NB II - Experiment 2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "import math as m\n",
    "from random import uniform as unif\n",
    "import networkx as nx\n",
    "import pandas as pd\n",
    "import os\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "from IPython.display import display, HTML\n",
    "%matplotlib inline\n",
    "\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "expid = \"./exp2_run8\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "if not os.path.exists(expid):\n",
    "    os.makedirs(expid)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Problem instance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Geographic data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from pkg.read_problem import read_problem, extract_problem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "xy_customers, xy_icps, xy_crcs, xy_pc, q = read_problem('./datasets/original')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "xy_icps = xy_icps + [[unif(20,100),unif(20,70)] for i in range(5)]\n",
    "xy_crcs = xy_crcs + [[unif(20,100),unif(20,70)] for i in range(5)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true,
    "run_control": {
     "marked": false
    }
   },
   "outputs": [],
   "source": [
    "I,J,C,B,K,V,W,DjUc,Dcj,Dc,FCV,FCT,FCR,U = extract_problem(xy_customers, xy_icps, xy_crcs, xy_pc, q)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Capacity constraints"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Q_icp = [200 for j in range(J)]\n",
    "Q_crc = [800 for c in range(C)]\n",
    "V = [Q_icp,Q_crc]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Adapting costs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# two-level cost\n",
    "FCRD = []\n",
    "for c in range(C):\n",
    "    FCRD.append(FCR[c] + Dc[c])\n",
    "\n",
    "f = FCT+FCRD\n",
    "c = [[[W[i][j1] + Dcj[j1][j2] for j2 in range(C)] for j1 in range(J)] for i in range(I)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot Problem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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4azTVFV1C9wMZeRl8+NuHfHvgW07lnMISYqFJzSbc0fkOEhpUoTFaK8l5o0im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\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x48b3828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pkg.lrp_nodes_graph import lrp_nodes_graph, lrp_draw_and_save\n",
    "G, pos, labels, colors, size = lrp_nodes_graph(xy_customers, xy_icps, xy_crcs, xy_pc)\n",
    "\n",
    "lrp_draw_and_save(G, pos, labels, colors, size, expid+\"/problem.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overwriting ./pkg/draw_solution_II.py\n"
     ]
    }
   ],
   "source": [
    "%%file \"./pkg/draw_solution_II.py\"\n",
    "\n",
    "def draw_solution_II(I,J,C,\n",
    "                    N_crc, N_icp,\n",
    "                    Y1,Y2, routes, expid,\n",
    "                    G, pos, labels, colors, size, path=None):\n",
    "    \n",
    "    from IPython.display import display, HTML\n",
    "    import pandas as pd\n",
    "    from pkg.draw_solution_II import draw_solution_II\n",
    "    from pkg.lrp_nodes_graph import lrp_nodes_graph, lrp_draw_and_save\n",
    "    \n",
    "    H1 = [j for j,vj in enumerate(N_icp) if vj > 0.5]\n",
    "    \n",
    "    ###############################\n",
    "    # DataFrames\n",
    "    display(pd.DataFrame(N_crc).transpose())\n",
    "    display(pd.DataFrame(N_icp).transpose())\n",
    "    display(pd.DataFrame(routes).transpose())   \n",
    "    ###############################\n",
    "    # Network\n",
    "    G2 = G.copy()\n",
    "    colors = colors[:];size = size[:]; labels = labels.copy()\n",
    "    # ICP-Cus\n",
    "    for j,vj in enumerate(Y1):\n",
    "        for i,vi in enumerate(vj):\n",
    "            if vi == 1:\n",
    "                G2.add_edge(I+i,j)\n",
    "    # ICP\n",
    "    for j,vj in enumerate(N_icp):\n",
    "        if vj < 0.5:\n",
    "            colors[I+j] = \"grey\"\n",
    "            labels[I+j] = \"\"\n",
    "            size[I+j] = 50\n",
    "\n",
    "    # ICP-CRC\n",
    "    for j,vj in enumerate(Y2):\n",
    "        for i,vi in enumerate(vj):\n",
    "            if vi == 1:\n",
    "                G2.add_edge(I+J+i,I+H1[j])\n",
    "    # CRC\n",
    "    for j,vj in enumerate(N_crc):\n",
    "        if vj < 0.5:\n",
    "            colors[I+J+j] = \"grey\"\n",
    "            labels[I+J+j] = \"\"\n",
    "            size[I+J+j] = 50\n",
    "\n",
    "    # Routes\n",
    "    for r,vr in enumerate(routes):\n",
    "        for c, vc in enumerate(vr):\n",
    "            G2.add_edge(I+vc[0],I+vc[1])\n",
    "\n",
    "    lrp_draw_and_save(G2, pos, labels, colors, size, path)\n",
    "    \n",
    "    return G2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Define Functions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## cflp_first_tsp_second"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "code_folding": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overwriting ./pkg/cflp_first_tsp_second.py\n"
     ]
    }
   ],
   "source": [
    "%%file \"./pkg/cflp_first_tsp_second.py\"\n",
    "\n",
    "def cflp_first_tsp_second(G,pos, labels, colors, size,\n",
    "                I,J,C,\n",
    "                W1,W2,W3,F1,F2,\n",
    "                U,Q_icp,Q_crc,\n",
    "                plots=True, expid=\"\"):\n",
    "    \n",
    "    from pkg.cflp_cplex import cflp_cplex\n",
    "    from pkg.tsp_cplex import tsp_cplex\n",
    "    from pkg.read_problem import read_problem, extract_problem\n",
    "    from pkg.draw_solution_II import draw_solution_II\n",
    "    from pkg.lrp_nodes_graph import lrp_nodes_graph, lrp_draw_and_save\n",
    "    import numpy as np\n",
    "    import pandas as pd\n",
    "    from IPython.display import display, HTML\n",
    "\n",
    "    #######################################################################\n",
    "    # Solve first problem\n",
    "    prob1, Y1, N_icp = cflp_cplex(I,J,\n",
    "                W1,F1,U,Q_icp,\n",
    "                relaxation=False)\n",
    "    \n",
    "    #######################################################################\n",
    "    # Prepare 2nd problem\n",
    "    H1 = [j for j,vj in enumerate(N_icp) if vj > 0.5]\n",
    "    \n",
    "    c2=[];    u2=[];\n",
    "    for j in H1:\n",
    "        c2.append(W2[j])\n",
    "        sqi = 0\n",
    "        for i in range(I):\n",
    "            sqi += U[i]*Y1[i][j]\n",
    "        u2.append(sqi)\n",
    "    #######################################################################        \n",
    "    # Solve Second problem\n",
    "    prob2, Y2, N_crc = cflp_cplex(len(u2),C,\n",
    "            c2,F2,u2,Q_crc,\n",
    "            relaxation=False)    \n",
    "    #######################################################################\n",
    "    # Preparing TSP\n",
    "    H2 = [j for j,vj in enumerate(N_crc) if vj > 0.5]\n",
    "    tY2 = np.transpose(Y2)\n",
    "    W3 = np.asarray(W3)\n",
    "    \n",
    "    #/!\\ Use H1 as a labeling array\n",
    "\n",
    "    S_tsp = [] #set of ICP in each TSP\n",
    "    for c,vc in enumerate(H2):\n",
    "        S_tsp.append([H1[j] for j,vj in enumerate(tY2[c]) if vj > 0.5]+[J+vc])\n",
    "    \n",
    "    w_tsp = []\n",
    "    for submat in S_tsp:\n",
    "        w_tsp.append(W3[submat,:][:,submat])\n",
    "    #######################################################################\n",
    "    # Solving TSP\n",
    "    routes = []\n",
    "    for c,vc in enumerate(w_tsp):\n",
    "        prob, X = tsp_cplex(len(vc),vc, relaxation=False)\n",
    "        # Extract routes\n",
    "        path = []\n",
    "        for j,xj in enumerate(X):\n",
    "            for i,xij in enumerate(xj):\n",
    "                if xij == 1:\n",
    "                    path.append((S_tsp[c][i],S_tsp[c][j]))\n",
    "        routes.append(path)\n",
    "    #######################################################################\n",
    "    # Draw solution\n",
    "    if(plots):\n",
    "        draw_solution_II(I,J,C,N_crc, N_icp,\n",
    "                    Y1,Y2, routes, expid,\n",
    "                    G, pos, labels, colors, size)\n",
    "    \n",
    "    return Y1, Y2, N_icp, N_crc, routes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pkg.cflp_first_tsp_second import cflp_first_tsp_second"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "code_folding": []
   },
   "outputs": [
    {
     "data": {
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w5MgR/Pz8mDZtGt7e3nTq1Inff/+dIps3q7OxLP19JCXR6cgRHKOi2L17N7GxsYwdOzZd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\natU+dwbQAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa735470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%time\n",
    "Y1, Y2, N_icp, N_crc, routes = cflp_first_tsp_second(G,pos, labels, colors, size,\n",
    "                I,J,C,\n",
    "                W,Dcj,DjUc,FCT,FCRD,\n",
    "                U,Q_icp,Q_crc,\n",
    "                expid=expid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "code_folding": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2567.3499647184453,\n",
       " 508.45410810172194,\n",
       " 179.82702709080942,\n",
       " 800.0,\n",
       " 1079.0688295259142)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pkg.objective_III import objective_III\n",
    "objective_III(Y1,routes,N_icp, N_crc,\n",
    "                I,J,C,B,\n",
    "                W,DjUc,Dc,\n",
    "                FCV,FCT,FCR)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## pmd_first_tsp_second"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "code_folding": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overwriting ./pkg/pmd_first_tsp_second.py\n"
     ]
    }
   ],
   "source": [
    "%%file \"./pkg/pmd_first_tsp_second.py\"\n",
    "\n",
    "def pmd_first_tsp_second(G,pos, labels, colors, size,\n",
    "                I,J,C,\n",
    "                W1,W2,W3,F1,F2,\n",
    "                U,Q_icp,Q_crc,\n",
    "                plots=True, expid=\"\"):\n",
    "    \n",
    "    from pkg.cflp_cplex import cflp_cplex\n",
    "    from pkg.pm_flp_cplex import pm_flp_cplex\n",
    "    from pkg.tsp_cplex import tsp_cplex\n",
    "    from pkg.read_problem import read_problem, extract_problem\n",
    "    from pkg.draw_solution_II import draw_solution_II\n",
    "    from pkg.lrp_nodes_graph import lrp_nodes_graph, lrp_draw_and_save\n",
    "    import numpy as np\n",
    "    import pandas as pd\n",
    "    from IPython.display import display, HTML\n",
    "\n",
    "    #######################################################################\n",
    "    # Solve first problem\n",
    "    prob1, Y1, N_icp = pm_flp_cplex(I,J,\n",
    "                W1,8,\n",
    "                relaxation=False)\n",
    "    \n",
    "    #######################################################################\n",
    "    # Prepare 2nd problem\n",
    "    H1 = [j for j,vj in enumerate(N_icp) if vj > 0.5]\n",
    "    \n",
