diff --git a/.readthedocs.yml b/.readthedocs.yml index 5ee23a7aed..0682f9864c 100644 --- a/.readthedocs.yml +++ b/.readthedocs.yml @@ -25,3 +25,6 @@ python: formats: - pdf + +sphinx: + configuration: doc/conf.py diff --git a/AUTHORS.md b/AUTHORS.md index c1ec649269..d4c7c4afff 100644 --- a/AUTHORS.md +++ b/AUTHORS.md @@ -1,3 +1,4 @@ +(authors)= # CLIMADA List of Authors * Gabriela Aznar-Siguan diff --git a/CHANGELOG.md b/CHANGELOG.md index 07d6e28697..df7ccc5ad5 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -277,7 +277,7 @@ Removed: ### Added - Convenience method `api_client.Client.get_dataset_file`, combining `get_dataset_info` and `download_dataset`, returning a single file objet. [#821](https://github.com/CLIMADA-project/climada_python/pull/821) -- Read and Write methods to and from csv files for the `DiscRates` class. [#818](ttps://github.com/CLIMADA-project/climada_python/pull/818) +- Read and Write methods to and from csv files for the `DiscRates` class. [#818](https://github.com/CLIMADA-project/climada_python/pull/818) - Add `CalcDeltaClimate` to unsequa module to allow uncertainty and sensitivity analysis of impact change calculations [#844](https://github.com/CLIMADA-project/climada_python/pull/844) - Add function `safe_divide` in util which handles division by zero and NaN values in the numerator or denominator [#844](https://github.com/CLIMADA-project/climada_python/pull/844) - Add reset_frequency option for the impact.select() function. [#847](https://github.com/CLIMADA-project/climada_python/pull/847) diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index f21b73e951..2ee11385ff 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -12,11 +12,11 @@ For orientation, these are some categories of possible contributions we can thin * **New Modules and Utility Functions:** Did you create a function or an entire module you find useful for your work? Maybe you are not the only one! Feel free to simply raise a pull request for functions that improve, e.g., plotting or data handling. As an entire module has to be carefully integrated into the framework, it might help if you talk to us first so we can design the module and plan the next steps. You can do that by raising an issue or starting a [discussion](https://github.com/CLIMADA-project/climada_python/discussions) on GitHub. A good place to start a personal discussion is our monthly CLIMADA developers call. -Please contact the [lead developers](https://wcr.ethz.ch/research/climada.html) if you want to join. +Please contact the [lead developers](https://climada.ethz.ch/team/) if you want to join. ## Why Should You Contribute? -* You will be listed as author of the CLIMADA repository in the [AUTHORS](AUTHORS.md) file. +* You will be listed as author of the CLIMADA repository in the [AUTHORS][authors] file. * You will improve the quality of the CLIMADA software for you and for everybody else using it. * You will gain insights into scientific software development. @@ -40,7 +40,7 @@ To contribute follow these steps: ```bash pylint ``` -6. Add your name to the [AUTHORS](AUTHORS.md) file. +6. Add your name to the [AUTHORS][authors] file. 7. Push your updates to the remote repository: ```bash @@ -83,4 +83,5 @@ It also contains a checklist for both pull request authors and reviewers to guid [docs]: https://climada-python.readthedocs.io/en/latest/ [devguide]: https://climada-python.readthedocs.io/en/latest/#developer-guide -[testing]: https://climada-python.readthedocs.io/en/latest/guide/Guide_Testing.html +[testing]: https://climada-python.readthedocs.io/en/latest/development/Guide_Testing.html +[authors]: https://github.com/CLIMADA-project/climada_python/blob/main/AUTHORS.md diff --git a/climada/engine/unsequa/calc_base.py b/climada/engine/unsequa/calc_base.py index 0c7b6d725a..901661f6ef 100644 --- a/climada/engine/unsequa/calc_base.py +++ b/climada/engine/unsequa/calc_base.py @@ -223,7 +223,7 @@ def make_sample(self, N, sampling_method="saltelli", sampling_kwargs=None): The 'ff' sampling method does not require a value for the N parameter. The inputed N value is hence ignored in the sampling process in the case of this method. - The 'ff' sampling method requires a number of uncerainty parameters to be + The 'ff' sampling method requires a number of uncertainty parameters to be a power of 2. The users can generate dummy variables to achieve this requirement. Please refer to https://salib.readthedocs.io/en/latest/api.html for more details. @@ -231,7 +231,7 @@ def make_sample(self, N, sampling_method="saltelli", sampling_kwargs=None): See Also -------- SALib.sample: sampling methods from SALib SALib.sample - https://salib.readthedocs.io/en/latest/api.html + https://salib.readthedocs.io/en/latest/api.html """ diff --git a/climada/engine/unsequa/calc_cost_benefit.py b/climada/engine/unsequa/calc_cost_benefit.py index 8d574b2c35..05fffbc9bb 100644 --- a/climada/engine/unsequa/calc_cost_benefit.py +++ b/climada/engine/unsequa/calc_cost_benefit.py @@ -53,8 +53,7 @@ class CalcCostBenefit(Calc): - """ - Cost Benefit uncertainty analysis class + """Cost Benefit uncertainty analysis class This is the base class to perform uncertainty analysis on the outputs of climada.engine.costbenefit.CostBenefit(). diff --git a/climada/engine/unsequa/unc_output.py b/climada/engine/unsequa/unc_output.py index d9c68fe69d..80a385395e 100644 --- a/climada/engine/unsequa/unc_output.py +++ b/climada/engine/unsequa/unc_output.py @@ -84,20 +84,9 @@ class UncOutput: samples_df : pandas.DataFrame Values of the sampled uncertainty parameters. It has n_samples rows and one column per uncertainty parameter. - sampling_method : str - Name of the sampling method from SAlib. - https://salib.readthedocs.io/en/latest/api.html# - n_samples : int - Effective number of samples (number of rows of samples_df) - param_labels : list - Name of all the uncertainty parameters distr_dict : dict Comon flattened dictionary of all the distr_dict of all input variables. It represents the distribution of all the uncertainty parameters. - problem_sa : dict - The description of the uncertainty variables and their - distribution as used in SALib. - https://salib.readthedocs.io/en/latest/basics.html. """ _metadata = [ @@ -192,6 +181,7 @@ def check_salib(self, sensitivity_method): def sampling_method(self): """ Returns the sampling method used to generate self.samples_df + See: https://salib.readthedocs.io/en/latest/api.html# Returns ------- diff --git a/climada/hazard/centroids/centr.py b/climada/hazard/centroids/centr.py index c4044f7dd5..e5e5a45bfa 100644 --- a/climada/hazard/centroids/centr.py +++ b/climada/hazard/centroids/centr.py @@ -23,7 +23,7 @@ import logging import warnings from pathlib import Path -from typing import Any, Literal, Union +from typing import Any, Literal, Optional, Union import cartopy import cartopy.crs as ccrs @@ -52,21 +52,7 @@ class Centroids: - """Contains vector centroids as a GeoDataFrame - - Attributes - ---------- - lat : np.array - Latitudinal coordinates in the specified CRS (can be any unit). - lon : np.array - Longitudinal coordinates in the specified CRS (can be any unit). - crs : pyproj.CRS - Coordinate reference system. Default: EPSG:4326 (WGS84) - region_id : np.array, optional - Numeric country (or region) codes. Default: None - on_land : np.array, optional - Boolean array indicating on land (True) or off shore (False). Default: None - """ + """Contains vector centroids as a GeoDataFrame""" def __init__( self, @@ -116,13 +102,13 @@ def __init__( self.set_on_land(source=on_land, overwrite=True) @property - def lat(self): - """Return latitudes""" + def lat(self) -> np.array: + """Latitudinal coordinates in the specified CRS (can be any unit).""" return self.gdf.geometry.y.values @property - def lon(self): - """Return longitudes""" + def lon(self) -> np.array: + """Longitudinal coordinates in the specified CRS (can be any unit).""" return self.gdf.geometry.x.values @property @@ -131,8 +117,8 @@ def geometry(self): return self.gdf["geometry"] @property - def on_land(self): - """Get the on_land property""" + def on_land(self) -> Optional[np.array]: + """Boolean array indicating on land (True) or off shore (False). Default: None""" if "on_land" not in self.gdf: return None if self.gdf["on_land"].isna().all(): @@ -140,8 +126,8 @@ def on_land(self): return self.gdf["on_land"].values @property - def region_id(self): - """Get the assigned region_id""" + def region_id(self) -> Optional[np.array]: + """Numeric country (or region) codes. Default: None""" if "region_id" not in self.gdf: return None if self.gdf["region_id"].isna().all(): @@ -149,8 +135,8 @@ def region_id(self): return self.gdf["region_id"].values @property - def crs(self): - """Get the crs""" + def crs(self) -> CRS: + """Coordinate reference system. Default: EPSG:4326 (WGS84)""" return self.gdf.crs @property diff --git a/climada/util/earth_engine.py b/climada/util/earth_engine.py index 2a35755e52..c0aa1646ce 100644 --- a/climada/util/earth_engine.py +++ b/climada/util/earth_engine.py @@ -26,148 +26,160 @@ # That's why `earthengine-api` is not in the CLIMADA requirements. # See tutorial: climada_util_earth_engine.ipynb # pylint: disable=import-error -import ee - LOGGER = logging.getLogger(__name__) -ee.Initialize() - - -def obtain_image_landsat_composite(landsat_collection, time_range, area): - """Selection of Landsat cloud-free composites in the Earth Engine library - See also: https://developers.google.com/earth-engine/landsat - - Parameters - ---------- - collection : - name of the collection - time_range : ['YYYY-MT-DY','YYYY-MT-DY'] - must be inside the available data - area : ee.geometry.Geometry - area of interest - - Returns - ------- - image_composite : ee.image.Image - """ - collection = ee.ImageCollection(landsat_collection) - - # Filter by time range and location - collection_time = collection.filterDate(time_range[0], time_range[1]) - image_area = collection_time.filterBounds(area) - image_composite = ee.Algorithms.Landsat.simpleComposite(image_area, 75, 3) - return image_composite - - -def obtain_image_median(collection, time_range, area): - """Selection of median from a collection of images in the Earth Engine library - See also: https://developers.google.com/earth-engine/reducers_image_collection - - Parameters - ---------- - collection : - name of the collection - time_range : ['YYYY-MT-DY','YYYY-MT-DY'] - must be inside the available data - area : ee.geometry.Geometry - area of interest - - Returns - ------- - image_median : ee.image.Image - """ - collection = ee.ImageCollection(collection) - - # Filter by time range and location - collection_time = collection.filterDate(time_range[0], time_range[1]) - image_area = collection_time.filterBounds(area) - image_median = image_area.median() - return image_median - - -def obtain_image_sentinel(sentinel_collection, time_range, area): - """Selection of median, cloud-free image from a collection of images in the Sentinel 2 dataset - See also: https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2 - - Parameters - ---------- - collection : - name of the collection - time_range : ['YYYY-MT-DY','YYYY-MT-DY'] - must be inside the available data - area : ee.geometry.Geometry - area of interest - - Returns - ------- - sentinel_median : ee.image.Image - """ - - # First, method to remove cloud from the image - def maskclouds(image): - band_qa = image.select("QA60") - cloud_mask = ee.Number(2).pow(10).int() - cirrus_mask = ee.Number(2).pow(11).int() - mask = band_qa.bitwiseAnd(cloud_mask).eq(0) and ( - band_qa.bitwiseAnd(cirrus_mask).eq(0) - ) - return image.updateMask(mask).divide(10000) - - sentinel_filtered = ( - ee.ImageCollection(sentinel_collection) - .filterBounds(area) - .filterDate(time_range[0], time_range[1]) - .filter(ee.Filter.lt("CLOUDY_PIXEL_PERCENTAGE", 20)) - .map(maskclouds) + +try: + import ee + + LOGGER.info("Google Earth Engine API successfully imported.") + ee_available = True +except ImportError: + LOGGER.error( + "Google Earth Engine API not found. Please install it using 'pip install earthengine-api'." ) + ee_available = False + +if not ee_available: + LOGGER.error( + "Google Earth Engine API not found. Skipping the init of `earth_engine.py`." + ) +else: + ee.Initialize() + + def obtain_image_landsat_composite(landsat_collection, time_range, area): + """Selection of Landsat cloud-free composites in the Earth Engine library + See also: https://developers.google.com/earth-engine/landsat + + Parameters + ---------- + collection : + name of the collection + time_range : ['YYYY-MT-DY','YYYY-MT-DY'] + must be inside the available data + area : ee.geometry.Geometry + area of interest + + Returns + ------- + image_composite : ee.image.Image + """ + collection = ee.ImageCollection(landsat_collection) + + # Filter by time range and location + collection_time = collection.filterDate(time_range[0], time_range[1]) + image_area = collection_time.filterBounds(area) + image_composite = ee.Algorithms.Landsat.simpleComposite(image_area, 75, 3) + return image_composite + + def obtain_image_median(collection, time_range, area): + """Selection of median from a collection of images in the Earth Engine library + See also: https://developers.google.com/earth-engine/reducers_image_collection + + Parameters + ---------- + collection : + name of the collection + time_range : ['YYYY-MT-DY','YYYY-MT-DY'] + must be inside the available data + area : ee.geometry.Geometry + area of interest + + Returns + ------- + image_median : ee.image.Image + """ + collection = ee.ImageCollection(collection) + + # Filter by time range and location + collection_time = collection.filterDate(time_range[0], time_range[1]) + image_area = collection_time.filterBounds(area) + image_median = image_area.median() + return image_median + + def obtain_image_sentinel(sentinel_collection, time_range, area): + """Selection of median, cloud-free image from a collection of images in the Sentinel 2 dataset + See also: https://developers.google.com/earth-engine/datasets/catalog/COPERNICUS_S2 + + Parameters + ---------- + collection : + name of the collection + time_range : ['YYYY-MT-DY','YYYY-MT-DY'] + must be inside the available data + area : ee.geometry.Geometry + area of interest + + Returns + ------- + sentinel_median : ee.image.Image + """ + + # First, method to remove cloud from the image + def maskclouds(image): + band_qa = image.select("QA60") + cloud_mask = ee.Number(2).pow(10).int() + cirrus_mask = ee.Number(2).pow(11).int() + mask = band_qa.bitwiseAnd(cloud_mask).eq(0) and ( + band_qa.bitwiseAnd(cirrus_mask).eq(0) + ) + return image.updateMask(mask).divide(10000) + + sentinel_filtered = ( + ee.ImageCollection(sentinel_collection) + .filterBounds(area) + .filterDate(time_range[0], time_range[1]) + .filter(ee.Filter.lt("CLOUDY_PIXEL_PERCENTAGE", 20)) + .map(maskclouds) + ) + + sentinel_median = sentinel_filtered.median() + return sentinel_median + + def get_region(geom): + """Get the region of a given geometry, needed for exporting tasks. + + Parameters + ---------- + geom : ee.Geometry, ee.Feature, ee.Image + region of interest + + Returns + ------- + region : list + """ + if isinstance(geom, ee.Geometry): + region = geom.getInfo()["coordinates"] + elif isinstance(geom, ee.Feature, ee.Image): + region = geom.geometry().getInfo()["coordinates"] + elif isinstance(geom, list): + condition = all([isinstance(item) == list for item in geom]) + if condition: + region = geom + return region + + def get_url(name, image, scale, region): + """It will open and download automatically a zip folder containing Geotiff data of 'image'. + If additional parameters are needed, see also: + https://github.com/google/earthengine-api/blob/master/python/ee/image.py + + Parameters + ---------- + name : str + name of the created folder + image : ee.image.Image + image to export + scale : int + resolution of export in meters (e.g: 30 for Landsat) + region : list + region of interest + + Returns + ------- + path : str + """ + path = image.getDownloadURL( + {"name": (name), "scale": scale, "region": (region)} + ) - sentinel_median = sentinel_filtered.median() - return sentinel_median - - -def get_region(geom): - """Get the region of a given geometry, needed for exporting tasks. - - Parameters - ---------- - geom : ee.Geometry, ee.Feature, ee.Image - region of interest - - Returns - ------- - region : list - """ - if isinstance(geom, ee.Geometry): - region = geom.getInfo()["coordinates"] - elif isinstance(geom, ee.Feature, ee.Image): - region = geom.geometry().getInfo()["coordinates"] - elif isinstance(geom, list): - condition = all([isinstance(item) == list for item in geom]) - if condition: - region = geom - return region - - -def get_url(name, image, scale, region): - """It will open and download automatically a zip folder containing Geotiff data of 'image'. - If additional parameters are needed, see also: - https://github.com/google/earthengine-api/blob/master/python/ee/image.py - - Parameters - ---------- - name : str - name of the created folder - image : ee.image.Image - image to export - scale : int - resolution of export in meters (e.g: 30 for Landsat) - region : list - region of interest - - Returns - ------- - path : str - """ - path = image.getDownloadURL({"name": (name), "scale": scale, "region": (region)}) - - webbrowser.open_new_tab(path) - return path + webbrowser.open_new_tab(path) + return path diff --git a/doc/_static/css/custom.css b/doc/_static/css/custom.css new file mode 100644 index 0000000000..18c791182b --- /dev/null +++ b/doc/_static/css/custom.css @@ -0,0 +1,39 @@ +:root { + + .navbar-brand { + height: 7rem; + max-height: 7rem; + } + +} + +.bd-main .bd-content .bd-article-container { + max-width: 100%; /* default is 60em */ +} + +.bd-page-width { + max-width: 100rem; +} + + +html { + --pst-font-size-base: 16px; + --pst-header-height: 7rem; +} + +.hero { + display: flex; + align-items: center; + justify-content: center; + text-align: center; /* Center-align text */ + background: linear-gradient(to right, #f39c12, #1abc9c, #bdc3c7); /* Orange, teal, gray gradient */ + color: white; /* Ensure text stands out */ + padding: 10px; + border-radius: 10px; /* Soft rounded corners */ + margin: 10px auto; /* Center the hero section horizontally */ + max-width: 980px; /* Restrict width to ensure readability */ + font-size: 1.1em; /* Slightly smaller text size */ + line-height: 1.8; /* Better line spacing for readability */ + box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1); /* Subtle shadow for depth */ + overflow-wrap: break-word; /* Ensure text doesn't overflow borders */ +} diff --git a/doc/climada/climada.engine.rst b/doc/api/climada/climada.engine.rst similarity index 100% rename from doc/climada/climada.engine.rst rename to doc/api/climada/climada.engine.rst diff --git a/doc/climada/climada.engine.unsequa.rst b/doc/api/climada/climada.engine.unsequa.rst similarity index 100% rename from doc/climada/climada.engine.unsequa.rst rename to doc/api/climada/climada.engine.unsequa.rst diff --git a/doc/climada/climada.entity.disc_rates.rst b/doc/api/climada/climada.entity.disc_rates.rst similarity index 100% rename from doc/climada/climada.entity.disc_rates.rst rename to doc/api/climada/climada.entity.disc_rates.rst diff --git a/doc/climada/climada.entity.exposures.litpop.rst b/doc/api/climada/climada.entity.exposures.litpop.rst similarity index 100% rename from doc/climada/climada.entity.exposures.litpop.rst rename to doc/api/climada/climada.entity.exposures.litpop.rst diff --git a/doc/climada/climada.entity.exposures.rst b/doc/api/climada/climada.entity.exposures.rst similarity index 100% rename from doc/climada/climada.entity.exposures.rst rename to doc/api/climada/climada.entity.exposures.rst diff --git a/doc/climada/climada.entity.impact_funcs.rst b/doc/api/climada/climada.entity.impact_funcs.rst similarity index 100% rename from doc/climada/climada.entity.impact_funcs.rst rename to doc/api/climada/climada.entity.impact_funcs.rst diff --git a/doc/climada/climada.entity.measures.rst b/doc/api/climada/climada.entity.measures.rst similarity index 100% rename from doc/climada/climada.entity.measures.rst rename to doc/api/climada/climada.entity.measures.rst diff --git a/doc/climada/climada.entity.rst b/doc/api/climada/climada.entity.rst similarity index 100% rename from doc/climada/climada.entity.rst rename to doc/api/climada/climada.entity.rst diff --git a/doc/climada/climada.hazard.centroids.rst b/doc/api/climada/climada.hazard.centroids.rst similarity index 100% rename from doc/climada/climada.hazard.centroids.rst rename to doc/api/climada/climada.hazard.centroids.rst diff --git a/doc/climada/climada.hazard.rst b/doc/api/climada/climada.hazard.rst similarity index 100% rename from doc/climada/climada.hazard.rst rename to doc/api/climada/climada.hazard.rst diff --git a/doc/climada/climada.hazard.trop_cyclone.rst b/doc/api/climada/climada.hazard.trop_cyclone.rst similarity index 100% rename from doc/climada/climada.hazard.trop_cyclone.rst rename to doc/api/climada/climada.hazard.trop_cyclone.rst diff --git a/doc/climada/climada.rst b/doc/api/climada/climada.rst similarity index 100% rename from doc/climada/climada.rst rename to doc/api/climada/climada.rst diff --git a/doc/climada/climada.util.calibrate.rst b/doc/api/climada/climada.util.calibrate.rst similarity index 100% rename from doc/climada/climada.util.calibrate.rst rename to doc/api/climada/climada.util.calibrate.rst diff --git a/doc/climada/climada.util.rst b/doc/api/climada/climada.util.rst similarity index 100% rename from doc/climada/climada.util.rst rename to doc/api/climada/climada.util.rst diff --git a/doc/api/index.rst b/doc/api/index.rst new file mode 100644 index 0000000000..eabfe4a5ea --- /dev/null +++ b/doc/api/index.rst @@ -0,0 +1,12 @@ +============== +API Reference +============== + +The API reference contains the whole specification of the code, that is, every modules, +classes (and their attributes), and functions that are available (and documented). + +.. toctree:: + :caption: API Reference + :hidden: + + Modules diff --git a/doc/conf.py b/doc/conf.py index 9656a27a25..8f9a44aa18 100644 --- a/doc/conf.py +++ b/doc/conf.py @@ -40,6 +40,7 @@ "sphinx.ext.viewcode", "sphinx.ext.napoleon", "sphinx.ext.ifconfig", + "sphinx_design", "sphinx_mdinclude", "myst_nb", "sphinx_markdown_tables", @@ -124,12 +125,30 @@ # The theme to use for HTML and HTML Help pages. Major themes that come with # Sphinx are currently 'default' and 'sphinxdoc'. -html_theme = "sphinx_book_theme" +html_theme = "pydata_sphinx_theme" # Theme options are theme-specific and customize the look and feel of a theme # further. For a list of options available for each theme, see the # documentation. -# html_theme_options = {} +html_theme_options = { + "header_links_before_dropdown": 8, + "navbar_align": "left", + # "icon_links": [ + # { + # # Label for this link + # "name": "GitHub", + # # URL where the link will redirect + # "url": "https://github.com/CLIMADA-project", # required + # # Icon class (if "type": "fontawesome"), or path to local image (if "type": "local") + # "icon": "fa-brands fa-square-github", + # # The type of image to be used (see below for details) + # "type": "fontawesome", + # } + # ], +} + +# Avoid section navigation sidebar in changelog page +html_sidebars = {"misc/CHANGELOG": []} # Add any paths that contain custom themes here, relative to this directory. # html_theme_path = [] @@ -155,6 +174,9 @@ # so a file named "default.css" will overwrite the builtin "default.css". html_static_path = ["_static"] +html_css_files = [ + "css/custom.css", +] # If not '', a 'Last updated on:' timestamp is inserted at every page bottom, # using the given strftime format. # html_last_updated_fmt = '%b %d, %Y' diff --git a/doc/guide/Guide_Git_Development.ipynb b/doc/development/Guide_CLIMADA_Development.ipynb similarity index 59% rename from doc/guide/Guide_Git_Development.ipynb rename to doc/development/Guide_CLIMADA_Development.ipynb index 08eb92c0cc..8e23160497 100644 --- a/doc/guide/Guide_Git_Development.ipynb +++ b/doc/development/Guide_CLIMADA_Development.ipynb @@ -2,288 +2,77 @@ "cells": [ { "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "fragment" - } - }, - "source": [ - "# Development and Git and CLIMADA" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "## Git and GitHub\n", - "\n", - "- Git's not that scary\n", - " - 95% of your work on Git will be done with the same handful of commands (the other 5% will always be done with careful Googling)\n", - " - Almost everything in Git can be undone by design (but use `rebase`, `--force` and `--hard` with care!)\n", - " - Your favourite IDE (Spyder, PyCharm, ...) will have a GUI for working with Git, or you can download a standalone one.\n", - "- The [Git Book](https://git-scm.com/book/en/v2) is a great introduction to how Git works and to using it on the command line.\n", - "- Consider using a GUI program such as “git desktop” or “Gitkraken” to have a visual git interface, in particular at the beginning. Your python IDE is also likely to have a visual git interface. \n", - "- Feel free to ask for help" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "subslide" - } - }, + "metadata": {}, "source": [ - "![](img/git_gui.png)" + "# CLIMADA Development\n", + "\n", + "This is a guide about how to contribute to the development of CLIMADA. We first explain some general guidelines about when and how one can contribute to CLIMADA, and then describe the steps in detail. We assume that you are familiar with Git, Github and their commands. If you are not familiar with these, you can refer to our instructions for [Development with Git](Guide_Git_Development.ipynb). " ] }, { "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, + "metadata": {}, "source": [ - "### What we assume you know\n", + "## Is CLIMADA the right place for your contribution? \n", "\n", - "We're assuming you're all familiar with the basics of Git.\n", + "When developing for CLIMADA, it is important to distinguish between core content and particular applications. Core content is meant to be included into the [climada_python](https://github.com/CLIMADA-project/climada_python) repository and will be subject to a code review. Any new addition should first be discussed with one of the [repository admins](https://github.com/CLIMADA-project/climada_python/wiki/Developer-Board). The purpose of this discussion is to see\n", "\n", - "- What (and why) is version control\n", - "- How to clone a repository\n", - "- How to make a commit and push it to GitHub\n", - "- What a branch is, and how to make one\n", - "- How to merge two branches\n", - "- The basics of the GitHub website\n", + "- How does the planned module fit into CLIMADA?\n", + "- What is an optimal architecture for the new module?\n", + "- What parts might already exist in other parts of the code?\n", "\n", - "If you're not feeling great about this, we recommend\n", - "- sending me a message so we can arrange an introduction with CLIMADA\n", - "- exploring the [Git Book](https://git-scm.com/book/en/v2)" + "Applications made with CLIMADA, such as an [ECA study](https://eca-network.org/) can be stored in the [paper repository](https://github.com/CLIMADA-project/climada_papers) once they have been published. For other types of work, consider making a separate repository that imports CLIMADA as an external package." ] }, { "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, + "metadata": {}, "source": [ - "### Terms we'll be using today\n", + "## Planning a new feature\n", "\n", - "These are terms that will come up a lot, so let's make sure we know them\n", + "Here we're talking about large features such as new modules, new data sources, or big methodological changes. Any extension to CLIMADA that might affect other developers' work, modify the CLIMADA core, or need a big code review.\n", "\n", - "- local versus remote\n", - " - Our **remote** repository is hosted on GitHub. This is the central location where all updates to CLIMADA that we want to share end up. If you're updating CLIMADA for the community, your code will end up here too.\n", - " - Your **local** repository is the copy you have on the machine you're working on, and where you do your work.\n", - " - Git calls the (first, default) remote the `origin`\n", - " - (It's possible to set more than one remote repository, e.g. you might set one up on a network-restricted computing cluster)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "subslide" - } - }, - "source": [ - "- push, pull and pull request\n", - " - You **push** your work when you send it from your local machine to the remote repository\n", - " - You **pull** from the remote repository to update the code on your local machine\n", - " - A **pull request** is a standardised review process on GitHub. Usually it ends with one branch merging into another" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "subslide" - } - }, - "source": [ - "- Conflict resolution\n", - " - Sometimes two people have made changes to the same bit of code. Usually this comes up when you're trying to merge branches. The changes have to be manually compared and the code edited to make sure the 'correct' version of the code is kept. " - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "## Gitflow " - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "Gitflow is a particular way of using git to organise projects that have\n", - "- multiple developers\n", - "- working on different features\n", - "- with a release cycle\n", - "\n", - "It means that\n", - "- there's always a stable version of the code available to the public\n", - "- the chances of two developers' code conflicting are reduced\n", - "- the process of adding and reviewing features and fixes is more standardised for everyone\n", - "\n", - "Gitflow is a _convention_, so you don't need any additional software.\n", - "- ... but if you want you can get some: a popular extension to the git command line tool allows you to issue more intuitive commands for a Gitflow workflow.\n", - "- Mac/Linux users can install git-flow from their package manager, and it's included with Git for Windows " - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "### Gitflow works on the `develop` branch instead of `main`" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "![](img/flow_1.png)\n", + "Smaller feature branches don't need such formalities. Use your judgment, and if in doubt, let people know.\n", "\n", - "- The critical difference between Gitflow and 'standard' git is that almost all of your work takes place on the `develop` branch, instead of the `main` (formerly `master`) branch.\n", - "- The `main` branch is reserved for planned, stable product releases, and it's what the general public download when they install CLIMADA. The developers almost never interact with it." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "### Gitflow is a feature-based workflow" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![](img/flow_2.png)\n", + "### Talk to the group\n", + " - Before starting coding a module, do not forget to coordinate with one of the repo admins (Emanuel, Chahan or Lukas)\n", + " - This is the chance to work out the Big Picture stuff that is better when it's planned with the group - possible intersections with other projects, possible conflicts, changes to the CLIMADA core, additional dependencies\n", + " - Also talk with others from the core development team ([see the GitHub wiki](https://github.com/CLIMADA-project/climada_python/wiki/Developer-Board)).\n", + " - Bring it to a developers meeting - people may be able to help/advise and are always interested in hearing about new projects. You can also find reviewers!\n", + " - Also, keep talking! Your plans _will_ change :)\n", "\n", - "- This is common to many workflows: when you want to add something new to the model you start a new branch, work on it locally, and then merge it back into `develop` **with a pull request** (which we'll cover later)." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "subslide" - } - }, - "source": [ - "- By convention we name all CLIMADA feature branches `feature/*` (e.g. `feature/meteorite`).\n", - "- Features can be anything, from entire hazard modules to a smarter way to do one line of a calculation. Most of the work you'll do on CLIMADA will be a features of one size or another.\n", - "- We'll talk more about developing CLIMADA features later!" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "### Gitflow enables a regular release cycle" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![](img/flow_3.png)\n", + "### Formulate the feature's data flow and workflow\n", "\n", - "- A release is usually more complex than merging `develop` into `main`." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "subslide" - } - }, - "source": [ - "- So for this a `release-*` branch is created from `develop`. We'll all be notified repeatedly when the deadline is to submit (and then to review) pull requests so that you can be included in a release.\n", - "- The core developer team (mostly Emanuel) will then make sure tests, bugfixes, documentation and compatibility requirements are met, merging any fixes back into `develop`.\n", - "- On release day, the release branch is merged into `main`, the commit is tagged as a release and the release notes are published on the GitHub at " - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "### Everything else is hotfixes" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "![](img/flow_4.png)\n", + "To optimize implementation and usefulness of the new feature, first conceptualize its data flow and workflow. It makes sense to discuss these with a CLIMADA core developer before starting to work on the feature's implementation.\n", + "- **Data flow**: Outline of how data moves through the system — where it is created or input, how it is processed, and if and where it is stored. This helps to improve the computational efficiency and to identify potential bottlenecks. \n", + "- **Workflow**: Plan about where and how the user and other CLIMADA components can interact with the new feature. This ensures that the new feature couples seamlessly to the existing code base of CLIMADA and that the new feaute is easily and clearly accessible to users.\n", "\n", - "- The other type of branch you'll create is a hotfix." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "subslide" - } - }, - "source": [ - "- Hotfixes are generally small changes to code that do one thing, fixing typos, small bugs, or updating docstrings. They're done in much the same way as features, and are usually merged with a pull request.\n", - "- The difference between features and hotfixes is fuzzy and you don't need to worry about getting it right.\n", - "- Hotfixes will occasionally be used to fix bugs on the `main` branch, in which case they will merge into both `main` and `develop`.\n", - "- Some hotfixes are so simple - e.g. fixing a typo or a docstring - that they don't need a pull request. Use your judgement, but as a rule, if you change what the code does, or how, you should be merging with a pull request." + "### Planning the work\n", + "\n", + "- Does the project go in its own repository and import CLIMADA, or does it extend the main CLIMADA repository. The way this is done is slowly changing, so definitely discuss it with the group.\n", + "- Find a few people who will help to review your code.\n", + " - Ask in a developers' meeting, on Slack (for WCR developers) or message people on the development team ([see the GitHub wiki](https://github.com/CLIMADA-project/climada_python/wiki/Developer-Board)).\n", + " - Let them know roughly how much code will be in the reviews, and when you'll be creating pull requests.\n", + "- How can the work split into manageable chunks?\n", + " - A series of smaller pull requests is far more manageable than one big one (and takes off some of the pre-release pressure)\n", + " - Reviewing and spotting issues/improvements/generalisations early is always a good thing.\n", + " - It encourages modularisation of the code: smaller self-contained updates, with documentation and tests.\n", + "- Will there be any changes to the CLIMADA core? These should be planned carefully\n", + "- Will you need any new dependencies? Are you sure?" ] }, { - "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ "## Installing CLIMADA for development\n", - "\n", - "See [Installation](install.rst) for instructions on how to install CLIMADA for developers. You might need to install additional environments contained in ``climada_python/requirements`` when using specific functionalities. Also see [Apps for working with CLIMADA](../guide/Guide_get_started.ipynb#apps-for-working-with-climada) for an overview of which tools are useful for CLIMADA developers. " + "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "(guide-pre-commit-hooks)=\n", "### Pre-Commit Hooks\n", "\n", "Climada developer dependencies include pre-commit hooks to help ensure code linting and formatting.\n", @@ -295,7 +84,7 @@ "- the correct sorting of imports using ``isort``\n", "- the correct formatting of the code using ``black``\n", "\n", - "If you have installed the pre-commit hooks (see [Install developer dependencies](install.rst#install-developer-dependencies-optional)), they will be run each time you attempt to create a new commit, and the usual git flow can slightly change:\n", + "If you have installed the pre-commit hooks (see [Install developer dependencies](../getting-started/install.rst#install-developer-dependencies-optional)), they will be run each time you attempt to create a new commit, and the usual git flow can slightly change:\n", "\n", "If any check fails, you will be warned and these hooks **will apply** corrections (such as formatting the code with black if it is not).\n", "As files are modified, you are required to stage them again (hooks cannot stage their modification, only you can) and commit again.\n", @@ -372,90 +161,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Does it belong in CLIMADA? " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "When developing for CLIMADA, it is important to distinguish between core content and particular applications. Core content is meant to be included into the [climada_python](https://github.com/CLIMADA-project/climada_python) repository and will be subject to a code review. Any new addition should first be discussed with one of the [repository admins](https://github.com/CLIMADA-project/climada_python/wiki/Developer-Board). The purpose of this discussion is to see\n", - "\n", - "- How does the planned module fit into CLIMADA?\n", - "- What is an optimal architecture for the new module?\n", - "- What parts might already exist in other parts of the code?\n", - "\n", - "Applications made with CLIMADA, such as an [ECA study](https://eca-network.org/) can be stored in the [paper repository](https://github.com/CLIMADA-project/climada_papers) once they have been published. For other types of work, consider making a separate repository that imports CLIMADA as an external package." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "## Features and branches" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "### Planning a new feature\n", - "\n", - "Here we're talking about large features such as new modules, new data sources, or big methodological changes. Any extension to CLIMADA that might affect other developers' work, modify the CLIMADA core, or need a big code review.\n", - "\n", - "Smaller feature branches don't need such formalities. Use your judgment, and if in doubt, let people know.\n", - "\n", - "### Talk to the group\n", - " - Before starting coding a module, do not forget to coordinate with one of the repo admins (Emanuel, Chahan or Lukas)\n", - " - This is the chance to work out the Big Picture stuff that is better when it's planned with the group - possible intersections with other projects, possible conflicts, changes to the CLIMADA core, additional dependencies\n", - " - Also talk with others from the core development team ([see the GitHub wiki](https://github.com/CLIMADA-project/climada_python/wiki/Developer-Board)).\n", - " - Bring it to a developers meeting - people may be able to help/advise and are always interested in hearing about new projects. You can also find reviewers!\n", - " - Also, keep talking! Your plans _will_ change :)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "### Planning the work\n", - "\n", - "- Does the project go in its own repository and import CLIMADA, or does it extend the main CLIMADA repository?\n", - " - The way this is done is slowly changing, so definitely discuss it with the group.\n", - " - Chahan will discuss this later!\n", - "- Find a few people who will help to review your code.\n", - " - Ask in a developers' meeting, on Slack (for WCR developers) or message people on the development team ([see the GitHub wiki](https://github.com/CLIMADA-project/climada_python/wiki/Developer-Board)).\n", - " - Let them know roughly how much code will be in the reviews, and when you'll be creating pull requests.\n", - "- How can the work split into manageable chunks?\n", - " - A series of smaller pull requests is far more manageable than one big one (and takes off some of the pre-release pressure)\n", - " - Reviewing and spotting issues/improvements/generalisations early is always a good thing.\n", - " - It encourages modularisation of the code: smaller self-contained updates, with documentation and tests.\n", - "- Will there be any changes to the CLIMADA core?\n", - " - These should be planned carefully\n", - "- Will you need any new dependencies? Are you sure?\n", - " - Chahan will discuss this later!" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "### Working on feature branches\n", + "## Working on feature branches\n", "\n", "When developing a big new feature, consider creating a feature branch and merging smaller branches into that feature branch with pull requests, keeping the whole process separate from `develop` until it's completed. This makes step-by-step code review nice and easy, and makes the final merge more easily tracked in the history.\n", "\n", @@ -475,8 +181,6 @@ "\n", " git checkout -b branch_name\n", "\n", - "### Follow the [python do's and don't](https://github.com/CLIMADA-project/climada_python/blob/main/doc/guide/Guide_PythonDos-n-Donts.ipynb) and [performance](https://github.com/CLIMADA-project/climada_python/blob/main/doc/guide/Guide_Py_Performance.ipynb) guides. Write small readable methods, classes and functions.\n", - "\n", "get the latest data from the remote repository and update your branch\n", " \n", " git pull\n", @@ -494,28 +198,15 @@ " git commit -m \"new functionality of .. implemented\"\n", " \n", "### Make unit and integration tests on your code, preferably during development\n", - "see [Guide on unit and integration tests](../guide/Guide_Testing.ipynb)\n" + "see [Guide on unit and integration tests](Guide_Testing.ipynb)\n" ] }, { "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "## Pull requests" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, + "metadata": {}, "source": [ + "## Pull requests\n", + "\n", "We want every line of code that goes into the CLIMADA repository to be reviewed!\n", "\n", "Code review:\n", @@ -523,17 +214,8 @@ "- lets you draw on the experience of the rest of the team\n", "- makes sure that more than one person knows how your code works\n", "- helps to unify and standardise CLIMADA's code, so new users find it easier to read and navigate\n", - "- creates an archived description and discussion of the changes you've made" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ + "- creates an archived description and discussion of the changes you've made\n", + "\n", "### When to make a pull request\n", "\n", "- When you've finished writing a big new class or method (and its tests)\n", @@ -543,17 +225,8 @@ "\n", "Not all pull requests have to be into `develop` - you can make a pull request into any active branch that suits you.\n", "\n", - "Pull requests need to be made latest two weeks before a release, see [releases](https://github.com/CLIMADA-project/climada_python/releases)." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ + "Pull requests need to be made latest two weeks before a release, see [releases](https://github.com/CLIMADA-project/climada_python/releases).\n", + "\n", "### Step by step pull request!\n", "\n", "Let's suppose you've developed a cool new module on the `feature/meteorite` branch and you're ready to merge it into `develop`.\n", @@ -566,17 +239,8 @@ "- Updated dependencies (if need be)\n", "- Added your name to the AUTHORS file\n", "- Added an entry to the ``CHANGELOG.md`` file. See for information on how this shoud look like.\n", - "- (Advanced, optional) interactively rebase/squash recent commits that _aren't yet on GitHub_.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ + "- (Advanced, optional) interactively rebase/squash recent commits that _aren't yet on GitHub_.\n", + "\n", "### Steps\n", "\n", "1) Make sure the `develop` branch is up to date on your own machine\n", @@ -600,17 +264,8 @@ " ```\n", "\n", "4) Perform a static code analysis using pylint with CLIMADA's configuration `.pylintrc` (in the climada root directory). Jenkins executes it after every push.\\\n", - " To do it locally, your IDE probably provides a tool, or you can run `make lint` and see the output in `pylint.log`." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ + " To do it locally, your IDE probably provides a tool, or you can run `make lint` and see the output in `pylint.log`.\n", + "\n", "5) Push to GitHub.\n", " If you're pushing this branch for the first time, use\n", " ```\n", @@ -621,17 +276,8 @@ " git push\n", " ```\n", "\n", - "6) Check all the tests pass on the WCR Jenkins server (). See Emanuel's presentation for how to do this! You should regularly be pushing your code and checking this!" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ + "6) Check all the tests pass on the WCR Jenkins server (). See Emanuel's presentation for how to do this! You should regularly be pushing your code and checking this!\n", + "\n", "7) Create the pull request!\n", "\n", " - On the CLIMADA GitHub page, navigate to your feature branch (there's a drop-down menu above the file structure, pointing by default to `main`).\n", @@ -642,13 +288,7 @@ " - Assign reviewers in the page's right hand sidebar. Tag anyone who might be interested in reading the code. You should already have found one or two people who are happy to read the whole request and\n", " sign it off (they could also be added to 'Assignees').\n", " - Create the pull request.\n", - " - Contact the reviewers to let them know the request is live. GitHub's settings mean that they may not be alerted automatically. Maybe also let people know on the WCR Slack!" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ + " - Contact the reviewers to let them know the request is live. GitHub's settings mean that they may not be alerted automatically. Maybe also let people know on the WCR Slack!\n", "\n", "8) Talk with your reviewers\n", "\n", @@ -656,17 +296,8 @@ " - Take comments and suggestions on board, but you don't need to agree with everything and you don't need to implement everything.\n", " - If you feel someone is asking for too many changes, prioritise, especially if you don't have time for complex rewrites.\n", " - If the suggested changes and or features don't block functionality and you don't have time to fix them, they can be moved to Issues.\n", - " - Chase people up if they're slow. People are slow." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ + " - Chase people up if they're slow. People are slow.\n", + "\n", "\n", "9) Once you implement the requested changes, respond to the comments with the corresponding commit implementing each requested change.\n", "\n", @@ -679,41 +310,21 @@ " \n", "12) Update the `develop` branch on your local machine.\n", "\n", - "Also see the [**Reviewer Guide**](../guide/Guide_Review.ipynb) and [**Reviewer Checklist**](../guide/Guide_Review.ipynb#reviewer-checklist)!" + "Also see the [**Reviewer Guide**](Guide_Review.ipynb) and [**Reviewer Checklist**](Guide_Review.ipynb#reviewer-checklist)!" ] }, { "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "## General tips and tricks" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, + "metadata": {}, "source": [ + "## General tips and tricks\n", + "\n", + "Follow the [python do's and don't](Guide_PythonDos-n-Donts) and [performance](Guide_Py_Performance.ipynb) guides. Write small readable methods, classes and functions.\n", + "\n", "### Ask for help with Git\n", "\n", - "- Git isn't intuitive, and rewinding or resetting is always work. If you're not certain what you're doing, or if you think you've messed up, send someone a message." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ + "- Git isn't intuitive, and rewinding or resetting is always work. If you're not certain what you're doing, or if you think you've messed up, send someone a message. See also our instructions for [Development with Git](Guide_Git_Development.ipynb).\n", + "\n", "### Don't push or commit to develop or main\n", "\n", "- Almost all new additions to CLIMADA should be merged into the `develop` branch with a pull request.\n", @@ -723,17 +334,8 @@ "\n", "So if you find yourself on the `main` or `develop` branches typing `git merge ...` or `git push` stop and think again - you should probably be making a pull request.\n", "\n", - "This can be difficult to undo, so contact someone on the team if you're unsure!" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ + "This can be difficult to undo, so contact someone on the team if you're unsure!\n", + "\n", "### Commit more often than you think, and use informative commit messages\n", "\n", "- Committing often makes mistakes less scary to undo\n", @@ -741,13 +343,8 @@ "git reset --hard HEAD\n", "```\n", "- Detailed commit messages make writing pull requests really easy\n", - "- Yes it's boring, but _trust me_, everyone (usually your future self) will love you when they're rooting through the git history to try and understand why something was changed" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ + "- Yes it's boring, but _trust me_, everyone (usually your future self) will love you when they're rooting through the git history to try and understand why something was changed\n", + "\n", "### Commit message syntax guidelines\n", "\n", "Basic syntax guidelines taken from here (on 17.06.2020)\n", @@ -763,17 +360,8 @@ " do it directly with the git command)\n", "- Put the name of the function/class/module/file that was edited\n", "- When fixing an issue, add the reference gh-ISSUENUMBER to the commit message \n", - " e.g. “fixes gh-40.” or “Closes gh-40.” For more infos see here ." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ + " e.g. “fixes gh-40.” or “Closes gh-40.” For more infos see here .\n", + "\n", "### What not to commit\n", "\n", "There are a lot of things that don't belong in the Git repository: \n", @@ -790,43 +378,16 @@ "To avoid committing changes of unrelated metadata, open Jupyter Notebooks in a text editor instead of your browser renderer. When committing changes, make sure that you indeed only commit things you *did* change, and revert any changes to metadata that are not related to your code updates.\n", "\n", "Several code editors use plugins to render Jupyter Notebooks. Here we collect the instructions to inspect Jupyter Notebooks as plain text when using them:\n", - "- **VSCode**: Open the Jupyter Notebook. Then open the internal command prompt (`Ctrl` + `Shift` + `P` or `Cmd` + `Shift` + `P` on macOS) and type/select 'View: Reopen Editor with Text Editor'" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ + "- **VSCode**: Open the Jupyter Notebook. Then open the internal command prompt (`Ctrl` + `Shift` + `P` or `Cmd` + `Shift` + `P` on macOS) and type/select 'View: Reopen Editor with Text Editor'\n", + "\n", "### Log ideas and bugs as GitHub Issues\n", "\n", "If there's a change you might want to see in the code - something that generalises, something that's not quite right, or a cool new feature - it can be set up as a GitHub Issue. Issues are pages for conversations about changes to the codebase and for logging bugs, and act as a 'backlog' for the CLIMADA project.\n", "\n", - "For a bug, or a question about functionality, make a minimal working example, state which version of CLIMADA you are using, and post it with the Issue." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "### How not to mess up the timeline" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "subslide" - } - }, - "source": [ + "For a bug, or a question about functionality, make a minimal working example, state which version of CLIMADA you are using, and post it with the Issue.\n", + "\n", + "### How not to mess up the timeline\n", + "\n", "Git builds the repository through incremental edits. This means it's great at keeping track of its history. But there are a few commands that _edit_ this history, and if histories get out of sync on different copies of the repository you're going to have a bad time.\n", "\n", "- Don't rebase any commits that already exist remotely!\n", @@ -834,17 +395,8 @@ "- Otherwise, you're unlikely to do anything irreversible\n", "- You can do what you like with commits that only exist on your machine.\n", "\n", - "That said, doing an interactive rebase to tidy up your commit history _before_ you push it to GitHub is a nice friendly gesture :)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ + "That said, doing an interactive rebase to tidy up your commit history _before_ you push it to GitHub is a nice friendly gesture :)\n", + "\n", "### Do not fast forward merges \n", "\n", "(This shouldn't be relevant - all your merges into `develop` should be through pull requests, which doesn't fast forward. But:)\n", @@ -852,17 +404,8 @@ "Don't fast forward your merges unless your branch is a single commit. Use\n", "`git merge --no-ff ...`\n", "\n", - "The exceptions is when you're merging `develop` into your feature branch." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ + "The exceptions is when you're merging `develop` into your feature branch.\n", + "\n", "### Merge the remote develop branch into your feature branch every now and again\n", "\n", "- This way you'll find conflicts early\n", @@ -871,96 +414,39 @@ "git pull\n", "git checkout feature/myfeature\n", "git merge develop\n", - "```" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ + "```\n", + "\n", "### Create frequent pull requests\n", "\n", "I said this already:\n", "- It structures your workflow\n", "- It's easier for reviewers\n", "- If you're going to break something for other people you all know sooner\n", - "- It saves work for the rest of the team right before a release" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ + "- It saves work for the rest of the team right before a release\n", + "\n", "### Whenever you do something with CLIMADA, make a new local branch \n", "\n", - "You never know when a quick experiment will become something you want to save for later." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ + "You never know when a quick experiment will become something you want to save for later.\n", + "\n", "### But do not do everything in the CLIMADA repository\n", "\n", "- If you're running CLIMADA rather than developing it, create a new folder, initialise a new repository with `git init` and store your scripts and data there\n", - "- If you're writing an extension to CLIMADA that doesn't change the model core, create a new folder, initialise a new repository with `git init` and import CLIMADA. You can always add it to the model later if you need to." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "slideshow": { - "slide_type": "slide" - } - }, - "source": [ - "### Questions" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ + "- If you're writing an extension to CLIMADA that doesn't change the model core, create a new folder, initialise a new repository with `git init` and import CLIMADA. You can always add it to the model later if you need to.\n", + "\n", + "### Questions\n", + "\n", "![Git and Github logos](img/xkcd_git.png)\\\n", "" ] } ], "metadata": { - "celltoolbar": "Slideshow", "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" + "display_name": "", + "name": "" }, "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.13" - }, - "vscode": { - "interpreter": { - "hash": "fe76ddefd4ac3b756bca82b2809865e7c67c346a46477cb9eec4ead581742ab6" - } + "name": "python" } }, "nbformat": 4, diff --git a/doc/guide/Guide_CLIMADA_Tutorial.ipynb b/doc/development/Guide_CLIMADA_Tutorial.ipynb similarity index 100% rename from doc/guide/Guide_CLIMADA_Tutorial.ipynb rename to doc/development/Guide_CLIMADA_Tutorial.ipynb diff --git a/doc/guide/Guide_CLIMADA_conventions.ipynb b/doc/development/Guide_CLIMADA_conventions.ipynb similarity index 98% rename from doc/guide/Guide_CLIMADA_conventions.ipynb rename to doc/development/Guide_CLIMADA_conventions.ipynb index 6b4e4c2909..9ce5b3285e 100644 --- a/doc/guide/Guide_CLIMADA_conventions.ipynb +++ b/doc/development/Guide_CLIMADA_conventions.ipynb @@ -49,7 +49,7 @@ " - Contact a [repository admin](https://github.com/CLIMADA-project/climada_python/wiki/Developer-Board) to get permission\n", " - Open an [issue](https://github.com/CLIMADA-project/climada_python/issues)\n", " \n", - "Hence, first try to solve your problem with the standard library and function/methods already implemented in CLIMADA (see in particular the [utility functions](#Utility-functions)) then use the packages included in CLIMADA, and if this is not enough, propose the addition of a new package. Do not hesitate to propose new packages if this is needed for your work!" + "Hence, first try to solve your problem with the standard library and function/methods already implemented in CLIMADA then use the packages included in CLIMADA, and if this is not enough, propose the addition of a new package. Do not hesitate to propose new packages if this is needed for your work!" ] }, { @@ -132,8 +132,8 @@ "\n", "Note that most text editors usually take care of 1. and 2. by default.\n", "\n", - "Please note that pull requests will not be merged if these checks fail. The easiest way to ensure this, is to use [pre-commit hooks](guide-pre-commit-hooks), which will allow you to both run the checks and apply fixes when creating a new commit.\n", - "Following the [advanced installation instructions](install.rst#advanced-instructions) will set up these hooks for you." + "Please note that pull requests will not be merged if these checks fail. The easiest way to ensure this, is to use [pre-commit hooks](Guide_CLIMADA_Development.ipynb#pre-commit-hooks), which will allow you to both run the checks and apply fixes when creating a new commit.\n", + "Following the [advanced installation instructions](../getting-started/install.rst#advanced-instructions) will set up these hooks for you." ] }, { @@ -505,7 +505,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.19" + "version": "3.12.6" }, "latex_envs": { "LaTeX_envs_menu_present": true, diff --git a/doc/guide/Guide_Configuration.ipynb b/doc/development/Guide_Configuration.ipynb similarity index 98% rename from doc/guide/Guide_Configuration.ipynb rename to doc/development/Guide_Configuration.ipynb index 69056eba61..ad8ccb36f4 100644 --- a/doc/guide/Guide_Configuration.ipynb +++ b/doc/development/Guide_Configuration.ipynb @@ -439,7 +439,7 @@ "source": [ "### Test Configuration \n", "\n", - "The configuration values for unit and integration tests are not part of the [default configuration](#Default-Configuration), since they are irrelevant for the regular CLIMADA user and only aimed for developers.\\\n", + "The configuration values for unit and integration tests are not part of the [default configuration](#default-configuration), since they are irrelevant for the regular CLIMADA user and only aimed for developers.\\\n", "The default test configuration is defined in the `climada.conf` file of the installation directory.\n", "This file contains paths to files that are read during tests. If they are part of the GitHub repository, their path i.g. starts with the `climada` folder within the installation directory:\n", "```json\n", @@ -509,7 +509,7 @@ ], "metadata": { "kernelspec": { - "display_name": "climada_env", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -523,7 +523,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.13 | packaged by conda-forge | (default, Mar 25 2022, 06:05:47) \n[Clang 12.0.1 ]" + "version": "3.12.6" }, "vscode": { "interpreter": { diff --git a/doc/guide/Guide_Euler.ipynb b/doc/development/Guide_Euler.ipynb similarity index 100% rename from doc/guide/Guide_Euler.ipynb rename to doc/development/Guide_Euler.ipynb diff --git a/doc/guide/Guide_Exception_Logging.ipynb b/doc/development/Guide_Exception_Logging.ipynb similarity index 100% rename from doc/guide/Guide_Exception_Logging.ipynb rename to doc/development/Guide_Exception_Logging.ipynb diff --git a/doc/development/Guide_Git_Development.ipynb b/doc/development/Guide_Git_Development.ipynb new file mode 100644 index 0000000000..b71d408fe5 --- /dev/null +++ b/doc/development/Guide_Git_Development.ipynb @@ -0,0 +1,303 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "fragment" + } + }, + "source": [ + "# Development with Git\n", + "\n", + " Here we provide a detailed instruction to the use of Git and GitHub and their workflows, which are essential to the code development of CLIMADA. \n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Git and GitHub\n", + "\n", + "- Git's not that scary\n", + " - 95% of your work on Git will be done with the same handful of commands (the other 5% will always be done with careful Googling)\n", + " - Almost everything in Git can be undone by design (but use `rebase`, `--force` and `--hard` with care!)\n", + " - Your favourite IDE (Spyder, PyCharm, ...) will have a GUI for working with Git, or you can download a standalone one.\n", + "- The [Git Book](https://git-scm.com/book/en/v2) is a great introduction to how Git works and to using it on the command line.\n", + "- Consider using a GUI program such as “git desktop” or “Gitkraken” to have a visual git interface, in particular at the beginning. Your python IDE is also likely to have a visual git interface. \n", + "- Feel free to ask for help" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "![](img/git_gui.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "### What we assume you know\n", + "\n", + "We're assuming you're all familiar with the basics of Git.\n", + "\n", + "- What (and why) is version control\n", + "- How to clone a repository\n", + "- How to make a commit and push it to GitHub\n", + "- What a branch is, and how to make one\n", + "- How to merge two branches\n", + "- The basics of the GitHub website\n", + "\n", + "If you're not feeling great about this, we recommend\n", + "- sending me a message so we can arrange an introduction with CLIMADA\n", + "- exploring the [Git Book](https://git-scm.com/book/en/v2)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "### Terms we'll be using today\n", + "\n", + "These are terms that will come up a lot, so let's make sure we know them\n", + "\n", + "- local versus remote\n", + " - Our **remote** repository is hosted on GitHub. This is the central location where all updates to CLIMADA that we want to share end up. If you're updating CLIMADA for the community, your code will end up here too.\n", + " - Your **local** repository is the copy you have on the machine you're working on, and where you do your work.\n", + " - Git calls the (first, default) remote the `origin`\n", + " - (It's possible to set more than one remote repository, e.g. you might set one up on a network-restricted computing cluster)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "- push, pull and pull request\n", + " - You **push** your work when you send it from your local machine to the remote repository\n", + " - You **pull** from the remote repository to update the code on your local machine\n", + " - A **pull request** is a standardised review process on GitHub. Usually it ends with one branch merging into another" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "- Conflict resolution\n", + " - Sometimes two people have made changes to the same bit of code. Usually this comes up when you're trying to merge branches. The changes have to be manually compared and the code edited to make sure the 'correct' version of the code is kept. " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "## Gitflow " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "Gitflow is a particular way of using git to organise projects that have\n", + "- multiple developers\n", + "- working on different features\n", + "- with a release cycle\n", + "\n", + "It means that\n", + "- there's always a stable version of the code available to the public\n", + "- the chances of two developers' code conflicting are reduced\n", + "- the process of adding and reviewing features and fixes is more standardised for everyone\n", + "\n", + "Gitflow is a _convention_, so you don't need any additional software.\n", + "- ... but if you want you can get some: a popular extension to the git command line tool allows you to issue more intuitive commands for a Gitflow workflow.\n", + "- Mac/Linux users can install git-flow from their package manager, and it's included with Git for Windows " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "### Gitflow works on the `develop` branch instead of `main`" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "![](img/flow_1.png)\n", + "\n", + "- The critical difference between Gitflow and 'standard' git is that almost all of your work takes place on the `develop` branch, instead of the `main` (formerly `master`) branch.\n", + "- The `main` branch is reserved for planned, stable product releases, and it's what the general public download when they install CLIMADA. The developers almost never interact with it." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "### Gitflow is a feature-based workflow" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![](img/flow_2.png)\n", + "\n", + "- This is common to many workflows: when you want to add something new to the model you start a new branch, work on it locally, and then merge it back into `develop` **with a pull request** (which we'll cover later)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "- By convention we name all CLIMADA feature branches `feature/*` (e.g. `feature/meteorite`).\n", + "- Features can be anything, from entire hazard modules to a smarter way to do one line of a calculation. Most of the work you'll do on CLIMADA will be a features of one size or another.\n", + "- We'll talk more about developing CLIMADA features later!" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "### Gitflow enables a regular release cycle" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![](img/flow_3.png)\n", + "\n", + "- A release is usually more complex than merging `develop` into `main`." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "- So for this a `release-*` branch is created from `develop`. We'll all be notified repeatedly when the deadline is to submit (and then to review) pull requests so that you can be included in a release.\n", + "- The core developer team (mostly Emanuel) will then make sure tests, bugfixes, documentation and compatibility requirements are met, merging any fixes back into `develop`.\n", + "- On release day, the release branch is merged into `main`, the commit is tagged as a release and the release notes are published on the GitHub at " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "slide" + } + }, + "source": [ + "### Everything else is hotfixes" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "![](img/flow_4.png)\n", + "\n", + "- The other type of branch you'll create is a hotfix." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "slideshow": { + "slide_type": "subslide" + } + }, + "source": [ + "- Hotfixes are generally small changes to code that do one thing, fixing typos, small bugs, or updating docstrings. They're done in much the same way as features, and are usually merged with a pull request.\n", + "- The difference between features and hotfixes is fuzzy and you don't need to worry about getting it right.\n", + "- Hotfixes will occasionally be used to fix bugs on the `main` branch, in which case they will merge into both `main` and `develop`.\n", + "- Some hotfixes are so simple - e.g. fixing a typo or a docstring - that they don't need a pull request. Use your judgement, but as a rule, if you change what the code does, or how, you should be merging with a pull request." + ] + } + ], + "metadata": { + "celltoolbar": "Slideshow", + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + }, + "vscode": { + "interpreter": { + "hash": "fe76ddefd4ac3b756bca82b2809865e7c67c346a46477cb9eec4ead581742ab6" + } + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/doc/guide/Guide_Py_Performance.ipynb b/doc/development/Guide_Py_Performance.ipynb similarity index 100% rename from doc/guide/Guide_Py_Performance.ipynb rename to doc/development/Guide_Py_Performance.ipynb diff --git a/doc/guide/Guide_PythonDos-n-Donts.ipynb b/doc/development/Guide_PythonDos-n-Donts.ipynb similarity index 100% rename from doc/guide/Guide_PythonDos-n-Donts.ipynb rename to doc/development/Guide_PythonDos-n-Donts.ipynb diff --git a/doc/guide/Guide_Review.ipynb b/doc/development/Guide_Review.ipynb similarity index 94% rename from doc/guide/Guide_Review.ipynb rename to doc/development/Guide_Review.ipynb index f996598b43..2ae95b7ed9 100644 --- a/doc/guide/Guide_Review.ipynb +++ b/doc/development/Guide_Review.ipynb @@ -39,9 +39,9 @@ "At least one reviewer needs to\n", "- Review all the changes in the pull request. Read what it's supposed to do, check it does that, and make sure the logic is sound.\n", "- Check that the code follows the CLIMADA style guidelines \n", - "- [CLIMADA coding conventions](../guide/Guide_CLIMADA_conventions.ipynb) \n", - "- [Python Dos and Don't](../guide/Guide_PythonDos-n-Donts.ipynb) \n", - " - [Python performance tips and best practice for CLIMADA developers](../guide/Guide_Py_Performance.ipynb) \n", + "- [CLIMADA coding conventions](../development/Guide_CLIMADA_conventions.ipynb) \n", + "- [Python Dos and Don't](../development/Guide_PythonDos-n-Donts.ipynb) \n", + "- [Python performance tips and best practice for CLIMADA developers](../development/Guide_Py_Performance.ipynb) \n", "- If the code is implementing an algorithm it should be referenced in the documentation. Check it's implemented correctly.\n", "- Try to think of edge cases and ways the code could break. See if there's appropriate error handling in cases where the function might behave unexpectedly.\n", "- (Optional) suggest easy ways to speed up the code, and more elegant ways to achieve the same goal.\n", @@ -126,10 +126,14 @@ } ], "metadata": { + "kernelspec": { + "display_name": "", + "name": "" + }, "language_info": { "name": "python" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/doc/guide/Guide_Testing.ipynb b/doc/development/Guide_Testing.ipynb similarity index 98% rename from doc/guide/Guide_Testing.ipynb rename to doc/development/Guide_Testing.ipynb index 319d8ada55..ad27d64011 100644 --- a/doc/guide/Guide_Testing.ipynb +++ b/doc/development/Guide_Testing.ipynb @@ -287,7 +287,7 @@ "## Testing CLIMADA\n", "\n", "Executing the entire test suite requires you to install the additional requirements for testing.\n", - "See the [installation instructions](install.rst) for [developer dependencies](install-dev) for further information.\n", + "See the [installation instructions for developer dependencies](../getting-started/install.rst#advanced-instructions) for further information.\n", "\n", "In general, you execute tests with\n", "```\n", @@ -334,7 +334,7 @@ ], "metadata": { "kernelspec": { - "display_name": "climada_py38", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -348,7 +348,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.15" + "version": "3.12.6" }, "vscode": { "interpreter": { diff --git a/doc/guide/Guide_continuous_integration_GitHub_actions.ipynb b/doc/development/Guide_continuous_integration_GitHub_actions.ipynb similarity index 100% rename from doc/guide/Guide_continuous_integration_GitHub_actions.ipynb rename to doc/development/Guide_continuous_integration_GitHub_actions.ipynb diff --git a/doc/development/coding-in-python.rst b/doc/development/coding-in-python.rst new file mode 100644 index 0000000000..39912c73ec --- /dev/null +++ b/doc/development/coding-in-python.rst @@ -0,0 +1,10 @@ +################ +Coding in python +################ + +.. toctree:: + :maxdepth: 1 + + Guide_PythonDos-n-Donts + Guide_Exception_Logging + Performance and Best Practices diff --git a/doc/development/dev-git.rst b/doc/development/dev-git.rst new file mode 100644 index 0000000000..44ba858fc0 --- /dev/null +++ b/doc/development/dev-git.rst @@ -0,0 +1,10 @@ +############################### +Using git and GitHub to develop +############################### + +.. toctree:: + :maxdepth: 1 + :hidden: + + Development with Git + Guide_continuous_integration_GitHub_actions diff --git a/doc/guide/img/CLIMADA_logo_QR.png b/doc/development/img/CLIMADA_logo_QR.png similarity index 100% rename from doc/guide/img/CLIMADA_logo_QR.png rename to doc/development/img/CLIMADA_logo_QR.png diff --git a/doc/guide/img/FileSystem-1.png b/doc/development/img/FileSystem-1.png similarity index 100% rename from doc/guide/img/FileSystem-1.png 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a/doc/guide/img/pylint.png b/doc/development/img/pylint.png similarity index 100% rename from doc/guide/img/pylint.png rename to doc/development/img/pylint.png diff --git a/doc/guide/img/xkcd_git.png b/doc/development/img/xkcd_git.png similarity index 100% rename from doc/guide/img/xkcd_git.png rename to doc/development/img/xkcd_git.png diff --git a/doc/guide/img/zen_of_python.png b/doc/development/img/zen_of_python.png similarity index 100% rename from doc/guide/img/zen_of_python.png rename to doc/development/img/zen_of_python.png diff --git a/doc/development/index.rst b/doc/development/index.rst new file mode 100644 index 0000000000..1c3c14ca35 --- /dev/null +++ b/doc/development/index.rst @@ -0,0 +1,17 @@ +.. include:: ../../CONTRIBUTING.md + :parser: myst_parser.sphinx_ + +.. toctree:: + :maxdepth: 2 + :hidden: + + Overview + Developing with Git + Coding in python + CLIMADA Coding Conventions + CLIMADA Configuration convention + Documenting your code + Writing tests for your code + Guide_Review + Guide_Euler + Authors <../misc/AUTHORS> diff --git a/doc/development/write-documentation.rst b/doc/development/write-documentation.rst new file mode 100644 index 0000000000..cfa4baa323 --- /dev/null +++ b/doc/development/write-documentation.rst @@ -0,0 +1,9 @@ +########################### +Documentation writing +########################### + +.. toctree:: + :maxdepth: 1 + + Guide_CLIMADA_Tutorial + Building the Documentation <../README> diff --git a/doc/tutorial/0_intro_python.ipynb b/doc/getting-started/0_intro_python.ipynb similarity index 100% rename from doc/tutorial/0_intro_python.ipynb rename to doc/getting-started/0_intro_python.ipynb diff --git a/doc/guide/Guide_Introduction.ipynb b/doc/getting-started/Guide_Introduction.ipynb similarity index 95% rename from doc/guide/Guide_Introduction.ipynb rename to doc/getting-started/Guide_Introduction.ipynb index 3f3a9ff134..aae2fa54d9 100644 --- a/doc/guide/Guide_Introduction.ipynb +++ b/doc/getting-started/Guide_Introduction.ipynb @@ -9,7 +9,7 @@ "# Introduction\n", "\n", "CLIMADA implements a fully probabilistic risk assessment model.\n", - "According to the IPCC [[1]](#1), natural risks emerge through the\n", + "According to the IPCC [[1](#references)], natural risks emerge through the\n", "interplay of climate and weather-related hazards, the exposure of goods\n", "or people to this hazard, and the specific vulnerability of exposed\n", "people, infrastructure and environment. \n", @@ -53,7 +53,7 @@ "

\n", "\n", "## References\n", - "[1] \n", + "[IPCC] \n", "IPCC: Climate Change 2014: Impacts, Adaptation and Vulnerability.\n", " Part A: Global and Sectoral Aspects. Contribution of Working Group\n", " II to the Fifth Assessment Report of the Intergovernmental Panel on\n", @@ -70,7 +70,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -84,7 +84,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.8" + "version": "3.12.6" } }, "nbformat": 4, diff --git a/doc/getting-started/Guide_get_started.ipynb b/doc/getting-started/Guide_get_started.ipynb new file mode 100644 index 0000000000..c4171d5005 --- /dev/null +++ b/doc/getting-started/Guide_get_started.ipynb @@ -0,0 +1,132 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "trying-bronze", + "metadata": {}, + "source": [ + "# How to navigate this documentation" + ] + }, + { + "cell_type": "markdown", + "id": "multiple-radical", + "metadata": {}, + "source": [ + "This page is a short summary of the different sections and guides here, to help you find the information that you need to get started.\n", + "\n", + "Each top section has its own landing page, presenting the essential elements in brief, and often several subsections, which go more into details." + ] + }, + { + "cell_type": "markdown", + "id": "68085d04-e4ee-45c8-8dbb-41e35bd072a1", + "metadata": {}, + "source": [ + "## Getting started\n", + "\n", + "The [Getting started](../getting-started/index.rst) section, where you are currently, presents the very basics of climada.\n", + "\n", + "For instance, to start learning about CLIMADA, you can have a look at the [introduction](../getting-started/Guide_Introduction.ipynb). \n", + "\n", + "You can also have a look at the paper [repository](https://github.com/CLIMADA-project/climada_papers) to get an overview of research projects conducted with CLIMADA." + ] + }, + { + "cell_type": "markdown", + "id": "future-distinction", + "metadata": {}, + "source": [ + "### Programming in Python\n", + "\n", + "It is best to have some basic knowledge of Python programming before starting with CLIMADA. But if you need a quick introduction or reminder, have a look at the short [Python Tutorial](../getting-started/0_intro_python.ipynb). Also have a look at the python [Python Dos and Don't](../development/Guide_PythonDos-n-Donts.ipynb) guide and at the [Python Performance Guide](../development/Guide_Py_Performance.ipynb) for best practice tips." + ] + }, + { + "cell_type": "markdown", + "id": "touched-penetration", + "metadata": {}, + "source": [ + "## Tutorials\n", + "A good way to start using CLIMADA is to have a look at the tutorials in the [User Guide](../user-guide/index.rst). The [10 minute climada](../user-guide/0_10min_climada.ipynb) tutorial will give you a quick introduction to CLIMADA, with a brief example on how to calculate you first impacts, as well as your first appraisal of adaptation options, while the [Overview](../user-guide/1_main_climada.ipynb) will present the whole structure of CLIMADA more in depth. You can then look at the specific tutorials for each module (for example if you are interested in a specific hazard, like [Tropical Cyclones](../user-guide/climada_hazard_TropCyclone.ipynb), or in learning to [estimate the value of asset exposure](../user-guide/climada_entity_LitPop.ipynb),...). " + ] + }, + { + "cell_type": "markdown", + "id": "cd831a52-ea5f-48ee-bf6b-4a6c1a1cdf15", + "metadata": {}, + "source": [ + "## Contributing\n", + "\n", + "If you would like to participate in the development of CLIMADA, carefully read the [Developer Guide](../development/index.rst). \n", + "Here you will find how to set up an environment to develop new features for CLIMADA, the workflow and rules to follow to make sure you can implement a valuable contribution!" + ] + }, + { + "cell_type": "markdown", + "id": "dc0f6303-e02b-429f-acc3-dd1b23d49be3", + "metadata": {}, + "source": [ + "## API Reference\n", + "\n", + "The [API reference](../api/index.rst) presents the documentation of the internal modules, classes, methods and function of CLIMADA." + ] + }, + { + "cell_type": "markdown", + "id": "6a25d260-336f-439a-8859-00b9b4c1251d", + "metadata": {}, + "source": [ + "## Changelog\n", + "\n", + "In the Changelog section, you can have a look at all the changes made between the different versions of CLIMADA" + ] + }, + { + "cell_type": "markdown", + "id": "efa8c4fc-706b-40e1-9567-c58dc9f59e0c", + "metadata": {}, + "source": [ + "## External links\n", + "\n", + "The top bar of this website also link to the documentation of [Climada Petals](https://climada-petals.readthedocs.io/en/stable/), the webpage of the [Weather and Climate Risk group](https://wcr.ethz.ch/) at ETH, and the official [CLIMADA website](https://climada.ethz.ch/)." + ] + }, + { + "cell_type": "markdown", + "id": "connected-anthony", + "metadata": {}, + "source": [ + "## Other Questions\n", + "\n", + "If you cannot find you answer in the other guides provided here, you can open an [issue](https://github.com/CLIMADA-project/climada_python/issues) for somebody to help you." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.6" + }, + "vscode": { + "interpreter": { + "hash": "fe76ddefd4ac3b756bca82b2809865e7c67c346a46477cb9eec4ead581742ab6" + } + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/doc/getting-started/index.rst b/doc/getting-started/index.rst new file mode 100644 index 0000000000..db2adfb269 --- /dev/null +++ b/doc/getting-started/index.rst @@ -0,0 +1,111 @@ +=================== +Getting started +=================== + +Quick Installation +-------------------- + + + +Are you already working with mamba or conda? proceed to install CLIMADA by executing the following line in the terminal:: + + mamba create -n climada_env -c conda-forge climada + +Each time you will want to work with CLIMADA, simply activate the environnment:: + + mamba activate climada_env + +You are good to go! + +.. seealso:: + + You don't have mamba or conda installed or you are looking for advanced installation instructions? Look up our :doc:`detailed instructions ` on CLIMADA installation. + + +.. dropdown:: How does CLIMADA compute impacts ? + :color: primary + + CLIMADA follows the IPCC risk framework to compute impacts by combining hazard intensity, exposure, and vulnerability. + It models hazards intensity (e.g., tropical cyclones, floods) using + historical event sets or stochastic simulations, overlaying them with spatial exposure data + (e.g., population, infrastructure), and applies vulnerability functions that estimate damage + given the hazard intensity. By aggregating these results, CLIMADA calculates expected + impacts, such as economic losses or affected populations. See the dedicated :doc:`impact tutorial ` + for more informations. + + .. image:: /user-guide/img/risk_framework.png + :width: 400 + :alt: Alternative text + :align: center + +.. dropdown:: How do you create a Hazard ? + :color: primary + + From a risk perspective, the intersting aspect of a natural hazard is its location and intensity. For such, + CLIMADA allows you to load your own :doc:`hazard ` data or to directly define it in the platform. As an example, + users can easily load historical tropical cyclone tracks (IBTracks) and apply stochastic methods to generate + a larger ensemble of tracks from the historical ones, from which they can easily compute the maximal windspeed, + the hazard intensity. + + .. image:: /user-guide/img/tc-tracks.png + :width: 500 + :alt: Alternative text + :align: center + +.. dropdown:: How do we define an exposure ? + :color: primary + + Exposure is defined as the entity that could potentially be damaged by a hazard: it can be people, infrastructures, + assests, ecosystems or more. A CLIMADA user is given the option to load its own exposure data into the platform, + or to use CLIMADA to define it. One common way of defining assets' exposure is through :doc:`LitPop `. LitPop dissagrate a + financial index, as the country GDP for instance, to a much finer resolution proportionally to population + density and nighlight intensity. + + .. image:: /user-guide/img/exposure.png + :width: 500 + :align: center + +.. dropdown:: How do we model vulnerability ? + :color: primary + + Vulnerability curves, also known as impact functions, tie the link between hazard intensity and damage. + CLIMADA offers built-in sigmoidal or step-wise vulnerability curves, and allows you to calibrate your own + impact functions with damage and hazard data through the :doc:`calibration module `. + + + .. image:: /user-guide/img/impact-function.png + :width: 400 + :align: center + +.. dropdown:: Do you want to quantify uncertainties ? + :color: primary + + CLIMADA provides a dedicated module :doc:`unsequa ` for conducting uncertainty and sensitivity analyses. + This module allows you to define a range of input parameters and evaluate their influence on the output, + helping you quantify the sensitivity of the modeling chain as well as the uncertainties in your results. + + .. image:: /user-guide/img/sensitivity.png + :width: 500 + :align: center + +.. dropdown:: Compare adaptation measures and assess their cost-effectiveness + :color: primary + + Is there an adaptation measure that will decrease the impact? Does the cost needed to implement such + measure outweight the gains? All these questions can be asnwered using the :doc:`cost-benefit ` and + :doc:`adaptation module `. + With this module, users can define and compare adaptation measures to establish their cost-effectiveness. + + .. image:: /user-guide/img/cost-benefit.png + :width: 400 + :align: center + +.. toctree:: + :maxdepth: 1 + :hidden: + + Navigate this documentation + Introduction + Installation instructions + How to cite CLIMADA <../misc/citation> + Python introduction <0_intro_python> diff --git a/doc/guide/install.rst b/doc/getting-started/install.rst similarity index 81% rename from doc/guide/install.rst rename to doc/getting-started/install.rst index d573b25f08..8a086db3f4 100644 --- a/doc/guide/install.rst +++ b/doc/getting-started/install.rst @@ -8,33 +8,46 @@ The following sections will guide you through the installation of CLIMADA and it CLIMADA has a complicated set of dependencies that cannot be installed with ``pip`` alone. Please follow the installation instructions carefully! - We recommend to use `Conda`_ for creating a suitable software environment to execute CLIMADA. + We recommend to use a ``conda``-based python environment manager such as `Mamba`_ or `Conda`_ for creating a suitable software environment to execute CLIMADA. -All following instructions should work on any operating system (OS) that is supported by `Conda`_, including in particular: **Windows**, **macOS**, and **Linux**. +All following instructions should work on any operating system (OS) that is supported by ``conda``, including in particular: **Windows**, **macOS**, and **Linux**. .. hint:: If you need help with the vocabulary used on this page, refer to the :ref:`Glossary `. -------------- -Prerequisites -------------- +--------------------------- +Install environment manager +--------------------------- -* Make sure you are using the **latest version** of your OS. Install any outstanding **updates**. -* Free up at least 10 GB of **free storage space** on your machine. - Conda and the CLIMADA dependencies will require around 5 GB of free space, and you will need at least that much additional space for storing the input and output data of CLIMADA. -* Ensure a **stable internet connection** for the installation procedure. - All dependencies will be downloaded from the internet. - Do **not** use a metered, mobile connection! -* Install the `Conda`_ environment management system. - We highly recommend you use `Miniforge`_, which includes the potent `Mamba`_ package manager. - Download the installer suitable for your system and follow the respective installation instructions. - We do **not** recommend using the ``conda`` command anymore, rather use ``mamba`` (see :ref:`conda-instead-of-mamba`). +If you haven't already installed an environment management system like `Mamba`_ or `Conda`_, you have to do so now. +We recommend to use ``mamba`` (see :ref:`conda-instead-of-mamba`) which is available in the installer Miniforge (see below). -.. note:: When mentioning the terms "terminal" or "command line" in the following, we are referring to the "Terminal" apps on macOS or Linux and the "Miniforge Prompt" on Windows. +macOS and Linux +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +* Open the "Terminal" app, copy-paste the two commands below, and hit enter: + + .. code-block:: shell + + curl -L -O "https://github.com/conda-forge/miniforge/releases/latest/download/Miniforge3-$(uname)-$(uname -m).sh" + bash Miniforge3-$(uname)-$(uname -m).sh + +* Accept the license terms. +* You can confirm the default location. +* Answer 'yes' when asked if if you wish to update your shell profile to automatically initialize conda. **Do not just hit ENTER but first type 'yes'** +* If at some point you encounter ``command not found: mamba``, open a new terminal window. +* If you encounter ``Run 'mamba init' to be able to run mamba activate/deactivate ...``, please run ``mamba init zsh`` or ``mamba init``. + +Windows +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +* Download the Windows installer at the Install section from `Miniforge`_. +* Execute the installer. This will install Mamba and provide the "Miniforge Prompt" program as a command line replacement. .. _install-choice: +--------------------------------------- Decide on Your Entry Level! -^^^^^^^^^^^^^^^^^^^^^^^^^^^ +--------------------------------------- Depening on your level of expertise, we provide two different approaches: @@ -51,14 +64,14 @@ Notes on the CLIMADA Petals Package CLIMADA is divided into two packages, CLIMADA Core (`climada_python `_) and CLIMADA Petals (`climada_petals `_). The Core contains all the modules necessary for probabilistic impact, averted damage, uncertainty and forecast calculations. -Data for hazard, exposures and impact functions can be obtained from the :doc:`CLIMADA Data API `. +Data for hazard, exposures and impact functions can be obtained from the :doc:`CLIMADA Data API `. Hazard and Exposures subclasses are included as demonstrators only. .. attention:: CLIMADA Petals is **not** a standalone module and requires CLIMADA Core to be installed! CLIMADA Petals contains all the modules for generating data (e.g., ``TC_Surge``, ``WildFire``, ``OpenStreeMap``, ...). New modules are developed and tested here. -Some data created with modules from Petals is available to download from the :doc:`Data API `. +Some data created with modules from Petals is available to download from the :doc:`Data API `. This works with just CLIMADA Core installed. CLIMADA Petals can be used to generate additional data of this type, or to have a look at the tutorials for all data types available from the API. @@ -116,7 +129,7 @@ For advanced Python users or developers of CLIMADA, we recommed cloning the CLIM .. warning:: - If you followed the :ref:`install-simple` before, make sure you **either** remove the environment with + If you followed the :ref:`install-simple` before, make sure you **either** remove the environment with: .. code-block:: shell @@ -211,7 +224,7 @@ For advanced Python users or developers of CLIMADA, we recommed cloning the CLIM If this test passes, great! You are good to go. -.. _install-dev: +.. _devdeps: Install Developer Dependencies (Optional) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ @@ -250,7 +263,7 @@ With the ``climada_env`` activated, execute pre-commit install -Please refer to the :ref:`guide on pre-commit hooks ` for information on how to use this tool. +Please refer to the `guide on pre-commit hooks <../development/Guide_CLIMADA_Development.html#pre-commit-hooks>`_ for information on how to use this tool. For executing the pre-defined test scripts in exactly the same way as they are executed by the automated CI pipeline, you will need ``make`` to be installed. On macOS and on Linux it is pre-installed. On Windows, it can easily be installed with Conda: @@ -259,7 +272,7 @@ On macOS and on Linux it is pre-installed. On Windows, it can easily be installe mamba install -n climada_env make -Instructions for running the test scripts can be found in the :doc:`Testing Guide `. +Instructions for running the test scripts can be found in the `Testing Guide <../development/Guide_Testing.html>`_. Install CLIMADA Petals (Optional) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ @@ -293,6 +306,10 @@ To install CLIMADA Petals, we assume you have already installed CLIMADA Core wit python -m pip install -e ./ +--------------------------------------- +Code Editors +--------------------------------------- + JupyterLab ^^^^^^^^^^ @@ -356,7 +373,7 @@ Test Explorer Setup After you set up a workspace, you might want to configure the test explorer for easily running the CLIMADA test suite within VSCode. -.. note:: Please install the additional :ref:`test dependencies ` before proceeding. +.. note:: Please install the additional :ref:`test dependencies ` before proceeding. #. In the left sidebar, select the "Testing" symbol, and click on *Configure Python Tests*. @@ -420,6 +437,56 @@ Therefore, we recommend installing Spyder in a *separate* environment, and then #. Set the Python interpreter used by Spyder to the one of ``climada_env``. Select *Preferences* > *Python Interpreter* > *Use the following interpreter* and paste the iterpreter path you copied from the ``climada_env``. +--------------------------------------- +Apps for working with CLIMADA +--------------------------------------- + +To work with CLIMADA, you will need an application that supports Jupyter Notebooks. +There are plugins available for nearly every code editor or IDE, but if you are unsure about which to choose, we recommend `JupyterLab `_, `Visual Studio Code `_ or `Spyder `_. +It is easy to get confused by all the different softwares and their uses so here is an overview of which tools we use for what: + +.. list-table:: + :header-rows: 1 + :widths: auto + + * - Use + - Tools + - Description + - Useful for + * - Distribution / manage virtual environment & packages + - **Recommended:** + Mamba + **Alternatives:** + Anaconda + - - Install climada, manage & use the climada virtual environment, install packages + - Anaconda includes Anaconda Navigator, which is a desktop GUI and can be used to launch applications like Jupyter Notebook, Spyder, etc. + - Climada Users + & Developers + * - IDE (Integrated Development Environment) + - **Recommended:** + VSCode + **Alternatives:** + Spyder, JupyterLab, PyCharm, & many more + - - Write and run code + - Useful for Developers: + - VSCode also has a GUI to commit changes to Git (similar to GitHub Desktop, but in the same place as your code) + - VSCode test explorer shows results for individual tests & any classes and files containing those tests (folders display a failure or pass icon) + - Climada Users + & Developers + * - Git GUI (Graphical User Interface) + - GitHub Desktop, GitKraken + - - Provides an interface which keeps track of the branch you’re working on, changes you made, etc. + - Allows you to commit changes, push to GitHub, etc. without having to use the command line + - The code itself is not written using these applications but with your IDE of choice (see above) + - Climada Developers + * - Continuous integration (CI) server + - Jenkins + - - Automatically checks code changes in GitHub repositories, e.g., when you create a pull request for the develop branch + - Performs static code analysis using pylint + - You don't need to do any installations yourself; this runs automatically when you push new code to GitHub + - See `Continuous Integration and GitHub Actions <../development/Guide_continuous_integration_GitHub_actions.ipynb>`_ + - Climada Developers + ---- FAQs ---- @@ -536,12 +603,12 @@ the level set to ``WARNING``. If you prefer another logging configuration, e.g., for using Climada embedded in another application, you can opt out of the default pre-configuration by setting the config value for -``logging.climada_style`` to ``false`` in the :doc:`configuration file ` +``logging.climada_style`` to ``false`` in the :doc:`configuration file <../development/Guide_Configuration>` ``climada.conf``. Changing the logging level can be done in multiple ways: -* Adjust the :doc:`configuration file ` ``climada.conf`` by setting a the value of the ``global.log_level`` property. +* Adjust the :doc:`configuration file <../development/Guide_Configuration>` ``climada.conf`` by setting a the value of the ``global.log_level`` property. This only has an effect if the ``logging.climada_style`` is set to ``true`` though. * Set a global logging level in your Python script: @@ -640,6 +707,6 @@ IDE .. _Conda: https://docs.conda.io/en/latest/ -.. _Mamba: https://mamba.readthedocs.io/en/latest/ +.. _Mamba: https://mamba.readthedocs.io/en/latest/installation/mamba-installation.html .. _Miniforge: https://github.com/conda-forge/miniforge .. _CLIMADA Petals: https://climada-petals.readthedocs.io/en/latest/ diff --git a/doc/guide/Guide_get_started.ipynb b/doc/guide/Guide_get_started.ipynb deleted file mode 100644 index 6fa55047b2..0000000000 --- a/doc/guide/Guide_get_started.ipynb +++ /dev/null @@ -1,135 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "trying-bronze", - "metadata": {}, - "source": [ - "# Getting started with CLIMADA" - ] - }, - { - "cell_type": "markdown", - "id": "multiple-radical", - "metadata": {}, - "source": [ - "This is a short summary of the guides to help you find the information that you need to get started.\n", - "To learn more about CLIMADA, have a look at the [introduction](../guide/Guide_Introduction.ipynb). You can also have a look at the paper [repository](https://github.com/CLIMADA-project/climada_papers) to get an overview of research projects." - ] - }, - { - "attachments": {}, - "cell_type": "markdown", - "id": "israeli-street", - "metadata": {}, - "source": [ - "## Installation\n", - "The first step to getting started is installing CLIMADA. To do so you will need:\n", - "1. To get the lastest release from the git repository [CLIMADA releases](https://github.com/climada-project/climada_python/releases) or clone the project with git if you are interested in contributing to the development.\n", - "2. To build a conda environment with the dependencies needed by CLIMADA. \n", - "\n", - "For details see the [Installation Instructions](install.rst).\n", - "\n", - "If you need to run a model on a computational cluster, have a look at [this guide](Guide_Euler.ipynb) to install CLIMADA and run your jobs." - ] - }, - { - "cell_type": "markdown", - "id": "future-distinction", - "metadata": {}, - "source": [ - "## Programming in Python\n", - "It is best to have some basic knowledge of Python programming before starting with CLIMADA. But if you need a quick introduction or reminder, have a look at the short [Python Tutorial](../tutorial/0_intro_python.ipynb). Also have a look at the python [Python Dos and Don't](../guide/Guide_PythonDos-n-Donts.ipynb) guide and at the [Python Performance Guide](../guide/Guide_Py_Performance.ipynb) for best practice tips." - ] - }, - { - "cell_type": "markdown", - "id": "c6ae7939", - "metadata": {}, - "source": [ - "## Apps for working with CLIMADA\n", - "\n", - "To work with CLIMADA, you will need an application that supports Jupyter Notebooks.\n", - "There are plugins available for nearly every code editor or IDE, but if you are unsure about which to choose, we recommend [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/), [Visual Studio Code](https://code.visualstudio.com/) or [Spyder](https://www.spyder-ide.org/).\n", - "It is easy to get confused by all the different softwares and their uses so here is an overview of which tools we use for what:" - ] - }, - { - "cell_type": "markdown", - "id": "25ab3b98", - "metadata": {}, - "source": [ - "| Use | Tools | Description | Useful for |\n", - "|:----------------------------------|:---------------------|:------------|:-----------|\n", - "| Distribution /
manage virtual environment
& packages | Recommended:
Mamba
Alternatives:
Anaconda|
  • Install climada, manage & use the climada virtual environment, install packages
  • Anaconda includes Anaconda navigator, which is a desktop GUI and can be used to launch applications like Jupyter Notebook, Spyder etc.
