diff --git a/examples/multiscale-migration/Multiscale Migration Model (Draft+Old).ipynb b/examples/multiscale-migration/Multiscale Migration Model (Draft+Old).ipynb
deleted file mode 100644
index 0d85d3d..0000000
--- a/examples/multiscale-migration/Multiscale Migration Model (Draft+Old).ipynb	
+++ /dev/null
@@ -1,9792 +0,0 @@
-{
- "cells": [
-  {
-   "attachments": {
-    "image.png": {
-     "image/png": 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"
-    }
-   },
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Multiscale Migration Model\n",
-    "\n",
-    "This notebook implements our model using `numpy`, `haversine`, and `pandas` (with `xlrd`). It has been tested to run on Python 3.6. To start, import the required libraries.\n",
-    "\n",
-    "![image.png](attachment:image.png)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### TODO\n",
-    "\n",
-    "+ Verify that the code matches the model as described in the paper.\n",
-    "+ Add visulazations for what countries are represented and perhaps for that matrices between countries."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "from math import e\n",
-    "from haversine import haversine"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "There are a number of datasets used in this model. They can all be found in the `/data` subdirectory."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "'A&B values for RTS.xlsx'\r\n",
-      " CIA_Unemployment.csv\r\n",
-      " Country_List_ISO_3166_Codes_Latitude_Longitude.csv\r\n",
-      " Freedom_index.xlsx\r\n",
-      " languages.csv\r\n",
-      " newPOP.csv\r\n",
-      " other.csv\r\n",
-      " PassportIndex.xlsx\r\n",
-      " UN_MigrantStockByOriginAndDestination_2015.xlsx\r\n",
-      " wb-codes.csv\r\n",
-      " wb-original.csv\r\n"
-     ]
-    }
-   ],
-   "source": [
-    "%ls data"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The following shortcut functions helps locate these data files easily."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def file_path(name):\n",
-    "    \"\"\"\n",
-    "    Shortcut function to get the relative path to the directory\n",
-    "    which contains the data.\n",
-    "    \"\"\"\n",
-    "    return \"./data/%s\" % name"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "These functions will be useful later when pulling in the data.\n",
-    "\n",
-    "- - -\n",
-    "\n",
-    "TODO: These could be better integrated in with the data processing and cleaning which occurs later."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def country_codes():\n",
-    "    \"\"\"\n",
-    "    Build country rows from their names, ISO codes, and Numeric\n",
-    "    Country Codes.\n",
-    "    \"\"\"\n",
-    "    return (\n",
-    "        pd.read_csv(\n",
-    "            file_path(\n",
-    "                \"Country_List_ISO_3166_Codes_Latitude_Longitude.csv\"),\n",
-    "            usecols=[0, 2, 3],\n",
-    "            index_col=1,\n",
-    "            keep_default_na=False))\n",
-    "\n",
-    "def freedom_index():\n",
-    "    \"\"\"\n",
-    "    Read data from the Freedom Index.\n",
-    "    \"\"\"\n",
-    "    # TODO: Add xlrd to requirements.\n",
-    "    xl = pd.ExcelFile(file_path(\"Freedom_index.xlsx\"))\n",
-    "    return xl.parse(1)\n",
-    "\n",
-    "def ab_values():\n",
-    "    \"\"\"\n",
-    "    Read generated A/B values for each country.\n",
-    "    \"\"\"\n",
-    "    return pd.read_excel(file_path(\"A&B values for RTS.xlsx\")).T\n",
-    "\n",
-    "def passport_index():\n",
-    "    \"\"\"\n",
-    "    Read data from the Passport Index.\n",
-    "    \"\"\"\n",
-    "    return pd.read_excel(file_path(\"PassportIndex.xlsx\"))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Modify this depending on how many table rows you want to see."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pd.set_option(\"display.max_rows\", 10)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Now that everything is set up, it is time to start processing the data.\n",
-    "\n",
-    "### A/B Values\n",
-    "\n",
-    "These values are used in the return to skill function. These values are based on each country's income distribution."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style>\n",
-       "    .dataframe thead tr:only-child th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: left;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>A</th>\n",
-       "      <th>B</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>BDI</th>\n",
-       "      <td>72.993</td>\n",
-       "      <td>0.0228</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>NER</th>\n",
-       "      <td>113.170</td>\n",
-       "      <td>0.0200</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>MWI</th>\n",
-       "      <td>58.581</td>\n",
-       "      <td>0.0226</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>LBR</th>\n",
-       "      <td>144.660</td>\n",
-       "      <td>0.0197</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>MOZ</th>\n",
-       "      <td>74.334</td>\n",
-       "      <td>0.0267</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>IRL</th>\n",
-       "      <td>20473.000</td>\n",
-       "      <td>0.0191</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>NOR</th>\n",
-       "      <td>28548.000</td>\n",
-       "      <td>0.0161</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>USA</th>\n",
-       "      <td>12001.000</td>\n",
-       "      <td>0.0260</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>CHE</th>\n",
-       "      <td>25711.000</td>\n",
-       "      <td>0.0193</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>LUX</th>\n",
-       "      <td>34726.000</td>\n",
-       "      <td>0.0189</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>162 rows × 2 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "             A       B\n",
-       "BDI     72.993  0.0228\n",
-       "NER    113.170  0.0200\n",
-       "MWI     58.581  0.0226\n",
-       "LBR    144.660  0.0197\n",
-       "MOZ     74.334  0.0267\n",
-       "..         ...     ...\n",
-       "IRL  20473.000  0.0191\n",
-       "NOR  28548.000  0.0161\n",
-       "USA  12001.000  0.0260\n",
-       "CHE  25711.000  0.0193\n",
-       "LUX  34726.000  0.0189\n",
-       "\n",
-       "[162 rows x 2 columns]"
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "wb_codes = pd.read_csv(file_path(\"wb-codes.csv\"), index_col=0)\n",
-    "ab = ab_values()\n",
-    "ab.index = [wb_codes[wb_codes.index == x][\"ISO3\"][0] for x in ab_values().index]\n",
-    "ab"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Distance\n",
-    "\n",
-    "The great circle distance between the average latitude and longitude of each country is used to determine distance between each pair of countries. A greater distance between countries corresponds to a greater cost of migration."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-       "\n",
-       "    .dataframe tbody tr th {\n",
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-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th>Alpha-3 code</th>\n",
-       "      <th>ALB</th>\n",
-       "      <th>DZA</th>\n",
-       "      <th>ASM</th>\n",
-       "      <th>AND</th>\n",
-       "      <th>AGO</th>\n",
-       "      <th>AIA</th>\n",
-       "      <th>ATA</th>\n",
-       "      <th>ATG</th>\n",
-       "      <th>ARG</th>\n",
-       "      <th>ARM</th>\n",
-       "      <th>...</th>\n",
-       "      <th>VEN</th>\n",
-       "      <th>VNM</th>\n",
-       "      <th>VGB</th>\n",
-       "      <th>VIR</th>\n",
-       "      <th>WLF</th>\n",
-       "      <th>ESH</th>\n",
-       "      <th>YEM</th>\n",
-       "      <th>ZMB</th>\n",
-       "      <th>ZWE</th>\n",
-       "      <th>AFG</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Alpha-3 code</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>ALB</th>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>2117.896267</td>\n",
-       "      <td>16895.879224</td>\n",
-       "      <td>1532.455828</td>\n",
-       "      <td>5950.929461</td>\n",
-       "      <td>8128.142279</td>\n",
-       "      <td>14566.535390</td>\n",
-       "      <td>8097.892316</td>\n",
-       "      <td>11958.506218</td>\n",
-       "      <td>2109.535029</td>\n",
-       "      <td>...</td>\n",
-       "      <td>9090.528503</td>\n",
-       "      <td>8519.542337</td>\n",
-       "      <td>8220.928041</td>\n",
-       "      <td>8259.970510</td>\n",
-       "      <td>16556.474987</td>\n",
-       "      <td>3558.506896</td>\n",
-       "      <td>3954.566537</td>\n",
-       "      <td>6311.648038</td>\n",
-       "      <td>6861.109417</td>\n",
-       "      <td>4047.637966</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>DZA</th>\n",
-       "      <td>2117.896267</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>18332.073360</td>\n",
-       "      <td>1617.263660</td>\n",
-       "      <td>4802.819207</td>\n",
-       "      <td>6775.280214</td>\n",
-       "      <td>13121.001344</td>\n",
-       "      <td>6693.230496</td>\n",
-       "      <td>9857.882861</td>\n",
-       "      <td>4052.615309</td>\n",
-       "      <td>...</td>\n",
-       "      <td>7533.250217</td>\n",
-       "      <td>10399.747037</td>\n",
-       "      <td>6894.644824</td>\n",
-       "      <td>6934.229126</td>\n",
-       "      <td>18378.419822</td>\n",
-       "      <td>1641.009707</td>\n",
-       "      <td>4844.395453</td>\n",
-       "      <td>5596.172091</td>\n",
-       "      <td>6075.793700</td>\n",
-       "      <td>5881.179067</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>ASM</th>\n",
-       "      <td>16895.879224</td>\n",
-       "      <td>18332.073360</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>16781.183743</td>\n",
-       "      <td>16887.897027</td>\n",
-       "      <td>12244.790481</td>\n",
-       "      <td>8413.753156</td>\n",
-       "      <td>12366.752989</td>\n",
-       "      <td>10536.725193</td>\n",
-       "      <td>15577.799241</td>\n",
-       "      <td>...</td>\n",
-       "      <td>11726.608348</td>\n",
-       "      <td>9822.031165</td>\n",
-       "      <td>12110.305759</td>\n",
-       "      <td>12072.597110</td>\n",
-       "      <td>679.219745</td>\n",
-       "      <td>17356.877464</td>\n",
-       "      <td>15931.725429</td>\n",
-       "      <td>16081.181640</td>\n",
-       "      <td>15615.277386</td>\n",
-       "      <td>14114.458021</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>AND</th>\n",
-       "      <td>1532.455828</td>\n",
-       "      <td>1617.263660</td>\n",
-       "      <td>16781.183743</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>6354.416344</td>\n",
-       "      <td>6597.394699</td>\n",
-       "      <td>14733.327780</td>\n",
-       "      <td>6570.436382</td>\n",
-       "      <td>10807.825417</td>\n",
-       "      <td>3599.228339</td>\n",
-       "      <td>...</td>\n",
-       "      <td>7577.478775</td>\n",
-       "      <td>9944.040798</td>\n",
-       "      <td>6689.088973</td>\n",
-       "      <td>6728.076233</td>\n",
-       "      <td>16761.295202</td>\n",
-       "      <td>2408.442044</td>\n",
-       "      <td>5364.385714</td>\n",
-       "      <td>7022.427946</td>\n",
-       "      <td>7535.391582</td>\n",
-       "      <td>5542.850159</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>AGO</th>\n",
-       "      <td>5950.929461</td>\n",
-       "      <td>4802.819207</td>\n",
-       "      <td>16887.897027</td>\n",
-       "      <td>6354.416344</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>9582.936994</td>\n",
-       "      <td>8617.606815</td>\n",
-       "      <td>9409.035715</td>\n",
-       "      <td>8550.718016</td>\n",
-       "      <td>6447.314494</td>\n",
-       "      <td>...</td>\n",
-       "      <td>9608.837040</td>\n",
-       "      <td>10126.835217</td>\n",
-       "      <td>9726.141831</td>\n",
-       "      <td>9755.766750</td>\n",
-       "      <td>16720.166333</td>\n",
-       "      <td>5349.419852</td>\n",
-       "      <td>4460.587618</td>\n",
-       "      <td>1272.594062</td>\n",
-       "      <td>1483.081009</td>\n",
-       "      <td>7064.158778</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>ESH</th>\n",
-       "      <td>3558.506896</td>\n",
-       "      <td>1641.009707</td>\n",
-       "      <td>17356.877464</td>\n",
-       "      <td>2408.442044</td>\n",
-       "      <td>5349.419852</td>\n",
-       "      <td>5213.689833</td>\n",
-       "      <td>12731.819101</td>\n",
-       "      <td>5115.224857</td>\n",
-       "      <td>8444.648781</td>\n",
-       "      <td>5616.394871</td>\n",
-       "      <td>...</td>\n",
-       "      <td>5907.694096</td>\n",
-       "      <td>12014.052233</td>\n",
-       "      <td>5340.400101</td>\n",
-       "      <td>5379.416508</td>\n",
-       "      <td>17856.688769</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>6422.698103</td>\n",
-       "      <td>6407.319367</td>\n",
-       "      <td>6792.169129</td>\n",
-       "      <td>7492.935440</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>YEM</th>\n",
-       "      <td>3954.566537</td>\n",
-       "      <td>4844.395453</td>\n",
-       "      <td>15931.725429</td>\n",
-       "      <td>5364.385714</td>\n",
-       "      <td>4460.587618</td>\n",
-       "      <td>11618.569554</td>\n",
-       "      <td>11675.467298</td>\n",
-       "      <td>11531.502329</td>\n",
-       "      <td>12943.625487</td>\n",
-       "      <td>2795.121151</td>\n",
-       "      <td>...</td>\n",
-       "      <td>12306.271594</td>\n",
-       "      <td>6194.728367</td>\n",
-       "      <td>11738.861689</td>\n",
-       "      <td>11778.419760</td>\n",
-       "      <td>15253.325565</td>\n",
-       "      <td>6422.698103</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>3878.379114</td>\n",
-       "      <td>4361.029497</td>\n",
-       "      <td>2635.372271</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>ZMB</th>\n",
-       "      <td>6311.648038</td>\n",
-       "      <td>5596.172091</td>\n",
-       "      <td>16081.181640</td>\n",
-       "      <td>7022.427946</td>\n",
-       "      <td>1272.594062</td>\n",
-       "      <td>10849.226608</td>\n",
-       "      <td>8339.619498</td>\n",
-       "      <td>10677.056632</td>\n",
-       "      <td>9440.607431</td>\n",
-       "      <td>6309.776583</td>\n",
-       "      <td>...</td>\n",
-       "      <td>10876.723675</td>\n",
-       "      <td>9027.092252</td>\n",
-       "      <td>10992.557684</td>\n",
-       "      <td>11022.688882</td>\n",
-       "      <td>15747.303180</td>\n",
-       "      <td>6407.319367</td>\n",
-       "      <td>3878.379114</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>555.974633</td>\n",
-       "      <td>6503.944667</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>ZWE</th>\n",
-       "      <td>6861.109417</td>\n",
-       "      <td>6075.793700</td>\n",
-       "      <td>15615.277386</td>\n",
-       "      <td>7535.391582</td>\n",
-       "      <td>1483.081009</td>\n",
-       "      <td>11008.120440</td>\n",
-       "      <td>7783.644865</td>\n",
-       "      <td>10828.497040</td>\n",
-       "      <td>9132.527940</td>\n",
-       "      <td>6850.711047</td>\n",
-       "      <td>...</td>\n",
-       "      <td>10933.761198</td>\n",
-       "      <td>9213.881278</td>\n",
-       "      <td>11150.519045</td>\n",
-       "      <td>11178.342101</td>\n",
-       "      <td>15332.596720</td>\n",
-       "      <td>6792.169129</td>\n",
-       "      <td>4361.029497</td>\n",
-       "      <td>555.974633</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>6967.328629</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>AFG</th>\n",
-       "      <td>4047.637966</td>\n",
-       "      <td>5881.179067</td>\n",
-       "      <td>14114.458021</td>\n",
-       "      <td>5542.850159</td>\n",
-       "      <td>7064.158778</td>\n",
-       "      <td>12093.714965</td>\n",
-       "      <td>13676.975977</td>\n",
-       "      <td>12086.880717</td>\n",
-       "      <td>15335.132302</td>\n",
-       "      <td>1944.126776</td>\n",
-       "      <td>...</td>\n",
-       "      <td>13118.058131</td>\n",
-       "      <td>4521.511712</td>\n",
-       "      <td>12170.907627</td>\n",
-       "      <td>12208.505814</td>\n",
-       "      <td>13480.274787</td>\n",
-       "      <td>7492.935440</td>\n",
-       "      <td>2635.372271</td>\n",
-       "      <td>6503.944667</td>\n",
-       "      <td>6967.328629</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>243 rows × 243 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "Alpha-3 code           ALB           DZA           ASM           AND  \\\n",
-       "Alpha-3 code                                                           \n",
-       "ALB               0.000000   2117.896267  16895.879224   1532.455828   \n",
-       "DZA            2117.896267      0.000000  18332.073360   1617.263660   \n",
-       "ASM           16895.879224  18332.073360      0.000000  16781.183743   \n",
-       "AND            1532.455828   1617.263660  16781.183743      0.000000   \n",
-       "AGO            5950.929461   4802.819207  16887.897027   6354.416344   \n",
-       "...                    ...           ...           ...           ...   \n",
-       "ESH            3558.506896   1641.009707  17356.877464   2408.442044   \n",
-       "YEM            3954.566537   4844.395453  15931.725429   5364.385714   \n",
-       "ZMB            6311.648038   5596.172091  16081.181640   7022.427946   \n",
-       "ZWE            6861.109417   6075.793700  15615.277386   7535.391582   \n",
-       "AFG            4047.637966   5881.179067  14114.458021   5542.850159   \n",
-       "\n",
-       "Alpha-3 code           AGO           AIA           ATA           ATG  \\\n",
-       "Alpha-3 code                                                           \n",
-       "ALB            5950.929461   8128.142279  14566.535390   8097.892316   \n",
-       "DZA            4802.819207   6775.280214  13121.001344   6693.230496   \n",
-       "ASM           16887.897027  12244.790481   8413.753156  12366.752989   \n",
-       "AND            6354.416344   6597.394699  14733.327780   6570.436382   \n",
-       "AGO               0.000000   9582.936994   8617.606815   9409.035715   \n",
-       "...                    ...           ...           ...           ...   \n",
-       "ESH            5349.419852   5213.689833  12731.819101   5115.224857   \n",
-       "YEM            4460.587618  11618.569554  11675.467298  11531.502329   \n",
-       "ZMB            1272.594062  10849.226608   8339.619498  10677.056632   \n",
-       "ZWE            1483.081009  11008.120440   7783.644865  10828.497040   \n",
-       "AFG            7064.158778  12093.714965  13676.975977  12086.880717   \n",
-       "\n",
-       "Alpha-3 code           ARG           ARM      ...                VEN  \\\n",
-       "Alpha-3 code                                  ...                      \n",
-       "ALB           11958.506218   2109.535029      ...        9090.528503   \n",
-       "DZA            9857.882861   4052.615309      ...        7533.250217   \n",
-       "ASM           10536.725193  15577.799241      ...       11726.608348   \n",
-       "AND           10807.825417   3599.228339      ...        7577.478775   \n",
-       "AGO            8550.718016   6447.314494      ...        9608.837040   \n",
-       "...                    ...           ...      ...                ...   \n",
-       "ESH            8444.648781   5616.394871      ...        5907.694096   \n",
-       "YEM           12943.625487   2795.121151      ...       12306.271594   \n",
-       "ZMB            9440.607431   6309.776583      ...       10876.723675   \n",
-       "ZWE            9132.527940   6850.711047      ...       10933.761198   \n",
-       "AFG           15335.132302   1944.126776      ...       13118.058131   \n",
-       "\n",
-       "Alpha-3 code           VNM           VGB           VIR           WLF  \\\n",
-       "Alpha-3 code                                                           \n",
-       "ALB            8519.542337   8220.928041   8259.970510  16556.474987   \n",
-       "DZA           10399.747037   6894.644824   6934.229126  18378.419822   \n",
-       "ASM            9822.031165  12110.305759  12072.597110    679.219745   \n",
-       "AND            9944.040798   6689.088973   6728.076233  16761.295202   \n",
-       "AGO           10126.835217   9726.141831   9755.766750  16720.166333   \n",
-       "...                    ...           ...           ...           ...   \n",
-       "ESH           12014.052233   5340.400101   5379.416508  17856.688769   \n",
-       "YEM            6194.728367  11738.861689  11778.419760  15253.325565   \n",
-       "ZMB            9027.092252  10992.557684  11022.688882  15747.303180   \n",
-       "ZWE            9213.881278  11150.519045  11178.342101  15332.596720   \n",
-       "AFG            4521.511712  12170.907627  12208.505814  13480.274787   \n",
-       "\n",
-       "Alpha-3 code           ESH           YEM           ZMB           ZWE  \\\n",
-       "Alpha-3 code                                                           \n",
-       "ALB            3558.506896   3954.566537   6311.648038   6861.109417   \n",
-       "DZA            1641.009707   4844.395453   5596.172091   6075.793700   \n",
-       "ASM           17356.877464  15931.725429  16081.181640  15615.277386   \n",
-       "AND            2408.442044   5364.385714   7022.427946   7535.391582   \n",
-       "AGO            5349.419852   4460.587618   1272.594062   1483.081009   \n",
-       "...                    ...           ...           ...           ...   \n",
-       "ESH               0.000000   6422.698103   6407.319367   6792.169129   \n",
-       "YEM            6422.698103      0.000000   3878.379114   4361.029497   \n",
-       "ZMB            6407.319367   3878.379114      0.000000    555.974633   \n",
-       "ZWE            6792.169129   4361.029497    555.974633      0.000000   \n",
-       "AFG            7492.935440   2635.372271   6503.944667   6967.328629   \n",
-       "\n",
-       "Alpha-3 code           AFG  \n",
-       "Alpha-3 code                \n",
-       "ALB            4047.637966  \n",
-       "DZA            5881.179067  \n",
-       "ASM           14114.458021  \n",
-       "AND            5542.850159  \n",
-       "AGO            7064.158778  \n",
-       "...                    ...  \n",
-       "ESH            7492.935440  \n",
-       "YEM            2635.372271  \n",
-       "ZMB            6503.944667  \n",
-       "ZWE            6967.328629  \n",
-       "AFG               0.000000  \n",
-       "\n",
-       "[243 rows x 243 columns]"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "distance_frame = pd.read_csv(file_path(\"Country_List_ISO_3166_Codes_Latitude_Longitude.csv\"),\n",
-    "                                       usecols=[2,4,5],\n",
-    "                                       index_col=0,\n",
-    "                                       keep_default_na=False)\n",
-    "locations = [(x[1][0], x[1][1]) for x in distance_frame.iterrows()]\n",
-    "rows = []\n",
-    "for i in range(len(locations)):\n",
-    "    row = []\n",
-    "    for loc in locations:\n",
-    "        row.append(haversine(loc, locations[i]))\n",
-    "    rows.append(row)\n",
-    "distance = pd.DataFrame(rows, distance_frame.index, distance_frame.index)\n",
-    "distance"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Freedom Index\n",
-    "\n",
-    "The [Freedom Index](https://freedomhouse.org/report/freedom-world/freedom-world-2017) comes from Freedom House. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style>\n",
-       "    .dataframe thead tr:only-child th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: left;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Country</th>\n",
-       "      <th>Total Aggr</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>AFG</td>\n",
-       "      <td>24</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>ALB</td>\n",
-       "      <td>68</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>DZA</td>\n",
-       "      <td>35</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>AND</td>\n",
-       "      <td>95</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>AGO</td>\n",
-       "      <td>24</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>190</th>\n",
-       "      <td>VEN</td>\n",
-       "      <td>30</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>191</th>\n",
-       "      <td>VNM</td>\n",
-       "      <td>20</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>192</th>\n",
-       "      <td>YEM</td>\n",
-       "      <td>14</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>193</th>\n",
-       "      <td>ZMB</td>\n",
-       "      <td>56</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>194</th>\n",
-       "      <td>ZWE</td>\n",
-       "      <td>32</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>195 rows × 2 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    Country  Total Aggr\n",
-       "0       AFG          24\n",
-       "1       ALB          68\n",
-       "2       DZA          35\n",
-       "3       AND          95\n",
-       "4       AGO          24\n",
-       "..      ...         ...\n",
-       "190     VEN          30\n",
-       "191     VNM          20\n",
-       "192     YEM          14\n",
-       "193     ZMB          56\n",
-       "194     ZWE          32\n",
-       "\n",
-       "[195 rows x 2 columns]"
-      ]
-     },
-     "execution_count": 8,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "#[cc[cc['Country'] == x][\"Alpha-3 code\"] if len(cc[cc['Country'] == x]) > 0 else x for x in fi['Country']]\n",
-    "codes = []\n",
-    "other_codes = pd.read_csv(file_path(\"other.csv\"), index_col=0)\n",
-    "cc = country_codes()\n",
-    "fi = freedom_index()\n",
-    "for country in fi['Country']:\n",
-    "    if len(cc[cc['Country'] == country]):\n",
-    "        codes.append(cc[cc['Country'] == country].index[0])\n",
-    "    elif len(other_codes[other_codes.index == country]):\n",
-    "        codes.append(other_codes[other_codes.index == country][\"ISO\"][0])\n",
-    "    else:\n",
-    "        print(country)\n",
-    "fi['Country'] = codes\n",
-    "fi"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Passport Index\n",
-    "\n",
-    "The [Passport Index](https://www.cato.org/human-freedom-index) comes from Arton Capital. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style>\n",
-       "    .dataframe thead tr:only-child th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: left;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Country</th>\n",
-       "      <th>Rank (1 = most welcoming)</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>KHM</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>COM</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>CIV</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>GNB</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>MDG</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>194</th>\n",
-       "      <td>AFG</td>\n",
-       "      <td>98</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>195</th>\n",
-       "      <td>PRK</td>\n",
-       "      <td>98</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>196</th>\n",
-       "      <td>SOM</td>\n",
-       "      <td>98</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>197</th>\n",
-       "      <td>SYR</td>\n",
-       "      <td>98</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>198</th>\n",
-       "      <td>TKM</td>\n",
-       "      <td>98</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>199 rows × 2 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "    Country  Rank (1 = most welcoming)\n",
-       "0       KHM                          1\n",
-       "1       COM                          1\n",
-       "2       CIV                          1\n",
-       "3       GNB                          1\n",
-       "4       MDG                          1\n",
-       "..      ...                        ...\n",
-       "194     AFG                         98\n",
-       "195     PRK                         98\n",
-       "196     SOM                         98\n",
-       "197     SYR                         98\n",
-       "198     TKM                         98\n",
-       "\n",
-       "[199 rows x 2 columns]"
-      ]
-     },
-     "execution_count": 9,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "pi = passport_index()\n",
-    "codes = []\n",
-    "other_codes = pd.read_csv(file_path(\"other.csv\"), index_col=0)\n",
-    "for country in pi['Country']:\n",
-    "    if len(cc[cc['Country'] == country]):\n",
-    "        codes.append(cc[cc['Country'] == country].index[0])\n",
-    "    elif len(other_codes[other_codes.index == country]):\n",
-    "        codes.append(other_codes[other_codes.index == country][\"ISO\"][0])\n",
-    "    else:\n",
-    "        print(country)\n",
-    "pi['Country'] = codes\n",
-    "pi"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "passport_index = pi.set_index(\"Country\")\n",
-    "freedom_index = fi.set_index(\"Country\")\n",
-    "#pd.concat([ab, passport_index, freedom_index])\n",
-    "data = ab.join(passport_index).join(freedom_index).dropna()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "political_barriers = 2/(1/data['Total Aggr']/sum(1/data['Total Aggr']) +\n",
-    " data['Rank (1 = most welcoming)']/sum(data['Rank (1 = most welcoming)']))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "political_barriers /= sum(political_barriers)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Shared Language\n",
-    "\n",
-    "Agents are assigned proficiency in languages spoken in their origin country. Mov-\n",
-    "ing to a country with entirely new languages presents a higher migration cost."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[{'Dari', 'Pashto', 'Persian'},\n",
-       " {'Albanian'},\n",
-       " {'Arabic', 'French'},\n",
-       " {'Somoan'},\n",
-       " {'Catalan', 'French', 'Portuguese'},\n",
-       " {'Bantu', 'Portuguese'},\n",
-       " {'English'},\n",
-       " {'English'},\n",
-       " {'Spanish'},\n",
-       " {'Armenian'},\n",
-       " {'English', 'Papiamento', 'Spanish'},\n",
-       " {'English'},\n",
-       " {'German'},\n",
-       " {'Azerbaijani'},\n",
-       " {'Creole', 'English'},\n",
-       " {'Arabic', 'English', 'Farsi'},\n",
-       " {'Bangla', 'English'},\n",
-       " {'Bajan', 'English'},\n",
-       " {'Belarusian', 'Russian'},\n",
-       " {'Dutch', 'French'},\n",
-       " {'Creole', 'Mayan', 'Spanish'},\n",
-       " {'Fon', 'French', 'Yoruba'},\n",
-       " {'English', 'Portuguese'},\n",
-       " {'Dzongka', 'Lhotshamka', 'Scharchhopka'},\n",
-       " {'Aymara', 'Quechua', 'Spanish'},\n",
-       " {'Bosnian', 'Croatian', 'Serbian'},\n",
-       " {'Kelanga', 'Setswana'},\n",
-       " {'Portuguese'},\n",
-       " {'English'},\n",
-       " {'Chinese', 'English', 'Malay'},\n",
-       " {'Bulgarian', 'Turkish'},\n",
-       " {'French', 'Sudanic'},\n",
-       " {'French', 'Kirundi'},\n",
-       " {'Khmer'},\n",
-       " {'English', 'French'},\n",
-       " {'English', 'French'},\n",
-       " {'Crioulo', 'Portugeuse'},\n",
-       " {'English'},\n",
-       " {'French', 'Sangho'},\n",
-       " {'Arabic', 'French', 'Sara'},\n",
-       " {'English', 'Spanish'},\n",
-       " {'Cantonese', 'Mandarin'},\n",
-       " {'Chinese', 'English', 'Malay'},\n",
-       " {'English', 'Malay'},\n",
-       " {'Spanish'},\n",
-       " {'Arabic', 'French'},\n",
-       " {'French', 'Kingwana', 'Monokutuba'},\n",
-       " {'French', 'Lingala', 'Monokutuba'},\n",
-       " {'English', 'Maori'},\n",
-       " {'English', 'Spanish'},\n",
-       " {'Dioula', 'French'},\n",
-       " {'Croatian'},\n",
-       " {'Spanish'},\n",
-       " {'Greek'},\n",
-       " {'Czech'},\n",
-       " {'Danish', 'English'},\n",
-       " {'Arabic', 'French', 'Somali'},\n",
-       " {'English', 'French patois', 'nan'},\n",
-       " {'Spanish'},\n",
-       " {'Makaasi', 'Mambai', 'Tetun Prasa'},\n",
-       " {'Spanish'},\n",
-       " {'Arabic', 'English', 'French'},\n",
-       " {'Spanish'},\n",
-       " {'French', 'Spanish'},\n",
-       " {'Arabic', 'English', 'Tigrinya'},\n",
-       " {'Estonian', 'Russian'},\n",
-       " {'Amharic', 'Oromo'},\n",
-       " {'Danish', 'Faroese', 'nan'},\n",
-       " {'English', 'Spanish'},\n",
-       " {'English', 'Fijian', 'Hindustani'},\n",
-       " {'Finnish'},\n",
-       " {'French'},\n",
-       " {'French', 'Polynesian'},\n",
-       " {'Fang', 'French', 'Myene'},\n",
-       " {'English', 'Mandinka', 'Wolof'},\n",
-       " {'Arabic', 'English', 'Hebrew'},\n",
-       " {'Georgian', 'Russian'},\n",
-       " {'German'},\n",
-       " {'Asante', 'Ewe', 'Fante'},\n",
-       " {'English', 'Italian', 'Spanish'},\n",
-       " {'Greek'},\n",
-       " {'Danish', 'English', 'Greenlandic'},\n",
-       " {'English', 'French patois'},\n",
-       " {'Chamorro', 'Englsih', 'Fillipino'},\n",
-       " {'', 'Spanish'},\n",
-       " {'Creole', 'French'},\n",
-       " {'French', 'Italian', 'Latin'},\n",
-       " {'Spanish'},\n",
-       " {'Cantonese'},\n",
-       " {'English', 'German', 'Hungarian'},\n",
-       " {'English', 'German', 'Icelandic'},\n",
-       " {'Bengali', 'English', 'Hindi'},\n",
-       " {'Bahasa', 'Dutch', 'English'},\n",
-       " {'Azeri', 'Kurdish', 'Persian'},\n",
-       " {'Arabic', 'Kurdish', 'Turkmen'},\n",
-       " {'English', 'Irish'},\n",
-       " {'English', 'Hebrew'},\n",
-       " {'German', 'Italian'},\n",
-       " {'English', 'English palois'},\n",
-       " {'Japanese'},\n",
-       " {'Arabic', 'English'},\n",
-       " {'Kazakh', 'Russian'},\n",
-       " {'English', 'Kiswahili', 'nan'},\n",
-       " {'English', 'I-Kiribati', 'nan'},\n",
-       " {'Korean'},\n",
-       " {'English', 'Korean'},\n",
-       " {'Arabic', 'English'},\n",
-       " {'Kyrgyz', 'Russian', 'Uzbek'},\n",
-       " {'Enlgish', 'French', 'Lao'},\n",
-       " {'Latvian', 'Russian'},\n",
-       " {'Arabic', 'French'},\n",
-       " {'English', 'Sesotho'},\n",
-       " {'English'},\n",
-       " {'Arabic', 'English', 'Italian'},\n",
-       " {'German'},\n",
-       " {'Lithuanian', 'Russian'},\n",
-       " {'French', 'Luxembourgish', 'Portuguese'},\n",
-       " {'Cantonese'},\n",
-       " {'Albanian', 'Macedonian'},\n",
-       " {'French', 'Malagasy'},\n",
-       " {'Chichewa', 'English'},\n",
-       " {'Bahasa', 'English', 'Mandarin'},\n",
-       " {'Dhivehi', 'English'},\n",
-       " {'Bambara', 'French', 'Peul'},\n",
-       " {'Maltese'},\n",
-       " {'Marshallese'},\n",
-       " {'Arabic', 'Pular', 'Soninke'},\n",
-       " {'Creole'},\n",
-       " {'Spanish'},\n",
-       " {'English'},\n",
-       " {'Romanian', 'Russian'},\n",
-       " {'Montenegrin', 'Serbian'},\n",
-       " {'English', 'French', 'Italian'},\n",
-       " {'Mongolian'},\n",
-       " {'English'},\n",
-       " {'Arabic', 'French'},\n",
-       " {'Emakhuwa', 'Portuguese', 'Xichangana'},\n",
-       " {'Afrikaans', 'Nama', 'Oshivambo'},\n",
-       " {'English', 'Nauruan'},\n",
-       " {'Maithali', 'Nepali'},\n",
-       " {'Dutch'},\n",
-       " {'French'},\n",
-       " {'English'},\n",
-       " {'Spanish'},\n",
-       " {'Djerma', 'French', 'Hausa'},\n",
-       " {'English', 'Hausa', 'Ibo'},\n",
-       " {'English', 'Niuean'},\n",
-       " {'English', 'Norfolk'},\n",
-       " {'Chamorro', 'English', 'Philippine'},\n",
-       " {'Norwegian'},\n",
-       " {'Arabic', 'Baluchi', 'English'},\n",
-       " {'Pashto', 'Punjabi', 'Sindhi'},\n",
-       " {'English', 'Filipino', 'Palauan'},\n",
-       " {'Spanish'},\n",
-       " {'Guarani', 'Spanish'},\n",
-       " {'English', 'Hiri Motu', 'Tok Pisin'},\n",
-       " {'Quechua', 'Spanish'},\n",
-       " {'English', 'Filipino'},\n",
-       " {'English', 'Pitkern'},\n",
-       " {'Polish'},\n",
-       " {'Mirandese', 'Portuguese'},\n",
-       " {'English', 'Spanish'},\n",
-       " {'Arabic', 'English'},\n",
-       " {'Romanian'},\n",
-       " {'Russian'},\n",
-       " {'', 'Kinyarwanda'},\n",
-       " {'English', 'Samoan'},\n",
-       " {'Italian'},\n",
-       " {'Cabo Verdian', 'Forro', 'Portuguese'},\n",
-       " {'Arabic'},\n",
-       " {'French', 'Pular', 'Wolof'},\n",
-       " {'Serbian'},\n",
-       " {'Seychelles'},\n",
-       " {'English', 'Mende', 'Temme'},\n",
-       " {'English', 'Malay', 'Mandarin'},\n",
-       " {'Hungarian', 'Slovak'},\n",
-       " {'Slovenian'},\n",
-       " {'Pidgin'},\n",
-       " {'Arabic', 'Somali'},\n",
-       " {'Afrikaans', 'English', 'Zulu'},\n",
-       " {'Arabic', 'English'},\n",
-       " {'Catalan', 'Spanish'},\n",
-       " {'Sinhala', 'Tamil'},\n",
-       " {'Arabic', 'English', 'Nubian'},\n",
-       " {'Dutch', 'English', 'Sranang Tongo'},\n",
-       " {'Norwegian', 'Russian'},\n",
-       " {'English', 'Swati', 'nan'},\n",
-       " {'Swedish'},\n",
-       " {'French', 'German', 'Italian'},\n",
-       " {'Arabic'},\n",
-       " {'Mandarin', 'Taiwanese'},\n",
-       " {'Russian', 'Tajik'},\n",
-       " {'Arabic', 'English', 'Swahili'},\n",
-       " {'Thai'},\n",
-       " {'Ewe', 'French', 'Mina'},\n",
-       " {'English', 'Samoan', 'Tokelauan'},\n",
-       " {'English', 'Tongan'},\n",
-       " {'English'},\n",
-       " {'Arabic', 'Berber', 'French'},\n",
-       " {'Kurdish', 'Turkish'},\n",
-       " {'Russian', 'Turkmen', 'Uzbek'},\n",
-       " {'English'},\n",
-       " {'English', 'Samoan', 'Tuvaluan'},\n",
-       " {'English', 'Ganda'},\n",
-       " {'Russian', 'Ukrainian'},\n",
-       " {'Arabic', 'English', 'Persian'},\n",
-       " {'English'},\n",
-       " {'English', 'Spanish'},\n",
-       " {'Spanish'},\n",
-       " {'Russian', 'Uzbek'},\n",
-       " {'Bislama'},\n",
-       " {'Spanish'},\n",
-       " {'English', 'Vietnamese'},\n",
-       " {'English', 'French', 'Spanish'},\n",
-       " {'Arabic'},\n",
-       " {'Arabic'},\n",
-       " {'Bembe', 'Nyanja', 'Tonga'},\n",
-       " {'English', 'Ndebele', 'Shona'}]"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "lang_csv = pd.read_csv(file_path(\"languages.csv\"), index_col=0)\n",
-    "# TODO: '' why?\n",
-    "lang_sets = [set([str(y).strip() for y in x[1] if y is not ' ']) for x in lang_csv.iterrows()]\n",
-    "lang_sets"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style>\n",
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: left;\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th>ISO3</th>\n",
-       "      <th>AFG</th>\n",
-       "      <th>ALB</th>\n",
-       "      <th>DZA</th>\n",
-       "      <th>ASM</th>\n",
-       "      <th>AND</th>\n",
-       "      <th>AGO</th>\n",
-       "      <th>AIA</th>\n",
-       "      <th>ATG</th>\n",
-       "      <th>ARG</th>\n",
-       "      <th>ARM</th>\n",
-       "      <th>...</th>\n",
-       "      <th>URY</th>\n",
-       "      <th>UZB</th>\n",
-       "      <th>VUT</th>\n",
-       "      <th>VEN</th>\n",
-       "      <th>VNM</th>\n",
-       "      <th>VIR</th>\n",
-       "      <th>ESH</th>\n",
-       "      <th>YEM</th>\n",
-       "      <th>ZMB</th>\n",
-       "      <th>ZWE</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>ISO3</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>AFG</th>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>ALB</th>\n",
-       "      <td>False</td>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>DZA</th>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>True</td>\n",
-       "      <td>True</td>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>ASM</th>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>AND</th>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "      <td>True</td>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>VIR</th>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "      <td>True</td>\n",
-       "      <td>True</td>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "      <td>...</td>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>True</td>\n",
-       "      <td>True</td>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>ESH</th>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>True</td>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>YEM</th>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>True</td>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>ZMB</th>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>ZWE</th>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>True</td>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>...</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>True</td>\n",
-       "      <td>True</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>False</td>\n",
-       "      <td>True</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>218 rows × 218 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "ISO3    AFG    ALB    DZA    ASM    AND    AGO    AIA    ATG    ARG    ARM  \\\n",
-       "ISO3                                                                         \n",
-       "AFG    True  False  False  False  False  False  False  False  False  False   \n",
-       "ALB   False   True  False  False  False  False  False  False  False  False   \n",
-       "DZA   False  False   True  False   True  False  False  False  False  False   \n",
-       "ASM   False  False  False   True  False  False  False  False  False  False   \n",
-       "AND   False  False   True  False   True   True  False  False  False  False   \n",
-       "...     ...    ...    ...    ...    ...    ...    ...    ...    ...    ...   \n",
-       "VIR   False  False   True  False   True  False   True   True   True  False   \n",
-       "ESH   False  False   True  False  False  False  False  False  False  False   \n",
-       "YEM   False  False   True  False  False  False  False  False  False  False   \n",
-       "ZMB   False  False  False  False  False  False  False  False  False  False   \n",
-       "ZWE   False  False  False  False  False  False   True   True  False  False   \n",
-       "\n",
-       "ISO3  ...      URY    UZB    VUT    VEN    VNM    VIR    ESH    YEM    ZMB  \\\n",
-       "ISO3  ...                                                                    \n",
-       "AFG   ...    False  False  False  False  False  False  False  False  False   \n",
-       "ALB   ...    False  False  False  False  False  False  False  False  False   \n",
-       "DZA   ...    False  False  False  False  False   True   True   True  False   \n",
-       "ASM   ...    False  False  False  False  False  False  False  False  False   \n",
-       "AND   ...    False  False  False  False  False   True  False  False  False   \n",
-       "...   ...      ...    ...    ...    ...    ...    ...    ...    ...    ...   \n",
-       "VIR   ...     True  False  False   True   True   True  False  False  False   \n",
-       "ESH   ...    False  False  False  False  False  False   True   True  False   \n",
-       "YEM   ...    False  False  False  False  False  False   True   True  False   \n",
-       "ZMB   ...    False  False  False  False  False  False  False  False   True   \n",
-       "ZWE   ...    False  False  False  False   True   True  False  False  False   \n",
-       "\n",
-       "ISO3    ZWE  \n",
-       "ISO3         \n",
-       "AFG   False  \n",
-       "ALB   False  \n",
-       "DZA   False  \n",
-       "ASM   False  \n",
-       "AND   False  \n",
-       "...     ...  \n",
-       "VIR    True  \n",
-       "ESH   False  \n",
-       "YEM   False  \n",
-       "ZMB   False  \n",
-       "ZWE    True  \n",
-       "\n",
-       "[218 rows x 218 columns]"
-      ]
-     },
-     "execution_count": 14,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "overlap = []\n",
-    "for s in lang_sets:\n",
-    "    o = []\n",
-    "    for i in range(len(lang_sets)):\n",
-    "        o.append(len(lang_sets[i].intersection(s)) >= 1)\n",
-    "    overlap.append(o)\n",
-    "lang_data = pd.DataFrame(overlap)\n",
-    "lang_data.columns = lang_csv.index\n",
-    "lang_data.index = lang_csv.index\n",
-    "lang_data"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Unemployment"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Macau\n",
-      "Falkland Islands\n",
-      "British Virgin Islands\n",
-      "Sint Maarten\n",
-      "Cabo Verde\n",
-      "Virgin Islands\n",
-      "Curacao\n",
-      "Saint Helena Ascension and Tristan da Cunha\n",
-      "The Bahamas\n",
-      "Federated States of Micronesia\n",
-      "Gaza Strip\n",
-      "Cocos Islands\n"
-     ]
-    }
-   ],
-   "source": [
-    "unemployment_data = pd.read_csv(file_path(\"CIA_Unemployment.csv\"), index_col=1)\n",
-    "\n",
-    "unemployment_data.index\n",
-    "codes = []\n",
-    "other_codes = pd.read_csv(file_path(\"other.csv\"), index_col=0)\n",
-    "for country in unemployment_data.index:\n",
-    "    if len(cc[cc['Country'] == country]):\n",
-    "        codes.append(cc[cc['Country'] == country].index[0])\n",
-    "    elif len(other_codes[other_codes.index == country]):\n",
-    "        codes.append(other_codes[other_codes.index == country][\"ISO\"][0])\n",
-    "    else:\n",
-    "        print(country)\n",
-    "        codes.append(country)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style>\n",
-       "    .dataframe thead tr:only-child th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: left;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Country</th>\n",
-       "      <th>Unemployment Rate</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Country</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>KHM</th>\n",
-       "      <td>KHM</td>\n",
-       "      <td>0.3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>QAT</th>\n",
-       "      <td>QAT</td>\n",
-       "      <td>0.7</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>BLR</th>\n",
-       "      <td>BLR</td>\n",
-       "      <td>0.7</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>THA</th>\n",
-       "      <td>THA</td>\n",
-       "      <td>0.9</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>GGY</th>\n",
-       "      <td>GGY</td>\n",
-       "      <td>0.9</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>COG</th>\n",
-       "      <td>COG</td>\n",
-       "      <td>53.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>DJI</th>\n",
-       "      <td>DJI</td>\n",
-       "      <td>60.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>BFA</th>\n",
-       "      <td>BFA</td>\n",
-       "      <td>77.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>LBR</th>\n",
-       "      <td>LBR</td>\n",
-       "      <td>85.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>ZWE</th>\n",
-       "      <td>ZWE</td>\n",
-       "      <td>95.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>195 rows × 2 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        Country  Unemployment Rate\n",
-       "Country                           \n",
-       "KHM         KHM                0.3\n",
-       "QAT         QAT                0.7\n",
-       "BLR         BLR                0.7\n",
-       "THA         THA                0.9\n",
-       "GGY         GGY                0.9\n",
-       "...         ...                ...\n",
-       "COG         COG               53.0\n",
-       "DJI         DJI               60.0\n",
-       "BFA         BFA               77.0\n",
-       "LBR         LBR               85.0\n",
-       "ZWE         ZWE               95.0\n",
-       "\n",
-       "[195 rows x 2 columns]"
-      ]
-     },
-     "execution_count": 16,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "unemployment_data.index = codes\n",
-    "unemployment_pd = pd.DataFrame([(x[0], x[1][1]) for x in unemployment_data.iterrows() if len(x[0]) <= 3])\n",
-    "unemployment_pd.columns = [\"Country\", \"Unemployment Rate\"]\n",
-    "unemployment_pd.index = unemployment_pd[\"Country\"]\n",
-    "unemployment_pd"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### UN Migration History\n",
-    "\n",
-    "This one is a bit more complicated because it is a matrix, not a list."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "un_pd = pd.read_excel(\n",
-    "        file_path(\n",
-    "            \"UN_MigrantStockByOriginAndDestination_2015.xlsx\"\n",
-    "        ),\n",
-    "        skiprows=15\n",
-    "    )\n",
-    "un_pd.index = un_pd['Unnamed: 1']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "names = un_pd.iloc[:,1]\n",
-    "un_codes = un_pd.iloc[:,3]\n",
-    "names_to_un_codes = {names[i]: un_codes[i] for i in range(len(names))}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "un_pd = un_pd.iloc[8:275,8:250]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[535, 830, 531, 534, 728, 729]"
-      ]
-     },
-     "execution_count": 20,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "codes = []\n",
-    "missing = []\n",
-    "for code in [names_to_un_codes[x] for x in un_pd.columns.values]:\n",
-    "    if len(cc[cc['Numeric code'] == code]):\n",
-    "        codes.append(cc[cc['Numeric code'] == code].index[0])\n",
-    "    #elif len(other_codes[other_codes['Code'] == country]):\n",
-    "    #    codes.append(other_codes[other_codes['Code'] == country][\"ISO\"][0])\n",
-    "    else:\n",
-    "        missing.append(code)\n",
-    "missing"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# We want to drop these\n",
-    "drop = [x for x in un_pd.columns.values if x not in names_to_un_codes.keys() or names_to_un_codes[x] in missing]\n",
-    "print(drop)\n",
-    "un_pd = un_pd.drop(drop, axis=1)\n",
-    "un_pd"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[728,\n",
-       " 911,\n",
-       " 912,\n",
-       " 729,\n",
-       " 913,\n",
-       " 914,\n",
-       " 935,\n",
-       " 5500,\n",
-       " 906,\n",
-       " 920,\n",
-       " 5501,\n",
-       " 922,\n",
-       " 908,\n",
-       " 923,\n",
-       " 924,\n",
-       " 830,\n",
-       " 925,\n",
-       " 926,\n",
-       " 904,\n",
-       " 915,\n",
-       " 535,\n",
-       " 531,\n",
-       " 534,\n",
-       " 916,\n",
-       " 931,\n",
-       " 905,\n",
-       " 909,\n",
-       " 927,\n",
-       " 928,\n",
-       " 954,\n",
-       " 957]"
-      ]
-     },
-     "execution_count": 22,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "missing = []\n",
-    "for code in [names_to_un_codes[x] for x in un_pd.index]:\n",
-    "    if len(cc[cc['Numeric code'] == code]) == 0:\n",
-    "        #codes.append(cc[cc['Numeric code'] == code].index[0])\n",
-    "    #elif len(other_codes[other_codes['Code'] == country]):\n",
-    "    #    codes.append(other_codes[other_codes['Code'] == country][\"ISO\"][0])\n",
-    "    #else:\n",
-    "        missing.append(code)\n",
-    "missing"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['South Sudan', 'Middle Africa', 'Northern Africa', 'Sudan', 'Southern Africa', 'Western Africa', 'Asia', 'Central Asia', 'Eastern Asia', 'South-Eastern Asia', 'Southern Asia', 'Western Asia', 'Europe', 'Eastern Europe', 'Northern Europe', 'Channel Islands', 'Southern Europe', 'Western Europe', 'Latin America and the Caribbean', 'Caribbean', 'Bonaire, Sint Eustatius and Saba', 'Curaçao', 'Sint Maarten (Dutch part)', 'Central America', 'South America', 'Northern America', 'Oceania', 'Australia and New Zealand', 'Melanesia', 'Micronesia', 'Polynesia']\n"
-     ]
-    },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style>\n",
-       "    .dataframe thead tr:only-child th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: left;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>Afghanistan</th>\n",
-       "      <th>Albania</th>\n",
-       "      <th>Algeria</th>\n",
-       "      <th>American Samoa</th>\n",
-       "      <th>Andorra</th>\n",
-       "      <th>Angola</th>\n",
-       "      <th>Anguilla</th>\n",
-       "      <th>Antigua and Barbuda</th>\n",
-       "      <th>Argentina</th>\n",
-       "      <th>Armenia</th>\n",
-       "      <th>...</th>\n",
-       "      <th>Uruguay</th>\n",
-       "      <th>Uzbekistan</th>\n",
-       "      <th>Vanuatu</th>\n",
-       "      <th>Venezuela (Bolivarian Republic of)</th>\n",
-       "      <th>Viet Nam</th>\n",
-       "      <th>Wallis and Futuna Islands</th>\n",
-       "      <th>Western Sahara</th>\n",
-       "      <th>Yemen</th>\n",
-       "      <th>Zambia</th>\n",
-       "      <th>Zimbabwe</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Unnamed: 1</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>Burundi</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Comoros</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Djibouti</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>265.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Eritrea</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>346.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>241.0</td>\n",
-       "      <td>324.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Ethiopia</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>145.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Samoa</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1588.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>17.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Tokelau</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Tonga</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Tuvalu</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Wallis and Futuna Islands</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>247.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>226 rows × 226 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                           Afghanistan  Albania  Algeria  American Samoa  \\\n",
-       "Unnamed: 1                                                                 \n",
-       "Burundi                            NaN      NaN      NaN             NaN   \n",
-       "Comoros                            NaN      NaN      NaN             NaN   \n",
-       "Djibouti                           NaN      NaN      NaN             NaN   \n",
-       "Eritrea                            NaN      NaN      NaN             NaN   \n",
-       "Ethiopia                           NaN      NaN      NaN             NaN   \n",
-       "...                                ...      ...      ...             ...   \n",
-       "Samoa                              NaN      NaN      NaN          1588.0   \n",
-       "Tokelau                            NaN      NaN      NaN             NaN   \n",
-       "Tonga                              NaN      NaN      NaN             NaN   \n",
-       "Tuvalu                             NaN      NaN      NaN             NaN   \n",
-       "Wallis and Futuna Islands          NaN      NaN      NaN             NaN   \n",
-       "\n",
-       "                           Andorra  Angola  Anguilla  Antigua and Barbuda  \\\n",
-       "Unnamed: 1                                                                  \n",
-       "Burundi                        NaN     NaN       NaN                  NaN   \n",
-       "Comoros                        NaN     NaN       NaN                  NaN   \n",
-       "Djibouti                       NaN     NaN       NaN                  NaN   \n",
-       "Eritrea                        NaN   346.0       NaN                  NaN   \n",
-       "Ethiopia                       NaN     NaN       NaN                  NaN   \n",
-       "...                            ...     ...       ...                  ...   \n",
-       "Samoa                          NaN     NaN       NaN                  NaN   \n",
-       "Tokelau                        NaN     NaN       NaN                  NaN   \n",
-       "Tonga                          NaN     NaN       NaN                  NaN   \n",
-       "Tuvalu                         NaN     NaN       NaN                  NaN   \n",
-       "Wallis and Futuna Islands      NaN     NaN       NaN                  NaN   \n",
-       "\n",
-       "                           Argentina  Armenia    ...     Uruguay  Uzbekistan  \\\n",
-       "Unnamed: 1                                       ...                           \n",
-       "Burundi                          NaN      NaN    ...         NaN         NaN   \n",
-       "Comoros                          NaN      NaN    ...         NaN         NaN   \n",
-       "Djibouti                         NaN      NaN    ...         NaN         NaN   \n",
-       "Eritrea                          NaN      NaN    ...         NaN         NaN   \n",
-       "Ethiopia                         NaN      NaN    ...         NaN         NaN   \n",
-       "...                              ...      ...    ...         ...         ...   \n",
-       "Samoa                            NaN      NaN    ...         NaN         NaN   \n",
-       "Tokelau                          NaN      NaN    ...         NaN         NaN   \n",
-       "Tonga                            NaN      NaN    ...         NaN         NaN   \n",
-       "Tuvalu                           NaN      NaN    ...         NaN         NaN   \n",
-       "Wallis and Futuna Islands        NaN      NaN    ...         NaN         NaN   \n",
-       "\n",
-       "                           Vanuatu  Venezuela (Bolivarian Republic of)  \\\n",
-       "Unnamed: 1                                                               \n",
-       "Burundi                        NaN                                 NaN   \n",
-       "Comoros                        NaN                                 NaN   \n",
-       "Djibouti                       NaN                                 NaN   \n",
-       "Eritrea                        NaN                                 NaN   \n",
-       "Ethiopia                       NaN                                 NaN   \n",
-       "...                            ...                                 ...   \n",
-       "Samoa                         17.0                                 NaN   \n",
-       "Tokelau                        NaN                                 NaN   \n",
-       "Tonga                          NaN                                 NaN   \n",
-       "Tuvalu                         NaN                                 NaN   \n",
-       "Wallis and Futuna Islands    247.0                                 NaN   \n",
-       "\n",
-       "                           Viet Nam  Wallis and Futuna Islands  \\\n",
-       "Unnamed: 1                                                       \n",
-       "Burundi                         NaN                        NaN   \n",
-       "Comoros                         NaN                        NaN   \n",
-       "Djibouti                        NaN                        NaN   \n",
-       "Eritrea                         NaN                        NaN   \n",
-       "Ethiopia                        NaN                        NaN   \n",
-       "...                             ...                        ...   \n",
-       "Samoa                           NaN                        NaN   \n",
-       "Tokelau                         NaN                        NaN   \n",
-       "Tonga                           NaN                        NaN   \n",
-       "Tuvalu                          NaN                        NaN   \n",
-       "Wallis and Futuna Islands       NaN                        NaN   \n",
-       "\n",
-       "                           Western Sahara  Yemen  Zambia  Zimbabwe  \n",
-       "Unnamed: 1                                                          \n",
-       "Burundi                               NaN    NaN     NaN       NaN  \n",
-       "Comoros                               NaN    NaN     NaN       NaN  \n",
-       "Djibouti                              NaN  265.0     NaN       NaN  \n",
-       "Eritrea                               NaN    NaN   241.0     324.0  \n",
-       "Ethiopia                              NaN  145.0     NaN       NaN  \n",
-       "...                                   ...    ...     ...       ...  \n",
-       "Samoa                                 NaN    NaN     NaN       NaN  \n",
-       "Tokelau                               NaN    NaN     NaN       NaN  \n",
-       "Tonga                                 NaN    NaN     NaN       NaN  \n",
-       "Tuvalu                                NaN    NaN     NaN       NaN  \n",
-       "Wallis and Futuna Islands             NaN    NaN     NaN       NaN  \n",
-       "\n",
-       "[226 rows x 226 columns]"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "drop = [x for x in un_pd.index if x not in names_to_un_codes.keys() or names_to_un_codes[x] in missing]\n",
-    "print(drop)\n",
-    "un_pd = un_pd.drop(drop, axis=0)\n",
-    "un_pd"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "un_pd.index = [cc[cc['Numeric code'] == names_to_un_codes[x]].index[0] for x in un_pd.index]\n",
-    "un_pd.columns = [cc[cc['Numeric code'] == names_to_un_codes[x]].index[0] for x in un_pd.columns.values]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style>\n",
-       "    .dataframe thead tr:only-child th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: left;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>AFG</th>\n",
-       "      <th>ALB</th>\n",
-       "      <th>DZA</th>\n",
-       "      <th>ASM</th>\n",
-       "      <th>AND</th>\n",
-       "      <th>AGO</th>\n",
-       "      <th>AIA</th>\n",
-       "      <th>ATG</th>\n",
-       "      <th>ARG</th>\n",
-       "      <th>ARM</th>\n",
-       "      <th>...</th>\n",
-       "      <th>URY</th>\n",
-       "      <th>UZB</th>\n",
-       "      <th>VUT</th>\n",
-       "      <th>VEN</th>\n",
-       "      <th>VNM</th>\n",
-       "      <th>WLF</th>\n",
-       "      <th>ESH</th>\n",
-       "      <th>YEM</th>\n",
-       "      <th>ZMB</th>\n",
-       "      <th>ZWE</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>BDI</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>COM</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>DJI</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>265.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>ERI</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>346.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>241.0</td>\n",
-       "      <td>324.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>ETH</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>145.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>WSM</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1588.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>17.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>TKL</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>TON</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>TUV</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>WLF</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>247.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>226 rows × 226 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "     AFG  ALB  DZA     ASM  AND    AGO  AIA  ATG  ARG  ARM  ...    URY  UZB  \\\n",
-       "BDI  1.0  1.0  1.0     1.0  1.0    1.0  1.0  1.0  1.0  1.0  ...    1.0  1.0   \n",
-       "COM  1.0  1.0  1.0     1.0  1.0    1.0  1.0  1.0  1.0  1.0  ...    1.0  1.0   \n",
-       "DJI  1.0  1.0  1.0     1.0  1.0    1.0  1.0  1.0  1.0  1.0  ...    1.0  1.0   \n",
-       "ERI  1.0  1.0  1.0     1.0  1.0  346.0  1.0  1.0  1.0  1.0  ...    1.0  1.0   \n",
-       "ETH  1.0  1.0  1.0     1.0  1.0    1.0  1.0  1.0  1.0  1.0  ...    1.0  1.0   \n",
-       "..   ...  ...  ...     ...  ...    ...  ...  ...  ...  ...  ...    ...  ...   \n",
-       "WSM  1.0  1.0  1.0  1588.0  1.0    1.0  1.0  1.0  1.0  1.0  ...    1.0  1.0   \n",
-       "TKL  1.0  1.0  1.0     1.0  1.0    1.0  1.0  1.0  1.0  1.0  ...    1.0  1.0   \n",
-       "TON  1.0  1.0  1.0     1.0  1.0    1.0  1.0  1.0  1.0  1.0  ...    1.0  1.0   \n",
-       "TUV  1.0  1.0  1.0     1.0  1.0    1.0  1.0  1.0  1.0  1.0  ...    1.0  1.0   \n",
-       "WLF  1.0  1.0  1.0     1.0  1.0    1.0  1.0  1.0  1.0  1.0  ...    1.0  1.0   \n",
-       "\n",
-       "       VUT  VEN  VNM  WLF  ESH    YEM    ZMB    ZWE  \n",
-       "BDI    1.0  1.0  1.0  1.0  1.0    1.0    1.0    1.0  \n",
-       "COM    1.0  1.0  1.0  1.0  1.0    1.0    1.0    1.0  \n",
-       "DJI    1.0  1.0  1.0  1.0  1.0  265.0    1.0    1.0  \n",
-       "ERI    1.0  1.0  1.0  1.0  1.0    1.0  241.0  324.0  \n",
-       "ETH    1.0  1.0  1.0  1.0  1.0  145.0    1.0    1.0  \n",
-       "..     ...  ...  ...  ...  ...    ...    ...    ...  \n",
-       "WSM   17.0  1.0  1.0  1.0  1.0    1.0    1.0    1.0  \n",
-       "TKL    1.0  1.0  1.0  1.0  1.0    1.0    1.0    1.0  \n",
-       "TON    1.0  1.0  1.0  1.0  1.0    1.0    1.0    1.0  \n",
-       "TUV    1.0  1.0  1.0  1.0  1.0    1.0    1.0    1.0  \n",
-       "WLF  247.0  1.0  1.0  1.0  1.0    1.0    1.0    1.0  \n",
-       "\n",
-       "[226 rows x 226 columns]"
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "un_pd.fillna(1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style>\n",
-       "    .dataframe thead tr:only-child th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: left;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>AFG</th>\n",
-       "      <th>ALB</th>\n",
-       "      <th>DZA</th>\n",
-       "      <th>ASM</th>\n",
-       "      <th>AND</th>\n",
-       "      <th>AGO</th>\n",
-       "      <th>AIA</th>\n",
-       "      <th>ATG</th>\n",
-       "      <th>ARG</th>\n",
-       "      <th>ARM</th>\n",
-       "      <th>...</th>\n",
-       "      <th>URY</th>\n",
-       "      <th>UZB</th>\n",
-       "      <th>VUT</th>\n",
-       "      <th>VEN</th>\n",
-       "      <th>VNM</th>\n",
-       "      <th>WLF</th>\n",
-       "      <th>ESH</th>\n",
-       "      <th>YEM</th>\n",
-       "      <th>ZMB</th>\n",
-       "      <th>ZWE</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>BDI</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>COM</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>DJI</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>265.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>ERI</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>346.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>241.0</td>\n",
-       "      <td>324.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>ETH</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>145.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>WSM</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>1588.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>17.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>TKL</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>TON</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>TUV</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>WLF</th>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>...</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>247.0</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "      <td>NaN</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>226 rows × 226 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "     AFG  ALB  DZA     ASM  AND    AGO  AIA  ATG  ARG  ARM  ...    URY  UZB  \\\n",
-       "BDI  NaN  NaN  NaN     NaN  NaN    NaN  NaN  NaN  NaN  NaN  ...    NaN  NaN   \n",
-       "COM  NaN  NaN  NaN     NaN  NaN    NaN  NaN  NaN  NaN  NaN  ...    NaN  NaN   \n",
-       "DJI  NaN  NaN  NaN     NaN  NaN    NaN  NaN  NaN  NaN  NaN  ...    NaN  NaN   \n",
-       "ERI  NaN  NaN  NaN     NaN  NaN  346.0  NaN  NaN  NaN  NaN  ...    NaN  NaN   \n",
-       "ETH  NaN  NaN  NaN     NaN  NaN    NaN  NaN  NaN  NaN  NaN  ...    NaN  NaN   \n",
-       "..   ...  ...  ...     ...  ...    ...  ...  ...  ...  ...  ...    ...  ...   \n",
-       "WSM  NaN  NaN  NaN  1588.0  NaN    NaN  NaN  NaN  NaN  NaN  ...    NaN  NaN   \n",
-       "TKL  NaN  NaN  NaN     NaN  NaN    NaN  NaN  NaN  NaN  NaN  ...    NaN  NaN   \n",
-       "TON  NaN  NaN  NaN     NaN  NaN    NaN  NaN  NaN  NaN  NaN  ...    NaN  NaN   \n",
-       "TUV  NaN  NaN  NaN     NaN  NaN    NaN  NaN  NaN  NaN  NaN  ...    NaN  NaN   \n",
-       "WLF  NaN  NaN  NaN     NaN  NaN    NaN  NaN  NaN  NaN  NaN  ...    NaN  NaN   \n",
-       "\n",
-       "       VUT  VEN  VNM  WLF  ESH    YEM    ZMB    ZWE  \n",
-       "BDI    NaN  NaN  NaN  NaN  NaN    NaN    NaN    NaN  \n",
-       "COM    NaN  NaN  NaN  NaN  NaN    NaN    NaN    NaN  \n",
-       "DJI    NaN  NaN  NaN  NaN  NaN  265.0    NaN    NaN  \n",
-       "ERI    NaN  NaN  NaN  NaN  NaN    NaN  241.0  324.0  \n",
-       "ETH    NaN  NaN  NaN  NaN  NaN  145.0    NaN    NaN  \n",
-       "..     ...  ...  ...  ...  ...    ...    ...    ...  \n",
-       "WSM   17.0  NaN  NaN  NaN  NaN    NaN    NaN    NaN  \n",
-       "TKL    NaN  NaN  NaN  NaN  NaN    NaN    NaN    NaN  \n",
-       "TON    NaN  NaN  NaN  NaN  NaN    NaN    NaN    NaN  \n",
-       "TUV    NaN  NaN  NaN  NaN  NaN    NaN    NaN    NaN  \n",
-       "WLF  247.0  NaN  NaN  NaN  NaN    NaN    NaN    NaN  \n",
-       "\n",
-       "[226 rows x 226 columns]"
-      ]
-     },
-     "execution_count": 26,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "un_pd"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'GAZ', 'ROM', 'SDN', 'SSD', 'TMP', 'ZAR', nan}"
-      ]
-     },
-     "execution_count": 27,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "set(ab.index).difference(set(un_pd.index))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "Index(['BDI', 'COM', 'DJI', 'ERI', 'ETH', 'KEN', 'MDG', 'MWI', 'MUS', 'MYT',\n",
-       "       ...\n",
-       "       'PLW', 'ASM', 'COK', 'PYF', 'NIU', 'WSM', 'TKL', 'TON', 'TUV', 'WLF'],\n",
-       "      dtype='object', length=226)"
-      ]
-     },
-     "execution_count": 28,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "un_pd.index"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 29,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'GAZ', 'ROM', 'TMP', 'ZAR', nan}"
-      ]
-     },
-     "execution_count": 29,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "set(ab.index).difference(set(fi['Country']))\n",
-    "# TODO: Replace values here."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style>\n",
-       "    .dataframe thead tr:only-child th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: left;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>A</th>\n",
-       "      <th>B</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>SSD</th>\n",
-       "      <td>112.74</td>\n",
-       "      <td>0.0305</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "          A       B\n",
-       "SSD  112.74  0.0305"
-      ]
-     },
-     "execution_count": 30,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "ab[ab.index=='SSD']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "replace = {\n",
-    "    'GAZ': None, # May need to be changed.\n",
-    "    'ROM': 'ROU',\n",
-    "    'SDN': None, # For now, Sudan\n",
-    "    'SSD': None, # and South Sudan are being dropped.\n",
-    "    'TMP': 'TLS', # This should be fixed.\n",
-    "    'ZAR': 'COD'\n",
-    "}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([[  0.00000000e+00,  -4.01770000e+01,   1.44120000e+01, ...,\n",
-       "         -1.19280070e+04,  -2.56380070e+04,  -3.46530070e+04],\n",
-       "       [  4.01770000e+01,   0.00000000e+00,   5.45890000e+01, ...,\n",
-       "         -1.18878300e+04,  -2.55978300e+04,  -3.46128300e+04],\n",
-       "       [ -1.44120000e+01,  -5.45890000e+01,   0.00000000e+00, ...,\n",
-       "         -1.19424190e+04,  -2.56524190e+04,  -3.46674190e+04],\n",
-       "       ..., \n",
-       "       [  1.19280070e+04,   1.18878300e+04,   1.19424190e+04, ...,\n",
-       "          0.00000000e+00,  -1.37100000e+04,  -2.27250000e+04],\n",
-       "       [  2.56380070e+04,   2.55978300e+04,   2.56524190e+04, ...,\n",
-       "          1.37100000e+04,   0.00000000e+00,  -9.01500000e+03],\n",
-       "       [  3.46530070e+04,   3.46128300e+04,   3.46674190e+04, ...,\n",
-       "          2.27250000e+04,   9.01500000e+03,   0.00000000e+00]])"
-      ]
-     },
-     "execution_count": 32,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "rows = []\n",
-    "for i in range(len(ab)):\n",
-    "    row = []\n",
-    "    val = ab['A'][i]\n",
-    "    for value in ab['A']:\n",
-    "        row.append(val - value)\n",
-    "    rows.append(row)\n",
-    "np.array(rows)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style>\n",
-       "    .dataframe thead tr:only-child th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: left;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>BDI</th>\n",
-       "      <th>NER</th>\n",
-       "      <th>MWI</th>\n",
-       "      <th>LBR</th>\n",
-       "      <th>MOZ</th>\n",
-       "      <th>MDG</th>\n",
-       "      <th>SLE</th>\n",
-       "      <th>GIN</th>\n",
-       "      <th>ZAR</th>\n",
-       "      <th>CAF</th>\n",
-       "      <th>...</th>\n",
-       "      <th>CAN</th>\n",
-       "      <th>SWE</th>\n",
-       "      <th>DNK</th>\n",
-       "      <th>AUS</th>\n",
-       "      <th>ISL</th>\n",
-       "      <th>IRL</th>\n",
-       "      <th>NOR</th>\n",
-       "      <th>USA</th>\n",
-       "      <th>CHE</th>\n",
-       "      <th>LUX</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>BDI</th>\n",
-       "      <td>0.000</td>\n",
-       "      <td>-40.177</td>\n",
-       "      <td>14.412</td>\n",
-       "      <td>-71.667</td>\n",
-       "      <td>-1.341</td>\n",
-       "      <td>-13.603</td>\n",
-       "      <td>-82.367</td>\n",
-       "      <td>-85.657</td>\n",
-       "      <td>-21.456</td>\n",
-       "      <td>29.739</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-12064.007</td>\n",
-       "      <td>-20362.007</td>\n",
-       "      <td>-20716.007</td>\n",
-       "      <td>-14738.007</td>\n",
-       "      <td>-25465.007</td>\n",
-       "      <td>-20400.007</td>\n",
-       "      <td>-28475.007</td>\n",
-       "      <td>-11928.007</td>\n",
-       "      <td>-25638.007</td>\n",
-       "      <td>-34653.007</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>NER</th>\n",
-       "      <td>40.177</td>\n",
-       "      <td>0.000</td>\n",
-       "      <td>54.589</td>\n",
-       "      <td>-31.490</td>\n",
-       "      <td>38.836</td>\n",
-       "      <td>26.574</td>\n",
-       "      <td>-42.190</td>\n",
-       "      <td>-45.480</td>\n",
-       "      <td>18.721</td>\n",
-       "      <td>69.916</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-12023.830</td>\n",
-       "      <td>-20321.830</td>\n",
-       "      <td>-20675.830</td>\n",
-       "      <td>-14697.830</td>\n",
-       "      <td>-25424.830</td>\n",
-       "      <td>-20359.830</td>\n",
-       "      <td>-28434.830</td>\n",
-       "      <td>-11887.830</td>\n",
-       "      <td>-25597.830</td>\n",
-       "      <td>-34612.830</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>MWI</th>\n",
-       "      <td>-14.412</td>\n",
-       "      <td>-54.589</td>\n",
-       "      <td>0.000</td>\n",
-       "      <td>-86.079</td>\n",
-       "      <td>-15.753</td>\n",
-       "      <td>-28.015</td>\n",
-       "      <td>-96.779</td>\n",
-       "      <td>-100.069</td>\n",
-       "      <td>-35.868</td>\n",
-       "      <td>15.327</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-12078.419</td>\n",
-       "      <td>-20376.419</td>\n",
-       "      <td>-20730.419</td>\n",
-       "      <td>-14752.419</td>\n",
-       "      <td>-25479.419</td>\n",
-       "      <td>-20414.419</td>\n",
-       "      <td>-28489.419</td>\n",
-       "      <td>-11942.419</td>\n",
-       "      <td>-25652.419</td>\n",
-       "      <td>-34667.419</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>LBR</th>\n",
-       "      <td>71.667</td>\n",
-       "      <td>31.490</td>\n",
-       "      <td>86.079</td>\n",
-       "      <td>0.000</td>\n",
-       "      <td>70.326</td>\n",
-       "      <td>58.064</td>\n",
-       "      <td>-10.700</td>\n",
-       "      <td>-13.990</td>\n",
-       "      <td>50.211</td>\n",