    "    c2=[];    u2=[];\n",
    "    for j in H1:\n",
    "        c2.append(W2[j])\n",
    "        sqi = 0\n",
    "        for i in range(I):\n",
    "            sqi += U[i]*Y1[i][j]\n",
    "        u2.append(sqi)\n",
    "        \n",
    "    print(u2)\n",
    "    #######################################################################        \n",
    "    # Solve Second problem\n",
    "    prob2, Y2, N_crc = pm_flp_cplex(len(u2),C,\n",
    "            c2,2,\n",
    "            relaxation=False)    \n",
    "    #######################################################################\n",
    "    # Preparing TSP\n",
    "    H2 = [j for j,vj in enumerate(N_crc) if vj > 0.5]\n",
    "    tY2 = np.transpose(Y2)\n",
    "    W3 = np.asarray(W3)\n",
    "    \n",
    "    #/!\\ Use H1 as a labeling array\n",
    "\n",
    "    S_tsp = [] #set of ICP in each TSP\n",
    "    for c,vc in enumerate(H2):\n",
    "        S_tsp.append([H1[j] for j,vj in enumerate(tY2[c]) if vj > 0.5]+[J+vc])\n",
    "    \n",
    "    w_tsp = []\n",
    "    for submat in S_tsp:\n",
    "        w_tsp.append(W3[submat,:][:,submat])\n",
    "    #######################################################################\n",
    "    # Solving TSP\n",
    "    routes = []\n",
    "    for c,vc in enumerate(w_tsp):\n",
    "        prob, X = tsp_cplex(len(vc),vc, relaxation=False)\n",
    "        # Extract routes\n",
    "        path = []\n",
    "        for j,xj in enumerate(X):\n",
    "            for i,xij in enumerate(xj):\n",
    "                if xij == 1:\n",
    "                    path.append((S_tsp[c][i],S_tsp[c][j]))\n",
    "        routes.append(path)\n",
    "    #######################################################################\n",
    "    # Draw solution\n",
    "    if(plots):\n",
    "         draw_solution_II(I,J,C,N_crc, N_icp,\n",
    "                    Y1,Y2, routes, expid,\n",
    "                    G, pos, labels, colors, size)\n",
    "            \n",
    "    return Y1, Y2 , N_icp, N_crc, routes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from pkg.pmd_first_tsp_second import pmd_first_tsp_second"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[150.0, 200.0, 200.0, 175.0, 150.0, 175.0, 290.0, 100.0]\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     0    1    2    3    4    5    6\n",
       "0  1.0  1.0  0.0  0.0  0.0  0.0  0.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>-0.0</td>\n",
       "      <td>-0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    0    1    2    3    4    5    6    7    8    9    10   11   12\n",
       "0 -0.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  0.0  0.0  1.0 -0.0 -0.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>(13, 1)</td>\n",
       "      <td>(6, 5)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(3, 2)</td>\n",
       "      <td>(14, 6)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>(1, 3)</td>\n",
       "      <td>(10, 7)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(2, 4)</td>\n",
       "      <td>(5, 10)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>(4, 13)</td>\n",
       "      <td>(7, 14)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         0        1\n",
       "0  (13, 1)   (6, 5)\n",
       "1   (3, 2)  (14, 6)\n",
       "2   (1, 3)  (10, 7)\n",
       "3   (2, 4)  (5, 10)\n",
       "4  (4, 13)  (7, 14)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 373 ms\n"
     ]
    },
    {
     "data": {
      "image/png": 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ZPn06Pj4+tG/fnj/++IO8W7eqs7HM/X3ExtL+2DHsHz1i3759REREMG7cuCRJnJ2d6d69\nO56enjg4OODo6IidnR3FihWjU6dOVrjSVwhzVvCy1JYV66G/iKmV0Z48eSIVKlSQKVOmWM2GXbt2\nSZ06dayWf2hoqAQEBMisWbPMSp8dq8W9SHBwsPj5+WVZeWfPnhVvb2+5efNmisdzQp3kBPz9/WXZ\nsmXyyy+/SOnSpaWIjY24glQBCYlf6hCQ6SBFQbxBvgQxxB/bBOIPov/gA5NlGY1GuXnzppw/f14e\nPXqUBVdnGV7q1RZzO+7u7qxbt44ZM2akOpQqs1izyeXx48c0bdqUrl270r9/f6uUYQ2ysl9BROjf\nvz8jR47U2mfNwMPDg4cPH2KnKKy3seEpMB9IPP9pBXAYOIo6o3B+/P4DqOttd1u2DG9vb6pXr87O\nnTtTLEdRFAoWLEjJkiXx9PS02vW8Srzygg5QqFAh1qxZQ//+/dm7d6/F87dWk0tkZCQtW7akbt26\njBo1yuL5W5OoqKgsa0NfsmQJDx48YMCAAVlSXm7gl19+4ZsBAyhhb48CVAQSj10ZhhoQoTBqcISE\nKeQ3gM1AfYOB27dvM3jwYNq0afNs8pCGddEEPZ5KlSqxaNEi2rdvz8WLFy2ad3R0tMU99NjYWDp0\n6ECxYsWYNm2a2cPPcgpZ5aE/efKEL7/8kjlz5qQ6skIjOdevXyegZEmUVAIMJF4j0h9IWAfSCSgC\n9NTpsLe3p1OnTvj5+VnFUdJIjiboiWjatCnjxo2jefPmFvUoLN3kYjQa6d69O3Z2dvz6669WWX3P\n2mSVhz5q1ChatmxJ7dq1rV5WbsLPz4/LT54gqax/cD3R5xAgoSGrAvELWJct++z4y+ZsvMy8fEpg\nZXr37s17771H69atLTbBwZJNLiLCwIEDuXnzJkuXLs3ycdyWIis89KNHj/LXX38x0Zw1hDWS0KtX\nL0b997/srVcPsbfnJPAg0fGpwCNUYZ8OvB+/v138/oU1amAwGFi2bBk3btygTp06WWr/q4om6Cnw\nzTff4O/vT7du3TCmFdPOTCzZ5DJ69Gj279/P6tWrs3wctyWxtoduMBj45JNPmDhxIt4vzFzUMM0X\nX3xBx44d+eDQIdzj4ugJJHZv2qDGo6wEtAASlpj3Ala5uvLtzp14eHgwadIkVq1aRd68ebP2Al5R\ntEbFFLCxseG3336jcePGDB8+nClTpmQqP0s1uUyfPp2lS5eye/duPDxMR//JyVh7YtG8efNwcHCg\nW7duphNrPCM4OPjZ55EjR/Lmm29SLygIhg5NEoi1OZDS0ugxtrYs8vFhw8aN6VoUTMMyaB56Kjg6\nOrJixQpWrVrFnDlzMpWXJZpcFi1axHfffcfmzZtzxUw6a04sunv3Ll9//TVz5sx5KfsXchz9+6uR\ntZ2ckNQ6lnU6cHHBYe1aSvbuTa1atTh06FDW2qmhCXpaeHt7s2HDBsaOHcu6desynE9mm1xWr17N\n0KFD2bRpE/7+/hnOJydhzSaXIUOG0LVrV15//XWr5P9KMnAgHDtGYPHiAOidnAiztVVXW/TygmHD\n4NIllKZNGTp0KLNmzaJ58+ZWm9uhkTJak4sJihUrxooVK2jVqhWbNm2iSpUq6c4jJiYGlwwu9h0Y\nGEivXr1Yv349ZcqUyVAeORFrdYru3LmT7du3c/bsWYvn/aojJUvy7u3b9Hj/fSKaNGHo9OnMWbxY\nXVT/Bc+9bdu2+Pv706ZNG86fP89XX32ljXbJAjQP3Qxq1arFzz//TOvWrQkJCUn3+RkVryNHjtCx\nY0f+/PNPiwQWyElYw0OPjY2lX79+/PDDD7iaCuqqkW4OHjxIVFQUXT/5hCdFinDOywvKl08m5glU\nrlyZAwcOsGrVKrp27UpMTEwWW/zqoQm6mbz77rsMHjyY5s2b8+TJk3Sdm5FO0fPnz9OyZUvmzp1L\ngwYN0nXuy4A1PPQffviBwoUL8+6771o0Xw2VuXPnYmNjw5tvvklcXJxZQ2YLFizIzp07iY6OpkGD\nBty9m62x5HM9mqCng88++4z69evTvn17YmNTD7z8IukV9OvXr9OkSRMmTJhA27ZtM2JqjsfSHnpI\nSAhTpkxh1qxZ2qu9FdDr9SxbtozGjRtjZ2dntqCDurri0qVLadCgAbVq1SIoKMjK1r66aIKeDhRF\n4YcffsDZ2Zm+ffuaveRuerzRe/fu0aRJEwYNGkSPHj0yY26OxtLDFnN7FKLsZseOHSiKQufOnQHS\nJeigDgUeN24cY8eOpX79+mzcuNFapr7SaIKeTmxtbVmyZAmnT59Ots5zapjroT99+pRmzZrRrl07\nBg8enFlTczSWHLb4qkUhyg4WLlxITEwM77zzDpB+QU/go48+YsWKFfTo0YOZM2daNQ7Bq4g2yiUD\nuLi4sGbNGmrXrk2RIkXo2rVrmunNEfTo6Gjatm1LtWrVGD9+vCXNzZFYqsklIQrR3LlzX8koRFlB\ndHQ0K1asoGbNms8mtOn1+gwvO1GnTh327dtHy5YtOXv2LNOnT8+5S1gYDXBrHZydAo9OgCGKt8QW\nVhaA4n2heB/Q5ctuK5+heegZxMfHh3Xr1jFkyBC2b9+eZlpTTS56vZ5OnTqRP39+Zs+e/Uq0AVuq\nU1SLQmR91q9fj7u7O++9996zfXFxcZlavbJo0aLs27ePq1ev0qJFCx4/fmwJUy3LxZ9guQ/s+wju\n7QV9OIgBW2Ih8joEjYeVfrC7A8Q+ym5rAU3QM0XZsmX5888/+eCDDzhz5kyq6dLy0I1GI7179yY6\nOppFixalGGsxN2IJD/3cuXP8/PPPfP/99xaySiMlFi9eTHh4OK1atXq2L6NNLonx8PBgzZo1lClT\nhtq1a3P58uXMmmoZROBQfzg6GGLvgz4s5XSGKDDGwM01sL4yRN7IWjtTINcK+o0bsGYN7NvnzQ0r\n1nP9+vX59ttvadGiBbdv30523GiE+/eLc+CAL9u3q3F0ExARhgwZwvnz5/nnn39wcHCwnqHAo0ew\nfj3s2ZOXc+esWpRJHjzw5fDhQmzcCGGp/F7SwlJRiKKjYetW2L07L4cPmx860xoEB8Pq1bB2LaRw\nK2UZBgPs2QMrVsDRo25s2LAFf3//JLOULSHoAHZ2dkyfPp2BAwdSp04ddu3aleT4vXuwbh2sWgVZ\npvcnv4YrC8EQaTotgDEWom7AlrchNpvfNMyJU2epLStiil68KNK4sYijo4i7u4izc5w4Ooo0aiRy\n4YL1yh07dqxUrVpVwsPD1R3BwXLynS/llF1FuUhROa5UlG8dhksZt+vy9dcier3I+PHjpXz58vLg\nwQPrGSYiDx6IdO4sotOpdeLiEidOTiKVK4vs3m3VopOxZYtI2bIiNjaR4uISJ+7uIk5OIr16iTx9\nan4+ixcvlooVK0pcXFyG7IiJEfnySxFX1+f3iauriL+/yNKlGcoyw5w8KfLWW8+/H3d39f5t2VIk\nJCTr7DAaRWbMEMmXT8TNTeK/m1ixsXkkDRtuFKPxedrp06fLgAEDLFr+5s2bJV++fLJgwQIJDRV5\n993nv2N3d7V+atUSOXLEosUm5dEpkT+dRBaT/m2Jg8i//axiFmbGFM1Vgn72rIiHh4iiqFeWeFMU\n9djZs9Yp22g0Svfu3eXDJk3E2KSJxNrqJBqHJEZE4SiROMpam5byRtnjUrRogNy6dcs6BsXz4IFI\nkSIi9vbJ6wREnJ1FNm60qgnP+OsvVbxTssPRUaRMGZGwMNP5PH78WHx9fWXfvn0ZsiM2VqRu3dRt\ncXYW+f77DGWdbg4fVh8qKdlhayvi7S0SHJw1tvTvr157SrbodHrp1k2eifp3330nn3/+ucVtOHPm\njPj71xIXlydiZ2dM9fvZs8fiRavs/1jkf7YZE/TFiPzpIhIXYXGzLCrogCewDDgHnAVqoy59vAW4\nGP83j6l8rC3or7+espgnFvVy5SxTVtmyZZNF/44NCZG7jo4Sq9imbgRIFA5yGX+ZNSbIMsakwUcf\npS7mCZubm0hkpHXtePQodQFNLOqDBpnOa+DAgdK7d+8M2/LDD6kL13MBE7l0KcNFmIXRKFK4cNp2\n2NqKvPmmde0QEdm+XcTFJW1bXFxEVq9W00+aNEmGDBliFVsaNowRiEvTlnz51LdcixL7NIl37p8X\nmdARKVMQ8XRGur+NRC1Qj638HKlYGHHTIcXyIxuGxgv6UleRS79Y2DDzBd3cNvTpwEYRKY0aL/Ys\nMBzYJiIlgG3x/2cbR4+qbWySRhuoCFy9CkeOZL68oKAg6tWr93yH0Yh98+bkNRiwF0Oa5+qI5SQ3\n+fW/NfH09MTHx4devXoRlpHG5DR4/BiWLYO4uLTTicBff1m06GT89huYGrwTEwPz50NagaIyG4VI\nBKZOTbK0d4oYDDB9eoaKMJvt2+HhQ9N2HD4Mly5Z15YpUyAiIu00EREwebL62VJt6C9y8ybs3euA\nqRHV0dFq27pFubUBlKTlLt4Hm4bD5WlwIRS+WQn/XoauP8HUD+HxPNg1CookjFzUh8OleRY2zHxM\nCrqiKB7A28CvACISKyKPUYOWLIxPthDI1jnq27ebFi6AuDjBxCjDjLFtGwQHo+j1ZiWPRM8oiePS\n7/9w9uxZbt68yZAhQyxq0oED4OBgupcvPFztjLMmK1eaFlEAGxs4cSLlY5aIQnTrlmkRBfVesrhg\nvMCWLWrdm8LGBnbssK4tO3eal+7AAfWhaC1B37ULzMk2LEzt4Lco0XfVDs5EDGgMft7g5QpftYUl\n++HXQPi4LjR+Xf1uXvOC0on75aPvWNgw8zFnIGlR4B6wQFGUisAR4FOggIiExqe5DRRI6WRFUfoA\nfQAKFChAYGBgZm1OkXPnCmMwFCU+RG2q6PVCYOB+nJ2PYWdnl+Jmb2+Pra3ts/9TGkrYqVMnhgwZ\nQqVKlViyZAnbfv+d+7GxlARWokZFV1BfbX4AngI9gMmoT9EPAQOx3B0/htOTxlG7dm1+++23NOvH\naDSi1+vR6/UYDIZnn1P6X6/Xc+JEYWJj3wFMz1K9efMBgYGnTKbLKPfuVQZMR1kyGPQcPHiK6Ojk\nC6Ddu3ePnj17UrRo0QzfR6GhOkSqA6aHh4aFRRMYeCBD5ZjDpUsBqHdK2uj1Bk6fvkxg4C2r2RIX\n9zbmDHozGoXt23dSsmRJbG1tLf57PnGiAHp9CcyRpuDgUAIDz1us7ELhZylmNCSpBb9EfoN/Xrj1\nCK4/gOaVUs8nOiqcA1bSOVOYI+h2QBVgoIgcVBRlOi80r4iIKIqSoisoInOBuQDVqlWTJM0UFuTh\nQ7XZwFSrhaurQrt2ZahVy5379+/z4MED7t+/n2R7cV90dDReXl7kzZv32RYREcGRI0cIDAzk+PHj\nbImLoxRwEnBOVN4K4DAQDjQCSgG94o/ZIhQ4coC99+6xc+dOXF1dWbt2LQ8fPuTBgwfP/iZ81ul0\neHl54e3t/exv4s9eXl74+Pg8+79atXwsXmx6KKSjIzRu7I21vhuAunXhwgUw9QKj19vRsWNlfH2T\n7r979y7ly5dn27ZtmQpcER0NvXqZTgdQtarOqnVy+TJs3Gi6qcPR0ZY2bUpSr15Jq9lSrJj6/Zii\nYEGFhg3rMXToUPLmzUunTp0saoezM8yaZV66Zs18qVfP13Ric7l8FQ4vBMPzm/R6osjYIfehYB5V\n5C+n4YTr3ApY9b5JC3ME/QZwQ0QOxv+/DFXQ7yiK4isioYqi+ALZui5mq1ZgzpwcGxuFbt3yYG+f\nx+y8Y2Njk4j8gwcP2LFjB46OjmzYsIEaZcsScPMmCmoHQ2KGofYeewGfAUt4LugAotcze9o09h05\nQrdu3ShQoABlypRJJtZ58uTJUNSjsmXN6zPo2zfdWaeLAQPUdnRTgv7WWyQTc7BcFCKdDurVu8n6\n9fmB1N/tXV3hiy8yVZRJ3n8fBqUUmDMFW6ytD198AYMHp/1wcXKCzz9XP2d2pmhqVK8OBQrAlStp\npzMawcSKG+kn35tA0v6v2VugZWVwdoTxq+D9WtC2GjSZpO6vXxZCH0NYdHyzi40D+L5jYcPSgTk9\np8BuoFT85zHA1PhtePy+4cAUU/lYe5TLggVpj15wdhaZP98yZfn7+8uWLVvEyclJTh08KGJnl6xA\nQE4n+n+L0qoVAAAgAElEQVQtSOkX0uwHyZs3r2zdutUyhr3Avn2m62TgQKsUnYzOndO2xcVF5MSJ\n5OcFBgZKoUKFJMycMY1p8PjxY+nVq5f4+tYQV9fYVEdE6XTqsEaDIVPFmcV335n+fpYvt74dEREi\nJUqkPiLKzk4dkfPkiZq+f//+MmPGDKvYsmlT2iOiXFxExoyxStEim2qnOMrFwxnp+hYSMV89tvwz\n5HU/xFWHBBRANg6LH+WyRCcSdtXiZmHhYYuVUFsOTqI2EecBvFFHt1wEtgJepvLJiolFP/6o3gyJ\nfyTOzuqPdPZsy5WTIOglS5aUlStWqAOGUxD0DYn+/xGkQaL/j4LkBRk3bpzExsZazrgX2LJFnZiR\neLyzo6NaJwMHZo1wiajjv7t1E3F0NApEJxk2mSdPymOLY2JipGzZsrJs2bJMlb1mzRopVKiQfPLJ\nJ/LkyRMJChIpVEgtO7FoOTmJNGsmkjA/zNoYjSITJ6rfR2IR0+niRFEiZdGiLPpyROTuXZGqVVXB\nVBSDgIiiGMXFRaR8eZGbN5+n7dOnj8yZM8dqtixfrtqReCilk5N6z44cKUkmOVmU6yvVoYfxgr7l\nP6RvHPqW+lYxy6KCbqktKwRdRB3z/MMPIm3bitSpc1emTVP3WZIEQZ8yZYq8/vrrcmHQIDE6OsoJ\nkPuJBL0ByEOQEJBSID/HHzsFkh/kq0qVpEaNGuLl5SVdunSRf/755/lsUwsSFSWyaJFIhw4itWvf\nk1GjRK5ft3gxZrFr13VxcZkhbm6B8v776oSj1J5nkydPlmbNmokxg7/ge/fuSefOnSUgICDZvAG9\nXmTdOnWsfq1a92TQIJHTpzNUTKa5d09k8mSRNm1E2rUTmT3bKLVrN5EFCxZkqR1Go8iBAyLlyu2W\nUqUuSrNmN2X37uQC2qNHD/nlF8uPt05MeLjIvHnqjNHWrUXGjRO5fduqRYoY4kTWVxFZ4pB+Qf/T\nWeTBYauY9UoLemJe/BFbigRB1+v1Mm7cOCni5yeuINVAricS9OkgRUG8QL4A0ccf6w6igLg4O4uL\ni4s4OzuLr6+vNGnSRNzc3KRVq1by66+/yt27dy1uu7XqxFzOnj0rhQoVkoYNG6aZ7tq1a+Lt7S2X\nMjDDx2g0ytKlS8XHx0c+//xzkw/J7K6TlNi3b5/4+flJpLVnfb2A0WiUwoULy+nTp1Otly5dusjC\nhQuz1K4sI/qByMqi6RP0P51U795KmCvouXZxLmsTHBxMo0aNsLW1ZeTIkVwNCSF4zBh2KgqFEqVr\nDlwBHgDf8Xyw3HwnJxY3aoRnnjz8888/REREcOvWLTZt2kRISAidOnVi48aNlChRgrfffpvvv/8+\n56xGl0mioqKwsbEhf/78aabLaBSi0NBQ2rdvz5gxY1ixYgXff/89Li4umTE5W6hduzbVqlVj9uzZ\nWVruqVOnsLW1pWzZsqmmsVanaI7A0QuaHSV4cW0aVXIhzaHQdi5g5wZ110KhNllmYmpogm4hgoOD\nqfbbbxxp3VodDmCTStXa2oKzM8rUqXywZQsLFy6kd+/eDBw4kMj4mTeenp58+OGH/PXXX9y+fZvh\nw4dz7tw56tSpQ4UKFfj66685evSo+or1EhIdHQ1AvnypBwbISBQiEeG3336jYsWKlC1blmPHjlGr\nVq1M25udTJgwgSlTpvDoUdatt7169Wpat26d5rr81ppYlGNw8ITGe6H+FijUDmx0YO8B9u7EKS5g\n6wKuxaHqdHg3FHxyRiB3TdAtQHBwMPXr1+eLL77grZUrYfduSAgG4OpKmI0NYTY26pi5Dz6Affug\nf38AGjZsyIkTJ3j48CFVq1bl8OHDSfLW6XQ0b96cuXPncvPmTX766Seio6N5//338ff3Z9CgQWzf\nvp04c6bJ5hCi4uf2p+ahJ0Qhmj17ttlBMEJCQmjWrBnTp09n06ZNfPPNNxka5pnTKF26NG3btmVy\nwpz7LCBB0NMi1ws6qGtV5KsNb/8Dba/D2yuh1kLO5Pka3jkArS5AQE/VS88pmNMuY6ktN7WhJ3Dl\nyhXx9/eXmTNnJj/44IHIli0yuFgxec/Ly2TP7JIlSyRfvnwybtw4k8vCGo1GCQoKkvHjx0v16tXT\n1ama3e3F69atk0KFCsnPP/+c4vGvvvpK3n//fbPyMhgM8uOPP0revHll/PjxGR4tlN11khY3btwQ\nLy8vuZ4Fvdg3b96UPHnyPKvH1OqlWbNmsnbtWqvbkxPJjnsFrQ3d+ly5coX69eszZMgQBgwYkDyB\nlxc0asRRf39WhoVhdHdPM79OnTpx9OhRAgMDeeutt7iUxopMiqJQtmxZRowYwb///suJEyeoVasW\nP/30E76+vrRu3Zr58+dz7969zF6mxYmKikKv16fooacnCtGlS5do0KABixYtYufOnYwYMSJXeo2v\nvfYaffv2ZcyYMVYva+3atTRt2tRkPb4SHvpLiCboGSRBzIcOHUr/+OaT1HB1dcXJyYm7d01Ppi1U\nqBCbN2/mgw8+oFatWsydO9estvJChQrRr18/Nm/enGqn6hVT0++yiOjoaPR6fbI2dBHzohAZDAa+\n++47atWqRZs2bdizZ0+aHXi5gaFDh7J69eo0Qx1agjVr1iQJNZcaer0+93aKvsRogp4BLl++TP36\n9Rk+fDj9+vUzmT5hDZbr16+blb+NjQ2DBg1i165d/PTTT7Ru3Zo7d8xfwe3FTtVhw4Zx9uxZateu\nTcWKFbl16xbHjh3Ltk7VqKgoYmJiknnoS5Ys4eHDh2k+IIOCgqhTpw5r167l4MGDfP75569EHFZP\nT0+GDx/OiBEjrFZGREQEO3fupGnTpibTah56zkQT9HRy+fJlGjRowH/+8x/+7//+z6xznJyc8PT0\nJCQkJF1llS1blgMHDlChQgUqVarEypUr022vTqejRYsWzJs3j1u3bvHjjz9iNBp57733KFKkCJ9+\n+ik7duxAb+ayv5YgKiqK6OjoJB76kydP+PLLL5kzZ06Knl9cXBzffPMN9erVo0ePHmzbti3dwxlf\ndvr168exY8fYu3evVfLfunUr1atXJ08e0+scaYKeM9EEPR1cunSJ+vXrM2LECD755BOzz9PpdHh4\neJjtoSfGwcGB8ePHs2zZMgYPHkzPnj0zHAjD1taWOnXqUKhQIS5evMj69evJnz8/Q4cOxcfHh27d\nurFixQoiTC3/l0nCw8MxGAx4eDxfTnfUqFG0bNkyxWGGR48epXr16uzbt4+jR4/St29fbFIbFpqL\n0el0jBs3jqFDh1rl7cqc0S0JaIKeM3n1fhUZJKEDbuTIkfRN59KETk5OuLm5ZUjQE6hTpw7Hjx/H\nxsaGSpUqZdpLUxSFcuXK8dVXX3Ho0CGOHTtGjRo1+PHHH/H19aVNmzYsWLDAsp2qd+7Ab79RftMm\nPrOzQ9m+HYzGVKMQRUdHM2LECJo1a8bgwYNZt24dfn6m1w/PzXTu3JmwsDBWWzgiidFoZO3atWa1\nn4Mm6DkVTdDN4OLFi8/EvE+fPuk+X6fT4eLikilBB3Bzc2PevHl8//33dOjQgREjRhAbG2v6RDPw\n8/Ojf//+bNmyhWvXrtGxY0fWr19P8eLFqVu3LtOmTePq1asZy/zYMWjXDooUgYEDabp7N9/ExkK7\ndkihQuxq04bJY8cmiUK0f/9+KleuzIULFzhx4gRdunRJc6LLq4KtrS2TJk3iP//5j0Wbyf7991/y\n5ctHsWLFzEqvdYrmTDRBN0GCmI8aNSpDYg6qh+7k5JRpQU+gTZs2HD9+nFOnTlGzZk2CgoIskm8C\nefLkoXPnzvz999/cuXOHoUOHcubMGWrVqkXFihUZPXp0sk7VcuXKpRy95q+/4M03YdUqNbpEeDj2\nRiNOAGFhKKGhfHLrFl1//hkePyYiIoLPP/+c9u3bM27cOJYtW4aPj49Fr+9lp1mzZuTPn5+FCxea\nTmwm6WluAc1Dz6logp4GFy5coEGDBowePZrevXtnOB+dToeDg4PFBB3UcH6rV6+mf//+1KtXj+nT\np2M0Gi2WfwIpdapGRkYm6VQNDAzkxIkTyaO0bNkC3burwUTTaPPVGY0op0/zpFYtqr7+OmvWrCE0\nNJRKldKI8/UKoygKkydPZvTo0c+Wi8gsmqDnDjRBT4UEMR8zZgy9zI1ZlgpOTk7Y2dlx9+5di74m\nK4pCr1692L9/P0uXLqVJkybcuHHDYvm/SEKn6tSpU5N0qg4ZMgQfHx+6d+/OypUrVZERgR49IH6a\nv0liY7G7cIFhNWrw2muvWe0acgs1a9akdu3azJw5M9N5Xblyhfv371OjRg2zz9EEPWeiCXoKnD9/\nngYNGjB27Fh69uyZ6fx0Oh2xsbHky5ePW7csH+i3ePHi7Nq1i/r161OlShWWLFli8TJe5MVOVScn\nJ1xdXZk5cyZ58uTBy8kJt5s3qQokvJcowAygGJAXGAIkfqdwFGHGqlUWEalXgfHjx/Ptt9/y8OHD\nTOWzZs0aWrRoka6RQ5qg50w0QX+B8+fP07BhQ8aNG8fHH39skTydnJyIjo7Gz8/Pos0uibGzs+Or\nr75iw4YNjB07lg8//DBLV+iztbWlbdu2NG3alICAAJYXK8ZjYD4pB80+CqyKP57ANOBtvZ4KmlCY\nRcmSJenQoUOy0UHpJb3NLaB1iuZUNEFPxLlz52jQoAHffPMNPXr0sFi+Op2OqKgo/Pz80j25KL1U\nrVqVI0eOkDdvXipWrMjWrVutWt6L/PLLL0ycOJF6qGu/V0SNVZhAQtDswjwPmg2qF/8zMNbZGTI6\nmuYV5Ouvv2b+/PkZvq8ePXrEoUOHaNSoUbrO0zz0nIn2iI3n3LlzNGzYkAkTJtCtWzeL5p3goZcq\nVSqJhx4TE0NgYCDHjh0jNjYWLy8v6tevT7ly5TJVnrOzMzNmzKBly5Z07979mRfn5OSU2UsxyfXr\n19UZnAZDiscTjyL3BxIaoD4DvgY8FAWycNbqy46vry/9+vVj9OjRLFiwIN3nb9y4kbp166Y7AIgm\n6DkTzUMHzp49azUxh6QeeoKg6/V65s+fz6FDh4iJiUFEePDgAatWrWLfvn0WKbdJkyacPHmSW7du\nUa1aNY4dO2aRfNPCz89PjayUylDDxA1OIUDCElzbUNvUfZ48wSf+7ah27dr873//s6K1uYMhQ4aw\nfv16Tp06le5zM9LcApqg51ReeUE/c+aMVcUcUm5DDwoK4tGjRxhe8GTj4uLYsWMHMTExFinby8uL\npUuXMmLECN55551Mt7ea4qOPPqJ///58ev06T4GTqOH3EpgKPEIV9unA+/H7LwAngK02NtjFB6YY\nP348bdpkf1ivnI67uzsjRoxI98JdcXFxbNq0iZYtW6brvIR79lVYFO1l45UW9DNnztCoUSMmTZpk\nNTGHlD30kydPphplyNbWNuOzMlNAURQ6d+7M4cOH2bJlC+fPn7foUrp6vZ6oqCgmTJjAt99+i7u7\nO3+Gh/Ma0BNIPHCxDVAVqAS0iD8OkB/wcXKi3KRJbNy8GYCpU6dSsmTJZytPvvjw03jOJ598wunT\np9m1a5fZ5+zevZvixYvj6+ubrrL0er3mnedQXllBDwoKolGjRkyePJmuXbtatayUPHRTk4CssfhS\n4cKF2bp1K56entSsWZP58+dnqpxTp07x5Zdf4ufnR9GiRenQoQNXrlzhzJkz3Ll7l7Dp0znk7GxW\n0GwUBTw8UHr3pnz58ogIFy9eZOvWreTPn59Bgwbx2muv0a9fP7Zv356lq0O+DDg6OvLNN98wbNgw\ns7/TzDS3aCNcciavpKAHBQXRuHFjpkyZQpcuXaxeXoKHXqBAAZ48eUJ0dDTlypVL1csxGAwUKVLE\nKrbY2NhQoEABduzYwYwZM2jXrp1ZgTcSuHPnDtOmTaNy5cq0aNECR0dHduzYwYEDB+jXr1+S9VgY\nNAg++QScnVPPENTA2V5esHMneHomOVSmTBlGjhzJ8ePH2b17N4ULF2bo0KEULFiQPn36sHnz5pcq\nnqo1+eCDD4iKijJrmWUR0drPcyG5VtDPnYPff4fNmwtw7tzz/adPn6ZRo0ZMnTqVjz76yOp2xMXB\nwYMePHzYhlWrbPDxKc6NGzeoWLEirq6uySZz2NvbU7t2bauMSLl1C5YsgY0bCxARUZ4DBw5SunRp\nKlWqxJo1a1I9Lzo6mr/++ouWLVtSunRpTpw4wXfffUdwcDDjx4+ndOnSqRf63XcYZ8zgTgrtrbG2\nDhjsdcQ0bA4nTkDJkmnaX6JECYYPH87hw4c5ePAgJUuW5Ouvv8bHx4ePP/6Y9evXp7vv4ckT+Ocf\n2LDBh23bUh2ckyWcOgWLFsEff8Dly+k/38bGhsmTJ5u1cFdQUBBGo5HXX3892bGYGFi3DhYsgL17\nvZNN9s1KQb92DRYvVuslC/r0X37MCTxqqS0rgkSfPClSvbqIk5OIq6uIk1OcODmJVKsmsmzZBfHx\n8ZHFixdb3Q6jUeS770Q8PUXc3AwCYeLuLmJjEyVt24ZITIxIRESELF++XMaNGydjx46Vb7/9Vv79\n918xGo0WtSU0VKR5cxGdTsTNTa0TFxeRYsVENm4U2bVrlxQpUkR69+4tYWFh8fYbZc+ePdKnTx/x\n8vKSRo0ayaJFi54dTw/Lli2TWjVqiHHTJrnS4GPZpmsu/yhtZaTNGAlwCRWdTqR9ezWmdka4du2a\nTJs2TerUqSN58uSRLl26yMqVKyUqKirVcyIiRD7+WK0Td3cRnS5O3NxE8uYV+emnjNmRUQ4cEKlQ\nQcTZWb1nXV1Vu958U+TChfTlZTQapUGDBjJ37tw0002YMEEGDBiQZJ/BIDJmjHqPuLuLuLiIODvH\niauryPDhInq9mu7GjRvi6+ubPsPSydWrIvXrq/WQUCfOziJlyojs2mXVok2Sk4NEmyXEQDBwCjie\nkDHq/JAtwMX4v3lM5WNtQT92TP3i1YVEXtyMoihhMn78eqvakMCgQeoNmJIt9vZxUr++SFycmlav\n10t0dLTFhVxEFXNfXxE7u5RtcXISWbZM5MmTJ9K9e3fx9/eXXr16SUBAgJQuXVomTpyYqWjzRqNR\nqlSpIqtWrZJff1XLS8kOBweRokVFHj7M3PXevHlTZs2aJXXr1hUPDw/p1KmTLFu2TCIiIp6liYoS\nqVpVFYuUbHF2Fhk9OnN2mMvu3anfJ4oi4uEhcu5c+vL8999/pWDBgkmu+UVq1aolmzdvfva/0Sjy\n0Uep2+LsLNKmjSr6wcHBUrhw4YxeskmuXhXx8hKxsUndlkSmZzm5RdDzvrBvCjA8/vNwYLKpfKwp\n6EajSMmSqYn5c1EvUUJNa00OHEj9h5GwubiImHCiLEKHDqmL+fMfiFFmz/5N3nrrLXFzcxNnZ2f5\n+OOPJSYmJtPlb9y4UcqXLy937hhSFdDEot63rwUuOp7bt2/LnDlzpGHDhuLu7i4dOnSQpUuXyn//\nG53qgyXxg+7MGcvZkhIGg4iPT9p2KIpIjRrpz7tjx44yfvz4FI+FhoaKh4dHku93wwb1njR1zy5b\nJnLx4kUpVqxYRi/bJPXrpy7mCVuePCKxsVYzIU1yq6CfB3zjP/sC503lY01B37/f9A2ZcFPu22c1\nM0REFVFTNySIBARY147791P3QpNuYVKp0mxZuXKlxMTESGhoqDRr1kyqVq0qZ8+eNVmO0WiU/df3\ny5JTS+TXo7/K30F/y/n750VE5O2335bFixfLpEmpe+cvel/h4Zavi7t378q8efPknXeaiaLcMWmH\nnZ1Inz6WtyMx69en9UaZuYfLhQsXxNvbW+7du6fuePpU5No1kdu35de5c6Vjx45J0tevb859ojZd\nnjlzRkqVKmWhWkhKcLB596ybm8jff1vFBJPkBkG/Gt/ccgToE7/vcaLjSuL/U9usKeiTJ5v2RBN+\nqJMmWc0MEVHbYc35cdjZqb8za7FundoWao4tbdsmPddoNMqcOXPE29tbZs6cmWJz0OOoxzL9wHTx\n+95PXCe4itsEN3EZ7yLuE93F6RsnKf1daclfL79ERkfKW2+ZZ4e7u3UfuCEhIk5ORrNsKVo06bmP\nHj2S4OBgefLkiUVsGTLEvDpxdhb5+ef05z+gb19Z0KKFqsL29mpGOp2E29nJqRYtVIGPx7wHv+qo\nHD9+QsqXL2+ROniRxYvNe8iB9R+4qZGTBd3cwaRvishNRVHyA1sURTmX+KCIiKIoktKJiqL0AfqA\nGpQhxag2FuDixcIYjUVRny2pYzQKFy9eJTDQeotkxcTUAUyPAlAUI4GB+3Bzs86Y6uPHvTEYymDO\nkj23b98nMPB0kn2lS5dm8eLFXL16lV9++YUiRYo8G91w5NERRgWNQkSINkanmOe5sHPoGugoOq0o\n7tGbgQom7TAY9Bw6dIqYmCcm02aE27d1iFTDnDqJiIgmMPAAMTExnDlzhvDwcBRFwWg04unpSZky\nZTI12uPq1QCSrm6TMnq9gTNnLhMYmHzp5e7du/PZZ58lCwaiCw1lyooV2D55og5bAXXIFeAClNm8\nGUOJElzt2ZMbHTui17+NOYPeRITr12/Qr18/i/yW9Xo9MTExz7YdOwoRF1cTcDB5bkhIKIGB5zNt\nQ3oJDw+3mo5lFkUV/3ScoChjgHCgN1BPREIVRfEFAkWkVFrnVqtWTQ4fPpxRW9NkzRro3BnCwtJO\n5+amDgvLwPBbs3nrLdizx3Q6b2+4exesFcD+yhUoV06N/JYWOh2MGAGjRqV8XK/XM2HCBGbPns2s\nWbNwqujE+3+/T6TevGg5NooN9gYPDHMOob8XkGZaR0cICYH8+c3KOt3ExKj1HhGRdjpFgVatYPly\nAzNnzuTp06ck/q3Y2NiQP39++vTpk+FYp4sWQf/+EB5uypZwfHx60qaNF++88w4NGjTA3d099RNu\n3IDKleHhQzAxgS3Q0ZEGsbGAM88vbzbQLcX0/v6wdOlBBg0axMGDB9M2HPXeCQkJ4fLly8m2K1eu\n4ODgQEBAwLPNzq46kye3JDo67WUFXFxgyhTo18+kCRYnMDAweXQuK6MoyhFRPZE0MemmKIriAtiI\nSFj85ybAWGA16rc+Kf7vqsyZnDmaNQMH0w91HBygeXPr2jJkCBw/nvYP1dFRGDRIsZqYAxQrBlWq\ngDlrfaUVYc/Ozo6vv/6apk2b0nFAR240u4HBxvwB20YxEmfzBGOXujDrHMS6pphOUaBxY+uJOagP\njI8/hp9+euawpoizMwwerK7CGRUVxYuOj9Fo5OHDh4SEhODv758hW957TxV0UxQu7MKaNaPYvHkT\nP/74I126dKFy5cq88847vPPOO1SpUiXpfIb33oNHj0yKOQAxMRQEqvp1Zc2NbxFJfRKYs7N6b784\nUzQiIiJFsb58+TLXr1/Hx8cniWjXqFGDYsWKERAQgOcLE8lADUN79mzaZhuNkAXTSF46zGlyKQCs\niPdC7ID/ichGRVEOAX8pitITuAZ0tJ6ZprGzU3+k3bqpISxTwskJ5sxR01qTFi2gUiU4fDg17ziO\nPHlg4EDrT86YNUt9Y0jNI3Vxgc8+S3VxxCTUqFGD8oPKc+3StXTbYYw0oqy7jei9AR9gIvBhkjSu\nrvDtt+nOOt2MGAF//gkPHqSseU5OULeuWm/r1wcTGxubYj5xcXFcv349w4Lu5ATff6/Wf1r37Ny5\nCq+/Xp7XXy/P4MGDiYyMZNeuXWzatIk33ngDJycnmjVrhl6v5/D+/Ty4dYuSwErUBh0FdSG0H4Cn\nQA9gMkkbWL7zPU5wHifOn3/eQpMYe3vB2zsaR8eV/PLLJi5fvkydOnW4fPkyT58+pWjRos9EumzZ\nsrRq1YqAgACKFCmCY/xia+by88/QtGnqdeLsDGPHQlovKa8qJqVNRK6gxil4cf8DoKE1jMooHTqo\nXlefPur/CR6ya7xD+PPPqvNibWxtYeNG+PBD2LxZnX0YF6c2reh0YGsbzLff3iBPnvpWt6VyZTVW\nc9u2anjPhCYpZ2dVzAYPhjFjzMvrTvgdtgVvS7mbwkCihVlSYD2InQG3oQWJ/v4P4mJbot5W5XB1\nBQ8PWLsWSqXZaGcZfHzg4EH1wRsS8jyGtYOD+h21awfz56tvDDqdDkVRknnooC6ill6xepHevdX7\nY/BgtbyEB6+bm/r/H39AkyZJz3F2dqZp06Y0bdqUFStWMGHCBFasWMGWLVsYFhXFl8BZUo4UFQ40\nAkoBCZFy7wJ1/t2PnU8h3N1b8ejRZAwGV0RsURQjItGInMTbexTbtuVFURS8vb2ZOHEiAQEB+Pr6\npit8nSneegtWrYKOHdWl8RPuWRcX9Z4dO1atL43k5LoVdj74QBWvv/6C3bshNPQWHTsWpGNH1dvJ\nKlxc1Jvy8mVYuFCdwpw3r2rf7NkTiIx8A7C+oAPUrq1O+1+/Xt2uXg2lWTNfunRRl1AxxfXr1/n0\n00/ZtH0T0bHRUB51IfOjwGuo695WQ328HwH2o7qC7sC7qAFEzwD9QNwfsewg/LdnG2Jjf+fttyfR\nti00bGi9voSUKFoUgoJg//6EV/zb1KnjQ/fuULjw83QVKlTgwIEDKU6lFxHKlCmTaVs++URtPvjf\n/9QHja0tNGgA775rXjNi/vz5OXnyJIsWLaL1sGEo588n88ASIkV58TxSVC+gNOrwNR/go7gY9un/\nxM/vXwoXnoXRWJD8+Q18/nk+6tSphTp/ENatW8fjx495++23M33tqdGoEdy5o/6GtmxRHaJq1dR6\n0jzz1Ml1gg6qcHfrpm6BgReoV6+g6ZOsRECA6lEkpnDhwlaLLZoatrZqJ1+rVhAYeJ569cxbMtVg\nMNCyZUsaNGhA5cmV2Xt9rxpm6CFwA1Xcv0SN9hwEBAKdUAX/IarX/gD1/T4vROujORexnw8/rEhg\nYCCzZ1v6Ss1HUeCNN9QtMPAc9eolb3fKly8fNWvW5N9//02yCJi9vT0NGzbE1TXl/oD04uqqvlkm\nvMTu9m8AACAASURBVF2ml4RIUUoq7RSpRYryid/Ew4P1v//OgTx5aNmyJYGBbwBqB2CdOkk7srNq\n+Vx7e/Wtu0MHqxeVa8i1i3PlZKwZLNrS/Pvvv9y6dYupU6fy1PhUHY2Z0GTsBtREFW17VI+9DqrX\nrqAGE/UEYoH4lgm9Uc+DyAe4u7sTZmpIUg6hUaNGvPfeexQtWhRPT09KlChB586dqVmzZnab9oxn\nkaLc3FI8nlqkqAQUEXB3fzYsMy205XNzLtq3kg28TIKe0OlnZ2eHo90L7cUeLyR+AuRJIRMHIFFH\nm5O9E0+ePMEtFfHJiZQoUYISJUpktxmp0qtXL0aNGkXZN96g+MWLnIqL4zWeB+ieivrsDUftIP0i\nfv8OoBhQWK/nRr58DOvTx2SUKG353JyL5qFnAy+ToPv5+RESEoJer6ewR+G0E3ugxpd7EW/UJpkH\n4GzvTEG3gpw4cSLTwbA1nvPFF1/QsWNHmmzciHtcnNmRoo4BbwAusbG80bAhFSpUYMaMGWmWpQl6\nzkXz0LOBBEEXkQxPSskqatSoga+vL8OHD6dL1y5sOreJiGupjIGsAmwCCqOu7pPQhu4JlAF2gOFd\nAwUfFWT16tUWC4b9KhMcHPzs88iRIxk5ciS0bAlbtyYZf9gcGJTC+V8An+l02Jw8CWa+gWiCnnPR\nPPRswM3NDQcHBx4+fJjdppjE1taWNWvWcOnSJXrW70nk5Eg4nUricsBbwD/ABOBPnruJLQA96Cfr\n6fdxP+bMmaN56NZi8WIoVMisITJx9vb01+k4bmrqbOJzNEHPsWgeejaR4KUnCdmWQylcuPCzsGZT\n905lTOAYddp/5RQSV4/fXsQZnLs4s73rdmoWyjmdibkSDw84dAhatUJ/+HCKM4WibG1x0umw/+MP\nGur1NGnShD///JMGDRqYzF6v12udojkUzUPPJhLapl82Pqv1GYUdCkM61xNztndmYI2BmphnFXny\nwJ49DKtdmws1alDc1VUdp+nggKFECYY4OBB64gS0bUuHDh1YunQpnTp14q+//ko1y/DYcHYG7+Ro\n5FGuO1/nwI0DGIzZGLNPIxmaoGcT2TEW3RKs+GcFD2Y8oJx3OZzszJup5WzvTLeK3ZjYcKKVrdNI\nzI0bN1hw4gQ+W7eq0y0NBoiJwfbCBeK6dGHB0qXP0tavX58tW7bwxRdfMGvWrCT5nL13lr5r+lLg\n2wK0/rM1S6KWsMllE01+b0