DST Analysis and Visualization.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This analysis investigates the effect Daylight Saving Time (DST) has on the american population's sunrise and sunset times. The purpose of doing this is to support my position in arguments with my friends that DST should be extended year round. \n",
    "\n",
    "To understand the effect of DST on the distribution of sunrise and sunset times in the United States, I estimate what time the sun rises and sets every day for each person in the country. The time differs from person to person due to changes in geographical location (eg the on 7/10/2021 the sunrises at ). We define geographical location at the level of census block. There are over 11 million census blocks and 365 days in the year, which equates to over 4 billion unique sunrise and sunset times. The primary challenge of this project, beyond identifying and joining relevant data sources, is calculating these 4+ billion sunrise/sunset times in a time-efficient manner. \n",
    "\n",
    "The steps are the following:\n",
    "1. Download shape file and population data for each census block\n",
    "2. Calculate centroid (latitude / longitude) for each census block\n",
    "3. Get timezone associated with each census block\n",
    "4. Calculate sunrise / sunset time for each day for each census block based on centroid\n",
    "5. Create plot of sunrise/sunset distributions with and without DST"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import geopandas as gpd\n",
    "import os\n",
    "import requests\n",
    "import pickle\n",
    "import importlib\n",
    "import datetime\n",
    "import time\n",
    "import requests\n",
    "\n",
    "# local imports\n",
    "import vectorized_suncalc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Download shape file and population data for each census block"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "save_path = r\"data\\2010 TIGER Shape and Demographic Files\"\n",
    "\n",
    "for i in range(60):\n",
    "    print(f\"i = {i}\")\n",
    "    url = f'https://www2.census.gov/geo/tiger/TIGER2010BLKPOPHU/tabblock2010_{i:02}_pophu.zip'\n",
    "    save_to = os.path.join(save_path, f'tabblock2010_{i:03}_pophu.zip')\n",
    "    r = requests.get(url, allow_redirects=True)\n",
    "    if r.status_code == 404:\n",
    "        print(\"Index f{i:02} resulted in status code 404\")\n",
    "        continue\n",
    "    else:\n",
    "        with open(save_to, 'wb') as f:\n",
    "            f.write(r.content)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2. Calculate centroid (lat/lon) for each census block\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Continue? (y/n): n\n"
     ]
    }
   ],
   "source": [
    "save_path = r\"data\\2010 TIGER Shape and Demographic Files\"\n",
    "inp = input(\"Continue? (y/n): \")\n",
    "if inp==\"y\":\n",
    "    df = None\n",
    "    for d in os.listdir(save_path):\n",
    "        if d[-4:]==\".shp\":\n",
    "            print(f'Adding in {d}')\n",
    "            poly = gpd.read_file(os.path.join(save_path,d))\n",
    "            poly['lon'] = poly['geometry'].centroid.x\n",
    "            poly['lat'] = poly['geometry'].centroid.y\n",
    "            df_i = pd.DataFrame(poly.drop(columns=['geometry']))\n",
    "\n",
    "            if df is None: \n",
    "                df = df_i\n",
    "            else:   \n",
    "                df = pd.concat([df,df_i], axis=0, ignore_index=True)\n",
    "    df.to_pickle(\"data/all_block_data_2.pkl\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>STATEFP10</th>\n",
       "      <th>COUNTYFP10</th>\n",
       "      <th>TRACTCE10</th>\n",
       "      <th>BLOCKCE</th>\n",
       "      <th>BLOCKID10</th>\n",
       "      <th>PARTFLG</th>\n",
       "      <th>HOUSING10</th>\n",
       "      <th>POP10</th>\n",
       "      <th>lon</th>\n",
       "      <th>lat</th>\n",
       "      <th>state_county</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>01</td>\n",
       "      <td>001</td>\n",
       "      <td>020500</td>\n",
       "      <td>1028</td>\n",
       "      <td>010010205001028</td>\n",
       "      <td>N</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>-86.411659</td>\n",
       "      <td>32.426078</td>\n",
       "      <td>01001</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  STATEFP10 COUNTYFP10 TRACTCE10 BLOCKCE        BLOCKID10 PARTFLG  HOUSING10  \\\n",
       "0        01        001    020500    1028  010010205001028       N          0   \n",
       "\n",
       "   POP10        lon        lat state_county  \n",
       "0      0 -86.411659  32.426078        01001  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with open(\"data/all_block_data_2.pkl\", 'rb') as f:\n",
    "    df = pickle.load(f)\n",
    "df.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Filter out blocks with no inhabitants\n",
    "df = df[df['POP10']>0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Get timezone (TZ) associated with each census block"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### - 3a. Find TZ associated with each state, for states which have a single time zone"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fetch_tz_from_coord(lat,lon):\n",
    "    url = f\"http://api.geonames.org/timezoneJSON?formatted=true&lat={lat}&lng={lon}&username=ryan&style=full\"\n",
    "    r = requests.get(url) \n",
    "    return r.json()['timezoneId']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "multi_timezone_states = ['Oregon','Idaho','Nebraska','Kansas','Texas','North Dakota','South Dakota','Florida','Michigan',\n",
    "                         'Indiana','Kentucky','Tennessee','Alaska']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "state_codes = pd.read_csv('data/FIPS State Numeric Code Table.csv', header=None)\n",
    "state_codes.columns=['name','code','abrev']\n",
    "state_code_dict = dict(state_codes.set_index('name')['code'])\n",
    "code_state_dict = dict(state_codes.set_index('code')['name'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "multi_timezone_codes = [state_code_dict[state] for state in multi_timezone_states]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "inp = input(\"Continue? (y/n): \")\n",
    "if inp==\"y\":\n",
    "    code_timezone_dict = dict.fromkeys(code_state_dict.keys())\n",
    "    for key in code_timezone_dict.keys():\n",
    "        if key in multi_timezone_codes:\n",
    "            continue\n",
    "        else:\n",
    "            state_code = f'{key:02}'\n",
    "            state_sample = df[df['STATEFP10']==state_code].sample(10)\n",
    "            lon = state_sample['lon'].mean()\n",
    "            lat = state_sample['lat'].mean()\n",
    "            tz = fetch_tz_from_coord(lat,lon)\n",
    "            code_timezone_dict[key] = tz\n",
    "    with open('data/certain_state_to_timezone_dict.pkl', 'wb') as f:\n",
    "        pickle.dump(code_timezone_dict_2, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('data/certain_state_to_timezone_dict.pkl', 'rb') as f:\n",
    "    code_timezone_dict = pickle.load(f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### - 3a. Find TZ associated with each county, for counties in states which multiple time zones"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['state_county'] = df['STATEFP10']+df['COUNTYFP10']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_county = df.drop_duplicates(subset='state_county')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "uncertain_state_codes = [key for key in code_timezone_dict if code_timezone_dict[key] is None]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done with 100\n",
      "Done with 200\n",
      "Done with 300\n",
      "Done with 400\n",
      "Done with 500\n",
      "Done with 600\n",
      "Done with 700\n",
      "Done with 800\n",
      "Done with 900\n",
      "Done with 1000\n",
      "Done with 1100\n"
     ]
    }
   ],
   "source": [
    "n=0\n",
    "inp = input(\"Continue? (y/n): \")\n",
    "if inp==\"y\":\n",
    "    for state in uncertain_state_codes:\n",
    "        state_code = f'{state:02}'\n",
    "        df_county_i = df_county[df_county['STATEFP10']==state_code]\n",
    "        # For each county in state\n",
