor with: + + from apd.aggregation.query import with_database, get_data + + with with_database("postgresql+psycopg2://apd@localhost/apd") as session: + points = [(dp.collected_at, dp.data) async for dp in get_data() if dp.sensor_name=="RelativeHumidity"] + +These can be called from any Python code, not just Jupyter notebooks + +# Analysis and triggers + +The aggregator allows for a long-running process that processes records as they are inserted to the database +and apply rules to them. + +This is configured with a Python-based configuration file, such as the following to log any time the +Temperature fluctuates above or below 18c: + + import operator + + from apd.aggregation.actions.action import OnlyOnChangeActionWrapper, LoggingAction + from apd.aggregation.actions.runner import DataProcessor + from apd.aggregation.actions.trigger import ValueThresholdTrigger + + + handlers = [ + DataProcessor( + name="TemperatureBelow18", + action=OnlyOnChangeActionWrapper(LoggingAction()), + trigger=ValueThresholdTrigger( + name="TemperatureBelow18", + threshold=18, + comparator=operator.lt, + sensor_name="Temperature", + ), + ) + ] + +This is run with: + + run_apd_actions --db postgresql+psycopg2://apd@localhost/apd sample_actions.py + +The optional `--historical` option causes the actions to be triggered for all events in the database. +If it's omitted then the default behaviour applies, which is to only analyse data that is added to the +database after the actions process has started. + +The possible actions are: + +* `apd.aggregation.actions.action.LoggingAction()` - Log data points +* `apd.aggregation.actions.action.SaveToDatabaseAction()` - Save data points to the db + +These can be wrapped with `OnlyOnChangeActionWrapper(subaction)` to only trigger an action when +the underlying value changes and/or with `OnlyAfterDateActionWrapper(subaction, min_date)` to +only trigger if the date on the discovered objects is strictly after `min_date`. + +The possible triggers are: + +* `apd.aggregation.actions.trigger.ValueThresholdTrigger(...)` - This compares the value of a sensor with threshold, using the specified comparator. + Any records that don't match the `sensor_name` and `deployment_id` parameters are excluded. + + +# Tips + +The `--db` argument to all command-line tools can be omitted and the `APD_DB_URI` environment variable +set instead. \ No newline at end of file diff --git a/Ch12/apd.aggregation-chapter12/Yappi Profiling.ipynb b/Ch12/apd.aggregation-chapter12/Yappi Profiling.ipynb new file mode 100644 index 0000000..153122f --- /dev/null +++ b/Ch12/apd.aggregation-chapter12/Yappi Profiling.ipynb @@ -0,0 +1,3140 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from apd.aggregation.analysis import interactable_plot_multiple_charts, configs\n", + "from apd.aggregation.utils import jupyter_page_file, profile_with_yappi, yappi_package_matches\n", + "import yappi\n", + "\n", + "with profile_with_yappi():\n", + " plot = interactable_plot_multiple_charts()\n", + " plot()\n", + "\n", + "with jupyter_page_file() as output:\n", + " yappi.get_func_stats(filter_callback=lambda stat:\n", + " yappi_package_matches(stat, [\"apd.aggregation\"])\n", + " ).print_all(output)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r\n", + "Clock type: WALL\r\n", + "Ordered by: totaltime, desc\r\n", + "\r\n", + "name ncall tsub ttot tavg \r\n", + "..futures\\thread.py:52 _WorkItem.run 7 0.000092 3.204091 0.457727\r\n", + "..tion\\analysis.py:373 run_in_thread 1 0.000053 1.844382 1.844382\r\n", + "..38\\Lib\\threading.py:859 Thread.run 2/1 0.000025 1.839877 0.919939\r\n", + "..rrent\\futures\\thread.py:66 _worker 2/1 0.000022 1.839852 0.919926\r\n", + "..lectorEventLoop.run_until_complete 1 0.000027 1.839745 1.839745\r\n", + "..ndowsSelectorEventLoop.run_forever 1 0.000088 1.839611 1.839611\r\n", + "..WindowsSelectorEventLoop._run_once 17 0.000408 1.839497 0.108206\r\n", + "..alysis.py:327 plot_multiple_charts 1 1.643447 1.839309 1.839309\r\n", + "..py:635 ThreadPoolExecutor.__exit__ 1 0.000005 1.837286 1.837286\r\n", + "..py:230 ThreadPoolExecutor.shutdown 1 0.000009 1.837281 1.837281\r\n", + "..8\\Lib\\threading.py:979 Thread.join 1 0.000006 1.837272 1.837272\r\n", + "..:1017 Thread._wait_for_tstate_lock 1 0.000011 1.837263 1.837263\r\n", + "..gation\\analysis.py:291 plot_sensor 1 0.000000 1.407986 1.407986\r\n", + "..query.py:88 get_data_by_deployment 1 1.397146 1.397188 1.397188\r\n", + "..d\\aggregation\\query.py:41 get_data 1 1.283592 1.375576 1.375576\r\n", + "..lchemy\\orm\\query.py:3232 Query.all 6 0.004333 1.362649 0.227108\r\n", + "..ctors.py:319 SelectSelector.select 17 0.000177 1.362418 0.080142\r\n", + "..tors.py:313 SelectSelector._select 17 0.000069 1.362216 0.080130\r\n", + "..y\\orm\\query.py:3400 Query.__iter__ 6 0.000054 0.958316 0.159719\r\n", + "..:3425 Query._execute_and_instances 6 0.000073 0.956631 0.159439\r\n", + "..ine\\base.py:916 Connection.execute 9 0.000062 0.740051 0.082228\r\n", + "..:589 PGDialect_psycopg2.do_execute 11 0.000042 0.738361 0.067124\r\n", + "..y:1159 Connection._execute_context 9 0.000307 0.734166 0.081574\r\n", + "..:291 Select._execute_on_connection 6 0.000047 0.731436 0.121906\r\n", + ".. 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import yappi\n", + "\n", + "from apd.aggregation.analysis import interactable_plot_multiple_charts, Config\n", + "from apd.aggregation.analysis import clean_temperature_fluctuations, get_one_sensor_by_deployment\n", + "from apd.aggregation.utils import profile_with_yappi\n", + "\n", + "yappi.set_clock_type(\"wall\")\n", + "\n", + "filter_in_db = Config(\n", + " clean=clean_temperature_fluctuations,\n", + " title=\"Ambient temperature\",\n", + " ylabel=\"Degrees C\",\n", + " get_data=get_one_sensor_by_deployment(\"Temperature\"),\n", + ")\n", + "\n", + "with profile_with_yappi():\n", + " plot = interactable_plot_multiple_charts(configs=[filter_in_db])\n", + " plot()\n", + "\n", + "yappi.get_func_stats().print_all()" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\r\n", + "Clock type: CPU\r\n", + "Ordered by: totaltime, desc\r\n", + "\r\n", + "name ncall tsub ttot tavg \r\n", + "..futures\\thread.py:52 _WorkItem.run 7 0.000000 9.515625 1.359375\r\n", + "..lchemy\\orm\\query.py:3232 Query.all 6 0.187500 6.390625 1.065104\r\n", + "..lchemy\\orm\\loading.py:35 instances 67353 0.046875 6.140625 0.000091\r\n", + "..esult.py:1257 ResultProxy.fetchall 6 0.000000 5.109375 0.851562\r\n", + "..py:1217 ResultProxy._fetchall_impl 6 0.000000 5.078125 0.846354\r\n", + "..38\\Lib\\threading.py:859 Thread.run 2/1 0.000000 3.125000 1.562500\r\n", + "..rrent\\futures\\thread.py:66 _worker 2/1 0.000000 3.125000 1.562500\r\n", + "..b\\asyncio\\events.py:79 Handle._run 29 0.000000 3.125000 0.107759\r\n", + "..WindowsSelectorEventLoop._run_once 17 0.000000 3.125000 0.183824\r\n", + "..lectorEventLoop.run_until_complete 1 0.000000 3.125000 3.125000\r\n", + "..ndowsSelectorEventLoop.run_forever 1 0.000000 3.125000 3.125000\r\n", + "..gation\\analysis.py:291 plot_sensor 1 0.031250 3.046875 3.046875\r\n", + "..egation\\analysis.py:313 4 0.015625 2.703125 0.675781\r\n", + "..and_clean_temperature_fluctuations 4 0.046875 2.687500 0.671875\r\n", + "..184 clean_temperature_fluctuations 4 0.062500 2.640625 0.660156\r\n", + "..sycopg2\\_json.py:164 typecast_json 67339 0.312500 2.562500 0.000038\r\n", + "..-input-1-879b7ebf06b8>:7 4 0.234375 2.515625 0.628906\r\n", + "..gregation\\query.py:116 subiterator 4 0.390625 2.281250 0.570312\r\n", + "..n38\\Lib\\json\\__init__.py:299 loads 67339 0.515625 2.250000 0.000033\r\n", + "..es\\psycopg2\\extras.py:686 67347 0.156250 1.796875 0.000027\r\n", + "..on38\\Lib\\uuid.py:130 UUID.__init__ 67347 1.000000 1.640625 0.000024\r\n", + "..\\decoder.py:332 JSONDecoder.decode 67339 0.718750 1.546875 0.000023\r\n", + "..d\\aggregation\\query.py:41 get_data 1 0.312500 1.531250 1.531250\r\n", + "..ion\\database.py:77 from_sql_result 67339 0.468750 1.218750 0.000018\r\n", + "..chemy\\orm\\loading.py:83 6 0.328125 0.984375 0.164062\r\n", + ".._collections.py:121 result._asdict 67343 0.375000 0.515625 0.000008\r\n", + "..thon38\\Lib\\uuid.py:231 UUID.__eq__ 67351 0.281250 0.359375 0.000005\r\n", + "..chemy\\orm\\loading.py:84 67347 0.343750 0.343750 0.000005\r\n", + "..y\\util\\_collections.py:112 __new__ 67347 0.234375 0.312500 0.000005\r\n", + "..\\matplotlib\\__init__.py:1535 inner 4 0.000000 0.296875 0.074219\r\n", + "..axes.py:1651 AxesSubplot.plot_date 4 0.000000 0.296875 0.074219\r\n", + "..xes\\_axes.py:1412 AxesSubplot.plot 4 0.000000 0.296875 0.074219\r\n", + "..gregation\\analysis.py:63 draw_date 4 0.000000 0.296875 0.074219\r\n", + ".._base.py:1842 AxesSubplot.add_line 4 0.000000 0.281250 0.070312\r\n", + "..oder.py:343 JSONDecoder.raw_decode 67339 0.265625 0.265625 0.000004\r\n", + "..s\\matplotlib\\dates.py:1911 convert 6 0.000000 0.265625 0.044271\r\n", + "..on_base.py:2063 vectorize.__call__ 4 0.000000 0.265625 0.066406\r\n", + "..b\\axis.py:1562 XAxis.convert_units 12 0.000000 0.265625 0.022135\r\n", + "...py:168 AxesSubplot.convert_xunits 48 0.000000 0.265625 0.005534\r\n", + "..lotlib\\lines.py:664 Line2D.recache 10 0.000000 0.265625 0.026562\r\n", + "..s\\matplotlib\\dates.py:401 date2num 6 0.000000 0.265625 0.044271\r\n", + "..tlib\\lines.py:1018 Line2D.get_path 10 0.000000 0.265625 0.026562\r\n", + "..68 AxesSubplot._update_line_limits 4 0.000000 0.265625 0.066406\r\n", + "...py:2154 vectorize._vectorize_call 4 0.046875 0.265625 0.066406\r\n", + ":1 DataPoint.__init__ 67339 0.234375 0.234375 0.000003\r\n", + "..tplotlib\\dates.py:210 _to_ordinalf 8467 0.140625 0.218750 0.000026\r\n", + "..alysis.py:327 plot_multiple_charts 1 0.000000 0.078125 0.078125\r\n", + "..ine\\base.py:916 Connection.execute 9 0.000000 0.062500 0.006944\r\n", + "..:291 Select._execute_on_connection 6 0.000000 0.062500 0.010417\r\n", + "..y\\orm\\query.py:3400 Query.__iter__ 6 0.000000 0.062500 0.010417\r\n", + ".. 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import yappi\n", + "\n", + "from apd.aggregation.analysis import interactable_plot_multiple_charts, Config, clean_temperature_fluctuations, get_data_by_deployment\n", + "from apd.aggregation.utils import profile_with_yappi\n", + "\n", + "async def filter_and_clean_temperature_fluctuations(datapoints):\n", + " filtered = (item async for item in datapoints if item.sensor_name==\"Temperature\")\n", + " cleaned = clean_temperature_fluctuations(filtered)\n", + " async for item in cleaned:\n", + " yield item\n", + "\n", + "filter_in_python = Config(\n", + " clean=filter_and_clean_temperature_fluctuations,\n", + " title=\"Ambient temperature\",\n", + " ylabel=\"Degrees C\",\n", + " get_data=get_data_by_deployment,\n", + ")\n", + "\n", + "with profile_with_yappi():\n", + " plot = interactable_plot_multiple_charts(configs=[filter_in_python])\n", + " plot()\n", + "\n", + "yappi.get_func_stats().print_all()" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "from apd.aggregation.utils import yappi_package_matches\n", + "import yappi\n", + "\n", + "yappi.get_func_stats().save(\"callgrind.filter_in_python\", \"callgrind\") " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "apd.aggregation", + "language": "python", + "name": "apd.aggregation" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.0" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Ch12/apd.aggregation-chapter12/pyproject.toml b/Ch12/apd.aggregation-chapter12/pyproject.toml new file mode 100644 index 0000000..4d3bb67 --- /dev/null +++ b/Ch12/apd.aggregation-chapter12/pyproject.toml @@ -0,0 +1,5 @@ +[build-system] +requires = [ + "setuptools", +] +build-backend = "setuptools.build_meta" diff --git a/Ch12/apd.aggregation-chapter12/pytest.ini b/Ch12/apd.aggregation-chapter12/pytest.ini new file mode 100644 index 0000000..8c26fbc --- /dev/null +++ b/Ch12/apd.aggregation-chapter12/pytest.ini @@ -0,0 +1,4 @@ +[pytest] +markers = + functional: these tests are significantly slower as they start a HTTP server + performance: very slow tests that provide performance guarantees \ No newline at end of file diff --git a/Ch12/apd.aggregation-chapter12/setup.cfg b/Ch12/apd.aggregation-chapter12/setup.cfg new file mode 100644 index 0000000..704c6bf --- /dev/null +++ b/Ch12/apd.aggregation-chapter12/setup.cfg @@ -0,0 +1,89 @@ +[mypy] +namespace_packages = True +mypy_path = src +plugins = sqlmypy + +[mypy-alembic] +ignore_missing_imports = True + +[mypy-alembic.config] +ignore_missing_imports = True + +[mypy-alembic.script] +ignore_missing_imports = True + +[mypy-alembic.runtime.environment] +ignore_missing_imports = True + +[mypy-matplotlib] +ignore_missing_imports = True + +[mypy-matplotlib.axes._base] +ignore_missing_imports = True + +[mypy-matplotlib.figure] +ignore_missing_imports = True + +[mypy-matplotlib.pyplot] +ignore_missing_imports = True + +[mypy-IPython.core] +ignore_missing_imports = True + +[mypy-yappi] +ignore_missing_imports = True + +[mypy-pint] +ignore_missing_imports = True + +[mypy-pytest] +ignore_missing_imports = True + +[mypy-setuptools] +ignore_missing_imports = True + +[flake8] +max-line-length = 120 +ignore = E501,E712 + +[metadata] +name = apd.aggregation +version = attr: apd.aggregation.VERSION +description = A programme that queries apd.sensor endpoints and aggregates their results. +long_description = file: README.md, CHANGES.md, LICENCE +long_description_content_type = text/markdown +keywords = +license = BSD +classifiers = + Programming Language :: Python :: 3 + Programming Language :: Python :: 3.7 + +[options] +zip_safe = False +include_package_data = True +package_dir = + =src +packages = find_namespace: +install_requires = + sqlalchemy + aiohttp + pint + psycopg2 + alembic + click + +[options.package_data] +apd.aggregation = py.typed + +[options.extras_require] +jupyter = ipywidgets +yappi = yappi + +[options.packages.find] +where = src + +[options.entry_points] +console_scripts = + collect_sensor_data = apd.aggregation.cli:collect_sensor_data + sensor_deployments = apd.aggregation.cli:deployments + run_apd_actions = apd.aggregation.cli:run_actions \ No newline at end of file diff --git a/Ch12/apd.aggregation-chapter12/setup.py b/Ch12/apd.aggregation-chapter12/setup.py new file mode 100644 index 0000000..6068493 --- /dev/null +++ b/Ch12/apd.aggregation-chapter12/setup.py @@ -0,0 +1,3 @@ +from setuptools import setup + +setup() diff --git a/Ch12/apd.aggregation-chapter12/src/apd/aggregation/__init__.py b/Ch12/apd.aggregation-chapter12/src/apd/aggregation/__init__.py new file mode 100644 index 0000000..3277f64 --- /dev/null +++ b/Ch12/apd.aggregation-chapter12/src/apd/aggregation/__init__.py @@ -0,0 +1 @@ +VERSION = "1.0.0" diff --git a/Ch12/apd.aggregation-chapter12/src/apd/aggregation/actions/__init__.py b/Ch12/apd.aggregation-chapter12/src/apd/aggregation/actions/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/Ch12/apd.aggregation-chapter12/src/apd/aggregation/actions/action.py b/Ch12/apd.aggregation-chapter12/src/apd/aggregation/actions/action.py new file mode 100644 index 0000000..99f8c31 --- /dev/null +++ b/Ch12/apd.aggregation-chapter12/src/apd/aggregation/actions/action.py @@ -0,0 +1,140 @@ +import asyncio +import dataclasses +import datetime +import logging +import typing as t + +import aiohttp + +from ..database import DataPoint +from ..collect import handle_result +from ..query import db_session_var +from .base import Action +from .source import refeed_queue_var + +logger = logging.getLogger(__name__) + + +@dataclasses.dataclass +class OnlyOnChangeActionWrapper(Action): + """An action that requires another action as a parameter. + The `wrapped` action will be delegated to so long as the previous + invocation's `data` attribute is different to the current one. + The first invocation will always be delegated.""" + + wrapped: Action + + async def start(self) -> None: + self.last_value = None + return await self.wrapped.start() + + async def handle(self, datapoint: DataPoint) -> bool: + if datapoint.data == self.last_value: + return False + else: + self.last_value = datapoint.data + return await self.wrapped.handle(datapoint) + + +@dataclasses.dataclass +class OnlyOnValueActionWrapper(Action): + """An action that requires another action as a parameter. + The `wrapped` action will be delegated to so long as the previous + invocation's `data` attribute is the value specified.""" + + wrapped: Action + value: t.Any + + async def start(self) -> None: + return await self.wrapped.start() + + async def handle(self, datapoint: DataPoint) -> bool: + if datapoint.data == self.value: + return await self.wrapped.handle(datapoint) + else: + return False + + +@dataclasses.dataclass +class OnlyAfterDateActionWrapper(Action): + """An action that requires another action and a date + as parameters. The `wrapped` action will be delegated to + for all data points with a date strictly after `date_threshold`.""" + + wrapped: Action + date_threshold: datetime.datetime + + async def start(self) -> None: + return await self.wrapped.start() + + async def handle(self, datapoint: DataPoint) -> bool: + if datapoint.collected_at <= self.date_threshold: + return False + return await self.wrapped.handle(datapoint) + + +class SaveToDatabaseAction(Action): + """An action that stores any generated data points back to the DB""" + + async def start(self) -> None: + return + + async def handle(self, datapoint: DataPoint) -> bool: + loop = asyncio.get_running_loop() + session = db_session_var.get() + await loop.run_in_executor(None, handle_result, [datapoint], session) + return True + + +class LoggingAction(Action): + """An action that stores any generated data points back to the DB""" + + async def start(self) -> None: + return + + async def handle(self, datapoint: DataPoint) -> bool: + logger.warn(datapoint) + return True + + +class RefeedAction(Action): + """An action that puts data points into a special queue to be consumed + by the analysis programme""" + + async def start(self) -> None: + return + + async def handle(self, datapoint: DataPoint) -> bool: + refeed_queue = refeed_queue_var.get() + if refeed_queue is None: + logger.error("Refeed queue has not been initialised") + return False + else: + logger.info(f"Re-fed {datapoint} to aggregation queue") + await refeed_queue.put(datapoint) + return True + + +@dataclasses.dataclass +class WebhookAction(Action): + """An action that runs a webhook""" + + uri: str + + async def start(self) -> None: + return + + async def handle(self, datapoint: DataPoint) -> bool: + async with aiohttp.ClientSession() as http: + async with http.post( + self.uri, + json={ + "value1": datapoint.sensor_name, + "value2": str(datapoint.data), + "value3": datapoint.deployment_id.hex, + }, + ) as request: + logger.info( + f"Made webhook request for {datapoint} with status {request.status}" + ) + return request.status == 200 diff --git a/Ch12/apd.aggregation-chapter12/src/apd/aggregation/actions/base.py b/Ch12/apd.aggregation-chapter12/src/apd/aggregation/actions/base.py new file mode 100644 index 0000000..0589ba4 --- /dev/null +++ b/Ch12/apd.aggregation-chapter12/src/apd/aggregation/actions/base.py @@ -0,0 +1,62 @@ +import typing as t + +from ..typing import T_value +from ..database import DataPoint +from ..exceptions import NoDataForTrigger + + +class Trigger(t.Generic[T_value]): + name: str + + async def start(self) -> None: + """ Coroutine to do any initial setup """ + return + + async def match(self, datapoint: DataPoint) -> bool: + """ Return True if the datapoint is of interest to this + trigger. + This is an optional method, called by the default implementation + of handle(...).""" + raise NotImplementedError + + async def extract(self, datapoint: DataPoint) -> T_value: + """ Return the value that this datapoint implies for this trigger, + or raise NoDataForTrigger if no value is appropriate. + Can also raise IncompatibleTriggerError if the value is not readable. + + This is an optional method, called by the default implementation + of handle(...). + """ + raise NotImplementedError + + async def handle(self, datapoint: DataPoint) -> t.Optional[DataPoint]: + """Given a data point, optionally return a datapoint that + represents the value of this trigger. Will delegate to the + match(...) and extract(...) functions.""" + if not await self.match(datapoint): + # This data point isn't relevant + return None + + try: + value = await self.extract(datapoint) + except NoDataForTrigger: + # There was no value for this point + return None + + return DataPoint( + sensor_name=self.name, + data=value, + deployment_id=datapoint.deployment_id, + collected_at=datapoint.collected_at, + ) + + +class Action: + async def start(self) -> None: + """ Coroutine to do any initial setup """ + return + + async def handle(self, datapoint: DataPoint) -> bool: + """ Apply this datapoint to the action, returning + a boolean to indicate success. """ + raise NotImplementedError diff --git a/Ch12/apd.aggregation-chapter12/src/apd/aggregation/actions/runner.py b/Ch12/apd.aggregation-chapter12/src/apd/aggregation/actions/runner.py new file mode 100644 index 0000000..b544c21 --- /dev/null +++ b/Ch12/apd.aggregation-chapter12/src/apd/aggregation/actions/runner.py @@ -0,0 +1,75 @@ +from __future__ import annotations + +import asyncio +import collections +import dataclasses +import time +import typing as t + +from ..database import DataPoint +from .base import Action, Trigger + +Decorated_Type = t.TypeVar("Decorated_Type", bound=t.Callable[..., t.Any]) + + +@dataclasses.dataclass(unsafe_hash=True) +class DataProcessor: + name: str + action: Action + trigger: Trigger[t.Any] + + def __post_init__(self): + self._input: t.Optional[asyncio.Queue[DataPoint]] = None + self._sub_tasks: t.Set = set() + self.last_times = collections.deque(maxlen=10) + self.total_in = 0 + self.total_out = 0 + + async def start(self) -> None: + self._input = asyncio.Queue(64) + self._task = asyncio.create_task(self.process(), name=f"{self.name}_process") + await asyncio.gather(self.action.start(), self.trigger.start()) + + @property + def input(self) -> asyncio.Queue[DataPoint]: + if self._input is None: + raise RuntimeError(f"{self}.start() was not awaited") + if self._task.done(): + raise RuntimeError("Processing has stopped") from self._task.exception() + return self._input + + async def idle(self) -> None: + await self.input.join() + + async def end(self) -> None: + self._task.cancel() + + async def push(self, obj: DataPoint) -> None: + coro = self.input.put(obj) + return await asyncio.wait_for(coro, timeout=30) + + async def process(self) -> None: + while True: + data = await self.input.get() + start = time.time() + self.total_in += 1 + try: + action_taken = False + processed = await self.trigger.handle(data) + if processed: + action_taken =await self.action.handle(processed) + if action_taken: + elapsed = time.time() - start + self.total_out += 1 + self.last_times.append(elapsed) + finally: + self.input.task_done() + + def stats(self) -> str: + if self.last_times: + avr_time = sum(self.last_times) / len(self.last_times) + elif self.total_in: + avr_time = 0 + else: + return "Not yet started" + return f"{avr_time:0.3f} seconds per item. {self.total_in} in, {self.total_out} out, {self.input.qsize()} waiting." diff --git a/Ch12/apd.aggregation-chapter12/src/apd/aggregation/actions/source.py b/Ch12/apd.aggregation-chapter12/src/apd/aggregation/actions/source.py new file mode 100644 index 0000000..dd3da9a --- /dev/null +++ b/Ch12/apd.aggregation-chapter12/src/apd/aggregation/actions/source.py @@ -0,0 +1,102 @@ +import asyncio +from contextvars import ContextVar +import logging + +from apd.aggregation.query import db_session_var, get_data + +refeed_queue_var: ContextVar[asyncio.Queue] = ContextVar("refeed_queue") +logger = logging.getLogger(__name__) + + +async def get_newest_record_id(): + from apd.aggregation.database import datapoint_table + from sqlalchemy import func + + loop = asyncio.get_running_loop() + db_session = db_session_var.get() + max_id_query = db_session.query(func.max(datapoint_table.c.id)) + return await loop.run_in_executor(None, max_id_query.scalar) + + +async def queue_iterator(queue): + while not queue.empty(): + yield queue.get_nowait() + + +async def get_data_ongoing(*args, historical=False, **kwargs): + last_id = 0 + if not historical: + last_id = await get_newest_record_id() + db_session = db_session_var.get() + refeed_queue = refeed_queue_var.get() + + while True: + # Run a timer for 300 seconds concurrently with our work + minimum_loop_timer = asyncio.create_task(asyncio.sleep(30)) + + async for datapoint in get_data( + *args, inserted_after_record_id=last_id, order=False, **kwargs + ): + if datapoint.id > last_id: + # This is the newest datapoint we have handled so far + last_id = datapoint.id + yield datapoint + # Next time, find only data points later than the latest we've seen + + while not refeed_queue.empty(): + # Process any datapoints gathered through the refeed queue + logger.info("Passing refeed queue") + async for datapoint in queue_iterator(refeed_queue): + yield datapoint + + # Commit the DB to store any work that was done in this loop and + # ensure that any isolation level issues do not prevent loading more + # data + db_session.commit() + # Wait for that timer to complete. If our loop took over 5 minutes + # this will complete immediately, otherwise it will block + await minimum_loop_timer + logger.info("Getting next group of data") + + +async def wait_for_notify(loop, raw_connection): + waiting = True + while waiting: + # SQLAlchemy isn't asynchronous, poll in a new thread + # to make sure we've received any notifications + await loop.run_in_executor(None, raw_connection.poll) + while raw_connection.notifies: + # End the loop after clearing out all pending + # notifications + waiting = False + raw_connection.notifies.pop() + if waiting: + # If we had no notifications wait 15 seconds then + # re-check + await asyncio.sleep(15) + + +async def get_data_ongoing_psql_pubsub(*args, historical=False, **kwargs): + last_id = 0 + if not historical: + kwargs["inserted_after_record_id"] = last_id = await get_newest_record_id() + db_session = db_session_var.get() + db_session.execute("LISTEN apd_aggregation;") + loop = asyncio.get_running_loop() + while True: + async for datapoint in get_data(*args, order=False, **kwargs): + if datapoint.id > last_id: + # This is the newest datapoint we have handled so far + last_id = datapoint.id + yield datapoint + # Next time, find only data points later than the latest we've seen + kwargs["inserted_after_record_id"] = last_id + # Commit the DB to store any work that was done in this loop and + # ensure that any isolation level issues do not prevent loading more + # data + db_session.commit() + + connection = db_session.connection() + raw_connection = connection.connection + await wait_for_notify(loop, raw_connection) + # Always yield new records from this point on diff --git a/Ch12/apd.aggregation-chapter12/src/apd/aggregation/actions/trigger.py b/Ch12/apd.aggregation-chapter12/src/apd/aggregation/actions/trigger.py new file mode 100644 index 0000000..d5c0def --- /dev/null +++ b/Ch12/apd.aggregation-chapter12/src/apd/aggregation/actions/trigger.py @@ -0,0 +1,77 @@ +import dataclasses +import typing as t +import uuid + +from ..database import DataPoint +from ..exceptions import IncompatibleTriggerError, NoDataForTrigger +from .base import Trigger + + +@dataclasses.dataclass(frozen=True) +class ValueThresholdTrigger(Trigger[bool]): + name: str + threshold: float + comparator: t.Callable[[float, float], bool] + sensor_name: str + deployment_id: t.Optional[uuid.UUID] = dataclasses.field(default=None) + + async def match(self, datapoint: DataPoint) -> bool: + if datapoint.sensor_name != self.sensor_name: + return False + elif self.deployment_id and datapoint.deployment_id != self.deployment_id: + return False + return True + + async def extract(self, datapoint: DataPoint) -> bool: + if datapoint.data is None: + raise IncompatibleTriggerError("Datapoint does not contain data") + elif isinstance(datapoint.data, float): + value = datapoint.data + elif isinstance(datapoint.data, dict) and "magnitude" in datapoint.data: + value = datapoint.data["magnitude"] + else: + raise IncompatibleTriggerError("Unrecognised data format") + return self.comparator(value, self.threshold) # type: ignore + + +@dataclasses.dataclass +class ValueDifferenceTrigger(Trigger[float]): + name: str + sensor_name: str + target_deployment_id: uuid.UUID + reference_deployment_id: uuid.UUID + + def __post_init__(self): + self.last_reference = None + self.last_target = None + + async def match(self, datapoint: DataPoint) -> bool: + if datapoint.sensor_name != self.sensor_name: + return False + elif datapoint.deployment_id in ( + self.target_deployment_id, + self.reference_deployment_id, + ): + return True + return False + + async def extract(self, datapoint: DataPoint) -> float: + if datapoint.data is None: + raise IncompatibleTriggerError("Datapoint does not contain data") + elif isinstance(datapoint.data, float): + value = datapoint.data + elif isinstance(datapoint.data, dict) and "magnitude" in datapoint.data: + value = datapoint.data["magnitude"] + else: + raise IncompatibleTriggerError("Unrecognised data format") + + if datapoint.deployment_id == self.target_deployment_id: + self.last_target = value + elif datapoint.deployment_id == self.reference_deployment_id: + self.last_reference = value + + if self.last_reference is None or self.last_target is None: + # We need to have seen both items before we can calculate a difference + raise NoDataForTrigger("Insufficient data processed") + + return self.last_target - self.last_reference diff --git a/Ch12/apd.aggregation-chapter12/src/apd/aggregation/alembic/README b/Ch12/apd.aggregation-chapter12/src/apd/aggregation/alembic/README new file mode 100644 index 0000000..7d90be0 --- /dev/null +++ b/Ch12/apd.aggregation-chapter12/src/apd/aggregation/alembic/README @@ -0,0 +1,12 @@ +Generic single-database configuration. + + +# Database setup + +To generate the required database tables you must create an alembic.ini file, as follows: + + [alembic] + script_location = apd.aggregation:alembic + sqlalchemy.url = postgresql+psycopg2://apd@localhost/apd + +and run `alembic upgrade head`. This should also be done after every upgrade of the software. diff --git a/Ch12/apd.aggregation-chapter12/src/apd/aggregation/alembic/env.py b/Ch12/apd.aggregation-chapter12/src/apd/aggregation/alembic/env.py new file mode 100644 index 0000000..4c8a2c2 --- /dev/null +++ b/Ch12/apd.aggregation-chapter12/src/apd/aggregation/alembic/env.py @@ -0,0 +1,80 @@ +from logging.config import fileConfig + +from sqlalchemy import engine_from_config +from sqlalchemy import pool +from alembic import context + +from apd.aggregation.database import metadata + + +# this is the Alembic Config object, which provides +# access to the values within the .ini file in use. +config = context.config + +# Interpret the config file for Python logging. +# This line sets up loggers basically. +if config.config_file_name: + fileConfig(config.config_file_name) + +target_metadata = metadata + + +def include_object(object, name, type_, reflected, compare_to): + if object.info.get("is_view", False): + return False + return True + + +def run_migrations_offline(): + """Run migrations in 'offline' mode. + + This configures the context with just a URL + and not an Engine, though an Engine is acceptable + here as well. By skipping the Engine creation + we don't even need a DBAPI to be available. + + Calls to context.execute() here emit the given string to the + script output. + + """ + url = config.get_main_option("sqlalchemy.url") + context.configure( + url=url, + include_object=include_object, + target_metadata=target_metadata, + literal_binds=True, + dialect_opts={"paramstyle": "named"}, + ) + + with context.begin_transaction(): + context.run_migrations() + + +def run_migrations_online(): + """Run migrations in 'online' mode. + + In this scenario we need to create an Engine + and associate a connection with the context. + + """ + connectable = engine_from_config( + config.get_section(config.config_ini_section), + prefix="sqlalchemy.", + poolclass=pool.NullPool, + ) + + with connectable.connect() as connection: + context.configure( + connection=connection, + target_metadata=target_metadata, + include_object=include_object, + ) + + with context.begin_transaction(): + context.run_migrations() + + +if context.is_offline_mode(): + run_migrations_offline() +else: + run_migrations_online() diff --git a/Ch12/apd.aggregation-chapter12/src/apd/aggregation/alembic/script.py.mako b/Ch12/apd.aggregation-chapter12/src/apd/aggregation/alembic/script.py.mako new file mode 100644 index 0000000..2c01563 --- /dev/null +++ b/Ch12/apd.aggregation-chapter12/src/apd/aggregation/alembic/script.py.mako @@ -0,0 +1,24 @@ +"""${message} + +Revision ID: ${up_revision} +Revises: ${down_revision | comma,n} +Create Date: ${create_date} + +""" +from alembic import op +import sqlalchemy as sa +${imports if imports else ""} + +# revision identifiers, used by Alembic. +revision = ${repr(up_revision)} +down_revision = ${repr(down_revision)} +branch_labels = ${repr(branch_labels)} +depends_on = ${repr(depends_on)} + + +def upgrade(): + ${upgrades if upgrades else "pass"} + + +def downgrade(): + ${downgrades if downgrades else "pass"} diff --git a/Ch12/apd.aggregation-chapter12/src/apd/aggregation/alembic/versions/4b2df8a6e1ce_add_indexes_to_datapoints.py b/Ch12/apd.aggregation-chapter12/src/apd/aggregation/alembic/versions/4b2df8a6e1ce_add_indexes_to_datapoints.py new file mode 100644 index 0000000..ef671bd --- /dev/null +++ b/Ch12/apd.aggregation-chapter12/src/apd/aggregation/alembic/versions/4b2df8a6e1ce_add_indexes_to_datapoints.py @@ -0,0 +1,32 @@ +"""Add indexes to datapoints + +Revision ID: 4b2df8a6e1ce +Revises: 6d2eacd5da3f +Create Date: 2019-12-02 16:07:41.123116 + +""" +from alembic import op + + +# revision identifiers, used by Alembic. +revision = "4b2df8a6e1ce" +down_revision = "6d2eacd5da3f" +branch_labels = None +depends_on = None + + +def upgrade(): + op.create_index( + op.f("ix_datapoints_collected_at"), + "datapoints", + ["collected_at"], + unique=False, + ) + op.create_index( + op.f("ix_datapoints_sensor_name"), "datapoints", ["sensor_name"], unique=False, + ) + + +def