{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Сезонные колебания"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Если есть регулярный вброс энергии или вещества в систему, то из-за экспоненциального спада возникает чередование периодов подъема и спуска.\n",
    "Биологическое значение обычно имеет одна из этих фаз.\n",
    "\n",
    "В северном полушарии, по сути, два сезона:\n",
    "- лето - активный плодотворный период в природе\n",
    "- зима - период покоя\n",
    "\n",
    "Остальные - всего лишь переходы, однако именно в переходные периоды идет активная учеба - осенний и весенний семестры. Учеба в общественных и биологических системах - переход от незнания к знанию."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Название активного периода становится единицей счета циклов для измерения интервалов:\n",
    "- активность летом - 5 лет\n",
    "- активность днем - 5 дней\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "На [странице](http://berkeleyearth.lbl.gov/stations/169045), откуда взят источник данных об истории измерений температуры на метеостанции Ростова-на-Дону, предложены классические графики с динамикой колебаний вдоль одной оси времени.\n",
    "\n",
    "Так как в дате содержится и год и месяц - мы можем визуализировать временной ряд в двух временных осях без вычисления каких-либо моделей."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": [
     "remove-cell"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%pylab inline\n",
    "import pandas as pd\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Поскольку информация о колонках указана в комментариях, то мы при импорте данных в таблицу указываем параметры\n",
    "- без заголовков ( `header=None,` )\n",
    "- свой перечень кратких имен (`names=[...]`)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>year</th>\n",
       "      <th>month</th>\n",
       "      <th>v0</th>\n",
       "      <th>dv0</th>\n",
       "      <th>q</th>\n",
       "      <th>br</th>\n",
       "      <th>v</th>\n",
       "      <th>dv</th>\n",
       "      <th>reg</th>\n",
       "      <th>dreg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1536</th>\n",
       "      <td>2013</td>\n",
       "      <td>7</td>\n",
       "      <td>24.486</td>\n",
       "      <td>1.279</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24.417</td>\n",
       "      <td>1.210</td>\n",
       "      <td>23.569</td>\n",
       "      <td>0.536</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1537</th>\n",
       "      <td>2013</td>\n",
       "      <td>8</td>\n",
       "      <td>24.419</td>\n",
       "      <td>1.928</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>24.408</td>\n",
       "      <td>1.916</td>\n",
       "      <td>24.037</td>\n",
       "      <td>1.720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1538</th>\n",
       "      <td>2013</td>\n",
       "      <td>9</td>\n",
       "      <td>14.553</td>\n",
       "      <td>-2.249</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.567</td>\n",
       "      <td>-2.235</td>\n",
       "      <td>14.302</td>\n",
       "      <td>-2.325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1539</th>\n",
       "      <td>2013</td>\n",
       "      <td>10</td>\n",
       "      <td>8.736</td>\n",
       "      <td>-0.628</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8.686</td>\n",
       "      <td>-0.678</td>\n",
       "      <td>8.523</td>\n",
       "      <td>-0.667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1540</th>\n",
       "      <td>2013</td>\n",
       "      <td>11</td>\n",
       "      <td>9.600</td>\n",
       "      <td>6.366</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</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": [
       "      year  month      v0    dv0    q   br       v     dv     reg   dreg\n",
       "1536  2013      7  24.486  1.279  0.0  0.0  24.417  1.210  23.569  0.536\n",
       "1537  2013      8  24.419  1.928  0.0  0.0  24.408  1.916  24.037  1.720\n",
       "1538  2013      9  14.553 -2.249  0.0  0.0  14.567 -2.235  14.302 -2.325\n",
       "1539  2013     10   8.736 -0.628  0.0  0.0   8.686 -0.678   8.523 -0.667\n",
       "1540  2013     11   9.600  6.366  1.0  0.0     NaN    NaN     NaN    NaN"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# u='http://berkeleyearth.lbl.gov/auto/Stations/TAVG/Text/169045-TAVG-Data.txt'\n",
    "u='d/169045-TAVG-Data.txt'\n",
    "D = pd.read_csv(u, delim_whitespace=' ',  comment='%', header=None,\n",
    "                 names='year month v0 dv0 q br v dv reg dreg'.split())\n",
    "D.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Как мы видим, данные обрываются на дате 2013-11. В 19-м веке сбор данных также был нерегулярным. Поэтому для целей визуализации отберем колонку \"Выверенные температурные данные\" за 100 лет."
   ]
  },
  {
   "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>year</th>\n",