-       "      <td>101.406</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-11992.340</td>\n",
-       "      <td>-20290.340</td>\n",
-       "      <td>-20644.340</td>\n",
-       "      <td>-14666.340</td>\n",
-       "      <td>-25393.340</td>\n",
-       "      <td>-20328.340</td>\n",
-       "      <td>-28403.340</td>\n",
-       "      <td>-11856.340</td>\n",
-       "      <td>-25566.340</td>\n",
-       "      <td>-34581.340</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>MOZ</th>\n",
-       "      <td>1.341</td>\n",
-       "      <td>-38.836</td>\n",
-       "      <td>15.753</td>\n",
-       "      <td>-70.326</td>\n",
-       "      <td>0.000</td>\n",
-       "      <td>-12.262</td>\n",
-       "      <td>-81.026</td>\n",
-       "      <td>-84.316</td>\n",
-       "      <td>-20.115</td>\n",
-       "      <td>31.080</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-12062.666</td>\n",
-       "      <td>-20360.666</td>\n",
-       "      <td>-20714.666</td>\n",
-       "      <td>-14736.666</td>\n",
-       "      <td>-25463.666</td>\n",
-       "      <td>-20398.666</td>\n",
-       "      <td>-28473.666</td>\n",
-       "      <td>-11926.666</td>\n",
-       "      <td>-25636.666</td>\n",
-       "      <td>-34651.666</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>IRL</th>\n",
-       "      <td>20400.007</td>\n",
-       "      <td>20359.830</td>\n",
-       "      <td>20414.419</td>\n",
-       "      <td>20328.340</td>\n",
-       "      <td>20398.666</td>\n",
-       "      <td>20386.404</td>\n",
-       "      <td>20317.640</td>\n",
-       "      <td>20314.350</td>\n",
-       "      <td>20378.551</td>\n",
-       "      <td>20429.746</td>\n",
-       "      <td>...</td>\n",
-       "      <td>8336.000</td>\n",
-       "      <td>38.000</td>\n",
-       "      <td>-316.000</td>\n",
-       "      <td>5662.000</td>\n",
-       "      <td>-5065.000</td>\n",
-       "      <td>0.000</td>\n",
-       "      <td>-8075.000</td>\n",
-       "      <td>8472.000</td>\n",
-       "      <td>-5238.000</td>\n",
-       "      <td>-14253.000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>NOR</th>\n",
-       "      <td>28475.007</td>\n",
-       "      <td>28434.830</td>\n",
-       "      <td>28489.419</td>\n",
-       "      <td>28403.340</td>\n",
-       "      <td>28473.666</td>\n",
-       "      <td>28461.404</td>\n",
-       "      <td>28392.640</td>\n",
-       "      <td>28389.350</td>\n",
-       "      <td>28453.551</td>\n",
-       "      <td>28504.746</td>\n",
-       "      <td>...</td>\n",
-       "      <td>16411.000</td>\n",
-       "      <td>8113.000</td>\n",
-       "      <td>7759.000</td>\n",
-       "      <td>13737.000</td>\n",
-       "      <td>3010.000</td>\n",
-       "      <td>8075.000</td>\n",
-       "      <td>0.000</td>\n",
-       "      <td>16547.000</td>\n",
-       "      <td>2837.000</td>\n",
-       "      <td>-6178.000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>USA</th>\n",
-       "      <td>11928.007</td>\n",
-       "      <td>11887.830</td>\n",
-       "      <td>11942.419</td>\n",
-       "      <td>11856.340</td>\n",
-       "      <td>11926.666</td>\n",
-       "      <td>11914.404</td>\n",
-       "      <td>11845.640</td>\n",
-       "      <td>11842.350</td>\n",
-       "      <td>11906.551</td>\n",
-       "      <td>11957.746</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-136.000</td>\n",
-       "      <td>-8434.000</td>\n",
-       "      <td>-8788.000</td>\n",
-       "      <td>-2810.000</td>\n",
-       "      <td>-13537.000</td>\n",
-       "      <td>-8472.000</td>\n",
-       "      <td>-16547.000</td>\n",
-       "      <td>0.000</td>\n",
-       "      <td>-13710.000</td>\n",
-       "      <td>-22725.000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>CHE</th>\n",
-       "      <td>25638.007</td>\n",
-       "      <td>25597.830</td>\n",
-       "      <td>25652.419</td>\n",
-       "      <td>25566.340</td>\n",
-       "      <td>25636.666</td>\n",
-       "      <td>25624.404</td>\n",
-       "      <td>25555.640</td>\n",
-       "      <td>25552.350</td>\n",
-       "      <td>25616.551</td>\n",
-       "      <td>25667.746</td>\n",
-       "      <td>...</td>\n",
-       "      <td>13574.000</td>\n",
-       "      <td>5276.000</td>\n",
-       "      <td>4922.000</td>\n",
-       "      <td>10900.000</td>\n",
-       "      <td>173.000</td>\n",
-       "      <td>5238.000</td>\n",
-       "      <td>-2837.000</td>\n",
-       "      <td>13710.000</td>\n",
-       "      <td>0.000</td>\n",
-       "      <td>-9015.000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>LUX</th>\n",
-       "      <td>34653.007</td>\n",
-       "      <td>34612.830</td>\n",
-       "      <td>34667.419</td>\n",
-       "      <td>34581.340</td>\n",
-       "      <td>34651.666</td>\n",
-       "      <td>34639.404</td>\n",
-       "      <td>34570.640</td>\n",
-       "      <td>34567.350</td>\n",
-       "      <td>34631.551</td>\n",
-       "      <td>34682.746</td>\n",
-       "      <td>...</td>\n",
-       "      <td>22589.000</td>\n",
-       "      <td>14291.000</td>\n",
-       "      <td>13937.000</td>\n",
-       "      <td>19915.000</td>\n",
-       "      <td>9188.000</td>\n",
-       "      <td>14253.000</td>\n",
-       "      <td>6178.000</td>\n",
-       "      <td>22725.000</td>\n",
-       "      <td>9015.000</td>\n",
-       "      <td>0.000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>162 rows × 162 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "           BDI        NER        MWI        LBR        MOZ        MDG  \\\n",
-       "BDI      0.000    -40.177     14.412    -71.667     -1.341    -13.603   \n",
-       "NER     40.177      0.000     54.589    -31.490     38.836     26.574   \n",
-       "MWI    -14.412    -54.589      0.000    -86.079    -15.753    -28.015   \n",
-       "LBR     71.667     31.490     86.079      0.000     70.326     58.064   \n",
-       "MOZ      1.341    -38.836     15.753    -70.326      0.000    -12.262   \n",
-       "..         ...        ...        ...        ...        ...        ...   \n",
-       "IRL  20400.007  20359.830  20414.419  20328.340  20398.666  20386.404   \n",
-       "NOR  28475.007  28434.830  28489.419  28403.340  28473.666  28461.404   \n",
-       "USA  11928.007  11887.830  11942.419  11856.340  11926.666  11914.404   \n",
-       "CHE  25638.007  25597.830  25652.419  25566.340  25636.666  25624.404   \n",
-       "LUX  34653.007  34612.830  34667.419  34581.340  34651.666  34639.404   \n",
-       "\n",
-       "           SLE        GIN        ZAR        CAF    ...            CAN  \\\n",
-       "BDI    -82.367    -85.657    -21.456     29.739    ...     -12064.007   \n",
-       "NER    -42.190    -45.480     18.721     69.916    ...     -12023.830   \n",
-       "MWI    -96.779   -100.069    -35.868     15.327    ...     -12078.419   \n",
-       "LBR    -10.700    -13.990     50.211    101.406    ...     -11992.340   \n",
-       "MOZ    -81.026    -84.316    -20.115     31.080    ...     -12062.666   \n",
-       "..         ...        ...        ...        ...    ...            ...   \n",
-       "IRL  20317.640  20314.350  20378.551  20429.746    ...       8336.000   \n",
-       "NOR  28392.640  28389.350  28453.551  28504.746    ...      16411.000   \n",
-       "USA  11845.640  11842.350  11906.551  11957.746    ...       -136.000   \n",
-       "CHE  25555.640  25552.350  25616.551  25667.746    ...      13574.000   \n",
-       "LUX  34570.640  34567.350  34631.551  34682.746    ...      22589.000   \n",
-       "\n",
-       "           SWE        DNK        AUS        ISL        IRL        NOR  \\\n",
-       "BDI -20362.007 -20716.007 -14738.007 -25465.007 -20400.007 -28475.007   \n",
-       "NER -20321.830 -20675.830 -14697.830 -25424.830 -20359.830 -28434.830   \n",
-       "MWI -20376.419 -20730.419 -14752.419 -25479.419 -20414.419 -28489.419   \n",
-       "LBR -20290.340 -20644.340 -14666.340 -25393.340 -20328.340 -28403.340   \n",
-       "MOZ -20360.666 -20714.666 -14736.666 -25463.666 -20398.666 -28473.666   \n",
-       "..         ...        ...        ...        ...        ...        ...   \n",
-       "IRL     38.000   -316.000   5662.000  -5065.000      0.000  -8075.000   \n",
-       "NOR   8113.000   7759.000  13737.000   3010.000   8075.000      0.000   \n",
-       "USA  -8434.000  -8788.000  -2810.000 -13537.000  -8472.000 -16547.000   \n",
-       "CHE   5276.000   4922.000  10900.000    173.000   5238.000  -2837.000   \n",
-       "LUX  14291.000  13937.000  19915.000   9188.000  14253.000   6178.000   \n",
-       "\n",
-       "           USA        CHE        LUX  \n",
-       "BDI -11928.007 -25638.007 -34653.007  \n",
-       "NER -11887.830 -25597.830 -34612.830  \n",
-       "MWI -11942.419 -25652.419 -34667.419  \n",
-       "LBR -11856.340 -25566.340 -34581.340  \n",
-       "MOZ -11926.666 -25636.666 -34651.666  \n",
-       "..         ...        ...        ...  \n",
-       "IRL   8472.000  -5238.000 -14253.000  \n",
-       "NOR  16547.000   2837.000  -6178.000  \n",
-       "USA      0.000 -13710.000 -22725.000  \n",
-       "CHE  13710.000      0.000  -9015.000  \n",
-       "LUX  22725.000   9015.000      0.000  \n",
-       "\n",
-       "[162 rows x 162 columns]"
-      ]
-     },
-     "execution_count": 33,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "a_data = pd.DataFrame(rows)\n",
-    "a_data.columns = ab.index\n",
-    "a_data.index = ab.index\n",
-    "a_data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style>\n",
-       "    .dataframe thead tr:only-child th {\n",
-       "        text-align: right;\n",
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: left;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>BDI</th>\n",
-       "      <th>NER</th>\n",
-       "      <th>MWI</th>\n",
-       "      <th>LBR</th>\n",
-       "      <th>MOZ</th>\n",
-       "      <th>MDG</th>\n",
-       "      <th>SLE</th>\n",
-       "      <th>GIN</th>\n",
-       "      <th>ZAR</th>\n",
-       "      <th>CAF</th>\n",
-       "      <th>...</th>\n",
-       "      <th>CAN</th>\n",
-       "      <th>SWE</th>\n",
-       "      <th>DNK</th>\n",
-       "      <th>AUS</th>\n",
-       "      <th>ISL</th>\n",
-       "      <th>IRL</th>\n",
-       "      <th>NOR</th>\n",
-       "      <th>USA</th>\n",
-       "      <th>CHE</th>\n",
-       "      <th>LUX</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>BDI</th>\n",
-       "      <td>0.0000</td>\n",
-       "      <td>0.0028</td>\n",
-       "      <td>0.0002</td>\n",
-       "      <td>0.0031</td>\n",
-       "      <td>-0.0039</td>\n",
-       "      <td>-0.0024</td>\n",
-       "      <td>0.0029</td>\n",
-       "      <td>0.0028</td>\n",
-       "      <td>-0.0028</td>\n",
-       "      <td>-0.0110</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.0014</td>\n",
-       "      <td>0.0064</td>\n",
-       "      <td>0.0062</td>\n",
-       "      <td>0.0020</td>\n",
-       "      <td>0.0076</td>\n",
-       "      <td>0.0037</td>\n",
-       "      <td>0.0067</td>\n",
-       "      <td>-0.0032</td>\n",
-       "      <td>0.0035</td>\n",
-       "      <td>0.0039</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>NER</th>\n",
-       "      <td>-0.0028</td>\n",
-       "      <td>0.0000</td>\n",
-       "      <td>-0.0026</td>\n",
-       "      <td>0.0003</td>\n",
-       "      <td>-0.0067</td>\n",
-       "      <td>-0.0052</td>\n",
-       "      <td>0.0001</td>\n",
-       "      <td>0.0000</td>\n",
-       "      <td>-0.0056</td>\n",
-       "      <td>-0.0138</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-0.0014</td>\n",
-       "      <td>0.0036</td>\n",
-       "      <td>0.0034</td>\n",
-       "      <td>-0.0008</td>\n",
-       "      <td>0.0048</td>\n",
-       "      <td>0.0009</td>\n",
-       "      <td>0.0039</td>\n",
-       "      <td>-0.0060</td>\n",
-       "      <td>0.0007</td>\n",
-       "      <td>0.0011</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>MWI</th>\n",
-       "      <td>-0.0002</td>\n",
-       "      <td>0.0026</td>\n",
-       "      <td>0.0000</td>\n",
-       "      <td>0.0029</td>\n",
-       "      <td>-0.0041</td>\n",
-       "      <td>-0.0026</td>\n",
-       "      <td>0.0027</td>\n",
-       "      <td>0.0026</td>\n",
-       "      <td>-0.0030</td>\n",
-       "      <td>-0.0112</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.0012</td>\n",
-       "      <td>0.0062</td>\n",
-       "      <td>0.0060</td>\n",
-       "      <td>0.0018</td>\n",
-       "      <td>0.0074</td>\n",
-       "      <td>0.0035</td>\n",
-       "      <td>0.0065</td>\n",
-       "      <td>-0.0034</td>\n",
-       "      <td>0.0033</td>\n",
-       "      <td>0.0037</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>LBR</th>\n",
-       "      <td>-0.0031</td>\n",
-       "      <td>-0.0003</td>\n",
-       "      <td>-0.0029</td>\n",
-       "      <td>0.0000</td>\n",
-       "      <td>-0.0070</td>\n",
-       "      <td>-0.0055</td>\n",
-       "      <td>-0.0002</td>\n",
-       "      <td>-0.0003</td>\n",
-       "      <td>-0.0059</td>\n",
-       "      <td>-0.0141</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-0.0017</td>\n",
-       "      <td>0.0033</td>\n",
-       "      <td>0.0031</td>\n",
-       "      <td>-0.0011</td>\n",
-       "      <td>0.0045</td>\n",
-       "      <td>0.0006</td>\n",
-       "      <td>0.0036</td>\n",
-       "      <td>-0.0063</td>\n",
-       "      <td>0.0004</td>\n",
-       "      <td>0.0008</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>MOZ</th>\n",
-       "      <td>0.0039</td>\n",
-       "      <td>0.0067</td>\n",
-       "      <td>0.0041</td>\n",
-       "      <td>0.0070</td>\n",
-       "      <td>0.0000</td>\n",
-       "      <td>0.0015</td>\n",
-       "      <td>0.0068</td>\n",
-       "      <td>0.0067</td>\n",
-       "      <td>0.0011</td>\n",
-       "      <td>-0.0071</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.0053</td>\n",
-       "      <td>0.0103</td>\n",
-       "      <td>0.0101</td>\n",
-       "      <td>0.0059</td>\n",
-       "      <td>0.0115</td>\n",
-       "      <td>0.0076</td>\n",
-       "      <td>0.0106</td>\n",
-       "      <td>0.0007</td>\n",
-       "      <td>0.0074</td>\n",
-       "      <td>0.0078</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>IRL</th>\n",
-       "      <td>-0.0037</td>\n",
-       "      <td>-0.0009</td>\n",
-       "      <td>-0.0035</td>\n",
-       "      <td>-0.0006</td>\n",
-       "      <td>-0.0076</td>\n",
-       "      <td>-0.0061</td>\n",
-       "      <td>-0.0008</td>\n",
-       "      <td>-0.0009</td>\n",
-       "      <td>-0.0065</td>\n",
-       "      <td>-0.0147</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-0.0023</td>\n",
-       "      <td>0.0027</td>\n",
-       "      <td>0.0025</td>\n",
-       "      <td>-0.0017</td>\n",
-       "      <td>0.0039</td>\n",
-       "      <td>0.0000</td>\n",
-       "      <td>0.0030</td>\n",
-       "      <td>-0.0069</td>\n",
-       "      <td>-0.0002</td>\n",
-       "      <td>0.0002</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>NOR</th>\n",
-       "      <td>-0.0067</td>\n",
-       "      <td>-0.0039</td>\n",
-       "      <td>-0.0065</td>\n",
-       "      <td>-0.0036</td>\n",
-       "      <td>-0.0106</td>\n",
-       "      <td>-0.0091</td>\n",
-       "      <td>-0.0038</td>\n",
-       "      <td>-0.0039</td>\n",
-       "      <td>-0.0095</td>\n",
-       "      <td>-0.0177</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-0.0053</td>\n",
-       "      <td>-0.0003</td>\n",
-       "      <td>-0.0005</td>\n",
-       "      <td>-0.0047</td>\n",
-       "      <td>0.0009</td>\n",
-       "      <td>-0.0030</td>\n",
-       "      <td>0.0000</td>\n",
-       "      <td>-0.0099</td>\n",
-       "      <td>-0.0032</td>\n",
-       "      <td>-0.0028</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>USA</th>\n",
-       "      <td>0.0032</td>\n",
-       "      <td>0.0060</td>\n",
-       "      <td>0.0034</td>\n",
-       "      <td>0.0063</td>\n",
-       "      <td>-0.0007</td>\n",
-       "      <td>0.0008</td>\n",
-       "      <td>0.0061</td>\n",
-       "      <td>0.0060</td>\n",
-       "      <td>0.0004</td>\n",
-       "      <td>-0.0078</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.0046</td>\n",
-       "      <td>0.0096</td>\n",
-       "      <td>0.0094</td>\n",
-       "      <td>0.0052</td>\n",
-       "      <td>0.0108</td>\n",
-       "      <td>0.0069</td>\n",
-       "      <td>0.0099</td>\n",
-       "      <td>0.0000</td>\n",
-       "      <td>0.0067</td>\n",
-       "      <td>0.0071</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>CHE</th>\n",
-       "      <td>-0.0035</td>\n",
-       "      <td>-0.0007</td>\n",
-       "      <td>-0.0033</td>\n",
-       "      <td>-0.0004</td>\n",
-       "      <td>-0.0074</td>\n",
-       "      <td>-0.0059</td>\n",
-       "      <td>-0.0006</td>\n",
-       "      <td>-0.0007</td>\n",
-       "      <td>-0.0063</td>\n",
-       "      <td>-0.0145</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-0.0021</td>\n",
-       "      <td>0.0029</td>\n",
-       "      <td>0.0027</td>\n",
-       "      <td>-0.0015</td>\n",
-       "      <td>0.0041</td>\n",
-       "      <td>0.0002</td>\n",
-       "      <td>0.0032</td>\n",
-       "      <td>-0.0067</td>\n",
-       "      <td>0.0000</td>\n",
-       "      <td>0.0004</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>LUX</th>\n",
-       "      <td>-0.0039</td>\n",
-       "      <td>-0.0011</td>\n",
-       "      <td>-0.0037</td>\n",
-       "      <td>-0.0008</td>\n",
-       "      <td>-0.0078</td>\n",
-       "      <td>-0.0063</td>\n",
-       "      <td>-0.0010</td>\n",
-       "      <td>-0.0011</td>\n",
-       "      <td>-0.0067</td>\n",
-       "      <td>-0.0149</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-0.0025</td>\n",
-       "      <td>0.0025</td>\n",
-       "      <td>0.0023</td>\n",
-       "      <td>-0.0019</td>\n",
-       "      <td>0.0037</td>\n",
-       "      <td>-0.0002</td>\n",
-       "      <td>0.0028</td>\n",
-       "      <td>-0.0071</td>\n",
-       "      <td>-0.0004</td>\n",
-       "      <td>0.0000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>162 rows × 162 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "        BDI     NER     MWI     LBR     MOZ     MDG     SLE     GIN     ZAR  \\\n",
-       "BDI  0.0000  0.0028  0.0002  0.0031 -0.0039 -0.0024  0.0029  0.0028 -0.0028   \n",
-       "NER -0.0028  0.0000 -0.0026  0.0003 -0.0067 -0.0052  0.0001  0.0000 -0.0056   \n",
-       "MWI -0.0002  0.0026  0.0000  0.0029 -0.0041 -0.0026  0.0027  0.0026 -0.0030   \n",
-       "LBR -0.0031 -0.0003 -0.0029  0.0000 -0.0070 -0.0055 -0.0002 -0.0003 -0.0059   \n",
-       "MOZ  0.0039  0.0067  0.0041  0.0070  0.0000  0.0015  0.0068  0.0067  0.0011   \n",
-       "..      ...     ...     ...     ...     ...     ...     ...     ...     ...   \n",
-       "IRL -0.0037 -0.0009 -0.0035 -0.0006 -0.0076 -0.0061 -0.0008 -0.0009 -0.0065   \n",
-       "NOR -0.0067 -0.0039 -0.0065 -0.0036 -0.0106 -0.0091 -0.0038 -0.0039 -0.0095   \n",
-       "USA  0.0032  0.0060  0.0034  0.0063 -0.0007  0.0008  0.0061  0.0060  0.0004   \n",
-       "CHE -0.0035 -0.0007 -0.0033 -0.0004 -0.0074 -0.0059 -0.0006 -0.0007 -0.0063   \n",
-       "LUX -0.0039 -0.0011 -0.0037 -0.0008 -0.0078 -0.0063 -0.0010 -0.0011 -0.0067   \n",
-       "\n",
-       "        CAF   ...       CAN     SWE     DNK     AUS     ISL     IRL     NOR  \\\n",
-       "BDI -0.0110   ...    0.0014  0.0064  0.0062  0.0020  0.0076  0.0037  0.0067   \n",
-       "NER -0.0138   ...   -0.0014  0.0036  0.0034 -0.0008  0.0048  0.0009  0.0039   \n",
-       "MWI -0.0112   ...    0.0012  0.0062  0.0060  0.0018  0.0074  0.0035  0.0065   \n",
-       "LBR -0.0141   ...   -0.0017  0.0033  0.0031 -0.0011  0.0045  0.0006  0.0036   \n",
-       "MOZ -0.0071   ...    0.0053  0.0103  0.0101  0.0059  0.0115  0.0076  0.0106   \n",
-       "..      ...   ...       ...     ...     ...     ...     ...     ...     ...   \n",
-       "IRL -0.0147   ...   -0.0023  0.0027  0.0025 -0.0017  0.0039  0.0000  0.0030   \n",
-       "NOR -0.0177   ...   -0.0053 -0.0003 -0.0005 -0.0047  0.0009 -0.0030  0.0000   \n",
-       "USA -0.0078   ...    0.0046  0.0096  0.0094  0.0052  0.0108  0.0069  0.0099   \n",
-       "CHE -0.0145   ...   -0.0021  0.0029  0.0027 -0.0015  0.0041  0.0002  0.0032   \n",
-       "LUX -0.0149   ...   -0.0025  0.0025  0.0023 -0.0019  0.0037 -0.0002  0.0028   \n",
-       "\n",
-       "        USA     CHE     LUX  \n",
-       "BDI -0.0032  0.0035  0.0039  \n",
-       "NER -0.0060  0.0007  0.0011  \n",
-       "MWI -0.0034  0.0033  0.0037  \n",
-       "LBR -0.0063  0.0004  0.0008  \n",
-       "MOZ  0.0007  0.0074  0.0078  \n",
-       "..      ...     ...     ...  \n",
-       "IRL -0.0069 -0.0002  0.0002  \n",
-       "NOR -0.0099 -0.0032 -0.0028  \n",
-       "USA  0.0000  0.0067  0.0071  \n",
-       "CHE -0.0067  0.0000  0.0004  \n",
-       "LUX -0.0071 -0.0004  0.0000  \n",
-       "\n",
-       "[162 rows x 162 columns]"
-      ]
-     },
-     "execution_count": 34,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "rows = []\n",
-    "for i in range(len(ab)):\n",
-    "    row = []\n",
-    "    val = ab['B'][i]\n",
-    "    for value in ab['B']:\n",
-    "        row.append(val - value)\n",
-    "    rows.append(row)\n",
-    "np.array(rows)\n",
-    "b_data = pd.DataFrame(rows)\n",
-    "b_data.columns = ab.index\n",
-    "b_data.index = ab.index\n",
-    "b_data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style>\n",
-       "    .dataframe thead tr:only-child th {\n",
-       "        text-align: right;\n",
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: left;\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th>Country</th>\n",
-       "      <th>AFG</th>\n",
-       "      <th>ALB</th>\n",
-       "      <th>DZA</th>\n",
-       "      <th>AND</th>\n",
-       "      <th>AGO</th>\n",
-       "      <th>ATG</th>\n",
-       "      <th>ARG</th>\n",
-       "      <th>ARM</th>\n",
-       "      <th>AUS</th>\n",
-       "      <th>AUT</th>\n",
-       "      <th>...</th>\n",
-       "      <th>GBR</th>\n",
-       "      <th>USA</th>\n",
-       "      <th>URY</th>\n",
-       "      <th>UZB</th>\n",
-       "      <th>VUT</th>\n",
-       "      <th>VEN</th>\n",
-       "      <th>VNM</th>\n",
-       "      <th>YEM</th>\n",
-       "      <th>ZMB</th>\n",
-       "      <th>ZWE</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Country</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>AFG</th>\n",
-       "      <td>0</td>\n",
-       "      <td>-44</td>\n",
-       "      <td>-11</td>\n",
-       "      <td>-71</td>\n",
-       "      <td>0</td>\n",
-       "      <td>-59</td>\n",
-       "      <td>-58</td>\n",
-       "      <td>-21</td>\n",
-       "      <td>-74</td>\n",
-       "      <td>-71</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-71</td>\n",
-       "      <td>-65</td>\n",
-       "      <td>-74</td>\n",
-       "      <td>21</td>\n",
-       "      <td>-56</td>\n",
-       "      <td>-6</td>\n",
-       "      <td>4</td>\n",
-       "      <td>10</td>\n",
-       "      <td>-32</td>\n",
-       "      <td>-8</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>ALB</th>\n",
-       "      <td>44</td>\n",
-       "      <td>0</td>\n",
-       "      <td>33</td>\n",
-       "      <td>-27</td>\n",
-       "      <td>44</td>\n",
-       "      <td>-15</td>\n",
-       "      <td>-14</td>\n",
-       "      <td>23</td>\n",
-       "      <td>-30</td>\n",
-       "      <td>-27</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-27</td>\n",
-       "      <td>-21</td>\n",
-       "      <td>-30</td>\n",
-       "      <td>65</td>\n",
-       "      <td>-12</td>\n",
-       "      <td>38</td>\n",
-       "      <td>48</td>\n",
-       "      <td>54</td>\n",
-       "      <td>12</td>\n",
-       "      <td>36</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>DZA</th>\n",
-       "      <td>11</td>\n",
-       "      <td>-33</td>\n",
-       "      <td>0</td>\n",
-       "      <td>-60</td>\n",
-       "      <td>11</td>\n",
-       "      <td>-48</td>\n",
-       "      <td>-47</td>\n",
-       "      <td>-10</td>\n",
-       "      <td>-63</td>\n",
-       "      <td>-60</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-60</td>\n",
-       "      <td>-54</td>\n",
-       "      <td>-63</td>\n",
-       "      <td>32</td>\n",
-       "      <td>-45</td>\n",
-       "      <td>5</td>\n",
-       "      <td>15</td>\n",
-       "      <td>21</td>\n",
-       "      <td>-21</td>\n",
-       "      <td>3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>AND</th>\n",
-       "      <td>71</td>\n",
-       "      <td>27</td>\n",
-       "      <td>60</td>\n",
-       "      <td>0</td>\n",
-       "      <td>71</td>\n",
-       "      <td>12</td>\n",
-       "      <td>13</td>\n",
-       "      <td>50</td>\n",
-       "      <td>-3</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>6</td>\n",
-       "      <td>-3</td>\n",
-       "      <td>92</td>\n",
-       "      <td>15</td>\n",
-       "      <td>65</td>\n",
-       "      <td>75</td>\n",
-       "      <td>81</td>\n",
-       "      <td>39</td>\n",
-       "      <td>63</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>AGO</th>\n",
-       "      <td>0</td>\n",
-       "      <td>-44</td>\n",
-       "      <td>-11</td>\n",
-       "      <td>-71</td>\n",
-       "      <td>0</td>\n",
-       "      <td>-59</td>\n",
-       "      <td>-58</td>\n",
-       "      <td>-21</td>\n",
-       "      <td>-74</td>\n",
-       "      <td>-71</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-71</td>\n",
-       "      <td>-65</td>\n",
-       "      <td>-74</td>\n",
-       "      <td>21</td>\n",
-       "      <td>-56</td>\n",
-       "      <td>-6</td>\n",
-       "      <td>4</td>\n",
-       "      <td>10</td>\n",
-       "      <td>-32</td>\n",
-       "      <td>-8</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>VEN</th>\n",
-       "      <td>6</td>\n",
-       "      <td>-38</td>\n",
-       "      <td>-5</td>\n",
-       "      <td>-65</td>\n",
-       "      <td>6</td>\n",
-       "      <td>-53</td>\n",
-       "      <td>-52</td>\n",
-       "      <td>-15</td>\n",
-       "      <td>-68</td>\n",
-       "      <td>-65</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-65</td>\n",
-       "      <td>-59</td>\n",
-       "      <td>-68</td>\n",
-       "      <td>27</td>\n",
-       "      <td>-50</td>\n",
-       "      <td>0</td>\n",
-       "      <td>10</td>\n",
-       "      <td>16</td>\n",
-       "      <td>-26</td>\n",
-       "      <td>-2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>VNM</th>\n",
-       "      <td>-4</td>\n",
-       "      <td>-48</td>\n",
-       "      <td>-15</td>\n",
-       "      <td>-75</td>\n",
-       "      <td>-4</td>\n",
-       "      <td>-63</td>\n",
-       "      <td>-62</td>\n",
-       "      <td>-25</td>\n",
-       "      <td>-78</td>\n",
-       "      <td>-75</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-75</td>\n",
-       "      <td>-69</td>\n",
-       "      <td>-78</td>\n",
-       "      <td>17</td>\n",
-       "      <td>-60</td>\n",
-       "      <td>-10</td>\n",
-       "      <td>0</td>\n",
-       "      <td>6</td>\n",
-       "      <td>-36</td>\n",
-       "      <td>-12</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>YEM</th>\n",
-       "      <td>-10</td>\n",
-       "      <td>-54</td>\n",
-       "      <td>-21</td>\n",
-       "      <td>-81</td>\n",
-       "      <td>-10</td>\n",
-       "      <td>-69</td>\n",
-       "      <td>-68</td>\n",
-       "      <td>-31</td>\n",
-       "      <td>-84</td>\n",
-       "      <td>-81</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-81</td>\n",
-       "      <td>-75</td>\n",
-       "      <td>-84</td>\n",
-       "      <td>11</td>\n",
-       "      <td>-66</td>\n",
-       "      <td>-16</td>\n",
-       "      <td>-6</td>\n",
-       "      <td>0</td>\n",
-       "      <td>-42</td>\n",
-       "      <td>-18</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>ZMB</th>\n",
-       "      <td>32</td>\n",
-       "      <td>-12</td>\n",
-       "      <td>21</td>\n",
-       "      <td>-39</td>\n",
-       "      <td>32</td>\n",
-       "      <td>-27</td>\n",
-       "      <td>-26</td>\n",
-       "      <td>11</td>\n",
-       "      <td>-42</td>\n",
-       "      <td>-39</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-39</td>\n",
-       "      <td>-33</td>\n",
-       "      <td>-42</td>\n",
-       "      <td>53</td>\n",
-       "      <td>-24</td>\n",
-       "      <td>26</td>\n",
-       "      <td>36</td>\n",
-       "      <td>42</td>\n",
-       "      <td>0</td>\n",
-       "      <td>24</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>ZWE</th>\n",
-       "      <td>8</td>\n",
-       "      <td>-36</td>\n",
-       "      <td>-3</td>\n",
-       "      <td>-63</td>\n",
-       "      <td>8</td>\n",
-       "      <td>-51</td>\n",
-       "      <td>-50</td>\n",
-       "      <td>-13</td>\n",
-       "      <td>-66</td>\n",
-       "      <td>-63</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-63</td>\n",
-       "      <td>-57</td>\n",
-       "      <td>-66</td>\n",
-       "      <td>29</td>\n",
-       "      <td>-48</td>\n",
-       "      <td>2</td>\n",
-       "      <td>12</td>\n",
-       "      <td>18</td>\n",
-       "      <td>-24</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>195 rows × 195 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "Country  AFG  ALB  DZA  AND  AGO  ATG  ARG  ARM  AUS  AUT ...   GBR  USA  URY  \\\n",
-       "Country                                                   ...                   \n",
-       "AFG        0  -44  -11  -71    0  -59  -58  -21  -74  -71 ...   -71  -65  -74   \n",
-       "ALB       44    0   33  -27   44  -15  -14   23  -30  -27 ...   -27  -21  -30   \n",
-       "DZA       11  -33    0  -60   11  -48  -47  -10  -63  -60 ...   -60  -54  -63   \n",
-       "AND       71   27   60    0   71   12   13   50   -3    0 ...     0    6   -3   \n",
-       "AGO        0  -44  -11  -71    0  -59  -58  -21  -74  -71 ...   -71  -65  -74   \n",
-       "...      ...  ...  ...  ...  ...  ...  ...  ...  ...  ... ...   ...  ...  ...   \n",
-       "VEN        6  -38   -5  -65    6  -53  -52  -15  -68  -65 ...   -65  -59  -68   \n",
-       "VNM       -4  -48  -15  -75   -4  -63  -62  -25  -78  -75 ...   -75  -69  -78   \n",
-       "YEM      -10  -54  -21  -81  -10  -69  -68  -31  -84  -81 ...   -81  -75  -84   \n",
-       "ZMB       32  -12   21  -39   32  -27  -26   11  -42  -39 ...   -39  -33  -42   \n",
-       "ZWE        8  -36   -3  -63    8  -51  -50  -13  -66  -63 ...   -63  -57  -66   \n",
-       "\n",
-       "Country  UZB  VUT  VEN  VNM  YEM  ZMB  ZWE  \n",
-       "Country                                     \n",
-       "AFG       21  -56   -6    4   10  -32   -8  \n",
-       "ALB       65  -12   38   48   54   12   36  \n",
-       "DZA       32  -45    5   15   21  -21    3  \n",
-       "AND       92   15   65   75   81   39   63  \n",
-       "AGO       21  -56   -6    4   10  -32   -8  \n",
-       "...      ...  ...  ...  ...  ...  ...  ...  \n",
-       "VEN       27  -50    0   10   16  -26   -2  \n",
-       "VNM       17  -60  -10    0    6  -36  -12  \n",
-       "YEM       11  -66  -16   -6    0  -42  -18  \n",
-       "ZMB       53  -24   26   36   42    0   24  \n",
-       "ZWE       29  -48    2   12   18  -24    0  \n",
-       "\n",
-       "[195 rows x 195 columns]"
-      ]
-     },
-     "execution_count": 35,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "rows = []\n",
-    "for i in range(len(fi)):\n",
-    "    row = []\n",
-    "    val = fi['Total Aggr'][i]\n",
-    "    for value in fi['Total Aggr']:\n",
-    "        row.append(val - value)\n",
-    "    rows.append(row)\n",
-    "freedom_index = pd.DataFrame(rows)\n",
-    "freedom_index.columns = fi['Country']\n",
-    "freedom_index.index = fi['Country']\n",
-    "freedom_index"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style>\n",
-       "    .dataframe thead tr:only-child th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: left;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th>Country</th>\n",
-       "      <th>KHM</th>\n",
-       "      <th>COM</th>\n",
-       "      <th>CIV</th>\n",
-       "      <th>GNB</th>\n",
-       "      <th>MDG</th>\n",
-       "      <th>MDV</th>\n",
-       "      <th>MRT</th>\n",
-       "      <th>FSM</th>\n",
-       "      <th>MOZ</th>\n",
-       "      <th>WSM</th>\n",
-       "      <th>...</th>\n",
-       "      <th>GNQ</th>\n",
-       "      <th>ERI</th>\n",
-       "      <th>IRQ</th>\n",
-       "      <th>LBY</th>\n",
-       "      <th>AGO</th>\n",
-       "      <th>AFG</th>\n",
-       "      <th>PRK</th>\n",
-       "      <th>SOM</th>\n",
-       "      <th>SYR</th>\n",
-       "      <th>TKM</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Country</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>KHM</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-95</td>\n",
-       "      <td>-95</td>\n",
-       "      <td>-95</td>\n",
-       "      <td>-95</td>\n",
-       "      <td>-96</td>\n",
-       "      <td>-97</td>\n",
-       "      <td>-97</td>\n",
-       "      <td>-97</td>\n",
-       "      <td>-97</td>\n",
-       "      <td>-97</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>COM</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-95</td>\n",
-       "      <td>-95</td>\n",
-       "      <td>-95</td>\n",
-       "      <td>-95</td>\n",
-       "      <td>-96</td>\n",
-       "      <td>-97</td>\n",
-       "      <td>-97</td>\n",
-       "      <td>-97</td>\n",
-       "      <td>-97</td>\n",
-       "      <td>-97</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>CIV</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-95</td>\n",
-       "      <td>-95</td>\n",
-       "      <td>-95</td>\n",
-       "      <td>-95</td>\n",
-       "      <td>-96</td>\n",
-       "      <td>-97</td>\n",
-       "      <td>-97</td>\n",
-       "      <td>-97</td>\n",
-       "      <td>-97</td>\n",
-       "      <td>-97</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>GNB</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-95</td>\n",
-       "      <td>-95</td>\n",
-       "      <td>-95</td>\n",
-       "      <td>-95</td>\n",
-       "      <td>-96</td>\n",
-       "      <td>-97</td>\n",
-       "      <td>-97</td>\n",
-       "      <td>-97</td>\n",
-       "      <td>-97</td>\n",
-       "      <td>-97</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>MDG</th>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-95</td>\n",
-       "      <td>-95</td>\n",
-       "      <td>-95</td>\n",
-       "      <td>-95</td>\n",
-       "      <td>-96</td>\n",
-       "      <td>-97</td>\n",
-       "      <td>-97</td>\n",
-       "      <td>-97</td>\n",
-       "      <td>-97</td>\n",
-       "      <td>-97</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>AFG</th>\n",
-       "      <td>97</td>\n",
-       "      <td>97</td>\n",
-       "      <td>97</td>\n",
-       "      <td>97</td>\n",
-       "      <td>97</td>\n",
-       "      <td>97</td>\n",
-       "      <td>97</td>\n",
-       "      <td>97</td>\n",
-       "      <td>97</td>\n",
-       "      <td>97</td>\n",
-       "      <td>...</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>PRK</th>\n",
-       "      <td>97</td>\n",
-       "      <td>97</td>\n",
-       "      <td>97</td>\n",
-       "      <td>97</td>\n",
-       "      <td>97</td>\n",
-       "      <td>97</td>\n",
-       "      <td>97</td>\n",
-       "      <td>97</td>\n",
-       "      <td>97</td>\n",
-       "      <td>97</td>\n",
-       "      <td>...</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>SOM</th>\n",
-       "      <td>97</td>\n",
-       "      <td>97</td>\n",
-       "      <td>97</td>\n",
-       "      <td>97</td>\n",
-       "      <td>97</td>\n",
-       "      <td>97</td>\n",
-       "      <td>97</td>\n",
-       "      <td>97</td>\n",
-       "      <td>97</td>\n",
-       "      <td>97</td>\n",
-       "      <td>...</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>SYR</th>\n",
-       "      <td>97</td>\n",
-       "      <td>97</td>\n",
-       "      <td>97</td>\n",
-       "      <td>97</td>\n",
-       "      <td>97</td>\n",
-       "      <td>97</td>\n",
-       "      <td>97</td>\n",
-       "      <td>97</td>\n",
-       "      <td>97</td>\n",
-       "      <td>97</td>\n",
-       "      <td>...</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>TKM</th>\n",
-       "      <td>97</td>\n",
-       "      <td>97</td>\n",
-       "      <td>97</td>\n",
-       "      <td>97</td>\n",
-       "      <td>97</td>\n",
-       "      <td>97</td>\n",
-       "      <td>97</td>\n",
-       "      <td>97</td>\n",
-       "      <td>97</td>\n",
-       "      <td>97</td>\n",
-       "      <td>...</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>199 rows × 199 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "Country  KHM  COM  CIV  GNB  MDG  MDV  MRT  FSM  MOZ  WSM ...   GNQ  ERI  IRQ  \\\n",
-       "Country                                                   ...                   \n",
-       "KHM        0    0    0    0    0    0    0    0    0    0 ...   -95  -95  -95   \n",
-       "COM        0    0    0    0    0    0    0    0    0    0 ...   -95  -95  -95   \n",
-       "CIV        0    0    0    0    0    0    0    0    0    0 ...   -95  -95  -95   \n",
-       "GNB        0    0    0    0    0    0    0    0    0    0 ...   -95  -95  -95   \n",
-       "MDG        0    0    0    0    0    0    0    0    0    0 ...   -95  -95  -95   \n",
-       "...      ...  ...  ...  ...  ...  ...  ...  ...  ...  ... ...   ...  ...  ...   \n",
-       "AFG       97   97   97   97   97   97   97   97   97   97 ...     2    2    2   \n",
-       "PRK       97   97   97   97   97   97   97   97   97   97 ...     2    2    2   \n",
-       "SOM       97   97   97   97   97   97   97   97   97   97 ...     2    2    2   \n",
-       "SYR       97   97   97   97   97   97   97   97   97   97 ...     2    2    2   \n",
-       "TKM       97   97   97   97   97   97   97   97   97   97 ...     2    2    2   \n",
-       "\n",
-       "Country  LBY  AGO  AFG  PRK  SOM  SYR  TKM  \n",
-       "Country                                     \n",
-       "KHM      -95  -96  -97  -97  -97  -97  -97  \n",
-       "COM      -95  -96  -97  -97  -97  -97  -97  \n",
-       "CIV      -95  -96  -97  -97  -97  -97  -97  \n",
-       "GNB      -95  -96  -97  -97  -97  -97  -97  \n",
-       "MDG      -95  -96  -97  -97  -97  -97  -97  \n",
-       "...      ...  ...  ...  ...  ...  ...  ...  \n",
-       "AFG        2    1    0    0    0    0    0  \n",
-       "PRK        2    1    0    0    0    0    0  \n",
-       "SOM        2    1    0    0    0    0    0  \n",
-       "SYR        2    1    0    0    0    0    0  \n",
-       "TKM        2    1    0    0    0    0    0  \n",
-       "\n",
-       "[199 rows x 199 columns]"
-      ]
-     },
-     "execution_count": 36,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "rows = []\n",
-    "pi.index = pi[\"Country\"]\n",
-    "for i in range(len(pi)):\n",
-    "    row = []\n",
-    "    val = pi['Rank (1 = most welcoming)'][i]\n",
-    "    for value in pi['Rank (1 = most welcoming)']:\n",
-    "        row.append(val - value)\n",
-    "    rows.append(row)\n",
-    "passport_index = pd.DataFrame(rows)\n",
-    "passport_index.columns = pi['Country']\n",
-    "#passport_index[\"Country\"] = pi[\"Country\"]\n",
-    "passport_index.index = pi['Country']\n",
-    "passport_index"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style>\n",
-       "    .dataframe thead tr:only-child th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: left;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th>Country</th>\n",
-       "      <th>KHM</th>\n",
-       "      <th>QAT</th>\n",
-       "      <th>BLR</th>\n",
-       "      <th>THA</th>\n",
-       "      <th>GGY</th>\n",
-       "      <th>TON</th>\n",
-       "      <th>LAO</th>\n",
-       "      <th>JEY</th>\n",
-       "      <th>VUT</th>\n",
-       "      <th>PNG</th>\n",
-       "      <th>...</th>\n",
-       "      <th>HTI</th>\n",
-       "      <th>BIH</th>\n",
-       "      <th>NPL</th>\n",
-       "      <th>SEN</th>\n",
-       "      <th>SYR</th>\n",
-       "      <th>COG</th>\n",
-       "      <th>DJI</th>\n",
-       "      <th>BFA</th>\n",
-       "      <th>LBR</th>\n",
-       "      <th>ZWE</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Country</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>KHM</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-0.4</td>\n",
-       "      <td>-0.4</td>\n",
-       "      <td>-0.6</td>\n",
-       "      <td>-0.6</td>\n",
-       "      <td>-0.8</td>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>-1.4</td>\n",
-       "      <td>-1.4</td>\n",
-       "      <td>-1.6</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-40.3</td>\n",
-       "      <td>-42.9</td>\n",
-       "      <td>-45.7</td>\n",
-       "      <td>-47.7</td>\n",
-       "      <td>-49.7</td>\n",
-       "      <td>-52.7</td>\n",
-       "      <td>-59.7</td>\n",
-       "      <td>-76.7</td>\n",
-       "      <td>-84.7</td>\n",
-       "      <td>-94.7</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>QAT</th>\n",
-       "      <td>0.4</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-0.2</td>\n",
-       "      <td>-0.2</td>\n",
-       "      <td>-0.4</td>\n",
-       "      <td>-0.6</td>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>-1.2</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-39.9</td>\n",
-       "      <td>-42.5</td>\n",
-       "      <td>-45.3</td>\n",
-       "      <td>-47.3</td>\n",
-       "      <td>-49.3</td>\n",
-       "      <td>-52.3</td>\n",
-       "      <td>-59.3</td>\n",
-       "      <td>-76.3</td>\n",
-       "      <td>-84.3</td>\n",
-       "      <td>-94.3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>BLR</th>\n",
-       "      <td>0.4</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-0.2</td>\n",
-       "      <td>-0.2</td>\n",
-       "      <td>-0.4</td>\n",
-       "      <td>-0.6</td>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>-1.2</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-39.9</td>\n",
-       "      <td>-42.5</td>\n",
-       "      <td>-45.3</td>\n",
-       "      <td>-47.3</td>\n",
-       "      <td>-49.3</td>\n",
-       "      <td>-52.3</td>\n",
-       "      <td>-59.3</td>\n",
-       "      <td>-76.3</td>\n",
-       "      <td>-84.3</td>\n",
-       "      <td>-94.3</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>THA</th>\n",
-       "      <td>0.6</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-0.2</td>\n",
-       "      <td>-0.4</td>\n",
-       "      <td>-0.8</td>\n",
-       "      <td>-0.8</td>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-39.7</td>\n",
-       "      <td>-42.3</td>\n",
-       "      <td>-45.1</td>\n",
-       "      <td>-47.1</td>\n",
-       "      <td>-49.1</td>\n",
-       "      <td>-52.1</td>\n",
-       "      <td>-59.1</td>\n",
-       "      <td>-76.1</td>\n",
-       "      <td>-84.1</td>\n",
-       "      <td>-94.1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>GGY</th>\n",
-       "      <td>0.6</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>0.2</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-0.2</td>\n",
-       "      <td>-0.4</td>\n",
-       "      <td>-0.8</td>\n",
-       "      <td>-0.8</td>\n",
-       "      <td>-1.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-39.7</td>\n",
-       "      <td>-42.3</td>\n",
-       "      <td>-45.1</td>\n",
-       "      <td>-47.1</td>\n",
-       "      <td>-49.1</td>\n",
-       "      <td>-52.1</td>\n",
-       "      <td>-59.1</td>\n",
-       "      <td>-76.1</td>\n",
-       "      <td>-84.1</td>\n",
-       "      <td>-94.1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>COG</th>\n",
-       "      <td>52.7</td>\n",
-       "      <td>52.3</td>\n",
-       "      <td>52.3</td>\n",
-       "      <td>52.1</td>\n",
-       "      <td>52.1</td>\n",
-       "      <td>51.9</td>\n",
-       "      <td>51.7</td>\n",
-       "      <td>51.3</td>\n",
-       "      <td>51.3</td>\n",
-       "      <td>51.1</td>\n",
-       "      <td>...</td>\n",
-       "      <td>12.4</td>\n",
-       "      <td>9.8</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-7.0</td>\n",
-       "      <td>-24.0</td>\n",
-       "      <td>-32.0</td>\n",
-       "      <td>-42.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>DJI</th>\n",
-       "      <td>59.7</td>\n",
-       "      <td>59.3</td>\n",
-       "      <td>59.3</td>\n",
-       "      <td>59.1</td>\n",
-       "      <td>59.1</td>\n",
-       "      <td>58.9</td>\n",
-       "      <td>58.7</td>\n",
-       "      <td>58.3</td>\n",
-       "      <td>58.3</td>\n",
-       "      <td>58.1</td>\n",
-       "      <td>...</td>\n",
-       "      <td>19.4</td>\n",
-       "      <td>16.8</td>\n",
-       "      <td>14.0</td>\n",
-       "      <td>12.0</td>\n",
-       "      <td>10.0</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-17.0</td>\n",
-       "      <td>-25.0</td>\n",
-       "      <td>-35.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>BFA</th>\n",
-       "      <td>76.7</td>\n",
-       "      <td>76.3</td>\n",
-       "      <td>76.3</td>\n",
-       "      <td>76.1</td>\n",
-       "      <td>76.1</td>\n",
-       "      <td>75.9</td>\n",
-       "      <td>75.7</td>\n",
-       "      <td>75.3</td>\n",
-       "      <td>75.3</td>\n",
-       "      <td>75.1</td>\n",
-       "      <td>...</td>\n",
-       "      <td>36.4</td>\n",
-       "      <td>33.8</td>\n",
-       "      <td>31.0</td>\n",
-       "      <td>29.0</td>\n",
-       "      <td>27.0</td>\n",
-       "      <td>24.0</td>\n",
-       "      <td>17.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-8.0</td>\n",
-       "      <td>-18.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>LBR</th>\n",
-       "      <td>84.7</td>\n",
-       "      <td>84.3</td>\n",
-       "      <td>84.3</td>\n",
-       "      <td>84.1</td>\n",
-       "      <td>84.1</td>\n",
-       "      <td>83.9</td>\n",
-       "      <td>83.7</td>\n",
-       "      <td>83.3</td>\n",
-       "      <td>83.3</td>\n",
-       "      <td>83.1</td>\n",
-       "      <td>...</td>\n",
-       "      <td>44.4</td>\n",
-       "      <td>41.8</td>\n",
-       "      <td>39.0</td>\n",
-       "      <td>37.0</td>\n",
-       "      <td>35.0</td>\n",
-       "      <td>32.0</td>\n",
-       "      <td>25.0</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-10.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>ZWE</th>\n",
-       "      <td>94.7</td>\n",
-       "      <td>94.3</td>\n",
-       "      <td>94.3</td>\n",
-       "      <td>94.1</td>\n",
-       "      <td>94.1</td>\n",
-       "      <td>93.9</td>\n",
-       "      <td>93.7</td>\n",
-       "      <td>93.3</td>\n",
-       "      <td>93.3</td>\n",
-       "      <td>93.1</td>\n",
-       "      <td>...</td>\n",
-       "      <td>54.4</td>\n",
-       "      <td>51.8</td>\n",
-       "      <td>49.0</td>\n",
-       "      <td>47.0</td>\n",
-       "      <td>45.0</td>\n",
-       "      <td>42.0</td>\n",
-       "      <td>35.0</td>\n",
-       "      <td>18.0</td>\n",
-       "      <td>10.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>195 rows × 195 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "Country   KHM   QAT   BLR   THA   GGY   TON   LAO   JEY   VUT   PNG  ...   \\\n",
-       "Country                                                              ...    \n",
-       "KHM       0.0  -0.4  -0.4  -0.6  -0.6  -0.8  -1.0  -1.4  -1.4  -1.6  ...    \n",
-       "QAT       0.4   0.0   0.0  -0.2  -0.2  -0.4  -0.6  -1.0  -1.0  -1.2  ...    \n",
-       "BLR       0.4   0.0   0.0  -0.2  -0.2  -0.4  -0.6  -1.0  -1.0  -1.2  ...    \n",
-       "THA       0.6   0.2   0.2   0.0   0.0  -0.2  -0.4  -0.8  -0.8  -1.0  ...    \n",
-       "GGY       0.6   0.2   0.2   0.0   0.0  -0.2  -0.4  -0.8  -0.8  -1.0  ...    \n",
-       "...       ...   ...   ...   ...   ...   ...   ...   ...   ...   ...  ...    \n",
-       "COG      52.7  52.3  52.3  52.1  52.1  51.9  51.7  51.3  51.3  51.1  ...    \n",
-       "DJI      59.7  59.3  59.3  59.1  59.1  58.9  58.7  58.3  58.3  58.1  ...    \n",
-       "BFA      76.7  76.3  76.3  76.1  76.1  75.9  75.7  75.3  75.3  75.1  ...    \n",
-       "LBR      84.7  84.3  84.3  84.1  84.1  83.9  83.7  83.3  83.3  83.1  ...    \n",
-       "ZWE      94.7  94.3  94.3  94.1  94.1  93.9  93.7  93.3  93.3  93.1  ...    \n",
-       "\n",
-       "Country   HTI   BIH   NPL   SEN   SYR   COG   DJI   BFA   LBR   ZWE  \n",
-       "Country                                                              \n",
-       "KHM     -40.3 -42.9 -45.7 -47.7 -49.7 -52.7 -59.7 -76.7 -84.7 -94.7  \n",
-       "QAT     -39.9 -42.5 -45.3 -47.3 -49.3 -52.3 -59.3 -76.3 -84.3 -94.3  \n",
-       "BLR     -39.9 -42.5 -45.3 -47.3 -49.3 -52.3 -59.3 -76.3 -84.3 -94.3  \n",
-       "THA     -39.7 -42.3 -45.1 -47.1 -49.1 -52.1 -59.1 -76.1 -84.1 -94.1  \n",
-       "GGY     -39.7 -42.3 -45.1 -47.1 -49.1 -52.1 -59.1 -76.1 -84.1 -94.1  \n",
-       "...       ...   ...   ...   ...   ...   ...   ...   ...   ...   ...  \n",
-       "COG      12.4   9.8   7.0   5.0   3.0   0.0  -7.0 -24.0 -32.0 -42.0  \n",
-       "DJI      19.4  16.8  14.0  12.0  10.0   7.0   0.0 -17.0 -25.0 -35.0  \n",
-       "BFA      36.4  33.8  31.0  29.0  27.0  24.0  17.0   0.0  -8.0 -18.0  \n",
-       "LBR      44.4  41.8  39.0  37.0  35.0  32.0  25.0   8.0   0.0 -10.0  \n",
-       "ZWE      54.4  51.8  49.0  47.0  45.0  42.0  35.0  18.0  10.0   0.0  \n",
-       "\n",
-       "[195 rows x 195 columns]"
-      ]
-     },
-     "execution_count": 37,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "rows = []\n",
-    "for i in range(len(unemployment_pd)):\n",
-    "    row = []\n",
-    "    val = unemployment_pd[\"Unemployment Rate\"][i]\n",
-    "    for value in unemployment_pd[\"Unemployment Rate\"]:\n",
-    "        row.append(val - value)\n",
-    "    rows.append(row)\n",
-    "unemployment_index = pd.DataFrame(rows)\n",
-    "unemployment_index.index = unemployment_pd.index\n",
-    "unemployment_index.columns = unemployment_pd.index\n",
-    "unemployment_index"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "195"
-      ]
-     },
-     "execution_count": 38,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "len(unemployment_pd)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Putting it all together (WIP)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'ABW',\n",
-       " 'AFG',\n",
-       " 'AIA',\n",
-       " 'ALB',\n",
-       " 'AND',\n",
-       " 'ARE',\n",
-       " 'ARG',\n",
-       " 'ARM',\n",
-       " 'ASM',\n",
-       " 'ATG',\n",
-       " 'AUS',\n",
-       " 'AUT',\n",
-       " 'AZE',\n",
-       " 'BEL',\n",
-       " 'BFA',\n",
-       " 'BGD',\n",
-       " 'BGR',\n",
-       " 'BHR',\n",
-       " 'BIH',\n",
-       " 'BLR',\n",
-       " 'BLZ',\n",
-       " 'BMU',\n",
-       " 'BOL',\n",
-       " 'BRA',\n",
-       " 'BRB',\n",
-       " 'BRN',\n",
-       " 'BTN',\n",
-       " 'BWA',\n",
-       " 'CAF',\n",
-       " 'CAN',\n",
-       " 'CHE',\n",
-       " 'CHL',\n",
-       " 'CHN',\n",
-       " 'CMR',\n",
-       " 'COG',\n",
-       " 'COK',\n",
-       " 'COL',\n",
-       " 'COM',\n",
-       " 'CRI',\n",
-       " 'CUB',\n",
-       " 'CYM',\n",
-       " 'CYP',\n",
-       " 'CZE',\n",
-       " 'DEU',\n",
-       " 'DJI',\n",
-       " 'DMA',\n",
-       " 'DNK',\n",
-       " 'DOM',\n",
-       " 'DZA',\n",
-       " 'ECU',\n",
-       " 'EGY',\n",
-       " 'ERI',\n",
-       " 'ESP',\n",
-       " 'EST',\n",
-       " 'ETH',\n",
-       " 'FIN',\n",
-       " 'FJI',\n",
-       " 'FRA',\n",
-       " 'FRO',\n",
-       " 'GAB',\n",
-       " 'GBR',\n",
-       " 'GEO',\n",
-       " 'GGY',\n",
-       " 'GHA',\n",
-       " 'GIB',\n",
-       " 'GNQ',\n",
-       " 'GRC',\n",
-       " 'GRD',\n",
-       " 'GRL',\n",
-       " 'GTM',\n",
-       " 'GUM',\n",
-       " 'GUY',\n",
-       " 'HKG',\n",
-       " 'HND',\n",
-       " 'HRV',\n",
-       " 'HTI',\n",
-       " 'HUN',\n",
-       " 'IDN',\n",
-       " 'IMN',\n",
-       " 'IND',\n",
-       " 'IRL',\n",
-       " 'IRN',\n",
-       " 'IRQ',\n",
-       " 'ISL',\n",
-       " 'ISR',\n",
-       " 'ITA',\n",
-       " 'JAM',\n",
-       " 'JEY',\n",
-       " 'JOR',\n",
-       " 'JPN',\n",
-       " 'KAZ',\n",
-       " 'KEN',\n",
-       " 'KGZ',\n",
-       " 'KHM',\n",
-       " 'KIR',\n",
-       " 'KNA',\n",
-       " 'KOR',\n",
-       " 'KWT',\n",
-       " 'LAO',\n",
-       " 'LBR',\n",
-       " 'LBY',\n",
-       " 'LCA',\n",
-       " 'LIE',\n",
-       " 'LKA',\n",
-       " 'LSO',\n",
-       " 'LTU',\n",
-       " 'LUX',\n",
-       " 'LVA',\n",
-       " 'MAR',\n",
-       " 'MCO',\n",
-       " 'MDA',\n",
-       " 'MDV',\n",
-       " 'MEX',\n",
-       " 'MHL',\n",
-       " 'MKD',\n",
-       " 'MLI',\n",
-       " 'MLT',\n",
-       " 'MMR',\n",
-       " 'MNE',\n",
-       " 'MNG',\n",
-       " 'MNP',\n",
-       " 'MOZ',\n",
-       " 'MRT',\n",
-       " 'MSR',\n",
-       " 'MUS',\n",
-       " 'MYS',\n",
-       " 'NAM',\n",
-       " 'NCL',\n",
-       " 'NER',\n",
-       " 'NGA',\n",
-       " 'NIC',\n",
-       " 'NIU',\n",
-       " 'NLD',\n",
-       " 'NOR',\n",
-       " 'NPL',\n",
-       " 'NRU',\n",
-       " 'NZL',\n",
-       " 'OMN',\n",
-       " 'PAK',\n",
-       " 'PAN',\n",
-       " 'PER',\n",
-       " 'PHL',\n",
-       " 'PLW',\n",
-       " 'PNG',\n",
-       " 'POL',\n",
-       " 'PRI',\n",
-       " 'PRK',\n",
-       " 'PRT',\n",
-       " 'PRY',\n",
-       " 'PSE',\n",
-       " 'PYF',\n",
-       " 'QAT',\n",
-       " 'ROU',\n",
-       " 'RUS',\n",
-       " 'SAU',\n",
-       " 'SDN',\n",
-       " 'SEN',\n",
-       " 'SGP',\n",
-       " 'SLE',\n",
-       " 'SLV',\n",
-       " 'SMR',\n",
-       " 'SPM',\n",
-       " 'SRB',\n",
-       " 'STP',\n",
-       " 'SUR',\n",
-       " 'SVK',\n",
-       " 'SVN',\n",
-       " 'SWE',\n",
-       " 'SWZ',\n",
-       " 'SYC',\n",
-       " 'SYR',\n",
-       " 'TCA',\n",
-       " 'THA',\n",
-       " 'TJK',\n",
-       " 'TKM',\n",
-       " 'TLS',\n",
-       " 'TON',\n",
-       " 'TTO',\n",
-       " 'TUN',\n",
-       " 'TUR',\n",
-       " 'TWN',\n",
-       " 'UKR',\n",
-       " 'URY',\n",
-       " 'USA',\n",
-       " 'UZB',\n",
-       " 'VCT',\n",
-       " 'VEN',\n",
-       " 'VNM',\n",
-       " 'VUT',\n",
-       " 'WLF',\n",
-       " 'XKX',\n",
-       " 'YEM',\n",
-       " 'ZAF',\n",
-       " 'ZMB',\n",
-       " 'ZWE'}"
-      ]
-     },
-     "execution_count": 39,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "set(unemployment_index.index)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "{'AFG',\n",
-       " 'AGO',\n",
-       " 'ALB',\n",
-       " 'AND',\n",
-       " 'ARE',\n",
-       " 'ARG',\n",
-       " 'ARM',\n",
-       " 'ATG',\n",
-       " 'AUS',\n",
-       " 'AUT',\n",
-       " 'AZE',\n",
-       " 'BDI',\n",
-       " 'BEL',\n",
-       " 'BEN',\n",
-       " 'BFA',\n",
-       " 'BGD',\n",
-       " 'BGR',\n",
-       " 'BHR',\n",
-       " 'BHS',\n",
-       " 'BIH',\n",
-       " 'BLR',\n",
-       " 'BLZ',\n",
-       " 'BOL',\n",
-       " 'BRA',\n",
-       " 'BRB',\n",
-       " 'BRN',\n",
-       " 'BTN',\n",
-       " 'BWA',\n",
-       " 'CAF',\n",
-       " 'CAN',\n",
-       " 'CHE',\n",
-       " 'CHL',\n",
-       " 'CHN',\n",
-       " 'CIV',\n",
-       " 'CMR',\n",
-       " 'COD',\n",
-       " 'COG',\n",
-       " 'COL',\n",
-       " 'COM',\n",
-       " 'CPV',\n",
-       " 'CRI',\n",
-       " 'CUB',\n",
-       " 'CYP',\n",
-       " 'CZE',\n",
-       " 'DEU',\n",
-       " 'DJI',\n",
-       " 'DMA',\n",
-       " 'DNK',\n",
-       " 'DOM',\n",
-       " 'DZA',\n",
-       " 'ECU',\n",
-       " 'EGY',\n",
-       " 'ERI',\n",
-       " 'ESP',\n",
-       " 'EST',\n",
-       " 'ETH',\n",
-       " 'FIN',\n",
-       " 'FJI',\n",
-       " 'FRA',\n",
-       " 'FSM',\n",
-       " 'GAB',\n",
-       " 'GBR',\n",
-       " 'GEO',\n",
-       " 'GHA',\n",
-       " 'GIN',\n",
-       " 'GMB',\n",
-       " 'GNB',\n",
-       " 'GNQ',\n",
-       " 'GRC',\n",
-       " 'GRD',\n",
-       " 'GTM',\n",
-       " 'GUY',\n",
-       " 'HND',\n",
-       " 'HRV',\n",
-       " 'HTI',\n",
-       " 'HUN',\n",
-       " 'IDN',\n",
-       " 'IND',\n",
-       " 'IRL',\n",
-       " 'IRN',\n",
-       " 'IRQ',\n",
-       " 'ISL',\n",
-       " 'ISR',\n",
-       " 'ITA',\n",
-       " 'JAM',\n",
-       " 'JOR',\n",
-       " 'JPN',\n",
-       " 'KAZ',\n",
-       " 'KEN',\n",
-       " 'KGZ',\n",
-       " 'KHM',\n",
-       " 'KIR',\n",
-       " 'KNA',\n",
-       " 'KOR',\n",
-       " 'KWT',\n",
-       " 'LAO',\n",
-       " 'LBN',\n",
-       " 'LBR',\n",
-       " 'LBY',\n",
-       " 'LCA',\n",
-       " 'LIE',\n",
-       " 'LKA',\n",
-       " 'LSO',\n",
-       " 'LTU',\n",
-       " 'LUX',\n",
-       " 'LVA',\n",
-       " 'MAR',\n",
-       " 'MCO',\n",
-       " 'MDA',\n",
-       " 'MDG',\n",
-       " 'MDV',\n",
-       " 'MEX',\n",
-       " 'MHL',\n",
-       " 'MKD',\n",
-       " 'MLI',\n",
-       " 'MLT',\n",
-       " 'MMR',\n",
-       " 'MNE',\n",
-       " 'MNG',\n",
-       " 'MOZ',\n",
-       " 'MRT',\n",
-       " 'MUS',\n",
-       " 'MWI',\n",
-       " 'MYS',\n",
-       " 'NAM',\n",
-       " 'NER',\n",
-       " 'NGA',\n",
-       " 'NIC',\n",
-       " 'NLD',\n",
-       " 'NOR',\n",
-       " 'NPL',\n",
-       " 'NRU',\n",
-       " 'NZL',\n",
-       " 'OMN',\n",
-       " 'PAK',\n",
-       " 'PAN',\n",
-       " 'PER',\n",
-       " 'PHL',\n",
-       " 'PLW',\n",
-       " 'PNG',\n",
-       " 'POL',\n",
-       " 'PRK',\n",
-       " 'PRT',\n",
-       " 'PRY',\n",
-       " 'QAT',\n",
-       " 'ROU',\n",
-       " 'RUS',\n",
-       " 'RWA',\n",
-       " 'SAU',\n",
-       " 'SDN',\n",
-       " 'SEN',\n",
-       " 'SGP',\n",
-       " 'SLB',\n",
-       " 'SLE',\n",
-       " 'SLV',\n",
-       " 'SMR',\n",
-       " 'SOM',\n",
-       " 'SRB',\n",
-       " 'SSD',\n",
-       " 'STP',\n",
-       " 'SUR',\n",
-       " 'SVK',\n",
-       " 'SVN',\n",
-       " 'SWE',\n",
-       " 'SWZ',\n",
-       " 'SYC',\n",
-       " 'SYR',\n",
-       " 'TCD',\n",
-       " 'TGO',\n",
-       " 'THA',\n",
-       " 'TJK',\n",
-       " 'TKM',\n",
-       " 'TLS',\n",
-       " 'TON',\n",
-       " 'TTO',\n",
-       " 'TUN',\n",
-       " 'TUR',\n",
-       " 'TUV',\n",
-       " 'TWN',\n",
-       " 'TZA',\n",
-       " 'UGA',\n",
-       " 'UKR',\n",
-       " 'URY',\n",
-       " 'USA',\n",
-       " 'UZB',\n",
-       " 'VCT',\n",
-       " 'VEN',\n",
-       " 'VNM',\n",
-       " 'VUT',\n",
-       " 'WSM',\n",
-       " 'XKX',\n",
-       " 'YEM',\n",
-       " 'ZAF',\n",
-       " 'ZMB',\n",
-       " 'ZWE'}"
-      ]
-     },
-     "execution_count": 40,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "set(freedom_index.columns)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 41,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "132"
-      ]
-     },
-     "execution_count": 41,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# What are the countries which overlap between all datasets?\n",
-    "intersection = set(a_data.columns).intersection(\n",
-    "    set(un_pd.columns)\n",
-    ").intersection(\n",
-    "    set(freedom_index.columns)\n",
-    ").intersection(\n",
-    "    set(unemployment_index.index)\n",
-    ").intersection(\n",
-    "    set(lang_data.index)\n",
-    ")\n",
-    "len(intersection)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Filter down the matrixes to only the intersections between the datasets."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style>\n",
-       "    .dataframe thead tr:only-child th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: left;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th>Country</th>\n",
-       "      <th>UKR</th>\n",
-       "      <th>MNG</th>\n",
-       "      <th>BFA</th>\n",
-       "      <th>BTN</th>\n",
-       "      <th>ESP</th>\n",
-       "      <th>DZA</th>\n",
-       "      <th>GAB</th>\n",
-       "      <th>PNG</th>\n",
-       "      <th>MYS</th>\n",
-       "      <th>CZE</th>\n",
-       "      <th>...</th>\n",
-       "      <th>KIR</th>\n",
-       "      <th>ARM</th>\n",
-       "      <th>SYC</th>\n",
-       "      <th>BLR</th>\n",
-       "      <th>AUS</th>\n",
-       "      <th>LVA</th>\n",
-       "      <th>USA</th>\n",
-       "      <th>MDA</th>\n",
-       "      <th>CAF</th>\n",
-       "      <th>GBR</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Country</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>UKR</th>\n",
-       "      <td>0</td>\n",
-       "      <td>-37</td>\n",
-       "      <td>-19</td>\n",
-       "      <td>-50</td>\n",
-       "      <td>-4</td>\n",
-       "      <td>-46</td>\n",
-       "      <td>-49</td>\n",
-       "      <td>-16</td>\n",
-       "      <td>28</td>\n",
-       "      <td>-4</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-15</td>\n",
-       "      <td>19</td>\n",
-       "      <td>45</td>\n",
-       "      <td>-5</td>\n",
-       "      <td>-34</td>\n",
-       "      <td>-4</td>\n",
-       "      <td>-31</td>\n",
-       "      <td>-18</td>\n",
-       "      <td>-42</td>\n",
-       "      <td>-5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>MNG</th>\n",
-       "      <td>37</td>\n",
-       "      <td>0</td>\n",
-       "      <td>18</td>\n",
-       "      <td>-13</td>\n",
-       "      <td>33</td>\n",
-       "      <td>-9</td>\n",
-       "      <td>-12</td>\n",
-       "      <td>21</td>\n",
-       "      <td>65</td>\n",
-       "      <td>33</td>\n",
-       "      <td>...</td>\n",
-       "      <td>22</td>\n",
-       "      <td>56</td>\n",
-       "      <td>82</td>\n",
-       "      <td>32</td>\n",
-       "      <td>3</td>\n",
-       "      <td>33</td>\n",
-       "      <td>6</td>\n",
-       "      <td>19</td>\n",
-       "      <td>-5</td>\n",
-       "      <td>32</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>BFA</th>\n",
-       "      <td>19</td>\n",
-       "      <td>-18</td>\n",
-       "      <td>0</td>\n",
-       "      <td>-31</td>\n",
-       "      <td>15</td>\n",
-       "      <td>-27</td>\n",
-       "      <td>-30</td>\n",
-       "      <td>3</td>\n",
-       "      <td>47</td>\n",
-       "      <td>15</td>\n",
-       "      <td>...</td>\n",
-       "      <td>4</td>\n",
-       "      <td>38</td>\n",
-       "      <td>64</td>\n",
-       "      <td>14</td>\n",
-       "      <td>-15</td>\n",
-       "      <td>15</td>\n",
-       "      <td>-12</td>\n",
-       "      <td>1</td>\n",
-       "      <td>-23</td>\n",
-       "      <td>14</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>BTN</th>\n",
-       "      <td>50</td>\n",
-       "      <td>13</td>\n",
-       "      <td>31</td>\n",
-       "      <td>0</td>\n",
-       "      <td>46</td>\n",
-       "      <td>4</td>\n",
-       "      <td>1</td>\n",
-       "      <td>34</td>\n",
-       "      <td>78</td>\n",
-       "      <td>46</td>\n",
-       "      <td>...</td>\n",
-       "      <td>35</td>\n",
-       "      <td>69</td>\n",
-       "      <td>95</td>\n",
-       "      <td>45</td>\n",
-       "      <td>16</td>\n",
-       "      <td>46</td>\n",
-       "      <td>19</td>\n",
-       "      <td>32</td>\n",
-       "      <td>8</td>\n",
-       "      <td>45</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>ESP</th>\n",
-       "      <td>4</td>\n",
-       "      <td>-33</td>\n",
-       "      <td>-15</td>\n",
-       "      <td>-46</td>\n",
-       "      <td>0</td>\n",
-       "      <td>-42</td>\n",
-       "      <td>-45</td>\n",
-       "      <td>-12</td>\n",
-       "      <td>32</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-11</td>\n",
-       "      <td>23</td>\n",
-       "      <td>49</td>\n",
-       "      <td>-1</td>\n",
-       "      <td>-30</td>\n",
-       "      <td>0</td>\n",
-       "      <td>-27</td>\n",
-       "      <td>-14</td>\n",
-       "      <td>-38</td>\n",
-       "      <td>-1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>LVA</th>\n",
-       "      <td>4</td>\n",
-       "      <td>-33</td>\n",
-       "      <td>-15</td>\n",
-       "      <td>-46</td>\n",
-       "      <td>0</td>\n",
-       "      <td>-42</td>\n",
-       "      <td>-45</td>\n",
-       "      <td>-12</td>\n",
-       "      <td>32</td>\n",
-       "      <td>0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-11</td>\n",
-       "      <td>23</td>\n",
-       "      <td>49</td>\n",
-       "      <td>-1</td>\n",
-       "      <td>-30</td>\n",
-       "      <td>0</td>\n",
-       "      <td>-27</td>\n",
-       "      <td>-14</td>\n",
-       "      <td>-38</td>\n",
-       "      <td>-1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>USA</th>\n",
-       "      <td>31</td>\n",
-       "      <td>-6</td>\n",
-       "      <td>12</td>\n",
-       "      <td>-19</td>\n",
-       "      <td>27</td>\n",
-       "      <td>-15</td>\n",
-       "      <td>-18</td>\n",
-       "      <td>15</td>\n",
-       "      <td>59</td>\n",
-       "      <td>27</td>\n",
-       "      <td>...</td>\n",
-       "      <td>16</td>\n",
-       "      <td>50</td>\n",
-       "      <td>76</td>\n",
-       "      <td>26</td>\n",
-       "      <td>-3</td>\n",
-       "      <td>27</td>\n",
-       "      <td>0</td>\n",
-       "      <td>13</td>\n",
-       "      <td>-11</td>\n",
-       "      <td>26</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>MDA</th>\n",
-       "      <td>18</td>\n",
-       "      <td>-19</td>\n",
-       "      <td>-1</td>\n",
-       "      <td>-32</td>\n",
-       "      <td>14</td>\n",
-       "      <td>-28</td>\n",
-       "      <td>-31</td>\n",
-       "      <td>2</td>\n",
-       "      <td>46</td>\n",
-       "      <td>14</td>\n",
-       "      <td>...</td>\n",
-       "      <td>3</td>\n",
-       "      <td>37</td>\n",
-       "      <td>63</td>\n",
-       "      <td>13</td>\n",
-       "      <td>-16</td>\n",
-       "      <td>14</td>\n",
-       "      <td>-13</td>\n",
-       "      <td>0</td>\n",
-       "      <td>-24</td>\n",
-       "      <td>13</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>CAF</th>\n",
-       "      <td>42</td>\n",
-       "      <td>5</td>\n",
-       "      <td>23</td>\n",
-       "      <td>-8</td>\n",
-       "      <td>38</td>\n",
-       "      <td>-4</td>\n",
-       "      <td>-7</td>\n",
-       "      <td>26</td>\n",
-       "      <td>70</td>\n",
-       "      <td>38</td>\n",
-       "      <td>...</td>\n",
-       "      <td>27</td>\n",
-       "      <td>61</td>\n",
-       "      <td>87</td>\n",
-       "      <td>37</td>\n",
-       "      <td>8</td>\n",
-       "      <td>38</td>\n",
-       "      <td>11</td>\n",
-       "      <td>24</td>\n",
-       "      <td>0</td>\n",
-       "      <td>37</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>GBR</th>\n",
-       "      <td>5</td>\n",
-       "      <td>-32</td>\n",
-       "      <td>-14</td>\n",
-       "      <td>-45</td>\n",
-       "      <td>1</td>\n",
-       "      <td>-41</td>\n",
-       "      <td>-44</td>\n",
-       "      <td>-11</td>\n",
-       "      <td>33</td>\n",
-       "      <td>1</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-10</td>\n",
-       "      <td>24</td>\n",
-       "      <td>50</td>\n",
-       "      <td>0</td>\n",
-       "      <td>-29</td>\n",
-       "      <td>1</td>\n",
-       "      <td>-26</td>\n",
-       "      <td>-13</td>\n",
-       "      <td>-37</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>132 rows × 132 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "Country  UKR  MNG  BFA  BTN  ESP  DZA  GAB  PNG  MYS  CZE ...   KIR  ARM  SYC  \\\n",
-       "Country                                                   ...                   \n",
-       "UKR        0  -37  -19  -50   -4  -46  -49  -16   28   -4 ...   -15   19   45   \n",
-       "MNG       37    0   18  -13   33   -9  -12   21   65   33 ...    22   56   82   \n",
-       "BFA       19  -18    0  -31   15  -27  -30    3   47   15 ...     4   38   64   \n",
-       "BTN       50   13   31    0   46    4    1   34   78   46 ...    35   69   95   \n",
-       "ESP        4  -33  -15  -46    0  -42  -45  -12   32    0 ...   -11   23   49   \n",
-       "...      ...  ...  ...  ...  ...  ...  ...  ...  ...  ... ...   ...  ...  ...   \n",
-       "LVA        4  -33  -15  -46    0  -42  -45  -12   32    0 ...   -11   23   49   \n",
-       "USA       31   -6   12  -19   27  -15  -18   15   59   27 ...    16   50   76   \n",
-       "MDA       18  -19   -1  -32   14  -28  -31    2   46   14 ...     3   37   63   \n",
-       "CAF       42    5   23   -8   38   -4   -7   26   70   38 ...    27   61   87   \n",
-       "GBR        5  -32  -14  -45    1  -41  -44  -11   33    1 ...   -10   24   50   \n",
-       "\n",
-       "Country  BLR  AUS  LVA  USA  MDA  CAF  GBR  \n",
-       "Country                                     \n",
-       "UKR       -5  -34   -4  -31  -18  -42   -5  \n",
-       "MNG       32    3   33    6   19   -5   32  \n",
-       "BFA       14  -15   15  -12    1  -23   14  \n",
-       "BTN       45   16   46   19   32    8   45  \n",
-       "ESP       -1  -30    0  -27  -14  -38   -1  \n",
-       "...      ...  ...  ...  ...  ...  ...  ...  \n",
-       "LVA       -1  -30    0  -27  -14  -38   -1  \n",
-       "USA       26   -3   27    0   13  -11   26  \n",
-       "MDA       13  -16   14  -13    0  -24   13  \n",
-       "CAF       37    8   38   11   24    0   37  \n",
-       "GBR        0  -29    1  -26  -13  -37    0  \n",
-       "\n",
-       "[132 rows x 132 columns]"
-      ]
-     },
-     "execution_count": 42,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "passport_index.filter(items=intersection).filter(items=intersection, axis=0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "all_data = {}"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "all_data[\"un\"] = un_pd.fillna(1).filter(items=intersection).filter(items=intersection, axis=0)\n",
-    "all_data[\"a\"] = a_data.filter(items=intersection).filter(items=intersection, axis=0)\n",
-    "all_data[\"b\"] = b_data.filter(items=intersection).filter(items=intersection, axis=0)\n",
-    "all_data[\"freedom\"] = freedom_index.filter(items=intersection).filter(items=intersection, axis=0)\n",
-    "all_data[\"passport\"] = passport_index.filter(items=intersection).filter(items=intersection, axis=0)\n",
-    "all_data[\"distance\"] = distance.filter(items=intersection).filter(items=intersection, axis=0)\n",
-    "all_data[\"unemployment\"] = unemployment_index.filter(items=intersection).filter(items=intersection, axis=0)\n",
-    "all_data[\"language\"] = lang_data.filter(items=intersection).filter(items=intersection, axis=0)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 45,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style>\n",
-       "    .dataframe thead tr:only-child th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: left;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th>Country</th>\n",
-       "      <th>UKR</th>\n",
-       "      <th>MNG</th>\n",
-       "      <th>BFA</th>\n",
-       "      <th>BTN</th>\n",
-       "      <th>ESP</th>\n",
-       "      <th>DZA</th>\n",
-       "      <th>GAB</th>\n",
-       "      <th>PNG</th>\n",
-       "      <th>MYS</th>\n",
-       "      <th>CZE</th>\n",
-       "      <th>...</th>\n",
-       "      <th>KIR</th>\n",
-       "      <th>ARM</th>\n",
-       "      <th>SYC</th>\n",
-       "      <th>BLR</th>\n",
-       "      <th>AUS</th>\n",
-       "      <th>LVA</th>\n",
-       "      <th>USA</th>\n",
-       "      <th>MDA</th>\n",
-       "      <th>CAF</th>\n",
-       "      <th>GBR</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Country</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>UKR</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.7</td>\n",
-       "      <td>-67.0</td>\n",
-       "      <td>7.4</td>\n",
-       "      <td>-9.7</td>\n",
-       "      <td>-2.4</td>\n",
-       "      <td>-11.0</td>\n",
-       "      <td>8.1</td>\n",
-       "      <td>6.7</td>\n",
-       "      <td>4.4</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-20.6</td>\n",
-       "      <td>-8.1</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>9.3</td>\n",
-       "      <td>4.2</td>\n",
-       "      <td>0.4</td>\n",
-       "      <td>5.3</td>\n",
-       "      <td>3.7</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>4.9</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>MNG</th>\n",
-       "      <td>-1.7</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-68.7</td>\n",
-       "      <td>5.7</td>\n",
-       "      <td>-11.4</td>\n",
-       "      <td>-4.1</td>\n",
-       "      <td>-12.7</td>\n",
-       "      <td>6.4</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>2.7</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-22.3</td>\n",
-       "      <td>-9.8</td>\n",
-       "      <td>5.3</td>\n",
-       "      <td>7.6</td>\n",
-       "      <td>2.5</td>\n",
-       "      <td>-1.3</td>\n",
-       "      <td>3.6</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>0.3</td>\n",
-       "      <td>3.2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>BFA</th>\n",
-       "      <td>67.0</td>\n",
-       "      <td>68.7</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>74.4</td>\n",
-       "      <td>57.3</td>\n",
-       "      <td>64.6</td>\n",
-       "      <td>56.0</td>\n",
-       "      <td>75.1</td>\n",
-       "      <td>73.7</td>\n",
-       "      <td>71.4</td>\n",
-       "      <td>...</td>\n",
-       "      <td>46.4</td>\n",
-       "      <td>58.9</td>\n",
-       "      <td>74.0</td>\n",
-       "      <td>76.3</td>\n",
-       "      <td>71.2</td>\n",
-       "      <td>67.4</td>\n",
-       "      <td>72.3</td>\n",
-       "      <td>70.7</td>\n",
-       "      <td>69.0</td>\n",
-       "      <td>71.9</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>BTN</th>\n",
-       "      <td>-7.4</td>\n",
-       "      <td>-5.7</td>\n",
-       "      <td>-74.4</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-17.1</td>\n",
-       "      <td>-9.8</td>\n",
-       "      <td>-18.4</td>\n",
-       "      <td>0.7</td>\n",
-       "      <td>-0.7</td>\n",
-       "      <td>-3.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-28.0</td>\n",
-       "      <td>-15.5</td>\n",
-       "      <td>-0.4</td>\n",
-       "      <td>1.9</td>\n",
-       "      <td>-3.2</td>\n",
-       "      <td>-7.0</td>\n",
-       "      <td>-2.1</td>\n",
-       "      <td>-3.7</td>\n",
-       "      <td>-5.4</td>\n",
-       "      <td>-2.5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>ESP</th>\n",
-       "      <td>9.7</td>\n",
-       "      <td>11.4</td>\n",
-       "      <td>-57.3</td>\n",
-       "      <td>17.1</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>7.3</td>\n",
-       "      <td>-1.3</td>\n",
-       "      <td>17.8</td>\n",
-       "      <td>16.4</td>\n",
-       "      <td>14.1</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-10.9</td>\n",
-       "      <td>1.6</td>\n",
-       "      <td>16.7</td>\n",
-       "      <td>19.0</td>\n",
-       "      <td>13.9</td>\n",
-       "      <td>10.1</td>\n",
-       "      <td>15.0</td>\n",
-       "      <td>13.4</td>\n",
-       "      <td>11.7</td>\n",
-       "      <td>14.6</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>LVA</th>\n",
-       "      <td>-0.4</td>\n",
-       "      <td>1.3</td>\n",
-       "      <td>-67.4</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>-10.1</td>\n",
-       "      <td>-2.8</td>\n",
-       "      <td>-11.4</td>\n",
-       "      <td>7.7</td>\n",
-       "      <td>6.3</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-21.0</td>\n",