downgrade(): + op.drop_index(op.f("ix_datapoints_sensor_name"), table_name="datapoints") + op.drop_index(op.f("ix_datapoints_collected_at"), table_name="datapoints") diff --git a/Ch12/apd.aggregation-chapter12/src/apd/aggregation/alembic/versions/6962f8455a6d_add_daily_summary_view.py b/Ch12/apd.aggregation-chapter12/src/apd/aggregation/alembic/versions/6962f8455a6d_add_daily_summary_view.py new file mode 100644 index 0000000..eb02455 --- /dev/null +++ b/Ch12/apd.aggregation-chapter12/src/apd/aggregation/alembic/versions/6962f8455a6d_add_daily_summary_view.py @@ -0,0 +1,38 @@ +"""Add daily summary view + +Revision ID: 6962f8455a6d +Revises: 4b2df8a6e1ce +Create Date: 2019-12-03 11:50:24.403402 + +""" +from alembic import op + + +# revision identifiers, used by Alembic. +revision = "6962f8455a6d" +down_revision = "4b2df8a6e1ce" +branch_labels = None +depends_on = None + + +def upgrade(): + create_view = """ + CREATE VIEW daily_summary AS + SELECT + datapoints.sensor_name AS sensor_name, + datapoints.data AS data, + count(datapoints.id) AS count + FROM datapoints + WHERE + datapoints.collected_at >= CAST(CURRENT_DATE AS DATE) + AND + datapoints.collected_at < CAST(CURRENT_DATE AS DATE) + 1 + GROUP BY + datapoints.sensor_name, + datapoints.data; + """ + op.execute(create_view) + + +def downgrade(): + op.execute("""DROP VIEW daily_summary""") diff --git a/Ch12/apd.aggregation-chapter12/src/apd/aggregation/alembic/versions/6d2eacd5da3f_create_sensor_values_table.py b/Ch12/apd.aggregation-chapter12/src/apd/aggregation/alembic/versions/6d2eacd5da3f_create_sensor_values_table.py new file mode 100644 index 0000000..b4a3545 --- /dev/null +++ b/Ch12/apd.aggregation-chapter12/src/apd/aggregation/alembic/versions/6d2eacd5da3f_create_sensor_values_table.py @@ -0,0 +1,31 @@ +"""Create datapoints table + +Revision ID: 6d2eacd5da3f +Revises: N/A +Create Date: 2019-09-29 13:43:21.242706 + +""" +from alembic import op +import sqlalchemy as sa +from sqlalchemy.dialects import postgresql + +# revision identifiers, used by Alembic. +revision = "6d2eacd5da3f" +down_revision = None +branch_labels = None +depends_on = None + + +def upgrade(): + op.create_table( + "datapoints", + sa.Column("id", sa.Integer(), nullable=False), + sa.Column("sensor_name", sa.String(), nullable=True), + sa.Column("collected_at", postgresql.TIMESTAMP(), nullable=True), + sa.Column("data", postgresql.JSONB(astext_type=sa.Text()), nullable=True), + sa.PrimaryKeyConstraint("id"), + ) + + +def downgrade(): + op.drop_table("datapoints") diff --git a/Ch12/apd.aggregation-chapter12/src/apd/aggregation/alembic/versions/d8cdc709086b_add_deployment_table.py b/Ch12/apd.aggregation-chapter12/src/apd/aggregation/alembic/versions/d8cdc709086b_add_deployment_table.py new file mode 100644 index 0000000..204e096 --- /dev/null +++ b/Ch12/apd.aggregation-chapter12/src/apd/aggregation/alembic/versions/d8cdc709086b_add_deployment_table.py @@ -0,0 +1,32 @@ +"""Add deployment table + +Revision ID: d8cdc709086b +Revises: d8d4cf6a178f +Create Date: 2019-12-17 16:28:58.585616 + +""" +from alembic import op +import sqlalchemy as sa +from sqlalchemy.dialects import postgresql + +# revision identifiers, used by Alembic. +revision = "d8cdc709086b" +down_revision = "d8d4cf6a178f" +branch_labels = None +depends_on = None + + +def upgrade(): + op.create_table( + "deployments", + sa.Column("id", postgresql.UUID(as_uuid=True), nullable=False), + sa.Column("uri", sa.String(), nullable=True), + sa.Column("name", sa.String(), nullable=True), + sa.Column("colour", sa.String(), nullable=True), + sa.Column("api_key", sa.String(), nullable=True), + sa.PrimaryKeyConstraint("id"), + ) + + +def downgrade(): + op.drop_table("deployments") diff --git a/Ch12/apd.aggregation-chapter12/src/apd/aggregation/alembic/versions/d8d4cf6a178f_add_deployment_id_to_datapoint.py b/Ch12/apd.aggregation-chapter12/src/apd/aggregation/alembic/versions/d8d4cf6a178f_add_deployment_id_to_datapoint.py new file mode 100644 index 0000000..88d5219 --- /dev/null +++ b/Ch12/apd.aggregation-chapter12/src/apd/aggregation/alembic/versions/d8d4cf6a178f_add_deployment_id_to_datapoint.py @@ -0,0 +1,34 @@ +"""Add deployment id to DataPoint + +Revision ID: d8d4cf6a178f +Revises: 6962f8455a6d +Create Date: 2019-12-03 20:58:11.285509 + +""" +from alembic import op +import sqlalchemy as sa +from sqlalchemy.dialects import postgresql + +# revision identifiers, used by Alembic. +revision = "d8d4cf6a178f" +down_revision = "6962f8455a6d" +branch_labels = None +depends_on = None + + +def upgrade(): + op.add_column( + "datapoints", + sa.Column("deployment_id", postgresql.UUID(as_uuid=True), nullable=True), + ) + op.create_index( + op.f("ix_datapoints_deployment_id"), + "datapoints", + ["deployment_id"], + unique=False, + ) + + +def downgrade(): + op.drop_index(op.f("ix_datapoints_deployment_id"), table_name="datapoints") + op.drop_column("datapoints", "deployment_id") diff --git a/Ch12/apd.aggregation-chapter12/src/apd/aggregation/analysis.py b/Ch12/apd.aggregation-chapter12/src/apd/aggregation/analysis.py new file mode 100644 index 0000000..eeb4ab2 --- /dev/null +++ b/Ch12/apd.aggregation-chapter12/src/apd/aggregation/analysis.py @@ -0,0 +1,401 @@ +from __future__ import annotations + +import asyncio +import collections +from concurrent.futures import ThreadPoolExecutor +import dataclasses +import datetime +import functools +import math +import typing as t +from uuid import UUID +import warnings + +import matplotlib.pyplot as plt +from matplotlib.axes._base import _AxesBase +from matplotlib.figure import Figure +from pint import _DEFAULT_REGISTRY as ureg + +from apd.aggregation.query import ( + get_data, + get_data_by_deployment, + with_database, + get_deployment_by_id, +) +from apd.aggregation.database import DataPoint, deployment_table +from .utils import merc_x, merc_y, convert_temperature +from .typing import IntermediateMapData, T_key, T_value, CleanerFunc, Cleaned +from .typing import ( + CLEANED_COORD_FLOAT, + CLEANED_DT_FLOAT, + COORD_FLOAT_CLEANER, + DT_FLOAT_CLEANER, +) + +# Static UUID to represent aggregation of other deployments +GLOBAL = UUID("bd02526e-7619-4a59-b04b-1fafd1c262d1") + + +@dataclasses.dataclass +class Config(t.Generic[T_key, T_value]): + title: str + clean: CleanerFunc[Cleaned[T_key, T_value]] + draw: t.Optional[ + t.Callable[ + [t.Any, t.Iterable[T_key], t.Iterable[T_value], t.Optional[str]], None + ] + ] = None + get_data: t.Optional[ + t.Callable[..., t.AsyncIterator[t.Tuple[UUID, t.AsyncIterator[DataPoint]]]] + ] = None + ylabel: t.Optional[str] = None + sensor_name: dataclasses.InitVar[str] = None + + def __post_init__(self, sensor_name: t.Optional[str] = None) -> None: + if self.draw is None: + self.draw = draw_date # type: ignore + if sensor_name is not None: + warnings.warn( + DeprecationWarning( + f"The sensor_name parameter is deprecated. Please pass " + f"get_data=get_one_sensor_by_deployment('{sensor_name}') " + f"to ensure the same behaviour. The sensor_name= parameter " + f"will be removed in apd.aggregation 3.0." + ), + stacklevel=3, + ) + if self.get_data is None: + self.get_data = get_one_sensor_by_deployment(sensor_name) + if self.get_data is None: + raise ValueError("You must specify a get_data function") + + +def draw_date( + plot: _AxesBase, + x: t.Iterable[datetime.datetime], + y: t.Iterable[float], + colour: t.Optional[str], +) -> None: + plot.plot_date(x, y, color=colour, linestyle="-", marker="", xdate=True) + + +def draw_map( + plot: _AxesBase, + x: t.Iterable[t.Tuple[float, float]], + y: t.Iterable[float], + colour: t.Optional[str], +) -> None: + lon = [merc_y(coord[0]) for coord in x] + lat = [merc_x(coord[1]) for coord in x] + + for axis in "x", "y": + plt.tick_params( + axis=axis, + which="both", + bottom=False, + top=False, + left=False, + right=False, + labelbottom=False, + labelleft=False, + ) + + plot.tricontourf(lat, lon, y) + plot.plot(lat, lon, "wo", ms=3) + plot.set_aspect(1.0) + + +def get_map_cleaner_for(sensor_name: str,) -> COORD_FLOAT_CLEANER: + """Given a sensor_name that represents a float, return a coroutine that acts as a cleaner + extracting that sensor's data keyed by the value of a Location sensor.""" + + async def clean_latest_coord_and_value( + datapoints: t.AsyncIterator[DataPoint], + ) -> CLEANED_COORD_FLOAT: + + # We will iterate over data points and build an entry in cleaned_data + # for each deployment. This lets newer data replace older data, as + # datapoints is assumed to be in date order + # IntermediateMapData is a typing hint for a dictionary with specific key/values + cleaned_data: t.Dict[UUID, IntermediateMapData] = {} + async for datapoint in datapoints: + # Get the existing data for this deployment, if we've seen it before + row_data = cleaned_data.get(datapoint.deployment_id, None) + if row_data is None: + # This is the first time we've seen this deployment + row_data = {"coord": None, "value": None} + if datapoint.sensor_name == "Location": + # Coord is a 2-tuple of floats + row_data["coord"] = ( + float(datapoint.data[0]), + float(datapoint.data[1]), + ) + elif datapoint.sensor_name == sensor_name: + # Value is a single float + row_data["value"] = float(datapoint.data) + # Store the info about this deployment back into the cleaned_data set + cleaned_data[datapoint.deployment_id] = row_data + + for data in cleaned_data.values(): + if data["coord"] is None or data["value"] is None: + # We only got a partial record, don't plot this + continue + yield data["coord"], data["value"] + + # Return the set up cleaner coroutine + return clean_latest_coord_and_value + + +async def clean_watthours_to_watts( + datapoints: t.AsyncIterator[DataPoint], +) -> CLEANED_DT_FLOAT: + last_watthours = None + last_time = None + async for datapoint in datapoints: + if datapoint.data is None: + continue + time = datapoint.collected_at + if datapoint.data["unit"] == "watt_hour": + watt_hours = datapoint.data["magnitude"] + else: + watt_hours = ( + ureg.Quantity(datapoint.data["magnitude"], datapoint.data["unit"]) + .to(ureg.watt_hour) + .magnitude + ) + if last_watthours: + seconds_elapsed = (time - last_time).total_seconds() + hours_elapsed = seconds_elapsed / (60.0 * 60.0) + additional_power = watt_hours - last_watthours + power = additional_power / hours_elapsed + yield time, power + last_watthours = watt_hours + last_time = datapoint.collected_at + + +async def clean_magnitude(datapoints: t.AsyncIterator[DataPoint],) -> CLEANED_DT_FLOAT: + async for datapoint in datapoints: + if datapoint.data is None: + continue + yield datapoint.collected_at, datapoint.data["magnitude"] + + +def convert_temperature_system( + cleaner: DT_FLOAT_CLEANER, temperature_unit: str, +) -> DT_FLOAT_CLEANER: + async def converter(datapoints: t.AsyncIterator[DataPoint],) -> CLEANED_DT_FLOAT: + results = cleaner(datapoints) + async for date, temp_c in results: + yield date, convert_temperature(temp_c, "degC", temperature_unit) + + return converter + + +async def clean_temperature_fluctuations( + datapoints: t.AsyncIterator[DataPoint], +) -> CLEANED_DT_FLOAT: + allowed_jitter = 2.5 + allowed_range = (-40, 80) + window_datapoints: t.Deque[DataPoint] = collections.deque(maxlen=3) + + def datapoint_ok(datapoint: DataPoint) -> bool: + """Return False if this data point does not contain a valid temperature""" + if datapoint.data is None: + return False + elif datapoint.data["unit"] != "degC": + # This point is in a different temperature system. While it could be converted + # this cleaner is not yet doing that. + return False + elif not allowed_range[0] < datapoint.data["magnitude"] < allowed_range[1]: + return False + return True + + async for datapoint in datapoints: + if not datapoint_ok(datapoint): + # If the datapoint is invalid then skip directly to the next item + continue + + window_datapoints.append(datapoint) + if len(window_datapoints) == 3: + # Find the temperatures of the datapoints in the window, then average + # the first and last and compare that to the middle point. + window_temperatures = [dp.data["magnitude"] for dp in window_datapoints] + avr_first_last = (window_temperatures[0] + window_temperatures[2]) / 2 + diff_middle_avr = abs(window_temperatures[1] - avr_first_last) + if diff_middle_avr > allowed_jitter: + pass + else: + yield window_datapoints[1].collected_at, window_temperatures[1] + elif len(window_datapoints) == 1: + # The item in the iterator can't be compared to both neighbours + # so should be yielded + yield datapoint.collected_at, datapoint.data["magnitude"] + else: + # Otherwise, let the window fill up, it will be yieleded later + pass + # When the iterator ends the final item is not yet in the middle + # of the window, so the last item must be explicitly yielded. + if len(window_datapoints) > 1 and datapoint_ok(datapoint): + yield datapoint.collected_at, datapoint.data["magnitude"] + + +async def clean_passthrough( + datapoints: t.AsyncIterator[DataPoint], +) -> CLEANED_DT_FLOAT: + async for datapoint in datapoints: + if datapoint.data is None: + continue + else: + yield datapoint.collected_at, datapoint.data + + +def get_one_sensor_by_deployment( + sensor_name: str, +) -> t.Callable[..., t.AsyncIterator[t.Tuple[UUID, t.AsyncIterator[DataPoint]]]]: + return functools.partial(get_data_by_deployment, sensor_name=sensor_name) + + +def get_all_data() -> t.Callable[ + ..., t.AsyncIterator[t.Tuple[UUID, t.AsyncIterator[DataPoint]]] +]: + async def get_all_data_inner( + *args: t.Any, **kwargs: t.Any + ) -> t.AsyncIterator[t.Tuple[UUID, t.AsyncIterator[DataPoint]]]: + yield GLOBAL, get_data(*args, **kwargs) + + return get_all_data_inner + + +configs = ( + Config( + sensor_name="SolarCumulativeOutput", + clean=clean_watthours_to_watts, + title="Solar generation", + ylabel="Watts", + ), + Config( + sensor_name="RAMAvailable", + clean=clean_passthrough, + title="RAM available", + ylabel="Bytes", + ), + Config( + sensor_name="RelativeHumidity", + clean=clean_passthrough, + title="Relative humidity", + ylabel="Percent", + ), + Config( + sensor_name="Temperature", + clean=clean_temperature_fluctuations, + title="Ambient temperature", + ylabel="Degrees C", + ), +) + + +def get_known_configs() -> t.Dict[str, Config[t.Any, t.Any]]: + return {config.title: config for config in configs} + + +async def plot_sensor( + config: Config[t.Any, t.Any], + plot: _AxesBase, + location_names: t.Dict[UUID, str], + **kwargs: t.Any, +) -> _AxesBase: + locations = [] + if config.get_data is None: + raise ValueError("You must provide a get_data function") + async for deployment_id, query_results in config.get_data(**kwargs): + if deployment_id == GLOBAL: + name = "Global" + else: + try: + deployment = await get_deployment_by_id(deployment_id) + except IndexError: + name = str(deployment_id) + colour = None + else: + name = deployment.name or str(deployment_id) + colour = deployment.colour + # Mypy currently doesn't understand callable fields on datatypes: https://github.com/python/mypy/issues/5485 + points = [dp async for dp in config.clean(query_results)] # type: ignore + if not points: + continue + locations.append(name) + x, y = zip(*points) + plot.set_title(config.title) + plot.set_ylabel(config.ylabel) + if config.draw is None: + raise ValueError("You must provide a get_data function") + config.draw(plot, x, y, colour) + plot.legend(locations) + return plot + + +async def plot_multiple_charts(*args: t.Any, **kwargs: t.Any) -> Figure: + # These parameters are pulled from kwargs to avoid confusing function + # introspection code in IPython widgets + location_names = kwargs.pop("location_names", None) + configs = kwargs.pop("configs", None) + dimensions = kwargs.pop("dimensions", None) + db_uri = kwargs.pop("db_uri", "postgresql+psycopg2://apd@localhost/apd") + + with with_database(db_uri) as session: + loop = asyncio.get_running_loop() + coros = [] + if configs is None: + # If no configs are supplied, use all known configs + configs = get_known_configs().values() + if dimensions is None: + # If no dimensions are supplied, get the square root of the number + # of configs and round it to find a number of columns. This will + # keep the arrangement approximately square. Find rows by multiplying + # out rows. + total_configs = len(configs) + columns = round(math.sqrt(total_configs)) + rows = math.ceil(total_configs / columns) + if location_names is None: + location_query = session.query( + deployment_table.c.id, deployment_table.c.name + ) + location_data = await loop.run_in_executor(None, location_query.all) + location_names = dict(location_data) + + figure = plt.figure(figsize=(10 * columns, 5 * rows), dpi=300) + for i, config in enumerate(configs, start=1): + plot = figure.add_subplot(columns, rows, i) + coros.append(plot_sensor(config, plot, location_names, *args, **kwargs)) + await asyncio.gather(*coros) + return figure + + +_Coroutine_Result = t.TypeVar("_Coroutine_Result") + + +def wrap_coroutine( + f: t.Callable[..., t.Coroutine[t.Any, t.Any, _Coroutine_Result]] +) -> t.Callable[..., _Coroutine_Result]: + """Given a coroutine, return a function that runs that coroutine + in a new event loop in an isolated thread""" + + @functools.wraps(f) + def run_in_thread(*args: t.Any, **kwargs: t.Any) -> _Coroutine_Result: + loop = asyncio.new_event_loop() + wrapped = f(*args, **kwargs) + with ThreadPoolExecutor(max_workers=1) as pool: + task = pool.submit(loop.run_until_complete, wrapped) + # Mypy can get confused when nesting generic functions, like we do here + # The fact that Task is generic means we lose the association with + # _CoroutineResult. Adding an explicit cast restores this. + return t.cast(_Coroutine_Result, task.result()) + + return run_in_thread + + +def interactable_plot_multiple_charts( + *args: t.Any, **kwargs: t.Any +) -> t.Callable[..., Figure]: + with_config = functools.partial(plot_multiple_charts, *args, **kwargs) + return wrap_coroutine(with_config) diff --git a/Ch12/apd.aggregation-chapter12/src/apd/aggregation/cli.py b/Ch12/apd.aggregation-chapter12/src/apd/aggregation/cli.py new file mode 100644 index 0000000..799325b --- /dev/null +++ b/Ch12/apd.aggregation-chapter12/src/apd/aggregation/cli.py @@ -0,0 +1,295 @@ +import asyncio +import functools +import importlib.util +import logging +import signal +import sys +import typing as t +import uuid + +import aiohttp +import click +from sqlalchemy import create_engine +from sqlalchemy.orm import sessionmaker + +from . import collect +from .actions.runner import DataProcessor +from .actions.source import get_data_ongoing, refeed_queue_var +from .database import Deployment, deployment_table +from .query import with_database + +logger = logging.getLogger(__name__) + + +@click.command() +@click.argument("server", nargs=-1) +@click.option( + "--db", + metavar="", + default="postgresql+psycopg2://localhost/apd", + help="The connection string to a PostgreSQL database", + envvar="APD_DB_URI", +) +@click.option("--api-key", metavar="", envvar="APD_API_KEY") +@click.option( + "--tolerate-failures", + "-f", + help="If provided, failure to retrieve some sensors' data will not abort the collection process", + is_flag=True, +) +@click.option("-v", "--verbose", is_flag=True, help="Enables verbose mode") +def collect_sensor_data( + db: str, server: t.Tuple[str], api_key: str, tolerate_failures: bool, verbose: bool +) -> t.Optional[int]: + """This loads data from one or more sensors into the specified database. + + Only PostgreSQL databases are supported, as the column definitions use + multiple pg specific features. The database must already exist and be + populated with the required tables. + + The --api-key option is used to specify the access token for the sensors + being queried. + + You may specify any number of servers, the variable should be the full URL + to the sensor's HTTP interface, not including the /v/2.0 portion. Multiple + URLs should be separated with a space. + """ + if tolerate_failures: + attempts = [(s,) for s in server] + else: + attempts = [server] + success = True + for attempt in attempts: + try: + collect.standalone(db, attempt, api_key, echo=verbose) + except ValueError as e: + click.secho(str(e), err=True, fg="red") + success = False + return success + + +def load_handler_config(path: str) -> t.List[DataProcessor]: + # Create a module called user_config backed by the file specified, and load it + # This uses Python's import internals to fake a module in a known location + # Based on an SO answer by Sebastian Rittau and sample code from Brett Cannon + module_spec = importlib.util.spec_from_file_location("user_config", path) + module = importlib.util.module_from_spec(module_spec) + loader = module_spec.loader + if isinstance(loader, importlib.abc.Loader): + loader.exec_module(module) + try: + return module.handlers # type: ignore + except AttributeError as err: + raise ValueError(f"Could not load config file from {path}") from err + else: + # No valid loader could be found + raise ValueError(f"Could not load config file from {path}") + + +def actually_exit(sig, frame): + click.secho("Exiting...", bold=True) + sys.exit(1) + + +def stats_signal_handler(sig, frame, handlers=None): + for handler in handlers: + click.echo( + click.style(handler.name, bold=True, fg="red") + " " + handler.stats() + ) + if sig == signal.SIGINT: + click.secho("Press Ctrl+C again to end the process", bold=True) + handler = signal.getsignal(signal.SIGINT) + signal.signal(signal.SIGINT, actually_exit) + asyncio.get_running_loop().call_later(5, install_ctrl_c_signal_handler, handler) + return + + +def install_ctrl_c_signal_handler(signal_handler): + click.secho("Press Ctrl+C to view statistics", bold=True) + signal.signal(signal.SIGINT, signal_handler) + + +@click.command() +@click.argument("config", nargs=1) +@click.option( + "--db", + metavar="", + default="postgresql+psycopg2://localhost/apd", + help="The connection string to a PostgreSQL database", + envvar="APD_DB_URI", +) +@click.option( + "--historical", + is_flag=True, + help="Also trigger actions for data points that were already present in the database", +) +@click.option("-v", "--verbose", is_flag=True, help="Enables verbose mode") +def run_actions( + config: str, db: str, verbose: bool, historical: bool +) -> t.Optional[int]: + """This runs the long-running action processors defined in a config file. + + The configuration file specified should be a Python file that defines a + list of DataProcessor objects called processors.n + """ + logging.basicConfig( + format="%(asctime)s - %(name)s - %(levelname)s - %(message)s", + level=logging.DEBUG if verbose else logging.WARN, + ) + + async def main_loop(): + with with_database(db): + logger.info("Loading configuration") + handlers = load_handler_config(config) + + # Set up the refeed queue before starting the handlers + # or source, so they all have access to it + refeed_queue_var.set(asyncio.Queue()) + + logger.info(f"Configured {len(handlers)} handlers") + starters = [handler.start() for handler in handlers] + await asyncio.gather(*starters) + + logger.info(f"Ingesting data") + data = get_data_ongoing(historical=historical) + + signal_handler = functools.partial(stats_signal_handler, handlers=handlers,) + + for signal_name in "SIGINFO", "SIGUSR1", "SIGINT": + try: + signal.signal(signal.Signals[signal_name], signal_handler) + except KeyError: + pass + + async for datapoint in data: + for handler in handlers: + await handler.push(datapoint) + + asyncio.run(main_loop()) + return True + + +@click.group() +def deployments(): + pass + + +@deployments.command() +@click.argument("uri") +@click.argument("name") +@click.option( + "--db", + metavar="", + default="postgresql+psycopg2://localhost/apd", + help="The connection string to a PostgreSQL database", + envvar="APD_DB_URI", +) +@click.option("--api-key", metavar="", envvar="APD_API_KEY") +@click.option("--colour") +def add( + db: str, uri: str, name: str, api_key: t.Optional[str], colour: t.Optional[str], +): + """This creates a record of a new deployment in the database. + """ + deployment = Deployment(id=None, uri=uri, name=name, api_key=api_key, colour=colour) + + async def http_get_deployment_id(): + async with aiohttp.ClientSession() as http: + collect.http_session_var.set(http) + return await collect.get_deployment_id(uri) + + deployment.id = asyncio.run(http_get_deployment_id()) + insert = deployment_table.insert().values(**deployment._asdict()) + + engine = create_engine(db) + sm = sessionmaker(engine) + Session = sm() + Session.execute(insert) + Session.commit() + return True + + +@deployments.command() +@click.option( + "--db", + metavar="", + default="postgresql+psycopg2://localhost/apd", + help="The connection string to a PostgreSQL database", + envvar="APD_DB_URI", +) +def list(db: str): + """This creates a record of a new deployment in the database. + """ + engine = create_engine(db) + sm = sessionmaker(engine) + Session = sm() + deployments = Session.query(deployment_table).all() + for deployment in deployments: + click.secho(deployment.name, bold=True) + click.echo(click.style("ID ", bold=True) + deployment.id.hex) + click.echo(click.style("URI ", bold=True) + deployment.uri) + click.echo(click.style("API key ", bold=True) + deployment.api_key) + click.echo(click.style("Colour ", bold=True) + str(deployment.colour)) + click.echo() + Session.rollback() + return True + + +@deployments.command() +@click.argument("id") +@click.option("--uri") +@click.option("--name") +@click.option( + "--db", + metavar="", + default="postgresql+psycopg2://localhost/apd", + help="The connection string to a PostgreSQL database", + envvar="APD_DB_URI", +) +@click.option("--api-key", metavar="", envvar="APD_API_KEY") +@click.option("--colour") +def edit( + db: str, + id, + uri: t.Optional[str], + name: t.Optional[str], + api_key: t.Optional[str], + colour: t.Optional[str], +): + """This creates a record of a new deployment in the database. + """ + update = {} + if uri is not None: + update["uri"] = uri + if name is not None: + update["name"] = name + if api_key is not None: + update["api_key"] = api_key + if colour is not None: + update["colour"] = colour + deployment_id = uuid.UUID(id) + + update_stmt = ( + deployment_table.update() + .where(deployment_table.c.id == deployment_id) + .values(**update) + ) + + engine = create_engine(db) + sm = sessionmaker(engine) + Session = sm() + Session.execute(update_stmt) + deployments = Session.query(deployment_table).filter( + deployment_table.c.id == deployment_id + ) + Session.commit() + + for deployment in deployments: + click.secho(deployment.name, bold=True) + click.echo(click.style("ID ", bold=True) + deployment.id.hex) + click.echo(click.style("URI ", bold=True) + deployment.uri) + click.echo(click.style("API key ", bold=True) + deployment.api_key) + click.echo(click.style("Colour ", bold=True) + str(deployment.colour)) + click.echo() + + return True diff --git a/Ch12/apd.aggregation-chapter12/src/apd/aggregation/collect.py b/Ch12/apd.aggregation-chapter12/src/apd/aggregation/collect.py new file mode 100644 index 0000000..2ec7c01 --- /dev/null +++ b/Ch12/apd.aggregation-chapter12/src/apd/aggregation/collect.py @@ -0,0 +1,118 @@ +import asyncio +from contextvars import ContextVar +import datetime +import typing as t +import uuid + +import aiohttp +from sqlalchemy import create_engine +from sqlalchemy.orm import sessionmaker +from sqlalchemy.orm.session import Session + +from .database import DataPoint, datapoint_table, Deployment, deployment_table + +http_session_var: ContextVar[aiohttp.ClientSession] = ContextVar("http_session") + + +async def get_deployment_id(server): + http = http_session_var.get() + if not server.endswith("/"): + server += "/" + url = server + "v/2.1/deployment_id" + async with http.get(url) as request: + if request.status != 200: + raise ValueError(f"Error loading deployment id from {server}") + result = await request.json() + return uuid.UUID(result["deployment_id"]) + + +async def get_data_points(server: str, api_key: t.Optional[str],) -> t.List[DataPoint]: + if not server.endswith("/"): + server += "/" + url = server + "v/2.1/sensors/" + headers = {} + if api_key: + headers["X-API-KEY"] = api_key + http = http_session_var.get() + + # Get the deployment ID in parallel to the sensor data + deployment_id = asyncio.create_task(get_deployment_id(server)) + + async with http.get(url, headers=headers) as request: + result = await request.json() + ok = request.status == 200 + now = datetime.datetime.now() + if ok: + points = [] + for value in result["sensors"]: + points.append( + DataPoint( + sensor_name=value["id"], + collected_at=now, + data=value["value"], + deployment_id=await deployment_id, + ) + ) + return points + else: + raise ValueError( + f"Error loading data from {server}: " + result.get("error", "Unknown") + ) + + +def handle_result(result: t.List[DataPoint], session: Session) -> t.List[DataPoint]: + for point in result: + insert = datapoint_table.insert().values(**point._asdict()) + sql_result = session.execute(insert) + point.id = sql_result.inserted_primary_key[0] + return result + + +async def add_data_from_sensors( + session: Session, servers: t.Iterable[Deployment] +) -> t.List[DataPoint]: + tasks: t.List[t.Awaitable[t.List[DataPoint]]] = [] + points: t.List[DataPoint] = [] + async with aiohttp.ClientSession() as http: + http_session_var.set(http) + tasks = [get_data_points(server.uri, server.api_key) for server in servers] + for results in await asyncio.gather(*tasks): + points += results + loop = asyncio.get_running_loop() + await loop.run_in_executor(None, handle_result, points, session) + return points + + +def standalone( + db_uri: str, servers: t.Tuple[str], api_key: t.Optional[str], echo: bool = False +) -> None: + engine = create_engine(db_uri, echo=echo) + sm = sessionmaker(engine) + Session = sm() + deployments: t.Iterable[Deployment] + if servers: + deployments = tuple( + Deployment(id=None, name=None, colour=None, api_key=api_key, uri=server) + for server in servers + ) + else: + deployment_data = Session.query(deployment_table).all() + deployments = tuple( + Deployment.from_sql_result(deployment) for deployment in deployment_data + ) + asyncio.run(add_data_from_sensors(Session, deployments)) + + if "postgresql" in db_uri: + # On Postgres sent a pubsub notification, in case other processes are waiting + # for this data + Session.execute("NOTIFY apd_aggregation;") + + Session.commit() + + +if __name__ == "__main__": + standalone( + db_uri="postgresql+psycopg2://apd@localhost/apd", + servers=("http://pvoutput:8080/",), + api_key="h3hdfjksfhwkjehnwekj", + ) diff --git a/Ch12/apd.aggregation-chapter12/src/apd/aggregation/database.py b/Ch12/apd.aggregation-chapter12/src/apd/aggregation/database.py new file mode 100644 index 0000000..f28b469 --- /dev/null +++ b/Ch12/apd.aggregation-chapter12/src/apd/aggregation/database.py @@ -0,0 +1,129 @@ +from __future__ import annotations + +from dataclasses import dataclass, field, asdict +import datetime +import typing as t +import uuid + +import sqlalchemy +from sqlalchemy.dialects.postgresql import JSONB, DATE, TIMESTAMP, UUID +from sqlalchemy.ext.hybrid import ExprComparator, hybrid_property +from sqlalchemy.orm import sessionmaker +from sqlalchemy.schema import Table + + +metadata = sqlalchemy.MetaData() + +deployment_table = Table( + "deployments", + metadata, + sqlalchemy.Column("id", UUID(as_uuid=True), primary_key=True), + sqlalchemy.Column("uri", sqlalchemy.String, index=False), + sqlalchemy.Column("name", sqlalchemy.String, index=False, nullable=True), + sqlalchemy.Column("colour", sqlalchemy.String, index=False, nullable=True), + sqlalchemy.Column("api_key", sqlalchemy.String, index=False), +) + +datapoint_table = Table( + "datapoints", + metadata, + sqlalchemy.Column("id", sqlalchemy.Integer, primary_key=True), + sqlalchemy.Column("sensor_name", sqlalchemy.String, index=True), + sqlalchemy.Column("collected_at", TIMESTAMP, index=True), + sqlalchemy.Column("deployment_id", UUID(as_uuid=True), index=True), + sqlalchemy.Column("data", JSONB), +) + +daily_summary_view = Table( + "daily_summary", + metadata, + sqlalchemy.Column("sensor_name", sqlalchemy.String), + sqlalchemy.Column("data", JSONB), + sqlalchemy.Column("count", sqlalchemy.Integer), + info={"is_view": True}, +) + + +class DateEqualComparator(ExprComparator): + def __init__(self, fallback_expression, raw_expression): + # Do not try and find update expression from parent + super().