11_projektna.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "65603bb9-c79a-479b-9235-b05334cea3f3",
   "metadata": {},
   "source": [
    "# Пример скупа података за пројектно учење"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7fca735-b0cf-4787-8f16-375fda21304a",
   "metadata": {},
   "source": [
    "Идеја пројектног учења је да оно што сте научили примените на конкретан проблем и онда пустите исти тај проблем да вас још нешто научи. При томе, наравно, нико не очекује да решавање проблема буде праволинијско. Има детаља којих ћете морати да се подсетите, док ћете неке трикове \"успут\" морати да научите. Тражење сличних примера и решења је увек дозвољено и врло пожељно."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38a0f19b-1d5a-49b7-92ec-473d7f430ccb",
   "metadata": {},
   "source": [
    "## Пројектни задатак – Квалитет ваздуха"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74eaf47a-1e15-4f9c-959c-6ff38d743dbe",
   "metadata": {},
   "source": [
    "Ваш пројектни задатак је да обрадите податке о квалитету ваздуха у Србији који су доступни на [Порталу отворених података](https://data.gov.rs/en/datasets/kvalitet-vazduha/). Оно што нас посебно интересује су различити параметри квалитета ваздуха прикупљени на свих доступним станицама у последњих месец дана. Јасно вам је да се ови подаци стално ажурирају због чега нема смисла да их учитавамо из унапред припремљеног фајла. Табелу са овим подацима морамо да преузмемо директно са веб сајта који објављује последње доступне податке. На овој страни имате више табела (скупова података) у различитим форматима. Осим табеле у којој су тражени подаци за последњих месеца дана ([_Kvalitet vazduha po stanicama_](http://data.sepa.gov.rs/dataset/11104dfd-b110-4b25-b350-9253e9233b6b/resource/0b6a7aa5-9b13-4a7c-8a80-f964e1c67bc3/download/air_quality_by_station.csv)) још две су нам потребне: [_Stanice za merenje kvaliteta vazduha - šifarnik_](http://data.sepa.gov.rs/dataset/11104dfd-b110-4b25-b350-9253e9233b6b/resource/dd7f4e4b-2375-4657-bb91-d541a2759891/download/station.csv) и [_Merene komponente - šifarnik_](http://data.sepa.gov.rs/dataset/11104dfd-b110-4b25-b350-9253e9233b6b/resource/7fa4ab3f-423a-4016-8508-37164b49c087/download/component.csv). Тренутно су ове табеле доступне на локацијама датим у хиперлинку. Могуће је да Портал промени локацију ових фајлова у будућности. Погледајте онда који је \"_Latest URL_\" за одговарајући скуп података и примените га. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c59e357-ad78-429f-8ad7-a2d31bdfa096",
   "metadata": {},
   "source": [
    "Конкретно, изаберите вама најближу станицу која прикупља тражене параметре ваздуха. Изаберите параметар или параметре које желите да анализирате и прикажете. Проверите да ли та станица стварно има податке за тражени период. Издвојте потребне податке из табеле са свим доступним подацима. Прикажите податке као временску серију (време мерења на _x_-оси, параметар на _y_-оси) и као хистограм за вредности изабраног параметра. Покушајте да протумачите резултате и видите колико често је квалитет ваздуха био одличан, добар, прихватљив, загађен или јако загађен. Ово тумачење није једноставно. Можда вам помогне текст [_CAQI, AQI, SAQI… Srbiji treba jasan pristup tumačenju zagađenosti vazduha_](https://balkangreenenergynews.com/rs/caqi-aqi-saqi-srbiji-treba-jasan-pristup-tumacenju-zagadenosti-vazduha/)."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "af44fae1-e258-4d42-a89a-2897c2ccf8ed",
   "metadata": {},
   "source": [
    "Прво увезите библиотеке за рад са табелама и цртање графикона."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "50bc9793-64e5-418e-9385-1a4283658415",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6be2c1a7-8f66-4677-b9cc-137d71d36689",
   "metadata": {},
   "source": [
    "На основу URL адреса које се завршавају са CSV, јасно је да су фајлови које треба учитати управо у овом формату. Оно што се не види из адресе јесте који је сепаратор коришћен за одвајање суседних вредности. Подразумева се да је зарез тај сепаратор, али то у већини европских земаља није згодно јер се децимални бројеви пишу са зарезом. Због тога се као сепаратор често користи знак тачка-зарез. У Србији се користе обе варијанте. Који је сепаратор у ком фајлу коришћен, углавном видимо тек кад учитавање са једним од њих не успе, односно кад добијемо нераздојене вредности. У сваком случају, учитајте све три табеле помоћу функције `pd.read_csv()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f9702284-24ce-46b3-9e61-98cb30ff4e5e",
   "metadata": {},
   "outputs": [],
   "source": [
    "stanice=pd.read_csv('https://data.gov.rs/en/datasets/r/dd7f4e4b-2375-4657-bb91-d541a2759891',sep=\";\")\n",
    "komponente=pd.read_csv('https://data.gov.rs/en/datasets/r/7fa4ab3f-423a-4016-8508-37164b49c087',sep=\";\")\n",
    "podaci=pd.read_csv('https://data.gov.rs/en/datasets/r/0b6a7aa5-9b13-4a7c-8a80-f964e1c67bc3',sep=\",\")"
   ]
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   "execution_count": 3,
   "id": "a5509b2d-f63e-4bae-b0d3-2e850ca94d73",
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   "outputs": [
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       "      <th></th>\n",
       "      <th>date_time</th>\n",
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       "      <th>0</th>\n",
       "      <td>2022-06-24 00:00:00</td>\n",
       "      <td>1</td>\n",
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       "      <td>21.328835</td>\n",
       "      <td>0.273868</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>4.07056</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>2022-06-24 00:00:00</td>\n",
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       "      <td>3.36544</td>\n",
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       "      <td>5</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.173505</td>\n",
       "      <td>23.608374</td>\n",
       "      <td>0.291652</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.82448</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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      "text/plain": [
       "             date_time  station_id        so2       pm10         o3  \\\n",
       "0  2022-06-24 00:00:00           1  10.549119        NaN  57.495900   \n",
       "1  2022-06-24 00:00:00           2   8.010171  18.566102        NaN   \n",
       "2  2022-06-24 00:00:00           3   5.254099        NaN  51.986375   \n",
       "3  2022-06-24 00:00:00           4  18.783293   2.522000        NaN   \n",
       "4  2022-06-24 00:00:00           5  11.628788        NaN        NaN   \n",
       "\n",
       "         no2        nox        co   benzene   toluene        no      pm2_5  \\\n",
       "0        NaN        NaN  0.178599       NaN       NaN       NaN        NaN   \n",
       "1  47.382533  68.781508  0.157805  0.227148  6.058088  13.80288  12.512833   \n",
       "2  15.060899  21.328835  0.273868       NaN       NaN   4.07056        NaN   \n",
       "3   6.210549  11.375379       NaN       NaN       NaN   3.36544   2.115254   \n",
       "4  13.173505  23.608374  0.291652       NaN       NaN   6.82448        NaN   \n",
       "\n",
       "         pm1  \n",
       "0        NaN  \n",
       "1  10.150000  \n",
       "2        NaN  \n",
       "3   1.659932  \n",
       "4        NaN  "
      ]
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   "source": [
    "podaci.head()"
   ]
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   "cell_type": "markdown",
   "id": "893e1d7a-b388-4039-a827-902fa0acb9a5",
   "metadata": {},
   "source": [
    "Примећујемо да су у колони `date_time` датум и време мерења. Судећи по првих пет редова, мерења су бележена на сваких сат времена. Да бисмо ову колону могли да користимо као континуалну независно променљиву, потребно је да буде препозната као тип `datetime`. То се вероватно није догодило. Пајтон при учитавању податка из фајла покушава да препозна тип податка који је заједнички за све елементе сваке појединачне колоне али ће целу колону оставити као објекат, односно колону стрингова уколико има икакву дилему. Проверите помоћу функције `.dtypes` који су типови податка у појединачним колонама табеле."
   ]
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  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8ae6ba6f-1aa6-402a-bd5e-1c5ae244f0d2",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "date_time      object\n",
       "station_id      int64\n",
       "so2           float64\n",
       "pm10          float64\n",
       "o3            float64\n",
       "no2           float64\n",
       "nox           float64\n",
       "co            float64\n",
       "benzene       float64\n",
       "toluene       float64\n",
       "no            float64\n",
       "pm2_5         float64\n",
       "pm1           float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "podaci.dtypes"
   ]
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   "cell_type": "markdown",
   "id": "1fbeb8c2-f41d-4665-b4dc-85bdb8566f54",
   "metadata": {},
   "source": [
    "Функција `.to_datetime()` ће претворити све текстуалне податке у датум и време уколико је то могуће. Урадите то за све вредности у колони `date_time`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "39a60db0-6b32-4d76-aeec-051f25b3119d",
   "metadata": {},
   "outputs": [],
   "source": [
    "podaci.date_time=pd.to_datetime(podaci.date_time)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6d8a965-57e4-4719-931f-28dee4ed51ce",
   "metadata": {},
   "source": [
    "Подаци у табели `stanice` садржи називе станица и њихове географске локације. То су кључни подаци помоћу којих ћемо закључити која је станица најближа. Пошто имена променљивих нису иста у свим табелама потребна нам је вредност `id` из табеле `stanice` која одговара вредности `station_id` у табели `podaci`. У табели `komponente` су пуни називи и јединице мерених компененти. Ови подаци су неопходни да бисмо могли да тумачимо резултате."
   ]