       "      <th>month</th>\n",
       "      <th>v</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>330</th>\n",
       "      <td>1913</td>\n",
       "      <td>1</td>\n",
       "      <td>-4.859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>331</th>\n",
       "      <td>1913</td>\n",
       "      <td>2</td>\n",
       "      <td>-6.552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>332</th>\n",
       "      <td>1913</td>\n",
       "      <td>3</td>\n",
       "      <td>3.278</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>333</th>\n",
       "      <td>1913</td>\n",
       "      <td>4</td>\n",
       "      <td>12.135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>334</th>\n",
       "      <td>1913</td>\n",
       "      <td>5</td>\n",
       "      <td>13.987</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     year  month       v\n",
       "330  1913      1  -4.859\n",
       "331  1913      2  -6.552\n",
       "332  1913      3   3.278\n",
       "333  1913      4  12.135\n",
       "334  1913      5  13.987"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = D.loc[D.year>1912, ['year','month','v']]\n",
    "X.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Готовим названия месяцев (для красоты)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "янв фев мар апр май июн июл авг сен окт ноя дек\n"
     ]
    }
   ],
   "source": [
    "# так можно получить готовый список 3-буквенных названий месяцев на английском\n",
    "# momo = pd.tseries.frequencies.MONTHS\n",
    "# momo = list(map(str.capitalize, momo))\n",
    "\n",
    "# а так мы получим локализованные названия\n",
    "import locale\n",
    "locale.setlocale(locale.LC_TIME, '')\n",
    "momo=[datetime.datetime(2016,i,1).strftime('%b') for i in arange(1,13)]\n",
    "print(' '.join(momo))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Группируя по году и месяцу, мы создаем двойной индекс, который методом `unstack()` разделяем на индекс строк и индекс колонок, получая таким образом широкую таблицу."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
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       "        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>янв</th>\n",
       "      <th>фев</th>\n",
       "      <th>мар</th>\n",
       "      <th>апр</th>\n",
       "      <th>май</th>\n",
       "      <th>июн</th>\n",
       "      <th>июл</th>\n",
       "      <th>авг</th>\n",
       "      <th>сен</th>\n",
       "      <th>окт</th>\n",
       "      <th>ноя</th>\n",
       "      <th>дек</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1913</th>\n",
       "      <td>-4.859</td>\n",
       "      <td>-6.552</td>\n",
       "      <td>3.278</td>\n",
       "      <td>12.135</td>\n",
       "      <td>13.987</td>\n",
       "      <td>17.212</td>\n",
       "      <td>21.268</td>\n",
       "      <td>23.346</td>\n",
       "      <td>16.663</td>\n",
       "      <td>7.469</td>\n",
       "      <td>4.932</td>\n",
       "      <td>1.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1914</th>\n",
       "      <td>-2.559</td>\n",
       "      <td>0.648</td>\n",
       "      <td>6.878</td>\n",
       "      <td>9.135</td>\n",
       "      <td>16.487</td>\n",
       "      <td>19.612</td>\n",
       "      <td>23.368</td>\n",
       "      <td>20.246</td>\n",
       "      <td>14.563</td>\n",
       "      <td>8.669</td>\n",
       "      <td>-2.168</td>\n",
       "      <td>-3.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1915</th>\n",
       "      <td>3.141</td>\n",
       "      <td>-2.852</td>\n",
       "      <td>1.978</td>\n",
       "      <td>10.835</td>\n",
       "      <td>13.987</td>\n",
       "      <td>19.412</td>\n",
       "      <td>23.568</td>\n",
       "      <td>19.246</td>\n",
       "      <td>14.063</td>\n",
       "      <td>7.469</td>\n",
       "      <td>4.332</td>\n",
       "      <td>-0.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1916</th>\n",
       "      <td>-3.059</td>\n",
       "      <td>-2.352</td>\n",
       "      <td>1.878</td>\n",
       "      <td>9.935</td>\n",
       "      <td>15.387</td>\n",
       "      <td>19.012</td>\n",
       "      <td>22.168</td>\n",
       "      <td>19.946</td>\n",
       "      <td>13.363</td>\n",
       "      <td>9.469</td>\n",
       "      <td>3.032</td>\n",
       "      <td>-2.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1917</th>\n",
       "      <td>-4.459</td>\n",
       "      <td>-8.952</td>\n",
       "      <td>-0.522</td>\n",
       "      <td>11.935</td>\n",
       "      <td>13.087</td>\n",
       "      <td>20.412</td>\n",
       "      <td>21.468</td>\n",
       "      <td>21.546</td>\n",
       "      <td>15.963</td>\n",
       "      <td>10.369</td>\n",
       "      <td>7.332</td>\n",
       "      <td>-0.84</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        янв    фев    мар     апр     май     июн     июл     авг     сен  \\\n",