    "        for i, row in df_county_i.iterrows():\n",
    "            county_id = row['COUNTYFP10']\n",
    "            # check if county already added...\n",
    "            if row['state_county'] in code_timezone_dict_2:\n",
    "                #print(f\"{row['state_county']} already exists in code_timezone_dict!\")\n",
    "                pass\n",
    "            else:\n",
    "                lat = row['lat']\n",
    "                lon = row['lon']\n",
    "                tz = fetch_tz_from_coord(lat,lon)\n",
    "                code_timezone_dict_2[row['state_county']] = tz\n",
    "            n+=1\n",
    "            if n%100 == 0:\n",
    "                print(f'Done with {n}')\n",
    "    with open('data/state_county_to_timezone_dict.pkl', 'wb') as f:\n",
    "    pickle.dump(code_timezone_dict_2, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('data/state_county_to_timezone_dict.pkl', 'rb') as f:\n",
    "    code_timezone_dict_2 = pickle.load(f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Add time-zone info to df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# time-zone by state\n",
    "df['tz'] = df['STATEFP10'].apply(lambda x: code_timezone_dict[int(x)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# time-zone by county\n",
    "def get_timezone(row):\n",
    "    tz = row['tz']\n",
    "    if tz is None:\n",
    "        return code_timezone_dict_2[x['state_county']]\n",
    "    else:\n",
    "        return tz\n",
    "\n",
    "df['tz'] = df.apply(get_timezone, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4. Calculate sunrise / sunset times for each day for each census block based on centroid"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4a. Sunrise calculation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2010-01-01 00:00:00+00:00  and  0.0  minutes in\n",
      "2010-01-02 00:00:00+00:00  and  0.9012767950693766  minutes in\n",
      "2010-01-03 00:00:00+00:00  and  1.8098193526268005  minutes in\n",
      "2010-01-04 00:00:00+00:00  and  2.757051686445872  minutes in\n",
      "2010-01-05 00:00:00+00:00  and  3.665884490807851  minutes in\n",
      "2010-01-06 00:00:00+00:00  and  4.589490191141764  minutes in\n",
      "2010-01-07 00:00:00+00:00  and  5.57680223385493  minutes in\n",
      "2010-01-08 00:00:00+00:00  and  6.464082682132721  minutes in\n",
      "2010-01-09 00:00:00+00:00  and  7.343773293495178  minutes in\n",
      "2010-01-10 00:00:00+00:00  and  8.242436961332958  minutes in\n",
      "2010-01-11 00:00:00+00:00  and  9.134606870015462  minutes in\n",
      "2010-01-12 00:00:00+00:00  and  10.0137469291687  minutes in\n",
      "2010-01-13 00:00:00+00:00  and  10.890794372558593  minutes in\n",
      "2010-01-14 00:00:00+00:00  and  11.747749098141988  minutes in\n",
      "2010-01-15 00:00:00+00:00  and  12.637877825895945  minutes in\n",
      "2010-01-16 00:00:00+00:00  and  13.507081091403961  minutes in\n",
      "2010-01-17 00:00:00+00:00  and  14.383098447322846  minutes in\n",
      "2010-01-18 00:00:00+00:00  and  15.265582636992137  minutes in\n",
      "2010-01-19 00:00:00+00:00  and  16.129739133516946  minutes in\n",
      "2010-01-20 00:00:00+00:00  and  17.009464395046233  minutes in\n",
      "2010-01-21 00:00:00+00:00  and  17.91238655646642  minutes in\n",
      "2010-01-22 00:00:00+00:00  and  18.79769031604131  minutes in\n",
      "2010-01-23 00:00:00+00:00  and  19.70875177383423  minutes in\n",
      "2010-01-24 00:00:00+00:00  and  20.730470263957976  minutes in\n",
      "2010-01-25 00:00:00+00:00  and  21.6201744000117  minutes in\n",
      "2010-01-26 00:00:00+00:00  and  22.48805802265803  minutes in\n",
      "2010-01-27 00:00:00+00:00  and  23.357227750619252  minutes in\n",
      "2010-01-28 00:00:00+00:00  and  24.24448616504669  minutes in\n",
      "2010-01-29 00:00:00+00:00  and  25.1166347583135  minutes in\n",
      "2010-01-30 00:00:00+00:00  and  25.986363315582274  minutes in\n",
      "2010-01-31 00:00:00+00:00  and  26.865882921218873  minutes in\n",
      "2010-02-01 00:00:00+00:00  and  27.746929057439168  minutes in\n",
      "2010-02-02 00:00:00+00:00  and  28.617500436306  minutes in\n",
      "2010-02-03 00:00:00+00:00  and  29.613533516724903  minutes in\n",
      "2010-02-04 00:00:00+00:00  and  30.51148966153463  minutes in\n",
      "2010-02-05 00:00:00+00:00  and  31.441925883293152  minutes in\n",
      "2010-02-06 00:00:00+00:00  and  32.407824687163036  minutes in\n",
      "2010-02-07 00:00:00+00:00  and  33.47261246045431  minutes in\n",
      "2010-02-08 00:00:00+00:00  and  34.4451102177302  minutes in\n",
      "2010-02-09 00:00:00+00:00  and  35.39351531267166  minutes in\n",
      "2010-02-10 00:00:00+00:00  and  36.32782038052877  minutes in\n",
      "2010-02-11 00:00:00+00:00  and  37.23814516862233  minutes in\n",
      "2010-02-12 00:00:00+00:00  and  38.21174091100693  minutes in\n",
      "2010-02-13 00:00:00+00:00  and  39.1247569958369  minutes in\n",
      "2010-02-14 00:00:00+00:00  and  40.05606305599213  minutes in\n",
      "2010-02-15 00:00:00+00:00  and  40.98523319562276  minutes in\n",
      "2010-02-16 00:00:00+00:00  and  41.92257673740387  minutes in\n",
      "2010-02-17 00:00:00+00:00  and  42.83458115259806  minutes in\n",
      "2010-02-18 00:00:00+00:00  and  43.74850877920787  minutes in\n",
      "2010-02-19 00:00:00+00:00  and  44.69010931253433  minutes in\n",
      "2010-02-20 00:00:00+00:00  and  45.62127680381139  minutes in\n",
      "2010-02-21 00:00:00+00:00  and  46.53675534327825  minutes in\n",
      "2010-02-22 00:00:00+00:00  and  47.46759425401687  minutes in\n",
      "2010-02-23 00:00:00+00:00  and  48.431103479862216  minutes in\n",
      "2010-02-24 00:00:00+00:00  and  49.3373240351677  minutes in\n",
      "2010-02-25 00:00:00+00:00  and  50.28314240773519  minutes in\n",
      "2010-02-26 00:00:00+00:00  and  51.18159622351329  minutes in\n",
      "2010-02-27 00:00:00+00:00  and  52.109108940760294  minutes in\n",
      "2010-02-28 00:00:00+00:00  and  53.04293363889058  minutes in\n",
      "2010-03-01 00:00:00+00:00  and  53.954961848258975  minutes in\n",
      "2010-03-02 00:00:00+00:00  and  54.9043452501297  minutes in\n",
      "2010-03-03 00:00:00+00:00  and  55.83666740258535  minutes in\n",
      "2010-03-04 00:00:00+00:00  and  56.76004812717438  minutes in\n",
      "2010-03-05 00:00:00+00:00  and  57.66159542798996  minutes in\n",
      "2010-03-06 00:00:00+00:00  and  58.64868249893188  minutes in\n",
      "2010-03-07 00:00:00+00:00  and  59.588792554537456  minutes in\n",
      "2010-03-08 00:00:00+00:00  and  60.70159537792206  minutes in\n",
      "2010-03-09 00:00:00+00:00  and  61.66474265654882  minutes in\n",
      "2010-03-10 00:00:00+00:00  and  62.63932615121205  minutes in\n",
      "2010-03-11 00:00:00+00:00  and  63.64716430902481  minutes in\n",
      "2010-03-12 00:00:00+00:00  and  64.5613621354103  minutes in\n",
      "2010-03-13 00:00:00+00:00  and  65.49780438741048  minutes in\n",
      "2010-03-14 00:00:00+00:00  and  66.40911600987117  minutes in\n",
      "2010-03-15 00:00:00+00:00  and  67.33234017292658  minutes in\n",
      "2010-03-16 00:00:00+00:00  and  68.28660120964051  minutes in\n",
      "2010-03-17 00:00:00+00:00  and  69.20958963235219  minutes in\n",
      "2010-03-18 00:00:00+00:00  and  70.13129946390788  minutes in\n",
      "2010-03-19 00:00:00+00:00  and  71.0634684364001  minutes in\n",
      "2010-03-20 00:00:00+00:00  and  71.96607725222906  minutes in\n",
      "2010-03-21 00:00:00+00:00  and  72.89438849687576  minutes in\n",
      "2010-03-22 00:00:00+00:00  and  73.82010056575139  minutes in\n",
      "2010-03-23 00:00:00+00:00  and  74.72736092408498  minutes in\n",
      "2010-03-24 00:00:00+00:00  and  75.63701145251592  minutes in\n",
      "2010-03-25 00:00:00+00:00  and  76.56496529181798  minutes in\n",
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      "2010-11-13 00:00:00+00:00  and  290.97596455017725  minutes in\n",
      "2010-11-14 00:00:00+00:00  and  291.8889072616895  minutes in\n",
      "2010-11-15 00:00:00+00:00  and  292.7971520940463  minutes in\n",
      "2010-11-16 00:00:00+00:00  and  293.71409662564594  minutes in\n",