KBbwswbuc47oTfyerL00gBTdCziZdppEsCixYt4rPPPmPruq0cHXiU\n/jX64+rgiqtDCpNrBFwdXPF19WV60+n82OLHHN8BnNv49ddf6dSp0/PhoYnqv0+fPsybNy/JmPOK\nFSuye/duZsyYwVdffUWsPpaJZydSdW5V5h+bT2RcJE9jnhJNNLFKLGGxYTyIesCEPRMoMr0IMw/O\nzOpL1HgBTdCziZdN0OfOncuIESPYtm0bFSpUwMHWgamNp3JvyD1mN59NFd8q5HPOh5uDG3l1eXG6\n5cTyjsu58cUNelXpZboADYui1+uZN28effv2TfF41apV8fLyYuvWrUn2Fy1alL1797J562aKjCzC\nzvs7idJHoZfU29ii9dFE66MZvm04o3eMtuh1aKQPrWcjm3iZBH3mzJl8++23BAYGUrx48STHdHY6\nulbsSteKXZ/tMxqN5M2bl/L/LY+N8v/snXdYVMfXgN8Fll4ERWwoamxo7MYaW6LmZyHWqNHYYk2i\nMSYx5TNK7C2WJBqNRsVojCWIJvYC2EXFTuwNRbGhSGd3z/fHBQRl2QV2QQ1vnvtI9s6dOTs799y5\nZ86cUzBnyA82bdpE6dKlqVHjubh6aQwePJhff/2V1s9E/3J3d6fyl5UJPRVqcNdoeuKS45h5cCbl\nXcvTp2YfwxcUYHIK7rZ84mVR6NOnT2fOnDkEBwc/p8z1YWFhQcOGDTl48KCZpStAHwsWLGDo0KFZ\nlunZsye7du3izp07GT6/EnWFdefXobM0XpmnErcvjgHtB2BjY0O/fv2yfX0BuaNAoecTpUqVIiIi\nIlszoLxERPj+++9ZsmQJe/bswcvLK1vXN2rUqGDjUD5x5coVjhw5QjcDsaKdnZ3p0qULy5Yty/D5\nTyE/oRM949KQU4sTqFuoadnZsPtjAaanQKHnE7a2tri4uBAZ+eJ5B4gI33zzDevWrSM4OJiSJUtm\nu44ChZ5/LFq0iD59+mBnRLzo9Iuj4eHhvNvpXeZ0nkPS5CTYhBIb4DdgK0pWjKCUC48BP6NsIPuZ\np+EbvSHhtQTOPNG3+6wAc1JgQ89HUn3Rixc3LpRtXiAijBw5kr179xIYGEiRIkVyVE+9evU4efIk\nCQkJ2NramljKAvSRlJTE0qVLCQ4ONqp8vXr1cHJyYseOHYwePZrX67+O42hHYpJjsh8mOR0RTyKU\nOPgF5CkFM/R85EWzo+t0OoYNG8bhw4fZvXt3jpU5gKOjI5UrVyY0NNSEEhZgiICAALy9valkZOon\nlUrF4MGDmT59OhEREfT7sh+WNpY5C5OcDh26gk1H+UCBQs9HXqTNRVqtlgEDBhAWFsaOHTsyTd6b\nXQrMLnmPMYuhz9KrVy8OHjxIiRIlsLPJxExjbJjk9AgFHk75QEGP5yMvygw9OTmZXr16cevWLbZs\n2WKyOOWNGjUq8HTJQ86dO0dYWBgdO3bM1nUuLi60bNmSy5cvU9i2MEnazJNiP72AzMMkp0NtqS7Y\nSJYPFCj0fORFUOiJiYm89957PHnyhL///hsHBweT1d2wYUMOHDiQaYLlAkzPr7/+yoABA7A2JhHp\nM3zzzTckJyfz69RfKW5THJJRUhtlRm3gAIqNXVBSDD5KOacFS40l3oW90Wq1JCQkZJqPtQDzULAo\nmo/kt0KPj4+nS5cu2Nrasn79+hwpgqwoU6YMKpWKa9euUbZsWZPWXUBG4uPjWb58OUeOHMnR9Y0a\nNaJs2bIcPnyYyLORikKvhhLX/lmqAnEoYZKjUeznnVP+3QPaYC0nU/5bsWIF48aNw9fXN0dyFZA9\nChR6PpKfCj02NhYfHx88PDzw8/MzS3xrlUqVZkcvUOjmZe3atbzxxhs57meVSsUnn3zCvn372BG0\ng6IzihKbnBIjPRthklUtVNTpVYcjg3L2YCkgdxSYXPKREiVKcO/evQzZ5PMCnU5HmzZtKFOmDL//\n/rtZkxUULIzmDTlZDH2W3r17s3XrVmIfxbKg9QIstNlXD042TqzsvDJXchSQcwoUej5iZWVF0aJF\niYiIMFzYRERFRXHhwgWqV6/O4sWLsbS0NHxRLihQ6Obn5MmThIeH07Zt21zVU6hQITp27MjPP//M\n9AHTeTvpbWwsbAxfiOLR4mzjzI4PdlCxcMVcyVFAznklTS4iEBKiJG25eLEkdnbwxhsZoofmGXFx\n8PffcPs2FCqkpHtM796durmoTJky+isxEYcOPaRbt19o3rw1vXsPQcT8HVKrVi0uXLjAkydP0rxn\nNBrYtg0uXQJbW2jdGvLLInPnDmzaBMePl+LBA2jXTpEprxGBffvgxAmwsIDGjaFmTeOuXbhwIYMG\nDTJJFiEfHx+6dx/Eu+8uorFHJ9zuLyHEfSqRcbeJ18Q/FxJApVNhZWlFA88GLHl3Ca+5GRfvx1jO\nnIE9e5RQ7tWqQfPm+XMfvzSISJ4dderUEXNz8KBIxYoiDg4itrYi1tYacXAQqVBBOZdXaLUiY8Yo\ncjg5idjYiDg6KjL17i0SG6uU69atm6xcudKssly9KlK3bqKoVPFiZZUoarVGnJxEihYVWbPGrE2L\niEjjxo1l165dIiKyZImIm9vTPrG3V/qkRQuRiAjzy5LKo0cinTsrMjg4SFqfODmJTJkiotPlnSy7\ndomUKfN0fNjaKv1SrZrIyZNZXxsdHS2urq5y8+bNXMtx9ux5cXJaJBAndnbJYmMjyr+2Oun4ySHp\ntKqLFJpaSNTj1WIzwUaKzigqrX9oLS06t8h128/y778itWuL2NkpR+r9U7KkyObNJm8uWwQGBuZ5\nm8BRMULHGq2MUfaJHQf+Sfl/N2AHcDHlX1dDdZhboR84oNwIynzn+cPeXmT//pzXr9FoRGfEna7T\nifTtq18WW1uRunVFEhJEPv/8c5k6dWrOhTLA9esirq4agWS9fbJ0qdmaFxGRL774QiZMmCCzZ+vv\nEysrkWLFRO7cMa8sIiJPnohUqaIoCX198umn5pdDRGTbNkVh6Ruzjo4iJ07ov37hwoXSqVOnXMtx\n/PhJsbHZKtbWSZnKYWcn8tZbIsnJGa+LiYmRQoUKSYQJn8bnzok4O4uoVPp/n/XrTdZctnmRFXp2\nbOifAv+m+/+vgV0iUgHYlfL/+YYIvP++YuLQR1ycUia7btFnzpzhp59+YuLEiUyaNImAgABis8iS\nvm8frFunX5aEBAgLg0WLzO/pMmBALFFRgj7rWlwcfPQRREebTQQaNWpEYOC/fPON/j7RaOD+ffjq\nK/PJkcqsWXD1aqa5kwFFxl9/hVOnzCuHRgO9ekF8vP4yMTHQR09ocRExyWLo4cOHad58JtCSpKTM\nF8jj4+HQIXg25aiDgwOdO3fm999/z5UM6RkwQMmYp+8+jYuDvn31/37/ZYxS6CqVqhTQDlic7uN3\nAb+Uv/2A7G1PMzF79yoKwRAPHig2OWMJCQlh48aNPHz4EFC2yJ8+fZpFixaRqGdEzZiR9YMFlPMz\nZ0KpUuZT6AcPXmHXLksMLZVYWMDy5WYRAVA2GO3fXxVlF4p+NBpFYZjz4aLVwo8/Kg/VrEhKgtmz\nzScHKLZ7Y5TSpUuZP1yOHDnC48ePefvtt3MsQ2BgIB06dKBEiTkkJmbt7RQbC9OmPf95//79Wbp0\nqUk2kF26BKGhhiddOh389Veum3vlMHaGPgcYjRJrLRUPEbmd8vcdwMOUgmWXgwcN36SgzDSM3Y2e\nnJzMzp07n3Mr1Ol0xMbG6g08deCAcW8BERHg5lbGLAo9LCyM9u19sbMzvIIUGws7dphchDSKFSsG\ntCIhwbAsarWyEGYubt3KekacilYLQUHmkwOUN7knTwyXE8l8zC5YsIAhQ4ZgYZEzZ7V//vmH7t27\ns2bNGq5ccTPqmjNnnh/bjRs3RqvVcvjw4RzJkZ7Dh8GYtd2YGAgMzHVzrxwGu06lUrUH7orIMZVK\n1TyzMiIiKpUqUxWmUqkGA4MBPDw8CDLTXXLpUml0Oi8MPaNEhEuXrhIUpG9f81OioqL0JqDQaDQc\nOHAg01l6UlJjlLB0WSOi4/HjJ/Tt29ek/RIfH8/FixcZPHgoP/1k3M1+7959goLMp0k9Patw6ZLh\nclptMseOnSEp6bFZ5Lhzxxadrh7PxXvNhPj4BIKCDplFDoDr18sDngbL6XRazp27TFDQU/dWrVZL\n9erVqVatWo7GTlRUFDdu3EhLbqHV6jBmficiBAYG8+wzZPz48Vy4cIEEY2ZVWXD2rAdabQWMccC7\ndSuCoKALuWovJ8TExJhNj+UaQ0Z2YApKRORrKDPxOGAFcB4onlKmOHDeUF3mXBTdulVZQNK3uJR6\nqNXxsmyZcStvV65ckSlTpoivr2+mx8KFCzO9rmVLw3KA4mWi0WjF2tpa4uLiTNIPISEhUrRoUVm7\ndq1cv64swBqSw85OxIzrsiIi0qpVqFhYZL7glv6wsRG5f998ciQlKZ4shuRQqUS6djWfHCIiq1YZ\nN2YdHEQOHcp47dy5c6Vnz545anfRokVSokQJOXXqVNpnNWsaN2YrVMi8zvDwcHF1dc31OD59OmvH\nhvSLxYsW5aqpHPNSL4qKyDciUkpEvFBC2u8Wkd7ARqBvSrG+wAYTPmeyTatWYExcKbU6mc8+q0qX\nLl3Ys2dPlna/UqVK6Z2hW1lZ8frrr2d67ssvwdExazlsbWHUKLC0tKBkyZLcvHnTsPAG2L9/P+3a\ntWPx4sV07dqV0qWhevUYMlrKnkcEPvww181nyWef2SCSdXxsCwvFT79wYfPJoVbDkCFgKGyNhUUC\ngwcbYQ/JBZ068dxMNzOKF1f2UaQiuVgMnT17NhMnTiQoKCjD+B092vD9Y2+vjO3MKFWqFPXq1WP9\n+vXZlik91apBhQqGy+l00LNnrpp6JcnNTtGpQCuVSnUReDvl//MNCwtYuhSyyrplZwdr1zpx48Y1\nWrZsyaBBg6hTpw7Lli3L9FVRrVbz1ltvPbc13sLCAgcHB2rXrp1pO61bQ5Mm+mVRq4VSpRTvEni6\nuSg3BAYG0rFjR37//Xc6dOiAiLBo0SIuXGiNra0GfQuS9vbg65txs5M5aN26Emr1L9jbZy6HSgXO\nzsqCsrn5+mtwd9dvq7W3F8qWPcPAga8bnfknJ9jYwMKFWY9Ze3tYsiTjZpq9e/cC8OabbxrdloiS\nI/aXX35hz549VHhGa3btCtWr699UZWMDFSvCBx/obyN1cTS3LF6sfG992NvDnDnGTeD+cxgzjTfV\nkRcbi/755+nGldTXMycn5bO//85YVqvVyubNm6V169bi4eEhY8eOldu3bz9X5+nTp2Xu3Lni6+sr\nEyZMkPXr10tMTEyWciQmKr7oNjZPzR5WVjpRqeKlVq2oDGaF3r17y9JcOINv2bJFihQpkvYq+OjR\nI+nevbu8/vrrEhYWJidOiJQrp7ympvr2Ojoqr/KzZ+e42WzTsuVb0qvXObGzU9pWfh+tODiIVK4s\ncv583skSESFSv75ibrK0fGp6srUV+fhjxd/6n3/+keLFi8tXX30liYmJZpNl1SoRF5f0Y1Yrjo4i\nHh4imb3d9+zZU+bOnWt0/TqdTkaNGiXVq1eXO1k4+sfGinTpkrohL3XMasXWVqRdO5Ho6KzbiY+P\nFzc3N7l+/brRsunjwAFlE5FiktKljVknJ5Hffst19bniRTa5vHIKXUS5GQMCRL7+WuT996/J+vXP\nb4h4lrCwMBk6dKgUKlRIevfuLUePHn2ujLEbi9Jz+7aiNEeNEpk0SWTChNXyzjvvZCjzzTffyPjx\n47NVbyoBAQHi7u4u+1N2TIWEhEi5cuVk2LBhGeyZOp1IcLCye7V79+uybNnT3ap5xXfffSfffvut\nREcr9s/3378lJUosksOH83ZnZnpOnxYZP16kW7fr8tNPz9vvIyMjpUOHDlKrVi0JCwszmxyJicqu\n3bffPia1am2WzZuV3cbPEhkZKYUKFZKHDx8aVa9Go5GBAwdK/fr1jb7mxg2RGTNEPvtMZPDgS3L1\nqvHfY9iwYTkey8+i1Yps3y5iYzNDPvkkTlauVDbj5TcFCj2PFXp6stv5Dx48kGnTpomnp6c0btxY\n1qxZI8mGngbZICEhQUqUKCGhoaFpn82fP18GDRqU7bpWr14tRYsWlSNHjohWq5WZM2eKu7u7rF27\nNsvr8mNAiihvEs2bN0/7/6tXr4qnp2e+yPIsWfWJTqeThQsXSuHCheXnn3/O9kM9OyxatEgGDBig\n9/y0adOkf//+RtWVlJQk3bt3lxYtWki0oem1HrI7VlInFNrMnkY5IC4uTqytrc3a59nlRVboBdEW\nn8HNzY3Ro0dz5coVRo4cyY8//kj58uWZPn162uai3GBjY8OoUaOYOvXpkkNOdosuX76cTz/9lO3b\nt1OmTBk6dOjA2rVrCQkJoWvXrrmW0xw0aNCAo0ePpvn1FytWjMjIyCwXpl8EUhMpHzhwAD8/P9q2\nbcudO3fM0paNjY3eDWs6nY6FCxcatRgaHx9P586diY2NZfPmzSZLK2iIunXrYmdnx759+0xS3+3b\ntylWrFhBOjsjKVDoerCysqJr167s3bsXf39/zpw5Q/ny5Rk6dChhYWG5qnvw4MHs3r2bixcvAtlX\n6L/++ivffvstu3btIioqilq1avH666+zd+9evLy8ciWbOSlUqBBlypThVMq2R1tbW+zt7U3yoMwL\nKlasyP79+6lXrx41a9ZkwwbTO3ZlpdB37tyJi4sL9eplklkiHU+ePKFdu3Y4Ojri7++PbR6Gj1Sp\nVCZbHAVFoZcoUcIkdf0XKFDoRlCnTh2WL1/Ov//+S7FixWjZsiVt2rRh8+bNet0as8LJyYlhw4Yx\nI8WlIzsK/aeffmLSpEns3LmT1atX07NnT3777TemTp1q1kQVpuLZ+OjFixc322zXHKjVasaPH4+/\nvz+fffYZgwYNIiYmxmT1Z6XQU10Vs5qtPnz4kFatWlG+fHlWrFiRL2Oid+/eBAQEmKRfbt++TfHi\nmeXBKyAzChR6NihWrBi+vr5cv36d999/nzFjxlClShXmzZuX7cE7YsQI1q1bR0REBK6urmg0Gh4/\nznp35PTp05kzZw5//vknQ4YMYd++fYSGhtKmTZvcfK085VmFXqxYMW7fvp3FFS8mjRo14sSJE2g0\nGmrVqmWSbe+gX6HfunWLoKAgembhfB0ZGUmLFi1o3Lgxv/76q9mTl+jDw8ODN998k7Vr1+a6rgKF\nnj0KFHoOsLGxoW/fvhw7dozFixcTGBhImTJl+Pzzz7l69apRdRQpUoQ+ffowZ+ZMVBs2sNDaGunV\nCz75BPz9lUhVKYgI48ePZ8mSJYwdO5ZOnTrRunVrtm/f/tIN9kaNGnEwXWCSl22Gnh5nZ2eWLl3K\nlClT8PHxYfz48bnOcK9Pof/222/06NFDry38xo0bNG3alC5dujBz5sx8tzmbyuwSERFRYHLJBgUK\nPReoVCrefPNN1q1bx7Fjx7CwsKBevXp06tSJ4ODgrBf7tFp8ra35es4cdB98QO9Hjyi0aRPMmwf9\n+kHRojB+PJKczLfffsvq1atp2rQp3333HevWreP//u//8m0GlhsqVKhATEwMt27dAl7eGXp6unbt\nSmhoKHv37uXNN9/k8uXLOa4rM4Wu0WhYtGgRQ4YMyfSaixcv0rRpU4YOHcrYsWPzXZkDtGvXjnPn\nznHJmAA+WVAwQ88eBQrdRHh5eTFjxgyuXbtG69atGTJkCLVq1aJkyZJs3749Y+GkJPjf/yg0bx5u\nIlg8a6558gSiopBp07hUtixbAgJQq9VERkZy4sQJmjRpkndfzMSoVCoaNmyYNksvXrz4S6/QAUqW\nLMm2bdvo0aMHDRo0YMmSJTny3slMoW/evBlPT09q1KjxXPnTp0/TvHlzxowZw2effZZj+U2NtbU1\nvXr1Sgv+lVMKFHr2KFDoJsbR0ZFhw4YRFhbG1KlTqV69Oh988AFjx459qrj69VNipxoImq6Ki+Py\nrVs8OneOCxcusG/fPj788MO02e3LSno7+stscnkWCwsLPv30U3bv3s2cOXPo2rUr940J0p+OzBS6\nvrgtISEhtGrVih9++IGBAwfmSnZz0L9/f5YvX45Wm3UMn6yIiIgoUOjZoEChmwkLCwveeecdtmzZ\nQnBwMPfv38fb25vRHTqg8/c3Lig3UAsIsrEh9sABIiIiqFChAsOGDTOv8GYmvUJ/FUwuz/L6668T\nEhJC2bJlqVGjBtu2bTP62mcV+tWrVwkJCaFbt24ZygUFBdG+fXsWL15Mjx49TCa7KalevTru7u7s\n3r07x3UUuC1mjwKFbma8vLy4efMmP/30E5988gmrduzAJTGROkCqo6IK+BEoBxQBvuRpfEQPwEuj\nQTVnDgCWlpa5tkvmN/Xq1eP06dPEx8e/UjP09Nja2jJz5kyWL1/OwIED+fTTT4k34iH+rEJftGgR\nffr0wS5dBK/Nmzfz3nvvsXr1atq3b28W+U1FbhZHExMTiY6OpkgmkeNEhEuXLuHv78/atWs5c+ZM\nrt4EXhWMyA1SgCmYNWsWAQEB7FSpqAicAtIHlFsPHAViUEJXVgJSX6JvaLVU9/MjevlyLC0tWbRo\nUZ7Kbmrs7e3x9vbm2LFjeHt7v3Iz9PS89dZbnDx5kmHDhlG3bl1WrlxJzZo1ny+YmAh//UXRVatY\nFRkJPj5oGjfGf/FiNqREVwRYs2YNw4cPZ+PGjTRo0CAPv0nO6NmzJ2PGjOHRo0cUKlQoW9feuXOH\nokWLPpeRSavVsnLlSm7dukVSUhIAly5dIjg4mA8//DBPN1K9aBTM0POIxYsXM/G776iUnIwKqAGk\nD/v9FeAGlAZGAqvSnSsNPFKruX/lChMnTqRy5cp5Jre5SDW7uLq6Eh8fb9Ts9WXFzc2NP//8k2++\n+YZWrVoxY8aMpxvStFoYN06J5zt0KLb//EOT5GT4+28YO5aTDx5Q6fvvISqKJUuWMHLkSHbs2PFS\nKHOAwoUL06pVK/78889sX6vP3HLgwAHCw8PTlDlAUlISDx8+ZPPmzbmS92WnQKHnEeHh4ZR/7TW9\nyUbTJyIrA0Q8W0AEtyJF6Nu3L++++26u/Z3zm1R/dJVKRbFixV45s0vVqlUzpClTqVT07t2bI0eO\nsHHjRt566y3Cr16FDh2UbOFPnjyXYNQqKQmblGzI0eXLM2/sWIKCgqhevXoef5vckVOziz4Pl5CQ\nkEzHv06nIyws7KW/N3JDgULPIzw9PbkcHo7oicqffuP/DeDZeUmySsWFW7fQaDTcvXuX6Ohoc4ma\nJzRs2JADBw4gIq+kHf3s2bM0b978uc+9vLwICgqidevWBHp7o9m1K1Nvp9uAD8o4UCUlERkVxSE7\nOyqWKwco9uUBAwbg7OxMsWLFmDVrllm/T25o3bo1N2/ezHYMJH0KPau3OZVKleu8pi8zBQo9j+jR\nowfDhg1jmkpFIooN/UG68zOAKBTFPhfonvK5P/CvhQV7X3uNps2a4e3tTdGiRTl79uxLPRPx9PRE\nrVZz5cqVV9LTJSssLS35pk8feut0WKUzG6THAngH+Cvl/9WA+s4d2LgRAF9fXy5evMj169cJDAxk\n+vTpbN26NS/EzzZWVlZ88MEH2fZJ17dL1NXVVe81lpaW2GeV7ugVp0ChmxGdTkdCQgITJkxgzpw5\neHh48KONDUWAD4H084x3gTpATaBdynmAW8D/dDo6pIQUaNCgAT169GDEiBEUK1aMPn36sHbtm+sH\nUgAAIABJREFU2pduxq5SqdLs6K/iDN3Ly4udO3ei1WqZPHky5cuXx8nJiTp16iiB2H75BcukpCy9\nmz4CMsRVjImBadMA8PPz47vvvsPV1ZUqVaowePDgXG/iMSf9+/fn999/z9YkRN8MvVmzZpkGHVOr\n1TRo0OC5RdT/Ev/db25GwsPDGT9+POXKlaNEiRL06NGD69evc/z4cSLu3uXJ8OGE2NtTKt01bYEr\nKLP2H4DUTf2f2NuzumZNnFxcmDNnDtu2bWPu3LkcP36c0NDQtF2JpUqVok2bNvz8889cv349r79y\njkhV6K/yDH3WrFmsWrWKzZs3Ex0dzZIlS5QZZIpNOdW7KRQly/oSQxWeOEHUxYvcvn07w87RGjVq\ncPbsWfN8CRNQqVIlypYtm623CH0KvVq1ajRq1AgrKyusra1Rq9VYWVnh7e1N06ZNTSn2y4cxWTBM\ndbwMGYtySlJSkvj7+0vbtm3F1dVVhg0bJseOHcu0rDY5WQJLlZJ4KysRJXuzXExNgJr+cHAQ6dZN\nRKORw4cPi7e3t3Tq1CnTvKfR0dHy119/Sd++faVIkSJSvXp1GTNmjISEhDyXPSa/MhY9y+HDh6V6\n9eqycOFCGThwYL7KYuo+KVOmjOzYsUMqVqwoAQEBzxewsxNAtqT7veeBtHxmDCSnjI+r6RLk3ti5\nUwCJj49Pq2779u1SpkwZk34HEdP2y8KFC6Vz585Gl69Ro0amqSBTiYmJkePHj8vRo0clKirKFCIa\nRUHGoleYixcv8vXXX1O6dGlmzZpF9+7duXnzJvPnz6d27dqZXjPW15fvypRha4cO3HF0BCA59aSV\nlZLOvFw5JbX56tVgackbb7xBaGgoVapUoUaNGvzxxx8ZYoU4OTnRuXNnli1bxp07d5g/fz5JSUn0\n7duXkiVLMnjwYP7++2/iDIQbyEtq1qzJ5cuXcXZ2fmVn6OHh4ZQvX/75EymB1Qx6Nz2LCI7OzgAZ\nzGyPHz/Os6xEOaV79+7s2rXL6HAIhnaJOjg4ULNmTerUqZNtH/dXlQKFngPi4+NZsWIFzZs3p0mT\nJmi1WgIDA9m7dy99+vTJclFm9erVrFixgsW//cbAPXt4cuwYh+fOJbhsWfi//4NJk2DbNrh0CQYO\nhHSR82xsbJg0aRKbNm1iypQpdOrUKVNFaGlpSePGjZk2bRphYWHs2bOHypUrM2