notebook_plot_PF.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 470,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "from pylab import *\n",
    "from matplotlib import colors\n",
    "from matplotlib.colors import ListedColormap\n",
    "from argparse import Namespace\n",
    "from pymoo.indicators.hv import HV\n",
    "from pymoo.config import Config\n",
    "Config.warnings['not_compiled'] = False\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 471,
   "metadata": {},
   "outputs": [],
   "source": [
    "conf = !python  parameters.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 472,
   "metadata": {},
   "outputs": [],
   "source": [
    "configs = eval(conf[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 558,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Experiment config\n",
    "configs.name=\"E500\"\n",
    "configs.n_devices=499\n",
    "configs.n_jobs=6"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 559,
   "metadata": {},
   "outputs": [],
   "source": [
    "def getWeithDecomposition(val):\n",
    "    print(val)\n",
    "    if val[0]==\"0\":\n",
    "        return \"(0,1.)\"\n",
    "    elif val[0]==\"1\":\n",
    "        return \"(1.,0)\"\n",
    "    return \"(%s,%s)\"%(float(val[0:2])/100,float(val[2:])/100)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DRL - average all samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 560,
   "metadata": {},
   "outputs": [],
   "source": [
    "## previou eval_trained_PF_ppo.py\n",
    "df = pd.read_pickle('logs/log_eval_PF_'  + str(configs.name) + \"_\" + str(configs.n_jobs) + '_' + str(configs.n_devices)+'.pkl', 'infer')\n",
    "df = pd.DataFrame(df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 561,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model</th>\n",
       "      <th>sample</th>\n",
       "      <th>time</th>\n",
       "      <th>cost</th>\n",
       "      <th>reward</th>\n",
       "      <th>iTime</th>\n",
       "      <th>iCost</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000</td>\n",
       "      <td>0</td>\n",
       "      <td>113.0</td>\n",
       "      <td>676.0</td>\n",
       "      <td>-113.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>720.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1000</td>\n",
       "      <td>1</td>\n",
       "      <td>117.0</td>\n",
       "      <td>815.0</td>\n",
       "      <td>-117.0</td>\n",
       "      <td>372.0</td>\n",
       "      <td>720.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1000</td>\n",
       "      <td>2</td>\n",
       "      <td>125.0</td>\n",
       "      <td>825.0</td>\n",
       "      <td>-125.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>720.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1000</td>\n",
       "      <td>3</td>\n",
       "      <td>109.0</td>\n",
       "      <td>647.0</td>\n",
       "      <td>-109.0</td>\n",
       "      <td>372.0</td>\n",
       "      <td>720.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1000</td>\n",
       "      <td>4</td>\n",
       "      <td>113.0</td>\n",
       "      <td>819.0</td>\n",
       "      <td>-113.0</td>\n",
       "      <td>272.0</td>\n",
       "      <td>720.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>0100</td>\n",
       "      <td>25</td>\n",
       "      <td>1016.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>-36.0</td>\n",
       "      <td>272.0</td>\n",
       "      <td>720.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>0100</td>\n",
       "      <td>26</td>\n",
       "      <td>1036.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>-36.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>720.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>0100</td>\n",
       "      <td>27</td>\n",
       "      <td>1096.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>-36.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>720.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>0100</td>\n",
       "      <td>28</td>\n",
       "      <td>1128.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>-45.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>720.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>0100</td>\n",
       "      <td>29</td>\n",
       "      <td>845.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>-36.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>720.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    model  sample    time   cost  reward  iTime  iCost\n",
       "0    1000       0   113.0  676.0  -113.0  322.0  720.0\n",
       "1    1000       1   117.0  815.0  -117.0  372.0  720.0\n",
       "2    1000       2   125.0  825.0  -125.0  322.0  720.0\n",
       "3    1000       3   109.0  647.0  -109.0  372.0  720.0\n",
       "4    1000       4   113.0  819.0  -113.0  272.0  720.0\n",
       "..    ...     ...     ...    ...     ...    ...    ...\n",
       "145  0100      25  1016.0   36.0   -36.0  272.0  720.0\n",
       "146  0100      26  1036.0   36.0   -36.0  322.0  720.0\n",
       "147  0100      27  1096.0   36.0   -36.0  322.0  720.0\n",
       "148  0100      28  1128.0   45.0   -45.0  322.0  720.0\n",
       "149  0100      29   845.0   36.0   -36.0  322.0  720.0\n",
       "\n",
       "[150 rows x 7 columns]"
      ]
     },
     "execution_count": 561,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "df.columns = [\"model\",\"sample\",\"time\",\"cost\",\"reward\",\"iTime\",\"iCost\"]\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 562,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>cost</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>model</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0100</th>\n",
       "      <td>953.733337</td>\n",
       "      <td>44.200001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1000</th>\n",
       "      <td>114.833336</td>\n",
       "      <td>702.200012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2575</th>\n",
       "      <td>251.066666</td>\n",
       "      <td>38.400002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5050</th>\n",
       "      <td>137.800003</td>\n",
       "      <td>74.366669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7525</th>\n",
       "      <td>112.166664</td>\n",
       "      <td>84.099998</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             time        cost\n",
       "model                        \n",
       "0100   953.733337   44.200001\n",
       "1000   114.833336  702.200012\n",
       "2575   251.066666   38.400002\n",
       "5050   137.800003   74.366669\n",
       "7525   112.166664   84.099998"
      ]
     },
     "execution_count": 562,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfmodel = df.groupby(\"model\").agg({\"time\":mean,\"cost\":mean})\n",
    "dfmodel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 563,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n"
     ]
    }
   ],
   "source": [
    "print(type(dfmodel))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 564,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0100\n",
      "953.73334\n",
      "1000\n",
      "114.833336\n",
      "2575\n",
      "251.06667\n",
      "5050\n",
      "137.8\n",
      "7525\n",
      "112.166664\n"
     ]
    }
   ],
   "source": [
    "for i in range(len(dfmodel)):\n",
    "    print(dfmodel.iloc[i].name)\n",
    "    print(dfmodel.iloc[i].time)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 565,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0100\n",
      "1000\n",
      "2575\n",
      "5050\n",
      "7525\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8,4))\n",
    "\n",
    "ax.scatter(dfmodel.time, dfmodel.cost, s=30, facecolors='none', edgecolors='blue')\n",
    "\n",
    "\n",
    "for i in range(len(dfmodel)):\n",
    "    ax.annotate(getWeithDecomposition(dfmodel.iloc[i].name), (dfmodel.iloc[i].time, dfmodel.iloc[i].cost))\n",
    "\n",
    "plt.title(\"Objective Space. Case code: %s\"%configs.name)\n",
    "plt.xlabel(\"Time\", fontsize=14)\n",
    "plt.ylabel(\"Cost\", fontsize=14)\n",
    "# plt.savefig(\"images/pf_nsgaII-0.pdf\",dpi=300,bbox_inches = 'tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 566,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5, 2)"
      ]
     },
     "execution_count": 566,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "avg_pf = np.vstack((dfmodel.time.values,dfmodel.cost.values)).T\n",
    "avg_pf.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 567,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[953.73334 ,  44.2     ],\n",
       "       [114.833336, 702.2     ],\n",
       "       [251.06667 ,  38.4     ],\n",
       "       [137.8     ,  74.36667 ],\n",
       "       [112.166664,  84.1     ]], dtype=float32)"
      ]
     },
     "execution_count": 567,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "avg_pf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 568,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "HV 328911.93373287626\n"
     ]
    }
   ],
   "source": [
    "ref_point = np.array([500., 900.])\n",
    "ind = HV(ref_point=ref_point)\n",
    "print(\"HV\", ind(avg_pf)) "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DRL one sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 569,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model</th>\n",
       "      <th>case</th>\n",
       "      <th>time</th>\n",
       "      <th>cost</th>\n",
       "      <th>reward</th>\n",
       "      <th>iTime</th>\n",
       "      <th>iCost</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000</td>\n",
       "      <td>0</td>\n",
       "      <td>113.0</td>\n",
       "      <td>676.0</td>\n",
       "      <td>-113.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>720.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1000</td>\n",
       "      <td>1</td>\n",
       "      <td>117.0</td>\n",
       "      <td>815.0</td>\n",
       "      <td>-117.0</td>\n",
       "      <td>372.0</td>\n",
       "      <td>720.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1000</td>\n",
       "      <td>2</td>\n",
       "      <td>125.0</td>\n",
       "      <td>825.0</td>\n",
       "      <td>-125.0</td>\n",
       "      <td>322.0</td>\n",
       "      <td>720.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1000</td>\n",
       "      <td>3</td>\n",
       "      <td>109.0</td>\n",
       "      <td>647.0</td>\n",
       "      <td>-109.0</td>\n",
       "      <td>372.0</td>\n",
       "      <td>720.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1000</td>\n",
       "      <td>4</td>\n",
       "      <td>113.0</td>\n",
       "      <td>819.0</td>\n",
       "      <td>-113.0</td>\n",
       "      <td>272.0</td>\n",
       "      <td>720.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  model  case   time   cost  reward  iTime  iCost\n",
       "0  1000     0  113.0  676.0  -113.0  322.0  720.0\n",
       "1  1000     1  117.0  815.0  -117.0  372.0  720.0\n",
       "2  1000     2  125.0  825.0  -125.0  322.0  720.0\n",
       "3  1000     3  109.0  647.0  -109.0  372.0  720.0\n",
       "4  1000     4  113.0  819.0  -113.0  272.0  720.0"
      ]
     },
     "execution_count": 569,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_pickle('logs/log_eval_PF_'  + str(configs.name) + \"_\" + str(configs.n_jobs) + '_' + str(configs.n_devices)+'.pkl', 'infer')\n",
    "df = pd.DataFrame(df)\n",
    "df.columns = [\"model\",\"case\",\"time\",\"cost\",\"reward\",\"iTime\",\"iCost\"]\n",
    "df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 573,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model</th>\n",
       "      <th>case</th>\n",
       "      <th>time</th>\n",
       "      <th>cost</th>\n",
       "      <th>reward</th>\n",
       "      <th>iTime</th>\n",
       "      <th>iCost</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000</td>\n",
       "      <td>0</td>\n",
       "      <td>113.0</td>\n",
       "      <td>676.0</td>\n",
       "      <td>-113.00</td>\n",
       "      <td>322.0</td>\n",
       "      <td>720.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>7525</td>\n",
       "      <td>0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>-98.25</td>\n",
       "      <td>322.0</td>\n",
       "      <td>720.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>5050</td>\n",
       "      <td>0</td>\n",
       "      <td>148.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>-119.50</td>\n",
       "      <td>322.0</td>\n",
       "      <td>720.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>2575</td>\n",
       "      <td>0</td>\n",
       "      <td>212.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>-80.00</td>\n",
       "      <td>322.0</td>\n",
       "      <td>720.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>0100</td>\n",
       "      <td>0</td>\n",
       "      <td>823.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>-36.00</td>\n",
       "      <td>322.0</td>\n",
       "      <td>720.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    model  case   time   cost  reward  iTime  iCost\n",
       "0    1000     0  113.0  676.0 -113.00  322.0  720.0\n",
       "30   7525     0  110.0   63.0  -98.25  322.0  720.0\n",
       "60   5050     0  148.0   91.0 -119.50  322.0  720.0\n",
       "90   2575     0  212.0   36.0  -80.00  322.0  720.0\n",
       "120  0100     0  823.0   36.0  -36.00  322.0  720.0"
      ]
     },
     "execution_count": 573,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CASE = 0 \n",
    "dfDRL = df[df.case==CASE]\n",
    "dfDRL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 571,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[113., 676.],\n",
       "       [110.,  63.],\n",
       "       [148.,  91.],\n",
       "       [212.,  36.],\n",
       "       [823.,  36.]], dtype=float32)"
      ]
     },
     "execution_count": 571,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "avg_pf = np.vstack((dfDRL.time.values,dfDRL.cost.values)).T\n",
    "avg_pf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 572,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(113.0, 676.0)\n",
      "1000\n",
      "(110.0, 63.0)\n",
      "7525\n",
      "(148.0, 91.0)\n",
      "5050\n",
      "(212.0, 36.0)\n",
      "2575\n",
      "(823.0, 36.0)\n",
      "0100\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8,4))\n",
    "\n",
    "ax.scatter(avg_pf[:,0], avg_pf[:,1], s=30, facecolors='none', edgecolors='blue')\n",
    "\n",
    "\n",
    "for i in range(len(avg_pf)):\n",
    "    print((avg_pf[i,0], avg_pf[i,1]))\n",
    "    ax.annotate(getWeithDecomposition(dfDRL.iloc[i].model), (avg_pf[i,0]+12, avg_pf[i,1]-2.5))\n",
    "\n",
    "plt.title(\"Objective Space. Case code: %s\"%configs.name)\n",
    "plt.xlabel(\"Time\", fontsize=14)\n",
    "plt.ylabel(\"Cost\", fontsize=14)\n",
    "# plt.savefig(\"images/pf_nsgaII-0.pdf\",dpi=300,bbox_inches = 'tight')\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# GA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 528,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_pickle('logs/log_ga_pf_'  + str(configs.name) + \"_\" + str(configs.n_jobs) + '_' + str(configs.n_devices)+'_0.pkl', 'infer')\n",
    "df = pd.DataFrame(df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 529,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>case</th>\n",
       "      <th>time</th>\n",
       "      <th>cost</th>\n",
       "      <th>executionTime</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
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       "      <td>0</td>\n",
