notebooks/ImageNet plots.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import seaborn as sns\n",
    "sns.set_style('whitegrid')\n",
    "\n",
    "import matplotlib as mpl\n",
    "import matplotlib\n",
    "from IPython.display import display\n",
    "\n",
    "import matplotlib.pylab as pylab\n",
    "\n",
    "from matplotlib import rc\n",
    "\n",
    "import torch\n",
    "from torch.nn.functional import log_softmax\n",
    "from sklearn.model_selection import KFold\n",
    "from scipy.optimize import minimize\n",
    "from matplotlib.lines import Line2D\n",
    "%matplotlib inline\n",
    "matplotlib.rcParams['text.usetex'] = True"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Read logs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "path = './logs_imagenet/'\n",
    "df = pd.concat([pd.read_csv(path + f) for f in os.listdir(path)], sort=False)\n",
    "dfbase = df.groupby(\n",
    "    ['dataset', 'model', 'method', 'n_samples', 'metric', 'info']\n",
    ").value.agg(['mean']).reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "colors = {'efficientnet_b2': 'r',\n",
    "          'resnet50': 'g',\n",
    "          'tf_efficientnet_b5': 'b'\n",
    "         }\n",
    "short_legend_augs = {'cf':  'CF',\n",
    "                     'ra':  'GPS',\n",
    "                     '5c':  '5C',\n",
    "                     '10c': '10C',\n",
    "                     'cc':  'CC'}\n",
    "model_names = {'efficientnet_b2': 'Efficientnet B2',\n",
    "              'resnet50': 'ResNet50',\n",
    "              'tf_efficientnet_b5': 'Efficientnet B5',\n",
    "              'tf_efficientnet_l2_ns_475': 'Efficientnet L2-475'}\n",
    "metric_names = {'ll': 'Calibrated log-likelihood',\n",
    "                'acc': 'Top-1 accuracy (\\%)'}\n",
    "# colors_augs = {'cf': 0, 'ra': 2, 'cc': 3, '5c': 5, '10c': 6}\n",
    "\n",
    "colors_augs = {'cf': 0,\n",
    "             'MGrid': 1,\n",
    "             'ra': 2,\n",
    "             'cc': 3,\n",
    "             'MTrain': 4,\n",
    "             '5c': 6,\n",
    "             '10c': 7}\n",
    "colors_palette = [\n",
    "    '#bcbd22', '#e377c2', '#9467bd',\n",
    "    '#8c564b', '#1f77b4', '#d62728',\n",
    "    '#2ca02c', '#ff7f0e', '#7f7f7f']\n",
    "dataset_names = {'ImageNet': 'ImageNet'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = dfbase"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SETUP1 - CIFAR IN-DOMAIN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 10000x1750 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mpl.rcParams['figure.dpi'] = 500\n",
    "f = plt.figure(figsize=(20, 3.5))\n",
    "\n",
    "for m, metric in enumerate(['ll', 'acc']):\n",
    "    params = {'legend.fontsize': 18,\n",
    "             'axes.labelsize': 18,\n",
    "    #          'figure.figsize': (6, 4),\n",
    "             'axes.titlesize':18,\n",
    "             'xtick.labelsize':18,\n",
    "             'ytick.labelsize':18,\n",
    "             'lines.markersize': 10,\n",
    "             'legend.markerscale': 1.,\n",
    "             'legend.labelspacing': 1.,\n",
    "             'figure.subplot.hspace': 0.3,\n",
    "             'figure.subplot.wspace': 0.05,\n",
    "             }\n",
    "    pylab.rcParams.update(params)\n",
    "    models = ['resnet50', 'efficientnet_b2', 'tf_efficientnet_b5']#, 'tf_efficientnet_l2_ns_475']\n",
    "    augs = [ 'cc', '5c', '10c', 'cf', 'ra']\n",
    "    sample_nums = [5, 10, 20]\n",
    "    datasets = ['ImageNet']\n",
    "   \n",
    "    i = 1\n",
    "    short_legend_augs_ordered = []\n",
    "    for aug in augs:\n",
    "        short_legend_augs_ordered.append(short_legend_augs[aug])\n",
    "\n",
    "    for dataset in datasets:\n",
    "        for model in models:\n",
    "            plt.subplot(len(datasets), len(models)*2, m*3+i)\n",
    "            i += 1\n",
    "            lines = []\n",
    "            for j, aug in enumerate(augs):\n",
    "                plot_augs = []\n",
    "                legends = []\n",
    "                for sample_num in sample_nums:\n",
    "                    marker = 'o'\n",
    "                    if aug != 'cc' or sample_num == 20:\n",
    "                        if aug == 'cc':\n",
    "                            sample_num = 1\n",
    "                            marker = '*'\n",
    "                        if aug == '5c':\n",
    "                            sample_num = 5\n",
    "                            marker = 'x'\n",
    "                        if aug == '10c':\n",
    "                            sample_num = 10\n",
    "                            marker = 'd'\n",
    "                        if sample_num == 5 and (aug == 'ra' or aug == 'cf'):\n",
    "                            marker = 'x'\n",
    "                        if sample_num == 10 and (aug == 'ra' or aug == 'cf'):\n",
    "                            marker = 'd' \n",
    "                        \n",
    "                        if model == 'tf_efficientnet_l2_ns_475' and aug == 'ra':\n",
    "                            for qw, inf in enumerate(['pols/resnet50_ll.npy', \n",
    "                                                      'pols/efficientnet_b2_ll.npy', \n",
    "                                                      'pols/tf_efficientnet_b5_ll.npy']):\n",
    "                                q = 'dataset==\"%s\" and model==\"%s\" and method==\"%s\" and metric==\"%s\" and n_samples==%d and info==\"%s\"' % (dataset, model, aug, metric, sample_num, inf)\n",
    "                                val = df.query(q)['mean']\n",
    "                                val = val.item()\n",
    "                                val *= 100 if metric == 'acc' else 1\n",
    "\n",
    "                                pos_x = j+qw + 0.2 * ((sample_num < 100) - 0.5) * (len(sample_nums) == 2)                    \n",
    "                                plt.scatter([pos_x], [val], c=colors_palette[colors_augs[aug]], marker=marker,)\n",
    "                                legends.append(plt.scatter([pos_x], [val], marker=marker,\n",
    "                                            c=colors_palette[colors_augs[aug]]))\n",
    "                        else:\n",
    "                            q = 'dataset==\"%s\" and model==\"%s\" and method==\"%s\" and metric==\"%s\" and n_samples==%d' % (dataset, model, aug, metric, sample_num)\n",
    "                            val = df.query(q)['mean']\n",
    "                            val = val.item()\n",
    "                            val *= 100 if metric == 'acc' else 1\n",
    "\n",
    "                            pos_x = j + 0.2 * ((sample_num < 100) - 0.5) * (len(sample_nums) == 2)                    \n",
    "                            plt.scatter([pos_x], [val], c=colors_palette[colors_augs[aug]], marker=marker)\n",
    "                            legends.append(plt.scatter([pos_x], [val], marker=marker,\n",
    "                                        c=colors_palette[colors_augs[aug]]))\n",
    "                            \n",
    "        \n",
    "                plt.title(r'ImageNet'+'\\n'+model_names[model])\n",
    "                plt.xticks(np.arange(len(augs)), short_legend_augs_ordered)\n",
    "                if model == 'tf_efficientnet_l2_ns_475':\n",