__init__(None, fallback_expression, None) + self.raw_expression = raw_expression + + def __eq__(self, other): + """ Returns True iff on the same day as other """ + other_date = sqlalchemy.cast(other, DATE) + return sqlalchemy.and_( + self.raw_expression >= other_date, self.raw_expression < other_date + 1, + ) + + def operate(self, op, *other, **kwargs): + other = [sqlalchemy.cast(date, DATE) for date in other] + return op(self.expression, *other, **kwargs) + + def reverse_operate(self, op, other, **kwargs): + other = [sqlalchemy.cast(date, DATE) for date in other] + return op(other, self.expression, **kwargs) + + +@dataclass +class DataPoint: + sensor_name: str + data: t.Any + deployment_id: uuid.UUID + id: t.Optional[int] = None + collected_at: datetime.datetime = field(default_factory=datetime.datetime.now) + + @classmethod + def from_sql_result(cls, result) -> DataPoint: + return cls(**result._asdict()) + + def _asdict(self) -> t.Dict[str, t.Any]: + data = asdict(self) + if data["id"] is None: + del data["id"] + return data + + @hybrid_property + def collected_on_date(self): + return self.collected_at.date() + + @collected_on_date.comparator # type: ignore + def collected_on_date(cls): + return DateEqualComparator( + fallback_expression=sqlalchemy.cast(datapoint_table.c.collected_at, DATE), + raw_expression=datapoint_table.c.collected_at, + ) + + +@dataclass +class Deployment: + id: t.Optional[uuid.UUID] + uri: str + name: t.Optional[str] + colour: t.Optional[str] + api_key: t.Optional[str] + + @classmethod + def from_sql_result(cls, result) -> Deployment: + return cls(**result._asdict()) + + def _asdict(self) -> t.Dict[str, t.Any]: + data = asdict(self) + return data + + +def main() -> None: + engine = sqlalchemy.create_engine( + "postgresql+psycopg2://apd@localhost/apd", echo=True + ) + sm = sessionmaker(engine) + Session = sm() + if False: + metadata.create_all(engine) + print(Session.query(DataPoint).all()) + pass + + +if __name__ == "__main__": + main() diff --git a/Ch12/apd.aggregation-chapter12/src/apd/aggregation/exceptions.py b/Ch12/apd.aggregation-chapter12/src/apd/aggregation/exceptions.py new file mode 100644 index 0000000..6200e90 --- /dev/null +++ b/Ch12/apd.aggregation-chapter12/src/apd/aggregation/exceptions.py @@ -0,0 +1,14 @@ +class NoDataForTrigger(ValueError): + """An error that's raised when a trigger is passed + a data point that cannot be handled due to an incompatible + value being stored""" + + pass + + +class IncompatibleTriggerError(NoDataForTrigger): + """An error that's raised when a trigger is passed + a data point that cannot be handled due to an incompatible + value being stored""" + + pass diff --git a/Ch12/apd.aggregation-chapter12/src/apd/aggregation/query.py b/Ch12/apd.aggregation-chapter12/src/apd/aggregation/query.py new file mode 100644 index 0000000..14144ff --- /dev/null +++ b/Ch12/apd.aggregation-chapter12/src/apd/aggregation/query.py @@ -0,0 +1,154 @@ +import asyncio +import contextlib +from contextvars import ContextVar +import datetime +import typing as t +from uuid import UUID + +from sqlalchemy import create_engine +from sqlalchemy.orm import sessionmaker +from sqlalchemy.orm.session import Session + + +from apd.aggregation.database import ( + datapoint_table, + DataPoint, + deployment_table, + Deployment, +) + + +db_session_var: ContextVar[Session] = ContextVar("db_session") + + +@contextlib.contextmanager +def with_database(uri: t.Optional[str] = None) -> t.Iterator[Session]: + """Given a URI, set up a DB connection, and return a Session as a context manager """ + if uri is None: + uri = "postgresql+psycopg2://localhost/apd" + engine = create_engine(uri) + sm = sessionmaker(engine) + Session = sm() + token = db_session_var.set(Session) + try: + yield Session + Session.commit() + finally: + db_session_var.reset(token) + Session.close() + + +async def get_data( + sensor_name: t.Optional[str] = None, + deployment_id: t.Optional[UUID] = None, + collected_before: t.Optional[datetime.datetime] = None, + collected_after: t.Optional[datetime.datetime] = None, + inserted_after_record_id: t.Optional[int] = None, + order: bool = True, +) -> t.AsyncIterator[DataPoint]: + db_session = db_session_var.get() + loop = asyncio.get_running_loop() + query = db_session.query(datapoint_table) + if sensor_name: + query = query.filter(datapoint_table.c.sensor_name == sensor_name) + if deployment_id: + query = query.filter(datapoint_table.c.deployment_id == deployment_id) + if collected_before: + query = query.filter(datapoint_table.c.collected_at < collected_before) + if collected_after: + query = query.filter(datapoint_table.c.collected_at > collected_after) + if inserted_after_record_id: + query = query.filter(datapoint_table.c.id > inserted_after_record_id) + + if order: + query = query.order_by( + datapoint_table.c.deployment_id, + datapoint_table.c.sensor_name, + datapoint_table.c.collected_at, + ) + + rows = await loop.run_in_executor(None, query.all) + for row in rows: + yield DataPoint.from_sql_result(row) + + +async def get_deployment_ids() -> t.List[UUID]: + db_session = db_session_var.get() + loop = asyncio.get_running_loop() + query = db_session.query(datapoint_table.c.deployment_id).distinct() + return [row.deployment_id for row in await loop.run_in_executor(None, query.all)] + + +async def get_deployment_by_id(deployment_id: UUID) -> Deployment: + db_session = db_session_var.get() + loop = asyncio.get_running_loop() + query = db_session.query(deployment_table).filter( + deployment_table.c.id == deployment_id + ) + return [ + Deployment.from_sql_result(row) + for row in await loop.run_in_executor(None, query.all) + ][0] + + +async def get_data_by_deployment( + *args: t.Any, **kwargs: t.Any +) -> t.AsyncIterator[t.Tuple[UUID, t.AsyncIterator[DataPoint]]]: + """Return an Async Iterator that contains two-item pairs. + These pairs are a string (deployment_id), and an async iterator that contains + the datapoints with that deployment_id. + + Usage example: + + async for deployment_id, datapoints in get_data_by_deployment(): + print(deployment_id) + async for datapoint in datapoints: + print(datapoint) + print() + """ + # Get the data, using the arguments to this function as filters + data = get_data(*args, **kwargs) + + # The two levels of iterator share the item variable, initialise it with the + # first item from the iterator. Also set last_deployment_id to None, so the + # outer iterator knows to start a new group. + last_deployment_id: t.Optional[UUID] = None + try: + item = await data.__anext__() + except StopAsyncIteration: + # There were no items in the underlying query, return immediately + return + + async def subiterator(group_id: UUID) -> t.AsyncIterator[DataPoint]: + """Using a closure, create an iterator that yields the current + item, then yields all items from data while the deployment_id matches + group_id, leaving the first that doesn't match as item in the enclosing + scope.""" + # item is from the enclosing scope + nonlocal item + while item.deployment_id == group_id: + # yield items from data while they match the group_id this iterator represents + yield item + try: + # Advance the underlying iterator + item = await data.__anext__() + except StopAsyncIteration: + # The underlying iterator came to an end, so end the subiterator too + return + + while True: + while item.deployment_id == last_deployment_id: + # We are trying to advance the outer iterator while the underlying iterator + # is still part-way through a group. Speed through the underlying until we + # hit an item where the deployment_id is different to the last one (or, is not + # None, in the case of the start of the iterator) + try: + item = await data.__anext__() + except StopAsyncIteration: + # We hit the end of the underlying iterator: end this iterator too + return + last_deployment_id = item.deployment_id + # Don't yield an iterator for unclassified deployments + if last_deployment_id is not None: + # Instantiate a subiterator for this group + yield last_deployment_id, subiterator(last_deployment_id) diff --git a/Ch12/apd.aggregation-chapter12/src/apd/aggregation/typing.py b/Ch12/apd.aggregation-chapter12/src/apd/aggregation/typing.py new file mode 100644 index 0000000..5db1337 --- /dev/null +++ b/Ch12/apd.aggregation-chapter12/src/apd/aggregation/typing.py @@ -0,0 +1,48 @@ +import datetime +import typing as t +from typing_extensions import Protocol + +from apd.aggregation.database import DataPoint + + +# Aliases for common types +# These type variables allow for generic functions. T_key represents the place +# in a chart that an item will be placed, and T_value the kind of data that is plotted. +T_key = t.TypeVar("T_key") +T_value = t.TypeVar("T_value") + +# Cleaned is a placeholder type, representing an async iterator of key and value functions +# Cleaned[float, float] is equivalent to typing.AsyncIterator[Tuple[builtins.float, builtins.float]] +Cleaned = t.AsyncIterator[t.Tuple[T_key, T_value]] + +# T_cleaned represents a placeholder for the result of a cleaner function, and is *covariant* +# because it can accept compatible types (such as ints where floats were declared). +# It is bound to Cleaned, so only items that match the specification for Cleaned (for any values +# of T_key or T_value) are valid +T_cleaned = t.TypeVar("T_cleaned", covariant=True, bound=Cleaned) + + +# CleanerFunc is a generic protocol, it matches any Callable that converts an async iterator +# of datapoints to its type. So, CleanerFunc[float] would be equivalent to t.Callable[[t.AsyncIterator[DataPoint]], float] +class CleanerFunc(Protocol[T_cleaned]): + def __call__(self, datapoints: t.AsyncIterator[DataPoint]) -> T_cleaned: + ... + + +# CLEANED_DT_FLOAT is a Cleaned represents datetime/float pairs, for simple charts +CLEANED_DT_FLOAT = Cleaned[datetime.datetime, float] +# and CLEANED_COORD_FLOAT represents (lat/lon), float pairs +CLEANED_COORD_FLOAT = Cleaned[t.Tuple[float, float], float] + +# The _CLEANER variants are functions that return their matching iterators from above +DT_FLOAT_CLEANER = CleanerFunc[CLEANED_DT_FLOAT] +COORD_FLOAT_CLEANER = CleanerFunc[CLEANED_COORD_FLOAT] + + +# When drawing a map we will be building a dictionary of UUID to dictionary +# That inner dictionary should contain coord (float, float) +# and value (float) only. Either or both can be None. +# This class is abstract, it's just for type checking. +class IntermediateMapData(t.TypedDict): + coord: t.Optional[t.Tuple[float, float]] + value: t.Optional[float] diff --git a/Ch12/apd.aggregation-chapter12/src/apd/aggregation/utils.py b/Ch12/apd.aggregation-chapter12/src/apd/aggregation/utils.py new file mode 100644 index 0000000..75bf6b3 --- /dev/null +++ b/Ch12/apd.aggregation-chapter12/src/apd/aggregation/utils.py @@ -0,0 +1,100 @@ +from __future__ import annotations + +import contextlib +import functools +import importlib +import io +import logging +import math +import os +import typing as t + +from pint import _DEFAULT_REGISTRY as ureg + + +# Set up mercator transform from https://wiki.openstreetmap.org/wiki/Mercator#Python +# Thanks to Paulo Silva and the OSM contributors +def merc_x(lon: float) -> float: + r_major = 6378137.000 + return r_major * math.radians(lon) + + +def merc_y(lat: float) -> float: + if lat > 89.5: + lat = 89.5 + if lat < -89.5: + lat = -89.5 + r_major = 6378137.000 + r_minor = 6356752.3142 + temp = r_minor / r_major + eccent = math.sqrt(1 - temp ** 2) + phi = math.radians(lat) + sinphi = math.sin(phi) + con = eccent * sinphi + com = eccent / 2 + con = ((1.0 - con) / (1.0 + con)) ** com + ts = math.tan((math.pi / 2 - phi) / 2) / con + y = 0 - r_major * math.log(ts) + return y + + +@functools.lru_cache +def convert_temperature(magnitude: float, origin_unit: str, target_unit: str) -> float: + # if origin_unit == "degC" and target_unit == "degF": + # return (magnitude * 1.8) + 32 + temp = ureg.Quantity(magnitude, origin_unit) + return temp.to(target_unit).magnitude + + +@contextlib.contextmanager +def jupyter_page_file() -> t.Iterator[io.StringIO]: + from IPython.core import page + + output = io.StringIO() + yield output + output.seek(0) + page.page(output.read()) + + +@contextlib.contextmanager +def profile_with_yappi() -> t.Iterator[None]: + import yappi + + yappi.clear_stats() + yappi.start() + try: + yield None + finally: + yappi.stop() + + +@functools.lru_cache +def get_package_prefix(package: str) -> str: + mod = importlib.import_module(package) + prefix = mod.__file__ + if prefix.endswith("__init__.py"): + prefix = os.path.dirname(prefix) + return prefix + + +def yappi_package_matches(stat, packages: t.List[str]): + """ This object can be passed to yappi's filter_callback to limit + by Python package.""" + for package in packages: + prefix = get_package_prefix(package) + if stat.full_name.startswith(prefix): + return True + return False + + +class AddSensorNameDefault(logging.Filter): + def filter(self, record): + if not hasattr(record, "sensorname"): + record.sensorname = "none" + return True + + +class SensorNameStreamHandler(logging.StreamHandler): + def __init__(self, *args, **kwargs): + super().__init__() + self.addFilter(AddSensorNameDefault()) diff --git a/Ch12/apd.aggregation-chapter12/tests/__init__.py b/Ch12/apd.aggregation-chapter12/tests/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/Ch12/apd.aggregation-chapter12/tests/conftest.py b/Ch12/apd.aggregation-chapter12/tests/conftest.py new file mode 100644 index 0000000..309a30c --- /dev/null +++ b/Ch12/apd.aggregation-chapter12/tests/conftest.py @@ -0,0 +1,127 @@ +import datetime +from uuid import UUID + +from apd.aggregation.database import datapoint_table +from apd.aggregation.query import db_session_var + +from alembic.config import Config +from alembic.script import ScriptDirectory +from alembic.runtime.environment import EnvironmentContext +import pytest + + +@pytest.fixture +def db_uri(): + return "postgresql+psycopg2://apd@localhost/apd-test" + + +@pytest.fixture +def db_session(db_uri): + from sqlalchemy import create_engine + from sqlalchemy.orm import sessionmaker + + engine = create_engine(db_uri, echo=False) + sm = sessionmaker(engine) + Session = sm() + reset = db_session_var.set(Session) + yield Session + db_session_var.reset(reset) + Session.close() + + +@pytest.fixture +def migrated_db(db_uri, db_session): + config = Config() + config.set_main_option("script_location", "apd.aggregation:alembic") + config.set_main_option("sqlalchemy.url", db_uri) + script = ScriptDirectory.from_config(config) + + def upgrade(rev, context): + return script._upgrade_revs(script.get_current_head(), rev) + + def downgrade(rev, context): + return script._downgrade_revs(None, rev) + + with EnvironmentContext(config, script, fn=upgrade): + script.run_env() + + try: + yield db_session + finally: + # Clear any pending work from the db_session connection + db_session.rollback() + + with EnvironmentContext(config, script, fn=downgrade): + script.run_env() + + +@pytest.fixture +def populated_db(migrated_db, db_session): + datas = [ + { + "id": 1, + "sensor_name": "Test", + "data": "1", + "collected_at": datetime.datetime(2020, 4, 1, 12, 0, 1), + "deployment_id": UUID("b4c68905-b1e4-4875-940e-69e5d27730fd"), + }, + { + "id": 2, + "sensor_name": "Test", + "data": "2", + "collected_at": datetime.datetime(2020, 4, 1, 12, 1, 1), + "deployment_id": UUID("b4c68905-b1e4-4875-940e-69e5d27730fd"), + }, + { + "id": 3, + "sensor_name": "Test", + "data": "3", + "collected_at": datetime.datetime(2020, 4, 1, 12, 2, 1), + "deployment_id": UUID("b4c68905-b1e4-4875-940e-69e5d27730fd"), + }, + { + "id": 4, + "sensor_name": "Test", + "data": "4", + "collected_at": datetime.datetime(2020, 4, 1, 12, 3, 1), + "deployment_id": UUID("b4c68905-b1e4-4875-940e-69e5d27730fd"), + }, + { + "id": 5, + "sensor_name": "OtherTest", + "data": "5", + "collected_at": datetime.datetime(2020, 4, 1, 12, 4, 1), + "deployment_id": UUID("b4c68905-b1e4-4875-940e-69e5d27730fd"), + }, + { + "id": 6, + "sensor_name": "Test", + "data": "1", + "collected_at": datetime.datetime(2020, 4, 1, 12, 0, 1), + "deployment_id": UUID("6e887b81-57c3-4f3b-a7e7-bad159e05e78"), + }, + { + "id": 7, + "sensor_name": "Test", + "data": "1", + "collected_at": datetime.datetime(2020, 4, 1, 12, 1, 1), + "deployment_id": UUID("6e887b81-57c3-4f3b-a7e7-bad159e05e78"), + }, + { + "id": 8, + "sensor_name": "Test", + "data": "2", + "collected_at": datetime.datetime(2020, 4, 1, 12, 2, 1), + "deployment_id": UUID("6e887b81-57c3-4f3b-a7e7-bad159e05e78"), + }, + { + "id": 9, + "sensor_name": "OtherTest", + "data": "3", + "collected_at": datetime.datetime(2020, 4, 1, 12, 3, 1), + "deployment_id": UUID("6e887b81-57c3-4f3b-a7e7-bad159e05e78"), + }, + ] + for data in datas: + insert = datapoint_table.insert().values(**data) + db_session.execute(insert) diff --git a/Ch12/apd.aggregation-chapter12/tests/test_actions_actions.py b/Ch12/apd.aggregation-chapter12/tests/test_actions_actions.py new file mode 100644 index 0000000..1429fb3 --- /dev/null +++ b/Ch12/apd.aggregation-chapter12/tests/test_actions_actions.py @@ -0,0 +1,118 @@ +import datetime +import unittest.mock + +import pytest + +from apd.aggregation.actions.base import Action +from apd.aggregation.actions.action import ( + OnlyOnChangeActionWrapper, + OnlyAfterDateActionWrapper, + SaveToDatabaseAction, +) +from apd.aggregation.query import db_session_var, get_data + +from .test_analysis import generate_datapoints + + +class TestOnlyAfterDateActionWrapper: + @pytest.fixture + def subject(self): + return OnlyAfterDateActionWrapper + + @pytest.mark.asyncio + async def test_minimum_date_before_passing(self, subject): + wrapped = unittest.mock.Mock(spec=Action) + wrapper = subject( + wrapped, date_threshold=datetime.datetime(2020, 4, 1, 14, 0, 0) + ) + await wrapper.start() + + data = [ + (datetime.datetime(2020, 4, 1, 12, 0, 0), 21.0), + (datetime.datetime(2020, 4, 1, 13, 0, 0), 21.0), + (datetime.datetime(2020, 4, 1, 14, 0, 0), 22.0), + (datetime.datetime(2020, 4, 1, 15, 0, 0), 22.0), + (datetime.datetime(2020, 4, 1, 16, 0, 0), 21.0), + ] + datapoints = generate_datapoints(data) + all_datapoints = [] + async for datapoint in datapoints: + all_datapoints.append(datapoint) + await wrapper.handle(datapoint) + + assert len(wrapped.handle.mock_calls) == 2 + passed = [call.args[0] for call in wrapped.handle.mock_calls] + assert passed == [all_datapoints[3], all_datapoints[4]] + + +class TestOnlyOnChangeActionWrapper: + @pytest.fixture + def subject(self): + return OnlyOnChangeActionWrapper + + @pytest.mark.asyncio + async def test_initial_value_always_passed(self, subject): + wrapped = unittest.mock.Mock(spec=Action) + wrapper = subject(wrapped) + await wrapper.start() + + data = [ + (datetime.datetime(2020, 4, 1, 12, 0, 0), 21.0), + ] + datapoints = generate_datapoints(data) + first = await datapoints.asend(None) + await wrapper.handle(first) + assert len(wrapped.handle.mock_calls) == 1 + assert wrapped.handle.mock_calls[0].args[0] == first + + @pytest.mark.asyncio + async def test_subsequent_values_only_passed_when_differing(self, subject): + wrapped = unittest.mock.Mock(spec=Action) + wrapper = subject(wrapped) + await wrapper.start() + + data = [ + (datetime.datetime(2020, 4, 1, 12, 0, 0), 21.0), + (datetime.datetime(2020, 4, 1, 13, 0, 0), 21.0), + (datetime.datetime(2020, 4, 1, 14, 0, 0), 22.0), + (datetime.datetime(2020, 4, 1, 15, 0, 0), 22.0), + (datetime.datetime(2020, 4, 1, 16, 0, 0), 21.0), + ] + datapoints = generate_datapoints(data) + all_datapoints = [] + async for datapoint in datapoints: + all_datapoints.append(datapoint) + await wrapper.handle(datapoint) + + assert len(wrapped.handle.mock_calls) == 3 + passed = [call.args[0] for call in wrapped.handle.mock_calls] + assert passed == [all_datapoints[0], all_datapoints[2], all_datapoints[4]] + + +class TestSaveToDatabaseAction: + @pytest.fixture + def subject(self): + return SaveToDatabaseAction + + @pytest.mark.asyncio + async def test_datapoints_are_persisted(self, subject, migrated_db): + db_session_var.set(migrated_db) + + wrapper = subject() + await wrapper.start() + + data = [ + (datetime.datetime(2020, 4, 1, 12, 0, 0), 21.0), + ] + generated_datapoints = [] + async for datapoint in generate_datapoints(data): + generated_datapoints.append(datapoint) + await wrapper.handle(datapoint) + + stored_datapoints = [ + point async for point in get_data(sensor_name="TestSensor") + ] + assert stored_datapoints[0].data == generated_datapoints[0].data + assert ( + stored_datapoints[0].deployment_id == generated_datapoints[0].deployment_id + ) diff --git a/Ch12/apd.aggregation-chapter12/tests/test_actions_runner.py b/Ch12/apd.aggregation-chapter12/tests/test_actions_runner.py new file mode 100644 index 0000000..f263420 --- /dev/null +++ b/Ch12/apd.aggregation-chapter12/tests/test_actions_runner.py @@ -0,0 +1,110 @@ +import asyncio +import datetime +import operator + +import pytest + +from apd.aggregation.actions.runner import DataProcessor +from apd.aggregation.actions.base import Trigger +from apd.aggregation.actions.action import ( + OnlyOnChangeActionWrapper, + SaveToDatabaseAction, +) +from apd.aggregation.actions.trigger import ValueThresholdTrigger +from apd.aggregation.database import DataPoint +from apd.aggregation.query import get_data +from .test_analysis import generate_datapoints + + +class AlwaysTrueTrigger(Trigger[bool]): + name = "AlwaysTrue" + + async def start(self): + pass + + async def match(self, datapoint: DataPoint) -> bool: + return True + + async def extract(self, datapoint: DataPoint) -> bool: + return True + + +class StoreAction(Trigger[bool]): + async def start(self): + self.data = asyncio.Queue() + + async def handle(self, datapoint: DataPoint) -> None: + await self.data.put(datapoint) + + +class TestRunner: + @pytest.fixture + @pytest.mark.asyncio + async def runner(self, event_loop): + processor = DataProcessor( + name="Test data runner", action=StoreAction(), trigger=AlwaysTrueTrigger(), + ) + await processor.start() + yield processor + await processor.end() + + @pytest.mark.asyncio + async def test_simple_flow(self, runner, event_loop): + data = [(datetime.datetime(2020, 4, 1, 12, 0, 0), 65.0)] + datapoints = generate_datapoints(data) + async for datapoint in datapoints: + await runner.push(datapoint) + + output = await runner.action.data.get() + assert output.sensor_name == runner.trigger.name + assert output.collected_at == data[0][0] + assert output.data == True + + +class TestRealWorldRunner: + @pytest.fixture + @pytest.mark.asyncio + async def runner(self, migrated_db, event_loop): + processor = DataProcessor( + name="TemperatureAbove20", + action=OnlyOnChangeActionWrapper(SaveToDatabaseAction()), + trigger=ValueThresholdTrigger( + name="TemperatureAbove20", + threshold=20, + comparator=operator.gt, + sensor_name="Temperature", + ), + ) + await processor.start() + yield processor + await processor.end() + + @pytest.mark.asyncio + async def test_real_usecase(self, runner, event_loop): + data = [ + (datetime.datetime(2020, 4, 1, 12, 0, 0), 19.0), + (datetime.datetime(2020, 4, 1, 13, 0, 0), 18.0), + (datetime.datetime(2020, 4, 1, 14, 0, 0), 21.0), + (datetime.datetime(2020, 4, 1, 15, 0, 0), 22.0), + (datetime.datetime(2020, 4, 1, 16, 0, 0), 21.0), + ] + + async for datapoint in generate_datapoints(data, sensor_name="Temperature"): + await runner.push(datapoint) + + # Wait up to 10 seconds for the runner to be idle + await asyncio.wait_for(runner.idle(), timeout=10) + + stored_datapoints = [ + point async for point in get_data(sensor_name="TemperatureAbove20") + ] + + assert len(stored_datapoints) == 2 + assert stored_datapoints[0].data == False + assert stored_datapoints[0].collected_at == datetime.datetime( + 2020, 4, 1, 12, 0, 0 + ) + assert stored_datapoints[1].data == True + assert stored_datapoints[1].collected_at == datetime.datetime( + 2020, 4, 1, 14, 0, 0 + ) diff --git a/Ch12/apd.aggregation-chapter12/tests/test_actions_triggers.py b/Ch12/apd.aggregation-chapter12/tests/test_actions_triggers.py new file mode 100644 index 0000000..275c8e8 --- /dev/null +++ b/Ch12/apd.aggregation-chapter12/tests/test_actions_triggers.py @@ -0,0 +1,127 @@ +import datetime +import operator + +import pytest + +from apd.aggregation.actions.trigger import ValueThresholdTrigger + +from .test_analysis import generate_datapoints + + +class TestValueThresholdTrigger: + @pytest.fixture + def subject(self): + return ValueThresholdTrigger + + @pytest.mark.asyncio + @pytest.mark.parametrize( + "comparison,expected", + [ + (operator.gt, [False, False, False, True]), + (operator.eq, [False, False, True, False]), + (operator.le, [True, True, True, False]), + ], + ) + async def test_simple_value(self, subject, comparison, expected): + trigger = subject( + name=f"TestSensor{comparison.__name__}21", + threshold=21, + comparator=comparison, + sensor_name="TestSensor", + ) + + data = [ + (datetime.datetime(2020, 4, 1, 12, 0, 0), 19.0), + (datetime.datetime(2020, 4, 1, 12, 0, 0), 20.0), + (datetime.datetime(2020, 4, 1, 12, 0, 0), 21.0), + (datetime.datetime(2020, 4, 1, 12, 0, 0), 22.0), + ] + datapoints = generate_datapoints(data) + + outputs = [] + async for datapoint in datapoints: + assert await trigger.match(datapoint) + outputs.append(await trigger.extract(datapoint)) + + assert outputs == expected + + @pytest.mark.asyncio + @pytest.mark.parametrize( + "comparison,expected", + [ + (operator.gt, [False, False, False, True]), + (operator.eq, [False, False, True, False]), + (operator.le, [True, True, True, False]), + ], + ) + async def test_magnitude_value(self, subject, comparison, expected): + trigger = subject( + name=f"TestSensor{comparison.__name__}21", + threshold=21, + comparator=comparison, + sensor_name="TestSensor", + ) + + data = [ + ( + datetime.datetime(2020, 4, 1, 12, 0, 0), + {"magnitude": 19.0, "unit": "degC"}, + ), + ( + datetime.datetime(2020, 4, 1, 12, 0, 0), + {"magnitude": 20.0, "unit": "degC"}, + ), + ( + datetime.datetime(2020, 4, 1, 12, 0, 0), + {"magnitude": 21.0, "unit": "degC"}, + ), + ( + datetime.datetime(2020, 4, 1, 12, 0, 0), + {"magnitude": 22.0, "unit": "degC"}, + ), + ] + datapoints = generate_datapoints(data) + + outputs = [] + async for datapoint in datapoints: + assert await trigger.match(datapoint) + outputs.append(await trigger.extract(datapoint)) + + assert outputs == expected + + @pytest.mark.asyncio + async def test_different_data_format_fails_to_extract(self, subject): + trigger = subject( + name="TestSensorAbove21", + threshold=21, + comparator=operator.eq, + sensor_name="TestSensor", + ) + + data = [ + (datetime.datetime(2020, 4, 1, 12, 0, 0), "21.0"), + ] + datapoints = generate_datapoints(data) + + async for datapoint in datapoints: + assert await trigger.match(datapoint) + with pytest.raises(ValueError): + await trigger.extract(datapoint) + assert await trigger.handle(datapoint) is None + + @pytest.mark.asyncio + async def test_different_sensors_are_not_matched(self, subject): + trigger = subject( + name="TestSensorAbove21", + threshold=21, + comparator=operator.eq, + sensor_name="OtherSensor", + ) + + data = [ + (datetime.datetime(2020, 4, 1, 12, 0, 0), 21.0), + ] + datapoints = generate_datapoints(data) + + async for datapoint in datapoints: + assert not await trigger.match(datapoint) diff --git a/Ch12/apd.aggregation-chapter12/tests/test_analysis.py b/Ch12/apd.aggregation-chapter12/tests/test_analysis.py new file mode 100644 index 0000000..053272e --- /dev/null +++ b/Ch12/apd.aggregation-chapter12/tests/test_analysis.py @@ -0,0 +1,525 @@ +import collections.abc +import datetime +import functools +import uuid +import warnings + +import pytest + +from apd.aggregation import analysis +from apd.aggregation.database import DataPoint, datapoint_table +from apd.aggregation.query import db_session_var + + +async def generate_datapoints(datas, sensor_name="TestSensor"): + deployment_id = uuid.uuid4() + for i, (time, data) in enumerate(datas, start=1): + yield DataPoint( + id=i, + collected_at=time, + sensor_name=sensor_name, + data=data, + deployment_id=deployment_id, + ) + + +@functools.singledispatch +def consume(input_iterator): + items = [item for item in input_iterator] + + def inner_iterator(): + for item in items: + yield item + + return inner_iterator() + + +@consume.register +async def consume_async(input_iterator: collections.abc.AsyncIterator): + items = [item async for item in input_iterator] + + async def inner_iterator(): + for item in items: + yield item + + return inner_iterator() + + +class TestPassThroughCleaner: + @pytest.fixture + def cleaner(self): + return analysis.clean_passthrough + + @pytest.mark.asyncio + async def test_float_passthrough(self, cleaner): + data = [ + (datetime.datetime(2020, 4, 1, 12, 0, 0), 65.0), + (datetime.datetime(2020, 4, 1, 13, 0, 0), 65.5), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert output == data + + @pytest.mark.asyncio + async def test_Nones_are_skipped(self, cleaner): + data = [ + (datetime.datetime(2020, 4, 1, 12, 0, 0), None), + (datetime.datetime(2020, 4, 1, 13, 0, 0), 65.5), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert output == [ + (datetime.datetime(2020, 4, 1, 13, 0, 0), 65.5), + ] + + +class TestTemperatureCleaner: + @pytest.fixture + def cleaner(self): + return analysis.clean_temperature_fluctuations + + @pytest.mark.asyncio + async def test_window_not_full(self, cleaner): + data = [ + ( + datetime.datetime(2020, 4, 1, 12, 0, 0), + {"magnitude": 21.0, "unit": "degC"}, + ), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert output == [(datetime.datetime(2020, 4, 1, 12, 0, 0), 21.0)] + + @pytest.mark.asyncio + async def test_window_exactly_full(self, cleaner): + data = [ + ( + datetime.datetime(2020, 4, 1, 12, 0, 0), + {"magnitude": 21.0, "unit": "degC"}, + ), + ( + datetime.datetime(2020, 4, 1, 12, 0, 1), + {"magnitude": 21.0, "unit": "degC"}, + ), + ( + datetime.datetime(2020, 4, 1, 12, 0, 2), + {"magnitude": 21.0, "unit": "degC"}, + ), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert output == [ + (datetime.datetime(2020, 4, 1, 12, 0, 0), 21.0), + (datetime.datetime(2020, 4, 1, 12, 0, 1), 21.0), + (datetime.datetime(2020, 4, 1, 12, 0, 2), 21.0), + ] + + @pytest.mark.asyncio + async def test_window_overfilled(self, cleaner): + data = [ + ( + datetime.datetime(2020, 4, 1, 12, 0, 0), + {"magnitude": 21.0, "unit": "degC"}, + ), + ( + datetime.datetime(2020, 4, 1, 12, 0, 1), + {"magnitude": 21.0, "unit": "degC"}, + ), + ( + datetime.datetime(2020, 4, 1, 12, 0, 2), + {"magnitude": 21.0, "unit": "degC"}, + ), + ( + datetime.datetime(2020, 4, 1, 12, 0, 3), + {"magnitude": 21.0, "unit": "degC"}, + ), + ( + datetime.datetime(2020, 4, 1, 12, 0, 4), + {"magnitude": 21.0, "unit": "degC"}, + ), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert output == [ + (datetime.datetime(2020, 4, 1, 12, 0, 0), 21.0), + (datetime.datetime(2020, 4, 1, 12, 0, 1), 21.0), + (datetime.datetime(2020, 4, 1, 12, 0, 2), 21.0), + (datetime.datetime(2020, 4, 1, 12, 0, 3), 21.0), + (datetime.datetime(2020, 4, 1, 12, 0, 4), 21.0), + ] + + @pytest.mark.asyncio + async def test_outlier_dropped(self, cleaner): + data = [ + ( + datetime.datetime(2020, 4, 1, 12, 0, 0), + {"magnitude": 21.0, "unit": "degC"}, + ), + ( + datetime.datetime(2020, 4, 1, 12, 0, 1), + {"magnitude": 61.0, "unit": "degC"}, + ), + ( + datetime.datetime(2020, 4, 1, 12, 0, 2), + {"magnitude": 21.0, "unit": "degC"}, + ), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert output == [ + (datetime.datetime(2020, 4, 1, 12, 0, 0), 21.0), + (datetime.datetime(2020, 4, 1, 12, 0, 2), 21.0), + ] + + @pytest.mark.asyncio + async def test_limited_to_DHT22_range(self, cleaner): + data = [ + ( + datetime.datetime(2020, 4, 1, 12, 0, 0), + {"magnitude": 91.0, "unit": "degC"}, + ), + ( + datetime.datetime(2020, 4, 1, 12, 0, 1), + {"magnitude": 91.0, "unit": "degC"}, + ), + ( + datetime.datetime(2020, 4, 1, 12, 0, 2), + {"magnitude": 91.0, "unit": "degC"}, + ), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert output == [] + + @pytest.mark.asyncio + async def test_Nones_dropped(self, cleaner): + data = [ + ( + datetime.datetime(2020, 4, 1, 12, 0, 0), + {"magnitude": 31.0, "unit": "degC"}, + ), + (datetime.datetime(2020, 4, 1, 12, 0, 1), None,), + ( + datetime.datetime(2020, 4, 1, 12, 0, 2), + {"magnitude": 32.0, "unit": "degC"}, + ), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert output == [ + (datetime.datetime(2020, 4, 1, 12, 0, 0), 31.0), + (datetime.datetime(2020, 4, 1, 12, 0, 2), 32.0), + ] + + +class TestWattHourCleaner: + @pytest.fixture + def cleaner(self): + return analysis.clean_watthours_to_watts + + @pytest.fixture(scope="class") + def huge_data_set(self): + date = datetime.datetime(2020, 4, 1, 12, 0, 0) + power = 500 + data = [] + for i in range(50_000): + date += datetime.timedelta(hours=1) + power += i + data.append((date, {"magnitude": power, "unit": "watt_hour"},)) + return data + + @pytest.mark.asyncio + async def test_one_entry_insufficient_to_find_diff(self, cleaner): + data = [ + ( + datetime.datetime(2020, 4, 1, 12, 0, 0), + {"magnitude": 1.0, "unit": "watt_hour"}, + ), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert output == [] + + @pytest.mark.asyncio + async def test_two_entries_provides_diff_with_second_time(self, cleaner): + data = [ + ( + datetime.datetime(2020, 4, 1, 12, 0, 0), + {"magnitude": 1.0, "unit": "watt_hour"}, + ), + ( + datetime.datetime(2020, 4, 1, 13, 0, 0), + {"magnitude": 10.0, "unit": "watt_hour"}, + ), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert len(output) == 1 + assert output[0][0] == datetime.datetime(2020, 4, 1, 13, 0, 0) + assert output[0][1] == pytest.approx(9.0, 0.0001) + + @pytest.mark.asyncio + async def test_nonstandard_units_can_be_used(self, cleaner): + data = [ + ( + datetime.datetime(2020, 4, 1, 12, 0, 0), + {"magnitude": 1.0, "unit": "watt_hour"}, + ), + ( + datetime.datetime(2020, 4, 1, 13, 0, 0), + {"magnitude": 10000.0, "unit": "joule"}, + ), + ( + datetime.datetime(2020, 4, 1, 14, 0, 0), + {"magnitude": 3.0, "unit": "watt_hour"}, + ), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert len(output) == 2 + assert output[0][0] == datetime.datetime(2020, 4, 1, 13, 0, 0) + assert output[0][1] == pytest.approx(1.7777, 0.001) + assert output[1][0] == datetime.datetime(2020, 4, 1, 14, 0, 0) + assert output[1][1] == pytest.approx(0.22222, 0.001) + + @pytest.mark.asyncio + async def test_fractional_hour(self, cleaner): + data = [ + ( + datetime.datetime(2020, 4, 1, 12, 0, 0), + {"magnitude": 1.0, "unit": "watt_hour"}, + ), + ( + datetime.datetime(2020, 4, 1, 12, 23, 42), + {"magnitude": 10.0, "unit": "watt_hour"}, + ), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert len(output) == 1 + assert output[0][0] == datetime.datetime(2020, 4, 1, 12, 23, 42) + assert output[0][1] == pytest.approx(22.7848, 0.001) + + @pytest.mark.asyncio + async def test_diff_happens_pairwise(self, cleaner): + data = [ + ( + datetime.datetime(2020, 4, 1, 12, 0, 0), + {"magnitude": 1.0, "unit": "watt_hour"}, + ), + ( + datetime.datetime(2020, 4, 1, 12, 23, 42), + {"magnitude": 10.0, "unit": "watt_hour"}, + ), + ( + datetime.datetime(2020, 4, 1, 13, 23, 42), + {"magnitude": 13.0, "unit": "watt_hour"}, + ), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert len(output) == 2 + assert output[0][0] == datetime.datetime(2020, 4, 1, 12, 23, 42) + assert output[0][1] == pytest.approx(22.7848, 0.001) + assert output[1][0] == datetime.datetime(2020, 4, 1, 13, 23, 42) + assert output[1][1] == pytest.approx(3, 0.001) + + @pytest.mark.asyncio + async def test_None_ignored(self, cleaner): + data = [ + ( + datetime.datetime(2020, 4, 1, 12, 0, 0), + {"magnitude": 1.0, "unit": "watt_hour"}, + ), + (datetime.datetime(2020, 4, 1, 12, 58, 0), None,), + ( + datetime.datetime(2020, 4, 1, 13, 0, 0), + {"magnitude": 10.0, "unit": "watt_hour"}, + ), + ] + datapoints = generate_datapoints(data) + output = [(time, data) async for (time, data) in cleaner(datapoints)] + assert len(output) == 1 + assert output[0][0] == datetime.datetime(2020, 4, 1, 13, 0, 0) + assert output[0][1] == pytest.approx(9.0, 0.0001) + + @pytest.mark.asyncio + @pytest.mark.performance + async def test_performance_of_cleaner(self, cleaner, huge_data_set): + import cProfile + + # Generate data point objects before profiling starts + datapoints = await consume(generate_datapoints(huge_data_set)) + profiler = cProfile.Profile() + profiler.enable() + + # Run the cleaner to completion + await consume(cleaner(datapoints)) + + profiler.disable() + cleaner_stat = [ + stat for stat in profiler.getstats() if stat.code == cleaner.