       "      <td>208.0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>0 days 00:08:40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
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       "      <td>194.0</td>\n",
       "      <td>0 days 00:08:40</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>99.0</td>\n",
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       "      <td>136.0</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   case   time   cost   executionTime\n",
       "0     0  208.0   45.0 0 days 00:08:40\n",
       "1     0  109.0  194.0 0 days 00:08:40\n",
       "2     0  164.0   63.0 0 days 00:08:40\n",
       "3     0  136.0   99.0 0 days 00:08:40\n",
       "4     0  127.0  136.0 0 days 00:08:40"
      ]
     },
     "execution_count": 529,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns = [\"case\",\"time\",\"cost\",\"executionTime\"]\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 530,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>54.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0 days 00:08:40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    case   time   cost   executionTime\n",
       "0      0  208.0   45.0 0 days 00:08:40\n",
       "1      0  109.0  194.0 0 days 00:08:40\n",
       "2      0  164.0   63.0 0 days 00:08:40\n",
       "3      0  136.0   99.0 0 days 00:08:40\n",
       "4      0  127.0  136.0 0 days 00:08:40\n",
       "5      0  118.0  155.0 0 days 00:08:40\n",
       "6      0  126.0  146.0 0 days 00:08:40\n",
       "7      0  137.0   90.0 0 days 00:08:40\n",
       "8      0  127.0  136.0 0 days 00:08:40\n",
       "9      0  164.0   63.0 0 days 00:08:40\n",
       "10     0  155.0   72.0 0 days 00:08:40\n",
       "11     0  154.0   81.0 0 days 00:08:40\n",
       "12     0  172.0   54.0 0 days 00:08:40\n",
       "13     0  172.0   54.0 0 days 00:08:40"
      ]
     },
     "execution_count": 530,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfga = df[df.case==0]\n",
    "dfga"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 531,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8,4))\n",
    "ax.scatter(dfga.time, dfga.cost, s=30, facecolors='none', edgecolors='blue')\n",
    "ax.scatter(avg_pf[:,0], avg_pf[:,1], s=40, facecolors='none', edgecolors='red',label=\"DRL\")\n",
    "plt.title(\"Objective Space. Case code: %s\"%configs.name)\n",
    "plt.xlabel(\"Time\", fontsize=14)\n",
    "plt.ylabel(\"Cost\", fontsize=14)\n",
    "# plt.savefig(\"images/pf_nsgaII-0.pdf\",dpi=300,bbox_inches = 'tight')\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# GA + DRL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 419,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8,4))\n",
    "ax.scatter(dfga.time, dfga.cost, s=30, facecolors='none', edgecolors='blue',label=\"NSGA\")\n",
    "\n",
    "ax.scatter(avg_pf[:,0], avg_pf[:,1], s=40, facecolors='none', edgecolors='red',label=\"DRL\")\n",
    "\n",
    "plt.title(\"Objective Space. Case code: %s\"%configs.name)\n",
    "plt.xlabel(\"Time\", fontsize=14)\n",
    "plt.ylabel(\"Cost\", fontsize=14)\n",
    "plt.legend()\n",
    "# plt.savefig(\"images/pf_nsgaII-0.pdf\",dpi=300,bbox_inches = 'tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "metadata": {},
   "outputs": [],
   "source": [
    "ga_pf = np.vstack((dfga.time.values,dfga.cost.values)).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "H - GA  0.0\n"
     ]
    }
   ],
   "source": [
    "ref_point = np.array([900., 900.])\n",
    "ind = HV(ref_point=ref_point)\n",
    "print(\"H - GA \", ind(ga_pf)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "H - DRL  48675.0\n"
     ]
    }
   ],
   "source": [
    "print(\"H - DRL \", ind(avg_pf)) "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Violin plot del PF según pesos del modelo DRL"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 420,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>model</th>\n",
       "      <th>time</th>\n",
       "      <th>cost</th>\n",
       "      <th>case</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1000</td>\n",
       "      <td>372.0</td>\n",
       "      <td>1620.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>7525</td>\n",
       "      <td>596.0</td>\n",
       "      <td>2500.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>5050</td>\n",
       "      <td>1561.0</td>\n",
       "      <td>1720.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>2575</td>\n",
       "      <td>3573.0</td>\n",
       "      <td>198.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>0100</td>\n",
       "      <td>3338.0</td>\n",
       "      <td>367.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    model    time    cost  case\n",
       "0    1000   372.0  1620.0     0\n",
       "50   7525   596.0  2500.0     0\n",
       "100  5050  1561.0  1720.0     0\n",
       "150  2575  3573.0   198.0     0\n",
       "200  0100  3338.0   367.0     0"
      ]
     },
     "execution_count": 420,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dtvio = dfDRL.loc[:,[\"model\",\"time\",\"cost\"]]\n",
    "dtvio[\"case\"]=np.zeros(len(dtvio),dtype=int)\n",
    "dtvio"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 421,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = list(range(len(dtvio)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 422,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['(.1,.0)', '(.7,.5)', '(.5,.0)', '(.2,.5)', '(.0,.1)'], dtype='<U7')"
      ]
     },
     "execution_count": 422,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def getWeithDecomposition(val):\n",
    "    if len(val)==3:\n",
    "        if val[0]==\"0\":\n",
    "            return \"(0,1.)\"\n",
    "        else:\n",
    "            return \"(1.,0)\"\n",
    "    else:\n",
    "        return \"(.%s,.%s)\"%(val[0],val[1])\n",
    "    \n",
    "fticks = np.vectorize(getWeithDecomposition)\n",
    "ticks = fticks(dtvio.model)\n",
    "ticks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 423,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(ncols=2, sharey=True)\n",
    "axes[0].barh(x, dtvio.time, align='center', color='red', zorder=10)\n",
    "axes[0].set(title='Time')\n",
    "axes[1].barh(x, dtvio.cost, align='center', color='blue', zorder=10)\n",
    "axes[1].set(title='Cost')\n",
    "\n",
    "axes[0].invert_xaxis()\n",
    "axes[0].set(yticks=x, yticklabels=ticks)\n",
    "axes[0].yaxis.tick_right()\n",
    "axes[1].yaxis.tick_left()\n",
    "\n",
    "\n",
    "\n",
    "for ax in axes.flat:\n",
    "    ax.margins(0.03)\n",
    "    ax.grid(True)\n",
    "\n",
    "fig.tight_layout()\n",
    "fig.subplots_adjust(wspace=0.23)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      1000\n",
       "50     7525\n",
       "100    5050\n",
       "150    2575\n",
       "200    0100\n",
       "Name: model, dtype: object"
      ]
     },
     "execution_count": 292,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dtvio.model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['0100', '1000', '2575', '5050', '7525'], dtype='object', name='model')\n"
     ]
    }
   ],
   "source": [
    "dfmodel #average DRL all cases\n",
    "print(dfmodel.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfrindex = dfmodel.reindex(['010', '19', '28', '37', '46', '55', '64', '73', '82', '91', '100'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "shape mismatch: objects cannot be broadcast to a single shape.  Mismatch is between arg 2 with shape (11,) and arg 3 with shape (5,).",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[295], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m fig, axes \u001b[39m=\u001b[39m plt\u001b[39m.\u001b[39msubplots(ncols\u001b[39m=\u001b[39m\u001b[39m2\u001b[39m, sharey\u001b[39m=\u001b[39m\u001b[39mTrue\u001b[39;00m)\n\u001b[0;32m----> 2\u001b[0m axes[\u001b[39m0\u001b[39;49m]\u001b[39m.\u001b[39;49mbarh(x, dfrindex\u001b[39m.\u001b[39;49mtime, align\u001b[39m=\u001b[39;49m\u001b[39m'\u001b[39;49m\u001b[39mcenter\u001b[39;49m\u001b[39m'\u001b[39;49m, color\u001b[39m=\u001b[39;49m\u001b[39m'\u001b[39;49m\u001b[39mred\u001b[39;49m\u001b[39m'\u001b[39;49m, zorder\u001b[39m=\u001b[39;49m\u001b[39m10\u001b[39;49m)\n\u001b[1;32m      3\u001b[0m axes[\u001b[39m0\u001b[39m]\u001b[39m.\u001b[39mset(title\u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39mTime\u001b[39m\u001b[39m'\u001b[39m)\n\u001b[1;32m      4\u001b[0m axes[\u001b[39m1\u001b[39m]\u001b[39m.\u001b[39mbarh(x, dfrindex\u001b[39m.\u001b[39mcost, align\u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39mcenter\u001b[39m\u001b[39m'\u001b[39m, color\u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39mblue\u001b[39m\u001b[39m'\u001b[39m, zorder\u001b[39m=\u001b[39m\u001b[39m10\u001b[39m)\n",
      "File \u001b[0;32m~/.pyenv/versions/3.9.7/envs/DGLenv/lib/python3.9/site-packages/matplotlib/axes/_axes.py:2629\u001b[0m, in \u001b[0;36mAxes.barh\u001b[0;34m(self, y, width, height, left, align, data, **kwargs)\u001b[0m\n\u001b[1;32m   2519\u001b[0m \u001b[39m\u001b[39m\u001b[39mr\u001b[39m\u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m   2520\u001b[0m \u001b[39mMake a horizontal bar plot.\u001b[39;00m\n\u001b[1;32m   2521\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   2626\u001b[0m \u001b[39m:doc:`/gallery/lines_bars_and_markers/horizontal_barchart_distribution`.\u001b[39;00m\n\u001b[1;32m   2627\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m   2628\u001b[0m kwargs\u001b[39m.\u001b[39msetdefault(\u001b[39m'\u001b[39m\u001b[39morientation\u001b[39m\u001b[39m'\u001b[39m, \u001b[39m'\u001b[39m\u001b[39mhorizontal\u001b[39m\u001b[39m'\u001b[39m)\n\u001b[0;32m-> 2629\u001b[0m patches \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mbar(x\u001b[39m=\u001b[39;49mleft, height\u001b[39m=\u001b[39;49mheight, width\u001b[39m=\u001b[39;49mwidth, bottom\u001b[39m=\u001b[39;49my,\n\u001b[1;32m   2630\u001b[0m                    align\u001b[39m=\u001b[39;49malign, data\u001b[39m=\u001b[39;49mdata, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m   2631\u001b[0m \u001b[39mreturn\u001b[39;00m patches\n",
      "File \u001b[0;32m~/.pyenv/versions/3.9.7/envs/DGLenv/lib/python3.9/site-packages/matplotlib/__init__.py:1433\u001b[0m, in \u001b[0;36m_preprocess_data.<locals>.inner\u001b[0;34m(ax, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1430\u001b[0m \u001b[39m@functools\u001b[39m\u001b[39m.\u001b[39mwraps(func)\n\u001b[1;32m   1431\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39minner\u001b[39m(ax, \u001b[39m*\u001b[39margs, data\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs):\n\u001b[1;32m   1432\u001b[0m     \u001b[39mif\u001b[39;00m data \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m-> 1433\u001b[0m         \u001b[39mreturn\u001b[39;00m func(ax, \u001b[39m*\u001b[39;49m\u001b[39mmap\u001b[39;49m(sanitize_sequence, args), \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n\u001b[1;32m   1435\u001b[0m     bound \u001b[39m=\u001b[39m new_sig\u001b[39m.\u001b[39mbind(ax, \u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n\u001b[1;32m   1436\u001b[0m     auto_label \u001b[39m=\u001b[39m (bound\u001b[39m.\u001b[39marguments\u001b[39m.\u001b[39mget(label_namer)\n\u001b[1;32m   1437\u001b[0m                   \u001b[39mor\u001b[39;00m bound\u001b[39m.\u001b[39mkwargs\u001b[39m.\u001b[39mget(label_namer))\n",
      "File \u001b[0;32m~/.pyenv/versions/3.9.7/envs/DGLenv/lib/python3.9/site-packages/matplotlib/axes/_axes.py:2397\u001b[0m, in \u001b[0;36mAxes.bar\u001b[0;34m(self, x, height, width, bottom, align, **kwargs)\u001b[0m\n\u001b[1;32m   2394\u001b[0m     \u001b[39mif\u001b[39;00m yerr \u001b[39mis\u001b[39;00m \u001b[39mnot\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[1;32m   2395\u001b[0m         yerr \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_convert_dx(yerr, y0, y, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mconvert_yunits)\n\u001b[0;32m-> 2397\u001b[0m x, height, width, y, linewidth, hatch \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39;49mbroadcast_arrays(\n\u001b[1;32m   2398\u001b[0m     \u001b[39m# Make args iterable too.\u001b[39;49;00m\n\u001b[1;32m   2399\u001b[0m     np\u001b[39m.\u001b[39;49matleast_1d(x), height, width, y, linewidth, hatch)\n\u001b[1;32m   2401\u001b[0m \u001b[39m# Now that units have been converted, set the tick locations.\u001b[39;00m\n\u001b[1;32m   2402\u001b[0m \u001b[39mif\u001b[39;00m orientation \u001b[39m==\u001b[39m \u001b[39m'\u001b[39m\u001b[39mvertical\u001b[39m\u001b[39m'\u001b[39m:\n",
      "File \u001b[0;32m<__array_function__ internals>:180\u001b[0m, in \u001b[0;36mbroadcast_arrays\u001b[0;34m(*args, **kwargs)\u001b[0m\n",
      "File \u001b[0;32m~/.pyenv/versions/3.9.7/envs/DGLenv/lib/python3.9/site-packages/numpy/lib/stride_tricks.py:540\u001b[0m, in \u001b[0;36mbroadcast_arrays\u001b[0;34m(subok, *args)\u001b[0m\n\u001b[1;32m    533\u001b[0m \u001b[39m# nditer is not used here to avoid the limit of 32 arrays.\u001b[39;00m\n\u001b[1;32m    534\u001b[0m \u001b[39m# Otherwise, something like the following one-liner would suffice:\u001b[39;00m\n\u001b[1;32m    535\u001b[0m \u001b[39m# return np.nditer(args, flags=['multi_index', 'zerosize_ok'],\u001b[39;00m\n\u001b[1;32m    536\u001b[0m \u001b[39m#                  order='C').itviews\u001b[39;00m\n\u001b[1;32m    538\u001b[0m args \u001b[39m=\u001b[39m [np\u001b[39m.\u001b[39marray(_m, copy\u001b[39m=\u001b[39m\u001b[39mFalse\u001b[39;00m, subok\u001b[39m=\u001b[39msubok) \u001b[39mfor\u001b[39;00m _m \u001b[39min\u001b[39;00m args]\n\u001b[0;32m--> 540\u001b[0m shape \u001b[39m=\u001b[39m _broadcast_shape(\u001b[39m*\u001b[39;49margs)\n\u001b[1;32m    542\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mall\u001b[39m(array\u001b[39m.\u001b[39mshape \u001b[39m==\u001b[39m shape \u001b[39mfor\u001b[39;00m array \u001b[39min\u001b[39;00m args):\n\u001b[1;32m    543\u001b[0m     \u001b[39m# Common case where nothing needs to be broadcasted.\u001b[39;00m\n\u001b[1;32m    544\u001b[0m     \u001b[39mreturn\u001b[39;00m args\n",
      "File \u001b[0;32m~/.pyenv/versions/3.9.7/envs/DGLenv/lib/python3.9/site-packages/numpy/lib/stride_tricks.py:422\u001b[0m, in \u001b[0;36m_broadcast_shape\u001b[0;34m(*args)\u001b[0m\n\u001b[1;32m    417\u001b[0m \u001b[39m\u001b[39m\u001b[39m\"\"\"Returns the shape of the arrays that would result from broadcasting the\u001b[39;00m\n\u001b[1;32m    418\u001b[0m \u001b[39msupplied arrays against each other.\u001b[39;00m\n\u001b[1;32m    419\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m    420\u001b[0m \u001b[39m# use the old-iterator because np.nditer does not handle size 0 arrays\u001b[39;00m\n\u001b[1;32m    421\u001b[0m \u001b[39m# consistently\u001b[39;00m\n\u001b[0;32m--> 422\u001b[0m b \u001b[39m=\u001b[39m np\u001b[39m.\u001b[39;49mbroadcast(\u001b[39m*\u001b[39;49margs[:\u001b[39m32\u001b[39;49m])\n\u001b[1;32m    423\u001b[0m \u001b[39m# unfortunately, it cannot handle 32 or more arguments directly\u001b[39;00m\n\u001b[1;32m    424\u001b[0m \u001b[39mfor\u001b[39;00m pos \u001b[39min\u001b[39;00m \u001b[39mrange\u001b[39m(\u001b[39m32\u001b[39m, \u001b[39mlen\u001b[39m(args), \u001b[39m31\u001b[39m):\n\u001b[1;32m    425\u001b[0m     \u001b[39m# ironically, np.broadcast does not properly handle np.broadcast\u001b[39;00m\n\u001b[1;32m    426\u001b[0m     \u001b[39m# objects (it treats them as scalars)\u001b[39;00m\n\u001b[1;32m    427\u001b[0m     \u001b[39m# use broadcasting to avoid allocating the full array\u001b[39;00m\n",
      "\u001b[0;31mValueError\u001b[0m: shape mismatch: objects cannot be broadcast to a single shape.  Mismatch is between arg 2 with shape (11,) and arg 3 with shape (5,)."