    "                    ww = [r'GPS''\\n'r'RN 50', r'GPS''\\n'r'B2', r'GPS''\\n'r'EN B5']\n",
    "                    plt.xticks(list(range(len(augs)))+[5, 6], short_legend_augs_ordered[:-1]+ww, fontsize=15)\n",
    "                if i % (len(models)) == 2:\n",
    "                    plt.ylabel(metric_names[metric])\n",
    "    handles = []\n",
    "    circle = matplotlib.lines.Line2D([], [], color=colors_palette[-1], marker='*', linestyle='None',\n",
    "                              label='1 aug. sample')\n",
    "    handles.append(circle)\n",
    "    cross = plt.scatter([np.nan], [np.nan], color=colors_palette[-1], marker='x', \n",
    "                               label='5 aug. samples')\n",
    "    handles.append(cross)\n",
    "    circle = matplotlib.lines.Line2D([], [], color=colors_palette[-1], marker='d', linestyle='None',\n",
    "                              label='10 aug. samples')\n",
    "    handles.append(circle)\n",
    "    circle = matplotlib.lines.Line2D([], [], color=colors_palette[-1], marker='o', linestyle='None',\n",
    "                              label='20 aug. samples')\n",
    "    handles.append(circle)\n",
    "\n",
    "    f.legend(handles=handles, loc='upper center', ncol=4,\n",
    "                bbox_to_anchor=(0.5, 1.15))\n",
    "plt.tight_layout()\n",
    "# plt.subplot_tool()\n",
    "plt.savefig('imagenet.pdf', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 5500x2500 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "params = {'legend.fontsize': 18,\n",
    "         'axes.labelsize': 18,\n",
    "#          'figure.figsize': (6, 4),\n",
    "         'axes.titlesize':18,\n",
    "         'xtick.labelsize':18,\n",
    "         'ytick.labelsize':18,\n",
    "         'lines.markersize': 13,\n",
    "         'legend.markerscale': 1.,\n",
    "         'legend.labelspacing': 1.,\n",
    "         'figure.subplot.hspace': 0.0,\n",
    "         'figure.subplot.wspace': 0.00,\n",
    "         }\n",
    "pylab.rcParams.update(params)\n",
    "mpl.rcParams['figure.dpi'] = 500\n",
    "f = plt.figure(figsize=(11, 5))\n",
    "# plt.subplots_adjust(wspace=2)\n",
    "\n",
    "for m, metric in enumerate(['ll', 'acc']):\n",
    "    poses = []\n",
    "\n",
    "    models = ['tf_efficientnet_l2_ns_475']\n",
    "    augs = ['cf', '10c', 'ra']\n",
    "    sample_nums = [5, 10, 20]\n",
    "    datasets = ['ImageNet']\n",
    "   \n",
    "    i = 1\n",
    "    short_legend_augs_ordered = []\n",
    "    for aug in augs:\n",
    "        short_legend_augs_ordered.append(short_legend_augs[aug])\n",
    "\n",
    "    for dataset in datasets:\n",
    "        for model in models:\n",
    "            plt.subplot(len(datasets), len(models)*2, m*1+i)\n",
    "            i += 1\n",
    "            lines = []\n",
    "            min_ = +150000\n",
    "            max_ = -100500\n",
    "\n",
    "\n",
    "            for j, aug in enumerate(augs):\n",
    "                plot_augs = []\n",
    "                legends = []\n",
    "                for sample_num in sample_nums:\n",
    "                    if aug == 'cf' and sample_num < 15:\n",
    "                        continue\n",
    "                    marker = 'o'\n",
    "                    if aug != 'cc' or sample_num == 20:\n",
    "                        if aug == 'cc':\n",
    "                            sample_num = 1\n",
    "                            marker = '*'\n",
    "                        if aug == '5c':\n",
    "                            sample_num = 5\n",
    "                            marker = 'x'\n",
    "                        if aug == '10c':\n",
    "                            sample_num = 10\n",
    "                            marker = 'd'\n",
    "                        if sample_num == 5 and (aug == 'ra' or aug == 'cf'):\n",
    "                            marker = 'x'\n",
    "                        if sample_num == 10 and (aug == 'ra' or aug == 'cf'):\n",
    "                            marker = 'd' \n",
    "                        \n",
    "                        if model == 'tf_efficientnet_l2_ns_475' and aug == 'ra':\n",
    "                            for qw, inf in enumerate(['pols/resnet50_ll.npy', \n",
    "                                                      'pols/efficientnet_b2_ll.npy', \n",
    "                                                      'pols/tf_efficientnet_b5_ll.npy']):\n",
    "                                q = 'dataset==\"%s\" and model==\"%s\" and method==\"%s\" and metric==\"%s\" and n_samples==%d and info==\"%s\"' % (dataset, model, aug, metric, sample_num, inf)\n",
    "                                val = df.query(q)['mean']\n",
    "                                val = val.item()\n",
    "                                val *= 100 if metric == 'acc' else 1\n",
    "                                \n",
    "                                min_ = min(min_, val)\n",
    "                                max_ = max(max_, val)\n",
    "\n",
    "\n",
    "                                pos_x = j*0.6+qw*0.7\n",
    "                                poses.append(pos_x)\n",
    "                                plt.errorbar([pos_x], [val], c=colors_palette[colors_augs[aug]], elinewidth=3, alpha=0.5)\n",
    "                                legends.append(plt.scatter([pos_x], [val], marker=marker,\n",
    "                                            c=colors_palette[colors_augs[aug]]))\n",
    "                        else:\n",
    "                            q = 'dataset==\"%s\" and model==\"%s\" and method==\"%s\" and metric==\"%s\" and n_samples==%d' % (dataset, model, aug, metric, sample_num)\n",
    "                            val = df.query(q)['mean']\n",
    "                            val = val.item()\n",
    "                            val *= 100 if metric == 'acc' else 1\n",
    "                            \n",
    "                            min_ = min(min_, val)\n",
    "                            max_ = max(max_, val)\n",
    "\n",
    "                            pos_x = j*0.6\n",
    "                            poses.append(pos_x)\n",
    "                            plt.errorbar([pos_x], [val], c=colors_palette[colors_augs[aug]], elinewidth=3, alpha=0.7)\n",
    "                            legends.append(plt.scatter([pos_x], [val], marker=marker,\n",
    "                                        c=colors_palette[colors_augs[aug]]))\n",
    "                            \n",
    "        \n",
    "                plt.title(r'ImageNet'+'\\n'+model_names[model])\n",
    "                plt.xticks(poses, short_legend_augs_ordered)\n",
    "#                 if model == 'tf_efficientnet_l2_ns_475':\n",
    "                plt.yticks(np.linspace(min_, max_, 7))\n",
    "                if metric == 'll':\n",
    "                    from matplotlib.ticker import FormatStrFormatter\n",
    "                    plt.gca().yaxis.set_major_formatter(FormatStrFormatter('%.3f'))\n",
    "                else:\n",
    "                    plt.gca().yaxis.set_major_formatter(FormatStrFormatter('%.2f'))\n",
    "                ww = [r'GPS''\\n'r'R50', r'GPS''\\n'r'B2', r'GPS''\\n'r'B5']\n",
    "                plt.xticks(list(sorted(set(poses))), short_legend_augs_ordered[:-1]+ww, fontsize=22)\n",
    "            \n",
    "                plt.ylabel(metric_names[metric], fontsize=20)\n",
