Network Graph.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import squarify\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "from itertools import combinations\n",
    "import networkx as nx\n",
    "import math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pd.set_option('display.max_colwidth', 0)\n",
    "pd.set_option('display.max_columns', None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".dataframe td {\n",
       "    white-space: nowrap;\n",
       "}\n",
       "</style>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%%html\n",
    "<style>\n",
    ".dataframe td {\n",
    "    white-space: nowrap;\n",
    "}\n",
    "</style>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "LIST_STORES = ['NB4', 'NB5', 'NB3', 'ZS1', 'TZ2', 'TZ1', 'WH2', 'BB1', 'NJ2',\n",
    "       'MAS', 'WH1', 'AQ1', 'NJ3', 'HZ1', 'JX1', 'YP1', 'YP2', 'JD1',\n",
    "       'JD2', 'JS1', 'MH1', 'MH2', 'NH1', 'WGQ1', 'CS1', 'CS2', 'ZJG1',\n",
    "       'CZ1', 'CZ2', 'CZ3', 'YZ1', 'ZJ1', 'DY1', 'DY2', 'WX3', 'NT1',\n",
    "       'NT2', 'HM1', 'SZ1', 'SZ4', 'SZ2', 'KS1', 'SZ3', 'WX1', 'WX2',\n",
    "       'WX4', 'SX1', 'NB6', 'WH3']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import Stores informations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Stores Province"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "52 cities\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Code</th>\n",
       "      <th>Province</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AQ1</td>\n",
       "      <td>ANHUI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BB1</td>\n",
       "      <td>ANHUI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CS1</td>\n",
       "      <td>JIANGSU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CS2</td>\n",
       "      <td>JIANGSU</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>CZ1</td>\n",
       "      <td>JIANGSU</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Code Province\n",
       "0  AQ1  ANHUI  \n",
       "1  BB1  ANHUI  \n",
       "2  CS1  JIANGSU\n",
       "3  CS2  JIANGSU\n",
       "4  CZ1  JIANGSU"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Code and City\n",
    "df_city = pd.read_excel('data/store province.xlsx', index_col = 0)\n",
    "dict_prov = dict(zip(df_city['Code'], df_city['Province']))\n",
    "print(\"{:,} cities\".format(len(df_city)))\n",
    "df_city.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import Transportation Plan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4,791 routes\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Month</th>\n",
       "      <th>Year-Week</th>\n",
       "      <th>Date</th>\n",
       "      <th>Capacity(T)</th>\n",
       "      <th>Total_tons(T)</th>\n",
       "      <th>List_Code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-9</td>\n",
       "      <td>2016-35</td>\n",
       "      <td>2016-09-01</td>\n",
       "      <td>3.5</td>\n",
       "      <td>2.91</td>\n",
       "      <td>['JX1']</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-9</td>\n",
       "      <td>2016-35</td>\n",
       "      <td>2016-09-01</td>\n",
       "      <td>3.5</td>\n",
       "      <td>2.14</td>\n",
       "      <td>['JS1', 'MH1', 'MH2']</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-9</td>\n",
       "      <td>2016-35</td>\n",
       "      <td>2016-09-01</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.50</td>\n",
       "      <td>['HZ1']</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-9</td>\n",
       "      <td>2016-35</td>\n",
       "      <td>2016-09-01</td>\n",
       "      <td>3.5</td>\n",
       "      <td>2.30</td>\n",
       "      <td>['YP2']</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-9</td>\n",
       "      <td>2016-35</td>\n",
       "      <td>2016-09-01</td>\n",
       "      <td>3.5</td>\n",
       "      <td>2.65</td>\n",
       "      <td>['NH1', 'WGQ1']</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Month Year-Week       Date  Capacity(T)  Total_tons(T)  \\\n",
       "0  2016-9  2016-35  2016-09-01  3.5          2.91            \n",
       "1  2016-9  2016-35  2016-09-01  3.5          2.14            \n",
       "2  2016-9  2016-35  2016-09-01  3.5          1.50            \n",
       "3  2016-9  2016-35  2016-09-01  3.5          2.30            \n",
       "4  2016-9  2016-35  2016-09-01  3.5          2.65            \n",
       "\n",
       "               List_Code  \n",
