{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "113058f9-8324-4368-99ed-b088987c683a",
   "metadata": {},
   "source": [
    "# Comprehensive analysis of Vostok station data\n",
    "Seasonal-diurnal diagram, hour crossections, adjustment using meteoparameters"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5870ae4e-8a7d-4d76-a7e0-a542ee0fbd25",
   "metadata": {},
   "source": [
    "### Import libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "c4d7641a-fe60-4177-95d8-9f10e7622f87",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.transforms as tf\n",
    "from matplotlib import cm, colors, colormaps\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import scipy.stats as st\n",
    "\n",
    "import datetime as dt"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb2d634e-ccc5-4fd2-a9e8-05d314f12c1b",
   "metadata": {},
   "source": [
    "### Loading source PG datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "80dbd8bc-6286-4b35-8de7-900551e1026e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# potential gradient values measured at Vostok station in (1998–2001)\n",
    "# units are V·m^(−1)\n",
    "# source: Burns et al. (2012), Table 2\n",
    "\n",
    "vostok_old_data = (\n",
    "    np.array([195, 201, 205, 192, 188, 195,\n",
    "              209, 198, 209, 195, 193, 192])\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "c63b60de-82fa-4dcd-af67-a3d4738a0431",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load hourly data derived from 10-second intervals \n",
    "# \"Field\" column contains PG values [V/m] without scale and calibration\n",
    "df_10s = pd.read_csv('./data/Vostok/vostok_hourly_from_10_s_without_calibration_and_empty.tsv', \n",
    "                     sep='\\t', parse_dates=['Datetime']).set_index('Datetime')\n",
    "\n",
    "# Add a new column 'Mark' to label this dataframe's data as originating from 10-second averages\n",
    "df_10s[\"Mark\"] = '10s'  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "d2d83b75-97ce-42ba-8a99-8597f84d7946",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load hourly data derived from 5-minute intervals \n",
    "# \"Field\" column contains PG values [V/m] without scale and calibration\n",
    "df_5min = pd.read_csv('./data/Vostok/vostok_hourly_from_5_min_without_calibration_and_empty.tsv', \n",
    "                      sep='\\t', parse_dates=['Datetime']).set_index('Datetime')\n",
    "\n",
    "# Add a new column 'Mark' to label this dataframe's data as originating from 5-minute averages\n",
    "df_5min[\"Mark\"] = '5min'  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "26da1848-ad4f-4e58-8a75-d02428fb12c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load earlier dataset (1998-2004) \n",
    "earlier_df_src = pd.read_csv('./data/Vostok/vostok_1998_2004_hourly_80percent_all.tsv', \n",
    "                             sep='\\t', parse_dates=['Datetime']).set_index('Datetime')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "d126b8b1-0b66-4817-a556-8f33860608ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Note that all the aforementioned files are averaged with 80% data availability, which \n",
    "# means that the hourly value was calculated only when at least 80% of the readings were \n",
    "# available (at least 288 10-second averages or at least 9 5-minute averages)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "7de3455e-ba80-420d-8fec-8f82ee28804a",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Combine the two dataframes: fill in gaps in the 10-second data with the 5-minute data.\n",
    "df_src = df_10s.combine_first(df_5min)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cf820033-d3f9-4432-a425-db7bcf047a4d",
   "metadata": {},
   "source": [
    "### Helper functions and variables for PG"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "42979690-97b5-4e86-8777-db48896539ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "def std_error(avg_val, avg_sqr, counter):\n",
    "    \"\"\"\n",
    "    Estimate the standard error from the average value\n",
    "    and the average value of the square.\n",
    "    \n",
    "    :param avg_val: the average value\n",
    "    :param avg_sqr: the average square value\n",
    "    :param counter: the size of the sample\n",
    "    \n",
    "    :return: the standard error\n",
    "    \"\"\"\n",
    "\n",
    "    return np.sqrt((avg_sqr - avg_val**2) / (counter - 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "b13f968c-f9db-4b05-86ae-0e997383f9c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "def pass_fair_weather(df, scale_factor=None):\n",
    "    \"\"\"\n",
    "    Filter and adjust potential gradient (PG) data to represent fair weather conditions.\n",
    "\n",
    "    :param df: The input dataframe containing 'Field' values for hourly averaged potential gradient (PG) data\n",
    "               and 'Count' values indicating the number of valid measurements per day.\n",
    "    :param scale_factor: A factor used to normalize the 'Field' values to mitigate external influences\n",
    "                         such as nearby metal structures (default is None, implying no scaling).\n",
    "\n",
    "    :return: A dataframe with hourly data filtered to likely represent fair weather conditions only.\n",
    "    \"\"\"\n",
    "\n",
    "    # normalize 'Field' values by the scale_factor if provided.\n",
    "    if scale_factor:\n",
    "        df[\"Field\"] = df[\"Field\"] / scale_factor\n",
    "    \n",
    "    # exclude records with non-positive PG values\n",
    "    df = df[df[\"Field\"] > 0]\n",
    "    \n",
    "    # exclude records with PG values > 300 V/m\n",
    "    df = df[df[\"Field\"] <= 300]\n",
    "    \n",
    "    # retain only days with complete hourly data (24 counts)\n",
    "    df[\"Count\"] = df[\"Field\"].resample('D').transform('count')\n",
    "    df = df[df[\"Count\"] == 24]\n",
    "    \n",
    "    # compute diurnal PG stats\n",
    "    df['Emax'] = df['Field'].resample('D').transform('max')\n",
    "    df['Emin'] = df['Field'].resample('D').transform('min')\n",
    "    df['Emean'] = df['Field'].resample('D').transform('mean')\n",
    "    \n",
    "    # exclude days with peak-to-peak amplitude over 150% of the daily mean\n",
    "    df['Var'] = ((df[\"Emax\"]-df[\"Emin\"]))/df[\"Emean\"]\n",
    "    df = df[df['Var'] <= 1.5]\n",
    "    \n",
    "    # # debug output for '5min' marked data counts per year\n",
    "    # (averaged from 5-min data) included in individual years \n",
    "    # z = df[df.Mark == '5min']\n",
    "    # print(z.groupby(z.index.year).count()[\"Mark\"])\n",
    "    \n",
    "    # remove temporary columns used for calculations\n",
    "    df = df[[\"Field\"]]\n",
    "\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "350d271c-0337-4e2b-89df-f90a42671d5c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def calculate_seasonal_var_params(cond_df, key=\"Field\"):\n",
    "    \"\"\"\n",
    "    Calculate parameters necessary for assessing the seasonal \n",
    "    variability of fair weather potential gradients (FW PG).\n",
    "\n",
    "    :param cond_df: DataFrame containing conditioned data with a datetime \n",
    "                    index and at least one numerical column.\n",
    "    :param key: Column label in 'cond_df' which contains the potential \n",
    "                gradient data (default is 'Field').\n",
    "\n",
    "    :return: A list containing arrays of:\n",
    "             monthly mean PG values, \n",
    "             counts of fair weather days, and \n",
    "             adjusted monthly mean square PG values.\n",
    "    \"\"\"\n",
    "    # compute the monthly mean of fair weather PG\n",
    "    mon_pg = cond_df[key].groupby(cond_df.index.month).mean().to_numpy().flatten()\n",
    "\n",
    "    # count the number of fair weather days for each month\n",
    "    mon_counter = cond_df.groupby(cond_df.index.month).count().to_numpy().flatten()\n",
    "\n",
    "    # sum((daily mean FW PG)**2)/(count FW days) over months\n",
    "    mon_pg_sqr = cond_df[key].pow(2).groupby(cond_df.index.month).sum().to_numpy().flatten() / mon_counter\n",
    "\n",
    "    return [mon_pg, mon_counter, mon_pg_sqr]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "89b9f394-8d80-4db6-bd44-0b9778b03f9d",
   "metadata": {},
   "outputs": [],
   "source": [
    "month_name = [\"J\", \"F\", \"M\", \"A\", \"M\", \"J\", \"J\", \"A\", \"S\", \"O\", \"N\", \"D\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f1c0ca4-15bb-448e-b85b-1f6b3a8a555f",
   "metadata": {},
   "source": [
    "### Applying fair weather criteria "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "d1f85dec-45b7-47b2-b421-d528776fff39",
   "metadata": {},
   "outputs": [],
   "source": [
    "# apply the fair weather filter to the newdataset with a scale factor of 3 to normalize field values.\n",
    "df = pass_fair_weather(df_src, scale_factor=3)\n",
    "\n",
    "# apply the fair weather filter to earlier dataset data with a scale factor of 1 for normalization.\n",
    "earlier_df = pass_fair_weather(earlier_df_src, scale_factor=1)\n",
    "\n",
    "# Optional: Adjust the scale factor to 1.35 for earlier data to equalize the mean values of FW histograms across datasets.\n",
    "# earlier_df = pass_fair_weather(earlier_df, scale_factor=1.35)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "fbce521b-c249-4441-b962-9a7991fb521f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# # debug figure for check the mean values of FW histograms\n",
    "# fig, ax = plt.subplots(1,1)\n",
    "# df.hist(ax=ax,bins=600, color='red')\n",
    "# earlier_df.hist(ax=ax,bins=600, color='blue')\n",
    "# plt.xlim(-200,500)\n",
    "# plt.xlabel('V/m')\n",
    "# plt.ylabel('Counts')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4aebf84b-3335-450e-8379-bee1f0f01c05",
   "metadata": {},
   "source": [
    "### Resampling daily fair-weather datasets "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "c339ed78-e4ba-43a9-89b8-85e8a0c57236",
   "metadata": {},
   "outputs": [],
   "source": [
    "# resample the filtered data to calculate the daily mean of FW PG\n",
    "# then drop any days with missing data to ensure completeness of the dataset\n",
    "\n",
    "daily_df = df.resample('D').mean().dropna()\n",
    "daily_edf = earlier_df.resample('D').mean().dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "a0aa9a7f-c65a-46f7-bb85-fcd1c133e4be",
   "metadata": {},
   "outputs": [],
   "source": [
    "# saving intermediate data for plotting in other scripts\n",
    "\n",
    "np.save('./data/Vostok/vostok_diurnal_2006_2020.npy', \n",
    "        df.groupby(df.index.hour)\n",
    "            .mean().dropna().to_numpy().flatten())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5a58975-053a-4162-bccb-f0dbef39b0ed",
   "metadata": {},
   "source": [
    "### Figure 1.2: Seasonal variation (new data) for different years"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "6d6b5a71-6dd3-4471-b081-f3606c7b6956",
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculate seasonal variation parameters for specific years: \n",
    "# average FW PG, count of FW days, the sum of squares of FW PG hourly values\n",
    "\n",
    "data, data_counter, data_sqr = np.array(\n",
    "    [\n",
    "        calculate_seasonal_var_params(daily_df),\n",
    "        calculate_seasonal_var_params(daily_df[daily_df.index.year <= 2012]),\n",
    "        calculate_seasonal_var_params(daily_df[daily_df.index.year > 2012])\n",
    "    ]\n",
    ").swapaxes(0,1)\n",
    "\n",
    "data_counter = data_counter.astype(int)\n",
    "\n",
    "# saving intermediate data for plotting in other scripts\n",
    "np.savez('./data/Vostok/vostok_2006_2020_results', mean=data, counter=data_counter, sqr=data_sqr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "4e29c652-918d-47e2-ac67-1d0c2a898eea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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hqFmzpkKdtra2sLCwUKgTAC5duqRQJxFRScbEk4ioBMs8uzlt2jRcv35d4Wxn165dcenSJdnQRwBISEjAmjVr4ODgIPsyHhUVJfc4XV1d1KxZE0II2TWVRkZGAICYmBiFNgBg8eLFctsXLlwIAHBzc1OIu0KFCjh27BiSkpLQoUMHhfY/FR0drXBmMDMpyBwmaWhomGV8mWcaP338p/EC2fcxK3l9bgvL9u3bMXr0aPj6+sqe66z06tUL6enpWLNmjWxbSkoKNmzYgMaNG6NixYoAAFNTU7Rv3x5btmxBXFycrOzmzZsRHx8PLy8v2bbevXsDANavXy/X1rp166Cjo4PWrVsDkCao7du3z/KWKSEhAV27dsWLFy9w6NAhVKlSJct+KBNfeno6+vTpg/Pnz2Pnzp1o2rRpts9Pz549ERISgufPn8u2HT9+HPfv35erk4iopNMp6gCIiKjoODo6olmzZti3bx8AKCSeEydORHBwMLp06YLRo0fD3NwcmzZtwpMnT7B7925oaUl/v+zYsSOsra3xxRdfwMrKCqGhoVi+fDnc3NxkQx4zz3JNmTIF3t7eKFWqFLp37466deti4MCBWLNmDWJiYtCqVStcunQJmzZtwpdffok2bdpkGXvlypVx5MgRtG7dGp06dcKJEycUlvzItGnTJqxYsQIeHh6oVKkS4uLisHbtWpiYmMgSXwMDA9SsWRPbt29H1apVYW5uDmdnZzg7O6Nly5aYO3cuUlNTYWtriyNHjmQ5zDe7PmYmpPl5bpVx6tQptGnTBgEBAZg+fXq25S5duoQBAwagbNmyaNeuHYKCguT2N2vWTHZGt3HjxvDy8sKkSZPw+vVrVK5cGZs2bUJYWJhC4vjTTz+hWbNmaNWqFYYOHYrw8HAsWLAAHTt2ROfOnWXl6tevjyFDhuC3335DWloaWrVqhVOnTmHnzp2YNGlSlsNes+Pr64tLly5hyJAhCA0NRWhoqGyfsbExvvzyS6Xj++GHH7B//350794d7969w5YtW+Ta7Nevn+zvyZMnY+fOnWjTpg3GjBmD+Ph4zJs3D7Vr18bgwYPz3A8iIo1XhDPqEhHRZ+DXX38VAISrq2uW+x89eiR69eolzMzMhL6+vnB1dRUhISFyZVavXi1atmwpypYtK/T09ESlSpWEn5+fiI2NlSs3c+ZMYWtrK7S0tOSWHUlNTRWBgYHC0dFRlCpVSlSsWFFMmjRJYSmKj5dTyXTx4kVRunRp0bJlyyyXPRFCiKtXrwofHx9hZ2cn9PT0hKWlpejWrZu4cuWKXLlz586Jhg0bCl1dXbmlVcLDw4WHh4cwMzMTpqamwsvLS0RERGS5/Ep2ffx0OZW8PreZy6ns3LlTbvuTJ08EALFhwwbZtgMHDsgtcZKdDRs2yJZayur2cZ1CCJGUlCTGjx8vrK2thZ6ennBxcRF//vlnlnWfPXtWNGvWTOjr6wsLCwsxcuRI8f79e4VyHz58ENOnTxf29vaiVKlSonLlymLRokU5xp0Ve3v7bPuR1TJSeYmvVatWOT4/n7p165bo2LGjMDQ0FGZmZsLX11e8fPlS6b4QEWkyiRAFnJWAiIiIPgsTJkxAcHAwHj58yEltiIjos8JrPImIiDTEyZMnMXXqVCadRET02WHiSUSfhfj4eAQEBKBz584wNzeHRCLBxo0bsyy7Y8cONGnSBGZmZihbtixatWqFgwcPKpTLyMjA3Llz4ejoCH19fdSpU0e2dAiRJrp8+TKGDh1a1GEQEREpYOJJRJ+Ft2/fYsaMGQgNDVVYwP5jy5YtQ58+fVCuXDn88ssvmDp1KmJjY9GtWzfs2bNHruyUKVPg7++PDh06YNmyZbCzs0Pfvn1ly2cQERERkXrwGk8i+iykpKQgOjoa1tbWuHLlClxcXLBhwwYMGjRIrlzVqlVhZmaGixcvytZMfP/+PWxtbdG2bVvZ7KwvXryAo6Mjhg4diuXLlwOQLofRqlUrPHnyBGFhYbJlMoiIiIiocPGMJxF9FvT09GBtbZ1ruffv38PS0lKWdAKAiYkJjI2NYWBgINu2b98+pKamYsSIEbJtEokEw4cPR3h4uNzaiURERERUuJh4EmmopKQkjBs3DgcOHCjqUFSqdevW+PPPP7Fs2TKEhYXh7t27GDlyJGJjYzFmzBhZuWvXrsHIyAg1atSQe7yrq6tsPxERERGph05RB0BEhcPAwACrV69GzZo1izoUlVq6dCnevn2L0aNHY/To0QCAcuXK4fjx42jatKmsXGRkJKysrOTOjAKAjY0NACAiIiLbNoKDg3OchOjEiRMwNjYuSDeIiIqNsmXL4vbt20UdBhEVc0w8iTRYw4YNcevWraIOQ6UMDQ1RrVo1VKhQAd26dUNcXBwWLVoET09PnD17FpUrVwYgPeOb1ZIS+vr6sv3Z8fHxgY+PT7b73d3dsX///gL2hIioeHB3dy/qEIhIAzDxJNJgixcvRteuXeHs7IxBgwZBR6f4/5f38vKCjo6O3BDiHj16oEqVKpgyZQq2b98OQHrGNyUlReHxycnJsv1EREREpB7F/1soEWVr0KBB0NLSwrBhwzB69GjY2toqJFwSiQQ3btwoogiV8/jxY/z5559Ys2aN3HZzc3M0b94cf//9t2ybjY0NTp48CSGE3HDbyMhIAED58uXVEzQRERERMfEk0mTm5uYoW7YsqlWrVtShqMSrV68AAOnp6Qr7UlNTkZaWJrtfr149rFu3DqGhoXLXuV68eFG2n4iIiIjUg4knkQY7depUUYegUpUrV4aWlha2b9+OYcOGyc5khoeH4+zZs2jevLmsbI8ePfD9999jxYoVcut4rlq1Cra2tmjWrFmR9IGIiIioJGLiSUSfjeXLlyMmJkY24+yBAwcQHh4OABg1ahQsLCwwZMgQrFu3Du3atYOnpyfi4uKwYsUKJCUlYdKkSbK6KlSogLFjx2LevHlITU2Fi4sLfv/9d5w9exZBQUHQ1tYukj4SERERlUQSIYQo6iCIqPC8f/8eK1aswMmTJ/H69WusXr0arq6uePfuHTZu3Ah3d3fZTLBFzcHBAU+fPs1y35MnT+Dg4IC0tDSsWrUK69evx8OHDwEALi4umDp1Ktq0aSP3mIyMDMyZMwerV69GZGQkqlSpgkmTJsHX17dAcXJWWyIqSfiZR0SqwMSTSIOFh4ejVatWeP78OapUqYK7d+/i6NGjaNu2LQCgWrVq6Ny5M5YsWVLEkRYv/BJGRCUJP/OISBU41JZIg/n5+SEuLg7Xr1+HpaUlLC0t5fZ/+eWXCAkJKaLoiIiIiKik0CrqAIio8Bw5cgSjR49GzZo15ZYUyeTk5ITnz58XQWREREREVJIw8STSYElJSbCwsMh2f1xcnBqjISIiIqKSioknkQarWbMmzpw5k+3+33//HfXr11djRERERERUEjHxJNJgY8eOxbZt2zBnzhzExsYCkM70+vDhQ/Tv3x/nz5/H999/X8RREhEREZGm4+RCRBqsX79+ePr0KX788UdMmTIFANC5c2cIIaClpYXZs2fjyy+/LNogiYiIiEjjMfEk0nBTpkxB//79sXv3bjx8+BAZGRmoVKkSPD094eTkVNThEREREVEJwMSTqASws7NT+ZDaZ8+e4fHjx4iOjkZWywF7enqqtD0iIiIiKr6YeBKVEPHx8dkmiXZ2dnmu59mzZxgyZAhOnjwJAFnWJ5FIkJ6env9giYiIiEijMPEk0mDJyckIDAzE+vXrERUVlW05ZZLEgQMH4vz585g4cSIaN24MU1NTVYRKRERERBqMiSeRBhsxYgQ2bdqEL7/8Ei1atECZMmUKXOeFCxfg7++PwMBAFURIRERERCUBE08iDbZnzx58/fXXWL16tcrqrFChgkoSWCIiIiIqObiOJ5EGk0gkaNCggUrrHD9+PNavX4/ExESV1ktEREREmotnPIk0WI8ePXDs2DEMGzZMZXUOGzYM6enpqFKlCnr16oUKFSpAW1tbroxEIlH5LLpEREREVHxJRFZTUhJRsfTu3Tu5+9HR0ejduzcaNmyIYcOGwc7OTiFJBABzc/M8t3Hr1i1069YNz549y7aMps9q6+7ujv379xd1GEREasHPPCJSBZ7xJNIg5cqVg0QikdsmhMC1a9ewfv36bB+nTJI4dOhQxMbGYvXq1ZzVloiIiIjyhIknkQaZNm2aQuKpatevX0dgYCC++eabQm2HiIiIiDQHE08iDTJ9+vRCb8PR0bHQ2yAiIiIizcJZbYk02JAhQ3Dx4sVs91+6dAlDhgxRqs7AwED8+uuveP78eUHDIyIiIqISgmc8iTTYxo0b0b59ezRu3DjL/U+ePMGmTZvw22+/5bnOM2fOwMzMDNWqVUP79u1RsWLFLGe1XbJkSYFiJyIiIiLNwcSTqASLiIiAgYGBUo9Zvny57O+QkJAsyzDxJCIiIqKPMfEk0jD79u3Dvn37ZPfXrFmDY8eOKZSLiYnBsWPH4OLiolT9GRkZBY6RiIiIiEoWJp5EGubOnTvYuXMnAOmZx4sXL+Kff/6RKyORSGBkZISWLVti4cKFuda5a9cudOzYESYmJoUSMxERERFpNokQQhR1EERUOLS0tLBlyxb07du3QPWUKlUKWlpa+OKLL+Dm5oauXbuiRo0aKoqy+OFi6kRUkvAzj4hUgbPaEmmwjIyMAiedAPDmzRts2rQJFStWxNy5c+Hs7AwnJyeMGjUKhw8fRkpKigqiJSIiIiJNxcSTiHJlZmYGb29vbNq0CS9fvsS5c+fQv39/XLhwAV27dkXZsmXRvXt3rF69msusEBEREZECJp5EGu6PP/5Ahw4dULZsWejo6EBbW1vhpgyJRILGjRsjMDAQly9fRkREBJYtWwZ9fX34+/vDwcEBtWvXxqRJk3Dv3r1C6hURERERFSdMPIk02O7du9GtWze8evUK3t7eyMjIgI+PD7y9vWFgYIA6depg2rRpBWrDysoKgwcPxs6dO/H27VscO3YMnTt3xv79+7Fjxw4V9YSIiIiIijMmnkQa7Oeff4arqyuuXbuGwMBAAMCQIUMQFBSEW7duITIyEo6OjrnWM378eNy4cSPXcjo6OmjTpg3mzZuH27dvY+LEiQXuAxEREREVf0w8iTTYnTt34O3tDW1tbejoSFdPSk1NBQA4ODhgxIgRmDNnTq71LFy4EA0aNECtWrXw888/IywsLE/tlypVKt+xExEREZHmYOJJpMEMDQ2hq6sLQDpBkJ6eHiIjI2X7rays8OTJk1zruX//PqZNm4aMjAxMmTIFlSpVQvPmzbFq1SpERUUVWvxEREREpBmYeBJpsGrVquHOnTuy+/Xq1cPmzZuRlpaG5ORkbN26FXZ2drnWU7lyZQQEBCA0NBRXrlzB2LFj8fTpU4wYMQLly5eHu7s7tm/fjqSkpMLsDhEREREVU0w8iTSYh4cH9u3bJ1tnc8qUKTh16hTMzMxgYWGBs2fPKn0dZoMGDbBgwQI8e/YMx48fx4ABA/D333/Dx8cHVlZWGDBgAA4fPlwY3SEiIiKiYkoihBBFHQQRqc/Zs2exZ88eaGtrw83NDW3atClwnampqfjjjz+wcuVKHD58GFpaWkhLS1NBtJ8nd3d37N+/v6jDICJSC37mEZEq6BR1AESkXi1atECLFi1UVt+HDx8QEhKCrVu34tSpUwCk144SEREREWVi4klUArx48QJnzpzB69ev0bNnT1SoUAEZGRmIiYmBqakptLW1lapPCIHjx49j69at2Lt3L2JjY1G6dGn06dMH/fr1Q9u2bQupJ0RERERUHDHxJNJgQgj88MMPWL58OdLS0iCRSFC7dm1UqFABcXFxcHBwwIwZMzB27Ng81Xfp0iVs3boVO3bswKtXr6Cjo4NOnTqhX79+cHd3h76+fuF2iIg+OwkJCTA2NgYAxMfHw8jIqIgjIiKizxEnFyLSYPPmzcOSJUswfvx4HD16FB9f0m1qagpPT0/s3r0713qmTZuGKlWqoGnTpli6dCmcnJywfPlyREZGYv/+/ejduzeTTiIiIiLKFs94EmmwtWvXYsCAAZg9e3aW623WqVMHf/zxR671zJo1C9WrV8eMGTPg6+sLBweHQoiWiIiIiDQVE08iDfb8+XM0a9Ys2/1GRkZ4//59rvX8888/qF+/vipDIyIiIqIShENtiTSYpaUlnj9/nu3+f/75B3Z2drnWw6STiIiIiAqCiSeRBvP09MSqVavw+PFj2TaJRAIAOHLkCDZu3AgvL6+iCk9OfHw8AgIC0LlzZ5ibm0MikWDjxo1Zls3IyMDKlStRr149GBgYoGzZsmjbti1u3LihUG7u3LlwdHSEvr4+6tSpg+DgYDX0hoiIiIg+xqG2RBosMDAQJ0+eRL169dCiRQtIJBLMmTMHU6dOxfnz51G/fn1Mnjy5qMMEALx9+xYzZsyAnZ0d6tatK1sTNCtDhgxBUFAQBgwYgO+++w4JCQm4du0aXr9+LVduypQp+OWXX/DNN9/AxcUF+/btQ9++fSGRSODt7V3IPSLSEB0lOe9P/+hvd2MgL6szHRG5l6FiKzExEb1790ZISEhRh0JEnxEmnkQazNTUFBcuXMCCBQuwa9cu6Ovr4/Tp06hUqRICAgLg5+cHAwODog4TAGBjY4PIyEhYW1vjypUrcHFxybLcjh07sGnTJuzZswceHh7Z1vfixQssWLAAI0eOxPLlywEAX3/9NVq1agU/Pz94eXkpvX4pERHlbtu2bfjjjz8QFRWFsmXLFnU4RPSZ4FBbIg1nYGCAH3/8EdevX0dCQgKSkpJw69YtTJs27bNJOgFAT08P1tbWuZZbuHAhXF1d4eHhgYyMDCQkJGRZbt++fUhNTcWIESNk2yQSCYYPH47w8HCcP39eZbETEdF/fv31V2RkZGD9+vVFHQoRfUaYeBJpsMOHDyM+Pr6ow1CZ9+/f49KlS3BxccHkyZNhamoKY2NjODk5YceOHXJlr127BiMjI9SoUUNuu6urq2w/ERGp1rNnzxAREQEA2V6nT0QlE4faEmmwLl26QFtbG3Xr1kWLFi1kNwsLi0Jrc8iQIShfvjzGjBmj8nYePXoEIQS2bdsGHR0dzJ07F6ampliyZAm8vb1hYmKCzp07AwAiIyNhZWUlm0wpk42NDQDIvhhlJTg4OMdJiM6fPw93d3cV9IioGMjlN5q0jy7X9LoB6ORySSgAgP9/NNayZcvw8uVLANJr92/dugVnZ+cijoqIPgdMPIk02IULF3DmzBn89ddf2Lx5M5YsWQKJRIKqVavKJaIODg4qazPzF+6lS5dizJgxmDlzpsrqzjx7GxUVhQsXLqBx48YAAHd3dzg6OmLWrFmyxDMpKQl6enoKdejr68v2Z8fHxwc+Pj7Z7nd3d8f+/fvz3Q+iYiW3yYXyg/9/ipW8/tAmhMCePXtk99+8eYP58+fzzCcRAWDiSaTRXF1d4erqivHjxwMA7ty5g7Nnz+Ls2bP4888/sX79ekgkEqSlpamszSdPniA+Ph6nT5/OcWba/Mi8JtXR0VGWdAKAsbExunfvji1btiAtLQ06OjowMDBASkqKQh3JyclydRERkWr8/fffiI6Oltt28uRJ2ecyEZVsvMaTqIRITk7G69ev8fr1a7x69QrR0dEQQsDJyUml7djb26NWrVoYMWKEwnWXBVW+fHkAgJWVlcI+S0tLpKamyiYbsrGxwcuXLyGE/LINkZGRcnUREZFqzJ07VyHxfPfuHQ4ePFhEERHR54SJJ5EGCwkJgb+/P5o1awZTU1O0b98eu3fvRo0aNfDbb78hMjIS9+/fV6rOGTNm4NatW9nuv337NmbMmFHQ0LNUvnx5WFtb48WLFwr7IiIioK+vj9KlSwMA6tWrh8TERISGhsqVu3jxomw/ERGpRmJiIq5fv66wPT4+HgsXLlR/QET02WHiSaTB3N3dsXDhQtjZ2WHv3r2IiorC9evXsXTpUnh5eWV55jA306dPx82bN7Pdf+vWLQQGBhYk7Bz16dMHz58/x9GjR2Xb3r59i3379qFt27bQ0pJ+rPXo0QOlSpXCihUrZOWEEFi1ahVsbW3RrFmzQouRiKik2b59O16/fp3lvkePHiEqKkrNERHR54YD7ok0mJubG86dO4cdO3bg/PnzaNGiBVq2bIkWLVooLDOiKu/evYOurm6+Hrt8+XLExMTIZpw9cOAAwsPDAQCjRo2CqakpJk2ahB07dqBnz54YN24cTE1NsWrVKqSmpmL27NmyuipUqICxY8di3rx5SE1NhYuLC37//XecPXsWQUFB0NbWLnhniZSUkJAAY2NjANIzQUZGRkUcEZFqLF++PMvr6gHg5cuXWL9+PSZMmKDmqIjocyIRn14ARUQa59atW7JJhc6ePYuIiAiYm5vjiy++QIsWLfDDDz/k+PgzZ87IJgqaPn06PD09UadOHYVyMTEx2L59O2xtbXHp0iWl43RwcMDTp0+z3PfkyRPZ7LuPHz/G+PHjcfz4caSmpqJp06b45Zdf4OLiIveYjIwMzJkzB6tXr0ZkZCSqVKmCSZMmwdfXV+nYPsZZbSm/imXiWRiz2h7hV4/iJLfPvKioKDg6Osre24D0R0hzc3MA0s9iCwsL/Pvvv4UeKxF9vph4EpUwKSkpCA4Oxpw5c3Dv3j1IJBKkp6fn+JjAwEDZ8FmJRKIwYc/HatasifXr18vNOqtpmHhSfjHx/H9MPIuVvHzmvX37FhkZGbL7/fv3x+bNm2X39fX1YWJiUmgxEtHnj0NtiTRcfHw8/v77b5w5cwZnz57F5cuX8eHDB+jo6KBJkyZo0aJFrnVMmDAB3333HYQQsLS0xKpVq9CzZ0+5MhKJBIaGhrJ1MomIqOQoV66c3H09PT1YWloWUTRE9Dli4kmkwRo2bIibN28iPT0dxsbGaNq0KSZPnowWLVqgcePGeV7L0sDAQFb2yZMnsLCwgKGhYWGGTkREREQahIknkQZzcHBA//790aJFC9SvX18242tB2NvbqyAyIg2V27DUj0e1uxsDeZnjisNSiYhIAzDxJNJgu3fvVnmdQgisWbMG69evx+PHjxUWCwekw27T0tJU3jYRERERFU9MPIlKkOjoaPTs2RMLFixA/fr181XHhAkTsHDhQtSrVw/9+vVDmTJlVBwlEREREWkaJp5EJciHDx9w6tSpLM9S5tWmTZvQs2dP7NixQ4WREREREZEmK/gFX0RUoiQlJaF9+/ZFHQYRERERFSNMPIlIKe3atcPly5eLOgyiYslIGxAdpDejvEwsREREpCGYeBJpqKSkJIwbNw4HDhyQbTMwMMDAgQNRvnz5fNe7YsUKXLhwAbNnz0ZUVJQqQiWiQpCQkACJRAKJRIKEhISiDoeIiEo4XuNJpKEMDAywevVq1KxZU7bNxMQEGzZsKFC91apVQ0ZGBqZOnYqpU6dCX18f2tryp24kEgliY2ML1A4RERERaQ4mnkQarGHDhrh165ZK6+zZsyckklzWKiQiKgQJCQkwNjYGAMTHx8PIyKiIIyIiorxi4kmkwRYvXoyuXbvC2dkZgwYNgo5Owf/Lb9y4seCBEREREVGJwsSTSIMNGjQIWlpaGDZsGEaPHg1bW1sYGBjIlZFIJLhx40YRRUhEREREJQETTyINZm5ujrJly6JatWoqrffZs2eYPXs2Tp48idevX2Pfvn1o2bIl3r59ixkzZmDw4MGoX7++Stskoo90zMNw9/SP/nY3BnKbRfeIKEhEREREOWLiSaTBTp06pfI679y5gxYtWiAjIwONGzfGw4cPkZaWBgAoV64c/vrrLyQkJGD9+vUqb5uINBiTaSIijcbEk4iUMmHCBJiZmeHChQuQSCSwtLSU2+/m5obt27cXUXRERERE9Dli4kmkQc6cOQMAaNmypdz93GSWz2sb06ZNg4WFRZbreNrZ2eHFixd5ro+IiIiINB8TTyIN0rp1a0gkEiQlJUFXV1d2PztCCEgkEqSnp2db5lMZGRkwNDTMdv+bN2+gp6enVNxEREREpNmYeBJpkJMnTwIAdHV15e6rUoMGDXDw4EGMGDFCYV9aWhq2bduGJk2aqLxdIlKOkTYgOhR1FKqliX0iIiopmHgSaZBWrVrleF8VJk2ahG7dumH48OHw9vYGALx69QrHjh3D7NmzERoaiuXLl6u8XSIiIiIqvph4EpFSunTpgo0bN2LMmDFYs2YNAKBfv34QQsDExAT/+9//lLpmlIiIiIg0HxNPIg2XnJyM3bt34+rVq4iNjUVGRobcfolEovTSJ/3794enpyeOHDmChw8fIiMjA5UqVUKnTp1QunRpVYZPRERERBqAiSeRBnv69CnatGmDsLAwmJmZITY2Fubm5oiJiUF6ejrKlSsHY2PjfNVtZGQEDw8PFUdMRERERJqIiSeRBvPz80NsbCwuXLgAJycnWFpaYvv27fjiiy+wdOlSLF++HIcPH86xjmfPngGQLpPy8f3cZJYnIiIiImLiSaTBTpw4gREjRsDV1RXv3r0DIF1CRU9PD35+fggNDcXYsWNx8ODBbOtwcHCQW6Il835ulFmihYiIiIg0GxNPIg2WmJgIBwcHAICJiQkkEgliY2Nl+5s2bYrx48fnWMdvv/0GiUSCUqVKyd0nIiIiIsorJp5EGszOzg7h4eEAAB0dHdja2uLChQvw9PQEANy5cwf6+vo51jFo0KAc7xMRERER5YaJJ5EGa9u2Lfbt24eAgAAA0qTx559/RnR0NDIyMrB582YMGDCgiKMkKnoJCQmyibbi4+NhZGRUxBERERFpFiaeRBps4sSJuHz5MlJSUqCnp4fJkycjIiICu3btgra2Nvr27YuFCxfmWMeMGTOUblcikWDq1Kn5DZuIiIiINAwTTyINsn//fjRq1Ajly5cHIB1q+/Hssvr6+li3bh3WrVuX5zqnT5+usC3zGk8hhMJ2IQQTTyIiUsqOHTuwbds2rF69GhYWFjmW/eOPP2Bvb4+aNWuqKToiUgWtog6AiFTHw8MDp06dkt13cnLC/v37C1RnRkaG3O358+eoXbs2fHx8cOnSJcTGxiI2NhYXL16Et7c36tati+fPnxewJ0REVJKsX78e79+/zzXpBIBly5Zl+aMoEX3emHgSaZDSpUsjJiZGdj8sLAzx8fEqbWPkyJGoUqUKtmzZgkaNGqF06dIoXbo0XFxcEBQUhEqVKmHkyJEqbZOIiDTbv//+i5YtWypsv3//PoYPH47ExETZtqZNm+Kff/5RZ3hEpAIcakukQVxdXfHTTz/h1atXMDU1BQAcOnQIL1++zPYxEokE33//fZ7bOHHiBObMmZPt/nbt2sHf3z/vQRMVto55WP7n42Vn3Y0B7TzUe0TkXoaI8iQqKgq2trYK28PDw7F69WrMmjULhoaGAIDy5cvneFwjos8TE08iDbJixQoMGDAAM2fOBCBNKrdu3YqtW7dm+xhlE099fX2cP38ew4cPz3L/uXPncl2ihYiI6GOlS5fOMpmMiIgAAMTGxqJs2bIApGtUZyahRFR8cKgtkQapXLkyzp07h8TERISFhUEIgcWLF+PJkyfZ3h4/fqxUG76+vggKCsLo0aPx4MED2bWfDx48wKhRo7B161b4+voWUg+JiEgTNWzYEEFBQUhLS5PbvmPHDmhpaWHfvn0ApJPa7d69mxMLERVDPONJpIH09fVhZ2eHgIAAtG3bFvb29iqre86cOXj79i2WL1+OX3/9FVpa0t+vMjIyIISAj49PjkNxiYiIPjV69Gi4u7ujcePGGDhwIAwMDLBr1y6ULl0aPXv2xIQJE3Dy5Ek8f/4cN27cKPDEeUSkfkw8iTRYQECAyuvU1dXF5s2b4efnh0OHDuHp06cAAHt7e3Tp0gV169ZVeZtUciUkJMDY2BgAEB8fDyMjo0Jpx0gbEB0KpWoiygM3NzcsXLgQU6ZMkV3+4eLigs2bN0NPTw96eno4duwYzMzMsGbNGnTr1q2IIyYiZTHxJKJ8qVOnDurUqVPUYRARkYYYM2YMvvnmG9y5cwempqaoUqWKbN///ve/IoyMiFSBiScRERERfRYMDQ3RqFGjog6DiAoBJxciIqX98ccf6NChA8qWLQsdHR1oa2sr3IiIiPLq9OnTePbsWa7l3rx5g8DAQFy+fFkNURGRKjHxJCKl7N69G926dcOrV6/g7e2NjIwM+Pj4wNvbGwYGBqhTpw6mTZtW1GESEeVbQkICJBIJJBIJEhISijqcEqFNmzbYvHlzruVMTEzw888/Y8OGDWqIiohUiYknESnl559/hqurK65du4bAwEAAwJAhQxAUFIRbt24hMjISjo6OStcbHx+PgIAAdO7cGebm5pBIJNi4cWOOj0lNTUXNmjUhkUgwf/58hf0ZGRmYO3cuHB0doa+vjzp16iA4OFjp2KiQdJTkfnM3/q+8u3Hu5YmoWJJI8vb/V09PD3Xq1MGlS5cKOSIiUjVe40mk4dLT03H48GE8fvwY0dHREELI7ZdIJJg6dWqe67tz5w5+/vlnaGtrQ0dH+hGSmpoKAHBwcMCIESMwZ84cDBgwQKk43759ixkzZsDOzg5169bFqVOncn3MsmXLchyaNWXKFPzyyy/45ptv4OLign379qFv376QSCTw9vZWKj4iIipcISEhePnyZa7l4uLi8OjRIzVERESqxMSTSINduXIFPXv2RHh4uELCmUnZxNPQ0BC6uroAADMzM+jp6SEyMlK238rKCk+ePFE6VhsbG0RGRsLa2hpXrlyBi4tLjuVfv36NGTNmwN/fP8uhvS9evMCCBQswcuRILF++HADw9ddfo1WrVvDz84OXlxevRSUi+oxcunSJZzKJNBiH2hJpsBEjRiApKQm///473r17h4yMDIVbenq6UnVWq1YNd+7ckd2vV68eNm/ejLS0NCQnJ2Pr1q2ws7NTOlY9PT1YW1vnufzEiRNRrVo19OvXL8v9+/btQ2pqKkaMGCHbJpFIMHz4cISHh+P8+fNKx0hEGkLVQ7w5zFslZsyYgfT09DzfiKh4YeJJpMFu3rwJf39/dO/eHWZmZiqp08PDA/v27UNKSgoA6XDWU6dOwczMDBYWFjh79iwmTpyokrayc+nSJWzatAmLFy/O9rqga9euwcjICDVq1JDb7urqKttPREREROrBobZEGqxChQrZDrHNr/Hjx2P8+PGy+926dcOpU6ewZ88eaGtrw83NDW3atFFpmx8TQmDUqFHo06cPmjZtirCwsCzLRUZGwsrKSiExtbGxAQBERERk20ZwcHCOkxCdP38e7u7uygdP8vKY+3cvJ/3X52YeCmf1uhTWbwzqaqso21FnW59Rn9I++tj0ugHo5OWEJj8TCqRVq1awt7cv6jCIqBAx8STSYP7+/pg/fz6GDh0KExOTAteXkpKCw4cPw8HBAXXq1JFtb9GiBVq0aFHg+vNi48aN+Pfff7Fr164cyyUlJUFPT09hu76+vmx/dnx8fODj45Ptfnd3d+zfvz+PEVO2CmN4YlavS2ENg1RXW0XZjjrb+oz6lJAOGJ+Q/r2zLmCUl8vBNfgzQR0/tJ04caLQ2yCiosXEk0iDxcXFwdjYGJUrV4a3tzcqVqyoMKGORCLB999/n6f6dHV14eXlhSVLlsglnury/v17TJo0CX5+fqhYsWKOZQ0MDGTDgT+WnJws209ERERE6sHEk0iDfTwkNnNm108pk3hKJBJUqVIFb9++VUl8ypo/fz4+fPiAPn36yIbYhoeHAwCio6MRFhaG8uXLQ1dXFzY2Njh58iSEEHLDbTNn4C1fvrza4yei4sFIGxAdijoKyvTixQtMnz4dly9fhoGBAdzc3DBhwgTZDOtEVDww8STSYPlZ1iQ3kydPxrhx4+Dl5YVq1aqpvP6cPHv2DNHR0ahVq5bCvtmzZ2P27Nm4du0a6tWrh3r16mHdunUIDQ1FzZo1ZeUuXrwIQDobLxERfd7S09PRpUsXAMCwYcMQExOD+fPn49WrV1i2bFkRR0dEymDiSaTBCmOihgsXLqBs2bJwdnZG69at4eDgoDBsVSKRYMmSJSpve/To0fjyyy/ltr1+/RrDhg3DoEGD0KNHDzg6OgIAevToge+//x4rVqyQne0VQmDVqlWwtbVFs2bNVB4fERHlz+vXr2Fpaamw/fbt27h16xZu3rwJZ2dnANJr9efMmcPEk6iYYeJJREr5eMju8ePHsyyT38Rz+fLliImJkc04e+DAAdlQ2lGjRqFBgwZo0KCB3GMyh9zWqlVLLimtUKECxo4di3nz5iE1NRUuLi74/fffcfbsWQQFBSlc60pEREWnatWqmDJlCsaOHYtSpUrJtuvq6kIikSAuLk627f3791lOHkdEnzcmnkQa7ubNm1i2bBmuXr2K2NhYZGRkyO2XSCR49OhRnuv79PGqNH/+fDx9+lR2f8+ePdizZw8AoF+/fjA1NVWqvl9++QVlypTB6tWrsXHjRlSpUgVbtmxB3759VRp3SZOQkABjY2MAQHx8PIyMjIo4IiIq7rZu3Ypx48Zh3bp1WLBgAbp16wYAqF69Olq2bImePXvCx8cH7969Q3BwMKZOnVrEERORsph4EmmwU6dOoXPnzihTpgwaNWqEa9euoW3btkhOTsb58+dRq1YtNGzYsKjDlMluTc6cODg4ZLtWqZaWFiZNmoRJkyYVMDIiIipMXbt2RceOHbF06VL0798fTZo0waJFi1C9enUcPHgQS5cuxcWLF2FoaIhNmzahT58+RR0yESlJq6gDIKLCM23aNDg5OeHevXvYsGEDAOnkQH/99RfOnTuH8PBw9O7du4ijJCIiAnR0dDBu3Dg8fPgQdnZ2qF+/PsaNG4fU1FRMnDgRe/fuRVBQEJNOomKKiSeRBrt69Sq++uormJiYyK5pTE9PBwA0btwYw4YNU3q4kpaWFrS1tXO8GRkZoVq1avj222+VGsZLRERUtmxZrF69GhcuXMA///yDqlWrYu3atdmObiGi4oFDbYk0mI6ODkqXLg0AMDMzQ6lSpfD69WvZficnJ9y5c0epOqdNm4Z9+/bh9u3b6NKlCypXrgwAePDgAf7880/Url0bbdu2xcOHD7FhwwYEBwfjzJkzqFu3ruo6RkREGufNmzc4dOgQXr16BRsbG3Tr1g2nT5/Grl274Ofnh9WrV2Px4sVo3rx5UYdKRPnAxJNIg1WuXBkPHjwAIJ1EqHr16ti7dy98fX0BAAcPHoS1tbVSdZYvXx5v377F3bt34eTkJLfv4cOHaN26NWrWrIl58+bhwYMHaNq0KSZPnoyDBw+qplNU+DpKct6f/tHf7sZAXiYIPsIzFUSUvSNHjsDT0xPGxsaoXLkyIiMjMWrUKISEhKBXr17o3r075s+fj65du8LNzQ3z5s1DhQoVijpsIlICh9oSabCuXbsiODgYaWlpAIBx48Zhz549qFKlCqpUqYL9+/dj2LBhStU5b948jBw5UiHpBKSJ7siRI/Hzzz8DAKpUqYJvv/0W586dK3hniIhIY33//fdwc3NDREQE/vrrLzx69AiTJ0/G+PHjAQB6enqYMmUK7t27Bx0dHdSoUaOIIyYiZTHxJNJgU6dOxY0bN2TXdw4cOBD/+9//4OzsjLp16+K3336Dv7+/UnWGh4dDRyf7wRI6Ojp4/vy57L6DgwNSUlLy1wEiIioRXr58iaZNm0JL67+vpq1bt5at65zJxsYGmzdvxpEjR9QdIhEVEIfaEmmwUqVKoWzZsnLb+vXrh379+uW7zlq1amHlypXo378/rKys5Pa9fPkSK1euRK1atWTbHj9+rPRwXiIiKll69OiBWbNmAQBq1qyJiIgIzJ07Fx4eHlmWb9q0qTrDIyIVYOJJVAKkpKTg6tWreP36Nb744guUK1cu33XNnz9fNqnQl19+KZtc6OHDh/j999+RmpqK3377DQCQnJyMjRs3okuXLirpBxERaaYVK1bA2toay5Ytw8uXL2FrawtfX19MmTKlqEMjIhVh4kmk4ZYuXYrp06cjNjYWAHD06FG0bdsWb9++RfXq1TF37lwMGTIkz/W1bt0a586dQ0BAAPbs2YOkpCQAgL6+Ptq3b4/p06ejQYMGsm2fDpOi4s9IGxAdijoKIs2QkJAAY2NjAEB8fDyMjIyKOKKioa+vj9mzZ2P27NlFHQoRFRJe40mkwTZs2ICxY8eic+fOWL9+vdwaaOXKlUPbtm2xbds2peutX78+9u/fj7i4OERERCAiIgLx8fHYv3+/LOkkIiIiIsrEM55EGmzBggXo0aMHtm7diqioKIX9DRs2xNKlS/Ndv5aWFq/fJCIiIqJc8YwnkQZ7+PBhjtdXmpubZ5mQEhERERGpEs94EmkwMzMzvH37Ntv9d+7c4RlLIqLC0lGSe5n0j/52Nwa0cyl/RORSgIjo88QznkQarGvXrlizZg1iYmIU9t2+fRtr166Fu7u7+gMjIiIiohKFiSeRBps1axbS09Ph7OyMH3/8ERKJBJs2bUK/fv3QqFEjWFpaYtq0aUUdJhERERFpOCaeRBqsfPny+Oeff9C5c2ds374dQghs3rwZBw4cgI+PDy5cuFCgNT2JiIiIiPKC13gSaThLS0usW7cO69atw5s3b5CRkQELCwtoaeXtd6czZ87kq92WLVvm63FERCUJ18UlopKCiSdRCWJhYaH0Y1q3bg2JJA8TZPw/IQQkEgnS09NzL0xEREREJQITTyINl5CQgN27d+Px48eIjo6GEPIzIkokEixZsiTbx588ebKwQyQiIiIiDcfEk0iDHT9+HF5eXlnOapspt8SzVatWhRAZEREREZUknFyISIONHDkSRkZGOHz4MGJiYpCRkaFw45BYIiIiIipsPONJpMGePXuGOXPmoEMH1c5ckZycjN27d+Pq1auIjY1FRkaG3H6JRIL169ertE0iIiIiKr6YeBJpsDp16iA2NlaldT59+hRt2rRBWFgYzMzMEBsbC3Nzc8TExCA9PR3lypWDsbGxStskIiIiouKNQ22JNNicOXOwYsUKXLlyRWV1+vn5ITY2FhcuXMD9+/chhMD27dsRHx+POXPmwMDAAIcPH1ZZe0REVHAJCQmQSCSQSCRISEgo6nCIqATiGU8iDdaqVSssXrwYTZs2RY0aNVCxYkVoa2vLlZFIJNi3b1+e6zxx4gRGjBgBV1dXvHv3DoB0CRU9PT34+fkhNDQUY8eOxcGDB1XaFyIiIiIqvph4Emmw3bt3o1+/fkhPT0d4eDji4uIUyiizRicAJCYmwsHBAQBgYmICiUQiN5y3adOmGD9+fIHiJiIiIiLNwsSTSINNnDgR1apVw+7du1G1alWV1GlnZ4fw8HAAgI6ODmxtbXHhwgV4enoCAO7cuQN9fX2VtEVEREREmoGJJ5EGi4iIwLx581SWdAJA27ZtsW/fPgQEBAAABg0ahJ9//hnR0dHIyMjA5s2bMWDAAJW1R0REueiYh5ErH6+c5W4MaGdbUuqIKEhEREQKmHgSaTAXFxc8e/ZMpXVOnDgRly9fRkpKCvT09DB58mRERERg165d0NbWRt++fbFw4UKVtklERERExRsTTyINtmzZMnTv3h0NGjRA7969VVKnnZ0d7OzsZPf19fWxbt06rFu3TiX1ExEREZHmYeJJpMF8fX2RlpYGHx8ffPPNN6hQoUKWs9reuHGjiCIkIiIiopKAiSeRBjM3N0fZsmVRpUqVfNcxY8YMSCQSTJkyBVpaWpgxY0auj5FIJJg6dWq+2yQiItUy0gZEh6KOgohKMiaeRBrs1KlTBa5j+vTpkEgk8Pf3h66uLqZPn57rY5h4EhEREdHHmHgSUY4yMjJyvE9ERERElButog6AiIiIiIiINBsTTyJSira2NrZu3Zrt/u3btytMYEREREREJRuH2hKRUoTIeVHx9PR0SCR5WMyciIg0Qnp6Om7evCl3KUZMTAz++ecf2X1TU1NUrly5KMIjos8EE08iUlp2ieX79+9x+PBhlCtXTs0RERFRUYmLi0Pr1q2ho6Mjt61jx44ApD9Y2tnZ4fr160UUIRF9DjjUlohyFRgYCG1tbWhra0MikaBfv36y+x/fypQpg82bN8Pb27uoQy6REhISIJFIIJFIkJCQUNThEFEJYWZmBmdnZ7x79052S01Nlf2dkJCAb7/9tqjDJKIixjOeRBqsRo0a6N+/P3x9fWFvb5/velxdXTFixAgIIbBixQp06NABVatWlSsjkUhgZGSEhg0bwtPTs6ChExFRMTJ27Fhcv34diYmJCvssLS3Rr1+/IoiKiD4nTDyJNFjFihUREBCAadOmoVmzZhgwYAC8vLxgamqqVD1dunRBly5dAED2y3Xjxo0LI2QiIiqGevTogXLlyuHZs2cK+2rVqgVjY+MiiIqIPiccakukwY4cOYLw8HDMmzcPSUlJGDp0KKytrdGrVy/s27cPqampSte5YcOGQkk64+PjERAQgM6dO8Pc3BwSiQQbN26UK5ORkYGNGzfC3d0dFStWhJGREZydnTFr1iwkJydnWe/69etRo0YN6Ovro0qVKli2bJnKYyciKul0dXXxxRdfKGw3NTXFhAkTiiAiIvrc8IwnkYazsrLC999/j++//x737t3Dli1bEBwcjL1798LMzAx9+vRBv3790KxZszzXmZ6ejsOHD+Px48eIjo5WmOlWIpFg6tSpSsX59u1bzJgxA3Z2dqhbty5OnTqlUCYxMRGDBw9GkyZN8O2338LS0hLnz59HQEAAjh8/jhMnTshNfLR69Wp8++236NmzJ8aNG4ezZ89i9OjRSExMhL+/v1LxERFRzvz8/HD06FG8fftWts3c3BytW7cuuqCI6LPBxJOoBKlWrRpmzpyJkSNHYsyYMdi5cydWrVqF1atXw8nJCWPHjsXw4cOhpZX9YIgrV66gZ8+eCA8Pz3ZplfwknjY2NoiMjIS1tTWuXLkCFxcXhTK6urr4+++/5ZLkb775Bg4ODrLks3379gCApKQkTJkyBW5ubti1a5esbEZGBmbOnImhQ4eiTJkySsVYpDrmYYma9I/+djcGcltO9UjOS+MQESmjfv36MDc3l0s8u3XrluMxhYhKDn4SEJUQCQkJ2LJlCzp37gw7Ozvs3bsX3bp1w44dO7B3715Uq1YNo0ePxvDhw3OsZ8SIEUhKSsLvv/+Od+/eISMjQ+GWnp6eYx1Z0dPTg7W1dY5ldHV1szwz6+HhAQAIDQ2VbTt58iSioqIwYsQIubIjR45EQkICDh48qHSMRESUs379+skSTSsrK4wdO7ZoAyKizwYTTyINlp6ejkOHDqFv376wsrLCgAEDEBUVhQULFiAiIgL79+9Hr1694O7ujpCQEPj7+2Pbtm051nnz5k34+/uje/fuMDMzU09HcvHy5UsAkFs/9Nq1awCARo0ayZVt2LAhtLS0ZPuJiEh1hg0bJvsR0crKCk5OTkUcERF9LjjUlkiDWVtb4927d7C1tcWoUaMwYMAA1KhRI9vyderUQVxcXI51VqhQIdshtkVl7ty5MDExkc28CwCRkZHQ1taGpaWlXFldXV2ULVsWERER2dYXHByM4ODgbPefP38e7u7ueYotLS0Nf/zxBwDp7MAfL7CulDzkyWkfvSxeNwCd3EbnZteHwsjJs2qrOLejzraKsh11tsU+ff7t5IGlpSXs7e0RGRmZ6wgaIipZJOJz+wZJRCozaNAg9O/fH23btpWbdKcg1q5di/nz5+Py5cswMTFRSZ2fyrzGc8OGDRg0aFCOZWfPno0pU6ZgxYoVcl9yvvrqKwQHB2e5ppydnR0aNGiA33//PV/xubu7Y//+/Xkqm5CQIFtGID4+HkZGRvlqMy/XeCakA8YnpH/HtwWM8nuNZ16uJ1VWVm0V53bU2VZRtqPOttinz7YdZT7zAGDnzp3w9vZGbGwsl1EhIhme8STSYJ8uR6IKcXFxMDY2RuXKleHt7Y2KFStCW1s+w5FIJPj+++9V3vantm/fjh9//BFfffWVwi/rBgYG+PDhQ5aPS05OhoGBQaHHp25G2oDoUNRREFFJ16NHDwwdOpRJJxHJYeJJpEGyWrg7L+zs7PJcdvz48bK/ly9fnmUZdSSeR48exYABA+Dm5oZVq1Yp7LexsUF6ejpev34tN9z2w4cPiIqKQvny5VUTSG5nGpSdaRbgbLNEVKzp6upi5cqVRR0GEX1mmHgSaRAHB4d8DalVZhbaJ0+eKF2/ql28eBEeHh5o1KgRduzYkeV1k/Xq1QMgHbbbtWtX2fYrV64gIyNDtp+IiIiICh8TTyIN8ttvv6nsWs7s2NvbF2r9uQkNDYWbmxscHBwQEhKS7ZDZtm3bwtzcHCtXrpRLPFeuXAlDQ0O4ubmpK2QiIiKiEo+JJ5EGyW0iHlV68eIFzpw5g9evX6Nnz56oUKEC0tPTERsbC1NTU4XrPvNi+fLliImJkc04e+DAAYSHhwMARo0aBS0tLXTq1AnR0dHw8/NTWIuzUqVKaNq0KQDpNZ4zZ87EyJEj4eXlhU6dOuHs2bPYsmULfvrpJ5ibmxfwGSAiIiKivGLiSURKEULghx9+wPLly5GWlgaJRILatWujQoUKiI+Ph4ODA2bMmJGvRcPnz5+Pp0+fyu7v2bMHe/bsASBdlBwAnj9/DgCYOHGiwuMHDhwoSzwBYMSIEShVqhQWLFiA/fv3o2LFili0aBHGjBmjdGz5xQl/iIiIiJh4Emm86OhoBAcH4/Hjx4iOjlZYg1MikWD9+vV5rm/evHlYsmQJ/P390a5dO3To8F9WZWpqCk9PT+zevTtfiWdYWFiuZZRdAeqbb77BN998o3QsRERERKQ6TDyJNNjhw4fRq1cvJCQkwMTEBGXKlFEoo+w1oWvXrsWAAQMwe/ZsREVFKeyvU6cO/vjjj3zHTERERESah4knkQb74YcfYG1tjT179qB27doqqfP58+do1qxZtvuNjIzw/v17lbRFRERERJpBq6gDIKLC8/DhQ4wePVplSScAWFpayq6zzMo///yj1LqgRERERKT5mHgSabAqVaogLi5OpXV6enpi1apVePz4sWxb5nDdI0eOYOPGjfDy8lJpm0RERERUvDHxJNJgs2bNwooVK/I0aU9eBQYGwsbGBvXq1cOAAQMgkUgwZ84cNG/eHF26dEGdOnUwefJklbVHRERERMUfr/Ek0iCjR49W2GZhYYEaNWqgQ4cOqFixosL6mhKJBEuWLMlzG6amprhw4QIWLFiAXbt2QV9fH6dPn0alSpUQEBAAPz8/GBgYFLgvRERERKQ5mHgSaZDly5dnuy8kJCTL7comngBgYGCAH3/8ET/++KNSjyMiIiKikolDbYk0SEZGhtK39PR0pdpo27Ytjh8/nu3+kydPom3btgXtChERERFpECaeRBrs2bNnSEpKynZ/UlISnj17plSdp06dwqtXr7Ld//r1a5w+fVqpOjVJmyvA2HtAhsi53IUYwOsGcCW28GNKzQCeJgEJyv3GAEDz+vO5tlWc+lSS3xPFpZ3P8TUiImLiSaTBHB0dsXfv3mz379+/H46OjkrXmzmLbVYePnyI0qVLK12npjgTDSx9BnjcAFIysi/XyAQ4FwtsjMx/W45ngV+zX9lGJjoNcPwLmBumfBua1h91t6WJfdLE94SmtaPO14iIKK94jSeRBhMi55+7U1NToaWV++9PmzZtwqZNm2T3Z82ahbVr1yqUi4mJwc2bN9G1a1flg9Ug7hbAH2+BDv8AIfUBkyw+aXW0AC8r4FhU/tt5lgzEpOZezlIXsNcHLuXzrIam9UedbWlinwDNe09oWjuA+l4jIqK8YuJJpGHev3+PmJgY2f2oqKgsh9PGxMRg27ZtsLGxybXOxMREvHnzRnY/Li5OIWGVSCQwMjLCt99+i2nTpuW/AxrAywoYXgHodRNodQX4sz5gpadYro4xsO5Fwdp6kQL88z73ck6GwKU8lMuKpvVH3W1pYp808T2hae2o8zUiIsoLJp5EGmbRokWYMWMGAGkyOHbsWIwdOzbLskIIzJo1K9c6hw8fjuHDhwOQDt9dsmQJ3N3dVRazJupUDjjeEHC7BjS7DBysD1Q3ki/z8gNQtlTB2lkVLr3lRgIgl8u9cqRp/VFnW5rYJ0Dz3hOa1g6gvteIiCgvmHgSaZiOHTvC2NgYQghMmDABPj4+aNCggVyZzLOTDRs2RKNGjZSq/8mTJ6oMV6O5mgJ/uQCdrwJNLgHLqwP9/v8E88NEYNkzoGWZgrXhaSkdUqcOmtYfdbWliX3KpEnvCU1rJ5M6XiMiorxg4kmkYZo2bYqmTZsCABISEtCzZ084OzurvJ3Tp0/j4MGDePr0KQDA3t4ebm5uaNWqlcrbKs6qGQHnXQGvm8CAW8CkB4CVLnA7AdCRALMqFaz++qWBAeVVE2teaFJ/1NWWJvbpY5ryntC0dj5W2K8REVFeMPEk0mABAQEqr/PDhw/w8fHB77//DiEEzMzMAEivGV2wYAE8PDwQHByMUqU4diuTtR5wphHwv0hg20vgbar0+quJDkAVo1wf/tnRtP5QwfE98fnja0RERY2JJ5GGi46ORnBwMB4/fozo6GiFmW4lEgnWr1+f5/oCAwOxd+9ejB8/Hj/88AOsrKwASNfvXLBgAebNm4cZM2Zg5syZKu1HcZHeIevtEgkwsLz0pipPmgNlCjm/17T+qLstTeyTJr4nNK0ddb5GRER5xcSTSIMdPnwYvXr1QkJCAkxMTFCmjOKFPDmtyZmVrVu3YuDAgZg7d67cdktLS8yZMwevXr3C5s2bS2zimel6HLDkGVBRD/BzAEoXwqetnUH2+669B+4lAnpaQFNT6dmOz506+6OutjSxT5+KSAYiPwBJ6YCBNmCjC5TXV03dmX16+0F6PaKVHuCYQz8L2k5hU/drpI7PISKivOJHEJEG++GHH2BtbY09e/agdu3aKqkzMjISjRs3znZ/48aNsW3bNpW0VZw9SwL+FyE9u7H2BTC7MjDYVrVtbIoArsUBi6v9ty0yBfC8AVyMlc6KCQDaEuB7e2BOFeXbOP1O+kU/ty/mbz4AK54DXcsBLqbKtwOopz/qbksT+wQA4cnA7CfA7tfS1/5TlrrSSXSmOAK2BUhCMwTwbSjwW4T0bwDoVBbYVVea6I66C5yKlq5R+V1FYJRd/tvKq62RQL9bQEY2ZxVzos7XCFDP5xARUV4x8STSYA8fPsS8efNUlnQCQIUKFXDq1Cl8++23We4/ffo0KlSooLL2ijMB6WySO14Co+5Jl1BYVl06y6QqLHkGuJrIb+tzE7iTAMyqDHxhCiRlANtfAfPCpGc9vlPyi3mbf4CZlYApTjmXM9EBfg4DXn3If+Kpjv6ouy1N7FNovHRdyMQMoFs5oJEJYKMnPVOXkiFNpK68l15LuPMVcNZFcQmPvFoVDqx/AbQ2B/pYSROy/W8A/wfAvQTgdDTQuSzwOhUYc086YU5va+XbuZ8AVFXDdY7qfD9kKuzPISKivGLiSaTBqlSpgri4OJXWOXDgQAQEBMDMzAzff/89KleuDIlEggcPHmDx4sXYuXMnAgMDVdpmcWagBUyrBHxtC/g/lK6lN8AG+KWK9KxQQTxMBIZ+dPbiUSLwdwywvhYw6KNruDqXk54dWhGu/JfYvA7E1tOSLkR/KVa5+j+mjv6ouy1N7NO4+4CpDnCtUc5nM18kS3+4GHsP+LNB9uVykpl0Hm/437avbIEht4Hj74Cl1f7rQ4/rwKJn+Us8a5yTzjbrawP4WBfe0HR1vh8+VpifQ0REecXEk0iDzZo1CyNHjkTfvn3h4OCgkjonT56MR48eYc2aNVi7di20tLQAABkZGRBCYODAgZg8ebJK2tIk5fWBzc7AqIrSL+LV/gamOgGjKwI6WvmrU0sCpH40V9TLD9KzG22yWJOvrTnw+5v8tRPyVlp3buLSpV+k80td/VFnW5rYp79igJ8r5z6E1lYfGGMHTHyQv3YA4GESMDuLoaHdygEbI4DuH62H6VZOeiY0P36wB4JfAuPvA34PgNZlAF9roJeVaq+LVOf7ISuF8TlERJRXTDyJNNjx48dhYWGBGjVqoEOHDqhYsSK0tbXlykgkEixZsiTPdWpra2Pjxo0YN24cDh48iGfPngGQruPZtWtX1KlTR6V9KG6eJkn/ff3/idrzZPn9VrrA1tpAUCQQ8AhY9wK40yx/bbmaAEEvpde2SSRAFUPpmnx3EgD7T67JvJMAlMvnbJqXYgt2JjOv1NUfdbaliX3S1ZL+yJAXcWnS8gXxPi2Lev+//eSM/7YlZ0iH4ubH3KrS2+l30udw1yvg5Dtg5F3pdcu+NtLEtqB9UddrpM7PISKivGLiSaTBli9fLvs7JCQkyzLKJp6Z6tSpU+KTzKw4/SU9gwFIh6m2vJJ9WQmAuwn5b+tHJ6DtP0D368DcKkBNY2CcPTAiFFhVA2hZBkhMB7a9kk78M6pi/tqZkYdrPFVBXf1RZ1ua2Kdu5YD5T6VJVLuy2Zc7HgXMeypN2PLL1QRYGQ70twEq/P8Z1uhUYNFTwEIXWPoM+LWGNMHdGAE4G+e/LQBoZS69/VodOPhWmpgdfAvseQ2Y6UgnTCrIGVB1vUbq/BwiIsorJp5EGiwjIyP3Qvl069YtHDp0CGFhYQAAR0dHdO7cWaUTGRVH86pK/70dD2yIkM7qWVjr9rUsA/yvFjAsFHA+L53gpaI+EJECdLn2XzkJgI5lgZmVCycOVVFnf9TVlib2aX5V4GY80OEqUNVQOkmNjR6gryU96xiZIj1Dfj8RqFcaWFg1/32a6gS0/weoeQ5oUQYoJZEO9dWVAPvrAe3+Afa+BuLTpbeQevlv62OltIAvLaW392nA7lfSM5UbI4FPlkJWirpeI3V+DhER5RUTTyJSSkpKCoYNG4bNmzdDCCF3jefEiRPh6+uLdevWQVe3ZM5YMc5e+u+BN9IzMF/bKg6hU6W+NtIvs6vDgaPvpLNzCkjPytjqAQ1NAG8rwM0i16qy1KpM4cb/qcLuT1G0pWl9stAFLrlK3987XgF/vAXepf6337yUdKKe8fbSCXMKcu1gyzLAyUbAz0+Aq++ldXUwB36qDDgZAicaAoufSSfiGVge6KqC5/BTJjrSJUgG20rXLN32Cjgclf/61PEaqftziIgoLyRCFOS3OyIqDp48eYI//vgDT58+BSC9HrNLly5wdHRUuq6xY8di6dKlGDFiBEaNGoVKlSpBIpHg4cOHWLp0KVauXInRo0dj8eLFKu7F58Pd3R379++X3umY9UVlcWnAkySghpH07IlSjmTxsZxNOwWSVTvqbKs4t6POtorB+yE1Q7oMiIFWHt/vxaBPKmmriNtR1eeQ3GceEVE+8YwnkYb74YcfsGTJEoVht1paWhg7dizmz5+vVH1btmxB//795a4fBYBq1arh119/xfv377FlyxaNTjzzorQOUKd0UUdBuUlKl07AUqaU9MxWcW+nqJTKa8JJasXPISL6nGjg4Y+IMi1YsACLFi1Cr1698MMPP6BGjRoAgNDQUCxatAiLFi2Cra0tvv/++zzXmZqaiiZNmmS7v1mzZjhw4ECBYy/O7icAy59Lr3Gz0gWGVgC+MJPuC40HzsQAJtqAuyVgpJ1TTXnzIhnY9wa4Hie9vi7zzJONnvQauy8tpMso5EfgI+kXVw/LnMuFxgMnooEeFv9NApNfhdmfTx2NAr68ARhrS6+DG2dfOAmUutoBCv/5Ox4lXWJniiNQLpcR9f/GScvY5HNdTHW1FZMqvZbT7pPhqH++lU5udC9BulZtczNgkmPB3+PqoO7PISKi3DDxJNJga9euhbu7O3bs2CG3vXHjxti2bRuSk5OxevVqpRLPTp064fDhwxg+fHiW+//880907NixQHEXZw8SgIYXpTNTWulKl3fY+lI66cmLFGBo6H+Tk1TUBy64Fmyxev8HwOKnQJoADLUBa13pF+SUDOB0NLD+BfD9PWmiM7uydAkHZcx4LL3+bE4VwM8h+3JWetJY3n4AAip9vv3JigTAyIrSGVh/i5BOhqOK6y2Loh11PH9Ln0sT2twSQQAYcw8orwdsyeecY+pqa8gd6QQ/F1z/27biOfDdXelZ6jrG0gR+3Qvpda0XXIFKhsq30+YK8JUt0M8m53LPkgCHv4CZ+ZxRWt2fQ0REecGBMUQaLCwsDJ06dcp2f6dOnWSz0ubVzJkz8eTJE3h6euL48eN4+vQpnj59imPHjsHDwwNPnz7FzJkz8e7dO7lbSfHjI+ki8ScbARGtgOctpROxLH4mXZy+qSlwuTFwsL70i+y0R/lva84TYH4YMKwCcLsZENcWeNAcuNVM+u/7ttLt31YA5oRJE578qGooTWj87mdfxryU9Ezaobf5awNQX38+JSCt8+EXQJdygOcNwO2a9Mu7KhV2O+p6/q6+BzpnsYzK82RpDMkfrfH5hRlw5X3+2lFnWxdjAfePfgRISgcmPpC+Ts9aSP8/X3CVPn+lJNL/D/lxJvq/NTZzYmcgPXN7KZ/9UefnEBFRXvGMJ5EGs7S0xI0bN7Ldf+PGDVhYKHfKJXO47r///ot9+/bJ7cucq6xmzZoKj0tPz+OK88XcxVjp4vAty/y3rb6JdC3Dw1HSpSgamEi3j6gAbIrMf1urXwDf2AJLq2dfproRsKQ68EEAq8KBCQ7KtzPVSTrU8KcnQFQqsK6m9Evtp74wK1jiqa7+ZMesFLC4GjC8AjDuPlD7AjDWTtp/VQ5FLKx21PX8vUkF7LIYavogEZj0UDqDqv7/98Pu/5cKyS91tfUuVXp2ONPNeCAhXTp77sevSWVDYIydNHHPr4R0IOpD7uXqGks/T/JDnZ9DRER5xcSTSIN5eXlhyZIlcHBwwKhRo2BkZAQASEhIwPLly7Fu3TqMHTtWqTqnTZsGiSrGN2qolx8A+yy+KGd+ea7w0XC2ivrAywJ8KY9MAVxM81a2kYl0WYX8mlEZsNQFxt6XDqfdXgcw+CRJShPSIX35pc7+fOrjeUKrGUnPBP35VpoYbomUDjX2zWV4ZFG3o67nT18LeJemuP3N/ydT8elA5knK1AK+J9TVlq0ecCv+v/u6EulrZZTFuDBDbenQ5fz6JUx6y40E8u8XZajzc4iIKK+YeBJpsJkzZ+L69euYPHkypk2bhvLlywMAIiIikJaWhjZt2mDGjBlK1Tl9+vRCiFRzlNcDTkVLJ/L42NkY6b834gBb/f/+Ll+A66qqGAJ7X0uvGcvNvtfSIbMF8Z2dNPkceBtodhnYWUd6BggAPmQA/4sEnI3zX786+zP4tvTfZ8nSf/3uA8ZZHBEbmkjb6n8rfwmhutoB1Pf81TEGdr9SPFsa8v9nu09FAwP/f5Keo1EFe53U1VYfa+m1sb2sgGZm0kmYqhgCv4ZLz05nSkoHNkT8d7YwP9qYA63M8v/4vFDn5xARUV4x8STSYIaGhjh+/Dj27dsnt45n586d0bVrV3Tv3p1nL1WstxUwN0w6oUdfa+nMpftfA0ueAYPKA9/eBSYnS89IrAqXDofLr8mOgO+/QMvL0uv2mphKrwvT1wKSM6RnwC69B1Y+l37h3JrPCV7k+mct7VvPm0Cd89LZbm30pENs7yYARxsUj/7seS2dXCX9/08p/RklPcOUHeN8DoFVVzuA+p6/b2yBQbcB92vSoa4G2sCuV8CrFKC9uXRCnivvpdcyhrwFNtbKf5/U1dZkR+BIFNDqivQ93cEcGGoLTH4IXIoFWpSRTtSz/430eTxSgPd52zL5mzBIGer8HCIiyismnkQlQI8ePdCjR4+iDqNEmO4kPYOw9Jn0Sx4gvR7yezvpMMreN4GRd6Xb65Uu2BdQb2tpEuP3QHqmLKtheRJIl37YVluaNKpCK3PgVlPpNXa7Xkm/kFfQBzbUAtplMRFMXqmzP7FtpP8eeAP0uC7tj71Bjg/5rNsB1Pf89S8PXI0Dlj2XJnuZdR5rIB2G6vOvdAkSQy1pQjegfP77pK62jLSBU42AKQ+lM9fufiV9/iSQXi+Zea1lAxMgyBloXian2oqeOj+HiIjyioknEZEK6WsDfzQALscC1+IAHYl06F516eW12FVXur5eUgZQVwULu/exlp7dOBcrXbcxIkU6HNBAWzp8rn5p6QyW+T2xfbIRUMNIcbu1njTR/K0mkJihusl3Crs/n7LWBb60lCYxhUld7ajr+VtUDRhVUZoUmupIJ7HR+//rIc+4SK+B1JWo5nVSV1tG2tJhtTMrAX/HSNe/jE+XroFqqycdDp2fJVQ+drIR4FhIPzx8TN2fQ0REecHEk0jDuLu7K1VeIpEozE5LBedimv1EL1WzSOQKQiKRziibuTi8KrXM5cyORKL6xecLsz+fcjEF9tTVnHYA9T1/TobSW1YyE8Pi2FZpHaBzOaCzaqsFkPv/J1VT5+cQEVFumHgSaZiQkBDo6+vD2tpatrxJTniNJxEREREVNiaeRBrG1tYWL168QLly5dC3b194e3vD2lpFF/cREREREeWDigeoEFFRe/78OU6ePIn69etj5syZqFixItq3b48NGzYgLi6uqMMjIiIiohKIiSeRBmrVqhVWr16Nly9fYteuXShbtiy+++47WFpawtPTE7t27UJKyue1Ynh8fDwCAgLQuXNnmJubQyKRYOPGjVmWDQ0NRefOnWFsbAxzc3P0798fb968USiXkZGBuXPnwtHREfr6+qhTpw6Cg4MLuSdERERE9CkmnkQarFSpUujRowe2b9+OV69eyZLRPn36YO7cuUUdnpy3b99ixowZCA0NRd262c/CEh4ejpYtW+Lhw4eYPXs2xo8fj4MHD6JDhw748OGDXNkpU6bA398fHTp0wLJly2BnZ4e+ffti27Zthd0dIiIiIvoIr/EkKgFSUlJw+PBh7Nu3D9euXYO+vj4cHByKOiw5NjY2iIyMhLW1Na5cuQIXF5csy82ePRsJCQn4559/YGdnBwBwdXVFhw4dsHHjRgwdOhQA8OLFCyxYsAAjR47E8uXLAQBff/01WrVqBT8/P3h5eUFbu5DXtiAiIiIiADzjSaSxMjIycPjwYQwaNAhWVlbw8fFBUlIS1q5di9evX6N///5FHaIcPT29PE2CtHv3bnTr1k2WdAJA+/btUbVqVezYsUO2bd++fUhNTcWIESNk2yQSCYYPH47w8HCcP39etR0gIiIiomzxjCeRhjl37hy2bt2KnTt3IioqCk2aNMHs2bPRu3dvlCtXrqjDK5AXL17g9evXaNSokcI+V1dXHDp0SHb/2rVrMDIyQo0aNRTKZe5v3rx54QZMRERERAAAicjLQn9EVGxoaWnBwMAAXbt2hY+PT56G1DZo0KDwA1NC5lDbDRs2YNCgQQrb//e//ymcsZ0wYQLmzZuH5ORk6OnpoVu3bggNDcWjR4/kyiUmJsLIyAgTJ07Ezz//nGX7wcHBOU5CdPXq1c/uOSMiKiyPHj3C7du3izoMIirmeMaTSAMlJSVh9+7d2LNnT47lhBCQSCRIT09XU2QFk5SUBEA6LPdT+vr6sjJ6enqyf3Mqlx0fHx/4+PioImQiIiIiAhNPIo2zYcOGog6h0BgYGABAlkvBJCcny5UxMDDIUzkiIiIiKnxMPIk0zMCBA4s6hEJjY2MDAIiMjFTYFxkZCXNzc9lZThsbG5w8eVJ2VvfjcgBQvnx5NURMRERERABntSWiYsTW1hYWFha4cuWKwr5Lly6hXr16svv16tVDYmIiQkND5cpdvHhRtp+IiIiI1IOJJxEVKz179kRISAieP38u23b8+HHcv38fXl5esm09evRAqVKlsGLFCtk2IQRWrVoFW1tbNGvWTK1xExEREZVkHGpLRJ+N5cuXIyYmBhEREQCAAwcOIDw8HAAwatQomJqaYvLkydi5cyfatGmDMWPGID4+HvPmzUPt2rUxePBgWV0VKlTA2LFjMW/ePKSmpsLFxQW///47zp49i6CgIGhraxdJH4mIiIhKJEFE9Jmwt7cXALK8PXnyRFbu1q1bomPHjsLQ0FCYmZkJX19f8fLlS4X60tPTxezZs4W9vb3Q1dUVtWrVElu2bFFjj6Q2bNggAIjLly8XehtZ3fz9/VVa/9mzZxX2Z2RkiAoVKggAws3NrcDtZfr1118FAOHq6qqyOoUouv583HZhvh/U3VZhvU4fu3nzpujZs6ews7MTenp6onz58qJ9+/Zi6dKlKm3n1q1bwtfXV5QvX17o6uoKGxsb0bdvX3Hr1i2VtZH5uujp6Ynw8HCF/a1atRK1atVSWTuZNz09PWFjYyM6duwolixZIt6/f1/gNoiI8opnPInosxEWFpancrVq1cLhw4dzLaelpYVJkyZh0qRJBYys+JgxYwYcHR3ltjk7O6usfn19fWzduhXNmzeX23769GmEh4dnuYRNQQQFBcHBwQGXLl3Cw4cPUblyZZXWr+7+aKrCfp3OnTuHNm3awM7ODt988w2sra3x/PlzXLhwAUuWLMGoUaNU0s6ePXvg4+MDc3NzfPXVV3B0dERYWBjWr1+PXbt2Ydu2bfDw8FBJW4B0hu5ffvkFy5YtU1mdWcn8XEhNTcXLly9x6tQpjB07FgsXLsT+/ftRp06dQm2fiAjgUFsiIo3SpUsXNGrUqNDq79q1K3bu3ImlS5dCR+e/Q8jWrVvRsGFDvH37VmVtPXnyBOfOncOePXswbNgwBAUFISAgQGX1A+rtj6ZSx+v0008/wdTUFJcvX4aZmZncvtevX6ukjUePHqF///5wcnLCmTNnYGFhIds3ZswYtGjRAv3798fNmzfh5OSkkjbr1auHtWvXYtKkSYU60/annwuTJk3CiRMn0K1bN7i7uyM0NJRLTBFRoePkQkRElGc+Pj6IiorC0aNHZds+fPiAXbt2oW/fviptKygoCGXKlIGbmxt69eqFoKAgldYPqLc/mkodr9OjR49Qq1YthaQTACwtLVXSxrx585CYmIg1a9bIJZ0AUK5cOaxevRoJCQmYO3euStoDgMmTJyM9PR2//PKLyurMq7Zt22Lq1Kl4+vQptmzZovb2iajkYeJJRKRBYmNj8fbtW7mbKjk4OKBp06YIDg6Wbfvjjz8QGxsLb29vlbYVFBQET09P6OrqwsfHBw8ePMDly5dV2oY6+6Op1PE62dvb459//sGtW7dUWu/HDhw4AAcHB7Ro0SLL/S1btoSDgwMOHjyosjYdHR0xYMAArF27Vjapmjr1798fAHDkyBG1t01EJQ8TTyIiDdK+fXtYWFjI3VStb9+++P3335GUlARAmni0atVKpUMF//nnH9y9e1eW/DVv3hwVKlQolLNp6uiPplLX6zR+/HgkJiaiXr16aNasGfz9/XHkyBGkpqaqpP7Y2FhERESgbt26OZarU6cOwsPDERcXp5J2AWDKlClIS0vDnDlzVFZnXlWoUAGmpqZ49OiR2tsmopKHiScRkQb59ddfcfToUbmbqvXu3RtJSUkICQlBXFwcQkJCCmWYrZWVFdq0aQMAkEgk6NOnD7Zt24b09HSVtqWO/mgqdb1OHTp0wPnz5+Hu7o4bN25g7ty56NSpE2xtbbF///4C15+ZSJYuXTrHcpn7379/X+A2Mzk5OaF///5Ys2YNIiMjVVZvXhkbG6s0kSYiyg4TTyIiDeLq6or27dvL3VTNwsIC7du3x9atW7Fnzx6kp6ejV69eKqs/PT0d27ZtQ5s2bfDkyRM8fPgQDx8+ROPGjfHq1SscP35cZW0Bhd8fTaXu18nFxQV79uxBdHQ0Ll26hEmTJiEuLg69evXCnTt3ClR3ZkKZWwKW1wRVWT/++CPS0tKK5FrP+Ph4lfeHiCgrnNWWiIiU1rdvX3zzzTd4+fIlunTpkuWkL/l14sQJREZGYtu2bdi2bZvC/qCgIHTs2FFl7QGF2x9NVRSvEwDo6urCxcUFLi4uqFq1KgYPHoydO3cWaCZdU1NT2NjY4ObNmzmWu3nzJmxtbWFiYpLvtrLi5OSEfv36Yc2aNZg4caJK685JeHg4YmNjVb78DRFRVph4EhGR0jw8PDBs2DBcuHAB27dvV2ndQUFBsLS0xK+//qqwb8+ePdi7dy9WrVql0uUfCrM/mqooXqdPZS4Rooohqt26dcPatWvx119/KazrCgBnz55FWFgYhg0bVuC2svLjjz9iy5Ytar3Wc/PmzQCATp06qa1NIiq5mHgSEZHSjI2NsXLlSoSFhaF79+4qqzcpKQl79uyBl5dXlsNdy5cvj+DgYOzfvx99+vRRWbuF1R9Npe7X6eTJk2jdujUkEonc9kOHDgEAqlWrVuA2/Pz8sGXLFgwbNgxnzpxB2bJlZfvevXuHb7/9FoaGhvDz8ytwW1mpVKkS+vXrh9WrV8Pe3l5uXdnCcOLECcycOROOjo7w9fUt1LaIiAAmnkRElE8DBw5UeZ379+9HXFwc3N3ds9zfpEkTWFhYICgoSKWJJ1A4/dFU6n6dRo0ahcTERHh4eKB69er48OEDzp07h+3bt8PBwQGDBw8ucBtVqlTBpk2b4Ovri9q1a+Orr76Co6MjwsLCsH79erx9+xbBwcGoVKlSgdvKzpQpU7B582bcu3cPtWrVUlm9f/zxB+7evYu0tDS8evUKJ06cwNGjR2Fvb4/9+/dDX19fZW0REWWHiScRUSETQgAAtLW