__code__ + ][0] + # Assert that the cleaner may take no more than 0.5 seconds to process + # these 50k data points + assert cleaner_stat.totaltime < 0.5 + + +class TestTemperatureMap: + @pytest.fixture + def temperature_data(self): + return { + (53.8667, -1.3333): -1, + (53.35, -2.2833): 1, + (52.45, -1.7333): 3, + (51.5, -0.1333): 6, + (51.55, -2.5667): 5, + (54.9667, -1.6167): -1, + (55.9667, -3.2167): -1, + (54.7667, -1.5833): 0, + (53.7667, -0.3): 1, + (53.7667, -3.0167): 0, + (51.4833, -3.1833): 4, + (53.2667, -3.5167): 2, + (52.0833, -2.8): 2, + (52.0167, -0.6): 2, + (52.6667, 1.2667): 5, + (50.4333, -4.9833): 9, + (50.35, -4.1167): 10, + (50.5167, -2.45): 9, + (50.8333, -1.1667): 9, + (56.45, -5.4333): 4, + (57.5333, -4.05): -2, + (54.5167, -3.6167): 3, + (53.0833, 0.2667): 4, + (51.35, 1.3333): 7, + (50.8833, 0.3167): 8, + (58.45, -3.1): 3, + (50.1, -5.6667): 9, + (58.2167, -6.3333): 4, + (55.6833, -6.25): 7, + } + + @pytest.fixture + def temperature_datapoints(self, migrated_db, db_session, temperature_data): + datas = [] + now = datetime.datetime.now() + for (coord, temp) in temperature_data.items(): + deployment_id = uuid.uuid4() + datas.append( + DataPoint( + sensor_name="Location", + deployment_id=deployment_id, + collected_at=now, + data=coord, + ) + ) + datas.append( + DataPoint( + sensor_name="Temperature", + deployment_id=deployment_id, + collected_at=now, + data=temp, + ) + ) + return datas + + @pytest.fixture + def populated_db(self, migrated_db, db_session, temperature_datapoints): + for data in temperature_datapoints: + insert = datapoint_table.insert().values(**data._asdict()) + db_session.execute(insert) + + @pytest.fixture + def cleaner(self): + return analysis.get_map_cleaner_for("Temperature") + + @pytest.fixture + def get_data(self, db_session): + db_session_var.set(db_session) + return analysis.get_all_data() + + @pytest.mark.asyncio + async def test_latest_coord_temp_cleaner( + self, temperature_data, populated_db, get_data, cleaner + ): + data = get_data() + deployments = 0 + async for deployment, data_points in data: + deployments += 1 + cleaned = {a async for a in cleaner(data_points)} + # There should only be one deployment + assert deployments == 1 + expected = set(temperature_data.items()) + assert cleaned == expected + + @pytest.mark.asyncio + async def test_newer_items_superceed_older( + self, db_session, temperature_datapoints, populated_db, get_data, cleaner + ): + # Pick any of the deployment ids and find the pair of DataPoints in that deployment id + deployment_1 = temperature_datapoints[0].deployment_id + existing = [ + datapoint + for datapoint in temperature_datapoints + if datapoint.deployment_id == deployment_1 + ] + old_location = [ + datapoint for datapoint in existing if datapoint.sensor_name == "Location" + ][0] + old_temperature = [ + datapoint + for datapoint in existing + if datapoint.sensor_name == "Temperature" + ][0] + + new_location = DataPoint( + sensor_name="Location", + deployment_id=deployment_1, + collected_at=datetime.datetime(2031, 4, 1, 12, 0, 0), + data=(-10.5, 150.1), + ) + new_temperature = DataPoint( + sensor_name="Temperature", + deployment_id=deployment_1, + collected_at=datetime.datetime(2031, 4, 1, 12, 0, 0), + data=21, + ) + for data in [new_location, new_temperature]: + insert = datapoint_table.insert().values(**data._asdict()) + db_session.execute(insert) + + data = get_data() + deployments = 0 + async for deployment, data_points in data: + deployments += 1 + cleaned = {a async for a in cleaner(data_points)} + # There should only be one deployment + assert deployments == 1 + + # We expect the newer one, not the older + assert (new_location.data, new_temperature.data) in cleaned + assert (old_location.data, old_temperature.data) not in cleaned + # The cleaner converts two data points to one plottable + assert len(cleaned) == len(temperature_datapoints) / 2 + + +def test_deprecation_warning_raised_by_config_with_no_getdata(): + with warnings.catch_warnings(record=True) as captured_warnings: + warnings.simplefilter("always", DeprecationWarning) + analysis.Config( + sensor_name="Temperature", + clean=analysis.clean_passthrough, + title="Temperaure", + ylabel="Deg C", + ) + assert len(captured_warnings) == 1 + deprecation_warning = captured_warnings[0] + assert deprecation_warning.filename == __file__ + assert deprecation_warning.category == DeprecationWarning + assert str(deprecation_warning.message) == ( + "The sensor_name parameter is deprecated. Please pass " + "get_data=get_one_sensor_by_deployment('Temperature') " + "to ensure the same behaviour. The sensor_name= parameter " + "will be removed in apd.aggregation 3.0." + ) diff --git a/Ch12/apd.aggregation-chapter12/tests/test_cli.py b/Ch12/apd.aggregation-chapter12/tests/test_cli.py new file mode 100644 index 0000000..5cab441 --- /dev/null +++ b/Ch12/apd.aggregation-chapter12/tests/test_cli.py @@ -0,0 +1,155 @@ +from unittest import mock +import uuid + +from click.testing import CliRunner +import pytest + +import apd.aggregation.cli +from apd.aggregation.database import Deployment, deployment_table + + +@pytest.mark.functional +def test_sensors_are_passed_to_get_data_points(db_uri): + runner = CliRunner() + with mock.patch("apd.aggregation.collect.get_data_points") as get_data_points: + runner.invoke( + apd.aggregation.cli.collect_sensor_data, + [ + "http://localhost", + "http://otherhost", + "--db", + db_uri, + "--api-key", + "key", + ], + ) + calls = get_data_points.call_args_list + assert len(calls) == 2 + details = {call[0] for call in get_data_points.call_args_list} + assert details == { + ("http://localhost", "key"), + ("http://otherhost", "key"), + } + + +@pytest.mark.functional +def test_add_deployment_to_db(db_uri, db_session, migrated_db): + runner = CliRunner() + deployment_id = uuid.UUID("76587cdd42dc489ea1d220e9b0bc62ca") + with mock.patch("apd.aggregation.collect.get_deployment_id") as get_deployment_id: + get_deployment_id.return_value = deployment_id + runner.invoke( + apd.aggregation.cli.deployments, + ["add", "http://otherhost", "Other", "--db", db_uri, "--api-key", "key"], + ) + deployments = db_session.query(deployment_table).all() + assert len(deployments) == 1 + deployment = Deployment.from_sql_result(deployments[0]) + assert deployment.uri == "http://otherhost" + assert deployment.api_key == "key" + assert deployment.id == deployment_id + + +@pytest.mark.functional +def test_use_stored_deployments(db_uri, migrated_db): + runner = CliRunner() + + deployment_id = uuid.UUID("76587cdd42dc489ea1d220e9b0bc62ca") + with mock.patch("apd.aggregation.collect.get_deployment_id") as get_deployment_id: + get_deployment_id.return_value = deployment_id + runner.invoke( + apd.aggregation.cli.deployments, + [ + "add", + "http://specifiedhost", + "Specified", + "--db", + db_uri, + "--api-key", + "an_api_key", + ], + ) + + with mock.patch("apd.aggregation.collect.get_data_points") as get_data_points: + runner.invoke( + apd.aggregation.cli.collect_sensor_data, ["--db", db_uri], + ) + calls = get_data_points.call_args_list + assert len(calls) == 1 + details = {call[0] for call in get_data_points.call_args_list} + assert details == { + ("http://specifiedhost", "an_api_key"), + } + + +@pytest.mark.functional +def test_list_deployments(db_uri, migrated_db): + runner = CliRunner() + + deployment_id = uuid.UUID("76587cdd42dc489ea1d220e9b0bc62ca") + with mock.patch("apd.aggregation.collect.get_deployment_id") as get_deployment_id: + get_deployment_id.return_value = deployment_id + runner.invoke( + apd.aggregation.cli.deployments, + [ + "add", + "http://specifiedhost", + "Specified", + "--db", + db_uri, + "--api-key", + "an_api_key", + ], + ) + + result = runner.invoke(apd.aggregation.cli.deployments, ["list", "--db", db_uri],) + expected = """Specified +ID 76587cdd42dc489ea1d220e9b0bc62ca +URI http://specifiedhost +API key an_api_key +Colour None + +""" + assert result.output == expected + + +@pytest.mark.functional +def test_edit_deployment(db_uri, migrated_db): + runner = CliRunner() + return + deployment_id = uuid.UUID("76587cdd42dc489ea1d220e9b0bc62ca") + with mock.patch("apd.aggregation.collect.get_deployment_id") as get_deployment_id: + get_deployment_id.return_value = deployment_id + runner.invoke( + apd.aggregation.cli.deployments, + [ + "add", + "http://specifiedhost", + "Specified", + "--db", + db_uri, + "--api-key", + "an_api_key", + ], + ) + + result = runner.invoke( + apd.aggregation.cli.deployments, + [ + "edit", + "--db", + db_uri, + deployment_id.hex(), + "--colour", + "red" "--name", + "New name", + ], + ) + expected = """New name +ID 76587cdd42dc489ea1d220e9b0bc62ca +URI http://specifiedhost +API key an_api_key +Colour red + +""" + assert result.output == expected diff --git a/Ch12/apd.aggregation-chapter12/tests/test_http_get.py b/Ch12/apd.aggregation-chapter12/tests/test_http_get.py new file mode 100644 index 0000000..df575c6 --- /dev/null +++ b/Ch12/apd.aggregation-chapter12/tests/test_http_get.py @@ -0,0 +1,188 @@ +from concurrent.futures import ThreadPoolExecutor +import datetime +import typing as t +from unittest.mock import patch, MagicMock +import wsgiref.simple_server + +import aiohttp +from apd.sensors.base import Sensor +from apd.sensors.sensors import PythonVersion, ACStatus +from apd.sensors.wsgi import set_up_config +import flask +import pytest + +from apd.sensors.wsgi import v21 + +from apd.aggregation import collect +from apd.aggregation.database import Deployment + +pytestmark = [pytest.mark.functional] + + +@pytest.fixture +def sensors() -> t.Iterator[t.List[Sensor[t.Any]]]: + """ Patch the get_sensors method to return a known pair of sensors only """ + data: t.List[Sensor[t.Any]] = [PythonVersion(), ACStatus()] + with patch("apd.sensors.cli.get_sensors") as get_sensors: + get_sensors.return_value = data + yield data + + +def get_independent_flask_app(name: str) -> flask.Flask: + """ Create a new flask app with the v20 API blueprint loaded, so multiple copies + of the app can be run in parallel without conflicting configuration """ + app = flask.Flask(name) + app.register_blueprint(v21.version, url_prefix="/v/2.1") + return app + + +def run_server_in_thread( + name: str, config: t.Dict[str, t.Any], port: int +) -> t.Iterator[str]: + # Create a new flask app and load in required code, to prevent config conflicts + app = get_independent_flask_app(name) + flask_app = set_up_config(config, app) + server = wsgiref.simple_server.make_server("localhost", port, flask_app) + + with ThreadPoolExecutor() as pool: + pool.submit(server.serve_forever) + yield f"http://localhost:{port}/" + server.shutdown() + + +@pytest.fixture(scope="module") +def http_server(): + yield from run_server_in_thread( + "standard", + { + "APD_SENSORS_API_KEY": "testing", + "APD_SENSORS_DEPLOYMENT_ID": "a46b1d1207fd4cdcad39bbdf706dfe29", + }, + 12081, + ) + + +@pytest.fixture(scope="module") +def bad_api_key_http_server(): + yield from run_server_in_thread( + "alternate", + { + "APD_SENSORS_API_KEY": "penny", + "APD_SENSORS_DEPLOYMENT_ID": "38cf2bae9adb445fad946c82e290487a", + }, + 12082, + ) + + +class TestGetDataPoints: + @pytest.fixture + def mut(self): + return collect.get_data_points + + @pytest.mark.asyncio + async def test_get_data_points( + self, sensors: t.List[Sensor[t.Any]], mut, http_server: str + ) -> None: + # Get the data from the server, storing the time before and after + # as bounds for the collected_at value + async with aiohttp.ClientSession() as http: + collect.http_session_var.set(http) + time_before = datetime.datetime.now() + results = await mut(http_server, "testing") + time_after = datetime.datetime.now() + + assert len(results) == len(sensors) == 2 + + for (sensor, result) in zip(sensors, results): + assert sensor.from_json_compatible(result.data) == sensor.value() + assert result.sensor_name == sensor.name + assert time_before <= result.collected_at <= time_after + + @pytest.mark.asyncio + async def test_get_data_points_fails_with_bad_api_key( + self, sensors: t.List[Sensor[t.Any]], mut, http_server: str + ) -> None: + with pytest.raises( + ValueError, + match=f"Error loading data from {http_server}: Supply API key in X-API-Key header", + ): + async with aiohttp.ClientSession() as http: + collect.http_session_var.set(http) + await mut(http_server, "incorrect") + + +class TestAddDataFromSensors: + @pytest.fixture + def mut(self): + return collect.add_data_from_sensors + + @pytest.fixture + def mock_db_session(self): + return MagicMock() + + @pytest.mark.asyncio + async def test_get_get_data_from_sensors( + self, mock_db_session, sensors: t.List[Sensor[t.Any]], mut, http_server: str + ) -> None: + results = await mut( + mock_db_session, + [ + Deployment( + id=None, colour=None, name=None, uri=http_server, api_key="testing" + ) + ], + ) + assert mock_db_session.execute.call_count == len(sensors) + assert len(results) == len(sensors) + + @pytest.mark.asyncio + async def test_get_get_data_from_sensors_with_multiple_servers( + self, mock_db_session, sensors: t.List[Sensor[t.Any]], mut, http_server: str + ) -> None: + results = await mut( + mock_db_session, + [ + Deployment( + id=None, colour=None, name=None, uri=http_server, api_key="testing" + ), + Deployment( + id=None, colour=None, name=None, uri=http_server, api_key="testing" + ), + ], + ) + assert mock_db_session.execute.call_count == len(sensors) * 2 + assert len(results) == len(sensors) * 2 + + @pytest.mark.asyncio + async def test_data_points_not_added_if_only_partial_success( + self, + mock_db_session, + sensors: t.List[Sensor[t.Any]], + mut, + http_server: str, + bad_api_key_http_server: str, + ) -> None: + with pytest.raises( + ValueError, + match=f"Error loading data from {bad_api_key_http_server}: Supply API key in X-API-Key header", + ): + await mut( + mock_db_session, + [ + Deployment( + id=None, + colour=None, + name=None, + uri=http_server, + api_key="testing", + ), + Deployment( + id=None, + colour=None, + name=None, + uri=bad_api_key_http_server, + api_key="testing", + ), + ], + ) + assert mock_db_session.execute.call_count == 0 diff --git a/Ch12/apd.aggregation-chapter12/tests/test_query.py b/Ch12/apd.aggregation-chapter12/tests/test_query.py new file mode 100644 index 0000000..9b6e0aa --- /dev/null +++ b/Ch12/apd.aggregation-chapter12/tests/test_query.py @@ -0,0 +1,111 @@ +import datetime +from uuid import UUID + +import pytest + +from apd.aggregation.query import ( + get_data, + get_deployment_ids, + get_data_by_deployment, + db_session_var, +) + + +class TestGetData: + @pytest.fixture + def mut(self): + return get_data + + @pytest.mark.asyncio + @pytest.mark.parametrize( + "filter,num_items_expected", + [ + ({}, 9), + ({"sensor_name": "Test"}, 7), + ({"deployment_id": UUID("b4c68905-b1e4-4875-940e-69e5d27730fd")}, 5), + ({"collected_after": datetime.datetime(2020, 4, 1, 12, 2, 1)}, 3), + ({"collected_before": datetime.datetime(2020, 4, 1, 12, 2, 1)}, 4), + ( + { + "collected_after": datetime.datetime(2020, 4, 1, 12, 2, 1), + "collected_before": datetime.datetime(2020, 4, 1, 12, 3, 5), + }, + 2, + ), + ], + ) + async def test_iterate_over_items( + self, mut, db_session, populated_db, filter, num_items_expected + ): + db_session_var.set(db_session) + points = [dp async for dp in mut(**filter)] + assert len(points) == num_items_expected + + @pytest.mark.asyncio + async def test_empty_db(self, mut, db_session, migrated_db): + db_session_var.set(db_session) + points = [dp async for dp in mut()] + assert len(points) == 0 + + +class TestGetDeployments: + @pytest.fixture + def mut(self): + return get_deployment_ids + + @pytest.mark.asyncio + async def test_get_all_deployments(self, mut, db_session, populated_db): + db_session_var.set(db_session) + deployment_ids = await mut() + assert set(deployment_ids) == { + UUID("b4c68905-b1e4-4875-940e-69e5d27730fd"), + UUID("6e887b81-57c3-4f3b-a7e7-bad159e05e78"), + } + + @pytest.mark.asyncio + async def test_empty_db(self, mut, db_session, migrated_db): + db_session_var.set(db_session) + deployment_ids = await mut() + assert deployment_ids == [] + + +@pytest.mark.usefixtures("populated_db") +class TestGetDataByDeployment: + @pytest.fixture + def mut(self): + return get_data_by_deployment + + @pytest.mark.asyncio + @pytest.mark.parametrize( + "filter,num_items_expected", [({}, 9), ({"sensor_name": "Test"}, 7)] + ) + async def test_iterate_over_all_items( + self, mut, db_session, filter, num_items_expected + ): + db_session_var.set(db_session) + num_items = 0 + num_deployments = 0 + async for deployment_id, items in mut(**filter): + num_deployments += 1 + async for item in items: + num_items += 1 + assert num_deployments == 2 + assert num_items == num_items_expected + + @pytest.mark.asyncio + @pytest.mark.parametrize( + "filter,num_items_expected", [({}, 5), ({"sensor_name": "Test"}, 4)] + ) + async def test_skip_first_deployment( + self, mut, db_session, filter, num_items_expected + ): + db_session_var.set(db_session) + num_items = 0 + num_deployments = 0 + async for deployment_id, items in mut(**filter): + num_deployments += 1 + if num_deployments == 2: + async for item in items: + num_items += 1 + assert num_deployments == 2 + assert num_items == num_items_expected diff --git a/Ch12/apd.aggregation-chapter12/tests/test_sensor_aggregation.py b/Ch12/apd.aggregation-chapter12/tests/test_sensor_aggregation.py new file mode 100644 index 0000000..655521d --- /dev/null +++ b/Ch12/apd.aggregation-chapter12/tests/test_sensor_aggregation.py @@ -0,0 +1,199 @@ +from __future__ import annotations + +import contextlib +from dataclasses import dataclass +import json +import typing as t +from unittest.mock import patch, Mock + +import pytest +import sqlalchemy + +import apd.aggregation.collect +from apd.aggregation.database import Deployment + + +@pytest.fixture +def data() -> t.Any: + return { + "sensors": [ + { + "human_readable": "3.7", + "id": "PythonVersion", + "title": "Python Version", + "value": [3, 7, 2, "final", 0], + }, + { + "human_readable": "Not connected", + "id": "ACStatus", + "title": "AC Connected", + "value": False, + }, + ] + } + + +@dataclass +class FakeAIOHttpClient: + responses: t.Dict[str, str] + + @contextlib.asynccontextmanager + async def get( + self, url: str, headers: t.Optional[t.Dict[str, str]] = None + ) -> t.AsyncIterator[FakeAIOHttpResponse]: + if url in self.responses: + yield FakeAIOHttpResponse(body=self.responses[url]) + else: + yield FakeAIOHttpResponse(body="", status=404) + + +@dataclass +class FakeAIOHttpResponse: + body: str + status: int = 200 + + async def json(self) -> t.Any: + return json.loads(self.body) + + +@pytest.fixture +def mockclient(data) -> FakeAIOHttpClient: + return FakeAIOHttpClient( + { + "http://localhost/v/2.1/sensors/": json.dumps(data), + "http://localhost/v/2.1/deployment_id": json.dumps( + {"deployment_id": "b29ba0ee10f14552b6b21327bb96d3fb"} + ), + } + ) + + +class TestGetDataPoints: + @pytest.fixture + def mut(self): + return apd.aggregation.collect.get_data_points + + @pytest.mark.asyncio + async def test_get_data_points(self, mut, mockclient, data) -> None: + # Mark the mock client as the same type as the one it's mocking + # So we can set it into the context variable without warnings + token = apd.aggregation.collect.http_session_var.set(mockclient) + try: + datapoints = await mut("http://localhost", "") + finally: + apd.aggregation.collect.http_session_var.reset(token) + + assert len(datapoints) == len(data["sensors"]) + for sensor in data["sensors"]: + assert sensor["value"] in (datapoint.data for datapoint in datapoints) + assert sensor["id"] in (datapoint.sensor_name for datapoint in datapoints) + + +class TestAddDataFromSensors: + @pytest.fixture + def mut(self): + return apd.aggregation.collect.add_data_from_sensors + + @pytest.fixture(autouse=True) + def patch_aiohttp(self, mockclient): + with patch("aiohttp.ClientSession") as ClientSession: + ClientSession.return_value.__aenter__.return_value = mockclient + yield ClientSession + + @pytest.fixture + def db_session(self): + session = Mock() + sql_result = session.execute.return_value + sql_result.inserted_primary_key = [1] + return session + + @pytest.mark.asyncio + async def test_datapoints_are_added_to_the_session(self, mut, db_session) -> None: + assert db_session.execute.call_count == 0 + datapoints = await mut( + db_session, + [ + Deployment( + id=None, colour=None, name=None, uri="http://localhost", api_key="" + ) + ], + ) + assert db_session.execute.call_count == len(datapoints) + + +@pytest.mark.usefixtures("migrated_db") +class TestDatabaseConnection: + @pytest.fixture(autouse=True) + def patch_aiohttp(self, mockclient): + with patch("aiohttp.ClientSession") as ClientSession: + ClientSession.return_value.__aenter__.return_value = mockclient + yield ClientSession + + @pytest.fixture + def table(self): + return apd.aggregation.database.datapoint_table + + @pytest.fixture + def daily_summary_view(self): + return apd.aggregation.database.daily_summary_view + + @pytest.fixture + def model(self): + return apd.aggregation.database.DataPoint + + @pytest.fixture + def mut(self): + return apd.aggregation.collect.add_data_from_sensors + + @pytest.mark.asyncio + async def test_datapoints_are_added_to_the_session(self, db_session, table) -> None: + datapoints = await apd.aggregation.collect.add_data_from_sensors( + db_session, + [ + Deployment( + id=None, colour=None, name=None, uri="http://localhost", api_key="" + ) + ], + ) + num_points = db_session.query(table).count() + assert num_points == len(datapoints) == 2 + + @pytest.mark.asyncio + async def test_datapoints_can_be_mapped_back_to_DataPoints( + self, mut, db_session, table, model + ) -> None: + datapoints = await mut( + db_session, + [ + Deployment( + id=None, colour=None, name=None, uri="http://localhost", api_key="" + ) + ], + ) + db_points = [ + model.from_sql_result(result) for result in db_session.query(table) + ] + assert db_points == datapoints + + @pytest.mark.asyncio + async def test_daily_summary_view_matches_query( + self, db_session, model, table, daily_summary_view + ) -> None: + await apd.aggregation.collect.add_data_from_sensors( + db_session, + [ + Deployment( + id=None, colour=None, name=None, uri="http://localhost", api_key="" + ) + ], + ) + + headers = table.c.sensor_name, table.c.data + value_counts = ( + db_session.query(*headers, sqlalchemy.func.count(table.c.id)) + .filter(model.collected_on_date == sqlalchemy.func.current_date()) + .group_by(*headers) + ) + + daily_summary_view = db_session.query(daily_summary_view) + assert value_counts.all() == daily_summary_view.all() diff --git a/Ch12/apd.sensors-chapter12/.pre-commit-config.yaml b/Ch12/apd.sensors-chapter12/.pre-commit-config.yaml new file mode 100644 index 0000000..d3b6ec7 --- /dev/null +++ b/Ch12/apd.sensors-chapter12/.pre-commit-config.yaml @@ -0,0 +1,26 @@ +repos: + +- repo: local + hooks: + - id: black + name: black + entry: pipenv run black + args: [--quiet] + language: system + types: [python] + + - id: mypy + name: mypy + exclude: (?x)^( + (.*)/setup.py + )$ + entry: pipenv run mypy + args: ["--follow-imports=skip"] + language: system + types: [python] + + - id: flake8 + name: flake8 + entry: pipenv run flake8 + language: system + types: [python] diff --git a/Ch12/apd.sensors-chapter12/CHANGES.md b/Ch12/apd.sensors-chapter12/CHANGES.md new file mode 100644 index 0000000..b2f6115 --- /dev/null +++ b/Ch12/apd.sensors-chapter12/CHANGES.md @@ -0,0 +1,44 @@ +## Changes + +### 2.2 (2020-01-17) + +* Add ability to store data points on query (Matthew Wilkes) +* Add v3.0 API to distinguish sensor errors and include historical data (Matthew Wilkes) + +### 2.1.1 (2020-01-06) + +* Cache DHT sensor connections (Matthew Wilkes) +* Force use of bitbang interface for DHT sensors, to improve reliability on Linux (Matthew Wilkes) + +### 2.1.0 (2019-12-09) + +* Add optional APD_SENSORS_DEPLOYMENT_ID parameter and v2.1 API which allows + users to find a unique identifier for a webapp sensor deployment (Matthew Wilkes) + +### 2.0.0 (2019-09-08) + +* Add `to_json_compatible` and `from_json_compatible` methods to Sensor + to facilitate better HTTP API (Matthew Wilkes) + +* HTTP API is now versioned. The API from 1.3.0 is available at /v/1.0 + and an updated version is at /v/2.0 (Matthew Wilkes) + +### 1.3.0 (2019-08-20) + +* WSGI HTTP API support added (Matthew Wilkes) + +### 1.2.0 (2019-08-05) + +* Add external plugin support through `apd.sensors.sensors` entrypoint (Matthew Wilkes) + +### 1.1.0 (2019-07-12) + +* Add --develop argument (Matthew Wilkes) + +### 1.0.1 (2019-06-20) + +* Fix broken 1.0.0 release (Matthew Wilkes) + +### 1.0.0 (2019-06-20) + +* Added initial sensors (Matthew Wilkes) diff --git a/Ch12/apd.sensors-chapter12/LICENCE b/Ch12/apd.sensors-chapter12/LICENCE new file mode 100644 index 0000000..053f694 --- /dev/null +++ b/Ch12/apd.sensors-chapter12/LICENCE @@ -0,0 +1,19 @@ +Copyright (c) 2019 Matthew Wilkes + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/Ch12/apd.sensors-chapter12/Pipfile b/Ch12/apd.sensors-chapter12/Pipfile new file mode 100644 index 0000000..44bfe74 --- /dev/null +++ b/Ch12/apd.sensors-chapter12/Pipfile @@ -0,0 +1,34 @@ +[[source]] +name = "pypi" +url = "https://pypi.org/simple" +verify_ssl = true + +[[source]] +name = "piwheels" +url = "https://piwheels.org/simple" +verify_ssl = true + +[dev-packages] +ipykernel = "*" +remote-ikernel = "*" +pytest = "*" +pytest-cov = "*" +mypy = "*" +flake8 = "*" +black = "*" +pre-commit = "*" +wheel = "*" +twine = "*" +webtest = "*" +sqlalchemy-stubs = "*" + +[packages] +apd-sensors = {editable = true,extras = ["scheduled", "webapp", "storedapi"],path = "."} +apd-sunnyboy-solar = {editable = true,path = "./plugins/apd.sunnyboy_solar"} +waitress = "*" +pint = "==0.10.1" + +[requires] + +[pipenv] +allow_prereleases = true diff --git a/Ch12/apd.sensors-chapter12/Pipfile.lock b/Ch12/apd.sensors-chapter12/Pipfile.lock new file mode 100644 index 0000000..ce04ef4 --- /dev/null +++ b/Ch12/apd.sensors-chapter12/Pipfile.lock @@ -0,0 +1,1092 @@ +{ + "_meta": { + "hash": { + "sha256": "8099f56a327cfc41360e2cbc7ff56ad2bab9f24325f631df83cc94d9f9196469" + }, + "pipfile-spec": 6, + "requires": {}, + "sources": [ + { + "name": "pypi", + "url": "https://pypi.org/simple", + "verify_ssl": true + }, + { + "name": "piwheels", + "url": "https://piwheels.org/simple", + "verify_ssl": true + } + ] + }, + "default": { + "alembic": { + "hashes": [ + "sha256:035ab00497217628bf5d0be82d664d8713ab13d37b630084da8e1f98facf4dbf", + "sha256:ba2d8937aef1ee14ffc983f9ab00a6f8e48907e46c7788218b561c100175025f" + ], + "version": "==1.4.2" + }, + "apd-sensors": { + "editable": true, + "extras": [ + "scheduled", + "webapp", + 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The available sensors are: + +* Python version +* IP Addresses +* CPU Usage +* RAM Available +* Battery charging state +* Ambient Temperature +* Ambient Humidity + +There are no command-line options, to view the report run `sensors` on the +command line. + +## Caveats + +The Ambient Temperature and Ambient Humidity sensors are only available on +RaspberryPi hosts and assume that a DHT22 sensor is connected to pin `D20`. + +The location and type of the sensor can be controlled by setting a pair of +environment variables: `APD_SENSORS_TEMPERATURE_BOARD` and +`APD_SENSORS_TEMPERATURE_PIN`. + +If there is an entry in `/etc/hosts` for the current machine's hostname that +value will be the only result from the IP Addresses sensor. + +## Installation + +You can install with `pip3 install apd.sensors` under Python 3.7 or higher. + +We recommend using pipenv to manage your environment, in which case you would +install using `pipenv --three install apd.sensors` and run the programme using +`pipenv run sensors`. + +## API server + +There is an optional API server shipped with apd.sensors. To use this you +should install the `apd.sensors[webapp]` extra. The API can then be started +with the non-production quality wsgiref server using: + + python -m apd.sensors.wsgi.serve + +or through Waitress (if installed) using: + + waitress-serve --call apd.sensors.wsgi:set_up_config + +Other WSGI servers will also work, you should use set_up_config as a factory +function. + +An environment variable is required to use the API server, `APD_SENSORS_API_KEY` +should be set to the API key required to gain access. One can be generated +using: + + python -c "import uuid; print(uuid.uuid4().hex)" + +The following endpoints are supported: + +* /v/3.0/sensors +* /v/3.0/sensors/sensorid +* /v/3.0/deployment_id + +## Historical data + +You can install optional functionality to periodically store sensor +values using the `apd.sensors[scheduled]` extra. In this case, +`sensors --save` will store the recorded data to `sensor_data.sqlite` +in the current working directory. + +The database connection can be specified with `--db sqlite:////var/sensors.sqlite`, +for example. It can also be specified with the `APD_SENSORS_DB_URI` +environment variable. + +The database must be migrated to contain the correct data first. This can be +done by running `alembic upgrade head` with the following alembic.ini file +in the current working directory. + + [alembic] + script_location = apd.sensors:alembic + sqlalchemy.url = sqlite:///sensor_data.sqlite + +### Historical data API + +An API to extract historical data is also available if installed with `apd.sensors[webapp,scheduled,storedapi]`. + +This provides the following three URIs, where start and end are a date/time in ISO format. + +* /v/3.0/historical +* /v/3.0/historical/start +* /v/3.0/historical/start/end diff --git a/Ch12/apd.sensors-chapter12/plugins/apd.sunnyboy_solar/pyproject.toml b/Ch12/apd.sensors-chapter12/plugins/apd.sunnyboy_solar/pyproject.toml new file mode 100644 index 0000000..4d3bb67 --- /dev/null +++ b/Ch12/apd.sensors-chapter12/plugins/apd.sunnyboy_solar/pyproject.toml @@ -0,0 +1,5 @@ +[build-system] +requires = [ + "setuptools", +] +build-backend = "setuptools.build_meta" diff --git a/Ch12/apd.sensors-chapter12/plugins/apd.sunnyboy_solar/setup.cfg b/Ch12/apd.sensors-chapter12/plugins/apd.sunnyboy_solar/setup.cfg new file mode 100644 index 0000000..0f5db10 --- /dev/null +++ b/Ch12/apd.sensors-chapter12/plugins/apd.sunnyboy_solar/setup.cfg @@ -0,0 +1,33 @@ +[mypy] +ignore_missing_imports = True + +[flake8] +max-line-length = 88 + +[metadata] +name = apd.sunnyboy_solar +version = attr: apd.sunnyboy_solar.VERSION +description = APD Sensor for reading data from Sunnyboy inverters +long_description = file: README.md, CHANGES.md, LICENCE +long_description_content_type = text/markdown +keywords = iot solar +license = MIT +classifiers = + Programming Language :: Python :: 3 + Programming Language :: Python :: 3.7 + +[options] +zip_safe = False +include_package_data = True +package_dir = + =src +packages = find_namespace: +install_requires = + apd.sensors + +[options.packages.find] +where = src + +[options.entry_points] +apd.sensors.sensors = + SolarCumulativeOutput = apd.sunnyboy_solar.sensor:SolarCumulativeOutput diff --git a/Ch12/apd.sensors-chapter12/plugins/apd.sunnyboy_solar/setup.py b/Ch12/apd.sensors-chapter12/plugins/apd.sunnyboy_solar/setup.py new file mode 100644 index 0000000..6068493 --- /dev/null +++ b/Ch12/apd.sensors-chapter12/plugins/apd.sunnyboy_solar/setup.py @@ -0,0 +1,3 @@ +from setuptools import setup + +setup() diff --git a/Ch12/apd.sensors-chapter12/plugins/apd.sunnyboy_solar/src/apd/sunnyboy_solar/Pipfile b/Ch12/apd.sensors-chapter12/plugins/apd.sunnyboy_solar/src/apd/sunnyboy_solar/Pipfile new file mode 100644 index 0000000..e69de29 diff --git a/Ch12/apd.sensors-chapter12/plugins/apd.sunnyboy_solar/src/apd/sunnyboy_solar/__init__.py b/Ch12/apd.sensors-chapter12/plugins/apd.sunnyboy_solar/src/apd/sunnyboy_solar/__init__.py new file mode 100644 index 0000000..3277f64 --- /dev/null +++ b/Ch12/apd.sensors-chapter12/plugins/apd.sunnyboy_solar/src/apd/sunnyboy_solar/__init__.py @@ -0,0 +1 @@ +VERSION = "1.0.0" diff --git a/Ch12/apd.sensors-chapter12/plugins/apd.sunnyboy_solar/src/apd/sunnyboy_solar/sensor.py b/Ch12/apd.sensors-chapter12/plugins/apd.sunnyboy_solar/src/apd/sunnyboy_solar/sensor.py new file mode 100644 index 0000000..190159a --- /dev/null +++ b/Ch12/apd.sensors-chapter12/plugins/apd.sunnyboy_solar/src/apd/sunnyboy_solar/sensor.py @@ -0,0 +1,73 @@ +import os +import subprocess +import typing as t + +from pint import _DEFAULT_REGISTRY as ureg + +from apd.sensors.base import Sensor +from apd.sensors.exceptions import ( + PersistentSensorFailureError, + IntermittentSensorFailureError, +) + + +class SolarCumulativeOutput(Sensor[t.Any]): + name = "SolarCumulativeOutput" + title = "Solar panel cumulative output" + + def __init__( + self, path: t.Optional[str] = None, bt_addr: t.Optional[str] = None + ) -> None: + self.path = os.environ.get( + "APD_SUNNYBOYSOLAR_PATH", "/home/pi/opensunny-master/opensunny" + ) + self.bt_addr = os.environ.get("APD_SUNNYBOYSOLAR_BT_ADDRESS", None) + + def value(self) -> t.Optional[t.Any]: + if self.bt_addr is None: + raise PersistentSensorFailureError("Inverter address not configured") + try: + output: bytes = subprocess.check_output( + [self.path, "-i", self.bt_addr], stderr=subprocess.STDOUT, timeout=15 + ) + except subprocess.CalledProcessError as err: + raise IntermittentSensorFailureError( + "Failure communicating with inverter" + ) from err + except FileNotFoundError as err: + raise PersistentSensorFailureError( + "Inverter control software not installed" + ) from err + + lines = filter(None, output.split(b"\n")) + found = {} + for line in lines: + start, value = line.rsplit(b"=", 1) + start, key = start.rsplit(b" ", 1) + found[key] = value + + try: + yield_total = float(found[b"yield_total"][:-3].replace(b".", b"")) + power_dc_1 = int(found[b"power_dc_1"][:-1]) + power_dc_2 = int(found[b"power_dc_2"][:-1]) + if power_dc_1 > 1500 or power_dc_2 > 1500: + raise IntermittentSensorFailureError( + "Received