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(ncols=2, sharey=True)\n",
    "axes[0].barh(x, dfrindex.time, align='center', color='red', zorder=10)\n",
    "axes[0].set(title='Time')\n",
    "axes[1].barh(x, dfrindex.cost, align='center', color='blue', zorder=10)\n",
    "axes[1].set(title='Cost')\n",
    "\n",
    "axes[0].invert_xaxis()\n",
    "axes[0].set(yticks=x, yticklabels=ticks)\n",
    "axes[0].yaxis.tick_right()\n",
    "axes[1].yaxis.tick_left()\n",
    "\n",
    "\n",
    "\n",
    "for ax in axes.flat:\n",
    "    ax.margins(0.03)\n",
    "    ax.grid(True)\n",
    "\n",
    "fig.tight_layout()\n",
    "fig.subplots_adjust(wspace=0.23)\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# GA MONO"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 479,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['0100', '2575', '5050', '7525', '1000']"
      ]
     },
     "execution_count": 479,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weigthRange = list(np.arange(0,11,2.5))\n",
    "combinationsWeightTC = list(zip(weigthRange,weigthRange[::-1])) #[(0, 10), (1, 9), (2, 8), (3, 7), (4, 6), (5, 5), (6, 4), (7, 3), (8, 2), (9, 1), (10, 0)]\n",
    "allCombination = combinationsWeightTC\n",
    "allCombination\n",
    "\n",
    "combinations = [\"0100\",\"2575\",\"5050\",\"7525\",\"1000\"]\n",
    "combinations\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 519,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "w0100\n",
      "\t (1, 6)\n",
      "w2575\n",
      "\t (1, 6)\n",
      "w5050\n",
      "\t (1, 6)\n",
      "w7525\n",
      "\t (1, 6)\n",
      "w1000\n",
      "\t (1, 6)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>case</th>\n",
       "      <th>time</th>\n",
       "      <th>cost</th>\n",
       "      <th>constraing</th>\n",
       "      <th>exec</th>\n",
       "      <th>model</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1168.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0 days 00:02:25</td>\n",
       "      <td>0100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>143.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0 days 00:03:29</td>\n",
       "      <td>2575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>105.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0 days 00:03:29</td>\n",
       "      <td>5050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0 days 00:03:12</td>\n",
       "      <td>7525</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>724.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0 days 00:02:39</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  case    time   cost constraing            exec model\n",
       "0    0  1168.0   36.0          0 0 days 00:02:25  0100\n",
       "0    0   143.0   36.0          0 0 days 00:03:29  2575\n",
       "0    0   105.0   54.0          0 0 days 00:03:29  5050\n",
       "0    0   111.0  128.0          0 0 days 00:03:12  7525\n",
       "0    0   108.0  724.0          0 0 days 00:02:39  1000"
      ]
     },
     "execution_count": 519,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dall = pd.DataFrame([],columns= [\"case\",\"time\",\"cost\",\"constraing\",\"exec\",\"model\"])\n",
    "for combi in combinations:\n",
    "    print(\"w%s\"%combi)\n",
    "    df = pd.read_pickle('logs/log_ga_pf_mono_'  + str(configs.name) + \"_\" + str(configs.n_jobs) + '_' + str(configs.n_devices)+'_0_w%s.pkl'%combi, 'infer')\n",
    "    df = pd.DataFrame(df)\n",
    "    df.columns = [\"case\",\"time\",\"cost\",\"constraing\",\"exec\"]\n",
    "    df=df[:1]\n",
    "    df[\"model\"]=[combi]*len(df)\n",
    "    print(\"\\t\",df.shape)\n",
    "    dall = pd.concat([dall, df], axis=0)\n",
    "    \n",
    "dall.shape\n",
    "dall.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 520,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>case</th>\n",
       "      <th>time</th>\n",
       "      <th>cost</th>\n",
       "      <th>constraing</th>\n",
       "      <th>exec</th>\n",
       "      <th>model</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1168.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0 days 00:02:25</td>\n",
       "      <td>0100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>724.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0 days 00:02:39</td>\n",
       "      <td>1000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>143.0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0 days 00:03:29</td>\n",
       "      <td>2575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>105.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0 days 00:03:29</td>\n",
       "      <td>5050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>128.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0 days 00:03:12</td>\n",
       "      <td>7525</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  case    time   cost constraing            exec model\n",
       "0    0  1168.0   36.0          0 0 days 00:02:25  0100\n",
       "0    0   108.0  724.0          0 0 days 00:02:39  1000\n",
       "0    0   143.0   36.0          0 0 days 00:03:29  2575\n",
       "0    0   105.0   54.0          0 0 days 00:03:29  5050\n",
       "0    0   111.0  128.0          0 0 days 00:03:12  7525"
      ]
     },
     "execution_count": 520,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dmono = dall.groupby(\"model\").sample(frac=1)\n",
    "dmono"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 522,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 1.0, 'Mono values')]"
      ]
     },
     "execution_count": 522,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "sns.scatterplot(\n",
    "    data=dmono,\n",
    "    x=\"time\", y=\"cost\",\n",
    "    hue=\"model\", \n",
    "\n",
    ").set(title='Mono values')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 576,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0100\n",
      "1000\n",
      "2575\n",
      "5050\n",
      "7525\n"
     ]
    },
    {
     "data": {
      "image/png": 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9IuKYFIhFRESKkJSUxDfffEN4eDjHjx+nbt26DBkyhGHDhtG+fXuHXd9VRApTIBYREbkiv3pceHg4u3fvxjAMevfuzfz58+nTp4+qyYnUUArEIiJSqxVVPa5NmzZMmTKFG2+8kQYNGti6iSJSxRSIRUSkViqpetx1111n6+aJSDVSIBYRkVrj6upxzs7ORVaPE5HaRYFYRERqNMMw+PnnnwkPD1f1OBEpkgKxiIjUSOfPn2f79u1s376dc+fOqXqciBRLgVhERGqMoqrHDRw4kBkzZhAaGmq3BTMMw3CIdYzT09OtPtcEuibHYBhGlS51qEAsIiIOraTqcf369cPT09PWTbym7OxsIiMjbd2MUouOjrZ1Eyqdrsn+VeWyhwrEIiLikEpTPc5RuLq6EhwcbOtmXFN6ejrR0dG0bNmy0B8aecD7zs6cL3AXL8gwuDc3F/u8L29W0jU5qpp4TVFRUVV6fgViERFxGFdXj/Px8WHIkCEMHz7coavHmUwmvLy8bN2MUvP09CzU3nXAw4BrgW3ZgBfwj2prWfkVdU2OriZdU1X/bCsQi4iIXVP1OPuXDEwHTJhDcD7Tle2jAB8btEuktBSIRUTE7hSsHvfNN9+QlJSk6nF2bCFwETCu2m4AiVf2L6ruRomUgQKxiIjYjfzqcdu3byc6OhpfX19GjBih6nF2bi3mMcRFyQPWoEAs9k2BWEREbKq46nEPPvigqsc5iAnAEooOxU7AxOptjkiZKRCLiEi1U/W4mmUOsBpIwHrYhAnwBWbbolEiZaBALCIi1UbV42omH8x3iCdQeJWJpWhCndg/BWIREalSjlo9TspmPJALnCuwrTFwr22aI1ImCsQiIlLpcnNz2b9/P+Hh4Q5bPU7KxgmYZOtGiJSTArGIiFSamlQ9TkRqDwViERGpkJpaPU5Eag8FYhERKTNVjxORmkSBWERESkXV40SkplIgFhGREql6nIjUdHYViGNjYxkwYECh7YsWLWLUqFFERkayYMECjhw5gq+vL/fddx/jx4+3HJeXl8frr7/Oli1bSElJISwsjGeeeYZmzZpV52WIiDi8zMxMfvzxR1WPE5Fawa4C8bFjx3B3d2fnzp1WkzB8fHy4ePEiEyZMYMiQIcyfP5+DBw8yf/586tSpw+jRowF488032bhxI4sXLyYwMJAlS5YwefJktm3bpvFsIiLXkF89bvv27Xz33XeqHicitYZdBeITJ07QsmVLGjVqVGjfu+++i6urK8899xwuLi60bt2aM2fOsHLlSkaPHk1WVhZr1qxh+vTpDBo0CIBly5bRv39/tm/fzq233lrNVyMi4hhUPU5Eaju7CsTHjx+ndevWRe6LiIigV69euLj82eQ+ffrwzjvvEB8fz7lz57h8+TJ9+/a17K9bty4dOnRg37595Q7EhmGQlpZWrufWZunp6VafxT6pnxxDVfRTWloaP/zwA19//TW//PILnp6e9OvXj2nTptGxY0dL9Tj9/iu98vaTYRhamk6kFIrLZJXxM2RXgfjEiRM0aNCAsWPHcvr0aVq0aMFDDz3EgAEDiImJoW3btlbH599JPn/+PDExMQAEBQUVOiZ/X3lkZ2cTGRlZ7ufXdtHR0bZugpSC+skxVLSf8vLyiIqKIiIigiNHjpCTk0NwcDD33HMPnTp1wt3dHTDfnJDyK08/aVifyLWVlMkq+jNkN4E4JyeHU6dOERwczD//+U+8vb35/PPPeeCBB1i7di0ZGRmFLjb/l3dmZqblL/KijklKSip3u1xdXQkODi7382ur9PR0oqOjadmypUq02jH1k2OoaD/99ttv7Ny5k++++46EhASaNm3KmDFjGDJkCA0bNqyCFtdO5e2nkydPVmGrRGqO4jJZZfwM2U0gdnFxYc+ePTg7O+Ph4QFAp06diIqKYvXq1Xh4eJCVlWX1nMzMTAC8vLwsz8nKyrJ8nX9MRf6jN5lMeHl5lfv5tZ2np6e+fw5A/eQYytJPqh5nO2X9eVJfiJROcZmsMn6G7CYQA9SpU6fQtjZt2vDjjz8SGBhIXFyc1b78xwEBAeTk5Fi2NW/e3OqYdu3aVWGrRUTsQ371uO3bt7Nr1y5L9bh58+bRt29fvS0vIlIMuwnEUVFR/P3vf+ett96id+/elu1HjhwhODiYkJAQPvzwQ3Jzcy3rX+7evZtWrVrh5+eHj48P3t7e7NmzxxKIk5OTOXr0KOPGjbPJNYmIVDVVjxMRqTi7CcStW7fmuuuu47nnnmP+/Pk0aNCAzZs3c/DgQT766CP8/PxYtWoVc+bMYfLkyRw+fJh169Yxf/58wDx2eNy4cSxduhRfX1+aNGnCkiVLCAwMZNiwYTa+OhGRyqXqcSIilcduArGTkxNvv/02L7/8Mo899hjJycl06NCBtWvXWlaXWLVqFQsWLGDkyJH4+/szc+ZMRo4caTnHtGnTyMnJYe7cuWRkZBAWFsbq1atxdXW11WWJiFSazMxMdu3apepxIiKVzG4CMUDDhg1ZtGhRsftDQ0PZtGlTsfudnZ2ZMWMGM2bMqIrmiYhUO8MwOHLkCB9//DE///wzaWlpqh4nIlLJ7CoQi4iI2dXV4xo0aMDtt9/OrbfequpxIiKVTIFYRMROXL58me+//57w8HAOHz6Mp6cnAwcOZNq0aTg5OdGxY0ctjyciUgUUiEVEbCg3N5f9+/cTHh7Ojz/+SFZWFt27d2f27Nn069cPT09P0tLSVDFTRKQKKRCLiNhAdHQ04eHh7Ny5k/j4eJo3b8748eMZOnQo/v7+tm6eiEitokAsIlJNVD1ORMQ+KRCLiFQhVY8TsZYHrAXOFdjWGJgAONmkRSIKxCIilU7V40SK9x4wGShYISAbcyD5h01aJKJALCJSaeLj49m5cyfh4eGqHidShGRgOmDCHILzma5sHwX42KBdIgrEIiIVkJmZyY8//qjqcSKlsBC4CBhXbTeAxCv7iy/PJVJ1FIhFRMrIMAx+/vlntm/fznfffcfly5dVPU6kFNZiHkNclDxgDQrEYhsKxCIipXR19biAgABGjRrF8OHDVT1OpBQmAEsoOhQ7AROrtzkiFgrEIiIlKK563IwZMwgNDcXJSfPiRUprDrAaSMB62IQJ8AVm26JRIigQi4gUUprqcSKVyTAM0tLSbN2Ma0pPT7f6XFbOwAvOzjzo7o6L8WckzjGZWJCZiXNuLqnA+87OnC+wLneQYXBvbm6VLMtW0WuyRzXxmgzDqNK12hWIRUSuOHPmDOHh4ezYsUPV46RaZWdnO1R57ujo6HI/tzsw18+PeNc/F15rmJ1Nt4QEIoFtfn7Mb9kSl7w/B1bkODkRd/o0tyYmlr/R11CRa7JXNe2aqnLddgViEanV8qvHbd++nWPHjql6nNiEq6srwcHBtm7GNaWnpxMdHU3Lli0r9E5Jx6I2NmpEMrDC0xOTYZBTYDiSyTBY0bIlDwYEVPqybJV1TfakJl5TVFRUlZ5fgVhEah1VjxN7YzKZ8PLysnUzSs3T07NK2vsccIkilmUzmbgILPfyqrJVKKrqmmypJl1TVd+cUCAWkVpB1eNE7J+WZRNbUSAWkRqtqOpxw4cPZ/jw4aoeJ2JntCyb2IoCsYjUOKoeJ+KYtCyb2IoCsYjUCIZhcOTIEcLDw1U9TsRB+WC+QzwBcC2wPRtYemW/SFVQIBYRh6bqcSI1y3ggE3gbSLqyzR34BhiLgotUDf27EhGHo+pxIjWXE7APOHjV9mOAG/Cv6m6Q1AoKxCLiEFQ9TqR2OIt5NQkA54QEmtx/P79t2oR7VBTbn32W0BMnaBMczPz58+nUqVPh5589y4033ljkudevX09YWBjr1q1j0SLr9Sruvfde5s6dW+Tz/v3vf7N8+XIuXLhAv379eP755/H19S3y2NjYWBYsWMDu3btxd3fnL3/5C0888QTu7u4AvPDCC7z//vtWz3n66acZN24cy5Yto0mTJtx1110lfIekKigQi4hdK6563E033USjRo1s3TwRqWR38ueEuoZLlnBp7FhM2dk0eeABkm+7jYDFi+n2wQdMmTKFHTt2FFpnNygoiB9//NFq2+LFizlz5gxdu3YF4OTJk4wZM4aHH36Y9PR0oqKiCA0NLbI9hw8fZs6cOcyfP5/27duzYMECZs2axTvvvFPoWMMwmDZtGnXr1mXDhg0kJSUxe/ZsnJyceOqppwD49ddfefLJJxk5cqTleflzHCZNmsTIkSMZOnSoloKsZuV+X3H8+PHs2rWr2P07d+5kxIgR5T29iNRiSUlJfPLJJzz00EPcd999/Pvf/+aGG27gzTffZN26dYwZM0ZhWKSG2nvls8vZs3h//TUpt92GzxdfkOfuTvzMmUS0bs2cOXOoU6cOX331VaHnOzs74+/vb/n4/fffCQ8P58UXX8T1SrnoX3/9lfbt2+Pv70/Dhg2pX78+derUKbI969ev5+abb+aOO+6gffv2vPTSS3z//ff8/vvvhY49deoUBw8eZNGiRbRp04aePXsybdo0/v3vf1uO+fXXX+nQoYNVG/Pf4apbty79+vVj48aNFfwuSlmV+g5xeno6Fy9etDzeu3cvQ4cOpUWLFoWOzcvL48cff+TcuXOV00oRqfFUPU5EAHoBe4D6mzZxuV8/DDc3PA4dIqNHDzCZ6I25aln37t05ePAgo0aNKvF8L7/8MnfddRetW7e2bDt16hQtW7YsVXsOHTrE/fffb3kcFBRE48aNOXToEM2aNbM61t/fn1WrVtGwYUOr7ampqZbPsbGxJb72kCFDePrpp3nooYc0H6IalToQp6SkcMstt5CRkWHZtnDhQhYuXFjsc/LfmhARKUp+9bjt27fz9ddfq3qcSA2QkQHvvw8bN0JiIoSEwEMPwcCBpXv+x0BTwOuHH7g0diwALhcukBUcjAn46Mpxfn5+REVFlXiun376iYMHD/LKK69YtsXHx3Pp0iU++eQTZs2ahZubG3379qV9+/ZFniMuLq7QO1J+fn7ExMQUOrZu3br079/f8jgvL4/169fTp08fwHx32GQy8fbbb/Of//yH+vXrM2HCBKvhE3369CE+Pp4TJ04U2yapfKUOxI0aNWLOnDns2bMHgG3bttG1a1eaNm1a6FhnZ2f8/PwYN25c5bVURGoMVY8TqZlSUmDoUNi7F4YPh3794JtvYNAgmD8fnnnm2udoDEzIyeGH48fJunJX1yk9HcPNjUlX9gO4ubmRlZVV4rk2b97M0KFDCQgIsGw7deoUYA61b731FgcPHmTx4sUEBQXxwAMPFDpHRkZGoXeoSvPaAEuWLOHo0aP83//9n+W1TSYT1113HePGjWPfvn08/fTTeHt7M3ToUADc3d1p1qwZR48eVSCuRmWaVHfnnXdy5513Aua/uiZPnlzsTM6KOn36NKNGjeLpp5+2vB0SGRnJggULOHLkCL6+vtx3332MHz/e8py8vDxef/11tmzZQkpKCmFhYTzzzDOF3tIQkeqn6nEiNd8//wmRkbBnD4SFmbcZBixYAE8/bQ7GAwZc+zwLk5IYkJdHWIMGuALx7u74ZmXxVoFjsrKy8PDwKPYcOTk5fP3117z00ktW23v16sXu3bst70A1a9aM48ePs2XLliIDsbu7e6Hwm5WVdc2VbZYsWcK7777LsmXLaNu2LQB33HEHgwcPpn79+gC0b9+e6OhoPvjgA0sgBqhfvz4JCQklnl8qV7lXmfjmm28qsx1WsrOzmT59OmlpaZZtFy9eZMKECQwZMoT58+dz8OBB5s+fT506dRg9ejQAb775Jhs3bmTx4sUEBgayZMkSJk+ezLZt2zT+UMQGiqse99hjjzF48GBVjxOpQS5fhnffhenT/wzDACYTzJkD69fDW2+VLhC7mkwArM3LozXwdEAA2fHxVqElPj6+xMm1Bw8eJCcnhxtuuKHQvquHYzVu3Ji4uLgizxMQEEB8fLzVtvj4ePz9/Yt97eeff54PPviAJUuWMHz4cMt2k8lkCcP5rrvuOnbv3m21LS8vT+OHq1mFvtvHjx+3vA0A5pmYffv2pV+/fqxbt67c512xYkWh/yg3b96Mq6srzz33HK1bt2b06NHcd999rFy5EjD/tbZmzRqmTZvGoEGDaN++PcuWLSMmJobt27eXuy0iUnbnz5/n3XffZdy4cUybNo2IiAhGjRrF+vXrWbFiBbfddpvCsEgNc/asORQPGVJ4n8lk3h4ZWbpz1a9fH2dnZ8tk/i5dunDgwAEMw7wgm2EY7N+/ny5duhR7jkOHDtGxY0fL+r/5tmzZwvDhwy3nAvPyjq1atSryPF26dOGnn36yPD5//jznz58v9rVff/11PvzwQ1555RVuueUWq32vvvoq9913n9W2Y8eOFRoqdvHixUIT86RqlTsQ79+/n9GjR7N69Wrgz+EMubm5uLm58eKLL/LFF1+U+bz79u1j06ZNLF682Gp7REQEvXr1wsXlz78P+/TpQ3R0NPHx8Rw7dozLly/Tt29fy/66devSoUMH9u3bV86rFJHSunz5Ml988QX/7//9P8aMGcOmTZsIDQ1l2bJlbNy4kYkTJ6qUskgNln/T9coQ3UJOn/7zmGtxcnKiffv2HD9+HIARI0aQnJzMggULOHnyJAsWLCA9PZ2bb74ZMI/zvXDhgtU5oqKirFaWyHf99ddz4cIFXnzxRc6cOcNXX33Ftm3bLEE1KyuLCxcukJubC8A999zDp59+ypYtWzh27BgzZ85k0KBBluGYKSkpXLp0CTBPmnvzzTe5//776dGjBxcuXLB8AAwePJh9+/axevVqfvvtNzZu3MjWrVuZOHGipX2pqan88ccfdOzYsXTfLKkU5R4ysXLlSurXr28Jrp999hkA7733Hm3atGH8+PFs2LCBv/zlL6U+Z3JyMjNnzmTu3LkEBQVZ7YuJibGMwcmX/1bJ+fPnLbM9r35eo0aNipwJWlqGYVgN3ZDSSU9Pt/os9qmi/ZSbm8uhQ4f4+uuv2bVrF1lZWXTp0oUnn3yS66+/3jK+r+DqNFJ2+nlyDOXtJ8MwMF0ZIuDIGjWCm26CZcvgrrugYL2MffsgPByuvKlbKv3792f//v2MHTsWb29v3nnnHZ599lk2b95Mu3btWLlypaUoxxdffMGsWbMsARrMwxpCQkIKnbdJkyasXLmSJUuW8MEHH+Dr68s999zDsGHDADhw4ADjx4/n66+/pmnTpnTr1o3nnnuO1157jaSkJG644Qaef/55y/kWLFjAH3/8wfvvv8/XX39Nbm4ub731Fm+99ZbV6x4/fpzQ0FBeffVVXnvtNV599VWaNGnCyy+/TLdu3SzHHThwgMDAQIKDg0v/zaolistklfEzVO5AfODAASZOnGh5y+DHH3+kRYsWlhmRf/nLX6yWOSmNefPm0a1bN2677bZC+4qa5Zn/NkhmZqblF1BRxyQlJZWpHQVlZ2cTWdr3eKSQ6OhoWzdBSqGs/RQbG0tERAT79+8nKSkJf39/hgwZQo8ePSzj406fPl35Da3l9PPkGMrTTzVlnsuLL5rHCPfpA088Aa1amVeZWL4cevWCsiw+NXr0aEaNGkV6ejqenp6EhobyySefFHnsqFGjCq1HvGrVqmLP3bNnTzZt2gRAWlqa1f/zvXv35o477rAaalHU+fMVfEf7gQceKHJiXkE33XQTN910U7H7v/jiC/72t7+VeI7aqqRMVtGfoXIH4oyMDMv4lvj4eKKiorj77rst+52dna3G51zL1q1biYiIYNu2bUXu9/DwKDTLMzMzEwAvLy/LnairZ51mZmZecyZoSVxdXfVXWjmkp6cTHR1Ny5YtK/T9l6pVln5KTk7m+++/55tvvuHEiRN4e3szcOBAbrzxRtq2bVsj7nDZK/08OYby9tPJkyersFXVq3t3+M9/zKtNTJhg3ubtDf/4ByxaBCUsClFI8+bNGThwINu2beOuu+6qmgYX4bfffiMxMbHESXNV5eLFi/z3v//l008/rfbXdgTFZbLK+BkqdyBu3Lix5Q7Qt99+i8lkol+/fpb9e/fuLTR8oSQfffQRCQkJDBo0yGr7s88+yxdffEFgYGChGaD5jwMCAsjJybFsa968udUx7dq1K9O1FWQymQrVSZfS8/T01PfPARTXT8VVjxszZoyqx9mAfp4cQ1n7qab9Mdm9O2zfDnFxcOkSNG1qPXyiLJ566inuv/9+7rjjjmr7fdOsWbNCwx2qy5o1a3jooYdUlKgYxWWyyvgZKncgHjBgAOvXryctLY3w8HBLdZa4uDjeeustvvzyS6ZOnVrq8y1durTQOMNhw4Yxbdo0br/9dj799FM+/PBDcnNzLeuV7t69m1atWuHn54ePjw/e3t7s2bPHEoiTk5M5evSoCoSIlIGqx4lIZWjUyPxRsXM0qva7pSaTyWoCf3V68sknbfK6UoFA/Pjjj1tmSPr4+LBo0SLc3d05e/YsH3zwATfccIPVrMlrKVhFpiA/Pz8CAgIYPXo0q1atYs6cOUyePJnDhw+zbt065s+fD5jHjowbN46lS5fi6+tLkyZNWLJkCYGBgZaB8iJSvISEBHbs2KHqcSIiUuuUOxB7eHjw1ltvcfHiRby9vXF1dQWgXbt2bNy4ke7du1daI8EcjFetWsWCBQsYOXIk/v7+zJw506r+97Rp08jJyWHu3LlkZGQQFhbG6tWrLW0TEWuZmZkcOHCADz74gAMHDqh6nIiI1EoVfk+gQYMGJCYmcvbsWdzc3AgMDKy0MFxw+RSA0NBQy6zQojg7OzNjxgxmzJhRKa8vUhMVrB737bffkpaWRkhIiKrHiYhIrVWhQHzmzBmeeeYZ9u7da9lmMpno3bs3zzzzTLFVX0Sk+p0/f57t27ezfft2zp07R0BAALfffjstW7akf//+mqwlIiK1VrkDcWxsLPfccw+JiYmEhYXRtm1bcnNziYqKYteuXYwZM4ZPP/20xDrjIlK1Ll++zPfff094eDiHDx/G09OTgQMHMmPGDEJDQ8nIyNA62yIiUuuVOxC//vrrJCcns2rVKqvl1sBcpOPBBx/knXfe4emnn65wI0Wk9HJzczlw4ADh4eH88MMPZGVl0b17d2bPnk2/fv20jq2IiMhVyh2I//Of/3DXXXcVCsMA/fr146677uLbb79VIBapJmfOnCE8PJwdO3YQHx9Ps2bNuPfeexk6dKjeqRERESlBuQNxQkJCiQUv2rZty5YtW8p7ehEphaSkJL755hu2b9/OsWPH8PHxYciQIQwfPpz27dvXuAX/RUREqkK5A7Gfnx9RUVHF7j958qQW8BepAsVVj5s3b56qx4mIiJRDuQNx//792bx5M8OGDaNXr15W+3bt2sXmzZu59dZbK9xAEVH1OBERkapU7kD86KOPsnPnTv7xj3/Qu3dvgoODAYiKimLv3r3UrVuXRx55pNIaKlIbqXqciIhI1St3IA4ICGDjxo3Mnz+fPXv2sHv3bsu+sLAw5s2bR+PGjSulkSK1SWZmJv/9738JDw8nIiJC1eNERESqWIUKc1x33XW8++67XLx4kd9//x2Apk2b4uvrWymNE6ktClaP++6777h8+TIdO3ZU9TgREZFqUOHSzWAu39ygQQNiYmL0H7dIGRRVPW7UqFEMHz6cJk2a2Lp5IiJ2LQ9YC5wrsK0xMAFwskmLxFGVORBv3LiRDRs28NlnnxV663bJkiX8+OOPTJ06lfHjx1daI0VqkmtVj3Ny0q9xEZHSeA+YDLgW2JaNOdz8wyYtEkdVpkC8ePFi1q1bh4uLC2fOnCk0qadOnTqkpaWxaNEiTp8+zbPPPlupjRVxVKoeJyIlMQyDtLQ0WzfjmtLT060+21Iy8KSnJyYgu8Ca6ybD4ElgRHo6PqU4jz1dU2WpiddkGEaVrq1f6kD89ddfs27dOvr27cvChQsJCgoqdMxzzz3HI488wrRp0/jwww8ZMmQI/fv3r9QGizgSVY8TkdLIzs4mMjLS1s0otejoaFs3gRVNmnDJ0xPjqpBkmEwkGgb/TErikXPninl2YfZwTZWtpl1TVa6zX+pA/MEHH9CsWTNWrlyJq6trscc1atSIVatWMXz4cDZs2KBALLWOqseJSFm5urpali+1Z+np6URHR9OyZUubv7P1hacnecX8PjVMJj4PDOSNevWueR57uqbKUhOvqaRicJWh1IH4yJEjjBs3rsQwnM/b25u//vWvfPLJJxVqnIijyMnJYffu3aoeJyLlYjKZ8PLysnUzSs3T09Pm7Z0ILME8se5qTsCkMn5P7eGaKltNuqaqvplU6kB8+fJlAgMDS33iVq1akZqaWq5GiTgCwzCIiooiPDxc1eNERKrZHGA1kAAYBbabAF9g9rVOEBUFKSmYXCplwS1xcKX+V+Dv709cXFypT5yYmEjDhg3L1SgRe6bqcSIitueD+Q7xBAqvMrH0yv5iRUVB27YAeALuH38MISFV01BxCKUOxJ06dWLnzp08/PDDpTo+PDycli1blrddInZF1eNEROzPeMwB+HMg5co2HyAL81CKYhexTEmxeujkACt8SNUqdSAeOXIkDz30EKtWrWLy5MklHrtq1SoiIyNZvHhxhRsoYiuqHiciYt+cMN8d/hTru8SfAm4UsRbxlWESXLWih+fp05gOHIBGjaBNmypssdirUgfiwYMHM2zYMF5++WV+/vln7r33Xrp06WKZZJednc2BAwdYv349O3bsICwsjFtuuaXKGi5SVWJiYggPD1f1OBERO5cMTMc8bji7wHbTle2jKDB0osAwiau1evrpPx+cOKFQXAuVaST5okWLMJlMlrDg7OxM/fr1ycvLIykpiby8PAzDYOjQoSxcuBAXDVQXB5GWlsZ3333H9u3bOXTokKrHiYg4gIXARawn1XHlceKV/YvyN141TKJYpT1OapQyJdY6derw6quv8uOPP7J161Z+/vln4uLicHZ2pmXLloSFhXHrrbfSs2fPqmqvSKVR9TgRkSqUnQrHlsHJlZB+DjwbQ/AD0P5xcK2cIWdrKXrZNa5sX0OBQOxTmrp1ZThOapRy3cLt168f/fr1q+y2iFQLVY8TEali2amwcyBcPIglsqafhZ/nwdmtcNP3lRKKJ1DyWsQTC25o08Y8HCJ/DPG4cZZdp59/nsDBg/HUGOJaS2MapFZQ9TgRkWp0bJl1GLbIM28/tgw6P134eWVU5rWIiwm76a1aYXTrBjWkiIWUnQKx1FiqHiciYiMnV1LiYIaTKyslEJd7LeKrhkXkeXmRhzlcnyuwvfGVc2sWSc2nQCw1iqrHiYjYgfRzFdtfBuOBXAoH2XtLelKB4RPpLi5kZmezwdmZBykcrF0oYvk2qXEUiKVGyK8et337dk6fPq3qcSIituTZ2DxmuKT9lcQJmFSeJ14ZPmGkpZF6/Diz3dxKt3yb1EgKxOKwiqseN2XKFFWPExGxpeAHzBPoipvuFvxANTeoZGuDgrhEKZdvkxrJrgJxQkICixcv5ocffiAzM5OwsDCeeuopWrduDUBkZCQLFizgyJEj+Pr6ct999zF+/HjL8/Py8nj99dfZsmULKSkphIWF8cwzz9CsWTNbXZJUMlWPExFxAO0fN68mUWhinRM06Greb0c+8/Mjr5jJ1YWWb5Maya4C8dSpU8nLy2PlypWWNY/vu+8+tm/fTkZGBhMmTGDIkCHMnz+fgwcPMn/+fOrUqcPo0aMBePPNN9m4cSOLFy8mMDCQJUuWMHnyZLZt26YJVA5O1eNERByIq7d5abUqXofYooJrHt+ekMD7AQFFhuJCy7dJjWQ3gTgpKYkmTZowZcoU2l4prfjwww/z17/+laioKHbt2oWrqyvPPfccLi4utG7dmjNnzrBy5UpGjx5NVlYWa9asYfr06QwaNAiAZcuW0b9/f7Zv386tt95qw6uT8lD1OBERB+bqbV5JohJWkyhRJax5PPH8ef4dEEAipVy+TWocuwnE9erV4+WXX7Y8TkxMZN26dQQGBhIcHMyKFSvo1auXVTnoPn368M477xAfH8+5c+e4fPkyffv2teyvW7cuHTp0YN++fQrEDqK46nGzZs2if//+qh4nIiLWKmHN4zp5eSzIyuJBd3ec+TMU5wG3AXUquclif+wmEBf09NNPs3nzZtzc3Hjrrbfw8vIiJibGcuc4X35VsfPnzxMTEwNAUFBQoWPy95WHYRikpaWV+/m1VXp6utXna/ntt9/45ptv+Oabb0hISKBp06bcfffdDB48GH9/f0B9URXK2k9iG+onx1DefjIMQ8WBKqKS1jwem5vLHszloPPff3S+8nggWnrNHhSXAyrjZ8guA/E//vEP/v73v7NhwwamTp3Kxo0bycjIKDQO2N3dHTCvNpD/C6ioY5KSksrdluzsbCIjI8v9/NouOjq62H2XL1/m4MGDRERE8Pvvv+Pp6Um3bt3o2bMnzZo1w2QyER8fT3x8fPU1uJYqqZ/EfqifHEN5+knzXCqgktY8TgU+wzxMomC81tJr9qOkTFbRnyG7DMTBwcEALFiwgEOHDrF+/Xo8PDzIysqyOi4zMxMALy8vPDw8AMjKyrJ8nX9MRd5md3V1tbRHSi89PZ3o6Ghatmxp9f3Pyclh3759fPPNN+zduxfDMOjRowf33nsvvXr1wtXVtYSzSmUrrp/EvqifHEN5++nkyZNV2KpaoJLWPF7i6spFtPSaPSsuk1XGz5DdBOLExER27drF8OHDLeOEnZycCA4OJi4ujsDAQOLi4qyek/84ICCAnJwcy7bmzZtbHdOuXbtyt8tkMuGl2ubl5unpiaenp6rH2TlPT0/9O3cA6ifHUNZ+0nCJCqqkNY/fd3EpaeCFll6zA8Vlssr4GbKbQBwfH88TTzzBqlWr6N+/P2C+NX706FGGDBlCw4YN+fDDD8nNzbUUXNi9ezetWrXCz88PHx8fvL292bNnjyUQJycnc/ToUcaNG2ez66rNkpOT+eijj/j22285ffo0DRo0UPU4ERGpXJW05vG9OTksd3UtLlZr6bUazm4Ccdu2bRkwYAAvvPACL7zwAvXq1eOdd94hOTmZ++67D3d3d1atWsWcOXOYPHkyhw8fZt26dcyfPx8wjx0ZN24cS5cuxdfXlyZNmrBkyRICAwMZNmyYja+u9sivHvfFF1+wf/9+XFxcVD1ORESqTiWteTwzO5v3XF1JQEuv1UZ2E4gBXnnlFV5++WUef/xxUlJS6NmzJxs2bKBxY/P4n1WrVrFgwQJGjhyJv78/M2fOZOTIkZbnT5s2jZycHObOnUtGRgZhYWGsXr1a41KrWFHV40JCQhg9ejR/+9vfLKuBiIhILREVBSkp4OMDbdpU/etVwprHPsASYAJQMDVkA0vRhLqazq4CsY+PD/PmzWPevHlF7g8NDWXTpk3FPt/Z2ZkZM2YwY8aMKmqhFFRS9bgGDRoQGRmpUsoiIrVNVBQUXCb1xInqCcWVYDyQCxRcl6IxcO9Vx+UAk4HfC2xrBqzCzoKVlJr6TcokLS2N77//nvDw8BKrx2m9YBGRWiolpeTHdswJmFSK4x4E3i1iuyvwr0ptkVQXBWK5JlWPExGRa8ofJnH1OrH5j6tr+EQVO4t5xYmirAbmY76rLI5FgViKdebMGbZv38727duJj4+nWbNm3HvvvQwdOlTjgkVE5E9XD5MoqOBKT8UMn0hISOD+++9n06ZNREVF8eyzz3LixAmCg4OZP38+nTp1KvLUSUlJ9OrVy2pb/fr12bNnT5HH//777zz99NMcPHiQxo0bM3v2bLp3717sZfXs2ZOUq+5w19+/H8PVleZ//zt/rFpFrp+fZZ8BjAZ2FXtGsVcKxGIlKSmJb775hu3bt3Ps2DF8fHwYMmQIw4YNIyQkROtliohIYaUdFlHMcUuWLGHs2LFkZ2fzwAMPcNttt7F48WI++OADpkyZwo4dO4pcf/bkyZPUr1+ff//735Zt+UP3rmYYBlOnTqVt27Z89NFH7Ny5k0ceeYSPPvqoyONjY2NJSUlh586dVgW/Ary8wGTi0rhxNFyyhNjFi62eV3QUF3unQCzk5OSwZ88ewsPD2bVrF4Zh0Lt3b+bNm0ffvn1VUlRERErmU8o1GIo47uzZs3z99dc899xzfPbZZ7i7uzNz5kxMJhNz5szhP//5D1999RWjRo0q9NxTp07RqlUr/P39r/nSu3fv5vfff+fDDz/Ey8uL1q1bs2vXLj799FMGDhxY6Phff/0Vf39/mjVrZrW9F+bQm3zbbfi/+CIJf/xBTpMmlv29r/1dEDukQFxLGYah6nEiIlJuOTnw+efwzTfg5NSGkatO0K9LCk7HI62HSaxfDyEhxY4h3rRpE/369cPNzY1Dhw7Ro0cPy7uRJpOJ7t27c/DgwSID8cmTJ2nZsmWp2nvo0CE6dOhgdae5R48e/PTTT0UG4pMnT9KqVatC2z8GmgKGmxtp119PvU2bSHjiCXN7gaLvN4u9UyCuZRISEti5cyfh4eGqHiciIuXy++8mRo+GX34xZ9zcXFi+vA09ekD4IvAreHBICJQwTveHH35g7NixAFy4cIHg4GCr/X5+fkRFRRX53F9//ZWcnBzuvPNOYmNj6dmzJ7NmzSpynsuFCxcKbffz8yM2NrbYc6enp3Pvvfdy+vRpQkJCmD17Nq1atWIi5gl0l2+4gfobN1oC8STME+q0LJvjUb/UAvnV48LDw4mIiMDZ2Zl+/frxwAMPEBYWpupxIiJSanl58Le/uXP5MuzdC2FhYBjwww/w97/Do3N82FjwCSUMp8jJyeH48eO0bt0agPT09ELD9Nzc3MjKyiry+adOncLX15dZs2ZhGAbLli3jwQcfZMuWLYX+byvu3NnZ2cWeOykpiSeeeAJvb2/+9a9/cd999/H555/ztrc3OcDJ1q25cOwYg3Nzae7szFtXnqtl2RyPAnENVVT1uI4dO/LYY48xePBgFcwQEZFy2bvXh59/duL7781hGMBkggED4J134K9/bcOsj07QueW1K9UlJSWRl5dnGabn7u5eKPxmZWVZTWor6PPPP8dkMln2v/baa/Tr149Dhw4VWj3C3d2dS5culfrcq1evJjs7mzp16gCwdOlSBg4cyLfffsttt93GOuBU/frcnJfHlkuX8Luy2oSWZXNMCsQ1TEnV45oUGPQvIiJSHocOedOokUH//oVXHbrlFvDygp1n2tC58JDfQvLHCufl5QEQEBBAfHy81THx8fHFLvV59Tr4fn5+1K9fv8hhEAEBAZw8ebLQuRs2bFjkud3c3KzuKLu7u9O0aVOrcxuGYXUdAHdiXn6tKFqWzX4VvTaJOJS0tDS+/PJLHnvsMe655x42bdpEaGgoy5YtY+PGjUycOFFhWEREKoWzs0FWlnnc8NUyM82T7VxdS3eu+vXr4+zszMWLFwHo0qULBw4csARNwzDYv38/Xbp0KfTc1NRUwsLC2L17t2VbbGwsFy9eLHJOTJcuXfjll1/IyMiwbPvpp5/o3LlzoWMNw+Cmm27i448/tmxLS0vjzJkzVue+ePEiLi4uVhPR917jmrUsm31SIHZQubm5REREsGDBAkaNGsWSJUtwcXFh1qxZfPTRRzz11FN07dq12PUYRUREyqN//yQuXTKxZUvhfevWQXY2/OUvpTuXk5MT7du35/jx4wCMGDGC5ORkFixYwMmTJ1mwYAHp6encfPPNAGRkZHDhwgUAvL296dGjB4sWLeLw4cP88ssvPP744/Tv35927doBkJiYyOXLlwHo1asXQUFBzJo1i6ioKFauXMnhw4e54447AMjOzubChQvk5uZiMpkYNGgQK1asYM+ePURFRTFz5kwCAwOtVqQ4fvx4oTX6rcuEFKZl2eyThkw4mN9++43w8HB27NjBhQsXVD1ORKQGMAyDtLQ0WzfjmtLT02nXLp2bb87i/vtdiYvL5u67c8jNhfffd+HZZ10ZOzaXwMAsSns5ffr0Ye/evYwcORInJydeffVVFixYwObNm2nTpg2vvfYaYL5D+9lnn/Hss89y4MABAObNm8crr7zC/fffT3Z2NoMGDWLmzJmW7+WoUaO4/fbbefDBBwF4+eWXmT9/PqNGjaJZs2a8/PLL1K9fn0uXLrF3714eeeQRPv/8cxo3bszUqVMxDIMnnniC1NRUevXqxauvvkpmZqal7Xv27KFv375WfbcBaOPpaR42UbCYlWFgAtanp1PVPZ2enm71uSYwDKNKi4OZjPz3JaSQn3/+GaDIt1OqkyNWj0tLSyMyMpKQkJAiqwuJfVA/OQb1k2Mobz/9/PPPxa6iYK/S051YuLA54eG+5OWZ/w9ydja4/fZ4Zsz4HTe30keL2NhY5syZwxtvvIG7u3ultvOXX37h119/5fbbb6/U84L5bvUjjzzCokWLChUGeb5FCz5t2NC8/EY+k4k7Llxg7m+/VXpbSisP+MzPj/gCY1oaZmdze0KCQwwZcHNzKzKTVUZe0x1iO6XqcSIitYerq2uh9XftUXp6OtHR0YSENOf//s+T337L4IcfnDCZYPDgPIKC6gDty3TOkJAQBg4cyKlTp4osvlER//rXv5g6daplWbei5F9Ty5YtC03SK8lHH33EoEGDGDBgQKF97wEPZ2dztsANq6aGwZt16uASElKmayiP4q7pfWdnXnB3x6VAUM8xmWgWFMS4ogaF25Hi1qKuLArEdkTV40REaieTyeRQd/89PT3x8vKifXtoX7b8W6TZs2dz//33c9ddd1XqDZ8VK1bgWsoZfvnXVBpZWVls2bKF1atXF/uc9UVtLO1sw0pS8JqSgTmYq+nlFAjqJmCOuzv3AKUswG0TVf1uuCPcIa/xEhIS2LRpE5MmTWLKlCl8++23DB8+nNWrV7Ny5UruvPNOhWEREamxGjVqxKefflrp736WNgxfS0JCAqNGjSI7O5ujR48yduxYzpw5w4MPPsiRI0eKfV5WVhYvvvgiAwYMICwsjKlTpxITE2PZv2PHDtq1a2f1MW3atGLP97///Y9bb72VLl26MH78eH7//fcij4uIiGDMmDF069bNct6wdu3wa9cO53PnAPB/4QXatmtHm3bt8G3Xjp7t2rF+vTnGL1u2jM2bN5fnW+WwdIfYRvKrx23fvp19+/apepyIiIidWrJkCWPHjiU7O5sHHniA2267jcWLF/PBBx8wZcoUduzYUeSd4tdee42dO3eydOlSfH19WbJkCY888ghbtmzBZDJx8uRJBg8ezPPPP295TnHjqM+dO8fUqVN59NFH6d+/P2+88QYPP/wwn332WaG7p126dOHNN9+kTZs2liETfR57jIz69clpbC4L4vbrr1x48kmSR44EoCEw+krRrkmTJjFy5EiGDh1aa27IKRBXI1WPExERcSxnz57l66+/5rnnnuOzzz7D3d2dmTNnYjKZmDNnDv/5z3/46quvihz//MknnzBnzhx69TIvxvb888/Tv39/zpw5Q8uWLfn1119p27ZtoUl5RdmyZQudOnVi4sSJACxatIgbbriBvXv30ru39WJurq6u1K9fn4YNG+Ll5cW///1vPE+c4LfwcMsxbr/+ysVJk8j198cJmADkjzauW7cu/fr1Y+PGjUydOrVc3zdHo0BcDYqrHjds2DCaNm1q6+aJiIhIMTZt2kS/fv1wc3Pj0KFD9OjRw3JH1mQy0b17dw4ePFgoEOfl5bFkyRI6dOhQ6JwpKSkA/Prrr1x//fWlasehQ4fo2bOn5bGnpycdO3bk4MGDhQJxQdnZ2SxfvpyHH3yQZ319SQBMqam4xsaS1bIlJsAXmH3V84YMGcLTTz/NQw89ZPuaBtmpkH0JclKAqln5S4G4iqSlpfH9998THh7OoUOH8PT0ZODAgcyYMYPQ0FDb/+MSERGpwWJiYNky2LQJkpMhNBQefRRGjbJeHvhafvjhB8aOHQvAhQsXCq0G4ufnV+QKCE5OToXC7nvvvUeDBg1o164dhmFw+vRpfvzxR9555x1yc3MZMWIE06ZNK3Is9YULFwrVG/Dz87Mak1yUL7/8kpSUFCaOHYsX5jvBdX79FcNkwv/tt/H4z39o5unJzhEjGHnrrdCmDWBeHzo+Pp4TJ07QvjJmTpZXdirsHIip/rMUXxS74hSIK1Fubi4HDhwgPDycH374gaysLLp3786sWbPo379/mZZzERERkfKJjob+/SE1FcaOhaZNITwc7rwTpk+HJUtKd56cnByOHz9uWbYtPT29UFh1c3Mr1TrSO3fuZM2aNcyfPx83Nzf++OMPy/mWL1/O2bNneeGFF8jIyGDu3LmFnl/e1968eTN33nknHh4ejAdygR9OnWK3ycTA667j+gEDqP/3v/P0qVN4P/MMQ/fvhzZtcHd3p1mzZhw9etS2gfjYMrh4EOpXbdkMBeJKoOpxIiIi9uPRR8HFBX75Ba7MIeOf/4Tly+Hxx+GOO+CGG659nqSkJPLy8iwTy9zd3QsF0KysLDw8PEo8z86dO3nssccYN24cf/vb3wBo0qQJe/bsoV69ephMJkJCQsjLy2PGjBnMmjWr0OT64l67bt26xb5uYmIiERERPP3004B5abFJwMQ77iBp8GDq168P+/fDpUtEu7ryQb16DL0ynAOgfv36JCQklHhtVe7kSswlRaqWAnE5FVU9bvDgwQwfPtxuq8eJiIjUdOfOweefw8qVf4bhfNOmwZtvwr/+VbpAnP9/eV6eOZAFBAQQHx9vdUx8fHyJN78+//xzZs6cyd13383s2dYjdevXr2/1uHXr1mRmZpKUlISvr6/VvuJeO6SEQh//+9//aNq0Ke3atSt0XfUvXIBTpyAyEoDrsrLY7eVleYyPD3l5ebYf4pl+rlpeRoG4jI4fP86aNWv46aefMAyDTp06MWXKFDp37mxZ77Cqq6k4goyMDM6ePYurq+s1/3KuqHr16hEQEFClryEiIo7ht9/MFZOvLOxgxckJwsLMQypKo379+jg7O3Px4kXAvJzZv/71LwzDwGQyYRgG+/fv58EHHyzy+bt27WLmzJmMHTu2UBj+4YcfmD59Ot99951lSGVkZCT169cvFIbzX/unn36yPE5PT+fo0aM88sgjxbb/yJEjdO/evdD2V599lgOrV7Pujz8s2465u3NdVhaMG2fZdnHAABo2bFjs+auFZ2NIP1vlL6NAXEZnz55l3759GFfKHh4+fJjDhw/buFW1m4eHB+vWrVMoFhERAgPNn48cMU+kK8gwzNuLWPihSE5OTrRv357jx4/Ts2dPRowYwcsvv8yCBQu4++67+fDDD0lPT+fmm28GzDeDUlJS8Pf3Jycnh9mzZxMWFsb999/PhQsXLOetV68e3bp1w93dnblz5zJ16lR+//13XnrpJSZPngyY5yUlJiZSr1493NzcGD16tKVg1+DBg3njjTdo2rSpZYWJy5cvk5mZaRWmT548yaBBgwpd1+BOnVjp5cXqBg0YmprKj15ebK1bl/fO/hk8U00m/oiNpWPHjqX7ZlWV4Afg53lV/jIKxGXUtGlTDMNg9uzZtGjRwtbNsVsZGRmcPn2aVq1aVekd4jNnzrBw4UKSkpIUiEVEhJYtYdAgWLgQbrkF6tX7c9+HH8Lhw/DSS6U/X//+/dm/fz9jx47F29ubd955h2effZbNmzfTrl07Vq5caSnK8cUXXzBr1iyOHz/OkSNHOHfuHOfOnaNfv35W53zvvffo3bs3q1evZuHChYwePZo6depw9913WwLx+fPnufHGGy3HNm3alBUrVrBw4ULeeOMNunXrxhtvvGEZ1rFmzRo++eQTvvnmG8vrJCYmFjnGOLRrV149d47XGjbkVT8/mmRn83JMDN0yMizHHPD0JLBhw0KralS79o/D2a2Yi0xrlQm7kf8Pr0WLFrRt29bGrbFfaWlpZGdnExwcXOra8CIiIpXhtddgwADo0gWmTIEmTcyrTHz4oXnViWHDSn+u0aNHM2rUKNLT0/H09CQ0NJRPPvmkyGNHjRplWY+4a9euHD9+vMRzt2nThrVr1xa5r2nTpkydOtWqct3AgQMZOHBgkcc/+uijPProo1bbPv7446L/D27ThpsOHOCmlBTzmOECwyRYvx5CQvhi9Wr+Zuu7wwCu3nDT9xiH92PKSbn28eWkxXBFRESkRuncGXbvNi+9Nn8+/