1iziSz19QUBD09fXRoUOHLPdraWnBzc0NQUFBiIqKkjsrVVyo8/1QWG2p+3WaP38+du7ciUOHDmHNmjX48OED7OzsMGLECPz4448quybXy8sL1atXx88//yxLNsuWLYs2bdpg8uTJcHZ2Vkk72alcuTL69euHTZs2qbTeadOmAZBeH2tubo7atWtj8eLFGDx4MCcWIiK1kYjMoxIRERWKpUuXYsyYMXj48GGhni2h4kGd7we+94iI6HPB5VSIiArZ5cuXYWRkBHt7+6IOhT4D6nw/8L1HRESfCw61JSIqJLt378apU6cQFBSEr7/+utAnC6HPmzrfD3zvERHR54ZDbYmIComjoyPi4uLg4eGBxYsXw8jIqKhDoiKkzvcD33tERPS5YeJJREREREREhYrXeBIREREREVGhYuJJREREREREhYqJJxERERERERUqJp5ERERERERUqJh4EhERERERUaFi4klERERERESFioknERERERERFSomnkRERERERFSomHgSERERERFRoWLiSURERERERIWKiScREREREREVKiaeREREREREVKiUTjw3btwIiUQiu4WFhRVCWERERERERKQpeMaTiIiIiIiIChUTTyIiIiIiIipUTDyJiIiIiIioUBU48RRCYPXq1ahbty4MDAxgaWmJwYMHIyIiQhXxERERFUs3b97Et99+ixo1aqB06dIwMDCAg4MDPD09cezYsaIOj4iISK10ClrB6NGjERISIrufnJyMjRs34vjx4zh//jxsbW0L2gQREVGxMn/+fEycOBHp6ely258+fYqnT5/Czs4O7du3L6LoiIiI1K/AiWdISAi6deuGBg0a4OTJkzh79iwA4Pnz5xg1ahT27NlT4CCJiIiKi99//x1+fn6y+zo6OvDy8kL16tURGRmJ48ePF2F0RERERaPAieeQIUOwfv16AEBAQADatWuHU6dOAZAefF++fAlra+uCNkNERFQs/PTTT7K/tbW1cfr0aTRr1ky2LSMjA8+ePSuK0IiIiIpMga/xHDhw4H+VaWmhf//+svtCCFy9erWgTRAVGQcHB3Tr1q2ow8gXBwcHDBo0qKjDICpREhMT8c8//8jud+/eXS7pBKTHSgcHBzVHRlQ4eJwkorwqcOJpZWWV4/3o6OiCNkHFnLu7OwwNDREXF5dtGV9fX+jq6iIqKkpl7Z47dw7Tp09HTEyMyupUpcTEREyfPl02QiA/Pvc+ZoqKisK8efPQsmVLWFhYwMzMDE2aNMH27duzLJ+SkgJ/f3+UL18eBgYGaNy4MY4ePZpl2XPnzqF58+YwNDSEtbU1Ro8ejfj4+CzLXr16Fe7u7jA3N4ehoSGcnZ2xdOlSpfpy/PhxDBkyBFWrVoWhoSGcnJzw9ddfIzIyMt/xxcfHIyAgAJ07d4a5uTkkEgk2btyoUFdGRgY2btwId3d3VKxYEUZGRnB2dsasWbOQnJysVD+ocERHR0MIIbvv6OhYhNFQccHjZNZ4nCz4cfLIkSP46quv4OzsDG1t7Wx/9IqIiEC/fv1QrVo1lC5dGmZmZnB1dcWmTZvkPtPy4u7du5gwYQLq1auH0qVLw8bGBm5ubrhy5UqW5V+8eIHevXvDzMwMJiYm6NGjBx4/fixX5vnz5wgMDISrqyvKlCmDcuXKoXXr1tlO1BYTE4OhQ4fCwsICRkZGaNOmDU+GfQ6EkjZs2CAAyG6nT5+W279+/Xq5/SEhIco2QRpm27ZtAoDYtGlTlvsTEhKEkZGR6N69u0rbnTdvngAgnjx5ku867O3thZubm+qC+sibN28EABEQEJDvOnLqY3Jysvjw4UP+A1ShAwcOiFKlSokePXqIxYsXi+XLl4s2bdoIAGLatGkK5b29vYWOjo4YP368WL16tWjatKnQ0dERZ8+elSt37do1oa+vL+rXry9WrlwppkyZIvT09ETnzp0V6jx8+LDQ1dUVjRs3FgsXLhRr1qwR/v7+ws/PT6m+NGzYUDg6OooJEyaItWvXikmTJonSpUsLKysrERkZma/4njx5IgAIOzs70bp1awFAbNiwQaHtuLg4AUA0adJEzJo1S6xZs0YMHjxYaGlpidatW4uMjAyl+kKql5CQICQSiewY+OWXXxZ1SFQM8DiZNR4nC36cHDhwoNDX1xfNmjUTFSpUEPb29lm2f+PGDdGqVSsxefJksWrVKrFs2TLh7u4uAIhJkyYp1ZcffvhBmJmZia+++kqsXr1azJ07V1SqVEloa2uLo0ePypWNi4sTVapUEZaWlmLOnDli4cKFomLFiqJChQri7du3snLLli0TBgYGwsfHRyxfvlwsXrxYNGjQQAAQv/32m1yd6enpolmzZsLIyEhMnz5dLF++XNSsWVOULl1a3L9/X6m+kGoVOPEcMmSIbF96errsSxMAIZFIFL6IUcmTmJgoSpcuLTp16pTl/q1btwoAYtu2bSptt6QfUD8njx8/FmFhYXLbMjIyRNu2bYWenp6Ij4+Xbb948aIAIObNmyfblpSUJCpVqiSaNm0qV0eXLl2EjY2NiI2NlW1bu3atACAOHz4s2xYbGyusrKyEh4eHSE9PL1BfTp8+rVDH6dOnBQAxZcqUfMWXnJws+6y8fPlytolnSkqK+PvvvxW2BwYGCgAKB3QqGo0aNZIdB3V0dMSFCxfk9mdkZCj8f6CSjcfJrPE4WfDj5IsXL2TJtZubW7aJZ3a6desmjIyMRFpaWp4fc+XKFREXFye37e3bt8LCwkJ88cUXctvnzJkjAIhLly7JtoWGhgptbW25hPfWrVvizZs3co9NTk4W1atXFxUqVJDbvn37dgFA7Ny5U7bt9evXwszMTPj4+OS5H6R6BU48AYhu3bqJgIAA0bJlS7ntPXr0KISQqTgaOHCg0NHREa9evVLY161bN1G6dGmRmJgohBDi0aNHolevXqJMmTLCwMBANG7cOMsz50uXLhU1a9YUBgYGwszMTDRs2FAEBQUJIYQICAhQeJ9+fOBJTU0VM2bMEE5OTkJXV1fY29uLSZMmieTkZLk2sjqgbty4UWhra4vx48fn2OfLly+Ljh07irJlywp9fX3h4OAgBg8eLIT47wzXp7fMg+uNGzfEwIEDhaOjo9DT0xNWVlZi8ODBcr/+5dZHe3t7MXDgQLmY8vLcnjx5UgAQ27dvF7NmzRK2trZCT09PtG3bVjx48CDHPitr6dKlAoC4efOmbJufn5/Q1taWS9aEEGL27NkCgHj27JkQQppM6ujoKJyxTElJEcbGxuKrr76SbVu5cqUAIO7cuSOEECI+Pr7ACeinzM3Nhaenp+y+MvF9LKfEMzs3b94UAMTSpUvzFTup1t69e+X+T5YqVUr07dtXzJgxQ3z33XeiVq1aYsyYMUUdJn1meJzkcTIrBTlOfio/ied3330nJBKJ7L1XEJ6ensLc3Fxum4uLi3BxcVEo27FjR1GpUqVc6xw3bpwAIN6/fy/b5uXlJaysrBSO80OHDhWGhoYK72FSnwLPatumTRuEhITIreUJALa2tli2bFlBqycN4evri02bNmHHjh347rvvZNvfvXuHw4cPw8fHBwYGBnj16hWaNWuGxMREjB49GmXLlsWmTZvg7u6OXbt2wcPDAwCwdu1ajB49Gr169cKYMWOQnJyMmzdv4uLFi+jbty88PT1x//59BAcHY9GiRShXrhwAwMLCAgDw9ddfY9OmTejVqxd++OEHXLx4ET///DNCQ0Oxd+/ebPuxZs0afPvtt5g8eTJmzZqVbbnXr1+jY8eOsLCwwMSJE2FmZoawsDDZ8kIWFhZYuXIlhg8fDg8PD3h6egIA6tSpAwA4evQoHj9+jMGDB8Pa2hq3b9/GmjVrcPv2bVy4cAESiSTXPn4qr89tpl9++QVaWloYP348YmNjMXfuXPj6+uLixYvZv9BKevnyJQDIYgeAa9euoWrVqjAxMZEr6+rqCgC4fv06KlasiH///RdpaWlo1KiRXDldXV3Uq1cP165dk207duwYTExM8OLFC3z55Ze4f/8+jIyM0L9/fyxatAj6+voF6kd8fDzi4+Pl+qFMfAWV1fNIRefLL7/E3LlzMWnSJKSnpyM1NRVbt26VK8M1POlTPE7yOJmVghwn8yMpKQkJCQmIj4/H6dOnsWHDBjRt2hQGBgb57MF/Xr58KdePjIwM3Lx5E0OGDFEo6+rqiiNHjiAuLg6lS5fOsU5DQ0MYGhrKtl27dg0NGjSAlpb8VDaurq5Ys2YN7t+/j9q1axe4P5QPymaqn57xfPTokVi6dKlwdnYWenp6oly5cmLgwIEiPDy8MBJlKqbS0tKEjY2NwhCQVatWyQ07HDt2rAAgd41CXFyccHR0FA4ODrJfr3r06CFq1aqVY5vZDa+5fv26ACC+/vprue3jx48XAMSJEydk2z7+JXfJkiVCIpGImTNn5trfzDMely9fzrZMTkOIsvplMTg4WAAQZ86cybWPmbF//EtuXp/bzF9ya9SoIVJSUmRllyxZIgCIf//9N6eu51lUVJSwtLQULVq0kNteq1Yt0bZtW4Xyt2/fFgDEqlWrhBBC7Ny5U+H5yOTl5SWsra1l9+vUqSMMDQ2FoaGhGDVqlNi9e7cYNWqUACC8vb0L3JeZM2cKAOL48eOybcrE97H8nPFs3769MDExEdHR0cqGToXo+vXrYujQoaJatWrC0NBQ6OnpCVtbW9G9e3dx5MiRog6PPjM8TiricbJgx8lP5eWM588//yz3Pb9du3bZnkFVxpkzZ4REIhFTp06Vbct8fWfMmKFQ/tdffxUAxN27d7Ot88GDB0JfX1/0799fbruRkZHcpYCZDh48KACIP//8swA9oYJQelbbQYMGQUiH6EIIAScnJ4waNQr//vsvkpOT8ebNG2zcuBG2trbKVk0aTFtbG97e3jh//jzCwsJk27du3QorKyu0a9cOAHDo0CG4urqiefPmsjLGxsYYOnQowsLCcOfOHQCAmZkZwsPDcfnyZaVjOXToEABg3Lhxctt/+OEHAMDBgwcVHjN37lyMGTMGc+bMwY8//phrG2ZmZgCAkJAQpKamKh3jx78sJicn4+3bt2jSpAkA5HtWtrw+t5kGDx4MXV1d2f0WLVoAgMJMc/mRkZEBX19fxMTEKIyMSEpKgp6ensJjMs9KJiUlyf2bXdnM/YD0jGRiYiIGDBiApUuXwtPTE0uXLsWwYcOwbds2PHjwIN99OXPmDAIDA9G7d2+0bdtWrh95ja8gZs+ejWPHjuGXX36Rve/o81C3bl2sXr0ad+/eRUJCApKTkxEeHo79+/ejQ4cORR0efWZ4nFQOj5O5Hyfzw8fHB0ePHsXWrVvRt2/fAtcHSM9u9+3bF46OjpgwYYJse27HyZzaTkxMhJeXFwwMDPDLL7/I7SvM54cKpsDLqRDlla+vLwDIhpyFh4fj7Nmz8Pb2hra2NgDg6dOnqFatmsJja9SoIdsPAP7+/jA2NoarqyuqVKmCkSNH4u+//85THE+fPoWWlhYqV64st93a2hpmZmayNjKdPn0a/v7+8Pf3h5+fX57aaNWqFXr27InAwECUK1cOPXr0wIYNG5CSkpKnx7979w5jxoyBlZUVDAwMYGFhIVuWITY2Nk91fCqvz20mOzs7uftlypQBoJolkkaNGoU///wT69atQ926deX2GRgYZPk8ZS4XkvllI/Pf7Mp+/KUk828fHx+5cpkH1fPnzwOQPu8vX77M8paVu3fvwsPDA87Ozli3bp1CP/IaX35t374dP/74I7766isMHz68wPURUdHicZLHyUyqOE7mh729Pdq3bw8fHx8EBQXByckJ7du3lyVr8fHx2R4nP3z4oFBfQkICunXrhri4OOzbtw/GxsZy/QCyP05m15f09HR4e3vjzp072LVrF8qXLy+3vzCfHyoYJp6kNg0bNkT16tURHBwMAAgODoYQQnagVUaNGjVw7949bNu2Dc2bN8fu3bvRvHlzBAQE5LkOiUSSp3K1atVCtWrVsHnzZjx58iTPde/atQvnz5/Hd999hxcvXmDIkCFo2LBhtmtMfqx3795Yu3Ytvv32W+zZswdHjhzBn3/+CUD6K6g6ZH7J+ZRQcj2vTwUGBmLFihX45Zdf0L9/f4X9NjY2Wa6Jmbkt8wBjY2Mjt/3Tsh8fiDL//nSdYUtLSwD/fUnw9PSEjY1NlrdPPX/+HB07doSpqSkOHTqkcA2KMvHlx9GjRzFgwAC4ublh1apVBaqLiD4PPE7yOAmo7jipCr169cLz589x5swZAMD8+fOzPU6eO3dO7rEfPnyAp6cnbt68iX379sHZ2Vluv7m5OfT09JTuyzfffIOQkBBs3LhRbqRRJnU+P6QcJp6kVr6+vrh16xZu3ryJrVu3okqVKnBxcZHtt7e3x7179xQed/fuXdn+TEZGRujTpw82bNiAZ8+ewc3NDT/99JPsF63sDpj29vbIyMhQGF756tUrxMTEyLUBSC/oP3bsGEqVKoV27dohIiIiz/1t0qQJfvrpJ1y5cgVBQUG4ffs2tm3blmN80dHROH78OCZOnIjAwEB4eHigQ4cOcHJyUiib1y8FgHLPbWH59ddfMX36dIwdOxb+/v5ZlqlXrx7u37+P9+/fy23PnKyhXr16AABnZ2fo6OgoLEj94cMHXL9+XVYOkH6ZA6SLVH8s87XMnGhiwYIFOHr0aJa3j0VFRaFjx45ISUnB4cOHs0xMlYlPWRcvXoSHhwcaNWqEHTt2QEenwPPEEdFngsdJHidVdZxUhcwznZlnkQcMGJDtcfLjM7MZGRkYMGAAjh8/jq1bt6JVq1YKdWtpaaF27doKx8nMvjg5OSn8qOvn54cNGzZg0aJFCqOYMtWrVw9Xr15V+AHi4sWLMDQ0RNWqVZV7EkhlmHiSWmX+ajtt2jRcv35d4Vfcrl274tKlS7Khj4B0mMaaNWvg4OCAmjVrApB+8f+Yrq4uatasCSGE7FoRIyMjAEBMTIxCGwCwePFiue0LFy4EALi5uSnEXaFCBRw7dgxJSUno0KGDQvufio6OVvjFM/NAkDn8I3MGtk/jy/wF9dPHfxovkH0fs5LX57awbN++HaNHj4avr6/suc5Kr169kJ6ejjVr1si2paSkYMOGDWjcuLFspj5TU1O0b98eW7ZsQVxcnKzs5s2bER8fDy8vL9m23r17AwDWr18v19a6deugo6OD1q1bA5AmqO3bt8/ylikhIQFdu3bFixcvcOjQIVSpUiXLfigTnzJCQ0Ph5uYGBwcHhISEcMgQkYbhcZLHSVUdJ5Xx5s2bLLevX78eEokEDRo0AADZ0NusbplDjQHpUOHt27djxYoVshmJs+vL5cuX5ZLPe/fu4cSJEwrHyXnz5mH+/PmYPHkyxowZk2Odr169ks2QDABv377Fzp070b179yyv/yT14M/kpFaOjo5o1qwZ9u3bBwAKB9SJEyciODgYXbp0wejRo2Fubo5NmzbhyZMn2L17t2xq7I4dO8La2hpffPEFrKysEBoaiuXLl8PNzU3261jmWa4pU6bA29sbpUqVQvfu3VG3bl0MHDgQa9asQUxMDFq1aoVLly5h06ZN+PLLL9GmTZssY69cuTKOHDmC1q1bo1OnTjhx4oTCVOaZNm3ahBUrVsDDwwOVKlVCXFwc1q5dCxMTE9kB3cDAADVr1sT27dtRtWpVmJubw9nZGc7OzmjZsiXmzp2L1NRU2Nra4siRI1kOX8quj5kH2vw8t8o4deoU2rRpg4CAAEyfPj3bcpcuXcKAAQNQtmxZtGvXDkFBQXL7mzVrJvulunHjxvDy8sKkSZPw+vVrVK5cGZs2bUJYWJhC4vjTTz+hWbNmaNWqFYYOHYrw8HAsWLAAHTt2ROfOnWXl6tevjyFDhuC3335DWloaWrVqhVOnTmHnzp2YNGmSUsNufH19cenSJQwZMgShoaEIDQ2V7TM2NsaXX36pdHwAsHz5csTExMjOFBw4cADh4eEApAdwU1NTxMXFoVOnToiOjoafn5/CBB+VKlVC06ZN89wXIvr88DjJ46Qqj5M3b97E/v37AQAPHz5EbGysbJmbunXronv37gCkx6u///4bnTt3hp2dHd69e4fdu3fj8uXLGDVqlML1vjlZvHgxVqxYgaZNm8LQ0BBbtmyR2+/h4SF7/keMGIG1a9fCzc0N48ePR6lSpbBw4UJYWVnJJrMCgL1792LChAmoUqUKatSooVBnhw4dZJfT9OrVC02aNMHgwYNx584dlCtXDitWrEB6ejoCAwPz3A8qBEUyly6VaJlTZLu6uma5P3PxZjMzM6Gvry9cXV0VFm9evXq1aNmypShbtqzQ09MTlSpVEn5+fgqLKc+cOVPY2toKLS0thYWxAwMDhaOjoyhVqpSoWLFinhfGvnjxoihdurRo2bJltgsqX716Vfj4+Ag7Ozuhp6cnLC0tRbdu3cSVK1fkyp07d040bNhQ6Orqyk0ZHx4eLjw8PISZmZkwNTUVXl5eIiIiIstp5bPrY04LY+f03GZOE79z50657ZmLeX+8zMeBAwdynLo906fLMH16+3TpkKSkJDF+/HhhbW0t9PT0hIuLS7bTn589e1Y0a9ZM6OvrCwsLCzFy5Ei5haQzffjwQUyfPl3Y29uLUqVKicqVK4tFixblGHdW7O3ts+1HVtPU5zW+nOrNfE2zW1A98/bp601ExROPk//hcbJgx8mc6v2470eOHPm/9u47KoqrDQP4s4B0AUEUEBFUrESxgCWWaDQWFCNqIvYYS8Ro1GjsGkvsGns3YsVKFE1RY4kauzHxM2oUO4IdEBAQ2fn+mLCyUnd3dpfdfX7ncHRnZu97Z3eYlztz516hXbt2goeHh1CsWDGhePHiwvvvvy+sX79ekMvl+db9Xb179853X96d2ubBgwdC586dBQcHB8He3l5o166dcPPmTaVtJk+enG+ZR48eVdr+xYsXwueffy64uLgItra2QtOmTfOduod0QyYIGj4BTUQm65tvvkFERASio6PZdYWIiOgdzJNEb/EZTyJS29GjRzFx4kQmUyIiolwwTxK9ZbINz+TkZEyePBmtW7eGs7MzZDIZwsPDc912x44dqF+/PpycnODi4oKmTZvmOnmyXC7HnDlz4OPjA2tra9SoUUMxJDqRMTp//jwGDBig72oQkRYwTxJpjnmS6C2TbXg+e/YMU6dOxbVr13JMzJvdkiVL8Omnn6JkyZKYNWsWJk6ciMTERLRr105ptCxAfHB99OjRaNmyJZYsWQIvLy9069ZNMSw4ERGRoWCeJCIiKZnsM57p6emIj4+Hm5sbLly4gICAAKxfvx59+vRR2q5SpUpwcnLC2bNnFXNBvXz5EmXKlEHz5s0Vo849fPgQPj4+GDBgAJYuXQpAHOa7adOmuHPnDu7evZvnRMNERERFDfMkERFJyWTveFpZWcHNza3A7V6+fIlSpUopTUDs4OAAe3t7pfnz9u7di4yMDISFhSmWyWQyDBo0CDExMUpzQhERERV1zJNERCQlyRqeqampGDFiBPbt2ydVkUXCBx98gF9//RVLlizB3bt3cf36dQwePBiJiYlKk9deunQJdnZ2qFq1qtL7AwMDFeuJiMh0MU8yTxIRmTILqQqysbHBqlWrUK1aNamKLBIWL16MZ8+eYejQoRg6dCgAoGTJkjh8+LDSRO1xcXEoXbq00hVfAHB3dwcAxaTwuYmIiMh3cIUjR47A3t5ek90gIjIYLi4u+Oeff/RdDckxTzJPEhFJwVDzpGQNTwCoU6cOrly5ImWRemdra4vKlSvD09MT7dq1Q1JSEr7//nuEhITgxIkTqFixIgDxSnZuQ2VbW1sr1uclNDQUoaGhea4PDg5GVFSUhntCRGQYgoOD9V0FrWGeZJ4kItKUoeZJSRueCxcuRNu2beHn54c+ffrAwkLS4vWiS5cusLCwUOoa1aFDB/j6+mL8+PHYvn07APFKdnp6eo73p6WlKdYTEZFpY55kniQiMlWSZrw+ffrAzMwMAwcOxNChQ1GmTJkciUQmk+Hvv/+WMqzW3L59G7/++itWr16ttNzZ2RmNGjXCH3/8oVjm7u6Oo0ePQhAEpW5EcXFxAAAPDw/dVJqIiIos5knmSSIiUyVpw9PZ2RkuLi6oXLmylMXqzePHjwEAmZmZOdZlZGTgzZs3itf+/v5Yu3Ytrl27pvT8ztmzZxXriYjItDFPMk8SEZkqSRuex44dk7I4vatYsSLMzMywfft2DBw4UHGFNiYmBidOnECjRo0U23bo0AHDhw/H8uXLleYnW7lyJcqUKYOGDRvqZR+IiKjoYJ5kniQiMlWG/3CJBpYuXYqEhATFSHr79u1DTEwMAGDIkCFwdXVF3759sXbtWnz44YcICQlBUlISli9fjtTUVIwdO1ZRlqenJ4YNG4a5c+ciIyMDAQEB2LNnD06cOIEtW7ZwUmwiIjI4zJNERCQZQWKJiYnCzJkzhY8++kjw9/cXzp49KwiCIDx//lyYP3++cPPmTalDqq1cuXICgFx/7ty5IwiCIGRkZAhLliwR/P39BXt7e8He3l5o1qyZcOTIkRzlZWZmCjNmzBDKlSsnWFpaCtWrVxc2b96scT3bt2+vcRlERIbC2M95zJPMk0REmjDUc55MEARBqkZsTEwMmjZtigcPHsDX1xfXr1/HoUOH0Lx5cwBA5cqV0bp1ayxatEiqkCaBw8QTkSkx5nMe86R2GPMxQ0T0LkM950na1XbUqFFISkrCX3/9hVKlSqFUqVJK6z/++GPs379fypBEREQGg3mSiIhMlZmUhR08eBBDhw5FtWrVlIZKz1K+fHk8ePBAypBEREQGg3mSiIhMlaQNz9TUVLi6uua5PikpScpwREREBoV5koiITJWkDc9q1arh+PHjea7fs2cPatWqJWVIIiIig8E8SUREpkrShuewYcOwbds2zJ49G4mJiQAAuVyO6Oho9OzZE6dPn8bw4cOlDElERGQwmCeJiMhUSTq4UI8ePXDv3j1MmDAB48ePBwC0bt0agiDAzMwMM2bMwMcffyxlSCIyMSkpKbC3twcAJCcnw87OTs81Iio85kkiIjJVkjY8AWD8+PHo2bMndu/ejejoaMjlclSoUAEhISEoX7681OGIiIgMCvMkERGZIskbngDg5eUleVeh+/fv4/bt24iPj0duU4+GhIRIGo+IiEhbmCeJiMjUaKXhCYhd4PJKfl5eXoUu5/79++jbty+OHj0KALmWJ5PJkJmZqX5liYiIdIx5koiITImkDc+0tDRMmTIF69atw/Pnz/PcTpXk17t3b5w+fRpjxoxBvXr14OjoKEVViYiIdI55koiITJWkDc+wsDBs2LABH3/8MRo3bowSJUpoXOaZM2cwevRoTJkyRYIaElGR95Es//XZ/x4PtgfMC1HmwZx3gMh4CIKAFStWICwsTN9VKRDzJBERmSpJG56RkZHo168fVq1aJVmZnp6ekiRmIiIyTn/88QeGDBmCvn37wtraWt/VyRfzJBERmSpJ5/GUyWSoXbu2lEVi5MiRWLduHV69eiVpuUREZBzmzJkDQRCwc+dOfVelQMyTRERkqiS949mhQwf89ttvGDhwoGRlDhw4EJmZmfD19UXnzp3h6ekJc3PlvnUymYwTbhMRmaBXr17hr7/+giAIWLx4MXr27KnvKuWLeZKIiEyVRg3PFy9eKL2eOHEiPvnkEwwYMAADBw6El5dXjuQHAM7OzoWOceXKFcyZMwdxcXFYsmRJrtswoRIRmaZt27bhyZMnAICHDx/i4cOHKFOmjJ5r9RbzJBERkUijhmfJkiUhkykPBCIIAi5duoR169bl+T5VRusbMGAAEhMTsWrVKo7WR6SilJQU2NvbAxCnbrCzs9NzjTRnZw4ILfVdCyoqli1bhvT0dADAo0ePsGzZMsyYMUPPtXqLeZKIiEikUcNz0qRJORKq1P766y9MmTIF/fv312ocIiIyLPfv30dsbKziddZznt99953Wc1NhMU8SERGJNGp4fvvttxJVI28+Pj5aj0FERIZnyZIlePTokdKyFy9e4OzZs6hfv76eaqWMeZKIiEgk6ai2ffv2xdmzZ/Ncf+7cOfTt21elMqdMmYJly5bhwYMHmlaPiIiMhCAIiIyMzLH8xYsXmDNnjh5qVDjMk0REZKokHdU2PDwcLVq0QL169XJdf+fOHWzYsAE//PBDocs8fvw4nJycULlyZbRo0QJly5bNdbS+RYsWaVR3IiIyHH/88Qfi4+NzXXfhwgWkpaUVyTk9mSeJiMhUSdrwLEhsbCxsbGxUes/SpUsV/9+/f3+u2zChksn6qIBnx7KPTxJsD+QcPDOng4ImNSLSiTlz5uTZ8Hz69Cl27txZ5KdWyQ3zJBERGSuNG5579+7F3r17Fa9Xr16N3377Lcd2CQkJ+O233xAQEKBS+XK5XNMqEhGREUlNTcWJEyfyXJ+WloYFCxYUmYYn8yQREZEEDc+rV69i586dAMQrqmfPnsXFixeVtpHJZLCzs0OTJk2wYMGCAsvctWsXPvroIzg4OGhaPSIiMjLm5uZYunSpUoPr+++/V5qn0tXVVR9VyxXzJBERESATBEGyfnVmZmbYvHkzunXrplE5xYoVg5mZGd5//30EBQWhbdu2qFq1qkS1NDzBwcGIiorSdzWoKCqgq21KJmB/RPx/cnNxDswCqdnVVrI5QwvqPqwOdh82KOqc8zp06KB0V7GoYp7UDuZJIjIlhnrOk3RUW7lcrnEyBcTnczZs2ICyZctizpw58PPzQ/ny5TFkyBAcOHBAMVk4ERGRIWGeJCIiUyVpw1MqTk5O6Nq1KzZs2IBHjx7h1KlT6NmzJ86cOYO2bdvCxcUF7du3x6pVqzh8PBERmRzmSSIiMjSSNzx/+eUXtGzZEi4uLrCwsIC5uXmOH1XIZDLUq1cPU6ZMwfnz5xEbG4slS5bA2toao0ePhre3N9577z2MHTsW//77r9S7Q2TQ7MwBoaX4U6hutkSkdcyTRERkiiRteO7evRvt2rXD48eP0bVrV8jlcoSGhqJr166wsbFBjRo1MGnSJI1ilC5dGp999hl27tyJZ8+e4bfffkPr1q0RFRWFHTt2SLQnRERE0mOeJCIiUyVpw3PmzJkIDAzEpUuXMGXKFABA3759sWXLFly5cgVxcXHw8fEpsJyRI0fi77//LnA7CwsLNGvWDHPnzsU///yDMWPGaLwPRERE2sI8SUREpkrShufVq1fRtWtXmJubw8JCnKklIyMDAODt7Y2wsDDMnj27wHIWLFiA2rVro3r16pg5cybu3r1bqPjFihVTu+5ElI+PZAX/BNu/3T7YvuDtiUwQ8yQREZkqSRuetra2sLS0BCAOfGBlZYW4uDjF+tKlS+POnTsFlnPjxg1MmjQJcrkc48ePR4UKFdCoUSOsXLkSz58/l6SuycnJmDx5Mlq3bg1nZ2fIZDKEh4fnuq1cLseKFSvg7+8PGxsbuLi4oHnz5jmuNsvlcsyZMwc+Pj6wtrZGjRo1EBERIUl9iYjI8DFPMk8SEZkqSRuelStXxtWrVxWv/f39sWnTJrx58wZpaWnYunUrvLy8CiynYsWKmDx5Mq5du4YLFy5g2LBhuHfvHsLCwuDh4YHg4GBs374dqampatf12bNnmDp1Kq5du4aaNWvmu23fvn0xdOhQ1KlTB0uWLMGkSZPg5eWFJ0+eKG03fvx4jB49Gi1btsSSJUvg5eWFbt26Ydu2bWrXk4iIjAfzJPMkEZGpspCysI4dO2Lx4sWYN28erKysMH78eHTo0AFOTk6QyWRISUnBDz/8oFKZtWvXRu3atTFv3jwcO3YMW7duRWRkJPbv3w97e3t8/PHH6N69O1q1aqVSue7u7oiLi4ObmxsuXLiAgICAXLfbsWMHNmzYgMjISHTs2DHP8h4+fIj58+dj8ODBWLp0KQCgX79+aNq0KUaNGoUuXbqoPFIhEREZF+ZJ5kkiIlMl6R3PkSNH4v79+7CysgIAtGvXDseOHUP//v0xcOBAHD58GH369FGrbJlMhmbNmmHNmjV49OgR9uzZg/fffx+bN29GUFCQyuVZWVnBzc2twO0WLFiAwMBAdOzYEXK5HCkpKblut3fvXmRkZCAsLEypzoMGDUJMTAxOnz6tch2JiMi4ME8yTxIRmSrJ5/F8V+PGjfH9999j3rx5aNasmcblvX79Gvv27cPGjRtx7NgxAOIzMdrw8uVLnDt3DgEBARg3bhwcHR1hb2+P8uXL5xiS/tKlS7Czs0PVqlWVlgcGBirWExERvYt5knmSiMgUSNrVNsvDhw9x/PhxPHnyBJ06dYKnpyfkcjkSEhLg6OioclcaQRBw+PBhbN26FT/++CMSExNRvHhxfPrpp+jRoweaN2+ujd3ArVu3IAgCtm3bBgsLC8yZMweOjo5YtGgRunbtCgcHB7Ru3RoAEBcXh9KlS0MmUx6t093dHQAQGxubZ5yIiIh8B1c4ffo0goODJdgjMjra+Dstt2OtkHHalxT/Db2sZhwVYqmEvz9UxDBPvsU8SURkGiRteAqCgK+//hpLly7FmzdvIJPJ8N5778HT0xNJSUnw9vbG1KlTMWzYsEKVd+7cOWzduhU7duzA48ePYWFhgVatWqFHjx4IDg6GtbW1lNXPITk5GQDw/PlznDlzBvXq1QMABAcHw8fHB9OnT1ck1NTUVEXXqeyy6pjfAA+hoaEIDQ3Nc31wcDCioqLU3g8yYtqYliS3Y01XcXQdi4okY25AME8yTxIRacpQ86SkXW3nzp2LRYsWYeTIkTh06BAEQVCsc3R0REhICHbv3l1gOZMmTYKvry8aNGiAxYsXo3z58li6dCni4uIQFRWFTz75ROvJFABsbGwAAD4+PopkCgD29vZo3749zp07hzdv3ii2TU9Pz1FGWlqaUllERGS