    "    handles = []\n",
    "    cross = plt.scatter([np.nan], [np.nan], color=colors_palette[-1], marker='x', \n",
    "                               label='5 aug. samples')\n",
    "    handles.append(cross)\n",
    "    circle = matplotlib.lines.Line2D([], [], color=colors_palette[-1], marker='d', linestyle='None',\n",
    "                              label='10 aug. samples')\n",
    "    handles.append(circle)\n",
    "    circle = matplotlib.lines.Line2D([], [], color=colors_palette[-1], marker='o', linestyle='None',\n",
    "                              label='20 aug. samples')\n",
    "    handles.append(circle)\n",
    "\n",
    "    f.legend(handles=handles, loc='upper center', ncol=3,\n",
    "                bbox_to_anchor=(0.55, 1.1))\n",
    "plt.tight_layout()\n",
    "plt.savefig('imagenet_tr.pdf', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "d=0.7\n",
    "colors_augs = {'cf': d*3,\n",
    "             'MGrid': 1,\n",
    "             'ra': 2,\n",
    "             'cc': 0,\n",
    "             'MTrain': 4,\n",
    "             '5c': d,\n",
    "             '10c': d*2}\n",
    "\n",
    "colors_augs = {'cf': 0,\n",
    "             'MGrid': 1,\n",
    "             'ra': 2,\n",
    "             'cc': 3,\n",
    "             'MTrain': 4,\n",
    "             '5c': 6,\n",
    "             '10c': 7}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "cfs = ['''0 74.360 (25.640)\n",
    "1 79.026 (20.974)\n",
    "2 80.236 (19.764)\n",
    "3 80.756 (19.244)\n",
    "4 81.028 (18.972)\n",
    "5 81.240 (18.760)\n",
    "6 81.460 (18.540)\n",
    "7 81.496 (18.504)\n",
    "8 81.504 (18.496)\n",
    "9 81.592 (18.408)\n",
    "10 81.692 (18.308)\n",
    "11 81.758 (18.242)\n",
    "12 81.720 (18.280)\n",
    "13 81.720 (18.280)\n",
    "14 81.720 (18.280)\n",
    "15 81.814 (18.186)\n",
    "16 81.842 (18.158)\n",
    "17 81.826 (18.174)\n",
    "18 81.880 (18.120)\n",
    "19 81.878 (18.122)''',\n",
    "'''0 74.190 (25.810)\n",
    "1 78.872 (21.128)\n",
    "2 80.086 (19.914)\n",
    "3 80.706 (19.294)\n",
    "4 81.020 (18.980)\n",
    "5 81.228 (18.772)\n",
    "6 81.384 (18.616)\n",
    "7 81.468 (18.532)\n",
    "8 81.572 (18.428)\n",
    "9 81.652 (18.348)\n",
    "10 81.684 (18.316)\n",
    "11 81.716 (18.284)\n",
    "12 81.700 (18.300)\n",
    "13 81.746 (18.254)\n",
    "14 81.766 (18.234)\n",
    "15 81.808 (18.192)\n",
    "16 81.820 (18.180)\n",
    "17 81.836 (18.164)\n",
    "18 81.840 (18.160)\n",
    "19 81.882 (18.118)''',\n",
    "      '''0 74.336 (25.664)\n",
    "1 78.922 (21.078)\n",
    "2 80.174 (19.826)\n",
    "3 80.714 (19.286)\n",
    "4 81.142 (18.858)\n",
    "5 81.298 (18.702)\n",
    "6 81.382 (18.618)\n",
    "7 81.524 (18.476)\n",
    "8 81.556 (18.444)\n",
    "9 81.612 (18.388)\n",
    "10 81.724 (18.276)\n",
    "11 81.752 (18.248)\n",
    "12 81.752 (18.248)\n",
    "13 81.826 (18.174)\n",
    "14 81.846 (18.154)\n",
    "15 81.806 (18.194)\n",
    "16 81.806 (18.194)\n",
    "17 81.822 (18.178)\n",
    "18 81.830 (18.170)\n",
    "19 81.846 (18.154)''']\n",
    "cf = np.mean([list(map(lambda x: float(x.split()[1]), cf.split('\\n'))) for cf in cfs], axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "cfs = ['''0 74.302 (25.698)\n",
    "1 79.034 (20.966)\n",
    "2 80.158 (19.842)\n",
    "3 80.804 (19.196)\n",
    "4 81.076 (18.924)\n",
    "5 81.290 (18.710)\n",
    "6 81.470 (18.530)\n",
    "7 81.546 (18.454)\n",
    "8 81.628 (18.372)\n",
    "9 81.682 (18.318)\n",
    "10 81.736 (18.264)\n",
    "11 81.780 (18.220)\n",
    "12 81.766 (18.234)\n",
    "13 81.810 (18.190)\n",
    "14 81.814 (18.186)\n",
    "15 81.822 (18.178)\n",
    "16 81.874 (18.126)\n",
    "17 81.880 (18.120)\n",
    "18 81.904 (18.096)\n",
    "19 81.878 (18.122)\n",
    "20 81.898 (18.102)\n",
    "21 81.904 (18.096)\n",
    "22 81.928 (18.072)\n",
    "23 81.920 (18.080)\n",
    "24 81.914 (18.086)\n",
    "25 81.918 (18.082)\n",
    "26 81.936 (18.064)\n",
    "27 81.882 (18.118)\n",
    "28 81.888 (18.112)\n",
    "29 81.878 (18.122)\n",
    "30 81.890 (18.110)\n",
    "31 81.890 (18.110)\n",
    "32 81.902 (18.098)\n",
    "33 81.902 (18.098)\n",
    "34 81.874 (18.126)\n",
    "35 81.924 (18.076)\n",
    "36 81.936 (18.064)\n",
    "37 81.950 (18.050)\n",
    "38 81.960 (18.040)\n",
    "39 81.906 (18.094)\n",
    "40 81.904 (18.096)\n",
    "41 81.932 (18.068)\n",
    "42 81.940 (18.060)\n",
    "43 81.922 (18.078)\n",
    "44 81.922 (18.078)\n",
    "45 81.948 (18.052)\n",
    "46 81.926 (18.074)\n",
    "47 81.910 (18.090)\n",
    "48 81.928 (18.072)\n",
    "49 81.950 (18.050)\n",
    "50 81.964 (18.036)\n",
    "51 81.962 (18.038)\n",
    "52 81.938 (18.062)\n",
    "53 81.944 (18.056)\n",
    "54 81.958 (18.042)\n",
    "55 81.948 (18.052)\n",
    "56 81.948 (18.052)\n",
    "57 81.922 (18.078)\n",
    "58 81.914 (18.086)\n",
    "59 81.922 (18.078)\n",
    "60 81.934 (18.066)\n",
    "61 81.936 (18.064)\n",
    "62 81.952 (18.048)\n",
    "63 81.966 (18.034)\n",
    "64 81.974 (18.026)\n",
    "65 81.950 (18.050)\n",
    "66 81.956 (18.044)\n",
    "67 81.968 (18.032)\n",
    "68 81.980 (18.020)\n",
    "69 81.984 (18.016)\n",
    "70 81.974 (18.026)\n",
    "71 81.978 (18.022)\n",
    "72 81.998 (18.002)\n",
    "73 81.986 (18.014)\n",
    "74 81.998 (18.002)\n",
    "75 82.014 (17.986)\n",
    "76 81.998 (18.002)\n",
    "77 82.006 (17.994)\n",
    "78 82.004 (17.996)\n",
    "79 81.992 (18.008)\n",
    "80 82.018 (17.982)\n",
    "81 82.020 (17.980)\n",
    "82 82.028 (17.972)\n",
    "83 82.034 (17.966)\n",
    "84 82.038 (17.962)\n",
    "85 82.034 (17.966)\n",
    "86 82.010 (17.990)\n",
    "87 82.020 (17.980)\n",
    "88 82.024 (17.976)\n",
    "89 82.024 (17.976)\n",
    "90 82.026 (17.974)\n",
    "91 82.022 (17.978)\n",
    "92 82.038 (17.962)\n",
    "93 82.038 (17.962)\n",
    "94 82.030 (17.970)\n",
    "95 82.040 (17.960)\n",
    "96 82.040 (17.960)\n",
    "97 82.048 (17.952)\n",
    "98 82.026 (17.974)\n",
    "99 82.044 (17.956)''']\n",
    "cf = np.mean([list(map(lambda x: float(x.split()[1]), cf.split('\\n'))) for cf in cfs], axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "ras = ['''0 70.978 (29.022)\n",
    "1 77.173 (22.827)\n",
    "2 78.834 (21.166)\n",
    "3 79.575 (20.425)\n",
    "4 80.068 (19.932)\n",
    "5 80.419 (19.581)\n",
    "6 80.551 (19.449)\n",
    "7 80.683 (19.317)\n",
    "8 80.899 (19.101)\n",
    "9 80.990 (19.010)\n",
    "10 81.054 (18.946)\n",
    "11 81.082 (18.918)\n",
    "12 81.142 (18.858)\n",
    "13 81.166 (18.834)\n",
    "14 81.226 (18.774)\n",
    "15 81.290 (18.710)\n",
    "16 81.300 (18.700)\n",
    "17 81.334 (18.666)\n",
    "18 81.320 (18.680)\n",
    "19 81.362 (18.638)''',\n",
    "'''0 71.024 (28.976)\n",
    "1 77.097 (22.903)\n",
    "2 78.888 (21.112)\n",
    "3 79.559 (20.441)\n",
    "4 80.022 (19.978)\n",
    "5 80.393 (19.607)\n",
    "6 80.635 (19.365)\n",
    "7 80.693 (19.307)\n",
    "8 80.759 (19.241)\n",
    "9 80.869 (19.131)\n",
    "10 80.960 (19.040)\n",
    "11 80.986 (19.014)\n",
    "12 81.038 (18.962)\n",
    "13 81.072 (18.928)\n",
    "14 81.114 (18.886)\n",
    "15 81.118 (18.882)\n",
    "16 81.148 (18.852)\n",
    "17 81.234 (18.766)\n",
    "18 81.284 (18.716)\n",
    "19 81.306 (18.694)''',\n",
    "'''0 71.204 (28.796)\n",
    "1 77.107 (22.893)\n",
    "2 78.820 (21.180)\n",