corrupt data from inverter" + ) + except (ValueError, IndexError, KeyError) as err: + raise IntermittentSensorFailureError( + "Received incomplete data from inverter" + ) from err + return ureg.Quantity(yield_total, "watt") + + @classmethod + def format(cls, value: t.Any) -> str: + return "{:~P}".format(value.to(ureg.kilowatt)) + + @classmethod + def to_json_compatible(cls, value: t.Any) -> t.Dict[str, t.Union[str, float]]: + return {"magnitude": value.magnitude, "unit": str(value.units)} + + @classmethod + def from_json_compatible(cls, json_version: t.Any) -> t.Any: + return ureg.Quantity(json_version["magnitude"], ureg[json_version["unit"]]) diff --git a/Ch12/apd.sensors-chapter12/pytest.ini b/Ch12/apd.sensors-chapter12/pytest.ini new file mode 100644 index 0000000..0b96d0d --- /dev/null +++ b/Ch12/apd.sensors-chapter12/pytest.ini @@ -0,0 +1,5 @@ +[pytest] +markers = + functional: these tests are significantly slower as they run the whole CLI script +addopts = + --ignore plugins \ No newline at end of file diff --git a/Ch12/apd.sensors-chapter12/setup.cfg b/Ch12/apd.sensors-chapter12/setup.cfg new file mode 100644 index 0000000..9fa1056 --- /dev/null +++ b/Ch12/apd.sensors-chapter12/setup.cfg @@ -0,0 +1,83 @@ +[mypy] +namespace_packages = True +mypy_path = src +plugins = sqlmypy + +[mypy-psutil] +ignore_missing_imports = True + +[mypy-alembic] +ignore_missing_imports = True + +[mypy-adafruit_dht] +ignore_missing_imports = True + +[mypy-board] +ignore_missing_imports = True + +[mypy-flask_sqlalchemy] +ignore_missing_imports = True + +[mypy-pint] +ignore_missing_imports = True + +[mypy-pytest] +ignore_missing_imports = True + +[mypy-webtest] +ignore_missing_imports = True + +[flake8] +max-line-length = 88 + +[metadata] +name = apd.sensors +version = attr: apd.sensors.VERSION +description = APD Sensor package +long_description = file: README.md, CHANGES.md, LICENCE +long_description_content_type = text/markdown +keywords = iot +license = MIT +classifiers = + Programming Language :: Python :: 3 + Programming Language :: Python :: 3.7 + +[options] +zip_safe = False +include_package_data = True +package_dir = + =src +packages = find_namespace: +install_requires = + psutil + click + pint + adafruit-circuitpython-dht ; 'arm' in platform_machine + +[options.package_data] +apd.sensors = py.typed + +[options.packages.find] +where = src + +[options.entry_points] +console_scripts = + sensors = apd.sensors.cli:show_sensors +apd.sensors.sensors = + PythonVersion = apd.sensors.sensors:PythonVersion + IPAddresses = apd.sensors.sensors:IPAddresses + CPULoad = apd.sensors.sensors:CPULoad + RAMAvailable = apd.sensors.sensors:RAMAvailable + ACStatus = apd.sensors.sensors:ACStatus + Temperature = apd.sensors.sensors:Temperature + RelativeHumidity = apd.sensors.sensors:RelativeHumidity + +[options.extras_require] +webapp = flask +scheduled = + sqlalchemy + alembic +storedapi = + flask-sqlalchemy + python-dateutil + \ No newline at end of file diff --git a/Ch12/apd.sensors-chapter12/setup.py b/Ch12/apd.sensors-chapter12/setup.py new file mode 100644 index 0000000..6068493 --- /dev/null +++ b/Ch12/apd.sensors-chapter12/setup.py @@ -0,0 +1,3 @@ +from setuptools import setup + +setup() diff --git a/Ch12/apd.sensors-chapter12/src/apd/sensors/__init__.py b/Ch12/apd.sensors-chapter12/src/apd/sensors/__init__.py new file mode 100644 index 0000000..127c148 --- /dev/null +++ b/Ch12/apd.sensors-chapter12/src/apd/sensors/__init__.py @@ -0,0 +1 @@ +VERSION = "2.1.0" diff --git a/Ch12/apd.sensors-chapter12/src/apd/sensors/alembic/README b/Ch12/apd.sensors-chapter12/src/apd/sensors/alembic/README new file mode 100644 index 0000000..98e4f9c --- /dev/null +++ b/Ch12/apd.sensors-chapter12/src/apd/sensors/alembic/README @@ -0,0 +1 @@ +Generic single-database configuration. \ No newline at end of file diff --git a/Ch12/apd.sensors-chapter12/src/apd/sensors/alembic/env.py b/Ch12/apd.sensors-chapter12/src/apd/sensors/alembic/env.py new file mode 100644 index 0000000..f508d4c --- /dev/null +++ b/Ch12/apd.sensors-chapter12/src/apd/sensors/alembic/env.py @@ -0,0 +1,77 @@ +from logging.config import fileConfig + +from sqlalchemy import engine_from_config +from sqlalchemy import pool + +from alembic import context + +import apd.sensors.database + +# this is the Alembic Config object, which provides +# access to the values within the .ini file in use. +config = context.config + +# Interpret the config file for Python logging. +# This line sets up loggers basically. +fileConfig(config.config_file_name) + +# add your model's MetaData object here +# for 'autogenerate' support +# from myapp import mymodel +# target_metadata = mymodel.Base.metadata +target_metadata = apd.sensors.database.metadata + +# other values from the config, defined by the needs of env.py, +# can be acquired: +# my_important_option = config.get_main_option("my_important_option") +# ... etc. + + +def run_migrations_offline(): + """Run migrations in 'offline' mode. + + This configures the context with just a URL + and not an Engine, though an Engine is acceptable + here as well. By skipping the Engine creation + we don't even need a DBAPI to be available. + + Calls to context.execute() here emit the given string to the + script output. + + """ + url = config.get_main_option("sqlalchemy.url") + context.configure( + url=url, + target_metadata=target_metadata, + literal_binds=True, + dialect_opts={"paramstyle": "named"}, + ) + + with context.begin_transaction(): + context.run_migrations() + + +def run_migrations_online(): + """Run migrations in 'online' mode. + + In this scenario we need to create an Engine + and associate a connection with the context. + + """ + connectable = engine_from_config( + config.get_section(config.config_ini_section), + prefix="sqlalchemy.", + poolclass=pool.NullPool, + ) + + with connectable.connect() as connection: + context.configure(connection=connection, target_metadata=target_metadata) + + with context.begin_transaction(): + context.run_migrations() + + +if context.is_offline_mode(): + run_migrations_offline() +else: + run_migrations_online() diff --git a/Ch12/apd.sensors-chapter12/src/apd/sensors/alembic/script.py.mako b/Ch12/apd.sensors-chapter12/src/apd/sensors/alembic/script.py.mako new file mode 100644 index 0000000..2c01563 --- /dev/null +++ b/Ch12/apd.sensors-chapter12/src/apd/sensors/alembic/script.py.mako @@ -0,0 +1,24 @@ +"""${message} + +Revision ID: ${up_revision} +Revises: ${down_revision | comma,n} +Create Date: ${create_date} + +""" +from alembic import op +import sqlalchemy as sa +${imports if imports else ""} + +# revision identifiers, used by Alembic. +revision = ${repr(up_revision)} +down_revision = ${repr(down_revision)} +branch_labels = ${repr(branch_labels)} +depends_on = ${repr(depends_on)} + + +def upgrade(): + ${upgrades if upgrades else "pass"} + + +def downgrade(): + ${downgrades if downgrades else "pass"} diff --git a/Ch12/apd.sensors-chapter12/src/apd/sensors/alembic/versions/0eeb2a54fea8_add_initial_sensor_table.py b/Ch12/apd.sensors-chapter12/src/apd/sensors/alembic/versions/0eeb2a54fea8_add_initial_sensor_table.py new file mode 100644 index 0000000..bb38e90 --- /dev/null +++ b/Ch12/apd.sensors-chapter12/src/apd/sensors/alembic/versions/0eeb2a54fea8_add_initial_sensor_table.py @@ -0,0 +1,45 @@ +"""Add initial sensor table + +Revision ID: 0eeb2a54fea8 +Revises: base +Create Date: 2020-01-16 17:43:37.298379 + +""" +from alembic import op +import sqlalchemy as sa + + +# revision identifiers, used by Alembic. +revision = "0eeb2a54fea8" +down_revision = None +branch_labels = None +depends_on = None + + +def upgrade(): + op.create_table( + "recorded_values", + sa.Column("id", sa.Integer(), nullable=False), + sa.Column("sensor_name", sa.String(), nullable=True), + sa.Column("collected_at", sa.TIMESTAMP(), nullable=True), + sa.Column("data", sa.JSON(), nullable=True), + sa.PrimaryKeyConstraint("id"), + ) + op.create_index( + op.f("ix_recorded_values_collected_at"), + "recorded_values", + ["collected_at"], + unique=False, + ) + op.create_index( + op.f("ix_recorded_values_sensor_name"), + "recorded_values", + ["sensor_name"], + unique=False, + ) + + +def downgrade(): + op.drop_index(op.f("ix_recorded_values_sensor_name"), table_name="recorded_values") + op.drop_index(op.f("ix_recorded_values_collected_at"), table_name="recorded_values") + op.drop_table("recorded_values") diff --git a/Ch12/apd.sensors-chapter12/src/apd/sensors/base.py b/Ch12/apd.sensors-chapter12/src/apd/sensors/base.py new file mode 100644 index 0000000..e822f4b --- /dev/null +++ b/Ch12/apd.sensors-chapter12/src/apd/sensors/base.py @@ -0,0 +1,59 @@ +#!/usr/bin/env python +# coding: utf-8 +import datetime +import typing as t + + +T_value = t.TypeVar("T_value") + + +class Sensor(t.Generic[T_value]): + name: str + title: str + + def value(self) -> T_value: + raise NotImplementedError + + @classmethod + def format(cls, value: T_value) -> str: + raise NotImplementedError + + def __str__(self) -> str: + return self.format(self.value()) + + @classmethod + def to_json_compatible(cls, value: T_value) -> t.Any: + raise NotImplementedError() + + @classmethod + def from_json_compatible(cls, json_version: t.Any) -> T_value: + raise NotImplementedError() + + +class JSONSensor(Sensor[T_value]): + @classmethod + def to_json_compatible(cls, value: T_value) -> t.Any: + return value + + @classmethod + def from_json_compatible(cls, json_version: t.Any) -> T_value: + return t.cast(T_value, json_version) + + +class HistoricalSensor(Sensor[T_value]): + def historical( + self, start: datetime.datetime, end: datetime.datetime + ) -> t.Iterable[t.Tuple[datetime.datetime, T_value]]: + raise NotImplementedError + + +version_info_type = t.NamedTuple( + "version_info_type", + [ + ("major", int), + ("minor", int), + ("micro", int), + ("releaselevel", str), + ("serial", int), + ], +) diff --git a/Ch12/apd.sensors-chapter12/src/apd/sensors/cli.py b/Ch12/apd.sensors-chapter12/src/apd/sensors/cli.py new file mode 100644 index 0000000..576c487 --- /dev/null +++ b/Ch12/apd.sensors-chapter12/src/apd/sensors/cli.py @@ -0,0 +1,118 @@ +import enum +import importlib +import sys +import pkg_resources +import traceback +import typing as t + +import click + +from .database import store_sensor_data +from .sensors import Sensor +from .exceptions import DataCollectionError, UserFacingCLIError + + +class ReturnCodes(enum.IntEnum): + OK = 0 + BAD_SENSOR_PATH = 17 + + +def get_sensor_by_path(sensor_path: str) -> Sensor[t.Any]: + try: + module_name, sensor_name = sensor_path.split(":") + except ValueError as err: + raise UserFacingCLIError( + "Sensor path must be in the format dotted.path.to.module:ClassName", + return_code=ReturnCodes.BAD_SENSOR_PATH, + ) from err + try: + module = importlib.import_module(module_name) + except ImportError as err: + raise UserFacingCLIError( + f"Could not import module {module_name}", return_code=ReturnCodes.BAD_SENSOR_PATH + ) from err + try: + sensor_class = getattr(module, sensor_name) + except AttributeError as err: + raise UserFacingCLIError( + f"Could not find attribute {sensor_name} in {module_name}", return_code=ReturnCodes.BAD_SENSOR_PATH + ) from err + if ( + isinstance(sensor_class, type) + and issubclass(sensor_class, Sensor) + and sensor_class != Sensor + ): + return sensor_class() + else: + raise UserFacingCLIError( + f"Detected object {sensor_class!r} is not recognised as a Sensor type", + return_code=ReturnCodes.BAD_SENSOR_PATH, + ) + + +def get_sensors() -> t.Iterable[Sensor[t.Any]]: + sensors = [] + for sensor_class in pkg_resources.iter_entry_points("apd.sensors.sensors"): + class_ = sensor_class.load() + sensors.append(t.cast(Sensor[t.Any], class_())) + return sensors + + +@click.command(help="Displays the values of the sensors") +@click.option( + "--develop", required=False, metavar="path", help="Load a sensor by Python path" +) +@click.option("--verbose", is_flag=True, help="Show additional info") +@click.option("--save", is_flag=True, help="Store collected data to a database") +@click.option( + "--db", + metavar="", + default="sqlite:///sensor_data.sqlite", + help="The connection string to a database", + envvar="APD_SENSORS_DB_URI", +) +def show_sensors(develop: str, verbose: bool, save: bool, db: str) -> None: + sensors: t.Iterable[Sensor[t.Any]] + if develop: + try: + sensors = [get_sensor_by_path(develop)] + except UserFacingCLIError as error: + if verbose: + tb = traceback.format_exception(type(error), error, error.__traceback__) + click.echo("".join(tb)) + click.secho(error.message, fg="red", bold=True) + sys.exit(error.return_code) + else: + sensors = get_sensors() + + db_session = None + if save: + from sqlalchemy import create_engine + from sqlalchemy.orm import sessionmaker + + engine = create_engine(db) + sm = sessionmaker(engine) + db_session = sm() + + for sensor in sensors: + click.secho(sensor.title, bold=True) + try: + value = sensor.value() + except DataCollectionError as error: + if verbose: + tb = traceback.format_exception(type(error), error, error.__traceback__) + click.echo("".join(tb)) + continue + click.echo(error) + else: + click.echo(sensor.format(value)) + if save and db_session is not None: + store_sensor_data(sensor, value, db_session) + db_session.commit() + + click.echo("") + sys.exit(ReturnCodes.OK) + + +if __name__ == "__main__": + show_sensors() diff --git a/Ch12/apd.sensors-chapter12/src/apd/sensors/database.py b/Ch12/apd.sensors-chapter12/src/apd/sensors/database.py new file mode 100644 index 0000000..a90ffba --- /dev/null +++ b/Ch12/apd.sensors-chapter12/src/apd/sensors/database.py @@ -0,0 +1,30 @@ +from __future__ import annotations + +import datetime +import typing as t + +import sqlalchemy +from sqlalchemy.schema import Table +from sqlalchemy.orm.session import Session + +from apd.sensors.base import Sensor + + +metadata = sqlalchemy.MetaData() + +sensor_values = Table( + "recorded_values", + metadata, + sqlalchemy.Column("id", sqlalchemy.Integer, primary_key=True), + sqlalchemy.Column("sensor_name", sqlalchemy.String, index=True), + sqlalchemy.Column("collected_at", sqlalchemy.TIMESTAMP, index=True), + sqlalchemy.Column("data", sqlalchemy.JSON), +) + + +def store_sensor_data(sensor: Sensor[t.Any], data: t.Any, db_session: Session) -> None: + now = datetime.datetime.now() + record = sensor_values.insert().values( + sensor_name=sensor.name, data=sensor.to_json_compatible(data), collected_at=now + ) + db_session.execute(record) diff --git a/Ch12/apd.sensors-chapter12/src/apd/sensors/exceptions.py b/Ch12/apd.sensors-chapter12/src/apd/sensors/exceptions.py new file mode 100644 index 0000000..725a0ad --- /dev/null +++ b/Ch12/apd.sensors-chapter12/src/apd/sensors/exceptions.py @@ -0,0 +1,32 @@ +import dataclasses + + +class APDSensorsError(Exception): + """An exception base class for all exceptions raised by the + sensor data collection system.""" + + +class DataCollectionError(APDSensorsError, RuntimeError): + """An error that represents the inability of a Sensor instance + to retrieve a value""" + + +class IntermittentSensorFailureError(DataCollectionError): + """A DataCollectionError that is expected to resolve itself + in short order""" + + +class PersistentSensorFailureError(DataCollectionError): + """A DataCollectionError that is unlikely to resolve itself + if retried.""" + + +@dataclasses.dataclass(frozen=True) +class UserFacingCLIError(APDSensorsError, SystemExit): + """A fatal error for the CLI""" + + message: str + return_code: int + + def __str__(self): + return f"[{self.return_code}] {self.message}" diff --git a/Ch12/apd.sensors-chapter12/src/apd/sensors/py.typed b/Ch12/apd.sensors-chapter12/src/apd/sensors/py.typed new file mode 100644 index 0000000..e69de29 diff --git a/Ch12/apd.sensors-chapter12/src/apd/sensors/sensors.py b/Ch12/apd.sensors-chapter12/src/apd/sensors/sensors.py new file mode 100644 index 0000000..c577dbf --- /dev/null +++ b/Ch12/apd.sensors-chapter12/src/apd/sensors/sensors.py @@ -0,0 +1,189 @@ +#!/usr/bin/env python +# coding: utf-8 +import math +import os +import socket +import sys +import typing as t + +import psutil +from pint import _DEFAULT_REGISTRY as ureg + +from .base import Sensor, JSONSensor, version_info_type +from .exceptions import ( + PersistentSensorFailureError, + IntermittentSensorFailureError, + DataCollectionError, +) + + +class PythonVersion(JSONSensor[version_info_type]): + name = "PythonVersion" + title = "Python Version" + + def value(self) -> version_info_type: + return version_info_type(*sys.version_info) + + @classmethod + def from_json_compatible(cls, json_version: t.Any) -> version_info_type: + return version_info_type(*json_version) + + @classmethod + def format(cls, value: version_info_type) -> str: + if value.micro == 0 and value.releaselevel == "alpha": + return "{0.major}.{0.minor}.{0.micro}a{0.serial}".format(value) + return "{0.major}.{0.minor}".format(value) + + +class IPAddresses(JSONSensor[t.Iterable[t.Tuple[str, str]]]): + name = "IPAddresses" + title = "IP Addresses" + FAMILIES = {"AF_INET": "IPv4", "AF_INET6": "IPv6"} + + def value(self) -> t.List[t.Tuple[str, str]]: + hostname = socket.gethostname() + addresses = socket.getaddrinfo(hostname, None) + address_info: t.List[t.Tuple[str, str]] = [] + for address in addresses: + family, ip = (address[0].name, address[4][0]) + if family not in self.FAMILIES: + continue + value = (family, ip) + if value not in address_info: + address_info.append(value) + return address_info + + @classmethod + def format(cls, value: t.Iterable[t.Tuple[str, str]]) -> str: + return "\n".join( + "{0} ({1})".format(address[1], cls.FAMILIES.get(address[0], "Unknown")) + for address in value + ) + + +class CPULoad(JSONSensor[float]): + name = "CPULoad" + title = "CPU Usage" + + def value(self) -> float: + return float(psutil.cpu_percent(interval=3)) / 100.0 + + @classmethod + def format(cls, value: float) -> str: + return "{:.1%}".format(value) + + +class RAMAvailable(JSONSensor[int]): + name = "RAMAvailable" + title = "RAM Available" + UNITS = ("B", "KiB", "MiB", "GiB", "TiB", "PiB", "EiB") + UNIT_SIZE = 2 ** 10 + + def value(self) -> int: + return int(psutil.virtual_memory().available) + + @classmethod + def format(cls, value: int) -> str: + magnitude = math.floor(math.log(value, cls.UNIT_SIZE)) + max_magnitude = len(cls.UNITS) - 1 + magnitude = min(magnitude, max_magnitude) + scaled_value = value / (cls.UNIT_SIZE ** magnitude) + return "{:.1f} {}".format(scaled_value, cls.UNITS[magnitude]) + + +class ACStatus(JSONSensor[bool]): + name = "ACStatus" + title = "AC Connected" + + def value(self) -> bool: + battery = psutil.sensors_battery() + if battery is not None: + value = battery.power_plugged + if value is None: + raise IntermittentSensorFailureError("Can't find AC status") + else: + return bool(value) + else: + raise PersistentSensorFailureError("No charging circuit installed") + + @classmethod + def format(cls, value: bool) -> str: + if value: + return "Connected" + else: + return "Not connected" + + +class DHTSensor: + def __init__(self) -> None: + self.board = os.environ.get("APD_SENSORS_TEMPERATURE_BOARD", "DHT22") + self.pin = os.environ.get("APD_SENSORS_TEMPERATURE_PIN", "D20") + + @property + def sensor(self) -> t.Any: + try: + import adafruit_dht + import board + + # Force using legacy interface + adafruit_dht._USE_PULSEIO = False + + sensor_type = getattr(adafruit_dht, self.board) + pin = getattr(board, self.pin) + return sensor_type(pin) + except (ImportError, NotImplementedError, AttributeError) as err: + # No DHT library results in an ImportError. + # Running on an unknown platform results in a + # NotImplementedError when getting the pin + raise PersistentSensorFailureError( + "Unable to initialise sensor interface" + ) from err + + +class Temperature(Sensor[t.Any], DHTSensor): + name = "Temperature" + title = "Ambient Temperature" + + def value(self) -> t.Any: + try: + return ureg.Quantity(self.sensor.temperature, ureg.celsius) + except DataCollectionError: + # This is one of our own exceptions, we don't need to re-wrap it + raise + except (RuntimeError, AttributeError) as err: + raise IntermittentSensorFailureError( + "Couldn't determine temperature" + ) from err + + @classmethod + def format(cls, value: t.Any) -> str: + return "{:.3~P} ({:.3~P})".format(value, value.to(ureg.fahrenheit)) + + @classmethod + def to_json_compatible(cls, value: t.Any) -> t.Any: + return {"magnitude": value.magnitude, "unit": str(value.units)} + + @classmethod + def from_json_compatible(cls, json_version: t.Any) -> t.Any: + return ureg.Quantity(json_version["magnitude"], ureg[json_version["unit"]]) + + def __str__(self) -> str: + return self.format(self.value()) + + +class RelativeHumidity(JSONSensor[float], DHTSensor): + name = "RelativeHumidity" + title = "Relative Humidity" + + def value(self) -> float: + try: + return float(self.sensor.humidity) + except DataCollectionError: + # This is one of our own exceptions, we don't need to re-wrap it + raise + except (TypeError, AttributeError) as err: + raise IntermittentSensorFailureError("Couldn't determine humidity") from err + + @classmethod + def format(cls, value: float) -> str: + return "{:.1%}".format(value) diff --git a/Ch12/apd.sensors-chapter12/src/apd/sensors/utils.py b/Ch12/apd.sensors-chapter12/src/apd/sensors/utils.py new file mode 100644 index 0000000..24f912b --- /dev/null +++ b/Ch12/apd.sensors-chapter12/src/apd/sensors/utils.py @@ -0,0 +1,20 @@ +from apd.sensors.base import Sensor, T_value +from apd.sensors.exceptions import IntermittentSensorFailureError + + +def get_value_with_retries(sensor: Sensor[T_value], retries: int = 3) -> T_value: + for i in range(retries): + try: + return sensor.value() + except IntermittentSensorFailureError: + if i == (retries - 1): + # This is the last retry, reraise + raise + else: + continue + # It shouldn't be possible to get here, but it's better to + # fall through with an appropriate exception rather than a + # None + raise IntermittentSensorFailureError( + f"Could not find a value after {retries} retries" + ) diff --git a/Ch12/apd.sensors-chapter12/src/apd/sensors/wsgi/__init__.py b/Ch12/apd.sensors-chapter12/src/apd/sensors/wsgi/__init__.py new file mode 100644 index 0000000..fbd2a91 --- /dev/null +++ b/Ch12/apd.sensors-chapter12/src/apd/sensors/wsgi/__init__.py @@ -0,0 +1,31 @@ +import flask + +try: + from flask_sqlalchemy import SQLAlchemy +except ImportError: + sql_support = False +else: + sql_support = True + + +from .base import set_up_config +from . import v10 +from . import v20 +from . import v21 +from . import v30 + + +__all__ = ["app", "set_up_config", "db"] + +app = flask.Flask(__name__) +app.register_blueprint(v10.version, url_prefix="/v/1.0") +app.register_blueprint(v20.version, url_prefix="/v/2.0") +app.register_blueprint(v21.version, url_prefix="/v/2.1") +app.register_blueprint(v30.version, url_prefix="/v/3.0") + +if sql_support: + from apd.sensors.database import metadata + + db = SQLAlchemy(app, metadata=metadata) +else: + db = None diff --git a/Ch12/apd.sensors-chapter12/src/apd/sensors/wsgi/base.py b/Ch12/apd.sensors-chapter12/src/apd/sensors/wsgi/base.py new file mode 100644 index 0000000..b3c73cb --- /dev/null +++ b/Ch12/apd.sensors-chapter12/src/apd/sensors/wsgi/base.py @@ -0,0 +1,47 @@ +from hmac import compare_digest +import functools +import os +import typing as t + +import flask + + +ViewFuncReturn = t.TypeVar("ViewFuncReturn") +ErrorReturn = t.Tuple[t.Dict[str, str], int] +REQUIRED_CONFIG_KEYS = {"APD_SENSORS_API_KEY"} + + +def require_api_key( + func: t.Callable[..., ViewFuncReturn] +) -> t.Callable[..., t.Union[ViewFuncReturn, ErrorReturn]]: + @functools.wraps(func) + def wrapped(*args, **kwargs) -> t.Union[ViewFuncReturn, ErrorReturn]: + api_key = flask.current_app.config["APD_SENSORS_API_KEY"] + headers = flask.request.headers + supplied_key = headers.get("X-API-Key", "") + if not compare_digest(api_key, supplied_key): + return {"error": "Supply API key in X-API-Key header"}, 403 + return func(*args, **kwargs) + + return wrapped + + +def set_up_config( + environ: t.Optional[t.Dict[str, str]] = None, + to_configure: t.Optional[flask.Flask] = None, +) -> flask.Flask: + if environ is None: + environ = dict(os.environ) + if to_configure is None: + from apd.sensors.wsgi import app + + to_configure = app + missing_keys = REQUIRED_CONFIG_KEYS - environ.keys() + if missing_keys: + raise ValueError("Missing config variables: {}".format(", ".join(missing_keys))) + data_file = os.path.join(os.getcwd(), "sensor_data.sqlite") + environ["SQLALCHEMY_DATABASE_URI"] = environ.get( + "APD_SENSORS_DB_URI", f"sqlite:///{data_file}" + ) + to_configure.config.from_mapping(environ) + return to_configure diff --git a/Ch12/apd.sensors-chapter12/src/apd/sensors/wsgi/serve.py b/Ch12/apd.sensors-chapter12/src/apd/sensors/wsgi/serve.py new file mode 100644 index 0000000..0b1f684 --- /dev/null +++ b/Ch12/apd.sensors-chapter12/src/apd/sensors/wsgi/serve.py @@ -0,0 +1,10 @@ +from . import app +from .base import set_up_config + +if __name__ == "__main__": + import wsgiref.simple_server + + set_up_config(None, app) + + with wsgiref.simple_server.make_server("", 8000, app) as server: + server.serve_forever() diff --git a/Ch12/apd.sensors-chapter12/src/apd/sensors/wsgi/v10.py b/Ch12/apd.sensors-chapter12/src/apd/sensors/wsgi/v10.py new file mode 100644 index 0000000..f3ae5da --- /dev/null +++ b/Ch12/apd.sensors-chapter12/src/apd/sensors/wsgi/v10.py @@ -0,0 +1,30 @@ +import json +import typing as t + +import flask + +from apd.sensors import cli +from apd.sensors.exceptions import DataCollectionError +from .base import require_api_key + +version = flask.Blueprint(__name__, __name__) + + +@version.route("/sensors/") +@require_api_key +def sensor_values() -> t.Tuple[t.Dict[str, t.Any], int, t.Dict[str, str]]: + headers = {"Content-Security-Policy": "default-src 'none'"} + data = {} + for sensor in cli.get_sensors(): + try: + value = sensor.value() + except DataCollectionError: + value = None + try: + json.dumps(value) + except TypeError: + # This value isn't JSON serializable, skip it + continue + else: + data[sensor.title] = value + return data, 200, headers diff --git a/Ch12/apd.sensors-chapter12/src/apd/sensors/wsgi/v20.py b/Ch12/apd.sensors-chapter12/src/apd/sensors/wsgi/v20.py new file mode 100644 index 0000000..4ab1522 --- /dev/null +++ b/Ch12/apd.sensors-chapter12/src/apd/sensors/wsgi/v20.py @@ -0,0 +1,40 @@ +import typing as t + +import flask + +from apd.sensors import cli +from apd.sensors.exceptions import DataCollectionError +from .base import require_api_key + +version = flask.Blueprint(__name__, __name__) + + +@version.route("/sensors/") +@version.route("/sensors/") +@require_api_key +def sensor_values(sensor_id=None) -> t.Tuple[t.Dict[str, t.Any], int, t.Dict[str, str]]: + headers = {"Content-Security-Policy": "default-src 'none'"} + sensors = [] + for sensor in cli.get_sensors(): + if sensor_id and sensor_id != sensor.name: + continue + try: + try: + value = sensor.value() + except DataCollectionError: + human_readable = "Unknown" + json_value = None + else: + json_value = sensor.to_json_compatible(value) + human_readable = sensor.format(value) + sensor_data = { + "id": sensor.name, + "title": sensor.title, + "value": json_value, + "human_readable": human_readable, + } + sensors.append(sensor_data) + except NotImplementedError: + pass + data = {"sensors": sensors} + return data, 200, headers diff --git a/Ch12/apd.sensors-chapter12/src/apd/sensors/wsgi/v21.py b/Ch12/apd.sensors-chapter12/src/apd/sensors/wsgi/v21.py new file mode 100644 index 0000000..16cbb72 --- /dev/null +++ b/Ch12/apd.sensors-chapter12/src/apd/sensors/wsgi/v21.py @@ -0,0 +1,47 @@ +import typing as t + +import flask + +from apd.sensors import cli +from apd.sensors.exceptions import DataCollectionError +from .base import require_api_key + +version = flask.Blueprint(__name__, __name__) + + +@version.route("/sensors/") +@version.route("/sensors/") +@require_api_key +def sensor_values(sensor_id=None) -> t.Tuple[t.Dict[str, t.Any], int, t.Dict[str, str]]: + headers = {"Content-Security-Policy": "default-src 'none'"} + sensors = [] + for sensor in cli.get_sensors(): + if sensor_id and sensor_id != sensor.name: + continue + try: + try: + value = sensor.value() + except DataCollectionError: + human_readable = "Unknown" + json_value = None + else: + json_value = sensor.to_json_compatible(value) + human_readable = sensor.format(value) + sensor_data = { + "id": sensor.name, + "title": sensor.title, + "value": json_value, + "human_readable": human_readable, + } + sensors.append(sensor_data) + except NotImplementedError: + pass + data = {"sensors": sensors} + return data, 200, headers + + +@version.route("/deployment_id") +def deployment_id() -> t.Tuple[t.Dict[str, t.Any], int, t.Dict[str, str]]: + headers = {"Content-Security-Policy": "default-src 'none'"} + data = {"deployment_id": flask.current_app.config["APD_SENSORS_DEPLOYMENT_ID"]} + return data, 200, headers diff --git a/Ch12/apd.sensors-chapter12/src/apd/sensors/wsgi/v30.py b/Ch12/apd.sensors-chapter12/src/apd/sensors/wsgi/v30.py new file mode 100644 index 0000000..549af12 --- /dev/null +++ b/Ch12/apd.sensors-chapter12/src/apd/sensors/wsgi/v30.py @@ -0,0 +1,117 @@ +import datetime +import typing as t + +import flask + +from apd.sensors import cli +from apd.sensors.base import HistoricalSensor +from apd.sensors.exceptions import DataCollectionError + +from .base import require_api_key + +version = flask.Blueprint(__name__, __name__) + + +@version.route("/sensors/") +@version.route("/sensors/") +@require_api_key +def sensor_values(sensor_id=None) -> t.Tuple[t.Dict[str, t.Any], int, t.Dict[str, str]]: + headers = {"Content-Security-Policy": "default-src 'none'"} + sensors = [] + errors = [] + for sensor in cli.get_sensors(): + now = datetime.datetime.now() + if sensor_id and sensor_id != sensor.name: + continue + try: + try: + value = sensor.value() + except DataCollectionError as err: + error = { + "id": sensor.name, + "title": sensor.title, + "collected_at": now.isoformat(), + "error": str(err), + } + errors.append(error) + continue + sensor_data = { + "id": sensor.name, + "title": sensor.title, + "value": sensor.to_json_compatible(value), + "human_readable": sensor.format(value), + "collected_at": now.isoformat(), + } + sensors.append(sensor_data) + except NotImplementedError: + pass + data = {"sensors": sensors, "errors": errors} + return data, 200, headers + + +@version.route("/historical") +@version.route("/historical/") +@version.route("/historical//") +@require_api_key +def historical_values( + start: str = None, end: str = None +) -> t.Tuple[t.Dict[str, t.Any], int, t.Dict[str, str]]: + try: + import dateutil.parser + from apd.sensors.database import sensor_values + from apd.sensors.wsgi import db + except ImportError: + return {"error": "Historical data support is not installed"}, 501, {} + + db_session = db.session + headers = {"Content-Security-Policy": "default-src 'none'"} + + query = db_session.query(sensor_values) + if start: + start_dt = dateutil.parser.parse(start) + query = query.filter(sensor_values.c.collected_at >= start_dt) + else: + start_dt = dateutil.parser.parse("1900-01-01") + if end: + end_dt = dateutil.parser.parse(end) + query = query.filter(sensor_values.c.collected_at <= end_dt) + else: + end_dt = datetime.datetime.now() + + known_sensors = {sensor.name: sensor for sensor in cli.get_sensors()} + sensors = [] + for data in query: + if data.sensor_name not in known_sensors: + continue + sensor = known_sensors[data.sensor_name] + sensor_data = { + "id": sensor.name, + "title": sensor.title, + "value": data.data, + "human_readable": sensor.format(sensor.from_json_compatible(data.data)), + "collected_at": data.collected_at.isoformat(), + } + sensors.append(sensor_data) + for sensor in known_sensors.values(): + if isinstance(sensor, HistoricalSensor): + for date, value in sensor.historical(start_dt, end_dt): + sensor_data = { + "id": sensor.name, + "title": sensor.title, + "value": value, + "human_readable": sensor.format(sensor.from_json_compatible(value)), + "collected_at": date.isoformat(), + } + sensors.append(sensor_data) + data = {"sensors": sensors} + try: + return data, 200, headers + finally: + db_session.close() + + +@version.route("/deployment_id") +def deployment_id() -> t.Tuple[t.Dict[str, t.Any], int, t.Dict[str, str]]: + headers = {"Content-Security-Policy": "default-src 'none'"} + data = {"deployment_id": flask.current_app.config["APD_SENSORS_DEPLOYMENT_ID"]} + return data, 200, headers diff --git a/Ch12/apd.sensors-chapter12/tests/__init__.py b/Ch12/apd.sensors-chapter12/tests/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/Ch12/apd.sensors-chapter12/tests/test_acstatus.py b/Ch12/apd.sensors-chapter12/tests/test_acstatus.py new file mode 100644 index 0000000..4f0e6ed --- /dev/null +++ b/Ch12/apd.sensors-chapter12/tests/test_acstatus.py @@ -0,0 +1,58 @@ +from unittest import mock + +import pytest + +from apd.sensors.sensors import ACStatus +from apd.sensors.exceptions import ( + PersistentSensorFailureError, + IntermittentSensorFailureError, +) + + +@pytest.fixture +def sensor(): + return ACStatus() + + +class TestACStatusFormatter: + @pytest.fixture + def subject(self, sensor): + return sensor.format + + def test_format_True(self, subject): + assert subject(True) == "Connected" + + def test_format_False(self, subject): + assert subject(False) == "Not connected" + + +class TestACStatusValue: + @pytest.fixture + def subject(self, sensor): + return sensor.value + + @pytest.fixture + def sensors_battery(self): + with mock.patch("psutil.sensors_battery") as sensors_battery: + yield sensors_battery + + def test_sensors_battery_called(self, subject, sensors_battery): + sensors_battery.return_value.power_plugged = True + assert subject() is True + assert sensors_battery.call_count == 1 + + def test_sensor_but_battery_unknown(self, subject, sensors_battery): + sensors_battery.return_value.power_plugged = None + with pytest.raises(IntermittentSensorFailureError): + subject() + assert sensors_battery.call_count == 1 + + def test_no_sensor(self, subject, sensors_battery): + sensors_battery.return_value = None + with pytest.raises(PersistentSensorFailureError): + subject() + + def