6mCeZJ4mITJWkDc81a9agV69emDFjBvz9/XOsr1GjBm7cuFFgOdOnT0exYsUwdepU3L59GydPnsSgQYPg7OwsZXUL5OHhASD3Z2NKlSqFjIwMxSAK7u7uePTokdIfEQAU87NllUVERKaLeZJ5kojIVEna8Hzw4AEaNmyY53o7Ozu8fPmywHIuXryIq1evYvz48fD29pawhqrx8PCAm5sbHj58mGNdbGwsrK2tUbx4cQDiXGyvXr3CtWvXlLY7e/asYj1RlpSUFMhkMsX0CVQ08XsiqTFPMk8SEZkqSRuepUqVwoMHD/Jcf/HixUJNjF2rVi0pq6WRTz/9FA8ePMChQ4cUy549e4a9e/eiefPmMDMTP8IOHTqgWLFiWL58uWI7QRCwcuVKlClTJt8/NIiIyDQwTzJPEhGZKkkHFwoJCcHKlSvRp08fODo6AoBi9LqDBw8iPDwc33zzjZQhNbJ06VIkJCQoRtLbt28fYmJiAABDhgyBo6Mjxo4dix07dqBTp04YMWIEHB0dsXLlSmRkZGDGjBmKsjw9PTFs2DDMnTsXGRkZCAgIwJ49e3DixAls2bKFk2ITERHzJPMkEZHJkgnvPmyhgcTERDRp0gR37txB48aN8euvv6Jly5ZITk7G6dOnUatWLRw/fhy2trZShdSIt7c37t27l+u6O3fuKLov3b59GyNHjsThw4eRkZGBBg0aYNasWQgICFB6j1wux+zZs7Fq1SrExcXB19cXY8eORffu3TWqJ0frMz4pKSmwt7cHII4KaWdnp15B2hgB9mAupwRdxdF1rAJI9j2RStQ553Xo0AF79+4t9PbNmjVDzZo1sWDBAsUdudycOXMG8+fPx+jRo1G3bl2V6pQb5knmSSIiTRnqOU/ShicgDoc+f/587Nq1Czdv3oRcLkeFChXwySefYNSoURy1Tg2GenBR3tjwzCOOrmMVgA1P/dBFw9Pc3ByCIKB9+/bYsWNHrtN8AMCbN29Qrlw5dOzYEUuXLlWpTnlhnpQe8yQRmRJDPedJ2tUWEIdDnzBhAiZMmCB10USGoTANp8xs/w+2BwrTw0zNxhPlQRvfE78jgxIcHIxffvkFLVu2xP79++Hg4JBjGwsLC3Tp0gW//fabZHGZJ4mIyBRJOrjQgQMHFJNJExERFWVdunRBVFQULl26hKZNm+Lx48e5blejRg3cvXtXkpjMk0REZKokbXi2adMGJUqUQN26dTF8+HBERkbi6dOnUobIoW/fvpgwYYLW4xARkfFp1aoVDh8+jJiYGDRs2BDXr1/Psc2jR4/g4uIiSTzmSSIiMlWSNjzPnDmDmTNnwtPTE5s2bULnzp3h5uaGqlWrYsCAAdi0aZNkV42zhIeHY8aMGahQoQImTpwoadlERLrA+UL1KzAwECdPnoRcLkf9+vWxefNmxbro6GgsWbIE9evXlyQW8yQREZkqSZ/xDAwMRGBgIEaOHAkAuHr1Kk6cOIETJ07g119/xbp16yCTyfDmzRvJYt65cwfJycn4/fffcezYMcnKJdPEwWQoOztzQGip71qQLlSuXBmnT59Gly5d0KtXL4wdOxalS5fGP//8AwsLC0yfPl2SOMyTRERkqiQfXChLWloanjx5gidPnuDx48eIj4+HIAioUKGCpHHKlSsHAKhevTrCwsIkLZtIW9igISp63NzccPz4cWzcuBHbtm3Ds2fP0KVLF4wZMwa+vr6Sx2OeJCIiUyJpw3P//v2KK7cXL15EZmYm/Pz80KRJEwwYMABNmjRB6dKlVSpz6tSpCAkJgZ+fX67r//nnH+zevRuTJk2SYheIiMgEZGZm5rpcJpOhd+/e6N27t1biMk8SEZGpkrThGRwcDHNzc3Tq1AkTJkzA+++/D0dHR43K/Pbbb1GxYsU8E+qVK1cwZcoUJlQiKroKmrqF0+sUKZcuXcK///4LS0tLNGzYEG5ubpKVzTxJRESmStKGZ1BQEE6dOoUdO3bg9OnTaNy4MZo0aYLGjRujatWqUoZSePHiBSwtLbVSNhUNfO6SiKS2YcMGXLp0CQsXLlQsi4uLQ0hICM6ePQuZTLxYYG5ujuHDh2P27NmSxGWeJCIiUyVpw3Pfvn0AxKurWV2Jpk2bhtjYWDg7O+P9999H48aN8fXXX+dbzvHjx5UGQIiMjER0dHSO7RISErB9+3a89957Uu4GGauC7joBqt954l0nIoO0aNEiBAYGKi379NNPcfXqVUyfPh3vv/8+UlNTsX37dsydOxdly5bFl19+qXFc5kki0jZesKeiSiuDC/n5+cHPzw+DBg1Ceno6IiIiMHv2bERFRWHfvn0FJtSjR49iypQpAMTnbSIjIxEZGZnrttWqVcOSJUsk3wciIjJe0dHRGDBggOL1rVu38Mcff2DdunXo06ePYnnr1q2RmpqK5cuXS9LwzMI8SUREpkbyhmdycjL++OMPHD9+HCdOnMD58+fx+vVrWFhYoH79+mjcuHGBZXzzzTf48ssvIQgCSpUqhZUrV6JTp05K28hkMtja2sLa2lrqXSAiIiNnZmaGjIwMxetHjx5BEAQ0a9Ysx7bNmzfHnj17JIvNPElERKZI0oZnnTp1cPnyZWRmZsLe3h4NGjTAuHHj0LhxY9SrVw82NjaFKsfGxkax7Z07d+Dq6gpbW1spq0pFCQdeIRPH6XV0LzAwEFu2bMGXX34JmUwGX19fWFhY4OrVq4rpR7JcvXoVJUuWlCQu8yQREZkqSRue3t7e6NmzJxo3boxatWrBzMxM4zLf/QOAiIhIUxMmTEDz5s3Rvn17zJkzB9WqVcOIESMQFhaGlStXokmTJnj16hW2bduG5cuXY8iQIZLEZZ4kIiJTJWnDc/fu3VIWBwAQBAGrV6/GunXrcPv2bcTHx+fYRiaT4c2bN5LHJiIi49SkSRNs3LgRAwcOhJ+fH9zd3VG2bFnExsaiTZs2iu1kMhk++ugjTJs2TZK4zJNERGSqtDK4UJb4+Hh06tQJ8+fPR61atdQq45tvvsGCBQvg7++PHj16oESJEhLXkugtdnkkMh3dunVDkyZNsGrVKhw6dAg3btyAIAgoXrw4ypQpgzp16qBr164ICgrSWh2YJ4lIZXxEidSQnJysGO1YX7Ta8Hz9+jWOHTuW69XXwtqwYQM6deqEHTt2SFgzIiIiwNPTE9OmTZPsjqaqmCeJyJBx6hbDcOXKFTRq1AgJCQl6rYfmD5doWWpqKlq0aKHvapAeZd2FFFqK/ycioreYJ4mIKD8LFy5EcnIyrly5otd6FPmG54cffojz58/ruxpERGQirly5gpYtW8LJyQnu7u7o168fEhMT9V2tPDFPEhFRXjIzM3Ho0CFkZmZi3rx5eq2LZA3P1NRUjBgxAvv27VMss7GxQe/eveHh4aF2ucuXL8eZM2cwY8YMPH/+XIqqEhER5er169cICgpCZmYmtmzZgnnz5uHAgQMICwvTuGzmSSIi0rWDBw8qHuc4evSoXgeak+wZTxsbG6xatQrVqlVTLHNwcMD69es1Krdy5cqQy+WYOHEiJk6cCGtra5ibK/e3lMlkRfpqNBERFS1XrlyBn59frssfPHiAw4cPo2LFigCAhIQETJw4UeOYzJNEZFAKGsQIUH0gIw5ipHPz5s1DUlISAODFixf46aef0KFDB73URdLBherUqSN53+FOnTpBJivEgU9ERFRIAQEB6Nu3L6ZPn640CqyTkxNkMhlu3bqlaHjevHlTspFimSeJihZjHByHI/RTloSEBPz777+K18nJyViwYIFxNDwXLlyItm3bws/PD3369IGFhebFh4eHa14xIiKibM6dO4evvvoKvr6++PbbbxEWFgYzMzOUL18enTt3RkhICFq3bo0XL17gxIkTWL16tSRxmSeJiEhXwsPD8fjxY6Vlt27dwvPnz+Hi4qLz+kg6uFCfPn1gZmaGgQMHwsHBAb6+vqhRo4bST82aNaUMSUREKkhJSYFMJoNMJkNKSoq+q6M37733Ho4cOYLVq1cr5sA8cuQIAGD79u1Yt24dvLy80LBhQ5w8eRJ9+/aVJC7zJBER6cratWtzPNP56NEjrFu3Ti/1kbTh6ezsjMqVK6NJkyaoV68ePD094eLiovTj7Oyscrn379/HF198gcqVK6NEiRI4fvw4AODZs2cYOnQoLl26JOVuEBGRiQgJCcH169cRGhqKjz/+GCEhIbhz5w66du2K77//Ht999x3q168vWTzmSSIyJpzyruj6999/8fTp0xzLMzMz9dZTRtKutseOHZOyOADA1atX0bhxY8jlctSrVw/R0dGKlnvJkiVx8uRJpKSk6K3lTkREhs3S0hJjx47FZ599hjFjxsDPzw/Dhg3D+PHjYWtrK2ks5kki02WMz5NS0bVgwQI8efIk13XPnj3Lc5A9bZK04akN33zzDZycnHDmzBnIZDKUKlVKaX1QUBC2b9+up9oREZGhunLlCnbv3o3Hjx/Dw8MDXbp0QXh4OAYPHoyhQ4di48aNmDlzJnr06KHvquaLeZIoHwWNzKrqqKwAR2alIi8zMxMHDhzIc/3Tp08xb948nd/51KjhmdWVp0mTJkqvC5K1fWFjTJo0Ca6urrnOT+bl5YWHDx8WujwiIqJNmzahT58+qFWrFqpUqYLr169j2rRpiIiIQEhICE6fPo0tW7ZgzJgxWLFiBRYtWoS6deuqHId5koiIdO3Fixdwd3dXDCAkCALu3LmD8uXLK7axsrLSeb00anh+8MEHkMlkSE1NhaWlpeJ1XgRBgEwmQ2ZmZp7bvEsul+fb1enp06d6+eCIiMhwffvttxg4cCCWL1+uWLZx40ZMnToVISEhAIDu3bujY8eOmDlzJpo2barWYEzMk0REpGuurq44ffq04vXr16/RrVs37Nq1S4+10rDhefToUQDi8zHZX0updu3a+OmnnxAWFpZj3Zs3b7Bt2zZJB34gIjJYnOy70JKSkuDl5aW0rFy5copJtrPY2tpi2rRp6Nevn1pxmCeJTATPv0QF0qjh2bRp03xfS2Hs2LFo164dBg0ahK5duwIAHj9+jN9++w0zZszAtWvXsHTpUpXLTU5Oxty5c3H27FmcO3cO8fHxWL9+Pfr06ZPnezIyMlCzZk1cu3YNc+fOxciRI5XWy+VyzJs3DytWrEBcXBwqVaqEsWPHIjQ0VOX6ERGR9vTp0wdTp07Fo0ePUK1aNcTGxmLVqlW5Nt4AsVGqDuZJ5kkiIhIV+cGF2rRpg/DwcHz11VeKCbx79OgBQRDg4OCAjRs3qvQsTJZnz55h6tSp8PLyQs2aNQs10uCSJUtw//79PNePHz8es2bNQv/+/REQEIC9e/eiW7dukMlkij8GiIhI/2bPno2KFSti+/btOHToEMqUKYNZs2ahd+/e+q6aypgnidSXNR0IEWmf5A3PtLQ07N69G3/++ScSExMhl8uV1stkMpWHdO/ZsydCQkJw8OBBREdHQy6Xo0KFCmjVqhWKFy+uVj3d3d0RFxcHNzc3XLhwAQEBAflu/+TJE0ydOhWjR4/GpEmTcqx/+PAh5s+fj8GDByuuLPfr1w9NmzbFqFGj0KVLF5ibc4IjIqKiQCaTYcCAARgwYIDOYzNPMk8SEZkiSRue9+7dQ7NmzXD37l04OTkhMTERzs7OSEhIQGZmJkqWLKmYv0hVdnZ26Nixo2R1tbKygpubW6G3HzNmDCpXrowePXrkmlD37t2LjIwMpW5aMpkMgwYNQrdu3XD69Gk0atRIkroTEZFhYp5kniTTxburZOokbXiOGjUKiYmJOHPmDMqXL49SpUph+/bteP/997F48WIsXbo03zllACi66GQN+pBfl53s3h0kQkrnzp3Dhg0bcPLkyTxHI7x06RLs7OxQtWpVpeWBgYGK9UyoRKRv/MNHv5gnmSeJiEyVpA3PI0eOICwsDIGBgXjx4gUAcWh4KysrjBo1CteuXcOwYcPw008/5VmGt7e30tDzWa8LosrQ86oQBAFDhgzBp59+igYNGuDu3bu5bhcXF4fSpUvnqKu7uzsAIDY2Ns8YERERiIiIyHP96dOnERwcrHrlDcUlLZSZ2+dlyHF0GUufcXQZi/ukfhxSG/Mk8yTpmDGef41xn8gkSNrwfPXqFby9vQEADg4OkMlkSExMVKxv0KBBjhHu3vXDDz9AJpOhWLFiSq/1JTw8HP/73/8KnPcmNTU113nSrK2tFevzEhoamu+IfsHBwYiKiipkjQ1QYYYgV1Vun5chx9FlLH3G0WUs7pP6cbTMmBsQzJPMk6Rjxnj+NcZ9IpUYap6UtOHp5eWFmJgYsWALC5QpUwZnzpxRTMZ99epVRYLJy7vDtOc3bLu2vXz5EmPHjsWoUaNQtmzZfLe1sbFBenp6juVpaWmK9UREZNqYJ5kniYhMlaQNz+bNm2Pv3r2YPHkyADEZzpw5E/Hx8ZDL5di0aRN69eolZUitmjdvHl6/fo1PP/1U0XUo6w+G+Ph43L17Fx4eHrC0tIS7uzuOHj0KQRCUrjzHxcUBADw8PHRefyIiKlqYJ5kniYhMlaQNzzFjxuD8+fNIT0+HlZUVxo0bh9jYWOzatQvm5ubo1q0bFixYkG8ZU6dOVTmuTCbDxIkT1a12nu7fv4/4+HhUr149x7oZM2ZgxowZuHTpEvz9/eHv74+1a9fi2rVrqFatmmK7s2fPAgD8/f0lrx8RERkW5knmSSqclJQUxQjPycnJsLOz03ONiEhTGjU8o6KiULduXcVVSi8vL6VR86ytrbF27VqsXbu20GV+++23OZZlXRkVBCHH8qwrp9pIqEOHDsXHH3+stOzJkycYOHAg+vTpgw4dOsDHxwcA0KFDBwwfPhzLly9XzE8mCAJWrlyJMmXKoGHDhpLXj4iIijbmSeZJIiISadTw7NixIzZt2oRu3boBAMqXL4+FCxdq9MDruxNpP3z4EEFBQfDz88OwYcNQuXJlAMD169excOFCXL16Nd/R//KzdOlSJCQkKEbS27dvn6KL0JAhQ1C7dm3Url1b6T1ZXYmqV6+ulGw9PT0xbNgwzJ07FxkZGQgICMCePXtw4sQJbNmyhZNiExGZIObJjxXLmSeJiAzHjh07sG3bNqxatQqurq75bvvLL7+gXLlySr1ZcqNRw7N48eJISEhQvL579y6Sk5M1KTKHwYMHw9fXF5s3b1ZaHhAQgC1btqBz584YPHgwfvzxR5XLnjdvHu7du6d4HRkZicjISABAjx494OjoqFJ5s2bNQokSJbBq1SqEh4cr6p31BwcRkalgNzkR86Qy5kkiIsOwbt06ZGZmFtjoBIAlS5bA3t4eO3bsyHc7jRqegYGB+O677/D48WNF8vn555/x6NGjPN8jk8kwfPjwQsc4cuQIZs+enef6Dz/8EKNHjy58pbPJa66x/Hh7e+foypTFzMwMY8eOxdixY9WqDxERGRfmSWXMkwSgcNN0ZJ92NtgeKMwN8YO5H3dEpLr//e9/+OKLL3Isv3HjBr7//nvMnz8ftra2AMSpwMLDwwssU6OG5/Lly9GrVy9MmzYNgJgst27diq1bt+b5HlUTqrW1NU6fPo1Bgwbluv7UqVMFDj1PRESkD8yTRERkiJ4/f44yZcrkWB4TE4NVq1Zh+vTpioanh4dHvhdUs2jU8KxYsSJOnTqFtLQ0PHnyBN7e3li4cCE6dOigSbFKunfvjsWLF8PJyQlDhgxBhQoVAAC3bt3C4sWLsXXrVgwdOlSyeERERFJhniQiIkNUvHjxXBuTWc/8JyYmwsXFBQDw6tUrRSM0P5JMp2JtbQ0vLy9MnjwZzZs3R7ly5aQoFgAwe/ZsPHv2DEuXLsWyZctgZmYGQBxcQRAEhIaG5tvFiIiIJMZucipjniQiIkNSp04dbNmyBaNHj4aFxdsm444dO2BmZoa9e/di+PDhEAQBu3fvLnBgIUDieTyzJsSWkqWlJTZt2oRRo0bh559/VgxyUK5cObRp0wY1a9aUPCYREZE2ME8SFY6dOSC01HctiEzX0KFDERwcjHr16qF3796wsbHBrl27ULx4cXTq1AnffPMNjh49igcPHuDvv/9GVFRUgWVK2vDUpho1aqBGjRr6rgYREVGRxDxJusARq4lMQ1BQEBYsWIDx48crxh0ICAjApk2bYGVlBSsrK/z2229wcnLC6tWr0a5duwLLNJiGJxEREREREenGV199hf79++Pq1atwdHSEr6+vYt3GjRtVLs8gGp6//PILFixYgD///BOJiYm5DtOemZmZyzuJiEgftNlN7uXLl6hZsyYsLS0Vyx4+fIjKlSsDAARBQJkyZXD06FHtVKAIYp4kIiJtsLW1Rd26dREbG4uLFy8iNTUVNjY2cHd3h4eHh0plFfmG5+7du/HJJ5+gevXq6Nq1K1asWIFu3bpBEATs3bsXvr6++Pjjj/VdTZPE7jZEpA8ODg6wsbHBtWvXlJbfuHFD8f+mTZvqulp6wzxJRETaEBMTgxkzZmD37t14+vRpjvWlSpVCSEgIxo8fn+vUK+8q8g3PmTNnIjAwECdPnkR8fDxWrFiBvn37onnz5rh79y7q168PHx8ffVeTiIh0qE+fPhg3blyud/FKlSqFr7/+Wg+10g/mSSIiktq1a9fQtGlTvHr1Cu3atUPdunXh7u4OKysrpKenIy4uDhcuXMDGjRuxc+dOnDhxAlWqVMm3TMkbnpmZmThw4ABu376N+Pj4HN19ZDIZJk6cWOjyrl69ipkzZ8Lc3FwxlG9GRgYAwNvbG2FhYZg9ezZ69eol3U4QEVGR9vnnn2Px4sV4+PBhjnWurq4FJj99Yp6kIksbUyUZ8TRJRMZsxIgRcHR0xKVLl/K9m/nw4UM0a9YMw4YNw6+//ppvmZI2PC9cuIBOnTohJiYm1+dLANUTqq2treI5HicnJ1hZWSEuLk6xvnTp0rhz545mFSciIoPi4uKCChUq5Gh4WlhY4PPPP9dTrQrGPElERIbg5MmTmDlzZoFdaMuUKYOvvvoKY8aMKbBMM6kqBwBhYWFITU3Fnj178OLFC8jl8hw/qg5uULlyZVy9elXx2t/fH5s2bcKbN2+QlpaGrVu3wsvLS8rdICIiAzBixAjFc+ZZSpcujc8++0xPNSoY8yQRkepSUlIgk8kgk8mQkpKi7+qYBEtLSyQlJRVq26SkJKUB//Ii6R3Py5cv47vvvkP79u0lK7Njx45YvHgx5s2bBysrK4wfPx4dOnSAk5OT4uD74YcfJItH/2F3GyIq4oKCguDs7Izk5GTFskqVKsHJyUl/lSoA8yQRERmCdu3aYd68eQgMDMSHH36Y53aHDx/G3LlzERQUVGCZkjY8PT098+w6pK6RI0di5MiRitft2rXDsWPHEBkZCXNzcwQFBaFZs2aSxiQioqLPwsICzZo1w4YNGwAAxYsXV8oXRRHzJBERGYJ58+bh8uXLaNmyJSpVqoTAwEC4u7vD2toaaWlpiIuLw7lz53Djxg34+/tjwYIFBZYpacNz9OjRmDdvHgYMGAAHBweNy0tPT8eBAwfg7e2NGjVqKJY3btwYjRs31rh8IiIybCNHjsTPP/+Mp0+fwtnZGa1atdJ3lfLFPEmGTptz9BJR0eHq6opz584hPDwcO3bswC+//IIXL14o1js7O6NWrVoYOXIk+vTpoxjcLj+SNjyTkpJgb2+PihUromvXrihbtizMzZX7X8pkMgwfPrxQ5VlaWqJLly5YtGiRUkKlvHFuTSIyJX5+fihZsiSePn2Kli1b5sg5RQ3zJBHRO7TxeBfAR7wkUKxYMfTv3x/9+/cHII6YnpqaChsbGxQrVkzl8iRteGbv6rN06dJct1ElocpkMvj6+uLZs2eS1I+IiIxP7969MW7cOIOYu5N5koiIDFWxYsXUanBmkbThqY3h2seNG4cRI0agS5cuqFy5suTlk/rY3YaIioJ+/fph2bJlRXruzizMk0REZOiuXLmC4cOH4/z587CxsUFQUBDmz58PR0fHfN8nacOzXLlyUhYHADhz5gxcXFzg5+eHDz74AN7e3rCxsVHaRiaTYdGiRZLHJiKios/FxQX379/XdzUKhXmSiIgM2evXrxEUFIQKFSpgy5YtSEhIwJgxY5CamootW7bk+15JG57akL0r0uHDh3PdxqQSakH94NkHnojIpDBPEhGR1K5cuQI/P79clz948ACHDx9GxYoVAQAJCQmYOHFigWVK3vC8fPkylixZgj///BOJiYmQy+VK62UyGW7dulXo8t59PxERkSFjniQiUg0f79K9gIAA9O3bF9OnT0eJEiUUy7PmiL5165ai4Xnz5k2lbfJiJmUFjx07hsDAQOzfvx8eHh64ffs2ypcvDw8PD9y7dw/29vZo0qSJlCGJiIgMBvMkEREZgnPnzuHatWvw9fXF0qVLFRc5y5cvj86dOyMkJASdOnVCs2bNsHTpUowfP77AMiVteE6aNAnly5fHv//+i/Xr1wMQBz04efIkTp06hZiYGHzyySdShiQiIjIYzJNERGQI3nvvPRw5cgSrV6/GggUL4O/vjyNHjgAAtm/fjnXr1sHLywsNGzbEyZMn0bdv3wLLlLTh+eeff+Lzzz+Hg4ODYl6yzEzxocN69eph4MCBher/q1RBMzOYm5vn+2NnZ4fKlSvjiy++UKl7kjHK6oogtBT/T0RERQfzJBERGZKQkBBcv34doaGh+PjjjxESEoI7d+6ga9eu+P777/Hdd9+hfv36hSpL0oanhYUFihcvDkDs/1usWDE8efJEsb58+fK4evWqSmVOmjQJNWrUgLm5Odq1a4dhw4Zh2LBhCAoKgrm5Ofz9/REWFoZq1aph/fr1qF27Nv7++28pd4uIiEgSzJNEREVXSkoKZDIZZDIZUlJS9F2dIsPS0hJjx47FjRs34ODgAD8/P4wfPx6vXr1SqRxJBxeqWLEibt68CUAcHKFKlSr48ccf0b17dwDATz/9BDc3N5XK9PDwwLNnz3D9+nWUL19eaV10dDQ++OADVKtWDXPnzsXNmzfRoEEDjBs3Dj/99JM0O0VERCQR5knShpSUFNjb2wMAkpOTYWdnp+caEZExuHLlCnbv3o3Hjx/Dw8MDXbp0QXh4OAYPHoyhQ4di48aNmDlzJnr06FGo8iS949m2bVtERETgzZs3AIARI0YgMjISvr6+8PX1RVRUFAYOHKhSmXPnzsXgwYNzJFNATOCDBw/GzJkzAQC+vr744osvcOrUKc13hoiISGLMk0REZAg2bdqEmjVrYt++fXj58iWOHDmCGjVqIDIyEgEBATh9+jRmzZqFsWPH4v3338eFCxcKLFPShufEiRPx999/K55b6d27NzZu3Ag/Pz/UrFkTP/zwA0aPHq1SmTExMbCwyPvGrIWFBR48eKB47e3tjfT0dPV2gIiISIuYJ4mIyBB8++23GDhwIC5cuIDNmzfjyJEjWLNmDaZOnarYpnv37vj333/RvHlzNG3atMAyJe1qW6xYMbi4uCgt69GjR6Fvv+amevXqWLFiBXr27InSpUsrrXv06BFWrFiB6tWrK5bdvn1b5W5KREREusA8SUSkRx/J8l+fme3/wfZAYQbqPChoUqMiKykpCV5eXkrLypUrh6SkJKVltra2mDZtGvr161dgmZI2PLOkp6fjzz//xJMnT/D++++jZMmSapc1b948tGnTBhUrVsTHH3+smKg0Ojoae/bsQUZGBn744QcAQFpaGsLDw9GmTRtJ9oOIiEgbmCeJiKgo69OnD6ZOnYpHjx6hWrVqiI2NxapVqxAWFpbr9uXKlSuwTEm72gLA4sWL4e7ujkaNGiEkJASXL18GADx79gwlS5ZUJL/C+uCDD3Dq1Ck0a9YMkZGRmDJlCqZMmYLdu3ejWbNmOHXqFD744AMAgLW1NWJjY7Fu3boCy01OTsbkyZPRunVrODs7QyaTITw8XGkbuVyO8PBwBAcHo2zZsrCzs4Ofnx+mT5+OtLS0XMtdt24dqlatCmtra/j6+mLJkiUq7S8RERk35knmSZV9JMv/J9j+7bbB9gVvX9BdHyIyebNnz8bChQvxv//9D4sWLcKpU6cwa9Yslaf8yk7Shuf69esxbNgwtG7dGuvWrYMgvL31XLJkSTRv3hzbtm1TudxatWohKioKSUlJiI2NRWxsLJKTkxEVFYXatWurVddnz55h6tSpuHbtGmrWrJnrNq9evcJnn32Gp0+f4osvvsDChQsRGBiIyZMno02bNkr7BwCrVq1Cv379UL16dSxZsgQNGjTA0KFDMXv2bLXqSERExoV5knmSiMgQyGQyDBgwAIcPH8Y///yDgwcPonfv3hqVKWlX2/nz56NDhw7YunUrnj9/nmN9nTp1sHjxYrXLNzMzk+y5FHd3d8TFxcHNzQ0XLlxAQEBAjm0sLS3xxx9/oGHDhopl/fv3h7e3NyZPnozDhw+jRYsWAIDU1FSMHz8eQUFB2LVrl2JbuVyOadOmYcCAAShRooQkdSciIsPEPMk8SURFl505ILTUdy2Ml6R3PKOjo/N9bsTZ2TnXRKsPVlZWBSZnS0tLpWSapWPHjgCAa9euKZYdPXoUz58/z9HvefDgwUhJSeF8aURExDzJPElEBECcf1cmk0EmkyElJUXf1dEJSRueTk5OePbsWZ7rr169ahQj6T169AgAlAaDuHTpEgCgbt26StvWqVMHZmZmivVERGS6mCeZJ4mITJWkXW3btm2L1atX5zra0T///IM1a9agb9++UobUizlz5sDBwUHpqnVcXBzMzc1RqlQppW0tLS3h4uKC2NjYPMuLiIhAREREnutPnz6N4OBg8YU28nJW2dnpKo4uYxlyHF3G4vEgbRxdxjLkOHnFMjLMkzrIk8aoEL9z7f9r44deLmSZpnIOMeR9Yk4xjFhqnnvevHmj+H+XLl3ynY/ZWEi6h9OnT0e9evXg5+eH9u3bQyaTYcOGDfjhhx+we/duuLu7Y9KkSVKG1LkZM2bgt99+w/Lly+Hk5KRYnpqaCktLy1zfY21tjdTU1DzLDA0NRWhoaJ7rg4ODERUVJb7Qxkh0WWVnp6s4uoxlyHF0GYvHg7RxdBnLkONki2XMDQjmSR3kSWNkyL/bzJPSxtFlLEOOo8tYap57UlJSYG8vjki9c+dO2NnZFfq9hponJe1q6+HhgYsXL6J169bYvn07BEHApk2bsG/fPoSGhuLMmTMazVWmb9u3b8eECRPw+eefY9CgQUrrbGxs8Pr161zfl5aWBhsbG11UkYiIijDmSeZJIiJTJfk93VKlSmHt2rVYu3Ytnj59CrlcDldXV5iZFa6Ne/z4cbXiNmnSRK33FdahQ4fQq1cvBAUFYeXKlTnWu7u7IzMzE0+ePFHqRvT69Ws8f/4cHh4eWq0fEREZBuZJ5kkiMnKFubOame3/wfaAeQHbHxQK2KDo02pnYldXV5Xf88EHH0AmK/xtcEEQIJPJkJmZWfDGajp79iw6duyIunXrYseOHbn2wfb39wcAXLhwAW3btlUsv3DhAuRyuWI9ERFRFuZJ5kkiIlMhecMzJSUFu3fvxu3btxEfH59j8miZTIZFixbl+f6jR49KXSWNXLt2DUFBQfD29sb+/fvz7ArUvHlzODs7Y8WKFUoJdcWKFbC1tUVQUJCuqkxEREUY8yTzJBGRKZK04Xn48GF06dIFCQkJeW5TUEJt2rSplFXK19KlS5GQkKAYSW/fvn2IiYkBAAwZMgRmZmZo1aoV4uPjMWrUqBxzjFWoUAENGjQAID67Mm3aNAwePBhdunRBq1atcOLECWzevBnfffcdnJ2ddbZfRERUNDFPMk8SEZkqSRuegwcPhp2dHbZv34569erBwcFByuIlN2/ePNy7d0/xOjIyEpGRkQCAHj16AAAePHgAABgzZkyO9/fu3VuRUAEgLCwMxYoVw/z58xEVFYWyZcvi+++/x1dffaXN3SAiIgPBPMk8SUQEAHbmgNBS37XQLUkbnvfv38fs2bPRsqW0n2JaWhp2796NP//8E4mJiZDL5UrrZTIZ1q1bp3K5d+/eLXCbd7tAFaR///7o37+/ynUhIiLjxzzJPElEpG2CIODx48eK8/Pr16+RmpqKuLg4xTa2trZwdHTUab0kbXjWqFEDiYmJUhaJe/fuoVmzZrh79y6cnJyQmJgIZ2dnJCQkIDMzEyVLllTMgUNERFSUMU8SEZG2PXnyBJUqVVKc+wVBQEJCAurUqQMAyMzMhKenJy5evKjTekk6j+fs2bOxfPlyXLhwQbIyR40ahcTERJw5cwY3btyAIAjYvn07kpOTMXv2bNjY2ODAgQOSxSMiItIW5knTkpKSAplMBplMhpSUFH1Xh4hMROnSpVG+fHnExcUhLi4Ojx49QlpamuJ1fHw8+vTpo/N6SXrHs2nTpli4cCEaNGiAqlWromzZsjA3V56URiaTYe/evYUu88iRIwgLC0NgYCBevHgBQGy1W1lZYdSoUbh27RqGDRuWY0ADIiKiooZ5koiIdGHQoEEYOnQoXr9+nWNd6dKl0atXL53XSdKG5+7du9GjRw9kZmYiJiYGSUlJObZRZe4xAHj16hW8vb0BAA4ODpDJZErdlBo0aICRI0dqVG8iIiJdYJ4kIiJd6N69O6ZPn64YiTy7KlWq6Pz5TkDihueYMWNQuXJl7N69G5UqVZKkTC8vL8UHZmFhgTJlyuDMmTMICQkBAFy9ehXW1taSxCIiItIm5kkiItIFe3t7+Pn55Wh4Ojg46O1ipKQNz9jYWMydO1eyZAqIE07v3bsXkydPBgD06dMHM2fORHx8PORyOTZt2qSXW8VERESqYp4kIiJd+eabb3DmzBmluaOdnZ3RokULvdRH0oZnQEAA7t+/L2WRGDNmDM6fP4/09HRYWVlh3LhxiI2Nxa5du2Bubo5u3bphwYIFksYkIiLSBuZJI/JRIbpEZ2b