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   "execution_count": 6,
   "id": "24c5ae40-ac64-4829-8797-bfb3d1795c26",
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       "      <td>19.841190</td>\n",
       "      <td>78.0</td>\n",
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       "      <td>urban</td>\n",
       "      <td>residential/commercial</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>Novi Sad</td>\n",
       "      <td>342.0</td>\n",
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       "      <td>RS1008A</td>\n",
       "      <td>RS0031A</td>\n",
       "      <td>1</td>\n",
       "      <td>1008</td>\n",
       "      <td>Novi Sad Liman</td>\n",
       "      <td>2015-06-01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>45.238642</td>\n",
       "      <td>19.835704</td>\n",
       "      <td>81.0</td>\n",
       "      <td>background</td>\n",
       "      <td>urban</td>\n",
       "      <td>residential</td>\n",
       "      <td>urban</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Novi Sad</td>\n",
       "      <td>342.0</td>\n",
       "      <td>Narodnog fronta 45</td>\n",
       "      <td>t</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>RS1009A</td>\n",
       "      <td>RS0001A</td>\n",
       "      <td>1</td>\n",
       "      <td>1009</td>\n",
       "      <td>Beočin Centar</td>\n",
       "      <td>2015-07-02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>45.208386</td>\n",
       "      <td>19.721709</td>\n",
       "      <td>87.0</td>\n",
       "      <td>background</td>\n",
       "      <td>urban</td>\n",
       "      <td>residential/industrial</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Beočin</td>\n",
       "      <td>15.0</td>\n",
       "      <td>Kralja Petra bb</td>\n",
       "      <td>t</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>RS1010A</td>\n",
       "      <td>RS1010A</td>\n",
       "      <td>1</td>\n",
       "      <td>1010</td>\n",
       "      <td>Sremska Mitrovica</td>\n",
       "      <td>2015-07-02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>44.972185</td>\n",
       "      <td>19.609349</td>\n",
       "      <td>82.0</td>\n",
       "      <td>traffic</td>\n",
       "      <td>urban</td>\n",
       "      <td>residential/commercial</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sremska Mitrovica</td>\n",
       "      <td>86.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>t</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id k_eoi_code k_airbase_code  k_network_id k_local_code             k_name  \\\n",
       "0   1    RS1002A        RS0034A             1         0001     Kikinda Centar   \n",
       "1   2    RS1007A        RS0029A             1         1007     Novi Sad SPENS   \n",
       "2   3    RS1008A        RS0031A             1         1008     Novi Sad Liman   \n",
       "3   4    RS1009A        RS0001A             1         1009      Beočin Centar   \n",
       "4   5    RS1010A        RS1010A             1         1010  Sremska Mitrovica   \n",
       "\n",
       "  k_start_date k_stop_date   latitude  longitude  altitude  \\\n",
       "0   2010-02-01         NaN  45.821483  20.454008      78.0   \n",
       "1   2015-06-08         NaN  45.245065  19.841190      78.0   \n",
       "2   2015-06-01         NaN  45.238642  19.835704      81.0   \n",
       "3   2015-07-02         NaN  45.208386  19.721709      87.0   \n",
       "4   2015-07-02         NaN  44.972185  19.609349      82.0   \n",
       "\n",
       "  aq_stationclassification aq_areaclassification          k_char_of_zone  \\\n",
       "0               background                 urban             residential   \n",
       "1                  traffic                 urban  residential/commercial   \n",
       "2               background                 urban             residential   \n",
       "3               background                 urban  residential/industrial   \n",
       "4                  traffic                 urban  residential/commercial   \n",
       "\n",
       "  k_ozone_classification k_main_emission_source             k_city  \\\n",
       "0               suburban            Agriculture            Kikinda   \n",
       "1                    NaN                    NaN           Novi Sad   \n",
       "2                  urban                    NaN          Novi Sad    \n",
       "3                    NaN                    NaN             Beočin   \n",
       "4                    NaN                    NaN  Sremska Mitrovica   \n",
       "\n",
       "   k_city_population         k_street_name k_report  \n",
       "0               38.0        Svetosavska bb        t  \n",
       "1              342.0  Bulevar oslobodjenja        t  \n",
       "2              342.0    Narodnog fronta 45        t  \n",
       "3               15.0       Kralja Petra bb        t  \n",
       "4               86.0                   NaN        t  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stanice.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d354b20d-166a-417b-9cfb-1c7447885615",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Najbliža stanica je Kikinda Centar sa vrednošću id= 1 .\n"
     ]
    }
   ],
   "source": [
    "print(\"Najbliža stanica je\", stanice['k_name'].iloc[0], \"sa vrednošću id=\", stanice['id'][0],\".\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4f50d35-3010-48f3-8f72-34068391080b",
   "metadata": {},
   "source": [
    "Обратите пажњу да неименовани индекс табеле није исти као вредност променљиве која се налази у колони `id`. Да бисмо издвојили, тј. филтрирали табелу `podaci` по тој вредности треба да је препознамо у колони `station_id`."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50e0596c-ed4f-4525-946a-1151230a260d",
   "metadata": {},
   "source": [
    "Сад кад имате све тражене табеле и податке одговарајућег типа, треба да издвојите само оне податке који се тичу вама најближе станице. У примеру који следи узета је станица са индексом 0. Ако ваша најближа станица није \"Кикинда Центар\", унесите прави индекс у приступник тамо где треба."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "ef04edd4-66cd-4231-8e4a-fa205d6f6eee",
   "metadata": {},
   "outputs": [],
   "source": [
    "podaci_najbliza=podaci.loc[podaci['station_id']==1]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9650a1be-03f1-4742-8555-89fbe8cff103",
   "metadata": {},
   "source": [
    "Користите функцију `.head()` да проверите има ли података за тражену станицу у последњих месец дана. Ако их из било којих разлога нема, онда узмите другу најближу станицу."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "861e8a65-4515-4be2-b308-b636d7277508",
   "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>date_time</th>\n",
       "      <th>station_id</th>\n",
       "      <th>so2</th>\n",
       "      <th>pm10</th>\n",
       "      <th>o3</th>\n",
       "      <th>no2</th>\n",
       "      <th>nox</th>\n",
       "      <th>co</th>\n",
       "      <th>benzene</th>\n",
       "      <th>toluene</th>\n",
       "      <th>no</th>\n",
       "      <th>pm2_5</th>\n",
       "      <th>pm1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2022-06-24 00:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>10.549119</td>\n",
       "      <td>NaN</td>\n",
       "      <td>57.495900</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.178599</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>2022-06-24 01:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>9.001029</td>\n",
       "      <td>NaN</td>\n",
       "      <td>46.433625</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.170444</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>2022-06-24 02:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>8.604286</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.881725</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.168619</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>2022-06-24 03:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>8.970360</td>\n",
       "      <td>NaN</td>\n",
       "      <td>29.526000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.158601</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>244</th>\n",
       "      <td>2022-06-24 04:00:00</td>\n",
       "      <td>1</td>\n",
       "      <td>8.687549</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28.728000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.203100</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              date_time  station_id        so2  pm10         o3  no2  nox  \\\n",
       "0   2022-06-24 00:00:00           1  10.549119   NaN  57.495900  NaN  NaN   \n",
       "61  2022-06-24 01:00:00           1   9.001029   NaN  46.433625  NaN  NaN   \n",
       "122 2022-06-24 02:00:00           1   8.604286   NaN  37.881725  NaN  NaN   \n",
       "183 2022-06-24 03:00:00           1   8.970360   NaN  29.526000  NaN  NaN   \n",
       "244 2022-06-24 04:00:00           1   8.687549   NaN  28.728000  NaN  NaN   \n",
       "\n",
       "           co  benzene  toluene  no  pm2_5  pm1  \n",
       "0    0.178599      NaN      NaN NaN    NaN  NaN  \n",
       "61   0.170444      NaN      NaN NaN    NaN  NaN  \n",
       "122  0.168619      NaN      NaN NaN    NaN  NaN  \n",
       "183  0.158601      NaN      NaN NaN    NaN  NaN  \n",
       "244  0.203100      NaN      NaN NaN    NaN  NaN  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "podaci_najbliza.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "34754f32-da12-4522-8154-1bc81c34ead7",
   "metadata": {},
   "source": [