    "3 79.692 (20.308)\n",
    "4 80.036 (19.964)\n",
    "5 80.343 (19.657)\n",
    "6 80.645 (19.355)\n",
    "7 80.775 (19.225)\n",
    "8 80.881 (19.119)\n",
    "9 80.913 (19.087)\n",
    "10 81.044 (18.956)\n",
    "11 81.148 (18.852)\n",
    "12 81.244 (18.756)\n",
    "13 81.254 (18.746)\n",
    "14 81.256 (18.744)\n",
    "15 81.276 (18.724)\n",
    "16 81.262 (18.738)\n",
    "17 81.320 (18.680)\n",
    "18 81.342 (18.658)\n",
    "19 81.354 (18.646)''',\n",
    "'''0 71.146 (28.854)\n",
    "1 77.236 (22.764)\n",
    "2 78.904 (21.096)\n",
    "3 79.673 (20.327)\n",
    "4 80.112 (19.888)\n",
    "5 80.357 (19.643)\n",
    "6 80.593 (19.407)\n",
    "7 80.741 (19.259)\n",
    "8 80.891 (19.109)\n",
    "9 81.026 (18.974)\n",
    "10 81.158 (18.842)\n",
    "11 81.184 (18.816)\n",
    "12 81.240 (18.760)\n",
    "13 81.252 (18.748)\n",
    "14 81.338 (18.662)\n",
    "15 81.302 (18.698)\n",
    "16 81.350 (18.650)\n",
    "17 81.386 (18.614)\n",
    "18 81.434 (18.566)\n",
    "19 81.462 (18.538)''']\n",
    "ram = np.mean([list(map(lambda x: float(x.split()[1]), ra.split('\\n'))) for ra in ras], axis=0)\n",
    "ras = np.std([list(map(lambda x: float(x.split()[1]), ra.split('\\n'))) for ra in ras], axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "ras = ['''0 71.242 (28.758)\n",
    "1 77.352 (22.648)\n",
    "2 78.940 (21.060)\n",
    "3 79.629 (20.371)\n",
    "4 80.008 (19.992)\n",
    "5 80.409 (19.591)\n",
    "6 80.621 (19.379)\n",
    "7 80.639 (19.361)\n",
    "8 80.817 (19.183)\n",
    "9 80.925 (19.075)\n",
    "10 80.960 (19.040)\n",
    "11 81.032 (18.968)\n",
    "12 81.128 (18.872)\n",
    "13 81.186 (18.814)\n",
    "14 81.290 (18.710)\n",
    "15 81.284 (18.716)\n",
    "16 81.320 (18.680)\n",
    "17 81.266 (18.734)\n",
    "18 81.338 (18.662)\n",
    "19 81.358 (18.642)\n",
    "20 81.460 (18.540)\n",
    "21 81.456 (18.544)\n",
    "22 81.422 (18.578)\n",
    "23 81.456 (18.544)\n",
    "24 81.480 (18.520)\n",
    "25 81.488 (18.512)\n",
    "26 81.464 (18.536)\n",
    "27 81.534 (18.466)\n",
    "28 81.532 (18.468)\n",
    "29 81.558 (18.442)\n",
    "30 81.575 (18.425)\n",
    "31 81.589 (18.411)\n",
    "32 81.641 (18.359)\n",
    "33 81.627 (18.373)\n",
    "34 81.643 (18.357)\n",
    "35 81.637 (18.363)\n",
    "36 81.653 (18.347)\n",
    "37 81.657 (18.343)\n",
    "38 81.659 (18.341)\n",
    "39 81.605 (18.395)\n",
    "40 81.562 (18.438)\n",
    "41 81.615 (18.385)\n",
    "42 81.649 (18.351)\n",
    "43 81.641 (18.359)\n",
    "44 81.631 (18.369)\n",
    "45 81.667 (18.333)\n",
    "46 81.699 (18.301)\n",
    "47 81.687 (18.313)\n",
    "48 81.685 (18.315)\n",
    "49 81.677 (18.323)\n",
    "50 81.687 (18.313)\n",
    "51 81.669 (18.331)\n",
    "52 81.663 (18.337)\n",
    "53 81.657 (18.343)\n",
    "54 81.663 (18.337)\n",
    "55 81.661 (18.339)\n",
    "56 81.671 (18.329)\n",
    "57 81.663 (18.337)\n",
    "58 81.665 (18.335)\n",
    "59 81.681 (18.319)\n",
    "60 81.679 (18.321)\n",
    "61 81.701 (18.299)\n",
    "62 81.687 (18.313)\n",
    "63 81.701 (18.299)\n",
    "64 81.687 (18.313)\n",
    "65 81.663 (18.337)\n",
    "66 81.669 (18.331)\n",
    "67 81.659 (18.341)\n",
    "68 81.657 (18.343)\n",
    "69 81.659 (18.341)\n",
    "70 81.663 (18.337)\n",
    "71 81.677 (18.323)\n",
    "72 81.695 (18.305)\n",
    "73 81.685 (18.315)\n",
    "74 81.721 (18.279)\n",
    "75 81.723 (18.277)\n",
    "76 81.721 (18.279)\n",
    "77 81.727 (18.273)\n",
    "78 81.687 (18.313)\n",
    "79 81.693 (18.307)\n",
    "80 81.691 (18.309)\n",
    "81 81.695 (18.305)\n",
    "82 81.707 (18.293)\n",
    "83 81.681 (18.319)\n",
    "84 81.701 (18.299)\n",
    "85 81.679 (18.321)\n",
    "86 81.689 (18.311)\n",
    "87 81.691 (18.309)\n",
    "88 81.715 (18.285)\n",
    "89 81.721 (18.279)\n",
    "90 81.735 (18.265)\n",
    "91 81.739 (18.261)\n",
    "92 81.723 (18.277)\n",
    "93 81.743 (18.257)\n",
    "94 81.721 (18.279)\n",
    "95 81.731 (18.269)\n",
    "96 81.717 (18.283)\n",
    "97 81.703 (18.297)\n",
    "98 81.705 (18.295)\n",
    "99 81.685 (18.315)''']\n",
    "ram = np.mean([list(map(lambda x: float(x.split()[1]), ra.split('\\n'))) for ra in ras], axis=0)\n",
    "ras = np.std([list(map(lambda x: float(x.split()[1]), ra.split('\\n'))) for ra in ras], axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "colors = {'efficientnet_b2': 'r',\n",
    "          'resnet50': 'g',\n",
    "          'tf_efficientnet_b5': 'b'\n",
    "         }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
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6fBz1jMN0x3ABAAAAAACAA8wYo273U9XqF1SvXVKn81HcJU1MJvO0iqVzKhXPK51+aiY/YQ8AiJ8xRtdudbaFQGud/XfbnkXH8iktlgcB0KVyQQuFFOdnAAAAADgECIUCAAAAAAAAY2RMpHb7imq1i6rVL6rfW4u7pMmwLOVySyqVzqtUfEHJ5LG4KwIAzKAgjPThRlsr1YYqN5paWW2o0w/jLmsqPTGXllceBEAXywUdySXjLgkAAAAAEANCoQAAAAAAADhQfL+mXu+GwrAjYwJZlivHySiVOqFEohRLTVHkq9n8hWr1S6rXLikIGrHUMWmWnVAh/xmVSudVLP6qXLcQd0kAgBnTC0JdWWsNO4E2dGWtpX4QxV3W1LEs6ZmjOS0u5LV0YjASvpBOxF0WAAAAAGAKEAoFAAAAAADAVOv3N7S5+Q9qtVbU7nyowK/d97FuoqRs5hnlcouam/vHSiaPTKyuMGyrXv+pavWLatR/oijqTexYcXKcrIrFF1QqnVM+/8tynFTcJQEAZki7H2il2tTKalPL1YY+uNlSGJm4y5o6rmPp9LG8lsp5LZULOruQVzrhxF0WAAAAAGAKEQoFAAAAAADA1DHGqNl8T+vrf6ta/UeS2VuHsMCvqe7/WPX6j3X9xr9TqXhOR4/+M+Xzz8uyrH3X5fubqtd/pFrtoprN92TMbI6vTSTmVSydU6n0onLZs7JtLiMCAMaj1vG1Um1ouToIgV671ZYhA3qXVMLW2YXCKAT67NGckq4dd1kAAAAAgAOAq7kAAAAAAACYKp3OJ7p27c/Ubl/d30ImUq12QbXaBWWzp3Xq1B8qnX7yoZfp9aqq1S6qVr84qGlGkyup9BMqFc+rVDqnTObZsYRoAQCHmzFG662+lm80VKk2tLLaVLXWjbusqZRLufKGY+CXygWdOpKVY3MuBgAAAAA8PEKhAAAAAAAAmArGhFpd/Y+qVv/DPTtwhlFfYdhWFLYVRh0ZE8gYI8uyZFmuHDsj28nKcbJy7OS2fdvtq1pe/l9UPvEvtXD8t2RZ9x+3aoxRp/OBarVLqtUvqte9PvbnOi2yuTMqFV9UsfSC0qkTcZcDADjgjDG6XusOAqDDbqC3Wv24y5pK87nkqAvoUrmgJ0ppPpABAAAAABgLQqEAAAAAAACIXRA0dPWDP1G7dWXbdiOjwK+p768rCJoPXkN37nfdvJKJo3ITJVkaBCyMCXXj+r9Tvf5jnX72j+S6hdHjoyhQq7WiWv2i6rVL8v1bY3x208OyHOXzv6RS6ZyKxReUSMzFXRIA4AALI6OPN9parja0POwE2uwGcZc1lcqltJaGXUCXThR0NJckBAoAAAAAmAhCoQAAAAAAAIhVv7+hK1f/d/W6N7Zt94O6ut1riiL/odcMgqaCoCm7l1A6fVIJtzi6r926rPcvf03PPPNH6vc+Va12UfX6jxWG7X0/l2lkO2kVC59VsXhexeJn5TjZuEsCABxQ/SDSB+utYQi0qfdXG+r5UdxlTR3Lkk7OZ4ddQPNaXCiolE3EXRYAAAAA4JAgFAoAAAAAAIDYBEFDV678b+r1qqNtkQnU636q/n26dVqyZDsZ2XZSlmwZRYqivqKwIyOz7bFR5KvdvqpkYl7p9FODzqNBXe32Va2vf0/ZzGnZ9uxdInPdgoqlcyoVzyuff162TRAFAPDwun6o91eboxDo1ZtNBaHZfcdDxrEtPXssp6VyQYsLeS2W88omZ+/nCwAAAADAwcBvpAAAAAAAAIiFMaGufvAn2wKhYdRTu31FUdTf9ljLspVIzCuRmJdjZ2RZ9j3WixRGHfn+Lfn+LRlzu3NZpG6vqm7vumw7KenOvp3OB8pmn5uJ8a3J1HGViudVKp1XNnvmnq8RAAAP0uj6WlltavnGYBT8h+ttGUMIdKeEY+vsMPy5VC7ozPGcUq4Td1kAAAAAAEgiFAoAAAAAAICYrK7+R7VbV0a3B4HQy9vGxVuylEweUypVlmU9OGxhWbZcJyfHySqRmFOnc02+vyFjwtFjjAmG49MHgckgbKnfX1MqtTDeJ/eYZDJPq1Q6r2LxvNLpJ2ci3AoAeHw2Wn0tVxtaqTZUqTZ0fbMbd0lTKZN0tLhQkHcir8VyQc8cycp1+PAFAAAAAGA6EQrFoeR53hlJr0j6kqQjks5ImpO0KWlj+L8/lPQdSW9XKpXNmEq9y5ba/5GkFzWo/3btV7b8+06lUnk7rjoBAAAAAHiQTucTVav/YXQ7MsGwQ+idQKhtp5TJnJLr5HZdz8goDNsK/Jr8oK4o6g3XSCsMO5IGXUONiRSGbTlOTtIgQNnr3ZDrFuU46fE9wUmxbOVziyqWzqtUPKdk8mjcFQEADghjjKr1nlZWG6rcaGi52tB6s7/7jodQKZPQYnkYAl0o6OR8hg9eAAAAAAAODEKhODQ8z5uT9MeSXtMgRHkvc1vue3H4WHmed0HSVyqVyluTrvN+PM97TdIbGgRY72VOg5pfHN5+3fO8TUnf0KD2qQm2AgAAAAAON2OMrl37s20dPHvdT7eNjHecjLKZM7Lt+1++MooUBE0FQU2BX1dkgrseY1mOHCerKOqMjmdMpCjsynYyw3WMut2Plc2encrAh2UnVMh/ZtgR9AW5bj7ukgAAB0AUGX2y2VFlOAp+udpQvePvvuMhdLyQ0mK5oKVyXl65oOOF1FT+TAAAAAAAwF4QCsWh4HneVyW9vmPzpqS3Jf0X3ekQekTScxoEL1/W9oDom8OQ5RuVSuUbj6NuSfI872VJX9f2MOimpK9o0MX0wvBxc8PHfE7Sl4c1