    • | Climada Users
    & Developers|\n", - "| IDE
    (Integrated Development Environment)|Recommended:
    VSCode
    Alternatives:
    Spyder
    JupyterLab
    PyCharm
    & many more|
  • Write and run code
    • Useful for Developers:
  • VSCode also has a GUI to commit changes to Git (similar to GitHub Desktop, but in the same place as your code)
  • VSCode test explorer shows results for individual tests & any classes and files containing those tests (folders display a failure or pass icon)
    • |Climada Users
    & Developers|\n", - "| Git GUI
    (Graphical User Interface)|GitHub Desktop
    Gitkraken|
  • Provides an interface which keeps track of the branch you’re working on, changes you made etc.
  • Allows you to commit changes, push to GitHub etc. without having to use command line
  • The code itself is not written using these applications but with your IDE of choice(see above)
    • |Climada Developers|\n", - "| Continuous integration
    (CI) server|Jenkins|
  • Automatically checks code changes in GitHub repositories, e.g. when you create a pull request for the develop branch
  • Performs static code analysis using pylint
  • you don't need to do any installations yourself, this runs automatically when you push new code to GitHub
  • see [Continuous Integration and GitHub Actions](../guide/Guide_continuous_integration_GitHub_actions.ipynb)
    • |Climada Developers|" - ] - }, - { - "cell_type": "markdown", - "id": "touched-penetration", - "metadata": {}, - "source": [ - "## Tutorials\n", - "A good way to start using CLIMADA is to have a look at the [Tutorials](https://github.com/CLIMADA-project/climada_python/tree/main/doc/tutorial). The [Main Tutorial](../tutorial/1_main_climada.ipynb) will introduce you the structure of CLIMADA and how to calculate you first impacts, as well as your first appraisal of adaptation options. You can then look at the specific tutorials for each module (for example if you are interested in a specific hazard, like [Tropical Cyclones](../tutorial/climada_hazard_TropCyclone.ipynb), or in learning to [estimate the value of asset exposure](../tutorial/climada_entity_LitPop.ipynb),...). " - ] - }, - { - "cell_type": "markdown", - "id": "0cc77b19", - "metadata": {}, - "source": [ - "## Documentation\n", - "\n", - "You can find the documentation of CLIMADA on Read the Docs [online](https://climada-python.readthedocs.io/en/stable/index.html#). Note that the documentation has several versions: 'latest', 'stable' and explicit version numbers, such as 'v3.1.1', in the url path. 'latest' is created from the 'develop' branch and has the latest changes of the developers, 'stable' from the latest release. For more details about documentation versions, please have a look at [here](https://readthedocs.org/projects/climada-python/versions/)." - ] - }, - { - "cell_type": "markdown", - "id": "growing-religious", - "metadata": {}, - "source": [ - "## Contributing\n", - "If you would like to participate in the development of CLIMADA, carefully read the [Git and Development Guide](../guide/Guide_Git_Development.ipynb). Before making a new feature, discuss with one of the repository admins (Now Chahan, Emmanuel and David). Every new feature or enhancement should be done on a separate branch, which will be merged in the develop branch after being reviewed (see [Checklist](../guide/Guide_Review.ipynb)). Finally, the develop branch is merged in the main branch in each CLIMADA release. Each new feature should come with a tutorial and with [Unit and Integration Tests](../guide/Guide_Testing.ipynb). " - ] - }, - { - "cell_type": "markdown", - "id": "connected-anthony", - "metadata": {}, - "source": [ - "## Other Questions\n", - "\n", - "If you cannot find you answer in the other guides provided here, you can open an [issue](https://github.com/CLIMADA-project/climada_python/issues) for somebody to help you." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "climada_env", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.15 | packaged by conda-forge | (default, Nov 22 2022, 08:49:06) \n[Clang 14.0.6 ]" - }, - "vscode": { - "interpreter": { - "hash": "fe76ddefd4ac3b756bca82b2809865e7c67c346a46477cb9eec4ead581742ab6" - } - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/doc/index.rst b/doc/index.rst index f2434e97dd..a9357b76c7 100644 --- a/doc/index.rst +++ b/doc/index.rst @@ -1,27 +1,105 @@ +:html_theme.sidebar_secondary.remove: true + =================== Welcome to CLIMADA! =================== -.. image:: guide/img/CLIMADA_logo_QR.png - :align: center - :alt: CLIMADA Logo +.. raw:: html + +
    + CLIMADA (CLIMate ADAptation) +

    + is a free and open-source software framework for climate risk assessment and + adaptation option appraisal. Designed by a large scientific community, it + helps reasearchers, policymakers, and businesses analyse the impacts of + natural hazards and explore adaptation strategies. +

    +
    + +CLIMADA is primarily developed and maintained by the `Weather and Climate Risks +Group `_ at `ETH Zürich `_. + +If you use CLIMADA for your own scientific work, please reference the +appropriate publications according to the :doc:`misc/citation`. + +This is the documentation of the CLIMADA core module which contains all +functionalities necessary for performing climate risk analysis and appraisal of +adaptation options. Modules for generating different types of hazards and other +specialized applications can be found in the `CLIMADA Petals +`_ module. + +.. grid:: 1 2 2 2 + :gutter: 4 + :padding: 2 2 0 0 + :class-container: sd-text-center + + .. grid-item-card:: Getting Started + :shadow: md + + Getting started with CLIMADA: How to install? + What are the basic concepts and functionalities? + + +++ + + .. button-ref:: getting-started/index + :ref-type: doc + :click-parent: + :color: secondary + :expand: + + + .. grid-item-card:: User Guide + :shadow: md + + Want to go more in depth? Check out the User guide. It contains detailed + tutorials on the different concepts, modules and possible usage of CLIMADA. -CLIMADA stands for CLIMate ADAptation and is a probabilistic natural catastrophe impact model, that also calculates averted damage (benefit) thanks to adaptation measures of any kind (from grey to green infrastructure, behavioural, etc.). + +++ -CLIMADA is primarily developed and maintained by the `Weather and Climate Risks Group `_ at `ETH Zürich `_. + .. button-ref:: user-guide/index + :ref-type: doc + :click-parent: + :color: secondary + :expand: -If you use CLIMADA for your own scientific work, please reference the appropriate publications according to the :doc:`misc/citation`. + To the user guide! -This is the documentation of the CLIMADA core module which contains all functionalities necessary for performing climate risk analysis and appraisal of adaptation options. Modules for generating different types of hazards and other specialized applications can be found in the `CLIMADA Petals `_ module. -Jump right in: -* :doc:`README ` -* :doc:`Getting Started ` -* :doc:`Installation ` -* :doc:`Overview ` -* `GitHub Repository `_ -* :doc:`Module Reference ` + .. grid-item-card:: Implementation API reference + :shadow: md + + The reference guide contains a detailed description of + the CLIMADA API. The API reference describes each module, class, + methods and functions. + + +++ + + .. button-ref:: api/index + :ref-type: doc + :click-parent: + :color: secondary + :expand: + + To the reference guide! + + .. grid-item-card:: Developer guide + :shadow: md + + Saw a typo in the documentation? Want to improve + existing functionalities? Want to extend them? + The contributing guidelines will guide you through + the process of improving CLIMADA. + + +++ + + .. button-ref:: development/index + :ref-type: doc + :click-parent: + :color: secondary + :expand: + + To the development guide! .. ifconfig:: readthedocs @@ -31,6 +109,8 @@ Jump right in: Use the drop-down menu on the bottom left to switch versions. ``stable`` refers to the most recent release, whereas ``latest`` refers to the latest development version. +**Date**: |today| **Version**: |version| + .. admonition:: Copyright Notice Copyright (C) 2017 ETH Zurich, CLIMADA contributors listed in :doc:`AUTHORS.md `. @@ -47,74 +127,15 @@ Jump right in: with CLIMADA. If not, see https://www.gnu.org/licenses/. -.. toctree:: - :hidden: - - GitHub Repositories - CLIMADA Petals - Weather and Climate Risks Group - - .. toctree:: :maxdepth: 1 - :caption: User Guide - :hidden: - - guide/Guide_Introduction - Getting Started - guide/install - Running CLIMADA on Euler - - -.. toctree:: - :caption: API Reference - :hidden: - - Python Modules - - -.. toctree:: - :maxdepth: 2 - :caption: Tutorials :hidden: - Overview - Python Introduction - Hazard - Exposures - Impact - Uncertainty Quantification - tutorial/climada_engine_Forecast - tutorial/climada_util_calibrate - Google Earth Engine - tutorial/climada_util_api_client - Local exceedance frequency and return period - - -.. toctree:: - :maxdepth: 1 - :caption: Developer Guide - :hidden: - - Development with Git - guide/Guide_CLIMADA_Tutorial - guide/Guide_Configuration - guide/Guide_Testing - guide/Guide_continuous_integration_GitHub_actions - guide/Guide_Review - guide/Guide_PythonDos-n-Donts - guide/Guide_Exception_Logging - Performance and Best Practices - CLIMADA Coding Conventions - Building the Documentation - - -.. toctree:: - :caption: Miscellaneous - :hidden: - - README + Getting started + User Guide + Developer Guide + API Reference Changelog - List of Authors - Contribution Guide - misc/citation + CLIMADA Petals + WCR Group + CLIMADA Website diff --git a/doc/misc/citation.rst b/doc/misc/citation.rst index 91570dbe49..9f4610db7a 100644 --- a/doc/misc/citation.rst +++ b/doc/misc/citation.rst @@ -19,19 +19,19 @@ If you use specific tools and modules of CLIMADA, please cite the appropriate pu - Publication to cite * - *Any* - The `Zenodo archive `_ of the CLIMADA version you are using - * - :doc:`Impact calculations ` + * - :doc:`Impact calculations ` - Aznar-Siguan, G. and Bresch, D. N. (2019): CLIMADA v1: A global weather and climate risk assessment platform, Geosci. Model Dev., 12, 3085–3097, https://doi.org/10.5194/gmd-14-351-2021 - * - :doc:`Cost-benefit analysis ` + * - :doc:`Cost-benefit analysis ` - Bresch, D. N. and Aznar-Siguan, G. (2021): CLIMADA v1.4.1: Towards a globally consistent adaptation options appraisal tool, Geosci. Model Dev., 14, 351–363, https://doi.org/10.5194/gmd-14-351-2021 - * - :doc:`Uncertainty and sensitivity analysis ` + * - :doc:`Uncertainty and sensitivity analysis ` - Kropf, C. M. et al. (2022): Uncertainty and sensitivity analysis for probabilistic weather and climate-risk modelling: an implementation in CLIMADA v.3.1.0. Geosci. Model Dev. 15, 7177–7201, https://doi.org/10.5194/gmd-15-7177-2022 - * - :doc:`Lines and polygons exposures ` *or* `Open Street Map exposures `_ + * - :doc:`Lines and polygons exposures ` *or* `Open Street Map exposures `_ - Mühlhofer, E., et al. (2024): OpenStreetMap for Multi-Faceted Climate Risk Assessments : Environ. Res. Commun. 6 015005, https://doi.org/10.1088/2515-7620/ad15ab - * - :doc:`LitPop exposures ` + * - :doc:`LitPop exposures ` - Eberenz, S., et al. (2020): Asset exposure data for global physical risk assessment. Earth System Science Data 12, 817–833, https://doi.org/10.3929/ethz-b-000409595 - * - :doc:`Impact function calibration ` + * - :doc:`Impact function calibration ` - Riedel, L., et al. (2024): A Module for Calibrating Impact Functions in the Climate Risk Modeling Platform CLIMADA. Journal of Open Source Software, 9(99), 6755, https://doi.org/10.21105/joss.06755 - * - `GloFAS River Flood Module `_ + * - `GloFAS River Flood Module `_ - Riedel, L. et al. (2024): Fluvial flood inundation and socio-economic impact model based on open data, Geosci. Model Dev., 17, 5291–5308, https://doi.org/10.5194/gmd-17-5291-2024 Please find the code to reprocduce selected CLIMADA-related scientific publications in our `repository of scientific publications `_. diff --git a/doc/tutorial/impact.rst b/doc/tutorial/impact.rst deleted file mode 100644 index 259cb0c9b1..0000000000 --- a/doc/tutorial/impact.rst +++ /dev/null @@ -1,14 +0,0 @@ -================ -Impact Tutorials -================ - -.. toctree:: - :maxdepth: 1 - - Impact Calculation - climada_entity_ImpactFuncSet - climada_entity_MeasureSet - Discount Rates - climada_engine_impact_data - Cost Benefit Calculation - Probabilistic Yearly Impacts diff --git a/doc/user-guide/0_10min_climada.ipynb b/doc/user-guide/0_10min_climada.ipynb new file mode 100644 index 0000000000..1e575c19eb --- /dev/null +++ b/doc/user-guide/0_10min_climada.ipynb @@ -0,0 +1,452 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 10 minutes CLIMADA\n", + "\n", + "This is a brief introduction to CLIMADA that showcases CLIMADA's key building block, the impact calculation. For more details and features of the impact calculation, please check out the more detailed [CLIMADA Overview](../user-guide/1_main_climada.ipynb).\n", + "\n", + "## Key ingredients in a CLIMADA impact calculation\n", + "\n", + "For CLIMADA's impact calculation, we have to specify the following ingredients:\n", + "- **Hazard**: The hazard object entails event-based and spatially-resolved information of the intensity of a natural hazard. It contains a probabilistic event set, meaning that is a set of several events, each of which is associated to a frequency corresponding to the estimated probability of the occurence of the event.\n", + "- **Exposure**: The exposure information provides the location and the number and/or value of objects (e.g., humans, buildings, ecosystems) that are exposed to the hazard.\n", + "- **Vulnerability**: The impact or vunerability function models the average impact that is expected for a given exposure value and given hazard intensity.\n", + "\n", + "## Exemplary impact calculation\n", + "\n", + "We exemplify the impact calculation and its key ingredients with an analysis of the risk of tropical cyclones on several assets in Florida.\n", + "\n", + "\n", + "### Hazard objects\n", + "\n", + "First, we read a demo hazard file that includes information about several tropical cyclone events. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from climada.hazard import Hazard\n", + "from climada.util import HAZ_DEMO_H5\n", + "\n", + "haz = Hazard.from_hdf5(HAZ_DEMO_H5)\n", + "\n", + "# to hide the warnings\n", + "import warnings\n", + "\n", + "warnings.filterwarnings(\"ignore\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can infer some information from the Hazard object. The central piece of the hazard object is a sparse matrix at `haz.intensity` that contains the hazard intensity values for each event (axis 0) and each location (axis 1). " + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The hazard object contains 216 events. \n", + "The maximal intensity contained in the Hazard object is 72.75 m/s. \n", + "The first event was observed in a time series of 185 years, \n", + "which is why CLIMADA estimates an annual probability of 0.0054 for the occurence of this event.\n" + ] + } + ], + "source": [ + "print(\n", + " f\"The hazard object contains {haz.intensity.shape[0]} events. \\n\"\n", + " f\"The maximal intensity contained in the Hazard object is {haz.intensity.max():.2f} {haz.units}. \\n\"\n", + " f\"The first event was observed in a time series of {int(1/haz.frequency[0])} {haz.frequency_unit[2:]}s, \\n\"\n", + " f\"which is why CLIMADA estimates an annual probability of {haz.frequency[0]:.4f} for the occurence of this event.\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The probabilistic event set and its single events can be plotted. For instance, below we plot maximal intensity per grid point over the whole event set." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "haz.plot_intensity(0, figsize=(6, 6));" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Exposure objects\n", + "Now, we read a demo expopure file containing the location and value of a number of exposed assets in Florida." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-01-21 15:38:13,269 - climada.entity.exposures.base - INFO - Reading /Users/vgebhart/climada/demo/data/exp_demo_today.h5\n" + ] + } + ], + "source": [ + "from climada.entity import Exposures\n", + "from climada.util.constants import EXP_DEMO_H5\n", + "\n", + "exp = Exposures.from_hdf5(EXP_DEMO_H5)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can print some basic information about the exposure object. The central information of the exposure object is contained in a geopandas.GeoDataFrame at `exp.gdf`." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "In the exposure object, a total amount of USD 657.05B is distributed among 50 points.\n" + ] + } + ], + "source": [ + "print(\n", + " f\"In the exposure object, a total amount of {exp.value_unit} {exp.gdf.value.sum() / 1_000_000_000:.2f}B\"\n", + " f\" is distributed among {exp.gdf.shape[0]} points.\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can plot the different exposure points on a map." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-01-21 15:39:38,249 - climada.entity.exposures.base - INFO - Setting latitude and longitude attributes.\n", + "2025-01-21 15:39:38,498 - climada.entity.exposures.base - INFO - Setting latitude and longitude attributes.\n" + ] + }, + { + "data": { + "image/png": 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", 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    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "exp.plot_basemap(figsize=(6, 6));" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Impact Functions\n", + "\n", + "To model the impact to the exposure that is caused by the hazard, CLIMADA makes use of an impact function. This function relates both percentage of assets affected (PAA, red line below) and the mean damage degree (MDD, blue line below), to the hazard intensity. The multiplication of PAA and MDD result in the mean damage ratio (MDR, black dashed line below), that relates the hazard intensity to corresponding relative impact values. Finally, a multiplication with the exposure values results in the total impact.\n", + "\n", + "Below, we read and plot a standard impact function for tropical cyclones." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from climada.entity import ImpactFuncSet, ImpfTropCyclone\n", + "\n", + "impf_tc = ImpfTropCyclone.from_emanuel_usa()\n", + "impf_set = ImpactFuncSet([impf_tc])\n", + "impf_set.plot();" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Impact calculation \n", + "\n", + "Having defined hazard, exposure, and impact function, we can finally perform the impact calcuation. \n" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-01-21 15:43:22,682 - climada.entity.exposures.base - INFO - Matching 50 exposures with 2500 centroids.\n", + "2025-01-21 15:43:22,683 - climada.util.coordinates - INFO - No exact centroid match found. Reprojecting coordinates to nearest neighbor closer than the threshold = 100\n", + "2025-01-21 15:43:22,686 - climada.engine.impact_calc - INFO - Calculating impact for 250 assets (>0) and 216 events.\n", + "2025-01-21 15:43:22,687 - climada.engine.impact_calc - INFO - cover and/or deductible columns detected, going to calculate insured impact\n" + ] + } + ], + "source": [ + "from climada.engine import ImpactCalc\n", + "\n", + "imp = ImpactCalc(exp, impf_set, haz).impact(save_mat=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The Impact object contains the results of the impact calculation (including event- and location-wise impact information when `save_mat=True`)." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The total expected annual impact over all exposure points is USD 288.90 M. \n", + "The largest estimated single-event impact is USD 20.96 B. \n", + "The largest expected annual impact for a single location is USD 9.58 M. \n", + "\n" + ] + } + ], + "source": [ + "print(\n", + " f\"The total expected annual impact over all exposure points is {imp.unit} {imp.aai_agg / 1_000_000:.2f} M. \\n\"\n", + " f\"The largest estimated single-event impact is {imp.unit} {max(imp.at_event) / 1_000_000_000:.2f} B. \\n\"\n", + " f\"The largest expected annual impact for a single location is {imp.unit} {max(imp.eai_exp) / 1_000_000:.2f} M. \\n\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Several visualizations of impact objects are available. For instance, we can plot the expected annual impact per location on a map." + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-01-21 15:44:16,514 - climada.util.coordinates - INFO - Setting geometry points.\n", + "2025-01-21 15:44:16,518 - climada.entity.exposures.base - INFO - Setting latitude and longitude attributes.\n", + "2025-01-21 15:44:16,771 - climada.entity.exposures.base - INFO - Setting latitude and longitude attributes.\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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xzDHH1JcrlUr84Q9/4MILL+Q73/kOF110Ea2trey///68733vqy93zTXX8PGPf5zjjz8ez/N417vexXXXXTfi/SxevJh77rmHD3/4w3zgAx8gn8/zxje+kSuvvJI3vOENw57Dr371q1iWxZe+9CW6u7vZd999ueWWW/j0pz894s9hrBiGwW233cZZZ53FmWeeieM4nHzyyVx55ZUce+yx9eUsy+K2227jwx/+MO9///sxTZPXvOY1/O53vxtzHtlkcsstt3DhhRfyzW9+EyEExx13HFdccUU9L2revHncfvvtnHPOObzjHe+gUCjwhje8gZtuuol99923YVupx+Bzn/scZ599NrVajWXLljUk6R9++OGcd955fP/73+e73/0uSinuuuuuTI1+KyDVRTvxxBP5wAc+MOhyqZG0fPlygAHzPNN5mzZtmuhhzjymu6wvI2O2k8oRXHbZZdM9lIxZTCpHsGHDhukeSsYcYig5go0bN2oppT7qqKNGtK1U8uKtb31rv799+9vf1oD+9Kc/Pa7xzgayHKeMjIyMjIytkHnz5vH617+eX//61/z6178ecJkXXnih/vMhhxzCypUr+fnPf94gpREEQT3Hrre3d66SheoyMjIyMjLmED/84Q/rhk36/4svvrj+996h9G9+85s89NBDHHPMMfWKYdu2Wb16Nb///e8pFAp1DTEpJd/+9rc55phjOPDAA/nQhz5EU1MT119/PX//+98544wzOPDAA6fwSKcHobXW0z2IjIyMjIyMjInh8MMP55577hn0730f+x0dHfy///f/uPXWW3nqqacwTZMlS5Zw4IEH8o53vKNfsvf999/PhRdeyJ///Geq1Sq77LIL73vf+/jQhz4056UIIDOcMjIyMjIyMjJGTJbjlJGRkZGRkZExQrIcp4yMjIyMjK2AWq2G7/tTsi/btudsj8TMcJoAHn7iqSndn9aaKAwxTHNOxJNdx6atqYhtSAwpMWwbFYaoKEJaFrVqFcswMCwLISWR7yMNA2maRL4PQmBYFioM0VojTRO0RkUKaRggQIURQkqEFOgoAiGQhoGKItAaYRg965gGaOL9GxKEgCSiLaREJ9on6c9aa4SMnbdaqfrPKUKIek5B3597o7XG8zwcxxnz59qwn2ScCIHWoFUUnw/i85EeW+/zEQUBWqn4M4giVBhi2jZaawLfx7QspGEQeB5G8pmEyY3YtG2iMIzXcRy0UqgowjBNECL5DARCSqrlCo7jYFgmKorQSifnXaOUjscWHxAI0XDetNbUPB8VKVw3biw8GiKlCKMe4VMpJGZyXoIoRKXnEFBKAxopJUrreJyG7BmnFPH1EYQgJcKQ6DCK1zcNVBjh12rYhTxCa1QQIS0jPh9+iDAlhmkSVmsIITBch8j30WGEmXNRQUBYqWEV8wghqXV0YuVczHyOWvtmpGliN5fwtnSigpDSwvkElSpeZyf5+fPQWlPZ0I7b0oyVz9H9/AuYrktuXiuVje2oIKS4ZBG1zi68jk6Kixeiwojyho04rc1Yrkt5/YtY+RxOazOVDe2gNPlF8/E7ugjKZXILFxD5PtX2zchigXypQOXFdqxiHrtUorJhI0IKcvPm4XV2EVarFBcuIKzVqG3aQm7BPKRhUF7/InZzCatYYHP7Zpqam9Fa07V5Czaa0qKFBJUKXkcnhmmSmz+PoFLB7+wmN68NIaDavgmrVMIwTQzH7vddHIxypcaLmzYTJJ8dgG1ZLFnQipPoZ111zhe4+ye/jO8ZQK65RLWjf4Pba/71K5rnteL5AWEUkXNsIqXZuLkDx7bwPJ+uysSqtO+x0/BNfWu12pT2kVu8eDHPPPPMnDSeMsNplhImhtNcQGtdN3jSB7s0TaRpxg/sMCSXz9eNCbNXqwkjuaml69QRAqPXTdOwev4mei2X7q/fOgIM2bi9+o+9tiukpLeJI3pvr/f83usPYRSlhtNYEYONUwC9jmew82FYVs/Pplm/xgTg9Lrp2r1uhmavz6BhHcNoOL/pPrXWBFFIzswDyWeQLiYERu9n3QDnTQhBznXwfZ9qrYZlWrju6M5Z38TO1DDrnfGpASEFIOKfhUAYoj4uafT6HNNjAzCNnp8NiacibAApkU7PwUknPtdKa2QyfqU1wrIQloUCsCzMZgudbM9ubQZi48/q9bPZVKyPyy4WsIuF+n6ali2t/1zapke4MDevp/GwUyrGxp2USNOkuHQxkVaEaJwFbSAEQRRhNTcBmiCKEHkX23WIknHa89soV6o4gNXWgpCSUEWYzbECeagiZM7BcixCrdCWidXWgpISjY7XMQ2U1uTzecrlMm1trfiFPJHvI4TAdF2kYcSfhZTJ7yYyOf9OSzPSshqu45FgWyaWaSXXAQRhiB8EbO7oxrEttIaWRfPTTxUhBPnmIrXO7oZEayfnUmiKj1crhYgvHExDYJkGUgjyOWfCDaeRMFWeppT169fj+/6cNJyyHKeMaUcmHoWMjJEihIhDAY5LGEVUKjWixPuXMTJEr++dSDyO6bzYYEwwDEiNcEPGv0M8z4w9ukgRGy8yNi2kZSISCzh9CYLEmLZiQzA2JG2QAi1E/LNhIAA35yKkpKurm0IhTwR4iafZyuUwXTf20FkWVj4Xe6ANAyufH7XRBGCaBgvntbB04TyWLGijVIiN+q5ylfYtnbR3dLLbqw6qK20PhDQMXvXW12Na8QufUgoh4tOTGnoKjes4dQ9nxuwkM5wypp1IqTg8ktlOGaNACIFpGuRzLqCpVKqEYZQZTxPEdJxFKQSGNPCCgIoX4Pk+YRjS0tJMpVIhCIMRfb4q8WKPFCEEtmViWxaObdHWXKJUyCOlqG+rsKCNV7/zTQOP2zAotTZzwofeidYaPwioeV7DC6EhBOjYq13IzT0vzNZEZjhlTDsqyReZljt1xqxGCIGUknw+h22ZVGs1fH9kD9eMmYMATGlgGbFnqqOrSmtTiZzrUk3yvwqFAuXuCn4w/OcbRiFhFBGEYZKnNoqxCIFjWyya31r3PKUcccbJHPNf7yLf3KsBsoBdDnwp53z/cvKtzURRRBCEOLaNkXjdtNYIIXAdC8OQ5HMOMvOyz1rmRpJMxqxGIJBSZHZTxriwbRtpGHg1jzCKcF2nIc8N0uT57EqbKRhS1g0IKSRCCCpVH6U1tm3i+5ogCKl5Pvmci3JzlMsVRLGANURxjCENlFKoSBGGEbZlYowyPCYgTv7vPU8IDjnpOA544+tY8+gThLWAhdttQ8viBUgh2Lili6aCi1Iay7Lwg4jUblNKYUiZGGY2OdehXK2N+pxNBGKS3ft6jn/HMsMpY9qRsrFqKiNjLAghMA0DmXPxPJ9yd5lCIY9MHlYZMwsBSGSDcau1plyrUcw5oOOiStuxqZQr5FwH17VBQGdXheamAmaSl9UXQ8bbDQiJghAQda/PUPRUbkLV8ylXB06oNi2LpTvvgJMkp2sVobQmDCPKFQ8/ikCXESK99jSmYZBzHbTWmKZBIe9S9fwh86YyZiaZ4ZQx7Ugp+r3ZZWSMBSEEhmGQy7kEYUilUsVxHEzTrF9jc9U+n+pvkBSiLt0wpvWlRPRJFgmjiEip2MBAg9REWmOYJlu2dNLS0oxtWciipKOrQlMxj2UObDzFY5RoYiMo59pxntEAaB17hMIoolKtUa351HwfDeQdF6UVQRgmoWERe8lURFtzkUI+R7UW52KhoRqENOVdNGAa8XXXValQrXps2NxBKZ/DMCSmYeLaFpWaN+ZzOGYm+2KZo9+xlMxwyph2DCHjcF3mFciYIIQQWKaJzAuqlRpBEOK6duJ9SpaJF+yteNBbsmvWMZVeNSEEpjQIE0/LaDGEqOcz9abq+VhGHL6r+T5aaYJAoXWEjhQ1r4Zp2hjSoJjP09FVpqmYx7EHrqQzDInr2ARB2GArxHIDcf6TBrTS1Hyfas0jCANMw8C2bUp5N0nk1lS9ACkkjmMhBbRv2kxToYBtW9imRblcYUt3hTCKEDJHS6lA4AfYtoXr2HRaZYIgwvMDgijCsa26NzTzts8uMsMpY9qRvfIcMjImijh0Z5LP5/A8n2q1Fuc9GQaWTAyNPpedIQ2MCXxd1lonSc+NXpHxPCc1GoGo/19pBRP84iEQmFIOeiYE8bmL85J6hEkNIet5PA1j1omBkobCiD088WZ6PIGeH2IaZt2YUFrjWAZ+GOeweTUfu2TT2V2mVMzj2hbVai3OlTJkvfS/YaypgGqyfy8I6OyKDRwpJUYigVCreTiWSVMxj22ZOJaF2cubVeqldxZFUYOMitbxsSitUErR2V3BMs1YUFbHcgfzWpqIErFZ3w8II5UVMsxSMsMpY9pJc5yyPJSMycAwDNycSxiEVCo1HNfBTgQT+wqTDhbKGSta6yTfZvBw0li22fvnMNSYhuiXCD8e4nMxfDK1IXsbg/GxmoMcqyYu60/lR4IojD1+xGE/z4+o1Dzmt5RihXkhYoNECnKujWvbVCpVfN/HNCWd3RXmt7awaUsHVd/Htk1kYoaJ5GVMJNtPc6HCSNHVXUVKQVuxhGHI2OuDwDQklmli2+aAxl/f8xMbXfE5V0pjGAatTSW6ylWqnsfmzi5ylombc/HD+FitRNRXA940GU0i+TfZzOUE8UyOIGNaSXNS4ntUZjxlTA5SCCzLJJ938T2ParVWV6yfbfR+yaj5AZ3lKn4YxobIBO9jIicpJFJILMOse7NSr5IXhHSVK5QKbqLUranUvFit3DSRUmKaBlIKwjCkVCji+z6eV8MyJDqK4vZHNHq2eo4H/v2PB/nIe8/m8D0O5OAd9+X1B7+OH333B0RBgJSCfM7FdWzCMKLm+URDJG3Xc51kT/K3IWPDK59zMQxJGEakW1BKNySBm0krKKtX7t1UIabo31wmM5wyppXUcJqFz6+MWUYsmGlSKBTisvdZLpgphMC1LRzLJAgUUTjzq7NSg8+QBrZhYkoDQwjK5Rp516G1VIwrI4WgpVQgZ9tUql6D0nZnZxdRFJF3bKo1D9uOjR2Ik9Uj3XMe/CCkUvP41S9+yylHv4U7f/VbgqT1yJpn1/Dlz3yR97z5NLyah22ZBGHEs+s2sGrtCzyz5gXCKGIg+hrdQgg8P+SRJ1bj2jYtxSJ5x6WYL+B7PoaUdS8nxC1emosFmkoF7DEonWdML5nhlDGtxErBcYJu5mzKmAqkFLiug2VZ1Dwfz/NnrfEkpSTvOuRce1a18Ui9L6ZhoBVEWlPMuw0tYCzTxDQlxbzbqNkkIAgCCsUiYaSxHYcg0tSqPiJR5pZSUq7W2NLVTfumDj511seJoijOMUpIjZ+/3vcAV1/5HQAiFbF5Syer177IPx5+gkrVY3NnN34Qe/M2d3THquZVL/lbF5u2dKEBwzRwHAuN5rn1G1m17gW8wMe0TMIo4sX2DspVD6U13ZUa3ZUanh+Qd5yp9c+IKZrmMFmOU8a00nMzzCynjKlDCIHjxFV2nuejlFevuptNpAaflGJWGE6hiohCFUsOOHFzaD8IsU1jwHNvmWbcUDkJZ0kpsUyLzu4y8+c5SAlRGGJKQbVaIQqDOKRnSKpeiGlI7vnVb6iUy4OOSSnFD7/7Q8748Jm4ts3L9tqJdS9s4p+PPEXNC3hi1XPstGIZi+a38M9Hn2LPnVbQvqWTp59dQz5foOYHbLNoHksXzWfj5g4WL2hj9boXqXk+tm2xeH4Ljzz+LJZloZRm791WsG7DZlY/9wLNxTy7rlw+u8s5t0Jm110iY86R5j7M/ah4xkzENA3yeRchoFyuEoThrPM+hVFEFM38MB1AteqzpatMd6VHMTvnOjSXCgMuL2WjQGYu5zJvXitCSrRW5ByHIAjqcgRBEBCEIWEQUsg55HM5nnj0cUxzaB/Bpo3trFuzjlqtRhhGCCGTPnWKjq4KXhCgtGZLZ3esD1aroZRmt5XbsvP227B63Qbat3SypbMb0zCY39bM/LZmdli2mFVrXkADyxbPJwgDnnu+ne5yFSlgl5XLKRZyU3rvy3Kcxk/mccqYVlLxy8zZlDEdpB5P13UIgpBqpYbj2Ni2NWsKFdLQ1ETywup1/Oev/0ZKyW4v34d5SxdOyHaLeZeca9NVrhIkrVBib1L/cx0rcYcYhlGvcpPJsTYX83R2dNHUVEIpRaFQoLNcxXVMDNMk5zp1D5zrOiMaW3NLEwhJzQuoVKtoDRKBUioOy3V0E4U9oT4pJZZtYoVxpZxKwoBSSlzbwvOD2KPpB3R2V3ihfTO2ZVHIO1R9n9bmIvNbm4iUiq+1KTLYp6yCeXa9f4yKzHDKmFYEcU6C1lMr4JeRkZJed3YiSOh5HlEU4Tj2qPubTQeGaUzYi0dH+2au+tjF/O3OP9YffEJKDnr9q3n/lz9Joak4ru2nbXFae21nsO+9Uoqa5+PYFoZtN/zNskwUJHIAum5U5XI5LNNo+NwOP/JVXPetawcdk5SSPffZk+bWFtas28AjTz5LEIS0tZTI5RxKxTyPP/McG9q3ECmNSMKG5UqVR59YTaXm0dpcpKW5FCe2G4JiIcez617kmTXPs83iBXh+gCElhbzLgtZWNneW0cmxCwS2ZVH1pkFBPGNMZIZTxrRiGFkfsYyZg2FIcjmXWs2jUqmRyzmJXMbMvEYnUnuqWq5wwZs/wLqn1zR4C7RS3Hf7nby4eh2fv+XbWIOodE80SmnQce6jH4RIGSeMQ1JR6Fh0l6s4toVSCsuMdZX6GrsHvfJgdt97D/7z8GMNyeE9+1Gc+dEPIoAF85rZRS1DRYoF81oo5FxesusObNzcQT7nsnzJAppLBZ5/sZ2WpiIL5jVjGAYL2ppxbJt9dt+RnGOzdNG8WNPKNGhraca1TWp+kPTbs9h+2aK6h0mI2GifKsNpawilTTaZ4ZQxrRiGRKORfZtWZWRMA2kYw3VdwjCkWvWwHRvbMkdkPMUCj1Hcp0zOfG9Vb+6++Xaee/LZAUNGKlI8+c9HuP+Xd3HICUdOyXhMQ2LbJirSSBlLGDT83TSpVH2amopIIcm5LrVqDavU6BUTQvCdG67m3SeeypP/eQLDMFBKIaRAK815F3+K1xzzWgDyrsP2yxY3rN/aXKS5qUBvVfK86xKWCmy3zaJ6flW15rNkYWsipOmyw7ZL2NTRRc3zEKakrdCEJQWmIZjXUmqoIHTsqXsUyykI1c3hKB2QGU4Z04gQAit5O8ya/GbMJKSMBTNN06BcrhCFUdyjbBhFaYBIaUCjxezypt55020M9cgTUnLnTbdNmeGktCaMVPxypXXDuYyUijW4VEi1WiPnOjiOw6ZqjWKfZQEWLl7Iz+65jbt/cxe/+cWvqZQr7LjLjrz5HW9l2bbL+m0/7mGnB1Vj33G7pXR2FrHMHmPOMg1qng92XHSgNXWl8CAM8T0fK2fjBwGu05h3ZVtWVlg3i8gMp4xpRWsNWX5Txgwk9T4VCnmq1RrVag3HcRr6l80lNr+wcUhXgVaKTes3Ttl4Yk+M1SAcWTdCghDTNIgikyCIQ2Ayaa+itB4wfGlZFq899khee2yP4aeUIggjzF4pA6m+U++WIX37AMYhwT6GcfKjFwREWte9jwKBY1mUax7SNPE9H8e2GzxOsUBmnFA+2WShuvGTxUcypg0BSTVQ/zfEjIyZgpSSfD6HbVvUat7Ie4zNsku6bcnCIb+H0pDM32bRlI1HJ1V1SvU0EfaDEN8PE3XxWI07bZgrDQPbNOrK4CNBqdgQ612VKIRADpArNex40biuTc514lAgUMzlqHl+XDksJUprTMvC72MgGYbEmSIF8bT1zWRPc5m5fXQZMxshMM3ZlQeSsXUihMCyLHI5hzAMqVSqPZ6JAXvezb63+iPedvyQBqGKFEecfPzUDUhDEEb4QYBSCs8PCMO4d10sTxA3cFZAFKk4Gduy8YKRtdHRWsfeoT4aWLG2nBhTLlC6rp2EeT3Po5JoQ0mga9MW/vbre7n7J79k9WNP1dczpMSeoqT7jPGTheoypg1Bogw8yx4wGVsnaa+7fKI2Xi5XcRw7zmdBN4a5ZuElffibj+G3P7yV1f95CtXXmJCSXfffm5cf9copG4/Suh4SizWRBLZtNpxnIQR516a7uwvXsTENSRCofjlLQxFG0aiWHxQde50QsfZTGClEEoKrVmv8+NJvcd/PfoPqpQW16/4v4b++egGLtl2KY1lYpkkQTlyz5oHIQnXjJ/M4ZUwbUor6m2NGxmxBSpn0ujOpVmvUPI9IKYIoJFRRfyNqluDkXC68+Rsc9PojGkJXhmXyqrcey6d++BWMYRS4JxLTkJiGEecgmUacQD3AcrZlEUaKMAyxbRsN9d5yw6GUquc5jRetNTXPJwgjIhUbbzU/wDJNvveJL/LnW+9oMJoAHv/7Q3zqhDPY/GI7tm025HNtbWzYsIEPfehDbLfddti2zdKlSznjjDNYv379dA+tH1vvp5QxAxDJQ2YWPmUytmrSXneGEYdjvMhP9I00hoglNjR61r3ZF1ua+Og3Ps+7P/thnvjHwwgp2GW/vWlqa5nScWitiSIVC5Ha1rD5Rq5jUS6XaWtrw5AmURih7aG9SEIICrm4sXC15sUNx8ehKyelxDQkSikMw8C1DfKuw8P3/YMH77l/wHVUFNHZvplffPcG3nH+h7Atk3J1TLsf+ThnoBzBhg0bOOCAA1i1ahWnnnoqBx10EM888wzf+MY3+N3vfsf999/PwoUTo14/EWSGU8a0ofTs6K+VkTEYpmkgZSyY6VVruK6LaRkoPfuMpt60LprPy486bFr2rbUmjCKCKMJNGjELIYbMW3Jsm46uClprXMehXO5GKXtY+Yg41OcQhBGVmoebaHaNBUNKpGURKYWUsh4CvOcnv0QaRr0lS19UpLjzptt456fOwrFtDCmJ1OTdGwVyCq7N0ZlOX/rSl3jmmWf44he/yHnnnVeff/zxx3PIIYfwmc98hm9/+9sTPcgxk4XqMqaV2fxwyciA+OGby7k4jkOt5uH7wYRf1X0T0cczzWS01gRBSBgqcolHbyTeESEEtiXp7Ooil3OJNIQjaHyctmqxLZNC3m3QZRotSmk2d3azuaObStXD8+Jk9vb1Gwc1mlK6NncmRp+9VRbM3HnnnQCcdtppDfMPPvhgdtppJ/7nf/6HWq020KrTQuZxyphWlIoyKYKMWU16/caJ4ibVapUwimLvgTEx76aagY2e3k7bOMGZQRPTBT0hmpn4nUsr57TWOElvOtXL86IZ3I8hhMC2bao1j1KxSKmQo1LzsC1zSGOxQUtpnOdEyFh3Kuc0eroWLls8pMcJoHlea2L8mbi2Pal6TlMZquvs7GyY7zixUGlfUqMon8/3+1s+n6e7u5uHHnqI/fbbb8LHOhbmrMfJyxomznx6vRXO9DfhjLlLLFQYJ/MqrYiiuCIq1Q8aDYYRaz6hNdVaDT8YoebTMEghMaTRbzLNnkkLjdIarRhwUkr3K72fCaTnPzUWzKQlSpzj1DOpSA0ZAZJCYgio1TwKhSJBIl/Qdzv17dXzqOJpvJ+TTBr2dnVXqXo+lapHuVLjlW86akijSUrJEW/rkXnoLY45GYgp+gewfPlympub69OXvvSlAce0++67Az2ep5Tnn3+exx57DIDVq1dP2jkZLaMynJ544gkuvPBCXvGKV7B48WIKhQK77747Z599Ns8///yA6zz55JOcdtppLFu2DMdxWLRoEUceeSR/+9vfRrzfq6++mv322498Pk9TUxOHH344v/nNbwYd4yGHHEI+n2f+/Plcfvnl/ZZZsWIFQgj23nvvhjealAsvvBAhBHffffeIx5gxelQiaAeZ4ZQxPWit46qqICQIQ4IgQGmFUhFBGOD7AZ4fUPN8fD9omMIwHPC6lVKSy+diJeiaRxAMvNzEH8tIl5t53zXfD2K5h3E0VE5FJr1EANN1barV/npb6aQiVa+qU2r8hlOlWmNLVzdburuJoggpBTU/YP7KFbzsdYcxUPmwNAxaF8/nyFNPrM/L5SbXcJpK1qxZQ0dHR33qnb/Um49+9KNIKfnABz7ATTfdxLPPPsu9997LCSecUG/MXKlUpnLoQzIqw+maa67h0ksvZenSpXzyk5/kK1/5CgceeCBXXXUVe+65Z90yTLnrrrvYZ599+OMf/8j73vc+vvWtb/HJT36SRYsWDWpo9eWss87ijDPOQCnF5z//eS688EI6Ozs56qijuOmmm/ot/6Y3vYmmpiZuueUWPvOZz3DBBRfw85//fMBtP/jgg3z/+98fzSnImGAipRLdk5l3M8/YOtAawjDC90OCIML3g9iQCiL8ICQMw7phFSlFpGJvRRCqulHU+6GrlGLTlk42dZZBSIIoolytTcjDedgDGYmg+Qx8KGvNoH3hRopIBHXDwMf3fQqFIjV/cKO193kQExC+iiURIlqbSvVjaS7mCaKIN57/QQ5/+wnYbq8wlYB9DjuAi2/9Dk4xXx+nZRj1xsGTQW+Bz8ma0nPZ1NTUMA0UpgM49NBDuemmm5BScvLJJ7NixQoOO+wwli1bxumnn17f1kxB6FF8k//617+ycuVKWltbG+Z/+9vf5swzz+Qtb3kLN998MwDt7e3stttu7LHHHvzqV7/Cdd1RD+7f//43L3nJS9hzzz35+9//jpVI0gdBwL777svzzz/PM888Q6lUAmDTpk3MmzePzZs309LSAsCHP/xhAL761a/Wt7tixQo8z8OyLLTWPP744+RyufrfL7zwQi666CLuuusuDj/88GHH+fATTw27zESitcar1XBcd0beBEdDU7HAvOYSQsTdzvuitaazs5OmpqZZf6zDsbUc60w6ztjoiXPtBv57rAkkRJzDIoTAlEbSkDX2mNq2leiRxceilGZLVzdhpCjkHAIv7nEXRhGO42CZ5oQfdzyWoCGPaTAmq9feWD9XrTXVmj+uxOze2+oqVynk8+RzLps2b46lARx70HXSFiuCkRuVAx1rmMgnmIakWvOJlCLn2nR0leksV5DSQEQRG//zNFEYsv2eO7Nw+dLk+L24yMB10FqzubOLjZs7hxlFD3vstHLYZTo7O2lubmae2zrpLVGUVrTXNtPR0TEqgyeKIh555BE2b97MDjvswLJly3jrW9/Kj3/8Yx5//HF22mmnSRz1yBnV2dtvv/36GU0Ab3vb24DY0En51re+xYYNG7j88stxXZdarTbqvKM03vn2t7+9bjRB3KzxlFNOob29vcGb1NrayoIFC/j85z/PU089xV133cWtt97Kbrvt1m/bjuPwuc99jueee44rrrhiVOPKmDiCIIzzMoYw38OOTipPPEmwafPUDSxjqyCuqooVwQeeDBzHwnYsHNvClDIJ78XXbGyENG5TSkFbc4mFbc3kXSfRfIqTYj3Pp1KbeO9Tqli9tZOG+2q1WAwpn8sPm2cmxNjaq/TFNOLqvFBphBTkcw5KQc6xWdDaQkupgDZMdjpwX15+1GEsXL60vn/bMonCsB6Wch1n2l8qpgPDMNhrr7145StfybJly/A8jzvvvJOddtppxhhNMEHJ4WvXrgVoEKi6/fbbKZVK+L7PQQcdRC6Xw3Vd9t57b/73f/93RNsdLtMe4M9//nN9nhCC73//+1x33XXsuOOOvPrVr+bggw/mjDPOGHD7p556KnvttReXXHIJ7e3tIzvYjAklTsaN6m/wvSk/8iiPvPt0Hj75nfzj1Ufxl73345F3nU750ccG2VpGxsSSPohNGTeVNU0Ty+qZbMvCkMN7cOKHo5V4FKBcrlCt1iZOr0cDeoiSuq0I17Hw/Li/nWkaSAHhEG1MBqtYHA0N4VqtYyPbMHAdi2IhT86NPY2tTQW6KhVUn/0ZhoGUkiAI476IhjFpTX9louQ02dNEcP7559Pe3s6nP/3pCdneRDEhhtNnP/tZAN7znvfU5z366KNEUcQRRxzBDjvswI9//GOuvPJKNm7cyJvf/Gauv/76Ybc7WKY9xPlT0D/T/uijj+bZZ5/lvvvu4+mnn+bGG28cVHVWSskll1xCR0cHn//850d2sBkTilKaMOz/8Oj657/493FvYvPd/9eT9ao1m++6h38f9ya6//3gFI80I2P8mKZJ3nVwXRchBeVyhVrNa3jwjkVzSWudmUwJqS5TuVLFsi1M0yYcompOKz1uD6AGOrrKvNi+he6kAbQfhHSVq2zq6Karu4IQ0FTMIxCUK42aRGkT6TCK4sRyQ+K6g4cX5yK77ror5557Lt/97nf56le/yuGHH87ll1/OBz/4QU499dTpHl4D49Zxuvjii/nJT37C8ccfz7ve9a76/K6uLqIo4uSTT24wkl7zmtew5557cu6553LKKacM+aZ2zDHHsPvuu/Ozn/2MT3ziE3VxrOuuu45f/epXwMCZ9sVikQMOOGBE4z/66KM54ogj+OY3v8nZZ5/NDjvsMKL1ejPVVSqzRcxuJMTHEZd+G0bPsT3x3+cRJS52LQQ6vU6UIgp8nvj4+exzx8BJ/7OVufS5DsXWcpww8LGKpLWHYdhYlkmt6qGUwnHs2GGUNIuNS7pFnF81jEcrLq5IDa+RjWuiGevnOhnXQy7nsKWzQlOpiGNbdJfLRKY5qK5WXKMyOkO1r5cpCCM6ussYRtyIOOfGTYe1jgCjbtwKAVXPo1TINWwzHZsfBDi2HSeIC4GeYBXxiUiEH3YfYzDjDzjgAH7yk5+wbt06bNvmpS99KTfffDNvectbJmGE42NchtMVV1zBZz7zGV75yldyww03NHwYuVyO7u7uBi8UwC677MLBBx/Mvffey3/+8x923XXXwQdnmtxxxx2cdtppXHbZZVx22WVAnNz9jW98gzPOOGNCMu0vvfRS9ttvP84//3xuvPHGUa8fheGQruAJR2sC348vzVkeB48MSbchCAO/rphbfvwJustl2GYpWgjUokUAiF431q7OTtb/45/kV47e0J2paK0pl8vAzKx8mii2luOEkR2r1ppK1cOQEssykwdbUiSnFRrqRlSvtRp+16jkwTz8+TQM2bB245YYcqxDMdbPVWuN54eYEyQWmuJVa2zYsAHHcejuruD7fiJ30LiciC1T5AgM1N5j7n2sWus4tUSFaBTlssb3amg0hpR1YzYKfLxalVCBa/Y/3ihS1GoeuZxLEIboIMALJlYMcyJDaYMz+u3Ppgr3MRtOl19+Oeeccw6HH344v/jFL/rlIS1fvpxHH32UJUuW9Fs3nbdp06Zh97N8+XJ+97vfsXbtWp5++mlKpRJ77703d9xxB8CAid+jZd999+Vtb3sbN9xwA//93/896vUN05zSruFaazRgz4GqOseyaG5qwrYtrKRHlPfcWuSa5wDqnia55rkGwwnAWLOWppfuM6XjnUzSt9eZUG02mWwtxwkjP9ZCIaJSqeK6DnavUvRIKYIwBB0LLKYPpNgj1fOg14m3aSSGkxD0MsZ6tpVuW8q4DcloGevnmlbVObbZb37qcRvYG5U2CR94u47r4vkhzc3NKOKXs3yu/z1TSNFQFTmS8UZRLKxZKBSQhoEA8oVi0uC3p7eeSgV+EwPKNE1CJaj6PqVSacB9dndXsJ24UjPSgs7yzNEvyogZ09P+0ksv5dxzz+W1r30tP/vZzxpK+VMOPPBAHn30UdasWcOee+7Z8Lc0L2lR4kkYCdtssw3bbLNN/fdf/vKXABx77LFjOYR+fOELX+AnP/kJH//4xznssNE1t5yOm3/qbp3tDx5pSEzLaGhRYBaLDUaS0Lo+9cYqFWf98fdlrnyuw7G1HCeM7FhN0yCXc/F8D01Py5HeujiN6/d5+I/yDb9xS43rasZ+Txvr5yqlHDQXdTj6GlVxxaPGNAw87RMEAc2lIus3bKyPsXEDJE2Zh29Hkr60xt5AUDpOFE41pNASpXX8uRnGgE9YrSFSmnLNI+/0b0Ts5hxq1RrFYgHbtpAVWTeMJ4KZGqqbTYz6teJLX/oS5557Lsceeyy33XbbgEYTUM93uvLKKxsu7L/+9a/cd9997Lrrrqxc2aM9sXHjRh577DE6OjqGHcMDDzzA1Vdfzate9SoOPvjg0R7CgKxYsYIPfehD3H333XWjLGNyEYBtmclNpueL1nzIKzCKxSHXNZqbaD74oEkeYUbG1BAnB5vkczmiMKLqxXlP6UNuKrPBpuuR19voGs0kpWyYUptASok0DLrKVQzDIOfY1AaRxNEqURIfTUI+EEYRnp9IqiRtYobT0hJS4Ps+L2zcNKA3yZBxPlQYRlhD5GWNlckWv0ynucyoPE5XXXUV559/PosWLeLEE0/kxz/+ccPfi8UiJ5xwAgCHHXYY733ve7nmmms48sgjOeGEE3jhhRf42te+hmVZXHXVVQ3rXnnllVx00UVce+21vPvd767P/8hHPsLGjRvZb7/9KJVK/PWvf+W6665j22235Yc//OHYjnoQPv3pT3PttdfywAMPTOh2MwZGSEHOSd+se24ORs5l2Yc/xLNfuGTQdZd/+CykO7AKbUbGbCROGjcoFPJUqzUqtRpu2rdsChPp50rKvhACyzQJozgHtalU4oUNG3EchWkYddFLiD1OKumHN5TXSQiBBJSMc6KkkFRqNUwjF8tSSIZN5i8Vcmzp6kIIiSn7e9mEANux8X0f17FjNfQhet1lTD2jMpz+8pe/APDCCy/w3ve+t9/ft9tuu7rhBLGi+F577cXVV1/NOeecg+u6HHLIIVxwwQXsv//+I9rnfvvtx9e+9jVuv/12qtUqK1as4GMf+xjnnnvuhEuwt7W1cd5553HuuedO6HYzBsY2Y1FBMYCK7TYfeB+qUmH1178JUiIsE8IIhGD5R85i6fv6X38ZGXMF13UIggDP9zGkjCu1poi55CywTAPf96l5cX5TPudSq/kUC7mGHDEJaDn6A5cC3DQnLTGkhtuKYUgKuRxKa1y3f1+6VDss8AMs08AcYwhz0DEn/zLGzqharmQMTNZyZWzMb2kin3OwbXtQ16734ous+eWvMdauxVm0iPknHIc9f/4Uj3RqmEmtSCaTreU4YXzHqrUmUnGVlVIK13FIk6UnE2kIjAE8IcMxnpYrNS8gN0G6RVEUNfS+LJcrmEkRiu/7bN7SQbGYT17aesYZqaG9Tb3HG0YRnR2dFItFgiDsMYB65aQNPj7Fi5s2U/UCFs5roeD2VwkPw4hqrUYhn6Ojs5uNHV1DyjWMpuXKtsUlU9JyZXX386NuuTJbmLrXmIyMXtiWhevaSCGHjIfbCxaw4I3HbxUP2YyM3qSeh3zOjb0mNQ/bsZEj6EeX0YPrOpTLFcJCAceJxUc9P8C2rQbv0GjycuL8KolpGmg0MmnFo/VIWt9ookgRhiG+75N3nH6rSCkxpMQPAnI5B9nVTRRlPo6ZQmY4ZUwLpUIOoalLEGRkZAyMlBLHcTBMk2q1hmFITMMck2TA1oiUEi0kQRBgmSb5nMOWjk7CMGxouDwSYzSWIojbRJGknskksWnEZpcQcdWkEGgtUFohMfougpQSFSlyORfXdihXq6M88oGZqTpOs4nsm5cx5Ti2hWtbE14tkpExV0kTnfO5XNzOw/cbwlEZgyOEoJBz6OrujnWWTAvLsvE8f0zb84Mg0daK1drjfnii7iUazABTKja6tNY0FXO0lAqgNXqAz7G3ZIDWmnwuK4SZSWRProwpRUpJMZ9DSjFm3ZaMjK0V0zTI53JYtoXneURqblRbTVTkMdVZ6otpmqjUU6QVhUKOmhdQqw0sTzAUjm2Rz7lJ2xxjFJ6/uCeegJ51BISD9MkzTROViG3mHHvC8pLGKvsw2mkuk8VJMqaUnGOTdx1MQ/YTfsvIyBgeKSWObWNIGYfuTBPLNBlL4vhYVcInEqUVcgwVbYMhhUBJGlpSCQTFQo7Ori4KSZeLfM7FDwLcAZKzByOVjBhLPz6ldaIsHmvXObaFaRjU/CBOVO97HDIO/2mtMQwD2zapjdFL1nAMUxCqm+stpzOPU8aUYRoGzaUihhTIMVTtZGRkxMRK1SaFQj7p9RbEfe1G8UBPjabp9hZoPbrE7KFIx2+kgpj1KjewLIsgjDAtC60V+VyOIFRT0mc0NZpUUpFXqXn4QYhhSHw/GPBzS48lUhopBXnXnfRxZoyMzHDKmDIKeRfHMkGIej+njIyMsZF6P/I5F9OQ+IEfh6JGQG+jabqRUiAnMN9x0PCRFJhSEAYBlapHEIYU8jm6y9VRe5BGS5r7FIQRQRBRSwynMIqQMjaeBsJ2bMIwQkA/+YSxkoXqxk9mOGVMCZZp0lTIEyk14YJuGRlbM1JKcjkX27YJoyjxYAy8rBCxAONMMZqARJJk8h9FUghM26Ja8xACyt3dOLaNBjx//CGw4RBCxBIIAor5HDnHxrYsioUcldrA+zekRKsIrTWWaeBY1oDLjYa0qm6yp7lMZjhlTAnNxTyCOLl1unMqMjLmIrZlkXPdpOouFs3s60kxDGOr8AgMhiElkYqbKNf8kCCMyDkOvheglJrUfaeJ3oaUOI5d79NpGjIW7RwkXJdimSaOPX7DKWP8ZE+wjEkn7zqx2GVSrpuRkTHxiCQEXijkMEwDz/eJlKpXmaVVrFur0dT7uB3HwZTQ3d2F47oopQiCgXONJgrTkNiWWRfeTL1+KlJDhutSpIwTysfrncs8TuMnq6rLmFQMKSnmXSQC0zS22pt2RsZUkH6/cq6LaYTUah5aaxzHntDKtdlOGq4E8DyPXC5HteZhWROTR9Sb1BiTQiLN/kaPkLEHsOYFuM7gbWfSUJ9pGvjB2L1jU+FxHIUc6Kwke/3PmFQKORfXtjOjKSNjijFNg1zeRStFtVobceL41oCUBhqJbdtUq1UsyyaM4mTtkaCUIlJ6UA+VUnEVnecHcb9Bb3BvlmkYWKZBGEXDhgtty8Q0sxzR6SYznDImDcs0KBXzyEQoLjOcMjKmjrTXXS7nYhgGlUqVMAonvYJsNhBFIaBiI0RIar5Pc6lIV/fwFXZaa8qVGu1bOhMZiMblldKUK1U6uipUax5hGKEY2iByHRvTMAjCoQ03Q0ocyxyXPycL1Y2fzHDKmBSkELQ1lTANiZkZTRkZ04aUEtexcXMu1UoN35/8ROiZjtYaKeL7lGOZeNVKIkwJ3jC5Rn4Qsrmrm5rnU67W+imVx5pRJq6TKowbGEIOapAJIZBSoNHDttERQuA6NiLLFZ1WshynjEmhVMzjOBaGzKroMjKmm7TXnSzk654n13FmlCzBVKKUrh+3ZZt4QYDn+zQ1NbF5y5a44m2Q+5ZpSFpKBYSKKObdfr6V1LjpjTVIeC2KFEqpOESoNbYVL6e1TsbYfx3HtmN19NEdcp3YYBzjyiNkrvs0sydaxoSTcx2KORcpRRaPz8iYQRhSUsjnkEm7lnCY0NBcRWtdF8WUQpJzbcrlSr0VSnWI1iaGYdBULNBcKuDY9rgMT6UUQRhSrtaQUqI1vRTGwwElCizTGFefTzFF/+YymeGUMaEYUlJwHQxDYhqZQzMjY6YRh+4cHMemVquNqdHtbCdSCiHj3EvDiHv/mZZJtVbDsV0CL5iSZHrTNLAtCw3k3MQIE6IuVzCQfIsQYkKEMDPGTmY4ZUwYQghKxXx8A0DAHH/ryMiYraS97nL5HFEUUalUiKJoViaOa62JlBqV90wrFXucEEmOkaRUyFGp1mLpBsMYMPF7okm9XkYvI04nXiil1KA5Tzl3cNmC4ZBMRYL43CZzCWRMGK5t0VIsAHHsPgzDuKJuK82jyMiYyaTNcPP5HJ7nxUaDbWNZ5oz/vvY2aJTWsYjkKPrdaa2S/K6eeVJKco5NR2cX+ZxLV3d3rJs0BS2ilNIEQYjhxELBduJpCqMIMcD907GtOAt9DIZdpuM0fjLDKWNCMKSkuVTEMCRhFOE4Vr3FQBRFmKaZdCmf21+ojIzZRPp9dF0X6ft4vo9SCscZX+4OwNMP/Yfbr76Jf997P0ppdj/opRz7nrey6/4vGfW2tNbJ1Div9/9HQ6RUv8RrIQSOY9FVrsZGlTTwfR8zlxv19kdCOu50GFFS6dj7vIdBiGma9eXThPHYO2UQheGkjC1jaDLDKWNCKOZd8q5DFMVNfKWUsUtYSCIVEgRBXNWTeZ8yMmYktm0jpYHnx94n13XG3CLprpt/wVXnfAEhJSrJFbr/l3fz59t+z7s+ezbHve+UUW8zioauI9NKjzj5RCuFGKC0zDAMTENSrXk0l4q0b95CznUnRU08UoooUnVphIHCckopTNMkjGLNqUhFSBF7ymzDoDoGw2kqdJb0HPc4zfVQZMYU4NgWzaUimvhtqHcZr5QC0zCxLYswjGZtHsV4id8W1Qim4ZfbGs9fxtRgGJKcG1fElssVgnD0gplrn3qWq/77i/E13yvBOv35+5/7Gv/524Mj3l763RnJcun9ZaAxN4T3lB4wnCSEwHUdfN8jiiIc26Kruzyh3zmlNJ4f1I2xuMFv/x6CaZ/B9LjQGtM0k5/Btsfm9xCkeU6TN81tsynzOGWMEykFzcUCpmEQRVHcwLLPDSCNqdu2FSc9BmG9BcvW4n2KlCJUQyevaq0JVIgfhYOeF0HcosEQmcxDxsSTfiddx8E0TGrVGpZlYdvWiPXYfvODWxBCDKrlIw2DX137Y3Z52V4jHlel5ieh/qTnmxz43qG0hiScNZCxo3SPNtJAHidIvl+mSblapVQo0L6lAzeMsK2JeVwKEb9sRslLkBeEgMbsk6MVBmF8HMk6ve+jGiZsPBmjJzvzGWNGAMV8nkLOJYrCYRtkpiJ8YRQRhCFWErvfKoynCXpjTd9AMzImEyEElmUijRzVqkdUi/OeRpIo/fB9/2jwNPVFRRGP3PePEY8lUoquShW0xkhV0G17QHFINCg9sHdKa42OFIaAoaJ+QgiKeZdNmzsJnJC861AuVzCbihMi5pve7wwpCUVskKUhu95jVVqBhqrnk+uVcyaSpHDLNBHEiuOj2j9TkByu5/Y9PTOcMsaMZZm0lApoNFKOrK1K2j8rEiKuIjFkPfkxIyNjZiGEIJ9zqXk+1RFW3ckRGFeDeXv6kobdmvK5urcobuE0svGrJI8oLflPhS8jDUMFlKSUFIs5ussV5re1siWM8PxwxDIAWmue/NejrHt6Nblinr0P2R833z/J3DJMTMOgUqs1GED1RHAp8QMPgSDnOvW/pxWRpmkQZAniU072xMoYE0IISoV8PURnWiPvR5caTwIIghCtqSuMz1nvU+pzz8iYYfT2dNS8uI+dILlctcJxHHKuQxiG1GoeWusBQ/Ip+xx2AM8+8jhqELeONAxeevhBIx6fIFbzHi1aQ6Q0QRJmC6MI3/dxXZc0fXmo+41j23ieT6VSwbFtal41acXSs45Simq1SrFYrM978l+PcuXHLmb1Y0/V57mFHCee9S7e9F/vathn2m4lijRmn+1GYYSbnPe+YTyIjbu4MfDoDKepSA6f601+M8MpY0yYhkExn0MphWGMrVJOSoltW/XQnWkaMECS5Fxg7h1RxlwijKJ6YUIYBvXEGtM0ELJHMLNQMJJedxFuojnU9/v62necwG3f+R+0GjhJGzRHn/aWEY9tjHJF9XUR1I0LnYhdMsJOb/lcjs5ylXktzVS9WK6ht+cHIAyjuifr2Uef5NMnnknYp1FwrVzlfy75FrVyhXec98F4LKSnWWBbsXxLubObO2/6Bf/309/QvaWT5bvswH6vP4J9Dj+w39gMKbFMg+oohd8zw2n8ZFV1GWOipalQ7+htGCP3NvUmVey1zNj1HwbhnO3anqUlZcxU0txDx7bI5xzcnIs0DJTW/V6KhBDkC3mkFFRrNYKgv7r2gm0W84nvXoLRp1GuNCTSkPzXFRew3W47jnhsY32RSit8tdaEURSnFPSp+h1u36ZpYJuSzq4uSsU83eVKP9kAIUS9p9wNl31nyPvYrVf9iPbnXwRA9sp1EgLWP/scH37V27juoq/y5D8fYf2q5/jb7/7IN//rAr533qX9thmnOWRFItNBZjhljBrbMsm7LipSmMb4VYbrndulTNzWW2fj0YyM6UYIgW2acWK4gCCI8P2AIAzr0gQyqbqzLRvPD6h5/V0e+x5xMFf+4Se88axT2XGf3Vm59668/vS38bV7bubQN75ulIMax/FA/GImEz9L0uJkxOsLgeM4hInRksvl6K5UG5aRhkSpiO4tnTzwm/8bMjEe4J5bf12/z1VrHl4Q98W74sxPsWXDpkbZhFQD6xe/57bv3NBvbGl18mhI2uFN+jSXyUJ1GaOmkHfRSmGYcW+liSL2XMlEiyV+o8oqyDIypg6t4xRl05RobREEIUGQaA0pwAbL6imLl1Lg+QHlSgXXcRq+r/OXLuJtnziTt33izHGNSSDqqtqpIGfv/QxfyWskQpIGvl9DiNHdsyzTwDIk5XKZYrHA5s1bCEIbrTXVmkel6qO1Zv3qtcPer6SUbFz3ArVqLT4OFRFpxUN/+hvrnnx2yHV//p0beP0ZJ2P0Sr63THPUXqcsVDd+Mo9TxqiwTAPXtghVNOE9nOLQXfwWFUXRqJp2znRGWzKckTEd+EGcAO77IUrF4SStNejYAOlrpBiGQS5RGC9XKnFIbIJfdoQQqF75UkrF3yYvCOtaSEOhtcZxbGp+QBDpeohsNPsv5HOEkUIrTalUon1TB1s6OvFqHo5t4jgui5ctQQwTBlRKsXCbxeTzOfL5HE2lIoV8jif+9hDGMAbQ5hc2sn7V2oZ5lmlOSS+9jEYyj1PGqHAdG0PISct27iuWGXcJn/1GhyElUve9qWroJdSn9FxvVJAx07FMA8sykKLHsxOEsWq1PUBlW10w03URQtLdVcbzPExzLCF8kf5XT5pO5iKlqL9IeX4sYyIAzw9whmnEK6XEMCTFfA7PC8akxSSlpFRw6ejsoq21JfH6SJqaivVjddpaOOCoV/KXXw8erhMCDnvT0Q1j8IMwbhczAvq+gJmGxBql4ZR5nMZP5nHKGDGGlLi2jWEak979Os17EkAUzf6k8fTm3TgZGNLANMx4ktmbY8b0oon1g8IoivNwlEJKgUyad/edercCMk0Dx7UJwqheedfTnHckU7ydKP1/FBGFijBUaK3QJEneWuF7HrWaR+D7eJ5HrVYbYvKoVmvUqjWMXsnhfhDnZ3l+gB+EBEGY5B4lx9SndYud3Pt836cpaWjeN3n9lE+8H9u1BzXO3nz2abQunNcwzzINdtpvT6JhPOzFtmbalixsGJOUEtsavVTDTKSzs5PPf/7z7LXXXpRKJebNm8f+++/PN77xDYIgGH4DU0jmccoYEQIoFvLkkpvCSN+QxrXPpARaSEkQhICY0JyqjIyMRsJR9kLs/fqUql1btkkUxknlOdfFGmFrkL771WiS/yDq8YJZScXfaEtVhdAYsidxWam0cbBO9hVP6ThiHavGfagooqMrYF5rC1qD74cNVcXLdtqeL9zybb75iS/x5L8era+Xbyrylg+fxvHv79/cOIwUux30MhZuu5SNa9cPrH8l4NCTjiNQir4ymrY1uheuRKFhUhnt9sMw5IgjjuDvf/8773znOznrrLPwPI///d//5ayzzuKPf/wj//M//zMpYx0LmeGUMSJc16GllEcQe54ipib/qK6QaxkEYQCYA2rHZGRkjA89BmNE9/k5FuQWmJaJUppqtYpSNpY1fK+7fj0u07gdoJVC96qGG4tvNooiql6ITDy7jmMl3preit19j6oRnbRA6a5UKRULdFdrOI6NYfSMbfs9d+bSX17LqkeeYN1Tq3GLefY8aF/sPvpPKaZh4No2H7ryIv6/0z5O1+aO+oupkBKtFHu+8gAOOfk4KpUaeddJ1NPjfQ4UQh2KmRiqu/vuu/nrX//Khz/8Ya644or6/LPOOouXvexl3HjjjVx11VW0tLRM7EDHSGY4ZQyLZZrMa46VcafDaBFCIIXEtiR+EGAYRsONIyMjY4YhBFKC5cbq22EU4TjOiBKZU49Pg36UFPh+gBASc8Q5SqKhLF5KWfd+CXSi8zRy/4tSmprn4Vgm3eUKWilytk2lVqNUyPdbfsXuO7Fi952G3a7WGtM0WLrDdnzhF9fyp1vv4N5bfk21u8zCFcvY77gj2P2VL0dKiR+GhJHCNIzEw6fnRHJ4R0cHAEuXLm2YL6VkyZIlPPjggzjOwIbndJAZThlD4to2rc1xc0sp+veVSxVzJ5v625UVJ42HWmOY5qgrZDIyMgZmuO+xV61R665QaGnCHEn4TcSZkLZjEwQhtcQ70ztx3PdDnnluPZVqDSklLU1FFs9vxbYbt6+U5unV6ykVC2yzsC02rtLh9nIQpcnTsYSB5tm18bYB8jmHbRYtIIpCzEQNvff9SydhwbgVS//DCYKQfz/2DHvvtgO2Y9PdXaatWOLF9k0U87kx3wfDKKK7WkMphV3Mccz73sbxZ76dIAyQhsmL7VvwkxyfIAwTnTsLlTQ/Ng0Dx+nfB28wpIinySTdfmdnZ8N8x3EGNIBe8YpXUCgUuPTSS9luu+046KCD8DyPm2++mTvuuIPPfvaz5HIjP8bJJjOcMgYl7zq0NZfiirBEJqA3UdLTqrcXStW6KD92J/76R0FI3G32JrfzYUh7/Bd9mohpmSZhGBEGAZY1eM+sjIyM8fPMQ//hp1//AX//3R/RSuHkcxz21mM44ax30TSvZfgNpKG7SFGtediWwnFshBB4QcAjTz5La3MJ17ZZ9+JqgjBkxbJFlCseQRTSVMihNWzc1AkagrYmPD/AMA08P6SUd4kiRbnmUSrk4qpfNGEU8vgzz+E6Ni1NBZ5YtRY/iNhrlxUYpklnd5V8ziWfNO7trlSp+gFN+RyuY6G1prtSwwviMYRRxPMbNrHT9tsQqQgVKZQKsQxBGEVYY2hWrpK8sEqlRltzCdMyqNZ8Qq2RpoFlGjiWVTeclNL4QUA+56K1plbzkVLS2jzyfYrk32SSbn/58uUN8y+44AIuvPDCfssvXryYn/3sZ5x55pmcfPLJ9fmu6/Ltb3+bM844Y1LHO1oywymjH3ED3xwtpWJsGBmywR1cr4JRusHLXVv9Dzbd8WV06PfMe+pPdNz/Q+a//gLsRTtPyPhil7tImnYGWLY1bMPOjIytmd6SFzXPxzbNAavCUi9MyiN//juXvuu/UUqhk8pWr1Lldz+8lb//9g9ceOu3aFnQWCU2EEIIDNOIw02JUnYu56ZJUazcdgnzW5v416NPs3FTJ1GkWb3uRcIwxHEsXrp7T4uW9Rs385+nnkNpjef5tDQXUZGis1xh0fw2XrbnTkhB/aWutbnE0oVttG/uIgwjNm7u5MHHnoZEWHPn7bdBCMFTz67DC0IMKXnZXjvxwobNPLV6HVJKFra1sN2yxfhBwN8efJxq1cO2JAcX8li2RRAEozac0srEStWPuzHkXaQQmNKgq6uL0FeAoKmUxzQNNnd1g9Z0dVcpFfJxyoJpUBlts7opZM2aNTQ1NdV/HyrcNm/ePPbcc09e+cpXctRRRxEEAT/+8Y95//vfz5YtW/j4xz8+FUMeEVmJUkYDlmnS2lSktakUJ4L3yifqcWlrhBQN88KO52n/1RcTo0n3mkD7VTb+/AKiypYJG6cQAjO5cfh+3NE9UxnPyBgYrTV+EFKteWzq6GZzV5lytTZAJVsPURjyjQ9/LlYN71PppSLFpvUbueGL3xzVOIQUWLYFAiqVKpGOv7fPvdDO48+sZXNnmdamIm3NJbZftohF81vZtKUbr1fT3CCIqPkBB++7O7us3Jb2zZ3stuN2vGzPnXhh4ya6qz0tUXw/4KnV6/j7w09SrtbI5xzWPL+BquezzeL5FAsuq9dtIJ9zWbZkAUsXzqO7UmPt8xv5z9Nr2GPnFRx+wEvYdeW2OLaFISU7brcNB+67O54fUK15iUjoqE5D/RxWKjU8z6epmK+nHaR5V7Zl1T+zIAwbEvfTSKVlmvUGxiP+DJLk8MmcUo9TU1NTwzSY4fTggw9y0EEHsfPOO3PNNdfwlre8hVNOOYVbb72VN7/5zXzyk5/kwQcfHP1JniQywykDiA2RpkKeBW3NlAo5BBrLMgfshRRFUaxBlGgRCSHofvB2UBEDVqRohQ5rVB793YSPOW0SHIax/spsN56kEP2mNH21/nvmWcsYJWkfSIC25iJBGA7YQLf3b/+88890bNg0qPSIiiLu+8Xv6d7SOeDfB0MIgWGZGJaJjuJtl7srVGseO267hO2WLeThJ1ax7sVNeEFQ75HXm2I+DrG5jklzsUA+Z1PMuwhB3TMG4Dg2e+2yA0e8Yl+WLVnAE6ueI4oUWzrLbNzcgYo0bS0l/vP0GlavfZFqzScKY/0qpWLFdIBIx5pVhmEwv62JQs5JWrkkwrVj+EqapoHj2Lg5G8ex+/090grXsQiiqKE/XhCGdHaViZTCGkPbKzlF02j4yle+Qq1W46STTur3t5NOOgmlFHfdddcotzp5ZKG6DGzLoqmYJ+dYSaduOWjzSK1jgby++U61p+8DPYRIpdZUn7mf0svePKFjF0JgGAIhzERZWDfoqsw2pBygYjB9Zoj0f7Pz2DKml0LOBeLvcBiqfteZ6vP9fe6JVUjDGLJpbRRGvPDsWootTYMuMxhSxi2WpBDsucv2LJzXXJcskIakUqlhhQZDyQOkpIndffH8gMeeepbnnn+RmhewbMl8lixopX1zJ5WqRz7nML+1iWrVo7OrQqXqIaQg5zpsv+1iHnr8GZ5YZbNofgvbLJ4fb1T02a9SCDn6R6mUkpxjD9iOSUpBLhfnbsUadj0ordnS3Y1tW9iWOSYl9JnGunXrAAYUukznhaP0rE0mmeG0FWMYklIhTymfi8NyppEYTgN7NbTWRFFUL+PtvYxWw1/UOvKHXWasSCkxTUEYBkl571haPkwvou5l6iP5MLsOI2MYlFIN1V/pBzySy3UirmkhRP2B27uqrFrzUCpW1DZNie066KFehhKcxCAbLRrI5WwOftnuFPI5yuUKrutgmiYv3X0l3eVq0ugbWpoK7LP7yqS6VzCvtRlpGCyaP4/mYol8LocQmle8bC9KhXg8tm1x8Mv2IPBDELFCdz7n4tom+++9MzUvwLYMSsU8+ZUu2yyenxhykkLeRUrB0oXzCENFseCScx0O3X+v2ADVsPtO29HWUiQI/DF/LkIILMOMvfh1A0jECfWG0RD26k0sjeCTc+1e641wn1OYHD5S9thjD37961/zve99jwMOOKA+X2vNtddeC8CBBx44oWMcD5nhtBUihCDn2LQ1l3CTN54wiEakjaSUHvANx16wI7XVfx/c6yTkhCWHD4aUAsuyCMMIP0nWHMwIzMiYLno3rB2JNyUlVtKfGM0e17Hw/QCtjTi8lXihXNtCK4VhSPZ97SFc/4Urh9zOwm2Xss1OK8Y8DoWmqSnVQDKoVGs4tk0h7ySht578Idsy67lOrpNDK4VtSuyiS3oeW0q99JS0pq2pkDQFjs95uRLrMNmmpKWpBSFk8rKlaGkq4NgWvQ3ZBW0t9Z8B5remnjWNKWN5Ft/3hm3uOxRaayKl6q1ThADLsvA8H9excWyLqtc/Abzm+XRXamPe70ziIx/5CD/84Q/5zne+w5o1azjmmGPqyeF//vOfOfHEEzn44IOne5h1Zr+PL2NUWKZJW3OJxQvayOfcuhEkxNBvs3GILjaKBgqFFfY6dphQnaKw59HjP4BhSB8uafVOljSeMVcQE3i3Thtpp19jTex58sMQP2msvWjbpbziDUcOaRS88ex3T9iLiWEa2I6NHwbUql7SM07T0dXNqufW8/Sa9XRXqoikZG5YofPEM75xSwdrX2hP7l+N/eeEIOkZKanWPPwgTO6FPX9vcP4mv6eVxWkNzFhPQRQpNnd2EQY9eVypRzBMQlSFXvfp3oRKEUWKUn50Ui+pjtNkT6Nh+fLl/PWvf+WMM87gkUce4WMf+xif+tSnqFar/L//9/+46aabRrfBSSbzOG0lSCHI51yainnyrptUboyOKFJIY2APjrN8H4oveQPd//pZfIdPjajk56aDTsWev8N4D2NEpBV3QsQd1Q2TOaGum7F1oxUT+qrbIEOAIJ806DVcGYe+peQDl55HUPP4yx33YBgGiNhjJoC3fuL9HPqmoyZuQMRVd7ZjE/gBfhCwuaPMI08+iyFjpexczmGXHZbRVCwQRrGx4SQGoJf0jQMwpCBUEZEWbGjvoLtSZcmCVvKuEzcuTu4PkVIEfoRhmNhWz/kIowg/COuyDX3PlyYxlkSP0TkWtNYUczk0sdepd6qEkJIoihPETcPAT5Pek4iBH0R4vo+Uo1PUHkvy9mgZy/a33XZbvvOd70z4WCaDzHDaCrAsk9ZigUIhN+ZWJekbljFIEqQQgqaD3429eBe6//Vz/PX/ASFwttmT4j4n4G6773gPY1TUjSeIk8b17E4an27ScMJAiayjQSAytfcxMpme07ShtpG08ogihRQSJ+fyiasv4cl/Pcoff/5burZ0snDbpRz6pqNoXTR/0sZj2Ra1asBjT6+hmM+x247LEUIShCF51+aZNc/zzJrn0RqWLGxjp+234e8PP4kfhLQ1FykWcjyxai2mYVCu1pjXUiJSiseeWkP75i5M02CPnbbDkIJ/PPI0lmnQXamy3TaL2HHFUh5+YhVbOsrkXIcD99kVwzT6Ze3Us9NG0D1hoDYyWmukIXEcm5rno1TchBjinE1DSoIgwHUd8q5bF8EsuC4tTQU2bNpCpVaj5vksmj95n0VGfzLDaQ4jpUzUv4vYlj0mLxOk+Q8hpjW04SGEILfyYHIrD06SSqc/vygVy0xLmtOWMdM9rtmGJlY4VuN8eAtAyJnh/auHWyaQvtdVXSx2AvYjJrtPRkI61LT8HmD7PXdh+z13JkzCXQIR5w1N4jiCIMTzA3bcbgk510EIQa3DJwgjWpqK7LbjCjZujsNwSxa2sWlLJ6VigaWL5vPAv//DkoXz2G7pAv7xyJMorXn+xU2sfaGdl+y2kvbNnTyxai3LlyygfUsHL997V2pewBPPPse81iY2bymzcF4LC+c1D2g06V55akrrQVOh02XSa6DuTUqERqNIxYnhpmxIl0iXiRIvk+uY0BVvMwxjkU7TNPGDEDXKT2EmJofPNjLDaY6Sc2yaSgWK+dyoqi763uCFEPXcJjmKJAsxkQkZ4yC9AaU97nonjWeMHIGIwyUqGp8RMIPOe6RUIiw4/m2l56SvenT6cAzDKDZ8xrEvrTRMoc2Zfsy9P28pBVrFj8WJNpvibfZg2ya2abJxcyfzW5vxg4jHnlrD0kVtrN+wCc8PiSJV99ZYpsnyJQvIuQ5BEDKvpURTqUCpkMMLAqo1n86uMqueW49SmlIhjxDQVCywaEErYRjxxKrnyLsO222zkFVrX+DF9i3Ma23CHUBnqZ4HxeAvYmEYJZIpMtaGiuJwXJx/FYflgjDEtq1+nthUeytM10nwggDfDynmcgThINp5QzBTQ3WzicxwmmNIISgV8jSXCknsf/gHlUCgVJz8rXq9IaFjyYIwjCasmme66N3jLghDLHNu6J9MFUIIJGBJg2A8xpPWqabgtJO2t5gIYsHEYUr3Z02NwkADjdvfmlISqmiSfU0xOddmxfJFPPrkaspJ9VhaddddqdFULBBFETUv7laQCuK6jsWiBa08/PgqXty0hedf3ERbS4n5bc20tZTIJX3ytttmIb4f9LPlKzWPas2nuVRg/Yub6sKhjfSE5yItBnwh6F0tl95/IDam4vttWG8qbMr+3nzDMCBpT6OJr9f0/lypeRQLLkqppOlvxlSSGU5zjKZiIW4WOYyhk36pgbouU5BUdvSOx4dhmAjVyVnvpUkr7qIoDgOYplEPRWQMT73JMhBE4RgfnXPdiT97SZX369//3u8VoudzEyL1RsUBu970qFKNHpHso/7yBixfsoB8zuXF9i0YUrJyu6XMaylhWTuyaXMnhmGweEEbxUKOXVYuo7WpgJSCPXbejrXr25FC0LLz9uRcm/mtTeyz+050dpUxTZO865B37aSdiollmuy5ywqaigWCMKRas9h2yQLybn+dqp73BkGflp11wihK7puN803TaMgZjLQmCCNybqNXS8r0nGukiLs0qEQEsurVsG1zTEZT2hZlMpns7U83c9Zw8jxvyIaCc5G869BcKoxIgj9WvI2zFAzDwLKs+pdZJTouUsi6J2quELvNDSCuuNOJV222G4VTSZp4HyZvwqNblzkp6Bkf1sQeWI+B0khvb1+kVF3xu94zTfRKRk7XIelvlswwpGwIvfeWG4HYeDF6uQZ7h5EMw6hHDDWacrmGHwbYlokhDSzTjO8buqdf5cDHl+TxRHHeVI80SnwmI6UQUrCgtYm25mJdXkBrTUtTnrxtIpMOB45tsc3CeXFqQaTIOTYrly9GGj3HWat5zGspMq+lGFfoubH+U9pcF2CHbZcghaCYd9HoQVsc6VSDQPQcy0CfU2+v9sYXNvCz62/h6ceewM25HH7MazjoiENwrFinScpE9LJPH1CBQIjY4Er70gVhxKaOrkHP7VBMxVdwDn7FGxhVrOKJJ57gwgsv5BWveAWLFy+mUCiw++67c/bZZ/P8888PuM6TTz7JaaedxrJly3Ach0WLFnHkkUfyt7/9bcT7ve222zjiiCNYvHgx+XyenXbaiQ9+8IOsWrVqwDEecsgh5PN55s+fz+WXX95vmRUrViCEYO+99x7QKLjwwgsRQnD33XePeIzTTd51mNfajG2NLH8n1SNJWx7E80SSdGhgyFgLSUpRF4+bK6Q5B5ZlEqkodoXPoeObCqSQWIY5amNhmGfp1CJoeECNb1tiUpK3hWTAp1AUxddtqlOmdY+wZvx/QPfSOkq9IhqEFqhIE4ZRfQrCMC6HTxpn25ZZ98b2TmjuO6lIEyqFlAZBEG9Lq7iQIE0eH2xSyYua5wd0dlepenHid+97stYahULIHv0kiPN/bMdGRarew9G0YumAtANC+v+0tYtSUZJwHXvX0mTs3hIARt14oz5/QNJE7yE+u/p5F4Kf/ugnHPfS1/Ddy77B72/7Db/88W2cc+pZvOPVb+a5Z9fi+T5CEN+PkvBbel9SSoHQWH2iCHPphXa2MSqP0zXXXMPXvvY1jj32WN7ylreQz+e57777uOqqq7j++uv54x//yK677lpf/q677uK4445j6dKlvO9972P58uVs2bKFv//974MaWn356le/ykc+8hFe+tKX8vGPf5ympib++c9/cs0113DzzTfz73//m6VLl9aXf9Ob3sSyZcu45ZZbWLVqFZ/+9KfZcccdOf744/tt+8EHH+T73/8+p5122mhOw4zDtkzmtTSRGyCBcSh63xhTBrxRzJQH3QRSDztZJmEQoXU4K9u0TAe9z5FpGHHOywgNz5nkcZJCIs0eb4vq08xWCBBSopUaWmiRxCMzwdfOYEZmaijBwBVSw46itxuq10whdENl10jH2FTIYRgGfhCypcMfdeWlbZsEQUhHZ4VSMYfjWEn13pB7RhgC27EIggBdU7g5tyeJeoBjUEolRStDh7dGcvxxJd3gRrfWuu7aue/uP/LFcy4ccLlnn3yGz7zvHL512w8wDQOVGL71sGiSIG4Z5oRp0WXJ4eNnVIbTm9/8Zs4991xaW1vr8973vvdxwAEHcOaZZ/LZz36Wm2++GYD29nZOOukk9t9/f371q1/hDhAnHgmXXXYZS5Ys4Y9//CO5XI9C6sqVKznnnHP48Y9/zIc//GEANm3axIMPPsi9995LS0sLAE8//TS///3v+xlOixcvxrIsPvvZz3LyySc3bHs2IYCWUmHAqo8h18sMBCDxnCRyBUEQYGZJ4yNGCIGRXEfhCHOe6mrLs4A0r28mpd6GY6ii6scAq6fZSmmy8kixrJ6HuWUadQ/LaI0nx7HxgoiaH2DZ5oi1voQUWLaFChWVShXXdfpVNvbGkIL+bWTHQHqiBhlnpBSC+Br6/levRhoSFfU3BqMo4unHnuD+e/7Mq456NbZlNlROCimo1TxMw6xXA2fe8elnVE+I/fbbr8FoSnnb294GwL///e/6vG9961ts2LCByy+/HNd1qdVqeAP02xmOjo4OWlpa+hk2y5YtAyCf7+lN1NrayoIFC/j85z/PU089xV133cWtt97Kbrvt1m+7juPwuc99jueee44rrrhi1OOaKZSKhaSsNjOExkpa8SKEJAjikEV2cxo5UogkbDcCkuu0t77RUNNUIZMQde9J1Mc6ZcMYlt7epolGKTXmg00/L9MwEsHZka+bhs5NQ8YSA+HoQlBCiDgsZxhUqzVqtVq/66cu6jlBVZRaD25i1vctwKtU+dufHhjQaEoxTIO/3fvnAQt6DClRSciudyhxPKTJ4ZM9zWUm5NV67dq1ACxcuLA+7/bbb6dUKuH7PgcddBC5XA7Xddl777353//93xFv+3Wvex2PPvooH/3oR3nkkUd47rnnuO222zj33HPZc88960YbxF+g73//+1x33XXsuOOOvPrVr+bggw/mjDPOGHDbp556KnvttReXXHIJ7e3tYzz66cM0DJpLhcxDMgGkFXemaRAGYZY/MArqYU9j+FBnXIAQ53GEKhp0itTU+XmGyuHZWhAi1Wkbq0guiU5aUpk7WlFGEYfsjDQRa7QIYhVu2yYMIyqVav0FKAhDKtUatSAcswhwX3QiejlQlpPWcY6XISW+PzL/VhSEA3rZ0tYrqZjwRKjuC3rCdZM1zfVvzoQ8cT/72c8C8J73vKc+79FHHyWKIo444gh22GEHfvzjH3PllVeyceNG3vzmN3P99dePaNvf/va3eeMb38jXvvY19thjD5YvX87xxx/PPvvsw5/+9CeKxWLD8kcffTTPPvss9913H08//TQ33njjoCXnUkouueQSOjo6+PznPz/Go58+ervDlVZT/pY+10gr7izLJIhCwjDMzucISTV0rAH0aPoSaU2kVZwEO8SUMbXECeJjK5SQMhaZzbk21jAdBgYiXV5KOWbjIL0G02rqWrVGEAQESQgwriqcmJfMHhXwAf5GbEga0qDUXGL+4gVDbktFip333HXQv1uWhSEllpm1jJopjFuO4OKLL+YnP/kJxx9/PO9617vq87u6uoiiiJNPPrnBSHrNa17Dnnvuybnnnsspp5wy7IWQy+XYaaedOPzwwzn55JNZuHAhf/vb3/jKV77C0Ucfze23305zc3PDOsVikQMOOGBE4z/66KM54ogj+OY3v8nZZ5/NDjuMvhHtVD9cG8MZikhFSeJjrO48efuaeiNiuvZtGmbsedIKcwSelIlgOs/zRCGEwBSSQA2e8zTS45wJ52G8n8ds+kyFEEkl3vjCdQKB1qM3fLXWhJGKq/T06A0cQY+vyrIswjCk5vlYpklLqciGoKOeIzTez0SpXlWDvbaVtqeSiLpx9db3nMK3Lvl6rPzed8wibmp89JtfP+h40u2nLxTjvZYyOYLxMy7D6YorruAzn/kMr3zlK7nhhhsaHi65XI7u7u4GLxTALrvswsEHH8y9997Lf/7zn4YqvL4opXjd617Hc889x0MPPUShUADgDW94AwceeCDHHnssF198MZdddtl4DoNLL72U/fbbj/PPP58bb7xx1OtHYeydmDK0JvB9TFPS3dWNbZl1/ZY4X3HiLlulkj51pjlhbu7RoLWmXC4DU5vQnuaSRFEIiW7RZO9/uo51oknPXTiY8aShUqnEYY5BDlPAiEJ/k02aGzOe9SuVCjA7PtPAH7umWXr9KqUIx2A41bwQz/MxpcIbgRZdX2SfCsPU4CiH8Xe4WgvYssWiXCnHRpYa+z27XK5Sq9Xo7OykUinT2Snr+/SDsEEW5vWnvJHHHnqUx/79SIPRIw0D0HzoUx9FC+js7BxwX34Q96ar+T7l7u5x11ZkVXXjZ8yG0+WXX84555zD4Ycfzi9+8YuGJG2A5cuX8+ijj7JkyZJ+66bzNm3aNOQ+/vCHP/CHP/yB//qv/6obTSnHHHMMpVKJ3//+92M9hDr77rsvb3vb27jhhhv47//+71Gvb5gmxhCVHBNNnEMArpun1FTCtUdXUTcalFIEQYhlTU+1WXqjaWpqmpYHT/oGmTYInsxzMN3HOpGkD62B2rOkOTDFUnHQ4xSAbY6sZdBkEn/+Y8+3So+9VCpN+7EMh0aPq49jeqz5QoFoDIaTaflYpkWpmMMYQ4snMUhSsiYWRFbUaG5uSkJ69HtmjQaFRBoGzc3N1PyQpqYmIOl/GIT9qpwv+vqX+Mm1N/Dj793A82vWIQ3JoUcexjs++B722u8lQ+7LD6JYJqJSxa76/f7uTOL9P2NgxvS0v/TSSzn33HN57Wtfy89+9rMBS/kPPPBAHn30UdasWcOee+7Z8LfVq1cDsGjRoiH3s27dOgCCoH+CnVKKcAI9PV/4whf4yU9+wsc//nEOO+ywUa07HTfEqUpinQnJstO5/1jryUqabUaYMKntZ6b7XE8UddVjIeqtJVK01rFAYXIeB3qDTj2nM+E8jHcMs+Uz9f0A0xifZ7l+rGMI1jh2rFVkjMW7q1OF7cb1IqXoKlfx68+QOC9pvN/h+DjjfCxD9uw3DCPsAXqEOq7D2z/wbt7+gXfj1TzM5EV044aNtG9sZ978eYOOxzBEUhHYf8zSMGgqFQZcbzCylivjZ9Svz1/60pc499xzOfbYY7ntttsG1T9K852uvPLKhjfOv/71r9x3333suuuurFy5sj5/48aNPPbYY3R0dNTn7bHHHgD89Kc/7eeduvHGG6lWqxx44IGjPYQBWbFiBR/60Ie4++67+eUvfzkh28yYG4gkVGckpdaZXMHIkVLGbSREGkgZ6F9P3oXs9XPG1GJIY6I1PEe3f8PAts0JV2APo4hytUakenKFPD+YkAIEpaKkEjAmrk4cevyWbXH1d67hJXvuw8oVO7LDdit5+b4H8KMf/GjA+4oUInYUDNCXzjYk1ijDmmKKprnMqDxOV111Feeffz6LFi3ixBNP5Mc//nHD34vFIieccAIAhx12GO9973u55pprOPLIIznhhBN44YUX+NrXvoZlWVx11VUN61555ZVcdNFFXHvttbz73e8GYK+99uLkk0/mxhtv5KUvfSlnnHFGPTn8e9/7HvPmzeO8884b+9H34dOf/jTXXnstDzzwwIRtczIRQuB7Pihdb1g7099qZyuphoq04mbIGFmPu5GSVh/Fnqckb0grIq0G9UxkZ3XqEcbgOWeDkaYN+EEwI70MqUcoJe5NHF91lXKFXM4d130zli3pbewML8dx5hkf4MYbbmzY5+OPP84Hz/wQDz/0MF+69EsN6wgRN1vXKm72q3qFQdO2WBlTy6gMp7/85S8AvPDCC7z3ve/t9/ftttuubjhBLCWw1157cfXVV3POOefgui6HHHIIF1xwAfvvv/+I9vnDH/6QAw88kB/+8Id8+ctfxvM8Fi9ezDvf+U4++9nPsmLFitEcwpC0tbVx3nnnce65507YNieTuAeTjFVp1cCu3IyJIw1D2LZFEIRo9JQkjc8FpJAYEsIp1GfKGDn1hrZjMH7CIMT3wxlZPahUXKmX0rvXnZQG5UoVx3Hqydwj/S6nfeq0ZsC8x97nofc2b7/tdm684cZ+y6Q/f+PKqzj+hOM56OCDGo8jUjSXCgRRSFe52ms9GK21myWHjx+hZ9qVPgvZ0rGemhfRVfHxg8nXn9Fa49VqtLa20Foqxm0Hkge4OcFaHzMhObyzs3PGJUxrrWPPE3HX8ok4NzP1WCeKVPwyiCK6urqGTJieS8nhwx3rTECj8cOQnOOMSp06DCOkFNR8H98PUWFAqak0puTw8dK3qg5ibar2LZ1UPR/Xdli2eAHd3V24ubgFmO+HRGGIYRq4jjOgF1knjZKF6OmwUqnW2LhxEwsWzCfwPZqaSgB4ieBlteZRKuZjY7TX9t7w+hO49557iQYIuwGYpskb3/RGrv7e1b1E3BW1modt22zYvKXBcHIsi3ktTbQmLcaGorOzk+bmZk6d/1LsCZat6YuvIn6w8R90dHTUE+fnElNXCjaHMaQk7wryrsmmTo9ydUK6IQ2LFNTdtGnybaodkjG51BsERxFBEMbJnrMgAXg6SXXGsle1mYdSccL+SK7e9F1bCEHV88m5dqIfNzbV8IlCCtko5pTQ17OjSUJ4hoFSsZBwFCmq1SqO62BbVn35IAh5avXzbOksIwQU8y7bLVtEFCrWb9hMsdSEbRk95wTo6Cqz5vmN7LnLCmSv6kCtNQ8//PCgRhNAGIY89OBDdHZXePTJuIjKkIJ5rc1ss3geaHBsCyklYRihtKbm9a+0G/I8kXmcxstcP74pI31gtjU5mMbUPTzTZMT0X+ZAnDp6J40HQTDqxqZbI2mumDFBCs4ZE8dI7h2e51Ou1ihXanRXqiBij5yVGAjTWTghZHJtGT2TaRjYloWdvNjoxH2UhuUc2yTnOrFH3TDwah7VpNcdQBBFPLNmPZ3dZVzH5vkXN/HgY6uQ0sB1HTRQqfloIIoUW7rLSCkpFXMI4mq4F9o3097RjVKaQn74CrhisUhXucrTa9ZhmrEx+O/Hnqa7XKWlqYhr22gNTcUCrU2lRA9q5Ex2u5WpMMymm8zjNIGkqrTFvM2WrtE3NB4tSum68GXmaZoe0galQliEQYA2zXqZfcbA1BPtB5EiyJgexAjqoSqeh+vYdTkJP4ioeT6ObVEs5OiKplAIuA+9c5TSCjTLMpnf1oxSms0dXXWPk0jCkUIILNPAkJIgCJC2hef5VJLcJ3RcBLJ4YRu7bL+MQj7Ho08+S2e5zPMvttPS0sxjT61mnz12xPcCnly9juVLFvLs2heZ19LEg4+vIkgSuxe0NfO6Y47hO9/85qC9MIUQnPimNwJgWybNTUWCMELpjSAET69ez+p1L5JzHbTW7L3bDjy79gUWzZ83+Sc4o85cNwynHCEEjjW58eO+SCmmzT2ekfbIivWeojDKetyNEMswsQxjQhqXZowPQ0oc2xpejkDHuURhFFdIuo5FUzHfYwxPY4VXuucwitjQ3sGG9ljaJvYKSywpUBoi3d88lFKgtMaQBoV8DmlIqtW4UXAYRaxdv5GHnljFY0+tZuH8VkzDwAsCTEPSXCqwZu2LPLd+A83FAkLEuU7rXmxn05ZOli6cT1tLE5s7ujn+TW/GdtwBcyKllCxYsICTTj4ZgK5ylcefXsOq1c/jOjYdXd2sWvsCTU0FFi9sI1IRG9q3sHBey6jPUyZHMD4yw2lSmJqHZhgpokjVe0xlTB/1JreWiSZOSs2Mp8FJvQOpvtPWxESJYU5otDN52g03LsMQGKbEEKKuhTSTvKtKKbq6K1Q8r+GWmApIxl1+Bj7/lmkSqQghBDnXJZfP1Y16rTSeF1CuVFmxbFHdwDQMyaL5rTy3fiNbOrtZOK+lnismhcT3Q7Z0xn1b57U2s+3y5Zx70ReZP39+sr5R7zoxf8FCfn77z2lubQGgpanI/nvvwsEv250gCHlh4xaEgI6ObsrlGoVcDgQ8+J9nRnWOUgHMyZ7mMlmoboLRWlP1pqbkOggDwijCwYr1cUjidhnThpQSSwiCMMT3AyzbjJNWMwYkDXXqSG8VOWJCgDQEaDFkkvBI0Do2niakgE3HxkEk1IDNaFOkkCilCaKI7kqNIAhoKhZmjPGkNXhBiGPbtDT15BNFUYg0hjbSjUTWJcU0DHwZYpsmy5YsYMcV26CV5tm1L7J88YLEqBI0NxWRUuLYNqVigS2dXQghWDCvhW0Wz4/zo2ybBW0tOI7Fyp135t+P/psfXX8jv/3tXRhSsvMee/Ga176G3ffYDaV0Pcn8L/98DMM0sC2T7ZctorWpyKq1L7C5s4u25hKtzSXWvdA+iWc0YyAyOYIJoKvrRaCnYmPdhjJqiJvPeEnlCBzXpblYoKmQQ8g4Odw0Tcwx9HkajEyOYGzEzWFjlfG0jcVw45+txzpaBjrOSMUtbdJvzZyVIxDUy/3H0jw4NTTTbY+nAfFY8Hwf17HrrXSCKMSQRpKwHH+ubj435Q7w9HpRSrG5sxvXsSnk3Pr1Uy5XQAgsy6G7XKatJZYPSB9/vV85e19zSim6KzVsy8R1bCo1jzCMMA2Dje2bWLhgPpGKYo+WEDi2hdIaz/MpFHL4fkCl6iGEIJ+LpR7K1RrNpQJRpChXa4RhFCeymwbFfA6VXHOdXeX62GzLopBzqHk+Vc8nihSmYWBZBkEYsWThgmHPUSpH8P75+06JHMG3N/49kyPIGJz6l0/Dhi3VAY0mmSRTRlE0oW/W3dUqzU2FpJP8wGJsGVOPSMqdQRAEAaZpZkrjQ7DVCGTqsRlMKUL0FKFMB4ZhIA0jDkVJiTVAc3M9jY5vKSXFfH818CiKcBy33/JaQ9Xz8PwAyzIpuE48P/mjEIJi3q2f83zy987uCrZl4TgOG9o3saCtGaUUkVLYloljx5IGOdchl6yT0mIVgVj/rXmAPnMCsEyD+W3NAFQqNRzXplrz2bC5gzCK4siqiHNbtWZEhlPv7U/2U2Ku3+Uyw2kC8PyImh/RXQ0GNJoMKWlpKpB3XKq+j+f51PyAYJwNioVIvvg1n1yzM/wKGVNKjyApiddCYw7woMnoqbRL27FkDMx0G96pYnY6joEMuOlJuRTJvgWu0/9eGEUK0zToa7Om381NHV1oYJsFbbiuE4dRdSxxoJVu8PQBuLbJli1+LAAqBEEYMVHvrH0/4zAMsLVFlDQa11qTyznkHRc/CGJZiIwpJbuLTwAvbh78wpVS0FQq0FwqYhoGrmsTRQovCGjf0jlq8bLeOLaNFIJKtUZLqZB5m2YohmGAEERhGItlTrC6+1yhd75TlkEwM7FGkAagU5ntKWS4ykylQUgDdI9HM85H9QkjhWUaBEFIperh9vIQDZbvZZpm8jdNIZ+jWq2Rz7uT973WsVCn1hrDkLQ1N5FzbMqVGpXa6KRvpqLqba7f3TLDaZKxLYuWUqGe15B6IQxDoptLrN+4eVBNj+GIooh8IU+56lHzAvK5zOs0U5FCIMxEaTwMMU2z4c09I0YQe54monP9cCg9uFijqAczZq4BNx0SJELIethqIO246TJ4h9uvImmdEkUYie5duVJl45ZOTMOgrbkJs5dHSdBH4qXP11Qk+UzVapV8LketVkVrPSkvr4YZ50imHq+mYh7HjguCvCAYd5FBxujJDKdJRibq0n1vMEII8q5DMefSWa6MadtBGFGu1oA41ynn2hP+INakL4/ZA348pJ+LZZqEYUQQBFimmRlOfRBCIJGIqdAD0qCiwR64M/9hJAbqLzKV+++VazU7ruO4VyICytUaW7or+EGIFBLLNOp5SUBDWG4wHCfOOyoVixhG/L02TcEIVh3dqBM1dkNIHMvGNi200nRXq2zpLI/6CsharoyfzHCaRoQQuK49ZsMJwA/ixpye5+MHYcOXf6ykFWEQV5VIKafa8z4nqRtPlkkYhgRhhGmM7Ca9NTFROkdznWnx7iQfS/r5DPg5TctnN/w+4wrJkO5Kjarnk/o0XddOxD9HN24pBaYpiVSEaVr4gY89AfffAccdKYIkT3JLVzfVmocXhCgVkc/1T3ofctxkhtN4mevHN+0YxtBNTYNxlDqnhFFIpDSeH0zIzTQMo6Sdi0YImRhO2YNsIjESNeMwDMdV7p6xlTGNX8NETWFI4lZw0xBCHMmTTIOKFLmcQzGfQ5J4OIWoy65Ua/6oxp/POXR2lbEtM86fmoTWV1rrOHJhGpiWgecHdJYreL6PEJLm4vD97zImlszjNIm4jp0kbQ/xRZoIQydSWKag5nkUck5SBj92lIqwLCtLNp9E0ioyYcbGs0bXq8oyMgajt4Cjpq+RIkbk7BnzNZb2Fhxk/VjHTtd/7lltZrx0xaNTNBWKUIw177orVfwgZOPmzljtH5jX0lSXHRgOyzToCiNMy0Rj4IdRPymEiUAIQOt+VdtKKyrVGm0to9gWWXL4eMkMpwkm1f1oKhZwLGvYMMxEhWkMIxZC84MQN/MQzQrSNi22JfCDrEXLdJBKegCJiGx/NINXVw1Gj7abrk+DjKDXXvrPH+hrnG4rUqpeWCLFyMPpaehnIANISEGUKFcL4qTq+h1KgBRR/aQ1JqfHOw+DiChQSEMCOi7nHywBf0LvUYNvq75/HTdGlzL2orc1F2kpFUhTxSIdq6YPpE01GFJKcq5NpVzFMg18z8cdQ9hveATSMAZ8ma2OsjI7C9WNn8xwmiCEIG4QmXOZ39o0Iq+PmiDl39SVaxhG3Kncsee8xT9XSPN5XMfG8/y4E7pS4/YaZgxP+gAdESP4ONIHdBCGsUihEIRRRJgII9a1j4iVoQ1p1HW9Gjw09MTE+lZe9r5fyORhOlqklNhDHPdgWTpCMKQ3RWtN4Fvkcna9zVBq3GndUwWo0Yg+Ib30L2PNbxtqDa01pog7+0ZK1D/z+PPvWc4cyYfcd79CYFsmlZpPS1OJ9k2bCKNoyPM7WqQ0EFKQcywqtkW5j25T1iR76skMpwnCtixam4q4jj0yo0kplJoYFXGZVOhJKSnXapSUGtMNNWN6saxY/T0Iw7o3KmP2UE+YJm5ejIhzakhebOoGgRD4KsQ2BfUo/iBJOqm21UBl/zM1N06KnvGahgF97kVa6/j+16vjQlzxplBKYxhjMJyGMB6UinWlJquZtGFIJPEx1TZupvLsWhbusC1uy8S0GmnwhE7E80LH02Qy2dufbjLDaYKI5fjdEfeJ88OQKIoIgvGphwPkcy6lYh6lFJ3lSiyymBlOsw6RSFcYhsQPgjjPLKswm3VIQyCkBA2BIZF9CixSEcORGMZiBD0Op4wJGkfajqjvHSqKImp+UK/kHQ1KKSIdhwYlIilq6emDGH+PJmT4/TAMgxceeJDfXvF9XvzrgwAIw2Dlca/iwPPOpGm7pePavkrUwkWaY9b376NU2pdo5CTLWIx2+xdeeCEXXXTRkMs899xzbLPNNuMZ1oSRGU4ThB+E+GE4rOGklKZS84hUhCHFiJR4B0ImLmIpJa1NsSq5lhLXtilXvXojzozZRZw0bmBIAz9RGTeynLVZReptAsi7DoHXo+ycOgxGInorZOKd0aoe+ppWJjkHT0qJY1n4UU918EiuewFUql6cJ5g8sHOOQyHv1ivmhGEgJukcPvvbP/K7d59L74ChjiKeuu0unrv3Ad70q6tp3m7sD3zTMutNlQcSu5wLqZEnnngiO+64Y7/5zz77LJ/+9KfZd999Z4zRBJnhNKGMxHUuBORduy6TbyWqsAP1uBsM17EpOBaLFsxvMNSEEBTyDps7urM8mVlM+rBwbAs/CNBKY5hGlsswS+j9sI/zaEQciUu+4mGkAFHvnzbg81yDVhAnA41AB2AqmOTrLw1LWpj4YTAKr5OgVMgNnnuldIMxOx76JrpHfsCdH/5CWkHQuGwU4XV088cLvsYx110y5v2FYYRtE18Tg/QGHA0zsapu7733Zu+99+43/zOf+QwA73vf+yZgVBPHDHiNmTuUK9Vhe8/VE0bDOEyntaaYz4344jcNg/ktTZQKuXrPs943DNe247GMsn9Rxswi/Vwt00JpTRCEE5IPlzF1pN4ipXRiBMUPOcs0yLtO/aVHqwGm5KM2RpPAPtlMwfUXX/MmtmHBCCNQcWMDUf/O9J4gEfEVE1PFFylFkITOAFb99g/UNncMLtEQRaz69R+ovNg+pv2pWBhryGVmhDdyEoiiiGuvvZZCocDb3va26R5OA3PzjE8TXZUqGzZ30NVdIQyjQctwpZAYhiQII7wkH2mkJbDFQg7XsQf9uxCCnOtQqdZG5cXKmJlIKbCs2Cvp+0H2mc4idFI127scflYzhR7Pej/PEVzvg8lI9KBHJN45EpSKJQvSRPaOZ55DDOfZ15rO1evGtr9IJYntgzNau1pO0QTQ2dnZMHneyF/of/WrX7F27VpOOukkmpomJtF+osgMpwmmWvN4cdMW1m1ox/MDokjFwmpa4wcB3ZUqnu+jlMY0DVzHxjJN8q47bChGyqFd0imFnEsUKfwgmMhDy5gm0n6HhiHxfZ9IDd6cNmNkKK0Iw4go8f5GavBJJSX1Kjnvg00pPfOY/cZSL6YyWph6nowkyR6l0clEr/M6ku9BHBk1x2X4KaXqUhBWkneotcYsFNAjyFezx6juLURcsT3UPX+0uVuCyTea0tEuX76c5ubm+vSlL31pxOP87ne/C8y8MB1kOU6TQqQUkeezqbOLBa3NCESSFO4TBCFCxHlKOcfGNE2Uim/gQgoYtOlo7KnqXeo7EHFllsQ0DWq+P6R3KmP2kFbcSSEIggAzeahkSeNjR6PraSl6yPTEQb6TIm1D0igdoXVS6TRO41ZMUHhpNtPbE6/p8fggUiXw4WUGNNRFO8dzOv3Ap+aF5HNOUpgjWPjK/RuaHfdDCFp2WE7rLtuPaZ+RUnGFJnHYbiCP80wO1a1Zs6bBW+Q4I1Nkf/755/nlL3/JXnvtxQEHHDBZwxszmeE0mST942zLxDJMmot5/CBkU0cnWmucJqt+c4wfipJoiMB+XJo8sl2PIDSeMctIy7ghLkTQUtfz3DJGyUR4g5LvmEY3hEuUikb23RPDj2Fr/mz7HruAWJ9ujDUvSoWYY7QxpJS4joNpmnVDRQjBvO2WsNu73sgj190y8A1Xaw745PvG/DmGQZjou5mxfE3YX75mtHbTVOo4NTU1jSnMdu211xKGIWecccYEj2xiyAynSaSQz2FImcgDaFzbwjJNmosFNNTVg5XSdU/RULJOqQLvUGgde7a0VuRzI7PuM2YXhmGAEERhRBiGWNbEd2Sf6/T9HvVNJhb1Fia63qYj/dvg20xL6CVCxHkwVc8fNKnf8wOEEDiW2TgewbD6XVmodvRoPT5DNA0fxtuKz79lmrzy8x9GKMXDP/hpfB0ZEhWGGI7DoV/4KCuPezVhFOH7caWgYUpMObIXHiFEPcQ4KqX7obbJzKuq643WmmuuuQbXdXnnO985YWOaSDLDaZKwLBPXsTBNk6rn076lg7bmJnKOjes4gKarXK1XTPlBSKmQwzAMuvtI6qc4ljVkw2CtNUEY0tldprlUxDZNlFL1nCqNwLFNzCHaJsQPhhkkupcxIIaUSEsQhmHdqwlbt4diPGitefyZtdiOxXZLF9LRVeaxp1az8w7LaG0usn7DZtq3dLLDtkuwk2q4ulK4SB/KsZ7bCxu3sHRhG14Q8s9Hn2b5whaa07futC+a0jz+zDoc22Tn7beJc2iUwnXs+rZm3mc508YzOsJoaGHNuEnx6FuYSNPksEs+zvanvpENd92P19GFMb+FPd5yFG5zCT8Iad/SSc3z6h0BWopFCoWevNah2thQN8hjw83zAxpz6kY13BnP73//e55++mne+c530tLSMt3DGZDMcJoECjmH+a3N2IknIOfYlKs1Nm7uYGFbC0IIKrUa5UoNaUhyjkNrcxGtNV2VGqVCHj8I8Pw4udu2TIr5HIWcO6QiuFKKzR3duI5DIecghEBpTc0LCMIIOzHmhiKK0gadGbMB0zQJwwg/yXuS/P/snXegJFWZ9n/nVOh80+Rh8pCjIgoqYkJWJK7oAgYUWflw3dVdURFUwICu6CKLCqsgggFcxZzWdVUWEwuIAXFgCDPMMDnd1KnCOd8fp6q6+96+oW+aO0M/cGfmdledOlXdVfXU+z7v887GG+7shzEYDFm3YRdLF8xlV28fjz+1mVwuTS6bZuPWHdiWJAhDdu/pN01iuwo4tsX2XX14fkhXR5ZyxeOBh9ZywrMOw3EsBosl/KCAHyj29A1Q9QM6chly2UxU+eoQBCG7evvpKuQZKJbp7S9iWZI5XQUy6eZR4731Ge+L3y0TONSEemzipJRGTtCUOH/AfBb+v/MQaAaLRbKdHfQNFBkslikPqSbb5u3B7rPo7sjTkc+NKMEQdTpGx7bozGepen5D4U+rn8lsdA6vxy233AIwa9N00CZO0wJLWg1RnUw6xdyuDrbu3M3WnbvR2qTpbNuiJ5ehs5AHoOL5pFMOczo76BsoscvvQ2sdNQ7uTMZvFqZXStE3WEJpTWchm2zbkpLuzsK45661bouO9xEkF1THkKfAD0wriya9zdpoRLPDs2j+HJ7csJX+wRL9A2UWzOumt2+QYqlC/0CRxfPn8OBfHo9S6iGFXJY5XQX+uOYJ5nR3ML/chef79A8UeXLDFhYt6EFEp2r/YJEnNmwhCEP6+ouc9LyjACiWKvxpzZOEocJZYfPQo+uxpEQpRWchx3OOOiia79BeddN6ePYrSAFaGR+00YiTiQZN/LyxhEarwJAdwPcDBgaLVLzh1c1xdsDz4+jRSOadCq1rmqq4qKieOFktpu/q7QKmCxMdf+fOnXznO9/h0EMP5UUvetGUzmkq0Q4tTAOK5QrlSjUhOFrr5LTw/AClFV0dOeZ1d5FNp5P1Uo5NT6cJ6QthzC6FlKTGqIzTWlOuVql6Hp2FbMsnUv04+3g0/hkL27awbcuU0Ddpy9BGI4YSD601nfks2UyKDVt2UKpWOXjlEkrlKjt390Wpbti1p9+UyVuS3b39eL5PLpPGkpKOQo5lixfQWcjx7CMOZF5P7WFHCEilHLKZFKVymXLVQyvNU5u28dj6TaxYssBYmezcg+PYuK7Dlh278fzmn6Xen3wOZggqDEeNpk+WOMXQ2uiRjM3F1H5OsUi9ngBOxZxnC7785S/jed6sjjZBO+I0LQiVIqgTkfpBwJ7+QUKl6CrkyGczpFynofEnRFVTwuhWtDaRKoTxZRoJWmuCMGSgWCGbTpNOpSYcbVBamRLr/ehEfCbBiqKcsSu9PUSLsz9gPKLoie6vlIKF83pYu+5p5nQVWDx/Dhs3bWft+k0snNtNIZ9FCOjIZ0mlzPnbkc9SyOXYtG0Ha9dt5JBVSwHdEBFQSvPEhi1IIejuKmDZVsLc5nZ3IqTkqc3bWTx/DrZt0ZHPksumWbygZ7+6Ke5tmObB03s8NRFBkxLHsXAcu2klXDInrVsmwWEYMtQ3rBXMZFVdq3jXu97Fu971rqmdzDSgTZymAUI0hk9ty6KQz0aaCHtYm5ShsCyLjkKWIDpBRhMrKqXp7S/iWBaFXGZS/cy00gjNmL4obcxeCCGwbXOxNuTJxjgn7x+fqUlxhMYCYEgdtox6nY3vRjJ8Ga1h2eL5PLlhM/PmdJFybZYsmkf/4yWWHTCfOZ0FVi1dyPqnt2E7FquWLmKwWGbtuk0EYciCuV1Gs1LI839/WMPqFQeQyaSwbUlnPscTGzZTqlRxLAvLssikXTo7shywcB4P/HktYoFg+QEL2LhlB5aUHLJqKXZbbzhlCMOpa5Zcb3zacG7piKBZEse2md/TxZ6+AQZKpaYeTFXPN9XSI8gjmp22Vb9RHD6ehtFtTC3axGkakEmlSNW5vUopyWcz414/jjyNlXLTWtNfLBKGAd09XZMqVY0rShiD1LUxu2FIuWkQ7PsBfmAiT9Z+8plWvYBtu/YQhgrXsUjkIRo6CzkKuQyhUk39keq/1xod+aKJhrYehVyav3nRcViWsQlZfsACFs3viQo9NMcctprDDgyQQuA4JqK3aP4cQhWSdk3hxYnHHYHvh6Rch0Xze/AqZTo7O1i9fDEydpy2LXq6CpEztM1LTjgaIQTLFs+n6vmJU/x+8rHtNegG+3Y1pdc2pQyJty2wEoKrCcMQ13IRQuA6NvN6uki5Dv3FEn7QmEqvej6Vqj9iy63m1lCNL7aaDZztdgT7AtrEaRogkj+mF+WqR6VSpbuzMGq13XhgKkpUkt5pY99HIhoPQmi4uO+7iFPTSik833glxcirKKU9wo1k6A1HysjxWzYuk4oqT5XWaKVwbAsdWYxrrZMoUNyCQwpjzBj/jtbYtkSpECu6GBhhrxk3dpq23NrlN+XWql3HqnydSoRKobTCsfbfW4HSOvLMNJ9DHPmZjOVDvF7VCwitkGydnML4ftXGlVLQkc+SSacolivs7utviD4Vy5VRG737fojGN2a3DP8et5yqm+VVdfsC9t+zZW9iBkiTHwSUqgG5bIaUO/m2KvGD2Wy272+jdViWBAFBEABW4jy+P8CyZANxmopoQoPmcILX/vrIbxwBHJqen/RctUYN6TIQp9h13Z+WsJLrURxZMwTahOmEEPiBKQqxpKyRvyGYKuH0TMMU5jQe+1CZVK9t2ZO6VFuWJOXYSe/IpJezrrVJiSGlJOVKQNPbL1HUok6VqhfpnIbPxmikjNGtCkPcKbjWtzF5tInTNEBPI3OKn7gHi2Vy+fyowvFWxkw6iLdzA/sVRJTyFY7A901J9mgGqLMZYz1ZJ9q8cbQyGecWp2KQaYHWw2fXTGSstELoxs+6RuwEQmgCNboJo3lv37w2xBKEoWna2FhSYQhlqJQxlZWtSRUsS+AHxhXcPJykIuf55mOUK/4wTVLcQLiZNMNxjImyjoySm8+v1YjT7LUj2FfQJk7TANN1XcM0RdwHS2VCpenI54ZV5k0UgQpxnPbXYX+EEAKJ6bIeBAEBIVbUMHhfg4oEuQZDepnVZCb7PcZbiaW0HtUUVUfLiOi4qihlL6gRLFNev+99V6DmvL3jD3/liZ/+Ctey6DnqIJafciJKWoRByGCxRKCMHUV9ynSscZXSlKseUgiCqJIujiyOdG5pjC28lRQymG/xSALveLyYjMVidMsyDd9j371WIJmBqrrpHX6vo32nnAYEYZBUxE31U1q56lEuexRyGVzHnrLxdaiRzv7+dX/mIk4ZOY6N7wcEyojGp6L31UxC1xGnZmnlZpVLz2SMeXnQGh2akngldBJ9qd2oDYGyJ9oddy+juH0XP73o/ez8w18RUaRVBQGZed284uZrWPy8o43jvtZJxXMME91XeJ6P6zo4kf4zVIpy2cPzA1LpNJl0CqU0YagIQo3juhTL1WHH3nVMg+BCNottWXiBj9KajOua3/2gwcYihhSSlBsXGwkyka9fXGjgOu303UyjTZymAUpplJp6E0I/COjrHySXTaHDUboBt4hAhW3vpmcIhBCRaFxF7vXsc+RpJGggCMNxP4GHoekPJ4VASDk9EbiZKGEadfNimL2IaSpei0JJSdTrUGBZGmmJhAhoMZ3Cg+mFCgJ+8vp3s2ftegB0GCZxusquPn58/rt47f/cRm7ZohGjagODJf7vT49w6KqlrFy6EIA9fYPc/6dHmdeT58jIsHiwVOYvj66nqyPPnO4OHnjo0WFjrV62mBVLFlKuVPnzI0/SP1hEA4VclpVLFuIHAY+tf3rYerlMmhOefZhxlNeaSsXnobXr8Dwf85lJzjz5xHEfF4FGTHNYdrrHHwnVapWf/vSn/OIXv+Chhx5i+/btCCGYP38+Rx11FC996Us55ZRTSKcnJ3FpE6dpQqjUCHK/iY/XP1DCcR1y2QzFwcEx1xlvtUW14pFOpWalH0gcEh9NNzC7IcZdUl7vDTPdsG2J7xvi0Nw/Zt861kKARLTUi6Tq+ziWRaCh4pWxLUk2nU6qD6fiYxhOW6YGrZgmxsLjeB5hGA5pyyOSNFD0G5Y02rg46rIvYt1Pf83uNU80fU8rRej5/OnzX+fFn3gP0Pw7b9sWe/oG2bB5O4sXzMG2LDZv28W6p7dSrXZy0MplpFyHHTv3sPa3D3LkgcvJHLySTVt3sWzxAuZ2dyRj5bNZHNvm939Zy+6+QQ5cvhghBFu27WJ33wCL5vUwr6cb3w9Y88QG5s/pYvH8OaRSdkLqtFaUqx5bt+9h4bweujvyVKpeS8dFMP2ptJm+emzfvp1PfepT3HrrrezZsyey/LDp6elBa80jjzzCL37xC2644Qa6u7t5y1vewqWXXsqCBQsmtL02cZomhMqEeJ0oHTKZG5HWmsFiGS8ImNPVMa4n49heYKyLv9IK3/MjXVYtvSiaeirM4FNEJH7VWlMqFY0OYyTdwKjTqqlox5q9EE1OeNF464uPTb07TNOx4p9RPvvh/cc05UoVyy4n74k4HTWCw7C5oMZlU8PnNNpXJSZpQX0VmBRIIRMdxVhmrTONkQhDNpMZ0QunKWIduZSkLJOKGiyWCVXY4Mmzr0MRpzVBCgsh4ohTfQVeZJFQt14DkdpHheFP/OAXCCnRIzwQ6jDk8e/8jJdc+96m78e94ZYunMf23b30D5Yo5DJs3r6L7o4c1YrHwECRx770LR688Q6C3n7uB6xMGo4/hgOueBuHHLoyGS/uQdg/WCKXTnPQ8gMo5NIce/jqpGhj3pxOiqUKT27czKL5PRx75GpM/Kbxgcq2JCuWLGDxgrlYz/BswbXXXsvHPvYxPM/j7LPP5rTTTuOFL3whK1asaFhu3bp1/O53v+P73/8+n/vc5/j85z/P+9//ft773uaf/2hoE6dpwmCxQrFUJZt2SadS2JYknXInlBbxg8D0octnkzz7WAhDU8I61vbKlSpKa1wpE8M9K+qRN4w2aZixm0lU3qsxac9sNtP84q3H9jUxPfhq0Yj4z73eKHV4w7TaT/LS6ClfFY6+E+PZxXpDPh2YFI1SCsdxcF3zM2tunNGxsaRMoiKI5NbSUnxHRu2NlJLme7bXvxDTB6U1AlNhV0+G64+WFMIUyQuTvlVaEXjhPtsXr9o/OCJpiuGXyqO+79gWi+b38OTGLfQNDOLYNoODZQ5ZtYyNm7fwu/f/Gzt+8quGdcJyBf73Pu7fsp3BT74P6ToRAZtLV0eeZYvn8+iTT/O/9/2JJQvnsmBuDwvmdkdVedQ9NJm/lTKR4VgQHj84C9O9mFC1dm5KPQM+TjN4Ln3iE5/gfe97H//wD/9AR0fHiMutXLmSlStX8rrXvY6BgQE+97nP8clPfrJNnGYT4v5Enu9jFctYliSfyVDIt+a7pLXG881YsUBwvKkcy7JwnJGrRLTWVD2fdNoimxmBmOxlaK3xPW9SPfj2FZgoYUg+n9tr+6q1jki3Id6lcgXf98lmM7NCC1X/1a+/oYehETbr8T59a3Bs20Rlo9+zafMdi4lkqDSmYF0gGTnyNhOp1anYXqgUEmP6GYYqsm1oHEtIE2GVUoASeEEwaXPdvYXu1ct4+n/vR4/U9FoIOpYdYCoPR4kKd3XkyWcz7NozQBgac8sVSxaw7Y9/YdN//br52FpTfXQdG7//czpe8UJymTRqwRxCpTjm0FV0deTZtmMPj63fzONPbeHoQ1dx+IFLo2hgUD8BoqdDgCR74Ychjz6xka3bd+P7AX/z4uMncaT2baxbt25UwtQMhUIhIVsTQZs4zQDC6IlhTzCIF/gsmNtjBKnjTLlVfd9UfkzxjSsO/zp2uyqjDYO4151t2yjlEIYhg4MlBoslspnMEG3MzCNOPZlzquYc7hEMDdaNC8POKU0SedLR1iSM6qgfBFNfCDIdMGXyskFHN5SEWYkFQXxtsJKoVP2yM/UdqBlLDv9g46q/kVKJh7/+TP588zdGHf+wN51tyHMslB9yXdZaU8hlmD+3iy07drOnf5CujjydhSyV3//FKOtHIWbqN7/nxMv/H1IKXMcx2iqlOXjlElYuWUjfQIm77/0jTzy1mYNXLomqF2vbt6KiBdObMZJQCDOvYqkC0FqKmv3Px6lV0jQV67aJ0wxCa02pXKW3f5DujvyYF5/YV6Va9eksTH0UQkXjs49qGNqYXsRPtx0deQaLJYrFEul0aq+l7jQ09JWrT8vpETRgk4UAbHvqbD+mCra0WxOIR7omPUQIF8vBY8QRba2NFsq2rMSgMYZlWTPSQy8uDAnDMLJ3qWXcVeRf5DjGDb/ZdHoOXcUx//gG/vTZrw4zRRVSMv/Ywzn09Wc0FMUIyx62b45js2BuF09t2kZff5ETnn2Yidrt3AOjpQK1ZmDDZvoHSgBkMykGS2UeeOgxliycy8J5PcY4MwxJp1wsa4yDWpdtcGybYw5bxdJF85FjrTd0mP24qq4epVKJvr4+Ojs7yWazUzp2mzjNMJTWDJbKZNIumTHST0prgiAgVIrUKCm3CWMmNUtt7JMwInGLQj5HuVylWCoThiHpdHrmTRFNtUDyq5Q1LxtLymmoXxPYsvlNOZnSXtBFjVy8MQpklIYdp89VTCYSfeAMIyZNQRDg+XHRSjS3aD+ErdF69NjGs9/1ZrIHzOfhz/8n/es3AeB25DnsjWdx9D+9ETmGbCI+1gvmdLNgbg+DpRKL588BINPV0aCdbIYw5XLP/X9Ga1i5ZCEHrVjMgrndrH96C3/55k/w/vc+9JYdhB05/u/skzn8Ta/GntNFV0eeTNrMLbaGQEMojK6vs5CLGnn7WC1qnPZnbNy4kY9//OP84Ac/YPPmzcnrBxxwAGeccQbve9/7WLp06aS30yZOewGe51OueKRT7qgX+1CZ0lPHtvaLBq1t7LuQUpLJpJGWpBwJajOZ9MxHYoRI0mtCxGePiKKzU5siEIycBopTWcZ2pDVR+l7F8D4tTV6sQWk9KYlA6PlsfehRlB8w56AVZOd0jX9dpfCDICkSidOMUEvhhaFipExVvMyBr3klK1/9Cipbd4LSpObPxUq5DJYrWGGRQi475vW1kM9y7BEH4gc++ZzxAFr6shN46q7/HnklKVn9t6dw+HOPAoxzfyGf5TlHrGbH9V+i/N+/NuxfKUoDg/z5pjv56+3f4VVfv57nHnMouUwKMH0mrSj66wUBqZTLkQevJJtxkZLWncOn+DwZaRszjd/+9recfvrp9Pb24rouRx55JB0dHfT39/Poo49y00038fWvf50f/ehHnHDCCZPaVps47QVooOp5hKFCjuLIG4YhxVKFwkgVZW20MYOQ0pRnCwGlYhmtdUKeZuL7KQTksxncKPpqWg+ZyIgKjcfNVN4SNJpAhdg0aqGSFJIKUWiG15/OXow3QialIAxNf7R0qjEqM56PWivFfTfdyf/deAflPX1mTNvisLNO5uUfegeZns4xx4hJsRDGh6pc8YxflyVwIsH6aFNRkSVMPp9jcHCAwtLFaAwhq/o+xXIZ27Io5GppnJH2TUrBnO5Cbf+0ZsmJz6Hn0FXsWbt+mABdWJJUR4FjL/47nIxDoVCgXPXo7S+y9vZvs+lnv4kn2XDMglKF/37L+7jgge9gJe1fdKJxDZWi6nkgNJWqefjOZjNjHsuGfWEGqupmOJNRLBY577zz8H2fG2+8kTe/+c0NJpeVSoUvfelLvPe97+W8887jr3/966TSd+0wxl6CH4RjtoewLOMBlY6ePNpoY29DCEHKdcnnc3ieT7FYStoLzcS2Hdsil0mRy6RwZqANSKwDHGpOKuJw1D6AUU1Vo5Rf477FRFiMqaOKS+VDpRrsHH72geu5+5qbEtIEoIKQv373Z3z1zEuo9o9t4Bs7nMeQlplPGJrq01ApvCAckTyFSqF13DqmzohWgyUspLCatu0ZLyzX4Yz/vJ6FUURJSImICF1h+WJOuePf6DxgIY6Ton9gAMeyyKQdHvvyd0dM72mlKG/fzZM/+d/aa3WL2raFEBrP9wm1ivrdteUWd9xxB08//TTf+ta3uOSSS4Y5g6fTad72trfxzW9+kw0bNnDnnXdOanvtiNMMwrEsXNehUvXIZdJjlvm6tk3KsXFbrJpoo43phBAC13WQMkepVKE4WCSbzeC2YLMxmW3X/7te8xuoEIepLJ03oxuLBhX5RdW6Auwr/kbJ/Ju+SSL+NsUA5uWkt+EY157+Yhm0xrIkjm0iJNv+8hh/uO3bzTcXKvas38QDt3yTF77rwrFmnswlDBW2ZSNtgdIKtKbs+VhSUsg1j7iUomKGcrmMtCSWZYHS+H6I61imQEeKRFw9kXRrZm43p37902z70yPsvO/PoBQLnnMEi19wLMVShcFiiVTKxbIdypUqum+Qyrado44pbZut9z/EQWedDERmyn6A6zhYUpKKe+FFhDcIFa2ceftbVR3Aj370I17wghdwyimnjLrcK1/5Sl7wghfwwx/+kIsuumjC29tv78jVapVUanZFajKpFN1dBSpVH8sS9BeLZFyXdLr5PIMgGLUMerqgtSIMTbNJISyktNqpwjaGwbZtcrkMlYoRjWvNjFbcOY5NOp3C9wMsS06b31CoI9KhTVsXtY8QphijBSQ02sgGlEqahpu+dbX3Y28nzXC9k+f75DNpLNtKvJAe+s8fIyxrRP8krRR//Mr3xkGcSLof+IFvvLoikiYikbSUzdPEvu+jlMJ1Xfr7B3Ci76VtCeysud66buPtb7xEOK52VlrjBwF+4LPkuCNZfvwxDcvt6h1g/dNbk0icbVmkvLHbo2hqgnytYcPmnezq7WfV0kXM7emgXPEiwpilt7/Epq0bOPG5R49r7gBCz0BV3QxHwR566CHe8IY3jGvZk08+ma985SuT2l5LxPCxxx7j6quv5oUvfCELFy4kl8tx+OGH8453vIMtW7Y0Xefxxx/nwgsvZMmSJaRSKRYsWMApp5zC73//+zG3t379+iRsPNLPNddcM2yOJ554Itlslrlz53LdddcNG3fFihUIITj66KOb9me7+uqrEUJw9913j+/AjBMV38e2LToLWaQUDAyW2bGnn1K52nT5ctVvySxzstBa43klqtUBgqBCEFTw/SKeN4hSU9dUuI39B5ZlkcmkcV2XYrFEterNWOognUrR3ZEnl03R3VEwBrGRwaNljf4jpUyWi2++Q3/2Je3SRBATBcuysKNjIoTA9wPKlSqhCjH3cBEdN2uYz1E+myGdckk5DipUFItldj/19MimkxEGt+2kUq7geT6eH5jq4TBEKeNaH7dYCpVmoFShWK4wWCqZLgq+T6XqoZRummpTWlMslshkMgRBQLVabWybNEIHwVYiTlobw9VqxTygW01I+849fTzy5EZ8LwANvf1F/rplJ6kDFo6a5tVByAEnHgeYddY8/hQPPfokjz65kWpkX5DPZhkYLPPbBx/miQ2bRxzrmYKdO3eOu1puyZIl7NixY1Lbayni9MUvfpEbbriB0047jde+9rVks1nuvfdebrzxRr72ta/xm9/8hkMPPTRZ/pe//CVnnHEGixcv5uKLL2bp0qX09vby4IMPjki06jFv3rwRmeH73/9+NmzYwJlnntnw+jnnnMOSJUv49re/zfr16/nABz7AgQceOGw5MCz19ttv58ILx37ymQoEYYgKTTf2MDCCRz8I2Lmnj7l0kkm7iY9K1TMRH8eZmaCg1hrfLzUlSForPK+I6+aRct90EW5j+iCEIJNOI4BSqYxSinQ6Na1O41prdu3pp1ypROXamk4rh0A0fRhKerM12BmM3otPaQ1hYyRCCIEthkhfx1niv7cgpQAhUUPTdVG7onTKbYgyBWGIUAIhJI5dRyabjJ2pE47HEfLcnO5RI04AbiGHxkSGGsw4RY2UxPYJriVwpINIuY2pWgAV0tc3kBhDEo2ZctNYlpU0fFXaRK5iF4d4f8IwqoqM9nG8jc61UlQqVTq7UliRaLv++NS3TXnWEavJZzMUyxV+88DD+Ke9hOoXvt50XGFJCksXsexlJ6CUZsPmbZQqVQ5euZRtO/fQ218kkzZE7cGHHyPlOJz4nCPGNecY+6M4fHBwcNxi70wmQ6lUmtT2Wrorv+Y1r+Gyyy6ju7s7ee3iiy/m+OOP55JLLuHKK6/kG98wTq27du3i3HPP5bnPfS4/+clPhom1xoNcLtc0/Pb444+zceNGTjjhBI466qjk9d27d/PQQw9xzz330NXVBcCTTz7Jz3/+82HEaeHChTiOw5VXXsl5551HJtNaZcJEoJVi555eCvkcGoVlCcKQKE8dECoTKg/CkHKlQi6TGbfD+KTnpsMxo0pBUMV1p9ZIrI19HyJqC5HJmJtVuVwhVIps9Pt0wAtMVMSPXLvD0NwY67maMcUcufKqXs/T9P1YLF0/gt473k1TgiEmkFIKQhUbWtb20basJO0ZWwEEYRjZMzT2URvqsi2E4IhXn8LD3/jJyNOwLI587StHlCgAlCqV6PMxhE9rHRGG5mTcmGIqSkVzQ3RTDoMDg7iui2VZWFLi+17y+Y33MwyCcIQHAJ00SA+DMGFkAiMSj7VhWsO6p7eScl36B4qUKlWe9Yaz2BWG/OWL3zTdl5VOvqRudycn3/qvKDSDg0U2bN7OovlzOPLgFfzs179n09adHHnICvr6i+zpG+C5Rx/CvDljVyju72j1nJzsOdwScTruuOOavn7++edzySWX8Oc//zl57T/+4z/YsWMH1113Hel0mkqlYipypkB3dMstt6C15q1vfWvD693d3cybN4+PfOQj/MM//AMbNmzgO9/5DldcccWwMVKpFFdffTUXXngh119/PZdffvmk5zUWNDBQqhAoRWc+R8qxqXo+nfkM2UzapDnQVKo+6ZSL48yQY7HWhOHYqTil/OTi2EYb9Yi/E6mokXW5XGFwsEQ2mx61X+JE4Xl+Y3RAaPMzhCb5QUC16pNJu02FztP9Xd7bp0qsx9Eq8pqqi8yYh7IQ22q0Woj9qcwLUQQqioTHAvLYGHXo7mkNS194LMteeCwbf/fHYU12hSVxcxmOu/jc5POLI3/1kJjUoVIK27bwA9+4hTPSZ6YJvADf9ykU8vieaVNVKIzdoWG0YzcwOEgqnRqWxosfdjPZTEJI69vDiMQcVrN1x25sy8L3Q6Qw4vYTP/rPiGcdxkNf+jbWjl3IbJrM8c9i/iknkjpgAWGo2LZzD3v6Bjho5RIy6RSL5s/h6a3bWbl0AUGUubAtCzVGWnQo9kdxOMAXvvAF/ud//mfM5Z588slJb2tK8kCbNhlH1vnz5yev/ehHP6JQKOB5Hs9//vO59957ATjqqKO46qqrOOeccya0rSAIuP322+no6ODcc89teE8Iwe23384b3vCGRNt07rnnDiNYMS644AKuu+46PvGJT3DxxRczZ86cCc2pVVSqPiocQErTzqBU9XGcgIFSGUsIhJRk0lPX1DYuGY4FlUPH9YMQxx5fiLqNNsaCbVtksyYcPjhYIpfLTvlDQJxiiSEQNAsvaaUZLFewbavlnl5Tg5mJGI+GJO0VKpRWiVYpFHqYJYrpVhA2OrRHJMmYUZrXldIN0cQk3YbRKJ1580f57/dcy9of3405BmYe3SuWcNqNV5NfNB9fmRu+Q80CIT5WSiuEFqYxsRBY0jJ980Y4lqEKKZfL5PM5wlARBAH5fG4KDt7YkYmYkNbPLRbKCwTHHXkwuWyaMFQ8+PDj/PWxDaxevpiOIw/GueBsXvr8Z9HVkUcIE/3LZ9P4fsCGzTsoVz0efWIj6zdupXdgkN7+Qbbv7GPh/B4Advf1c6Bc1OpOzUBLlJmPyt5zzz3cc88941p2sufklFxJrrzySgDe8pa3JK+tWbOGMAx5+ctfzllnncWll17Ktm3buOaaa3jNa17DV7/6VV7/+te3vK0f/OAHbN26lUsuuYRcbviJceqpp/LUU0/x8MMPM3/+fFauXDniWFJKPvGJT/CqV72Kj3zkI1x//fUtz2ciMI17AwQBUlpUqh5+0IvnB2TTKRbO7WjZDbb5dmpPnKGOOqM3EUYqpWASfiZttFGPWEycy2UplSsMFovkc7npj6A2uVbbtkU27c58e5hZhqrnUfEC40Ydpd+I+tHFUEqZ1JQQCEsawjXEFV0pjTXSsYxTgRrcXJbTb7yavg1bWP+/9xF6HvOPPJgDnnc0Sc+8CL4KEZiHujhFqDQ4UmLVRczqfaZiGDNSxWBkiaGUplqtkMvlmkayJoLJusILIRFCEoQBnhdEac/amEacb5tXNPi+Yk9fkS3bd7N04Xy6OvIA5LJppBA8uXELSxbNZdXyRTy5YQuWlJx0/LMnNcd9HevWrZvR7U2aOH30ox/lrrvu4swzz+RNb3pT8vrAwABhGHLeeefxta99LXn95JNP5sgjj+Syyy7jda97Xctf7JtvvhlgxCgSQD6f5/jjjx/XeKeeeiovf/nLuemmm3jHO97BqlWrWpoPTDxfqgFHCqSAqucjpCCfTSf9t0bb3lh5+lgQGUZPdbHeo74ZajKG1gisMffDstxkvZnCePZ1f8H+tq9CCHLZDKUS9PX3k06lyEYu+JPdT6WG3EwxN1DLGvI91qbcHkyZ/bDGwOM4zyaLvfmZJqm3UOPaVuRDFaIRSGgwLw3CEJNmkjVyo0GhEs8gFSoEVk3EHW1HRWmq+BoTo2PpQo5+w5nD50RjU2aNRirQyQOjwgt8BCLRV1WqpgzfdWup3zAMGRwskkqnUMroggod+SQaNpnjHh+3ZmOMda5qItKecbn7vj+ax1VhIlFHHLQC2zJttFKuw29//5eG++DBK5fi+wE9nXmec9RB9HTlCQONZQnWrnuaPz3yJIOlCkccuAKtYP2mrZzUwn5JPQPi8Bn+vi9fvnxGtzcp4nT99dfzwQ9+kJNOOok777yz4cPPZDIMDg42RKEADjnkEF7wghdwzz338OijjzZU4Y2FjRs38tOf/pRjjz2WY489djJTb8C1117LcccdxxVXXMHXv9682mE0hIEpp50IqpUKAJYUdBTyqDCgv79/1HW01hSLRWDkkGNc2mtZNlorAh3lxOs8mUKlKJcrSNuiXKmADoDm+XIhBI4DQvgT2s+JYjz7ur9gf95XFYbs3LkL13UizaPpdzfR/SyVyvhe1RAoDWUUAo1j20maBAwZ6OsfoJDxETpoIE4y8gEaCaHSwzQ6rUIIQblcSv4904hTdOWqh21JKlJiSZmQIj8IUYE5p4PQRJukZWFJkUR6lFZJtaLSJgJlWZJKRdaCTEpTqVapVirY1ti+bxrd4IclMG7esfg7DFXkH1XLloW+jxBQqYhkmVKphJQSP/Apl8q4KZdqpZJcVyd77IrFEmHTSmOdVGY121chBD0dpi9dPH8pJblsinw2w8DAAIW0y3OOWGWq/eJyPyCfTVP1fRbMKeBYUBwcxPMCUimHno4sRx+8nEq5hAp8Dl6xiKULelrar/1V4zQatNbs3LmTefPmTcl4EyZO1113HZdeeikveclL+OEPfzisFHDp0qWsWbOGRYuG51/j13bv3t3SNm+99VaUUlx88cUTnXZTHHvssZx//vnceeedvPvd7255fcu2sSahnxAIOgs5eroK4zLxi59yOjo6RrxABZEewXEcE4JXoTGME7XwtdE72HWlxhohNFKqRNRqTmoL180g5dhRqYncHOLoGIhhVYTj2df9BfvzvhYK2hhlFksIIUinMxQKhQlbFripNPl8IRIyh1i2xLYktnQaUkl+EOIp6MxnSQ/xRIv9nEaCefiY3JOzEAIpxV77TLXWeH6AtKvYtm2iTnXWDH4YkHJcE/+JirukJROpQKjUcBsDaNA3xRpK27aNMWOhMK59FdIIpZU2/f7sqMXUePdrcLBIJpMhX8hTKpbp6ckkEc2pgIkoCbJNnMnjc3W0fR36nqAmhA+VwnEdFthzkFLiedWm0pN4W9WqRzqdwnE9tLSNwafWOI7L3J6uSe/rvo7169fz4IMP8rKXvSypqAdjhP2ud72LL33pS1SrVebPn8+nPvWpCcmE6jGhu/21117LZZddxite8Qq+973vNS3lP+GEE1izZg0bN27kyCOPbHhvw4YNACxYsGDc21RKceutt5LL5Xjd6143kWmPimuuuYa77rqL97znPbz4xS9uad2JnKi5TDqqFjHdv7s68uN6Uqvf5mgeNPXlwlJK3LqO8jWiEr1vSWzbSqJR5oJR976MdQcqccwdtj3AklbD0/54odFJXyohhusSxtrX/Qn7674KIchk0jiOTbFYYnCwSEdnB5KJnT/plEs65SZl6EppfN9HyiEiXWnIuNbDtzPWcTbvtzy1ptuY7Gcan3ITGcKyJNl0OqmGq0cYlcI7Vlw+bxT2Spl+aBBF5oasN9TvyFgVyOT8HWtfpTSC8zA0PeeMGen4rCu0NiQ8CHw6OzuoVKo4rk0um53y86Z+X+ofGsf7uTY8aJoPICnSkULgeR6ZqKI6k8mOaLcQbyd2eveDAEvKSI7RWlR0f/Rxuv766/nSl77Etm3bGl5/5zvfyRe+8AU6Ojo4/PDDefjhh3nTm97EypUrecELXjDh7bX8uPfxj3+cyy67jNNOO40f/OAHI/ofxXqnz372sw1fngceeIB7772XQw89lNWrVyev79y5k0ceeYS+vr6m4/33f/83GzZs4Nxzz6VQKDRdZjJYsWIFb3/727n77rv58Y9/PKVji6hs1HXs5IRJpxy6Cjlsy2JOZyFpdzAdaHaCm6fN0JCg6L344mXbNo7j4jgutu2YsuDIy8W0QNBNfyZaSRE7+Y5EytrYPyCEwLZt8vk8UgoGB4oNlVqtjhUTm7j1Bnq4kFdH6agRe7VNKwSeH+D5E9vHevhBQO84GuMm1XN1kTLjpl678fYPlsz7wjRNjh/YhBD4YWiE5FWvJXF1bF453ktYHFmSkefReIth4uhLtVqlUChQqVZRWpFOpZOee2Gd+/hU6QaVMp5NlUoVfxKfZ3x44geJUJlraiaTZXBwcMxxG96OBitXx27j0jgHPSM/M4nf/e53nH766Q1+kTt37uTWW29l+fLlPPbYYzzwwAP84Q9/oKOjg89+9rOT2l5LEacbb7yRK664ggULFvDqV7+ab37zmw3v5/N5zj77bABe/OIXc9FFF/HFL36RU045hbPPPptt27Zxww034DgON954Y8O6n/3sZ/nQhz7El770Jd785jcP23YsCp/qNF09PvCBD/ClL32J+++/f8rGtC2LQi5DIZvBcRyK5QpBEJDNpCiVq6Rdl2ymdXPQyUIK03VrvGRHIpDWGF+XSfA+S0rs/SzK0sZwxBV32WwGKSWlYolUOk3KnVjlWxJZBZpRIyHAdWxsSyakHyGatuqYDJKIQH0ZvxT89