      "2010-11-17 00:00:00+00:00  and  294.60139536062877  minutes in\n",
      "2010-11-18 00:00:00+00:00  and  295.5210297028224  minutes in\n",
      "2010-11-19 00:00:00+00:00  and  296.44156155586245  minutes in\n",
      "2010-11-20 00:00:00+00:00  and  297.34642541011175  minutes in\n",
      "2010-11-21 00:00:00+00:00  and  298.26048916975657  minutes in\n",
      "2010-11-22 00:00:00+00:00  and  299.1776819785436  minutes in\n",
      "2010-11-23 00:00:00+00:00  and  300.08221324682233  minutes in\n",
      "2010-11-24 00:00:00+00:00  and  300.97100812594095  minutes in\n",
      "2010-11-25 00:00:00+00:00  and  301.8917544563611  minutes in\n",
      "2010-11-26 00:00:00+00:00  and  302.8127231359482  minutes in\n",
      "2010-11-27 00:00:00+00:00  and  303.76053953170776  minutes in\n",
      "2010-11-28 00:00:00+00:00  and  304.77710328102114  minutes in\n",
      "2010-11-29 00:00:00+00:00  and  305.7053795297941  minutes in\n",
      "2010-11-30 00:00:00+00:00  and  306.61065598328906  minutes in\n",
      "2010-12-01 00:00:00+00:00  and  307.5102942466736  minutes in\n",
      "2010-12-02 00:00:00+00:00  and  308.4416857759158  minutes in\n",
      "2010-12-03 00:00:00+00:00  and  309.336094446977  minutes in\n",
      "2010-12-04 00:00:00+00:00  and  310.2380134344101  minutes in\n",
      "2010-12-05 00:00:00+00:00  and  311.16001828511554  minutes in\n",
      "2010-12-06 00:00:00+00:00  and  312.07779261271156  minutes in\n",
      "2010-12-07 00:00:00+00:00  and  312.97459581295647  minutes in\n",
      "2010-12-08 00:00:00+00:00  and  313.88112206459044  minutes in\n",
      "2010-12-09 00:00:00+00:00  and  314.7686033010483  minutes in\n",
      "2010-12-10 00:00:00+00:00  and  315.68598172664645  minutes in\n",
      "2010-12-11 00:00:00+00:00  and  316.61303457816445  minutes in\n",
      "2010-12-12 00:00:00+00:00  and  317.52330305973686  minutes in\n",
      "2010-12-13 00:00:00+00:00  and  318.44136433204017  minutes in\n",
      "2010-12-14 00:00:00+00:00  and  319.35809108416237  minutes in\n",
      "2010-12-15 00:00:00+00:00  and  320.26568214098614  minutes in\n",
      "2010-12-16 00:00:00+00:00  and  321.15335683027905  minutes in\n",
      "2010-12-17 00:00:00+00:00  and  322.0719574888547  minutes in\n",
      "2010-12-18 00:00:00+00:00  and  322.9670839746793  minutes in\n",
      "2010-12-19 00:00:00+00:00  and  323.86919183333714  minutes in\n",
      "2010-12-20 00:00:00+00:00  and  324.7744495987892  minutes in\n",
      "2010-12-21 00:00:00+00:00  and  325.6994234363238  minutes in\n",
      "2010-12-22 00:00:00+00:00  and  326.59895780086515  minutes in\n",
      "2010-12-23 00:00:00+00:00  and  327.50238788525263  minutes in\n",
      "2010-12-24 00:00:00+00:00  and  328.42861318588257  minutes in\n",
      "2010-12-25 00:00:00+00:00  and  329.331986216704  minutes in\n",
      "2010-12-26 00:00:00+00:00  and  330.23948088884356  minutes in\n",
      "2010-12-27 00:00:00+00:00  and  331.1586941639582  minutes in\n",
      "2010-12-28 00:00:00+00:00  and  332.0710188190142  minutes in\n",
      "2010-12-29 00:00:00+00:00  and  332.9942789991697  minutes in\n",
      "2010-12-30 00:00:00+00:00  and  333.9102690855662  minutes in\n",
      "2010-12-31 00:00:00+00:00  and  334.8030811429024  minutes in\n"
     ]
    }
   ],
   "source": [
    "dt_i = pytz.utc.localize(datetime.datetime(year=2010, month=1, day=1))\n",
    "bins = np.arange(0, 24*60, 1)\n",
    "sum_counts, _ = np.histogram([], bins=bins)\n",
    "sum_counts_no_dst, _ = np.histogram([], bins=bins)\n",
    "sum_counts_all_dst, _ = np.histogram([], bins=bins)\n",
    "\n",
    "counts_list = []\n",
    "counts_all_dst_list = []\n",
    "counts_no_dst_list = []\n",
    "\n",
    "t0 = time.time()\n",
    "while dt_i.year==2010:\n",
    "    print(f'{dt_i}, and {(time.time()-t0)/60} minutes in')\n",
    "    lon = df['lon'].to_numpy()\n",
    "    lat = df['lat'].to_numpy()\n",
    "    df['sunrise_utc'] = vectorized_suncalc.get_sunrise_vec(lat,lon,dt_i)\n",
    "    \n",
    "    invalid_cutoff = dt_i-datetime.timedelta(days=90)\n",
    "    df_valid = df.loc[df['sunrise_utc']>invalid_cutoff]\n",
    "        \n",
    "    for tz in df_valid['tz'].unique():\n",
    "        tz_bool = df_valid['tz'] == tz\n",
    "        df_tz = df_valid[tz_bool]\n",
    "        local_time = utc_to_local(df_tz['sunrise_utc'], tz)\n",
    "        \n",
    "        dst_offset = local_time.sample().iloc[0].dst()\n",
    "        if dst_offset.seconds > 0:\n",
    "            local_time_all_dst = local_time\n",
    "            local_time_no_dst = local_time - dst_offset\n",
    "        else:\n",
    "            local_time_all_dst = local_time + datetime.timedelta(seconds=3600)\n",
    "            local_time_no_dst = local_time\n",
    "        \n",
    "        local_min = local_time_to_min(local_time)\n",
    "        local_min_no_dst = local_time_to_min(local_time_no_dst)\n",
    "        local_min_all_dst = local_time_to_min(local_time_all_dst)\n",
    "        \n",
    "        counts, _ = np.histogram(local_min, bins=bins)\n",
    "        counts_no_dst, _ = np.histogram(local_min_no_dst, bins=bins)\n",
    "        counts_all_dst, _ = np.histogram(local_min_all_dst, bins=bins)\n",
    "        \n",
    "        sum_counts = sum_counts + counts\n",
    "        sum_counts_no_dst = sum_counts_no_dst + counts_no_dst\n",
    "        sum_counts_all_dst = sum_counts_all_dst + counts_all_dst\n",
    "        \n",
    "        counts_list.append(counts)\n",
    "        counts_no_dst_list.append(counts_no_dst)\n",
    "        counts_all_dst_list.append(counts_all_dst)\n",
    "        \n",
    "    dt_i = dt_i + datetime.timedelta(days=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('data/sum_counts_no_dst.pkl','wb') as f:\n",
    "    pickle.dump(sum_counts_no_dst, f)\n",
    "    \n",
    "with open('data/sum_counts_all_dst.pkl','wb') as f:\n",
    "    pickle.dump(sum_counts_all_dst, f)\n",
    "    \n",
    "with open('data/sum_counts.pkl','wb') as f:\n",
    "    pickle.dump(sum_counts, f)\n",
    "    \n",
    "with open('data/counts_no_dst_list.pkl','wb') as f:\n",
    "    pickle.dump(counts_no_dst_list, f)\n",
    "    \n",
    "with open('data/counts_all_dst_list.pkl','wb') as f:\n",
    "    pickle.dump(counts_all_dst_list, f)\n",
    "    \n",
    "with open('data/counts_list.pkl','wb') as f:\n",
    "    pickle.dump(counts_list, f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4b. Sunset calculation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 327,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2010-01-01 00:00:00+00:00  and  0.0  minutes in\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\volpir\\Documents\\Notebooks\\For Fun\\dst_analysis\\suncalc.py:243: RuntimeWarning: invalid value encountered in arccos\n",
      "  # import time\n",
      "C:\\Users\\volpir\\Documents\\Notebooks\\For Fun\\dst_analysis\\suncalc.py:138: RuntimeWarning: invalid value encountered in arcsin\n",
      "  \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2010-01-02 00:00:00+00:00  and  1.042784078915914  minutes in\n",
      "2010-01-03 00:00:00+00:00  and  1.979592764377594  minutes in\n",
      "2010-01-04 00:00:00+00:00  and  2.9042262196540833  minutes in\n",
      "2010-01-05 00:00:00+00:00  and  3.831922260920207  minutes in\n",
      "2010-01-06 00:00:00+00:00  and  4.77773224512736  minutes in\n",
      "2010-01-07 00:00:00+00:00  and  5.699843072891236  minutes in\n",
      "2010-01-08 00:00:00+00:00  and  6.642824641863505  minutes in\n",
      "2010-01-09 00:00:00+00:00  and  7.585805181662241  minutes in\n",
      "2010-01-10 00:00:00+00:00  and  8.512916274865468  minutes in\n",
      "2010-01-11 00:00:00+00:00  and  9.450480445226033  minutes in\n",
      "2010-01-12 00:00:00+00:00  and  10.38129365046819  minutes in\n",
      "2010-01-13 00:00:00+00:00  and  11.286774301528931  minutes in\n",
      "2010-01-14 00:00:00+00:00  and  12.237963859240214  minutes in\n",
      "2010-01-15 00:00:00+00:00  and  13.15569152434667  minutes in\n",
      "2010-01-16 00:00:00+00:00  and  14.118072895208995  minutes in\n",
      "2010-01-17 00:00:00+00:00  and  15.05960417985916  minutes in\n",
      "2010-01-18 00:00:00+00:00  and  15.978097375233968  minutes in\n",