7f7A9YJ7nlqKDqk1NQ0SUn6ZNm6JEiRJKDc/WrVvnGFtAVyRteC5ZsgTt27dH7dq18cknn0hSppeXF7y8vBSvra2tsXbtWqxdu1aS8omIiHSFeZKIiHTFzMwMwcHBWLRoEQBxUKERI0borT6SNjy7d++ON2/eIDQ0FP3794enp2euo/X9/fffUoYlIiIyCMyTRESkS1999RW2bduGx48fw9XVFb6+vnqri6QNT2dnZ7i4uGi0Q1OnToVMJsP48eNhZmaGqVOnFvgemUyGiRMnqh2TiIhIF5gn9S8lJUUxqXpycjLs7Oz0XCMiIu3x8fFB6dKl8eTJEwwYMECvdZG04Xns2DGNy/j2228hk8kwevRoWFpa4ttvvy3wPUyoRERkCJgniYhI1wYMGIChQ4fqfaA5SRueUpDL5fm+JiIiMmXMk0REpIoePXrg8OHDepm7M7si1/AkIiIiUltBo82qOtIsoPZos3bmgNBSrbcSEUnG0dERkZGR+q4GzPRdgYKYm5tj69atea7fvn273oYEJiIi0jfmSSIiMgRFvuEpCPlfZczMzIRMVoi5tIiIiIwQ8yQRERmCIt/wBJBnwnz58iUOHDiAkiVL6rhGRERERQfzJBERFXVFsuE5ZcoUmJubw9zcHDKZDD169FC8zv5TokQJbNq0CV27dtV3lYmIiHSGeVJ9Wc9dCi3F/xMRkW5IOrhQ1apV0bNnT3Tv3h3lypVTu5zAwECEhYVBEAQsX74cLVu2RKVKlZS2kclksLOzQ506dRASEqJp1YmIiLSOeZKIiEyVpA3PsmXLYvLkyZg0aRIaNmyIXr16oUuXLioP3dumTRu0adMGgDjR8xdffIF69epJWVUiIiKdY54kIiJTJWlX24MHDyImJgZz585FamoqBgwYADc3N3Tu3Bl79+5FRkaGymWuX7+eyZSIiIwC8yQREZkqyefxLF26NIYPH47hw4fj33//xebNmxEREYEff/wRTk5O+PTTT9GjRw80bNiw0GVmZmbiwIEDuH37NuLj43OM4CeTyTBx4kSpd4WIiEhyzJNERGSKJG94Zle5cmVMmzYNgwcPxldffYWdO3di5cqVWLVqFcqXL49hw4Zh0KBBMDPL+8brhQsX0KlTJ8TExOQ5ZDwTKhERGSLmSSIiMhVaG9U2JSUFmzdvRuvWreHl5YUff/wR7dq1w44dO/Djjz+icuXKGDp0KAYNGpRvOWFhYUhNTcWePXvw4sULyOXyHD+ZmZna2g0iIiKtYJ4kIiJTIukdz6yuPps3b0ZUVBRevXqFOnXqYP78+QgNDVWaRyw4OBjjxo3DsmXLsGrVqjzLvHz5Mr777ju0b99eyqoSERHpHPMkERGZKkkbnm5ubnjx4gXKlCmDIUOGoFevXqhatWqe29eoUQNJSUn5lunp6Zln1yEiIiJDwjxJRESmStKutkFBQTh48CDu3buHmTNn5ptMAaBr166Qy+X5bjN69GisWbMGL1++lLKqREREOsc8SUREpkrSO57h4eFSFgcASEpKgr29PSpWrIiuXbuibNmyMDc3V9pGJpNh+PDhkscmIiKSEvMkERGZKo0anvfv31frfV5eXoXeduTIkYr/L126NNdtmFCJiKgoYp4kIiISadTw9Pb2hkwmU/l9qoyud+fOHZXLJyIiKgqYJ4mIiEQaNTx/+OEHtRKqKsqVK6fV8omIiLSFeZKIiEikUcOzT58+ElWjYA8fPsTx48fx5MkTdOrUCZ6ensjMzERiYiIcHR1zPM9SkOTkZMydOxdnz57FuXPnEB8fj/Xr1+e6T9euXcPw4cNx8uRJWFpaIigoCAsWLICrq6vSdnK5HPPmzcOKFSsQFxeHSpUqYezYsQgNDdVk14mIyEAxTzJPEhGRSNJRbbVBEASMGDECPj4+6N69O0aMGIEbN24AEJOit7c3lixZonK5z549w9SpU3Ht2jXUrFkzz+1iYmLQpEkTREdHY8aMGRg5ciR++ukntGzZEq9fv1badvz48Rg9ejRatmyJJUuWwMvLC926dcO2bdtUrh8REVFhME8SEZEhkHRUWwCIj49HREQEbt++jfj4+Bxzi8lkMqxbt67Q5c2dOxeLFi3C6NGj8eGHH6Jly5aKdY6OjggJCcHu3bsxbNgwlerp7u6OuLg4uLm54cKFCwgICMh1uxkzZiAlJQUXL15UDPYQGBiIli1bIjw8HAMGDAAgXmmeP38+Bg8erBjcoV+/fmjatClGjRqFLl26qHy1mYiIjA/zJPMkEZEpkrTheeDAAXTu3BkpKSlwcHBAiRIlcmyj6rMua9asQa9evTBjxgw8f/48x/oaNWrgl19+UbmuVlZWcHNzK3C73bt3o127dkojDLZo0QKVKlXCjh07FAl17969yMjIQFhYmGI7mUyGQYMGoVu3bjh9+jQaNWqkcj2JiMh4ME8yTxIRmSpJG55ff/013NzcEBkZiffee0+SMh88eICGDRvmud7Ozk5rk2Y/fPgQT548Qd26dXOsCwwMxM8//6x4fenSJdjZ2eWYDDwwMFCxngmViMi0MU8yTxIRmSpJG57R0dGYO3euZMkUAEqVKoUHDx7kuT571x6pxcXFARC7G73L3d0dL168QHp6OqysrBAXF4fSpUvnuFKd9d7Y2Ng840RERCAiIiLP9adPn0ZwcLD44pKqe1EIWWVnp6s4uoxlyHF0GYvHg7RxdBnLkOPkFcvIME8yT6ocR5exDDmOLmPxeCj6cXQZS9/HgwGRtOHp6+uLpKQkKYtESEgIVq5ciT59+sDR0RHA225IBw8eRHh4OL755htJY2ZJTU0FIHY3epe1tbViGysrK8W/+W2Xl9DQ0HxH9AsODkZUVJT44iMtDMufVXZ2uoqjy1iGHEeXsXg8SBtHl7EMOU62WMFGkFzzwjzJPKlyHF3GMuQ4uozF46Hox9FlLD3EMdQ8KemottOnT8fy5ctx9+5dycqcMmUK3N3d4e/vj169ekEmk2H27Nlo1KgR2rRpgxo1amDcuHGSxcvOxsYGAJCenp5jXVpamtI2NjY2hdqOiIhMF/Mk8yQRkanS6I7n0KFDcyxzdXVF1apV0bJlS5QtWzbHCHUymQyLFi0qdAxHR0ecOXMG8+fPx65du2BtbY3ff/8dFSpUwOTJkzFq1CitJaus7j9ZXYmyi4uLg7Ozs+Lqrbu7O44ePQpBEJS6EWW918PDQyt1JCKioot5knmSiIhEGjU8s4ZDz83+/ftzXa5qQgXEq6ATJkzAhAkTVHqfpsqUKQNXV1dcuHAhx7pz587B399f8drf3x9r167FtWvXUK1aNcXys2fPKtYTEZFpYZ70V7xmniQiMm0adbWVy+Uq/2RmZqoUo3nz5jh8+HCe648ePYrmzZtrshv56tSpE/bv3680cMPhw4dx48YNdOnSRbGsQ4cOKFasGJYvX65YJggCVq5ciTJlyuQ74iARERkn5knmSSIiEkk6uND9+/fh6uqaZ5ee1NRUPH36VKXR9Y4dO4Z+/frluf7Jkyf4/fffVa4rIF6JTkhIUIykt2/fPsTExAAAhgwZAkdHR4wbNw47d+5Es2bN8NVXXyE5OVkxIuFnn32mKMvT0xPDhg3D3LlzkZGRgYCAAOzZswcnTpzAli1bOCk2ERExTzJPEhGZLEkbnj4+Pti0aRO6deuW6/qoqCh069ZN5au5+U2mHR0djeLFi6tUXpZ58+bh3r17iteRkZGIjIwEAPTo0QOOjo4oW7Ysfv/9d4wYMQJjxoyBpaUlgoKCMH/+/Byj882aNQslSpTAqlWrEB4eDl9fX2zevDnPz4OIiEwL8yTzJBGRqZK04SkIQr7rMzIyYGZWcO/eDRs2YMOGDYrX06dPx5o1a3Jsl5CQgMuXL6Nt27aqVxYo9KiC1atXx4EDBwrczszMDGPHjsXYsWPVqo82ZciB2HSgpCVgZyQXlY1xn3TFGD87Y9wnY2Tq3xPzJPOkLmMZ4+8b94n0wRjPD/qg8XQqL1++xP3793H//n0AwPPnzxWvs/9cvnwZ27Zty3WS6Xe9evUKT58+xdOnTwEASUlJitdZP8+ePYOVlRW++OILrF27VtPdMFg+J4Blec8brhD/BvA5Ccy5q/UqacwY90lXjPGzM8Z9Mkb8nvLGPKlfujw2dRXLGH/fuE+GsU/GxhjPD0WZxnc8v//+e0ydOhWA2NVn2LBhGDZsWK7bCoKA6dOnF1jmoEGDMGjQIABit6RFixYZ7ESp2nY/DUjIKHi7UpZAOWvgXKL266QpY9wnXTHGz84Y98kY8XvKG/Okfuny2NRVLGP8feM+GcY+GRtjPD8UZRo3PD/66CPY29tDEAR88803CA0NRe3atZW2kclksLOzQ506dVC3bl2Vyr9z546mVTR6D9OBiy8L3q68LXCuENsVBca4T7pijJ+dMe6TMeL3lDvmSf3T5bGpq1jG+PvGfdJ+fSgnYzw/FFUaNzwbNGiABg0aAABSUlLQqVMn+Pn5aVyxd/3+++/46aefFIMclCtXDkFBQWjatKnksQzNyhjxpyAyAPk/XVR0GOM+6YoxfnbGuE/GiN9T7pgn9U+Xx6auYhnj7xv3ifTBGM8PRZWkgwtNnjxZyuIAAK9fv0ZoaCj27NkDQRDg5OQEQBwwYf78+ejYsSMiIiJQrFgxyWMbipBSQLCrvmshLWPcJ10xxs/OGPfJGPF7KhjzpH7o8tjUVSxj/H3jPpE+GOP5oaiStOEJAPHx8YiIiMDt27cRHx+fYwQ/mUyGdevWFbq8KVOm4Mcff8TIkSPx9ddfo3Tp0gDEecnmz5+PuXPnYurUqZg2bZqk+2FIahUHennouxbSMsZ90hVj/OyMcZ+MEb+nwmGe1D1dHpu6imWMv2/cJ9IHYzw/FFWSNjwPHDiAzp07IyUlBQ4ODihRokSObfKbayw3W7duRe/evTFnzhyl5aVKlcLs2bPx+PFjbNq0yaQTKhERGQbmSSIiMlWSNjy//vpruLm5ITIyEu+9954kZcbFxaFevXp5rq9Xrx62bdsmSSxDdKcRUMLIek8Z4z7pijF+dsa4T8aI31PhME/qni6PTV3FMsbfN+4T6YMxnh+KMkkbntHR0Zg7d65kyRQAPD09cezYMXzxxRe5rv/999/h6ekpWTxDI0B8ALkgGXLgtQDYmgEqXkzXOS8b5dexaUDcayA1E7AxB9wtAQ9r/dStqDPGz+7dfaKiyRjPRdrAPKl7ujw2dXUONsbzoinkL2PYJ2Ojy++IeVLihqevry+SkpKkLBK9e/fG5MmT4eTkhOHDh6NixYqQyWS4efMmFi5ciJ07d2LKlCmSxjQk5U8CZayBA7WAqvZ5b3fxJdDwPPBDdaCPmn3LT8QDc+8CN14BpS2BQWWBrm7iugPPgGPxQHELoJc74KnhL2lMGjDjDrD7CfD0dc71pSzFB7TH+4j7r47br8RhrRu/09NtVQyw/AHw7yvAygxo5ARMqwDUdlAvDsDPTpPP7vcXgI9NwX9oPX0txm5bEghwVD2OsR4Puoqly3ORIWOe1D1dH5u6OAcb43kR0P5np+v9AXRzPBhbTjG24y4L86TEDc/p06dj8ODB6NatG7y9vSUpc9y4cbh16xZWr16NNWvWwMzMDAAgl8shCAJ69+6NcePGSRLLUMWlA00uAPtrAfXySCz1nYAGjsCPT9Q7iC8kAh9eBMxl4oPRcgCf/wNkCsDdVGDirbdXcebeBc7XAyraqrc/15KBpheAV3KgXUmgrgPgbiWeZNLl4v5eeAlsjAN2PgZOBABV7FSPM/g68EYADtV5u2xitHjyqWQHhLqJV7x+ewE0Oi/GqaPGyY2fnfqfHQA0uygmlvHl89/OwQKYeRd4/Fq9P7CM8XjQZSxAN+ciQ8c8qR+6OjZ1dQ42tvMioJvPTpf7o6t9MsacYmzHXXamniclbXgePnwYrq6uqFq1Klq2bImyZcvC3NxcaRuZTIZFixYVukxzc3OEh4djxIgR+Omnn3D//n0A4vxkbdu2RY0aNaTcBYM0oyKwOQ5ocRGIrAm0dMl9u3auwIpCzB2Um29vA87FgJMBb08iceniCef3eHFo6B+qA09eA20vAROigW1qfjUjbgCOFsCluvlfWXqYJibfYf8Cv9bOe7u8/JkEjCz39nViBjD3njja2A/V3nZvSMgA6p8DxkerF4efnfqfHVC4bimAmCRq2APnEtWLY4zHgy5jAbo5Fxk65kn90NWxqatzsLGdFwHdfHa63B9AN/tkjDnF2I677Ew9T0ra8Fy6dKni//v37891G1UTapYaNWoweebBwwo4Xhdo/5f4s6E68Klbzu1KWQIvMtSLcSkJ+LKs8pUrdyugc2ng1+fAlAriyci5GPCFJ7DkvnpxAOBkAjCzYsHdGcpYA195AWNuqhcn6Q3gku0h739SgNdy4Jtyyn3qnf7bp8m31YvDz079zy7L/mfAo1y6v+SoVyZw65V6MYzxeNBlLEA35yJDxzypH7o6NnV1DgaM67wI6Oaz03Xu0sU+GWNOMbbjLjtTz5OSNjzlcrmUxSm5cuUKfv75Z9y9excA4OPjg9atW0s6QIMhcywmdkn49DLQ7X/ArVRgnI/yNqcTxGdC1PE8Q/kkkMX5v2XZ1zlbaPbLYmkmJsrCSHojbq8OHxvgdCLQt4z42tFCvIqcW+hMAXhnqr1C42en/meX5Vyi+lfsC8sYjwddxsqi7XORoWOe1B9dHJu6OgcDxnVeBHTz2ek6d+lin4wxpxjbcfcuU86TEnx82pWeno4+ffqgZs2aGDNmDFavXo3Vq1dj9OjR8Pf3R69evfD6dSEu+ZkAKzPxtn2/MmJf+JYXgUPPgSvJwLTbQHgc0LW0emX72AD7nuZc/tMz8WTwR8LbZacTNRtxr11JYN494PDz/Lc7/FzsehFUUr04fTyA8Fhg+yPxdXV7sW//9NviiGJZHqUDy2OABk7qxeFnp/5nl2VqBSCzZeF/ivI+6fJ40GWs7LR5LqKcmCcLT9vHpq7OwYBxnRcB3Xx2us5dutgnY8wpxnbc5cZU86Skdzyz3LlzB7/88gvu3bsHQHzOpE2bNvDx8SngnTmNHj0aGzduRFhYGIYMGYIKFSpAJpMhOjoaixcvxooVK+Ds7IyFCxdKvBeGyUwGrKomPmQ9+ibQ6k9xuQzAB87ASG/1yu3jDoyLBjr+BXRzB4rJgKinwIZYcZSvgdfELhiP0sUHsKdUUH8f5lUCLicDLf8EKtkCgY5iVw5rMyDtvwe9zyWKI6r5FwcWVFIvznAv8ZmE0P8B8++J/exbuQBz7gG+f4gPfb/KFLcxlwFzfNWLw89O/c9Ol4zxeNBlrHdp61xkLJgn9Uebx6auzsG6ostzvS4+O13nLl3skzHmFGM77vJiinlS8obn119/jUWLFuXoTmRmZoZhw4Zh3rx5KpW3efNm9OzZU+m5GACoXLkyli1bhpcvX2Lz5s0mm1B7ewAVcrmiNMBTHN76l2fAswygqh3Q3Fn9OKO8gTupwLpY8SQDALbmwOIqwOCy4mhg8+8BcgHo4Kr8ULiqXC2Bc4Hi1a4dj8V9yN5dw7mYOJrayHLiVTELNe/bW5gBUf7A4vvA9/eBWXfernuQJv5YmolXw2ZUBHzVHMWMn536nx0ANC0BlNNBdxNjPB50GUtX5yJjwDypW7o8NnV1Dja28yKgm89Ol/ujq30yxpxibMddFuZJQCYImvZgf2v+/PkYNWoUOnfujK+//hpVq1YFAFy7dg3ff/89du7ciXnz5mH48OGFLtPR0RGzZs3CoEGDcl2/YsUKjB07FgkJCVLsQpEUHByMqKgo8cVHhR3LTgUHczkE8ojzKF28MmQhE38RS2Trz5+aKT787ZhL//884+QTK7sMOZAqB2zMgGKF+aVXYZ+yXE8BbqQAyf9NGlzGCvCzF0+qUsTR6LNTMVZ2BvXZFSKWWorgPunyeJD891Yb31G2WErnPCPDPKkdRSlPvkulc7CpnBe18dmpEUut/VEjTnbaOh6MOafo+nvSxd9NaskWx1DzpKR3PNesWYPg4GDs2LFDaXm9evWwbds2pKWlYdWqVSol1FatWuHAgQN5JtRff/0VH330kUb1psKLTRevBJW2FOcHy04GwF4rnbfFX/xC/fJroIqdZnMzFYSfnWExpuNBX8ce5cQ8aXp0cQ7WFV2f67X92ekjd2lrn9ysxJ8s6XLg52fAvyni3cHGTurN5ZobXecUYzvuTJmkh8bdu3fx1Vdf5bm+VatW+PXXX1Uqc9q0afjkk08QEhKCwYMHo2LFigCAmzdvYtmyZbh37x62b9+OFy9eKL3P2dlI71G/4+JLcSjoHm6Ai2X+28ZniFdvrAu6QpSLdDkQ/Jf44LMMgADxYe/DdYDkN0D3K8DZRLH8LzzFPvMyLd0UkcrdVOBeKlDbASiuxT+8jfGzy+7lG/Fh+Lj0t1cI3a2A9+yl/VyjngAf/w2UtRaf58ht+HFNGOPxoMtYujoXGTrmSd377bk46MngskDJAo5NTX32D1DTHhhWQBfDSy+BrY/EgUUqF+GLdro6L2b3OB3Y+1Q8p8T+l1fszQFfW+BDZ/FZP3XPU89fAzHpYlkF3i2T0MM0cZ/+SsqZK/2LAx+7Ah4FTOeRl3l3gfMvge3ZZlK6lizOo3kv7e2crwKALqWBLX7qdxc1xr9nDj8XpyYa71Pw+eF/SeI27lb5b5cXXZ6LiipJTyOlSpXC33//nef6v//+G66uriqVmdUN6X//+x/27t2rtC6rl3C1atVyvC8zs5BjIxu4uXeBv5PFuYUKEnRJ7De+rrrqcebdFX9h+niI/dDNIfbvH3UD+CdZfOj6y7LipMHf3xdP6l+UVT0OADS7AHxeBujhnv9291MB75PAtArA+PKqxxlzEzgeD9xtnHNd/6tA9DvzngkAjtVVPY4xfnaA+IfT+GjgtxfAG0F5QnMBYhecFs7ADF8xsUpBBqBJCaD3P+LEyosrAzUkKtsYjwddxtLVucjQMU/q3rx7wOPXwORcBjppfF55JM4scjVHgd0YK54bbr4CllbJ+4/uynbAD7HiOW2OGoOVfPYP0KmUOMl8fh6kAU3OA994A4PU+N3W1Xkxy/TbwHd3xAZO9gYTIL6ef09s6GzyUy+vTLgF7HoM3M9lf97V+W9x0Jexqo/3pWT0TWDhPTFP2poDbpbiiKbpcnFwnHUPgeH/AiPKic8rqtpQWx8LfOSivOyT/4nlb/YD3ncSG7rbHwHT7gDV7uT+u1AYusopG2KB2sWB9wr4jh+lA2OjgW5u4gBE6lj8QLwYUJiG4Ff/ivNwblZzhipdnouKKkkbnl26dMGiRYvg7e2NIUOGwM5OvIyXkpKCpUuXYu3atRg2bJhKZU6aNAmyon65RI/OJoq//O96+ho48gIIKfW2u0CTEuKQ1+rY9hj4uBTwQ7Y/FD90AYZeB/5MEk9u3f5r7FhfAVY9VP8P2OPxYoOlIF424lWncy/Vi3P+JRDqlvucTF7WwKY4cYQzAEh8IyZwdRjjZ3cqAWhxUXzofkQ5cZhzdyvASgakC+JJ/OJL8TNseA74rQ7Q0Em9WNkJAKZXAL4tD3x9A6hzFhhQBpheUfkZE3UY4/Ggy1i6OhcZOuZJ3ftfMvC5R+7rPi0N/J0k/iENiH84HyxgWoWCNHQCVsaIcx5u8su9y56tudhwPPQi57rC2BgLVLQpuOFZ1lp8Lu5ovHoNT12dFwFg+QNg8i2gkRPwWRnxOb7Hr4Ftj8Tzy44aQIYATLklXmS9WA8ob6tajOPxYm8Zm3fudmbI/+s6mu1OVilLsVGlScNz9h2xsTa4LBBWNvfuotdTgBUPgNl3xTz2jbdqMe6mAtWylXstGbiaDOyqCXQs9Xb55AriHeSNceo3PHWVU/r+I04XVFDD09US2PFInBtS3Ybnny+Bz3I5PzxIA7bGiRdTs3rnvO8E7HysXhxA9+eiokjShue0adPw119/Ydy4cZg0aRI8PMRPLzY2Fm/evEGzZs0wdepUlcr89ttvpayi0Xn8GiifywhZ/0sWh6F+2vRttzdva/WTwp1U8SrWuz50BpY+ABqXeLuskROw+4l6cbKkZIpdYgpS015MSOp4lA5UzCNpvWcv/nupvjhUd0Sc2IVEHcb42Y2+Kd6xOh4A2OXRXalzaWBCeaDpeeCbm8DJAPVi5aaCLbDHX+wiM/wGUOkP8e7tQE/1u/UY4/Ggy1i6OhcZOuZJ3XueITaScuNtI54z5/iKf1xujdP8j71BnuIfsoOuAfF/AT/WzL1bZ6CD2KhSV2FHhgx0LPp5EgCWPABauAAHaisv7+EO9Loi3lW7UB/4oIR40XHCLWCrineeHqQBVXLZnxMJ4sXU7OepKnbi3VFNrHoI9C8jjvqalyp2wKIqwGtBvGChasPTxlx85CVL/Bvx2KiTS6OtjgOwIU618rPT9d8zBTGXid3A1b2IDgBP8zg/3Hwl3k3tV+Ztw9PLWmy8q0vX56KiSNKGp62tLQ4fPoy9e/cqzU/WunVrtG3bFu3bt+dVWYlZyICkXHpLJfw3FHRqttH6zWRApppjGFvKcv9le/RfAyc+Q7yyCgAJb/JujBTWrLviT0GynjFQh5lM+fPJLuW/UdnS5OK+aDL0szF+dn++BBZULriudubiMOHD/1UzUAE+dAH+rg+sfihOwLzqIbCkMtCoRMHvfZcxHg+6jKWrc5GhY57UPTvzvP9YjE0Xf5+l/sg/LwOULCZedPngAvBTLfHuTHaJb8RnF9U1/Y54l6wgr+XinUJ16Oq8CIiNmqF53B3rWArYHCeOmGpjDvRyF7stqipDEPfpXWn/nbveZNuJ4ua5n9NUEZde+AF96jqIU3qoqkkJsdt2WFnxs6lmJ85BeSZR7N2U3ZlE8U6yunSZUzY/EutbkPtpwEMNGoPWZsCLNzmXP/1vn5IzgaybqRmC2NhVlz7ORUWNVh4V79ChAzp06KCNoukdle2AA8/F7o7ZHf6v+875l4DnfyeBUwm5zx9UGE1KiFezOriKV5cA8dmOWXfEMqfdBsL9xF/UlTFi33xNNHMGmjppVkZBqtoBkU9yfnaAeJWztKXmjUDAOD+74haFv2MVk5Zz1DtVTLkl/ns9Rfz3+/tAiVzK6+EOrH0INLmg3jMRxng86DKWrs5FxoJ5UnfqOYjPwQ0vpzxAzhs5sOyB+LtvpYURLDuUAg7WFgdjqX1GvDuXdUfoRYZ4oSzr91Id1e2AmhI9454XXZ0XAfHZx7+Scl/3d5LY3Tfre3IpBiTl0lgoSDlr8Vz0rkv/xb3x6m1322spbxtR6vK1BX58Il6IKMjeJ2+7LatiSnmg3jmg0Xmx509zZ/Gu2eDr4t3PJk7i/JrbHokN22lqdrMFdJtTnrwWn1MtiLks77uIhVHDHtj9OOed5v3/PQ5yLB7o/V++OvRcve8oi77ORUUJB9E3cD3dxbtJQ66L3TlszIBdT8RBPgIcgMHXgNuvxJHNNsUB31dWL87UCkDjC0DgOaCyLVBMJp6UK9gCe2sCjS4AjkfFSYMtZOJzLZpoXkL9QW8Ka0AZYOA1oOtlsUtoBRuxMTX/njj63AQNBxTIYoyf3aelgQX3AB8b8cpzbs8wZcjFZ0nm3hM/a3VNva38esn9/LdX92KhMR4Puoylq3MRkapGewMf/gnUOgMM8xKP/wdp4h/QV5LFUT61pVEJ4I8AoMPf4p3PBk5iA+tEgtj4jFBzkBJAfG7aWPIkID7bNu222Kjs7ykO4vLktdjlcMYd8ZnYrLuV99PeXshSRTtXYPF9oF1JoFNpcdnpBLFxXdZaHExpcRVxJN/VD3N/9k8V43yA7v8TB3j6whOo7yiOh2BtJt4pjksXu4mueCAeE6p2HQbE5yB/rgX0uAK0+0v8jEoWE7vfDrr2djszmfh9jtHgO9NlThnhpf3jGxDzVZ9/gOBLYrdaG3PxosrjdHHMjC+vAxdeisfE/mdAuAaD4unzXFRUaNzwDA4OVml7mUyWY9Q9Ut+XZYGjL8SH8pc9EJfZmwOH6oj/Bv8lPl8HiAMEhHmqF6dGceBCILDogdjN0kImXgEd5S0OMHM2EFjzEHiVCXR3A+pKNFeUNvXzFB+GXxUD7Mj2HIcMYsNqUrYTnocV0FrNB9eN8bOb4Ss+/zDgqtjY8C+eM5n+lSR2xWpdUtxeXU8/EP/99ZmYWC/W1+zqZl6M8XjQZSxdnYsMEfOkfjV1Fgc9+epf8SeLUzFgRVUgNNso4LWKA7M0OF/lpqo98Fd9ceTnbY/FeRWr2gHfVhCfuSvKdHVeBICJ5YFbqWL34eyPi8ggDti0vOrbZQ/SxPiqGucD7HkCdLksDuRjbSbmqx+qi11rQ/8HBJwVY1a0FafY0ERXN7GsUTeBnldy744sg9iI3vYe8Ima04R94Az8+754V/PQc/HOrYOFeAGwjJV4nH3qBvjZa7AzMM6/Z3p6iMf4kgdiwzLr+/ittvhsduj/xFH0bc3E46eXBhcj9H0uKgpkQtZY62oyMzODtbU13NzcUJiiZDIZbt++XeB29FZwcDCioqLEFx/lfj/n2Avxioyjhdi9p9R/z5LIBbEbhFOxt8tyOJjL95ZHHI3kFieXWMfjxTtpGnVxUWGf/koCDjwTHzB3LiZe4Qos7MlSV59dIWPp+rM79Fz8Y+SvpP/mW/vv+RsPK/Gk+UlpcbCIQsfJJ9bvL8SBhH6qpcYcWvo8HvKJpRF9/t7mE0eKc5HSOc8IME9qX2Hy5Bs5cDFJ7AboXEz8Y7zQ3doK+XtwP1V8lvPdUVMLrZDnkI2xYldHjRoSRTRPXnwp5panr8XGYUMnsfuoVLHiM8S7nuf/O091dwPa/jc68I0UsWulkwXQ3jWf71HFfRIE4FRi3rmygWMez/aZaE7p+494R7+gUZuliJXl9iuxAepoIXYpzn5uSJeLz7fm+fylip+d2ueibHEMNU9qfMezTJkyePjwIUqWLIlu3bqha9eucHOTeGZ3KtAHzuLPu8xkQKUiPDl1bpqoMTCMJvyLSzfPpL7p+rNr6aL+EOaqauoM/Flf+3GM6XjQB2M6F0mFebJosDAD6mn5Dsy7g7loiyZ3XdShy/NiHQft3gkuUSzv6UQq2WnnPCWTiVNxvO8kfdnG6AcNurOqq7xt3tPzSP3cpS7ORUWVxh/lgwcPcPToUdSqVQvTpk1D2bJl0aJFC6xfvx5JSXk8JU5ERGQimCeJiIgkaHgCQNOmTbFq1So8evQIu3btgouLC7788kuUKlUKISEh2LVrF9LTNRjrWM9u3ryJrl27wtPTE7a2tqhSpQqmTp2KV69eKW136tQpNGrUCLa2tnBzc8PQoUORnJysp1oTEVFRwTwpYp4kIjJdkt48LlasGDp06IDt27fj8ePHiiT76aefYs6cOVKG0pkHDx4gMDAQZ86cwZdffomFCxeiQYMGmDx5MkJDQxXb/fXXX/jwww/x6tUrLFiwAP369cPq1avRpUsXPdaeiIiKEuZJ5kkiIlOllelU0tPTceDAAezduxeXLl2CtbU1vL29tRFK6zZt2oSEhAScPHkS1auLnc4HDBgAuVyOjRs3Ij4+HiVKlMC4ceNQokQJHDt2DA4O4sMJ3t7e6N+/Pw4ePIiPPvpIn7tBRERFCPMk8yQRkamR7I6nXC7HgQMH0KdPH5QuXRqhoaFITU3FmjVr8OTJE/Ts2VOqUDr18qU403Dp0srjdru7u8PMzAyWlpZ4+fIlDh06hB49eiiSKQD06tUL9vb22LFjh07rTERERQ/zJPMkEZEp07jheerUKXz55Zdwd3dHUFAQoqOjMWPGDMTGxuLnn39Gjx49YGdnuEMZfvDBBwCAzz//HH/99RcePHiA7du3Y8WKFRg6dCjs7Ozwv//9D2/evEHdunWV3mtpaQl/f39cunRJDzUnIqKigHmSeZKIiCSax9PGxgZt27ZFaGhooboK1a5dW5OQOjd9+nTMmDEDqampimXjx4/H9OnTAQC7du1Cly5dcPz4cTRu3FjpvZ988glOnDiBuLi4PMuPiIhAREREnuv//PNPg/vMiIjUdevWLfzzzz/6roZkmCeZJ4mIpGSoeVKSZzxTU1Oxe/duREZG5rudIAiQyWTIzMyUIqzOeHt7o0mTJujUqRNcXFzw008/YcaMGXBzc8OXX36pSLRWVjlntbe2tlZKxLkJDQ1VGoCBiIiMC/Mk8yQRkanTuOG5fv16KepRZG3btg0DBgzAjRs34OnpCQAICQmBXC7H6NGjERoaChsbccbo3IbCT0tLU6wnIiLTwzzJPElERBI0PHv37i1FPYqs5cuXo1atWopkmiU4OBjh4eG4dOkS3N3dASDXbkJxcXHw8PDQSV2JiKjoYZ5kniQiIonn8TRGjx8/zrXLU0ZGBgDgzZs38PPzg4WFBS5cuKC0zevXr/HXX3/B399fF1UlIiLSOeZJIiIqDDY8C1CpUiVcunQJN27cUFoeEREBMzMz1KhRA46OjmjRogU2b96MpKQkxTabNm1CcnIyJ8cmIiKjxTxJRESFofGotsbu+PHjaN68OVxcXPDll1/CxcUF+/fvxy+//IJ+/fphzZo1AMQR9Ro2bIhq1aphwIABiImJwfz589GkSRMcOHBAz3tBRESkHcyTRERUKAIV6OzZs0KbNm0ENzc3oVixYkKlSpWE7777TsjIyFDa7sSJE0LDhg0Fa2trwdXVVRg8eLDw8uVLndd3/fr1AgDh/PnzWo+R28/o0aMlLf/EiRM51svlcsHT01MAIAQFBWkcL8uyZcsEAEJgYKBkZQqC/vYne2xtHg+6jqWt7ym7y5cvC506dRK8vLwEKysrwcPDQ2jRooWwePFiSeNcuXJF6N69u+Dh4SFYWloK7u7uQrdu3YQrV65IFiPre