       "year                                                                        \n",
       "1913 -4.859 -6.552  3.278  12.135  13.987  17.212  21.268  23.346  16.663   \n",
       "1914 -2.559  0.648  6.878   9.135  16.487  19.612  23.368  20.246  14.563   \n",
       "1915  3.141 -2.852  1.978  10.835  13.987  19.412  23.568  19.246  14.063   \n",
       "1916 -3.059 -2.352  1.878   9.935  15.387  19.012  22.168  19.946  13.363   \n",
       "1917 -4.459 -8.952 -0.522  11.935  13.087  20.412  21.468  21.546  15.963   \n",
       "\n",
       "         окт    ноя   дек  \n",
       "year                       \n",
       "1913   7.469  4.932  1.66  \n",
       "1914   8.669 -2.168 -3.44  \n",
       "1915   7.469  4.332 -0.34  \n",
       "1916   9.469  3.032 -2.54  \n",
       "1917  10.369  7.332 -0.84  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "W = X[[\"year\", \"month\", \"v\"]].groupby(by=[\"year\", \"month\"]).max().unstack()\n",
    "W.columns=momo\n",
    "W.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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jJGtbkGUt5mwW9j5QOkKyY5bUbw3aTVsqW42wwvzJMDMzMxtHpcZgjiePwTQzMzOzMVWkginp/BGOL5O0QtKKb1816kL0ZmZmZrWjtrYslyop1UX+ZuBzQw+2bnH02eujXoMwzczMzPbBZOgiz1bBlLQaOGi4u4Du0c7fuLVeb/7hh00d/UEVM39a/RaH76VekyMOOubQ0hGS9WzYXDpCso33ri8dIcnBpx9bOsKE1zdl1F8zlXN419rSEZL1HrO0dIT9UK9JgXWVswXzLOCGiPiNqYeSfpKxXDMzM7PKmgzLFGXrsI+I+xh5VfjLcpVrZmZmZmVlHYMZER8e4fjHcpZrZmZmVlUegzkGJHVGRO+QY8+LiKv2dt6iuYN5g42xdRurNXtrX0TU7wPe2zXcsN7qiq3bS0dItmv95tIRknXPqtcY6Bis1/cbwK6f31o6QpJZ85eUjpAs5tdvbG7HlMWlI9RS1WZ855DtFUp6pqQHgQckfU/SCS13j+mG6mZmZmZWHTlbMP8ReGJE3CfpmcBVkl4XEdfRmEluZmZmNum4i/zAdDcn+hARX5d0B/AlSX8GeI1LMzMzswkqZwVzu6RjI+IugIhYKelZwNeA31i6aKhVa+tVu9+woad0hGTrFk4vHSHZMR27SkdIUsdxNmtveaB0hGSzDplTOkKSraseLh0h2eyj6jWmsW/q7NIRkt0/cETpCMluurujdIRkT39M6QRuwTxQFww9EBEPSDoXeHnGcs3MzMwqyxXMAxARt4xw/G7gXbnKNTMzM7Oycs4inyPpUklrJG2RtFLSlZKelKtMMzMzs6pTW1uWS5XkTPMJ4HbgCODRwI+AS4BLJJ2TsVwzMzMzKyjnGMxjI+Lc5vVVkk6KiOslPQ+4unkZ0bTueo1POPWMgdIRki3pXlc6QjJtr9cC1R1zZ5eOkOzoZzyudIRk2x+o12e5ai0N+6Jr8YLSEZL0tneWjpBsYecjpSMkO/nI7tIR9kP5nz/vRX5gtkg6CUDSacBOgIhYD8zIWK6ZmZmZFZSzBfNC4ApJ7UAncD6AJAEfzFiumZmZWWV5FvkBiIibgBMkzYuIDS3HQ1K9NrU1MzMzGyN1HCaTKmcLJgCtlcsWHwBO3/t5efLkct3P6jfe57QT6rVwMsDiuL10hAmvd/O20hGS7dywvXSEJHOOqd/PXvuSg0tHSNI/0F86QrJBppSOkGxqR/3eZ6jf4vB1lK2CKelmht9zXMBRuco1MzMzqzJ3kR+YC2jMFD91yHEBV2Us18zMzMwKyjkG80ZJl0fE/UPvk3RlrnLNzMzMqswtmAcoIt4ywvGLRjt39YM9Yx8ooyk1XNNqelf9xs5M2bqrdIQ0gzUbTAxsXT3csOlqa2uv19i1Lfc9XDpCsu55d5eOkKRzzsLSEZJtmfrY0hGS/fAX9Zt/8DuPLp1gckzymfiv0MzMzMzGVfZZ5GZmZmb2K5Ohi9wtmGZmZmY2ptyCaWZmZjaOJsMYzMpWMBfMr9fA4bXr6jUpCeDWVdNKR0j2mMUHlY6QpLO7q3SEZPNPPLx0hGQP/viXpSMkWXDiIaUjJGufVa+fvR3z6vc5ntdevwl2Rxw8q3SEepK7yM3MzMzMklS2BdPMzMxsIvIkHzMzMzOzRJVtwezprdcC1T/65k2lIyRb8uozS0dItmHWEaUjJJnzwOdKR0gWNVwcvr27o3SEJHd947bSEZKd+jdDd/2ttpmrby0dIdm6o59dOkKyrTsmfktcDpNhks/Ef4VmZmZmNq4qVcGUtEzSCkkrbrhmeek4ZmZmZmNObcpyqZKsXeSSXgScC8wF1gI/Ay6LiG3DPT4ilgPLAd57RQ376czMzMxGUaqLXFI38BXgSGAAuDwi/kHSPODTwFHAvcDLImLjgZSVrYIp6RLgcODjwHzgFcB04MeSnhURK/d2/sJ5lWpcHdULL6jfeManH7+mdIRki9f+rHSEJAMzppeOkCz6+kpHSDbYP1g6QpL27soOfx9Zz+7SCZL0H3xU6QjJulS/9ZQH6vWjZw3vjYhrJE0FfiTpa8DrgS9GxMcl/RFwcfPYfsv5Lfcs4OSICABJb4uId0v6OvBvwNkZyzYzMzOrpFLd2RGxG7imeX2XpLuBRcDTgDc1H/ZfwE85wApmzmbCQRotlkiaD3QCRMSNwGEZyzUzMzObdFrnsjQvy/by2EXAmcANwLyI2AwQEVtoDG08IDlbMN8D/FDSj4CzgEsAJAn4YcZyzczMzCorVwtm61yWvZYvdQH/A/xVRGyWNHTeywHv152tghkRn2lWLh8HfDgibmseD+DVo53/0MMDuaJl8dBDO0pHSHbG0TNLR0i2fU699heeOvi90hGSbbhtr8OjK2nT/ZtLR0iy+ORFpSMkG9g+7NzMytItNzD42CeWjpFk5fYlpSMk66zXErTVUXAdTEmdwOeBr0fE5c3DmyTNiIjtkmYBBzTBBzIvUxQR9wJf3lO53EPS83KWa2Zmk1vdKpdm40HSNODLwPcj4j0td30HOL95/SXAtw+0rGwVTEnPlPQg8ICk70k6oeXu94x0npmZmdlEJinLZR+cDjwVuEDSHc3Le4C3AudLuhN4IfAXB/oac47B/EfgiRFxn6RnAldJel1EXAdUazVQMzMzswkuIq4Fuka4+5yxLCtnBbM7Iu4DiIivS7oD+JKkPwO8iLqZmZlNSpNhL/KcFcztko6NiLsAImKlpGcBX6Ox5tJeHXlIvd78O2/fVTpCsns2LCwdIdkJXetLR0gyWMMNqQb66rdy8sGPP7h0hCRRx8/Ftu2lIyTp2LahdIRkBy2t3++ROTNruGlABXbJrtq2jjnk/GRcMPRARDwg6Vzg5RnLNTMzM7OCci5TdMsIx+8G3pWrXDMzM7NKmwRd5Dlnkc+RdKmkNZK2SFop6UpJT8pVppmZmZmVl7OL/BM01lV6A7AEeC/wEeASSRdHxNV7O3nFzTszRht7x504v3SEZB3t9RsH1tc1o3SEJFOXLi4dIVn9RubC5jvvLx0hybQFs0pHSNZ58NLSEZL0zTjgne7G3bQpPaUjJFtxy0gTkqvrvDO6S0fwGMwDdGxEnNu8vkrSSRFxfXOR9aubFzMzMzObYHJWMLc0K5W3SToN2AkQEesl1asZyszMzGyMSBN/DGbOCuaFwBWS2mlsmn4+gBpLzX8wY7lmZmZm1eUu8v0XETcBJ0iaFxEbWo6HpFtHO3/WrHqN6zhk8ZTSEZIdMmtL6QjJpuzcXTpCkt33P1A6QrLBvv7SEZL1bKvX52Kgt37v8fSjtpaOkKStv37jGbva6pd51qyO0hGsorKvkNpauWzxARr7YZqZmZlNKt7J5wBIupnh9xwXcFSucs3MzMysrNw7+VwNnDrkuICrMpZrZmZmVllepugARMSNki6PiN9YoE7SlbnKNTMzM6s0zyI/MBHxlhGOXzTauVu39o59oIymtNVvks+2nvKLzaZq79leOkKStkPqtTg1wNZbf1k6QrKOqZ2lIyTp21Wv7zeAGBgoHSFJ+9aNpSMku7NrSekIyR5zzGDpCPuhfr+v6yj7JB8zMzMz+5XJ0EU+8dtozczMzGxcuQXTzMzMbDx5maJyznliZaMN66a7onSEZEfOq9+ivv0D9dpltGOwfuOT+nbUa9FygIdueah0hCQzFk4vHSHZlOn1yrzz4ONLR0h21NT1pSMku3PjwtIRaqmxqeHENvGr0GZmZmY2rurVTGhmZmZWd5Ogi3ziv0IzMzMzG1eVbcH8+vfrtU7cUUd2lI6QbCDqtxZYtFX2IzuswR07S0dINvPwxaUjJOu67cHSEZJ0z6rfGrQbbri5dIQks489pXSEZJ3TDi0dIdnimTtKR9gPB5UO4GWKzMzMzMxS1