    "Ми ћемо, примера ради, да прикажемо измерене вредности из прве колоне са параметрима квалитета ваздуха, `so2`. Ви изаберите који желите. Иако нам је за цртање овог графика потребна само једна линија кôда, ми ћемо да би побољшали изглед графика употребити још три. Подразумевани график у Џупитеру је прилично узан због чега није погодан за дуже временске серије. Да би график покрио ширину расположивог екрана, ставићемо да су димензије графика 18 пута 6 инча. Друго, временске ознаке садрже велики број карактера па се зато преклапају ако их све хоризонтално испишемо на _x_-оси. То можемо да решимо ако ознаке исписујемо вертикално. Треће, ставићемо мрежу (_grid_) преко графика како бисмо лакше очитавали вредности."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6e7d0c14-74b5-4e05-bb79-83d116bdb5b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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jcKA+Zog9DoiIaAxMIscxEhGlSzKlCpcBuBvAYUEQDmgf+zqAHwP4pyAIHwVQB+A9aTlCoilmIjIOgpIapIjXHDFtGQfSgIwDTlUgIqIxEI1xjAwcEBGl2rCBA0VRtgJI1NDgmtQeDhHpKZbjWqoQVh/TYop9q4uCkLbAgZ5xoJdHhHijR0REY2CUKjDjgIgo5ZLqcUBE40fPOAiMY+AgIMUfxygIAtK1ntd3hMwmERaTgBAzDoiIaAwipQoTfCBERJMQAwdEGUbfiQ/Lyrg1eAppQQqraXBzRCVtGQfqY5oEAWZRZKkCERGNiRY3YKkCEVEaMHBAlGGigwXBcVpMBxNkHKg9DtLzmPqNnUkUYDYJbI5IRERjIrI5IhFR2jBwQJRhondKAqHxCRyEhpiqkO4eB2aTAItJNDIQiIiIRsPEHgdERGnDwAFRhokJHEjjM5IxKMkQBMAsxjZHFNI4jjEm40AUEGbGARERjUFkqsIEHwgR0STEwAFRhpGiVurjNVkhKMmwmkQIwsCpCuqf6ehzYDRHFNWMg/EqyyAioslJ1O5qx6s/EBHRVMLAAVGGick4GK/AQVge1BgRiB5tlfrHjGQcqFMVmHFARERjYWKPAyKitGHggCjDyBORcRCWBzVGBCIZB+m4CQtHZRyY2eOAiIjGyChVYOCAiCjlGDggyjDRO+/jFTgISfKgxogAjNKFdAQOJH0co9bjgFMViIhoLIwsOZYqEBGlHAMHRBkmepE+rqUKcTMO1JuwdGze6AESvcdBmD0OiIhoDCKlChN8IEREkxADB0QZRpIV2LRF/PhlHCiwmIRBH09nqUL0VAWLiRkHREQ0Nvo1S2LkgIgo5Rg4IMowkgI4rCYAQHCcxjEGwjKsZtOgj6ezOaLR48Ck9jgIMeOAiIjGQGRzRKJJa/Opdrx1pmOiD2NKM0/0ARBRLEmW4bSY0IMQAqHx63FgjZNxIIxLxoE6VcE/Tj8rERFNTqY09uUhool134ZTsFtMuHRe0UQfypTFwAFRhpHk6IyDDOlxkIbDiJmqIIoIS+HUPwgREU0ZxlQFxqGJJh1/SDbe4zQxGDggyjCyrMBpVd+a49UcMSTJsFnGdxxj9FQF9jggIqKxErXLGDMOiCafQFiCOU52LI0f9jggyjBhWYbDomYcjNtUhQTjGNNZLxqdcWBhjwMiIhojjmMkmrwCYXncmoZTfAwcEGUYWQFcNjVw4A+OT3PEYFiGNU7gQEhjc8ToqQpmk2gEEoiIiEZD73EgMeOAaNIJhOVxK+Gl+Bg4IMowkqwg224BAHjHK3AgybDE7XGg/qmkI+NA0jMORFhEgRkHREQ0JpEsuQk+ECJKuUBI4r3iBGPggCjDSLICi0mEzSzCGxyfhoHBsAxbvFKF8cg4MAkwmwQjkEBERDRaosBSBaLJKBCWEQrzvT2RGDggyjCSrMAkAi6bGZ5xChyEEvU4SGNzRL00wSSopQqMIhMR0ViZRIGlCkSTjKIoCEosVZhoDBwQZRhJUWASBTgspvErVUgwjlFA+pojxkxV4I0eERGlgCgInKpANMmEJAWKAoTYHHFCMXBAlGFkWQ0cuGwmeAPjEzgISUrcjAPB6HGQ+seMnqogigIklioQEdEYiYLAUgWiSSYQVu+HA8w4mFAMHBBlGElRYBIEOKzjV6qQKONA73GQjsBBTI8DZhwQEVEKmEQBXFsQTS76ePKQJKelYTclh4EDogwjSQpEUYDLaoJvHEoV9LqxuIED7UPpKVWIZByYRI5jJCKisROF9FyziGji6IEDRYncP9L4Y+CAKMNIigKzKMBpNcMzDoGDkFYiYDUJg/4tMlUhjc0RRQEmkRcCIiIaO1FkjwOiySYQitwPs0HixGHggCjDSLKaceC0muAbh1IFfZpB3OaI4zCO0SyKMIkiJFlh+hkREY2JSRAYiCaaZAJRTRE5knHiMHBAlGEkWe1x4LKZxiXjIKidjIcax5iOBb2ecSAK6o0ekJ4ABRERTR1qxsFEHwURpVIwKnAQkMancTgNxsABUYbRxzE6rWZ4A+nPOAgOkXEgpjXjQIZZFCAIAsxamURYZvoZERGNniiAUxWIJpmYjANO4ZowDBwQZRBFUefUmrRSBW9ISnv6fjIZB+nqcWDSHkD/k3EDIiIaC5PAKT1Ek40+jhEAQmHeLE4UBg6IMogxolBQMw4UBfCH0nuC1DMObEP2OEjDVAVJbQIJREoVmHFARERjweaIRJNPIOpemM0RJw4DB0QZxKj71zIOAMCb5gaJenPE+BkH6oI+Hfdg8TIOJntDqzdPtWPdDzfgREvfRB8KEdGkJAoCSxWIJpnoUoUgMw4mDAMHRBlE3yUxxQQO0tsERj8BW8e5VEGSFZi1x9R7HEz2wEEoLKO9P8COwEREaWISBbAEmmhyiS5VYMbBxGHggCiDREYUCnDZzAAAzzhlHIx3c8TojAP9cSZ74EDUfsVMoyUiSg9R4DmWaLIJxoxjZOBgojBwQJRB9BJ/URDgGKeMg8AQzRGFtGYcyEaPA/3Pyd7QKp09I4iIiKUKRJmk1xtKyffhVIXMwMABUQbRmwOaRAEuq5px4A2kN3Cgn4CHyjhIx2SHeD0OwpP8YpDODA4iItJKFXiSJZpw9V1erPnBa9hX1z3m7xVbqpDe+2JKjIEDogyi77iPZ3PEoXscpG+hK8lRUxWmSHNEvWdEukdsEhFNVYIgMDhLlAHa+gOQZAWN3b4xf6+YqQrsEzVhGDggyiB6qYJ5Ipojxs040I8rPRkH4sDAwSRfUAtgxgERUTqZRJaDEWUC/f7Sl4L72NhSBfY4mCgMHBBlEKNUQRj/5ogWbbJBNCGdGQeSEtXjQD0VMeOAiIjGwiQIDBwQZQB9+oEvlIrAQVSpApsjThgGDogyiNEcUYw0R0xFpHYoQSlxc8R0LnTVHgfqY+oPPdl7HKQzEENEROp5drIHoYnOB0bGQUoCB8w4yAQMHBBlED1V3yQCTosaOPCkuTmivliPP1UhnT0O5KgeB6L2OJP7Zo8ZB0RE6WUSmXFAlAn0wEEqSm6D4cg9Y5CBgwnDwAFRBtF3SUyiCLNJhNUswhtKb6mCXh5hjlOqIKZzHKMS6W2gXwzCk3yXSO/pMMl/TCKiCWMSBCN7j4gmjj79wJ+ijIMsu1rCy1KFicPAAVEGMQIH2k6/y2oat3GMFjFxxkE61rnRGQeiMVVhcl8M0hmIISIiQBAmf6NdovNBapsjSsjSen+FJnlZayZj4IAog0QyDtT/d1rNaW+OGJYmJuMgLCmDMg4me/ZZpPSDFz0ionQwiUJaJgER0cgEtQV+SnochGRk2y3q92XGwYRh4IAog8hKpFQBUCcdpLvJk14eED9woGUcpKNUQVaMxzQZpQqT+2IQ+X1O8IEQEU1S7HFAlBlSPY7RYREhCmyOOJEYOCDKIAMzDkyikPa6f2McY5xSBX2hm471fOxUBT3jYHLf7LFUgYgovQRBADOZiSZeaqcqSLCZTbCaRTZHnEAMHBBlED1IoC/YzaIIKc13QGFJgShE+gxEE9LZHFFWoqYqTI3AgQA2RyQiSieTAJYqEGWAVGYcBMMybBYRFpPIUoUJxMABUQaJlCpEmgamPeNAlmGOM4oRiMo4SMMhqBkHA3scTO6bPT0Qw3GMRETpwVIFosygT1Xwpmiqgs0swmoSWaowgRg4IMogkVKFyII63ZMGQmEFljjZBgCgVy+kp8dB1FQFYYqMY0xjIIaIiLRSBZ5kiSacnhngT1GPA6NUgRkHE2bYwIEgCA8JgtAmCMKRqI99VxCERkEQDmj/3ZjewySaGgaOYxyPHgfhTMg4MOm9FCb3zV46AzFERKReP5lxQDTxQimdqiDBalZLFZhxMHGSyTj4C4C3x/n4LxRFWaX992JqD4toaoqXcZDuG6CQpMCSMHCg/pn2HgfMOCAiohRQSxUm+iiIKJDS5ohaqQKbI06oYQMHiqJsBtA1DsdCNOVJ2gJdjGoaGE57c0QZljijGAE15RNIT+AgLHGqAhERpZbA5ohEGSHV4xhtZpPWHJHv74liHsPXflYQhP8AsAfAlxRF6Y73SYIgfBzAxwGgtLQUmzZtGsNDTh1ut5u/q0kk2efzYFtY/XP/PvRVm9Df50NIQlpfCw3NfoSDctzHaPGoJ/2jx44jt+d0Ut8v2Z/V6/Ojva0FmzZ1o82rP84x5PUm9zjno2a39nMePYbs7lMTfDTJ4blo8uFzOrnw+YzV0e6H2xv/mna+4HM6uUzV57OhyQ8A8AbD2Lhxo7EZNVKKosAXDKOlqQF+r4TWkGfCf59T9TkdbeDgdwC+D0DR/rwXwEfifaKiKH8E8EcAWLt2rVJVVTXKh5xaNm3aBP6uJo9kn8/A0RZg316sW7sWy6bl4qHqXejzhVBVdVnaju2Jpn1oDfbFPb7aTg+wZRMWLVqEqjWVSX2/ZH9W09YNqJxWiqqq5Wjo9gKbN2L+gkWoWjd9ZD/AeaSmwwNs3YRFixejavW0iT6cpPBcNPnwOZ1c+HzGerb1ABoDXef174TP6eQyVZ/Px+r3AC2tkBXgsivWw2oeXU9+TyAM6ZVXsGLRXLQfb4VZFFFVdXGKj3ZkpupzOqpnUFGUVkVRJEVRZAAPALgwtYdFNDXJcacqpLtUQTGaEw6Uzpr86KkKZq1kQZrkKfwsVSAiSi9BEJDmYURElITo6QdjKVfo8gQBAAUuK5sjTrBRBQ4EQSiP+t93AjiS6HOJKHn6wtkU3eNgPKYqiPFPBUIaF7phWTECE/rPO9mbIwpQf07GDYiI0sMkTv5+OUTng+gmhmNpkNjhDgAAirKsbI44wYYtVRAE4TEAVQCKBEFoAPAdAFWCIKyCWqpwDsAn0neIRFPHwKkKJkGAlOatE3WqwtAZB+kYHxgzVUFvjjjJLwbpDMQQEZF63eI5lmjihaKaGI4lcNDp1jMObFpzxMl9r5jJhg0cKIryvjgffjANx0I05RmBA30n3jROGQcJxzGmr1QhLCswmQYEDib5vZ4+LYP3tERE6SGOwxhjIhpeQEptqUKhS804YKnCxBldlwoiSouBGQdmUUj7WKmQFNn5HyidNfnRGQdmYxzj5L4YsMcBEVF6mQQhLcFuIhqZYFiGTWuI6AuFR/19OjxqqUJhlhVWE0sVJhIDB0QZRF9QiuPZ40CSYUmQcQB9oZviY1AUBZKswKT1VpgqPQ7SmcFBRERqgJY9DogmXjAsIddhAQD4gqNf7He5g3BYTHBazbCYhJgSCBpfDBwQZRB94Wwez6kKcuKpCnrTxFQv6PVvN7DHQbqzKyYaexwQEcW3t7YLn398/5ivA+I4ZOoR0fCCkowcPXAwlh4HniAKs6wAwOaIE4yBA6IMot/sRKYNiGnfhVdLFeKfCvSZu6luRBPWShKim0CqH5/cN3vpbDZJRHQ+e+tMJ5450ATvGBYYgF6qwHMs0UQLhmUj48AbHH2pgho4sAEAmyNOMAYOiDJIvB4Hac84kOSEUxVsaQocSAMyK0RRgDAF0ktZqkBEFJ++ixgYY+BAFAVjtDERTZxgWEaeFjjwj2mqQgCFLjXjwGU1wxsMcwNmgjBwQJRB9KkCpugeB2lOyVJLFeKfCszagj7VaWHhAQES/bHGEjjYeLIN9756cszHlk76T8vdMCKiWHqAOjDGQLUoCJjkfXaJzgshSYnqcTC2cYx64CDLboasjK30gUaPgQOiDCINTOEfh4yDkCTDkmCqgiAIagfbVGccSLEZB8DYf9aXDjfjkR21Yz62dIqUKkzwgRARZZhAigIHJpHBWaJMEAxH9zgY3ftaURR0eYIo0HocZNnMAAC3f/SlDzR6DBwQZRB9Y1+v+TePy1SFxM0RAbXPwVhv5AY9pp5xEJXpYBLG9rN6gxLCUmbfLAraj8ubWiKiWEapQniMpQoCSxWIJpqiKGpzRLsZggD4RtnjoD8QRlCSUeRSexxk29XAQR8DBxOCgQOiDBIZx6j+/3hkHIRlOWGpAqD2OUh1qcLAHgfA2H9WX1DK+E67zDggIorPKFUY5c6kThQEKAqb0BJNJP1+zGYxwWYW4R/lBlSXOwgAxlQFPXDgDjBwMBEYOCDKIJEFtaj9mf6dk5CkJCxVAJCWUoWBUxUAwGwSxxQ48AYlhDI+cKD+yYwDIqJYISlVpQpsQks00fT7RqtJhN1iGnWPg9Y+PwCgSJuqkGVTSx9YqjAxGDggyiBhYxyj+v8mUYSiIK0zqcPS0BkHVnMaehzEyTgQR1iqoCgK3jrTYewqeUMSFCWzJzNwqgIRUXyR5ohjLVVQ/8zkawHRZGcEDswi7GbTqKcqNPX6AADT8h0AonocBEIpOEoaKQYOiDJISJK1SQZajwOt90A6+xyE5OF7HKQ+4yD+VIWRBEj21/fgrj/txK6aLgCR+rlMzjoQmHFARBRXykoVjIwDnmeJJkpI6zllNYtwWE2jLlVo7FYDBxW5auBAL1XoZ8bBhGDggCiDtPUFUJxtM/5fX1inc+ckLMmwiMNkHKStx0FUc8QRNoJs6lEvJj0+Ners1dLgMrnPQaTHAW9oiYiiBVNVqiAwcEA00fRAoMUkqj0ORplx0NjjQ6HLCofVBICBg4nGwMEk0tLrx0Nba7goOY+19ftRmmM3/l9P5Q+naSi1LCuQFQydcZCOHgfS4IwDtTli8o/T0R8AAONipNfPhVJ8rKnEUgUiovgCKStVSH/AnYiGFpTU97HVrPY4GGngoMcbRH2XF409fqNMAQBcNjZHnEgMHEwif99Vh+89fwzt7sBEHwqNUkuvH6U545dxEJIjEeFE0tnjYGCpwkimKXZonXb1gIGecZDu8ZVjof+0jO0REcWK9DhIUalC5saQiSa9QExzRHHEJUg/feUk3vP77Wjo9hplCoB6v2q3iAwcTBAGDiaRM239AIBObUFF55/WPj/KojIOTGJ6exzoO//moaYqmE0IpDj9X8+giGmOONKMAy1A5gtJkGUFPi2aneogRyqxxwERUXyRHgdjyzgw8TxLNOH097NNyzjwjfB93dLrR0ufH9XtnpiMA0CdrMBShYnBwMEkcrrVDYCBg/OVLyihzx9GSZzAQboyDozAwVAZB2koVUiUcRAeQcpBdODAH5XamtnNEQUIAnscEBENlKoeB3rGQbpHGdPU8+rRFvxjd91EH8Z5YaxTFXp9kakJ0/JiAwc5djMzDiYIAweTREiSUdPhAQB0eliqcD7SZ9WWxelxkP5ShcQZBzazOOaa04H0yLPe7AZQgwgj2SHSSxX8QSlmPnAmlyoAav1thh8iEdG4C6UqcKD3kuGJllLs77vq8ODWmok+jPOCHgiMTFUYfeCgYkDgIMtuRr+f4xgnAgMHk8S5Do+xYOpgxsF5qUULHJTGZByob9G0ZxwMN1UhxRkHnoB6AXFZzcbHRjpVITrjwBsVOMjkUgVAnTHOFFoioljBFDdHZNyAUs0bkEaccj9ZvXq0Bada+xP+ux4I1Hsc+EfY4yA6cFA5qFTBDDdLFSYEAweTxOk2t/H3LmYcnJeMjIPcSHNEc5p7HOgn9vGequDRUsxcttiMg2QDJIqixAQOoi/kmVyqAKjlCryhJSKKFelxMMZxjNqdLUsVKNW8oTB8wcy+xxgvH39kL972i80JSy+jSxVsoyhV6PGFMLvIhWy7GTMLnTH/lmVjqcJEYeBgkjjd6oYgALkOC3scnKda42Yc6KUK6blQ6QGJIUsVLKKRcpYq3qB6wndGZRyYRxA48AQlI3rtC8oxGQeZX6rAHgdERAOlbKoCSxUoTbxBacQL4MluV01X3I8HonscWEwjCgj6QxKCYRnvvqASB779NmTbLTH/rpYqMHAwERg4mCRqOtyoyHWgIs/BUoXzVEtvAE6rCVm22MU0kM6pCvp0g/FtjujRFvrRGQeikHypQkd/JKvGH5KMQAQAhDK+VGFkvRyIiKaCgJTqUgWeZym1fEE1w3EyBv97vaGkN2+iP+/P287F/ZzggHGMQUke9P3vfnAn/rJtcM8IvUwh12GJaaKty7FbmHEwQRg4mCTcAQk5DgsKXVY2RzxPtfaroxgFIXKSNMYxjmDawEiEpOEzDtLR48AbCEMQAIclEjgwm4Skd4j0MgVAK1WI7nGQ6aUKYO0tEVE0RVFSlnGQ7mlENHV5AmFIspLx9xkj5Q9JuPz/3sBT+xqS/nxddYc77udEN0e0a/d60V/nD0nYeqYDR5r6Bn1tdOAgHr1UYTIGcDIdAweThD8kwWERUZhlZanCeaq9L4CibFvMx/TeA2lrjignkXFgFhGWlZSmfboDElxW84AgiZh8xoH2GndZTfAFY5sjpivIkiqiIIDXOiKiiFDUeXusPQ70cYyMG1Cq6f2U/JOsz0G3N4j+QBhn2uIHAQaKvueK/ns0vZdVls0Mu1m9x4wOHDR0e6EoiNn40Q0bOLCbIckKG1VOAAYOJgl/SILDakKhy4YuDwMH56NOTwBFWdaYj+kpl+lrjqhNVRgm4wBI7U6+NxiGM2oUIwCYhOQDJHrGwfQC56CMg8xvjsgUWiKiaNHXl7GXKqh/8jxLqRSSZOOeabItWPV+Ae3u5DKW9Xsum1lM2POhzxeGSRTgtJqM0dv+qGyi2k6v+r3ifH2vVw0c5DkTZxwA4GSFCcDAwSThC0mwm00ozLLCHQizect5qMsTRKFrQMZB2scxqidxi2noHgfA2NNHo3mCUkwvB0DNOEj25+zT5veW5tgH9TjI9BRCURSYXkdEFCW6N82YSxUElipQ6kXvrKczcNDrDeGuB3agvsubtscYqF+7p2rvTy5w4A2p91yFLmvcjAFAvU/LsauZpfFKFc5pgYPo+zfdcBkH2Xaz8Rg0vhg4mCR8IQl2qwmFLnXHupNZB+eVsCSj2xtCgSs24yDdtZp6JoM5TvMZnU3POEhl4CAQhtMWm3EwkqkK/qAEQVCj0b6QBG/o/CpV4P0sEVFEbMZBqkoVeKKl1Ile4CZaLKfC4cZevHW2E3tq408rSIc+n5ZxkGTgQP/5C7Ns8CZoFtnnCyFHW/jbzIMDB3WdnpjvFW24wIF+r9zlYeBgvDFwMEn4gxIcFhMKs9Qd684k040oM3RraVmFA0oV0t3jQE/rNw+VcZCGUgVPIBwzihFQgyThJMdO+kLq692p9Tg4n0oVRJYqEBHFCIZTWaqgXjdvun8rtpxuH9P3SqeXj7SM664yjc14ZRy0aKO5x7Nfmb5z3zHCUoUClxWKEj/Y1+sLIUcbo2i36D0OokoVuhKXKvRogYOBYxh1enYu1zrjj4GDSUJfSOVr9UD6QpTOD/okjIGlCsZUhSQX1CMVTnKqApDajANvUIJrYI8DMfmdeP31breY1IyD8yhwIDDjgIgoRvTCY6zNEaPj4Pe+empM3ytdFEXBPY/tw9921k70oVCSojco0plx0KoHDsYxc1jvcdDpCRolrEPR77n0LOd4v48+fxg5DnWDKF6pQp1RqhDna30hZNvNcUcxAjD6gXUwu3rcMXAwSfi05