7GyshJiYmJyrG/atKlQvXp1yeJk/VhZWQnu7u7CRx99JCxatEgv50PSDebJvGMwTxaOKeRJXeZj5knVME/qhiTTqRi7wMBA/PzzzwVu16hRI/zxxx86qFHRMXXqVPj4+Cgt8/Pzk6x8a2trbN26FY0aNVJa/vvvvyMmJibXofk1sWXLFnh7e+PcuXOIjo5GxYoVJS1f1/tjrLT9PZ06dQrNmjWDl5cX+vfvDzc3Nzx48ABnzpzBokWLMGTIEEniREZGIjQ0FM7Ozvj888/h4+ODu3fvYt26ddi1axe2bduGjh07ShILEEcUnTVrFpYsWSJZmbnJOi9kZGTg0aNHOHbsGIYNG4YFCxYgKioKNWrU0Gp80j3mybwxT6qGeVIazJPqYZ7UMn23fEl6urySq60YWeWHhIQIJUuWzHHVvH///kKdOnWEcuXKSXbl8/bt2wIAITIyUnB1dRW+/fZbScoVBP3sz7uxjeWOpza/pyxt27YVXF1dhfj4+BzrHj9+LEmM6OhowdbWVqhSpYrw5MkTpXVPnz4VqlSpItjZ2Qm3bt3SOFbW9+Lv7y9YWVkJDx8+VFov9ZXc3L7/w4cPCzY2NkK5cuWEV69eaRyLSBPMk+phnjSMOMyTqmOe1A0OLkRFWmhoKJ4/f45Dhw4plr1+/Rq7du1Ct27dJI21ZcsWlChRAkFBQejcuTO2bNkiafmAbvfHWOnie7p16xaqV68OJyenHOtKlSolSYy5c+fi1atXWL16NVxdXZXWlSxZEqtWrUJKSgrmzJkjSTwAGDduHDIzMzFr1izJyiys5s2bY+LEibh37x42b96s8/hExop5kt7FPKk+5kntYsOTNJKYmIhnz54p/UjJ29sbDRo0QEREhGLZL7/8gsTERHTt2lXSWFu2bEFISAgsLS0RGhqKmzdv4vz585LG0OX+GCtdfE/lypXDxYsXceXKFUnLzW7fvn3w9vZG48aNc13fpEkTeHt746effpIspo+PD3r16oU1a9YgNjZWsnILq2fPngCAgwcP6jw2kb4wT6qGeVJzzJPqY57ULjY8SSMtWrSAq6ur0o/UunXrhj179iA1NRWAeEJt2rSppBOOX7x4EdevX1cktUaNGsHT01MrVwl1sT/GSlff08iRI/Hq1Sv4+/ujYcOGGD16NA4ePKiYl1BTiYmJiI2NRc2aNfPdrkaNGoiJiVGafkJT48ePx5s3bzB79mzJyiwsT09PODo64tatWzqPTaQvzJOqY55UH/Ok5pgntYcNT9LIsmXLcOjQIaUfqX3yySdITU3F/v37kZSUhP3792ul+1Dp0qXRrFkzAIBMJsOnn36Kbdu25ToxuiZ0sT/GSlffU8uWLXH69GkEBwfj77//xpw5c9CqVSuUKVMGUVFRGpeflSCLFy+e73ZZ61++fKlxzCzly5dHz549sXr1asTFxUlWbmHZ29tL+gcCUVHHPKk65kn1MU9qjnlSe9jwJI0EBgaiRYsWSj9Sc3V1RYsWLbB161ZERkYiMzMTnTt3lqz8zMxMbNu2Dc2aNcOdO3cQHR2N6Oho1KtXD48fP8bhw4cliwVof3+Mla6/p4CAAERGRiI+Ph7nzp3D2LFjkZSUhM6dO+Pq1asalZ2VKAtKLIVNvKqaMGEC3rx5o5dnWJKTkyXfH6KijHlSdcyT6mGelA7zpHZwOhUyCN26dUP//v3x6NEjtGnTJteH2dV15MgRxMXFYdu2bdi2bVuO9Vu2bMFHH30kWTxAu/tjrPTxPQGApaUlAgICEBAQgEqVKuGzzz7Dzp07MXnyZLXLdHR0hLu7Oy5fvpzvdpcvX0aZMmXg4OCgdqzclC9fHj169MDq1asxZswYScvOT0xMDBITEyUf1p+ImCeJeVJKzJPawYYnGYSOHTti4MCBOHPmDLZv3y5p2Vu2bEGpUqWwbNmyHOsiIyPx448/YuXKlbCxsZEspjb3x1jp43t6V926dQFAkq437dq1w5o1a3Dy5Mkc89UBwIkTJ3D37l0MHDhQ41i5mTBhAjZv3qzTZ1g2bdoEAGjVqpXOYhKZCuZJYp6UFvOk9NjwJINgb2+PFStW4O7du2jfvr1k5aampiIyMhJdunTJtRuPh4cHIiIiEBUVhU8//VSyuNraH2Ol6+/p6NGj+OCDDyCTyZSW//zzzwCAypUraxxj1KhR2Lx5MwYOHIjjx4/DxcVFse7Fixf44osvYGtri1GjRmkcKzcVKlRAjx49sGrVKpQrVw4WFtpNB0eOHMG0adPg4+OD7t27azUWkSlinjRtzJPSY56UHhueZDB69+4teZlRUVFISkpCcHBwruvr168PV1dXbNmyRdKECmhnf4yVrr+nIUOG4NWrV+jYsSOqVKmC169f49SpU9i+fTu8vb3x2WefaRzD19cXGzZsQPfu3fHee+/h888/h4+PD+7evYt169bh2bNniIiIQIUKFTSOlZfx48dj06ZN+Pfff1G9enXJyv3ll19w/fp1vHnzBo8fP8aRI0dw6NAhlCtXDlFRUbC2tpYsFhG9xTxpupgntYN5UlpseBohQRAAAObm5nquSdG3ZcsWWFtbo2XLlrmuNzMzQ1BQELZs2YLnz58rXW0zFLo8HrQVS9ff07x587Bz5078/PPPWL16NV6/fg0vLy+EhYVhwoQJkj1r1KVLF1SpUgUzZ85UJFEXFxc0a9YM48aNg5+fnyRx8lKxYkX06NEDGzZskLTcSZMmARCf+3F2dsZ7772HhQsX4rPPPjPaARPIsDBPFh7zpGHEYZ7UDuZJacmErN8CMhqLFy/GV199hejoaK1eBSLDoMvjgcceERkCnqsoO10dDzzuyNRxOhUjdP78edjZ2aFcuXL6rgoVAbo8HnjsEZEh4LmKstPV8cDjjkwdu9oakd27d+PYsWPYsmUL+vXrp/WHoKlo0+XxwGOPiAwBz1WUna6OBx53RCJ2tTUiPj4+SEpKQseOHbFw4ULY2dnpu0qkR7o8HnjsEZEh4LmKstPV8cDjjkjEhicRERERERFpFZ/xJCIiIiIiIq1iw5OIiIiIiIi0ig1PIiIiIiIi0io2PImIiIiIiEir2PAkIiIiIiIirWLDk4iIiIiIiLSKDU8iIiIiIiLSKjY8iYiIiIiISKv+D/c1hpO9G9H8AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1000x1400 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10, 14), constrained_layout=False)\n",
    "ax = [None for _ in range(3)]\n",
    "ax[0] = fig.add_subplot(4, 4, (2, 3))\n",
    "for n in range(2, 4):\n",
    "    ax[n-1] = fig.add_subplot(4, 4, (2*n + 1, 2*n + 2))\n",
    "\n",
    "low = [100] + [80] * 2\n",
    "high = [180] * 3\n",
    "step = [20] * 3\n",
    "coeff = [1] * 3\n",
    "caption = ['Vostok station, 2006–2020',\n",
    "           'Vostok station, 2006–2012',\n",
    "           'Vostok station, 2013–2020']\n",
    "col = ['orangered'] * 3\n",
    "\n",
    "for n in range(3):\n",
    "    for axis in ['top', 'bottom', 'left', 'right']:\n",
    "        ax[n].spines[axis].set_linewidth(0.5)\n",
    "    ax[n].tick_params(length=6, width=0.5, axis='y')\n",
    "    ax[n].tick_params(length=0, width=0.5, axis='x')\n",
    "    ax[n].grid(color='0.', linewidth=0.5, axis='y')\n",
    "\n",
    "    ax[n].set_xlim((-0.5, 11.5))\n",
    "    ax[n].set_xticks(np.arange(12))\n",
    "    ax[n].set_xticklabels(month_name, fontsize='large', va='top')\n",
    "\n",
    "    ax[n].set_ylim((low[n], high[n]))\n",
    "    ax[n].set_yticks(np.arange(low[n], high[n] + step[n] / 2, step[n]))\n",
    "    ax[n].set_yticklabels((np.arange(low[n], high[n] + step[n] / 2,\n",
    "                                     step[n]) / coeff[n]).astype(int),\n",
    "                          fontsize='large')\n",
    "    ax[n].set_ylabel('Monthly mean fair-weather\\npotential gradient, V/m',\n",
    "                     fontsize='large')\n",
    "\n",
    "    ax[n].set_title(caption[n], fontsize='large')\n",
    "\n",
    "    ax[n].annotate('', xy=(12, np.min(data[n])), xycoords='data',\n",
    "                   xytext=(12, np.max(data[n])), textcoords='data',\n",
    "                   annotation_clip=False,\n",
    "                   arrowprops=dict(\n",
    "                       arrowstyle='<|-|>,head_length=0.8,head_width=0.3',\n",
    "                       patchA=None, patchB=None, shrinkA=0., shrinkB=0.,\n",
    "                       connectionstyle='arc3,rad=0.', fc='black',\n",
    "                       linewidth=0.5\n",
    "                   ))\n",
    "\n",
    "    ampl = (np.max(data[n]) - np.min(data[n])) / \\\n",
    "        np.sum(data[n] * data_counter[n]) * np.sum(data_counter[n])\n",
    "    ax[n].text(12.2, (np.min(data[n]) + np.max(data[n])) / 2,\n",
    "               f'{ampl * 100:.0f}%',\n",
    "               fontsize='large', ha='left', va='center', rotation=270)\n",
    "\n",
    "for n in range(3):\n",
    "    ax[n].bar(np.arange(12), data[n],\n",
    "              yerr=std_error(data[n],\n",
    "                             data_sqr[n],\n",
    "                             data_counter[n]),\n",
    "              width=0.8, color=col[n])\n",
    "\n",
    "for n in range(3):\n",
    "    ax[n].text(-0.3, 1.05, chr(ord('a') + n),\n",
    "               fontsize='x-large',\n",
    "               fontweight='semibold', ha='left', va='bottom',\n",
    "               transform=ax[n].transAxes)\n",
    "\n",
    "fig.subplots_adjust(hspace=0.3, wspace=1.6)\n",
    "\n",
    "for n in range(3):\n",
    "    for m in range(12):\n",
    "        ax[n].annotate(f'{data_counter[n, m]}',\n",
    "                       xy=(m-0.15, ax[n].get_ylim()[0] + 3),\n",
    "                       rotation=270, ha='center', va='bottom',\n",
    "                       fontsize='large', color='0.')\n",
    "\n",
    "fig.savefig('figures_two_parts/pg_total_partial.eps', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1b65fb2a-8f3d-4d23-a8a2-c4c7f031c910",
   "metadata": {},
   "source": [
    "### Figure 1.3: Diurnal-Seasonal Diagram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "19eddef4-207d-4fcd-af6b-fc3fcd02e7cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a seasonal-diurnal array by grouping the data by hour of the day and month, \n",
    "# then calculating the mean FW PG for each combination.\n",
    "\n",
    "sd_df = df.groupby([df.index.hour, df.index.month]).mean()\n",
    "sd_df.index.set_names(['hour', 'month'], inplace=True)\n",
    "\n",
    "vostok_pg_data_to_plot = sd_df.values.reshape(24,12).T\n",
    "vostok_mean_pg_data = vostok_pg_data_to_plot.mean(axis=1).reshape(12,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "5b03d7c6-e23e-45a9-8188-024d21851176",
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculate seasonal var params (avg FW PG, count FW days, \n",
    "# sum of squares of FW PG) for specific years\n",
    "data, data_counter, data_sqr = np.array(\n",
    "    [\n",
    "        calculate_seasonal_var_params(daily_df),\n",
    "        calculate_seasonal_var_params(df[df.index.hour == 3]),\n",
    "        calculate_seasonal_var_params(df[df.index.hour == 9]),\n",
    "        calculate_seasonal_var_params(df[df.index.hour == 15]),\n",
    "        calculate_seasonal_var_params(df[df.index.hour == 21]),\n",
    "    ]\n",
    ").swapaxes(0,1)\n",
    "\n",
    "data_counter = data_counter.astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "762c01a9-2ee5-428c-b331-ef44d1801127",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1100 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig_width = 10\n",
    "fig_height = 11\n",
    "\n",
    "height = 12\n",
    "heightC = 1\n",
    "heightB = 12\n",
    "hsep1 = 3.5\n",
    "hsep2 = 5\n",
    "hsep3 = 3.5\n",
    "hsp = 3\n",
    "tot_height = height + heightC + 2 * heightB + hsep1 + hsep2 + hsep3 + 2 * hsp\n",
    "\n",
    "width = 12\n",
    "widthM = heightC/tot_height * fig_height/fig_width\n",
    "widthB = 12\n",
    "wsep1 = 1\n",
    "wsep2 = 3.5\n",
    "wsp = 4\n",
    "wshift = 1.5\n",
    "tot_width = (width + widthB + wsep1 + wsep2 + 2 * wsp) / (1 - widthM)\n",
    "widthM *= tot_width\n",
    "\n",
    "fig = plt.figure(figsize=(fig_width, fig_height), constrained_layout=False)\n",
    "\n",
    "ax = fig.add_axes(\n",
    "    (\n",
    "        (wsp - wshift) / tot_width,\n",
    "        (hsp + 2 * heightB + hsep2 + hsep3 + heightC + hsep1) / tot_height,\n",
    "        width / tot_width,\n",
    "        height / tot_height\n",
    "    )\n",
    ")\n",
    "axM = fig.add_axes(\n",
    "    (\n",
    "        (wsp - wshift + width + wsep1) / tot_width,\n",
    "        (hsp + 2 * heightB + hsep2 + hsep3 + heightC + hsep1) / tot_height,\n",
    "        widthM / tot_width,\n",
    "        height / tot_height\n",
    "    )\n",
    ")\n",
    "\n",
    "axB = [None for _ in range(5)]\n",
    "\n",
    "axB[0] = fig.add_axes(\n",
    "    (\n",
    "        (wsp + width + wsep1 + widthM + wsep2) / tot_width,\n",
    "        (hsp + heightB + hsep2 + hsep3 + heightC + hsep1 + height) / tot_height,\n",
    "        widthB / tot_width,\n",
    "        heightB / tot_height\n",
    "    )\n",
    ")\n",
    "for n in range(2):\n",
    "    axB[3 - 2*n] = fig.add_axes(\n",
    "        (\n",
    "            wsp / tot_width,\n",
    "            (hsp + n * (heightB + hsep3)) / tot_height,\n",
    "            widthB / tot_width,\n",
    "            heightB / tot_height\n",
    "        )\n",
    "    )\n",
    "    axB[4 - 2*n] = fig.add_axes(\n",
    "        (\n",
    "            (wsp + width + wsep1 + widthM + wsep2) / tot_width,\n",
    "            (hsp + n * (heightB + hsep3)) / tot_height,\n",
    "            widthB / tot_width,\n",
    "            heightB / tot_height\n",
    "        )\n",
    "    )\n",
    "\n",
    "caption = 'Pot. gradient, Vostok station, 2006–2020'\n",
    "\n",
    "for axis in ['top', 'bottom', 'left', 'right']:\n",
    "    ax.spines[axis].set_linewidth(0.5)\n",
    "ax.tick_params(length=6, width=0.5)\n",
    "ax.tick_params(length=0, width=0.5, which='minor')\n",
    "ax.grid(False)\n",
    "\n",
    "ax.set_xlim((0, 24))\n",
    "ax.set_xticks(np.arange(0, 25, 2))\n",
    "ax.set_xticklabels(np.arange(0, 25, 2), fontsize='large')\n",
    "ax.set_xlabel('UTC, hours', fontsize='large')\n",
    "\n",
    "ax.set_ylim((0, 12))\n",
    "ax.set_yticks(np.arange(0, 12.5, 1))\n",
    "ax.set_yticks(np.arange(0.5, 12, 1), minor=True)\n",
    "ax.set_yticklabels([])\n",
    "ax.set_yticklabels(month_name, fontsize='large', ha='center',\n",
    "                   minor=True)\n",
    "tr = []\n",
    "for label in ax.yaxis.get_majorticklabels():\n",
    "    tr.append(label.get_transform())\n",
    "for label in ax.yaxis.get_minorticklabels():\n",
    "    label.set_transform(\n",
    "        tr[0] + tf.ScaledTranslation(\n",
    "            -0.05, 0, fig.dpi_scale_trans\n",
    "        )\n",
    "    )\n",
    "\n",
    "for axis in ['top', 'bottom', 'left', 'right']:\n",
    "    axM.spines[axis].set_linewidth(0.5)\n",
    "axM.tick_params(length=0, width=0.5)\n",
    "axM.tick_params(length=6, width=0.5, which='minor')\n",
    "axM.grid(False)\n",
    "\n",
    "axM.set_xlim((0, 1))\n",
    "axM.set_xticks([])\n",
    "axM.set_xticks([0.5], minor=True)\n",
    "axM.set_xticklabels([])\n",
    "axM.set_xticklabels(['mean'], fontsize='large', minor=True)\n",
    "\n",
    "axM.set_ylim((0, 12))\n",
    "axM.set_yticks([])\n",
    "axM.set_yticklabels([])\n",
    "\n",
    "lbl = ax.set_title(caption, fontsize='large')\n",
    "pos = (width + wsep1 + widthM) / 2 / width\n",
    "lbl.set_position((pos, 1.))\n",
    "\n",
    "# the bounds of the colour bars (in V/m)\n",
    "pg_bound = np.arange(100, 191, 10)\n",
    "\n",
    "# the colours\n",
    "colour_hour = np.array([[71, 71, 255], [1, 14, 236],\n",
    "                        [4, 79, 149], [6, 143, 63], [49, 192, 13],\n",
    "                        [142, 220, 7], [234, 249, 1], [255, 184, 0],\n",
    "                        [255, 92, 0], [255, 0, 0]]) / 255\n",
    "\n",
    "pg_cmap = colors.ListedColormap(colour_hour[1:])\n",
    "pg_norm = colors.BoundaryNorm(pg_bound, pg_cmap.N)\n",
    "\n",
    "pg_cax = fig.add_axes(\n",
    "    (\n",
    "        (wsp - wshift) / tot_width,\n",
    "        (hsp + 2 * heightB + hsep2 + hsep3) / tot_height,\n",
    "        (width + wsep1 + widthM)  / tot_width,\n",
    "        heightC / tot_height\n",
    "    )\n",
    ")\n",
    "pg_cbar = fig.colorbar(cm.ScalarMappable(norm=pg_norm,\n",
    "                                         cmap=pg_cmap),\n",
    "                       cax=pg_cax, orientation='horizontal')\n",
    "pg_cbar.outline.set_linewidth(0.5)\n",
    "pg_cbar.ax.tick_params(length=6, width=0.5)\n",
    "pg_cbar.set_ticks(pg_bound)\n",
    "pg_cbar.ax.set_xticklabels(list(map(str,\n",
    "                                    pg_bound.astype(int))),\n",
    "                           fontsize='large')\n",
    "pg_cbar.set_label('Potential gradient, V/m',\n",
    "                  fontsize='large')\n",
    "\n",
    "ax.imshow(vostok_pg_data_to_plot,\n",
    "          cmap=pg_cmap, norm=pg_norm,\n",
    "          extent=[0, 24, 12, 0], aspect='auto', interpolation='none',\n",
    "          rasterized=True)\n",
    "\n",
    "axM.imshow(vostok_mean_pg_data,\n",
    "           cmap=pg_cmap, norm=pg_norm,\n",
    "           extent=[0, 1, 12, 0], aspect='auto', interpolation='none',\n",
    "           rasterized=True)\n",
    "\n",
    "low = [100] + [80] * 2 + [100] * 2\n",
    "high = [180] * 3 + [200] * 2\n",
    "step = [20] * 5\n",
    "coeff = [1] * 5\n",
    "captionB = ['Vostok station, 2006–2020, diurnal mean',\n",
    "            'Vostok station, 2006–2020, 3–4 UTC',\n",
    "            'Vostok station, 2006–2020, 9–10 UTC',\n",
    "            'Vostok station, 2006–2020, 15–16 UTC',\n",
    "            'Vostok station, 2006–2020, 21–22 UTC']\n",