fGnUUFAV1fnuNJQzSrHALbv6uX3f17LWaeM/pRc9Xw2b9tNT1c+qcaqRZ9hT1+R3zz4V0487gi6CjnQNaNGYarqE7KXdl0yaYf4xB77pm7k3iYaFauf6t6PNiSkQPs6eVXr5N0xq/9j4pTP56hUqlQqFbK5LJ7nRQ9dwngoRdtJqtWE0dk1BICkifzElXn178Wflema4KNCY4SZTqeolKuEQUAYhIRBGInpzUaEMNsaTyWelJJ0KkW5XCWbTeN5miAIRvU9a5QymL/7B0ssmDvm5vZrbNiwIeEeMX7xi18QBAHvfOc7E23ToYceyhve8AZ+8IMfTGp7LRGn++67D4Bt27Y17Sy8fPnyhsl//vOf56ijjuKWW27h0ksvJZ1Oc+KJJ3LVVVfx3Oc+d9zb3b59Oz/4wQ846qijxl0tNxH09PRw+eWXc9lll03JeLZl0dWRoyOfS7RLHfmsyc+Xyiit6cpPvxN3XMUymZvFdLbP2B9TU22MDiEEuVzWRA8qVVQYksmkp/x7JoUgm07hBwHFShWlFI5t05HLMpUSVilF07n3D5YQula55vsBm7btYufuPnK5DEsXzmVXbz8CQXdnnk3bdjG3p5O+gSK79vSTSadYscT44/lBwGPrNtE7MMjiBXOY19PF1h272bZjD9KSrFiykMFihT/+9XHm9XRy+MErEMBTm7YRKsWSRfOQQhD4Aeuf3oZSmvlzOlm6aD69/QNs3LKdUGmWLJzLnK4OduzuY+36p3Edm2WL55PPpkckNkIKVKgIg5AgCBsiTxoaSKVQgpBwbAG5Nin8OFoWBAGe59PRUcD3fTzPI5vNGkG7tHDtSIOZmFHq2jwi64UGYotEi6hh75D3hIgrBM0fjuNgWZYx4XRsQt8nDBW+7yOERIv6aBKR5cOQYxRF/uoRhiGeV6VQyGNZNqVSmUw2apND7aEgHktaYkJyiHrsj6m6PXv2sHDhwobX7rvvPoQQvOxlL2t4/fDDD08CMRNFS8Tptttu47bbbhv38pZl8c53vpN3vvOdYy579dVXc/XVVzd9b/78+Xjj6Co9Xqxfv37E99773vfy3ve+d1LjSyEo5LOmAabrDDOKq3rGzTify7Qkhoxdu0ctg40vTlIi4jD1kPfA6ELiQm6r7qJvPJ1Iyo/rtVIjbasebQLUxnhhnuBNW5a4x10ciYLWvku67s+h20g5NrvLplEsQMp1yGdnPsqrtWb9pm2sXbeJRfN62LR1J8Viia7OAg+vXU9PVwdKKRbNn4PvB2TSLk9v2YFA01HIU/UDduzpY6BYZtuuXk567lEEoWlts23nHh59YiOLF841REVKwjDkL2vXI4SgkMswUCzTkctQLFfpHyyBhi3bd5HLZHjkyY3YliSTSfHwY0+xcskCntiwlZ7OAuVKlUeeeJpnH7Eax26uQ1KRQN91HVJDespRRyqSF6hL64nG+ExSvKIUQRhiRxYT5XKFdCZNqBTFYonOzg7cFpqgj6fv59BrWqVcxU252Lad9NUTgG3b2LZFOpM2KU7fBw22Yyf7MhLJDCMLiGq1SqVSjYihaRekoiSA1iQ6U6h9s8Ow3tVdR62G2vKGefPmsXXr1obX7r33XhzH4bDDDmt43XXN5zkZ7A0r3f0OPZ0dlCtVgtAIvfO5DN2d+YbSf4j0B0pRKpdJp13TzX2cN4f+wRJbd+5GKW30UpYgn883fAFikvTnNeuY292BELBzTz+Hrl6K69gNaQApJJbQpFyHMNBs37mb/sEihVyGeT1daDQPr32Kg1YeQHcU8m+GIAh5aO16FszpZtGCnrqLRvOUQxttxIiJuRs9XJRKJfr7B5Ob71hNeEdCvI7SKrrjmKd3KYzyIkl3jFfUO76tjjofMOXz23f2Mre7gyMOWs7GrTt4fP1mVixdRCadYvO2nTzvmEORUrCnb5BytcpAscRgqUI+lyWTcnnWYavZ0z/I/X96FM8P2NM3SF//IH2DRUrVKgevWkJnIcfi+T24jk1ff5ETn3sUnYUsSmsGBkvkcxkOXb2UfDbDz371e7bv7mXrjj0UchmqXkCl6rNxy0527O6LHtQUUkgqFQ+30DxCrrVJ/1tRoclUnvdxBZ3j2LiOzcBAkUIhPy2tfICkYa8h8JE/VfRdTbl2FCXyE88qIURU3BILvw25jMX49eLy2CbG931Cpejq6qBcrkQPw+A4zjCCpzWo0AQOhFaJfihUimKl0vJ3dX+MOB1yyCF84xvf4L3vfS+WZbFp0ybuv/9+jj/++GHfk/Xr17dUmNYMbeI0BZjb3ZHkyf0gHJUQlcqmZ1/abe2k37G7j4ceWc+8nk6qnke5XMFNpZg/p4s9/UWCIKSQz5B2XXb39ZPJuKBhx+5+DloRUtWa3v4iQkB3Zx7HtrEtG98Puf+htQwWy3R3Fti0bRelSpUFc7vZvquXFUsWUPV8+gZKaK3pyGfJpFNUPY++/iIIo78o5LIopegvVqhUq6RTLh35qa9yaWP/Qr1oO5s1WhXP96lUqzi2jeO6NJc+xRVvJpyhlEoMMUUk1hFaEGqFkJJCLkM2nTIWHFKOWL1Uj1buR6O1Ey6Vqzy9dSfZdIp0ymVP/wBbduxm555+MmmXgcES5YpHV2eBLdt3M1iqsLtvgMMPXJbcaDUaS0qymRSDxTJCCMqVKuue3sIRBy0nnXbZ0zeYeDTt7O2nkDdu7Zu37aRcLkSFH2bfHceKDEHBtW1y2RQdhSw9nR2JOWNff5E5XR2kUw65TJp8brQo3fQ8KGmlKVcrKKUpFLIUiyWy2cyEmsaPua0oQq+i9lQi0kxprfAjTZaWxiBUWhKtFIEfJLk527bxPB/bUni+j2PbUW9Ws24Qhgz0D1CtVnFdl86ODvwgwHVdgiDADwI6mtgbaF3rfWfZFtKSENSMjVvFTIi3Z1oc/ra3vY3Xvva1vOhFL+LEE0/kBz/4AUEQ8MY3vnHYsnfffTdHHHHEpLbXJk5TgFK5iu1IHMsmnRq5pLbq+Xh+QD6boX+whGPb5HPj9x1xHZvFC+bg+QEbn94CwLqnt7Fp607j0eLaHLLygGHreX7AY+s3UyqbsPDcng4OXrkES0qe3rKTwWKZ5x97OD1dBcoVD0sKKnWVGtt39fL01l2UK1W6OnIcsmopf31sA/2DRdIpl77+ojEY293PI09uJOU4+IHPkYespKdz6isg29g/YITiIqqCigS6AtyUS+AHqFBRKVfQWiVP5EBCUWKCFJMlgUkZJZYW2hAa00tMY0sAc356VQ/f84ede3GUKokmUOdZJGr6EpGogM3vkto86tHTWeDpYol1G7fR3Zln0YIetIaNW3ZgWRYHrlhCX/8gh6xaSk9XgfVPbyWfTdPTVWDnnj5Srkt3Z4FMOsWCecbo0HUdFszrJp/LsGzRfHr7jGnq3J5OMmmXxfPnsG3HHjw/4NDVS9m0dRe9A0UWzetmXk8nC+Z2J5VsC+f3MLenAykFm7ftZldvH3O7O1mycA7liiFw5apFLpOaVp1jM2gdtYopl+nq6qRcnlij+Hq5Qozk+9MEMvK70+jENiEIjKeX0sr4TQmJtOLIkgAt8L2oEXFUpRgqQ6BcxwGM63omkyGVcg2JdWyKxSLZXJbAD7BtawRCX5u9+dwswFhF7FnzBL2PPMkR713dZL1nDs455xze/OY3c9tttyV9cc8+++xhWuy1a9fym9/8hk9/+tOT2l6bOE0BypUK3oARCeayGdIpE82pPzH9IGCwVKaQy+LYFqWyYE//ILZtk0mPL09fqXqsf3obvh+gwoBQKR59ciO5bIZM2uWpTduY18QMrW+gxBMbNrNq6SK01qxdt4kDly3GkpKBUplCPktH3thKlMtV+geLdBRqZmyWZeE6FqWyYvO2XWSTtMIh+EHItiiFuO7pbQwMlpizZAEbNvezaduuNnFqY1TEVh1aaoIAEGBLC8d2Itfo4eXkhgzVIkIqSiXV91VLogdRBCHK0RmRsDaO2GiF0vFTu2FLJt1eU/bGFgq1qqnonI5/j/dDDu8BKS3JAQvmUMi45HI5hDTl/wevXIwXhDiWaVybTTvJzfWQVUtwHZt5c7pQSpm0F8YjrXCIOUcLuTRHHbKCdMrlmENXUfV9bNsCbfRbB604gKUL50U36C4WzutBhYpsNoVAcOTBK0i7DtKSHH7QCvLZNB35LPPndKO0IpdJY1mSg1cuoVSpIoUgk26NrEwVwohsSCmpVj26uztbrr7UGF1RPaSQwzy/4n9bSWotXlYk66vAFNmoJBIojLt6VLEppSQMAmOHUPdAQOQjlkqlEMLcD4KoIg+MUWNHR/NrZT3pk1ED+O2Pref+D15P36PrADj9vZeM+3gY5/DpTtXNPG699Vbe+c538uijj7Jq1SqOO+64YctYlsW3v/1tTjzxxEltq02cpgDZbJqcTlONqj4GiiWCIMC2jIDQkhZBGJLPpRO/pkzaRSk9otiy6XYyaQ5dtRSN5qE1j7F9Vy9BoCJ9geCABXPJZdLR43JtvdjXRaNxHZulC+dBJDLvzOd4eutO9vQN0tWRZ1dvPzt295GLxLOVqsfjT22mu6tAIZelUvWpeD5CmgrBqu/jRPopU2EiomqdblPu3EYb40Bc1ZRyRk5z16JOtdRHbKIYE7Bh65gVm4zVsETjNoa8HKgAoYUhJ83mruO+eTUzzjhKZkmBY0tsxwKlCKPWTLYAFQZUwwAEVMplQCOkhadCpGVax2ilCMOQalgFAb29tUiwX60m+xFGZpW1ViOacugnUTIhBOVSGcuysKTED0JEGOLYMikISbmGIGqtCYIQBGTTcX9KxRDu0fBZhKGZZ/y3iU41NxAdT4Q9vp5YUpLJZOjv76dQyI3bJHPoRIemtOLU6lAtZsPc6rywpBSEgfGGCoPAEPJQRWm9JvsWRR+lAB15SqlQgdCkIkG77/tYlp30Jh1x33TjvMK+Ae75fx/EH4evVzMIYTpETCcmmqrr7+/n2muv5Vvf+hbr168nk8lw0EEH8U//9E+84Q1vGHP9Y445hmOOOWbE91evXt3gHzlRtInTFKBa8dDo6KIocKTAcY1xpJFgKHJpB1tK/MCEZGPn43qMJqiOK0vWbdpGGAQIIZjb3YltWWzZvodK1cd1bVKuSdnFfbjSKYfuzhwL5nUzWKyQdh3mzelCSlMJsnhBD7v6+rnvT4/S1ZGnWK4wt9voHNJpNzHFrFQ8wtCEqefP6WJP3wAPPPQYjmNTrfqkXIdlixfw8GPrKZWrBGFIZ5s4tTEG4pJzPwxwRyFN8bLlSjW5JKvIcRpEdNMfvq4JDE1cd6O1xqYuJTPCMqOtH4YB2UwarYxWRURkJn6YSdqbhCFhqNm+u5fN23cTBCE9XXmWL15AOm2jiXvHRRVX0d9JVM5sMCI/1CJu0XuxQ3+MUBkfIitqdaJ0pNmhpqlvIKMRUVLKtLlREUmKj42pGK4aV2vLNjpKx27wHhqvG7vWhlikUi7VatW0ZYkqBWOM+F2J0rajfZeUVuggIjeJD9PQcWokMuW6ePgEEWnSKm5SHEWg4mMTkXnbsaNl4v0xn71SOhrDECo5LP3bfB71wcw/3noXft/gpPsozjZs2rSJl770pezYsYMLL7yQI444gkqlwtq1a3nqqaf29vQa0CZOU4DOjjyhUlSrXi29EL2XeHBo0wQzCALclIvrOE1vFLHnkqCxmmhuTwdHHrLChHcBx4KF87tZMK+Lro4Cnu/T3Vkgm0mxetlishkXKST5bJpCLsvxxxzC9p29CCFYOL87uglVcB2XY484kK3b91CqVMlmjOBcAIetXsac7g46C1l27OrDcRxsW9LTWeB5xxzCth29pNMuyxbPo6uQI5NJkUm79A8WyecySdSqjTZGgsZUBzmWzVj306rnY9l2y4UVk8F4oiPjWUYKgZaRaD0iEkLGth8RtxOCTdt28vDa9SyY301HPsvAYIXBUgXXsRkslbGkJJ/L4vsBu3r7SLsuPd0d9PYN0pHPkEmn2d0/QMpxUBp27Rkg5TrMn9MJQrB1+278IKSnM08hn2uMliCSlFNc8q4x0RYR6cWIiK4hD7VS+DjKJgR11zVDJMJooPjalhAwIySrxSaGRIYyUcqwWPIQCAYHS4mnEwxPj8aWFHHVcN2whmA2++wY2aMuJvXVqkfVM2kex3UiUhcghKyFLgVJZR1CGJ2dpc1ymH6mWmu8apXAl2SzWQLGGX1TmvoA0SPf+umkSNNsraq74IIL6O/v549//CPLly9vad2hXk31MC7tGVatWsU555zTcku1ZmgTpylAnCaws83bz0B8EhqxoO/7FIslqrZNLptpCNEGKiTuPl6v18ikXZYsnJv83j/Qb3o8RUQofoqVwoi/41B0Ou2ihSabSbFyac0gzPcDgkCRSUtsy2LRgh6sqPw7vngdsGAOAJmUS0d+ePQov7xxf7U2hnrz53S2q+naGBcE4IyjfN0PTPl2KhLqNoNlSZTS46qYm0nUiFJzv6IoF4Pvh2zYsp2F87t59uEHmjRkdI6vXbeJvz7+FAcsmEtXR56nt+4g5dpUqwHdu3splqt0FLKsXLKIx9dtJp9Ls6d/kHwuQ7lSpViuYEnJhs3b6ekqkHKdYRHhOPoCjU18x2MvUK1WCYIQ27aShsaxx1CcYo2JF9RF1+v0SkZbZshKsVgilUpRKpWM/UpmZB1ogz9dlBYLQhMZSghb3QNpzR+pdq3TUZPxZvsZR7ksKxZvGzPOINIpxfumQlONVw1MEU59IDIIaxE6Y3Ypa9q6MaCJmXV0rHsHxlxnNIgZIE6tpup+85vf8Itf/ILrrruO5cuXE4Yh5XKZfH5kK5x63H333eNa7nOf+xxvfetb+Y//+I+W5jcUbeI0QzAVRBZpKXEdBz8IGCiW8foH6e7MJ94flrSSMHwcKoaoS3ndRSf5N+akd+oa7IZhmOgxQlXTQMXjxU9jlmUqQ0zzXoVF68aDQ/exjTZawXi+M34QUKp4pBxnxP5pGuORU/UDMpEH1GxBQhLqXqs/l016TRKGiiAIyUUmoH4Q8MSGzaRclzAMSTk2Rx68nDVPbEQKydGHrmLnnn6eeGoz8+d2sWX7bno6C8nx2t07QCadwvMCduzqY053gf6BIvmomERG1gX1aBBLT6KHUiKKTsY144lIQhD3kbOs2nUrFvIP9A9QKOQSr6NCId9Uvzb0+Nb/Xq9ta9S5JTmAZJs1XtK4v37UVsVx7ajCUiTky7JtfD8glU7VsgyWuRYrbb6LRlIhzXGOUqSWZeF5Hr7vE4QhWds8fIZhSF/fAEKA47pYliT0AizXIvCD6LhJ0JCbP4fBLdtb+0D2Evr7+xt+T6VSTasif/SjHwFw0EEH8Xd/93d897vfxfd9Fi1axD/8wz9w+eWXj6pvW7du3ajzKBaL/PWvf+WGG27g5ptv5qSTTuJ1r3vdBPbIoE2cZhjxU13KdXEch/7+QYrFMoUoomMIjtVwQasnTSPBXIhCDDuKCBh1zt/NLoKR3kBQM21rhsZWBGLY7220MV1Q2qQ7bNvCcawhvbpq0BqwLFJC4AXBqCLzvYH4YUfXRcRU2EgsXNdhTlcHGzZtp7OQw3XsiPB0gBDkc1nyuQwp12FPOEjV86lGtiFLFsxjy/bdPPrE08zp6SCXSbN1h6mAzWXSFPJZcpkUqZTLY+ue5tEnA4494iDcUdy0hRzeIqTZfmlNQ3TFXB9q2qEkmyUaK9eaXdPKJSNutyyLgYFBcrncmA2Ah37O4/3cldJoZUTw9QSu9n4ItsS27YQECoxruNYa37PJRs7h9dCYCFxxsIjrumSzGfwgRIrYHNQck2KxGGnUwLYdCoUcxe27+f11X+KRO3+I1zeIW8hx4GteyVGXnIu1eAFaaw4991U8cMPtDeL1VjCT4vClS5c2vH7VVVc17RCyZs0aAC666CJWrFjBLbfcghCCG2+8kQ9+8INs2LCBL3zhCyNubzypvcMPP5wzzjiDZz/72dxyyy1t4rSvIT7RJJDLZRgYLOL5flJtAY1ER+mx89mJJkHHWoMo/193Tsfh6pj4xDehsXrYqegpyor8cWKRaf2FZjbdpNrYP5BUdyHIxMUWIyA5p0Ts56SxrNnznayv1FO67twRJP+2JBy4YjGeH7Dm8Q1Gz4NkTlcHpUqVQj6D1rB00XwGiiXWPL4BPwhZfsACujpyLD9gARs2beOABXMp5DLs3NNH70ARS0o6Czl27elnT/8guUyaOV0dI1YJNs569GNYn45LCsog0T81isIbSa815Jph+twFZLOmh6Ft26RS42+p0iqEqOmbmu2l1uY6LIVAWlbDZ1i/P8OIG5BOpUinUjWiaFkEoTJ6PtuKehua93w/wHFsipu28a0z/h/lHXvQUXrQGyiy5svf4cnv/5xX/+DzdK1ayjFvfjWPfOe/KT69FR3OboH4xo0b6ejoSH4fyYNrYMCkH7PZLPfcc0+y3Lnnnsvhhx+e9Ls95JBDJjWfVCrFueeeyw033DCpcdrEaS9DRiHYIGheVRSTllYw0uIxmapPHYwVjq/3wkm0I1FOP7mQiMmF9dtooxm01vhBQGoM0lSP+HwKVTjrtE7154lpXiuT6Iyx8YBsJsWzDl9N/0DRpKlyWTKZFNVqlblRL7tCLs2zDl/NwGDZaIoKWRCwYvFC5vV00lnIYUnBsw8/kIFiCdu2jU0J0DNQQkgo5DJjNouNBd9Gr6OT9JuITCHr1473I46q1ZOS2JZgrIpJz/MMobAklYEKPT3dEz/WY8Avltl4z/14xTI9h6xk/lEHN5kTxs6hyXdvaHow9Hye+O9fs+fxDbj5LAeeehKFA2ptPYwOVhIEofluSisZw/ONk/j/vPsTDaQp2VaoqPYO8It3fJRX//DzFHq6eMnNH+WPn/wim37+u5aF4lJo5DRHnGINVUdHRwNxGgmZjElZvu51r2sgV67r8vrXv54Pf/jD/PKXv5w0cQI44IADGBycmJVDjDZx2sswBnum3N9xHBzbbkiHhROonmh2faqRJXORDgnHVaatqemp4iulEAIjXW+TpTamB6a03cexrFH1Lc1gRfqgMFSzhzzpxnMw1hsOTX1rZbyferoKiJiwhKaNk+vUUkaubSetnuKxHUfS7RoxrYrGmdPVUZP1CEF3V67BPHTMaWuNFyh6+wfxoka2+VyGzkIu0ldGj1BxtCkhW3ZLRpWmf1tAOp2iOFgkm80OOzbQSL4Gt+xg+x/+irAki557NOmezrH3Ryke+PRt/OFzXyUoVZLX5x1zKC+7/v3MOazm8aO1Mu1NxsCTP/sNP33nNVT29CFtC60Ud1/57xz5utN52ccuxYqqQONrvR+EKBVEH0kkNN/Ry8b/vW/keYchWx94iF1rnqD7kFWkuzp5zofewZH//Cb61q4fc471mI0+TnFKb9GiRcPei1/bvXv35CcGPPXUU3R1dU1qjDZx2ssQwoR1VWgaPzpR095QKQLVvHpoPGjmLaO0SohTLIocc34InLhpJbWwu4yE5O3O3G1MB/zIKHI8He2HIm6HYnyeWm8SPD2oI0d1554QUWXZkNNIa40Oh5e6i7qCEV33QBOTjLh6V2vww5B01D5EoRJNzETOWEtKXMdGCgvbtpK+gLUBa1El03Jk/GMbrytFGAY4To5iMQQJXhAksxWI5NpY2d3H3e+9lnU//t8k2iIdm0PPfRUv/PA/44xig/LbD32GP33+P4e9vvMva/n2GZfw2p9+ka7Vy6Ld0mM+HG76vz/x/Qvfl4i/VV3F51/u+CEqCPmb69+fvGaOj6RUqgAmBYiG3keeGPtARfPMrDggIdCp7k7mH3/MuNadzTjhhBO46aab2Lhx47D3NmzYADDpxrxgxOq33XZbU1fxVtAmTnsRcepM2hLXdSgWy6RcF9u2x5IWjIq4HDfeRiwUD6LSZguJ5wfjeipolsOPX9dGBNCOPLUxpQjCEM8PyKZTEyI9QpjWJlXPb/kmPl0Y6UwzVVemr9lYBpsjPqQk/EkkRSWlchXXtRMjS6Fr75ntGo2NqidAI8C2JD1dhWRbSZRpyBxi092RrhmjIQgDLMuiXK5g2Q6iScUfmBTbd//27ex5/KmGFJXyA9bc8UP61m3ijG9cn/SRq0f/U5v50xeGkyYw6bCgXOGBT3+Jkz97lXlNj02c7v23L8alzU0G1fz1P3/M8f/8JrpWLGncjyDAlqadD4B0x6flslIpqn4wqQdWyQyk6lqc31lnnUVXVxdf/vKXueKKK+jsNNHDgYEBbr/9dhzH4ZRTThlx/XvuuWfU8cvlMg8//DA333wzmzdv5vOf/3xL8xuKNnGaBdDadNa2LEm5UiWdwZyM8UWt1admDWH0xY0vH1prJEYI7nk+gR+QmeCNKUYiQG+jjSmC8Tob20V8LMQ3cRN1mkCbjhlC7LcGk4ve1gux444FFc9LqriaQQiBtGq91povVHswSkryo23V+z+CnhRB9TyfbC5LuVRukCfIuDovmsOaO3/I7rXrmhIVrRSbfvN7nvrZb1j5ypOGvf/oXf9lCNkIgmodhjz+3f/hxddelkStRvsOVnoH2PibP4ya9xSW5NHv/pzj//lNtX0Spg2PViqpGFx8wjHYmTRBuTLSUEjHZsmLjqMaWctM1IppRlJ1LY7f2dnJv//7v/OmN72J5z3vefz93/89Qgi++MUvsnnzZq655pphFXr1eMlLXjLm9SLuJfjhD3+YV73qVS3NbyjaxGkvw4iva467A6UyQZSmqC/jHbpOtVzFls3bTMR9nsy6ok4rYMLpYRjiuu60Vqy00UarMHqaqJebNbbp4lhwbDuKOjX3fpoN0Eqj6owhR4OQda1aomhv/c2zPjIVKoUfBCQei/Hu160TV/c199SOFo9v0vG/6yJLMYzpZDz4xBD4Po5doAx4XkAqHeLaTvI9iD+/NV/7PqMxBmFJ1tz5o6bEqbRtF0JINCPrRlUQUu0bwMmma8d4BHjF0pj7JaSk2tdoWGkiog6VSjVpgePkshz91r/jwc98pfl3QQiOuOBvSXd34A2WmBRzmqW44IILmDdvHv/6r//Khz70IZRSHHXUUdx5552cd955Y6472jmeyWRYvXo1Z599drtX3f4EKSSZTJqK52FJSS6XBWJdUmMvpdg0r5DPN5QAozVh1GS0wSMGc0FRWmNbEtuyo+7gs/Nm0sYzDzWdiyKdcqfkuxm76YehGkfp/fRCKz3iQ044tHvuiIPoxEAyDMMGwTnUoiNh5GDt+b5JgUmnITIci851qNFSJPqcWCs1dNz6EvzYzkQMtTCJLFBinVX9fIbub7PX4gc7rcGSgpTjNsw5JoODW3eMyhd0qBjctLXpe9kFc9BjWLtI2yLVWUtLNrcpMPNNd3cgHRvl+SOOp4KQzuWLh29HxpWUtfk8771/T3HrDh79xk8QtoUOFcKS6CBk1ekv4QVX/SNgjs9kzg4h1LCH8amGEBOzSTj11FM59dRTW17vtttum9D2Joo2cZotiELimXQaz/NACNMCRYtE0B3DOO7Kpq0Q7Oik9iKNUwypBb2Ra7Dtzt7URRvPTGgwXmaOM6bh4XghhDBNqGdB1GnC245Xi4JSWtcE5SpU+EGI5/ukU25SfWjbFna0v8VSGTtvJePYttUgTo8bD9dX5w1F3Fw4FrGPVLUbKkWoNEFoUlD1PlpKKSqen7TLSadMzzcVpat05G2iNUlD5J19faRc0+5JhSG7+gZIz+k2LUdGiNAJKcktnNf0vUNe80ru/9QtIx9qy+LAs09uEJePxNGUUsiUy0FnvZy13/7ZMAuBGJZjc8jfvmLY6yadWiNOGtPP7uU3fJCj//7veOQ/f0Rx604yc7tZ9epTOOB5Ryf2EbY92TS2RsqZsSPYX9EmTrMAsVZICEHKNSFcr+qRyaTHNKccc2yIunfrqP3K/v2FbmPfhO/7SCmnPDIUO/Xv7ajT0BtdLMxuqIhr5gQ95CWlFULXqvAqnke5UiXlOonHknkAS5kGtb6HF/gopbFtyzyMyUaNTLzduJ+eHkUwHruBN5umVppiqYLGoqujsQ+e1sbo0fN9EALHsQgDE2HMpOuMf0VcFahxbJtK1SOdcimVK3h+wLJXv4KHPvnFEY+zVopDzzut6XsdyxdzzMXn8afPf33Ye8KSONk0x/7LhWP65sWVg7ZlceJlF7Pxf++jvKuvkTyZ8kde8pF/Jh1HsOpgdFuaMAyi72eYPDDMO/oQ5h1t/IpUlHKtP+SOYyobg/FGKvdzDAwMUCgMP8bTue4sMTl55iJ27q4XdqZSDqEKJ0RyYj2ALSWWlDiWnTyJmpRfOz3XxuyCH4QEoRq1/cdEEVePTfR8mrqJ1P6ZiKvr0mLjnVusiUzGQCTtQup7eUkhyGZSZNNpevuLBGGIG3nEjWQZkAjMh7zeEO1mhLlqQ9IGS6WmNiqWJenIZ5nT3cncrg7SdVVkcfVcPLdSpUqoNJ35HFII9vQNopRpdL7qtafSsXIJoknVnLAkC593NCtf+aIRj98LrvpHnnfZW3FyjQ3K5x51CGd853MUli82febC8X1fCovnc/6PbuagV70YUef51HPgck67+aMcfcHZTder1566rptoUkdath5SiIbj1ypicfh0/8wUVq5cyac+9amWTC2LxSLXXnstq1atmtA22xGnWQhTlluNmvVO7COq7z0Xl2d3dWZbNhNso43phDE+9HGd5oUOUwFLWol+aq9qneLoUuTPZKJO5q3xEicTPa7pR9IpFyKTWiHAEqZpt0mpGbdq13GoegFl2yOTdrF03MxbNmh+DBEbNuXa3ImiUyN8TqHSSGmRGaHopH78WHg9UCrTXyxi1w2Zy6RJpxzC0HRNqHoehVwHmXSKPil48e2f4E9Xf4YNv/hdTX9lSQ46+xWc9Il3I0e5ZgopOe5fLuSY/3c+m37ze/xima6DV9B9yMok0hUT2vEKgTqWLOS0L3yE8q5e+jZuwc1n6V69bNTvc82TC1Ipl/7+AXLZ7LDlxnCgaAN417vexYc//GGuvvpqzjnnHE4//XROOOGEYVV4GzZs4N577+VHP/oR3/72txFCcPnll09om23iNMsQn2x6Emm1kXyXUo4zqbm10cZUInYHl5ackiq6kSCEieSaSM1eirrG+qEoKGFaroiavmgcGLqsaX0i8YKAUOkofWT0QsYC1Giaujpy9A4UKVWq5ji7EguGpaRiQb4lJVpG5duRuD4IQoIgjPRTVoN4Px6mkM1AdwfZtNvAOVQkgHeGEJp0ymX+HAe0pre3L3ndsqykHU1HPsvc7o5k3XwmQ2F5ltO++in6N2xm2+8fRkjB4hOeNaK2qRmcbJoVr3hhNP9I8G7VjH7Nd6a170lmTheZOV3jWjYW2JuqupG/A828rKCxQXSrEFIjplnj1Kpz+GRwxRVXcOGFF3Lttddy22238dWvfhUAx3Ho6elBa82ePXvwfR+tNV1dXVx88cW85z3vYeHChRPaZps4zRJorQnCMIkIjRgSnyDaKbo2ZhPiBr5KazK2O63fzzjiWq5WsSxrWHPZGYOoRVrqXjJRqKhibdSKsVigrWrrWpbEkhblqkcukzZRDGWq72o6JkEunaZYrrC7f4BCLkt3Rz5KYeokOlVf9i8A6qrkylWP/sEShVyWQjadWCKY5SPPOMsy84lIcFwpWfV9fN8nnUrhOLbRWUXFL4abNHpAxdu0LCvpsRfPK10XzepcfgCdyw+Y8MeRfAZCjPj9a6o7mwKEEfEJwyCpqozJ9HgwGX2TFIrpLqiWE6yqmygWLVrEpz/9aa655hp+/OMfc/fdd/Pwww+zY8cOAA477DCOOuooXvrSl/I3f/M3SW+8iaJNnGYJQmX6QeWyGbRWkUBzb8/qmQV/z9OU1vwMf8/TSCdDetXzyax8HsJqR+qmGlpD1ffJpFIzYosRO+iHYYgc5UY5XfB8D8uyaue0BGjUJY0H0uTjABPJEUDKtfH8AD8IjU7MqmuvFJWuW5HFQ7FSwbGshPRY0jKVXaMcDsuSZKKqPce2Tf82Eaft4qVEg2t43NbJD0LQmpTr4vm+qbaTcaqwlq6qR7niEShTOYyGdNrdKxKD6TL3jR8awoh8agxJDMMQKYffkpvNQ41hq/BMRTab5TWveQ2vec1rpnU7beI0ixAqRV9/kVTcKqGdyZ4xDPz+LvrvuwNjh6xASMpP/JaBrgOYe+aHsHI9e3uK+w201nh+QCabnVHNkevYlCrVYSmjiUIpZVyutUgiJrG4Ok73NCwbKiwratRrRaKhqA3SuNN1daJik44TpFOpqNpNJFYlWpnquPp5uI6NZWVxbDvxxKz5MjW5OSudpDnTKZd0qvF9GX12JnpSsyNI2sdosG2JwNghWJYpwff9IEo7gk78n2rjplybVNTVQBCV7jdBHKWXUial+lNJiGPfqumAFwRJqlQKgeu6VKvVJr0Zm5tJGQ3ryILy0bC/per2BtrEaZbAkpL5PV1owPd8BgcDJuPE28b4UXrsV4Y0gSFNdX8HfVvY9eNrmPeaT7XTnVMEPwgQgmmpohsNIiqD94JgyvR+lhxfLzzLsqhWveTfGgXaEI+xyt+HQtfdcM0cJK7tUP+gFeuSbNs2holSYEsLe4i2WyCQVq2lST2BM8JxaWwORCMRHHouaG1Md3VEEJPX6tqKBGFIGCpDlqTEtm2KpUoUdapt17Kshu+GUoogbuYrhrqWm5J+I4S3ELJ2XCYL0+R3eqB1LWIIhlT6vjd8OUXT20B3Z56q5xHWHd/xwhCn6Y1Y7e/EqW1HMEsQ+81YkTbBdErYv798swFaawYe/BYjklSt8Heuw9v88IzOa39FEIamwek0isFHgy1j/6DJe+DEUeH69NTQn2RZTZIqVKFx94/P7lZSQnF1oCEgtXR+fZpMRS7lJpqnk+uKjvqv1M8t1hPVE5cwiqRpDHna0z9AuVI1r48hStbatHQqVaqUK1Uqnk/FC6h6AV4QUvZ8YjG0FbmgB8Hopf9+ELC7d4Cde/ooV6p12zL74zhxqjIYc36tIHYzn2rExp9DP/dW+HMm5dJZyCMQZDPpsVdoY0rRJk5tPKOhyr0EuzcwalpUWFQ2PDhjc9pfYawHAtPyZy+1+0miTn445g17TOjxP9qYFF5EVtBJ6i7+PRzox9+5Y0QH6tomdd2PWTuOCBlhtoxE5jqyIrCTZscq0k3Gwm5D3nTkI1fbhoz9pZSxOegbKDJYKpuIUmR9MMohwfcDgsAQGYAgDNBoHEuSy5i0omPboHUyv9GgtCFzQ8l2TADD0Gi7HNser4PAhKB0zZxzctBorbCGVCaGUYPrMFRjbkcIQSbtIiLi2QriVN10/+zPaKfqZiHEtMkS2xgKPaQ1TVMI0OHE9ARtGBg9ioliuLZtOrzvBcQpHa1NJANRq/JqFRoYj89ffP+zLCsiMCpJofX/6n/ZeuNnKD5wn1mmp4e557+R+W99GzI9PJIghpCMuGWHUipKuRFVq5loTqwDEnXibVVXeBKneep13sbCSCRVgPEYSmkcyxr96iS08YzyqobMQEM7mKQ/mxCEscWBZSUkayiUUqA1PZ0FHHs4cZLCEEUhoOoHWFJMqZC84XsREeXJf3NF8vmHQS1KFwvEDWkC0ARBkLiU16ZhbDz6B0pRK5vhKb5RITWTbEgxDrSJUxszjPqn0zamF1auB5nuQFX6R15IhbjzD5y5Se2HCKPWEZnU9FoPjAexPQFRdZOwBaZ12HTOS6BUiGPXSMKeb/0nGz/wPqjTqIS7d7Ptps8w8Ntfs/pLXxtGnrQyqbc4SqO1BIbrj+Lfk750dftX7wel6oThKlo2rrKzpJVUb1U8j1K1Soc91MPJpMvqfw9UiCUNObWaeHTJSI4QELceGVnmHCqFY9sj9ho0ETaoVj1c255WeUOSuhvH12RotGjo3E0vv/hz1/hBQDaTScThSQo2iiQOHTsIQsrVFglTG1OGdqpulmJv31yeKRDSInfkqSMTVSEQqTyZVc+f2YntR1BKUa36UVpmdlxyhBC4rmNu2lEvtwmlYMZxmsZmilIayaxt2YS7drHxqvebBdQQXY5SlP70B3bc3tiTTUQRo3o9U0yiRtJYxe/HUTUTpRE1F++YKGkT6dbUnLN1FGGxLAsVanoHBukbLFKpekNE5HWkDCjk0qQjiwTPHymSZCor4yikGccQk6HXviAMRvxs4v2wpDQVjlMZqx8SXhpvpXOcRg3CMJpTI4QwxqQq8ojSGlQYJp9v/BkP/VwbBhA1S4Jck8jkaBBCIeQ0/8ywj9NMY3ZcxdpoRJszzSgKx74ad9ER0W/1FygLpM2cv3kvwp54b6hnOqqej2VNbQplquC6DlqD5/soPX7ylERyxpmqq0+hWbbNwPe/PboaWCl2fu3LDfOR0Q21FdT60jWuF1ee1YThkQllndicaM5zuzuZ291JV9407vWDYMjUa6V6sYYKIXAdJ7FIqEcYhoRBgNIhlhRIAdIyjcgN0avdlqSUlCs+nh8MIWs6aVyulI70asMjPZPB0LFMg+Rx3jJFVAHY5IGsRoR09DtYtj1uzV08ZpymzWZSoy3+jMWvf/1rPvjBD/LWt76VRx55BIDBwUHuueceent7JzV2O1U3CyFoZ+pmEsJymHv6Bymu+R+KD/2YoG8Lwk6ROfCF5I85E6d7yd6e4j6JWBuj0aScvZ+iawYhBCnXoVzx8P0Ax7GRjD/i28ouSSmRlnF8rDy+lrGekILt21ClEnYhn8w1xlg32PHNvy6lVz9mZEoppSFQhbqGuEl7kgbpj05IoJQSx7FRQDryV0oiSpEzemyOacwwjbg75bomysVwsmLbhqzEn0+SWgxDQOBHnkgpZ2qJufHKGiJGr9/nERAvF7UEHPZZKKXwfJ+061KtVEAIE4UMAhihz9/QeTVUD7Z4WokZ0DiJvejerLXmggsu4I477kg8ys4//3wOPfRQbNvmzDPP5H3vex/ve9/7JryNNnGalRC0w04zC2E55I88lfyRp+7tqew30JFztG1NXwPfqYAQgnTapVyporXGdZwxNU+t3BYagzNRlCGdGZt1SYmbzSRRDqV0EmUwbzdbv+YWPhaMlLLxWiOiTi2I2NVbR6m4+vWGWy0kqToR+0rZ2Faj/qpemG6aFQdYQhIEHrls1lhENDmwppWMIUiVqkcmcuIMQuNh5Dg2jm3mHYat+xqNdYwmvm7zlY0YXCfaJYFJ3VUq/rjOE61N2jRGOKGqupZWaRl7kzh9+tOf5o477uATn/gEZ5xxBocddljyXjqd5uyzz+bHP/7xpIhTO1U3SzGL7zNttDEmYndwKQSuM/uJkyUl2XTKWCaMR/OkTSn/RE/Uzlf8DYxW0WlZdL705Uh35EidJiZT9T9q3KyumSbKilJlsb4mVIq+gUEGi2U8zzel8k2Oi5DSVPVF48R6tngciCIlSjFYruAFRg5urA8MiQjDsOnhTFJ3QiSkyRwiw/JUqJKo09Cqw9kIjTaO6g0EtAWNXZQGjTFYrkz9JPdh3HrrrZx//vm8+93vZu7cucPeP+yww3jiiScmtY02cZqNmP3nfhttjIg4laCUmnF38MlASkkq5RKGJpUSBHFp+ChpmXGM22yZwgtPInP4EdBM9xWRiYWX/CPAFHkHTQxKafb0D9JfLCW91Ybuj4akB52Qw8XdYITtpi0MCZFONEki/nv0ozk0kiTjMUZZZlYiirgBWHUi8fFCCsGcrkLye9VrzSplf/dxeuKJJzjppJNGfH/u3Lns2rVrUtvYB75lzzwYjVObPbWx76LiebiuM2uq6MYLS0pSroOKvHJiQ8KhmHCqLoKQkpU3307msKgowbLBtqMqzhQr/v1GnCOPYk//IHv6B/EDoxVT0X9GfDy+0vjJwJKSuT2ddHXkcZxYZD5ko7rxejUsklVXHWZFppyu45jGwbaFbdsEYWhK8Ue47sXRsIbfo9YsrmMnXlH7wnVTSpH0aLQtG98fKrYfG0LICRdb7O/EKZ1OMzAwMOL7GzZsoLOzc1Lb2HceB9too41ZD601laqH69izsopuLMQ35IyU+H6AFxjX60CFOLadlPIPC3WMNuYIrztz5nLQN79H8b7f0ffzn6GrVdKHHEb3mWdjFzroL5bY0zcAQpCO/K903aCxtcDw1NnYNo3jjWBJKShkM6Muo7VKCLJWNSfz+unEom+BaHDMBnAdB8/zyWbScce8cc0txr5AluphTFijqkXLolKt4kTC9pEaLo8w0jTNcN/Gc57zHL7//e9z6aWXDnvP8zy+9rWvccIJJ0xqG23iNBuxj10I2mgDYjG40e3Y1r57aUl0Oq6D7dhUqh4CTTX0jBmjbSW9JMcl5h1jW/njX0D++BcMey/l2ORzGVKOg2NZI4wkRnh9jDnpmg9Q/UhDb8b1ZfOI5qJzpTSWPfFrlmVbeIPFaF665dTVvob674zxyDLVgbGVg2WN91hO8DhZGqb9mWbvfYbvfOc7Oeuss/jHf/xH/v7v/x6AUqnE/fffz/ve9z7WrVvHzTffPKlt7LtXt/0YQjB+v5A22pglCJUiCMIoRbfvk38hBJYQZNMpo3mKtE9WZLQ4nfuo0biuw7xMOoniTK0rtkYPISjNDB6FFA3L1arnaqJmpVVTv6LxQtQdy72p55pulCtVNDS458f7bSocTfSuQUEzwmGVAlKuQ1BuvVn1/l5Vd8YZZ3DNNdfwgQ98gJtuugmAs846K3n/Yx/7GC996UsntY02cWqjjTYmDaVrfbX2CYFuCxBRFZMlTfWWH4RRY9zp28/YtTvupagBW0qkMOkwovtrMzJlqu3iaFIzA8bmrw9fbvgyMamp/ztpRTIJCCFb1vnMFvhByIZN20inUyyeP2fEhMFTm7YThIpDVy1JNE5CGL2TEKaaUUdO7k+u38KWn9zNlu/9D72PPQUpl5Wnv5Tn/ePr8Ts72Lp9N0sWzSMI9m+H7oni8ssv5/TTT+fLX/4ya9asQSnFIYccwgUXXMCzn/3sSY/fJk5ttNHGpBD3zlJKk0nPbuuBiSK+wUkpsEJFsVwhUALHtht8iiY6drMoi0Y3kAkZtU1RKPyo6W4zqCZi9mHblEaoHVe2TTTKE5f/JzYAdWLwkdAQ6YodtqVs8Kja22jleIRhyNPbdpFTGnvzNpxclsKqpQwWK5QrFfK5LIVcmoHBEl4Q4vkBvQNF0ikHx3YYLPt4QYlcNo2UktAP+P27P86u3z5oQktKQ7nCY3f8gHXf+ikHfexSUoeswrat5DNsBft7xCnGUUcdxSc/+clpGbtNnGYlxmdg10YbswFKK3zfI5NO75ekqR5SShxh3LQrVY+q50cRg5gAtL7/ceXZsNcjItEsqiTq/x5jk03vYZqI4ECoxi90HzZM7HcV2QnIyABzpGiVBrSobS++0o1EHvcWNIxba1ratpONn7yF/t/+nvsi0ppbtpjO17ySwgufw0CxzEtOOAaAIAhYu/5p+vqLHLZ6GWvXPUm5WgWgqyPPMYet4qHPf51dv/uDGbyeZCpFUPV49MrrefEPbub3f15L32AJ27ZYteyAqdr1NsaBNnGahRBiJFfgNtqYXdBR2b7ruvuc9cBEYaqiLPLZDFXfJ4jMFx3LwrKsBtPH8Y1H02M3UsozJm+mLcroxzx2/o5+i7fYuH1EAxMTiIbKOC100gA4Xl7IiOioegfyRiuC+jkoZbRQIvovHqu+hctQsfpeRdS/byyUtu/i+2e9jdK2XQ3NmosbN1O87lYOUorgmMPY02ecvjdt28XTW3byoucdSblaZfvuXlYsWYhSmt29ffT2DfDQF785ch9DpVADRXb96j52LTuAk553FLlsi01+JTMQcZre8UfDW97yljGXEULwxS9+cczlRkKbOLXRRhsTRtX3J+Ups68j5TiEMsQPQoKo9YWUQy6r0xBJEUKMKyDSuFzzKNDQFFkc+YkJkcBqWrsnhDB3SN187HpIacbRUVPeYXOUctKFWHFvxDBUdf0+RRJZq9UNNh67oUQveX0ccf/7/u1WytsbSZOZjPnr8c98hZ5/uyI5xrYl6ejMsXHzDhbO7yYIQzzPtJFZvGAucrBoSNhosCwG1zyBXrqYIAjx/VEc6JtB6Ol3cNyLlZG33XbbmMu0idP+DN2qr0cbbcwM4puUChWpUdqCPBNgokwSPwjxgwAhxaw3Y6xPi5mmswFam8+yPooVp9diz6hh41DjhXH0aKSUm+EuQ6sRdfJe7EfVbIzEZTweKyJCWptIluf5hEGAtCRSWslKKtmETiba4FZem8Kw7YUqZHCwZOYrSATcsUVDpVhm7Td+gh5FU6aVovp/f4DnPwspBYsXzGX1skU88NBaursKLF88n+27ekm5LiuXLCCdSY04Vv1xzGQzdC6Yw71/XIMUgnNOfcmY6z1T0EwrF4YhTz75JNdeey0PP/wwP/3pTye1jTZxmsWY2vLjNtqYOsSCcMe222llDLFwbAvbspKebrO53YwGVBhGfk4RCdYal8aHNU0k5h7FW0gpxcDAIFprisUyIOOuMU0xPAIWfZccByEk1Wo1id41Xy9aS9eiYq7rJiahtd549Xs7xvEYZREdpe1U7DGlNaHSFHftIax6o44rLMkBtsUBC+eycF4PWoNtCV78vKOxIm+uuErTsgSWJeg6aAW9jz814qR0GLLiZScw5+AVHLT8gJbvEjOSqptlWXvLsjjooIO4+eabOfXUU7n88sv57Gc/O+HxZu+Z3casEku20UaMuIEvYNpktAHU0mKplEMQhFR9P+lJNutQp32yLQvHtihXPYIwxLVrn6lAIK3RNVtSCgqFXBQpUnR05JOy+mao10HJSCu1Y8fOyMNJkU6nyQ5xK48bBGutG8TnYbSNZoL0kVKUNd2XTkw963Va44HWmsKcbkZliNEm8nO6cF0bFSqTmvNDslFkqVgskstmCAJFpVImlUpx5CXn8utLP9F0OGFZdK1eyoIXPJvdfUVDglotJZJMf6puln7tAU477TQ++tGPtonT/oboVN7b02ijjaYIQ0UYKjJpd29PZVZCCIHj2Egl8T3TLFhIsTfNlIdh6ENZYxqqhvFEE7U234lYLxWGalQSkgjCpQBl2shYlj1mhF1KWQszYVy2TfubMafYAKU1KlSJ8/vQPnjjhZvLsOwlz2Pj/943YrpOhyEHnn0ypVKFiucRhpqde/rRSiEtSXdHnmxOoFRIKpUinU5z5PlnUN64ld9ffzvCstBhmBiR5g9YwOlf+zcGyxWToqwEeJ7P0sUtT/8Zi1KpRF9f36TGaBOnWYo2bWpjNkJrTcXzSD+DqugmCktKhOtg2RI/CLClFZkczg4GFc9DRVVtuXQ6IkoTv/rE5GmY4LqJulwrTYghUK7rjK2hj9KKIrJS0HXu50Kb7+K4okZaN1T1TQbHXfoWnr7nfnSzyJMUHHjmy+k5ZBWVqodjO+zp7eXRJzeycG43pUqV9Ru38sLjjiCbThFGWjPXsXneZRez4tQX85cvf5fetetJdeZYfcbLWH3GywktiQgUmVSKR9dtpFiqtjTnZ2KqLsYDDzzAv//7v3PUUUdNapz9ljhVq1VSqbGFdrMWs1hY2sYzE3EDX8e2saxZemWcZYijGSnHIQiN8/jerkCsJwu9g0XQkE2nGFoMOBEopfHDANd2Gl6vtysYVsWndFRVNzqJUcposmLPKyklWqmWe7wJKaM6QWCc/QZHwoJjj+BVX/0UP3/7hyjv6kXYpnIQDYe89lRecu17EVGT5pSr2b7LpEYPXL6YXb39PLZ+M34Q8tTmHTz65EYA5nR1cPhBy9hk2VRPfxl2EHLQ6iUsmj+H+9c8wY5dfaRch2MOWw3Ajl29Lc1ZWOZnOrE37QhWrVrV9PXdu3czMDCA4zjcfvvtk9pGS1e/xx57jKuvvpoXvvCFLFy4kFwux+GHH8473vEOtmzZ0nSdxx9/nAsvvJAlS5aQSqVYsGABp5xyCr///e9bmuhdd93FS1/6Urq6ushkMhx44IG8+c1vbjrHE088kWw2y9y5c7nuuuuGLbNixQqEEBx99NFN8/BXX301QgjuvvvuluY4VagXZrbRxmxA3MBXa3DsZ6b1wESRNA12bBzbJlDhsPf2wqwQUpBLp0mnHEKlKJZKBC24dw9tyKu1puL7lCrDBdPGAqD5fmodR45Gv+JZlpW4t6vIdFNKmfyMFwKTgjQ/ctKfwbKXHM8Ff/w+f/PFj/Hcd1/ECz/0Dt543128/N8/gJUy6ex6XVf/YIn7//woDz/2FOm0i1KKv6xdR3dnnkNWLWXT1p30DhRZMLebww5czpzuDp54agubt++mb6DIic87muccdXCik2pmnvpMxrJly1i+fHnDz4oVKzj55JN53/vex9q1azn55JMntY2WnjG++MUvcsMNN3Daaafx2te+lmw2y7333suNN97I1772NX7zm99w6KGHJsv/8pe/5IwzzmDx4sVcfPHFLF26lN7eXh588MERiVYzvP3tb+fGG2/kjDPO4CMf+QiZTIbNmzfz61//etiy55xzDkuWLOHb3/4269ev5wMf+AAHHnggZ5555rBlH3roIW6//XYuvPDCVg7DjME09myn7drY+1BK4QchKcdpp+gmgPimadsW0pL4fmCEznshcieEqd5CmUaxOroNeJ6MRNaMaeI5lDSFSlEqVxgsllFIuvK5xuWbNBVufJ9RedOwuSiNFtQ5lrdiODr1V1TLsVl92kuGva61TvRf8e/dHTlOet7R9A0Wue+Pj7Bzdz+eH1AsVRhIlVg0v4cwVDy6biOFfJZyxSOIdIWObeNYEm3V9rmnq9DaZKUwP9OJvXiJmImAR0vE6TWveQ2XXXYZ3d3dyWsXX3wxxx9/PJdccglXXnkl3/jGNwDYtWsX5557Ls997nP5yU9+QjrdmrtpjK985SvceOON3HTTTVxyySWjLrt7924eeugh7rnnHrq6ugB48skn+fnPfz6MOC1cuBDHcbjyyis577zzyGQyTUZso4024io6S8p2im4KIKPoU2zWGEfxZjryJIVA16W30mkZFaboEaMw9U19Yz8jIcAPAgbLFVKOjWNbFMtVMiknKrkfYnHQJCUnxyhOU1FFnZASGVUvaiLNE8xqaUMYhliWhYra0/QXS9z/0KNUPR8NdBayrDhgPtt39TFQLJFNp8llUhQrVRzXJgxDhBDM7epg3cYt3PvHNVhScsiqpQCzt3JzP0ZLR/y4445rIE0xzj//fAD+/Oc/J6/9x3/8Bzt27OC6664jnU5TqVSoVlsTsQF85CMf4eijj05I08DAwIhlrt3d3cybN4+PfOQjPPHEE/zyl7/kO9/5DocddtiwZVOpFB/+8Id5+umnuf7661ueVxttPFMQe/ykXGdWmzruS4jbtriODVpT9byEHMzkHGTdjyVlQ/qqGeIIShiG0U/NDqCQy1DIZcllUgyWygR1lWYCsCxDvEeKWIajpAmVMhGuWtQqagsTt3CZJYL7kVD1PBSKeT2dnPCsw1l+wEIOXb2MFz33SObP7eLwA5fx7MNXs+yA+axesYiezgIvOPZwVi5ZyDGHr+a4ow5ibk8HL3zOERy4bDEHrTiArkKeAxbMY/WyRS3NJRaHT/dPq6hPZw79+ctf/tL6gNOIKRGHb9q0CYD58+cnr/3oRz+iUCjgeR7Pf/7zuffeewHTsfiqq67inHPOGXPctWvX8thjj/H2t7+dT37yk1x33XVs3bqVdDrNqaeeynXXXceKFSuS5YUQ3H777bzhDW9ItE3nnnsub33rW5uOf8EFF3DdddfxiU98gosvvpg5c+ZM9BBMD2Jb3vbNqo29hDBUVKs+uez+38B3phHfFBxHoJTG83ysun53sxFD6UlMWCwpSbsuoefhOg5dHfYQLZyoNfJttmtjfLdklEIc+lor3RW01lQff5DB334fVerHXbyawkvOw+qcO+o6jdNs7XNRWlOuepSrVRzbwZKCRfO7iQ9CuVREhSG+57FgbncdWYCezjy6I4cdHUchBNlMiqrnU/U8duzpNWnVVs/LWZyqe9GLXsTFF1887PWlS5eOuM5IYvDRIITgiSeeaHm9GFNCnK688kqgsbnemjVrCMOQl7/85Zx11llceumlbNu2jWuuuYbXvOY1fPWrX+X1r3/9qOOuWbMGgG9+85uUy2Xe//73c/DBB3P33Xfzmc98hv/7v//jj3/8I/PmzUvWOfXUU3nqqad4+OGHmT9/PitXrhxxfCkln/jEJ3jVq17FRz7ykVkXedItCDXbaGOqoZSJhKRcZ+yF25gwYs2REA5+EKKCYNY6stfPyPSqI9Fu6UisbVsWvu9RUYp0ysW2pKmao14I3hqGHot624S4LQyjEChVHmDrp95C+Y+/ACu67SnFrq99hLkX/SudfzNc5xqnJcMostWMvI0FAWTSKdIpl4Fiid6BCsVyxTiI2xZSKzKRWagQNKTChSBK79X2X2tN1feo+n5rE9lHsGrVKt7whje0tM6yZctm/KFu0sTpox/9KHfddRdnnnkmb3rTm5LXBwYGCMOQ8847j6997WvJ6yeffDJHHnkkl112Ga973etG3eGBgQEAtm/fzk9/+lNOOeUUAP72b/+Wjo4OPvrRj/LpT3+aj33sYw3r5fN5jj/++HHN/9RTT+XlL385N910E+94xzsmxF6nI0xc3/dpeN+m0XtC7U9o7+veg+d7aEBatZveVGC27ed0opV9jdu2+H6A7/s4jj3ronyq/rq0ZzNhuYjKz4VctmGZ3oFBLEuSdmvFBLE55ojXM6UgikqNdbwS9+/IH0ppNaqJ5dbrL6H00D0RG6lr56JCdtz8HqzO+eSOf9XwbZDQM6Dmtj7ezzWupgzCMImMlStVbMtYetTGadaPz1QPhipE1PsH6Mmdi6ap8vR+rybztfV9n0qlQqEwPtH73qh+nxRxuv766/ngBz/ISSedxJ133tlwkmcyGQYHBxuiUACHHHIIL3jBC7jnnnt49NFHG6rwhiIWbC9evDghTTHe8pa38NGPfpRf/OIXk9kFAK699lqOO+44rrjiCr7+9a+3vH61Wp2Qfms0lCtVwjBEKTXsacv0hCoC01MhMpvQ3teZ23a83fgp2w9M+43Am9rvdvszHXsdzzd+T7NFjK9ioqI15b/8msGff4VgS5TqcFJkjj2F/CveRAUbPwioViukXYdSuUSlEjfjhcAPKFXM98myJK5tUpN+EBAGQRS9svF9b9TjFSrT3kUKGUVm1IjaLG/LOvasewS6ljQfTEg2//QrLDjsxKZv158b9a8Vi8WESA1t2RIqTRAEyX4qpenrL5JOuWRzJu0tEJRKVQYGBJVKJdJ/Nc4/DBVKq6RptO+HVCtlqt4kIk6zOFV311138dWvfpUwDOns7OT000/nox/9aIMkZzZgwsTpuuuu49JLL+UlL3kJP/zhD8lmsw3vL126lDVr1rBo0XDhWvza7t27R91GnNeczBjjwbHHHsv555/PnXfeybvf/e6W10+lUlNutmnZZXzfp1DID3uSik/kjo6OZ8SNB9r7Ot1QSkUmguYpuFiuks/Z0xL1aH+mY0PVuUjPBvuHMFSmme//fJnyrZdhCYmla1IC9dMnKP7hJ6TffQdCFjhg4XxTOh/1MlRKUypXkJaF7TqUKj6ubeO6NumUSxCE+F41IU7ZbGYM4mQE6rZl3NjDMByROO3+6c9I9T7dGGkail3ryHgD2HMWj1svBVAoFJKoVP3nFIQhBO8StQAAoi5JREFUlUoVNFi28Z/yQk02naKzkEuMQKUUZDIZpBR0dBSGfdYmNecb01lpHOhLfoCWU/swM13o7+9v+H20e+Vxxx3HOeecw8EHH0y1WuVXv/oVN998Mz/5yU+GWR3tbUyIOF177bVcdtllvOIVr+B73/te01L+E044gTVr1rBx40aOPPLIhvc2bNgAwIIFC0bdzlFHHUU2m2Xjxo3D3hvvGOPFNddcw1133cV73vMeXvziF7e07nRc/IdWFYz2/v6O9r5OP7SGiudFlVUSx7ZIpaavF137Mx0dUkocxyYIFe4oVW4zBSEEamAXvbddgdAa9BASEgaE29ez52dfYdHfXUo201hMIKVx7FZBiOu6CCFxHTtJgzUco3EcLykEWsokJyRGM7KsFo3NwhjpraBcxGL8YutkexFziqO1ccpNSpl4XZlzyqZc9SjkspGmrfnP0G24tkn12ZaV/NQv12qfPSFnIFUXTWmoqPuqq67i6quvbrrO/fff3/D7+eefz6te9SrOOOMM/vmf/5n/+q//Gvf2161bx3XXXcf//d//sXv37mGV+JMVh7f8KPPxj3+cyy67jNNOO40f/OAHI/ofxXqnz372sw352AceeIB7772XQw89lNWrVyev79y5k0ceeaSh+V4mk+Hv/u7v2L59e+IPFeMzn/kMAKeffnqru9AUK1as4O1vfzt33303P/7xj6dkzMlCa922D29jRmBZkpTr4Pk+fhCQnkbS1MbYEJE9gBCCIAj3qh7MbFtTvOebMFrBigrRf/45oT/cOdzoe8xPEASESjFYqjJYriaWBrWb2/j0YJa0Ir3P6Jofd+mhEI6e2hJuBtmzcMztNkej1inWMyWpVmFeAyiWTbPfVmD6G9bSkY5tJyJ9IQQdQ8xGxx5QzMwPsHHjRvr6+pKfyy+/vKWpnn766TznOc/h5z//OZVKZVzrPPzwwzz72c/mC1/4ApVKhSeffJJsNku5XGb9+vVYlsWyZctaO2ZD0BJxuvHGG7niiitYsGABr371q/nmN7/JV7/61eTnu9/9brLsi1/8Yi666CJ+/OMfc8opp/C5z32OK6+8kpNPPhnHcbjxxhsbxv7sZz/LYYcdxne+852G1z/2sY+xZMkS3vjGN/Iv//Iv3HTTTZx77rl89rOf5VnPehb/9E//NPG9H4IPfOADdHV1DWO+ew1j2enOEsSNPZ8JYt/9FXGKRERl5W3sfcReT0qrvX5+aY3RNI0V3aiWkKW+aJ1GAbUUEsdxzVhBSLlSBa2xImIQR2nGE12LS/Z1RFrEKP0Vcs8/C5ntGFmxLC3yLz0fO5Mfc7vNYKRfGi/wCZVKmhybhxHXVBFqjW1F5p0T6AVh21ZCoA0ZiyJtCBx79rac7ejoaPiZiKRl5cqVBEEwblnOVVddhW3b/PGPf0w00DfccANbtmzhxhtvpLe3l5tuuqnledSjpSN+3333AbBt2zYuuuiiYe8vX76cs88+O/n985//PEcddRS33HILl156Kel0mhNPPJGrrrqK5z73uePa5qJFi7j33nu58sorufPOO9m9ezeLFy/mXe96F1ddddUwbdVk0NPTw+WXX85ll102ZWNOBmofIiLlcoVU2sW2Zu9J3Mbo8P2AlG1j7wUX6zaaQ0pzY/SDAGcvpjalJZG5znEsKZDpLMViyaTPILEiEAJsSyCFg+tYZDMp7JiI6TBJVWmlCcNmeiQR/58gjlIJIRC6OamTqQzz33ETW6+9wKxcr3WSFs7ClfScd3nLKa+EyNa12fN8HxWGIIy2icic07TYAbtO9zVexEalQUSeq55f88+KNIktYRaLw5th7dq1OI4zbp/FX/3qV/z93/89hx12GLt27QJqn9Ull1zCr371Ky6//PJhQZpWIHQ7TDBp+P7UC/WKpTKVSpWuzkLStiCG1pr+/v5ZI67VWlMqlbFdm5QztdGK2bav04m9va/VqocQEseZXuK0t/dzJjFV++oHAUrpveqpVXn8QTZd9ooR39eWjT7+1Sx5+/WJnkhDogPSdZEzUwmXLIGOhOa2bVP1/KRn3rBjJswfkQsBEBth6ki3U9cKZkhkp/rYAwx853qqf/qleT9TIPeyN1A465+Qua66gFRM0GpEteZZReRUDn39/eTzuajlidE2hUpRrVYi8XdUjSclWikqVZ9qELJ4Xg+OY6OUoq+vn0wmQ7FYpLu7a2RX9VBRqVaxbZtdvf0MlsoApFMuuUyahXVehiOhv7+fzs5O7j89T96Z3vNu0Nc894eD9PX10dHRMeby27Zta6pX/spXvsIFF1zAGWecwfe///1xbTuVSnHTTTfxlre8hYGBATo7O/ne977HGWecAcBNN93EBz/4QXbu3NnaTtWhHR6YzWhz2jZmENEtbG9Po40msC0LX4cEQZg4Sc800gceS+bZL6f8p7ubVKiZG3H+pa9PtFnJO9LotUKlTNpKmP0BQzSkkPi+z+DgoDHQtC3SqVTUoqXW6y7+ZurI9EhFf8cNFtCm75/GaEM1quES6qw+lp53fxlVKaKrJUOWbENE48hVbSuN5KwGnSgoPK9KtWJHIvBa5EsrDdL8rQGJikiUxpGCqucRhIHxn4q3G1mAJNseQhqFwDjKR95OMSwp0S36JAspZ0Ac3tp15OMf/zg/+9nPOP3001m+fDme5/HrX/+ab33rWyxatKglc+r58+cnkaZCoUAmk2HdunXJ+6VSadL2QW3iNIuh607jNtqYbtRlHNqYZYj1TkEQoJTYaxYF3e/8POoz/0D19/8N0jLlU6GPzBaY+483Eqw8crhOKfqnqYQTDVe1+oa9yeJCROk90fha7RcAJkwfs80LmnRdlCz+I3ktIWQRUVNG/O1G/RvrCZoV6dKICG4YKJAKy7KRlq4ZgsbkL9q2HwSEwqQ346hcbW4kvfnqfaXSKRcpWjwSszBV97KXvYxHH32UO+64g507d6K1ZsWKFbzrXe/isssua2jnNhaOOOKIhr65z3/+87nppps466yzUErxhS98gcMPP7y1CQ5BmzjNYuh97k62f6dd2mhjb0IIozUKQoUd6V5mGtrN0PUvXyR4+lEqD/wXYXGA3Oqj6XjBWQg3TX9/P1KOZqESk4BI1C0FKlTUX+gERlg9kb0br0P7aK+Lxj9G3I7jOJG1gmh4PRaIA/hBiGVLXEtS7h8kDDWu42BZJgLnVavmc02KMprHuZRSVCoBUiqy6TQq0oG5jtOyNms24swzz+TMM8+ckrHOOussPvWpT1Eul8lkMklRWtwVRAjB9773vUlto02c2pgS2LaFPUtcjttoY39EbFGglEnZOXtRxG8vOYT8kkMIqj65fA5p24Y0hLU2JSPNLQxVwkkMP6mrNIu1RKOsPxJiD6VQ1dqb1I+hlDJNlKf5mNV7MmXT5hYbBgH6of+l+uAv2eFKsoc9j8wLzxzidRVfP5vPz7JtEIKUaxOGLsVyBSlEy6nbmfRxmin8/Oc/5+UvfzkAb3vb23jb296WvHfSSSfxu9/9jjvuuAPLsnj1q1/NCSecMKnttYnTLMW+pJkVQky5c3obbbQxHEk/uyBIiMDenVDjr0EQUCpXyGUyaPSIBEggENKQhSAITWprCqBp9FSKNVUCMUx7NR0QQmBFBDeGv3U9Gz9wDsGmx8GyGQAGfvIl5Bcup/NfvkDmOS8d59hQqfrs6u0nVGHkKj8BcwMhpp/ZiJltUP+KV7yC1atXc9FFF/HmN7+ZhQsbPbme85zn8JznPGfKttcOEcxW7FMpujb2F+zvVW77A2p6p8ndnJQ26Z5WCqtlXaWZJa2GyjU/CKh4Prt6B+gdGIxScM3nH/0r+V1GN3JZ5749FGP5xcVl+zJyv7ek6YNnRUaUUsoZT2+qapmNl51OsCUSJ4eB+QFUsZ89//pGgs3jc7COj3W9xkkO0Yw9U/HmN7+ZrVu3csUVV7Bs2TJe/epX85Of/GTavM/axGmW4pnSjqKN2Yf29272Q0QpGq/O06dVGCLSnKSMuN2YlFiWSfdIgef5Ubm8T9Xz6chlIhG1HvG7NFAq0TcwyJ7+AUqVatIQt252w+ZarXoMFMuJ0/hIkEImNXGiLu03Hd/rWNNU/xPbEAAM3PNtgu0bm/fJ0wrCgP4f3jzmNlSUekynHBzHRBnNPsqG7Y0LM+gcPlO49dZb2bJlC//xH//Bs571LL773e8mFXof+tCHkhZtU4U2cZqtaN+82mijjREQRxuENGLxiZCnWrSolXX1sG35fsBAqUzadZjb3UFHPott24nlwAjDkE655DMZcpkUjmNsAUYiN1pD1Q8S7dIow6JUSKhCgtD8TKfrehy1q/+pP56Dv/7+6GkxFVL89dhGjLGrerw/MQTGhb0NyOfzXHzxxdx33338+c9/5u1vfzulUokPfehDrF69mtNOO43vfve7I5irtoY2cZqlEKLNndpoo42RYcTixjxyproMxBVj9Sk+LwjwPD9Kh4koGlbTE/l+QKVa6xUnpcBxbFIpF8exI/uCsbdtIl2SoQ1bh0JImaT+4mjNTFri1W9KlQYYy2hJV8pjjxkRRt8PCeKIm/H7pFJtrfedEDLycprGn5lWhw/BkUceyQ033MDmzZv52te+xkknncR//dd/cc4557BkyZKWe+YNRZs4zVKI+Kxoo42ZQttwdZ9DrOVpRatkqt9UHQGZ2HUm3qZlmcaztWbENc1S3VaTfwkhyGXSTfVGxvR7+H5IKchn09i2TTgaEYn8jpRWCXGLmxTvDaRWHmH8rkaCkDjLDh5znNjXSQztdac1YTtVNyJc1+X888/n5z//OY8//jivfe1r2bZtG9dee+2kxm0Tp9mK2fG9a+MZhzZ52tcgpSEqLQm9o+cyEYmLxxuxanZZsqSkkMtEvdlMZVul6lGuVKlUPYJQtaAvGilVZ+aXcmzcUZraCmFE61YsDo80WZMN3w9tWFz/78bZi8SkUmtNxyvf1FzflAys6Dzt75MxR96+Gb3eI0sQfX5TVJG4v8LzPO644w4uuugivvnNbwIMq7prFW3i1EYbbbSxD0MIYxipIrH3eJaXUcm8FVWajZdWOJaNYzu40Y8tLZTWFMuVRGsTjxUTJsexcMfZ2Nb0eDMC6/p2JFprgiDSLI1AMGrVZiTpPxnt42RoU7z9sC5FGAvr0SSVe1JKLNtKOJpSGnv5YXS//n3Rzg253QpB+rhXkH/puYa8jpKCjI0y66NnIiLMLT/qSDkzP3sZf/7zn3nHO97BokWLeOMb38g999zDqaeeyne+851Ji8XbPk6zGe2oUxtttDEOGH8nGz8IEsIw1vITgW3bDTeNwPfJ2Clsy0q0RFJKujryExofIAhDPN9HIMhkUgRhSLliGtwGQYiUgpTrYFvDDUDDOiG4IRV6SvyblNIorRK7hPh3pRS2ZUfLqIaIlFKmsrDrvPeQXnowu795PdUnTCsQe+4BZE99C4Uz/h/StiO39JFTelrXCLKUglCZ/VN6AsL3GTDA3FupuoGBAe644w5uueUWHnzwQbTWLF26lHe84x1cdNFFLFmyZEq20yZOsxSihafANtpoow0gaaY7k3YmUkgcxziHVyZ5wxTCtJkulatowI9IU6lSJeU6pFMuOjARoHyTnnP1KTSFMs1+p6DQRggQWiTErNbXri5lN6Rpbz0KLz6HwovPIezfjQp8yPdQqpQR4zQwjecv6z7X2MvLH2bl8MzDb37zG2655RbuuusuSqUSlmVx1lln8da3vpVXvvKVU34utInTLIWo+7ONNtpoYyyIyPxRRSXrw5rtThOmWhUXKk0m7eAHQRTBEmRSLhoolirks+mEYA3dOxFFhOLHTiHFlFQnCyERonl0p6G8XQ+P5tX/bnX0IKO0YyuRIiFEpGEDIhNSrTVVz08iXuPGTKTSZjhT96IXvQiAVatWcdFFF3HhhRdOWsc0GtrEqY022mhjP0HNVTwEoZFMt6GpQIiacHok0fRQaIA6T6Z6N2xjsaCxLUk6ZZNJO4RK4dg2nucnhLDZXiVpOqvmEj7Z/TckrRZhGt/OResimjbhlZGT+XihtI58qQK0VpGpp0nVOS32qtsfidNrX/taLr744qRf3XSjTZxmLabmSamNNtp4ZkEIgWUbiwI53b3shEApY28Q63rCIVV0WmujqdE1YqMil20nqpCrbx/jWBbZdKrmmA2kXZdQKbwgxJKKbLp5b0xDUkRSuj9lu4mJfGklxkUM6yNeTd8faitAzaupGbRSpoGy0kljZMe2SLk2Qbu5Ov/5n/85o9trE6dZCiHbDphttNHGxCCFQAlBEIQ446xomzA0wyJO9dEUo7ciqYZTSuEHIeWqRy5Dg8O4MKEUpBRYlk25WqXiBZQqHkEY4AchPR0FglAhlW7Yt+nUdcVjj9d1Oo5MjaRUjb2mrPGGZoTAtiSlMDLAFAIpzTFtJXJlhpp+/dv+3rapTVXbaKONNvZDxCmicAyn7cmiWfpqKJEyPfHMckGo8PwQPwiN1qdhzUjLE/2WchxSrk256lH1fBzbwnXtSDDu4fk+pXKFSrU65W1Vhs4bar36Rm/7Mo5U5ZCU5lhkJiZglaqXROHCULOrt7/dcmUvoB1xaqONNtrYTyEtiQpVQzXWlKMJYRmNxDi2hRQC2xakHLchJiOEQCtNsVjBti3SaTfydgopZDM4jp1YL5SqVVTJRK4K2Qwpd+p3LVRhklaTsk6vNMLuxQQnJk/N3NGV1khR3xZmdPsIk+qEQCn8ugbHSpvIXasRp/1R4zTTaBOnWYq4q7fx6ZiZ6pg22mhj/4EQAgloKQjCsKnv0aS3gRifYHrIvKxoLjLSAMV/E5k8eoGPFwYgjFt4Rz7bqAESJholpSCXzbQukG4BKrJ30KqOOI2goaoXu9dHppSqtUwhei/eZ6Ua0201Lyjzux8EWFHlXOMmBa5tt4nTXkCbOM1SxE8klWo1EVC20UYbbbQC4xIuCVQwqvh44uObP+Lmu/WCbisiB3GEZTjqyU6tqs5xbPL5HFLKyPgRCrlsw5q5THpK9yOZRdIQuNaGJjlmOja5bKwCjI02RWQTUN8nLx4zCEPjMC6GG10OXR5ocE73/QDbtilVKnh+EL2vGCyVsaSJxrUxs9jPeeG+CyEEqbRL4Ad4Xmvdr9too402YkhpLAriyrepRuwf5Qch/YMl/CAgDEMqnk8YKnr7B9Fo01NvhJ9krPo/TcffYTojqOmPwqg9y1SgpseqI0e6UY8URqLu2LHbEEWV/F3/es1hXDcaZQ7brmpaCKRCH+VXCIMAKQRVr2Z0qQHP95umAsfEM6jJ73ShHcqYxXAdhzCTplypIi1rWkLtbbTRxv4PGTmKK6WxrKm9huiIuDi2RT6bIZNyEVJSqXiUKlVDjMZN2ETSuiUZX2uUJvGkUlqjIq2PEGJKHv/jSJPWRhcmAImFhqR58mikM05ZDk1dmjYtEktYCckJwrCB8MTELxZ5l//yK/p/+FkqD90DaOSCVXSe+lb0s88ctl0TlWvx8zQ9eVpbp1WI/btZeDviNIshhCDtGnFkuVQZtQlkG2200cZosC2LULXmWN0KpJS4rtHcSGH6yWXSLtlUatw6HMMn6hrZxv3Z6o0yY5PLqOfaZGlgTJrCyHE9JoL126rNTwxLOzak5ZrovYy1Qk3vNDRKpGMNldb0//zLbP/4a6k8/GviY6C2rWPPbVdQ/uI/w5C0nFmvfV+YabQjTrMcUkpy2QwDg0UqlSrZJv2Z2mijjTbGA8uypk8oHlXuxT+2bQEWOK2N0eC8HXlRxdVtaPCDECEgnXIjB+3J74eO0mlgyI9SUUVdXQRJyJqeCdW4z/Uap9igsr79SoNDeiQQN6JwE4FKpVOo3ZvY86X3mYEbCFJ0QP78M8TBL0Q/96y6d8bn1F4P0z5memMmYj+POLWJ0z4Ay7JIp1NUylVc1xm1i3YbbbTRRjPEVXYKIy4e6zqilDK6qBGq5mIN0nQjVArPC0zT3yDEkoJsJk0Y9eOzpqQZXe2fcRUdNEaQtNaEujFiF0egFLXUYRxdi3vp1ZOm2FNLhSoxrwyDAMfO0ffLryWEq/kcBeK3dzYQJ9u2WzdKngkNUlvj1MbehhCClOvi+wHFYplCIbe3p9RGG23sgzC94IzeSSjdIMxuuqwlR76PAyHTT54c2+g7Naadi6yLajWk8IbMfbzQMEwG0Ww84yfVuJyI2rvE6T2BSEjYUNIUpwEtyyJQAUIIXNelWCwhhcB76i/DUnFDJgVbHgMgk0pjWRLXspPqvzZmDm3itI9ASkkul2FgoEi5XJk2nUIbbbSxfyM2dFRaIUbxiBtPaw4hBTqc6mtR43j1+qihDXPr5xcECj/wcRynJV+nmODEOiP0yO7f9anE+jSeFLKhP17t8txoAWG0TtE+/v/2zjvAjrLc/5/3nZnTt2XTCykk1NBBOgFBpBsgXspF6qWoF7wqiKACCsgPFC4CchWFgIggTVBpUgIoEDrSQhISAgmQusmW06a9vz+m7Dm7Z/tusrvM594j2XNm3nln5pyZZ57yffzcqmD/hJEAIaGznCXdi3u6yiGu6SDAsXuY47RRdJyG9/0pSg4fQmhSI5lIUDRNbNvueoWIiIiINgT5R5XK/HtDb0doLf0vH8GrYqu8Tts8qlIs2wvntTWuupxHm7l01EIG1WosBf/XOkbrX54EQfsQp/TbtQTjCQG5XA7DMBBCkN7lq50aTUpqqG0OQAiBZTterzspyRULPdrfiL4TGU5DjFjMwDAM8oUilhUZTxEREb1D1ySO0/eKrO4YX0opTMvCtCws28aybWzfQLIdp1/6rUkpMHSvcnAgwldCeB6jQBG8NFQIJWlSIvDqla4rysJ2lu2g6Qa2bYeNitN7fg195ESQFbxlwbqzTiaTSjCqrobaqjRCtg8fdkngcRro1zBmeO/dMCWVTCClpGAWKz6xRURERHSFp4nkGS69R3ZLo8l1FY7jtxoRMvS+SL9STfRDMrFCkcsXvfYmHS2jVK+NqmAtTUp0TUProNlvqzCmKvu79H3btkgk4mXhPWHEGfej+9Hrx3sDSc0L3QkBegxOugYmbI2hGyQTcQzDwLYd7J4av95GB/41jIlynIYYQdVGzDCwTBtTt4gPRHfLiIiIYY+uSyzbRu91pa7qwU1SeZ4lX2TScbybvlecV1kZvCdIIUjEY14ieQeCla5SXiJ3adJ7G3XwTqbvTVO0Kk153Vm8XCUXhQjbsihs2w0No1KPkxtoafmDBQroAHL0FMb/8kXyrz9G9o0nccwi8Wk7kNj766x3dIqm6bWxcV10TWJadqTvtwmIDKchiFeNYWDEYmRzeTRNYhg9EEuJiIj4wiOEQPi5O4HCdU+Ronuq4FJ61yzXcT3RSiHQNUkcgaNcbLM87cCr+uuJQeB5s5SrcDrxoAVil25gKCmFkLLMkBFetjiEGkmtieNKubguoFzfi+aUJYR7ukrCX0f5+k4lThjfSBMIisWiJzEgBK7j+rlWgNCI7XoEcseveg2BNR3btlHNLSil0KT38Gw7LqZl9eAYeQgpEQMcShPDPDk8MpyGMIl4HMdxyReK6Lre74J2ERERw5vAg23bLjGj5zfTntweW0v2CSUFwGttgl5uJOma1qPKuG7PoW3j3jbvlzbwDcQlw0o6QagbpZRE1yRGm+uuUgpXel4tKbVwLCmkLwgKdt7BMAxc18XQNWIxo+K1u6BcXNclZui4ykG5ikwy6QlY4oVYi2Yv8lyjqro+E+U4DWGEECQTcSzLJl+IKisiIiJ6RlkZfq/W7/k6HRT6d7stS1/oSGIhqKxrXc6roAtysmjzeWdIX/8qyOMSQpTlcDmu6wlXdjKfUjRNoms6mq5RnUmF63kJ9lGB0KYg8jgNcTRNI51Oks8V0GTHTy8RERERldCk8FuMuD0u5Q/0tXu2Tse6UZuMElXvQOcqEAcN85+6Mb22gpfhWKXVd6ojlajOx9WERNM18JPBe+Vtgsjj1A9EhtMwIBaLYTsO+UIBTZNoA9CHKiIiYnjieT38Niy9uKH2NIm7IzZpdbAoN5KUn9cUCKMrlNenzs+Ras1/6mzI1r51yiyghETG4khNw7HtbnvrlFJkc3lsxyGXLwKCmKFj9laORkjvNZBEveoiBjPBjzMRi9Fi2uTyBTLpVGQ4RUREdIswXBQkLvfk2hEYFz1AoVCWSfb1x7E+W4JIZojvdDBurKZnA/UjUgiQskzxm9IGv34pndPNhPWgtU3j43fRcO9NmB+9D0Bi2y9Rdcy30Hc9yGtno8C1LXKvPkbzc/fibFiNPmozqg/8T8SWe4ZVeYZhUFOlYxhaqJFpRSLIm4zIcBom6LpOKp2kpSWHaVkk4vFNPaWIiIghgq5pmJaF6qQFSyV6sqwX+hLk3vgHa/7vPNyW9aDp4Lrwh59g7DmHum/9L1AqRxAkahNWvQXq3oH6djuxbeEbQqVeIzqz7/xKOgT5QoFX31mE4zhIKRk1opYtp01olwemUKHyekdaTiuv/Q6Nf7+9zLtTWPAahctPpfqkC6k54bsUmxpYd/3pWB++7uk2uQ7FJW+RffEvxHY6mOpv38z7S1awvrEZKSQ1VWmmT56AYWhIIUklEj3XpfKy83u2Tk8Z5s/tkeE0jDB0nUQiRiFfxND1LrufR0REREDQg02ESdI9MYhsy6GxsdkLKWVzKNU24Tm4sQusxa/QeO0pEIT3SpKbrZfuZ42hET/xSrK5XFnYMBxPtDbSFYFBomj1fLlB8xMVVvHRZiqVQmzBW5bjsnrdBraYOomEpvj4teepapjEuO13pano0pzNUZVOETN0GjY0k0gkQUiaWrIYuka+aJJKJGh47iHsv9/uD15i2fkeq6Y/Xg3bz0LNuwVr6Vv+Z07Zf823nqTlgV+wYvIRjB09gpqqDEs++RQEbDt9MomYQXMuTzwW6fhtbCLDaRjhVdklME2blmyOqkx6o1SqREREDH103ROllHrPbguarlGVSfsGiUt1daZE7NHFcdywP95nj9zor1XBS6IULc/dA/ufTvXUrYnFYmGCdn+hlPJbvZRW0JW0QskWEI5N7KnfYLx8LzMKzTjA8niaddscSnbWGeQsxbjRI1i7dh2xeIJ4PMZ7i5cxfkw97y5cRnVVmpEP/Y6UkIgOes8pqfHpnddQ99mLiI5SxZWi8PQfECd9Gdetw7ZtHMclZhg0NLbw6tsLqavJ0NySZ+qk8d0/CBslObzv47uuy1577cXLL7/MrFmzePbZZ/s+r34iMpyGIZl0kuaWLMWi6cv6D3O/aURERJ/RNA3Lcnp+VyjxULVVyS7922laS+H9FzofS2pYbzyKmLZNKJTZ31QaM/Bi4bpMeeaXxJe/WmbQiGKW+rceYKKzjrVzrmLthhaq0ik++Xw11Zk0McOgpioNwI5bT2PD2mWoThr2Cteheu3ijo2mADNPcvViNqRrME0Lze9jt2LlWpRSjKyr6XGobuMIYPZ9/Ouvv5733nuvH2bT/0TuiGGGEAJN04jHYuQLRewogTAiIqKbyN40je0mbrap64WEROUa0TS50dJkSg0p+5151C9/paJBI5Si8PY83DceR9c1xo0ewZp1jXy2eh0Tx45E97WbMukkIpbocrtK07olSyCVwxbTJrLztjOoq87w+eoGXOV67VaUCrWdhhNLly7lJz/5CVdcccWmnkpFIsNpGCKEIJGIY+gaLdkcThvXdEREREQlNF3redPYDihLplYKrW406F20hnJt5MjNCBO7Nwa+3ABA8fm7cTsp1VdCYrz6IFtMnUhNVZrqqjSuqxg/ZiRCCOIxA4EgsdfhKNlxjqlC0DRtz66NQ03HGjODN95bzDPz32LdhiYmTxjD5pPHM6I2w8crVrJufXPP9jeQIxjoVx8466yz2GabbTj33HP7NM5AEYXqhilCCJKpJC0tWQqml7AYERER0RnSlzfpboJ4sERQYea63stbVYVhJCFAJjJk9plDy/P3tiZCtx1PjxHb9fB+2ZeO8MJdrQnrSrk4QSL5mmXIzkJsyiWdW0V1JsXyFSsxLYupE8cQM3Tqa6v5yj47Y+gao77+bfJP/BFlqfZlf1JDZmqY+Z2fs/aXy7E+fKPy8ZAaiT2+xr4HzMJ1XaQm0aT3asrm2GW7Lfweez00MDdijlNTU7mXMR6PE++i4vv3v/89zz33HK+++uqgzdEdnLOK6DNeE02NZCJBsVDENHveDDIiIuKLh9QkTg+9To7j4DiOnwzu+C83rGwLeujW/8cP0arrvdL7UnwjbcQpP0ekqvtlPyrh9eYTYTsU6edRBWrmWlV9l31ktKoROI5LU0uW0SNqmDZpHEGTYa+vnEIbtxljr7wHkfTynpBauM9aTT11l91Fqn4UqTOuQ6sd3cZD43UE1idsQdVJl5EvFjFtG13TiBk6juvQ2NzCmoYNrF3fyNoNG/r/QPUTkyZNoqamJnxdddVVnS7/+eefc8EFF/Dd736XHXfcceNMshdEHqdhTjwew3Y8YUxd1yKJgoiIiA4JBHV7knDckzQAvX48E6/8B2vv+inZ+X8NPS36+BkkjvgO1fv/Bw0N63o67T4h/N5yAJn9vk5x4SudLUxmv+PIpBPMmDqBmprq9p45/3gkd9yXyXe/Q8vT91N472WQGqldZpHe92tki96DrFY/njH/7ykK8/5I0zN34zStRa8fT+bAb2Ds8x8Y6SqK2QIxQw/73pmWQ5/E2jeix2n58uVUV7cawl15m771rW9RV1fHZZddNpCz6zOR4fQFIBH3GgEXCkWSqWRZ36SIiIiIUoSQIDzvUVehEk8Ysmfj6yMnMPY7t+Cc/v+wVn+CTFbBqM3IZvN9mHXvKa0CrJ71HzT+7Wbs1Z+0D59JDa1uDJkvn9jav47ORUBlqorqI0+j+sjTfDVxDaVchOmErWpEuo7aY79P3ZzzAS/saVompml61YiOS8zQ0TXpN/ftY87qRmm54o1fXV1dZjh1xr333stDDz3EE088QSo1uBPeI8PpC4CUkmQiQUs2h9QkiXgkURAREVGZ4NLgKtVvuRxBP7xStKoRaFUjgMHTPkQmM0z42V9Z+YtTKS5+3fecCHAdYpO3Ycz5c9GrRrTLAQsMKSEEQkpkBcNG+sfAsh0ct9X4KRQKxGKtSfNCgBTSDx8KdF2i61qoXt5Wh2o4YJom5513Hl