oj6Ii/TZ9lTrC4tsjywVMGc9lKFJKYqmNQTfkpLFYJhuAb0ODCPJOMgKMNuMcFhMWk9DiQj+BHK+FIFsMcBEVEU/fpit4hjL1WImtazojJ3TN8rXQJhtdGeh7PozxvjlnHQqwYOuiYg40BRkFSDdb08VL9njff76POFjFKDgYEDSVZQ3+2N+Vi03qivjSdfL8seRcZBbaeHmT5jwMDBJCDLCvwhdSGlLwDTtUNN6aFHlgeWKkQyDtI7VWG8Mw48gTBcA0oVRFGAlOSi3xdSpzI4LCaEJAX9/shFJtMzDtQeB1MncBAIS0mnPxLR1KSXwmXbLSkrVQCQsaNv3VrAINEoO8o8MT0OxqNUYQIyDgCgLYk+B369VEG7Z42bNeCPlCo4jMCB+j7fW9uNkKQg12GJ+7XDBQ4sJhF5Tsuoyjm++sQhfPlfB0f8daRi4GAS0KPzDovpvNl1pVh61LRwUMZBmqcqyCPpcZC6C6U3IMVvjpjkXZ4vqGbY6CN+ujxB4wKV6YEDAZl7M5tqiqLg8v/biAt/uGGiD4WIMpgemM62m8eccdDtjSwmPHE6tmcCPdOAE7AyW3t/AN999iiCYTlmgesfh1KFLs/4Bdz7o6YTJDOSUQ+i6Pes8V7Hfb6w0Rwx0uNAQiAs4etPH8a0PAfefUElfHGaK/b6QglHMeoKXVajzHckOtwBHG3qY+bnKDFwMAnokU+HRUx7TTylR5cnqE0JGJhxoP6Z7oyDoaYqpHoco6Io8ATDccYxJj9VwReSYNd6HADq7y/byDjI7IvBVOpx8OLhFrT3B6bMz0tEoxMJHFjG3OPgqkUleN+F01GSbYM3kJkLc492XMw4yGwbT7bhL2+dw+m2/vEvVRjnjAP9Xi+ZyQqRUgW1L9fA30cwLMMXkpBjH9DjICzh3/sbcabNje/fthRFWba4zRU73QHkOWLvhwcqzLKhYxQZB33+MNyBMBq6fSP+WmLgYFLQI30OqwkW7Y2f6SPpKFanJ4h8p3VQIxiTkXGQ7uaIw2ccpCpwEAjLkBUkmKqQbOBAhkPrcQCoF1inxQSzKGR8xoEoTp0eBw9sqTb+nspSFyKaXPTzdrbNjEA4/ni3ZOXYLfjR7StQkefI3IyDIEsVzgf6Ilqd3pT+UoWwJBulfeM6VcEXwsxCJ4DkAgc+bSS23pC9qceHL/7jAHq0bB+9Z4KRcWCOlCo8e7AJs4tcuGphCRxaJkL0+6DfH0JtlxcLSrOHPIaiLOuoehzoox5PtvSP+GuJgYNJQT+B2S0mY3xfunaoKT063cFBZQpAdHPE9DxupFQhccaBLcU9DvTaTpdt8FSFpDMOglqPg6hShSy7GRaTmPmBg0ne40CWFTxzoBGSrMSkDEenQhLR1LLldPuQ3doDUaUKspKaexiXzZSxC3OWKpwfjMDBgOlN6XpdtbvVDL2KXDv6A+Ex9/tIVr8/jOJsG7Jt5qQDB+pIbHUDaOvpDjy9vxGvH28DoAYiABglpDYtQFDf5cX2s524ZUU5BEEwvj46EHO4sReKAqycPnRj00KXbcR9IPwhybiXPdHSN6KvJRUDB5OAPgbFEd0cMcMXTxSryxMcNFEBiDRHTFfGgdEccaipCikOHOipowObI5qEkZUqRGcchGUFswqdsJiEjC9VENNYqiDJCm799Va8fKQlPQ+QhD213fj84wewo7oTnkDYqG3s82fmzh8RpdfJln7c/eAuvHasNeHnRJojqteFZDLc2vsD+Oa/DydcfDss5gwOHLBU4Xyg1/t7gxI82nNlNYtpC/joZQpLKnIAAN2e8Qm49/vDyLFbUJxtS67HQUiK2bxp1o57T203gMj1Xm9waDOLEATg6f2NkBXglpUVAAC79vXR2RyHGnoBACsq84Y8hsIsK3q8oRFtFvVFbWCcSHPGQSAs4ZHt51Db6Unr44w3Bg4mgdhSBTZHPB91egKDJioAkYyDdGWQ6AGmoTIO9Lq3VAUO9BTNQRkHphGUKgRl2K2RHgcAMKc467zIOBAEpC3joNcXwsGGXuyr6074ObtquvCD54+l9HH/97mjeHp/A4BIY7JeXwiegITyXAcAZhwQTVV6l/hznYlHoEX3OACAQBILs00n2/C3HXU40tgb99/VjIPMDFjq10EfAwcZbWCpgsNigstqStvzpjdGXFKh7raPpvnfaPT5Q8i2m1GUbUs648BuMcFpBA7UfgF7a7vU76dnHDjUQKAgCLCbTej1hTCjwIn5WhmC06IHDiL3bYcaejC9wBF3My1aYZbaX6F7BFkHfT71fScK6Q0ctPX7cfXP3sS3njmK5w42pe1xJgIDB5NApDmiKWqHmoGD80nnsBkH6ZqqoDVHHKpUwaJPVUhR4EBL0RzY48AsCpAH/Jx9/lDcm0K/nnFgjQocFLnOi8CBKAhpm6qg1+4NVRv54uFm/GlrTUpvfP61pwGbT3UAiNww9PlC8IUklOfaAcSOeyKiqUOvQ27sSSZwoF4Xnt7fOOwCRh8bp+92DuS0mo2d/UyjXwf1+zd/SMITexumTP+b80XHgFIFp1VNz09Xj4NNJ9thFgWsrFQDB+PVILHfH0a2lnHQkWTgwBm1eaNnSpxqdaPXG4r0OLBHJiPo2YerpucZH9MDD9EBvoP1vcNmGwBAkXbPPLBBoqIo+Ov2c2joHny+0Y9rYVkOznV40nZvvaO6C409Ptz/vtX4zFXz0vIYE4WBg0lAXwDYLZHmiCFOVThvyLKCXl8I+c54GQfpbXapL7KHLFVIecaBVqowMONAGJxx8OOXTuD2374Vc1FRFAVevcfBwIwDs5DxjUHFNGYc6I2JhhrjpKchtvXHv9keKW9Q7VCsvz70AEFrn/o4ZTl64IAZB0RTkb74aRyii3lQq+XWMw5+8MJxfG+YzCh9d1b/cyCXNYMzDgJ6c0T1z5eONOPL/zqIk61s2JZJ9OCVNygZY6DtFjHlgQNfUMLrx1vxjz31+OClszCryAVgfBokSrICdyCMbLsZxVnJlyo4rGZj4e+J2ojYV9dtbGLozRGByL3ByqjAgV6qoE9p6PEG0djjw4ppQ/c3ACIZBwOzMhq6ffj2M0fxzX8fMT62s7oTn350L860ugEAi8uyEZYVoxFlqrVoGRhVC4shCIk35s5HDBxMAjHNEfXU9gxfPFFEfyAMRYnUgkUzmdKccSApMIkCxKHGMaa8x4FeqjBwqoL6OLKsQJIVBMMyXjjUjKAkGzVvgJr5ICvq610PHGTbzSjKssIiiinLjEiXdDZH7NEzDobYpeg0AgepuWC2aQECvSZZj+i3aoGJMi3jgD0OiDLLtjMdqOlIf/2tfj5q7BkicDCgxwEADJEIByBy7kmYcWBTexwMzGQbD55AGIcb4pdQAJGFlj8kQ5YV1GplHL1eBlgzhS8ooV/PDAmG4QmG4bKa4UhDqcIHHtyJjz68B4UuKz53zXyjWXaia3ldpxentCCToij4y7aaId9fQ3Fr1+Ych5px0O8PD9vDwRcMw6ltVurrjpJsGxwWE1460myUBERnHOgbQ6uimh7qgQe/9vs8264u7OeVZA173Hp578Dgil6CsOlkO3ZWd2LjiTa894878OLhFrxwuBkAsKhcLZUY7e9s86l2nOxK/Dtq6vEjy2aO+fknCwYOJoHoHgcmI3CQ2YsniuiLE5nVpbvHQUiWjcdIxAgcpKpUIRi/OaJeLtHrC+Gqn23Crb/ZZkSt99f1GJ/n12rhHBYT7Fb12OYUZ0EQhPOiVAFA+koVvMOXKuhpfYl26UZK353QXx/6rkJbX2zggKUKRJnlc4/tx69eP532x+lyRzIOEqXiG6UKUQHl6B428ejByZaEpQqR2fHj7eHt53D777YZmQUDRX/cF5KMmfLuBJ9P4y96N1ovVXBomY6JAgchScYTextG1DxRlhUcaezFzSvK8dLnr0Suw4IcuwUWkxBznW7t8+N3m86iyxPE1546hE8/ug+Auvj97nPH8Nft50b8M9Z2evCrN9RzQLbdjOJsdRd/uDIh/XcBwNjAKc+1451rpuGZA02o7fTAahKN8oRoS8ojgQP9a71G4EANZM4tHj5woG+29Q3IZjze3AdBAIqybPjD5mo8e7AJOVpAUg+2LCpTm082aYGDjSfbRhREvfe1U3jubORxw5KMR3fWGiMem3t9RpnmZMPAwSQQPVVBXTwJCLHHwXlDXxzHyzgQhfROVQhLilHekoheqpBMl+tkRHocxN4U6j/rr944g7ouL4439yHfacH0Agf213UbN5y+qECZftGZq6X1nR+lCumbqhApVUgi46AvtRkHeqqxHgjTSxVKWapAlHH8IQmdniAaRrnjNhL6rqknKBnXu4GC2nk7Okg+cEEwUCTjIP7P4NLTqCegz8HZNg9CkmIsTAaKPiZfSEJ9l5pxwMBB5ojOytNLFfS6/kSlCt9//hi+/K+DeOVo8pONmvv8CIRlXDK30Fi4i6KAxeU5RtbKwfoeVP10E/7v5RN4aGsN9tV142y7G76gZCyGjzaOfLzg47vr8aetNQCAnOjAwYAU/j9vq8H3nz8W05vDCBxof+Y4LPjgJbMQCMt4en8jchzmuGn60b2p9L/rv8/qdg8sJgGV+Y5hjz1LCzIO3JQ40dKHmQVO3LaqAltPd+DNU+1Yv7AEBS6rkZ20uDwSOJBlBZ/+2z4jgJKM5h4f8u2Rjdr/eGgXvvH0Efzy9VPqv/f6UZ43/M9wPmLgYBLwhyM7sIBaFz9ZmyO+eLgZs772wqRK5+sbInAwHlMVhmqMCKjdcK0mMWWlCocaepFjNw/6efWf9aFtNbhtVQV+/4E1+Nl7VmLdzAJsr+7ExT96Hc8dbDJqQvUmRflOC1bPyAMAWEznQamCiLQ1wNJLFdTdkcE3oCFJRrc3tpRgrPReCQN7HOid1HPsFrisJmYcEGWQ4RbdqRTdc6UhQZ+DYFiG1STimsUl+MT6OVhQmmWkO8ejKIpx7kmccaAuLCaiz4EeCGgacGwhScbJlv7YjINgJOOA58nMEb3r7gtGmiM6LKa4GQVNPT78dXstgOHLS6PvAc5pO92zC10xn7N6eh4ONvRAkhX8dtMZ2C0iFpRm4ZEdtfCHZCgKcLK1Hydb1PT+w429Ce8tnj3YFHd0aV1XpIGgy6b2OBj4swPAn7edw4Nba3DHH7ZDlhU1iGKJDRzkOa1YWJaN9104AzMKnLht1bSY7/Hkpy7BU5++NOZjDmOqgh44cGNmoQvmYTa0AHXEo8UkGMG2I429+OELx3C0qQ+LynLw9mVlCEoyujxBXDG/yMgAsJpFFGfbkG0zo6nHj6ZeH3whySgXilbd7jayJ3XBsIx2dwAFWuDgZGs/3jrbCUAtPQLUUoXyHGYcUIbS33A2LaXcLArnRbr2SLgDYfiCEr7w+AEAQH2cbqnnqyEzDkRBHd+XpsBBry9kRG2HYjWnJnAQlmS8fqIV1ywuHXRhMEWVTHz4stl4+7JyXLO4FKtn5KHfH0ZrXwA/fumE8fuya1NEtvz31bjropkA1CaPmf7aT2uPg6iAWrxyhehMhJRlHGg3GPoIWH2XUE/zzLKZkW23MOOARuT146340UvHh911ptHRA3stvf609wDo8gQxW8sKS1RTHAzLsJpFOK1m/M8Ni1GR5xjyue/2hhCSFGTZzGjtD8RdqOkNeCci40BfkA3MOHjuYBNuvH8LzkXNdu/1hYwADjMOxtfnHtuPX7x2atDHv/3MEXzyb3sBqJsUvpAEX0iCU+9xECdw8PeddcbfvQlKGXq9IXzgTztx++/eMq6Jeoq83hBRt3pGPrxBCRtPtOG1Y62488IZuH5pWUzWzonmPiPjoNcXihuYe2pfAz732H78bUcdPvfYfvhDEt77h+34y7YaNHR5sWxaDv7zitm4YGY+SuKUKviCEuq7vZhT5MLRpj4caOiJW6qQq41e/NHty/Haf63HN29eEnMcF8wswJoZ+TEfiwT3tMBBhwdzBvweEhEEAVk2s9Gj4al9jXhgSw1qO71YVJ6NNTPyjQwKNXCgZgDofQfK8+xo6vEZ5RG1nbGlCpKs4M4/7sD3Xzge8/G2fj8UBShwqPes+u+80GVFhzuAQFhChzuA8jwGDqac+i4vbv3NNmw+1T7RhzIkf0iC3SIaDe7MpsxP1x6pjz28G+97YIexm5zopHw+GipwAKiBoHRlHJzr9GJW4fAnaatZRFAa++98T203erwhXLekdNC/RQcOllbkGH9/x8pp+OT6ufj5HSvR2OPDw2+dAxC5WGXZzMbXng+lCkIaSxWibyjilStE12ymaqpCpFQhNuNAj404bSZk29XI/t0P7kS11vyIKJEHNlfjow/vwR/erMYN921Jaq44jYweOAhJ6essruv0BLFc65KeaLJCUJKMfjqAej1MVNYARM5fy6flQkrQHX2iMg78Icn4/TYPCBw09fggyYqx2APUxZJ+TXAz42DctPT68ezBJvxtR21M4KnPH8Lju+qN/5+W54A3KMETiExzitfj4JWjLcbrPN6/P76rDjfevwU7azpxuKEXn350HxRFwbkOD2xm0ZhApNPHFn7j34cBAB+4eCauXFAMAKjMd8BlNeFESz9OtvSjSMsUiDe++o+bq7F8Wi6+fuMivHqsFe/5/XbsrOnChuNtqO/2Yfm0PHzjpiVwWs0ocFkhCLGBg7PtbigK8PEr58BqEvHioWZjwgQQlXHgGDwZbDgmUYDVrE6pCEsyajs9mJNEfwNdtt1iBNtqOiL3FovLcyCKAu66cIYRNKjQFvI5WoCjIs+Bpl4fzrSpX9fhDsYE7vbWdqOtP2BkD+n0cocCW2zgYOX0PHS6g2jtVX93FbksVZhyTKKAg/U9CWvUMoVPm2mvM5tEhCfZOMZjTX04UN9j/P9k2r0cLnBgEoW0lZ7Udnows9A57OelqlTh5SMtsJpF4+IXTV/8L63IiclGyHVa8LUbFuGdq6dhTpELG463AYitk9OdD80R0zmOsdcXgh5/iR84UD9WlGUzehCM1cDmiAN3CdWMAzN21nRiy+mOjA/E0sTq8Qbx89dO4epFJfj7xy5CS58ff3jz7EQf1qQTnX472s7iyQiGZfT7w5hXkgW7RYz7WEebetHU4zf66QDqrmDfEIED/fylj3aLN1khMiN+fDcaond9G3tij0svFZOjJimdjgoiMONg/Lx6TO1D0OkJYm9tt/HxjSfaEJRkfPiyWfiPS2Yi226GT+vPkeuwqD0OghL8IQnX/vxN3PDLLfjpKydwus2N21ar6fkDMxKa3TK+9tRhFGZZ8ejHLsY3blqMLac7sPtcN2o6PJhd5Bo03WpmoRP5Tgta+wK45+r5mJbnwKrpech1WHDxnEIsLMvGkcZenGl346blZTCLAg4PCBzIsoJznR5cNLsAH79yLm5fPc34nIMNPejyBDG9ILLANZtEFLqsMT0O9EkHa2bm44r5RXhem3bltKgLcP19lugedjhqICaMhm4fQpKCucXJZRwA6v2FvllR0+HBpXML8cn1c3HF/CIAwBevW4BHPnoRAAzKOKjIc6Cpx2/8fEBs1sFLR9QJDANLofQ1YYFDPV81dHvhspowt9iFDncATVr2EDMOpqA8p/ri6hni4pUJfMHYwIFFFIy04cmgxxtEnz8cc1NxPqev6hccXa8vBJMoDGoWqDOLYsoyDhp7fPjxSyfQ3h9ArzeEbm8oqYyDtbPyMTOJzxtKpzuAf+6px9uXlsUtj9Ab+62dmT/o3wB1p35GodO4sXLE6bhtFkWjyVamEgUhbVMVerxBVOargaB4O3B6Y8QlFTkpm6qgL0AGZhzoXFqpgn5OOhenjnAs9tV1Y9l3Xolbq32mrT9t/SQA4OG3zsXd4aHkhSUZgaiu94/vrocvJOHLb1uIS+cV4dZVFfjbzlr84Plj2FvbNYFHOrlE3wwnGmeYCt3aeb3AZUVZjt3YiY920/1b8caJtpiP5TjM6POHE75/9fPOykp1h/e5g00xC3Bg4jIO9B1Kq1kcdF7Sfx8AUKSNk9M7sZtFgT0OxtErR1swvcABq1mMaWb46tFWFGfb8K2bluB7ty6D02pGlyeIQFhGntNqlC7squnCmTY3/CEJv9moBjdvWFYGq1kcFKyq71evjz+6fTkunF2A966bjmybGY/vqkNNpyfufZggCHjfhTNw98Uz8YVr5wNQN0ee/NSl+MaNi7G4PAd767oRDMtYXpmHOcUunGqNzehr7ffDH5KNMojv37YMH7t8Nj54yUzjtTY9P3bzqCjLFpNxcLrVDZMoYFahCzevLDfew85BpQqjCxzov88abdE+O8lSBQDIspvhDoQQDMuo7/bhgpn5+NoNi4z3frRIxoF6nNPyHOjyBHG0sdcYA3vvq6dw9c82od8fwitH1NdEuzsQM6lOP3fqPQ4aun2ozHeiKMuGQFjGaS2DoZwZB1OPw2KCxSTE1A1nIl9Igt0am3EwmZoj6guNn7x7BZ745CUAzu8GQh99eDf+56nDxv/rUex43WeB1GUc7Kvrxs33b8Hv3zyLz/59H85oUdZkMg5+fdcafOaqeWN6/D9sroY/JOFz18yP++/6c3rpvKKE36MiqkttvIwDq1nI+FGk6cw46PGFjIvuUKUKSyty0O8Pp2QWtX6DEQjLUBRlUDaQ02KKmc0+sI5wrDadbIc7EDbSDXX767px7c8348l9jSl9PJ0/JOG7zx3F33fVDf/JaXr8o03nf9Divg2nseZ7r+Ev22pwtKkXf9pSg0vnFmKJVq50z9XzISvAn7bW4Hebqif4aMdfXac34Ui/sWjp86NAmxWfzqxK/ZxT6LKiLNeO1jjNAqOPSZdjt0CSlYTZAnpvlQtnF6Ai144Ht9bgQa07vG6iehzo/Q1WT88bFJSJvp/U669Pt6kLs5mFTrgDmX2/Ga3HG8Q/99SnNTiban3+EPac60J9lxc7qrvwjpUVuHxeEZ472IR+fwi93hA2nmzDdUtKjQwAu8Vk7CLnOy1w2cwIywr+vb8RVrOI5++5HJ+7Zj4+dOksVOQ51IXwgGBVg1uGKETGDDqtZrxjVQVeONyMuk7voP4Guq++fRG+f9uymPvDeSVZyHdZcfOKCswtzkJxtg0XzipAea5j0IZATXvsYtxlM+ObNy/B9UvLjM+ZXhB7D1icPSBw0NaPmYVOWM0i3rFyGmZony9pz7s+NjXXOfqMA29QMs5DlfnD35PqsrWMg7ouLyRZwZwhshUiGQdm7XHU/z/c2Gtkwb5xog3VHR58/K970dTrxyVzCrVSqMj9VHOvH9k2Mxzm6MCBA4V6uYg2CWPKjmMUBOEhQRDaBEE4EvWxAkEQXhME4bT2Z/wtwvOcIAjIdViHrLPLBP6BpQqTrDmivtBYWpGDZVr92HgGDnxBCb947VRKdi0kWcHe2m4cbOgxPqYHDhJRexyM7fls7PHh43/dg2y7BV9+2wLsrOnC9547CmBwQ57hjGQ+cViS8crRFiiKgmcONOL6pWWYVxK/fu3TV83DT961Am+L0/9ANy06cBAn4+B8KFUQkL6Mg15vCBV56g5KolIFq1k0mg+Ntc9BSJKNUWvBsAR/SEZIUoxyCafVBFEUkG2PvL5ru1KbcaCXMA1s9qjXEP/slZMpCZAMVNflhaKkrsnkSFS3u3Hrr7fhpvu3YuOAndrzzfHmPniCEr773DHcdP9WKIqCb9y02Pj32UUu7P76tbhxeRmON4983Nj5zB0I4+2/3IzfbjqTsu+nZ8i09vkxryQLTqsJTT3pyTh4aGsNbrp/K4DEGQd6E9dsuxkfuWy28XF9VzDR/VdDtw/5TgsKs2zY9rWr8eZXqvCpqrkxn5Mo4+DL/zqIN060juEnS+yVoy149VgLHBYTlk/LRVOPL2ZhHZtxoC40ajs9KM+1I99pPa9KFZ7Y24CvPnFo0C53Ojy6sxa/3JD8uLx4gmEZH/7zbrz799vxkb/shsUk4O6LZ+GzV89DuzuAH710Ag9vPwdvUMIHtIbLgHod0wM+eU4Lrl9aCkEAntrfiAtnFcBlM+O/rluA775jKYDIQjhao1vd9bdH3bd88NJZKHRZsaIyFzctLx/xz3PJ3EJs+K/12P2NazGj0InSHBta+/zYdqYDn3tsP9r7Awl38ReWZRt/nz5g9GFpjh31XV6jaeqZNjfmaQEPkyjg4Y9ciLnFLqybVWD8foAxlCpY1dKPxm4fzKJgBNSSoWYchI0Gk7OLEvdH0Bfy+rnlbUvKsHpGHmRFzVzS3482s4jt1Z1YVJaNj1yunpOiM4eaenwxZQgN3V4tcKAGYnef60JJtg2uJBqPn4+SyTj4C4C3D/jY1wC8rijKfACva/8/KeU5Lej1JZ6JngkG9zjI/AZxI3GuwwtBUKOidosJVrM4rqUKLx1pxi9fP41tZzrH/L3OdXoQCMuo7/IaO+O9vpBxIotHHGHGQac7gO1nO6EoCp472IReb8hYPD30obX47NXzceHsAhzUoqIzCpKP7j5zoBFvv2+zMT5oOM8ebMInHtmLXTVdaO0LYFFZTsLPzXVYcMe66QkzL4DhAwdmUcz4Mh0hTRkHiqKgxxdSb6Zd1pgIua7DHUBxls0oOzk9xhs+vdFZrsOCoCQb2QYl2epFVb9x15sRAWoab6oyohRFwUEtcDBw7nRtp3reaOnz44m99XG+enT0961+o5KqJpPDURQF9204hX113fj84wfQ1u/H9AIHvvXMkQkZN5cqbf0BrF9QjMf+82J84dr5ePrTl2FpRW7M5+Q6LVhakYvGHl/GB/JT6fXjrfAGJSOVfay+/cwR3PyrrXhibwNa+vwoy7GjIs+RtpGM0enfhVlWlOba0dYXiFlI6++fn9+xCt++JdKFXV+EJLrWn9PqwgF1k2dmoWtQOZ2RcRC1iAuEJTyxtwEvHGpBqv30lRP4xCN7se1MJ+aVZGFavgOBsBwTxI3OONAXKrKipotn2c1JNUcMhmXsre1K+zSM4eglGQPr6tPhsV11+MfusWV3/fy1U9hb2405RS6cbnPjI5fNRlmuHWtm5ONjl8/G33fW4b4Np3DNohIj4wlATBlpntOKeSXZuH6JumOv19JHc1hN8A7YYGnol7GwNDvmYwtKs/HW/1yDpz59GZZXxp7zRqMsx44OdwB7znXj2YNNcFpNCRsvFmbZUJxtg9NqMjKPdFfML0KnJ4h9dd1o7w/gXKcXi6ICDbOLXHj9S1W4QCsr1e/F8saQceALqRkHZbn2mEbZw9GnKuiNEYcqcyjLtcMsCihwqj+vw2rCnz+0DnddpE6rmFXohM0s4ifvXgFBAP7nxsVGeUN0JkdLnx9lWvZCry+Efn9YLVVwqe/n6g4P5pcm3+DxfDNs4EBRlM0ABhYW3grgYe3vDwO4LbWHlTlyHZbML1WI6m4K6DXxmb3rOhK1XR6U59iNSG2O3TyuGQd67WX0TsFo6TeAIUlBfbcPp1v70ZdMxsEIFsO/euMM7vrTDvxxczXueWw//r6rDkcae3HJ3CLMK1FP/u+7cDoANQJrj7MAT2R6gRN9/jBu/91b+Pf+xmFvXLZrs231GbcVY2wWk0ypQqZnHKSrx4E7EIYkK8hzWlDgssbMTte19wdQmKXucFhMAvZENYSKxx+S4A4mPth9derXr5tVgGBYNm7y9Wh8lk1/z6qv71XT8xCSFPzs1ZMxo6tGq6bDYywkB3ber+vyYkaBWnd4qCE1N7ZffeIgrr9vM3xByQgcpKpXBKDu7Nz1wA6j30e0gw29uG/Dadz5hx043NiLb928BD++fQUaun14+UjqF0EDnWzpx09fOQFFUdDpDuD2327Dva+eHPH3OdTQg7U/2ID/eGgXTrf2o7XPj9IcGy6ZW4gvXLsAMxKUTi0pzzGOY6C/bKvBR/6ye8THkin8IQk3/HILXj0a+zw+d1BtzlWTZKA2EW8wjF5vCC8caobdIuIrTxxEY7d6k16eax+yVKHfH8JvNp5Bd5wMpoEae3y4/bfbjF32Ey39WFqRg/ULilGZ70RZjt2Yq67TM3ZKBuwy6ueMPl/8a73aUG7om3O7OdIcUZ89rz/2uQElU2fa3EldOxKl5e+q6cJvNp7Fneum49UvXok/fXCtkRodndERfR8RvbM6vcChNnpLkHHw/KEm4xz3/KEmvOt32/G2+zbHPVeMl3otcJzuPi+SrOB0qzvh2M2Bajo8eOdvt+HW32wzPhYMy3h8dx1uWlGOpz59Kb5+46KY8sv/fvsifPeWJVhSkYMvXrcg5vtF3yPla4vOz187H3OLXbhh2eBMAac1MnVBUdTXXZtXwYIBgYNUK821Q1aAPbVdKNZ2vGs61P4JAxsvAsCKabmYW5w1aLPmmsWlsJpFvHC4GU/ua4AkK3jHqmkJH9ehbRCMJeNALVXwx2wOJSPLrr5nqts9KMqyDnkMFpOIv37kQnzw0lnGx/KcVvy/dy7HzEIXPnbFbHz7liW4ddU07P3mdVi/oNgIuESXHDX1+FGhZS80aKPhozMOABgZGpPRaPMoShVFaQYARVGaBUEoSfSJgiB8HMDHAaC0tBSbNm0a5UNOjLDXj46AMu7H7Xa7k37Mjm4fFIdgfL7P60NryHPe/a4TOVTtQ44Jxs9jkkM4W9uITZvGngEwHElW8MYx9cSw5/BxlLhH191bfz5fOR25yH/1b1uwp0WCxQSsLjYlfL5CwQCamluwadPQizzd64d9UBTgRy+dAABs2H8a1e0SFmYFjMdwSQpcFiDPFBzx6+Sra0z43cEAvvCPAzh09BiurIw9UUe/djdpv7uX96m/t47aU9g0yt8hAHT41Bs7AcD2rZsHXfDaWgLw+sMZ/drv7fUhJCHlx9juVX83LXXVQCCMOs/gc8ipRi+mZ4vYsW0LpmcJeONQDS52JF50/v14AHtaQnBZNkIQBJzoklDmFOANAye7JNT2yXCYgaxQF2QF2LhtFwDAHFQXd0rIj02bNqG5Tl3cz7N7cADA7zadRblLQIVvbDXrbzWpN9kmAThypg6bNkXS9o+e8yHbIsBhVbDvTFPS759EdrWE8c8D6o37Nx95He0+9Qa2rS+ANzZuhDhEpkyy/nosgLfqwnjg2c1YVxZ7ef7nySBMAmARZZS7ROT1nkagF7CbgOd3HEVBX/Lp7MlcX4KSgn+cDOK6mRaUuUT8dLcPRztlzFOa8LM9fjS6FZxr68UF1uakH1dRFPxghx/+gIxtp9tx79M9aO8PwdfdOuzxdPvV1/ezm/fCWxt7zvnnbh+Odcr4+/NvoCJr5K2btjWG0OZVcNk0M0qcka93BxUIAuCyjP25Hcr+tjCONwfw1JZDsLar521fWMGmE16YBKC2wzPka2yo59MfVvDVzT4oUBAIA/9zoR1PnQ7iZLeM/rYGyB4Z9V1S3K/3hRXcu8ePMz0yDpw4i/cvHjqF+LXaEPbVBfGn57diaaEJvb4Qbpkl4NqZEnZs24LOFvX9+vwbWzEzR12Mba1Xzw1nj+5D99nI776mV114bdu9D97a2PdCIKygpc8PpX/4143NBDy89Qz+suUM/JKCL6xRb/hPN3cbX9vhk/GVN334xAobLq4YfFtc3SPBJAJne2Q8fTqIH17hRI419rm4f58fWRbgqrxONB3vQhOAc13qz7Bl5x50njFBVhT0ekMocgjo8CnoaIj0ZAj3tqLfp6CrT30ujja7ceSJ17GsyIQjHWH8bE8AV00344NLbdhZo/7OzrS58aun3sQVlaNbsI3ViQb1+r71WB025aRvWk6LR0ZAa7777KsbkW+PvE5kRUF1rwxvSMGKYjN+ttuPI52R3X79fbO/LYwebwgLLF04sOstLACwe3tsJtosAF9eDnSc3o9NUVURHS2R+7bjB3ajWXv8b10AVB/ehYFXsaDXhyaf+p58oTqIZ86GoAAId9Zh06amFPxG4mtvU99fu6o7MCNbVF9HdV5UZIlx3ye3lMkIlcS/F1laIODpPbWwiMDCfBENx/ag4Vj8x21rCkIAcGjPDpw0jeJc6Q3gbHsYVlHAwoLE98LxdDQFEQzL2HemEXnm5O6rjjbE/7gdwDQAmzZF3peKosAsALuPnMbsUC1kLXDu6WqB2xbE3i1qwLq1+hiOtEW9LnubsWlTR9I/x/kk7QUYiqL8EcAfAWDt2rVKVVVVuh8ypZ5tO4Cd1V0Y7+PetGlT8o+5ayMqy/NQVbUaAPCr42/BYTGhquqi9B3gOPry1tdw7eJSVFWtAACUHtkKh8uKqqoL0/7Yu2q64A1vBwAUlM1AVdWiUX0f/fn8e90e5Du70O0NYW+rBAVAUALmz5qGqqrlcb82a/dGFJVEnt9one4AHt5ei7nFLty4vBzegISGV141mts4rSYc6VQgKcC165aianUkavzg7E5k2cxG34iRuOMGBUu+8zLMBZWoqloc828/e3wD7rjkQljMAtpffgMAUKNtEt5YdfGYpjOEJRlf3fwS7BYTrrrqqkH/vsV9DDtb68f9/ToSD57dCU8gjKqqy1L6fY809gKbt+Ki1cvRKjbheFNfzO8hJMnoePVlvPuiWaiqWoQt7mN4ZEctLr38ypj56dF+fGAzugJhzFt5EdyBMD5y/xa8a00lJFnBU8cakW0346K5xVg0pxA4cwLT5y8Fdu7FynkzsLulBqUFeaiqugSVbf3oMp/CPW9fhCd+ugkA0OEHLr/iypjRmyO18+UTMIvVWFGZC8EsoqrqEuPfPv/mq7hsibob9MyBJqxfv37IMpihhCUZX//JRiyflouSbBteru7UGjj1QwGw7IJLUJIztmyaQFjCFza/rj5e7jTjfSXLCtr6Azi6ezsum5+Hn717BWxmk9GIas2ZHWj3h1FVdXnSj5XM9eXXb5zG63WnsGTeLCxfUIajL6u16lkzlqJx0x4AgNNhT/q99tiuOjyyvRZne2X85N0r8Ps3z6JdsUJBNy5cvhBVF88c8usVRcH3dr0GKTtyLdB9ZdsGAAEcDhbjiWN9+OE7l2NxeeKyqGiyrOBz33sVff4wtrWK2P4/VbBor8n3/mE7qjs8ePrTl46oYVe8Y5dkJeFr/YV/HQTQAGtusXGe33amA2FlJ25aUY4XDjVj/sqLBjUx0w31fD66sxZ9wSOwW0Qsn5aNj7/zMnwgKOFXb5zBBy6egUd21GL31nNx3x8/evE4qnvVGfBbm9z4v/+4dFBa8xN7GzCn2IU1M/Lxr7/vA9AMOacC+bOLgDf34Nb1Fxi10Ll13fj1gbdQOX8ZqharvWwObDgF4dhp3HJd5PcOALM6PPjf7Zswc94iVK2pjHnMY019wIYtuGbdclStGLo2XHr1RfQGFLWcKhiClDcDwCn0BYELLr4M2XYLXjzcDAX7UDR9DqqumBP79bKCL/1wg5FNFZKAvpw5eEfU67W204P9r2zCZ6rm4fprFhofz6nrBna9hSXLlqNqYQm6PUEor7yGC+eV4sXDLbhg5VI8fuoQfCEJV6xZgmNNfdjdVoeqqir8309eQthsxqduvxI/vn8LgAD2dwr4w+VX4rB0Fjh5CgDgKJ6BqqqFGG+KoqD79VcASGjwjP1cPpSXjzQDW/YBAGYuWY01MyJt1b777FH8Zcc5AMDz96zDkZe34rZVFSjOtuGBLTW44OLLkeuw4MnH9iPf2Y5P3351zOssGUfk03i+Wv1933htVcLrpe7P1bvQ4w1i/frL8M2dG2E2yZAVCXe9/bIR76iPRFFjL+7btxUBCVgxpwJXXLkCHa+9hFvXzhrxvaulsgMf/vNuBCUZ37ptFapWViT83CVr/LiuthvXj6JPAwA0Omqx5ekjABSsWThrRK/nWus5PHn6KFp86pjvqqo1ozqGoZTvfgOW3HxUVa1Gvz8E5ZVXsWLhPGTJdXj7mtXwuRrxzmvmI9dhQfbmV9AfCOOGy1bj0rmJG32fz0b7Lm8VBKEcALQ/z+/uTEPIdVgyqqbSH4od5XemzY26Li+WT4vcJJnGsTliMCzjmQPDp6yPljcYRoc7GHPDlD2OpQo7qjshCOpjpiIl8GRrPy6ZWwiX1YToX9lQ6VUmUUg4jvHfB5pw/+un8fnHD+DlIy3YU9sFRVEnUNz7npX46OWzjUj9wJqri+cUjipoAKh9F3IdFvQOKON5+K1z+PWBAH6x4RR2VqsVTnlOC4JhGYKg1piNhdmk1urF62+g/nvmlyoIgoB0vF30dPW5xVnId1oGjZFt6PZBkhVj7NPamfkIhmUcSdCZ3xsMGw0GD9T34HvPHYOsqAuaHdVqtk+/P4y1M/ONUal6B/Vy7eZIrzGeV5KN377/AkzPdyLHbkaO3YyQpIx5frwvKMFlM6Ms1x5TqtDrDaHXF8LMAhfmFWeh3x8eVMowEptOtqOp14/PXDUP/33DIniCEk629hs1na0paJD4xvE29HhDsFtEHKqPPCd/fuscLv7R66jt9OKGZWUoybHHdK9eOT0Px5v7RtS0dDitfX5jvNiB+h78Zds549/2a+UpC0uzB9WrJ/LGiVZ8/Wl1ksw9V8/Du9dUYlahC4e0JrED09TjEQQBi8tycKw5tlSh2xM0ntvHdtVhX10P/rSlBqdb+3GmLfZzZVnBX7efw+ce248/vHnWSKfv84dxw7IydLgD2HZG3SXq9Yaw+1wX2vsD+NjDe8Y0reWxXfV4zx+2x72GhCUZG46rjfqiy170957e/Xxgav3XnjyEx4aZ6KEoCh7ZXovF5TnY+t9X4+GPXAhBEOCymfG1GxahMt+J4iwbgpKMvgHX1E53AH/dXotbVlbg53eshC8kDXo8X1DC/zx1CPe+ehKKomB3jXrOP9rUi+Pa8xTdhE0//0c3SGzrD6DAaR20mBuqOWKkEdrwQWj92vmtm9X+CdHnu1ptYpOeaj+wMaGiKDhQ341OTxDluQ5ML3BiRoETLx6OzbL5154GCADuviQ2+KWfF/W+O3qZwlULS/Cl6xZg/YJio+Ruer4TWTYLvEEJYUlGg1tGry+EvbXdONHSj3esrECPN4Q3T7XDF5JgFgXMKnTiXKcHbX3+ceu1outwB+ELSVg5PQ/+kIyz7WMrp5FkBX9482zMyFVZVnD/66djRnU29/ghywp+9OJxbDndjsd31xm9A/TPu2lFBeZrZZl63523znTg6kWlIw4aAJFSBZfVNGzQAFBLFbxBCQfqe9DQ7cP/vmMpfnetM61BAyD2/mqWVvL16Mcuxh1rKxN9SUKXzSvCwe+8Da984UrcMkxwriTHjhtGGTQA1BJGXcVISxW0BoR9/nDafr/lOQ6jVEE/R+iTopZW5OKbNy8x7uGLtGtZoibgk8FoAwfPAvig9vcPAngmNYeTefIcapfbkSxGnjnQiA3HUt+xV5YV3P3gTvznX/cYH3tibwNMooDbonaSLabEC81Ue+5gEz7/+AHs1xqUpZr+Zo2ujc+2WdA3TsGc4819mFngREWuI26X+pHwBNSRMYvKcjBbGxmj3zAP3eNAhJSgx8GZtn6jcU9dlxe7znXBYhJwyZxCvOuCSizXAgPRY4BSZWBQ7UxbP7773FGYBGBndSc2n2pHntOCqxaqlUzFWTbYzMn3U0ikIs+RsC+D9TyYqiAKietkR6utz48Ht9bglpUVmFeShTyHFT3eYExA79yAm+1VM/IAAEeb4neqP9zQawQ4HthSje3VnWqX8F4/mnr9mKu9htfNKoBFu5nSO6Tr9X8DuwqLooCnP3MZfn2XuitQHVW7/frx1hGPF/QF1cawJdl2YzwboPZFAdSeHHpfj4HjGqNtP9uJm+7fkjC48OjOWpRk23DN4hIsKM3Gddrkj4tmqzupY71p7/eH8KOXTmB6gQO3r6nEkcZe47l7en8D5ha78KXrFuC2OHWmq6bnISwrONrUh5Akp6Tnwl+3n0MgLOGK+UU4UN+D14634rJ5hQCA/XU9AIDVM/KMenW9zjOe6nY3Pv/YASwuy8GTn7oUX3rbQoja6Dl9QVWaZLbGovJsnGzpi6lz1hfY+nMxu8iFFw434d2/347PPXYAgLoo+f2bZ3H7797Ct585ii2n2/Gjl07gbLsHu2rUINhXrl+IbJsZLxxSF4Vvne2ArAAfuWw2TrT046n9jfj1G6fR0uvHI9vP4QfPJ8jbHeBgfQ++++xRZNstMdNFAHUj4FvPHEW3N4Rsu3lQ4CDfacG6WeruanRD2up2Nx7fXY8fvXgcPd4gPKHB55NAWMLXnz6MEy39+OAlM1GUZRuULQBEGvR1DGgu+vBb5+APS7jn6nmYX5qNeSVZ2DegJ8re2m6EJAW7a9TFbVt/ANk2M4439+NYcx8q8x1GrwJAvQaIAmJGMrb1BeJ2UddHpsXrcaA3QptVNHwWyJ3rpuP9F83AlQvU3b8jjZHznR6AOKKdA/XGhJKs4NdvnMbq77+Gn7x8EiZRwHP3XI4NX1yPW1dVYEd1J1452oJfvX4azxxoxL8PNOLy+cWDXscWI3CgXpP0YG5Rtg33XDMf2XaLEQSfXqA2RwTUyTP9QbUxpP68fPzKOch3WvDykRajIfbMQhdqO7349KP7cOcfd4zrKOJ67T1/wzI1sJVsnwNFUfC9547hxl9uwW82quVVsqzgK/86iB+9dALve2CnMfHibLsbP3/tFP65pwGF2mu3udeHmk4P/rC5Gh98aBf8IRlf16aw6IGDWYVOoyFvny8MWVbQ7Q2Oejye3uQ3zzn4/ROPXrP/3MFmWE0irl9WBssIGv6NlhqAUx9nRqETJlHAhbMLMGeU934OqwkLy7JHnamXrIWl2cb7YKR9sLKiRj2na/xhWa7daCCrb1pGP260QpfaZ6E4K/nJEOebZMYxPgZgO4CFgiA0CILwUQA/BnCdIAinAVyn/f+kpO8ojWSh+n8vncCv3hjb2Jh4njnYiN3nuo0OtpKs4Kl9DbhqYbHRxRzQmiMOcQHxBMIpG/mzXdt5TNcc6BbtBkNvMgSoHdrHK+PgeHMfFpfnIN819iaZp1r7oSjqDsysQhcEAbjnmvkARp9xcKrVjWXTclHgsqKxx4cDdT1YWpFrLKyXaoGDmYWuETVBTMbAwMETexshCgJunmNBU68fLxxuxtULS4wd7mn5qYkGr5qel3BWr8UkQlYwbAOlXTVduPOP23H9LzYjGB7fQIOYhoyDH710AiFJxpe0pk55TgtkBTGNtqoHBA70BUOXttiv7/Li9eORgKc+MrTUKeBQQy8KXFb85N2RFPFf37UGD35wLS6cXQCbdoPcqTVk1Hc+XNbBF9e5xVlGpos+Y1pRFHzxHwfwwYd2D1rADMUXUhvDFmfb0O8PG7vu+k7izEKnEfk/0x4JHHS4A7ju52/i4bfOoaXXj8/+fR+ONvUZ57Nozx1swsaT7bjzwhnGQuCzV82DSRRwjZZuPdKMA0lWYoIUP3n5JBq6vfjFHauwenqe2uypw4NzHR4caezD+y6cgXuumR+3IehqbbfmjROt+NjDe3Dtz99EIBw/+0CSFfxtR63RDyOekCTjH7sbcPWiEty8ohz9frU2+M51M2ASBeN1sVoLPP1xczWqfrppUNCl2xPEj146jg/+eRfMJgF/uPuCmOOfFVWyVJqT3E3W4vIc+EOyMaIXiAQO/t/ty/HQh9bil3eugj+k7tYeb+lDjzeI3248gx+/dALBsIwf3LYMz35WLet481Q7dtZ0YVqeA3OKs3Dd0lK8crQFwbCMzac7kK3tzM8rycLXnjyEn716Cv/aU4+/76rHn7bWoLo9fjBKURS11ECS8Z1nj6Ioy4pfvnfVoG7hP37pBB7fXYePXT4b776gEq1RGRynWt2YX5qN0mw77BYR5zojwZlnDzZBENRdtvU/3YQvv+mFOxDGmbZ+fO3JQ3h0Zy3uf/00HttVj09VzcV71k5P+Ds1AgcDnr+XjrTg0rmFRuBtaUXOoCDj9mo1OyMoybj3VTWV+91rK+EOhPHG8TYsGzAdw2wSUZxtQ3OvH409Pjy1rwGtff64ZT5mkwiX1RR3qkJNhxdlOXZjQTeUH79rBX74zuUoctlgFgU09viMxVVtpweKouCodk/l0aaTPLarDj979RQkScHOmi6sm5WPXIcFoijgHSsrIAgCPvHIXtz72il8/vEDaOj24Z2rB6dy64+jX2P0jJP8qAWoU9vFLs6yIVsLtOoBGn9INt5XhVlWTC9wotMTgD8kwW41YVahE9Xtbhyo70F1uwdP7ktQvJ2EBzZX46GtNTEfe+ZAo5EVNJA+UeGK+UWwmkWcbE1u8kdLnx8PbatBfZcXv3z9NFr7/Hj+cDOe2t+IT6yfgzlFLnzj6SMISzKaogJMF8zMh8NiQnOv3whSCIKA1TPycOX8ImTbzTjY0GNM4NKDdP3+EPoDYcjK6Lv+65sz+a7kvt5pNcEfkrC3tgvrZufHBM/SSRQFYy0wawwloePNbBKNTa6RZg1kR21OjDRbIVkzCpxo6vEjGJYjgYMEoxYvmlOAaxeXpj3YMpGGPesqivK+BP90TYqPJSPpC7oeXwiFSUSQuj1BNPX60ecPQ1GUlL547ttwGoKgjvPp9gTR3OtHW38ANw1II7KYhEEj6YJhGT9+6QQ+dsVs/PeTh6AowN8+NvYeCDu13ZpUdhaPpgckoiOJ2XaLkX6WTp5AGLVdXty+phLHm/tweogdy2To3cAXlWUj3zkLKyvzcOuqCmw41oq1Wg1oPGaTACnOlAxFUXCqtR+3rqowZuDWdHiwfkGx8TkVuXbkOS1YkIbRMLkOi9EtWpYVPHOgEesXFOPCwn48czaEQFjG25aWwqct5lJ1Uv/GTYsTvq/M2o1aSJJhEuMHStr6/fj4I3vgCYQRkhS09vkT1g6ng5jicYwbT7Th6f2N+Nw18zFLCwroOyO93sjEjnMdHmTbzcauo8UkIttmRrc3iC2n23H3g2pjw4PffhtynRYcqO9BZb4Di3NCeK02jDvXTceismyU59oRlhUsKss26sj19M1uLbimLwQSzTHOd1qQ67CgWts5bO8PaKnSYXz7mSP47fsvMD53y+l2mEQhbr2gNyjBbjEZ0f32/gCmFzhRp93Uzihwwmk1IctmNt5/kqzgM4/uw+k2N57e34hTrf1wB8KwmkUcqu/BO7RazhcPN+PrTx9Gny+EdbPy8emoGfErp+dhzzeuRbbdjP9+8tCg858sK3hiXwPMooDbB9Rnbzndjh88fxxn29147p7Lsbg8B68da8WNy8uxdlaBkaJ9sL7HSOm+cYg00JIcO66YX2SUFgDAvtoe9HiDuGx+kXHT2uUJ4n+fO4pnDjRhTq6INscZ1HZ68JN3r4z5fq8da0WHO4D3XzTTeM9aTAKqFhajOMuGlj4/XFaTEZB5+WgLwrKCui6vsXMcCEv4z7/uwYH6Hiwsy8a971k16D02U0ulFYTI4nU4i7Vxrseb+41dtFOtbmTbzJhT5MLc4iwoioIvXKsGZO/bcBoPbq3Bbzedxa2rKnDfe1cZ5465xS68dqwFp1vdxjnzpuXleGpfI3ZUd2LL6XZcPLcQVrOIe66eh88/fgAOiwk7a7qMYMUDW6rxnrXTsXp6Xsw5aUd1F97/p5346OWzcaC+B199+0LkR+32f+bRfRAEdVzhnetm4Js3L8EDm6vhC0no84eRYzfjVEs/bls9DaIoYFahy8g4UBQFzx5owsWzC1GUbcNrx1rgDwM7znbiC/84AHcgjH/uqYfFJOLWVRX477cPXdtclK0eV/T41rpOL063uXHnhTOMjy2tyMEzB5rUcb/Vnbhvw2n4ghKWVuTgbLsbG463Ytm0HNy2ahr+vO0cwrKMz2vPQ7SyXAeePdiEfx9oNO5T3n1B/FTqHEf87MKGbu+IxggD6qKqNMeOxh4fSrLtCMsyznV60doXQKeWTegOqNeqI429KHRZ8Y9PXIJ3/nYb3rEykukzvzQbO/7nGtR0eFCea8eX/3UQJ1v7jZKSaHqgMaht5HR71J8lP2oB67SaUJnngCgKxi7mPi2rB4gs0PMcVjUFPiAZmVYzC13GqMlsmxm/3ngG710Xec6S1e8P4d7XTsJqEnH3JTON4/784wcAADU/unHQNbdBm6gwq9CF+SVZON4cP3MNUDMQ+/xhXLek1Ajqfu3GRfj2M0fxi9dOYX9dD+aVZOG/r1+E1dPz8Mm/7cPGk+3o1ILIH7p0Fq5fWoYz7W5j59dmFvHcPZcjz2GBIAiYU+TCwYZeTNMyEvVUcjXwqT6/Y+n6D8QGfIb8fIuacdDhDqY803M4Zbnqa/x8ChwAwJqZ+dhT2zXyUgV7+gMHs4tckGQF9d3emFKFeKGyr1w/uj5o55O0N0c83+m1pMn2OTimnTzdgTBa+wKjqumu7/IiNGBLUlEU1Hd5tRrPPlR3uI3U21XT82M+1yyKg3ZcjzT14qFtNajr8mDrmQ5jjulYNHR7Ud+lnsRbetMTONC/b3QKYLbdDE9QgiQrI5r3OlInWtQMgcXlOWju9Y+5x8GJFrWsYHq+EzMLXbhQS619+CNDN3lMlHHQ1h9Avz+M+SXZaO8P4HBDL9r6A8biEVAj8r97/wUoSXJHbyRyHBajjnV7dSeae/34+o2LkdV1EgUutcTnivnFxg1FqurPhgrGWaNSQxNlWPzvc8fgDUr477cvwg9eOI52d2BcAwdAascxPri1BjMKnPjMVZGFbZ52g9TtDRrj7c51qnPPo39/eS4LerzBmBr2Tk8AWXYzdtV04bJ5RZhv7sT+ThEfuHgmBEHAV65fiLAUGxTVAwc93iAcUTdteo+DgQRBwOwil5EqrAfl5pdkYdPJdkiyAlEA/t+Lx/HAlhpcPq8obuDAH5LgsIgo1l7fbXrgoNOLoiybEbi4aHYBHt9dj2XTcrFqeh521nRhZqEThxp6cK7Tg2uXlKK5x2fspiuKgvs2nEK23Yz3XzQD/3nFnEGvJ30hWJRlM0oVZFnBbzaewQuHm3GipR9Ws4hrFpUa15Gfv3oS979xBrOL1AygX71xGt+6eQla+vxYq83EnlechWy7GXtqu3GypQ8rp+cNe0P00IfW4bcbz0KBWhP8i9dOYde5Lnz/1qW4+5JZeO1YKz71t70IywquXlSCN0604aevqCnY379tWUwJ0Rsn2lDgsuJKbTGdZTNj9Yw8ZNstKMu1o6XPj/I8h3FO1hcC0deA32w8iz213fj1Xatx84r4TbX0G9tC1+D69kTml2bBJAo40dKHm1aUo7XPj+3VnVgQlU4rCAK+cO0CBMISfrfpLH71xhkUuKz4wW3LYl6z6xeU4KFt6g7rLVqw6LJ5RbBbRNz76kk0dPvwOS0j7NZV03Dl/GL84IXjeGp/AxRFHaX32K56PLarHn/+8DpctbAERxp7cbbdjT3n1B3jB7Ud3OiRbcGwjJeONENW1MXP57XHKNXuFdr6/PAG1RFjesB3RoHT6HHw8pEWVHd48MmquXjXmkp0eYJY98MN+NPWargDYfz+Axfghy8eQ0uvH19+2/BNxuKVKuip4tcsigzMWqplD9z/+mk8sqNW66Wk4NZVFajIc2BfbTf+cPdaFLqsmJbnwIcvmxW3QeVXr1+IFw83w2k1YdPJdpxucyfscZHrsMQdg+wNSnHLG4ajL6qKsqxwWtVgor6jbhYFeLRFwblOD2YVuTCvJAt7v3ndoLr24myb8fiPfuwi9PpCcbMf9K/TSxX0nyU65X1lVH23voup9xEB1JIAq0mE3SLCZTWjtd8PX0iC02qKKdV477rp+NPWGjUbYYTZhS8eboY/JMMfkrGzuguXzy+KKXN762wnLpsXe/5t6Pai0GWFy2bGorIcbD6deKrCva+eQocngOuWlBpB3cvnFeHOddPxqDaW9773roKoZXGVZNvw6M5arKjMgyComwUWk4jyXDuae/3ocAexpCInZrzhbC1woAckjXGe/pCRLZrswn8gPY0++VIFM3whCZ2eQNzyoHQqy7Uj12GJ6YNzPvjU+rm4cn5Rws2GRLLGIeNAv6c+1+GBP6S+l7PtlriBg6kgPS1QJxH9BnxgE7hEjkWl8g1VU5tIc68P1/z8TTx5KvZi6QlKkJXIRaa63YODDb3Itpsxc8Cix2wSEBqwQ63vVmw43gZFATo9wTGVKzy6sxaf/ft+AOpirXUMjceG0tTrR6HLGnMh1FPQ3GkuV9AXvIvLs1HgsqDbGxpTbfrJln4sKM2OO093KCZBGBQI6veHjNn080uzUJHnMNL65gxoGHXJ3MK0RL1zo3aDHn7rHPKdFly3RE3Ret+F0/GBi2bCZTNjdpELFpMwLnNtLQOaUQ2kKAo2nmjDHWsrcfEctWZ7YIqu7my7O+W9CIDUZhyEJRn76rqxfkFxzOJPT6mMbpCozj2PfW3kO63o9obQ7g5AX1N1e4M4UN+DDncQVy8qwbIiM/Z88zrjonz7mkrcsS429VkP2PT6QnDZTMiymnHVwmIjOBbPnCKXUapwWtvBvW31NHiDEmo6PHhkRy0e2FKDuy6agT99cK3xdfVdXuN5MUoVojIOALXHgX4DCQC/fN9qrJ6eh5+9ctK4eb9j7XTIiprBdcOyMqycnocjjX0ISzK2nO7AqVY3vnDNAnzl+kVD3jCW5tiMRfPm0+2497VTsFlM+MxVcxEMy3j2kDp+q98fwu83V+OGZWV46fNX4MOXzcKLh1vwj93qSLDVWqdwURSwdmY+tpxux8GGXlyu9RYYisUk4vPXzscXrl2AFZV52HVObTCm94jZc64LoiDgpc9fgQc/uBaXlJuwbFoOJFlB9YCmZocaerCyMhcmUYBJFPCb96/Bd25RG8vpM60r8hwx5XFApOFdWJLx+K46XLOoJGHQAFBLl0xRqbXJsFtMmFPkwvFmtZ/Drb/ehrouL/5jQFM6ALCZTUb39U+tnzuov8DblqplJl+8dgGu0hbIdosJl88rxsGGXrisJtwUlemR77Ji1fRcI+j3+w+swf/csAguqwmvaT2NfrfpLD7/+AE8tS9Sk72oLDvmfVfX5YWsAHdfPBO/v/sCY3OhVFuItvT5cULLjtEXRpX5TjR0+9DvD+G7zx3FkvIc3L56GkyigOJsGwrtAnZUd8FqElG1sBiP/efFeOw/L04qIJrvtEIUYgMHG463YU6xKyYIvUQLAjysNVvc/NWr8Mn1c3H3JTNx7x0r8coXrzR2e7f+91X42IDpBLrL5hXhh+9cjm/ctASfuWoegMTNMaflOYyd7Wh6jf9I6a/fwiwb1s3Kx9GmXrx2rFWdOFGZa9xTnOvwGueP4ZrhmU1iwmxU43pklCqEYBIFo38DAHzv1mX43q3LAER2T/XnHwDqunzI0XbVHXrGQUgNjOsTiuaVZGGulgEUrxeTLCvYUd2ZsITvib0NmFXohMNiwitH1Sa70eWgD2wZPDa3vsuHSu31tbhc3bw43do/6PEVRcHJ1n5j8V7X6YVJFFCR58D3bl2GB/5jLf7rugW4WcuctZhEvHP1NGw53YFzHR6UZNuM32N5rgON3T4cbezFigGNnWcXqT+//pqNyTjQroNjLlVI8uv1z/eH5KQylVPp01VzY0oKzxe5TgsunTd4c2A4+nndZhaTfn5GSr+nrunwwB1QX0uJShWmAgYOhhEpVUhut/lYcx9c2kljYEfnZDy4pQbBsIw3G8LwBiMnbj01f0lFDiwmAdUdHhxq6MGKytxBC1GzKCA8YOEUXR+pf7qeApeMXl8Iv9t0Fve/fhpbTrfju88eRUO3FxfOLsDK6bkxzY5SqaXXNyhrQ78gxKt9HMq5Dk9SvRheOtyMj/5lN+7bcBrZdjOm5TmQ77RCkpVBnaeTpXbv7sOiqA7TyRqYceALSnjHr7fhE4+oTTIXlGbH7ObPTlD/n2q5Dgv6A2HUdHjw2vFW3HXRDCPA85XrF+Hb2mKjMMuGV7+4HrevGdzYLdWiSxUAdXcvut673R2ANyhhfkm2sWPUHqemfm9tN66590387s2zg/5tpH704nFsOhnpCi0KY8840He2Xz/RBm9QwtpZsVlHuQ510aJnyYQlGU09vkHpvWrgIIiO/gAWaLXMne4gXj/eCpMooGpBCZIRKVUIwmk1QxQF/PnDF+KK+cUJv2ZuSRaaev3o9YVwus2NHLvZaKT5ytEW/OCF46haWIwf3rbMeF09c6ARV/xkI145qi7UjOaIRsaBeh6q64xNZc6ymXHJ3EJ0e4NGuvD6BcXItpthM4u4amEJVlbmwReS8IMXjuNzj+9HSbYNN68cvlP0nKIsHGvug6IoeHxXPQpcVvzzExfjy29biEVl2XhijxoYeO1YK4JhGR/Tshc+ctlsuKwm/OqNM7CaxZjd2bWzCowpGCMd6XR51M2X3jCypc+P0lwbFpfnqPXZK+342XvUEoVTUbXJnkAYZ9rcWFGZZ3xs/YJio85dPxdX5NphNYsoyooEVFq0FOJNJ9vR1h/Ae9clrqsH1AXC9HzHiLPyFpXn4HhzPzrdQbT0+fGNGxfj1jhNIwHg5pXlWFSWjQ/EGfV48ZxC7Pifa/C5a+bFfPy6Jepr8JaVFYN2v/TsvqIsG9bMyMcn1s/FFfOLsfFEGxRFwWntmu8JSvjGTYuxtCIH778oNnVcz7J51wWVxusdiPxuz3V4cO+rJ5FlM2NxhfqaqMx3wBuU8M89DWjtC+A7tyyJGX03O1f9+6oZebBbTKjMdw5Z/hbNJAoocFmNwEF1uxvbznbg5gHlMfkuq9H09Pu3LUN5rgNfu2ERynPVBojR5SbJlmjevKIcX7thUcJSnOkFasCk2xPE33bURgKGwZHvqgOR33Ghy4qL5xRCVoCn9jdi3awCFDjVLDlfUEJLnx+zU5DqHck4iExVyNWCAPFEBxTmaM9pQ5cXuVqjP5fVDE8wDL923qvUgm9rZ+YnbHIJAG+ebsedf9yB7z13FBuOtRqvQUB9ve0+14071k3H+gXFRhBMv+edU+zCppPtMVkQgJoJMV3rW7RIKyG6+Vdb8elH96K1z4/vPHME/pCEtv4Aen0ho1lvXZcXFXl2WEwiTKKA65aU4nPXzI95Pa+ozIMkK9h6piOmv1VFngNt/QF4gtKgiVB63yN9mkB0j4OeOJkeI6GXKiT79c6oXi6F45xxsLQiN27ZzGSlrwem5TnS1lcgX2t4WNPhGbY54lTAwMEwomuFk3GsqQ8Xzi5Ajt0c04xrOIGwpDVdqsOismz4wurscZ3eWbjAacWMAidONPfhZEt/zA2ezmwa3BzxXIcHlflq+qCeflmn7dwdbuiN2yAuJMnGhfrJvQ34v5dP4OevncLdD+6C3WLCS5+/Ev/8xCUoz3XEjFdKpeZef8yFA4hcXEfSIDEsybjrgR34n6cOIxCWsPlU/LS6vbXd+NSj+3CipR+rpufiv65bAEEQjBS30ZYrtHoVdHtDowocmE0COtwB3PPYfiz99st4zx/eQk2HB2U5dlTmO4zUUN141bbpqYAPbKmGSRBw98WzEn7u7CJX2mY8R9N3vvWU00/9bS/u/tMu43Uc3TSvwGWFICBuJ/0/aynM979+ekQBtoH21nbhD5urcbgh0nFaFIfPONhb2z3kqLX99d346Ssn8cV/HACgNo6Kpu+s6Ls8bf0ByAoGvZfynWoqcIc7aIzr7PYG8frxNlw4qyDpdMdIqUIo5qZpKHozpCONvTjT5sa8kizML82C1STi12+cQViS8f/eudy4GTjd2o+vPamO9NuljX3zhyQ4rGYUuWywmkU0dvsQCEto7vMPCpLkOa2QlUg38AKXFR++dBY+fNlsuGxmYyTUX946h+XTcvHoxy5KagrIxXMK0doXwO5z3dhwvBXvWjMNNrMJgiDgXWsqcbChF409Pjx3sAnT8hxYozUVzHdZ8cFLZ0GSFSyryInZ2dQzNaxmcdBzO5ybV5ZjaUUOpuU5jN4LLb1+Y7dVN7vIBZMo4HBDL77w+H4caexVpzkowMrp8ce06uUJ+usoOltAz254cl8DirJsxi7+UO69Y+WwNfgDLSrLRmOPz0h5Hmoiw/svmomXv3Bl3KaSgLqQHHizef3SMlw2rxAfvXz2oM9fWJYNq1nEispc4+uuXlSiNWzrQ02HBxfNLsDl84pw/dIyvPC5K3D3JbNivoc+EWDgwlT/Ob71zFEcberDL+9cZZxj9cyBN060wmISsGbAa0JfZF48RIbPUIqybGjq8eOv28/hZ6+ehEUU8YE4WRzvv3gm7rl6npHJMVZmk4hPrp8btzkioAZM3IEwfr/5LL757yNGVp2eqj9S0RkHq2eoY2QlWcHl84qQZVcX5frramYSox6HYzRH1O7H3IGwsdCJpzjLDkEALplTiA8sVu85+gNh4z7UaVNr5/VMK5vZhD/efQE+d818I4inBw62n+00mojq43Mf3l6Lj/11D/7fi8eNx3xyXwNEAbh9dSVWz8hDS58/Jr3/C9cuQIHLip+/dsq4jkqygqYen/G61EduBsIy9pzrxkNba/Dw9lpsPtVu9JaRFXWzp7bLi5kFQ/9uF5er36/LE4zpsn/XhTPwgYtnYN2s/JheToDag0MUYDTktJpF2Mwi+rTmrsDoMw707JZkd7Sjs2HGu1RhqrGZRZhFAeUjnMYwUnpppb7uiNf4eaqYuj95kvRF6sCZ6PH4QxLOtLtx3ZJS9PhCIypV+Mfuenz7maOozHfgt+9fgw/+cQse2lqD966dDlEUjN31HIcZs4uysPlUO0KSMihdC9CaIw5ISavV6pu/c8tS9HpDuG/DaRxt6sPfdtRiy+kOfP/WpVgzMx/t/QFULSxBry+Ea+7dhHuuno8PXjoL++t7UJ5rxz8/cQn+34vHccPycmPHtizXjpaj/pQ3gwTUm9F1A3ZOoiPJydLnsEuKgif3NuLrTx/Gq1+8Entru9HU48P1S8uwbFou/rj5LHIdFrz6xStjdpv01O8uT9BID0zW9rOd+MZWH8yigIvnDp92PJBJFFHd7kFdpxdVC4vx+ok23LluOr77jqXwBSUIgmBMLKjItad8ekIiejbOrpouzCvJGlU/j1TTS3m2V3fCZjHhjZNqac7Omi5cPKfQKNmZVeiCxSSiwGkdFDho7fPj5SMtuGWl2rjyNxvP4MfvGnnqnycQxo9ePIHibBs+ekVkESIIwrCBg/997ijOtrnxvqjmZD94/hiae/34xXtXYeMJNfDlDUooy7EP6h+hl1jpN0zNxnSS2Ocoz2lFc48fYVnR0qKbUdflxcnWfnzl+uHro3X6orffH076hn5FpXruOtSgBg6uXazO2F5Ylo3Djb24Yn5RTM3imTY3ClxWuGwm7K9Xd798Wo8DURSMtOb6Lh8UBTGlCtG/E/1mOs9pwX9F1YDPKnLh4Y9ciOn5jhGNr7pUe09/5YmDCMtKTHOyy+eru/8vH2nBltMd+Mjls2POkR+7Yg4e2VFrlM3olk/LhdUk4oIZ+SN+Py8qy8ELn7sC//nXPUbQq7XPP2iHzmZWu7L/fVcdvEEJd6ybjqPaqLp4AWkAKMtVz/n6TVpZrh3HmtVMqtY+9Rqw+1w3rlxQlFTfggtmjnyhq7+Gj2mjO4uzU3tjnue04tGPXRz336xmET+4dRnmlkSuAVWL1AXMn7fVICQpuGPtdLwrQbM/QM04KHRZBwXlop/nz141z5jYAagLaEA9184pyhr0u11UYIIghLB+YXIZQgMVZdnw5ql2vKkF1O9YWxm3hEQvLRgvlfnqe1jfBdcDwr6glDAYNBT9GlWUZYXDasKq6WpZz2XzilDb5YXbHzZ6ScwqHL7MYzgWMbbHgTc4dIlFrtOCo/97PZxWM55++Y3Ix7Vzl1Mb8+cNhuGwqMenv070MoQOdxD9/hA+9OddWFSWjX9/5jLsOdeNVdPz8I6VFfjnnnqj/EOWFTy1Tx0lWZZrNwIB9V1e4553Wp4dn7lqHr7//DE8ta8R77qgEi19foQkBdO156c424bbV0+D3WrC33fW4S9vnQOgXoejr01dniDqOj14+7KhM7lmFrrgsJjgC0kxwe6yXDt+cNvyuF8zpzgLO75+TczrNsdh0TIOzDG/x5GqyHPgivlFg87TiUT3uyjMYuAgnQRBQJ7TarwW02V2kQs7qzuxuDwHWTZzWvurZToGDoZhNonIdVjQ2hfAppNt+NfeBtx/5+q4L5pTrf2QZAVLKnLQ2ufHxpOJm8UM1Njtg9UsYvNXroIoCrhhtgV/POTGGyfacO2SUqOWPNtuwfzSLGw43orl03KNG9OYYx7QHFFRFNR0ePCOVWq9aa7