ResultsFig1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib\n",
    "matplotlib.use('Agg')\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.backends.backend_pdf import PdfPages\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "from scripts.parsing_utils import *\n",
    "from utils.utils import *\n",
    "from scripts.parse_results import *\n",
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [],
   "source": [
    "BASE_DIR=\"/home/pari/learned-cardinalities/all_results/inl_fixed_scan_ops/nested_loop_index7/test_diff/\"\n",
    "\n",
    "RES_DIR1 = BASE_DIR + \"final_results4\"\n",
    "#RES_DIR2 = BASE_DIR + \"sample_bitmaps_load\"\n",
    "#RES_DIR3 = BASE_DIR + \"sample_bitmaps/\"\n",
    "\n",
    "assert os.path.exists(RES_DIR1)\n",
    "assert os.path.exists(RES_DIR2)\n",
    "\n",
    "SAVE_DIR =\"./figures/results1/\"\n",
    "make_dir(SAVE_DIR)\n",
    "FN_TMP = \"{ERROR_TYPE}-{SAMPLES_TYPE}-{PART}.png\"\n",
    "\n",
    "COLORS = {}\n",
    "COLORS[\"Flow Loss\"] = \"blue\"\n",
    "COLORS[\"Flow Loss Bitmap\"] = \"lightblue\"\n",
    "\n",
    "COLORS[\"Q-Error\"] = \"green\"\n",
    "COLORS[\"Q-Error Bitmap\"] = \"lightgreen\"\n",
    "\n",
    "LINESTYLES = {}\n",
    "LINESTYLES[\"Flow Loss\"] = \":\"\n",
    "LINESTYLES[\"Q-Error\"] = \"--\"\n",
    "LINESTYLES[\"Flow Loss Bitmap\"] =  \":\"\n",
    "LINESTYLES[\"Q-Error Bitmap\"] = \"--\"\n",
    "\n",
    "# LINESTYLES[\"Flow Loss Bitmap\"] =  (0, (3, 10, 1, 10))\n",
    "# LINESTYLES[\"Q-Error Bitmap\"] = (0, (3, 10, 1, 10))\n",
    "\n",
    "ALG_KEY = {}\n",
    "ALG_KEY[\"flow_loss2\"] = \"Flow Loss\"\n",
    "ALG_KEY[\"flow_loss2_bitmap\"] = \"Flow Loss Bitmap\"\n",
    "\n",
    "ALG_KEY[\"mse\"] = \"Q-Error\"\n",
    "ALG_KEY[\"mse_bitmap\"] = \"Q-Error Bitmap\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [],
   "source": [
    "def arg_filters(df):\n",
    "    #tmp = df2[df2[\"template\"] == \"all_job\"]\n",
    "    #tmp = tmp[tmp[\"partition\"] == 2]\n",
    "    #tmp = tmp[tmp[\"alg\"] == \"flow_loss2\"]\n",
    "    tmp = df[df[\"flow_features\"] == 1]\n",
    "    tmp = tmp[tmp[\"hidden_layer_size\"] == 512]\n",
    "    tmp = tmp[tmp[\"feat_rel_pg_ests_onehot\"] == 1]\n",
    "    return tmp\n",
    "\n",
    "def get_alg_name(cols, x):\n",
    "    name = \"\"\n",
    "    for col in cols:\n",
    "        if col == \"sample_bitmap\":\n",
    "            if x[\"sample_bitmap\"]:   \n",
    "                name += \"_bitmap\"\n",
    "        else:\n",
    "            name += x[col]\n",
    "    return name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "skipping obj:  all_qerr.pkl\n",
      "skipping obj:  cm1_jerr.pkl\n"
     ]
    }
   ],
   "source": [
    "#df1 = get_all_objects(RES_DIR, \"query_qerr.pkl\")\n",
    "#df1 = get_all_objects(RES_DIR, \"all_qerr.pkl\")\n",
    "#df2 = get_all_objects(RES_DIR, \"cm1_jerr.pkl\")\n",
    "#df3 = get_all_objects(RES_DIR, \"flow_err.pkl\")\n",
    "#plan_err = get_all_objects(RES_DIR, \"plan_err.pkl\")\n",
    "\n",
    "RES_DIRS = []\n",
    "RES_DIRS.append(RES_DIR1)\n",
    "#RES_DIRS.append(RES_DIR2)\n",
    "#RES_DIRS.append(RES_DIR3)\n",
    "\n",
    "df1s = []\n",
    "df2s = []\n",
    "for res_dir in RES_DIRS:\n",
    "    qerr = get_all_objects(res_dir, \"all_qerr.pkl\")\n",
    "    jerr = get_all_objects(res_dir, \"cm1_jerr.pkl\")\n",
    "    if \"bitmap\" in res_dir:\n",
    "        qerr[\"sample_bitmap\"] = 1\n",
    "        jerr[\"sample_bitmap\"] = 1\n",
    " \n",
    "    df1s.append(qerr)\n",
    "    df2s.append(jerr)\n",
    "    \n",
    "df1 = pd.concat(df1s, ignore_index=True)\n",
    "df2 = pd.concat(df2s, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'flow_loss2', 'mse'}\n",
      "{'flow_loss2', 'mse'}\n"
     ]
    }
   ],
   "source": [
    "cols = [\"alg\", \"sample_bitmap\"]\n",
    "set(df2[\"samples_type\"])\n",
    "print(set(df2[\"alg\"]))\n",
    "#df2[\"alg_name\"] = df2.apply(lambda x: x[\"alg\"]+ str(x[\"sample_bitmap\"]), axis=1)\n",
    "#print(len(df2))\n",
    "\n",
    "#df1['alg'] = df1[cols].apply(lambda row: get_alg_name(cols, row), axis=1)\n",
    "#df2['alg'] = df2[cols].apply(lambda row: get_alg_name(cols, row), axis=1)\n",
    "print(set(df2[\"alg\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_PDF(arr, num_bins=600):\n",
    "    \"\"\"\n",
    "    usage: x, y = compute_PDF(arr):\n",
    "           plt.plot(x, y)\n",
    "    \"\"\"\n",
    "    values, base = np.histogram(arr, bins=num_bins)\n",
    "    cumulative = np.cumsum(values)\n",
    "    return base[:-1], cumulative / float(cumulative[-1])  # this is for CDF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#TITLE_FMT = \"{SAMPLES}-{PARTITION}\"\n",
    "TITLE_KEYS = {}\n",
    "TITLE_KEYS[\"job\"] = \"Join Order Benchmark\"\n",
    "TITLE_KEYS[\"test\"] = \"Test Set\"\n",
    "TITLE_KEYS[\"train\"] = \"Train Set\"\n",
    "\n",
    "def plot_cdf_jerr(samples_type, df, part):\n",
    "    #plt.gcf()\n",
    "    fn = FN_TMP.format(ERROR_TYPE = \"postgres\",\n",
    "                       SAMPLES_TYPE = st,\n",
    "                       PART = part)\n",
    "    fn = SAVE_DIR + fn\n",
    "    plt.figure(figsize=(5,4))\n",
    "    plt.style.use('ggplot')   \n",
    "\n",
    "    df = df[df.samples_type == samples_type]\n",
    "    algs = set(df[\"alg\"])\n",
    "    \n",
    "    plt.title(TITLE_KEYS[samples_type] + \"-\" + str(part))\n",
    "\n",
    "    for alg in algs:\n",
    "        if alg == \"true\" or alg == \"postgres\":\n",
    "            continue\n",
    "        tmp_df = df[df.alg == alg]\n",
    "        \n",
    "        alg_label = ALG_KEY[alg]\n",
    "\n",
    "        losses = tmp_df[\"loss\"].values\n",
    "        x, y = compute_PDF(losses, num_bins=len(losses))\n",
    "        #x, y = compute_PDF(losses, num_bins=600)\n",
    "        plt.plot(x, y, label = alg_label, linestyle=LINESTYLES[alg_label], linewidth=3, color = COLORS[alg_label])\n",
    "        #plt.plot(x, y, label = alg_label, linestyle=\"--\", linewidth=3, color = COLORS[alg_label])\n",
    "\n",
    "        plt.xscale(\"log\")\n",
    "        plt.legend(frameon=False)\n",
    "    plt.savefig(fn)\n",
    "    plt.show()\n",
    "\n",
    "def plot_cdf_qerr(samples_type, df,part):\n",
    "    fn = FN_TMP.format(ERROR_TYPE = \"qerror\",\n",
    "                       SAMPLES_TYPE = st,\n",
    "                       PART = part)\n",
    "    fn = SAVE_DIR + fn\n",
    "\n",
    "    plt.figure(figsize=(5,4))\n",
    "    plt.style.use('ggplot')   \n",
    "\n",
    "    plt.title(TITLE_KEYS[samples_type] + \"-\" + str(part))\n",
    "    \n",
    "    algs = set(df[\"alg\"])\n",
    "    for alg in algs:\n",
    "        if alg == \"true\" or alg == \"postgres\":\n",
    "            continue\n",
    "        #alg = ALG_KEYS[alg]\n",
    "        tmp_df = df[df.alg == alg]\n",
    "        \n",
    "        alg_label = ALG_KEY[alg]\n",
    "\n",
    "        #losses = tmp_df[\"qerr\"].values\n",
    "        losses = tmp_df[\"loss\"].values\n",
    "        x, y = compute_PDF(losses, num_bins=600)\n",
    "        plt.plot(x, y, label = alg_label, linestyle=\"--\", linewidth=3, color = COLORS[alg_label])\n",
    "        plt.xscale(\"symlog\")\n",
    "        #plt.yscale(\"log\")\n",
    "        plt.legend(frameon=False)\n",
    "    plt.savefig(fn)\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'df2' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-2-bb6f680f9934>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mst\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mSAMPLES_TYPES\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m     \u001b[0;32mfor\u001b[0m \u001b[0mpart\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf2\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"partition\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m         \u001b[0mtmp_df\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdf2\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdf2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpartition\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mpart\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m         \u001b[0mplot_cdf_jerr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mst\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtmp_df\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpart\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'df2' is not defined"
     ]
    }
   ],
   "source": [
    "SAMPLES_TYPES = [\"test\", \"job\"]\n",
    "#SAMPLES_TYPES = [\"test\", \"job\"]\n",
    "\n",
    "for st in SAMPLES_TYPES:\n",
    "    for part in set(df2[\"partition\"]):\n",
    "        tmp_df = df2[df2.partition == part]\n",
    "        plot_cdf_jerr(st, tmp_df, part)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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38NxzBkJDc3F2Nu9Xpb29x235b2epuDIwiBCNoNHAlClXLmY98kgZhw5ls2yZweyJU7R/HWZIOiHMYd68Ug4dsmbq1AoefbTxdxWIzkeSpxBX0Whg924dcqpdNEQ+IkJcQxKnaAxpeYpO77ff1BQWqrGyUnB1NeDoKOc3RcPkf6zo9P7xD3uCg924/XZ39uyxa3gDIZDkKQTV1Vd+b8lN76JzkeQpOr3KyiuPFVpZSZddNI4kT9HpVVdfSZ7W1pI8ReNI8hSdnnTbRXNI8hSdnnTbRXNI8hSd3tXddhsbSZ6icSR5ik7v6m67lVXr1UO0L5I8Racn3XbRHJI8Radn2m1vxYqIdkUezxSdXteuBhwdDVRVya1KovEkeYpOb8+epk2qJgQ0MnkePXqUnTt3YjAYCA4OJjQ01OT10tJStm/fTnZ2NlZWVsyfPx9vb28A9u/fT3x8PIqiEBwczNSptXN4nzt3jh07dlBRUUGPHj1YtGgRdnZ25OTksHjxYuMIzn5+fsydO9ecxyyEEC3WYPI0GAxER0ezcuVKXFxcWL58OQEBAXh6ehrLxMTE0KdPH5YuXUpmZibR0dGsWrWK8+fPEx8fT0REBFqtloiICEaOHEnPnj158803efzxxxk0aBAJCQl8+umnPPzwwwB4eHgQGdm4ec2FEKI1NHjBKD09HQ8PD9zd3dFqtQQGBpKSkmJSJiMjgyFDhgDQu3dvcnNzKSwsJDMzEz8/P2xsbNBoNPj7+5OcnAzAxYsX8ff3B2Do0KEkJSWZ+9iEEMJiGmx56nQ6XFxcjMsuLi6cPn3apIyPjw9JSUkMHDiQ9PR0cnNz0el0eHl5sXfvXkpKSrC2tiYtLQ1fX1+gdqbMI0eOMGrUKL799luT+dpzcnJYtmwZXbp04eGHHzYm2avFxcURFxcHwIYNG9Bqtbi6ujb+wJtYvqUstT+J2/LYcXG1V9ttbGDsWKXOvZ7t7b1ob3EtGduidW6oQH2Ta147uX1oaCi7du1i6dKleHt707dvX9RqNZ6enkyfPp1169Zha2uLj4+PcSrh+fPns3PnTj766CMCAgLQamur4uTkxLZt23BwcODs2bNERkYSFRWFnZ3pOIshISGEhIQYl/V6vcye2YnjNje2osBDD3lQWlr7uTx1Kotu3Uw/8+3tvWhvcS0Zu1XnbXdxcTFpFebn5+Pk5GRSxs7Ozjhfu6IoLFy4EDc3NwCCgoIICgoCYM+ePcZWbO/evVm5ciVQ24VPTU0FwMrKCqvf//X369cPd3d3srKyjC1WIczp//0/W2PitLVV6NJFblUSjdPgOU9fX1+ysrLIyclBr9eTmJhIQECASZmysjL0ej0A8fHx+Pv7G1uKRUVFAOTl5ZGcnMz48eNN1hsMBj7++GPuuusuAIqLizEYDABkZ2eTlZWFu7u7OY5VCBOZmWpefNHRuPzAA+XyeKZotAZbnhqNhtmzZxMeHo7BYGDixIl4eXkRGxsLwKRJk8jMzGTr1q3Grvq8efOM20dFRVFSUoJWqyUsLAx7e3sADh06xBdffAHA6NGjmThxIgAnT57kgw8+QKPRoFarmTNnjnEbIcylogLmzHEmP18DgLt7DcuXF7dyrUR7olLqO6nZDlVVVck5z04ct6mxV6xw5F//6gqAVqvw/vv5jB1b1eK4TSFxLR/bkuc85dl20emkpVkZEyfAqlXF102cQlyPJE/RqRgM8NJLV85zhoRUMHt2WSvWSLRXkjxFp/Lvf3chLa12rg1ra4X/+Z8irrnzTohGkeQpOpXYWFvj73PnltKnT00r1ka0Z5I8Rafyj38U8Pe/FzB8eBWLFpW2dnVEOyZD0olORaWCGTMuce+9l6S7LlpEWp6iU5LEKVpKkqcQQjSDJE/R4aWkWHP//S588YUtNXJ9SJiJJE/R4e3Y0ZXDh22YPduZDRscWrs6ooOQ5Ck6tMxMNZ99duX2pPvuu9SKtREdiSRP0aGlpVljMNReHRozppKBA/WtXCPRUUjyFB1aXt6Vj3j//pI4hflI8hQdmk535SPu4mJoxZqIjkaSp+jQLo/XCZI8hXlJ8hQdWn6+tDyFZTTq8cyjR4+yc+dODAYDwcHBhIaGmrxeWlrK9u3byc7OxsrKivnz5+Pt7Q3A/v37iY+PR1EUgoODmTp1KgDnzp1jx44dVFRU0KNHDxYtWmScuiMmJoaEhATUajWzZs1i+PDh5jxm0Ylcfc7TxUVu8hTm02DL02AwEB0dzYoVK9i0aROHDh0iIyPDpExMTAx9+vThtddeY+HChezatQuA8+fPEx8fT0REBJGRkaSmppKVlQXAm2++yaOPPkpUVBSjR4/m008/BWrngE9MTGTjxo28+OKLREdHG+c0EqKp5JynsJQGk2d6ejoeHh64u7uj1WoJDAwkJSXFpExGRgZDhgwBamfFzM3NpbCwkMzMTPz8/LCxsUGj0eDv709ycjJQO2Pm5fnYhw4dSlJSEgApKSkEBgZiZWWFm5sbHh4epKenm/WgRech3XZhKQ0mT51OZ5wuGGqnItbpdCZlfHx8jMkvPT2d3NxcdDodXl5enDp1ipKSEiorK0lLSzNOY+zl5cWRI0cA+Pbbb43rr92fs7Nznf0J0VgvvVTMypVFzJ9firOzJE9hPg2e86xvfjjVNUPShIaGsmvXLpYuXYq3tzd9+/Y1zqQ5ffp01q1bh62tLT4+PqjVtfl6/vz57Ny5k48++oiAgAC0Wu1191efuLg44uLiANiwYQNarRZXV9dGbQs0uXxLWWp/EvfGsa+ayBVo3n7b23vR3uJaMrZF69xQARcXF2OrECA/Px8nJyeTMnZ2dixYsACoTX4LFy7Ezc0NgKCgIIKCggDYs2ePsVXZu3dvVq5cCdR24VNTU+vdn06nw9nZuU69QkJCCAkJMS7r9XqZPbMTx7VkbIlr2biWjN2qs2f6+vqSlZVFTk4Oer2exMREAgICTMqUlZWh19c+vREfH4+/v7/xynlRUREAeXl5JCcnM378eJP1BoOBjz/+mLvuuguAgIAAEhMTqa6uJicnh6ysLPr379/UYxZCCItqsOWp0WiYPXs24eHhGAwGJk6ciJeXF7GxsQBMmjSJzMxMtm7dauyqz7uqrxQVFUVJSQlarZawsDDs7e0BOHToEF988QUAo0ePZuLEiUDtudBx48axZMkS1Go1YWFhxq6+EEK0FSqlsScZ27iqqirptnfiuPXF/ve/u/DGG/a4uBiYNu0Sf/5zuVnimovEtXxsS3bbZQ4j0WGdP6/h5EkrAIYPr2rl2oiORvrDosO6cOFK20Du8RTmJslTdEiVlfDFF1cGQR4ypLoVayM6IkmeokOKi7OlsLD24+3pqWf0aOm2C/OS5Ck6pI8+6mL8/f77LyE3bAhzk4+U6HDy89UkJFzpst9/f/OusgtxI5I8RYezb18X9PraR4gDAqro21eGohPmJ8lTdDgffnily/7AA9LqFJYhyVN0KD/+qOXYMWsAbGwU/vQnmWpYWIYkT9GhaLUwY0Y5trYGJk2qwNGxQzxAJ9ogecJIdCj9++v5+98LWb9eRVGRquENhGgmSZ6iQ7K3V7C3l1ansBzptgshRDNI8hQdxu9DygpxU0jyFB1CeroWLy8rnn22OwkJNq1dHdEJyDlP0SF89pktOp2KDz+0o7RURVBQZWtXSXRw0vIUHcLnn195HPPuuytasSais2hUy/Po0aPs3LkTg8FAcHAwoaGhJq+Xlpayfft2srOzsbKyYv78+Xh7ewOwf/9+4uPjURSF4OBgpk6dCsC5c+fYsWMHVVVVaDQannzySfr3709OTg6LFy82juDs5+fH3LlzzXnMooPJz1dz9GjtjfFarUJIiCRPYXkNJk+DwUB0dDQrV67ExcWF5cuXExAQgKenp7FMTEwMffr0YenSpWRmZhIdHc2qVas4f/488fHxREREoNVqiYiIYOTIkfTs2ZPdu3dz//33M2LECFJTU9m9ezdr1qwBwMPDg8jISIsdtOhYDh2yNv4+YkQV3bvLLUrC8hrstqenp+Ph4YG7uztarZbAwEBSUlJMymRkZDBkyBCgdkrh3NxcCgsLyczMxM/PDxsbGzQaDf7+/iQnJwO1c79fulT76Fx5eXmd6YyFaKyDB69cILrtNhm3U9wcDbY8dTqdca51qJ1X/fTp0yZlfHx8SEpKYuDAgaSnp5Obm4tOp8PLy4u9e/dSUlKCtbU1aWlp+Pr6AjBz5kzCw8N55513MBgMrFu3zhgvJyeHZcuW0aVLFx5++GH8/f3r1CsuLo64uDgANmzY0OTJ7ZtavqUstT+JC4mJVsbfp061xdXVvFfb29N70R7jWjK2RevcUIH6JtdUqUwfewsNDWXXrl0sXboUb29v+vbta5yGePr06axbtw5bW1t8fHyM0wjHxsYyc+ZMxo4dS2JiIm+88QYvvfQSTk5ObNu2DQcHB86ePUtkZCRRUVHGeeAvCwkJISQkxLis1+tl9sxOGPfCBQ2//OIOgJ2dgq9vLuaudnt5L9prXEvGbtXZM11cXMjPzzcu5+fn1+li29nZsWDBAqA22S5cuBA3NzcAgoKCCAoKAmDPnj3GVux///tfZs2aBcC4ceN48803AbCyssLKqrYl0a9fP9zd3cnKyjK2WIW42tVd9vHjFaytb1BYCDNq8Jynr68vWVlZ5OTkoNfrSUxMJCAgwKRMWVkZ+t8f74iPj8ff39/YUiwqKgIgLy+P5ORkxo8fD4CzszMnT54E4Pjx43h4eABQXFyMwVA702F2djZZWVm4u7ub41hFB3Tw4JVsOXGizJApbp4GW54ajYbZs2cTHh6OwWBg4sSJeHl5ERsbC8CkSZPIzMxk69atxq76vHnzjNtHRUVRUlKCVqslLCwMe3t7AJ566inj7U9WVlY89dRTAJw8eZIPPvgAjUaDWq1mzpw5xm2EuFZxsRqVSkFRVAQFyVV2cfOolPpOarZDVVVVcs6zk8YtKFBx+LANjz1mj07XPuoscW9ObEue85QnjES75+SkMGVKhcyQKW4q+bgJIUQzSPIUQohmkOQp2qVTp7S89poD335rTZU8VCRagSRP0S598YUtmzY5cN99rqxY4dja1RGdkCRP0S5dfXP8uHHS9BQ3nyRP0e5cuqTiu++u3Bw/frwMfCxuPkmeot1JSbGmqqp2fAU/v2o8POTJInHzSfIU7c7Vj2ROmCCtTtE6JHmKdufAARm/U7Q+SZ6iXSkoUHHsWO2oW2q1wtix0vIUrUOSp2hXDh+2QVFqz3cOG1aNo2OHGJpBtEOSPEW7Yjp+p7Q6ReuR5CnalasvFt12myRP0XoaNfWwEG3Ftm0FHDxow+HDNgQEyMUi0XokeYp2ZfBgPYMH65k3r6y1qyI6Oem2CyFEMzSq5Xn06FHjlBnBwcGEhoaavF5aWsr27dvJzs7GysqK+fPn4+3tDcD+/fuJj49HURSCg4OZOnUqAOfOnWPHjh1UVVWh0Wh48skn6d+/PwAxMTEkJCSgVquZNWsWw4cPN+cxCyFEizXY8jQYDERHR7NixQo2bdrEoUOHyMjIMCkTExNDnz59eO2111i4cCG7du0C4Pz588THxxMREUFkZCSpqalkZWUBsHv3bu6//34iIyN58MEH2b17NwAZGRkkJiayceNGXnzxRaKjo40TwonOq6qq9h5PIdqKBpNneno6Hh4euLu7o9VqCQwMJCUlxaRMRkYGQ4YMAaB3797k5uZSWFhIZmYmfn5+2NjYoNFo8Pf3Jzk5Gaid+/3SpUsAlJeXG6czTklJITAwECsrK9zc3PDw8CA9Pd2sBy3an7Q0awYP7klgoBsREQ6tXR0hGu6263Q641zrUDuP++nTp03K+Pj4kJSUxMCBA0lPTyc3NxedToeXlxd79+6lpKQEa2tr0tLSjPOvz5w5k/DwcN555x0MBgPr1q0z7s/Pz88Y29nZGZ1OV6decXFxxMXFAbBhwwa0Wi2urq6NP/Amlm8pS+2vs8Q9fbr2//yvv2opLbXD1dWmTpm2VmeJ2/qxLVrnhgrUN7mmSmXafQoNDWXXrl0sXboUb29v+vbta5yGePr06axbtw5bW1t8fHxQ/z5LV2xsLDNnzkuH/ngAACAASURBVGTs2LEkJibyxhtv8NJLL9W7v/qEhIQQEhJiXNbr9TJ7ZgeOe/hwdy5/XAcMKCEvr9xssRsicS0b15KxLTl7ZoPJ08XFhfz8fONyfn6+sYt9mZ2dHQsWLABqk+3ChQtxc3MDICgoiKCgIAD27NljbMX+97//ZdasWQCMGzeON998s9796XQ6nJ2dGz5K0aF9//2Vm+OHDatuxZoIUavBc56+vr5kZWWRk5ODXq8nMTGRgIAAkzJlZWXo9XoA4uPj8ff3x87ODoCioiIA8vLySE5OZvz48UBtd/zkyZMAHD9+HA8PDwACAgJITEykurqanJwcsrKyjFfhRedUVKTil19q/89rtQr+/pI8RetrsOWp0WiYPXs24eHhGAwGJk6ciJeXF7GxsQBMmjSJzMxMtm7dauyqz5s3z7h9VFQUJSUlaLVawsLCsLe3B+Cpp54y3v5kZWXFU089BYCXlxfjxo1jyZIlqNVqwsLCjF190TldHkUJYODAamxtW7EyQvyuUfd5jhw5kpEjR5qsmzRpkvH3AQMGsGXLlnq3Xbt2bb3rBw4cyCuvvFLvazNmzGDGjBmNqZroBH74Qbrsou2RJp1o877//krLc+hQSZ6ibZDkKdq8H364kjyl5SnaCkmeok3T6VScP197dsnaWuEPf5DkKdoGSZ6iTfvtNw19+tTeyTFoUDXW1g1sIMRNIkPSiTZt0CA9hw7lUFioIi9P/teLtkOSp2gXundX6N69prWrIYSR/CsXQohmkOQphBDNIN120WYdO2bFoUPWDBtWzeDB1Tg4yDTDou2Qlqdos7780oaXX3bk/vtdiYyUMTxF2yLJU7RZV4+kJE8WibZGkqdokxRFniwSbZskT9Em/fabmpwcDQBduxro10/fyjUSwpQkT9EmXT2S0pAh1Wg0rVgZIeohyVO0SVePpCRddtEWSfIUbZLp+c6qVqyJEPVr1H2eR48eNY76HhwcTGhoqMnrpaWlbN++nezsbKysrJg/fz7e3t4A7N+/n/j4eBRFITg4mKlTpwKwadMmLl68CNROPWxnZ0dkZCQ5OTksXrzYOPGSn58fc+fONdsBi7ZPUWQMT9H2NZg8DQYD0dHRrFy5EhcXF5YvX05AQACenp7GMjExMfTp04elS5eSmZlJdHQ0q1at4vz588THxxMREYFWqyUiIoKRI0fSs2dPFi9ebNz+7bffNs55BODh4UFkZKSZD1W0F5mZGnS62pOc3boZ6NNHnmkXbU+D3fb09HQ8PDxwd3dHq9USGBhISkqKSZmMjAyGDBkCQO/evcnNzaWwsJDMzEz8/PywsbFBo9Hg7+9PcnKyybaKonD48GHjxHBCXNvqvGamayHahAaTp06nM04XDLVTA+t0OpMyPj4+JCUlAbXJNjc3F51Oh5eXF6dOnaKkpITKykrS0tJMphUGOHXqFI6OjvTs2dO4Licnh2XLlrF69WpOnTrVogMU7c/AgdW88EIxU6Zc4s47K1q7OkLUq8Fuu6LUfZ5YdU1TIDQ0lF27drF06VK8vb3p27evcSbN6dOns27dOmxtbfHx8akzE+ahQ4dMWp1OTk5s27YNBwcHzp49S2RkJFFRUSbdeoC4uDji4uIA2LBhA1qtFldX18YfeBPLt5Sl9tcR47q6wpgxl5e6/P5jntjNIXEtG9eSsS1a54YKuLi4mLQW8/PzcXJyMiljZ2fHggULgNpku3DhQtzc3AAICgoiKCgIgD179pi0YmtqakhOTmbDhg3GdVZWVlhZ1Xbb+vXrh7u7O1lZWfj6+prsMyQkhJCQEOOyXq8nLy+vcUcNuLq6Nql8S1lqfxLX8rElrmXjWjJ2S+NevnBdnwa77b6+vmRlZZGTk4NerycxMZGAgACTMmVlZej1tU+AxMfH4+/vb2wpFhUVAZCXl0dycrJJK/PYsWP06tXLJKEWFxdjMBgAyM7OJisrC3d398Yeq2jndDoV9XR2hGhzGmx5ajQaZs+eTXh4OAaDgYkTJ+Ll5UVsbCxQO397ZmYmW7duNXbV582bZ9w+KiqKkpIStFotYWFh2NvbG1+7tssOcPLkST744AM0Gg1qtZo5c+aYbCM6JoMB3n3XjoiIbrzySiHTpsm5TtG2qZT6Tmq2Q1VVVdJtb6dxf/pJy7Jl3TlypPaRTDe3Gr7+OgdHx6Z9NDvCe9EZ41oytiW77TIYsmg1ly7B6687sH27PXr9lYuQdnYKWVkaHB1lMBDRdknyFK3iwAFrXnihO+fOXfkIarUKCxaUsmhRCV0ad4FdiFYjyVPcVLm58Mwz3fn3v01vPQsIqOLVVwv5wx+ktSnaB0me4qb59ltr5syxQqe7Mtxct24GVqwo5tFHy1HLMDWiHZHkKW4aPz/TVuW0aZf4n/8pws3N0Eo1EqL55H+9uGlcXAxs2FCDp6eed97JZ/v2Akmcot2SlqewiKQka1JSrFm4sNRk/RNPGAgKyqdLlw5xh5zoxCR5CrMqKFARHt6N997rikqlMHZsJQEBV8bjVKmQxCk6BOm2C7NQFIiJ6cKdd7rx3ntdf1+n4uWXHVu5ZkJYhrQ8RYudO6dhxQpH/vtfW5P1kydfYu3aolaqlRCWJclTNFt1Nbz5pj2bNjlQUXHlCSEPjxoiIor44x/l+XTRcUnyFM3y3XdWPP98d06dujLqu0qlMHt2GcuWlWBvL+c1RccmyVM0maLA+vXdTBLnLbdU8+qrhQwfLpO1ic5BLhiJJlOpYP36IqysFLp0MfDSS0Xs358riVN0KtLyFA3KzFTj5mbA6kpDEz8/PZs3F3LrrVV4ecnslqLzkZanuC69Ht58syt33unGjh11B6QODb0kiVN0WpI8Rb1SU1Xcc48ra9c6Ul6uJirKnvPnNa1dLSHajEZ1248ePcrOnTsxGAwEBwcTGhpq8nppaSnbt28nOzsbKysr5s+fj7e3NwD79+8nPj4eRVEIDg5m6tSpAGzatImLFy8CUF5ejp2dHZGRkQDExMSQkJCAWq1m1qxZDB8+3GwHLG6stFTFq686sHOnFoPhyu1HffrUUFoqE6gLcVmDydNgMBAdHc3KlStxcXFh+fLlBAQE4OnpaSwTExNDnz59WLp0KZmZmURHR7Nq1SrOnz9PfHw8ERERaLVaIiIiGDlyJD179mTx4sXG7d9++23jhHEZGRkkJiayceNGCgoKePnll3n99dfrTFkszC821oYVK7qTlXWlhWlrq/DssyXMm1dqcs5TiM6uwYyUnp6Oh4cH7u7uaLVaAgMDSUlJMSmTkZHBkCFDAOjduze5ubkUFhaSmZmJn58fNjY2aDQa/P39SU5ONtlWURQOHz5snAguJSWFwMBArKyscHNzw8PDg/T0dHMdr6hHVpaaOXOcmDXLxSRxTphQSXx8Dn/9qyROIa7VYMtTp9OZTA3s4uLC6dOnTcr4+PiQlJTEwIEDSU9PJzc3F51Oh5eXF3v37qWkpARra2vS0tLqzL9+6tQpHB0d6dmzp3F/fn5+xtednZ3R6XR16hUXF0dcXBwAGzZsaPLk9k0t31KW2l9L4yYnq5gyRUtJyZUueY8eClFRCg8+qEKlcjJHNY0s+b631fdY4rZebIvWuaEC9U2uqVKZnvsKDQ1l165dLF26FG9vb/r27Wuchnj69OmsW7cOW1tbfHx86nS/r51+uLGTeYaEhBASEmJc1uv1MntmM/TqBa6ubpSU1H4UHn64jBdfLGbAAJc2Wd/WiC1xLRvXkrFbdfZMFxcX8vPzjcv5+fk4OZm2Ruzs7FiwYAFQm/wWLlyIm5sbAEFBQQQFBQGwZ88ek1ZsTU0NycnJbNiw4br70+l0ODs7N1RN0Uy2trBhQyEvvujIhg1FjBtX1dpVEqJdaPCcp6+vL1lZWeTk5KDX60lMTCQgIMCkTFlZGXp97RQL8fHx+Pv7Gy8AFRXVjqqTl5dHcnKySSvz2LFj9OrVyyShBgQEkJiYSHV1NTk5OWRlZdG/f/+WH6ngq69seOEFR65t3N92WxXx8bmSOIVoggZbnhqNhtmzZxMeHo7BYGDixIl4eXkRGxsLwKRJk8jMzGTr1q3Grvq8efOM20dFRVFSUoJWqyUsLAx7+ys3W1/bZQfw8vJi3LhxLFmyBLVaTVhYmFxpb6HcXDWrV3fjk09q/6EFBlYybZrpiEdaedZMiCZRKY09ydjGVVVVyTnPaxgM8N57doSHd6Oo6Mo/oD/8oZq4uNwbzlbZVs9BtUZsiWvZuJaM3arnPEX79PPPWp5/3pHkZBuT9TNmlLNmTbFM8ytEC0ny7GAqKmDLFge2bbOnuvrqJ4T0rF9fxO23V7Zi7YToOCR5diAHDljzwgvdOXfuyp9Vq1WYP7+UZ54poUuXVqycEB2MJM8O5MMP7UwS5623VvHqq4UMHKhvxVoJ0THJma8OZPXqYrp3N+DgYGD9+kL27cuTxCmEhUjLs506e1aDq6uBq588c3Ex8I9/6OjfX4+7u6H1KidEJyAtz3amqgo2b7YnJMSN9eu71Xl9/PgqSZxC3ASSPNuR5GRrJk3qQWRkNyorVbzzjh3ffitjbArRGqTb3g4UFqoID+/Gnj1dTdYPHVqNvb0kTyFag7Q82zBFgX37unDHHW4mibNrVwNr1xbxn//kMXhwh3hATIh2R1qebdT58xqWL3fk669tTdbfffclXn65iF695LymEK1JkmcbdOqUlnvucaWi4krHwMOjhvDwIu6+u+IGWwohbhbptrdBAwfqGT68GgCVSmH27FK+/jpHEqcQbYgkzzZIpYJXXilixIgq/vOfPF5+uRgHBzm3KURbIsmzFSkK7N9vy+OPO1Ndbfpa//56/vOfPEaMqK5/YyFEq5Lk2UoyM9XMmuXMnDnOJCTYsmOHfZ0yKrkLSbQDXl5e3HXXXcafCxcukJiYyBNPPGG2fVy4cME4nU9b0agLRkePHmXnzp0YDAaCg4MJDQ01eb20tJTt27eTnZ2NlZUV8+fPx9vbG4D9+/cTHx+PoigEBwczdepU43afffYZn3/+ORqNhpEjR/LYY4+Rk5PD4sWLjYOQ+vn5MXfuXHMdb6urqYF//rMrr77qQHn5lf9du3fbMWeOTPEr2h9bW1u+/PJLk3UXLlxopdrcPA22PA0GA9HR0axYsYJNmzZx6NAhMjIyTMrExMTQp08fXnvtNRYuXMiuXbsAOH/+PPHx8URERBAZGUlqaipZWVkAHD9+nCNHjvDaa6+xceNG/vSnPxnjeXh4EBkZSWRkZIdKnMeOWXHPPa6sWeNokjgfe6yMzz7LlcQpOqSCggJmz55NSEgI99xzDydPngQgODiYoqIiFEWhZ8+efPjhhwD89a9/5ZtvvmlU7AMHDjBp0iSCg4NZsmQJlZW149VGRERw5513cuutt7J27VoA/vOf/xAUFERISAgzZsxo8XE1mDzT09Px8PDA3d0drVZLYGAgKSkpJmUyMjIYMmQIAL179yY3N5fCwkIyMzPx8/PDxsYGjUaDv78/ycnJAMTGxjJ9+nSsfs8Yjo6OLT6YtqqsTMXSpRqmTHHlhx+sjesHDKhm3748XnmlCEdHuSAkWi4qygEbG2t69+7V4M+yZXW/c8uWOZqUiYpyaHCfFRUVxi57WFhYPXWKYvDgwcTFxfHCCy/wzDPPALWTPaakpPDTTz/Rt29fY25ITU3l1ltvbdR+Fy9ezPbt24mPj0ev1/P2229TUFDAZ599xldffcV3331n3N/mzZt59913iYuLY+fOnQ3Gb0iDyVOn05nMbuni4oJOpzMp4+PjQ1JSElCbbHNzc9HpdHh5eXHq1ClKSkqorKwkLS3NOK1wVlYWP/74IytWrGD16tWkp6cb4+Xk5LBs2TJWr17NqVOnWnyQrSkuzoaJE3uwZYsGg6H2JKaNjcLzzxfzxRe5jBolM1aK9u1yt/3LL78kOjq6zuvJycncd999ANx2220UFBRQXFzMmDFjSEpK4ttvv2Xu3LmcOnWKrKwsnJyc6Nq1a5041zpz5gze3t74+voC8MADD5CUlISDgwM2Njb87W9/Y9++fXT5fRTwgIAAFi9ezLvvvktNTU2Lj7vBc571zQ+nuuZKRmhoKLt27WLp0qV4e3vTt29f40ya06dPZ926ddja2uLj42OcCdNgMFBaWkp4eDhnzpxh06ZNbN26FScnJ7Zt24aDgwNnz54lMjKSqKgo41TGl8XFxREXFwfAhg0b0Gq1uF49PltDB97E8s31448aMjM1xuWJEw1s3aqnf39bwPb6GzaSpY6jvcW1ZOz2FNfOTtNwod/Z2Nji6mp1zTrT7e3s7HB1rZ0H63r1ValUddY7OjpibW2Nq6srGo0GJycnYxmNRoOrqyuTJ0/m3XffJS8vj/DwcD755BO+/vpr7rjjjjrxSktLjdtd1r17d6ysrIzrLu/Tw8ODpKQkEhIS+Oijj9i+fTtffPEF0dHRJCcn89lnnzF58mSSk5NNGoZN1WDydHFxMbYWAfLz83FycjIpY2dnx4IFC4DaZLtw4ULc3NwACAoKMl4l27Nnj7Gyzs7OjBkzBpVKRf/+/VGr1ZSUlNCtWzdjV75fv364u7uTlZVl/O9yWUhICCEhIcZlvV7fJmfPfPJJ2LvXjZISDS+9VMh9911CpQJz7bqtzjp4s+NaMnZ7ijt/Prz0UuPjXlvs5Zdrf+orc736KopSZ31RUZFxRtuAgACio6NZvHgxiYmJODo6UlVVRZcuXcjOzqa8vBxvb2+GDx9OVFQU4eHhdeIVFBRQU1Njst7FxYWzZ8+SkpJC3759+ec//8nIkSP59ddfuXTpEqNGjWLMmDH4+/uTl5fHuXPn6NevH08//TSffPIJx44dY/DgwTd8f1o0e6avry9ZWVnk5OTg7OxMYmIiixYtMilTVlaGjY0NWq2W+Ph4/P39jS3FoqIiHB0dycvLIzk5mXXr1gEwatQojh8/zi233MLFixfR6/U4ODhQXFyMvb09arWa7OxssrKycHd3b6iabcLJk1q6dVPw9LzSJbC1hR07dAwa1B1FudSKtROidSxZsoQlS5YQEhKCra0tmzdvNr42YsQIDIbacRpGjx7N+vXrGTVqVL1xzpw5Y3IudM2aNWzcuJGnnnqKmpoahg0bxuOPP05hYSGzZ8+msrIStVrN6tWrAVi3bh2//PILiqJw2223ccstt7TouBo1b3tqair/+te/MBgMTJw4kRkzZhAbGwvApEmT+Pnnn9m6dauxqz5v3jzs7WvvW1y1ahUlJSVotVqeeOIJ44UlvV7Ptm3b+PXXX9FqtTz++OMMHjyYb7/9lg8++ACNRoNareaBBx4gICCgwQNpzXnbL11SsWmTPW++ac/tt1fy9tu6OvdotqfWS3uMa8nYEteycS0Z25LztjcqebYHrZU8v/7ahuXLHTl//kojfvt2HdOmmT6H3lY/HB0lriVjS1zLxrVkbEsmTxlVqZlyc9WsWdONfftML2SNGVOJv79MuiZERyfJs4kMBti7147w8G4UFl6506t7dwMrVxbz0EPlqOWhVyE6PEmeTXD6tJbnn3ckKcnGZP2995azZk0xrq4yQLEQnYUkz0bKyNAwaVIPqqquXAny8dGzfn0Rd9xR2Yo1E0K0BulgNpKnZw1/+lPtrUZarcLChSXEx+dK4hSik5KW53UoSt0h4VavLqagQM2KFcVyUUiI3128eJEXX3yRn3/+GYPBQFBQEKtWrcLGxvT01vvvv8+6devw8PAwrvvf//1fBgwYcLOrbBbS8ryGosCHH3bhrrt6UFxsmj1dXAy8845OEqcQv1MUhTlz5nD33Xdz6NAhDh48SEVFBeHh4fWWnzZtmvE5+C+//LJO4rz2mfPGPoOu19/876Qkz6ucPavh4YddePZZJ06dsmL9+m6tXSUh2rSDBw9iY2PDQw89BNQ+t75mzRo++ugjysrKGhUjMTGRSZMm8fTTTxMcHExiYiL333+/cfny6EnBwcFMmjSJQ4cOAbUt2blz5zJz5kweeeQRix3j9Ui3Haiqgu3b7Xn9dQcqK6+0Nr/6yoayMhVdu3aI5whEJxD1XRQbUzc2quyjAx/l1QmvmqxbdmAZ7/74rnF5ycglPHfrc9eN8fPPPxufGrzMwcEBT09PfvnllzrPjn/66afGoecuLwOkpKQQHx+Pt7c3iYmJHD16lISEBLy9vXnjjTcAiI+PJz09nUceeYQDBw4A8N133xEXF1dnvI2bodMnz5QUa55/3pGffroyuoxarRAWVsbSpSWSOIW4AUVR6oyydnl9faZNm1Zvl37UqFHG2ScAhg8fblxOSUlh1qxZAPTv3x9PT0/Onj0LwO23394qiRM6cbe9sLB24NfQUFeTxDl0aBX79+exZk2xJE4hGjBgwAB++OEHk3UlJSXk5eVx5MgR4yDJv/322w3jXDvk5NXLN3qC/NrtbqZO2fL8/HNbVqywIjv7yqjudnYGli0rYdasMrSd8l0RHcFztz7H+j+ub/bz3K9OeLVOV/5GJkyYwPr16/nwww954IEHqKmpYe3atfzlL38x/rTUmDFjiImJ4bbbbuPMmTNkZmbi6+vLsWPHWhy7JTply7OwUEV29pWuxqRJl/j66xzmzJHEKURTqFQq3nrrLf7v//6P8ePHM3jwYFQqlXHqi2t9+umnJjNtXjulT31mzpxJTU0NwcHBzJ8/n02bNtW5Dao1dMpRlRQFHnnEg9OnFV5+uYjJkyssPs1vWx01pqPEtWRsidv4uCkpKTz99NO89dZbDB061Kyxm0NGVTIzlQp27tRTXZ1Pt24d4n+HEG3CqFGjTK6md2SdstsO4OWFJE4hRLN12uQphBAt0ahu+9GjR9m5cycGg4Hg4GBCQ0NNXi8tLWX79u1kZ2djZWXF/Pnzjfdo7d+/n/j4eBRFITg4mKlTpxq3++yzz/j888/RaDSMHDmSxx57DICYmBgSEhJQq9XMmjWL4cOHm+t4hRDCLBpMngaDgejoaFauXImLiwvLly8nICAAT09PY5mYmBj69OnD0qVLyczMJDo6mlWrVnH+/Hni4+OJiIhAq9USERHByJEj6dmzJ8ePH+fIkSO89tprWFlZUVRUBEBGRgaJiYls3LiRgoICXn75ZV5//XXjlMVCCNEWNJiR0tPT8fDwwN3dHa1WS2BgYJ3bCzIyMoyPaPXu3Zvc3FwKCwvJzMzEz88PGxsbNBoN/v7+xpPJsbGxTJ8+3TjNsKOjI1B7tS4wMBArKyvc3Nzw8PAgPT3drActhBAt1WDLU6fTmUwM7+LiwunTp03K+Pj4kJSUxMCBA0lPTyc3NxedToeXlxd79+6lpKQEa2tr0tLSjPOvZ2Vl8eOPP7J3716srKx4/PHH6d+/PzqdDj8/P2NsZ2dndDpdnXrFxcURFxcHwIYNG9Bqtbi6ujb+wJtYvqUstT+Ja/nYEteycS0Z26J1bqhAfbeBXvssa2hoKLt27WLp0qV4e3vTt29f4zTE06dPZ926ddja2uLj42PsfhsMBkpLSwkPD+fMmTNs2rSJrVu33vBRrKuFhIQQEhJiXNbr9a029XBr7k/iWj62xLVsXEvGbtX7PF1cXMjPzzcu5+fn13kQ387OjgULFgC1yXbhwoW4ubkBEBQURFBQEAB79uwxtmKdnZ0ZM2YMKpWK/v37o1arKSkpqbM/nU6Hs7NzY49VCCFuigaTp6+vL1lZWeTk5ODs7ExiYiKLFi0yKVNWVoaNjQ1arZb4+Hj8/f2ND+wXFRXh6OhIXl4eycnJrFu3Dqi9mfb48ePccsstXLx4Eb1ej4ODAwEBAWzZsoV77rmHgoICsrKy6N+/f4MHYm1tfcP/EvVpavmWstT+JK7lY0tcy8a1ZGxLxW3wgpFGo2H27NmEh4ezePFixo0bh5eXF7GxscTGxgKQmZnJkiVLePbZZzl69KjJYABRUVEsXryYV155hbCwMOzt7YHaFml2djbPPfccr7/+Ok8//TQqlQovLy/GjRvHkiVLCA8PJywsrFFX2l944YUmHXhTy7eUpfYncS0fW+JaNq4lY1uyzo26z3PkyJGMHDnSZN2kSZOMvw8YMIAtW7bUu+3atWvr37FWW6cFe9mMGTOYMWNGY6omhBCtQm6eFEKIZtCsWbNmTWtXwlz69etn0fItZan9SVzLx5a4lo1rydiWitthhqQTQoibSbrtQgjRDJI8hRCiGSR5CiFEM3TKkeTrU1FRwVtvvYVWq+WWW25hwoQJFt1fdnY2H3/8MeXl5Tz33PXnxW6q5ORkUlNTKS4u5o9//CPDhg0zS9yMjAz2799PSUkJQ4YMMblVraUqKipYvXo1Dz74ILfeeqtZYp44cYL3338fT09Pxo8fzy233GKWuAaDgffff59Lly7Rr18/7rzzTrPEBTh16hQHDhzAYDCQkZFhfKCkpfLy8oiOjsbBwYFevXrVGVKyuTIyMvjggw9wcHBgyJAhjB07tkXx6vtOmON7WV9cc3z/2n3yvNFYo9u2bSM1NRVHR0eioqJuuE1ycjJjx44lICCATZs2NeuP1JT9ubu7M3/+fJNy5og7evRoRo8eTWlpKe+8884Nk2dT4np6ejJ37lwMBgNvvvmm2eoL8MknnzBu3Dizvg8qlQpbW1uqq6tNBrZpadwjR46g0+mwt7dvMG5TY/v7+xtHHrs8gI454l68eJGRI0dy1113sXXrVrPFTUtLY/Lkyfj7+/PKK6/Umzxb+p243veypXGb8v27LqUdq6mpURYuXKj89ttvSnV1tfK3v/1NuXDhgvH1EydOKGfOnFGWLFnS4DYff/yx8ssvvyiKoiibN29uVn2asr/LXnvtNYvE/de//qWcOXPGrHFTUlKUF198UTlw4IDZ4n7//ffKwYMHla+++ko5cuSI2eLW1NQoiqIoBQUFyuuvv262uDExMUpsbKyiKJb720VFRSll9+pFbwAAAtpJREFUZWVmi1tcXKysWbNGWbNmjZKQkGC2uIWFhcqOHTuUt99+W1m5cqXZjv/q9/V630tzfdca8ze8nnZ9zrOhsUYHDRpkfBy0oW2uHpBEaebdW03Zn6XiKorC7t27GT58eIP3tzW1vgEBAaxbt44DBw6YLe6JEyc4ffo0Bw8eJD4+HoPBYJa4lx/ptbe3p7q62mz1dXZ2pmvXrgCNemy4qe9xXl4ednZ2xrEhzBH3q6++4oEHHmD16tWkpqaaLa6joyNPPvkkjz76KA4ODmY5/mtd73tpqe9aU7Trbntjxhpt7DaTJ0/mn//8J6mpqWY773aj/ZWUlPDee+9x7tw5YmJiuPfee80S97PPPuPYsWOUl5fz22+/Nfnc5PXinjhxgqSkJPR6PSNGjGhSzBvFDQsLA+Drr7/GwcGhyTMGXC9uUlIS33//PWVlZdx9991mq++UKVP45z//yY8//oi/v3+T494oNkBCQgITJ040a9y77rqLDz/8kIMHD9KjRw+zxc3JySEmJobKykqmTZvW4nj1fSdGjx7d6O9lU+K29PsH7Tx51tdCvHas0cZuY2traxxWz5yutz8HBwfmzp1r9rhTpkxhypQpZo97yy23tOiiS0N/q+ZeeLle3DFjxjBmzJhmxbxRXBsbG+bPn9/suDeKDfDggw+aPa63t3eLLkpeL66bmxtPPfWU2eLV951oyveyKXFb+v2Ddn6rUmPGGjXHNje7jhK348a1ZOz2Ere91LMh7Tp5Xj3WqF6vJzExkYCAALNvc7PrKHE7blxLxm4vcdtLPRvS7p9tT01N5V//+hcGg4GJEyeaDGW3efNmTp48SUlJCY6Ojjz44IMEBQXdcJuWsNT+JG77jNse62zuuO2lns3R7pOnEEK0hnbdbRdCiNYiyVMIIZpBkqcQQjSDJE8hhGgGSZ5CCNEMkjyFEKIZJHkKIUQzSPIUQohmkOQphBDN8P8BIGXmBVuMiU4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 360x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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lSYszF8/w2nevXfd9u1igYGMNjm3tiHkwxvDarFmWaK0Ow9CFgAKO6P+oLpf/398qhYg/FfoqHrx/3/uG8mq1CrrGkxkUzfj4K8sP11T/291uJ3JYpNF7F3s0lnWH19X62VOpVIT4hPDakNeMyv8j6R/E/Rp33Z/55X1P+T/FlH5TjMqHx4eTcCqBSl0lOkWHDp3h34a/Ff3fbw99mwl9JxiVH/bxMH7X/k5d3O17NwNdBhp+9gCf/PoJ54tqXn/rak5OTji2qXm1g2tdHedG3HDyrK7LrFKpatxfXyEhIYSEhBi263MT7e7fdzPnhzl1OvYu97sY2mGo0b4dv+4gKjmqTuXHdB/DIMdBRvu+PPola4+urVP5F/q+wO3tbjfat/XIVrb/ub1O5dvRjlva3GK0b8t/t5ByPqVO5f0d/HFXuxvt23h4I2cKz9Sp/AMeD9BeZzzT+IbDGyjTldVQwtjUXlNRtVMZbmDOLc1l8y+bay/4P5EDIrHR2Bi2/9L+xefHP69T2fbW7Y0+V15eLmgvZkHPL2steyAXcpUeRuVtbFwpcz5Dicc3fPtn7fHLy8urfK6PZx7n+7++r1P9Xa1dq5T/9dyv7M/YX6fy/Tv2r1I+LTuNo1lH61T+Qu6FKuXbWbarU1mA/Iv5ZGdnG928rujqNhS3tLAkKyeLSpvK2g/+H1PdJH/DydPZ2dmoIjk5OTg6OlJRUUFOTk6V/QAODg7k5ubi6OhIbm4u7drV/Y2+maio/y+b5qQhvyxNFvsG3rt77y3FMaeU3Q0s/9//nufT3/OZ9UODq2D2n71FPW7E0SlVFxns0q4LFy9dpI2mDWoLNRqVBrWFGksLS7o6dKW9dXssVBaoUNHVoWuV8jNvn0lxRTEqVKhUqip/d2jTgVDvUNpo2lQp21RuOHkGBgbyzTffMGTIEE6cOIGtrS2Ojo60a9eOzMxMLly4gJOTE4mJicyYMcNQ5ocffiAsLIwffviBO+6444YbUp0ejj148Y4XKSkpMfowXv5PfXmfSqWiS7suVcr369iPF/q+YHTs1Z9pQ3lU9HbuXaX8UI+hdGrfieKSYqNjr60HQKBrYJXyYb5h9HHuU32drzpPW7u2BDgEVCk/odcE7ve5v0pbq6tHL6deVcrP7jebovIiw/Ft7dpSVFRUtTwqvNp5VSkfMThCf52qmnpfWw8nGyejsraWtqy4Z4VRHa9X3tLCeBXKTnad+DDoQ8rLVHz+rzb8uNcGRVGBcrneKmxsdAwbVsbY0AqjsjNmFJJd4k3K+Zhq36urqVDR1rLqipJ3dr6TL8d+ycWLF6u8V9fWv7111bWBRvuO5rYOt1Xb1qv3tXdoj2WZZZXyz/Z5llFdR10OWrX8VfVwta261Mq8AfOYeftM1Co1FioLnJ2cuZh/EQssUFuoUaFCbaFGrVJX2/4VQ1dU2Vcf43uPv6HyTaHW+TxXrFjBsWPHKCgowMHBgbFjx1JRof+whYaGoigKMTExHD58GCsrK6ZNm4avry8AqampbNiwAZ1Ox7Bhw3j44YcBKCgoYPny5Yau+uzZs+t8q5I8226eGE0Vx5Qx9u2z4rXX2vPHH8Z9hBEjdIwZk8c995RiY1NDYRNoae+XueO0tGfba+151rbwk0ql4tlnn632tYCAAAICqvaI7O3tmT9/fh2rKET95OaqmD27Pbt2GQ/p7rmnlFdeKSAkxIHs7FIz1U60Fi1uViUhamNlBWfOXPlo29vrWLDgIo8/XowZL8OKVkYezxStTtu2Chs2aHF1reSRR4r5/vsLPPGEJE5hWtLzFK2Su3slP/xwAXt7WaJLNA7peYoWb88eK+Ljravsl8QpGpMkT9GinTqlZsoUJ5591omdOxvxq3MhriHJU7RYxcUqnn3Wifx8C8rLVSxY0I6yuj3QJMQNk+QpWiRFgfBwB44f198gbmWlsGpVLlZWZq6YuGlI8hQt0ldf2fDFF7aG7cjIfG6/vfw6JYQwLUmeosU5d86CuXOvPNL42GPFPPFEsRlrJG5GkjxFi1JZCZMnO5GXp//ourtX8NZb+WaulbgZSfIULcrWrbakpOgvbFpYKKxYkSe3JAmzkOQpWoyiIhVLl9obtl96qZDBg+XrdWEekjxFi7Frlw0XLqgBcHOrZNo0WSRImI88nilajIceKqFz50oWLWrHM88U0aaNDNeF+UjyFC3KgAFl/Pvf5pufVYjLJHmKFsdCLjaJZkA+hqLZM9Oy3EJcV516nocOHWLdunXodDqCg4MJCwszer2wsJBVq1Zx/vx5LC0tmTp1Kl5e+jVttm/fTkJCAoqiEBwczMiRIwE4deoUq1evprS0lA4dOjBjxgxsbW2rxBY3t+xsC554wpmJE4t45JFiefxSNBu19jx1Oh0xMTHMnTuX5cuXs2/fPtLT042OiYuLw8fHh2XLljF9+nTWr18PwOnTp0lISCAyMpKlS5eSmppKZmYmAB9//DHjxo0jOjqa/v3789VXX5m+daLFW7OmLceOWfLyy+157jmn2gsI0URqTZ5paWm4ubnh6uqKRqNh8ODBJCcnGx2Tnp6Ov78/AO7u7mRlZZGXl0dGRgbdu3fH2toatVqNn58fSUlJgH4hNz8/PwBuvfVWDhw4YOq2iRauqEjFhg1XVmZ89FF5BFM0H7UO27VaLc7OzoZtZ2dnTpw4YXSMt7c3Bw4coGfPnqSlpZGVlYVWq8XT05PY2FgKCgqwsrLi4MGDhpU1PT09SUlJ4Y477uCnn34yWuP9avHx8cTHxwMQFRWFi4tL/Rqo0dS7jCk1RfymamNTtyUuzoKLF/W/37t1U/jb3+xQq+u2ympdYzQm+dk3vximjFNr8qxuZeJr164OCwtj/fr1hIeH4+XlRZcuXbCwsMDDw4PRo0ezePFibGxs8Pb2xuJ/X5VOnTqVdevW8fnnnxMYGIhGU31VQkJCCAkJMWzXd2lSWXq4ZcW5HENR4IMPOhj2P/30RXJzi0wao7HJz775xagpTqMsPezs7GzUK8zJycHR0dHoGFtbW6ZNmwbok+306dPp2LEjAEFBQQQFBQGwZcsWQy/W3d2defPmAfohfGpqar0rL1qv5GQrw1ydbdroGDNGhuyiean1mqevry+ZmZlcuHCBiooKEhMTCQwMNDqmqKiIiooKABISEvDz8zN8c56fr5/xJjs7m6SkJIYMGWK0X6fTsW3bNoYPH266VokWb8OGK3dePPxwCQ4Ocr+SaF5q7Xmq1WomTpxIREQEOp2OYcOG4enpya5duwAIDQ0lIyODlStXGobqU6ZMMZSPjo6moKAAjUbDpEmTsLPTX7Pat28fO3fuBKB///4MGzasMdonWqCsLAu+/rqNYfvpp00zXBfClOp0n2dAQAABAQFG+0JDQw3/7tGjB++99161ZRctWlTt/hEjRjBixIi61lPcRLZssaW8XH9dPTCwjD59KsxcIyGqkieMRLOi08E//3llyP7MM9LrFM2TJE/RrBQVwd13X8LBQUf79jpGjiwxd5WEqJYkT9Gs2NvD3/+ez8GD59i6NRtra3PXSIjqSfIUzZK1NXKtUzRrkjyFEKIBJHmKZqOy0tw1EKLuJHmKZqG0FO64w5Vx49R89ZUNOp25ayTE9UnyFM3Cnj3WnD+v5vPP1SxZ0o5rpk8QotmR5Cmahe3brzxRNGJEiSRP0exJ8hRmV14O335rY9geObLUjLURom4keQqzS0y0Ji9P/1H08lLo27fczDUSonaSPIXZ7dx5pdc5erROhuyiRZDkKcxKUWD37iuPET3wgHzNLloGSZ7CrP74Q83p0/rJvdq21TF4sMzbKVoGSZ7CrL7//sqQ/c47L8nSwqLFkOQpzOr7768M2YcOvWTGmghRP5I8hdlcXltQo9H/Q5KnaEnqNJP8oUOHWLduHTqdjuDgYMLCwoxeLywsZNWqVZw/fx5LS0umTp2Kl5cXANu3bychIQFFUQgODmbkyJEAnDp1itWrV1NWVoZarebZZ5+lW7duJm6eaM5UKvjkEy0FBSpSUqzw8pKH20XLUWvPU6fTERMTw9y5c1m+fDn79u0jPT3d6Ji4uDh8fHxYtmwZ06dPZ/369QCcPn2ahIQEIiMjWbp0KampqWRmZgKwadMmxowZw9KlSxk7diybNm0yfetEi2BvrzBsmPQ6RctSa/JMS0vDzc0NV1dXNBoNgwcPJjk52eiY9PR0/P39Af2SwllZWeTl5ZGRkUH37t2xtrZGrVbj5+dHUlISoF/7vaREP0t4cXFxleWMhRCiOat12K7Vag1rrYN+HfcTJ04YHePt7c2BAwfo2bMnaWlpZGVlodVq8fT0JDY2loKCAqysrDh48CC+vr4APPPMM0RERPDJJ5+g0+lYvHhxtfHj4