-       "      <td>-8.5</td>\n",
-       "      <td>6.6</td>\n",
-       "      <td>8.9</td>\n",
-       "      <td>3.8</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>4.9</td>\n",
-       "      <td>3.3</td>\n",
-       "      <td>1.6</td>\n",
-       "      <td>4.5</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>USA</th>\n",
-       "      <td>-5.3</td>\n",
-       "      <td>-3.6</td>\n",
-       "      <td>-72.3</td>\n",
-       "      <td>2.1</td>\n",
-       "      <td>-15.0</td>\n",
-       "      <td>-7.7</td>\n",
-       "      <td>-16.3</td>\n",
-       "      <td>2.8</td>\n",
-       "      <td>1.4</td>\n",
-       "      <td>-0.9</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-25.9</td>\n",
-       "      <td>-13.4</td>\n",
-       "      <td>1.7</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>-1.1</td>\n",
-       "      <td>-4.9</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-1.6</td>\n",
-       "      <td>-3.3</td>\n",
-       "      <td>-0.4</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>MDA</th>\n",
-       "      <td>-3.7</td>\n",
-       "      <td>-2.0</td>\n",
-       "      <td>-70.7</td>\n",
-       "      <td>3.7</td>\n",
-       "      <td>-13.4</td>\n",
-       "      <td>-6.1</td>\n",
-       "      <td>-14.7</td>\n",
-       "      <td>4.4</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>0.7</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-24.3</td>\n",
-       "      <td>-11.8</td>\n",
-       "      <td>3.3</td>\n",
-       "      <td>5.6</td>\n",
-       "      <td>0.5</td>\n",
-       "      <td>-3.3</td>\n",
-       "      <td>1.6</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>-1.7</td>\n",
-       "      <td>1.2</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>CAF</th>\n",
-       "      <td>-2.0</td>\n",
-       "      <td>-0.3</td>\n",
-       "      <td>-69.0</td>\n",
-       "      <td>5.4</td>\n",
-       "      <td>-11.7</td>\n",
-       "      <td>-4.4</td>\n",
-       "      <td>-13.0</td>\n",
-       "      <td>6.1</td>\n",
-       "      <td>4.7</td>\n",
-       "      <td>2.4</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-22.6</td>\n",
-       "      <td>-10.1</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>7.3</td>\n",
-       "      <td>2.2</td>\n",
-       "      <td>-1.6</td>\n",
-       "      <td>3.3</td>\n",
-       "      <td>1.7</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2.9</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>GBR</th>\n",
-       "      <td>-4.9</td>\n",
-       "      <td>-3.2</td>\n",
-       "      <td>-71.9</td>\n",
-       "      <td>2.5</td>\n",
-       "      <td>-14.6</td>\n",
-       "      <td>-7.3</td>\n",
-       "      <td>-15.9</td>\n",
-       "      <td>3.2</td>\n",
-       "      <td>1.8</td>\n",
-       "      <td>-0.5</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-25.5</td>\n",
-       "      <td>-13.0</td>\n",
-       "      <td>2.1</td>\n",
-       "      <td>4.4</td>\n",
-       "      <td>-0.7</td>\n",
-       "      <td>-4.5</td>\n",
-       "      <td>0.4</td>\n",
-       "      <td>-1.2</td>\n",
-       "      <td>-2.9</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>132 rows × 132 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "Country   UKR   MNG   BFA   BTN   ESP   DZA   GAB   PNG   MYS   CZE  ...   \\\n",
-       "Country                                                              ...    \n",
-       "UKR       0.0   1.7 -67.0   7.4  -9.7  -2.4 -11.0   8.1   6.7   4.4  ...    \n",
-       "MNG      -1.7   0.0 -68.7   5.7 -11.4  -4.1 -12.7   6.4   5.0   2.7  ...    \n",
-       "BFA      67.0  68.7   0.0  74.4  57.3  64.6  56.0  75.1  73.7  71.4  ...    \n",
-       "BTN      -7.4  -5.7 -74.4   0.0 -17.1  -9.8 -18.4   0.7  -0.7  -3.0  ...    \n",
-       "ESP       9.7  11.4 -57.3  17.1   0.0   7.3  -1.3  17.8  16.4  14.1  ...    \n",
-       "...       ...   ...   ...   ...   ...   ...   ...   ...   ...   ...  ...    \n",
-       "LVA      -0.4   1.3 -67.4   7.0 -10.1  -2.8 -11.4   7.7   6.3   4.0  ...    \n",
-       "USA      -5.3  -3.6 -72.3   2.1 -15.0  -7.7 -16.3   2.8   1.4  -0.9  ...    \n",
-       "MDA      -3.7  -2.0 -70.7   3.7 -13.4  -6.1 -14.7   4.4   3.0   0.7  ...    \n",
-       "CAF      -2.0  -0.3 -69.0   5.4 -11.7  -4.4 -13.0   6.1   4.7   2.4  ...    \n",
-       "GBR      -4.9  -3.2 -71.9   2.5 -14.6  -7.3 -15.9   3.2   1.8  -0.5  ...    \n",
-       "\n",
-       "Country   KIR   ARM   SYC   BLR   AUS   LVA   USA   MDA   CAF   GBR  \n",
-       "Country                                                              \n",
-       "UKR     -20.6  -8.1   7.0   9.3   4.2   0.4   5.3   3.7   2.0   4.9  \n",
-       "MNG     -22.3  -9.8   5.3   7.6   2.5  -1.3   3.6   2.0   0.3   3.2  \n",
-       "BFA      46.4  58.9  74.0  76.3  71.2  67.4  72.3  70.7  69.0  71.9  \n",
-       "BTN     -28.0 -15.5  -0.4   1.9  -3.2  -7.0  -2.1  -3.7  -5.4  -2.5  \n",
-       "ESP     -10.9   1.6  16.7  19.0  13.9  10.1  15.0  13.4  11.7  14.6  \n",
-       "...       ...   ...   ...   ...   ...   ...   ...   ...   ...   ...  \n",
-       "LVA     -21.0  -8.5   6.6   8.9   3.8   0.0   4.9   3.3   1.6   4.5  \n",
-       "USA     -25.9 -13.4   1.7   4.0  -1.1  -4.9   0.0  -1.6  -3.3  -0.4  \n",
-       "MDA     -24.3 -11.8   3.3   5.6   0.5  -3.3   1.6   0.0  -1.7   1.2  \n",
-       "CAF     -22.6 -10.1   5.0   7.3   2.2  -1.6   3.3   1.7   0.0   2.9  \n",
-       "GBR     -25.5 -13.0   2.1   4.4  -0.7  -4.5   0.4  -1.2  -2.9   0.0  \n",
-       "\n",
-       "[132 rows x 132 columns]"
-      ]
-     },
-     "execution_count": 45,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "all_data[\"unemployment\"] "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Here I start working on fitting the data to the model as described in the paper. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: left;\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>UKR</th>\n",
-       "      <th>MNG</th>\n",
-       "      <th>BFA</th>\n",
-       "      <th>BTN</th>\n",
-       "      <th>ESP</th>\n",
-       "      <th>DZA</th>\n",
-       "      <th>GAB</th>\n",
-       "      <th>PNG</th>\n",
-       "      <th>MYS</th>\n",
-       "      <th>CZE</th>\n",
-       "      <th>...</th>\n",
-       "      <th>KIR</th>\n",
-       "      <th>ARM</th>\n",
-       "      <th>SYC</th>\n",
-       "      <th>BLR</th>\n",
-       "      <th>AUS</th>\n",
-       "      <th>LVA</th>\n",
-       "      <th>USA</th>\n",
-       "      <th>MDA</th>\n",
-       "      <th>CAF</th>\n",
-       "      <th>GBR</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>UKR</th>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>-289.907838</td>\n",
-       "      <td>572.829633</td>\n",
-       "      <td>125.974021</td>\n",
-       "      <td>-4118.167851</td>\n",
-       "      <td>-607.485035</td>\n",
-       "      <td>-461.148471</td>\n",
-       "      <td>296.770653</td>\n",
-       "      <td>-412.580539</td>\n",
-       "      <td>-6837.986706</td>\n",
-       "      <td>...</td>\n",
-       "      <td>378.022297</td>\n",
-       "      <td>-274.985279</td>\n",
-       "      <td>-1363.184580</td>\n",
-       "      <td>-1109.056592</td>\n",
-       "      <td>-10989.490063</td>\n",
-       "      <td>-2358.983246</td>\n",
-       "      <td>-7123.223327</td>\n",
-       "      <td>113.962604</td>\n",
-       "      <td>400.582193</td>\n",
-       "      <td>-8882.844003</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>MNG</th>\n",
-       "      <td>399.240133</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>1003.038048</td>\n",
-       "      <td>440.068286</td>\n",
-       "      <td>-4548.547463</td>\n",
-       "      <td>-333.880109</td>\n",
-       "      <td>-274.612416</td>\n",
-       "      <td>601.307508</td>\n",
-       "      <td>-255.204139</td>\n",
-       "      <td>-7628.392781</td>\n",
-       "      <td>...</td>\n",
-       "      <td>742.948193</td>\n",
-       "      <td>18.875351</td>\n",
-       "      <td>-1343.613184</td>\n",
-       "      <td>-916.366848</td>\n",
-       "      <td>-12580.973914</td>\n",
-       "      <td>-2462.910599</td>\n",
-       "      <td>-8103.081270</td>\n",
-       "      <td>518.859474</td>\n",
-       "      <td>657.547106</td>\n",
-       "      <td>-10106.269247</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>BFA</th>\n",
-       "      <td>-834.296077</td>\n",
-       "      <td>-1060.810716</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>-458.659647</td>\n",
-       "      <td>-5563.770173</td>\n",
-       "      <td>-1525.154650</td>\n",
-       "      <td>-1113.542302</td>\n",
-       "      <td>-170.635628</td>\n",
-       "      <td>-976.383551</td>\n",
-       "      <td>-9079.959870</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-169.313178</td>\n",
-       "      <td>-1045.651128</td>\n",
-       "      <td>-2180.308924</td>\n",
-       "      <td>-2143.242278</td>\n",
-       "      <td>-13921.395843</td>\n",
-       "      <td>-3485.059605</td>\n",
-       "      <td>-9131.716733</td>\n",
-       "      <td>-667.261723</td>\n",
-       "      <td>91.699936</td>\n",
-       "      <td>-11384.317201</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>BTN</th>\n",
-       "      <td>-235.115223</td>\n",
-       "      <td>-596.410931</td>\n",
-       "      <td>587.753960</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>-5793.402374</td>\n",
-       "      <td>-1053.120200</td>\n",
-       "      <td>-786.973444</td>\n",
-       "      <td>256.415339</td>\n",
-       "      <td>-698.487061</td>\n",
-       "      <td>-9574.989434</td>\n",
-       "      <td>...</td>\n",
-       "      <td>340.156572</td>\n",
-       "      <td>-576.827419</td>\n",
-       "      <td>-2013.139297</td>\n",
-       "      <td>-1741.944485</td>\n",
-       "      <td>-15199.032883</td>\n",
-       "      <td>-3402.577496</td>\n",
-       "      <td>-9882.243263</td>\n",
-       "      <td>-71.111071</td>\n",
-       "      <td>436.861044</td>\n",
-       "      <td>-12322.519954</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>ESP</th>\n",
-       "      <td>8128.759819</td>\n",
-       "      <td>6519.566873</td>\n",
-       "      <td>7540.405551</td>\n",
-       "      <td>6127.088932</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>6863.381711</td>\n",
-       "      <td>4779.229273</td>\n",
-       "      <td>5569.067666</td>\n",
-       "      <td>4082.177333</td>\n",
-       "      <td>-1543.023779</td>\n",
-       "      <td>...</td>\n",
-       "      <td>6625.733420</td>\n",
-       "      <td>6568.295264</td>\n",
-       "      <td>3299.115446</td>\n",
-       "      <td>6283.162871</td>\n",
-       "      <td>-9019.720178</td>\n",
-       "      <td>2903.503809</td>\n",
-       "      <td>-4793.437112</td>\n",
-       "      <td>8001.440744</td>\n",
-       "      <td>4379.581693</td>\n",
-       "      <td>-6008.427385</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>LVA</th>\n",
-       "      <td>4031.871653</td>\n",
-       "      <td>3056.724204</td>\n",
-       "      <td>4089.755332</td>\n",
-       "      <td>3115.947350</td>\n",
-       "      <td>-2514.108374</td>\n",
-       "      <td>3033.378325</td>\n",
-       "      <td>2088.974272</td>\n",
-       "      <td>2943.438308</td>\n",
-       "      <td>1772.886261</td>\n",
-       "      <td>-4939.877007</td>\n",
-       "      <td>...</td>\n",
-       "      <td>3517.148880</td>\n",
-       "      <td>3090.003629</td>\n",
-       "      <td>804.150784</td>\n",
-       "      <td>2439.383891</td>\n",
-       "      <td>-11182.784093</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>-6726.219397</td>\n",
-       "      <td>4038.294540</td>\n",
-       "      <td>2416.828384</td>\n",
-       "      <td>-8403.089867</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>USA</th>\n",
-       "      <td>17311.315060</td>\n",
-       "      <td>14299.783440</td>\n",
-       "      <td>15237.411839</td>\n",
-       "      <td>12867.947595</td>\n",
-       "      <td>5901.742900</td>\n",
-       "      <td>15487.104169</td>\n",
-       "      <td>10838.899321</td>\n",
-       "      <td>11434.795841</td>\n",
-       "      <td>9284.828423</td>\n",
-       "      <td>6513.431091</td>\n",
-       "      <td>...</td>\n",
-       "      <td>13568.669626</td>\n",
-       "      <td>14382.171762</td>\n",
-       "      <td>8979.500000</td>\n",
-       "      <td>14970.249909</td>\n",
-       "      <td>-3459.709006</td>\n",
-       "      <td>9564.068693</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>16876.851818</td>\n",
-       "      <td>8752.849191</td>\n",
-       "      <td>-65.778426</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>MDA</th>\n",
-       "      <td>-122.470522</td>\n",
-       "      <td>-404.897150</td>\n",
-       "      <td>492.346313</td>\n",
-       "      <td>40.945546</td>\n",
-       "      <td>-4356.293662</td>\n",
-       "      <td>-746.017715</td>\n",
-       "      <td>-559.818982</td>\n",
-       "      <td>230.025521</td>\n",
-       "      <td>-497.940685</td>\n",
-       "      <td>-7210.133563</td>\n",
-       "      <td>...</td>\n",
-       "      <td>300.055331</td>\n",
-       "      <td>-389.845878</td>\n",
-       "      <td>-1491.713870</td>\n",
-       "      <td>-1267.850000</td>\n",
-       "      <td>-11489.043975</td>\n",
-       "      <td>-2539.132429</td>\n",
-       "      <td>-7462.891712</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>357.760961</td>\n",
-       "      <td>-9305.953922</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>CAF</th>\n",
-       "      <td>-1816.958034</td>\n",
-       "      <td>-2165.737881</td>\n",
-       "      <td>-285.579853</td>\n",
-       "      <td>-1061.687782</td>\n",
-       "      <td>-10063.884426</td>\n",
-       "      <td>-3018.676012</td>\n",
-       "      <td>-2195.207787</td>\n",
-       "      <td>-519.568754</td>\n",
-       "      <td>-1920.712022</td>\n",
-       "      <td>-16365.615527</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-557.196066</td>\n",
-       "      <td>-2140.162349</td>\n",
-       "      <td>-4068.744750</td>\n",
-       "      <td>-4114.713338</td>\n",
-       "      <td>-24839.757096</td>\n",
-       "      <td>-6413.826786</td>\n",
-       "      <td>-16336.130816</td>\n",
-       "      <td>-1510.000192</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>-20364.674325</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>GBR</th>\n",
-       "      <td>14014.910188</td>\n",
-       "      <td>11578.569688</td>\n",
-       "      <td>12332.483599</td>\n",
-       "      <td>10416.889496</td>\n",
-       "      <td>4802.626942</td>\n",
-       "      <td>12541.705769</td>\n",
-       "      <td>8777.730570</td>\n",
-       "      <td>9255.613265</td>\n",
-       "      <td>7519.293272</td>\n",
-       "      <td>5314.143433</td>\n",
-       "      <td>...</td>\n",
-       "      <td>10982.669638</td>\n",
-       "      <td>11645.180239</td>\n",
-       "      <td>7277.804114</td>\n",
-       "      <td>12126.277367</td>\n",
-       "      <td>-2735.031989</td>\n",
-       "      <td>7757.029616</td>\n",
-       "      <td>42.703971</td>\n",
-       "      <td>13662.493832</td>\n",
-       "      <td>7083.738103</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>132 rows × 132 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "              UKR           MNG           BFA           BTN           ESP  \\\n",
-       "UKR      0.000000   -289.907838    572.829633    125.974021  -4118.167851   \n",
-       "MNG    399.240133      0.000000   1003.038048    440.068286  -4548.547463   \n",
-       "BFA   -834.296077  -1060.810716      0.000000   -458.659647  -5563.770173   \n",
-       "BTN   -235.115223   -596.410931    587.753960      0.000000  -5793.402374   \n",
-       "ESP   8128.759819   6519.566873   7540.405551   6127.088932      0.000000   \n",
-       "..            ...           ...           ...           ...           ...   \n",
-       "LVA   4031.871653   3056.724204   4089.755332   3115.947350  -2514.108374   \n",
-       "USA  17311.315060  14299.783440  15237.411839  12867.947595   5901.742900   \n",
-       "MDA   -122.470522   -404.897150    492.346313     40.945546  -4356.293662   \n",
-       "CAF  -1816.958034  -2165.737881   -285.579853  -1061.687782 -10063.884426   \n",
-       "GBR  14014.910188  11578.569688  12332.483599  10416.889496   4802.626942   \n",
-       "\n",
-       "              DZA           GAB           PNG          MYS           CZE  \\\n",
-       "UKR   -607.485035   -461.148471    296.770653  -412.580539  -6837.986706   \n",
-       "MNG   -333.880109   -274.612416    601.307508  -255.204139  -7628.392781   \n",
-       "BFA  -1525.154650  -1113.542302   -170.635628  -976.383551  -9079.959870   \n",
-       "BTN  -1053.120200   -786.973444    256.415339  -698.487061  -9574.989434   \n",
-       "ESP   6863.381711   4779.229273   5569.067666  4082.177333  -1543.023779   \n",
-       "..            ...           ...           ...          ...           ...   \n",
-       "LVA   3033.378325   2088.974272   2943.438308  1772.886261  -4939.877007   \n",
-       "USA  15487.104169  10838.899321  11434.795841  9284.828423   6513.431091   \n",
-       "MDA   -746.017715   -559.818982    230.025521  -497.940685  -7210.133563   \n",
-       "CAF  -3018.676012  -2195.207787   -519.568754 -1920.712022 -16365.615527   \n",
-       "GBR  12541.705769   8777.730570   9255.613265  7519.293272   5314.143433   \n",
-       "\n",
-       "         ...                KIR           ARM          SYC           BLR  \\\n",
-       "UKR      ...         378.022297   -274.985279 -1363.184580  -1109.056592   \n",
-       "MNG      ...         742.948193     18.875351 -1343.613184   -916.366848   \n",
-       "BFA      ...        -169.313178  -1045.651128 -2180.308924  -2143.242278   \n",
-       "BTN      ...         340.156572   -576.827419 -2013.139297  -1741.944485   \n",
-       "ESP      ...        6625.733420   6568.295264  3299.115446   6283.162871   \n",
-       "..       ...                ...           ...          ...           ...   \n",
-       "LVA      ...        3517.148880   3090.003629   804.150784   2439.383891   \n",
-       "USA      ...       13568.669626  14382.171762  8979.500000  14970.249909   \n",
-       "MDA      ...         300.055331   -389.845878 -1491.713870  -1267.850000   \n",
-       "CAF      ...        -557.196066  -2140.162349 -4068.744750  -4114.713338   \n",
-       "GBR      ...       10982.669638  11645.180239  7277.804114  12126.277367   \n",
-       "\n",
-       "              AUS          LVA           USA           MDA          CAF  \\\n",
-       "UKR -10989.490063 -2358.983246  -7123.223327    113.962604   400.582193   \n",
-       "MNG -12580.973914 -2462.910599  -8103.081270    518.859474   657.547106   \n",
-       "BFA -13921.395843 -3485.059605  -9131.716733   -667.261723    91.699936   \n",
-       "BTN -15199.032883 -3402.577496  -9882.243263    -71.111071   436.861044   \n",
-       "ESP  -9019.720178  2903.503809  -4793.437112   8001.440744  4379.581693   \n",
-       "..            ...          ...           ...           ...          ...   \n",
-       "LVA -11182.784093     0.000000  -6726.219397   4038.294540  2416.828384   \n",
-       "USA  -3459.709006  9564.068693      0.000000  16876.851818  8752.849191   \n",
-       "MDA -11489.043975 -2539.132429  -7462.891712      0.000000   357.760961   \n",
-       "CAF -24839.757096 -6413.826786 -16336.130816  -1510.000192     0.000000   \n",
-       "GBR  -2735.031989  7757.029616     42.703971  13662.493832  7083.738103   \n",
-       "\n",
-       "              GBR  \n",
-       "UKR  -8882.844003  \n",
-       "MNG -10106.269247  \n",
-       "BFA -11384.317201  \n",
-       "BTN -12322.519954  \n",
-       "ESP  -6008.427385  \n",
-       "..            ...  \n",
-       "LVA  -8403.089867  \n",
-       "USA    -65.778426  \n",
-       "MDA  -9305.953922  \n",
-       "CAF -20364.674325  \n",
-       "GBR      0.000000  \n",
-       "\n",
-       "[132 rows x 132 columns]"
-      ]
-     },
-     "execution_count": 46,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# formula: Ae^(Bx)\n",
-    "x = 40\n",
-    "all_data[\"a\"] * e ** (all_data[\"b\"] * x)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 47,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style>\n",
-       "    .dataframe thead tr:only-child th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: left;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>UKR</th>\n",
-       "      <th>MNG</th>\n",
-       "      <th>BFA</th>\n",
-       "      <th>BTN</th>\n",
-       "      <th>ESP</th>\n",
-       "      <th>DZA</th>\n",
-       "      <th>GAB</th>\n",
-       "      <th>PNG</th>\n",
-       "      <th>MYS</th>\n",
-       "      <th>CZE</th>\n",
-       "      <th>...</th>\n",
-       "      <th>KIR</th>\n",
-       "      <th>ARM</th>\n",
-       "      <th>SYC</th>\n",
-       "      <th>BLR</th>\n",
-       "      <th>AUS</th>\n",
-       "      <th>LVA</th>\n",
-       "      <th>USA</th>\n",
-       "      <th>MDA</th>\n",
-       "      <th>CAF</th>\n",
-       "      <th>GBR</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>UKR</th>\n",
-       "      <td>1.088543e-06</td>\n",
-       "      <td>-0.000112</td>\n",
-       "      <td>-1.135147e-06</td>\n",
-       "      <td>0.000043</td>\n",
-       "      <td>1.741077e-07</td>\n",
-       "      <td>-1.563125e-05</td>\n",
-       "      <td>-7.986923e-07</td>\n",
-       "      <td>7.214698e-06</td>\n",
-       "      <td>9.013634e-08</td>\n",
-       "      <td>-6.424665e-07</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-0.000730</td>\n",
-       "      <td>-2.538145e-07</td>\n",
-       "      <td>0.000002</td>\n",
-       "      <td>4.204624e-06</td>\n",
-       "      <td>-4.006950e-08</td>\n",
-       "      <td>0.000019</td>\n",
-       "      <td>6.061405e-09</td>\n",
-       "      <td>2.303753e-05</td>\n",
-       "      <td>-0.000030</td>\n",
-       "      <td>6.625019e-08</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>MNG</th>\n",
-       "      <td>-2.432780e-05</td>\n",
-       "      <td>0.006276</td>\n",
-       "      <td>5.007893e-05</td>\n",
-       "      <td>0.000743</td>\n",
-       "      <td>-4.065737e-06</td>\n",
-       "      <td>3.045886e-04</td>\n",
-       "      <td>2.256187e-05</td>\n",
-       "      <td>-3.255976e-04</td>\n",
-       "      <td>-6.959533e-06</td>\n",
-       "      <td>1.007683e-05</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.034752</td>\n",
-       "      <td>3.065737e-06</td>\n",
-       "      <td>-0.000536</td>\n",
-       "      <td>-1.967016e-04</td>\n",
-       "      <td>2.176108e-06</td>\n",
-       "      <td>-0.000557</td>\n",
-       "      <td>-2.258060e-07</td>\n",
-       "      <td>-1.049946e-05</td>\n",
-       "      <td>0.001188</td>\n",
-       "      <td>-1.706212e-06</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>BFA</th>\n",
-       "      <td>5.653936e-06</td>\n",
-       "      <td>0.001104</td>\n",
-       "      <td>3.556910e-05</td>\n",
-       "      <td>0.000648</td>\n",
-       "      <td>3.082736e-07</td>\n",
-       "      <td>1.390236e-04</td>\n",
-       "      <td>-1.609369e-05</td>\n",
-       "      <td>4.025941e-07</td>\n",
-       "      <td>-1.661555e-06</td>\n",
-       "      <td>-6.403348e-06</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.001238</td>\n",
-       "      <td>-4.673806e-07</td>\n",
-       "      <td>-0.000060</td>\n",
-       "      <td>-4.848069e-06</td>\n",
-       "      <td>-1.573853e-07</td>\n",
-       "      <td>-0.000060</td>\n",
-       "      <td>-1.058419e-08</td>\n",
-       "      <td>9.452159e-06</td>\n",
-       "      <td>0.000219</td>\n",
-       "      <td>-3.676127e-07</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>BTN</th>\n",
-       "      <td>-3.625838e-07</td>\n",
-       "      <td>0.000029</td>\n",
-       "      <td>7.668287e-07</td>\n",
-       "      <td>0.000032</td>\n",
-       "      <td>-7.963649e-08</td>\n",
-       "      <td>6.746989e-07</td>\n",
-       "      <td>2.775988e-07</td>\n",
-       "      <td>-4.168086e-06</td>\n",
-       "      <td>-5.546007e-08</td>\n",
-       "      <td>1.030225e-07</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.000440</td>\n",
-       "      <td>4.354963e-08</td>\n",
-       "      <td>-0.000004</td>\n",
-       "      <td>-2.136746e-06</td>\n",
-       "      <td>3.350159e-08</td>\n",
-       "      <td>-0.000006</td>\n",
-       "      <td>-3.311235e-09</td>\n",
-       "      <td>-1.423725e-06</td>\n",
-       "      <td>0.000010</td>\n",
-       "      <td>-2.125943e-08</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>ESP</th>\n",
-       "      <td>-7.213387e-06</td>\n",
-       "      <td>-0.000560</td>\n",
-       "      <td>-1.540497e-05</td>\n",
-       "      <td>-0.000543</td>\n",
-       "      <td>-5.816558e-07</td>\n",
-       "      <td>-3.987183e-05</td>\n",
-       "      <td>2.434781e-07</td>\n",
-       "      <td>1.258697e-05</td>\n",
-       "      <td>6.139015e-07</td>\n",
-       "      <td>7.922655e-06</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-0.001494</td>\n",
-       "      <td>-5.956653e-07</td>\n",
-       "      <td>0.000117</td>\n",
-       "      <td>-3.163963e-06</td>\n",
-       "      <td>-1.123182e-07</td>\n",
-       "      <td>0.000005</td>\n",
-       "      <td>4.976309e-09</td>\n",
-       "      <td>-4.195928e-05</td>\n",
-       "      <td>-0.000204</td>\n",
-       "      <td>2.062428e-07</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>LVA</th>\n",
-       "      <td>1.694033e-05</td>\n",
-       "      <td>-0.000791</td>\n",
-       "      <td>-2.538517e-05</td>\n",
-       "      <td>0.000424</td>\n",
-       "      <td>3.045905e-06</td>\n",
-       "      <td>-2.214296e-04</td>\n",
-       "      <td>-1.236825e-05</td>\n",
-       "      <td>1.317640e-04</td>\n",
-       "      <td>2.369582e-07</td>\n",
-       "      <td>-9.580287e-06</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-0.013542</td>\n",
-       "      <td>1.111478e-06</td>\n",
-       "      <td>0.000060</td>\n",
-       "      <td>1.121238e-04</td>\n",
-       "      <td>-8.479572e-07</td>\n",
-       "      <td>0.000335</td>\n",
-       "      <td>9.058222e-08</td>\n",
-       "      <td>2.341512e-05</td>\n",
-       "      <td>-0.000587</td>\n",
-       "      <td>1.157347e-06</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>USA</th>\n",
-       "      <td>-1.056635e-07</td>\n",
-       "      <td>0.000020</td>\n",
-       "      <td>3.997209e-07</td>\n",
-       "      <td>0.000001</td>\n",
-       "      <td>-3.489516e-08</td>\n",
-       "      <td>2.508865e-06</td>\n",
-       "      <td>1.397903e-07</td>\n",
-       "      <td>-1.540345e-06</td>\n",
-       "      <td>-3.324803e-09</td>\n",
-       "      <td>7.278445e-08</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.000129</td>\n",
-       "      <td>1.625233e-08</td>\n",
-       "      <td>-0.000006</td>\n",
-       "      <td>-8.903684e-07</td>\n",
-       "      <td>9.956763e-09</td>\n",
-       "      <td>-0.000001</td>\n",
-       "      <td>-4.868586e-09</td>\n",
-       "      <td>5.091451e-07</td>\n",
-       "      <td>0.000013</td>\n",
-       "      <td>-6.581807e-09</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>MDA</th>\n",
-       "      <td>1.811724e-05</td>\n",
-       "      <td>-0.000767</td>\n",
-       "      <td>-1.173642e-05</td>\n",
-       "      <td>0.000183</td>\n",
-       "      <td>1.959146e-06</td>\n",
-       "      <td>-1.728003e-04</td>\n",
-       "      <td>-7.863703e-06</td>\n",
-       "      <td>7.995196e-05</td>\n",
-       "      <td>5.416268e-07</td>\n",
-       "      <td>-6.254032e-06</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-0.008631</td>\n",
-       "      <td>4.057047e-08</td>\n",
-       "      <td>0.000028</td>\n",
-       "      <td>3.565803e-05</td>\n",
-       "      <td>-4.963120e-07</td>\n",
-       "      <td>0.000164</td>\n",
-       "      <td>6.348163e-08</td>\n",
-       "      <td>1.175538e-04</td>\n",
-       "      <td>-0.000332</td>\n",
-       "      <td>6.894577e-07</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>CAF</th>\n",
-       "      <td>2.177755e-06</td>\n",
-       "      <td>0.000418</td>\n",
-       "      <td>9.414563e-06</td>\n",
-       "      <td>0.000247</td>\n",
-       "      <td>1.296655e-07</td>\n",
-       "      <td>5.212633e-05</td>\n",
-       "      <td>-6.261755e-06</td>\n",
-       "      <td>-9.215140e-08</td>\n",
-       "      <td>-6.278690e-07</td>\n",
-       "      <td>-2.468890e-06</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.000498</td>\n",
-       "      <td>-1.685368e-07</td>\n",
-       "      <td>-0.000023</td>\n",
-       "      <td>-1.724477e-06</td>\n",
-       "      <td>-5.212001e-08</td>\n",
-       "      <td>-0.000022</td>\n",
-       "      <td>-3.554844e-09</td>\n",
-       "      <td>3.844280e-06</td>\n",
-       "      <td>0.000081</td>\n",
-       "      <td>-1.375046e-07</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>GBR</th>\n",
-       "      <td>-5.886479e-07</td>\n",
-       "      <td>-0.000026</td>\n",
-       "      <td>7.087793e-06</td>\n",
-       "      <td>-0.000012</td>\n",
-       "      <td>-7.862801e-08</td>\n",
-       "      <td>4.167423e-06</td>\n",
-       "      <td>1.281654e-08</td>\n",
-       "      <td>-1.726319e-05</td>\n",
-       "      <td>2.039852e-07</td>\n",
-       "      <td>7.991862e-08</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.001473</td>\n",
-       "      <td>-2.695472e-08</td>\n",
-       "      <td>-0.000042</td>\n",
-       "      <td>-5.058340e-06</td>\n",
-       "      <td>1.454802e-07</td>\n",
-       "      <td>-0.000044</td>\n",
-       "      <td>-9.272919e-10</td>\n",
-       "      <td>-8.007931e-06</td>\n",
-       "      <td>0.000085</td>\n",
-       "      <td>-2.171862e-07</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>132 rows × 132 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "              UKR       MNG           BFA       BTN           ESP  \\\n",
-       "UKR  1.088543e-06 -0.000112 -1.135147e-06  0.000043  1.741077e-07   \n",
-       "MNG -2.432780e-05  0.006276  5.007893e-05  0.000743 -4.065737e-06   \n",
-       "BFA  5.653936e-06  0.001104  3.556910e-05  0.000648  3.082736e-07   \n",
-       "BTN -3.625838e-07  0.000029  7.668287e-07  0.000032 -7.963649e-08   \n",
-       "ESP -7.213387e-06 -0.000560 -1.540497e-05 -0.000543 -5.816558e-07   \n",
-       "..            ...       ...           ...       ...           ...   \n",
-       "LVA  1.694033e-05 -0.000791 -2.538517e-05  0.000424  3.045905e-06   \n",
-       "USA -1.056635e-07  0.000020  3.997209e-07  0.000001 -3.489516e-08   \n",
-       "MDA  1.811724e-05 -0.000767 -1.173642e-05  0.000183  1.959146e-06   \n",
-       "CAF  2.177755e-06  0.000418  9.414563e-06  0.000247  1.296655e-07   \n",
-       "GBR -5.886479e-07 -0.000026  7.087793e-06 -0.000012 -7.862801e-08   \n",
-       "\n",
-       "              DZA           GAB           PNG           MYS           CZE  \\\n",
-       "UKR -1.563125e-05 -7.986923e-07  7.214698e-06  9.013634e-08 -6.424665e-07   \n",
-       "MNG  3.045886e-04  2.256187e-05 -3.255976e-04 -6.959533e-06  1.007683e-05   \n",
-       "BFA  1.390236e-04 -1.609369e-05  4.025941e-07 -1.661555e-06 -6.403348e-06   \n",
-       "BTN  6.746989e-07  2.775988e-07 -4.168086e-06 -5.546007e-08  1.030225e-07   \n",
-       "ESP -3.987183e-05  2.434781e-07  1.258697e-05  6.139015e-07  7.922655e-06   \n",
-       "..            ...           ...           ...           ...           ...   \n",
-       "LVA -2.214296e-04 -1.236825e-05  1.317640e-04  2.369582e-07 -9.580287e-06   \n",
-       "USA  2.508865e-06  1.397903e-07 -1.540345e-06 -3.324803e-09  7.278445e-08   \n",
-       "MDA -1.728003e-04 -7.863703e-06  7.995196e-05  5.416268e-07 -6.254032e-06   \n",
-       "CAF  5.212633e-05 -6.261755e-06 -9.215140e-08 -6.278690e-07 -2.468890e-06   \n",
-       "GBR  4.167423e-06  1.281654e-08 -1.726319e-05  2.039852e-07  7.991862e-08   \n",
-       "\n",
-       "         ...            KIR           ARM       SYC           BLR  \\\n",
-       "UKR      ...      -0.000730 -2.538145e-07  0.000002  4.204624e-06   \n",
-       "MNG      ...       0.034752  3.065737e-06 -0.000536 -1.967016e-04   \n",
-       "BFA      ...       0.001238 -4.673806e-07 -0.000060 -4.848069e-06   \n",
-       "BTN      ...       0.000440  4.354963e-08 -0.000004 -2.136746e-06   \n",
-       "ESP      ...      -0.001494 -5.956653e-07  0.000117 -3.163963e-06   \n",
-       "..       ...            ...           ...       ...           ...   \n",
-       "LVA      ...      -0.013542  1.111478e-06  0.000060  1.121238e-04   \n",
-       "USA      ...       0.000129  1.625233e-08 -0.000006 -8.903684e-07   \n",
-       "MDA      ...      -0.008631  4.057047e-08  0.000028  3.565803e-05   \n",
-       "CAF      ...       0.000498 -1.685368e-07 -0.000023 -1.724477e-06   \n",
-       "GBR      ...       0.001473 -2.695472e-08 -0.000042 -5.058340e-06   \n",
-       "\n",
-       "              AUS       LVA           USA           MDA       CAF  \\\n",
-       "UKR -4.006950e-08  0.000019  6.061405e-09  2.303753e-05 -0.000030   \n",
-       "MNG  2.176108e-06 -0.000557 -2.258060e-07 -1.049946e-05  0.001188   \n",
-       "BFA -1.573853e-07 -0.000060 -1.058419e-08  9.452159e-06  0.000219   \n",
-       "BTN  3.350159e-08 -0.000006 -3.311235e-09 -1.423725e-06  0.000010   \n",
-       "ESP -1.123182e-07  0.000005  4.976309e-09 -4.195928e-05 -0.000204   \n",
-       "..            ...       ...           ...           ...       ...   \n",
-       "LVA -8.479572e-07  0.000335  9.058222e-08  2.341512e-05 -0.000587   \n",
-       "USA  9.956763e-09 -0.000001 -4.868586e-09  5.091451e-07  0.000013   \n",
-       "MDA -4.963120e-07  0.000164  6.348163e-08  1.175538e-04 -0.000332   \n",
-       "CAF -5.212001e-08 -0.000022 -3.554844e-09  3.844280e-06  0.000081   \n",
-       "GBR  1.454802e-07 -0.000044 -9.272919e-10 -8.007931e-06  0.000085   \n",
-       "\n",
-       "              GBR  \n",
-       "UKR  6.625019e-08  \n",
-       "MNG -1.706212e-06  \n",
-       "BFA -3.676127e-07  \n",
-       "BTN -2.125943e-08  \n",
-       "ESP  2.062428e-07  \n",
-       "..            ...  \n",
-       "LVA  1.157347e-06  \n",
-       "USA -6.581807e-09  \n",
-       "MDA  6.894577e-07  \n",
-       "CAF -1.375046e-07  \n",
-       "GBR -2.171862e-07  \n",
-       "\n",
-       "[132 rows x 132 columns]"
-      ]
-     },
-     "execution_count": 47,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# inv(migration history)\n",
-    "pd.DataFrame(np.linalg.pinv(all_data[\"un\"].values), all_data[\"un\"].columns, all_data[\"un\"].index)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 48,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Political barriers\n",
-    "freedom_index_inverse =pd.DataFrame(np.linalg.pinv(all_data[\"freedom\"].values), all_data[\"freedom\"].columns, all_data[\"freedom\"].index)\n",
-    "political_barriers = all_data[\"passport\"] + freedom_index_inverse"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
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-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: left;\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th>Alpha-3 code</th>\n",
-       "      <th>UKR</th>\n",
-       "      <th>MNG</th>\n",
-       "      <th>BFA</th>\n",
-       "      <th>BTN</th>\n",
-       "      <th>ESP</th>\n",
-       "      <th>DZA</th>\n",
-       "      <th>GAB</th>\n",
-       "      <th>PNG</th>\n",
-       "      <th>MYS</th>\n",
-       "      <th>CZE</th>\n",
-       "      <th>...</th>\n",
-       "      <th>KIR</th>\n",
-       "      <th>ARM</th>\n",
-       "      <th>SYC</th>\n",
-       "      <th>BLR</th>\n",
-       "      <th>AUS</th>\n",
-       "      <th>LVA</th>\n",
-       "      <th>USA</th>\n",
-       "      <th>MDA</th>\n",
-       "      <th>CAF</th>\n",
-       "      <th>GBR</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Alpha-3 code</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>UKR</th>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.265692</td>\n",
-       "      <td>0.255263</td>\n",
-       "      <td>0.275819</td>\n",
-       "      <td>0.150666</td>\n",
-       "      <td>0.171183</td>\n",
-       "      <td>0.296550</td>\n",
-       "      <td>0.620155</td>\n",
-       "      <td>0.458456</td>\n",
-       "      <td>0.060171</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.668515</td>\n",
-       "      <td>0.072155</td>\n",
-       "      <td>0.321401</td>\n",
-       "      <td>0.026450</td>\n",
-       "      <td>0.655093</td>\n",
-       "      <td>0.050558</td>\n",
-       "      <td>0.459039</td>\n",
-       "      <td>0.015862</td>\n",
-       "      <td>0.240818</td>\n",
-       "      <td>0.120499</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>MNG</th>\n",
-       "      <td>0.265692</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.515443</td>\n",
-       "      <td>0.121865</td>\n",
-       "      <td>0.409778</td>\n",
-       "      <td>0.435939</td>\n",
-       "      <td>0.520534</td>\n",
-       "      <td>0.358363</td>\n",
-       "      <td>0.246291</td>\n",
-       "      <td>0.315931</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.413527</td>\n",
-       "      <td>0.242099</td>\n",
-       "      <td>0.374049</td>\n",
-       "      <td>0.268988</td>\n",
-       "      <td>0.432742</td>\n",
-       "      <td>0.268792</td>\n",
-       "      <td>0.524627</td>\n",
-       "      <td>0.280798</td>\n",
-       "      <td>0.452409</td>\n",
-       "      <td>0.349608</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>BFA</th>\n",
-       "      <td>0.255263</td>\n",
-       "      <td>0.515443</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.482627</td>\n",
-       "      <td>0.151481</td>\n",
-       "      <td>0.087944</td>\n",
-       "      <td>0.109426</td>\n",
-       "      <td>0.832303</td>\n",
-       "      <td>0.633728</td>\n",
-       "      <td>0.220894</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.922379</td>\n",
-       "      <td>0.275352</td>\n",
-       "      <td>0.335359</td>\n",
-       "      <td>0.260717</td>\n",
-       "      <td>0.759869</td>\n",
-       "      <td>0.272009</td>\n",
-       "      <td>0.480889</td>\n",
-       "      <td>0.239432</td>\n",
-       "      <td>0.131097</td>\n",
-       "      <td>0.229540</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>BTN</th>\n",
-       "      <td>0.275819</td>\n",
-       "      <td>0.121865</td>\n",
-       "      <td>0.482627</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.424969</td>\n",
-       "      <td>0.422504</td>\n",
-       "      <td>0.450710</td>\n",
-       "      <td>0.358215</td>\n",
-       "      <td>0.182912</td>\n",
-       "      <td>0.335632</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.462968</td>\n",
-       "      <td>0.220414</td>\n",
-       "      <td>0.260114</td>\n",
-       "      <td>0.291288</td>\n",
-       "      <td>0.381234</td>\n",
-       "      <td>0.302397</td>\n",
-       "      <td>0.638927</td>\n",
-       "      <td>0.286748</td>\n",
-       "      <td>0.384157</td>\n",
-       "      <td>0.388888</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>ESP</th>\n",
-       "      <td>0.150666</td>\n",
-       "      <td>0.409778</td>\n",
-       "      <td>0.151481</td>\n",
-       "      <td>0.424969</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.074562</td>\n",
-       "      <td>0.243267</td>\n",
-       "      <td>0.768019</td>\n",
-       "      <td>0.606136</td>\n",
-       "      <td>0.094226</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.775355</td>\n",
-       "      <td>0.207395</td>\n",
-       "      <td>0.394538</td>\n",
-       "      <td>0.141466</td>\n",
-       "      <td>0.796557</td>\n",
-       "      <td>0.141709</td>\n",
-       "      <td>0.384311</td>\n",
-       "      <td>0.138473</td>\n",
-       "      <td>0.223248</td>\n",
-       "      <td>0.078742</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>LVA</th>\n",
-       "      <td>0.050558</td>\n",
-       "      <td>0.268792</td>\n",
-       "      <td>0.272009</td>\n",
-       "      <td>0.302397</td>\n",
-       "      <td>0.141709</td>\n",
-       "      <td>0.184202</td>\n",
-       "      <td>0.330170</td>\n",
-       "      <td>0.627068</td>\n",
-       "      <td>0.484509</td>\n",
-       "      <td>0.051421</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.650370</td>\n",
-       "      <td>0.119732</td>\n",
-       "      <td>0.371880</td>\n",
-       "      <td>0.024373</td>\n",
-       "      <td>0.683319</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.409855</td>\n",
-       "      <td>0.057633</td>\n",
-       "      <td>0.280478</td>\n",
-       "      <td>0.086649</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>USA</th>\n",
-       "      <td>0.459039</td>\n",
-       "      <td>0.524627</td>\n",
-       "      <td>0.480889</td>\n",
-       "      <td>0.638927</td>\n",
-       "      <td>0.384311</td>\n",
-       "      <td>0.449651</td>\n",
-       "      <td>0.589569</td>\n",
-       "      <td>0.638643</td>\n",
-       "      <td>0.734380</td>\n",
-       "      <td>0.414488</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.498985</td>\n",
-       "      <td>0.529539</td>\n",
-       "      <td>0.774446</td>\n",
-       "      <td>0.432822</td>\n",
-       "      <td>0.766749</td>\n",
-       "      <td>0.409855</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.460633</td>\n",
-       "      <td>0.598973</td>\n",
-       "      <td>0.351367</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>MDA</th>\n",
-       "      <td>0.015862</td>\n",
-       "      <td>0.280798</td>\n",
-       "      <td>0.239432</td>\n",
-       "      <td>0.286748</td>\n",
-       "      <td>0.138473</td>\n",
-       "      <td>0.155643</td>\n",
-       "      <td>0.281735</td>\n",
-       "      <td>0.634079</td>\n",
-       "      <td>0.468824</td>\n",
-       "      <td>0.052428</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.684377</td>\n",
-       "      <td>0.075666</td>\n",
-       "      <td>0.317421</td>\n",
-       "      <td>0.033783</td>\n",
-       "      <td>0.664112</td>\n",
-       "      <td>0.057633</td>\n",
-       "      <td>0.460633</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.227209</td>\n",
-       "      <td>0.116030</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>CAF</th>\n",
-       "      <td>0.240818</td>\n",
-       "      <td>0.452409</td>\n",
-       "      <td>0.131097</td>\n",
-       "      <td>0.384157</td>\n",
-       "      <td>0.223248</td>\n",
-       "      <td>0.151385</td>\n",
-       "      <td>0.068382</td>\n",
-       "      <td>0.707467</td>\n",
-       "      <td>0.510490</td>\n",
-       "      <td>0.240729</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.844367</td>\n",
-       "      <td>0.220480</td>\n",
-       "      <td>0.204272</td>\n",
-       "      <td>0.259512</td>\n",
-       "      <td>0.631202</td>\n",
-       "      <td>0.280478</td>\n",
-       "      <td>0.598973</td>\n",
-       "      <td>0.227209</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>0.282916</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>GBR</th>\n",
-       "      <td>0.120499</td>\n",
-       "      <td>0.349608</td>\n",
-       "      <td>0.229540</td>\n",
-       "      <td>0.388888</td>\n",
-       "      <td>0.078742</td>\n",
-       "      <td>0.147000</td>\n",
-       "      <td>0.314468</td>\n",
-       "      <td>0.704564</td>\n",
-       "      <td>0.571155</td>\n",
-       "      <td>0.064787</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.696614</td>\n",
-       "      <td>0.191454</td>\n",
-       "      <td>0.423239</td>\n",
-       "      <td>0.099311</td>\n",
-       "      <td>0.769958</td>\n",
-       "      <td>0.086649</td>\n",
-       "      <td>0.351367</td>\n",
-       "      <td>0.116030</td>\n",
-       "      <td>0.282916</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>132 rows × 132 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "Alpha-3 code       UKR       MNG       BFA       BTN       ESP       DZA  \\\n",
-       "Alpha-3 code                                                               \n",
-       "UKR           0.000000  0.265692  0.255263  0.275819  0.150666  0.171183   \n",
-       "MNG           0.265692  0.000000  0.515443  0.121865  0.409778  0.435939   \n",
-       "BFA           0.255263  0.515443  0.000000  0.482627  0.151481  0.087944   \n",
-       "BTN           0.275819  0.121865  0.482627  0.000000  0.424969  0.422504   \n",
-       "ESP           0.150666  0.409778  0.151481  0.424969  0.000000  0.074562   \n",
-       "...                ...       ...       ...       ...       ...       ...   \n",
-       "LVA           0.050558  0.268792  0.272009  0.302397  0.141709  0.184202   \n",
-       "USA           0.459039  0.524627  0.480889  0.638927  0.384311  0.449651   \n",
-       "MDA           0.015862  0.280798  0.239432  0.286748  0.138473  0.155643   \n",
-       "CAF           0.240818  0.452409  0.131097  0.384157  0.223248  0.151385   \n",
-       "GBR           0.120499  0.349608  0.229540  0.388888  0.078742  0.147000   \n",
-       "\n",
-       "Alpha-3 code       GAB       PNG       MYS       CZE    ...          KIR  \\\n",
-       "Alpha-3 code                                            ...                \n",
-       "UKR           0.296550  0.620155  0.458456  0.060171    ...     0.668515   \n",
-       "MNG           0.520534  0.358363  0.246291  0.315931    ...     0.413527   \n",
-       "BFA           0.109426  0.832303  0.633728  0.220894    ...     0.922379   \n",
-       "BTN           0.450710  0.358215  0.182912  0.335632    ...     0.462968   \n",
-       "ESP           0.243267  0.768019  0.606136  0.094226    ...     0.775355   \n",
-       "...                ...       ...       ...       ...    ...          ...   \n",
-       "LVA           0.330170  0.627068  0.484509  0.051421    ...     0.650370   \n",
-       "USA           0.589569  0.638643  0.734380  0.414488    ...     0.498985   \n",
-       "MDA           0.281735  0.634079  0.468824  0.052428    ...     0.684377   \n",
-       "CAF           0.068382  0.707467  0.510490  0.240729    ...     0.844367   \n",
-       "GBR           0.314468  0.704564  0.571155  0.064787    ...     0.696614   \n",
-       "\n",
-       "Alpha-3 code       ARM       SYC       BLR       AUS       LVA       USA  \\\n",
-       "Alpha-3 code                                                               \n",
-       "UKR           0.072155  0.321401  0.026450  0.655093  0.050558  0.459039   \n",
-       "MNG           0.242099  0.374049  0.268988  0.432742  0.268792  0.524627   \n",
-       "BFA           0.275352  0.335359  0.260717  0.759869  0.272009  0.480889   \n",
-       "BTN           0.220414  0.260114  0.291288  0.381234  0.302397  0.638927   \n",
-       "ESP           0.207395  0.394538  0.141466  0.796557  0.141709  0.384311   \n",
-       "...                ...       ...       ...       ...       ...       ...   \n",
-       "LVA           0.119732  0.371880  0.024373  0.683319  0.000000  0.409855   \n",
-       "USA           0.529539  0.774446  0.432822  0.766749  0.409855  0.000000   \n",
-       "MDA           0.075666  0.317421  0.033783  0.664112  0.057633  0.460633   \n",
-       "CAF           0.220480  0.204272  0.259512  0.631202  0.280478  0.598973   \n",
-       "GBR           0.191454  0.423239  0.099311  0.769958  0.086649  0.351367   \n",
-       "\n",
-       "Alpha-3 code       MDA       CAF       GBR  \n",
-       "Alpha-3 code                                \n",
-       "UKR           0.015862  0.240818  0.120499  \n",
-       "MNG           0.280798  0.452409  0.349608  \n",
-       "BFA           0.239432  0.131097  0.229540  \n",
-       "BTN           0.286748  0.384157  0.388888  \n",
-       "ESP           0.138473  0.223248  0.078742  \n",
-       "...                ...       ...       ...  \n",
-       "LVA           0.057633  0.280478  0.086649  \n",
-       "USA           0.460633  0.598973  0.351367  \n",
-       "MDA           0.000000  0.227209  0.116030  \n",
-       "CAF           0.227209  0.000000  0.282916  \n",
-       "GBR           0.116030  0.282916  0.000000  \n",
-       "\n",
-       "[132 rows x 132 columns]"
-      ]
-     },
-     "execution_count": 49,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Distance\n",
-    "\n",
-    "all_data[\"distance\"] / max(all_data[\"distance\"].max())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 50,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style>\n",
-       "    .dataframe thead tr:only-child th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: left;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>UKR</th>\n",
-       "      <th>MNG</th>\n",
-       "      <th>BFA</th>\n",
-       "      <th>BTN</th>\n",
-       "      <th>ESP</th>\n",
-       "      <th>DZA</th>\n",
-       "      <th>GAB</th>\n",
-       "      <th>PNG</th>\n",
-       "      <th>MYS</th>\n",
-       "      <th>CZE</th>\n",
-       "      <th>...</th>\n",
-       "      <th>KIR</th>\n",
-       "      <th>ARM</th>\n",
-       "      <th>SYC</th>\n",
-       "      <th>BLR</th>\n",
-       "      <th>AUS</th>\n",
-       "      <th>LVA</th>\n",
-       "      <th>USA</th>\n",
-       "      <th>MDA</th>\n",
-       "      <th>CAF</th>\n",
-       "      <th>GBR</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>UKR</th>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.996074</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.979624</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.998382</td>\n",
-       "      <td>0.999773</td>\n",
-       "      <td>0.987573</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>MNG</th>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.999999</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.999983</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.999941</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.999993</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>BFA</th>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>BTN</th>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.999998</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.999989</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.999997</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>ESP</th>\n",
-       "      <td>0.993575</td>\n",
-       "      <td>0.999965</td>\n",
-       "      <td>0.999905</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.995410</td>\n",
-       "      <td>0.999977</td>\n",
-       "      <td>0.999998</td>\n",
-       "      <td>0.999950</td>\n",
-       "      <td>0.999291</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.999210</td>\n",
-       "      <td>0.999997</td>\n",
-       "      <td>0.999678</td>\n",
-       "      <td>0.999503</td>\n",
-       "      <td>0.999669</td>\n",
-       "      <td>0.996695</td>\n",
-       "      <td>0.998643</td>\n",
-       "      <td>0.999987</td>\n",
-       "      <td>0.974372</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>LVA</th>\n",
-       "      <td>0.997221</td>\n",
-       "      <td>0.999991</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.999986</td>\n",
-       "      <td>0.999999</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.999983</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.999936</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.996186</td>\n",
-       "      <td>0.999988</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.999950</td>\n",
-       "      <td>0.999858</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.999905</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>USA</th>\n",
-       "      <td>0.971304</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.992169</td>\n",
-       "      <td>0.998531</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.994725</td>\n",
-       "      <td>0.994115</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.992950</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.995295</td>\n",
-       "      <td>0.993540</td>\n",
-       "      <td>0.997955</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.996889</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.940664</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>MDA</th>\n",
-       "      <td>0.994688</td>\n",
-       "      <td>0.999992</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.999989</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.999724</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>CAF</th>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.999999</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>0.999955</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "      <td>1.000000</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>GBR</th>\n",
-       "      <td>0.998057</td>\n",
-       "      <td>0.999849</td>\n",
-       "      <td>0.999980</td>\n",
-       "      <td>0.999962</td>\n",
-       "      <td>0.992433</td>\n",
-       "      <td>0.997774</td>\n",
-       "      <td>0.999967</td>\n",
-       "      <td>0.999900</td>\n",
-       "      <td>0.993761</td>\n",
-       "      <td>0.996547</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.999985</td>\n",
-       "      <td>0.999851</td>\n",
-       "      <td>0.999678</td>\n",
-       "      <td>0.999607</td>\n",
-       "      <td>0.988731</td>\n",
-       "      <td>0.994519</td>\n",
-       "      <td>0.982394</td>\n",
-       "      <td>0.999716</td>\n",
-       "      <td>0.999986</td>\n",
-       "      <td>1.000000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>132 rows × 132 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "          UKR       MNG       BFA       BTN       ESP       DZA       GAB  \\\n",
-       "UKR  1.000000  1.000000  1.000000  1.000000  1.000000  1.000000  1.000000   \n",
-       "MNG  1.000000  1.000000  1.000000  1.000000  1.000000  1.000000  1.000000   \n",
-       "BFA  1.000000  1.000000  1.000000  1.000000  1.000000  1.000000  1.000000   \n",
-       "BTN  1.000000  1.000000  1.000000  1.000000  1.000000  1.000000  1.000000   \n",
-       "ESP  0.993575  0.999965  0.999905  1.000000  1.000000  0.995410  0.999977   \n",
-       "..        ...       ...       ...       ...       ...       ...       ...   \n",
-       "LVA  0.997221  0.999991  1.000000  1.000000  0.999986  0.999999  1.000000   \n",
-       "USA  0.971304  1.000000  1.000000  1.000000  0.992169  0.998531  1.000000   \n",
-       "MDA  0.994688  0.999992  1.000000  1.000000  1.000000  1.000000  1.000000   \n",
-       "CAF  1.000000  1.000000  1.000000  1.000000  1.000000  1.000000  1.000000   \n",
-       "GBR  0.998057  0.999849  0.999980  0.999962  0.992433  0.997774  0.999967   \n",
-       "\n",
-       "          PNG       MYS       CZE    ...          KIR       ARM       SYC  \\\n",
-       "UKR  1.000000  1.000000  1.000000    ...     1.000000  0.996074  1.000000   \n",
-       "MNG  1.000000  1.000000  0.999999    ...     1.000000  1.000000  1.000000   \n",
-       "BFA  1.000000  1.000000  1.000000    ...     1.000000  1.000000  1.000000   \n",
-       "BTN  1.000000  1.000000  1.000000    ...     1.000000  1.000000  1.000000   \n",
-       "ESP  0.999998  0.999950  0.999291    ...     1.000000  0.999210  0.999997   \n",
-       "..        ...       ...       ...    ...          ...       ...       ...   \n",
-       "LVA  1.000000  1.000000  0.999983    ...     1.000000  0.999936  1.000000   \n",
-       "USA  1.000000  0.994725  0.994115    ...     1.000000  0.992950  1.000000   \n",
-       "MDA  1.000000  1.000000  0.999989    ...     1.000000  1.000000  1.000000   \n",
-       "CAF  1.000000  1.000000  1.000000    ...     1.000000  1.000000  1.000000   \n",
-       "GBR  0.999900  0.993761  0.996547    ...     0.999985  0.999851  0.999678   \n",
-       "\n",
-       "          BLR       AUS       LVA       USA       MDA       CAF       GBR  \n",
-       "UKR  0.979624  1.000000  0.998382  0.999773  0.987573  1.000000  1.000000  \n",
-       "MNG  1.000000  0.999983  1.000000  0.999941  1.000000  1.000000  0.999993  \n",
-       "BFA  1.000000  1.000000  1.000000  1.000000  1.000000  1.000000  1.000000  \n",
-       "BTN  1.000000  0.999998  1.000000  0.999989  1.000000  1.000000  0.999997  \n",
-       "ESP  0.999678  0.999503  0.999669  0.996695  0.998643  0.999987  0.974372  \n",
-       "..        ...       ...       ...       ...       ...       ...       ...  \n",
-       "LVA  0.996186  0.999988  1.000000  0.999950  0.999858  1.000000  0.999905  \n",
-       "USA  0.995295  0.993540  0.997955  1.000000  0.996889  1.000000  0.940664  \n",
-       "MDA  0.999724  1.000000  1.000000  1.000000  1.000000  1.000000  1.000000  \n",
-       "CAF  1.000000  0.999999  1.000000  0.999955  1.000000  1.000000  1.000000  \n",
-       "GBR  0.999607  0.988731  0.994519  0.982394  0.999716  0.999986  1.000000  \n",
-       "\n",
-       "[132 rows x 132 columns]"
-      ]
-     },
-     "execution_count": 50,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Migration History\n",
-    "\n",
-    "# max(all_data[\"un\"].max())\n",
-    "1 - (all_data[\"un\"]) / max(all_data[\"un\"].max())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 51,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-       "\n",
-       "    .dataframe thead th {\n",
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-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th>ISO3</th>\n",
-       "      <th>UKR</th>\n",
-       "      <th>MNG</th>\n",
-       "      <th>BFA</th>\n",
-       "      <th>BTN</th>\n",
-       "      <th>ESP</th>\n",
-       "      <th>DZA</th>\n",
-       "      <th>GAB</th>\n",
-       "      <th>PNG</th>\n",
-       "      <th>MYS</th>\n",
-       "      <th>CZE</th>\n",
-       "      <th>...</th>\n",
-       "      <th>KIR</th>\n",
-       "      <th>ARM</th>\n",
-       "      <th>SYC</th>\n",
-       "      <th>BLR</th>\n",
-       "      <th>AUS</th>\n",
-       "      <th>LVA</th>\n",
-       "      <th>USA</th>\n",
-       "      <th>MDA</th>\n",
-       "      <th>CAF</th>\n",
-       "      <th>GBR</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>ISO3</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>UKR</th>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>MNG</th>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>BFA</th>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>BTN</th>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>ESP</th>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>LVA</th>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>USA</th>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>MDA</th>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>CAF</th>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>GBR</th>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "      <td>1</td>\n",
-       "      <td>1</td>\n",
-       "      <td>0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>132 rows × 132 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "ISO3  UKR  MNG  BFA  BTN  ESP  DZA  GAB  PNG  MYS  CZE ...   KIR  ARM  SYC  \\\n",
-       "ISO3                                                   ...                   \n",
-       "UKR     0    1    1    1    1    1    1    1    1    1 ...     1    1    1   \n",
-       "MNG     1    0    1    1    1    1    1    1    1    1 ...     1    1    1   \n",
-       "BFA     1    1    0    1    1    0    0    1    1    1 ...     1    1    1   \n",
-       "BTN     1    1    1    0    1    1    1    1    1    1 ...     1    1    1   \n",
-       "ESP     1    1    1    1    0    1    1    1    1    1 ...     1    1    1   \n",
-       "...   ...  ...  ...  ...  ...  ...  ...  ...  ...  ... ...   ...  ...  ...   \n",
-       "LVA     0    1    1    1    1    1    1    1    1    1 ...     1    1    1   \n",
-       "USA     1    1    1    1    0    1    1    0    0    1 ...     0    1    1   \n",
-       "MDA     0    1    1    1    1    1    1    1    1    1 ...     1    1    1   \n",
-       "CAF     1    1    0    1    1    0    0    1    1    1 ...     1    1    1   \n",
-       "GBR     1    1    1    1    1    1    1    0    0    1 ...     0    1    1   \n",
-       "\n",
-       "ISO3  BLR  AUS  LVA  USA  MDA  CAF  GBR  \n",
-       "ISO3                                     \n",
-       "UKR     0    1    0    1    0    1    1  \n",
-       "MNG     1    1    1    1    1    1    1  \n",
-       "BFA     1    1    1    1    1    0    1  \n",
-       "BTN     1    1    1    1    1    1    1  \n",
-       "ESP     1    1    1    0    1    1    1  \n",
-       "...   ...  ...  ...  ...  ...  ...  ...  \n",
-       "LVA     0    1    0    1    0    1    1  \n",
-       "USA     1    0    1    0    1    1    0  \n",
-       "MDA     0    1    0    1    0    1    1  \n",
-       "CAF     1    1    1    1    1    0    1  \n",
-       "GBR     1    0    1    0    1    1    0  \n",
-       "\n",
-       "[132 rows x 132 columns]"
-      ]
-     },
-     "execution_count": 51,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Shared Language\n",
-    "\n",
-    "1 - all_data[\"language\"]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 52,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
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-       "\n",
-       "    .dataframe thead th {\n",
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-       "\n",
-       "    .dataframe tbody tr th {\n",
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-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th>Country</th>\n",
-       "      <th>UKR</th>\n",
-       "      <th>MNG</th>\n",
-       "      <th>BFA</th>\n",
-       "      <th>BTN</th>\n",
-       "      <th>ESP</th>\n",
-       "      <th>DZA</th>\n",
-       "      <th>GAB</th>\n",
-       "      <th>PNG</th>\n",
-       "      <th>MYS</th>\n",
-       "      <th>CZE</th>\n",
-       "      <th>...</th>\n",
-       "      <th>KIR</th>\n",
-       "      <th>ARM</th>\n",
-       "      <th>SYC</th>\n",
-       "      <th>BLR</th>\n",
-       "      <th>AUS</th>\n",
-       "      <th>LVA</th>\n",
-       "      <th>USA</th>\n",
-       "      <th>MDA</th>\n",
-       "      <th>CAF</th>\n",
-       "      <th>GBR</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Country</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>UKR</th>\n",
-       "      <td>0.005000</td>\n",
-       "      <td>-0.065722</td>\n",
-       "      <td>-0.082938</td>\n",
-       "      <td>-0.282732</td>\n",
-       "      <td>0.149381</td>\n",
-       "      <td>-0.362113</td>\n",
-       "      <td>-0.392577</td>\n",
-       "      <td>-0.062474</td>\n",
-       "      <td>0.064330</td>\n",
-       "      <td>0.149381</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.082680</td>\n",
-       "      <td>0.022938</td>\n",
-       "      <td>0.286959</td>\n",
-       "      <td>-0.225773</td>\n",
-       "      <td>0.014742</td>\n",
-       "      <td>0.114381</td>\n",
-       "      <td>-0.014794</td>\n",
-       "      <td>-0.082784</td>\n",
-       "      <td>-0.466495</td>\n",
-       "      <td>0.149227</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>MNG</th>\n",
-       "      <td>0.075722</td>\n",
-       "      <td>0.005000</td>\n",
-       "      <td>-0.012216</td>\n",
-       "      <td>-0.212010</td>\n",
-       "      <td>0.220103</td>\n",
-       "      <td>-0.291392</td>\n",
-       "      <td>-0.321856</td>\n",
-       "      <td>0.008247</td>\n",
-       "      <td>0.135052</td>\n",
-       "      <td>0.220103</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.153402</td>\n",
-       "      <td>0.093660</td>\n",
-       "      <td>0.357680</td>\n",
-       "      <td>-0.155052</td>\n",
-       "      <td>0.085464</td>\n",
-       "      <td>0.185103</td>\n",
-       "      <td>0.055928</td>\n",
-       "      <td>-0.012062</td>\n",
-       "      <td>-0.395773</td>\n",
-       "      <td>0.219948</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>BFA</th>\n",
-       "      <td>0.092938</td>\n",
-       "      <td>0.022216</td>\n",
-       "      <td>0.005000</td>\n",
-       "      <td>-0.194794</td>\n",
-       "      <td>0.237320</td>\n",
-       "      <td>-0.274175</td>\n",
-       "      <td>-0.304639</td>\n",
-       "      <td>0.025464</td>\n",
-       "      <td>0.152268</td>\n",
-       "      <td>0.237320</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.170619</td>\n",
-       "      <td>0.110876</td>\n",
-       "      <td>0.374897</td>\n",
-       "      <td>-0.137835</td>\n",
-       "      <td>0.102680</td>\n",
-       "      <td>0.202320</td>\n",
-       "      <td>0.073144</td>\n",
-       "      <td>0.005155</td>\n",
-       "      <td>-0.378557</td>\n",
-       "      <td>0.237165</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>BTN</th>\n",
-       "      <td>0.292732</td>\n",
-       "      <td>0.222010</td>\n",
-       "      <td>0.204794</td>\n",
-       "      <td>0.005000</td>\n",
-       "      <td>0.437113</td>\n",
-       "      <td>-0.074381</td>\n",
-       "      <td>-0.104845</td>\n",
-       "      <td>0.225258</td>\n",
-       "      <td>0.352062</td>\n",
-       "      <td>0.437113</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.370412</td>\n",
-       "      <td>0.310670</td>\n",
-       "      <td>0.574691</td>\n",
-       "      <td>0.061959</td>\n",
-       "      <td>0.302474</td>\n",
-       "      <td>0.402113</td>\n",
-       "      <td>0.272938</td>\n",
-       "      <td>0.204948</td>\n",
-       "      <td>-0.178763</td>\n",
-       "      <td>0.436959</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>ESP</th>\n",
-       "      <td>-0.139381</td>\n",
-       "      <td>-0.210103</td>\n",
-       "      <td>-0.227320</td>\n",
-       "      <td>-0.427113</td>\n",
-       "      <td>0.005000</td>\n",
-       "      <td>-0.506495</td>\n",
-       "      <td>-0.536959</td>\n",
-       "      <td>-0.206856</td>\n",
-       "      <td>-0.080052</td>\n",
-       "      <td>0.005000</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-0.061701</td>\n",
-       "      <td>-0.121443</td>\n",
-       "      <td>0.142577</td>\n",
-       "      <td>-0.370155</td>\n",
-       "      <td>-0.129639</td>\n",
-       "      <td>-0.030000</td>\n",
-       "      <td>-0.159175</td>\n",
-       "      <td>-0.227165</td>\n",
-       "      <td>-0.610876</td>\n",
-       "      <td>0.004845</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>LVA</th>\n",
-       "      <td>-0.104381</td>\n",
-       "      <td>-0.175103</td>\n",
-       "      <td>-0.192320</td>\n",
-       "      <td>-0.392113</td>\n",
-       "      <td>0.040000</td>\n",
-       "      <td>-0.471495</td>\n",
-       "      <td>-0.501959</td>\n",
-       "      <td>-0.171856</td>\n",
-       "      <td>-0.045052</td>\n",
-       "      <td>0.040000</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-0.026701</td>\n",
-       "      <td>-0.086443</td>\n",
-       "      <td>0.177577</td>\n",
-       "      <td>-0.335155</td>\n",
-       "      <td>-0.094639</td>\n",
-       "      <td>0.005000</td>\n",
-       "      <td>-0.124175</td>\n",
-       "      <td>-0.192165</td>\n",
-       "      <td>-0.575876</td>\n",
-       "      <td>0.039845</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>USA</th>\n",
-       "      <td>0.024794</td>\n",
-       "      <td>-0.045928</td>\n",
-       "      <td>-0.063144</td>\n",
-       "      <td>-0.262938</td>\n",
-       "      <td>0.169175</td>\n",
-       "      <td>-0.342320</td>\n",
-       "      <td>-0.372784</td>\n",
-       "      <td>-0.042680</td>\n",
-       "      <td>0.084124</td>\n",
-       "      <td>0.169175</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.102474</td>\n",
-       "      <td>0.042732</td>\n",
-       "      <td>0.306753</td>\n",
-       "      <td>-0.205979</td>\n",
-       "      <td>0.034536</td>\n",
-       "      <td>0.134175</td>\n",
-       "      <td>0.005000</td>\n",
-       "      <td>-0.062990</td>\n",
-       "      <td>-0.446701</td>\n",
-       "      <td>0.169021</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>MDA</th>\n",
-       "      <td>0.092784</td>\n",
-       "      <td>0.022062</td>\n",
-       "      <td>0.004845</td>\n",
-       "      <td>-0.194948</td>\n",
-       "      <td>0.237165</td>\n",
-       "      <td>-0.274330</td>\n",
-       "      <td>-0.304794</td>\n",
-       "      <td>0.025309</td>\n",
-       "      <td>0.152113</td>\n",
-       "      <td>0.237165</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.170464</td>\n",
-       "      <td>0.110722</td>\n",
-       "      <td>0.374742</td>\n",
-       "      <td>-0.137990</td>\n",
-       "      <td>0.102526</td>\n",
-       "      <td>0.202165</td>\n",
-       "      <td>0.072990</td>\n",
-       "      <td>0.005000</td>\n",
-       "      <td>-0.378711</td>\n",
-       "      <td>0.237010</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>CAF</th>\n",
-       "      <td>0.476495</td>\n",
-       "      <td>0.405773</td>\n",
-       "      <td>0.388557</td>\n",
-       "      <td>0.188763</td>\n",
-       "      <td>0.620876</td>\n",
-       "      <td>0.109381</td>\n",
-       "      <td>0.078918</td>\n",
-       "      <td>0.409021</td>\n",
-       "      <td>0.535825</td>\n",
-       "      <td>0.620876</td>\n",
-       "      <td>...</td>\n",
-       "      <td>0.554175</td>\n",
-       "      <td>0.494433</td>\n",
-       "      <td>0.758454</td>\n",
-       "      <td>0.245722</td>\n",
-       "      <td>0.486237</td>\n",
-       "      <td>0.585876</td>\n",
-       "      <td>0.456701</td>\n",
-       "      <td>0.388711</td>\n",
-       "      <td>0.005000</td>\n",
-       "      <td>0.620722</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>GBR</th>\n",
-       "      <td>-0.139227</td>\n",
-       "      <td>-0.209948</td>\n",
-       "      <td>-0.227165</td>\n",
-       "      <td>-0.426959</td>\n",
-       "      <td>0.005155</td>\n",
-       "      <td>-0.506340</td>\n",
-       "      <td>-0.536804</td>\n",
-       "      <td>-0.206701</td>\n",
-       "      <td>-0.079897</td>\n",
-       "      <td>0.005155</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-0.061546</td>\n",
-       "      <td>-0.121289</td>\n",
-       "      <td>0.142732</td>\n",
-       "      <td>-0.370000</td>\n",
-       "      <td>-0.129485</td>\n",
-       "      <td>-0.029845</td>\n",
-       "      <td>-0.159021</td>\n",
-       "      <td>-0.227010</td>\n",
-       "      <td>-0.610722</td>\n",
-       "      <td>0.005000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>132 rows × 132 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "Country       UKR       MNG       BFA       BTN       ESP       DZA       GAB  \\\n",
-       "Country                                                                         \n",
-       "UKR      0.005000 -0.065722 -0.082938 -0.282732  0.149381 -0.362113 -0.392577   \n",
-       "MNG      0.075722  0.005000 -0.012216 -0.212010  0.220103 -0.291392 -0.321856   \n",
-       "BFA      0.092938  0.022216  0.005000 -0.194794  0.237320 -0.274175 -0.304639   \n",
-       "BTN      0.292732  0.222010  0.204794  0.005000  0.437113 -0.074381 -0.104845   \n",
-       "ESP     -0.139381 -0.210103 -0.227320 -0.427113  0.005000 -0.506495 -0.536959   \n",
-       "...           ...       ...       ...       ...       ...       ...       ...   \n",
-       "LVA     -0.104381 -0.175103 -0.192320 -0.392113  0.040000 -0.471495 -0.501959   \n",
-       "USA      0.024794 -0.045928 -0.063144 -0.262938  0.169175 -0.342320 -0.372784   \n",
-       "MDA      0.092784  0.022062  0.004845 -0.194948  0.237165 -0.274330 -0.304794   \n",
-       "CAF      0.476495  0.405773  0.388557  0.188763  0.620876  0.109381  0.078918   \n",
-       "GBR     -0.139227 -0.209948 -0.227165 -0.426959  0.005155 -0.506340 -0.536804   \n",
-       "\n",
-       "Country       PNG       MYS       CZE    ...          KIR       ARM       SYC  \\\n",
-       "Country                                  ...                                    \n",
-       "UKR     -0.062474  0.064330  0.149381    ...     0.082680  0.022938  0.286959   \n",
-       "MNG      0.008247  0.135052  0.220103    ...     0.153402  0.093660  0.357680   \n",
-       "BFA      0.025464  0.152268  0.237320    ...     0.170619  0.110876  0.374897   \n",
-       "BTN      0.225258  0.352062  0.437113    ...     0.370412  0.310670  0.574691   \n",
-       "ESP     -0.206856 -0.080052  0.005000    ...    -0.061701 -0.121443  0.142577   \n",
-       "...           ...       ...       ...    ...          ...       ...       ...   \n",
-       "LVA     -0.171856 -0.045052  0.040000    ...    -0.026701 -0.086443  0.177577   \n",
-       "USA     -0.042680  0.084124  0.169175    ...     0.102474  0.042732  0.306753   \n",
-       "MDA      0.025309  0.152113  0.237165    ...     0.170464  0.110722  0.374742   \n",
-       "CAF      0.409021  0.535825  0.620876    ...     0.554175  0.494433  0.758454   \n",
-       "GBR     -0.206701 -0.079897  0.005155    ...    -0.061546 -0.121289  0.142732   \n",
-       "\n",
-       "Country       BLR       AUS       LVA       USA       MDA       CAF       GBR  \n",
-       "Country                                                                        \n",
-       "UKR     -0.225773  0.014742  0.114381 -0.014794 -0.082784 -0.466495  0.149227  \n",
-       "MNG     -0.155052  0.085464  0.185103  0.055928 -0.012062 -0.395773  0.219948  \n",
-       "BFA     -0.137835  0.102680  0.202320  0.073144  0.005155 -0.378557  0.237165  \n",
-       "BTN      0.061959  0.302474  0.402113  0.272938  0.204948 -0.178763  0.436959  \n",
-       "ESP     -0.370155 -0.129639 -0.030000 -0.159175 -0.227165 -0.610876  0.004845  \n",
-       "...           ...       ...       ...       ...       ...       ...       ...  \n",
-       "LVA     -0.335155 -0.094639  0.005000 -0.124175 -0.192165 -0.575876  0.039845  \n",
-       "USA     -0.205979  0.034536  0.134175  0.005000 -0.062990 -0.446701  0.169021  \n",
-       "MDA     -0.137990  0.102526  0.202165  0.072990  0.005000 -0.378711  0.237010  \n",
-       "CAF      0.245722  0.486237  0.585876  0.456701  0.388711  0.005000  0.620722  \n",
-       "GBR     -0.370000 -0.129485 -0.029845 -0.159021 -0.227010 -0.610722  0.005000  \n",
-       "\n",
-       "[132 rows x 132 columns]"
-      ]
-     },
-     "execution_count": 52,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Political barriers\n",
-    "\n",
-    "# This goes over 100. Is that a problem?\n",
-    "# Check with John about this one.\n",
-    "(0.5 * all_data[\"passport\"] / max(all_data[\"passport\"].max())) + (0.5 * (1-all_data[\"freedom\"]) / 100)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 53,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style>\n",
-       "    .dataframe thead tr:only-child th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: left;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
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-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th>Country</th>\n",
-       "      <th>UKR</th>\n",
-       "      <th>MNG</th>\n",
-       "      <th>BFA</th>\n",
-       "      <th>BTN</th>\n",
-       "      <th>ESP</th>\n",
-       "      <th>DZA</th>\n",
-       "      <th>GAB</th>\n",
-       "      <th>PNG</th>\n",
-       "      <th>MYS</th>\n",
-       "      <th>CZE</th>\n",
-       "      <th>...</th>\n",
-       "      <th>KIR</th>\n",
-       "      <th>ARM</th>\n",
-       "      <th>SYC</th>\n",
-       "      <th>BLR</th>\n",
-       "      <th>AUS</th>\n",
-       "      <th>LVA</th>\n",
-       "      <th>USA</th>\n",
-       "      <th>MDA</th>\n",
-       "      <th>CAF</th>\n",
-       "      <th>GBR</th>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>Country</th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "      <th></th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>UKR</th>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>121.548754</td>\n",