z2nwvF/3PO9rlUrljcdVNwAAAAAA99Nsvqd2++roth/U1fdvjW47duq+gVCjSL5fHwRBg7qMiXY9nmXZsu2MwrCt2x1DI+PLMglZ1uAYQdhWGLamJnDpOFkViy+oVDqvfP4zcpxU3CUBAKZcEEb6YL2tlWpDy9WmVlYb6vTD3Xc8hJ6cy2jpREFLC3ktlQuazyXjLgkAAAAAgLEhFIqZNhy1/h3dCVRuSvpzSV+tVCpX9rD/ixp0F31luGlO0tc9z3tV0pf3ssZ+eJ73dQ27lW7xRqVS+drOxw47gV4Y/vvGsPY3dee5v+553iuSvjTpugEAAAAAeJD19f80+nrQpfPa6LYlS+nMqbsCoZEJ1O+vqd9f39ZhdK8sy5bjZBSGrTtrRl05zp0QaN+/GWsoNJGYH3QDLZ1XLnv2gV1SAQDoBaGurLW0XB2Mgr+82pIf7v5hicPGsiw9czQrr1zQYjmvxXJB+RTnWAAAAADA7OK3XsysYQDyzS2bvqFBoHLPY9SHXThfHYZL39Sd0ewvS3rX87wv3u7UOW6e57275XjSIND60l4DncO6nvM8703dCbWe0YTrBgAAAADgQfr9DdXql0a3A7+mKLozyjaZPCbXyY1uR1Ffvf6afH9jT11BH8SyHNl2cjSm3phIxviyrMSwlrqilC/bTuzrOA8jlX5SpeI5lUrnlck8w6haAMB9tXqB3l9tqlJtaKXa0AfrbUWRibusqeM6ls4cz2upPOgC+tzxvNIJJ+6yAAAAAAB4bAiFYiYNR67fDoRuStpXCHIYxHzJ87zXJX11uHlOg4DlS+MMWA7HwH9X2wOhVzQIhO450HpbpVJ5dUcwdCJ1AwAAAACwF5ub/yBtCXf2/fXR15ZlK5UqS5LCqKd+f01+f0NG4wu8WFZKki8N14wiX44zCIEaGfn+LaVSC2M73j0KUDZ7RqXieZVK50bPFwCAnWptX8urgy6gyzca+mSzI0MG9C7phKOzwzHwS+W8nj2WU8Kx4y4LAAAAAIDYEArFzNkyMl56yO6au6lUKl/zPE+6EwyVpO96nnf6UQKb97G1I+ltr+5n/WEw9LLujJKXxl83AAAAAAC7arVWRl+HUV9B0BzdTiTmB2Piu5+o729KYwyD3mZZlizLlTGD7qTGBJIiSYPwyNbx8uM7pqN8/pdUKp1TsXhOiURp7McAABxsxhjdbPZHo+CXq02t1rtxlzWV8mlXS+WCFhfy8k4UdHI+K8em0zYAAAAAALcRCsUs+s6Wr18dVyD0tmEw9Kik14eb5jQIcn5pv2sPO5G+vGPzG2Pq6PklSZe33L7dkfSlMawNAAAAAMCetDsfjr4Ow/aWeyKFYUfNZkWTCINuZdtJheGdkfXGhLKsYSg06oznGE5axcJnVSyeV7H4K3KczFjWBQDMBmOMPq11tXzjTgh0s92Pu6ypNJ9LyisXtDgcB/9EKS3LIgQKAAAAAMD9EArFTPE87zXd6Yb5jUql8vYkjlOpVN7wPO+VLcd62fO8VyqVyluPuuaww+lXd2zerFQqX3vUNbeqVCpXPM97S3fGyEvSi57nvVapVL4xjmMAAAAAAPAgvl9T4NdGt6OwLZlQYdSTMYGMsR5TyGP7SNlBKHQwQj6KfEWRL9tOPPSqrltUsXROpeI55fPPP9IaAIDZFEZGH220R6PgV1abavWCuMuaSuVSelsI9Fg+FXdJAAAAAAAcKIRCMTM8z5vT9lDlzoDluL2hQYfQ274s6ZFDobp3vV/Zx3r38hVtD4VK0lc9z/tzxsgDAAAAACat17sx+joIW+r2ro+6hVqW89i6fg1GyDsyJpQkGRNtuz+KensOdCZTx1UqnlepdF7Z7JlRx1EAwKOptX1dr3fU6YcKIyPHtpRJOnqimFEpe3DC9v0g0tWbLS1XG1qpNvT+WlM9P9p9x0PGsqST81ktlQvyTuR1dqGgUubg/P8MAAAAAMA0IhSKWfIHGoxEl6Qr4x4bv1OlUnnL87zNLcd82fO8uUcJV3qe96LuDmtK+wuZ3qVSqVzwPO+K7nQ4lQb1/7EGIVcAAAAAACYmCNoKgoZ6/VUFQVNR1Nty7+MOU9qSwuHX28fV7wyJ7pTJPqNS8ZxKpReVSj3BCFsA2IeNVl9/f2VdK6tNfbDeUq3t3/expWxCzx7NaXEhr187c1RHcsnHWOmDdfqh3l9tamW1oUq1oatrLYWR2X3HQ8axLZ0+ltNiuSCvXNBzCzllk/ypCgAAAACAceI3bcySV7d8feExHfPPJb225fYfSHqUUexfvse2SQVb35L0+o5tr4lQKAAAAABgQowxajR+omvX/k+12lt/1Z2WsMzOOnaEQi1b+dyiiqXzKhXPKZk8+tgqA4BZZIzRL6439LeVVV38aFPG7O18UGv7+lF7Uz/6eFP/9sInOv/0nP65t6BfeqLw2AP69a6vlWpTK9WGlqtNfbTR0h6fxqGSdG2dXchrsVzQUjmvM8fySrp01QYAAAAAYJIIhWKWvLzl643HdMx3d9x+7hHXee0e2yYVbP2O7g6Fznme93KlUnl7QscEAAAAABxCxhjV6hdUrX5T3c7H8v3ajkdMS4fNnXXYsuyECoVfVql4TsXiC3LdfCyVAcCsuXarrT/7/ge6stba1zrGGF348JYufHhLZ47n9Ie/cVpPzWXGVOXdNlp9VW40tLLa0HK1oeub3Ykd6yDLplwtLuS1NAyBPn0kK9chBAoAAAAAwONEKBQzwfO8Mzs2HXlMh94ZPt1Zx648z7tXIFQahDcn4Yf32f5lSYRCAQAAAAD7Zkyozc3/ourqX6nXvT7ablnOjkduDWM+eGT7+G093qAOy3KVSBT11Mn/TuWF35Ztpx5zTQAwu8LI6Fs/va5/f+nTe45V7wWR2v1AnX6oth8qCI2MjCxZch1L2YSjTNJRNukqtaPT5JW1lv7nf/8z/atzT+lffPaEHHt/Hzowxqha72m52hj9W2/297XmrCplE1oajoI/u5DXyfnMY+/aCgAAAAAAtiMUilmxM4z50OHMR7S54/bcI6xxr9Hx0v3Dm/tSqVQ2Pc/b1N21vuJ53lylUtn5nAAAAAAA2JMoCnRr8wdaXf0r9Xtrd92/M2RpWfZo1K4xoYwxjyVIYoyRMeGohkSipEzmaTlOTpYsHTv6BQKhADBG9a6vP/mb93V5tblte2SM6t1AN5s9tbrBfffv+dp2fy7t6lg+pVImMfp4QRgZ/b8XrunH1zb1R184q2I6sef6osjo2q2OlqsNVaoNrVQbajygnsNsoZjS4kJh1An0eCFFCBQAAAAAgClDKBSzYmcI9EXP885UKpUrEz7uzmDlQx3P87w5SS/e675KpTKp8fHSoM57HfcPJH1jgscFAAAAAMygKOprY+P/0+rqt+T7t+77ONtOyLYTiiJf0r06h0aSdm4bP9t2ZUxKtuVKlqNU6km5zmA8fCIxp0SiOPEaAOCw2Gj19W++XdGN2vZx6/Wur2u3OvKDh+8U3eoGanUDJVxbJ+cz2wKg76829dVvvaf/6Tc9Hckl77l/EEb6YL2l5WpTy9WGVlab6vbDh67jMDg5n9HZYSfQpXJec9l7v6YAAAAAAGB6EArFrNg5xl2Svi7pSxM+7s4w6uWH3P/l+2yfdLfOH+reodAviVAoAAAAAGCPwrCn9Y3/pLW1byvw63vax7Ez9w2FRpEvx5lEKNSS42SVSBTluiX1e2ujTqGS5DjZ0deZzDMTOD4AHE71rq//9dsVVbcEQoPI6NPNjm617j2O3bKkdMJRyrVlWZaMMeoFkbp+OOoufZsfRLq61tJ8Lqmn5jKjsfE3al39m29X9MZvP69iOqGuH+rKWms0Cv7KWkt++PBh1FlnWZaePZrVUrmgxXJei+WC8in+jAQAAAAAwEHDb/OYFfcKUb7sed7LlUrl7Qked2fo9GG7e94vtDrpDqf3C6/es2spAAAAAABbhWFbN2/+rdZufkdh0HqofR0nJz+4HSC1ZVmujBmM6DXGlzHjGUNryZLj5pVIlOS6RdlWYniMUH5wp5up6+bl2He6nuVyZ/d9bADAYJz7n/zN+9sCob0g0pW1pvo7uoNatqX5bEJHskllko7se5wHImPU6YfaaPd1q+3LRHcSordafbV6gc4czyvl2goio0q1of/h/7qgxXJeH210FEXmrjUPu4Rj68zx3CgE+tzxvNKJyXfsBgAAAAAAk0UoFLPifiHKNz3P++IkRrEPR79v7fS5+QgB1M/dZ/u9Op+O0/06kZ7xPG+uUqlMulMpAAAAAOAACoKmbt78rtZufldR2HmkNRKJOfV6N2Q0COfYdkJhGAzvNTKmJ8tKP9LaluXIdQty3ZISbuEe4+mlXq8qY+6EkZKJo1sWsDU//2uPdGwAwHbf+ul1XV5tjm73gkjvrzYVbO3QaUnH8ymVi+lRl8/7sS1LuZSrXMrVkyWjar2rtWZPMlIko2Yv1I+vbSqXcgddQIcZ0I1WX+Xio51XZk066ejs8by8E4NR8M8czSnh2HGXBQAAAAAAxoxQKGZCpVK54nnepqS5HXfNSXrX87wvTaBj6Gs7bj/K2PX7deacdCjzQaHTz0maZHdVAAAAAMAB4/s1ra19R+vr/0lR1NvXWradlJsoyvdrkiTLSsiyeqOgZhT1h9v23qnMsVNKphaUSMzJ0v3DLUHQUr9/c0stCbmJ0uh2qXhOicT8wz4lAMAO12619e8vfTq6HURGV9a2B0KTrq2nj2SVe8jx5Ga4XjrhKJd0tdbsbRsF3wsi5VKObA1CptV6V6VM4lB2wCykXS2WC1oqF+SVCzo5n5G9S/gWAAAAAAAcfIRCMUvelvTKfe77jud5X6tUKm+M40DDLqF/vGXTpqSvPOQaDxrVPunx8Q8Knb4oQqEAAAAAAEn9/obW1v5a6xvfk4n8sa2bTBwdhUIlybbTCsP26HYYduQ4WVnWg7uXOU5aqWRZbqIkSw8OuURRoE7341GHUklKp09u2+/osX/+sE8FALCDMUZ/9v0PFG4Z1/7pZmfbyPh00tFzx3Jy99Cl0kjq+qFavUCtXqBmL9wWLk27tqLIKDSD40XGqOdHygxDoMZIH99q6+zxvKx7jKWfJUfzyeEo+EEItFxMzfxzBgAAAAAAdyMUilnyFd0/FCpJr3ue94qkV8cwTv5Ptb0r6RuPMHL9zAPuW3/4kh7KgzqF/qMJHxsAAAAAMOV6vTWtrn1Ltza+L2PCsa/vOHm5TlbBMAhqWa5sK6HI3A6eRoqijmw7c89gqONklUqV5bqFXcOg0iAQ2u5c2dblNJmYV8Itjm5ns6eVz3n7e2IAAP3iekNX1lqj27Wur1ut/uh2MmE/MBBqJHX6oZrDEGirHygMzT0fKw3GymeTjlr9UNEwGNoPI7mOrcSwK2a7F6rZC1VIz9afRE6U0vJOFLS4MBgHfzSfirskAAAAAAAwBWbrCggOtUqlcsHzvAu6/0h2aRDEfHc/XUM9z3tN28Onb1UqlUcZHf+gUOikPSjAGmddAAAAAIAYdbufanX1W7q1+Q+SiXbf4RFZlqV0+qRarZVR507bScuE4WiMvDGhwrAtx8mMRsm7Tk6pVFmOm99TGFSSgrClTufjbYFQ204qnX5qSz2OTp36Q7qpAcAY/G1ldfS1kfTJrc6dOy3p6fnsXYFQI6nZ9XWr7avW8RVF9w+B3ottWcokHLX7wagfdNcP5abc0dlivdU70KFQy5JOHcnKKxe0WM5rsVxQMZ2IuywAAAAAADCFDu4VEODe/rWkd/fwuNtdQ79cqVT2PCrd87yXJX19y6YLlUrl1Yes8bbnHnG/SZvb/SEAAAAAgFnS6Xys6upfqVZ7dzBn9zFwnIxSqbK6vRvDLZYcJ6swbI+CoVKkMGzJdYvKZp9Vwi3teX1jQvV6VfX7N7eNjLfthLLZM6OgqSSVT/xLpdNPjuFZAcDhttHq6+JHdz6PXuv48reMjT+eTymXuvNnia4faqM96CS6dST8o3BtS0nHVm+4TmSMgjBSYhhArXV8+VtuTzvHtnT6WE5L5YKWygWdXcgrk3R23xEAAAAAABx6hEIxU4bdQr8m6fU9PPyMpO94nveNSqXy5d0ePAyRvrll09uVSuVLj1iqJB3Zx7779aDx8XQKBQAAAIBDot2+qmr1L1Wv/ziW4yeTCwqChoLw9phhe1sw1LJc2fZgFG6n85ECd16J5Lyc+4yVNyZSGHXk92/JD25tCZcOV7eTymbPyLHvjNfN5p7TwvHfmthzBIDD5O+vrMts+XDBzeadLs2WbalcTMuPIm0Og6CdfjjW46dcW/0wGn0UoL81BGqkW62+ForpsR5zXFIJW88dz49CoKeP5ZR0D0aAFQAAAAAATBdCoZg5lUrljWFHzweNkd/qNc/z/kDSq/frGup53uuSvrpl056CpLt4UPjyQePdAQAAAADYl2ZzWdXVb6rZ+HmsdViWpUzmWbXblxVG3eFWR6nUk5IiBUFz9FhjIvX9dfX9dVmyZDsZ2XZSlmwZRYqivqKws60r6FbJxLzS6ae2dQhNpU/o9LN/tG0bAODRrazeed/uBZFa3UDSYDx8xrX14UZbja6v+7xV75tlWUo4g2CoJAWRUWiMHGswRL415hDqfuRSrhYXBmPgvRMFnZrPyD0gXUwBAAAAAMB0IxSKmVSpVF7yPO9d7T0YOqdB19C3JP3rSqWyKUme553RoDvo1nVerVQqb42hzKkd0+553plKpXIl7joAAAAAAONjjFGz+QtVV7+pVnM57nJGbNtVNnta7c5VOXZGydSCHHvQxc0P6up2rymK/G37GBmFYVth2N7D+gml0yeVcIvbtqfSJ/Tcmf9RrlsY35MBgEPug/XW6Ot2P1AQGflhJD80CiMj17YmXsPWUKgkhZGR4wyOO+7OpA+jlE3IG3YBXSzn9dRcRpY1+dcDAAAAAAAcPoRCMbOGwdA3Jb3yELu9Iullz/P+tQadPLd2B90WGB2DOMfH72ZqA6sAAAAAgIdjjFG98WOtVr+pdvtq3OXcxbIcHTv+RR2Z/6/16fX/R+3Wnc8oJtyi3PwvKKNacAAAIABJREFUKfBr6vvr2zqH7sZ180omjspNlGRpe+gmm3tOp5/9IwKhADBGtbavWttXN4h0q93XJ7c6avcHnUItSc5jyj861uB4t5uRRlvG2Q8CqltGyk/QQjE1GgW/WM7reD5FCBQAAAAAADwWljETmtMCTAnP816R9Kd69KDjFUlfvt9o+Ufled4t3b+mcXUjfdDxH/TN/6VxP9+4vfvuu+9I+vWt22zbViaTiamig6HT6SiKom3beN0AAJhdnPuB2WKMUb//C3W6f6cwqMZdzt0sV+nUi0qn/ys5TkmSZEyobvf7anf+s2Tu