/96lfZYostWLZsWdnnhUKBZcuWkU6nGTVq1KaZZAmR4fQFQAhBLGaQchMUCmaoKh4ZTxEREW0JrwvdvDcLgd93zcvz0bQKbVtE/1TJBQZDaKS0NV68f/id5kTY660n48sR4xj/88cpfvgm+ff+BUqR2mYvYlvs6u2sAle5ft6WlwxvWRaFokkqlUDXNNxwmZKDKCSa5nmdZEl4MJ6Ihz3wtJLcKwDbdtA1LYwSuK7qu8SMFAPfcqWH4+dyOVatWsUTTzzB1KlT233+8ssvM3XqVI499ljuv//+/pplrxm2hlOxWOwynvpFIjCeTNMiXyiQSac39ZQiIiKGBV6ytWc4iTAXp78JvC1e+MohZrSXNnBdN9ShklKitQlJdSe85rouKDCm7Yg+dQffMNS8kKTfLw+lcJWXAK9pAttxKVo2CVehJGWhvHDbvuiB4ziAZ2yVqpZ7y5TOFd+71NoSxnacbjcaHkqk02n+8pe/VPzs6KOPZtttt+WKK65gwoQJG3lmlemRJ3bx4sVcdtll7L333owdO5Z0Os0222zDeeedx+eff15xnQ8//JDTTjuNiRMnEo/HGTNmDAcffDCvv/56t7Z56qmnlqnPlr7OP//8inPcZ599SKVSjBw5kuuuu67dMlOmTEEIwfbbb19R7O2yyy5DCDGoJN77A03TSKUSWJZDsWhu6ulERERE9AiBoFC02NCcoyVXoGi2v451ZBQF3iE7qPjrINzlhdtalc/xQ2SB90pK0aqMLfAUzqUIx1RK4XYiaZDP50mn014Ok+9hCrYl23jQHNcNc5sAHNcNc6N6TZAcPtCvHmAYBrNnz674Ahg5ciSzZ89mt91269u+9xM98jjdeuut3HDDDRx++OF8/etfJ5VKMX/+fG6++WbuuusuXnjhBbbaaqtw+Xnz5nHkkUcyfvx4zjrrLCZNmsSGDRt44403OjS0OuLOO+9s994222zT7r1jjz2WiRMn8uCDD7Js2TJ+/OMfM336dI466qh2y77zzjvccccdnHbaaT2ay1BG13WSqQT5fBFN19CjkF1ERMQQQPiNfKUAQ9fQdRm2IAnQpESpVq9RgGfMqDB85uKCkkjaG1qu6yIQoVRB2faDf+OHAv1MJOk/zHeVepTL5cMH/6JpkUwkvH0QhEaTZ3z5ifduoIXlebosy/Y9Vn1giPSqG8z0yHCaM2cOF154IXV1deF7Z511FrvvvjvnnHMOl1xyCffeey8A69at47jjjmO33XbjscceI9FHAcaTTjqpy2UaGhp45513eP7556mtrQU86fann366neE0duxYDMPgkksu4fjjjyeZTPZpfkMFIQRxw8AqmuSyeTKZVCRREBERMahwHCdMiC5Fk5JkIo7UJLqmoZXcoFuXVX7OVet6SnljSiGRfm6R6EC3Wy+5Hnb0UOl5nmS4HU1KaqvT4XzajS08g8w0i1RlMhSLJrGYQSJRnk6i/Nwo2w/nSYGnRu57vfrF4zTEGIyJ8D0yC3fdddcyoynghBNOAODtt98O3/vNb37DmjVruO6660gkEhQKBYrFYq8nqpSiqampU2u7rq6OUaNGcfnll7NkyRLmzZvHX/7yF7beeut2y8bjcX72s5+xYsUKrr/++l7PaygipSSVSuIql0LRHJRfzIiIiE2HKmlDMvDbap8P1JIvhGrkpQTempiuh56Z0nGCHCfXVWXzV6gwzBaob7f1KJWOL0IPku+pCsYtrZ7zV3Uch2yuQC5foGCaYZhOlf6fUuRzeQzdwHUVhWKBZDLZbnvge8toTXCXJXOxbafv50WIjdByZXhHMfrFn/bpp58CMHr06PC9Rx55hKqqKkzTZM899ySZTJJIJNh+++154IEHeryN2tpaampqiMfj7LHHHhUTyYQQ3HHHHdx+++1Mnz6dL3/5y+y1116ceeaZFcc8+eST2W677bj66qtZt27jiq5tajRNI56IY5rmsCxvjYiI6D1BKGpjEBg8pZimRb5Y7NC7IqXE0PWw+izA8Y2bttV0gtZ8pV7NL/T2uCXv+0KhQuCisB3l9QVtY7QB4QN/LGaQLxRIp1Jl3rK2eG1rWlXOvffcvofpwDNsBjq/aaB1ojYx/bJ3l1xyCQCnn356+N6CBQtwHIcDDzyQadOmcd9993HTTTexdu1a5syZw1133dWtsceMGcO5557LjTfeyMMPP8xVV13FZ599xjHHHMMvfvGLdssfeuihfPzxx8yfP5+lS5dyzz33dBiKklJy9dVX09jYyOWXX96LPR+6CCFIxGLouk5LS/YL5/6NiIjohECRYKM8UIl20geGoZOIx0LDKPBKBUZW8G+nxAvkLRgM2dbsExU9W91BCs9L1T402Cp5EI8ZJBMxYoZRMbxn27Zn7BkGjm13WPGtvI7DuI5DUK0YHAPHVf3WgDmib/RZjuCKK67g/vvv56ijjuKUU04J329ubsZxHI4//vgyI+mggw5i5syZXHjhhZx44oldJiZfffXV7d47++yz2XHHHfnRj37E8ccfz6RJk8o+z2Qy7L777t2a/6GHHsqBBx7I//3f/3Heeecxbdq0bq1Xysb21pReRPqCEIJUMkFzS5Z8Pk8qlRx0ieL9ta9DgU29rxtr+5t6PzcmQ3Vfgzm39eh0tFzbV0+2E4TkSl9xwyhLlm5dxtNPklKE4bhAkVsILz9I4MkVlFepqdDo6c25aCctEO6n919d10Lpg0rHwHVdpBDkcjli8Vin8wjkFoSmhZ6rIExnWXbfv0tRcnif6ZPhdP311/OTn/yE/fbbj7vvvrvspptMJmlpaSnzQgFsueWW7LXXXjz//PMsXLiwrAqvu1RXV/O9732Pc889lyeeeIL/+q//6stucM0117Drrrty8cUXc8899/R4/WKx2Kf8rZ6ilCKbzQI9E3frCMu0aCoUyBfyFbVRNiX9va+DmU29r8WihdQEhj6w8m6bej83JkN1Xy3bBkXFBO1SvP3LI4Tq1b66rvJDUC5F00TgiSFKKWl2W4UeHccNjQjhB0oUrv93kCPUqnfkf4AgqEfruRBmIHDZ8X77nnrhC2cKzzvUdhVXueRzeZLJBNms91/HtivOxwsLQi6bQ/qREts0EQJyBZNsLtt3w2kjtlwZrvT6Cnndddfx/e9/n/3335+///3v7XrLTJo0iQULFjBu3Lh26wbvNTQ09Hbzobro6tWrez1GwM4778wJJ5zA3XffXVEbqivi8fhGFdsMfjjV1RUaTPZyvGwuh2U5pFIp9AG+cfaE/t7Xwcym3td8oYimyQE3njf1fm5Mhuq+Bv3QjG4YTiCprq7qdF878kYFKQK246AXilRXV2PbFlVVVRiGXr6+/2+BXyXnOmWyAZXmGRhmBGE1KbrVq9PpQmgy0GpKZ9LhdoUQ7eRdPE+RZ4RWV1cjpEYqmSTmV8o5rhM2VA7EDWzb9jxoUuK4LlWZFFIIlMgSy2+8B/SIjunVHfKaa67hwgsv5Ctf+QoPP/xwxVL+PfbYgwULFrB8+XJmzpxZ9tknn3wCePlLvWXRokWAJyvQH1x55ZXcf//9XHDBBcyaNatH626KC2LbSoy+kkomaXGymJaFruuD6iLf3/s6mNmU+7oxtx2d08FNoATenXmXLtPZvnrhtvbrtl2v0hhtjRHHb8Cr8MJ4pWrlgYcpXN7PG1IodFp7vlWaR+vfEiE69+yUzjPvCwpXVUh3sCybWCxGsVhESll2fS31aikUAoFlWSSTCUzbJl80vTCfpmG7Tv98h6JQXZ/p8d5dddVVXHjhhRx++OH87W9/61D/KMh3uummm8q+pK+99hrz589nq622YvPNNw/fX7t2LR988AGNjY3he9lslpaWlnZjr1q1il/84hfE43EOOeSQnu5CRaZMmcK3v/1tnn32WR599NEerRuUq1ZKXuzOa1MTaJLEE3EKBZNiJFEQEfGFRgCD1cxre91UUOYdchwH2/Y8Ru1L91vzphzHq1Lrj8IYXXoBxErX9EKhQDqdapVJKPlc1zR0qaEJze9hJ3FdT4dKIEgl4uia5s3X7oeKuoh+oUcep5tvvpmLL76YMWPGcMwxx3DfffeVfZ7JZEKJ9FmzZnHGGWdw6623cvDBBzN79mxWrVrFDTfcgGEY3HzzzWXr3nTTTfz0pz9l7ty5nHrqqYDXPmXWrFnMmTOHrbbaitraWhYtWsStt97Khg0buOmmmxg/fnzv974NP/7xj5k7dy6vvvpqj9azbAtFq/Kr6z+pdccq9dy7mz40JoQgHouhXEUuX0DTtQHPdYmIiIiAnhppnXvBBMJXBgcpSqvgRNmGAr0lrR8sRMMwsG0H23UxSjxagayAbXtJ3ZqulRT+eRsurfo2LStcJujLB/h/99PDbORx6jM9ujO+8sorgOfxOeOMM9p9Pnny5NBwAvjtb3/Ldtttx+9//3u+//3vk0gk2Geffbj00ku71XNm7NixfO1rX+Oll17igQceIJvNUl9fz6xZs/jud7/Lfvvt15Ppd8mIESO46KKLuPDCC3u0XvB1dkufgpSiO88HQoE+SIS7hRDE4zFM0yRfKKCn0/3jGo6IiBhSeBVq/et1rhQi8z4gVPPu3jh0KDETeI8CL7onHqmFKwp/GQWtKt/9dI0LBTilBE/eiVwu5yWDO46XvC4Elm1hdHDRt0wLQ9f9HC5FKuGrhveXhhNBGHJgDZuBHn9TI1QUk+kzzbnmXq8r8FRwe1rx0dTU1C4Js7unstzN7cXVg/+C50FracmTSMRJJROb1HjqaF+HI5t6Xzdmcnh0Tgc3rvLEHLvqZentXzPpdBqlFM3NzVRVVVVcRwjRmm/UNklcKXK5PDXV1TQ0rEM3Yp7XQilSyThGF9fIIERmlxgXmqaFqtttl3NcN/y8Eo7jhnlUHW2v7b5alh167gMZh/Xr11NVVUWxaFI0LfKmhZSScaNGtNsfVyly2Zz38Go5FC2LuuqMN7btsHzlmg6Np21nbF7x/VKampqoqanhnRuOpio5sL/x5rzFduf9hcbGRqqrqwd0W5uCKBazyelfu9Vy7L4/KQpBLGZgWhaxmBE1Ao6IiOiQIMG5O3mbHX3WVjk8mYyjazobmlvIFYrUZLq+VXWUeF7pPb2L/pw9vdwFHiLXdYjFDIKGvFLzWsPk8nli8QTC6tgYsy0bIaWvFG6j+QnvgServzxOUaiu70SGU0RFdEPHdhyy2RyZTLrLC01ERMTwYWO2XKmEFF4DXUPXuzULLzQHSgUaT6pD7aaBeggUiPA66bqKfD5PIh6j1YhySacSJBOxdpV/EDQhDpoHa2GrFaDMk9b3iUY6Tn0lMpwiKiKkIBY3KOY9cU8tOfhUxSMiIgYOhedJ2ZQ/+3Qy4WkwdWMSQgh0XSvTfOotgep4Z857Ufp/AhLx1vCXXeL5z+bymI5LQgqq0sl24UNo9cQF79uOSyJWOl7UamUwERlOER2iaRpGzKBYNInFYlHILiLiC4IQ3fc3iRLDpiMdp06r4IRACy2UcktF01ob3HaHQM+pr1cpIQSaLwNQCa8djecVK983r3LPMi2kkOi6QaHQhO2qsOddJVzlNRI2NK+iznXdcN8B7P6UIpAS5ABHEOTwlk6IDKeITtENHcd2aGnJUVOdiQyniIgvDKqbOgECTZNhb7v2xkT5spVwXa85SqXVgmTuQMSytZFKOR1W2pXkXVXy9nREZ+Kfwb5KKbEdl6JpkUklws9c1/Xbr3jbNXSNeKzjhOxA+0/XtTB5vnTbtm13uG6PiXKc+szw3ruIPiOEIJ6MA17VS3+XKEdERAxOVMn/dkVHyt/dfYHC9fN4vPcqTgbHdnD8FiaeoKQbGh0d7odSKFcNWMZW0IQ4wHVdbMdG13Wam5tRQCIeI5mIdTYI+MaS67qYll3mZYpCdYOLyOO0iVHgSen34GcdqN6WCqQFow2EuoQQwgvZFUx0wySxEfvyRUREbBpU8D8b0cnsOC5SamUbLW1PYju+kSQEluN4zX9dl3jM6NDjJIUAbSBb3iiUajVsXNfFdVzimTi5XI5c0aRK7175vxACy7Y948m2PQ8UUXL4YCMynAYBnTWTrIRSCls5/de7qBtouoama+TzBTSplTXgjIiIGIZsAu9y0TQ9MUwR5DW1ep9c5VIwLcAPyymwfO+T3knblIG8RgbeJsu2sWwLKTUcx0UIgWmaFC0HEJ0KfAahyKCKzrIdYrFYKJIZNiqOGDREd7+IbiFEa5VdwSyGeQxRztPwo21+RcQXlI1kNwXClZquY1sWMijpVwpQCOV9F6UQxGO6X/YvcV2XuK77nrFNm0LghdhAoCgUCiSTKYrFAqlkHMt1ScZineZLWZZNIuF58h3HJWbooQfNdpz+jSSIjZDjFHmcIiI8vJCd7oXsNN3XKIkYLoguyq8jvlgotdFsJ0zTIpVMks/nwc9JCudBa6l+oGovhCAWK799bQpT39OP8qrlDF8KwXEcdF2SL7joukF1Ot2pGr9SoFw31IByHAcMHddVSM3vc9efk46Sw/vM8N67iH5FCIFuGBgxg1wu339KthGDgqALe2Q7RUBJjtNGwPa7FJQiZHtTSJPSK+kX7RPSN5XglK7pZNJJpJQUTRNd1zFNE6UgHot5LWOMjsv/Aw9v4JGSUpKIx9D98J7tuJvcoxZRTuRxiugxhmHg2C75fIFMJmoEPFyQUqIse9OrHkYMErorR9B3vJYiLvjK2UCZ12kwI6VA4hlGxUKRTCaNZVnomoaua13nsPrHODCgVJC7JXQ0Kb0E+P6ccJQc3mciwymixwSq4oV8gXy+QHITNwKO6C8UjuugMKI8pwh84aSNhu0nendUHVeGag3hhW9tIq9Mqeq363qJ4W7JfgQ95zrCLWTJP38vTa8/iptrRo2ZjvaVb6BvvTvgJYv3675Fobo+ExlOEb1CapJYLOa7pjUMw4hutEMcr2WF4ZWE61FvwoiOk5kHAtd1UaiyHm3gh+RkuWCL7TgUC8Uyw04IQVVVZkDm1hHBsXCVCnOUAt0oaXTdacFa9TGfXfY1nLUrPC+vUvDR26x78T7sI77FiG9chu30o/hlRL8QGU4RvcLLd9JxXJd8oYimad17UowYtAi/aqlgWkhXRI2dv8AELUWgvN1JkPfkKuV7Uvp3m1TydFboVafrOul02jOmfIMjm83232S6ieMqlOuWyQ0IIZFS4rqd54Aq1+Xzq07Aafjcf8O3Av31Gv9+M/qEGbgzD+nfSQvNew0kAz3+JiYynCJ6jZBelV0hX6RQLJKKGgEPC2K6jmlZYaf2iC8erusihcBVLrblhOEml1YjSqr+lSPxEqTbf9+UqyiYhW54uvo2F6UUSsGGphZWrl0PeK1SxoyspTqTrrjO6rUbaM7m2HLaxNZZCBDC015SrkvLSw/T9I/bMJd/gEikyOx1NDVfPQPrsw+xPl3U6f40PnwjbHNwydgiDAmWvtcjpNgIobrhfR+IDKeIPqFpGvF4nKJpYhgGhq5HxtMQxiuv9hLFLdshZkRaXV9EXKUQCN9oscikkkjh/e3ifRZ8LTyHT3+F7xTKHz98Ryl0vfNblQBErO/fU9d1+XzNehYtXc7okXUUTYuPlq9k+62mMWZkLa5S2LaL5vehW9/YwpqG9WwxbSKWZZNKp1BKYRgxisUin//vGeRf+btnqLguZDfQ+MhvaHrydlI7HwKaDh2G4hT2yqWMcLKo2pEoPC+w4zgIIUPBTSMKq290IsMpos9oukTaglwuT1UmHYXshjhCCAxDxzItbNtB17vO1YgYPoRGkAhyGXUE4LgObqipFPxPsA4ELZ9Kjaiefm8qtXnxcu+6/x1UtkX29cfJ/XseODbx6TtTtc8cZLLr/CeF1w+vuirFdltOQUrB2x98xAdLlyOEZMGHH5PNFUgkYkydMDJMUF/f2MK/31+K4zrU11Xjugrz6dsY/coj3q6UVta5Dm4xT+61R7olM/D6vxew1a67I4XkjcUfki8UiBkGe+2yLWsbGvlgySd87eB9u3VsgCg5vB+IDKeIPiOEIJ6Ik88WyOXypDNprz9UxJBF+jcry3KQmkQysK0rIgYPnt0i/FCdwrFdXE0hhQTlhfAQ5cnaQaNd11V+v7mgs0APNiy8bbdrj9eDMczPlvD5z/8De80nIL3bW/Ozd7Puj5cx9vu3k9p+/26N4wla6iTiBmPqa/lgyXLu+vP9fLLsY7bYfDMmTtuSlaslyZQXwnMdl6aWLJMnjmHzSeNYt74Jd8Hf21X+hbukXJRZgE4kFxRA1Ujioyay+KNP2Xnb6UyZOAaB4I33FrP881V8tqqBCWNHdv8ARfQLkeEU0W/E4gamaWFZFrGoym7Io2kart8OIh7rXpPSiKGPJ4KqkFLDtmyas3kMQ0PTDYQSaFqr9yfwLgWepuDlum6PPc8iCNC11RHrZhTQLWT57PKjcTas9t9oDYGpYp7Pr/5PJl09j9jELbo9p6Jp8fg/nuaaKy/j888+Dd9PJBJ87/wLOOqY48L3YobBuFEjcJTiw3ffZtqGlZ3bfJqOMHRcs4hQlbSeBIUv/QeJZJJEPMbylWtYva6RETVVKBSm6eC6ilQy0e398YaNdJz6SmQ4RfQLQgivEbDtkM3m0apkl3kJEYMfQ9dxHBPbcTCi8/nFoKTXipSCqnQSXdNwldvDKrp2vqNOEcLbdtt8qe4+gLW88GBrhVq7qbjgOqx5+NdUfePKTubgbeuz1et44fV3ef/dd/nhd7/drkLONE3+fPef+PzzVRx/8umAd6xQnvcpWyh2a86p3Q6h6eXHkXar8aSERCiX5s12Y8N2s3FbcoweWcfahkayuTyJeAylIJGIU1eT4YMln7DNjKnd2p43Uc17DSQDPf4mJroSRvQbQghiiRjFQpF8vkg6LaOqrGFAzNApmhYyaHcRMWxRvrcncBgopUgl4qEXSvTw/Offn8/6v/ya3JvPgOuQ2Hp36r72LdJf+mq4jBuEq4TXl81xnNBbFWg4dYfsq4+2aiFVwnUw33iM1NnXdTiGAiZNGE0iEUMIuPInF6KUW1bFVsof/zCXb33721RlUmy/1TRSyQTJhMGue+5B8dnNYdVSOnSZOTZV+3wd66BzKc77A+qtRxFmntj4GdQc+l/UzPwK1dkCuqZRk0lRnU4yoiaDrmmMGVlHdSbFuFF1jK6v7dbxieg/IsMpol/xEosNCvkiuqmRTPTQjRwx6BBCYOg6pumF7OQwLzX+ouM4blipJfAMF9lFW9Ogb6XCC9MJKWh+9HbW/d/5fkWZ93n+7X+Sf+tZqud8l7r/vBjwDKfWnnMSpZSnIo4neSJdQXe6Ytr5li6TrZVVRO+gCi3wdNVWpaitSrFq1Spe+Nc/Ox9PKZ564jG22+Y71FWncBwHpVyqUnGqjv4Oa39zXuUVpYY+ejNSOx1IEkFx8ytw1RVkcwUyqSTJRAzbcVFCkssXaGhsDr363u9RoyWXQymFpvXwYSYK1fWZyHCK6Hc0XcOIGeTzBQxdj0J2QxwhBJrm9cyyHQdDRFV2wxVFa880728v4VvgSVRI0V6eQvl5cFJ6cgVCCOwVH7LuN+fjx65aF/b/3XT//6JvtTuJ7WeFbUqkkJ6oruO2eqF8pGy/XddVZUa8PnFrzEWvlm+vFCGJTdqqw31vO/66dQ0dLhugaRpr1q5GSoFlWRhGzJ+bS+3+x2N/tpgNf73RC125DkHoUqsdzbgf3o2QGsp1cRwHVwniMYNE3Ai1mgpFk6JlhduzKigX2D1tth5V1fWZ6I4WMSAYMR3XVxXPpKMb7VDHU4rXMC0bTUm06HwOW8pOrVKhBIFyQVZI+BZSkkjEASgWixiGTtOTd3heB9XBTV1q5B+fS81uB3vXiXzBS0gXEiOmt0ss1zTNk0coSUo3LTsMIcdjOiMOPYPcU3M73jHlUnPIf3X7OIwZM9pXAO+4Sa9t20ycMMGfk4uUrQaYEIL6/7yE9O5H0PTkHZifvIdIVpHZ4yiq9v16mTyCIqhMbB3bdd2uGwRHbBIiwyliwDAM3WsErGkkE/HIeBrieCXaGpZlIWOx6HwOQ1ynbTWcIMjRCZS1K512L9yGH2ZzyC94uWPPj7chioteK9d8wpe/7CDc5uVZtRommpQUTYtsPs/o+jqSE7ZgxImX0PCnn/lGm290CC9pO73HkWT2PrbydFzPPGw1zGDEiHoOP/wwHn30sTAU2RZd15nz9Tlt5lm+TGL6ziSm79zxsSjZ90DGAcD2Pbz9TtRypc8Mb39axCZDCOGJ58VjFAoFTNPaZN3LI/oPr/WGxKoUM4gY8jh+q5VKhMG7UukB/7N2v2yta/kKobXpMtCpIe41zm1d1Asf19dWM3pELbqf51P3tXMZe8GdxGfsEi6rj5nCyNOvYsx3bukwud31JRTAM3wc1wEUl1/+M5LJZIfSCpdeegn19fX+nGR39Cw73kOlcHw9LO/fTqferl4jZGu4bqBeUY5TRETv8BR/dRzHIV8soBt6JKQ4xBFCEDN0CkUL23GiRsDDjvZ3/kBfycu7UeXLhOGzcumB1K4HU1z4WqvXpy1SI/Wl8ua1Qgi/lYsoa5pb+sBV6vEKBDbjsVjZrNO7HkJ610NwizlwHEQy0+U1Rwrp5Uz7+xL05tt66615Zt7TnHfud5g/f364/KhRo7jwwgs4+eSTw3CapmnYjkOsF8UTgXhoICQKYNtfnDDdihUr+NGPfsQbb7zBp59+Si6XY8KECey+++5ceOGF7LDDDpt6imVEhlPEgOI1Avaq7PL5AulUclNPKaIfMAwNy7ajRsDDCNd1K55LGRhEinZK2IF+uOO62K4bJnanvnwiG+7/X1QxX8F48tapPfJsP+lbto4VbMpPMlfeRr3qOk1UVIXqqMpTxlPd2/E2YwQVfgE77rADzz//LAsWfMCSJUuorq5izz33JJfLlY1hGAbZbNavSFRlSfadoZTnTZNCgvQzypTC7rCHXR8ZhE1+V69ezdKlSznkkEOYNGkS6XSaZcuWcfvtt3P//ffz97//nYMPPrjrgTYSkeEUMeBomkYiGadYMIk7MXQtShYfyggh0KTEEVEj4OFEXzyISimvfN7/W9SMYsLPHuDTS+egCtnWxB8pQWqM/cGtJKZuG67rd3EBvLCZUsqXQfA+CHrhVQq1lX73AtVypfDzhTpetqv9abve1ltvxdZbb9Xu8yCEaBh6a2hNeFWoHUkflG6nWDTRNA1HOUiheYWIqC+Ux2nnnXfmn/9sL/1wzjnnMHnyZK688srIcIr44qFpGlJKstkcmUw6CvEMcYKQXdFsDdlFxtMQJ9Sh7P55DDxQmpToMQ2zUPDyg4Qgve2eTJ37Dk1P3kXuzXkoxyaxzR7UHHIqev24Dgb0pQ806Xlh/JCd283koSBPCAJPmQjzl7p7zWmbv+WFDzucbkhw3KSUaJqO49hdGk6uL0WQTCWxsvnQCHNdhWUPkMdpCCWHjx07lmQySUND19IQG5PIcIrYaMTiBoVckXwuTzqdikI8QxxP7FTHNK0wJyRiaOIphkOPOurieVsC74vwmwJ7Xkjv1qJVjaDumHOpO+ZcwAvpoTxDSKhA6Lt1/YDSsF0wv+58v4QQoYEUhNwk9Fjx3JuB3+64k81W6sknhKew31ElXoBSinyhQCweQwp/a8ILG9qOGpiKOtioOk5NTU1lb8fjceLxeIerWZZFY2Mjtm3zySefcN1119HS0sLhhx8+oNPtKdGdK2KjIaUknohhOw6WFVXZDQc0KdE1T98pYujiVdO1D211F1HyD6/izTMmSivw/DfC913X9bxBZcnfpcu3Llu2jc7m4efcBWX9rRIKrfpPZfNpQ/C+lME4omxd120zX7wwY2lSdxB27KoizrJsBF4BjVIK13F8D64bVtcNdSZNmkRNTU34uuqqqzpd/oknnmDUqFGMGzeO3Xffnccee4zzzz+fn/3sZxtpxt0j8jhFbFSk5l3Usrk8mq5HnophgK5rOKaLZbWGJqJzOrQIPDq9PW9KKaQEQ9NIxmNovjEd5Ca1FhGUyg/gJZy7nn5RqUHjJWu3zieoxg3m6QaeLr+qr6N5B9ICnjK5KGnv0v472ioD4IZ5fG3zpxw/gb5UWT3wDAV9HFUY8uyqTY2N1DyBTctyQEgScR1NCvIDFaaDjdpyZfny5VRXV4dvd+ZtAthjjz148sknKRQKLFq0iLvuuot8Po9pmsRisQGdck+IDKd+IPhplarMSulF2IMfoRQSUDi+UFzwt6v8aooeoJRCFxq61BACHL9EWArptUgYxE8qQSPgoMouk+5+5UvE4MXQdYqWRdG00HWNeMyIjKchQmcencBY6YxKBoLrumRzeYSQSAGW7VBbnQ4NpFAVyvWMH1338uX8GbWbmzemlzCO33DYdV0vF6qDubcOEk4UKT3DpqM9ChLTK2tZibDtTDA3b/6ibPnAwDK6aDUVREdt2/MuaQJihpcLatsDFKYDr/2LHOAcJ3/86urqMsOpK0aOHMlBBx0U/n3aaaexww47sHDhQp588sl+n2ZviQynfiBulFjCbb6PbXMDu8gV7BZBY8cgIbd0TKUUlmMPeuMpHo9RyBcomiaJLp5CIgY3XmhGYCiNvG3TkjORUoZ5LhGDm0o5RgFSCjqLOHXmpdI0zRNK1VrzhIJlXT/XyZuAZzjl83k0KXFdz/Dw8oS8B0/PMlKgvDyqIETm+aW6sY94gpJdyWeEggclxlFr0nfr1rywHWFYMHhQDvbNcRzi8a49JJ7WnYbrCr8q0asgtJ0vTkVdZ9TV1XHUUUfx61//mg8//JDp06dv6ikBkeE0LJF+kuZgRmoSzdDJ5QpoUsOIbrJDHl3TiBkGjqNoyeapq+laeDBi0+P6LpiKGk5SdppH7IXo2p9j1/W8McLPE4rpnqp24NEpc/kI73rgFhySiQSmaXrJ0mHIy/d8+a1TPKNJlIXMuiIwDl2lPC9Ymzm39aq5jouSIgy/dTAqWkk+VRBd8FTOvaTxoDpPhvlW7ZPcXeX6x8VFKW/9AUsMh40aqusP8vk8AOvXr++3MftKlBwescmIxQykFOSLhaiZ5TAgqLIzDB3bccjlzU09pYiNQJCAXYoQYOgaMV1DkxIlPIPHdVy/L1zr/3mDeP+JxQxs28aybM8rVaLt5DqtaRCd5TUFc/KSz91y0c5Q5bzy8uGyoSJ6Oa7rhi8VVt35veVsp6wJcmD8WZaF43heqMAgUr7XTPrGWRB6dF1vVGcgPU5BqG6gXz1g5cqVFd9ftmwZDz30EFVVVcycObM/9r5fiB7zhyVD4ylfAEbMoJgvUtSKJBOJyEMxxNGkJBH3bn7ZfB5d90J20XkdnAR5On05Pb6Ppfw934j2txIu57uM2o/he5ACA8b7W/hVbG4YOquU1F0Jt1Qmwd+y1GRozLR9UCv1ggXVhV5hn7d94csFBGOq0JBr3RnHttB1vaznXjZXIBbTW6v7CEKVntElpUbRshC0etBcV2ENpMdpEPLDH/6Qt956i69+9atMmTIF13VZsGABd955Jy0tLcydO5dkcvB0nYgMp2GIq9yhYToJr1FnLG5QyBfQNc9bEd1khzaalCTjMRzXJZsroFWlIsHTQUpgqMRiXTfl7RDfygjK8EtVwP0F2i3elqD8v1g0W2UEwlUDK6YnlDcErjDdspkJf9LBokHCuQKU0732KaEUFp6HzHFcNF2GIdBWWQPvGg1ermpcxLBsG9dV6Do4jjOwUgSDMFR37LHH0tjYyJ///GdWr16N4ziMGzeOI488ku985zvstttuAzTR3hEZTsMQKSSOGhpPLEIIdEPHcVwKxSK6HilQD3U8b4NBwnEpWhaFokk6mQDa55JEbHo84cXe/+a6U3nXnTGEENi27XmeKPUYEbZd6a4QZlfb0iqMYdtO69jhf7yHu9BDFVhTAj/82DGuUl4ujFLl1qIgLO0TQuA4Npbt4CivVYxt270wFIc2Rx55JEceeeSmnka3iQyniE2OEAIjpnuq4vkC6UiiYMgjBMRjBuC1jjBNCyElzS05UsmoinK40T+GkxfC0jQNx3EwdB1RUq3WlexAq7hlL+0Ov2hOQJhrJAKdqOCDMlcUnVpOqiRHq+08S5FSYug6ruvJINi23alB1mf8foEDyjDvChEZThGDgtJGwIZhRSG7IY7ww7CG7mmNNWfzmLZNNl9AKRctOrWbnKAx72ALo+qGjmVaOEYM0WHRSHurReGV8Qf93toipQQBmp/D5IXLygcI7CLHLfd4BUZUYPM4ysW2bQpFE2l5X2bLstANL+TpOA5Fy9OlEsILy9m2gyYlqWTCy3EKEt9dl6ZsDk0IdC3mSxEMnOkkhOxSnLM/tjGciQyniEGD1CS6rpHPF9C0VLseUBFDiyBk57iKZDJOdn0RpRTN2QLJmOdV0LsQCYwYWFzHxYj3Ib+JyjlLfUFKDSldjB6K3nk5Rk47Lb0Ot1NycxdStGo4AVqFtiylMi9KKex4nGQiHkobKOUSWENenl+5Z9XVXSzHwbQtXMdF02QYelRKYfuVdrbjftEidUOO6KoVMWgQQqDHdAq5Avl8gVQqGXmdhjhCCOIxg0LRojqdYq1pYdoW+XyOZCpJTVVmU0/xC09ff2MDcZMPqvR6Nrfe5T8ppbwoXUnFnhfuC1TKW0OJosRJFVT+BesEbYH9D8uEM4MQpGHoOI5DzrSQmvTU0gVUp5Oe90rKgdVwAhAbQTlcDO+H3uHtT4sYckgpicVjWJaNbQ9wdUnERkFKz5OoaTLMb7Idh3zR6rIRasTA4bqu18KkjwjZ9+TwsvH6Idm8p9uD1v53QRK68rK3W9XRe2uU+RLnyk8SD2QHYroRvm9alt+mRRv430RQVTfQr2HM8N67iCGJpmtITZLN5aIb6zDB0DVihk4qEQ97eLXk8jQ253xNm4iNTb/mN/WjY9jrrrKRvxO+XeQH4nwNp9J2NJXn1JqQ3vl8NSnRNK31MCmFrmskYjGkkL5HSvr9RiMGO1GoLmLQIYQg7jcCzuXykddpmGDoGm7MIJWMk81mcRyHDc0tGIZOOhmPwrIbGaX6JnwZ0IFN0duR+uatqCAMHmhCBftbqam6t5rwBS/br+8Eb1YY33GckhBf24q51gMchALdNvIEmuZ5oAxd75YR1mc2YpPf4UpkOEUMSoJGwPl8wSvP7Qf9lohNh1dlp6HrikQ8FjYAtmybQrFIMhFD0vd8m4ju0drLsj8sp+6P0aqO7bbrNSel9N6voHfpWgXyy+aT+/hl3GIWvWos6en7Eh+3bXkFV5v1pBSYluO1MPEVBQy9fbsWb3veyhV7fba1m/zVbcfBblxH7vn7KX6yEJFIk9n3aBi3RXBwPMPLb1asaRLbtst6czqOi2k7VKVTvvhlNw9mbxmEAphDjchwihi0COl1Ds+2tGBZdre6jUcMbgxdI24YJBNxCpaXBNuSyyOlJJWIk4jO8UbBth3fcOn7WKKHxpdSivVNLWRSyYrn25tTq/VgZxtY98wvcbLrwvecljUUP/s3iUm7ULfnGYgKHg6lFNlCkULBxFUqDJXFDaNdOKzUy9O2fUzrxPzJuV5zXwQ0PXkn6397IThW6GXJ/u3/0Lfbj6qL70Smqnz9y7DhDLZlk0y1tg9xXBelvFCdaVuRh30IMLzNwoghjacqbiCEpFAoDnwrgogBRwhBzNDRNa8tC4Bp2axvbGZ1wwYam70QXsTAopTqslFut8eiI8XJ9mNbtkO+UCRXMLHsjs6zLOsJt/5f/4eTW99mo17uY2H567QseLziJpVS2Jbj9btzFY7t/7uX1xApBNL3GgkhyL8/n4abvwe26e2/Y3svwH7vBVb/8r8qjuMGhlc4z1bDzXE2YqhuEDX5HWoMW8OpWCxu6ilE9ANCgB7TsB2bomlu6ulE9ANSShKxWJlOl+O6mJbt30yjcN1AEpTf9/dhFlKGgwbl++237WLoOqNH1JLuQEG+dD1r3VKs9Z+EhlIlWhY+jWv7YpMlNoeUkkw6RX1tNTVVaepqMtRWZ9pVEkopvAbAUnT88o0lUWJsNj82t+OQlOuQf/1Jih+927pfVD7kqnTfXIeBFL+M6B96ZDgtXryYyy67jL333puxY8eSTqfZZpttOO+88/j8888rrvPhhx9y2mmnMXHiROLxOGPGjOHggw/m9ddf79WEb7rppvDLu2zZsopz3GeffUilUowcOZLrrruu3TJTpkxBCMH2229fsWrrsssuQwjBs88+26s5RvQfQW5MLGaQzxWwLHtTTymiH5BSUJ1JoWutlyBD00kl4/0uqBhRmZ6G2DodSwi/ckyi67oXFhOBqdD6UgryRRMhvNwe27ZxXbfsOix9eQOlFIWVH3SZL6PMLGaTd/8RUngvX2NJCoHtOEgh/O15+U6izDCSnjdJyo5fbbxzdsNKzOULOzXokBq5lx8JjTKhVd4Px1V+eyKwFe2S0/sb7/4pB/g1vH/EPcpxuvXWW7nhhhs4/PDD+frXv04qlWL+/PncfPPN3HXXXbzwwgtstdVW4fLz5s3jyCOPZPz48Zx11llMmjSJDRs28MYbb3RoaHXGJ598wkUXXUQmk6GlpaXiMsceeywTJ07kwQcfZNmyZfz4xz9m+vTpHHXUUe2Wfeedd7jjjjs47bTTejyXiI2LETNQCgqFYtSOZRgghCARj1FTlaahsQWlFEXLZPW6DVSlk1SlktF5HiA8PSH6zePUkQEWGC+tf4NSGvmiSb5QJOafX13zU4dKjCelFK6f+9O9OXjICj3SNK01j8oL27mhIRRsR4iehS3dQrbrhaSEQhbZhQaU15fP8746PdjnXhNV1fWZHhlOc+bM4cILL6Suri5876yzzmL33XfnnHPO4ZJLLuHee+8FYN26dRx33HHstttuPPbYYyQSiT5P9uyzz2bLLbdk66235o9//GO7zxsaGnjnnXd4/vnnqa2tBWDp0qU8/fTT7QynsWPHYhgGl1xyCccffzzJZLLdeBGDCyOmk8+2qopHDG00KalKp8gXTHIFL7RuWhbrG21My2b0iFq0qKndgNBhAnSvx2o/XqVwnZQC5SoKto30jefWZUoStH2PkzFy8869OoAwUsRqxnXL8BHCa+1UiusqpOxZRadePx5hdHFPsy2MSVtWrAj2+gR6YWnL72EHoFwVtVsZAvQoVLfrrruWGU0BJ5xwAgBvv/12+N5vfvMb1qxZw3XXXUcikaBQKPQp7+gPf/gDTz75JL/73e867GFWV1fHqFGjuPzyy1myZAnz5s3jL3/5C1tvvXW7ZePxOD/72c9YsWIF119/fa/nFbHxEEIQT8YoFk1M04wSxYcBhq4zoqYqlCcAr1Q+m8vjROKnmxTXKlBcuYDC5+/h5Bs7XK6npm0qmSCVjKP7DaA7wxg5Ha16XCfhOkF6xv4IrXv99oSf4F1myIjyBO3uIONJ0nsc3olnRbRKE1TAVQrLsrEsC4HnKVOu8gsjBjpWp22c1zCmX5LDP/30UwBGjx4dvvfII49QVVWFaZrsueeeJJNJEokE22+/PQ888ECPxl+9ejXf+973+J//+R922mmnDpcTQnDHHXdw++23M336dL785S+z1157ceaZZ1Zc/uSTT2a77bbj6quvZt26dRWXiRg8hFpAhk6hUIxUxYcBQgiSiTi11ZmyMIuueYZUZBwPEJ4iZOWPXIemtx9i1cMXsu65G2h4/iZW/e0iGl78PU6xUopE900nr6pSI5WIE48ZoZZTaz5U++Vr9joLGc+0+dz7d3zctmS2OaxXwpGhKjiVE9m7oubQUzHGTWtvPEkv9jj6f/4PLVneizEIkwaimoauo+uaZzQpd+M8LEQtV/pMv+zdJZdcAsDpp58evrdgwQIcx+HAAw9k2rRp3Hfffdx0002sXbuWOXPmcNddd3V7/P/+7/+mqqqKn/3sZ10ue+ihh/Lxxx8zf/58li5dyj333NOhh0pKydVXX01jYyOXX355t+cz2BnOeSFe09gYruuSzxci42kYIISgKp0ik2wNfUgpwlyYiH5GiA7zkpRSrJ9/Gy0LnkA5ZukHFFa8ybqnr8W18r3etPRbj3i9C8tfpYZz6e9az4xmxME/Jr3d19CqxyOTtRijZlCz++nU7P1NlNB6LTHgKhU26u3pdVNLVzPpF09Q+7VvIlLV/ruCxPb7Uv2je0jvfaT3Tsm4wSw93bIYmuZJLySTsT5JJURsXPosgHnFFVdw//33c9RRR3HKKaeE7zc3N+M4Dscff3yZkXTQQQcxc+ZMLrzwQk488cQuv6wPP/ww9913H4899hipVKpbc8pkMuy+++7dWvbQQw/lwAMP5P/+7/8477zzmDZtWrfWK2VjPxV31h+pu72ThgqV9kfTNBLJBLlcHt3SiRnGsDAWh9u564hK+ymA6kyKfKGIadsUTBPRnAXh3WSCqqOhxmA8p6Vzajuv4upF5Je/0cGKLlbzKloWP0/V1geXjdXaILf3+9p2XmXfDyNFaouDSG1xUMkawg+xuZ6idwdVax3huK5nyWiV26V0Z66yqpaRZ1xB/SmX0rDiI1I1I9DSNTS3NKNcF9UmWT3cN783Xb5QxNA1NCkxLRvXHXitOiG1ioKh/b2N4UyfDKfrr7+en/zkJ+y3337cfffdZTevZDJJS0tLmRcKYMstt2Svvfbi+eefZ+HChWVVeG1pbGzkW9/6FieccAKHHHJIX6baKddccw277rorF198Mffcc0+P1y8WixtVN0opRTbrVXVUSjp0FTju8CjbVwpyuRyUuNN1qSMEmMUiuVyOVDKJ1sOL5mCks/M6nOhoP11XYWiCbK6I6yqKhQKgcC2T6ky6rEJrqDAYz6mrPDFI6csHlNL04WsUtbpOE7KLS99ETdgDaK1+KxRMXNfp077m8wW/75v3m/dyoDobp70eU48q43w9q+6sU8mYKd1X11XkjQzKEdDURKFQ8LxobcZ2lSKXy+M6LpomaMnmiccMmmwL07TJZXOYAy25ErVc6TO9Npyuu+46vv/977P//vvz97//vZ03aNKkSSxYsIBx48a1Wzd4r6GhodNtXHbZZaxfv57/+Z//KdNsCqQIVqxYAcDkyZP7dFHaeeedOeGEE7j77rs5//zze7x+PB4nHq8s5jYQBD/i6urqioYTgOX07MfnXUAkrnLDJpjev0XYx0n6TxFB+a73rgqX94w274Jb+l7QgwpU2TaCcuBgjKB/Vdv90aRGdXVVuM0gwbO6WtHc3IJSkMmkK5YiDyU6O6/DiY72UylFVVUGIxajKZsPwzWu0NBj8SHZCHgwnlNXKWzLRtNkuzQGs7AK6azvYE2ffJbq6urwT9t20LQ8VVVpoPf7qusGjmNTVVVFoVCkqqqq24aTkBWSvrsgzHHqxjqe1pRqZ5xVV1ejlKKlpYWqqioy6RTrNzQyavRoDEOvaDhJKanKZBBCYCtBJpUgZhhkcwVaihZCGx4PvcOZXhlO11xzDRdeeCFf+cpXePjhhyuW8u+xxx4sWLCA5cuXM3PmzLLPPvnkEwDGjBnT6XY++ugj8vl8h2G3fffdF/DCgplMpuIy3eXKK6/k/vvv54ILLmDWrFk9WndTXBDbqti2nUtcDmDPrw5yxvq8ToVllFLomkTX2j99CiFIZ9K0NGcxTYtEYujdWNvS0XkdblTaT+GLENZVVyGFpCmbw3FdLNtmfWMTmqwhOQTP8WA7p0Ez5Upz0pLVCCE79TjJeKbdeROi1dtd+mAVUOqwEaJyXnpQth+8XAWitIdcu7L+kvC9kH6+uyLwTns94sKtljuoSj4snYoo+6Pkn1KiCeXnhwUhN89o9CIOJjU11ViWBUrhug6Wqdrtq9fqpkQgUrSeC0/UYSM0M490nPpMjw2nq666iosvvpjDDz+cBx54oENPyymnnMLcuXO56aabOOSQQ8Ivw2uvvcb8+fPZaqut2HzzzcPl165dy9q1axk3bhw1NTUAXHTRRZx66qntxr7hhhuYN28ev/3tbxk9enS/aDBNmTKFb3/72/zv//5v6IKNGPxoUhKLGRQKRXRdwzCGZi5MRCuxmEEmnaRo2eQKBQAKpsXa9U3UVmdIJmIYetSfvC90lEWTmrI7heWddHUQktTUvdq97TguhUIB0yxSKLTPOQwMDdd10XSJQNDWRrFsO1SSV4oKXQIUoTWjyrWjKhnhnje7ZBslUwqNmeAfJQZR2aRKN91mf4rFoi+o6VBT4xmczc1NxGIxHMfFFSoMLwcGme04CCm9cKnjolyFZTsYuh4eo4jBT4+uPjfffDMXX3wxY8aM4ZhjjuG+++4r+zyTyTB79mwAZs2axRlnnMGtt97KwQcfzOzZs1m1ahU33HADhmFw8803l61700038dOf/pS5c+eGxlJHnqaHHnoIgIMPPpgpU6b0ZBc65cc//jFz587l1Vdf7bcxIwYWIQTxRBzHcSgUimGrh8HydB/RO+Ixg3QyjmlZvlAgFEyTtesbyaSSjKipQteH91PtgKK8nDJEq2dJALEx2xAbNQNz7Yft3UJCIuMZkpvPKiubd5UXYo/FYsQMg3g8VvH3Z5oWoDB0o9Uo8T1ECHBdpyQcDzFDoyOpAykFjutSLFp+fre3XCJe7mkP9ZlKDKRgZwWexyi4XgRGTpDoXhqWU+H/eP+wLBvTNInFYmFosqWlhVQqRSzWuv/Cb+kS7IVpmjiO632vbS/8Z9uO571ynI1SJey1RBng5PAox6mVV155BYBVq1ZxxhlntPt88uTJoeEE8Nvf/pbtttuO3//+93z/+98nkUiwzz77cOmll7Lbbrv1beYDwIgRI7jooou48MILN/VUInqAFIJEIkE2m6NomiQ2Yr5ZxMAQSBSYlk1LiRim7Thk8wXSyUSY2BwZyb3DVW6p3YQSXr+5Eft+i8bX/kT+k9codbUYIyZTu/tpyHgaVdoexQ/NBT3qdL1yqxzbtrEdAEU2b5JJJXxDJij8KK9uCzSeKuHlSkLR8nrP6bpGOhlDb+OJVErhOm47D5smJUGnY+F7mwJFcdt2KBRN4vFYuwT1QAYll8+TSCSoqspg215SvKdVGG9vb1L6HQ08Zt51S5OSeNxAKeU3uN4YbAydpeFtOAk1mOpkhyiWtXG1ZpRSNDU1DaqE04GiJ/uaz3vtWKqq0kMyZPdFOa/d3U+lFIWiyboNzeQLhTINnEwqQTqZJJ1MDOpqu8F6Tk3TQqHCVh8A+DfyYJ5Obj3FVR+gXIfYiCkYdRM94Uan/AbvOA7Fokk6k6a5qYmampqKobpCoUjRssBvM5KIG2XenaJpkjAMMpk0a9aspa6utovvBxRNi6ZsjphhUFuVbueFVH5IrC2B4VQemvMMqJZcgVXrNjCqrprqTKr1eDgOuZynYZVKJWlYvx5N09GkJJnyGla7bTr0tk1aLxZN7/gJSUuugFIudTVVSClY29BIUzbX4f52xrYzNu9ymSb/3Cx44y9hIv9A0dycZeudj6axsbGskGC4ECUKRAwbEol4+EQoNa38phAx5PDETg1qMmmEhFy+EObJNLXkyBdMNE2SSkQexn6hzTO0lqojNXXPsvcqCjT6lbeu43SYoxNkJMVjBo3NeSzH9g0NT4dJagIJOFaR3Efv4KxeRq6qjsTEndDS9RXH9FTIdarTKc8zJNsbawo8J0/FabcaNK6vr+Q1Hdaorc6QiLWG/RzHJZvNIaQgnUqRzxco5AsYhsHo0aN8SYL2BpqgQu6VEN6+K9dLpK+w3EAS6Tj1nchwihg2BPlOTY3NFAtFksnEoHrKj+g5UkrSqThCKizTxrRbE4Zt2yaXL5DsIKcmojL9HWQQHVkmZcu05hTFYzoxpXn5RygMQ8N1XZxVb1N89wFwiiAkWaXIvvMQial7UbXTce1uxkIIdEP3Q4Pl23N9fSnwQmKlC5REBcvnF1QkxwziMd3rH+ePk8vlvOuLKrLhvZexLZv4yOkILYHrBDIqAiFLZA5UUEFYUhno/9fQNarSSXJ5M/xko32DIx2nPhMZThHDCk1KUukUuWzOU5yObqpDHikliXicZCKO2dJqOCkgmyuQSSXbJQVHVCZQ+UaA7E0eSi+NrtCz4ypSiThF0wvZmbaFJjXUusVY//5TyXZavTeFj14EoHqXE8vHpLIR1DqG/18/XNad+Xn/9gYNVL6zuRzKzmPNv52mBc+AclEI8onR1G62JerAc3Fj6dZk8grbchzXn6eXqF40LZoDrTJfiiBqtzJ0iAyniGGFEIJYzMBxYhRNE93Qy/I2IoYmuqZRnUlRMC2KZmsPtaJl0dicxdD1YaEevzFwHE81XPp90vqKKpUJ6AJd99TKU0kvvJoS3n8bFj5KhzE1FIWPXiS91VfLwnYKhf//AGXGkRQCpBdC7O0v3zOa8ijHpvD4z7E+/6CNvpWiuPgF1m/4lPrjrgXp7UtHosQoUG6r5IBSbokswsYznITQNkJV3fAO1UVXmohhhxReI2CUIp/LR9oowwQv3ymFYZQ/77XkC2TzhUHVD24wopTydYQEuq6FVV2lr64eMCp+rjyhR68Krrvr+v4ipXBa1mKv/4Suwn3FT99su1kvudx/tc0xClrK9OahyXVdsrkcoJCfvoH12fuVRUGVi7V6CfkFz1T0NLXVkgrQNI1kIoHhJ7MrNmI/w0AAc6Bfw5jIcIoYlui6TjKVxLYdT803YsgjhCCdSpCKx/y8Gg/HcWjO5kK9p4jKBPk6uq/QH/xd+urq5t2xESI6lQ/wVw7V4b12LxKpaeCYHa8TritxrUKni/SX3aGUIpfPg4JUMknL24/Rud9KkHv38Q5bvqg2lXaBARbTdTQZnAvaVeRFDF6iUF3EsMXQdeLxGPlCAV3X2vXlihhaBBVP1Zk0roJsLh+GN3L5AusbW6irqUIL1JpF+X+/yAQ6QYavsRR4n0oNpeD4dklHPVO6SWk1mwCMzAgvmbiTNi8oBy09suOPlcLyCwf6IoyqlCKfL6BcRTqdIl8o4jSvoXNvmMJpXhuub1o2rusSj7VXUEcIEBJdkxSKpt9ixTOaNl6oTg64QGUkgBkRMUQRQpBMJrAsm2w2NywaAX/REUKQiMcY4Xsu8oViqLi8obmFomkRM3QM3RNi1HWtNRTVy7DNUEcphW3b4f47jotlW+TyxdCREtN1L7zdDbquoesZMpYmMWlnCsvf6NB4EnqcxMSdOx2nr3ZHcJzy+TzV1VWYlkU+n0fLjMRpXtnJBgRaxsu9cl3FmoZGlHIZNaLGP6ZttJ385HPHN640Kb32K1GoecgQGU4Rw55UOkm2JRuqin8Rb57DCSEE8XiMUYaBaVkUiiamZVE0vVe+2CpIq0mJYejE/D6GccMgnjAwtC/Opc/xlbM1KTFNi+ZcHl3TkFLD0DVPgFITYcPZrulv0wmqdziW4qqFKDPbxnjytlW184kIvXPDLpAP6AtF0yQW99S/W5qz6IaO3OrL5D97p5O1FKmZXw3/qskksV1FzBfhLZuSX6ln2zaO45Dwq343ao6TkDDQyduRxykiYugShB9isRiFfBFd16MGscMEKT3vUyIew3VdTMvGtGwKxSL5ooVpWTiui1M0KRQBPIMhkYhRnU6TSsQHtep4fxDkLum6BkqxrrGJbL5IOplg1IgaDF2naFnk8oWydiVKeaGjQN+orYhjUK7vtSzp+U0yaGgrfGNNT4+g9oDzafr3X3BWvh0aT3rdJNLbHEF83LZdjqlJ0SdzLmjcW1tTSzaXBeElcae2OQD7/cexVi1u7xETEn3ERFLbfsWbgybJpFO4rurQkFOuS0u+QL5okk75Deo3ZlVdJIDZZ6I7SMSwx/NQxLEdh3wujxaF7IYdntZTzG8OnMBxXU8gs1Akmytg2naY19OSzVMomKSSCWozKWJ+Lspw80R6++siNYmUEtd1sWyXZDxOXXUGTfOEJ4sFE01qGJpGczaPoWvEDN3rR1fRaFIIITEtm6Jp4rpuWGUUaB91MKNwGaBVoFLTEICerie500nENYf1Kz8hXTOCRM2YsnXK51GOaTvEjd7f0mzb9nrguQ62ZaNrOvF4HEPXGDnnKjY8dQP5hc+VuJAEic13p+7g7yL0uGf4uMrTjZKiw4o65W+rUChiWRaJmIFboZVNxOAlMpwivhBIKUilkrS0ZCkUi6SSyU09pYgBQAiBpgk0TWLoGolEnNqqDPmiSb5QpGCa2LaD4zg0tWTJ5vLUVKXJpFKextAw0vxyHK+dR2vrIYGha2EStQBPF8uyqavJ4Dgu+WIRxzFavbIVNImCRPNcvkgqGUcKQT5f8D8nFHX0thGsr8LqsrYGRWBAKb8/ijDSiMw4ZCqD7Tg4jiJm6NiOg2XZxGIGuqa1q/5XSuG4Lr3tUpnL5cmk01iWjasU6aQnFyCEQMTTjDj8Ipz9zqT46bso1yVVO5W6cVPCZHvX74knSvu8tLGcbNubv5CSxpYcQZKZW6Gf3kARJYf3nchwivhCEITs4vEY+XwBQ9eHZCPgiO4Tdr3XNar0JFXpJI7rki8UyRdMipaFaVo0NDbTkisQjxmkEnGS8ZgnEOkrQQ9FQ8p1XRzXJWa0VtGZluUbIi5FyyIej+Eql+qM9xDRnM2hS4kWeky0MJTmKoVtO0AgYaAomCaJRKxdT0jlh/i8lSlP8lEVWr60KkLS2tVOoVwXR3k94CxLkS+aWL7RW1qxFo6mXGzLpRC0PAlPW+t5DPYnCDcGi7h43kipSZSliMVioSeyFK1qJKmt9ve8eU1NZZ8FxYbC/4c3jfJ9dRyXeFyjqOxQDgLYuFIaQtsIOU5RqC4iYtiQiMexbYdcvkAmagT8hUOTkkwqSTqZwHYcCkVPiTxfMMnm8mRzBRJxwzOc/CT0VCJOPDZ0jGzXdbEdp6ws33EcsrkCqUQ8VFlXSpFOJrEdB9O0kFKQTCQ8T5VvZITj2Q6O6yKFIJcvUiiaSBl4fbxk55aWLPmC12A76A9XanYElWNBSKq9/eT6n3nJ2bZto+tBzzhIxAySiViZLaJoNZA0KRGAq9zyD1RrW5Ngo6rUWBPe3KQQKNelUCiiadI7hprWpeGslOdNEwiE5u2z8PObVImh6LhumCKQLxTL1v+ia5AtXryYu+66iyeffJIlS5bQ3NzM5MmTOeigg7jooosYN27cpp5iGZHhFPGFI5mI09zcQrFYJJmIGgF/ERFCYOg6uqaRTsaxMw6W7ZAvmOTyRfJFT3Fe5gs06RrJeIJMOkFiAPKhgkRsoMueau3XBS/nqEQQ1PUMn1JBRiklVemkJ0egXPSSkKRSXiJzKpnwCimM1j5tQRK41GRoiMUdg1jMQNMkMV3HdZX371iMmGGElaulgpqe8KXryyK0N0YCb02xWPSOuxSk06lORDUF3T1UKjSWStq0lPY6AVpasiQSifB4JhIJCoUiKEU8EW9nQLXN5XKVQrbpiRd4+oLlbcvyDFbAMu2Scdio+U1CSsQAPzD2dPxbb72VG264gcMPP5yvf/3rpFIp5s+fz80338xdd93FCy+8wFZbbTVAs+05keEU8YXCy4HRSKWSZLN5pIgaAX+RCYygmJQYuk4yHqe22stzyeWLZAsFbNvLh2rKZkknE9RWpf1yfhF6EHrz/QlyfRzboSmbAzyvifTDZUJIZBcSAbbtULQsYoYRVs5JX4KhdD1PrdtLFo/p5Z/pmhYKX5YaU67rIoQMjQTH9yDF423Dc16yeCxmYBh6uO22SuSWpZDCM8AqFWe4rusHzzxDUG8zz97SHY+RkAKpeS1jdF3HiBnoho7ruJimRcEplHnINF2G+9fWoCo9hsE6gSRELGYAAsPQsRzH0yBTLo7rraeVta3xZApcV4XnNTAwHX/bUgrK/XrdOR6Dr1fdnDlzuPDCC6mrqwvfO+uss9h9990555xzuOSSS7j33nv7e5q9JjKc+hnvyco7rK5rlzxxyTY/KBcpg/wDF9ft7IkjuEB7FxXHsTtZtn9ona+3Ta8h5UCXy5brw3hzgFgsXrZMcNHt7Zy8RsAxL9ejWNwkjYCFkEipga/f4iVTKrRBqi/khWVk2XfVMzpav5fKL9Wu9P2vPGZpkmrlXmPeOK3vt27TX8v/LZX+vtrTOnal32H52CDxDIlkIk6dm6FYNMkWTAqmF6JakcsTM3TiRqvnJUiF1nWNeMzosP1G6bxNy8Z2bJSrPAHKeM/DgUZMx4iVfmdE2MajLVJKDCHaGS2dzpNAcqD9/PFDW8rPTQpyn1zX9VX6W3/PIvh9lRiCFStb+yZK3imu69Kczfu5WmAYGplUCgTomo5S0NRSwHUV+YJJ3DBCT5zjKrK5Ao7jkEzEESgKBRPLtrFtF9txSafiaG0OVK5QxDS934GmacRjAsPQGF1fSzZfxPGrPD09Jxkc3DBHrDRPzXG8EGzQssY0TXRdx7KGfphv1113rfj+CSecwDnnnMPbb7+9kWfUOYPzKj2E0TSDN998k6qqKjbffHOUctE0Hdu2+eCDhUgpmTFjBobhGQP33nsvxx57bFguXGk8KSWrVq1i5cqVbL755mQyGVKpgfuxaJoXjvj0009Zu3YtEydOZOTIkWVud9d1cZz+6wGn67HQtW3bFpqmYZoWxWKRTCbjG0kuum6Qy+VwHIfq6mosq9j14BUIRBRN06KQL5BOp/ptXzRNR0rNf2q3OzivOk899RTTp09n6tSpzJs3j/HjxzNlyhS6Ky4opRYaWo5jd2F89x4pNVxX8de//oVjjz3Wf9L1vAYrV65k5cqV4XcE4P3338e2bbbbbjtsu30fMikl9fWjUEqxcOFCXNdlxowZxGJxHMfydYdi5PN5AJLJJLZthsZmc3MzAIZhkEp5582yLLLZbMX5x2Ixkslk6EX54IMPiMfjbL755gDYtkVHx1xKSTKZIJGI4zgpiqZNoWhSKBbJ5guhpwi8VGND10inklRnkqEAout6eT1StsoCZPMFiqaF7pf+a5rE7S9RSeUg0Fvr2fwcGuX6ZpBwwrl1SJijJEJjxnuO8hOf8fSIgvGDY1v6MCOkQDneOtL/bbcP0VWSOxgYLNvhjfc+9DSrNB0hYNK4UWwxdSJKSD5YspyVa9Z7D1aGzrTJ4xk/up58vsiipStY09AIKFLJBFtOm0g8EcNxFP9esJSiabHdVtNIJ4zw924YBkuWfcZnq9ehaxouirqaDNtvOc3rMKMUn61u4OMVq5i55VTSqTiLl33G+g3NFE0LQ9cwdJ3tt57Gug1NrPh8tXeMgUljRzN10lgaW7Is+mg50zab0O3jsDE9Tk1tEujj8TjxeLzSKhX59NNPARg9enT/Ta4fiDJj+xEpNUzT5LTTTmPEiBG+90nj+uuvZ9q0aXzzm9/knHPOYdq0afz1r38F4NVXX+UPf/hD+JReiqYZrF27lqOPPpo999yTH/7wh2y55ZZcdNFFxOOJ0Avg3cS08GZWaV7BsgHt19P8ZXWampqYNWsWBx98MD/60Y/YfffdueCCCxBC8NZbb3Huuef6N4Fg+7LNOKJs/PYXS9luWSEEs2fPZt26dei6gZQav/jFL6itreXPf/4zmubtm23bbL755kybNi0cq3xbXe9j6/HVSKWTWJaNaVp43qzOx2kb/igdO1hHSo3/9//+H4888ohvhJbPUdO8J8gHH3yQhQsXAnD77bfz0ksvEY/H/c/Lt1/pb03T+f3vf8/tt9/eZp2u9yP4rrR9jwpuf03T+e1vf8u7774b/r106VK+8pWvhN/LPfbYgyOPPBKA2tpaTj311NBYKD/3Al2PceeddzJjxgxOO+00zj33XKZNm8bcuXPD4wWwxx57sOOOO5bss2DNmjXU1tbypS99iZkzZzJp0iT+9Kc/8c9//pOddtqJnXbaiWnTpjFu3Ljw72uvvRaAe+65hxkzZvDf//3fHHfcceyyyy688sor6HrXnp4gdJROJRhRW8Wo+lrGjhzBiJpq4oFCNArTttnQ3MKqtev5fE0Dq9ZuYO36Rv/f69nQ1EI2n6dgWsTjhqeXhMLyDZv+JKiky+YLWLaN63uFvIT4bjz0KEWQ/B34nqQf6pZ+2E7Q6pUiqFQrM4Rk+XdAiDD/JQhVlucLBVV1/Y/jumxoaqG2OsO2MzajtibDwqXLacnlWbzsU5Z+8jlTJ41lmxmboesar7+ziIbGZt5f/DGfrl7LlIlj2Hr6ZKoyKSzLRgpJS67ARytWsqZhA6vWrAe84xMktTdlc2iaZJsZmzFt0lg+XbmWFSvX4DgK23b5aPlKGluyLFuxkmQ8wWbjRrPltInkCgXq66rZZsZmNDZneW/xR4yqr2HmFlOYOHYkC5d+wmer1+G6LqvXbRiQ49UfTJo0iZqamvB11VVX9Wj9Sy65BIDTTz99IKbXayKPUz8ipeTee+/mS1/6EvX19SiluPvuu7n22mt5/vnnmTp1KgCrV6/m3//+NwDf/OY3OeKIIzj11FPDUAgEIT/JiSeeyPTp01m0aBG6rtPQ0MCXv/xlRowYwQUXXIDjOGH1ydKlS5k0aRLxuPfkHtxcGxsbWbNmDVOmTEHXY+HTYVA989FHHzFp0iQSfqL0r3/9a0aOHMlzzz0XeoFWr14dzv3NN98kn88jhCCRSJDP50kkEqxatQrHcZgwwXv6WbZsGclkkjFjxuC6XhmxrhsopViyZAmjRo2itrYWANM0mT9/Po2NjaTT6fCpZLfdduP222/nxBNPRErBo4/+ncmTJ7NkyRKAcLxPPvkEx3GYPHkymqbjug5CeO7sWCzG0qVLGTduHOl0Gtu2wuNs6DqJRJxEIoGuGyxfvpx8Ps/mm2/uj+0Cgs8++4xEIkF9fX3Z2Lqu09LSwrp165g8eTK6bmDbNu+//z41NTXk83kMw/PgBefpww+XMGHCBK6//voytWaAxsZG1q9fz+TJk8MndMuyygzVIJHUcRw+/PBDDMMgn897uRm+J7OxsZENGzYwadIkhAi8Xyo850uWLGHq1KnE43E+++wzNmzYwGabbUYmk8G2zbIQs+M4XHfddbz44osE7SK++tWvcsYZZ/DEE0+ECsl33303AOPHj2fLLbfkb3/7G7Nnf63M4yalzjPPPMN3vvMdnn/+ebbbbrtwvv/85z/DROU33ngD0zRJp9O88MIL7LXXXmVhtQULFiCl5IknnuBrX/sajY2NLF26FICbbrqJZ555hgcffDBc/uWXX+ass87in//8Z2iM3X333Rx22GF8+OGHVFdXddtj53kkPK2jZCLutTKxLVCCQrGI5dgULZuCaeGpUZu4QhIzDDRNI4ZOVSqJiycZUEolr0xvUEpRNG10zcunEXgPCrbt4Lp2l9sIQo+BthKoMJcmNHjCjRHm55TlfXkHC9dxEdKXAvDKzDxPk+99k37FW2sVWp93v0OUAtOyyRaKNDbnSKeSFIoWn3y6mm23mMqUCaNwXZeRdbU89cIbLP5oBavXbWCrzSexxdQJCCGYMKYeUDQ3t7Dk488YO2oEI2qrWLFyNZPGjSSV9JLjNc07DslEnFH1tdiOy5KPPyeXL6JJyeqG9Ugh2HHrzXn/w09Y39yCJgX1ddXEYwa1NRnGjhrBsy//m1Ejatl8swm05HJMnjCWlmyBJR9/xrYzJmP0VPSzTbh7QPC/X8uXL6e6ujp8uyfepiuuuIL777+fo446ilNOOaXfp9gXIsOp3/C+jHfffTfnnHNOeAG85ZZbuPjii5kyZYp/Q4KRI+s56KCDcF2XadOmoWkab731FjvssD1OIKImNJYuXcoLL7zAfffdh5QCyzKpq6vlpz/9KT/4wQ84//zzee655/if//kf6urqSKVSvP3229x+++185SteC4Af//jHPPDAA0ydOpUPP/yQu+66i912240//vGP3HrrrbiuSywW47333uOJJ55ghx12oKmpCV3Xw30QQjBmjKfg+6Mf/YgPP/yQww8/nOnTp3PLLbew2Wabcdhhh7Fo0SIOPvhgzj77bObMmYOu6xQKBcaPH899992HYcR48803+cY3vsH48eNZsWIFhx12GL/85S+59tpraWho4OSTTyYej3P77bcDnuH0/PPPs3z5ciZNmsTcuXM57bTT+PGPfxwe+YMOOghd13Fdl48//pi//vWvbLXVVrzzzjt87WtfY8aMGeEx/tWvfsWcOXPCEJIQgnQ6w6pVq/j617+OaZqMHj0apRSPPvooa9as5Wtf+xpSShobG9l222354x//iGFoHH/88SSTSZYuXYppmiQSCZ577jmef/55nnzySV555RXuu+8+vvWtb5FOp/nJT35CIpHAMAyuvPJKrr32Wv7zP/+TY445BoCHH36Y3/zmN2iahuM4PPbYY9TV1XH66adzyCGHcOKJJyKE4Ic//CHjx4/noIMO4p577kEIwQsvvMB//Md/cPbZZ/O9732Pv/71r0yePJlVq1bx0EMPMWPGDO64Yy633347uVyOTCbDzTffzI9//GNWrFjBhAkTWLBgAc8++ywjR9ajlGdESCn5179eYMyYMYwdOxalFE8//TSu63LhhReilItleUb6CSecEBpcRx55JHfffTdHH3102a9ESsktt9zCueeey8yZM8PfRFVVhsMPP9zP/dO47bbbOOWUU0gmk8ydO5d99tkHx2l/R91tt90oFousW7eOsWPHtjMIbNtC1w1uu+02Tj/9dHbYYYcw7HfCCSdw44038pe//IXTTju1x6HOICeqriYDeIasZSUxbZt8wSRfNMMyc13XqEqnSMRj6LrEqpCnGJS1a1rfwyiWZYNyUUpDoSiYFlIILNsmbniNZ12lOqzi8x7cWj/zBLFbdZOCa0OpodeqldTqdfLyzygxxFsrzUJnlvDCfq2VWGrAjCfXdVnf2IzruBSLJslEnGwuj6sUibiXr1YoFEmnDWKGhml6ofaYoQOenpXUJChFNl9gxcrVbDF1Irqms76xhdUNjUyeMNpL6/Z3YtXa9bz6zkJaWvK05PKMrq/FNE0+XrEqbBmklOKTT1cxffKE1u+wr59p2Q6pRAIhvLw4r+rPYN0GBztMqO8+QmyElit+qK66urrMcOou119/PT/5yU/Yb7/9uPvuuwc0hNsbolBdPxGc2FdeeYXtt98e8C4WL774IrvssktJ7N9L7rZtM8wR2mGHHXj55Zfbxfrnz5/PtGnTqKur8y/qnqdol112YdGiRTQ0NABeTsnvf/97HnvsMe655x7OPPNMXNdl3rx5PProo7z11ls8+uij3HLLLXz7298Ot7F48WL+9re/8fTTT/O9732Pm266CfCqGV577TWmTZvGySefzD333BN6OX75y1+yww478Mwzz3DLLbeEY22//fa89NJL/PSnP+WCCy7gP/7jP3j++ed5+eWXSafToSF0+umnc9111/GPf/yDt99+m+eee44XXniBiy66iPr6eu677z6eeeYZNttss/A4nHzyydx5552sWbOGt99+mwMOOKDs2D/yyCM88cQTPPnkk1x00UVceeWV4WcfffQRl19+OY8++ihPPvkk3/rWt8jn82VhO03TOP/88znggAN49dVXeeSRR3j44YcBuPzyy9lll1144YUXeOutt1i1ahVz584N15VS8tJLL/H6668D8OSTT3LggQdy6KGHcsEFF/DMM88wZ86c8DzdddddzJs3j7322qvdd2jDhg288MILvPDCC8ycOZPrrruu4y+c/735xje+wWmnncYzzzzDOeecw1NPPcVjjz3GO++8wzPPPMMpp5zCd7/73XCdBQsW8PjjjzNv3jySySRvvvkm8+fP58EHH+T9999n1KhRZQndQojwOx28/+KLL7Ljjjv6LTsCTR4X2zaxba8FR/CdDsYo/W9nvwmlFIVCgXvuuYeTTjqJE044gQcffJCWlpaykM/jjz/Oww8/zNlnn82sWbMYN25c+Bspx/v7pZdeYtdddy3Jx3FQSrHLLrvw4osvUilE2VM0zVMqr0qnqKvOMHpELSOqq8ikk1SnUqSTcQwBtmVVdKtYjmdwuX0M2SmlkH51WCAZoGsaylUk43EQ0JwtYFldJ+8HcgBhrpM/fhCm6yoJPhwjfEeEIWIBoXEmhPBzsFrnMxDGk6HrbDl1EgfsuQO7zJzB6nXrKRQtqjMpPl66iPXvPIr9wcN8+vK9mE2rmDCmnupMmmUrVtGwoZlC0WLl6gYaGlv4bFUDlm2zcs16Vqxcg6sUy1Z8Hnrzg2umlBJd08jmi9TXVVOdSbO+Kcva9U00Z70wIUqxeu16bNvGKNHfcl3F2JF1rFrbQHNLgfraaq9A4fO1jB5RW7bscOG6667ju9/9Lvvvvz+PPvpomMc4mIg8Tv2IUop169b54Sf/Uaob69TW1rJ27dp2y3e3Ymz77bdnxowZuK7DvvvuSzab5bPPPuPpp5/GMAwuueSSMDz31ltvhT/offbZh9raWpRSzJw5k3nz5gEwbdo0Fi5cyL/+9S/++c9/ctFFF/Hggw+2Kwd1HDtMTj7mmGPCp8+nnnqKTCbDD37wAwDWrFnDa6+9xjHHHMPbb7/NU089xVNPPQV4T4CvvfYae++9d9m4pYbNSSedxIEHHkgqleKEE05olzfz5JNPcuutt7Jy5UpaWlrKfmgTJ07kS1/6Eq7rsO222zJq1CgWLlwYGreBy/rpp5/m8ssvp1AooHCJGV4n9n/961/ccMMNYaXQnDlzeOGFFzjrrLMAOPzwwwnKsWfOnMmKFSvanEM3vAHssssuTJ48Gcex0LT2eTWzZ89GCO+YHHPMMWFuTncIjv2//vUvjjzySJLJJK7rcNxxx5XlFcyaNSsMN44bN45UKsURRxzBYYcdxuzZs5kwYUKbqk1BQ0NDWZlwN2ZDbW0t69atC8fofsK75KGHHmKnnXZi0qRJgPc9vf/++zn11FPD5e677z6y2SzPPPMMDz30EEF1KvTsRjIQlaJe1aaBobxWIbgOqUQcu7EJR9cws3niI2pBF2WNXS1fmDUeMyrmPPaEVq+VwFr9Ibm3/or9+buAQBu/HdqWB6Nltu5w/VY5gsCwwfeA+HpTJb/B7hh6gbHkeaA8syvMavK/u25JYrmUIsydUq5D/pNXyS35F06uAZmoJjV1L5JT9kDqsR4dF9OyeP/Dj1mxcg0F0yKTSjJ2VB216+ajlj+DVyYgMFDsiiK2dh3JaQfz5vsf8fyr7xCP6aBg8oSxrF63numTJzJh7CikEDQ0NrLgw09oaslRnU7iOC6uUoyur2XX7bZgZO0q3ln4EWvXN7Fi5RpGj6hh2y2mIIQkm8/z3qJlrG1opLoqjVKeNlS+UGSzCWNYt76Jl954j0w6SUuuQCaVYMbUiTSXFCd0l8HccuWaa67hwgsv5Ctf+QoPP/wwyUHaGisynPoRIQS1tbU0NzdTXV2NlIK99tqL119/nd12282/uOMLwEk/V0bQ3Nzsi3uVPm0p9txzT5YsWUJDQwO1tTVhsu1rr73GFltswYgRIzqch+u6ZLNZtthiC//m7nHUUUeFF8Mg3uxV/mlluSi6rrP//rM44IADOP7449lyyy1paWlpt52AdDpNYCxms1kOPPBAxo4dC8ARRxzB6NGjyWazGIbB4YcfHq57xBFH+JVklccFGDduHFOnTuWKK67g5ZdfLpvnokWLOPfcc3n00UfZYosteOmllzjvvPM6PUeVqtxKw5K5bJ5YTeULctv14/F46DVpewz9NQjOq3eM8AUDO5xiu+0E/w7mVyy2ryQsLbFv67ksTb4N5uA4NvF4nNdff52nn346DNPOmzePbbfdpsx4qq2tZdmyZeH4e+21F3feeaf/fdTCnK/AiFbKpbm5mZqamrI5BnMIfhPBdzH4TQTG8m233cbLL78chofz+TyNjY1lhtOtt96KlJLHH3+cE088kYULFxKPtz9nwb7vueeevPbaa5x00kkEkgZCCF5//XXOPPNMumvY9YQgz0XTBJoQmJZFekQdiUwaoWm0rG/CyudIjKjFFeDajn/u+j6X4LuSffcxGp/7DQgN/PCr++HzWIufI3HAf5Pe9itl67X1NpZWz/lN2DqkIyO0zPii/LsavLwcKQ3Z5obr2kXWPXsD1toPCX5LTraBxnUfkV30DHX7fxc9Wd2tUE7M0Nl9x60omhZCeLlfddUZxIqXKCx7OnxsFSXH31z2ItVGkv33OJyGxiaKps3oETXomoahKcaPG0PS14Grr6umOp0mHothNSylsOSfbLF+BWhxcotXMWHSbsTjWxAzDKZMHEsqEaemKkXBtIjFNHbadjpSejmFO22zOalkgqZsFikkO26zOc25PLl8gWQiQXUmjeM6xOMGO207vct9Lz8fg0/HCeCqq67i4osv5vDDD+eBBx7oUT7UxiYK1fUTwUVj55135r333gvfO/vss7nyyiv56KOP0PUYhhFjzZq1PPnkk+GN5r333vNDF6WGk8O0adPYZ599uOiii3AcF8OIsX79Bi699FLOPPPM8GLxzjvvsGTJEqTUePHFF0kkEkyYMIHDDjuM1157jZ133pn99tuP/fbbj5122qmyfkoJ7777LsViMXxqWL58Oel0mmQySSaTYcOGDQDtqtSC+R922GEsXLgw3Oa+++7LhAkTmDRpEtOnT2ft2rXhZ3vvvXdYxh6MXakS79JLL+WnP/1pWEIesGzZMiZNmsQ222yDruvcf//9ZZ+vWLGC119/HSk1PvjgA1avXl1mpAZzPuCAA7jjjjuIx+OMGFEfeon22msvHnjggbCM/MEHHyzzjnVEsC9Bkn938CotReh1CcJ5kyZN4r333kMIQaFQ4Jlnnmm3nYC9996bRx55hGKxiJQa999/P3vvvXe74ymlRi6XQ9M0DjvsMH71q1+x//7788Ybb1Dq+VTKDb/TgRF24IEHomle5SAIDMOrBLzrrrsIKkmD73QwRoDrupx99tnceOONvPPOO+Fvoqmpmb/97W98/PHHvPjii3z22WesWrWKVatWsWbNGt59910WL15ctg+u63LIIYew9dZb85vf/KbDqkClFKeffjq33XYbb731FroeQ9N0/vSnP7F48WKOPvroisZ0f6LFDNJjRiF1DT0eR2oaqdpqMqNGohsG5E2MokltKolEoOzOw2jdwVyzxDOaIDSavH+7gGLDvJuw1n1Sto7rSxe4vjEjhUD6Vn5ZblIJQrQaRT2hvQxBIGngvWdbNhte+zPWuiXBxMv+azevYsPLczH9noNdvVzHYUx9LZuNG8WksSMZU19LTBO0vP9op/PMf/gsultg3Mg6pkwYRSoZxzA06moySOFJYdi2l1M2ckQ19oKHaJp3Leby16D5M9jwES1v3EPjP37G6KRNPGYwekQNVemk3wPQq8R0UdiuQ0NjE1KT5H3dsGw+z/qmZqQU1FRlMHSNxuYW1jc1k8sXynLRhio333wzF198MWPGjOGYY47hvvvu449//GP4euihhzb1FMuIPE79hvfkNGfOHB577DEOOeQQHMfm+OOPZ82aNey///5sttlmSCn5+OOP+fWvfw3A559/TkNDgx9Oar14B67yP/3pT5xzzjlsueWWzJgxg3fffbdd3spWW23FqaeeSl1dHa+99hq33XYbmqZx0EEHMXv2bHbccUe22247GhoaqK+vL6s2qkSQpzNz5kwcx2HhwoX87ne/Q9M0dthhB0aMGMG2227LjjvuyF133dVuztdeey3HHXccf//735k4cSIffPABP/3pT5kzZw533HEHJ510Er/+9a+prq7m/fff589//jM77bQT3/zmNznssMMYOXJkOwNo1113Zbfddms313322YdsNssBBxxQVtEXMGXKFC688EJSqRSvvfYaN954I6lUqkRfyMt3ufbaa5k9ezb/+Mc/GD16NLZt88gjj/CTn/yEww8/nFmzZtHY2Mi0adO6VRp7wgkncNJJJ3HXXXdx/vnndyvUlU6n2XvvvYnH47S0tPCPf/wDgDPOOINZs2bx5ptv0tzczMSJE8N1jj76aI488kjmzZvHKaecwne+8x322WcfdtppJzbbbDM+/vjjMF+rFCEEixYtYvbs2ey8885ks1kaGho44ogjygwdpVz23Xdfli5dSkNDAzU11ei6xuOPP863v/1tfve737HFFluwZMkStttuO0466SQAHnvsMebMmVM2FniimAcccAA33HADRx99NCNHjiSVSvHhhx9yxRVXcMcdd3DkkUdSVVXl6yt5Xr1jjz2W22+/vcybGORXXXrppRxzzDGcffbZoUet7TZ33313brnlFubMmcPEiRNpbm5GSskjjzxCbW1tRb2p/kS5Lna+gJFJ+z3TvHCU8CuiZFUaNx4DXcctFrHyBfSaKoRfmUUvWmRk334ETzCoA6NQSLLvPkrtrHNa56kUrnI955LwH2ACL6bwJQgqWEmO45DPFygWi2HFbVts2ynxMHnvBWG+wPNo2w6Bo8HMbqDw8fyOk52Ui7VqAcX1nyPTo7o+IEqhRGvFIIDdsBRltnS2FigH8/N3MSbuUlJJqLAtG9M0MXQ9LOoxP5lPYfEz4fxKcQvNbHj+JhIH/DB8T+AJW7qOG45RCVcpCsX231HL7rkY8mAM1b3yyisArFq1ijPOOKPd55MnT2b27Nn9MbV+QaiBl4Me9gQijN5TfJ499tiDl19+mWQygRfC8UrUFy1ahKZpfqm7d8G84oorSKfTfPe7360o5hgIYK5evZpVq1YxdepUMpkMuVyWeNyr4rr88st58sknWbRoEVOmTAkNgyB80tTUxEcffUR9fX1407Usy+syHo+FT3m2bZNIJAAoFAosWbKEeDzOpEmTfKVtO9T/CZ6ykskk2WyWVCqF49gEIpVCeAZiYGxkMpkwpANeiM1xHDbffHPi8bhXluyXtedyOV/00A7L05VywhCLlBr5fJ50Oo1SKizLHzFiBPX19ZimSTKZ5J133uGEE07g7bffZuHChUyYMIHq6uoyOYIAT8vHK9M3TZMtttgirNQLjIza2tpQWkEI6amO67r/xC1D2QCvp5cXKglkAgI5gng8jm2b6HosXF/Xdd9DJGlubmbdunWhZ81xbHTdoFAo8NFHHzF16tQw4Tbo+RV4ojxRT++uE+R7ect7NyXHcfwqSiOUhsjlcixdupRUKuXLZajQYAkwjDiXX345VVVVfOc73/EFSj2dmsArNGHCBOrr6wFYv349++67L6+99hqGoberVvNCWJ6MxOLFi3Ech+nTpxOLxSgUCv4x1MPiCU3Tw2TbeDxOLpfzv2+Wf+5iZLNerzFN00q+2/GS6knpy1S4LFy4kEQiEcqDdCaA2VeUUjQ1NZEyYuRWryExdgy267dF0SvfXFzbxi6aaLEYrm2T39BIvLYaI5HwHq668DAIwNB0Vt95Fk7z6s6XrRnPyONvbBXr9A2b0qTvtuG70n1TChzXIZvNEjNitLS0UFWVKVvOex50sW3P0xX8HtrbVoJcLuf3icsTz62g8V+/7nT+ALVfOpnUtK69wI7joHwhzyAHrPDZuzQ8f2OX61btdDzJzfcrOxbNzc0kkklPSdz3xq7++09wmld1OlZi9zNJTtjBb3MjaGppoaGxuU/fwG1nbN7lMk1NTdTU1LBihSe/MZA0NTUzceJ0Ghsbe1VVN9iJPE79iOs6VFVVcd111/HRRx+x7bbb+krIXthryy23ALwLTlAmXVNTw1lnndVhGxXHsXAcQX39COrrvZwm0yyEhlOArutstdWWKKVCA0wpB9d1SKdTzJw5k+CmGLSoMAwtvEkGf3vrem0BttpqS390Fd6AHMfFdb1S5VhMx7YtX1XZCi8qtu3lEEycOME31BSWZRLcnISQTJ++eTh2MF/H8S7WiUQc13V8F7SgsXEDVVVV/j55bR0SiXg4phCSGTOmh+PFYuU3ayklW265pV86X1lp3JuzZOrUKf5cHNat24Cu66RSqXC+pRpHhhHoI7mAE+q2lB7TWEz3H5iVf7zNcJxgfcsqout+B/hEnGnTpvpVX264bCymh98f7zio0HAIzp1ShOdv1KiRjBo10q92a203ousynJ9lmcTjMbbeeit/n+12BmXw/ve//33uuOOO8Ibnfa8FI0fW+6FW7zsSiGPeeOONZYZLKa7rsmGDJ2RZ+j1oPSaUqdI7jh3uo+vaFb5vJolE3D+/dsl3u3XbQdVf6e+wo/0dCGTMIFZXx4aWLNl8EUPXGDOyrqIcgNR1Yv6DldQkidoa9FgMu1Akt249yRE1aMmEl3vUWVVbN576pdBwHQVG8Hf7uFvb8VtVwr3j77oumtSI+7/JRJvG2YGXqVj0pCfiiVi7ZrgBlfL3+gvPa0ZZ+b5ePbZb62rV41rHoFxeIcAtNHdpNCE07NWLWJeehq5JMqmk13euJzvSRwLl/aG+jU1J5HEa4qxcuZJ3332Xgw46aFNPZdARlPeXJsdHREREfBEpFApMnTqVlStXbpTtjR07lo8++iiMYgwnIsMpIiIiIiLiC0ChUMA0BzafLyAWiw1LowkiwykiIiIiIiIiottEcgQREREREREREd0kMpwiIiIiIiIiIrpJZDhFRERERERERHSTyHCKiIiIiIiIiOgmkeEUEREREREREdFNIsNpmDOQgnIRm47ovA4/onM6PInO6/AjMpx6SVNTE5dffjnbbbcdVVVV1NfXs9tuu/HrX/8ayypvWWHbNldffTVbbrkl8Xic8ePH881vfpN169Z1a1vLli0LW2t09LryyivL1lm8eDH77LMPqVSKkSNHct1115V9vtlmm1FbW4vjlLfDADjuuOMQQoR9x0pxHIeamho222yzbs19qLExzyvAqaee2uE5Pf/889stH53XnrOxz+nPf/5zZs2axbhx44jH44wZM4Z99tmHO++8s+J5ic5p79jY5/XJJ5/km9/8JnvssQepVAohBLfffnuHy0fndRijInqMZVlq1113VVJKdcopp6jf/OY36le/+pXab7/9FKBOOOGEsuVPOukkBagjjjhC3XLLLeqiiy5SyWRSbbPNNqqpqanL7bW0tKg777yz4muzzTZTgHr77bfL1tluu+3UoYceqh566CF1/fXXq0wmox5++OHw85NPPlkB6uWXXy5bz3VdNXr0aGUYhho/fny7ucyfP18B6pRTTunBERsabOzzqpRSp5xyigIqntvXX3+93fLRee0Zm+KcHnnkkerUU09VV199tbr11lvVddddp7785S8rQJ188sntlo/Oac/ZVL9VXdfV9ttvr770pS8pQM2dO7fD5aPzOnyJDKde8OSTTypAfec73yl733EcteOOOyohhFq/fr1SSqmnn35aAeqoo44qW/b+++9XgLrkkkt6PY/FixcrIYTaY489yt5ft26dAsI5KKXUeeedp84777zw7zvuuEMB6qqrripb95133lGAOvPMMxWgFixYUPb5z3/+cwWoP/zhD72e92BlU5zXwHDqDtF57TmD5beqlFKHHHKIAtTSpUvD96Jz2js2xXldsWKFyufzSiml5s6d26nhFJ3X4U0UqusFjY2NAIwfP77sfSkl48aNQ0oZdqn/wx/+AMD3vve9smWPPfZYpkyZEn7eG37/+9+jlOLMM88se7+uro5Ro0Zx+eWXs2TJEubNm8df/vIXtt5663CZAw88EIBnnnmmbN1nnnkGKSU/+tGP0DSt4ucAX/7yl3s978HKpjyvSimampoquu0DovPacwbLbxVg6tSpADQ0NITvRee0d2yK8zphwoRutxCJzuswZ1NbbkORzz//XKXTaVVfX6/uuece9fHHH6tFixapK664Qgkh1KWXXhouu9VWWykppSoUCu3GOeGEExSgVq1a1eM5WJalxo4dq6qrq1VLS0u7zx999FE1YsQIBShAHXfcccq27bJltthiC5VKpVSxWAzfmz17ttppp52UUkrttttu6thjjw0/KxaLKplMqi233LLH8x0KbIrzGnicqqurFaA0TVO77767evDBBysuH53XnrEpf6vr1q1Ta9asUQsXLlQ33nijSiQSavLkye3Gj85pz9nU1+CuPE5KRed1OBMZTr3kqaeeUtOnTw9/FIBKJBLqlltuKVsuk8mo0aNHVxzjggsuUEDFXJauePDBBxWgzjnnnA6XaW5uVvPnzy8LDZRyzjnnKEA999xzSinPzV1XV6e+//3vh/Orr69XrusqpZR69tlnFaC++c1v9ni+Q4WNfV5/8IMfqHPPPVfdcccd6uGHH1bXXHONmjRpkgLUNddcU3Gd6Lz2jE31W62vrw+3J4RQBxxwgPrggw8qLhud056zKa/B3TGclIrO63AlCtX1kvr6embOnMnpp5/Ovffey1133cUhhxzC2WefzS9+8YtwuVwuF7qM2xK4fXO5XI+3/7vf/Q6gXZiulEwmw+677x6GCNrS1lX81ltvsX79eg444AAA9t9/f9atW8e///3vsuWC9YYjG/u8Xn311dxwww2cfPLJHHXUUVxwwQW8++67TJ06lR/96EcsX7683TrRee0Zm+q3+uCDD/KPf/yDuXPnMnv2bACam5srLhud056zqa/B3SE6r8OUTW25DUXefvttlUgk1A9+8IN2n339619XUsqwyq2zp53zzz+/V087n3zyiZJSqp133rnnky9h7dq1Sgih9t13X6WUUr/4xS+UpmmqsbFRKaVUU1OT0nVdXXvttUoppfbZZx8lhFBr167t03YHK5v6vJZy4403KkD97ne/6/G60XltZTCd0//+7/9WiURCLVq0qMfrRue0nE19XrvrceqK6LwOTSKPUy/43//9XwqFAscdd1y7z4477jhc12XevHkATJw4kbVr11YUQfv000/DZXrCbbfdhuu6nHXWWb2YfSv19fXssMMOvPzyy+RyOebNm8cuu+xCdXU1AFVVVey8884888wz5HI5XnnlFXbccUfq6+v7tN3ByqY+r6UET6irV6/u8brReW1lMJ3TU045hUKhwB133NHjdaNzWs5gOq99ITqvQ5PIcOoFn332GUA7kbXS92zbBuBLX/oSrusyf/78dsu+9NJLTJ48mdGjR3d7267rctttt5FOpznxxBN7M/0yvvzlL2OaJs8++yz//Oc/21Vq7L///vzzn//k2WefxTTNYV3JsSnPa1sWLVoEwNixY3u1fnRePQbTOc3n8wCsX7++V+tH57SVwXRe+0p0XocekeHUC7bddlvA8/yUopRi7ty5AOyxxx4AfOMb3wBopxr74IMPsmzZsvDzgLVr1/LBBx+E5bZt+cc//sEnn3zCcccdR1VVVZ/3JYiVX3PNNTQ3N4ex9YD999+fpqYmrrnmmrLlhyMb+7xms1laWlrazWPVqlX84he/IB6Pc8ghh/RqX6Lz6rGxz2lTU1NoILXd3q9+9SsA9tprr17tS3ROW9mU1+D+JjqvQ5BNGigconzyySdq1KhRClCHHnqouvHGG9V1112n9txzTwWoY445pmz5oOT1iCOOUL/73e/UxRdfrJLJpNpqq63CWHbApZde2mns/JhjjlGAmj9/fr/sS3Nzs9J1XQHKMIx20galn+u6rpqbm/tlu4ORjX1e33zzTVVdXa1OP/10dc0116hbbrlFnX/++aqurk4JIdSvf/3rXu9LdF49NvY5nTdvnqqtrVX/9V//pa655hr1u9/9Tv30pz9VM2fOVID66le/2q4kvbtE57SVTXEN/ve//60uv/xydfnll4fX4WOOOSZ879///nev9iU6r0OPyHDqJR9//LE688wz1eTJk5VhGCqZTKodd9xR/fKXv1SWZZUta5qm+vnPf65mzJihYrGYGjt2rDrrrLPU6tWr243bmeG0atUqZRiG2m677fp1X/baay8FqL333rvi57vvvrsC1F577dWv2x2MbMzz+vnnn6tvfOMbauutt1Y1NTVK13U1ZswYNXv27LA8uS9E59VjY57T5cuXq3POOUdtt912qra2VmmapkaMGKH2339/dcstt/TaaAqIzmkrG/saHCSEd/TqS6J4dF6HFkIppQbCkxURERERERERMdyIcpwiIiIiIiIiIrpJZDhFRERERERERHSTyHCKiIiIiIiIiOgmkeEUEREREREREdFNIsMpIiIiIiIiIqKbRIZTREREREREREQ3iQyniIiIiIiIiIhuEhlOERERERERERHdJDKcIiIiIiIiIiK6SWQ4RURERERERER0k8hwioiIiIiIiIjoJpHhFBERERERERHRTf4/MJzD8kiloOEAAAAASUVORK5CYII=", + "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "imp.plot_basemap_eai_exposure(figsize=(6, 6))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Further CLIMADA features\n", + "\n", + "CLIMADA offers several additional features and modules that complement its basic impact and risk calculation, among which are\n", + "- uncertainty and sensitivity analysis\n", + "- adaptation option appraisal and cost benefit analysis\n", + "- several tools for providing hazard objects such as tropical cyclones, floods, or winter storms; and exposure objects such as Litpop, or open street maps\n", + "- impact function calibration methods\n", + "\n", + "We end this introduction with a simple adaptation measure analysis. \n", + "\n", + "### Adaptation measure analysis\n", + "\n", + "Consider a simple adaptation measure that results in a 10% decrease in the percentage of affected assets (PAA) decreases and a 20% decrease in the mean damage degree (MDD). We apply this measure and recompute the impact." + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2025-01-21 15:49:48,642 - climada.entity.exposures.base - INFO - Exposures matching centroids already found for TC\n", + "2025-01-21 15:49:48,643 - climada.entity.exposures.base - INFO - Existing centroids will be overwritten for TC\n", + "2025-01-21 15:49:48,643 - climada.entity.exposures.base - INFO - Matching 50 exposures with 2500 centroids.