-       "      <td>-524.136397</td>\n",
-       "      <td>-33.471509</td>\n",
-       "      <td>1661.845736</td>\n",
-       "      <td>267.615598</td>\n",
-       "      <td>147.418128</td>\n",
-       "      <td>-83.622780</td>\n",
-       "      <td>101.007241</td>\n",
-       "      <td>3434.883816</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-219.352184</td>\n",
-       "      <td>131.392834</td>\n",
-       "      <td>407.666098</td>\n",
-       "      <td>107.256114</td>\n",
-       "      <td>4824.318609</td>\n",
-       "      <td>452.651133</td>\n",
-       "      <td>2042.086734</td>\n",
-       "      <td>-20.856149</td>\n",
-       "      <td>-54.593130</td>\n",
-       "      <td>3271.257611</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>MNG</th>\n",
-       "      <td>-305.714681</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>-1262.634948</td>\n",
-       "      <td>-147.309068</td>\n",
-       "      <td>2678.751519</td>\n",
-       "      <td>226.581819</td>\n",
-       "      <td>128.867960</td>\n",
-       "      <td>-203.525082</td>\n",
-       "      <td>76.838801</td>\n",
-       "      <td>5816.299621</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-530.219398</td>\n",
-       "      <td>-12.949312</td>\n",
-       "      <td>552.788115</td>\n",
-       "      <td>499.403309</td>\n",
-       "      <td>6786.327537</td>\n",
-       "      <td>1312.187149</td>\n",
-       "      <td>3223.639056</td>\n",
-       "      <td>-341.949524</td>\n",
-       "      <td>-137.474940</td>\n",
-       "      <td>5570.552689</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>BFA</th>\n",
-       "      <td>-91.749985</td>\n",
-       "      <td>-58.175621</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>-65.515757</td>\n",
-       "      <td>107.193767</td>\n",
-       "      <td>-827.713390</td>\n",
-       "      <td>-288.969381</td>\n",
-       "      <td>-4.173188</td>\n",
-       "      <td>-22.769564</td>\n",
-       "      <td>-1182.859236</td>\n",
-       "      <td>...</td>\n",
-       "      <td>45.066666</td>\n",
-       "      <td>8.290627</td>\n",
-       "      <td>-92.722458</td>\n",
-       "      <td>-625.318539</td>\n",
-       "      <td>47.119512</td>\n",
-       "      <td>-171.295224</td>\n",
-       "      <td>-525.529021</td>\n",
-       "      <td>-122.245222</td>\n",
-       "      <td>19.630755</td>\n",
-       "      <td>-1097.048816</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>BTN</th>\n",
-       "      <td>268.860572</td>\n",
-       "      <td>481.675661</td>\n",
-       "      <td>-1002.289420</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>4924.726602</td>\n",
-       "      <td>1065.522840</td>\n",
-       "      <td>528.178816</td>\n",
-       "      <td>-131.992020</td>\n",
-       "      <td>310.377860</td>\n",
-       "      <td>10925.135252</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-348.515514</td>\n",
-       "      <td>574.210021</td>\n",
-       "      <td>1177.039189</td>\n",
-       "      <td>1500.314599</td>\n",
-       "      <td>11973.893466</td>\n",
-       "      <td>2711.967883</td>\n",
-       "      <td>6071.918558</td>\n",
-       "      <td>70.995217</td>\n",
-       "      <td>-135.865310</td>\n",
-       "      <td>10223.787360</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>ESP</th>\n",
-       "      <td>-5471.755590</td>\n",
-       "      <td>-3722.846798</td>\n",
-       "      <td>-9983.005764</td>\n",
-       "      <td>-2098.853435</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>-3360.485848</td>\n",
-       "      <td>-1908.511590</td>\n",
-       "      <td>-2163.365504</td>\n",
-       "      <td>-1380.289269</td>\n",
-       "      <td>974.008603</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-5640.424380</td>\n",
-       "      <td>-4373.802275</td>\n",
-       "      <td>-1318.933457</td>\n",
-       "      <td>-2505.550446</td>\n",
-       "      <td>5562.270421</td>\n",
-       "      <td>-1380.689595</td>\n",
-       "      <td>637.542717</td>\n",
-       "      <td>-4336.286124</td>\n",
-       "      <td>-671.327924</td>\n",
-       "      <td>2619.074475</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>LVA</th>\n",
-       "      <td>-1445.544601</td>\n",
-       "      <td>-1834.592442</td>\n",
-       "      <td>-5505.397948</td>\n",
-       "      <td>-1188.857489</td>\n",
-       "      <td>1458.806344</td>\n",
-       "      <td>-1913.245945</td>\n",
-       "      <td>-972.776791</td>\n",
-       "      <td>-1191.787542</td>\n",
-       "      <td>-629.426681</td>\n",
-       "      <td>3522.930618</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-2955.550269</td>\n",
-       "      <td>-2168.698754</td>\n",
-       "      <td>-352.855535</td>\n",
-       "      <td>-284.500494</td>\n",
-       "      <td>7147.144851</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>2698.526859</td>\n",
-       "      <td>-1048.502639</td>\n",
-       "      <td>-476.744141</td>\n",
-       "      <td>4341.787377</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>USA</th>\n",
-       "      <td>-22467.543764</td>\n",
-       "      <td>-14355.797992</td>\n",
-       "      <td>-29695.727332</td>\n",
-       "      <td>-8987.774753</td>\n",
-       "      <td>-3700.242345</td>\n",
-       "      <td>-16826.275125</td>\n",
-       "      <td>-8254.313773</td>\n",
-       "      <td>-4166.524729</td>\n",
-       "      <td>-3448.106086</td>\n",
-       "      <td>-8185.536421</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-11305.453728</td>\n",
-       "      <td>-17175.621801</td>\n",
-       "      <td>-6764.254135</td>\n",
-       "      <td>-14232.291949</td>\n",
-       "      <td>2172.799950</td>\n",
-       "      <td>-8524.473129</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>-19129.143839</td>\n",
-       "      <td>-3130.310023</td>\n",
-       "      <td>33.583852</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>MDA</th>\n",
-       "      <td>40.438641</td>\n",
-       "      <td>200.264114</td>\n",
-       "      <td>-497.043652</td>\n",
-       "      <td>-13.152254</td>\n",
-       "      <td>2009.815982</td>\n",
-       "      <td>386.980423</td>\n",
-       "      <td>206.697149</td>\n",
-       "      <td>-76.059788</td>\n",
-       "      <td>142.715367</td>\n",
-       "      <td>4251.438592</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-196.706366</td>\n",
-       "      <td>216.410436</td>\n",
-       "      <td>517.731852</td>\n",
-       "      <td>212.845643</td>\n",
-       "      <td>5844.359058</td>\n",
-       "      <td>623.831045</td>\n",
-       "      <td>2498.558268</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>-58.210753</td>\n",
-       "      <td>4020.709148</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>CAF</th>\n",
-       "      <td>3949.261684</td>\n",
-       "      <td>3799.396423</td>\n",
-       "      <td>716.300237</td>\n",
-       "      <td>1217.647747</td>\n",
-       "      <td>15552.920691</td>\n",
-       "      <td>3117.150305</td>\n",
-       "      <td>1551.446140</td>\n",
-       "      <td>585.158714</td>\n",
-       "      <td>1852.680865</td>\n",
-       "      <td>34748.894996</td>\n",
-       "      <td>...</td>\n",
-       "      <td>1209.338461</td>\n",
-       "      <td>4104.450220</td>\n",
-       "      <td>4487.329852</td>\n",
-       "      <td>6704.168478</td>\n",
-       "      <td>41039.226256</td>\n",
-       "      <td>9769.692258</td>\n",
-       "      <td>19066.069032</td>\n",
-       "      <td>2832.310772</td>\n",
-       "      <td>0.000000</td>\n",
-       "      <td>31333.943414</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>GBR</th>\n",
-       "      <td>-10729.614335</td>\n",
-       "      <td>-7268.450395</td>\n",
-       "      <td>-15970.615559</td>\n",
-       "      <td>-4274.754908</td>\n",
-       "      <td>-2700.181456</td>\n",
-       "      <td>-7881.435326</td>\n",
-       "      <td>-4067.611758</td>\n",
-       "      <td>-2251.448734</td>\n",
-       "      <td>-1624.022758</td>\n",
-       "      <td>-3855.730075</td>\n",
-       "      <td>...</td>\n",
-       "      <td>-6662.457994</td>\n",
-       "      <td>-8400.256214</td>\n",
-       "      <td>-3265.647913</td>\n",
-       "      <td>-6279.034111</td>\n",
-       "      <td>1119.610699</td>\n",
-       "      <td>-4075.117551</td>\n",
-       "      <td>-8.947114</td>\n",
-       "      <td>-8823.021038</td>\n",
-       "      <td>-1434.341644</td>\n",
-       "      <td>0.000000</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "<p>132 rows × 132 columns</p>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "Country           UKR           MNG           BFA          BTN           ESP  \\\n",
-       "Country                                                                        \n",
-       "UKR          0.000000    121.548754   -524.136397   -33.471509   1661.845736   \n",
-       "MNG       -305.714681      0.000000  -1262.634948  -147.309068   2678.751519   \n",
-       "BFA        -91.749985    -58.175621      0.000000   -65.515757    107.193767   \n",
-       "BTN        268.860572    481.675661  -1002.289420     0.000000   4924.726602   \n",
-       "ESP      -5471.755590  -3722.846798  -9983.005764 -2098.853435      0.000000   \n",
-       "...               ...           ...           ...          ...           ...   \n",
-       "LVA      -1445.544601  -1834.592442  -5505.397948 -1188.857489   1458.806344   \n",
-       "USA     -22467.543764 -14355.797992 -29695.727332 -8987.774753  -3700.242345   \n",
-       "MDA         40.438641    200.264114   -497.043652   -13.152254   2009.815982   \n",
-       "CAF       3949.261684   3799.396423    716.300237  1217.647747  15552.920691   \n",
-       "GBR     -10729.614335  -7268.450395 -15970.615559 -4274.754908  -2700.181456   \n",
-       "\n",
-       "Country           DZA          GAB          PNG          MYS           CZE  \\\n",
-       "Country                                                                      \n",
-       "UKR        267.615598   147.418128   -83.622780   101.007241   3434.883816   \n",
-       "MNG        226.581819   128.867960  -203.525082    76.838801   5816.299621   \n",
-       "BFA       -827.713390  -288.969381    -4.173188   -22.769564  -1182.859236   \n",
-       "BTN       1065.522840   528.178816  -131.992020   310.377860  10925.135252   \n",
-       "ESP      -3360.485848 -1908.511590 -2163.365504 -1380.289269    974.008603   \n",
-       "...               ...          ...          ...          ...           ...   \n",
-       "LVA      -1913.245945  -972.776791 -1191.787542  -629.426681   3522.930618   \n",
-       "USA     -16826.275125 -8254.313773 -4166.524729 -3448.106086  -8185.536421   \n",
-       "MDA        386.980423   206.697149   -76.059788   142.715367   4251.438592   \n",
-       "CAF       3117.150305  1551.446140   585.158714  1852.680865  34748.894996   \n",
-       "GBR      -7881.435326 -4067.611758 -2251.448734 -1624.022758  -3855.730075   \n",
-       "\n",
-       "Country      ...                KIR           ARM          SYC           BLR  \\\n",
-       "Country      ...                                                               \n",
-       "UKR          ...        -219.352184    131.392834   407.666098    107.256114   \n",
-       "MNG          ...        -530.219398    -12.949312   552.788115    499.403309   \n",
-       "BFA          ...          45.066666      8.290627   -92.722458   -625.318539   \n",
-       "BTN          ...        -348.515514    574.210021  1177.039189   1500.314599   \n",
-       "ESP          ...       -5640.424380  -4373.802275 -1318.933457  -2505.550446   \n",
-       "...          ...                ...           ...          ...           ...   \n",
-       "LVA          ...       -2955.550269  -2168.698754  -352.855535   -284.500494   \n",
-       "USA          ...      -11305.453728 -17175.621801 -6764.254135 -14232.291949   \n",
-       "MDA          ...        -196.706366    216.410436   517.731852    212.845643   \n",
-       "CAF          ...        1209.338461   4104.450220  4487.329852   6704.168478   \n",
-       "GBR          ...       -6662.457994  -8400.256214 -3265.647913  -6279.034111   \n",
-       "\n",
-       "Country           AUS          LVA           USA           MDA          CAF  \\\n",
-       "Country                                                                       \n",
-       "UKR       4824.318609   452.651133   2042.086734    -20.856149   -54.593130   \n",
-       "MNG       6786.327537  1312.187149   3223.639056   -341.949524  -137.474940   \n",
-       "BFA         47.119512  -171.295224   -525.529021   -122.245222    19.630755   \n",
-       "BTN      11973.893466  2711.967883   6071.918558     70.995217  -135.865310   \n",
-       "ESP       5562.270421 -1380.689595    637.542717  -4336.286124  -671.327924   \n",
-       "...               ...          ...           ...           ...          ...   \n",
-       "LVA       7147.144851     0.000000   2698.526859  -1048.502639  -476.744141   \n",
-       "USA       2172.799950 -8524.473129      0.000000 -19129.143839 -3130.310023   \n",
-       "MDA       5844.359058   623.831045   2498.558268      0.000000   -58.210753   \n",
-       "CAF      41039.226256  9769.692258  19066.069032   2832.310772     0.000000   \n",
-       "GBR       1119.610699 -4075.117551     -8.947114  -8823.021038 -1434.341644   \n",
-       "\n",
-       "Country           GBR  \n",
-       "Country                \n",
-       "UKR       3271.257611  \n",
-       "MNG       5570.552689  \n",
-       "BFA      -1097.048816  \n",
-       "BTN      10223.787360  \n",
-       "ESP       2619.074475  \n",
-       "...               ...  \n",
-       "LVA       4341.787377  \n",
-       "USA         33.583852  \n",
-       "MDA       4020.709148  \n",
-       "CAF      31333.943414  \n",
-       "GBR          0.000000  \n",
-       "\n",
-       "[132 rows x 132 columns]"
-      ]
-     },
-     "execution_count": 53,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "# Here we try running the actual migration model...\n",
-    "# Migration occurs if ExpW_d > Wo + beta*C\n",
-    "cost = ((all_data[\"distance\"] / max(all_data[\"distance\"].max())) + (1 - (all_data[\"un\"]) / max(all_data[\"un\"].max())) + 1 - all_data[\"language\"] + (0.5 * all_data[\"passport\"] / max(all_data[\"passport\"].max())) + (0.5 * (1-all_data[\"freedom\"]) / 100)) / 4\n",
-    "# formula: Ae^(Bx)\n",
-    "x = 100\n",
-    "(all_data[\"unemployment\"] / 100) * (all_data[\"a\"] * e ** (all_data[\"b\"] * x)) - (all_data[\"a\"] * e ** (all_data[\"b\"] * x)) * cost"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Population.\n",
-    "\n",
-    "(Still figuring out how this integrates with the rest of the model)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 54,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['Aruba',\n",
-       " 'Afghanistan',\n",
-       " 'Angola',\n",
-       " 'Albania',\n",
-       " 'Andorra',\n",
-       " 'United Arab Emirates',\n",
-       " 'Argentina',\n",
-       " 'Armenia',\n",
-       " 'American Samoa',\n",
-       " 'Antigua and Barbuda',\n",
-       " 'Australia',\n",
-       " 'Austria',\n",
-       " 'Azerbaijan',\n",
-       " 'Burundi',\n",
-       " 'Belgium',\n",
-       " 'Benin',\n",
-       " 'Burkina Faso',\n",
-       " 'Bangladesh',\n",
-       " 'Bulgaria',\n",
-       " 'Bahrain',\n",
-       " 'The Bahamas',\n",
-       " 'Bosnia and Herzegovina',\n",
-       " 'Belarus',\n",
-       " 'Belize',\n",
-       " 'Bermuda',\n",
-       " 'Bolivia',\n",
-       " 'Brazil',\n",
-       " 'Barbados',\n",
-       " 'Brunei',\n",
-       " 'Bhutan',\n",
-       " 'Botswana',\n",
-       " 'Central African Republic',\n",
-       " 'Canada',\n",
-       " 'Switzerland',\n",
-       " 'Chile',\n",
-       " 'China',\n",
-       " \"Cote d'Ivoire\",\n",
-       " 'Cameroon',\n",
-       " 'Congo',\n",
-       " 'Democratic Republic of the Congo',\n",
-       " 'Colombia',\n",
-       " 'Comoros',\n",
-       " 'Cabo Verde',\n",
-       " 'Costa Rica',\n",
-       " 'Cuba',\n",
-       " 'Curacao',\n",
-       " 'Cayman Islands',\n",
-       " 'Cyprus',\n",
-       " 'Czech Republic',\n",
-       " 'Germany',\n",
-       " 'Djibouti',\n",
-       " 'Dominica',\n",
-       " 'Denmark',\n",
-       " 'Dominican Republic',\n",
-       " 'Algeria',\n",
-       " 'Ecuador',\n",
-       " 'Egypt',\n",
-       " 'Eritrea',\n",
-       " 'Spain',\n",
-       " 'Estonia',\n",
-       " 'Ethiopia',\n",
-       " 'Finland',\n",
-       " 'Fiji',\n",
-       " 'France',\n",
-       " 'Faroe Isladns',\n",
-       " 'Federated States of microstesa',\n",
-       " 'Gabon',\n",
-       " 'United Kingdom',\n",
-       " 'Georgia',\n",
-       " 'Ghana',\n",
-       " 'Gibraltar',\n",
-       " 'Guinea',\n",
-       " 'Gambia',\n",
-       " 'Guinea-Bissau',\n",
-       " 'Equatorial Guinea',\n",
-       " 'Greece',\n",
-       " 'Grenada',\n",
-       " 'Greenland',\n",
-       " 'Guatemala',\n",
-       " 'Guam',\n",
-       " 'Guyana',\n",
-       " 'Hong Kong',\n",
-       " 'Honduras',\n",
-       " 'Croatia',\n",
-       " 'Haiti',\n",
-       " 'Hungary',\n",
-       " 'Indonesia',\n",
-       " 'Isle of man',\n",
-       " 'India',\n",
-       " 'Ireland',\n",
-       " 'Iran',\n",
-       " 'Iraq',\n",
-       " 'Iceland',\n",
-       " 'Israel',\n",
-       " 'Italy',\n",
-       " 'Jamaica',\n",
-       " 'Jordan',\n",
-       " 'Japan',\n",
-       " 'Kazakhstan',\n",
-       " 'Kenya',\n",
-       " 'Kyrgyzstan',\n",
-       " 'Cambodia',\n",
-       " 'Kiribati',\n",
-       " 'Saint Kitts and Nevis',\n",
-       " 'South Korea',\n",
-       " 'Kuwait',\n",
-       " 'Laos',\n",
-       " 'Lebanon',\n",
-       " 'Liberia',\n",
-       " 'Libya',\n",
-       " 'Saint Lucia',\n",
-       " 'Liechtenstein',\n",
-       " 'Sri Lanka',\n",
-       " 'Lesotho',\n",
-       " 'Lithuania',\n",
-       " 'Luxembourg',\n",
-       " 'Latvia',\n",
-       " 'Macau',\n",
-       " 'Saint Martin',\n",
-       " 'Morocco',\n",
-       " 'Monaco',\n",
-       " 'Moldova',\n",
-       " 'Madagascar',\n",
-       " 'Maldives',\n",
-       " 'Mexico',\n",
-       " 'Marshall Islands',\n",
-       " 'Macedonia',\n",
-       " 'Mali',\n",
-       " 'Malta',\n",
-       " 'Myanmar',\n",
-       " 'Montenegro',\n",
-       " 'Mongolia',\n",
-       " 'Northern Mariana',\n",
-       " 'Mozambique',\n",
-       " 'Mauritania',\n",
-       " 'Mauritius',\n",
-       " 'Malawi',\n",
-       " 'Malaysia',\n",
-       " 'Namibia',\n",
-       " 'New Caledonia',\n",
-       " 'Niger',\n",
-       " 'Nigeria',\n",
-       " 'Nicaragua',\n",
-       " 'Netherlands',\n",
-       " 'Norway',\n",
-       " 'Nepal',\n",
-       " 'Nauru',\n",
-       " 'New Zealand',\n",
-       " 'Oman',\n",
-       " 'Pakistan',\n",
-       " 'Panama',\n",
-       " 'Peru',\n",
-       " 'Philippines',\n",
-       " 'Palau',\n",
-       " 'Papua New Guinea',\n",
-       " 'Poland',\n",
-       " 'Puerto Rico',\n",
-       " 'North Korea',\n",
-       " 'Portugal',\n",
-       " 'Paraguay',\n",
-       " 'Gaza Strip',\n",
-       " 'French Polynesia',\n",
-       " 'Qatar',\n",
-       " 'Romania',\n",
-       " 'Russia',\n",
-       " 'Rwanda',\n",
-       " 'Saudi Arabia',\n",
-       " 'Sudan',\n",
-       " 'Senegal',\n",
-       " 'Singapore',\n",
-       " 'Solomon Islands',\n",
-       " 'Sierra Leone',\n",
-       " 'El Salvador',\n",
-       " 'San Marino',\n",
-       " 'Somalia',\n",
-       " 'Serbia',\n",
-       " 'South Sudan',\n",
-       " 'Sao Tome and Principe',\n",
-       " 'Suriname',\n",
-       " 'Slovakia',\n",
-       " 'Slovenia',\n",
-       " 'Sweden',\n",
-       " 'Swaziland',\n",
-       " 'Saint Maarten',\n",
-       " 'Seychelles',\n",
-       " 'Syria',\n",
-       " 'Turks and Caicos Islands',\n",
-       " 'Chad',\n",
-       " 'Togo',\n",
-       " 'Thailand',\n",
-       " 'Tajikistan',\n",
-       " 'Turkmenistan',\n",
-       " 'Timor-Leste',\n",
-       " 'Tonga',\n",
-       " 'Trinidad and Tobago',\n",
-       " 'Tunisia',\n",
-       " 'Turkey',\n",
-       " 'Tuvalu',\n",
-       " 'Tanzania',\n",
-       " 'Uganda',\n",
-       " 'Ukraine',\n",
-       " 'Uruguay',\n",
-       " 'United States',\n",
-       " 'Uzbekistan',\n",
-       " 'Saint Vincent and the Grenadines',\n",
-       " 'Venezuela',\n",
-       " 'British Virgin Islands',\n",
-       " 'Virgin Islands',\n",
-       " 'Vietnam',\n",
-       " 'Vanuatu',\n",
-       " 'Samoa',\n",
-       " 'Kosovo',\n",
-       " 'Yemen',\n",
-       " 'South Africa',\n",
-       " 'Zambia',\n",
-       " 'Zimbabwe']"
-      ]
-     },
-     "execution_count": 54,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "population = pd.read_csv(file_path(\"newPOP.csv\"))\n",
-    "[x for x in population[\"Country\"]]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 55,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "SyntaxError",
-     "evalue": "invalid syntax (<ipython-input-55-cd06a24c807b>, line 1)",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-55-cd06a24c807b>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    len(set(cc[\"Country\"])  set(population[\"Country\"]).union(set(other_codes.index)))\u001b[0m\n\u001b[0m                              ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
-     ]
-    }
-   ],
-   "source": [
-    "len(set(cc[\"Country\"])  set(population[\"Country\"]).union(set(other_codes.index)))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "len([x for x in set(cc[\"Country\"]) if x not in set(population[\"Country\"]).union(set(other_codes.index))])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "population[\"ISO\"] = pd.Series(None)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "other_codes"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "\"Albania\" in list(other_codes.index)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for i, x in population.iterrows():\n",
-    "    if x[\"Country\"] in list(cc[\"Country\"]):\n",
-    "        # TODO: Is this working on a copy?\n",
-    "        x[\"ISO\"] = cc[cc[\"Country\"] == x[\"Country\"]].index[0]\n",
-    "    elif x[\"Country\"] in list(other_codes.index):\n",
-    "        population[\"ISO\"][i] = other_codes[other_codes.index == x[\"Country\"]][\"ISO\"][0]\n",
-    "# How many aren't null?\n",
-    "population[population[\"ISO\"].notnull()]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "population[population[\"ISO\"].isnull()]"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/examples/multiscale-migration/Multiscale Migration Model.ipynb b/examples/multiscale-migration/Multiscale Migration Model.ipynb
deleted file mode 100644
index 7a95364..0000000
--- a/examples/multiscale-migration/Multiscale Migration Model.ipynb	
+++ /dev/null
@@ -1,995 +0,0 @@
-{
- "cells": [
-  {
-   "attachments": {
-    "image.png": {
-     "image/png": 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A+6WXXorvEctdWCYZwXUo16LlXor7Q9tPKkMx/k2aNPGL5rZYq5lfdwm7FM2PtpyX3yr53UIe8ptWTH5MSwIkQAIkQAKlIkCxpVQkaYcESIAE6hCBkNAiIgu+IbRcVgGhBcjat2+f8fPVV+XZJreUzYWXWLw81wc3bdo09/DDDyeKKWgPtNlzzz1XJ6oLQcMKFNVQ8M6dO7v+/ftnLAqEmJVWWsntv//+8T3Stm1bL76Ik/bKaGhpoG1btDP8inFDhgxx+JTThQSXUWPHU3ApJ3TaJgESIAESKJoAxZaiEdIACZAACdQtArkKLR9XcNehfv36uSlTpgQ/6667bsGABw0a5HbccceC0+ea8LLLLnMffPBBHB35nn766bkmb3Dx0CZg1KJFi7LWHQLYscceG4/SQX6VyDdbpSD+zJ8/3+G6ue2224LRIeDdfvvtDvfGd9+lthafPXu2g0gD8SXXEVu4DuvDtWgFl7POv9JRcAleOvQkARIgARKoEgIUW6qkIVgMEiABEqgEgXyFlpuuubTs2zuj3h06dHDt2rULflZccUWPRqY84EUVx3oKBF6qO3Xq5EcK6OkvdpoE0iKOTCWx00okPqZG6OkmGEmAUQbIA9/6RRfx3nzzzdiulFVPI8JUEKTp2bNnWrmlzWUqBuJhJAbyyWVEBsqF/EPl0nWTY8sD+QsTlA0ct9hiCylWTt86n9GjR8dlgV1dH5RVpoTZ6SzICHmjDKg34oWmvyAd+Atb5I1jpAMDHEv7Iy5ECbiTTz45DtPtijDExzURun5yZRgCJXVE2WzdDz74YJ9kwIABvv1C7s477/Te5557rlt99dX9McSiZ5991o88ShIhwQ6M8LEcQ/loP5myJCxCo7VQLwmv9IghLbig3BRcsrUow0mABEiABGqVQNTxpiMBEiABEmggBKIpQkv++9//LoleqpZEU4WW/PTTT0t+/PHHJdFL05IFCxYsOfPPly3Zca/D/GfgoMcqQiV6CC6JXh6z5oV4zZo1W7L55psvif7b77/hd9ZZZ8V+CO/Ro0dsS9uOpjr4+IiD9GILftGoGp8Gx2I3GhGxJBpJsETSISx6Afb2dRqUHeeIL/WQc9gEZ5zjg3wRD8fIXxz8kK/UDXWS+HEkc2DLJeWWuuAbNlBehKFsEgf1ghs1alRcFtRNyqDrZ/MXu1JXnQ/qgTrqfHWdX3zxRW9OmElZpb6SXsqBcx0fzPBBWeGEJeqo2xRhsC3hCAMvKavki/JIW0j9cW7rJu2j4whDXxDj9DUkPGAXaZC3+OE76dqX9kXdcA0lOWEp4fb6RNktR6m/rivKhnN8UH6xg7YRJ+2keaM9xH5SGUvtj9+qg3udHP9WDRsx0v+W/fzzz/63Db9x+K3Dh44ESIAESIAEaosA5rvSkQAJkAAJNAACIrTgRUSElsWLF3uh5dtvv11yaf8bKi60ALu83MlLof6Wl2qJh7jy4ilCgRZXEF/H0S+T8oIr6fGNF0XE0S+fONcv0nghR5nET8QT2BOn85GyyguovDSjvOLET/JFXNjQcVAvLcjEiZceZCuXCAt4cRYnfsJVXtT1y7x9Wbf5ig0rSOiXcqkP6ikO9ZM4VmxBmOYpfKxIoNs6mlrj7aEuUn5r155L2TV3K2bgXPLJhaHlg3LZ60GEDBGbbDmsDTkX0Qb2wAJ1tSKPFlvkGtfc5X6CTctDl1OEJ0mr7w/hi+tRt7PUQ9opqR7l8KfgUg6qtEkCJEACJFBKApxGFPU06EiABEigvhOIHhxxFbEALs7xkUVxBz32jHvz3dSaI384qbc74dgjqxJJ9BLsZFpR48aNfRlPOeWUuKzNmzf3x6FFdd9++22n08POcccdV6OeiKPXEtlvv/382hmYMoEpLo8//rhPM27cuBppQx5vvPGG99bTczbZZBPvp9d5iV5k0+Lsvvvufl2PJJdruU477bTYBKZqwUUv1P4bU1KiF/qYKfwOP/xwH5av69WrV5xkp5128sdbb7117AeukVBQw6xMVenSpUsc1q1btxrx4KEXlMXUGqx3cuCBB7rx48f7tpk4caJPp7kGDUWemDITiQ8OnOWaQlxcT5gWpqfIZGJo7b/77rveq3v37nGQXE8vv/yyjZ7xfMyYMS4S4Py6LTNnznRnnHGGa9mypZ82ZR3YXH755b5tC1mn6MEHH/TXAhbMlet833339dmAL3jgetT1kutp5MiRtjhlP8eUokcG3uW277KNz4tTisqOnBmQAAmQAAnkSSA1ET7PRIxOAiRAAiRQdwiI0CLiihZZILZAaLn/4Sd8hWpLaMFCocccc0xWqGuvvXbWOEkR8AId/Qc+LRgvjhBStLN54CVTxBF5+UzKI+QPUQYvsdrJyzfEDqm3tY11bDK5XMslAlTIFsQGyyRT/JAN8dNriIgNWWsEcSxXSTd27Fh/qBlp8UPnadcpgSiA8kPIgX0IErk6EeSSOMvaL7CXD5Nhw4b5IogQIeVBOV9//fVcixfHg0iHD+4RCER/+tOf/DWLtVO0qAKhpRiHawHOXg/ww1o8Im6uv/76admAPe6t2nCNVl3V3dD/Enf+Zde4j4Z94gWX22+40m25+aZeSF5++dT/FJdbbjkvLuObjgRIgARIgAQqRYAjWypFmvmQAAmQQC0Q0EKLHc2Cl5GH/v10rQstlcKCl0L7Mg6xI5uTkTMTJkzwO9lgUdN83GabbeZEUJB08iIfGlmTq21ZZLXQciEfvFjbUQkyOiTXchQbT0QWzcguvKpHU2iGKD8+GD2EtslnVI4IN7a+c+fO9VlYYcfWEyNMQk5G6NjFZSFm7LnnnqEkNfwgqiC9FnwQCSLUTTfd5ONDfNEOI1pwjWNUkU1XI4OARzQ9yGF0lfxO6G8IgiKc2a2ia0tokSqsuMIKXnDhCJdAo9KLBEiABEigVglQbKlV/MycBEiABMpHwAotMmVIvhuS0ALKmC6ip4fghT7btA68tOIluU2bNvHUIpkmApECL8Xi7Eu7+B922GH+UO9gBIEErmvXrhItr2+UC3XBaBgZJfPcc895G7ZcmQzjxRwih4gbqA+mW1XSyQgQ3RbCOFM5Hn30UR8sghXK/uSTT3q/XKYRQbiAUIP6SjuKDT3dLKkMIQEIcXfdddcaZRDhRU+1SrIL/2ixV7/FM0ZU6WsMYUOHDvVJcT1rh1EuYALBZLfddquRLi1y4ATXEqYJaZEIuyhhhydcH7jOYBsCpZQJI16qwVFwqYZWYBlIgARIgAQsAYotlgjPSYAESKAeEKhrQotsx4xtcO1HixTFNA1ED7wsrrHGGj6Ptm3berEik8MLJkZeQJCQrZ+xtgVGAcBvn3328cnx0o5pHKHtmjfddFNvA8IGbCBvHNu1YTKVw4ZJuSAESbnwEmzLZdPZc7yUw4EJ2qBVq1Y2SkXOUW5MjcGWwuADxuCTyWGtFjhs74yyY30YjEJCG8MWhAIZSXLRRReliV1iF2ugQLRCvaX+OD/vvPMyZZ0xDCNAUHZcE6gL7LZv396XS69hk8kIbDz//PNe/EDZ0Ma4tuTaga1TTz21hglcF0iHaxPTjfJxEIlgF2VFmbEuDNhCwFpllVW8qXPOOceLj6gHygLxyE6RyyfPUsal4FJKmrRFAiRAAiRQCgIUW0pBkTZIgARIoIoIhIQWPSWg2ka04KU0l5dQxJOFV4EbL6Tw02tI4Bh+MuVBh2Pdi1mzZvmXRUzBwTGm+IgtfNs84PfOO+/4BUrxX/xoxxY/XeXaa6910c4w8Tok1113nU8LwSPaSjvNDkZQYKFTTPPAArGYfoJFT2XhXOSBeum6wU/qguOQy1auEB/Y0XUEk2j3HF8/rC2DETcYHaEZ2rytXTkX5ogfKruuo20nLHQb7WzjxZGLL77YPfDAA27EiBFx1qE8MCIGTFFWOIz4wHQXjIpBfbCAKsQHWYsE9qwdjAZB/e+++24/wgUiA86x+DCcrasUyF53cUGXHqBtUR+0NbiinPPmzYuvS1sOmx7nKBuuE5QJ141MeUN9cO3KiCbLGulwbSINRqlk4q7rgTKhjCgryow1cJDXK6+8Ei8gjKlLqBfuV9xDGEWFxYOlDUL1qKQfBZdK0mZeJEACJEAC2QgsF3XAl21RkS02w0mABEiABKqaQJLQoqcODRyU2k2nthbDrS2AEEvwwfQPvKjjRRT/xceLIgQUutojgFEoCxcudAcddJAXJDCSA6NTCt1Zp/ZqwpyrgcCv//1vvGguyvOXKy5w23ft7BfIxaK5+JbFcrlobjW0GMtAAiRAAvWTAMWW+tmurBUJkEADJEChJXOjY50TrIGBqRmY5jFjxgw/3QL/qbc7x2S2xNBSE8BUMRnZI1OKMD0FI4LoSKAQAlZwOfm3R7vjjjmcgkshMJmGBEiABEigIAKcRlQQNiYiARIggeoikI/Q0uvwA90Jxx5ZXRWoQGkw7QLTZPSUB0yToNBSAfhZssDUF5kShO2R0UaYskVHAoUSsFOK/vV//3YPPvKk320JI/1kaiXsc5B3oZSZjgRIgARIIBMBjmzJRIdhJEACJFAHCOQjtGB7VGyTihcROhIgARKo7wQ4wqW+tzDrRwIkQALVS4AjW6q3bVgyEiABEshKIJvQMvTjT5ys0UKhJStORiABEqhnBDjCpZ41KKtDAiRAAnWIAEe21KHGYlEbBoFihzNzsb+GcZ2gltmEltHjPnPnXNTfA6HQ0nCuC9aUBEigJoFqH+HCZ3/NNiulj+bLflIpydIWCZBAJgIrXBm5TBEYRgIkUH4CmTpZElZM56CYtOWvfeVyyMS5rjGi0FK564Y5FU8AOz/9+uuvbrXVViveGC2QQAEEsAvRPj13i7axXsF9/MkoN+LTMW6FyG+rLTaLdyaC2UruUpTpmcRnfwGNrJJkYmst17Xnvy0/z0mABKqXAMWW6m0blqyBENAdAts5sGE4t3GSMM2YNdt9/8MP7rvv8fnerbH6amkdyqR09dE/V2aoe13odEl95HqQxR5le2eOaKmPV3Hp6vTII4+46dOnuw4dOqQZhSDy8ssvuw022MCtvPLKaWFIs2jRIte2bduCCtKsWTP35ZdfuiOOOKKg9ExEAqUisM2Wm9cUXDptWnHBxT7fdf1sWK7P/m+//db99NNP7ueff/YfuY/rwnOtVO1rn/f2XOdjudjzUpWJdkiABBougRUbbtVZcxKoXQK2M4XSwG/hom/d4sWLg6LK2q1a+kKHOg+2k3DU8aemVfC9V55IS2fj1y6N8uQeYiw5YUj5nLnz4oybNlnTNVp1Vc+omtlInfCND4WW8lw79dnqBx984G6//Xb3yy+/RC+dy7oBN998s/fHDk3HHHNMjAAiDHYHuvPOOx12DaIjgbpOQHZj+/t9gxx2KYLDttAY/SKuXM+B0HPph+gfI/+Nnklw9vm+xhprxGWyYQjQ5fz3v1N1kQS/+93vfLhOV656xYWsxYMQW/HDsxKc8ZERdo0bN45LqznVZ0a12DzMmgQaJAGKLQ2y2Vnp2iZgOwRyju87/vYv9+pb72UtYrdtt3L77rm7223nHdwK0c4y2TpTEi6dCHueNcM6FsEyRvHFb2Y06ufiq25wEydPiWt15YVnu56775zWMa22Dpe+TnBMoaWOXZRVUtx9993XiypDhw5NE0/gBzdgwIA0sQXiDNxuu+2WVoM5c+a4pk2bpgk2torfR6PqVl99deudeI6pRgsWLHDYpjuTy9duJlsMa5gEQoLL8cemRl7p52QpnwP2uSTnH330kZs8eXLWhlhnnXXcxhtv7Nq3bx8LQ9qmNaDD6vuzXz8fwUHOMcJn+PDhbuzYsRaPP992223dFlts4VZaaaVYuELaUrZ7MGN6kgAJNAgC3I2oQTQzK1lNBEKdLfjFnxwLO3T4p67fDbe7o084zb3x9nv+v9T65duakTCdF+Kk5a3LUaXHtl6hc93p0vXDi9wd997njoqYaaHFc1AsQjZr2y+pTpw6VNstU/d/dKzSAAAgAElEQVTy33XXXX2h33jjjbjwo0eP9scYvfLmm2/69VXEPfvss/4QLyTwf+mll1ynTp1cy5YtXatWrVz//v0dhBdxeEmB31prreX0f+XjCNHB+++/719mEA8O4gmOYQ92e/fu7eOIwzQmxJfvU045xZcFaeCPD8qEdLBFRwK5EIDg8oeTevuoGOHywMOpEaDy3IC//PbmYi9THG0n9NzNlFbCvv76a/fuu++6xx57zH3++ef+HpBnQKic8KsPz/5MbEJtJX4YxfLEk08mCi2wCyHm+eef9yOLQrYy5c0wEiABEshGgGJLNkIMJ4EyEbCdrUwdpkxFmDt/gev/1zvc9bfeE3e6YMs6yS/U8bJlyfXc5mHPQ3ZsnNB5pnQ2TKcPdZTE7/MvpjpMrXrs6RdDWcYd6pCNYIJa8NR1l+sF31yjpRYaow5niZEmm2++uRcuxL3zzjsO66qceOKJ3mv8+PH+Gy9zmFZ01lln+XOMhtl///3d1ltv7UaNGuXuvvtud8stt9QY9XL55Zc7jKBBWusgouy0006uX79+7rLLLvPBBx98sEOazp07uyFDhvi1YxBHCy6IB3EFgtDFF1/sy4I0PXr08GXB1CfkByGGjgRyJQDBpdfhB/roEFyeeeHVtJfuXO3kGs8+w0LP62y2fvzxRzd48GD33nvvZXzu6+d9pZ79tn44z8VlShcK089q2Jd8dFwIUz9Ggot2jRo1qlGcefPmuZEjR5ZMWKuRAT1IgAQaLAFOI2qwTc+K1waBUGfAdiJsuR6892bXqmVqSP0irOcSLX437rNJ7tGnXnAQEMS9/vYQ1+ek3zisPRJy6GjJf4CRJ+ysFuh0hNKK34/RWjJwWNsETuqTKY2EYS2aJms2ziuNpEW+K0dDfPX6EnZItC5DiPOgx552EKbgmjdr6jZov74bNuLTZcmWjuSBXUkvx5JXLvUsRxzN2V4vFFrKQbz+2zzttNPcGWec4UekYMrOk9F/f4877jg/5QdCzFNPPeVHsojo0qtXLw8Fo2EgygwaNMifIw4c1nTRU3sQ54EHHqgxxQgjaKzQgnQYTaPXisFL0pZbbunz0+vEoNynn366zxMjbOAgwKAc+Jx66ql+ahMdCeRKYOHChdHo0CE+Op4Nu+60vX8GlPL3P/RMsr/lurwQH2X6HRa8heiJe3XcuHFu/vz5cdQpU6a47bbbzq2yyirBaS/6uY/66EVzc+WDUbNwmGYjLtdnP0QhiBu5xtdlQr6YIo2PuKRncYgv8sZIIO0OOOAA//sALlgkHGKVOKxNtc0226TlJddBmhGekAAJkEAeBCi25AGLUUmgHAR0h0v+86Tz+d//UsOA4bfGGqv7zy47dnM77dDFHXD0yWlFGhFtZ7nbLt2DxYTtWbPnutejKUf4zx2Eh7322NkdesA+bovNOwbToIM3fsLn7ta7/1lj2s3eUdrfn3Csk0V7rQEIJJ+OHuf+M3yke/Od92Oho9t2W7udd+jqt9vcoF3yziYfDRvhXntrcPRZ1hnqsGF713nrTtFChkekiUoh4aVGR3bpf9eOOHhf94cTe7t33x+aJrbINCLUI6lDZ+tYifNQJxJtic/osZ+5cy5KTcNosVYzd0P/S9yKqmNaifIxj7pJ4MADD/Riy3/+8x+HaUUQO8477zxfGYwQwagXjDrBiBc4jGSBwygWjFjRrnv31G8O/jMswgjiaHEU8RGOvCDEXHTRRbEJ+MNhrRiZsoRzxHv77bfj0S/wQ7nF7bXXXj4OxBuMbjn88MMdRCGbb5yAByRgCEBoOb7PuW7OvPleaLn3tuvcmo3XiEe26GdLKZ8L9rlvG0Z+4+EPkQMf7AS23nrrObsILgSFdu3aBZ9byOe7777z68FMmDDBQYDAbmObbbaZn7IXqhPynj17tvvwww8dRnxot+GGG3pxJ2l6IASSmTNnuq+++sp9EQlBMqpk3XXX9eVv3bq1v2eTHHYsmzRpkvviiy/iKJiOiHT4Dcq0dbx95uupjTDWsWNHt+aaa/pnJ+oNltqhrvK8FX/Eo+CS1Fr0JwESyIUAxZZcKDEOCZSAQOil2Xa4fBwz5DYkwKA46AQcesBe7ukXXotL978oLeKH3PwFC13v36emAkg4hAx80Ml8/MG/1Uj2/MtvuFsioSXksIgvPpedf5brsWvNHUrOOO9yN0ktQCs2hn480uEDd+df+7lOm22SZh7zpjElSossEgHrrOCD/0IO/NvNfqSMdIa0Edvpwnnb9dZ1D/3jNteqRfNUh8pyWjqyxda1GjpbofqMGTfBnX1RP19cCC0PRCOgKLTY1uN5EgG8nOGlB9N1mjRp4qN17drVfx922GF+eg5GnGCaEIQM+S+7/q+6tY0X10wOC1QiT9jQi/NKut133z1tO2r8dz+Tg6gya9Ys99prr7kHH3zQi0f42N2UMtlgWMMloIWWtZo2cffcfLUXWrST53ZIlMiVXKZnvzzfJY7YlN/8UB7Ysn3ixIlxEOKKgGDjQ1zBKDXtIGTg02i11dzRRx1lk/jRbFiwN+Qg2uAD0RSijXUvvvhiDYEGcSC+4AO33377eaFHO5Qfo0y0yCLhEEHwgXhz6CGH+JEycNImmq+kgR8WE8YaTli3BfYx+kfHhb92egROMe2dZpQnJEACDZ4A12xp8JcAAdQmAdsJ8x0vU6D/LUmNYkCY/diyN2qU2rrYdtwQ78Irr7fR43OMcpkcTUnS9iHOWKFl4w3aOXy06x8t0vvNjFlpaUeNHZ8mtHTpvJW79Lwz3Mm/PdoLO+KuuPZmh3x0voMeeyZNaEH8Iw/ezx15yH5p5T2o18l+aLV0SvW3Lp/49z7qkFho8X6GRlJ6E62ip7odbT0feOSJuCxn/vHE+IW5ogVkZnWaAKYNQaTAVB1MHZIdgDbddFNfr4EDB/qFJTFiRBzWbpGRKOInu3yIWAN/jDaxDqINxBF8Y4SK/OdZFuzFSyRG1diPtaPPIbjg5Q3TmvDfeIg5suhupnQMa9gErNBy5439XGM1okV+b8tBSX7XJQ88/6xDmH3ey7l+LiAd7oGk8mLEWpLDqBOIGDofiDNWaME9ZUejYJrft99+m5YWI1r0SBiIHTvvvLMfkaLXScFoNeSj8/3000/ThBbEx++Q/BahDigvhFS9RbZmiTiaA44hmmA0DD7Y1lszhOCrHQRo2zaWdRJL+pMACZBAEgGKLUlk6E8CZSAgHYFM3wEVoEanC+l//HFx2qgWFHfjaJqNdCZs8bG+ywnRtpbPDBrgsA6MFU3e+3BYWj53Dbg/zcR2nbd0d/z1Knd39N+/E3sfmRY2ZtxnaWlvv+e+OLxLlO66K873W1Qfe+TBfnSJCC4Qee6O8pEyQ3jBAoXiZFh3n5N/4/548m/dP+5IF4yw5oruXCGdZRvy8x0ofJTDme1oSdq0iBU+0fXTdet/yZ/cttGUKrgrrr0lmlKUWtC0wsVjdnWYAKbcYJQJRrFA4BCHlzcIIrJ4razXgnBMD4K4IuulQDCBYKPFGsRr3rx5DTJrr722fzF89NFHfRjsQjCVdWIgkmDdBDhMY8L0Ab2IrzWI/1pjByLYgINYhAV26UggEwErtNx2/RXx1KFM6YoJs8+l0Lm2j2ci4ljBBdN0MM1GO6xBInFtGXF/Yy2jo6IRLAcddFAN0QT3m87DChBt2rRx++yzjxc0t9pqqzTzEFd0Wi3SIN0ee+zhpzch/0MPPTQWXCC0IB9Ji3Mt4EJowdoqmK7UpUuXtKmDKABGyFh+8Ld+oXOpAKZezZgxI60+IrZIOsuS5yRAAiRQCAFOIyqEGtOQQAkI4IEOZzsEkU+a9anTv/KdEu3efu9D99Bjqe1YxX/Dduu5xtF6LmLXFvH03x/nDtinh/9PT4vma7mbrrnUHdJ72a4dz774mjvmiIPiMkGYOfrQA9zUaV9GJVridu7ezadFWXrs2t0NHPR4nMW4CZP8ooJwWMR3klq4d9iIUV4I2KxjB58en7/dco2Pu2Y0DQhO6jc8WnNGuz4nHuvWWH01H4507dqu60Uisf/3gQ+7btttU2MqkWULm5ZzjbEtGO2y9IP4yA/n1TKc2JYf5brywrPdFdfd4kZ8Otb1Oftid++t10br76RGJaSB5AkJBAh069Yt9u3Zs2daDIxmwX/F8R9tGfGCCBiFgl2EsCORTAmC0HLjjTempdfTHGzWsPfQQw95G9ddd50XdSDAQHxp3759bBf299xzT5s8Pr/99tvdJpts4tezwAK9mBKFl0vsZkRHAiECWmhpFi0mf/O1l/rpqJVy9tmknzm6DIsWLarx3J82bVq8YLXExRTAlVdeOX5G2npAfMSIMTiM7sB9/vjjy57duE8hWIrDotRYz2XBgtRi8lhnBWXEMxhiBEagiMNIsvXXX9+fYhFfPcXwm2++8Wu34F6XZygEGzhM54GT575dxBaL1EKUlec+6ii/NUiHrZrteiveoHIhzgiGP8o6bNiwtPgQeES0kue+fv7rY5MVT0mABEggIwGKLRnxMJAEyktAOlq6w2Wnt/S74Y6cCvGHk3rHYkFIIGi//nppQsxKK63oME99XjSaBA6jTNC5kU4KBBm4tuut44ftYnFdONie/mX6Cv8LFi7rGGIB343at3WfT5nu48Odc/HV7vhjDndY4HaTDhtE/0VMF1kkHnZZ0u7Lb2a6twd/EOUZ5Rv9+QPlsCaMlDctIDoR/zS2SlBZqnUtS6bErxA/a7/c57pe9ljqhGHRl/35TIdr5JNRFFzK3Sb1zT5eaLDGAl5AZQFcqSO2gMboFHmZEn+MQoE4cu655zpMJYDQgpcw7TDU36azfnjxkryxNgz++z1mzBg/suWDDz7w55hCIIvdYhFe2NDCD46xowjWecAuR5gWBTFI1pepb+3F+hRHoIbQcs0lsdCC3/xK/e7L77f+tjWzW57bcDnHfaufdTYehAoRLRCGZwaEBYwmgZPpPDiGHZnu0zh6RiMd7k1xEIC0Wxwtgi+CCQRPiBUi0iDe66+/7u9jCCX4LbEii9iCaKMdpidhlyW4ULvohWx1m1meoXPkhd8XCC7iUGeM2NO7HqUViCckQAIkUAQBii1FwGNSEsiVgH5ZRprQuXQMakwjypIJBJO/XHlBtD300oVfE+KL2IJ8pIOyzZabuTcHfxinkK0WxeOdIR+6x59+0U2e+mWC1aXeS0UMiXRCNM3o8mhqi3YPPPJkfIoyY0pRz912cquuumzRugWmM6fTJBUAnUV0lrJ1lC1zO4JIphGJHc0JedvzpPKUyz++PpaWRcqEDuIlfzrNXX3jnW7k6PF+hAuEtxOOTZ/qVa5y0W7dJiD/bba1gGChpxaFwpPShtKF/ELpIdxY8QZ5J/mjnLLtsy0jz0lACFih5carL4rWuVozfpmXl3r7XSxB/dyp+QxK/6dAPnlBuBBhMWRXbGH3HQmXZ5iIlBIH/0zRz0+MoPnss8/ShJNQ2fQzCeEYFTN48OC0qBBBxck6LFhYV9aZQRhEG+10mrQAdSJbWCfVPcQdQq6dJoUy7b333vHiuVInYSXfSeWgPwmQAAlkI0CxJRshhpNAmQjoh3p6FnZsS80CbBBNGeretbPbMpoyskE0imTVpcNyJWaoA7LKKsuGGi/rSKTbnj1nbiTatPCel0eL12J6Si4Og0LkP1yIv+3WW0Q7DV3lLr/m5njkjLaD0TR3/eNB/3n8/rujjs7KqeAaw02y575w0XdebNGdIql/6DvERnLRbYLOp7aZvSSViWHLj3P8t/Kic051195yT7Td9nj39/sG+cJQcKlMmzAXEiCB6iVghZa/9o+ElkiE0MIKSi/nuiYhv2Jqqp8x9rc8m12MUsEWyC1btvQLomM0STYHMV7ylGeaTYPRK7KlMnYEwvSfXBzs6uc+1mTCduwY8aZHjogt/GNkxIgR/oM1XGQkSb4cYA/2Q/WXuiKOPsaoOXy0wxQpTKUURlpw0vFgJykszSBPSIAESCBAgGJLAAq9SKCcBPLtWNwbrW+CUSvaFfLgt/n6jogZRiOdp1mz59QQWn4TjUQ56rADfDGmRdOIzrrgqrhIsKM7XQhYP5p+NPCeG6PpR3Pc+Imfu2HDR7m3h9TcTvLp519xRx9+oLdlZabrr7rAbbbJxnEnGKICHL7xAQd8S2dI1zFUX594qbN1j4zo4Bo2C2GeZrBEJ7Yc8jKADuOFffu4v9z6N/fpmM8ouJSIN82QAAnUXQJaaEEtLo5GAWKNFv38kN/Q0LfU3P7u5kvEPo+ypYdoIQKIjltIOWze9lymD3/33Xc1hBa9IxBYvvXWW2lFt899TD/C4rYQcObOnesXof3yy5ojYzFyBmvDhNxuu+3mpx2F2iP03A/Z0HXEujRWaMHOaRgtVwjPUH70IwESIIEkAtyNKIkM/UmgygiEOh7WL1ORpfOBb/lYdeN//0uFDftk2dBf2MR0oyOirZelUzZ63IT0rCKdQtvVx1j7ZecdurqzTzvZPf3Qve68M36flvbF1952mL4E2x07bJQWNjla98XaRYS4/EuP0wtT+Fkam8LNlCWltDWMSwdRf+MYQ7PPP+sPbstOHX0ZMMLl/oeXLYZYloLRKAmQAAlUIQErtPS7qK/DdNp8hJZqeBm3z/nQeRJ++6zEuXXww/PX7s6DETRYgBph+MhW7ZLePpv1OUabYhFb7CaEUSzYWUi7yZMn+13EYBe7jmmHhXatbYTbuoifLk+aoegEZcZIGu0gtNg1pWw6fV4N10Cm8jGMBEiguglwZEt1tw9LRwLBoc6FYqnZ0ao5mgNxvv4mfUvEHrt09x0dOMzvHvrxJ6YIKZEGnlhId8Kkye6bGbPct999744/9vC0hed2iKY/6YV5MaXo50hsWTUaodI+2m1Iu4+ifPbbc7fYC2V48dW3ogV2V/cL5q4eLca7fZfOcdmK6RTVGOlialjpU9RFmNvjUGdb/LzgcuYp7oY7BrhRHOFS6WZjfiRAAlVA4NfoOdX3wn5uzrz5vjQQWjZdOkpSRkba79Dvam1VRcqC/OW5VtTzTYksth8gIgZGtmgHsUTihsQYxJVwjGTBwrX4xnoq2OEIfMWts846fhFrmV6Eb1krBuvKaAfRZ8MNN0zzw+gUWWAX31I2/WyU8ui+yhtvvJFmB6NpMH2IjgRIgAQqRYBiS6VIMx8SyJlA+o472IBHOi26s6WPQy/lNjvbKbHhOJdOFwSR515+M47yVDTVZ71127jpX33jBj70uJsf7T6k3dtDhro+J//Grx0zN+rc3njnP+JgbN2MbaExFQrbQr/6ZvoCen848Ri3SrR1JfLu2GFDt8H667ovpn3l038SrRlz7sX9/YKv67Zp7d4fOtzd88//i22fGC3EC7ElF/dutF22nzjlNaMlkWC0bAtL+Hz08cioU5uamgRWrdduFW2j3LFqhhlL59t+62lUCMM55rJDcBn02DPupTcGc0pRLhcI45AACdQLAhBazr/sGjfx8y98fURowW8jpltqkUV+T0N+8ozVz9pKAdK/88hTl0EfZ3v2S1yJl1R+hLdp08ZhxIm4zz//3GFaEHYHGjt2bI11WL766isn2zRjPZaPP/44TotnEEQNTIWCsCK7C0kELGota6VgZAvWoMFIJLhZs2b56UrYaQn5YxrSyJEjY9tYiHfdddfN+mwOrT2DqU2ySK5tX5x37NjRYe0ZcbXR9nHmPCABEqgXBCi21ItmZCXqEgE8vHUHyXaqsMOxdugEShz4hx7+ukMVChd7Oiyp8wX/TaPpPM2inRpEVIH4ce4l18TFQthJvznS3Xz3v2K/Y3/X1+22YzfX99STHASUvw98xIcNeuI5/wk5jHDZa/ed04L6/vEkd3a0VbQ47IR04ZU31Ei+3TZbuN5HHVLDP8kDi8dmcm+++4HDR9xeu+/kxRbwyJVvJvuFhuk209eKPbaiC0a4HBdtt43dWh5/9mUKLoU2QInTYVg7tkQ96KCD4heIHXfcscS51B1z+E/4c88953nU9pbN2G4XO7GEdk4KEc01vrT5nnvumbZ1dcgm/YojIELLR8NSoy+10KIFFbzo4zdU/OzvqZQi0/M0n5LCTtJzXz9jdL4oG5yUIVQW8QvZkLRJYZKXlAuiB0aNyOgTbPWMxW7FIQxbvetpOS+88IIXPjBNCAKK7CSENVnwCTnYad++fVpQ586d3dtvvx37QXjReUsAFghGPrk4O1IHaSDkZHIQiORayBSPYSRAAiSQKwGu2ZIrKcYjgRITsB2nuENl8lku6nAl/ddNOgXyrTuOtrhh0cYoO1EiKQfElJCD0HLzNZe4nXbo4kWVNBeZQ/r99tzdHbRvj1Dy2A9Cy2m//23aFCMErh9NJcKiwNhxKclBaLnigrPj4FDnKOSXZK9a/UPXiG5r2+76OpH2PuzAvd2RB+/rq8g1XGq3pbFGARZ/HDBggBcW7rzzTv/J1SE9ti+tFgcRAWUqxsHGscceG3yxKsZuIWnRFihLri7X+E2bNnX9+/d3vXr1ytU04xVAIJvQIqNarNCS9HwtoAhZkyT9ptuEiBf6PZfffPstcZPs6OdhqAxIB3+IKSEHgQS/XZi+ExI7kHajjTaqMf3H2oIdjIZBebXDVCIsCowRLkkOQssuu+zig20dbBoISHaNGRuH5yRAAiRQCQLLRT9INVfLqkTOzIMEGhgB3Gr6I4vNyuJzCMPx2PET3ezoBURct+06u1VXXSXjf7cQV25l+X53yIfehKxFskv37VPnS295xBs3YVKU11xE8m67zlv6KT0+XRSOOdUTJ09xH0TTd9q0Xjva0rmTa9UitUsA4qC8c+ct8PHhsIUzdnoQh/QInxAN5x424tNorZU1osV2N3cbbdAuLV6cIDqQThTSfvn1jGjq0tdR2lFuvXVau+7dtnPtIjEGozYQT4tLoc6XMMX3W+++j0q5//n6pyoMNlj7Jco1soe8o1FEKEzUEWy9dku/tbZ0UvW3Lm+5j3W7Sn3kmgl9g5v1R7onn3vFj3CBw7Qsbgtd7paraf+uu+5yZ5xxhps9e3ZBIxzwX2MMoZdromYOlfXBPYHpAdjVo1AH8ah99F/uhx9+OOcRJYXmlS1d7969fTly5ZtP/GqqZzYOdTE8V6HFChiZhJbQM6UQNrie5JP03MdvAkZ5ybWH0SJ4zsFJOZLKo58RGJmlr18sBKvDYQ8ixA8//BCXCdOHZEoPwvEMWbBggfv666+9KIxpNbIzEsqAOuitnZFW1lNBeoQvXrzYr+GC9VcQ1qpVKwfRUcdDXHFSN6TFtCV8kBbTiCDwQITRIhniW8EGtjRrjGLB6BZdfxxrnnIMWzhGOZFnqG+RxF/Xg8ckQAIkYAlQbLFEeE4CZSKgOwFyrDte8MM5nO4s4RwPed1BCBXRdihCdnQcCc+UzpZDyqK/dVlCnZFMNkL1CNmTjpBwwLftDGlGum7CVX8nlUnbTLIfqmO2ehQbrtvIdtatsCLnVnSBDQouxbZE4enxgoPdPTAa4vTTT/eGMA0FDtOI8DL+wQcfuCOPTI0oe+211/wLhkwxQvhFF13kxQB8unfvHoscGF0i8bt16xa/pOnSIi/89xj/lZa8MF1GpvFgiguOUQaZRoMy/+c///FrKSCd/Ecb8QYOHOiFo379+rmePXvG5USeEIXwSZoahPyxBgT+k411HzKJLaGy6rJoW/JyKvVGOTEVARxDXIQb/psPwSgkniAO1ngAA5RX52Hjiz1MRcALW4sWLdKmRvXt29fdfvvt/gWwtqdM6Wujrh8XK7TI8ySX52whrPDbqz+h577+jdd55FImnTbJjvWX8iAveyx+tq7y7As9A62f5GfrYm2GznU+8uxHPDm2/QGxoRnLsbCWOsHf1iPpua/zQXpbx1DZ6UcCJEAClgDXbLFEeE4CZSaAB7Z0RORYHuJ4uEtnINQpsJ0EKapNI/alw4F41p5Ok1Qe8ddIdIcjl85HLnFgPxRP1xfH8rGdLfEPldOy0czkWNtOKou2XeljlE/aAnVHBxJ+OM7kNFOkOfygfXx0ruGSiVp5wiBawB144IFxBjKFCIIKRA5MYcECjpdddplfrBKCRLNmzfx/iCFKvPxyamTSs88+67cuhUCA6SmXX355bBPx77777lgwgRix//77x7b23Xdfhx05kAaiikzjgeCA/ODgD3Fmp5128ucSJmXBYphPPvmkD8M6C1jrAXVAmlNOOSW2g/CzzjrLXXvttV5cgBhx/PHHe7EINr/55ht33HHHeTtJTrhAMDnvvPNi27CBsAcffNBhq1g4GTGk80GZEY5vsNJCF9oCYSgLFuPUi3DCHkYioS3EPvwgLkH0ssIO8sR/xcWesHzxxRfdfvvt58t37rnnerEF+TTkdXo8jBK5ahdaQtWU33P5fZbnfuhZJXHst9i1aeQ5gW99rNPrNBLHlkn8dfn180QfJ9Ux5G/9QnZ0WeU4n2e+ZYNzqTOObT1C+dly2XNbD56TAAmQQBKBzD31pFT0JwESKIiAfajj3H6kU4HvXD5Ir+PJufXXnRUUXvLNJY+kOKE8kuJm8rc7REhcXRddXssM57pOunFCcZP8xIb+tsfadqWPdT2lPTNxRRjYygfnEFyOOoRruFS67YYNG+azzDblBkLJ9OnT3ZgxY/woGLy8QxDByzmEErhBgwbFo2EgmkCkGTVqlB8xAYdzjOqAgzAAh+k+EG0gbmhxxgdGDuIAbPwSbcMOByECAsWQIUN8WfCNskBQwYiNf/0rtTg2vkXAgNACAQVCCOxAaIG4gAVw4caPH+/DUC/YRD1lQU0fIYODqAKRAnZRLtQRo3tQJ9QNDgsPw2EkiowAkqkEYIeROLLGjAhaEGhQFnARgQQ2EA/xIXqh7sgXogzYoR7WIU/wgRiDcqJMqL8IZIgPbnB6gVFrh+e5E6hLQkvSM0f753pstz4AACAASURBVPqbbp+P9hw27XNB8gFdObZx8jkP5ZFPesTN9bkvXCTPJJa6bvrY1l3CNLekOLlfjYxJAiRAAmECFFvCXOhLAhUjoDsO0qnQnQDrpzseusMT6vzozkmoI2TztnFsZyjbuU2f63lS50n8tZ1QXDQW/MXJscSV8FBa66dt2fQVuygCGdk64Tx0bQgr21aa4WEH1hRc8OJCVz4CGAHSo0fmRaOR+4033hhPMZFRMCIi2NI9//zz3gviC6bWYPTIQw895P1klAZEB4zaEJEHoywgVlgH8QI2ZMQGRAx8IPKIQIE0WBMi5CDuQKxAmTAyBnZuuukmnxdG4sC98847/lvEGZQXAk0uDsIHbOIjopNMuZK6ST5vvPGGNynlQD7XXXed9xOh5M0333SnnXZaLIDI6BMpi2wPizKDAfLFKBW4kECENTbg0M5Y7wJluu222/xHHMohAlbsyYOCCNQloSVUQfvcsb/lSb/vOp38pktc+5y0Nm242NL+Sc8P+zyx5yEb2fwsA3seSq/jgKtNI6ytfz7nYld/h9qQfiRAAiSQCwGKLblQYhwSKAEBPOzhkh76umOEONLRsMf5pg91WJL8JC+dh45r/ZPKksk/Ke+Qv+7QWZvCK8RU/KTZbFpdT7ETilOCZi+pCZTR1lfKb/khrh7RYsOt4HL+Zdc4Ci4lba40Y3i5D+3iYXNM2g3ExsM5RlzY6wFThuAwekJ24zj44IPTkotYoT1tHAgsmB6E62illVaKpxSlGVInsk0rph7JvYR0GO0BwQcO5bVCTy5MkHaTTTZJyxrClZ3KIxEeeeSRGrsKyagSiCeYWgXXvHnzGjbFQwQbrLMj9WkfLeQLJ6KOTgxxBaNa0M5IA3aYhiQjjCQu2OvRLmkF4ElOBOqa0BL63dbPG/0bDn/5rbbHOk0u6e1vfqZzycva1WWRMClvUnlC/pnytmGZnvvWtr5gbJg9t5xtuD7P6UJkJBIgARLIgQDXbMkBEqOQQDkI4MGe5HSYHIf8JH1ofjXCkEbPzxYbei434tnzTHYzlTupPuKfb1pbd5zbj9RT550tH81FpxfbhZY3W/1LEW7LDpvoRIauAazTgjBdR10GCC5wjz3zsvto2CcOgssN/S9xK0ZTj+hKTwBTVkrp8FIPJ9NotG2MosDuH3BYB0a7mTNnpp2HTrC2CoQSCCSygK+IDaH4WBQWDlOE9Lo0Nq5e/wRhmEqUi4NoIYIJ4mOHlCSHLWLt+itYZwZuww03TFycVk/v6dChg4+P0SxWAEta3BZTkzBtC6NiwAGjccAPI4+0w9QkusIIWKHl6EP3d5tusnEsUGQbcYHfTy0WoBTZnheFlTQ5Vab8dJgch/zEeuh3X+pULc/+TPUNUbL1xrl8pG752ERcsBAe2oa2bY+lbPnkFaoP/UiABBo2AY5sadjtz9pXmEBSJyL0Hxf5bw/S6M6hPradgyQ7YkOHi339bTuqMjJCf4fSZfPLNMIiW1pbZlvnpHM0rQ2TvDTTTPa1DTmu8CUTzE5fR7Yutj1129v2RVw9wkUEF45wCWIvyhPTdKwAUJTBKHGvXr1iExhZgY9MZ4EwgZEfEApkBAwiY8QKRl9kcliXRdY8kYVcsRgtnBVusEUrnIxQwdQjKYtM7xGRREbP6NEekydPzlSUxLBMo0NkkV49/WnChAneVteuXWPRZuLEibF9xNVCEHZnEmfZioilC4dRRPiAOZhBYMGaLeCo6yvTuhIrxoBEAiGh5chD9qsTQov+zUYF5dmknz/aL8nfPtOy2dG///rYPnfts6EUz/5yPPeFS4iD5ppLvZNsyAWIcDoSIAESKJYAxZZiCTI9CeRJQB7goW/bQcjWGZCsQ/F0p8Qe645ctk6XDc92XopOWlIewkfqi/ondYg0X8tH19/a1KySbOfZ5GWJruuHDJLaNNSJtnwpuJSlidKMYnoN1jTRAkC+uYpYgWkyeIHHWixwmDoEgeSll17yO+JgBIqsKwLRAC/42HYYcZDGTuWx5RC7WNMEdrCLDz5Ih9Easkgu0mGnIZmWA3Hhlltu8XGRDlsjoyzYJhoOC9rCYRtmsQt7hTg7QkbbkHxQD+QDXrLgrwg/EL+w2C2m+ggXbQPxUN/f/OY3Pr3EQX2wzbZ1jz76qJ8+hDaA6AImWGsH+chImKRpXdYWz8MEHvr3U34EHhxGtNQVoUVqY3+z7TNKfpft80rOQ1RCcfUzzB4nPSfgb58V9jmR63mxIovNxz6jQ3W2frbeYkNs52ITvBGPjgRIgASKIUCxpRh6TEsCBRKQB7jtIBR7juIk2dCdD93xSOqE2A5Prueh/HNNa+OJLVv2UB7Wz7KwNnRewiDJhtgqsLnLlsyW1/JLOg91qim4lK2ZvGFMX4HDFs6FOiwIKzvxYIcfvMRjChEWgoWYA9FFds+RkSZYpBZiC3bzwdQWrFkSWrNFlwl2ZVvqLbfc0mFKDUblYKckOIQhDtYogW0IEHAQXhAH4gTSIQwCTJ8+fXw4RojIKBuEY10T7IBUaod8YBdCE/KBaHTOOec4Gd2C/AYMGODLD7EHI2EwXQjCiHaY4gThCOnBFzstoe52MV2kwaK/qCvaAKILmGA6kyzMizgzZszw5nNdp0aXpaEf3//w4+7v96WmY9VFoUXaD7/ZcPa3u9jzTDaTnn3y3JPnhD1Pen5k85e6ZIuXFK7TJ3EJ3Q+huFL3UB2lvpJOGIZs048ESIAECiWwXDSHcUmhiZmOBEigOAKh20/PKw5ZR8dAu5ANhOfjnxQ3lH+p/Gw9crFr09jzkA2pm65jqL5iS9sM+YXyqAY/XU8cY70W+cZx6PPfaAci6//ZpMnuquvv8FXavss2XMOlRI2LkShY1wRbDOsdakpk3o+Y+emnn4LrkWCUhUzpwTGEAAgLdi0RW5ZMNm1ce67ztGE4B4+ktU9C8Qv1Qz6rrLJK4mK6uZQDHLDDkF4zJlN5MIKlUaNGNeqHkT6Y/oTtqJMW981kt6GG1RehRbdf6BkEv0zPNBsWsoE88vFPiluua83WIdd8Qs/lUFpdHznOVEf7jM81n1De9CMBEiCBEAGKLSEq9COBChPI1BlAUXLtoGSzU2x4sVhyrUemfKwNey5pQ52uUJj4JXWykuxnKmNthenOJY4puNRWS4TzxRQTjHzAqItKjW7ANBmM3sDaLRjNIeuZYESMCDDh0tK3lARqo+1LWf7aslUfhRbNMtMzOddnTyYbkle2ONnCi2n/XOuRKY98bWR6/ks+1mZSHyBTuRhGAiRAAtkIUGzJRojhJFCHCeTbgco3fr5obOcm3/Q6fi62bH3suc3f2rTnNn41nksd8Y0PBZfqaqXaGN2AqTBYn0QcpruUY3RNdZGuntKUe1RT9dS0tCWp70JLKWlle7Zly6vY9Jnsl/I5mslWqA4hP11Wa8+eZ6oXw0iABEggFwIUW3KhxDgkUA8JZOuEVFuVC+0Eheqp/UJ2Q37VxiNTeaR++MaHgksmWpUNw5QUrNuCXYMqOZUEL/yY3oKpMJWYvlNZqtWdm7DP1OYzZpZ2W3BNZMq06e67qP2LdXvuvkuxJnJOT6ElZ1Q+Yug5l5+F6oyd77O4UA755lOdtFgqEiCBaiRAsaUaW4VlIgESKCmBfDpg9aXTRcGlpJdQgzGG7XXnzJlXtvqOHje+bLbHjp/oFixcVBb7sCs74ZQlgzpgdMirT1aklBRacsecy7Mtn2daLvZyKV0+eeZiL9c4+ZS/tsqYa10YjwRIoH4QoNhSP9qRtWggBJI6Euw0ZL4AkrjZVPWNY30RXBYuXOh+XPyzba6izn/48Uc3ecrUomxkSvz+Rx9nCi4q7OMRo9ycefOLssHEDYfAlptt4tZsvHrGCqftlKBOFn37nRsdiVhwlRBbKLRkbCYfmOvzTCzl+1zL174ucb55Za9t7jFyLXdtljH32jAmCZBAfSFAsaW+tCTrUa8JJHUiPvzPCHfepVf7uj9w7y1uw/br+2N2JpZdDpbdSaed5yZ+/oXbp8eu7rIL+qZdN/WRW30QXPpdf6t75Y1309qKJ3WTwE7dOpet4K3XbuVat2pRFvurrdbItVm7ZcG2E39blu4u17L5WjVsJ6apETN/D/u7qH8nYE3OZ0ejnM67/C8+g3KLLRRaMrejbTOJ/csvv7jXX389np4Y2po8n2spKR/kh+3QX3nlFZ91r169XOPGjdMKnU8+mWube6gt78yZM91zzz3nDRx00EFu7bXXrvUy5l4bxiQBEqhvBFasbxVifUigPhGwnQh9/sAjT7gBAx/21b3n5mtioQXnNp1mUhudoUq3SVL94X/u6b93p557iXvlzXfdKSf1jl7OCn+BqnS9CskP7Y16S7svv/zyfg0XfOfjOm68obvigjP9ttCYTvH7M853t/3lctekSZN8zFRN3KZrNnadNt24bOXZeMN2bo3VM48mKDTztZo1dc2arllo8mQxNrpWVll55WgUxBo1bDeE340alS6TR4hlyK9M2aeZld9K/S2/F6nvSpTCOS207L/Xbu7IQ/bzv1H4rLDCCvGx+OlvsMM5voVjbfEsFy39TNPHP0Yj9J56+mn34w8/+HWg9t577/j5rxnoNMWUEes+iQut/ST5lJN/trq0atXKNW/e3M2dO9e9//777rDDDiumykxLAiRAAkURoNhSFD4mJoHyEUjqXMH/gUeedP+4PyW03H3z1W6LzTsGBZZQhydbR6V8Naody7a+YNVhw/Zu4uQpbsB9D7lLz+8bd9ARN8SsdkpeulxRJ123JMEFcUSEsdxQGi24YHTQ8X3OjUZU3VwxweXU3/3Gde+yjQej26k+tlnpWr+mpSReSf6Wd02L9MmHQIhzyC8fm4XGlftcf+NYzitRLi20bNWpo/vt0YcGxRWUxYot4odvKWslylwo70LS2TaCDfhBaHn6mWdioWWvvfbyDBAm35JfIUxCz4A2bdq43Xff3ZvVeVj7obSF1D2XNKG8unfv7p5//nkvuMyYMcO1bt06NiV8crHNOCRAAiRQLAGKLcUSZHoSKAOBpM4VsvrwP8OXCS039Y/+O7+JH6lgOzu5FKuQNLnYra04oU4XymL9zznj9+60cy+NRrcMdr8/MTW6RVjU146Y7oSDSZLg8t9ogVQRXELtqAUXrBtSScFl+aUvVKiLtJe9hu15qA70W0YgxCvkR2b5E8jE0YbZc5tbtnAbP+nc/hbqZw2O7We5PEfAJeWb5P/U8y+5v983yAdDaLnw7D+6lVZaKSiq2BEuYNJQRrTYPgF+pzGdByNa1llnHdezZ8808cM+x+x5UntYf3u9YNQIPnAShnaw8bSdTNdupnS2LJnOrR2Uca211nLz5s1zI0aMcPvuu69PnqksmewzjARIgAQKJUCxpVByTEcCZSJgO1X6fOGib931t9zjc95rj53d5pHQYuPrzoTuDJWpuFVn1na6UEDrB4GqeTQVY+78Be6xp553Z/Y5Kf5voMSvj50y6RRL3eqa4BL16RNfrupje5Xy5srEx4bZc1uObOE2Ps/TCYT45epXCpb69xDH+hmCY9kqHsehcpWiDLAxeux4d+PtA7y5JKEFAgvKwBEtqeeYtNWnn37qhQQ4jOIAI7QbnLSZbT977iNncPo6yRAt7dmp4+lyhNLnaj+U1vqFbIHLCy+84Ney+SESpVZbbbW4rPmysPnxnARIgARyJUCxJVdSjEcCFSAgHQb9rY+vvuE2LxBAKDjntN/FnSsUDR2ba2+605fykP33dhtusH7Umf3MvfbWYLcw2rZ0p+5d3Q5dOjssIgmH/4yNnzDJvTX4A/fY0y+6vXvsEgk4u7jtozhJbsbMWW70uAlu3GcT3ZvvvO82italOLH3kW6zjh38vPpMDkLR24PfdwMHPeE2isp21KEHuE032dg1idbO0A71feaFV31HfIvNN3UH7tvTl3PGrDk+3+OOOSItzcxZs92cufN8+IdDP46mtKzp9tilu+u0WcfYbKgj1mO3HX29R4wckybGlPPlIhOfSoWhfrqjWZcEl+WWS63hgDroD9jZdrPnleJb1/IJccrVr67VtRrKmwvbUJxSl90+Y3AuH+SFY7+2U3TPlcPh973P2Rd701Zo0SNYwAK/USE/+Q2AjUowKweHJJu6fRBHt8/8+fP9aA24bt26Oaydop9xs2bNcuPHp7ZY33XXXd2CBQvctGnTHBaOxRpbWNsFo2FkBOPixYvdF1984SZOTO061a5dO9exY0e36qqrxsWD/e+++84NHz48zhfhwl3KhzVdYOuLKVPcppEN5NOiRYsaoyUxBWro0KGxLRx888037qeffvJ+m222WZw3rkOIJajXokWLov7MQl+PTp06+VFQuu5xouhAL96L6USNGjXy5UV8/a3T8JgESIAESk2AYkupidIeCRRIIFPnCiY/GjbCDR3+qbf+57P6uFVWWSWtkzFj5uxIWHnPh0NUueLam70wI07Svvj4QLdy1EHpdeLpaeGvRlNq8Ln8/LNcj912itPJwYOPPun++cCjaf5zP17ghn480vudc/rvvMijHTo0EHX69L3Ir5Eibm40/UTSddt2K3dD/0vS0t0/6HFftrZt13X3/PNBL4qIO/2UE+J6h8qEeIgPQeq0U453PXbdMc22nEDogUO5fv31V7fiiqmfw4bQEbMdzWIFl9v+NtBvRVzuKUWYRiTTBuQbbahfuuQ82Oj0TCMQekG1fvacCAsnEGKZq1/huYZT6ueNvCjjW6ak2lESYSuF+WYSWuwIFjmXexzfcu8LuxDDwkpWXamS2ujVV1/1BW3WrJnr0KFD2j9d4D9u3Dg3JRI7IHR88sknbuTI1DMaYV9//bUP32abbdxWW23lhZHBgwenVRwjZiDWHHHEEWkiCcQOxIfbaaedYgEI/BH20ksvpdmBICSi0J577unWW2+9OByiyeTJk/05hB2dFgKRXH8Qgp6NdhbCdCnrYHuDDTZw2267rVtjjZqLeqOP1CgazYK0s2fP9iITXH29XiwfnpMACVQHAYot1dEOLAUJpBHQnV/pcMnOQxtv0M51iQQK6YxIwinTpsc27v7Hg16s2HP3ndwxRxzsRo0Z7277230+/F+RYDJl+lc+HHZOOPYI9+DDT8RCzgfRmjB7GIFi9LjPYqEFu0TstvMObr112rh3hnzobr37X97uLXf9020ZjUTZoF3buBwQMS6+6novaED8OD7Ka8tOm0b/pfrRPfPiq14cggiEETidNktNiULnSkSidwZ/6CZ9MdXb2yuqy8EH7B3X+/8efcr96//+7cMwygd2sUvL2+994J5/6Q2frv8Nt+Nfgr4+wlE6Whusv6zjN3vO3HjED8IpuMRN6MUy+Q/oMt9lR1jD5brL/+wu6vfXsgsu333/YzydQL98yTFKxY50qJVq+oU45epX0xp9CiVQW8z1izzKHhJa4N+o0bLRDb9GvwUrZhnBmI2DFVou6NvHC91JIkvIv77f79I20i66PwBRA6NC4DCqReJo7hjBAocRMBBXMKJju+228yNgMP0II0ggwkCgeO+91D9odt55Z59GzpHH999/79OIw7bPcBB54ESYQ5lefvll7wfxY8MNN3RNmzb120RPmjTJT3fCiBiIP+L0rkYiBqGcm2yyiY+HOqMvgC2cURasv9K5c2e/yxD8J0yY4AUhiD9YABfCkL6X5Lh1tO0z4mB0j3YN4RmfVmGekAAJ1BoBii21hp4Zk8AyArrjqztWcoxRKyI6HLDPHvF/lDRDxBHXrGkTN/Cem6LRLyt7r3bRCJEhHw1zw0aMco8/+5IXPm6//spozZcOvoNy9WV/9osUIkw63WJrcTSs98prb/GnEHpOOeHYeMrQgfv0dFtE03V+f+YFPnxyJHAgL3GDHnvGiynI7x933pA2/Qd5fzEl6oxFaW69+5/u3tuu88m+mTErTo+wS847w+26YzefJ8qK8t0TiUkoK9ztN1zpFwkWd9C+e7r999rD7X3Ycd6r/1/v8OKQOOlk6SHSCJM2wHHoBSg2UI8OhKfUFy82fuqAWRQzm+CCqWDlFFx23H4798ob70ai3Rdu/+V3TxRcGlLbleMyDF33Ib9y5F3fbSZxDPmH/ErNxz5zcN/jN1aL+Iijp3nOmTMvEqVbFlyUkNBiF8O1i+BasQVshE8lOBVc2QIT6nYRE9IPwDdGpYiDAGHjo/1EjMH3jjvu6Nq3bx+ngWABsQUOwgpGlWCUi4zsxJSfp6OtpOFgW18PmL4Dt+aaa8b5Qvh45513vP/WW2/tttxyy7h9YBtlhBADwQWiTMuWqesHx+JQzqOOOsqP1hWH6UQitGAHpD322CN+LiFe165d/fQk1AHpMZJH1zM2pA4a4jM+xIF+JEAClSVAsaWyvJkbCSQS0B0BHQn+WHdF3J6771KjQ4ywMdHoE3Gnn3JcNJd5xbSOmO40//Gk3tF6KRvFog06rY0bp4bhTpn6ZZp9TDm6/283uQXRui9Nok4W4uoO2KpLBR3krTtnU6PRMzLy5Pp+F7nGa6yelg7xf3d8L/fGO0P8OjG//PKL7+xPnjItrkeXzlu63ZcKJdLhxIu/CC2X/Ol0t3m0Xoxlhw76ntGaLK9H68rAoUOoO3K+rNGfuAhxg3VoT/CTF5dqFVykgUIvXwirzy9gpb44k15SQ/4hv1KXp6HYS2KZr38xvPRvpfymyv2vhVb44V6zv62F5p1JaLECC3iE7nP4N6T7XNoHzHH8888/e1EBDlOAwELHgT/WNhGHESjrr79+PAIF/iKq4BgjSSC0yG8+/FZeOfUPGhxjZAviwOHawEgZOEzHkT7AkCFDvNiBvDbffPO08qB8EETaR2IPBBqkkXRSD9jr0qWLX3tF9ys+//zzWDTCVtNSV8SXaxK2xWGaENaasS7T9SvXvXzbtDwnARIggVIQoNhSCoq0QQJFENCdAek46W+YfvbF13wO20Xiw8orpy8IJ+nfePcDH2ej9m39wrNiw3tGbsrUZdOMsFiuDZ/25Vc+HkaT2DB0hFo0X8uHo0P0008/u4XRnGu4aV9+7b/hdLpPR6f+A4dRLRjtousp8bt03srhI2H4Hhcthiuu7x9PSuuAocM1fsLncfgOXbdNC48DooPtt9smFlsgFLVquaxjhnhTp30ZR9fl1p066dhru/XxWOos9a1WwQXl0y9hOBc/tIuUv6G0WzHXYhKjfP2LKUNDTlubnPXvrfz2iZ8ILuKP+61Yl0lo0fezjF4MCS3wayj3d6h90AZ6NAjWP7FthzgQHcRhLRNtC/4QUMTJIrRa5NBizbfffutHpth0IpzAtoySwdQhOMlPH2N0jTiEY3qxOIg0EE10OoRhByE4jJSBQxntPYPdhcThHyrWBsJkwV0c23BrLzbGAxIgARIoIQGKLSWESVMkUCgB2wnAuXxmRLvtyBom+0S7BYm/5IXzRVGnSNx+0RQaaw8dlc+jKTtwxx9zuO+0SAdLOhwQJOB67to9DhM7U6d/6f795AtuQZTP8E9Gx3nZAwgyYhfTluB223n7GnlJOltO+I8cNdYHb7fNFq5li+Y1Om8jPh0Th1vhSeziWw9WWbNx4xpMvv9+2X8AdTlwrDth9lznUZ+OUWdd12oUXCJppYbYIi9hKL9uN3akc786k1gl+edumTFDBLJxzRYespmPn/ze4Vt/YEPO7f2Uj30dN5vQIqNaQkKLlEELPuVmU2g9S5HOPod0eyBMFpSFP3bjkbaSePjGrjvi9DQj8ZPRKTjH+io6T/hhhKk4rI8i4VrEkd2PZFoR4mMEjb52YiPmAHEg4ojDWjJa7IE/Rq+KiCOiEvylLKFrQG8YgHDExbfYyaVsSWWmPwmQAAkUQ4BiSzH0mJYEiiSgOzrSGRA/Occ8eXEtW9Sco42wL7+eEcfBWijWzVY2dujaOa2DJfl9vFREwTbO0vnBCJY77r3PvRktVKvdHrssWwMF04UmLxVysEAt0iKd2Ns0WkBV10l3lHT9YR+dLBGFsBaMDUec6V+lRtI0jdYJCYVLOb/6JrVIIM5DoowOb9G8Wdw50/VsaMe6k4q6V53gslyqTPolLCS2hDrjDa0tc61vNlbZwnPNh/FqEqgttvo+l+eMjBzA/QQ/uceKKWOuQovcw/iWD/IVf5CT8tSkWL989DPNHou4gdEg0m72GSiiyNrRwrChtpOFaREuba0J6oVksa6Z2BcRB9OKkA5Oj7TB9CNbllDLII4WfOy21UiDUSriIMJZuzi3ddPCkKTVIg7EKTixZdPHGfKABEiABEpMgGJLiYHSHAkUS0A6UbCD41nRyBZxay3tZNk8Zs+ZE3utt07rGp0TmSKESE2bpBa3052NmbOWpW/fbtkuPfc//HgstBx39KHu4P33cquuumwRO9i7+c5/xGILxCA4GSWDY6yNYjtLPhLCovppp0UhLMZrw1OJUim6bbd1OHypQeygBNcjGqmj7aDeOBfRBtOc0KGDg39D74QJH+GAjjU6rdLBXoo36y5FoUVz773tL0UtsBlpLfFLl30xQ3l12zX0dpR2yvWbvHIlVXi8amUsv3tyD9nvQmqshZZm0TMHuw7pxXDtOi36ftaCi72vCylLXUuD9pBnlj4WkUIvUGvrJmKJ7Ohjw0WMaRwY7Ym42L0IDrsJgb2UQ0QaPeVH7yikn7E2T32OeJIH/O1aavDTtiDuJNnWU6JEbJEy41sWCoZN1DfkYLta78tQeelHAiRQ9whQbKl7bcYS1zMCuiORdCxVXnXVZYvXwU/iD/34Ux9lw0goCf23atLnU2Jqa0YL4drOy9xopwBx67Re24fj8+6Qod6789ad3NGHHxj7S1yMRHnrvdSoF8QRu3bxWZsf0uMFHv76JV6LQuut2yYuU/pBSqD56ptZNeoh8fTIGr2Aru6IffJparpS561Si/qxw7WMgJuOEgAAIABJREFUsuYE31IJLkcc18fde+u1botoi/DCHOSWZYKLvIjZ78JsN6xUvN5rt71rkz9+d+Uel/sp03e+pKzQcv1VF6QJLSFhxfrpe1qXLd+y1JX4oWek9tPHIlDYcC0+QCyxNrFWiggQoSlGYLVo6VpseqQIntUi4rRuveyfOZkEDOGOPoL8M0P8RPDB1CM4W059jjVX7M6BksaKNtaOHqWDXZAQLvedPpZy8ZsESIAEykGg+JXPylEq2iSBBkDAdpTsORDAb9zEZQvCNl4jtWOQxTNyVGox2nZtl41KQVr5fPl1aqtHiDHidLhM3UGYiDEzoxE18xaktnrcIVpsVseXY1k/Bek6qUV5W0VrrYj7OhJFtJO0N90xwB3Y63duwMCH4+BJn0+Nj5uvleosai7azvMvv5FmV598NGx4fNpj1/TF+RAw7rOJ8To4B+23V5qdpPwSM6unAdIplZcevAzhWL8U6XUWkv5TLSNcmjZJ/Wexz9kXO7yMFeP0ixiPUyN68uWg+eeblvELY665FXP9F5sW5YALlUfCJA97ni3vGTNnO9zjcBjRAqEFoynt74YVV/S5LVe+ZchWxroQrp9DONYLvUIICTkRShBmBQrY0NNzZJchbQeiikxVEjEG6fSiuRhVAwf/NVR/RNsWmyjzE0884V599dVYrEEeIvgkja7RZcKW0dJnsGWVNWw6deqUNjpV4k2YMMEfoq6YrkRHAiRAArVBgGJLbVBnniSQgYB0suQbi7tmcosX/xSLIl232yqOqjusb733kfffslN4RMH4z1I7AO0RLWYrHRs9vPeVN96pIbaMi9L0u+GOOL/WrVP/OZL0Iuw8/8obPq12U6KdgN4cnNo9qWOHDeKg0eNTU382jHZUsv8Nk0i779LdH0IIWrho2UJ7Ej42ElL+cuu9/vT4XofXmPaEsrz7fmrEDuJ07JDaRUHSN8SOvdTdftuXsloXXKJ3xFD72Jcz1EP78TizOGDbnef1n0DoPiq21nhR79P3Qm8mm9CC/EOCi72Xy1HOYutZrvT22Y98xC/peShlASe9FooWQhAH4RAuxNlRKchHiypabNGL7uo1VrRgI6KIZjNixAh/CtFFRBq7NbWOL8fIAyNz4GbMWLYenfQt8D106NBYwOnYsWMNM8hTFsfdYostaoTTgwRIgAQqRYBiS6VIMx8SyIFAqLO1TutWiSnRgcJuReIwssV2TrUgYYUFSSdTgTbtuLH3QjkwwgVTbOAmT8VuRM+7WbPnRqNCJrlb7v6nu/CqG1yfk3q7tZqmFp4bNnxUFD7Hb+uI9Mcfe4RPC1HkzPOvcP8ZPtJBCPn7wEHutD9d5sO2jaYe7brTssV2h49M7TS0VSQK2U63TxA5bBWNdHDH/u4s98jjzzmsOfNFtLX1C5Gwc+2Nd/kwxDny0P1qCD2YYvTU86/6OCf2PjJR1PER6OLrSdqj1gWXqE3kGrfXujRXkj+bkwRIIExA7m+EJv32hlOmfCG0HN/nXDdn3vychBY7Ek7nme3+zlSOuh4mfQDUQx9nE1sQXy9+i3PbjlpswcgX/TuJYx2uhZTvvvvOY8U/YFZccdnqA1i/RUSR9957z40dO9YLIBMnTvSjWaZNm+bT7bLLLvF0YS3cyKK1PpJxsl30pEmT3GuvvebrhmlSEFA+/PDD2PbOO++cViaYAbepU5eNkpXpSjqLhnyNWdY8JwESKC8BrtlSXr60TgLFE1CjQiAUrLJK+rot07/8Ks6jzdopYUZ3or6ZsWxXnk2jqT46DAm1GLPB+m29LcRBh+WYIw5yI5aubfLQ4886fMT95siD3X577ua++/Y77//2kI/858F7b3aYOrLNlpu7Q/bf0z3z4ut+Ad0rrrs1TouDjaLRK38+q0/spxfp3dSMNklLGJ2c+YcT3Umn/9l7P/Dok/6jHXZLOue03wWFlI+Gpf7bhvg7br+dT8aOVxq+GidyPQgnCC7FLpq7YOG3frpBoWu4JLWZvb5rVIYeJEACORPI5X4qhdAiIm7SfZ1zgRtARBE/bFVnzkw96yGChNpNxJi2bdvGz3gdT0bGQARBe4gTEQbrnsDpNuratauD0IKpQWPGjPEf7Tp37hwLMvDXgk6mqT0YmQORBIINBJy33norzS7EoB122MHJgr26Hui7jBuXmlrdpk2bGlOq0gzxhARIgATKTIBiS5kB0zwJZCIQ+i+W9kPazdRWzhjF0q7tusbkcq7H0qk1dqcgHzESTnos3aq5ZfO1gp0w2cp53WgnI3RaUAZ8Y3FZiCfvf/Sxw1SiJtHc+57RGiibRNs5t2qZmje+TyS4tGnTKraL+flw+A/YKSccG40w2SISbMa4UWNS63Tst9cebufuXaKRM+nToxo1WiWuxzZbpUauSEWlIyVs1m7Vwj31f/dGIs40N/iDoe7pF17zdcQORR07bOwQLnF1J2zRt99GU4z+5s323G1H1379ZWvYSF72O9RptXEawrm+LlDfYgSXm6++2N3yt4HRNfFZUYJLQ+DOOpJAJQnk+3tHoaX41rHP/JBFtEu7du38iA1ZYFZ+kxEfC9HKCA5s62wd4mIqDz6wg3OdHvExtQhCDNaE0dcBtnVeb731vH3xl2+MbNlrr73c9OnTHUatQBzp0KFD0I7kATsyrciWU59DTNlmm228bSyGi6lB6667rmvVqpUXcPQoG53uk08+ideF6datWxxky54pb4aRAAmQQKkILBf9yKcvplAqy7RDAiSQkQBuPbn98C2788i3hGNazv5HneRtnfGH490Be/eIxZCMGZjAXG51XR5JnuSnO2OIazsy9lwXx6a19cgUbuthz60tfX7pNTe54Z+Mdtju+cG/3+J3yEBe+OjpMbrsmcqSKa/6GqavB3vd4trFBx1/CcOx+OtvXNd/jbYNh+ACl2mEy+tvD3ZXXHuLgyh4ft8/+hFLehqCtKF811f2rBcJlJIA7lH90fev3Lf4PvjYU3y2Tzx4b9rW7RRaStMa9jdV2sT2BT777DM3bNgwn+nRRx+dJojkW5JMz00dFoqnn4lJz8ck/3zKGco7W3pck6+88oqPtt122zms56Kf7/ZYylmK8mYrG8NJgAQaJgGu2dIw2521rmIC9qGPl8qeu6YWhX3p1dRQWt1BwHEuH70YoY0vOMRfD+kO+dlwK1Ro+xq1+MPPlgHnUsZQWKgcSfmEmvfdaIoThBa4a684P/6vmC2TZhGy09D9wAvOtoe+vnDNSnvatRkkHoSuP5/x+2jR5tTihqXYpaihtw3rTwKlJiD3u7VrhZYb+l2YuOsQbNjfAfl9SPr9tfnx3PlRHeIwHUfYFfKd9KyFfW1P/66H0uh2ybUcIZsh2/DTNrNdAxBn3n33XR+tWbNmbuONU2vQwYY4fZzJL1teDCcBEiCBXAlQbMmVFOORQBkIJHUCrP+JvznK544tmsdPmJRTJ8t2aJI6UPAPdWokvu4EWZs2zHaMbJ7apg4LlcGGJ9kKxbNNhUV9ZfrQCdHCvRu0S81ZR1o4sWHT8TxMwHKT60dfH9kEF9iA4HL+madQcAljpi8J1BqBTL+JVmj5a/+LKLQU0VLye2pNWH+scYIpQHAff/yx/7bPv6RntPWXdNoffvqZbm1LeUL+8gwIhdm8io1jOeEcQsvgwYP99CGs57LHHnt4gU/KLKwss5At+pEACZBAKQlQbCklTdoigSIJ2I6BnLdq2cKvMQJ354AH41EDutOS1JmyHR3bmQqls3EydY4kDGWz5dF5h45DfrY8tjOYVJakzt74CZ+7P116jWcHoaX3UYf4Yzhd9qTzODIP0giAnWYm/HV75SK4YN49BRdeXCRQPQTk3g6VKCS0YEF03Pe43+Ujvwcc0RKimOxn2dvnHdYwgcNitrKzT+g5qtPZZyrCQs9V8bPh+tlqj3WaTOXQ6UL2rR2Jb9NZHmAhQgu2iYbQsu+++/qdk8RJGpzjmI4ESIAEKkmAYkslaTMvEsiBgO0MSEfhJBnd8sVUv/2y7tjYDol0XOy37qjoMOn8yLfu6NhjazPp3NrSeSSV3dZDl9fa0+UKxYMfRgGde8nVnvqZfU7wQouOC397Ln45NFWDjwJ2mmHoWskmuCANRrhccNYf3FZZphSxm9zgLzkCqDABuceR7ZRp09O2d77x6pojWuR5gHQUWvJrLPt7alMjHLv0yOgWrN+S9MzUz2X9jMOxDdPnoWObRrexth3yt372PKlskmemb4TBYUSLFlpkW2uxjTj2WNiKDcua5yRAAiRQKgJcILdUJGmHBPIkIIu/4TvbR0zPnD3HHzaN/pOIDgWc7izk23HQC9AlHSMPHeYzTXBJ+Vv/fM9DZdD8dLj2X7ToW7c42sFg1ei/XNhJCU46XfobHUA513ESqklvQ8Bey3qx52yL5uprH4vmXn/7392no1M7V8miubJALnacOv/sU+P/oid13NlAJEAC2QnIfRu6X/VC1wccfXINY7dcc4lbP9oZD7+b8vup70cKLTWQZfSwv6H6d1Efw8gvv/zid+aBg/gCp5+p2Z6vkpdPaJwOyxTPprPntgwIz1RGSR/KX+qPOJoTzrGAM6YOwWFUi4j79jmvn/H2WS9585sESIAEykGAWz+XgyptkkAOBPDAl45DqGMSMtG6VUvvLfEzdV6szVDHSeIgLHQsZQilDZVPly0Ubstk44fCxY4uX8i29UN8CCxNogBJq79xLB+dNlMZbB48TxEAM30NoWOLlzV8a4eOsfgJexFjEA9Tii7s28ddf9u9bmQkuGDR3PPOOsU1XvpCIbbYRrzySKB0BPT9q38Tk+6zay87z7VX614hnhZa5Nj65WK7dLWq25akTUK/eRgJiA+ctJH91mGWhG5X+2yXMP17btPne67z08chO7reoWPth/R4ZljBCXHsR/LKpyyh8tGPBEiABPIlQLElX2KMTwIlJmA7D9p8UsdE/O030mZLgzj5dLAK7XQllSOpjJniWyZSpkzfoXw0Lxzbj+STa1l0uRr6MZjlK7ho3vLfdbygXXTOqe66W//mRo4a5268fYBrsVazpVE5kaihX2esf2UIyG+jfVb8JdrJbdNNNqrx2ykjByi0FNY+9vfTPnftuf7txLF+tunzbKWxzzppb+svduz1kGQ/lD7kF0ovLHQd9bNe6hcqq+aAY/vJh02obPQjARIggXwJUGzJlxjjk0CZCOTaEUH2ukOhz+1xUlF1XrrzJP4hvyRb+fgn1THJP5ttpNOdMHtu0yNcs5Nz7WfT8Dx3Apo/UuHFK9sIF8ST6013ni859zR37c33uE9GjXVz5s2PC2HbDwG6XXMvLWOSAAkIAX3v6t9Fud8Q74arLvRCC5yNg3MKLaW9njR7sSzPO2kD8be/gaG0uZROroOkuIXateVNsq/rE3q2ix0dpm1J+ULXpy57MfXIVnaGkwAJkIAmQLGF1wMJ1CIB6djoDk62ToDuTEjRS92J0Pa08JIPqmz10LbyiZtrGTRTpLHccB7iVo6y5Frm+hBPuAvHJMEFIoy0gRzjWpMP5t5f9ucz3NU33ulGfDo2hWbpwBa2UX24UliH2iYg96qUQ5/LvSn32o1XX+w222TjtKmv+h63I1sQJn4Sj/dt5hYX/sJJi8+hlJar5lsMaylHKM9C/Yopj87Tli10rrnguFRcCq0705EACTRsAlwgt2G3P2tfBQS0mCEvmiiW+FuxQ3ckpPi2I2PPQ9W0dkNxtF+m+Lnkl81+rjZsOUKcLNNQZ0t3wkJMs5WX4ZkJ6HbBcdIinDoMx/KR9kG6fjfc7oaPHON67trdXXDOacH/oLMNM7cHQ0kgREDfc3Is6yjJvTll6nTXdr110pLL/SnfWmyBH4WWEO3sfvp3E7GTzhGW9FyTXHR49pyXxZA880mTS9x8yqPLkMQgqZz6WZB0nEt5GYcESIAESkGAYkspKNIGCRRJIKkzAbO2QxHqYCFePh2ZQopryxGyUe4y2DxDHbIQM51Od74st0qX39anvp3r6xrHmQQXhOuPtA3aBP633P1Pv4bLI/fdWWMxTsSRT31jyPqQQDkJyD2HPOw9qs/1vazvNzm2You+H/m7ml8LatbSLmJBwnCuuSYd55dz5WNLuXW9dCm0fyYuOo3Y1N/Wr/I1ZY4kQAINlQDFloba8qx31RGwHQkUMKkDkqljVUzHNim/csIqpLy2nNnOdfmT2BVSjnJyqS+29XWNYyu4aD8c6/hoE2kXvMy9+Opb7uD994r/a67/e67j1hd2rAcJlJuAvT/lHpT7VM4lnpRH7jf9be9HxOXvamEtqNtFLNg2sHwta3v+/+ydB7gmSVWGf5SoBMkrcZYsOS1IzjnnJeckUTKSliwCEiTnnFl2WYK7wLKElRxlUYKACgIiIBlBXfst5vv97plT3f3fuXfm3plTz9NPd1WdOuGrqu6u01XV69Nk11KZHrtS/X/KevVwOdl1Tw/Ji+eI15jOlVcIFAKFwEYhUM6WjUKy+BQCG4BA9kLRY+svMOt9menx3k7p8YUrxjNbCrsMlc1LU51w5nCHi8eV73VIXelgMNf7gi6azbOiOBcC+yYC6ndY533QHS7K8wGs9814Df3+/FzaiJbi90G/jrz39edZD4ceJhke1RZjq6l4IVAI7CkEytmyp5AuOYXATAR6LxBZ8XqB+H9UergpPcMqS8twrrTdR0D1wFlHdLp4niTGQVzP2QJ91efu11Nx2P8QyPomKMT+SFx9LPZL9T/P3/+Q3HiLVTdzOO/r97+IRYw7Ro7Fvo7LnLZRNIVAIbD3EKi/Ee097EtyIZAioBeDuS8SKZP9MNFfqBy77EUrS9sPIdujJoO5D9YQjuNEdcXZD1eOshy+TGGPKl/CCoF9GIGsb8b+531X90/1S49Tru6vG9dYhGXvfWB/wjpiMcf2OTQbV1vFqRAoBAqBXRGomS27YlIphUAhUAgUApuEQM+5MsfZ4oM7v0bVeqnepAortvsFAt4vMTjGIwjqb+qHyq9+GJGq+GYhUA6ozUK2+BYChcBGIlDOlo1Es3gVAoVAIVAITCIQB3LR0ZK9RGeOFgT5oG9ScBEUAoVAFwHvd71r73PV/7pQVkYhUAgUAoVAIdAQKGdLNYRCoBAoBAqBPY5Az+GCIp4XnSn+JT3m7XEjSmAhsI8hEB2dMY65cfZKjO9jkJQ5hUAhUAgUAoXAuhEoZ8u6oauChUAhUAgUAruDQPb1PDphxD86VmJ8d/SosoVAIbAWgczJEjEqJ0tEpOKFQCFQCBQChcBaBMrZUi2iECgECoFCYK8ikDldUEjpPqjrXe9VA0p4IbAPI5D1w33Y3DKtECgECoFCoBDYMATK2bJhUBajQqAQKAQKgfUikH1JHxvk1Vf19SJd5QqBQqAQKAQKgUKgECgE9gQC5WzZEyiXjEKgECgECoHZCGSOFxUuJ8tsGIuwECgECoFCoBAoBAqBQmAvInDCvSi7RBcChUAhUAgUArsgUA6VXSCphEKgECgECoFCoBAoBAqBbYbA720zfUvdQqAQKAQKgUKgECgECoFCoBAoBAqBQqAQKAS2NALlbNnS1VPKFQKFQCFQCBQChUAhUAgUAoVAIVAIFAKFwHZDoJwt263GSt9CoBAoBAqBQqAQKAQKgUKgECgECoFCoBDY0giUs2VLV08pVwgUAoVAIVAIFAKFQCFQCBQChUAhUAgUAtsNgXK2bLcaK30LgUKgECgECoFCoBAoBAqBQqAQKAQKgUJgSyNQzpYtXT2lXCFQCBQChUAhUAgUAoVAIVAIFAKFQCFQCGw3BMrZst1qrPQtBAqBQqAQKAQKgUKgECgECoFCoBAoBAqBLY1AOVu2dPWUcoVAIVAIFAKFQCFQCBQChUAhUAgUAoVAIbDdEChny3arsdK3ECgECoFCoBAoBAqBQqAQKAQKgUKgECgEtjQC5WzZ0tVTyhUChUAhUAgUAoVAIVAIFAKFQCFQCBQChcB2Q6CcLdutxkrfQqAQKAQKgUKgECgECoFCoBAoBAqBQqAQ2NIIlLNlS1dPKVcIFAKFQCFQCBQChUAhUAgUAoVAIVAIFALbDYFytmy3Git9C4FCoBAoBAqBQqAQKAQKgUKgECgECoFCYEsjUM6WLV09pVwhUAgUAoVAIVAIFAKFQCFQCBQChUAhUAhsNwTK2bLdaqz0LQQKgUKgECgECoFCoBAoBAqBQqAQKAQKgS2NQDlbtnT1lHKFQCFQCBQChUAhUAgUAoVAIVAIFAKFQCGw3RAoZ8t2q7HStxAoBAqBQqAQKAQKgUKgECgECoFCoBAoBLY0Aifc0tptEeWOP/74WZqc4AQnmEVXRIVAIVAIFAL7LgJjz4x6Tuy79V6WFQKFQCFQCBQChUAh4AicYHgpnOdJ2Mdx2wwYtspLdbRtK+gVdcqa11bQM9Or0vYcAlk72RPtYm/JzZCNuuwJ+zM9Km0cgVhP49S/y626nINS0RQChUAhUAgUAoVAIbA9EdjvlxHxguwvyf/8z/+84PjBD37Q0seOn//854tvfetb7eBatLe+9a0Xv/d7v7d44xvf2C2/kc1lTMdsAEAaOvOif/DBB6/RcSP1irxcz5j393//94sLXOACDbfnP//5LTuzK5bbjvHMLqXRZqiXsbYzVp48ym/0IK4nc6Pxj3LEX32KvhlpFF9Vlx4f0rMwVjcZ/Sppq+jS67s9vVfRo2hXR8DbH+2Texjt1euU54na8NWudrXFf/zHfzRBVWer410lCoFCoBAoBAqBQqAQ2C4I7LfOljhAU/zAAw9ccJzxjGdc40CJg6H//d//XTzzmc9cnOMc52jHEUccsXy5VuVHGd4oIr/dicfGFnnJKRTTYznikWbV+JiNERfxRr+LXOQii3/4h39oJB/96Ee7DqBV9VmVPsNklbQ58lbhtzu0U7r0eGflRMtg8he/+MWy6Bhtj3+WLj7Ki3yn0smPZabirkekZXCcOVxdzlj5yG8q3uM11Xfh2ys7JTPmr2G0jkjkt9nxVVTcHV16csRT+R6P8u53v/st3vzmNzfSD37wg2ueFdBWKAQKgUKgECgECoFCoBDY9xDYL5cR+cttvD7hCf9/G5t3vetdi2td61qt1rMX4jOc4QyL//zP/2z5r33ta9ssEQWfWbDRswyWQpKLTM8XvOAFi/vf//6L//7v/96lxHp1W0+5iLWUeeOb3rS4/e1ut/iTP/mTxWc/+9nFiU50ojV6rkfWLoZOJPRk9NJhl2EdxcyhiWU2Kz5mS0+m60/7OclJTrJ4/etfv6atq+wY/ywvw0ZpMe82t7nN4i1vecvi61//+mLHjh1r1M149+zJ0qMsaC50oQu14w1veENWpKWNyR3Lo2wmU4I8b6zvin5KVmbA3DI9ujH9M3lbMa1nQ8/mng3OJ7Zf4vSbP/iDP2jFP/ShDy0ud7nLtWvJieeenEovBAqBQqAQKAQKgUKgENheCOx3G+ROvRh79T30oQ9dXOMa11hToyr/sY99bOloEQGzXQi8PEOnl2iVWfUlfpWm1Bs4wIOZIoSMZr26uX3r0VNy23nnl10Gt7//+7+/cByl92Zil+kf6y6j8bQM2x7mKtcr43z3hN0uI9NJad/+9reXqmV0GUZzcHRea9rFwDDKIZ61D2SvB6vIHz6//e1v2ywr2mOU5TZSdlWZmTzxzPLUd9GjJ2u9evT4TbWHWM+Z3pFmK8ZX1XsMF/Hi7NfY7f3moIMOam1KvDir/tZTj1sR19KpECgECoFCoBAoBAqBQuB3COzXy4iAQC/H/pJM+p/92Z+1ARdLCXg51iE67SsCHeF//ud/Gg355N32trddHHvsscs00r/4xS8uHvCAByxOd7rTLa5+9as3OgZ2nPlyr2ULLB0gzsG18pkBAh/kfPOb32zp8GE2Dk6hNw35ru+///u/Nx7MCCCIJ+kc6MiXc/GUjciBFr4cXEtXx+t5z3te24cAmey5gm2nPe1pm07vfe97m20RX49TDh2e/OQnN/3Ax3VCLnGWrUQM4EP5Bz7wgW1QjJ7gShl0cZuEA/oJT81coLxwR2fsweEDLXtj9EK0CzqVBwN4sDfDE5/4xCUOwle2/N3f/d2CAx0ufOELN52JYzNnt0FtB73Vdn7zm98scVFbQ4bqWTqSRxr1ha3odMELXrDVFdeyH3qFaB86X//612/ZT3rSk5ZtQnTU9ROe8ISmG7ZzINP3nolYqizpzgccwQNbkUX9STfvi1yTB360f9oAZag7lXE7wAt90EtthrLI87q57nWv21Q96qijlu2RfOHIWXyFNfb/7d/+7bLf0I5wlCDTdaYc7cr7rvqL6kF2wVt9lzZBXH2Xa3hIj9i2pvouZdERflN9t1dvkk3/BHPVPe0K3uqHXgdcgxX3Kmh0gEmkI672KjrvT6JXnrdj5clOvyeI3u+91J3KUG+ioR1jF/r6/UyYuM7Uge5pqhvur3e/+92XEN7xjndsbQo6DuRQl/Ah6Bwxr3ghUAgUAoVAIVAIFAKFwDZEYHi525JhGBQf/573vOf44YV0w/QbXoaP5xgcI+0YpncfP7xAHz8MiNrxX//1X7zxtuPoo49u57/+678+/pe//OXxw4BieaATeec73/mOf8UrXtGuX/nKV7Z8aG9+85u3tFe96lXH//rXv27HsE5/yVtlOV/5ylduB9df/epXmw6ux33uc59luWGpUsv78Y9/vEz7oz/6o+NvcYtbrOENdr/61a+O/9d//dc1edBxDF9aj//KV77SyhBHP8mVLuTBGxu5lq7YKNpb3vKWLf1xj3vckmZYCrTmGnz9EM7wQA/kSwZn4s961rOa/pL72Mc+dnktDIblRk0/103XnEWHHHCNdG4XOg977jQaeDrfd7/73cv2onbjZ9oQh7AQD8cBWdSZcFN9xXqjHl796lc3PTjvbtuRPLCAJ3UrvVw/8r7N2oyBAAAgAElEQVT3ve81O3TEOnvOc56zSz09+9nPXrbHWO+OIdhk2KkPqh/GOr3Sla60rBPV16c+9anWxzg+/elPr6krb0vYdMwxxywxBwvXCVpv6/RV2hxtz+V6e4Qm1o3aleoyth/6r99fvvOd7yzbYq/vUu/qG84fGfTp2Hehp8/Hvhv7h/dd6TvWd6mXqXr7/Oc/P9kPZT9n6s319HZDO3Vabw+OK9fDcrLlfUUYeZrar/ql8xZ9dl/x+y16er/m2vuI2q2eIbQxrxts5fmh9oRccOegHXzta19btgVw1vNpwx54xWjTEFBdcd6s4DI2U85m6V98C4F9FYHYN8fi+yoGZVchUAjMR4AvaVsuDF+fly+hvKCe//zn3xAddUOMjhYNShm06EX8Jz/5SRtEnPKUp2wDGY6f/exn7ZCD5RnPeMbymjTRydmCA0YDQw0WNWBiYIdDxwdEvKiT7o4G9GGQRx6yydNA6TGPecyS//B3i6WT57DDDlumM8CSTdKF8z/+4z+2dHQlDl8NJtGJa9nLtfR0B5L0gA8DFxwKDJZ8EDHsabNmAAXWciIgE9mSK4eVHFvSO8PA8RRuP/zhD48HE5UjjgwNgEinHIP0mE4ezgNhcd/73rfx4RwHWNEhMezD0GjBiEEd7UAONumJXqpbx22YGdXawb/8y7+swQJMYtth0CZbKeMOo6ztSJ4cOOjIABLnE/VE3WgwiTMlOljcMeaOIHQTb84atKIPDg4NqsFC7ebDH/5wiqNkUkb2YCftE/uH2WXLdo3+3s+c/qc//Wmj58wAV/WhPkOdK0200IM76Rw4UWl7yCRO33Anq9qp6gbbVZYz6ar7d77zncs8tXfO3neFIe1U9wzvu+gn/tKDtNh3vQ9N9V1kuh7wH+u7U21fzhLakZzF3AfckfGjH/1o6dBVe5DDITpDwS2m0Y7kKJLjEL1Jo90II+47ctZExyblVA9eZzjXaBu0E+pOefQn8QBf2Ulf7/UT+Oteo7YDzy9/+ctLvvBSHYATer32da9rfaMcLhvymN9QJmODqDl5qygzh1+kWYX/nqSNenp8T+pRsgqBuQiMtdmxvOyDxBi98ubqtdXo5tgGTYW9j0DV1d6vA2mw5ZYRDS+dbWq1h+FltS2r2J0wGNyK6xx5DY2yTfdXYPr48JVzMQzMFl/60pfWLAV42tOe1shuetObLsuoPGeXRZwp8sMAqZVhWvlpTnOaVu7Sl770YnAwLGWqrOs4DNIXw8BkcZaznKX9UhSae93rXq0cm95K7klPetKFlj884hGPWKY7L9eRawVoiP/lX/5lSxoGSk0m8ji4HgYlLY9lF+Ij3sMAavEXf/EXi5Od7GTN9rOd7WyLYVDf6IdBdro8ADo/oJUeSm8MhnDve997DQYRT7BBJzDAdvQhDAO3pa47WbWlF8PAqaWf9axnXQyDopY1DNwbrtQ7NrMsgvC6171uUn+W5xBYDgA/7TtD/VLfBJY/RNxIP+SQQxaXutSllm1C9SJalnmo7bzmNa9p7YA8eGOLguhVnnThiJ0Kg4Ow1Q901Nftho2JCS960Ysm7VR9q57gwTXLhwjYil6kg+EwAG9LJghgFOtccehZUiE7hwFoqxvSsReMFCTb28Dhhx/e9sCQ3Hvc4x6tPtm8+pOf/GRLH2aENBb89Yq4DvoivL7//e+39qN0aKOd5BE4K68lDIF6x171UZaeKLCERXxX6buSBx/HytPFV32XfjrWd6U3ZwI4PfKRj2xtAV7edz/ykY9064zy4KY/iA0OrmW7Akd4wptAP4SetqwNxWnLug9S14Ojqukt/XS/5/4HtuBKHjQs3eQ8OCuazgrCQvUjG8kfXoh3qbPBqbe45z3vuRic+a3PDk6hxor7Bxvaig+y0WeYjbK4zGUus6YuVC9+dn2UrjSnA/Nb3epWi4OHo8LGIiCcB+dWe4YPTrjRtpzVo7cf/iD1spe9rG3Q3aNVumj/6Z/+aZI2azekIQd5g1N9yYNr0uCbtSfXd2PRnM9N9qgEfQj8B2fmsq9G/OZzn6aMvIm/9KUvbQd6ZPlbAbdpy/YORQ+vXvre0XK+1J7epHu/7XFUefVPvx9Q/uUvf3nrn6JTn6X9xxB1gZZ22rvHxPIbHY/6xDj9B/24B00F7FW/i3ymym6V/Ki3x6kj7NP9eavoLD2kq+sV9aceabOEaOsq9sSyivfa8yq89yXaLeds4YU2Cx/4wAey5HWl9RoH6Qq8JMjpo0E7+fz6dvh6ubjCFa7QBgsqw1kv+86DNA1ISPcBHXnnOc95ljIzHuyT4fpShsE5AxD+2oM+DHrYsFcvYQxapQvnqA9pmd4a7GpjUJfLgITAYCnayW9NxVPnK17xio2eAZHz6V1Dq7zIX04t5UtPysjB42XZK4XADTHyYq8S11UDQh/owetMZzpT44G9wop4pj+DaQL1Qr7zP+CAA1oegcGmY3+zm91sF/1FKznD1/tleQ1OZRO/HI/0rqvo/viP/3hJRz26Dec973lbHm00s83tEW+nwyYF5EQeYEJgz5mYJyxIH2YALPlEO1UXEEgftQHaGY4U+oAfF7jABRo/+gdyrnKVqyyGWWrtbzA3vOENmwN1mKHQ8pAnZ4MwkzLS2euNvEiHU0lpOsuR584WnFHqu9KXgfwqfRf+BMdTeMgZ57qM9V10Eabip76rth/rTXG/V7NPizCSbPoa4ROf+ETLG2aQtLhkOl8cVcMMrLb3Dun8CY5wznOec2mn+OIwhNbvv414CLEO4EVwG3eSrnEcUw5+6Ibd3AOPPPLI1i4pS/twG1132a00yXMayYxpXkblRFvn1RGIeH784x9vv9zmPhCx73F3uqyMpzGocEeO12FWNpMZ5UWaaFOkJ66Q5c1JizJXjbuOLu8b3/hG23eKOhiWHKZ1MEe/uTRR7x42Gd1cGW5r5LOe+Kpy9yT9mD20fZ6hHjLdxnjsiTzXaY6umQ0xTXwy3lNpkVekj/FMVuQxF8dYblVZc3QRz0gb42O6bIW8OW2lZ1OvPtZrV49fT0fXiw8OHFldR3169kS6jFdWljRoMz3XJO4HkS33N6Izn/nMKewaQKeZ60jsNR6xIp+BC4PRpzzlKYsHPehB7esnG2wS3MHgZfR3CaXxIo7DgcDgmrgHn3WggYLnaxaGpw1LhhZ3utOd2qyRXohyoPNG7/lc0xkVznWucy07ptLOfvazL/MZgPzhH/7hkt+BBx64pNdfNuQIEM4q7PHYYSOtyvggnjR9gc7wJJ8ZPm9/+9vbTImIqTtn0FV6cnZ8qGsPnqdr6auBrmZ1UE40tCEFHCcM2E5/+tO3pOtc5zq7tAfVC+W5xolGYAAc6xReOBD44sAR84lrxgc8fJDrf1ZpAoYwZmPMlyx3ktCWvQ6R8ad/+qeNNxg5f+dHuhySOL9ku+iZWXDuc597MSwTWfJXn6IPMDDuBTnc0IXZYLQZykgvcH3Uox61uOQlL9n6d9TLMZU+alOOIfoJb+kietlD+rBkaMEmqav2XemR3V+87+KciHrEvsusDelGu3d60tUnSBed46s0nEUE2pX0c5vph29729uWTsbPfOYzjZ46dlzF2/F8//vf35LpU5kOKuN5PX2h9TpQWRzdrgfyH//4x7eNfXGo3/jGN26k6MuMuRvc4AbLXziLZzy7Po6FZGb5XqdZv1TZOo8jkGGbpcU+lHGNbQ6n8SUucYnFSU5ykuU9iHsf7ZsZUv68o81yPxHtlLwoi3imd9Qh0ztLm9Omog7wmVMOOpXNdMbJrvAPwzOAjc+5n2eYzJXnMjN7YxqOXILue5nsWKYXdx0jZnP0j2V6cnrpU+WjDlP0PTm9dPEblkI2BxozmHnmZEG6uA5Rv6zclM5zeMA38pmKuy5TtIxTeO75/UBlOMdncNRnyoYoP8NprC1m9DGtJ6OXHsvHeK8c6eia5U/hEGVsdjzTMdad6wC9yuxufYzZ1tMrK+O0XL/jsMMWfzx89OU5pRBpMj5Ki3U0povn6TnIBANPF78MtzE9tnvelnO28BcNliWwlEWBL7OXvexldxtrVW5saL10BD7kIQ9pf5MYNuZcs3SDhks5ldWLvqfp2vm7bPj3dJGxMZ+Blc9oYCobjZrAHzUe9rCHqegu58hL8kk/8YlPvKTHljgY8hkW0Mf8aJcLH5NLXpbv5eMgqkevdDljxNvp40NQeVGPnv7CzLETbZTncqERrryIe5lMVsYr8vM4MzUYFGY3/DEbnUd2LT1cR6+PWIa464ATaK6t0FE+8ieNZT4EYaj2h5ODX7THB4JkygFEHIctjkpmWLzjHe9YvPjFL25ODw6WvajvuE3iwzmzVfnCKSsrm+i7OCYVVum7UY70gbf33UyP2HfH7Ij29uxxOtdF137O8qf4arlR5kSEX1bfwjnaEOUrP7uv4BzlXs/S1WHT7KXTFuc2y4uGPX7ajMKefRHbSBd1ww7KRHuyNC9b12sREO4Z/qqDjCbDPdYRcX9h1L1HbdTbHfwYgBFUt5I7V5b01Vny4CseSlN8rL2M5bmt0llpPb29TMTU4zyHOZgxBn7/9m//1hyvZzzjGdM2H3UZi0tOj8bz+TDkNnlerJMev7F0r4MxOs+b0j/ymUs/ly7yn4pHvnqukx7bYsZrPRjBR3K9nqIumTylOW3vOtLqHaPHFz7625/b73xI1yE73JZoQ9RNcS/T02c96VG+dBzjFXXMaKPeme1Zuc1My2xdRV5WPmKhe73zXW+bn9Ktd8+KOsGHWZe/Sma1eD15/yU98s/sz3SMdNzzFWKe0h23KDeTsZ3TtpyzBTBZp88+HXzdZCq6pqNvBNBZgyQtawykMfuA8JKXvGRx8pOfvM0gePjDH96+gscyWZy0U53qVI3HoYce2tZ0etBeEqRlekSe/qWI6blMu1fQ137xWiNoJ3+lRb4+k4MZBMya8DD8rWYZ1ewQ5xX5xbjrJDszmqgzcZaROK1mAzF7JeIJvWaDaPZC5Om84nV2g8z0dBtwvLE8BbkM1pSHXAZnCsJU/CLfLK5lONo/w23BceAvPeRFe3pp4pPRe57bktF6O2Gmg8+igA9OIIKck+IdZWg5EzMKHB+u+YIcnTbqU7RVZqsQ/Gbt16437RznJMdTn/rUxV/91V+1vYq4Zi8Qn7Hltrvezi/akdGJvtd3yV9v36UsX4oV5vbdiHGmN2mZrUrX8iS+7jNryHlCM/xNrrGlbsm7+MUv3uK0Ze6hY4E6pX/TfpjdF0OmF20kpr/vfe9rRUmPeVma5CAT+5jRwjIo9uBhcI0jhn1bFNxm5xdlRfp9/cUi1tdmxr0OVNd+1rXoWPqg5y4fLoaNjZsjgLQznOEMC2Z2MpjyOmNPKe5DzErkHvHNb35zMfwxrJFwj4c37wfkQ4uDk2vS/JkCDxwOyOJlFOcr91D14cwWvQxzv6c8OsIXO6TDUtnhwtsWdNB7e3QdKIfzAx3kBBEv+ODklkxmUSo4P9eZa8WHDbMbOXie4hSnaBizlIh3FnhDF+/T0g0MqRd053mPHTGoHun3ww8NGi02nPrUp15DylImAnzkKCWu8sjCNpb88lzBKU6acIaWOkM3vXsQB3v05/4mmWP92jFjEMQ9n5mOYIws5EfdZQhtAPxoAwTeiURLW1I9i54zs0+++93vNluEJfXgs22h8/5Ae8ROeGKj+oPjBp7qP972oSVgJ7qyzAgetHP6GU43+MzFCP2pW3hx72U/Mezm/XOMh3RoygyB/brgAS/0oY6ZLe39TrQ6e12RRl1TZ3zYYFaL2ik2M2OK4P2A+optO/KMsrx8pAVv4Qlf2qHap+SonWMbGMWgeoa39wVwBp/YTuCjWWhuX7yOdsR85IE7bZ0zHy1Od7rTLd+VoXeshC20BNoOZbwNSiZn9U21SWZaUbcZBtQL76mylbaALrQrf4+Cr+qAvsN7KWfqPt4nva5inbuetB/1K3hhE/cd3pmjbJ4vBGxXP6avIZs+mtnmOgtz6pXA/VFL1Xfs2LHmecA+qbQtltpCx7sPbSvrY34fgpYPADzn4r0VmaoXb6e0UbVf9UtkX+xiF9vledAU34fClnS2gC8d4OCDD96jUMcbHMK5kd7kJjdpX8IVmJbqN0bSicfySuPruwIdzm8avv9ItomWyom3bijsGcMAwGUy6In0y4ThgputXiK9nK7hyUCIQaFmy6g8X3oJGhRz7RhwTSfqYSA+WX7MizT+4gutT1vl5SM6hrQEQTeVaKtuIjHd42M3Tdkuem5OOFvYjFVLdWQTN2kCN1VfvhFtjjzhzeE3VW5e9AvpxhIZDxG3yFPxrJ4yPj18NHOIMtikpUzc2Hkh8gAmBPZQGdPP7VSdCgO9YMJHaWx0q8BDIso97rjj2oMJvAg8qNCFpSHeB5gVw6AeJwV7p8Q+FTGQzMyWpULJBfSx72oQBfl6+q6woLz6LjJ6fdfvQ66i2xLbRs9O0vVyDS/qiJdpgsrI0aEH6UUvetGWr02zW2RnYNNxlmuyYS0OMByl1Iv6lNPigKRfHXXUUc3WnmOGOo9OOucTr7Hhc5/7XHMK+T0FGc985jMXD37wgxcvfOELl0vQVL6HUeS/v8d5aey9JG4UNqoL9Q2vG/U3XnS1jA/HyXvf+96leF5yca7RVn15IssceUm//OUv316OP/rRjy7LkM7B84Z2I1r6pDtvebbqJVqFtXzyqle9anuRJ0h30cgG14H7rtvRw48Xc39m8LIblzBqkIZzkb7ly3x47vOSzp5XDNRjyPAmjfcc2caAQR9zeEbQDhiA+72Ga56hER90Y98d+qA7XeFNPXmQHczw1EcK8mUv9wnpAXbMXEMXD+DFfQ3eLDHVQBoc0I3nw7HDc4KvxQo8a7j3sb9eb0AoWrDhPYxN4D1I95Pxvjk8o6JTaPjLXxsQK3CfQib3KnBTPSufdwVv16RLBgNLZnlqgAcG8CBgb68/sASM4O0HTDgknwHYUYOD2/GhDPhh2y127jXYGHUCGOGI8nduSKU/fUxLPDssWjJ8cGQdPWwCGvURfnxMjY5O7hP+bOZ9+Atf+EK7d1HH5IEv94DLXe5yrd/7fUb9V2fp4n0leweVLdBJPudjjz027RfQ0x7lPON5B0Y8b/WsdZ44P2nXtBt0lh3HDHXv+KgP0k74SJbN5KTs2Duy48H9g3tfDOip9zjRo5/uG6LX2IP+6LP66Ue0VX8fpYzayY1udKPlzzJIx4nznoE+tgXywAQnsM/G4B7B+4ffk9CNdnzenXtteh1H+zzea4fQwO9Gwz6CLpv+TqA/6qMVcWzjoy736uxjsrcx3bMoB0aqA55fTseyadq3Avai0zUHPPSzEfLAAx5+HyK9YTL0DZYUaosEpcPLn4O6z+gDtWTSFgjo5e1K+fvCecttkLu3QPWbgypdHYllRARu/gxY4sBOOoveeXGN109fhXhZo7Nwo2AZgdYTw0MvZrG8dwy9cNDouXmQx2Dzrne963KpBbzw3EofyY4PL+hcZ/4oRGA5xVvf+tamIwfXDDQIw++R1zxYWuIQxMfjut7ds+vINQ9bNs0l8LDkZUw43OUud2kDLGym80e9pOuUTpTrBc/jmv17CCxLYdDIV03hRr0Q2NTT7SAt2qU0z8NWDZJ50GOrePMnE4XI23nNpelhpXS1+yc96UlNB6UzECWwyTBtBU85GDBjhMEpAYwct6gfdsqRR53yAosMnBDUo4Jk+gsXOPCQVh5lucGzJ4f6AX9u4i9F/DXHdScfRwvtRbNv9IJNf1EfWyowXEzhFPMVV9/lweZ99253u9uavqs8ynnfFR/HUWnqu9yren33uc997izdHWu3O9YBgzjNKrr2ta+97Ie89Hk/ZKBGoM6oFwKDIW1aipMDRwuBP8Bhk/5iRZ/ifkR7ol1hG44W2go8oNVG5k9/+tOb04c0+gm8FBwzT3NMP/vZz7Y2zF4zyFMecpnZSOBvUl5GfD1tKXTiAl1vf/vbt2NfDeDCrEheGPmzG7M76Z8bFSL+8M3qgsFBls7LOgMP9uPhWawN05mBwZe3OPASH9qcZvGxxJk/0XGWPtJD5XnZ1Us7smjffMSRPAZ/ooUH15GH20o+XxXhoYNnIgf8FXj5FT8+5migzGCMvkvbYxDDyzUv59ybHCe3J9aZ6xPLyLHM13E5OHz2omwVnswmET7c//lLHgebmRO4v6M/9MMv2ZeOFpxUYMmB3bz08wELvCVDertMOVqg552MD3vUBTrIiRN1hE/7kDPUD5jx3GGwC3ZaMiUcHKuIjRx1OCjggZ3YzICPgSD3T8mmLPclDXAYnKIrOiNTH5ZUx8JHDhMGzLQJ8AEr7IUXM/RchvRVf1BbUvvknit69KXPENCDOE4HdIAOG2Qb+5OhL88+0rE94uFxZBDXoAwnG7rf4Q53WLZTZkhg+xyseddGLu2dPkt7xzbaJTzYRyy2E8eSGS1ytFzvetdrjgenF63r4mnCdcxmz4Pe5TMQVr+g/vSHR9UL9S8s1L/+0erK65j3HIL2ZKQfUd/gAx7cR7iPXeta11q2E/CL+kUdVWeiU1y2M1ZhFhbvCGCvGVnc87yf0r/laKHf066wV44jb7PI4LlCedo09zLaCHWsdww+xugezjOcZxC2Qs+9gnYpfeADvfDi7M7xgw46qPVV2jv9n+eDwlTdgvO73/3u38ke7hXcO8AZPdEFnaJs8f7g4HShD3N/QF85JbhXT/1hD/5N3yHQX+lHyPX2y0cC2jeOVOxjjIE8dPI+Txnui7oPqS2iE7xx2NKWsNXbnLcVcFL45HD/4X6H0+ia17xma2P7etivnC14zNxr1rum0kXLmY3xFOR0mNMwvBNqY11ueNy0+erOVHqmqXOTU8ganfhAQ+PU4JvBL95NOiADQ/7goen53Hj10vCmN72pseerMQ1cgxyXiQwGL3IsMTDlpszBNQ4M9NRX80zPpREjFxHzGKdorKeMHU4NBqLgyY0Ivbgpy6GEzZrB4F+pM16rpMlur1ucaRrsMmjkCyWzb8AS3LjpTX2J8TqO9YKDi+Bth7pkqZ3aDg+DVcOqdUj7oF1hEy/xciRhGzaSjuOAZRY8vJ/85Cc3lR796Ecvp187buS53fz+l4CdPCRo59hJX9FDw8uANV9bhQty+fKhv+nwgKPukcGvt+GHAwh+fPWAXi8tDNS9vQhX+ph4NOVWCI4v17xwogM4wRf5fJlU31XfIy/ru+jBi3gW5vbdWOcxHnl7fXldQUccJyL9kAczL2nozoObQTWBTRS15BJ6lhuJHjroNVONupZDD4eXfjGJw4V6wn7aFwHHkYL280IHeMEDXZCnFy9oo/5LBjsv+LLFvQ4+9GHuKbyU8XIoB0/2Ncn5+P0s8ifu9zZeUHAesRx0XwvCmhev9w9/EtSfqxgoMYOMl9vNCLG9ugzy0MenbPOiyECRNslAyp/1ctRShrIE8VA8pjmtXjo582XTZVGOF20treNLvpwJzgM68WkKDEE60JbgwUF/4YwDRn/94h6sTaDh4RtUszRbTmX0usbOX9UzUNNv0pGFo5v7PPfVDNuYprhkIUf6qp/zhV2DIOXpJV7LO2Qr9cIAgPu/XuS1fxf9koGB09LvNfAXb+ULR9qeZrTQv3GacY+i7uUYdpwp54FnB2XAmvvE+Xb+0Y+vzmoXGS7wYYBEwBnBe5v22+LepkElzkjpis2a/cygBDzRFfnxnUJlNBMALPmAQLsgEOfdkwBPdIlti+c7+KldaXAHXj4wlp0qL6ylKzNZsY10zuxTx3OZdyW1rwwj0pDD8g4w4nmJvbR14sKa+3GvvHSRztir2VXkYRvPBZ4bvA+7DdCqPLNX9Gtc2gX3B+VxJqgsZ6W5Xo1oCFFXl6m8SEv9aMYB/ZD60ywT8NQ7H/0JfthCYKDsdUWeYwGOpPXaCW1RDlsG42onsi+zJ9rntNxf+IABX7CnDWILQbqjD+820NLX9ZGJtqNtJLCBvi/stCyGfPiSrr6BM4D+rHuGZtRJPvyh5R7I+IF07sHijyzdk9CX+yjvh/Q7OYBjfWUYkKaPC9TPzQZnk+432KqPxtSXZDt2px6WGuHQ5X0bLHhnwrlLoExPZtam1EZ1lv6899BfwQNcNCvQfzACHrQFAvchtUXKcL9QfVInqh/xly6kK9BGNW7TzCzZ7fYvC+wDF1t2GdFmYsuN2ytUcc54SGNlc4PjhZ8NaGkg/kLNw4OHntbyorccEqSJFzdGXoKYWs9AChoaKc4Tnxonu9UJiUueePEbbDzODGIJDCDpAAwy6MDw86lvyGIgp+UYesliQBkHD/Cic+OsQA4DU6ac4jEmXbpwVlkNkKQ7Z24oLKkhL+rvca6hBffIRxjw1StucsdXdTo2MxW4KTBg5QHKgJZNjbV8BF1YzyxeXnfkITOTDR36R3risX3ABycXL4X8kpb/y3PjRCbefPh4oF2AvbcZ8VWe9n6hHDc/Xl7xftN2eCHixYWHSRxMwsfbDuXBjjQG924P1+RJP8/Dxmg7vBgsM8AmqP55CPF7dGah4N3maws68hBkVoJ+w9wKjQT6AgMx+MODBwt95M53vvOCaZXUqbcn4rx0gTm4HDbsus6DmnpQf5A4dIQnfZhDv5Kn3oQltLL5Oc95zhpNSfd6Ex0b66KD+pQX0t4eXs/oQN9lXyqC9136My9RGiyRvxF9FweC7knS2/turGcwVt9FB+Vn7Z4XKB7C7J2EXThZ1A9ZouX9EF7E+VILPV85eZnEUUmdaQ8Y6AjoQBr1S/0zGOHrFS++yFUAewbzfLmi7bAMgJdFvuLQnnhR8X0K1D+yOsOBx8sHOmILMwFwAtGe3OG3FL7zQhjRn7if+H2V+49mAEGnA/nMpFFZr4dYJ1HeVo57O8FhwYtVDNyz4/0o0kzFJYfz2AEf8vWy5xtK7xiel8H1ngUAACAASURBVM4HWp5HfJBghpnamdOITybfdXaZ+nBBfiyP04AXfQZ3yJa+OkNPe5A8vSwr7nnsccQ+HfBkoO0vuHpZ5v1APJFBee2BRV0xq0tLhsQ7a489zHn28XLOAEaDWeTwIi9beRfRrBvyhDO6s2zD9+mjnAZf6K17Cpg5LfogU+8RbjsyiHNofyxoGSw4nb66Q6/6c3wZVFBG2EOnQS6DV1++RJ746ExZ3UfEX7yEuRxv3EvkFKI89xHXFf05oBEv6DSriAEiwcvwLqs6YGYAzirPB7uIGzZTL+ybonbSGA/B2wBx9R2erwxStVwMDBnkExzPlmABfsjgGSJcJINyq2BNP4eeQSlOE97FJJt6UDuJNmA/fVHvCLz/UjeOi3RDdb9W3HWPaWst/l0s8kOW1z37+bh87NCSDe6lchIz84lnKo4LcBRfjQfUfuHFvZmgenH+OKTVTmhPfp+UPWN2OK/YxtQWaFNyMJBGe+VA59gG1a5wSOteAV/uacx4YbmLL2EBLwV4ydnPfVizk8hHLoG2wHscbYX7C8vhFLj3wEO0vq2B2s6SOFyQL16+p5HXt/owzxtke168V8P+gGFMRn1yuM3kebuLunncZaidkIaN2uAdfuDNfUiOJ9oE+Mf6oQ1pfyhNBnBdHD/4Qg+OrofwJX9fDCcYjN335+8MNecVr0bHmUbgZ7+mwr2cx70xZBDGNOK8xNCQ5Z2mcZGmFyw6uhpcbHjOL+oUG2YsG/PnxjM+Mc315VoHMiKt5Eb9ice0HtZR94hzzO/pEfUW3ar6S3c/RzsznbI0xyDiwTRI0rhBSXe+EOmhwkOLduV2uYxoV4z39Ik6yc6M3jFUvuRkemW8dROPeI7JFW/J7+k2N9359XhGmox3rEPFofXrKGMO70xelhZ59eJeT1ldOW+vC6+naJPsjDIzPbdq2lxcvK5jvYuH6pm49sWI11PYb1WcpFds4wwcNcvJdWdwoK/m620fEfPYFokzFZwXcr5i6zmLHpppitPbXy7Jw2mIswWHmQZkLEtjAIPTEj7wxsHrabJPtJR3mTzr0YWZbdkMNZeHfPgjX4475yu9HDsGzyy/4OWdgaXbxbOCWVQEnhmeJx5ykvDllpdqb/uyLZ6Fud6jhDn6s+dNdKAyCxFHJjr48lBk4RilHAEbcFJjJwPmuIEkX4tZYqHAII2BJB+HYntidh0Bhy4DB8rilIK3b3YtXqIH9x2DM46gNkG71XIN4cPHILVxls1E+Vk7ZTCPrbQFBikxSFecUhr0M2UfXs5ferkt0h+M3bEoGc0BOjhoZB/tkmVVBJy/OCI8eH9QeyaNNhzbOFiIFzyoExzd1CXYC7M1Aiyi9kR7pT0zW4jBqhxkXg6sCY5HxBqcjzzyyGUxMGE2EvpEbNS/GCyyfEgYqa+5bPVP7gfK1/1AmKCXsGM2lDZ57unL3izwpU3Ck2t0IrgOKs+sDf24gg8LBBxifHSg//iMeT6CgSEfEuSs0Iz3qXaCPvQD6vbwww9vcrTMt0UsCH8cB3yAI/Dhwz+Wkkab11IxLQMmHZuwm3ZP3bnDiXx0URvknuP7LnHPoq35Xn2UIXBvob1qU1rSvB7oh+ADb2SgA3UHT2bLKaiM7lXggvNU6c6TMuDBR0jsgE7PDvEjX7y4h8lppudTbDfw1zMNh4V/bJU8neGNQwb+yKVdqn7Ufql7zQR33XUPUd1x3+aDL+0qOniQx+wn2iL5mvmje4TaD/zf8IY3NNPBAkcSadkBTcSyFdzGYb+Z2ULFqaHF61ixPNh101fdqowagHh5WU/zNkE6X1P5ksuXdGZewIcBtG54fH1Wo8va0yoNbxXaTFbEI6ORDD+7/jE/4zGVJh4Zrl6XU3yUH3XK4pkNGf+xtrQe3Xoy0IeZU7QdblBaJkW6lt2Q3nO0uI296zn1HfXL6kT8HW/hGfMkEz7K07WnuVzoXG7kGeNR56l4r3wvfb383HZdRzuneM/JH9NbeX7mOqa7frFd9+pprI6k95huc2xzmqwtrsqjh78wmcIls8fTMpwd793Rd6uU9XrQNS+rTHuOs1t8k/NeO1rVLvjEI2snrqeWOmR0vTR3CEMjmVFfT9fLLTS8kPrgSYNfp5eOnPVV0NMkS2kMSDV44eXWv95C64MW/7Ng1Jk4H318Y0TSsvYtff3MfljYSsAZ4g4Rl4UO6KRNIeHB7F8GBcxWYdDIEgMO8NISTNnLskJ0ZBAAL9Eig1kx0clDuvRkYEXA4SR+rht1I2dYrGvaS/xK6zwyfkrjzKCE2XJysDDYUVtgoInDy3Vl0EoAg6gL6TgzhHeUPVXPlJeTrAkZAvUsOUobs0+YihY9cRShE3WPnRzMPuBeEDchVTnOksNGnMwAlYMFG2kX6OaOl2iv85BezBRgCQs/E6CNgAkHA0cG5ThhNVNB/GhTCgxWcc5EZ59jo3J+jrpJH2zwvFhWNohecjLn7FLJ4YK+RHtmFgZ1QBz8mLVAuxKW1EHkPdVORB/19ntCtMNp4+wx9PbNttXeaCfaUyoO6v0e6e2TGafMvGGvF2zkoN1RnvugZsFpFg/3FY5eQG/vE5rRlN3/xEO2xrolnzzde6mbDEt0pL/Q7h03lY/3G8n1mYFRF8nx9MibPHc4ZvnCQ/hhy1hbJD/yibpEXcewFe2+cN5vnC2qLHWIWMExHiuXBuM0kV75amix4+HdZFo8e43gwaaj8HVFN3am3Itn5O26uB5jdFH/3Y1ntkd9iWdpc2QLL5X3Dqs855OlxTJe5/E62uN6x7we/qSrPjJ95tgtHpxdrpdV28HhwteK2HZe8YpXtLJRb7dJ/LO0THZs79GWzN6eDlFm5CVZ7uSM/Ug8vI4zvXtpUeZ64o6vymdprqPjGG2IZWN8PTr2yjjvWB8ej3nOb6P02yg+PVvnpEsHb2ex3HpxoVzWTiM/4n5E+ftCHBxuMAy+eNaxzIKBCzNaeIn1vrG7tsY+R1wHvOO15MV6cj28jNOrjJ+jfKfnZVl7MJHOclyfWaI/kUGngUSPt2zxF3AGUxqk8KU9Lt2jjP/pggGKnCmxDSruOEhmTFO666p9FDLamMYA2JeYcv/H+cDBwIOBEwNkHBDMhuGrtexATwbhHHy0YgDFjBVoaWssMciWvICbZhggQ3irb6KjBhNZ2+i1CdnWK0M+snBkuaOFL9O6X/A1X84WtQUtdcwGMfDU8ohMLz7CgE8vyOZM56zMWBuHXvzQGecYDi8cW8xQwLlBffJ3F32Nj/LFn+Uccg4wO4o2Ilrel7XMNtNbacKDM+2AvZFYhoqzhlkraivIyhxzLF9GDvXBQD7SRDk9vHqYOb3r6umaAU8aM44UvK16mmSBPf0FJwQDfDnjtDQv4qZ2onTxdznSMeqdxSMt/TPOIvJlOpKLQ06BPVd8NoxmS0XduY9qiSb9mfrCWUt/YWYFjjbuK9wTwYO25HWZYYkOkiMnQ5TLPdfpYr7sIF3LsbQML+IjGXG5kvj3eGcfCrxMlJPpG2kiHsrnPqKZQThMeyGWdxy4xmlKexRf6GVfr2xP1nZL3++cLaqgWLFxoKfGoIYQz17R5MUblOhFx42TX5mydt9/G8mMFzbdZQAdebiOsUH29Il2rbdBZnw8racr6Rldpgd0ws7P0EbbY1w0jnPUuadH1N3ljenv9ez2yA6liX9sA1E/6J0n14qLJ/Fe2+GlhT0//Be80j/amMVdn0w3tzGzVzpGPr14T4b49ORF2VN0PTlT5bL8jFeWlukY6xaaXhtS+SnemY69tMgr1gvllNY7Z7x7NsBDeZxjiPrE/PXGJXdO+ahDjItHxCriM0dW5AWPnjxod0fGKvpsNq3qXmcGt0xh1nKYjZQf21mMS1bUaUwH5xHLEc9kZGU0YPaBBfuo+EsySzg8jPF2XXSNDBw5DC4YdDBLIuPh7Y6XezlberQxPWu3mc3aDJKp6ThNCJEXTgX20GCJCDNUslkDDKIoz7Ot/UVksA9HThz0wp8v5yyNYNDIu5UGyNqnSviCFbpoEMfgS3usSEevD9Lk1MpsFd9oXy9Ouga+fHlHX4IcPj7DQLq6Uw4MiHtdaH8W+EgudcvADn6qA+mancdsc77xWvFor/QjnQEkzg7uAcy8YhkdM5jcLudLGdmE7gyOnb+cS5kusg367CAfXTTDAOcvbYplYXr/hwb9kM3eYOzvBh2YuuMqYobNSpNsryenl56yIeapvM/+YIZTXIrT488SKZwt6i/+y2/HjZk99BXaCde9INuizRm9dPc87jdxKZY2ikYuZeh3mgFCv/CZXDg2lAet+kuUz30V27lnaHkRtlFvam+0H/0VUlhk97bo5PQ2zbX3VXTy+o960d5wHiJb92fHUs5d1W/E2eNefzFdeaoDnaVPjHt61Flx3Ycki/sKS/yye3bkoTKZXH0QIC9iG/nsK/H9ytmiDuEdI+toqlyn84YTK195pKthUTbKY9CsdYzKd1lKy3TytNhAoz5Z+UgzNx55eVzXvTMyYvmeXOjcLuiEq3hk8Sn+mb5RrzH9Xd9MlvQWnccz+mi/26ay3oakq7cdpUXbiCstXrvNTuN6R91UxnF33WJ9Rb69eCbH0xwH0iVf50ib8XNssnznNUWb5WdpLsex0XXEK9OLtCnevXJZeuTlcV33zhk/6RfrAh6r2Bn16snK0qNsxyzmZXKytEyO86XMnHKOQ+TpPHQ9l2/ktd3iU21/Kn/M3ljnoo3pMQ4dX0PjwM/LZ2WiLtnv4Xs8NI1f+Rp8E2dQIXlRboxDTxozBRgwMWhhQ/GMTrIYiDCQZbND1svzwqz2imx+xcpHH/8DjNvh7d/l6Nr3WNC6/sweMABzBlD8nYoXbxxGbMbIBwSvD2QeOOxNwIAXXhxcYwODYp+9Ai2DZPAQrfQXXqQz+FGIg1ivjx4P8XI8pnASDrKNWTrCnjyWX2kTSvFnoOMzkrCLOoSesvrhgdOTzuCSQRF1zcwQHxjBkxkEOKfANW5S6XjF6wwPtX3kMqjWzCYcq7IPPj6Yx1ZwECbS389c79i5ebXoaKO+v41wcD7Og8EtM1doX3LSyQbav/7Gk9lFGgNubTjLvhds+Dt2r4h4ZfGoq2hiOnHqHnnYTdvAmeqB+md2GOm+jwZl5HCjPdMWiPtGqPBh4E+bop0w68XvB7QTluPg8FE7yezxtGiD8tDdfxIAb5xuBNoh5XyfIOrLefmyFe4X8AITfr/MPYNZMN7GcbpgK05u1S0zKsEKLLJ7Prwox8wLnB5+X0F/Zsqpv2fLkJAT7weyAdnUA/owy8d1RRcFYRH5kN/Dtpe+ZLrzQksnla5ywkcyIz/lu6NR92yXQR3hUKKtxBmFLtP5c617hNus9GjDdo/vV84WKitWbq9inc5pYmOEp9K88Xi65GaNBTl+iDbrcCof5WV8x2T26HvpUReP6zpLm6ODcBat4yueSnMZvXrLbJirW2ZLxq+XJltcXo/W02O7EZ8Mi8gPWj+EY7QlxkUnflM6SyeV6+HvfKTXqjK8vqOcOZhEjLJ4tDfGY5lefkzvtVX4ue6RfxaPvDOaOWmRT6wjeGRpU7wp423X41Nldyc/2gOvDPcoIysXaYhHOsXHMJqq28iDuA6XGWVn+lXaWgTAbAr/Hma8/PYGUL0ypCOPl36WJLDMgJdRvsZmvHj51KCJKe4MjlhOwmCGQQMDb/hoJgJ8PGS2kcbAlnIE9sBhA8sswI8XfAaODKwYYL3rXe9qyyoYBDNgZuCJfBwRGgwgg80Y2beCmSrxr4nkSzfOLN8hsNGjBk9Z3cAffZEJdjhYGPixvJo/7rFJpP46A04aFOMEQg4DS9JYOuW0DLz818MRN+mLjaoPNk8Ff2zjLy4MiJQnLCOfmB7zs7hkM/Ci3lkOwxd4BrMMxMCBuAaJ1AW0DLjRjdkJOElYVkU9EqfuXVfJBSf40baYFYQTDt7UM+k4xQhx8E5a1F22RpvRibZEPTAQxWmDE406YDCKXtBgH3po7x4NZjM5wojZTiwBoS5xeNB+Kc+ADmeaZhXgTGFg7g4p8eVM28YZCT0fq+ijDKjBgX5IwBkV26j0IJ12ioMHzJg1xWwXHyxHfDYiLhuQQ91R77RN9GBWBniAM/2FQD1GPHGeoK+WLzIIdmwoR7sHB+qHDXTBXUv4wF5O3KydrGIn7QR9abcENv1FJkEzPdxBQl0jkzLMlKNPUg+635Gney19ABzAhb4DX+pb/Zh2j904MtS3uPdBD0+cHdzf4E0+s5yg5/6lmT/w515PGXChHSLfZ9vE57ZjTVno6RcsGWXGHbLowziACNBkM1vIg1fk3wolIbYD/YUR2ejNfVUbVVNcfGO5yBo8sJ/7BzPU4KvlfbRL2id9nja0ir5RjuuU5W3ntP3O2RIrSzdab8yeFhtO1ii9Y8GfuMrpWnKdH3J0qJF5PGt4Xj7TJdoX43M77VS5iJfoI/8Yj3xj3Oll31haD4Mod5V4pI06qg6gcx3H6qbHM5YhHnkq7ulqG/AVb117XHR+jte9uot2q1zUOfJzW6MuGU/nG69ls8pFDHr81pveqyfxWzU/w2oV3abkzeUV+WR1JOzn8nQ6+E3ZGnVYjxyVie1iLu+5dLG+e3g5XWybMe74wk88V9Vpd3DbW2WnbJzK7+lNOW8LHo91FmlVH1l6T57XN4M3vcAfffTR7QU0/vpXurFnBANIXrA1rR9eOBoYpDIrgAEdzoaxNfGuF18YFfjy3gv8mYgXbF6G2S8GBwqDEt9HhrIMtPyXyz1+WTrLAjTrgEFMxFR1ITwYdDNQZ0CHLsjWgCqzhYEaL/yUZ+CkF/+MlnqISy5cZ3jwNZw6QzYDNjmtcCrJkUEZaMfaJnlj+VEu7UWzDjRYhoa6wXGEI4D6YRBM/TJ7hzohnfbBIFL78zCwYXmO9jCRvuDEvjwMirDP98KAhkEkfLMwZYvycXJoMMpmtgSWyLMEhMEsg0gNJF0ODr4MU+/D1J8woo4UaCPUD4N3+tIxxxyz2DHMfmF/FQ/iBQ4s22MpELO2ODyAOQ6tqcAsHZxyYI8jQE4DL+e4ZW1C+RFft1v8nIZBOH2LwSz1L0eZaLnfZHs0+UwXaOlvMeDMufKVr9ycH9iGDA4F6oD6jDo7H+WpTjN7kEFdaQNvytMPaAv0U5UhDg2OD82QgvaSl7xkc0Ywkwc9qQv+EISjTH8tirhQLs7qoi1gK7Q4DTgUaAvci92RRh953/ve1/oQbY6DQDvEwchSLcIYPsrnHszSSZwe8b6LbfoLkfRZ7xldvA7oAzx70FVyff+fOfqLH44o2qKcXrpnSlf6CThO4QE9Hwa0TK7XN9aLwVYtt9/8+jlWgDdIXffOKpvdSHp8nFekiY2LuB9zOgA0mT7RzvXGex0mpk/F58gfs6OX10t3eVG3Hq6RLsajDbE+vS7m6NXjF9uMx12G9FObkV0x7vZGm2I86uTxzF7XR7SRp8djXsa/Z69oM2zH+I7ZNJU3xXcqP8Mn039Kjx62c8tFuqj3VDyWV3xum+iV36x01yva5jLH8jLdnH6s7FQdq2w8I3OMb6bTVkvL2kSGR4blemz3+wXXvr5ccV6s2Q+AAbrW4ZOnqekMtnwKO5jyAh/LeJoP5HkRx2FA4CsqL5sMknmZ1FdE8lwfpltDy2BGL/fwYS08QV9NmdWBLP2qNfJVvBUaCZp5IBLk8NLPl1kG8Qyame3BDAH0oS44uMYJBBboq41lxQebdMgZQJyBnee5aqQrUAfEsU+Y8qWZL9rUGy/2GnjrjzHOSxvjYovsYJAZl8YwSEIOzjGva9oLGIAz9vPyj80MihjUMThDN7AAa2gZ2OrLsHACT+0lwgyCGISFt0/slP0M3mibotMMJXSlPSmQDjbM0sBBh658aefA8eJ7UVAGfrQ1ymEj9Uyb9AERNoCjljBlg/LY9lWHavvE5URCLnZiH5jhgAMz+hntR/086++yX2fqlQEd5dCd9kk58uHPNXXtM1uElfOifqhPDuwEb/jFTVtj/xIv5NAH5NykP2E7cbVdaMCJNkKa+oraDe04a8OqJ/Slr9GO470KGuRRh9Qn9Uc791lo0ERMsZUlW5rVI1mOEdfUF7y9naCDb0YtOto5cnbs/CW6eIm32gZtirojzuwV6kv6E8+codCiCzZSFsywUbjBU3u9aLYecqkb+gX3YeqU+lFfdv10TTtgWQ360B/8/gON44hM6oW2CG/NPqOeqW/q2ffbjHg4LtimviTZ9BsOD8hnthtn+g32Sye1M+oKudk92WUKU91r1E9pZ7RN7juZs07LKblnxnZLXXJ/hgeY9NoitqKn3zPVx/Q80fPGnztu6xpgtnlkv3W2qEGq/nSTGEv3unZ6L6N0P8e02JiIe5qu4evXma5jOnne2HUmw+l7+Vl6ljYmO+IYacfye3mZDlkaslZJj/JivcIv0kR7evFee8lkSGc/c52lS15mZ5aW6ec2rWKf8x+Tldm4XpmZ/mNpY3p5ubl0mawxzMjbHd6ZvDl692T20qOcaFOMR/rNiK8qc65tUddYLsadfkonL9u7jvK3Uzzry+iftfPdtd9lca0jOl08T7rMrTOvz6m67eVn6VkaOsW2FeO9tjCXzmWoDOd4ZC+/UbZscHyz6wxz8VoF3yi/F4/Yup4qw0CBQQNOC9kuPQ8//PBGdsMb3rANVB0b0iNWjqPyJUeyvU1yTYjtlLRMVwbaHNrDQ7yRy+wYBj7M+JBjSPmcpVvv2vO93Nh1D1+XEeVGzJzWbfb2A02UFflE7Hu8YnrGu2fzHIyiHrIv6htlRL3UNrJ2oLKZrMi3F3c8I7ZeRnq7TLdF9nmZrO6iHZnMLA2+vfSoZ8/WsfSpOo35Ho91mtWH2y1cZNMcuxxf5+/1EnWUvZk8TxvDxXlIX6eX7lH2nLjjFp36PUzn6LpdaPbrZURqIDSg7FoNPusc3rhUfuwcG67Kq5FJlvP1a29QrmuWrrRM7zH6rNH2dJC+WZlV0iKOsWxPvtfZlE09HlPlxnTpyY9l1hNHX29L8Ih1KZs4965Xta+n6xz8srJZOaW5PRldpnvEIJM5N21KJnzm0MyRl9mscnNkzKGZo0eGaVZuSt5mtv1Mn15a1DO2j5jf4zOWviqPOfSRJsbH9Nkuedg01sfd5o22f4qf8qVfjAvj2M57dKJ3udH2rG1Gmli3U3ZkciOPLB6xJx6PqXLCJrPLy8I3o43YU0a0mexV0qb4MNNByzL4wuqbuWpvEb4Az3W0CM+svtx+2aA0BhwaXI/p/K1hM1FmrzCjgeVPmnHAjB0tn2CWFDxchznXwn0Ovlmdeb317JdefnZ5WTnyvd2IJvKK+vd4OT9osjY5R6cMpygz2hnjGQ+l0SbifSHGY/ko3/PH+qfKTdGILtrhcoVn1M1x55oymbyYlvHxtDGbe2WnyszNdxwcm6hfxMTjPXtdh8jb5QrLnq1ZOuV7ckWvfJftZTKMsjTxcz6uf2aL0jLd94W0/XpmS6zArCFmaWPlRN87e9nYEMnzhjvWiKEd020sL+of5cb8KT3m0q+q06r00qOnby99jv6ZLp6W5Ue+vXjGR2ljfMfaz1xbo05ebkx2LDcWn6NLlDUVH5M3N29Mr7G8ufx7dNG2jG4z5UveRsmYY09m43ZJ2yicor2bxTfK2dPxrD2Qltmbpa2ir2RxnjrgG3WL8UgT82N8SteMPksTnx4evfQp+b188eM8dcAjky87HHfh53k9TB2HMUx6NkylR/6uE04O9nuRswKnC8sLmEFCuP71r79ctjIHK8pkGMl2x8hntMR8xf3MNftLaPNfLTtAX8JlL3vZtkTK5bvOjSiELL+nP0VjfYpdVm+RN3E/KBtlOX9h5XIlL+Mzh9cYzwDNuqJjNsveqKfb5+1jStcoa67Csa5i3Pm4jAxz2aQysf5k25Qtme5jemX0md5TdHPzvc6Eg2yP8chzDBPRuq1RluTE81T9R8xdj56OvfRV6sL1jzpHzOLMyV4bi3pt53g5W5LaW6WBUdzps+sxfrHjZB0uUbElZXznpoln7CBT6T1dPL3HM9NtjN8q9JnMLG2OnnN1ivrF+BifLC9rO9BFvlkbie2IclP2T/GOcjOdx9LmyPfyUV6Mi7aXPqaL50W9Yly0vfS5cjK6VXXfDB0yvUhbRdaqdvRkbmR61GkVe1bRY4xv1CHjO1Y+o9+OaXsKB8nhrAO8PO40wjLqF+Pi0aP3OsnKxvKbWYertCen5VpxXcczeo/xj/i73WO4O2a9693BLNZJpiczXJjJwt4CclywSS9/U4r7izguwiRLizpHucSzQ7iNYcbGlPozDvTsacGfWdgTwesx6uD1l9GN1a/z6mEa5QkfnV1mJl+26xwxE/8en6h/Vt4xd3mZ7nPTotxoG3Edjonzj7rGeKZLT25G27M11mUsu6otsd0Kb8l3eWOyx/KijlPxiFOkn8p3esejdx35T2ES6Ym7TvE61kmkF7+sDUVdMtmxPPGxesvwi2kRK+K9Q/ZEHmO6bqe8crasWFtTN4Ne48zKxc4kVdbT2HpyY4eJ5maysrRYbjPjGVZT8qLOMU75LG2Kb8xfL86RTxbv2e3p0YasDUWaTJbSnLc2xdKmXGPl5uStogf8Mvvnps3RRzQZZuStou8c2kz3VfTcaNo5Ou+OzK1m7+7YstlY7Y5u26ls1iY2GlvJ4KwDjHrXjl+mn8qKbg5NVidZuSwtK7tejOaUi/c/xTn7gV6eF/V03HuYub3ZdZYW5awnPqVbzJet0d6ISYZPLOP6uhyus0PYZVh4WsShJzemKy4bxSfSRf6ZHdI10krPTJYwc/lO5zwjXlGO65zJmuKV1XuUsWo802OOzZKjNiHd5+gY8VtF57E25XwyrD0tyszqzm3zuumVjem7E5+D0Rwa6bA7eLjtU/hn/W8J9wAAIABJREFU7YnyEfue7rH9xHgP00yvqbKZDjEt4kbcD9kW7evpuV3Ty9myQTU31lAlItJ4o+w10DH1Ir9ex7jNbW6zeMtb3jLGak2e/oowu8CKhOzwrV32+U0gv0XLAr9uY/rsIYccsnj0ox+dkSye9KQntfyrXOUqC/2CEMKIZy8tZTqS6JjrmunI7PbNL9yY0psFaFhjzfrwbMd/yvCLwh8Pu55fZoQms2OsHWW6KC22H/2pgV9f9nQc4xfzsjqINB6P+pAX0xTHMfSx4Td+Nxime2c72ztf2htTsL903HGLUw9r2/mVoHZg72G3qu5jdlVeIVAIbB4C8blHPEuTBvGe4prFvBifwwOa9ZYT/426//T4KD07e1qvfIaZ2xztz/KytI1oJRlf0nT06jA+C4hnB+WVrms/j2Hjerg+UT+3Ieqb1cmUPtG2TN+ML3QZnpmNniZefo5pTu9yIi49vplNmb7SP56j/N2JR9t68UxG1Mvtz+iV1quvsTJZu3LMIk/ic23J7HDePdlO09NdZaN+PXrSV6Gdy8exiLhkPKYw8TKub9Q9kxVpxKsnk/yxOoj6O+1YuZ4e0Z6IXYwjv8cr6rYd4/v1BrkbWWHeSLIbA2mxIcX4qo2N8mOdAH5T+RkGma4Z3VRaTzaD+lve8pbNAYSD4VrXutYurJjai6OF8OY3v3nxqEc9aknjuD372c9u6Te5yU124bHRCb2bD46W2972tu3Xi0xLzuzmzwb8Pu6+973v4lnPetYuquEQYJ044XOf+9ya/KydiGAsbxchlpDpqOyxvDGeMS/yaQ6Sj31scfDBB0fSNB7LexxHy+1vd7v2K0f90jUyQdbjHve4xTHHHBOzWpy6eOYzn7nm13qkrxfTVEglFgKFwKYioOegn8cEev+O95iYF+8Foo/pUV7kq/xeeiw/xT/ST8Uzfkqbe85kYM8Y74iX0yuvx8OxymRk+pDm5bgWf117fuSBHD/IZ38BQkxX3HnM0ROaqEsWh29PV8nJzq5DzJeuGY3bkdkkHJUn3TKbM/49XTK50EZ5UfdMxhSvHs+s3KppmX1ZWuTrtko/r3e/nsI68vZ4z/aYnuGapfVk9ezJdBcPtzHjq/wxHrHcKrSxbIxn9ittFTmOTZRBPPIai8e8yM9lrYqft4nYPqIcxTN9PE3XnLPrHt99Jb1mtmxyTU7dRLIOtqpKktE74wjAsfHa1752catb3WoX9lmH2IVohYQpm8k/8sgjFze4wQ0W7KKvTepchPKVhqNCg2rpi0OGjeEIrGdmNkbs8Btlm9sUr9HjgAMOaHq4ntIdJ8O5z33uFo32Sr8vfelLi4td7GKN5le/+tWaf9tHm8SXc8yLcafVddRf6Sc84e98r1//+tcXO3bs2KXoHN6ZDOn5wAc+cPE3f/M3i97MqazdeNovfvGLxQ9+8INWzzjgbn/727cNDXFUfeUrX1lc4AIXWOqMo+XKV75yi+PYo62Rf9yXv7w44p3vXM70oj+84Q1vWIPjKnbuAlIlFAKFwF5BID7/UKJ3r8sUzO4/ohvLc15TdFP5zmuz7kORr+K9c4ZVltazLaaP1UmkzeTMTRMvP3Md0yPmjgPXfkCruK7n6BNlRp3gMZbm+S63V2cxPdNVNNI/xpUe6yTGx+yfo0eU4/yjrIxfZlvGI/Ia03u9eY5h7zrjvYq+vXrK+E6ljWGS6T8lW/wyezJdxuRn9KumTem7Xn4ZNhmvMRyU19Mxpsc48mJaxHNMfqZvTIv8Yn4Wjzq5nsqL58yWjPd2TquZLZtce1nD22SRjX120/N06YB+3ul1zcD2iGGJDyFbqoGD4f0f+EDLv9lNb7r4zW9+szjiiCNanAEs+Z/5zGfazBWWzjDQ9cHwxS9+8UaLc+Kb3/zmLstWXvOa17T8W9ziFou3vvWtiw9/+MPLGTDoiN6f/vSnGw0OjLOd7WxrXqyFu2ihcztbwZkhu2E5vqc97WmbDtjy+c9/fnGZy1ymcRYNS4gI5zvf+dpyFn7lqGU60u+www5rNPe5z33WOFpa4khw+yCD9znOcY7mQIgzSMbscBHQ8ccEMMRRFHXN1Pntb3+7+M53vrNLPYoWnlrmpd9dZnxwnIAjy3yivrQBZgiBIw4UwpOf/OTFc5/73FbmRz/6UXPI4WhhWRnhnYNjRTOnsOeCF7zg4uChfT7nOc9pjjqcNsxIesxjHrPLgyvTr9IKgUJgayKgZ1l275+jsT+r/d5DWc8jHvPFP9IpXfS9/Dn6rUozR5bTzLnOdFjVNuTEMjGeyVklDX6yh2vFdQ0vyXRaleHsB/SK61r6OG6Zjs7fy7hOUR/XTWWU5vJd3yy9p2PUOcbdDuVJvtO6TlmZufIzjGRPJjfqG3WK+Y5NT2fXf9XrKG8qPod/5NErM4euZ/Ocsk4zh75Xb5kOU/yyMo7DVPn1YhblRjm7i4m3x6hjlKX8XvpcPFS+19fEx213mRGTuXr1sOqlRzz2lfjv5kfuK9bsx3aoI3DODqBhkKtDNP4bQr9m0MoMAJZq3OMe91jyVPlb3/rWLe/jw8CWZUHf//7320wDjmOPPXZxpjOdqc0meP7zn9/ScK6wv4rKn+Y0p2mDZgKDY/Rx/XCwEChDwGnjunP93ve+t+Xd//73X+r3xje9acHxpp0HzgIcAX6jiPg0JiE4jbIcY9KcBh0IzCCKGL/iFa9oeY94xCPaGedLxJ1BPwHHgPi2hCHEuNJj/piNMU98M/4/+9nPFte85jUXJzrRidrfDjgzOwocvY64pq7ZW+dkJzvZkpY46aKlLuChZWFcc8gu+FLP17jGNRofnCDwII1ZT8Lqcpe73OJVr3pVy3vCE57QzMfRwp5ERx99dHNSQXvve9+75eFAwQ6vJ13jzGG/IJwyBx10UKOvUAgUAtsbgfgCR1xpup46g8AUDUtL4jFWZhVa8YllVo2P6ZPlud3xeqxVZLyUNgfLsfLrzRNWlJ+6dlwjveRD47qsB58eFuLt51jXrldGr3w/93R3O3Td020qfw52PXnepnpypvTycuKXyfO0iO1GxKdkjtkR82JbG+M9l1Y2jvGak9fDuJc+F/ee7Km66ZUjfazsWLms7Bh9z/ZYr2M85uStyq9HP9Vmevk9PMd0j7xcp3gtHPfVcy0j2sM1y4wPgjbo3F3xGkT7gDwOLnF24LxgoKoZAWrokk+HieGXv/xlmyXBrAFmCDBwJTB4vtOd7tRmc7DpK4Nc9i0573nPu2RBHvupMOPkpsPMF3gQGBRf+tKXbtcvfvGLFw960IMWN7/5zRevfvWrl2WPGzYyvdSlLtUGzc94xjPaBqjwY+aE66llRR/84AfbbBLytPHuktnOCwbV6J1tjBppYzw6KjLMWQZ0iUtcounJPiKiYabGqU51quZY+shHPrI4/elPv7jSla60eM973rO0hWVDzI4hMDtDG77KVpbdjM0akb7Q41zi95Wvf/3r0yVj0Lr+ipN20pOetLHSLB05w5iNQ2BZDk4vBRxEd7zjHZd55z//+RdfHpbpaI+Uww8/vDmPcKY9/vGPX6YzW4nAsjbCC1/4wsWf//mfL2esoMsTn/jElgdWcqoRJ4+2xmwU5SMH5w1BeHNNX4tYClN/QECreGOyM67rOhcChcD2Q8Dv2z3t59D0ysb09fBaT5nsWR11WTUeeca4+MX0VfXP6LM05MX0KHuOjeLhZ+fbk+HPA8mN5znyM5oo023NdMvoY304NhGnGHedxvIy3cfSpOfu8pxjb7R/jl5jNJuRt14cxuzv6TlH1nr4Im8O70yv9cqD13rLrlfXTP+ptFVlrWLTqryndO1h6nJW0W+OPNH0bOmlr8J7O9HWzJY9VFsMAvkCz2Cbg2vSNiJ4J+E6O5DDl3wGyDpwPvg1cQbrKs8sg0MPPbSpeIc73GHx85//vB04WggMovFcagZDSxwCzhOWs3DGacI1g2YCTh/xZ6YC4W1ve9uaWRM4JQjMdID3ve51r+asQTfJ4loBJwc8ySMwmGcvD5Y24fjAbjadxRnEjAtChhGzJdA15kX6GEfuec5znsYXPZmNIX0++clPtvSb3exmzZlxxStecfGhD31oaS90LI8h4NwAc83kYHkVszs0a+TCF75wcxixxCvqKHmcCf+7sx20yM6gMkSz8k77gWGJGMvAPvvZzy5Y4oQDBicKGwCjH9g+4AEPaEWYQYLzCMcY53e84x0tHUcMjiKcay960YuW7HGsccAHrHC0EJDD7B+On/zkJ00mWLFPi+qdWTDwoy4/8YlPtPynP/3py3w5hoTlmJ2OB/KF3VLRuigECoFtiwAvc1MvdKKJ5/UYHXn04s67RzOWvh7dxspMYaSyGV2m55QslXG+c3hHvpnsOWn+tVXX+mpL+fg11vXN9Ix6TcVdx4hBpn9Pn1565NHTZ8yWyMMxGOO3Ks+MV49HTI/xjBdpPVs2O31O3boOq9JnZXsY7A4OznMMsyh7jHYsb3d0HeO70XlzcJlDk+kVsVwvJlPyp/Iz3VZN68nIbNyX08rZsodq933ve9/ijW98Yxvsc3BN2u4EHxxmg8o4mMSpcec733nNgePEDy1x0eCWgS0zTHAiMKjG8UHgLy5nOctZloNcOTrIu851rrPGCcNLAc4aArMYxFuzE0hniYnSX/KSlzTai170oi0NpwsB5w5x7GJQT8CJwzImpbfEIch2ZowwI0dLWO55z3vu4mhQGRxPzORhXw9wwBnmGArvDGtsxLlEYPaObGHWDYE/DZHG0ioCTgHRsBkw4e53v/vSDpw+OBRwAD32sY9tjg/0YpYSe+CoLG3JHWja64YlYDhmtKxqzMkU2wn1TL2TjpyrX/3q7SDI2YIzhjaBU+PhD3/4Uh+cK9Ce8pSnbPnenhqDIUh3ztAxG4ZD7Qm54Hm1q12t0dMepMvb3/72xU9/+tPmrKH94MRi0105oJgVRQCrWE+ke1oj3BlUt55W14VAIVAIZAhkL5wZXZamslneetMyfWLaenmvWg656wm9cnzg+cY3vrFmI/0ebZQruoiFx3tOC3i5nLkyXQeW5LJ5P8ussxB5Si/JnqN/pMnkKI2PG2AJpusJEcf18JgqA17oyIeVXoi4QRd1i/Eer41MjzIzPXvysrJZ+R7detJ7uvTwXJU+03+MR0/uXNumeG9EfqZLj+9c2owupvVkjKVHHuuJj/Hv5fXk9Oj3h/TaIHcP1TJLJhg4X/ayl20SuSYNx8RGhjjA9EEkA9PrXve6TRydIQsaZJOnsk996lObAwFHCYHZGThtGCwr+DXLSeLglVkKGS3OD5YTffSjH22DZwbN/FmGQTj7usAHpwuBATYODdJYOkJgdgWy3R7HQOnwgpblLLxo+PIS6YojgRkzj3zkIxdXuMIV2swK6glHkegzfJXGbCUcWu9+97vb0inC8573vHbmb0TQaTYPs3ewlzQ2syVc+9rXXjoD2MCV8KlPfaptLIwdOFJwbrDEBpxYRvWCF7ygzRaKgb1jOBRYPuYb90pn8v2a+IEHHrimbknTEjFsow5Y7qSyOHuEYUscAvuu4BQBT9pUr63woouT5Yc//OGCpVhylogPZzmmuEY2S8JwoiHzpS99aZvFxOyfMZvEL7Z76R3TXX5dFwKFQCGwLyKgJZ848LmHKnBfjPfELG1PYIKDAD35EHKGM5xhZZHY4ff5aEfvGRDtnytY/KB33c94xjPOZbGGTnpshJ581MCRwZLgk5/85LvUsdc/1+vFIDPUccnySYNGbZJ3Fi0P7tErfY6ec2im5OyN/M3UezN5r1I3ewPXvSVzT2C+kbZtN3030vaN5FXOlo1Ec4QXMxJwdOhrPbMVNDtjpNhklh5gnOO10pSOY0ezL8Y6kA+MKQvtQx/60MXd7na3pg/OBwbJ/vD0axwTHqeMfn0cDbrJTW7SnC3MXoC/ZmDwJx3xwFGC84XlNlp6paU3/C7Z7Rb/6IAhneVGhC984QtLx0PUhz1m2O8EfZ71rGe1WSVsfovzgxk+jmm8liON2RfYxMwOHA44bph9A71weNnLXtZmsrD/CDTYd9aznnXpOJLTgSVnkoOuvHAS+OsRL8jMMNL+JqTjqLrRjW7U9L7dsIGxgteJ143bIFo5hoiLFgeM4mCL04WAvLve9a4qusuZ8tD3nC3MhGEDYdpXL7j90MgJxzXtUI4W4jj6CMwak+5q6253IxpC7AfQkKaz6OpcCBQC2w+BrM9HK+bQZGX83rEqj7n0P/7xj9vzCoe1O0Om9PH8MVk4qtkP7JKXvGR6v+zdH6N8xcdkjdHEch7Xh45oU9Stp1NM1/1d6XP5RB0j349//ONtfzZ9RIEvziE5NqbKR34xnumZpcVyY3HXKeO1uzqPyVbeXBnQSUe/niOjaAqBQqAQ2JsI1DKiPYQ+S2GuetWrtpkNHFxreczuquAPK11z1uH8PX3uNRuNytECLza1ZXaIBtFxMJ3x5YVOwfNZ7kH42te+1niyRwgBJ4zT8UtkArMcmO1BYLaFBt7R1kyHVmgIpzjFKdbwzmiZNcLyGG3iihMIuiyoPC/DmgGCE0K2sIRINDhdsI187GXfEQJ/dwJH0cmZxAwOT9fmwixPghb7cdLokHycMjiOdGgmiGzIbJZtnudpfo2jh4AjicFA78DBJ34Zf5YjydEiXjipOJj1tGrQl08cWGzwnNniPKGpUAgUAvseArrXuWVj971VEZi6t4zJHctzvt/97nfbTARf0hrvp+KV2TaFgTaUd5696yk5U7IyvWNaJjvOuJGcTN6qdThGP8ZfeurMRwNmjXzve99bPq95bvMewYcK/2gyxhd9pvLHdJ6bF/Ufi8/l2aMb4+3vNqITn1guYuM47QnMevZVeiFQCBQCUwiUs2UKoQ3K5489DCwZYLPhJ9ekbWTQA2fOQyg+yHpx9NOGuE972tPab6AZyN7vfvfbxWEhW/jCE/nx1yICMzg8j5eRy1/+8i2PZTzMCiGwdMbpWLpEYLNbNkUlsFTHaVriEKJsxeWkYUBOWo+WJTosz9Hvq9lwV7+gzng7H83yYFmU9uRhxouXY7kQAXv1px0cMM5H9saXbDmt+JV2povsEq/GNASnUZanyQHk/DULi42ASdeyMJwsLAPqHXF5j/QSb/YuItAuqHvN7iFfebFMtMfjOJXkcOK30GOBJU7YwW+tqfMKhUAhsP0R0L1Flvh9jMEdf38jeLpfM3Bmrw2eWZzZDDyjJY8jGzBShjx3+CpN/ChHPksykekyuO/znGXmI4EZLq4L1+SrDOXhJZ0iP6eLNI6F2yI6lytMx84RK8qzZwln6YWciJvi4BMxifWV4es6SXeV8zz0U5jauwRaZKG/6jLqTbqey8wUkmyVzRz/bqvj47rFOqPdKp/9TGgT6DYniJdoYx0RRyf0Z0kv+Hs7Fb3amPIyPrIfXo6V+hVYgknEUW1COiruMryNOx5ZPc/BpWgKgUKgENgTCGzsaH9PaLzNZTAlNpsWu1Fm6WEMP79eL3+W07C/CEtL5HRh41X+OIPTgH1goix+D6zlSpIrp8KtbnWrXfRi01icE095ylPaDBdk4YSR/kwdxflCYE8UBWaMKGS2ehovEGykyp4nmuWxLLzzgqU7L3/5y5ujhUH4IYcc0v6oo19MZzIo6unak4VlQgq+LIc0/tBEAFv9uUdLnEiHH0u1mN3y7W9/u81OUcC5QfDlU8qL06MdvyWDnRfk+eH5n/70p5d74ygdB6HLPeigg1qclyby4q/Msf9CF7pQW7rFumvHiBdFORrZp4XAH4W01Iq4lle1TAvZVOdIg5OFX07TnpCvX5Y7HS9tqgemfWuZ1Rz+UV7FC4FCYGsg4PcZXTP4O2Jw0v/KZrFx/+TvbixD9MDsBDmbPZ2Zgsy8c3rtiYUznv0vPHD/4qMAsxr0Jz7SuKezBxl7Wh0z5EsnzYrkXsT9kvuTPjzA9/3vf39jjw7w1HOQJaM8nxkck85f3LjGWa8lp64Xe4jwLD7Z8Hy9+c5nN/l6FnDNYBgMtC+Xyp/5zGdutowtZxIvzjirsp8AoKew9PrSzFb+wqfABw9/NjIA5x4tLLFR+C4LDReqm7vc5S4tWXIYoIOXz7RFHz6IxP1BmFHE88lxALfzDh8cVE/w5o+D0pm/H6reWDqOnrzbyGbo0f/Xv/510wP6GNhgHqwV+BAAT2ax8iw76qij1ujE0lnej5ix6yHrC54GlgroQxuLdY691xuWv6t9s3yZnw0gU7NsXSb4CnveAXnO42T56le/mvYr+gL9sBfULsGAmcLeNigDJtTdAQcc0FhAX8/wHpqVXggUAnsLgZrZsreQ32S5/lCVKGYnsJnq1MELAuX58v+whz2sFecBymCY44gjjmhpLPNgoK0HouTgQGCZCTNc4MULoZwK+iuRaDnf+MY3blFeSgjaXLZFhgB/BsM4LXDGcBC0P8dOsjUDetnPF0L+YsQDnaUpOFKUJ70V56WOl22W6ODk4M9L2cwMycvO6MQsDenJjJU4g4nZG9AIE9/TRTzlCGD5lOoDPNmwV5sHR7uxA8cQLznIiPnEZWumu9LYb4aXVOTSBm54wxsu64Z6hQf8VW/oqt930x5wnPD75gc+8IGNJfQMLrRfzStf+crGF6fLQx7ykEbDb6Mpyws35XkJAxcCL4G8bM15iYIGHeUE5MwyJV76sQcMH/zgB7eXNNoD6+kf/ehHNzkVCoFCYPsiEO/rWILjgGcXTg36PANEzp/73Ofacwynhr6gkyZHC4NM0cKHgfdbhmenf20XUv4c4Vpf7JUfy7CEtH18GGi5d+7YsaPNtmEg+/Wvf72V55mhGZ/wuchFLtLiOLX9Ho7zRgNk9gvRRwmcBNJF8jlrsHqe4VlKwGnO8/akJz1pk4se6AZPHAoM/HHcMPjHQYEDKXuGRAz4o4wcLfCBh55p6MA9XThxpp5wKJDHMxe9Tn3qUzdnhBxNkuH4utyIM/Z5Gk63w4e/9GGH6hdnDjIPHd5ZmCkiftAKWz2L+FBDO/J6gj+zPalDAjprqXjUU7rwTvLOQQ85WigLNnJiYS/4ib4xHgJ1wo8BOKMTDkMC7yz89S/WAXlZGunSjTMzUOgj8EUH2hkHtmAvz3bwgFZ2IhM7pKPOal/0Md5FSOe9Tv0KO+ENlgSwBGfn0zKG4Gm8u8Kb+qIt0S5510LnDww/b9BMG8pl7VM861wIFAKFwN5AoGa27A3UN1Emg00eNjq7qEMPPXTBMRX4wwuDY81OYfmQBu/w5iHHHi7a5DXyZPPU613veu3wwAA8OkjI5wUSBwKzJAhaQhMH18hkHxUCy5l6gZc2/yoIHTNV5AQiLozEgzhf4tAlyu3JUbroxZPNffX7ai0Zch7QM8MHnAl8AXM9yGc2C39Fev7zn99miLA0hpd0zszYIEQbKIf+/LXJbYjXUy8jvHjxJwA5g5CFo4RZPz7jiL9UsZ8PL1M4pvRbcOipT14oNVuF87Of/ezWJmgHHPzOmhkoBJxTegEjzgsZdciLK3ksU/riF7/YcJmqH/Jx6LAhNcu6XvSiF7UjBhxZOHYqFAKFwPZGQPc0P+M4YNkpwWcLcJ/B0cJAjZmCDABx/DLwI3DvOfvZz95msTCQxgmMk4GBJzNDtP+GEEMmA0MPrgfXOqBBL3RgsIwM7q3MfMDJj7OFOPdLn9HIIFczYl0WDgLuoejEfU+/y8U2Bsg+i5Zy7HlG0HLQE5/4xMsZHeiowTLy2DQXnhzgwdLnHw3y4M1gumcvcj65c181nAI8vxTggzMBRwMOFvQmaDCOXOoDZxM4gL3PjoE3+vgzTHjoueB5YKBZGZoNBH/sB3uepzxXqHtmmuidhQ80BJxc2leO+jjnOc/ZNofHmaWPB8yUxQnwrW99qz33VG/o5e1AujPLgzaAMwNnv+roXOc6V3vWsdyZZyN80NHrG6cYH6+oNwJ28DdAHEg4HOLsHJcPfdSHODNVCDhaeGYig8A7H7NJeO9AHzn7oEN/6kb11woMAdsIvOehNx9J1KaYlSN62gF94LDDDmu48aFL+wftZLV0tjBbC3nIBS/6BnWNA5AyOEn5kELdYI/ah9qD+NW5ECgECoG9hUA5W/YW8ntArh46zEDRL6b9RaSnAi8jfPXXxqU83GNghoWm9zLg9sAXQb5W8BBnSREzDRg0Z44WleOFgQcmgZczPSj9gckXOL3kISM+TIlr+Q7Xykc+D3k5CpSuB7N0iEthos2KR7mk+0P+vve9b/syRxrOloyegb6W4vCCIxqnxbHEzA9ewnnhfNWrXrXE0OnitfPKZGdp6CrseCliQ2CcKMwE4a9J+iopWzmDF7OkGIjwQsULM/XCtF5eAuOMHniwBI22ReAllRdNNoHkpY4XL16YaCv6QovtfGlFZw0oor2uU2O8M+AwYiNqXsbQjxdodMOZx4seL6bwUp/IcHF+dV0IFAJbDwH1Xz9zrecSA2EGZp7PoA3HCYNWBoXaUwzrGAgSNMjlPsdvexkU8kzjvuUBvj4gJk+ynI+ncX/jfqNyWgKC84SBZW/g6fcrePNRxDc/ZyAqxzyOG3d0aCkog3zZHe3QYB1HCEtLGHijN3LZ0F/3SDkOoq3QMvDHMcXgGEeFYwN/BvHYieMH3ZkhQZygpV0qQz5LRNh4Ft5+SLbXq9ujfA36lac9vVSOZ4EcMkpjaRL1oBk/4iU68mg/emdwHaR7rCthpqWzPOvAyPHB6YazBf48J/lI5Pk4r3iuSp63E+rMlx/J3oiZ48I1ziYCfYL6cXlqS7R9nt/k42SiDM9U7wvMkFE90ufgo35Fn4LWeWM76cxyhb+cV9IbWg7aNAFM6NP++2/aCw4xYV3PcKFX50KgENhKCJSzZSvVxjp18QcND1Y9cHSNs4FBsB7Qc8WwFt1fIlSONB66vi+LHCWi4WWSFy0kqgWZAAAgAElEQVR4zHkAoqP+cNPTj8E2/GJw/gywCUqbOo/x6untGFM+xvUHoAw76cZLhl7qe3Kg5cWKl1x/0ZXOmW2eFvNVLmsH0IIdeRzUH84xHC2yUeX9TDvgqzFBtBmd0kQbedA+5UBDF+nOy63akKdHzDzu9tFmtGcLM2/Ew+md75julVcIFAJbC4F4j9X9i7OcLcw20CBP9BroYQ1pmlWJ88LvH8pnUM2AUJt++v0D3l6Ga5cXZePsiINa9FFgdoqcHkpzu7wGNEOFfOnE84fZCMyS0KwMyjCoJTA49UGv88MRopkLzApiI3YGt3FPGi8j23UGIwKOCpZfRVqcOARhySBagZkcslVpYC9nC3pjp+MtW+IzgfLkccgJAPbRdgbzPIu93iiLI4OZGejH2WWSz5JXffzxPMcjXhOXvXxoiLr4nkDo7G0XmTzPhIHs3THMBlLbjDpShjQ/PE11QRqOEeFEPAb0Bivk4WyhjeH8kc6awQKWmpEjBx91rXznK32hw/kU88h3pwz71TDjCucny+biBx2VVxvJ2kS0q+KFQCFQCGw2AuVs2WyEN5m/P1R4MOnh4tdzVYgP6lUeVE6rhy9pq/CYq2fk6fF4rXhMl6zIayqdfMo4vhmPqfyerZHXHL3dxuw6yooyYj5x6e+2qn0oLSuXpbm82MacPtOdND+gn9I/6ud8M/2UNsV3rGzlFQKFwN5FgHuLDjTRDDoG67rvxDN0pLFUgQBtHACTrr+vMQvPZZCnAX1jkATJFF9msWR6qKjynFWUqbyMFzbIYcIgljiblDI4JjBQzWSQxz2QmYUsG2HgzQxHAvxw7LD/ipawtIwhuC1cs98ZgfLsJdIL/ptk0cA76uZLoYSDy5TzIZaTbqSzQT4B50Wky+I4V1hWpDaU2SBdJMflid55c009KFB3WVvDWaZ92lwG5eSM6unj6ZItHlEXZGvZGeU04ybjTRrlKUNboD+ADbiqf7HPDIHlULJLMqlrjlWC245TB4cNPNjAX4FZSXwYkjNQz3DK6j2gnuuroF60hUAhsBkIlLNlM1DdSzzjgyaqEQehMT/G9cAi3ctmfPyBxnV8wMV4lDUnHnnMjYsunl1m5CWb5+jlX+P8xcZfbsb4zJHtNNl1tI14xndMjywvtgFoPC0rM5bmOmV8om2yw8/wz2xzvMmPcZXLyo7pXHmFQCGw9RBQ/87us57GIC3Sxris40t/xs9/EazBvcpAH/n5YDMbUGcyMn4xLbt3ZfrgFGFpJjMeWL7BEhOCluiOyYeefWBwQOE44cBxwv42OAH8L4DwdNu5xqFBYBaJluw4Xcu04LqM6ZXVo/iqHPg43lxz+EyhrD5cP3jpJwCks1cJNjGLAmeQ9n6Bzm2XSZkNTjtGR55mWa1SJuPpumW83GaumckaZzB5e3NHGH+Bon0xW4rZPdQNzhccMdS7eEsHHHw7dm4iLF397HvBuS0qzywjlv8ihxk47PmD44WlaBy3vvWtW92IPusnmdxKKwQKgUJgTyBQzpY9gfImyfABpR4uPGyyB43T9tRxHtB4GfFVWpTNBqYK4tM79+TPSc9sk65e3umiHhl9ltaTNVdPPfihn/MS0JOX2eL6RvtiPOrbayOiozw0oot29PSMcqbiPT6k9w63O/KX3jHd7fJryXD6Kex6vCu9ECgE9h4Cul/5mSUXLF1gdof2g9C9LJ5ZPsTAEVq/38kiOVuYdSAZyuOPRr4MiHTNpHBEIt8YF23kT3qWpnQ/iwd7fzAYZikRG/FqVovvXZOVc31xMGg5MDN6jjnmmOZ00SbBrq/rqAE7tNos1m31azlDxAucfSYL6fprDw4jyvpzI+JCXLOUXCfNTKKOe8uVpZfPZsEBwZ4xWZ7LVn6mj+PkurPMDSdDDJKPg2iMXywnez1d5f3suPgsJfoLjjbXMV7DhzQcJ2pfLEvTMnKcMHoOCxPpk70jRl1jPPLA6cLMGfozs3L0cwb+joQekd71z/CqtEKgECgE9gQC9evnPYHyJsuID0TivYNZGL08pUMjunj2sk7HiwHreDmg8XKRn+LrOWfyJcv1z66n7I75VFtMI95Ln2OPsMlox2zLMBQvP2f6zk1zGWN6ZrqvmpbplPGIdKrrSCs65U9h4l1SZTe5mxb7QqAQ2AMIaGCpL+wapPuAkxkfbNZ95JFHtgGaBr0MdLOZD9p7wjckZRBOiPtcUB4nByEOcj1NUIgmxn3g6HyydJXV2f9mxEa5zALA4aBlNFGm9MJRoaVSrivOKO0xBnae59eOJena68T1Z/YQOCvNB/wMoKOtOHc8uIy43IY8LWNSGdJ8xgYb8roMdGSDfv7CR56cNeDFDCGnVTtw3uRH/bwMeR5nlgcBh5ync+0zqGi/yh/jL/mZHmsUCxHoaSdq01k/YdkTNsd6oZzbof1Y3BkJf/3FEt7wivbSDrDZ06Wm0sjHSSYdyCePdsOG9wT9BSliINpgekULgUKgENijCJSzZY/CvbnC4sA0G4CODaJj+d7A1ge0XoaXMb54xHIuc0x+NoCOaR7P9O3pFmmzOLUT01VjSncav84G+dI12ixec23LeItn1Ndpe/ZQJuZFGT3dx3Te6Lwx3MbsdlsiXRb3OtZ1nQuBQmDrI+CDKw3OpDVf6rV3CbMyNKhjsM+SGAIbwBK0zwnX7NUhWgZ4xx133HIZDl/UJUezZdgEVsuPKKdfSDfGnZANLjNSNgElaHPbbDBJfrSdNGYAEJiBQGB/i6kALtjPTAHJ4owTQk6P6ICIspkdoZkk7K+h2ULQgRN/1uP3z3KUsDxHsx6QLWcPWPKHO224i+6SJUcaadATcHKBk/YOET1nHAqaZcMvrLV5MnbpbzzM4sGJoL/d4AhQvdIOwETtBp78Vlr6aGYTjh54jgXVA04w2pacCOCkPW7AQ06oXp1nMqRPLBPryOn4ow8BhwlLdNwpAjb8bZC/EnoZrtW+wAWsqHMtBxItWP4fe2cCtd013v27/Up9lqFoiaEVcxdFidAQQyQiZCJEIhGKEhoatKgxqaGGKjU0NdaYSIiQkSS8IUPThobyUbOukhpWG8vQKl1LvvPbff+3/3O9197n3M/wvu/zPHuvddY5e+9rX8N/77PP2dfZex85c2gLOLYI5FO/tAP+WOnON9klHtTpZZddVv4m6I5Q8rUBM/cxcQKOHe53/mSoNPHs545AR6AjsCMQ6MuIdgTqqyiTwSMPFD87e6WTpkF27QGkfJUXX+JRxhgPDWpdbuS/qD7Oy22sXbu8muysbKR1mlae9Mtwy+RMSavZMCU947+I/m4HvGI84z81LbafzB7SlB6vp8qhnGRlMuATMYnxqbI6XUegI7BjEIj9CXEO9ttgcM+AnH04tLEnWnKNk0WB5SUM0BisnXPOOUtooWFJiS9xUVkGimecccYSegaiDKYJUTfSpvYxu+++exmQ8stpDn6/K6eB9Pazy/KNcqHRb4Ez2erb+SMcf3xhs1QOZP3sZz+bb27KB5Vs6YvslHz4XHjhhQVLDjmNNGsEh41mPVCGvV1wVOBYweHAAF1OFmixXTL0LKA8zgEcNHJ28MccnCY4MaJObLKKQ4P62rJly5L60gbA6IKTA33R9eyzzy7OFzmacJTgxEMme4VceeWVZUNhHEyioy0QDjnkkHKOgXrBmYK92IXDgD83CRtsR9fVDP4cjHxpFzg0sBGHyOc+97lio2YIoQ/1qaA6llOK+iX4r5tFw4cP7iscNtr/h3LUreyljat9RN2I0zbAGTkf+MAHyowadNLSJerOZ9TgxJETZq+99ios1b4z/j2tI9AR6AisNQL/5/ghrLWQzn9tEdDLUzaYjHl6UfF0T1uN6zgrAp6abbDorIPV0GdRHqqtrJzXZMSQPJXx64zPWqVNkSuasbPrOIXvSm3ytpG1oTH+y8U/w4G0HjoCHYH1gYA7GaQxaTgG2L+EGQo4DRiIMShkMMtvkdWHUwZHCr+fZQ8WZhtAy0COJTQMGOUcEH/6KJwqyGApETMakMWvaSkDHwbW2gcDnnyZV1pElpkSDDoZ/DLbA90YhBM4k8fSJfSMtJGX7EIn7VkDX7dX/SlldY1c/VYX/K644oqCA04WMGPgq346yvQ4g2GwlM0aWDPzFecJ/KSL7CSNemKDWGZX4JBgvxlsBkvs1wwYytzkJjcpeThYcKCAM/TsGyJ8oGG2CvTYhgx4Uw+UwVbKsN8HdAoM3sEcfRjoIxcnDvUrZxP5lCEP/sjHicBSLQ7SsR955Ok30cggj6VNYEnboZzsY2kM+HldYaPyVVfKR0ccRLQ5eMZ8rxf4sOQbu2UH9NhLu5I+1AE6Yi/4+FIv8YeWdPihm9oG8kTDNXKoE2STDp5gRxqYorfrTN2Bn+4D5KAvzlGu2cOH9kTdI5N2KbzgozbH7Cf0F046Ox79uiPQEegIbA8Efml4oC5dcLo9pHYZq46AVyPXisf0RQXXymf8/WHmD9sd9bCLD9cYB4ssbQyj7JaJaTE+xnNqfsRY5Ra1I6u/Wl1P1W0qXQubzL6s/UR7I88Yj7plcqCJfGO5Hu8IdAR2DgS4x70fYwCttNoZzb3MVEvG+pMWn0X7lJasRXiBBzMzGOTuvffexckzpbzL17UcEZSv8cgwF94tzGv8MkwXoV2kfGZzS2fnnemUpWX61NK8fLz2OtB1S57Xi+4Rr5fMzihzzF7xi/Y4rjGvpXOkVVz24sCSQ5I0OQB19jTJWY68mh49vSPQEegILIJAX0a0CFo7MS0PEn+wrdaDxflm15mc+HCL8e0JY9QvxldLF8cGnsuVU3s5ifw8HvMy+S2+yhMf4hnP1cRqjFdmX0snz5uqf+QX42M69vyOQEdgxyLAPRv7L2mUpbf6hlofCb/V6Bum8GjptyjSLKXA0cLsHDlasn51zG6V4ezXrk9N75iu8jF9im1T8JvCJ6NZDu9YJsYzObEtOfat8o471zEe+Up2xFnlPJ20SKfyrlNLP5dfs6kmo4ZTTHf57mhxLDK9I58e7wh0BDoC2xuB7mzZ3ohvB3l6eCIqXkfxYw/AmO/8Ii/Fxx5+Yw9t5+sP7pq8WnqUE+O1cstNXw7/aN8i+KJnJjNLa9kUZS5avsV7uXlRh7E4coRlpM0wdr0i/XJ17uU6Ah2BnQcB3dfxGVbTMPaDNTpPj32L53m/kvUxnhb7rhbfll5sQPovw9+C2CQWRwthv/32K88JHaRlurkOLl+0Xj7yaOmU5WV4ZHSrnVaTW7N3Stup8Zyie6us4+54qx5q+S25zPzAJh2iFa8p9rouKp/hF/Vo2RppW3G3P2KR6dbi1fM6Ah2BjsD2QKA7W7YHyjtYBg8kfxi6OmMPwJi/nIex84j8xqBx+poNGY+pciLdmIyx/EyXVlqrblrlFslbC50Xkb8S2lg/Me68a22lVqaWvhJ9e9mOQEdg+yMw9V72/jbre8Vnap85Ra5oWrSeN/UZK5RdV34nzMa2Cvvss8983xdktHRxPHTtenl5+I/Zg146nPdKWkdN5lh91cpJF+U7n6n10OLdyvP6y+g8Tdec/drrIePRwhp61Y/TRT4xnvGs0aykXmJZlyEcsnOmX0/rCHQEOgI7EoG+Z8uORH+VZccXBWcfH1wrEZ3x8jR/KLLLPb9vZINB3xRvEfknn3xyIT/ssMNGi/Fl76yzzip0hx9++BL67IUgpmW2wSRLj2mR16iyWwla9YY9Zw72/Nbw5wX+RMHmcPyGcY899ih4EhaV+973vreUEz7RDulN3SGLjSHZtG85ssRrOedoV4wvh2cv0xHoCGwcBNR3cdaBdR53GuVFBGp9YKSL8Vq52FcpHtPH+NX418qxlwUbprJ0iFkMBGS6/EV1EA8/x+sM4ywt6r0j4tH+DOOYpngsG3FYqT2Rf2w3tXhNj6wOSNNBObeV65oOi9oWMVy0fI0+tmXi2SFMImY1vj29I9AR6AisFQLd2bJWyO4AvvGhyWD5Oc95TtHkne98Z9lQrBVOOeWU2emnn15+cfn85z+/RbqN88Fl+8P65IHnUY961OzEE08szpL4IG8JEU/pzZ8VxgI28xcEAi+eHhaRrXJuF391+KM/+qNtVOD3ove5z31mtxh2vmdH/0XkxDpzuXyhvOtd7zqXx69ELx2cHzU8W3JdjjY7HMOT9nDkkUfOHvGIR8xOOumkokdLxjbArDAhyorxFbLvxTsCHYF1joD6NT+PXctk7xOnwrBoGfVZWd8V02rPgpZuY2VcfqaL0qJdHnc9Mx7ol+lBmtIj/5ZNOzov05W0WF+uZytvUXsir4h5Vh/IiOW8XrweYp1k9q6GbWN8F8Ul6uS4cN2K1/BZiQ69bEegI9ARmIpAd7ZMRWonp/MHm64ZTPPLPH5xeOGFF85nJ9RMYRM9aOUYqdGRHh+kMa4HH84PHAT3tJkYlM9eDDK+pLmzJcqJOiKP33ES3JlQe0GJ5RXP8HRHTq0cv7Vk5gi/FSVMsVOy4pnfUX7xi1+c3e9+95s9/vGPnx1wwAGzf//3f18ys2URu8QfJwoBJ4oCTrEzzzij1L2C6u7mw28hNbNFeTW75oVX4SKTkaWtgqjOoiPQEVinCMR+k3iWhnmt50crb7nQLNI/S0ZNjynpTiPZY2e3rSYDmjFbapiP4b5cbJejd0tWy3byWs+eVl5LZisvwztLi3VTw8XrJ9ZVSw/lTbWxheMUORlNhr+363jt8RY+maye1hHoCHQEVhuB7mxZbUR3AD9/uMXrl7zkJbPjjz++DKx9IB3V/Lu/+7vZfe9735KMw4UZGlND6+EaH9AxHmVkvK52tasVsp/97GeRfJs4DoLb3OY2Jf1//ud/yrn2gqLCyo+y4wsJGw/CG+fHm9/85rnsH//4x7OLLrpo9sY3vnH2z//8zyWdpUwPfOADt5FPQkuOy2QqOOFLX/rS7LcGh0ftBSKml0Ih1GSKbN999519/OMf3wbjWF8xHuWsJF7jHdNjfCUye9mOQEdg/SMQ++qx+GpbLHm1vmksPfbP6JelSe+YF+Nunz8f/LqGQcYr6h/jUV/nkfGryXY+LmNRHi3+i+a57MzuRfnV6DPenla7Fr+sfFYPWZp4kJfxqem8lum1Os9wUBpnv25hs5a6d94dgY5AR8ARaK8r6VitKwT0cPKH1NFHH12cLe973/uKg+Ca17xmahP5hOOOO67QiAczKb7yla/MfvjDH87ufe97p+WhYW8RZsZQVmXYV4R04r/xG7+xhK+U0L4gd7jDHWa//du/vc1Sp/hi8F//9V+zb3zjG7PPf/7zZbkTS3dkEw9Zp//5z39eHrxKY1nRFVdcUWaL/OAHPyj7ntz0pjcdlRlxxRacHx7Q/0lPetLspS996exFL3pRmYXy2te+dvbkJz95m5dmbEYHnFqUwwYcK5LDciXsVPjWt741wxZ0xeHEJojCE/twAnGWLY6pZtiIFzLIJ7DnC/WDPBwtBLAl7LrrrkUH8nC8IY8gHVUP/2+oB8Iew548GZYuCzrNloE+7jnjdQcdTiyWUml/Gr1EQafrIryHjkBHoCMwIKD+PjtvD4Bq/dKi6aupq2Rz9utFZET9Y3wRXlNpt4eMqbqIbi11ynh7Wu26ZUPGs0VP3nLKjPFczfwMB6XF83qwZzWx6bw6Ah2BnRSBYeDSwzpGYBiEX8UxOBLKMSydKccwq6Mcw+D8qmEmxlVD87vqr/7qr6766U9/Oj+GjfSu4rjyyitLPscwi6Kkcf61X/u1ebryB4fIVZdffvkSPoceemihG/aFuYp80cKHNOURl/wXvvCF2/CGDl7DILvooEP8tmzZsk0ZdPzmN7951fCby3Kgt8sXj8HJskQ30VB+WGI1xy3DTzqLNzpKXjwjT7bB220BZ9WF5Ov8oQ996KrBgXHV4Py46uEPf/g2dkL3hS984aq3v/3tJe8d73hHoUe+eFxwwQXb1Bn1MThSUjwpC5+oC3HPw17hCBZnnHHGNnIog72DY660OR3i/YlPfGKbMsOSq6tId3raFulRJ3h//etfL21dxzq+bbvqHYGOwCoj4H1D7Xmo5+L2Out5orP0ys6RNj6Los7eb/pzPV5Dp7IuI9Mh4jamd3z/cJ7Rnqj/zhh3TMeu11L/iB3xWF/e3seuY9nIf6otsVwtPpXfSuhqbbm/I6xyx9rZdQQ6AitG4H+3qx9GNj1sHwSYScCxlmFoFYW9zsxsIbDMhbThYVQOrjmGwXPJZ2YJf54h7+CDDy4zL/7wD/9w9qlPfWo2ODrKMiNmZBxxxBFzHtBSjnDssceWfOKUkwzyJIs0ZmIw+4PAhrwsk3nNa14zGwbUs/e///2zyy67bAn/QjiEQw45pNAMDofZq1/96pKGjne84x1Tetk5PNDLDBLtf8IsDmbrsLSK8mxui07SsXbeqsYSuoglcfCRbsgUP/Y9QTY2DM6Voo82In7IQx4yO/fcc4sdf/EXf7Hk952f+9znSvzGN77xvE6FLWeFhz70oeVycKAsqS9m3Hi9i56y+++//+yDH/zgnAfLoDjEnwyV5XzJJZfMDjrooIIb9Tw4eMrmy9hEGsusmJEiXMSY9kSAlvbGUizkMPNKtNQTGwKTPjisSh0Nzpcy2wre6AqNAvr00BHoCHQExhBofa0nr3bwR58ph8pH2si3pWeNdiwdnpHG9eFaNE67qC6ZDPGIeZlOEZssHvls7zg61WRGfWt0q5Ge4RrTWvUX88Z0atntZSPfWnxM3mrne3tznGr69fSdE4Hs3Xvn1LRr1RFYDIG+jGgxvFZE/eIXv7gMIgk4G17wghesiJ8Gm37Oru92t7uVwTCDewbuOB58oPryl7+86PGyl72sDHz/9V//tfximDKkaYPac845pywpgQ/OCS2lueUtb1nKMyD+2Mc+Vn7zTFDHqWs5BoYZNiUfp8k+++xTrp/4xCfO7nSnO8323nvv2Z/92Z/Nzj777JLugXwG6vqbDn/KYXCPXPacQa47H3R96qmnFhqcRWeeeWaxh4f9u9/97rLx69Oe9rTZc5/73Nl73vOeubgWthC5bYqr8M1udrM5H/Bm01x+oQxuYPqZz3ymLLki/Omf/mnR6wEPeEC5xtF0/etff3a9611vzgMHmIJsQj7X2OGB5TeqL7C67nWvW+R+73vfKzK93il/jWtcY3bb2952zkK6kxdpkYUTjYCTCMwI97jHPWY4eg488MDZMFOlOM70FyzXjXYl3XC2XOc61ym6sVQJ3fhFOAEnjngjk6VQYILzhSVVuw5LnGQ3OkYMXGa/7gh0BDoCNQSyviNLq5VXeqtMK8/5en9LOuViWpY+xt/za9dj9i2aP6YT/Gq2LSprNehdlym6S2akzWxaVL/IsyZrLD3Krdmo9EzumD1ZmSh3kfiYPHhFmWPxReR32u2PwFid19rt9te0S+wILB+BPrNl+dgtVBJnAI4WZgZwcE3aWgQ6Jz9wTsjJw0wSDaQ54zRhwMvAlwEwaQy4GdQOy3PKVz3SdDzsYQ8rKl966aVLZJAGj7vf/e5L6NVRchaPV77ylbMf/ehHs2G5zBJaOW0YsDu9MHrMYx5THrTig6MAJwUBJ4PSRa84m9UScKpgj7AhHx0I7FkTccvi4p3leRqzZQiygxk7BBxsN7jBDZbYvdtuu5U8ZvhgB2U8eFzX4osNCsccc0xxREkPrjXr6B//8R+LzMirhpnTih9ptBUCjhWV5Qyu1A8hzowh7SlPeUpVN2ZOwYMZSgRksPeN5HJm7xucODj4lF6Ie+gIdAQ6AtsJAZ4/ix6rpRqz+pg1OCzxnMzSB6JxUDqZSUIoDJKsyUkZjpMLrzJhhg3PJPDm8OB6RzUyPtSXZntGesXFc1iuW96NfAZnrQzpmbwafY12al3W8KjJIx1bOCi7nCD8a+WjTTG+HJmxjL+H6Jo969jHcFhqttO9j2T6ehp6cwxLy7Z514y2r2U8vscRRyfpR1w4635wO9ZSt9XiHesiYk/+SkLkT3tEBritJVbO2+trJbZs9LJ9Zst2qmFmfDzykY+c/0KXmS2kxV/qLledeNPpJlb6UUcdNXvGM54x4+9Ef/Inf1IGx4STTjqpnBkMR0cES0TOO++8ssEt4fzzzy83MgG+egBK1lOf+tR556000XD2ByazHJjJ8OEPf7jwY3YDv6dWyB6uzBARX9FpVgazbnCceD48ePh+4AMfKOQM1pm94g9kv6ZD18wL6IVd7TrqIjrOzF4h4LhCD+wjsEmxnGy18jFderiuSnNaOcuKoK1By6e+//3vb/PCE+uPIs5P15yhpc4U2Nw21pFm4DCTiFlLzkvOmV9oNpsv7ZJuOM+YMUMbZRYYDqphv5jZXnvtVe4TTXV2PcEEOWvxkuW69uuOQEdgfSDg/dawz1NR2vuiq1/96sXhrU3J10vfwccPlqHyUUJ/DmzVyGrZtVp8WrrujHkMWPTjgMc97nHLVpH3HNohy5avda1rNfkwAxZanuW3utWtmrTbOzPDY+zZK/yw/drXvvbCKnv5MewWZt4o4P2Fkymdj43UE+8mqiflbc/7paandM7yTznllJJ92GGHlTrJaNbSBpcXr/nQCq78FEEzwuP9IN2i3mupc6OpbJMV9fK6EPbcD3ycJkT6lh2R1nlzzcdaPSNomzWsoG3JEd9MP8/j+uKLLy4/HvF7YRH+kd9GjXdny3aqWZbHMLuEDkTXzHBZy8CNqYMXS2al4HjAycPyHbzIr3/964sKvEzoRsa5wv4YzLQg8Bci/kZDmY9+9KPF4QJtHKzfYvirThyAu33i/5a3vGX2zGc+s2TR4cCXZSTMBpHDJetUWMIU0zUbBl6yVTIjrTtzXC9d02H4X4ZUPvLNZLlMeeNJ22WXXYpe7LlCAFPhqjJ+rsly/rEce9mcdtppsxvd6Ebb4OPlIh7kRXlOE689jmNEzizJwIGqlyQAACAASURBVFaF2A74Y5TSYidPunj98R//cdm3hWVIzHDB6cLB8qt3vetds/3226+IiDzmgvtFR6AjsOkQyPo2QODFsxV4fjBY8qWa8Kr1L628lpxWXqZ7ltbisZy8ldiyPfRbjk3LLZPZ86u/+qspuxpuGY+UQZKosjzDCZljIZObpSXsF05axJYWrd7P/CNWTZkWn1qZ5aZPlZXReRrXHqffyMrE/iSjWa4tKhd58vGQD2T0bVe72tXm7FUnmgWtDNcx8pqqW7QzlovYka80nMk4VlgyzkfUqEOMU3Y1dI46rlY809d5x3zZEtMzfTIa0mK/EelWilfkh244/vloz8f5m9zkJuUjBgHasfaQ2bZR07qzZTvVLF/mmc2Cw4WD69Wa1ZKZQEOPBxul4mzhl8Q4fD75yU/Of+nMlz7dSG9961vnDgG+LGipDnJYJiRni+SqXHYjeh7Xww7ySxwtwx92yj4wBK7vda97lesar5j+ta99rdD70pySMIRIywa07OtCBxCPWhl4ZPZlaeLhTjQedNAywwQnC0uonvCEJ4h0fnZ+UW+Pt+gym1t6kqcjsz/K9TibPEfnF/v8ZHykV9Ql48/sFbVNfnHNDCH2+OGLx6Mf/ejiQfcXB3j3Dn0Oe7/oCGxqBGr9413ucpclX9Z5sec397zcc/Dbepz+2g9MfLK+xfutDOysjOjGysY+0fmPlY26QJ+9XI+lZXxi2lrEF7VvpTpEeTEeZ7kiT9hF2kyXKTSxHB/jOAgqP1ZfLqfW9lq6xDzFa+mum/SvyWWGjkLkN8+wixoN6RzI0Vl6RHyiLjWeUX5GF9NiXDpwlm7iG/WK8pYbz3RwPcT3O9/5TnE2MzvY2zKDY+kmXlH3KbpFnDMdMj6S6WccQxdv/fh8//vfvxQjXzR8jKvZnclQWqZji34leVG/VtxtW65M569rOTqE33J5t8pFu/jwyn1OW+PwD6LQbs86aOm9o/P+dy3JjtZik8hnvw46FY6Vbo7bgizeDKJlI1NmkjDDg9krLNcgsBmpbn7OJ5xwQklnw1YGvspj/SyblCoofZ4wXDgf10Pp7M+hgB7MeFAe09Eib+fBdNzIX7NE2AQ46qO4/tJDRxDLe9zt4Npl+7XrGMuwXOjxj398SWZ2hvhrQ1w26J2iw5i8TO+sjOunMq5/K038RONff+Pfm+D5T//0T4U1M20Ubne7282vaxeut9uFA40BEMuJ9LcjlihlYcz2rExP6wh0BNY/At5PYU3s00jjay4zL3cdNtfmYBYlS2u1QTtOXPrmuNbd+6PsOkMvo4s6tWhaeW5fRhf1cRrltdJi+SnxKXrU+LR0yfiuZlrEI8PWabL8lj7R5hZtLS+TGdNcTotPjS7yq/Hw2ao1mpWmR12kM3zH8pzW9ajZHXWN/GO+BvxKhz6meV4svz3j6KWl65ncqHeMZ2U8rYWV4x2v4aGyzoMPlD8Zlu3haNSYwNtbS15N1+WUqfGakt6SF51F8FsUc9fBy0Y+0oP0mDfFjuXQ8GzlgwXPUX0Aj3Ud28Jmi/eZLdu5xqdMp1xNleKNg5OHJTzMsLjooouK8wUnjG4MZDOVldkrv/u7vztPZ0YK+6IocEPFm0myXH+n4VrT3JDrvzJmpgQzahT0hxrn9ZGPfKT8kloY4jBiSRSBjWCjfMnG08rvjRmoszeNZtJQjjXS7DXz7Gc/u6ypjsFxqV1TBnyYKcQ6WLDDvqc//emFHeX4tTP7tYA5TivXATsOOOCAssxLy6uiHlkcvvGLhOsYy8Q8xT096iYeosFxBZb8whlnneqC+mN5GOFBD3rQvG1oRg/lo3zXjzza5F//9V+XdfL6oxU0eOsZFMnRIj7dYx5ruMc7ApsbAfUzWV/jL+/k038w7Zn+ir3DrrzyyrKPmO+HAh1/svvsZz9bZsCU/mj4QHDjYckkf8fjr3Hwod/UhwQcOdoTzWuD8kzr5/mqjcvRCd6stUc+AZ1wbDOVXnxi/xntY8kls3U0uxAddx321brzne9c/jinwGat/PHuhje84ezWt7717PLLL5/xBRzZTN/nmczsVd8fy23g2mXDjxlC3/3udws+yL3l8OLNyzdLjzMcIj948BwGFxxh2PLFYRYogy+cZMKZvb2+/OUvlxmw6MqG6tBHGWDKs1h2IQ969hmDl9PLFuqPTeSxBVqWE2vDdtmsNkOcjWxpK98Z7EZPAjYzWARbD5KhOmw9t9hcnj8H0j7gQxn9TZB6Qa7aIlj/7lC/yPUv2pE/PD796U+Xdnz7299+m+XGWmrHcjr2mHN9JRt9GAQr8L7D0mjaM20HHVRPbjszUgm8Y3o7JE22ih75DNiygE60b+qeNk4d8YGNe0XLYaLd4iN7iMPjq1/96vxehg/Ln+N9Ai3l+DCKTPDTPYWdHmhvyJat1JNjBS11Ch/auGYuiQftX39iVBp4eZ0qnR9XcHxj+LEF7Q68qDPwB190po1wj9M2CewRyJI46pB7kndQAnpSRrixuSp2ijc08Kcv4qOZ3zfYgk20J2ylLagcy8DpW2p79Og+gD/X6kOIUzbmi044k0/7096HyAIDtSc5bD7/+c9vo1NcZgNv+hU+FKp/J41+B9t0L7fqT/065fTsUJuDN/crfwWljSnIRs7gn/UnWuLqfzf18uhEv8n9x30ADtSv03AtWfwZlX6a+4d6hZ7+k74S+bQHb3PUC7p7e6CdqZ+YC9p6sfvuuxdnC/0JfP2+lJ2xzGaKd2fLOq5tblLd1LXraB6/S2ZAz8Cf8LznPW/Jw5U0NtA9+uijy++YofvBD35QyuBEYAkMg2qWI/FSqT/pRDnEvdNUnI6Q3zUzQ4bNUHFC4GRhAL/nnnuW3/xy/apXvar8gcb3UMGxwgsog3I6Mf1eGH50jFlAB2SgPzLpZCh30EEHlb9CsZwLu1xOxsfT2Cg4bpjHnikKOFpwTPmaWB6eshtZ4MiyGB5awpaOTKH24hB1U/2TLryzsqKL9JRDH7BnZtGxxx5bpgFqKqDkqRxL4Kgf2sXJJ59c2gIPaP0+HNtxiLmcqHOMS+/HPvax5VfjyKaO0Qlb+JsUjhZmt/isGcqp3Wc2Rzk93hHoCGx/BLhPGbQw+OVlMBtELFcr79dinxP7ulo+sw550WSAwmAbRy9LFelTiGuvLwZbuwyDJZ6HWn6EE5iXYQYt3x7s09fZOODGvn8Y+JOvDwNgIicP+bwwE3gZ5mBgyKaD7nAhHzvkOOJ6y5YtS5ws/3fQhRdwBh//Mjhx9h3+2EffqXpAdwbeDJSRg10EynCQRx8c8SpEW4Py+AAiJxGDAzBgkMPBwIkPNlnf7LzBAZ1++tOfFqeTBtNgRTo89xqmqetjDwNkZDK4ZQDBs1wBXqcNzyc5QKJt8Me5po8E4Mhzj3omwJtBBstXwUHBB3p8CNLG/vDnuU2bgAcHNmtA7rZjcwtTZDEYggdtSgMnpTFg41lL0FdkNmrlYJYWGz8TMhkMtsGSdionDrTYqkEmAy61QfGQEwDHBgNcAvev2hzORuI124UrbUF74IAl73JyDKo+oQVLlpLT5t0OnIJeR9Q/P28A/0cefnjRqxbEh4Ep71sEZHJP0LZIY0C5/4MfXD4Gih55vs/fNQYMyz010Mpx5HUqHLFV7UU68Ucm5cvRSh7vsecOdtBesUX3LrQM3LW/CrT0B+yZKP2vN+jP4JaDenrUox41x63YwLE1SE/OXidK52PZOYPDWfdNuQ+G9iD+9BX0deqL1CapU/oYypUyw5m64uDDHO0jBuEr2dIHXLhf1Ld5OWj9HmR/ENoP7RVnGXlTdOLd1HWiPt3RBT/4gj8H7+2MWWgXSouztXFIqA/kOaJ2js70N9hHutsle7CRvuOMM86Ym6v+RPJYAitHDeW4TxmDSKZ0Rm+cHATnz3V8Rlw5tDv6T2TrnkCOdKfvO2+QofaQ9XHuPEIm9xNtmDKUpz34+zl6ZM+CueEb/KI7WzZABccGjUlZGul4oXGkvOlNbyqWH3HEEXMEVIYXLQ2+D9/6IGMQffbZZ5fOB88ljosHDw8nHv7cRFmo3VjswcFvmBmoazYE8lguQofBYJ50ZnwoH/7I5UHG3jMEdGJQHh0DURderPC842jCq8+AnkM8kOczKWL5GMc5484V5eNM4Y9IzGhBZsSFlzocTLLbbXvFK15R1tZmoYZjTCce0yI/z1d9Q/O6172uYPuhD32oHHzli/pDx1cOXkCYiQMOzORRYCPlt73tbUvWB0f5rTiDHl5i4fmsZz1rCSkPYuoszgzb7B14C8+e1xHYkQio/+AZQX+iwPJAXnq1P8pq6ohMP8RbaZks8jTIJJ+XWfoZvvRqsEU+ewkw2OClmRkWOGcY/DDQJf12w4cABum8JGvZqOTx8ukDGXjgkOCFmcEqf2vTiy4DMD4E8CxkYOcv0G4P1+QXh8nAgw8fzN5AFxwCDCLgxZdLlgN73y+5fDDRn+UYIDH4YIBFfjZQcvnYWfgM7xSHDs89DcT44opj5J+HGSrMDsnq2Z8tukYuL+g4T3hxpx7AFxnw44Wfr6cMdMGTAQp5OGm0jxdfu8vAdcADBwG2ERj0MPMIenDHGYJcZGqwxzuIPrpADx5uL/TUG+8hBNqEO8PgwzOeNsBHIerT7XRec8aVi6y9ch/hyOHDEo4V8MHhgE04Mn2Zb2SrjXeh80Gf/jQJXrQjHCpydDAAJ5CHPO4HAg4a6mDfffedf/mnHWI7uGlGUKxjDZZ5j0Rn8OOvM8zyhTd76oEhdtFePZBO/VCftCfqnD+7UNcs/dPSkyWFtkbQg5lLPqiUMwznHM4q+DMg5b2G4Pc+9x/vvfQJ3FfcT9xXBLUJv7dUd5yVLiy8XtGJdy0CA13qlXYM79NPP70MhrEL5xjYXTK0bQLOQjnihBtl5HygrnBY0YdQLwz0NZPa655r1QkYgCX3PO/C8CdP9w28wcg/CKIL/QVpvDsyk0V40jcg2/uQ2B6EhbYmYFad9HO8kEM6afzUg/vv29/+9vz+I995u044SbyOsYe+hUC6HC2yAXrqRc498pnJwb0sRwKOM82QgYf6QPCjfeAIlT7IIwjPEhmCY6+ZRupP1F+CN30W9z1ON9Uhs3CKzOG+pP+GN3pgN44fl0H7Qyfqj0BbYJxCAHeeYR6kNzphjz/3oGNWFW2FPk46edvXMxC76cvhNzYmWaLABo50Z8sGq1zv3DWYjo39uOOOmx1zzDHlRnBPvm4M0ngI8LDlxuLFwWd+kAZPvN10LDhduPl4iY5Bee7l5MEFDx5+dFzMZlFHwsOUFwBk81An6KUHHchDLi+7bMoUB9/o4y9Jsh2beDlj01WWQNGh8oDgpdjx8WvhoQ6Ir036qxBpSkdH4eNpEQt0xanCTBZeSOkA8ZLXZtVoDxT/yuNYS1ccTscff/x8wOA2kEd9UzdqD/ottdOBu+NGJws2pGlKrOhZGkRHjf6cecjyoJQdzhd7ke+DGeGiPLUbyvGgp6PW5rikUU88UPQyH3Ht8Y5AR2DnQcD7QF4C3dGCltzbDHIZHHpfsagFkuPy1C/H/tnjsY9GB1/iwMCFPo8vyQQGDJoersEAz0T6PmixRdPt6Vt56aXP8v6KgQdBX7UZ1KofxonDIEu8+YjAV0MGFfS/8dmC/qIVtrxAy7FAPn03z0c+JNBPM4BlwKBy6MJAF72xnxd8XsT50gk9+mpQUhS3IPx4ZvOyrwEVvOGlgaAGHwwWYz3HOhN7dGAGB/kMGsGCgQWBPOFEOoMN3hHI14BDjhMNQsQXbOCJQ4B93njnIFBXBJw4/hyGno8JzJwhCDewQSayY5ugDIMRsENGdDp4GyxMkyBcvI6Vhkx4giX6UJ84UbAfudlyA4lQXULLAF3vTSypIeCoAxucWJoFxUwBAssNkOd1hrOCNiZcaEcE7gfwkeOwJA5BttMOVZ+0Tzkv0QcHQe39atfB4QG+2A4v+DNApr45ajObJVt8maUAhtKHds+PKuCBXryP0q7BU0E/sqAM7yp8VNJMBOHi2Ii3ZPuZa2EmfLFDbYU82j9ONe5F3lNp2ziXdhv6FIJ+BsE1uGE7jhUG5f5HSOkET8ksDLYGpeOs0mCcNs27sujBnDrDwQSG3seID+/z3KuUQR8G4ThbWAqIHR6kkzDiLMcV97RjF8vBn/4ORwvvm75kJ5ZDJ/UV1DHtkzp2Z6DaPvcVjjwCMuj7ieOYpC0z3qCObj6MP1hWR73pnVhtmbrh/uFZoO0R6OOxDf7UqdeBsGc8I/uz/gRHCTLRlTZCOeIEnhviy33EeznPLPiBB7TcL/RHBO4hOVqIc03dykGDI4uDduf3KLTSnfd4nn2MTRi/4dD2oKVj9CPooH5f1562pOAmiHRnyzqvZD18MCNeKy2ayMNEnUXM8zidrv+JyPOQpYcfnZo8vZEf6b4/iefTOdRuPsqJpzp4lSUe05SHLp6nm50zBy8IPLwU93PUXXiKB502vNGZEM+kiTby8jzkc+iFL6MlLbODziyuhdXLbcYHe/0rK/rVsIvpsV7dNuFMBx6D07V08zxhqbLoTNtT/UQZxFt5GX1P6wh0BNYWAd3HksKLcRZ4edSf2lp9ZlY2S0NulO10nh/psjgvlxpwaRDrL8vwRn85W3jBZrCA44E0XqLp38SbjwMEvpKSplkDpPnghjj56ht52ecF2B3u0EgXfZBArl6uJdMHvLz8wtNtQF+VoQ4oRxoDL70sIysG8dezhTg6ShfoeSmHjwaLNR4R+zgogQ+DBezj+Sv90RdHA3kMkBmAMnBQiJiSLqc+esrhoPbpzi7x4AORguQy6CVomZjrQzrPQ3QCP2ErHtga7Z0LCBcZLYMjpau+9GzH9hpvpePoY6Am5xTp6Erb9ZkvGvQycJSt2OJtx9sSurhjkXqP7wUqDy4Ed9DJdOTi2EAvl0W+v3eJHh0YQPPhKu6DIhrOstP5CBOdwYC2wEAa3dWWJNexdcesdPU+zPVXusvTtZy5mgnhMvzdECxwZuj9DDr007IulaPfibhhs9ed60k6Zb0vwrbIQ+/v4u88qEccLJRROvcwAWer26S60Jk8zZYiTTPBSFc5P+P4pB9FJs5UgssVLXXpfQV0uk8cI9oNgbYfbVY69wfPAbDHGcv9ozg0clLh5EIvnDk42tFFDgueE8K6CByC6oT+kaC6dXtIhy8y1Z94XUUbocdOOVuEo+ykL4126j6krHSS3uCo9HKxNajvwykjGap78WO/INUHxbzNOK/NdN2dLRuktmnMNO7Y6Y81ci8jHro5/GZxmMRzjLfKtPhO5bFINUWeHuc6O2RzJifiCk2WprLCLeqR8Z5CI3lZ+ZbeTl/r+GLdRBta/Kfo3qKp6SSZWT2Jn/Ntyahh1tM7Ah2BtUNA97Y7el2aL3lo9aWLaggvP1ReacS931G+BtHEcWb7yzEv+ZrhkOnDwEcvyThTmPrNS7oG97woazaEHBz+0sxyiFaIyyTQH3k+UKl9Febll5dvXur9izjyfHCjvtbrK8OJco4lg12+NrujxW0Rjl7Hzle2UEYOFecvXu64ijoo7phmzhPtaQI9+vpHIE15d938o4YGKXJAMKjSEgG3V9fUjXj52fln5ZQmDBwLOZD0zNbZy2Q8JZPBFgM3nAq0BQ30SAcbvsBjHzNZKCP7qBfXAxmakRJ1IC+2M9LiQE7tNepbw8fbt9qq12etnNLVPtljqBWgR1f9yUczNiJ/zaoSvb+DyFbHxsurLdFHEKKD0fXzcvQz3Gutdife8PCyuo5p0OOsJGig7DxI9/sABw+DfPGhv4Ne9pMeZbg90kt03u/KoezlVZaZFKpDOaIkN9JHp0Jso6KXbOiV5rxox9QRacjS7DAccsKI2Y20BfobnPI8J9CTdqP6JT1iKvuF/Vh/Qj4zEZ0PcsVHOKGzlmWJlv6fIKeY10fs4ygjXOgnTjrppFh98zj5EXvNmNOSWS8Mrd8nVcYbNKM7WzZAxXpn4o251bBVJj4QppZ32FpyoIs3mejjjQrtGK/lVhd8dUhOlub8o96ZLeLltjimNX2jnTE+pkeNby29hnlmo2yq8crSM/2zNC9b00npnP2QXmN8M/16WkegI7C2CGT9OV9KGcjqa5k04Cthre9ZrpYun+sYh29MlyyWjCigM3R66eQlm8FeLfCCK744kXC2sIyEpUAMIDRdna/vegH2QUZtGan0rw1CWKKloMFv1FGzW+CFbMcEWtKy/hT9Ii30SuPMtHkNKPgKylR7AgMNBkd6yfcyUT/hRnrmMBJecmh5efGFhkPOJw1Coiy3U3iIRnq4zZINDY4Jd84gozVrE1nOy+VwHXWJ+cRdPnHXMdaZ4yhezpN86cuyCJae6Iu3ZqkwKGSg5rOU+OovW1r2uMyIreuu+0yOvliuFhfPDLcx250n7dSdNNJNNNr7RbYyGNV1zf6YrvtM6d4WatjEunaducZhq02ZiTN7QnbQNrVMystJvvSJPIWb6KgT10NYu33cY153OBnEB/rY7iM26CB6XUuvLF3l3ZlL/6pNrpXvcuVodf3dLl1r9hI8MvyFr/Lhx0wTLS9Cb/RixhnldX8xq5F2ppmRWhYZ6wa+0mGsP4m4Zdh5Gvc4S6k86LnmaT4jTfjr2VTTKbsHxVP1EfUj7vXhPJYouYEj3dmygSo3dnRZgxYNN4Wu/SbIbpoaRJF/jOuGq/HP6F3WWH5Nr4yHeHH2A1rPG+MJrWMn+mjjFN0zGk/zB0nUSzrE9FY8lvG4d5ItHp4X9Y9xaLM08WjZp3Kc/VDZyDfGp9rQ6ToCHYHVRUD3tc5seMoLKGu8cSzgkOCF2EPsm6ZqFGVRrtaXkV7L4+WUgGOFvgQ6vWwz4NKLa62f0cs6dumFHJtxxGgJEYNZyXfnDfsaZIPPiI/bijxfCswgNptFxMCAwBdHyvugghd95Kp/5cVbg2Fsz7BSGgNyOVp4KWcfC724Q+OOloyPbCNP+Qz+PR7LxXjkoa+0DIBwROkrq+h85oscWMpjSjx2eCAtysBxSFsBNx/MZO0i6uvxVh4yMxw8zfXKrt0OlWNGgmY6UX/a34YZM9DgWMHZgt2yXTMXpFMmK9oebXN79OWduqYtetmsnNsRdYg2Ehc/8Yo8cQjK0VvjRxk5KZnhoqV/kofecSaXy+HabSOuGQwuU/0E90rNmSu+WnJEefbq4F5Xnjb+JS9zGkAXcZAepKsvVl8hO1WGmXUKsV+Itopv5DFnYBdRJ8XjmSK0Tf0xjpkl7FHDUqJY3y5XfNSfR53oL8CefjruiwWtZrFxLV44UcCJfccUqEfyNfOFGR/cZ7QR9NZMILeXeuLw2URT+hPnof7b01wvT0dXHHaxnflHBunkTuXsxyHxnnV8o8w5SP1i9ssdg42BgG4AzvHgxUIHeWPXTu+0Xpb0LF6THWkzGTFtOWUij5qtUc8Yp1XEtBgXBp7ekh/tifHIL/KKcqI+Y3HHwmVnekTZWTyWk3ynbelUoxMOEQ/n5fWzMe7gbkVHYP0i0HrJ4r7lq59+ZaqvoVjbKjcVjZXwoCx/p9HsExwfpHFo8M5AR2m8kOqaAWsmW5sQUk4vv7x0y6ECD/o2BV72nW/kr7hkeVyDQhwJns61lokgR0tQXF+9aKscOmiQLV0jT8XRWeGBD3xguZQNmZPC5UIb+ZLG4MTTRVeYDyGWcZ5cM6hRwHYNHnTW0hBoNGgUfprJAx/R+2wspWlgysCFJRVRJ59tpLy5Ultt8DjXsiPaUysfZYpfpI90xPUrYeqPQaNmrmCf2rs274evlhC5nrp2vcd0ly44Wwjau8V15H7i98wc2o+kZlumT00vZGhPDAbQLlP8qTdP13497gBUvjsfSFO712AbbJWuPPUvLkN4i97z2AuEP2vh/CKdnzoQWOKlZVqij04B5+OYKF02SzdfSsI9qHTRy3HqvFSPGX/KKfi1p4m3O4wjrq4vzhDuWTay5p7F4QI2UVfpM1dguIh2Ky7HA/1CpKG8+gufuah9THCocBCod/SgT+fa+305YqSn9JI89Sf0P8waEh1nDj1jRO99HG022u99nPK05xjPIvFRnjZpRi+lSQZtwftk6e62RNzU92tpFvk9/C8C3dmyQVtCbZDrA9lsAF3L5was0a9lerRjUVmZQ0A2Ki/KWCTe0sf5iE5pMR71nJrfkh/zomzhEOkWiWd6Z3JqPDNa8fSz6Py8QW/dblZHYN0jUHvJqqWvlsFT+fPCyIspAzucLQRm3PiLtS/vYfCjF0vOvASfddZZ5a8kDBw9TzNMGAxo/bz2wdCLKgMHvQQzEIuDPdL4kxC/Ho0vtOiqNP3ClmVLOBiUDj/NqGEau5ZHIF9BcsWPQZ+WlmQbkgpbzvoiq7LiiQ7+pT0bSDqfuTJbL5Q3lu50shlMtRcQOjim6KXNLFnOpTJyPvBHDuGHbPTmjyoKotdgi3TyfTDENQPkM888s5SPwe0Wv5q9sSxxlcnyPM3pnD/XWuqgNo89otfMHgaRLIGg3cgZFWWLr9KjnIyeNNqhnBL8PcfbI/vJaIaZZmJFWzN5ovE8l881f1AhsPdFdPQwQ4B77fzzz5+3GTlbGARTRvxoU75kR3Zz1jI6zRiSXrQ7nwkjXvyJkgDWPuAHE/7+AxbCSn97kpMQHrQ3twde3uYl3/WXvsojTp1oU9vsvpE9zCxRefQgRH7i62fZq3NGQ1qme+SDXM224G9u3vdG2jF5csDBQ/uPqAz1oTqTw4Q87hHuCfJxqnDtdcL9RJtRG1FZdHOsJIf7UfcYMwWjc4Wlmu9///vnbRDnjOqKP9XJ4Us52kvm6JZjTO1MzyBsiH0ceWyAK514RkSdLrzwwtn73ve++d5BXr9agqT2Ee2u1f1mSO/LiDZQLTMQpeH72c2r5auMaL1T8PKRTnnwbYWsXE1Gi0+WsK6mCQAAIABJREFUNyY76ij6eI68W3yFYywT4y0eU2mnyor8srjrI/wzHWt1k9G6nLH8KTpFHsSVpmuPwzOWyeT0tI5AR2D7IcA9mfUja32v1uQyePEXQA3qhAjOEDkulMYgBIcIL6kMonkB5Y8ufIFkAMWLJYP7uNknHybgx4uqpv/zAis8dOZvfPDGwYHThuUKDPI02EVe1MlrED4MwnAU8JLN5p/oy5dq/dIYevaOibJ5YUcug0z0YJmDMGFAIYdRrEPiHDjPtQziE5/4RBlo4mDAXsozIMKRRRx9sC0u60G3yL/VQmu00omy+v0puHLgWME27Z8Ajf7qwzXOFrCiLs8999wS5wsweFJncj5JNnVLnYA5+9LgINDvvDXwZbaI/8moZZPyarZlZWu0pPv9JTrHRwM1fdHWchTRqr0jV46Y+Lwlb8p9nOlJGo6Pyy67rDgiqRfw436STmyAGm3JcPC0Gr1sZ8Cp9ko948TA2UFd6x6lXWjJB/c+9YhO9B04Lvlar/sKHDWwlWyww4FEmfPOO6/cEzgqGMwjT04e6e0ytmzZUuRRHwyAGehTRs5feNOf4AxBHn0ZbY92y/3NQB19cKJBSznuOfBFZ/2xizQFYUOcX/h+7GMfKzNoOOAJNrpv6Ivk8PFyzqt2PdZWaA/gy+A/LnPxOtY1tnFfY9dFF11U/loZ/xDl7SHqS5wA/vTf1D98wJs6A2M5DciXo1rywVczjeISM/oM9KIuwCyWjfbQj/JLZ/ahif0JM89oS7QL/bGK8vRxPI/IO/300wsWtBl09j4LWmzVX5TIx1GivwnRt8FXM6OEE/UlnbAF3thJm+PZhFywAhvHGXlyzOlDQrR3M8f7zJYNVvv+YOS6dmhWA/k+6yDGlaf0LD/K8DI1+tpMh0XTo+wYj7rE/EXiNcym6NyS43xbdKuZ18KlZk8m32nH8iPfSB9xiHHRc8ty7ecNdht3czoC6woB3Y+Z0tm9mqVlZaektWSrPC+uOBN0kK4Xxn333Xd2l7vcpTwHnRfXrKNn6jqBl1VeiHkB5Ws4DhXlRT39ZZMXVXjHwNr4/ffff/6lkhd4BnYMcHghxmHhg4hYnjh8WcajF2gNrsjjZZ/9HXzKv3jgAIE/NAzQ5GhhQMrmkwQNSlQmxhmgMRBgEMkgk0ELcQY/vOBzMGjkDyoavIjXcs7e/9fKg+l+++03/zJLXWnACEbg7U4f7Gd5mwIDCwZKDKbjBpOioT75Mwhl1SZoF+DAAGTPPfdM69vbvGxZzn0Qy9awyO4L6lDL3ChHO1C9cqb+FRiItfRzPTJZWRq8aRd77bVXEUP7kIOCtkN7ZclbLOt6SO5c0YkX7PEh26krZo3hwMA5iHPBZ7LBkt9QoxOBNiFHC+3Fl96QD3a0h3vd615zxwp20T7oW5gVIjoNajmz15HkMojVjCMG1Pq9MeVwwrCpMTLQg/ZGQG/aI8sfCXKWgBFlpD/3gO/nUYgt0N733nvv+X0Df9039GXcU96HxfoRq5ge41EucdnPvTe1bnGUoTP40mf6DKmpPJBNHwjWBPVj6qvANPYB1Jn37cJXdsmZSRw66dLCgT6Duo79Ce0BHjxjHHucoP6rc9oDOkOrdoB8tTMciNxX7mSBHgzZaFlBy4coh06UkU44oJhJhE7SN9rEDBgtrfIZgHMBW3Xy+Ga6/qUB2L6oagPWuFdr7RqzW9XfyhNk8YZrQTmFX1Z+0XI1nTy9dj0mf1FdnN9KymZ6rVaa9Krhtlw5Y/xaeGT1o7R4Xq5+vVxHoCOwegjofvb7unaPx3t4rK/ItHR5XGt6tF/70hp4qAxfHD1M0VkbrDJY5yW0pTO6aA8CaGvLIqQDU+j9bzqZc4Yp47wkS37ExGWKJuKPDXx1Z1Cwzz77FBuQy4syg07KkaYjyoBfPNAL5xPlfU8B5xPr2/VCtvYGUL0onzx4wzf+TQMswK2Gr/DCBtFEexRHnurX+SkN21QnXu9ghxzScPQ4brp2fB2f2P7QwW2SPNLAwW0Qb9LhT71ljrlYV8RVX1zry7vwpg1p+YTXJ/LI099TZKvboD2CKCeMWvhBk+U7T91D6BmxnWo7/LBP/QNx7XEEprU6UbnsvlOa6kT2Sne1C202KlxrfYfj7huUql6kC2dwrvUBkt86j2FOe1Ob9nYvnthKPjbS5sBPdcMZndVOcJxlwdsl9cjSO9odjm85LMQHWdn9Kx3gj57EndbrpIavdEOWcCFNdZDhL17kZfh4vx/b1hTswcN1yPAjzft76eFp2OD6U4Y49QueBHRl+Rzh4IMP3uYeI139F9fRESqMOeOwZ8YNjhY9W9Qu9CHD6YvQTRS6s2WDV3Z2s0WTI03MX6v4SuROKeudrWzwtFZ+xj9Lm4rNSsoiY6Xlp+iZ4TGl3FrRZB1zlrZW8jvfjkBHYDoC3kfVrmv973L6HsngrEMDKk/za6zxcpl1rnuWr7QxnSOfGHfe5I3xa+mivCgj2srLNcsoCA972MOKTHcitF6KHUe/zvSCrx/Q1OxzHaO+lIs2uTzPa9FlOtb4KL3GL2vDSvNzzf6IQ7Q/w0A6tfhDk/GGX3ZA7+kZRlFeRpOl1bDLdIzlow1eJtPH08Qrw7Rma0vXVp7rnek8xdZoey3uekzVqcZrkfTMLk+L9UE81kfkoXpAD12zjIzliMw21EBd+S19M11a9IvmtXCv1YPrVKNp6RHxatFm/JWmMw4eZjWx0TFL9Jw/M1VY7sksNmbQxPpEdlan0kl5OGROO+20knzooYcWJ5zy/Jni/Ft2bcS8vmfLRqxVs0mNWzfeIjfyWkMTdRuT5x3LcuzwMrF8K74S7GLZrHMcs3u186Otzr+V19JjuXWT4VGrJ6UvV8eW/j2vI9ARWBkC3Jfe38W+r9bPrOb9vBJeLX2Xg0zUxfGJ/CJtzF+tuMvhWnG/lqyoU4y7TrH/F78Wf8pTrsUXGvKdf5S1WthM5eP6un2Om9sfabz8FPtdr4hn5B1tcFkxb0x2i3eN76L3UManlRbtV/uItmU8Is0UXeHj7S3y8Dqv5bXSx/ScomPGP+M7xY7IK+MDjdeD4qTpcJoaT9eH/XDYsJZ9j1hOxBLNKaGmX0t+xreGTYt/xifSx3hNjnhF+kzGomnMemGJJ3u0MCuKpWfMjCLOEk8CS4pi3UkXT496Ku/SSy8tWSyj06wZ4mthj3RYb+fubFlvNbZMfb3Rxxt+R94Q6DImP3vgRBuWCcukYtJvOTJXUlbKLUfuJMMCUayHGG/p47RslMhma6ztZN3zlMBD9tLBw37HwfOO9z2Gmi6Rrsc7Ah2BHYcA92nsr73/ivdxjC9X81Y/W5PhuiI3xqVLS/8p+q60/BQZoqnZ4Pl+DX3rWER2jS/pkjGVX7TD4zEvtrepMpwOnoRF6kplxEfxDE9onD6WnapzJiPynspL9krfVjmXO0bnGMZ3lzEMarhE+a5zrQyyszzSol4x7jZmPFoYLFJ2Ed4rbeeLyKrZl9UDtKoPnZWW8cnqhTT2tcEBwF92cL5o6VjGI6ZF22I80sc49LU2UMN9URktTKI+U+MtvcWDpUy8h7PEB3y1bEj5bAquv17F+os2Ku5n3t3Zq4XlQ7cYNhgWj1h2qk0bla4vI9qoNbtCu2odzwrZTi4e5Xv8lFNOmf186Jz3GDa30u/bnDHrNk/9wAdK0oEHHDBff+k3P1Pr+HVnoTnwwLIWcSxEnZzeeWd0StMZ+c9//vPnfzuAF7+B02/tiHMd7cODHP+k0dI748Fv6d72treV3d8JbLZ162En/kMf/vD55oFjHWVmo/Rgg8QLLrig4MvGarXgPHDQ0FnT6dN5a81s1rnX+PX0jkBHYOdAoNU/SMOxPmaKJd6vcl074BX7YOef6ZulUWaq3rXytfQp9rZoanw9nfX6bNyJDTwbOLeOKM/xFaaOq/fX4ivMMtxcN+dTq5toY1Y+6jwlHvlOKSO7amfH1WlqvIUt+RkWjq34xbSMt/hm50yW11PGP6tH5xOvM50ijxhXmZhOPOoUaVTWMYzYTm03i7aLmi5TMMhoIpZT9BnTYQoP1yXyq+GvunH6WFb2ZHVDGu+B/JmIDYi1ca3XZ8ZvTF4N17H0iFOMx7rJdGvJcH6LlnVMogzH1nVkia02PcY5AsZsTqyxj9dr1Id4hjNpl1xyScljI2jnRVq2LDXyjvpv1Hh3tmzUml2HdrU6M8874ogjyn/ejzvuuOKw8A6Fa5wJ2mWbAT9/a1DQjc5vMg8YHDEM7rVL+1gnkOkX+UZdlB87wH8ZftXnfwWYKxgu+C3iK1/5yrkNj3rUo2bvf//7a+TbpL/73e+ePeIRjyjp7HTPDuPxF4QqBBbvec97ioNkOVhg48mDI+yoQUdknnjiidvo00rwsieddFIhlR7x3OLT8zoCHYGdA4GszxzrWxbR3PvVeE1cBzxjH1yTk+lco100fa14O9/addSVeoiH79+S1VPEM8PU+2rxR3bGz+vFrzOcYtpUO6PdHo88lVdLjzZ4PNotmz19ii6OcUafYRr1ijZm9RbTVCbymqp/CzvhWeMd7Yx0wrJ1jjwk089ZWq1cTCce20WmZ1bO9Z6SH+Vksmt8lO66ZfzGysd85xfbBPGYltlcqwvSp9RNZlsmZ5F6Ec8WRrU8T19UZuS5kvIRu2hTLT/W2Vg8Yu308Zp4PGL52MY2crw7WzZy7a5T27wTyjoJOUqYkaFf4GGqaF/60pfOXvSiFxXrjznmmNlrXvOacu2d2dOf/vTZG97whtnxxx8/e8ELXlDy1ypEG4gzewNny/3ud7/Zm970pm1Ef+lLX5q9+tWvnn384x8veVdeeWWZVonn33dNJ+/oo48udC984QtnRx555BJezJbRHx74LdwXv/jFIvPNb35zmTUDJujy2te+tuBBwEly2GGHVV+OocnqyGen4EzyXfWXKGUR58Mu7DjJ+JWdZsXUOv8av57eEegIbC4EvH/luhVvIeN9UYtuuXlrxT/ydfulq9Os5KU4Ygv/KJ80vWTr2s8ZfpnOThdleDzmqVxmc5bX0ifm+TuE8mrPqIhz5BXjEYPMrjFZkSdx+Djv2nW0J8Yz26O8qHOMR56xfCbD06L9lM/KiG+0nfSIs9NGfabkjelQ49nSO5ap4RjplhuP/Gu6tfBv5aGXy4j14nUSdanZFHWM8Vq9TOXvcmtlPD2TX9M94iG6qTwilirfwrFmg8vkOtZjjLtNnhevxSuWd3ktfDZaXne2bLQa3QD2ZB2JdyIa1GPqFVdcMfv1X//1JR25nArMCsG5wNRpBd3o/HOeGR7sgM6mTjF/tWDMOj/ScEawKRU7d7/rXe9KX1jRAecDNuAwetKTnjRXyzF6zGMeU2a7vOMd7yhOkiyw9Ah5BGbyZMt0mDXDjKG99tprybpO7xyzuoEn6fy2D6cPv/HTbCIcOQR3+pSEIfDbTuoP/louJSeULydSp02ZzdpRC7N+7gh0BJYi4H0sOcSzfld5y8UPnsvtf7zfXK78rFzkm/XPkQY+/gKs/tXPTuNyM6wdV8dnkX476hjj0iFLj2kxnuE2llbjkdV/tDniW8My0yHim9F43Sk/0yti5ryjHLc38srsy/SKmMV4VibKatlT06PGA141O6NuMR51Hctv6R15ZfHMhqkyM36LpLXkZHopLauPjD7TBZleN7GeKKO0Fs+YF+OZ7EXTWvh43qKya3zH+MRyEbsxXKP9yJPMeA1tpk9Mi+UiP49H+Zsl3jfI3Sw1vY7tjJ3HDW5wg5kcKeymzR4hBOj4tSXOiYcP+48w8+XFL35xcWywNpEbHhocD1pKw+/OxF/5NaiYUYKjJ4bY8ZCPPAX4Rxs8j7WUtfCyl71s9tCHPrTMfnniE5+Ykok3mX7txMwWIeDEwA52KI9646xhGZFwUH7kmclj/xccLfCnPmQTTqITTjihzKZhRpKHZz7zmSUPhxNyCeDGEiScPhdddNF8zxdkRr2WMOuRjkBHYFMi4P2C+gkHwvv1rI+v9XMRzNhfxvxWfCVlM77qgyPfrG+O/Tf8VI6zDqV7XpRNXuyLI+auU4uX8xZfpTkP0lr2Om3UJeo/Jd7iEfXK9M1srpWr6RPxaMkhb7n8Vc7bSMYrS4u6R15TytR4tNIj3xiPZT0OrddvrV15mVZ7aMmKcqfSiq5ml9cVtDW6KfKm2hZleLx2XZPfkqn6yeyKOozhVKPP9IqYOk2Lz9h9k8kirYVBza6srTof5fs5S4v8sU82xutIm9njZcnPeGXlNltad7ZsthpfRXtrzoe4qetyRXpHxrUO+LF0hqVADOD32Wef+csYA3TC/vvvXwbuOFvOPvvsMitEHRO7chNwALBTN44BOgjvuApBCGcMMzbYj2RKYJPeWqfndsArxklTh6VfszHbxPHIdMCOmuMGW+WgOuqoo2ZnnHHG7GpXu9oSm1mvPyZD+sYzzhHCU5/61CU8XvGKV8y2bNlSHDHnnntumf1CYP8YHC04Z974xjfO64A8zbB51ateNZ8h03rgFYY9dAQ6ApsWAe+/1Y/X+vNaX+LpU/pBwK7xWqQipspynplctzdeZ/rAQ3ziteijHOdLXg1jxybyyHQRfQ0L5+E0SlfaVFmZDlN4tPh7Xu06k1tLmyqrVlek1+orto9MVkyL8ah3Db8pdRp5tWxq6aG8qAvpnhbzo3zXuSUvlls0PpX3VH2m8JvKq2ZLlDEWdz7IjvTkx/qZKtvpMr41PjF90bJj7afGf9FyY/Z5Xeqas3D2NHg5vWzmXLuOdmRxL6v8yJP0RTHOZK3ntO5sWc+1t4N1Z7nIIx/5yG208Bt6m8wFE9RxxPOee+5ZOLGRKhvISiYDegK/kbvpTW9ark8//fQyK4SbHTr9hQhnjRwt/m94yuCcuM997jN79rOfPf9Dz+/d4x4zNpxdEgaeWfBOLl7LFspxnTlISGcGzute97rC/pBDDlnSUbpM8Re/TB+cSq9//evLrBOWTt1jsAUHDs4PliqxzEedIfyEVcQFhw0zT8DTl2+xIzlBTiHphAPngx/8YJllxHKnr3zlK7OrX/3qxUFG+MDw1yhm2bgNqlscNArKl16bveOeA9MvOgIdgYKA9w3qw0iP11PgyvoX55PxyMpkdDFtueXEx/vGzN4Wf/KU73S1Mp7ewiMrn6VJX9lSo3HMROPPjFo5p4m4x3iNR6TLdBlLI3+MfwvPmg5jPD3f+ZMe201NxhTdRZPhPaZjTe5YuVZ+zIvYuv2Z/Fgemsy2rGwrLePbolfecstlvCOv5dgVeSAnS4vyRaP6iPUS6cf4jsms5S/HZtetxjfTfzXKRb7x3o14Kp+zY+x2ywbOfu2YL2Kn09b4RTs2U7w7WzZTba8DW72TqKkLze1vf/vZda5znbIcSMuESGeWBOFmN7tZOeMwwbnA/iC/8iu/MmMT1lNPPbXksbeLOiPibJTLrArC3//938+OPfbY4uzg70Y4Fn7zN3+zHFmnUgpZiHZIDmefgQJvzQpR8Q9/+MNFJpvkEthr5W53u9vow95tWarN/8ZwsDBb5C1veUtZasXxN3/zNyUTJwqOJX6DzX4u8JKdvvEuOj360Y8uuIARs4egFaa77LLLNs4jMPvbv/3b2eMe97jZk5/85Nkee+xR6o1rdJLDSzrjfFFgeZI7gjK7elpHoCPQEQAB+izvu3St/tj7bqWtBnLxpTTGV0NGjYdsVr7HpUdmq/Jc10X0nkK7WjQt/Wu4rFX6FJu8LqbqkfGNdmc0y+Hv98jU8lPoon5R/8gj0sd8jy9CG/morOuzKL+MR5QzNb6o7Kl8F6WrtYNavWV6T02L2Lvsmh5TeS9qd8Z3UR47kj62ReLCMDuja8RYPMbOi9jpuK53jBexewptd7ZMQanT7BAE6BxaB7+AxrnCoB/nCjNBCD4LBOfBhRdeOPvkJz9ZBvY4GAg4MNj7xQf6t7rVrebOFDkRfv/3f3/253/+57O//Mu/nGMQOy0HJ3aC5NVsIA/nxR/8wR84i/k1OjIbBb3FJxKOdWjI9vCEJzxhxsHeNiynOv/888vsIHB57GMfW5b1sEmtlhhR9pa3vGXBhcD5tNNOK7+QZinXOeecs8QJFGepUAYd2EOHPWFwynDgKGM/mqif68r1j3/84+Lo0sOENH+wRPoe7wh0BDoCEQHvP2LelLj6qVbfLz5Zn5yljfV9U/TKZK6mjovokOmynPJZmYjfVOxiOXhPKZuVy/Sqpa20/FpiCe9F74eWPS08W+Uy7Balz3i00sS/pXOrvLAbo1kv+TU8aukrsSu2OY/X6j1Lz9JWotdGKSs8W+doq7CsnSP9lLjXT6+rXyD2y1PA6zQdgZ0BAR6QekhyftjDHlbU4m8+BG3Aevjhh88dHPe85z1LHktSKKM9XRj8i5/z5FozT3Da4HzAGeG02XURMgTlxXgmCxpm3nz2s58tv7Dm4BonhgLLoSLPeeZWeVNkRZ1xiuy9994z9lT55je/WWbSEJhxwtIeMIi4iAc6MROGGUNOR3kcWNAJQ+WT9vznP3+uOr+11h4xUTfi1I8H6bIksUc6Ah2BjsAyEKA/efvb314OHLoKSvvRj360pM///ve/X/a5Ou+88+bPEsp8/etfn518yiklTZuQT1WHF9G1eBnN+CqtlVfTGyze9ra3lWMjBuqQmZdatrrSOllp+Y2I8RSbNiNu8b4ci0/BcTVpeMejT+QeiYF3O+6b1e4XHANkRkyiHh6PtDt7vGXLlLyafbxbk+dnruMRaWr8FkmX3pTp4RcIdGdLbw3rAoFs4P87v/M7RXdmrrBMiBkuBF8exHIjAg4Ewlvf+tZyPuCAA6rOFg3+6ZhYQoMDgrRThpfqa17zmuXAWcFxjWtcoxzsbeKHOxCQFx0KRYkhaHkSM2k4mDnykpe8pMz8YNYLv3RWEI95woSLMSeFeCJX2DztaU+bscEvD1OC6y7nCbN9CMyCcRkqF+0l3R0oz3jGM8qfnSKdTJLzZzk2T4Clk3QEOgKbCAHvo2Kf6H2QIPG0n/3sZ2XfryuvvHL2b//2b7NvfOMbpd/67ne/WxzOPxlmCTIYIU6I/HdmmDfbC/F6qpudud2onU/FU/cT5XhXw4Gn94vtbedUnddCL9m+FrxXk6fXl/P19B/84Acli3fsiKno4rmlowb0oolx0scG/S3+O2PemD1T8yNmXk4Ol4xXLS/jNxW/rN6mlt3IdH0Z0Uau3TW2jSU67Jey1kEdeTzj7Lj3ve9dZqvgmGB5C04Kza6QXvw6mU1acQxoHxQ2bI0PCOhjGi/Y973vfUs6Dok444IydC6tEHnqAeRlRAMv9pbB8cFGtCz5YckONo3JqenAkqETTzxxdumll5bNhLWxrfNDPpvbEn74wx/O+JKLTAJ58cWIfLAGE7fvW9/61pLfXkun5z73uYUvs3bYfwa7WKLF1+IsaLmX8pAhff06K9vTOgIdgY5AhoD6Ku+zuFZcsyV9OSQz/wikPfjBD55v6K2ZLDxL7nznO8/T6adiH+XyMr1WO2015GU8Ylr2TIo02BbpMprVxsD5bU95NVkRg7W0d7Vtr9mU2dCiJY9N8nkX4f3m2te+dsZiVdJaeqyKgMBkTB71z98l+RPj4x//+IVUGOO9ELOG3lGO4px1rf5N7/76EYXnT9EnymqV2VH3TkunnSnP8cnqQXXmOmeYZmkq08rbmbDYWXXpzpadtWbWgV5spMqxI4I6FDZrxdnCfiOEww47rJzJVwez3377FWfLU57ylJKnZTqxs/cHCnQ4GJhZor1F2Dvl937v9woPdTycvRPy68i/FLSyimdp/CWIZUw4kLCNPz8p1Dq9qIswYCYO5ZkBxIP+4osvLg6d2CkLQ5Y2Xf/615/nR1zYZBinDRsKiwf7y8iRhZ5u+xe+8IXZm970puKcYdYOsln6hT7ge+ihhy6hh7/CTW5yk/l11Ff2zQn6RUegI7CpEfB+R9fef3kafRzOdIJo6CsVfGYfaTe60Y2KQ8XpucZ5zaxGl61nD/m1/rowsuDlY95axafIzDBFH7dR+tVsnSJnio0r5ZOVj2nEa3ZM0bFGE+UsR0bkUZNVS19p+RrfLL0mSzPAKONYj+Fe45fJjmkrKRt5LTeODt/+9reXFJdetbYwpvdY/lRdnU+Np9I5oy8fLwnx4+ZUmVPpathMLb/Z6Bwvr8sajrX0zYbbWtvbnS1rjXDnvxAC3Pixs2+laTYGX0sI/FI4luc3zgTt18JeJR78IaIHCQ6CZz3rWcWBoH1flhRKIrHTyvRWsUjr7JT3hje8oThb5JTgS1BG5zxdpq45n3zyyWWfFdnDDB2cKjhAsBXnh/B53vOetw2GwohNiHFawQdnkALXOKXYN4dZKwr/+Z//OXvQgx5Uomymyy+oCdjG12BoqUMGLApy2qCvnEItvOYF+0VHoCOwaRHwfp9r79cZ4DET5brXve7sxje+cfnDGX0fzhacKnKssESIoL/ZMavliiuuKGk//elPy3Ihziwd1TR6/phGuNa1rjW73vWuN2N2H/0VG4vHZ8D3vve9skfMDW94w0KvgHyWKDH7Dxr0xNFMv6g+c048XDCzkEObwsOPQQ/nLEguuv73f/93mX0IDnIexTIRS+WTDq//+I//mMELPdERvKKtlMFW6DkIYAJGbMCeBekpu25961sXu3ymkcqBGV/XZZN0ATemx8eA7tjO84UzX+RxoEVbox3x2QM9y0FoG+gLL/Z2oz3tuuuu28jWfhfY/pOf/KQMuLEP2be4xS2KbVFGprvS4MESXNoZssEIXjVMoadtoS/tg9motL1aW0SO6lj1RpnYZqFT/ZKHoxK9kIVe2EbbUF2IVnjwzkadwRvdI+4RA+IsP/L2RLtr2Y5M7n1sdxtku2S7LHCVHO5z+oqMzvHD2cr9qP7jtre9bWlJVyZhAAAgAElEQVTnCrJdzt2vfe1rJYv70H9G4HromnbOexTyaDe0NfUNYOvtt1Yf9FmxPsRf5dH/y1/+cnEc0zdoZjN00LgcllZiCw5r6NFRbZh0lt5rRiBtgxlM6lMlV7qSRzuh7ZBG26ROe1g5AmP9ysoldA5TEejOlqlIdbodjoAexn6mk+Ylh5dUAr8Vjh2MXqxFU3Oe8Fcg/RkInk996lNnJ5xwQnmJ8CD+nP26BlDUxx9alIn54sOsIZYTSS+cRO6UGJOHHGEFL2a0HHPMMcWpgmOEw8NtbnObJX8/Up7jQtpznvOcUtYfxjhuSGMGDQ4U2QiG4P7EJz6xrO0lkMdLBrNjcGjtv//+5c9IOFYIWlrEjJceOgIdgY7AIgj4wIAXf5zVGuTA5zOf+Uxx9CpAL2fLJZdcUpIPOuigcqZfUmCQHL9Mk8eSRw4G2rvttluRR2Bwoz5YPHBsM9hkaapmhTIYvGDYYJe9Xzx8+tOfLoMZ9hdz5wFOo3/4h39YQqsB7F3ucpfZHe94xzk9dtHfaxCoQqJ/wAMesM0gCBr1344l6ZSTfa4AcoWpnmfoiL0eFOd5gfNBAefFhz/84SX1JHmcDz744G0Gr2cPzvuIGbRgxsxQBsoKOBwuu+yyJTiA4/8dnu23GwbGhGir7BAW4sWg92NbtqSy4Xfw0HZctjbepb7PPvvsuU5gyXKaO9zhDvMZs/PM5AI9KCN+IlFdspxNfy50XfnI4bTsbUd9MSCmXYCtbBxrL7HeaLu0B+QyuMbRoiB8Hzx8bJFzx9sO7ZvAc37M4QAd9+3ll18+58+F2hPysd9DbKvCiQ9y8KFP4KOT3juw/aMf/egSG5wfSwjdmYls8MtsJ4+2cNCw1J62IJzET3XIBzTkx3dAb3Nsyi2Hr8oLh6hTqz6k0/6DHXL0Iod2QB8R+0gcOle/+tWLSOjUR6KrZif5MnJo+MED9RTrgTj9Icv+1ZdJV/oBMFJd8nG0O1uWQNgjGwCB7mzZAJW4EU3whw+duJwG8YztPHjw/BP8a6HjwlpZ9i6p0Xzuc5+bkyODr158UZQe2TlLc5m6ji9rlMPLj8xsGZbz5WGMA4mA00d5LoffUr/4xS8ujpgsH1p0uPnNb16cIWDFIIKHrAKOEI5YnoenBx7AejlROrzZhwUHFQ9tvlCgC5viHn/88bPjjjuufImJgWVLD3zgA5fIZAkRv7smHHnkkbFIj3cEOgIdgW0QUB/LWQcD+A996EOFltkDODj4ks5ggD+/KTCI0EDC0+ib6X/5mswAlSWkOInVR5LGVHoGq9CSrmcMfFwnlfE0yYQPTgM2dmc5Jl+K6aMZiDN4/eQnPznjD3AEBvpytEB/q1vdqjzzGPQxkGUAy5diBjYEZn2Qx8Bv38Gxwtd2cGGgw8Dy4sG5dMiwp1mcFeF6FkZbAzLgwQATmy+44IIySEMu2PDVnbJ8odbgCXx4NvA1nFkl4P/JT31qriOs0Qc+6KnBOXoyywCZDDjZT4eBGrjhtMCBQr3iMEIX+Esf6HFSKfBckiMALBngwZv3Ah8cyu5fWLz0Cp3OOuuskoiuuw/PPZ7l8MIm6hHZ7GcXw5YBK56D1Bt4gAW4sbT3rne963x2h8pFXbBPg3ScHhroMkuCNoFzAyeK6hJd5WgBJwaxvEPwviRHB7J4nqst8ryXY46PO3xQYcD9qcE2HIrUBXXMQZCOyEcGtjGrgw8spDFbAicM7Zr3D5Z96++R1Cdtl3sD+fHdw/GjPbmDAb28PX12eJdCrgbyzKyRYwfn25577llw4Z6i3TvGbrvaiGznHZD7E0zAkv7AHWnwke04EmgL3KO8X9EWeBfiXRJHLB/99KMG9qtze2NdC1t44WihreEkoZ0LC3Si7eI8cnu41nub6oOZOujJPfbVr351dqc73akUwXY2/yZQf7xroidtyd/91EeiM7rK+bPLLrvM2w626l4Cc/oI+iL6L3TloP9Fttv+7e98p2DFPYmThVk1HpDXahtLiHukI7CTItCdLTtpxXS1foGAOnh1uPHMwF4zPrKHFvQ8QLIgXv4SDZ137i4vyibutFFGLY8XHB7A0lfnTC4vKQoZv5ptURfFeWDzgjolgMtYQCdeZPgFIF+pHve4x5XZKdjYKk8Ztw05zJrhRe3JT37yvGxm85hOPb8j0BHYHAh4H8q1ju8ML/EEnAMsZaQfIY+BLYM8BtoEDSQcLecT+YtO6ZR3GuX7AFL5XpY0BoX6osxgg4Et6QyKGVAziJeOnPXHPZZYMoNFefSj7E3GMk4GN+pXNXBm9gYDHXSiX2YAyCCNAStO8cyB7vZJb55Z97rXvebPPL6sbxlmeTBIxXmw++67F1IG2NAy+MSxQMA2dGZAxuAKB4ueXQwACXcc/jCI7ejJQBc72CAePTnggSw5WnCo4JwiMFjU/mxgKv4sodAgGnpwIKAfOPCXQYVYT/OMrRf6Sx6DSWzX4B49cX6wEb3L9vLXGxwUYK7nGTOBvjhgBhaUyZaAebuiPeNEY7CP7go4GTSrBMegHG1aigUuLPOVXOqP9yXaCUHtH+eMBstg7oNePqZo4MwgHOcFwfHCQSEnDPz5eMNMMZYLMWtW999c8VDe0+M1dYldWipGvtoT7Q4McSyqPWmDfZxbOJlUT9jFDF+WnxH4uIP9bjsfgPydCltpY/BElhwVbru3Q3DjT5ngi8OReLR9rJ0pn9k42I1TlftCfQOODO5t3Qu6B1SO+wOdvD5wvHCv4iDWnzx9JtJDHvKQeRththWOj/PPP39eFfAWf7Ut+lb1c3IC067VN1GY9oYNyKZ9wduXP8nRjH7gpHaKrP7uN4e/X6xzBLZd2LrODerqr38Esg7WO2Fd02HrUBnl+dkRyfJXmgb/Go9aHnp7GbeDdOVHupqcRdKd52q2Fhw4vNTxFfHUU0+tYiJdY73wwvjGN76xvJy//OUvH1UNPj10BDoCmxcBvfxrIOBn7YvAAI2+QoNKaHwg6WWEpNNyTeAMrc4uW/SihV60Th/L4HDW3g4so8HxIV5yNjCQVxozBQg4W1wm1+LDQEu/1pWzm0EOA1bXn4Eog3YGao5B1Fc6I5cy0S4N7uEvnRhcM8hy3ZUnR4F0JF1/NGGpj/Mhj7qCD1gR11d1ZChNOlPPspl9ZUj3GZw8W2Qree5kcgxq19qnB8dIbFPEcWwQZJtjhzPN8Uf+Llv3pmBQXZOpdDBi9gSOD68D+OhjE8454hxy1GlJhtI5uzMBhwMHjj8FzVhQGeTJGYSjwnVVGbB1GdKJNoszQ3miJ+54ZParDG0GpweDeJfBtezD+aA81ZP+duRl1P7Qg3Rsl8OT+mPWjOiln/oL9jRRnuzQHkQuQ04Olh26jSqT2Zql4bykzpHv/Okb1Na4H8hTea8PpZEvpyf1QR9BGrNRCHLOut1y4Agn5dG2KU8bIcg+9U20E9eVa//rFLP2KOOz6XAmRftlRz93BDYCAn1my0aoxQ1qQzaYztIwn3Q6a4LOSs/gyfh4mq4XPWeyPE16Skfxj3GVyXSK/BR3u6Mejk8sn5XL8Mno4OXpbHzLNF3/ZXXUJconzguPfrvK1xStY472Z3q1+Pe8jkBHYHMhQH+kA8sZ9PlghDxfCpANVKAhPQbSYl/qsrwvVPlID08NRrhm8EyfxwDmve99bxlY4dRgAOQDHi2VpQxfkbWfQtTR+WM7g0EGQueee24ZoDETgoFSNpOCsrIh4kieBqKSiW1yWCBD+JDPjBIG5gzoWA6BfR4cY2bAoBs0LMNBZ9LQU4N2ylJGTgGc8xpUO1/tzcZMBxwyLLsg6Cs81/4cAWf9WcXrz3lyTZ5mS6lNKV20YAovbIav81MZtQfXAZsy2bEumN2DYwbbcEq1MFVZ1Znz9+XLaovulNLeRY4BshXUtsVTTjWn9/uHDzzeNoSb0rQBrGMCbznyuFZ7oi1l7QkMJRNsCLG9kuYDf9mOA0GhZbucFNKfM+0U/Rzf2L/EPsBp54LtQvmcab84jmnraqdeBhrhqHJqey7H64O6xNmh+0eOMrfL9yp0++SgkeOVPO+bmDXosuDpvJBNvpw/ypeusQ14PMOqp3UE1gMC3dmyHmqp6zhHIHa8eojRUWcvMQ5dLKu8mK64p8e0GIdX5COdsuqTruRldJFXjGfyohzJcFm1tFg2i0sHf4BLD6XxEsesFn71zPr5sU1uxZMvRkxtZSNdzm4v1zGe6dfTOgIdgc2DgPocztkhJHBK8HLv9N6fkB4HB5EeXi7D+9SMVvR+jteSyYCQ5S8MqFhyoY14WeaEs4Ev+nzJdh2zjXq95tk7RssOWLrCAJ0Dhwj71XBoj4a4n1aGq3ijh/Jljw9ehRH6feITnyjFkOMzKXAUaNDqNrF8g+UJ7BeBnhzsa0Z5NrWX04WBNkE00i2eVa/SFz1jG3BbKK+416/4kifnBrOlZKvnoyMDYh/4K1/thHLxWQo+URfpo3TsZh8S6aDZCNAxaMZZAq0wVTnN/nH+3v4pS1vBmaGQDernmcMFOuAIdFlRf4/HPHj5fYNzJHNyMAuFsix34X2CQHuQo5A8tSe3XRhB622M8u4IoYwO8ig3ZnvEl3eeKCOzPUsrBiVBOrFsScu9sMUdeFrK4za4blEnj8PL7cYG6ed6SjWn1dJEnMIZrdobZb2dy5nKfevOHejEJ953Xt7bbAJZT+oI7NQIdGfLTl09m1c5dbq1Dlb5sZOu0Ucka3SeruvaGZ41Pp6HjlPooo6K18rGdOLZw8/5eP4YfUufKEe6kM5GkhytEHVnHe873/nOglM8Wnx6XkegI9ARAAH6nniQ7rMGlM+yBgUf9ClNdOLr/GO/mdFCr8GN93WadSGZ6kcZfOiPPtCwNICNU5lJgfNB+xxIP/7g5rMT5sbYheQzq4DfAzPLgwE1SyZYrsPAkg1D2asBGg8ZjuSzzCnOqPHZJZIJfwX28vAv22zSTnDciMMXPTmoMwbg7A+Cnmw4is3oiWOCgSaYcLSCy2BQLry9jJwMystopK9mCWnAGG1ABsEdO5IFLfjEZx/5wjTKFn/OwgJ6HGg46UTPXj6amRJ5MGsj6umOFa7Ry5dU6SNJpqvbo2vOUa7HdR3TFGeJFO0wBrUn7VVEPhtd+6wIZkLJuSJ+GtgzK4T9dTz4LAz4616EhnLaWLlme7STclmaZCpPtjh97Eu8DH2UO1pwRqqdwCs6W6IeLZ0iLbNV/M9C5DPb2PURTpo1JGdu5AW+6pvcdtWRL+Vz/o5FDfu5Qv2iI7DOEOh7tqyzCtus6tL51g7f34TrKUfkpTJKd56RdpE49VWjl4xaPulTaFS+JctlOM8pWEVspFem21R+0eaMV6Qh3kNHoCPQERACepmvnaHTtHdodGhpCfmeHpHNBiy1NPERDxwTpGlAx+CJQXqmu5dlMMJvbNlglIDTJTo5sMn1jtduh+cxWOOLNBsGa0DKl/xY3vXhWkEOC6fX8hL2ziAdbDWwYlaOf0XHiaI8eNbkMmuCvVf0pxXKMFsGLDXg1F4VPmDO+MnRAw+3RddaGiR9anqRrkE+sjNZGgBrpkCUF8vMgW1gQRkGvprNxMxPHAjizdkH3tGBgPNOA2XluYNMOvmfHKlD0lUu6u2yZUONxnGt2cuMKerJD7Ub6k1tho84PljnnnInk+zTLCj27ZEdypNDDF2UhiwCcuSczWx3/XXtWDgGni+amJZhpjTtI0MZNsum3Ssv1p/saPGHJuarzuVwE39o/c9qlCNPzmIcoq67O1PRLdrlvHDqKR++2muKtB46AhsVge5s2ag1uwHs0uDaz9kAXIP/Wl6WHp0ConHHQnRKZHyAuZbueTW6lpOhlTcFE9dLvPw8xj/a5dhkeEV64iqTnT0/lq01X7e7RtPTOwIdgc2DgF7S/cw1SxAIDK4YlOkFn4Ere4lMCepvWrQ+SGCQrYGb7wNBeW1YyrV0YdDO0hAcKnFwx4aqCuhMGfaHIMRNSsljkMgMAJYiwYsyzFzhy78cP5LLwE2beM6FbL1we/yabJZziAdnx1IzLRg4K2ifEg0GfYkGDiPSGeC6nqLljJ76e5DkajNdBtkaGJMn/PQ7ZTkMVN71l36+T4nSos2kR9kMKIWpyqGLgs86mSduvXD8XFZMj3Hxkf2y2Z0HOL5UjpkKBO1vInyoM/a7URAfltdojyCvZ5VDDu1L8qLu0c4Yd3ryYpuM9npcvFg+5OnxnlLb0WwWdBUm5CHT26B4+UwZOWhcDnjQrqKz0TF0e6Ot4iUa9CDU6Dyd/kQzpUinbLTbdXWdPN15cg0e6gNoD34vwcOXKqqsNqf2epAdam84L9VfSb7+eoQt8dfZxMU/nmVLP3cE1jsCfRnReq/BTaI/L73qiHUdX4SdBlhi/hhUkd7jrbyMr+hdZ+iIR15Z+dXQv8U306tGH9MjzsvRNSsjXPzM9VS8op493hHoCGxMBNR/ybrsJV37OjCY/+AHPzi7+93vXhwh/JmHwTIDS19OMYbUWF8kHXCS4DxhKQ2zEPjqy1IgBjSaTQItB86IT33qU2XJDHsdsEcLejEVn/1KCPyRRHulsGyGgRb0bKrLZrcM7LFRdmkPFfTlGrnsh0FZBlcMPhn8MJhCn7h8AJkRX2EDL5xI/N6VwOBbGDIThXJykIAxNvAVnDJgQhp/HmF/GuwgDx2wmTh6MqsHBwl8GbiprjTAw17yST/rrLMKPTwZLLKfBLzJv93tblf0ATtm3WhfEOylDL/yZUaI6kR2x+eNty3KQo+Thr3J+AUvstgPR7+vhiYuoxB+8Ir8a3h7HfCxgmVgONJoS/yNinrEAQAOOOHULsAaPMGJAI5nn312KYNe+n21dMIe6cXffmiP7OmDQ4/f8dKGmGXBUi6C/hwT9Y5txuPiz5l2Au78FpnfKDPY1mwH6aQz9LQn1R/2yna1d7UnHCE42LAHhxQ2gM0ZZ5wx22233QrLaDv8OWgj8KVNYCd4cr9QV9wn8CJQB7LFdW3ZLjqw1zI0+CFPtkde8INWbZM6p1/hfmA5GXXK/YAjDecRziXVI7xkF9et9qY/OUFHG6E+6COEXewj5TTxpVmyHbzoH7gPqAvaDkH1xDXti7bsIdt/KtN5SaEe6QisMwS6s2WdVdhmU5dO1x9kivtZmMSH4CIddkY7Na1VJ5n+0EebajwyHWq02zM92jUm2+2ILyYqK5p4Jn9nxWHM7p7fEegIbF8E1L8wgLr//e8/27JlS1HA9xFhUMEAkgEq57j/Cf1N7Ic8zS2KdGw4yxdgBuQXX3zxnJQ9WZjNoVkF6Mkgk1+78gc3bVzrvBns6K8fyEFP9qzAycFAUns6qAyDRBwJCgzo+FKPo0F7LTh/BkRx8OP58fp+97vf7OMf//h881vycTbAR0tnSCPOBrk4PvQLbtL58xLOCHBAf/6QxF4UDMJI059motw4SBMG0DMQ5VAAMzB1u3BY4ZxikAr+qgOcW+B7+eWXl+Jjzxny2cyYzVqpX/4K5QHbNLCPNozF/f1FbZizDpxHtFccFVo2BE/qm3omkM9MJnRAF3C46KKLSh6OBIW99tprvuGsZhogh/aDE4cBPfWjTY5VDoellujE+yFi53G/R6hL2cCZpUG7bp2FNldwuPD3BAbytF+vO2ixG/vVnrD9AQ94QGmLbjubTRNoG9j+kY98ZC5KuOO4JIATOHJ4oO3HfkL5LdudB7byi3c2vuXg9+iZ7ZSh/eLU5F7HUcmhwLIinDHYwT2g+2iJwkMk6pXlcy+pnql3BbDFeQUO8GFGDW2C+x0cFSSDJUnYg8M0azvYot+yRz0ir5g/Zkek7/GOwM6GwC8NHU1fKLez1UrXZwkC/uJBhjfZKc13Skc9RjOW36qyKTq2yse8KbpkMlu4ZfRRboxPKTOmq/NwWl3Xzugyxjvq2+PbF4Ep7WMtNertYy3R3bG81bY4c2ipg591zawPBmo4HRig8MWf2SbEmUHCV1oNojTAYl8TbRgKDQ4Zyvlgi/LsRcBgXbTe5hhw4HRBJjIYoPDFlzLSQSiiK/shcPClF/l8ufZBjWiRwcCHwT4Hg2O+UPNF2jc5hV764GRgvxi+jMMTenTQkh/XO2LLkgBwIZ3BEjNIwAN+xLMBqOoEGsoyiGephvRDf+31whd5OUbAmpkZ1BczGsCWmRU1hxAYgxn6gHGsI7cLmeAPZvAGXwaOtA8G6wwW2Xw36zciJtQXZdQ2kA0vDg/wYuYBZ2ZbYL/4c4YH7QG5vmcKPNS2/Sy54EN94/jQrBCWymmWEXWsdMowaAYn9NT+GqeffnpRVc4JrqUbdaO/PQkr1YPjg+7YQBuQE0b2g7eWnfifk8jXvadZK770TOUdc9Joh3Icqu5Uzm3HeaT2gg7oSDsBY3Ah7Zxzzili2HQ51gkYapZMy3bZgN1yNAqbzHbyOLi/0Ve2x3tW9a3+C17UN20cu+lPJAddySdOvdKW0atWH5qZollowho+kkEbUZ+oPo44+OOI4p5sOU10n3FvwFf8wF56c8Y+2gfXt7jFLUqdce1n9CNN5aRvP3cE1hsC3dmy3mpsE+obH7qKA4VfR2iyDjpLi+U8vih95NXSL9Jm8ZXKzzBq4bdSfTMbSMvsyGQ5XXYd+cR4TX5PX1sEsrpcVOKiPBap+0VoF9W7029/BNRWOOtwRwtpHkdD0enaz7IgaydZGvRZekyrtemYHuNjiEY5Me7lYx5xHTU7MnzF0/OinjU7auktPSPvWjzaF+livscdixomNSxIn2KXYyzZU+qAcpLBWe05pkf9ZD/0DL45tKRIeTggmOlD0F+AXDfSa3hEPMVzyjlirzIZT7dddJ7mdpPvcWzXZswM9j3gdGJ2Fo4xZqvEOoF2NW2P/DOco/7RzhhfYtBWfaW3nyOdx4WXy27RR71btMpDRq3O5VQhP3O01OpgitxO0xHY2RDoy4h2thrp+qwZAt7pr7YQf3CtFu/V0hc+rl+ML6rvauq1qGzZwVeW7KvZaum2qF4biX6lbXlHl/e6UHuI7X9qfUVb9DVbXzOn8NGGjPHL9pSynSZHIOvDlOZnSsd4bB9xQKD8rC/J0nIN/zd1UfoWr6h3K57xkS6c/RraqXrWsMrkLZo2VYfId6xclh+xyDCRHPKi3R6PfURWLxneLjOrA/GN8qMulI1pxHE4sHSOWR0sQdJSI2atsF8KgX1AMt2kT8xz2zKdY36Mi18s6+nkRXtqaeIvep2ZXcHG08yscNtxMsl2Zo95HbSuox1R/yxfaY5hlOHlyIt2exuYKmMR3Vo8Y95YW4j00f5oq7DIzjVePb0jsF4R6DNb1mvNbRK99bDRGbO5ZoriGWee2UThl4eHl8Jhhx02+YWyyTTJRJczt+ry8Ic/fD5910mhYTM/Aroo6AFGnEEZf2UgHH744auub8QQOZ7GxnC+Flw6bnMecP2tYT34Pe95z22yVjPBsRFfpYGV/taB4yWjjWVWUzfHDb4t+SuVG2WN8VuOLjUZTD+nbe+xxx6zXZN19egSy2bt6OcsQRjajP6kEaeVw+fkU04ZM21J/oEHHLBkCQN2swEiL9n6kstUe/aJwDkScYlxMY/2KB0s9NcKvo7Gaf9RefE54ogjZqcMtr3+9a+fHXPMMXOymvzIp8dzBIQvZw59+W9dO6esnqfWyVS6TEZuzbb3UUY3Va7KOr2uOfs1tJFvxBYa4TxmU5afpWU6ZjZnaVHfjKaWFu1XPGLi5SMeMZ7Jch0j7ygzK0+aYx6vY77ifuae4J2CfT0I9L/6SMGyDvaf8aVkmZ6l4NaQ2eT5Y9e18p4Ojwxf2a/8Go10YGYL+zVpaZXbrlktPruCcqoXXbs9Nd2dJrt2TMfwjTZ6nxbtlqzIs6az0oVb1LWWDl2sn1qadIy8FRe+8ZzNcmnZVePf0zsCOysC3dmys9ZM12vJIC4+hBhs8yeDqUHrWqfSj9H5gwne2mSO3fL5A0UMJ5xwwuzYY48tyawZzwZqL3nJS2bHH398ccacdNJJhTZ7yEXeU+Ous7+oqPyRRx45e9/73jeJ3SMe8YjZiSeeOIl2pUQZBtQ/fyEg4GyJISsTaTZiXH+McEdYC4v4ghXjcmrxt43MQeIYqizOhbF2xKaGz33uc2cHHnjgfA8B1rAvEvgzh3Rig8BnPetZSzbKdF6ve93rZkcfffSS9fljsiIWcprQ7nGGTg04Wlnjzn0Pju60atXNVP6blc77MK798EFKzIv1GvGLdRLjkX5KvCazlj7GM9MpS3M+yufs19DEso4t+Y6h4s57uXaM2TklP+peK+N0EYMYjzwcj+w6YhJlkZ9hPqa7455du1zlR114PvJXKPbfYJYLThY+VPBnHf6AFXWLOmV6Oz6R3vOy60gf49EmxWMb9PRaGRwtbPrKHixcYzszXeiD414t0U7h4vplukYbM5rIO8bFI9Zh7MdcFrRRVo1v1FFt2OVGmhifyrvG08tzrcMdLZ6+qLyob493BHYmBPoyop2pNrouKQLxAQSRHhZMjX3e856Xlht685KuGS4qEx9QeeFfpI49mHhYsPaXdcB8VWfn/Bj49agCf51gpoDrwTVfvwlHHXXUnHa5Okf50QbylRbzDj300NlBBx0UWSyJa4O1RbFsMq1kRv1cd655IZmqx1Q6VyWTX1F1hyfvueeexVnn7ev/s3fmQbsV1bl/c29VrPuHSfwn4lSBaIxJFBWnilGDYEACojijICpqHHGe53nGoVQURwZnJIAogoIaxVJB44jzUMYoZnAoU3FI5d77/lqeN895zuq99/t93zmc49ld9b57d/fqNfXavfdau7v3FP4dhnOCNgRs3EY4Z+YI11xu6pd9Ql52RP94evOb39yuFez8wQ9+8OL4449v1SeddNI2cGSOOeaYVva0pz1tFVwTELNXwM3UcII2JGaOQBdHguNvx/sAACAASURBVA3/+BLH05/+9MVxxx23OOGEE9qnUuVgAJ/2kLpSnq8+cH1ynRNoTDjxVB3ZuPDUU09dHLaciXPssce2L8+INniShwrHXLa9BtCb+iF1mHm19jbbY/xNSbbNfK9dlruNOI5eubefAjOFr4RRnqOfJ++9PG3cZnv6p/0610iPnspTjoQfq3f4ng6m6kM6AGeeV3QE57JMpeVt0Gf1S5rqH+mfsVqf4XVekoep+ZTH6Y+dZz9lPuVN/ab81AvGYcUjL8H4glLqKPXgfLge/Nx5G5NzjF6Fy68r1fNsmTJTJ9jk2/MuY/Ir23BcOnfYxK+6pFPhT5jUK/mh3xD/SW/OzxrY1TUwB1t29R7aQ/nTzSBvClnOZwRxrpSqmwNlfiNznGPqTVjekOirD16HE4UDiUPHG3RPzHyhjna83eatP58AdJ7Aq89X8slC4ZY8yccY30P1jkvnHHWOU+g6BVfvxtnjCwe4+koFb/n1pYQpPDKzQrMXnJY78M676wse2Bwwv5IwRLeq68mILJWM7Osxhaa+WDA2Y8R50lcXki5yQ5fk+lDb7D+Vu2w6xxb5tCT2ymwn0ste9rLF+eef32y0mr2ltk4bO2JZXSaCFXyi8/a3v/2CGV8vfOELW/CG8kwKtsBH9QUE6MEbiU9YvuQlL2nnyEswhsAn19N+++3XeOdTrXyeUjBV36ZO6OejjjqqtWHGWeq3p9vW4LLE522Rj2v/He94R5sZA54cm7zNfD5NA9Ih0OqLyklRv3l/qQ+cUtWfVVlyl7aUdAQ/hEs4EibzU3D1ZBKuPCZ82mfqr9IdOHoypL7WyffkT56n4nTZUw9TaEEHuJ4OxIfjqs6n0BKdnmxZ73z1+kJ0kycv78FUsvV465Wn3Jnv2Z7rXfboNFwXjkPtkveUkXxVlu2rfE/WHs0eDpdhSLZsn3wnXeXdZtN+ZS9Jt8KVfQZMz96yvfTcO6Zszs98Pmtgd9TA/9odmZ553jM0oIFbN9U8SgtZ3sszlRanjh8OpcOxH4M+owgczjx53oJ/97vfXTziEY9onw7ks3e8qT7ooIMaDuD48cabxKfxwKVyjmxSR+INOw4syxAoh77gLr744gbD8go+hdmTIcvhAf78R4Ci+qktdHrnXpe0qjaU4QizBAQeHvnIR7bPEyIn+mJpFDDMlOATk5QjH+XeB5yja37oTziZoQBu9E+gwXlqClsm6ZA6Nv979rOf3fCwLnuvvfZq/c0SEO8vffb0wgsvLHUNDtayP+tZz2r1sht4gxfy4CfYd8455zQeXv3qV7c8NLGdty+d6rQF4N7+9re3et66sRQKXLe5zW3aunq3G+CoAy98AoPuFARBp9IH8kGXhENPuyOPPLKULXXoec6Ri31a6AMtFXvVq17VZKN8n+VnGrNN4oQPlblMOkcWbdhIEKfCB6xShUNl7373uxsYwRunyTkwf/7nf972Kfj617/eZvxQprqkqzqvf8tb3tKCpMyuwW6oIwCDXfL7zne+s02/YYPYPjZIYEU0Xv7ylzc+0Su2TKLOjy0zp8kacAcjz/NBngCM/6jPMk1pz3LH1avLcuHq0RH8EG6H2Sj+lEn8+DF1VcE4/TGZKl53dFklQ5alPqv6LOvpwsuzTfat+mCoTeKo8mM6zDa9PhuCq3SUZS6P46r4q+qzrOKHMtHpyZHliTfrXQ7BVjxXZeKnwpH8V3y4PEPwVdtKDxWPVVnScv4dPuWrcCV8D/c6NBzH5IF/Bpw1sJtpYA627GYdtiewKwcEWdMZUl56cKcoz7MtThcOEykd0fvf//7NoWIZAnC0JThAog6Hl8TbcxIzVdibBacLB43PC8p5ZJmQO4ds0kbijTp0oeOONbRYpkB6wAMesI3M7vylPORZHoHD7j/WY1c/8DuOKt+YWKYxuomHNvCOntAbukDOZz7zma0MvX3uc59ry0ZIlB9wwAErPfnnGY844ogFDjQ4pFNmQBx66KEreOgriVecXDbBI0BCoq1m6BCAIJBFQIi20CcRRPC+0rkcY2Y0OC0+k0kb7ISgB7MlWMLCEhP25AEWmsh+9LItS2RcVwRqWD5DPXDYI3xiTwTyFOgTH/DIbCkCCcCgP+giDzqVHRFEkK6oZxYF9ua0p54TZCKYSMBJCZzMIKF8SjCQdtBzO0qb+sEPftDQs39R1Qeu9yHe2auF9KhHPaoF9UTHj9gXM2OSh6QrOt72ta99bcNPHwieQJmWPbHkzgOBJ554YutfArD0sXARqMEGSdiN5MtjA5jTZA3wsE7SQ7ufuwPRO6+ciiHHY8zBAJ9gKtxeP0Qn2w7BjtFLnlKGqXnhSd6UH+Kx12ZnlruexKsfp+oh26YMTqeney+fQjfxTNG58PauieS7l5e84mEILmVxWK9z/rNN6tfp92j36PRwT8GT8grX1LZqX/EwVpa0p9JcV1dT8a7Dj+up0lklu2zUj5NvAjPgrIFdXANzsGUX76BdmT3e2vYGzc3y7Y6WcKXD5eXpRLmj53U4ZnJuCXBQx6wBnHscyhe84AUrh0r4cXJ5u81skfe///1tpgpfJcGhOuuss1ZO5Z3udKfWhOCKO3Fszkm69rWvvbjjHe/Yznmb7w4dwQQSSx4o52253pzzhpwABjA+mwEZcahxrKf80qlLfare+RKM6zB13RhfJvR00UUXNSf0s5/97GovDma7sJzkn/7pnxYvfelL20Z9JGYBEawSjcvQtKAMOgTHZz7zmQV7fJA++tGPtmCJdCt45U877bTWJ/Qj7VnucvLJJy++9rWvtcAEdaecckprr31xCFo4D/DCviTAYifXWH5BB3ilSy+9tOFjucoPf/jDVfANfAQjRFMzlcAv/uBd/Qx/6OkJT3hCsyftW0LAQPDqD/T0sIc9bMEXcKBLkELLc4T/gQ98YMNFwlaQmzJwYT+amcPsFAI+2H7OFEp7IOiFHiQLMzISpspLV247shnJxjIi5CHxtaAKj+QHpldPOQENNj0kMWPm+c9/frtOFAgFxnVK3q8vrjOCSPSN7EttmA2F/knMsnI+oEs/UE+ABRq0x96xwfe85z3b8a3g3xlnnLGd3Tcic9qUBvJ+5M6OnIp0RjLvcJWjUDknFd0Kz1R8FQ2XZQrPPZ6ch4TJvDtYyXvC9njqybJO+VbjFr6UAePLssx7P1R97HJ52+y/KbTUPmluVh8bae9tUifr5lMXvfaC86Prt3fOC4Nencp7MJKz0n3W9fiuyiuZfbDzNhuRW7w5nUoHLpfqvW22ybqqveNJ+KShepdX14IfN3UjmBvPGtjFNDAHW3axDtnT2RlysKQbOTzkcbB5M86PfSz4scxHP97CM+tAzhYDP44e6V73ulebGYBTSfrkJz/Zlo6ks4tDxU91vJ1mSRDpfe97X4OHJ/aoIPGJV/HIPho4lQRiuLnoS0VqJweNdgRwwO3ynXnmmc25xwn/+Mc/3px7luCIR/aHIYDgPzbjrH7uKFbnjflluu9977udTlOvoi/ZaYeONCsI/Ne//vWFss2SQPfAMytAM4QIrAiXgAlasKmdcIOXPURIfMYy4aFFmT6t/dSnPnXVnvKrX/3qq/5i1ot4kOPLpsbCyVGbGRO8SFqUKQBDHbMdlJjZI3gcc5IvKyOoR0IeyQc8ekHnJAJKmq2CXEr3u9/9Vvqjzd/+7d+2Kpx8yS945YFTGTNoWEbDLA1mWDBj55BDDtku0CS7ILAILwSB6FNm+hBMIxAlmN5RPH/rW99aLWfj2iIwxI/rREFHAkLIP4arV69yAlcKuDz3uc9tASeWs93oRjdqMiugpv6RXnR9ESCh3wmIMpNIuqMdCXvNsQEcwBJY4fpEZ8x8IhEgxM6Tb31BS/szSVfiR0eVz8dpGmBsVdK5P8xjY1OchCFHwfHluTsoQ3UO5zwN8ZZtlE86Pd4r+Ip2hQ+dVuVjtCqee3imyj7WfqvqexY3hH+o/9XO7TPLxmhmP3j/6dz7Ocuyruofb6P+zb5JHSSerO/lK130YJ0H5ytpr5sXrp7crrPkYYzXqj77sIJRWQUrPiu+nVfXQ0Wjqh/Tnctf6SXrK7peJvncDrKsd03M5bMGdkcNzBvk7o69tofxnA6L8qiBAIWW+7haGNiVcLbdicHZxBm6z33us9p0kxkXwOFkkZyG9nkQPurY14KE86k2WiJAcIU34uzLwcwM0m1ve9uGk+APTiFvvV//+tc3Bw5HlMS+G+CCd/ELLM49SyAkJwEDnEjexpOAdXlboSXVuQ4knx/VBJrV56upr3CpHQEA6YIy7SHCueuWvDaQxUHNrwXg3DseeGQPDBIOvBzmVrBMgkWnpL2Xn3WUXOTh2XlhHxWCRzj6zGh6/OMf32ZEkNi0VstnWLbkOqNeQZIGvEw42iQCKILlyAOJEsET+o8AG4l6fXlqBbQ8wZaxHfjT3iDU0x8sg3Febnazm7WmBHPcZoXPeaGMZWXwwI/NmflsOrNxWCJHoI7kbbA5AoX6HDMzp1j2pX4QnWzneifowa9KyERQEjlEd8iGE4frgjquPWYIwTOzi9jI9vTTT2+Bykc/+tHt96Y3vWn1JSG1J7iCbfLDBriuCLwwYww7VFAEudMmoUtABRvi2tT1jy2Qly5cLnRPcj2RX0f2hmBO22nAx00fp3Tu9ds1vqxgrB/S7io86+AYgs06aGdZRZ+yqXAVrMsoPElbMJuh0+N9rNxpTumPIXzJf+az7UbkThwbycOX90H2h+rVn0Ow3ue0k8x57nyO6UWwFVzVRxVcTy8VrOTNo+RO+ZM/lzn1MVXuiq+eDOuWC3fVz2O41uFL+tsqnFOvD+dxHX7H+JzrZw3sqhqYgy27as/swXz5zTnPyeuHinBqcKJIQwO4O0rA4hCxxwbOLU4uMwsSRl0g5528+FGAAGfOE7MbmG3AEhh4Y5NTEvBqy1t9ljrgyPG1FO39QLlgxAtHyYt8BAJw7s4999wyyOS8+A3by12HeQ7cYx/72NVeJ3kjrHC6TnQOHp0zq8fLK168DMdWfKmcYBXBFC3pSByex4GW/vQwgTOtRCCMoBd9Qt+Dk4AIdFkGRSIQgyOdNkEQwnmTXMwSSVjRo1z9SBlyKDC0YspOWCbky5cIfCVuz3MuOUEDTyrz/hOvHJnNRaAIWyLY4nViBR0JH3gIkI31o9djz+jFE8EdEtcstg98ZVPbNFpmgKtoZxlBKoIm/KDxve99b/GMZzyjBV5YPoXcLKmS/hIv+/HQlk9GEzz5yU9+0lhJOOePgBE2rj4FR/IleJWjd20+7HV5vaUe5vywBvw6ALLKD2MYrh3rn16/O9YxHD0OxtqN1U/lIXVW8TNGa6y+wrmRsh6dsX7Idpnv8eJwYzR6ODZa7v2i8ywT7ix3XiXD1GOP36m6cF56uJzvCkb8iyZ55195jl7n50kj5Xe6LlvFz1h91WYjZS5vr/1medls+9TbZq6Lqr96cs/lswZ2Jw3MwZbdqbf2IF5105TIGsBV7gP6Rgb3Sy65ZLVnBAEX9tDQ7AanBX0crqTx85//fNUbXgcOgi0EWTjHySPJSeecGRMEW1hWQmCAZSwknOqUT3nqOQcPSxrgl/zrXve6tj+F0tCNM2cwrBoVJylvAbJdkfMqfgUk5zb5o00GEpjZ4RvmAiOnV3idP84dr/MhOD4B7byIJnuhMPviDW94Q5vhwrI00r3vfe/t+pzy5FX4K5qi53WUPfGJT2yf/q0ScvisFmCyvcoSf+rE4ahTAIZyZlSxNAg5CT5B05Pj2qaiyIi/5JNAC0u1HBf9yL5J1H37299ugQ/Vp20UpCbx6PiYycMMmg996EPteicg4jyhE+9TAnDMuuGaIjBU6TT5IqDjwTOus/z8O21cPgJdqWPVU76uLpKnOf8/GkCXqeut0s+PfvSjNhONJWtXvOIVG1rvxyl02DuJ/axIzECbkrh2SNwP2JNpnbQVtjVEfyvwryPPFFj6iIA6y43pK0+XF788Q8CT284UWRxGtoN9Yztu673xhP2qqOMFhGYuCjaP6/IzBV62w9g81XZdLmi4bMpr3Ewd9O4vwpE8Z/mQ7WRb9Sl2duUrXzmrtySf/G0J0hEkFc2NjqnYvI+Zeb/L/Ahrc/Wsgd1CA/OeLbtFN+2aTLK8hqBC/raaWw3q1eDuZXlOvvqxD4OW4Mi55s20PkWc/LM8IfEQrCHxptzrtEyA/R9YIlPBaD8PPqOrmTFsjMs+M46LtkmXPDMwbn3rW7c6nEJk0I835r2fy1Xh9XqdAze1XQ8nenV8gqMs8VPGTBPB4Ahzzl4tJC3H8nacAye9fupTnxK51dF58Nkp2isFh5wlROzvkXvnOK3cTFcE2DjZ5XIGVK6lU8inpSsc4Uc/8m4H2wkSBdhNJtFzfrJMMuH0a0mc4+Hhyn+qU1nS7OVFR/RZukc/Efh48pOfvGpWPcz1cHo5my1j+9g8SfR0Th598vUoErPIKFOApacXAi7UaUZUBUcZNsMsOdKLX/ziZjvMDOPar9poU2AtM2oN57RbaoD+5T7AXkY4EOTXSbKPX/7ylw3HN77xjcnNgef3q1/9atVmXfo9Yokn87Sr6Pfw7ezyil/6B551397ZPFX0xBPHKcnl0jn9z95aF118cRdFjq3M2uMFj9tOt/FAhey30rc3y3rZDnafdZkfIN+qXLY8Vz7vWZkfo7GO7awDO0ZX9evqZCreywvOx8yejG5bU84vL1lmurMGpmpgDrZM1dQMt50G/uiP/qi9oc/fdoA7oKB3A9LAXJFUHY4XTs+LXvSiNvuEvVNIOII+sAvHhz/84e0cJ/YOIckpUzv2ArnFLW7R6pg1Qbrb3e62TXtmFQDDQw97xZDYP8Nl0nneaHDucC615IivJPGWgB+BAH7/+Z//uWAmh3481PAj9fTWKjspeajynaatmAe7bOPwqlMZS7ASnr1xSDi/Ca+8vgbFUqBsrwCMAjKixYwOAhbYAzNOSGywm+0Fz54gnoDL1OPvwAMPbKAE4hI/byixwy9/+cvbfCVIuBNedH3JlcoUNOzxAU7q2FAYG2V2yVDSw2nvYXaordfRHloKcDIriwevCm8+EPd4YGYBmwczC0V7H6WuuDb06XY2xfaUsPQvAVDGNOqucpWrNHD6pUpvfOMbGzzXIzyQJ7HnC9eqkugkHvVZhXsu2xoNbJWO01bgjn199t9//21mSwiux32v/gpXuMJ240LSzLYpW+YrHno4K9yOz9ulXTudKTxUfG1F2UZoT2lT6Wwdfqv2zGiR7fR46Onc9c/5Xls8i6LHz1SZk+/Mp704vUpXG+WnureMyZD0K/iEUV596h8K6MEOlXNf47mTZ7lMapflOzNf9Ufy9etf/7oFOJHFZWV5rtu9cKU+1pFnM23XoTPDzhrYqAbmYMtGNTe32yU0gHPENP788WbZf/qqyAc/+MG2tIcgiYIrvJnGCSf4oa+7+I2DN47sy4Azh+N27LHHtk9Ek1gSlElfJeLrQaQb3OAGCbL6cpG+fMPNJ29WNPKbCDfeww8/vL09v8lNbtJw6ka1HYHLCvxhoweT5UzzTX1W+f/+7//Opts5C9sBRIHLpyp0i47pP+gyW0hvfffbb7/t9CQd0J8k+pAZK7Qn8PDCF76wOcIkPsOdN+YnPelJrQ7nn8RGxRtJ3n/eLypn+ZeCPcwKIwBEHTw+5SlPacuYCPgQjKvaJ9/w6GXaRwg7xlY1u0JwOhLYYRkbQY/b3e5224jqQY2xcxpWMELoARO3Q4K0WmbDTCXsyGEdbw+X8LHvi/qPTXwJWBHAIdDB2MDyoeOOO261ZJDlS95PLjzwLKtibGCKO0nLqwio5KeyCZywJIrrUXbFjBUCLwTv4CtpMUuOdPOb33xF2vva+ZnP19dA6jLzUzDmdeZ9mHUEO/fZZ5+24XbW9fLwkHVVWcJ43uXI8mw3ReaKflUm3MJJ3uG8PGGn8rEOXCVrllV8ZFmvTcKN8dbDU+kSm5HtVPXg6pVXfFVlat/j22kkbA9f4qpkTr49r/beLuHFV9Lqte3BbUW58zLGF8uHWM6VL0HG2qUO//mf/7kts+U+lnqqdNCD2Qr5x/pGtBOOZ2VkYGmV8zdlzExcqZ9ePnWzVfLPeGYNbFYDw680N4t9bj9rYAdpQM4Wsyb23XffUSo4YSy9wYkn8Qla3y+CZQgERXB4WWIkZwtYPvdLAECf2xUxynmDkenggw9ubUg4Ylo+4nDQcBg5dXlTfuhDH9ra4wgiK7M3+HQ1DjlJevBjljld6pKG13P+vOc9r/3G0he+8IVt9DSGV/j85uxlOpe+FYhSOV/zGZqFgZ74yg8ziWib7cHrX78RXr5Aoy8B4ShrGQ/1HiQY00cli5fB+1lnndV4IHiUgToCMQRA0M+Ufkp+WIqkDZoJYpB++tOftiOzuFjqRGJ/ketc5zpNP/7VqZ7dVDpYh8eqPfbF8kOCEmxkC98VXGPYkmCc/mMe85i29IwgKsGPKqFbvkCWS7SY5cbD3xe/+MXWJ/DhG9wCz8wnAlgXL6fqa5kgD8Ha/Jf9fnCcSPClPWII3mFPakO9NsXNjYMrnuey9TTg4w/nLBUjyM5Yqc2eCZixb9CVrnSl7n4RwsPMQBwFbIBZJ+wv4TTgjhmD2ILqk2PV44SRxBPnKku+hZdrgzHNv2yW+LXMEjrAY4fCC2w1jiCXZjoiU8UHeAnuozs2CifRjrzvsyE9c+QexV5bXj9En75At85vykdevMAP8PxcJ9kn5HH0SIKtdFzxJvou/zq2Q3toTbEd5Gcpj9uOj4HgwLY2YjvYLc8UQ7YjWZ1X9h1yXaWOZDved6n/qbYj2+X4s5/9rO15JNtJus4rtpu2kzwInkA+dtOzHcH5sWc7otGjBQ71qV83VT/res3rW7DMBiFpv8C0eergM+VKvdFf6MBtjHbVNSc7GBobXU86B5/f/8Tbd7/73QbCuIQ98vzDWKIxkfNqzx4fn7j2dP057UqntON6Ylk0du+68OuqkmEumzWwozUwB1t2tIZn/JvWgAZNHzy5SeHkTE289ecGpk88E0zxmyaOKs4oGxX+8Ic/3CaIgPPOrAmmRH7gAx9ozpM+DVvR5829ZsiAtxrogdFMCngRjI48KBEgUp4bIAEibroJ6/lenfhE5oof6vkCzzpOoIJIOKi0I++4yWuJlusJGLXJzz4Dx54XBLbYj4bZHwQOmBUiesCgH+nPaTKz4Otf/3rrK5xjZo3AG0ExTe0VL2rHQ8D1r3/95lBrBgwwjldyXPWqV3VRWuCEOvWz6xcbEK9qhAzMkmCdPZ8o5hwc2BObJcMLdPlRDo7KhqRbllU5n0972tNWs55EU9eJ8PLpcOxP7XrH1NM2gg9ksCP6r+pbmkEPOQkaMjtEfLkcQq8vjREQqeqB48GWQApBEjad1hfA0CdvGdFf8oL9qH/Ay7IhZpvoodHFY+kRtkE/K3DCF6MYS3iw0yfZ1QYc55xzTpth4xtpa1Nu9snxYO6AKueqiRpIR4g8gRbGTGyA/sfefvzjH68wMh4QqFcwQRU4OOctZ0D+Iqbwc+0zA9EdBMYnNhy91a1u1WYpKLF3Azbgienz0NRmyiwdJTnv8MdsLBwHEo4GM61oWzkmBFO5bwle9Lj+WNIg3Ng4MKmDRmN5T7l1LIXCgYJPZEJ/559/fkNN4HHvvfcWmeZc8hIAHSjRBr1yz/LxEPpcR7y19wT9WyzHP/9iHPU44QT0GSczoUdmd+aYQN/RxvnhJQrBdBL8SCeJ0/PYDi9gttJ20J3bGs8a6M5tB942ajvInrbDGNizHeCHbEf62pG2w32wZzvQV/+O2c7Vrna1bboT2yGAXtkO+sB2cMjdFriWhmxH+hiyG2bi0qdcA/QryfuZe8NHljJrbMH2/3TZR9e73vUaP7TnGlGiP0m8ZCNAIvtwHIJlfGJZvAINlDM+MbuE5yeehbRnkWYBoyfoadNi4UKf17rWtcoNu2kDX34dQxt5CeqQtFSY88997nPtx7jBNcDsTvqcc/REHyMX+q/GXXR06HI88wARs3bR8w1veMMFtC9YLrlynf7Z8mUSL5K0AbSPQ5JxPs4a2Jka+J2lEW6/6cDO5GCmNWvANIA5yiQ56u1HngvOYddRpJt9Dwd7QfD2nxs2zikpH+6m0HRaFbzjrPBTpvLq6GVZX9Fz3VV6TH0I55gcFa1eWaV/fYGITU+npEpXU9olDA8g7KHDAzl78SjgkXDr5FOHmU9cLov3t+CGZBXuHkzah/A7nTwfo+vyuA3lOXjSbpwf6jOfusl82k7yIvghnfd0lbTI83aZQAwzXwjmVLPZXI7EAS3GMQI1BNcYUwggSud6OO71QeKb89tqIPtZeQJdOMw8rJN4GMfx5u3nV77ylVbmThF5HvjPPPPMVsdD/DWvec0Gj+OGw0fw4/a3v/0q8IGjwhtcxg9wCQebnysR6MF2cG7BSaCOdNRRR7Ujjj02QXBCM05wkqGrT90TDMYhUzr11FPbKfzAF3gJSH7+859fwchBo4CgyNve9rZVHbwSKMQJUiIoDQ4SPBFsAS98K5jDrE1gkj6OFMEd6QkcBJM0s6KiD7xvZp4BKw/icG/AeWLvLgXMvO/ocxxlyYNemLFIMBf+pSdvMzQGEGzCYRyyHXTjgdZ1bQeHk6XGm7Ed+DzjjDNGbcdnMGp23e5uO1/gmrwsILoR2/HN5de1ndVFEyf0qYItwk8gg37m+pYtci1///vfX40FsgGuO4JtWoLOdc+MHwKRBFtlM5BldirXIuOElqdir3deBmZ03eka8nGHa0nLhxlfuAZpt++SJ4Ilfo3xEsIDhIxPZy5n56J3tSGYxJiGPRGM5kUQfOraY2yAfwJN8ItsOWYit77ciWxaeu/jk49n0jM8OC8ElBQ4Yqm27tXrPmP0+ncunzWwUQ3Mm7tr6wAAIABJREFUM1s2qrm53Q7TAAOjHpjzHKLUDT0o9RhzJ03tp+DarBO0EV6RwW8QebOoeKpgerqoysd0sVE5RKvSf8XHEJ2pdU6rokE9TjROkN7ysG9Kvj3u0fNyp6Vz74uUO3lzXNmHFe9Z1uMROOocp/JJM2FEo8I9ZifeVrDZJuWs6KScibdXn33Qg5tKE5vA8eRtvX+yuoc3dQcdZugQaGF/KBzWOW2NBtTXfswyHsZxNu5wWZCEet5u83DPQzuBNHdO4IyNkXEcVI6TjuPCrEcCsgRDPIGTgBp9zYw6Ek4Gs9MI3rKMDYeETS9JOF7Ak3TEEWMWC29rFYDDocKZZrYe+xNViSWpmvnFTAz2a4LPb37zm6slttqYGT0Q9NMMLvDj0OB00U5LG8UTPKMLyvWWWHXi5Q53uEPDh+zohWuF4AzBB2ZxkUQfR4/lvKIPv7zMgD5OHjoj4XxpxoMHgajHoWLWEPXoClzwJMeMWQvoG36Ax7GT8+r9lGOS5OEoG3Lb0RJW2Q664R6yUdtxO03bQecEECrbwalVHwhH2g4yYEsEDqfYDvC7u+3ohZjbDkFtBRCpx3aYnTxmO/RpZTvSt9uKn3u/5HVCHxE8IQAAfmwU2ySAz5gCf/QtR9krPGhGB7b2mc98ppHL4Ct9d+6557ZgDjNONNNH/GKrBH25PqFNOQFQBTsPWepJdKDJ9UgAh6Mv08eeuLYZN2584xs3XOAkWEWglxnFBEWQgWCSAsyayYlO0u6RR1+bhDZjpsYa6DAuoSdkVwBLuuX6FC/McOUcXTDzhvst1yrjgK51HXv9N5fPGthRGpg3yN1Rmp3xbpkGGCz1A6nnN3ou5sbaJ72p7YbwVopJeD1s65j1G82nPBvFs5F2KUvqMvOiQTv9hui6XofgqONmzJtVlu8QcGHDVpY2TeEh5XDekk/pu4KpysR31U+uh+ShkncIpsJf4Rgq6+FIucDhvOh8iL8hulmXeDzf4xEc6yQeFrER1p8ff/zx3abOm2gzU+vRj350mzl1wgknrNoKtotsrpikAX9455yfP9SDBIeBh3GVsxxTCQeMcmbC4ByQ/KGeOmxKARaCApSl4wBdHCICHSScKWiKn7322qsFWZSEQ44D5TgKJLWR04GDwywGySccTPVnSZxw4axphg1Otso144U6OevUyUEBH7KjA7URDeQm4CienF+W6Dk+6q582ZdxmOpPnnaij6OZ9HGISMxYEX0CRST05fKBj7z0yFt1ynCwlSS/+M0le9KhyyOZdaROiVkI6kfK4V9pR9kOM1E2Yzvw6XvtVP3qtiO5dmfb0fWhL0ViI8yy8P4mP8V2ZAcK4Ki/HVd1vjKM5Ynbl8oJgDHuq6326mPMIPghuoInz54rsnHNMCPw6jaLrWgWB8FL6qDhSX2rdrS5y13usjjiiCPa8nTHp8ALAQvxyteFRF8zT0SHIAfPUgQ/GQOdvuuBctcleXBqzEQGjdPUca5xl4CLeHTZuD7RqWgyY4/EeMKeNqmHbZQyZ2YN7CQNzDNbdpKiZzLTNKAbEdDpEDFoZpmwersepcStQVjHIdzOj+B68D36Y3QqmZ1W0q3qEofzKP319Oj6STzkp/A/VXbnxXFXdIVzSN9ZJ16dHy/jhszmpXoTwgPHkH4ruQSfekk9q76n95Q5+ahoZ5sejMOBVz+VV2VjuFPXTps610dPZsdR4cuyqj8rmUU/jxXsumV8wYn9YNjc98gjj9xu35XkWXo85phjGqmTTjppNQMgYcf0sS6vewK82wTn+XMdcL2rXuXMHuAhHycNp8cddpx4JdEBjoRzIKdBdRwpcxw4u+lc4OwR1OCXOMBNQM75dLuQo7FibHmiWR2UCVazRsSnfzVOgQvHoX0WKMOh8pkTlBE8cTm8LcEW6vx6Y/YMjhGbniKL00e+xOXBC3RMXht7ExBPeOiDBx2y5IKgAUclAmPeBt7U19ItPGnJlstDcIvAkrfHdpKHrbAd4VzXdir7k07SxiVbVT7bzva2I0ffrzv1NbrU9e42U+lYfepwBHnSNn2JDoEBXbuOU9eXbJzrg6Uyec/Q/mBcF+pv8UuQR7I5T9g6eGhL4FCpGut8/zEFAoGHBnkFNZN26kI8IRc/gkxKjEVqrzLXETygI+FAp7pec/yjPYEfwZL3cWpFdD6ZNbATNDAHW3aCkmcS62tAg6K39JtLhVH1PrgmnMP4wFu1YdNNfiS14+h8jPEk+uCfCuv0krbyzk8PXrR1dPrr8pM0EvdYvqItHnRk1gBJfVHpqyqraCecy8s5U+n9KzE9fWb5GK2k4/JIrjEcznvKUbWdgtfl4HwoD40xutl3yRftHcbrE3fmp9AXPtFRm+TL+yN5HMpXOmVKP1OjnfYQDvHEJpTSee8o2DF8c/22Gqj6iTJ+7iC7o6E2viwGWHcmNK29p2+cdRwnJdFzfjJoAaz2pXK8apObtidtyeXlBE/UniP25cETfY1FbajLwAF18IoTj6OmgE/FY/JEO/Gla9Hxc+4BKHfwHRdOE7QV+NK9AH1V/BLQYeYMSbrnXDqUTsST+hoY8OHYam8e54MlZXsvlzJ4P1ZO6lbYjvcbPDnNynaYGcUsTE9qsyNsByfYA1I922GpGMGx3wbbka3pWuLInikEZTn3PtqmI5aZrBescOpLTz0c2V62KngduU5ZWjOU8prpXUdcQ2NjnXDRvyRm+yT+5NFl9Dq/10teBVbBzXWVuD0Yq+tEOH2sokz3V+mm4kNjgvMypMu5btbAVmhgDrZshRZnHDtMA2MDogZOZ6DXxgfeHHDzZiF8wtU7AtejR53wDsEMKa9Ht1c+hR+11c0p6Us3WV7l15HLdSHazkOlq8Sf+YonL0ucPZldb0O67el3iI7qaOvnzqfLlTJmPuUbqu/J5TKuI2/K6bz0zof469X1ykWDetel8tWxx1eWJ77Us+sy2zpfVR186VfVz2XraSCvKfL6CVMPJuurtuwbkHDOIW9MtbSGcuFwRyEDMimhO3aOQ3B5DaR82Ua273InTWaueDBG9VoeIJkSh+edr9Sx06MOGT0o1aMvp14yisdKZmiwKajkd51TXvGkMuEjgJJLRMCnt+aOI50/50nnDj/VdqQr4XA6BDq0R4zg0J3SzrAd7zfo9vpOzvKOth1s1Jf/SRdpOwQ1fHbHSmmXnfRshwCf96PaCZ489XldqtzpVHZRwWWbnq25zXFtsGdLxQf4KB+zD+CYyaJACwEN9giiD8GP/i644ILGXuJKPbnOKv0ljoT3YErV3mfGCZfgXM89fQi2V9+EnNOsgR2sgTnYsoMVPKNfXwN6aNQRDFsxUDoOBmDlde5lzrXgeschCXt8VzeVxONth2hnXeJJWXpyVnAVrp5Mgs36vNlV+u7xlLgyX/GnssTpedf/FD2P0fX6iq7zVPGc+DNftaGsgkv6Did4P2ZZD6+Xe58mPfD1dO1yVLyrfqhuSBeimzx5fw+19zpvU/FTlVW4e7qmXL/NyF3R3BPL6C//ZV+qrtINDoU+D0w9swjS2c12tJGNcO55YHNZALD+uVS1dTvTua6hSgYv+7d/+7eVA6q2LN9RosydGRwlljJ5InCkVL19d/6AS349X/HtwR1mD2WwB/oK9mimjmaOsLFn7rkCD8itBH311Y9+9KPtHGZ48iUSwIOfr5VUifqUsYJTGf3uM5bYkDVlzPayF8or2yGA4EtK4GdPtx2upwy2uO3kjKx1bYegEXrWeK1zX6JGf7lteL/27EbwWV/ZROJ2O5GNE/xhA1wFw/z+kjiTP8/7Ukm+quU2y3WkJJ60v4wCWdQnv96mJ7fKNV4S+FRifPBPPFOusYFzyex0wSMdrBDZScWjxqmhdhWuuWzWwEY1MAdbNqq5ud0O1UAOhgyYvYFRsOsw5G2SVuIR3TwC1+PJcfQGe8FkfYXTyzbKh+i5vKlX1U2VzXGm3rJOcjp9YJKHxJP6yHzCVzidRo9e6jXzKY/T9T50/qbQBU/KlPlKRpWN2Y/jSpkyX/FS0Va7SpdZBmxPPxXuqmxIH45f5w6f/FT4vUy8DtGscIzBq56jfsIz1raiN5f9jwbcvjjXTxBDedW5s4Hj5Ut0wIOTw5IY7b2Q+geP4yDo4YEO4H0ZmuxSvKcMiZ+8w5Bn82X2TfGkN/Es7xA8fIl/OU1qo/0ZyGsvhim8AC/dca5rT3i9zpeaJH1fqiL67JPxve99rwVJUmbwU0ciKEY9QSIl8Kkf4IlZIu6wOT6/7qbILBg/cp4OI8Ecx7cVtpMBI9f3OrxLT1NtR32H7WffbYXtqA9SBuWn2g57e9BmM7ZDP7nteDAHvYG/Gqspd/5XxlicDMFlnePVl71Aydjiecqwe/jnWpA9Cl/Fn+yJzW7TXjOwh8y+FI9+92AgtM9afhKadPjhh28D6zykOgiU+J4syEVAzXWswCr9knwKX+rNy6mr+ix5mfOzBnakBuavEe1I7c64t0wDGiw5btWPKDk/8Pm58Ge94Lx+Ci+iMQUWGFLCepnOt0K5SWcj+XXlm6JH6T77Zyqt7E9vl/2aeecv6WVe+kp6VblgqmMFP7UvKnxe5rgTp9uS6taxK9ooJW7P9/Q21GadOseftMb0k7rKfI+PxNuDozx5kt7UZiuv6XX6b0+B9YfxfDCXI8KD/NWvfvWmEj4jzEO+6nAK+KTzRz/60bY5ZTovytOf+qIQnyolKMA0eJyT888/fzt1V7xsB3RZQcJSTPCGL/cQUIAO9C666KLWgq8hiS99vpXPubJPA7DU4XBRRkJ2OTMVLWDcXpVvjZdJtLytyvT1Er6Uwr4jTv8Tn/hEa0/QSPRxmEnanwL9gYuj71fB3i2U46DJSeYT3Vragnz63HZDeFkSjxXPDufw3ibLN2I7ycOY7fhyFui7nqu8y5Gw1PHVnksuuWQb2+ETviS3Hc5JO8p2vA+cT5VfXrbD9V9ds00ZlpLnXh3lVT8I3u1Btsz1LD1gY/qkM2OLj09cTx/72McWH/zgB1u/qs22nP4mp7q9l3sTkdiPRtcj1w3XlwcnueYY/wjg6PPzLDFSEIQ67IjEZtUKymiGCl9FG5Ibu9cXyXzMhCfGKn3JTGOYyzaE9zfS/uYfOGbr8GlqeHVdO9x8PmtgR2lgntmyozQ74920BhiEGRSHjhARzEYJCj/tOc/kZTqv4LKd8hV/fpOYgmsKTI9+Ve4yu9wVr1X7qmwzPPbaVuVVmfNT6dblUn3iUd7Le+eV/NKj0xec48n65KOHe7PlSWdd2Xr0haen11659NXD2ysfwjdF3z28zk9Fw21oCEdVV+k6+6NqN5dtjQbQdV53iZl6Pl3KVHbesOK04/TwNRotJeDrODgpwuVHnfPlD94M4xwToOFHAhdLV9gnQc6U80DApLITwVT2QoAEZ4QffGoJEeU4ZuKJPI4bDgfBGDnVcuDhR04t9Ib4SL1VfDkMPDAbiK8aQZ9A1le+8pX2Rlv8Qp99KMQvSyb4lOyFF164YLkDP81wEG42s/UZJWx4TjAMnB/60IeajpGP2UU4gfqcdMqX8lT5Mfu5vG2HoFu14btkqfoIm6Af+O0qtlPxybK3rbadm9zkJqXtEGwgYIHNEHDgSLApNyZ2G5FtcEz+e3nZeeKRbbLvD+PPV7/61fa70Y1u1IKRBBwIcmh8ot+wc41P9Cm23ktOF3xci1yTbOLu1+Nf//VfN5zYxjnnnNPGBvBe85rXbMFmdEPAJa9JPsOsxDgILLyefvrpLZh705vetGQNHbPsC1jGJwI0mqlDA/qf9vDvek79JnLg1YagEfyQ9JnshJ/zswZ2lAb+9zOWaUchn/HOGthqDWhw9UFWg28OvF4+dA6PvfqhN+a9NiqX7Jl3elP1U8mWbRMm66fkx2Qaqh/Dn3pwXK6TIRrVDIEKPvEN5Xs4szxxTKFbwVxeZcm/58f6bkp99qfaDMk7BW/CVPhStgqmV9Zr2ytPPGkn1E9t67Ap55xfTwPpwJBn6QMOAQ/qStI5DgMbQhJA0dR1+pKNU4HhnD1OwEH7a1/72u0hHQdfNoBDQGBAOKBBnn1GwAkPTLfHSSGYoLe1zNwgGOK8gBcHSLjFL8EfnCpmcYCbesrYo+LGN77x6k0yy0LgkyDEPvvss91sKnjSW2ecGnjBiaGcIJA+Ayue2CMDuuAUTzo6T9mOpRfgZskJe5lIl+gV+sjAvg8EQobosxwCHRGE0jIcHEv0QCAml7Sgb/qBPqHPoAMsjiq44AnHXe1SJtc7/eby0xfSC8etsB30xG8rbId+Wtd2cOhlD7uC7aBXzdCin3wfHNkOtvbjH/94ZTtcR7e85S23s90x22FfHR8PuEbpB/oV24EOtkNQBlzM3oAn7XfktuO2Af/0qfOv66Fne7qWsHVd39gyGxLDF9c51wll1DOWQEe6AD8BYIIhzBBh3BJPXAvAqb3bOLigyTV16aWXtmALs/IYpwiO8kMX0Ic2/UEb6EAfnRCcAvd+++3XAjLYk2jAh/bB4sh1xy95gldgkYG2nNPHGp+gR5DJx13RF07JK9roFP65JuCZpH6AX/rfr+cGMKdZAztQA7+zvKn8vx2If0Y9a2BTGpB5uplWZU6Eeg2kmyK+bJx4xvLQq3jNcvE19fJLumrfK9+o3IlvKn/r0uvhTfqZh05V5vR7uHvlybvjH6OVbXeX/O4u19S+HOuPITxDdY630mW2TZg9wcbGdL+ZeumXY++nDRi9HpreVvksczjOs//Ee/ZzlVdbjkzxZ1YJThGBG8q8fkgnFe4h+I3UTeVlCu7kN3U6hKOnb29T4Vd9T44sTzqJs7ILl4P6CsbxJA3x2KPl9c7vnmQ7PVtJnVU21NP3kO14mzwn77+KpttA2sMUnnttprR1G4G3zI/x29O12iW+KfpNnC7HFJlcjh4fVXn21Vi+olPpay6bNbBZDczLiDarwbn9TtcA6zhJetNXMSAYvS1IGKLc2gQMGKL/vZQ3l8z32lXlvCnQWtfqplN9jhI83/3ud7dDN8QHb1F8AzMa86ZA6/O19tfhKnxeVvGLHpl6z3RTpqRe97rX3Y7PquCe97zn4l3vetfilFNPXdz9bnfbzplxuhVfFU6VVfDwXpVXeCq4qqxqW5VVeqvgvGwdelPwr4NvjLddob6SZ4oekvcKDzDr2EviJN/Dm+WZr3DNZcMakA6z/4d0u27/DuESd8LJkpcvfvGLbVYISztoy4/gj+5LvEVX+RTcQzY1rJ31aitevCzrU+eeT9h1ONlM20pXwtc7TuVN8q3D3xTYPdF20PmOsJ8p+s7+rtpQlr/KthLXlHzKXdGfgsf5Sdsear8OvXXwOs11aFS8Zvte3vnjPOEq3JStew/o4ZnLZw1M0cA8s2WKlmaYy0UDfkPSOUfWheLcy1GHOYclmEEghvTZz362BQByAP7Sl77UggQkTQVvGUvZhqqqLNv1+AbuHe985+Loo47KJtvk999//8Uzlqv7mMKqKZDVGv8hJKeccsribssgBgl9vO51r1u84hWvWG0c6G3f+ta3Ntgx2fIBARyvec1rFscdd9zirne96+Jtb3vbEEvb1LF+du/ltFGmobJBW07rBTj5yfwUYhXPtPPyHt5e+RS6M8zuqYGevWy1NLNtbV6jfk/gvPfT9Z71OQ44vnW4y3YEVdj0lT0PCKqwTIclAV/72tdWyx9YBqNp8dBye6hsw+1yZ9goPCQfmXcd9fjbGbwO9ZV4Hjsmjkqe7OchmMTXyyfOynZoS/BOy65+m20HWVMnPd3tqHK3fZ1nWdJ2niv+h66DCt71kOdOO+1adUPXaoVviL8KV1WWOqnoOMy6NFO2lN37Ctih/Fh/VrLMZbMGNqOBeWbLZrQ3t90pGsib0QMf+MDFwx/+8MUb3/CGxd2WTr6S4HwH+TPPPLMFW3xgZ6Bl8zESgQ0FNFyYqTeTIQUk3y1/2ao99hEgSMH6W6YHEzwisTkbO6bDF78PfOADrfwud7nLdqTe/e53t7LrXOc6LQDliXX2PLghxwEHHNA2WQMOmgceeGDbi+Dcc89dnLqcWcIsk+c85zltF3rtIJ/Eqhsjs23AhywnnnhiNtku7ziYdQPtww47bHHkkUe2DQ17aTN94W3TBnYEvR7OuXz30EDaWmX3kiRhexKCYypsD8dcPqyBnn4pzz5UmfqFo/dRD1dyILzCpyNBFPZNUcBFm1fSntkubBKZgRbR7NF2OSqZUsbktZdPehUfDpPwrgNouE7JV7z2eNnq8pSll6/oOt86z6PazbbzG02kftX/0lPaDuWuu+p6ch1X/bTVZS5DnlfyVfQrO0nZ/Xqt4BNvts968hV/2S51TDvvgwqvyir8TldwlWyO13kYopd0nVby4vmsUzvKUx9T6M8wswa2QgPzzJat0OKMY8s1oAG5OvqsFG2E5wP80UcfvfBAhD4dp0GXI5uAEeA4++yzFwcffPB2g3BvUO6VSwHOB+fOP+csnYE/gicnn3xyqTe+VkGAhMSUdG3KlsDslE96y1vesprFkjBf/vKX2yaKJL5YAC6/GTGtXZ921AwXYAXj8lDu+b/5m79pgSHpMGlPyQtHRbu6aU7BOcPMGpg1sGdoIMdXpK7G3Syr4FQ2prkcEwWfNAh26xOqHNlkUsFsH9vkBAyNd04z6Wd+jP+sz3taxUfCJI7kQfksn6LjMVpOO/Fn25Ql8+BSWeLyfnW+U7bMp26G+O3h3ZNsx3UwdJ79Uek5+7+CSTzZxm2kd5540wYqW6rKkpfEk/pIuuSHbLqCT5wVzWy3ERpJp8o7HdfPUJ+4zH6ePPb6rgeXMs/5WQNbqYF5ZstWanPGtUM04DcDzv/iL/5iRYcZG/4ZN3YpJ9Ci2R6cs2TF9y/hKwOaScIu6iTw+gCfeREUL3kz8BuF8CXf5Nk3xeuzHXhZ58+n9/iMJfwzkydTtsu84N/0pje10wc/+MFtN3ng+Il/priz1IrP7vHVBqXE53nOmRWjGTgETPRw+IAHPKCheO5zn9u+6uGJMvR+u9vdbhUcYukRS76YXXOnO92pnGW0DZI5M2tg1sCsgdCAxjMf2xyEeh/7BKdxLY9DCs6xEFjhEx2O/BRc4agy4HXuR5WLdiVTypf5Ib6H6kTLefCy5M1xJQ+u28SRsEM8TalL/Km7Sh61ybaZVz9T7nyrvHcc4lttgPFz8Sk60NxTbMf7KPVc9UmWDel7qC7xeF7nHP3ceXXcwIh3PyZMxU/KDMyQnSSOHt8Jp3zSU/u0xyHeU3c9Wg7ndB1edBNnL5/9Aa4pZYmvx/NcPmtgR2jgN98I2xGYZ5yzBjaoAQ2+ecMhrx/BA9IZZ5zRynD2+RF8Id35znde3OMe92jnH/vYx1qd4NjIlURAhpkeFT3qRYtlLkz79h+fp/Of6pxHx6HyRviyJJ68jeTgc4YkPoGnMj+KZ2CqepXd8Y53bHjYW4UZM05T5wSvDjrooPZZPudl6FzLmx71qEet2vDpwEMPPbTN3iGgwj4F4uPCCy9cPPOZz1ycd955DUa40T9f5iB96lOfasc5zRqYNTBrYIoG/AGa8+rHOK1ynVfHHNN7ecb6qi5pOw2dI5PDCQ9lidN5TlxVPtuvk3f6Oq/op4zZrpJDvPbq1uFzKmzy5fpK+SqZqvY9HFN5GoKDnttG6izrxXPFU7Z1GPGwlX1R6cr5c35S166TCk+v3su36ryn0+R5LF/pO3l0HDu6T6bo0HWfvFbyjMEnjh58rzzbV3lvO9Qnuq78OOXeMsPMGthKDcwzW7ZSm3sYLtakv+pVr9pO6nU2St2ucRT0HH6CKQQQmPnx+Mc/fhUwYY8WEjNDrnWta7VzZmHc5ja3WT3kaokRm7p60KLHyy1ucYteVVkunD3eaeR1QsINg8TsF/ZQIRG0mMJjD+ZmN7tZmzHynve8p+3VwkwXjr6cCDq0F/1GOJLLxBeIXv3qVzcINhl22ugUGszKef3rX99m5QB/xBFHNHj2ZmH5k7f5u7/7u8UjHvGIxQUXXND2OiAN8dIA5jRrYNbArIGlBhgrNJ7o3I9SUpb5mAbM2Bjoyq7aUiYcfk47jWcch37Oa9LzMXEdXh3P2Lnz6bBOO3G47lXn/O0oXpOPKu98V7L15JJM4ATGZUzZgJkqo+PVOUf9hMvxOd+c936S32VyvoVbMgneeVLZ2LHSm5eN1TsvyXfqMmUY420r6l3nqa9KtnVpJo6ezJWeklbiyrzDe53TnGoDFe6qzGmmbFmX8vT4pTz7RbAVD1nm+awb4mGumzWwFRqY92zZCi3uoTje8Y53tM1NM00duLMdebX1I+f8fFYGe7XoCzaXXnppWw8PDJsP8tUHvnBD2TWucY1Ghs88a4Bl81i+gnPxxRe3JUmU9wZx8bju4Cye8/jO5deI7nvf+y5udatbrQIWrgcCQ8961rPaBra/93u/1/ZZ6SXqSW984xvLDXTV7nvf+14LYoDT04Me9KA2A4ivHvHp654Osk/A96d/+qcNFf2Qia8fsScOOmZPmBe84AWLE044YQG9l770pStw6Z19ZVjCRKCGvVuq/lhX/8nTnJ81MGvgt1sDGqeQcjPnY1qqcOc4nzyQ17hWHVXvx4oPp+31vfIKh5dV46qXVfU9nBUPVVmvfa98IziSb+XXkS3pDuWzrpIlYcirTOeez36q7CblSrmFo6Jd8bjRsqSb+Y3gTZ43gmMzbdaxlYrORviv2lRlaRtJf0z/iTPziW+M3hB8r24qzZRl3X7J9vCTZZnv8TyXzxrYqAbmmS0b1dzcbodrgMHYfxBUntkRBCz4qtAXvvCFFmRhLxYCLSzB0ewJgkF89pgv57CHyL//+7+vPn/M8hUf8HH6CVzc9ra3bbM//CtFwOWAnHnx53xWMlAP3/kFIVcos1H4/HNFd4riXS4CTmy0e8kllyzMZmeqAAAgAElEQVTe+973ttlIBF4IgPAjsckuwQ5kUlv0wSwVNhC+9a1vvdIHQS8Sy7CcjvhiOdKb3/zmNpuFtvoS0vOf//wWMEu9aW06y4/4QpFSwq0q5pNZA7MGZg2EBnzsGjqvFJdjjY9rqlOZw2p8Fz3loZFjIzBqq3M/0ib5SF6TF9WPtUs8VT5xZL5q42UVb44j9TGGb6tkSzky3+Mjec925Hs2UeGs8PXsp6IFTsqrn+oqutBIfBXcZst6NHrlY/Sy3UbtZ4xOrz7pD+nYcVQ2kTQct8tVlVd8JL6pvKmdcA7xOtVupvKX/Te1XfLcy1c6mVo2pIepOGa4WQNDGpj3bBnSzly3y2iAwVADos6POeaYxh9LZEjsC0LS0hvgtHyF/UDIsxksieUtwsORoACzKwgUPO5xj2sb6hKgcZgp5+AegwOGmSEss/GfZqq8+MUvbkEfNq8dwtcql6miV5UTXEI2Ai/M/OEz0wR1SPe+973bLBXNHiLgxFeM0AfLtOCNWSrUM0uIxKwg0dbeLDoSrELH2kOHPmKvgx6vDeGcZg3MGpg1sAkN+AN871zoqdePMs/nPgfUDZVlfbXHQG/vA6d7eZ2LtyGdTOEt9djT6ZB+xuhMNQ/hyf4e4jHlz7bUe6p47cnmOk5bEJ6ejVFf1VX8DsldyVPJkDh6Oq/op356bRNuiI+Uf0jHG6mraFf8bZUsFe7kYay/BS+4bD8lP9RWNlrhmcqbtx2iVdEYkw8dDrUb6is9hyaMP59m3ZyfNbAZDcwzWzajvbntTtEAAyDJB0jOmW1Bevvb396WqhA8ILFkRm2ue93rtrKTTjqpLZnRfjJ8QUcwzIhhj5GLLrqoBRCe+MQnLg455JDF+9///hYwYGNZls6QGNyVuHlkYnZIpgwwUA8d+Ml0//vff/HsZz+7fZ2HZVAkp5nwvbxkq+qpY+YPs4EIqKA3Zrq8/OUvb7ITdEEfBKjgk2ALm9qiD7425MuRvE+cFvvO+N4955xzTmtbJX3Cmjrxjcx5vhE9VPTmslkDswZmDbgGqrGlKutprTde+RhGW+Hk6Ode16Mxxq/XD43/Y7TWkTt5ndq2x1/VPmEdJuvET+LxfNalDI6jwk/7qryHZ2q/Jd6KZ8r0836sZKrwrcu38K7bboouen21Ttutgq30t1W4e3iGaKa+h2DdDnq0hsqhNYZ/qP26dRuhtdE2qcfktaqvbH4j9JPWnN+zNTAHW/bs/t+U9My8qPZs2RTSCY01QPIpY2Zc4Pz/67/+64I9ZEh/8id/snoYImBBni8SsVGrZsF4QObjH/94a8cnpIWbpUgsP2LzVjbiPe200yZwtljtnQIe/YYaih6DOcGXk08+ufH60Ic+tC3h6SUf/IduBASJ+Kwzn5POBG0CRiz5YYYLn2Z+whOesKj0wSbBJ5544oJg0LWvfe2GCp33ZCRoQ7/wNSQ2xX3MYx7TZg9pto7zUu37At4huVKWOT9rYNbArIGt0sDY2OMP5DkWa+yqxjDB9o5byb/uLeD8xS9+0TZev8IVrrD43d/93S6ZMbm7Ddes6NFxnoUSWJUzc5KXI8yS9CB9RV6w1F3xilesQCaXoT+eH/jiHvubjaX/+q//WvzqV78a5FNy9XQBDd0btdQW2B1tO2OyrVOvfhuSMfGtA5ttN5KvbK6Hh3795S9/2ZZUj9nfOrBJb2fqIGlN1Ue2Sxk8PxWn2qyDe4huVce4kNdVNVboGva+3iq+pupjq+hVepjLdq4G5mDLztX3bxU1Ahb6eszOEkyDlI4PechDFuwFwtIbnHuCAlquwkAFHIEEZr5oc1b2evEv4vzkJz9p7PsAeKUrXWlVRjtmm5A0+G1kEOwNsI6Lr/zsu+++i9NPP73NquFrQkpDNL0OOuRZ9gMuEl9g4otMiQtYNqUloRfylT7+4A/+oMFQLznYd6aS6ZOf/GQLVBEII0Dz4Q9/uAWhCCbxBStuXuob5xsY8b5idD6ZNTBrYNbABjTgY5POeaj+2c9+1sY4yq585Su3GYTVLMUpJDWOAVuNaYnDx7uh8TzbbTbPLMVvf/vbi/33339xzWtec7PoLrf2BFrY2+uP//iP2xcHe4m+FSwwbEq/rr7dfrinSX/QHkss1f3IRz7S+ETn6yTxCX29HGLJdGU7U2XCcdTsXHhx2QjkcA1wXyaYlAm510kEtv7wD/9wmyYE+tg4nxc/PBfAN0E/linzLDZVjnX42ApY1xP40KH6VTOre3TWge3huDzKvS9S/p3Bz460BeQh0MIYQrrPfe7TjpTpK6WMFSTvvwMOOKCV0X5d/jajw17bdXlozE9MlwfNiazt1mBzsGW37r6Z+cMOO6wFW9j7hHT00UevlKKBkSUwBFuAI9GGpEFFN01mavBJZBJBAYIDwPBAwE8DXO/YGg6kaoD0Ms6Z+UHg6LGPfWyjz74xvlFvDwflkkfnbAgsXAQyuJnc4AY3aDICy2bBBEII7JCe/OQnt3Lpg4cj5CaxlIqgFfVXvepVV1Jyk2J/G6X/+I//aBvtklg6ROCLJVvMcIEOgSv2jSGJT3CT9HUp9VsrnNOsgVkDswYmakBjoI4045yZCQSHtd9Uorva1a7WllV6kL0aa7Nd5tVmbAzbCG7R+vnPf774l3/5l27QxGVXG4JKJBxr1Vc8jPGd8u6MvMvD/YQARjrz6AO9UOd94PxJtikyVjpcR1b0DC/S+zptBVv1T1XWw+0yMBuDpcFjidnC++233+IqV7nKKgDJdbNO2meffVb3coKbX/ziFxdfXX644BfLQFmV/vIv/7LtYUff7kppzAbG6l2Wyua8/Tr9uivpaAovU/T0ox/9aMGz41YFgpOm8hwJrHNtcu7lkiXbUq4y+snrs9+qtj28CoAyluXMu29961ttjPNypzVEZ0qfbATmm9/85oqnlHsj+Pa0NnOwZU/r8V1c3hzMYFdlHP2cAUefIJZYPDAraUDQsheVs/mrJy1H4nPFLKlhrxHeKGlTXeGBntPfBkmR8QGpGhy93psfe+yxize84Q3ty0pPetKTWsAkU9U2y6DJPil8gYgHJgIuJC29cpws+2H/FtpIHwRmWMqEPgiUaANiHopYZsVyJ8oIpigdd9xxbYYRdFmWRQLnK1/5yobjec97Xgt2sReMEvvpkKo9bFZA88msgVkDswYGNKAxNo88yF+wDCrj7DG2sayUh9hf//rXbbPw73znOy0I84Fzz13c/vDDV3tlabwfILllVeI5x/CKAA+9bPQ+ZYaF2rM5Oj9Pfk/a6L2q4m8jZc6L2nuZzpl5wYwW8l7/j//4j23WBI5+r73fu6fKnnwl3UpW6OAoZUAoYRN31m8mL9x5BCczWElOn0AVzxs//vGP27Jf7v2aFcu9PhNfKgQWfV/96ldfVSM7L1+Emz6hb0jgJIjDLBqCn2zSTxCGly3QPnx57fms5KRZ5XeEDhOn5/N8yvUqvr1ttkualazZpoLZbNkUPip5NksXG2EMJtgCDxuVNflX3o+MIbLprEcOAoTQ5+iy+vgxRQc9XtSWa45ZUoxnemEJDa4NypkR58sHE9+QztfV3xhueOXFLC9iuXcKfl06Qzz/ttfNwZbf9h7ejeXT4IYI1TllzPp40YtetGCaLwMAszl84ACGNdYEE7ihk6p9Q5hBwmBCYIFPGvMArlkuqcIpA4zzK/45wh8zRKo9VIQXmQhAEGRhtg3ToX32iPhRcAJ5nCfk9zzBFh6Ozj777BZE4iFQs0gIVrE/TOJn6rD0AQwP+NIH+B/2sIe1YAv75CjYomnKzGJh/xdP4GdmDZsZI9sLX/jCVs2NRW/c9HC3TcNlJuXJ+jk/a2DWwJ6tgXxYVJ5Ay/ve976mnBve8IYLNkz3JUM4izx4MzbiPJ533nnta3b6Ekfi3SotC2/eS6bQ416lNAV+jOe8Vwk+7yk9PClDD87Le3xnufJenjDgxaknUccvZaraiO+qrqLr+HsyJt0enJdX9HvtJJvqMy8e8+g09t57722ekYTrete7XntG+vSnP92O5EnAZyJIyfXCjLCqXo7qZz7zmdaUF1ngk85xInkGIfjCxvvgwq41M6mSSzwM6WuoTu2n2LXjqc5VNkSvapd6zPzQtTRECzxDbZPOFF322oyVj/FZtc8Zh8IxJFOPTqX37K9eHt4qvFVZJYeXZZvM55gl2ixz1bnzOaQLp7uRMagnt/D+9Kc/3YYn8QJ/U/lyHvfE8znYsif2+m4gswaMvKiV9wucWRT8cjBzGJau5KDguAhwEDQ46KCDttFO0hvLq3FvECLIon1YevxCg5kfmvEBzuSdMq/vdan4AB8zTbSEpwevcunDZ61QJ56JcDNDhtkqT3/601sAix+frK4S/INL+Mjzoy3p+OOP3y7gIzyV7BWNuWzWwKyBPVsDjE/+gMqMFhJBFYK5Xi9NaWw6axmU/v3f//22fl9vFIFhf5cvfOELbc8OJZxCxlOfvQBuBY6ZYZkbaLLU5Stf+UqbYaJ2l1xySVsSRBlvDKHznaXTySwcgkME57W0Se1xTEkEw0ncT3hbK9oEj1i6oQdkZjJ89atfXdFxnnGK+QofQSnhxYG+xjWuUS7tYKr/l770pQV8uy6uda1rtTakarxGN7y9hif0puU16ive5JJ4s62ZEqpDZ8jOSxB0BL846rwEICjw/e9/X6y0GZwkdKD25Nkz5Otf//riG9/4RpOz6r8VkuWJ2xDljgt7ANd3l3uikfZdBhHgm5c6wCG/+gpd+4wiLathPzX4YEku+kDn4v0mN7lJuXcKeiDIgZMGPDNF0KPzJhkoSxlUl2/sKVefcZ0QbOElCDLopYzaVseKPnBcR9r/zZdtQEt6Aj97yfEyRteL6np44Z+XYbzc0TUJfvoAffgGxsiBPPSD7IW+U6BIy6o9AAtdaBD8uejii9u1qPHD5e/xJxjXf8LCNy+wFGT4P8vZPlfZa6+2lFFfoaS/uS65tnz2EPg/97nPtWsJeX0GMXUsQUE3tMsXi8kH8FzL0FGiTTVrjmUvBM8uvfTS1VjBTEHGKMbW3PeKsYKXfD5WyNYlj2xatDWmaSl78ov9ozd41CwYtZW+GMPpV9c/tswYCF0CfNgEiT0e1d/CQ14/yvxctkt5jnPAMRbxk440lhKQZIwGhiV90L/0Mp0zniIX4xs8KtjCGE4/agxBj7x81XiCvD9c9sVhy20SdL+CNjYl+vQPds/Y7Pc0ycoRW/T9nHStqD+5r+hewv0JWOxOs9ORKXXh+Ofz32hgDrbMlrBbaEAXcx7FvF/wYxd+hSPbeD7hyWdZKpH63iDkdQkzRDdpTMmLFrA6h+ZGk/jjgYY9Wf7qr/6q7dHCciLfW2YKfm4er33ta9tNhqVTc5o1MGvgt08DjDc8qPMAiHOVQYjNSqzxjKOfs0xI+0SwD4XXi6bGRL7Sc5flHlka34SHB1qWkyrhHOPs8oDKj+C5O9Ny/m50oxu1Jj7WMhWbeh745cTi5OA4Qh/nHX6hwZHgBL9DDjmkPXDjAFZjt+QCDwlnA2eMhJNIPc4G9dDWDEW+rvP3Z5yx0hGwfEGHh3V+PFTjCOuhG8f5zDPPbHjlHPIQLl3wooKH+opH2iAjsDgdyCM4HGLpDUcEB0V1HKV/ghDwjByUszxYsjemLKWuzj///NZvOB/wLp4JaOGYZb+DqsKNHhUUIQiGTuCPH/dBOVT0tfoDZwf88CQ+wM91QHst2xE8PKUOP//5z7c+hXdkUB+hq3xBJN5dBsdXyaV6+IQv+qSCcx33dCQYggbSEcE0lm+nQw4sMpB0LeqYtJRH13LgaSv7oU+5dlwf2AvlOKXYMzaADvlJh1y//gIMmz7vgx9cXRfom77h5/vVZR/1+KWcvpeNfXCJW7MaqMsx5fa3v33TGwEoeIeO6MqOWEZIQi6en4SbMhxigkkecGjAlsAJTzj9rkt0Ak2CkgQ8FEDkujzzrLO20ckvlmXQ4UfwkOXh6l+uE2YskdD1XkvnnCACtPgxrjAW9Wysp1s2VYY/+kgBPImlvf/Iawaj8DCOEzxjjOO+wDlJH/hweuorL0t+KhtlLGdmJAmboc9kY8y858ut0mfDx2+ZpAMdsWdPlMOTxnBecmp89/bUY1sk6CMrNote+N3znvfc5kt04MRWtMyPNvQl+uVaIWCtD4nQ15ngS3bn5wk353+jgTnYMlvCLqcBH8j8JuIXNIO6D4oaqMaEcXy9c3BUdSrrHafQFp+SsaLTo+/4vR3l4FUS7h4/Y/XezvEmPiLbfBqbLw+x1w2fhR5L4pu+00MRn7v2t1Ep2xjOuX7WwKyBXU8DGjt4mNNyArhk020ciq3YEFM0fJzSGMubVRIPjDh+U+8R0iQP5XL0eXAlYMPbQb2Z5EGWh2ge+vNzyqLVGz+TF5wB0cDJIjiF3njTyd4WBGf0k1PuszcYT5V4EOeNLV+QAxd1rifdN3nrTFAH/bB0Sl+iwZGRQwgtveVmRgDJnVPywJ+xDNp87OMfX9x5uUS292lkzWYh6Ob8shE7CacMJxQHmfs7POsTrfAIXvqERB042H+HN9VnLR1BEputen3LLBNBDZx9zerB2SJowp4hLHFR8v7yPlI5/YFzSxv4oZ+wEfChK+0h5/oWHhwvOdn0DzMCSLzZdudJtrpiannCDB6+iIIDx3WDzgl8gRM9yMl1urQX7TG5dM9l03w5ezj83s75Sfm8TufAsA8ctoT+CbgQSMJZzGt/6jMJ17SCA/e4xz1WtkY5zj36Zdm1bFl8tqDrkh8Cl1xH0KO/sAH6jkCeEmXAIz/7ZnANYYdcj3LSpddKbpefc+8DZibIBgiGcC1hR+BnVgfOLsuzuR71cQL49H12cKpJXC/wSX8hLzJhj+Agoeeh/qNfsB+cbMYS5MWWoA9N9AkfJGwcWsASmEKHwBJkuXg5+weatGEMIyl4yrXi+yhit1yrvJy7wx3u0K4Xgi766la1n1BDeFmSTqBHYFhjDcFNTwR2CDqTmNWmfiPQTDsljUM+Hnl/ATelr6GhWZTIgB5oR38Q6IA+s1eY4cK9gpeU6IByZvtQTuqVOw+MFeBnXEYf9D1jJvciEn2pfmA8hD59w4wg+kOJQBv3nexT+pNxjn5in0fk4ZoAB0EsZhT59To/r69UOnjyvwZr58pZA5ejBvIiJu8/HjCU59x/U8oTn7fp1VXlqCjLq7LEP8Rzwjp+lzvp9uDUxo9T8SQNNwk2vOWmzteTuJkkbC//kpe8pG2k+6AHPWg1HVGwl6PJzaRnDcwa2KQG/MFQU88dJQ4dD/k9R2Cj5P0hmXM5HVpqkfVVnodulTOekXD0cY5xgqlnhobe+lHPQyzl+cDuuFwngqXMl2ngmEKLepxRvc3G0VYbp5H4pTeCDzjyOBvMNqxoE7RgtgQJJx4HQDQYzxW0IBCgcoIkJOR32gR17n73u7fNhZ1e6hc4Ev0CfeFl2QPpRstgFgmHTHVa3kBQBXwpv/hoDZdJ7XRUOQ4ijongtbwCBxJH3XWUfJP3hJNM/4DL+4kp/N5WbcQLjg0JB1OBFvI4MHK2yAve6TKLB3vgHkk5fYuzTcKBT/4pTzka8DJJB6KD/ARDWLagN+PQg5Zge7gqOg6LziUrQUMcePZ548hbdQKXybvnxbPKsBucP4Im2Jpk4NpUMK93vTDbTF9ipJ2WvRGswCYpw2nX0ic2LlXgiyOOsKfUSZV3/sGPE0uiv+l32RHX/YEHHtjq4IUf16SWEMKXZFWw5saXzZ5T/1OvMQ9nW7ZS8UVwgDGYxNJurk3akwgq0x4bIzCBfnC0SehAOgQ/cmgWH066eCQIQ9JYoXLGFhx4ZsHk2AR8xauXQVPjInYrvBqrGadJ2JrqGE9I+sKW5KRMMNBQoixt0HkALvkk2HGT5fjNfQG7El76VzO3CG6rXDSEqyp33pxn2mizaY3b3GcJ2iB/0lfghevN6Wg8IqDofQpu+pXrieszaVe6kRwrJc4n22lgntmynUrmgl1BA7pRcBxKfkMRLINBtsu8cGb5UF51eezhcr7FZx5TNqefvEyhk/jIJ03lVac2fsNxWlnu7blh8saTt5MM2D2ek3feSjGtkRsRbbJdlmV9JedcNmtg1sDlq4EcK3jorRIBCi052ei1LVr58Cd6OCFKyddQue4f2gsk98UQLsp5M8mbZs1ocLxJ0/N6gNWbWZwqPTgLhz77ibOTD7zAVGWU48T16qQrn6qOA5TwvO0k4Xiqjrf/TJPnzSp4kFnLZnCqSClzK7Ry7UuAU0igCXgcORyhvZZ7VpAI6uitvPpAy6jEC+38/DIyK/qSU+XI47xhc5o2T//5BvGCEw5vR+BA9z/KOVcQSQ6ey+t86lpQ0Ee8ccSBxJZIOMK8CHG6vrRK7Viage5wOj1oV/HvuAh2DCXsh37yNj146aiqV3uW2jGjRXus4PxiV/xwALF9gmGadQQu6Vh4hYsAALZBov81e428bFDBOspcBuwr7VzLnOAFvD7rQYFP8GiMwk7Vj0P6cb1wLrrqY/rbeQG/z47j+oAfxkh4I+iIA0xCj/CmYBFBEwXiFIjJ/Uxaw8sS/GjGGEUKiKge3B5YclgFnA3dagYJY4pmpdHfn/jEJ1pAF3oES9U/jHnIO3T9Ov48ZyYMctIPGicUbOH6/Phyhh3jBnvWkBSwpe+gWfWbl1X14Bkq5zlYQUXg0AUBEJKW1/tY6vjgKe1S9ZTL9kQfm3XbBJagju/Po77w68jvI8z+UmIMd/rg8v6Hrmj7udqLvxXC+aTUwBxsKdUyF+5qGqguaA0kPqDAd29QlEwVLuqy3PM6r8oqXSWujcD0cPTKKxpZRlv0Ixyuq8SrOrVxXA7Lg6o/rCae5IG8HhyArX5qMwVXhX8umzUwa+Dy04DGjt4X3eQowKGPR5vhWA+COoq2HKix+4LTFg7K0jESHA6unKd8WK4eoEXfH/hVxpTwxOH8VrxXZfCmAAjngvEjdPS2Fxi+EDeUeEjHUUKfcjZxovjx4M9DvgIvPTyijy61NIqgg5xlynHCcCLkKNFGb8i1xKHSietN9XnPAjdwBDGUcNRwamlT4fVy5z/LHad4qXgSXQIGTo9yLY/gHOeW+6nf+/xzq7R1+cb4z/rcNJUZprIHlvfJKRa/ySvlVVlVLjgCCTi//NAN/U4wlCVBOKFswslXDHH0KtwuA/AsweDYS9BAR94PsgFvgw2AR/iFE3tzPjgHH/2Ak+/89HjI9g6HDYhH1xsOOzZPW+oJLBCQ4pqg3wjEwaOCu1yPwDMrjja6XggkOn6nDZyWIuXYJp6ddwUruDaxddcpeN1ecOIJyMA38Iwd8K+gGuUeeMnr1OlWeqUeHCyBIcACLnRCn6A7eJMOKQe/ZvCgK5Lzr/Oqr1wXnCev4k9wzAJh827dEyr+U3fA0L5HX3U+FlTBcfBwn2MvH9lARV903M7FQwWfZcmr+JJ+En7O/48G5mDLbA27rAY0uPlRzFZ1Gkh8YBoTroLNMs/3znt0qkFIvPfaUJ48OOxQXeIULaeZ59km9egyqC7brMOTy0e7ddsm7Tk/a2DWwOWvgWpswPHXDALnEMehGhvXlSJpktfPZxwILuFz7PF67Q8CT9mOMs1KoS4dG+dDMjkP+ZCfs1pok05BxWvFl/ipdCm+/A1+Ot/ZDj7UjqUVOOf6KggP7fwIvDCFXcsXEofaawYGb+eZ9q7ZR9oDAkeKzTlxlPT2VctDhAPceV7p2HWjcy/zNlk+hN/rKjm9DNh0sLR0JOEy7zz5Pbui2eNfvHo9M008D38s68Expl9wYEmCSbtL+g6bMlSwOOwEG7g+6VvRxkmU/fTkYYkQDi0JZ943gWW2A/y7zHkN0a6SBzjZOTA+26OCB6bST/INnGC9TkGlhFde/OgLMuiGfmMJJkmzdAiWEGig75hBwbVIoAj+E3fFC+OO4PKYfCuIm3gz2Eg9Ojt8uayQIB77txAs0ljBcjVk0Qs376PELZ6dd/qdH3Izk4bxiKSZKwpAQZvABDPlgNesHKfh+nZalFdwyY90RPCQfauUWFql+wP3EW1o3MNZye08cE5AVoG4hCdAe/bZZ6/osxTMZ0ppPxfputq3JmVzu096glV57xpJnHtyfg627Mm9vxvJroeNoYt6qG4dUROP57MOvFUZ5QxEvTqVV4NYr82QDFPaANMbHJ2P5C3bVbDO2xAvqRPBcszfkLxz3ayBWQO7pgY0PujIQzfLDHmLjWPPrBY9iEqCHBfWkYy2+nk7n22HU1/NsvGxTG8E4Y+xSA+r8O4zcURDS1zIJ305S86P3rDyoJs8Z/uUX/U+trqTkvCeT9y00zIh4HAMNNW9N3bTRnW83WdzdJab4EiwXADHh02Q6V8cyUzigTot3SCgoo00NXMDJ5JgCxtc6hOozIZQP0lml0l10NQ5vHq56rJMfA6VZ91QXnV+THhsMQNcPtMo4V0uznuypc7H8qIDPmyA5RdsAosD79dO4tF92svdbpx/zS5wh9zlwRlmfxiWIrOnhRzwpAlOfiw/VDr44IO3cSi1H4rjd168T4Qj6zUuaX8ix8W5HHvnr9dfTk/8qx1jCs5/JgUfHR5bIYikYBhttMeKgpfg03il5ZmJW3lwq3+ZhcEXeVIPwKpMG80SLFF/Om7XiQdvgKEtS4r4ERBgXxdmyhCcRa60ZWhmmdOSXpARm4G2Zt4QjKBeY5v2sqG9z+AZC+6Ih6RLvuINeL8XEEjy65vxTMn17GWV/ql3XmSbVSBN9xbasCTP94TymS6SXTPp6NOKpxXDxYnD+3Wf/BZN9+iieYPcPbr7d33h/WLWuR853+hGr7Rd94fGsk1Piw5XtdvZvA/RQ4f6iW/Xa54nrHCP9cVQfU+Pc/msgVkDu7YGhh7YGBtwCpgVwXIZHhoFP9RuSOIp7aCj6fbavLqbn2oAACAASURBVLGHE8eeoAE/OVp6IGXKPfTyJydDe3A4T8zKIK+gC06KHnpxjFL+/JoGfFYyVmUpU/LpbeCHvAdE9MAtftUenr0ted4kO350zGa6WjKA7ir6XqavG6EH4H3mit7k40DJgdCSDvEueR3nUJnrJ3kb0rPLPqbjIfqiqeUWGWyj3p17yen0K76TpyE5K1m4LkkcmQ2ggCJLdAQvGMF5vkdfMHzBhK/MsORjKOktu39BKOUlzzINLX9gk1wFF6gj6KeAFflKh0M8iJ72SQJWm/eqHTC9IIzjrnStMtk613vKyB4fBFRI2pgXOdQvzGrBqedak+wK2DCrR7z5J5WTRiWnX9PQI884yFds0IHPkNC45ngVFIVvzdoCh2+CDV74JhCgsaJajuV4U6dep2AG9qBxQjNXXCdaLpU68T7lvOozp+/wlU61yTezwsQbcMitfXREJ2Ws8PVgKdf+U95OX3QjMI29QFc/py85vE8Jgjku2tH32EB1DUzVVaW/PblsDrbsyb2/m8ieN3zYpizLVbbZo+OvaKTaevQcroJRfdb1yhOuyk9pOxTwqHDuyDLp2nXuMqSu5/ysgVkDu74Geg9kvfKtlMgfHFlmQOKhnE9Z+maPlBNA4GGUz66Srne967Up+uDgbSgJJ443y554uJVzp70AaKMlGNoLhDY8vPobeedPOAncVOXbEI2MHrATRjoWPupd75zzsI0DQoI3HD2nzywDHGWmn1OOrKeddlpb8pFfkNGsGHBpw0rHled6Gw8NEvoDBj3JUcIRw7Hkzbi/LU85hLshWiacPKWEXVUUJz1YLy+arXTmNJ0nl11BJr4Cwlt35KWec23wWfHudB2feHN6wCbPun9X/FOnpM8f09c4j6rz9omrejYAH+W69rh2/O2+80E/KwjJtZe8Sx7KsQMcdZK+ZIMOsV2Wpij1rqXUnetN5wR8FBRjJp76iKM+Oe38J7/KO27BU7b3ZZvcMqMLPQuOcUhf/IG+Zq5QT7CRxJfBCMYwLqkdwQ1guVZkQ7m/j2CdJ3SpcYsNrxVYBQZc9BlBMHgRP/DA3is+VrB8RnuUsCwQPSHX6aefvjjnnHO2G1d49lRAyTd1lo4U+CHvfGdeS0SZzURgVrN51IaxjXLsi+TjkmjpSJspqYJzeo6Dct1bNPOG+hw7KUOH3jeyP5bMkbwuaahOQULlnb6PLQryYTcK4tGnPm4CT/9zHYkXHX0GjXhznubzvgbmZUR93cw1u4EG/GFhXXZz8BzCVdVVZc5D1q9Db11ZBC+aTsv5UHnypvbUV3W98iF515GhorlO+xl21sCsgV1HA1zPOd7B3c64zp0GThmfNmWJBA/5BFxw2PjyCW9e9TAObwRZcBiUcPw1hZ+gAw/2+yw/iYpTpEALD6x6Cw1d8jiWLMcADnjaQZu32nrwlm4qHUGf8l4dS3gIVPD2EYeFL4/kEilvO4QHx0q/P/uzP1sws0SBDh6wtScGzgpLAZCLzwMTONADPk4B8jGLqFoasVLoZSdaliBdIIPLi6OkafkKxCQOz8Mz/clbbDZahS/4djuYYneVnnp2PMRP1gkvgTicbJxY+EROnB9mBqB7fYpVs46SH/JDciQ8fPT4r/BwrbAEAltnjwm+gKTlJhW85FSd6IsmDjFLzQgSYDf8uJ7oU4KR9LEcb65JX9om3CkTtg4+bB97Q5fSG8Ei9nNBnwQQoeN8O3/ZR+SlX2wbnODA8WS2AP0Gr8ika1/6rXD1yuhzghvg4joij73KLmi37777bvM1KgIjXDOaQZJfR6OfGA/AyZIsAhouT/IiPXA9c62zHAc5sUGfIcPSMsGiW/gFTrDYrq5TxgoFmuH1+te/flsq9JGPfKQFh7hGGW89MCM7Z8xg7GVmDjQYa6AnB1+yuBy0VRvKFSAXv+ThjTGBsQF41aVNDdl26s7zjod+JODH2E858mJD2Cd6ZVYSvKATbJjZkNgvPAKHLinD1oAn2El/f+xjH2tBRmYEOf8pA8uGCAZqhg30ue+IvvqK6xr83C8IbiqwQnCF+woBfI3L7PuixHWMnaj/0BmzyzaquyG9/rbWzTNbflt7dpZrVAMMFP4bbbBJgJ1Nq2J3TGbVq60G0yx33L26MVpJI/n1G0reXBJ2I3lwjv02gndPajOmvx3Rb3uSfncXWTVOVPz6GKL6qqxq2ysbopdtcOZvd7vbNUeEhFPC23QFWnD0WArDw2MmfbaWcpwsHEY5Wzgr1Cth67yFplxOGfC87eVTmr6OXm0qOXzcTH7I85Cuqfg8JDO1P/F4PuuEE0f60EMPXX26lYd+nFUcABw6ZHOHBydBn1PFucMp58esIXSLg1WlHAPIyzEDHsfAYXCUlDS7iLz0ojrPo3MSDgr881Z53ZR67+lNvPhxjBby4fAdeOCBq08cy1HlM8uuD+k86Tt/WSdeXCcOU8FXPGOjmjVAgDLxeT7bJyz1BC+5rtSn2OunPvWp5pRyHXK9EAzlK0geJNB9BRx+j5GuaEvQApvFuWeJIoFOHEkSTq02k3U+p+gBnoUHZ1WBDH0S2/HlufD39ISMXHOaVSBnW7NIkCP384GGXwea6aJrxj+Z7ddO8pZ59Hab29xmNZbgiGtZH5tdK9AGHWAPOeSQ1XiA3mW/jBX0oQIawBNkZrxgJofGFfqE65MxRGOxeNKsKsYSxmafaQFM1W+amUO99mvhHPoEbJTQifoj+8X7S/AVLep65dRBD/tgXCZIh22SGBOxJY1P6EyzxrjONLuQctkrs6sU9CfwQUDE+ea+lbzQV9Bi7AA/unb6BFJI4NMsS4JcBx100Gq2GHZOPf15y1vecpsAPngJApIIuGgWViuY0yQN/M7SMKfNoZqEbgaaNbD1GkgTJX+Pe9xj8c53vnPx1re+dXH3u99964kGxhzcVN0r35EMpT56tKbA9WC8PM/f9a53LY4++ujFXe5yl6Z/T1P0kTS9jTYB400PD34VvqqspwPKk94QLHVHHnlksy1uKJWDBMy6PIzR3Bn1Q3rgAYcHpLvd7W6jn4Lt8Sq9ve1tb5t8Te6OeuzJv6eXy75yvKj0on7PYwXbK4OO0+Rc0/5VlzyRx7Hxh3keVHs8e7kvQfINRB3GeQWe2QJynpl+D23KtD8FfGSZcIBXb/6nzBjRfgo88OPUOV84PdDiLamW/aiePhAf0IaWHF/x4tcp5+Bj+jv4cBoo06/qL2j5D3qaUq+ZQbQDhj70GQ/Oi3Tieu3xCB5owBc0nEfO0TtBGfonN2wWLzqCh/4D1t+SUw8O+IU32ZL0CSxtvC8kI/yJLlP2zz///AZ72GGHrXhVIA286gPJwUwB6Ezh33l0W4IX4UUWzoFFNyTtoaO6VrhMuoag7cvIqJOs6m+VgRc9KkFLckkm6pyfxOXLeri+ZM+uX9eTbAC8fs2KB8nh14Xq3H68LX1Nv+ka7vFL3wArGwCv86k+odzt2WHEi8YO8uoT6Qx46ZW+hV6lT8ebfaPxQfYtutmGch8/K72prY7AM7OCcSl5B0a8+lHlzod41pH+0ZinMUT8+hhCP6ETJa8TP65DL4N37+uULXkiz7hYXffe1m0mcfbqdF2CW1+G8raSHfrV+ChYvz4oI0//g1+6cpiqfyirtiMQvpRpzi+v8VkJswZ2ZQ1Ug33yC0xvgErYKu80qAcXN0rWJzJFk4FLMElns7QrfraiLGXaKE4eaFhT3gs6bBTvWDvxX+l9M7JlW4IMTKmsHsSAdXjv+8QzJo/sagrcEMxG6PbwOa7e+UZkTr0lfcfJWxLpP6+tbDfndw8N0I+yJz/Pa2Ar+nsIv7QFL4LjWD2oihfB0jbPqzEiZfIeSnge2t2ph2aWqT11/BLHkAUA69ex61eOodc7LefD2zmM0wYfzpP49DbZ3vUoHNxTJVvyhDPhTr7T5QG/V5c8gAcaKndeOUcGr3M6lc1UQRPaiF+XA316UAs6OEHMHODIW3APUmj/Hd50O5/iX2XQ0zn8VA6/y6Hz5FG8Sn7BkYcv2anX+7noVrR6Zd7nwuWySjZvT31l07KDtB3HQdshWwJ26LrItuIV+9N5yip+OSIvsPCY+iaf+BOX56vr03WXdkKd68LPZduU6dzHxErfzsuQzioZgE87df5cjiG9Jm5sYMoY4vKCw/UuPnJcoRw49F7p0nUnvqQ39Xny63nhrGB6ddiTjxk9uN74KFqVPNk/wKovesfEpzaVTHPZHGyZbWAX1oAP+nnueUTIfG8gqmBTBeBi6iPrvpmS5zMsRMfxV2WJc6vyKafjHaqbKnfie9nLXrZ45jOf2dbbCj9v3li/uY4jMCQ/eKVPTXEkyOV67cmW/dyDq+QnkMT67FNOPXVx9+WMDqWXv/zli+c973mrjeiGeB+qm8JbwlR8DtGo6oZ0IPiEYZoytp4OW8IN0RPs/132J2+PKtm8PYEu9K+ZMN7fFZ25bNfWAP1dXbOVHXhZVb8RSYfwwFfylzQEk+VD+aHrY4ifxJmwmU/4KXnnrcJXlYG3Vy6aqueon9p53VQex+iN8dRrn7w4v1k3RmNIlinjFjC8fSfYoi+ksJwC54mXOpr2z3hY6bQqq3iWXGnLlA/ZquNyffbOh/Qxpc71n7J5+5RDdb3ynhxjPLmcFax49KPTqtpUZVNspWrn/ee6Ew+b4a+ny7SXMR1VfGeZ46h4TtnUvscj9UN10k/ykeVJ13kTrPMumq4j1W+Un4pHlVW632j/VLhSfpfZdVGdpy6H5NiT6+aZLXty7+8msmtQySPs49jloDMm1hC8BsovLjdhI5HXrxqkgPFBVrR7sEO0nW9vP9Qm6zJf6aIHU5WzkZ/0IFw8NGqmi9pUOqho98qEh/XKU3EB5zxX/Ite1pHXZmJLJNvgYf2vPvua+Hv9WslF2wp+at/2eE9aKVvWe74Hy1sc/2ThEI5RvuyaAbbSAeVsckiCJ9dVT2+iOx93XQ34NVldx2kLmV9HMtGq6AjPVPwbtTmXN3nvXWvAOV/JY+YT71De+UneNiJjxYvKOOonmbxuiE+vc77UXrqr6A/hTfpVPnmu8PXoUu68iXeOYzwzxt785jdvn1RlmSw/T7zc0ZIs16f4dVlSrpRhjP9evej6UbiH2iT9Xj5xSLYhuhUu2lV2k7BOL2kDqz7Lc+FJ/pQXrgpn8iDZnFaPnmB7OLLc+cnzCpfb6xjv1A/xPNY+eVXedZc8e12vfVUuuSqek27VXroa42cdmd0+K5pTcA3BVLKO0XQ+HHfq3fOpk0qWuWxcA3OwZVxHM8TloAEN8n7MssbW8iGHXcDf+MY3Li644IK2udMRRxyxuP/977/NlDtAWRrEuuhTTjmltQGWzdvucIc7tJ24SUznfcQjHtF2RSfd7373axvaPeQhD2mbKJLYyOpDSzyfWm4OCD42GbvrXe/aNqjy9Z4NeIMpb3KOJutYz3viiSe2L26cd955bdMrNuViXxXfbE2ygestb3lL+7Tne9/73tbmnve85+LYY49tegA/jvBrXvOatps86aijjmpHdKc6Hhwf9KAHtfITTjhhceGFFy6YFcKMkVOXs0X+4R/+oeFDN+gO/UOTviI94xnPWOm0FSyT6LziFa9oy0uEV/XV0feNgcanP/3pZgsKFCEXGxMqgCI6+lzkc5/73PaVkgc/+MGNH2jyQIws+soHNxz097rXva7NenI9P/CBD9xmMzHgHv7wh7dNMR/wgAe0zyCeeeaZzeae8pSntM06x2YFsScR6ZWvfOU2fDMb5IlPfGKrO+mkk7axcb7S8PznP7/RRRb6kTXNwNE34pn6e93rXtt8OUS2QZ36FBqs46XP0A+J+mOOOabh5TpxG5BdMquF/W7oF/b3QffoQzPEgKOfpf/nPOc5Df9DH/rQhp8EzNCDRgOa0y6pgXwI7PVjr3yjQlX4tsqOUibnUXRzXK7aJI9j+Y3owuk6bxU/Ff7kSTBernOO+gHn5RXuqsz5kg4rWtl2jM/kJXl2fhN3lXc+dV6V0bZndwRcGOO0uSibXbK55t7LTwLnkiftiSA+odWTqeI3yyqeHUa4VTaWT/yV3K6fhO/JknSzHXl0g45lL37t9fBWeATr/ZV9B4zjzHyF18tcB3letc1xpIKhrOJJvHldr/2UcufXaU7lMWkkz8K5Wb6Tz6TrefGgstSV86LzxOc4RFv2qGPFU9JOHpJOj2/p3/HJbqf0TfKvfkh+UheC65UP8b+n180b5O7pFrALyq/Bwo95jrP27ne/e7H//vuvAgIuCuVnn332au0xziFBGAUPUmyccxxtdsvnaw+ZcFjZPJQNz/ZZbiSqL1I4fd5KsZu31lVWAytyvPrVr27O75T09re/vYHlAKo8+6kQWBE/+WlCnNiDDz64tcdRl2xsbov+MrFsiOABG8Te+973zurmZONAUweOk08+ucHgvIOvh/fDH/5wW46U+kd/H/jAB1Z0tHaUz4ninBMwqvh0xljipITj/qxnPatl+XIBn79TQhfsmk9iv4FMBKAIDCHLaaedtoAHzbQhwIT9+Gcf1R6dYz8K2KFndttHNlLKTBm71bNuv2cjBDJe9apXNRum/0j0IYEgAhckvh4gmuQJGiE7QSICJvBx05vetMszwQ4Fk2T39B8BNRKBK5aMiX/ZFkeCQQTjgOfaIElvd77znZv+Mgk3MrPfQCZtdl09CCTsnN89NJDjFlxXNr8ZafLeAC7K9FPej0mv4lMwG6kbagPeIRvfjH4qul5W1acukr+sF39+rMpSf9kfyounHm9D+ujVJT+SKct77SveKUtePV/VuUxZLxrwkLpUWVXn/TPGv/Oc56JfHRNv5qs2iT/lzf51mSuZUnfk85c0KzxeVvGdfDpM9otwJe/eJvGJ5+Q19VHxlmW08b6QfSSfPf4qnTpfWV/Rz7J180O8jvGdulQ+dT7Gk+sQ2ORJZdJvD5/TrXir2iVthxmqq3BR5n2WedVVeL2sOk+dDOWlrx6Pc/kyQDwrYdbARjXA23+crvxtFF/VzgfTHFiBxxHEqeaTlezoj8Oscmad8Jad3/HHH99gcRT1uUyHP+CAA9rGdWxQx8wMHEYSzjoBBhxF6D/5yU9uziszB9ip/Nxzz22fMcSxppzPGlYDsA/EBFoIZkz5VQO46wHHG7rogIAGa8IJEsAv6fDDD2/7raAD8UU5AYynPvWpbbYGMvLJQdIjH/nIBk/7Sy65pJWR0A0/xwM+6Ve4wYtuwMtPfNz61rduvBGUIGDFkUSfCC+4PIGTYA7w/NgxnR/wyEt62tOetuKB2RQKtPC5PD67Sh/T96THPe5xK1j6+M1vfnMrf9Ob3tRw0ufOg2RDHwq0QNdlII/+uQZSF8iGzByxlR/84ActmEPy5Vnenzq/7W1v2+DOOeecbfD+/d///erTnOAQTY4EwUh3utOdWjl9///ZOw9oXYoqbX+jYkZFjCgjICAggmREMIFiAkwggpgVFRQMGMaswJhQ1BEDYkBExDCYEAPqiAFdIBhAxYA6jnFE1DHr3L+fGt7vf8++u7r7O+fcyzn3Vq3Vq7t27drhrU61u6oa22hbcOA8pk3wEzqBJ+mT396mjJbSNcN1xXXDqCF+pUighUSwRnUKoUsEWhjBQ7AHnLk2SZwb5NFFO+rcILjD6CyCmbLnClFtt8wR0Muq7xfbpfgSiPxMbx8t+7OC+Cmrlass8kR6zCPb63h5n51DZZmcMXoy+9zGKJe8yh2nMfZFXKPuWfJjbZS9Q/bVyqO/WT7DSHx+TrotWXlmg2RHzDPeSIv6PJ/x9pVH/lny0Q6vO1ancOjbO0aOm/RF2lhZLjceZ75k/rruPr2zlNX8mqVtXEaUF/2YxbYh3kxvZne0IbNxSNfYcmSLN9oS6dEu5cfqinw1fZHP7fAyt32Invnp9qs82qS87mm+X+xn+5okrwVb1qTWXM2+0Pli5EXcFsMMdd5dVuyUquy8884rozu4UdCRpPNMopOtzuhLXvKSQiM4csMb3rDQxa9pE3TsY4fZO7Mc77TTTiWgQ6eePDYx+oGpKiQ6/6Roq+f5gs+oATqYvkGLm9eLclloTx1spj3tuuuuxaYNN9yw2KiAxKc+9ampX8W4LoHRM5/5zLJKPAEmRlAoKUjlbSBfvTMcbaM+Q6Jf8YpXlJEjbEcdddRULiN6dtlll2ILewW0WBzQ5VLBcVeZaIyqUYDoWc961pRXwTPs9zZmKg+JOvz9RnLkH3uniS6fkacRLeeff/4cHwiuSTYjRSRHThOUwFdkYpOmSb3xjW8svI6h7GC/++67FxGcK6IT9CH4QWCHtj3jjDOm+ggW4R/BRPQQCFWeKUyc6+ijvRWQdDzkMzrlA8EWEtcRbYWMrbbaahoooozrwf2AxjnAuYUdyOXaVDCPET3ypwi/IgkHz3Psdhl7O2wIVBHgZZBUe0ms0ePLaeTz8ngs3ho9K4cm/qw8yqrlo52ZLNeFnCwfaeLrk6c6NRu83OVlNtT8G6Jndrt8tz/aO9/zJPMr+hTtkh2uU8djfOzDeNaymr5Z5Yzhj+0e81GGLuxIj/VqPgzRJdf5oNXqqczrRdv68rJb8vt01WwQnWloNZ5on/S6bZE2ZJvLrOmNdPcv81W0jM9tzY6H7I221PLRrmhTpruP5rhGG6MNUXcs97zrzOh9dWNZplfyxes49Pmr+5au1bYfRqCt2TKMUeNYjQh450odsGwvk/iKHztqBBtI+vJPUEiJjh+dQ0+suaJ1OljrxZN3nrn50OlkTyLYwYgL8te//vULjc7w2A7iWD63R3XY80cgJdYjUacXGuVMMaFDTTCKdTPcbzrNnoef9WtYZ+Wyyy6bdrgl3zH2ejqWXRrhoXo3vvGNpzbe7na3m4ONpjRpuov7GX2hDJxZk4SREwQbmBbkdtG2bNAIYvGXB9mFLkb8MB2I4BLJbRef9pRzDA94kMDHfYfGQ0qyWTNF+kuFLslnZGG/fGbdF9clftEIiuAj7cd5xggS6pAYkUIggylT+MkLGEEMEmsLYSOYkmh3bIx2c92wFovwcCx0jG4SU5G8PkE0+fzRj360jKTxxNor8OOv/CF4xPWIXQSfvAzeiIWusTmCW6YhUEFA51O2r1TpJdfOv3ieSkjkF1+kZ/w1nl4DrbCvfmaH7kV98vtketmY46iPOqJ5WQ1b7OyzJ/Mj8tfszNojkycbHM+aD06Px8iJfmKb7NNxtq/hEH2t2b8Y9Joux2WsnqE2cTnwDmEf+ZWv2axytZHns7reRpT3yZW9vqdO1CU9C9nX7OqzT/b7+ek02VOTEc9ht9/rRNsyvoynpjfilOEbefryUU+0JZZnuLgNwqUPn0xGn41ZWWZXPLdifkhvlNmHhfPGepm9jdZ+/dzOgWWAADeNbMN01lmJHUmmOShRxogHJb661xI6vNMJH3nvODIVg84kHdVa8hut34jiDTjyxXLVdbqO2atDzagB77CK5173ulexk06z/JDNjMaJuBEoULAFGZle9znyUMZIB6/ni9ISQHCd/OaSlMmBJl7hQGCAAANtyPSaGESgDiN8HvnIR85p85rNsjPqF12YMXWGRMDA7ZddrL1CIEdyxENwRj5nDyTHSTa6TYzIeepTn1qmqRFcOeeccwobf61gIWawIABD4IwpZCSdC/qFNu0/dK76uSHcOc+VCK7Ec+Uu3bQ59xle2U67Cgv5vXG3zpF4ot/ipTxeLxluU8PaQUPAEOBc4Vzyvc65xTqP+uT4ed3HJ5MznoyWNXK8hjIeaC5PdTJa5M3kZbaJlpUN+eltNUb/WB63vWZXjR797uOjTD5QL/qj+5rTnT/ig7xsi3732RTtjzpq5QulD9kkrGr2DNX3eo5nX72+MvdXfNn10Yf9LPKjbOUzO0TLeIbwy2wa8k8+RhvdtmhLpsf5a8exnvKRntV33uwcEC2rO0SL+mexy2VTbyF29NnpNsb2oF4sjz5lsms8Th9znMlutLkItJEt7YxYcgj4jSQek3ca0ybijSfW8Y7iiSeeWPWX0Sk1WdCZ4sMXfQVvXvnKV5ZOMCNIWN+ETj5JMnTjdYWUETRgTRESthE0qCXWsSBJZtxTxugR6L4N1VlnnXWqvqquYzF07HY5r/sV6V4ndubli2zhD1Ba3JbgAUEcrw8f0140bWmPPfYoC7ZyfrBYLXm1W7TDsXJ7dcwixKT11ltvDmZRP3n8EF3t4jKj7loeuqYSsUYMQSz8I9CEHVoYlwAMQTIFVLbffvuiH7xInK9Pf/rT3YQ5xwqOuR0cs7aKEovZRjs575ViWZTlCimL/JRDi9dL7UVgjgMt0xBIEPBzKR4n7KuVlJ3XGS0zStfOWH7J0PWVyXRaTe5Yeo0v2u32ePtE+2ryIt9QfkjOUHm0v0+f+6PjeH+L8uCTDTp2GvpqNtbofTau6rLsfBuys688O1+cP+IZ/avJVr0+fF1WlBPz4o3+w7dQXdEn5aMNMV/jG7Kxz++aLbPQo50xX5OVnQvw1upHP2tyazjV5Io/yo9tXdM3JLdWL/PVz62svE+Wl9VsivSYHyt/beZrwZa1ufUX6DujIx7ykIcsUEpenZuH30B0HOlnnnlmmdrjvL///e+nQqHrd8wQ+SKvP7CIqaaHcsroQHNz0SK80FmXhClH0Nn47XImb0q0A9YuGfrDjti1oCp5951jTZdiWg1rsMRyRn+Q8NeDANCYYuO/hYamoIICWBkuRWBPchsim8uLZTHvcvj9MKM5SG95y1vKtJZMD2UkAhL8bpnpNSQCBwq0xFEclEdZslN0RgghjyBZnGZG/Ysuuqjo8XqFYLQsn+HhMhixQqJ9GU3FSJJDDz200Bhtgp/8upupW5QRUNJonx122KHw4W+c5lMKLLn/OvYRSSyqe+1rX3tOHf1tKNYVk/zQQ9l9zXCiHvTsIV6jz/Wi5RoCzUAm0wAAIABJREFUdQSy87DOPb+S7NzNJA3x9dnqdf2ayvQ4rSZzrC2ZrCFaLHd7oz2ZHRltyM/5lI/RE+2Vnr66lPl9UPexeD+TDPbZMboyPRltPv6vijqZbVn7j9WdyfO6Cy1fLDtq50Vs87H6hvj6/M7K+tpgVdlY8yGzr9amfXYjx8ujviE9q5u/do5EO66sfB9efWVXlr3LRW8LtiyXllqCdu62224TtlWddCONe/Sy+Ct0v9mq86tpHze/+c2nJjK9wjuSFLC4KGuvxCAMZS5bo1Ho1NLpdp1aTFR1+h4A/F6ZKSBjkutw/zm+9a1vPRVBh1ijDWQzC7aS6HhHjFjYVcEaCeFPNySN8Ml0u19R5tSYkQfRH6+mMkYT8ScggiUEGmJgTXUIqBBwIDEVikCLZAgH8bpeaDU/RNd6PARcTjrpJDezjHaSXoIjETPPS1e0Y45As4e6/IabxXRZG4XEGiyy68ADDyyjXfQbcRbflT7ZzBQkzu/rXOc6c9RwvhBU02/Ko90wE8wB93iu4DNylTIfI815Y5n80bnVHuhzmqplVhMCs5538TweMjPKj/lY38szXX3PmChL+fnUcVmZzRmtpj/S3Z6FyIlyV1VeNg61h/xyn5ym+iqv7fFjsXFZbHk1rCNGfXr7yiTf5Y3hr9nl9IXIGaq7Kuzt82nInuxcWt021uwfsj0rXx22Z3prPqwOe2q6Z7GzJqNGX5WyazrXRHp9/sKa6G3zadkjEB/gdAZf9rKXldELlJ177rnTaROHHHJIodHx5q81pGOOOabwQGdjgVHW3GC6BYEbJQVeTjvttCkv0zVI/D2HeiQ6rS996UunHW5osqUwhIROgj2MKiHYoY28tlvd6lYTbaouv2W35GjUAlNFWAgYOp3rl7/85dMOMb8RVj3JYwqUcMAX+JVY9wN+nyoCLynKifmMZyrYDuSPl0Ua+eOOO66sVUKAi7aL/NLPyIvNNtusFPOLZej4xRou/B5bSYvdkldAAiwkx20QDTy01g9/2QFfylgzh1+Bk9CtxXEzP2ryp4ZdceB8HGvqFCOpSIzwEQ+BFxK/WSap3Sh3m/kTl2ymjGAk5/tNb3rTOfQipEuSr790MTULHKHjcxwpA92T6kvWnELLaPFkBZJqfI3eEBiDQDwPGeFIcDQGSPtkid9HR/bxZ2Usks5Iwx/84AdZcaEt9gss1zc69Se+quIrCvjtPZvuC/grmurOKnNIZ1bOWlNDWGX1Fkrzdvb71ULlervqmH089r9/eNks+mV3PO9nkbEqeeX3mP0YO2pysro13iF6JgtaVq/GK3pWR20d69Z4Z6FHmWPyffLH1B/D06dDZWPkRJ4+uZF3KF+TNVTPy2syrkz6WPv7bBwro/H1I9CCLf34tNIlhgA3BU90wgl2ELTYb7/9Jve+973L13g6vwQZSLyM0HGExnQfePhNMvx0OOGnE+2jBtTRJJBD4IVpE3Sm9bWfUSX77LNPGV1y7LHHls6oOqGMqGHKSXwJ8vwsZX1NQJBF02a22Wabyb777luwABMSoyIIRMQXSvy+z33uUxYYZk0T/CTxtx+NCmE0xJve9KZCh5cgkf/ZqRQkadYHaLQNkdDo2GOPEgvGPupRj5pu/G6bTUEjrddC+9K2+MXoD3zlTzikZz/72ZPHPOYxRT5TysCOURq0cZwiJLsY/UHQjcRIEvAFZwIfGtHknZzYtlMHBg6yetttt12pxegZbMVO8W277balDPtVJhXYrN95s04Rwbv73e9+pb1ZuJdEkCOOeJHP7PnFts53cGThW9aE+frXv14wJCkoGW2P16ns8v0LX/jCkuXa4hwlONpSQ2A+CMTzL8tHWtQTy/1ayI5j/dWZlz2z6MzqRJ+Rl/FJT42/z45MXkbrk7E6yrI2rtHG2uP3wb57Yl9Zny6CZHxUiHb21VnTyrKO4nx9zGTNt21qNmQ6arxXJj2zcz60K8OHWe28MmxcHTrH4rA6bFnbdbRpRGv7GbBE/ecmoRc7P5a5BEsoJ0jA1zHWJ2EaDFNOnvKUp5Q/tHiiU8kICabwMO2CoMsvfvGL0sHm1890vP2hSueUQMNZZ51VxOh3wazbwhfAE044oYxwYWrLwx/+8LJg6QYbbND7xTL6hFx8cL3QYt79iMf4ecEFF5SRKSykio90kAkcHXnkkQUH4ag9Mlgz5vjjjy9rkRBsIkBEZzeOWiBoIQyoR6eYETjgTBvIVo5JcWoStAc84AGlzBP14KXM6yCXpDVClK9h4j5hK6NVXve615X2xSem1kAn+EBAjaAFstigvfvd7y7rwJD0S2L5wjkjvZSB84tf/OLJ2WefXeQTvLvrXe9aRlL5FDTq4Zd+8yx97CljKhQp+kTe/YGHABfnGOvpyK5SuUvoFD4KLKqMPW3Pn6gYHcTXdtkMDgTP/BoBb4Ix+KmEfAJZH/zgB6fTmA4++OAJa9iAG2nHHXcse2yXfVoLyP1TW/s6QWBBkIp1l+BlSmLEZGpMO2gIVBDwayZeP5UqVbLWyIprFNUq9OmjjG2h5/SQf9IjG4dsgk/TMVn7Kasf70WS6XT3q09nn11MZfTyxcSq1mbQvZ3d9tWlv8+2WFbDliDL6aefXt5dCITX2iP6hLxIizpbviHQEGgINAQWD4F/6m68c8eAL57sJqkhMDMCOh3Zs2lhVz8WTX+w8TooHHNKj3nZqMlRXfbZxhBh58lAyGQP2SRM4t5x8LKIBaNStHCqFsONOmNetkf6kH/Ui3WiPY5LhkeGW4026zmQ2VbztabT6S4vO4YWt5pcb0Nf2Nh9XCheUXeU5/lYhh8EKc8444wShPGgW+Z71BVx0PUC3Y/FR32XG+W1/NqNQO3aZxoMnVGS/hS3KpCK1wej6C699NIygkzraun8HXN9yMbaNRj1kcdXLVrNiLRaqslk6qv+aMZf3EhOk8zMj5quSI92U85UK/BSwEB1+q73TE7U5flZ+Pv09unIyjK9GY2689H7m9/8ptyDmTIagy0ZjlFHzGc+NFpDoCHQEGgILByBNrJl4Rg2CasQAV4I9IKiY3/ho2zWl4ax/K7bXXT9okPT5jSOXZ/7ksFWs63PT9kZ63qd+JJX8yHaFGVGf2I+43eZslW0Gh41erRvPvloY8yPldlXz/HlOOYjbo6Hy9VxbH/osU3H2p3xuTzpYmFcRsAwgodFh7WYLkE7XvJJt7vd7eac9xkmmT/CJMMl2pfJjDwtv3YioGsg2/v1wTGd05///OeTX/7ylyUIwkhEgnuetM4KI7HWWWedaRGjCBgJyV/crnnNa5bRfYyiIyBB4Jr1h/yvd6r417/+tej94Q9/WEisDwafX2/x/I52E0hBN3r+/Oc/F13Yh5zoNzrifYE6P/vZzwqdxeKxX37e4ha3KH4qYE99jt2+TCZ+/eQnP5naBBaMhMswLY53iSkvWrwevfC6/TqOulUfHGi7m9zkJuVvbKyXRlCLX9O7bha7//Wvfz35/ve/X0akxrZEXq2dqQtWLIaPnpvd7GaT9dZbr+hFPzS1s2jIZ9Fw6jFKVH/OQw/ysBN7Lr/88nLeYStt4Ak8tQA/gRPajLXHZMemm24653zEfvFrrTbsYsosiTakvs4d9CGXddiufvWrFx7wjufeHKNapiHQEGgINAQWBYEWbFkUGJuQVYmAXgjiywEvytC0ecdtsewZehmhvG+LdkR50SfxO594Ml6nxXJkQCNx7B0L2awytzPaGH3wOs4b68W825Idi5bZlNmQ2Syfo+5Yf6g88o/JRyyUZ6+thp3L93aM57iXLZYPsS2EISNWmHLEVD2+0j/taU8rnRut78NeHcfou/zJ6H24OFaL5d+Ytms8yw8Bnbe+92MPIFx88cWTr3zlK1MnOY/psDN1jY6/EkFFEtMp9et4Or78Yp2OsxJTCgkW0JlHLlMWNfVINlCP6a2+KDd20OEliOmd3qngKw4kg+l/caFd8gRCd95558kWW2wRq06fhxSAwSc+8YkS8Nloo43K2k3QmFJIYuogfjpW6NYm4R6A+e1vfzv5RLdG2Z/++MeVdOPbPe5xjznBBHADIzBX+ta3vlXw0xRS6dO9ZyXBHYGAE6NgWDsNGwgmeILOumUfoq2usE3YMVVY7Yyu2M7oJXhCeympLutmESjRaCWtc0VbQmONMKZCKmktLeQ5nXJhsPHGG0/XzoLOuSKbkO92UIf2JoiDbLdfMuFBJoE4Aixndn7E9sHea3XTRe/ftbmfq+0+O226dtAQaAg0BFYJAi3YskpgbULni4C/bMWXgJh3HfElTS+r4umrO2RrTRYy+zbkjtE7xOPl2OJ5OuPxRVk21ezGLgVearrH0MXjvLV6joX7oONIi23SJ9f9jPhEOZ7vk9lXT2VZ/YiJ52tlURd8se2EH/SsjPJZfHcZ7kfU/YxnPKOcbyy4zG+oSawJ9MQnPrEsVExZn4+y233M6rgNztuOGwJjENB1ofM6XiN09umoMrKAUSosKs16Rl/96ldLpz8mv85Y/JyAAcEZpmHCz9pPrNlFEIMEv9+HoaGTOgRiCCowMuULX/xiCb4wwsbXqsr002lWh591p9Zdd93CRqCB4A/yCSDQOXfdOmbPembYyIgWFsd2v5AFT7Td68suBVvYE7wBD3BAJkEjRvgQwME3yu973/tOXQI/BZzAggAVo2LOP//8OQGT2GYZJtAI1BA0YHF8/LrkkksmF154YaEzggjM79wFhwko0L7QCDTAG5Pw+Mc//jENcFAfvxjFQpCDwJpSxAo6GBNkgp9gBxgJe8oJimk6GX6fd955Jeiy0047rTTCBX4CLQROWIycxGggzlV8ZG04nt2sr8b6bPgGpvCT0As/gRZo6MYfgjmcs/hCwEprfOl+3+6/Bb6WGgINgYbAKkGgBVtWCaxN6EIRiJ0+5GUvBM7nL2vZi1tWf4ydfbLUccz2kl3Ti1zVG2NHjScb/ZDJ5iWXP8k4lm5btDPmM3/Ek/FGmnAUXTb63m2r+RvprqfvHIj2zEdX1J3JiJh43m3I7Iny4cEnbcpHPvLCcags4xNuKpMe9gRc2BgO/8fuJZ4OHlOKol+e78NFZVl9aKJnMjLfGm3tRcCvd6Hg14todGz5OxmJzieBFzqwBCKYwqEpcuJXh5kRDQqoMBqFKSskOr1ML2HkCQmdClK4Tfy1jGk6nNMEJ7bppt3RgafTrUXXpdP3yGAkB4l6BDilh2Nswa6//OUvxR/phke206lmagtBBtb1UJnbJ95IE2+p1CX48IHOPYEWfGedFQXtsYkO/KmnnloCK0wZIgiEfQq07L///tPRPBt1o2ywi4XaSWqzK9QN7vbqFoBncV3qMTWLQAQJ23yRd9odmxkF09fOWsMMGYz2wS/8veMd71im/bAwPkntTJkwQyfBME07g05ATFN6mAIknBhZhC7OPYIfTMMkefsxWkkLj1PGMfwkzkdfVBiabOIYu8TLAu2cr8hmjy6dRwSX9NdB+eL33aKspYZAQ6Ah0BBYFARasGVRYGxCFhOBMQ9/8fCiEflFy2ya9YWiJkty2Mdjz2NDn86+Mre/Zkf0UVjAr5dBeDjmZZCXPSW3PbOzZpvTa8fRrii/1m5eT/bX7Ig6Ikbuf9TvGEQ588nXcBC9tu/TRR3HQMfRrz4ZKvM6GZ4uO8qXHXy1zfyE5ptjnenKsFD9xW6XMdg0nuWHgJ+jHNc2eca5Kx5odH4JUtBRZpRJHN2iIIQCLdSJa5sgUwnZCkiIRjBBIw91fhOkIDEKQ3/+8mvEr0PJJ1BAkJyggnyg06yRLrJVeskzuoFAC2uOMAKEpA59DTvVdxucBp01a0gKFHmQADrPlx/96EclUERARfyUgZ/L1jQqyrxtyGfJdbFGjOelV9OkVN/X3aGtNQVI5cKONVVIGv3ibennBvRoK3XiVCzah0ANCX63lVEwJAInons5beZ52UV7cj564M3lcx6hi6lUnC8E2xhhxHot0CmnTXQuiqZ9MaqlhkBDoCHQEFh0BFqwZdEhbQIXG4HYYdNLBXp0LJ6hF4coa8hW5NWSZOnlRfY4vVZXvL7v462VyX+9APoLlNuuY/c/2plhk9GizZEn5t32aIfysd1iXjKibPexryza7DbFejWsh+guJzvOaDWZ8Ma2VH33uVZf9Ih3jd/bITuOeMkW9nGD133N8k5TfdFcds3eRm8IRAQ4b+Mmntg5h860DwIDjL6InVt1wkVnQdzI4wGbTC+d8HitEuBRimXQ/dpjGgydY0ZmMGWJ6TJMPcIWddgzWd/85jcnbOiKgZap8isO0CdfVUaea9D91TFYkViclcVgYxKNkS3UkT8xgIB8ysBIi/dmeET55MFE9sQ6BEZiO0mGfHWZ8l0jWxRQk33wepuRj7qZrpPphAZOBJzAxQN3bhPH7gfnpZ8HlPv6PpRpkz3o0n2TkTEEWwguMXIIjLVQcLaIs+ohM963ZWfbNwQaAg2BhsD8EWjBlvlj12quQgT0suP7qC7yUD72ZWEsX3yZcxtcBsfKR3q0uy8f7ZL+SJevbh88egnTMXz+EpXZltkdbazpr+ER6ysvOe6X29fnbyYzs6smI+PNZA7R+uTEsgxv5Ee+sTodq1jHz4WId+T1vGT63o/hddmynb1v8Ck/pD/DZVZM+nxqZWs+Ajon2cdjp4GEd4adVyj5+Q1N9TXiQbSIKqNP9DeYKIMOcqR5Ppa5Duln9AvBBa0PQ8CFjQAAnWqmqJDcPwItJDrb2EZwxpPrpZ50iUf5zFZGAZEYlcFWS0yjcbkZFtSNQYSaPJfVZ1/0xeVlZZGmAJn7jgyNglJwBpp4ogzyrGFz9tlnTxdVpg00EonAC9jBp3bz9uM+6PmoK5apXPZQnxEtBBK/973vlWAWG1OhGFl197vffaUAEjKoh4x2HwaNlhoCDYGGwOIh0IIti4dlk7SKEBjzEiCezISFvDyMqes8Op61XmY3NJfT9yLkGOnlz2kuP7Mx86FmU7Qry/fVFb+/HPqx163RxaPyzL+aDWPapla3j57JHYOreGrtq3YUbuzn43dmu+uUTPbxOPpGXpvs8rzbmumN5ZIf9dTqNnpDAAT8OojXxFBeCPr5HmkaQcKIjoxP66rEa0a29dlAmV/b7o/kMXqGUSGs/8Eip6x/QrAFvSx2yvpJWpdDtsPLdBoW0GV9GHzQ9JnMHmhZMCqjIRvd/KFHgZ7aNeuYEGCIurHXR8d4uWQ6LR4rH+XGfGxT5YW3Ak7kmbKVBacIXIk/yicfAyCsx6I6YLXZZptN1TLFS4GqKEs63EY/rvFDd8wYFbXVVluVDYz5MxILEjO6hvOCv8vV2q2mu9EbAg2BhkBDYH4IXGV+1VqthsCqR8BfBmJnzPPxmLw2LXTnNC+jvG/L6vXJBxXZU0NI9WvlNbrXG2tX5ht1hYvKHZOxsof4ZimvtVO0L8qM/sVy+ep8Gc9CaTX7Xa7aNerK6DqPxBvlj/E76snyLjc7jrjFvPDNZA/R3O/aOd/oDYFZEaDjGTulWV408TsPnWdt6CfAoE65+Al+aFpNLRhD3cwe+eS6M3u8nKAKgRfWAtFCvd///vdXks+fdJhupD/gfPrTn56wIGrmZ2ZbxifftQYKQQnxOVaZP/iqkRzRR02tYfFtYRUxG2uj1+vzQTbAL780vYZFdKM/jNLxFO1xeeJjAWQS7UBQymUy2oTkNlKuFOWLN5ZHLP38dPms8UKwR4skMy2NP125fteZ+TM1rh00BBoCDYGGwMwItGDLzJC1CqsTATpsMYnmZbWOHXVrZYtBz+RHexc7P8bvxfCtJqPP51odpwuPSBtTVzw1TGeRsZi8mU/I9zRke8brdWr2Zu1R4x1Dj4Ed1Yn0mI+yo121Nmv0hsDqQiDryKI70pXXYqTwxA6qRrVQxnQj77z2yZSvWafWZXzhC1+YnHXWWWVEi8vjOttkk03m0FwW5eT5+wxr1RAIYgqJeJw32iw9stH38GrUB77zC22vT0CHXy0zcgObKWP9EaU4moOpLUoKVkX9fTZnNkbaGF/dTvzydv773/8+/f02smPATfocB44VPCHg5XXAQAE6RpvEAInb7zIzPaKxPo78RP655547+dKXvjSnvSnX35GiLPxl84BPxLHlGwINgYZAQ2D+CLRgy/yxazVXMwK8RMaU0cSjzl/MR3qU6fnYgczyffWzMn8BzMrH0NxvHfs+szOjocvp0p3xZny1+n0+SI7z1GTX5AzZN0beLDKivFrdPntrZWPomb6srcbIijyZbGgKpsSgSsz3YZO1cdRfyy/GdVKT3ejjEYgdyVp+vMRVy8n5WEtZmc4zP9/kI8EWAhakj3/842UKBkEC1uMggKERJow4mc/5GrF0u1nslc7zJz7xiTK9hQAGG/r5mxGJUROS4XWh4av+iMNIC0ZUjLUx8kkHfgqPD37wg5OLL7642ISdLMrKSBsCT/xKmzqsySL+T37yk2XqEyNFCAicc8456doh+JH55P7F45q9GZ/zSg+/U1Zb0s74xWibj33sY8UnlWW2SYbvNapI/iKLKV2f+tSnpiOOCLpAJ2gV2y7KzMoVcONcAFMCJozQYXQOC/Oim3YhYMQaMugnMcWMaUbCgUV0Tz/99MIjWsQz4tjyDYGGQEOgITAegbZmy3isGucyQiC+VA/lZ3VtoS8jqh/tmtWOyI+8KDvmVSfTndGijsXKu60us0av6T3ooINK0Wtf+9rJjW50oxpboS+Gf4sho9fIkYWyYz7novvg9YW976M5XlfHcR/r9OXRz5fYf/u3f5scfvjhk912223O+gOL1W59NqzqsjFttFTOq5qtTmfxzVe/+tVlesL2229f2qx2n1mV2Oo81TniNsTz1MtqPkZb4cM3puLQOSZooSki/OnnBz/4wZzfG3t99M+3TdFLp5g/2bDI7ec///loWvnFL0GNWkI3v/3daaedSmCIYAj3RwU/hFm0MeYlX/Tdd9+9yGNkBh19BX7gIyix4447lkCtEjgRaAE/Aj6aRkPgZqNu8V8CLzFJV62dKPeyaPNQfelDBhv8d7vb3abt/O1vf3vCRmJ9E9pZ7S7+mm3UIdhCkIYgCAEWJUb60G4EocCN3zPTPvqVtviiP1MB3YHKaEvqYZd0PPCBDyzywNux9vq3v/3tpz73+eB12nFDoCHQEGgIzB+Bf+putvV/285fbqvZEFg0BPwU1bHT+G0ji78x5PqUU04pX3Puete7ljnKvEDtvPPOE801x6i+F5n5GN13CVHG3yE+97nPTf793/998pnPfKaoePCDHzzZd999J8yv56V6PnZluCCnzx75V8OgRle9bD+fOpJTs7VGz/Srw8HLrbDM+KAtxNaazLH0WXVn/Bku2XnAtcAfKUice3QYPbnsPplZmeRIRtz34VGT9573vGdy8MEHT971rndNDjzwwKmIDINM/li+rK5oNdv66iyVsoX6n/menVf4yxdw77BzLzv11FPnXFsLtWcIV9nGni1O74DGiAG+8JOY/iJeyea58cc//rEsIMuXfhKBDRKBAI3OED9+s0Ato134swwBhS9+8Ysl6MB9nD/Z4DcjO1gcdf311y8jDUgaDcZUGwIo8BFocJxqPjFyhEVOmeJCwIIONvhf9apXlWll9IKm5cQFXmHiL0LggZ/YJVywGTlZfaex9oiSbCaQ4CMqkKuFeKfM3QH8yAJvns0EGviLEz6AE3iBE7TsvBEu4I8M+YAOlTFChLZ0G1Smv0Xhq9oU/ymX/5LFeaSFiH1hYUa4gP3ee+9dfFQ7R53uN8f4e+mll5YpPPwJSCNk0IPvyCEghl1+rlLXscA//Jd/8g2bNJ2N85IROko6XzkvCMwwUgqc44hF1nBBF+eN/g5FPmuL6F/My6751I2yWr4h0BBoCKwpCLRgy5rSkmuwH/4Sipue52VXC79RxssGL1D+tY3AC51PdcoX+0VA9qgJPH/00UdPXvjCF05bB1s0vFfEj370o5N73vOeU55ZD/r0R1mndZ3aQx760PLym6WIzRFHHFFGHMAfy7x+X1mmB1q0W3w1eiYHXr6yk/gzhwfVMv7VQePXmnQcYgc06h7CLCvPsPHrQTpYr0HXwF3ucpc5X1ejHVk+kxn5ZF/cRz7lJRPb2AisKOm8fGcXLD2w67z3ycxwqems8WY41mT00RdLTp+Omg99dSgbUy/a7/naOcA97UUvetFkyy23nLzpTW+abL755uV8V+pruyGbx5a7bRzPsqEj+hnlOQ/TM+iA01n2ERv4SQCddJ/73Kd0mqFpky/kfeod9MgjXtmRBY/cR7dPdV1fpPXlHQvZ1sc/5rzy+s4fjyNeKtc+tpP89vZyXdEXlQm7Wn3KCU4QAFE7ywYCZB/+8IeLqPvd734lOFVrP7dFunwfy91fxybyxXzmp2gRY+o6zjr2czI7P1Uv6vZ8ZkeNfxb/ajIavSHQEGgILEcE2poty7HV1iKb40uV5z3Q8oIXvKB82eIr49e+9rXyRYkhugRfGE3y9Kc/fYraLC8IQ1BHWW7fS17ykhJowQZs4csi8+/5OsfXJmwm8aLOYojUzbZoQ+RRuejOH2kf/tCHSnG0O8pQPdYmqJW5jKjHbciOo373KfJ7WTyGl9EsbPpqGXmirih/PvlMBzTaWKOXyNc6TeiMMqIdY+wWj+/5UkmghSAL595nP/vZcs5JX9QT82P0xjryp0aXTPBREEj4lIUZO6xKuuIaEG4ZfhnNsXQbnD4rT62u6EN2ZPWpE7eMz2mz6Bnje4ZDtCHDX3azngXp8Y9//GTXXXctX9tV//+a8P/aEtrqSLWOb9bBdJps6+PDZ54nrC/CdBfy8hE6idEKfYGWmnzqZmUZPaNldb3TTDl5bRl/5In1vc6QnJp8pxfAKn7Ph69Pp3QN6aOcc5X2ZLoWa5sgl0SghY8jJEZc+k9qAAAgAElEQVQUeaClT/d8yooSS1FGX1nkVZ468iXWn6vt/3Jjr1m/3qkZ7x9995hMb6M1BBoCDYE1GYGrdp3BF67JDjbfli8C8cGvPHuG1W699dbFuQ91AYRHPvKR06+H0BjhwLDYe9/73uXrKwsbMuLAh0MvJjJuK8cnnHDC5FnPelbp7NL53XjjjecEOBgKzVxwhqUz/50X+cc+9rFzvpwyYuNj3Yict7zlLYXnv7pFEhkG7EP45QOdWOZpn3feeWXaEhtDlzU8nkDUBz7wgTJKha+0m9/mNoVn424oO1j5Cxn2S/eb3/zmomKz7us1/Fvf9rZT2PgSyJddbQS4GO4+FmMCTATMNupsYFE/1kV497vfPflpF4i6aTe3/VrXulbBDL3vf//7ywKVO+yww3T0il7omIICD3J8DQPOEb5InnHGGSUQp2Hkp512WuG/becLMmgfRj6BLVgwVx9baDMFcPCVNkAeIzIYss0wcx9Jw7oj73znO0tgjU4ZX0jBg5d0nR/Y9KFOBm1KWxF0o518+D1tgT7aq2B+xXkez1fJ9D3HtDE2sIYNbcEx8jnfSN6+/9XN99dijlE+OPFnERZilJ/ClMUxwQYMaDvOAXjBNEvopK2FD50Yrg3hg5+0E1+OwR1s3vGOdxSZW2211Zx2lQ/yg7Y78cQTSx2+TNPOsjfraAgv5Pi5IN+OOeaYMo2Ejfrw+6agKefj8ccfP/lrd+2BIwE/OqTi1XmFj+DEeUUgDv+QQftShh785FoDBzru+ouLny+M3GIEieQ7zmDLRn02EjIy/1XPcVD7fL67Jv/12GPL1ASm5fk1wDXEdYjd2MVUGka86ZqXDdA1naFPv9s/67Hk9smvlWV1oTm/83D9cp8mkP+d73ynHNNeYEDiuULAxWVwPCbQQf1YL8OiZpvqxn1f0MR9i/Vkd0afL839ma8Mt1mYxb1k1zD1cq/LMXhxffEratqZa5URTd/4xjcKjfZlsWE9K2WPfHO/Mn/dJvF6G8XyWvtFvpquzK4M+0yPdPhe8uI9w/V7mevPjvtoLrMdNwQaAg2BNQGBNo1oTWjFNdQHPby9c6FjOjr77LPPZIsttphceOGFBQF/AfKXMwIHdFI017oG1+tf//p0IcL4YsVL12GHHVbEZC8f0O5xj3uUEQV0Lg844IA5fC6PzgqdeBa182lOdPgYqp8lyVQZnXw6qQQ7YmIRyyc+8YkleMJw/5hYRM/XOZE/BDAOOeSQyD79bSXBof322y/VySKnr3zlK+cEIiTI8WJhW/6CwAif17zmNSvJYm2DY7uOH+3iCX9e9apXTUl0Tkl0hOQLmDBlKyZ+TcqCniT9ghMb8PX5z39+sUcLI15yySVFXg07ZIDvE57whCIPGfzZwdP+++9fzgHaXHrmMFyR+chHPjKdSgYvOvW3CX1N93p+bUDXdUGQhiAPibUQ6CATcKHzy0KKnGPIp6NI54LEeaOgXCF0iQ67cEIm9TgnX/ziF4ul7JHLOkmsD8N57lODnBH7WJMl4vOgBz1ocvLJJxdsHvGIR5R2JeigNpAM2uY5z3nOVCTBmpe97GXpNaLRPExV6kvYpGuOgJRPR5RvBJVYj0MJ3Aj+ZNcaI4nwX4s0q705B0jynfOUdJsu4BnLpIdzhnZDpiem7nBuq72wZ88995wzbVL8nP++OOccQVdkwIB7ENMYuV/FBJbIwGfWvuB+GxM+4PdDu+mJ+Kx1d/weHOssRr52DehaQIeOnXfMsdflmPONjjd4s04JiSAl91Q9V9xfjmsbdf0ZUIRZcptrx7FOls90yPeMP9Ky+pFnlnw8HyI+yOrTmbW363ffsmNhqTqOLTTy3GtjO3PNbdQF8rN27rM52pthlWGS8UVabMeYd7tcR8Q8y2d1hY/syHwTflwfCnbzbM4C37V2rtGj/4udz/BbqI4ry5eF2t3qNwQaAqsAge4m01JDYF4IdF9uV3QjEVba5iUsVOpeelawdS+5Zes6BCu6r8Eruq/oZeuCHYxRX9F9/V7RjdSYs3Wd6BVs4qUeGzL6tq6zWGQObfC5HMmXvq5zOpXRfR0rtnVf3Ods0WbPd1+/p/W7jsyKrnNWtq6jM6WTl8yuQ1Tob3/721d0HewVv//971d0X/lXiA4vdrCXb10gYcV3v/vdFV1nfIqTcGPffc1b0Y0YmqOP+tjZfdmd0rGp66St6DqfK7qgyZTerb2RYu1YOd7I6RY5LlvX6S1yZP/znve8Fd3igcU/2d99TS+2s4nWddBLvusQzbEPu8EDHZJJHeHncikHc2jgCI9w7wIrBV/kd0GBqY2ypeuMrsBWZOMD9kDDJse+CxyUPBh3IyOmtnaBrylm3d8vpnRdC9k14dcF7cb1IP3o5VyU/d1onanPtKPotLO3PWXYiJwusFXquP1dAKDkOU8dT9pT14DvJbsL9szBp5tOtIJ7COfmW9/61jlt1o2UKdh524C92kx+YiP2cz5zDvo5hR991zvnos4d9rQ7crqAXDmW/35tCjP8xgZs4ryQHOrVzqsusFT84fzh3FAdZEHnHNf5Q5nORWHt7SWb1E7o7X4PXPDnPKKNkNEFPgYx8OsWefjUBZmmbYsdYMvWTSGaXp/Yih/YTRsLe3Tqvs05uyqTXxNqa39OYJewol10z+C6YOPc4zrnGmbrOopTX7k+2bjfsYGLNtEoFzbURQbykCsd6EQ3duhayK4T0WSz7JWdma3olF75sNh7/FnMrYaN4+P3o3hvgk/XmLdpbNuIm9rZ25i2q7UxbV1rZ29rYYNf2hwvb4+svXS+qK7Om2zvOnQ+9LVNlBnPRc45fzfiutU15ddt37OnGwm0opuCtaL7YLCiC5SvtHVr0q3oPpxM70O6N/g9Ij7jsvxC7yOZzCFatLWWH5Lj5Qv1o9VvCDQElh8CbWRL90ba0vwQYAj+Qx7ykJUqd5fBSrRZCZLB3jfkkN92223L12+mDsSpC/FLDnXGfGVgXYvuBSc11evzhct/Mey2UpnpA3y1JnUvg2U/CyaMKMA3vvKzLoKnhz3sYZP3ve99k66jM3n2s59dRkDw5Y3EFyWfRsNQd9ZXYF0FplThA1NzSN0La9m7X24jx3zJlh/dS1rhJ/3rv/7rhPVouoBCmX6DDMnR1/yug1aGY/fJ50s4X/vhZTqN9DNqhr9IkZiewighJaaFMQqBr+34RdIfP/AVP7HhUY96VLHvzDPPnNblQPU5VlvDzzQ0UtQHBmBNOumkk6bTisiz1k60RboZsdF1oEs90lFHHTV5wxveUEbBHHfccYUm3KA/5SlPKaOlmJaiMt8XoiU/5zjuXuYKftttt105d1hrZ5dddik1mF52//vff4qH2oT1gxgRpXZ0+UzFYfQGo664vrzN+TOHZHQvn2WaFLyMcGCUS0zYJXsZMcUIFvB529veVlgp4zx49KMfXfLI0toI5OHnnPe2YVQOfEw94g8hJOFJvowqu2Kx3VKYJPTq7xsaLSU7/dzvOkul3f28FA2x1IFf1yHXJPcHP6+6YFIZ+aMEP6NFSKwR4VPFNKWMdgFrJY3+wVZGjpH4wwg4cB9Ehtql6yiWc4DrIf6JSvKwm+ua64+EDNlEWdcJK/XxlelDTJchca2Qxyf+RITObDqC2oM6fh8oQhYpqb0Qx7Hy2T6jeb1YP8qOJrtP8rW2p+5YPDI7Ik12S260bVXl5bNjM6Qrtr3yvo+0msyhNol2eZvHY8fU8XQZ3mbCOrZxzVaXGY8dk5rvEbeab65fPH3yM/vdz2iP4yY/RGNUsda06cOBMu7Ze+2115y/aEUfXUZf2RBfxCraNlSeYSoa73RMd2bkKSMPx9i5WDzRj5ZvCDQElgcCbYHc5dFOa5WV/nCPD0XybJpmQEdbNK8nwDKag+l1WeiRzgUbQ/h1HPe+IGSUT55h/iQ6lJJP3nXVjungyLd73eteKy2mSbCAxO986WBjpwIiTKvqvmRN69BxwgZ43I6xtsg3+NGlTQucMo1IctXZVwefDiDBjJqfLpvpRKrPnjUslG5/+9vP0a0OLZ1V2SNe5fm7Ewlet5vj3XfffSrbbRORqRfQVY9AAkEBNjqULs8716LLL5fBMQEV0pOe9KQ5vlJPbcp0qWiT4yQbXYf0sGfqD+cO68mwto1s0lQUAkMEwERnOhwJup83DKOn/eiEC0O1+cMf/vApPugEE3XCkeX2x2PpdT5o4oPOujK8mMovyjWNh0AOeXzAPhLtJRmS/9SnPrWUfaibAhRtiPnC2CW/VpFDuysx/QqaOhXw+rmATOfnN7uySTIIzMk+95dyrl+VsdfaOvwa3uk634QDcpiyR6ITQ5BXelkjhuAO2EWfPd+NoJr6yf3CcSfgpbaFL9pNxUz2VOBqOPBOjDqMtb0CQtkemjbq+zFu1GQ6r8vw+uLpkzFUltlG+7BlelcVTXbOIt99o57jofyQ/6432hDlu0zXF4/lQ9TtvlGm9s98li9Z2UJpQ3ZFO92WiJf77nL93NaxX7Zc3yTf65r3QAtBX+5XBx54YJlOy4cU8jvvvPPkWlf8Vp1pwax55veR7P6R6XI+t0+2RTlDPLV6UY7up07XWk2SkfFEe6Nc5d3OyBN9aPmGQENg+SLQgi3Lt+3WGsvjQ4i8vsby1dUfXByT+mguz3mjnqGHaORXXp1DvsZHXd5oWX1eSJQYXRB59OVZi7BiIwuikgjAsNjqjjvuOHnpS186/QON+yHZyCVF+eTFz975xYtfpG222WalDqQvjkunOJMfMcHemt4Y2GJtG1LmEzTR4UGuaKIr8CAZzv/ABz5w2ol2GwlE8DcrOsws/kkwg+2Nb3xjsUW2uyynEUBTov0IEDJ6gY21NxQskyztkeHHsimjUcZIAxJr4PByLX6ODz300FLGwryyk/n0+EziLxziZ50SEiN6RFOb076qrz24KIm/tp8ydgeq737xok5e7caxLwgN3UefUSYcwZKRW7zkR3ti3nVSpnPF7Y51WACWxIgq2ed2KkhCsEp0+KGDtWTHMjo/joV+o0wwzPVoBJfueZQRvCMRfOqmGZXFgQk2EXirtYHTv/71r5f6jKKJ/MjX6DECyBGzyE+e5HyilYJVlNQhRrx3jr1TGTubsaMaO8becVVZTV6sK12SoXpOd1l+LB8yGZme5UZzTORj3NewqeHoGDiPyx3CKbZVlBPrR1ti+ZBu6ct8z2SJltnlsmLdml+So71fOzr26xmaX+8eaGGUMUFv7sVaUB655Hm27dOt56URtSwu3U07nN7js3sIoyW7qWMr3Y8y3sWmSbff16MOBVuc3sfv93avEzFVGfRYJlrbNwQaAssTgastT7Ob1Ws6Ajx4+hJfcukA+h9QqMNDXnvqZzTJlQ7tmUak6TKiIS+mOI3I5VGPAIESHW2f2hNlRT95MCvx4uR56JpiwLHKCEDw5xCmuRB46dZ4KIuZstGZ1nQN1x31UhbxcB6OYx1GoESa63CZfccqi/ojfUhGpjv6QDBKCfx0vogW/WQEDaNh6OCSHvCAB0wYcUR6+ctfPunWvZm+FEKL+pDPC6YSHW91pkXTOea2RLvE4xjJVvYEIDR65mlPe9qELUuve93rShlfxElMU2NKCAv9stAqCR4SU4/i+ac2R6dsUjCEIIP7X4R0ye10+71cx+J3f6PPbpOCRdRTHR3rzz0Ry8wG8JCdskE26UVaeQK9zit6nFrIV10S9IiL8l4GzX3gWvd68ptzSMd0YhhJQyCIaYVc+/wJjY3gC3/88nuGbI0+ghUyIwYKaovfsRSvaL53fte5qo7dLj93HE/pjudDtF9+qT3UBt4WksteG/I97zwqG/I/2htt8/J4vgzJXkrl7kfEaSF2SpbayvPC0tvV2xq93sayg3ra1I4xn9nsPmZt5eWSm8lxWpQ5n3qS4bLicTznHSf+VKafErAYuxYiF0/0gT85aaoqI2BYMNflo5tpzfx6+4fdFMw/2ZTljbs/tzGyWIvFu2xGW2ILz3SN+ou6xcOf2cSDbj5WkaBxD2ZUID8L6Na9moqgjCC8sOH9kJGj3G9J7CWHez0BdfTxkQl9BJq4H5P/eWfnrt20XqaYkhh5SvA8Owewj7/WkXjeasRh5C0MLTUEGgLLAoEWbFkWzbT2GOkvO/6Aj8d8TSfYwh9JWCuCxMMIPj2UdMxXWb6m0FGOgQ9/6LNuhr7g9yGOPv4WouQyoNEBopPC12Z+k8pwfvfLZUPHB6YA8VD1v8LwcPfADfV4eJMYWeEJvmc84xll48sLgRfW2aAjzd/db9X9VSemaJPnOfa8d3D1EsEaFrwIefK1XaIM+CJWojlvtMPle33nkxx1GMnzy87Iw/oYUV6fTOZmK9DC+hl86Rc/wRa3X8dOg9fbkDbRGkOcp74RXHN7/eUqwwSa6AzPJnFeMPUj+k0Z69zgC79B14gF/XGIUVGM4OGlEx469ExFkRy1OfgxusWvMdb6IPEimunNbC8VuiQfxOM+icf3sZzAENdMxFH5zB7pzeS6HSqXTgVTWMeGqUQx6c8/tEG0U3K9TWN92VXDQvQYoOF+Q6AMu1hPgMALgTLag99n629ZUR/yFChj/R7ZLD3wI5Mk371M8qDhl/ZRz+rK+zmJznj9eN7tdftVT1iQ13Gs7zp0vmkfy4SByxAt22dY9vk3Rn7Wdplut71WPkSv6Yr+D+UzPRk2kS/Dyts5HsvezO7YpjHfh5fkRT/d3qwso0XbnCeWSX4mx2mxPOaRg2zfLr300qn5BBky3e43x9w/eA8i8CKZEkIwwtemcmzQxcZHjz322GM6YhM7CbRoGiSB5SyJBxvgkV3ygfsfARYFj1wGgRP4NZq2W4i42KLEvVZ51kpj/S/08Qwlkff3DZ4Lshe9jAhCfsQcPMSnkczIy3inxrSDhkBDYEkj0IItS7p5lrZxzM9luzISv5ol0aFgAVBfN8LtIfCg3yKzaKR3PPTgZc/GwpOaEuEPtvgwJMCgutLlsqCxuC2LtB555JElyKORBJGfhUEZgUKnkUAKHVwlRk3EYEv3t55SzMge6WRUAy8xCiQp8II8gj2MdlFgIOqfKrviIGISy8kTLEC21nFwHl8DYginqCtiityMJn2xTIEWdQwJsrHoqrefL5gb62f6FHx71rOeVRacVdAJzGkf1YntH2XxokUQg5EGvEBhk+tn+LJ+kel46lg+RD3k2TjPSIxi0giVyMsUIoJxnG8KznBeMiKCIBC28WtskqanSIbaXKN0pBe7zjnnnKnJ0KOtFIp/ythDE7943Q+OmSqjxDQYD4BBZzSZ/25U9V13piPqifx85XzTm9406f7qtNJ5ye+TFZTjd8Dub8336B/5eK+JNsQ8ers/ikyDtAReuDewmDABF37VHRfZdr2awobtnNMa7i8stFaPpvqNsY+6Y/iiL4uVz86/aI/bSJnyaivRnB7tk0z22rwNvTzWVd7tEuYuI6sXfcl4Mtp862Wyhmiz6JqFdwgbt6vvPESn8PZjbwPJ8nasHdfwcN+i7DF+i6fPl1kwiT5lednJPtoMv9aU47gvCBsxJvgQ5XGvVqBF9y6elXx4YCoR7xmMeCGowbpYGuHittXsxD7X5x+M5LeP0CFwxLsTI1ZYn4uPNfhKIAV7+GDFxq/tSRt1vwPXumdRFx/akINPjLzBdj4KMFIHX5DPgub6wOLngt6hWOvGR2dKx5jzRv61fUOgIbA0EGhrtiyNdmhWzIgADy6tUUGngk6QJx7idLZZqI0HH6NRaoEW1aMzzdcXeLM9NC06OWQuARZ1sAk68GVGiRcAAj/+BxaO1dGh80uiU8xoA71MMARVnf973vOehd79arE8yJ/znOdM+eILjb7MuM2MSqkl1ffpB77Y7WMf+9hSlSAO07hkHzyveMUrSpkvOKpyyY32wZ/RiqCQXIbkikV5gmsk/saCn6ITlIMW+V23eLVnLjqJ4dKi0Sb8jUmJl0WVaaQAejgHRRcuJ554YjkXpJM9I5sYCcWfekSnE017a9ix5EyVdgfi5TxXR19r0rh81dGfwwiOMGpKMhW4BB8W6SUhR+XsFVQ9+uijpwtAYyPnLaNiol3SD91tB0cSo0Cor5Txu37nI0DENU/i/Kc9SJLBmi0EZLRuier63vVFeiyTHZo+RpANv0WnnXUv4prXWks1vY6HH7sPOnYM3C6Oufbo7PDFFiwzXg/KRj/Jc29SwIURcJJDGdeOAmm6h7gN0T8C25yv+gNbpi/WWZV5OiW+ua5Y5nk6QOR9nYy4FkbM98mT3oynz6a+epms5Urrw6DPp6Fzp1a31r7e5t6+2blQk91Hn+WckZxVdQ5EuX1YZvcV+Ak2kGr3hHi/cjkEPtj4g6ASf5HTiF3KCM4wRUnBdUZQcn/y0auqG21UPpZL71Rpd8Dac9zb9WGLqTuMOlHiL4+q5wEbdMgWP6YegRoS74EEZMCK5xaBeCV/BksO/gmTLbppSJwHpBrGU2HtoCHQEFjSCLRgy5JunmZcHwI8yBRAYR0KggP82YXfI/PQ5vfB/AKWjqP/hrdP5lCZHn59fDwYGWXCV3c6MnSEGRnArw8f0f3Glo4LXzj0q1tGHOgvPsjlDyPYT0eHQAqdcUbJ8AJAR4/htKxVgR7ksaYInU46mXTmCcjAz6gW5Khjimz9MhYd1PWV9d0nZPPyAcYkMMRe+BlJgA34xW9l+RUsC/LyBUfBIL6o970g9JW5HTqG3+vU6kNnaozWWWGkEvay8ZtaXqxcZvQ56lbg5pnPfGYZHUTQhK9b4Kz52vx+GDqdfkaVbLbZZgUbAk4EprCJPzfRFrQf7QiWdNhpJ4JqlCnoAT8LJfNihqyYIhZaqJeAm0Y3Zecp1wftRmKameQw0gb9nEPY/bjHPa68GKqcPfjBQzkvptjNV8bHPOYxk3/5l38pMnVOetv4MTwEr3RNcG7pGsjsLUIr6fnPf36xh3Nc1whtAoYEf/gaqSBCtKEispeMDAK8XIskMOKaReemm2464fwgnXbaadM26NMrf2OnKjPC24Fy5VlTgMAsbcILPbZxTnEtcm2TuA69juo6jalGJALWtCk+8ZVXwSVGdWkaYtZOonEPBnONmipCr0hZPS9fHcfC2vduX1YeO93i8Q47NOX7ZM/qY7Rn1vpLnT/6R36WlNXPaGPaWG0Y2zG2fyyf1eY+/zLba7Q+OX1lUV6NV/cuv/dwrMA29RhN23ePg8fvN9LlMjU6lCA891cvU4BD93GCuPro4wGXaCN51XX7VMcDJthEIEQyVE8fBSjXgr41X1QnyuW54HI5Jjiuj2qMmpFN4vP3Mfx2nfLFfRKmbd8QaAgsbQRasGVpt0+zrgcBpgrQyWVerNZQoANMh5+RAkwLYqExOmRxrRa9JPnLUqTV8j0mlSLV48GKbXSC1cFkDRWGxrImBrYzKiGu/0CnmOlC6hjTeWJaAIlOHjKU8IuOMwEVgjt0+uCBn84soy58ChOdYzqpPOjBiRcYx0D2i6YRGbwUgaPWZOFYI3A4PvbYY4tJ+MWc5biWy9TgKw4ynZHH8/Brk42RXy8hYEKnU75iO4EgOpOvfe1r51QbenEhyEAnlqAHezYCOZxnBCmYfkOCzmglEsEzEn/4UbthE3ZoVBCYccwID/4UxFBpvYSVypb0wuUvXiqmPRTg0sgVlTlmwluBERZQVUIu56gSwUqSY8M5yfodCtxhP6MmwFlBoqkAO5Be7TkX1QYEE8Entmu0W3VdPucX9ugaoR2OOuqoMveeaTP8Uakv8JTJlPy+MtqMoBSJ9uR6I9ih4KiCpu6Ty83olLvP7udQGetMcb3z0g4GnPNci/wyndE2tevQ25bzmPuQgmlMNVMnCF/Jux3RH5VldkfaUsvH9sjOPdFixzu2mcuKZYvhd59ty7FsMTAZIyNri4iXB1H62jnKinIWkh/ji3jmq2dIh98Xsmcjo01iyp5LNR7JVB2NgiPIC80DFzrmvqTEeiYKULiOWC/apHysyzOXII/qi8/9HDuyJeok2OJydaw123hPYlSkbGKvNWCYTs6Cu6QoN2Lb8g2BhsDSR+Cfugu5/7cvS9+HZuEahoA/XOJDMpbhuk5hXkD4MsBef3zRS0kNItWNDzSXSV3k+N7luQy3J9IpYwqJf93IeCRbZdThhSCu3+I26Bj/eYGhk+U2+8tZzQ/Z7lgM4UId1vGg0x+/fmeYRX8lP9Iz32TfmL3zyA5oLA7LaBeCBgRFxp4f1GWKBH9TiOvv1GyF7rr9mMAMec4F2RC/mjJihc40c9djctxqx1mdSMvyagvK4rG3qewm2EMnn4ANoyBI7qvL8fPJZcuOjDZkB+V8fQRL/eFBtvleOjJ7IoaZzkjjKyvtyDkRg7nwRgxi3u0Zc1zDRrq4Brn+ufcRHKv5Ll2xLcgzjJ1RVdxrfLFu9yfDNnZWo+6F+j4Gn8XiiTjHvPRkPmW0xbKryVlcBGK7xvza2M7CQPcG9goGiHbKKacUaHjeM7KVNPa8d4z5A5E+RtzhDneoBoZ5/jF6lAQfetHHQuA8k8nzASQmdLEOmXioq8QIRBLvSdCztj/99NOnPFpQHkKNThlTLzVlnJG0So4P/shv8GNkDQkbWJCfxAhZRmxm91rKx+JdhLXUEGgIXOkItAVyr/QmaAZkCPAw8QdgfLhQJh4v89/qxjoxX5Ph9qhOrKu8ZKhOlOnlHPuX5lgXGZnPCmJ4WYYZNPx3Wzn2DR7lo4xoT7Rddb0esrRIXpQdMcvqSxa8jp3ryDARf/SBPJ3gI444ohTxtV/2UUfri7DmjeOQ2Up91zO2Hfpkyd4YDMzqvOhFL5ocdthhqjJnn/E7g9st/IbqUD+2ueTQiSeQApas26IFaOmcn3zyyUU1U81I0lPT57bBn7VvEWTJ67hcHfcFFzM7JK+vzG3L+AjssGUp44evRtUpms4AACAASURBVM9kuP4xdQmOqCOCHm1Z3djO0k/QqDYaRnJcdnZc82W50NVGY6+bWdt0ueCwptvp7ebXQ83vtaWd/X7sx8KF4ACjMhhlMkuSLO3jml2ZLuT7wvHwZHwEhIZS1C/+TJ7Lqul0enZuuFyOxcOoGdaFYQFdFiDXs8unEFHu9f2elOka8r2VNwQaAlcuAi3YcuXi37TPiEDtBckfZs6j4+wBldHcnExONHfsQ9DtizJqZf6wpc6QvZnt1PFNcsbIgifaEG2XbJc7BrcoJ7Nduh1j8QmzzEY6wEzbYmoPf45hKhTrWzCFhqk9DEtmbR+3fYw90uX+RRtrcryOeKQ/2zNigj9sZWuaeJtEubJR+Pi5lbVlpEV5spXgEJgypY0paIccckhZh4i1W5gWRxl/rHHfsjaNNLePsmiP+CNflEMe2+PoIPlT88vlSEbEV/k+G6IcyRI96o95l13TL1kZRu4nx75FG2p+UCeT7fUlV/7V8tG/DJ+lTlsTfFjqGC8V+1pbr9wStXuBpvVoDRVfSH9lKf+fwogOgrnco5HdN23W5fDxRIl6vjk9062AkOpkPEMyKK9hUaNnepyXqZsEW1hIlxE+TBliQXcS68rpXqzzUnXbeZoh22gNgaWPQAu2LP02Wuss1IMGx+PDJXvoQBOf7zNaBNPrelnUG/NRjuclsyY71o3+uo/+gI71avkaBtAjJn0yXLfbGHGKch2rMbhJtniH2tjx1XG0j3UmmPfMmhqsiaLEiyLr2Ky77rpT2hAmsidrzzH+SVGGCzRtjisjSDRcO+rwvNvkGOhYvNH2GsayIfJD1yKwBFz8d88EWs4666zyIl3zxzHw9nP74Im+up06dpzE73rjcSbXZUTbxI++aGtmQ5TlPtSOx9aJ7eD5WFbDok9Xn+9e5seOr7CK7Sa676MdLd8QaAgsTQTifU73QvYbbrhhWR+OxFpjmkrU5wmjWHjuEqDhD0MEGzzxcQG5Sq6fdU2UtIiu32+0pkq8BxHcyf6MFu2MvsZytynqiGVjZfFuQrAJ+773ve+VP8r97Gc/K+KYPqR7e7zH12xr9IZAQ2BpI9CCLUu7fdZq63iw6eFVe8hFgLzDQVnMOy0+yFxfTW6kL1Y+8zXaN4su97t2XJMXcVBecmI96Noc33gc66ncXyxindj+8SWEvDbZIB4WM2VtFtay0IsMc6+H8FB5pls+qMx9quFT46nZUcMy4lfTB10YUMePs3ykOcaxLgEXFuJFxyWXXFLWK2GES21ESbSZvNsXMXb+qDvDXPK0F3a+l8waXm5T7ThiCl+f7dIVdca8+xuPYxvKNul2Wa4vO46ya/nMT9cbZWf5WXys2dHoDYGGwJWHgO4DssCvaX4+wHRF1kJh26ibVsSfFvsSwXkFPm5yk5tMWVm4/he/+EVZkFsjOqIc1pBS0rRN7o36YHL55ZeXtabiullaO8XlxWcKZfKt9nxR/YhJtHOWPM9LFgW+6KKLJt///vcn+vsRe/BtqSHQEFizEGjBljWrPdcYb+JLf/aQxNmM7h0AeGJeII3tFAzxyYbMllkaJD7MM719LwTOH32u5d2+TD/63L/In+Gb2RFxcF06jv5meMr/aJfyXgd5WhuFF8LY7pT7ltkoWrRFdmR4RDkxn+EjmuyhjtOijFqeOkO2RtudX8e+F79oWrtG64PI1iE8I1bRTpVLn+NEWbRb/DXsHEvZ6DqifPG4767X9UfboyzP95XJHt9H/6OumB/CIdPhNOxzn6O9mXxhFbH3vHiG9LfyhkBDYOkgEO8HmWVMGSXQQuLPfJtvvvlk++23L0F3JdZRYcQKQXl97GBUC6M2dY8jUE+whcTi9VtvvXVZ3JsED6NTCEaQGPGh301jIwEeghUkAi6+dhzrn/zgBz8oZSRk+X1To0qmDN1B7b4XeQiGoI9F2XfYYYfpiE74xsiQ77e+9a2L/QShNFKUES61ezGyx8h3e9txQ6AhsDQQaMGWpdEOzYoBBPwFAFY9kET36nogxT0883lYzafOgDulWHL18I32OV3yhmzx8ngc82NslE3+ohLrIVeyo30x73Upk49+nPka+cTvLyYci0/H0We3k2NtEfvoY2yLmB+qH+VFfrfLy6L9Lke+i98xghbzorlMl+F1HL94LBscv+zY+aLdstl11vgzu12ey8ra0/3NdFAuHIb2fbb0tVVmQ/Qhsy1rnz5ZKot7x0jnhWiZz25b5leUTz7Sav41ekOgIbA8EOCa9vuFrCbowZpirIFGsICAChsjM7Smi4IxqkNwnuCKyyNAotEtBC/YmGLE6BcCGiwgq7/xEahRQoZPBWZtNuRs1H1UYQ0x6hIUkWzq+X2MjzDZyJdaq6guerEZ+aTzzz+/BICYAuU/Z6jJkR3IoZ7bRxkBJSW3t09eK2sINASWPgIt2LL022ittVAP+mzfB0r20u8PrtpDLHZsoo5YTy8Nka56spu8H9fkxpeamtxYP8tn/ma0rK7bK7u9boaTymfREXVHfx0PlUm37+Ox512H28hxzMvvaFfmb2yrWt0oK+ZreGV49tWV/hpmquv6ol9D2CIj+i0cs30NE3gzO6N/ykc7ZYf74roidpEv6snky0bf1/S6PNeV6c1o0R7pcZ8y3TUMo/9RjnyKeqOvY/yKuob8jzpbviHQEFjaCNTuWQQX9tlnnzIqQyNXWPCVLSYCJSxSr+T3oD322KOs/cJUItLFF19cNiWCEne7292mI178nrPddttNLrjggsLKCBmNkmHkyp3udKfJhRdeWMpqPgyVqdzrM/2HdVZIBIdI66+/ftl78jrZcxOaj+zZbLPNJle96lXn2Brvr1FHyzcEGgLLA4F/6i74FcvD1Gbl2oqAn6I6zmjCp/bC3/fAXSi2NXuyyyujuf6h8pqtmX81LCJWmc6aT1F/3wtBZtOsvkbblPd97dh1RTvJa4NvyFZ4oi0RizH5mp6+tqrVifqG7MvKI418H56U9WE5FtOoN/qS5Wt1oj3U7cPTZbvMKN9xyOzJaFlbZbSsbs2uMfRZfI66a35ndtdwrdGjrpZvCDQEli4C3Av8vsd0INF8jwfiY/TJ7373uwl/DtIaK0zz4bfNjN4giOD8fr/RMXWZ/nPZZZeVP/SwHhgL4rKYrE9PQo7faxhl8qtf/apMWWK0C3oZXUMd5LFmG3IYkaJ68KKPKUs+/chbhZE52Ka6XoZ9CuwgExnIkj54Nd225jd0bP/EJz5RRO+1115ldAzysF3roM2yHloR1FJDoCGw5BBowZYl1yTNoAyB7OEsPi+DFjsIMZ/xRJ3IzOplfE6LtsQ8vBlNMvrKom7l++yMZTFfkxntiPma7pr8SK/Jy+zJeEXzfXYc5ckO9trgcfsWYmvUNzYfdUabXE7kzfDp01vjzzBFDvRY5vYNYRrtddtqtvTZ31cWdcV8X1352mffkL2Zvow2ZIfK+/TFsqgn5vt0Rlk13kxmpMV8TVajNwQaAksPgXi/HxNswYsx95DsOZLVy2iO1HzvMao3tn5mb63F+mTX5LDeDUEbRuLst99+03eSFmypodzoDYHliUCbRrQ8222ts1oPMh5a/qCM+b4H8tgHLDLG8rpdsV5mW0ajnh7GY/VmJ0Bf3b6yIVk1m1WvJrtGjzhJjjCI9kQ5NXvgizjGvHRLZtxntrncaNti5qOfQ/iOLXcba9g5j/yN+1pd+BzHDNOIa9Yui4VlhmNGkz4/75wv81eYZLb26egry2RBy/TX2imTMVbnkJ4ouya3Ro/1W74h0BBY2gj4fa52Xeu+MeZeXuPxe0/tPrxQpNx+Hcd91OH2ul3OF++bNZmRT/IYBaPRMdtuu20RjQxtyjs92tnyDYGGwPJAoAVblkc7NSuvQMBfAiDVXgQcsDE8CwXYdWQvFn0096P2YM/sG+PXGJ5M9nzxW4i+Wt2IifjiS4xspjzD23GWjLh3GUO4rKryGg6ztEnEbDH8ctyjPMfR7a/5UqMvJqZjdUS+2rmDbbVzLuKxGH5Eu6LMaMsQf6y/WDbPV2/NnkZvCDQElgYCQ9e234Nqzxw8cTleB7rfb/tkzAcR16vjuM/kul2yP9rWJzuTCY3pVqxv88Mf/nDy85//vLDd7GY3m/zzP//znA8W0un7msxGbwg0BJY+Ai3YsvTbqFkYEIgP7gwg58nKVyVNuv3hnNkcH+gLtek973lPEfHgBz94oaKWRX3HT8fCOWLr+GfHTsucHyrP6iwmbVb9Y/lr52i03fHMZIsW95KT1Yk6snx8wc14nDZfPX0yxmIUbVsMW1xmxKJPfl9ZtJN8lJ3xzCozk9FoDYGGwNJGgOuc+4H2bm3tHiD+MZ5FuZ6X/DH3oyFdbquO475PhuzyvfizZ4Lry+RS56tf/er0l9nwaCFfLYyLjLiNwT/T12gNgYbA0kGgrdmydNqiWbIGIxBfHpSP9IMOOihFYZ99953cYdddy+JvV7va/4+R+gNe9L///e9TGUMvAKmyBRBXhb6IUTRvqDzyu43R3piPdRcrH21eXXqH7I92eT6WSVYNz6Xi05DPrbwh0BBoCDQE5iLA/V73fB335eeDX3ymxPx8ZMY6eg7FPXx9zyj3VTJr9o2Vw6+i+TU1QZZNN910wh+I+NsS9bX5grh+PGRv9LvlGwINgaWDQAu2LJ22aJas4Qj4g7r2IGf1/qH0ghe8YPLc5z53DhsP6oWObCFY8653vauMjOl7eRiyL5aPkVV7iYmysnxf3Uz3WFqma22iRVxj3rGImMb82oRb87Uh0BBoCCx3BOI7Cnlt+BaPF+Jv37NljNysvj+DdBz3Q7IjBs6vsjHPuiGskKEt+/uQyobsbeUNgYbA0kSgTSNamu3SrFoDEeCBmb0U4Gqkf/e73y3zeEn8uvBrX/va5L3vfW8JhrzoRS+asIr9mWeeWX6tqPoHHHDA9LgcdEkvAlG+yrVnsTbS/3YvVKQh/li/74VjVllR9iz21OzI6Bkt07220uK5MwavMTxrK57N74ZAQ6AhsFwQ4F7Os9v30fbIE8vJ154J/l7gPDV6lD2Gz+XWjqNcz0ffh3TW/BjSIZyon2199VtZQ6AhsPQRaMGWpd9GzcI1HAE9wP1BzrHyDDndtZtCxPbsZz97svXWW08++9nPTrbffvsShNEDPhvZgowf/ehHk3PPPXeKInJudatbTfNf+tKXJp/+9KdL/sMf+hCRlhLoucMd7jCV/Yc//GFy6aWXTr550UWT9W5wg8kOO+wwudGNbjSVgR7scBuYzvTtb397clFXZ8MNN5zstttu1ZZEvgI+3/rWtyZ77bXXhHnMMUnmxRdfXIqQu/nmm8+xxev4yw/0mI/yW35lBIZeIBumK2PWKA2BhkBDYLkjwL1dz3btaz7pOaD3lqHnQu25MlRP+sfywe+8sV7Mw+/vYpRnPmW0GjayweW6H+jItvn42mdDK2sINASuHATaNKIrB/emdS1FQA9b9tqAQsfM3yUxr1cjWyJUBCUIMpA+85nPlKAI6ZrXvGbZs+I9icDEy172ssmLX/zikvd0gy5g8pOf/KSs/3LIIYeUUTOe9t9//8kpp5xSSMccc0wqY8stt5ycffbZcwIdV7/61UsdRt7st99+k8svv3wqFp3vfOc7J3vvvbermhDsufOd7zyHdpe73GVy6qmnzpFN0GinnXaaI1OVNP2JvF6e4ktUzM9R2DINgYZAQ6Ah0BBoCKyEgAcJ/B0GxiyAsJKAVUjo0x+f+TGPWRktk5nR+twag5m/q8Rj8k7r09XKGgINgaWNwFWWtnnNuobA6kfgm9/85uSII44oD7o999xz8rGPfWxRjIgvKRIKXVtG+9///d9pOXzrr7/+ZIsttiisBEliXfjZvvzlL5cgCUGO8847b/Kf//mfZaTJgx70oBKweMQjHlH4jjvuuMnznve8Io89gZ5XvepVpYxfFCpYQ2CHAA17giGMQGEki/SxVyJ4wi8N4WUUDsEbdB511FFzfDnhhBOmgZbnP//5k0suuWTy6le/utTZYIMNJt/4xjeK/L/+9a/TQAu64WN7zWteU1QefPDBk7POOmuqPx5kL1SRp+UbAg2BhkBDoCHQEJiLgD8/a0EBBQdW9541TmrbGFuyth5TbyE8sndIBrY59pmtjdYQaAgsfQTaNKKl30bNwtWMAAvEapoK02vY/ud//mdynetcZ9Es8QDLULAFpf6VhPxd73rXEjj55S9/WYIRnpTXaJXDDz98wigU0nrrrTd5y1veUoI1m2yySfHrhje84WTjjTcu5dBuectblmPkfPGLXyzBGfh32WWXQmf/xje+sdBYP+bQQw8tdE+UXXDBBVMS046wRzZrCtKRRx5ZeAgEifb4xz++6IX/pJNOmrzyla+cvO997yvBGuR++MMfLiNyeAmBF9v37f7WRCDnnve8Z8GqvaCEBmnZhkBDoCHQEGgIzBMBPVOX+/N1Pu8Gq9pnBV1omvnYN88mbdUaAg2B1YRAG9mymoBuapYHAoxqUaDFLX7729++IAcULIlBk5rQWjBGdKbTkJxPsjTS5AEPeEAhHX300SV4AR1+vqo84xnPKEEUph55sIZy1WcPz9ve9rbJM5/5zDl0BWQIgDD6RXbIhsc97nFz+NGp0TiMskE29UiMvCHg43oZ8fKrX/1q8opXvKLQP/KRjxRe7ECW23m3u92tlDHShjpKY7GeVmgHDYGGQEOgIdAQaAhUEfDAAEzKL5e9O1azOTpf46vRHZexGIkv20d7Wr4h0BBYXgi0kS3Lq72atasYAaa+ZIkRH4uV+gIp0pEFUVz/V77ylZK98Y1vPCdYAk1BBkag3OY2tynTgu5+97uX4wc+8IGTffbZZ7LVVlvNcUcBFwU8vPDHP/7x5LTTTiujUt7//vfPqSd9HrCBdsc73nGOXbyUsLAvMn7zm98UG1mrhcRiuB4Y4Vjrz0gu04lIL33pS0vgxb/++DEjdTRCxumlcksNgYZAQ6Ah0BBoCCwYAX++Dn3YGHoWD9VfqLFD+qP8Pv4xtqo+vNlxTZ/r7bMh1m/5hkBDYGkj0IItS7t9mnWrGQE66gQi4uiWO93pTqvMEh7I2qQko7kBrIVC2nHHHecEKqC5LPj4RTQjRAi6HHvssWUj8PLWt751ctvb3rbI0QtEtIPpO495zGMKD3U0WoZFeZm2Q8oCNEy58pcSl8sxdVQO5jFYUwR3STzYTmKv4ytY2q4h0BBoCDQEGgINgSsJgYUGBhZaf1a3/d2EurPoH8Mr+WMCLbK9BVpmbcXG3xBYPgi0aUTLp62apasJAUaNsCgs65uwZ7rLYq7XIjdiYCO6pwe2+LTnb0QKOBAYinL8ReLa1752mQrEr59Z3Pb000+fXO961yv1WRg31sUG1/fUpz61mEUdRpSwhgpBmvvf//5Tc51fRAVgFFRxmyI/gS2n6djrKsjDejO//e1vy/a73/2urDnDb6P/+Mc/Tv785z+XPzhFXRHXlm8INAQaAg2BhkBDYPkikL0zjKVFr8fWy/iiLPIETmLwRDTRPR9pmcxGawg0BJYvAi3Ysnzbrlm+ihAgsMJfeS677LKyv9WtbrUomvqCAHqIS5F4vQ5l/M55u+22K2yMNInTgbx+fDEg8MKUHUa6kD7wgQ+UNU5ct9fhV8sENEjU4S9IlBMEYdFg1zXNJAcu0+vsvPPOJfu5z31upWALulkglyCRJ+yIfsU8/O7THAEt0xBoCDQEGgINgYbAkkUgPtOzfJ/xGf9CaPPVVavngRh4YnCmVq/RGwINgeWJQAu2LM92a1avQQjwEpAlpxNkIfDAiBIFQPS3oViXen/7299KkIjNAyqUMfqDkSok1kbRSwj5k08+eZpn0VoljRiBl0AI05KUfvrTn64U3PAXmynjFQfyC5kEjEj4go9Khx122OSxj31s+UsR/ByTCBCxhowSZRdddFEZecT0qFqSzlp5ozcEGgINgYZAQ6AhsPoQ8PeEoXeGGu9SpI9FsAVZxiLV+BoCyxuBFmxZ3u3XrF9DESDY8KhHPapsLGjLuib81vicc84pgRKCEwQrSHrZEBTk+TUyU24IzLCuC8GRL3/5y+UXygcddFCh82cfRrvAj2zS5z//+cmJJ55YAjuUabrQnnvuOfnkJz9Z1m9hZM1Nb3rTaQDkzW9+cynzgEY89rzb/PSnP73oJZjCOjD4hb/yc7/99ityWex3jz32KLzbbLNNwQWdLJjL4r8kfodNiroKsaWGQEOgIdAQaAg0BFY7AnpHiXsZEunzyWdTl10OU41///vfTzfegdicpmN4ve6Q7FntHfJ7tTdQU9gQaAisUgT+qbtJ5J/VV6naJrwhsHYhoIcxXnPs65H48XWve90qMAQbdt9998nhhx+eriHDL5RJ+tsPxwROjjnmmOloGAknuPHc5z53jhz+NHTooYcWFoIs1GUUyZOf/OQShFGi7Pjjjy9Zn2LFtCuNhrnwwgtLMMiHyz760Y8uI1OQyzoySgRNDjjggGmeA34d/bCHPWzONClGvmAzwR1PjI4h6LL33nuX30KjU3v/chSH7s4R0jINgYZAQ6Ah0BBoCCwYgb5uRVaW0TAio7NOG0nBE44ZvcuabaS//OUvE0bbroq0wQYbTK5xjWsU0YwKvslNblKOeW/j45SOy0GXau8cGT2jDclReds3BBoCSxuBFmxZ2u3TrFtDENBLg4IutYALU3QiL1NkWC9lKGl6zYYbbjhllSwWkeX3yTe/+c2nI2L65MWXHOr/+te/nvCywagZkl4O/CWBaT8k55MeFvblBQlfsgWHZX+sK1uk5x//+Ed5mSLPr6+RRXClFmiBr+9Fpg+HVtYQaAg0BBoCDYGGQB2B+L4gzoweaTHPFGgCJ5dffvnkr3/9a5lKTLr00kvrBizBko033rhYxfsYARo+hrFfZ511Cj2+k8R8xiM3M16VtX1DoCGw9BBowZal1ybNojUQgRhAIc/mo1pE0x4Y4otIDZoan9NrPJnMMbz+wNfxYr8EuB1RNvkYYIkjWuDRlvnZaA2BhkBDoCHQEGgIjEeg9n4Q6X3vH3w04ePLL3/5yzKVh+DKcguojEdsLieBGIIv6667bpmSzagY3l1I2XuVasd3oCH6fO1r9RoCDYHFRaAFWxYXzyatIZAioJcO9tmmoAuVvTwVViH2vdh4FfH5gzu+JIm/Rqdc9WsvABUzF0z2AIofe6DFj93WBStvAhoCDYGGQEOgIbCWIZC9C0Ra7R3EAytM+fnFL35R/va4kHSta12rBCpITOlhXTslAhkaQSLa1a9+9emo3JpevcswuoZRNZ6gERBSYqSupi7hz5/+9Kea2FF0pmDjD6N1PQAza/Bldb+PjXKuMTUE1nIEWrBlLT8BmvurBwG9hPi+NqoFi+CLLzJjLY26VG++8mQP+9qDvO+FoGb3fOyRnizgUhvV0md3zbZGbwg0BBoCDYGGwNqMQHxG9+W9jEAFa8cx9ZhpQPNZQ0XBlOtf//plBAjThRkBMiZoUmuzaH/kq73fRL6Yj0EaRuww9ZoRO7/97W9LcGk+wRimVDMNiUDSeuutVwJIfe9amf0ZLdrf8g2BhsCqRaAFW1Ytvk16Q2CKgAdBOK5tVHBeCRh6aPqLRHypiPm+Zsl4nVZ72A/Z16dzljLpYe9bDLYgU+WzyG+8DYGGQEOgIdAQWBsRiM//2nuF0xm5wkgPgiqsvTbLqBUFVW55y1uWYApbtqabt0W08cpup7HvPgRhFIj5yU9+MnMQhtEv/JSAIAwBGL3zyP/au1lWfmVj1vQ3BNYmBFqwZW1q7ebrlYqAXhDYx2PRnD5fY+OLSMz3yc14Iy2+WAw94Pv0ZWVRn3iiHuU94BKPqRvtzXQ2WkOgIdAQaAg0BNZGBOIz1/PZMTT+CsR6K9/73vdGj1xhdAZ/8CFQwHQZAi1aqwTcox1rWltk7yKMcGbUC8EqNkbBMCpoTNLIF/78yB+R4juSZES9MT9GV+NpCDQE5o9AC7bMH7tWsyEwMwJ6mWDvG4KU13EmfOghGV9WYj6TKVqNN6NHO2JeMmv0TGafbZk8ZGujvJYfI7fxNAQaAg2BhkBDYG1BID6DPZ8dM3qFKTH8MfE7l1wy+VM3SmMo8fdDRqwQYCEYwJ8Mo94hGfDX3iOG6l5Z5fOx2X38+9//Pg1mMQLmZz/72aAr1+pGBG1xm9uUkS9MvfJRLy47Yhnzg4oaQ0OgITAzAi3YMjNkrUJDYP4I6EXD9xxHOhpEm+/DsPZSU6NnXvXxRrtiPpO3GLT44qA8ez9G1+qyaTH8ajIaAg2BhkBDoCGwKhGIz/S+dw9ND+IvQd/61rcGzfLgyvWud73q8zfaUBO8GM/v+coYa2PNduhjZdRsFJ3RL7/73e/KSKKxwZctt9xysskmm6w03ch11Y77fGplDYGGwOwItGDL7Ji1Gg2BBSHgLzfxRWfMw7n2YMaorH5GyxwYy5fV7bOpVjarvihH+aF9Zm+jNQQaAg2BhkBDYG1AID5rPZ+9g9CpHxNgYRrQZpttNrnZzW42WX/99VcKrkS9Q1jHZ3zkHyqP/OTnU4d6s9o+ps6sMqPtyhN8YfFhphxdwiijgT8heeDlqle96hSm+O6kgqg3w7XRGgINgfEItGDLeKwaZ0NgURDIXm4QPPZBPPQgrMmp0d2pMTzOP2QLvGN45gNs9qLgulaV3vnY2uo0BBoCDYGGQENgdSIQn+d97x6swcL6K0NThDR6hekq/BnIU9Q35GvtGT0rPeqp1Y98Y/Nj/arxzUqv2RX9Uv4vf/lLmd41NOrFpxqxfk57h6oh3egNgcVFoAVbFhfPJq0hMIiAP3jjyw+V44M5PmDhyWhZXRkTZQ7RB51IGGo2OesYnkR0SoqyhvKpkEZc1gjovI5tv7qcql1X0n9l2bW6/G96GgINgaWHQPaOuQL2rgAAIABJREFUgZXxfYNfNPNr5m9+85u9fxAiwLLFFluUtVd8ZITLHINCdj+MtJiX3Bqd8r6yMfXd9qF7+pDPtfqRHvNDciO+0WflWe+FkUnf/va3e9d64c9GW2+99YTFdRU0c5m142hHyzcEGgLDCLRgyzBGjaMhsKgIDL0I1ZTFh2uNz+ljH+gZn+TMR6/bsND6Y2T16egrG4PhUuLpa6fFaq+l5O/QubxUba3ZtSadizUfG70h0BBY/QjEZ4Py2Z6/3TD9pG8dllqAJerp8zS73/V14iN/zEtXje62jOHps11lY/yt8UR6Xz6WoT+j1WyO/pIfG3jZeOONJ9tuu+0kG+2Sya3Z0OgNgYZAjkALtuS4NGpDYJUh4A9QP/7hD384OffccycbbrjhZLfddltJf3zorcRQIcQHdsxT7Rvf+Mbkoosumuy6665lNXtP89XLsNYvdf7cqvtykvmzGDokY742Uv+0004rYg488MCyX4isImCBKWufIZEF6y99qXylGsJ6SJaXX9lYYEuGx9/+9rfJ+973vmKq2m2s3Zk81R3rb5Shc2ifffaZXOc613FTSrv8+Mc/nuy1115lsULSWD1zBLVMQ6Ah0BAICMR7kfJxz2K3vGP0jWKhs73pppuWdwCfIhR11Boh3tf68rOUoS/yZzaM4cnqjaWNwSHjcVosH1uGjbFun92OBceaasRUsdqvpRntcrvb3W6y0UYbTUcwSU7ENub7bGllDYG1HYEWbFnbz4Dm/2pHoPZwpcN28MEHTw444IDJqaeeupJd8324xQd0zP/hD38oD9fLL7988tOf/rR0CNVBxIisA+nGUf/DH/4wb0OTf7ZA0Wnvec/kkIc+tOqPy5ivbyuBNA+ChkWz6NxipFl88baoBUxie2V8Once/OAHzzl3ZrFlMXzPZMzXhui351//+tdPnvzkJ08OP/zwyfHHHz/hHPzIRz5S1CtYmemVjC9+8Ytl+Dzpvve97zRAQlCEc/8Od7jDSkFH94368Co4+Z7uXK9dux//+Mcn97nPfSZ3vetdJ2efffZUTGaf62jHDYGGQEOghkDt/ig6ezYC03xIufDCC2uiJrfpfhlMkIVfBitF+VnleA/ry3vZmGPpizKjHUPlkX+x8kP4ZOVOm/UYu6PMmK/5Joy0512P9V2+9rWv1apMtttuu3JeXLv7pTQpylDFKwv/quGtoCGwBBG42hK0qZnUEFgrEIgPWzqJBFp233331H/4F/pgizpR9LjHPa4EWt75zneWPwqQ6Myefvrp5ZhAyt57712Os/SOd7xjcsQRR5Qi7KejSiLwQv6Od7zj9CWhZr/bVePJdA/RXO5///d/lw76a1/72ukIA+oToCDVXlxmtacmZ8hWOvB02LFHGGZ2EQxQx57OPknnjmM9pM/LCeA8+9nPXgmbmoxZMBnCQ7JqfKJrj6204w1ucIPJ0UcfPSFIxhx1MCFB/9WvflVtT4ZW3+lOd5q6xpc+vVB+oWsDAoTvete7yiihWnrd615Xro93nnJK4dt///0nJ554YqE97GEPm3O9cO1wHVD27ne/u4zEwWf5MwuWNXsavSHQEFg7EIj3yXh/JM/Ggrfnn39++atQlhjFstVWW5X7l9+DovxYN96vPK/jjIacGj2WSWfUNWRLLF+VeWzrwyord5p8Q8aY4+iL16Osz5ZYRlCN5yTtzx+NLrjggpVGu0BjY4rRjjvuOFl33XXnmOA2UzDUVtH+lm8IrE0ItGDL2tTazdcrHYH40MMg0ehgq5Md+eKDTY70PeBcRnYMja/udAJZ/O7+979/sUWbdBx33HGTe9zjHlXs3vCGN0zLvC6BAPcne9HIhEbfM54xtCjnvPPOK74ee+yx06ASck7pOsykyD9GRx9PX9v06XMMo03kfQSOysFagRevE+vX7CXIwOiMY445ZsJQYlKf/ZncPn7pzXgyWY6PyrVnJArp5JNPnvDrUegug8DhF77whXQ6FXxf/vKXZU7Zg+cU067caW5vaqe1BwGaW9ziFpOHdsEaOjg+neg1r3lNOfcICGk6UYbFHMNapiHQEGgIXIFAvP/E+6LugwSeWYulFmRhFOttb3vbmUax+L1q6HioHHfivS/mM57aiZDVrfEuJh28h3RTHtst0jw/dFwrj35FnSqPdOSxNg8bo0NZVJfNE+cR2wYbbFBGu9z0pjedUy4MJHsIk2hryzcE1gYEWrBlbWjl5uOSQyB7UeJhxxd5Omla24HRGNDpxK2zzjplwbOvfOUrJe9rq0D/r//6r+Kn6P5g5RhZfO268Y1vXDqp1OErPOmDH/xgmaMbH8Z8sX/ve99b7JJNAhNehifzcCZYo4e0ZPzxj38s9a573euWutD9wcwIBfKyFz+xEV51VOFxn+D505/+tJIt1GPBP14IHBd0CpuPfexjRdbFF19cOtfggJ6ow18WkPvzn/+8YIuP4H61q829bWLTr3/966nd6PvOd75TdLHa/1CKmMMPzek1HgUJwBpb/dyRHzqHkOvl5IXNSW95SzGTl3R0RT8lX23tbVQqdonh6uAkHegH22tc4xplvjh0MM+S2iDqld/Cg4AQNnJeEgCU/+J74hOfODnhhBNKYEPBp6jvBS94QSHpnKUucrA3BrIy3KkrOvyyjSAVo7we/vCHl9FijBRDJuWMGGMUDKNmGNnyqU99ano9UN5eUGMrtXxDoCHg9xqhoXuP7zkmyMKab5dddtlKwPG832yzzSZbbrnldC2O2r2NyvF+5Hkdx32sl9WJPDI06huir+RghVCTW2FfiTwLRitV7ghj7+3YKV1Dx/JJsvtsxKZaeaQzunOHHXYoC+XynOd9inctJaaYs/Gc4yNaX9BlobhnWDZaQ2A5I3CV5Wx8s70hsJwQ0MPN9xxrY7oOL0RMyYFGR45jaMyvPfLII0uQ5M53vnOZX73NNtuUL/jwsd361rcu29e//vWSd9kcM2R08803L0NDydPhYxTAgx70oMktb3nLqRzVBdsnPOEJBWI6r9KjPTJOOumkUk7nkiS74fnQhz5U5vxit2SyZ+oHi+/hF34QRGLaEv6TZy/bybMxxQY+hr4KH3hYq4QOPF9mwAWZ/J4SHfzWEh6ww290kPbdd9+Sxz7soQ6bdEIjKHXQQQcVuXzNYTQFdoD/WWedNQdb7AV35KHjmte8ZnlhYSPYAk2y+/b/29mqJLyEp9vGQoeRD9345FjjA3kCUPKRY/DBB3Swbglln/3sZ4tI1uchD51yAihgCe74LxzIv+QlLykYw8cGzvDQPgQ8CEqBC+cOe9Yscb/kEws2brLJJpPtt9++2NCH0ZOe9KTCo+lDwge5pJ133rkEUdDvtslGMPmP//iPcq4oECabtEcOGKtOti/KuhTbh9FhJAUo3d8Hd1OJSJ/5zGdKEJJEfd+XTEsNgYZAQ6BDQPcHwPD7ot+zmAbC/f/MM89cKdDC84qppfvtt1+53+mDissV0HSQtUHT8VWucpWVjkXLyqhXo7uOMcd+EozhjzwLPYmivDH5hdrch1+Ga9YWNTszPHReUcYx5wjnyv3ud78ypZ3pZp4I5n30ox+dnHHGGWUKkp+X/jzLzrFMf6M1BNYGBFqwZW1o5ebjTAgwUoEO8p577lk6lIwMWOwUH3Dk1WFEl3f8yNOJxCa+6PP1nA4lX/jpNNIh5gF52GGHFTM/97nPTR+A6ijytyECKyTm30LX9BmmNkh/1MtD93rXu97khS984UqdTzqzTCGi3Nd08c4p+lw2nfwXv/jFxQ58ePvb3172BAUUuIn14T300ENLHTr5fFWBhy95hxxySPHr1a9+9YRpQs973vNKHh2MAIKPLzFve9vbih7Sc5/73KJ3l112WQlz6UYHHWYSmPMy+//Yew9wa4vq7nvnteaLgg0kQAwCAipIEzVWUKlSVFQiSmygWLEreFlQE8WCqIiCvQOKNBsgoIKAoCioCFKsgMTYMGosed9v/yb8d/57nTX3Lmef85xznpnr2tc9Zc1qc+8p614zA20CBgk3clGHwBYcDA3Qwath++23L23E+SI6QyROTDzdV1TBozzxEtuGdITL8pATowPvCBOjiy66qHf44YcX/WBwQn94XKALBfRHmnzoP/nJT07bC3h0fNhhhw3xSz7GPp3hgw7QFwFdoAfwutyf+cxnSjnvgA4rzvTETRrwTjtgHJReBAsODsl96UtfWvBxGK2/S8T57xBksCMeYcjL6Mc8h5NM8I/xksB5CbGO/qN46HibCVep2ELTQNPAaq0B79O8D/G+k0VvzcjCAhnPP4y/eHqyIHecrlwtzMlTPFvAx7xs4d9lKKgZAMhXGAdmqb8Y48jQBRPboEuntTbxtqnRinqM7xz1eHd22223Mr9rRpeosZZuGhhfA20b0fi6apCriQYwbLDVhHDWWWf1XvWqVxVPBz+DYVJV+MJKcXDExVgtj0Umrp06tBMDAF4K5LOIZQ/23nvvXQwy73nPewbGCeFjmxCBBbgmXro6V8YLyeSDLgtyvAkwJOCBgHeMgm5W4UYYJoGi5XHPY/B++ctfXuDg4yUveUnxfkCWo48+uvfCF75wDg7RQna8LbQdCRqcvULAQCDPGtyk8b7AkHLooYeWLzB4mrAAZiEOHjxWpEfxCh7FOdND22UwUgiW22fe199uA594/Fx88cUDfolQh/eFq5eRlUNRMQbh2YEXEQffkk+I74DTxzCmq4QzOL5gCkfUtfCwPQhDCwEDg7aAoR88onhH4Ake0Q1GFcLjH//4gbwYN2Rwoq21LYc6p59+evnyhcEFYxIuyOIVPYATg5OCtvdA9xWveEWBlS44z4SAQc3/J+R5moOBCbxv/o4Sdz0wOSRggMNg6oF3gsA7zxkrBOr6e0Ae743rvgBa4L9AiLTJw3jJfwtj5o477jhUj3ed/yjGOnRCkB6GAFuiaaBpYLXTgPc53vd5vOvgWxbE22233dA221o/pn7H+5+YNyod+6+sL8vyYr3VpaEzXah9Yhn5sW0cNsaBVZ3s6Tp23PH9iHipx/yBcZUPj3zE8quj5enCfBQvquwgXce5urR1k7NpwDXQjC3tfWgaMA0wmMjQ4ophqwhnLcwqMPjEn+OOiz+8JbTVBzgGVhbGeJZgEGARjYGBr/4sdjGS+BkrWtDiZQBdvHcUskNGNTjCB0YCjC18RZN3CHWFE70InmdmABBNeCPAO0YfYJGFQ0VlbMkWvxg38FBxvbzpTW/q8SN4PttkCGwXkfdGybgpOH6fzEgGGSnY9uI6h99ddtml8IkcGCMwxKge6DFISCZwc+UvxhZ0h5FCweu47igHt4wfgwqViOR2nROHV7ZwcXbMi170ooH3CrQwJvIjuN6UVh77/wl4LgEvucDBIa8K55xzTjEqOC68S5RGD095ylOK8YeboLj1SBNDaGAwxAMGb5qoC09fffXVhSRbjsjXjzyXH+Paox/96B4eMyxMdNMQ/+sbb7yxlOmdpy54VF9PjCUyRkrO7Ok8qJxtWATePfGvMralETBWeQDOJ8tDhS3RNNA0sKI14P2E93kIrT4GT1K2APNxJYZxjSw+3ik+6RPaGR7x5GXOZy0/yrK6paNe1P5ZfsyLuqI8jiVd6Vg/ew+FE9guowvnufBx5q7924swurBVPLZ/TbbIR0s3Daw0DTRjy0pr0SbPvDTAYahZ4ADUaYNPnjRxqj2hEcvI23///QeLXcHwBQtjC4tz0eCrP+dZsFCUt4cWmSy+McqwoNSZEcLVJRsGBeriSfKCF7ygeKNglGK7EgtLrh3G60PB5RV+aLL4VGDR6YtxFsgs6lkMU58yn1iwQBa884rRCC8EFuI6lFb0gWOxrdt1lA8e4XdY4Veetls5PYwvCsCrrcjDIBT5dvxdcS/DoKWtMOBVmZ54tmBIcNriiTzJwfXEuJKfcMIJxSuJd4g92HgnOT2Xz3Wjd16GFJ90UYfrk3kH+LIlXQiX3jPVgSZy8a7Ci85LwRBF4NDaTD8uuy8yJHtNDt59dIR3yQEHHFBoaLsU6UiLtMvH+yYPGcnkzze/+c3lfRMfXibc2s6X1cfA5DyMmkRnOFpe00DTwPLXgPdxSONp4vSt9DXxFjVgxzGyeN8S40qP+4Rm7KtiOoNZ/q20eBJk+tT4VOOCOnpvFB/3WcMZ30un0WV00e1FnHWHF622BouO81Wj3fKbBlaaBpqxZaW1aJNnXhpgEcgExt0kQTiup8GkxBnQ/Kf6WjArjceK4MjzgU/eFOSzhQZjyzHHHDNYZOJVQZDBhrgG0gc96EGlTOmSsKDFN94RLGA5FwUPGp2z8eIXv7hMBsWv8+g4I35tZXLaeEu4EcHrrLPOOnOMLUw+2a6CgUYBoxBbqhScnyHB+onIk9IYJwg1o4SMQnzJwdAk2THORJxdNAUrHpWGf7abOO+Ol7j07zgiLbxR+NLEli0m62zbIsA/ebRnjV/ytW1n1113HeJFdOQ5JT79nXW+9K5iQHrqU5/a+8AHPtB7y1veUg6hlRdRpjvH4XyqXTzPYeGDw3aR873vfW9579lWxX+CPMqAqeFHPmTW2StRr6QxeGXGPcrkSUPceSTN+6JAmU+sY3oA2CJNA00DK04Dtf6LfJVx6DhbDv1WGBSBZx79GP0JYykh9jXetyiePbM88GX11Qhe1pWnsvacnwbiOBHbZ9TYQX3BZO9JzHN8KtNTuOBBRpfrr7++zBf8PcUL6/L+vOMB/W3VGvcyPrJ3aX7aarWbBpaeBtoBuUuvTRpHq1gDfJF+6EMfWrhgYcfCaj7ntWTiMHD5IJYNdg4DDqV5+mLRDTF4FGj7CN4nwOHhQOAsiRguueSSIbxOw2myfYbAghUYzi4hkN/Fp+PwxTiH+jot4lwj7fDksTiOeaqH1waGFmCuvPLKYiBjwOeMHQVoSlfUI8S0YIUXjw0C/EQeScu4gwFIOIHHwyODpyzL9zzBFMIJvPLjM+KIdTFgsVUH/eBFgnEN/jF8sA1K9YXX9cV7RGCPtss5igfquY5FQ54ieGPR/hzYS+DMHb8lw3mKegOe66XFT01+8HEmCnJzrTRGQgLeR6JVMvoho6GyUc9ML75FL+J2j7JIW7QynKP4aOVNA00Dy0MDtf7N+8zf/e53Zash52pFQwserdwuxOGlWrx6n0GeFrCK+1OHp+qZwYzKQ9MRZnlof/lzmek95ilda+P4DtTgIl60l72/eCrr9iLX8B//8IfyDnNmIO90nBdEfMu/dZoETQO5BpqxJddLy12NNYB3CweCsgWFBTdfuWcZfGKkeBzAIr1sARbxCAdf8glsJYJ/FtcssnWIqdPSth3glZ/hpS43G5x44okFJ4tY6DjOGg7yGWT5GqfAFzsfeNG1PAUcj58NMqjcj8gwQx43u+j2HHh3d+sumVwPoslTHhsyBjgOFu4K0pOeGHocNsMf+Yk4HLfKYp7XyfBlPKAfdInXjryZOCy2i0f2XhO4qhi4OFGSd82aa645ZIyRZ1Hkg/Y/8MADC04OEtaZKBxYTIjwkbcC1A/yOovwKle+zsjhsGHeWwIeal6PvFF4KO8KNT6joRAcf+hPPhVG4e2i2cqaBpoGlp8G9J+PfY7SeOCxXZJ+Gs9JDxtssEE594utuyyCY/8RF8ZdC+qusoiHtIKXLT/tr0yOszZRHhLH9uxq+1gWcTve+A5ThgGQM/78bD944EMUXrbf/va3i0drrAtMfJ9XZms1qVZXDTRjy+ra8k3ukRqYpTdLNpD4xCtjJtbJBijqRTiMIgS2UHADD+GQQw4ZwAHv2xnkZVIA+yHiE10ZcdiqROBQW+fppupz8gTDgl8LUHlLaAGvG3ZEX3UynJE/3KkFjyx+C07GHy6vWb5o6yBkDAIYgRxWBiHkQIeRF4fNyiSPaPnTy2JcuCLOCCd8eLM87WlPKwcIO094dXBArYLKlObrk/L4gkrAsBavrnZjFzf7RDzOp9N/0pOeVHAeccQR5bwhJmXSY5RNadWXZ0zc4hfrgZ888LIwgQ7/BeIY0iK80ynM3VRfcaVdDi9TXOVaJGHcclrE5RXFddwZnQxvy2saaBpY3hpQ3+D/eeVpDKRfwwAdz2ZhWzOeLNxyF73ywBcX01m6axGdwZOnEBfcy7slVjb3sa1qbZvlj/uO6J1zTfrYyDu67bbbFk8XHQgvWLYWHd83unDGX9d/YmW3UpNuddRAM7asjq3eZF5lGvDFV2QiLsy83AemWI+0D3YYNfBeYJHMNbMEBr+IQ7em/OAHP8hQljznCaMGgQUj1/v5NdCOwHmJ+bo9iGt+WaQzseTMGdKZJ4DoO07ibJcS/4cffnjZKsKhpZtssknv0ksvLQfBElhos52KOjvvvHPJO+igg8qkVgO+eBQNbsbR1iRk1AR4zz33HNwoBC2C6yfimbRM9Sd9RjqkaSu8s974xjcWQwPGEXSEEUbGJLbvKLzsZS8rUYxyfFUFFmOFDHdsDUJm8GDEkX65yltGyYyPmKdtbmzVI+j6bsGpDWI9YHUFt7awFQRJ8LqcESMDB9dUZ3gTFNUsTVKrAP0CbsEi6Kpshz333HNLUlvViM+XJ8ff4k0DTQNLSwNZ3+b9HLcM0S+z1cK3DOEJiIEFz1p5kEbJZBSJi+ds4ZzlxXoZvkizpZeHBiYxvGTvRpZXe1+kEX+veWfZjs+tju7VrK1FX/7yl3u8+17H/yvLQ8uNy6aB8TTQjC3j6alBNQ3MRAOazGTIvCzCaZDL6nmeBq7nPve5JZuFJgtibhCKQZ4q3ObjocYHOHxRHvF1pcUXt7zoph2MQVyNzJOtHjonR3hqi1Dh4iBgjCKHHXZYMS7wpIyJK54TBBbm8uTg66CMKBh38K7JAnJyxgfGHPTHQbJMeLVQPvroozsPT42TnEgjtmVsa8Fn7ZDBZnnIwGSGQPuzuEdH6BuZuJFHxgtgON8E3SAjutEWKoxVMrhguMGIpy054OB2qlEhtqMO6qUeV0TG8ho+DtEl4F3k56JE+V2/XDGugHEv6p4y5XmZcGZlNf6ULwMnshH0vhLH8EXAcDeu3KVCC00DTQPLSgP+v9d/XXl4s/BjOy39Kbe0eWA7xh577FG2ZWT9hPdLWhSTl8U9L/ZnsT+M6WWl8MZsVQOxXWvvgfIzQ0vXexQJ6z3niXcL453OgBMstxad0L8xsHm5RO219ErUwN/0/wzdG9JXotRNpqaBRdRANtHySRdxJl4sINmuwRcBvFMIeGVwzgNp8hkMFYDnel6uNo5bntj7re0M1I3lwo1BgUX2VVddNTDIwI/oMlC6oQaalK277rpD+aIHjzrzRPzxVUN5zjveE7e5zW3Koh+5WLyfdNJJ5SpneQUgH/JHeuCBT+hiGGHSikHB6YAfvOKJODyxf5gALHrRtdVufKBcMskog6fHRhttNETDcTodTVr4UokMlK211loSf+ip9yO2v783XoGtTeB0vSrP9a86yIf3EtcNIwNeTvE6RmDV5sR5Z2ibGg70EOVhLzbvXOTB31nw4SmEoY/zW/CWUXl8RvlJY6h5z3veU4xo3Bak/xFtxcG5oq260EP+yBP5yEv78G7d4ha3KKLyrqFbvRslMwnA0F76b4ketLbccsvyn9J7pXfhpz/9abkpiwOtKYNmNqmFXNRZwkLLahpoGliiGsj6LlhVf0VfiWGbBacH+nQ8ATgHS4E63h+oP6E8i8d+tJYW/tbXDDXBapPwMRKhu95ZlTtMLZ4pUO/Yb3/72955550357ZPzofj48Qtb3nLUn3UO5vRaHlNA0tZA83YspRbp/G2IjQQByXSfthoPHg0DnrzUUIcUCMuvrTjaYL3Bp4hhFgnph1HV1mGC2MS2yzYTqJtSBpYtXhnIYoRJA64kfeoJ+dF8QxHlhdxj5uOE1XS8Qcuh1M847cmU8wXfxmOcXgf1W6R5wxnJnsG53ksMphY4V2DBxLGB+kro+lyE8fAwVYx6mMg9DNYajJl+Vl7TCpPxIvBhwkjnjdc16qDgsHLfxwDImV8yWZLW/YVWjxEXkbptZU3DTQNLA0NxL6atP8wenPTULxliC//W2211dBVzrV+KvaZMe19aa1PaX3M0nhfVjUXcRzztL/LXfGsDLnIj+8weYzd8p6V/H/b/yCla6K73udVra9Gv2lgGg20bUTTaK3VaRqYpwbiYKL0tM8aO6PwcaYFX+G50vmyyy4rA2N0FyWtX8TnZRlMxMWXPK7f5VwUDqAFHx4G2qrCWSAytERak6QzXly2yNckuB1Wes/0Rh4h4vY6sUzpGn+eH3HXZM5oZO0W87J6XfzV3kHy8RjhCkjaGUPJ3nvvXTW0iIbkczl5Nz74wQ8WUry7Xtals0y2qK9xcGU6KUz0A3xhTGEblgwtKuPmJsrwxuHKcvAoeHyQ2SJNA00Dy0oDMqjAtOL+xNB8ySWXpGezsMWVbZ70SaqjfsH7nNjHxfSo/tlxLSvlNmYXTAPxnVAagrV3L753Me3vbvxfUIZHNQfocvizgs5y4SNMvLEIGMezYMpoiJsGFkgDzbNlgRTb0DYNuAbigJN5s0SYWH8cjYJjkgA8RhYO4uPLGi6e2RaTcXFG+kr7k0NxOTskBow+HMDqxpYIU0tHuhFOg3/MJ10r68LpdXxiUYt30aHMaWU6E9/jlGUyLlReTXeRnrvFs93rlFNOGWxDA4d+sZ5043IT5/3hkF0O8423/mQ4anmxHWM6q5e1Ad5YW2yxRTFcYlDkPyRcbFvDm4eyq6++euj97pqkZrRbXtNA08DS00DWJ5CnfLxYzjjjjMEWVkng3iyCjX1QHFO60uD1ctFxnEtPe42jpaYBvYvwFeP+rse4v/Medzz+fgJT83J5xG67lYsYgI/vdHufl9ob0/gZpYFmbBmloVbeNDADDdQGJQ1IcZDywWkc8sIfYWv5EY5DX/mi8OLwliYSAAAgAElEQVQXv7h6y1Csk6Uzei675GJxylkonLeCZwsHmeLtwjkWPpD6QNtFL6Mb4SPeWD5pOk4AqN81MeiaIET+o86kt+zpeS5DxDmpfDX4mhwx3+mzRW3DDTfsbb755r0NNthgcIaQ9FVrZ9eDx9mOxrvK1jPdcFXjtys/eyeiHFn9yBfycchl/P+AizNqOIcIjy4d0psZWVwH4/CQ8dXymgaaBhZXA7GvJu0/zhPjZjgP8WwW4dD/3p+xX1A6g4WG9x2tH1ncd2GlUfMxPMbjGJils/+Gv6N6P/F8Pe200+ZsrePDDOfDxf9AfM9Xmt6bPCtPA83YsvLatEm0BDWQDTo+IYsD1SQi+CCoelkeZbX8Seh1wUY5I82sPE4aqRMnlMpz2i5LlIt0nGiOStfkynAJtov3yHOkH+llMggmkzXLy+AjnVmkR8niNMSn11H7SkeZHilTXZ5Z3GGc5rj8RZ6Eo1Y/6tx5ynhxuSSzP93oEnXh8rR400DTwNLSQFdfQBlbITiXIrtp6L73vW/xfuvqG2t9R+wnsr6z1n8tLQ02bpaLBuK7Dt/ZeEye/wSnPMkb33veV/LOP//83o9+9KMhteAVitFFHtcaPwXU3vXl8hat3nw2Y8vq3f5N+kXUQNfgBBtxQFLeJCyKRqxTy3ca8x20nEaMu+yRN0/7QjSbRNbqdsmnOpl8WV6X3iO8p7N4F3yUJZMh6lF1avldvEd6801H2SK+TB5gYruSjnlRTnDFd6iGX3VH8ZfBjVMn46PWHi6X5Ox6un6iPlu6aaBpYGlooNYHKL+2bYjtulznTBBs7Puy/gF4z1fanzFeiLTQNDBDDWTjnP8XiGfpmA9Ltff/+uuv75199tlDXHN4bttWNMOGbKgWXQPN2LLoKm8EV2cNxIEIXXiepyfRkw+CsV4si+kIr/Q4C0+v63gzmbI8p6HJJDhrE8uM13HlEd4Mx6R5UTdRji4dxrqj+I/lmZ5FL8JOKtdCwEeeJH9s79g+mZzkxfyoT5ehq6wGV6sT6VLf+YnlLqdkk8zx6bLX6C9E2zScTQNNA5NpQP9zf6of4Pnv//7vvS9/+ctDWyLYNrTjjjuW7ZPeT3gfoT6h1ld4vvcXMT6ZNA26aWByDcSxDgxxLPT/RPZfUR1R9//Cn//857L17te//vUQc5zP9g//8A9tW9HkTdZqrGINNGPLKm6ARn710kBtkEILXiatZHnjaizWjelIs7bIq+U7H5lcwh8H2hrdOPEEzieg48gdZcx4z/JquIWvVifLj3kxXaPVlR/lGpXuwjVOWcSvthinrmAyHF7f23aUjvwdAkcN9yg8ol+Dq+VHmWr8ZHwJp+SNcsf0JDpusE0DTQOLp4Hsf0+eftw29O1vf3uIITxZuPZdNw1R6H2C0pP0D7H+4mmgUWoa+F8NZP8HSv0/EdMqU74//b3mEolvfetb5RY/D1tvvXXvXve6V7qtSPWHKrRE08AS0EAztiyBRmgsrF4a8AVZLT6JRhxHVi+Wx3RWZ9pBKw6+4I6Da41+XHRmE8qMrxq+KFdWN8JMkq7hq+U77nFgHD6TMcuTvieRY6Fga/xBL7Ztlz4cTxbP6mZ5XXJOAi8eeHpc+J3HTE7Py8q7+GxlTQNNA6tGA7X/Pfmcz3LuueeWm8g8aNtQrU/g/x/7A+XFfPB6P+XxVaORRrVpYPjDR9d/RONlhMnGUH/3s21F6667brmFULf++f8o/k9aGzUNLAUNNGPLUmiFxsNqqQGfgHl8GmWMqh/LY1o0swlcluc8RlykM9k8P9bxwRXcPnhG+kpHHF16izi6YMctmw/O+dQVf6PkH1U+rpw1uChDRi/LU/v6M8adZvYu1XjqwlOrE+WowZGf8aK8mqzOk2jx9Pg0fHfx2cqaBpoGZqcB/4/HeHY+i982JPjs/65+wJ/qC7I+ovUTs2vThmm2GqiNjeTH/4zyYhkcZf+X7LYiznHZa889e/zXsv+K/m+zlbJhaxqYTgPN2DKd3lqtpoGZaMAHqGkRjoMjg4l52eCU5UU+HY/iMS/md9HOBk5ojsNL5M3Tk9SP/HXRz/Bm9cfhJcPl9brwdpV16WUhy2o8Sc4ob0zH92gUr7H+KHiVj1sv40d5NVn93Ylyx/S4/Da4poGmgYXXQPy/k/bff/7nf/Y+//nPD53P8vd///flivdb3epW6cKR/7z/75X2Z1efsfBSNwrTaiCOAeOOK9PSW2r19N+Ar2xcjP8fh8nq+v/kr3/9a++cc87p4emigMFlh+237935znduBpel9jI0foY00Iwt7YVoGlgiGogD9azYquGN+bWJQS0f/hxHFvfB1OEjbcqcjg+y0kMXH6N0NW1d+KzVreW7nP/xH//R+/3vf99bb731eje/+c2H2PzlL3/Zu93tbjeU34VTlTPdjZJ/VZR38Zm1dRePXbhivXF0GOtMms7edXBkfNZkVf5i8DupfA2+aWB110A2dpGnHwfhfuELXxhSUzyfJf7HSWc/kHi+0kLe+oi5b2PW186FWro5k7TpOLJOgm9arYzDB7iz8dH/O/DqaY+rvuOQbORddtllvUsvvXRIhHZw7rQt2uotlgaasWWxNN3oNA0sogZGDYq18mkHbOGLT0Q+77zzej/5yU+K9I94xCN6/1//awQh8gBtvl6ccMIJ5TBBTp1nz7vCcccfX6J77L57udUhCxg2Tv3sZ+fAzUcu6iLDT3/603LQIRPqUfgk27777ts7vs/31VdfPbjyE+Ze//rX917zmtcUPvfZZ5/eJz7xiYE44+IeVFimkUxOz4vvRxST8gyHw40qjzhHpSNPo9Lgizx4OpaNot/KmwaaBhZeA3EcI+2/a665pnxl98Chnfe85z0H45r+2zxjXHn+BJfDeXrhJV4+FLzP/ctf/tL7r//6r+JFdItb3GKOEL/73e96t771rXu3vOUt55RlGSzkCfe4xz2y4kGeeBgFzxazCy64oOCT90Un4gkKoX3DDTf0dthhhwlqzQY00o7jIHIzlyPQNvFDk/5LlMf/VvbfA87/Gz/+8Y97X/va14aE4eDcrbbaas5/zesOVWiJpoFF1MDwp9ZFJNxINQ00DSycBrJFnA+IWXnkZhRMHGBjfaXf9a53FYMD4ZRTTunttNNONdDeRRdd1HvSk55Uyh/72McWd2zxsd8Tn1jyr7rqqqqxBU+SceCqDFgB8jFhgB/x//GPf3zIaJLhcb088IEPLCAYmJSP4UaGlrvf/e69BzzgAaVMcgou6n9cfWc8req8KIvzUyvz/Ez2WC+mF0LmrjbyNoy0M96yvFivpZsGmgYWVwPqa/xJXL/sxiGudb7Tne5UNbTwX9cPaTwd8yVt6x/mtruPA8RZdHPN9h3veMfeXnvtNacC4/b2/W0mG2644WB8nQNkGbHtI6zTpwxjB4FxPAvMHzDM3eUud+mtvfbaGcjUeT//+c8LbuTLwnzenyhnxJ/R9joYmOAtBoxOGCRvc5vblCLqiE+e+o95O3i+4nzw4oPb6aefPiDBzUXU85uKKFSd+egjytHSTQOTaqAZWybVWINvGlimGsgGGw1qWdkoMTWIdcHFQfvwww+vGluAPfTQQ+egizg0IM8B7Gc4bIyPK6Pqffe73y1fjX7zm9/MIQVMhi/SfOYzn9nj5zyfddZZBd/jHve4HsYbhYjTcc1hYJ4Z88GdyZ2xMwpuVLnjnAQ242XWec6PdDkuj+PCzZrnhq9poGmgWwP6L3t/rTjPaGjhcE4M6jK06L/tT+LZD06Ur7g/uzldvUp9vPI2khbYkov3KEaVGLz9YlnUt4wmGY2srvK4pjgLyo88zGcMcF1AM6YzPibNGxdnTU/Kf8xjHlNIIy+eKHjEYKiRYcz/JzWa5AMnHUoW/nPgweCCJw2Ba9fZ3qebigSr+vPRu3C1Z9PANBr4P9NUanWaBpoGVoYGfLI3K4nioKk055PwFeoXv/hFOkHgxHnKPTBZiYMsedkvwik96RP61MEtFUPLqaeeWgwjhP/bz1eoyan6eroMxOV6vMceewz0MAmPuE5Lh6PqcYDjj370o4EOo44GwliErVj8asFpwgc0xg1631bSpGeUTF6+kuQet80bXNPActCA+nPv3zTO4KFwxhlnlMWcAoaWXXfddSpDS+wT1C+0/qH7TfE2AlLpu971rr2vfOUrvT/96U9DcwPBqB2BZ2sRnhe05Z///OcheAwB/Bwe7xXggQU/Rp0bb7yxwPi7IjieXl88UBfvC+rzPsX6v/rVrwodYJgLOW68ecUbfGNckOzgwdjE3AnaqiceoPu9732vd/755w+VO37qU37xxRcX2byMOLKLd645l5cOZVEO0gp4n/DDsxcDyB3ucIcecjJniO989p+IecIr/sC72267lRuJFK677rrel770pXIdu8uhdhgAtkjTwCJqoBlbFlHZjVTTwOqigTjIIfdzn/vcIv6nPvWpoUFQk4IPf/jDpRxPEEKcNJTMm0KcDPig7zDgxnCAweGHP/xheXb9MGQIN+651157bW/nnXd20oNJDplRzsjXE57whLJnWTTZP478hP3226/sJedcF+pFfORRj/3OwGDY4AmOddZZp0xevvjFL/acZ+qQZusW9dZcc83eRhttVOJbbLFF+eojffP85Cc/Wcp48uUJGOrwO+igg8pBdA5PnIkSfFAPPoDFjRua2qc9pLCbEqvDQiJODlcHmbO2bnlNA8tJA7X+l3wWbXgjsohTkKFFNw75wpE4Z475L+sXlAdOjy8nvS0Gr9420ItjLHlsNyZgMKBcYxZ5itOObGP+9Kc/3fv+979ftiDhWYqRRjDk8yONkQJ4bpviXJjPfOYzxRjx1a9+tRgtgIEWH2TAQT0MFRyaTFx8wgPGkhNPPLH329/+ttRnDvDrX/+64GDMPPa443onnXRSMaLA17HHHluMJ+KLOmf30/By+eWXl3wFaDNOw+PnPve5YhQRb3zY+djHPta7pF8OP5/tn2f3yT5uGYuAg67ThreTTz55QBsjDzguv+KKMn84vl+uM/hER7IqLd68nHkYhhZuEJJxxP831Mn+J/yPPF+49V4wh+LDFbeAKTSDy0AVLbJENNC2ES2RhmhsNA0sdw1kkyCXac899+y9853v7B1zzDG9ZzzjGUNGC+B0jsnee+/de/e7312qalKhQVl5PtlwGp6vSRcThyc/+ckOVo3/4Ac/KPurCRgyCD5p6ieG+Ha+NPjHp3DwFQa3Wr66MEHbbLPNinGDQ4BFw/GpHs/vfOc7ZV849Z797Gf3zjzzzDIZ00+TTWD52ko+ARp45bz2ta8tE8B11123h4zsefbw/ve/v9TB++hZz3pWObAX4wk/jCs64I7JIF8R4QNYzt/hzBkMM8973vPKhJIvwMiBTFGeIaIt0TTQNNA0sIo14P11HMMyQwuLugc96EHFmOJ9XLZQjHmIqj4xPlexGpYFeW8fZ/hmN7tZ2c517rnn9jbZZJPeWmutNSQP9RizWOxvt912ZbsRdRiv+Ahzn/vcpxywq3eBeQQGFeD52AI+8s4+++yCFzjNNTCagFM0McxhXIEPwTBuMv+Bxr3vfe9ibOFDCueL8BHlj31PFsoZUwnwxAHM973vfQcH/wLz0Ic+tNxsKB54Mh4rjzpX9I0i4IVHjDDMAaDJ+8oBsnxYwVhCvnTCnEH8izY882EH4w4eKXiQOA7noRYHF4Gz9DAiYWR5QH++k/13CmA/aO5Qa2vBqRx42vIhD3lIMXrpamgZXNgK7gckt7mJNNiei6mB5tmymNputJoGVqEGOJj1YQ97WPkRn1XQBCXi04RE5RyK9pznPKcM3pyHosGSCQlpvrZgaMFbguD13YiC0UCeIvEJbgXqgINbjTBy8OPQ3a6fDrL1gT6TT3kOJ55jnvKZsHzoQx8qHimEl7/85T28eQ488MCBLiJd0UFmDmFk8vfWt761uEC/7W1vK3g4BBBZ+eGdIqMJkz6+ch188MHFzRi5Ca94xSsG8CwmCNQBHzcugZ8nkxfChRdeOIDnzB0ZWjAAfeQjHyn84wGEMYjJKO9WprOCrIWmgaaBpoElooGsH1cfXDO0PPjBDy6LRYIbTKJhJaYFr3yvv0TUseTYiO3jDKqdyCPOB4Tb3/72xXNE23Qo09gobww+OOAFgqcFh7USGFc1XwAXcQwEBLw2Nf/AsEEQjPgTDHCcJSKvFdVbY401ikGANAt/DHZ4iVBfxgFgxCs4CIIhjqGCeoIpAP3ALUfSxfrrr1+8Y9gqxZMf8srAwEeTDTbYoPezn/2s1MFrhaAzh8Ct+Rfyk8ZgwhyKQB1woGelXReKl8J+YB4BLQwt8EJgnqd5h/RH/jT/JepJduozZ4keLp/pG9SgLTjRdNqFsRaaBhZQA82zZQGV21A3DSwVDTDY4YGAhwOBOF9UNKhPw6cPVj6QxbhwM3Dj7sm1x3ib8GVF4YMf/GCJPv3pTx9MbEgL1wCwH3n0ox/tyWqcutDk6xA/n+RSSYO7I8joeTn4gNHkJRL3+rW46oyiBRwwCi972cvKlyZ4IOimo6OOOqr3lre8peQRJ7DNB5kFSz3ani9q/N73vveVSZPjf+pTn1oWEdThyddbvhQBz5c/wute97ry5FA6jEfAogsmm4ccckjZGoXxR946wGZ6LkhaaBpoGmgaWEUaUN+nftifLNYxVmshDIvyaPFFIfkaV0Y9BevPVST6siWbtRXCKJ8xi60/nFEiQwpljFMs/AnMPWLA2OJzIeqw5QcDh8ZQ6rBlliB6xDHy+LvEGSUEzRWIY/DQWAksBhfmZOTJ01TlwPPRh8BHDIwfGDcwxogOZYrz3olH5WHY0bY3GXEKwn7QtmbqYMyJMsoLhNuEqEvgQ5lkhh56wKDkct+Efkg3zAP0f+HcF7Y58fHmtre9bQ/DkLYIUTf+r4Sv9oQ2dZwH0tHDBY+gk/vvxF59zyE/20V1RbdGp+U3DcxCA83YMgstNhxNA0tcA3gksM3jla98ZeGUrxZs1VF6VuxrQPancJOHgWXTTTftHXnkkb0XvvCFZcHPxPboo48uA/u22247mCQIR+SNL4s1IxFy8WWLoMmOBmXyanHnMdJTPT01uJP2gdr5jbzHsgxXF13K7na3u6WTOso02cKARsBtWHzqiXFEga9NbJdSGd4pMrQIRjc76JwXvGMUcFfGG4mADvjpqkd5LbVJzEBdLdI00DSwhDTgfaP6Zp70o/JoyQwt03i0qB+MzyWkjiXNitoKJr2tsnyMFGzNxetSW2VVRx4n++yzTyqv5gsUEsfogEeExlbyMTAQmLdE+IhUdMnH00RpvQcYUcjDawRDkMNzXgxBMDJ6uMyiF/VAPrzLMIQHj4w3lOHVg/GGeuDFO1g4eMrrBAMRhhmC+BdN8ef1VJbxQxk8sSWL82E4JBhji2DRCfFJ/yOq43yAY/v+eXsY3NhGRZDB5dGPelTbUqSGas9F1UAztiyquhuxpoFVowHO9OAsDhlXPvrRjw5dOzwtVwxy/gNPzPOBkPjTnva03ktf+tLeRRdd1Lvf/e5XviAS4C8u+FXXF+4YanSuSuQbV2H2KzsfEaYrLXrjwGiCEGGjPkbx0kVTZWyBkhuy6Hk9TQjxRCHgieKTRPL8qw5l+lGGcSbCe5o4Ey6FF73oRYN4jLAlKcoUJ1OxTks3DTQNNA0shgbUN3k/TZw+bhxDi3ikT/OfvtL7glHlXmcxZFxpNNRWLpfneVuynZUzUzi/RYFyjBpXXnllOYNM3ifc+oMHC7frML46vD5OYJzAM5SgM0hk/HB+vG6M+w2MvGd4tWy++eblncNjCu8WPjqJB7baEODBx1KPRxqkVc5TxpbotQNtDFLAsAWJwNgunWicx1uV9xeDT+RfhshJeEPWr3/964UevKmu/18oU7oA3hQ0f6BOnKeQp3LAlZZnkxtc2ObcroV2zbb4YmmgGVsWS9ONTtPAKtQAi3WMGRzapsAZILMOPtiDOxuM2eKCsYXD6TAKvOMd7yhsPOUpTxkYalTX64tX8rL8SE9pBngO5Y0hG9TZjsMkQ8EH8Vg/40G8+VN8RHjBiEZGS3W0d9tx+KRD+UwCmajhQszkMtKUDKLtMkVYpWM+dTiXBddieOanhYa+/FIn02/UYUs3DTQNNA0slga8T1MfyLNmaGGhydYU78vU5/nTDS2CVTmytb5w8hbOxh1h6SoDho9L+oijdtY5I+TjtYl3BkYZPkIwF8LQ4e+HDBFsS9p6662LoYXtLwR/d5SOEgqGfMZktt7i1csBtgQMPMDoYFpuCtpyyy2LMQjvUA7LxRvE8bjczqtoxzzme9/85jeLJzOePdxORJBXiXRy6qmnlg9foo1OdH4d9eCHCwPQGzio59uIIn2lMewQeP85T0+eQfKaFZye8X8Wy0nHD3LkITd1XX7S0eCiQ3ObwSXTbMtbSA00Y8tCarfhbhpYIhrA7ZUvHDoYl720CxV8wPPJAfRIM4hz3ggGELYS8QWKLTIYOSj3OrG+cGT5KpNcgsFF94QTThhL3EMPPXTAhw/ejlN4p51AR95JZ7ScYbZGdelGOJlEcEXlJZdcMjhnRXh0QCBpuRWrnvMU+QOexYgOziMNjLyL4N2NLdKL8Ei2afXlemjxpoGmgaaBaTTgfZ36Un9yuLhvHaK/Y8sq/Zb6LsX9GQ0tDgufrd+bprX+R2/ZWOTY8MbQmSlqS8oxELDQxsihsY524Lw3PlxgPMDYsvHGG/fucY97lO0y1NeHFuIYOh75yEcWrxOMFRjd8PrwLT/+YQa61MNQoQ8dzLuIQ4d85l98EOHMPHmS8P5wMQDns2CQwIuGW4c0H5I8UYvZVmpw+vZgjEQykGAkwetHNzHBKzrhtkLmBjXa6BHDE9deo4fdd9+9HJKPXmpBvOvcGOgg90YbbVR+uvmJfPHhuGr5gkFn1IsfnFRPbUEa/vHMibcUNYNLrfVa/kJooBlbFkKrDWfTwBLVwKyMLNkkyPOyOHn6cUPOIx7xiN7+++9fNMXBq9mgq7yM3jgqpp5uItKkV0/qe5y0+FM8gxHdyK/L53gcl+pGHBFX5CVLR1yk+XJG4GucdCs4JnMEJj0+kVO5dJw9yePLH0YxXLExkDFJ1eSG8gsuuKDcPMAkhq910m0mm2i2Z9NA00DTwEJrwPs09c16smBjqwFnVyi4oUV59Gfx1wwtC91y/4PfxxJyNO4wzmB0UPtSpjiLbM+njPaibf3DgdfBKKI023owKmC8wIjBe3L++ecXw4y2FQm+VOoH+IIneasAi8eIgryJ47yDsRU62fZoYBl3YyAv5qMLN8IgP+O98wCeqJcabdEE50477TR4/yMdwald8KjhJzrxfwO8dBB1EXEJZwYXjS7QE7zkJI3RlA9W0eCCTB66aA0BtkTTwIQaaFc/T6iwBt400DQw10iR6SQO6A7DQbgE7avWZKCrTkZjofLYy83XHv34GkbgYDfycI/lCb/6zYeXOJHI9JDliaZ4ePazn12yOMCWLVrwiWvwYYcd1tt1111LGVulIt8xDZzTU/kRRxxRcHAzEjjBz6SUw+j4OsdWMIwxsX7JaKFpoGmgaWCRNaB+zPs4j2NowRNQIRpa6JuzXzO0LE5D+tiodoBy1iZe7jBd8DUpOL+HDwhsn2ErMmfM4Q3DmXA6W6VGr4s3lyejHes6TCzz9Di4Yv2sTqQXdRfTkQfXSaQXYTP6wCgo7s8Mp/8XxZ9w8F+nHINLvBb629/+9tBcyPuKjLeW1zQwrQaasWVazbV6TQNNA0Ma8EGSgph2YNxrWbAT8MCQS22sEwfbUSqPA3WsHwftiE/lXFHIAXb8+EKmG464tpoblXSrUqyvNHgiL5Rl+Q7n9Wu4le/GEPJI4zL9hS98oXzNwuix1VZbFTfmN77xjaXaAQcc0Ntrr71GGogibtHkOmnh4skec75ysUcel+0PfOADc76iuRw1vA7T4k0DTQNNA7PQQLZ4ckMLnnhuaGGrB31ZHCfUb+vZDC2zaJ3pccT2qKVFoVae5VOHfLxX8ETBO4X3hB+eLOutt17V0DOpRBn9Wl72zjnsqHLBOo81WjFfOnEcEWacdNYewl3THXgdpkYH+aWDiFMGF66FdoMLxhYZXKijvkHxGk8tv2lgUg38Tf/l+n+TVmrwTQNNA00DPmlVHFdb4noSx/UWLxA8K3ToGvnso+X2nO22227o/A+uKsRggGsri3sCA+ynP/3pEgePjDOxFfBI+fznP1+y2aYEvThBoEwDeKyv7hB++aoVYePAn10j6XqR7PCi24AuvPDCog+8eXQ9ZeRHfKALDD+k2Q6lfPiiTLJS5gFvFnT4jW98o+gZnbHth58H+IAfvGCiCzNlfNHDWEM7iDa8UsaXPr764TKNO+4222xT4j7pySaAQwy0RNNA00DTwAJpQH0x6BXX2IS3Iv2XAv3zLrvsMrgatmtRF8vAEceGBRJptUOrccfH1XHianN/jqM8H2M97mN0HK+9/cehIZhJ8GSwkZbzG8uUHgcm40/vfFaW4Y668/8H8drcoCanvwfQ63oHdJaLYJxnPJY4EJhrvRWYH+HR5jI6vwPAFmkamFIDzdgypeJataaB1V0DPvgRzwwtnh/hfTCWLn2grcXH1Xsc3KnXNYA6P5G3Gi7n0eWDFmn/RRl9YK/JpPqOT/GsTuRbMJ7vPGc4PK+mE/HueolGlmwyNQntUby18qaBpoGmgZoGsr6TPMYprvONC6499thjYJwHp/dxHo/9mmD9WeOp5U+uAY1BtfZUvj+h4vVE1ccz52Sc/Gzs8vFvEskyXNTP8rO8cWjVZBqlC5U73ShnF09Ol3hWl7zsV9NB1lZZu3tel8Hlz3/+c/lQNY7BpUvWcdqhwTQNlPe6/3I2z5b2LjQNNA1MrAF1HXGAi4Oge7l4HYonUDwAACAASURBVAjG7icOzM7UpIOeBnNweFzpKHDkLZY7bxGfw0p+8lwXgol4Ip1RuITXn104ItwkevT28Xim22hsEQz5hC6djeK/lTcNNA00DYyrAe/LvQ9mLGKhdfLJJw8ttDjU2w8WVV8Vn83QMm4LzBYutifYvV2zMVcw4sTHr1qe06lJUBu/JxlXI+6s7rh5o+TM5I70XR8uH/nORyzrwhPL9F8STqVjfqyXpeP7kL0L5HUZXPCCph9QwLONLdY8nTfxm/HR8poGxtVAM7aMq6kG1zTQNDCkgUkGvDgYjqvKcQd3xxcnBxGHl3u9bKJF3iT4Ig7JDZ0uXDV9xPoRv/BmctRwxvyoD9GIcFnaJyVaiMSnYKgfaWU4W17TQNNA08C0GvA+Uv2nnhzmfdZZZw1d8cxh7X6ri/dpWf+W9WetX5u2tcar52NS15gY217Ya2NaxAt8lke+2rj2dJjxpJoLlb1HWV7kM2KqySC4TB+RTpb2dz/SjGmn4TqrxSfVX2xr0tmvy+DCVdZnnHHGgPW/7W87f2x/SzYHIEtW5zfK2NJNA+NqoBlbxtVUg2saaBqYo4FJBjwNhCDxekKaDWo+4MfBfw4zlhFxxXRWN+OJvIwHDcQZniif8DrsOPzU8NT4jLxkdAUzri4dh8edf+L+xTd+/XVdjUs3ytLSTQNNA00DozTgfSPx+OMwXD8Qd4sttigHoHu/qP5Kz6w/A37cPnwUz618PA1kY1FsbzB5njB73SyvC7dzF8c9yuKYFtPOk3BlMBkdz4PHWj295w6veFfZKJqU63+guNeR16rTynhQXoZrPv+j2P6SNT4zg4vocpEAZ9sprLvuukPXXGc8d8nYypoGMg3cPMtseU0DTQNNA9NooDYZYFD2AVBwPsnxuuPER/EXB/Eab8JDuSY04ivWmRQnuIXX+Y14RsnieJxP8sWz4sIVeY80RpVHvBGedPxlC5NpZI28tnTTQNNA00CXBtRnx4WW0tdcc82QoYVbSe5+97sPUMa+jHTWn1Gh9WldLbEwZejcx2Ufn9Qmyhs1dgEvXLW4l0ui2O4xXZNccFl5V5nDCy7jS/I7vAwMXkYe22c8cImAG01q/Hg+8QjneRmPgo94Mt4zPWV54IrvQQaHfMgu2tRRvU022aT3u9/9rnfFFVeUqtddd125oYibHD2MSyuj3/KaBpqxpb0DTQNNA1NrIA5AGmQ18GpQi08R9EE5G4SB8/wuRjM45dVwgy+bGNToRHwZza7Bv4uPjCbwwhef4l04M7o1/YpWxr/KvG6mI+q6PpTuwpnJ2PKaBpoGmgZmoYHYT5Hmx+1s55577oAE5zI86EEPmnNVrPdhzdAyixaZLY6usc4pdcHpHfFxSnk8vW7k3sc7yhzHrMY95wEa8Z2OdFTOoc9sk7vhhht6f/rTn3rctnXjjTf2fvWrXxUxIp4om9J4dnDdNbcQ3vKWt+zd/va3L2niBOhHHmJaH9eEM5bPUm/gijrLZBOcZKCOdLL11lsXXV1//fWlKsYWjLHcUKQgGuPQyui3vNVbA20b0erd/k36poGZaECDlg9gIFba8wWrcjEwywG5NphHGqLt/Ee+XEGqX8Mf67qsjod45EXpWCfTVxdM5CHjvysv4q7hc10Q10+yRV1FeaM+WrppoGmgaWAaDXj/TVy/2oG4j3rUo8oCstaHNUPLNK2w+HXiWBXT4mjcfH+PatKMM67Fsa5GHxqCdZha3HnCGwODyk9/+tNiYPn9739fYznN7+Ipq3DHO96x94//+I89jDF3uMMdBsYXl0Fx4XY9RJ3EehnNSfK87YhnP/oD5YObuPjKbijaZ599BjeUxb5iEt4abNNAM7a0d6BpoGlg3hqoTQ7iwFaDEwNxQI7pGlxNgFGDveplfHleRrfGW+RlFJ4In6Uz/jLeycvoOc5x+fY6NZzZBIQ8zxeeaehmumh5TQNNA00D0oD6Jp7xx+LqS1/60tCBuA95yEPKV2uC+ip/NkPL8nu3auOTS9IF0zW+ZtrIxjfBTTrO1Wj7ew3u//7v/+5xoOsPf/jD3ve///2MrZF5UQcxXUMQZSLN9qNNN920t/HGG/due9vblqoOl9WJMDV60+a7zojHnxtbHFa8/va3vy1XQitgVNp9993bgbnTNkirN9BAM7a0l6FpoGlgJhoYd9IAsVGDfByoqZPldTEe4WNadSMvNTkiDzV8kaeIf9x6jifiiGlgs7zIi6fH5WMUXuFxfFle1F8Xb62saaBpoGlglAbUN/HMfixKL7roogEaPxCXPir7RWOL91vj9pmj+G7lC6+BUeOWc5DBZnn+LsT4pBI5fn+PwaM0xoFJDSwYEvHaWmuttYr3CU/hBO/f/d3fDc0V8IhxXn7xi1+UbUjQxWOG7UmSNRvjKcMowdknG2ywQTHCCN6fJbMfFvo/5Lok7r9obHFY8fXjH/+4d95554ndcq7Tfe973zl9xWLIMmCiRZa9BpqxZdk3YROgaWDpaMAHbbjydFeZSzBqMB5VPgkuhx2Hv4x2lhdlz1qoVi/CRr66eFZZV51x6Y7DR4Yr5sV0xKv0uHC1+i2/aaBpYPXSgBZSSE3cF1O//vWve6eeeupAISxC/ZwW+pv4a4aW1e/9qUmcjaG1MaqWn+HO5kTK0/v8l7/8pXfVVVf1Lv3Od3p/DAfaOk7OHvKtPRhZYnB6+n8Ak8lHPjAE/ReIc6guxpef/exnPYwRLm+UnW1GnIFy5zvfueBReYSL6QI8oxD7BaV5jjK4wNfXvva1IqfCwx/+8N76668/p7+gfCHlmJE6GpoloIFmbFkCjdBYaBpAA7XBr6adpdrJZ3LEvJiuyUj+JHJOAlujOS5vs6BV46GWPy5vXXCz4jujUcNdy6/J6fnzqTsO/gbTNNA0sPw0oP6Hp35aSHH+wsknn9z74x//WARjUbrLLrsMzmmhT8l+vsBUOfVbH7T83o9pOM7GtBqeSd8Jx+3vLvj1/uJFcvnll5cDWmsBo+Fmm23W4wwVN65kvNfoiCZPDo/GkMLhsPJqIR/ca6+9djmj5U53ulPxiEFm/iMYXzC8XH311T2Mmpku8HbZdtttS/2b3exmqdElq1eTe5L8mtzkZ8YWh4cn0t5/QJvzW+hHan3EJPw12NVPA83Ysvq1eZN4iWmga5CMrHYNTl1lEc9CpseVJ4NzviaRZxxYhxlFe1T5OPQWUsfgHsXjtPSjbOPSAS7WnRUPXXhmRbOLRitrGmgaWDoaUJ/E039aSJ1zzjm9H/3oRwOG+TLNglF9Bc/oxUJe/IGg9S9Lp92XKyf+viJDfH8xDp5//vnlPJYsyMCC8ePmN7/5WGO/PFT0/xBdpTkD5jt9zxkMJuMEzmbZcssth84v4T/EOScc0nvllVcOjJuO72/724rus912vQ033HDw/6Lc/1cL8R/LdE5ezdjieoIfDEoYXBQwGtGPyHCkviLKMo4uG8zqp4FmbFn92rxJvIQ0oAEBljxOmsniBRdcULi95z3v2VtjjTV66623XhlsPWjiSJ4GWOILMYANEU4SUQaBZPkx77jjjus94QlP6D3ucY/rfeITn+jkn33GfI3ha4tPohOWBlmuj0g71htVvip0OymPEX7c9DiyZfrxvH333bd3/PHH9z7+8Y+XL0IeaDsWQ0zS1lxzzbInWu0InNMH9pe//OVQeSYHcFxV+Zvf/Kbc0DDuO5HhanlNA00DS1cD6md4Zj++uJ911lkDAThLYpttthkytGih1GVwAcE4feHS1VTjbFVrwMdEvavwpDjztSuuuKL39a9/PWX1Xve6VzkLRdcuCygbfx2B/0dEj6cMDYyTXIXO2SyTBLxdHvzgB5froAnx/8Oc7MILLyzeLjHg6UJdngT9t+Iz1ptPelRf4e0AHaXFUzy/hbNbmK+o/9DT5ZkPv63uytXA/1m5ojXJmgaWrgaygVd573rXu3qbb755b6ONNirGB35bbbVV+TLAPli+RsT6ktQHYcEs5jPTuPOU8ZnVIS+rJ1j24qOP5z3vebXqc/JdD3MKJ8zo4m1CVCsO/Itf/GIxtGy//fbFcCa9895ihMHAwgn/vNc8ec/XWWedHu89e9W9nWjnu971rr3nPve5Q/lRaRhrMOwQZNwRngjb0k0DTQPLWwPe/3p/wTYMN7SwKGTsVIiLpFoa+GZoWd7vyKrmXu+ov58exyj4qU99ao6hha0q2/U9QRg7mQdiaIk4MtlqC3/VxdDCD0PImWeeObGhBZoYZ84444zer371q8KTcMqIw9am3XbbrYzrG/QPy/VAnZNOOql39tlnF68Rlwk4pTPZps1zQ07tv+56gw5ptR3n4ejmMsowinHldtYeqjMtr63eytZAM7as7PZt0i1xDaiD1pMFKQYEblF49atfXQ74wz2T57Of/ezy5Z7J4/Of//w5g5VEjQPBQgxiNRpZvjfBpLxEfLXmlP5q5ePkO61J4SOfWXocnBlMhmsW8ma0yJu0jVSHJx4mT3ziEwtqvJOc9x122KEYYdhv/tGPfrT3gx/8oPflL3+596pXvap3u9vdrnfQQQeVL9DsIY881NKOnzMZMLQwmfvkJz9ZeCAspK4GRFqkaaBpYME14P9l7xMUx2POwwMf+MA5X9/HWXQ1Q8uCN+WKJZC9lz5OYbA4/fTTy5XkOlNIyrj//e/f22uvvXp3u9vdynaVOO5Fpfm7rLLauwsutg5x+Ot8A/+zv/71r0Nbcnx7Dh9UHvCAB/Qe9ahHzTG6sFXq2GOPLduXXC/6b+s5Xx6jPkb9793o4gYXDtXGAKbA1dDocdZ8zkrehmdpaqAZW5ZmuzSuVrAGfFDxwYbB6g1veEORHMPKwQcf3Ntxxx3L9giehx9+eO+II44o5UceeWRZ2GqAk7p8wHPclMeBbT5p0ZsURxcf/7c/GYh4Bxk3RUQv5jverKyLbg3nuLLV6MX8cfFFOMcTy2adHpdWhJN+DzjggGIQxJjCVy74451kgkc+RpVvfOMbvcc85jHlvb7f/e7XO+SQQwZf9zAyYliUXP5OjNOGxxxzTGENrxkO+wOP13O+W7xpoGlg+Wgg/pdj38cijkM+FTicU4d6di20BC98pCPupZSetMW6eJ8UV4Pv1kDtHdW8DG+WE088sXfdddcNIdp0002LJwueFL7QFz4H7nqXM+OL1/3mN785lUdLlBqDka5U73q/uAYao8vOO+882HokXF/5yld6p512Wo/zahwH5f5fjLSnScsANanu4AOjF9uHFDCQZbKL72n4a3VWvgaasWXlt3GTcAlpwAdjsaWBBeMJX/4JT3nKU+ZM+Biwn/GMZ/ROOeWUss9XB6WRr8A2jNe97nW9LbbYopSzbQNvGWDi7xP9r/+Pf/zjy2DC72EPe1iPPNwkBQtvfIWgnDL2+VKHhbQbdj7Zh+HwMGjygz7eN3FbiOqcd955PfYj3+IWtyiGJGj0BR7I4XqKg7k3ZzYoR3ilv/vd7xbd4JqLPMhA2rdlCZZy5ODrjesTGTEcSC54UR1t/6IucNoWI/0KTvpEj5FXzukBPpbh7YGBjfaUjomzZSfqGJwYGijn7BLgaQvkpJ1I83Ta4AcGnYhfZCjtUgkZ77y/GFQe/ehHD+E/6qijChZwss9bdfWe3eUudylGGN5rPGD0nsR3ItL0NPhZXL3jHe8otF7/+teXJzAtNA00DSx/Dfh/2f/7LP4YmxRw/cc7QIss5ce044tlXdqK/RBfum+88cbBwrGrrsocPuIjzSIUGHB7yGC78jJeGOOzcaMLz6zKkCluIxmFO5NhqeUhA8Gfkos25Py96M3Ce4onCx6djLuqr3r+To4yFPgtOdKN1+eMNIw9swrg4gOKQlcbMg/B8xRPMw8YnU74zGfKAbuxvnQxK35rugR/TbeiTTthCFPgxii2Yy00z7OSveFZ9RpoxpZV3waNgyWoAQ76YqHJAn2hQuyocVV87GMfW8ixKGXB7AYSwVPG4pTBebAovYlJ9sseeuih5WwXDvJi8bv11luXRbnTQ7b9+ts9KOeLCp40bOkg75/+6Z/mLIZBf0H/tPyHPOQhpQ6DrPCxSKce9cHFjy+MGAcYpFjISw4MShhZwIMXA+d6cM3hfvvtV87lIIBXT+f5JhFL+aQeD/AAzde85jVFN895znMKOtJsy+JQVde1aP3Lv/xL0ScBfSIjePCgKHzcZMSiTbT9C7if//znJS1PJepLlsyDpyrnTfjx/mCbDe7HtBV6ox3YG/3GN75xqG15b9T2tBOwtAXvwSk6Xd94B5791cAQaD/qsB0HDxGMTc6f4pJJT9xrCcgtg4rezwMPPLCUffrTn+5haIvvNWl4xtuFA/QGurrJkJjppyC0IBj9h975zncOvUuAAtNC00DTwPLSgP9vs76IQzk94DGnxZUWUipXmgUTX9d1mKf6Kp7035z9Iu9R768E53zoQ8lPfvKTwbiQ8el59N+Cz2ApA8Y9WDO4afI4jJ7DP2PdWb4VEbfS0NbB/6PocUMP45B7P4yqsyrKkY2QyYwnxOc+97lypbOHe9/73uXAWLw/vC5xf2drhgA/nJa46nm+88U4P+sg71HR0X8u0wdlGCzwatUBu/Dzx/75LZwDwzxQ+iPfccySb/EofUX9Oi3KxAfeLb6diK1UcTuRYGfJb8O1MjTQjC0rox2bFDPUAIvatdZaq3gX4KGh09PnSyIbgDS46Pnud7+7kGGRjAGCxbUmRZrwARvj4o1JIt4BGFe+9a1v9T784Q+Xoo985CODOhgeZNhgIkPZW9/61nLoGedpYATJjA94arCQxRVVBhRct8knCBf4rrnmmrJgR463ve1tgwnohz70oTLpgA6eDF/4whfKVw3y/StJNqF1XfURSuR0ghP1+opXvKLgx5CATpH3hhtuKOeFEN7znvcM4RFyYDGwMBHgx/YYAu0kGkw4gCFQjlyCffvb3y5U/2sYG8E7FdTGPPF2kY7RK7zTvvpKBQ0mo3on8OiQkUXvAm1L23EgH4FJgnTM+w48Hinf+973igy491588cUlD4OUT6qibuO7S7s7DHSYqDDJImCYOvroowst7fsWL97uipdK/VCjG/PxzoFvAsZSylXfnyWzhaaBpoElrQH///p/HaZJX3vttUPXPHPuxa1vfesiU7ZolbCMcYxfX/3qV0s/JHzgxPODvpb82L94Wn2UPpSsv/76A3j1x7Wn0wNnBucwKu/ip1YWcUsHEd7731g2SbqGp0Y3w82YxRjB2R6c7xXrDjJWcQTevZ1cFt4jPJGRRYF3hY9ieF65lyfl2ftKngwo2TPmuTpkWODJrX6zDhGndBHpuE448BcvFw4B9sB8lXmUGzBctxHnNGnXR4xL9/4UDfjg46ZvJ6JNmV8RJJ/DT8Nfq7NyNdCMLSu3bZtkU2iAr0jahqDqfPnia/xCBR+IGIg/8IEPFFJ8deOAsXvc4x6929zmNr0Xv/jFPb704P6ryYye4g0vB7wDNLnS4MAiWwtytpHwZYkvd5QLlgnqQx/60IKKxbBPmIT/pS99afFAYICnXMaEV77ylXNwYcAgvPa1rx3gAi/hZS97WcEDDgYxjCBaIFPuOvEBNw5qGWysSxpvC4wHnHvjuttzzz0LP7S59MBTgUOKXUdMkggYjGSQ4qsMAWMGBgXphjhbeRTgQzTI83RGW3kYDxjU2SdMGykfIyA6w1DCl13la8sOhiS9C9STIc9p434vAwzvBDilH9473fYUjVHeDorry93GG288kM3bAqMNe9MJL3rRi4pe4f8Rj3hEMYDpYFzJQV0PWbtmedTZaaedStXv3OSZFnENIW6JpoGmgSWtAf//+n+eMY0xUYEv5hg8CNnCVfmC/9u+V4EWTd4Hy9vA8xRnG4wfDprBKA/+5JXB028ygQdkAZZ+GA8Il008Chd1ZST3PpI65KtcOIGBT21FEox06Tz6tp6Im7KubT/Au05IuxzEnXfJFWFimvGE8Yhx6NuXXLIkF7TSZeQdHfAx54QTThg6BBfPDuYcHCCrOrV31Y0sHo/GlWgkkH5lTNB7NsstRKIhnE7L/3eCi20ODMamRz7ykUPeIhg/mU/F900yRHzTpMVr1NuofNorbidi3hX/t3onpuGt1Vm5Grj5yhWtSdY0MLkG6OyzgNWdL2bzDd4R+2DrgzWLdowsTDY4LJQBm8U2C15+a6yxRjH+sJWIAcJxsiWGgZ5AGYttBQZG6jBYMyHlul28Jtg3i8syAQMCAe8W54k8vFGY+Ai/w7NoBlctUAZtLcjvc5/7DPiGDrzi8cA2E9cL+KKM5ImHCJvRBwbaPNkextklfDEj8NWMgMHC9Sg8u+666xA/+mJKOcYBJoOf/exnCzj7kl035PnXG+dZ+DOalLnuobneeuuV7U/okXdBru/kwTv56JRJigLt6/yAh3eHiTH4KfMJGGm1oSYicve97LLLUv2IVwyACjoYV7JJFt5Frk7ka9hn+vu0ebf5qqwf9d/85jeXc4kUIg5/FzQ5Eg9KUwfPtBL6cdJeJhxef0CwRZoGmgaWjAa8f/Q+UfFL+otwv9GFMyF09oWEUF/2P93B//YFpNfp95+3utWtSr/EQpg4QdsFoEO/yI9bARk7oEf5JptsUj4YcO4YH2no06B/1/5V9fSHjHV4OBK40QSDCosztqYqcFU1Y5DOm1l33XXL1hJwqu9m0Y5BiXEaumw/2XDDDQsKDCjwdEXf84PtGATmABi16e/hi/kDY7fGXtGHH7x3Lup7YlKXsYzxDtoEPE75QCGvDHhj/JcxC/6gfXl/PBJt9KGPQ+BgbGK7BTgwbO3Q93okSK+xD1Z7oz/GHLaDMZ4QRw+Md1l/XpAuchCv2XvJPAqvXQ9sn77nPe9ZslSXuHTgz1HxWM95oS5zPN4NBdp6oQJGRN410XXeRDPypzRGTT62cA6eDrfmPT+57w20V98o5dt2wO9tPx95hCvyWnsfHZ6PRHibK/B+c+5gxDUrXucjZ6u7dDTQjC1Lpy0aJ0tAA0xKssA5HbMKGpzBl8XJY7Bk4sIPbxIW0UxsMMSwWMbjQh4wzpcWusLtZeDVBI6vBxzCC64sOKwGRiYKvngnH+MIAU8FfrWgCavKGWSFSwMZXiPCB5zo1nAqvwtOZWyDYVsW3kK14LIJholllk+5ZBINDCsR1t1OKfNB23Usel6fODBMmt73vvcVz6ZaEC8ylgEXeQcXW+PktUQdXPAVmCR3hZqeyffbFUjrBz6Pk+Yd5dai/fffv8hGXbxeWKy85CUvKVdHc9Ct6oonp+96zMqZpLP16qqrrlLxzCZqA4Qt0jTQNLDgGvD+RH0AT4zdMiDABMYPPEAJ9A9aOPkCKvYb4ME4weKJRROHxJOnflj9KkZoDCUYnzGcYOzG05BDR7ltRfCqi3Hg0ksvLcYXDOXwiWGI4H08OIHhynr6brbiYlTAsCFZ+TDA2AIdaMInxmT6SAzvGHTAgbEIOdjmi5EDPoUD+uCQsQI+MDBRj0Ut8GxNxaDEHAhjB4e5YjjiwHIChhfyGEMwyDB28BEK/TFPAQfbQNAJH06gjcciC+a99957gKNE+oFy8ac8PdVvY1Ri2wmGGnS69tprD0C9XWP9hU6Lb8ngz8zQwtZavCIkN8/4fsZ3NqazOp4HD3q/FZce0OFCBRkoXR5vG+fL/3+Kwxv64d2RwQXj3UIbXKQ7fwczvoFzGTDm8uFV3u7MX/hoxcekiCu2w0K1QcO79DXQjC1Lv40ah4uogXJTy0239IgskwtusJlP8E44w6Nynvo5HMYJFu58LWHSxdcqDC5+QjrwPpGLdDRxZJKlyQ9fwLSNhMnb+9///t6//du/lao+4RSuiF+eEtw+oy02kS5pbU9RGW7F8SwceZkAI3344Ky6Ma+mW9cpni0KLOqZmCuobSVbHHCdn0Glm3h0fUi/zqcbs7K2dd6JC5/iPN/0pjcN2gSvDw7IJWC0YNsXE27BOz9MmONXXh2SJ17wflHAoENAfulAcTyXxKvKlBYu4fF0LANG9YjDH5MUDEBM5NEXB9uyzSwGrxfLPA1/rsfId1fdVtY00DSwNDQQ/+/qS5TP4kyBBT2LfkLsv4Dn40UMwsOYj3EWQweLJhbFPvYRxwOEfBaFBPpDDAIYYagb+236WcZmjTt8qODcEbxivI8Gp2DUF0OLuPjDkIHXJGHLLbcsNDH2IDPeJxv0DzfXPICnruV1IxB0NOaJPoYj4NEX+MGHoQA4ebNgoNFimjievzIGMV6DV+Mn4xHXbeOlw/Ylxh/k5cB9cEOHQ9rlPRzb19sHQxUGJ9oGuE37PGFUYpHL4lx1wUlcY5LjWKi4aPOMv8zQgtcD+nWe4U08x/c1Szt8V1xl4KCdnT+Mfv5xZRb6iVv2oBv/a2oj6KmtxJe3H0Y9zlij7QkLbXBx2pku1D4qU/uR5n+D97c8jDG8sG1c861RuDN6LW9la6AZW1Z2+zbpptDAP//zP/f22GOPsr0Bl159ZZ8CVWcVH7QBZILG1yMmGUywCN7Bk8b7gC9WfN3CKBQXpRHeGVCZJqkYStgCo4kcsDK8aDCM9SN+vmBhvMBoIwOO14n1ldbedS/XV8o4EAPjA5/zkPEpnCrTliHyMSjo6yXpzJPG8fvE2Hkl7pMZ0tzGo0NgSYPHb7PyCbnKI353PVYZhjUC7c7htxrQcRGPB7S5AUvbxkrlfoAf2orAF1Lay+mzkNC7oEkTT+me+ppE3IRy8H76djUvow5eWUyw+QLE+yJeBMcTmWgbzu7B2Ic3V4RTu0QeHI/KMCgS2Bceg+SI+S3dNNA0sDQ0oP86T/3gTHH6Ey10yGcLZTQse9/lUgm38ugDMW5gOKDf4IY39Ys8MRrwxR0DC9sdFITHDSjA460HPIfre/+KV4YOuxcOxvo4BoCXPOVjxFBc2yrYDot3C3QwWQf+qQAAIABJREFUcDgOnTfDGKuAh0qkoy/xkgOvFwJpDDoE386k7UUs2LWlla0xjlf84eWiLa23ve1tB+2W3bwjHvXEYIFO0R/eSwR0QMDzhjlQHJMijoVKS1d6D/05qaFFMug99fc1xpFH8DEuWTX21fCiw1kbW5gvOK8ytDgP8f/mbRPLZLhbCgYX17f4dB0zH+PwYwLvK57nGENjO1DXcbn8Lb76aGCuuX/1kb1J2jRQ1QAGFvYvL5ShJRKmQ2axyQJ45513LmeL+ECkOGerYGghTLq1CRxMjGTU4IYeeZyQz8SGyYwC8PpleZTpwFM/a0V1mKRivPGbbLT4ZdIqmfSUUcFp1eiqDuXOp+LIo7jOJYFXjCFeV9cVg8dvnhiHrmjznhCyG3t0G5RgoS1DGlu5nHd4fu9731twiUeeMqhghGAyQx6wmpAInjwmuxjRCEysyNNPXi2UESefRYbgMfQ5XcoxVHFeQO1WDue1EO0H3l0PpPnKyhY4yaxybwveR8LTn/708pRuFI95BSgE4ZOs/h44PscZcbR000DTwKrXgPdF+u/yxJjBVhkFjCQYLQha+MUnZV39CWV4jrJo0jkrqiM6eHhASz/GE+rQJ9PPCD7GRVdbOdQfA48BQ+WqF+lSrjqukwjv8gmHw6tcecxtPM/5EIzoOs+UuYxZPfJ0TojgeerWJ+cv8qDxji1WfBzgd+aZZxb9Xtpf0Dp8yewH8av0QjxFI/JLmrlTPKPFPVri+6g07w4/L4/prK7k8zLlOZ+uBw7lnXXwK5xlUOCZyaV88QwvquM8Y3DBiKeAhwvb0fjfx7afRbuLB/HjctTi0OX/o/kvdek3OIMpvh+z1nnDtzw10Iwty7PdGtcrTAN06pxXgScNWyk4T4WFqRaueDFwW8xznvOcIjkLZNyXu4I6fcEwQSKPa/cIHFDKJAGjCMYSXJ218GZLRzawxYHkWc96VsGFkeZpT3va4MsdeNnywsIZ7xvVQ0YCngtsj4E2cvFl0sO4gyiLagxQ/PhqGH9M7nSwMZM44MCNAeGpT31qmchJZt//P8RMJSGZZPTCCIYcfBWED24dkGEMFJKJL3PafnX88ccXHQCPnjTRBF7txfkmBLyQgOWLJQfJcrCceMdTRRPiZz7zmQWebV3oGHiMXriBCx5XcfjhazC4CJyhAhw0COyRZ8KIN4p7HZXCm4Jk4qnzjnCv9/bDTR4+CHiu8K7pQGbaB4MOssvQ96QnPcko/E+UdkZHtXaGZ+dFN0TNd/vfHEZaRtNA08CCasD7Dv9PK04/4YficmAsQYs4X8wp38sdP/mk+WEMYZsL+OUBIKOCvD7wBok/+lDnE/p4wXB2ieOX0V/0SqHRV9rziQsPccnNIpcFLXRkOFf/L48WDO9Rf05b8YwfDEoEXzwSJ1CGjBjqnTZ45ImCN4u8UfxmGXm7RNpK048zfrKI3WuvvcptNfy4MICxlYU325gkF/x4vDC4AEE0Mr45INY/2kA+GlrIi++np+drYHGR3UDg+bTH4OD4GeiI/4Q8W8R/TY6a8UV64ek6jgYXtrUxl4r693rzFUl6E0+19nI6eLJ4wEAoHj1/Md7R+crf6i+sBpqxZWH127A3DYytASZteEKwIGZgYWGKJwgTKyZ4eFCQT178ijIuETp9GVvYW83eUxbeGEb4msC+cvBj8MHrRYviGn7cgjXROPHEE8tkFZdlFtikOYCPK4c12DCBIo/whje8oQz+8IOR4ZBDDhmQ8YGvRpt8DrzVQcIYMfTDcMSPCS6TTs6UISAj+kSXGFrQ41ve8pZShvcFBpiu4IOmBlUmMS9/+ctLNSbq6IDzA2irww47bIDOJwpsVSNgSGHCAl+cW+KeLYJnskkAF221QX+PPnEMVjp0Ea8odMnk96CDDhroGDjgDzzwwELj4IMPLrjIE36MQtCgzTGOgYdJPHvtyeMgZt2iQF2XoyC7KcgoxDa12H7gxe2WQBz98H4xYSMfuXnvjznmmNKGrmfqsKUPHtS+anM9dUAv9TBQ4saODM73gNEWaRpoGljSGvA+xuMsbDEsK/ihuMqLiyTy1R+Bi7hwUuZxDmSl3+DgVy9jPGORj8FXh3rzsQKjrr64Ozx9OttKgMdIgUHZDUTi1fs5xZ0f4BiT9VEEAzhBHgWMtTJEwwdxzpLYeOONC1zEH3ErHZ9alLMNFoM4P22JpQx4xpCf//znRUYMPdBmPNLZM9oyRD3456fxSrxFujq3DWML9fnhQcBTMsXtxkXQBQyxXZxndM4cxNuWjy86o8UNH3ova4aHmqEC0fyd7hLV6UU4yrbZZpvBGTyxfJI0Xl58vBFf1K0ZXGpyuUyKu64xuPCOKbB1ULdkKs/hJ+E/wmbtlOWpHmXQZs7ut5TCowyO/p5Eei29+mmgndmy+rV5k3iJaEAdtj9ZuHPoHZMzJnPu6cAkhy88mUeLjAkumvBSh6DJD0++HuFRcuyxx5aJATAMxBxId9xxxw0mtPCDoadGFxq4UsMnxhWdlcEkGC8dBkxwKhDHyHHSSSeV2w8IyIPBhZskwKPBS4PdoLJF4Cmej5LBSWYMBhgD4JFJIfQ4kA3Z2fMuWjp7JNOn8FPGQKr96cS5KQjjGF82cHHnXBiMFVxzjNeGPEqAhRZGELx84AuZ0QHbx+DXdQ08+mWSgc7QL7rFawWjGPrE44V3hsAVjEwAaEMmgbi2snhAp8BzqLKCeAEeYwf6wUAEP/AIDWj7hIe63i6a7JDPFjhupOKAW/ShSZZgeJ8xhHAQpYyFKsP4hcFH74q3R3YWUHw39BWXfJ3Ds++++w5kbZGmgaaBpa8B70/glrTyePoWH8r1ZZn/vX6SMvYRyo80PJ8+i0WkPONEn8NFWUSxddONPRjoxafwYHjAyMsWIfprFqZ439CXko70Y1r4lI+RgQM4MSATMDjjhQMdHYgLXo29wGOElqdLxCc+I9/Khy56wAjOORTqTxlHGKcoA4bDUZETAwpzCQJGKc5aUUBmdMYYRR+NvnRjXiY38x4MSdq+LRjaErq0N/QYW3Vwr/Nda/MBQxNGRJ9njJNGP34TH7LrJinx4u9mFoclf3ddhlnLg8746OLv8IQqKeDRaBP5H5dv4FyvSuvJ+4Znl85n4h3nI40Meg4/jRxex3nJ8MKn5PI4c0bebRncmN9w5iPB9eB15strq7/8NPA3/Rfg/y0/thvHTQPLTwP81bKftotQ5vFRf03vyKM2vK7TBE5lDuO4iCvdRUM0M3yRH9KRhueJZkbb60W5ajxkPEkPkbdJZKWu4xZOvvzheu63HoGX7Vh4sWDIkJfLKJ1G3mtp5yXKhFGDCUA8c4jJLtt8vvGNbwx4jboflY60nL8nP/nJZXKOtxAeLJF3r6uyiC/qJ7ZPTHt9jFu6mQMDFAbKmjzUi7QiLy3dNNA0sHgaUH/qT42JGDswlitgJNcNO/yP/Qs68RgiTjxOwK3bbVROPWiRpg/1PgJ4Fn4Ytb1vJZ/FFotZDMaqg+eDeMEDFHxsy6ScrcGChyb0yKM+PDGmwKPo0Lex0Mz6LPGFQcQ/bogv+HW+oKM86UlnrEQjBkYeDEdxLFH/DQ22eUTa4IVXlfPhBl3gnUSAvgfxivEfHiQnT/3QCVuN+CgBnLe56A0hnWfC3wnF4ZM47wEfEBTYXsUHJvEqfpz/LO5wwpW1cZco8d2GR368f/ximrGRDzhqiy7cXka78AGNMRbd86Md1BbKk0yqm+nRefY4dUhLB9qmJWOGPJX5j7g+I81xZXI46Ip+5E/tHnkFnvmWDLSk+XiE126tvafhrdVZ3hqYOyItb3ka900DS14DPhjD7KjBWQJlHfeoPMc/CnZVlI/Dnzdo1FVsbMqF05+T0OnSQ4aTfehM+pn8c3W2YBiAjzzyyJJmD7fX7aIx3zJ44IsjXwF15g848SjC0MJ+eoxCXXTEa4QpQoTgOj/qqKOKFw+eLSwOYn3B1vBn5YL1J/Fs0oNnDeFDH/rQ0C1bJbOFpoGmgSWrAS10xKD+36SJa2sPaRZcG220UQFVH1OLe//kcRaOvuB3PHhZ8vPFPOWkMRpEwwP55MnQQT9Lf8sWIIwDeHdgzPBtjQ5fBOkH8mTsAJfTgS48xEUg9cQXi14vF1/KdzoxD7rR0AI8RpQor/CINh6ibuTJyuGFUKMjXmkTHwccl4w+ahcvm3Xc38eocwwYfEhR4H3EEyh7F5U36gkurz+pPNJZxoO/x8TxSmK7kw6WHocW3l147DJ3AIfjjLyrrUfJnJULl/SPUUVbzynD6IKnV2wTyrzNxpEpwkQdZrqMdUjz/vthwfJgi/zEdIar5a1MDTTPlpXZrk2qJagBdbRxkCCdWc27OmYNCjUxIy3BiTZpx++DDGWj8I+im5VHnLWBLfIScblsUY5aWZcuhX8UXdHKaLA1CfdzzjjRti19hWUSdvLJJ088kcro1OR1HVEPzxIOpcWwwgQJ92wdQPu5z32uHDaYTSRcBz6Z6tJN5JObA9ABX/mgFXXv6ViGHBmt+O44nGTnyxJ0mUSyTUny6ekTdJfdddfiTQNNA4uvAe9DiOuncTF6tbCNZIP+eQ61/3jsQxwncR9vPY7kkRfXRtZfeR3BAscWE51BQj5GBq6V9n4owydea3hFA751+CxPHT6rcukGY4mubWY8iH1pTLu8s4p7ezi9Gm10ENtQ8vCMY1OEnS/f8R1Qm/BE72zD8QP12XLLR4Yop/OcxeFzFrw7v/Ji8Wf0blEZ9fgoonPOiHuQEYEnP3mw8ITv6NHiRhjJ5ryR57rsigtW+mH+gkeuAh5iGI1qeh0SZMJEF1/eX0g20I/r3QKsZJqQrQa+jDXQjC3LuPEa68tLAz7odHXmKkM678wlrXfUWacd6QiP4xqFlzrjTAKc1jitEXl3Gj5oOn3Hm8mQ8RDzYjrqMsrqfEaapLM8BlvOZ5GRhckX1xlz6K67cEfaUW+OW2WR/0g/0xHXZb7yla8cGFn4OsQBueyjz/QuHNlENupnFF/cKMR2Is6k4crnjL8od0x7G6gsy6NM3kW4vHOoc9f2oZoskX5LNw00DSyOBrx/ywwhHOb+o/45YwS8CDjU2/upLA4s/3XHrb47o1Hr110DWd9MecxXH8N5ExjgMXZwzgSL0yzE/jzywtd8FsKcD8K2CunCcdX6xgiD5wi8MD6x9YUv8rqWOuMt1o+ydtWJfS3pyGdMC7/nq17XEz4iri7euspc/3FxzW1Pvp0Nr1bOMHFZu/j0soXgGd4zg4vy3PAiOfXUIdCc66ItdOKX/1jNuCIjS5w7SMdqU39G2rW0dET5aaedNji/Bf74uKItZ+JzFjqt8RnfBfEsOTmHUOfLsOWPs1vgy/UyC/663t1WtjQ10IwtS7NdGlcrVAPeOdcGF+/oUQPpOImI6aiuOFgIT8Qd6/mEgbJRdFRfeCO+WtrpxHjkwXE4nSjLuGWRp0gvkzmjRV6WL/wRr+szoxH5irgzPWQyC87LnF7Ut/NLWe3n/EcakdcuvmLdKLfzM2mZeBevk8hSo9XymwaaBhZOA953qE/1RQ2LWw4HVxjHq8X7AdUTbp5x0aQ0sIJzib0/E0ymEcHV+tsazogfgwoLX4wq3Pija5czmrU856EGo/zbczPc2msXbyE8GFgcxjAJPtVVHX9GPDFd06HaND6hFelE3idJi37tfeGwXy2oWfDjrSRPj8ib0lpsi9dZ8ivZnO+asQWYWIbxhfeMg4f5r+EZyqGvLosbUyRLzFO6Jpu/467bceLSGx9V8BJWuGv/Ni74jXoX/CTtHmHFF/nSW+2dUN3m3RK12NLSQLuNqL0LTQOLrAEGBg08GpicBS8n32EifEw7XuLClcUzsYUvPjNYzxPdCOf5kVenkcWj7JKBfI9Hmp6W/MJf42eUvI4nyuq8RHriZRz5BCv8Gc+RtmCkE396GfmZjJEv0vFXqztpG3i7OM6YP006yhZlUHoa3K1O00DTwOJogD5FfZzifgMRi1s/hFz9SO3/rj4q9qksDFl0KpB22uBzPtS/CD6mI/4IJ9zC6/XFowwsbE+5/vrrCwrBRXoDxi2ig8EjLFczR35i/d/2PQL5ccYM/Gy22WblqmU/hyLD4XqKOJ0P4pkskVdwxDyvF8scPiuLPI2bVnv5O0Gcm5RkaAEX23FHGVqi7JkexuWrCw68/h6SjkYz3nnlcdgwW910HTd1ORcIgxtBfAtPfLqxxWVU3cir5Nb7Hss9LTmES2kORsbrhjOQCFy1jFcR2+McVrS6aIxTFnWqOlEG8adtV357UnYz0Ti0G8zK0kAztqys9mzSLHENqPOuDTy1zj2KVRtMIl4NChoMePpAFvGSFo74zGDJiwOPw0VakW+nMYperFvjRzKIdtSJ13OcXfSjHoGVLv0p2rVnFw3xFeV0HWbxUTrO9AQN/cRrV57Lk+EblecyRX5rdaMeBJfVd9goh+MXXA13jZeW3zTQNDB7Dei/rD4UCh7nrBbfMrPVVlvNOa+DOvH/L05lRFE5uBV3A4toRj7ET5Q864MijOqqP3LcMvSQh9fKFVdcURa+CrF/Is32HwxNLIhZdGJ44hkX1ODw+ve///0LDAf1ckgv25ow7LAlqbYdCX74QZNrq1mACydP16Pky+R3Prx+5DGrG3XhdLpoduEaVRbbNb4XF1544QAFxi22YUUZxZs/JW/UwSh+pimPNEj7O8J2NAyYGCoIKuO50047Dba6ZXIIlwwtGcwonqnj708GLxkoE6yeXGvO+S26nejiiy8e3ALleEfRyOh6Xo3PGl69O/xfdDMRt3RhoONmIpVDo4Z7FE+tfPlqoBlblm/bNc5XgAZqna7yo4g+CFEW0+TFAUd5PmhFvJ52nBF/TI8aeCJ8xrNg4rOLx6yM+nFAk+wZ3ZiX8So6wq2n0xGemOf4qRfl66Ln8kW8So96Snan4zxEnrJ0lEF8ebsr7nnOf4yPK3esp7Tqu16ijDU5azhbftNA08Cq1QD/Z+/TiF9zzTUDpsb1aqFC1sd4v1ErjzyIuPc15I1Ky5gCHfWLXoc450vhVYDRw71snLdNN920LOg5/8Fv6Yn0s5aLMnIuCz95Ady1vwWDc7wwaLH9AV3Lo0b4MM587Wtf633zm98sN+5xmxLnz4Db+9iazmN+rJPx7XkuQxfNKOsovFm5dKp3IKY5BN+9WvCwcHliXPz6M+oj42M+edAS3+IHwwheLNdee23v/PPP73GOEEHGRtHj9kK/dSryDTx5bmhRnmiNK5/49Houd8z3toDm1ltvXW4kIkTvFuGJOBz/uHHxWeMt+x9yXhweYc27ZVwtrx5wzdiyerRzk3IJacAHGjrr2sDjHX0cOGI6DgY+4DoN4OIAofKIoyutsshHHOhHqd3rR1wxLVwZv5Efl7GGJ9appcmPOlTbkK+f8oCPcinNM5aJbvbMZJVso57Od5RNfGR8OY/Oq/NXy89kGCdvFD5vzyhLxB/5dxmBHUUr4mvppoGmgdlrwPuvLK4rk0U582qp/df9fw6M8Gf/fZWrr439Kriy/sfzPI7hxGmqPjAYL/Bg+dnPftbjrAxCPJyWBS8eLBhaWFgqeD/mcjjtKF9Mgyvm6ZrrDfreKyzK+RKPEUiLRfDjfcPWDX7crgesFtoRZ8Q/qjyDHwh9U8RhXA8Rd6w3nzRyS7e01Ve/+tUBOrxa0Jvow9Oo30LyGuWUjvgP0Zbf/e53izcT8riRBTjyOMAfY4u/b5lsbnDJ5I18dKVF29tW8FkeZWoPzpTB+Bq9W6KOga/h6uItlmU4Mtx6Z5p3S9RgSzdjS3sHmgZWoQbigBM78KyTjwNKxr7wClZ4Hb8GroxGzIvpjOY4fNXqef4oWiofl3/BRdoZnSwvaxPHKV1ncK4T4eaZ0Yn8ZfrM2k+8xCf1u3jK+PG8jH7kUfgjX6PkG1Ue6Qje9R5hIr+u5yhXVrflNQ00DawaDfC/9v6L+Pe///0hZvyslvjf9v4k61vIE37FhSPLh3Dsa2I6wsh4woJUccFggOHsFDxERoXf//73RXYOK2UBrNtWoO+8C4/rIsOd6SODIw+vlfXWW6/88Hhhoe5nvgAjT5cddtiht3b/UN1IX/Qi3VHpGk/kz6duF16VxXcvptl6o4U9dfBqEV+Sv+uZyTAOX9PAwAfbV7gmWVuF9O4IHzCSkfd1xx13LAYXD13yZGXTyOh8eBt73P93gu/ybonw0+gw6kE4M34jPdLRu4VzmPivTKOj+fLf6i8NDbTbiJZGOzQuVjMNeAeN6J6OZZT74BNVVRuYBFfDndGJuGu0u/jJcHheF9354B1Fd77l4jt7Rh3HiYPS8VnTb+Q16izSA568LD/SgAfnYxyeBBP5EF3xG8tJd7VpV1nUgacjHZU5vnHk6qLRypoGmgYWTgPqr/TEIKE4HhbcQKQF7hZbbNHbfPPNBwt7/tv6yh7jcNzVr9T6yFq+NJD1OcoT79oOxFN5yHD22Wf3MKJME/hKjqEJmfQbJWNNfvjNyiRHLCON4QjjD14tUQdsReJMGHnnOH/iMeIcxXtNRxFPTNfqjZMvuXj6e6j4mWeeOdhixbau7bffvqCVvP4ukqe0w4zDx6xgPv/5zxfvKUL2XpOn/PXXX78YW2LbSr+SMT6jbNO2h+tefPnT20P5ouVXLeP5hnHS9e8yzEe3NR4z3gQbbyZ63OMeV85YqulxPvy1uktfA3Pvd1v6PDcOmwZWtAZ80FLHnAmcldXyVD/izvCOynMco2Cz8ozHDG6h8+KgF9ORvuTOnl63tgiI+CfRQ61uzI+0Sce8WAc5XSbFlR/TUS+edljHG+tE2SNPs0hH+pG3yFNLNw00DSyOBuLCLi5mfvKTnwx5EnAopoL3HbEfGYf7rH7sb2K/SbnyvIw4wZ/IokUY24ZOO+20qQ0t4MZDAc8K8RCfzhc34/DL+KdeVxnlWaAOxq7HPOYxPYwrrisO2D3xxBPLlqNMhzFPaejUymr54s1xZPxOmhffPeprUU+crTh+lg1GP4LzmaWVV4AXOeyyyy5lYR95gGfliX9dnVx7v8mvtYnjm1bEjCdv44y22sz7BQ79lUeZ2i/2M/Pl0XUX48IteTjImq1OCrrpa1Y8TStLq7dqNNCMLatG741q08AcDRx77LFlMrTvvvvOKZtvhgaAGp4nPvGJ5esUE0MF3If1w0q/EAG3ZX4Kn+zrAHfWcXQADLDRzdn5zAZqH8jHlem4444rtI7tPwlxkuE4OciQHwuGDDbS7OKxxqu3ZxcvogUv8CSX9HH4inzON82XXdoaF1v/yhtlGaUP8Z7BiUfXW02H85Wn1W8aaBqYXgNxQeSL3ssuu2yAmPMx/Hpd//97PPaDoziL/ULWn9SMFsD6IhTeyfNFOh4tJ598clmszzdccMEF5TwVl1f0eUYji/h244rzG/WWyS59infGDg7U3X333cuBvarDeS6nnHJK2XIkz57YFhn+cXUS645bb1o4fw+Jx0OaXfbIW0xLz9PyMm092nrvvfceMrh4mxBnPsP5O7rRSu9MfAKrvIWSL/IW6ahc+lAarxwPGMX8P6iyWRk4nA/xqDb2tPI4UFoBYxDn5hBi3zcAapEVq4FmbFmxTdsEWy4aiAOBp2PZQsiEgeX4448vrrFc/QfNJzzhCb273e1u5bfxxhuXGxFkGGGQft3rXlduMJg2jCOXD0h8QWMP+WINUuPwJ9njADytTmZRL+MlTlQmoRPfxUn0Eulw8ONHP/axcgvHAQccEIsHBqw5BSFDk5quZ9Y2o/C28qaBpoGF10DsQ7xPJ07/4Le++IIlW+DMl+OsH4k4u2C8f5UsN9xwQ++EE04Yu0+L9GIaDxlukolBBpSYrzS8ZQtlN7y4bBFPtk1izTXX7OE5oXNLVOdb3/pWufIW74I4TpPOdDhOXuRpIdIZv6LD4bIKzIXQnYeud9LfjYXguwsnXhUYXGr8/W3f80WHMEcDS5bWuySZHG8XH+OWZbrKeHc4jIkYYxW4Blr9S2zTcfmowTndjK+s3oYbbjiUzdYu8eUF4jnD0fJWhgaasWVltGOTYhlpwDtWj/PF6Oqrr+694x3vGJIm65wB8MEkxqM6RCfS/sMf/tDbb7/9CvgxxxwTq/U222yz3mMf+9jyY88pP8Khhx7aW2eddXrPf/7z59QZlZENLOJ/j74Orrzyyt4RRxwxkI8vg0xyTj311KGB6u1vf3vvqquu6q277rqDATbSzmhFGNKj9JfVmVVeF4/ia760OPAQN1Z+k4YuHiLvMR1p/fM++/Rw/cW4x37rWtBkZtQzq+91svKW1zTQNLBqNeD9LZx4v0GfrsAVqizua4ub+F/3BdF8JBy334FvGSQwMhDnfJPTTz99yGNzPryoLgYcxmvx5gYTxkg8TOThIo8WlyMaXWoGF9e1DAveXsSBYWsRcxY/hwLPgi996UtlC5i3BfFRY8MsdDQpjsiT5AQPcT7w+MG4cRuVZHSdUddln5SnWcLfeOONA3TOE+262667DjzGMuNK9n4I2ULKJ11mz0w3XEmugJGW/0FsV8qzvAzfqDyX3ds98gse/ocYtBQw3IkPvWuz4msU36181WqgGVtWrf4b9dVMA96xxk5XVz6y1zPriD1vnA464oj0SONhwJfEt73tbeUQPvLkCkzTHHzwwb2PfvSjQz+2gJBHOPLII+d4nEQ+Y5p6ylPzK83XGPhwHeigN68HPDDAMqBlOCPuyIenBRtpCOb/9ulFmKGMjgQ4hFdgyvN0xl+GVnAZLsfrdPFG4guQfwXy8hjPeMnyJFfGUw0eoxmBrWvTHhop2eMEZyEngVlbtLymgaaB8TXg/ZNqed+D0eCKK64YIHTviWxhMz7l2UF29ZVsFbjooovKGCpDxewo93q/+MUv5qCDH/pUPGl8y5L3jV4pLhYpU552qztKAAAgAElEQVSeLqPqeh9PnJtu1lhjjd6ee+5ZPF8Vrrvuut7J/W1FGIZ8DKA8a3/nbVXEMx7F57XXXjtgCcMfczQP2TsZdbkqZIImxjm2d2277baljQjil3kA8vg70hX3uv7+zFq2Gu6MN9G+053uNHQ2ijyu/X2dFZ/OXy0eaW200UaDLIxBzLcz3pbifyPK0tLTa6AZW6bXXavZNDC1BtSx+pMrFdm+c9RRRw06Y+KcTcIki60073rXu8oXJfKAV6fNF5jHP/7x5Re32wjm9a9/fcF/3nnnlXrg+9SnPlW8V/bff/8yQXS3YYRTXZ4qw7iBpwv1COecc84QHBM++IYW53PwxAPmO9/5zhCcDy6ig0wswqkPPZ6aKPzrv/7rnDJwoxvnkzheE5z/Al1+2ZYn4OAJnT784Q8veiWe8iljS//pfEtHehGiTGzRQh4WDTzhSzDOMzLAL+0KH2zTQhfoMsLpHaEO5/xIB6SlO+KcM/PCF76wyK4yyoVPvCPvC17wgoEO0Jf4dNroBv54b+BNaeHBcKJ3DDrwBQ/ku8wYyF796lcPjHxeJj22Z9NA08DK1kDsh5DWDeuk8Z7UQot0La6yhdaY+iofJ8kjzZklurGntmicL386k8LxkAd9PlTgGUsQn4JzvUVdUUb9rsUj5cJLnDkG25qgg1HpwQ9+cG+DDTYQud4f+4YWDC54hfgYkvE2qLQEIj4WEff3kfNBpMeoT8kV32mX3csWWlS202BoYe60zTbb9O585zuXpwLXdkdZRqUXmmfHn/FCuevd31e/Fp4zdvz9Xwi9i3bkx3lSHM88DFsKOiiXdPxvLKaOG63F1UC7+nlx9d2oLRMNcBjd5ZdfXk7fj18zphXBB3JwxIGZhSlbejBkfKx/tgWBRev/z96ZAFtTlHf/5KtokiqJfmXFWDFaGHeJCghqRERRwQ1RcEVR3EVF474rbqC4i7viLrhvEXEHd0UUFaFcUCsSCTEVq4jGWJXke7/za9//yf/+3+45c84955573ztdNTXT3U8/W/f0dD/zdDcGEZbVHH/88WWC6oElGQzwCEzocWHmL9ehhx66Bo4Jrzp8BhB4hZxwwgllUv+sZz1r9NSnPnUN/AMe8IDRBz/4wdHb3va2ydIh/8AArPLOLxP729zmNkV3tYBHDPAKbNpK4C8YgeUlxxxzTDn54B3veMdIfEwKjB/IA8/973//ohv+hPKnBv6otyc/+cnFEODhCle4QtGp9ILupW/gyCdIv89+9rNHz3jGMyYo4Iu6ga6WUjl+/7hLJnb5/+IXv+hg5dlxwzM6YymUAnVKPRLgiz9UCmwGjNusdIj8BP0NxmW1Kw9Y3GwVqMPnPe95k7g/IOfb3/72spEegbaIHjCUsIxMgf0Ezj///DWDOfb/OeusswoIMtDmfCNkNuxlPyDykD/blg9aJoSGh0EDgwa2tAZ8csGzGy145sha7deSxz1rWUNrecOy+wz/fotvLR3Co4VL6Ww2vwzPFgwqTKCRVXo4/fTTy/ePDWwxnPN9VMBYz7cEQxA/RzCIYEi/293uVsY1v/rVr8rSUgzoeKdw0g6eAoTTTjuteEUgN5N3+mo2yCV84hOfmNA47LDDRpe73OVKnO+AxiPE2Rfk8DG/8A3Pushbdn1NGGw8eH16W/S6RQcKHI/MzyPxXWuH+R1L0ilzxhO+FteyJj/pxuGoq29/+9ulnWBk8UA63kh8ezdzUD8Bj95HZJ+hOsSbysdajA3V1ryewDePzlNX4i/vyav4Y9zGT04FxlK1pX6L4i/5HeKr18Dg2bL6Ohg42GQawFuAiSGDPQYRj33sYxfOoX9M/FmE9FFRHG8DPvZMqpksayLLQIoBE538wx/+8AKeXgnk4X1CYMDFbvrg10SdwZJ4AFYfDOD9Y6J03VX+Tne6UynDBZ8YWhiYYSzhDxg8ywCAwQLDj+ALUzvp1NKY7L/1rW8tMNzBB94aj6TBC4YW5GTQgSGAO0YU1pefccYZpSw8HHfccQUvA5BLLrmkXGeeeWZJwwDBzvGuF9JF1/XCMyHrkY8/hiyMCRzdKR2AW9444FP7euQjH1nqlo0G0RkywLfW+Tp+dM9mhOgHt3UGyuKDdK6Xv/zlo7PPPrv8HYaOgmRgACBDCwYgaHOhD+oPwwrr71Uvwo+hBWMK9QCfpL/lLW8p6FmOxmAQPf/mN78ZIRMyYABz3fEnChrkMdB3HUqXE4aHh0EDgwa2vAb0jkuQ7DfpC3xj3Kte9aqdE3SfNC1iAtWl4OSVuPozDBkYWkjjwujhf9q78M6a554ilOV7+Otf/3rERpxc5LunJ3xhTOEbj6HFJ6QYYthb5tJLLy0b42Mg4rtBXIFvAT9p+HYSMKSwR8tNb3rTEscgz6RfsrOZsS/9wsMFGq1NcyeEVvQgvr1+xQrfbA+MmwjAZnsTHn0ruTPG0FjH8/UsXKLdVwV8k/mh5t90yhL/whe+MLpgPCa8973vvYuhBRiMZ76spS/NVcC54UqGE/iovfdpVPJ+ZFb99pVVPLV4czx5ahLGIYK3hb50B7itqYHB2LI1623gekkawDOCpTge2LDWd6Sfh7R/zL3zV2fLnQEJgWefmIoek1sGoOQz2MFzgnDOOecUeG10i7EBQ4E+/MC/7GUvK7AnnnjiBL+8T/CkaNHMdHAySMMgpfK4pJLOIE8GGI6qxLjAXwV4hnfxy7PwSraUV/lKB440T1dZpTGwkREKg4Xkuv71r1+8SeCHjzDwGKjwFuJoTuCkK9er1t7XeBAvfheP4uuZz3xm8RhiMIq3C4YR/XWUEYPBMgYi8jB8iA90Jm+jU045ZU1dCr9kQj7+klCWwKQFI8/DHvawkfLgM/WF/AQMItASbfTx4Q9/uORhJJGMEwTjB7ye0Cd8Uu7UU08t2XiCqT6oe9ob7QT8qTOMhwS5ozuPTmt4HjQwaGD30EBXf3nRRRdNhNTGuErICY1PxJatGfVL2X8Rx+sQg4YChhaWpObkb1E8Ci/yQ59lQ9pLhG8ARnf3KoU/vFVYyoDHCs8EympCyhIgvlE69UnfSODwgKAcBhaM/3gk1oK+PeCFhhtcMAbxvUv9iY8avlWkZT3DtwJ6laeSdC95ND5QW8C4hEcwYyQunvkBxpiENi54lZ9FD9Q3P4X4kYEXmPTOHRoXj5eUHXnEEaM99tijqcJleFw1ic2Z4QYVR+HvvcPQ9n2pjrdh6Vf1OydLa4rV+EveiCstN8rlHc36XyR/i5BxwLFYDfzBP3yxOAdsgwa2rAZay18+8pGPTAYqixAuBx7Z0XpHDL1jjz22dM4E3Q8//PDyp4qlLUxcGTBp6QoeDTe72c0KPEt09EfrgAMOKB9oHzQlrVJoZzjppJPKR90Df7+0yz1GHHnKYDhQSG8K0pn4wwf7feTRvz5YAxaeutI04AFWgxcNetFB6sqXSQGPkUB/G4DFyyMDHhf6g+f1w7N/bDNPePjT6LqlDG7IhG9+85vF8IIRxt2+MQJpSZX+rMlzSbIKP3uiSEekOR9HjAdcnld75i8YgaVang+feHUReB+yHtCv/maKLl40D3rQg0Z3vetdR7wrtDMG6AwyXM+uO4w6tSCY1HMNdkgbNDBoYPNrwPsmcas09ZGcQqdA36nJSk5iUlrvizNvvfHkkbi+N3iR+IlqMrSw7JK+bxlB31r1yXjV0Bd/6UtfKvrCM5E9XPhuwQeTTjxWpGMtiSaubx4/HDzw110bqbPcWKFWh14OnpjIc8fggleplreCEy9c+nzVF/h41n0Z+mrhTFm8nlWGn0cK2gBYcOKbfH4+8T1nvNAKyC9vBuoQA5c2qKWM42u1Z+h85StfKSTQMfXMWGq//fabeORiaGktL2rxtpnTUxfeZpJvxnQyILJsWeOvFo4sP0/ccXs7zvYFbrzd/H3gXdU4qoVnHp6GMptTA4OxZXPWy8DVijTAn/laYB+SeYN/yHnuukQjJ7j777//mgkxcNpnhAm84FnGg0EDzwMGXNDSEqJHPOIRZXIv+uBg/a7KunzimY+DPhDKZ18QjDvsIcOgTPi0zwj5TLLzg8OgIOVT3HXUSnO+/Rn+uWRswajjMuWHTPhZX87a3pTP9QAeHwjV+AS+lo7+nQ9gpAM3oGBcedSjHlVOk6gFygmP4+ND7XF/ZiCnuPMPfqVLbhmUarRJg1f+bkrGNM4Ag3HnxS9+cdEl+wEQaAdPecpTiudObfLBYIjAJEu4iefgqAANYdDAoIHdQgPZjyMUE1s/YtdPTSNffQJ3v5apEO/TxbPueKGyASlBaQcffHBZQoR37LImvP73XpN3loYoXOUqVyl7Q3B8Nnt7aTKnPh+PV/HMDxH6a5acEGrfOcFK5xNCjQd0ITz77LNP+SmDUYCAQQJDA3QJglt1f+/17GLphxL51KvLpmdOZMJjxdtuQzWTZDxmPvrRjxYvUL69/m3s0gn6U/0JGUvEMKYxrmNp71bwWpmmH+V7e8w0tRmvE+0bBCxL4TxvI9pY8gQfzoNOTVJbwTiHt1jKuRG89q2DAW5xGhiMLYvT5YBpN9AAf35YRuQbozHAufnNb74U6eiM/YKI4k6QwZJPngXHnQm88v7u7/6uFHv9619fNrDF6KHlIkyIwU0Q/N57713irfDmN795zYa2CSd8pOuvAq7OPvFXGcHW5APGcTlMn3LQa8E5XvGCq++RRx5Zohic8MzAeMUA9dGPfnT5UyicLb6Ey3l32OQp4cjH0OKb477kJS8pf7xucpObjDjJ6ElPelKRS/p0vaaOa3KKZsrgvD/96U8vf4FagYGc03J+VIYBAt5UF1xwQVlPzpHgDOTxdiGwDI8/O7WBhIx0LfpD+qCBQQNbXwP0G61+yD0L8SKgz6Gv0IX0tb6jlrYITYlP51nP3PmB4RuNH3jggWXDWQwtBGD40+5LoxbBF98o+mIm1fxcwCjlhin0wRIT+uFrXvOaxZOSZ31jeCZghNHEHGMIBhA8U9mTRUuHxC84vd5Il94pS30JxuFIY9yEZ6wmmCzzZRyCgcDxUG5ZdSk58u51m3nEfSmKNg12+TB2+aanNRxdaRhqMAqwHw5eSNKh31UeXmrfSdoCdcaeO7uToSX15m2w9aw6Ut1xV7vKdpn454k7H16+xR8wvFvaQJqfTBhbam2/ljYPj0OZzaOBYc+WzVMXAyebRAPsP8Gu/uyhgZumr91dFIv5ofePATSI+2RdaUr3POHiziBVG+Uy+WUAheEAgwIT+KRLnkLm9ZHVaYOfAE5P1zNH8rWCwyc/5BG4S26lOT55JTGgdLjkhTyMLQT0wga6LOVhrT0GAV+DX6OX+Jw/5wdjg2CVrqM5tZxIbsHksyEtR3BjBGKg7m71iQf4Fh9985CdgN6Qv3Xp76jLWaNNGm7i7PPCMjUGFaLBO5Vl9F4x2Ez5RKswOIRBA4MGtqwG8l32d51nvETcuxCDtwcmL5qI61nxZSrF+fa+iyUKvmSECRTGDGCcT4wdMiosgk88WORhQd/JBBzazhvfK4wv2ihXe6fg6YqBCG9DAmXgjeU+jBPw0uGEITyM5DHb4hkZ+U7yXQAnfb1kl/zSHTR0ghH44IvvrfMsWN1bdJeZ7jzwzLjJ06R3jQeQA72tN2g/G/ByEVIPpLPnTVdgPOOnFnbBbrW8fPfhv/b+6xRIyccefoTU56Ll9z6pxqvT841yeX9lsF02j4uWecA3uwYGY8vsOhtKbAMNMAhhk9FlebRIhXSy/lH3dFdzrTP2co5HyzjY5BRjEYFlKh5yLXYN/5oCPSLuDlvbSE/LmdhLxvkFteLOR8LIcyZhFZeXjow9bijBkMGHjjvwb3zjG4tEnKKDLjTYYcmMuw87b10qEN/OP4PKlE17AjFwhqZgqDOMPS4bm+sR/HQJDcgSb+rK8bTytDSOP48Jz/pw/ja7PJI/0/ibS31nnTMgR98EvKzEs/Dor6Djq/Eq+OE+aGDQwNbWQL7rSJOnvvgmsNMmL8vQRvLofSOGIfot8YVhguUywCSveCv4RrHr4RVvRwz04o0lExztqzTvN/kOs3QT3nhmye8d73jHsk8I3hgynsMPS26BxSMWozenFuENSyDON1OysURJpxIh66GHHlri/BjAq8InnZQXr3gcuB7w0JDXiGBc5+vRU5+ySSvjjkN5yOe8sqRnlqVDXXxRJ36alWBFjx80bHo/LXAC4KJ4mkZro/JrRhV/z8QHaWq3qb9l8triL/sCxdmo2pcYYrT1dsVzV3tcpiwD7uVqYDC2LFe/A/ZBA7tooNaZelotHyTqiGsdsucxkcejAIMCJykRWM7hMG4Y4WhiggZLJbIz1D4mnu/PDKpwESZg3NFknb9E7OchY8tRRx014UXlWzKTzweKwIS9S3aMJrhzE5CXQTzw/OF4/OMfX4wo8r7hpB6C/2XjBCUGngoMXmqhi1eHZykNpzNhjMB4wTObxxK0iS8DYALLbGTw4QOMvmT0QW+Ur8le429amvBwmhEBnuDNabDx8Y1udKPRgx/84AldbbhMGW9LDJwZTPMH0w1DwMiN3pewqaxO+MIo01en02Qb8gcNDBrYfBrwviv7MU5XUWBJipZUKK31DWqlr0d69UPev/kzRyH78iGMFG5o4NkvjCRdSzT78IpXBX3xmWeeWfbsQO7LXOYyI9+jQnjEP0uamHziHUnfjqcuz/TH2vxccsGvThxyPOCQbKRTL9DUOAFPAuLQUZrfKSN+MOr4JJNvmp++mPz30cuiYcQrePnpoqA9ZhRnvORtdhF8YGzBUOJtDbwsM9YJi9Po8P1ez7Kmafg3U77efbU38eZ7GmnfFvK8bmvx9cqW7R58rf4JbzQFeS8nf8vgcb0yDuXXp4HB2LI+/Q2lBw1sOg0wKOI4XwKDmlyDLYZlcGCDt9aHAdjaQKoFz+lFGHqgywCRv2/8HeMIYAJGhNpmqS18lGHjN3BifGBQmCcZSR4+WC960YsKLF4917jGNcqfOly8KcuEXxN7NnklAM8mdRgS+PuHK65c2sGB0UbGKNHpe2fvH3iFD1zKpW9ORsIwBL/77rtv4ZePLoNyTvMhDT2dfvrpk81mMWTwN22RAV3olCZ4g0eMK3vuuedEj294wxsmAxUfyDgf1AkeLOiYtkadozfuqis2U/bAIFJePmz+PIRBA4MGdj8N5CTC45pY+nJJLQVFEz6Rym/QMjUlHsWf7vw0oM/St4rlTvwIEG9paCGOIYI+EQPHPEuK8CbBAxE6nO7DSYAyuHR9M6Uf+nQMHezjws8Hnn0pQ0uPwu2GJKVlXTiM4xM8+uPZlxOxfEZ/9V3PlM820+JxEele14lPeRi2xBc/RJaxrBz8jDPcGxf6vsw4+SMuHeNdxUbH6znIoYZ/s6R5m+viScuWgcnNhDeqXYlX1Y+/N6TlUiLftHqjeOzS4ZC3HA0MG+QuR68D1kEDTQ3Q+WanShqTX/brcA8CPeOqq05biEnDgFAbPDHRZ6d6Qss4Qdk3velNo5NPPrlssKYAHf7Yiaekm4K5LEy8cRN+7nOfW/7EYXThA8gSmSc84QllsOfBPR5EjyU1yC26DHYwPLCJLUE60V3GG+DBz99HTsFhnTP02cMGnGwkLF7ZWwTPFQwqbALMQBBDA8YOZMArSMd6et3U6gGegBFu6pDnF77wheX0BdbLM6HAsIERjD+GgsUwxp+r973vfWVTY/Y5gXcMP8iiIyelM2TVMjHRVR545VmUdUaeynkeG/AyUGPJEh4uXNI/d//LTDtrtTdc1TG4oHNwyfDytKc9rbia07YJklvL2/DwgYbykm/JNtwHDQwa2Noa8O+Enlke4UsftNmq9wO1PqGWtl7t1PgjTRc/JRTw6qDfFB8YHJgoc1fQN4F0DP4s+eHbqNN5uvjFMMPPCrwqZMzgW8JEn+8SHpza90s8iH/dlb7XXnuNuDLUdKg0z6s918qCXzKnDojzXWU/GW2GfM455xTDvpZ/qGwLd/K/nrjqtIaj1g5kBAF+3p8vNVqexnIhjGuij3eN9gZKnRBnXIQXM/vwyCA0jcbukO/txOuqJRsw0l++G60ys6Rnm1dZT/dneWoLjnpmbLVMHmeRZ4Bdjgb+aNz4/rD75HLwD1gHDWx7DegV4+6X/8XIPxoobZ5Xc1oZ/2izvOWAAw4onhy4n2pA5jDwkXFVqMuVaX0rvcVvKz3xJm8ZT3jiyXeNFnh0UYZnDaQ9PeX2+m3RSXoZT54lU97FV8K34kkn4yqXOsx4C3/qooYPXLR1Bt0YZNhkkL++0q906zqelf40/ob8QQODBjZGA9kf5neOb8+3vvWtwgyec3e4wx0m/a57TKT3xDL6hORVcXjGq0VLQOEVQ78MyNKkZKt91z0P4xJLLzE0ceF1gnEFb0cMKBjU0QVGCF+ic/75509+oECTHxgYMDyI50xbA7QzUtOh0jzP02r5idt58GfKsqRXR2ZTDqMRHp0tD5rEvYh4rZ6zztjMnzERsPxg0SmPxPlBs6xw9NFHT8YZ73nPe6peTOzFw/Jj2oob95bF06rxonOC11vWl2D4iSNjHj/Q8JSj3WX7Iq3W/ueVtYvH7POgwfJ1eVBjcKQ+a3wCu0g+55VvKLd+DQyeLevX4YBh0MBcGlAnSkc9T4eqDt6Jz4IHjwLWc7Pfxu1vf/vyF6XvevmkmbxkvKWgWfit4ZinfJ8ywAiu67nGk6fNW7epX+LOzzS6tfw+cjudGo4+adPovPSlLy2GlmOPPbachjGEQQODBnY/DeQ3wOM8c1144YUTwX1vE+9DpvUni9BcjVfxyN29Wth7BEMLEzjBwKPiPDPBUgBGacCwzwmehngxeABGePKZcrmMEy9CPArT4EJZ8QX+afpTvsO10sRXDW/q0GUTP8id3i3Ek8eMr1HUgiPOtybGLO1wHQpm2RvQsh8Q7Ys9XFg25npgUo53braDBatj06FDB9m2amnJOPpT0DuYMIuK1/hRmupQPHCn/5CxhXE3Pz0Ze0vOjWz/i9LBgKdbA8OeLd36GXIHDaxbA95x8qxLiD3Nn1vWeMHU8vsw6zQY6LDMhskvm6K26EPLL+Bq9Fuy9El33vvAA5OhT7m+egeuL74uuGn0usrWdFzDl/VTK5f6atGdR6eOK8s7v2zQyPIqlpaxX47ntcoN6YMGBg1sbQ34ZEnPeFbqhDmk4xQi9SNd/UL2Y4vSDHzpAqee2XhUyzlI59Q7+lcCvKiv9T4YjxTi8k7hzmTK44JJOMWB1zN3LRtyednDhWUt4kO8OF+u0+zznXfldaV5efGR5Vowgmd5lALGC21k73Lx7G0m85YRd3q+LEfpGGLm2XenL68YWaCLp5NOKYQee7ixxxyGte1maOmjO9pbLaC7jW5D8OH8tJ6zHhl/O6/eD9VkG9K2ngYGz5atV2cDx1tUA3S83qG2PhIOJ5jWRyPzE2etXMIw6T3ttNPKJPi+971v+fNEEFzCKw/c4rV2VzXVeKhVodPpW8bx1Pis0Um+auVcdp7zcv100XBd8Sydtfiu5QvWecq0Gg8pl+Puo98sn/GkOQ0ng1X2syEwSZAXleOVnvvIl/SH+KCBQQObSwP0Cd4v+LOf+ALXLI9Q8H7A+95lSJf9lvPMM0tKFJgQ+75d6ruA41lldcdwUVvyIJq6u4x6dsMJaXiF1AJ9Kd6pOl7Z+dAz5cSrP9fSpsGqjJdNvshDbsG4vMjB3jzau4Z9bPxYa+Hqwp/0FhkXr+Cs1SseSb/85S8XSbLg0jHbbIq7x/hnxH7j/VjYXF9LyRZOcIsjzLbt9bYq0cSTt3vnU3xRp/4O/OIXvyiGNLU357+Wtir5Brrza2Awtsyvu6HkoIG5NNA1iPDO2j8erTI+WIOZ/OC0yjnj/E3BnZs/TLgkU0bl8q5yPghJnhUXbB8eHC/PrTJ9dDKtrPPbpTPg8nLeWnRcbvit6UowXXI7fj3nvUtXTsM/2P7cRd/lSH5r8T56Zb07cOk6Lj3PSrPGx5A2aGDQwGo1kN8huFGa+sSLL754wqQM/CTU+r1lSyOekkfo6oh6njndh4mS+PT+WP0fecKHwQGjCUHGB+UJznGpH8y7DC977LFH8XzIwMbqlMHgov7dZUn4lo5b6eKxdk/cijsupUl2TnKSsQWvIfZEwRMhy0iWFo1Z06UT52caDvGsO0bBZRhb2AyZZUR4/rChMnWutjONxyF/ugayLWV8OoZuCH//Bak0tWvR5E4d6x3A2MK7m/DdFIfcraSBwdiylWpr4HXLasA7UX3wc2CRnXXmu/A1HK2Ph2BbyoMOf5tYSy6a3P05y6Y8tXiN38ST8Wkyt/Jb6Y5fMK6nls5cdp79Es4WTdLB63WUNFPuWh05fuenRr/Fi/OgckkrdVDDVUurySDZyfNn4niyyMhCnl+Ja4gPGhg0sHtowPtC73uYYCjgLaD+QGm1PqeWth4t1fpCpXHH+8b36dhzvG8GwXn1CTHPlJNhBTh5tigv9SGZsj+UgUV38q90pStVjS3w9L3vfa/wxmRdfbruJcOC67H1LPBWvvh1WpQh3elKn56HYcHDv/zLv0w8O2o41gAvIeI8gj7jTpJToZYR8Gz4kz/5k7JB8hDqGlDbqufWU1vvQB168anOsz+zbFKB5ZQsV5TBMd+ZxXM1YNxoDQzGlo3W+EBv22qgNgjRQCY7ZCmp9dFXOYerpZHv6YnP83h2fvy5VmniuXZP/pO3Gr6utFk+mC1akl35XThbenDcDpN6ld5JdzpdNBNHjZbXZ8rp8ZTV6yPhEo/T8DqpwZHvtCRfi75wg6t1Oc3hedDAoIGtqQH1feLe+zc2r8wjn11K9TXeRyxLC+LL73q+5JJLJmSZDOuHRPLlBhflSX7ivpTI9cCz96vZJ7qhhTyMUhwR3AoYXNDrTW960+IV4bSyTNLtylderYzT4RQKqzkAACAASURBVBlZCYL1MuKHNE6hO++88wosS4m0jIa81Evytuy47/fBiVEe2G8MrwT3zFovPxjIOE5cwdui62+9dHb38r40cZn763TpsdXe813ES80D7Qxj6qrbfpdsQ978GhiMLfPrbig5aGBuDbQ+oBpoCHELzgnXOudM80FOjWnR4e7PwGacNMcnWk5zGr0aD5kmHM5DwswSz4/gNN126aTFE2VSHy6HPyfvmZf81eohYYSzlu71A1zGk58ajoRxPaTcouFlajpNnDU5E2aIDxoYNLB1NJB9G0tWPfBHX2EV77/4y76aDb0V/LQk5xV+dSldeGRkIZ80v4D1Pjj7xjS0kJ8eIRPm7OHHP/5x2fD+kEMOmboMRTRdnhrOWn6WldHF0/2b4DhIRxYZW1hO8T//8z+TE1lqOLr4WnTe5S9/+VKn8HnppZfugh5DERsTc2rRegPtiuVpHrwtrBf/dirvBlzqcNVBbQg+/Jk474ufzMU7wN5Fglv1O7Bq3e1u9Adjy+5Wo4M8m1oDtc7XO1WYV9wHfl1CZafsOHj2AV0Lp3Bw9+fEJT6cpmTSfR4ZavI5jVr+vGmuD+GQXpJmH1106Sh1n/idfuZ1xbvyUibFVaYla0ufSSvhHJ/kzbTkAZyuW48n/iE+aGDQwNbRQH5jPM4zV22/luwTUuJp/VDCT4u3+BSPeN/4aUm+1Em8+h163qeBRwYI4RTNvNf6whqN1ia5KSt7f7Aswb0lHKamy1pa4q3BiE9k8nyPZzp4ayeykAas102NZvK16DhLXj2gT/eUoF7333//0Ve/+tV1kWZy/bd/+7frwjEU/oMG/KhnUrION0JP2Xah6Wl61p09Ev/xH/+xsCavHL03+T5tBP8DjeVpYDC2LE+3A+ZBA1UNZOcLUK1j7TPI8EEJeLyM8lr0xJyX4VnxTK8K0+C9hrtVflp6yjgNflp+S0deLnXfVxfgSH17PTiNvgPKLl5EryazyqX+WumJI+lmvuKOT7LXdOD49My9L50W/SF90MCggc2pAe97/Nnd/WXEyP5kIySCJ13Qcx7T+8YNHd5v6Tn7MfWBLRqtfjnxeZzNeVub5AJH3i1vectyjLaC00keXcddebW6mAavfOhLF17HeSILS7ZaJ7LU6C8zLY1UeN0478jDciI29v32t789l4cLmwRf5zrXWXPa0DSdLlPmrY7bvVqQxb3lViGbt/nWs7+neLbgCacNuOFZ7+7QLlZRg4ulORhbOvSpj0QHyJA1aGAuDdQGHy1EOShzuEV1wsKTd2hNo5EfkpSji/+ErcXBP42HWrlMq/EhvF15wjMLD31g+8CkDIsq06dv60vLBwQ1vJkmvH3xpw6G+KCBQQObWwO8896n+vN///d/r/EY4e9+rY+lf9C1EdImv+59w8Q4+anxVuvTSHN9JB1k83KOV+l+58hYP5GIPE4UvMlNbjKCT7wuWqHGXwu2ViddsJJT8khOpYu20rn7iSz/+q//Orruda87Mcw4r8DOw3sXv57nvCudZU5qAywZutrVrjYpIniWqhx00EGjH/3oRyPf8LmLLjJjZGH/Hx3rDD7Jt0w5u/jaCnn+7iS/vrcO74jCKvSp9iHaar/Jf+7bwpI1NmBedntP3Q3x5WtgMLbs1HG+BFJ9K30VL/Dym8NAYTNqoE9bq7XT7OiRTXA1nErzvBpcTUfA1XgAti+OGt5FptX46NLHNNo1fCrjuuiCSxqpw2llp+UnPtGbVi756ooLF7R4dp0qLcurTBcfLd6XIUPyN8QHDQwaWIwG/D3mOY8ulseI9wVd/cIiuMq+RXHuembfEwXc/T14/8WzLmCUl30f6Z7mzy3Za+lM1tmXReFmN7vZiCUUbDJ7vetdryS7fF267MpzeWd5bskpnjzfT/bhCOgDDzxwoqO+MszDm8qIF8ehNDwj9Pwf//EfkzrWSVPaDBiDyfWvf/2y/wbLzjDM/PrXv554u7D86IpXvOKINsRJQzxTtrYnD/T8gq+so69//euFXTZTBTdHUeOJg8FtOwf3RMPrKEPqMeMJP2+81qaEK/NoA3hzabkiBiNfSuc8Zn8yL39DudVoYNsbW7xDVxXU0sjLhu8v0Gqqr021JQMl+Bgcd9xxpfB73/veNpIV5Djfzudpp522CzfL6ix3IbTghK66mZdUduKOx/Oks2UMZOblfZ5y89Z96r5Lb/B11FFHFfZe/epXl83Llh1mkWsW2GXz3YX/9a9/fVnb/qhHPaq4XU8LfQcV4Dz55JNHt7jFLQpuD1tFN9N0MeQPGthKGsj+VXHues4/0Lyr/r7W3t1a2qL04jw6Ttz6FZjQEpzXGk81Obw/I1/0auVFw/lIOBl+MKxw6hCTbPZn+c53vjPiCGWWJmQZx7fMZ5dPdJRWy/uzP/uzNey0NsldJs813PCKUQsDEM8X/dM/lb1V0tCiPXnAgdEQYwrlCMhC2fRekaHF78BxuQEGHKT5nWfq/FOf+tToBz/4QcnzgKcEBizaK0YdvGfgaXc0xnhfw7N7FiH7Zgre9vWsO+NKGVv+adzOrn71q68xOKoNbCZ5Bl5m18C2Nrbky4r6fL2c1MlHjd3HafT3v//9R7e97W3L5kt6CRzP7FWw3BI13rDSv+997yuE3Ygx7aWu4erDfRfeLpzO56mnnrrLAKKrbB++tjOMd/iL0MNWqQvJ3Vfm97///QX0xBNPLH+mvC1L5q723ZfO7gyHUQQ9HnbYYb2MLeiiT3tiYzn1Y4985COrddNXr0Md9tXUADdooJ8GWu/wL3/5ywmCNGD7e7jsdxL+WjzmZpsYBVq8TeMzvxnT4FFOFwwT6Hvf+95lfxYFDC43v/nNR5/93OdGR93nPp1LifrV3vqgajKj60xPYwv7buTSivVx0r80vMlbBV7dw+DSsccBy98Y93u78WfKK84dXATS/ZKRRfSU54YWpdW4B+72t799MbhgXIMvBbzGuLTpapbHCMPSJwxCeOOIx4SbJ956l1q4vC20YPqkQzePkle/sigaffiowahNkNd6ZsmTPNWozwzS66plSb6G+GwaaC/unA3PloNWA/bOsdVZ4J7JZIGB/Z3udKey8RIW7z5lN1IxyU9NRvhxOfO5hkNp88rSF2cNzmnW8mtp8/K5jHIbxZ/XY8rRlZewGe8q67JlOcVr8pP229/+thg28ayqwbTwbUR6l1zKcz5WxX+NbqbV9OUw9xkPzLlqctXKttIo3wqvfOUrRz/5yU+KsSWDl0vePc6fKueTvDvf+c6jn/70p6NXvepVBW1X+aSb8Syb+UN80MCggX4ayL4k+wbfHNeXkfhkQs8bNcEQj+IdTxEPraOpZ+UT+D5Xl6YxrNQMElpC9POf/7yr+IbndemIyb4bXNizwuvCmc12tChBam2MtPRmgjfVHXwzftFFuowomedwehaMl/N2gWyK1+SUwYWlS7MEvMowxDCpX4Shxb+b8JHf0VY8YbtkqNV7thFfQkR7wiDp9Vqr4y6ai8gTTa/HGh9+RDXGRh0nXpN7EXwNOFajgW1pbMkXNTsMqoLJAdZSLhr/7373u9EXv/jFSQf8ile8Yk3HQplWx7IZ07251eRv5a9HlloTT9qJX2UyfavGW+2kpptpaam7Vp11pQtHwmS8pu8+ddPC893vfrdk/b+dfxeTj6TneHhO+MxvxSnnweOOM/E7P04/8bXoZnrKN0s8cbXiLZwOL88dpbXKTEt3neQzHkFsLuiDatfvNNxsnKilBw6LezR4McTg3l3DM6tcCd/S7ZA+aGDQwHQN8E76O8XSCj81xCcagqtNSKZT6g/hPFFK8UxPjNMmTcvmO/nJOJPnvxvv3/L1b3yjLCtaVajpwSeeyRd7jyjkRHNanSSuRcSdf4wZqneWqbhBRc81Y0pXWs3QIlyi5TzU9ImclDn44INHN77xjWcWm82U1xvy3fbvL9/j2pXfaPEwSz3XYN3A6BsZr1fGecu36gx8mZfHufMOtPqkmuzz8jiU21gNbLtlRNmI/eV31WvwThowdGxsRPbOd75zdJe73GX0mte8ZvSCF7ygrNMUjF4i9ho555xzRljC99prr7LWsxVwAfzc2PUTWMJVr3rVsqt864x4cONydtFFFxV4eMIlsAYvWdlQiw8FvLDbu7+wehbvrZeZdCzi3xh/yBWgzbGNSVsujMpTOf4UaAnWBMn4ocYP+Z5OfXjA+IUuCNAhyD2ZDyR6ZYf4888/f2odpE6pgxvd6EaTugV3ylQI7gy1PPHueRjuoIWLI+2GZVJf/vKXS92jl1ym4jT0nPWDh4j0AIzXR8JKzl/sbDtXMzm9HQAHb/CTHwL06nqGJgM70oClDDA//OEPRz8Y6/5vd7Z/5wWdfOELXygiXTg2auIlprJMrKlb0QYWXPvtt1/hSXC19gos6TW+UxfEwYesyvP3qAZPGuukz7/gghG6w227K6jdA3Ob29ym8JUBXbFOV3LBD0dg8ufS/1p5e2aCwkaCWTeJG/qSQ/gFg545spLAn2bqgJB/ysDB4J3wf8fvL/1HwpAnWnr3kImTHNijRe2Jdo+BxEPR57idEOgfGDi6nij7+c9/vuTDJ4MqyaI+QHFk9YGM3j3K8qerpn/ynAZxeNYgW/rLARJwQxg0MGjgfzVQ6zOVq7w8nlUGWL1ftfeslrYovSfPwkv/qKDlJIovk5/1ynWNa1xj9L3vfW903nnnjfbdd9816Fhessq9O9Cb+mi/O5O1+lhGH6w6dNxJG5i//uu/Lt82nhlD77///uW5i/8aHsrk5YYb5ZHmcH3ag+pZ3/NpZZgH1DyjppVTvsvHs+L5LHiN3SUb6ZJRepROlZe8OI3kA/z+rU9jC7gVRDfxb0Q8ZfQ25Jvk0vcwXhnCbqaBcYVvqzB+MXdwjf+w7BhPYHaMJ4o7xpbEHeOJ8I7f//73/IYp13iivmM8mJ9c40HCDq5xhzaBGU/WSjnKc429YSZ5wqP7c57znELLr3e96107xhOMahnyhJc7vIzd5auw4BhPbCbw8MR11lln7RhPjtaUAfYf/uEfJmniB120rrExYMc973nPKu2x6+oa2vAqmc8999wdt7rVrXYpd/zxx+9CS3y4zGOj0qSsZOI+3kxqordnP/vZRVbX/XijuF1oSm7HzzN6zrpSHD0JXvWETF6HF1544aR8rc6Ea/xnfkILONpD0r31rW+9Y+zeWdpm16V6Gk/Kd2k/8AmPKWer7cCDy4lsqut3vfvdu7RZl1c0+uietqg6TLmJ3+Me9yj53ImjI9qWYMU/afCYbXV8csQEtkuHKjf2rNlFd7wrtDn6AV2i//GPf3wXeHStOvc2MfaA26VuwVNrg9IdOve26G2p692jPrJfoZ+qlUGH6rOk56wL1RHttfbuAg9u+iNvY8Lj7x400KNovf3tb5/olf41+ybhgK70X+NTeN/xjncUPavtiHd4G2+Yu0sdIL/zzfOjH/3oXeBUV7QpfS+4D2HQwKCBtgZybEX/wDtJf8S7Rt8zNq7ueNOb3lSuD3zgAyWNPGCApYz6d76By3jvNP4DP7SgSX/T4vPTn/70hE/gxCffEvG4DD7bmu7OYVyAftErAfnGP/9K2viUnO7CC8rt0xbGP4p2jA1AO9Cv2sTY8F7qQXpeRluYhbexgX/HG97whh3jTd7LNf4psYPxMN9HXYxHdaHf8ZKWNRdp5Aue8siN/NTRotrU2Bg00aP0WbvTHuYNrrvaHIp3A/kvuOCCHZ/5zGfKmEY88PzZz352x/jnWYHJd731Lul99fmav6/UicuJnmv9yka/r7V+JvvD2jvwzW9+c/IOSEct3cxbj0O5jdfAplxGhJXy+c9/ftlV+7GPfezoa1/72nj8u/4wVm9Bwr11icr4RSkwfuf5lFNOKSBsyIil1mEe+tCHlrzx4L/sDM+lUzKe+9znln0qwMHF89FHH13c48cGg+KtwsX+BgTy+JMr+Be96EVFFwRo47WBJZs/zeDgr7zwwxM6G09ailcAMHgSQAdY3zuhj06QS8sNJBu4COxnw0Zb44/iRKclYxzGxoPReJI9GhsWii7GE5uSPja2jLDeturAdboT1UQP485ndNe73rXIcdBBB42e/OQnT+pAsLhWjie2o3HnPhpPkic6etKTnrSGJhuMoWcC8OPJW7l4JqAn9Ag/j3nMY0oaS8lUJ9w/8YlPlHQCtMQ7eeiGgP75o0QaAZq0h/GktegF/UDzzDPPLPsCqU4K8M6QumLT0Stf+cpFD9QzeNR29tlnnwkeyoFTbQdeoJNy+h5ERrRaRzWelCbdg991/4hHPGKiN9oudUd41rOeVdryy1/+8ol+SOe0LPQHv7xDbExNII22gy5dJ2ODUcmnzbGsJPXlcep97733LrqjPdNGac+8K9e+9rVH48HfpHxBOg6iz/JB9Mc7SHnqkravNkH7PPzww0sZcFIv4IeO4JM3YL///e9PTggDlr9PwOH5oXcPfdJH0K7RC7rA087bI33GHe5wh1Im3wHaAHtO4dGDvj/2sY8VPqkL6oBLvOG1Bt/ggC551CkB3A94wAPW0C0Z40D9E8AJLdXTzuxJmWc84xlF38DRN4EffpALutrL52Uve1lpI8JJGdJaeGkbd7zjHUevfe1rq/KTh/zIiUchHorISPth6SjvN+8ldUU9Aqfgz5PE4WHQwKCBXTSgdyXfGfpHhdrxrOTN+md/F+I9E5K3nsUKmP8tn6XcRsByGhEeLuOfLmVPq7GRuzwTOJJ4FUH6mqY32sd66qWPbMlDF2+M29yz6Wc/+1mpe1/24x4qPOcSIvdWUb7fE59kSD6nyYZHNl73rQA+vNDx0NFYtAVbS/d3mmcfd/A8NmaMxoaC0Yc+9KHRV77yleIt655sPOOZ+qUvfanA4CVPGeEBZ/YbtbbgaTzjxaWAR6p72rfqdlbd1vTRJ61Gv0Zb3vng1CoHx++66UN3gNl8GtiUy4gYEI8to0VbHLnKxYvLi7TekC+qGnE25ne/+93lg6XA0VxvfvObJ8YLJoNeBn41ORlbwifu8i996UuL0WT8F6d89Jh0EuhwCEwunva0p03oPPzhDy9Hf0GfiRUGJzqk5z3veQWGiYfDn3322aMHPehBow9+8INr8B977LEFnjuGGjZVYwLFhOuAAw4ochBqnVnJ2BnQuyZ7TIhUB2zGBi50xMQE+e51r3t50ZLOx13LHZjgMXlET+AUjxQSH65T502dOR8TJkRM0pgE8tHKiRdLct72trdNOl0ME0zAkZm60FIXTaCZOL/4xS+ewN/tbncbPfWpTx297nWvK4YQJucYNKiDj3zkIyPqSIE4gQkyOmDyK3ngk/Cwhz1sFx7h/ZBDDikfbpaZsUwG4w7H5LpeCoJxyHYrYwUTb2gTMHoxMGAijBEAoxRBx3xjKFD7oy0cccQRo6c85SlFTia/4HL96yNYkOwM/pHWs/OG7qUDivDRRd+ue1xztVv83/zN3xRXXYLXI22HSbiOCkZP0jHvF/xCVx8ulvQRMOR1tWkMI2ysSsAQgs4IHKdIOYy86M9PzCAfwxYDCd4jwcMfcpEOn/Ay/sNS2n2+1+BnUAA87VFyiVfSkc91hz4e97jHFXrU3e1ud7vyrKWG17nOdUqbxIioAQbvFe8XuN761rdO0lUP5LFfDvWPYYlAXVAH8A9NBkHwgxECI5CW3+Cei+54l2jr6Jw01zeyjz1CJqe6eZ7aE4Mu+isC8go/PGDoesITnjAae+wUXuj/6A+dT569bUKDOPy35FcfgPxMPNA/hloCG+yiW8ojD67i2kcAA3ZutlcKDWHQwKCBXTTAu9jV//ryHJ9g1CYguyBfUkIXv05SPNZ4raUtid1eaBmn0edrHC0ZWd7p49peyOYAQh999ToH+qUVyXpEBpaU890gjzEwP7P8NFIxI3lTbm83POtyI4zSwJU8zCIshjbGyIwvM8AXY3+WBXPtM/7hxBhAY5qE97jLxrMujdmYH7Hc140rXfjIY4zBj3W+vb5RNnmt9uO65XnsKTLyI9qRx3U8jYeNzG/JBA++JJw51y1ucYuJDtbTHjZSvoFWtwY2nWcLk3F9IJx1n4R0i1TPzZe01Xmo9Atf+MJixNDFBIDOgb8xWGWZGGjADy6MD/wxZZKGNdw7JP7wEvBmUBlNdMDJZF7p3JmwYphh7xbi8pAAB5Njh6XDvu9971vwM9FUHngJRx55ZJn4qFOEN/19Jt/5rD2z3wuBiRcTEZcZXPBKwMNDNErCONz97ncvm2IqnbsMAAwEavQyTbgoi+wYMG55y1uWv+AytIgnwaIPz4NPeavwNxt4Bh1MDAkYIxwefaE3gjxZtAkZcf6+gIM7ceTEQEigoxQ/8hzgA5e6Yb8PyUoeE2eCPDdSDw7rA1b06XkYiqh7rpST4wLFG3dkxuBDYPKcPCYPme+4CpJxwFDk6ehefzD5GCsP3ATumUY6ZdC5aHKXEQrjq/OGPvSOMFFOvj1OvSvwzoo+9yc+8Yml78GbSTwJ1t8j8YQXDQHDgfBgiBu7BhdDjstGGe3dJF0nDff+Ud4nP/nJ0q/IYKd0GaigrzYNPYwKBNqjt2neQ9om8sGHZAA2+cT4RuD0HwZBosmd+pShT3qivAJ1BF3H6fk8g0PtPT2DoIehVP0c8CqffNbS5QWZ8kOTgR3t5AY3uEGRifeWgNHYvaV4/zEMc7HHTPIwEXZ4GDQwaKCpgXzviftJRJe97GV3KbvKiYXec+d7FwY3eQL7svA94kdAzUvEJ6YbLYrqtquO1Sa8b98IPls8kY4Bw3nnJB+H55mL7x4X34989jTlqZzuyOl05pUbfu93v/uVU1M9YCRi3sHR4AeNx9AYjphnMFbhJ1QreF3oHfExAT9UGf/PYmgRLcpgGAKHxiT5va29l+JJRyaDjzFOHiVPutdVS8ZlpNfotuo3j0BHFwpbuT9ahl63Ks5N59nCX9Fa8E1Aa/mzpvkL7B2I8Dz96U8f8dedPL0g3/rWt0ZvfOMbi4u/JvxYuP1lYDLDho/AalKMAYnA5F60mCwxgcNwwwSZZ4wyTOD40+s4VZ4JCktCMuDaSEB3vKR+DBp/5ElDBsnChFSh60UmT8tkMKqoA/Ay/CHWZFOy8VefyT5GD9EWPTwvarRVljw9e4eDdw4eDejgox/96EQe8eKwGDI8Dk42KEXXwJPn+Zxzn/DoTYFJKoYm6hyPJGTDI0IGLSZ22t0d4xR1izeSPFs0qRevTFZlhNKH1jf14m96buapstz1gcGAJEOaeOVD7hNx99CQgRBYtWkZ/UijvXrbk64mihg/iA/SarpnIp8w/LmQ7snzfK8LtVFwuwFMvGJ8QWbeI7xEREueVyzbQR+O33nnWcZDPD+cF57RHV4jySPl8DpSGxGfvEd4Imm5n2hR92yQjbsuxhLxo/fY4Z1X5FPc0zHeYETBBbe2pJK/SuJZHiN40mSbxpChP0j5DhCXnkUbPr2/Ub7aJh49MlhI9mOOOWYXuspzmieffHIx9PE+wSv9Hzrm/aYOPbhOXCbXUcovQ7XwwDseOb7EjKWBeOfQnjDAHHXUUYUnbT4seb1d6ll5axgdIoMGBg3sogF/T31CppOI/D3LwpvhPWNctRn4SN14HL3qG9EFhzGGPpRv3VYMtKVF1YXjEV5vq9IPY3y+S1pmywbEjBO8/DSevI3z3IpDcxquPvXG+JIxDuMuvXN46BA0zkEGDEe0G7YkYPzBd7Dm6aLvK3faj+6MdXTYQR++WjDjvVwmPzjVNlt1rToa72NSlv4qXPOa19wFfZeedwHe4AR4k4xpbKHOWh61Lb1sMPsDuRk1sOmMLa0jybQfyozyrQHXS1q7k6Z0Ct373vcuHaoHJhZM6PAMYZLAn3q59jMhYk8SOrcM+otLuiYL0HrJS15SOnH2DaCcypKGZ428RsQXnf2DH/zgRL8mDn4+qApMsCWbOh6Xy2VOxK4TDAoeV7k999yzFIN3/kgTMEZhiGAiLzju6lxEx2knbo8Djz4I/JnBCp4GKcfFX2zpOT9cmvBp0o0HBTA+iYOOGyxUhmVH1DsTfQxKWgrGpFvwp59+evloyBCDp4NkEQ145zl5c724PKS7HrXzessA5mUFi6HIaQoGQ5MCRh4m404r9eJxPbd0P0G88wG4WhmnpzIsHRG/5KMrPsK8Y0yQWSLDEj3SMYASMO45L0mfOAYCAn9AgNVFWvLhuOThIDhvyy4Xhk48iHhXM3g/INyuT+RLfTMIeeADH1gMqR4SF+Uw7ijw8U55lJe0Uw/qh7xPSlmIyxvE81JP5Lke9Uw//5a3vKX0gejqhBNOKBfvI3v04EmopVFZXu+Ny9clf/KgOP0ES6ZYTsS7jEePvHpYWojBXe3Eabbe25qOhrRBA9tBA/6O+vvm6XjoeeDP+0a/Sy0+s478DznGbIWN5jf5asXR5XjT0Fb2mnTqYT2n0fQi0hNos+hT33P/riOC4kzk+U4RxzjPz1/aiButHEeKLzn9XkvLcuuJMwZgLMqYtGVEwQuG5eRudOEnFhdtJL+9xP1iHD2PR0vKBQ683Rmniqb04995f2apsdO+1rWutQZt6leZSk8elhlX24BG7TmNn8wnMbYgr+BXwfcydbLdcG868zYDbP4Gay8FPCHYl6Dr+ORZKs1fVspl5yFcmU6cAT2TCZYbELRvBHn8qdUkhc6LDlnXQx7ykAmL4OCiDBNbBvp4pmBdZi8WWdAx7DD5FR8gYCKCVb3rmhDa+aCjbV0en5TV5PQ04WNvGPGtO3AaiKiTJE2hhntaXpYRPH+buZh0MvnUhryOr4bb9e35al/gS5rEfamO8m813reFIG8FJosEeUPAH20AeI50JmAQUfmSsDPUaE7L9zLA4ilBmteL1w3P+nvIZFKw0gl3X1ajsl08ApP5tbSUjzLJfy0NGMfvxIraDAAAIABJREFUspEnYyMeJbQB6gnDFsYv7R/iOJKG/n7gtZE8ui4TR+JxXakcaez3IkMLhgTvBzBCCo/XgeT1NJ55/xksydDC/inCp+VCjs8NZ/qDKT5bunddC4Z2TGDpkvc10PfrpJNO2qVt1+jUaACHbBg7+DvFflh6v0888cSyN5LXh+R0XQuv7mwSruCbkadePY7XGZvjctz2hz/84clSNYwu7Jfj8vDssiT9IT5oYNBAfw34H/RhItFfbzVIJmv8EPNvQA2ONF/K1YJZdrrX97SlZcvgpdbelMZdl2gzjmK8Lhh+2miCTBrPuvPcuhzWaQjvomXF4MJ8JA0RSUdGF+YdGDDYoB6PFcYR+Q3UN5ixvx+5nDhnjeNB7l66/v3Xt1e8MPZj9YACxiR+nmTdLUuvs8gmHqbx4nuSpmEaepJ9FtoD7ObRwKYztqAaTtNgKQ4vHx2xTqNYtNq6Bs/5cnuHowms3ArJYx8GAhMG/rQ7vG9W5el6xm2eyT8bXTLw1x4XeLwAo43kmHRhfMKLgotJfl5MJHwJDBZrBdHDcOJBsiYc6dqLhBNYiOfkDcMDQZ4mokGay+rxGp2EdzzkYcQ49dRTi24wZHAiSfLjePWcd+H1Dan4YDivyOjuiVqKxccW+kzK+AhR/ywB0+RNG6/SbnU6DsusWnrwdJ49ZJ7HWXZBwMhQg0MeLlw8c5lQ6kMeOKTTllS/xGVg88mpPEPIF+3E6fEuuRxH4vJyLiN1gGGPwMdWy2aYoDsOL+N05EqbS98EjyGSC92lHDVZHIYy8nZigIOR1fmg3SgkrjXEdkYwbCicccYZa5Y4ueeMaDDAU9+RnjXAYJiiXfj+JOAnz+tYy4RYEun9TPY77gXl/LvMLmemK87SHgwv7HHE+0TAs0V7DnThcP7pRyW/t1PxxgCGuvUlqeKBQSlLDdkoW/VEn559g+i5vMPzoIFBA/+rAb2v/t6S6z8wfDmx6y4nS6vSq59oAg9sxLnZgwwuGJ3zT7nzfvHFF28aUfju1JaWbRSDbvCAZteEmEMMBINRgLmJl+e5ZXQRnPJ9At5FcxF6kOGnDy7eSzz38ewnsIyYsb+MLj5O4AfFogM4RaP23Yce6b6PJWn8NMnges16TtiNinu9d9F0T+bsR7vKDXmbVwOb0tgideUxXstQY77QTqM2aCANjxYCJ9YQ55JRg70mtG8E6Vhg5eVAGcEzkGcTSAYgpGmSy8SDP+IEDAyks3eMAktYhEOdEq58/BkWLp90aCNalWedI51nBuHkTtBdS46YvNUmoDp2DY8clRFux5l4u+irnMMgE15FuEQS2MOFJRbJdxdt8gTPHyCfmPpHBBh5//BHwOuT5QUEjpYlsGREONkrhsBfAeqcsr6somTuDKkb6YfsWp6nMZgS7xgllUdZ4je84Q3LhVux/+nSCS+Oi31ACP7ng41RCezAn0HH/5LueASXvAuuBZ9/Mmp6SNx6/zg5iE2nCWxCrLLJg6fzB0RBXl/iDS8L9MZO8K7TSYHxQ01mlXd3c5YbAqt2pf1aBKu8mryioQ8uda09WISP91rBeVL79HdDfQuGU4xNMpyKtjykhIc2TYCGjrV2GvRd4E/PO8lW48vT0DNeMdwdL3zq+HJkV9tQWfVvXiZpSX4McQ7HM55p1C/yY0Th5CiMK+Jb8Hij4SlFYFCndME5TT0P90ED210D/p5M04W+X8D1nYBMw7nI/NxcdBFLJRbJXwsXE2v2/tt7fNJMK6xyk1zxRFshpF5z74qWDItOz4m541cePyFkJCTNvb01mZ92B6/DLFqOReFjCRH7R97znvcsKPnm8wPRPYIXvY8mdBx/jsnJp90wJvEfUYzpcp+3rvpclI764HE+psH76WzyuJ9WZsjfOhrY1MaWVaqRTUiZcDMo152/4RyZKq8HjjrWAEOD/NNOO61MQrgwgLBswSey+lPKRILBPqcd+d9TJhR4uBCYeIAfAwyGGQITYYwx4KfTYXJNGvlMpvUR04SUdC7gmdzgaZF/xVPPwsGdSZ72h2AfDTpd6GLgYd8F6ULyqyw4/aMuPSUt4l6mlq804LBgY2ghoH+fMDuc6PldtEijc5ZRiw8Kz/rzjUeCjndWHQiPlliIBz/9RvWsPHk2iG6XbJ6X+lBcPMC7jnOmPmlHGMJoCxgACaRh6AFWaXiMMcmlLdDmaHs6/tZP+NGxxLQT6tvh3XBYk6el99SBJvTsxSNvA5dbeFTO8/jDxGAdLxI+Spzik6fGJG8qD5yOv1Z7lu44lYaA11T+2azJlfXCe4qhhoAxTsuc0DntRnns9UQeoSazaKkekJF3l8CddsU7rfaG14vKsMSOQP1zBCT1TLumL+Jdpczhhx9eYLRZLukYdoGFL8ppMgTfeImAn3ZAH8jyInhgXTGhJUPJtCA4jh7lHcH4gzzQJI960BHe4JeHjf5aYfyBF18Gmfhp0wSXH76JIydyIT9GSHSCRxQGF7ntwgdtHo8vYPnLJ75Z9kC7wJiqNJd9rbRDbNDAoAHXgG/cj8v/KkJOfjLuPPmPCpZvErbK+85mp/vuu29VxZdeemk1fRWJ+oaIdpdHzqL5y7qXEQQ6LYOIvGqBYe9Axkg1PImjhW/RMi0Kn9o5RhcOdGDzeH68fvrTnx4x3sAo0voOr4cHxiD6OQQP/p0lnZBeq+nVovrw+hRPWVfr4XXess5fC8dW8KRr8T6k1zUwGFtML/4iMgHnqDQ+WLqYgGvCwoBbe3XQIcgCzF4D7B3BxYSItZBMUOUeD04mLOw9weSLCQRpTCy4+KPK5IpJkTYLBj8TQeDJY3Kw53hjWgYDdISkQUvLS4DHAKPJHRMNlgBo4042Fp0l0Lky8YAOGxWzMRgTd01eyPdlOcJ90UUXTch0dXJdec6n4JiISTaW7uCtM0/g2GTVC5M/6oy6oA4JyCqdCr+OgFbcNxtmIia+yNexyjz3lVF49aGpDe5IY5mFPvwYTGg7eHfQ1qgrHbENLHtPuJzwzJ8vyYkxBjlFk7pkGQwBHdB28IhgeYVO/hGfedcHzu+CcR3An9oUOoeHaTqSLjAgsVGuwjHjE3BcT6kzxXXnXZHuaEsYSdAdAyfeO+nOZUu5Um7pTkYwJvK8+7zPvK+0C07RAj/vEXlaepi4FKceVG8YJuCTv5bUG/2PTgoiTh6BdqANg2kL1DP1LWPtq171qokhCfxKp2+jbeNiTjvWckbecWiCF09DGSE5aUvGkJaeUi7VL+XgnwBulumRxh29EZ75zGdOirPEUnoAvjWJoAB6oL8j1ORHZ8jHJS85DC78VeKkKy7tC4Sh1b3aJgwND4MGBg10aiD7YIB9A1f1V+oTan1/La2T6IIzfRNZXwK1YDJLQ9dlcPFDFJbGQA/EGCwUfM+KVtFltAl920WziwY/a5xPvFvwzukaf7Rk2QrpkosfDXiQMM/hW7mIE4ha8tNPuMFFRhZ44XRNlukr0Mb5Rvv4jDyvw6zfFt1VpsMjYw8F/ajOsavya/3rKvkfaE/XwKY7jWg6y4uD0EtIw+WZuyaZUCHOJEBrNUlj4K/NVZ0TOmCssp/61KfKRTkmbWzsy8CeAb3+5uvoZYwueNBwnCtHLLMUCfpMKNwbBt7o7JhU8Vf8c5/73GTfAXBilPGjg4FnIsXkmAke/BD4U86kjz/6eKa0OiHpQvIxMGK5CfgwrChgcME1X5MuyokOd+eJOPmksQmXltwIl8oqzp0PG/rw+hEc+7fIE4Fj6/AgUt3VcOGRRHCeqBc2uWV96rnnnjvx+MGThzqgnrNTowwTVOoNA4XT4pnJtk4y0EdZMEx64ZF6IM0veNMxutSd400egOUDQ93y0WNJB4NB+Mb45JZ+8NB2TjnllCIn7UcDR8F7exZd4FkKw+QUIwT6O/TQQ4v+/B0B3o0zzndR+DhIXuRXPjzhmaL9g2S0qsEKj9+Rk9NrkCHfx+SBuLch2iv7g+DNIa8Q6NImtYmtaEnWNCaCU/WpdgANlqCgL95V/sCwZw5tSXvnoE9t7EY52jiGBPAl3+CjHjCssXwLLy7KYMRTO8ZokX8qaUfIQZ8CL7RHaNAXIYfaE/Qe//jHT5Ypkq5JEDrFVVf9GbDoDfkw4PAsfrlLT8ijoPysU+hwihvvHPUPDdolnmrsmwJ86tv7T/BL/8ild1v0kB/+W/KLP2TgPf7ud79b+lQMSBjBOHUMPtSvAQ/PHKXpBjK1qYnAw8OggUEDazTAO6IrVZP9XeZvdNz5gWd+ZtE/EOhHWydlbjSfs9BjMooXEWNMBbxH8NTJ02a68C6irvTd0R16vn+MnwDl35YuvhaZJ5qtfl188w1mrK+AUZ9vsvKn4Vkkz8vE5fWlZ8adfFsZbzP+XEa47GUvW9B6O+GZPdx0iij5LOnqc9zzMnhcL07aiLcznplbDGH31cAfjSt57c6cu6msiClRZTVt3QXnZaapZZFqnAeXfwzz2T9cPPuFXA5P3OXW8yy6cJx96NVoqm6U53eeFVw2T89n1+ks+p0FNvVY461WF/Bag3UZ+tZJjd9MSz5TV13x9ZRNPmp0+uJnwMMAGC8tvC0oN68O4aMPbzV+Vc7Lt55dtuQ15a7hmMZj4sh4Tc4anZqcyS8wtbRaWaXVdFXjqQuH0024mrwJ04pn3RDXpU0N2b8FrxqMMnhCKb+Lpxa9IX3QwO6kAd5tv3JsRR4/SBQwQDNZ4h3yTUP9vVvP+9yl2+SVeC5dII0/7L6pOYZdDBQ1njd7H8CPPD+oAYO5fiL6N6Cmt6yHjNfKeF8vfWebYPmoe8rivZltotY2avTWk9a3PbieMLa4AQtPWfZWzPbbR1fr4X1ZZV0n1BsXHuRceuZnkp9muQhe+IHFzxZt6uv6YxWAG7kOO+ywcjxyTee1drOqupAu0Y/3M9nnZH/DzycMTy6LnsG1KnkWUc/bEce2MqXROGn4tXs2XsEJdrM3Dn/xas+1DsllVhnJTZ4/u/z+0Um9CI/fnbbgnccanRo/Scv5r+V5mtObBjtvftLweJdeUo7EM0udZNmaLH1gauVqaX1wqb30ga3R8DQ+9niNYGhhGdL97ne/NUWgUaPTR4fTaJOfbb9Gq4XHYfXcKt9Kb+EmPctkvKvstLwav7PinxW+xlMXjq68Gq5aGjjyEhxecLj6bsU/3DVZh7RBA4vQQPaJime60/JjnxfBwzw4eM+7eGSTXCb/2viciaW8Rv17Mg/tjS6Dxx6TU52SiEdJrZ5cH+pPXVbX2Sz9bY2WbyiPPrRP2Cx4F6VHp5l1K95d9j3Hy/jx9tZyD7x0r3SlKxVjnIfEtSh+NwpPrd5Emz3fFm1skWctdDFGYFwgMN5j+ZDqCY+t9H4FjnzB6HkV7SnrR23H21DC5KbcePJgbNnqbSjl3K7xbWVs8Ur2F1Lp+VJ6JzutgXS9RNPKZn5fXMkveFpppOdVg6+VT/5acZWt3UXby3bRopNF/3614Fvpyecs9amyLdzCVdNhTUaX3/WTukq6s3a0zlfiSn3MGq/popaWeIERXzX+avryNMqwkR77tPifRpYBacmV9NvSZ/I0b9xlEY4+deR6Sp1l3PHy7DJ16a+Fp0u/Lk9NNqefz8mb0/Fn57mmqz50W7gXle46bumRP278bXJ335o8i+JpwDNoYDtpgPdO17Llzj4n46LPfmUyCnAAAXtwqX/wfq3VZyxbjr744RWDC8tvWV7pp77wA4OlqMjJPnuXjL1g/nP8vWUyTRmMCGlIcH21ZIemruSTdJ3gSR5LtjSxFmwLb+JabzzrPus349CDf4zuLEdWOP2TnxwdMfZ+0jIY0oV7o2RZry5q5dXOvb0Dd/nLX74Gvq407VsiWhhcWD7N0mvpEAMom+wTz0s6T31nfF1MrrOwtwnXaR8eBd8Hdp1sDsUXrIFtY2xRA9fLyJ2G6422NsHPDsb1n3mLfAHWi0vl/c6zX66L9barpCfcSb9Gp4+swKS+nf9pOFTXNRw1nrrquaa3pJ/66BtPXlQu22rCKd7SUws+052e42zB1XSRsK771FPCtuKcZCFDy5FHHllOFGKvDvDllTz10d0seksdedmk1ZJX6a18l0FtNnG3dNWF08sIr8NPK1uD7VvG24H4qPHQkkvp0+hNgxPNGhy4hV/PurM5YIt237qZJtuQP2hgd9RAnq4hg2XrfdooHUA/+wOnzabd5513XknCGMGpZVq6sFE8roeOy8YzRhP2nsLgwkbtLOX5zGc+s8sRzNBk81oulk8Srj7e60/7jIkn6a9PPYoX7kyi/fhe9l5TP5vyttITbr1xl8H781b7YH8y9iw866yzCmkMVGeeeWbZ14y9TRRm0dF6ZVhGeekl6xgvFPbZWdTm0bRNcLru6TdYtqvA0eDoPA1z5Hs70XPyvAz9DDgHDfTRwLYxtkgZ6vj0cnYpyWEdTp1vvsitTrmLxiLzkh/FuzqhLNPFj/TRKuP0XN+19Bod4fc8pXkHnPmJy/nLuso6yji4snxL3i66jiflb8UTX5+49OO668tvF/4uHJ7XBSf8ziNpNZ1P40V/GLXzvPQL7ryU14Wzlpd81mCcf8mutun3VlnnTeW7dJi4nX7SSDwZd/h8n/rUSfLr+KfRktxON3lIefrGu2jXcLTga/K1YGt4h7RBA9tdA+pHsj/hWHcP6TK/ke8ZtJw/xWvpbJLNBI8TZwg//elPy8l8tb6rlrbK9pB1QZwLb1D2RznnnHPWnOwyjVeWzXCxP4k8Cyjj+iOede9pyrvkkkvWkGOi7SH74jWZS4jU6l58Z9t0+f7qr/5qdMMb3nBiOGJ5FkYkTlhU2GztYhb1Zd1mHbGHGcamNKbOQgNY+gOWBqmNii4b1ztu9koDlvy8wKO05HNWfhYJn21LfKpdeHvyZYsYsTDsDmH30MC2MrZ4o88ONKszOxl/IVplax+ZxLuseIsnpasT8rhe+hZPqS//aLT04XxMo6X8mt5Eu9YhJb9OsyvP+Qcu41l2mn4SPvno0oXjznKJV7CpJ+nI75KrhsPT+tDswlGTrQveeU85WuUcrrbGHx5ql/D1kVG6m7WM4L19it40+WbRXeLsarN95K3J2YWz1mZm4Z/yDj9PO2i1j1r6LDqolXd+wSV8el4E/hbdIX3QwFbVwLQ+rybXZnqX4CX7QcnE6SuceEjgdCKdMKn8WtmavBuZJt509804WTb0yfGSFxmQZuWL/UmYCOoUOMp36SB5AZ49OBQ4tdGXZnqf67xtRHtJGh7P+oY30mgf6EP7t3C6HeXYhJggHNm+XLat+ozhA2OTTlicVw50pf1J0BNtlHbGqVnSH8YYPyHQaQHj7cbj8/K0inLsW5R7GcHH7th2VqHfVdLcVsYWKZoXsdV4lef5LVivuD4wy6xodTRJwzsg8rJDSvha3PWFnI5H8E6/9tzir4VL+hQ93Wv81XAkXNZPxhN+nnhNxtR/6qtWpot21oXHKbcMuVr6Td4zLn68fBd/WcfCl+mOD5i8Un/Jl3QmuKST8IlPcZfFeazxmzhEow+tFp3E6Xqp5bXS+vCbuJ3vPjJQPuXoW67F96LTWzKR3pW3aD4GfIMGtqIG+vYjKVutH6ilZbn1xkVjGt94ccjYgoGCE1F08gw8eL+2Xp4WWV5yydCiOx4t8xpaxB9Li65ylauMOD1GAX2KJndd0pHuv/3tbyeGCdKufe1rL1LsuXE5/y6T+G61F9IPPPDAsvmw9HruuecWFDK48Cz8G9G251ZCR0H4Fu965s6+Pnh70SbY1HWWgLFmr732GrFcT4F2iqEF7yctF8Igd61rXauAOO0aT05/K+haPGY/lPFZ9DrAbj4NbDtjCw1bH8f8SCpPLzT3hKlVYR+YWrlFpnV1Kspr3bv46NJXlksePJ55WVbxGj3Xr3dANZy1tKyfjLd4mSe9Rr+lhxrsLDRdV5RbplxeP87jNBk8v8ZfV30qT3ImD8LNXRcw03hyPF30k57DOh2Xqyaj6ytxTuN3HtzTcDo/fflNnKnjjCeNlr5qutkMaS6PtzPxlvJmfDPIMPAwaGCzamAzvS/won4w+3j0x9JVJns6dhbDy57j02gol3LM0p8uq25chjS0cHrMD3/4w4WQ/upXvzq6y13uMmLvkq4gfnTH80OBJVpsxEtwfaZeu/AvOi9pt+qUdOXRRlia5R5DW93g4u+FdKI60v6WxHlmmTdLwc4///wRS6n6BC3BwqNF+GmvX/7yl9cYWjDEsDm92ojjFj+1ex8eVgkj/Xbx0Gp7XWWGvM2pgW1nbNELq0Zca8z+EqgTaFVfrXwLdiPSW/x6up5bsC0+W51D4pkWb+EnvaZPp5u4VZ9dOL1MDX9X2fXkJa/T4n1ppT6QqY8e+uIXXJeuUhaVyXTx5vzV0pw3l6dLNmiJXj5Pk1U6VHnnqaXLlA241FHiqcG0dFXj2flz+jU6Nf5qOD1tljIJm/EWreQ7yy1Klhb99aaL3+R7vXiH8oMGdkcN1N5nNmLdTIF32flUXO94ysCfexlb8F7gGFo8XhJu1X2E+OGuSwYX9s35xje+sbBqQA/f+973yqa5uWGp9On8QJgNhqVH4uxt4vuwkeb9Lc8bqVNvF9km4K1W36QBi5fG7mpwUb0gJ3XNMh89Iz9pGE3w5MHbiROEWALEwQYe2ACXfUlYDsSz2g04aJ9f+9rXyilVqnNOqTrooIPWtAG1ibx728nnNUzsJhG1u91EnN1ejG1pbNGLWGus2cFm55otQp2Cp3eVqcEnzkXHnWZf+i24Vrp47srPvJqeBJN100qfVVfOQxf9WfH2gU/5++hMMDV9iP9pMvXhrQZT47dvWsrmuk4cWQ/ku2yZD27hmHZ3uVyHXfp0/DW9eJrod9VFypvxaTTET5cOWzjmodXCpfQWTk+v1VlNDqVNo7nqfJct29yqeRvoDxrY7Bpg4qXABEqh1ZesQh54afXjbCpb825JQwF857dlo2WBfl4YXDjeeb3Lh1IWvGQ4Aln0NHmGnoJ0yl3eHuTh1eLLkEhr9a0b3U7UFvKblny4bPCPweVOd7rT6PTTT1+zpOj3v/990ZNOKWq1tYnSVvyQ/BH3y41r0gEs8yxDitqElhZpY1vVs/ARZxPcM844oyxDko7pJ9iMWXFoJh81XIJfsQqr5F2vVYCORPS5mWXrYH3IGmtg2xpb9JKqo1Ajzgbd1bi9k/HW1FVmva1uUbin4XHZHDb105JnGn7pn3tNj5RvpXvZFv0+6X147INnXphZ6GcdLEoHs/I+C8+Ou6tcq667eHN8PGe8VlYw2YazD6iVVVpLjpoMCdt6p7roKS95Jr3r/ajhTH5qMF1ps5Tvgq3pKul2lU/YeeM1/U3DtRF8TeNhM+X30eGgs81UYxvHC21Dl1PNk4g2jqNdKXnbVB/batPp3cLeJ3h1JPyq2rvz4brnGeMHxzgvI7AHyx577FG+wTUeoEk6m8imV4tP2qW3VelPupEcyUfGXZfSN2l4eKTBhf1MMHbd7na321LHQnud8JyX6k/p8qJyfWBUI+76k+EEfeEF8/nPf77kK12GFjeweP0kHx4Hzml5Pa3yGZ6kB39PpvGUupsGP+RvTg38n83J1sZxlS+lXto+HHS98MvK68NXF0xf+VIvwtmV7jJ38TBPXurTcWTevPF5+JqnTN86aOHuKt8lewvftPRWvXbR6psn2kljWnnKZRml9ZHHYcDTJ0yDq/GcdATTh16WzTJ9cbXgavwuIy35Jj6NTq3MotNqPHTRAH47BQ2Yu+6pDx9kK8/LJ/wQ39oamGXSME3SjX6/kp7i6heSX7xbfDNXvDo4OaSmg1pa4ltG3N8/PWsC3HcvjVn5QgcsK4FeTrbFA/nsxaHAUhK8WrwPJi/rIOtoVt7mha/RTV4dt8MjswwuGBoU0P/7P/CB0W9+85tJm1E7WVV76dKPyyTZMX74RbrieO3k5XlZDto/+clPdjG03OAGNygeLfICAi5173E9Sxbnu0u+IW/QwEZqYNsbW/Qip9LzBc78ZcVrncgi05bB97I6N63vxfLdCoukPU3PLR6mpfM3h4s1qYsMy2ijyWtLv630WeVLnVM+5ZoWn5VmX3jqS/roWybhkvfM79PGvUyNp1raNLyt+sv68Hjy3jfeopXlu2gvK6/GQ6YRn6bPWpmtkKbJkO78fWY/Cv5WdwXggQGWMipPX807Q57jJA0dKq0L95C3dTWwGSeNfbSpPsr7quz7kI09RnwCjQFBhgZ/l6C5Sl2IFxk/uLMXxjICRgToaekQd9GVHtg41ZcwcWw0k28PtTogv+/3Yxmy9f3uiE/VOXcMLne9611HvmzuP3/3u9EHxgaXCy64YNIXeplVtpmW/mo6SMNJzeBCGgYTh9Uzy4a++MUvjr7zne+U+iWdOxvhcrS6L7eq0c93c9XtpKW7aeldbXvWk52m0RryV6uBwdiyWv1vWuob1ek7HQ0QdP/EJz5RNqF7zGMes9KBy3oriWMiuX75y18WVBul23n4Rt9c4nUeHOsp4x+f/BDpA7se/LOURQfSx7Lqzdt4F29qM/B0zWtes1x6T0jjWEQupf3D+N0B5rGPfWzv9pb67uJnu+VRT7zD9EW1sJnf6eTX+1nPI/24444bXf3qVx99/OMfXzMZ8DKSlaNO0QlllM8zaeQpDZ0Jp+htJX2l/ob4dA3MWr/qe1bRByXt1ndG6ey9oYBXB8uJ1NZX1b5d3/muKu8KV7jC9IqbA4LThERTBhfQKO1Xv/rV6Pvf//4EM95B8CJ9ur6zLuZgZ2FFnJfktRaHMOnSN3fit7rVrUbXve511/DFRsWf/vSnR78bG1+8vqS3hQmxTkTSgWQjLsNIGlE8L+FcX//8z/88+uhHPzrZCFf4bnOb24z2jFO+EqfjyWfxuE6RN01xjHVD2H00sK33bJlWjf4Bmwa7qHwz/ju6AAAgAElEQVTR9E5uvbjXKwd/Jb/+9a+PDjvssBGutBn0Ucn0rrjzxPOnPvWp0Z//+Z9Pjnij7P8bp3uo0VmGvpJmlxx98u51r3sVMNedyz9vXc9SrwmrdbKsLRZf8HHPe95zDa81nQOwbL330es8MKkHl0X4BNOCnbW+Wnig5228Bpcycsym6ij5ddirjV20gTvggANKsuqR3f4JN7/5zTfsj2GXXO9973vLO8/mk5s54PLu+pROuas9dMm5SNlmbX/Oq/NR49fTavlenuM+pROfZAGTf7ZVTu1QPM0jyyJ1OeDa3hqg/Xk793c526bgOLLWN8tlOdFVrnKVsixGMF14lqVxaOel94x0jCLL8G65/OUvX+jyziO3LtLwYPDlQ3gFsfdNBi8n3aX+s8xGx52fFm/ITF7eb3zjG484dpuNcuXhzE8Svn+ctoMxGm8OL4d8LTqrlB3aGEjgVTyq/ydNeTwLhjvejpxexXzC6xvPH8YAf/qnfzpJF41sF634ZtLVRtfNQG9raGAwtozrSR2Cqizjq6jKVfDQovnOd75zdPzxx49++tOfjpjsKfiHQGWnfRychnfEd77zncvAHffSSdjZmRMHVh+xLvpZV9P4SXjFW7pw+D4wwL/nPe+ZFPMy4q0vnhavtfQaTk973/veV7weLrzwwomxhXzntaaL1GeNTo0f0rJsC27WdHiYVZfJdy3uaXruaoPJdw2nw5T8aOOJI+Ps9P+ud72rJAt/jU/eI67k9y53ucvokEMOKcYWynmdZDxp942n3CmzaGLwu+9971vaHJOXRdGfl8/k2+MMBtGZ6z3bXKt9J96+/Dlc1lMrL3WdtGptRTCehxGwi2/y0AkXz4Jdg2Pn5KvGU0tXye8QHzSwERrI9yvf7WzD9K14bGhpDBt8HnnkkWWjWA/g4Z1YRXvXe6n7X/zFX4zYqHXR4XKXu9wufQU0mYBjaPHlQxj///iP/3jNZFu6qemolrZo/rvwef1539bFl+rb7yy1xNjAsqJvfvObI7w7FFhOc/a3vjU6+Na3Hv3lX/7lJF20ldBFs0uG9ea5DhIXhhXqWd4pMrLL8IYOWA5Du8O7CVzAEnjm+7Hn2JtFccno3jOkta7kZ4gPGtisGtjWy4j0EVLlZPy//uu/JgNJ5U2706nOWg54X+M+jQbr5IHpE6bh6kP7rLPOKqSm4WLyNE12x6Nn5KnhL4k7A7TVkaPjGrzzp3JeH4au+SgcTldpyObyOYzvWzBNT5k/S91n2VocHqmLWp7SvvrVrxb20an02gVPHoE79TWtnoVLOuJvjsup9LyLjmh5vnCq/SfsNP49H7zIUKs314foqyx5Tr+LJmWRuUtXiR98Lb7Iy7oi7iHznT/2zNARrMl38rEGac+IcAq8plvyBPejH/1oF8yJI/lKvp3GLsgaCUlD77XzBozHMy/56Mqv5XXVcRduyeuiJXzK18r3dLWbCd6x/LVy2V94e/O2mO0QvPLicv54HsLurwH36lyGd8V6NJiTWE3swFmb5Cn9Dne4wxqyn/zkJ4thId8bgFbVzkWXe80reT16oyxeLego+wHosR+HGxXw7sDgk6Gm46yTLLORcfHS4rOWDn+kqy2w1OyGN7xh+Vl58MEHj25xi1usEYG9XDgumqVF2kDX+0mAM75KHUg+ZJRhxPUggwpjJZaU/uAHP1gDh5cVP37w6HEc4O1jaJHsXjcbqY9V0VpVP7IqeXcnutvW2OKN1jsxBsH88b/iFa9Y3Nr4k/z85z+/TJqOOuqoYpVnoqoy3BkA//3f/33Ju/KVr7ymXE60wAXcaWP3wfPOO6/ghA4fLZ7BnWWgQTq7dFOWfSS4s8YxeQFWO4LT0cEX5cAtnpHlta997ei2t73thDYwr3vd69ZMJHFxvMxlLjOSsYU9IIhrsskHFtnBAz9Y5TnWkQ2uwJ/B9cwz5fmzzV4TBDYOozyXBvWkS8fAQh+3Q2jWZAcW/shHD87Ta17zml0GQuIpeUN2yquepCvqiuef//znhUfk5CMKT9wpl/UHz6430aIseqfuaWfUFfpUO4O2YJWGbKpTdK58aHqd0g7hR3WqwRDlWQv6/ve/v+ic9dPo+7TTTivykMcFnA+gaKve/vRuQDPldV6pI/QFDZaJwTsf3tS76191jrspMqIb9KL6Vzt83OMetwtt4Lmm6QmcuDRTb9QNuoIvlxmeFHhnoI9uRJ94zVDmtFmbDh2OfMSbCIOH2jb4eVZgic+hhx5aBsW4pdN2qT/pBlh0gu65nFfhEE7aITD3u9/9SnnuWrNP3atNqt/S3i6SWTSpWwY/XFqC5LQEp3LU933uc5/Sf9IGqQtokO5yk86JAwT0yLtGuS7c5Hk71/tNf0O/it6Tnxo+0oBT25Q+abe0c+REP95nqi+EV3ggHxnUB2W7IV39sPLEO20PmtIPdNIwhSzg593hz6C+SbQJ5OV9nWa8lC6QF34op/eDO22S5ZvJO/CqK93hAY8o3mPaKOnwoDaWbVG0SZ+Esc7VTlQHfv9fwOFpd9OAe8S6p8NmktMni/ClePKoCR7vAu+QAnKx15EMLqT7O+htP3EuOl6jBb9MbhcZ9tprr8k77X0vXgzuRUNfp3Ge67lrsqy8RfI7Ly7nM/lvxaFFHgYn9vZh3xbpCE/Oe9zjHhOvDvHF0iK+z9rTxHWqOvW0eeWZt5x4kMyJh3Q2jKb+P/ShD5VvKUvJBM9YCA9RviF4RElHuvcxtLi+vXzyMsQHDWwmDWxLY4t/iLIDu/3tbz9iUs4fYCYne++99+i5z31ucf/DbZTABE2DUCbd17ve9UoZ4OlAuShPOf5+MFAVPBM1wjfGe6Dceuw2SMfK8hnK8sz6zbPPPnvNpIEJF+l8vNhsC3hoYgRhwsJEwwfHamBPe9rTCl/66KmTfsYznlEG79/97ncn/AJDGvuyCBdr8pFFAdrE6TCBYXKKLuADfuALGHCxKSIThfww5MeDjlebh6GDu9/97uUSHLSZAPFRxxgDfuCgiU6YBIlfytC5o+MzzzyzypNP6CaCjR+SL/2FPeGFLyz1BD1oE3hGTy94wQuKzviYIgNyH3300aMnPvGJ1fpwPlWWMmpn1BV6RN4Sdk5OvL2yhCTrFLwnnnhi4QXeqKNHPepRhR/S9t9//yIfcNSd1yk6JO7rzf9A+g8TI8owIdt3330n7Uh1QBsHP2086xkcD3nIQ0r7pC60z8jJJ59c1mzn5DL17/UPHeqN9wM9y2iAHmh/buz5g+JGZalN6gmc8ArPer+lC9bdI6Mmn8jter/b3e5W2h/01V7hZ8+xC6y/3zzvt99+hTbln/3sZxd46oW28fKXv3xSF06DOlc7o04I8IhBifdfsNwV1J5qaeJd9Y67rvB6P/WgBz2ooIPfmuEIl2cCZXzJiOtGdUVfSH2jF/rN5zznOaUcuKHNuyl+1IeBW30HLubC1bp/4IMfLH2L2hQ0aOfHj5c5sjErbUHBcWQaxge1TfJoB/BNGwBnwkvH4MSIwvtFoIzoeH0gMwFcyve2Bz0u9ft674WDMgTaDeXU1oFDXow+vF8KNX2RRzoGUWSlHHqHJ95h9WMyMno7cnzolL2dgKceffNc0Rffzg9pkqPG54T54WHQwIo1oMm02GhNoB2Oto3hFEO9Qs3gQp7eg3wfliV2yiM617/+9YvBdBGB/hbjb/Y9fMt8Q1yOeab/SZ766HgRfC4Kh/jv4jvz6AMZz/PtlLeH9MWPG755LKFHRx5+/etfj9iUnR9uLN9nXJF6Bj7TFtm+Ejf0kK9Gg3R+JFHvGOG5u5GFfN6Tww8/vIyZ+IZKV+B175iap0zq1etijeKGyKCBTaqBbWdsUUeRHQlxOggGlIRXvvKVZUMrJl8M7C+55JJJHrAaXGK40MSNjwz7m3DhIi+jAOt5RU8DWgbA/NGkUwUeWo985CMLbf6ACz8TICaHBCZu8AP8ueeeO3rHO95R0jHq1I7VZCJAGXihDDiZEMnrhE5c/P7bv/1bmRQy6UQOYG9605tOaEDnDW94Q4lr93n4JzBw/9Z4zSkT3M985jPFfRTZ0Zv+/Nb0TdrDH/7w0VOf+tSCB+PM29/+9nL54B2eyPvFL34xwlUX48YrXvGKUuYtb3nLRLd8kOjMCfCE62byhE6QL/mhjKeNIwUPExTVE7pHTwR4et7znld4veiii0p9fOELXyh5p5566lpcJfUPARoY7ShLeNaznlXKg/vb3/52+dsNTYJ0wJ1yBOpadUrbII+/4MKnun7pS19aeGVyRPvURJe6g2cF6pQ4da22SZ7aOBOt+9///gWcySH44ZX2SltQG0/PBeB5l+CVvzrvfve7y8771AuBNi6ZSsI4ZJ14PoZB6HGHtuof2TAIeHsBV01P8AhP8Kx6Ayd1iYGPcMwxx6wxnpTEcWBjt7e97W1l4MRFefAk/Te96U0ljXeJNoHBk/cBGgQMoxo4ZRunrjgSEndijFHHHntsKfP4xz++GEK8HZCuOvJ6E4zrjjTeM+qawLuk957BN3IQvvSlL01wqi54Xwh4p9Tqx9M0+afOcYdGdvpN1RX9jviD/tOf/vSCmzvtg/6viwY6OHrsoUNgnTtl1P+qXX3uc5/rxCH8j3jEIwoe2jTtiX6CO3F9A1y/qU+1F2TKgTB9sfSGqzQyq+1Bk35C+ufdR24Z1qQfr1PaPDxxpx0iOwEabmhs6e71r399gcdIgpzUC+/wxz72sZKOhyR4vH257A984AMLTRlaGAwnrOClp+QFOt42Ha4wMYRBA5tAAzmpg6VM80ki7ZgfQV0GF38XwKe2vxHi+qSUZya52ntqPfQx2CC3gmRiDC1DC/TwfKNvhi7BdelxPQuf+J4Q2CQPqc9W21A6/Tvhale72hpDhbcJPJsxZrO0KI0uLC+i76ff5s4SvNpR496msu+dNy6VZ3lPx7jI2IZvC54sfJ9SJ3hTMS6XZ7TXrbxmSfPnxJFxeHA8m6R5LJ2N7Sjz0pW6QQS2nbHF9ZqdCK57BD4OTE40qGQi+pSnPGVSlEEjF5Nm/nYTOK2Hiazy8ArRJOPJT37yJN07RSbIWg4ALQ3g9ScbXHRiBAa6GCWEHzz8pdTfaiZ/yhOjTKIYWNPRKw/3fgwjDPhFmzye+bNOeOtb3zqBJ09BOLgzwdcEEh75mCqfjzB/mgn+51f6Bi51D6znO13ynvSkJ02MPOThgURgEsAaV9KYBDNpIbBOFJ2Ckzx4ktHKDTTJh/NQEI0DE2TpCs8QeTZwZ3KmdsKaZAI80Dakj51oJnEmhArUqcrz15q6VxDv0hfp1CmeM3iiqJzq9CMf+ciIdie68HzggQcWdLRT4XHdCraLV+mUSRt0gYU2sjNxJ/hEuiSMg9qfPKFoI3iIEeC1pXtP34mqGD9p76LN++kTXteR06btQxecMjJiPFO9kY7OpHdk5f1IfeCRAj0NCFx2f1+POOKIEcZV5AMHAyPu0FCgnQo/9BXgTx5G1N1JJ500yWOjweRJMjsObzMU9jjwCirLXZ4cyOg4mYDLQwN91+pL8BgcMDRhvOI9E6/ctbcBbVDLqMCFbgjOS42Gp2G4goaMg5SlfT34wQ8uuOgnXAYvq3QMzjJo4mHG+0Me/be/f86bdCx80JeRyusTPBitCPQPvNOkyXCGjp132hOGQQJ9GUY28VkSx4G2TxnR9k3EMTRO05n2TcC47rrm3UUPGJnUl7icwJ5wwgmjD469ia5znesU44z3qYKFx+SZPNK4FJxP0ry8P08KDA+DBjZQAz6R0bNP8pIVh68ZXHhfmBz7O1V7TryLiqc8koVlqnhfznu0LMuBGXeCT+807zkG5YlX7lgIvmH0/fKkcV3WnpHbeV6UHhaNRzzWZPA09qnjhwvekm5sEj/eH/LM0iKMLoxtOfEqw89+9rPi7cJYn6OjMeTo6GhgE5/HE1fGu8p6Ht9sfhDzU4j2jWc5vPBDzeuOZzyY8UjGe4clQ9INtHnmW+LP0/SZ5VOGIT5oYDNrYFudRpQfuoxrAM7fXg0SBXOTm9xkUo8aRPrmX6RhgPCgwThGCeHTnYG4JvAqg1eDguDkxs/GYo5fHRu8MkjnQ/ewhz1sDX3+MPtgl0xoMgEg8KeYjVQVtExKhoI1yMYRyU06xiUCcjBY946SdFnoOWmECYaCOm7itY+B8+vPmrRIbp+8wi+TaumHgQA8EbyDVn1oz5AJUzt5EU9+Vz053+x9IG8ItQ/K6IPKM7pl8uZB+lOdcnoBafAoPLjmKgheMpP+6Ec/ejIxEpzqFB1lnapN96lTpwNteOIjSmCAle2V9Dve8Y5lMpYeBeTxoZXeJKOMP/ylcd0B73HXN3lMML09kI9rLrRV1sujJ4cHRrAMNB0/z8imwL4oLL3qan/II9ldt9S5+grSMS4QqBcmrHgGgV8wzrMMWRNGxg8qQz2oPShfZVNv5HueZPU0tTvkYD8XlitRJ0z26WuAxYhAwPiMQcLpJA+0ffiXsYh3UfAXX3yxwIsRUn/5HEcN96TQTnkwqtDGuTAEsb5dbfYnP/lJAae9t3ApnXIKrnPyoSGdE/dLZdQuaGMYazBcYwxRUH9HfyxYGabpg/I7QTner3//938f/fjHP54Y7YUPI7h4547M1An1hbcf70EtqMyrXvWq0QMe8IBSx6TR76g+ZZzN8vDN0kSMLfB2xhlnFLr5DlJOOtJzK03p6gu8v0n6Q3z31gDtyL+Xm0naVvvM9qp3UXd5esizg7/+eOJi1OQ9y/ItOvPqwvHx7Bc4Pc7yH8ZJjIV8HDuNNuPNPXcuAxEsfTGes3gxSkYZWrgnL11x8TmNj1Xn96k7+nzkx7NDfTF8+3MtTt/MJroYUjBq6NvhMpPOpcC4ke/qla50pfJe8R1TEK9evvacfAHDuIUfKdQtxh3/dpKv+uaZ8tDlpyPtXe+3vyfAycjixpY+bUK0nCb4dqfA2FBB85XdSb7tLMu2MrZQ0dmhEOfDz115PhlTGntDKGDd5eJDpeAulbUGpUGq8DFY9oErZTyuZxlAmChqsljD7/wrn5c1aQD32c9+doRrOIP7WpBOMg9cwufLafgAtwKeApIZGOezxrPDCifeAtBtfTTEF39uCRifZFCq8dU10U/6GFaST8HwEU39il5Nh+KTCS2BSV3iZrKuiRcwyhdNPqrOo57nrVOnn3jhl6OhCWwEKFn9Y6d2Tz2nLmSw8IGJNkWTbAX5ziD6eSdbXmMOL4Me7wVLfAhMJHFnRU/Jj8rWjKnQpJ19+MMfLn9uXC+UkwFCvCET7r8EPBlEi37hlFNOKR4WrVDTOUYo17/Kol8MNGrbWUfAuZw1HEpLONKRgwESXjvoEe8TltiQrqUweKnV8EJb6dxp1yx56uqn4AFYruSn9X47Hfpc2qK8rWo6nsarBjQYUsWDeAIfxg2dlKR8h+MZXjEmYmx59atfPfF8ZICsZT7sqwJeGdzArT1yanyTJkOcyyDDCGl69/xUj2ny0q4xnDBop3646GOOO+644hHkhjThwnsIbyoCRiUtHXW+nW7y4PpUGcFIDpenpY8hfffQQJ6EgyHCvwWbRUr/ViVPevfyHfAy9Nd4jLCEWYEfEYwV6OPdw8HfA2Br+JOHVtx58PeKdCa1fJd4VhwYNvnG+wDe8PZjTMfEOgPHWfOtZS88jAfgEy76YcZTlFMaY2UMwHi01GQSnPIcpgaf/GyWOLzW+jDS0QdLavDsacmk/hB5/Fny8V1mLoJnDGMS+u+aoR54vF64PNBnMwfAAOM/k4AhjaD5hegzJ7j00ktH/zw2rrCEqU+ATw5awMACPeHSXXqScQWcagP+rDS/12D78LQ7wPB+DmH30cC2MbZkx0Y8L69WnxSQ7h0F1t3cUPTNb37zmlaRHSwWYgYc4kMbhHoh59EH98CwwZj2zsjmBy0tFfI8/p5nx8dkSnsrMNnXkhTcFlnG8MY3vrGgcF6EU/ryfHBoYlnrJBOX8+P6d7jk2enr2e+JRzzVOvQuXI7Heci6qOkj5VT76eKTPOGuyUVe5tfSMLRoSQ2TqJe97GWlfVKnbFbGRCv5a8mQ6TKqoQ9o68MpOOcv6831qfdBMDV+vLzKig4fnkyTsQMY8Zb8K+533sPUa/KVembgKBrSQU121iZrksokl8EWxh0mFtQRHhjSpeuAjRZThuSdfOdT9DPN6yh1JpqiRT7wLBPCSMJSIJ4xGmhJjYzPyY9wgQODAn2Qwkte8pIyQKcP4s8ppw8QpFe1B8ehsq22wj4wbFRMoJ2zvIZ2zslN7JOE4cPxOQ2li4bu0o906Ppq6U7pDC7lBXP++eeXev7KV75SUNNfyxPM2wnLOt17TXyIVxkos22pnpxvnr1NpN4kExM89oPCkIOnIfWMMemF482/udi/xT1zwEsbltEX7xbc21tG9dSZeOQuHpxvtU9vp1lXjmN43noa8Lrtw73ad7bzPmUXDdNql602mrxz6g59H99lBYwZl4yXFB0yNhSnxyswLZrzyObvF8+6tAQQft2rgO8oRhfS8VKh76c/Z+LMd4/+QzjEK/0OXoT0e+TJAIOhhb5PZQTvfOSzYOaRddVlXNfOC57fjL8wamQfCJy3GS9Xg4UG4wP2dKGfZu6Atyj9edfJXhhouNIIswid0b753vP91TgM3nWJhuREBoLf8znbRa3MIngfcAwaWIUGto2xJZXrHUN2ECzzYKKkQL67d+G6zSRCe3cAxzpL/b1RJ6HyijudpAksaa1AHn/tE7fDZ3niPmgHlkkQgYE06z/pxAnAyi2xxhswPlGSixuDdk30a50laTW+RCNpJWxhbid/es67cPCHhsDkrsaTBgQtfM6L86HnvLfwiL+UJWXlT4XDoCuWdcnjaBo/osMyAQUmVV6nckFN2s5j8im5SGeijQFO+8wkLIMtAu1JE/gu3F4+cSkuXr3tsiyMD7sHBhIE3tWUrxZXWd5v4XKaeLUQ9HfG+aNedHyp0uVdIfoMUGVo4S8mG9LqTyZ5WurivAkXe7xoLyF40HuuP097jl23WzI5ny6P5K2lkefl6M+oQ9oL3iNa+sMGzsjgsMIrHOTJyEAaG94+9KEPLWDkpetx8kO8C7/wsPm2As+a/Ou9UV4XLvI0ONT7l/S10bXo+j2fMVxjbGbfKr4HehfZuFd8+LG3++yzz2SfI/Hrfbp/K5Sf79VECeMH8Z79rMukZ9o8+4fBL0ZUeKGv+f/svQecLkWV/t/+1wQiKFkRJEoUkQyChNUVSbqAghnjGkFdMKJiXDChIOyKYA6YFQRERBREooAgQVFEggKuCroqrv7cf3975nnnmXNP9dvvzNx7J3R95p3uqjp1UuXTFVjhoq2N4pmBPWd38TvqqKMaoyF13VcouJ793fmLvJbi+vD5oYFYDueyVC4L715Po1yUf8HzpA9mvMY2Ik2IWSnAOResumW1QnZmStRfG83IA37Bi189tbrFjSzEqW2R0Rae+NFGOi7hJowtrbQXtB3CTzwHoLKyx8dZkY+S33nP5JrNYcik/IdPzhDEwMG5btKh5GtrJ9vaU+G/973v3Rhx+DE2YwUNYxP6a8YKGhPNtL6gx8cFjCyMj+BDTnxnsmVlSLqIZSGGy+/PmZZraeNznXHGT+/mrwYWlLGFgq0fWep+3jVJ1TYPj9dgVEWBOB94sk8Xq7M3vFQerj8rLZeNjZP7NdHEiMMkkEkcXx1o5LwB5ysEXx8Ib8MH36TX8ngmdr41A141UZRuYrF3ffg2HQxRHO7l9Fm2SkcQlw+XcDst4fFnlC3Coy9NohgE+NkTgmXCi+60wkh6zOhF/FEXLkfGmwYvEQ/hfOUnT3XOiWDAowm5h3l+Kw8cr/KNbR9xuw2TeFy2kkoytPGvs3HQqcqfaMMXg0mcDsqN/OFXnRA9pY9+z4eIRwYS55VzJHCqt47PV3WJnrYJcQAuA2Gn51s9kLmNvvJDZ5oAS75ybpIcKwj0JZEwPzhQ8APg8RfyyGUhWEYMynbkKaaX33VEmMspv8I8bzCsyHCgMsVhwo4vqzPEs+oEx0oPJvROU9tqIi+ON+MnwutWHQzGfoYTaVnZIud4B4H1C+H84FGOZdlakUMcKxDbjGLOE7pAP+iMg4S5DlqH4zKZch3wtRu8fO3m9p/IF/vh4YM0Xt+d3qREwRNldtq0z2wVYqWNwmknMMyKL2DcmMk15cBwzgtlkB/bpnSD0TC+pOsoi9Jl4W3y9XGzUwPefgzjkImazgCjrYvjoqzsD8O5uOIll7d3w8qs+NcTowUrHTkkVeMu+GVFCD9WgPCBSAYQl8XpKnwYfc8L3vmpD9I7/ZTowafH49e4M+YF6RnPIQtthQwqPGlXOGOE/BVdeBaMwrJnV9kWVz7PFF7pnidbyFi9yPYrLxO8u7zud33H8Mijw7LiV8YXwZFPjOcxvvCuDzbEwx9zEn1MIW00+jH+kcGNp7azlPjy8Mgr9ERXTw+LZSLCeNqIe775yRd32dxpvsm8kORZELcRZY0BYfGn1SxMhPmaT0MFDKdt+xkMdEj8aJR0uwrbiOiEHCfL8RnAHn300YPwtsIVGzP8unWHdJxLwYRXNJgYMOlggqYVBhG/88MXagbXOA+H7yOOOGKQ1K+qdp6YXCoddIWLMx3gRXhp6LnylCWU2pbUxpdWyaD3qVh3xRODF77O45iQRZ5Y7cJZB5oYKp3zloVF3iN8KT7ikgFGN0hhwKBs8CUEWMrZIYccMkDneRRpepwOnWWpssKRXVdqk5bJkpcd6cnzlLSRDpNGwfK1HryCY6msVrywkiGmB1fUQcQfZXS/4+MMCa9fTFpFm60lbXREUwdIs7VKS7xJh6GFG6dwyMoX/4iPa4adPnnF9dU4VjcA77fEgFM4MBRpKxfwDMYkm8vIdd0y+pBXvjVRh6A6vPB7WMNQcJEWBn5F/t8AACAASURBVDjlo3CQhDNGcMcdd1xjNMAoQfuV0QTO6WO8wrGqTOG0n2xb9Bt+mHREfqkDcpGW09F1zUzYBIe+SO/nT0mHzp/LiUwq07Rd5Ct1k6fXvyij+/XOqhXqn1aIEM5WIa0GEl22a+IoM5Qd5w3/FltsUa1dr16i/XNem0RDXNSn/Dypo6wAoq3W7U/Cz00pOHShM8kiLuTAMA8MbQhlMuMvptMgOmNdg+0srg+bPxrwiZTK2VySrm1SGCeJXt55V33gnQNDOWfKDy1FD7SxX6jbR87jom6qXsWndFYKjzqNvGH0iIYP/NRt3yIkuPiEN1Y9c1snq7odF23WPvvs06x2iHSH+eFbOo4yzEU/stA3saqF7aAl+WNeOJx0kunF291YFtyPcYR+ibzhLBVWKerHzUAYxjgUnx/vHs876egPwKGPuBq7ZnSzvCrJrvA2HUh2wWb452KY2gQ9u8qQlYWuaXu42aOBBWFsier2wu4VgK/hmrhy1TMDVCZQ7FfffPPNB2j8AF0ZKeiI6FSZeDHRJx2TGgapHEgoF2l74xX5xE+DxzJuHDxxijxXwvLOkk0G+UwiGVALV4kW4Rr0H3jggc2kgEkyE2oG0XyBhl9wIocml2yHwPHFGnhN0DkIFKcrh4mHNyaGDCQwfrQdCKnGlAZfX5sxeEF7lIZWOqVjYCKN4+wJjGFMxNCVeMLQAb3Y4EV/g2REF/OW5BEvfr4y06nh4I/lt3wB43Yb8ldxSuv5muHjAEsccipPWWlEnnI1nyaWGJsYLOG0nQwjInmardyCFjrVpB+dMlHFiAN+thZQVqgzbmhoCBRc5N/BXM6YnMPm2FfOgZ4Y8rgGWrSpdxEv8gifnqw808SY9PjRAysKXEZfIis+mJhCB11hYKVNgD5thFYxUFeVd8K94447NrCk5yBBHBNuttrIGEgYecSkFr3CE4MkGcvI1+wrhw9oor7cr84aOcUfdQBd+io++Kf+yXDhZ/1E/Ua/rj0nLe0o7QC6weDMtcdqwzDY6UpryjtyY4BBR9yYgyuVAxl0OEOEdgW98GUOP9uK1IZQznVbmvQgfoWbVTw4DLwqVxg8MDRrC5R4EQ5/etliG45w8UQWpRVdeEf36Id42iMOcoaW4Kmr2naW0WwLc36cNuUQIxV0GUSjM85soWyhfxzlLW4V8zzAmE/dx5HOb8EQT10GhF1ghK9/zm8N6CtubEdmm9Qqs97WDntHBmC8TlKH6ON9HAkcW4tkdKFe6QOfp43v0lEMly697goWfqLRRRPeaFwRLEZrDMG0kXzM8wkyhiNWs9KOMbmPOmmbTDtP0pXC5vqTPp6+kFUtUSfSq4eX9CS9ABud5ztxWTloM45k8B6WpY10Ik/ObyZ3SXaXXzj8mdGZy2Gqo5kMHEwsp8sfHC4rCxmePmx2amBBGlvICi/03jnRsWC8kNEBWCYd+oods5EJDJ2kJjEYXZhAM7liIM1XbG39USMkHLFRirjxA4Px4fTTTx9Mmpm4MDlkYs7gl0MOHWeGR2FMFOEP+cCBoYSVAXT4GGwYVBOHHPAOfdJIH4Sz5J1w4FlWqhUupGXig2OAz6A+m7hGPTDQ50YPHNsXyANvWKKePC7KyuTi8ssvH/BE3ml1DRM9lsHHrVjgEM7Im8dFWorztBmMwrzM6aBidMdEiPKiiZDg4xklGW5oc7MVeQou5SnlkTxlQMTZPOQfX6bZQkMaJqSiTRnQ9d2i4XoAhw7MJJ581oSciTP5hcvyJYZFv+hl9dHDKIvIBK/aWkO5ZcKa5af4kRx6wqsm+2zrkBEA/WBsZItHxiNnckCf8g99dEedZFLvZRz9U1/RD3S4MhE/5Y76gkGSlUwYVnx/NUYy8IFXKzWQj7Lsq+oy3qRDl9nD/J1VKzhkR5dsQ5Seefr18RiKYr5keIGhDnP9J8Ys6ZX2iTIN/wcffHAjG3Ey4mBAOuaYYxqU6L7tFiNgZMDCqKLVh9DjgFcMPB//+Mebck65x0Csdt3lUxhtM/kj47qMLAyWMaZ3deQHtxfJIaOfJaNw4JBP5Q09YIhS+8t2PG1DjXkc/V15Exz6xrjH10raQmSnDpM/8MMk0OsJ6eQXDr7Mywi19957N+UmuozPGCa/PyNMxNv755YGSvnpN2jJyDsXJItlVXWj7YlcSqf2Bz+3G2J4Xbs2fLrD6MIHAlZQYuCg32hb7eJtW/buuDP+xZ/LQBgr61idSJvKuCGuZAEeAwttBvmp9EyahbNkQIj6ErzzOhfeWXWq7XDOL2M4zsLi41mmc9eLDFyuv6ifzB/1E/Pe42NcV38JR6Sd8TcsbFjZmKtlIupmKn4MrXKMY4Y5lbFhcH387NDAveoKWD6VdXbwOG0uJCJPWW21FUhhbs2Nll0Y4Cu0bpFgmSATBW9QgcFP50QcXzI8vkvFcD4ldMwedYasjsh4yJTlOHiXH14ZgJcMIiUeFB7lwwiD3OzFjo2u8+Vyip9M9kwW56ktDfQZrDBw6MJTlHUYX67TkmyERx6jP9Olzu3gi78GNCVdOB+8k6d87c/yFByjlMMMPpa/DCaTSeWhDd51zjsrq/QlUIYJBjrkqcvo5bCkJw+XzhjwM2lUHYi6zHhFfvY7+807kSZ4BBcNZlH/mZ8vihyqGM9viXTcH/G0wWZxyh8MPUzOMQSw4sEH0ZFGzC/aTRxtJXqljYoOHBGnBmDADqMhmhwK6Af1eRmQLJF25Jd4b+sFj1EBIwiGCT9oO+Mv0nC/lyfC3Q//6EjlKPIvPDHcZfO4jK7LKzkpW9DmPBadXaW0wEf9Rz7a5I38iFc9lc/xGdO10ejjZp8GYr3CH8dQhHE+EgZZHMYGDJ3kfWkCNtskVf3N5C2FIYPS8e5lnbrIis3Sdb7As12b29boC7QNqVRHXV8lXuO4Fzg+DNAXMq6lb40OepzLwmpLDN3DVrKU8tPbA9dFpDfb/bSf9A/MB/ggQb6g18/Wq6ofXa+OZMV5KS8Ij3mjMA+PcMLnZaktzOl3KS9Oz9MOw+O4Yzvu/tI7+GO6Ev25HJ61D7GN5Fw/zv7E0T7ycc/rTFav5rJOFhrvC87YQqFXh6PCLj8dDltjuKWCCYcmC6Rh9QorCPhyqYMwYyMxzN+1cHmDmjWuWQM3Cu5h+MHlNDIeHEYNgsJio5DxJpzeCAGX0crCHDaLjzzhn0m+Is2SX/I5v0xGOewTYwrlTJNqeKSc6ewZBmGxTEVdOt3IQ4RV/mThHuZ4vBwIJgvL4px3z4+MD+nJn25s8XM6YvrIT/SLt6gf+eMTeIWVcLm+4rvTiTQjvmH+iDv6Y/oYP4qfr3Lrr79+k4SvqwwalW8lOsovHziQXuGRvuPL3iO84xLOiFu8RV4V7vmrtKzsYFXSQQcd1Kz+UjhGMiYVTEBYtYcxRHjhpaSHEt8eHsuCx7kMCo90S/KVaEd9ud5cJ0pPWEm+UrjzGuVxfnn39jdri4fRyOTsw5a+BrJyFicSwHB7mw5Uh2sOYVYZmSvlwetNJncMQ85Y17yc886WKla6aaJVytFlOIej3mbKBxi2qbDtkx+6y1zklS/nGPHpS3nn4xjnvIlHwTsuDAlsr6Qd1BbHUp7FOh7rv/zgn8t1ndv66DswOvHxh21DzBXOr1e8Pr0+RDzLD9dtLA8qMzEfIpzilT9ZfnneLY73mG+xLHvexvwWbHzO9fLQRc/KY3/GNpIV04x5cZyts059eLbXodhGxrzowkcPs/Q0sKBuI4pqprB6g6VJL1ZrlpWzjJKvf5wHwDJ/HAdpxsbC/TFONLtWDPgZBpvFZ2Eur+QEjl9bQx1xye9pHEbvwh2fUe+j+jP6w/TUhacoZxtOYKP8JX8bHmTXLQxsSeGa2dNOO60ZPLHFhxUFOLY8SY9t+ooyzATsKDih57p2f4xrw5vFeViMj/5Iq00PxHkegUt+5WmGXzhjnJeDSDfCenyMi/6Iy2XM4qYahmGFbTzHH398g4ItJ2yFgR/9MtpRh6JfKv+Sz/HG9yjDKPlR4pXwyBNn09CWsx2JW7wwovNVF+MLhha+KnF+juTukjeR95hGfDhchIn0okzRH2l28UddKE2Jly44Iw7xWXpKzphuFFo97OzRgMpOW1uoW03Edakczh6pFuVEdTirK4tCT4RIVp7epvHOjS9sL+LqeD748bEvW2HCViPdZJTRYnIWHfh1cL7rnXfXv+QSDAZn8OnsCMnLU8YE1W35s8lgrP/gH1V3Uaal7cdIhcPQgsP4guNQ3MzQQpxkLpV517/e47Mh0sGpfHUAbQXJ8imGyd/2LMVBPOJrZWieRqpN0Nb8TMxeT5lW5lbYgl3ZQgHXihZ/J/u0lD5mJcvLdd5A1oDECpHBRJzyewOp97ZG02lFOk5jVLwl/trCod/2y9K6jCV5S/JH+BKc8xQHAvAU8yvqKtJpyyuPy9IRFsOZ5OpwTNcRy6s5I6QtX6NOSzqIcNP1R50NK4cuQ0zrvLhupCus/AxCcXwVzVzUURsN0sc8KOlN4cPwZTIorIQ74oz+TM4sbKrpIl+6DQwarKryc42g4b9MXvDFrzSRhtJFfNEf5VRZIFzvHka49CBcHiZ8MS1+loNz81Qc5LBfmgMhZRR1PZfene9M9ixMaTKcUZYuMrqsvGcyR30638Penc8M1nkkXjLwjO2vxwk2w9mHzX4NqGzz9HYgtgn4OQRajjOpdJBoLB+zvUy4zF3qmcMjm7cHWf1nCzRX9mIo4SwQVqRM1UXawuPhGFVYMcOlAnx0zIwG3ibF/Ip+h43vU5VjtqTjnDg/b018UZbZvs34jfOw2pznf8yfWLbAk8HHcNFz2DYehsV5uRRsVlYV1vUJrgz3MH7manzMT/xZO/nZz352ICLnZPpxDG31a67qZaHxvSBWtlCxvQGSX50Acf7OoY4MwpngKR1biHy7R2xYvDHK4rxx0XvkCRziRU+FecEchj/Cio7L7bSnU+idF+kwPofhL/HlOhOOqBfXl2C68pThz3Qn/iJ+54U458X1G+GEhxt8OHiWrUPihUMy9UUdONel86b3mLcZjPCU4kYNj3pzf/bu+dFGy3VGGlaV6QYaz4OIL/pdXsepcM8npe2SXy5blCPL4zb4mL4Nti1OeIbBRN26n8N62TLEYNG3zYAz/iLf8jMYAGf8Obx4jDjdX8Ifw0kjGSJeYF0fWd4AQ3vOAcTnnnvuAD2TDd2s5Xgdp+OOfEXa+Ev0Pe0wWtLRKHxkvCm9ePJykPFTwuF8ZOk8T0vvbbj7uLmpAfK6VKbiBJ4zi2hzcF3qyGzSiOT0tkBhbW1glFWwhLvu2LLDWWL8CGerEds8WfFyzz33tJ7zEvUUeb3f/e5Xrb766o3ueWJoUd543sV04PVJX/QD7z/JJB3pGfmba/7M0IIMHHyPO+uss5pzdjg4PZZ5ySpdDCv3ngfKG4WBy98dd6kOCmbYM8srD4t5Gv3izZ/xfRgPCyFe+aTb2STzdM5cXAh6m4syLoiVLWSMCjVPWRVLT4eNnU9sMGIjg7+tURpWSJy2YGPDmeGPfGVpM9zD+BkW7/K77Hp3XiOuyE+U0+Fj3LC0JV5ieImG0yvRLunYeeO9jVfiXIfC6fpr02GJ/6hr/F3xABtlztJ62LD3LH3kXboq6c/ho86iP5Pf5XL5oqzRL