      "2010-01-19 00:00:00+00:00  and  16.9249440908432  minutes in\n",
      "2010-01-20 00:00:00+00:00  and  17.85479701757431  minutes in\n",
      "2010-01-21 00:00:00+00:00  and  18.85799906651179  minutes in\n",
      "2010-01-22 00:00:00+00:00  and  19.815645249684653  minutes in\n",
      "2010-01-23 00:00:00+00:00  and  20.728653367360433  minutes in\n",
      "2010-01-24 00:00:00+00:00  and  21.827908527851104  minutes in\n",
      "2010-01-25 00:00:00+00:00  and  22.7685733238856  minutes in\n",
      "2010-01-26 00:00:00+00:00  and  23.729239571094514  minutes in\n",
      "2010-01-27 00:00:00+00:00  and  24.667998798688252  minutes in\n",
      "2010-01-28 00:00:00+00:00  and  25.5789808233579  minutes in\n",
      "2010-01-29 00:00:00+00:00  and  26.53580509821574  minutes in\n",
      "2010-01-30 00:00:00+00:00  and  27.46255563100179  minutes in\n",
      "2010-01-31 00:00:00+00:00  and  28.40988895893097  minutes in\n",
      "2010-02-01 00:00:00+00:00  and  29.340973818302153  minutes in\n",
      "2010-02-02 00:00:00+00:00  and  30.250922513008117  minutes in\n",
      "2010-02-03 00:00:00+00:00  and  31.204328481356303  minutes in\n",
      "2010-02-04 00:00:00+00:00  and  32.12741419871648  minutes in\n",
      "2010-02-05 00:00:00+00:00  and  33.089514501889546  minutes in\n",
      "2010-02-06 00:00:00+00:00  and  34.04376713434855  minutes in\n",
      "2010-02-07 00:00:00+00:00  and  34.96958436568578  minutes in\n",
      "2010-02-08 00:00:00+00:00  and  35.89746593236923  minutes in\n",
      "2010-02-09 00:00:00+00:00  and  36.81739753484726  minutes in\n",
      "2010-02-10 00:00:00+00:00  and  37.75116905371348  minutes in\n",
      "2010-02-11 00:00:00+00:00  and  38.690747141838074  minutes in\n",
      "2010-02-12 00:00:00+00:00  and  39.61190526088079  minutes in\n",
      "2010-02-13 00:00:00+00:00  and  40.55004106760025  minutes in\n",
      "2010-02-14 00:00:00+00:00  and  41.47881845633189  minutes in\n",
      "2010-02-15 00:00:00+00:00  and  42.40692514181137  minutes in\n",
      "2010-02-16 00:00:00+00:00  and  43.332987447579704  minutes in\n",
      "2010-02-17 00:00:00+00:00  and  44.27561628421147  minutes in\n",
      "2010-02-18 00:00:00+00:00  and  45.196116491158804  minutes in\n",
      "2010-02-19 00:00:00+00:00  and  46.2090741554896  minutes in\n",
      "2010-02-20 00:00:00+00:00  and  47.32015738884608  minutes in\n",
      "2010-02-21 00:00:00+00:00  and  48.34316890239715  minutes in\n",
      "2010-02-22 00:00:00+00:00  and  49.336669290065764  minutes in\n",
      "2010-02-23 00:00:00+00:00  and  50.27554628054301  minutes in\n",
      "2010-02-24 00:00:00+00:00  and  51.25149589379628  minutes in\n",
      "2010-02-25 00:00:00+00:00  and  52.191233392556505  minutes in\n",
      "2010-02-26 00:00:00+00:00  and  53.11810306310654  minutes in\n",
      "2010-02-27 00:00:00+00:00  and  54.0567883292834  minutes in\n",
      "2010-02-28 00:00:00+00:00  and  54.9844407081604  minutes in\n",
      "2010-03-01 00:00:00+00:00  and  55.91087485949198  minutes in\n",
      "2010-03-02 00:00:00+00:00  and  56.84311297337214  minutes in\n",
      "2010-03-03 00:00:00+00:00  and  57.79122164646785  minutes in\n",
      "2010-03-04 00:00:00+00:00  and  58.712760563691454  minutes in\n",
      "2010-03-05 00:00:00+00:00  and  59.62836872736613  minutes in\n",
      "2010-03-06 00:00:00+00:00  and  60.56081181764603  minutes in\n",
      "2010-03-07 00:00:00+00:00  and  61.48704748948415  minutes in\n",
      "2010-03-08 00:00:00+00:00  and  62.440251636505124  minutes in\n",
      "2010-03-09 00:00:00+00:00  and  63.3613920489947  minutes in\n",
      "2010-03-10 00:00:00+00:00  and  64.29010913372039  minutes in\n",
      "2010-03-11 00:00:00+00:00  and  65.20561057726542  minutes in\n",
      "2010-03-12 00:00:00+00:00  and  66.11856699784597  minutes in\n",
      "2010-03-13 00:00:00+00:00  and  67.05743357737859  minutes in\n",
      "2010-03-14 00:00:00+00:00  and  68.00191846688588  minutes in\n",
      "2010-03-15 00:00:00+00:00  and  68.92945581674576  minutes in\n",
      "2010-03-16 00:00:00+00:00  and  69.8800233165423  minutes in\n",
      "2010-03-17 00:00:00+00:00  and  70.79679004748662  minutes in\n",
      "2010-03-18 00:00:00+00:00  and  71.71740469535192  minutes in\n",
      "2010-03-19 00:00:00+00:00  and  72.665267932415  minutes in\n",
      "2010-03-20 00:00:00+00:00  and  73.59728892246882  minutes in\n",
      "2010-03-21 00:00:00+00:00  and  74.53544597228368  minutes in\n",
      "2010-03-22 00:00:00+00:00  and  75.46433218717576  minutes in\n",
      "2010-03-23 00:00:00+00:00  and  76.37422337929408  minutes in\n",
      "2010-03-24 00:00:00+00:00  and  77.313835867246  minutes in\n",
      "2010-03-25 00:00:00+00:00  and  78.26360333363215  minutes in\n",
      "2010-03-26 00:00:00+00:00  and  79.18286403814952  minutes in\n",
      "2010-03-27 00:00:00+00:00  and  80.10106245279312  minutes in\n",
      "2010-03-28 00:00:00+00:00  and  81.04535984595617  minutes in\n",
      "2010-03-29 00:00:00+00:00  and  82.04034531116486  minutes in\n",
      "2010-03-30 00:00:00+00:00  and  83.14131138324737  minutes in\n",
      "2010-03-31 00:00:00+00:00  and  84.08628852367401  minutes in\n",
      "2010-04-01 00:00:00+00:00  and  85.0110142548879  minutes in\n",
      "2010-04-02 00:00:00+00:00  and  85.9525379061699  minutes in\n",
      "2010-04-03 00:00:00+00:00  and  86.8766684015592  minutes in\n",
      "2010-04-04 00:00:00+00:00  and  87.8110646923383  minutes in\n",
      "2010-04-05 00:00:00+00:00  and  88.7700336098671  minutes in\n",
      "2010-04-06 00:00:00+00:00  and  89.70583529074987  minutes in\n",
      "2010-04-07 00:00:00+00:00  and  90.66064794063568  minutes in\n",
      "2010-04-08 00:00:00+00:00  and  91.58320968548456  minutes in\n",
      "2010-04-09 00:00:00+00:00  and  92.544389017423  minutes in\n",
      "2010-04-10 00:00:00+00:00  and  93.47395447889964  minutes in\n",
      "2010-04-11 00:00:00+00:00  and  94.42058022022248  minutes in\n",
      "2010-04-12 00:00:00+00:00  and  95.38953264156977  minutes in\n",
      "2010-04-13 00:00:00+00:00  and  96.32996449073156  minutes in\n",
      "2010-04-14 00:00:00+00:00  and  97.25258677403131  minutes in\n",
      "2010-04-15 00:00:00+00:00  and  98.19083550771077  minutes in\n",
      "2010-04-16 00:00:00+00:00  and  99.15045359532039  minutes in\n",
      "2010-04-17 00:00:00+00:00  and  100.07298945585886  minutes in\n",
      "2010-04-18 00:00:00+00:00  and  101.00015143156051  minutes in\n",
      "2010-04-19 00:00:00+00:00  and  101.9368148525556  minutes in\n",
      "2010-04-20 00:00:00+00:00  and  102.8692280014356  minutes in\n",
      "2010-04-21 00:00:00+00:00  and  103.84207003513971  minutes in\n",
      "2010-04-22 00:00:00+00:00  and  104.76554298798243  minutes in\n",
      "2010-04-23 00:00:00+00:00  and  105.69547088940938  minutes in\n",
      "2010-04-24 00:00:00+00:00  and  106.75530999104181  minutes in\n",
      "2010-04-25 00:00:00+00:00  and  107.7985741575559  minutes in\n",
      "2010-04-26 00:00:00+00:00  and  108.74514606396357  minutes in\n",
      "2010-04-27 00:00:00+00:00  and  109.70942660967509  minutes in\n",
      "2010-04-28 00:00:00+00:00  and  110.649472061793  minutes in\n",
      "2010-04-29 00:00:00+00:00  and  111.62607473134995  minutes in\n",
      "2010-04-30 00:00:00+00:00  and  112.54837848345439  minutes in\n",
      "2010-05-01 00:00:00+00:00  and  113.48322848081588  minutes in\n",
      "2010-05-02 00:00:00+00:00  and  114.41342799663543  minutes in\n",
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      "2010-05-06 00:00:00+00:00  and  118.14894571701686  minutes in\n",
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      "2010-05-08 00:00:00+00:00  and  120.09716771046321  minutes in\n",
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      "2010-05-10 00:00:00+00:00  and  121.96866348584493  minutes in\n",
      "2010-05-11 00:00:00+00:00  and  122.91372708876928  minutes in\n",
      "2010-05-12 00:00:00+00:00  and  123.86396877765655  minutes in\n",
      "2010-05-13 00:00:00+00:00  and  124.83370248874029  minutes in\n"
     ]
    },
    {
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   ],