+OJj48HICoqChcXl/o1UKOpdxlTaor4TdXG1tIWeb+aZ5yW1pZak6eiVL3vTnXNIyBhYWGsX7+e8PBwvLy86NKli2EZ4tGjR7N48WJsbGzw9vbGwkLf2d21axfPPPMMAwcOJDExkY8++og333yzSqyQkBBCQkIM29nZ2fVqoIuLS73LmFJTxG+qNpoyzp491uzbZ8XAgWXccUcZdnaKyWPUpCW+X+aM0VRxzNmWzp071/s8tSZPZ2dncnJyDNs5OTlVhti2trZMmzYN0Cfb6dOn07FjRwCCgoIICgoCYMuWLYZe7A8//MCECRMAGDRoEB9//HG9Ky9arv/8x4bNm9uyciW8/PJFZs0qNHeVhKiXWq95+vr6kpmZyYULF6ioqCAxMZHAwECjY4qKiqio0K83k5CQgJ+fH7a2+uVj8/PzAX2PMSkpiSFDhgDg5OTEsWPHAPjll19wc3MzXatEs5eYeOX+zkGDysxYEyEaptaep1qtZuLEiURERKDT6Rg2bBienp7s2rULgNDQUDIyMli5cqVhqD5lyhRD+ejoaAoKCtBoNEyaNAk7OzsAJk+ebLj9ydLSksmTJzdSE0Vzc+6cBX/+qf/oWVsr3HabJE/R8tTpPs+AgAACAgKM9oWGhhr+3aNHD957771qyy5atKja/T179mTJkiV1radoRX766UqvMyCgDBub6xwsRDMlTxiJJpeYeOUBdhmyi5ZKkqdocj/9dHXylJvjRcskyVM0qfPnLTh50hLQX+8MCJCep2iZJHmKJnV1r/P22+V6p2i5JHmKJrV/v9yiJFoHSZ6iSe3ff6XnOXCgXO8ULVedblUSwlT+7/9y2b/figMHrOnXT1bJFC2XJE/RpG65pYJbbqlg/Phic1dFiBsiw3YhhGgASZ5CCNEAkjxFkygthTNn1FQzw6EQLZIkT9EkUlKsGDjQlcBAVyIj7c1dHSFumCRP0SSSk/W3KJ07pyY/Xz52ouWTT7FoEpeTJ0D//nJzvGj5JHmKRldZCT//LMlTtC6SPEWjO35cQ2Gh/qPm5laJh4eszy5aPkmeotFdPWS/444yrlk/UIgWqU5PGB06dMiwZEZwcDBhYWFGrxcWFrJq1SrOnz+PpaUlU6dOxcvLC4Dt27eTkJCAoigEBwczcuRIAJYvX87Zs2cB/brttra2LF261JRtE81EUtKVyUBkyC5ai1qTp06nIyYmhnnz5uHs7Mzrr79OYGAgHh4ehmPi4uLw8fEhPDycjIwMYmJimD9/PqdPnyYhIYHIyEg0Gg2RkZEEBATQqVMnZs2aZSi/ceNGw4JxonVRFEhKMu55CtEa1DpsT0tLw83NDVdXVzQaDYMHDyY5OdnomPT0dPz9/QFwd3cnKyuLvLw8MjIy6N69O9bW1qjVavz8/EhKSjIqqygK+/fvN6yqKVqXjAw1586pAWjbVoefn0wGIlqHWnueWq3WsNY66NdxP3HihNEx3t7eHDhwgJ49e5KWlkZWVhZarRZPT09iY2MpKCjAysqKgwcP4uvra1T2+PHjODg40KlTp2rjx8fHEx8fD0BUVBQuLi71a6BGU+8yptQU8ZuqjQ2J8+23V34/DxwIbm7XL3+zv1/NMUZTxWlpbak1eSrVPE+nuuaKf1hYGOvXryc8PBwvLy+6dOliWIZ49OjRLF68GBsbG7y9vbGwMO7s7tu377q9zpCQEEJCQgzb2dnZtTbqai4uLvUuY0pNEb+p2gshDC8AACAASURBVNiQOMXFNvTubc/x4xpuv72Q7OxCk8eor+b8fjXHGE0Vx5xt6dy5c73PU2vydHZ2Jicnx7Cdk5ODo6Oj0TG2trZMmzYN0Cfb6dOn07FjRwCCgoIICgoCYMuWLUa92MrKSpKSkoiKiqp3xUXLMHp0KaNHl1JQoKKiwty1EcJ0ar3m6evrS2ZmJhcuXKCiooLExEQCAwONjikqKqLif/8zEhIS8PPzM3wBlJ+fD+h7jElJSUa9zCNHjtC5c2ejhCpaJ3t7BUdHmRVEtB619jzVajUTJ04kIiICnU7HsGHD8PT0ZNeuXQCEhoaSkZHBypUrDUP1KVOmGMpHR0dTUFCARqNh0qRJ2NnZGV6rbcguhBDNlUqp7qJmM3b53tC6kmueLStOa4nRVHGkLaaJ0yjXPIVoqLfeaoe9vY7AwDIGDCjD2rr2MkK0FJI8RaMoK4ONG9tSWqq/MyM19Ryurjoz10oI05Fn20WjOHLE0pA4vb0rJHGKVkeSp2gU104GIkRrI8lTNIqrn2eXyUBEayTJU5icokjPU7R+kjyFyZ08qUar1U8G0r69jm7d5NEi0fpI8hQml5x85Z6kO+4ow0I+ZaIVko+1MDkZsoubgSRPYXLyZZG4GUjyFCaVlWXBn3/qn72wtla49VZJnqJ1kieMhEk5OurYvj2L5GQrcnIs5JFM0WpJ8hQmpdFA377l9O0ry22I1k2G7UII0QCSPIUQogEkeQqTyc624Nw5+UiJm4N80oXJbNpkS79+bgwc2JHY2Dbmro4QjapOXxgdOnSIdevWodPpCA4OJiwszOj1wsJCVq1axfnz57G0tGTq1Kl4eXkBsH37dhISElAUheDgYEaOHGkot2PHDr755hvUajUBAQE89dRTJmyaaGopKfr7O8+c0aBWm7kyQjSyWpOnTqcjJiaGefPm4ezszOuvv05gYCAeHh6GY+Li4vDx8SE8PJyMjAxiYmKYP38+p0+fJiEhgcjISDQaDZGRkQQEBNCpUyd++eUXUlJSWLZsGZaWloaF4kTLVFl5JXmC3BwvWr9ah+1paWm4ubnh6uqKRqNh8ODBJCcnGx2Tnp6Ov78/AO7u7mRlZZGXl0dGRgbdu3fH2toatVqNn58fSUlJAOzatYvRo0djaWkJgIODg6nbJprQr79qKCjQf5xcXSvx8qo0c42EaFy19jy1Wq3R0sDOzs6cOHHC6Bhvb28OHDhAz549SUtLIysrC61Wi6enJ7GxsRQUFGBlZcXBgwfx9fUFIDMzk19//ZXY2FgsLS3529/+Rrdu3arEj4+PJz4+HoCoqChcXFzq10CNpt5lTKkp4jdVG68X5/jxK7+H77wTOnRoWH1ulverJcVoqjgtrS21Js/qFtdUqVRG22FhYaxfv57w8HC8vLzo0qWLYRni0aNHs3jxYmxsbPD29sbif1Ps6HQ6CgsLiYiI4OTJkyxfvpyVK1dWOXdISAghISGG7fqurierZzZNnN2723P549S3byHZ2UUmj2EqzeH9akkxmipOq1s909nZmZycHMN2Tk4Ojo6ORsfY2toybdo0QJ9sp0+fTseOHQEICgoiKCgIgC1bthh6sU5OTgwYMACVSkW3bt2wsLCgoKCAdu3a1bsRwvyungxEZlISN4Nar3n6+vqSmZnJhQsXqKioIDExkcDAQKNjioqKqKjQT3ibkJCAn58ftra2AIYvgrKzs0lKSmLIkCEA3HHHHfzyyy+Afi32iooK7O3tTdcy0WQyMtScPav/PWxrq6NXL3k0U7R+tfY81Wo1EydOJCIiAp1Ox7Bhw/D09GTXrl0AhIaGkpGRwcqVKw1D9SlTphjKR0dHU1BQgEajYdKkSdjZ2QH6HumHH37InDlz0Gg0vPDCC1WG7KJluHr+zn79ytHIjAniJlCnj3lAQAABAQFG+0JDQw3/7tGjB++99161ZRctWlR9YI2GGTNm1LWeohkznr/zkhlrIkTTkSeMxA3r1KmSW27RD9UDA+V6p7g5yABL3LAXXyzkxRcLyc1VYWtb9e4MIVojSZ7CZBwdJXGKm4cM24UQogEkeQohRAPIsF00WFkZTJniyG23ldO/fxkDBpQhd5uJm4UkT9Fg33xjw86dbdi5sw1eXhXs33/B3FUSosnIsF00yGefteHFF688pjtkiNzfKW4ukjxFvSgKrFrVlpdecqSiQj9G9/CoYNasAjPXTIimJcN2UWc6Hbz1VjtWr7Yz7PPzK2fTphzc3HRmrJkQTU+Sp6iTsjKYMEFNbOyVxDlw4CXWrtXi4CD3d4qbjyRPUavCQhXPPefInj1XFiYaMaKE99/PxcbGjBUTwozkmqeo1fvv27Fnz5Us+be/FfHRR5I4xc1Nkqeo1axZBQwYoP82/eWXL/LOO/myOqa46cmwXdTKxgbWrdPy888dCAoqNHd1hGgWpOcpqjh5Us21S1c5OCiMHSvfqAtxmSRPYeTLL20IDu7IRx+1NXdVhGjW6jRsP3ToEOvWrUOn0xEcHExYWJjR64WFhaxatYrz589jaWnJ1KlT8fLyAmD79u0kJCSgKArBwcGMHDkSgE8//ZSEhATDgm9PPPFEldnqRdOKiWnLggXtUBQVixc70LlzJaNHl5q7WkI0S7UmT51OR0xMDPPmzcPZ2ZnXX3+dwMBAPDw8DMfExcXh4+NDeHg4GRkZxMTEMH/+fE6fPk1CQgKRkZFoNBoiIyMJCAigU6dOAIwcOZJRo0Y1XutEnSgKREXZs3LllQX4uncvJzBQFnIToia1DtvT0tJwc3PD1dUVjUbD4MGDSU5ONjomPT0df39/ANzd3cnKyiIvL4+MjAy6d++OtbU1arUaPz8/kpKSGqclokHKy2H27PZGibNfvzK2bcvG3b3SjDUTonmrteep1WoNa62Dfh33EydOGB3j7e3NgQMH6NmzJ2lpaWRlZaHVavH09CQ2NpaCggKsrKw4ePAgvr6+hnI7d+5kz549dO3alaefftqwsubV4uPjiY+PByAqKgoXF5f6NVCjqXcZU2qK+A2NUVwMTz6pYceOK79DR4zQsXkz2No6Vzm+ObelucVoqjjSFvPFqTV5Ktd+7QpVlggOCwtj/fr1hIeH4+XlRZcuXQzLEI8ePZrFixdjY2ODt7c3Fhb6/6ihoaGMGTMGgK1bt7Jx40amTZtWJVZISAghISGG7ezs7Ho10MXFpd5lTKkp4jckhlar4plnnElNvZI4H3usmL//PY/iYn1iNUWc+motMZoqjrTFNHE6d+5c7/PUmjydnZ3JyckxbOfk5ODo6Gh0jK2trSHxKYrC9OnT6dixI6Bfnz0oKAiALVu2GHqx7du3N5QPDg5myZIl9a68aJiMDDVPPulEWpqlYd+LLxbw6qsFMpmxEHVU6zVPX19fMjMzuXDhAhUVFSQmJhIYGGh0TFFRERUVFQAkJCTg5+eHra0tAPn5+YC+x5iUlMSQIUMAyM3NNZRPSkrC09PTNC0StTp3zoL0dP0jQiqVwttv5/Paa5I4haiPWnuearWaiRMnEhERgU6nY9iwYXh6erJr1y5AP/zOyMhg5cqVhqH6lClTDOWjo6MpKChAo9EwadIkw3XNTZs2cerUKVQqFR06dOD5559vpCaKa/XrV85HH+Uydaoj776bx6hRcjuSEPWlUqq7qNmMnT17tl7HyzXPmmVnW+DiUvenhppzW5pbjKaKI20xTZyGXPOUJ4xuAlu3tuHkyaozedQncQohjEnybMUUBZYvt2P2bEfGjXPm/Hn5cQthKvK/qZWqrITXX3dg2TL9469nzmh45512Zq6VEK2HTEnXCpWWwosvOrJ9exvDvrvvLmXx4nwz1kqI1kV6nq1Mfr6Kp55yNkqcDz1UzIYNWuzsWtR3g0I0a5I8W5Fz5yx45BEX9u+3Nux77rlC3nsvDysrM1ZMiFZIhu2tRFqamnHjnElPv/IjffPNfKZMKTJjrYRovSR5tgJ//AFhYS7k5upvR9JoFKKj8xgzpsTMNROi9ZJheyvg7Q1DhpQB0KaNjvXrtZI4hWhk0vNsBdRqeO89/VwBkycXEhAgkxgL0dgkebZAiqL/Y3HVuMHaGj7+OLfmQkIIk5Jhewuj08Fbb7Vj/vx2VVa4FEI0Hel5tiBlZTBrVnu++EI/3V/HjjpmzJB11IUwB0meLURhoYrnnnNkzx4bw75ffrFEJ3N7CGEWMmxvAbKzLXj0UWejxPnMM0WsWpVrdN1TCNF0pOfZzJ06pb/5/dSpKz+q8PCLzJxZKDO/C2FGkjybsSNHLHnqKSeys/U3v1tYKCxZks+TT1azOpsQoknVKXkeOnSIdevWodPpCA4OJiwszOj1wsJCVq1axfnz57G0tGTq1Kl4eXkBsH37dhISElAUheDgYEaOHGlU9quvvmLTpk2sWbOGdu1kyrTL9u61YtIkJ4qK9ONyGxuFDz/M5d57ZckMIZqDWq+Y6XQ6YmJimDt3LsuXL2ffvn2kp6cbHRMXF4ePjw/Lli1j+vTprF+/HoDTp0+TkJBAZGQkS5cuJTU1lczMTEO57Oxsjhw5YtZ11Zuj0lJ46SVHQ+J0cNARG5sjiVOIZqTW5JmWloabmxuurq5oNBoGDx5McnKy0THp6en4+/sD4O7uTlZWFnl5eWRkZNC9e3esra1Rq9X4+fmRlJRkKLdhwwbGjRtXZR34m52NDcTEaLG11dGpUyVxcdnccUeZuaslhLhKrcN2rVZrWGsd9Ou4nzhxwugYb29vDhw4QM+ePUlLSyMrKwutVounpyexsbEUFBRgZWXFwYMH8fX1BSAlJQUnJyd8fHyuGz8+Pp74+HgAoqKi6t1L1Wg0Zu3ZNjR+SAh88UUlXbsqeHq2v+6xTdXGpojTWmI0VRxpi/ni1Jo8q1tc89qeYlhYGOvXryc8PBwvLy+6dOliWIZ49OjRLF68GBsbG7y9vbGwsODSpUts27aNefPm1VrBkJAQQkJCDNv1XV2vJayeWV4Of/2lplu3SqP9vXvr/66t+rKCYvOL0VRxpC2midOQ1TNrTZ7Ozs7k5OQYtnNycnB0dDQ6xtbWlmnTpgH6ZDt9+nQ6duwIQFBQEEFBQQBs2bIFZ2dnzp8/z4ULFwgPDzec89VXX+Wdd96hffvr97Jam5ISFZMnO/Lzz1Z88UU23btXmLtKQog6qPWap6+vL5mZmVy4cIGKigoSExMJDAw0OqaoqIiKCv1/+oSEBPz8/LC11T9CmJ+vXzcnOzubpKQkhgwZgpeXF2vWrOGDDz7ggw8+wNnZmSVLltx0iVOrVTF2rDMJCTbk5Vnw5JNOaLVy17sQLUGtPU+1Ws3EiROJiIhAp9MxbNgwPD092bVrFwChoaFkZGSwcuVKw1B9ypQphvLR0dEUFBSg0WiYNGkSdnZ2jdeaFiQjQ82TTzqRlmZp2PfIIyU4OsrzlkK0BCqluouazdjZs2frdXxzvOZ5/LiGp55y5tw5/c3vKpXCokUXmTixYUtmyHWv5hejqeJIW0wTp1GueQrT+uknKyZMcOLiRf3w3MpK4R//yGXUKLmHU4iWRJJnE9qxw4YXXnDk0iX93Qp2djpiYrTceafcwylESyPfTjSRTz6x5fnnryTODh0q+de/siVxCtFCSc+zifzxhwadTp84fXwq2LIlB2/vylpKCSGaK0meTeTNNy+SnW3ByZMaNm7U4uIi36oL0ZJJ8mwiFhYQHZ1HebmKtm1b1A0OQohqyDXPRpCfr+Ldd+2ovGZUbmWFJE4hWgnpeZrYuXMWPPWUM8ePW3L+vJo1a8xdIyFEY5CepwmlpWkYNcqF48f1Tw1t2tSWn36S6faEaI0keZpIaqolYWHOZGToO/Majf7m90GDZJguRGskw3YTSEiwZvJkR0pK9L+L2rTRsXp1LsOGXQLamrdyQohGIT3PG/Tpp22YMMHJkDidnCr57LOc/yVOIURrJT3PBlIU+PBDOyIjryxa5+lZwebNOfj6ys3vQrR20vNsoA8+ME6cfn7lfPlltiRO0Sq1adOG4cOHG/6cOXOGxMREnn76aZPFOHXqlGHi9JZAep4N9MADJaxe3ZbsbDWDBl1i7Vot7drJl0OidWrTpg3ffvut0b4zZ86YqTbNg/Q8G8jHp5JNm7Q8+mgxmzblSOIUN7Xc3FwmTpxISEgIDzzwAMeOHQMgODiY/Px8FEWhd+/efPbZZwC8+OKL7Nmzp07n3rt3L6GhoQQHBzN79mwuXdJ/nxAZGcnQoUMJCQlh0aJFAPz73/8mKCiIkJAQHn744UZo6RXS86yjykpQq433+fuXs2JFnnkqJG5a0dH2vPuu/VV7ap7Id9y4Iv7+93yjfa+84sDmzVfuApk9u4A5cwquG7OkpIThw4cD4OXlRUxMzDV1iqZPnz6sXbuWH3/8kZkzZ/Ltt98SGBhIcnIyHh4eeHt7k5SUxKOPPkpqaipRUVG1trW0tJRZs2axdetWfH19mTFjBhs3bmTMmDHs2LGDPXv2oFKpDMv9rFixgs2bN9OpUyfDvsZSp+R56NAh1q1bh06nIzg4mLCwMKPXCwsLWbVqFefPn8fS0pKpU6fi5eUFwPbt20lISEBRFIKDgxk5ciQAsbGxpKSkoFKpcHBwYNq0aTg5OZm4eaZx6pSaSZOceOedfPr3lynkxM2numH71ZKSkli9ejUAd955J7m5uVy8eJEBAwZw4MAB0tPTefrpp9m0aROZmZk4OjrStm3tt/GdPHkSLy8vw5Lljz76KBs2bGDChAlYW1vz8ssvExwcbFhhNzAwkFmzZvHggw9y//33m6DlNat12K7T6YiJiWHu3LksX76cffv2kZ6ebnRMXFwcPj4+LFu2jOnTp7N+/XoATp8+TUJCApGRkSxdupTU1FQyMzMBGDVqFMuWLWPp0qUEBATw+eefm751JnDkiCWjR7vw66+WjB/vxK+/SmddiGvVtET55eSZlJTEoEGDcHZ25uuvv6Z///4NPi/o117/+uuvGTFiBN988w3jxo0DYMmSJbzyyiucPXuW0NBQtFptwxtVi1ozQVpaGm5ubri6ugIwePBgQzf8svT0dB566CEA3N3dycrKIi8vj4yMDLp37461tTUAfn5+JCUlMXr0aMPqmgCXLl2qshZ8c7BnjxXPPutEUZH+d8ylSyoyMtT07CnLAwvzmTPnyjC7Iev+/P3v+VWG8jdq4MCBbNu2jVmzZpGYmIiTkxP29vbY29uj1WopLy/H29ub/v3789FHHxEREVGn83br1o0zZ87w559/0qVLF/71r38xcOBAioqKKCkpITg4mICAAO68805A/419QEAAAQEBfPvtt5w9e7bRRrS1Jk+tVouzs7Nh29nZmRMnThgd4+3tzYEDB+jZsydpaWlkZWWh1Wrx9PQkNjaWgoICrKysOHjwoKH7DfDPf/6TPXv2YGtry4IFC6qNHx8fT3x8PABRUVG4uLjUr4EaTb3LAHz6qQUTJ6opL9cndUdHhW3bKhg82B6wv35hE8Svj6aI0VRxWkuMporTVG0BqsRxcHDAysoKFxcXIiIieO6557j33nuxtbVlw4YNhuMHDRpEZWUlLi4uhIaG8s4773DfffcZ5RXQX988efKkUa906dKlxMTE8MILL1BRUWEYlmu1Wp588klKS0tRFIVly5bh4uLCtGnTSEtLQ1EUhg0bxj333FOlY2aq96zW1TP379/P4cOHDcsJ79mzh7S0NCZOnGg4pri4mPXr1/Pnn3/i5eXF2bNnmTx5Mj4+Pnz33Xfs3LkTGxsb3N3dsbKyYvz48UYx4uLiKC8vZ+zYsbVWuClWz1yzpi0LFjgYtjt1qmTz5hxuuaX+Pc7WvuqgxDBvHGmLaeI0yuqZzs7O5OTkGLZzcnJwdHQ0OsbW1pZp06YB+msU06dPp2PHjgAEBQUZbnzdsmVLld82oL/AHBUVVafk2ZgUBd55x54PPrjSs+zevZzNm3Nwd5eZ34UQV9T6hZGvry+ZmZlcuHCBiooKEhMTCQwMNDqmqKiIigp9rywhIQE/Pz/DNc3LtwtkZ2eTlJTEkCFDAAxfHAGkpKQ0KPObUnk5zJrV3ihxBgaWEReXLYlTCFFFrT1PtVrNxIkTiYiIQKfTMWzYMDw9Pdm1axcAoaGhZGRksHLlSiwsLPDw8DAM8UF//1dBQQEajYZJkyZhZ2cHwObNm8nMzESlUuHi4sLzzz/fSE2sm/h4Gz777MqXWCEhpXz0US5t2sjN70KIqmq95tncNOY1z4gIez780J7HHy9iyZJ8NCa4K6m1XyuSGOaNI20xTZxGueZ5M5k7t4C+fcsZObKUZnjnlBCiGblpn21PS9NQUmKcIVUqeOABSZxCiNrdlMnzp5+sePBBF6ZMcaRC7ncXok7Onj3LhAkTGDJkCIMGDeKNN94wTNJxta1bt+Lv7280hd3vv/9uhho3rpsueW7fbsOTTzpz8aIF8fE2vPVWu9oLCXGTUxSF5557jvvuu499+/bx448/UlpaWuOTQqNGjeLbb781/OnRo4fR65XXrMt97XZNKppRb+emuua5caMtb7zhgE6nH5d37FjJY48Vm7lWQjR/u3fvxtramsceewzQ34WzcOFCBgwYwKuvvlqnST4SExN59913cXV15ejRo0RGRhptp6SkMGvWLP773/+iVqtZsGABQ4YMYevWrSQkJHDp0iWKi4sN09qZ202RPBUF3n3XeBqvLl0q2LIlBy8vmfldtCzRP0fzbuq7dTp2XM9x/P2uvxvte2XvK2z+dbNhe3bAbOb0m3Pd8xw7dgx/