vWLIoVK8alS5dY\nvHhxvtutra2tqV27Nvfv3893WcyFp6dn5tEXPRVVbsi76VkkKQnXypUpXrw4J0+eTPv85MmTVK1a\nNdfymhMXFxfat2/PypUrDZbVaDQ8fPiQokWL5oFkrw4FCj0bnDx5kuHDh1OqVClWrFjB8OHDCQ8P\nZ8aMGUZt9jl27BiffPIJAQEBLFu2jM6dO1OhYkUO6HSc/t//YOJEGD0aGjfOoMifpW7duhw9epTX\nX3+dmjVrsmLFiiwj+1WoUIFRo0YRGBjItWvXcHNzY+fOnVSpUoX69eszadIkTp06lS+5PRs2bMjV\nq1df2Rn6wIED+e6777h48SIiwqlTp3jw4AGkREbU590EkACkBgFIBGKBABH+99571KtXj/HjxxMV\nFcW///7LokWL6NevX559r5xirE96ZGQkRYoUeSlDROcrxthlTHW8jDb0x48fy4IFC6Ru3bri6ekp\n48aNk2vXrmW7ntu3b4unp6esW7dObt++LW5ubhIeHi4iIgMHDpR58+blSL5jx45J9erVpUOHDnLr\n1i2jrkntk8TERNm5c6eMGDFCvLy8pEyZMjJ8+HDZvn27JCYm5kie7BIQECCtWrUStVptskzxOcFc\nNnSNRiMTJkwQLy8vcXR0lLp16yq/e0yMADIXpCyIG8goEE06+zmZHPHBwbJixQpp2bKlWFtbi1qt\nFldXV5k5c6ZJ5U/F1P2i1WqlTJkycvz48SzLhYSESO3atU3atql4kW3oBQo9E3Q6nezfv1/69+8v\nLi4u0rlzZ9m8ebNoNJocyZCQkCANGzaUcePGiYjIxx9/LKNGjUo737BhQwkKCspR3SKKYh47dqy4\nu7vLsmXLRKfTZVk+sz7R6XRy+vRpmTRpkjRo0EBcXFykW7du8vvvv8uDBw9yLJshIiMjxcXFRQoX\nLiyRkZFma8cQ+XSTykVb2+cXwzM77O1FevcWSffb3rhxQyZNmiQVKlSQypUry5QpU+TmzZsmldEc\n/TJ27FgZMWJElmU2bNgg7dq1M3nbpqBAoeexQtdqRbZtE5k4UeTDDy/Ltm3KZ4a4e/eu/PDDD1Kl\nShWpWLGiTJ8+Xe7cuZMrWe7f10nDhkulSpU/ZfZsrezde13c3Nzk7t27IqIoUmdnZ7l3716u2hER\nCQ0NlRo1akjbtm0zvbGPHBGZOlWkf/8rsmaNSEKC/rru3Lkjv/32m7z77rvi7OwsTZs2lZkzZ8r5\n8+dzLWdcnMjKlSLffy8ybZpIhZLvSHtPT7m4bJnI6dMiSUm5bsNYLl0S+eEHkX79rshvv4k8fpxn\nTSsK/ZtvROztRZeVMndwEOnSRW+/pE5ABg0aJK6urtKmTRtZtWqVxMXF5UiuyEiRefNEfH1FRoy4\nIBERufmWz3PlyhUpUqRIlm+BCxYskA8/HCj79olMniwyYYLI+vUiycmmlcVYnjwRWbZMGbNDh16S\nc+fytv3/rELfuVOkeHERR0cRCwsRCwudODoqn+3Y8Xx5rVYr27dvl/fee09cXFzkgw8+kODgYIOz\nXEMkJ4t89JGIlVWyqFRxAjqxsRGxsEiS1147L48eKeXCw8PFw8MjV22lJzExUXx9fcXd3V2WLFki\nOp1OwsJEvL2VSZ7iKakTJycRZ2eRhQsN1xkXFyf//POPDB48WIoXLy6VKlWSL774Qvbs2SPJ2bjD\ndDpFeTo6KkclzskvqqESg708xkES7RxEnJxEChUS+fZbERPPNtNz965IixYitrYi1tZKnzg4iNjZ\niXz5pXETgNwCyMWLF2XP5L2yS91a4rGVaBwlDht5goPEquwltlw1kRUrMszMsyIuLk7++OMPad26\ntbi5ucmQIUPk4MGDRo3n+HiRPn1EbGyUfgARa2uN2Ngo3rOxsbn9xk9p3ry5rFu3Tu/5QYN+EVfX\n++LgIGJpKaJSKUPD1VVk1SrTyWEInU5k3DilPxwdlT6xstKKra1Io0YiRlo5c43JFDpgC4QAJ4Gz\nwPcpn7sBO4CLKf+6GqrL3Ap9586nAzGzw85OKSOiKNLx48eLl5eX1KxZU+bNm2cyX1adTqRTJxEb\nm+RM5bC21om3t3KDbNmyRVq2bGmSdtNz4sQJqVmzprz55gBxctKKSqX/Tf7HH42vV6vVypEjR2Ts\n2LFSs2ZNKVy4sHzwwQeydu1aeZzJ9Nbb2zvtFdXXV2kPRIYyT2Kxk0TUmQtmY6PMTP/5x0Q98pSo\nKJGyZUXUepq2txfp39/kzWbK+vVPx2xJwuUD/OQTfpT+/CY1OC729iKHDuWs7hs3bsjkyZOlYsWK\nUrFiRZk8eXLaus2zaDTKA07f/WNrK1K/voipllaWLVum16Ry4oSIlVV8ltan5ctNI4chhg9/Omaf\nPaysREqUUCYH5saUCl0FOKb8rQYOAw2A6cDXKZ9/DUwzVJc5FbpWK1KypH5lnnq4usZJ27btxdXV\nVYYOHSpHjx7N9Wz8WXbsELG312Yph52dyIwZIjNnzpThw4ebtP1UkpKSpHz5SwKaLGWxtRXJqZn8\n+vXrMm/ePGnTpo04OTlJ69at5eeff5br169nKBcerrQDIoNYKDHouUueOSJsbaVDgwZSvHhxAeTq\n1au57pevv1aeF4ZM1iEhxtcZFxcnR44ckeDgYLl48aJRYyopSXkZMdQNlSvn4suKYpI5cOCADB48\nWFxdXaV169byxx9/ZDDJrFmjPD8NWX6WLMmdLKnExMRIoUKFJCITe06dOiKQ9f3j4KCY7szJ2bNZ\nTxBBmRR8/LF55RAxoULPUFjJyRAK1AfOA8VTPi8OnDd0vTkV+o4dT1+JsjosLGLkiy+2SkxMjNlk\nadkyyeCABMUM1K9ff1mwYIFZ5Lh507DiSlVepnCSiI6OlnXr1qXtVq1Ro0babtWvv1ZMTiW4KXEY\nuRAIcgdknq2tHAgKMolCT04WcXExZpyI9OhhXJ0nT56UiRMnyqRJk8TX11cmT54sc+fOzfSNJT1r\n1ihmBEOyODgo6x+mIC4uTlatWiVt2rQRV1dXGTRokOzfv1/q1NEZ9ZPk9uGSngEDBsi0adMyfBYW\nZliJgnKvL11qOlkyY/DgVBOl4d/H3A8XYxW6UX7oKpXKUqVSnQDuAjtE5DDgISKpzsN3UOIJ5RtH\nj4IxUTNVKgfc3NrgoCeMbW7RarXs3RuPMS7+Dx7AqVPXzLYh5PhxMCZ5S1wcBAfnvj0nJye6dOmS\ntlt13rx5/Pjjj3Tp0oWZMw+RmDiZeKpRlIRspeD7yMqKeiaIT6PT6Th79hGJiTojysLBg4brvHv3\nLv/88w8ajYbkZGW/b1JSEo8ePWLVqlVZXnv48HMh0PXKcvSo4XLGYGdnR48ePdi6dSunTp2iXLly\nDBgwgNDQzKM+PsuFC4o8iYmJ7N69mx9++IGpU6emZRDKDv3792fZsmWpk0UAjh1L20SbJTExSp51\nc7JvHxgTS8zCAlJi5+U7RqWgExEtUFOlUhUC1qtUqmrPnBeVSiWZXatSqQYDgwE8PDwyBP03JVeu\nlEbEC0OKVETHlSvXCAq6YRY5bt68ibV1V5KTDZfV6XR06dKFxMREs/TL6dOF0WiqYMzP/ODBfYKC\nzphcBjs7Oz799FOmT9/O3bvr2IKWN8heCj5iYojx9QXg0KFDXLt2DRFBq9Wi0WgyPbRaLcnJyc+V\nefy4EDpdH4x54CYkxBMUdDjLMufPn09T5OkRkTRl75gS3uFZbtwoh5KPKmt0Oi0XLlwmKMhgYIBs\n06BBAxo0aEDr1lZGjVkRYdeu3Rw/foyEhIQ0ZXzp0iWuXLlC1apVcXNzM7r9jz76iM2bN6dNsP79\n1wOttgLGjNnbtyMICrpgdFvZJTa2HmB44qfRaAgJCeXu3fwPq5GtnKIi8kilUgWihGqOVKlUxUXk\ntkqlKo4ye8/sml+BXwHq1q0rmQX9NwU6HaxapTy5s8Le3oKePcvRvHk5k8vg5+fHhAkTaNDgPXbt\nMly+SBEdP/00iW+/Nf2NClCxIowfb7icvT1061Yk04QMucXW1pYaNWqg0XxMEatveV0zNC0FX3pS\nU/C58TQF38B0562vXQNg9OjRREdHExsbi6urK4ULF6ZIkSIULlz4ub/d3d0zfK7E0bbip59ATxjy\nNCwt4e237Qz2Sabb+lNQq9WUK1cOb2/vTM8/eABbtxqepVtaWvLBBxWpVati1gVzQa1aEBJiuJyH\nx0MsLVUkJydnmFmDMkG5evUqnTp1QpXFTuf0HDhwgL///psFCxYAULw4zJqV6dwwA46O0L17CZo3\nNxQsIXuICCEhIfj7+/Pw4S3gPZSlQ/1YWlrRo8cbRr0NmxuDCl2lUrkDySnK3A5oBUwDNgJ9gakp\n/24wp6CGaNECChUyrNALFVLKmpqDBw/y5ZdfEhQUxJ071hw6BLGx+svb2UGHDhe4etV88TdKlIDm\nzWH7duWBpw+dDsy9azwuLpxieKAj8/dpQ0Gq1CnKY/ny5dSoUQMXF5ccx73++GMl61tiov4yNjYw\napThutzc3Lh169Zzyg0UBZdV0CgfHyUWmyG8vBSFa05Gj4a+fbMes1ZWCXh4+BEQcI3ChQtnWiYp\nKYnIyEiKFStmVLt9+vShevXqzJ49Gzs7OypVgvLl4zh71g5Db1HvvWdUEwbRarXs27cPf39//P39\ncXR0pEuXLvj51aJXLyuySndrbQ0DBhhn2swLjLkjigOBKpXqFHAExYb+D4oib6VSqS6ivCVPNZ+Y\nhlGpYMUKZbapD3t7+P33LMOk5IibN2/StWtXlixZgre3Ny1aQNu2+mWxsYEKFcDLaxvVqlXLvJCJ\nmDcPnJ31f2d7e5g1S3nQmZPSpT1p3ukh1mQ+NTYUpEqV0pleXl64urrmKonB6NFQqpRyM2aGvT30\n7g16gmVmoH79+nrjjTg7O2cazzsVtRqWL1ce7vpwcAA/P8Ny5JY337xPjx678fHZRNWqZ7G01GY4\nb2OjpXDhW9y4MQ27LARWqVTZSmJRqlQp6tWrx/r169M+GzbsKJaW+hfE7Oxg0aLcKdHk5GS2bdvG\nkCFDKFGiBCNHjsTd3Z3t27fz77//MnHiRDp18uajj1R672MrKyhaFMaOzbkcpsbgXSEip0SklohU\nF5FqIjI+5fMHIvKWiFQQkbdF5KH5xc2aZs1g82YlkJ2jo9LhlpY6HB2VG3jTJmXGakri4uLo2LEj\nI0aMoH379oCiPFetgkGDlEHn4KAsnNjZKcq8bVtlweXCBfNHyCtXTnmVrlFDad/aGiwsdDg5KUp8\n3jwYNsysIgBKkKpj56Zy3L0sgvEp+AASVCoS33kHUBbjcpsz0tlZWZBs0UL5fWxtwcJCcHRU+mjU\nKPjlF+PqKlGiBG+//TZWVlZpDxlra2scHBx4//33DZoe2reHdeugWLHUMStAEg4OUL48BAZC3bq5\n+roG2b17N4sWLaRMmf3Urn0UH5+NjBjxEy4u0djYaLGxga5dLbl6tTwREdcpWbIkOj2vfDqdDg+P\n7PlHPBuwy8bmIu3bT6NcuSQgFrVasLICJycoUkSZlPXokf3vGR8fz4YNG+jbty/FihXD19eXChUq\ncPDgQY4fP86YMWOoUqVKhmtmzFCCoDo4KL+PpSWo1VpsbaFJE2WxWs/LSr6gyuxV0VzUrVtXjppq\nuT4LRBSvjSNHFBtn9+7lad7c9DNzEeH999/H0tKS33//PdOb9/Fj8PeHO3fAxQU6dlRMIaBETfzp\np59o2LChaQXTw8mTsGsXnDt3hbZty9GunTJLNCdeXl4sXryYFi1aMGXKFH778Ufu37tHZZSF0FIo\nXi5zgTnAY6AfyiaH1HlvZj+bqcbt9evw999w8uQVmjYtR6dOyo2bXR49esTJkyeJiYmhVKlSVK1a\nFStj7Ckp6HSwcyfs3v2QhQt/YdOm/6NhQ9OP2We5fPkyq1evzmRhV4VKVZJHj5rw9deVSB/F9vHj\nx/zyyy8kPmOz0mg0HDhwgEqVKjF8+PDnlKM+EuLj6ejhgX+TJtifO0f0/fvE29hwpmpV1hRqQcU3\nx6HRQLVq8M47xnnBpPLkyRM2b96Mv78/27Zto1atWnTp0oWOHTtSqlQpwxWkEBcHAQHKeImIuMjI\nkRXILMy9uVCpVMdExOCj/ZVU6OkJCgoyy2IfwOTJkwkICCA4ODjL19DM0Ol0ODk5ERERgYuLi1nk\n04c5+8QgiYkklyqF5f37aa+HKpTtxq9lUlysrFDVrKm8ZphRu+Vrn6Tj0qVLvPPOO1y6dClP2lux\nYoXehV0rKytq167N//73v+fO3bt3jw0bNnDnzp20N5OHDx+yZMkSXFxciIqKonbt2nz22We0bdtW\nfxjckyeha1cSr11DrdVikU4fxQM21tZY9OgBv/6qvN4aQVRUFBs3bsTf35/AwEAaN25Mly5d8PHx\nMUl89fwYK8Yq9Gx5uRTwlA0bNjB//nwOHz6cbWUOpMUlz2tlnt9sCwrie62WIHt7rBMSslyt1VhY\ncF+lwm7VKlzMPVV9QdBoNHkaAzwqKkrvOUtLS73mLXd3dwYOHEhsbCxJSUlpi9QTJ07k77//ZsmS\nJezZs4cBAwagVqvR6XQsXrw4zSwJwKFD0KoVxMSQmaq2A8Udae1aOH8egoL0Gs4jIyMJCAjA39+f\nQ4cO0bJlS7p164afn99/KptRQYKLHHDmzBkGDhzIX3/9RcmSJXNUx9mzZ1/4DDOmZunSpfTt25fp\nGzdiffIkVKqkGCefxcoK7OywbNKEuX370mHAAOKzcjV4hdBqtdky1eSWIkWKZCmLocmKg4NDhkVq\nW1tbunXrxpYtW7h06RJfffUVtra2xMbG0q1bN3r16qUks753T7GfGHJLA4iPZ/KxYzg6OeHo6Iij\noyN2dnZYWFgwadIkmjVrRqVKlQgKCmLgwIHcunWL9evX07t37/+UMocChZ5tHjx4wLvvvsvs2bOp\nX79+juv5Lyl0EcHX15cJEyYQHBxMkyZN4LXX4OxZ2L0b6dYNR0tLtC4uyor2hx9CaCiq4GAmLVyI\np6cn3bt3z5b3xMuKRqPJU4XeuHFj1JkspFhYWFC6dGlsc+FKUrx4cb744gsuX75MYGAgPXv2ZP36\n9dSuXZs5VauiycZD+luNhhhLS07v2MF3332Hm5sblpaWXLp0iS+++II7d+6watUqunXrpncj13+B\nAoWeDZKTk+natStdu3ald+/euarr7NmzZndZfBFITk7mww8/ZNOmTRw8eJBKlSo9PalSwRtvIKtX\nU1atJuHWLbhxAxYsgJSUfhYWFixduhSNRsPAgQP1ele8KuS1yaV06dJpXjpWVlaoVCqsra1xd3en\nS5cuJmlDpVLRtWtX3n//fe7fv0/njh2Ze+8erklJRoeAAEhISmJtq1ZcvXoVnU7HggULWLp0KR06\ndMjVg+dVosCGng1GjhyJvb09kydPznVdZ8+eZfjw4SaQ6sUlOjqabt26oVarCQoK0hs/5+HDh9jZ\n2ek9b21tzdq1a2nVqhVffvklM2fONHon4stGXptcAN544w2qVq1KWFgYCQkJeHp6UqZMGbP08bx5\n8zgTGspWe3sqxsVlKwSErQhflizJ3vffZ8WKFXTv3p0CMlIwQzeSBQsWsHv3bv74449cz6C0Wi3n\nz5/XuyX8VSAiIoKmTZtStmxZAgICsgyGdvPmTYMuZA4ODvzzzz9s376dadOmmVrcF4a8Nrmk4uDg\nQL169XjzzTfx8vIy2wNz8eLFTOzbl0pqdVoIiPRu3KkhIErzNAREelSPH+Pn50fXrl3/06YVfRTM\n0I0gODiYcePGsW/fvmx7peh0OqKiolCr1Tg7OwNw9epV3N3dX9kBefbsWdq2bcuwYcP46quvDCoH\nYxQ6KNvst23bRpMmTShcuDCDBg0ylcgvDHltcslrwsPDKV+2rN7zhkJAxKnVrF27lg0b8jXSyAtL\ngUI3wNWrV+nevTsrV66kQoUK2bo2JCSEoKAgNBoNIoKbmxs+Pj6cOXPmlV0QDQwMpHv37syePZte\nvXoZdY2xCh2UnZnbtm2jWbNmuLm5mczO+6KQHyaXvMTT05PLOh3V9ATSCQdS74znQkCoVKx3d8fN\n0vKF2DPwIlJgcsmCJ0+e4OPjw7fffsvbb7+drWtDQkLYuXMn8fHxJCcno9FouHv3Ln5+fq+sQl+5\nciXdu3dn9erVRitzyJ5CB6hQoQKbNm1i2LBh7N69OyeivrDkl8klrxg4cCDfzZjBxSZNsh0CQmtr\ni5+lJX369Hll11ByS4FC14NOp6NPnz7Ur18/24uXWq2WwMDATONkazQaHj58+EopdBFhypQpfPvt\nt+zevZsW2QxnmV2FDlCrVi3Wrl1Ljx49yOvdx+bkVTe5jBo1ivfee4/WZ8/iDHyIsiM0lXeBOkBN\noF3K+VRCExPZeexYpjtXC1AoUOh68PX15d69e8ybNy/bs4GHDx/qda8TEWxsbF4Zha7RaPjoo49Y\nvXo1Bw4cyJEr5s2bN3O0QatZs2YsXryYDh06cO7cuWxf/yLyqs7Qr127xttvv42lpSWff/45zdu0\nYYGrK4ft7Ej/KG8LXEGZtf/A03g+ODjgHRjIl19+SYcOHfjhhx8ynTD91ylQ6JmwZs0a/Pz88Pf3\nx8bI+BHpsbKyytJfOjEx0ejARS8ysbGxdOrUicuXL7Nnz54c75rNyQw9FR8fH6ZMmcI777xDeHi4\n4QtecF51G/qVK1do1KgRiYmJvHvtGhYDBijxivVNmmxtlfCYW7bg0LQp06ZNY//+/ezcuZPq1auz\nY8eOvP0CLzgFCv0ZQkND+fjjj9mwYUOOA/kUKlRIrzeMhYUFV69eNVtO07wiMjKS5s2bU6RIETZt\n2pTmwZNdRITw8PAcK3SAfv36MXz4cNq0acODBw8MX/AC86rO0AE2b95Mw4YN6d+/PytXrsTR2Rl+\n/hm2bYMOHZRCjo7g4kKMhQXJzs7w1VdKHJc330yrp1KlSmzevJlp06YxdOhQOnfuzLWUjFb/dQoU\nejoiIyPp2LEj8+fPp2bNmjmuR6VS4ePjg1qtzmCusbS0xMrKymShX/OL8+fP07BhQ9q1a8eSJUsy\n3TpuLNHR0ahUqhw/EFL5/PPP8fHxoW3btsQYEx/kBeVVtKFrtVrGjRvH4MGD8ff3Z8SIERnNmE2a\nwIYNyN27vLZlC6xdyxwfHxZ+/z34+irB4p8h9R47e/YstWvXpm7duvj6+v5nYv7oo0Chp5CYmEjn\nzp3p378/3bp1y3V9pUuXZuDAgVSpUgU7OzucnZ1p1KgRycnJL7W5Zf/+/TRr1owxY8bg6+uba2+D\nVHOLKbwWpkyZwuuvv07nzp2fi9X9svCqmVwePnxI+/btCQoK4ujRozRu3Fh/YXd3Rbm3aoVH27aE\nhIYarN/W1pYxY8YQGhpKWFgYVapU4a+//nrpJ005pUCho7z2Dxs2jGLFijFu3DiT1Vu0aFG6devG\n6NGj+eyzz2jZsiVhYWEv7YLounXr6NSpE35+fgwYMMAkdebGfv4sKpWKBQsW4OjoSJ8+fdBqtYYv\nesF4lUwuoaGh1KlTh6pVq7Jz506j84wC1KtXjyNHjhhdvnTp0qxZs4YlS5Ywbtw4WrVqRVhYWE7E\nfqkpUOjA3LlzOXbsGH5+frnKVWkML6sP+uzZsxk5ciTbt2+nTZs2JqvXlAodlAXpP/74g3v37vHx\nxx+/dDO1V8XksmTJEtq0acP06dOZOXNmts1yVatWJTw8nOjo6Gxd17JlS06cOIGPjw+873/hAAAP\nyklEQVTNmjVj1KhRPH78OFt1vMz85xV6amyQDRs2mH0rfnJyMpcvX6ZySiTBlwGtVsvIkSNZvHgx\nBw4cyNXaQmaYWqGD8hoeEBDAkSNHGPsiZfA1gpfd5JKQkMCgQYOYMWMGe/bsybH5Uq1WU6NGDY4d\nO5bta62srBgxYgRnz54lOjqaypUrs3Tp0lc+Uif8xxX6hQsX6N27N6tXr8bLy8vs7V28eBFPT88c\nZTjKD+Lj4+nWrRsnT55k3759lC5d2uRtmEOhAzg7O7NlyxbWrFnD3LlzTV6/uXiZTS7Xr1+nSZMm\nPHr0iJCQkFyvFWXX7PIsRYsWZfHixWzcuJEFCxbQqFGjXNX3MvCfVeiPHj3Cx8eHiRMn0rRp0zxp\n82VKanH//n3eeust7Ozs2Lp1K66urmZp59atW2ZR6KDc0Nu3b2fmzJmsWLHCLG2YmpfV5LJt2zbq\n16/P+++/z5o1a3Bycsp1nblV6OnrOXjwIEOGDMHHx4eBAwdy9+7dXNf7IvKfVOharZaePXvSqlUr\nBg8enGftviwK/fLlyzRq1IhmzZrx+++/52hzlbGYa4aeSpkyZdi2bRtffPEFmzZtMls7puJlM7no\ndDomTJhA//79WbNmDaNGjTJZnBVTKXRQ9n/079+fc+fO4eTkRNWqVfnxxx9fuSxY/0mF/vXXX5OU\nlMSsWbPytN2XQaGHhITQpEkTRo0axZQpU8y+SGxuhQ7g7e1NQEAA/fr1Y//+/WZtK7e8TCaXqKgo\nfHx82Lp1K0ePHjX5m+5rr73Go0ePuHfvnsnqdHFxYfbs2QQHB7Nx40Zq1apFUFCQyerPb/5zCn35\n8uX4+/uzZs2aXG2IyQkvukLfuHEj7dq149dff2Xo0KFmby82Npb4+Hjc3NzM3laDBg1YsWIFnTt3\n5tSpU2ZvL6e8LAr9xIkT1K1bl/LlyxMYGEiJEiUMX5RNLCwsqFu3rlns3t7e3uzYsQNfX1/69etH\n9+7dX4nQEYhInh116tSRvCApSWTNGpHPPhPp2vWGrFkjkpgocvDgQXF3d5czZ87kiRwiIuHhItOm\niQwdmiyWluMlNDQxz9pOj04nsnOnyOjRIp0735BFi0Sio5+e//nnn6V48eISEhJidlmiokTmzxfp\n3fuhFCr0i+zdq8iXF/z5559SokQJuXz5soiIhIaKjBkj0rFjuPzwg8idO3kjx7PEx4usWCHSoMFB\nqV9/rwQEiCQn548sV66ITJok8vHHIgMGXJYLFzKe9/PzkyJFisgff/xhVjk0GpFu3ZZKo0b75NNP\nRZYtE4mNNX07sbGxMnbsWHFzc5MJEyZIfHz8c2Xu3ROZM0fkk09EevW6Jnlwm2QAOCpG6NhXTqEH\nBIgUKiTi6Kh8O1D+dnbWiqtrf9m4caPZZRBRbtCePUVsbJRDkSVZ7OxEGjUSuXs3T8QQEZFjx0Q8\nPTP2iYODiJ2dyNSpWvnyy9FSsWLFNCVnLnQ6RXna2orY26fKohUHB5HXXhM5e9aszacxf/58KVOm\nsVSrliT29iIWFoostrbKMXCgMinIK/z8RJycMv4+Tk4ihQuL7NiRd3I8eSLSoYPSB2q1IoelpVZs\nbUVatRKJjEyQoUOHSsWKFeX06dNmlWXPHhEPDxFb26QM97GDg8iCBeZp88qVK9KxY0cpV66cbNiw\nQXQ6nWg0IsOHK/ewnZ0ih0qlE3t7kapVlYdfXmAyhY6SFSoQCAPOAp+mfO4G7AAupvzraqgucyv0\njRufdnpmh1qdJHmhz7Vakbff1i+LWi1StqzI48fml+X06YyK4tnD0jJePD0XyP37980uy+efp1fk\nGQ+VSsTFReTSJbOLIffuiTg6RgskZyqLvb1Ily5589awfLn+PkmVJTDQ/HIkJYnUq5d+8pHxsLbW\nip3dRfHxeU8em3ngHjpkuE/mzzdf+9u2bZNKlSrJO++8I506PdIri4WFSJEiIrdumU+WVEyp0IsD\ntVP+dgIuAN7AdODrlM+/BqYZqsucCl2jESlaVP8gSD3c3ZWy5mTTpqyVaOpscNIk88ohItKsmeE+\nsbXVmf2N4coV5TtnJYeFhUinTuaVQ0Rk1CgRa2tdlrI4OIjs329eORISlJm4od+nXDnzP1xWrFC+\nc1ZyqNWJ8ssv5n/Kvf664T6xt1feKMxFYmKijBy5TCA2SzmsrEQGDTKfHKkYq9ANLoqKyG0RCU35\n+wnwL1ASJbmIX0oxP6Bjbmz5uWX7djAm0FpCghKt05zMnAmGAv4lJMCcOcqwMBfXr8Phw4bLqVQq\nFi0ynxwA8+eDoY16Oh1s3gz375tPjqQkWLQIkpKydq2Li4MffjCfHAB//WVcuchIOHTIvLLMmAGx\nsVmXSU62ZuZM86Z+O3UKLl82XE6lgpUrzSeHtbU1jx71xdIy602AGg2sWGG47/KKbHm5qFQqL6AW\ncBjwEJHbKafuAB4mlSybnDih3ISGiItTyppbFmN4/Fg5zMWpU2CMC3l8PBw8aD45AA4cUJSpIWxt\nwZzJhyIiDD9YQHnQmjuz3dGj8OSJ4XI6nfnH7L//Glfu6lXj+i+nnDwJxnjKxsYaN1nJDYcPg1Zr\n+AFmZQUvSjh2o/2jVCqVI/AXMFJEotNvHhARUalUmc41VSrVYGAwgIeHh9l8Pq9d8wTKYvgZpePa\ntWsEBd0wixwAWm1jwLBLpFarY//+/Tg4mCcq4Jkzbmg03hjzM0dF3SMo6KxZ5AB48qQmUMhgOY1G\nw4kTp9BosheUyVgiI23QaN4gXXIzvSQlxRMUZD6tERFRDjAcTkGn03Lx4iWCgm4bLJtTRJpizPxO\nRAgODtabYCi3nDtXFK22IsaM2bt3IwgKumAeQYD4+HqA4UQ0Go2Go0dDuXfPiBmluTHGLoOinbYB\no9J9dh4oLk/t7OcN1WNOG/revYZtgKm20T17zCaGiIi8+66yyGdIFi8v89pGIyMN261T++Tnn80n\nh4jIhAnGyWJnl9Gd0tRoNMpCliE5LC1F+vc3nxwiyiK+MTZ0OztlcducNGliWA4QqVnTvHJcvmzc\nOHFyEjGz16R8+ulTb5+sDmdnxS3anGAqG7pKmYr/BvwrIum3Vm4E+qb83RfYYKJnTI5o3Bg8jDD6\npMbQNydffgmG4m/Z2yvlzDXTAShaFNq0AUOhQUSgTx/zyQFgTIQFtRp69AAThAHRi6UlfPqpYtrJ\nCmtrGDXKfHIAtG1rWA6VCipXhhzk3s4WX30FhrIiOjgo5cxJuXJQr55hs4uFBXTpYl5Zhg83fO/Y\n2MCQIcp4eSEwpPGBJoAAp4ATKUdboDCwC8VtcSfgZqguc7stHj6c9SzdwUFxiTI3Op3Ihx/qd72y\nsxNp0MD8T3URZWNTkSLKjFOft8CKFeaXQ0R5C9DXJ2q1SMmSeeOfHxsrUq2afhc9BweRL74wvxwi\nIrt2Ze3K6eRk/tm5iDJmO3fWL4u9vUibNub3EBMRuXBBcWHV95Zrby/y99/ml0NE5Pvv9feJtbVI\nhQp5437Mf3VjUUiIiLe38iPY24vY2GjE3l6kShXJ091dWq2Ir6/ivujkpChxR0fldbJ/f5G4uLyT\n5cYNxX0xdUOPjY1GHB1FSpQQ8ffPOzlEFL9rd/enfeLgoCjWNm3ydpdmdLRI9+5K246OItbWGnFy\nUhTJDz/k3c5VEZGgIGVjVepmL3t75d/atfNGmf9/e3cXInUVh3H8+6DV+gKmFbKpqRdmmVDKglqB\ni1p5IXZXgoIU3SVplJF1Ed1H1E1B2MtFYYRJiZAWppA3hi8XbepaaL6h7oJQIAhqvy7Of3Vwdtu9\naOe/nfN8QNY581/m8OzsM7Nnzvynz7Vr6YFs7NibP58xY9Kb4data+2brbq7IxYsuHmf7buvTJ8e\nsWtX6+YRkfa8T5yYMklvPrsabW1pi+2lS62Zw1ALXenY1ujo6IgDw711oHLoUNpF0N3dzerVs5k/\nvyU32+TKlbQV78IFmDAh/Zk9TGeiHdSJE7BnD3R1HWflyvtZvHhoOwr+a9evw+7daXtaWxssWwbT\nprV+HgC9vbBzJxw8eJwlS+5n+fJ6/nyOSLsq+nZ5LFo0/MssA7l8Od1ne3rg4sUjbNw4Z1iXwf7N\nsWOwb1/aHjh3blpaHc5lyoFcvZq2Rp86BadPH2XDhgf7++zqYSPpYER0DHpcroXeZ+/evXR2drb0\nNkc6Z9LMmfTPuTSrI5OhFnpxZ1s0M8uVC93MLBMudDOzTLjQzcwy4UI3M8uEC93MLBMudDOzTLjQ\nzcwy4UI3M8uEC93MLBMudDOzTLjQzcwy0dKTc0nqBU617AaTu4Fh/Njh/yVn0syZ9M+5NKsjk+kR\ncc9gB7W00Osg6cBQzlJWEmfSzJn0z7k0G8mZeMnFzCwTLnQzs0yUUOgf1T2BEciZNHMm/XMuzUZs\nJtmvoZuZlaKEZ+hmZkXIptAlTZO0R9IRSb9KWl+NT5L0g6Tfqq81fURzfSSNknRY0o7qsjOR7pS0\nVdIxSUclLSo9F0kvV787XZK2SGorLRNJn0jqkdTVMDZgBpI2SfpdUrekp+qZ9U3ZFDpwDXglIuYA\nC4EXJc0BXgd2R8QsYHd1uTTrgaMNl50JvA/sjIgHgIdJ+RSbi6QpwEtAR0TMBUYBqygvk8+A5beM\n9ZtB1S+rgIeq7/lA0qjWTbUfEZHlP+Bb4AmgG2ivxtqB7rrn1uIcppLuhEuAHdVY6ZlMAE5SvYbU\nMF5sLsAU4AwwCRgN7ACeLDETYAbQNdj9AtgEbGo4bhewqM655/QM/QZJM4B5wH5gckScr666AEyu\naVp1eQ94Dfi7Yaz0TGYCvcCn1VLUZknjKDiXiDgHvAOcBs4Df0bE9xScSYOBMuh7EOxzthqrTXaF\nLmk88DWwISL+arwu0sNoMdt6JK0AeiLi4EDHlJZJZTQwH/gwIuYBl7llKaG0XKp14adJD3b3AuMk\nrWk8prRM+jPSM8iq0CXdRirzLyJiWzV8UVJ7dX070FPX/GrwGLBS0h/Al8ASSZ9TdiaQnkmdjYj9\n1eWtpIIvOZdlwMmI6I2Iq8A24FHKzqTPQBmcA6Y1HDe1GqtNNoUuScDHwNGIeLfhqu3A2ur/a0lr\n60WIiE0RMTUiZpBevPkxItZQcCYAEXEBOCNpdjW0FDhC2bmcBhZKGlv9Li0lvVBcciZ9BspgO7BK\n0h2SZgKzgJ9rmN8N2byxSNLjwE/AL9xcL36DtI7+FXAf6UyPz0TEpVomWSNJncCrEbFC0l0Unomk\nR4DNwO3ACeA50hOcYnOR9DbwLGnH2GHgBWA8BWUiaQvQSTqj4kXgLeAbBshA0pvA86TMNkTEdzVM\n+4ZsCt3MrHTZLLmYmZXOhW5mlgkXuplZJlzoZmaZcKGbmWXChW5mlgkXuplZJlzoZmaZ+AdXbM0l\nHp9++AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa7167b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%time\n",
    "Y12, Y22, N_icp2, N_crc2, routes2 = pmd_first_tsp_second(G,pos, labels, colors, size,\n",
    "                I,J,C,\n",
    "                W,Dcj,DjUc,FCT,FCRD,\n",
    "                U,Q_icp,Q_crc,\n",
    "                expid=expid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2570.2545338487184,\n",
       " 505.48641114820595,\n",
       " 185.6992931745982,\n",
       " 800.0,\n",
       " 1079.0688295259142)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pkg.objective_III import objective_III\n",
    "objective_III(Y12,routes2,N_icp2, N_crc2,\n",
    "                I,J,C,B,\n",
    "                W,DjUc,Dc,\n",
    "                FCV,FCT,FCR)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Capacity problem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "run_control": {
     "marked": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overwriting ./pkg/capacity_vector.py\n"
     ]
    }
   ],
   "source": [
    "%%file \"./pkg/capacity_vector.py\"\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "def capacity_vector(Y,U,J,Q):\n",
    "    cluster_q = np.zeros(J)\n",
    "    violated = []\n",
    "    for i,vi in enumerate(Y):\n",
    "        for j,vj in enumerate(vi):\n",
    "            cluster_q[j] += U[i]*Y[i][j]\n",
    "    \n",
    "    for j,vj in enumerate(cluster_q):\n",
    "        if cluster_q[j] > Q[j]:\n",
    "            violated.append(j)\n",
    "            \n",
    "    return cluster_q, violated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pkg.capacity_vector import capacity_vector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([   0.,  175.,  200.,  200.,  175.,  200.,  200.,  180.,  110.,\n",
       "           0.,    0.,    0.,    0.]), [])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "capacity_vector(Y1,U,J,Q_icp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([   0.,  150.,  200.,  200.,  175.,  150.,  175.,  290.,    0.,\n",
       "           0.,  100.,    0.,    0.]), [7])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "capacity_vector(Y12,U,J,Q_icp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "code_folding": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overwriting ./pkg/capacity_balancing.py\n"
     ]
    }
   ],
   "source": [
    "%%file \"./pkg/capacity_balancing.py\"\n",
    "\n",
    "import numpy as np\n",
    "from pkg.capacity_vector import capacity_vector\n",
    "\n",
    "def capacity_balancing(Y,W,U,J,Q,N_icp):\n",
    "    l = len(capacity_vector(Y,U,J,Q)[1])\n",
    "    max_loop = 10;\n",
    "\n",
    "    # capacity balancing\n",
    "    Ycp = np.copy(Y)\n",
    "    Wcp = np.copy(W)\n",
    "    while l != 0:\n",
    "        max_loop +=-1\n",
    "        j = capacity_vector(Ycp,U,J,Q)[1][0]\n",
    "        i = np.argmax(np.multiply(Ycp,W)[:,j])\n",
    "        # Find condidate and update\n",
    "        Ycp[i][j] = 0\n",
    "        Wcp[i][j] = 100\n",
    "        stop = False\n",
    "        while not stop:\n",
    "            candidate = np.argmin(Wcp[i])\n",
    "            if N_icp[candidate] > 0.5 and candidate != j:\n",
    "                stop = True\n",
    "            else:\n",
    "                Wcp[i][candidate] = 200\n",
    "\n",
    "        print(i,j,candidate)\n",
    "\n",
    "        Ycp[i][candidate] = 1\n",
    "        Wcp[i][candidate] = 100\n",
    "\n",
    "        l = len(capacity_vector(Ycp,U,J,Q)[1])\n",
    "        \n",
    "    return Ycp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([   0.,  150.,  200.,  200.,  175.,  150.,  175.,  290.,    0.,\n",
       "           0.,  100.,    0.,    0.]), [7])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "capacity_vector(Y12,U,J,Q_icp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from pkg.capacity_balancing import capacity_balancing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "code_folding": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "44 7 5\n",
      "48 7 10\n",
      "(array([   0.,  150.,  200.,  200.,  175.,  200.,  175.,  200.,    0.,\n",
      "          0.,  140.,    0.,    0.]), [])\n",
      "Wall time: 4 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "Y12_capa = capacity_balancing(Y12,W,U,J,Q_icp,N_icp2)\n",
    "print(capacity_vector(Y12_capa,U,J,Q_icp))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Swap problem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "code_folding": []
   },
   "outputs": [],
   "source": [
    "def swap_list_icp(Y,W,Djj,N):\n",
    "    cluster_len = np.max(np.multiply(Y,W),axis=0)\n",
    "    swaps = []\n",
    "    for j,l in enumerate(cluster_len):\n",
    "        for j_bis,n in enumerate(DjUc[j]):\n",
    "            if n<l:\n",
    "                print(j,j_bis,n,l)\n",
    "                if j != j_bis and j_bis < len(N) and N[j_bis] == 0:\n",
    "                    swaps.append((j,j_bis))\n",
    "    return swaps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 1 0.0 14.4826137144\n",
      "1 16 14.13263774226811 14.4826137144\n",
      "2 2 0.0 14.8802150522\n",
      "2 9 10.337929561903117 14.8802150522\n",
      "2 18 7.482673759893286 14.8802150522\n",
      "3 1 19.250000000000004 19.3784416298\n",
      "3 3 0.0 19.3784416298\n",
      "3 11 8.497009728444942 19.3784416298\n",
      "3 13 19.288696171592314 19.3784416298\n",
      "3 18 17.510145069848114 19.3784416298\n",
      "3 19 10.107094984064222 19.3784416298\n",
      "4 4 0.0 19.7355516771\n",
      "4 13 6.104506532063019 19.7355516771\n",
      "4 15 14.188810205423438 19.7355516771\n",
      "4 18 17.220624751003797 19.7355516771\n",
      "5 5 0.0 15.7393043048\n",
      "5 14 6.953855045943942 15.7393043048\n",
      "5 15 13.688337303365547 15.7393043048\n",
      "6 6 0.0 13.7835409094\n",
      "6 12 5.484802870614965 13.7835409094\n",
      "7 0 6.394137940332537 13.0944148399\n",
      "7 7 0.0 13.0944148399\n",
      "7 8 9.673516819369118 13.0944148399\n",
      "7 17 8.584729908095179 13.0944148399\n",
      "8 0 3.941822900731233 12.0260779607\n",
      "8 7 9.673516819369118 12.0260779607\n",
      "8 8 0.0 12.0260779607\n",
      "8 10 10.250598522658924 12.0260779607\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[(2, 9), (3, 11), (6, 12), (7, 0), (8, 0), (8, 10)]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "swap_list_icp(Y1,W,DjUc[0:J][0:J],N_icp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def swap_list_crc(Y,W,Dcc,N):\n",
    "    cluster_len = np.max(np.multiply(Y,W),axis=0)\n",
    "    print(cluster_len)\n",
    "    swaps = []\n",
    "    for j,l in enumerate(cluster_len):\n",
    "        for j_bis,n in enumerate(Dcc[j]):\n",