   "source": [
    "dt_i = pytz.utc.localize(datetime.datetime(year=2010, month=1, day=1))\n",
    "bins = np.arange(0, 24*60, 1)\n",
    "set_sum_counts, _ = np.histogram([], bins=bins)\n",
    "set_sum_counts_no_dst, _ = np.histogram([], bins=bins)\n",
    "set_sum_counts_all_dst, _ = np.histogram([], bins=bins)\n",
    "\n",
    "set_counts_list = []\n",
    "set_counts_all_dst_list = []\n",
    "set_counts_no_dst_list = []\n",
    "\n",
    "t0 = time.time()\n",
    "while dt_i.year==2010:\n",
    "    print(dt_i,' and ', (time.time()-t0)/60, ' minutes in')\n",
    "    lon = df['lon'].to_numpy()\n",
    "    lat = df['lat'].to_numpy()\n",
    "    df['sunrise_utc'] = vectorized_suncalc.get_sunset_vec(lat,lon,dt_i)\n",
    "    \n",
    "    invalid_cutoff = dt_i-datetime.timedelta(days=90)\n",
    "    df_valid = df.loc[df['sunrise_utc']>invalid_cutoff]\n",
    "    \n",
    "    for tz in df_valid['tz'].unique():\n",
    "        tz_bool = df_valid['tz'] == tz\n",
    "        df_tz = df_valid[tz_bool]\n",
    "        local_time = utc_to_local(df_tz['sunrise_utc'], tz)\n",
    "        \n",
    "        dst_offset = local_time.sample().iloc[0].dst()\n",
    "        if dst_offset.seconds > 0:\n",
    "            local_time_all_dst = local_time\n",
    "            local_time_no_dst = local_time - dst_offset\n",
    "        else:\n",
    "            local_time_all_dst = local_time + datetime.timedelta(seconds=3600)\n",
    "            local_time_no_dst = local_time\n",
    "        \n",
    "        local_min = local_time_to_min(local_time)\n",
    "        local_min_no_dst = local_time_to_min(local_time_no_dst)\n",
    "        local_min_all_dst = local_time_to_min(local_time_all_dst)\n",
    "\n",
    "        \n",
    "        set_counts, _ = np.histogram(local_min, bins=bins)\n",
    "        set_counts_no_dst, _ = np.histogram(local_min_no_dst, bins=bins)\n",
    "        set_counts_all_dst, _ = np.histogram(local_min_all_dst, bins=bins)\n",
    "        \n",
    "        set_sum_counts = set_sum_counts + set_counts\n",
    "        set_sum_counts_no_dst = set_sum_counts_no_dst + set_counts_no_dst\n",
    "        set_sum_counts_all_dst = set_sum_counts_all_dst + set_counts_all_dst\n",
    "        \n",
    "        set_counts_list.append(set_counts)\n",
    "        set_counts_no_dst_list.append(set_counts_no_dst)\n",
    "        set_counts_all_dst_list.append(set_counts_all_dst)\n",
    "\n",
    "    dt_i = dt_i + datetime.timedelta(days=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('data/set_sum_counts_no_dst.pkl','wb') as f:\n",
    "    pickle.dump(set_sum_counts_no_dst, f)\n",
    "    \n",
    "with open('data/set_sum_counts_all_dst.pkl','wb') as f:\n",
    "    pickle.dump(set_sum_counts_all_dst, f)\n",
    "    \n",
    "with open('data/set_sum_counts.pkl','wb') as f:\n",
    "    pickle.dump(set_sum_counts, f)\n",
    "    \n",
    "with open('data/set_counts_no_dst_list.pkl','wb') as f:\n",
    "    pickle.dump(set_counts_no_dst_list, f)\n",
    "    \n",
    "with open('data/set_counts_all_dst_list.pkl','wb') as f:\n",
    "    pickle.dump(set_counts_all_dst_list, f)\n",
    "    \n",
    "with open('data/set_counts_list.pkl','wb') as f:\n",
    "    pickle.dump(set_counts_list, f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5. Create plot of sunrise/sunset distributions with and without DST"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {},
   "outputs": [],
   "source": [
    "def group_every_n(bins, counts, n):\n",
    "    bins_group = []\n",
    "    counts_group = []\n",
    "    count_group = 0\n",
    "    for b, c in zip(bins[:-1], counts):\n",
    "        count_group += c\n",
    "        if b%n == 0:\n",
    "            bins_group.append(b)\n",
    "            counts_group.append(count_group)\n",
    "            count_group = 0\n",
    "    return bins_group, counts_group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 376,
   "metadata": {},
   "outputs": [],
   "source": [
    "def hour_to_text(hours):\n",
    "    labs = []\n",
    "    for hour in hours:\n",
    "        h = int(hour)\n",
    "        m = int((hour-h)*60)\n",
    "        labs.append(f\"{h%12}:{m:02}\")\n",
    "    return labs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 543,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_hist(ax, bins, counts, min_h, max_h, sum_every=10):\n",
    "    bins_group, counts_group = group_every_n(bins, counts, sum_every)\n",
    "    hours = [b/60 for b in bins_group]\n",
    "    hour_marks = np.arange(min_h, max_h+1, 1)\n",
    "    hour_labels = hour_to_text(hour_marks)\n",
    "    ax.bar(\n",
    "        bins_group, \n",
    "        counts_group, \n",
    "        width=sum_every, \n",
    "        align='edge', \n",
    "        color='#404141', \n",
    "        edgecolor='k', \n",
    "        snap=True,\n",
    "        linewidth=0\n",
    "    )\n",
    "    ax.set_xlim(min_h*60,max_h*60);\n",
    "    ax.set_xticks(np.array(hour_marks)*60);\n",
    "    ax.set_xticklabels(hour_labels, fontsize=14)\n",
    "    ax.set_yticks([],[])\n",
    "    ax.grid(axis='x',linestyle='dashed', linewidth=1)\n",
    "    return ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 544,
   "metadata": {},
   "outputs": [
    {
     "data": {
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F9JMdDBxMDU+VdJCkdWZ2j5ndI+lIpY3cPWY2rc9zfixpZs/heKY0Oc7mrmV6R7dmVj97R8Ky5O7blUaKf2fAIiXncIuka3vu+76k3xrynJLzkJnNlHSCpI9OsOiPlQ7PO6jn/pkansNB1fNyzuHdkv7F3T/j7mvd/ZOS3ivprCHPKXa9cPfr3f1ISQconYf7FKXDb28c8JRis+jSmWG/XxuG1T+Zdg9aZpy50EcOQT9JP9mNflIS/eRO9JEDldZPSmLgYKq4UNLhkh7fdVsp6TPV7/2+YblaaSPw1K77nippf8V5SVdL+j0z27drmc6MuBtHV/74VLUfqrRz0E/JOXxL0iE99z1a0qYhzyk5D0k6RWnE/DMTLLdK6XDMYzt3WLrE1GHaNYfDbNdLTx1bvf6qURU8Bg9Q9U1Bl19peH9R+nohd7/d3W8xswOVDllePGDR4rNQ2iH8sXZd//eV9Hva9bzVXpNp99Xdr9u1zLDX3VP0kUPQT9JP9qCfpJ+8F/rIeymtn0w8g5koue39m3pmjJb0aknre5b5qtJlchYqfajXatfLpUxX+lB8RumSIc9Xmgk0q8ul9LTpX5S+STpY0gKlmXF/Lmn2VMqhqvvJSp36W5TO7X2h0jmKr5pq60VVt0n6gaSP9nnseUqXJ3t4133nKM1y+wyly1Jdqf6XmVpSPf6MavncLzN1ntIhh3+oNBHP8yTdKuk9U3S9eKbSud4HK3XMq5XOAb9fyVko7dh1/oi+Q9LfVL//VvX4G6uan1+14TPqucyU0iXsLqjTbqXJne5R+ubu0OrnL7WXLsfYVcdSTcE+sqqbfjLqpp/ctV30k04/2dOmKdlHVnVOuX6y8dC5NXPTvXeKzlY1P0nXfb8h6VPVCvvz6vcZPcscrjRD6F1K30a8XZleKqWqt/OhvbvqnL4g6TFTLYeuuv9Q6Xrld0m6TuncNOt6fMrkIelopfPDntLnsZOrx+Z03bevpA9J+knVYVysrktKVcv8ltJO9x3Vch+SNK3ptk6QwwMlvV/pG7U7lSYF+0dJ+07R9eJPlC7Rt6Oq+cOSppeehdI1ub3P7bzqcavafkvVhmWS5vW8xlJJS+u2W+ma2OuVttPfl/T8Btq/VFOwj6xqpp/ctW76yaiZftLpJ3vqnZJ9ZFXjUZpi/WTnepAAAAAAAAD3whwHAAAAAABgIAYOAAAAAADAQAwcAAAAAACAgRg4AAAAAAAAAzFwAAAAAAAABmLgAAAAAAAADMTAAYBJM7PzzKzRa7ia2Twzu8fMjm2yjhKZ2UYzW9qW161Zw+PN7NdmdmSTdQDAOFhytZn9R9O1lMTM3MzOa8vr1qzhuWZ2t5n9TpN1oD0YOAAyYWZzzexcM1tvZneY2c/M7FozO9/Mjm66voy8V9K33P3r/R40swPN7K6qU37JoBep/ph1M/uJmU0bsMziahk3szm7W3D1Xut29/k5MbOlXZlMdDu56Xo73H21pAslvcfMrOl6AOyZUvtMMzvZzM7cjaeeJOnJks7ueb2NPdvlu6v7PmZmj+xZtrPM2iH1rekstxs19r7Xl/fkNXLRJ+Nht6OarrfD3S+UtFbSu5quBe2wT9MFAJDMbL6kZZJ+KekCSd+TtJ+kR0t6jqRfSLqysQLD6ZL+v6be3MyeKulYSc8dstiLJd1f0o2STpX0qSHL3iXpNyQdL+m/et5rlqRnV8vsu/tVF+cfJH2s698HSXqfpKsknduz7Lern4dIavRIlcr7lT5nz5Z0ScO1ANhNLeozd8fJkuYoba/q+BtJF7v7D/o89iNJZ1W/P1DSUZJeIenZZvY4d7+ta9m7JM0zsye7+/90v4iZPUnS40S/2OtMSQd0/fswSW+W9CVJX+xZ9vvVz/0k/Wr8pU3oA5LON7PHuvv3mi4GeWPgAMjD2yU9QNITqm9GdzKzV0v6zUaqSu9vkvZ39+3u/kulHbWmvFLSTyR9ZcgypyrtMC6W9H4z+213v37AstdL+rWkU9QzcCDpZdXPiyW9cLcrLkzvkR7VkRjvk3SDu/cdpHH3HeOvbFKukrRRafCLgQOgvbLtM5tgZscoDdCeNWCRbT3b53PMbIukVyv1f+/ueuwqSU+s7t9l4EBpsOE2Sd9VGsSHdn5zv1N1VMGbJV0zpF+8ay+UNhlflHSOUr/4moZrQeY4VQHIw+9I+knvDpAkufuv3f3mzr/NbE51uNvZvcua2dm9h9V35iUws+lmdo6ZbakO5f+WmS3oef5RnUPMzexVZnat0jcLf939Wj3PeaSZfdzMNpnZjur1v21mL+9ZzszsL8xsVXVY6S/M7MrJHlJqZvsoHWnw9WoAo98yT5T0eEnnS/oPpUGOUyZ46U9IOs7MHt5z/8lKf1xumUx9o2Jmp5nZd8zsTjPbZmaXmdnTByx7tJldYul0i7vM7AYz+3czO6hrmVdWr3FTdYjqLWb2KduDUy92o033muOgc5+ZHWFml5vZ9mrd+Rcz28fM9q1+v6lq2zfM7LA+rz3NzN5sZt+rlttqZheb2RN6l3V3l/Q1SX9gZgf0Pg6gNXLpM83MzjSza6o+7edmtqHaDt+vZ9n5ZvYlM7ut6is3mNlbqr6ts8xGSUdKmm31Dm9/odK315dNsFy3r1U/H9Vz/91K/edJZrbzqAJLp/SdVD12d4332SNVf/BGS6eh3FX1d18ys8MHLP+Cat9ia7WvscHMPmhm968ev0+V+zfM7MdVv/i/1f/1g/diu+41x0HnPjNbZGm+ijvM7Edm9sbq8QOrdWtL9diXzexhfV57upm9y8x+WK1rt5rZp81sbu+y7r5dabCIL0gwIQYOgDxcL+nBZvb8Mb7H1yQ9QtLfSfonSfMkfcXMHthn2TMlvUnSZ5RGoFf0e8Fqh+frSh3OZ5SOCHinpOsk/V7P4p+U9GFJP5T0BqXzMKdL+rqZHT+J+p+kdCjgfw9Z5lRJt0v6grv/ROkP/5eb2bBt3SeVjjroHGEgM1so6TGSPj6JukbGzN4l6aNKAx5vlvSeqo4rzezZPcv+uaQrlA4bPUfp/+k/lHJ6RNeif630DdEHJb1K0uckPU/St/fmTtIAj1Baf76vVOc3Jf2V0ukQn5f0BKX16V1K7bqw+/+y2jG/VOnbx6slva5a/jGSvmXpcOZeVysdbdd3MAZAK+TSZ75V6YirjZLeKOn1SoenP1XSzrlzqu33t5ROpXiPpNcqbYv+TtKnu17vTEnrlbbZL+26fV/DHSnpe+5+e432dSbEu63PY/8uaYZSX9HxPEkHai/3i0r92juVTrd4vaR/k3S0pKt7B4jNrNN3zFT6fzlTaW6bZysdoSKlUxlfL+kHSkdavFapHzpV0tLOAEODnqB0BORSpf7wB5LeaWZnKPX5ByrtP/2bpD9QOlVnJzObrnSa4CuV9oFeo7TvtUjSCjOb3ec9r5Y0y8wOHX1zUBR358aNW8M3pZ2Mu5XOA79OqWP+C0mH9Vl2TrXc2X0eO7t6bE7XfedV932kZ9kXVvf/edd9R1X3/VTSzD6vf56qL26rfz+uWv4NE7TvedVyf9Zz/z6SVirNR2ATvMYp1WscP+Dxfau6z+u674TqOc/qs/xGSeuq378g6bqux86V9OOqvg/3Zrob/78732vIMocoDWB8U9L9u+5/mKSt1Wvct7rvEZJ2SLpW0ow+r3Wfrt/37/P4Mf3+36r3WFqzbZ318bwhy9zrdav7XNILe+5fVeWwuHudUNq5c0nP7Lrvdb33Vfc/SNL/9muL0oCBS/qr3f3/5MaNW7M35dNnfkfStRPUum/Vn3xD0j49j3W2YUd13bdU0sYaWdxX6WiDLw54fKPSwMNB1e1gpf50q9Ig9byuZV3Sl6vfV0m6rOuxyyStrH7/srr2BXbz/3Dnew1Z5thquc/29AePk3SPpKu67ntKtewSSfv2vI51nl/9vl+f9zq1ev6f9KnzvJptO2rQOjfsdav7fi1pQdd995d0S3X/B3uWf2/1nEO67vuApDslHdGz7GxJP+/XFkkvqV7nBXvyf8qt/BtHHAAZcPerlb5RPV/pW/hTJH1E0rVmdlW/w8t2w/t6/r2k+tnvMjwXuPtkDtHfVv082sxmDlnuJUqTVV1oZgd1bkrfaFystGM30eWAHlL9/OmAx5+vNBJ/ftd9nVMNXjHBa39c0u+Y2dPMbD9Jf6qUwT0TPG+UTlDaoflnd995GKinQ27PU+r0O9+uvFBpZ+Jv3X1r7wu5+6+7fr9d2nl45vQq9zVK/3cLep+7l93k7r1zS3xTKYcPuXv3aTFXVT+715OXKH07t6pnvbq/0jdIT6/+P7v9pPo5bH0FkLGM+sxtkh5uA04nqxwraZbSaXEzerZVnfl6jtuDOh+sdATxoL5Rkg6VdGt1u0Gpz7tN0gnuPuiKPx+XdIyl0xEfqTTgvLePNugc8fAP3f2Bu1+jNHjxdDPr7Bu8uPp5lvfMH+CVrt/vlCQzu6+Zzaj+Lzr/v033i1e7+86jPKv9gf9W6hc/2LPsLv2imZlSDt+QdFPPuna7pOXqv67RL2JSmBwRyIS7r1U6r17VoWRHSjpN6ZD/xWb2pO4/KHfDDT3v95PUx6jf4erXTbLmTdWhgWdJusXMVisdSvdfvutszIcpzeS8ecjLzZrgfTs7DYMupXeq0k7Rj8ys+5zNr0t6oZkd5LvOHN3tUqUR/VMkzVX6xvoTQ2oZh4Orn/1mNe7s2M1VOkKjs+P63Yle1MwWKc22vUD3ngX7wPpljtSNfe772YDHOvd3r6+HKc1MfeuQ9zhI0v91/buz/uRwlQcAuymTPvPNSofCX2VmNysdLXCJpM93vXdnbpZhf3TP2oM6J+obpXTUwenV73dLutndfzjB6/6n0mkVL69e+27telrF3nCw0jft/U7VWKc04H6wUh/wO0pZrJnoRc3sT5ROA3iCpPv1PNx0v3hDn/sm2y8+pPr9OA3uF3/d5z76RUwKAwdAhtx9k6QLzOyTSiPKT1M6DO+bGr5hH/iZdvdBl/3pt7NxxyRLlbu/1cw+LukPlXbYTpP0ejP7Z3d/Y9d73CrpRUNeatC3Hh2dTvA3eh8ws4OVznk0DR58eIkGXN7K3X9lZhconRP4WEnL3X2ic0pHbdhO36Blh3byZvZkpcNLf6g0Z8WNSocwutKcFE0fdTbsUlSTWV9N6RrUfznkdXp3nn5jwP0AWqqpPtPdrzaz35b0TKU+6Gilfu6tZvZ0d/9p1/Kvl3SvyRwrNw+4fzJ+ovTH4L36xi63u/vldV7U3X9mZhcqDc6YpAvd/WfDnzVydfvFCf/wrebF+KzSt/hnKA0s36V0yselyrhfnMQ62fl5udLcQJNFv4hJYeAAyJi7u5mtUNoJ6sz63zkcsd9OwigOz6zN3W+Q9CFJH6pmYf6apDeY2XuqUx5+oDQp1HJPM/jujs7AQr9TGk5R6jBPVzpvs9c7lI5IGHZd7I8rTW61UNKf7WaNe6JzycjHdv3e8ZjqZ+ebiA3VzycoZTvIi5R2hp7l7ju/qTCz/dX8tyqj8AOlb1iWdJ+eMYHO0SgTDVQBaJkm+syqT/tCdZOZvVLSvyr1Oe9WbKMn+8d7rW993f3XZvZ9TXy63+74uNKpe1K6XN/edr3SoMxhkq7peazTL3b6tg1KkwU+TsMnUX6p0kDB0e6+80uSQiYGvFVpH+hBNQeK6BcxKU2PqgGQZGbHWtclmbru309xPtq1kuTuv1CaaGlRdT5bZ9m5Spcr3Guqc+Z3OcyvOrew821954/TC5S2N/804HUmc5jmd5Um9lnY89z7KH0jstbdP+bun++9KR1eOa/6Br4vd79O6duHv1X6NmJvu0hph/H13Zma2UOVBkY2KU5N+LzSYaNvN7MH9b5Q13rR+Xai91ubN6uM7f8FStdr73vEwYD1aqHSpFrfGmNdAMYolz7Tui592+U71c/OQMXXlObaeZOZ9Ttibr+eKzVsl3Rgd62TsFTSYf36gz10uaS3VbcrRvzak3Fh9fOsnv+7eZKOl/RNd+98S/6f1c9/tHTpyF309Iuurj6weuytI659r6sG0P9D0lPM7I/7LTNgPqqFkja7+4Y+jwE7ccQBkIf3KV1a6iKlQ6/vkPRIpW+MH600Ud/aruU/rPQt+lerQwkfpvRtwDpJA/84HoOjJZ1rZl9QGu3frjRh1WmSVnQ6IXf/vJl9QtKrzeyJSpMa3aZ0dYCnKo12D/3mpzqd4IuSTjCzae6+o3roOKWs/n3I07+gNHv2qZL+Z9BC7t478dBAZuaSNrn7nEk+5SFmNmjH5BPuvsHM3q10qcpvmNlnleaF+DOly1C+uHOYorv/yMzOVPpWa211msUmpW/YTlCaDHK10mXBXqd0CbFzlQYbjlX6RmbQfA9t8gGl9ry7msthidLg0m8pTeR1l9I6KmnnzuGzJF26B0e+AGheLn3m981sudIli2+W9FClbfbdSqeDyd1vN7OXKf0RvKE6te+HSpMDH6o0se/zlP74l9IEdn8k6cNm9m2lP3SXTDBh8X8pXW73D5QuuTsS1R+i75jMsmY2R+nb/2XuftQk3+JRQ/rF97n7183sc5JOVBpM+bLSYPGrlLbvr+2q9b8tXdL4jUoT5n5WacDoYEl/rHTqylalgfcXSFpS9Z33UxpAeoDK8BalI24+V2W3XGl9nK10WcpVquYGkSQzO0DpNNO9PfElWoiBAyAPf6n0B9/TlTq0GUqzNV+jdJ7aeT3Lv0tpJumXKl3251qlP4qfpL07cLBG0herGl6sdFj8/0r6R6VJlXZy91eY2ZVKO1VnKc18/2Olb2fOmuT7naPU4f2RqsNCldqtqo6+3H2dmV0n6UQze11nRuXd1fXt0E01njZT0t8PeOxypSsMvNHMfqg018I7lTr7FZJe5O5XdT/B3c8xs+uVzpt9rdI1w29W+lbo/6plvmVmL1D6tujvleY3uFxpErFv1Kg9S+7+SzP7Q6W8Xqp0tIiUcvhv7XqFDUn6faVBhVfutSIBjEMufeZ7lP4Ye231+luU/lD7J3ffOUmfu3+tOuLtTUrz7TxEaWK765Uuqdd9GP77lQbS/1hpcOM+SgOgAwcO3P9fe2cev9dw/fH3yb5JIhtBhISIJN98k0jt+hNJUKHSUCWWUrSltraqaG1Fq0oXilK0ilaVKq0tSCmaWoJELCGWSEhCIrtYM78/zrmeeW7uvc/zXZJvxPm8Xvf1PPfO3JkzZ86cOXNmueEhEXneytdojoM6oj794lbk94tXo18COBi1Ew5H+b0ceAg4I+UcIoRwqohMAY5DnfDN0P7wLuzsphDCTdaHfxe4CK2Hf6J1s4DPOEIIi0VkJ/TwxwPQdvIxMBs98+Pq1Cv7oU6TK9cknY7PJpJvmjocDsdnAiJyD9A+hLBLE9LwZeB2YGQIYWKl+I61AyJyG+o4GB6883M4HOsQRORA4AZgYFMsOReRE9CB/UDb+uf4DEBEJqOrJ8c1NS2OtR/uOHA4HJ8piMhAdKXDXiGECU1Ew2XApiGEfZoif0fdISJD0FmrESGEh5qaHofD4WhsiMgk4LUQQtEXjFZX3ncCr4YQjl/TeTvqBxEZi65QGRhCKDpo2eEA3HHgcDgcDofD4XA4HA6HowDrwqnaDofD4XA4HA6Hw+FwOFYT3HHgcDgcDofD4XA4HA6HIxfuOHA4HA6Hw+FwOBwOh8ORC3ccOBwOh8PhcDgcDofD4ciFOw4cDofD4XA4HA6Hw+Fw5MIdBw6Hw+FwOBwOh8PhcDhy4Y4Dh8PhcDgcDofD4XA4HLlwx4HD4XA4HA6Hw+FwOByOXLjjwOFwOBwOh8PhcDgcDkcu3HHgcDgcDofD4XA4HA6HIxfuOHA4HA6Hw+FwOBwOh8ORC3ccOBwOh8PhcDgcDofD4ciFOw4cDofD4XA4HA6Hw+Fw5MIdBw6Hw+FwOBwOh8PhcDhy4Y4Dh8PhcDgcDofD4XA4HLlwx4HD4XA4HA6Hw+FwOByOXLjjwNFgiEhrEflIRNav5/s/E5GzGpuuzztEZKKI7N/UdKxOiMjDIjIuJ2yQiLzUSPm8KiLDGyMtR3Uo4nlRvX8eICLnisglBeGzRGRII+TxmzrE31BEnhGRng3J15EPEektIi+JSKHtJiLbicizIrJYRA5fQ+Q5GgEi0llE3hGRdlXG31JE/iciiz5LdpQoZopI36amJQ8icq2InNzUdKwO1FXO1maszWURkRYi8oGIdMsJnyUiw9Y0XSka6mTfuuNgHYKIrC8iQUSWich7IvKWiJy0BrIeAMwNISysgsbRIvJM/CyEcFoI4ZzVRt0ahIi0MSdK56amBa2XZ5uaiNWMQcBzWQEhhGkhhH4NzcA6o17A81XEXUW+P48QkaNF5B8NeL8SzwcBL9Q3/XUANeSU3xy4G+aF1wGDgWnVRBSRlsCtwLkhhDkNzDdOd18ReVBElojI0xnhvxGRaSLysYicWCGtHUVkgogsEJF3M8IvE5H3rf9Mrt4WJiKyNBV2TxX0t7cB/MLU8/1FZIqVa5qI7JEKHygi/xSRd0VkoYicCxBCmAnMBXarkPUVwHkhhE4hhD9WorMxISLDReR2EZljg9nbRKRrFN5MRM6y8HnpwW6lOjXevWhp/1VE2q+Jcq0phBAWhRC6hxDeq/KVnwO3hRA6N9SOMofTvSLytsn7MyIyqiFp5iEoeocQXlkd6TcSPhM2VDX9rYg8KiJjk/t6yNlagzVZFhE5X0R+3YAktgDeDSHMz0i7M7AR8GID0m8Q6mLfJnDHwbqFIcA7IYQOIYR2wDHAr0Rkk9Wcbw0wtcq4tXWI+1nEIGBOCGFRUxIhIl2AjsDLTUlHEUSkRQPf3xRoB8xoHIpysTXwapWd0mdCvhvK+yrQUD7k8lxEegHtWYtlew2ghvyOvgZ4KYTwQSPkUa3RfBywLIRwawPzTGMJcDbqlChzHIiIAJOB8cB76fAMfAxcjA6qs5x7Q4GDrP9MrpkWtiXwSSpszyro/4PlOyWie2fgarR/7gRcCtwqIq0sfAjwL+Aq1AG0CfDnKM0ngNF5GZruHwTclhPevAq6G4LNUNr7Ab2BDYDzovALgD2Abew6XkR2MtoK61RExqD8OhzoaWmvicmRtRkjgZsbmoiIDADuQmVzY5S3PwLmNTTtjLxWd//TWNiaz0B/ToX+1tpVXez0tRZNUJaG2jIDC96vAd5oYudNXexbRQjBr3XkAr4L3BndbwgEoI/d74MaMEuB/wEDo7j3oQbVE8BiYCLQ1cJGA8+k8poBbG//LwIuiMJ+C7wBLEONgFp7fjZqRL1vYZcDrYGPgM4WpxPwe+Ad4G10BitJd0/UkDgHeBN4FzisgB+dgN8Ar6IG6H+AZlXw4k1gSHT/Y+B39r+/5fudKN1TLWwk8IGVZxnwrD0/EJ0VXwLMAY7JoXcn4FFgITAfuLDasltZ/2DvTQO+BTxZwJux6MBjEWocPAiMtbAWwGnowGwh8BegXfRuEe82Af5p7z0CnAj8w8K2sPxOBmahxnFL4CbgLePZQ0CvKL2XUfmaavy7BWhtYXujA5szgdkZPLkBOCm6391oWmjxD8zhzUao0b0Q+C9wCnBLfeQ7kp8Z9uwFYLeCetkXeAptg3NS9Dezss4yfu0DfAisXx/eR3KQ5PdfYMsovwFWH4uNH+cDv7aw3HoDrgU+AVZY2A/t+S4R/6cC21bL8xSP9kLb0w9RHTEbGBeFvZiKfyxwV05abYCzgJdQeZ4MdLOwGuBeYIHV317RezehcnmnvfdMVP5m6EApkZEZwHZV6Ja2qA5+y9J8Dtgsg+b2wErgMKN7IfDTKPw44KbofkdgEtp+pgG7VCo/sJ7V4XoWb7zF+UIGPR1NtoZHzy5AqOxuiQAAIABJREFU9dGfLWxGXO66XqjuPj4nrI/xo1OVaf0JuDj1rJnV1eY573wNeKiONJ+KDsKuBH4TPT8X+Gt0vynab7Sx+yeArxWk+13y28YItN19QqmPPQqYAFxjsvIDtN+9ENUxC4zGlpGuWAwcbzK8wMo/0uR8KVF/XwUffg7cY/83tPd7R+F/x3REpTpF9caB0f0JwN0FeQ+3ss9HdcV37XluH0dBH2/hw9D+cqFdf7DnR2P6NspjBbCh3Y+iZF/NJ7JtUjSfR0nPFtkbfVDnykqr60SnHwlMt3wmABtXWU/nEtmPGeE3Ut4f7Uyka9GVMMejdsoyq9fmUdmfNVmYB1xmcpn0T0U6sysqu7Pt3R8V0NgfuB+1HxcB1wEtqqzX1qhjca6FHwzMz8mnvjq+XrJlYZl9NTn9bfReG7TNJXKyDLUVYjmrc5tH5f15e+9uoEcl3mTwcVPUpptn9XtEES9WR1mAHsAdRsMS1IbqaGEPoGOo5ZbXQUV1EdXxAxZ2JyrzF+WU/1jgHuCXVsaXgZ2j8CJ7sA9qQ8+38PuisEaxtXLbWV0i+7V2X6hBdJb974wq2ycBQQdNb6AdaTPU6J4cvTvXBLQPagj+DzjHwn4AXBfF7YAOkDrY/QRgvP1vBnzd0miJzg7EzowngC9F98OAmfZfgIeBX6EzyT2B14B9LPwUVDkejHbKXwdm5PCitZX9MrTjaQ3sYWG5vECN5g+BVlFafwOOtf8HoI6Bky3N/0ONvqSD/AXw8+jdrdCB2lZ2vzGwdQ7Nu6LLg5uhXsCFmKFeVHbj20R0QNsJVVwLgGtz8hmFKultLa2rrAx9LfwqVJn1RAcpdwGnV8G7tuiSq8QwTYzYcy18HNrBnYl2AK2tbsZZfbdDjY3LLH47i38PanBuiHa2R1j46Zb+IVnygCrMEfZ/fyvzrkBzVM4HZfCmLdoZnm7572J5nFkf+bZnh6By1czq8bmCNjwG7fgENcw+pNQh/wQdQG2Mtu9JwKwG8H688XOY8eQsYILF3wDVCQdbOQ8zGTnSwnPrzcLfJpJz4ItoxzzC+HA4OitekecZPDrVwo+2chyOGomtge6UD3jbowPx2py0/onOZm9sMrSH8WIr1Pj8qt2PQjvoZHDxPGog9zf+3Av82MKOQh2xXaysNUZXJd1yHqrDO1ie2wFtM2jeHjVmLkXb+yDUsEkM16sotdfBVo+7ozJ1kN23qFD+HVHd2xw1ah7FBkAZ9BwGTE09u8ve39Z4cA1wdUT/ooJrZEYei4gcHqmwccArdegnpwKHpp71N56+afy5BmgfhV+AGpXvou3s0Ap57IEaZa2Bx4BvpOh9DV0e2gF1LhxnYcMxRxDwCqprj0ulfSbwp4K8j6XcMXEJaix/2eqiNepUvQnVI52AxzHHjNH3ETqT3xJ1QM5DBygd0ZWNVTlq0DbwBvDtiPa/p+LchtktRXWK6pC3E9m1ZycC/87Jewe0DY+1cmyIGdAU93GV+vhZqNNWUAdbkuZlmM1k9wOBt+1/J6MlHgxnOtLQQcwRVdKyF/BY9O6RqHNwS7TtXgbcWmW7OB2V8eOxyaZU+HNYf2r33wFutv/d0fZzo9V5d6urnS38e1aOI60uWqNymdiYeTqzDTpJcQHaT/Q2edoxpwzbWL23QHXaK8BXq+TlH9F+rDs6uHoZmJiTT311fH1lK7evtvCy/jaD3jGxnGTIWZ3avMnKZNRWaYXqsKuKeJNBU3ery+Mtz1pUv/auwIvGLssW6ORoa6N5EvADC+tgacV9QSW7aTbwDePLeJOHzAlO4Heovk901FnAC6l6y7MHH0HbYHO0newU6clGsbVy5akukf1auy/UIFpmje8Va8w9LewpYP8obi/UwG5OydiODf1TsFkrtDP4fhS2I+UDtDlkDMIsbDfgv/a/Oeoh7xmFHwHcYf+/jHqlJQq/hlLncgM2i2v3XyDfcXAcarA1ywgr4sVobKVAFP4SpQ7wPKLZS+Pdh5RWMkzAvJJ2v4cpkqH1qM//ALtXKjuqXGdgnU9Ex0k56U4Gvh7djzC5EVR5L8FWgFj4Qegeykq8Ox54MJXXTOAA+38OOUZeFP8bwJ+jMi7FVr7Ys8sxbzE6S3RJDk9aWb10RRXyHGBMFTw/Pk2j1f/Y+sh3Rvw+wFt1kIE30CW/3Y0XW0Rh5ySyWFfeG0/mAaOiZz2Bhfb/YspnO5qjne22OXTG9baR8SGWx6eAo1LvvI8a1HXl+Y1ETjGT29jx9To2yEQHKjfkpLO3xW2fEfZ3dI94/Gw62j6SVVKxF/8ySo6Dn6GG00ap9yvplr8A1wNdKsjE0ahRG/P3PkpOnUnA3lE5Tk7x6iNg8wrl/5alMxHtR1oV0HMjcH7q2exE9uz+B5jjoK4XpdnnjjnhP6HKGRNKemFQ6vlGqLOoGdreXqZ8tdtQ1KhsgTpKPgb65eTRFzXONrb0lgPDovBextuVdl2H9XnGp4AOLNqi/ccnQP/o/T+RM1tt4Z86juz+IaKBucnw20ROKeAMbHIA1RX/isJGok6zZEVES6O7kpx2R53311PqH/8DnJCK9xSRYyWvTu1Z2unwS3KcKJb3dzKeV+rjcvt41MnwMbAfUfuzeI8C+0X344H77f9WaB+7K5F9k0P3zEReimix+9MpDdhao/1cvPJnF2xipoq20drk72mTwSmUBmttrdxxX3wVJZ03Cp2saBOFP0dJt11H1J9EcpmsFMvTmScAj6SeXQmcWGWZ/gR8s4p6rUGdk51S5ft1Trr11fF1li0q99Wr9LcZ9J4OXFkgZ1W3eXSG/j0i/Yc6a54u4k0GTb8k1TejDvh983jR2GXJoesn2Ao+dKwzvQ51UWY32bMPyHcSPko0aEf7jJVEztFU/DcSvqNt/QRSfTONaGvlXX7GwToCEWmNzlL3DyF0CSH0DSEcFUKYYwcT1aJeuQTd0PMQPkGV5nMhhPgwrR5oYwM1mqZEYbXJvZ0U2gU1qhGRbexgpNkishhdSpOcbL8Vug92TlZa6KzY7cGkOaIziT8YnSFLMIj8fb77oIbqyvhhFbwoK6sdvNSH0h6lLBpejPKJywOqQH8H3GwHOn07i1gRaS4iP7JDod4VkUWohzHhXVHZx6BGzydR+AZk7E8WkQ1RT2l8kE4XtP4DOoPRBnjdDp9ahHbUS6rg3RhW3VfbJaKjFvW+x/SMFJH7k4O0UMdAUuYaVMktiF6J5bKIJwOAefbucGBlCOHOND8yMAZdOhejR1KGusq3iGwhIjeKnh69GJWjzC892EFqvxCR6RHvN0AHdyNRp0h8nkPM27ryfjhqtNwS5fUCajwl6cUy0hk1Jp8zWovqbQgwLZFH0VP2hwIXJ3nZOyvRDq2Q5xmoScXviA7oEjl5AhgmIt3RjvWMnHT2AW4MISyPH4qeWL8ncEKK3l6owTQQWBBCeDx6LZa9i9EZ5WdEv/6wqz2vpFtOt3K8LCJ3i8jggvKn23s34B3b/zmIEu9GU16PnVAn0Ny88hsGG20boIb3hzm0gM4CfiqXtse+JzqLm6BIV1fCEHT2eUlBeKXzDWI6PiF1GFUI4a0QwvQQwsoQwkvoKozaKPzpEMK7IYSPQwh/QnXQoHTidtDUzcDRIYQ3USdEK0rtpg+6EuFcez4U+BK6sgX0fIBHQggXhRBWhBDuRQ3VvvZ+C1RHFx3MOITyPmgw5W1/NDpDtiJ6FveztaheSzAAHQC/b/f90SXcqxwwGfGhHyXH02FR/7gp2jaSeF3Q9vRgRhnSdVr2ruH/LI90/j0tjWszyMvt4yw8t4+3tnIQ6th5U0R+KXogsth7mXZSCGE6Oij/jeV7hr2Tpjs5LC058LeSvRHXdQ16DseTUfy4XgsRQvgghPCLEMJQVNfNRbdOJWnPTfXFsa0zmEhG7LyOLSm1+VX6fsr5laczx6C6PNbDB6NOmFUgIseIyGTRA1AXUdpileSXx8sx6FLvxVF4pg1Vgd562495skXlvrqsv81BmU7IkLO6tPmRqO56PKLnHnS5fBFv0hgDjE3V7c7A8gJeNHZZEJGvih62+LbRcCrltkzcpquxmz61TUQPKm5G/kHFgyi3ZboBi0MIH1ewB0Hbwb7AWyJyjYh0WQ22VibccbDuYBDa4GZnhPUA3k8Zf/uiS11Aldk7SYDo4UljgbvMUOlHuWCNplwRvhhCSL4kMAH1LvcLIXRClWRyEFUtqx5KFXc+3Skpn0QhfBF41Dqi/pQbE0Mz0kvQhVJjjlGJF+lTdEeiy8ETo6I2jwZrtB0xJwqAdQjnhRC2RJcVXWHGUhpnot7NfdBZ8h1QBfJ6FWXvhs4gYXRsjcpD1oEs3YD3Uh3kPlHcLuh+3M7R1TGE8HUq864buiw8oWNX1EObKOFadLVDEt4P3QP9U3R5WmdUFpJypeWyE1ofdxtP+lFe/zFPYrnKk4UspHm5BzqYe7Wu8m2G4X0of2os/mXky+zlRusIYH2043zB+J3mbXN0wJHUW514b/lMTtVz5xDC5lnpoTLyaghheRX1lm7nXdCOulMqr3ZBD/DL5XmaQaKn9yfbCBKMQ1d9JHX8BOocOxNdBZEebMR0ZclFW7t6ZtD7crp8Vs+fPgshzA8hfBMdPN+PLsmFCrolhPBaCOEgdNZhNuUHysWoobxd9EVXEDxkvytDCDNFpC261DJu62OBp2zQWNQuatAlnm+hezSL0IZyQ74WeDmEED+L9eQOUv51gvSVPsV9CPltpprwdNxpIYSPK8TLdXSIyAZoX5VlDI5AeXeriMxFnQTN0MFiZ3Sp9gMhhLvMCTEFbZfJJyw/IXIsWl/RnZKjY19Ke1OzaGtmtCeO/d6oHogdJel+tgVqUCZ6PN1+0/wdTAG/RQ9/fBg9R+KU1ETA++iqlwRHofxIt/WsOi17V0S2RR0tf80gowvwQco5Eofl9XFQ0McDhBD+FkLYAe2j90cdEb3Reo6/EBDbSYQQrggh1KIrK0+xdNOoRWc4P4jui2yeXPvJEPfPVcPsyPvQmV9I6S7RSYRhlMqXpnMQ6mh413T21pT3/b2JdHyBzuyCbjWI66pDCOGaNM0icgS68u0QlBd90Nn8uF+q1obqiq4mzDzUrr46vgINebJVqa/OsqvTSMfJkrNq23wXSl/xSK5OIYRdK/AmjS7o1p04nfYhhPsLeNGoZRGR3dD+7STU+ZDIQZEtU8luejuKfwA5BxVbG+jIqrZM4vQosgcJIUwMIYxEZa4W3ZLQaLZWEdxxsO5gKDmfpUOF4j0R+YroN0X3R/dBJp/uqQGGikg/EemIesVnoQeeCDrT2An0U0hoZxQ7DhJFuTkqhE8CK0Xke+hSo1jhpL3ssdf5cWBfEVnPBuE3otsYnkU7nndDCHNTZZ5CNp4GDrW0WonI7jbYqsQLicq6Mbq3bqrdd0KN+rgziWnogi4Dbm3xO4jIoSLSyQy6TdClRgsz6B2C1t9sK+v1UbqVyj4d2E9EOorIZujyvHdCCLFCSjBTSZP9jC9Hol75pA4no17gLawMXUVPsqYK3r0AfE1EWovIQHSlxfMhhE9MrnpR3qkORGd5pgCtReRCdE90Ii81wP+Jfh++K3o4zu0hhOdQZZkuY8yT2KCaBvQRkZFWpl4isk0GbxJejrcyDEaV9zQzfusq351Rw/a/wAoROQRdKpbXySezbPPQ/e2xk+EFYEfRFQwd0c64LyV+1pX3zwL9RT8fKSLSztpIxyi9g62ed0KXHybvV6q3ZG9jgleBFtYWmhmNO0rpay9FPE8jWU5+kKW1HTp7e3oU5wl06ex48gffoLz+qoj0sHLuKCLrB53tmA6cKCIt7RoqIv3tvfTAqQ9a76+JSI2IjBJdAdYKNZ6SuEW6ZScR2d6M7Daoky5PTmqAMaIzEhuj7f1CcwZ+qo9t0DQNrcfmIjICrcfTisof5TEZPVRqrIgcVsDHV1C9mKCMP1JaDZfMvk4K5V8nSF/323stRWeZhgLTRGd2m0XpthFdQdULeMHyScJGi36SuJndt7C0hgHP2rvNLayfiAwxeeok+nnAbdCzdpLVNT3s/9boaoTrgq3QE/1cV7ICYCI6Mz7Err+iMzxDzLE1H9hORDa2OtkPPfPhbnv/YWA303ldUJ13awjhFSvLT9D9t1ltA7R9rAilCYRadOl0vPLucWB3q/f1UV0xG7gz0hVxH5c2vNOr6j6FiBwI3I5um7kiI8okVE+1FJEvobN7Z0fv59apvbuX6Gen+1HaspHlHHgV+FhEDrN67W56DAr6uEp9vIgcHOmt9dG2+hzatpuhAwGsbxhKqX0fKboCCrRtf0i2sR6v5qxES3u0D0jCpwC9RWRb06XHoM6LizPyKYOIHChqE3Q0fu2MOg6vTKJEZWuD6ujlofTVkbROjPvixH6Jv85Qi56LEirozMnAUSLS2fqpzURkl5xiDEG3GL2Gys+f0TMmFlXiJarvv2TtrgcqW+3IsKkboOPrK1uV+up0f5uFdJxYzura5p8CRojIsOR90c/mSgXepDEZONbK01xEthKzywp40dhlqUXHOi9aPtei7TNxGqfzqlQX04HDrL/5InqgdNHKyY9R26eZiOyFbhH8SUR3pj0oIuNEZEsRSc5/WN/CGtPWykeow74Gv9beCz0Y78qC8NHWOJahA5nto7DHUY/bK+hy3z9RvtcrObX+P+jS33cp7Se+BjjF/jdDDZ330KWr30JnUJIvJmyKNujlqJevl/1P9uu1QWdzF6LL5C7E9u8Ah2InM9u9oIOXvjnl7YEu0V1k9N5WJS++YHx40mj5dD8pul8wfWL7NOwwL3QJ8D+idDdBvf3JoVr3kX8WxM5oo1+K7ku+ltIpsYVlR72IE+3ZU+jevAlZ+Vj8I1Cv4zx0GfeLlA5WEVTZvWnleJXyPVhFvOuDDtqWocuUfw38PuLdCyk62qKnzr5nfDwJ2ytm4W8D3zRa5ll6rSOe3F3AkweITiZH9yXPQOVtBrBnDm+2QDu0xej+s79Q2kNaJ/m2Z+dbnc5Cl6rOIX+/2zgLX4i2qzsoP0X3ckvrJXSf+3JK7aNOvLfnX4vqci46IEoOaRpuYUtQB96N2GnWVdTbsIjXyaFQI9FOcCnqYb+H0tcLcnmeQfNB6CDrErRtTyP62oHF6YguzTu7gs5shx6ItcDyfpjSPsghlLfdhygdcPoA5Se77w88bP/3MJqWG0//GMlHkW45FDV6l6Py/isyzhVADbH30AM656KOyB9S2iN/BuVnoQw13i5HjaUxlcrPqnp5gNXZDjl8PJJoryoqu/FJ4cOxQzzrchm9IXVtF5UrHRYfKnUJ0f5kdDCbjv81C/uK1ctytJ+7nujrClae+ajsPo8un22RovMrOWV4iGhPP2pM/w7VbYlcxadoCzrYmGf1eyl2BgXwfSqc5YB+weff0f0ZwBWpOIJ+EWS+Xb+ndIp4WR+HOkrfJzoYE92HfHBG3knc5KtCyfW3KM5GaPtZiuqDeK9wpTptYbQuRB3ghfvc0e1G04yGWcDhUfkz+7h0+e1Z3Mf/FW0LS63ex0bxfm90PYj2Wx+ikwht0b5/IdrOJpF/0Oc1lH+FpoiW7YHXUuEHGw1L0dVxW0VhfyT1NZEobDyqlxYbnU/EdYw6wR9FHcp3ozZhovOyZOS3lOu2u1P5fSqXFOvMrsbzBai+L+N5Ks3+6CBtmcnYRZS+2lCJl21Qu2sJOkD9FXagXEY+9dXxDZGtor56lf42g+YfGf+Woo6NXDnLqc+yNo+OA14zemZXU5cZNG1msrQIlbnHKNmhRbxotLKg44T/Gr2PoXL5TBR3T0p6Yu8q6mJbdHD+Ltr+Pj10NaP8pxntN6Pt7nGir09QYA+i50MkX7Sajm6NS95rFFur6EqMDcfnFDaLsRT9lFjunkXHugnRWdRJ6Km3lZbv1jXtSehnaOr8bXfR5cAvBV3e70hB9KyMMSGEfXLC6837jLRaoYPacSGEyZXiNzVEv0f+b/QgyaVNTc+6DhFZDx1Qbx3Ktyc0FT0dUUfqLiF7Nrox8xqBHux36GrOpwZ1Wu66NvDY4XA4HJ9P+FYFRx9gqTsNPh8QXaa/vS2z6k/pRPQGOw1suVob0S0aP0e9+HdUei8HNeQfKPO5g9VZb1t+NhpdznZ+FN6YvE+WwrUU3SJyNbrc+bPgNGiDztKe6U6DNYMQwtIQQq+1ZUAbQlgSQhi+up0Glte/V7fTwPJ51sq0VvDY4XA4HJ9PuOPAUUPqhGnHOo2e6DKw5ejBfn9Blz01Bk5AlxTOQA+L2juE8FE903K5LMdQdBvKInSf+uEhhP9F4Y3Ge9FD9c62vKaiS+MPqTflawgicgK6xPtVdLuOw+FwOBwOh6OR4FsVHA6Hw+FwOBwOh8PhcOTCVxw4HA6Hw+FwOBwOh8PhyIU7DhyfOYjIMSKyynd8o/BrReTkeqa9nYg8KyKLReTwehO5FsE+BZW7311EHhaRsY2Qx+0NSSMjzT8X1YGInCsied8Irmte14vISY2RVh3zLSyjQyEifUVkUVPT0VSwM0lmikjfpqalKdDU9S8im4rIUvv8VWOlOUhEXmqs9BpAR6X+4RHR752vThp+JvoZzLzwiaKf/80Lf1hExjUSLd1F5D4RWVhkZ6wOVOKDw+FwNDXcceBYoxCRo0XkHw1JI4RwRQjhyIIoA8j/dmolXAGcF0LoFEL4Yz3TWNtQ6aDBQdSfXwkGo5/haTSEEMYndWDfzU1/D3gwGd9ZricG03AeFCJL9uMyrg0QkUcb6kRaTSisHxF5U0SGrEF6GhU2UFlm1yci8n50//ug6B1CeKWpaW0iVN0+s9qZiNzYQMfgAOrzvesChBCmhRD6NVZ6DUBh/xBC2DmEMHF1EhBCOC2EcE5BlE/7dBE5X0R+nQofROP1BacBL4cQ1s+yM0TkThFZEbXPhfblmQajCj5Ujc+6TnQ4HGsn3HHgWNOoRQ9cy4SItGiEPLYuyqMg7y6oAXJbI9CwNqEG/VzaKhCRTYGW6IFyDc1jdQ68s+SmhkYwFk3m+mek39golP2mhs2m1rB20phLl4h0Q7/H3GRf4bAVAfXuT0MIo0MIHUIIHdDvsB+U3IcQjm48StcMRKR5IydZF7nMamcNbXsDG/j+2ozc/mFtgPXLHdFPwkKqLq0Pa4ceDNsYGAX8rSB8KLBj1D7XDyF82Eh5NwrWBp3ocDjWUYQQ/FpHLkCAH6KG5yLgZqCThbUDPgaORDuTZcBlBWk1A45HZ5GXUvpON8CmwC3oCeazgSOi9y5Avzf9Z6NhBjDEwq4FPgFWWP4/BLaweCcDs4B/WdwjgenAYmACsHGUx+wozdbAxcBcdPB7MDC/oFw7ApOAJVa2Xez5CKPrE6Pt8jXBEws/wZ4tBd4AxtrzR5P/dn8IcE9Ey3kWf5m9v11Omd8GjgGmGD+vBZpb2N7A/6K4Wxu/FwOvAOOqLP87wCD7P8ro+VIGLb8FzrD/XdCvO/zQ7nsC84EOJhcL7fnZqOy+n9QNsB6wEjgKdR5UkucW2EwSsBD9mkQ7CxsEzI3idgWusXqcB/zInncCAtAtijvA4nREHTA3AW8ZPQ8BvSrI/sIorY1Rg3Uh8CZwTBT2beBO4DLj0VvA6ILy5slUO+Ai4HX0KwyXWr22sbgrjb5lwCYZ6Wa+H4V/w/JdZPX2OvCFSvJs9z