f3+jffb29nh4ePDnn3/Sp08fo9e++uorkpKSjLZBP0Pbd999h5eXF4mJiUbbq1atAvT3iqelpfHEE0+wd+9eAH7++Wfi4+OrPKBjTq0+eVZWwquvGs9feNttZWzcqMXZWW5+F6IuFEWpdvKemu50HDVqVLVD+ttuu80wXeW124mJiYbZkbp164aHhwd//PEHAHfffXezSpzQyq95lpTA449rjBLn0KGlfPppjiROIeqhV69e/Pe//zXaV1BQQHZ2NikpKYYvhs6dO3fd81w9m9q129e75fzacs1Bq+155uermDDBiQMHrvx+ePjhYqKj87CyMmPFhLhBc/rNMQyzGzQl3V1/rzKUr01QUBCvvfYan332GY8++iiVlZUsWrSI8ePHG/7cqDvvvJO4uDjuvPNOTp48SUZGBr6+vhw5cuSGz90YWm3Ps6JCxYULV9bNmDy5kH/8QxKnEA2hUqlYs2YNX3/9NUOGDKFPnz6oVCpmzpxZ7fFfffWV0a1KycnJtcaYMmUKlZWVBAcHM3XqVJYvX26YC7g5atWPZ54+reaRRzoyadJFpkwpasRa1ay1P9YmMcwbx1xtSU5O5oUXXmDNmjXceuutjRKjscjjmXXg5VXJ4cPllJWZJ3EK0VrdcccdRt+m34xa7bD9snZyD7wQohG0+uQphBCNQZKnEEI0gCRPIYRoAEmeQgjRAHX6tv3QoUOsW7cOnU5HcHAwYWFhRq8XFhayatUqzp8/j6WlJVOnTjU8crV9+3YSEhJQFIXg4GBGjhwJwCeffMLPP/+MRqPB1dWVadOm1WlyASGEaA5q7XnqdDpiYmKYO3cuy5cvZ9++faSnpxsdExcXh4+PD8uWLWP69OmsX78egNOnT5OQkEBkZCRLly4lNTXVsPDbrbfeSnR0NMuWLaNTp07ExcWZvnVCCNFIak2eaWlpuLm54erqikajYfDgwVWeFkhPTzfMuOLu7k5WVhZ5eXlkZGTQvXt3rK2tUavV+Pn5Ge4N69u3L2q1/gmgHj16oNVqTd02IYRoNLUO27VardFa687Ozpw4ccLoGG9vbw4cOEDPnj1JS0sjKysLrVaLp6cnsbGxFBQUYGVlxcGDB/H19a0S47vvvmPw4MHVxo+Pjyc+Ph6AqKioBj0JYO5ljZsiflO1sbW0Rd6v5hmnJbWl1p5ndU9vXjs1VVhYGEVFRYSHh7Njxw66dOliWIZ49OjRLF68mMjISLy9vbGwMA65bds21Go1d911V7XxQ0JCiIqKIioqqj7tMnjttdcaVM5UmiJ+U7WxtbRF3q/mGaeltaXWnqezszM5OTmG7ZycnCrz6tna2jJt2jRAn2ynT59Ox44dAf1sLEFBQQBs2bLFqBf7/fff8/PPPzN//vxq5woUQojmqtaep6+vL5mZmVy4cIGKigoSExMJDAw0OqaoqMiwtkhCQgJ+fn6G+ffy8/MByM7OJikpiSFDhgD6b/C//PJLXn311WY9c4oQQlRHvXDhwoXXO8DCwgI3Nzfef/99vvnmG+666y4GDhzIrl27OHnyJL6+vpw6dYq3336bnTt3UlxczLPPPovV/+Z+i4yM5KuvvmL//v1MmDABT09PACIiIigrK+Onn37i22+/5dSpU/Tr169RGtm1a9dGOW9zit9UbWwtbZH3q3nGaUltaXFT0gkhRHMgTxgJIUQDSPIUQogGkOQphBAN0Kpnkr9WaWkpa9asQaPR0Lt37xrvLW0s58+fZ9u2bRQXFzNnzvXXyb4RSUlJpKamcvHiRe6991769u1r8hjp6els376dgoIC/P39CQ0NNXmMy0pLS1mwYAFjx45tlC8Vjx49ytatW/Hw8GDIkCH07t3b5DF0Oh1bt26lpKSErl27MnToUJPHADh+/Dh79+5Fp9ORnp7O4sWLTR4jOzubmJgY7O3t6dy5c5W5LkwlPT2dTz/9FHt7e/z9/Rk4cKDJzl3d/8X65ocWnzw//PBDUlNTcXBwIDo62rC/uslMkpKSGDhwIIGBgSxfvtwkybM+8V1dXZk6darRcY0Rp3///vTv35/CwkI++eSTOifP+sTw8PDg+eefR6fT8fHHHzdaWwC+/PJLBg0a1GgxVCoVNjY2lJeXG92HbMoYKSkpaLVa7Ozs6hWjvnH8/PwMj0FX9zSfKWKcQP2gaAAAA2ZJREFUPXuWgIAAhg8fzsqVKxutLQcPHuT+++/Hz8+PJUuW1Jo8b/T/Yr3zg9LCHT16VDl58qQye/Zsw77Kykpl+vTpyrlz55Ty8nLl5ZdfVs6cOaNs27ZN+fPPPxVFUZQVK1Y0efzLli1b1iRxNmzYoJw8ebLRYiQnJytvvPGGsnfv3kZry+HDh5Uff/xR2b17t5KSktIoMSorKxVFUZTc3FzlH//4R6PEiIuLU3bt2qUoSv1//g352UdHRytFRUWNEuPixYvKwoULlYULFyrfffddo7UlLy9PWb16tbJx40Zl3rx5Jj33ZVf/LOqbH1r8Nc9evXphZ2dntK+myUyuflpKMdEdWvWJ31RxFEVh06ZN3HbbbfW6n62+bQkMDGTx4sXs3bu30dpy9OhRTpw4wY8//khCQgI6nc7kMS4/MmxnZ0d5eXmjtMPJyckw5eK1jyibMg7oh9W2traGB1VMHWP37t08+uijLFiwgNTU1EZri4ODA88++yzjxo3D3t7epOeuTn3zQ4sftlenpslM7r//ftauXUtqamqj3ZB/vfgFBQX885//5NSpU8TFxfHQQw81SpwdO3Zw5MgRiouLOXfu3A1dj6wpxtGjRzlw4AAVFRXcfvvtN9SO68WZNGkSoH+U197evt6Jpy4xDhw4wOHDhykqKuK+++5reCOuE2PEiBGsXbuWX3/9FT8/vxuKcb04oJ9oZ9iwYY0WY/jw4Xz22Wf8+OOPdOjQodHiXLhwgbi4OC5dusSoUaNMeu7q/i/279+/XvmhVSbP6n5rXL6udfkZfHPEt7e35/nnn2/0OCNGjGDEiBGNGqN3794m/WKlpjiXmeILlppiDBgwgAEDBtzw+a8Xw9ramqlTp5okxvXiAIwdO7ZRY3h5eZn0C8+a4nTs2JHJkyc3yrmr+79Y3/zQ4oft1anLZCatIX5TxJG2NL8YTRWnNbSlMc/dKpNnXSYzaQ3xmyKOtKX5xWiqOK2hLY157hb/bPuKFSs4duwYBQUFODg4MHbsWIKCgkhNTWXDhg3odDqGDRvGww8/3KLjN0UcaUvziyFtaR7nrk6LT55CCGEOrXLYLoQQjU2SpxBCNIAkTyGEaABJnkII0QCSPIUQogEkeQohRANI8hRCiAaQ5CmEEA0gyVMIIRrg/wFuzhA5g/g6tAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 360x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "SAMPLES_TYPES = [\"test\", \"job\"]\n",
    "#SAMPLES_TYPES = [\"test\", \"job\"]\n",
    "\n",
    "for st in SAMPLES_TYPES:\n",
    "    for part in set(df1[\"partition\"]):\n",
    "        tmp_df = df1[df1.partition == part]\n",
    "        plot_cdf_qerr(st, tmp_df, part)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'plan_err' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-114-11f9532f2b5f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mst\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mSAMPLES_TYPES\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m     \u001b[0mcur_df\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplan_err\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mplan_err\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msamples_type\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mst\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mpart\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mplan_err\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"partition\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m         \u001b[0mtmp_df\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcur_df\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcur_df\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpartition\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mpart\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'plan_err' is not defined"
     ]
    }
   ],
   "source": [
    "# for part in set(df1[\"partition\"]):\n",
    "#     tmp_df = df1[df1.partition == part]\n",
    "#     plot_cdf_qerr(\"test\", tmp_df)\n",
    "\n",
    "for st in SAMPLES_TYPES:\n",
    "    cur_df = plan_err[plan_err.samples_type == st]\n",
    "    for part in set(plan_err[\"partition\"]):\n",
    "        tmp_df = cur_df[cur_df.partition == part]\n",
    "        plot_cdf_jerr(st, tmp_df, part)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'qerr'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m/usr/lib/python3.8/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   2645\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2646\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2647\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'qerr'",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-115-b207642eac4d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf1\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"qerr\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnotnull\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/usr/lib/python3.8/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   2798\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2799\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2800\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2801\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2802\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3.8/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   2646\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2647\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2648\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2649\u001b[0m         \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2650\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'qerr'"
     ]
    }
   ],
   "source": [
    "df1[\"qerr\"].notnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'feat_rel_pg_ests_onehot'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m/usr/lib/python3.8/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   2645\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2646\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2647\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'feat_rel_pg_ests_onehot'",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-116-1b61ba249b4e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mtmp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtmp\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtmp\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"alg\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"flow_loss2\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mtmp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtmp\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtmp\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"flow_features\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mtmp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtmp\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtmp\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"feat_rel_pg_ests_onehot\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/usr/lib/python3.8/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   2798\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2799\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2800\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2801\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2802\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3.8/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   2646\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2647\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2648\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2649\u001b[0m         \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2650\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'feat_rel_pg_ests_onehot'"
     ]
    }
   ],
   "source": [
    "tmp = df2[df2[\"template\"] == \"all_job\"]\n",
    "tmp = tmp[tmp[\"partition\"] == 2]\n",
    "tmp = tmp[tmp[\"alg\"] == \"flow_loss2\"]\n",
    "tmp = tmp[tmp[\"flow_features\"] == 1]\n",
    "tmp = tmp[tmp[\"feat_rel_pg_ests_onehot\"] == 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "108\n"
     ]
    },
    {
     "ename": "KeyError",
     "evalue": "'num_hidden_layers'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m/usr/lib/python3.8/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   2645\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2646\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2647\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'num_hidden_layers'",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-117-4426bdb23ae3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtmp\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtmp\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"num_hidden_layers\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtmp\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"hidden_layer_size\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtmp\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"flow_features\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mset\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtmp\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"feat_rel_pg_ests_onehot\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3.8/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   2798\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2799\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2800\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2801\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2802\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3.8/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   2646\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2647\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2648\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2649\u001b[0m         \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2650\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mindexer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 'num_hidden_layers'"