test_str_representation_is_formatted_value(self, sensor, sensors_battery): + sensors_battery.return_value.power_plugged = True + assert str(sensor) == "Connected" + assert sensors_battery.call_count == 1 diff --git a/Ch12/apd.sensors-chapter12/tests/test_api_server.py b/Ch12/apd.sensors-chapter12/tests/test_api_server.py new file mode 100644 index 0000000..8aa4cea --- /dev/null +++ b/Ch12/apd.sensors-chapter12/tests/test_api_server.py @@ -0,0 +1,332 @@ +import datetime +import os +import typing as t +import uuid +from unittest import mock + +import flask +import pytest +from webtest import TestApp + +from apd.sensors.base import HistoricalSensor, JSONSensor +from apd.sensors.sensors import PythonVersion +from apd.sensors.wsgi import set_up_config +from apd.sensors.wsgi import v10 +from apd.sensors.wsgi import v20 +from apd.sensors.wsgi import v21 +from apd.sensors.wsgi import v30 + + +class HistoricalBoolSensor(HistoricalSensor[bool], JSONSensor[bool]): + + title = "Sensor which has past data" + name = "HistoricalBoolSensor" + + def value(self) -> bool: + return True + + def historical( + self, start: datetime.datetime, end: datetime.datetime + ) -> t.Iterable[t.Tuple[datetime.datetime, bool]]: + date = start + while date < end: + yield date, True + date += datetime.timedelta(hours=1) + + @classmethod + def format(cls, value: bool) -> str: + return "Yes" if value else "No" + + +@pytest.fixture(scope="session") +def api_key(): + return uuid.uuid4().hex + + +def test_api_key_is_required_config_option(): + app = mock.MagicMock() + with pytest.raises( + ValueError, match="Missing config variables: APD_SENSORS_API_KEY" + ): + set_up_config({"APD_SENSORS_DEPLOYMENT_ID": ""}, to_configure=app) + + +def test_os_environ_is_default_for_config_values(api_key): + app = mock.MagicMock() + os.environ["APD_SENSORS_API_KEY"] = api_key + os.environ["APD_SENSORS_DEPLOYMENT_ID"] = "8f1b57faa04b430c81decbbeee9e300c" + try: + assert app.config.from_mapping.call_count == 0 + set_up_config(None, to_configure=app) + assert app.config.from_mapping.call_count == 1 + for key, value in os.environ.items(): + # There may be keys in the config not from the environment + assert app.config.from_mapping.call_args[0][0][key] == value + finally: + del os.environ["APD_SENSORS_API_KEY"] + del os.environ["APD_SENSORS_DEPLOYMENT_ID"] + + +class CommonTests: + @pytest.mark.functional + def test_sensor_values_fails_on_missing_api_key(self, api_server): + response = api_server.get("/sensors/", expect_errors=True) + assert response.status_code == 403 + assert response.json["error"] == "Supply API key in X-API-Key header" + + @pytest.mark.functional + def test_sensor_values_require_correct_api_key(self, api_server): + response = api_server.get( + "/sensors/", headers={"X-API-Key": "wrong_key"}, expect_errors=True + ) + assert response.status_code == 403 + assert response.json["error"] == "Supply API key in X-API-Key header" + + +class Testv10API(CommonTests): + @pytest.fixture + def subject(self, api_key): + app = flask.Flask("testapp") + app.register_blueprint(v10.version) + set_up_config( + { + "APD_SENSORS_API_KEY": api_key, + "APD_SENSORS_DEPLOYMENT_ID": "8f1b57faa04b430c81decbbeee9e300c", + }, + to_configure=app, + ) + return app + + @pytest.fixture + def api_server(self, subject): + return TestApp(subject) + + @pytest.mark.functional + def test_sensor_values_returned_as_json(self, api_server, api_key): + value = api_server.get("/sensors/", headers={"X-API-Key": api_key}).json + python_version = PythonVersion().value() + + sensor_names = value.keys() + assert "Python Version" in sensor_names + assert value["Python Version"] == list(python_version) + + @pytest.mark.functional + def test_unserializable_sensor_is_omitted(self, api_server, api_key): + value = api_server.get("/sensors/", headers={"X-API-Key": api_key}).json + sensor_names = value.keys() + assert "Temperature" not in sensor_names + + @pytest.mark.functional + def test_erroring_sensor_shows_None(self, api_server, api_key): + from .test_utils import FailingSensor + + with mock.patch("apd.sensors.cli.get_sensors") as get_sensors: + # Ensure failing sensor is first, to test that subsequent sensors + # are still processed + get_sensors.return_value = [FailingSensor(10), PythonVersion()] + value = api_server.get("/sensors/", headers={"X-API-Key": api_key}).json + assert value["Sensor which fails"] is None + assert "Python Version" in value.keys() + + +class Testv20API(CommonTests): + @pytest.fixture + def subject(self, api_key): + app = flask.Flask("testapp") + app.register_blueprint(v20.version) + set_up_config( + { + "APD_SENSORS_API_KEY": api_key, + "APD_SENSORS_DEPLOYMENT_ID": "8f1b57faa04b430c81decbbeee9e300c", + }, + to_configure=app, + ) + return app + + @pytest.fixture + def api_server(self, subject): + return TestApp(subject) + + @pytest.mark.functional + def test_sensor_values_returned_as_json(self, api_server, api_key): + value = api_server.get("/sensors/", headers={"X-API-Key": api_key}).json + sensors = value["sensors"] + + python_version = [ + sensor for sensor in sensors if sensor["id"] == "PythonVersion" + ][0] + assert python_version["title"] == "Python Version" + assert python_version["value"] == list(PythonVersion().value()) + + @pytest.mark.functional + def test_erroring_sensor_shows_None(self, api_server, api_key): + from .test_utils import FailingSensor + + with mock.patch("apd.sensors.cli.get_sensors") as get_sensors: + # Ensure failing sensor is first, to test that subsequent sensors + # are still processed + get_sensors.return_value = [FailingSensor(10), PythonVersion()] + value = api_server.get("/sensors/", headers={"X-API-Key": api_key}).json + sensors = value["sensors"] + + failing = [sensor for sensor in sensors if sensor["id"] == "FailingSensor"][0] + python_version = [ + sensor for sensor in sensors if sensor["id"] == "PythonVersion" + ][0] + + assert failing["title"] == "Sensor which fails" + assert failing["value"] is None + assert python_version["title"] == "Python Version" + + +class Testv21API(Testv20API): + @pytest.fixture + def subject(self, api_key): + app = flask.Flask("testapp") + app.register_blueprint(v21.version) + set_up_config( + { + "APD_SENSORS_API_KEY": api_key, + "APD_SENSORS_DEPLOYMENT_ID": "8f1b57faa04b430c81decbbeee9e300c", + }, + to_configure=app, + ) + return app + + @pytest.mark.functional + def test_deployment_id(self, api_server, api_key): + value = api_server.get("/deployment_id", headers={"X-API-Key": api_key}).json + assert value == {"deployment_id": "8f1b57faa04b430c81decbbeee9e300c"} + + @pytest.mark.functional + def test_erroring_sensor_shows_None(self, api_server, api_key): + from .test_utils import FailingSensor + + with mock.patch("apd.sensors.cli.get_sensors") as get_sensors: + # Ensure failing sensor is first, to test that subsequent sensors + # are still processed + get_sensors.return_value = [FailingSensor(10), PythonVersion()] + value = api_server.get("/sensors/", headers={"X-API-Key": api_key}).json + sensors = value["sensors"] + + failing = [sensor for sensor in sensors if sensor["id"] == "FailingSensor"][0] + python_version = [ + sensor for sensor in sensors if sensor["id"] == "PythonVersion" + ][0] + + assert failing["title"] == "Sensor which fails" + assert failing["value"] is None + assert python_version["title"] == "Python Version" + + +class Testv30API(CommonTests): + @pytest.fixture + def api_server(self, subject): + return TestApp(subject) + + @pytest.fixture + def subject(self, api_key): + app = flask.Flask("testapp") + app.register_blueprint(v30.version) + set_up_config( + { + "APD_SENSORS_API_KEY": api_key, + "APD_SENSORS_DEPLOYMENT_ID": "8f1b57faa04b430c81decbbeee9e300c", + "APD_SENSORS_DB_URI": "sqlite://", + }, + to_configure=app, + ) + return app + + @pytest.fixture + def db(self, subject): + from flask_sqlalchemy import SQLAlchemy + from apd.sensors.database import metadata + from apd.sensors import wsgi + + db = SQLAlchemy(subject, metadata=metadata) + db.create_all() + + wsgi.db = db + yield db + wsgi.db = None + + @pytest.fixture + def store_sensor_data(self): + from apd.sensors.database import store_sensor_data + + return store_sensor_data + + @pytest.mark.functional + def test_historical_with_no_data(self, api_key, api_server, db): + value = api_server.get("/historical", headers={"X-API-Key": api_key}).json + assert value == {"sensors": []} + + @pytest.mark.functional + def test_historical_with_data(self, api_key, api_server, db, store_sensor_data): + store_sensor_data(PythonVersion, [3, 9, 0, "final", 1], db.session) + value = api_server.get("/historical", headers={"X-API-Key": api_key}).json + assert len(value["sensors"]) == 1 + assert value["sensors"][0]["human_readable"] == "3.9" + + def test_historical_sensor(self, api_key, api_server, db): + with mock.patch("apd.sensors.cli.get_sensors") as get_sensors: + # Ensure failing sensor is first, to test that subsequent sensors + # are still processed + get_sensors.return_value = [HistoricalBoolSensor()] + value = api_server.get( + "/historical/2020-01-01/2020-01-02", headers={"X-API-Key": api_key} + ).json + assert len(value["sensors"]) == 24 + assert value["sensors"][0] == { + "collected_at": "2020-01-01T00:00:00", + "human_readable": "Yes", + "id": "HistoricalBoolSensor", + "title": "Sensor which has past data", + "value": True, + } + + @pytest.mark.functional + def test_sensor_values_returned_as_json(self, api_server, api_key): + value = api_server.get("/sensors/", headers={"X-API-Key": api_key}).json + sensors = value["sensors"] + + python_version = [ + sensor for sensor in sensors if sensor["id"] == "PythonVersion" + ][0] + assert python_version["title"] == "Python Version" + assert python_version["value"] == list(PythonVersion().value()) + + @pytest.mark.functional + def test_erroring_sensor_excluded_but_reported(self, api_server, api_key): + from .test_utils import FailingSensor + + with mock.patch("apd.sensors.cli.get_sensors") as get_sensors: + # Ensure failing sensor is first, to test that subsequent sensors + # are still processed + get_sensors.return_value = [FailingSensor(2), PythonVersion()] + value = api_server.get("/sensors/", headers={"X-API-Key": api_key}).json + second_attempt_value = api_server.get( + "/sensors/", headers={"X-API-Key": api_key} + ).json + + sensors = value["sensors"] + failing = [sensor for sensor in sensors if sensor["id"] == "FailingSensor"] + assert len(failing) == 0 + errors = value["errors"] + failing = [sensor for sensor in errors if sensor["id"] == "FailingSensor"] + assert len(failing) == 1 + + python_version = [ + sensor for sensor in sensors if sensor["id"] == "PythonVersion" + ] + assert len(python_version) == 1 + + sensors = second_attempt_value["sensors"] + assert second_attempt_value["errors"] == [] + failing = [sensor for sensor in sensors if sensor["id"] == "FailingSensor"] + assert len(failing) == 1 + python_version = [ + sensor for sensor in sensors if sensor["id"] == "PythonVersion" + ] + assert len(python_version) == 1 diff --git a/Ch12/apd.sensors-chapter12/tests/test_cpuusage.py b/Ch12/apd.sensors-chapter12/tests/test_cpuusage.py new file mode 100644 index 0000000..445f4e5 --- /dev/null +++ b/Ch12/apd.sensors-chapter12/tests/test_cpuusage.py @@ -0,0 +1,45 @@ +from unittest import mock + +import pytest + +from apd.sensors.sensors import CPULoad + + +@pytest.fixture +def sensor(): + return CPULoad() + + +class TestCPULoadFormatter: + @pytest.fixture + def subject(self, sensor): + return sensor.format + + def test_format_simple_percentage(self, subject): + assert subject(0.05) == "5.0%" + + def test_format_multiple_decimal_places(self, subject): + assert subject(0.031415926) == "3.1%" + + def test_format_multiple_decimal_places_int(self, subject) -> None: + assert subject(1) == "100.0%" + + +class TestCPULoadValue: + @pytest.fixture + def subject(self, sensor): + return sensor.value + + @pytest.fixture + def cpuload(self): + with mock.patch("psutil.cpu_percent") as cpu_percent: + cpu_percent.return_value = 50.1 + yield cpu_percent + + def test_cpu_percent_called_With_interval(self, subject, cpuload): + assert subject() == 0.501 + assert cpuload.call_count == 1 + assert tuple(cpuload.call_args) == ((), {"interval": 3}) + + def test_str_representation_is_formatted_value(self, sensor, cpuload): + assert str(sensor) == "50.1%" diff --git a/Ch12/apd.sensors-chapter12/tests/test_dht.py b/Ch12/apd.sensors-chapter12/tests/test_dht.py new file mode 100644 index 0000000..85c3e34 --- /dev/null +++ b/Ch12/apd.sensors-chapter12/tests/test_dht.py @@ -0,0 +1,61 @@ +import pytest + +from apd.sensors.sensors import Temperature, RelativeHumidity, ureg + + +@pytest.fixture +def temperature_sensor(): + return Temperature() + + +@pytest.fixture +def humidity_sensor(): + return RelativeHumidity() + + +class TestTemperatureFormatter: + @pytest.fixture + def subject(self, temperature_sensor): + return temperature_sensor.format + + @staticmethod + def to_degc(magnitude): + return ureg.Quantity(magnitude, "degree_Celsius") + + def test_format_21c(self, subject): + assert subject(self.to_degc(21.0)) == "21.0 °C (69.8 °F)" + + def test_format_negative(self, subject): + assert subject(self.to_degc(-32.0)) == "-32.0 °C (-25.6 °F)" + + +class TestTemperatureSerializer: + @pytest.fixture + def serialize(self, temperature_sensor): + return temperature_sensor.to_json_compatible + + @pytest.fixture + def deserialize(self, temperature_sensor): + return temperature_sensor.from_json_compatible + + @staticmethod + def to_degc(magnitude): + return ureg.Quantity(magnitude, "degree_Celsius") + + def test_serialize_21c(self, serialize): + assert serialize(self.to_degc(21.0)) == {"magnitude": 21.0, "unit": "degree_Celsius"} + + def test_serialize_negative(self, serialize): + assert serialize(self.to_degc(-32.3)) == {"magnitude": -32.3, "unit": "degree_Celsius"} + + def test_deserialize_21c(self, deserialize): + assert deserialize({"magnitude": 21.0, "unit": "degree_Celsius"}) == self.to_degc(21.0) + + +class TestHumidityFormatter: + @pytest.fixture + def subject(self, humidity_sensor): + return humidity_sensor.format + + def test_format_percentage(self, subject): + assert subject(0.035) == "3.5%" diff --git a/Ch12/apd.sensors-chapter12/tests/test_ipaddresses.py b/Ch12/apd.sensors-chapter12/tests/test_ipaddresses.py new file mode 100644 index 0000000..bd61d85 --- /dev/null +++ b/Ch12/apd.sensors-chapter12/tests/test_ipaddresses.py @@ -0,0 +1,59 @@ +import socket +from unittest import mock + +import pytest + +from apd.sensors.sensors import IPAddresses + + +@pytest.fixture +def sensor(): + return IPAddresses() + + +class TestIPAddressFormatter: + @pytest.fixture + def subject(self, sensor): + return sensor.format + + def test_format_single_ipv4(self, subject): + ips = [("AF_INET", "192.0.2.1")] + assert subject(ips) == "192.0.2.1 (IPv4)" + + def test_format_single_ipv6(self, subject): + ips = [("AF_INET6", "2001:DB8::1")] + assert subject(ips) == "2001:DB8::1 (IPv6)" + + def test_format_mixed_list(self, subject): + ips = [("AF_INET", "192.0.2.1"), ("AF_INET6", "2001:DB8::1")] + assert subject(ips) == "192.0.2.1 (IPv4)\n2001:DB8::1 (IPv6)" + + def test_unusual_protocols_are_marked_as_unknown(self, subject): + ips = [("AF_IRDA", "ffff")] + assert subject(ips) == "ffff (Unknown)" + + +class TestIPAddressesValue: + @pytest.fixture + def subject(self, sensor): + return sensor.value + + @pytest.fixture + def gethostname(self): + with mock.patch("socket.gethostname") as gethostname: + gethostname.return_value = "localhost" + yield gethostname + + @pytest.fixture + def getaddrinfo(self, gethostname): + with mock.patch("socket.getaddrinfo") as getaddrinfo: + yield getaddrinfo + + def test_getaddrinfo_used_for_value_collection(self, getaddrinfo, subject): + getaddrinfo.return_value = [(socket.AF_INET, 0, 0, "", ("192.0.2.1", 0))] + assert subject() == [("AF_INET", "192.0.2.1")] + assert getaddrinfo.call_count == 1 + + def test_str_representation_is_formatted_value(self, getaddrinfo, sensor): + getaddrinfo.return_value = [(socket.AF_INET6, 0, 0, "", ("2001:DB8::1", 0))] + assert str(sensor) == "2001:DB8::1 (IPv6)" diff --git a/Ch12/apd.sensors-chapter12/tests/test_pythonversion.py b/Ch12/apd.sensors-chapter12/tests/test_pythonversion.py new file mode 100644 index 0000000..43ad806 --- /dev/null +++ b/Ch12/apd.sensors-chapter12/tests/test_pythonversion.py @@ -0,0 +1,61 @@ +from collections import namedtuple +from unittest import mock + +import pytest + +from apd.sensors.sensors import PythonVersion + + +@pytest.fixture +def version(): + return namedtuple( + "sys_versioninfo", ("major", "minor", "micro", "releaselevel", "serial") + ) + + +@pytest.fixture +def sensor(): + return PythonVersion() + + +class TestPythonVersionFormatter: + @pytest.fixture + def subject(self, sensor): + return sensor.format + + def test_format_py38(self, subject, version): + py38 = version(3, 8, 0, "final", 0) + assert subject(py38) == "3.8" + + def test_format_large_version(self, subject, version): + large = version(255, 128, 0, "final", 0) + assert subject(large) == "255.128" + + def test_alpha_of_minor_is_marked(self, subject, version): + py39 = version(3, 9, 0, "alpha", 1) + assert subject(py39) == "3.9.0a1" + + def test_alpha_of_micro_is_unmarked(self, subject, version): + py39 = version(3, 9, 1, "alpha", 1) + assert subject(py39) == "3.9" + + +class TestPythonVersionValue: + @pytest.fixture + def subject(self, sensor): + return sensor.value + + @pytest.fixture + def python_version(self): + import sys + + return sys.version_info + + def test_data_value_is_sys_versioninfo(self, python_version, subject): + assert subject() == python_version + + +class TestPythonVersionSensor: + def test_str_representation_is_formatted_value(self, sensor, version): + with mock.patch("sys.version_info", new=version(3, 4, 1, "final", 1)): + assert str(sensor) == "3.4" diff --git a/Ch12/apd.sensors-chapter12/tests/test_ramusage.py b/Ch12/apd.sensors-chapter12/tests/test_ramusage.py new file mode 100644 index 0000000..197ad06 --- /dev/null +++ b/Ch12/apd.sensors-chapter12/tests/test_ramusage.py @@ -0,0 +1,47 @@ +from unittest import mock + +import pytest + +from apd.sensors.sensors import RAMAvailable + + +@pytest.fixture +def sensor(): + return RAMAvailable() + + +class TestRAMAvailableFormatter: + @pytest.fixture + def subject(self, sensor): + return sensor.format + + def test_format_bytes(self, subject): + assert subject(15) == "15.0 B" + + def test_format_kibibytes(self, subject): + assert subject(15000) == "14.6 KiB" + + def test_format_mibibytes(self, subject): + assert subject(15000000) == "14.3 MiB" + + def test_format_gibibytes(self, subject): + assert subject(15000000000) == "14.0 GiB" + + +class TestRAMAvailableValue: + @pytest.fixture + def subject(self, sensor): + return sensor.value + + @pytest.fixture + def virtual_memory(self): + with mock.patch("psutil.virtual_memory") as virtual_memory: + virtual_memory.return_value.available = 1024 + yield virtual_memory + + def test_virtual_memory_called(self, subject, virtual_memory): + assert subject() == 1024 + assert virtual_memory.call_count == 1 + + def test_str_representation_is_formatted_value(self, sensor, virtual_memory): + assert str(sensor) == "1.0 KiB" diff --git a/Ch12/apd.sensors-chapter12/tests/test_sensors.py b/Ch12/apd.sensors-chapter12/tests/test_sensors.py new file mode 100644 index 0000000..0f74084 --- /dev/null +++ b/Ch12/apd.sensors-chapter12/tests/test_sensors.py @@ -0,0 +1,126 @@ +import json +from unittest import mock + +from click.testing import CliRunner + +import pytest + +import apd.sensors.cli +from apd.sensors.exceptions import UserFacingCLIError +import apd.sensors.sensors + + +def test_sensors(): + assert hasattr(apd.sensors.sensors, "PythonVersion") + + +class TestCLI: + @pytest.mark.functional + def test_first_sensor_is_first_two_lines_of_cli_output(self): + runner = CliRunner() + with mock.patch("apd.sensors.cli.get_sensors") as get_sensors: + get_sensors.return_value = [apd.sensors.sensors.PythonVersion()] + result = runner.invoke(apd.sensors.cli.show_sensors) + python_version = str(apd.sensors.sensors.PythonVersion()) + assert ["Python Version", python_version] == result.stdout.split("\n")[:2] + + def test_failing_sensor_shows_error(self): + from .test_utils import FailingSensor + + runner = CliRunner() + with mock.patch("apd.sensors.cli.get_sensors") as get_sensors: + get_sensors.return_value = [ + FailingSensor(10), + apd.sensors.sensors.PythonVersion(), + ] + result = runner.invoke(apd.sensors.cli.show_sensors) + assert ["Sensor which fails", "Failing 9 more times"] == result.stdout.split( + "\n" + )[:2] + assert "Python Version" in result.stdout + + def test_mocked_failing_sensor_shows_error(self): + from apd.sensors.base import Sensor + from apd.sensors.exceptions import IntermittentSensorFailureError + + FailingSensor = mock.MagicMock(spec=Sensor) + FailingSensor.title = "Sensor which fails" + FailingSensor.name = "FailingSensor" + FailingSensor.value.side_effect = ( + FailingSensor.__str__.side_effect + ) = IntermittentSensorFailureError("Failing sensor") + + runner = CliRunner() + with mock.patch("apd.sensors.cli.get_sensors") as get_sensors: + get_sensors.return_value = [ + FailingSensor, + apd.sensors.sensors.PythonVersion(), + ] + result = runner.invoke(apd.sensors.cli.show_sensors) + assert ["Sensor which fails", "Failing sensor"] == result.stdout.split("\n")[:2] + assert "Python Version" in result.stdout + + +class TestSensorFromPath: + @pytest.fixture + def subject(self): + return apd.sensors.cli.get_sensor_by_path + + def test_get_sensor_by_path(self, subject): + assert isinstance( + subject("apd.sensors.sensors:PythonVersion"), + apd.sensors.sensors.PythonVersion, + ) + + def test_invalid_format(self, subject): + with pytest.raises(UserFacingCLIError, match="dotted.path.to.module:ClassName"): + subject("apd.sensors.sensors.PythonVersion") + + def test_invalid_module(self, subject): + with pytest.raises(UserFacingCLIError, match="Could not import module"): + subject("apd.nonsense.sensor:FakeSensor") + + def test_missing_sensor(self, subject): + with pytest.raises(UserFacingCLIError, match="Could not find attribute"): + subject("apd.sensors.sensors:FakeSensor") + + def test_invalid_sensor(self, subject): + with pytest.raises(UserFacingCLIError, match="is not recognised as a Sensor"): + subject("apd.sensors.sensors:Sensor") + + +@pytest.mark.functional +def test_develop_option_finds_sensor_by_path(): + runner = CliRunner() + result = runner.invoke( + apd.sensors.cli.show_sensors, ["--develop", "apd.sensors.sensors:PythonVersion"] + ) + python_version = str(apd.sensors.sensors.PythonVersion()) + assert ["Python Version", python_version, "", ""] == result.stdout.split("\n") + + +class TestDefaultSerializer: + @pytest.fixture + def python_version(self): + return apd.sensors.sensors.PythonVersion() + + @pytest.fixture + def serialize(self, python_version): + return python_version.to_json_compatible + + @pytest.fixture + def deserialize(self, python_version): + return python_version.from_json_compatible + + def test_serialize_deserialize_is_symmetric( + self, python_version, serialize, deserialize + ): + value = python_version.value() + serialized = serialize(value) + json_version = json.dumps(serialized) + assert ( + json_version == f"[{value.major}, {value.minor}, {value.micro}," + f' "{value.releaselevel}", {value.serial}]' + ) + deserialized = deserialize(serialized) + assert deserialized == value diff --git a/Ch12/apd.sensors-chapter12/tests/test_utils.py b/Ch12/apd.sensors-chapter12/tests/test_utils.py new file mode 100644 index 0000000..34a82f8 --- /dev/null +++ b/Ch12/apd.sensors-chapter12/tests/test_utils.py @@ -0,0 +1,57 @@ +import typing as t + +import pytest + +from apd.sensors.base import JSONSensor +from apd.sensors.exceptions import ( + IntermittentSensorFailureError, + PersistentSensorFailureError, +) +from apd.sensors.utils import get_value_with_retries + + +class FailingSensor(JSONSensor[bool]): + + title = "Sensor which fails" + name = "FailingSensor" + + def __init__( + self, + n: int = 3, + exception_type: t.Type[Exception] = IntermittentSensorFailureError, + ): + self.n = n + self.exception_type = exception_type + + def value(self) -> bool: + self.n -= 1 + if self.n: + raise self.exception_type(f"Failing {self.n} more times") + else: + return True + + @classmethod + def format(cls, value: bool) -> str: + return "Yes" if value else "No" + + +class TestRetry: + def test_default_retries(self): + sensor = FailingSensor(3) + value = get_value_with_retries(sensor) + assert value is True + + def test_n_retries(self): + sensor = FailingSensor(5) + value = get_value_with_retries(sensor, retries=5) + assert value is True + + def test_insufficient_retries(self): + sensor = FailingSensor(5) + with pytest.raises(IntermittentSensorFailureError): + get_value_with_retries(sensor, retries=4) + + def test_permanent_failures_not_retried(self): + sensor = FailingSensor(3, PersistentSensorFailureError) + with pytest.raises(PersistentSensorFailureError): + get_value_with_retries(sensor) diff --git a/Ch12/listing12-01-clean_passthrough.py b/Ch12/listing12-01-clean_passthrough.py new file mode 100644 index 0000000..4cb58a5 --- /dev/null +++ b/Ch12/listing12-01-clean_passthrough.py @@ -0,0 +1,9 @@ +async def clean_passthrough( + datapoints: t.AsyncIterator[DataPoint], +) -> CLEANED_DT_FLOAT: + async for datapoint in datapoints: + if datapoint.data is None: + continue + else: + yield datapoint.collected_at, datapoint.data + diff --git a/Ch12/listing12-02-sum_ints.py b/Ch12/listing12-02-sum_ints.py new file mode 100644 index 0000000..317ff88 --- /dev/null +++ b/Ch12/listing12-02-sum_ints.py @@ -0,0 +1,18 @@ +import typing as t + +def sum_ints(source: t.Iterable[int]) -> t.Iterator[int]: + """Yields a running total from the underlying iterator""" + total = 0 + for num in source: + total += num + yield total + +def numbers() -> t.Iterator[int]: + yield 1 + yield 1 + yield 1 + +def test(): + sums = sum_ints(numbers()) + assert [a for a in sums] == [1, 2, 3] + diff --git a/Ch12/listing12-03-process_own_output.py b/Ch12/listing12-03-process_own_output.py new file mode 100644 index 0000000..438de25 --- /dev/null +++ b/Ch12/listing12-03-process_own_output.py @@ -0,0 +1,17 @@ +import itertools +import typing as t + +def sum_ints(start: int) -> t.Iterator[int]: + """Yields a running total with a given start value""" + total = start + while True: + yield total + total += total + +def test(): + sums = sum_ints(1) + # Limit an infinite iterator to the first 3 items + # itertools.islice(iterable, [start,] stop, [step]) + sums = itertools.islice(sums, 3) + assert [a for a in sums] == [1, 2, 4] + diff --git a/Ch12/listing12-04-wrapper_generator.py b/Ch12/listing12-04-wrapper_generator.py new file mode 100644 index 0000000..f53b5cc --- /dev/null +++ b/Ch12/listing12-04-wrapper_generator.py @@ -0,0 +1,36 @@ +import itertools +import typing as t + +def sum_ints(source: t.Iterable[int]) -> t.Iterator[int]: + """Yields a running total from the underlying iterator""" + total = 0 + for num in source: + total += num + yield total + +def get_wrap_feedback_pair(initial=None): # get_w_f_p(...) in the above diagram + """Return a pair of external and internal wrap functions""" + shared_state = initial + # Note, feedback() and wrap(...) functions assume that + # they are always in sync + def feedback(): + while True: + """Yield the last value of the wrapped iterator""" + yield shared_state + def wrap(wrapped): + """Iterate over an iterable and stash each value""" + nonlocal shared_state + for item in wrapped: + shared_state = item + yield item + return feedback, wrap + +def test(): + feedback, wrap = get_wrap_feedback_pair(1) + # Sum the iterable (1, ...) where ... is the results + # of that iterable, stored with the wrap method + sums = wrap(sum_ints(feedback())) + # Limit to 3 items + sums = itertools.islice(sums, 3) + assert [a for a in sums] == [1, 2, 4] + diff --git a/Ch12/listing12-05-enhanced_generator.py b/Ch12/listing12-05-enhanced_generator.py new file mode 100644 index 0000000..bede34f --- /dev/null +++ b/Ch12/listing12-05-enhanced_generator.py @@ -0,0 +1,25 @@ +import typing as t + +def sum_ints() -> t.Generator[int, int, None]: + """Yields a running total from the underlying iterator""" + total = 0 + num = yield total + while True: + total += num + num = yield total + +def test(): + # Sum the iterable (1, ...) where ... is the results + # of that iterable, stored with the wrap method + sums = sum_ints() + next(sums) # We can only send to yield lines, so advance to the first + last = 1 + result = [] + for n in range(3): + last = sums.send(last) + result.append(last) + assert result == [1, 2, 4] + + +test() + diff --git a/Ch12/listing12-06-mean_finder.py b/Ch12/listing12-06-mean_finder.py new file mode 100644 index 0000000..5e344b2 --- /dev/null +++ b/Ch12/listing12-06-mean_finder.py @@ -0,0 +1,29 @@ +class MeanFinder: + def __init__(self): + self.running_total = 0 + self.num_items = 0 + + def add_item(self, num: float): + self.running_total += num + self.num_items += 1 + + @property + def mean(self): + return self.running_total / self.num_items + +def test(): + # Recursive mean from initial data + mean = MeanFinder() + to_add = 1 + for n in range(3): + mean.add_item(to_add) + to_add = mean.mean + assert mean.mean == 1.0 + + # Mean of a concrete data list + mean = MeanFinder() + for to_add in [1, 2, 3]: + mean.add_item(to_add) + assert mean.mean == 2.0 + + diff --git a/Ch12/listing12-07-wrap_enhanced_generator.py b/Ch12/listing12-07-wrap_enhanced_generator.py new file mode 100644 index 0000000..5327e96 --- /dev/null +++ b/Ch12/listing12-07-wrap_enhanced_generator.py @@ -0,0 +1,50 @@ +import typing as t + + +input_type = t.TypeVar("input_type") +output_type = t.TypeVar("output_type") + + +def wrap_enhanced_generator( + input_generator: t.Callable[[], t.Generator[output_type, input_type, None]] +) -> t.Callable[[t.Iterable[input_type]], t.Iterator[output_type]]: + underlying = input_generator() + next(underlying) # Advance the underlying generator to the first yield + + def inner(data: t.Iterable[input_type]) -> t.Iterator[output_type]: + for item in data: + yield underlying.send(item) + + return inner + + +def sum_ints() -> t.Generator[int, int, None]: + """Yields a running total from the underlying iterator""" + total = 0 + num = yield total + while True: + total += num + num = yield total + +def numbers() -> t.Iterator[int]: + yield 1 + yield 1 + yield 1 + + +def test() -> None: + # Start with 1, feed output back in, limit to 3 items + recursive_sum = sum_ints() + next(recursive_sum) + result = [] + last = 1 + for i in range(3): + last = recursive_sum.send(last) + result.append(last) + assert result == [1, 2, 4] + + # Add 3 items from a standard iterable + simple_sum = wrap_enhanced_generator(sum_ints) + result_iter = simple_sum(numbers()) + assert [a for a in result_iter] == [1, 2, 3] + diff --git a/Ch12/listing12-08-shared_state_by_return.py b/Ch12/listing12-08-shared_state_by_return.py new file mode 100644 index 0000000..b16850b --- /dev/null +++ b/Ch12/listing12-08-shared_state_by_return.py @@ -0,0 +1,33 @@ +import typing as t + + +def mean_ints_split_initial() -> t.Tuple[float, int]: + return 0.0, 0 + + +def mean_ints_split( + to_add: float, current_mean: float, num_items: int +) -> t.Tuple[float, int]: + running_total = current_mean * num_items + running_total += to_add + num_items += 1 + current_mean = running_total / num_items + return current_mean, num_items + + +def test(): + # Recursive mean from initial data + to_add, current_mean, num_items = mean_ints_split_initial() + for n in range(3): + current_mean, num_items = mean_ints_split(to_add, current_mean, num_items) + to_add = current_mean + assert current_mean == 1.0 + assert num_items == 3 + + # Mean of concrete data list + current_mean = num_items = 0 + for to_add in [1, 2, 3]: + current_mean, num_items = mean_ints_split(to_add, current_mean, num_items) + assert current_mean == 2.0 + assert num_items == 3 + diff --git a/Ch12/listing12-09-mean_with_enhanced.py b/Ch12/listing12-09-mean_with_enhanced.py new file mode 100644 index 0000000..15fde89 --- /dev/null +++ b/Ch12/listing12-09-mean_with_enhanced.py @@ -0,0 +1,30 @@ +import typing as t + + +def mean_ints() -> t.Generator[t.Optional[float], float, None]: + running_total = 0.0 + num_items = 0 + to_add = yield None + while True: + running_total += to_add + num_items += 1 + to_add = yield running_total / num_items + +def test(): + # Recursive mean from initial data + mean = mean_ints() + next(mean) + to_add = 1 + for n in range(3): + current_mean = mean.send(to_add) + to_add = current_mean + assert current_mean == 1.0 + + # Mean of a concrete data list + # wrap_enhanced_generator would also work here + mean = mean_ints() + next(mean) + for to_add in [1, 2, 3]: + current_mean = mean.send(to_add) + assert current_mean == 2.0 + diff --git a/Ch12/listing12-10-coroutine_and_queue.py b/Ch12/listing12-10-coroutine_and_queue.py new file mode 100644 index 0000000..8ea91a2 --- /dev/null +++ b/Ch12/listing12-10-coroutine_and_queue.py @@ -0,0 +1,52 @@ +import asyncio +import itertools +import typing as t + +async def sum_ints(data: asyncio.Queue) -> t.AsyncIterator[int]: + """Yields a running total a queue, until a None is found""" + total = 0 + while True: + num = await data.get() + if num is None: + data.task_done() + break + total += num + data.task_done() + yield total + + +def numbers() -> t.Iterator[int]: + yield 1 + yield 1 + yield 1 + + +async def test(): + # Start with 1, feed output back in, limit to 3 items + data = asyncio.Queue() + sums = sum_ints(data) + + # Send the initial value + await data.put(1) + result = [] + async for last in sums: + if len(result) == 3: + # Stop the summer at 3 items + await data.put(None) + else: + # Send the last value retrieved back + await data.put(last) + result.append(last) + assert result == [1, 2, 4] + + + # Add 3 items from a standard iterable + data = asyncio.Queue() + sums = sum_ints(data) + + for number in numbers(): + await data.put(number) + await data.put(None) + result = [value async for value in sums] + assert result == [1, 2, 3] + diff --git a/Ch12/listing12-11-dataprocessor.py b/Ch12/listing12-11-dataprocessor.py new file mode 100644 index 