OMfsG+feV3id98t2zrEzZs3Z+DAgWzbtq3qGlyE1NRUdu3aVWRhj0rRpg107w5XT8oLCeFiq1b89+efueeee6rmtcvK1Rtc64NX8yp7CbsKxJcuXeKZZ55hwIABdO/enXvuuYeIiAjL/l27djFq1Ci6dOnCiBEj+Pzzz62en5mZyfz58+nbty/dunXjySefJDExsbovQ0RERGysXTt4/324fBnS0uD4cfMqE1etZlYqTz31FBs2bCjVesOVxdvbm/fee6/IIh2Vysen0OM1a9bw0EMPWZabqw3sasjEE088wYULF3jllVfw8/Pj/fffZ9KkSXzyyScYhsGUKVOYMGECS5Ys4bvvvmPmzJn4+vrSt29fAObNm0dERAQrVqzAzc2NZ599lmnTprF+/XobX5mIiIjYgrMzVLQuVqNGjfj0008rp0FlkL96VZVq0wZOnICUFHM4btOGJ598supf187YTSA+c+YM//3vf9m4cSM9evQA4Omnn+aHH35g27ZtJCQk0K5dOx5//HHAvFbf0aNHWbVqFX379iU2NpatW7fy9ttvW2p9v/LKK4wYMYIDBw7QrVs3m12biIiIiN26Uq65NrObIRMNGjRg5cqVdO7c2bLNZDJhMplITk4mIiLCcic4X58+fSzrAeevzdenTx/L/latWhEQEMC+ffuq5yIqQR6wGni+wMdqqqNGi4iIiEjtZDd3iOvWrVto5mR4eDhnzpxh9uzZfPLJJwTmLy54RaNGjUhPT+fixYvExsbSoEEDq9mY+cfExMSUu12GYZCWlmZ5nHFlSZKMjAyr7ZXlfWdnHnR3x8X4c2mRHJOJ3MxMxuXmVvrrVZX09HSrz1WlqvujpquufpKKUT85hvL2U36RBxEp2dWZrOD2iv4M2U0gvtr+/fuZNWsWw4YNY9CgQWRkZBQaWJ7/OCsri/T09CIHnru7u5OZmVnudmRnZxOZX8YQc2EOgNOnT5OdnV3u8xYl1cmJpzp1wmQY5BToWJNh8JSTE+2PHqVOnmPdK44ubTmgcqrK/qhNqrqfpHKonxxDefqpyidOidQAV2eygir6M2SXgXjnzp1Mnz6d7t27s3TpUsAcbK+e3Zn/2NPTEw8PjyJnf2ZmZlpKIpaHq6ur1aLU+QPcW7VqVemLVT/t6kqKiwvGVX/lGCYTyS4ufNapE885SOhLT08nOjqali1bVuj7fy1V2R+1QXX1k1SM+skxlLefTp48WYWtEqk5rs5k+SrjZ8juAvH69etZsGABI0aM4MUXX7Qk/qCgIOLi4qyOjYuLw8vLCx8fHwIDA7l06RJZWVlWfyXExcVVqIKZyWSyWtQ6v+qah4dHpRecWE/xY4XzTCbed3VlaXXMOK1Enp6eVVqYoyr7ozap6n6SyqF+cgxl7ScNlxApnaszWcHtFWU3k+oANm7cyPPPP8/YsWN55ZVXrIJtz5492bt3r9Xxu3fvpnv37jg5OdGjRw/y8vIsk+vA/DZ6bGwsYWFh1XYNFTGB4jvECZhYjW0RERERqS3sJhCfPn2ahQsXMnToUKZMmUJ8fDwXLlzgwoULpKSkcO+993L48GGWLl3Kr7/+ypo1a/jqq68sNb8DAgK45ZZbmDt3Lnv27OHw4cM88cQT9OrVi65du9r24kppDuCLuVp3QaYr22dXe4tEREREaj67GTIRHh5OdnY2O3bsYMeOHVb7Ro4cyeLFi3nzzTdZsmQJ7777Lk2bNmXJkiVWS7E9//zzLFy4kEceeQSAAQMGMHfu3Gq9jorwAZZgvlNccGBENrD0yn4RERERqVx2E4gffPBBHnzwwRKPGTBgAAMGDCh2v5eXFy+88AIvvPBCZTev2owHcoFzBbY1Bu61TXNEREREajy7CcRi5gRMsnUjRERERGoRuxlDLCIiIiJiCwrEIiIiIlKrKRCLiIiISK2mMcQiIiI2lJ2djWEY/Pzzz7ZuyjUZhgGYK4PVlIIiuib7YRgGOTk55ObmWn3k5OTg4+ODs7Nzlb22ArGIiIgNOVJgMZlMVkWzHJFhGGRnZ+Pq6orJZKoR13Q1e70mwzAKBd2Cj/Py/qzXazKZcHJywsXFBXd3d0tfVRUFYhERERvq1q2brZtQq6SlpREZGUlwcLBKoVeynJwc4uLiOH/+PDExMZw/f97ydUxMDImJiZZjXVxcCAgIICgoiMDAQMvn/K/d3Nw4duwYISEh1dJPCsR2Lg9YS+F1iUsq8ywiIiJS2fLy8khISCgUdvM/X7hwwXKX12Qy4e/vT2BgIM2aNaNXr15WgdfPz6/EIRBpaWnVdVmAArHdew+YTOHKdS7AP2zSIhEREamJDMMgOTm5UNjN/zo2Npbs7GzL8Q0aNLAE3I4dOxIUFGS509uoUSNcXV1LeDX7okBsx5KB6YAJcwjOZ7qyfRQq5ywiIiKld/nyZau7uld/Tk9Ptxxbp04dS8jt27ev1fCGgIAAPD09bXgllUuB2I4tBC4CxlXbDSABuAUYemWbhlGIiIhIVlaWZcxuUcMakpOTLce6u7tbhjF06dKFESNGWAJvUFAQ3t7eNryS6qVAbMfWYh5DXBQD+AHYfeWxhlGIiIjUfLm5uVYT164OvgkJCZZjnZ2dCQgIIDAwkDZt2tC/f3+rYQ0NGjRwqFVOqpICsR2bACyh+FAMfw6l0DAKERERx5eXl0diYmKhwJv/OS4uzmriWsOGDQkMDKRx48b06NHDalhDw4YNq3Tt3ppEgdiOzQFWYx4ecfWwiasZQCLmYRaLqrhdIiIiUj75E9dKGsdbcOJa/fr1LcMa2rdvbzWkoVGjRna53rAjUiC2Yz6Y7xBP4M9VJrKLP5w8YA0KxCIiIraUnp5uGcZw/vx5YmNjrYY1FFxSrE6dOpaQ27t3b6vAGxgYWKMmrtkzBWI7Nx7I5c91iLcDPxZzrAmYWB2NEhERqcWysrIsIbfgnd38r5OSkizHurm5WUJup06dGDp0qNWwBh8fH43jtQMKxHbOCZhU4PFjgD+QWcSxbsDsamiTiIhITZabm8uFCxeKrLZ2/vx5EhISMAzzYEYnJyfLxLXrrruOG264wVKBLSgoiAYNGuDkpDWg7J0CsR06dw5Wr4ZDh6BuXbjrLhg2DJycIImiwzBXtqegSXUiIiIlMQyDxMRETp8+XagARWxsLLGxseTm5lqOz5+4FhQURNeuXa2GNfj7+2viWg2gQGxnPvkE7rkHXFzg+uvh2DFYuxaGDzfvu/MaQ4lGA7uqpaUiIiL2KyUlpch1eM+dO1do4lrdunUtwxjatWtn+Tr/QxPXaj4FYjty6hTcfTf89a+wapX57rBhwFdfwZ13wsyZsHdFyefYUz1NFRERsan09PQSC1BcvnzZcqynp6cl5Hbv3h2A0NBQWrRoQVBQEF5eXra6DLETCsR25J13wMsL1q0zfwYwmeDmm81h+KWXoMcyiCih13pXS0tFRESqVnZ2tqUAxdXDGmJiYrh06ZLlWFdXV8vd3I4dO3LjjTdardRQt25dy8S1tLQ0IiMjCQkJURAWCwViOxIRAUOH/hmGC/rrX2HePHj2ENzeo+h1iU3AR1XcRhERkcqQm5tLfHx8sevxxsfHW01c8/f3JygoiJYtW9K3b1+rYQ1+fn6auCYVokBsRzw9IT6+6H3521u4mZdWW13EMZOAxlXUNhERkbIwDIOLFy8WO6whLi6OnJwcy/G+vr6WkBsaGlpo4pqLiyKLVB3967Ijo0fDpElw+DCEhv653TDgtdegbVvo2BHeBnKA3ws8txnwVvU2V0REarnU1NQi7+7mr9aQkZFhObZu3bqW5cj69+9vFXgDAgJwd3e34ZVIbadAbEfuvhuWLzcvsfb88zBiBPzxByxdCtu2webN5qXXnIB1Nm6riIjUfBkZGUUWnsj/nJqaajnWw8PDauJawTG8gYGBeHt72/BKREqmQGxHPD1h50544AGYMsV8ZxigWTPYuBH+9jfbtk9ERGqWnJwcYmNjix3He/HiRcuxrq6ulgIU7dq1Y9CgQVZ3eevVq6eKa+KwFIjtjL+/eb3h336DI0fMS6/16WNel1hERKQs8vLySEhIKLLaWkxMDBcuXCAvLw/4c+JaYGAgzZo1o1evXlaBVxPXpCZTzLJTzZubP0RERIpjGAZJSUlFrsMbExNDbGysVQGKBg0aWIYxdOzY0WpYQ6NGjXB1dbXh1YjYjt0G4nfeeYcff/yR999/37ItMjKSBQsWcOTIEXx9fbnvvvsYP368ZX9eXh6vv/46W7ZsISUlhbCwMJ555hmaNWtmi0sQERGpsMuXLxc5YS3/64IT17y9vS0B9/rrr7eM383f5uHhYcMrEbFfdhmIN2zYwPLly+nZs6dl28WLF5kwYQJDhgxh/vz5HDx4kPnz51OnTh1Gjx4NwJtvvsnGjRtZvHgxgYGBLFmyhMmTJ7Nt2zaVXRQREbuUlZVVbLW1mJgYkpOTLce6u7tbAm7Xrl0ZMWKE1bAGTVwTKR+7CsSxsbE8++yz7Nmzh5YtW1rt27x5M66urjz33HO4uLjQunVrzpw5w8qVKxk9ejRZWVmsWbOG6dOnM2jQIACWLVtG//792b59O7feemv1X1A1ygPWAucKbGsMTMC8KoWIiNhGbm5ukRXX8gNvQkKC5VhnZ2fL0mRt2rRhwIABVis1NGjQQBPXRKqAXQXiX375BVdXVz777DPeeOMN/vjjD8u+iIgIevXqZbUwd58+fXjnnXeIj4/n3LlzXL58mb59+1r2161blw4dOrBv374aH4jfAyYDBUd/ZWPu4H/YpEUiIrVDXl4eiYmJREdHs3//fg4ePGhVga3gxDWTyUTDhg0JCgqiSZMm9OzZ02pIQ8OGDXF2drbxFYnUPnYViIcMGcKQIUOK3BcTE0Pbtm2ttjVq1AjA8pc2QFBQUKFj8veVh2EYpKWlWR7nj9XKyMiw2m5LycCTnp6YgOwCdw5MhsGTwIj0dOqePAkpKeDjgxEcXOVtSk9Pt/pcVeyxPxxJdfWTVIz6ybYMwyA5OZm4uDjLRLX8pcpiY2OJi4uzmrhWt25dyx3dNm3aWJYqCwgIwN/fv8SJa5mZmdVxSbWafp4cQ1n6yTCMCr9zYleBuCQZGRmFxgHnV7XJzMy0fMOKOiYpKancr5udnU1kZKTl8dmzZwE4ffq01S9AW1rRpAmXPD0xrvrHYJhMJBgGrxw4wIv9+lm2H/n4YzKraQmL6OjoKj2/PfaHI6rqfpLKoX6qOhkZGVy8eJHExMQiPwoGVQ8PD3x9ffH19aV169aEhYVZHjdo0KDYimtJSUkV+v9IKpd+nhxDafuponPFHCYQe3h4kJWVZbUt/xeUl5eXZeZsVlaW1SzazMxMPD09y/26rq6uBBe4o5r/l32rVq2sttvSF56e5BX3l5HJxP+umlXculEjjJCQKm1Teno60dHRtGzZskLf/2uxx/5wJNXVT1Ix6qeKy8rKstzhvfpOb2xsrNXENTc3Nxo1amSpuFbwDm9AQADe3t5F3o1SPzkG9ZNjKEs/nTx5ssKv5zCBODAwkLi4OKtt+Y8DAgLIycmxbGte4O5nXFwc7dq1K/frmkwmvLy8LI/zw7aHh4fVdluaCLwEGFdtD46KwiclheBjx6y2e0ZHm8vi+fhAmzZV2jZPT88q/T7ZY384oqruJ6kc6qfi5ebmcuHChSKrrcXExBAfH285Nn/iWkBAgGXiWsGVGio6cU395BjUT46hNP1UGRNNHSYQh4WF8eGHH5Kbm2uZcLB7925atWqFn58fPj4+eHt7s2fPHksgTk5O5ujRo4wbN86WTS+77FQ4tgxOroT0c+DZGIIfgPaPg2vhJXXmAK8BBUfZBEdFEXXVmGuLgt+PEyeqPBSLiFSUYRgkJiYWuQ5v/l3f3NxcwPyfo5+fnyXgduvWzaoAhb+/vyauiYgVhwnEo0ePZtWqVcyZM4fJkydz+PBh1q1bx/z58wHzW1zjxo1j6dKl+Pr60qRJE5YsWUJgYCDDhg2zcevLIDsVdg6EiwcxL6YGpJ+Fn+fB2a1w0/eFQrEPhTvSJyWlVC/3r5QUnNDybCJiW4ZhkJKSUuSyZPlfFxw2V69ePYKCgggICKBdu3aWsJu/TWvPi0hZOEwg9vPzY9WqVSxYsICRI0fi7+/PzJkzGTlypOWYadOmkZOTw9y5c8nIyCAsLIzVq1c7VinKY8usw7BFnnn7sWXQ+elCT3sIWMKfwyZSfHxK9XLLfHyIRMuziUjVS09PL1SAomDgvXz5suVYLy8vS8ANCwuzGtIQGBiot7pFpFLZbSBevHhxoW2hoaFs2rSp2Oc4OzszY8YMZsyYUZVNq1onV1I4DOfLM+8vIhDPBdYACZhD8ck2bWhz4gR1U1IIi4zk7QLDJMatX8/RkBBSfHw42aYNJmA6MArz3WYRkfLIzs4uNJShYOC9dOmS5VhXV1dLyO3YsSM33nijVeCtW7euClCISLWx20Bca6WfK9d+H2BZdirHjy1jysmVBKWf47xnY94JfoC+7QZbHXs0JIQD3btbHhtAIrAQWFShxotITZabm0t8fLzVsIaC43nj4+MxDPP7VE5OTjRq1IigoCBatWrF9ddfb1mbNygoCF9fX5ycNFBLROyDArG98WxsHjNc0v6iZKcydudAjIsHcbpyh7lp+lnm/zwPk9cmcAeuLKNZ1HCKPMx3mBWIRWovwzC4ePFioWENBSeu5a/oA+ahbPkBt0uXLlaBt1GjRpq4JiIOQ4HY3gQ/YJ5AV+SwCSfz/qIcW4bp4kFMVz3PiTxIi4Tw/8drPuN5/cowiauZMC/fJiI1W2pqaqGwW3B4Q8ECFPkV14KCgmjTpo3VkIaAgIBiC1CIiDgaBWJ70/5x82oShSbWOUGDrub9RbnW2OPEjxg9cDmPlfDSj5a5sSJibzIyMopchzc/+BacuObh4WEJuT169LCE3fwQXKdOHRteiYhI9VEgtjeu3tBqNazsBkOABoB7ILR/uNh1iIFSjT1eUcJuA7geaH3lcTNgFfoHImJvsrOzLZXWihrWcPHiRcuxrq6uliprISEhDBkyxGq1Bk1cExExU96xR2l5sBXzB8BPn0Pn7sUfD6Uae7yWwtXsCjpz5SOfK/CvazZWRCpTbm4uCQkJViH3999/5/Tp06SkpJCQkEBenvndICcnJ/z9/QkMDKRFixb07t3baliDn5+fJq6JiJSCArE9iYqClBSIjLTenv+4pFLLpRh7PAHzWsXFDay42mpgPlDMND4RKQfDMLh06VKRy5Llr9hQcOKar68vjRo1ol69evTs2ZPmzZtbTVxzcdGvcRGRitJvUjuRuCcK3z4VKLVcirHHczCH3Py1iq/FAEYDu0pxrIj8KTU1tdhxvDExMWRkZFiO9fHxsYzbveGGG6yGNAQEBODh4UFaWhqRkZGEhISoIIWISBVQILYD778Pb05OKV3wLK4ks6u3uazzsWXmCXbp58zDKIIfsIw99sF8h3gC5uEQANnXeLndwI1oTLFIQZmZmUWuw5sfeFMK/Jx6eHhYQm63bt0KVVzz9i5mXoCIiFQb5Rsb+9//4L77YMYdPvBxKZ5QUklmV29zFbsiKtnlGw/kAvlT8FYCJYw8BuCb/NOjMcVSO+Tk5BAXF1fozm7+14mJiZZjXVxcCAgIICgoiHbt2jFw4EBL2A0KCqJ+/fqauCYiYucUiG3slVegfXtYuKUN/HrizzHEBYdJrF8PISEljyEuJSdgUoHHk4CmlG4IhcYUS02Rl5dnNXHt6sB74cIFy8Q1k8lkmbjWtGlTevbsaXWX18/PTwUoREQcnAKxjX3/PUydCk5OFBt2M68Lwb37NVaZKKfGmAtyrC7FsRpTLI7CMAySk5OLLEARGxtLTEwM2dl/Dhhq0KCBJeR26NDBKvA2atQIV1fXEl5NREQcnQKxjbm4QIH5NWZXDYsw1S1hmEQleBvIAX7nz+ERxdlTxnPnYQ7bBVdJbox5HLMWg5KKSEtLK3Id3vyv09PTLcfWqVPHEnD79OljVYAiMDAQT09PG16JiACQnVriPBiRqqRAbGO33AIbNsAzz4Bl8nibNuRGnmD8qBTc/XxY07FiwySuxQVYd+XrPpQcenuX8dwbnJ15kD8n8YF5Ip8L8I8ynktql6ysLKuAe3XwTU5Othzr7u5uCbehoaEMHz7cquKaT0lj70XE9rJTYedA65WS0s+alxM9u9U8aVyhWKqQArGNPfkkfPCBORgvWQI9esCxYzB3bhs+PQHfXOuWbQkuXDCv0lavHnTsCKWZ1/MxxY8pNgEfleH1U52cmO3mhok/V7MIjoqibkoKb/r4MKpNGyoUU7JT4eQq89dfhcEvdXU3wYHk5uZy4cKFIoc1xMTEkJCQYDnW2dnZUnGtTZs29O/f32riWoMGDTRxTRxP/trzlTA/xOEdW1bEsqGYH188aN5fwoRxsR95eXDoEKSnQ4cOUL++rVtUOgrENhYSAl9+CePHQ1iYeSxxXh74+8PmzTBgQNnPmZgIjz5qfn7++v6dOsHSpTB8eMnPLWlM8STKNqFubVAQl/gzXAdHRRHV9s+1lpeeOMH08v4nkH834eRJoDuQp7sJdiYvL4+LFy8WeXf3/PnzxMXFWU1c8/PzIygoiMaNG9OjRw+rwNuwYUNNXJOaJSoKCvw+LHaN+dri5EqKLxuVZ96vQGz3Nm2C2bPh1CnzY09P+Mc/4OWXC7wLbqcUiO3AgAHw66+wYwecPg1BQXDzzeDuXvZzpafDjTfC77+b7zgPGwZnzpjD8C23mMP30KEln6PgmOJ8zYC3ytiWz/z8yCtw187nqjWUv0pJYXoZz2lhuZtw9U+Y7iZUl/yJa8UNa4iNjSUrK8tyfP369S3DGNq3b19o4pqbm5sNr0akml29pnxxa8zXFunnKrZfbG7DBvMCWXfcAatWQcOG8NlnsGiR+e+/7duvLCBgpxSI7YSzM4wYUfHzbNhgfqti/37o2tW8rUMHcwi+8UaYNQtuuqnk4RMFxxRXxO0JCbwfEMB1J0/ik5JCyFUlqe8vTUnq4uhuQrVIT08v8u5u/tdpaWmWY728vCwht3fv3oUKUGjimgh/DpO46vchFfl9WBN4Nja/y1fSfrFbOTnwz3/C3/5mvkucnzE6d4aePc35JjzcfLPPXikQ1zCbN5vDb34YzufiAo89BqNGmd/KaN266tsy8fx5jiYns7dduyL3/700JamLo7sJlSI7O5sLFy6QlpbGxYsXCwXfpKQky7Fubm6WO7ydOnVi6NChVsMafHx8NI5XpCRXD5MoqCK/D2uC4AfMQ96KvNHhZN4vdmvXLjh7FqZPL3zDbdgw8zymTZsUiKUapaYW//u28ZU/sKvrnbk6eXk8VaCiV4nK2ijdTSiV/IlrVwfd/M8JCQkYhnmUt5OTk2XiWqtWrbj++uut7vI2aNAAJ3t+v0vE3pX291xtHD7R/nHz/I9CE+ucoEFX836xW/n/ZIOCCu8zmcz5w97/WSsQ1zBdupjH7GRnw9W1BL78Ery9q+fucL4RpR1FX9ZlsSx3E4pSe+4mGIZRaOJawdAbGxtLbm6u5fiGDRtaQm7Xrl3x9fUlMzOTXr160bx5c01cE6lKpf09VxuXCXT1Nk+G1jrEDqlzZ/P44C+/hAeu+u83KQn++1/zkAp7pkBcwzz8MLzzDkybBsuX/zkx79tvzRPrJkyo3t+1puBg89t/lV2SOv9uQvzJq3bUvLsJKSkpxQbemJgYMjMzLcfWrVvXMoyhTZs2VmN4AwMDC01cS0tLIzIykoCAAIVhkarWpk3V/D6sKVy9zXM/NP/D4TRrZp5MN3euecWsbt3M21NTYdIk8+pZkyfbtInXpEBcw3TuDP/6l/kvtI8+goED4bffYO9eGDQIFi+2QaOK++UeEgLlLUmdfzfBeA7YBziBZ1OHvJuQnp5e5EoN+Y8vX75sOdbT09MScHv27GlVfCIwMJA6derY8EpE5Jqq4vehiB145x3zHKbu3c2rZzVsCDt3QmamefxwUcMp7IkCcQ00aRLccIP5H+eRI9C8OTz1FNx+u3lync1cfWu6oreqXb0heDKwD0bsK37wtI1lZ2cTFxdXbJnhixcvWo51dXW1hNwOHTowZMgQq3G8devW1cQ1kZqgsn8fithYw4bmyXVbtphvyKWkwNSpMGUKtGhh69ZdmwJxDdW+PSxbZutWXKXg24U16G3B3NxcEhISig288fHxlgIUTk5O+Pv7ExQURIsWLejTp4/VkAY/Pz9NXBOpDWro70Op3Tw84N57zR+ORoFYqpcD/tI3DINLly4VuQ5vfsW1nPySgICvr68l5IaGhloNa2jUqBEuNr1NLyJ2wwF/H4rUVPqfWQRITU0tMuzmV1zLyMiwHOvj42MJuP3797cawxsYGIh7eUoMioiIiM0oEEutkJGRUWiFhoKfU1NTLcd6eHhYQm737t0LrdTg7e04E/ZERETk2hSIpUbIzc3ljz/+KHK1hvPnz1tNXHNxcSEgIICgoCDatWvHoEGDrCau1atXTxPXREREahEF4nI6c+aMrZtg1zIyMjh79iyurq54eHhU2evk98PUqVMtFddMJhP+/v4EBgbSrFkzevXqZRV4NXFNREREClIgLqN69erh4eHBwoULbd0UucLV1ZX77ruPdu3aERgYSKNGjXC9ukyfiIiISDEUiMsoICCAdevWkZSUZOum2LWMjAxOnz5Nq1atqvQOMZj/SAkICKjS1xAREZGay2Tkv88shezfvx/DMAqVu5VrMwyD7OxsXF1dNR7XjqmfHIP6yTGonxyD+skxlKWfsrKyMJlMdK9AtUfdIS6BflDKz2Qy6Q8JB6B+cgzqJ8egfnIM6ifHUJZ+MplMFc5sukMsIiIiIrWaptqLiIiISK2mQCwiIiIitZoCsYiIiIjUagrEIiIiIlKrKRCLiIiISK2mQCwiIiIitZoCsYiIiIjUagrEIiIiIlKrKRCLiIiISK2mQCwiIiIitZoCsYiIiIjUagrEIiIiIlKrKRBLmVy6dIlnnnmGAQMG0L17d+655x4iIiIs+3ft2sWoUaPo0qULI0aM4PPPP7d6fmZmJvPnz6dv375069aNJ598ksTExOq+jFrj9OnTdOvWjY8//tiyLTIyknHjxtG1a1eGDBnCe++9Z/WcvLw8XnvtNfr370/Xrl25//77+f3336u76bXG1q1b+ctf/kLnzp255ZZb+PLLLy37zp49y5QpU+jevTv9+vVj+fLl5ObmWj1/w4YN3HjjjYSGhjJmzBiOHj1a3ZdQ4+Xk5PDqq68yePBgunXrxtixYzl48KBlv36mbO+dd97h3nvvtdpWGf1yrXNI2RTVT9988w2jR4+mW7duDBkyhBdffJGMjAzL/tLkhmtlj1IxRMpgwoQJxq233mrs27fPOHXqlDF//nwjNDTU+PXXX42TJ08anTt3Nl555RXj5MmTxqpVq4wOHToY//vf/yzP/+c//2ncdNNNxr59+4xDhw4Zd9xxhzF27FgbXlHNlZWVZYwaNcpo27at8dFHHxmGYRiJiYlG7969jVmzZhknT540/u///s/o3Lmz8X//93+W561YscLo3bu38e233xqRkZHGxIkTjWHDhhmZmZm2upQaa+vWrUaHDh2M9evXG2fOnDHefPNNo3379sb+/fuNrKwsY9iwYcYDDzxgHD9+3NixY4fRq1cv49VXX7U8/+OPPzZCQ0ONTz/91IiKijJmzJhh9OrVy0hISLDhVdU8r732mnHDDTcYP/zwgxEdHW3MmTPH6NGjhxEbG6ufKTuwfv16o3379sa4ceMs2yqjX0pzDim9ovpp3759RkhIiPHWW28Zp0+fNr777jtjwIABxj//+U/LMdfKDaXJHqWhQCylFh0dbbRt29aIiIiwbMvLyzNuuukmY/ny5cbTTz9t3HnnnVbPeeKJJ4yJEycahmEYMTExRvv27Y3vvvvOsv/UqVNG27Ztjf3791fPRdQiL7/8sjF+/HirQPz2228b/fr1M7Kzs62OGzZsmGEYhpGZmWl069bN2LBhg2V/UlKSERoaamzbtq16L6CGy8vLMwYPHmwsXrzYavvEiRONt99+29i2bZvRqVMn49KlS5Z9H374odG9e3fLf9jDhg0zXnrpJcv+7OxsY+DAgcbbb79dPRdRS9x+++3GokWLLI9TUlKMtm3bGuHh4fqZsqGYmBhjypQpRteuXY0RI0ZYBa3K6JdrnUNKp6R+evLJJ4377rvP6vhPPvnE6Nixo5GZmVmq3HCt7FFaGjIhpdagQQNWrlxJ586dLdtMJhMmk4nk5GQiIiLo27ev1XP69OnDTz/9hGEY/PTTT5Zt+Vq1akVAQAD79u2rnouoJfbt28emTZtYvHix1faIiAh69eqFi4uLZVufPn2Ijo4mPj6eY8eOcfnyZat+rFu3Lh06dFAfVbLTp0/zxx9/cNttt1ltX716NVOmTCEiIoKOHTtSr149y74+ffqQmppKZGQkCQkJREdHW/WVi4sLPXv2VF9VMj8/P7799lvOnj1Lbm4umzZtws3Njfbt2+tnyoZ++eUXXF1d+eyzz+jSpYvVvsrol2udQ0qnpH6aOHEiTz31lNU2JycnsrOzSU1NLVVuuFb2KC0FYim1unXrMnDgQNzc3CzbwsPDOXPmDP379ycmJobAwECr5zRq1Ij09HQuXrxIbGwsDRo0wN3dvdAxMTEx1XINtUFycjIzZ85k7ty5BAUFWe0rro8Azp8/b+mHq5+nPqp8p0+fBiAtLY1JkybRt29f/va3v/HNN98A6it7MmfOHFxdXbnxxhvp3Lkzy5Yt47XXXqN58+bqJxsaMmQIK1asoFmzZoX2VUa/XOscUjol9VOHDh1o37695XF2djbr1q2jU6dO+Pr6lio3XCt7lJYCsZTb/v37mTVrFsOGDWPQoEFkZGRYhWXA8jgrK4v09PRC+wHc3d3JzMysljbXBvPmzaNbt26F7jwCRfZR/i+azMxM0tPTAYo8Rn1UuVJTUwF46qmnuPXWW1mzZg033HADDz/8MLt27VJf2ZGTJ0/i4+PDG2+8waZNmxg1ahTTp08nMjJS/WSnKqNfrnUOqVw5OTnMnDmTqKgonn32WYBS5YZrZY/Scrn2ISKF7dy5k+nTp9O9e3eWLl0KmP+BXv2PL/+xp6cnHh4eRf7jzMzMxNPTs+obXQts3bqViIgItm3bVuT+ovog/5eKl5cXHh4egLnf8r/OP0Z9VLlcXV0BmDRpEiNHjgQgJCSEo0ePsnbt2jL11dXHqK8qz/nz53nyySdZt24dPXv2BKBz586cPHmSFStW6GfKTlVGv1zrHFJ5UlNTeeyxx9i7dy+vv/46oaGhQNF9ANb9dK3sUVq6Qyxltn79eh599FEGDx7M22+/bfmLOSgoiLi4OKtj4+Li8PLywsfHh8DAQC5dulToH25cXBwBAQHV1v6a7KOPPiIhIYFBgwbRrVs3unXrBsCzzz7L5MmTCQwMLLKPAAICAixvHxZ1jPqocuV/P9u2bWu1PTg4mLNnz6qv7MShQ4fIzs62mjsB0KVLF86cOaN+slOV0S/XOodUjri4OMtShqtXr2bgwIGWfaXJDdfKHqWlQCxlsnHjRp5//nnGjh3LK6+8YvU2Rc+ePdm7d6/V8bt376Z79+44OTnRo0cP8vLyLIPkwTyOMjY2lrCwsGq7hpps6dKlfPHFF2zdutXyATBt2jQWLFhAWFgYP/30k9Vatrt376ZVq1b4+fnRvn17vL292bNnj2V/cnIyR48eVR9Vso4dO1KnTh0OHTpktf3EiRM0b96csLAwjh49ahlaAea+qlOnDu3bt8fPz49WrVpZ9VVOTg4RERHqq0qUPzbx+PHjVttPnDhBy5Yt9TNlpyqjX651Dqm4pKQk/vGPf5CYmMiGDRsK/UyUJjdcK3uUWtkWz5Da7NSpU0bHjh2NqVOnGnFxcVYfycnJxokTJ4yOHTsaS5YsMU6ePGmsXr260FqATzzxhDFkyBBj9+7dlvUECy7BIpWv4LJr8fHxRlhYmPHUU08ZUVFRxkcffWR07tzZ+Pjjjy3Hv/LKK0avXr2MnTt3Wq3NmZWVZatLqLHeeOMNo1u3bsa2bdus1iHevXu3kZGRYdx0003GpEmTjMjISMs6xCtWrLA8f9OmTUZoaKjx8ccfW9Yh7t27t9YhrkS5ubnGPffcY4wYMcLYtWuXcfr0aWPZsmVGSEiIcfDgQf1M2YmnnnrK6v+SyuiX0pxDyubqfnrqqaeMjh07Grt27SqUK3JycgzDuHZuKE32KA0FYim1t956y2jbtm2RH0899ZRhGIbx/fffG7feeqvRqVMnY8SIEcbnn39udY7Lly8bc+bMMXr27Gn07NnTeOKJJ4zExERbXE6tUTAQG4ZhHDp0yLjrrruMTp06GYMHDzbef/99q+NzcnKMl156yejTp4/RtWtX4/777zd+//336m52rbFmzRpjyJAhRseOHY3bb7/d2LFjh2VfdHS0MWHCBKNz585Gv379jOXLlxu5ublWz1+1apUxYMAAIzQ01BgzZoxx9OjR6r6EGu/SpUvGvHnzjEGDBhndunUz/v73vxt79uyx7NfPlO1dHbQMo3L65VrnkLIp2E85OTlG586di80V+X1RmtxwrexRGibDKMMibSIiIiIiNYzGEIuIiIhIraZALCIiIiK1mgKxiIiIiNRqCsQiIiIiUqspEIuIiIhIraZALCIiIiK1mgKxiIiIiNRqCsQiIiIiUqu52LoBIiJSshUrVvD666+X6tiRI0fyySefsHbtWq6//voqbpmISM2gSnUiInbu2LFjHD9+3GrbokWLuHjxIi+99JLV9qCgIM6fP8/111+Pv79/dTZTRMRhKRCLiDigIUOG8McffxQKyiIiUnYaQywiIiIitZoCsYhIDbJixQratWvH//73PwDOnj1Lu3btWLVqFe+//z7Dhw+nc+fODBs2jA8//BCADz74gBEjRtClSxf+8pe/sHXr1kLnPXXqFE888QR9+/alU6dODBs2jOXLl5ORkVGdlyciUiU0qU5EpBZYv349eXl5jBs3Dg8PD9asWcOzzz7Ld999x7FjxxgzZgweHh6sXbuWf/7zn7Rs2ZKuXbsCcPjwYe677z68vb0ZO3Ysvr6+HDx4kLfffptdu3bx3nvv4e7ubtsLFBGpAAViEZFaICEhgS+++IJmzZoB5sl3jzzyCHv27OGrr74iICAAgGbNmvHggw/y3Xff0bVrVwzDYPbs2dStW5etW7dSv359AMaMGUNYWBhz587lvffe4/7777fVpYmIVJiGTIiI1AJdunSxhGGA6667DoDu3btbwjBAixYtAIiNjQXg+PHjREVFMXDgQPLy8khMTLR8DB48GHd3d3bs2FGNVyIiUvl0h1hEpBZo2LCh1WMXF5citzs7OwOQvwDRqVOnAPjwww8tY46v9scff1RqW0VEqpsCsYhILZAfgK9mMplKfF5+ML777rsZPnx4mc4tIuIo9FtMRESK1bRpU8AcjK+ufJeXl0d4eLjVUAwREUekMcQiIlKsTp060aRJEz799FNOnz5ttW/Tpk089thjfPTRRzZqnYhI5dAdYhERKZazszMvvPACU6ZM4c477+Tuu++mRYsW/Pzzz3z00Uc0b96chx9+2NbNFBGpEAViEREp0fXXX8/mzZt56623+Pjjj0lJSSEwMJAxY8YwZcoU/P39bd1EEZEKMRn5MyZERERERGohjSEWERERkVpNgVhEREREajUFYhERERGp1RSIRURERKRWUyAWERERkVpNgVhEREREajUFYhERERGp1RSIRURERKRWUyAWERERkVpNgVhEREREajUFYhERERGp1RSIRURERKRW+//WzbWYANmCegAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 800x400 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from mpl_toolkits.axes_grid1.inset_locator import zoomed_inset_axes, mark_inset\n",
    "fig, ax = plt.subplots(figsize=(8,4))\n",
    "\n",
    "axins1 = zoomed_inset_axes(ax, zoom = 2, loc='center right')\n",
    "axins1.scatter(dmono.time, dmono.cost, s=30, facecolors='none', edgecolors='blue') #MONO solutions\n",
    "axins1.scatter(dfga.time, dfga.cost, s=30, facecolors='cyan', marker=\"H\" ,edgecolors='cyan') ## PF\n",
    "axins1.tick_params(labelleft=False, labelbottom=False)\n",
    "axins1.scatter(dfDRL.time, dfDRL.cost, s=35, facecolors='red', marker=\"P\" ,edgecolors='red') ## PF\n",
    "axins1.scatter(dfmodel.time, dfmodel.cost, s=30, facecolors='orange', edgecolors='orange')\n",
    "\n",
    "for i in range(len(avg_pf)):\n",
    "    # print((avg_pf[i,0], avg_pf[i,1]))\n",
    "    axins1.annotate(getWeithDecomposition(dmono.iloc[i].model), (dmono.iloc[i].time+12, dmono.iloc[i].cost-2.5),fontsize=10)\n",
    "\n",
    "\n",
    "#axins1.plot(y,datapts.T,'mo')\n",
    "x1, x2, y1, y2 = 60,250,10,220 \n",
    "axins1.set_xlim(x1, x2)\n",
    "axins1.set_ylim(y1, y2)\n",
    "\n",
    "\n",
    "ax.scatter(dmono.time, dmono.cost, s=30, facecolors='none', edgecolors='blue') #MONO solutions\n",
    "ax.scatter(dfga.time, dfga.cost, s=30, facecolors='cyan', marker=\"H\" ,edgecolors='cyan') ## PF\n",
    "ax.scatter(dfDRL.time, dfDRL.cost, s=35, facecolors='red', marker=\"P\" ,edgecolors='red') ## PF\n",