     ]
    }
   ],
   "source": [
    "print(len(tmp))\n",
    "print(set(tmp[\"num_hidden_layers\"]))\n",
    "print(set(tmp[\"hidden_layer_size\"]))\n",
    "print(set(tmp[\"flow_features\"]))\n",
    "print(set(tmp[\"feat_rel_pg_ests_onehot\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [],
   "source": [
    "def shaded_plot_cdf(samples_type, df, error_type, err_bar):\n",
    "    plt.style.use('ggplot')   \n",
    "    fig  = plt.figure(figsize=(5,4))\n",
    "    ax = plt.gca()\n",
    "    df = df[df.samples_type == samples_type]\n",
    "    algs = list(set(df[\"alg\"]))\n",
    "    print(algs)\n",
    "    #clrs = sns.color_palette(\"husl\", 5)\n",
    "    \n",
    "    fn = FN_TMP.format(ERROR_TYPE = error_type,\n",
    "                       SAMPLES_TYPE = samples_type,\n",
    "                       PART = err_bar)\n",
    "    fn = SAVE_DIR + fn\n",
    "\n",
    "    plt.title(TITLE_KEYS[samples_type] + \"-\" + error_type)\n",
    "    print(fn)\n",
    "\n",
    "    for i, alg in enumerate(algs):\n",
    "        if alg == \"true\" or alg == \"postgres\":\n",
    "            continue\n",
    "            \n",
    "        alg_label = ALG_KEY[alg]\n",
    "        tmp_df = df[df.alg == alg]  \n",
    "        Y = []\n",
    "        X = []\n",
    "        \n",
    "        for part in set(tmp_df.partition):\n",
    "            tmp = tmp_df[tmp_df.partition == part]\n",
    "            #tmp = tmp[tmp.loss >= 5e6]\n",
    "            x, y = compute_PDF(tmp.loss.values, num_bins=20000)\n",
    "            Y.append(y)\n",
    "            X.append(x)\n",
    "    \n",
    "        X = np.array(X)\n",
    "        X = np.mean(X, axis=0)\n",
    "        Y = np.array(Y)\n",
    "        print(Y.shape)\n",
    "        maxs = np.max(Y, axis=0)\n",
    "        mins = np.min(Y, axis=0)\n",
    "        stds = np.std(Y, axis=0)\n",
    "        means = np.mean(Y, axis=0)\n",
    "        \n",
    "        ax.plot(X, means, label=alg_label, color=COLORS[alg_label], linestyle=\"--\")\n",
    "\n",
    "        if err_bar == \"minmax\":\n",
    "            ax.fill_between(X, mins, maxs ,alpha=0.3, facecolor=COLORS[alg_label])\n",
    "        else:\n",
    "            ax.fill_between(X, means-stds, means+stds ,alpha=0.2, facecolor=COLORS[alg_label])\n",
    "\n",
    "        plt.xscale(\"log\")\n",
    "        plt.legend()\n",
    "        \n",
    "    plt.savefig(fn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5, 600)\n"
     ]
    }
   ],
   "source": [
    "Y = []\n",
    "test_df = df2[df2.samples_type == \"test\"]\n",
    "for part in set(test_df.partition):\n",
    "    tmp = test_df[test_df.partition == part]\n",
    "    x, y = compute_PDF(tmp.loss.values, num_bins=600)\n",
    "    Y.append(y)\n",
    "Y = np.array(Y)\n",
    "print(Y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['flow_loss2', 'mse']\n",
      "./figures/results1/Postgres Plan Error-test-minmax.png\n",
      "(5, 20000)\n",
      "(5, 20000)\n",
      "['flow_loss2', 'mse']\n",
      "./figures/results1/Postgres Plan Error-test-std.png\n",
      "(5, 20000)\n",
      "(5, 20000)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "shaded_plot_cdf(\"test\", df2, \"Postgres Plan Error\", \"minmax\")\n",
    "shaded_plot_cdf(\"test\", df2, \"Postgres Plan Error\", \"std\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['flow_loss2', 'mse']\n",
      "./figures/results1/Postgres Plan Error-job-minmax.png\n",
      "(5, 20000)\n",
      "(5, 20000)\n",
      "['flow_loss2', 'mse']\n",
      "./figures/results1/Postgres Plan Error-job-std.png\n",
      "(5, 20000)\n",
      "(5, 20000)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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IDBRq/4Z12b9/PxiGQUpKSovr9XqP8YUXXsC8efPQqVMnDB8+HL/99hs+/PBDfPDBBx5tR6lUYuPGjZg4cSIeeOABzJ07F3FxcTh79ixeeuklSKXSJtt88MEHsWjRIjz00ENYtmwZjEYj5syZ4zBsGzlyJNLT0zFx4kS88cYb6NmzJyorK5GZmQm5XI5//etfzbad53lcvXoVAGAymXDo0CF89tlnuP/++wHYegkvvviifYV29OjR4DgOp0+fxokTJ/DGG2+41Y5KpcK8efOwZMkSKBQKjB49GkajEdu3b8cLL7zQbLubQ2JiImQyGVavXo158+bh0qVLeP75593qWdVl/vz5kEqlmDBhAr799luHH9zm8sILL2D8+PFYsWIFJk6ciKysLCxZsgTz5s2DVCpFdnY21q1bh/HjxyMhIQFFRUX4448/0KdPHwBAZGQkVCoVdu3ahW7dukEmkyE8PBzz5s3Dfffdh/79++OOO+5AZmam3S+1sc/bsWNHTJ8+Hf/617/w5ptvYtCgQdDr9Th27BjKysqwYMEC/PDDD8jNzcXQoUMRFRWFY8eOIT8/v1UiUJ9OnTqBYRisXLkSkydPxsmTJ7F06VKP1Q/A/r7XJTw8vNGerCueffZZ9OnTB8888wwef/xxXLp0CU899RQmT57scijuLl5x1xEEwb70/u9//xtLly7F66+/jpSUFLzxxhtYsWIFHn30UY+3O27cOGRmZsJsNmPs2LHo0KEDHn/8cQwcOBBHjx61u1c0hFKpxPbt21FRUYH+/ftj8uTJmDt3LqKjo+1lGIbBjz/+iIkTJ+KZZ55Bly5dMG7cOPzyyy/o0KFDi+y+dOkS4uLiEBcXhy5dumDx4sWYPXs23n//fXuZRYsW4d1338X69evRs2dPDBkyBO+++66Di4g7vPrqq1i2bBnef/99pKamIiMjA8ePH2+R3c0hMjISn3/+OXbv3o1u3bph/vz5ePvttx3meN1l9uzZeOedd3DPPffghx9+aLFNY8eOxSeffIJNmzYhNTUVc+fOxaxZs/Dyyy8DsA2/Ll68iPvvvx+dO3fGPffcg8GDB2PNmjUAAJZl8cEHH+Drr79GQkICevfuDQCYOHEi3nzzTaxYsQLdu3fHF198Ya+z7tDZFR9//DHmzp2LZcuWISUlBaNGjcKmTZvsPdjw8HD89NNPGDNmDDp37oznnnsOCxcuxPTp01v8HOrTo0cPrF69GmvXrkVKSgrefvttJxe71sDzvP19r/vvp59+apGtP/74I/bv34+ePXtiypQpGDduHD766KNW2ciQlqxaNEFGRgbatm2LjRs3erpqCuW6ZOnSpXjvvff8stuE0nw8OpQuLy/Hf//7X+zfv5+KIuWmxWq1YuXKlXYn9X379uGtt95ymMukBDYe7TGOGDECFy9exJQpU/Daa6/ZXQwolJsJjuNw55134tixY9DpdEhOTsbUqVPx7LPP2qeYKIGNV4bSFAqFcj0TEEEkKBQKJZCgwkihUCj1oMJIoVAo9fDrTHBRUZE/m2+QyMhIlJeX+9uMFkFt9w/Udv/gyvY2bdq0ul7aY6RQKJR6UGGkUCiUelBhpFAolHoElLcpIQQmkwmCIDQ7uIAnKSkpgdls9lv7raGkpAQmkwksy0Iul/v1OVIo1ytNCuN//vMfHD9+HKGhoVi5cqXTdUIINm7ciBMnTkAmk2HWrFkOIZuag8lkgkQi8fvuALFYfN3u2qm1neM4mEwmh8hAFArFPZocSg8fPrzRRE4nTpzA1atX8f777+Pxxx/H+vXrW2xM3ag8lNYhFovdChhKoVCcaVIYU1JSGg0JfvToUQwdOhQMw6Bz587Q6/WorKxskTF02OdZ6POkUFpGqxdfNBqNQ74JtVoNjUbT2mr9RkJCAkaOHInRo0dj9OjRyM/PR2Zmpkdz8Obn5zvlqaBQPEF2tgi7djUv2Ks30Vv1KKopgsFqgJEztig3kz9o9bjV1QdtqKeyZ88e7NmzBwCwYsUKpwQ+JSUlfh9Ky+Vy/Pbbbw7nioqKwDCMx2yrnb/01metrVcmkzWZJCmQEIvF15W9dfG07YQARiOgVNr+72oQJpfbrgsCUFVlO/fjjyKsXCmC2ex+MnpvPXczZ0ZNTQ04cChHOUAABaNAsDQY8SHxTd7vTg4mb9ne6m+mWq128DyvqKhoMK9Keno60tPT7cf1PdbNZnPALHrUTbrO8zwIIeA4DpWVlZg3bx6uXLkCuVyON9980x5pedu2bQgJCUFqaiqWLFmCSZMm4amnnsKkSZMcMiXW5gip2wYA/PHHH3j11VfB8zx69uyJ5cuXQyaT4fXXX8euXbsgFosxdOhQLF68GD/99BPeffddsCyLkJAQbNu2DYDtRamt12w2X1c7Gm60HRjNxWoFrFYGWi0wfnwURo0yY8WKanAckJjovJtj1iwdXnpJh6oqBt26xdnPi8WkWba0xnZO4KCz6OzHcrEcZt4MKStFkd7WU6xLFapQwpagqKzhXW9asxZrTq5Bvi4f5zTn8POEn9Ejqofbtnti50urhTEtLQ2//vorbr31Vly8eBFKpbLRhFPN4d571U7n7rzTiGnTDDAaGUyZEuF0fdIkA+67zwiNhsXjjzva8e23TUdPNplMGDlyJAghaNeuHTZs2OBwfeXKlUhNTcUnn3yCgwcPYs6cOdi9ezfS0tJw5MgRxMfHIzExEYcPH8akSZNw/PhxrFixwq12586diy1btqBDhw6YPXs2PvvsM9x7773YsWMHDhw4AIZh7Im5Vq1ahS+++AJxcXEOyboogYvZDDAMUFFhm8EiBDCbGZhMDEQiYMcOOY4eleK332zpD86etS2esSywdKnz37hHDysAQC4nDteTkjinst4gX5cPvVUPC+/cO2UYpsFh8+LMxcitznU498aQNxAXFIftedux+uRq+/k+0X2QFJrkUbvdoUlhXLVqFc6ePQudToeZM2fin//8p71HkpGRgd69e+P48eOYPXs2pFKpQ2a065HaoXT93lwthw8fxrp16wAAQ4YMQWVlJbRaLQYMGIC//voLBQUFmDp1Kj7//HMUFxcjPDzcrdSNOTk5aNeunT1vzKRJk7Bp0yY88sgjkMlkmD9/PkaNGmXvcaelpWHu3LkYP3487rjjDg99eoq3MBoZZGeLUasVViuQlSXFL7/IMWaMCb16WXDunMQuik88ocO8ebaeGMsCjz6qb7Buubzx695AZ9Gh2lLdoOdDXVE0WA34u+JvtFW1hVquRpW5CmXGMoxuN9peRiayzYtGK6Mxut1opKpT8XSfpyFm/TO11mSrTz/9dKPXGYbBY4895jGD6tJYD0+hII1ej4gQ3OohNpeG5lQHDBiATz/9FPHx8ViwYAF27NiBX375pcHk8e7UC9iGxr/88gsOHjyIH374ARs3bsQ333yDN954A8ePH8fevXuRkZGBXbt2OeSXpviemhoGLAuUlrIwmWxzYxIJgVwOaLUMCAF+/FGOLVuU0GiuTRl17sxh0CAL/vEPA556SovERAEqVeAuUnACh7zqvCbLWXgLnjnwDHKqcwAAU7tOxQO3PIAHb3kQXSK64JbwW5zuGd9+PMa3H48oZZTH7W4O1GmwmQwcOBDbtm3D3LlzkZmZiYiICHteY41GA6vVisTERPTv3x8fffQRli1b5la9HTt2RH5+PvLy8pCcnIytW7di4MCB0Ov1MBqNGDVqFPr06YMhQ4YAsGUW7NOnD/r06YPdu3ejqKiICqMPEATbENhksg2BlUoBGg0LnQ7IyxODZQkE4dqCwenTEpw9K0FWlgSvvKJFp04cxGJgzBgj5HKCjAwTkpJ4KJUCbr/d6vPPwws8CrQF0NRoEC4Ph1x0bbeUzqKDVbDCxJmgkqogYSWw8BaUGkrdqvuH3B+QU52DKEUU0tulI6OdLQ/48ITh6BjmXo5rf0GFsZk888wzeOaZZ5Ceng65XO6QVrJ37972oUX//v2xfPly9OvXz2U9OTk5DgnSlyxZgnfeeQczZsywL75MmTIFVVVVmD59OsxmMwgh9jScr732GvLy8kAIwZAhQ9CtWzcvfuqbG6vVNhQGgPx8EXi+7kqpredXe65WFB96KAImEwO9/ppHHMcBXbty2LTJ5s4WESEgNFRAcLB/eoecwEFr0aKGr0GVsQrlxnKEykKhs+ggYSUgIPb5w3Kj+4szJs4EmUiGEEkIAGD18NUIlYXar4uYwFhgbQy/5nypH4/RYDBAqVT6yZpr1F3Zvd6oa3ugPE93CcRVaasVuHxZBIOhcZdfjgvD3r0maLUMJk0y4qOPgsDztuHzoEFmJCbyiI3lER4uICTE9pWTywl86YRBCEGluRJiVgwRI0KBrgBm3oywsDBU1fr7tBILb8GEnybg23HfwsgZkV2VjYFxA+3XRawIYbIwtFW19Uh7AbsqTaHcCAiCbR4wOPiaWGk0DDQaEQyGhn3pzpwR488/Zfj2WwkACVJSrMjIMGHmTNtiiFJJEBoqQCQiYFkgLMw//RBe4HGx6qLLFeTWUqwvxva87TDxJvyc9zMAYF/+PtzZ/k5EKhx9DDuHd4aElXjcBk9DhZFy05OfL4JOx6B2gZX9/51DjnMURK2WwfHjUgBAjx4WREQQbN4chJMnpZBKCfr0sWDWrBokJvKQSGwCaBNG/y+k5GnzPCqKJfoSrDm5Bq8OfhUVpgr8lPcTCCEQs2KMiB+BscljHcp3Cu8ECSvx2ypzc7k+rKRQvMTVqywqKx2HyXU9UAgB3nknGAcPyuwrzSxLMHSoGU8+WYOHHjLgscf0SEtToapKC5mMoF073pcfoUksvAVGzuix+k6Vn8KCgwsA2BZoUtWp+H789y7LhsnCECGPcFjUuR6gwki5qdHrXX9Zz50TY9cuOebMqUG3blaUlrLo2JFDdDSPvn2tkMkIgoIIUlMdV5IjIgIropGJMyGnOsdje5S3X9qO1Vk2B+zxyeMRLA1utHyUMgoK8fUX+o4KI+WmhefhsKiyc6ccq1erwDDXhtH//ncNxowxYcwYU6N1MQzQtavVp4spDaG1aMGChVKiRIWpArzQuh4sJ3Aw82YESYJwsPAgAGBu77nISMxo9D6WZe2O29cbVBgpNzSCYOsVqlQEDAOYTDYxNJmAigqRfScKxwHffacAzzOYMMEAmcwmdFJp4/W3bcsjJERAdDRxGejBE7gTTAEAjJwRudW5EIgAESOCVCR12qvcHP66+hcqTZV4L+s9RCuisfzW5Zjfdz4krKTRnqJUJEVSSBJkItl1NXyuCxVGFxQVFWHBggW4cOECBEHAyJEjsXjxYshkjr9+W7ZswWuvvYbY2Fj7uQ8++ACdO3f2tcmUBtBqGVy5IkZ0NA+FguDKlWvb8gDg999lOH1agn/+04BVqypRUiJCYqJjD4tlr61U137Pg4IIlEqCiAgBDAOv9hQ1Jg2qLdUIl4VDZ9HZh6e1gllmKIPGpIGZv5aOgyMcOKHlLmenyk5hyZ9L7McR8gjEBcW5JXRiVgy5WN7itgMBKoz1IITgkUcewdSpU7Fx40bwPI/nnnsOy5Ytw9KlS53K33XXXY3ubuF53iFiUP3jhuA4zu8h2G4EaucQS0udn/nx4xK88YbNCXnsWCNiYuAgimq1AIVCgEQCnzphGzkjTJwJAhFg4k2oNFdCEATUWGoAADqrDkqxEhbeApVUhUpTJQTS+rnNSlMlsquz0S+mH5QSm//rW0PeQhtVG0TI3dtVJRfL3S4byNBvXj0OHjwIuVyO++67D4AtduKSJUswYMAALFiwwK2AEJmZmXjnnXcQExODM2fO4PXXX3c4/vXXX/HCCy/g1KlTEIlEePnll3Hrrbdiy5Yt2Lt3L8xmMwwGA7755htvf9wbFkEALBY4rTjXotMxWLHCJopvvVWJDh1sghgWJkChsIlgZKQAX48ETZwJ+bp8mLiG5zR5gbeH+jIbW5e0rcZSg8KyQhy+fBgf//0xAOD5tOcxKG4QVg1b5XI/c13ErBhS0bX5hnBZOBVGb3Pvz/c6nbuz/Z2YljINRs6IKb9Ocbo+qfMk3Nf5PmhMGjy+53GHa9/e+W2TbV64cAE9ejjGfgsODkZ8fDzy8oAg+YkAACAASURBVPKQmprqcO3HH3/E4cOHHY4BICsrC7/99hvatWuHzMxMh+OPPvoIALB3715kZ2fjgQcewB9//AEAOHbsGPbs2eOx0G03G4QAFy6IYTYzTvuWAZtgMgywdq0KOh2LW26xIjX12pBTrRYQFOQ/v8NyY3mjouhpXj/yOk6UnbAfD207FMPihwFAk6LIMAwiFZGIVkZ71UZ/ENDC6A8amuhuyN2hoaF0r1690K5dO5fHR44cwSOPPALAFjwiPj4eubm2+HRDhw6lothCrFagoEAEs9lx33ItZWUspk5VY+1aDebN0yEjw4T27a+JolIpQC73nygaOSOqzJ7ZmtcUJfoSBEuD8drg13Co4hCkvBQdQju41dtjGAZtVW0RKg2FiA2AZXgvENDC2FgPTyFWNHo9Qh7hVg+xPp07d8aOHTsczul0OpSXl+Po0aOYO3cuAGDz5s2N1lN/j3Ld48Z8yq6nvc2BBscx0OlcD505Dpg61Rb4OD9fhHbteHug11piYgS/uNsIRLDFKDSUeWSusDEuay/j1b9eRaG+EInBiVg5dCXGdRzn1l7pEFkI4lXxYBkWLNPqdFEBzY396VrAbbfdBqPRaJ/f43keS5cuxbRp0zBt2jTs3r0bu3fvdliJbi4DBgzAd999B8AWZaewsNAeoJbSMsrLWZSUuH6dCwtFGD/eFt+vTx8Lbr3VcWscyxKEhPguyo3eqkeVuQpGzohyYzmyq7LtAR283e7M32aiUF+IVHUqJneZjCBJ03PmABAsDYZaroaYFd/woggEeI/RHzAMg40bN2LBggVYtWoVNBoNxo8fjzlz5rgsX3+O8fXXX2+yjYcffhjPP/88Ro0aBZFIhHfffdfJFYjiPmYzUFTUcFfv119triNjxxoxc2aN/TzLEtxyCweJj2MaFOuLW+Vf2FJq4yiOTx6PWT2bF2k/XB7e5C6XGwkadswFdUN3HTlyBE888QTWr1/vtCgTiNxMYccMBgalpSy02sZ7MITYeo3x8ddccSQSmyiyHur8uGM7L/C4rLsMg9Xg9SFzXU6Vn0KlqRLD4ofho1Mf4bHUxxyCOTQVdkzEinBL+C0BGQCChh3zE/369XPoEVICB62WaVQUjx2TYOHCMCxbVoU+fWzziSxLIJHYot54ShSbghM4lBnLYLAaoLf6NjcLAGy9uBXVlmoMix+GmT1mun2fWqFGtDIaLNgbdpGlIagwUq5L9HoGVVWNK9u2bbbeclTUtd5ZZKSA2FjfBnooqiny2Wpzfb69+C0Olxxu0vWmPmJWjGBp8HURO9EbUGGkXJdotQwsloa9r3keOH5ciuhoHgkJtiG0SuUbUaw02TZNy8VyWAUrqi3+SW97vvI8Npyxpf99tNujbt3DMqxdFEOkId40L6AJKGH043TnDcmN+jxzc0WoqWm4t2g0XnPNGTXK5iwtlRKvhgTTGDXQWXQQs2IU1hT6dA6xIf66+hcAWySc7pHd3bonOTTZ7ZXqG5mAEkaWZekeYQ/BcRxYX02i+RCLBY2mGgAAmQx49lkddu6U45//tK3+hoUJXkkrUGmqhNaiBWNlUFXtn+GyK8y8GQ/e8iDuan8XwmRhbt93sw6d6xNQCiSXy2EymWA2m/0arkgmk8Fs9q5PmbeQyWQwmUxgWRZy+fUd4cQVOh3rtKOlFkKARYtCMXy4CenpZvTvf81fUan0vChyAocifRF4gUeYwn3x8TY84TF5x2SsGrYK8cHxbt3DMAxilbEBufLsDwLqKTAMA4XC/9F+AzFbnbtcz7a7Q216AVesXx+EY8ekUCoFpKc7/rBJpZ4Rxto8yyzDolhf3OogsN7gVNkp6Dk9DhQewINdHmy0rJgVI0IRAdbE+j3JfSARUMJIoTRFQ6kIAGDPHlsPed48nf0cw9h8Fj3lxF1qKEWFscIzlXmJ2ig5wxOGN1m2raotksKSUM7duD+mLYEKI+W6geNsc4yu+M9/VNBqWUycaIu+XUtYmGBflW4JGpMGV/VXAdhGNIHYQ6xLmaEMl7SXoBAr0CbItaMzy9hSDoTJwq7LfCy+gAoj5bqhoqLh+UWVSkBiIoeJEx2z4clkrRtCG6yGVkXC9jWVZpur0IK0BQ2WiVJGIUYZ4yuTrkuoMFKuC/R6BuXlzqvshABHjkgxdaoBU6c67z8OCWmd24xVsDZdyI9c1l7G5nOb8VfJX5jfZz76x/bHssHL0Ce6T4P3NGeV+maFCiMl4OE4ICfH9au6apUKmZkyLF9ejY4dHXt2IhFBaxbmz2nOwcoHpjCaOBNWZ63GbwW/AQDkIjnUCjUUYkWjohguD4eUbSLDF4UKIyXwqahw7Y+5bFkIDh60TSgGBzv3DENCWjaMLjWUotpcHbCiCAA51Tk4XnYcADAtZRru63xfk/dIRVIES4Ov28x9voQKIyXgceXQbbXCLoqffVbhsB+6lqio5i+UlBnKUGooDYidKw1RYaxAN3U3fDzqYyjFSrcCPEhFUnQM60j9FN3kxtsaQbmhsFgAo9FZGAsKbGLwzDNal6IYHc03exht5IzQmDQBLYoCEfDQzofw8emPESwNdjvqTaCGDQtU6JOiBCyCYBNAjnMWxuRkHuvWaaBUOouYXE5cimVjXNVftQdyDVTyqvOw5uQaAEBcUJzb9wVJgujwuZm4JYxZWVnYuHEjBEHAqFGjcPfddztcNxgMeP/991FRUQGe5zF+/HiMGDHCKwZTbh6qqhiXwSIqKlgUFYnQpYvVyXGbYYB27bhm5W6psdQEvCgeLz2OlzJfsh/fnni7W/cpJUq0D23vLbNuWJoURkEQsGHDBixcuBBqtRovvPAC0tLSEB9/bQ/mr7/+ivj4eDz//PPQarWYM2cObrvtNhoMgtIqeN71EPpf/7JlstuwoQJt2jj2DENDhWYPoTkS+H6KUlaKpJAk/LPTP3Frm1sdcjk3RJAkCB3CaC6hltCkcmVnZyM2NhYxMTaH0MGDB+PIkSMOwsgwDEwmEwghMJlMUKlUN2RkF4rvsFgAnc5ZGDdutIXEGj/e6CSKAFoUWiyQd7NoTBqcKj+FAbED8OHID5t1742Q+N5fNKleGo0GarXafqxWq6HRaBzKjBkzBoWFhZgxYwbmzZuHRx55hAojpVXU1DgPowkB/vxTiogIHrNm1TjdI5cTBAU130XH29n5WsP2vO144+gbOKs52+x73elVUlzTZI/RVbDT+hO5J0+eRGJiIhYvXoySkhK8+uqr6NKli1Mipj179mDPnj0AgBUrViAyMrI1tnsNsVgcsLY1xY1iu0YDhIU5vmcXLgCCwGDkSFsCp7rIZECnTgTSFmhBAVfQ6t0gIrHIyabWUKovxQfHPsCu3F0AgEHJg6CUNC+xWVRklFv33CjvjEfrbaqAWq1GRcW1aCIVFRUIDw93KLNv3z7cfffdtphusbGIjo5GUVEROnbs6FAuPT0d6enp9uNADY91PYfuuhFsNxqBggLncDhRUcDnn7MIChJQP6ldp05WaLXNb9PIGaGp1DRdsAmayrTXXD488SF2Xd4FMSvGkz2ehEVvgQUNRNBwgVKihEFigAFNp2m9Ed6ZuvgkS2CHDh1QXFyM0tJSREREIDMzE7Nnz3Yy7vTp0+jatSuqqqpQVFSE6OjoVhtHuTm5fNn1a8kwgFrtPIfIsi3rKQJAiaGkZTd6maHxQ9EhtAPGJo9tUYJ7uejGC1LsS5oURpFIhOnTp2PZsmUQBAEjRoxAQkICdu2ydfEzMjJwzz334D//+Q/mzZsHAJg8eTJCQm7eRDqUlkMIGkxytWhRKPr3N2P8eJPD+aAg0iz3nFo4gYPOomu6oI8oN5Zjys4pGBQ7CIsGLELvqN4trksioikKWoNb/jR9+vRBnz6OG9MzMjLs/4+IiMDChQs9axnlpkSrdS2KVVUMjh6VAiBOwtjS6Nw6iy5gEoYRQvDv3/4NAIhWRrfaIZsGimgd1NGQEjAIAlBc7Nz1s1qB2bNt89rOvUUBbds230Wn0lSJIn1Rywz1MBbeguVHlqPGWoOE4ATM7DGz1XW6u1WQ4hoqjJSAQBCAnBzXw+jPPw9CWZkIwcECevS4tgDBsgTt2rnvg6gxacCAgc6iQ5U5cDL6mXgTSo22nTcrb1vpkTqV4uatYFMcoc6GlIDAZGJcOnQDwMCBZiiVAj7/vMJhV4tC0bxcLsX6YuTr8gNKFLdf2g4GDJ7u/TS2T9iOYGlwq+sMkgTRgBGthD49it/heeDKFRGUDXRykpM5fPFFhdPKc3N2nFaZqwJqh8uak2tw5OoRlBpL0TaoLXpG9fRY3XIxXZFuLVQYKX6nrIyFxcI4CaNez+Ctt4IxeLAZGRnOu1PczRWtMWlQrC/2hKmtJqssC4sOLbLnkekR2QOdwjp5pG6WYZEYkkiH0R6ACiPF7+h0rmd0Dh+W4q+/ZDCZGCdhDAsT3AotZuJMKNIXQRACI8aiWq7GkDZDECwJxrSUac3ezeIKESuCUqxElCIKKqnKA1ZSqDBS/I61gQwCP/9sS+358svVTtfc7S3mafMCQhStghU/5PyAO5LuaDSDX3NgGAZquRptVK3f6UFxhAojxa9Yra7Di1VWMjh7VgKFQoCiXupjmYwgLKxpsdOYNAGRt2Xd6XXYlrMNgC1Xi6eEMUQaQkXRS1BhpPiVkhIRXPlY15578UXnDdBt2/JNLrwIRICRMzZeyAds+HuDXRTvSLoDs3rM8ki9DMPQ3NBehAojxa/U1Dj3Fi9fFkGttrnn1N8bLRYTqFRND6OL9cWoMFY0Wc7bGDgDWLD4v7H/hxCp57bJKsQKuvrsRagfI8VvEAJYrc7C+OabIcjOFrsMGOGOKBo5o19FkRd4bL24FWbejLva34VNt2/yqCgyDIO2qrYeq4/iDO0xUvyG0cg4DaOvXmWRmyvGiRNS9OrlPD8okzUujFXmKpQZyjxpZrMwcSY8ue9JFOoLUWYs88j2vvrIRDIoxIqmC1JaDBVGit+oqnLuLe7caRse9u3rOvZgYztdyo3luKq/6rf0pxbeglf+egWF+kIkhyRjWso0r7RDI3N7HyqMFL9hMDjP5Fy+LEZMDI8ePVyvJje2Gl1lrvJrTuhDxYeQVZYFAFg9YjVEjHcCOTCgqVC9DZ1jpPgNV/6LBQUitG/vOmufXE7QUCohf61C51Xn4ePTH8NoNaJrRFeMSRyDr+74ymuiCMCj85UU19AeI8Uv6HQMeBdblx980IDgYNe9Prnc9fxijaUGRfoin8dWNHJGzNpnc78JUgRhcsfJmNN7jtfbpavR3ocKI8XnGI02lxxBcB4SDh/uOmOfVEoaFMxyUzlMnMnlNW/xd8XfePaPZwEAD97yIJ5Ie8KjOV9cIRFJ0CaoDWQimVfbodChNMUPCALjUhQ3b2bxxx/OCwsMA0RGCggPd+4RCkRAjdU5laq3WXV8FQBgTOIYPNTlIZ+0Ga+KR6gstEU5YCjNg/YYKT6F54HKSucvtskErF3LQq1W4bbbHLP2xcXxDosuOosOGpMGBs4AESPy2V5ogQjYX7AfIxJGYM2INThXea5VeVmag5gVI0gS5JO2KFQYKT5EEIDsbDHMZufe4jffKMHzDP79b+feX3CwYN8CWGooxVX9Vfs1K7y7F5oTOBg5I7KrsrHu73XI0+ZBa9FiQocJPhNFAIiQR9Ceog+hwkjxGWYz41IUeR748ktbb6h3b0f/RaXSMQOgL6NvE0Kw5M8lOFZ6zH6uU1gn3NX+Lp/ZANh6i1GKKJ+2ebNDhZHiMwwG1/53BQU25RszRoCs3rpCRASPGr4KFTUVkLASny2yEEJg4AxY2H8hfsr7CVJWir7RfREfHO+T9uuiVqhpcisfQ4WR4hPKyliXASMAICpKwGuvVaFXL+c5NJkMKDaWw2A1eNtEB7668BVyqnKwcMBCTOo0yadt14emQvU9VBgpXofngaoqFkajszDW1DDQ6xn06WNFeDhQ6/ESHi5ALicwQ+czUeQEDuv/Xo/9hfttqUxVCT5ptzFYhqX7ov0Anc2leJ2rV0UuRREADh2SYto0NQoLrw0VGQZQqwVERvIoNRf4ykz8XfE3fsj9AVXmKgxrOwxP9XrKZ227QiKSIEIeQR26/QDtMVK8hiDYQouZXftsAwAuXRJDIiGIi7u2DUYiIVAqCXSWGlh418EkPGonEWDiTEhVpyItOg3P93ve764xYlaMpJAk2lv0E1QYKV6B54HqagYaDQuTqeGgB8eOSdGuHeew8iyTEVgFK8qN5T6wFHjz6JtQSVV4uOvDASGKABCtjKai6EfoUJriFYqKRCgoEMNgYF3ucgFsYccuXxYjNtbRQTsyUkC1uRo6i87rdhJCcKLsBHZe2gmlWBkQoqhWqKGWq/1txk0NFUaKV3Dlr1if/fttvjkZGY4uOLZhtPdFEbD5RWotWjya+mjAuMREyCPAMDS0mD+hQ2mKxzEaGZjccDe86y4TkpN5dOtm271i5PS4aroMtlLjs7iKX134CgCQGJzok/Yag2VYdA7vDAnbSDReik+gwkjxOFeuuI6cUxdCbKvPdQPSlpkLoSeVPg02K2bEYMGie2R3n7XpCpZh0UbVhkbnDhCoMFI8isHAwGJpehi4a5ccX3+twMqVVRCrqlBlLYOYYSFvIqeLp+kf2x+9ontBzPr3q5AUkgSVVOVXGyjXoMJI8RgcZ1t0cSde7K5dcmg0LCQqHYrNV0AgIBjBDqvT3uSvq38hTBaGnlE9fdNgI7AsS0UxwKDCSPEYJSVsg/uh66LTMTh7VgJ1lAlF5hyHa8og7/cYz2nOYcmfSwAA69PX+z0VaZg0zK/tU5xxSxizsrKwceNGCIKAUaNG4e6773Yqc+bMGXz66afgeR7BwcF45ZVXPG4sJXARBNdxFl2xZYsSADDp8XNO18Re/Kl+P+t9/F3+N/Jr8gEAK25d4XdRVCvUiFRE+tUGijNNvoaCIGDDhg1YuHAh1Go1XnjhBaSlpSE+/lqUEb1ej/Xr1+Oll15CZGQkqqurvWo0JbAgBMjNFTe54FJLTo7tteuU6hiQlhXZdr14iwntJ6DMUIZ4VTzuSL7Dr8NoqUiKeFU8FGJFwLgJUa7RpDBmZ2cjNjYWMTExAIDBgwfjyJEjDsJ48OBBDBgwAJGRtl++0NBQL5lLCUT0esatIXRpKYuQEAGzF59ChUHrJIJKBYHECz3GCmMFCAgSQxLx6uBXPd9AM2EYBsHSYDqvGMA0+RpqNBqo1de88NVqNS5evOhQpri4GBzHYcmSJTAajRg7diyGDRvmVNeePXuwZ88eAMCKFSvsQhpoiMXigLWtKfxhu14PhIU1LYzvvCPC6dMEyzbkIlLpvMNEpWIRFua5+TZO4PDZ6c+w7sQ6SEVS/HLfL14TI5FY5JbtaqUaiaH+95msC33fXdTbVAFXKSnre+XzPI+8vDwsWrQIFosFCxcuRKdOndCmTRuHcunp6UhPT7cfl5f7Zi9sc4mMjAxY25rCl7ZbrYBWy7q9En3pciiC1Qboja53tbSTqTyWaY8TOMzcOxOF+kKESEPwbN9nwRk4VBm8EwE8LCzMLdsjSATKrYH1bt1o73t93WkJTQqjWq1GRUWF/biiogLh4eFOZYKDgyGXyyGXy9G1a1dcvnzZIwZSApPKSgaFhU07ctfCCRwKChn0Gew6o59cTqBUAJZGIvE0h9zqXBTqCzG07VA8l/YcRIz/5/HCZGF0+Hyd0OQyYocOHVBcXIzS0lJwHIfMzEykpaU5lElLS8P//vc/8DwPs9mM7OxstG3r39U+infQaFhkZ9sCRLgjijzhUWLOx29HymHUS5DQQeuynEzm2d0ukYpITO06FTO6z/C7KEpFUkQro9EupJ1f7aC4T5M9RpFIhOnTp2PZsmUQBAEjRoxAQkICdu3aBQDIyMhAfHw8evXqhfnz54NlWYwcORLt2tGX4EajrIxFdbV7voq1XDVfhoHXIXN/CgCgU7dKl+VEHgxnYuJMiJBH4IFbHvBcpS2AZVjEBcUhXB5OM/xdZzDE1SSijygqKvJX041yo825eILCQhEqKpr35bYIZlw2/g8AoNdJUFKoRPsuzq5cDIDoGB7tEkJbPceYWZSJd0+8i+fTnkffmL6tqqs5uJpjDJIEoUNYB5/Z0FJutPfdJ3OMFArH2eYUm4uRvzafGBRsdSmKABAWxiMkuHW/zwIRsOHMBmzL3oYQaQiSQ5NbVV9rqXXJoVyf0P49pVF4vnnO23WpFcYLp8Oxc2sSTEbXc30KZatMBADszd+LbdnbIGWlWJ++HhHyiNZX2goUYgWildF+tYHScmiPkdIoZjPTaGqCxjAKNmFcu7wXOI7FqLsuuywnlbZ+NqfSVIlgSTA+Tv84IHpqIdIQf5tAaQVUGCmN0lgiq8Yw8DXgCAdDjRhWiwi9BpVA7GK7n0jkmd0u/WL6oU1QG4TJ/B+QQSVVUWG8zqHCSGmUmprmzbZYBQtKLQUw8DYn7oI8W+9t0KhCl+WVitb1Fq/qr4IjHJJDk/0+r1hLkCSIpjy9zqFzjJQGMRoZtyPmAAAnWHHFeN4uigBwOdvWc2rXwfVuF7m8dcK4+dxmPLnvSZh5D3mGt5Lk0GREKaL8bQalldAeI8WJqioGRUUiCM30ua60lkFAvYx/sUYMHFmIoGCrU3mxmCA4pOXC+O3Fb/FbwW8YET8CMpGsxfV4iqTQpICY36S0HiqMFAd4HigocH+rn/0+wqGaq3A633tQKXoPKnV5T3S00GLH7v9p/ocNZzagU1gnzOwxs2WVeIjaCNzBAhXFGwU6lKbYEQRbFO6WuOaUW4pA6vUWN7/fDVl/unZZUSoEKFo4v8gJHNaeXgsAmNJ1il8XOliGRfuQ9uis7kxTnt5A0B4jxY7ZzKC8vHn7igkhKDFfgY6/tutDrxPj9acHQVctQ3iUCb0GOvcY5XICtoU6ImbFWNh/IQBbBGx/EiQJglLiAUdMSkBBhZFix+o8DdgkWk7jIIoAcOZYFHTVMvQaWIKMiXku75O0IEtoXnUevr74NR7u+jBig2KbX4EHkYqkaB/a3u8BKijegQ6lKQBs6Qk0mua9DoQQaKwlTudPH41EaIQJ0545DWm9qDlKhYDQUKHZbjpF+iJsOrcJf139y++pTgGbA7dUJKVpCW5Q/P+GUQKCigoWWm3zhLGaqwBHHLuZhABGvRipaeVgXVQXoSZQNNNFZ/O5zfjy/JcAgHs63uP35FGhslDEKGP8agPFu1BhpEAQgKtXmyeKPOFRaXWeO2QY4MmXT0Dgne9hmeaLYr4uH/93/v9wa9ytGJc8zq8JrBiGQZQiyu/DeIr3ocJIgcnENHslusR82am3CAClxQpExxnhaoTZkpCE8ap4LOy/EL2ievl1kYNlWcQp4/y+2EPxDVQYKc0KPAsAGksJ9LzzTpbvP+uI335MwphJuRh7X67TdVEzp+P0Vj2CJEEY3GZw8270IGqFGqHSULAMS1efbyLo4stNjlbLoKTE/ddAa9WgwnrV5bXTh20+iw1F0VEq3d9Ksy9/H+795V7subLH7Xs8CcMwSAhOQLQyGiqpioriTQbtMd7klJez4Hn3eoxaqwYllnyX1wQBKLuqxKgJlyBTuJhgBBDi5va/gpoCvH3sbQBAN3U3t+7xFGGyMCjECgBAuDy8idKUGxUqjDcxggDo9a57izzhUW0tRzVXDp4IYMGAh2vBA4D/Zdnm3iJjjA2Wkbnhu7jhzAb8kvcLRKwIG0Zt8NlCB8uwkIvliFZG08g4FCqMNzM1NYxTPmiBCKi0lqLSWuawxa9hSfz/MEB0Gz16utjlAthWpJuCF3jkVOUgSBKEF/u96NPVX7VCjbigOJ+1RwlsqDDexFRVOfYWbfOHxeAI1+y6UnpXIKX3oQavN7bwQgiBgTMgjA3D67e+Dp7wPt1REiILoaJIcYAuvtykEGJbeAFskXEKTbkoseS3SBQJAQ7siG+0jFjsusdYaarE/D/m495f7sV3578DAJ9vs1NJVD5tjxL40B7jTUpNjc130cTrUdyAT6K7XL4Ygm83dEF8sq7BTICiem8aJ3A4WHQQWy5sQWFNIR7q8hDS4tKAZsaAbC2dwjvZF1solFqoMN6kVFezqLKWodxSDILWRdHe/V0SACA0ouEo2tJ6+V42ntmIbTnbwDIsZnSfgbva34WwEOfczN5CJpIhUhFJRZHiEiqMNyFWK5BdUoYKi2t/xOZgMorw99EodEyphDra5LKMREygqjdand5tOgbFDUKXiC5+CQoRLg+nu1goDUKF8SYkv0LjEVGsLJfhlVm3ghAGd9yX02A5iZRAJrP1GPVWPa7qr6JDWAekRqa22obmIBfL0SaoDUSsKCBSIVACFyqMNxnV5mpklxWhpetuNVoJ8nODYTSI0XNAGf75r/8h51w4OqY0PARmWdvK8xnNGXxw8gNUm6vxyehPfOovKBVJEaWIgkpKF1ooTUOF8SaixlKDS9VXGnTqbgxtpRT/3dMWO7Z0AAAkdqpGn8GlGDy6CINHFzXcJleNi1VZOJl/AAcKD0AlUeHFfi/6RBRZlgWIzTezjaoNzfVMcRsqjDcJRs6IS7pL0NUAAml+ToEtH3fB6SO2vdDj7s9B9/6uHbmd2uX1+DjnLViJFVO7TsU/OvzDZz3FGEUMIhWRMPEmOnSmNAsqjDcBRs6I3OpcmMwCSkta9ifvNagUHVOq0HtwCcLU7udwjpK1wYp+HyIqTOrzxY4gSRAYhqErz5RmQ4XxBsfEmZBbnQuDkUdpGdtsx5xzWRFgWaDf0OYt1hyv/h1ndYdxb9wTSI2Lg6/z57EsSwWR0mKoMN7A1IqiledRrWVgMjV/bnHLx12R1Kkat/TQuH1PmbkIO0o/AwCEKmU+F8UgSRCildE0nSmlxdAtgTcotaLICRxKS1hUVzd/m92ZUdORsQAAGYlJREFU42poShVo38V9p+u/KndhefZjqLKW4+7YGZDLfStOKqkK7UPbI1ga7NN2KTcWtMd4A1JXFHkB0DczQjcAnMiMxsZ3eoAVCeg1yL2Fln3lW/Hd1Y/QOag3psQ/h1BJJFSqJuPyeASJSAKVRIUYZQztKVJajVvCmJWVhY0bN0IQBIwaNQp33323y3LZ2dl46aWXMHfuXAwcONCjhlLcw8yZ7aIIAOVlbLPzuRAC7N+eAEWQFfNXHEZImMWt+3qGDIGR1+P26AchYsRgGdLsNKktJVYZSwPLUjxGk8IoCAI2bNiAhQsXQq1W44UXXkBaWhri4+Odyn3xxRfo1auX14ylNA4ncMiuzLaLYlEx2yyfxdrYjAwDjLzrCpJvqUJwaNPBJa4YzyNOlowIaQzGxkwFYNsbHRbmu4gQdKGF4kma/NZkZ2cjNjYWMTExEIvFGDx4MI4cOeJUbseOHRgwYABCQqgTrT8QiIBL2kswczZXGrOl4ejcDfHxip6Ye99InD2hRo/+ZW6J4r7yrXgnZzZ2l/2f/RwDIDaOR2ioj3qLQbE06jbFozTZY9RoNFCrr/mfqdVqXLx40anM4cOH8fLLL+PDDz9ssK49e/Zgzx5bcqMVK1YgMtK/idMbQiwWB6xtDZFbmQtpkBQisQhhYWEoKweCVe4PoQkBss9EQCon6JIqIFjV9OLFvpJt+O7qR+gbMRzjE6dBKbZttxOJgZjo5otire3NgWVYdI7u7JdAFHW5Ht+ZWqjtLuptqgCpH/secJrc/vTTTzF58mTbFqxGSE9PR3p6uv24vLzcXTt9SmRkZMDa5oqimiKUG232hoWFoaS0CgX5ombtcCnOD4LZJMI9j5wHK6mErqbx8qe1h7D5ytvoFjwQD8U9D95EoIMtpapSKaCqqvnD6LCw5ocd6xTeCVUa34Qqa4zr7Z2py41me5s2bVpdb5PCqFarUVFRYT+uqKhAeLjjJHdOTg7ee+89AIBWq8WJEyfAsiz69+/fagMpjVNhrEBhVQWsVgaaShZaHVBW1jxR5DkGX6xJgVJlQe/BJU2W5wQrvilajXh5B0xLeNEp4rZE4pshtJgV07lFildoUhg7dOiA4uJilJaWIiIiApmZmZg9e7ZDmQ8++MDh/3379qWi6AO0Fi2uVBehsOhaClSDnmn2KrTZJEJQsBXp/7iMkPCmV6DFrARPJL8BFixkrLMwuZMN0BNIRT5qiHLT0aQwikQiTJ8+HcuWLYMgCBgxYgQSEhKwa9cuAEBGRobXjaQ4Y+SMyKm4gqKrjNt5oV3B8wxkch4zX8pCY+5/POFxoeYEztUcwT9iZyJGltBgWcZH2wYiFdfnvBgl8HFrxrpPnz7o06ePw7mGBPGJJ55ovVWURrHwFuRV5+FqKVq0za+W6kopVr2UhomPXED3fs5zTIWmXOwr/xaFphyUmPPBESsUrArD1RMRIY1xKs+yBFFRAlQq7w6lRawIncM7Q8JKvNoO5eaF7ny5zuAFHpn/u4JqAwHHta5r9tPnHVFdKXPI1WIWjOAEK4LEIdBaK3BaewjJyhTcouqLZEVXpAQPgIR1PYSVSICQYO/PL0YpoqgoUrwKFcbrCBNnQp6mEBVaC9DK0AzaSimOZ8Zg4MgitOugQ57hLA5pduCEdj+GRIzHhNh/oYsqDUu7fOlyHtEVDON9UWQYBmo5zdVC8S5UGK8DTJwJpYZSVBiqUFAgQmtFEQB+/soWifuWjL346NJ/cLbmCGSsAr1DhqFnyBAANhGSMe6v+rJe3qLMMAwSQxIhYn2bd5py80GFMYAxcSaUGEpQbbblar5awsJibb368DyDv49EYcDwYmSxn+FSzf8wIeZfGKIe73bv0BXe1CsxK0aYLIymJ6D4BCqMAYiRM6JIW4rcYh3EYgKeZ6HTMi1KSVAfq2DBnootmPRiGdoq20EeNROTWCmUotYniVLIvTeUTg5Npj6LFJ9BhTGAMHJGlBpKUW2uRmEhC4PRs34vZ3VH8G3xGpRbinBnjBi9ox4AEOGx+iVecCtkGAbB0mAqihSfQoUxAKgriLwAlJR4VhRruGp8XfQesrR/IIRPRNwf25B2XyQA93O3uMP/a+/Og6O87zuOv59nn70vaVc3EhiJw0IFR5gEG8eJOO2ScYaamE6TJrUd27EThkywG5tJJqiu7RIXpo7reooHQtwcDhPXBLehCeVyRTAYwhFzOEZOTDiEdYJWQlpp93n6x4IsrSTYlXb1rOD7mtGgfZ7V7kfM8uE5fz8tDbvSY71j8dv9qX9hIa5CitFEvQsxHFa4cEGlrS01u8y97Wr6T46G9lLlepjf/8v3QbOSFdyb0vcAsFhSuyvtsrqkFIUpTC3Gjy5d+75cM3S3ddN8KfE5ToaiI9JBa7iVcFihvsFCZ2fqT+l2Ri/hsLi4O/fL3OKZzY+e/BLN5108/ORhrjHeR9JUxUBLwadJURR8Nh9FniK5VlGYxtxibM/MYgxbw1xoT/+ILboB58+n5kxz39eN8quPfsTBi2/xRNlLuCw+try4iIbzLv7mseMD3uUyXHZ7arYWnZqTcb5xKXktIYZKdqVNEO6C7i6FtjYl5aV4KRLilVPf43jbO8zK/hx21Ymhw+RpzaiqwW1zzqX0/a5wpOiMtEtzpeR1hBgOKcYRFg4rnDmjpvw4IsSmGPhJ7fPUd55lSdEyPh24p2dd1edOU/W50yl/zyucKeizLEcWVkV2n4X5pBhHSCQSm62vsSE9pQjwq49eJax3sHT880xwTwPgv18rw2qLctfiD9PyngBWzRjWFmOWPYs8Vx7F2cWjdsBUcX2RYkyzaBTqG1Ta2tIzFtfpjpN4tCyyrbl8cczjBLxBop3QHrKy/c2xbN98E3felb4tRYdDJzvLwDKEX89ldWFVrRR7i1FHaqwyIRIgxZgm0Si0hhSam5OfvjQREb2b3zT8lP9teI3p/tl8peQp/NYgLs1LiBCbfzyBvTvGMPWT9dzzpdqUv7/dbpCdHRtibCi/ndvqpiyrLOW5hEgFKcYUC4ehuUWlvU0lXTfInemo5Sdn/5lznX/kk1nzWFz49T7rG847+d3uAipnnef+bx296gC0iVIVg7x8HZvNwGod3oARqqJS5Bn+vBxCpIsUY4qEw9DSohJK0y7zFe+27mH9n5/Go/l5eOzTTPXd3j9Lh4UxN4X4q787mZJShNjtft4kB6DVVA2fzYfNYsOiWHBoDrqiXdgtdrnFT2Q0KcYkRSLQHVHo6oLuLujsVAiHU3+3Sm/tkVYuRpoocoynzD2NTwfu4S/zvoxb6zvSzOk/ehk/QaV4/EW+9eyBlJWiphmUlEST+pkcZw4F7oJ+xw7dVndqQgmRRlKMCejqhlCrQmtIJRJJ86CDvdSHz7Cr6Q32tWwl3z6Wvy/7N1wWD18o6jt9RFdY5devj2fH5nFU3NrKw0+mphRtVgOPRycrK/HjiIqiUOItIcue3PzQQmQSKcZBGEBrq0JrqzKseVWG4tSlP7C14WccDb2Nqmh80j+Xqpx7+83nDXDqpI8f/2sF9efc3DbnLH/7WB3Jz+jcn1UzKC6JJnW2WVVVSjwlcn+zGPWkGOPoBjQ1w6lTlmHNvpesqBFBN3Ssqo268J/446WjLMj9IncGPo/POvDQYPV1Tl7+x0qc7gjf+N5BJk9rxu3xEmobXha3O3a2OdFSVBSFgCNAvisfTZWPlBj95FN8WTQKFy4oXLio4nYNb0rSZHRE23m7ZQtvNW2iKriY2TmLmeGfy3R/FTbV0e/5XWGV/W8VcseCs2QFwpSUhvjiN44TyO0cdhYFsDsMCgv1hHadXVYXbqubgCOA3WIf9vsLkSlu6GKM6tDZodB+CUKt6bsjZSDNXR+xq2kTb7f8D2H9EhNc0yhyjAdiE9rDwLfGbXp1Er/dWsys+Wex2XWWVh9MWSab3aCkOLGTLB6bh1J/acreW4hMYmoxPrD1gX7Lvjb1a9xWeBsnmk/w/IHn+61fVrmMytxKDjUc4sVDL/Zb/+0Z36Y8UM7eur2sfXdtn3WGAY9PXUmeVkrN2V388tyGfj+/dPzzePGyp3kLWxte67d+edmL+LRsdja+wVtNm/qtXzHhFewWJ7+p/ylvt/y63/rqyT8G4GdnV1Pb/nsq/VXMzlnMWOekfs+Nt+PNsfx2azGfXfjnlJ1x7s3tTuzopKqojPGMSX0AITKEqcU4JTCl37Irkx25re4B13ut3p4/B1rvVN2Ew2DV/Uz0VqDrseOG0YiCAbRfcNNqU3EqAUrdf9Hv5zUlNj5/ljWHCe6pA6yP/ZUFbHkDrlcuX56SYysccP0V9xZ+HafqJtuWN+hzejt+KMgv/2MSn7j9IxZ95WRCP5MMVTXw+Qa+TtFmsZHtyEZTNELdIbxWr+w6i+uaYhhG+icDHsSvj/TforqaqA6RbohEFSKRy99HFLq7YzPfRSKkZHfY6/ESagsN+3VSpT2k8fQ37iA7p5Pl/7Qfm33wLbuhZFcVg8IiHZfz44+Coij4bX6CzuCIXXuYk5MzageRkOzmGCh7UdHw76oydYuxtja5tzetwU3w9vYifv9OLg8sfxe3N8Jff+09yspbrlqKybJYDAoKdOw2A6tVxWlx4dAcODUnPptPzjCLG5apn/wbqegS1dZqZfdvitmysYycgks9y6ffkdrRzm1Wg9KxGtkuL367H5fmGvA6SSFuRLJJkEEa6pysWfEpLrVZmfapeh5Y/i4Wbfj/fSgo+LUgNtWBptjwuTXKJ6ky1JcQg5BizCDbfnkTAI+veoexZa0pOfPssnjJtY3Bpn58siTbG0VVUrdLLsT1RorRRJfaNd7YMIlxEy9y511nue+h9/jswj9TNK592K+tKVZybEV4tY/vWVYUcLl08vKkFIW4GilGExgG1B7L5uf/Xk5Tg4Mx42L38GlWY9ilqCgKWVouQVv/kW1ycqIUFkopCnEtUowj7GKzjVdfmErt8Wz8gU6W/cPvKL354pBfz6bYsakO7KoTu+ok31NIm96/XB0Og6wsOd0lRCKkGEfYmT95Ofuhh8UP/oHb555N6vIbCxY8WtblEnRgU539tgq1ASap1zSDCRMiqHKuRYiEJFSMhw8fZsOGDei6zty5c1m0aFGf9TU1NWzevBkAh8PBQw89xE033ZTysKNNNKJw4nCQ/f9XQHeXhUeeOkLFrU2sfPm3uDyRhF/HqXrwW4N4LP4hXVITDOpSikIk4ZrFqOs669ev57vf/S7BYJAVK1YwY8YMiouLe56Tl5dHdXU1Ho+HQ4cO8corr/Dcc8+lNXgm27OtiLe3j+H8GTfhDg23r4tPzKzvWZ9IKWqKhlcL4NMCfc4oJ0vTDHJz5biiEMm4ZjHW1tZSUFBAfn4+ALNmzWL//v19inHy5Mk930+cOJGmpqY0RM1MhgFN9U5OHApy653ncbkjRCMqNnuUT322jptvaWJKZVPC1yO6LF58WmDIW4e95eVFCQRka1GIZF2zGJubmwkGgz2Pg8EgJ08OPojBjh07qKysHHDdtm3b2LZtGwCrVq3C6/Emm3dEqBa1Xzbj8kAUmtUgdFFj25v5NNQ5eP+ol6b62Badx2vjM3c1sPALF1n4hd4nVDxXfT9N1fBZg2TZcoa1dQhgsVjIysqisNCgsHBYLzXiNE0jJyfH7BhDItnNka7s1yzGgcaYGGxL5ujRo+zcuZOnn356wPXz5s1j3rx5PY8zaaCG3q4MxNDc4OD9d7N5/90A7x8N8Jm7T7Ng8YdcbLXxXz8bgy87zPhJF5jz+RYmVLRQUNye1OjZLosXvxbEbfGhRBUudXZwiY5hZQ8Gs4BmrNYoo21cgOttMIPR4nrLPiKDSASDwT67xk1NTWRnZ/d73qlTp1i7di0rVqzA601sS3CMIzMnXPc4/Hx9qZsPamNbb/6sKFOndXDLZA9jHGUUFcDPN32I1vO3ZwcKknoPq2LDqtpSmhugqMhAUZKb0U8I0dc1i7GsrIy6ujrq6+sJBALs2bOHZcuW9XlOY2Mjq1evZunSpUm1tcty9V3MkWAYUFenUlNj5/x5C9/8Zhs+u5fpld3MndNGZWUX48ZFL9+eZ6FntzgDL3RSVYNAAFpazE4ixOh2zX/eFouFBx98kGeffRZd15k9ezYlJSVs3boVgAULFvD666/T1tbGunXren5m1apV6U1+DYYBoZBCY6NKY6OFlhaV5maVxYsvYbPBpk1O3nzTSUuLSjgcOzQwZUo3XV2xn3/wweHfljeSVNVgzJgoFovZSYQY/RLa7pk+fTrTp0/vs2zBggU93z/66KM8+uijqU12DZ2dUFdnoaHBQmOjSkNDrADvv7+dYFDnjTecrFvXf4t0zpxO8vN1srN1br65m0BAJy8vym23dZGfPzova1EUKCuL4HSanUSI64OpO4SrVvU/FnnPPR1UVEQ4dcrCa6+5epbHtgBV7r+/nUmTIuzda+f73/f1rI/tRupcuKAQDMItt3TzyCNt5OZGCQZ1AoFYGdouH9arqgpTVRVO+++Ybj6fTn5+VEpRiBTKuBG8Q6HYRXcdHUq/9R6PQVdXbLe3oqKbFStayc2NkpMTK77eu5ETJkSYMCHxu0tGiyv/AWha7P7nweZpEUIMnanFuG7d4GcJbr45ctX1ubk6ubmjf4svGapqUFoaxeWSMhQineSeiFFk3DgpRSFGghTjKBEI6Hi9UopCjIQMvBpP9HblmKKMui3EyJFizFCqauB2GwSDupxgEWKESTFmGJfLwOfT8fl0HA6z0whxY5JizDD5+VE5liiEyeTkSwZxOg0pRSEygGwxmsjt1nE4DBwOsNsNHA4pRSEygRSjSfLzo6P23mwhrndSjCNMUaCoKHb/thAiM0kxpoGixAa9uEJVDWw2sFoN8vN1uXtFiAwnxZgiFouB32+QlaXj8RixOWKiYLHAMOe0EkKMMCnGIVBVA1UFqxU8ntgF2C6X0acAFYVeUx8IIUYT+ac7CKvVwOk0sNkM7PbYWWObzUDTkOlIhbjOXZfFGNtaM7BaYwWnqqDrsS/DuPKn0rNMUWLXEF75KikxuHDh+hvLUQiRmFFXjPGlF/uKfW+zfbxuOMf1ZBdYiBubqRUwcWJ3Us/XtNiXnMwQQqSTqcUo85QIITKRnEYQQog4UoxCCBFHilEIIeJIMQohRBwpRiGEiCPFKIQQcaQYhRAijhSjEELEkWIUQog4imEYMmqqEEL0knFbjGvXrh3S+oGWxy/r/Xig76/8+dRTTyUWNsFsyeYcaJlkTy5Xos+5Wtb4x5I9sVyJPMfs7Ndiqa6urk7LKw9DUVHRkNYPtDx+We/HA31fVFTEtm3bmDdvXsJ5E8mWbM6Blkn25HIl+pyrZY1/LNkTy5XIc8zOflWG6OfJJ580O8KQSXZzSHZzpCt7Rm4xZoLS0lKzIwyZZDeHZDdHOrLLyRchhIiTcSdfhBDCbFKMQggRR4pRCCHiyLRPCThz5gxbtmwhFAoxdepUFixYYHakpHR2drJy5UqWLFnCrbfeanachB07doyNGzdSXFzMHXfcQUVFhdmREqbrOhs3bqSjo4PS0lKqqqrMjpSwEydOUFNTg67rnDlzhmeeecbsSAlrbGxk/fr1eL1eioqKWLRo0ZBe54YtxpdffpmDBw/i9/tZs2ZNz/LDhw+zYcMGdF1n7ty5LFq0iOLiYh555BF0XU/ootZ0SyY7wObNm7n99tvNittHMtkVRcHhcNDd3U0wGDQxdUwy2Q8cOEBzczMej2fUZS8vL6e8vJx33nmHsrIyE1PHJJP93LlzTJ8+nfnz5/PSSy8N/U3TchHQKHDs2DHjgw8+MJYvX96zLBqNGkuXLjXOnz9vdHd3G0888YRx+vRpwzAMY//+/cZ3vvMdo6amxqzIPZLJfuTIEWP37t3Gzp07jQMHDpiYOiaZ7NFo1DAMw2hpaTF+8IMfmBW5RzLZN23aZGzdutUwDMNYvXq1WZF7JPt5NwzDWLNmjdHe3m5G3D6Syd7a2mpUV1cb1dXVxo4dO4b8njfsMcYpU6bg8Xj6LKutraWgoID8/Hw0TWPWrFns378fgBkzZvDMM89QU1NjRtw+ksl+7NgxTp48ye7du9m+fTu6rpuUOiaZ7Koa+3h6PB66u5ObajcdkskeCARwu90APb+HmZL9vDc2NuJyuXC5XGbE7SOZ7Dt37uS+++5j5cqVHDx4cMjvecPuSg+kubm5z25PMBjk5MmTHDt2jH379hGJRKisrDQx4eAGy/7Vr34VgF27duH1ejPiH2m8wbLv27ePI0eO0N7ezt13321iwsENln3hwoX88Ic/5L333qO8vNzEhIMbLDvAjh07mD17tlnRrmmw7PPnz+cXv/gFu3fvJjc3d8ivL8XYizHAte6KolBRUZHxB/4Hy35FJh/8Hyz7zJkzmTlzpgmJEjdYdrvdzmOPPWZCosRd7TOzZMmSkY6TlMGyjx07lscff3zYr595mw8mCgaDNDU19TxuamoiOzvbxESJk+zmkOzmSHd2KcZeysrKqKuro76+nkgkwp49e5gxY4bZsRIi2c0h2c2R7uw37L3SL7zwAsePHycUCuH3+1myZAlz5szh4MGDvPrqq+i6zuzZs7n33nvNjtqPZDeHZDeHGdlv2GIUQojByK60EELEkWIUQog4UoxCCBFHilEIIeJIMQohRBwpRiGEiCPFKIQQcaQYhRAijhSjEELE+X/pgTEAdGJAbgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 360x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "shaded_plot_cdf(\"job\", df2, \"Postgres Plan Error\", \"minmax\")\n",
    "shaded_plot_cdf(\"job\", df2, \"Postgres Plan Error\", \"std\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['flow_loss2', 'mse']\n",
      "./figures/results1/Q-Error-test-minmax.png\n",
      "(5, 20000)\n",
      "(5, 20000)\n",
      "['flow_loss2', 'mse']\n",
      "./figures/results1/Q-Error-test-std.png\n",
      "(5, 20000)\n",
      "(5, 20000)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "shaded_plot_cdf(\"test\", df1, \"Q-Error\", \"minmax\")\n",
    "shaded_plot_cdf(\"test\", df1, \"Q-Error\", \"std\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['flow_loss2', 'mse']\n",
      "./figures/results1/Q-Error-job-minmax.png\n",
      "(5, 20000)\n",
      "(5, 20000)\n",
      "['flow_loss2', 'mse']\n",
      "./figures/results1/Q-Error-job-std.png\n",
      "(5, 20000)\n",
      "(5, 20000)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "shaded_plot_cdf(\"job\", df1, \"Q-Error\", \"minmax\")\n",
    "shaded_plot_cdf(\"job\", df1, \"Q-Error\", \"std\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.8.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}