\n", + "2025-01-21 15:49:48,645 - climada.util.coordinates - INFO - No exact centroid match found. Reprojecting coordinates to nearest neighbor closer than the threshold = 100\n", + "2025-01-21 15:49:48,648 - climada.engine.impact_calc - INFO - Calculating impact for 250 assets (>0) and 216 events.\n", + "2025-01-21 15:49:48,648 - climada.engine.impact_calc - INFO - cover and/or deductible columns detected, going to calculate insured impact\n" + ] + } + ], + "source": [ + "from climada.entity.measures import Measure\n", + "\n", + "meas = Measure(haz_type=\"TC\", paa_impact=(0.9, 0), mdd_impact=(0.8, 0))\n", + "\n", + "new_exp, new_impfs, new_haz = meas.apply(exp, impf_set, haz)\n", + "new_imp = ImpactCalc(new_exp, new_impfs, new_haz).impact()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To analyze the effect of the adaptation measure, we can, for instance, plot the impact exceedance frequency curves that describe, according to the given data, how frequent different impacts thresholds are expected to be exceeded." + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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+XDRp0kS4urpqvMflOb+V+R4XuXHjhjA0NBQAxI8//ljm+1qkaLWUhYWF+Pjjj8XOnTvF33//LaZNm6ZeLSWEEF27dhW2trZi2bJlYufOnWLmzJnqz87jE4pLOl9FlEqlcHd3F+7u7gKA2LlzZ7E+n376qTA0NBRvvvmm2Lhxo4iMjBTr1q0Tb731lnj//fe1Oi4iJjcPPJ7c/P777wKAMDc31/gxNDQsccZ+WcnNp59+qtF28OBBAUAkJiZW9mGQlspaLYVHlqy++uqrwszMrNiqnj/++EMAEN9884267fr162LkyJHCxcVFGBkZCTc3NzF48GBx+/ZtdZ/MzEwxe/Zs0aRJE2FsbKxetjx16lSNJd5CSAlJmzZthLm5uTA1NRUNGjQQw4YNE8eOHVP3UalUYt68ecLDw0MYGxuLFi1aiM2bNxdbySOEEJ999pnw9vYWCoVC+Pr6imXLlqmXkj9K2y9eIYSIjY0VgwYNEvb29sLY2Fh4enqK4cOHi9zcXHWfO3fuiEmTJgkfHx9hZGQk7OzsRFBQkPjf//6nThhLU97kRghpFc/IkSNFvXr1hJGRkXB0dBTt27cXH3/8cbHYn3vuOWFiYiLs7OzEqFGjxN9//10suVGpVOKLL74Q9evXFyYmJiI4OFjs3bu3xPdY2/Nb2e9xkdDQUGFnZyeys7NLfG9KkpqaKiZPniw8PT2FkZGRcHJyEs8//7w4d+6cuk9iYqIYOHCgsLOzE9bW1uLVV18Vx44dK3dyI4QQ7777rgAgPDw8hFKpLLHPX3/9JcLCwoSVlZVQKBTCy8tLDBw4UOzevVvr46K6TSaEEFU/PlTzyWQybNy4Ub3iad26dRg6dChiYmI0VsUAgIWFRbGJesOHD8f9+/eLrbjq1KkTAgMD8e2336rbNm7ciMGDByM7O5uXpYhqkMjISISFhSEiIqLW7c2VnJwMLy8vTJw4EV988YWuwyHSKc65KUVgYCCUSiWSk5PRsWPHCj9Pu3btsHnzZo22nTt3Ijg4mIkNET21Gzdu4MqVK/jyyy8hl8sxefJkXYdEpHN1eil4ZmYmTp48iZMnTwKQlm2fPHkSCQkJaNy4MYYOHYphw4Zhw4YNiI+Px9GjR/H5559j27Zt6ueIjY3FyZMnce/ePaSlpWk8HwCMGTMG165dw7Rp0xAXF4cVK1Zg+fLlmD59ejUfLRHpox9//BGhoaGIiYnBzz//jHr16uk6JCKdq9OXpYqGoB8XHh6OVatWoaCgAB9//DHWrFmDmzdvwt7eHu3atcOcOXPg7+8PQFpJUFR061GPvq379u3D1KlTERMTAzc3N8ycORNjxoypugMjIiKqw+p0ckNERET6p05fliIiIiL9w+SGiIiI9EqdWy2lUqlw69YtWFpallomnIiIiGoWIQQyMjLg5uYGubzssZk6l9zcunULHh4eug6DiIiIKuD69etP3PC1ziU3lpaWAKQ3p6Sda4mIiKjmSU9Ph4eHh/p7vCx1LrkpuhRlZWXF5IaIiKiW0WZKCScUExERkV5hckNERER6hckNERER6ZU6N+dGW0qlEgUFBboOg0iDkZFRsV3qiYhIE5ObxwghkJSUhPv37+s6FKIS2djYwMXFhXWaiIhKweTmMUWJjZOTE8zMzPgFQjWGEALZ2dlITk4GALi6uuo4IiKimonJzSOUSqU6sbG3t9d1OETFmJqaAgCSk5Ph5OTES1RERCXghOJHFM2xMTMz03EkRKUr+nxyThgRUcmY3JSAl6KoJuPnk4iobExuiIiISK8wuanDIiMjIZPJnrgyzNvbGwsWLKiWmIiIiJ4Wkxs9sHjxYlhaWqKwsFDdlpmZCSMjI3Ts2FGj74EDByCTyXDhwgW0b98eiYmJsLa2BgCsWrUKNjY21Rm6BiZRRERUGZjc6IGwsDBkZmbi2LFj6rYDBw7AxcUFR48eRXZ2tro9MjISbm5uaNy4MYyNjVkvRYc4IZiI9NGt+zmIS0zXaQxMbvRAkyZN4ObmhsjISHVbZGQk+vbtiwYNGuDw4cMa7WFhYerbRZelIiMjMWLECKSlpUEmk0Emk+HDDz9UPy47OxsjR46EpaUlPD09sXTpUo0Yzpw5g86dO8PU1BT29vZ44403kJmZqb4/NDQUU6ZM0XhMv379MHz4cPX9165dw9SpU9WvXxqZTIYlS5agd+/eMDMzg6+vL6KionDp0iWEhobC3Nwc7dq1w+XLlzUet3nzZgQFBcHExAT169fHnDlzNEa75s+fD39/f5ibm8PDwwPjxo3TOIZr166hT58+sLW1hbm5OZo3b45t27YBKHnU66+//tI4jg8//BAtW7bEihUrUL9+fSgUCgghkJaWhjfeeANOTk6wsrJC586dcerUqVKPn4ioJsrKK8TXO88j7KtIvPX7KShVQmexMLl5AiEEsvMLdfIjhPYfjNDQUERERKh/j4iIQGhoKEJCQtTt+fn5iIqKUic3j2rfvj0WLFgAKysrJCYmIjExEdOnT1ff//XXXyM4OBjR0dEYN24cxo4di3PnzgGQEp8ePXrA1tYWR48exR9//IHdu3djwoQJWse/YcMGuLu746OPPlK/flnmzp2LYcOG4eTJk2jatCleeeUVvPnmm5g1a5Z6BOvR19+xYwdeffVVTJo0CbGxsViyZAlWrVqFTz75RN1HLpdj4cKFOHv2LFavXo29e/dixowZ6vvHjx+PvLw87N+/H2fOnMHnn38OCwsLrY8RAC5duoTff/8d69evx8mTJwEAzz//PJKSkrBt2zYcP34crVq1QpcuXXDv3r1yPTcRkS4oVQLrjiYg9KtI/N/eS8grVMHCxBD3s/N1FhOL+D1BToESzd7foZPXjv2oO8yMtTtFoaGhmDp1KgoLC5GTk4Po6Gh06tQJSqUSCxcuBAAcOXIEOTk5JSY3xsbGsLa2hkwmg4uLS7H7e/XqhXHjxgEAZs6ciW+++QaRkZFo2rQpfv75Z+Tk5GDNmjUwNzcHAHz33Xfo06cPPv/8czg7Oz8xfjs7OxgYGMDS0rLE13/ciBEjMHjwYHU87dq1w3vvvYfu3bsDACZPnowRI0ao+3/yySd45513EB4eDgCoX78+5s6dixkzZuCDDz4AAI2RJR8fH8ydOxdjx47FokWLAAAJCQkYMGAA/P391c9RXvn5+fjpp5/g6OgIANi7dy/OnDmD5ORkKBQKAMBXX32Fv/76C3/++SfeeOONcr8GEVF1OXQpBXO3xOJcUgYAwMveDLN6+qJ7c2edTnlgcqMnwsLCkJWVhaNHjyI1NRWNGzeGk5MTQkJC8NprryErKwuRkZHw9PSs0JdyixYt1LeLEqCibQDi4uIQEBCgTmwA4Nlnn4VKpcL58+e1Sm6eJp6i5y9KOoracnNzkZ6eDisrKxw/fhxHjx7VGKlRKpXIzc1FdnY2zMzMEBERgU8//RSxsbFIT09HYWEhcnNzkZWVBXNzc0yaNAljx47Fzp070bVrVwwYMEAjDm14eXmpExsAOH78ODIzM4tVxM7JySl2WY2IqKa4lJyJedvisOec9D1gZWKISV0aYVg7bxgb6v6iEJObJzA1MkDsR9119traatiwIdzd3REREYHU1FSEhIQAAFxcXODj44NDhw4hIiICnTt3rlAsRkZGGr/LZDKoVCoA0qW70jL0ona5XF7sMtvTTKh9NJ6i1yiprShGlUqFOXPmoH///sWey8TEBNeuXUOvXr0wZswYzJ07F3Z2djh48CBGjRqljvP1119H9+7dsXXrVuzcuRPz5s3D119/jYkTJ2p9fI8mgEVxubq6asyXKqLLlWtERCW5l5WPb3dfwNp/E6BUCRjKZXi1rRcmd2kEW3NjXYenxuTmCWQymdaXhnQtLCwMkZGRSE1Nxdtvv61uDwkJwY4dO3DkyBGNSzWPMzY2hlKpLPfrNmvWDKtXr1aPcADAoUOHIJfL0bhxYwCAo6OjxjwapVKJs2fPalwiq+jra6NVq1Y4f/48GjZsWOL9x44dQ2FhIb7++mvI5dL/On7//fdi/Tw8PDBmzBiMGTMGs2bNwrJlyzBx4kQ4OjoiIyND4z0omlPzpLiSkpJgaGgIb2/vCh8fEVFVyitUYs3ha1i49yIycqWFGF19nTGrV1M0cCzf3MPqoPuxI6o0YWFhOHjwIE6ePKkeuQGk5GbZsmXIzc0tcb5NEW9vb2RmZmLPnj1ISUnRWEJelqFDh8LExATh4eE4e/YsIiIiMHHiRLz22mvqS0adO3fG1q1bsXXrVpw7dw7jxo0rVjzQ29sb+/fvx82bN5GSklL+N6AM77//PtasWYMPP/wQMTExiIuLw7p16zB79mwAQIMGDVBYWIj/+7//w5UrV/DTTz9h8eLFGs8xZcoU7NixA/Hx8Thx4gT27t0LX19fAECbNm1gZmaGd999F5cuXcIvv/yCVatWPTGurl27ol27dujXrx927NiBq1ev4vDhw5g9e7bG0n4iIl0QQuCfM4l4bv5+fLItDhm5hfB1tcIvr7fBj+HBNTKxAZjc6JWwsDDk5OSgYcOGGvNcQkJCkJGRgQYNGsDDw6PUx7dv3x5jxozBkCFD4OjoiC+++EKr1zUzM8OOHTtw7949tG7dGgMHDkSXLl3w3XffqfuMHDkS4eHhGDZsGEJCQuDj41Ms0froo49w9epVNGjQQGNeSmXo3r07tmzZgl27dqF169Zo27Yt5s+fDy8vLwBAy5YtMX/+fHz++efw8/PDzz//jHnz5mk8h1KpxPjx4+Hr64sePXqgSZMm6snGdnZ2WLt2LbZt2wZ/f3/8+uuvGkvpSyOTybBt2zZ06tQJI0eOROPGjfHSSy/h6tWrVTJXiYhIW6eu38fgJVEY+/MJJNzLhqOlAl8MbIEtEzugfUMHXYdXJpkoz3pjPZCeng5ra2ukpaXByspK477c3FzEx8fDx8cHJiYmOoqQqGz8nBJRVbp1Pwdf7jiPjdE3AQAmRnK80akB3uxUH+YK3U3TKOv7+3G1YzIJERERVamsvEIs3ncZS/dfQV6htBijf2A9TO/eBG42pjqOrnyY3BAREdVhSpXAn8ev46udF3AnIw8A8Iy3HWb39kULdxvdBldBTG6IiIjqqJKL8DVF9+a1e99BnU4onjdvHlq3bg1LS0s4OTmhX79+OH/+/BMft2/fPo09gh5f1UJERESlu5SciVGrjmLoj//iXFIGLE0MMft5X+yc2gk9/FxrdWID6HjkZt++fRg/fjxat26NwsJC/O9//0O3bt0QGxtbrNhZkfj4ePTq1QujR4/G2rVrcejQIYwbNw6Ojo4YMGBANR8BERFR7fF4ET4DuQyvtfXCpC6NYFeDivA9LZ0mN9u3b9f4feXKlXBycsLx48fRqVOnEh+zePFieHp6YsGCBQAAX19fHDt2DF999RWTGyIiohKUXITPCe/09EVDp5pZq+Zp1Kg5N2lpaQCkmiGliYqKQrdu3TTaunfvjuXLl6OgoKDYNgF5eXnIy8tT/56enl6JERMREdVcQghsP5uEef+cQ8I9qTCrr6sVZj/vi2dreK2ap1FjkhshBKZNm4YOHTrAz8+v1H5JSUnFips5OzujsLAQKSkpcHV11bhv3rx5mDNnTpXETEREVFOdun4fH2+NxdGrqQAAR0sF3u7WBAOC3GEgr91zap6kxiQ3EyZMwOnTp3Hw4MEn9n18olNRHcKSJkDNmjUL06ZNU/+enp5eZpVeIiKi2qymFuGrTjVi+4WJEydi06ZNiIiIgLu7e5l9XVxckJSUpNGWnJwMQ0ND2NvbF+uvUChgZWWl8UOSyMhIyGSyYns8Pc7b21s9x4mIiGqmrLxCzN95Hp2/jlQnNv0D62HvW6GY9lzjOpPYADpOboQQmDBhAjZs2IC9e/fCx8fniY9p164ddu3apdG2c+dOBAcHF5tvU1csXrwYlpaWKCwsVLdlZmbCyMgIHTt21Oh74MAByGQyXLhwAe3bt0diYiKsra0BAKtWrYKNjU11hk5ERE9JqRL4/eh1hH4ViYV7LyG3QIVnvO2wacKzmD+kZa2rLlwZdJrGjR8/Hr/88gv+/vtvWFpaqkdkrK2tYWoqnYxZs2bh5s2bWLNmDQBgzJgx+O677zBt2jSMHj0aUVFRWL58OX799VedHYeuhYWFITMzE8eOHUPbtm0BSEmMi4sLjh49iuzsbJiZmQGQRmvc3NzQuHFjANJIGJVOCAGlUglDw7rzPx4iqj0OXUrBx1vjEJcoLZbRlyJ8T0unIzc//PAD0tLSEBoaCldXV/XPunXr1H0SExORkJCg/t3Hxwfbtm1DZGQkWrZsiblz52LhwoV1ehl4kyZN4ObmhsjISHVbZGQk+vbtiwYNGuDw4cMa7UW7cT96WSoyMhIjRoxAWloaZDIZZDKZxq7W2dnZGDlyJCwtLeHp6YmlS5eWGVNoaCgmTpyIKVOmwNbWFs7Ozli6dCmysrIwYsQIWFpaokGDBvjnn380HhcbG4tevXrBwsICzs7OeO2115CSkqK+f/v27ejQoQNsbGxgb2+P3r174/Lly+r78/PzMWHCBLi6usLExATe3t7q3b2vXr0KmUyGkydPqvvfv38fMplM/d4VvSc7duxAcHAwFAoFDhw4ACEEvvjiC9SvXx+mpqYICAjAn3/+qdX5ISKqbJfvZOL11VIRvrjEdL0rwve0dH5ZqqSf4cOHq/usWrVK40sbAEJCQnDixAnk5eUhPj4eY8aMqcoggfws3fyUY8P20NBQREREqH+PiIhAaGgoQkJC1O35+fmIiopSJzePat++PRYsWAArKyskJiYiMTER06dPV9//9ddfIzg4GNHR0Rg3bhzGjh2Lc+fOlRnT6tWr4eDggP/++w8TJ07E2LFjMWjQILRv3x4nTpxA9+7d8dprryE7W1qemJiYiJCQELRs2RLHjh3D9u3bcfv2bQwePFj9nFlZWZg2bRqOHj2KPXv2QC6X48UXX4RKJW3ytnDhQmzatAm///47zp8/j7Vr18Lb21vr97HIjBkzMG/ePMTFxaFFixaYPXs2Vq5ciR9++AExMTGYOnUqXn31Vezbt6/cz01EVFGpWfn4cFMMun+zH7vjkmEgl2F4e2/sezsMr3esD4Whga5DrBE41v4kBdnAp266ee13bwHGJVdqflxoaCimTp2KwsJC5OTkIDo6Gp06dYJSqcTChQsBAEeOHEFOTk6JyY2xsTGsra0hk8lKvFTVq1cvjBs3DgAwc+ZMfPPNN4iMjETTpk1LjSkgIACzZ88GIF1e/Oyzz+Dg4IDRo0cDAN5//3388MMPOH36NNq2bYsffvgBrVq1wqeffqp+jhUrVsDDwwMXLlxA48aNi43QLV++HE5OToiNjYWfnx8SEhLQqFEjdOjQATKZDF5eXlq9f4/76KOP8NxzzwGQEqr58+dj7969aNeuHQCgfv36OHjwIJYsWYKQkJAKvQYRkbbyCpX4KeoaFu65iPQ6UITvaTG50RNhYWHIysrC0aNHkZqaisaNG8PJyQkhISF47bXXkJWVhcjISHh6eqJ+/frlfv4WLVqobxclQMnJyVo/xsDAAPb29vD391e3FdUrKnqe48ePIyIiAhYWxf+iXr58GY0bN8bly5fx3nvv4ciRI0hJSVGP2CQkJMDPzw/Dhw/Hc889hyZNmqBHjx7o3bt3saKP2ggODlbfjo2NRW5urjrZKZKfn4/AwMByPzcRkbaEENgRIxXhu3a37hThe1pMbp7EyEwaQdHVa2upYcOGcHd3R0REBFJTU9WjCS4uLvDx8cGhQ4cQERGBzp07VyyUx1aiyWQydWJRnsc82lZ0TbjoeVQqFfr06YPPP/+82HMVFWfs06cPPDw8sGzZMri5uUGlUsHPzw/5+fkAgFatWiE+Ph7//PMPdu/ejcGDB6Nr1674888/IZdLV2HFI5f7CgoKSoz90b3NiuLbunUr6tWrp9FPoVCU+R4QEVXU6Rv38fGWOPx39R6AulWE72kxuXkSmUzrS0O6FhYWhsjISKSmpuLtt99Wt4eEhGDHjh04cuQIRowYUerjjY2NoVQqqyPUErVq1Qrr16+Ht7d3iauT7t69i7i4OCxZskS9xL2koo9WVlYYMmQIhgwZgoEDB6JHjx64d+8eHB0dAUhze4pGXB6dXFyaZs2aQaFQICEhgZegiKjKJabl4Mvt57Hh0SJ8HevjzZAGdapWzdPgu6RHwsLCMH78eBQUFGh8CYeEhGDs2LHIzc0tcb5NEW9vb2RmZmLPnj0ICAiAmZmZegl5dRg/fjyWLVuGl19+GW+//TYcHBxw6dIl/Pbbb1i2bBlsbW1hb2+PpUuXwtXVFQkJCXjnnXc0nuObb76Bq6srWrZsCblcjj/++AMuLi6wsbGBXC5H27Zt8dlnn8Hb2xspKSnqOUFlsbS0xPTp0zF16lSoVCp06NAB6enpOHz4MCwsLBAeHl5VbwkR1SFZeYVYsu8ylh64gtwCacS4f2A9TO/epE7WqnkaNaJCMVWOsLAw5OTkoGHDhhr7b4WEhCAjIwMNGjQoc+uJ9u3bY8yYMRgyZAgcHR3xxRdfVEfYam5ubjh06BCUSiW6d+8OPz8/TJ48GdbW1pDL5ZDL5fjtt99w/Phx+Pn5YerUqfjyyy81nsPCwgKff/45goOD0bp1a1y9ehXbtm1TX5JasWIFCgoKEBwcjMmTJ+Pjjz/WKra5c+fi/fffx7x58+Dr64vu3btj8+bNWhWeJCIqC4vwVT6ZEOVYb6wH0tPTYW1tjbS0tGJbMeTm5iI+Ph4+Pj4wMTHRUYREZePnlEh/HL6UgrmPFOHztDPDu71YhK8kZX1/P46XpYiIiKrZ5TuZmLctDrvjpNWiliaGmNylEV5r58VaNZWAyQ0REVE1Sc3Kx7d7LmLtkWsoVAkYyGV4ra0XJnVpBDtzY12HpzeY3BAREVWx/EIV1kRdZRG+asLkhoiIqIqUVISvqYsl3uvdjEX4qhCTmxLUsTnWVMvw80lUO5y5kYa5W2PxX/zDInzTuzXGwCAPFuGrYkxuHlFUPTc7Oxumplx6RzVT0Uajj1eAJqKaITEtB1/uOI8NJ1iET1f4Lj/CwMAANjY26r2OzMzMuBSPagwhBLKzs5GcnAwbGxsYGHBFBVFNkpVXiCX7r2Dp/ssswqdjTG4eU7Qj9pM2hSTSFRsbmxJ3bici3VCqBNafuIGvdpxHckYeAOAZbzvM7u2LFu42ug2ujmJy8xiZTAZXV1c4OTmVuqkika4YGRlxxIaoBjl8KQUfb41DLIvw1ShMbkphYGDALxEiIiqRVITvHHbH3QbAInw1DZMbIiIiLbEIX+3A5IaIiOgJWISvdmFyQ0REVAoW4audmNwQERGVgEX4ai8mN0RERI94vAifwlCONzpJRfgsWISvVuBZIiIiQslF+F4MrIe3WYSv1mFyQ0REdVpJRfhae9ti9vPNEOBho9vgqEKY3BARUZ1VUhG+WT2boocfi/DVZkxuiIiozimpCN+kzo0wrD2L8OkDJjdERFRnlFSE79U2npjctTGL8OkRJjdERKT3SirC16WpE2b1YhE+fcTkhoiI9JZUhO82PvsnDlcfKcI3+/lm6NCIRfj0FZMbIiLSS48X4XOwUODt7izCVxcwuSEiIr3CInzEs0xERHqBRfioCJMbIiKq1ViEjx7H5IaIiGqtw5dT8PEWFuEjTUxuiIio1rlyJxOfsggflYLJDRER1RoswkfaYHJDREQ1Xn6hCj8duYaFey4iLacAAIvwUemY3BARUY0lhMDO2NuYt41F+Eh7TG6IiKhGOnszDXO3xOJfFuGjcmJyQ0RENUpSWq5UhC/6BoRgET4qP35KiIioRsjOL8SSfVewhEX46CkxuSEiIp1SPSjC9yWL8FElYXJDREQ6c/hyCj7ZGoeYWyzCR5WHyQ0REVW7K3cyMe+fc9gVyyJ8VPmY3BARUbW5ny0V4fspikX4qOowuSEioipXUhG+zk2d8G6vpmjoZKnj6EjfMLkhIqIqU1oRvv8974uOjRx1HB3pKyY3RERUJUoqwje9W2MMCmYRPqpaTG6IiKhSlVSEb3TH+hgTyiJ8VD34KSMiokpRUhG+fi3d8HaPpqjHInxUjZjcEBHRUympCF+wly1m926GlizCRzrA5IaIiCrs8SJ8HnammNXTFz1ZhI90iMkNERGVW0lF+CZ2bojw9t4swkc6x+SGiIi0xiJ8VBswuSEioidiET6qTZjcEBFRqViEj2ojJjdERFQiFuGj2orJDRERaWARPqrt+CklIiIAD4vwLd1/BTkFSgAswke1E5MbIqI6rqgI31c7z+N2OovwUe3H5IaIqA6LunwXH2+NZRE+0itMboiI6qD4lCzM2xaHnUVF+BSGmNiFRfhIPzC5ISKqQ+5n52PhnktYE3VVXYRvaBtPTO7SCPYWCl2HR1QpmNwQEdUB+YUqrD1yDd+yCB/VAUxuiIj0mBACu2JvY94/5xCfkgWARfhI/zG5ISLSU2dvpuHjrbE4coVF+KhuYXJDRKRnktJy8dXO81h/gkX4qG7ip5yISE9k5xdi6f4rWLKPRfiobmNyQ0RUy6lUAhuib+LLHedYhI8IgFyXL75//3706dMHbm5ukMlk+Ouvv8rsHxkZCZlMVuzn3Llz1RMwEVENE3X5Ll74/iCm/3EKt9Pz4GFnikVDW+GPMe2Y2FCdpdORm6ysLAQEBGDEiBEYMGCA1o87f/48rKys1L87OnLGPxHVLSUV4ZvQWSrCZ2LEInykQ/lZQEYSYN9AZyHoNLnp2bMnevbsWe7HOTk5wcbGpvIDIiKq4UoqwvfKM56Y0pVF+EjHbscCx1cCp34DHJsAr+/WWSi1cs5NYGAgcnNz0axZM8yePRthYWGl9s3Ly0NeXp769/T09OoIkYioUrEIH9VIBblA7N/AsRXA9SMP27PuADmpgKmtTsKqVcmNq6srli5diqCgIOTl5eGnn35Cly5dEBkZiU6dOpX4mHnz5mHOnDnVHCkRUeVgET6qkVIuSaM0J3+WkhgAkBkATXsBwSMBn1BArrtpvTIhhNDZqz9CJpNh48aN6NevX7ke16dPH8hkMmzatKnE+0saufHw8EBaWprGvB0iopqGRfioRinMB85tkZKa+P0P263cgaDhQOCrgJVrlb18eno6rK2ttfr+rlUjNyVp27Yt1q5dW+r9CoUCCgWvQxNR7XE7PRdf7mARPqohUq8Cx1cD0T9Jl5sAADKgUTdplKbRc4C8Zk1ir/V/S6Kjo+HqWnWZIhFRdWERPqoxlIXAxR3SXJpLewA8uMhj4Qy0Gib92HjqNMSy6DS5yczMxKVLl9S/x8fH4+TJk7Czs4OnpydmzZqFmzdvYs2aNQCABQsWwNvbG82bN0d+fj7Wrl2L9evXY/369bo6BCKip6ZSCWyMvokvd5xHUnouABbhIx1JuwmcWCP9ZNx62F4/TBqladITMDDSXXxa0mlyc+zYMY2VTtOmTQMAhIeHY9WqVUhMTERCQoL6/vz8fEyfPh03b96Eqakpmjdvjq1bt6JXr17VHjsRUWWIunwXn2yLxdmb0kpODztTzOrpi55+LpDJOK+GqoFKCVzeCxxbCVz4BxAqqd3MXppH0ypcpzVrKqLGTCiuLuWZkEREVFVYhI90LjNZmkdzfBVw/+FAArw6AMEjAN8+gGHNmbNapyYUExHVJizCRzolhLTS6dgKaeWTqlBqN7EGAl6RkhrHJrqNsRIwuSEiqgYlFeELa+KId3v5opEzi/BRFcu+J9WkObYSuHf5Ybt7a2kuTbN+gLGZzsKrbExuiIiqUElF+Jo4S0X4OjVmET6qQkIACUekujQxfwHKBzXfjC2AFkOkURoXf52GWFWY3BARVZHiRfiM8Va3JhjMInxUlXLuA6d/ly493Yl72O7SQhql8R8IKPR7tJDJDRFRJXu8CJ+xoRyjO/pgbGhDFuGjqiEEcOuElNCcWQ8U5kjthqaA/wApqXFrBdSRFXj8W0ZEVElKKsLXt6Ub3u7eBO62+jOfgWqQvEzgzB9SUpN0+mG7o6+U0LQYDJja6Cw8XWFyQ0T0lEoqwhfkZYvZz/si0FM3uyKTnks6IyU0p/8A8jOkNgMF0LwfEDQC8GxbZ0ZpSsLkhojoKRy5chcfb31YhM/dVirC18ufRfiokhXkADEbpaTmxtGH7XYNpMnBAa8A5va6i68GYXJDRFQB8SlZ+OyfOOyIYRE+qmJ3zktLuE/9AuSmSW1yQ6Bpb+nSk0+nOj1KUxImN0RE5ZCWXYCFey9iTdRVFChZhI+qSGEeELdZGqW5duhhu40nEDQcaPkqYOmss/BqOiY3RERaKFA+LMJ3P5tF+KiK3L0sbYdw8mcg+67UJpMDjXtKl54adAbkHBl8EiY3RERlEEJgd1wy5m2LwxUW4aOqoCwAzv8jjdJciXjYbukGtBom/VjX0118tRCTGyKiUpy9mYZPtsYh6or0P+iiInyDgtxhaCDXcXRU692/DpxYDZxYA2TeftAoAxp2kebSNOoOGPBruiL4rhERPeZ2ei6+2nEef7IIH1U2lRK4uEsapbm0CxAqqd3cEQh8DQgKB2y9dRqiPuDfUiKiB7LzC7FsfzwW77vMInxUudITgeifgOOrgfQbD9t9OkmjNE2eBwyNdRefnmFyQ0R1HovwUZVQqaQ5NMdXAue2AUJKmGFqC7QcKhXbc2io2xj1FJMbIqrTWISPKl1WChC9VkpqUq8+bPdsJyU0zfoCRiY6C68uYHJDRHXS1ZQszGMRPqosQkj1aI6tAGI3ASqpXAAUVkDAS1JS49xMtzHWIUxuiKhOYRE+qlTZ94BTv0mjNCkXHra7tZLm0vj1B4zNdRdfHcXkhojqBBbho0ojhLS307GVQMwGoFCapwUjc6DFIGmUxq2lTkOs65jcEJFeYxE+qjS56cCZ36Wk5vbZh+3OflL1YP/BgImV7uIjNSY3RKS3Ym6l4eMtLMJHT+nWSWkuzZk/gQIpQYahCdC8v3TpyT2YG1fWMOVKbs6fP49ff/0VBw4cwNWrV5GdnQ1HR0cEBgaie/fuGDBgABQKXrMmIt26nZ6Lr3eexx/HNYvwjQlpAEsTI12HR7VBfhZwdr00SnPrxMN2h8ZSQhPwkrSkm2okmRBCPKlTdHQ0ZsyYgQMHDqB9+/Z45plnUK9ePZiamuLevXs4e/YsDhw4gPT0dMyYMQNTpkypsUlOeno6rK2tkZaWBisrDh8S6ZOcfCWWHbiCxfsuIzufRfioAm7HSpODT/0G5EnlASA3kpZvB48EvNpzlEZHyvP9rdXITb9+/fD2229j3bp1sLOzK7VfVFQUvvnmG3z99dd49913yxc1EVE55BYocf1eNq7ezca1u1m4djcbu2Jvq4vwtfK0wezezdCKRfjoSQpygdi/pUtP1488bLf1AYKGSwX3LDg/qzbRauQmPz8fxsbal4Uub//qxJEbotojM69QnbhcvZuFaynZuHZP+j0xLbfEx7jbmuKdnk3xvL8ri/BR2VIuSaM0J38GclKlNpkB0LSXNErjEwrIOTerpqj0kZvyJio1NbEhoppFCIH72QW4ejcLCfeycTXlwSjMPenPlMz8Mh9vqTCEl4MZvOzN4W1vhkZOlujh58IifFS6wnzg3BYpqYnf/7Ddyl0apQl8FbBy1Vl4VDnKNaFYpVJh1apV2LBhA65evQqZTAYfHx8MHDgQr732Gv+XRETFCCFwJyMPVx+MviQUjcI8uJyUnltY5uPtzY3hZS8lMF72ZvC2N4fngz9tzYz47w5pJ/WqtGll9E9A1p0HjTKgcXdplKZhV0DOpFhfaJ3cCCHwwgsvYNu2bQgICIC/vz+EEIiLi8Pw4cOxYcMG/PXXX1UYKhHVVEqVQGJazsPLR4/Mg7l2N1u9w3ZpXKxMiiUuUkJjxtVNVHHKQuDiDmkuzaU9AB7MwrBwAVoNk35sPHQaIlUNrZObVatWYf/+/dizZw/CwsI07tu7dy/69euHNWvWYNiwYZUeJBHpXn6hCjdSs6VLRilZuHo3W7qUdDcLN+7lIF+pKvWxchngbmumTli87c3haWcGbwfpT15GokqVdhM4sUb6ybj1sL1+mDRK06QnYMCkWZ9pNaEYALp164bOnTvjnXfeKfH+Tz/9FPv27cOOHTsqNcDKxgnFRKXLyVeqExaNy0f3snAzNQeqMv61MDaQw8POtITRF3PUszGFsSEnZlIVUimBy3ulujQX/gHEg2TbzF6aR9MqHLBvoNsY6alU+oRiADh9+jS++OKLUu/v2bMnFi5cqH2URKQT6bkFxea9FC2nvp2eV+ZjTY0M1CMvRYmLt70ZvBzM4WJlAgM5579QNctMlubRHF8F3E942O7VQdoSwbcPYFgz665R1dE6ubl37x6cnZ1Lvd/Z2RmpqamVEhQRVZwQAvey8tUrjq6mPLx8dO1uNu5llb0CycrEEN4O5urEpejykZe9GRwtFJzAS7onhLTS6dgKaeWT6sGkdBNrqSZN0HDAsYlOQyTd0jq5USqVMDQsvbuBgQEKC8te9UBElUOlEkjOyCvx8tG1lGxk5JX9d9HBQiElLiWMwtiYsZQD1VDZ96SaNMdWAvcuP2x3by3NpWn+ImBkqrv4qMYo12qp4cOHl7qtQl5e2cPZRFR+dzPzEJuYLk3efeTyUcK9bOQWlD6BVyYDXK1MpITFwQyedg8uHz2YD2Oh4J65VEsIASQckerSxPwFKB981xhbAi0GS5eeXPx1GiLVPFr/CxceHv7EPlwpRVR5bt3PQZev95W6jNpALoO7ranm5aMHyYy7LVcgUS2Xcx84/bt06elO3MN2lxZA61GA30BAYaGz8Khm0zq5WblyZVXGQUSP+fvkLeQUKGFnboxWnjYPk5gHf7rZmMLIgCuQSI8IIe3AfWwFcGY9UJgjtRuaAv4DpVEat1bcuJKe6KnHpq9du4asrCw0bdoUcu7BQVRptpyW6nO81a0xhrbx0nE0RFUoLxM484d06Snx1MN2R19pLk2LwYCpjc7Co9pH6+Rm9erVSE1NxZQpU9Rtb7zxBpYvXw4AaNKkCXbs2AEPD1Z7JHpa8SlZiLmVDgO5DD39uM8N6amkM9Lk4NO/A/kZUpuBAmjeT0pqPNpwlIYqROuhlsWLF8Pa2lr9+/bt27Fy5UqsWbMGR48ehY2NDebMmVMlQRLVNVtOSaM27RvYw86cq5dIjxTkACd/AX7sCizuABxbLiU2dg2Abp8Ab50D+i8FPNsysaEK03rk5sKFCwgODlb//vfff+OFF17A0KFDAUgVikeMGFH5ERLVQVtOJwIA+rRw03EkRJXkznlplObUL0BumtQmN5SK7AWNAHw6MZmhSqN1cpOTk6NR7vjw4cMYOXKk+vf69esjKSmpcqMjqoMu3s7A+dsZMDKQoXtzF12HQ1RxhXlA3GYpqbl28GG7jadUaK/lq4Bl6cVhiSpK6+TGy8sLx48fh5eXF1JSUhATE4MOHTqo709KStK4bEVEFVM0atOxkSOszbi5H9VC965I2yFErwWy70ptMjnQuKc0l6ZBZ4ALUKgKaZ3cDBs2DOPHj0dMTAz27t2Lpk2bIigoSH3/4cOH4efnVyVBEtUVQgj1KqneLTiRmGoRZQFw/h9pGfeViIftlm5AUDgQ+BpgXU938VGdonVyM3PmTGRnZ2PDhg1wcXHBH3/8oXH/oUOH8PLLL1d6gER1ybmkDFy+kwVjQzmea8bheqoF7l8HTqwGTvwEZBZNTZABDbtKdWkadQcMWBGbqpdMCCF0HUR1Ks+W6UTV7csd5/B9xGV0a+aMpcOCn/wAIl1QKYGLu6S6NBd3AuLBViDmjtIITVA4YOut0xBJ/5Tn+5vpNFENIV2Skubb9A7gKimqgTKSpBGaE6uBtOsP2306SXNpmjwPGLJ0Aeme1smNXC6HrIRlelZWVmjSpAlmzJiB/v37V2pwRHXJ2ZvpuHY3GyZGcnRp6qTrcIgkKhUQHynNpTm3DRAP9joztQVaDpWWcTs01GmIRI/TOrnZuHFjie3379/Hf//9h1dffRWrV6/GoEGDKi04orqkaCJxl6bOMOeu3aRrWSnSaqfjq4DU+Iftnu2khKZZX8DIRGfhEZVF639B+/btW+p94eHhaNasGb766ismN0QVoHFJiqukSFeEAK4dkurSxG0ClPlSu8IKCHhJSmqcm+k2RiItVNp/D7t164bZs2dX1tMR1SnR1+/j5v0cmBsbIIyXpKi65aQCp36TLj2lXHjY7tZKmkvj1x8wNtddfETlVGnJTU5ODkxMOERJVBFbTkmjNl2bOcPEyEDH0VCdIARw45iU0MRsAApzpXYjc6DFIGmUxq2lTkMkqqhKS26WLVuGwMDAyno6ojpDpRLYdqbokhRXSVEVy00HzvwOHFsF3D7zsN3ZT6pL4z8YMGGZDKrdtE5upk2bVmJ7Wloajh07hsuXL+PAgQOVFhhRXXHsWiqS0nNhaWKITo0ddB0O6atbJ6W6NKf/AAqypDZDE6B5f+nSk3swN64kvaF1chMdHV1iu5WVFXr06IFx48bBy8ur0gIjqiuKVkl1a+YChSEvSVElys8Czm6QLj3dOvGw3aGxlNAEvCQt6SbSM1onNxEREU/uRETlolQJbDsjlazvHcBVUlRJbsdKozSnfgPy0qU2uZG0fDt4JODVnqM0pNdYTINIh/69chcpmXmwMTNCh4a8JEVPoSAXiP1bSmoSoh622/oAQcOlgnsWjjoLj6g6aZXcjBkzBv/73//g4eHxxL7r1q1DYWEhhg4d+tTBEem7zQ9q2/Ro7gIjA7mOo6FaKeWSlNCc/Fla0g0AMgOgaS9plMYnFJDzs0V1i1bJjaOjI/z8/NC+fXu88MILCA4OhpubG0xMTJCamorY2FgcPHgQv/32G+rVq4elS5dWddxEtV6BUoXtZ7lKiiqgMB84v1WaSxO//2G7lbs0ShP4KmDFy5xUd2mV3MydOxcTJ07E8uXLsXjxYpw9e1bjfktLS3Tt2hU//vgjunXrViWBEumbw5fvIjW7APbmxmhb307X4VBtkHoVOL4aiP4JyLrzoFEGNO4ujdI07ArIOSmdSOs5N05OTpg1axZmzZqF+/fv49q1a8jJyYGDgwMaNGhQ4qaaRFS6LaekVVI9/V1gyEtSVBplIXBxhzRKc2kPACG1W7gArYZJPzZPnjJAVJdUaEKxjY0NbGxsKjkUorojv1CFHTEPVknxkhSVJO2mNEJzfDWQcethe/0waZSmSU/AwEh38RHVYFwtRaQDBy7eQXpuIZwsFWjtzUtS9IBKBVzeK43SXPgHECqp3cxemkfTKhywb6DbGIlqASY3RDpQtAN4L39XGMh5SbfOy0x+MEqzCrif8LDdq4O0JYJvH8BQobPwiGobJjdE1Sy3QIldsbcBAH1YuK/uEkJa6XRsBXBuC6AqlNpNrKWaNEHDAccmOg2RqLZickNUzSLP30FmXiHcrE0Q6MHS93VO9j3g5C9SbZq7lx62u7eW5tI0fxEwMtVdfER6oNzJTefOnbFhw4ZiE4rT09PRr18/7N27t7JiI9JLRXtJPd/CFXJekqobhACu/yuN0sT8BSjzpHZjS6DFYOnSk4u/TkMk0iflTm4iIyORn59frD03N5e7ghM9QXZ+IfbEJQPgKqk6ITcNOLVOSmruxD1sd2kBtB4F+A0EFBa6i49IT2md3Jw+fVp9OzY2FklJSerflUoltm/fjnr16lVudER6Zu+5ZOQUKOFpZ4YW7ta6DoeqghDSDtzHVkg7chdkS+2GpoD/QGmUxq0VN64kqkJaJzctW7aETCaDTCZD586di91vamqK//u//yvXi+/fvx9ffvkljh8/jsTERGzcuBH9+vUr8zH79u3DtGnTEBMTAzc3N8yYMQNjxowp1+sS6cqWU9IqqedbuLLwpb7JywTO/CHNpUk89bDd0VeaS9NiMGBqo7PwiOoSrZOb+Ph4CCFQv359/Pfff3B0fLi7rLGxMZycnGBgUL6y31lZWQgICMCIESMwYMAArWLo1asXRo8ejbVr1+LQoUMYN24cHB0dtXo8kS5l5hUi4nzRJSmuktIbSWeAYyuB078D+RlSm4ECaN5PSmo82nCUhqiaaZ3ceHl5AQBUKlWlvXjPnj3Rs2dPrfsvXrwYnp6eWLBgAQDA19cXx44dw1dffcXkhmq83bG3kVeoQn0HczRztdJ1OPQ0CnKAmI3SpacbRx+22zWQEpqWrwBmLM5IpCvlnlA8b948ODs7Y+TIkRrtK1aswJ07dzBz5sxKC+5xUVFRxTbm7N69O5YvX46CggIYGbEUOdVcRaukevOSVO1157w0SnPqF2myMADIDaUie0EjAJ9OHKUhqgHKndwsWbIEv/zyS7H25s2b46WXXqrS5CYpKQnOzs4abc7OzigsLERKSgpcXYsP9efl5SEvL0/9e3p6epXFR1SatJwC7Lsg7eLcO4CrpGqVwjwgbrOU1Fw7+LDdxlNKaAJfBSycdBcfERVT7uQmKSmpxCTC0dERiYmJlRJUWR7/H68QosT2IvPmzcOcOXOqPC6isuyMSUKBUqCxswUaO1vqOhzSxr0r0nYI0WuB7LtSm0wONOklJTUNOgNy7uZOVBOVO7nx8PDAoUOH4OPjo9F+6NAhuLlV7f9IXVxcNJagA0BycjIMDQ1hb29f4mNmzZqFadOmqX9PT0+Hh4dHlcZJ9LiivaRY26aGUxYA5/+R5tJciXjYbukGBIUDga8B1ix5QVTTlTu5ef311zFlyhQUFBSol4Tv2bMHM2bMwFtvvVXpAT6qXbt22Lx5s0bbzp07ERwcXOp8G4VCAYWCG86R7qRm5ePQpRQAXCVVY92/DpxYDZz4Ccgs+g+UDGjYVZog3KgbYMDdaohqi3L/bZ0xYwbu3buHcePGqSsVm5iYYObMmZg1a1a5niszMxOXLj3cWyU+Ph4nT56EnZ0dPD09MWvWLNy8eRNr1qwBAIwZMwbfffcdpk2bhtGjRyMqKgrLly/Hr7/+Wt7DIKo222OSUKgSaOZqhfqOrEZbY6iUwMVdUl2aizsB8WAlqLkT0Oo1oFU4YOul2xiJqELKndzIZDJ8/vnneO+99xAXFwdTU1M0atSoQqMjx44dQ1hYmPr3ostH4eHhWLVqFRITE5GQkKC+38fHB9u2bcPUqVPx/fffw83NDQsXLuQycKrR1KukuAN4zZCRJI3QnFgNpF1/2O4TIlUPbvI8YGisu/iI6KnJRNGM3DoiPT0d1tbWSEtLg5UVa41Q1bqTkYc2n+6GSgD73w6Dp72ZrkOqm1QqID5SmktzbhsglFK7qS3Qcqg0QdihoU5DJKKylef7u0IXkY8ePYo//vgDCQkJxTbR3LBhQ0WekkgvbT+bCJUAAtytmdjoQlaKtNrp+CogNf5hu2c7aS6N7wuAkYnOwiOiqlHu5Oa3337DsGHD0K1bN+zatQvdunXDxYsXkZSUhBdffLEqYiSqtTZzlVT1EwK4dkiqSxO3CVA++A+YwhoIeEm69OTkq9sYiahKlTu5+fTTT/HNN99g/PjxsLS0xLfffgsfHx+8+eabJda/Iaqrbqfn4ujVewCkjTKpiigLgMzbQMZt4Pq/0gThlAsP768XJF128usPGJvrLk4iqjblTm4uX76M559/HoC0zDorKwsymQxTp05F586dWTCP6IGtpxMhBBDkZQs3G1Ndh1P7KAuAzGRpaXZGEpCRKCUwGYkPfk+S7stKAfDY1EEjc6DFICmpcWupi+iJSIfKndzY2dkhI0Pa+bZevXo4e/Ys/P39cf/+fWRnZ1d6gES11aN7SdEjlIVAVnLxZOXxJCbrDoolLaWRGwGWLtKWCH4DAP9BgAkXDBDVVeVObjp27Ihdu3bB398fgwcPxuTJk7F3717s2rULXbp0qYoYiWqdm/dzcCLhPmQyoJd/HUlulIVSQlJaslLUXq6kxRCwcJESl0d/LFwAS9eHv5vacSsEIlIrd3Lz3XffITc3F4C0tYGRkREOHjyI/v3747333qv0AIlqo60PRm2e8baDs1UtX42jUmomLRmPJi2PJDFZdx4WwnsSmcFjiUpRsuL8MGmxcAHM7Jm0EFG5VeiyVBG5XI4ZM2ZgxowZlRoUUW33cC+pGjxqo05aHklWMm8XT2KyksuXtFg4PzbS4vqg7ZGRFjMHJi1EVGUqVOdGqVRi48aNiIuLg0wmg6+vL/r27QtDQ+69QnTtbhZO30iDXAb08NNBcqNSSpNsS0tWitozb5cjaZE/TFpKG2mxdH0w0mJQtcdHRPQE5c5Gzp49i759+yIpKQlNmjQBAFy4cAGOjo7YtGkT/P39Kz1IotqkaNSmXQN7OFpW4qatKhWQnfLkibiZtx9W4H0SmVzaS6nMkRZXwNyBSQsR1RoV2hW8efPmOHbsGGxtbQEAqampGD58ON544w1ERUVVepBEtcmWyircpywA/pkJ3Ip+eHlIVajlg2WAhVMJIy2PJTHmjkxaiEjvlDu5OXXqlEZiAwC2trb45JNP0Lp160oNjqi2uXwnE3GJ6TCUy9CjucvTPdmp34Bjyx9rlEkJyeNJyuNJjLkjYMDLxERUN5X7X78mTZrg9u3baN68uUZ7cnIyGjbkxnNUt205JY3aPNvQAbbmT7GztEoJHFog3W47DvAf+EjSYvT0gRIR6bEKbb8wadIkfPjhh2jbti0A4MiRI/joo4/w+eefIz09Xd2Xu25TXVNphfviNgN3LwEmNkDYu4DC8umDIyKqI8qd3PTu3RsAMHjwYMhkMgCAEFJBrj59+qh/l8lkUCq1nNRIpAfOJ2XgYnImjA3k6PY0l6SEAA7Ol24/8wYTGyKicip3chMREVEVcRDVekWjNp0aO8Da9CkuHV3eCySeAozMgDZjKik6IqK6o9zJTUhISFXEQVSrCSEqb5XUwW+kP1uFA+b2TxkZEVHdU6HlFLm5uTh9+jSSk5OhUmkWAXvhhRcqJTCi2iTmVjriU7KgMJSjazPnij/RjWPA1QPSRpDtJ1RegEREdUi5k5vt27dj2LBhSElJKXYf59lQXVU0ahPWxAkWiqdYgn3gwVybFkMAa/dKiIyIqO4p9+YuEyZMwKBBg5CYmAiVSqXxw8SG6iLpktSDVVIBT7FKKjkOOL8VgAx4dnLlBEdEVAeVO7lJTk7GtGnT4Oz8FEPvRHrk1I003EjNgamRATo3dar4Ex1cIP3p2xtwbFwpsRER1UXlTm4GDhyIyMjIKgiFqHbackoateni6wQz4wpekkq9Bpz5Q7rdYVolRUZEVDeV+1/i7777DoMGDcKBAwfg7+8PIyPNJa+TJk2qtOCIajqVSmDbmUpYJRX1nbTZZf1QoF6rygmOiKiOKndy88svv2DHjh0wNTVFZGSkupAfIE0oZnJDdUn09VTcSsuFhcIQoU0cK/YkmXeAE2uk2xy1ISJ6auVObmbPno2PPvoI77zzDuTycl/VItIrmx/sJfVcM2eYGFVwd+1/fwAKcwG3VoBPp0qMjoiobip3dpKfn48hQ4YwsaE6T6lxSaqCq6Ry04H/fpRud5wGPDISSkREFVPuDCU8PBzr1q2riliIapWjV+8hOSMPViaG6Niogpekji0H8tIAhyZAk+crN0Aiojqq3JellEolvvjiC+zYsQMtWrQoNqF4/vz5lRYcUU1WVNume3MXGBtWYCSzIBeIWiTd7jAF4GgoEVGlKHdyc+bMGQQGBgIAzp49q3GfjEPqVEcUKlX450wSAKB3QAVXSZ38GchKBqzcAf9BlRgdEVHdxl3BiSrgyJV7uJuVD1szI7RvUIHNLZWFwKFvpdvtJwIGT7GLOBERaeA4OFEFFF2S6uHnCiODCvw1itkI3L8GmNkDrYZVcnRERHWb1iM3/fv316rfhg0bKhwMUW1QoFRhe4x0SapPRVZJCQEc/Ea63WYsYGxWidEREZHWyY21tXVVxkFUaxy8lIL72QVwsFCgTf0KXJK6uBNIjgGMLYBnXq/8AImI6jitk5uVK1dWZRxEtcaWB4X7evm7wEBegUn0Bx6sKAweCZjaVmJkREQEcM4NUbnkFSqxM/bBKqmK7CV17TBw/QhgYAy0HVfJ0REREcDkhqhc9l9IQUZuIVysTBDsVYFRl6JRm5avAFYVrGpMRERlYnJDVA5Fq6R6+btCXt5LUomngUu7AJkceHZyFURHREQAkxsireUWKLE79jYAoHdABUZdDi2Q/mz+ImBXv/ICIyIiDUxuiLQUcS4ZWflK1LMxRaCHTfkefPeyVNsGADpMrfTYiIjoISY3RFracvrhDuDl3mrk8EJAqICGzwEu/lUQHRERFWFyQ6SFrLxC7Dn34JJUeVdJpScCJ3+RbnecVsmRERHR45jcEGlhz7lk5Bao4GVvBr96VuV78JHvAWU+4NEW8GpfNQESEZEakxsiLWw5Ja2SKvclqZxU4NiDApgctSEiqhZMboieICO3AJEX7gCowCWp/34E8jMBp+ZAo25VEB0RET2OyQ3RE+yKvY38QhUaOJqjqYul9g/Mzwb+/UG63WEqUN5JyEREVCFMboie4OEqKbfyXZI6sQbIvgvYeku1bYiIqFowuSEqQ1p2AQ5clC5J9SlP4b7CfODw/0m3208CDLTeo5aIiJ4SkxuiMuyISUKBUqCpiyUaOpXjktTZP4H0G4CFM9ByaNUFSERExTC5ISrD5tMPV0lpTaUCDi6QbrcdBxiZVH5gRERUKiY3RKW4m5mHw5fvAijnKqnzW4GU84DCGggeWUXRERFRaZjcEJVie0wSlCoBv3pW8HYw1+5BQgAH5ku3n3kdMClnwT8iInpqTG6ISrHl1MNVUlqL3wfcOgEYmgBtxlZRZEREVBYmN0QlSM7Ixb/x0iWp5/3LMd/m4DfSn62GARaOVRAZERE9CZMbohL8cyYJKgG09LCBh52Zdg+6eQK4EgnIDID2E6s0PiIiKh2TG6ISbKnIKqmDD+ba+A8CbDyrICoiItIGkxuixySm5eDo1VQAwPPaJjd3LgBxW6TbHaZUTWBERKQVJjdEj9n6YLuF1t62cLU21e5BhxYAEECT5wEn3yqLjYiInozJDdFjivaS0noicdoN4PQ66XbHaVUUFRERaYvJDdEjrt/Lxsnr9yGTAb20TW4OfweoCgHvjoB7cNUGSERET8TkhugRW89IozZtfOzgZKXFtglZd4ETq6XbHaZWYWRERKQtJjdEj3i4SkrLwn3/LgYKsgHXAKBB5yqMjIiItMXkhuiBqylZOHszHQZyGXr6uTz5AXkZwH9LpNsdpgEyWdUGSEREWmFyQ/RA0ahN+wb2sLdQPPkBx1cBuWmAfUPAt0/VBkdERFpjckP0QNEqKa0K9xXmAVHfS7efnQLIDaouMCIiKhcmN0QALiVn4FxSBgzlMnRvrsUlqVO/AhmJgKUb0GJI1QdIRERaY3JDBGDzgx3AOzZygI2ZcdmdVUrg0LfS7fYTAMMn9CciomrF5IbqPCFE+VZJxf4F3LsCmNoCrcKrNjgiIio3JjdU5529mY7Ld7KgMJTjuebOZXcWAjjwjXS7zRhAYVH1ARIRUbkwuaE6b/2JGwCAbs1dYGViVHbnS3uA22cAI3PgmTeqIToiIiovnSc3ixYtgo+PD0xMTBAUFIQDBw6U2jcyMhIymazYz7lz56oxYtInBUoVNp2SLkn1b1XvyQ84OF/6M2g4YGZXdYEREVGF6TS5WbduHaZMmYL//e9/iI6ORseOHdGzZ08kJCSU+bjz588jMTFR/dOoUaNqipj0zb7zd3AvKx8OFgp0bOhQdueEf4FrhwC5EdBufPUESERE5abT5Gb+/PkYNWoUXn/9dfj6+mLBggXw8PDADz/8UObjnJyc4OLiov4xMGCNEaqYDdHSJam+Ld1gaPCEvw5FozYBLwHWWozyEBGRTugsucnPz8fx48fRrVs3jfZu3brh8OHDZT42MDAQrq6u6NKlCyIiIqoyTNJjadkF2B2XDECLS1K3Y4AL2wHIpKJ9RERUYxnq6oVTUlKgVCrh7Ky5OsXZ2RlJSUklPsbV1RVLly5FUFAQ8vLy8NNPP6FLly6IjIxEp06dSnxMXl4e8vLy1L+np6dX3kFQrbb1TCLyC1Vo6mKJZq5WZXc+uED6s1lfwKFhlcdGREQVp7Pkpojssc0GhRDF2oo0adIETZo0Uf/erl07XL9+HV999VWpyc28efMwZ86cyguY9MaGB6uk+reqV+pnDgCQehU4u1663WFq1QdGRERPRWeXpRwcHGBgYFBslCY5ObnYaE5Z2rZti4sXL5Z6/6xZs5CWlqb+uX79eoVjJv1x7W4Wjl1LhVwG9G35hEtShxYCQgk06Ay4tayW+IiIqOJ0ltwYGxsjKCgIu3bt0mjftWsX2rdvr/XzREdHw9W19I0OFQoFrKysNH6INpy4CQB4tqEDnK1MSu+YcRuIXivd7jCtGiIjIqKnpdPLUtOmTcNrr72G4OBgtGvXDkuXLkVCQgLGjBkDQBp1uXnzJtasWQMAWLBgAby9vdG8eXPk5+dj7dq1WL9+PdavX6/Lw6BaRgiBjdFScjOglXvZnY8sApR5gHtrwLtDNURHRERPS6fJzZAhQ3D37l189NFHSExMhJ+fH7Zt2wYvLy8AQGJiokbNm/z8fEyfPh03b96Eqakpmjdvjq1bt6JXr166OgSqhY5fS0XCvWyYGxugW1nbLeSmAcdWSLc7TAPKmpdDREQ1hkwIIXQdRHVKT0+HtbU10tLSeImqjpq14Qx+/S8BA4Pc8dWggNI7Hvga2PMR4NgUGBsFyHVe0JuIqM4qz/c3/7WmOiW3QKneAbx/YBkTiQtygCMPikl2mMrEhoioFuG/2FSn7IlLRkZuIdysTdC2vn3pHaPXAll3AGtPwG9A9QVIRERPjckN1SkbH2y30C+wHuTyUubQKAuk5d8A8OwkwOAJO4UTEVGNwuSG6oyUzDxEnr8D4AnbLZzdAKQlAOaOQOCr1RQdERFVFiY3VGdsPnULhSqBAHdrNHSyLLmTSgUc/Ea63XYsYGRafQESEVGlYHJDdUZR4b4Xy5pIfGE7cCcOMLYEgkdVU2RERFSZmNxQnXDhdgbO3EyDoVyGPgFuJXcSAjg4X7rdehRgalNt8RERUeVhckN1QtGoTWgTJ9hbKErudPUgcOMoYKAA2o6rxuiIiKgyMbkhvadUCfx9smi7hTIuSRXNtQl8FbDUfvNWIiKqWZjckN47cuUuEtNyYWViiM6+TiV3unUSuLwHkBlIy7+JiKjWYnJDem/9Cam2Te8ANygMDUruVDRq49cfsPWunsCIiKhKMLkhvZaVV4jtZ5MAlHFJ6nYsEPu3dLvD1GqKjIiIqgqTG9JrO2KSkJ2vhJe9GVp52pbcad9nAATg+wLg3Lxa4yMiosrH5Ib02sZoaSJx/0B3yGQlbLeQdObBqI0MCJ1VvcEREVGVYHJDeispLRcHL6UAKKNwX+Rn0p9+/QHnZtUUGRERVSUmN6S3/jp5E0IAz3jbwdPerHiHW9HAuS2ATA6EvFP9ARIRUZVgckN6SQiB9celVVIvljaROGKe9Kf/IMCxcTVFRkREVY3JDemlmFvpuJicCWNDOXr5uxbvcOMYcHGHVNcmZGb1B0hERFWGyQ3ppaLtFp5r5gxrU6PiHSI+kf4MeBmwb1CNkRERUVVjckN6p0CpwqZTZWy3cC0KuLwXkBsCnaZXc3RERFTVmNyQ3jlw8Q5SMvPhYGGMjo0ci3eI/FT6s+VQwM6neoMjIqIqx+SG9M76B5ek+gS4wcjgsY94/AEgfj8gNwI6va2D6IiIqKoxuSG9kpZTgF2xtwEAA1q5a94pBBDxYNQmKByw8ajm6IiIqDowuSG98s+ZROQXqtDY2QLN3aw077wSCSQcBgwUQMe3dBIfERFVPSY3pFeKVkn1b/XYdguPjtoEjwSs3HQQHRERVQcmN6Q3Eu5m47+r9yCTAf1aPrZK6tIe4MZ/gKEpd/4mItJzTG5IbxRtkvlsAwe4WJs8vEMIIOJj6XbrUYClsw6iIyKi6sLkhvSCEAIboqXtFvo/XtvmwnZpHykjc47aEBHVAUxuSC+cSEjFtbvZMDM2QPfmLg/vEOJhNeI2bwDmDroJkIiIqg2TG9ILRROJe/i5wFxh+PCOuM1A0hnA2AJoP0lH0RERUXVickO1Xl6hEptP3QLwWG0blQqIfLDzd9uxgJmdDqIjIqLqxuSGar29cclIzy2Ei5UJ2ta3f3hH7F9AciygsAbajddZfEREVL2Y3FCtV7TdQr/AejCQP6hto1ICkZ9Jt9uNB0xtdRQdERFVNyY3VKvdzcxD5PlkAI+tkjq7Hkg5D5jYAG3H6CY4IiLSCSY3VKttOZ2IQpWAfz1rNHa2lBqVhQ9HbZ6dBJhY6y5AIiKqdkxuqFbbcKKE2jZnfgfuXQbM7IFn3tBRZEREpCtMbqjWupScgVM30mAol6FPwIO9opQFwL7PpdvPTgYUlroLkIiIdILJDdVaRbVtQho7wsFCITWe/AVIvQqYOwGtR+suOCIi0hkmN1QrqVQCf0U/3AEcAFCYD+z/UrrdYSpgbKaj6IiISJeY3FCtdCT+Lm6l5cLSxBBdfJ2kxuifgLTrgIULEDxCtwESEZHOMLmhWqnoklTvFm4wMTIACnKB/V9Jd3Z8CzAy1WF0RESkS0xuqNbJzi/EP2cSAQADilZJnVgNZNwCrOoBQeE6jI6IiHSNyQ3VOjtjbiMrXwlPOzMEedkCBTnAga+lOztNBwwVug2QiIh0iskN1TrrH9S2eTGwHmQyGXBsBZB5G7DxBFq+quPoiIhI15jcUK1yOz0Xhy6lAHhQuC8/Czj4jXRnpxmAobEOoyMiopqAyQ3VKn+fvAmVAIK9bOFlbw78twzIugPY+gABL+k6PCIiqgGY3FCtIYTA+uOP1LbJywAOfSvdGTITMDDSYXRERFRTMLmhWiM2MR3nb2fA2ECO5/1dgX8XAzn3APtGgP8gXYdHREQ1BJMbqjVWHboKAOjazAnW8mzg8P9Jd4S+AxgY6i4wIiKqUZjcUK1w5Mpd/HFcWiU14lkf4MgPQG4a4NgUaP6ijqMjIqKahMkN1Xi5BUq8u+EMAODlZzzQ2lkGRH0v3Rn6DiA30GF0RERU0zC5oRrv+4hLuJKSBUdLBd7p6Qsc/g7ISwec/QDfvroOj4iIahgmN1SjnU/KwA+RlwEAc15oDmtVujSRGABCZwFyfoSJiEgTvxmoxlKqBN7ZcBqFKoGuvs7o6ecCHF4I5GcCrgFA0+d1HSIREdVATG6oxlp75BqiE+7DQmGIuf2aQ5aVAvy3VLoz7H+ATKbbAImIqEbi+lmqkW7dz8EX288BAGb0aAJXSwWwfhxQkA3UCwIaddNxhEREVFNx5IZqHCEE3v/7LLLylWjlaYNX23gBe+YAMRsAuSHQ7WOO2hARUamY3FCN88/ZJOyOS4aRgQyfDWgB+bEfgUMLpDtf+D/Aq71O4yMiopqNyQ3VKGnZBfhgUwwAYGxIAzS+Fwlse1u6s/NsoOUruguOiIhqBSY3VKN8tj0OdzLyUN/RHOMb3QPWvw5AAEHDgY7TdR0eERHVApxQTDXGkSt38et/1wEAC7pYQPH7IKAwF2jcA+j1NefZEBGRVjhyQzXCo1ssjA60QIvIkdKO326tgIEruDEmERFpjckN1QhFWyx4WagwM/U94P41wNYbeOV3wNhc1+EREVEtwv8Ok84VbbFgACX+sF8Gw6RTgJk98OoGwMJR1+EREVEtw+SGdOrhFgsqrHH8FU639wOGptKIjX0DXYdHRES1EC9LkU4VbbEwXfE3OmVsA2RyaY6Ne7CuQyMiolqKyQ3pTNEWC4MMIjFB9rvU2OsroGkvncZFRES1G5Mb0omiLRaCC0/gM6MfpcYO04DWo3QbGBER1Xqcc0M6se1MEhLP/YvfjRfAACqgxRCgy/u6DouIiPQAR26o2qVlF2Dx3xFYZfwFzGV5gE8I8MJ3LNJHRESVQufJzaJFi+Dj4wMTExMEBQXhwIEDZfbft28fgoKCYGJigvr162Px4sXVFClVlm83H8GCgo/gKEuDyqk5MOQnwNBY12EREZGe0OllqXXr1mHKlClYtGgRnn32WSxZsgQ9e/ZEbGwsPD09i/WPj49Hr169MHr0aKxduxaHDh3CuHHj4OjoiAEDBujgCKgkBUoV0nIKkJ5TgLSin+w8ZGemI/VeCnrGTEcDeSLyzN2gePVPwMRa1yETEZEekQkhhK5evE2bNmjVqhV++OEHdZuvry/69euHefPmFes/c+ZMbNq0CXFxceq2MWPG4NSpU4iKitLqNdPT02FtbY20tDRYWVk9/UE8oCwsRPLNy5X2fDWNSqlERto9ZKXdQ07GXeRlpqIg6z5Ezn0gLw2GeekwVGbDSJkNE5ELc+TCDLkwk+XCHHkwk+VpPF+O3AKmY3YDTr66OSAiIqpVyvP9rbORm/z8fBw/fhzvvPOORnu3bt1w+PDhEh8TFRWFbt26abR1794dy5cvR0FBAYyMjIo9Ji8vD3l5D79Y09PTKyH64lJTbsF15TNV8ty1iuzBTylUkCNd4QLFwCVMbIiIqEroLLlJSUmBUqmEs7OzRruzszOSkpJKfExSUlKJ/QsLC5GSkgJXV9dij5k3bx7mzJlTeYGXIVcUT670hYAMWTJz5BiYI8/QEoVGVlAprCAzsYaBmQ2MzGxgbG4NhZkVTC2sYGJuDQOFhbQvlLE5YGwBKCwgNzSBDScOExFRFdL5UnDZY190QohibU/qX1J7kVmzZmHatGnq39PT0+Hh4VHRcEvl4OIJzEmp9OetSUx1HQAREZEWdJbcODg4wMDAoNgoTXJycrHRmSIuLi4l9jc0NIS9vX2Jj1EoFFAoFJUTNBEREdV4OlsKbmxsjKCgIOzatUujfdeuXWjfvn2Jj2nXrl2x/jt37kRwcHCJ822IiIio7tFpnZtp06bhxx9/xIoVKxAXF4epU6ciISEBY8aMASBdUho2bJi6/5gxY3Dt2jVMmzYNcXFxWLFiBZYvX47p06fr6hCIiIiohtHpnJshQ4bg7t27+Oijj5CYmAg/Pz9s27YNXl5eAIDExEQkJCSo+/v4+GDbtm2YOnUqvv/+e7i5uWHhwoWscUNERERqOq1zowtVVeeGiIiIqk55vr91vv0CERERUWVickNERER6hckNERER6RUmN0RERKRXmNwQERGRXmFyQ0RERHqFyQ0RERHpFSY3REREpFeY3BAREZFe0en2C7pQVJA5PT1dx5EQERGRtoq+t7XZWKHOJTcZGRkAAA8PDx1HQkREROWVkZEBa2vrMvvUub2lVCoVbt26BUtLS8hkskp5zvT0dHh4eOD69et1Zr8qHnPdOGagbh43j5nHrK9q8zELIZCRkQE3NzfI5WXPqqlzIzdyuRzu7u5V8txWVla17sPytHjMdUddPG4ec93AY649njRiU4QTiomIiEivMLkhIiIivcLkphIoFAp88MEHUCgUug6l2vCY6466eNw85rqBx6y/6tyEYiIiItJvHLkhIiIivcLkhoiIiPQKkxsiIiLSK0xuiIiISK8wuXlKixYtgo+PD0xMTBAUFIQDBw7oOqRKM2/ePLRu3RqWlpZwcnJCv379cP78eY0+w4cPh0wm0/hp27atjiJ+eh9++GGx43FxcVHfL4TAhx9+CDc3N5iamiI0NBQxMTE6jLhyeHt7FztumUyG8ePHA9CP87x//3706dMHbm5ukMlk+OuvvzTu1+bc5uXlYeLEiXBwcIC5uTleeOEF3LhxoxqPonzKOuaCggLMnDkT/v7+MDc3h5ubG4YNG4Zbt25pPEdoaGixc//SSy9V85Fo70nnWZvPcm07z8CTj7ukv98ymQxffvmluk9tO9dlYXLzFNatW4cpU6bgf//7H6Kjo9GxY0f07NkTCQkJug6tUuzbtw/jx4/HkSNHsGvXLhQWFqJbt27IysrS6NejRw8kJiaqf7Zt26ajiCtH8+bNNY7nzJkz6vu++OILzJ8/H9999x2OHj0KFxcXPPfcc+o9y2qro0ePahzzrl27AACDBg1S96nt5zkrKwsBAQH47rvvSrxfm3M7ZcoUbNy4Eb/99hsOHjyIzMxM9O7dG0qlsroOo1zKOubs7GycOHEC7733Hk6cOIENGzbgwoULeOGFF4r1HT16tMa5X7JkSXWEXyFPOs/Akz/Lte08A08+7kePNzExEStWrIBMJsOAAQM0+tWmc10mQRX2zDPPiDFjxmi0NW3aVLzzzjs6iqhqJScnCwBi37596rbw8HDRt29f3QVVyT744AMREBBQ4n0qlUq4uLiIzz77TN2Wm5srrK2txeLFi6spwuoxefJk0aBBA6FSqYQQ+neeAYiNGzeqf9fm3N6/f18YGRmJ3377Td3n5s2bQi6Xi+3bt1db7BX1+DGX5L///hMAxLVr19RtISEhYvLkyVUbXBUp6Zif9Fmu7edZCO3Odd++fUXnzp012mrzuX4cR24qKD8/H8ePH0e3bt002rt164bDhw/rKKqqlZaWBgCws7PTaI+MjISTkxMaN26M0aNHIzk5WRfhVZqLFy/Czc0NPj4+eOmll3DlyhUAQHx8PJKSkjTOuUKhQEhIiF6d8/z8fKxduxYjR47U2FxW387zo7Q5t8ePH0dBQYFGHzc3N/j5+enN+U9LS4NMJoONjY1G+88//wwHBwc0b94c06dPr/UjlWV9luvCeb59+za2bt2KUaNGFbtPX851nds4s7KkpKRAqVTC2dlZo93Z2RlJSUk6iqrqCCEwbdo0dOjQAX5+fur2nj17YtCgQfDy8kJ8fDzee+89dO7cGcePH6+VFTDbtGmDNWvWoHHjxrh9+zY+/vhjtG/fHjExMerzWtI5v3btmi7CrRJ//fUX7t+/j+HDh6vb9O08P06bc5uUlARjY2PY2toW66MPf+dzc3Pxzjvv4JVXXtHYUHHo0KHw8fGBi4sLzp49i1mzZuHUqVPqS5e1zZM+y/p+ngFg9erVsLS0RP/+/TXa9elcM7l5So/+zxaQkoDH2/TBhAkTcPr0aRw8eFCjfciQIerbfn5+CA4OhpeXF7Zu3VrsL05t0LNnT/Vtf39/tGvXDg0aNMDq1avVkw71/ZwvX74cPXv2hJubm7pN385zaSpybvXh/BcUFOCll16CSqXCokWLNO4bPXq0+rafnx8aNWqE4OBgnDhxAq1ataruUJ9aRT/L+nCei6xYsQJDhw6FiYmJRrs+nWtelqogBwcHGBgYFMvkk5OTi/3vr7abOHEiNm3ahIiICLi7u5fZ19XVFV5eXrh48WI1RVe1zM3N4e/vj4sXL6pXTenzOb927Rp2796N119/vcx++naetTm3Li4uyM/PR2pqaql9aqOCggIMHjwY8fHx2LVrl8aoTUlatWoFIyMjvTn3j3+W9fU8Fzlw4ADOnz//xL/jQO0+10xuKsjY2BhBQUHFhut27dqF9u3b6yiqyiWEwIQJE7Bhwwbs3bsXPj4+T3zM3bt3cf36dbi6ulZDhFUvLy8PcXFxcHV1VQ/XPnrO8/PzsW/fPr055ytXroSTkxOef/75Mvvp23nW5twGBQXByMhIo09iYiLOnj1ba89/UWJz8eJF7N69G/b29k98TExMDAoKCvTm3D/+WdbH8/yo5cuXIygoCAEBAU/sW6vPtQ4nM9d6v/32mzAyMhLLly8XsbGxYsqUKcLc3FxcvXpV16FVirFjxwpra2sRGRkpEhMT1T/Z2dlCCCEyMjLEW2+9JQ4fPizi4+NFRESEaNeunahXr55IT0/XcfQV89Zbb4nIyEhx5coVceTIEdG7d29haWmpPqefffaZsLa2Fhs2bBBnzpwRL7/8snB1da21x/sopVIpPD09xcyZMzXa9eU8Z2RkiOjoaBEdHS0AiPnz54vo6Gj1yiBtzu2YMWOEu7u72L17tzhx4oTo3LmzCAgIEIWFhbo6rDKVdcwFBQXihRdeEO7u7uLkyZMaf8fz8vKEEEJcunRJzJkzRxw9elTEx8eLrVu3iqZNm4rAwMBaeczafpZr23kW4smfbyGESEtLE2ZmZuKHH34o9vjaeK7LwuTmKX3//ffCy8tLGBsbi1atWmksk67tAJT4s3LlSiGEENnZ2aJbt27C0dFRGBkZCU9PTxEeHi4SEhJ0G/hTGDJkiHB1dRVGRkbCzc1N9O/fX8TExKjvV6lU4oMPPhAuLi5CoVCITp06iTNnzugw4sqzY8cOAUCcP39eo11fznNERESJn+fw8HAhhHbnNicnR0yYMEHY2dkJU1NT0bt37xr9PpR1zPHx8aX+HY+IiBBCCJGQkCA6deok7OzshLGxsWjQoIGYNGmSuHv3rm4PrAxlHbO2n+Xadp6FePLnWwghlixZIkxNTcX9+/eLPb42nuuyyIQQokqHhoiIiIiqEefcEBERkV5hckNERER6hckNERER6RUmN0RERKRXmNwQERGRXmFyQ0RERHqFyQ0RERHpFSY3RKTXrl69CplMhpMnTz7V84SGhmLKlClP7NepUyf88ssvT/ValaV169bYsGGDrsMgqnZMbohqseHDh0Mmk0Emk8HQ0BCenp4YO3ZssU3/ylJZX/41lYeHBxITE+Hn51flr7VlyxYkJSXhpZdeqvLX0sZ7772Hd955ByqVStehEFUrJjdEtVyPHj2QmJiIq1ev4scff8TmzZsxbtw4ncSSn5+vk9ctTX5+PgwMDODi4gJDQ8Mqf72FCxdixIgRkMt1+09r0Xl4/vnnkZaWhh07dug0HqLqxuSGqJZTKBRwcXGBu7s7unXrhiFDhmDnzp0afVauXAlfX1+YmJigadOmWLRokfq+ot3eAwMDIZPJEBoaCqDkyzD9+vXD8OHD1b97e3vj448/xvDhw2FtbY3Ro0dj1apVsLGxwY4dO+Dr6wsLCwt1AlaayMhIyGQybN26FQEBATAxMUGbNm1w5swZjX6HDx9Gp06dYGpqCg8PD0yaNAlZWVllxlPSyNS+ffvwzDPPQKFQwNXVFe+88w4KCwvV92dlZWHYsGGwsLCAq6srvv766zLPAQCkpKRg9+7deOGFF9RtI0eORO/evTX6FRYWwsXFBStWrAAACCHwxRdfoH79+jA1NUVAQAD+/PNPdX+lUolRo0bBx8cHpqamaNKkCb799luN5xw+fDj69euHefPmwc3NDY0bNwYAGBgYoFevXvj111+fGD+RXtHx3lZE9BTCw8NF37591b9fvnxZNGvWTDg7O6vbli5dKlxdXcX69evFlStXxPr164WdnZ1YtWqVEEKI//77TwAQu3fvFomJieqN8kJCQsTkyZM1Xq9v374aG/F5eXkJKysr8eWXX4qLFy+KixcvipUrVwojIyPRtWtXcfToUXH8+HHh6+srXnnllVKPo2jTP19fX7Fz505x+vRp0bt3b+Ht7S3y8/OFEEKcPn1aWFhYiG+++UZcuHBBHDp0SAQGBorhw4eXGU/RBpHR0dFCCCFu3LghzMzMxLhx40RcXJzYuHGjcHBwEB988IH6ecaOHSvc3d01YrGwsCj2fjxq48aNwtzcXCiVSnXboUOHhIGBgbh165a67e+//xbm5uYiIyNDCCHEu+++K5o2bSq2b98uLl++LFauXCkUCoWIjIwUQgiRn58v3n//ffHff/+JK1euiLVr1wozMzOxbt069XOGh4cLCwsL8dprr4mzZ89qbPi5aNEi4e3tXWrcRPqIyQ1RLRYeHi4MDAyEubm5MDExUe8EPH/+fHUfDw8P8csvv2g8bu7cuaJdu3ZCCFHsy7+ItslNv379NPqsXLlSABCXLl1St33//fcaCdfjipKb3377Td129+5dYWpqqv4Sf+2118Qbb7yh8bgDBw4IuVwucnJySo3n8eN79913RZMmTYRKpdKIz8LCQiiVSpGRkSGMjY1LjKWs5Oabb74R9evXL9berFkz8fnnn6t/79evnzohy8zMFCYmJuLw4cMajxk1apR4+eWXS32tcePGiQEDBqh/Dw8PF87OziIvL69Y37///lvI5XKNpItI31X9RWgiqlJhYWH44YcfkJ2djR9//BEXLlzAxIkTAQB37tzB9evXMWrUKIwePVr9mMLCQlhbW1fK6wcHBxdrMzMzQ4MGDdS/u7q6Ijk5+YnP1a5dO/VtOzs7NGnSBHFxcQCA48eP49KlS/j555/VfYQQUKlUiI+Ph6+vb6nxPCouLg7t2rWDTCZTtz377LPIzMzEjRs3kJqaivz8/BJjKUtOTg5MTEyKtb/++utYunQpZsyYgeTkZGzduhV79uwBAMTGxiI3NxfPPfecxmPy8/MRGBio/n3x4sX48ccfce3aNeTk5CA/Px8tW7bUeIy/vz+MjY2Lvb6pqSlUKhXy8vJgampa5jEQ6QsmN0S1nLm5ORo2bAhAmtAaFhaGOXPmYO7cuepVMsuWLUObNm00HmdgYFDm88rlcgghNNoKCgpKfP3HGRkZafwuk8mKPZe2ipIQlUqFN998E5MmTSrWx9PTs8x4HiWE0EhsitqeNk4HB4cSV6kNGzYM77zzDqKiohAVFQVvb2907NgRANTnZ+vWrahXr57G4xQKBQDg999/x9SpU/H111+jXbt2sLS0xJdffol///1Xo39px33v3j2YmZkxsaE6hckNkZ754IMP0LNnT4wdOxZubm6oV68erly5gqFDh5bYv+h/+0qlUqPd0dFRYxKwUqnE2bNnERYWVmWxHzlyRJ2opKam4sKFC2jatCkAoFWrVoiJiVEnchXVrFkzrF+/XiPJOXz4MCwtLVGvXj3Y2trCyMioxFhCQkJKfd7AwEAkJSUhNTUVtra26nZ7e3v069cPK1euRFRUFEaMGKERi0KhQEJCQqnPfeDAAbRv315jBdzly5e1Pt6zZ8+iVatWWvcn0gdcLUWkZ0JDQ9G8eXN8+umnAIAPP/wQ8+bNw7fffosLFy7gzJkzWLlyJebPnw8AcHJygqmpKbZv347bt28jLS0NANC5c2ds3boVW7duxblz5zBu3Djcv3+/SmP/6KOPsGfPHpw9exbDhw+Hg4MD+vXrBwCYOXMmoqKiMH78eJw8eRIXL17Epk2b1JfgtDVu3Dhcv34dEydOxLlz5/D333/jgw8+wLRp0yCXy2FhYYFRo0bh7bff1ojlScu7AwMD4ejoiEOHDhW77/XXX8fq1asRFxeH8PBwdbulpSWmT5+OqVOnYvXq1bh8+TKio6Px/fffY/Xq1QCAhg0b4tixY9ixYwcuXLiA9957D0ePHtX6eA8cOIBu3bpp3Z9IHzC5IdJD06ZNw7Jly3D9+nW8/vrr+PHHH7Fq1Sr4+/sjJCQEq1atUi8BNzQ0xMKFC7FkyRK4ubmhb9++AKRlzOHh4Rg2bBhCQkLg4+NTpaM2APDZZ59h8uTJCAoKQmJiIjZt2qQeWWrRogX27duHixcvomPHjggMDMR7770HV1fXcr1GvXr1sG3bNvz3338ICAjAmDFjMGrUKMyePVvd58svv0SnTp3wwgsvoGvXrujQoQOCgoLKfF4DAwOMHDlSY05Qka5du8LV1RXdu3eHm5ubxn1z587F+++/j3nz5sHX1xfdu3fH5s2b1ednzJgx6N+/P4YMGYI2bdrg7t27WtcxunnzJg4fPqwxWkRUF8hERS8wExFVksjISISFhSE1NRU2Nja6DqfCbt++jebNm+P48ePw8vJSt2dnZ8PNzQ0rVqxA//79qy2et99+G2lpaVi6dGm1vSZRTcCRGyKiSuLs7Izly5cjISEBgDRh+NatW3jvvfdgbW2tUeCvOjg5OWHu3LnV+ppENQEnFBMRVaKiy3oAkJCQAB8fH7i7u2PVqlXVsgXEo95+++1qfT2imoKXpYiIiEiv8LIUERER6RUmN0RERKRXmNwQERGRXmFyQ0RERHqFyQ0RERHpFSY3REREpFeY3BAREZFeYXJDREREeoXJDREREemV/we1BdrnHidPtgAAAABJRU5ErkJggg==", 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    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ax = imp.calc_freq_curve().plot(label=\"Without measure\")\n", + "new_imp.calc_freq_curve().plot(axis=ax, label=\"With measure\")\n", + "ax.legend()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "hide_input": false, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/doc/tutorial/1_main_climada.ipynb b/doc/user-guide/1_main_climada.ipynb similarity index 99% rename from doc/tutorial/1_main_climada.ipynb rename to doc/user-guide/1_main_climada.ipynb index 7a9b45ab83..ee18d9e062 100644 --- a/doc/tutorial/1_main_climada.ipynb +++ b/doc/user-guide/1_main_climada.ipynb @@ -22,7 +22,7 @@ "\n", "The model core is designed to give as much flexibility as possible when describing the elements of risk, meaning that CLIMADA isn't limited to particular hazards, exposure types or impacts. We love to see the model applied to new problems and contexts.