    "ax.scatter(dfmodel.time, dfmodel.cost, s=30, facecolors='orange', edgecolors='orange')\n",
    "\n",
    "mark_inset(ax, axins1, loc1=2, loc2=4, fc=\"none\", ec=\".25\")\n",
    "# plt.title(\"Mono GA \")\n",
    "ax.set_xlabel(\"Time\", fontsize=14)\n",
    "ax.set_ylabel(\"Cost\", fontsize=14)\n",
    "# plt.savefig(\"images/pf_nsgaII-0.pdf\",dpi=300,bbox_inches = 'tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 475,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>case</th>\n",
       "      <th>time</th>\n",
       "      <th>cost</th>\n",
       "      <th>constraing</th>\n",
       "      <th>exec</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>105.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0 days 00:03:29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>105.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0 days 00:03:29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>105.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0 days 00:03:29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>105.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0 days 00:03:29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>105.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0 days 00:03:29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>0</td>\n",
       "      <td>105.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0 days 00:03:29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>0</td>\n",
       "      <td>105.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0 days 00:03:29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>0</td>\n",
       "      <td>105.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0 days 00:03:29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>0</td>\n",
       "      <td>105.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0 days 00:03:29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>0</td>\n",
       "      <td>105.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0 days 00:03:29</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>88 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    case   time  cost  constraing            exec\n",
       "0      0  105.0  54.0           0 0 days 00:03:29\n",
       "1      0  105.0  54.0           0 0 days 00:03:29\n",
       "2      0  105.0  54.0           0 0 days 00:03:29\n",
       "3      0  105.0  54.0           0 0 days 00:03:29\n",
       "4      0  105.0  54.0           0 0 days 00:03:29\n",
       "..   ...    ...   ...         ...             ...\n",
       "83     0  105.0  54.0           0 0 days 00:03:29\n",
       "84     0  105.0  54.0           0 0 days 00:03:29\n",
       "85     0  105.0  54.0           0 0 days 00:03:29\n",
       "86     0  105.0  54.0           0 0 days 00:03:29\n",
       "87     0  105.0  54.0           0 0 days 00:03:29\n",
       "\n",
       "[88 rows x 5 columns]"
      ]
     },
     "execution_count": 475,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_pickle('logs/log_ga_pf_mono_'  + str(configs.name) + \"_\" + str(configs.n_jobs) + '_' + str(configs.n_devices)+'_0_w5050.pkl', 'infer')\n",
    "df = pd.DataFrame(df)\n",
    "df.columns = [\"case\",\"time\",\"cost\",\"constraing\",\"exec\"]\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 469,
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: 'logs/log_eval_PF_default_3_9.pkl'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[469], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m dfDRL \u001b[39m=\u001b[39m pd\u001b[39m.\u001b[39;49mread_pickle(\u001b[39m'\u001b[39;49m\u001b[39mlogs/log_eval_PF_\u001b[39;49m\u001b[39m'\u001b[39;49m  \u001b[39m+\u001b[39;49m \u001b[39mstr\u001b[39;49m(configs\u001b[39m.\u001b[39;49mname) \u001b[39m+\u001b[39;49m \u001b[39m\"\u001b[39;49m\u001b[39m_\u001b[39;49m\u001b[39m\"\u001b[39;49m \u001b[39m+\u001b[39;49m \u001b[39mstr\u001b[39;49m(configs\u001b[39m.\u001b[39;49mn_jobs) \u001b[39m+\u001b[39;49m \u001b[39m'\u001b[39;49m\u001b[39m_\u001b[39;49m\u001b[39m'\u001b[39;49m \u001b[39m+\u001b[39;49m \u001b[39mstr\u001b[39;49m(configs\u001b[39m.\u001b[39;49mn_devices)\u001b[39m+\u001b[39;49m\u001b[39m'\u001b[39;49m\u001b[39m.pkl\u001b[39;49m\u001b[39m'\u001b[39;49m, \u001b[39m'\u001b[39;49m\u001b[39minfer\u001b[39;49m\u001b[39m'\u001b[39;49m)\n\u001b[1;32m      2\u001b[0m dfDRL \u001b[39m=\u001b[39m pd\u001b[39m.\u001b[39mDataFrame(dfDRL)\n\u001b[1;32m      3\u001b[0m dfDRL\u001b[39m.\u001b[39mcolumns \u001b[39m=\u001b[39m [\u001b[39m\"\u001b[39m\u001b[39mmodel\u001b[39m\u001b[39m\"\u001b[39m,\u001b[39m\"\u001b[39m\u001b[39mcase\u001b[39m\u001b[39m\"\u001b[39m,\u001b[39m\"\u001b[39m\u001b[39mtime\u001b[39m\u001b[39m\"\u001b[39m,\u001b[39m\"\u001b[39m\u001b[39mcost\u001b[39m\u001b[39m\"\u001b[39m,\u001b[39m\"\u001b[39m\u001b[39mreward\u001b[39m\u001b[39m\"\u001b[39m,\u001b[39m\"\u001b[39m\u001b[39miTime\u001b[39m\u001b[39m\"\u001b[39m,\u001b[39m\"\u001b[39m\u001b[39miCost\u001b[39m\u001b[39m\"\u001b[39m]\n",
      "File \u001b[0;32m~/.pyenv/versions/3.9.7/envs/DGLenv/lib/python3.9/site-packages/pandas/io/pickle.py:190\u001b[0m, in \u001b[0;36mread_pickle\u001b[0;34m(filepath_or_buffer, compression, storage_options)\u001b[0m\n\u001b[1;32m    124\u001b[0m \u001b[39m\u001b[39m\u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m    125\u001b[0m \u001b[39mLoad pickled pandas object (or any object) from file.\u001b[39;00m\n\u001b[1;32m    126\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    187\u001b[0m \u001b[39m4    4    9\u001b[39;00m\n\u001b[1;32m    188\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m    189\u001b[0m excs_to_catch \u001b[39m=\u001b[39m (\u001b[39mAttributeError\u001b[39;00m, \u001b[39mImportError\u001b[39;00m, \u001b[39mModuleNotFoundError\u001b[39;00m, \u001b[39mTypeError\u001b[39;00m)\n\u001b[0;32m--> 190\u001b[0m \u001b[39mwith\u001b[39;00m get_handle(\n\u001b[1;32m    191\u001b[0m     filepath_or_buffer,\n\u001b[1;32m    192\u001b[0m     \u001b[39m\"\u001b[39;49m\u001b[39mrb\u001b[39;49m\u001b[39m\"\u001b[39;49m,\n\u001b[1;32m    193\u001b[0m     compression\u001b[39m=\u001b[39;49mcompression,\n\u001b[1;32m    194\u001b[0m     is_text\u001b[39m=\u001b[39;49m\u001b[39mFalse\u001b[39;49;00m,\n\u001b[1;32m    195\u001b[0m     storage_options\u001b[39m=\u001b[39;49mstorage_options,\n\u001b[1;32m    196\u001b[0m ) \u001b[39mas\u001b[39;00m handles:\n\u001b[1;32m    197\u001b[0m \n\u001b[1;32m    198\u001b[0m     \u001b[39m# 1) try standard library Pickle\u001b[39;00m\n\u001b[1;32m    199\u001b[0m     \u001b[39m# 2) try pickle_compat (older pandas version) to handle subclass changes\u001b[39;00m\n\u001b[1;32m    200\u001b[0m     \u001b[39m# 3) try pickle_compat with latin-1 encoding upon a UnicodeDecodeError\u001b[39;00m\n\u001b[1;32m    202\u001b[0m     \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m    203\u001b[0m         \u001b[39m# TypeError for Cython complaints about object.__new__ vs Tick.__new__\u001b[39;00m\n\u001b[1;32m    204\u001b[0m         \u001b[39mtry\u001b[39;00m:\n",
      "File \u001b[0;32m~/.pyenv/versions/3.9.7/envs/DGLenv/lib/python3.9/site-packages/pandas/io/common.py:865\u001b[0m, in \u001b[0;36mget_handle\u001b[0;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[1;32m    856\u001b[0m         handle \u001b[39m=\u001b[39m \u001b[39mopen\u001b[39m(\n\u001b[1;32m    857\u001b[0m             handle,\n\u001b[1;32m    858\u001b[0m             ioargs\u001b[39m.\u001b[39mmode,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    861\u001b[0m             newline\u001b[39m=\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m\"\u001b[39m,\n\u001b[1;32m    862\u001b[0m         )\n\u001b[1;32m    863\u001b[0m     \u001b[39melse\u001b[39;00m:\n\u001b[1;32m    864\u001b[0m         \u001b[39m# Binary mode\u001b[39;00m\n\u001b[0;32m--> 865\u001b[0m         handle \u001b[39m=\u001b[39m \u001b[39mopen\u001b[39;49m(handle, ioargs\u001b[39m.\u001b[39;49mmode)\n\u001b[1;32m    866\u001b[0m     handles\u001b[39m.\u001b[39mappend(handle)\n\u001b[1;32m    868\u001b[0m \u001b[39m# Convert BytesIO or file objects passed with an encoding\u001b[39;00m\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'logs/log_eval_PF_default_3_9.pkl'"
     ]
    }
   ],
   "source": [
    "dfDRL = pd.read_pickle('logs/log_eval_PF_'  + str(configs.name) + \"_\" + str(configs.n_jobs) + '_' + str(configs.n_devices)+'.pkl', 'infer')\n",
    "dfDRL = pd.DataFrame(dfDRL)\n",
    "dfDRL.columns = [\"model\",\"case\",\"time\",\"cost\",\"reward\",\"iTime\",\"iCost\"]\n",
    "dt = dfDRL[dfDRL.model==\"5050\"]\n",
    "dt[\"reward2\"] = dt.time*0.5+(dt.cost*(0.5/3))\n",
    "dt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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