0000000..3074098 --- /dev/null +++ b/Ch12/listing12-11-dataprocessor.py @@ -0,0 +1,44 @@ +@dataclasses.dataclass(unsafe_hash=True) +class DataProcessor: + name: str + action: Action + trigger: Trigger[t.Any] + + def __post_init__(self): + self._input: t.Optional[asyncio.Queue[DataPoint]] = None + self._sub_tasks: t.Set = set() + + async def start(self) -> None: + self._input = asyncio.Queue() + self._task = asyncio.create_task(self.process(), name=f"{self.name}_process") + await asyncio.gather(self.action.start(), self.trigger.start()) + + @property + def input(self) -> asyncio.Queue[DataPoint]: + if self._input is None: + raise RuntimeError(f"{self}.start() was not awaited") + if self._task.done(): + raise RuntimeError("Processing has stopped") from self._task.exception() + return self._input + + async def idle(self) -> None: + await self.input.join() + + async def end(self) -> None: + self._task.cancel() + + async def push(self, obj: DataPoint) -> None: + return await self.input.put(obj) + + async def process(self) -> None: + while True: + data = await self.input.get() + try: + processed = await self.trigger.handle(data) + except ValueError: + continue + else: + action_taken = await self.action.handle(processed) + finally: + self.input.task_done() + diff --git a/Ch12/listing12-12-trigger_and_action.py b/Ch12/listing12-12-trigger_and_action.py new file mode 100644 index 0000000..bb483f6 --- /dev/null +++ b/Ch12/listing12-12-trigger_and_action.py @@ -0,0 +1,60 @@ +import typing as t + +from ..typing import T_value +from ..database import DataPoint +from ..exceptions import NoDataForTrigger + + +class Trigger(t.Generic[T_value]): + name: str + + async def start(self) -> None: + """ Coroutine to do any initial setup """ + return + + async def match(self, datapoint: DataPoint) -> bool: + """ Return True if the datapoint is of interest to this + trigger. + This is an optional method, called by the default implementation + of handle(...).""" + raise NotImplementedError + + async def extract(self, datapoint: DataPoint) -> T_value: + """ Return the value that this datapoint implies for this trigger, + or raise NoDataForTrigger if no value is appropriate. + Can also raise IncompatibleTriggerError if the value is not readable. + + This is an optional method, called by the default implementation + of handle(...). + """ + raise NotImplementedError + + async def handle(self, datapoint: DataPoint) -> t.Optional[DataPoint]: + """Given a data point, optionally return a datapoint that + represents the value of this trigger. Will delegate to the + match(...) and extract(...) functions.""" + if not await self.match(datapoint): + # This data point isn't relevant + return None + + try: + value = await self.extract(datapoint) + except NoDataForTrigger: + # There was no value for this point + return None + + return DataPoint( + sensor_name=self.name, + data=value, + deployment_id=datapoint.deployment_id, + collected_at=datapoint.collected_at, + ) + + +class Action: + async def start(self) -> None: + return + + async def handle(self, datapoint: DataPoint): + raise NotImplementedError + diff --git a/Ch12/listing12-13-valuethreshold.py b/Ch12/listing12-13-valuethreshold.py new file mode 100644 index 0000000..3ac364d --- /dev/null +++ b/Ch12/listing12-13-valuethreshold.py @@ -0,0 +1,34 @@ +import dataclasses +import typing as t +import uuid + +from ..database import DataPoint +from ..exceptions import IncompatibleTriggerError +from .base import Trigger + +@dataclasses.dataclass(frozen=True) +class ValueThresholdTrigger(Trigger[bool]): + name: str + threshold: float + comparator: t.Callable[[float, float], bool] + sensor_name: str + deployment_id: t.Optional[uuid.UUID] = dataclasses.field(default=None) + + async def match(self, datapoint: DataPoint) -> bool: + if datapoint.sensor_name != self.sensor_name: + return False + elif self.deployment_id and datapoint.deployment_id != self.deployment_id: + return False + return True + + async def extract(self, datapoint: DataPoint) -> bool: + if datapoint.data is None: + raise IncompatibleTriggerError("Datapoint does not contain data") + elif isinstance(datapoint.data, float): + value = datapoint.data + elif isinstance(datapoint.data, dict) and "magnitude" in datapoint.data: + value = datapoint.data["magnitude"] + else: + raise IncompatibleTriggerError("Unrecognised data format") + return self.comparator(value, self.threshold) # type: ignore + diff --git a/Ch12/listing12-14-webhook.py b/Ch12/listing12-14-webhook.py new file mode 100644 index 0000000..16345f3 --- /dev/null +++ b/Ch12/listing12-14-webhook.py @@ -0,0 +1,34 @@ +import dataclasses +import logging + +import aiohttp + +from apd.aggregation.actions.base import Action +from apd.aggregation.database import DataPoint + +logger = logging.getLogger(__name__) + + +@dataclasses.dataclass +class WebhookAction(Action): + """An action that runs a webhook""" + uri: str + + async def start(self) -> None: + return + + async def handle(self, datapoint: DataPoint) -> bool: + async with aiohttp.ClientSession() as http: + async with http.post( + self.uri, + json={ + "value1": datapoint.sensor_name, + "value2": str(datapoint.data), + "value3": datapoint.deployment_id.hex, + }, + ) as request: + logger.info( + f"Made webhook request for {datapoint} with status {request.status}" + ) + return request.status == 200 + diff --git a/Ch12/listing12-15-loggingaction.py b/Ch12/listing12-15-loggingaction.py new file mode 100644 index 0000000..4021ae6 --- /dev/null +++ b/Ch12/listing12-15-loggingaction.py @@ -0,0 +1,19 @@ +import dataclasses +import logging + +from apd.aggregation.actions.base import Action +from apd.aggregation.database import DataPoint + +logger = logging.getLogger(__name__) + + +class LoggingAction(Action): + """An action that stores any generated data points back to the DB""" + + async def start(self) -> None: + return + + async def handle(self, datapoint: DataPoint) -> bool: + logger.warn(datapoint) + return True + diff --git a/Ch12/listing12-16-get_data_repeatedly.py b/Ch12/listing12-16-get_data_repeatedly.py new file mode 100644 index 0000000..140edea --- /dev/null +++ b/Ch12/listing12-16-get_data_repeatedly.py @@ -0,0 +1,25 @@ +import asyncio + +from apd.aggregation.query import db_session_var, get_data + +async def get_data_ongoing(*args, **kwargs): + last_id = 0 + db_session = db_session_var.get() + while True: + # Run a timer for 300 seconds concurrently with our work + minimum_loop_timer = asyncio.create_task(asyncio.sleep(300)) + async for datapoint in get_data(*args, **kwargs): + if datapoint.id > last_id: + # This is the newest datapoint we have handled so far + last_id = datapoint.id + yield datapoint + # Next time, find only data points later than the latest we've seen + kwargs["inserted_after_record_id"] = last_id + # Commit the DB to store any work that was done in this loop and + # ensure that any isolation level issues do not prevent loading more + # data + db_session.commit() + # Wait for that timer to complete. If our loop took over 5 minutes + # this will complete immediately, otherwise it will block + await minimum_loop_timer + diff --git a/Ch12/listing12-17-actions_cli.py b/Ch12/listing12-17-actions_cli.py new file mode 100644 index 0000000..efbf891 --- /dev/null +++ b/Ch12/listing12-17-actions_cli.py @@ -0,0 +1,61 @@ +import asyncio +import importlib.util +import logging +import typing as t + +import click + +from .actions.runner import DataProcessor +from .actions.source import get_data_ongoing +from .query import with_database + +logger = logging.getLogger(__name__) + + +def load_handler_config(path: str) -> t.List[DataProcessor]: + # Create a module called user_config backed by the file specified, and load it + # This uses Python's import internals to fake a module in a known location + # Based on an StackOverflow answer by Sebastian Rittau and sample code from + # Brett Cannon + module_spec = importlib.util.spec_from_file_location("user_config", path) + module = importlib.util.module_from_spec(module_spec) + module_spec.loader.exec_module(module) + return module.handlers + + +@click.command() +@click.argument("config", nargs=1) +@click.option( + "--db", + metavar="", + default="postgresql+psycopg2://localhost/apd", + help="The connection string to a PostgreSQL database", + envvar="APD_DB_URI", +) +@click.option("-v", "--verbose", is_flag=True, help="Enables verbose mode") +def run_actions(config: str, db: str, verbose: bool) -> t.Optional[int]: + """This runs the long-running action processors defined in a config file. + + The configuration file specified should be a Python file that defines a + list of DataProcessor objects called processors.n + """ + logging.basicConfig(level=logging.DEBUG if verbose else logging.WARN) + + async def main_loop(): + with with_database(db): + logger.info("Loading configuration") + handlers = load_handler_config(config) + + logger.info(f"Configured {len(handlers)} handlers") + starters = [handler.start() for handler in handlers] + await asyncio.gather(*starters) + + logger.info(f"Ingesting data") + data = get_data_ongoing() + async for datapoint in data: + for handler in handlers: + await handler.push(datapoint) + + asyncio.run(main_loop()) + return True + diff --git a/Ch12/listing12-18-config.py b/Ch12/listing12-18-config.py new file mode 100644 index 0000000..1f4c54d --- /dev/null +++ b/Ch12/listing12-18-config.py @@ -0,0 +1,22 @@ +import operator + +from apd.aggregation.actions.action import ( + OnlyOnChangeActionWrapper, + LoggingAction, +) +from apd.aggregation.actions.runner import DataProcessor +from apd.aggregation.actions.trigger import ValueThresholdTrigger + +handlers = [ + DataProcessor( + name="TemperatureBelow18", + action=OnlyOnChangeActionWrapper(LoggingAction()), + trigger=ValueThresholdTrigger( + name="TemperatureBelow18", + threshold=18, + comparator=operator.lt, + sensor_name="Temperature", + ), + ) +] + diff --git a/Ch12/listing12-19-dataprocessor_stats.py b/Ch12/listing12-19-dataprocessor_stats.py new file mode 100644 index 0000000..96c7d78 --- /dev/null +++ b/Ch12/listing12-19-dataprocessor_stats.py @@ -0,0 +1,43 @@ +@dataclasses.dataclass(unsafe_hash=True) +class DataProcessor: + name: str + action: Action + trigger: Trigger[t.Any] + + def __post_init__(self): + self._input: t.Optional[asyncio.Queue[DataPoint]] = None + self._sub_tasks: t.Set = set() + self.last_times = collections.deque(maxlen=10) + self.total_in = 0 + self.total_out = 0 + + async def process(self) -> None: + while True: + data = await self.input.get() + start = time.time() + self.total_in += 1 + try: + processed = await self.trigger.handle(data) + except ValueError: + continue + else: + action_taken = await self.action.handle(processed) + if action_taken: + elapsed = time.time() - start + self.total_out += 1 + self.last_times.append(elapsed) + finally: + self.input.task_done() + + def stats(self) -> str: + if self.last_times: + avr_time = sum(self.last_times) / len(self.last_times) + elif self.total_in: + avr_time = 0 + else: + return "Not yet started" + return ( + f"{avr_time:0.3f} seconds per item. {self.total_in} in, " + f"{self.total_out} out, {self.input.qsize()} waiting." + ) + diff --git a/Ch12/listing12-20-stats_signals.py b/Ch12/listing12-20-stats_signals.py new file mode 100644 index 0000000..54e5958 --- /dev/null +++ b/Ch12/listing12-20-stats_signals.py @@ -0,0 +1,11 @@ +import signal +def stats_signal_handler(sig, frame, data_processors=None): + for data_processor in data_processors: + click.echo( + click.style(data_processor.name, bold=True, fg="red") + " " + data_processor.stats() + ) + return + +signal_handler = functools.partial(stats_signal_handler, data_processors=handlers) +signal.signal(signal.SIGINFO, signal_handler) + diff --git a/Ch12/listing12-21-better_stats_signals.py b/Ch12/listing12-21-better_stats_signals.py new file mode 100644 index 0000000..71b7216 --- /dev/null +++ b/Ch12/listing12-21-better_stats_signals.py @@ -0,0 +1,32 @@ +def stats_signal_handler(sig, frame, original_sigint_handler=None, data_processors=None): + for data_processor in data_processors: + click.echo( + click.style(data_processor.name, bold=True, fg="red") + " " + data_processor.stats() + ) + if sig == signal.SIGINT: + click.secho("Press Ctrl+C again to end the process", bold=True) + handler = signal.getsignal(signal.SIGINT) + signal.signal(signal.SIGINT, original_sigint_handler) + asyncio.get_running_loop().call_later(5, install_ctrl_c_signal_handler, handler) + return + + +def install_ctrl_c_signal_handler(signal_handler): + click.secho("Press Ctrl+C to view statistics", bold=True) + signal.signal(signal.SIGINT, signal_handler) + + +def install_signal_handlers(running_data_processors): + original_sigint_handler = signal.getsignal(signal.SIGINT) + signal_handler = functools.partial( + stats_signal_handler, + data_processors=running_data_processors, + original_sigint_handler=original_sigint_handler, + ) + + for signal_name in "SIGINFO", "SIGUSR1", "SIGINT": + try: + signal.signal(signal.Signals[signal_name], signal_handler) + except KeyError: + pass + diff --git a/Ch12/listing12-22-time_taken_callback.py b/Ch12/listing12-22-time_taken_callback.py new file mode 100644 index 0000000..00b2f60 --- /dev/null +++ b/Ch12/listing12-22-time_taken_callback.py @@ -0,0 +1,25 @@ +class DataProcessor: + ... + + def action_complete(self, start, task): + action_taken = task.result() + if action_taken: + elapsed = time.time() - start + self.total_out += 1 + self.last_times.append(elapsed) + self.input.task_done() + + async def process(self) -> None: + while True: + data = await self.input.get() + start = time.time() + self.total_in += 1 + try: + processed = await self.trigger.handle(data) + except ValueError: + self.input.task_done() + continue + else: + result = asyncio.create_task(self.action.handle(processed)) + result.add_done_callback(functools.partial(self.action_complete, start)) + diff --git a/Ch12/listing12-23-refeed_getdata.py b/Ch12/listing12-23-refeed_getdata.py new file mode 100644 index 0000000..23b8a57 --- /dev/null +++ b/Ch12/listing12-23-refeed_getdata.py @@ -0,0 +1,43 @@ +import asyncio +from contextvars import ContextVar + +from apd.aggregation.query import db_session_var, get_data + +refeed_queue_var = ContextVar("refeed_queue") + + +async def queue_as_iterator(queue): + while not queue.empty(): + yield queue.get_nowait() + + +async def get_data_ongoing(*args, historical=False, **kwargs): + last_id = 0 + if not historical: + kwargs["inserted_after_record_id"] = last_id = await get_newest_record_id() + db_session = db_session_var.get() + refeed_queue = refeed_queue_var.get() + + while True: + # Run a timer for 300 seconds concurrently with our work + minimum_loop_timer = asyncio.create_task(asyncio.sleep(300)) + import datetime + async for datapoint in get_data(*args, inserted_after_record_id=last_id, order=False, **kwargs): + if datapoint.id > last_id: + # This is the newest datapoint we have handled so far + last_id = datapoint.id + yield datapoint + + while not refeed_queue.empty(): + # Process any datapoints gathered through the refeed queue + async for datapoint in queue_as_iterator(refeed_queue): + yield datapoint + + # Commit the DB to store any work that was done in this loop and + # ensure that any isolation level issues do not prevent loading more + # data + db_session.commit() + # Wait for that timer to complete. If our loop took over 5 minutes + # this will complete immediately, otherwise it will block + await minimum_loop_timer + diff --git a/Ch12/listing12-24-refeed_actions.py b/Ch12/listing12-24-refeed_actions.py new file mode 100644 index 0000000..970fc68 --- /dev/null +++ b/Ch12/listing12-24-refeed_actions.py @@ -0,0 +1,18 @@ +from .source import refeed_queue_var + +class RefeedAction(Action): + """An action that puts data points into a special queue to be consumed + by the analysis programme""" + + async def start(self) -> None: + return + + async def handle(self, datapoint: DataPoint) -> bool: + refeed_queue = refeed_queue_var.get() + if refeed_queue is None: + logger.error("Refeed queue has not been initialised") + return False + else: + await refeed_queue.put(datapoint) + return True + diff --git a/Contributing.md b/Contributing.md new file mode 100644 index 0000000..f6005ad --- /dev/null +++ b/Contributing.md @@ -0,0 +1,14 @@ +# Contributing to Apress Source Code + +Copyright for Apress source code belongs to the author(s). However, under fair use you are encouraged to fork and contribute minor corrections and updates for the benefit of the author(s) and other readers. + +## How to Contribute + +1. Make sure you have a GitHub account. +2. Fork the repository for the relevant book. +3. Create a new branch on which to make your change, e.g. +`git checkout -b my_code_contribution` +4. Commit your change. Include a commit message describing the correction. Please note that if your commit message is not clear, the correction will not be accepted. +5. Submit a pull request. + +Thank you for your contribution! \ No newline at end of file diff --git a/LICENSE.txt b/LICENSE.txt new file mode 100644 index 0000000..89d8143 --- /dev/null +++ b/LICENSE.txt @@ -0,0 +1,27 @@ +Freeware License, some rights reserved + +Copyright (c) 2020 Matthew Wilkes + +Permission is hereby granted, free of charge, to anyone obtaining a copy +of this software and associated documentation files (the "Software"), +to work with the Software within the limits of freeware distribution and fair use. +This includes the rights to use, copy, and modify the Software for personal use. +Users are also allowed and encouraged to submit corrections and modifications +to the Software for the benefit of other users. + +It is not allowed to reuse, modify, or redistribute the Software for +commercial use in any way, or for a user’s educational materials such as books +or blog articles without prior permission from the copyright holder. + +The above copyright notice and this permission notice need to be included +in all copies or substantial portions of the software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS OR APRESS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. + + diff --git a/README.md b/README.md new file mode 100644 index 0000000..148455e --- /dev/null +++ b/README.md @@ -0,0 +1,16 @@ +# Apress Source Code + +This repository accompanies [*Advanced Python Development*](https://www.apress.com/9781484257920) by Matthew Wilkes (Apress, 2020). + +[comment]: #cover +![Cover image](9781484257920.jpg) + +Download the files as a zip using the green button, or clone the repository to your machine using Git. + +## Releases + +Release v1.0 corresponds to the code in the published book, without corrections or updates. + +## Contributions + +See the file Contributing.md for more information on how you can contribute to this repository. \ No newline at end of file diff --git a/apd.aggregation/HEAD b/apd.aggregation/HEAD new file mode 100644 index 0000000..cb089cd --- /dev/null +++ b/apd.aggregation/HEAD @@ -0,0 +1 @@ +ref: refs/heads/master diff --git a/apd.aggregation/config b/apd.aggregation/config new file mode 100644 index 0000000..4638757 --- /dev/null +++ b/apd.aggregation/config @@ -0,0 +1,7 @@ +[core] + repositoryformatversion = 0 + filemode = false + bare = true + ignorecase = true +[remote "origin"] + url = git@github.com:matthewwilkes/apd.aggregation.git diff --git a/apd.aggregation/description b/apd.aggregation/description new file mode 100644 index 0000000..498b267 --- /dev/null +++ b/apd.aggregation/description @@ -0,0 +1 @@ +Unnamed repository; edit this file 'description' to name the repository. diff --git a/apd.aggregation/hooks/applypatch-msg.sample b/apd.aggregation/hooks/applypatch-msg.sample new file mode 100644 index 0000000..a5d7b84 --- /dev/null +++ b/apd.aggregation/hooks/applypatch-msg.sample @@ -0,0 +1,15 @@ +#!/bin/sh +# +# An example hook script to check the commit log message taken by +# applypatch from an e-mail message. +# +# The hook should exit with non-zero status after issuing an +# appropriate message if it wants to stop the commit. The hook is +# allowed to edit the commit message file. +# +# To enable this hook, rename this file to "applypatch-msg". + +. git-sh-setup +commitmsg="$(git rev-parse --git-path hooks/commit-msg)" +test -x "$commitmsg" && exec "$commitmsg" ${1+"$@"} +: diff --git a/apd.aggregation/hooks/commit-msg.sample b/apd.aggregation/hooks/commit-msg.sample new file mode 100644 index 0000000..b58d118 --- /dev/null +++ b/apd.aggregation/hooks/commit-msg.sample @@ -0,0 +1,24 @@ +#!/bin/sh +# +# An example hook script to check the commit log message. +# Called by "git commit" with one argument, the name of the file +# that has the commit message. The hook should exit with non-zero +# status after issuing an appropriate message if it wants to stop the +# commit. The hook is allowed to edit the commit message file. +# +# To enable this hook, rename this file to "commit-msg". + +# Uncomment the below to add a Signed-off-by line to the message. +# Doing this in a hook is a bad idea in general, but the prepare-commit-msg +# hook is more suited to it. +# +# SOB=$(git var GIT_AUTHOR_IDENT | sed -n 's/^\(.*>\).*$/Signed-off-by: \1/p') +# grep -qs "^$SOB" "$1" || echo "$SOB" >> "$1" + +# This example catches duplicate Signed-off-by lines. + +test "" = "$(grep '^Signed-off-by: ' "$1" | + sort | uniq -c | sed -e '/^[ ]*1[ ]/d')" || { + echo >&2 Duplicate Signed-off-by lines. + exit 1 +} diff --git a/apd.aggregation/hooks/fsmonitor-watchman.sample b/apd.aggregation/hooks/fsmonitor-watchman.sample new file mode 100644 index 0000000..e673bb3 --- /dev/null +++ b/apd.aggregation/hooks/fsmonitor-watchman.sample @@ -0,0 +1,114 @@ +#!/usr/bin/perl + +use strict; +use warnings; +use IPC::Open2; + +# An example hook script to integrate Watchman +# (https://facebook.github.io/watchman/) with git to speed up detecting +# new and modified files. +# +# The hook is passed a version (currently 1) and a time in nanoseconds +# formatted as a string and outputs to stdout all files that have been +# modified since the given time. Paths must be relative to the root of +# the working tree and separated by a single NUL. +# +# To enable this hook, rename this file to "query-watchman" and set +# 'git config core.fsmonitor .git/hooks/query-watchman' +# +my ($version, $time) = @ARGV; + +# Check the hook interface version + +if ($version == 1) { + # convert nanoseconds to seconds + $time = int $time / 1000000000; +} else { + die "Unsupported query-fsmonitor hook version '$version'.\n" . + "Falling back to scanning...\n"; +} + +my $git_work_tree; +if ($^O =~ 'msys' || $^O =~ 'cygwin') { + $git_work_tree = Win32::GetCwd(); + $git_work_tree =~ tr/\\/\//; +} else { + require Cwd; + $git_work_tree = Cwd::cwd(); +} + +my $retry = 1; + +launch_watchman(); + +sub launch_watchman { + + my $pid = open2(\*CHLD_OUT, \*CHLD_IN, 'watchman -j --no-pretty') + or die "open2() failed: $!\n" . + "Falling back to scanning...\n"; + + # In the query expression below we're asking for names of files that + # changed since $time but were not transient (ie created after + # $time but no longer exist). + # + # To accomplish this, we're using the "since" generator to use the + # recency index to select candidate nodes and "fields" to limit the + # output to file names only. Then we're using the "expression" term to + # further constrain the results. + # + # The category of transient files that we want to ignore will have a + # creation clock (cclock) newer than $time_t value and will also not + # currently exist. + + my $query = <<" END"; + ["query", "$git_work_tree", { + "since": $time, + "fields": ["name"], + "expression": ["not", ["allof", ["since", $time, "cclock"], ["not", "exists"]]] + }] + END + + print CHLD_IN $query; + close CHLD_IN; + my $response = do {local $/; }; + + die "Watchman: command returned no output.\n" . + "Falling back to scanning...\n" if $response eq ""; + die "Watchman: command returned invalid output: $response\n" . + "Falling back to scanning...\n" unless $response =~ /^\{/; + + my $json_pkg; + eval { + require JSON::XS; + $json_pkg = "JSON::XS"; + 1; + } or do { + require JSON::PP; + $json_pkg = "JSON::PP"; + }; + + my $o = $json_pkg->new->utf8->decode($response); + + if ($retry > 0 and $o->{error} and $o->{error} =~ m/unable to resolve root .* directory (.*) is not watched/) { + print STDERR "Adding '$git_work_tree' to watchman's watch list.\n"; + $retry--; + qx/watchman watch "$git_work_tree"/; + die "Failed to make watchman watch '$git_work_tree'.\n" . + "Falling back to scanning...\n" if $? != 0; + + # Watchman will always return all files on the first query so + # return the fast "everything is dirty" flag to git and do the + # Watchman query just to get it over with now so we won't pay + # the cost in git to look up each individual file. + print "/\0"; + eval { launch_watchman() }; + exit 0; + } + + die "Watchman: $o->{error}.\n" . + "Falling back to scanning...\n" if $o->{error}; + + binmode STDOUT, ":utf8"; + local $, = "\0"; + print @{$o->{files}}; +} diff --git a/apd.aggregation/hooks/post-update.sample b/apd.aggregation/hooks/post-update.sample new file mode 100644 index 0000000..ec17ec1 --- /dev/null +++ b/apd.aggregation/hooks/post-update.sample @@ -0,0 +1,8 @@ +#!/bin/sh +# +# An example hook script to prepare a packed repository for use over +# dumb transports. +# +# To enable this hook, rename this file to "post-update". + +exec git update-server-info diff --git a/apd.aggregation/hooks/pre-applypatch.sample b/apd.aggregation/hooks/pre-applypatch.sample new file mode 100644 index 0000000..4142082 --- /dev/null +++ b/apd.aggregation/hooks/pre-applypatch.sample @@ -0,0 +1,14 @@ +#!/bin/sh +# +# An example hook script to verify what is about to be committed +# by applypatch from an e-mail message. +# +# The hook should exit with non-zero status after issuing an +# appropriate message if it wants to stop the commit. +# +# To enable this hook, rename this file to "pre-applypatch". + +. git-sh-setup +precommit="$(git rev-parse --git-path hooks/pre-commit)" +test -x "$precommit" && exec "$precommit" ${1+"$@"} +: diff --git a/apd.aggregation/hooks/pre-commit.sample b/apd.aggregation/hooks/pre-commit.sample new file mode 100644 index 0000000..6a75641 --- /dev/null +++ b/apd.aggregation/hooks/pre-commit.sample @@ -0,0 +1,49 @@ +#!/bin/sh +# +# An example hook script to verify what is about to be committed. +# Called by "git commit" with no arguments. The hook should +# exit with non-zero status after issuing an appropriate message if +# it wants to stop the commit. +# +# To enable this hook, rename this file to "pre-commit". + +if git rev-parse --verify HEAD >/dev/null 2>&1 +then + against=HEAD +else + # Initial commit: diff against an empty tree object + against=$(git hash-object -t tree /dev/null) +fi + +# If you want to allow non-ASCII filenames set this variable to true. +allownonascii=$(git config --bool hooks.allownonascii) + +# Redirect output to stderr. +exec 1>&2 + +# Cross platform projects tend to avoid non-ASCII filenames; prevent +# them from being added to the repository. We exploit the fact that the +# printable range starts at the space character and ends with tilde. +if [ "$allownonascii" != "true" ] && + # Note that the use of brackets around a tr range is ok here, (it's + # even required, for portability to Solaris 10's /usr/bin/tr), since + # the square bracket bytes happen to fall in the designated range. + test $(git diff --cached --name-only --diff-filter=A -z $against | + LC_ALL=C tr -d '[ -~]\0' | wc -c) != 0 +then + cat <<\EOF +Error: Attempt to add a non-ASCII file name. + +This can cause problems if you want to work with people on other platforms. + +To be portable it is advisable to rename the file. + +If you know what you are doing you can disable this check using: + + git config hooks.allownonascii true +EOF + exit 1 +fi + +# If there are whitespace errors, print the offending file names and fail. +exec git diff-index --check --cached $against -- diff --git a/apd.aggregation/hooks/pre-push.sample b/apd.aggregation/hooks/pre-push.sample new file mode 100644 index 0000000..6187dbf --- /dev/null +++ b/apd.aggregation/hooks/pre-push.sample @@ -0,0 +1,53 @@ +#!/bin/sh + +# An example hook script to verify what is about to be pushed. Called by "git +# push" after it has checked the remote status, but before anything has been +# pushed. If this script exits with a non-zero status nothing will be pushed. +# +# This hook is called with the following parameters: +# +# $1 -- Name of the remote to which the push is being done +# $2 -- URL to which the push is being done +# +# If pushing without using a named remote those arguments will be equal. +# +# Information about the commits which are being pushed is supplied as lines to +# the standard input in the form: +# +# +# +# This sample shows how to prevent push of commits where the log message starts +# with "WIP" (work in progress). + +remote="$1" +url="$2" + +z40=0000000000000000000000000000000000000000 + +while read local_ref local_sha remote_ref remote_sha +do + if [ "$local_sha" = $z40 ] + then + # Handle delete + : + else + if [ "$remote_sha" = $z40 ] + then + # New branch, examine all commits + range="$local_sha" + else + # Update to existing branch, examine new commits + range="$remote_sha..$local_sha" + fi + + # Check for WIP commit + commit=`git rev-list -n 1 --grep '^WIP' "$range"` + if [ -n "$commit" ] + then + echo >&2 "Found WIP commit in $local_ref, not pushing" + exit 1 + fi + fi +done + +exit 0 diff --git a/apd.aggregation/hooks/pre-rebase.sample b/apd.aggregation/hooks/pre-rebase.sample new file mode 100644 index 0000000..6cbef5c --- /dev/null +++ b/apd.aggregation/hooks/pre-rebase.sample @@ -0,0 +1,169 @@ +#!/bin/sh +# +# Copyright (c) 2006, 2008 Junio C Hamano +# +# The "pre-rebase" hook is run just before "git rebase" starts doing +# its job, and can prevent the command from running by exiting with +# non-zero status. +# +# The hook is called with the following parameters: +# +# $1 -- the upstream the series was forked from. +# $2 -- the branch being rebased (or empty when rebasing the current branch). +# +# This sample shows how to prevent topic branches that are already +# merged to 'next' branch from getting rebased, because allowing it +# would result in rebasing already published history. + +publish=next +basebranch="$1" +if test "$#" = 2 +then + topic="refs/heads/$2" +else + topic=`git symbolic-ref HEAD` || + exit 0 ;# we do not interrupt rebasing detached HEAD +fi + +case "$topic" in +refs/heads/??/*) + ;; +*) + exit 0 ;# we do not interrupt others. + ;; +esac + +# Now we are dealing with a topic branch being rebased +# on top of master. Is it OK to rebase it? + +# Does the topic really exist? +git show-ref -q "$topic" || { + echo >&2 "No such branch $topic" + exit 1 +} + +# Is topic fully merged to master? +not_in_master=`git rev-list --pretty=oneline ^master "$topic"` +if test -z "$not_in_master" +then + echo >&2 "$topic is fully merged to master; better remove it." + exit 1 ;# we could allow it, but there is no point. +fi + +# Is topic ever merged to next? If so you should not be rebasing it. +only_next_1=`git rev-list ^master "^$topic" ${publish} | sort` +only_next_2=`git rev-list ^master ${publish} | sort` +if test "$only_next_1" = "$only_next_2" +then + not_in_topic=`git rev-list "^$topic" master` + if test -z "$not_in_topic" + then + echo >&2 "$topic is already up to date with master" + exit 1 ;# we could allow it, but there is no point. + else + exit 0 + fi +else + not_in_next=`git rev-list --pretty=oneline ^${publish} "$topic"` + /usr/bin/perl -e ' + my $topic = $ARGV[0]; + my $msg = "* $topic has commits already merged to public branch:\n"; + my (%not_in_next) = map { + /^([0-9a-f]+) /; + ($1 => 1); + } split(/\n/, $ARGV[1]); + for my $elem (map { + /^([0-9a-f]+) (.*)$/; + [$1 => $2]; + } split(/\n/, $ARGV[2])) { + if (!exists $not_in_next{$elem->[0]}) { + if ($msg) { + print STDERR $msg; + undef $msg; + } + print STDERR " $elem->[1]\n"; + } + } + ' "$topic" "$not_in_next" "$not_in_master" + exit 1 +fi + +<<\DOC_END + +This sample hook safeguards topic branches that have been +published from being rewound. + +The workflow assumed here is: + + * Once a topic branch forks from "master", "master" is never + merged into it again (either directly or indirectly). + + * Once a topic branch is fully cooked and merged into "master", + it is deleted. If you need to build on top of it to correct + earlier mistakes, a new topic branch is created by forking at + the tip of the "master". This is not strictly necessary, but + it makes it easier to keep your history simple. + + * Whenever you need to test or publish your changes to topic + branches, merge them into "next" branch. + +The script, being an example, hardcodes the publish branch name +to be "next", but it is trivial to make it configurable via +$GIT_DIR/config mechanism. + +With this workflow, you would want to know: + +(1) ... if a topic branch has ever been merged to "next". Young + topic branches can have stupid mistakes you would rather + clean up before publishing, and things that have not been + merged into other branches can be easily rebased without + affecting other people. But once it is published, you would + not want to rewind it. + +(2) ... if a topic branch has been fully merged to "master". + Then you can delete it. More importantly, you should not + build on top of it -- other people may already want to + change things related to the topic as patches against your + "master", so if you need further changes, it is better to + fork the topic (perhaps with the same name) afresh from the + tip of "master". + +Let's look at this example: + + o---o---o---o---o---o---o---o---o---o "next" + / / / / + / a---a---b A / / + / / / / + / / c---c---c---c B / + / / / \ / + / / / b---b C \ / + / / / / \ / + ---o---o---o---o---o---o---o---o---o---o---o "master" + + +A, B and C are topic branches. + + * A has one fix since it was merged up to "next". + + * B has finished. It has been fully merged up to "master" and "next", + and is ready to be deleted. + + * C has not merged to "next" at all. + +We would want to allow C to be rebased, refuse A, and encourage +B to be deleted. + +To compute (1): + + git rev-list ^master ^topic next + git rev-list ^master next + + if these match, topic has not merged in next at all. + +To compute (2): + + git rev-list master..topic + + if this is empty, it is fully merged to "master". + +DOC_END diff --git a/apd.aggregation/hooks/pre-receive.sample b/apd.aggregation/hooks/pre-receive.sample new file mode 100644 index 0000000..a1fd29e --- /dev/null +++ b/apd.aggregation/hooks/pre-receive.sample @@ -0,0 +1,24 @@ +#!