7uZmjK/IdGWG/2RCGZlB6NNyZFlpWVZatpWWZSXucUxH2cw/Uzr9eUbGA0Oc/zEObT/Sdy/X9W9/WlM3GPz31PEHo9ulQVAzk3h8o9E7fqRwePXTsSxlEnfCmKdKSWXHXYsllXOunplL6pn5pJ6ZS6qQ4jwDYHpx/gcA4HDh3A882L2+RyT94KWXXvp8HPWMA51CMfMqlcpbnue9Lem72vs4+a3emFBAkm6cMYuiSK1Wa/cHYhteNwAADhfO/cDBY0ykIPy5fP8HiqKbcZdzF0tJuYlzSiT+saS8ul1J2vo+86KSiVPq9b6lMPr0rr2ljKSMLGtwc2fPNWM0DAIF27Y79pNKJX9HUXRM7XZ3nE8JmDmc/7EXQWR0+Zavn6z2dXnDV70XqePf+QPK1oYUliw9zv4U1pZeocYYGXPnbOEHoQJ7f8XYlvRE3tGpoqunSwk9VXSUcW8HTSMp6KoVPHAJAJg6nP8BADhcOPcDs41QKA6F4cj3lzzPe13bR8LvxZue5417dDwAAAAAAGNlTKgg+Kl8/x1FZvp+fbWslBLuS0okPifLyj7wsbZ9XOn0f68o+ki+f0FBuCLprk9q74Et11lUIvGibPtpxvYCwD4ZY/RJI9RP1/r6+Vpf3eBOuHJnzHJaZpSNoy7Xlp4quMMQqKsnC66SDucUAAAAAAAwnQiF4lCpVCpf8zzvgqTvPOSur0h62fO8NyqVyjcmUNq0oYspAAAAABwQxgQKgh/J9/9ekanHXc5dLCujhPuPlEi8KMtKP8R+lhznGTnOM4qiuoLw54rCTxRGN2RM4wH7FeTYJ2Q7T8l1flm2XRjH0wCAQ22zG+qnq339ZLWvW917h/R3RiTv9OqM173q2k3atXSy4OpUaRAEfSLvyLEJgQIAAAAAgIOBUCgOlWGn0D9+xN3nJH3d87xXJX25UqlcGV9lAAAAAAA8HGP68oNL8v2/lzHTN+rJsnJKJH5NCfecLCu5r7Vsu6ik/etSYnA7Mi2ZaF3G9DUYE+/KspKy7KOyrdy+awcASN0g0i9u+vrpal8f13efhb4zM2ltSYVGMtpbHHM8oi1x1J1Nou+V7cwlrFEA9FTR1ULOkU13aQAAAAAAcEARCsWh4HneK5L+VHc6YG5KekPSDyW9KenMQyz3sqTLnue9WqlU3hproXisbNtWJpOJu4yp1ul0FEXbuz/wugEAMLs49wMHQxR11e39g3rdH8hEHTm2NE2XeGy7qHTmN5ROnZdlJSZ0lJykhQmtDRwunP+xVRgZvb/R04+ud/TeWk9hNAhXuo6z674Zy5ZlhaPbtm0pDAf7GyNJRtZjCFoaY4bHu1PH1sNmkq6O5lw9O5fUM8N/R7POY6kNAKYF538AAA4Xzv3Ag93re+Sgm56/GAAT4HnenAahz5e3bP5apVJ5Y8vt54YdRL/6kMu/6XnezrVwgGQyGT3//PNxlzHV3nvvPbVa2zsO8boBADC7OPcD0y0IGlq7+V3dvPldOXZXuawjKR93WSPJ5DEtLPyO5uc/L9vmkhNwUHD+hzFGH6639f3L6/qHq+tqdAcdmDPZh38vTzeNgnDwR5RIkYLoTkjUsl25j2EEexAZyQpGfUkTjqNc2lUu5epEKa3/4789ryO5/XWwBoCDjvM/AACHC+d+4MHu9T1y0HGFHjPL87yXJX1ny6Yrkl6tVCoXdj62Uql8zfO8tzQIkL74EId53fO8M5VK5dX9VQsAAAAAwL35fk1ra9/W+vp/VhT14i7nLqn0iUEYdO4fy7J27yQHAJgOG62+fnBlXd+/fFPXN7tjWTObdFTvDEKhzo4AqB9Gcu3Jnyf8MJJjW3JsS65t6zNPFJRNDv4Ucu7UHIFQAAAAAAAw8wiFYibdo/PnBUlfrFQqm/fbp1KpXJH0kud5r0n6+kMc7hXP875TqVS+9GjVTqX7vk4AAAAAgMej31/X6tpfa2PjezJREHc5d0lnTqq88LsqlV5i5C4AHBBdP9S7H97S9y/fVOVGY9uY9XHIJR3VO74kybEsubY16NypQVgz7drjP2dYUi456ASaTtj6aKMtDZ9XLu2OAqGSdHZhejpsAwAAAAAATAqhUMwcz/O+Lum1LZvefpjAZqVS+YbneW9rEAx9ebfHD73sed7XK5XKlx+i1Ac5MqZ17snzvLlJrg8AAAAAeHS93qpWV7+lW7fekTHh7js8ZtnsaZXLv6dC4VcIgwLAARBFRj+/Xtc7l9d14aNb6gfRxI41l03qer07CmUmHXs0Qt5I6gWR0on9dQu1bUvZpKN8ahAEzSYd2cPz0aebndGxJelYPjX62rIs/fqZo/s6NgAAAAAAwEFAKBQzxfO8r2p7IPTCo3TwHHYN/dKwa+hXJe0lRPnasGPoW3s8zOYe143DRtwFAAAAAMBh0+1+OgiDbv69xt66bQxy+SWVF35X+fwvEQYFgAPg44223rm8rh9cXVet7T+WYyZdW6VMYnQ817FlB5Gi4XmtH0ZyHVuuvffziGVJhXRCuZSrfMpVJunoXnu3eoHWmr3R7cSwltvOPz2neUbHAwAAAACAQ4BQKGbGMMD5+pZNm5K+uJ81h11D/1zSn0p6ZQ+7vOl53vyDxtQfEAe9fgAAAAA4MDqdj1St/pVq9QtTGQbNFz6j8sLvKJ/34i4FALCLWtvXD66u653L6/p4ox1LDUdzyVEo1JKUTjhq9wNJgyaeHT9Ubkt3z/vJplwdySU1l0nI2SVEGoTRtrHxknRyPrMtPPqF5xce4dkAAAAAAAAcPIRCMRM8zzujwbj3rV4dRzhzuMarnue9okE4dLfunn8s6Y09LL2xh7XiQqdQAAAAAJiwVuuKVle/qXr9x3GXck/F4gsql39X2ezpuEsBADxALwh18aNNvXN5XT/7tBb75wvyKVfZlKN2bzA2PmFbSjq2+uFgbH1kjNr9cNvY99uSCVvz2aTms0mlXHtPxwvCSJdvttQPotG2+VxSxfSdLqFnjuf0/InCfp8aAAAAAADAgUAoFLPizR2336pUKm+P8wCVSuUtz/PeHh7r5Qc89HXP876yh0DqFUlnxlbgwznyoDtnoNMpAAAAAEytZrOi6uo31Wz8Iu5S7mZZKpVeUnnhd5TJnIq7GgDAfRhj9N6Nht65vK53P7ylrh/GXdKIZVk6OZ/VSrUxCqimEraCyIzGyIfGqNUPlUk4SiVszWUSms8llU269xwNfz+tXqCPNtrbAqFJ19ZTc5nRbce29Ie/cVrWLp1JAQAAAAAAZgWhUBx4nue9LOnFHZv30qnzoQ3Dkl/yPO+r2j6qfqc/kPSNXZa78oD74uwgSiAUAAAAAMbMGKNm8+eqVr+pVmsl7nLuZtman/s1LSz8ttLpJ+KuBgBwH59udvTO5XX94Mq6Nlr9uMu5r0zCUbmY1o1aV5Jky1I26ajdD0fBUMeyZCTNZxM6UczsOiJ+qzAyqta7Wmv2to2Mdx1bZ47nt631r849tS0kCgAAAAAAMOsIhWIWfHXH7bcqlcqDApf7VqlU3vA8b/0ex77tVe0eCn1Q+PLoIxW2dw8KnTI6HgAAAADGxBijev1Hqq5+U532B3GXcxfLcjR/5De0cPxfKJU6Hnc5AIB7qHd9/cOVDb1zZV0f3GzFXc6eLRRSqncDtXuBpEEI9Fg+qY4fSrJ0ezj8zUZf6y3//2fvzqLbvO80zz8vVhLcQAIEJcuSLULJ387i2PImMpWqxJa8yWdmTpXd1Zd1MbGruudiLrqiJHczF5PYM9VzU6en7ZyaM3VXKas6p7sjJ47lJLWElDd5Sxy/LgOULMkWQQAEN5DE8r5zAVAiCUqiwOXl8v2cowPgBfD+H+iCPHz58PdXdySonkhIrStsKy/Vtp2fLVWVL5Y0XizLddwlz4cCtULo4m3nDyXa9dhX9mzURwQAAAAAANiSKIViWzPG9KtxSugLm7G2bdvPG2OklYuh19tefkHqOs9t9KTQ620ff3aD1wYAAACAHc91HU1MnNVo5qeam73kdZwGli+oWM831Nv7qEKh6/2ICADwQqni6P2LBQ2lcvrg0oScZQXI7cCyLB2MRXSxMKtw0K/uSEgtgdo28p8VZjW+aNKp67jKT5eUny7JsqSWoF/hgE+WZcl1Xc1XHM2Vq1e2o1+uuy2kfdGl00b3dLXof3no0E1NIAUAAAAAANgJKIViu3tq+QHbtk9v1uL1YugxrVACNcZE69vNX8v1pplu9G/krlc6fXOD1wYAAACAHct1qxoff12ZzMuanx/1Ok4Dny+sWOyb6u09pmCwy+s4AIBFXNdVamxaQ6mc3hjJa7ZU9TpS01pCft13W7cGk3HF20P6j69+fGUr+YDP0oGeiKKRoC6Oz6pccZa813Wl2VJ1VZ8/GPDp1u5WdbYElxzf09Wi//CIUcey4wAAAAAAALsBpVBsd8eWPd60QugiJyS9vcLxfl1/6uZb13mOSaEAAAAAsI04Tlnj48PKZH6mUinrdZwGPn+reuMPKx5/WIFAu9dxAACLZCbnNJzOaTiV09jUvNdxmmZZlr6yr1ODybju3h9VaNE27icev0N//ctPlMpMXznW2RLUnXuDmpgtKzs9r5m5yqrXam8JKNYeVldrUMvngB5KtOvfP3SooSgKAAAAAACwW1AKxXZ337LH15u+uSFs2z5rjDmrxm3srzvt07btgjEmrVp5dLmNnhSavM5z1yurAgAAAAAWcZyScvl/1ljmFZXL417HaeAPtKk3fkzx+Lfk90e8jgMAqJuZr+jNc3kNp3L6ZFFRcjva3xPRYDKmB/tj6mpduYjZ2RLUicfu0M9/e1n/9d1Lqjq1feAtSdHWoKKtQc1XHBVLldqU0HJVFceV47ryWZYCPkutQb9aQ35FQgGFFxVOF/h9lv6ne/bp0S/vYct4AAAAAACwq1EKxXa3fKJmypMU0o/VWApdjbNauRS60rH1dK3zF26w5T0AAAAAQFK1Oqdc7h81NvYLVSqTXsdpEAh2KtH7qHp6/kh+f9jrOAAASZWqow8uTWgoldN7FwpXipHbUTQS0pH+Hg0kY7q1e3V/dOD3WTp+1159bX+X/nbonNJjM0ueDwd8CgdCWuXplujvbdOfff2g9kVbb/7NAAAAAAAAOwylUOw0XhUaV9q2Pr+K970q6akVjm/09vHXKoUyJRQAAAAArqNaLWos+5qy2ddUrczc+A2bLBjsViLxuHp6vi6fL+R1HADY9VzX1Uh2RsPpnF5P5zUzv/ot0reacNCnwwe6NZCM6c49nfI1OY3z1u6Ivv/Enfro8pR++VFG73xakOvefEHWsizdcyCqh+5I6I49HbIspoMCAAAAAABIlEKx/RW0tEB5vW3RNzrHcqvZyn6lMqkkyRhz2Lbts81Huq5rlUJf3aD1AAAAAGBbq1SmNDZ2WtncL+VU57yO0yAU7lUi8YS6o0fk83G5BwC8lp2e15l0TkOpnEYntt73jdWyLOnOvZ0aSMZ0+EC3WoL+dTqvpTv3durOvZ3Kz5T0ejqnTzLTOpcrqlAsXfN90UhIt8ciOpRo15H+mLrb+AMIAAAAAACA5fgtAba7tJZu277R266v2mq2YbdtO22MSevaW8hvVCn0WpNIT27QegAAAACwLZXLBY2N/ULZ3D/Kda5dUvFKuGWv+hJPKBq9X5a1PkUdAEBzZktVvXU+r6FUTh9fnvI6zprcEm3V1w/F9ODBjS9e9rSF9PhX9155PDFb1ucTs5orO6pUHQX8PrUEfdrb1aqu1uCGZgEAAAAAANgJKIViu9sqpdDl695MufIFSc+tcPz+mzzPqhhjjl7jqbRt26uZbgoAAAAAO16plFNm7BXl8/8s19l6W/22tO5XX99xdXUeZrtcAPBQ1XH14WeTGkpl9c6nBZWrjteRmtbREtCR/pgGk3Ht72n17PtLV2uQ8icAAAAAAMAaUArFdvempKcWPT5sjImuZkrnOlteCv3xTbz3Ra1cCr1WeXOtjl3j+AsbtB4AAAAAbBvz8xllMi8rPz4suVuv2BOJHFRf35Pq6PgqZVAA8IjrurqQn9VQKqvXR/KanC17HalpQb9P9xyIajAZ15du6ZTfx/cWAAAAAACA7Y5SKLa7lQqVz0h6fpNzPL34gW3bq57wadt2wRhzUkvLrdLSCajr6Vpl0xc3aD0AAAAA2PLm5j7TaOZlFQpvSK7rdZwGbe1fVF/iSbW330EZFAA8Mj5T0usjOQ2lcro0Put1nDX54p4ODSZjuve2bkVC/JoAAAAAAABgJ+FqD7a1eqHytJYWHb+nTSyFGmP6l63fzNo/UGMpVMaYo7Ztn2422wrni2rlsumLHkxXBQAAAADPzc5+qtHRU5qYfGdLlkE7Or6sROIJtbd/0esoALArzZWrOvvpuIZTOf3+88mt+K1i1fq6WjSYjOlIf0zx9rDXcQAAAAAAALBBKIViJzgh6e1Fj6PGmOds2z6xSesvnlRaaGZd27bPGmPOqrGweULSupVCVZuiulyhvg4AAAAA7BozM2mNZk5pavJ9r6OsqLPza+rrO65I5KDXUQBg13EcV7+/PKnhVE5nPx3XfNnxOlLT2sIBPXCwR4PJmA7G25g2DQAAAAAAsAtQCsW2Vy9UvqilhcfvGGN+bNv22Y1c2xjzjJZO+Pz2Gk73bS0tt0rSUWNMv23b6TWcd7HvrbQuU0IBAAAA7Aau62pm5mONjv5U09MfeR2nkWUp2nWvEonjam291es0ALDrXCrMajiV03Aqp0Kx5HWcpvl9lr62P6rBZExf3delgN/ndSQAAAAAAABsIkqh2BFs237WGHNUUv+iw68ZY+5dx0LlEsaYw5JeWHToRdu2TzZ7vmuUW6XaJNKnmz3vgnqBNbrs8Om1ZAYAAACA7cB1XU1N/06Z0VOamfnE6ziNLJ+6u48okXhcLeE9XqcBgF1lYras19M5Dadz+jRX9DrOmhxKtOtIMqb7b+9Re5hL/wAAAAAAALsVV4awkxxTbdLmQvExKultY8zD6z0xtF5AfXXRoZO2bT+71vPWy633aek28k8ZY55aS3nTGBPV0gKrVNs2fs2ZAQAAAGCrcl1Xk5PvaTRzSrPFc17HaWBZfnX3fF2JxOMKh+JexwGAXaNUcfTuhYKGUln99tKkXNf1OlLT4u1hDR6KaaA/pkRni9dxAAAAAAAAsAVQCsWOYdt22hhzr1Yuhp6wbfv59VjHGPOClk7zPG3b9poneS7ysKQRLZ3q+SNjzNk1TD19baV1NmqKKgAAAAB4yXUdFSbeViZzSnOzl7yO08DyBRXr+YZ6ex9VKNTjdRwA2BVc19XHo9MaSmX11vlxzZWqXkdqWmvIrwcO9migP6ZDiXZZluV1JAAAAAAAAGwhlEKxo9SLoQdVK0Eunrb5nDHmTyWdsG37dDPnrm+//pyWljWft237RNOBV2DbdqFebn1VUn/9cNNTT40xb2vp/4UkHVvv6akAAAAA4DXHqahQeF2ZzM80Pz/qdZwGPl9Ysfg31Rt/RMFgp9dxAGBXuDwxp+F0VsOpnHLTJa/jNM3ns/TVfV0aTMZ0161RhQI+ryMBAAAAAABgi6IUih3Htu2CpHuNMd9RrcS54LCkV40xadW2Uj99vWJkfcv1+yQ9raWTQaXa1utPN1swvZFFU09fknS0fvimpp7Wt7h/QVeLpVIt900XSwEAAABgK3OcssbHh5TJ/FylUtbrOA18/lb1xh9WPP6wAoF2r+MAwI43PV/RmyN5DaWySo/NeB1nTW6Pt2mgP6YH+nvU2RL0Og4AAAAAAAC2AUqh2LFs237eGHNStWLoU4ue6q8fkzGmICktKa9aYVKqlS/7tbRMuaAg6QeSXqyXTzdM/fzHjDFPSfqRrk4ofc4Y8716jtOS0gtZjDGHVSuyPqvG6aAv2rb97EZmBgAAAIDN5DjzyuX/RWOZn6tc3tAf0ZriD7Srt/eY4rFvyu+PeB0HAHa0ctXR+xcLGk7l9P7FCVUd1+tITetuC2mgP6aBZEy3RFu9jgMAAAAAAIBthlIodjTbttOSnq5P/XxGtbLk4rJnVI3lyZWkJb2wmgmd68227ZOSTta3rz+hWv6oFk1BNcZc7xQvSnqu/n8BAAAAANtetTqnXO7XGhv7hSqVKa/jNAgEu5TofVQ9PX8ovz/sdRwA2LFc11VqbEbD6ZzeGMmrOF/xOlLTwkGf7r2tR4PJmO7Y0yHLsryOBAAAAAAAgG2KUih2hfokzeclPV8viB6VdEy1qZpRST3124JqU0PT9fuvSvr7jZ4Kuhq2bb8o6cX6NNCjku7X1YJov65mL6g2QfRNSae3QnYAAAAAWA+VyoyyuV8qO3Za1WrR6zgNgqEeJXofU0/P1+XzhbyOAwA71tjUvIbTOQ2nsspMznsdp2mWJX35li4NJGO650BU4YDf60gAAAAAAADYASiFYteplyRP1v9tO7Ztn5V01uscAAAAALBZKpUpjY29qmzuV3Kqc17HaRAK9yqReELd0SPy+bjUAgAboViq6M1z4xpO5fSvo1tvSvTN2N8T0ZH+mI709yga4Y8IAAAAAAAAsL74TQUAAAAAANiSyuWCMmOvKJf7J7lOyes4DcIte9WXOK5o9D5ZFtPdAGC9VaqOfvfZpIZSOb17YVyVqut1pKZ1tQZ1pD+mgWRM+3siXscBAAAAAADADkYpFAAAAAAAbCmlUlaZzCvKj/+LXKfidZwGLa371dd3XF2dh2VZltdxAGBHcV1X53NFDaVyemMkp6m5rfd9YLWCfp/uva1bA8mY7tzbKb+P7xkAAAAAAADYeJRCAQAAAADAljA/P6pM5mfKjw9LruN1nAaRyEH19T2pjo6vUgYFgHWWnynpTDqnoVRWnxfmvI7TNMuSzJ4ODSbjuve2brUEmSQNAAAAAACAzUUpFAAAAAAAeGpu7jONZk6pUHhTcrfe1sBt7UZ9fU+qvc1QBgWAdTRXrurt8+MaSmVlX57ait8CVm1vtEWDybiO9MfU0xbyOg4AAAAAAAB2MUqhAAAAAADAE8XieWUyL2ti4qzXUVbU0fEVJfqeUHvbF7yOAgA7huO4+vDzSQ2ncnr7/LjK1a03GXq12lsCevBgTIPJmG6LRfjDAQAAAAAAAGwJlEIBAAAAAMCmmplJaTRzSlOTH3gdZUWdXXerL/GEIpGDXkcBgB3jQr6o4VROZ0ZymiiWvY7TtIDf0t37uzWYjOnLt3Qq4Pd5HQkAAAAAAABYglIoAAAAAADYcK7ranrGVmb0p5qetr2O08iyFO26T4nEE2ptvdXrNACwI0wUyzozktNwKqcL+aLXcdbkC30dGkjGdP/t3YqEuKwOAAAAAACArYurVwAAAAAAYMO4rqupqd9qNHNKxZmU13EaWT51dx9RIvG4WsJ7vE4DANvefKWqdz4taDiV0+8+m5Drep2oeYnOsAaScQ30x9TbEfY6DgAAAAAAALAqlEIBAAAAAMC6c11Xk5PvajRzSrPF817HaWBZfvX0/IF6E48pHIp7HQcAtjXXdfXR5SkNpXJ6+3xe82XH60hNi4QDeuBgjwb6Y0r2tsmyLK8jAQAAAAAAADeFUigAAAAAAFg3ruuoUHhLmczLmpu75HWcBpYvqFjPHyqReFTBYLfXcQBgW/usMKvhVE7D6ZzGZ0pex2ma32fprlu7NJCM6a5bowr6fV5HAgAAAAAAAJpGKRQAAAAAAKyZ41RUKLyuTOZnmp8f9TpOA58vrHj8W4rHjykY7PQ6DgBsW5NzZb2Rzms4ndO57IzXcdakv7dNg8m47j/Yo/Ywl8oBAAAAAACwM3ClCwAAAAAANM1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tj/EcbOZNj9scpZQWWnEBAIBd+5+xBwAAAADMRltDGgAAM5FSOouIi9jfQCgMoW+g9tetjgIAAOARQqEAAADAUBYppafQNgQA8CSllK7iabRLQquU0lH0D0X3aRQFAAAYlFAoAAAAMCRtoQAAM1QaQo963vwuIm5jO9t2w9hWCXoeppQOtjYSAACARwiFAgAAAEPSFgoAMDPl57vaz3i30QTlnuecf8g5/5Rzfl7+/SEinkfEq4g4D0FR9t/xirfXFgoAAOzUj2MPAAAAAJidt9H8wR8AgD2XUlpExFnlJm9yztWf/XLOt9EERy8j4s0EmhOv1vy6+4HW/3Tc9l3Utxi/7jGO/y3/9t2q/KEvlWOegzWklFYNhEY0geq3Q44DAACgRigUAAAAWMd1tG8fepBSOukKBwAAsBfeVY69zDlfr3qHJSQ6mpzzZTQB1a1JKb2JepDxKuf8cZtjqPEcrK2t9fMy2htEFymlw5zzzZbGBAAA8B3bxwMAAADrqLVFRWjCAQDYe6Ul9LTl8Md1AqGwr8p8aHtj3D86vtwW8gAAwM4IhQIAAADruI2IWqvPwZpbKwIAMB2/Vo6939koYBpOWj5/mXO+i3rzatvXAgAADE4oFAAAAFhXVxDgw05GAQDAtrxq+fxNCcHBU9LW9vnbg38f5U1zAADArgiFAgAAAGspQQBtoQAA89W2VfbtTkcBI0spHUbEwWPHcs6X9/+tsIU8AACwE0KhAAAAwCa0hQIAzFBK6dEAXCEUylPzuuXzD4OgtWDoUUppMdB4AAAAWgmFAgAAAGsrbaHnlZtoCwUA2E+1UCg8NSctn3+4ZXx1C/mI+HWAsQAAAFQJhQIAAACbettx/N1ORgEAwJA0GkJEpJSOomU+PNwy3hbyAADAFAiFAgAAABvp0RZ6WP6QCgDA/ng29gBgItqCnG0B0OvKfR2mlLTwAgAAWyUUCgAAAAzhw4bHAQAApui45fNtW8WfddyftlAAAGCrhEIBAACAjeWcb6O9KSdCWygAALBnUkptgdDWreJ7bCF/stGgAAAAOgiFAgAAAEN523FcWygAALBP2lo9a1vEdx1fpJQO1xwPAABAJ6FQAAAAYBDaQgEAgLlIKS0iou33l64t4m0hDwAAjEYoFAAAABjS+47jXW2iAAAAU9C6zXvXFvG2kAcAAMYkFAoAAAAMJud8E/WtEo9slQgAAOyBdbeO73W7lNLxasMBAADoRygUAAAAGFpXG+iHnYwCAABgDeWNbActhy963o0t5AEAgFH8OPYAAAAAgHnJOd+klG4ioq0R9CildFhaRdmilNJRRLxsOXyWc76tfO0iIpZf/3NELOLbH8ZvI+Ku/HsVEde1++oxzsN7j3UQEc/K4y0f40tE3ETEb86b8dx7nZ7Ht3Ni+VotLV+z5cdVzrlvm9amY/s5vp1DD8d2/5z9LZpz9m7LYzL/dujB9/Esvp0Hy+/jj2jOx67tfPdSSukgmnDRwb2P5fe/fA2vIuL3bZ/798Y02TVjah55/ZZz/v7z8yUiLp7i8wMjeV059nufO8g5X6aUajc5SiktdrUuAwAAT4dQKAAAALANb6MJn7T5EO1hqUlIKX2IiNOWwzc55xcDPc5xVNqGcs4/bHD3L6P9e7iKJmTycDwH0bx+J5X7XYbTDiPiuHzddUS8XSU0llI6jYh38X1A6L5FfAsXH0XEaUrpNiI+5JzP+z4O6yvhxHfRvM5tbVn3LV+z5et2WsIQN9FsoVoNQ644tuNoAht9tl4d5Jxdkfm32ljaWqSvc86PXi/KOfCmjK/N/XPyZE5rSJmfJ/EtTPiYRXwLGB5HxNk2n4MprxlTU+b78hyuvX73n5+TlNJdNIG0QQKiHfOv5rR8bR8/Cb2xh9quxau+seQ66tepXyNi769JAADAtNg+HgAAABhcCSnUwklHpUGMCUkpnUXE56gH0tocRcSnch9dj3OcUvozmhBKWyCtzUE0oaarEj5iS0rY589ogo19wl01h+V+PqeUPpUw3zpjWqSUzsr5cxH9AqFtlufsxRTOJfOvn3IOnN47B2pBm8csv4dPU3jd11Xm0L+jeR1XnZ/L5+DzkNfiKa4ZU1XeePI51nv9lmHgqzIXN32ugQdK23fbNaLv1vF9b28LeQAAYHBCoQAAAMC2vO04vk4rFVuQUjpIKa0bRnvoJKXU+sfvcuwiVg+jPXQUEf/e51DXVN07H7Y1Rw8j4iKl1Pt8K0HAD9EEzk5i8/PnvuNozqVRgurmX3/3QofrBFofOozme9irQF2ZC1cxzOt4EE2YeKO5PsU1Y6ruhbL7Nmx2OYomOOtnKhhWLajZa+v4FW5/uG/XIgAAYPqEQgEAAICt6NkW6g+gIytBuE+xeavbfcePBVRKkGnItrdFRPzfgPf35JWQ39DnQ5s/VrjtRQwXonrMIppw3E7bCM2/fkro8FMMHzpcvu57cS0q4/x3rN6O2uW0T8vsYya8ZkxOmZdDhHkfc7rv7bcwMW3Xy1W3jo9y++uOm2kLBQAABiUUCgAAAGyTttAJuxdIawuR3EXEbfl3VafLoFVptvsU9SDTuo9zWNoDGcb/Rf/A0k00IYebWOP1yznXQuMP3a5w2+V5exMRl+Wj72Nd7CogaP719nM0z9O2mlwXsfpWwDvX43x5aHn+9HWyZtvkVNeMSSktvavMlXXm/2HsUcgZpqqjlXjd60XX1+19EzIAADAtP449AAAAAGC+cs7XKaWbaA/zHKeUDnLOqwRXGMZhRLx75PO30YR5/9KEVIImx9G0GfUJnbwtt/2/+Os5cBcR5xFx9vD1L01nRxHxOvo1G76LiI89bkdFCUHUgnd3EfE+mnOjNZxVXr+fI+JlNK/jY/e56pyvBaPuotma9So6GrzK99i19fhVSunFqk1gKzL/+quFZs8j4l8RcZNzvi1jP4jm/HsT/YOkhymli5zzq41HuwXlte8TvryM5jX9rpHu3px8E/XX9DSldPXw6yvjmvKaMRmlpber3XV5Pj/6/N+b/6+j/pwfRBMMfbHCz1Y35bEf83Pl8ZZrb6ctr6cwtNq1YNWt4+9/Xa2ReZFSOuq7/gIAAHQRCgUAAAC27X3U23E+RP2Pr2zHY41wb3POreGuEjD5GBEfSxioa7vhk5RSxF8DJdc555eVx7mL0vBY2vEuoh6CW6SUTnLObaEW+qk1+172Dczd2yb1OqI1zLhq499/HvncdUR8WCVAkXM+Tyn9Ho8HJZcOogk6djUdb8L8W99dRPwj53z58EAZ+035OC/n3kX0C4cep5QOJ9pGeRX1QOh1RLxpCwHen5PlOTmL9pDiRUT81HNcU14zJiGlVHuuI0pwtjb3I/4y/4+ieQ3b5uUimnPmeZ8xljX00XW0Y635I+dsy2tm5d4bIx6z8tbxSznnu5TSdeW+I5rfh4RCAQCAQdg+HgAAANiqEtyptVUd2+p0El51hVLuKwGw1mDZPQ+3w7ysBdIeeZybiHgR3S1xve+Tvyrhv7Z5eLNJg2LO+Tbn/DHn/DyawMNtNO2Oq7j/+l9HxPOc88t1GrVyznc5565z6rQEQ3bF/OvnOiL+9lgg9DHl3HsR/QO+/1x7ZFtSQoW1a+THMhd6tUKW5+RltLe7Ljq2Tl6Oa+prxuhSSsdR3xL6NiJerDL3I5oQZ3luakHsg7JlPbCabWwd3/frbSEPAAAMRigUAAAA2IWuQM5jrXnsznnfkNV9JZC3Spjldp2gUGll6vq6rq15qas9f4M1ZuacL3uEmR5zVz5erRKA69AVZNzVumT+9XNeXvuVW9pK6K7P935YWhgnoYylFhJ6m3Nea36Wr2ubh33O/amvGaO611Lb5jrn/HyTtay0dNaaOo/7BHyB79Tm1Lpbx/f++hImBwAA2JhQKAAAALB12kIn7W7D7V/fr3DbtR+nNBbWWiEXO252nJtf2g6s08bZZY1g3x+xQkNkzzHcRrNNeptfh3qsCvOvn+tNt6ku506fYOiU3qTQtm13RNP6ulLD5EPlOX1sW/Y+baFTXzPGVnvtlm2tGyutwbXz4My1Efrp0YC80TpUvr5rfdzoWgcAALAkFAoAAADsSldz2GDNYqxklVDZX/T8A3dE88f0TYNCtZBNRH2LZeraQkOTCGKVLd+3MZba+b8oAZFtMv+63Q0YoLuM7sbJwym8SaG0xbWNY63W1xZt99N1TZ70mjGmsm7UmlQHOZ+XSuvrY+HepXdDPh7M2OvKsa5rYF9dW8gfCXIDAABDEAoFAAAAdqJHW+iJP4Lu3qZNc8VVj9tsFH4raqGXCKHQTTxr+fys52RpwKzZ6lbi5t8o3kZ3cHEK2/fWgnyDNcmVxtzHAsMHHdfkJ7lm9FRrm/24yZbxFbWQ8Kmfr6CXWkPyplvHr3I/u2gqBwAAZk4oFAAAANilrm15p7Rt71Mw1Ba/nc1wQ2z73SNI0xZSolvrc7uDtsyx1ebB85EedxXm3wpKu2pXC+ao2/eWOdc274ZofX2o7dpbC0U/5TWjVY+W0CEC2n9RCfcuaQuFipTSUbSH2jfeOn7JFvIAAMCuCIUCAAAAO5NzPg9toVPStYVlX11hsa6GwaE4d9ZXew3nHiaqBT222X5p/o2kXIuqr/vI16LaFsZdgdaVVUKmtXE85TWjphbm+jhUsKxF7dyoNSAC9bn728CP1XX9P0wp7Uv7NgAAMFFCoQAAAMCuaQudjj8Gup8vHceHbLXbZqDmKftX5dhxSmnO87J2/m6z/dL8G9d5x/GfdzKKx7VtX3+3hZbQpcfut9b4+ZTXjJrats9DB8u+k3O+ifY5unjKDa7QQ9u6G9F9vVhVny3ktYUCAAAbEQoFAAAAdqpHQ5u20B0pAZJd+Lyjx2FNPbYXP00pfSrbq85NbT3a2lpk/o3urOP4KAG6cv