    "            if n<l and j_bis != j:\n",
    "                swaps.append((j,j_bis))\n",
    "    return swaps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "code_folding": []
   },
   "outputs": [],
   "source": [
    "def swap_list_byRoute(Y,W,DjUc,N_icp):\n",
    "    \"\"\"\n",
    "        find the 2 routes and arcs to swap\n",
    "        find wich elemtent to swap with which other element\n",
    "    \"\"\"\n",
    "    cluster_len = np.max(np.multiply(Y,W),axis=0)\n",
    "    swaps = []\n",
    "    for iroute,route in enumerate(routes):\n",
    "        for iarc,arc in enumerate(route): #chose on element\n",
    "            if arc[0] < len(cluster_len): # do not select CRC\n",
    "                for j_bis,n in enumerate(DjUc[arc[0]]): # look at other\n",
    "                    if j_bis < len(cluster_len):\n",
    "                        if n<cluster_len[arc[0]] and cluster_len[j_bis] == 0 and arc[0] != j_bis:\n",
    "                            coarc = [[iroute, ico] for ico,co in enumerate(route) if co[1] == arc[0]]\n",
    "                            swaps.append(([iroute,iarc],coarc[0],arc[0],j_bis))         \n",
    "    return swaps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[([0, 1], [0, 2], 3, 11),\n",
       " ([0, 3], [0, 1], 2, 9),\n",
       " ([1, 0], [1, 1], 6, 12),\n",
       " ([1, 2], [1, 3], 8, 0),\n",
       " ([1, 2], [1, 3], 8, 10),\n",
       " ([1, 4], [1, 2], 7, 0)]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "swap_list = swap_list_byRoute(Y1,W,DjUc,N_icp)\n",
    "swap_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3, 2)\n",
      "(1, 3)\n"
     ]
    }
   ],
   "source": [
    "print(routes[0][1])\n",
    "print(routes[0][2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[(13, 1), (3, 2), (1, 3), (2, 4), (4, 13)],\n",
       " [(6, 5), (14, 6), (8, 7), (5, 8), (7, 14)]]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "routes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "swap = swap_list[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "def swap_arcs(swap, routes, N):\n",
    "    routes_swap = np.copy(routes)\n",
    "    N_swap = N_icp[:]\n",
    "    remove = swap[2]\n",
    "    introduce = swap[3]\n",
    "    \n",
    "    print(\"swap: %s %s\" % (remove,introduce))\n",
    "\n",
    "    arc1 = routes_swap[swap[0][0]][swap[0][1]]\n",
    "    arc2 = routes_swap[swap[1][0]][swap[1][1]]\n",
    "\n",
    "    if arc1[0] == remove:\n",
    "        arc1 = (introduce, arc1[1])\n",
    "    else:\n",
    "        arc1 = (arc1[0], introduce)\n",
    "\n",
    "    if arc2[0] == remove:\n",
    "        arc2 = (introduce, arc2[1])\n",
    "    else:\n",
    "        arc2 = (arc2[0], introduce)\n",
    "\n",
    "    routes_swap[swap[0][0]][swap[0][1]] = arc1\n",
    "    routes_swap[swap[1][0]][swap[1][1]] = arc2\n",
    "    N_swap[remove] = 0\n",
    "    N_swap[introduce] = 1\n",
    "\n",
    "    return routes_swap, N_swap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "swap: 3 11\n"
     ]
    }
   ],
   "source": [
    "routes_swap, N_icp_swap = swap_arcs(swap, routes, N_icp)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[13,  1],\n",
       "        [11,  2],\n",
       "        [ 1, 11],\n",
       "        [ 2,  4],\n",
       "        [ 4, 13]],\n",
       "\n",
       "       [[ 6,  5],\n",
       "        [14,  6],\n",
       "        [ 8,  7],\n",
       "        [ 5,  8],\n",
       "        [ 7, 14]]])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "routes_swap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2570.7318175210671,\n",
       " 508.45410810172194,\n",
       " 183.20887989343095,\n",
       " 800.0,\n",
       " 1079.0688295259142)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pkg.objective_III import objective_III\n",
    "objective_III(Y1,routes_swap,N_icp_swap, N_crc,\n",
    "                I,J,C,B,\n",
    "                W,DjUc,Dc,\n",
    "                FCV,FCT,FCR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
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G2aBtW4suW2nUSE0/adIk+eijj6xux/Xrll2znp4ia9daufCry0WWe6YR9CrF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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xaab9f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pkg.draw_solution_II import draw_solution_II\n",
    "g3 = draw_solution_II(I,J,C,N_crc, N_icp_swap,\n",
    "            Y1, np.zeros((3,8)), routes_swap.tolist(), expid,\n",
    "            G, pos, labels, colors, size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "run_control": {
     "marked": false
    }
   },
   "outputs": [],
   "source": [
    "# swapp customers\n",
    "def new_assignement(W,N_icp_swap):\n",
    "    H1 = [j for j,vj in enumerate(N_icp_swap) if vj > 0.5]\n",
    "    asso = np.argmin(np.transpose(W)[H1],axis=0)\n",
    "    new_Y = np.zeros((I,J))\n",
    "    for a,va in enumerate(asso):\n",
    "        new_Y[a][H1[va]] = 1\n",
    "    return new_Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     0    1    2    3    4    5    6\n",
       "0  1.0  1.0  0.0  0.0  0.0  0.0  0.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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BQUEZtTlFzp0rjMlUjPgQtRYxGoWgoH14eh7FxcUlxc3V1RVnZ+cn/6c0lLBz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\nsNAlqQkLXZKasNAlqQkLXZKasNAlqYlfxXa1VM+pUsAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xabaa208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pkg.draw_solution_II import draw_solution_II\n",
    "\n",
    "new_Y = new_assignement(W,N_icp_swap)\n",
    "g3 = draw_solution_II(I,J,C,N_crc, N_icp_swap,\n",
    "            new_Y, np.zeros((3,8)), routes_swap.tolist(), expid,\n",
    "            G, pos, labels, colors, size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2574.1133152877787,\n",
       " 511.83560586843345,\n",
       " 183.20887989343095,\n",
       " 800.0,\n",
       " 1079.0688295259142)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pkg.objective_III import objective_III\n",
    "objective_III(new_Y,routes_swap,N_icp_swap, N_crc,\n",
    "                I,J,C,B,\n",
    "                W,DjUc,Dc,\n",
    "                FCV,FCT,FCR)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## final Algorithm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 1\n",
    "\n",
    "Find the initial solution SO"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "best_solution = []"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "solving with Cplex two p-MD problem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "44 7 5\n",
      "48 7 10\n",
      "Wall time: 41 ms\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# %%file \"./pkg/pmd_first_tsp_second.py\"\n",
    "\n",
    "# def pmd_first_tsp_second(G,pos, labels, colors, size,\n",
    "#                 I,J,C,\n",
    "#                 W1,W2,W3,F1,F2,\n",
    "#                 U,Q_icp,Q_crc,\n",
    "#                 plots=True, expid=\"\"):\n",
    "    \n",
    "from pkg.cflp_cplex import cflp_cplex\n",
    "from pkg.pm_flp_cplex import pm_flp_cplex\n",
    "from pkg.tsp_cplex import tsp_cplex\n",
    "from pkg.read_problem import read_problem, extract_problem\n",
    "from pkg.draw_solution_II import draw_solution_II\n",
    "from pkg.lrp_nodes_graph import lrp_nodes_graph, lrp_draw_and_save\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from IPython.display import display, HTML\n",
    "\n",
    "W1,W2,W3,F1,F2 = W,Dcj,DjUc,FCT,FCRD\n",
    "plots = True\n",
    "\n",
    "#######################################################################\n",
    "# 1 Solve first problem\n",
    "prob1, Y1, N_icp = pm_flp_cplex(I,J,\n",
    "            W1,8,\n",
    "            relaxation=False)\n",
    "\n",
    "#######################################################################\n",
    "# 2 Prepare 2nd problem\n",
    "H1 = [j for j,vj in enumerate(N_icp) if vj > 0.5]\n",
    "\n",
    "# 12 Prepare 2nd problem\n",
    "Y1_capa = capacity_balancing(Y12,W,U,J,Q_icp,N_icp2)\n",
    "\n",
    "c2=[];    u2=[];\n",
    "for j in H1:\n",
    "    c2.append(W2[j])\n",
    "    sqi = 0\n",
    "    for i in range(I):\n",
    "        sqi += U[i]*Y1[i][j]\n",
    "    u2.append(sqi)\n",
    "\n",
    "#######################################################################        \n",
    "# 3. Solve Second problem\n",
    "prob2, Y2, N_crc = pm_flp_cplex(len(u2),C,\n",
    "        c2,2,\n",
    "        relaxation=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Solving with Cplex p TSP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>4</th>\n",
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      "text/plain": [
       "     0    1    2    3    4    5    6\n",
       "0  1.0  1.0  0.0  0.0  0.0  0.0  0.0"
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "      <th>8</th>\n",
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      "text/plain": [
       "    0    1    2    3    4    5    6    7    8    9    10   11   12\n",
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>(13, 1)</td>\n",
       "      <td>(6, 5)</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>(3, 2)</td>\n",
       "      <td>(14, 6)</td>\n",
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       "    <tr>\n",
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       "      <td>(1, 3)</td>\n",
       "      <td>(10, 7)</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>(2, 4)</td>\n",
       "      <td>(5, 10)</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>(4, 13)</td>\n",
       "      <td>(7, 14)</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         0        1\n",
       "0  (13, 1)   (6, 5)\n",
       "1   (3, 2)  (14, 6)\n",
       "2   (1, 3)  (10, 7)\n",
       "3   (2, 4)  (5, 10)\n",
       "4  (4, 13)  (7, 14)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 272 ms\n"
     ]
    },
    {
     "data": {
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ZPn06Pj4+tG/fnj/++IO8W7eqs7HM/X3ExtL+2DHsHz1i3759REREMG7cuCRJnJ2d6d69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\nHp9++AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xace51d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%time\n",
    "#######################################################################\n",
    "# Preparing TSP\n",
    "H2 = [j for j,vj in enumerate(N_crc) if vj > 0.5]\n",
    "tY2 = np.transpose(Y2)\n",
    "W3 = np.asarray(W3)\n",
    "\n",
    "#/!\\ Use H1 as a labeling array\n",
    "\n",
    "S_tsp = [] #set of ICP in each TSP\n",
    "for c,vc in enumerate(H2):\n",
    "    S_tsp.append([H1[j] for j,vj in enumerate(tY2[c]) if vj > 0.5]+[J+vc])\n",
    "\n",
    "w_tsp = []\n",
    "for submat in S_tsp:\n",
    "    w_tsp.append(W3[submat,:][:,submat])\n",
    "#######################################################################\n",
    "# Solving TSP\n",
    "routes = []\n",
    "for c,vc in enumerate(w_tsp):\n",
    "    prob, X = tsp_cplex(len(vc),vc, relaxation=False)\n",
    "    # Extract routes\n",
    "    path = []\n",
    "    for j,xj in enumerate(X):\n",