\n", "\n", - "CLIMADA provides classes, methods and data for exposure, hazard and impact functions (also called vulnerability functions), plus a financial model and a framework to analyse adaptation measures. Additional classes and data for common uses, such as economic exposures or tropical storms and tutorials for every class are available: see the [CLIMADA features](#CLIMADA-features) section below.\n", + "CLIMADA provides classes, methods and data for exposure, hazard and impact functions (also called vulnerability functions), plus a financial model and a framework to analyse adaptation measures. Additional classes and data for common uses, such as economic exposures or tropical storms and tutorials for every class are available: see the [CLIMADA features](#climada-features) section below.\n", "\n", "\n", "### This tutorial\n", @@ -31,9 +31,9 @@ "\n", "### Resources beyond this tutorial\n", "\n", - "- [Installation guide](../guide/install.rst) - go here if you've not installed the model yet\n", + "- [Installation guide](../getting-started/install.rst) - go here if you've not installed the model yet\n", "- [CLIMADA Read the Docs home page](https://climada-python.readthedocs.io) - for all other documentation\n", - "- [List of CLIMADA's features and associated tutorials](#CLIMADA-features)\n", + "- [List of CLIMADA's features and associated tutorials](#climada-features)\n", "- [CLIMADA GitHub develop branch documentation](https://github.com/CLIMADA-project/climada_python/tree/develop/doc) for the very latest versions of code and documentation\n", "- [CLIMADA paper GitHub repository](https://github.com/CLIMADA-project/climada_papers) - for publications using CLIMADA\n" ] @@ -57,7 +57,7 @@ "CLIMADA's `Impact` object is used to analyse events and event sets, whether this is the impact of a single wildfire, or the global economic risk from tropical cyclones in 2100.\n", "\n", "CLIMADA is divided into two parts (two repositories): \n", - "1. the core [climada_python](https://github.com/CLIMADA-project/climada_python) contains all the modules necessary for the probabilistic impact, the averted damage, uncertainty and forecast calculations. Data for hazard, exposures and impact functions can be obtained from the [data API](https://github.com/CLIMADA-project/climada_python/blob/main/doc/tutorial/climada_util_api_client.ipynb). [Litpop](https://github.com/CLIMADA-project/climada_python/blob/main/doc/tutorial/climada_entity_LitPop.ipynb) is included as demo Exposures module, and [Tropical cyclones](https://github.com/CLIMADA-project/climada_python/blob/main/doc/tutorial/climada_hazard_TropCyclone.ipynb) is included as a demo Hazard module. \n", + "1. the core [climada_python](https://github.com/CLIMADA-project/climada_python) contains all the modules necessary for the probabilistic impact, the averted damage, uncertainty and forecast calculations. Data for hazard, exposures and impact functions can be obtained from the [data API](/user-guide/climada_util_api_client.ipynb). [Litpop](/user-guide/climada_entity_LitPop.ipynb) is included as demo Exposures module, and [Tropical cyclones](/user-guide/climada_hazard_TropCyclone.ipynb) is included as a demo Hazard module. \n", "2. the petals [climada_petals](https://github.com/CLIMADA-project/climada_petals) contains all the modules for generating data (e.g., TC_Surge, WildFire, OpenStreeMap, ...). Most development is done here. The petals builds-upon the core and does not work as a stand-alone.\n", "\n", "### CLIMADA classes\n", @@ -65,32 +65,32 @@ "This is a full directory of tutorials for CLIMADA's classes to use as a reference. You don't need to read all this to do this tutorial, but it may be useful to refer back to.\n", "\n", "Core (climada_python):\n", - "- [**Hazard**](../tutorial/climada_hazard_Hazard.ipynb): a class that stores sets of geographic hazard footprints, (e.g. for wind speed, water depth and fraction, drought index), and metadata including event frequency. Several predefined extensions to create particular hazards from particular datasets and models are included with CLIMADA:\n", - " - [Tropical cyclone wind](../tutorial/climada_hazard_TropCyclone.ipynb): global hazard sets for tropical cyclone events, constructing statistical wind fields from storm tracks. Subclasses include methods and data to calculate historical wind footprints, create forecast enembles from ECMWF tracks, and create climatological event sets for different climate scenarios.\n", - " - [European windstorms](../tutorial/climada_hazard_StormEurope.ipynb): includes methods to read and plot footprints from the Copernicus WISC dataset and for DWD and ICON forecasts. \n", + "- [**Hazard**](../user-guide/climada_hazard_Hazard.ipynb): a class that stores sets of geographic hazard footprints, (e.g. for wind speed, water depth and fraction, drought index), and metadata including event frequency. Several predefined extensions to create particular hazards from particular datasets and models are included with CLIMADA:\n", + " - [Tropical cyclone wind](../user-guide/climada_hazard_TropCyclone.ipynb): global hazard sets for tropical cyclone events, constructing statistical wind fields from storm tracks. Subclasses include methods and data to calculate historical wind footprints, create forecast enembles from ECMWF tracks, and create climatological event sets for different climate scenarios.\n", + " - [European windstorms](../user-guide/climada_hazard_StormEurope.ipynb): includes methods to read and plot footprints from the Copernicus WISC dataset and for DWD and ICON forecasts. \n", "\n", "- [**Entity**](#Entity): this is a container that groups CLIMADA's socio-economic models. It's is where the Exposures and Impact Functions are stored, which can then be combined with a hazard for a risk analysis (using the Engine's Impact class). It is also where Discount Rates and Measure Sets are stored, which are used in adaptation cost-benefit analyses (using the Engine's CostBenefit class):\n", - " - [Exposures](../tutorial/climada_entity_Exposures.ipynb): geolocated exposures. Each exposure is associated with a value (which can be a dollar value, population, crop yield, etc), information to associate it with impact functions for the relevant hazard(s) (in the Entity's ImpactFuncSet), a geometry, and other optional properties such as deductables and cover. Exposures can be loaded from a file, specified by the user, or created from regional economic models accessible within CLIMADA, for example: \n", - " - [LitPop](../tutorial/climada_entity_LitPop.ipynb): regional economic model using nightlight and population maps together with several economic indicators \n", - " - [Polygons_lines](../tutorial/climada_entity_Exposures_polygons_lines.ipynb): use CLIMADA Impf you have your exposure in the form of shapes/polygons or in the form of lines.\n", - " - [ImpactFuncSet](../tutorial/climada_entity_ImpactFuncSet.ipynb): functions to describe the impacts that hazards have on exposures, expressed in terms of e.g. the % dollar value of a building lost as a function of water depth, or the mortality rate for over-70s as a function of temperature. CLIMADA provides some common impact functions, or they can be user-specified. The following is an incomplete list:\n", + " - [Exposures](../user-guide/climada_entity_Exposures.ipynb): geolocated exposures. Each exposure is associated with a value (which can be a dollar value, population, crop yield, etc), information to associate it with impact functions for the relevant hazard(s) (in the Entity's ImpactFuncSet), a geometry, and other optional properties such as deductables and cover. Exposures can be loaded from a file, specified by the user, or created from regional economic models accessible within CLIMADA, for example: \n", + " - [LitPop](../user-guide/climada_entity_LitPop.ipynb): regional economic model using nightlight and population maps together with several economic indicators \n", + " - [Polygons_lines](../user-guide/climada_entity_Exposures_polygons_lines.ipynb): use CLIMADA Impf you have your exposure in the form of shapes/polygons or in the form of lines.\n", + " - [ImpactFuncSet](../user-guide/climada_entity_ImpactFuncSet.ipynb): functions to describe the impacts that hazards have on exposures, expressed in terms of e.g. the % dollar value of a building lost as a function of water depth, or the mortality rate for over-70s as a function of temperature. CLIMADA provides some common impact functions, or they can be user-specified. The following is an incomplete list:\n", " - ImpactFunc: a basic adjustable impact function, specified by the user\n", " - IFTropCyclone: impact functions for tropical cyclone winds\n", " - IFRiverFlood: impact functions for river floods\n", " - IFStormEurope: impact functions for European windstorms \n", - " - [DiscRates](../tutorial/climada_entity_DiscRates.ipynb): discount rates per year\n", - " - [MeasureSet](../tutorial/climada_entity_MeasureSet.ipynb): a collection of Measure objects that together describe any adaptation measures being modelled. Adaptation measures are described by their cost, and how they modify exposure, hazard, and impact functions (and have have a method to do these things). Measures also include risk transfer options.\n", + " - [DiscRates](../user-guide/climada_entity_DiscRates.ipynb): discount rates per year\n", + " - [MeasureSet](../user-guide/climada_entity_MeasureSet.ipynb): a collection of Measure objects that together describe any adaptation measures being modelled. Adaptation measures are described by their cost, and how they modify exposure, hazard, and impact functions (and have have a method to do these things). Measures also include risk transfer options.\n", " \n", - "- [**Engine**](../tutorial/climada_engine_Impact.ipynb): the CLIMADA Engine contains the Impact and CostBenefit classes, which are where the main model calculations are done, combining Hazard and Entity objects.\n", - " - [Impact](../tutorial/climada_engine_Impact.ipynb): a class that stores CLIMADA's modelled impacts and the methods to calculate them from Exposure, Impact Function and Hazard classes. The calculations include average annual impact, expected annual impact by exposure item, total impact by event, and (optionally) the impact of each event on each exposure point. Includes statistical and plotting routines for common analysis products.\n", - " - [Impact_data](../tutorial/climada_engine_impact_data.ipynb): The core functionality of the module is to read disaster impact data as downloaded from the International Disaster Database EM-DAT (www.emdat.be) and produce a CLIMADA Impact()-instance from it. The purpose is to make impact data easily available for comparison with simulated impact inside CLIMADA, e.g. for calibration purposes.\n", - " - [CostBenefit](#Adaptation-options-appraisal): a class to appraise adaptation options. It uses an Entity's MeasureSet to calculate new Impacts based on their adjustments to hazard, exposure, and impact functions, and returns statistics and plotting routines to express cost-benefit comparisons.\n", - " - [Unsequa](../tutorial/climada_engine_unsequa.ipynb): a module for uncertainty and sensitivity analysis.\n", - " - [Unsequa_helper](../tutorial/climada_engine_unsequa_helper.ipynb): The InputVar class provides a few helper methods to generate generic uncertainty input variables for exposures, impact function sets, hazards, and entities (including measures cost and disc rates). This tutorial complements the general tutorial on the uncertainty and sensitivity analysis module unsequa.\n", - " - [Forecast](../tutorial/climada_engine_Forecast.ipynb): This class deals with weather forecasts and uses CLIMADA ImpactCalc.impact() to forecast impacts of weather events on society. It mainly does one thing: It contains all plotting and other functionality that are specific for weather forecasts, impact forecasts and warnings.\n", + "- [**Engine**](../user-guide/climada_engine_Impact.ipynb): the CLIMADA Engine contains the Impact and CostBenefit classes, which are where the main model calculations are done, combining Hazard and Entity objects.\n", + " - [Impact](../user-guide/climada_engine_Impact.ipynb): a class that stores CLIMADA's modelled impacts and the methods to calculate them from Exposure, Impact Function and Hazard classes. The calculations include average annual impact, expected annual impact by exposure item, total impact by event, and (optionally) the impact of each event on each exposure point. Includes statistical and plotting routines for common analysis products.\n", + " - [Impact_data](../user-guide/climada_engine_impact_data.ipynb): The core functionality of the module is to read disaster impact data as downloaded from the International Disaster Database EM-DAT (www.emdat.be) and produce a CLIMADA Impact()-instance from it. The purpose is to make impact data easily available for comparison with simulated impact inside CLIMADA, e.g. for calibration purposes.\n", + " - [CostBenefit](../user-guide/climada_engine_CostBenefit.ipynb): a class to appraise adaptation options. It uses an Entity's MeasureSet to calculate new Impacts based on their adjustments to hazard, exposure, and impact functions, and returns statistics and plotting routines to express cost-benefit comparisons.\n", + " - [Unsequa](../user-guide/climada_engine_unsequa.ipynb): a module for uncertainty and sensitivity analysis.\n", + " - [Unsequa_helper](../user-guide/climada_engine_unsequa_helper.ipynb): The InputVar class provides a few helper methods to generate generic uncertainty input variables for exposures, impact function sets, hazards, and entities (including measures cost and disc rates). This tutorial complements the general tutorial on the uncertainty and sensitivity analysis module unsequa.\n", + " - [Forecast](../user-guide/climada_engine_Forecast.ipynb): This class deals with weather forecasts and uses CLIMADA ImpactCalc.impact() to forecast impacts of weather events on society. It mainly does one thing: It contains all plotting and other functionality that are specific for weather forecasts, impact forecasts and warnings.\n", "\n", "climada_petals:\n", - "- [**Hazard**](../tutorial/climada_hazard_Hazard.ipynb):\n", + "- [**Hazard**](../user-guide/climada_hazard_Hazard.ipynb):\n", " - [Storm surge](https://climada-petals.readthedocs.io/en/stable/tutorial/climada_hazard_TCSurgeBathtub.html): Tropical cyclone surge from linear wind-surge relationship and a bathtub model.\n", " - [River flooding](https://climada-petals.readthedocs.io/en/stable/tutorial/climada_hazard_RiverFlood.html): global water depth hazard for flood, including methods to work with ISIMIP simulations.\n", " - [Crop modelling](https://climada-petals.readthedocs.io/en/stable/tutorial/climada_hazard_entity_Crop.html): combines ISIMIP crop simulations and UN Food and Agrigultre Organization data. The module uses crop production as exposure, with hydrometeorological 'hazard' increasing or decreasing production.\n", @@ -101,12 +101,12 @@ " - Drought (global): tutorial under development\n", "\n", "- [**Entity**](#Entity): \n", - " - [Exposures](../tutorial/climada_entity_Exposures.ipynb):\n", - " - [BlackMarble](https://climada-petals.readthedocs.io/en/stable/tutorial/climada_entity_BlackMarble.html): regional economic model from nightlight intensities and economic indicators (GDP, income group). Largely succeeded by LitPop.\n", - " - [OpenStreetMap](https://climada-petals.readthedocs.io/en/stable/tutorial/climada_exposures_openstreetmap.html): CLIMADA provides some ways to make use of the entire OpenStreetMap data world and to use those data within the risk modelling chain of CLIMADA as exposures.\n", + " - [Exposures](../user-guide/climada_entity_Exposures.ipynb):\n", + " - [BlackMarble](https://climada-petals.readthedocs.io/en/stable/user-guide/climada_entity_BlackMarble.html): regional economic model from nightlight intensities and economic indicators (GDP, income group). Largely succeeded by LitPop.\n", + " - [OpenStreetMap](https://climada-petals.readthedocs.io/en/stable/user-guide/climada_exposures_openstreetmap.html): CLIMADA provides some ways to make use of the entire OpenStreetMap data world and to use those data within the risk modelling chain of CLIMADA as exposures.\n", "\n", - "- [**Engine**](../tutorial/climada_engine_Impact.ipynb):\n", - " - [SupplyChain](https://climada-petals.readthedocs.io/en/stable/tutorial/climada_engine_SupplyChain.html): This class allows assessing indirect impacts via Input-Ouput modeling.\n", + "- [**Engine**](../user-guide/climada_engine_Impact.ipynb):\n", + " - [SupplyChain](https://climada-petals.readthedocs.io/en/stable/user-guide/climada_engine_SupplyChain.html): This class allows assessing indirect impacts via Input-Ouput modeling.\n", "\n", "This list will be updated periodically along with new CLIMADA releases. To see the latest, development version of all tutorials, see the [tutorials page on the CLIMADA GitHub](https://github.com/CLIMADA-project/climada_python/tree/develop/doc/tutorial)." ] @@ -128,16 +128,16 @@ "\n", "Hazards are characterized by their frequency of occurrence and the geographical distribution of their intensity. The `Hazard` class collects events of the same hazard type (e.g. tropical cyclone, flood, drought, ...) with intensity values over the same geographic centroids. They might be historical events or synthetic.\n", "\n", - "See the [Hazard tutorial](climada_hazard_Hazard.ipynb) to learn about the Hazard class in more detail, and the [CLIMADA features](#CLIMADA-features) section of this document to explore tutorials for different hazards, including\n", - "[tropical cyclones](climada_hazard_TropCyclone.ipynb), as used here.\n", + "See the [Hazard tutorial](climada_hazard_Hazard.ipynb) to learn about the Hazard class in more detail, and the [CLIMADA features](#climada-features) section of this document to explore tutorials for different hazards, including\n", + "[tropical cyclones](../user-guide/climada_hazard_TropCyclone.ipynb), as used here.\n", "\n", - "Tropical cyclones in CLIMADA and the `TropCyclone` class work like any hazard, storing each event's wind speeds at the geographic centroids specified for the class. Pre-calculated hazards can be loaded from files (see the [full Hazard tutorial](climada_hazard_Hazard.ipynb), but they can also be modelled from a storm track using the `TCTracks` class, based on a storm's parameters at each time step. This is how we'll construct the hazards for our example.\n", + "Tropical cyclones in CLIMADA and the `TropCyclone` class work like any hazard, storing each event's wind speeds at the geographic centroids specified for the class. Pre-calculated hazards can be loaded from files (see the [full Hazard tutorial](../user-guide/climada_hazard_Hazard.ipynb), but they can also be modelled from a storm track using the `TCTracks` class, based on a storm's parameters at each time step. This is how we'll construct the hazards for our example.\n", "\n", "So before we create the hazard, we will create our storm tracks and define the geographic centroids for the locations we want to calculate hazard at.\n", "\n", "### Storm tracks\n", "\n", - "Storm tracks are created and stored in a separate class, `TCTracks`. We use its method `from_ibtracs_netcdf` to create the tracks from the [IBTRaCS](https://www.ncdc.noaa.gov/ibtracs/) storm tracks archive. In the next block we will download the full dataset, which might take a little time. However, to plot the whole dataset takes too long (see the second block), so we choose a shorter time range here to show the function. See the [full TropCyclone tutorial](climada_hazard_TropCyclone.ipynb) for more detail and troubleshooting." + "Storm tracks are created and stored in a separate class, `TCTracks`. We use its method `from_ibtracs_netcdf` to create the tracks from the [IBTRaCS](https://www.ncdc.noaa.gov/ibtracs/) storm tracks archive. In the next block we will download the full dataset, which might take a little time. However, to plot the whole dataset takes too long (see the second block), so we choose a shorter time range here to show the function. See the [full TropCyclone tutorial](../user-guide/climada_hazard_TropCyclone.ipynb) for more detail and troubleshooting." ] }, { @@ -257,7 +257,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now, irresponsibly for a risk analysis, we're only going to use these historical events: they're enough to demonstrate CLIMADA in action. A proper risk analysis would expand it to include enough events for a statistically robust climatology. See the [full TropCyclone tutorial](climada_hazard_TropCyclone.ipynb) for CLIMADA's stochastic event generation." + "Now, irresponsibly for a risk analysis, we're only going to use these historical events: they're enough to demonstrate CLIMADA in action. A proper risk analysis would expand it to include enough events for a statistically robust climatology. See the [full TropCyclone tutorial](../user-guide/climada_hazard_TropCyclone.ipynb) for CLIMADA's stochastic event generation." ] }, { @@ -444,9 +444,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "See the [TropCyclone tutorial](climada_hazard_TropCyclone.ipynb) for full details of the TropCyclone hazard class.\n", + "See the [TropCyclone tutorial](../user-guide/climada_hazard_TropCyclone.ipynb) for full details of the TropCyclone hazard class.\n", "\n", - "We can also recalculate event sets to reflect the effects of climate change. The `apply_climate_scenario_knu` method applies changes in intensity and frequency projected due to climate change, as described in 'Global projections of intense tropical cyclone activity for the late twenty-first century from dynamical downscaling of CMIP5/RCP4.5 scenarios' (Knutson _et al._ 2015). See the [tutorial](climada_hazard_TropCyclone.ipynb) for details.\n", + "We can also recalculate event sets to reflect the effects of climate change. The `apply_climate_scenario_knu` method applies changes in intensity and frequency projected due to climate change, as described in 'Global projections of intense tropical cyclone activity for the late twenty-first century from dynamical downscaling of CMIP5/RCP4.5 scenarios' (Knutson _et al._ 2015). See the [tutorial](../user-guide/climada_hazard_TropCyclone.ipynb) for details.\n", "\n", ">**Exercise:** Extend this notebook's analysis to examine the effects of climate change in Puerto Rico. You'll need to extend the historical event set with stochastic tracks to create a robust statistical storm climatology - the `TCTracks` class has the functionality to do this. Then you can apply the `apply_climate_scenario_knu` method to the generated hazard object to create a second hazard climatology representing storm activity under climate change. See how the results change using the different hazard sets.\n", "\n", @@ -474,7 +474,7 @@ "\n", "The `Entity`'s `exposures` attribute contains geolocalized values of anything exposed to the hazard, whether monetary values of assets or number of human lives, for example. It is of type `Exposures`. \n", "\n", - "See the [Exposures tutorial](climada_entity_Exposures.ipynb) for more detail on the structure of the class, and how to create and import exposures. The [LitPop tutorial](climada_entity_LitPop.ipynb) explains how CLIMADA models economic exposures using night-time light and economic data, and is what we'll use here. To combine your exposure with OpenStreetMap's data see the [OSM tutorial](https://github.com/CLIMADA-project/climada_petals/blob/main/doc/tutorial/climada_exposures_openstreetmap.ipynb).\n", + "See the [Exposures tutorial](../user-guide/climada_entity_Exposures.ipynb) for more detail on the structure of the class, and how to create and import exposures. The [LitPop tutorial](../user-guide/climada_entity_LitPop.ipynb) explains how CLIMADA models economic exposures using night-time light and economic data, and is what we'll use here. To combine your exposure with OpenStreetMap's data see the [OSM tutorial](https://github.com/CLIMADA-project/climada_petals/blob/main/doc/tutorial/climada_exposures_openstreetmap.ipynb).\n", "\n", "LitPop is a module that allows CLIMADA to estimate exposed populations and economic assets at any point on the planet without additional information, and in a globally consistent way. Before we try it out with the next code block, we'll need to download a data set and put it into the right folder:\n", "1. Go to the [download page](https://beta.sedac.ciesin.columbia.edu/data/set/gpw-v4-population-count-rev11/data-download) on Socioeconomic Data and Applications Center (sedac).\n", @@ -578,7 +578,7 @@ "\n", "Impact functions are stored as the Entity's `impact_funcs` attribute, in an instance of the `ImpactFuncSet` class which groups one or more `ImpactFunc` objects. They can be specified manually, read from a file, or you can use CLIMADA's pre-defined impact functions. We'll use a pre-defined function for tropical storm wind damage stored in the `IFTropCyclone` class. \n", "\n", - "See the [Impact Functions tutorial](climada_entity_ImpactFuncSet.ipynb) for a full guide to the class, including how data are stored and reading and writing to files.\n", + "See the [Impact Functions tutorial](../user-guide/climada_entity_ImpactFuncSet.ipynb) for a full guide to the class, including how data are stored and reading and writing to files.\n", "\n", "We initialise an Impact Function with the `IFTropCyclone` class, and use its `from_emanuel_usa` method to load the Emanuel (2011) impact function. (The class also contains regional impact functions for the full globe, but we'll won't use these for now.) The class's `plot` method visualises the function, which we can see is expressed just through the Mean Degree of Damage, with all assets affected." ] @@ -679,7 +679,7 @@ "\n", "They are stored as `Measure` objects within a `MeasureSet` container class (similarly to `ImpactFuncSet` containing several `ImpactFunc`s), and are assigned to the `measures` attribute of the Entity.\n", "\n", - "See the [Adaptation Measures tutorial](climada_entity_MeasureSet.ipynb) on how to create, read and write measures. CLIMADA doesn't yet have pre-defined adaptation measures, mostly because they are hard to standardise.\n", + "See the [Adaptation Measures tutorial](../user-guide/climada_entity_MeasureSet.ipynb) on how to create, read and write measures. CLIMADA doesn't yet have pre-defined adaptation measures, mostly because they are hard to standardise.\n", "\n", "The best way to understand an adaptation measure is by an example. Here's a possible measure for the creation of coastal mangroves (ignore the exact numbers, they are just for illustration):" ] @@ -889,7 +889,7 @@ "\n", "The `disc_rates` attribute is of type `DiscRates`. This class contains the discount rates for the following years and computes the net present value for given values.\n", "\n", - "See the [Discount Rates tutorial](climada_entity_DiscRates.ipynb) for more details about creating, reading and writing the `DiscRates` class, and how it is used in calculations.\n", + "See the [Discount Rates tutorial](../user-guide/climada_entity_DiscRates.ipynb) for more details about creating, reading and writing the `DiscRates` class, and how it is used in calculations.\n", "\n", "Here we will implement a simple, flat 2% discount rate." ] @@ -979,7 +979,7 @@ "metadata": {}, "source": [ "Note: the configurable parameter `CONFIG.maz_matrix_size` controls the maximum matrix size contained in a chunk. You can decrease its value if you are having memory issues when using the `Impact`'s `calc` method. A high value will make the computation fast, but increase the memory use.\n", - "(See the [config guide](../guide/Guide_Configuration.ipynb) on how to set configuration values.)\n", + "(See the [config guide](../development/Guide_Configuration.ipynb) on how to set configuration values.)\n", "\n", "CLIMADA calculates impacts by providing exposures, impact functions and hazard to an `Impact` object's `calc` method:" ] @@ -1115,7 +1115,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "`Impact` also has `write_csv()` and `write_excel()` methods to save the impact variables, and `write_sparse_csr()` to save the impact matrix (impact per event and exposure). Use the [Impact tutorial](climada_engine_Impact.ipynb) to get more information about these functions and the class in general." + "`Impact` also has `write_csv()` and `write_excel()` methods to save the impact variables, and `write_sparse_csr()` to save the impact matrix (impact per event and exposure). Use the [Impact tutorial](../user-guide/climada_engine_Impact.ipynb) to get more information about these functions and the class in general." ] }, { @@ -1226,16 +1226,16 @@ "source": [ "## What next?\n", "\n", - "Thanks for following this tutorial! Take time to work on the exercises it suggested, or design your own risk analysis for your own topic. More detailed tutorials for individual classes were listed in the [Features](#CLIMADA-features) section.\n", + "Thanks for following this tutorial! Take time to work on the exercises it suggested, or design your own risk analysis for your own topic. More detailed tutorials for individual classes were listed in the [Features](#climada-features) section.\n", "\n", - "Also, explore the full CLIMADA documentation and additional resources [described at the start of this document](#Resources-beyond-this-tutorial) to learn more about CLIMADA, its structure, its existing applications and how you can contribute.\n" + "Also, explore the full CLIMADA documentation and additional resources [described at the start of this document](#resources-beyond-this-tutorial) to learn more about CLIMADA, its structure, its existing applications and how you can contribute.\n" ] } ], "metadata": { "hide_input": false, "kernelspec": { - "display_name": "climada_env", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -1249,7 +1249,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.15" + "version": "3.12.6" }, "vscode": { "interpreter": { diff --git a/doc/tutorial/climada_engine_CostBenefit.ipynb b/doc/user-guide/climada_engine_CostBenefit.ipynb similarity index 100% rename from doc/tutorial/climada_engine_CostBenefit.ipynb rename to doc/user-guide/climada_engine_CostBenefit.ipynb diff --git a/doc/tutorial/climada_engine_Forecast.ipynb b/doc/user-guide/climada_engine_Forecast.ipynb similarity index 100% rename from doc/tutorial/climada_engine_Forecast.ipynb rename to doc/user-guide/climada_engine_Forecast.ipynb diff --git a/doc/tutorial/climada_engine_Impact.ipynb b/doc/user-guide/climada_engine_Impact.ipynb similarity index 99% rename from doc/tutorial/climada_engine_Impact.ipynb rename to doc/user-guide/climada_engine_Impact.ipynb index a342a43b39..150d76e0da 100644 --- a/doc/tutorial/climada_engine_Impact.ipynb +++ b/doc/user-guide/climada_engine_Impact.ipynb @@ -11,7 +11,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### Goal of this tutorial" + "## Goal of this tutorial" ] }, { @@ -30,7 +30,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### What is an Impact?" + "## What is an Impact?" ] }, { @@ -44,7 +44,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### Impact class data structure" + "## Impact class data structure" ] }, { @@ -97,7 +97,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### How do I compute an impact in CLIMADA?" + "### How do I compute an impact in CLIMADA?" ] }, { @@ -141,7 +141,7 @@ "By default it is set to 1e9 in the [default config file](https://github.com/CLIMADA-project/climada_python/blob/main/climada/conf/climada.conf).\n", "A high value makes the computation fast at the cost of increased memory consumption.\n", "You can decrease its value if you are having memory issues with the `ImpactCalc.impact()` method.\n", - "(See the [config guide](../guide/Guide_Configuration.ipynb) on how to set configuration values)." + "(See the [config guide](../development/Guide_Configuration.ipynb) on how to set configuration values)." ] }, { @@ -2039,7 +2039,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.13" + "version": "3.12.6" }, "latex_envs": { "LaTeX_envs_menu_present": true, diff --git a/doc/tutorial/climada_engine_impact_data.ipynb b/doc/user-guide/climada_engine_impact_data.ipynb similarity index 99% rename from doc/tutorial/climada_engine_impact_data.ipynb rename to doc/user-guide/climada_engine_impact_data.ipynb index 40ead3d807..6f6972f3b3 100644 --- a/doc/tutorial/climada_engine_impact_data.ipynb +++ b/doc/user-guide/climada_engine_impact_data.ipynb @@ -62,6 +62,7 @@ "metadata": {}, "source": [ "### clean_emdat_df()\n", + "\n", "read CSV from EM-DAT into a DataFrame and clean up.\n", "\n", "Use the parameters countries, hazard, and year_range to filter. These parameters are the same for most functions shown here." @@ -184,11 +185,11 @@ "### emdat_to_impact()\n", "function to load EM-DAT impact data and return impact set with impact per event\n", "\n", - "##### Parameters:\n", + "#### Parameters:\n", "- emdat_file_csv (str): Full path to EMDAT-file (CSV)\n", "- hazard_type_climada (str): Hazard type abbreviation used in CLIMADA, e.g. 'TC'\n", "\n", - "##### Optional parameters:\n", + "#### Optional parameters:\n", "\n", "- hazard_type_emdat (list or str): List of Disaster (sub-)type according EMDAT terminology or CLIMADA hazard type abbreviations. e.g. ['Wildfire', 'Forest fire'] or ['BF']\n", "- year_range (list with 2 integers): start and end year e.g. [1980, 2017]\n", @@ -196,7 +197,7 @@ "- reference_year (int): reference year of exposures for normalization. Impact is scaled proportional to GDP to the value of the reference year. No scaling for reference_year=0 (default)\n", "- imp_str (str): Column name of impact metric in EMDAT CSV, e.g. 'Total Affected'; default = \"Total Damages\"\n", "\n", - "##### Returns:\n", + "#### Returns:\n", "- impact_instance (instance of climada.engine.Impact):\n", " Impact() instance (same format as output from CLIMADA impact computations).\n", " Values are scaled with GDP to reference_year if reference_year not equal 0.\n", @@ -322,10 +323,10 @@ "\n", "function to load EM-DAT impact data and return DataFrame with impact summed per year and country\n", "\n", - "##### Parameters:\n", + "#### Parameters:\n", "- emdat_file_csv (str): Full path to EMDAT-file (CSV)\n", "\n", - "##### Optional parameters:\n", + "#### Optional parameters:\n", "\n", "- hazard (list or str): List of Disaster (sub-)type according EMDAT terminology or CLIMADA hazard type abbreviations. e.g. ['Wildfire', 'Forest fire'] or ['BF']\n", "- year_range (list with 2 integers): start and end year e.g. [1980, 2017]\n", @@ -334,7 +335,7 @@ "- imp_str (str): Column name of impact metric in EMDAT CSV, e.g. 'Total Affected'; default = \"Total Damages\"\n", "- version (int): given EM-DAT data format version (i.e. year of download), changes naming of columns/variables (default: 2020)\n", "\n", - "##### Returns:\n", + "#### Returns:\n", "- pandas.DataFrame with impact per year and country" ] }, @@ -430,9 +431,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.12" + "version": "3.12.6" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/doc/tutorial/climada_engine_unsequa.ipynb b/doc/user-guide/climada_engine_unsequa.ipynb similarity index 99% rename from doc/tutorial/climada_engine_unsequa.ipynb rename to doc/user-guide/climada_engine_unsequa.ipynb index a7f6fabd6c..d1f60722fe 100644 --- a/doc/tutorial/climada_engine_unsequa.ipynb +++ b/doc/user-guide/climada_engine_unsequa.ipynb @@ -3085,7 +3085,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "For examples of how to use non-defaults please see the [impact example](###Compute-uncertainty-and-sensitivity-using-default-methods )" + "For examples of how to use non-defaults please see the [impact example](#compute-uncertainty-and-sensitivity-using-default-methods)" ] }, { @@ -5778,7 +5778,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.18" + "version": "3.12.6" }, "latex_envs": { "LaTeX_envs_menu_present": true, diff --git a/doc/tutorial/climada_engine_unsequa_helper.ipynb b/doc/user-guide/climada_engine_unsequa_helper.ipynb similarity index 100% rename from doc/tutorial/climada_engine_unsequa_helper.ipynb rename to doc/user-guide/climada_engine_unsequa_helper.ipynb diff --git a/doc/tutorial/climada_entity_DiscRates.ipynb b/doc/user-guide/climada_entity_DiscRates.ipynb similarity index 100% rename from doc/tutorial/climada_entity_DiscRates.ipynb rename to doc/user-guide/climada_entity_DiscRates.ipynb diff --git a/doc/tutorial/climada_entity_Exposures.ipynb b/doc/user-guide/climada_entity_Exposures.ipynb similarity index 99% rename from doc/tutorial/climada_entity_Exposures.ipynb rename to doc/user-guide/climada_entity_Exposures.ipynb index a57079ef20..aa1b39fd38 100644 --- a/doc/tutorial/climada_entity_Exposures.ipynb +++ b/doc/user-guide/climada_entity_Exposures.ipynb @@ -12,21 +12,17 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### What is an exposure?\n", + "## What is an exposure?\n", "\n", "Exposure describes the set of assets, people, livelihoods, infrastructures, etc. within an area of interest in terms of their geographic location, their value etc.; in brief - everything potentially exposed to hazards. \n", "\n", - "\n", - "\n", - "### What options does CLIMADA offer for me to create an exposure?\n", + "## What options does CLIMADA offer for me to create an exposure?\n", "\n", "CLIMADA has an `Exposures` class for this purpuse. An `Exposures` instance can be filled with your own data, or loaded from available default sources implemented through some Exposures-type classes from CLIMADA.