Tghy7GX/aUg1vUILNLOJoUx82HG/D3tpu7P/2dfjn7np0uIN47VirGjio68aaGfmYXuDE7z5wQczjlebYEQzL6PGGYjpHj5U3GEavLzQokhhpepN8xoG+e9vaF8DWM+rzsvV0B37wwjHICvC3HbV48EPr8OqxVnymat6gFNVIxoH6PCiKgnZ3ACXZdtR3ebUFTfy30+vHW2ESgFf+a/2gGvNkmLXX2sVzCvGnD65DU48PJdk2mE2icbOpL7DGq0wBiFyEz7ari75MML8kC9MLHHj9eBu6PUEoihr8+/2bZ3HxnELUavWVeqClONs2KHDw241nICsKvvK2hTAJ6liy7926bNha12j76rrx/gd2wheS8OPbl8fcSAxXqnCipc/oX+ENhuG0muENhvHIjloEwjLMJgGnWt1YUp6Dhm4vLppTMChgZ9YmJujppnon6oHvpXyn1SiDmV7ggN0i4rC2eBy48B6KNWohk2xzozwte+rlI83o9EQyHpZNy8Hhxl68a8A0ghuWl+PqxSW499VT+Mtb5xAMyzG1zpX5DjT0+IzF8sDj14N/5zq8sJiEuDfwA3exkjGz0InyXDtqO724fmmpMW0AUOdT5zktuP/10wjLCm4ZUPNf4LLijS9VGTPHdXaLCd99x1LMHcP7uSTbhr213VAUBS19/rjv0QWl2Tjb7sHcYhcumVOIv++sw7Q8R8IpB3qvFP1nXFSWjYZuLxaVZWNPbTda+wLocAeMbJJ00BcGeiPiQtf41hAP7O9Rkm3HispcPHtQzRCMfv7jqW4f3GtEpzd/u+fq2GkE+vkqJCmYF2dCzpw8E/Z+87pR727qu9ULSrPwuWvmx5S7TKTpBerPrffh8ATCkGUFvlE0AASiShW0n/fmleXo84ewRFsMuANhI7g80g2CeERRgFkUjIxOX3D4TAn9WuG0RM7pkcCBGZKsoNcXHvTzR5cqbDjeikBYxsGGXrxytBWHGnrwkctn4yOXz0Z1hxvPH2oGoPZSaOzx4YvaGN8ZUYGDgHbMuQ4rPnTpLLx6tAXfeuYI1szMNzKZ9OcHAH7+3lXwhyQ8safB+NrtZzuNADEA1Hf70O0NDXttMYkCFpVnY39dz6Bg91AGBruy7Wb0+cOwW4LItpmTbsI6kN1iwiMfTX4KWWypwvien6ai339gTdoaI+pmF7nw9P5GtPcHpnR/A4ClCklZPSMPO6s78fBb5/DCoWZsP9sJf0jC958/hpcONxufpzdGXFKeg3klWehwB5IucWjrD6A4y2b0K7ioTK1f+8NmtcZaT4/JsZvx8Svm4KEPrcW/P3PZoIZPgNYcMapUoccbQp8/HJPCPqPQCW9QwsrKXKycnofTbW6caOmDOxDG7pouI2K8p7YLjT0+NHT7jLndA+npf9HlCiFJxl+21Rhz1Ucj0S6pftFPtjzCH5Kw8WQbFmqNpjYcV+vNH9lRC1kBvvr2hej2hnDnH3cg32nFhy6bNeh76IEDvbHaMweacOmP3sCJlj7ceP+WmNS/gao7PCh1iaMKGgCRkZR66m9FnmNQ2n+hy4osmxnzS0ZeCjFa+o6ZoiDt0d5kCYKAaxaVYuuZDjy6sw4XzS7AJ9bPxaaT7dhZ3YlznR5My3MYNxBFWbaYHgdn2tz42846vO/CGZhR6MTNKyrQ6wth25mOER3HP3bVw2wS8M9PXBIz0gwYvjniv/ZEZnF39Kuvt82n2hEIy7h+aSmeOdCE4819uHllOZ797OX47i1L436fXKcF5zo8eHRnLZq1sZnlObEX14KoudRFWTYUOK3GjOyRXIijgyojaVq2ojIXBxt6YTEJuEGrc75uSSmWT8s1mtdFs5nVXcJgWMbx5j611tkaCRw0dnuNjIKBjeH0dN/aTg9yHdaUZUcJgoBLtKyDT1fF7siKooCLZheg16feLC+bNrjLfHG2LW5JxF0XzcBFSe5wxVOSbUeXRy1D8YfkuBlB87VF7vsvUidmFGXZcPUQJQYrKvOw8ctVRqr6f123AM9+9nKU5aplEXqH+nQGDkq1fhZG4CADdvSuXlRiBODmDhc4iNOkVPf8PZfj8HffNihImWO3GIvHBQnO8WNJidYXnXeum4GbV1SMuhY81Qa+hz0ByViUjqY54orKXHzs8tlYr/Vu+Y9LZuHlL1ypjkK0mREIyzjbrmY2jXZ3eiCLSYzKOAjHnb4Qj1WMlDrox6L3z+r2BuGwxr5GHFYTXFYTOt1BPHewGRW5aibafz95CCFJwTotu6c814Eebwi+oGQEAGZr0xn063h9l8+YJJbntMAkCrjvzlVYNi0XYUk2grMDr/t2iwlrZuYBUEebnmjpx86aLqM842B9DwAMaugdj97zZSwLQnV0t1qqMJ5TBqKzYQoy4Pw02a2dVZD2wIEeVDvZ0j+l+xsADBwk5fJ5RajuUMcYAsBf3qrB3Q/uxINba/D7zZFus8ea+5BlM2NGgdPYdUi2z0Fbvz9mZJ5JFHDLygrsr+tBWJKjShUsyHdZcfWi0oSpMgObI9Z0RtKzdfqb4B2rpmF+SRaONvWitU9dQH3z30fQ2OPDuy+ohD+kBgAAJA4caOmr2850oFvrqLvhWCu++5ya2jZaR7VAjN4tV1eea0ee04Kjjb3xvgyAmhGg1+M1dPsgKzC69urR/5oOD0RBvXm4cXkZQpKM+967Ku5umx440DsGP3ewCWFZwbf/fRT9/jBePNwc01fiTJsbb/vFm/j5a6dQ3e5GmWv0ixS9w/JVCxPvhgqCgMf+82Lcc/X4pZJG31jNKEjvSXskrltSimBYhj8k4cvXL8RHLpuNilw7/ve5Y6huj+22PzDj4McvHYfDYjJ2YK5YUIQcuxnPHWoa9DgA8MKhZnzgTzuNCSXBsAxJVvDGyTasXxB/qoAoCIjX3DoQlnC23Y1Hd9YaXcb1oMbLR1qQ77TgN3etwVffvhDZdjPevrQMs4pcCbN88p1WbDzZjm88fQSvHW+F02oatLMdvUAoyrKhIMtqvMZHMj5zNBkHQKRc4Y61043Hu3pRKZ675/KEN9h6L4J9dd0IhGVjATEtz4EOdxAnW91wRk1a0OmlCk29fqPZWKp85qp5+Mm7V8SMVtPp6a03ryhPW/OmePTriR4Iihc4uHpxKS6cXWCk1n/3HUvx/duWDfl9oxe9etZTWY4NIUnBm6faIQpqE9900XcVT7W4YTOLGbH7owdbKnLtQx5Pvz+E9v5Awsywwixb3M0AILK7uyBOxsFYzS/NQo7djHeuTn8D25GIDpgAgCcYhk/bjBhNjwOb2YRv3rwkbpBFP28db+5P6Xhei0kwmiN6R1BiIQiCkV0ZnXEAqGUJ8QInhVk2VLe7sflUO25ZWYH77lwFh8UEi0kweqXoPQOaen1GPwT9nkfPRq3r8hrZlfpjl+c68M9PXIL5pdmo7/ZBEOIv6t9zwXRcMb8IH9Y2YGo7vbhNa16qBw6S+f3qgYORZBwMlGM3o09rzDja/gajob82bWbRCPbQ+U3P+jrb7s6Ia85Emto/fZLUzuDHEZIUzC/JwobjbbCaRKyblY/9dT1GFPlYUx8Wl6vj9vTAwdk2d1LNrdr6AkZXWN2MAifCsoLW/kBUqcLwT5nZJCIcNY7xDW2HPfr7zynKgiioN7P6DF9APeFVd3iwdmY+vnHjYjy5rwGP7qyDWRSMOc4D6U2dfvDCcRxt6sMv3rsK286qQZbXjrXgrgFdpaNJsgJ3IBw3ur/pRBvynZZBu1eCIGD5tFwjnTueL/3zIGq7vHjik5egQWuGdtGcwqi6OTUldEmFmqL403evxKer5g2qA9blOMzIdVhwoqUfnkAYW7Qgkj72rNsbwq6aLlw6rwi+oIT3/mE7Oj1BBMKNaOj2Yfns0b/VLp1biLfOdg6bsbC8Mn27fPFEP2epvNEaq0vnFuJX71uNi+cUGn04vnHTEnzm7/sAqGPQdHrg4LebzsAfkrHheBu++vaFxo2UzWzChbMLYsas6k629ONL/zoAf0jGA5ur8YVr5+N9D+xAhzuA9v5Awp1bAYCC2MjBkcZe3PqbbbCaRDgsJvzfu1bgw3/Zjfb+AGo7PXj1WCtuXlEOs0nEp6vm4RNXzh22xi76xnr3uS7MKXINWrhGz7UuzrYZ/28xCYMW3kOJ3iEdyQ39NYtL8erR1hHVTpfn2pFtNxvPSaRUIdJAbk7x0D9rqndU5xZnJRx5+ralZfj3/kbcsXboKQOppu/M6+fJgc0RATUI889PXDLmxyrT6pA3HG/F3OKspHdVRyPHoU7CCITltHbSHollFbkoybZhwTDNb71BCdcvLcWKaXkjfozKPCeONPZhfmnqs8ruWDsdt6ysSOvzNlqV+Q5j99sbjEybGk3GwVCybPo0LDeqhgjSj5TVLBrNEUfa1DHXYUGnJxgJHNgiXxvv5y/KsmLb2U6EZQXrFxZj3awCvPpfV6Kl128EmPWeAc09fnS4g9rXRc710wucqO/2wmJSg3Lx0vsburwoz7HHLd971wWVeNcFlZBlBd+8aTGWTcvFkoocPLKjFge1jKQZSZTBvWNlBfp8oYT9VpKRY7egqUcNcuSPYxaN/twUulKX2UYTSw+ShWUlqXXYZMaMgyQsKM1CSbYNDosJv7xzNdbOzMdfP3ohPn3VPIRlBfvreiDLCo439xmzjivznbCaxRFkHAQG1WfpDZEaurzo84dhM4tJdfm2RI3ve+tMB36z6QzeuXpazMLzP6+Ygyc+dSlKc+wx6e3/ccksiIK685TvsmLNjHyIgoDv3LIkYU3htDwHvn7jIizSmpqpj6t28d12thOfe2w/frcp/li73795Fut+sAH/3F0PRVEQCEuQZQWyrGDTqXasX1Acd4G0fFouTrX2wx9S0+2iR4q9erQFT+1vxN7abhxv7ke91ggoOhPkPdpN/Fotfc9lMycMGgBqsOLKBcXYdLIdm0+1IxiWcYXWX+LaxSVwWEx48YhatlLb5UGnJ4gl5Tmo7fRCkhWUu0b/Vvvzh9fh8HfflnEXoNiMg8wJHAiCmq1THDUb/KYV5fjMVXMBxO6YFmfZEAjL+MnLJ3H/66cxLc+Bj1wW2029LNdulMX0+UO464EdePVoC770rwPIsllwxfwi/GlLNZ471Iy9td2o7fRCEICqBI3KBEGAPGCIyRN7G2ASBVw0pwC/eO8qLNV2bJt7ffj0o/tgMYnGNBQASTXm2VPbrT2eWk4ycKICEOkyLWo3VvpOXFmufdCY16GMNnAwtzgLT3zq0hGlGepTTvTnxBFVqgCofUyuivO7V2ehq39PVRpyMqblOfDMZy9PSc30SOjXk8ONPQCGnj4wVvouZmtfevsbAOrzX5oTW6s+0URRwEMfWofvJCgb0pXm2PGHu9fG7U00nLklLrisppQ07RtIEISMDBoAsenw7oBklD/aU7yTm2VTzwkDG/KNldUkIhSONEccyfkxWztP6efp6K+N9/MXZtkQDMsQhEiD0xy7RWt8q6rQfjY948BhMcVkic0ocKoZB75gwvNkfbcXlcNc80VRwMeumIOL5xQi22aGWRTQ4Q4i32kxMimGkuuw4DNXzRtTE7psuzlSqjCO53z9msQyhcmjNNtmvBaneuBgav/0SRIEAZ9YPxdufxhLKnLwxKcuBaAuIkQB+L7W7dwTlIwUTZMoYE6RK+5kBb1Zod652x+S0OsLGenJOn0HraHbh35/CDlJnvjMJhGKou7m/3NPPQpd1pixZoCakqbXqeqL6aIsG770tgW4S6vvBoC/fHgdBEEYMjVHEAR8/Er19/PrjWdQ0+FBdYcH1y0pxWvHWvHswSa8crQFn6qaO+hrXznagpAs46tPHsJPXjmJHm8Qt62ehg9cPBNdnmDCkV4rKnMRlhWcaOnHMwca8bcdtdj19WuR77LiZ6+exJxiF+o6vfj3gUYIgnrxLsm2YWFZNs60uXHH2kr8cfPZIet5B7pqYTGeO9iEH798AgUuK75zyxLc+MutuGPtdGTbLXhibwM+VTXP6Elw+5ppOPaCuis6llIFm9mUVMBovNktJnU3JSwbr9VM9uW3LcSaGfkx5QN6cKEoy4afvnsFKvMdgwJkZTl29HhD8Ick/HN3Pd4624mdNV2QZAW/vHMVVlTm4ab7t+Bzj+1HrsOC/3vXcrT2BRLWHIsCjDIaQH2fvnC4GVcvLMHv71Ybj4Yl9eZvw/FWHG3qw73vWTni3/En18/BbzaexRXzi7H5VHvclE99F6rApV4U9V2ZihHeOMcGDtJ/WVEb1qqBA/350lMJAbVcZSCTqKb+9vpCRtnCZKZfT/SMg3QGDpZW5OJrNyxCfZcXd65LnGGWKiXZNtR1ecd9RvpQhgo8p8In18/F7Wsqx2WsbSb5wMUzsbg8B7/YcAreQBi+oJYdmeKMA1fUbn5FCke7WcyRHge+oASHJfnzoz7Va2CpApAo40B9z88vyUp4z1aqlZaqGQcBFA2YSjKjwInXT7RhZoEzYXp/ls08okCoIAjId6kTjGaMYwA1x2FBnz+EkCSPc6mC+rtnY8TJw2wSUZZjR2OPj6UKE30A54t4M51z7BYsLs/B0aY+XDS7QGssFblhnVuSFTO/XffGiTZ89OE9+NtHL8Ll84uMGuvoHgdA5OLV0O1Dny9sXESGY9Ya6oQkGWfbPVhcnjNkXV1pjg3ZNjMWl2fDYhJj0sgS1VvGs7AsB7ICoyfC56+Zj0BYxuZT7UbQIyTJeHRHLd65phKSrOBwYy/uuXo+ZhU6selkO9r6/XhyXwPOtLlhNYu4cn78lMHlWjR9y6l2/GN3PW5ZUYF8l1Udidnmxmevno9jTX145kAj1szIx7R8B0RRwOevmY93ralEZb4Th75z/Yg65a9fUAxBUGv2fnz7cswrycbeb12LbLsFS6fl4qUjzfj+c8dw2Ty1pvn6pWX4ySsnEQzLKHNOzps9/YZmNKOxxpsgCDEjzoDI4urDl81KGKTSF1zNvX48vP0cFpaqc+QXl2fjHSsrIAgCfnPXGnzsr3tw10Uzhh01NbDHwc6aTrT3B3DLykjHfbNJRKHLij3n1KyBi+aMfGzdV65fhC9dtxD3v3E6YeCgQAsU6F3V9YXYSPobALE9DkZTezxSuQ4LDjWovVv0G+iSbDssJgGFLlvCXe98pxo4SDYIez4rzLJBFNRstkKXdUTnupEyiQI+uX5wYDhd9PdkoukPk1G23TKi6/Fkcfn8Ilw6txD3vX4KnqBk9DhI9TUnejGQykZranNEteeSWtaa/HHr56lIc8ShAwfF2nl8VZxeKzqb2YSiLBuatYyDge+hygIngmEZp1rdmFUUP1j95w9fmPTPoNNHH49ndmK2zQx/SIY/JI9rqYL+HGdSYJPGblq+A409vil5Ho7GwMEYffzKOdhX241v3rxkUC3YvOIsvHi4edDM4ddPqD0HHtpWowYOtAY1A0sVbGYTSnNsaOj2om8kGQdidODAPWxtrSAI+MZNi8dcp76oXE2He2xXPSrzHVhakYO/fuRC/HLDafxiwykEwhKe2NuA7z53DDYtPU5R1J381TPycfuaSvR4g7ji/zbiQH0Pvn/r0oSN3ypy7ZhV6MS9r50CoM5DB9RFvawA/7+9uw+S6yrvPP57unt63vQymhm92NLoxZJlGbAlWbKwLWxL2BgI2CYJGGeBIHDihFqyFJslJPtS2exWsayzlQpsFnadsLVJSGJiEkMq2XV4swybhd3YmBiMLTsyNsj2WK+WNC+a0WjO/tH39vTI05K6+869fc/5fqoopGlJc65/M3dOP/ec56xf2qsNyyrHVu7dd0jb11ZWVwz191Svs9GJ9MCCTr1hw6CKBdO7oyO54hvIyr5ufWDnOn12734NLiyro1g5V37Lqj49e3h01tFKPlnc3ZHqEsCkXb2uX79562uqec4lfpPypcde0E+Ojuv3/slWXbV6iRZ3d1RX8ezetEz/++O75zz7/GyFwuxTFR6NigO7N80ukg0u6NThkUn1losNv5Gf+VxWbdh30Rz/Rne5qM5SobryIv5+a3TiPKtwkEI1fnF3R/U41ngCXSyYrrlkQFetXlJ3W8/inrJ0ZCzVp09ZKRZMFy2uTHTmatqYZ/HX60BAhYOQFQqmno6iRidmehw0cxzjudQu12+lId/ZOoqVfhwTU9Oado0VPM5ujlj7d+f6d+Lvhy1D5+6rdXFfl148fkqHT06+qt/ApqhPxwuvjJ+zANGo+J57IScqJKV2SXma85TOUkEdRdPgQu5PPlnV163/J7HiIOsB5N3tW1bq9i1zdyLesa5fzkl3//Ej+sx7rtLCrg455/TNpw+pWDA9tO+gnj8yqoPRaQZL57jJrFrSowPHxjV2+swFL68tFSqT+APHxjU2eea8x0NJetWRcc1YO9BbbVr1ji0rq5P3eOXEc4fH9OmvP1P59ZFRHR2p7KGrbX7T11PWJ3/2Sj398km9t6aJ3dnMTH929zX6tS8+roHecnWLyP6op8T6pQu0eqBHHUXT+OkziTXv+8MP7JCLPv/ZtkdNMB966pAuWlxZ4fDxt15WyffIvkQ+f7v54M51ud7v1VEs6AM7X72aqFbcjf5rT74sSbp+w9I5j3a60H2xdtaKg5dOnNJAb/lVS/yXLuzUU8MntXHFwpb6W1yzbkB3bF9Vt+HXiujYLmnmSLdGCwfxeeVT0y6VLtK1//1rJ9DnO2t7SfT3QtiqIFX6o4xPnpn3vgNpm1lxwBO9UPR0ljQ2OVXtcZD0yqb5WnFQjo7HHp9sfNzxKTjx/a52O8VchZO1g70yk3asO3fh4KLFXXr20KiOjk7qqrOad28Z6lNvuajRyTOJHmEY/2xJc8VB7cO2es2954OZ6bPv2abXznH8LvIrvi/kec6bhLCvfp7t3DCoe372Sv3GA9/X3zz+ku7csVrPHRnTgWPj+tCu9frs3v36+pMHq2f1nt3jQKo0/Pruj4+po1DQ0JIL+2EW/3txw8D1dY5+SlqxYLpsxUI9fuC43rF1Ztl1/M32B996Vi+fmFBnqaDnD4/pR4dHtW3Nklc1v3nblRfpbTr3cm+p8kbt7DcKz0aFg0uW9qqnXNI1lwzoW88crjZOa9W5GsZtihpjvvDKuK6NjmDbFjVf3LvXz8LBuU7M8EX8JuWJF09oxaKulidTZ/c4GD5+as795/GpBpe12EW9u1zUPe/cXPf1z73/6urToPhJW70lqudSLhU01WDzr2bVPj1q5MljXDBI80zvLG2chw787WBZdcUBhYNQLOgsaXSiZqtC4qcqVKbDpYIlugWmslVhWmNNFDzW9PdqUVdJfd3l6O+ee6vCDZcO6psf233eByUbotPBpp2rbm+oHe+16wf1tSdfTrTAGp9kcyEnKiRl54ZB3bb5Yr3v2jW6em3j2/1acfMcfXaQb3EfpdBXHPi58bqN3HH1kP7XR66vLoV+eF9lm8K7tw+pWDAdGZ3QwZMTKtjcyy6HlvTopVdO6ejYZEPNESXpqeFK4WBDnWPC5sPNly/XzZcv14aakxriwsGDTwxrYWdJ164f0D8eGtH+QyO67DzHVzVq/6FRXby4q/oD9pbo5j2UQvO+i6Nj4qRkn1ggW4u6SurqqHxPJfH1WulxMLtwsGKOpbHxMsf5fvO3YdmC6kR5y1Cf/uJD11ULX42It/6k1Rwx1shEPJ68xhNx5FM8gUuy+z3aW0853qowPz0O4q0Kyxd1tdTJ/2xx4WA8PkaygfvjHdtX6eGP7a65t557q4KZXdDqyh3rBnRm2sk5zbmc/saNlVM/ktzS1d8bbVVIsXCwfFGXPv1zW1MvGsBPK6srDsJ48FBP2GWTlNRO/B947AVtWrFQawd7taSnrKOjk5qerhQN5vphNdTfralpp1fGTl/QETbSTI+DfcMntbCzNOcWiPlSe2RcLH6KefLUlHZuGNDagV7t3XdI0sx+uqTsPzQya2vGbZtX6vEDx7VzQ+PHXzXKzLRpxUL9/XPHtDLBrszIlplpxaIuPXdkLMHCwczvh0+c0pbVfa/6c9UVBwl/j5yLmWnbmnMvc60n7nNQu5x2vtQWDhp58hhPhENojuiz16/r1x99cIdev443BKHoLZc0OjlVXfKfdI+DcqmgcqnQdD+Zc/27Y+NnqgWPRk6DKBULs/o8dRQLKhcLmjwz3dKKi21rlqhg0rSbu8HojRuXqVT4oVb2Jfcmf9dly3Tg2LiWX0AfIKAdbVndp5s2LdNVa/qyHkqmWHGQoh++eEL/cOB4dfVBf2+Hjo5O6uDJU3NuU5A06zzyeL/b+cSnKuwbPqlLli1oaX90Ero6itW9qFuG+madQ53k01TnnPYfHNH6mhUWi3s69Nvv2lz3aLykxW/yWHHgl3grQVJfr/FWhVOnz+jo6KRWzLFVYevqPl0y2Dvvx7wlJX4q1shxY82qXULbyJPHuLN2CM0RfWZmumHj0sx/tiE9PZ1FjU2embceB1KlC/9FCRf9O4oFnZ6anikctDjunqgw28qKiwWdperPlbkKB6sHerT3Y7v0ltetaPpznO3qtf361J1bz7ndE2hni7o69Lk9Vwe/0o3CQYr+/JGfqFwq6Ke3Vpop9vdWVhwcGpmoWzhYtqhLN2ysNDWrWd18TnFzxJeOj+uieTy7uxHxG+nNq/qq5/+WCjbrTX6rXj4xodHJM6n1dJjLphWVPgcrE+qpgPYQbyVIYoVMwaz6vRw3Rp1rq8L2tf36xr/YlZtTK+LCQdorDhp58vjGTcv0cztWp9qgC0DrejtLGom2KpQK9qpTrJLwW7e/VnffcEmi/2a5NLs5YqtbLOIVC632eNgRLd+v12B01ZKeRLdsAPADWxVSVCyYfnrLyuo+2/7esvYNn9TJU1N67UX1nyp+5KZL9c2nD2nDBZyOIM00R5x27fNk7eLF3Xr8wHFtGerTyERlr9+6wd5EzxZfurBT3/jVGzN9o3Xz5cv17f1HEj3GCNm7uK9bpYJd8PfguVSWiFYqB8MnTknSnCsO8ibeqpBGj4NFTW5VGOrv0X/4mSvmY0gA5lFvuaixqDli0o0RY2+/8uLz/6EGdURbC2ZWHLR2f4yPu231v8EdVw/p6OhkYidOAQgDhYMU/Zu3v2bW7/t7yzp0ckIjE1Natqh+H4Jta5bo+//2lgvu5BmvOJDSPbv2XK5dP6BXxie1bFGX+qamVbDk924XC6ZLUmwEOZcVi7v0X95zVaZjQPJ+4Q3rtGvj0kT21RYKMz0OXjo+LinZc8OzMlcDr/kS39eKBasWSgH4qyfqcXDq9JnEGyPOp5mtCpUHJi1vVSi3vlVBqmy7+513b2np3wAQHgoHGerv7dSJU5UfJudrYNhIF89SzUS6XZqAvf+6tXr/dWslVd5g/Ootl2l7k03YgLQNLOic89STZljNioOXoxUHy30oHBTjHgfzP6mPV1J1dxTZ5w4EYEFnSWOTlSaDeSscTJ5xM8dIJlQ46ExwtSYAXCgKBxnqr9lGUK/HQTNq9/61y4qDs/3T3RuyHgKQidoeBy8dP6XeclELPTgXuFwqqKdcTKX51YLOkooFS7yzOoD21NNZ1Jlpp2Njp1MpTialXKz0OEiqOWJvuUTBFEBmKFlmqL/mCebSBI+oqW1o066FAyBUtT0OXnxlXCsWd3kxCYwLB2kwMy3qKqm7zI8wIAS9UW+AIyMTuSoYlkuFWYWDrlJrY+8uF3O14gKAX5h1Zai/Z6abbbIrDigcAO2qYFYtHDxzcCSRhovtoFwspNIYMba4uyNXTx4BNK+3My4cTKZWoExCR7EQnaowpe6O1ldkXbZ8oS715GcGgPyhcJCh/t6ZwsH5ehw0oh2bIwKoMDM5SadOn9Fzh0d12fJkm4Rm5Y2blunWzRel9vkW95QpHACB6I2KBYdHJnL1fV8pHDiNTp5JpODxKzddqi/80rUJjAwAGpf/jbU5FhcOFnd3JLr0rrY5YrscxwigwiQ5J+0/NKJpJ21M+HSRrNy5Y3Wqn2/XxqWamp5O9XMCyEZ8DOHUtFNXjlYcxKfNnBg/zRYDALlH4SBDS3orb+qT3KYg5aM5IhCqQtTPYN/wSUnyZsVB2j76po1ZDwFASnpr3nT35GrFQeV+f3z8dK62WADAXNiqkKHOUlELOkuJblOQZjdHbOQYRwDzL/72fGr4pDqKprWDvdkOCADa3PJFMw2kCzlqJhs/yDk+flrdKfaAAYD5QOEgY+sGexNvjtYR9ThY2FWaVUQAkL24OdaTL53Q+qULZq0QAgC82lB/j/79O14nSVqcoy2YtYWDPK2UAIC5UP7M2Od/4fXqLCX7xiHuccA2BaD9xA/L/vHgiK5asyTbwQBATrzvmjXasqpPq/t7sh7KBYt7HBwfP60NSzkNAUC+UTjI2Hy8uadwALSveJntyMSUFnVxCwaAC3XFqsVZD6Eh5ZoVB4uYkwHIOdbIeijeqkDhAGg/8e6hianp6qQSAOCfeKuCc9LggvJ5/jQAtDdmrR4qRisOOIoRaD/xioPJqWn6GwCAxzpqjsceXJBsI2wASBuzVg+x4gDIh46E+5sAANpH7T2ewgGAvGPW6qG4xwH76YD2U3uUGCsOAMBftdvRBhM+ehsA0sas1UOlgun2LRfrxkuXZj0UAGepPSG1XOS4VADwVW1xmB4HAPKOlt4eMjN96s6tWQ8DwBwKBVYcAEAIyjVbFZayVQFAzjFrBYAUGVsVACAItc0Rl/Sy4gBAvjFrBYAU1W5VoDkiAPgr7nGwpKeDQjGA3OMuBgApqm2OSI8DAPBXXCzgRAUAPqBwAAApmrXigCdQAOCteFUZhQMAPmDWCgAposcBAIQh7nHAUYwAfMCsFQBSVLs5gcIBAPirXN2qQGNEAPnHrBUAUjSrx0GJHgcA4KuujqI6iqaVfd1ZDwUAWlbKegAAEJJCTbmWFQcA4K+ujqK++MvX6dLlC7IeCgC0jMIBAKRo9qkKFA4AwGebh/qyHgIAJIJZKwCkaFZzxBK3YAAAALS/llYcmNlzkk5KOiNpyjm3PYlBAYCvao9jZMUBAAAA8iCJrQq7nXOHE/h3AMB7BY5jBAAAQM4wawWAFNWuOIjP+AYAAADamTnnmv/LZj+SdEySk/TfnHP3zvFn7pZ0tyQtX75823333df05wvJyMiIFiygC68vQsozpGttxqMvT+k/PzYhSfrtG7q1tKe967fk6R8y9Qt5+odM/UKe/vE50927dz9ar/1Aq1sVdjrnXjSzZZK+amZPOee+WfsHomLCvZK0fft2t2vXrhY/ZRj27t0r/lv5I6Q8Q7rWZkw+MSw99qgk6YY3XKfli7oyHtG5kad/yNQv5OkfMvULefon1ExbetTlnHsx+v+Dkh6QtCOJQQGAr+hxAAAAgLxpetZqZr1mtjD+taRbJP0gqYEBgI8KNXddehwAAAAgD1rZqrBc0gPRmeQlSX/qnHswkVEBgKeMFQcAAADImaYLB865ZyVtTnAsAOA9tioAAAAgb5i1AkCK4uMYiwVTscBWBQAAALQ/CgcAkKJ4xQH9DQAAAJAXFA4AIEXxTgW2KQAAACAvmLkCQIpMlcpBZ4nbLwAAAPKBmSsApKjAigMAAADkDDNXAEhRoRD3OOD2CwAAgHxg5goAKZpZcUBzRAAAAOQDhQMASJEZKw4AAACQL8xcASBF8XGMZZojAgAAICeYuQJAimiOCAAAgLxh5goAKSpUtyrQ4wAAAAD5QOEAAFJkrDgAAABAzjBzBYAUmaIeBxQOAAAAkBPMXAEgRYXorsuKAwAAAOQFM1cASFG1xwGnKgAAACAnmLkCQIpmTlWgOSIAAADygcIBAKTIjB4HAAAAyBdmrgCQonirQpmtCgAAAMgJZq4AkKICxzECAAAgZ5i5AkCKqs0RKRwAAAAgJ5i5AkAGyjRHBAAAQE5QOACAFBUKrDgAAABAvjBzBYAUVXsc0BwRAAAAOcHMFQBSRI8DAAAA5A0zVwBIUVww6O4oZjwSAAAA4MKUsh4AAISkv7ese9+3TdeuH8h6KAAAAMAFoXAAACm75bUrsh4CAAAAcMHYqgAAAAAAAOqicAAAAAAAAOqicAAAAAAAAOqicAAAAAAAAOqicAAAAAAAAOqicAAAAAAAAOqicAAAAAAAAOqicAAAAAAAAOqicAAAAAAAAOqicAAAAAAAAOqicAAAAAAAAOqicAAAAAAAAOqicAAAAAAAAOoy51x6n8zskKTnU/uE+TYo6XDWg0BiQsozpGsNAXn6h0z9Qp7+IVO/kKd/fM50jXNu6VwvpFo4wIUzs0ecc9uzHgeSEVKeIV1rCMjTP2TqF/L0D5n6hTz9E2qmbFUAAAAAAAB1UTgAAAAAAAB1UThoX/dmPQAkKqQ8Q7rWEJCnf8jUL+TpHzL1C3n6J8hM6XEAAAAAAADqYsUBAAAAAACoi8IBAAAAAACoi8IBAAAAAACoi8IBAAAAAACoq5T1ADCbma2TtFXSD51zT2U9HjTHzBZIeoukIUlTkp6R9BXn3HSmA0uJmb3JOffVrMeB1nA/yr/Q70U+MrMVkuScGzazpZKul7TPOfdEtiNDM8zsNlW+J09lPRbML+ZGfgh5bsSKg4yZ2Zdqfn27pG9IulXSl81sT0bDQgvM7A5JD6kyWf+wpB2S3ifpe2Z2RZZjS9Hnsh4AGsf9yC/ci/xjZr8k6duSvmNmH5L015LeLukvzeyuTAeHZn1B0gEz+2Mz+ykzK2Y9IMwb5kY5xNxoBscxZszMHnPObY1+/X8kvcc59yMzG5T0defc5mxHiEaZ2eOSrnHOjUU5/olz7s1mdqWk/+qcuy7jISbCzP6q3kuS3uic601zPGgd9yO/hHIvComZfV/S6yV1S3pe0oZo5cESSQ8557ZkOT40zswek/RGSe+UdKek10l6QNKfOeceznJsaBxzI/8wN5rBVoXs1VZuSs65H0mSc+6wmbGUNJ9M0nj061FJyyTJOfe4mS3KbFTJu17SeyWNnPVxU+XJJvKH+5FfQrkXheS0c25M0piZ7XfODUuSc+6YmfEkKJ+cc+6YpN+X9PvRVpQ7JH3SzFY554ayHR4axNzIP8yNIhQOsrfZzE6ockPpNLMV0dODsiSWq+XT/5T0oJk9LOmtku6XJDPrVyVnX3xH0thcT0TMbF8G40HruB/5JZR7UUimzazDOXda0tviD5pZl9h+mlezvhejYtCnJX3azNZkMyS0gLmRf5gbRdiq0KbMrE/S5c65b2c9FjTOzH5K0msk/UPcCMfMCpI6nHMTmQ4OaBD3o/ziXuQXM1st6UXn3NRZH1+pyvfo17IZGZplZrucc3uzHgeAxoQ4N6JwAKBl0RPMeLklPECmfiFPAADQCpa1ZczMhszsPjP7lpn9SzPrqHntSxkODU0KJVMzWx1d5yFJ/1fS35vZwehjazMeHppApn6pyfOgyNN7UeNEeIRM8yeUOWBIyHQGPQ6y998l/YUqe6LukvSwmd3qnDsiib1t+RRKpl+Q9LuqdJc9I0nRMVLvknSfpGuyGxqaRKZ+IU/PmNnP1HtJ0oo0x4JkkKl3QpkDhoRMI2xVyJiZfa/2+CQze6+k35B0m6T7nXNXZTU2NCeUTM3sGefcpY2+hvZFpn4hT/+Y2WlJf6LZXb5j73TOLUx5SGgRmfollDlgSMh0BisOstdhZl3OuVOS5Jz7vJkNS/pbSZz1mk+hZPqomX1G0h9K+kn0sSFJ75f0WGajQivI1C/k6Z/HJf0n59wPzn7BzG7OYDxoHZn6JZQ5YEjINMKKg4yZ2UclfffsY1vMbKuke5xzb8pmZGhWKJlGx9DcJel2SStVWVZ5QNJfSfocHdvzh0z9Qp7+MbPrJT3vnPvxHK9td849ksGw0AIy9Usoc8CQkOkMCgcAAAAAAKAuTlVoQ2b23azHgGSFkmko1xkSMvULefqHTP1Dpn4hT/+EmimFg/ZkWQ8AiQsl01CuMyRk6hfy9A+Z+odM/UKe/gkyUwoH7elvsh4AEhdKpqFcZ0jI1C/k6R8y9Q+Z+oU8/RNkpvQ4aDNmtkjSpZKedc4dy3o8aB2ZAgAAAMgzVhxkzMw+b2aD0a/fLOkJSf9R0vfM7F2ZDg5NCSVTMztqZn9gZjeZWZBLtnxDpn4hT/+QqX/I1C/k6R8ynUHhIHubnXOHo1//pqTrnXM3S9om6V9nNyy0IJRMD0n6nqR/J+mAmX3KzK7JdkhoEZn6hTz9Q6b+IVO/kKd/yDRC4SB7hWgpuyRNS/qxJEVvPEuZjQqtCCXTUefc7znndkq6VtILkj5jZs+a2ScyHhuaQ6Z+IU//kKl/yNQv5OkfMo1QOMjeb0l6yMw+KOnvJN1vZj9vZv9D0oOZjgzNCiXT6nIt59yPnXP3OOeukvRWSRPZDQstIFO/kKd/yNQ/ZOoX8vQPmUZojtgGzGyDpF+UtFGVJ9IHJH3JOfe3mQ4MTQshUzP7HefcP896HEgOmfqFPP1Dpv4hU7+Qp3/IdAaFAwAAAAAAUJdP+61zK+q8/w5JKyU5SS9K+rJzzqdl7UEJJdNQrjMkZOoX8vQPmfqHTP1Cnv4h0wpWHGTMzH5XleXsf6TKcnZJWiXp5yU945z7SEZDQ5NCyTSU6wwJmfqFPP1Dpv4hU7+Qp3/IdAaFg4yZ2dPOuY1zfNwkPe2cuzSDYaEFoWQaynWGhEz9Qp7+IVP/kKlfyNM/ZDqDUxWyd8rMdszx8aslnUp7MEhEKJmGcp0hIVO/kKd/yNQ/ZOoX8vQPmUbocZC9PZI+a2YLNbP8ZUjSieg15M8ehZHpHoVxnSHZIzL1yR6Rp2/2iEx9s0dk6pM9Ik/f7BGZSmKrQtswsxWqNNwwSQecc8MZDwktCiXTUK4zJGTqF/L0D5n6h0z9Qp7+IVO2KrQN59ywc+5R59wjkn456/GgdaFkGsp1hoRM/UKe/iFT/5CpX8jTP2RK4aBd3Zb1AJC4UDIN5TpDQqZ+IU//kKl/yNQv5OmfIDOlcNCeLOsBIHGhZBrKdYaETP1Cnv4hU/+QqV/I0z9BZkqPgzZkZgXn3HTW40ByQsk0lOsMCZn6hTz9Q6b+IVO/kKd/Qs2UFQdtwMzebGZ3mdlaSYq/EM3sg5kODE0LJdNQrjMkZOoX8vQPmfqHTP1Cnv4h0woKBxkzs09I+leSrpD0dTP7lZqXP5zNqNCKUDIN5TpDQqZ+IU//kKl/yNQv5OkfMp3BVoWMmdn3JW11zk2ZWZ+kP5W0zzn3UTN7zDm3NdsRolGhZBrKdYaETP1Cnv4hU/+QqV/I0z9kOoMVB9krOeemJMk594qkWyUtMrP7JZWzHBiaFkqmoVxnSMjUL+TpHzL1D5n6hTz9Q6YRCgfZ229mN8a/cc6dcc7dJWmfpMuzGxZaEEqmoVxnSMjUL+TpHzL1D5n6hTz9Q6YRtipkzMy6Jck5Nz7Hayudcy+kPyq0IpRMQ7nOkJCpX8jTP2TqHzL1C3n6h0xnlLIeQOjiL0Iz63DOnT7r5YkMhoQWhZJpKNcZEjL1C3n6h0z9Q6Z+IU//kOkMtipkzMx2m9kBSS+a2VfiYz4iX8loWGhBKJmGcp0hIVO/kKd/yNQ/ZOoX8vQPmc6gcJC9eyS92Tm3VNK9kr5qZtdEr1l2w0ILQsk0lOsMCZn6hTz9Q6b+IVO/kKd/yDTCVoXslZ1zT0iSc+6LZvakpL80s1+XRAOKfAol01CuMyRk6hfy9A+Z+odM/UKe/iHTCIWD7J02sxXOuWFJcs49YWY3SfprSeuzHRqaFEqmoVxnSMjUL+TpHzL1D5n6hTz9Q6YRtipk79clLa/9gHPugKRdkj6ZxYDQslAyDeU6Q0KmfiFP/5Cpf8jUL+TpHzKNcBwjAAAAAACoixUHGTOzxWb2STN7ysyORP97MvpYX9bjQ+NCyTSU6wwJmfqFPP1Dpv4hU7+Qp3/IdAaFg+z9uaRjknY55waccwOSdkcfuz/TkaFZoWQaynWGhEz9Qp7+IVP/kKlfyNM/ZBphq0LGzGyfc+6yRl9D+wol01CuMyRk6hfy9A+Z+odM/UKe/iHTGaw4yN7zZvZrZlZtumFmy83s45J+kuG40LxQMg3lOkNCpn4hT/+QqX/I1C/k6R8yjVA4yN67JQ1IetjMjpnZUUl7JfVLuiPLgaFpoWQaynWGhEz9Qp7+IVP/kKlfyNM/ZBphq0IbMLNNklZJ+o5zbqTm429xzj2Y3cjQrFAyDeU6Q0KmfiFP/5Cpf8jUL+TpHzKtYMVBxszsn0n6sqQPS/qBmd1e8/InshkVWhFKpqFcZ0jI1C/k6R8y9Q+Z+oU8/UOmM0pZDwD6RUnbnHMjZrZW0hfNbK1z7lOSLNuhoUmhZBrKdYaETP1Cnv4hU/+QqV/I0z9kGqFwkL1ivOTFOfecme1S5QtyjQL7YvRIKJmGcp0hIVO/kKd/yNQ/ZOoX8vQPmUbYqpC9YTPbEv8m+sJ8u6RBSVdkNSi0JJRMQ7nOkJCpX8jTP2TqHzL1C3n6h0wjNEfMmJmtkjTlnBue47Wdzrm/y2BYaEEomYZynSEhU7+Qp3/I1D9k6hfy9A+ZzqBwAAAAAAAA6mKrAgAAAAAAqIvCAQAAAAAAqIvCAQAAAAAAqIvCAQAAAAAAqOv/A4cz0F3RovCJAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1296x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(18,6)) # dimenzije grafika\n",
    "plt.plot(podaci_najbliza['date_time'],podaci_najbliza['so2'])\n",
    "plt.xticks(rotation=90);   # date_time podatke ispisujemo vertikalno\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2030c588-72d6-4ed8-b6c9-e51f555fae99",
   "metadata": {},
   "source": [
    "Исте ове податке можемо да прикажемо и хистограмом. Тада ћемо јасно видети расподелу измерених вредности, односно колико је пута очитана која вредност за месец дана. Са друге стране, изгубићемо информацију о времену када је вршено мерење. Да би график био прегледнији нагласићемо да интервал од 0 до 30 делимо на 30 делова."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d15cec4e-305f-4696-982d-1eae1129ee17",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "podaci_najbliza['so2'].hist(bins=30,range=(0,30))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "430f2a01-475b-421f-923c-a0ae2c4a5935",
   "metadata": {},
   "source": [
    "Овде анализа података о квалитету ваздуха тек почиње. Тумачење ћемо препустити вама. Још боље би било да то радите тимски са професорима у школи. Из мало труда, могли бисте да на направите \"контролну таблу\" где би у рeалном времену били исцртавани актуелни подаци са упозорењем када је квалитет ваздуха испод прихватљивог нивоа. "
   ]
  }
 ],
 "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": "3.9.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}