    "\n",
    "col = ['orangered'] * 5\n",
    "\n",
    "for n in range(5):\n",
    "    for axis in ['top', 'bottom', 'left', 'right']:\n",
    "        axB[n].spines[axis].set_linewidth(0.5)\n",
    "    axB[n].tick_params(length=6, width=0.5, axis='y')\n",
    "    axB[n].tick_params(length=0, width=0.5, axis='x')\n",
    "    axB[n].grid(color='0.', linewidth=0.5, axis='y')\n",
    "\n",
    "    axB[n].set_xlim((-0.5, 11.5))\n",
    "    axB[n].set_xticks(np.arange(12))\n",
    "    axB[n].set_xticklabels(month_name, fontsize='large', va='top')\n",
    "\n",
    "    axB[n].set_ylim((low[n], high[n]))\n",
    "    axB[n].set_yticks(np.arange(low[n], high[n] + step[n] / 2, step[n]))\n",
    "    axB[n].set_yticklabels((np.arange(low[n], high[n] + step[n] / 2,\n",
    "                                      step[n]) / coeff[n]).astype(int),\n",
    "                           fontsize='large')\n",
    "    axB[n].set_ylabel('Monthly mean fair-weather\\npotential gradient, V/m',\n",
    "                      fontsize='large')\n",
    "\n",
    "    axB[n].set_title(captionB[n], fontsize='large')\n",
    "\n",
    "    axB[n].annotate('', xy=(12, np.min(data[n])), xycoords='data',\n",
    "                    xytext=(12, np.max(data[n])), textcoords='data',\n",
    "                    annotation_clip=False,\n",
    "                    arrowprops=dict(\n",
    "                        arrowstyle='<|-|>,head_length=0.8,head_width=0.3',\n",
    "                        patchA=None, patchB=None, shrinkA=0., shrinkB=0.,\n",
    "                        connectionstyle='arc3,rad=0.', fc='black',\n",
    "                        linewidth=0.5\n",
    "                    ))\n",
    "\n",
    "    ampl = (np.max(data[n]) - np.min(data[n])) / \\\n",
    "        np.sum(data[n] * data_counter[n]) * np.sum(data_counter[n])\n",
    "    axB[n].text(12.2, (np.min(data[n]) + np.max(data[n])) / 2,\n",
    "                f'{ampl * 100:.0f}%',\n",
    "                fontsize='large', ha='left', va='center', rotation=270)\n",
    "\n",
    "for n in range(5):\n",
    "    axB[n].bar(np.arange(12), data[n],\n",
    "               yerr=std_error(data[n],\n",
    "                              data_sqr[n],\n",
    "                              data_counter[n]),\n",
    "               width=0.8, color=col[n])\n",
    "\n",
    "ax.text(-(0.3 * widthB - wshift)/width, 1 + 0.05 * heightB/height, 'a',\n",
    "        fontsize='x-large',\n",
    "        fontweight='semibold', ha='left', va='bottom',\n",
    "        transform=ax.transAxes)\n",
    "for n in range(5):\n",
    "    axB[n].text(-0.3, 1.05, chr(ord('b') + n),\n",
    "                fontsize='x-large',\n",
    "                fontweight='semibold', ha='left', va='bottom',\n",
    "                transform=axB[n].transAxes)\n",
    "\n",
    "fig.savefig('figures_two_parts/pg_diurnal_seasonal.eps', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b9bf03b-5a1b-4ccf-aaf7-c47337cd05a1",
   "metadata": {},
   "source": [
    "### Data adjustment using meteoparameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "d16e0930-f81f-4288-a7a9-2f3942194f81",
   "metadata": {},
   "outputs": [],
   "source": [
    "# load and filter meteorological data from CSV files into separate dataframes, \n",
    "# then join them with the daily FW PG dataframe to create a combined dataframe\n",
    "# without missing values\n",
    "\n",
    "temp_df = pd.read_csv('./data/Vostok/vostok_daily_temp.csv', parse_dates=['UTC']).set_index('UTC')\n",
    "temp_df = temp_df[temp_df[\"COUNT\"] == 4][['T']]\n",
    "\n",
    "wind_df = pd.read_csv('./data/Vostok/vostok_daily_wind.csv', parse_dates=['UTC']).set_index('UTC')\n",
    "wind_df = wind_df[wind_df[\"COUNT\"] == 4][['Ff']]\n",
    "\n",
    "pressure_df = pd.read_csv('./data/Vostok/vostok_daily_pressure_mm_hg.csv', parse_dates=['UTC']).set_index('UTC')\n",
    "pressure_df = pressure_df[pressure_df[\"COUNT\"] == 4][['Po']]\n",
    "\n",
    "meteo_df = temp_df.join([wind_df, pressure_df, daily_df]).dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "c45bdf6b-d725-4d87-b033-d90fa4f3f9a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculate anomalies for potential gradient (pg_anom), temperature\n",
    "# (temp_anom), wind speed (wind_anom), and pressure (pres_anom)\n",
    "# based on a moving average window of +/-10 days.\n",
    "\n",
    "pg_anom = np.zeros((len(meteo_df.index)))\n",
    "temp_anom = np.zeros((len(meteo_df.index)))\n",
    "wind_anom = np.zeros((len(meteo_df.index)))\n",
    "pres_anom = np.zeros((len(meteo_df.index)))\n",
    "\n",
    "halfwidth = 10\n",
    "for i in range(len(meteo_df.index)):\n",
    "    sum_pg = 0\n",
    "    sum_temp = 0\n",
    "    sum_pres = 0\n",
    "    sum_wind = 0\n",
    "    counter = 0\n",
    "    for j in range(max(i - halfwidth, 0),\n",
    "                   min(i + halfwidth + 1, len(meteo_df.index))):\n",
    "        if abs((meteo_df.index[j]-meteo_df.index[i]).days) <= halfwidth:\n",
    "            sum_pg += meteo_df[\"Field\"].iloc[j]\n",
    "            sum_temp += meteo_df[\"T\"].iloc[j]\n",
    "            sum_pres += meteo_df[\"Po\"].iloc[j]\n",
    "            sum_wind += meteo_df[\"Ff\"].iloc[j]\n",
    "            counter += 1\n",
    "    pg_anom[i] =  meteo_df[\"Field\"].iloc[i] - (sum_pg / counter)\n",
    "    temp_anom[i] =  meteo_df[\"T\"].iloc[i] - (sum_temp / counter)\n",
    "    wind_anom[i] =  meteo_df[\"Ff\"].iloc[i] - (sum_wind / counter)\n",
    "    pres_anom[i] =  meteo_df[\"Po\"].iloc[i] - (sum_pres / counter)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "326311ea-74ce-4de1-ac7a-e30e173fc8d8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Temp: a=-0.38283510510880514\tb=-0.003032362343859364\n",
      "Temp: Correlation coefficient: −0.08\n",
      "Temp: Significance level: 0.01.\n",
      "Temp: Number of points: 2252.\n",
      "Temp: Critical value (((P − 2)r^2/(1 − r^2))^(1/2)): 2.578016182019777.\n",
      "Temp: Threshold correlation coefficient: 0.0542692606142489.\n",
      "\n",
      "\n",
      "Wind: a=2.9091927832965867\tb=-0.005838450636248837\n",
      "Wind: Correlation coefficient: 0.13\n",
      "Wind: Significance level: 0.01.\n",
      "Wind: Number of points: 2252.\n",
      "Wind: Critical value (((P − 2)r^2/(1 − r^2))^(1/2)): 2.578016182019777.\n",
      "Wind: Threshold correlation coefficient: 0.0542692606142489.\n",
      "\n",
      "\n",
      "Pres: a=0.029923256849570173\tb=-0.008695565945834233\n",
      "Pres: Correlation coefficient: 0.01\n",
      "Pres: Significance level: 0.01.\n",
      "Pres: Number of points: 2252.\n",
      "Pres: Critical value (((P − 2)r^2/(1 − r^2))^(1/2)): 2.578016182019777.\n",
      "Pres: Threshold correlation coefficient: 0.0542692606142489.\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# calculate linear regression coefficients for temperature, wind speed \n",
    "# and pressure anomalies against potential gradient anomalies.\n",
    "\n",
    "temp_coeffs = np.polyfit(temp_anom, pg_anom, deg=1)\n",
    "wind_coeffs = np.polyfit(wind_anom, pg_anom, deg=1)\n",
    "pres_coeffs = np.polyfit(pres_anom, pg_anom, deg=1)\n",
    "\n",
    "\n",
    "for title, anom, coeffs in zip(\n",
    "    [\"Temp\", \"Wind\", \"Pres\"],\n",
    "    [temp_anom, wind_anom, pres_anom],\n",
    "    [temp_coeffs, wind_coeffs, pres_coeffs],\n",
    "):\n",
    "    P = len(meteo_df.index)  # the number of points\n",
    "    a = 0.01  # significance level\n",
    "\n",
    "    q = st.t.ppf(1 - a / 2, P - 2) # critical value\n",
    "    r = q / np.sqrt(q**2 + P - 2) # threshold correlation coefficient\n",
    "\n",
    "    corr = np.corrcoef(anom, pg_anom)[0, 1]\n",
    "    print(f\"{title}: a={coeffs[0]}\\tb={coeffs[1]}\")\n",
    "    print(\n",
    "        f\"{title}: Correlation coefficient: −{-corr:.2f}\"\n",
    "        if corr < 0\n",
    "        else f\"{title}: Correlation coefficient: {corr:.2f}\"\n",
    "    )\n",
    "    print(f\"{title}: Significance level: {a}.\")\n",
    "    print(f\"{title}: Number of points: {P}.\")\n",
    "    print(f\"{title}: Critical value (((P − 2)r^2/(1 − r^2))^(1/2)): {q}.\")\n",
    "    print(f\"{title}: Threshold correlation coefficient: {r}.\")\n",
    "    print(\"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "dafd0f56-b6fa-4883-9640-01fdcb4a3870",
   "metadata": {},
   "outputs": [],
   "source": [
    "# update the \"CorrectedField\" column (copy of \"Field\" column) in the `meteo_df`\n",
    "# by subtracting the linear regression adjustments \n",
    "# based on temperature and wind anomalies.\n",
    "\n",
    "meteo_df[\"CorrectedField\"] = meteo_df[\"Field\"]\n",
    "meteo_df[\"CorrectedField\"] -= temp_coeffs[0] * (meteo_df[\"T\"] - meteo_df[\"T\"].mean())\n",
    "meteo_df[\"CorrectedField\"] -= wind_coeffs[0] * (meteo_df[\"Ff\"] - meteo_df[\"Ff\"].mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0590fff-4817-440f-9550-d4438b742769",
   "metadata": {},
   "source": [
    "### Figure 1.5: source vs adjustment PG for new and earlier Vostok datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "9a209c25-af4a-4c67-80cc-2ce657f9cb3d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculate seasonal variation parameters for specific years: \n",
    "# average FW PG, count of FW days, and the sum of squares of FW PG hourly values\n",
    "\n",
    "data, data_counter, data_sqr = np.array(\n",
    "    [\n",
    "        calculate_seasonal_var_params(daily_edf[daily_edf.index.year <= 2001]),\n",
    "        [vostok_old_data, np.zeros(12).astype(int), np.zeros(12)],\n",
    "        calculate_seasonal_var_params(daily_df),\n",
    "        calculate_seasonal_var_params(meteo_df[[\"CorrectedField\"]], key=\"CorrectedField\")\n",
    "    ]\n",
    ").swapaxes(0,1)\n",
    "\n",
    "data_counter = data_counter.astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "84050d4a-259f-4513-8d91-0ffc9840f542",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1400 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(10, 14), constrained_layout=False)\n",
    "ax = [None for _ in range(4)]\n",
    "\n",
    "for n in range(4):\n",
    "    ax[n] = fig.add_subplot(4, 4, (2*n + 1, 2*n + 2))\n",
    "\n",
    "low = [140] * 2 + [100] * 2\n",
    "high = [220] * 2 + [180] * 2\n",
    "step = [20] * 4\n",
    "coeff = [1] * 4\n",
    "\n",
    "caption = ['Vostok station, 1998–2001',\n",
    "           'Vostok station, 1998–2001 (adjusted)',\n",
    "           'Vostok station, 2006–2020',\n",
    "           'Vostok station, 2006–2020 (adjusted)']\n",
    "col = ['orangered'] * 4\n",
    "\n",
    "for n in range(4):\n",
    "    for axis in ['top', 'bottom', 'left', 'right']:\n",
    "        ax[n].spines[axis].set_linewidth(0.5)\n",
    "    ax[n].tick_params(length=6, width=0.5, axis='y')\n",
    "    ax[n].tick_params(length=0, width=0.5, axis='x')\n",
    "    ax[n].grid(color='0.', linewidth=0.5, axis='y')\n",
    "\n",
    "    ax[n].set_xlim((-0.5, 11.5))\n",
    "    ax[n].set_xticks(np.arange(12))\n",
    "    ax[n].set_xticklabels(month_name, fontsize='large', va='top')\n",
    "\n",
    "    ax[n].set_ylim((low[n], high[n]))\n",
    "    ax[n].set_yticks(np.arange(low[n], high[n] + step[n] / 2, step[n]))\n",
    "    ax[n].set_yticklabels((np.arange(low[n], high[n] + step[n] / 2,\n",
    "                                     step[n]) / coeff[n]).astype(int),\n",
    "                          fontsize='large')\n",
    "    ax[n].set_ylabel('Monthly mean fair-weather\\npotential gradient, V/m',\n",
    "                     fontsize='large')\n",
    "\n",
    "    ax[n].set_title(caption[n], fontsize='large')\n",
    "    if np.sum(data_counter[n]) == 0:\n",
    "        ax[n].text(0.5,\n",
    "                   1 - 0.01\n",
    "                   * (ax[n].get_position().x1 - ax[n].get_position().x0)\n",
    "                   / (ax[n].get_position().y1 - ax[n].get_position().y0)\n",
    "                   * fig.get_size_inches()[0] / fig.get_size_inches()[1],\n",
    "                   '(after $\\it{Burns~et~al.}$, 2012)',\n",
    "                   fontsize='large', ha='center', va='top',\n",
    "                   transform=ax[n].transAxes)\n",
    "\n",
    "    ax[n].annotate('', xy=(12, np.min(data[n])), xycoords='data',\n",
    "                   xytext=(12, np.max(data[n])), textcoords='data',\n",
    "                   annotation_clip=False,\n",
    "                   arrowprops=dict(\n",
    "                       arrowstyle='<|-|>,head_length=0.8,head_width=0.3',\n",
    "                       patchA=None, patchB=None, shrinkA=0., shrinkB=0.,\n",
    "                       connectionstyle='arc3,rad=0.', fc='black',\n",
    "                       linewidth=0.5\n",
    "                   ))\n",
    "    if np.sum(data_counter[n]) == 0:\n",
    "        ampl = (np.max(data[n]) - np.min(data[n])) / np.mean(data[n])\n",
    "    else:\n",
    "        ampl = (np.max(data[n]) - np.min(data[n])) / \\\n",
    "            np.sum(data[n] * data_counter[n]) * np.sum(data_counter[n])\n",
    "    ax[n].text(12.2, (np.min(data[n]) + np.max(data[n])) / 2,\n",
    "               f'{ampl * 100:.0f}%',\n",
    "               fontsize='large', ha='left', va='center', rotation=270)\n",
    "\n",
    "fig.align_ylabels([ax[0], ax[2]])\n",
    "fig.align_ylabels([ax[1], ax[3]])\n",
    "\n",
    "for n in range(4):\n",
    "    if np.sum(data_counter[n]) == 0:\n",
    "        ax[n].bar(np.arange(12), data[n],\n",
    "          width=0.8, color=col[n])\n",
    "    else:\n",
    "        ax[n].bar(np.arange(12), data[n],\n",
    "                  yerr=std_error(data[n],\n",
    "                                 data_sqr[n],\n",
    "                                 data_counter[n]),\n",
    "                  width=0.8, color=col[n])\n",
    "\n",
    "for n in range(4):\n",
    "    ax[n].text(-0.3, 1.05, chr(ord('a') + n), fontsize='x-large',\n",
    "               fontweight='semibold', ha='left', va='bottom',\n",
    "               transform=ax[n].transAxes)\n",
    "\n",
    "fig.subplots_adjust(hspace=0.3, wspace=1.6)\n",
    "\n",
    "for n in range(4):\n",
    "    if np.sum(data_counter[n]) > 0:\n",
    "        for m in range(12):\n",
    "            ax[n].annotate(f'{data_counter[n, m]}',\n",
    "                           xy=(m-0.15, ax[n].get_ylim()[0] + 3),\n",
    "                           rotation=270, ha='center', va='bottom',\n",
    "                           fontsize='large', color='0.')\n",
    "\n",
    "fig.savefig('figures_two_parts/pg_corrected.eps', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "83a15933-44ff-4abc-baf6-7a99a069f8ca",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.11.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}