/bin/sh +# +# An example hook script to make use of push options. +# The example simply echoes all push options that start with 'echoback=' +# and rejects all pushes when the "reject" push option is used. +# +# To enable this hook, rename this file to "pre-receive". + +if test -n "$GIT_PUSH_OPTION_COUNT" +then + i=0 + while test "$i" -lt "$GIT_PUSH_OPTION_COUNT" + do + eval "value=\$GIT_PUSH_OPTION_$i" + case "$value" in + echoback=*) + echo "echo from the pre-receive-hook: ${value#*=}" >&2 + ;; + reject) + exit 1 + esac + i=$((i + 1)) + done +fi diff --git a/apd.aggregation/hooks/prepare-commit-msg.sample b/apd.aggregation/hooks/prepare-commit-msg.sample new file mode 100644 index 0000000..10fa14c --- /dev/null +++ b/apd.aggregation/hooks/prepare-commit-msg.sample @@ -0,0 +1,42 @@ +#!/bin/sh +# +# An example hook script to prepare the commit log message. +# Called by "git commit" with the name of the file that has the +# commit message, followed by the description of the commit +# message's source. The hook's purpose is to edit the commit +# message file. If the hook fails with a non-zero status, +# the commit is aborted. +# +# To enable this hook, rename this file to "prepare-commit-msg". + +# This hook includes three examples. The first one removes the +# "# Please enter the commit message..." help message. +# +# The second includes the output of "git diff --name-status -r" +# into the message, just before the "git status" output. It is +# commented because it doesn't cope with --amend or with squashed +# commits. +# +# The third example adds a Signed-off-by line to the message, that can +# still be edited. This is rarely a good idea. + +COMMIT_MSG_FILE=$1 +COMMIT_SOURCE=$2 +SHA1=$3 + +/usr/bin/perl -i.bak -ne 'print unless(m/^. Please enter the commit message/..m/^#$/)' "$COMMIT_MSG_FILE" + +# case "$COMMIT_SOURCE,$SHA1" in +# ,|template,) +# /usr/bin/perl -i.bak -pe ' +# print "\n" . `git diff --cached --name-status -r` +# if /^#/ && $first++ == 0' "$COMMIT_MSG_FILE" ;; +# *) ;; +# esac + +# SOB=$(git var GIT_COMMITTER_IDENT | sed -n 's/^\(.*>\).*$/Signed-off-by: \1/p') +# git interpret-trailers --in-place --trailer "$SOB" "$COMMIT_MSG_FILE" +# if test -z "$COMMIT_SOURCE" +# then +# /usr/bin/perl -i.bak -pe 'print "\n" if !$first_line++' "$COMMIT_MSG_FILE" +# fi diff --git a/apd.aggregation/hooks/update.sample b/apd.aggregation/hooks/update.sample new file mode 100644 index 0000000..80ba941 --- /dev/null +++ b/apd.aggregation/hooks/update.sample @@ -0,0 +1,128 @@ +#!/bin/sh +# +# An example hook script to block unannotated tags from entering. +# Called by "git receive-pack" with arguments: refname sha1-old sha1-new +# +# To enable this hook, rename this file to "update". +# +# Config +# ------ +# hooks.allowunannotated +# This boolean sets whether unannotated tags will be allowed into the +# repository. By default they won't be. +# hooks.allowdeletetag +# This boolean sets whether deleting tags will be allowed in the +# repository. By default they won't be. +# hooks.allowmodifytag +# This boolean sets whether a tag may be modified after creation. By default +# it won't be. +# hooks.allowdeletebranch +# This boolean sets whether deleting branches will be allowed in the +# repository. By default they won't be. +# hooks.denycreatebranch +# This boolean sets whether remotely creating branches will be denied +# in the repository. By default this is allowed. +# + +# --- Command line +refname="$1" +oldrev="$2" +newrev="$3" + +# --- Safety check +if [ -z "$GIT_DIR" ]; then + echo "Don't run this script from the command line." >&2 + echo " (if you want, you could supply GIT_DIR then run" >&2 + echo " $0 )" >&2 + exit 1 +fi + +if [ -z "$refname" -o -z "$oldrev" -o -z "$newrev" ]; then + echo "usage: $0 " >&2 + exit 1 +fi + +# --- Config +allowunannotated=$(git config --bool hooks.allowunannotated) +allowdeletebranch=$(git config --bool hooks.allowdeletebranch) +denycreatebranch=$(git config --bool hooks.denycreatebranch) +allowdeletetag=$(git config --bool hooks.allowdeletetag) +allowmodifytag=$(git config --bool hooks.allowmodifytag) + +# check for no description +projectdesc=$(sed -e '1q' "$GIT_DIR/description") +case "$projectdesc" in +"Unnamed repository"* | "") + echo "*** Project description file hasn't been set" >&2 + exit 1 + ;; +esac + +# --- Check types +# if $newrev is 0000...0000, it's a commit to delete a ref. +zero="0000000000000000000000000000000000000000" +if [ "$newrev" = "$zero" ]; then + newrev_type=delete +else + newrev_type=$(git cat-file -t $newrev) +fi + +case "$refname","$newrev_type" in + refs/tags/*,commit) + # un-annotated tag + short_refname=${refname##refs/tags/} + if [ "$allowunannotated" != "true" ]; then + echo "*** The un-annotated tag, $short_refname, is not allowed in this repository" >&2 + echo "*** Use 'git tag [ -a | -s ]' for tags you want to propagate." >&2 + exit 1 + fi + ;; + refs/tags/*,delete) + # delete tag + if [ "$allowdeletetag" != "true" ]; then + echo "*** Deleting a tag is not allowed in this repository" >&2 + exit 1 + fi + ;; + refs/tags/*,tag) + # annotated tag + if [ "$allowmodifytag" != "true" ] && git rev-parse $refname > /dev/null 2>&1 + then + echo "*** Tag '$refname' already exists." >&2 + echo "*** Modifying a tag is not allowed in this repository." >&2 + exit 1 + fi + ;; + refs/heads/*,commit) + # branch + if [ "$oldrev" = "$zero" -a "$denycreatebranch" = "true" ]; then + echo "*** Creating a branch is not allowed in this repository" >&2 + exit 1 + fi + ;; + refs/heads/*,delete) + # delete branch + if [ "$allowdeletebranch" != "true" ]; then + echo "*** Deleting a branch is not allowed in this repository" >&2 + exit 1 + fi + ;; + refs/remotes/*,commit) + # tracking branch + ;; + refs/remotes/*,delete) + # delete tracking branch + if [ "$allowdeletebranch" != "true" ]; then + echo "*** Deleting a tracking branch is not allowed in this repository" >&2 + exit 1 + fi + ;; + *) + # Anything else (is there anything else?) + echo "*** Update hook: unknown type of update to ref $refname of type $newrev_type" >&2 + exit 1 + ;; +esac + +# --- Finished +exit 0 diff --git a/apd.aggregation/info/exclude b/apd.aggregation/info/exclude new file mode 100644 index 0000000..a5196d1 --- /dev/null +++ b/apd.aggregation/info/exclude @@ -0,0 +1,6 @@ +# git ls-files --others --exclude-from=.git/info/exclude +# Lines that start with '#' are comments. +# For a project mostly in C, the following would be a good set of +# exclude patterns (uncomment them if you want to use them): +# *.[oa] +# *~ diff --git a/apd.aggregation/objects/pack/pack-0e7fa669b06f00ec17d49d52ad382f7f44ef9af1.idx b/apd.aggregation/objects/pack/pack-0e7fa669b06f00ec17d49d52ad382f7f44ef9af1.idx new file mode 100644 index 0000000..52e4a5d Binary files /dev/null and b/apd.aggregation/objects/pack/pack-0e7fa669b06f00ec17d49d52ad382f7f44ef9af1.idx differ diff --git a/apd.aggregation/objects/pack/pack-0e7fa669b06f00ec17d49d52ad382f7f44ef9af1.pack b/apd.aggregation/objects/pack/pack-0e7fa669b06f00ec17d49d52ad382f7f44ef9af1.pack new file mode 100644 index 0000000..133e6aa Binary files /dev/null and b/apd.aggregation/objects/pack/pack-0e7fa669b06f00ec17d49d52ad382f7f44ef9af1.pack differ diff --git a/apd.aggregation/packed-refs b/apd.aggregation/packed-refs new file mode 100644 index 0000000..36b3545 --- /dev/null +++ b/apd.aggregation/packed-refs @@ -0,0 +1,22 @@ +# pack-refs with: peeled fully-peeled sorted +e68339c178aa92e93e161ceec8e6ad5984d231f2 refs/heads/chapter06 +8a533e7cfedcc03e10a911262af38cc3e2624e94 refs/heads/chapter07-aio +3bc819a982acd8c3e307ae2c9f1894e118e6acb5 refs/heads/chapter07-multiprocess +3b82b4b08833b25bdb87b6f141272d64fa3b0631 refs/heads/chapter07-nbio +9ac7aa52764b5af12994dd7ab22fab778583e120 refs/heads/chapter07-simple-threads +633120e5e65cb767e8f2e89c4ee84d17ee7815dc refs/heads/chapter07-threaded +3fd6023bbfb5e0d75420c7374bf2bc9b52985565 refs/heads/chapter08 +1120f3a255accd407f4c6ee143950967c3304500 refs/heads/chapter08-aio +f96f51f4c3c221c63505765fe8dfb3e0ad397d54 refs/heads/chapter09 +8ae1bb836c545a61871bdcc9a21ab29a24c50904 refs/heads/chapter09-ex01 +938a338266b5d69a348c388791e4de7b56b3b7cc refs/heads/chapter09-ex02 +b889fd324e327fff10d91f63bcd12e69a5ebb462 refs/heads/chapter09-ex03 +376f6b78e758c9974eea7f2f27cf98f2d1aa5b0e refs/heads/chapter09-ex03-complete +4f32c03d95615bf75e3d4ce464a17e21bff28696 refs/heads/chapter10 +50b773a254605eb4ebacd6ecc63c0460e059b168 refs/heads/chapter10-cprofile-profiling +983f8601b003956cb0cfb456fc3f0a248b8c4697 refs/heads/chapter10-ex01 +0be751b296ef5a8c1afa000963db5ebdfa1ef10b refs/heads/chapter11 +855bd8f7bcd627514ebd28b6b3741d40c16209f2 refs/heads/chapter12 +18ca722479360f4661d1a8899ca7057f7019df70 refs/heads/chapter12-ex01 +41918a77904da095a85879064d7c9ac5229e133e refs/heads/chapter12-ex01-complete +523e1bc410b6211a73c2e48948031ac8fe05bd97 refs/heads/master diff --git a/apd.sensors/HEAD b/apd.sensors/HEAD new file mode 100644 index 0000000..cb089cd --- /dev/null +++ b/apd.sensors/HEAD @@ -0,0 +1 @@ +ref: refs/heads/master diff --git a/apd.sensors/config b/apd.sensors/config new file mode 100644 index 0000000..5b38365 --- /dev/null +++ b/apd.sensors/config @@ -0,0 +1,7 @@ +[core] + repositoryformatversion = 0 + filemode = false + bare = true + ignorecase = true +[remote "origin"] + url = git@github.com:matthewwilkes/apd.sensors.git diff --git a/apd.sensors/description b/apd.sensors/description new file mode 100644 index 0000000..498b267 --- /dev/null +++ b/apd.sensors/description @@ -0,0 +1 @@ +Unnamed repository; edit this file 'description' to name the repository. diff --git a/apd.sensors/hooks/applypatch-msg.sample b/apd.sensors/hooks/applypatch-msg.sample new file mode 100644 index 0000000..a5d7b84 --- /dev/null +++ b/apd.sensors/hooks/applypatch-msg.sample @@ -0,0 +1,15 @@ +#!/bin/sh +# +# An example hook script to check the commit log message taken by +# applypatch from an e-mail message. +# +# The hook should exit with non-zero status after issuing an +# appropriate message if it wants to stop the commit. The hook is +# allowed to edit the commit message file. +# +# To enable this hook, rename this file to "applypatch-msg". + +. git-sh-setup +commitmsg="$(git rev-parse --git-path hooks/commit-msg)" +test -x "$commitmsg" && exec "$commitmsg" ${1+"$@"} +: diff --git a/apd.sensors/hooks/commit-msg.sample b/apd.sensors/hooks/commit-msg.sample new file mode 100644 index 0000000..b58d118 --- /dev/null +++ b/apd.sensors/hooks/commit-msg.sample @@ -0,0 +1,24 @@ +#!/bin/sh +# +# An example hook script to check the commit log message. +# Called by "git commit" with one argument, the name of the file +# that has the commit message. The hook should exit with non-zero +# status after issuing an appropriate message if it wants to stop the +# commit. The hook is allowed to edit the commit message file. +# +# To enable this hook, rename this file to "commit-msg". + +# Uncomment the below to add a Signed-off-by line to the message. +# Doing this in a hook is a bad idea in general, but the prepare-commit-msg +# hook is more suited to it. +# +# SOB=$(git var GIT_AUTHOR_IDENT | sed -n 's/^\(.*>\).*$/Signed-off-by: \1/p') +# grep -qs "^$SOB" "$1" || echo "$SOB" >> "$1" + +# This example catches duplicate Signed-off-by lines. + +test "" = "$(grep '^Signed-off-by: ' "$1" | + sort | uniq -c | sed -e '/^[ ]*1[ ]/d')" || { + echo >&2 Duplicate Signed-off-by lines. + exit 1 +} diff --git a/apd.sensors/hooks/fsmonitor-watchman.sample b/apd.sensors/hooks/fsmonitor-watchman.sample new file mode 100644 index 0000000..e673bb3 --- /dev/null +++ b/apd.sensors/hooks/fsmonitor-watchman.sample @@ -0,0 +1,114 @@ +#!/usr/bin/perl + +use strict; +use warnings; +use IPC::Open2; + +# An example hook script to integrate Watchman +# (https://facebook.github.io/watchman/) with git to speed up detecting +# new and modified files. +# +# The hook is passed a version (currently 1) and a time in nanoseconds +# formatted as a string and outputs to stdout all files that have been +# modified since the given time. Paths must be relative to the root of +# the working tree and separated by a single NUL. +# +# To enable this hook, rename this file to "query-watchman" and set +# 'git config core.fsmonitor .git/hooks/query-watchman' +# +my ($version, $time) = @ARGV; + +# Check the hook interface version + +if ($version == 1) { + # convert nanoseconds to seconds + $time = int $time / 1000000000; +} else { + die "Unsupported query-fsmonitor hook version '$version'.\n" . + "Falling back to scanning...\n"; +} + +my $git_work_tree; +if ($^O =~ 'msys' || $^O =~ 'cygwin') { + $git_work_tree = Win32::GetCwd(); + $git_work_tree =~ tr/\\/\//; +} else { + require Cwd; + $git_work_tree = Cwd::cwd(); +} + +my $retry = 1; + +launch_watchman(); + +sub launch_watchman { + + my $pid = open2(\*CHLD_OUT, \*CHLD_IN, 'watchman -j --no-pretty') + or die "open2() failed: $!\n" . + "Falling back to scanning...\n"; + + # In the query expression below we're asking for names of files that + # changed since $time but were not transient (ie created after + # $time but no longer exist). + # + # To accomplish this, we're using the "since" generator to use the + # recency index to select candidate nodes and "fields" to limit the + # output to file names only. Then we're using the "expression" term to + # further constrain the results. + # + # The category of transient files that we want to ignore will have a + # creation clock (cclock) newer than $time_t value and will also not + # currently exist. + + my $query = <<" END"; + ["query", "$git_work_tree", { + "since": $time, + "fields": ["name"], + "expression": ["not", ["allof", ["since", $time, "cclock"], ["not", "exists"]]] + }] + END + + print CHLD_IN $query; + close CHLD_IN; + my $response = do {local $/; }; + + die "Watchman: command returned no output.\n" . + "Falling back to scanning...\n" if $response eq ""; + die "Watchman: command returned invalid output: $response\n" . + "Falling back to scanning...\n" unless $response =~ /^\{/; + + my $json_pkg; + eval { + require JSON::XS; + $json_pkg = "JSON::XS"; + 1; + } or do { + require JSON::PP; + $json_pkg = "JSON::PP"; + }; + + my $o = $json_pkg->new->utf8->decode($response); + + if ($retry > 0 and $o->{error} and $o->{error} =~ m/unable to resolve root .* directory (.*) is not watched/) { + print STDERR "Adding '$git_work_tree' to watchman's watch list.\n"; + $retry--; + qx/watchman watch "$git_work_tree"/; + die "Failed to make watchman watch '$git_work_tree'.\n" . + "Falling back to scanning...\n" if $? != 0; + + # Watchman will always return all files on the first query so + # return the fast "everything is dirty" flag to git and do the + # Watchman query just to get it over with now so we won't pay + # the cost in git to look up each individual file. + print "/\0"; + eval { launch_watchman() }; + exit 0; + } + + die "Watchman: $o->{error}.\n" . + "Falling back to scanning...\n" if $o->{error}; + + binmode STDOUT, ":utf8"; + local $, = "\0"; + print @{$o->{files}}; +} diff --git a/apd.sensors/hooks/post-update.sample b/apd.sensors/hooks/post-update.sample new file mode 100644 index 0000000..ec17ec1 --- /dev/null +++ b/apd.sensors/hooks/post-update.sample @@ -0,0 +1,8 @@ +#!/bin/sh +# +# An example hook script to prepare a packed repository for use over +# dumb transports. +# +# To enable this hook, rename this file to "post-update". + +exec git update-server-info diff --git a/apd.sensors/hooks/pre-applypatch.sample b/apd.sensors/hooks/pre-applypatch.sample new file mode 100644 index 0000000..4142082 --- /dev/null +++ b/apd.sensors/hooks/pre-applypatch.sample @@ -0,0 +1,14 @@ +#!/bin/sh +# +# An example hook script to verify what is about to be committed +# by applypatch from an e-mail message. +# +# The hook should exit with non-zero status after issuing an +# appropriate message if it wants to stop the commit. +# +# To enable this hook, rename this file to "pre-applypatch". + +. git-sh-setup +precommit="$(git rev-parse --git-path hooks/pre-commit)" +test -x "$precommit" && exec "$precommit" ${1+"$@"} +: diff --git a/apd.sensors/hooks/pre-commit.sample b/apd.sensors/hooks/pre-commit.sample new file mode 100644 index 0000000..6a75641 --- /dev/null +++ b/apd.sensors/hooks/pre-commit.sample @@ -0,0 +1,49 @@ +#!/bin/sh +# +# An example hook script to verify what is about to be committed. +# Called by "git commit" with no arguments. The hook should +# exit with non-zero status after issuing an appropriate message if +# it wants to stop the commit. +# +# To enable this hook, rename this file to "pre-commit". + +if git rev-parse --verify HEAD >/dev/null 2>&1 +then + against=HEAD +else + # Initial commit: diff against an empty tree object + against=$(git hash-object -t tree /dev/null) +fi + +# If you want to allow non-ASCII filenames set this variable to true. +allownonascii=$(git config --bool hooks.allownonascii) + +# Redirect output to stderr. +exec 1>&2 + +# Cross platform projects tend to avoid non-ASCII filenames; prevent +# them from being added to the repository. We exploit the fact that the +# printable range starts at the space character and ends with tilde. +if [ "$allownonascii" != "true" ] && + # Note that the use of brackets around a tr range is ok here, (it's + # even required, for portability to Solaris 10's /usr/bin/tr), since + # the square bracket bytes happen to fall in the designated range. + test $(git diff --cached --name-only --diff-filter=A -z $against | + LC_ALL=C tr -d '[ -~]\0' | wc -c) != 0 +then + cat <<\EOF +Error: Attempt to add a non-ASCII file name. + +This can cause problems if you want to work with people on other platforms. + +To be portable it is advisable to rename the file. + +If you know what you are doing you can disable this check using: + + git config hooks.allownonascii true +EOF + exit 1 +fi + +# If there are whitespace errors, print the offending file names and fail. +exec git diff-index --check --cached $against -- diff --git a/apd.sensors/hooks/pre-push.sample b/apd.sensors/hooks/pre-push.sample new file mode 100644 index 0000000..6187dbf --- /dev/null +++ b/apd.sensors/hooks/pre-push.sample @@ -0,0 +1,53 @@ +#!/bin/sh + +# An example hook script to verify what is about to be pushed. Called by "git +# push" after it has checked the remote status, but before anything has been +# pushed. If this script exits with a non-zero status nothing will be pushed. +# +# This hook is called with the following parameters: +# +# $1 -- Name of the remote to which the push is being done +# $2 -- URL to which the push is being done +# +# If pushing without using a named remote those arguments will be equal. +# +# Information about the commits which are being pushed is supplied as lines to +# the standard input in the form: +# +# +# +# This sample shows how to prevent push of commits where the log message starts +# with "WIP" (work in progress). + +remote="$1" +url="$2" + +z40=0000000000000000000000000000000000000000 + +while read local_ref local_sha remote_ref remote_sha +do + if [ "$local_sha" = $z40 ] + then + # Handle delete + : + else + if [ "$remote_sha" = $z40 ] + then + # New branch, examine all commits + range="$local_sha" + else + # Update to existing branch, examine new commits + range="$remote_sha..$local_sha" + fi + + # Check for WIP commit + commit=`git rev-list -n 1 --grep '^WIP' "$range"` + if [ -n "$commit" ] + then + echo >&2 "Found WIP commit in $local_ref, not pushing" + exit 1 + fi + fi +done + +exit 0 diff --git a/apd.sensors/hooks/pre-rebase.sample b/apd.sensors/hooks/pre-rebase.sample new file mode 100644 index 0000000..6cbef5c --- /dev/null +++ b/apd.sensors/hooks/pre-rebase.sample @@ -0,0 +1,169 @@ +#!/bin/sh +# +# Copyright (c) 2006, 2008 Junio C Hamano +# +# The "pre-rebase" hook is run just before "git rebase" starts doing +# its job, and can prevent the command from running by exiting with +# non-zero status. +# +# The hook is called with the following parameters: +# +# $1 -- the upstream the series was forked from. +# $2 -- the branch being rebased (or empty when rebasing the current branch). +# +# This sample shows how to prevent topic branches that are already +# merged to 'next' branch from getting rebased, because allowing it +# would result in rebasing already published history. + +publish=next +basebranch="$1" +if test "$#" = 2 +then + topic="refs/heads/$2" +else + topic=`git symbolic-ref HEAD` || + exit 0 ;# we do not interrupt rebasing detached HEAD +fi + +case "$topic" in +refs/heads/??/*) + ;; +*) + exit 0 ;# we do not interrupt others. + ;; +esac + +# Now we are dealing with a topic branch being rebased +# on top of master. Is it OK to rebase it? + +# Does the topic really exist? +git show-ref -q "$topic" || { + echo >&2 "No such branch $topic" + exit 1 +} + +# Is topic fully merged to master? +not_in_master=`git rev-list --pretty=oneline ^master "$topic"` +if test -z "$not_in_master" +then + echo >&2 "$topic is fully merged to master; better remove it." + exit 1 ;# we could allow it, but there is no point. +fi + +# Is topic ever merged to next? If so you should not be rebasing it. +only_next_1=`git rev-list ^master "^$topic" ${publish} | sort` +only_next_2=`git rev-list ^master ${publish} | sort` +if test "$only_next_1" = "$only_next_2" +then + not_in_topic=`git rev-list "^$topic" master` + if test -z "$not_in_topic" + then + echo >&2 "$topic is already up to date with master" + exit 1 ;# we could allow it, but there is no point. + else + exit 0 + fi +else + not_in_next=`git rev-list --pretty=oneline ^${publish} "$topic"` + /usr/bin/perl -e ' + my $topic = $ARGV[0]; + my $msg = "* $topic has commits already merged to public branch:\n"; + my (%not_in_next) = map { + /^([0-9a-f]+) /; + ($1 => 1); + } split(/\n/, $ARGV[1]); + for my $elem (map { + /^([0-9a-f]+) (.*)$/; + [$1 => $2]; + } split(/\n/, $ARGV[2])) { + if (!exists $not_in_next{$elem->[0]}) { + if ($msg) { + print STDERR $msg; + undef $msg; + } + print STDERR " $elem->[1]\n"; + } + } + ' "$topic" "$not_in_next" "$not_in_master" + exit 1 +fi + +<<\DOC_END + +This sample hook safeguards topic branches that have been +published from being rewound. + +The workflow assumed here is: + + * Once a topic branch forks from "master", "master" is never + merged into it again (either directly or indirectly). + + * Once a topic branch is fully cooked and merged into "master", + it is deleted. If you need to build on top of it to correct + earlier mistakes, a new topic branch is created by forking at + the tip of the "master". This is not strictly necessary, but + it makes it easier to keep your history simple. + + * Whenever you need to test or publish your changes to topic + branches, merge them into "next" branch. + +The script, being an example, hardcodes the publish branch name +to be "next", but it is trivial to make it configurable via +$GIT_DIR/config mechanism. + +With this workflow, you would want to know: + +(1) ... if a topic branch has ever been merged to "next". Young + topic branches can have stupid mistakes you would rather + clean up before publishing, and things that have not been + merged into other branches can be easily rebased without + affecting other people. But once it is published, you would + not want to rewind it. + +(2) ... if a topic branch has been fully merged to "master". + Then you can delete it. More importantly, you should not + build on top of it -- other people may already want to + change things related to the topic as patches against your + "master", so if you need further changes, it is better to + fork the topic (perhaps with the same name) afresh from the + tip of "master". + +Let's look at this example: + + o---o---o---o---o---o---o---o---o---o "next" + / / / / + / a---a---b A / / + / / / / + / / c---c---c---c B / + / / / \ / + / / / b---b C \ / + / / / / \ / + ---o---o---o---o---o---o---o---o---o---o---o "master" + + +A, B and C are topic branches. + + * A has one fix since it was merged up to "next". + + * B has finished. It has been fully merged up to "master" and "next", + and is ready to be deleted. + + * C has not merged to "next" at all. + +We would want to allow C to be rebased, refuse A, and encourage +B to be deleted. + +To compute (1): + + git rev-list ^master ^topic next + git rev-list ^master next + + if these match, topic has not merged in next at all. + +To compute (2): + + git rev-list master..topic + + if this is empty, it is fully merged to "master". + +DOC_END diff --git a/apd.sensors/hooks/pre-receive.sample b/apd.sensors/hooks/pre-receive.sample new file mode 100644 index 0000000..a1fd29e --- /dev/null +++ b/apd.sensors/hooks/pre-receive.sample @@ -0,0 +1,24 @@ +#!/bin/sh +# +# An example hook script to make use of push options. +# The example simply echoes all push options that start with 'echoback=' +# and rejects all pushes when the "reject" push option is used. +# +# To enable this hook, rename this file to "pre-receive". + +if test -n "$GIT_PUSH_OPTION_COUNT" +then + i=0 + while test "$i" -lt "$GIT_PUSH_OPTION_COUNT" + do + eval "value=\$GIT_PUSH_OPTION_$i" + case "$value" in + echoback=*) + echo "echo from the pre-receive-hook: ${value#*=}" >&2 + ;; + reject) + exit 1 + esac + i=$((i + 1)) + done +fi diff --git a/apd.sensors/hooks/prepare-commit-msg.sample b/apd.sensors/hooks/prepare-commit-msg.sample new file mode 100644 index 0000000..10fa14c --- /dev/null +++ b/apd.sensors/hooks/prepare-commit-msg.sample @@ -0,0 +1,42 @@ +#!/bin/sh +# +# An example hook script to prepare the commit log message. +# Called by "git commit" with the name of the file that has the +# commit message, followed by the description of the commit +# message's source. The hook's purpose is to edit the commit +# message file. If the hook fails with a non-zero status, +# the commit is aborted. +# +# To enable this hook, rename this file to "prepare-commit-msg". + +# This hook includes three examples. The first one removes the +# "# Please enter the commit message..." help message. +# +# The second includes the output of "git diff --name-status -r" +# into the message, just before the "git status" output. It is +# commented because it doesn't cope with --amend or with squashed +# commits. +# +# The third example adds a Signed-off-by line to the message, that can +# still be edited. This is rarely a good idea. + +COMMIT_MSG_FILE=$1 +COMMIT_SOURCE=$2 +SHA1=$3 + +/usr/bin/perl -i.bak -ne 'print unless(m/^. Please enter the commit message/..m/^#$/)' "$COMMIT_MSG_FILE" + +# case "$COMMIT_SOURCE,$SHA1" in +# ,|template,) +# /usr/bin/perl -i.bak -pe ' +# print "\n" . `git diff --cached --name-status -r` +# if /^#/ && $first++ == 0' "$COMMIT_MSG_FILE" ;; +# *) ;; +# esac + +# SOB=$(git var GIT_COMMITTER_IDENT | sed -n 's/^\(.*>\).*$/Signed-off-by: \1/p') +# git interpret-trailers --in-place --trailer "$SOB" "$COMMIT_MSG_FILE" +# if test -z "$COMMIT_SOURCE" +# then +# /usr/bin/perl -i.bak -pe 'print "\n" if !$first_line++' "$COMMIT_MSG_FILE" +# fi diff --git a/apd.sensors/hooks/update.sample b/apd.sensors/hooks/update.sample new file mode 100644 index 0000000..80ba941 --- /dev/null +++ b/apd.sensors/hooks/update.sample @@ -0,0 +1,128 @@ +#!/bin/sh +# +# An example hook script to block unannotated tags from entering. +# Called by "git receive-pack" with arguments: refname sha1-old sha1-new +# +# To enable this hook, rename this file to "update". +# +# Config +# ------ +# hooks.allowunannotated +# This boolean sets whether unannotated tags will be allowed into the +# repository. By default they won't be. +# hooks.allowdeletetag +# This boolean sets whether deleting tags will be allowed in the +# repository. By default they won't be. +# hooks.allowmodifytag +# This boolean sets whether a tag may be modified after creation. By default +# it won't be. +# hooks.allowdeletebranch +# This boolean sets whether deleting branches will be allowed in the +# repository. By default they won't be. +# hooks.denycreatebranch +# This boolean sets whether remotely creating branches will be denied +# in the repository. By default this is allowed. +# + +# --- Command line +refname="$1" +oldrev="$2" +newrev="$3" + +# --- Safety check +if [ -z "$GIT_DIR" ]; then + echo "Don't run this script from the command line." >&2 + echo " (if you want, you could supply GIT_DIR then run" >&2 + echo " $0 )" >&2 + exit 1 +fi + +if [ -z "$refname" -o -z "$oldrev" -o -z "$newrev" ]; then + echo "usage: $0 " >&2 + exit 1 +fi + +# --- Config +allowunannotated=$(git config --bool hooks.allowunannotated) +allowdeletebranch=$(git config --bool hooks.allowdeletebranch) +denycreatebranch=$(git config --bool hooks.denycreatebranch) +allowdeletetag=$(git config --bool hooks.allowdeletetag) +allowmodifytag=$(git config --bool hooks.allowmodifytag) + +# check for no description +projectdesc=$(sed -e '1q' "$GIT_DIR/description") +case "$projectdesc" in +"Unnamed repository"* | "") + echo "*** Project description file hasn't been set" >&2 + exit 1 + ;; +esac + +# --- Check types +# if $newrev is 0000...0000, it's a commit to delete a ref. +zero="0000000000000000000000000000000000000000" +if [ "$newrev" = "$zero" ]; then + newrev_type=delete +else + newrev_type=$(git cat-file -t $newrev) +fi + +case "$refname","$newrev_type" in + refs/tags/*,commit) + # un-annotated tag + short_refname=${refname##refs/tags/} + if [ "$allowunannotated" != "true" ]; then + echo "*** The un-annotated tag, $short_refname, is not allowed in this repository" >&2 + echo "*** Use 'git tag [ -a | -s ]' for tags you want to propagate." >&2 + exit 1 + fi + ;; + refs/tags/*,delete) + # delete tag + if [ "$allowdeletetag" != "true" ]; then + echo "*** Deleting a tag is not allowed in this repository" >&2 + exit 1 + fi + ;; + refs/tags/*,tag) + # annotated tag + if [ "$allowmodifytag" != "true" ] && git rev-parse $refname > /dev/null 2>&1 + then + echo "*** Tag '$refname' already exists." >&2 + echo "*** Modifying a tag is not allowed in this repository." >&2 + exit 1 + fi + ;; + refs/heads/*,commit) + # branch + if [ "$oldrev" = "$zero" -a "$denycreatebranch" = "true" ]; then + echo "*** Creating a branch is not allowed in this repository" >&2 + exit 1 + fi + ;; + refs/heads/*,delete) + # delete branch + if [ "$allowdeletebranch" != "true" ]; then + echo "*** Deleting a branch is not allowed in this repository" >&2 + exit 1 + fi + ;; + refs/remotes/*,commit) + # tracking branch + ;; + refs/remotes/*,delete) + # delete tracking branch + if [ "$allowdeletebranch" != "true" ]; then + echo "*** Deleting a tracking branch is not allowed in this repository" >&2 + exit 1 + fi + ;; + *) + # Anything else (is there anything else?) + echo "*** Update hook: unknown type of update to ref $refname of type $newrev_type" >&2 + exit 1 + ;; +esac + +# --- Finished +exit 0 diff --git a/apd.sensors/info/exclude b/apd.sensors/info/exclude new file mode 100644 index 0000000..a5196d1 --- /dev/null +++ b/apd.sensors/info/exclude @@ -0,0 +1,6 @@ +# git ls-files --others --exclude-from=.git/info/exclude +# Lines that start with '#' are comments. +# For a project mostly in C, the following would be a good set of +# exclude patterns (uncomment them if you want to use them): +# *.[oa] +# *~ diff --git a/apd.sensors/objects/pack/pack-2c612f3627aa76525a86c082a5393616dab67f82.idx b/apd.sensors/objects/pack/pack-2c612f3627aa76525a86c082a5393616dab67f82.idx new file mode 100644 index 0000000..5945600 Binary files /dev/null and b/apd.sensors/objects/pack/pack-2c612f3627aa76525a86c082a5393616dab67f82.idx differ diff --git a/apd.sensors/objects/pack/pack-2c612f3627aa76525a86c082a5393616dab67f82.pack b/apd.sensors/objects/pack/pack-2c612f3627aa76525a86c082a5393616dab67f82.pack new file mode 100644 index 0000000..33d67fd Binary files /dev/null and b/apd.sensors/objects/pack/pack-2c612f3627aa76525a86c082a5393616dab67f82.pack differ diff --git a/apd.sensors/packed-refs b/apd.sensors/packed-refs new file mode 100644 index 0000000..caeb497 --- /dev/null +++ b/apd.sensors/packed-refs @@ -0,0 +1,23 @@ +# pack-refs with: peeled fully-peeled sorted +9cdd7e08582bdf6c8d5cdb18044c56bd38cc40ff refs/heads/chapter01 +9bfe9c45b7e8601bbceb75f999fd85f7f54e95d0 refs/heads/chapter02 +5c63f2b9e1fa290514c33a4ab2c51533e28c8ee9 refs/heads/chapter02-ex01 +502ac10401067564123e4053e88323b03aceb3f5 refs/heads/chapter02-incorrect +a492d5a75a86245ad722ca1f008fdf9caa010981 refs/heads/chapter02-pyi +89298845361f18395f4324108461c0a11567e468 refs/heads/chapter03 +14fb06d963b1df89af6be63b237c6c1b745cc714 refs/heads/chapter04 +b5b8bba6928b6cb19d586dad8804d5464395bf15 refs/heads/chapter04-click-parsing +7457322b961eb00095d386121d2e846831e774b5 refs/heads/chapter04-click-subcommands +577a5e07457842629d4d67c43341bd0da970982c refs/heads/chapter04-configparser +f9bc4212f19c3fffc646a44fd1160df5a03f5850 refs/heads/chapter04-configparser-local +41eff0a016d15695686063ea39b59faa2ac7da7d refs/heads/chapter04-ex01 +d7a8bea379bbbf65df17f205f8758c79a4b746d2 refs/heads/chapter05 +d7a8bea379bbbf65df17f205f8758c79a4b746d2 refs/heads/chapter05-pintbased +f1bb92520e5ebbc75047e083180b9fe4a4e679ef refs/heads/chapter05-thirdpartywsgi +50175b31a34973bc9896132cbd019df1bd684508 refs/heads/chapter08 +6d670cab57cd4b51e79f3ac18c085d19f01b359d refs/heads/chapter10 +2e8e0d3c2c90cb10b86176c37f4dfc7fe8029491 refs/heads/chapter11 +653dd033e3d8d2fe275b31ba8d9127d48818a4c7 refs/heads/chapter11-ex01 +a0477f2290843792c977c771d34b06af4dd6a852 refs/heads/chapter12 +a0477f2290843792c977c771d34b06af4dd6a852 refs/heads/master +fe2b5e7ebb0199031b674e747a780b6793bdd1c6 refs/heads/old_master diff --git a/errata.md b/errata.md new file mode 100644 index 0000000..11e10af --- /dev/null +++ b/errata.md @@ -0,0 +1,13 @@ +# Errata for *Book Title* + +On **page xx** [Summary of error]: + +Details of error here. Highlight key pieces in **bold**. + +*** + +On **page xx** [Summary of error]: + +Details of error here. Highlight key pieces in **bold**. + +*** \ No newline at end of file