    \n", "If you have your own data, they can be provided in the formats of a `pandas.DataFrame`, a `geopandas.GeoDataFrame` or simply an `Excel` file. \n", "If you didn't collect your own data, exposures can be generated on the fly using CLIMADA's [LitPop](climada_entity_LitPop.ipynb), [BlackMarble](https://climada-petals.readthedocs.io/en/stable/tutorial/climada_entity_BlackMarble.html) or [OpenStreetMap](https://climada-petals.readthedocs.io/en/stable/tutorial/climada_exposures_openstreetmap.html) modules. See the respective tutorials to learn what exactly they contain and how to use them.\n", "\n", - "\n", - "\n", - "### What does an exposure look like in CLIMADA?\n", + "## What does an exposure look like in CLIMADA?\n", "\n", "An exposure is represented in the class `Exposures`, which contains a [geopandas](https://geopandas.readthedocs.io/en/latest/gallery/cartopy_convert.html) [GeoDataFrame](https://geopandas.readthedocs.io/en/latest/docs/user_guide/data_structures.html#geodataframe) that is accessible through the `Exposures.data` attribute.\n", "A \"geometry\" column is initialized in the `GeoDataFrame` of the `Exposures` object, other columns are optional at first but some have to be present or make a difference when it comes to do calculations.\n", @@ -1689,7 +1685,6 @@ }, { "cell_type": "markdown", - "id": "5d078d09", "metadata": {}, "source": [ "Optionally use climada's save option to save it in pickle format. This allows fast to quickly restore the object in its current state and take up your work right were you left it the next time.\n", @@ -1727,7 +1722,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.9" + "version": "3.12.6" }, "latex_envs": { "LaTeX_envs_menu_present": true, diff --git a/doc/tutorial/climada_entity_Exposures_osm.ipynb b/doc/user-guide/climada_entity_Exposures_osm.ipynb similarity index 100% rename from doc/tutorial/climada_entity_Exposures_osm.ipynb rename to doc/user-guide/climada_entity_Exposures_osm.ipynb diff --git a/doc/tutorial/climada_entity_Exposures_polygons_lines.ipynb b/doc/user-guide/climada_entity_Exposures_polygons_lines.ipynb similarity index 100% rename from doc/tutorial/climada_entity_Exposures_polygons_lines.ipynb rename to doc/user-guide/climada_entity_Exposures_polygons_lines.ipynb diff --git a/doc/tutorial/climada_entity_ImpactFuncSet.ipynb b/doc/user-guide/climada_entity_ImpactFuncSet.ipynb similarity index 99% rename from doc/tutorial/climada_entity_ImpactFuncSet.ipynb rename to doc/user-guide/climada_entity_ImpactFuncSet.ipynb index 6df482925f..fd349487cd 100644 --- a/doc/tutorial/climada_entity_ImpactFuncSet.ipynb +++ b/doc/user-guide/climada_entity_ImpactFuncSet.ipynb @@ -11,7 +11,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### What is an impact function?\n", + "## What is an impact function?\n", "\n", "An impact function relates the percentage of damage in the exposure to the hazard intensity, also commonly referred to as a \"vulnerability curve\" in the modelling community. Every hazard and exposure types are characterized by an impact function." ] @@ -20,7 +20,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### What is the difference between `ImpactFunc` and `ImpactFuncSet`?\n", + "## What is the difference between `ImpactFunc` and `ImpactFuncSet`?\n", "\n", "An `ImpactFunc` is a class for a single impact function. E.g. a function that relates the percentage of damage of a reinforced concrete building (exposure) to the wind speed of a tropical cyclone (hazard intensity). \n", "\n", @@ -31,7 +31,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### What does an `ImpactFunc` look like in CLIMADA?\n", + "### What does an `ImpactFunc` look like in CLIMADA?\n", "\n", "The `ImpactFunc` class requires users to define the following attributes.\n", "\n", @@ -52,7 +52,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### What does an `ImpactFuncSet` look like in CLIMADA?\n", + "### What does an `ImpactFuncSet` look like in CLIMADA?\n", "\n", "The `ImpactFuncSet` class contains all the `ImpactFunc` classes. Users are not required to define any attributes in `ImpactFuncSet`. \n", "\n", @@ -77,7 +77,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### Generate a dummy impact function from scratch.\n", + "### Generate a dummy impact function from scratch.\n", "\n", "Here we generate an impact function with random dummy data for illustrative reasons. Assuming this impact function is a function that relates building damage to tropical cyclone (TC) wind, with an arbitrary id 3." ] @@ -187,7 +187,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### Loading CLIMADA in-built impact function for tropical cyclones\n", + "### Loading CLIMADA in-built impact function for tropical cyclones\n", "\n", "`ImpfTropCyclone` is a derivated class of `ImpactFunc`. This in-built impact function estimates the insured property damages by tropical cyclone wind in USA, following the reference paper [Emanuel (2011)](https://doi.org/10.1175/WCAS-D-11-00007.1).
    \n", "\n", @@ -289,7 +289,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### Plotting all the impact functions in an `ImpactFuncSet`\n", + "### Plotting all the impact functions in an `ImpactFuncSet`\n", "\n", "The method `plot()` in `ImpactFuncSet` also uses the the [matplotlib's axes plot function](https://matplotlib.org/3.3.2/api/_as_gen/matplotlib.axes.Axes.plot.html) to visualise the impact functions, returning a figure with all the subplots of impact functions. Users may modify these plots." ] @@ -321,7 +321,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### Retrieving an impact function from the `ImpactFuncSet`\n", + "### Retrieving an impact function from the `ImpactFuncSet`\n", "User may want to retrive a particular impact function from `ImpactFuncSet`. Using the method `get_func(haz_type, id)`, it returns an `ImpactFunc` class of the desired impact function. Below is an example of extracting the TC impact function with id 1, and using `plot()` to visualise the function." ] }, @@ -354,7 +354,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### Removing an impact function from the `ImpactFuncSet`\n", + "### Removing an impact function from the `ImpactFuncSet`\n", "\n", "If there is an unwanted impact function from the `ImpactFuncSet`, we may remove it using the method `remove_func(haz_type, id)` to remove it from the set. \n", "\n", @@ -423,7 +423,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### Reading impact functions from an Excel file\n", + "### Reading impact functions from an Excel file\n", "\n", "Impact functions defined in an excel file following the template provided in sheet `impact_functions` of `climada_python/climada/data/system/entity_template.xlsx` can be ingested directly using the method `from_excel()`." ] @@ -471,7 +471,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### Write impact functions\n", + "### Write impact functions\n", "\n", "Users may write the impact functions in Excel format using `write_excel()` method." ] @@ -570,7 +570,7 @@ "metadata": { "hide_input": false, "kernelspec": { - "display_name": "climada_env", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -584,7 +584,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.13" + "version": "3.12.6" }, "latex_envs": { "LaTeX_envs_menu_present": true, diff --git a/doc/tutorial/climada_entity_LitPop.ipynb b/doc/user-guide/climada_entity_LitPop.ipynb similarity index 99% rename from doc/tutorial/climada_entity_LitPop.ipynb rename to doc/user-guide/climada_entity_LitPop.ipynb index b41728bf2d..613d3b3e28 100644 --- a/doc/tutorial/climada_entity_LitPop.ipynb +++ b/doc/user-guide/climada_entity_LitPop.ipynb @@ -29,7 +29,7 @@ "\n", "*Note*: All required data except for the population data from Gridded Population of the World (GPW) is downloaded automatically when an `LitPop.set_*` method is called.\n", "\n", - "**Warning**: Processing the data for the first time can take up huge amounts of RAM (>10 GB), depending on country or region size. Consider using the [wrapper function](climada_util_api_client.ipynb#The-wrapper-functions-client.get_litpop()) of the [data API](climada_util_api_client.ipynb) to download readily computed LitPop exposure data for default values ($n = m = 1$) on demand.\n", + "**Warning**: Processing the data for the first time can take up huge amounts of RAM (>10 GB), depending on country or region size. Consider using the [wrapper function](climada_util_api_client.ipynb#the-wrapper-functions-client-get-litpop) of the [data API](climada_util_api_client.ipynb) to download readily computed LitPop exposure data for default values ($n = m = 1$) on demand.\n", "\n", "#### Nightlight intensity\n", "Black Marble annual composite of the VIIRS day-night band (Grayscale) at 15 arcsec resolution is downloaded from the NASA Earth Observatory: https://earthobservatory.nasa.gov/Features/NightLights (available for 2012 and 2016 at 15 arcsec resolution (~500m)).\n", @@ -50,7 +50,7 @@ "\n", "### Downloading existing LitPop asset exposure data\n", "\n", - "The easiest way to download existing data is using the [wrapper function](climada_util_api_client.ipynb#The-wrapper-functions-client.get_litpop()) of the [data API](climada_util_api_client.ipynb).\n", + "The easiest way to download existing data is using the [wrapper function](climada_util_api_client.ipynb#the-wrapper-functions-client-get-litpop) of the [data API](climada_util_api_client.ipynb).\n", "\n", "Readily computed LitPop asset exposure data based on $Lit^1Pop^1$ for 224 countries, distributing produced capital / non-financial wealth of 2014 at a resolution of 30 arcsec can also be downloaded from the ETH Research Repository: https://doi.org/10.3929/ethz-b-000331316.\n", "The dataset contains gridded data for more than 200 countries as CSV files." @@ -62,7 +62,7 @@ "source": [ "## Attributes\n", "\n", - "The `LitPop` class inherits from [`Exposures`](climada_entity_Exposures.ipynb#Exposures-class).\n", + "The `LitPop` class inherits from [`Exposures`](climada_entity_Exposures.ipynb).\n", "It adds the following attributes:\n", "\n", " exponents : Defining powers (m, n) with which nightlights and population go into Lit**m * Pop**n.\n", @@ -830,7 +830,7 @@ "metadata": { "hide_input": false, "kernelspec": { - "display_name": "climada_env", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -844,7 +844,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.18" + "version": "3.12.6" }, "latex_envs": { "LaTeX_envs_menu_present": true, diff --git a/doc/tutorial/climada_entity_MeasureSet.ipynb b/doc/user-guide/climada_entity_MeasureSet.ipynb similarity index 100% rename from doc/tutorial/climada_entity_MeasureSet.ipynb rename to doc/user-guide/climada_entity_MeasureSet.ipynb diff --git a/doc/tutorial/climada_hazard_Hazard.ipynb b/doc/user-guide/climada_hazard_Hazard.ipynb similarity index 99% rename from doc/tutorial/climada_hazard_Hazard.ipynb rename to doc/user-guide/climada_hazard_Hazard.ipynb index aebd85792c..412346d041 100644 --- a/doc/tutorial/climada_hazard_Hazard.ipynb +++ b/doc/user-guide/climada_hazard_Hazard.ipynb @@ -6,17 +6,13 @@ "source": [ "# Hazard class\n", "\n", - "#### What is a hazard?\n", + "## What is a hazard?\n", "A hazard describes weather events such as storms, floods, droughts, or heat waves both in terms of probability of occurrence as well as physical intensity.\n", "\n", - "
    \n", - "\n", - "#### How are hazards embedded in the CLIMADA architecture?\n", + "## How are hazards embedded in the CLIMADA architecture?\n", "Hazards are defined by the base class `Hazard` which gathers the required attributes that enable the impact computation (such as centroids, frequency per event, and intensity per event and centroid) and common methods such as readers and visualization functions. Each hazard class collects historical data or model simulations and transforms them, if necessary, in order to construct a coherent event database. Stochastic events can be generated taking into account the frequency and main intensity characteristics (such as local water depth for floods or gust speed for storms) of historical events, producing an ensemble of probabilistic events for each historical event. CLIMADA provides therefore an event-based probabilistic approach which does not depend on a hypothesis of a priori general probability distribution choices. Note that one can also reduce the probabilistic approach to a deterministic approach (e.g., story-line or forecasting) by defining the frequency to be 1. The source of the historical data (e.g. inventories or satellite images) or model simulations (e.g. synthetic tropical cyclone tracks) and the methodologies used to compute the hazard attributes and its stochastic events depend on each hazard type and are defined in its corresponding Hazard-derived class (e.g. `TropCylcone` for tropical cyclones, explained in the tutorial [TropCyclone](climada_hazard_TropCyclone.ipynb)). This procedure provides a solid and homogeneous methodology to compute impacts worldwide. In the case where the risk analysis comprises a specific region where good quality data or models describing the hazard intensity and frequency are available, these can be directly ingested by the platform through the reader functions, skipping the hazard modelling part (in total or partially), and allowing us to easily and seamlessly combine CLIMADA with external sources. Hence the impact model can be used for a wide variety of applications, e.g. deterministically to assess the impact of a single (past or future) event or to quantify risk based on a (large) set of probabilistic events. Note that since the `Hazard` class is not an abstract class, any hazard that is not defined in CLIMADA can still be used by providing the `Hazard` attributes.\n", "\n", - "
    \n", - "\n", - "#### What do hazards look like in CLIMADA?\n", + "## What do hazards look like in CLIMADA?\n", "\n", "A `Hazard` contains events of some hazard type defined at `centroids`. There are certain variables in a `Hazard` instance that _are needed_ to compute the impact, while others are _descriptive_ and can therefore be set with default values. The full list of looks like this:\n", "\n", @@ -779,9 +775,7 @@ { "cell_type": "code", "execution_count": 13, - "metadata": { - "scrolled": false - }, + "metadata": {}, "outputs": [ { "data": { @@ -1009,7 +1003,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3.8.13 ('climada_env')", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -1023,7 +1017,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.18" + "version": "3.12.6" } }, "nbformat": 4, diff --git a/doc/tutorial/climada_hazard_StormEurope.ipynb b/doc/user-guide/climada_hazard_StormEurope.ipynb similarity index 100% rename from doc/tutorial/climada_hazard_StormEurope.ipynb rename to doc/user-guide/climada_hazard_StormEurope.ipynb diff --git a/doc/tutorial/climada_hazard_TropCyclone.ipynb b/doc/user-guide/climada_hazard_TropCyclone.ipynb similarity index 99% rename from doc/tutorial/climada_hazard_TropCyclone.ipynb rename to doc/user-guide/climada_hazard_TropCyclone.ipynb index 28d80d1ac0..c58cc4a300 100644 --- a/doc/tutorial/climada_hazard_TropCyclone.ipynb +++ b/doc/user-guide/climada_hazard_TropCyclone.ipynb @@ -13,7 +13,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### What do tropical cyclones look like in CLIMADA?\n", + "## What do tropical cyclones look like in CLIMADA?\n", "\n", "`TCTracks` reads and handles historical tropical cyclone tracks of the [IBTrACS](https://www.ncdc.noaa.gov/ibtracs/) repository or synthetic tropical cyclone tracks simulated using fully statistical or coupled statistical-dynamical modeling approaches. It also generates synthetic tracks from the historical ones using Wiener processes.\n", "\n", @@ -2183,7 +2183,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### REFERENCES:\n", + "## REFERENCES:\n", "\n", "- Bloemendaal, N., Haigh, I. D., de Moel, H., Muis, S., Haarsma, R. J., & Aerts, J. C. J. H. (2020). Generation of a global synthetic tropical cyclone hazard dataset using STORM. Scientific Data, 7(1). https://doi.org/10.1038/s41597-020-0381-2\n", "\n", @@ -2195,6 +2195,13 @@ "\n", "- Lee, C. Y., Tippett, M. K., Sobel, A. H., & Camargo, S. J. (2018). An environmentally forced tropical cyclone hazard model. Journal of Advances in Modeling Earth Systems, 10(1), 223–241. https://doi.org/10.1002/2017MS001186" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { @@ -2213,7 +2220,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.12" + "version": "3.12.6" }, "toc": { "base_numbering": 1, diff --git a/doc/tutorial/climada_util_api_client.ipynb b/doc/user-guide/climada_util_api_client.ipynb similarity index 99% rename from doc/tutorial/climada_util_api_client.ipynb rename to doc/user-guide/climada_util_api_client.ipynb index 215f8b6d0f..29d6bf0a02 100644 --- a/doc/tutorial/climada_util_api_client.ipynb +++ b/doc/user-guide/climada_util_api_client.ipynb @@ -620,9 +620,7 @@ { "cell_type": "code", "execution_count": 18, - "metadata": { - "scrolled": false - }, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -1331,7 +1329,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.13" + "version": "3.12.6" } }, "nbformat": 4, diff --git a/doc/tutorial/climada_util_calibrate.ipynb b/doc/user-guide/climada_util_calibrate.ipynb similarity index 99% rename from doc/tutorial/climada_util_calibrate.ipynb rename to doc/user-guide/climada_util_calibrate.ipynb index 0efefc5a2d..bd08946fff 100644 --- a/doc/tutorial/climada_util_calibrate.ipynb +++ b/doc/user-guide/climada_util_calibrate.ipynb @@ -7,17 +7,17 @@ "source": [ "# Impact Function Calibration\n", "\n", - "CLIMADA provides the [`climada.util.calibrate`](../climada/climada.util.calibrate) module for calibrating impact functions based on impact data.\n", + "CLIMADA provides the [`climada.util.calibrate`](../api/climada/climada.util.calibrate) module for calibrating impact functions based on impact data.\n", "This tutorial will guide through the usage of this module by calibrating an impact function for tropical cyclones (TCs).\n", "\n", - "For further information on the classes available from the module, see its [documentation](../climada/climada.util.calibrate).\n", + "For further information on the classes available from the module, see its [documentation](../api/climada/climada.util.calibrate).\n", "\n", "## Overview\n", "\n", "The basic idea of the calibration is to find a set of parameters for an impact function that minimizes the deviation between the calculated impact and some impact data.\n", "For setting up a calibration task, users have to supply the following information:\n", "\n", - "* Hazard and Exposure (as usual, see [the tutorial](../tutorial/1_main_climada.ipynb#tutorial-an-example-risk-assessment))\n", + "* Hazard and Exposure (as usual, see [the tutorial](../user-guide/1_main_climada.ipynb#tutorial-an-example-risk-assessment))\n", "* The impact data to calibrate the model to\n", "* An impact function definition depending on the calibrated parameters\n", "* Bounds and constraints of the calibrated parameters (depending on the calibration algorithm)\n", @@ -4049,7 +4049,7 @@ ], "metadata": { "kernelspec": { - "display_name": "climada_env_3.9", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -4063,10 +4063,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.18" - }, - "orig_nbformat": 4 + "version": "3.12.6" + } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/doc/tutorial/climada_util_earth_engine.ipynb b/doc/user-guide/climada_util_earth_engine.ipynb similarity index 99% rename from doc/tutorial/climada_util_earth_engine.ipynb rename to doc/user-guide/climada_util_earth_engine.ipynb index 10811ce4d7..bf773ef7d4 100644 --- a/doc/tutorial/climada_util_earth_engine.ipynb +++ b/doc/user-guide/climada_util_earth_engine.ipynb @@ -88,10 +88,10 @@ "metadata": {}, "source": [ "If you have a collection, specification of the time range and area of interest. Then, use methods of the series **obtain_image_type(collection,time_range,area)** depending the type of product needed.\n", - "#### Time range\n", + "### Time range\n", "It depends on the image acquisition period of the targeted satellite and type of images desired (without clouds, from a specific period...) \n", "\n", - "#### Area\n", + "### Area\n", "GEE needs a special format for defining an area of interest. It has to be a GeoJSON Polygon and the coordinates should be first defined in a list and then converted using ee.Geometry. It is possible to use data obtained via Exposure layer. Some examples are given below." ] }, @@ -558,9 +558,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.12" + "version": "3.12.6" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 4 } diff --git a/doc/tutorial/climada_util_local_exceedance_values.ipynb b/doc/user-guide/climada_util_local_exceedance_values.ipynb similarity index 93% rename from doc/tutorial/climada_util_local_exceedance_values.ipynb rename to doc/user-guide/climada_util_local_exceedance_values.ipynb index c8a4d156f7..b463124829 100644 --- a/doc/tutorial/climada_util_local_exceedance_values.ipynb +++ b/doc/user-guide/climada_util_local_exceedance_values.ipynb @@ -340,7 +340,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "2025-03-14 16:42:11,754 - climada.hazard.io - INFO - Reading /Users/vgebhart/climada/data/hazard/template/HAZ_DEMO_FL_15/v1/HAZ_DEMO_FL_15.h5\n" + "2025-03-31 17:21:06,957 - climada.hazard.io - INFO - Reading /Users/vgebhart/climada/data/hazard/template/HAZ_DEMO_FL_15/v1/HAZ_DEMO_FL_15.h5\n" ] } ], @@ -508,7 +508,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "2025-03-14 16:42:35,716 - climada.hazard.io - INFO - Reading /Users/vgebhart/climada/data/hazard/tropical_cyclone/tropical_cyclone_10synth_tracks_150arcsec_HTI_1980_2020/v2/tropical_cyclone_10synth_tracks_150arcsec_HTI_1980_2020.hdf5\n" + "2025-03-31 17:21:31,356 - climada.hazard.io - INFO - Reading /Users/vgebhart/climada/data/hazard/tropical_cyclone/tropical_cyclone_10synth_tracks_150arcsec_HTI_1980_2020/v2/tropical_cyclone_10synth_tracks_150arcsec_HTI_1980_2020.hdf5\n" ] } ], @@ -546,12 +546,12 @@ "name": "stdout", "output_type": "stream", "text": [ - "2025-03-14 16:42:37,253 - climada.entity.exposures.base - INFO - Reading /Users/vgebhart/climada/data/exposures/litpop/LitPop_150arcsec_HTI/v3/LitPop_150arcsec_HTI.hdf5\n", - "2025-03-14 16:42:37,282 - climada.entity.exposures.base - INFO - Hazard type not set in impf_\n", - "2025-03-14 16:42:37,283 - climada.entity.exposures.base - INFO - category_id not set.\n", - "2025-03-14 16:42:37,283 - climada.entity.exposures.base - INFO - cover not set.\n", - "2025-03-14 16:42:37,284 - climada.entity.exposures.base - INFO - deductible not set.\n", - "2025-03-14 16:42:37,284 - climada.entity.exposures.base - INFO - centr_ not set.\n" + "2025-03-31 17:21:32,751 - climada.entity.exposures.base - INFO - Reading /Users/vgebhart/climada/data/exposures/litpop/LitPop_150arcsec_HTI/v3/LitPop_150arcsec_HTI.hdf5\n", + "2025-03-31 17:21:32,768 - climada.entity.exposures.base - INFO - Hazard type not set in impf_\n", + "2025-03-31 17:21:32,769 - climada.entity.exposures.base - INFO - category_id not set.\n", + "2025-03-31 17:21:32,769 - climada.entity.exposures.base - INFO - cover not set.\n", + "2025-03-31 17:21:32,769 - climada.entity.exposures.base - INFO - deductible not set.\n", + "2025-03-31 17:21:32,770 - climada.entity.exposures.base - INFO - centr_ not set.\n" ] } ], @@ -584,10 +584,10 @@ "name": "stdout", "output_type": "stream", "text": [ - "2025-03-14 16:42:37,339 - climada.entity.exposures.base - INFO - No specific impact function column found for hazard TC. Using the anonymous 'impf_' column.\n", - "2025-03-14 16:42:37,340 - climada.entity.exposures.base - INFO - Matching 1329 exposures with 1332 centroids.\n", - "2025-03-14 16:42:37,342 - climada.util.coordinates - INFO - No exact centroid match found. Reprojecting coordinates to nearest neighbor closer than the threshold = 100\n", - "2025-03-14 16:42:37,346 - climada.engine.impact_calc - INFO - Calculating impact for 3987 assets (>0) and 43560 events.\n" + "2025-03-31 17:21:32,824 - climada.entity.exposures.base - INFO - No specific impact function column found for hazard TC. Using the anonymous 'impf_' column.\n", + "2025-03-31 17:21:32,825 - climada.entity.exposures.base - INFO - Matching 1329 exposures with 1332 centroids.\n", + "2025-03-31 17:21:32,827 - climada.util.coordinates - INFO - No exact centroid match found. Reprojecting coordinates to nearest neighbor closer than the threshold = 100\n", + "2025-03-31 17:21:32,830 - climada.engine.impact_calc - INFO - Calculating impact for 3987 assets (>0) and 43560 events.\n" ] } ], @@ -606,7 +606,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "2025-03-14 16:42:37,379 - climada.engine.impact - INFO - Computing exceedance impact map for return periods: [10, 50, 100, 200]\n" + "2025-03-31 17:21:32,861 - climada.engine.impact - INFO - Computing exceedance impact map for return periods: [10, 50, 100, 200]\n" ] } ], @@ -651,7 +651,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "2025-03-14 16:43:03,117 - climada.engine.impact - INFO - Computing return period map for impacts: [1000, 10000, 100000, 1000000]\n" + "2025-03-31 17:21:58,100 - climada.engine.impact - INFO - Computing return period map for impacts: [1000, 10000, 100000, 1000000]\n" ] } ], @@ -683,6 +683,169 @@ "# plot local return periods of impacts\n", "plot_from_gdf(local_return_periods, title, column_label, smooth=False);" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# When to use binning in calculating local exceedance frequencies or local return periods\n", + "\n", + "When using `method='extrapolate'`, local exceedance frequencies or local return periods are extrapolated beyond the observed data range. The used extrapolation technique is the one of `scipy.interpolate.interp1d`: the two extremal interpolations at the lower and upper limits of the data are simply extended beyond the data range.\n", + "\n", + "In specific cases (see below for an example), this can lead to undesired behaviour. For this reason, the user can pass in an additional integer parameter `bin_decimals` to bin the intensities (and sum up the corresponding frequencies) according to `bin_decimals` decimal places. \n", + "\n", + "As can be seen in the following example, this binning can lead to a different and more stable extrapolation (which might be desriable in particular for the `local_return_period` method), and to a smoother interpolation. Note that, due to binning, the data range of observed return periods for `method='interpolate'` may be reduced.\n", + "\n", + "As an example, we consider a hazard object that contains one centroid and five intensities, all of which occur with a frequency of 1/10years. Importantly, the two maximal intensities are very similar, which strongly affects the extrapolation behaviour." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "hazard_binning = Hazard(\n", + " intensity=sparse.csr_matrix([[80.0], [80.02], [70.0], [70.0], [60.0]]),\n", + " frequency=np.array([0.1, 0.1, 0.1, 0.1, 0.1]),\n", + " frequency_unit=\"1/year\",\n", + " units=\"m/s\",\n", + " centroids=Centroids(lat=np.array([1]), lon=np.array([2])),\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "test_return_periods = np.arange(1, 12, 0.1)\n", + "interpolated = hazard_binning.local_exceedance_intensity(\n", + " return_periods=test_return_periods\n", + ")[0]\n", + "extrapolated = hazard_binning.local_exceedance_intensity(\n", + " return_periods=test_return_periods, method=\"extrapolate\"\n", + ")[0]\n", + "interpolated_binned = hazard_binning.local_exceedance_intensity(\n", + " return_periods=test_return_periods, bin_decimals=1\n", + ")[0]\n", + "extrapolated_binned = hazard_binning.local_exceedance_intensity(\n", + " return_periods=test_return_periods, method=\"extrapolate\", bin_decimals=1\n", + ")[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
    " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# plot different extrapolation methods at at centroid\n", + "import matplotlib.pyplot as plt\n", + "\n", + "fig, axes = plt.subplots(2, 2, figsize=(9, 6))\n", + "plt.subplots_adjust(wspace=0.4, hspace=0.4)\n", + "\n", + "for axis, local_exceedance_intensity, color, title in zip(\n", + " axes.flatten(),\n", + " [interpolated, extrapolated, interpolated_binned, extrapolated_binned],\n", + " [\"teal\", \"g\", \"r\", \"orange\"],\n", + " [\n", + " '\"interpolate\"',\n", + " '\"extrapolate\"',\n", + " '\"interpolate\", bin_decimals = 1',\n", + " '\"extrapolate\", bin_decimals = 1',\n", + " ],\n", + "):\n", + " axis.plot(\n", + " test_return_periods,\n", + " local_exceedance_intensity.values[0, 1:],\n", + " color=color,\n", + " )\n", + " axis.set_ylabel(\"Local exceedance internsity (m/s)\")\n", + " axis.set_xlabel(\"Return period (years)\")\n", + " if title in ['\"interpolate\"', '\"extrapolate\"']:\n", + " cum_freq = np.arange(5, 0, -1) * hazard_binning.frequency[0]\n", + " intensities = np.sort(hazard_binning.intensity[:, 0].toarray())[::-1]\n", + " else:\n", + " cum_freq = np.array([5, 4, 2]) * hazard_binning.frequency[0]\n", + " intensities = np.sort(\n", + " np.unique(np.round(hazard_binning.intensity[:, 0], decimals=1).toarray())\n", + " )\n", + " axis.scatter(\n", + " 1 / cum_freq,\n", + " intensities,\n", + " s=10,\n", + " color=\"k\",\n", + " )\n", + " axis.set_title(f\"method = {title}\")\n", + " axis.set_xlim([1, 12])\n", + " axis.set_ylim([40, 100])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Given the hazard distribution in the hazard object (black points), the user might prefer flat extrapolation behaviour to large return periods, in which case the default `bin_decimals=None` is the correct choice. \n", + "\n", + "However, in the inverse problem of computing a return period for a given hazard intensity, not binning the values can lead to an unbounded extrapolation, and binning the values might be a good choice." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Local return periods [years] without binning:\n", + "\n", + "| | geometry | 55.0 | 75.0 | 85.0 |\n", + "|---:|:------------|--------:|--------:|------------:|\n", + "| 0 | POINT (2 1) | 1.76331 | 4.11019 | 5.09816e+73 |\n", + "\n", + "\n", + "Local return periods [years] with binning to bin_decimals=1:\n", + "\n", + "| | geometry | 55.0 | 75.0 | 85.0 |\n", + "|---:|:------------|--------:|--------:|--------:|\n", + "| 0 | POINT (2 1) | 1.76331 | 3.57665 | 6.84921 |\n" + ] + } + ], + "source": [ + "test_hazard_intensities = [55.0, 75.0, 85.0]\n", + "print(\"Local return periods [years] without binning:\\n\")\n", + "print(\n", + " hazard_binning.local_return_period(\n", + " threshold_intensities=test_hazard_intensities,\n", + " method=\"extrapolate\",\n", + " )[0].to_markdown()\n", + ")\n", + "print(\"\\n\\nLocal return periods [years] with binning to bin_decimals=1:\\n\")\n", + "print(\n", + " hazard_binning.local_return_period(\n", + " threshold_intensities=test_hazard_intensities,\n", + " method=\"extrapolate\",\n", + " bin_decimals=1,\n", + " )[0].to_markdown()\n", + ")" + ] } ], "metadata": { diff --git a/doc/tutorial/climada_util_yearsets.ipynb b/doc/user-guide/climada_util_yearsets.ipynb similarity index 100% rename from doc/tutorial/climada_util_yearsets.ipynb rename to doc/user-guide/climada_util_yearsets.ipynb diff --git a/doc/tutorial/exposures.rst b/doc/user-guide/exposures.rst similarity index 52% rename from doc/tutorial/exposures.rst rename to doc/user-guide/exposures.rst index c5a278fc75..846846e67f 100644 --- a/doc/tutorial/exposures.rst +++ b/doc/user-guide/exposures.rst @@ -2,6 +2,10 @@ Exposures Tutorials =================== +These guides present the `Exposures` class, as the main object to handle exposure data, +as well as the `LitPop` subclass which allows to estimate exposure using nightlight intensity and +population count data. We also show how to handle polygons or lines with CLIMADA. + .. toctree:: :maxdepth: 1 diff --git a/doc/tutorial/hazard.rst b/doc/user-guide/hazard.rst similarity index 62% rename from doc/tutorial/hazard.rst rename to doc/user-guide/hazard.rst index 248dacd828..8c22fdb45d 100644 --- a/doc/tutorial/hazard.rst +++ b/doc/user-guide/hazard.rst @@ -2,6 +2,9 @@ Hazard Tutorials ================ +These guides present the `Hazard` class as well as subclasses +that handle tropical cyclones and winter storms more specifically. + .. toctree:: :maxdepth: 1 diff --git a/doc/tutorial/img/UncertaintySensitivity.jpg b/doc/user-guide/img/UncertaintySensitivity.jpg similarity index 100% rename from doc/tutorial/img/UncertaintySensitivity.jpg rename to doc/user-guide/img/UncertaintySensitivity.jpg diff --git a/doc/user-guide/img/cost-benefit.png b/doc/user-guide/img/cost-benefit.png new file mode 100644 index 0000000000..6e10294106 Binary files /dev/null and b/doc/user-guide/img/cost-benefit.png differ diff --git a/doc/user-guide/img/exposure.png b/doc/user-guide/img/exposure.png new file mode 100644 index 0000000000..ae87af65c1 Binary files /dev/null and b/doc/user-guide/img/exposure.png differ diff --git a/doc/user-guide/img/impact-function.png b/doc/user-guide/img/impact-function.png new file mode 100644 index 0000000000..02c377a09f Binary files /dev/null and b/doc/user-guide/img/impact-function.png differ diff --git a/doc/user-guide/img/risk_framework.png b/doc/user-guide/img/risk_framework.png new file mode 100644 index 0000000000..8b193893f9 Binary files /dev/null and b/doc/user-guide/img/risk_framework.png differ diff --git a/doc/user-guide/img/sensitivity.png b/doc/user-guide/img/sensitivity.png new file mode 100644 index 0000000000..37900c93d2 Binary files /dev/null and b/doc/user-guide/img/sensitivity.png differ diff --git a/doc/user-guide/img/tc-tracks.png b/doc/user-guide/img/tc-tracks.png new file mode 100644 index 0000000000..e5c170826a Binary files /dev/null and b/doc/user-guide/img/tc-tracks.png differ diff --git a/doc/user-guide/impact.rst b/doc/user-guide/impact.rst new file mode 100644 index 0000000000..d37454f084 --- /dev/null +++ b/doc/user-guide/impact.rst @@ -0,0 +1,22 @@ +================ +Impact Tutorials +================ + +These tutorials show how to compute impacts with CLIMADA, and all related aspects such +as impact functions, adaptation measures and discount rates and cost-benefit analysis. + +The first tutorial presents an end-to-end impact calculation, +and subsequent ones present each aspect in more details. + +Additionally you can find a guide on how to populate impact data from EM-DAT database. + +.. toctree:: + :maxdepth: 1 + + Impact Calculation + climada_entity_ImpactFuncSet + climada_entity_MeasureSet + Discount Rates + Using EM-DAT data + Cost Benefit Calculation + Probabilistic Yearly Impacts diff --git a/doc/user-guide/index.rst b/doc/user-guide/index.rst new file mode 100644 index 0000000000..85c7d70328 --- /dev/null +++ b/doc/user-guide/index.rst @@ -0,0 +1,27 @@ +==================== +User guide +==================== + +This user guide contains all the detailed tutorials about the different parts of CLIMADA. +If you are a new user, we advise you to have a look at the `10 minutes CLIMADA <0_10min_climada>`_ +which introduces the basics briefly, or the full `Overview <1_main_climada>`_ which goes more in depth. + +You can then go on to more specific tutorial about `Hazard `_, +`Exposures `_ or `Impact `_ or advanced usage such as +`Uncertainty Quantification `_ + +.. toctree:: + :maxdepth: 2 + :hidden: + + 10 minutes CLIMADA <0_10min_climada> + Overview <1_main_climada> + Hazard + Exposures + Impact + Compute local exceedance + Uncertainty Quantification + climada_engine_Forecast + climada_util_calibrate + Google Earth Engine + climada_util_api_client diff --git a/doc/tutorial/unsequa.rst b/doc/user-guide/unsequa.rst similarity index 100% rename from doc/tutorial/unsequa.rst rename to doc/user-guide/unsequa.rst diff --git a/setup.py b/setup.py index 0ed12b9f50..cef54a6f9a 100644 --- a/setup.py +++ b/setup.py @@ -19,6 +19,7 @@ "sphinx", "sphinx-book-theme", "sphinx-markdown-tables", + "sphinx-design", "sphinx-mdinclude", ]