as5yMzMzKzuJsFWkdleoaR7JH1e0qkJ5yyTtELSip9f/4lc0czMzMzKaVOeS4XkrEL3Ah8DPiTpG5KePNoJEbE8Ik6LiNMe/aQ/yBjNzMzMzHLJ2UUeEXENcI2k3wEuktQJvDsivjHayU88bVrGaGNvV0+1/nKYqDTYXzpCkrauztIRkm296/7SEZKt/cEjpSMk6Xt8X+kIyeYdd0jpCEnat9Rv0fL2BfX7XHz1hvpNWHvCCaUTgNxFPjYi4rsR8TTgL4E3SLpxPMo1MzMzs/GXswXzN5r0IuJ64JmSTstYrpmZmVl1VWy8ZA45K5hnjXRHRKzIWK6ZmZlZZWkS7OSTrYIZERskHQY8EhE7JR0NPAb4WUTcO9r5u3rq9eZP7RosHSGZiNIRkrXVbAzmwM5dpSMk69lSv4WTF54xp3SEJDOXlF/oOdW0o48oHSHJpsP3eQGTytg0UK/PMcDTf6te38kN9RsbX0c5lym6GPgu8L+SXgh8AXg28HVJL89VrpmZmVmlSXkuFZKzi/xlwAnAEuAW4NCI2CppAXAd8OmMZZuZmZlZIbmXKeoF7pf0XxGxtXlwvaT6bdxtZmZmNhY8BvOArJX0mIi4OSL+aM9BSccB20c7+ac/25Yx2thbvKRe63YCdB01tXSECa9j0aLSEZJNnfdQ6QjJps0b9SulUvp21W+9w/7NW0pHSNIWA6UjJJs9ZXPpCMluWzuvdIR6qlh3dg45K5jnA8N9i3YBf5KxXDMzMzMrKOcs8rUjHL8lV5lmZmZmVTcZlinKOYt8jqRLJa2RtEXSSklXSnpirjLNzMzMrLycXeSfAL4DvIHGTPL3Ah8B3i/p4oi4em8nH3nkjIzR8jjx8HqNq+pqr9/6Ze3r6re/cN20T+0qHWHCW3DCwaUjJGufN7d0hCQH3XQ1Wx93TukYSab3bmZH5+zSMWw8TIK9yHNWMI+NiHOb11dJOikirpf0PODq5mXCqFvl0sxsIqtb5XKPo44+unSEJEfVK64Bkh4PXB4RpzRvvwb4ELCu+ZAdEXHAOxXkrEJvkXQSQHPv8Z3QWKYIqF/zpJmZmdlYaFOeyygkfQC4ht+s/306Io5vXsZkG6ycLZgXAldIaqexL9P5AJIEfDBjuWZmZmaVpUJd5BHxZkn/Anwld1nZXmFE3BQRJwBnRsThEfGj5vEAbs1VrpmZmdlkJGmZpBUtl2X7eOrLJN0l6RpJJ45FlpwtmABExIZhDn8AOH1v5934k2FXOaqsHTsXlo6QbO7sztIRkh119Ji03I+bufd8pnSEZIMD9VugesHJh7H+1lWlY5iZ7Zt96M7eHxGxHFieeNpngf+IiJD0YuByRqmj7YtsFUxJNwPDvYMCjspVrplNTidd9Z3SEaxCvE+Z2b6JiJ6Wm1cAHx+L583ZgnkBjZniQ5ucBFyVsVwzMzOz6qrQMkWSngz8JCJ2AS8EVozF8+bcyedGSZdHxP1D75N0Za5yzczMzCqt0F7kkv4WeD5wtKQVwJuBJwL/KWk3sAZ43ViUlXUMZkS8ZYTjF+Us1/bNsUt7Rn9Qxcxdf0fpCEkGd+4qHSFZ3/b6Zd69aTvbXvXs0jH2Wd+u+q2be9hzziodIUk86pTSEZLdt+CM0hGS3bJucekIyV76pDKVuyqIiHcC7xxy+DrgPWNdVvZJPvvrP/9uSekIE963bqnfTj5mZma1573IzczMzMzSVLYF08zMzGxCqtAkn1wqW8H85PdKJ0gzvXuwdIRkx87dXDpCstjVUTpCkimzZ5WOkKxr1ubSEZLtfGRb6QhJFpx0WOkIyfo3bCwdIYna67fOb7vqN2xp+67JO57xgGRaB7NKJn4V2szMzMzGVWVbMM3MzMwmpEnQRT7xX6GZmZmZjSu3YJqZmZmNp0ILrY+nylYwH94YpSMk2bGjfpN8uk+YWzpCsrlzjigdIcm8OatKR0jW1rmmdIRk7V2V/Sob1o619ZowAzD/yHpNTNo5dU7pCMke2LGwdIRkK++v34Yd0FU6wKRQr29lMzMzs7rzQuvjS9IySSskrfjR1ctLxzEzMzMbe1KeS4Vkq2BK+mjL9emS/lHSVZLeLql7uHMiYnlEnBYRp515zrJc0czMzMwso5xd5L/Tcv1SYBPwfuBFwEeBP9zbye1T8gXL4XHHl06Qbv60naUjJDtoywOlIyTpvfuu0hGSrf3p3aUjJGvvrtcC/BtXbigdIdmMQ+o1NnfTWUtLR0g2u79+38mLFs4uHaGeJsEyReM1BvNM4MSIGJD0A+AX41SumZmZmY2znBXMgySdS6MbfmpEDABEREiamrFcMzMzs+qaBJN8clYwrwGe27z+LUmdEdEr6enATzOWa2ZmZlZdFZuQk0O2CmZEXDDC8WtoVD736he3bRrzTDlNn1a/NSWPn725dIR0Ua/1UTuXLCkdIdmMRfVbu3PjvetLR0gy7+j5pSMk61q0oHSEJDP76rfWqDrqt57yk48fKB1hP9Tv56+OxrWNVtIl41memZmZWeWoLc+lQrK1YEr652EOv1xSF0BEvD5X2WZmZmZWTs4xmEcChwMfBPb0a56Hx1+amZnZZOYxmPsvIn5f0ttorHv5qojYIOltEfEfuco0MzMzqzzPIj8wEfFeSdcDX5P0lpRzL3h+vVZaf3Br/QZn/2jN4aUjJPv9g1aWjpCk5/Y7S0dItnlV/SZHTJ0zrXSEJD3bdpeOkKxt+vTSEZJM3Vm/xexXTj26dIRkV/+0fqsOPu7Y0gkmh+wLrUfEDyQ9B/gYsD13eWZmZmZVFu4iHxsRsR544XiUZWZmZmZlZRsEIGmOpEslrZG0RdJKSVdKelKuMs3MzMwqz8sUHZBPAN8B3gAsAd4LfAS4RNLFEXH13k7++epZGaONvftW9ZSOkOz3zugvHSFZf3SVjpBkSldn6QjJ5h69sHSEZJtW1muh9ZmLDyodIdngtnqNcOrtmlk6QrJFnfX6HAOcemL9xvLb+MhZwTw2Is5tXl8l6aSIuF7S84CrmxczMzOzyaVirY055KxgbmlWKm+TdBqwExrjMSXNyFiumZmZWWV5ks+BuRC4QlI70AmcDyBJNBZfNzMzM7MJKOdC6zcBJ0iaFxEbWo6HpFtHO//TH/thrmhZnP/aJ5SOkGxq+67SEZJN/+m1pSMk2TkwUDpCss6D6rWmJMC0ufXK3DG9u3SEZLvWrC0dIUlHX/2+33Z31W9NyTnd9VvTFSrQiToJusizv8LWymWLD+Qu18zMzMzKyNaCKelmYLhBBgKOylWumZmZWaV5DOYBuYDGTPFThxwXcFXGcs3MzMyqy3uR77+IuFHS5RFx/9D7JF2Zq1wzMzMzKyvrVpER8ZYRjl802rnn/cGZYx8oo81b6zeZo6Otr3SEZOqo18Ll3Uvqt2j5xptuLx0h2ba1W0tHSLJlzZbSEZI96oVnlY6QZO3M+i0A3jNYr+83gA076zcxqQomwzJFE7+N1szMzMzGVdYWTDMzMzMbYhIsU+QKppmZmdk4Clcwyzl4/mDpCEm6FtcrL8AtDy4oHSHZEccOXZSg2rpu/d/SEZJNWzyvdIRkfTt7SkdI0rOtXnkB+rdtLx0hySF3fLN0hGSrjn9W6QjJ2tpml45gFVXZCqaZmZnZhORJPmZmZmY2UUh6vKRbWm7Pk/QNSXc2/507FuW4gmlmZmY2jkJtWS6jkfQB4Bp+vf53CfDFiHgU8EXg4rF4jZXtIt+yc0rpCEn6+uuVF2DOzPqNG20bqNfYtYEtm0tHSLb57jWlIySr2zqYaqtf91jn4nqt6brzsJNLR0j2vfsOKx0h2eYt9fs98pzHl05AsS7yiHizpH8BvtJy+GnAm5rX/wv4KfD6Ay3LLZhmZmZmk9e8iNgMEBFbgDHpIq9sC6aZmZnZhJRpmSJJy4BlLYeWR8TyUU6LIbfHZEupbC2YkhZIeqekcyW1SbpI0peax4bdW0rSMkkrJK249sujvR9mZmZmtkdELI+I01ou+1KZ2iRpBoCkWcDGsciSswXzs8ANwAuAFwP30hhIeh5wKXDB0BOab8RygFf9zUPxnW8/lDHe2Lr3Z3eWjpDstW9+SukIyXYvrNcajV07d5WOkKy/p790hGTb1+0oHSHJ1Ln127+5Z3V9vo8BtPrTdP7O00vHSPLUI1aWjpDsrq2HlI6wH8qPDqzYXuTfAc4HLgNeAnx7LJ40ZwXz4Ij4K0mdwEMRMQ9A0g+AOzKWa2Zmk1zdKpdm40HS3wLPB46WtAJ4M/BW4NOS3gbcB7x8LMrKWcGcIqktInolvWTIfWPSv29mZmZWO4W2ioyIdwLvHOauc8a6rJyv8AvAbwNExDUtx18I3JaxXDMzM7PKCpTlUiXZWjAj4i9HOH4FcEWucs3MzMysrGwVTElnR8S3Wm6fABwP/DQiVo12/kvOnZ8rWhaLXtlVOkKyHX31mhgBIOq1qO+U6dNLR0g2c+mYLIE2rnZvqddkqpmLDyodIVlbV71GNk1ZfW/pCMnWzv690hGSrdvSUTpCLe3Lrjt1l/MV/vOeK5JeS2P7oecAV0t6acZyzczMzKygnJN8WgcD/BnwuxHxoKQFNKbEfzZj2WZmZmbVNAlaMHNWMFtXhu+OiAcBImK9pPr1J5uZmZmNgYqtg5lFzgrm8ZK20mjJ7JLU2Vyy6Ghg1AFTG3fUaxfLyz+9tXSEZM98zhGlIyQ7Zc7O0hGStM+v11higP5b6rdpwMKT6rXY89bVG0pHSNZ17LGlIyTpX3x46QjJNu7sLh3BbMzknEU+UvtvAK/JVa6ZmZlZlU2GST7j3kwYEfWb2mdmZmZm+yxbFVrSHEmXSlojaYuklZKulPSkXGWamZmZVZ6U51IhOVswP0FjtvgbgCXAe4GPAJdIujgirt7byWvWxd7urpzXvmpp6QjJRE/pCMm619RrfODOO+8qHSHZYP9A6QjJHvnlQ6UjJInBen2/AWz90Y9LR0iiV/xW6QjJDuvcWDpCsmmd9VvTFcrPM3YX+YE5NiLObV5fJemkiLhe0vOAq5sXMzMzM5tgclaht0g6CUDSacBOaCxTBMzIWK6ZmZlZZXkv8gNzIXCFpHagEzgfQJKAD2Ys18zMzMwKyrlM0U3ACZLmRcSGluMh6dZc5ZqZmZlVmcdgjoHWymWLDwCn7+28w5ZUq6l3NAOD9coLMK2zv3SEZNsOf0zpCEm676nfJJ/pi3tLR5jwenfUb4LdQU84o3SEJDsH6vc53tlfv4XW+wYmfkXJ9k+2Cqakm2HYAQECjspVrpmZmVmlVWxJoRxytmBeQGOm+KlDjgu4KmO5ZmZmZpUVWedYV0POMZg3Sro8Iu4fep+kK3OVa2ZmZmZlZR2DGRFvGeH4RaOdO3tavcYH3r5q3HfdPGDHH1o6QbqpvetKR0jTUb/PxUBPX+kIydbdtrZ0hCRzj5pXOsKEt2nGwaUjJFv3yLTSEZLd+2D9WuKe+bjSCSAmQRd5/T4ZZmZmZlZp9WteMTMzM6sxL1NkZmZmZmOqarvu5FDZCmZPf71q9/Nn1+/DMhD1y9zffVDpCEm6Z80uHSFZz5bbS0dI1j2rq3SEJFPn1nC33N27SidIsnTVDaUjJNt5yPTSEZIdPad+v0dgYekAk0JlK5hmZmZmE9Fk6CKf+K/QzMzMzMaVWzDNzMzMxtFkWKbIFUwzMzOzceRJPgU9snVK6QhJ5s4cLB0h2ZGzHikdIdmUjTtKR0gy2NtTOkKyGIzSEZJ1z5paOkKSbQ9uKh0h2bz+ei3A33fQ/NIRkvUOdpSOkGzd9pmlIyQ7pXSASaKyFUwzMzOziciTfMaZpGWSVkha8b2vLC8dx8zMzMz2Q5EWTEnnR8Tnhh6PiOXAcoB//zb166czMzMzG4XHYObzZuA3Kpit2mr23u/sqVlgoF39pSMka9+xuXSEJAM7dpaOkKx9ar0WLQfYsX5b6QhJ6jZmFKBv7brSEZK0H761dIRkM2bVa4w5wAMDs0pHsIrapwqmpLnDHP4n4GDgryPif4c5ZzUw3LYrArpTQpqZmZlNFJNhDOa+tmCuBVbDr7XpLgUeBTw8wjlnATdExKKhd0j6SUpIMzMzs4nCXeS/ck9EnNB6QNLtEXH/SCdExH2S3jPC3Zfta0AzMzMzq5d9rWDOk/Qm4H7gexGxHkafhBMRHx7h+MdGO/eBB+u15tpxR9Vvxaefr19aOkKyxXMOLh0hzbYflk6QrGvW9NIRkvVsf7B0hCTt3fVb73Cgp7d0hCTqqt/nuI36rae8ev3E7+rNYTJ0ke/rK/xroAN4NvADSV8F5qQUJOkZkv5c0pMSM5qZmZlZjexTs1tz+aD/I+kJwFWSvsPIk3y+DZwdESHp7TQqp98E/l7SFyLiXw48vpmZmVm9eAxmC0kdwHE0usZXAI+l0ao50iSfwyNiTzf6q4FTI2KHpPcCNwKuYJqZmdmkE5r4Fcx96iJvdmvfTqNSeGnz+lERcX9E7BrhtFslndO8vjUi9izw1QbUa6NxMzMzM9tn+9qC+c/AMyLibgBJxwD/DTx+L+csA66RdB6wtTlJaBPwWpq79exNT0+9BjuvXlu/jYeOPqR+mTVYr8Xhu444rHSEZJu++YPSEZLNPnR26QhJZiwZbmnhauuYU7MFtQfqNVEUYNdg/RbgP/6Qek3+aii/mUREuRZMSdcCRwC7m4c+FRF/P9bl7GsFc/qeyiVARNwtadreToiIhyU9Dng+8Dga3eubgHdExHX7mdfMzMzMDsx5EbEiZwH7WsG8TtJy4NPN2y8FRm3miIhB4AvNi5mZmdmkF/u8iE997esrHABuBd7QvNwO/MneTpA0R9KlktZI2iJppaQrJT3xwCKbmZmZ1VegLBdJyyStaLksG7Z4uELSLyX9s6QsC3nv65M+NSL+hMZYzH31CeA7NCqkS4D3Ah8B3i/p4oi4em8nz5/fmVBUeTt2DpSOkGzu9PqNndnaWa/F4eft/o0VvCqvY1r58Umptj20uXSEJDG4oXSEZFOX/sauv5W2Zc6RpSMka1O95h4AzOjcPfqDKqd+33H7qrms5GjzXJ4ZEbubQx3/g8acmUvHOkvO7WeOjYhzm9dXSTopIq6X9Dzg6ubFzMzMbFIpuQ5mROxu/rtT0peB38pRzr5WMI+SdEvLbQEREafs5ZwtzUrlbZJOA3bSOGm9pBn7mdfMzMzM9oOkbuDMiLi2ub75ucAVOcra1wrmcfvx3BfS6ONvBzqB8wEkCfjgfjyfmZmZWe0VbMEU8LeSDgV6gS8Dn81R0L5uFXl/6hNHxE3ACZLmRcSGluMh6dbRzn/0EfUaH9jZXr8xmIu6N5aOkGzBPTeUjpBkYHdP6QjJpi6cUzpCsod+9kDpCEk6pnWUjpBs6Zx6fS7mrbm5dIRkPYc/oXSEZHdtXVA6giVobo7z5PEoK/s8+dbKZYsP5C7XzMzMrIpyzSKvkmyTfCTdDMO+WgFH5SrXzMzMrMpK7uQzXnLOIr+AxkzxU4ccF3BVxnLNzMzMrKBsFcyIuFHS5cON35R05Wjn37G6Xutg3rtyR+kIyZ7+xHqtawdwWPf00hGS7Fi1pnSEZG1TppSOkGzeMfNLR0jSs61+Y3MHd9TsO+7OX9D2qBNLp0jSF/X6vQfQNzDxW+JyqFp3dg5Zx2BGxFtGOH5RznLNzGxyq1vl0myiydlFbmZmZmZDTIYWTFcwzczMzMbRZKhgZl+myMzMzMwml8q2YPb1l06Q5pST6rf75YLpm0tHSBY99RoEX8cJM7s3bi0dIVkMRukISabOmVY6QrLejZtKR0ii2UtKR0i2Y6B+n4vBSbDcTg6TYZkit2CamZmZ2ZiqbAummZmZ2UQ0OAnGYLqCaWZmZjaOJsMkn8pWMO+8Y3PpCEmmz6zX2ECARbPqtWg5wOCUjtIRkkw7+vDSEZLteuTm0hGSddXss7x97ebSEZItXLq4dIQk27sOKh0h2dwpG0tHSBYxp3QEq6jKVjDNzMzMJiJP8jEzMzMzS5S1ginpRZI+I+kbki6X9EZJM/fy+GWSVkha8YsbLs8ZzczMzKyIQFkuVZKti1zSJcDhwMeB+cArgOnAjyU9KyJWDj0nIpYDywH+9RvUamG7QxfWbOFOYNGM+q132HHHfaUjJOnfsbN0hGRT588uHSHZ3d/8eekISQ4988jSEdINDJROkKSzd3vpCMk2dtVrnCvA1h3VqtRYdeQcg/ks4OSICABJb4uId0v6OvBvwNkZyzYzMzOrpMkwBjNnBXOQRovldknzgU6AiLhR0mEZyzUzMzOrrKp1Z+eQs4L5HuCHkn4EnAW8H0CSgB9mLNfMzMzMCspWwYyIzzQrl48DPhwRtzWPB/DqXOWamZmZVZm7yA9QRNwL3Asg6RnACcCPI+L60c5dPLdeA8pXPjSldIRkM4/oLh0h2a7DTy4dIUnX+nWlIyTr39VTOkKyzhn12uhg6+r6LajdNWt16QhJdHq9PhMA7fSVjpDs8IW9pSPsh/p9Nuoo2zJFkr7d7A5H0tuBvwZmAH8v6c9ylWtmZmZWZYOZLlWSswXz8D0zyGl0iZ8aETskvRe4EfiXjGWbmZmZVdJk6CLPudD6rZLOaV7fGhE7WsqsX3+ymZmZme2TnC2Yy4BrJJ0HbJX0JmAT8Fqai6nvzX9cfk/GaGPvrLOPKR0hWW9//er5U9feXTpCkr7tO0Z/UMWs+/mq0hGS9W6v1ziwXRt3lY6Q7ODffnTpCEm0c1PpCOmmLimdIFn/oHec3h9epugARMTDkh4HPJ/GTPLjaFQw3xER1+Uq18zMzMzKyj2LfBD4QvNiZmZmNul5DOYBkDRH0qWS1kjaImmlpCslPTFXmWZmZmZVFyjLpUpytmB+AvgO8AZgCfBe4CPA+yVdHBFX7+3kk08/ImO0sfeE43eWjpCsu71e49YAaKvXuFF11W+9tUPOfFTpCMm2rV5fOkKSaYvmlI6QrPPYen0udndMLR0h2ca+uaUjmI2ZnBXMYyPi3Ob1VZJOiojrJT0PuLp5MTMzM5tUBmP0x9RdzulfWySdBCDpNGAnQESsp7HgupmZmZlNQDlbMC8ErpDUTmNfpvMBmrv7fDBjuWZmZmaVVbXxkjnkXKboJuAESfMiYkPL8ZB0a65yzczMzKysrMsUAbRWLlt8ADh9b+dNaatX7f7eR+rX63/swi2lIyTbevDJpSMkmbn+odIRkvWvXF06QjLV7PuiZ9O20hGS9f7yjtIRknTvrt9i9p2n1Gsxe4CuKd2lI+yH7FWfUU2GZYqyvcuSboZh24AFHJWrXDMzM7Mqi0kwySdnNf4CGjPFTx1yXMBVGcs1MzMzs4JyjsG8UdLlEXH/0PskXZmrXDMzM7MqG/QknwMTEW8Z4fhFo5376OPqtaD29voN96Gzrb90hGQzV9Rr+dRtt9Vr3BrAquvvKh0hWffsei2qPW3utNIRksXAQOkISXYcd0bpCMl29tdvPOOmnR2lI1hFlR/pamZmZjaJTIZJPjkXWjczMzOzISLyXPaFpGdJulXSLyW9I9drdAXTzMzMbBKQNB34V+Bs4CTgmZIen6OsynaRb95er7rvz26u35qSs6fPLh0h2YmLlpaOkOSgjsr+iI3oqKn1GwfWt21n6QhJ+nf3lo6QrOuwQ0tHSLK6+8jSEZJ11GycK8DsafUby9/YXLCsgjv5nA7cGBFrASRdATwLuHGsC6pXLc7MzMzMhiVpmaQVLZdlQx6yFHi45fZ6YHGOLPVrXjEzMzOrscFMC61HxHJg+WjFD7mdpUnXFUwzMzOzcVRwFvlaYH7L7QXNY2OushXMOo5pPPfpXaUjJFk49ZHSEZLFjnq9xyw6lIFf3Fw6RZL+HfVb1HXHuk2lIyTrOqhma2HmanLJ5Oif/CcbH//M0jGSHNRW2V/JI/rfVSeUjpDsnMeUTlDUDcBlkhYCG4HzgL/OUVD9Ps0VVbfKpY2PulUubXzUrnJZQ3WrXNrkUmov8ojYLunPgO8CHcCnIuK6HGW5gmlmZmY2SUTEl4Ev5y4n2yxySfdI+rykUxPO+b/ZT7f98PJc0czMzMyKGURZLlWSc5miXuBjwIckfUPSk0c7ISKWR8RpEXHaSU94TcZoZmZmZpZLzi7yiIhrgGsk/Q5wkaRO4N0R8Y3RTj7uuIMyRht796+v1l8O++Low1aVjpCsf8ac0hGSdBx8SOkIybp37CgdIVnPlnpl7pw5tXSEZP2b6jWRasb2h0pHSHbvQVk2VMlq1sz6/e6rglJjMMfTuCy0HhHfjYinAX8JvEHSmK8Yb2ZmZlYHEcpyqZKcLZi/8Uoj4noa+16elrFcMzMzMysoZwXzrJHuiIgVGcs1MzMzq6yaLSu7X3JWMP9U0k3N6fDJ2qdUq6l3NNO66/dp2cmM0hGSzVW9PhcbvvP90hGS9e3YXTpCssH+gdIRkjz405WlIyQ7cl69xj/3Tq1X3rpaOqe3dIT9kGVnRBsi5xjMPwJeIukmSS+RalYzMDMzM8sgIs+lSnJWMLdGxMuB84FnALdJ+kNJXtzdzMzMJq1AWS5VkrOCGQARcWdEvAZ4DnAGcIek12cs18zMzMwKGrdZ5BFxL7BM0mHAX4x28sr7duXKlUXP0u7SEZLt6llaOkKypXPuLB0hydzfekzpCMm2/vz20hGSPXLHmtIRkix+zOGlIyTrWDCvdIQkHT/5Br2PGXV/j0rp6qrfeMb5U7eVjrAfyn+WPcnnwHxguIMRsQq4MGO5ZmY2ydWtcmk20WSrYEbEv+d6bjMzM7O6qtqEnByyjcGUNEfSpZLWSNoiaaWkKyU9KVeZZmZmZlXnWeQH5hPA7cARwKOBHwGXAJdIOidjuWZmZmZWUM4xmMdGxLnN66sknRQR10t6HnB18zKiJzy+Xguhzqjh4OxpHX2lIyTrY1rpCEk6++v3HtdtwgzAzg07S0dIMuW+h0tHSDZ1UfmJESm6N6wqHSHZwIzjS0dIVrWlcepisGL7hueQswVzi6STAJp7j+8EiIj1UMMtZMzMzMxsn+RswbwQuKK5sHonjQXXae7o88GM5ZqZmZlVVtXGS+aQcxb5TcAJkuZFxIaW4yHp1lzlmpmZmVlZ2bdtbK1ctvgAcPreztuyc0qeQJn85Of9pSMke+pp9RrnCjB9872lI6QZHCydINnS048rHSHZjoceKR0hyWD/QOkIydo66rXL7+55h5WOkKxnsH7fybv6u0pHqCW3YB4ASTfDsKN/BRyVq1wzMzOzKvNOPgfmAhozxU8dclzAVRnLNTMzM7OCco7BvFHS5RFx/9D7JF2Zq1wzMzOzKotJsExR1kE1EfGWEY5fNNq5bTV77885o35j7Q7qrNfagQBtD2wqHSHJrnXrS0dI1rdzV+kIybau2Vg6QpLZRywsHSFdW85V7cbelN4dpSMkG+iu19wDgLXbppaOYBVVr1HbZmZmZjXnST5mZmZmNqYmwySfevV5mJmZmVnluQXTzMzMbBy5i7ygJbN7SkdI8tO7u0tHSPbUE+s3maNvYb0WT46Bn5WOkGxgd2/pCMnauztKR0jS1l6/yRyDu3eXjpCkjt1zUv1qHfc8UDrBfjijdIDJobIVTDMzM7OJyC2YZmZmZjamPMnHzMzMzCxRZVsw713XVTpCktWrt5eOkOymmXNLR0j2qFn1G7tWN+1T6/WzBzBj8ZzSEZI88MO7S0dI9ug/Pbd0hCQ7ptXrMwHQ1Va/8c9HHlw6QT1Nhi5yt2CamZmZ2ZiqbAummZmZ2UQ0WL/dpZNla8GU9NGW69Ml/aOkqyS9XdKwa/pIWiZphaQV3//a8lzRzMzMzIqJyHOpkpwtmL/Tcv1SYBPwfuBFwEeBPxx6QkQsB5YDXHNzT0B91sJc+3D9xq0dv2RH6QjJOu6q19i1KYfWb4DSw9//aekIyQZ6+0tHSNI5o7N0hGT9GzeUjpBEgwOlIyR7cMfs0hGS9fardASrqPHqIj8TODEiBiT9APjFOJVrZmZmVilVa23MIWcF8yBJ59Lohp8aEQMAERGSpmYs18zMzMwKylnBvAZ4bvP6tyR1RkSvpKcD9euDMzMzMxsDk2Gh9WwVzIi4YITj19CofJqZmZlZBUi6HHg6sK156LsR8cf7+3xZx2BKOgPojYibJB0CvBi4IyK+Ntq5312RM9nYW7igfis+/fSe+mWec9ILS0dIcsR3Pzr6gyqmc+a00hGSbXtwY+kISRY/5vDSEZK1z59fOkKSXVPqN5FqXtfO0hGSrd08u3SEWopsgzAPeNLVGyLiirFIkq2GIekiGl3kmyT9tHn9KuDpkk6NiL/LVbaZmZlZVU2GST45d/J5GfCEiDgbWAgsj4i/pFHRfEnGcs3MzMwmndb1xJuXZQmnB/BhSXdJ+qSkmQeSJWcf6WBzUk8H8NvAX+85DnRkLNfMzMyssnLt5NO6nvhwJH0LGG68y7OAP46I3c162/uBvwH+Yn+z5Kxgfl3S/wCzaax7uVzSZ4DnANeNdvLs2a6D5vY7x68vHSHZ4RvqNThXnfUbBzbYV69FywH6d/eVjpBkx7pNpSMkm7lrV+kISTr66pUX4KFdB9RgVMSUnP2gNuaavcqjPaZP0ueBtx5IWTlnkb9J0guBbuC/gNOAVwA30NjJx8zMzGzSqeIYTEnn8KtVfl4E/OhAni/rNOKI+HzLzRuaFzMzM7NJq6LrYP5/wL8Bu4H/BT50IE+Wcxb5HODdwPOAGcBG4GbgfRHxv7nKNTMzM7M0EfGCsXy+nC2YnwC+A7wBWAK8F/gI8H5JF0fE1Xs7+ZBFB7yW07iaP7NeY8AAprXtKB0h2ZRd20tHSNNRv7HEA731+yxPnVOvtTu3P1yzzzEwuHt36QhJpvTXKy/A0plbS0dI1t42o3SE/VD+e7mKXeRjLWcF89iIOLd5fZWkkyLieknPA65uXszMzMxsgslZwdzSrFTeJuk0YCdARKyXVMc/eczMzMwOWGQbhFmd3t+cFcwLgSsktQOdwPkAkgR8MGO5ZmZmZpVV0Uk+YyrnMkU3ASdImhcRG1qOh6RbRzu/f6A6tfB9Ier3aZHqlznappSOkKRen+KGmYctKh0h2bqb7i0dIcncoxaUjpCsrbu7dIQ0t95A3ylPKp0iyaK2h0pHSLaWR5WOYBWVfYnU1spliw/kLtfMzCavulUubXKJyHOpkpzLFN3M8A04Ao7KVa6ZmZmZlZVzDOYFNGaKnzrkuICrMpZrZmZmVlmDk2AQZs4xmDdKujwi7h96n6Qrc5VrZmZmZmXl3iryLSMcv2i0c9un1Kt2/5M7sr6VWeiEg0tHSLaoc9T5YZVSrylJDZvuXF06QrKN924sHSFJ5/TO0hHSqV5T1roeeaB0hGTrpg/t8Ku+Tdvr97uvCqo2XjIHfzLMzMzMxtFkqGBmn0VuZmZmZpOLWzDNzMzMxtHgJGjCrGwFs61mbasL59dvtN2SaQ+XjpCsf/Cg0hGStLd3lI6QbObB80pHSDZtwazSEZJsvm996QjJVLMv5Z75h5aOkKxrSm/pCMn6+ksnsKqqbAXTzMzMbCKKwdIJ8nMF08zMzGwcxSToIq9Xn4eZmZmZVV5lWzD7+uu15lpnZd/JkW3rn1Y6woQ3uG1r6QjJpkztLh0hWe+2TaUjJOma2VU6QjJNm146woR39a0LS0dIdviSOvb1lm9bG6zj25ao/LtsZmZmZhNKDdvdzMzMzOrLYzAPgKQFkt4p6VxJbZIukvSl5rGpI5yzTNIKSSu++6XluaKZmZmZFTMYeS5VkrOL/LNAF/AC4NNAJ3AJMA+4dLgTImJ5RJwWEaf9znOXZYxmZmZmZrnk7CI/OCL+SlIn8FBEzAOQ9APgjtFOPmTOrozRxt5P76rfoP2D59Yvc/fDK0tHSBKdnaUjTAo9W3eWjjDhrf/2D0pHSLLguTNLR0j2xOO2lY6Q7Md31+99roKoWnNjBjlbMKdIaouIXuAlQ+7zb10zMzOzCSpnBfMLwG8DRMQ1LcdfCNyWsVwzMzOzyorIc6mSbF3kEfGXIxy/ArgiV7lmZmZmVlbWZYokTQPOAuYCa4HbImL9vpz7yI56jQ982qM3lo6QbEbbjtIR0qleC/AP7Kjfe7zupntKR0jW3zNQOkKSGQtnlI6QbNYxh5aOkKRv7pLSEZLN7dxSOkKyRfM8BnN/DE6CMZjZKpiSzgY+CvwYmAEc3Tis64E/iZgMW72bmZmZ/Tqvg3lg3gs8PSJeSWOSz4aIeDSwBnhPxnLNzMzMrKCcFcypEbGqeb0XWNS8/vfAuRnLNTMzM6usGMxzqZKcYzB/KOk9wFdoVChvBIiIkNQ92skrbunJGG3sbTl6fukIyRbNnl06QrIlB80rHSFJz9p9GnJcKbMOq99n+b7v3V06QpLp86eXjpCs89B6jcHsaa/XOH6AqYPbS0dINlixSo1VR84WzD8FdgJ/CQwCrVvznJKxXDMzM7PKGozIcqmSnMsU7Qb+boT7Nucq18zMzKzKPMnnAEiaI+lSSaslbZG0UtKVkp6Uq0wzMzMzKy9nF/kngNuBI4FHAz8CLgEukXROxnLNzMzMKmtwMLJcqiTnJJ9jI2LPbPFVkk6KiOslPQ+4unkZ0bRpWdeAH3N1bO2eO3V36QjJ+nvrtahvx0H1W1B7x9r6bRqw9HEHl46QpGP6qPMcq2d6vX722vrrNVEU4OHBxaUjJOsfqNfmFzZ+crZgbpF0EoCk02hM+KG5k0/9fuuamZmZjQHvRX5gLgSukNQOdALnQ2MrH+CDGcs1MzMzq6yoWHd2Djlnkd8EnCBpXkRsaDkekm7NVa6ZmZmZlZV9oGNr5bLFB4DT93bewvkdeQJlMiXnYINMprQNlI6QrPvhlaUjJOmbMqV0hGTbHtpcOkKyXZt3lY6QZMbCeo1nBJh11y9LR0gyZVa9NmUA2NZbv7G5U7vquNJ6+V/YVVuzModsFUxJNwPDjf4VcFSucs3MzMwsnaQjgOsj4uCWY1NprAx0KvAI8PKIGLW1J2cL5gU0ZoqfOuS4gKsylmtmZmZWWVUcgynpz4G3A9OG3PVW4L6IeKmk3wP+CXjuaM+XrZ04Im4ELo+I+4dc7gOuzFWumZmZmaWJiA9FxMJh7noa8F/Nx3wTOL05YXuvso7BjIi3jHD8otHOPXRh/9gHymh6R1/pCMmWtK8tHSHd6J/pSuk45tjSEdJ956bSCZL1bO0tHSHJzMX1+hwDqKNe4+KjZt8VAFt2d5aOkOzw2dtKR9gPs0oHqGQL5l4sBR5uub0VmEeju3xE9VrN3MzMzKzmctUvJS0DlrUcWh4Ry1vu/xYwf5hTnxURD+7lqYfO5hr1ryFXMM3MzMwmgGZlcvle7j97P552LY1K6brm7dnA+tFOKj9X38zMzGwSicHIcsnk28BLAJqTfG6LiFHHBbqCaWZmZjbJSfpjSSuAqZJWSHpp8673AcdJugu4GHjdvjxfZbvIH9la2WjDmj6vfpN8Nkb9FiJetG1L6Qhp+uv3uZh7zOLSEZIN9NVrsee29votwN82bWrpCEn6ug8qHSHZwqk7S0dItrO/fhOTqiAquNB6RPwr8K/DHN8JvDj1+epVizMzMzOrucF6zSLfL+4iNzMzM7Mx5RZMMzMzs3FUxS7ysVbZCubSOfVaOHnTrq7SEZIdOq1+C63H7LmlIyTRur0tK1ZNnbNmlI6QbMNdm0pHSLL08fX7vhjcsaN0hCT9nUN3u6u+2R31W7T8K7+o35jtp55cOsHkUNkKppmZmdlEVLOdfPbLuI/BlHT+Xu5b1pwav+JrV/z7eMYyMzMzGxc1Wwdzv5RowXwz8Lnh7mhdgf7qm3ur9U6ZmZmZ2T7JUsGUtBoYbhEyAd378hwr19Vrba3Tj9zrnu+V1BH1GucK0DdrYekISTrW3F86QrL+HbtKR0jWv3WgdIQk0xfOKh0hWd/W7aUjJOnordeYUYCOqfVbN3frtv7SEfZD+frFoCf57LezgBsiYtHQOyT9JFOZZmZmZlYBWSqYEXGfpPeMcPdlOco0MzMzq4OqjZfMIdskn4j48AjHP5arTDMzMzMrL9skH0lTgDOAxTQGPKwFftzc03JUc2bWa2/hL/5v/fa9PfPR9RsHdmbn+tIRkvT88p7SEZJ1zKzf+oFHP/OI0hGS9G6r357Tc+bXaw1afnodOvFxpVMk2X3QPk1RqJQzT/GGgPvDC63vJ0nPAP4JuBXYM/tlAfA4SX8SEV/PUa6ZmRlQu8qlTS6TYS/yXC2Y7wOeEhG/tlWMpCXANYArmGZmZmYTVK4KZufQymXTuoxlmpmZmVXeZJjkk6uy90lJ3wP+B3gYGATmAy8CPp2pTDMzMzOrgFzLFL27WcF8GvDo5uG1wLsi4rp9eY4FM3pyRMvmCafUr2H2UbMeKB0hWef6TaUjJGk75ojSEZLtXv1g6QjJOqZ1lY6QZMf6baUjJOvfUq/MU9rLL6adqkv1+r0HMEUTvyUuB0/yOQAR8X3g+7me38zMzKyOYrBeK+XsjyzrC0iaLemfJN0p6RFJWyXdJ+kySfNylGlmZmZm1ZBrAavPALcBp0XE/Ig4CDgF+DEeg2lmZmaT2OBgZLlUSa4u8qMiYnnrgYjYCnxc0p9nKrOoex6q3xjMiENLR0i2ZPePS0dIsuu++o1z7d9Vv3Fgvdt3l46QZKC3v3SEZP3bd5SOkKSjZ1fpCMkG822ul82s7nr97DV0lA4wKeT6NK+U9DpJ/7e9jaRpkv4AuD9TmWZmZmaVFxFZLlWSq4L5cuBM4BZJ2yRtAX4BnAW8LFOZZmZmZpUXg5HlUiVZKpgRsRH4Z2AZcHBEzIqIIyLiD4A35SjTzMzMzKoh117kbwdeDNwDXCbpHRHxyebd5wJ/NdpzfP6a3hzRsjniiHqtwwdw1Kx1pSOkq9dSfHQvWVQ6QrK11/+sdIRkalPpCEnW3fxI6QjJFp96TOkISdRfv7HEolotUPsiol4/e1VRtdbGHHLNTHk18NiI2C1pEfDfkpZExPsAfxrNzMzMJrBcFcz2iNgNEBHrJJ0DXCEpoIZ/opmZmZmNkcHwQuv7a6Wkp+25ERE9wAuAs4EjM5VpZmZmZhWQqwXzOUMPRESfpOcDp2cq08zMzKzyPAZzPzVbLIc7vgu4bl+e47W/X69JPqu2TPwPSxX0z6zXTqO9a9aWjpCsZ1v9Fk7edN/m0hGS7H64Xt9vAIO9faUjJFHF1gTcFxt7Z5WOkOyhbdNKR6ilyVDBrN+2AWZmZmZWafXb39DMzMysxqq2604ObsE0MzMzszFV2RbMr904t3SEJCcfXTpButl960tHSNa5dmXpCEnajz68dIRkB22q2Wr2QH/PQOkISdq7K/vVO6Jphy4tHSHJYHtn6QjJ5nduKh0h2X39M0tHqKXBwYm/TFH9vuXMzMzMasyTfMzMzMzMErkF08zMzGwcxSTYyaeyFcyZM+rVuDq1o37r2q1vr9eYKoCpi48qHSFJx/r6rYO5aWX9xuZuXrWldIQkR5xVvw3N+jbX6z3u3r2jdIRkbarXWGKA6V11rChNKR1gUqhsBdPMzMxsIpoMYzCzVTAlvQg4F5gLrAV+BlwWEfWbompmZmY2RiZDBTNLP7SkS4AXAZcB/w+YB0wHfixpxL4hScskrZC04vpvLM8RzczMzMwyy9WC+Szg5GguVS/pbRHxbklfB/4NOHu4kyJiObAc4F++OgmWuTczM7NJZ7Cik3wkHQFcHxEHtxx7KvAVYHXLQ8+MiM17e65cFcxBGi2W2yXNBzoBIuJGSYftyxP09NSrfrlhR/0W9Z3d3VU6QrL2nu2lIyTpXftw6QjJBnr7S0dI1rO+r3SEJD+/7LbSEZL91l8cVDpCksGlx5WOkGz+5ntLR0i2dOac0hH2Qx0z5yfpz4G3A9OGufvaiHhOyvPlqmC+B/ihpB8BZwHvB5Ak4IeZyjQzMzOrvCqOwYyIDwEfkjQmLTlZxmBGxGeA5wHfAM6LiMuaxyMiXp2jTDMzM7PJrHUuS/OybIye+imS7pZ0vaSz9uWEnMsU3Q8sBo6T9GgaM8l/HBE7M5ZpZmZmVmmRaS/y1rksw5H0LWD+MHc9KyIeHOG064FZETEo6UnA5yQdumeezUiyVDAlPQP4J+BW4JHm4QXA4yT9SUR8fbTnmDq1Xgut37myfuPWFszsLh0hWV93vcaBtXXUb6nZ6Qtmlo6Q7JGOzaUjJDnmBUeUjpBs6sJ5pSOkqdl4bYDr23+3dIRkc2NX6Qi1VKqLPCKGnWQ9yjl9Ldevl9RLYwnKDXs7L9dvv/cBT4mIX9vGRNIS4Bpg1AqmmZmZmZUl6QzglxGxWdKZQG9E7LVyCfkqmJ1DK5dN6zKWaWZmZlZ5VdyLXNIfA38ITJW0AvhARHwWOAH4jKQBYCPw8n15vlyVvU9K+h7wP8DDNJYtmk9j8fVPZyrTzMzMzPZDRPwr8K/DHL8cuDz1+bJUMJuLqn8PeBrw6ObhtcC7IuK6HGWW9vC6HaUjJOvtH26pq2rrvnNF6QhJtqwbtRehcvp29pSOkGzGwVNLR0iydc2W0hGSLdxer/mZUx9eVTpCsmOOW1M6QrLv3T/i5nyVdfrxpRPAYAWXKRpr2bqrI+L7wPdzPb+ZmZlZHeWaRV4lufYiny3pnyTdKekRSVsl3SfpMkk1m4poZmZmZilyrQX0GeA24LSImB8RBwGnAD/GYzDNzMxsEovByHKpklxd5Ec1F/v8PxGxFfh4c6/LfXLw/IExD5bL7cDzz67XupJ9A7Bw+rbSMZINLt6n7ewr4uf07dhdOkSygb76dd90zugsHWGfDfT0M6WrfgtqTH3UMaUjJNl5yAmlI5hNWrm+4VZKeh3wuWbFEknTgJfQ2OFnVHWqXEL9KpfgyuV4cOVyfNSpcgm4cjkO6li5nLtjNXNP+e3SMZIcc3TpBPVUxWWKxlquLvKXA2cCt0jaJmkL8AvgLOBlmco0MzMzq7zJ0EWepYIZERuBfwaWAQdHxKyIOCIi/gB4U44yzczMzKwacs0ifzuNRTmXAbdJemXL3efmKNPMzMysDmJwMMulShQx9k2qku4AHhsRuyUtAv4b+GpEvE/S7RFRdHCMpGVDJyFVWd3yQv0y1y0vOPN4qFtecObxULe84Mw2/nKNwWyPiN0AEbEOOAf4bUlvBaowSGBZ6QCJ6pYX6pe5bnnBmcdD3fKCM4+HuuUFZ7ZxlquCuVLS0/bciIge4AXA2UD99pUyMzMzs32Wa62M5ww9EBF9kp4PnJ6pTDMzMzOrgCwVzGaL5XDHdwHX5SgzUd3GdNQtL9Qvc93ygjOPh7rlBWceD3XLC85s4yzLJB8zMzMzm7xyjcE0MzMzs0nKFUwzMzMzG1OuYFaIpBObE6HMkkl6paR3S5oj6Wel85iZ2eTlCma1nAg8v3QIq60vAo8HfgT8Q+EsZmY2iU24CqYaLpV0p6Q7JPVJOk3SrS2PuVjSW5rXr5V0X/Oxd0o6uVz6epB0hKSQdGbz9vOat+dLuqX5Pt4l6c0tj18t6QvN9/kbkg4qkPvXsjVz9TQz3SHpkSGfi09LuknS3ZKeOs5Zk99j4EcR8cyIOA54laSLxzlv68/YV5o/Z9c2f/5mSLpC0j2SbpR0RvNxr5H0kZbzrpV0WpUzt+Tc87npkzR/PDK3lD/SZ/n25uf1qubjnippe0vWvxjPnC15XyHp55LulfQ1SUslfUnSrZJWSDq1+bj/+/8f+v9TIPOFzffzdkl/0jy2vfnv4ZJ+Iekdkm5uvreDzX9/WCjv0M/zxZLeIulkST9sZvufPd+9kp7dfA23t373jWPe4yTtaObaIum8vXwuLpd0XvP6ZZJuGc+stn8mXAUTOAk4AzguIo4H1uzDOec1H/sl4Jic4YYj6S8k3Q38LfD05hfxDyUtGeWLuGTFeDXwxub11wAbm9fPjohHAY8D3iZpWvN4O/Cm5vu8kjI7NPxaNmAacFdEHN/M9ZEhj/9+RDwOePkw942H1PcYAEkvovEzUMQI5b8D2B0RRwOvAj4pacq4hxvB/mRu+dzsy3fMWBvps3wCjZ6Qo1qyXrsna0S8b7yDSjqBxnv5lIg4CvhX4FLg4og4mcZ3wb+Md669kfRY4LXAac3LMkmntDzk7cC7IuIfIuIxzc/BzuZ7/ITxT7xX/wm8rZnxFuCdzePvAl7S/MyU+H6bA3y3meuLzWN7/Vyo8Qf3kog4Bau8XAutl7Qb6G5edrUcP1aNPdIB5gP/2HLfFZL6gC7gn8clZVPzS+s8GhXj5wLPiYhXS3oH8NfAwTR+4G6U9HgaXwRPbJ5+XkSskPR+GhXj8fxr/x5gmqRzaPyC3dY8/hJJLwbmArOABc3jj0TEfc3rXwZeMY5Z9xgp20hWAETEDZIWSOoaaY3XTFLfYyTNAv6cxudE45h1b+VfAcwDXgYQEbdK2gYc3rz/5ZLObl4/bBzjAvuV+V7K/3E+3Gdgz3fcHODrwGDBfK3OBr4QEXv+QPoq8FngeOn/PqIdLY+/QtLu5rHW7/Dx9BTgqojYAdBsEX5q8/pxwO8Cry+UbW+G/p77Z2BeRHyveexTwOeb13fT+KyUchxwf8vtNhqflZE+F500fideOi7p7IBNuApmRNwt6XJgg6RVNCpo0Pjr/mRodB0MOW1PRe084L3AS8crL/Bk4GsR0SMp+NVe7VcB/w48mr1/ERepGDd9FPhvGi0/zwGeCZxP46/iB5rdNcNVcjoZ518ckl45TLb7Rznt154CGMgSbu9S3+N/AN4NnADMGOesI5V/HvBnQx7XmvnTEXEhNFrmcwccxv5k7hyHXMPay2f5rog4WY0viy8B5wDj+QfRSMSvvtf23O4DTojhF2Le8318BPCVcci3L/a8hmnAT4F/i4jespGGNfT33NDv39b/izcCn5PUDkzn1xtdxsOTgW+23B7tc/ExYAvw3XHIZmOg9F/huUwBLtuP7qs2fv2LcDyM9ItqCo2/MPf8wO3p4jq65THnRcSxwJtoVIzHVUR8k8YvuV82Dy0Cbmj+0jucxl/Qe3RKmtLstnsVcM04x523l2wjmQog6VzgxojozxlwOInv8WE0Wiu+Ot4596H8a2m0uknSScBM0ir4uSRnlrSYxs9mKfvyWR5aqSvp+8ALJc1u3n4C8GPgDQCSOlpasKvie8BzJU1rDkH5/eaxncCxwO9KurBkwH20A9go6azm7Zfyq930tgFbgVMZ5y5ySdOBs/j1PyAG2Pvn4g9otHx/beiwIKumCdeCKel5wNOB30s47QpJvTR+2Ma76/YnwIckvZtGBXdPpf/FNH7B9dL4gfuwpA4a45i+NeQ5SlSMAYiIb7TcvBz4iqQ7gV/QqBzvsRD4IY0uva8CnxuvjE2f2ku2kXyo+UX4CPDqnOH2JuE9Dhp/bJSyt/I/CfwWcAeNX3qvjoiBlpb5UpIy0+hS/CHwJ+OSbngjfZb3dI920mjl+RaNVqKiIuImNSZy3SCpDbiRxvjGSyX9MY3hTP9NI28lNDNfTiMrwEcj4mZJRMRDkp4CfEnSYERUvcv21cDyZgX/NuAPJc2l8R38qoh4pMDP4TXAUuDGZtlLaPzePpO9fC4i4i8kvRX4sqTfj4id4x3c9p23iqwASR+mMf6yl0YryWYa4+9eDsymMebkUTR/4CLirc2uxCOa52wFXhERd1BBe7q69nTd1EHz/X1LRKwoncXMbCKRdF9EHDHaMau3CdeCWUcR8Ubgjc0xoM+JiNe03L2NRvfM0HOeOi7hzMzMxtZwq4iUWFnEMnILZoWMUME0MzMzqxVXMM3MzMxsTE3UWeRmZmZmVogrmGZmZmY2plzBNDMzM7Mx5QqmmZmZmY0pVzDNzMzMbEy5gmlmlSDpCEkh6czm7ec1b8+X9FxJP5F0u6TPSOpqPuYVkn4u6V5JX2see7akXzQf+4iktzSPXyvptJaybi31Ws3MJjpXMM2sSlYDb2xefw2wkcYOVq8HnhwRJ9DculPSCcA7aGyfehTwr83z3kVj//YTGOc9ls3MrME7+ZhZldwDTJN0DrCGxk5WTwUeC9zU3Le4i8b2qGcDX4iIjQAR8eXmc+ymsWe4mZkV4gqmmVXNR4H/Bs4AnkOjp+V/IuKPWx8k6fXAcDtFvBH4nKR2YDrwj1nTmpnZb3AXuZlVSkR8k0YX9y+bh24HnivpaABJiyU9Fvg+8EJJs5vHz2o+fhuNFs5TcRe5mVkRbsE0s8qJiG+03FwJXAh8QdI0oA/4s4j4tqSPADdIagNulPQL4HPAqyLikWaXeqsrJO0GOoBDJF0YEa6EmpmNMe9FbmaTkqTXAEdExMWFo5iZTThuwTSzyeqHgJcqMjPLwC2YZmZmZjamPMnHzMzMzMaUK5hmZmZmNqZcwTQzMzOzMeUKppmZmZmNKVcwzczMzGxM/f/tfYYqXJ4StwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "figure(figsize=(12, 8))\n",
    "sns.heatmap(W, yticklabels=8, cmap=\"coolwarm\")\n",
    "xlabel('месяц'); ylabel('год'); title('Среднемесячная температура'); "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Обратите внимание:\n",
    "- параметр `yticklabels` задает интервал между отметками по оси, чтобы метки не наезжали друг на друга;\n",
    "- дивергентная цветовая карта позволяет сосредоточиться на особо жарких и холодных месяцах;\n",
    "- зимой 1941-го и во время оккупации данные не собирались."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Если у нас стоит задача заполнить недостающие данные или спрогнозировать колебания в будущем, то в условиях стабильных предсказуемых циклов, какими являются сезонными, хороший результат дают средние значения для той же фазы цикла. \n",
    "\n",
    "В данном наборе данных для прогнозирования температуры в августе в будущем году или заполнения пропуска в 1941 году можно взять среднюю температуру в августе."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Оценка аномалий\n",
    "\n",
    "В наборе данных есть колонка, показывающая насколько текущее значение отклоняется от ожидаемого, т.е. показывает аномальность температуры в этом месяце."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "W_anomal = D.loc[D.year>1912,[\"year\", \"month\", \"dv\"]].groupby(by=[\"year\", \"month\"]).max().unstack() #делаем широкую таблицу\n",
    "W_anomal.columns=momo\n",
    "sns.heatmap(W_anomal, yticklabels=8, cmap=\"coolwarm\")\n",
    "xlabel('месяц'); ylabel('год'); title('Аномалия в температуре'); "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Сравним аномальность из таблицы с отклонением значений от среднего."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "W_dev = W - W.mean()\n",
    "sns.heatmap(W_dev, yticklabels=8, cmap=\"coolwarm\")\n",
    "xlabel('месяц'); ylabel('год'); title('Отклонение от среднего'); "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "По идентичности рисунков становится понятно, что \"аномальность\" именно так и рассчитали. Небольшие расхождения в нашем примере связаны с тем, что мы взяли среднее не за всю историю наблюдений, а за 100 лет."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Обратите внимание, что в зимние месяцы температура нестабильна из года в год."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "W.std().plot(kind='bar', title='Вариативность среднемесячных температур в течение года');"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}