1ra4jrEyRa19Ujn2ttqnvia8ajSrNf65qxozlfCw0/mdcCVpFSqjXpDrZ1/FLPLeS1+wIAABv5cewBAAAAAE/S26gHct7FFrbI5Tu7bPzr2t6aaTiPegjhMCKuUkp30YTTriLiesvbIc+V+TeynPNtOZfbQnwvI+LjDoe0VAvunXSElwaXUlpU5rg143u1psFtNbw+dFUZx+sY55yGqXtVObatht+LqK/3i5TS0RbboQEAgJnTFAoAAADsXI+20FNtoVvXteX0vj4W63sb/cKKi2iCYBcR8WdK6c+U0kVK6fQptQJuyPybhj8qx1pbMrdsrMdtUxuPNeN7LyvHdtUMXDunbR8PD5TfN2rrUFcr8rr6ND/XwqoAAABVmkIBAACAsWgLfTrm2go3Kznnu5TSP6IJbq1iEU0z3XFEREopomnFu4qI8xm3Au4Lz3+7m2gPAz3b5UDueT7S47ZpfR6sGX9RO2f+s6MxVJ+7juZXeIq62pc/lzVqDCcR8WasBwcAAPabplAAAABgFNpCR7fLUIimwj2Rc76MYcLYRxHxIZpWwLOx5nJKaZFSOkopnZRWwg9lPFcppU8ppc8RcTrC0My/afhcOTbW9WesMGqb6vMwtzVjQ7VW1dtdDCDn3PU4U2uihbFNOnSZUjoeewwAAMB+EgoFAAAAxvS+4/i7nYwC+K+c88dotiwdKrh4Ek3Qq6uNa2MppeMSKPuUUvoaEX9G0z54Fk3g7LSM5yiarZQFpJ62KQZm9y4Muc9rxsBqr91U2jmnFjqG0aSU9uHngEmHVgEAgOmyfTwAAAAwmpzzx5TSu2gPUpyELeRh53LOlyml62i2hW7bXntVZymlFznnwQIOpU3wJCJeRxPyhFVMcavtttDeXUT8fZcDKXo1XO7LmrEte9RsOvUAHOzS5NeWiDga6VoEAADsOaFQAAAAYGzvo2nwe8wipXRaWsiAHSoBhJelSetDDBP0OkkpHeScX25yJymlg2gC4/vWJMi0dG2NPkYIp629dJFzvtnpSFY05TVj23LOdyml2k2mEhqdYjsujOXXsQfQ00lE+F0IAABYiVAoAAAAMKoebaHvwh9CYTQliPayNOEdRdPKeRTrh5yOUkofcs4rtwCXMOhZDNdEyNNWa00cq5WttZlzX9riprRmTMhOtm0va2TN5M8f2IWUUm1Nuo32N6xtw9uoX49eh9+FAACAFQmFAgAAAFOgLRQmroTRLsvHcrvknyPiZTRbt68S1DxNKf22SvNhSuk0Vg9p3JaPm4j4XP77SzTBqC+l3e9DRJyueL/Mw/9Wjo3VqFgL7T3rOD4pY68ZI7iN9mDX8x2NoSt42xo6hiemtnX8h5zz+a4GUtbG2s83h6Ux2fwFAAB6EwoFAAAApuA86n8M1RYKE1MCX9flIyL+27z1JiKOe9zFh2jCYZ1SShc97/MuIn6PiKuIuN6HVkVGdVg5Nta587ly7DD2ONS3yzVjJLVzpqvBcyhdj2P7eGjU1pzfdzaKRtfvQRHNOrnPbckAAMCO/c/YAwAAAAAoQZFa6HORUjrZ1XiA9eScr3POryLip+gOch/12Oq4byD0OiJe5px/yjm/yTlfCoTSQ62pcqzw5R+VY693Nood2caaMaLaObNKK+omqqFZ6yJEdPxOsfM3lJTH62pB9nsQAACwEqFQAAAAYCredxxfddtoYCQ557uc89vobvWrbd+6DG7UAqG30YRBX+acryu3g++klGotoRFN2+zOle3R2wJJuwoW7txQa8bIfqscW+wo0Fo7Ry538PiwD15Vjp3tbBSrPe6iNCsDAAD0IhQKAAAATELPttA+28vum2djDwC2pQQ1a9uddgUcw+kq7AAADBdJREFUamHwy5zzc2FQ1tTVujnmedX22IseYda9NsCaMaauc2arY08pLaK+ffwoQWeYkjJPWudiznmU8HTO+bzHzaYcigcAACZGKBQAAACYkqfYFroYewCwTTnnWti7NeBWQuBt8+O2bDkNKyuhoNPKTe5yzmNtHx9RD++929koRrLumjG2HltA18KuQ+g6N37f8uPDPqhtwz52m27X48/xzXEAAMCWCIUCAAAAk9GjLfRghm2hv4w9ANiBWlCqTW0b6TkGxNmdWigoYvzwXO3xj3e0DfnY1lkzpqC2hfzBlrd/rp3XN+VnLHjqam2bY20d3/vxZ/h7EAAAsCVCoQAAAMDUPLW20ClvhQtDWad1sRZ8G3Jrb2290zbo61MClV3XkVGDQT3eIDF2cGkXxmxq3cR5RNTCl1v5GSaldBr1ubLtllKYvJTSYbT/bHGXcx7yZ4uVlcfvCm/bQh4AAOhFKBQAAACYlBKGOa/cZNtNW30MElIqf5wWSOMpGLrZcMh58xRaF/daWSuHctFx/CbnPIWWytobJI5SSl1tp/tuL+dl+Rmm9todlgDnYMr8qIVNb8YOu8FE1AKVtd89dqlrHEcpJb87AAAAnYRCAQAAgCnqarQauy302UD388+B7gc2klI63PKWpG2hvlr4rtaW9fMGY/mvEqwYO2ROt3dD3ElK6UO0n4tLXW3VO9GnLXRb28inlBYppU+1N2BMdM2YhJzzx+hoC91x0FlLKDR+rRz7bWejqOszjrm/KQAAABiAUCgAAAAwOT3aQg930Bb6n8qxjRt6SphmyFAIbOIoIi5KEGzQ87KjFe+PyrF/VY4NFXLqClMxDcebBhBLILSrofE253y5yeMM7H3Uw4XbmK/HEfHvaK5PtdDpFNeMKelao6qh2z6W4d2ov07XWkIhosy3tp/fbyfSEB1lHLcdN3u9i7EAAAD7TSgUAAAAmKqx20Krf5DdJART/jAtjDZBKaWLlNLnto+xx7dFv5R/D6MJKw3SQljmSa3lsTYPagGNg5TS2XqjaqSULkJL6D65WDdEV86VPlt2v1rn/relvEHi75WbLGKAcGHEf5s/r6KZk8vg1PPKl0xxzZiMnPN5dG8DfbVu2Lk815+i/uaS25jYOQ0jqm0dv9HPE1vQNZ7DbTVFAwAA8yEUCgAAAExSn7bQLQ+hqzForVBqaUC7Wudr2YmDjo+5ejifTiLic2kBPCnbrK8kpXQSTWip1szV2mBXjtVaEk9SSlerji2ldFwCvtvc+prtuCrhw16veUrpKKX0Z/TbavfjVJri7itj6nqTxFUJtK8zT49L2+Sn+GtIunZ/k1szpibn/Ca6f5a4KOtYr+tLaQf9EBGfo/ua9Kr8LAXUr/ldAe5d6zOeWsgVAAAgfhx7AAAAAAAVH6JfmGdwOefblNJdtIdTjkrT4D/6hC5KG9i7sGU809QWLjqMprHqLKV0E83WzZ/Kv3cR8WV5/pdQ02FEvIwmXNYVWOoTrP496mvAUUT8mVI6j4iLxwJjJZx2EM12q8c9xsW0nUTErymltxHx+8P1t5yHx9EEZvq+1rc5567g5Whyzh9TSr9EPdR0HBHHKaXLiPgtmm3D/3JtKs/PUXybp7Xw5rPKsamuGVPz94j4d9Sf56NoArU3UV67aM7Ju7J+PYvmeVquYX28mmLIGcZQAudtHl0rx1Tm/nXU28xPovsNAwAAwBMmFAoAAABMVglmXsZ4jX7XHY+9DOFcR9MG9jkivpRjz6IJgbyMiJ+jPRByHiMFXyHiv8GsPg7jkVBzSmmdh70u2yt3eRv95sdJNM2hESV4Vj4vADpPi/gWPNz09b6NiBdDDWxbcs6vUkpn0T0fjsvHcm7els8vr0mrePT5nPiaMSkl3PW3aIKxXc/bd8/Xms9TRMTLfWpUhR2otWpe7GwUqzmLeih0kVI6MtcBAIA2to8HAAAApm7MFpz3PW93FBGn0fwB96J8nEXTatbWxHYXTWBUyw9j23Vw8i4iXvW5YWnvWnWOLJtB+35f2vSm7S6a0GZbk9uqr/d9txHxYmotcW3KduSrzoflc7NqIPQympbLtvvcpd5rxhTlnO9yzs+jeaPJNt1Fcz4LiUFR2nZbW/qnGjbPOV/2uJkt5AEAgFZCoQAAAMCk5ZxvowmnjPHYN9E0eQ7tPCL+JrjBFJTz8EVsP7AU0QQw/7ZKCC/n/DG2M7abaL7vvuFvRlLW4r/FsOfBTexRIHSpzIeX8a0BdGi30TRNvmp7bqa+ZkxVznmbbwS5jOZ5EnKH79XalUf5/WIFXeMbaycFAABgDwiFAgAAAPtgtDbN0sw2VPDlMpoQ0ps5BFyYj5zzTQksPY/tBKEjIs5zzmuF8MrYhpqHNxHxpoxFgGpPlLbFl9E0Rm66fr5Z91ycgpzzdWmeHPLaeBsRb3POz/u8YWHqa8ZUlVDvTzFcGK0zxAtPXK1N82xno1hP5/hSSoKhAADAo4RCAQAAgMkbsy20PP4m7V435Wt/KqENITQmK+d8W0LLP0TTRngemzUS3pX7+KkErDcZ2ybz8LZ87fMSMpvkdrF0yzlf5px/iub1XOXcXJ6Lz+fy+t8LGK76XNx3GU2o8Hm5v1XHMNk1Y6pKwPlVNG2r57FeyPkyIl71DfHCU5RSOoyIg5bDd1OfO2V8XevDLNdJAABgcz98/fp17DEAAAAA7IWU0iIifo0m+HIYEc8iYlEO30XEl2hCoP8q//6huYs5SCkdRMRRNK2Ay5DFY+f/8t+riLjeVgg6pXQSTWPkQXwLfNwfw22UeTj10AcRKaXTiPjQcviuhEDbvvYgmi10f4lv58MimnNg+XGVc576NsEbe+S5WMS3+XEb38+Nrc3Pe2OZzJoxdSW89jq+ncPL12/5mn2JJ3QuAwAAAJsRCgUAAAAAYDSbhEIBAAAAgO/ZPh4AAAAAAAAAAABgBoRCAQAAAAAAAAAAAGZAKBQAAAAAAAAAAABgBoRCAQAAAAAAAAAAAGZAKBQAAAAAAAAAAABgBoRCAQAAAAAAAAAAAGZAKBQAAAAAAAAAAABgBoRCAQAAAAAAAAAAAGZAKBQAAAAAAAAAAABgBoRCAQAAAAAAAAAAAGZAKBQAAAAAAAAAAABgBoRCAQAAAAAAAAAAAGZAKBQAAAAAAAAAAABgBoRCAQAAAAAAAAAAAGZAKBQAAAAAAAAAAABgBoRCAQAAAAAAAAAAAGZAKBQAAAAAAAAAAABgBn74+vXr2GMAAAAAAAAAAAAAYEOaQgEAAAAAAAAAAABmQCgUAAAAAAAAAAAAYAaEQgEAAAAAAAAAAABmQCgUAAAAAAAAAAAAYAaEQgEAAAAAAAAAAABmQCgUAAAAAAAAAAAAYAaEQgEAAAAAAAAAAABmQCgUAAAAAAAAAAAAYAaEQgEAAAAAAAAAAABmQCgUAAAAAAAAAAAAYAaEQgEAAAAAAAAAAABmQCgUAAAAAAAAAAAAYAaEQgEAAAAAAAAAAABmQCgUAAAAAAAAAAAAYAaEQgEAAAAAAAAAAABmQCgUAAAAAAAAAAAAYAaEQgEAAAAAAAAAAABmQCgUAAAAAAAAAAAAYAaEQgEAAAAAAAAAAABmQCgUAAAAAAAAAAAAYAaEQgEAAAAAAAAAAABmQCgUAAAAAAAAAAAAYAaEQgEAAAAAAAAAAABmQCgUAAAAAAAAAAAAYAaEQgEAAAAAAAAAAABmQCgUAAAAAAAAAAAAYAaEQgEAAAAAAAAAAABmQCgUAAAAAAAAAAAAYAaEQgEAAAAAAAAAAABmQCgUAAAAAAAAAAAAYAaEQgEAAAAAAAAAAABmQCgUAAAAAAAAAAAAYAaEQgEAAAAAAAAAAABmQCgUAAAAAAAAAAAAYAaEQgEAAAAAAAAAAABmQCgUAAAAAAAAAAAAYAaEQgEAAAAAAAAAAABmQCgUAAAAAAAAAAAAYAaEQgEAAAAAAAAAAABmQCgUAAAAAAAAAAAAYAaEQgEAAAAAAAAAAABmQCgUAAAAAAAAAAAAYAaEQgEAAAAAAAAAAABmQCgUAAAAAAAAAAAAYAaEQgEAAAAAAAAAAABmQCgUAAAAAAAAAAAAYAaEQgEAAAAAAAAAAABmQCgUAAAAAAAAAAAAYAb+H/OGENjrVLjeAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 3000x2000 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "params = {'legend.fontsize': 14,\n",
    "             'axes.labelsize': 14,\n",
    "             'axes.titlesize':14,\n",
    "             'xtick.labelsize':14,\n",
    "             'ytick.labelsize':14,\n",
    "             'lines.markersize': 10,\n",
    "             'legend.markerscale': 1.,\n",
    "             'legend.labelspacing': 1.,\n",
    "             'figure.subplot.hspace': 0.01,\n",
    "             'figure.subplot.wspace': 0.05}\n",
    "pylab.rcParams.update(params)\n",
    "    \n",
    "x = np.array(range(1, 21))\n",
    "idxs = [0, 1, 2, 4, 8, 19]\n",
    "plt.plot([0.9, 21], [80.402, 80.402], '--', c='gray', lw=2, alpha=0.7, label='Central crop')\n",
    "plt.plot(x[idxs], cf[idxs], '-o', ms=5, lw=2.5, label='Scale, Crops, Flips', alpha=0.7, c=colors_palette[colors_augs['cf']])\n",
    "plt.plot(x[idxs], ram[idxs], '-o', ms=5, lw=2.5, label='RandAugment', alpha=0.7,  c=colors_palette[colors_augs['MTrain']])\n",
    "plt.xlim(0.9, 21)\n",
    "plt.gca().set_xscale('log', basex=2)\n",
    "plt.ylabel('Top-1 accuracy (\\%)')\n",
    "plt.xlabel('Num. samples of TTA')\n",
    "plt.legend(fontsize=12, ncol=1)\n",
    "plt.savefig('example.pdf', bbox_inches='tight')"
   ]
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