1wlGUrwpXDwlXCNGp7pJMoe+XD9ZunhofRz+CyfCBsVv/SRyZ7RAD7KFPPfcWU4xCd9QJuLehBsxmuGJ/KZwUj+iLskUwx3nE5PMmbyk6aNt67yRd7ldx4zHSosS+/y9O+zXwOxfMVxlcohknDenc4lYKveOvVWFU3g9YxlaDZrIMoOr17vSnVQcC4bsLHetfnRs1a88OTA3cytsMIKzfkiOM54cdoZ/44jq8fEEy5DQqzfWd2OcjiNufSOQcVXZ2W8szKSj7PDVrd4Wm+Ls3fPJ6VzOMKivwSX8aywLJ88bNj7sPg22vM1LtYx/LGN5BxHVhTLPeMZzxiMwdzAGdvI+aqz+SjXgljZEjOOBoEC709gvKFQfKnxUbg/s4YmC4v8yJ/xpDBPE3mK/gzW8ZQa5RJfw8Khn+nBw4fhIB74Em9RRsHFp+hEfqJf9Ep8OS+RL+cl6tVhifNfxJPxAIz/BOM0M56djyw+6qUNpg1X5MP9pbgY3kZb8no5iHoTPn+28QFO4ROcl5vIn9OOvEZYxx1h2+IyPJI9w1OCb4ONMosfcHke+3vUKX792vhTHLj0y3iOuKI/k8fDnHfn23nL6JbwMngRv4Ip6SDK34VOW1mKPDndEi2n2YW+8HhZ0Hsbb11xR51l+QAu/RQfZY266P1zXwPkcSxj3GojxzY+jC1yXkbnivSScVh9yXThMsa2LOok0wc4ZTzhySGtkY+o/0hTftVPh1cYMMJbMrAIJksTecpkmSth2ZX3zvtUDC2u31I5UPnRM6bJ/OJrqvqP6dzf5d15miv5uyT4zOokZzXJ6ZykyEvMjxjf+2e3BhaksYUsUcEtNRpZtnma0rvSlfBmFSYOMrwyZhXT+c/oRd5jA17Cn/EWcWX+qEv8JflL6aMOMjjCHM7linpq46kkZwzHX+Irhkde5OfpP2RwvC6n603vMWyYXkrxCo8yRniXI8aVcDjOiD/6M5xtNKWrjIbC4jOj4eljXjl8Gy/AZfKQps3F+AyHpy/Fl8JLtCO8ZPNnlIk0+iku+jN6wEhOf4+w4inDGfnN0kYZMv5jui7+SFv8RT4zuAy/6yKLz8Icd5f3YThiPDgz/UU4+aOsJbgMXmn9GcOG4evj54YGVK68HMQ2T3F+Hfzdd989NwQcwmVbvVKc6yPqK6Iv6Q64GBfraIxvw+1plS5uewFGP3Bl71mY6Eb+Ij9zzR/P93L+2fox6oqWKL/01dZ/ANNWnrJ8jXRK/lJ+eXiEGeYv0VpI4covzzd/Z2WLHAa73s0/DSwYY4saqNgQud+/cmadlmB5Zu8UDw/34hIbpFIcdAXr71nRy3B6WqWJYV3wZ7jBV+Ipyh3TR3/EQ3ym8yh3xnvEFeUexluk0cXvfAAf+YIn8aV3PaVHPTP+CPNflMl5LMnfJodoCka6j+ElHBEu+kmXhZXwZeGkdx06Pr2Xnhm+KGOmN8Fk6RXmfGQ4YtqIc5he2uLb4iJd+V3uTJ9RL9BwvbrfaTiM4wUmyhx152ljXCYH8BnvGT9Z+rYw4QYmyq2wqJM2fI5nGFyMF/0Mh8fFdCV/prcuZVb4pkozpne9xrgS73343NeAlx/KnR/GrRt3RimPs1UjXs+8fHs7KBhvb0vtpMvZBtMW16arWK+z+kl6D4/v0e/w8b2Nl7kUp6uyI88YWvbcc8+Rtg5FHO6P9WaYPlW2Is6YzzG+iz/iiH7hKIV3obEQYNrqqhueOe8n6rTX7dwvIQvG2JJlVVsB9sZLcP7MwqARcUZ/xoeHZXSJj512xBPpyJ+l884+4hnmL9FRumHxJTjCneeIJzZUbbAug+MpvZdkhmbkI8I6H5JB6Xi6ruV3vJEG+PwnnG18eFxJT5Hv6G/DH2GjP6aN/gjv/ih/CVY441P68ecwHJ4nwLrORuG9C82Iv2uaUeBKsJmcyCedx3jpDRjXc6ZzwcZnhj/jryvOLD9Ew/mNfHTxC08sg5n8Gb8ZbyW6sV5GuIgr+keVNcqkPMjCIy9T8Wf8eliMj/6p0OzTzF4NkL9ZmfdtRHDPwZAxbPZK1c6ZyrS3rwqL+pA/hrdTyGNdz6PWqwjvfucdyvg9LPrFXcSZcz03Q2+//faU8Zk0tEQCrs9Snx3TzKS/lJ+l8JmkPV9xxbZR51ghLxdDDHO97odpaPbFL1hjS1thJS4OSgXPM3snax1nG/5hxUD0Ha4LPodpa5QzuGE8Kb4LH1EXXXG30SjRzXRV0lsJR4m/LvBtepYegPEBmNJ4GRMtnqX3TK7YaHfhOcufiKekk4yHEmwbL663Nh16/rpeRDMLK/FTCo98er6U0gwLd31G/FnaLjCk6won2MiHZPNn5CfT6TC6Wfx0wiJP0Z/hjjAlP2mRv1TuXP7sfdR8mAr8VNNIZvE91bI8Hf1GHjwfZgJvKV/78NmvAW7CcccXXYwtWTnNwma/hGNtdIl3b3v0jkz+3kVGtV1KOyxNqd7FcPnbnm1xw/iYy/E61DTKsO+++87YipaIO/pjfhFf6sdi2un6M9rTxbnQ05N33C7mjgOt0XWv7/lVOhaUsYXCq07QOyuyNBZsh/X4UTuaiLet+GQdaORT6Yfh9fgSjkzuNv66xg3jrYRn1HSjdDIZ7izMeWvTWykfVL6ijNByfjM48cPT39vyaZgMkY+Sfzp4ppI204fzJpzSk9Movbfpibi28uJ5XZKnFJ7pdBTYLP1MhbkepR/pNCuDoqt0mRxZmNK14XSZ2nCUZJ9KmmH8gDPLe5c/08VUeCnJ1TV8FJptZb2N3ig02vDEuMWFN9Lp/UtPA8pjr08ZN1xnrC1EXKM7yq0tGb7ZGOa6yMo+YV5HY5s8TIejypzxAA4P1/uoz4hnVN7mCjy3PXFdNldwyy1JQ0tJT6W8BX7UctSGq0S/Dy9rINO/wvSM5wBFg3SfJ2X9zqWYBWVsyTJGBVmdncOM0hEp3XQqBmlj5ZwOPucp4o38luKjztr46Yoj4pyq3/OuDUfGcxYWcXTFH9NFvStfnabCYj7gFxzPmKZEK4ZnedFFZvBkaZ2vSGsm/W16iTyMqhuHjzJ21U0m67C0kVaGoy1sGP62tKLtetW78E6XP6ffpuOYfyW+S/JOhU+XMeKNehA/gis9I98Rr+Knw2+kMaq/lA8lXkfF3wa/JGi00e/jlq4GvF5ldWDllVceGFu4hWOjjTYarOyYb2VH8qCHTLaoK/eTi57OdSm4LjldoutpBROfwMQwx5fh7sLTkobJyuEoPCDnXXfdVXE2i9zee+896w2FcyV/RsmL+Qb7+9//fiDSwx/+8KJ45GWfn0X1zPqI/2/Wc7gYGYydBvvmsCq6ZdELuOBLBb4UPpMiPP3pT6/+6Z/+qTrllFNGQotVnkEOnYXvDxyG5Mc//nGThrR///vfh4E38XRs+jGYetrTntbwzI/rHhei87IT3zN/LEvRP0yHwMffsDSKj+nk75p+ccGNqoM2PmYCV1e9TEefM8Fnmx6GxVH3+d10000D0KkOXKcrS0mPbeHDyjSHonv66Pc4cGW0SjrMYIeFlXBNJ3wYzZmOnw6vfdr5rQHK2uqrrz4Q0tsVAn3sMJ80oTqGTLG+qc2JT+C8PYptE379MpxtcYJ3mIg/0s9kmE155GUnvk+XT/Axlr3jjjsaVBhaKMdT7Quny0+ffm5qQOXFy41f+7zKKqtMEow6h9Nzbkrdc40GFrSxZVgRWNIFvK3hbotrk0Odzgtf+MLGMn/sscdWbj1VfAnHZpttVj31qU9t0h522GElsGKnc8ghh1Rf+MIXBum44qwki3gpxWfER4H19F3TdYXLeGsLi4OjrKxlYW0450rcsDKHHBgEMRAuaYdBEdp/+9vfljTpGac3Stn985//PMkIm6X1sC55OOMCTQGhTxBIntW7tkmLD3bma32cglr7JL0GJmkg1g2vN1FVbMVwxyG5WXsT0y0t/0y2dd4eldoiwqORRf42A0pbO5bFRRriJzO6zMZ2UPkS8yeGz5RfKxD22muvSStaIv6lVU57urNTA14+Mg794/eDH/zgDKQPmwcaWNDGlpnu4GOjXyofsXGO6bJ4cEW4LviB+eY3v9kYPDbeeOPqRS96USlZMfwjH/lIc2Xjhz70oeYLd4k/5xEYJqwytHzjG99o/GuttVZrejGR0cjCikxbRElvpXDnoQ3/MH6IX9ou43EqYdORI9JzXJ4HesfIsv7661en1WUmwpb4GFXXGTxh1113XbXuuutWt9122yLlNNLOcESY6fqj7kbxl3TnfOv9mGOOqTbYYIOivldaaaUmTvQF2MZPlD2DjTCikYVPJ8wnOeDJJjoxzOGmQ7tP22tgIWtAE3XpIJ5JMMpq16wNWdxh4rsLna757O2Rt8eePurNDR6Z8SS2XyWYzGBT4nuU9t71U8I3nfCof3DFsKn4OfhWh9+2pb/xxhsH1zuX4DKepiNzn3b+acDrOx+f3S2//PIDr9d3h4ntwvzT0PyUaEGd2aJC7oVd2VoKywo2sApXOofLcGXFpw0ui4u8ZDBOh3gmrs985jOb4NNOO63ZyjMsXeR12WWXrT796U83Syf5XXHFFZP2rgLvOPV+6623DlA9+tGPLtJFrmE8jSp7lCHSGDW/hvHXFt8WF/l0P+mi3G3wipsqvTbcEWfkK8ZnuDKYGIafQU3j6nfFi577PW3MX5JHHsdQLmoAczznnXfeOOkybccd+WsSD3FR5iHgU46OdNwf37/3ve81dGIaEacNKMUJJuq7BF+qezE84puyIixhLEclnIuDdolWH95rYD5qwNvkWO9YXasvuhw4+rCHPaxpX2K9YxIcw0bVlbdDfPDh9g+uV8XoMMwNg4e3v/zlLw2aZZZZZmgbmdFz3TivyM74jbaXcRvOdSpco+gng83CoM1qx6inLm06fAkuw53poC0s0mzzR/0hAytS/vd//7f64x//2KzQxnELFoczt7kVV1yx4mYYHB8bt9hii+aDIXjIE60AEo6YN56vDtNGs49bOBqgrFIO5Wg/ZIieiXqzcDQ5NyRdUMaWLEu8cY6NOPAK4+vLl770peqCCy6ovvWtb1X/8i//Uu1TX/l2wP77DwwPP/jBD5rVH6uuumr1gQ98ICNXnXDCCdX3v//96klPelKzPUfurLPOalagnHPOOdUjH/nIZhUKMGzjwcWG/B91Rc1clOGNb3xj08G85S1vGawqQZZXvvKVTXL45DwWdxdeeGF1/PHHN0EYWXBPeMITql133bX67ne/W32x1sNBdQMmogAAIABJREFUBx44KQ0e0eb5n//5n9XXvva1AYzTu+GGGxr8O+20U/Wc5zynMQJdfPHFFbyKF7ZzfOnLX65OO/XU6uqrr250sskmm1RPfvKTq0033XQSbXRKvnzwgx9sBienn3564ycf9thjj4Z3HPnzne98p/r85z9fHXHEEdU+++xTLbfccovIoYCoSzraL9c8ff3rXx+JJxpRZDz55JMb1G9961urbbbZZhGDFXEMPFkNBP+Sm3JwwAEHNPBtjXDkl1VIF110UUOTPEUX/DJH2eN3zTXXNNHoOMJDW2X8MY95TPWyl71sEVScKQQfn/3sZ5s4yhpbyXCf+cxnBnmAjJR/DIHa1kY6/MiNe+c731mdWuc/tF760pc2ZQq9QHeNNdaoLqxlo3yoXFKegMsGOW9/+9ubVSsvf/nLqx133HHSoBi68HnooYc2dRv3ghe8oCk/0Nphhx0GOFVPXU9PfOITB3ot5U8pb8gX6vyBdX3afffdq2233XaRcoFOcZ/85CebFTeU769+9atNHsGn2ogGyBx6pA4it8oSZX7/us3S4F11nXqNk2GWlS7eLsA/eX/uuec25ZO8e/GLX7wIjOQHFjjOfMLtsssuTZlXXRSbKk/k3cEHH9zUE8rsm970pgHukk6FYyrPxYFzKnz0aXoNzCcNZG1vJh/tt4wtN998c/WoRz2qAaOdAYe3l7Q5+Gkj7nOf+0xCd+211zbnaOy2224ZmUl4AIAWbR3tV1vfL2TD4OGLNhSX8ZC1M7EvEC0P552xzBe/+MXqKU95SsNrhou0pXDh9WfULXGi63igTfs97BBYjDL0Sfxu/MUvqofU55jQn9N3sl1M+Znx0hYWdRR1o7QKl2HqzjvvrG655Zbq9rpM/KU2tEzHYYxpM8gsUxtcVl9ttWZcTf6wBeS+971vSjKrF6PkW4q0D5zVGohlGGYVpqduZSOOckSZ8HLRl5FZncUjMXevOtPzWftIaGY/cCzk+D2Mdzr/RzziEY0wfvc5nWnWkQKHxZsOn4kJE2WuNcRRiaIRAwMCE28cX5KZxGF5x4CgyU4TaQ5jyEte8pImhIrHZIhO8FOf+lR10EEHBehFv0zT6WFsufzyyycZKV796lc3Bg8mqkwg5eBnnXXWadIw0dXkCNrAMVnDEPSjH/2oSBtdPutZz2oGCtH95Cc/aQYnTKwYRHA4lGT/6U9/Wq299trNxBfDir5CRBwYVaQT4qQTDEoYMqJDV7XyqmeNr/DxeOQoTVaBUxmhbGy11VYj8wSv8BVlodz8oh6c8IVEjkkmk9LMAY/uYpmKsOIXwwkDpegwNrznPe9pJvTAUibJC233ivCUjzPPPHNg6MFQ9YxnPKMZrMqg4ml0Wr/OPEFvbAnCUeYz+SgHTR7V7v73v39koSknxL/rXe+q3va2tzUGwiOPPHKgU3jCWIHjK2M0SjFw1Beq22+/faBDr//wGbfRgA+6yIqe2KtdqqfAACv5FxFiPEDtzHrrrTcAIW+9fFAHHY8GcKXyTXoGme5e9apXDQymkRdu//j2t7/d6IGDtsn/6K6//vpm0Kz2iglP1gbCE0ZSOfR0+OGHF2ljvFL5o005pc476qUMzyqHrG6iLcD1A46YO72/18Ds00AcT2lrRrZFgzaZ8YUcYxn/qqs6z/OjH/1oA8aYYPvtt58kOO0xbcVzn/vcoQqBP/olzojRyoRhicDNakc+dkTjjHjUqkD6yqk46c3TEsYWAz7wydhCWKktLIVHnO6PaZwPaDN+o79mW22EBQ/5evbZZzeGFgwPG9f9Cv2YVqbSJ+t8nix9pivnYdg7K0wwrPD71a9+laFb4mGshqHf5IfxxcuxmIm6iP4lznRPcLFooNQeEq428Stf+cpgZRwfAZl7UR700+opX0XVl5fFkl2LHenwdZSLnYUlTyA24vj1EzfyMyjQJIOOhwYeQwwTXzpXOhe+SANPQ6sOlwkgFcp/rN7AMTli4k7c+9///sEEjgkNuKHBl2UcK0L4OtzGY4yTn6/ZmsQxUBEvxB999NENHwxWmHApjVbC0MGzekfhpOXLEo4v5ejF6Toc75/4xCcqJmxywPNjubAcnfmVV17ZfMVmtchDH/rQhkf0Cd/wBx7SYYiBJxwrEDBUuDyEY2hB/+QNXyRY5YDD8MOEjokeeH/2s58N8okVApkcMQzDhXhiUt2VJ3jl1HrylgEMTya74Dr//PMHtMEnQwQy4GfpK6uAMG4BT3jkK/NjRJOhhckwk3cGI6Rn5RVbutTYs1JHE9w3v/nNzZJbfrzjKB8M+KDT6Lt+ykXaxMc4D0M+8kT5Q97iKAcMgsEHrx//+MebcJ74gSOOQR8OQws6hUcG7MhKWcFhRBCv4u/UerUEDvk5eyTyjZ+vCtQ9lTF4xHDDIBsZqC8ytEhP5An6xaFD6SnD72HKG8oj8vFlFtzwh5MMStME1o7yDX/UBcqHJivwgdFS8KwW0co04JGFsoc+0RP1knYFuTByUJ/lVE/XXHPNJl6ONpB6ysAWozL1VTxRTlUXyUvRVnuGXuEV2sRR5pRH9UuDB/2xqgh9cr4TX76BwenZeHrXa6DXwJzXgIy4EoQ2LPYfxKnuM5nnoxb9utoant5GeBtLu4phHeOv4D3ecfBOGyxY0vKL+AVHnNovwciPIQe6TivyBa3/9//+3yS+SPeHP/yhCeNJHyzZxCsGdfok4fNCAO+swhHfzpfgScvWbuJwwqsn7Ti8OX69R1j4/XzdZv+u5pOPEE+t+xlWJ9GHcfsk+UW/C07PR+fZ3zOaSuf6o+9hzIiRnv6WMfUwQwtlDcM9P8aw/Bhj/uu//mvrDxjBM2YjfSy3UR7KJ9vsWVEKj7yrbLkcJXkjvt4/tzVAPqscSxKFUYe0BZG4/iaiuZ3Xw7hfsNuISg2fFKYOSUYPGlsmDCy/J47JCEvesdwzeWHLBhM2Vowwcfqv//qvZpm9VzRNal/xilc0eJgA8aUex/aW7bbbrsFN3L/92781nR74/uM//qP5aq5KCrw6U/HrT8FpOwYTLskjfrCU8lWCpf0YBOgwMWCwJYdJEVs2SONWVDoa9IC8dHiszIl0vXFxmqV3Jq7IKjrwLCMN71rJgb7ZRoFjQseKkfe9732NXzTh+4wzzhiw9LrXva6RB4cOgIcOuJi4MvnHKOZf5geJx1/AzUocTUgxfHXhyfFccsklg5UK5DG8sK2F1SePe9zjGlAvZ0w2dbYOZeqHP/xh8yUOHihnfDWRvrxMgAe/DABazUQYhsAPf/jD1YYbbtjol4EbclAWcUzEtToE/+tf//rGuMGqB2DguaFpnYfnaYPEnOKcPwwt733vewdQ4GQbGQ4d6aull6HsnYE5XwS8/CEzqznIY4yE7t5Vb0fCkefOj+MmPsbh58fAWPUUIwP4pf83vOENzcodDHoYT7RCYxID4x5wMRhmEMdqKsoA9ZBwygW6pu1Ahsc//vGLoKDuqQ4QCQxlmC+rtC1qb/TEOEI+ysEbRle2KjFQJS8oAy6356nXfVaSUZ/koE19Iy+Ud8DTluDQE/kpfMCTZ8jO9jCMWDinR/5ABzxevnmHR+dnwEj/0mug18Cs0IDqqZjxeuttDOG0e35uCyuBmWw4nAu1eb2NmA8PrDJhrKJ2UzBqR2iPLrvsssEEnDELW5BZtUgaDBJs42aizXkkwLM1FeMGsHwE01lzTLTFDxPmn//8582KZRwT75133rl5BwajAvF8QMHR3xKvjwD0txgGfFsKZ4DwIYWtURixSYvBgtW24N9yyy0bXPDGGIC+H8dHKdpT6PKRAiMU4zE54hnX6WYT0l966aWTjBLQpi+ANv0b4xrhgHdf5ZnlCWNXtumAhz7E857VqfvUH0AYw/i2r6wNd9x6j090i4zX8wGkptnmkHm1emsPK8xZzUqeku9Tcb7qWFudwYOsfJTgvA2MP+StbwcRLXjF2MIPx0cuxgrZljDXX9/PTSW3Zm+arIzDrR8MTjmlPZLry8Dszc+pcja1Vmiq1JZSOm+8eW/7iUVg6MA1yX7Na14zMLQQzo9GXJMGviaThqVgOE1ogSOcDkMTf85gINyX/jMBEl49OasC51/MXYVtchCnjl0TKsELPx09K2sYcDBB0wSPL89U/gw/Axcc2y6yeOlNceJXNGM4W1KUBhid8UA6Ov2oE02wMU4pTjT4ouLwukGFeAZozpu2e5G/w+TQIAc84HRZeBdPnOPhccAzGZbhRHQ0iKI8KEzl7LWvfe1gIAku4knPFyMchh/nlzD5gWcQQH7iGCAIB0+MTPpqhxzIJVgZ+lx/Mn4Aw8oJ4hiYyWV6i3GeBxogKh/Ap/pDeYq6czjJAX5ObNfKMIVj7MBhrND14sTBN7pl0Lv11lsPzWto4pwXP+iZbYYexzuDVhx6Qv+ZXhRGvcKYgtGEd8clPBhPFM5TjpvEPJx3DfiZMChOxkqMZVGH2iYJTr7AKo1oOO9Om7xzXnmXoZByRDr0rvLEIDzCq+2QcVq0RPvZz372IrobCN+/9BroNTDnNKBJA0/9JASr1+R0fovaK8K9fWCLEcYPxjSs1KNt8Xi1NRgsWOmA0YKPB7T7GIMJ87ZO73zYwDjAuWhskaa/UHvm8PTtjNHY7rT55ps3MHyVBgYHftJCc7/99mt4Y6WecLBFGDrEMeZBFowbGHEkC3gwBmHMV39MGJN12lO2S2NEgZZo04aDh36AeM7jwqEH0cbQwupCzhZz2tAChjEBODDmE89Yg9WTOOEQj9K5xpaMo7zP0DsTRx9DNsjMed5l6QnTik/OekMHmaEF4wp5zfgO3e+5557N2AC+MJbIiFGiMdVwcEODjyZ8QGBMz4pVN3JFmZGBD4V8iMC4xlgq0ieN6ybi6P1zQwPkYXQexns8r0XwbmiJRpfojzR6/+zVwIIwtrSp3xs7dZzAq3Nh9Qfuec97XmOR9h8dCoYQnJa30qHqPBEaVnVWdPg4VhawlYZwHaxGeMSNnw5Xzjs7wmIjnfkxAOHoAIgXLw6LXI997GMbOWR0YVAhmNjJeoMh3jLaCoswwkc4dOOEU504gwbx60+fsLk8LqfzLvoyZjmvkbeSHGw7wmlLCXBOm69QOJ8kS0+s3Ihy+IRXcSpH7D2nI0cv/ChPDDQxIsiV+CTcv3BpsON56LyIR/AycIhxbNWRYyJNvPMQ5fJ4xcGDHMZDhUuH4sF1KjyRH8Ey8cfQSbzSQUeGG7YdKU6ryTikNxq9Mj1m8rGsW063Zrgcyn9gMKBmeJ1XDK+cP4OBiLzWjzIq5/hdfxG3tlYBQxz5JJcZcNGbrhfEsCk6ka74VTgGRZfB45UHbjzG0Ef7SDvGk58vk1Ua5SkDfcq6/KIreSXfQLj+pddAr4FZqYFoVCkxqRWi1HFWX/iSem9rVPdZqUA7wVjKDR3EA0+7Ch76BwzXtHW0W/QNnCuiya3gRY/2GxjgY9/s7ZP6Mq1y0AcIwWCEoY8BDvrghyee8MXYz3FgKIAvb4PBwQcK9W/wShtPHwEcq1ZwSsf5KOART+AHnnEU2xT4YZyBH/CSN8DS32tLEUYNHCsvcOK/8dRObbDyBD/GAhwfxBQf22rpRTh4xn4jpiUe3BgkWKXMauvokJcxLf09xhUMHpQN7989jfMVcU3FH3nGj17hgfIDT6zaxaCmj3pOh7xhXvGFen6AAaY3ukwlF+ZuGtULjMZylJPYbvaGlbmbxxnnC3IbkTeWKCX6PcwnWpkCFYZh44UvfGHj5esF23BOPPHEQRirRXDcHkKHgvPOqAlocXwx92VmSu8V0vFF3JIxkqBTpyPWlbcyzEQ46ICDwQ6ODiPScxoleuKbJ4MtdCEZSCMDkcM5L955SY+K90ELYVE3kV+lIzzCOn2tOtEyZ+HRMw7QnN/IE3HOd5TB02bvbGWjI3fn/OgLIStqYn4ojeAZsMkhv3hRXvvyW+JI5/y6PiOvUUfEazKdpXNeS7J5OvgQn3qyOgujFfWOrWk4tk7htIJqGG7RkLz4fWWL6Hp50QAa3MPkoB6zlFgrQDA4Uq5Ur9hiIzz+5J36H8uL65k4JgBy8CV41x1fAnVe0AA4efE04HLZYn2BTuQtQTkIcv0SyIAZ/MpLATqdNnx9XK+BXgOzSwOxfY7cafUDKytwLKtntYvqvLc/vNNm0HbRx7GSjxUYgiFORgN9IHN6GILV5hBOOl27qtWqhKstFz3hdxiFYcjwNo/+kjh+OpCdcZIOi8fI7fD4mXBh5BBOtqULRk99CBHfepKGFTYYcRyvVpPqwxXwkTZ0WL2CMYr+TXKDB/2Lf8kDDneKR4faKuXxsX/wOMnqT97JI1YAOd9KB39sbWKsxcenDEfkMfqVZtQ+RekiPvljn0U4h9pj1OLHeTqMtdgK5gZFbTNie9Q29apbDkfFYOTO61CJfh8+OzUQyzdcKkzjP3HORSYx3+WnDIxaZmenRhY2VwvG2BIbZxX8rEIojicdGR3iu9/97sZw0lbo1eGxTUQdKZMfOiPOcMHpalze1WHxHitfA1w70YsNeiYP8Fk4YfoJr2DZCwtv8Ith6fnPf35zILC+OkW62qLA1wXRcrp6j/Qc1nmIaVnqimFKX40irK4y9nRtfCh9Gz+RN/n1ZIkqk3htE4o8j8JTTCsaKmfc1MJ2ETWw8enyxHdw6VpsrbIp0SOcvc1yGAG0R1k8sR/ZafiADhj3A+d7UGMc8TEPFBbjXP+8qw7EcMXpyUoOyjFllAE58vCOIYMtVEo/ECrw5PHOqxvTZPQUTXjTVz7hbaPD9iHVdc4KYnui4LW6CTyZ/ghTeKYT4twYyRJzvjxGvnSuAO2P4xRd4XY9xXfXj95dblaDqQ2JafFDA9olnXsatUFd+Mpo9WG9BnoNLBkNqK6KmtfdrF2kXdYXXlZp6GDsiEdtBQYN2nn6N1ZsCCdPTVTZ/hrPooptlLc9rD5xgzm8Z/CSqdRmKQ1waqe5VU6HxDpNwUYjtmjH3CKt2kzBiAa49A6MjDuOw2FiOH6MAC4X/ZzoeLjS6mYmDFwax6p9Fi34kF5jfjpuVmOyLTwzsjDOZAsVq2wdv9K7LNm70mRxXcMijqiP6I94KZcyvDCuxciFkUwOowsfOy+tzxrKjC6uu8hLpNX7Z5cGvGz4u68CpoyXbrHs83t25ed0uFlQ24iygk+YwmM8fp2jwGG4+NVhKp2nUUbQuHJYJg7DAXtmceyrZQKotH4IGY1vhtN5U7zoZH7ilIbljDjoO26906HuscceDQyHtTLRx3FQZ+RFOBuAgov8OFikr7iYRh23VtpEUhxCh2P1UHQRVxf6kQ/JmfELT4THMqD8LfGU8RnpykjCypVIe5gfXIJZe3xLE2HxXB34ZmsbPzp9P0+F5bpOh/QcSCin5cIa1JE+8sWASS7iIpywNhfTRPxZ2gjDobA49nlrNRkHTAvOcTg/pXjCfSCOQSnS9BVCGDtivPu5pQDHihYOv3Ye/HDnTNbIe8azfxnDOCgYlVlf+UL7k+FwfkVTt0VlccCA37edcTgzYfrFdJkfPDFcYeKD+N71Gug1MPc04BMHvWv7C/WaMZC2VKifcSnVNtDG0tawnUiGDOJkWGbFCW22tuNioNG4S3h5aqUwW3zUTnETpLc1am94elsGjPgRvAwWhMugjlFCRmd9QAIPMPjXW2+9SXgUJ3qi40+nTRvuW1dJL6MFkzh+ON1uJJ5JQ1rgZaB32r4dVWn8qfN2MKqzcsPjoMchxNxyqZUcxDvf0CLvMJrxkSEaWuCJj1yMYbXFwtMLX4PUHOXKf21xEXaYP9KSP+qH8KgP+SkLbKfmfBlWabmT0YXtRVo5nuEp8dGHzw4NlMqmlxcdTUAYbYCcyiD+aGiJ/tkhbc9FVw0sKGNLVErsALJ4Vhng+BqsgzfVADIB4SsLX6fj5FPXu2Kk4RYOHDeXKK06OZ2f4NcvC4ZJMYe2ZctivUIL3vn3ePEW5eOWGVazsOUCoxLnhbDCAqMCWzFwjpt3HWAr44BgIu4u/ogbv98wg/yC4Yn+9fWfFTCZ3M6z68DxxHRZnNLypNOXgyd3+ipDWImnDL9wKE5GL8pZPGSVwQwDjn//939vDrPL8IlfBplM5HFnnnnmpIEQK3DYxsYkH6MWA1AZ/DjnxCfUDIRUbikTGqzqXBEZnsQLPOoqZ2i7oUeyAoNz/hXn+tY7/GewwqG0jpPtQrjjjjuuuX0Ip6vbHS6jp3ieuqYdOIxS0hNXZ+vLIXHIJKMOe8i1b7whXDvnn3cZb9nP7XvMmWhI36Rj0K+0JVySIcbr1iBW4+msHeHiOmYc7Y4MQ1rRRLjyTTj1xKAUZRF9hSMPt3zgPve5z00qT8DokEmu0c5clId2C370lTVL04f1Gug1MLs14JMEn0zAddyCotWRtAUybgAX2x7Os2Miz+o9xYOb1Zqs1qXt4FBYzsRgDMbT2xfe2eqBu+qqq5rDKqGtc/ScXkY/huGHBu0teLhRCF7giXYROVllSRx9Bm0h/LN9IPI1Cm2MThh2wAde5IaOG755hx9WAok2aQSjMSjjA9pa+iJ0mMko3pCJyyAw4nCjIzdHgpN+grEBH2/4uKgtP6RzQxpbl+jv4pksGIc4IJg+mzFKzPeGqXE3zDji8RjcRoGPsJBUOfYy7HknviLPmR/9sXWWMYN/IAMHRhc+ypAf8TwX4oVP9Prn7NeAyglPjMHUGznaAC9ThKuszX7Jeg67amBBG1uikrzh1DuTS020uIaXiQwdGwYQDnHl6z9nejAh80aVm35Ix+RZhgu2F3lDydIxrSY56qijmgNxaWDp6HTeC+l9G0PkOfpdBk2MmQzGW2Q4KZ/Ojo72He94R8MXHYCMCYcffngz0ZEjnoGCthHFDkJwpUaiFE4655mvTbqulm1byIC+maBxOCn6hmduD3B9C4/rN+qmq1/8CBcGDAxlOIwVGMCmwlPGm8LodFXO+GqEvCpn++67b2MUYyAVlzu7/oRLqzvIQ/KJMkV50rXE3Iajif6xxx7byEU8Z4lo5QvvKrcf+MAHBnlEHdDgjLyhHpAGOn44b6ZrTdg9LuqaOJXbr371q9VJJ500KJOCVfpMnxgOMDahL36c40I9i2mdB5VNYLQaDMMWcmF0IZyrxnHohC8R1B/JrS2CXA+dyeO0dVsERhvSkzccUs1SaQwwKgPHH3/8wIDrvJbepQueWlVHXWGZPgZK6LC8XmfZ0O5o6Sptl4wkGGCB56aKrs5lVtkj75BFuJAV3cIT+VPKDw9/5Stf2ZRJbgzBldJ05bOH6zXQa2DJaKDr5IF+yLc+aoWvuMzqPGFM4nWgq9oGwuk7OCCWA+UxsrASlskMB+tr5YbDM7HnzCzOigNWN0AKxvkAv36Og3faWfqFc845p9kSQx/JygXBY5igXyeOcRd9IeM9bcd2OUt0nLboY9ABD8YLxnngh476UOCgjU4YSwIDLGlk6IFX/IwvaGsxyuijV+RL+uDJqiTOhmNMgsGKtNDnHBf6MsYEpJfRjHcmmqxm+fa3vz3p/BJwkBd8sPKDk50+NFWu1Ge7P3vHyNJmaFF8fJbKr48VxI/rJNNXlm8Kw+BH/jz5yU9u9OWOeQWrXOiLBe+4om4mJe49S1UDypssv3xeRbmPBmeVMZUv9y9VoXri09LAgjmzRQXXGygKMX5/ZtqkAz300EMrJhAf+chHmp8c1nt92Y5pmZhzlS8OI022L49JNpWNZYVMRDTZEy629zDBnYrTxJhJJ/ui+aqOvBhN4A3H1gXni0EDX8RZrcNBrHzpYUCEjvhyg6OD9cN6ox6BKXVWistgGuS10/YKDFDohI5bjgnc6aefPuDZ6USag0Qz8HLYYYc1WLhFhkGLD8ooAyeffHLK0yikGahwpg/42frCTw6dQ6OLY+BEmWRiT95r1QxpWamFoUqOQRllgHKnVU6KYxCGsUO3YqnuwMcRRxzRlCndAAQsW0eYHJdc1mkoTHkHDcothjbqmeoPZTFLDy3VYdHlLBRtQ9OWuFJawiUX72wFwxCBbOgKIwQ6Qgd8waOeoicZrqCJ7OibuhP5EW6e/LiWEmMD/OlAbdJQxsHBIXrkBWUfHrrmuWSHBgYn6iplJuYpcBwaLKMPfuSGHu0by7/5YVTydqekP6fLO2mQjZV9GW3aQZUnpe2fvQZ6DcwfDcQ21dtXvbu0fExigo+jzaONVH/Ak48b2U1lGBC0Oo+04GbyyjiH1ZisHMEow/hFjtUc3FYkfLSVW9eHk2LsZhyklTLAgY8nRgD/yEHaJzzhCQ0OnLaD4GcVLCsfoSuekIHxEnwhKytu4mUH3DJEPy2cpIVmDCMeg4TgwE2by49VKaTBcCDaPJEf2pxdhk4ibeGg/2JFq3RDG+78NEiDw7BDH0k65AYePaoMoEP1Hax84SMF8isMdOQVhrPIt5Ny+FHfhcfTRTlKfslBvJddwtElK1C1RYstY4wFKFOSxfHG9O4nDWNKygfjP22/YpULhik+3jIO0VhcenX9lmTow5eOBjx//d0vXNDHNXFYKttLR4Ke6kxq4F51IZj3m+AlIk/fl6p3PeksVBHigZqkxcLM5J9Og3iWAdJxxUZc9FiyqUEEX4+BLambjhL8rGoBHw2r9jPj1w9DCQ0xX2N8+b8KhcvKO5MoDAR80dbyfZa66vDTaMiBjvPNahIZY7ipiMHQRz/60WbSGZ3TJg486JNwp4Os0If/7BBN4UFW9jYqT9AJg5JotAKO7TXSseeHvtAjhzoq4lnpo3vupWeXBx6yH3xTBnQoaleeHDd60Tkfmf7BzSFqlDN44zpIlbNY1sAb81x8i1d0QHllEFta5UGZwOKu5cNsE4O2BmaRLn5W3gDPQE7byqRvz6csTPrga5ryzsuzl0FoaTCJTPCUlRvhZDUKg2EGcZfVh86p7hAf5XCVQXaeAAAgAElEQVTdqR2gfFKmiGPw6rRiPUXudepbBLwdyGjEsgQebtTAseLEB8DoCxzog3repj/VpcgneFXPde08+cTXzEx30FM9Ii0w8NRGW3nHWQnOP7KKL51nlLVn0KEtU54iK3zoSyThlA14juU/6hhcves10GtgdmggtnfZuEttL209hmwcYRg3fHsNdZ30ckon+Ow5LEy4WNHC+SUYImjH+eDB6gsMPBpnxLYm+sHVNWwgxPiLyxLjMn9GJ4MbNSzilT8+S7J6eMRFHNuFMCIQp3hW9fDhgba9pIc2+m1xkj/jZZhuxIs/9c64kXGS35qZ4eMDKv16PDw4g41yQIsPVzo4WmnoZ6kbGie5LqciZ8ZLHzY9DcQyg9/nmcwx1dZBiQ9r1APlpZ7ZaqzpcdanXtoaWHDGFi/8sSLg14+MUcXx96xRi2GeLmZwW1xsdEnrldD9enf8wu0yMJBh0sxXZjoInRnhMmW4hFf02Q7AdgcGJUxm9fVCcJmu2vTpfHd9F42o77b0UaeZPqP8UY8uR5Z/hI3CU9S98+/8Oq9693xxPCWeM1pRhuiPNEp+53sm3rvyMYwWKye0KoQVKKU8L+lM+R3163nclk8Zf8LpT+EfJneXspXhyMIy3giLNKI/S+f683inW9KZaHreZO8adDh8xm/GXx/Wa6DXwNLRQNbeucElxrPtRx+m+LrPahNvO7xNUVqXLGuL2sJISzw8scXSbwfh4wZbgryNd1pZ25iFKU0W5/KMkkMZrlHSd4FVOwys04u05UcW3vV0GuiXDx70x4LniY4Zi0acUWeexvmJ4cN4dZ5KNIGJZcbLLB8v2ALl52043uydj0SsfNLHgqgrLwdRJj40nHXWWZO3W9UfYHavz7Pxcz5iuoyPPmzJaMDLj9oXf1IP9AEKjg466KBJH4HJy8zQ0lZml4xkPZXpamBBGlso/F4B4rviXbmEzUSBV2WMGZfhJsx/pJE/ppdf+CUDT1YgsAeXJY58YfBltSU8TotGX4epsY+UDiTjt42HGNdGt6SjtjSKi3zJ70/XYYSPfLoevVxMh8dhcnie+7vyJEsfecv8WTrCSrKUdJPhGQU2pi/Rj3Btfr440ZFxSC/bZCjrrMqQUTCWA8eV5bHrTzqKMmb5BGyEk3wZHeHOZIt4Mpi2sK56HUZH8W34PM7fHXd8z/TXFua6HcZzm176uF4DvQYWrwZim+dfd7N2EEMLBhcc8WyndUOr4xOMPyVN1g5lYZ4W3lhhR5tCf6EtQGpjYlsT/eDKwsTTXHg6/2qDo1xRRvxRtw7DCiFWs2gFseJYnZGtrnR6gvVnFibdOt3Ip+u/LS6WWZVTygfGOLZATdWxJUvXZYsH8Pt7pm9g2MrPWUbu2HbF1jfS65fpYqr89ummpgGVGX+6wY4jEGSs4xwpfp6HbmiJH5qmxlGfarZoYEGc2UJhVkOaNXRkRqkRjumUcSX4mLFK3zWd8PrTG1N/z2hlsrLVgTMaOBOCw1E5ByPylelAPOjATdK6oaWrDjKeoJfxID5KcVHmNh48Tu/Sn/sjTvnhoQ1/KV1bGpcrypjx2IVf57MLzyW6JXkU3iZXW9wwvDHe+euK16+xxtDC7Qtxu5l0Gem1+cWL89GWT+DqyjOww/JrVFwuyyhplS6T0+Ni2WnTT6bXYbrL0vRhvQZ6DcwtDVDPYzuuti62IcBy0Ku3M2wh5Os9LmvHIv5SOxRpejq98/GJiXCp7fM2a5RcyPgupY86iXCj4Ipph/kzuaPMJfrSofjXxBJDCwcU81Qe6kwbGbOcr4xeW5hw+lP4Sry2xcdyIljk4YwZjEbTcZz7yPk7LjuT6Vhu3S+eOMuFLba+IkLnJ2Jwcaf8GKaD6cjSp801kNVhD6Mc+aoonfEnbF7e+/zLdTyXQxfEyhYySIWe5yi/xZG5WaX0ylXqZGJ45C2TUbJzhgLLc9lOxPXNsaIDFys4fpbYcqCctg/d5z73GcBFePHjfETdR56j33WT6UnwJdolOTyctF10mekzyhb5b+PLYUuyOV/icxi/kU/pPPLaVbejytQmc1uc+BS9kk4UH3E5PNeWMyDhbBCMi/xKuozygSfqUGEe53y04S7xGXFGupmcjiviLeltmB6j/BneLCymc515nMIjjqgz0hCmX/Rn4YKJvPT+XgO9BmaXBrL2LlvhAtfA+lYitj5z8HxsQyRhbHuiXzijRrxtLLWT3k7F9CV+SuEx/Uz7Z5JulNtxl+j4V3vJxqTy1FNPbQwtSscZbhzYjoFBLqNHmIdnMKRv463Ea0n3XnZimWUb/plnnjnS1qESHQ5z5owaX7EALDThOZbhKDu3PHGmkA7PJW1c4eK6GVUPJb778G4aiOXI6wbvvoWIM/Y4WFrlXc9+C1E3Xc9FqAVpbCGjYqOa+ZWh3ilPpwFzPFlhyToQb3Bj4xtxSAaXL757mmFyRXpqEMDh7xkfCosdSBtsTBNhM39bfsS4yL/kyPBmuhwmi/BEuhF/13IgfjO+HWfkS7zHcNJktD1sGO9OdxjssPhMhqirYf4oj2hGnbkuI07Xk+tO7xEef8QX6WWylXBHGYRfOLrqMcMDDoUPwxPjo1/8RDruj3GOQ+/Z08N4j37XSYkv13n/3mug18DS1YDaAp6ZkYVw/eCUA7fPPvvsQXvFeQb+cUfSkIY2wPF7XHxva5+GaSi2VcC3tT9tccNoLY34yG9XeT3vlLfwjyGAK6C1ooUwbZeQfDK4QMt/xJf8ihMO5zvKkMEoLHt6GYzllMsZzj333CzZlML4cMlHoSgP/lie5fc84dYntqK4wQVDFnh1AYTjLulmSsz3iVo14HVC725wYWeB8q3fQtSqynkZuSC2EanxoQLQ+OhZylHBEJ/BTqUBy/Bk9LNOxBtbyRLTDsPvMintMDkyullY5MVp6T3Sih2JcLgcgsnwdwnL5OzCv6dzHqIsMS7yVPK7LqKMzl/pvYQ3hju/mb6zsExnEW/JH/MYuCzM00ceoj4cVrgyvRPmtHiP+vOwkgwuPzhFK8Mv+ZxOF5mBF+6IN5N3mA5Jk+ER71n6LMxlb9OPp3W6GQ+RTklXhGdxMX0bX31cr4FeA7NTA9RjtQ9qlyKn3Lbijlv5OKhWztsH4SNO+GL743QcxtNEHvB7mxPbpBg/LH0WPxvDhsksuZk8yqFT/QhjIhm3DrHtfJ36lidWiGhbb8wn4Y75G3Wf8ei69PiuOo5lxGVCVm7om0nH1eKZsSXqRH7xo/LO1eZcL+4GF847wkjJDYzSpaefil5mUuaFgMvLkeT1MM78cQMZuwzcDSvbC0GH813GBWNsyRohhSmT1aB1bai6NmKxIR1WqLKKp7CuNIfR6BIfabo/xkV80qXr3WGiHGqYFD5MZzF9G27PX/ETwyL/0S95olxd6EZc8pdkbNNzm9yRl1I5jrou8Ud4V3oZji5pHaakj4g7psn4BEY/xbteI075gYGPqCP5nVbEn/GhMMeXvWf8RP1Fv9JkesvCIo0SvpIcMX2k4TpyWKeTvcd8iX7np43nyF/v7zXQa2DpaYC6GttNtRlZW8GXeb74Xn311Q3T1157bXorkLcP3gZFnO7XexYWNdTW/gDbpQ3qAhPpLm2/85y9Y3wg3L/W650VFxwgi1HFL2C47bbbKn6k4zpk8pftNPijnmNYjM9073xOVX+UCf8hk34Y/GbSsVJms802m7SdCvxa7RPLMHHiDVl5x+Cy7777Vuedd97g8GEMLldeeWVzaC5OsDOhn5mUf6Hgiu0M14XLcdUzZ/eovHsexfyK/oWiv/ko54IytngGthXirKGi8rSlEe4I0zVdVriEq60yZulKYZG3ElwMj3zg78ITMGp0MtreIHn8KDrL8Eb+8bfxG3FE+pLDnxkNhUV8EbZNJxGHcLXx72nEe3wKJsoWeZuKv03etjjnifcusMDFcuN+xwM+118XHYonYIU36izD43RKOhTOiLsE31Unzo/0E8OmQ8NxRd0Lb9RRpBf5yfTlMFl8xNn7ew30Gpj9GlBdLvVJSEAcP1ZCyNhy1113VfyYnPjWE0kMXm+P/F0wCsvgMnjSxban1HZ10XxM2yXN0oSJ/MofDS1ucOGGy0suuaQxUHjeRDnISw6aJY+32mqrinMr/IwK6X4m9R95aPOrDOr5t7/9rSl/M+k4IJWbE+NWqowGfHh+yM8Tg9auu+7anHOk2544NBdDDFdqS5c8I56MVh82fQ14+ZHeCaMccV6LHNeeR0c+e7mP9TDC9/65p4EFZ2yhEFMBcHrvUrAjTPSXsn4YnHhR+gg/zJ/JEhtXl7nEZ1u4eCg9pcs2HFmcy+Z6UHjUTUlHGe42nqJOY/q2+BJvbWlK+IfJ5zjb8HeJ83IS+Zlpfxs/Ga0IX9JLzP9hMoFXuP098uAwqjvxWUpDuKePcNEPbIbbZY76iP6IU/5h+iili+Ft9LI4yRPxRH9MO11/xN/7ew30Gpi9GohtX+SU+BVWWKFiOxGTUtqVG264odpuu+0Gbaa3GbzLn7VBHubta+kdfiJ+8VgKjzLMRX9sh6PMbmhRHDoknPNM2BoTdRdxuF5uvvnmit+ee+7ZGNKUj+IjPku4p6vrWA7w6yeZp0sjSx/LUizD+J0XcKgsO8/AcVORbym6+OKLK25mjLd7KX3GTx82PQ14ngiTh914442TCKy++uqT/LE8TI+bPvVs1cCCM7aQEWrM/F1hXvDbMq0rXBsO0W+DGUZH8cMaU5c5o5s1GM6X6JSeJRmG8a90LkcMK+EeNTzjJQsTXtfpMP2NygvwEWcmd+Qv+p1X4cxwe7phee14piJXlmYY356mK2xJf1EPmT/jMatD4sV15vxFXqMfOhle8SS8WbosLONbYRE+y+cI04ava1yGsyRzide28Ax/V956uF4DvQaWngaou7Ht9LYha6M23njj6gc/+EHTP/JFmBtXtOweSdQeeLug90gL+NgWOUxGP8PrdEvanG/tlPKOp1atoC9tsfnpT39a3XHHHZMMVNJNF11gqGE7DKsxYrosj0t6n0448pR+4MVogYwz5Zhol1a1tG0lgr7KsfglLDvD5YwzzqgOOOCA6oEPfOCAbc/LmZKlxzOhgViGlF+Ec/urHNvodJAxeVJqa4DvUof6PJg7GliQxpZS9qhB6lrQl2RlKNHSYCHyroZZskZ/1EHE73gFK5j47KqvSNN5izRKsDMVHuXN8Aom00UG3zVM+IBv48Pj2uAinmH8DsPVVY6ZgBuFF2Bdd230hXeqOoy42/hsi4t5U8LbtUzE9G3+YXy1pZ1uXBfaw2CGxU+Xxz59r4FeA0tGA6rLPg6JYxL8/NZcc81JTPFlGAOMO+GLbYTTEXwMwz9KHxlpRLxLRoNLnoryQ08ZWXhy4Ofll18+YKqkozauOTCUc0d23333Rc4wId1UcLbR6xIXZcY4MpPGFm4Oiq5UliMcftUZ8UkYBheuSefMHDmuiN57770nnZ+jcr809JrJMh/C1I5EWRTODWt+MO469WHR0ZEfXgb6/Ikamh/+iUvv54c8S0yKtgqhylN6dmXS03dJ4zx5BSZtiZcSjZi+jb7TbYMrxWW8lWCXRvh05Ys8zzS+6eLP9D8bw5BzVL48TZf00uUodLqmaYPjq5Z+o9Cey7Cx3Mb8yeL7sF4DvQbmrgbUXqmuR0mIpx3kC7AcZ7j4eSAKd1wZntg2+vkgpfZ2lDY40pzr/qgvyeMGCPIBv1YeteVBF31w3shvf/vbSaDgXJJOE+P4hAe25Myk4yYi0ZGcUe/D/PCjtMqblVdeudptt90GrP7ud7+rLr300kVW7QBQMhDMpJwLFZfyQ3q+6qqrBqrgYOjllltukmq8rC/pcr9Q82hpyd0bW5aC5mNjGllQfAyfij/SGrVC3/2HP1bvfO9xzW9JulH5HMbbO979wYrf3X/4QwM6mzucr59+VvWO9xxb/fjaiUO1ZoLfrCzMtJ5jPpRoZuExbfTHNMTHMPk9Tni6yBrxRR7cPwpsxmsb7hgXaWX+mGa6/oxGKawrrVJ6hXfF08P1Gug1MDc0QN2OTmEe5+8bbLDBIAlfhrmCN+sDCRvWpkQaEb5keIltUkw33/yeR9K1Jo8YWTjYlSc3D/31r3+NWTplPwYXz1unPWWkIyYsla3ll1++itf0joh6AM7BtZxJJKeyqyfhXcuUYHkqj5jMr7322gP811133SKrcjI5Bwn6lylpQPqPZfhPf/rT4PBiEHMLFc7z2P1O3NusKTHVJ5p1Gui3EVmWUFke+8SndM6kJ+y+c3XEaw7tDD/TgFNtOKnIXdPec8891dnnfr9h/YjDD0lF8M4iBZgFgWd95/yGixcc/PRqheXHGHIdtDVuXXXVRcwuuK6ujSzfqvndYdutqs022XCAdnHpuU32LjLNFExXPqTDrvBd4TI5ppM2w+dhM417pvEN439xyjIK7R6210CvgdmvAW+f1Jd5n+Z9I2e0PPzhD690ZepFF13UnO/hbf8o/WFsG4f1wxF+9mt3ZjlEP/ppNYu2EbGFaCYdh+VyQ4voCfeSzIM2Whj+OLCZLSFTdauuuurglqCIA9qi73zwjk6yVV3gUPlXWea5/fbbNwYWbV3htqL99ttvcC6O8Ctt5KX3d9eA9O4pvAxzO5QcN2+x+ii6mN9t5TCm7f1zSwMLcmWLVxJvqEbNOu40aqtwqnjDnpHuMHiv0Eqbhf3rM17UrJDo4kh//Ec+Ue2614GDTm8M50TqKKv7I89daM40TOTB/V/8xAkVv5VXfPAinTp8tKWdLp+OW7gIu/2OOxvj3re/+/1J9F/+ooMbXnfcbqtFeG3jM6MzXd5nY3oNTto6pi4ws1G2nqdeA70Geg3MNw3Etlp+tdOZvL6igMmuT/Lp66bjvH/I3qeDe66n1eQeHcvAoierW6688soZFZG8ZbWMxi8zinwGkLHy6VGPetSkg3xHQctqlh122GFoklgnVC7btraBFDjVB959OxFGl2uuuWaR8S3ppluHhgq0AADieFx6pTzfdNNNAw1wyLfyl0Bv9/x9AahswYq4oFe2qLGJT0rD5z/2oWq1VVdZpGB4hVFkbLQymEUQTSMg0nNUimP7z29/9/uUSkwv/xVXXTuAjzCZrN7IE+9yZ+m76iVLm4WlwtWBGeyqq0xYlYmPvJdwzVR45An/TTffOoa+fvf45R+4XHWv5cdOko/pIj9Rp4KP4aRri4t454o/k3Ou8N7z2Wug10CvgYWmAfW93gdnYwe+BHM1MJNx3I9+9KPq8Y9//KRxBuHqzxeaHhenvL6aBf1iZOH5xz/+sckPv0FoJvjAoAB+/VQelkTeenl0WQjX7/73v39zkC8T6Ouvv76TyOho0003rdZaa63BDTQklGzxqTjJHGVHR9kqlwiPcWfDDTdsbvLCYRxbb731BrcTleTtJFQP1GgAnWdO4WzhkmNViw79VnlSnMqA48rCMlp92NzSwIIztqgy+DOGkYXUpVKF8ooimL/U223+UBs4lq8nycvWlQv/z3/xy+qOO/+72vrRm9dbVyauYYtFxGFXW3Xlar11Hl4tUzfuJff3v/+9+u/f/m4QDU2Hv+PO31Q/vu6nTfxdd/+hWUFBZxF5EO9/rq3ft9/xm+pnN97UpPn17WPLRFde6cGTGnfg6XR/csONtVy/aba4ZAYpMYbB55bbftXogF3b6669VrX66qtO4lUNS6ZryQnvGCDQ01U/vq76nz/9udps40dMog2ty664qlq91t+GG6w36RR2+IFf3MorrdjEQU+0pa/lHrBstflmGzf8KT9db9BgW5X0TToMJkrjct9ay317LXfGz5hcv68u/eGPmiS33X5Ho//ll1+uoT1G56/1/t7J+QosccKNH/zrhvKCXOSpyqNw/uSGn1f/8z9/qrbecvPqQbZ3WHz3z14DvQZ6DfQa6DWwuDTgEz3vg7P+Hx623nrr6uyzz27Y+dWvflX3X/8z6UpbwvvJyczllueD3t3wgsGL8ctMGlu48UflAkmgK9pLOm+hJ5p615Nx49r1mShsCcLogtEJ41N0nJ0CDDcP3ec+9xkYbCLumE5+6cKfimszuEh3pGMlDtuztJ2Im6Me+9jHDvQsGb0Olvjpw3MNqIyqvOrJqhY/GHf99ddf5LatWBbcn1PrQ+e6BhacscUzzBt1f1ejpcpUymRvqH5w0WXV2959bPX43XZqjBDHHH/ypGTbbvWo5syTaPA4/E3vrC4Zn3R7AuDf9sZ/n2SYgB5GgLe8833VJZdPnHJNuje95pBq98fu2DSmTz34ZQNU4MYPX2887BWTeJJ8F178w+rt75k4APfpLxg7m+UzJ31wEvzV115fHfKaIyeFbbDu2tUbDnt5tc7D15xknPrO9y5o9BHdSvU2nve/602N4QUXdez+3/z3bxve/6XmfftttlwEHzK97lUvrV5/5FGT9LHSgx9UveaVL6623WqLAfkDn/vy5v2Uj7JiaWyVC4OGN7z16EV0+fxnH1itsfpqg/yU3o778Mea82veXOv6c186tbph3DjFKqj713vMyZtnvuDQ6re/vyuK3ej/9a8ey5ff/Pfvqqc9fyIvTv7k5yt+R9R63K3Ow+NP/Hh19ncvqN50+Cuq3Xd5zEBPp55xdnXMCZPLlQi9+22vH8iLDi+48NKG/ze/9tDqV7Ux56RPnDKJp+3q8vX2Nx3eGAa9HC/CeB/Qa6DXQK+BXgO9BqapAU0ehaZtwqdxAKtb+DKcTRqdnb4Pm2bmjCdHj/pp65D8nAWCkSFbXTEd6hgloPG3v/2tmZSqXEwHZ9e0KpOa7OopowZ+rbrR+wMe8IDmKnJ4xtjCypEtt9yy+aAp44pv/XGZIh3JGmV2vmLZjrCxHgCPYYitK9wahfvFL35RbbTRRtVqq602STVtuLrqcKHBoV+57D2ufKKsxHwnvcJcf31+zN/S9E9H1m62icdX/i+fdmZjVGDC/YBll6l8G8hU+VXFUOfhFUZhn/jcl5vg/fd9YrVsTdcrE+GltL/45S3VeT+4pLq7XknynfMurNavjRBve8Or61UW61Y3/OwX9aqRXzb4HrnJRgMc59QGCSbtwL62Ng684NkHVWuu8ZABPAaMtdd62ACeVQ0Hv/jV1c/qFTMYFN799tdXuzxmu+qmX95afe30b9Vffn5d7bTDNtUTH79bsyriih9dU21br2LAuLHjdls3BoGscVh9tVVqvjas4Af36ROPqfbbZ4+Gxp///JfqK6ed1YRfctmV1RoPWa16zaEvrh63607VXXVHw405t/369sYv3N857wfV28cNLU950p7VW153aLXXE3avV8zc0aye+do3zqr2fPyu1QPqlSTReR79T32a95e+fkazOueb53yvkeV1r35pUx6u/cnPqhtvurl+3lBdWhueDnjSE6uXvOBZ1W/qFT+sKOIclGcftN9Ad58cz1fkIj10jjrmhOr8Cy9rWHj8ro+pXvWyFzTpT//mdxq5MJ5s+ahNm5VJwH+/zt8bb7qluu1Xtw/yYK8n7FZtv/Wjm87txI99pvrhlT9uVo68vubz2Qft3xjXrrjqmobXrbZ4ZLOy5v73u2+dR7tWN99yW20IubN67jOeUh12yIuqDdZbu8Hz/YsubejsvOO2g/zHePXuD3644XX9eiXLW9/46mrrGt819Qom+MQI9Nga/kEPGjvtvimPF1zcyHFBbQjE2IPhZeWVV6x+WdPFULRq/b7RI9af9CUn5kfv7zXQa6DXQK+BXgMzpQGfUGTjkUgHY8stt9zSBN91110VX4q1skK4solLxNP72zXgeZGd1cJhqzJ6zeTKFiajGDD4+HX66ac3NMjzZZddNp2QtksxtVgfdwoDYZQr1wtxhMHrtddeW33/+99vVruwTeTe9753Y5jJfozrSEcc723GGNH3si26/nRJxaOn4SYlVoOxGhvHddA6B0n1JdJwnP17Nw2ge+mfVS3nnHPOICFX2GvllutcBjiViT4fuul6LkPNypUtr6tXKtzw8180ev38V77R/L78qQ9XGAWm62LnroriFQYaN918S/WP//vHJHKqEApcZXxLCn7hZVWDVigQvslGG9TbPNaqXnH4W6qv1UaLpx/wpCY523FO+Mgnm/dDXnxwtelGj2je997jn6t11l6zWUHyuS9+vdp1p+2bcNyH6hUP4D+gNgS96LlPH2yVWfNhD62eUa+oYDXEc595YLNy46GrrdqkYbIvQ1WUUf771XtLwSG3ysorDeRxfe2y03bVS57/rAYMXTxy0w2rvZ76vOqy2thBB4lln05Icj3vmU+tnnngvw7gj37r66sTTvpk9cWvnVGd9Z3zqmcduN+Apl5Eb4y3sVBkBtcznvrkJmCTDTdoDDDfruWF9rFHH9noGXfUW15bPe5Jz2jef11vn1rNzmohjDxlIMHVhaTH/cdbXlNts+WjmnfSn3v+hfVV1x9q/GxJGvDUhFSNoQW4berVIXLw86Wvn9l4X/XS5w/oPu2AfauH1OX2ne87vvreBRfVxo31Gt1RdrTKiXj8hGvJboOobsTxU1b+8+RPN0GHvvi51T5PfFzzvnFtKMEgc+LHPlt96dQzq89+6euD1TP/V6fDobujjnxttV1tEMJhvHsoq3aO/mB16hnfrp689x6NfNDWswHsXa+BXgO9BnoN9BqYQQ2onxFKjamyvkf9LhPZuLpll112aVB4ugzHDLK+IFCNjbsmJo/ys33r7rvvHugAA4mfSzFV5ZC3uqWF8SMHyZ5xxhnNahFupOK6XIxrnN0z087Lot7jKhbGX4TJMb798Y9/3GwTUXnjSmcMKDjwaCLtRhfi3MACnH7CrbowIDaOT3RUH0QnwuF3WOj5NjyMLWx9WnHFFQdj2gyX4+3fJ2tAeVCqJ9mqlphPyueY/72u57cGZp2xhRUAMrS46i8JUVcAACAASURBVM/6zveq5zztgCnnhjdUXlHiuwi84W3vGUrr0x/5wGBS7fh32HbLiQl6PZFlcotj4nt7fc4HBgAawlPq7SdyvjRT8Ezq3WDAShXck/d5wmBijn/V2jgCL7gV6nM/4OUf9Q/Hf3Crghf1MD45J43z8o9/TCyZ27leNaM48LFkktUvyHXl1dc221jurFciaRvNfjWfDk+aJz5u18bYwraWZz51zBDTMFq7yJvz8c/1yhP3r7nGhHFo4w3Xb9Iq/VaPfmT1wyuurgcDP210MwlvLSd4flSf/SK3xSM3mYR7p+23HsTViSfixnVK5BabT07zwOUeUH3rq58apHNe11pzjSb8y6d+s3rhc542CTee/6sNQM4/NMfCx2izuks63aZeNeP5iU633GKzxtiC8ei1r3zJIK0IPXrzTSel2XTcMMXqFs526bcSTWRJ/9ZroNdAr4FeA4tXA3E8gt/7aafOWCluiWDbxgMf+MBJ4xrhXLycz0/sUfc+HuGdbShyrJhYY401Gt2zumOqjoNct9lmm0nJ2eZC3nJtLqsEeGJ40WGzHPQ604YXlRs93bgi5tABB85ecsklTTkFhifXQmMkchy8u8HF/bzrB25/L+kRGGiJRoSLcZ6X8ZDpq6++ujm7RbRdvhL+SK/3T2jA6wnllTON5FjVohVNMZ8zXWdhva7njwZmnbHlz3/+c6pdtufMtFNF8adoPOdp+9fndoytDmnC6gYvuhXqzn7QsI1PkNnigRHCJ9tMxOVYcaA40v71r/9bXXTp5c12Eg6TjU7wHHaqCffyyy03CT9ptDKC94ancX4G/nHE4tef3mBEeJfjEeuvO5i0K/0Wj9y4OqfeNvWn+uBVVmD8qT68Vu4H9VkwjdbQ3bj+PH6guxJvjalozCGf88lqENw/77LjIrp4UH2YLg54558wjEeEwS8OYxGNnMPhX69eXfTzeivPRZddUT223qrV4Gv+j9GMacajmoN4r6+3OF1fHyJ89x/Gyuxddd7JOR1lEc88fKys+GHIbEOK+cXWM7m77rq7OcBX+Y/hSQMDwaxYyyxHveIAXTX04O4b/YF6+pdeA70Geg30GphBDdC/qO8f1u8IjhtdmHhrG8u5555b7bvvvp3xzCD78xaVxhXSufycpcJqDjluusGxPYLxOue4jOpYtfKYxzxm0qoR4dhiiy0a446vpJHhhTKAY2UNho5VVlklxdGVn6wsEsaPcRPjMuS78MILm+04PpZCP5tvvvlgQg1NpVV6x0VaGWEULj5VD0p8i88MLgsDj8Zy6MrPbtl+++2bVUPKZ/Fdot2Hj2kg1ovoH7aqRXpWfqkMlPKv1/v80sCsM7ZsVG8Rydzee4xtncjiuobFykE6dSh6CheHza66ykqLoI4Vww0nAD98zYcOJs6C9UYNeKU5/azvVMed+IlFaDDRl8M4QHrO5ZC7733vM6mhVDhwapQ12ebp9IF1WSfkn2DD4evUgwjhnhQ/Hs1KmqZj+uXNA/h3vf+ECaTJG2fz6LBa8ZU9Mag4zw2qcUsFtxS5oaKJGqclnsa948nG9KGVP4+qbx+K6ZHz4fV2G4wt0I157GHCDcxJnzyl+uo3vuXkmnfPT/zSn3RbUwh5NCaBZOY2JxwrWBbRQx3uxpM//+We+ovf2Oom0jyoNrwojRr37MuN+FIex3LeMNC7XgO9BnoN9BroNTADGvDxUTZWchLE77TTToObidgSwYGtrITI8MwAewsGhY/nENrHGLzfdtttk3ShVS3ofd11121WmlxzzTXNSpQubu36jBOuRGbCH8cZ+Bmf7L333tVnPvOZIjq2MGkb00wYXpwPvWPwwUiBkQU9xI9WD3vYw+oPW8s3PCoNT//JuOKGlpLBpSjseAR4lVeih99597xUuK4dFn70xm1FzjfvEdcwfhZ6vNcTttkNW9USy4jrz/Nwoet1vso/64wt9673Pr73HUdUH64PG2U70cr17TX71Yef6vaa6WaEN0Z6V6WJce4fRndi4myT6XGDgKfVyoo7f/PbgaFlxfpQ05OOO7pejni/BpTJ9XNfdnjzDg9jBpoJo8df6gn1/epDVt3FyirowdN4yeSOsgt3KXyC3mRKK9UrL+Q+fsJ7m1dg679BJ4SHsJVWfNAkI4Pz1SQc55nzUKJBRMaSu+/+4yQcnk5mF5eBM1vwY+DA3XTLrWkHBk3hGqQfF/VhD33IIjR/eOXVA0MLh+oe+bpXDvbxXlcf4nvYm/6jQTdJjnF8/zduUBsjOBC75nHMMLTMMmPXgF9eH77rsgiesjSRdqy8DIxk4+Unlg/B+xeOEswAef/Sa6DXQK+BXgO9BmZQA+p36NuyPkh9HqsYmNhz7gTu4osv7le3zFA+NGOi8Z9QSu9MzsfGcPdqDljlIFjGMcorzgDZcccdqzvvvLO5bpiJZ+a4dYhtQA960IMmrQYBVviVjjN69thjj+qss8YuZ8jwKUyGF/DvtddebaBpHLS97P3mN7+pzjvvvKacKRwYySy9YLDws1qEXLK4oSWGubzSY8pcEih+ifK0sf6IT/hgBY6uI2Y7FH7HE3ElZBd0kHSpOhL9WnGFkii7nOOTOeWX8n/UvM9w9mFzQwOzzthCId5+m0c3P8434bBWDDAz7byyCLfC2miVYAa2jHoCXYIBryrrpZdfOSDznne8od6TOrH16Lqf3jCIk3FgxQeP3TJDBIYAXxFCGFtW7rnnf6sHrbD8mNFmwFDOjzcaehfRSfwX8EzobyyVVpE8ePw2HELhffVVx7b7eOPi71FXzoviuOEnwsmQMvEU9x6z6DYi8NBpYuDAcevPhCxjYWyHunz8fBxCJmiPxT9k9ZUX4efyH40ts8Vw9u8vf+HgKwhpz6+vYZZzOdxA54MXSSAjzMN8m1Cd9/H68JtvnfjypNVYyrbJ/E/unOGJlUHwpEZf7x42YL5/6TXQa6DXQK+BXgPT1IBP9PTufVCGnj4pHvh54403NqsrYv+Vpe/DFtWAj0eIRY/+QYixEDdAya2zzjqDbTvKN+DZOo+xgx+O1SBcQgDMcvW2d61i8RUexPGLKz2Ul2wdY9VK3J6xqBRjIbvvvnspamg4NG+//fbq/PPPb27twYkPJfaVLaxoechDJj66CVYy+TOTz+GHMmcApCuNzYQz5inJOWRYxha24v2pvumTfIlylnCPwuNCgEVP0jNGOd9KxzY48txdLBcxbiHobKHLOLlELGVtxEaCifriMLRITKcXaUd/d9WML1coJJDx5A9/GLP+71bf8KNDXFWBuVXIeSScyvvoesUE7rIrfjQw2hB3zz1/rQ563iHVwS89rDHEOO+DFRrGTybbYCVEDUcHK14c1sP0PjBtjDc+nCcjd/1PfzbgRfCcEXLaN7/dnG2S4RO9CfzGePIqY8IkfsezAINDxMMZOIRx/ozc7fWtRY0BZlyGW261s3MMx4b17T84x6s0vxxPc+D+ew8MGMJ5/g8mjC2cZr8IT+N0BzKMMybZOFdG7tbbfr2I3m4f32a07sMnrgmfyJdB0nHeJzoJj4k8TU7V+3oN9BroNdBroNfAzGogTlLBHicmgiGO1S1McuUuvfTSSX23wunPetdNA9KVjwEUxlYtzyNWFuGX0YSVHbrK2N+5ynmllVZqfhwgK3gZHjIDBNw6LfysmGElzDC36667NisKpuMuv/zywaop8SJZVSbhm5U9O++8cyN3Sf4obyzTwj8Vfr0+xPeMDjQwdvnBwrpKnTjP/6nwM9/TxPoR/RdcMDFfowxq25bnhed3DJ/v+uvlG9PArDK2LK1MyTpmDqtlOw+ra/jxnv0wTIy5yZ27Oi7vwCQfYdttvUXjPff7FzeGBybm11z/0+rQ1x5ZcesQE2fcZVeMXTFHmqftt08TdvxJn67ee+yJ9WFx99QrWv5YffKULzfhu++8/eCcmbXWGrsBh1Uh4L3zN2PnfjSAtYsyc9gvqzJwXMsM/IRs44laHjIMsL3p8ENe2EC++4MnVqd86dSGTxz6e8mrj6iO+/Anqkt+OHFqdwvaYpQbhwS0iK7HB1wuq2BYMSUdv+CQ11enf+vc6vf14bKf//Jp1cvra7elC4wWSvPA5ZZN+SH+SePXMZ/17e81Bi+MKudfeEl18EsOa9II309+duMA3xoPGbul6iv1LUXoe2Aos6IEbjruZz5l7Mrw17zlqOqUL4/plHLzvuNOHFwLzbXcknVCPxP8xzyHLw97x3uOrXbe44Dq7fW10L3rNdBroNdAr4FeA4tLAz5RFA1NREo06a+23XbbQTRf6XUNr/rprJ8r4evDxzQwGDeMf/hRPvz6178eGL/YQsRYJBoZMD54mPvd6CCjjMKiYYBwnJcL0nB+S5vjoF4Oy52uY9uSr5JyXrxcsooHmplswC1OQ4vLKD25HsWzxymNn91yww03TPro19eZ9tLj+lE7QwrOM9K2Rvwc+kyZdedlx8s2MNE/KWHvmVcamHXbiJakdinoVBw9nfab33VMJ1Y+dvx7FtnS05pw3ADAdcBM9m/85a0Vk313Rx35msb7uiPfXU+kP9P8TjvlpIorjp/51CdVn/7C12sjzUXNz92zDtp/4F17rTWrf37sDs1NQa96/dubcL+aOMrMuSAvPPjA6ugPMHkfO5js5OOOGqkxUIO0wzZbVrvWK3a+WxuSPvWFrzU/d1wJvfce/zzQ/aTIjp56AeoikFEmALJORGFvqc9VQTe/q40sH/7YZ5sfbtf69qGtt3xk9d7jTqo2Gl/N0kSM512Gd9ONHtGAkJ/P+rdXN+84jCwfOOrN1VXXXNfgI0/B/6qXPb/af98nVmee/b0mzQsPfcMgXGmd9/3rc4tu/fXtjU7Jf37uKBeUD7kJ/Uw+sG1SonGP6sBE3Ji1Z9HwLHUf1mug10CvgV4DvQampoE44Sj1O+oPWTXBjTicPYHjamD8yy47+WNICc/UuJx/qeLYSH7yg49/fGxjUo4jjBVFGBKAE6zelYcRJ+H+c0OLGyV4j7DSuM5v+eY3v5lmAitfuDEJI8h0HDw87nGPqy677LKKVS64WIbgkdt8MCjJZTDSh2QC1sOmw6dweX75u/Pt+cGBvtpKhIEAnXGlduQ/+qfL61xO7/rjXX6e1A9uqZKjfnDVtvIn5n1WJuaybnreR9PArDK2UBi9cKtwjibS1KBFe/faQDF2nU23ZajLLDN2qO0qdSXbfecdBhNe5x2ZdqtXnYCX5Xxy73vnERUrHbgimW1FTPA333TjwZkcWiHiZoWD9t+3Wn/ddaqf33hTM/nm/I6ddtim2rDeFoNF1em++HnPrLbf+tGLKESyRh532r7+YvTKCZvCCvW+1L/WJ8zvtlPNe+2y/NhofILPlcSKpyN69cteUO35+N2qq6+5vuETGTapDRKP3GTDas2Hje3rzfA5b1xJjMGInFC4hBnT9/a1vjcY0FUnMcHTSoN0GH+i/tHdCe9/e3XtdTdUV159bb21aJ1mexHn4Zz8qc83pJYbX80CfWiii1XqpbGRdw43/tpnPlxdcdU11fcuuLhipdAWm29SbVLzR9yO225VHfaKMTmkK/T01je8qtKZKwpvjCa1wlh9IwcsZ8HsWtOHBleB4zisd7v6fKO169uTxBPPleu0lMdNNs6/+Dxu18cMcPOC7sgb3Gbjz0kAvafXQK+BXgO9BnoNzKAGYr8OavVjPhZ0koRzOKmMLcRdcskl1S677DKJs9hHzyDb8wYVutQPofydc1fk0KVWc0S4qAzPN9Jlv7bVH05T76zK4PwW3UCkcLYZ/fKXv6w+/4UvVHvtueekrTKRr65+zgXCEHH11VcvMrYEh1b4CJ/GnZFvH4/FuK68DINT/fGy7vzonecKK0yc+whetohhgBGOxcXjMBlme7zXEX/nOnRdRY8MlJvY5qjsE+/vs13mnr+Z18C96sLTzarw/7d3ps9Xldee31fACXAqERBEBUUQVFCIQ3A2VtRovImVa1KV6he5r29Vv+mq7hdd6fS7W/2q6/4DXZXqWJWYck4pyiCgUSCKtoiggiACojgwKARNn+8235N11m89e+8z/s7w3VXU3s+0nrU+zz7n/NbiGTrfd08l8kOCTrmXht2nwz7bumVK+g9XCifzU/eoHys7evZ9M80PtU17+dZGPnvdfJsoXdQm0gN5/F8MyPM2eHleT6sD63q9qshgna9rm7hN+vs0WKvPf/6vv8lnnPzHv/86u+Ti747i9rpGulidIv2KZHg7fDpqa/N8OfnTLqaL7rau5eF1UVoEREAEREAE2iVgf6/5ex/9bYZ+7G8qnGwczcvrnnvuyQMC9vcNZf53sV19h6U9WaeYY/Nh7keB2SNYZmPHyo+H5cJ6/m8N1PGBFv49aGe3ROOGd+LRRx+tb9iLsb7//vvzv+cRhMG7sHjx4uz666/P+2j1QhDiiSeeyIM7W7dubRCDU3y4jM3/feffM5v2Za3q5tv5MYzGknloC0b43OCCHQsWLMg/H0Uzi3yfo5K277rnihO3/vjHP9ZRIAB33XXX5Wn/zlu2Rb7PqHAdVTtb/0YaQGL8wrN3/+w/KNZMX1YlzfZV6jZbp13dov6iL4uoXjN5XmaKia/XTB+om5Lr5bCf/1HbmwQbC//P//Uf+ZRAysDJQgi04MIsHN++X9NV7bes6tD0IAIiIAIiIAI9JhD9HvE3FqrYZ6saTquxm36uWbMm3NzfO8U9Nq8vu/NMbJrPcCg5Njh9x44FAyZ2rxbuyRLl+fpM0/m0Din78eBQx+7fgiU/uJC/aNGi7KGHHqrN+H4/e+yxx7LDh7+b+etllKUZaHnggQeyFStWZHfeeWf9/YNd1157bT1tbbD6V3l3y/Roptz/PYq2qc8Ml7mgDo7q5lX0PjSjy7DVZZCFd9iH59WrV9dNxTI3zLTD5cfCp4eNj+ypTmDCr2tX9erDV9N/yVgL+UPjPzBlaciwdZi2+VFemVz/BVq1flSPdtJGb3fUxuel7Ixs922tLUXPVk/qWiQ/4hrZChk315ZfbX9vZ/b6G1uzHbU7luf8eeNr2f/53XcR61/X9nS5qHbsstXPcoqYeSa2bfSDnOLC/LI2Xofojxffh9cxlbYsU3YrXwREQAREQATaJcDfd8qJAgC2Dp5xOhH3FcGm9Cdqy55nzfrucADKqfL73a7ug9yenK1DCXvAFcc+gx+CGZjdYv8eif428Xk+IGHTkMs0x7VsrLAvC/S4+OKLs+nTvztggOzh9GJmC49wRr3zzjuv8tAw0IIZCtxwF+2x1AZL1ubNm5cfoVykI8uK6lRWqEJFcvNVU34N8jFjCRfGFjN1cEX8U7J9X8OYjvjx87Fz585s+/btdbMxQ4jvmR3/1GfF8h5GdrIpJtBXe7bEKnYmFx8CfoD4JYJ02RdKUXmqzMrnB8t+eG2/Ud2Uxba/6Bl5zPfPKZk+3+vjy20adWlXxNLrgjS/gMjF3lN92X68flE6Ym1lsB/sg/Lf/8u/Zb/59/9dC7T8Y7ooNrX91//0L9nya69u4JnSj/kRA5ZRzzIZVcq9LMvZ8ozybV70XKV/1REBERABERCBThGwv8985u+T/V21v+3oG/tQ2M1ysZzkyiuvzDgTg/oV/TZ3yoZBkwMmnqe1ActNOBZTpkypL81BHscG9e3YWd4s453torutW8YRQY/Uhb8v77jjjjxYsvbFF/MgyQ9+8IPSzXNtoAWzV+yFoA5muvgNmC2DlD69zK/yefHHY3OTXOrJ96HfbOslRzLg58PesY/R+vXr6+pgU1zMsMPl32ubZ/UfZba9HMd+62tkgi0Eb38Yil56Wy8atKK2ti8884eeH1r7pWjLfbsqacqy9ygvZYP9cqV+qFvFPupu774fyIn+2T6ivpBnuTHtmVj9qQfrRtx9GdK/+W/fnR50/PiJ7Iva9NMZF0wb88cEbfD22TR14b2orrW/rF5U7pkx3erd6uNlR/0rTwREQAREQAQ6SYC/z/wN4m+474N/q6De0qVLs927d9c3q8QUfyw3sUewerle3qin7d8seIYTzgvsEGjgmCCggcv+neD5+r/LWBd3/8/KsjLbGRNsqPvTn/wkX+6BzXNvveWWzB59bGUXBVpYz8+iaUe3brQlf7K0/O0zTvKy1/HanoXRiUTd0HGQZPL7xd6h/7p16xrMuPHGG0Nfie84x8OmB4mDdO0cgZEKtvgvpAgj6+BDZuvzQ+PbFP04+C88K4/yIY/1KDuS6fNsms9ld6+77S+yN1Wf+UU2WNnQy+pm06jnbfP9otz2FfWPPGsD29g8PDNtZTIPx1+feeYZ9e6pp9ff62f75nNUJ5VXZn+VdlYGn1N3yCsqY3+t6pXSV/kiIAIiIAIiEBHA743/m4K/QchPXaiDTVGxZwuuQ4cO5TMasMGpba/fs+8IepY2zWc44fbCzBZcDLTwjrwqXO3fG9FzVTkNSpUkMIvj3toJRW+99VaGY6OjzXOrBFpKuhn3Yn5urCI2Lypn3WPHjtVO3JxS93XsWNrP4rgb2SMF/GcD3SIP/7D8at++fXVNsNyMp8uCW+qfH5cemaJu+ozASAVbyJ5fPvaLJTUuqTqp/NQHix9if2d95qfkRvk2j8+pe5F9/FK1X672OdWWOqPcPkcMoBf/odzryTwrB3UsF98HZdg61IX64+6fbZ7VhXpbXSOdfRvKZ9/Ui/Kq3Ftp4/VIMbV2NfNcRW/VEQEREAEREIFOE7C/ianfR/72Yjo/9vHgSSuvvPJKPpNh6tSpuVr2b4WUrE7rP2jy/N9XVn/7d1ArS8HtGFhZNr8bvDBDZ+7cuflSj8drJwxhA90f15YE4b347LPP8lOH4DT7pUPd0KUXMsHW/j0cPdvPydGjRxvUsn/L9kLffuqDrOgf8A4d/fIhbMyN/Xtw8X229yi/n2yVLr0nMHLBFn4Z2Tux+y8q5PsfINSxl0/7IfRfdqzv5fp2Pp3qx+ZHz0XtvA6R/V6PyPaiL2j2n7pDXkpHllmGtv8on7qwDHf+s/JsubcJ+lh9mU7pafMpt4hbqiwlP1Wf9nj9mbbyvOxm00U6qEwEREAEREAE2iWA3yX+hlOWTUe/ryzH7BacsPLVV1/lTVetWqXlRCUDkvo7KNWMfwuh3G4AWjRGzfwdkuq31fwtW7Zku3btyn5SW1L0i5//PEMQ7pFHHsmWL1+e/b/ajJdhDLTwMwRm9rmIYdFnrqjdsJXZ7xc8859fPnTzzTfXZ3l5BvYzUvTu+3ZKDzeBkQu2YDjtD7r/krHlfE69AvaDVFbH92M/1Km2UX6qz9SHOlXfyrZfyFXqe73KbKFMf/dyynj7+pRn+6ct/s4vTciwX6LeXqStnkxX0Z2yvZ7tpr2OkTxbx9f3ads+Kovyoj6VJwIiIAIiIAKdJIDfH/+bDvn+byj2yd9z7NHilxNt3Lgxz+NvWkpGJ/UfBFn276Fm9CVHO7sF7ZHPsrK/H8rKm9GnqC5mtbz22mt5laeffjq77777MuyxgdOqnn322XwTZSw1G8bLf4ZoYxX2o/gZsd839rOB52j5EDdL5nufuoN7FebD+A7KpkYCIxls4Qeg6Esl9WWV+vD4D5T98No2zI/q+7yylzWqH+V5nSMdiuytqkfKZra3uqX09H0VjZGXa+1iO3vHs0/7/qgX7vxHfl5npq3dvg7aWr18f+2ko76iPOpf1leqbVk7lYuACIiACIhAJwnY3yP7d4D9vUV/qMc8v5wIpxNdeuml9WOC7W+2fu/So3Xw4MF6IZimLjAkRzsOtv54cWagBbrgOGgsGcLJRC/WNjldsmRJBht/+9vfZj/84Q+Tm+em7O63/Ig986Iy6m/35rGfsX6zr5v68LsDd//vyJEjY04fwmlY0TvtPws+3U0bJLv/CYxssAVDY7+EUh+eVocwksc+cY/+YKjSV0qubVtWJyrvxBdtJLcZvSL7y2SyjdefY8s72fPL1OZHOrJf3PmP9bxONu3Hlf1GtkV5XnZUpyiv2fbN1i/qW2UiIAIiIAIi0C4B/C7Z31L7O+V/s1iPfwNg9oJdTrS6tnHuT/75n/NTV3hRvpfVrt6D2N5y5t9HkR1kFTErKotkdTsPs1qwhMheCK5gRsvCBQvy5UO43nvvvXzzXDjQt912W3JpyLfffpvXtxsDNwjv84T/PFl1/d/OfW5KR9Wz3x1893n/5ptv8tOs7IXvFnsaF7hG/2yb6PPSUSMkbCAIfHeG20Co2h0l+UGhdP/B8b368lbTdr1rmQzU5b+yutEHO2rTLbuivpiXYlzUxrf1eleVSd6Wo2dq66BfX15Fz2bGlfKaHd8iPTyforoo0yUCIiACIiAC/UjA/35BR5/HNMroON5+++11c76qnbgCp8kHFfrR3vHUyfKZNm1aXRV7Ast46tdM3y+99FJY/dNPP83sprAIsjz88MPZ559/nv3f3/0u3zQ3uiAvJTOq3y959m+86O89G4DsF53HWw8GWzZt2tTwPtx66615wNZ+30BXm/bfTeNti/rvHwIjH2zhUPgPkM9PldsPm/+g+bQfdl+eSvt2qXQz7VN1kZ+yvUobr1vUxtepko7kpPJS+ttASCro4gMuUR9l+kZtivLK5JWVd1N2Wd8qFwEREAEREIFOE8DvGq/oN873Z+ufffbZ2bJly+pVPvroowxLiuhIscAGGLw8pf9BwLLtdy6HDx/OZ6ykLhwLjj1ceOFkogcffDC7srZ/yx/+8If8qGjOZEGdHTt25O8O/u3Zsycldijy/efBp4fCyL8bQdv4nWC/Gz788MNs27ZtdXNxghOW00XfSajE7yc+s+EgfW6GaWz70RYFW9yoRD/qZXlVB7ZMTrvlVfUoq2e/OMrq2i+VSP+q7TtZz+oBudYePEdBFx988baU2enr9zLdSXaSJQIiIAIiIAL9QgC/pbz8b7n/rUc9HnHXcgAAIABJREFUOlE4mtXuN4KTaA4dOtSwL4Ot3y/2dlMP7zz7dFnfdizK6o5X+csvv5xhCUjRheCbDbjg7z8c//zQQw9lr73+er60CKdaYabLmtoyNF7ML5I93mWtjFGz78F429hu/6lAC/Ix7jjJjNcZZ5yR3XDDDfV02d/2qNjKGLRrk9r3NwEFW/p7fKRdmwSK/jgr+9K0X5hWTpsqqbkIiIAIiIAIiEAJAf4GV/mttr/RcJqQvummmzI4S7yeW7kyd6b4v9jW6SpRZaiLh8XZxqwWzEqocp122mnZyZMnG6qee+652b/87GfZ6aefnm+e+/jjj48R9dRTTzXMfBlToc8zPvjgg7qGkydP7nNtO69e9Jnn9wGCdE8++WRDp9hUOfr7v+g7CQKs/9B5KyRx0Ago2DJoIyZ9WyJgvxj5RZj6smQHvk1LHauRCIiACIiACIhASwSs08Lnot9mWx97LKxYsaLeL/ZvefHFFxuc5WEJNLQEN9EIgQh72X1OEk36InvDhg2ls1pmzJiR/eIXv8juuuuubOLEsWeETJo0Kd8sF4EXbLTrL+zvMoj7t3g7kOYRxlHZKOQxyAJb8bx+/fo8GMsLwVowSn3v8HvIs7LfQb5M6dEkoGDLaI77SFtt/1Areh5pSDJeBERABERABPqEQOTwRL/fVJdBFGz2unz58roVWELy6quvav8WM67eOfRBiGO1IFW/X5jVgrFNXQiy/Kw2a+X+++/PymZ0IJiS2iwX8gd1/xYfNPNBtRS7Ycm3s1oYaOEde7Ts2rWrbur8+fOzOXPm1NPRd02UNyysZEdnCSjY0lmekiYCIiACIiACIiACItAhAqlAC8RHDg/r05G6/PLLG/ZvgbOMTVStwwVZdMY6pPZAi8GmoLy8k96Phq1bty6c1XLeeefVgyzYOLns4oa4ZfUGYf8Wb4MPmvmgmq8/TOmiQAuOikcAlhdmNS1dujRP8vuFZfb7xpbb76hh4iZbOkNAwZbOcJQUERABERABERABERCBLhCgMwPRUYDFd2nrw9HC0a12/xYsJzpw4MBIBVwsE3L03Jg+66yz6kXcB6Vfg1GYhbJ///4GU6ZMmZL96Ec/yn76059mVYIsaOw3xE2xYf7zzz/fd/u3FI2Rna1jg2n+vSize9DKiwItWDb0pz/9qW4SviNwdHxRkMUHXNh42DkO2rj3k74KtvTTaEgXERABERABERABERCBkEBZ0MU6SXimo4Xne++9t0Hm6tpJM9owN8Sc4UhkXl988UVcqYe5dhaSf37uuefyzW6RzyDLz3/+84bZTFVU3blzZ5Vq9ToI8Lz22mt52uuUSjfVQYcrHzx4sC4Ry+v85YMFPu3rD0LaBlqsvsjHZ99viItAC/Z6ou38PvHfK1H+IPCQjuNDQMGW8eGuXkVABERABERABERABCoSSDlA3vGxjhFE0+HCHhV33313vTdsmPv4E080bISacs4qqjiw1bxjff7559dtwYyIaLPYbhtrAxYcRx/EQMADm9ZyY9uHH344w/4sHMdmdMTxz7/61a+yBx54IF9GAqe76EIfmzdvbphV4/VjmnK8TUXy2y2zDPBsTyJCUGpULnKw7L/99ttwQ1w7Cyr1veLzwdF/fkaFreysRmDsVtzV2qmWCIiACIiACIiACIiACPSMAJwaOE3eufFpq5B1eBFEwIa5GzduzKsg4LJq1aoMR7xOmDAhlxvJ75mBPe6I9qJbMoT9dskVyrCvxezZs0M2neZF5xj9WkfZomE+gh1YIjZv3rzslFNOqdeP7KqCFjKmT5+e/1u2bFm+tGjv3r3Z1q1bM8zwgVw46vbCMhRsvosjo1OXZ9Sqfin5RfnoGwEpe8E+6FD0uSmSOQhl/j1Cmv9eeOGFhg2Vr7rqqgxLqzhOZFN0J4NhZjgI4zwIOirYMgijJB1FQAREQAREQAREQAQaCNBp9Q6PdbSsY4t8bJh7/Pjx7I033shl4RSblStXjgm4oMzLHRX8CDrgRJbt27fXGSHY0s3LO8foi84xnk+cOJHvs/Ppp5/mwQMu+0GQ48iRIxkCaQgi2BkpduxbGUtslop/ixcvzpedIPDy1ltv5XpQP+iF9wfL1PBeoR6CNHv27MnrXHTRRfmyLBwjbJdnoaxd/ahD3pG5yJJ37ruDKrDHb47bChvfZz+lrf18h3jfsmVLQ6Bl5syZ+fiiPOKAPPuPdkZ1+4mBdOkfAv9Ue7n+1j/qSBMREAEREAEREAEREAERSBMocqYw88A7WJBknSk8r127Ntu3b1+9EywdWbJkyRjHapicKsuNnCJeqIcgFBjx+uUvf5nPHgEP3smmXUZWL44VxxDp999/P8OmxlWuW265JZ/pQgcZbTqhp9URy6oQ9HnnnXfyfyhDIAWBlqLrjFrA5Y7aviAXXHBBx/SzeuHZjiefn3nmmfpx1pixc8UVVzS853ZcyavdMS3i0M0yz4PvEe4ItLz++uv17hFowfvi3xWm/btu8+171U17JHvwCUz4de0afDNkgQiIgAiIgAiIgAiIwCgQaMd5psOEZQNwmDErAhf2/0AZ9vzg1U4/gzAO1jH1+mImBmZx8AIvLC8iE8vI53lZRWmvg3WODx8+nD377LPZtm3bikQ0lGFvkt27d2ezZs0as9kpKraiq3fgIQN80AcCFdh8Fpv0ll0na0EaHC8N/RBwsTzbedc8Q+jBPMy2+ctf/lJXDcvouAks+rSBM6Tb0aPM/m6X+3EiB+Rjds/LL79cV4EnD2G/H2szGfA+THy6zV/yYwLaIDfmolwREAEREAEREAEREIE+JVDkIHmHyTqRdMiQd/PNNzfsT4LTZfA/39bhh/ls06co2laLLK0g7GGDJSe8ECToNAfvHGMmBv8h0PLoo49mhw4dato+tPn973+fffnllw0zPVoZS68j3w3ouW7duny2RLMX9HvsscfyAJ+didKKfuzbjo195lIw1EOAYfLkyaG60TsQVuzTTDtOngn2HMI+LbzA4Z577smwaTav1HeGzbd1+xSD1OpDAgq29OGgSCUREAEREAEREAEREIHqBKo4S3Qo6Zhh7wrstWE3hEXABftt0KmOnLjqWvVXTe9Q23T0vHDhwroBmF2C5TPWkWdhlFdmueVqWSP4gFki2Li43Qsyvvnmm5bHMtKRwSAsbdq1a1dbKq5evbqt48cj7jYPtnNvIiiK/YrsxTEvei/aMrBHjf1n1L5P2F8Hmxjbq5lAS8SrR2apmyEhoGDLkAykzBABERABERABERCBUSIQOYs+6OJ52IALnDL87/Z9993XEHB5/vnn8xN4eHlnzssc5LQPsti03xSXm77SmYXdkcNflYflimcGMt588836HiNVZUX1MIMEwYZW9PW6UQbuX3/9dX50cLsX9njB8eOdCAj5sYCeWK5krwULFjTs1YIyH2hp16Zet7fjRAYcqyjQgpPH/IwW6uy/O3x6GHj1enzUX5Yp2KK3QAREQAREQAREQAREYCAJ2ICLd46itDWSThn2sEDAxV5PP/10fupM5MwNJCijNLmU2YGlRFdffXW9GvZwIQ9k2ucyWbbcy0DaLh+yszGakRvVxbIwLEniWLNOFd3ZhnfqyJOQov6azcPx49iM2OvXjBzb1j7jyGpe2HPHn0Jk+6j6TjSjV6/qcizteKUCLTi1ivX9dwf0jb4zbL1e2aR+hoeAgi3DM5ayRAREQAREQAREQARGjoB3mlIOk80nJDpoCLjcfffdDeyGOeACQz236MW55JJL6tk40hibwdJZtfWjvEge86xjbJ/tjKKi9s2UMWhm+ylq721B2u6t0mkdsXmr5wH9vB5lvG196Ijx4rVo0aIGk21whe9BEZN+K/NjadNFgRbYYe2t+l0xiIz6bcxGVR8FW0Z15GW3CIiACIiACIiACAwJgWYdKFufjhr+13vYAy7eafTc+DowHxuq2o1y//znP+dVouBAlVfJBgTwzNkivO/du7eKmKbqIJhB+b7/lCBvn9UVJx518oI8318V+VEb5GFZ0saNG+siMH7nnHNOPZ0ac1Tw70cVPXpdh2No72RRFmihrrCz7F+v7VJ/w0lAwZbhHFdZJQIiIAIiIAIiIAIjR4DOYpkjxXILCA5bKuDy3nvv1WcaeGdv0CF759s73MuWLaubiNkS2Bw2usglKvOcfaAAaQREMHOm01c0s6VVXbFJcKcv7N2Cf2QC+UX6pcqYj71a7KwWjp9/5/04d9qubsijjfZObu0GWuwxz9Ddfpd0wxbJHA0CCraMxjjLShEQAREQAREQAREYagKRc1Ql6AIo1nmLAi5r167NcFJR5OwNIlTLBfp7R9zaBB7Y84PXpk2b6pu6Mi8VALBybB3vLCPQgo1nEXTo9AWZkO3HDv14vb2OSPMfdIRD3o0Ly9hsX2VcPT+mT5w4kW3evLmuIsYN42cvO9b+PeiGbZ2S6cfP8mo30OLf/+i7pFN2SM5oEejON8ZoMZS1IiACIiACIiACIiACfUDAOklVAi3WybKONgMu/ljoYQi4kBGHy6Y9M9ZZsmRJfXQRvOCxx3R4WWgZpl6HlNNsT4lJtW01H8EMXL7vSJ514qPn6dOnR81azpszZ0490EIhKY423z8jvWXLloaA1TXXXDNmjxL2UfQetGxMlxr6cbPj8vbbb4853hmnDqWCTP4dt98B/rlL5kjsCBFQsGWEBlumioAIiIAIiIAIiMCwE6ATmXKqivLpxIERnDV/LDSCLfhnj+tF3ZRz3O+sPauUvmeeeWbD7JZ169bVT/lhmyoMrNNsHWY+X3DBBSkVWs6fMWNGw6wR77iXCfZ6Yh+bTl4XXnhhQxCoTL+IIfTB0qFt27bVVbvqqqsy6uqDCHbcO2lLN2R5HnY8cNrUK6+80tBtK4EW+50AYeTTDXskc7QIKNgyWuMta0VABERABERABERg6An4IEJRgMU7ooBDB4/HQvsZLitXrmw4oca2GRS4nhH0LnIyr7/++gbTVq9ePYZBsxzoOLPdeeed13F8CJrZflrpgO8D2s6ePbsVEck2nIFh+4gq23L/jODfCy+8UG+G93XhwoVjxNjxteM/pmKfZNBOjh/vsBdBFgRbeMHme++9t+kZLfa7AbKKPgN9gkVqDBABBVsGaLCkqgiIgAiIgAiIgAiIQDUCPphgnarUs5VMRy8KuODUnOeeey47duxYgyNPZ7CahuNXK3IoU7yo5YQJE7KbbrqprvShQ4eyd955J5w1UmSZdZw972nTphU1banMzpZJ9e0FWyff64iA0KxZs3yTltJY5oNZQ+wvJcTqY23g84YNGxqWD61YsSLDePn3HPKZZ/uK3oeULr3It58jby8CLc8//3yG5UO8GGg5++yzG9Tz9helyaYX9qmP0SGgYMvojLUsFQEREAEREAEREIGRIpAKIJQ5XYREJxcBlwcffDCbOXNmnd9HH32UPf7EEw0Bl5ST3q/QLQfoGDniVndsuGo3y8XsgsOHD48xryx4YPlaZjii+Kyzzhojr9UMON8IjljnvYqssuADgiTt7jEzb9687PLLL8/VKesPdSxTG4BA4I976KDe/Pnzx8zuYB/R5yFXoI8u+z5YO/GM4OZTTz2V4bPHi4EW7svD/KLPuC9Dmypj0EeYpMqAEFCwZUAGSmqKgAiIgAiIgAiIgAg0TyByML2z5dPsxTp7qHPbbbc1BFy+qjl/jzzySMbjhdHOOovNa9u7Ft65JANokOKBMiwnssuqnvnTnxqOLm7Wfjs+mI2xfPnyjkG48cYb8xOEvK3tdjBx4sTsuuuuy7yDX1UuZsZgT5UqVxRkQTvkf/zxx9mqVavqYs4999yMmxlHY2jHmI06zaaKTak6/t2xnz/sSfNELbiJGVW8EPz88Y9/PGYcvO1FacjqJwYpNsofTAITfl27BlN1aS0CIiACIiACIiACIiAC5QSsQ19e+x810A4On20/d+7c3IFHgIXXjh078pkOWAbj++p3R87q5x37iBVsx4wRzqY4+de/5g7wpZde2uC0eg6UVeRQowyBDCyt2b9/f9R95bxly5bl48Fgi79DP/6LhEZ6oh4DAKeffnp20UUX5bMtjhw5EokYk4dZO9ALM1qoD3TAs01bvfBsgw58xnHWWMp28uTJej/YHBZ64bL2+b68/DGKjkNGxJu2fvjhh/mJQ9bWK664Ig/8wTbUs++btb3omZzGwVx1OSIEFGwZkYGWmSIgAiIgAiIgAiIwygSsM9YMBzq7bI+2OP4XAYfdu3fXRcEhPH78eIbTZXxftm0zfXe7bkovOr6p/qdOndoQcMJSooMHD2aXXXZZQwCDjq6XEznWqEPnGvK//fbbhlkMXkZRGo64DWj4QIsPbESyqAv1Yh2bDzl4F7BUCbNyECjC0dj2gi3YBBfLexYvXpyfEMTgir2DFdP+/WGfvKOPJ598sqEvBFrwTpK5v3ubU2MTsehmnn3XPHPsz4LjrF966aUGFa6++uqMx1qjjeXl7U6lIZDtummfZI82gX+qvaB/G20Esl4EREAEREAEREAERGBUCPBPX+/AFqXBJnLqPvnkk+zFF19scHqxROTmm2/OZ2d4h7YfnTvPA0EOsrDPth7tghNs9wtZunRpvozFOrhgh7TlC7n2H5xqpHG3z5gxgqO2o31hovcVM0ewDAkBDgQ/GLywzzag4ceHNtJu6kidUmnPiQEX+w54XbiBLfK9ftSL75wdD+iCpUN23xIsaUJwie+X5W+DLPaZ4xJx7FUeefPzRTuRPnHiRIYTr6ydyLdHO5MPbbF2Fz2zfq/sVD+jS0DBltEde1kuAiIgAiIgAiIgAiNJwAYObBCg6Bmg2M466XAKn3766YaAyxm1QMsdt9+ez3qwDjBkMN1P4FN2+yAC69EGpNeuXZvt27evbk4q4MIKaFMUbGHQhXWwdAQziDBz5tNPPw2xYeYITh265JJLskmTJuVBlirBlmg8aKMNuPhgi9ffc6Jc7/D7gAvKU4EWyrD64F0DbxuAwN4vixYtyrn4/pC2ARZfHsLsUSY/S/ZOW7E/C5ZI2VlC2Cfo7rvvzoOYuFDXfra8bak0OfXITHUz4gQUbBnxF0Dmi4AIiIAIiIAIiMAoEoicPDp7qTs4sR2dOeatWbOmIeiA/BtuuCG78sor+z7gErHwAQTPhI4uAgDPPPNMg2NcFHChHBuwsMGMKLBh+8ZSraNHj+ZMp0yZku+Vw6CCDWZUmTlCG+z7b/uyARcbBEIdq79nQ3nW4WfQg8EVr3Nq1gllY28Yv3QIG8TeeuuteXep4AL7seWsb+3u5bN939Cv5ff+++/ns8XsBTsxWwzs2JZjV2S3Lxtvu3vJWH31BwEFW/pjHKSFCIiACIiACIiACIhAjwlYp887zKk0VEw5fNu3b882btzYYAWWFd1xxx35jAs6f6xAh7HHZofd0V4U+kCLT5MB9a8acPG8UwGXokCGZe+daRtsQZmf4RLN8vAwrI4+2OL18lwsQ8hN6UfdqC/rIc12Vo9UoOWWW25peKdsf95WK3u83jvaxPfH2ogg27p167KdO3c2DAn2Z1m4cGF9I1zyifh63jZt2zV0oIQIdJGAgi1dhCvRIiACIiACIiACIiAC/U3ABwDoMBfdYRHb0XGlYxft4zIIy4pSHFIBBWs/bI+WU2GzYOyxwVkmfBPY1gYvokCGDXZY/SDHcmdgwQYv+OzvHCcrw7+hfuyjoIutw3L7XlB+FADweqZ0Qh846WnlypUNM4ewdAib7aLcv3+U5YMtqT687d1K+/EjP/QHG5+DjbXZO/by+7OQaRHbiLdt1y37JFcEIgIKtkRUlCcCIiACIiACIiACIjAyBKwj6B3tKA0w1ln0Di8CD+vXrx+zrAhLir73ve/lwQdcbOefxwt8xKEo2ML6dHCjgAsCTT9+4IH6hsFsQ4ZRIMP2aZ/ZxvKKggtRgIV5bGvZe95FHBgUQh3Pxupn+2FfUVDIBkHYhu/W22+/nb366qsN6lUJtEBmFGyx3LzN3Ur78SYj5GM2yzvvvJO98sorDd2fe+65+WywU089tR7UtLqTWZX7eNjcLZaSO3gEFGwZvDGTxiIgAiIgAiIgAiIgAh0mEDnYdHr9HV0zj890qHlH+bvvvjtmWRGOCb7rrrvy/UZ8G6Y7bFpT4qxdPpiAMj+LA3nWDgRcNmzYMCbQdN999+UbBtuLzH0AI+qXdXm3fXqn288c8YEH6FDG2vZHJtTL6mefI9CRbuwbenldyBjBOr+kxgZa2M7LZ9rabOtGOnYrz4+ZTWPzW2yCi1kt9sKx3djzB3awvmWUsjfK92y7ZafkikCKgIItKTLKFwEREAEREAEREAERGCkCkYNNR9veASVKI5+ONJ2/L774InvhhRcaloGgHjbPXbBgQd/NciliEAVByMLaDRk4snnbtm0N7w+c6GuuuSZnxPqerw+8WH3Yl+fMdDRzpJVAC5WmbuzX6lrEgu2jAIDlZO1gHzju2m84jJN4VqxYkeHUJfJIyU7l+76oYzfu1JE2eX7RJrjWRtuOeltuKRttfi/t7QZDyRwOAgq2DMc4ygoREAEREAEREAEREIEOEPDBBoj0AYEozXq4e8cQjvnLL7+c7dq1q0HDfp3l4hnQ3ijAYOt6uz/44IN8OZW9YPOdd95Zn9nTTF9FjOlo2+BKO4EW21dKR3JhXdajvZ4H8qkn27AOltRs2rQpw9Ihe2FJze21Y8Rx6hLlVwk2jFfgwbLyDDGbBScN2aOrUceeNmTbFPErYgAZbNsAUwkR6DEBBVt6DFzdiYAIiIAIiIAIiIAI9DeByGGkY526wyLbzjqKKEN63759edAFTqe9+nGWSxRISAVbUnbD5tTMHuxfs3z58nxvEV5Rn5635VbkcPsyjkED+AoJ2oaqkS5eZy8SeqAO9bF6MP/AgQPZmrVrx2wQiyU1S5YsGXMSj7etKN2q3d6OsrTnZHnhvcEsJ783C+rcdNNN2SWXXNLw2bE6F9nmmSLdK3vLeKhcBPJ3sfbB+JtQiIAIiIAIiIAIiIAIiIAI/IMA/0S2dzrWqTta+3bWAcTzyZMn8//dR+DFXpjxceutt2aYyeCdRqZ7OT7eDqSLgi22vtcfszbgaPuZPdg89/bbbqvv5QIZaEu+5Ol1IQfPFmn+Qx3/3A4/r4PXkbraPmiP1dfmHT58ONu8efNYLrVlQ9dff30+48PKtfZVeSaDduyu0jZiQ71h46pVq8bszQLbEGjBJrjWRqtzFRvtO9Are6swUR0RyN/H2odDwRa9CyIgAiIgAiIgAiIgAiLgCEROJJ3s6I7mzOcz7pFDmJrlghkf1113Xd0JZVsrp1cDZe2P7I3yvN3W9pTNCDDB8Z42bVo92EIb2QflUCfPxfYT8e4EM/ZtbbSMrM6p/lD/66+/zoNP2ADX2oE2doNY9uNtQ7rsH9p62SmdWs1P8UA+AnM4Sckvi0Jf3//+97OLL764MDBJ/Zuxs9v2tspJ7UaXgIItozv2slwEREAEREAEREAERKCEgHco6fzzjuY+z5exC+80Y5ZLasbH92pLbObNmzcmUANZvXQqaX/KxiifTKyudJpTNkPOrFmz8qOxzznnnDFBFzJEPcqKuHo+3WBFJlanKM9ywPNf//rX7K233so3D/Z6YYNY7M0C2+37498Z2p66034vn7p24m5t5bO979mzJ9vw0ktjlkVhuRACiXb/Gbbzdlo7ymxl3U7YJhki0EkCCrZ0kqZkiYAIiIAIiIAIiIAIDB0B71xax5KOcXQHCO+MWqcS5Uh/8skn+dIiv5dL0dIitu0F7Kr2kgHtjhxp6n3s2LFszZo12WeffdZgAtrA7mXLlmUXXnhhw54u3lY64RGLbgYbvH3Ui/aynPePP/4427p165jlQihHkOXaa6/NLrroonoAJuKWCjjYfMuhG/an7KOdqSVDsBH7EmHpkJcRfR68rbQryrc241mXCPQTAQVb+mk0pIsIiIAIiIAIiIAIiEDfErBBByjJ4ELqbuvwmcZZJxPPkIFZD1u2bBlj/9y5c/M9PM6s7XHi2yPdDcfaK2FtT9lr8629yE851QcPHsyXm0RBF9iL47GxtAYOu7UzZbPdcNfb0E6a9nsZNt8+nzhxIsMMj1c3bhwzwwMybJAFOlu+KPfvhw80+LRv4/VsJ52ykeONGTsbNmzIl0X5i8uivI1+LKvYSxt9W9+n0iLQLwQUbOmXkZAeIiACIiACIiACIiACfU/AO8V0OP0dhtg8pu3dOpjIRxozPrC0yB+Pi/KlS5dmixcvDvdzYXvcu3VZ2729Pg0dmEd9kI5sRh7sxfIaH3RhW+zrAscdS1Fs4MXL65bt1gbbB5kgD2OHfWl27NgRjh/qQPfLL788w948PgCBcm8P0mX/onZWx1afrW2Q4ccf+7K88cYb+bj5C7NYGCC0coreAdqRsrdbdnrdlRaBThFQsKVTJCVHBERABERABERABERgJAh459EGFfjs7wDj6xGWdbDpaGLGR7S0CCf4XF/b1wRBhwkTJuQi2N7L68ZgeIfb2xmV03avTxRYwJIqHBPsTy6ybbHMCPZjk9UpU6ZkkyZNGsMg1ZfPj9J2fH25LUOw4fPPP8/279+fbd++fcyJO7YtgkU4xnnGjBm5rlYOn/17gPapwAPzWcfevc7Npr39dkwhC6dLYRbLxk2bxszasUuGUNe2te+p19/bSXt8vU7a2SwX1ReBZgko2NIsMdUXAREQAREQAREQAREYeQKRs4y81D8AYxmf7d062shHGvV3796drV+/fgzv8Qy6WAfaP0f2e9u9MZHtWJqCoAuCGH4vG98ewRcEMWbPnp1vMIvlR3Y5kXXyfdtU2o4v6xw5ciSfeYOAEPZh2bt3b6p5Pf+aa67JLr300mzy5MnhTB9UjIIQlokPRNg0O2rFRq+8t9mOLeoWBVkzIpqXAAAPc0lEQVRQvvzvmzrbGTvIh5xojMtsRFvaimem+ay7CPQ7AQVb+n2EpJ8IiIAIiIAIiIAIiEDfEvAOqQ82QPGiPJbjHjndyMcJPgg8RPu5jFfQpcxua7NlYO3Fs70ihxzlX3zxRR50qhJ4oTxwmVkLwODi5rOnnnpqHowpujBT5fjx43kVzC7CMc37ajNXvqotEapyYWbH/Pnz8+APgkDRUiEb1IhstsEU+4z+o3QVvYrqWH1Qz49tWZAFy7swawczrawsPKfeaW+jty3iUmSDykSgHwko2NKPoyKdREAEREAEREAEREAEBoaAdzCts2qDDnyGYf6ZebinHE1surqxtuFqtMQmCrpYWf65E3C9U15ma1Q/0sPbb3XHvigffvhh/i/a1yaS1+08LBGaM2dOHtQ5++yz8+78O+HzvI027QMRTJODb9uqfZGOVs+yIAuWciHIwo2b/fh6PSM7vK20Mbq3aqfaicB4EVCwZbzIq18REAEREAEREAEREIGhIRA5rs0EH6L2kbMKYAg4YFPSoqALHH/M5OBFWUjb53YHwDvYtBlyvf02j8/s39rPvJTOzMeeKZh5giU9CL4cOHCgdMlRu/Zi5sr06dPzJUtYGoSZMhMnTqyLjcaRtnruUfABdX0AwtdjnVZtKdIRMrsRZInsKsqjbZ5ZqzarnQiMBwEFW8aDuvoUAREQAREQAREQAREYSgLNBB9sXd8OcJAXBRzofB89ejQZdEH7a6+9Nj/BJ3VkNOp0wpmNnHfk2X+0h3lM27t/RhqX17EozQAMlgNhJtDhw4fzZUi4cE+ddvT3rjLMUuHsFNynTp2aB63wjEAL94KJbIYMm0+Z9u7Hk2mOKe9oY/OYtvdIfirP6xXpD144fjw6XQhyUzNZUAZ53jbqmrLJ51vbrKyUTcoXgX4noGBLv4+Q9BMBERABERABERABERgoApEjawMPLE/lwVhbB+kiR7Ys6DJ37twMG7UikEA53pn16VaAe51pH+2J0pGtNs/rEekZ5XlmXk4zaTuekW6+PJLtudu0f0ba51FmytaozyJd7VghAIX9gHDCUHSVBVnQpsy+yCaf16qNkc7KE4F+IKBgSz+MgnQQAREQAREQAREQAREYOgJR8AFGMuhg7zbfPzONu3W2vUOOjV2xiWy0kS7aYsPWq6++Op+hwGOjvcwojbyqlw08WPtpK+QUPbOfSE5Kh6IARFFZSp7Nt3pUyWcd328rwQjI8u1a0TliiaVC2PNm8+bNySOreZKS35MFOlCmfx/9O0kbkG//RbZ5ZlVsVR0R6GcCCrb08+hINxEQAREQAREQAREQgYEmEDm6DDawzKZ9HoyPZHgnF/XozFY5NhlLjC6++OKG2S6UYYG36gBbO6wNtJV5RfW87VHa6srnVnWOZDHPjkGqnu+3aIwgg+NV9MyyVJ8p/aJ3BnWxrOrdd99NLhXiaUoLFiyoB+QiWVVti2xk20hGmZ0qF4FBIqBgyyCNlnQVAREQAREQAREQAREYSAJFQQUGIGyd1DONZ3nksNLBxf4l+2vHFmMPjtReJZztUrShLvu0fVUZhMhJT9lVxMfbnEoX6dSO7pHcSJ7P80EFm049oy/fLuofeZZvxITl2Itlz5492ZtvvpmcxYIlZosWLaofk+3lV3nfqLu3DWlvk0+nbFS+CAwyAQVbBnn0pLsIiIAIiIAIiIAIiMDAEEgFH3wAoigNYyM5dF5R7h1ZpLE57I4dO7Jt27Ylec2aNSt3uC+88MLCZUa2j6QwUxDp6220dtmyKJ+irVyvB8ssF1+n3bSXXTYG6M+OTTRO1MnLLrI54otlQp988kn2+uuvZ3v37k2aihks8+bNy09V8tzRKOIY6Y08b5tPW/v9c1JBFYjAABNQsGWAB0+qi4AIiIAIiIAIiIAIDB6ByDlGHv/BolSaZfZun8scfiwxgvO9devW5CwHyLvyyiszbKx7/vnnNwReUBYFAqI81LVXym7UsY6+d/qjdrYN+7D1XNdtJyP7fJ4NLrDDooBDUf0im7ydTGMm08GDB/ONbjG+qQuzWC677LJ8fHFstedt2Za9T96+orRnktJP+SIwLAQUbBmWkZQdIiACIiACIiACIiACA0XAO7mpgAPyU2Uw2DrffC5zktEOs112796d3FCXMBF4mT17djZ9+vT8GGR7+YADy1L5KX2LWNg2ka3s05Y1KNmBRGRPEWN0aQMPRWlblrLB5zON4BmWijGAVmQqNrzFcjEebe2Zo23V96coqFJmd5GOKhOBYSKgYMswjaZsEQEREAEREAEREAERGCgCUfDAOsF49mkY6POZV2R8KjiAGRGHDh3Kdu3aVbjMCLKx1GjOnDm50z5lypQxs1yioATaRfmR7dYOHwzw6chmK7OIRTNlke4plpSbCjikWHh9vB02jU1usQcL/hUtEYJMnDx1xRVX5CdRnXLKKWFgzvddZIMPstAe5Ldrc0oP5YvAoBJQsGVQR056i4AIiIAIiIAIiIAIDA2BKPDgAyo22JB6JhDvrHtQ3jFGOfIwUwJLUbC3C44GLrrOOPPM7NKaM4/lKFia4me9UGZKBnWIbEebKN/abeVG9kZ5KV2ifBtQYbnNK3v25VYfLzvSlXkYEwTDsDxoZy0g9tWxY5G69byZM2dmCxcuzKZNm5YvE8KV4uYFeZ1RjjzmV32mXG+n709pERhmAgq2DPPoyjYREAEREAEREAEREIGBIpAKMFhnuewZBrfjXKM9nGQGXhB0KdpYl4AxewIzKbDB7tSpU7Mza8GY6Eo54D4YEbGwtvnnKB3130qe1zkKSni5vg3KkWftsm2Y/9VXX2VffvllHuz64IMPCvfWYXtsdAvuUYClChfq6u2y+VWeqU9ku+ejtAgMOwEFW4Z9hGWfCIiACIiACIiACIjAwBGIAg3IKwu0+CBLJKcIRsrpxlIjLF/BHi/bt2/PEBAouzjz5YILLsjwD8GXCRMmJJtZB90HJHzaConKorxkx4mCKGAAuT4gYZtHNqTkoB24HqvNVDlw4MB3m9tWmLmCdmeccUY2f/78bMaMGfUlQsj3458wLc+O7PDjj7TNSz2zn8jWIh1UJgLDTEDBlmEeXdkmAiIgAiIgAiIgAiIwsAR8wMA60lWeYbiV0SlHHHJPnDiRHy2cL22p/at6YfYLAgQIvmDpEfZ9mTRpUoPjb2VR5yiIEdWrqkckt0pbH0yAHJ8HOcyz/DFT6MiRI/lMFSzVwsa2eK56YdYQNirGMqHTTjstDKzY/lJybcCEdXwe0jYv9Wz7iDikdFC+CIwCAQVbRmGUZaMIiIAIiIAIiIAIiMDAEvAOtA2a+AAK0lE5jPd1bV4RHOtEe6cc7ZB39OjR7OOPP843bG0m+MJ+se/L6aefXg/CIACDmTDY1NX271mwPfM75fCn+onkoy7zOVOF+6wgqPL1119n77//fhHisIzBFSwNmjx5cl6nm2Nox9aPM9I+j0pHTEKDlCkCI0ZAwZYRG3CZKwIiIAIiIAIiIAIiMJgEfADAO95I27yoHJZbOb5OGRnrWBc9I/jy+eef5/uOIAjTzAwOrwNOQMJMjnPOOSc766yz8mLMjKEtmB2Dq9NOv+eNWSm8EETBheOz8e/48eOlJwPVGwcPmOWDo7Wx7wqOZvbBFTSJxi0Q1ZAVBUh8nk2nniHU8/XpMl1ULgKjRkDBllEbcdkrAiIgAiIgAiIgAiIw0AR8EMAHTJCO8mC0LWPa3v1zCpR3tMvS6Bf7vCDwguVH3AA2Jb/VfOwTM7O2TKmqHUX97K/to1J28k9R+1QZAioIGp1//vn5hrbYfwUzeCKdU2Odko18OxY+sMJyn68gSxFRlYlAawQUbGmNm1qJgAiIgAiIgAiIgAiIwLgSSDniVQItqTowyMr1fUQG+0AL6/j8KI0ZMFhy89lnn+VBGMwQaWUZUqTXeOdhGRCWRiGogtk3CKpwxornHKWpf7NjUBRsqRJU8YGY1HiON1/1LwL9TkDBln4fIeknAiIgAiIgAiIgAiIgAiUEUgESG1RJBVhS+egycvSjPKueD6qUlUX10QdmwjAQAxnYDwYXlu20syzJ6tPqMzb65ZImbFqLC0uBJk6c2DBTBfkRryiPuhSVoU7Eq2qAxbZHmyiwEsmibrqLgAhUJ6BgS3VWqikCIiACIiACIiACIiACfU3AOurRM/Oie1F9a3QUDIjybJsoQFClPGpn8xCMwclIvHCMMvZWidqxDmbP4MI+MP6ydmA2Cjbp5XXqqafmJydFtkZ5aJfKp8yy8siOojwfKGG67A59fFvPRmkREIHmCCjY0hwv1RYBERABERABERABERCBvifgnXimbT6efb4vh6EpWYTgy6M2KWBR4MDXTdVJ5fv2nUpHdla1NdXW6xbZ5PNS6ShYwjzci8qph5ft9VNaBESgOgEFW6qzUk0REAEREAEREAEREAERGDgCUQAFRqQCLVH9KI8gfCDBp21fVeFVcfqr1KnaX5V6kV2+XZU6tk1kg88rSrOsKJBSVAe6ePneJqVFQARaI6BgS2vc1EoEREAEREAEREAEREAEBoqADwREARTmFZXB6KicMIr68cB8XV9u0/0WFOiU7t6uonRRUAWsfGAlqk+mvp8i9ioTARFonoCCLc0zUwsREAEREAEREAEREAERGGgCPlAQBU+iPBjN/LJyAvJ9WRlFEKN2vn6vAgad0iXS1+elAiQ+kAIWUd0oj9x8X56n0iIgAp0joGBL51hKkgiIgAiIgAiIgAiIgAgMFIEoiFAliBLVifIIw/fj06l6AwXTKJsKavj8onTV4Aq6LQqw+PJBZSq9RWDQCCjYMmgjJn1FQAREQAREQAREQAREoAsEogCIz0sFVJrNh/pediqvyNRIRlH9Vst8UKRMTlTf56UCJM3mQxcvO5VXprfKRUAEOkdAwZbOsZQkERABERABERABERABERgaAlEgw+d1IshSJLMMpm9bVr/V8iiYkZLl6zaTrhJoQb9eZiovpaPyRUAEuk9AwZbuM1YPIiACIiACIiACIiACIjDQBFJBDZ+fCr7A+KplFpSXH0GsUidqVzUvCmz4tqk6Pj8VTIG8qmW2by/f66W0CIjA+BFQsGX82KtnERABERABERABERABERhIAqkAh89vNg0Yvg0BpfItwCp1qgCvEsRI1YnyfV6zaers21WxRXVEQATGh4CCLePDXb2KgAiIgAiIgAiIgAiIwNAQSAU5ovx28iywSE43gZYFOqLydvJgS9S+mzZKtgiIQOcI/H/iPBttoKvTsgAAAABJRU5ErkJgggA="
-    }
-   },
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Multiscale Migration Model\n",
-    "\n",
-    "This notebook implements our model using `numpy`, `haversine`, and `pandas` (with `xlrd`). It has been tested to run on Python 3.6. To start, import the required libraries.\n",
-    "\n",
-    "![image.png](attachment:image.png)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# These libraries are used later to supply mathematical calculations.\n",
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "from math import e\n",
-    "from haversine import haversine\n",
-    "import ipywidgets as widgets\n",
-    "from ipywidgets import *\n",
-    "# Visualizaton\n",
-    "import matplotlib\n",
-    "import matplotlib.pyplot as plt\n",
-    "from mpl_toolkits.basemap import Basemap\n",
-    "from matplotlib.patches import Polygon\n",
-    "from matplotlib.collections import PatchCollection\n",
-    "from matplotlib.colors import Normalize, LogNorm"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "plt.style.use('ggplot')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The datasets used in the model are found in the `/data` subdirectory. Many are formatted as either CSV files or XLSX files."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%ls data"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "The following shortcut functions helps locate these data files easily."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def file_path(name):\n",
-    "    \"\"\"\n",
-    "    Shortcut function to get the relative path to the directory\n",
-    "    which contains the data.\n",
-    "    \"\"\"\n",
-    "    return \"./data/%s\" % name"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Importing and cleaning the data.\n",
-    "\n",
-    "In this step, we define some helper functions that will help all of our datasets talk to each other."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Reduce some visual clutter by only printing ten rows at a time.\n",
-    "# This can be adjusted to match personal preferences.\n",
-    "pd.set_option(\"display.max_rows\", 10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def country_codes():\n",
-    "    \"\"\"\n",
-    "    Build country rows from their names, ISO codes, and Numeric\n",
-    "    Country Codes.\n",
-    "    \"\"\"\n",
-    "    cc = (\n",
-    "        pd.read_csv(\n",
-    "            file_path(\n",
-    "                \"Country_List_ISO_3166_Codes_Latitude_Longitude.csv\"),\n",
-    "            usecols=[0, 2, 3],\n",
-    "            index_col=0,\n",
-    "            keep_default_na=False))\n",
-    "    other_codes = pd.read_csv(file_path(\"other.csv\"), index_col=0)\n",
-    "    column_names = [\"ISO\", \"Code\"]\n",
-    "    other_codes.columns = column_names\n",
-    "    cc.columns = column_names\n",
-    "    cc.index.rename(\"Name\")\n",
-    "    return pd.concat([cc, other_codes])\n",
-    "country_codes()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def get_codes(name):\n",
-    "    \"\"\"\n",
-    "    Given a country name, this function translates it into an ISO code.\n",
-    "    \"\"\"\n",
-    "    cc = country_codes()\n",
-    "    if name not in list(cc.index):\n",
-    "        return None\n",
-    "    return cc[cc.index == name]\n",
-    "\n",
-    "# Test this shortcut function on \"Albania\"\n",
-    "get_codes(\"Albania\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def missing(countries):\n",
-    "    \"\"\"\n",
-    "    Helps keep track of which country names did not get translated into codes.\n",
-    "    \"\"\"\n",
-    "    m = [x for x in countries if x not in list(country_codes().index)]\n",
-    "    if len(m) == 0:\n",
-    "        print(\"No countries ignored\")\n",
-    "    else:\n",
-    "        print(\"The following countries are ignored: \", ', '.join(m))\n",
-    "    return m"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Visualization\n",
-    "\n",
-    "The country in this model represents a lot of countries. It's easier to read that graphically rather than just looking at tables. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# https://stackoverflow.com/questions/7404116/defining-the-midpoint-of-a-colormap-in-matplotlib\n",
-    "from numpy import ma\n",
-    "from matplotlib import cbook\n",
-    "from matplotlib.colors import Normalize\n",
-    "\n",
-    "class MidPointNorm(Normalize):    \n",
-    "    def __init__(self, midpoint=0, vmin=None, vmax=None, clip=False):\n",
-    "        Normalize.__init__(self,vmin, vmax, clip)\n",
-    "        self.midpoint = midpoint\n",
-    "\n",
-    "    def __call__(self, value, clip=None):\n",
-    "        if clip is None:\n",
-    "            clip = self.clip\n",
-    "\n",
-    "        result, is_scalar = self.process_value(value)\n",
-    "\n",
-    "        self.autoscale_None(result)\n",
-    "        vmin, vmax, midpoint = self.vmin, self.vmax, self.midpoint\n",
-    "\n",
-    "        if not (vmin < midpoint < vmax):\n",
-    "            raise ValueError(\"midpoint must be between maxvalue and minvalue.\")       \n",
-    "        elif vmin == vmax:\n",
-    "            result.fill(0) # Or should it be all masked? Or 0.5?\n",
-    "        elif vmin > vmax:\n",
-    "            raise ValueError(\"maxvalue must be bigger than minvalue\")\n",
-    "        else:\n",
-    "            vmin = float(vmin)\n",
-    "            vmax = float(vmax)\n",
-    "            if clip:\n",
-    "                mask = ma.getmask(result)\n",
-    "                result = ma.array(np.clip(result.filled(vmax), vmin, vmax),\n",
-    "                                  mask=mask)\n",
-    "\n",
-    "            # ma division is very slow; we can take a shortcut\n",
-    "            resdat = result.data\n",
-    "\n",
-    "            #First scale to -1 to 1 range, than to from 0 to 1.\n",
-    "            resdat -= midpoint            \n",
-    "            resdat[resdat>0] /= abs(vmax - midpoint)            \n",
-    "            resdat[resdat<0] /= abs(vmin - midpoint)\n",
-    "\n",
-    "            resdat /= 2.\n",
-    "            resdat += 0.5\n",
-    "            result = ma.array(resdat, mask=result.mask, copy=False)                \n",
-    "\n",
-    "        if is_scalar:\n",
-    "            result = result[0]            \n",
-    "        return result\n",
-    "\n",
-    "    def inverse(self, value):\n",
-    "        if not self.scaled():\n",
-    "            raise ValueError(\"Not invertible until scaled\")\n",
-    "        vmin, vmax, midpoint = self.vmin, self.vmax, self.midpoint\n",
-    "\n",
-    "        if cbook.iterable(value):\n",
-    "            val = ma.asarray(value)\n",
-    "            val = 2 * (val-0.5)  \n",
-    "            val[val>0]  *= abs(vmax - midpoint)\n",
-    "            val[val<0] *= abs(vmin - midpoint)\n",
-    "            val += midpoint\n",
-    "            return val\n",
-    "        else:\n",
-    "            val = 2 * (val - 0.5)\n",
-    "            if val < 0: \n",
-    "                return  val*abs(vmin-midpoint) + midpoint\n",
-    "            else:\n",
-    "                return  val*abs(vmax-midpoint) + midpoint"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "%matplotlib inline\n",
-    "\n",
-    "COLOR1 = '#46bcec'\n",
-    "COLOR2 = '#eeeeee'\n",
-    "\n",
-    "def map_plot(title, color, data, normc):\n",
-    "    fix, ax = plt.subplots(figsize=(20, 10))\n",
-    "    m = Basemap(resolution='l',\n",
-    "               projection='robin',\n",
-    "               lon_0=0)\n",
-    "\n",
-    "    m.drawmapboundary(fill_color=COLOR1)\n",
-    "    m.fillcontinents(color=COLOR2, lake_color=COLOR1)\n",
-    "    plt.title(title, fontsize=50, y=1.08)\n",
-    "    m.readshapefile('../migration/visualization/World/World', 'world',drawbounds=False)\n",
-    "    l = [country['ISO3'] for country in m.world_info]\n",
-    "    df_plot = pd.DataFrame({\n",
-    "        'shapes': [Polygon(np.array(shape), True) for shape in m.world],\n",
-    "        'country': l,\n",
-    "        'values': [data[x] if x in data else None for x in l]\n",
-    "    })\n",
-    "    df_plot = df_plot.dropna()\n",
-    "\n",
-    "    cmap = plt.get_cmap(color)   \n",
-    "    pc = PatchCollection(df_plot.shapes, zorder=2)\n",
-    "    norm = normc(\n",
-    "        vmin=df_plot['values'].min(),\n",
-    "        vmax=df_plot['values'].max())\n",
-    "\n",
-    "    pc.set_facecolor(cmap(norm(df_plot['values'].values)))\n",
-    "    ax.add_collection(pc)\n",
-    "\n",
-    "    mapper = matplotlib.cm.ScalarMappable(norm=norm, cmap=cmap)\n",
-    "    mapper.set_array(df_plot['values'])\n",
-    "    cbar = plt.colorbar(mapper, shrink=0.7, #label = None,\n",
-    "                       orientation='horizontal')\n",
-    "\n",
-    "    fig = plt.gcf()\n",
-    "\n",
-    "    #plt.show()\n",
-    "    return plt\n",
-    "\n",
-    "#map_plot(\"Estimated Number of Immigrants (x=90)\", 'Greens', norm2.sum(axis=1)+1, LogNorm)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Importing Data\n",
-    "\n",
-    "### Freedom Index\n",
-    "\n",
-    "The [Freedom Index](https://freedomhouse.org/report/freedom-world/freedom-world-2017) comes from Freedom House. "
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def freedom_index():\n",
-    "    \"\"\"\n",
-    "    Read data from the Freedom Index.\n",
-    "    \"\"\"\n",
-    "    # TODO: Add xlrd to requirements.\n",
-    "    xl = pd.ExcelFile(file_path(\"Freedom_index.xlsx\"))\n",
-    "    xl = xl.parse(1)\n",
-    "    xl.set_index(\"Country\")\n",
-    "    missing(xl[\"Country\"])\n",
-    "    return xl\n",
-    "\n",
-    "fi = freedom_index().set_index(\"Country\")\n",
-    "fi.index = [get_codes(x)[\"ISO\"][0] for x in fi.index]\n",
-    "fi.columns = [\"Freedom Index\"]\n",
-    "fi.plot.hist(bins=10)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "map_plot(\"Freedom Index\", 'Greens', fi[\"Freedom Index\"], Normalize)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### A/B Values"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "def ab_values():\n",
-    "    \"\"\"\n",
-    "    Read generated A/B values for each country.\n",
-    "    \"\"\"\n",
-    "    return pd.read_excel(file_path(\"A&B values for RTS.xlsx\")).T\n",
-    "\n",
-    "ab = ab_values()\n",
-    "m = missing(ab.index)\n",
-    "# TODO: This can't be run twice. Why?\n",
-    "ab.index = [get_codes(x)[\"ISO\"][0] if (x not in m) else x for x in ab.index]\n",
-    "ab = ab.drop([x for x in ab.index if x in m])\n",
-    "#ab.sort_index()\n",
-    "ab.plot.hist(subplots=True, sharex=False)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "map_plot(\"A Values\", 'Greens', ab[\"A\"], LogNorm)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "map_plot(\"B Values\", 'Reds', ab[\"B\"], LogNorm)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Passport Index"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "def missing(countries):\n",
-    "    m = [x for x in countries if x not in list(country_codes().index)]\n",
-    "    if len(m) == 0:\n",
-    "        print(\"No countries ignored\")\n",
-    "    else:\n",
-    "        print(\"The following countries are ignored: \", ', '.join(m))\n",
-    "    return m\n",
-    "\n",
-    "def passport_index():\n",
-    "    \"\"\"\n",
-    "    Read data from the Passport Index.\n",
-    "    \"\"\"\n",
-    "    pi = pd.read_excel(file_path(\"PassportIndex.xlsx\"))\n",
-    "    pi.set_index(\"Country\")\n",
-    "    return pi\n",
-    "\n",
-    "pi = passport_index()\n",
-    "missing(pi[\"Country\"])\n",
-    "pi.index = [get_codes(x)[\"ISO\"][0] for x in pi[\"Country\"]]\n",
-    "pi = pi.drop([\"Country\"], axis=1)\n",
-    "pi.columns = [\"Passport Index\"]\n",
-    "pi.plot.hist()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "map_plot(\"Passport Index\", 'Greens', pi[\"Passport Index\"], Normalize)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Shared Language\n",
-    "\n",
-    "Agents are assigned proficiency in languages spoken in their origin country. Moving to a country with entirely new languages presents a higher migration cost."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "lang_csv = pd.read_csv(file_path(\"languages.csv\"), index_col=0)\n",
-    "# TODO: '' why?\n",
-    "lang_sets = [set([str(y).strip() for y in x[1] if y is not ' ']) for x in lang_csv.iterrows()]\n",
-    "overlap = []\n",
-    "for s in lang_sets:\n",
-    "    o = []\n",
-    "    for i in range(len(lang_sets)):\n",
-    "        o.append(len(lang_sets[i].intersection(s)) >= 1)\n",
-    "    overlap.append(o)\n",
-    "lang_data = pd.DataFrame(overlap)\n",
-    "lang_data.columns = lang_csv.index\n",
-    "lang_data.index = lang_csv.index\n",
-    "1 - lang_data"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Unemployment"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "unemployment_data = pd.read_csv(file_path(\"CIA_Unemployment.csv\"), index_col=0, usecols=[1, 2])\n",
-    "m = missing(unemployment_data.index)\n",
-    "unemployment_data[\"Unemployment\"] /= 100\n",
-    "#unemployment_data\n",
-    "unemployment_data.index = [get_codes(x)[\"ISO\"][0] if (x not in m) else x for x in unemployment_data.index]\n",
-    "unemployment_data = unemployment_data.drop([x for x in unemployment_data.index if x in m])\n",
-    "unemployment_data.plot.hist()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "map_plot(\"Unemployment\", 'Reds', 100*unemployment_data[\"Unemployment\"], Normalize)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### UN Migration History\n",
-    "\n",
-    "This one is a bit more complicated because it is a matrix, not a list."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Population\n",
-    "population = pd.read_csv(file_path(\"newPOP.csv\"))\n",
-    "population = population.set_index(\"Country\")\n",
-    "m = missing(population.index)\n",
-    "population.index = [get_codes(x)[\"ISO\"][0] if (x not in m) else x for x in population.index]\n",
-    "population = population.drop([x for x in population.index if x in m])\n",
-    "population"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "map_plot(\"Population\", 'Greens', population[\"Population\"], Normalize)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "un_pd = pd.read_excel(\n",
-    "        file_path(\n",
-    "            \"UN_MigrantStockByOriginAndDestination_2015.xlsx\"\n",
-    "        ),\n",
-    "        skiprows=15\n",
-    "    )\n",
-    "un_pd = un_pd.set_index('Unnamed: 1')\n",
-    "un_pd = un_pd.iloc[0:275,7:250]\n",
-    "drop = [x for x in un_pd.index if x not in un_pd.columns]\n",
-    "un_pd = un_pd.drop(drop)\n",
-    "m = missing(un_pd.index)\n",
-    "un_pd.index = [get_codes(x)[\"ISO\"][0] if (x not in m) else x for x in un_pd.index]\n",
-    "un_pd.columns = [get_codes(x)[\"ISO\"][0] if (x not in m) else x for x in un_pd.columns]\n",
-    "un_pd = un_pd.drop(m)\n",
-    "un_pd = un_pd.drop(m, axis=1)\n",
-    "\n",
-    "\n",
-    "# TODO: Should we be using the UN numbers for this?\n",
-    "\n",
-    "un_pd = un_pd.sort_index().fillna(1)\n",
-    "un_pd"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "(un_pd.sum(axis=1) - un_pd.sum()).sort_values()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "map_plot(\"UN Migration History\", 'coolwarm',(un_pd.sum(axis=1) - un_pd.sum()).sort_values(), MidPointNorm)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "drop = [x for x in un_pd.index if x not in population.index]\n",
-    "un_pd2 = un_pd.drop(drop).drop(drop, axis=1)\n",
-    "drop = [x for x in population.index if x not in un_pd2.index]\n",
-    "un_pd3 = pd.DataFrame(un_pd2.sort_index().values / population.drop(drop).values,\n",
-    "             un_pd2.columns,\n",
-    "             un_pd2.index)\n",
-    "un_pd3"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "(1 - un_pd3).min().min()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Distance\n",
-    "\n",
-    "The great circle distance between the average latitude and longitude of each country is used to determine distance between each pair of countries. A greater distance between countries corresponds to a greater cost of migration."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "distance_frame = pd.read_csv(file_path(\"Country_List_ISO_3166_Codes_Latitude_Longitude.csv\"),\n",
-    "                                       usecols=[2,4,5],\n",
-    "                                       index_col=0,\n",
-    "                                       keep_default_na=False)\n",
-    "locations = [(x[1][0], x[1][1]) for x in distance_frame.iterrows()]\n",
-    "rows = []\n",
-    "for i in range(len(locations)):\n",
-    "    row = []\n",
-    "    for loc in locations:\n",
-    "        row.append(haversine(loc, locations[i]))\n",
-    "    rows.append(row)\n",
-    "distance = pd.DataFrame(rows, distance_frame.index, distance_frame.index)\n",
-    "distance / distance.max().max()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "## Putting it all together\n",
-    "\n",
-    "$$RTS = Ae^{Bx}$$\n",
-    "\n",
-    "This is a constant vector based on the A/B values.\n",
-    "\n",
-    "TODO: Add this to the front.\n",
-    "\n",
-    "Run `jupyter nbextension enable --py widgetsnbextension`."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "skill = IntSlider(min=0, max=100, value=90)\n",
-    "display(skill)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "rts = pd.Series(ab[\"A\"] * e ** (ab[\"B\"] * skill.value))\n",
-    "rts = rts.dropna(how='all')\n",
-    "rts"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "political_barriers = pd.concat([fi, pi], axis=1).dropna()\n",
-    "political_barriers = (political_barriers[\"Passport Index\"] + (100 - political_barriers[\"Freedom Index\"])).sort_values()\n",
-    "political_barriers / political_barriers.max().max()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Cost\n",
-    "cost = ((#distance\n",
-    "    (distance / distance.max().max()) + # distance\n",
-    "    (1 - un_pd3) + \n",
-    "    (1 - lang_data) + \n",
-    "    (political_barriers / political_barriers.max().max())\n",
-    ") / 4)\n",
-    "cost = cost.dropna(how='all').dropna(how='all', axis=1)\n",
-    "cost"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for x, y in cost['SYR'].dropna().sort_values().iteritems():\n",
-    "    print(x, y)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "map_plot(\"Costs (SYR)\", 'Reds', cost['SYR'].dropna().sort_values(), Normalize)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "map_plot(\"Costs (FRA)\", 'Reds', cost['FRA'].dropna().sort_values(), Normalize)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "# Expected wage\n",
-    "#pd.concat([(1 - unemployment_data), pd.DataFrame(rts)], axis=1)\n",
-    "return_to_skill = pd.DataFrame(rts)\n",
-    "return_to_skill.columns = [\"RTS\"]\n",
-    "return_to_skill\n",
-    "expected_wages = pd.concat([unemployment_data, return_to_skill], axis=1).dropna()\n",
-    "\n",
-    "wages = ((1 - expected_wages[\"Unemployment\"]) * expected_wages[\"RTS\"]).sort_values()\n",
-    "wages"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "map_plot(\"Expected Wages\", 'Greens', wages, Normalize)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "country_list = list(set(wages.index).intersection(set(cost.columns)))\n",
-    "drop_0 = [x for x in cost.index if x not in country_list]\n",
-    "drop_1 = [x for x in cost.columns if x not in country_list]\n",
-    "cost = cost.drop(drop_0).drop(drop_1, axis=1)\n",
-    "drop_2 = [x for x in wages.index if x not in country_list]\n",
-    "wages = wages.drop(drop_2)\n",
-    "drop_3 = [x for x in rts.index if x not in country_list]\n",
-    "rts = rts.drop(drop_3)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "map_plot(\"Return to skill\", 'Greens', rts, Normalize)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "wages.sort_index()\n",
-    "rts.sort_index()\n",
-    "\n",
-    "matrix = []\n",
-    "for x in wages.index:\n",
-    "    vec = []\n",
-    "    for y in wages.index:\n",
-    "        vec.append(wages[x] - rts[y])\n",
-    "    matrix.append(vec)\n",
-    "    \n",
-    "beta = 50000\n",
-    "mig = pd.DataFrame(matrix, wages.index, wages.index) - beta * cost\n",
-    "mig"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pd.DataFrame(matrix, wages.index, wages.index).sort_index().sort_index(axis=1)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "mig = mig.clip_lower(0)\n",
-    "mig"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for x, y in mig[\"SYR\"].sort_values().iteritems():\n",
-    "    print(x, y)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "mig / mig.sum()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "(mig / (mig.sum() + 1))[\"USA\"].sort_values()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for x, y in (mig / (mig.sum() + 1)).sum(axis=1).sort_values().iteritems():\n",
-    "    print(x, y)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "norm = mig / (mig.sum() + 1)\n",
-    "norm = norm / norm.sum(axis=1).max()\n",
-    "norm"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "drop = [x for x in population.index if x not in norm.index]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "population.drop(drop)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "0.15 * (1 + norm.values) *  population.drop(drop).values"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "norm2 = pd.DataFrame(0.15 * norm.values * population.drop(drop).values, norm.index, norm.index)\n",
-    "norm2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "norm2.sum(axis=1).sort_values()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "x"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "map_plot(\"Estimated Number of Immigrants (x={})\".format(skill.value), 'Greens', norm2.sum(axis=1)+1, LogNorm)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "map_plot(\"Estimated Number of Emigrants (x={})\".format(skill.value), 'Reds', norm2.sum(axis=0), Normalize)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "(norm2.sum(axis=1) - norm2.sum(axis=0)).sort_values()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "map_plot(\"Net Migration (x={})\".format(skill.value), \"coolwarm\", (norm2.sum(axis=1) - norm2.sum(axis=0)).sort_values(), MidPointNorm)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "for x, y in (norm2.sum(axis=1) - norm2.sum(axis=0)).sort_values().iteritems():\n",
-    "    print(x, y)"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.4"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}