    "        for i,xij in enumerate(xj):\n",
    "            if xij == 1:\n",
    "                path.append((S_tsp[c][i],S_tsp[c][j]))\n",
    "    routes.append(path)\n",
    "#######################################################################\n",
    "# Draw solution\n",
    "if(plots):\n",
    "     draw_solution_II(I,J,C,N_crc, N_icp,\n",
    "                Y1,Y2, routes, expid,\n",
    "                G, pos, labels, colors, size)\n",
    "\n",
    "#######################################################################\n",
    "# Compute objective funtion\n",
    "obj = objective_III(new_Y,routes_swap,N_icp_swap, N_crc,\n",
    "                I,J,C,B,\n",
    "                W,DjUc,Dc,\n",
    "                FCV,FCT,FCR)\n",
    "\n",
    "best_solution = { \"obj\" : obj,\n",
    "                  \"Y1\" : Y1,\n",
    "                  \"Y2\" : Y2,\n",
    "                  \"N_crc\" : N_crc,\n",
    "                  \"N_icp\" : N_icp,\n",
    "                  \"routes\" : routes}\n",
    "\n",
    "objective_III(Y1,routes,N_icp, N_crc,\n",
    "            I,J,C,B,\n",
    "            W,DjUc,Dc,\n",
    "            FCV,FCT,FCR)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 2\n",
    "\n",
    "Swappinf loop for ICP"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "scrolled": false
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     "text": [
      "Wall time: 2.01 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# ICP swaping\n",
    "swap_list_icp = swap_list_byRoute(best_solution[\"Y1\"],W,DjUc,best_solution[\"N_icp\"])\n",
    "swap_result = []\n",
    "for iswap, swap in enumerate(swap_list_icp):\n",
    "\n",
    "    routes_swap, N_icp_swap = swap_arcs(swap, best_solution[\"routes\"], best_solution[\"N_icp\"])\n",
    "    new_Y = new_assignement(W,N_icp_swap)\n",
    "    new_Y = capacity_balancing(new_Y,W,U,J,Q_icp,best_solution[\"N_icp\"])\n",
    "    draw_solution_II(I,J,C,N_crc, N_icp_swap,\n",
    "            new_Y, np.zeros((3,8)), routes_swap.tolist(), expid,\n",
    "            G, pos, labels, colors, size, path=expid+ \"/\"+ str(iswap) +\"_swap.png\")\n",
    "    plt.clf();plt.cla();plt.close()\n",
    "    swap_result.append(objective_III(new_Y,routes_swap,N_icp_swap, N_crc,\n",
    "                I,J,C,B,\n",
    "                W,DjUc,Dc,\n",
    "                FCV,FCT,FCR))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(2601.0351282595684,\n",
       "  532.88515275643454,\n",
       "  189.08114597721973,\n",
       "  800.0,\n",
       "  1079.0688295259142),\n",
       " (2623.3759550209788,\n",
       "  541.46527710161422,\n",
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       " (2603.4585548398918,\n",
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       "  191.81095429758474,\n",
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       "  1079.0688295259142),\n",
       " (2572.280187513205,\n",
       "  513.38433089648129,\n",
       "  179.82702709080942,\n",
       "  800.0,\n",
       "  1079.0688295259142),\n",
       " (2560.2776014580104,\n",
       "  505.79077954457165,\n",
       "  175.41799238752444,\n",
       "  800.0,\n",
       "  1079.0688295259142),\n",
       " (2563.9381074214953,\n",
       "  510.99082980754741,\n",
       "  173.8784480880337,\n",
       "  800.0,\n",
       "  1079.0688295259142)]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "swap_result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(swap_result)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_Dcj = [Dcj[j] for j,vj in enumerate(best_solution[\"N_icp\"]) if vj > 0.5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "run_control": {
     "marked": false
    }
   },
   "outputs": [],
   "source": [
    "new_DjUc = [[DjUc[c][j] for c in range(J,C+J,1)] for j in range(J,C+J,1) ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "range(13, 20)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "range(J,C+J,1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Step 3\n",
    "\n",
    "Swappinf loop for CRC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 20.49899998  22.58428657   0.           0.           0.           0.           0.        ]\n"
     ]
    }
   ],
   "source": [
    "swap_list_crc = swap_list_crc(best_solution[\"Y2\"],new_Dcj,new_DjUc,best_solution[\"N_crc\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0, 2), (0, 5), (1, 2), (1, 3), (1, 4)]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "swap_list_crc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import copy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[(13, 1), (3, 2), (1, 3), (2, 4), (4, 13)],\n",
       " [(6, 5), (14, 6), (10, 7), (5, 10), (7, 14)]]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_solution_route_to_swap = copy.deepcopy(best_solution[\"routes\"])\n",
    "best_solution_route_to_swap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0, 2), (0, 5), (1, 2), (1, 3), (1, 4)]"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# swap_crc1 = swap_list_crc[0]\n",
    "swap_list_crc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14 15\n"
     ]
    }
   ],
   "source": [
    "# swap\n",
    "best_solution_route_to_swap = copy.deepcopy(best_solution[\"routes\"])\n",
    "swap_crc1 = swap_list_crc[2]\n",
    "\n",
    "remove    = swap_crc1[0] + J\n",
    "introduce = swap_crc1[1] + J\n",
    "print(remove,introduce)\n",
    "\n",
    "N_crc_swap = best_solution[\"N_crc\"][:]\n",
    "\n",
    "N_crc_swap[swap_crc1[0]] = 0\n",
    "N_crc_swap[swap_crc1[1]] = 1\n",
    "\n",
    "for iroute,route in enumerate(best_solution_route_to_swap):\n",
    "    for iarc,arc in enumerate(route):\n",
    "        if arc[0] == remove:\n",
    "            best_solution_route_to_swap[iroute][iarc] = (introduce, best_solution_route_to_swap[iroute][iarc][1])\n",
    "        if arc[1] == remove:\n",
    "            best_solution_route_to_swap[iroute][iarc] = (best_solution_route_to_swap[iroute][iarc][0], introduce)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[(13, 1), (3, 2), (1, 3), (2, 4), (4, 13)],\n",
       " [(6, 5), (15, 6), (10, 7), (5, 10), (7, 15)]]"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "best_solution_route_to_swap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
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       "    0    1    2    3    4    5    6    7    8    9    10   11   12\n",
       "0 -0.0  1.0  1.0  1.0  1.0  1.0  1.0  1.0  0.0  0.0  1.0 -0.0 -0.0"
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       "         0        1\n",
       "0  (13, 1)   (6, 5)\n",
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     "data": {
      "text/plain": [
       "<networkx.classes.graph.Graph at 0xad49f28>"
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     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
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55xw+fJjjx49Tu3ZtZs2ahZeXF126dOH333+n0Pbt6mwsU38fSUl0OX4c+wcP2L9/P48e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      "text/plain": [
       "<matplotlib.figure.Figure at 0xad49e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "draw_solution_II(I,J,C,N_crc_swap, best_solution[\"N_icp\"],\n",
    "        new_Y, np.zeros((3,8)), best_solution_route_to_swap, expid,\n",
    "        G, pos, labels, colors, size, path=expid+ \"/\"+ str(iswap) +\"_swap_crc.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "code_folding": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0, 2)\n",
      "13 15\n"
     ]
    },
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   "source": [
    "# This is the loop vertion for creatinf picture of the each possible swap\n",
    "for iswap,swap_crc1 in enumerate(swap_list_crc):\n",
    "    print(swap_crc1)\n",
    "\n",
    "    best_solution_route_to_swap = copy.deepcopy(best_solution[\"routes\"])\n",
    "\n",
    "    remove    = swap_crc1[0] + J\n",
    "    introduce = swap_crc1[1] + J\n",
    "    print(remove,introduce)\n",
    "\n",
    "    N_crc_swap = best_solution[\"N_crc\"]\n",
    "\n",
    "    N_crc_swap[swap_crc1[0]] = 0\n",
    "    N_crc_swap[swap_crc1[1]] = 1\n",
    "\n",
    "    for iroute,route in enumerate(best_solution_route_to_swap):\n",
    "        for iarc,arc in enumerate(route):\n",
    "            if arc[0] == remove:\n",
    "                best_solution_route_to_swap[iroute][iarc] = (introduce, best_solution_route_to_swap[iroute][iarc][1])\n",
    "            if arc[1] == remove:\n",
    "                best_solution_route_to_swap[iroute][iarc] = (best_solution_route_to_swap[iroute][iarc][0], introduce)\n",
    "\n",
    "    draw_solution_II(I,J,C,N_crc_swap, best_solution[\"N_icp\"],\n",
    "            new_Y, np.zeros((3,8)), best_solution_route_to_swap, expid,\n",
    "            G, pos, labels, colors, size, path=expid+ \"/\"+ str(iswap) +\"_swap_crc.png\")\n",
    "    plt.clf();plt.cla();plt.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 4\n",
    "\n",
    "Go back to step 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Run experiment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'result1' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-62-a251556947c9>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mobjects\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;34m'UFLP'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'CFLP'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'TUFLP'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'TCFLP'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m \u001b[0my_pos\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobjects\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0mperformance\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mresult1\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbest\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mresult2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbest\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mresult3\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbest\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mresult4\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbest\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbar\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0my_pos\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mperformance\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0malign\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'center'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0.5\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'result1' is not defined"
     ]
    }
   ],
   "source": [
    "objects = ('UFLP', 'CFLP', 'TUFLP', 'TCFLP')\n",
    "y_pos = np.arange(len(objects))\n",
    "performance = [result1.best,result2.best,result3.best,result4.best]\n",
    " \n",
    "plt.bar(y_pos, performance, align='center', alpha=0.5)\n",
    "plt.xticks(y_pos, objects)\n",
    "plt.ylabel('Time s')\n",
    "plt.title('Best processing time for each subproblem')\n",
    " \n",
    "plt.show()"
   ]
  }
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