+2d5ehOmq3KOwW4NsZ9GxBebtdYHXdCfg9KpdvA+cW8OrmqMzLrI6Ps7BdgEesTqYC20bvPYK2wcdR2exOQfupUod3tDrok3p+FPCPSOd9BHwLeMnq7bvogOa/dn9D6v2x6Fc5FlucLSvJSgZt+0ZpzAFOStE3AW03C4EfUNDmMtLeyfh1pdHwDuU6tKz+yekfyG5nM6xOk/rd0eIejeqwxcDdQI8o/e2Bx9B+4o92HZdD+40pXuwMvFhFW7wheQ/YE3gaOAdt++8Ch0VpdLb4C1AH7WHAMwVy9FtKfcNkoLYgblH/MCrOB+1njzdalwF/T+JmpHsycE2kf2cBV9h9G1Rv9qYkz50jPfsHVM9NQ+X8SQt7wOpyueV/ENqHPQuciersMt5l0JWpZ4FWVv5EVp7NeLcH8CHQqsr2/AhwFtrmlqFtoAf6ZaMlwBNA91S7TvhwE6pr7zTZeYZSf7IlUd9hz+4HDiRfJ+bqbqAbap/Ms7wmk9NW/fLLr8/31eQE+NWIlalG7IPoAKwNcDtwsYVtixqkvwTaW+fyIRmDA4t/KfAfoB86sNgBHeh1Rw2S49GBUq111L3tvbuA1yy/ZqgheXWU7tvYYNPux1kHd6bR3Bo1CqdZ59gcHSzdavG7xB03atD93ejaCDVSJ+aUaTBq+OyOOlkOsvsWFn4s8NcC/jY6T1DD7FlKhm9fYDOjbymweZT/L4AL7f9RwH2WfzN01qh7Bs0boobQLajBsglqXH3Nwk8HrrL/PdEB6SGW5s5GQ48K5d8oqRPUWJwG9Mvh4blRGU5FP7l4gd2fBfwqkouHoveeIHJEoA6glajTqBp5vgq4x8rY3urkdAsbD0yw/21QA/oCoC1q2L5BabAxC/hilO5twPfsf1eju51dfydyZpAt+w/Z/3boIPBkVIa2Qgffgy38clSm9jDenwXcn1PWTJmy//cA16EDkW6ogTjewsYAj1XQMUXvH271OYiS/vnYylYoz3Z/iKXZDDgFeC4KeympgwyavkPUbi2vh4FfWd490fa3TxU69HvooKgL8EXUkB5hNB0OvBTlsRgdGCSDn8L2U6UO/6KlK6nnlwDn2P9hqOz/HJXRfdEBws3ABujAaFkkO+NRp+owVJ+eRUnec2Ulg7YxaDsTK9uHlHTDJVbWL1vZW1PQ5jLS/jbaDxxn7x6ADg7bp+ufgv4hp50NBOal8jsdld0tUL11NSU9mDgD96Hk/PiQqN2n0noOGJGSx5uraIvRmz4gAAAPB0lEQVRTk/dQeV+BOr5bAF8HZkSy9hDwG+PjAHTgd30OPc3s/cSZeSlwZ07cSv3D94Dr7H93i3sjpX7vbWDnnLSPjPhwIKobbrL7rwO3RfI8MyrrRNTx0QldCbYAuNbCO6CD6/apulyBtrsy3mXQVEnPDkjLSur9PS3/uWhfcBGR4zQVN9ER9xqfk3b5iJW5NfA/4IQ0H+z+eVQX9Tda7wV+bGFfJeof7dk7wIAsnViF7r4B/cxvK8srU9b98ssvv5qcAL8aqSLVYFwMbBQ9O5TSwOQo1NiSKDxvVrHGOtONMsJ+yaqzWfcC+9r/2cABUdgPKA2SN0K/Cd88Cj8H+Hd03xqdzRoePdslMixGAFNTdHaK4l4F/DqHR38HTo7uxYyAzaN38wzb1cWTb6ED1S1S72+JzkjE9TUBOMT+/wx1HKxCTyqd0UZ3x+jZ77EZWHQG5LioHL9Nvf+K8byo/HtYvJuAf8Z5ZcT9PjoIboE6eY4Gfmf3bwB9I7m4xP43N7npGaWT8K0aea41XnaOnh1EyXD9GXCR/T8BeCT1/pXAifb/buBb9n9bdHVP65yyfgP4cwXZvyTK91+p9x/AZl1RY/OUKOyr5DsO8mTqy+hsU4vo2WmUnDWnA1cW1F3u+1Z/c4kGEaj+ebkaec7Iqw/wlv1vizog1suJexVwWorOZ1N5XYMNvAvKdyI6q9fV7p8CjkrFeR8d0GyODt5jR0hu+ynKNxX/BOA/Gc8fwlYvAEegg87kc8p9jT8bRvFnoQORluggeFQU1pPSap5MWamS1jcwB6HRd1a1bS4jrcuwthI9W44Omj6tfyr3D1ntbDxwb3Tfw+L0i57tADxt/29NlWUjdMC8fgbdCW1dU/KYDPDy2mIr1BmRyNoNRKvcgC9Qchzsg+rKlil5OCVNTw5vdwP+mxNWqX+4jpJjdBQ6iG8TxX2OfMfBfsBd9n8SOqBPVsw9RslpcgRwR1TWGan6m0BpZcaOwPRUPn/B9Giadzntq0jPfupEznm/L+oobAYMN34cmRN3c7Lb5f7R/fURf2M+JKsP4hVOl0VydT5waUpG36c0CZKlE18nX/dPQh2R7fPK7pdffvkVQvAzDtYh7IIurXsretYNNbJAZ9vvDCHoiFlkc3SG582MtPa2uG9lhI0BxorIouRCZ5+W217EnujMUoJBlPZPDkEPmPokCq+lfD9hDfBJCOHJnHLUooZYQst9IYTFUdwNyN9rPxqID83qhA5K50b0TUm/ZFhdPLkedQA8KCKTRWQ/ez4UdZCE6L247Bejs6jP2InSu+bQXYMaQkuiZ93Q2QkoP3Rsd8r5AzqLPqdC+QejM0Ej0b3ZSzLiJFhocfdDl67PANZHZ02nhNLhb3FZtwKWhRDmROkMRg3AauR5L3QG/PWofq5EBzZpHowBhqXq8mDUKQHqfBtg/38GnB1C+MBoGCki94vIHHvvcnR2C/JlPyljFu9jua9BnTIJYhlKI0+mxqAzX/Ojsp2OzhQnNObJf6X3h6MD2Uei+F0p8bVQnkVkCzvAbqaILEYHxgnvBgGzQghLyUaa7t2B21N5xbxcBSJyHDpDOiqEsEBEehrNF6dkYSVqoNeiy6dfS+Wb136qRS3qvEhjMKUy1qIDsqR8A4yWuVaW9VCd8xJaL92BW6IyvIAOFiFfVsogIu1F5BciMj1KZwN0MJLQF+vxSm0ujRp01jvJrwM6KJ9Pef1X6h+y2llaPkaiA/fHI9ruARbbORVfimmxcs4OISzMoXtuCGFB9Cxu13n8TWa2k/cGk9++x6C67qMovAs5/ZyIbCMit4vIbGtL/6LUlrLoL+of4rIMRp2V71s+rVCH4PMiskN0WODrFn8h0EFEtgU+QFerrS8iw9Bl8P/OyGMM6lyK6y/u07N0VBHv0qikZwt1YAjhlRDCzBDCSpPB+4z+LCQ6ImmXHdF2eXcUZwC6EiOJn+Q9EFgQQng8p1xDU3TWoiu0Ps4pRyXdfwy6zWqWiPxNRHrn8cDhcHy+4Y6DdQfd0RUHMfZFZypBO5ano7Ch6EAtsCq6UDIss8K2CyF0jq72IYT7LY+XQwjLovhDKRnCWUZxLbpkri7lSDrEbuhSSQBEpCs6u7LKIVYi0hYdsMZpjwWeCiGsMINxEPlGw2rhSQjhvRDCyUAvdHnmHyxO2ZchRGQwusTwRXtvfgjhm6ghcj+6VDgLNZSMQESkE7ArcLcZfv0o8auM9+aMeB+d7Soqfw267PxfwLV2yF4eFqH1cCK69HaJletYdEltgries+SmJvWskjz/JlU/HUMIX4/SmhrF/WoqbocQQvJZrmnAABEZhfL+TwAi0g/dI/pTdItKZ6O/kuwnZUzzfgt0GfUT5hRpge7pjsubNcAskqkuwPdTZVsvhHBmAY0xit6PBxsJxlGS4Vx5Nnm5D23jNSGETujsWkJL0cGISbuN6U7zsjO6BeDRnDSOQWeGR4YQ5kdlnR/06ypxeduZoyitt7Ly3ZVS+6kWqwxczIhvQekA03Q9DUndDwZeNZ3TBZicKkPnEMLmUCgraVxuaY1AHX0HAS+EED40+lpSGgBB5TaXRpmeAvZHB13zKa//avqHrHYWP+uCDk5j2jqFEHZFl7K3JepX0G0TeQcjpuW6K7rSYyoU8jd2mrVCV1ak++eE5m7oFpgkj77kHABssj4BXSnQz9rSPzN4kqCof2hp+cR6OKYxORvm3RDCpFA6LHAzC090/Xcp1/XfIV/Xp/v0rS2fqVHcZ6LwpA9L9wV55c3Vsxm0FML01gDynRRpuatFV0Ist/eb2/txH5HZ51le8bP0l6NGYzzK0YmFuj+E8EwI4cvoNsl26MpAh8PhWAXuOFh38ASwg+j3rjuIyE9QT/21Fp450MpJ62lgLxHZTESaiUitnVwMaiwfKyLtRKS5iGwlIttY2GDKO7vWlBseyX78JLwjalDFHeAUoLeIbCsirc2oH43OsEN55zod+JKIbCgiPdBBXDsyTtoPIaxAB30HG90j0Bnj0yzKVsCKEMLsNcUTEeklImNFpD06MNg4iivoiojEGLwUPXDrIxGpEZFRllYrdABbZBjuJiLdRFc//AH4WwjhZUqzXslM2uPAeCvDEHTf7+k2+1NU/mQ29NtofZ6RQwvoLNQOwMchhKdRY7LWynCflbcjutc2qccuxo90uWL5LZLnyeiKkC0s/a4iMsb+d7a8X4jiHiUinUWxmYjsEqWVrDj4acQb0BmiJUZDaxG5EN3KkNRLluzHZXwcOEBEWtnA4C/o9okF2IqIEMLKSuWtIFOTUfnvaXF7isjo6PUyGjNQ9P50oJ+IDBednT4PXVacfKIzV57RwcRm6AFiK0TkEPS8kITuok/xtbUrpvtxYF8RWc9ovRFdArxKGiLyTXRf/agQQjxwfRVoISKHmry3FpEdRWQTC88aoBa1n4oQ/brHwIx0aylfrTGYYsdBrCOfBfqLfs5UTEftLiIdK8hKGkNQHTAP2I5yx04tq8pnbpvLKHcvdBvCgSLSQkS2R9vXqVF5k7qr1D9kyXD62VPACNGZb4wX+4qImLPlLeBwo+UrqJMzT/4EPUsAEWmDOliWhxBmVuBvXEdbA+8mM9OGuH2/AHzF5HlTtJ9bEkLIWl2VOBmfBFaKyPfQ1V316R+2Rp1niTMtLXdFOhdU128KbAPcgerHDdGtbTdE8eLVNNOB/axONrOyvhO1zXRdDkiFV6KrSM9CgeNARIaKSD9rR93RLXbNUSdNFio5+LYClobSKr6YD2le90FlLVnhFOvTnVDHZ+JcydKJubpbRPYQkcGiDod2qPOmyIHscDg+zwhrwX4JvxrnQr3Ec1BP/9+wPeHoYG455aef/4v8vXlC6UsFS1EjZBML2wxdarcINQweA3aysGuAU6N0hqNLTJP7YejS9OXoDPMu6KxVOv+D0b3jS9HZk63seQt0Bm8Du2+DnluwBB2A/Qo7vCynXEPRDnQ52kGPicIOJDprYU3wxOh5El0G/46VJTk1eXPUWJ2C7rn9JXYYFmp4TbNyzEUPiOycQXMzdC/vocbPOehhTsnBkodie1CjPB+08r1E+anmmeXPqJMN0b3P++XwcQi6X3g/u9/A7o+N4pTJBWp8TrXy/pz6yfP56DaGZeig8Mwor+ejuF3RE68XWH1OofxLAG3RLRGTUnm0RU+/fs/q5iTKv5hQKPuW7x3obNgb6GFpyR72M4DfpeJ+RMbZChVkqjW6XPxttM28iJ3XYOE/sjIvJTo3JAqv9P6Z9v5MdFbxA6B/JXm28PMt31noGSBzKH055QGiM0Iy6LrC6Jkd6YXr0LY4F7iQnFPQjd7kax3JdaiFjUQHy0uNl/dgX9RAzy7YLpVWUfsZhZ51knkCvcUZlFWvVv/JafRZsj8D2D66vxL7Eojdf83qapnx41Z0wJMrKxm0jbM6WYjqtDso7Tv/lL5q2lxG2nuhev5KSl9r+XIUXlb/5PQPWe3Mnh1u5VuGrmgB3ev+mj2bHdOPLuuehcr5LegBduNzaO+MrmR5Ae0DzgQerqItPkDpAMJDKf+6iKDynJz30tXiL0Oda2ehW/Sy6GmGDv7fMz58C9VX9e0f7rb/iZ6P9+v/luKvlXRGdfv3o2crKD8QtUye0UHrRCv/U+he/QlR/D0jmdo7pjGLdxk0FenZDVGd1TLn3WPsneXoFp3LyDiQOIr/CuXt8mrKbYGDknrM4MMDwIFR3P0TubL7I40Pj6Jn5UxFV0zl6cRc3Y3q25lRuWLdMRE7vNEvv/zyK4TwqcJ0OBwOh6NRILp1Yyp62FZVM+6OzydE5IfooasnNjUtnwWIyE3AEyGEiytGdjgcDoejEeFbFRwOh8PR2NgaXcnhTgNHJdRQ2irkSEFE9rJtCm1E5ARgJ3TVh8PhcDgcaxTuOHA4HA5HY2NrSucbOBxFqKH8YEVHOQ5AtxHMRpfqjw4h5B1U63A4HA7HaoNvVXA4HA6Hw+FwOBwOh8ORC19x4HA4HA6Hw+FwOBwOhyMX7jhwOBwOh8PhcDgcDofDkQt3HDgcDofD4XA4HA6Hw+HIhTsOHA6Hw+FwOBwOh8PhcOTCHQcOh8PhcDgcDofD4XA4cuGOA4fD4XA4HA6Hw+FwOBy5+H86O7nI7op9cwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1152x720 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(3, 2, figsize=(16,10), sharex='col', sharey=True, gridspec_kw={'wspace':0.05, 'hspace':0.07})\n",
    "\n",
    "min_h = 3\n",
    "max_h = 10\n",
    "\n",
    "axs[0,0] = plot_hist(axs[0,0], bins, sum_counts_no_dst, min_h, max_h)\n",
    "axs[1,0] = plot_hist(axs[1,0], bins, sum_counts, min_h, max_h)\n",
    "axs[2,0] = plot_hist(axs[2,0], bins, sum_counts_all_dst, min_h, max_h)\n",
    "\n",
    "min_h = 15\n",
    "max_h = 22\n",
    "axs[0,1] = plot_hist(axs[0,1], bins, set_sum_counts_no_dst, min_h, max_h)\n",
    "axs[1,1] = plot_hist(axs[1,1], bins, set_sum_counts, min_h, max_h)\n",
    "axs[2,1] = plot_hist(axs[2,1], bins, set_sum_counts_all_dst, min_h, max_h)\n",
    "\n",
    "axs[0,0].set_ylabel(\"Never DST\", fontsize=18, rotation=0, ha='right')\n",
    "axs[1,0].set_ylabel(\"Normal DST\", fontsize=18, rotation=0, ha='right')\n",
    "axs[2,0].set_ylabel(\"Always DST\", fontsize=18, rotation=0, ha='right')\n",
    "\n",
    "axs[2,0].set_xlabel(\"Sunrise (AM, Local Time)\", fontsize=18)\n",
    "axs[2,1].set_xlabel(\"Sunset (PM, Local Time)\", fontsize=18)\n",
    "\n",
    "axs[2,1].set_xlim(15.75*60, 22.25*60)\n",
    "axs[2,0].set_xlim(3.75*60, 10.25*60)\n",
    "\n",
    "plt.suptitle(\"Population-Weighted Distribution of Sunrise/Sunset Times in U.S.\", fontsize=24,y=0.95)\n",
    "plt.savefig('DST Graph v2.jpg')\n",
    "\n",
    "plt.figtext(0.121, 0.01, \n",
    "            \"Population counts and geographic data aggregated by census block (n=11,155,486) from 2010 census info. Sunrise and sunset times estimated based \\n\"\n",
    "            +\"on centroid of census block with elevation set equal to zero. Times are plotted using a bin-width of 5 minutes.\", fontsize=12,style='italic')\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}