       "0  ['JX1']                \n",
       "1  ['JS1', 'MH1', 'MH2']  \n",
       "2  ['HZ1']                \n",
       "3  ['YP2']                \n",
       "4  ['NH1', 'WGQ1']        "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Address Data frame\n",
    "df_plan = pd.read_excel('data/delivery records.xlsx', index_col = 0)\n",
    "print(\"{:,} routes\".format(len(df_plan)))\n",
    "df_plan.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "#### % per Truck size for each month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Count shipments per size/month\n",
    "df_count = pd.DataFrame(df_plan.groupby(['Capacity(T)', 'Month'])['Total_tons(T)'].count())\n",
    "df_count.columns = ['Routes']\n",
    "df_count['%Routes'] = (100 * df_count['Routes']/df_count['Routes'].sum()).round(2)\n",
    "\n",
    "# Pivot table\n",
    "df_count = pd.pivot_table(df_count, index = 'Month', values = 'Routes', columns = 'Capacity(T)').fillna(0)\n",
    "df_count\n",
    "\n",
    "# Bar Plot\n",
    "ax = df_count.plot.bar(figsize=(10, 6), edgecolor='black', y=3.5, color='tab:blue', legend= True)\n",
    "df_count.plot.bar( edgecolor='black', y=5.0, color='tab:green', legend= True, ax = ax)\n",
    "df_count.plot.bar( edgecolor='black', y=8.0, color='tab:brown', legend= True, ax = ax)\n",
    "plt.xlabel('Month', fontsize = 12)\n",
    "plt.ylabel('Number of routes', fontsize = 12)\n",
    "plt.title('Number of routes per truck size', fontsize = 12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Calculation\n",
    "#### Mapping with columns (boolean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12 Months\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Month</th>\n",
       "      <th>Year-Week</th>\n",
       "      <th>Date</th>\n",
       "      <th>Size</th>\n",
       "      <th>Tons</th>\n",
       "      <th>Stores</th>\n",
       "      <th>NB4</th>\n",
       "      <th>NB5</th>\n",
       "      <th>NB3</th>\n",
       "      <th>ZS1</th>\n",
       "      <th>TZ2</th>\n",
       "      <th>TZ1</th>\n",
       "      <th>WH2</th>\n",
       "      <th>BB1</th>\n",
       "      <th>NJ2</th>\n",
       "      <th>MAS</th>\n",
       "      <th>WH1</th>\n",
       "      <th>AQ1</th>\n",
       "      <th>NJ3</th>\n",
       "      <th>HZ1</th>\n",
       "      <th>JX1</th>\n",
       "      <th>YP1</th>\n",
       "      <th>YP2</th>\n",
       "      <th>JD1</th>\n",
       "      <th>JD2</th>\n",
       "      <th>JS1</th>\n",
       "      <th>MH1</th>\n",
       "      <th>MH2</th>\n",
       "      <th>NH1</th>\n",
       "      <th>WGQ1</th>\n",
       "      <th>CS1</th>\n",
       "      <th>CS2</th>\n",
       "      <th>ZJG1</th>\n",
       "      <th>CZ1</th>\n",
       "      <th>CZ2</th>\n",
       "      <th>CZ3</th>\n",
       "      <th>YZ1</th>\n",
       "      <th>ZJ1</th>\n",
       "      <th>DY1</th>\n",
       "      <th>DY2</th>\n",
       "      <th>WX3</th>\n",
       "      <th>NT1</th>\n",
       "      <th>NT2</th>\n",
       "      <th>HM1</th>\n",
       "      <th>SZ1</th>\n",
       "      <th>SZ4</th>\n",
       "      <th>SZ2</th>\n",
       "      <th>KS1</th>\n",
       "      <th>SZ3</th>\n",
       "      <th>WX1</th>\n",
       "      <th>WX2</th>\n",
       "      <th>WX4</th>\n",
       "      <th>SX1</th>\n",
       "      <th>NB6</th>\n",
       "      <th>WH3</th>\n",
       "      <th>#Stores</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-9</td>\n",
       "      <td>2016-35</td>\n",
       "      <td>2016-09-01</td>\n",
       "      <td>3.5</td>\n",
       "      <td>2.91</td>\n",
       "      <td>['JX1']</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-9</td>\n",
       "      <td>2016-35</td>\n",
       "      <td>2016-09-01</td>\n",
       "      <td>3.5</td>\n",
       "      <td>2.14</td>\n",
       "      <td>['JS1', 'MH1', 'MH2']</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-9</td>\n",
       "      <td>2016-35</td>\n",
       "      <td>2016-09-01</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.50</td>\n",
       "      <td>['HZ1']</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-9</td>\n",
       "      <td>2016-35</td>\n",
       "      <td>2016-09-01</td>\n",
       "      <td>3.5</td>\n",
       "      <td>2.30</td>\n",
       "      <td>['YP2']</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-9</td>\n",
       "      <td>2016-35</td>\n",
       "      <td>2016-09-01</td>\n",
       "      <td>3.5</td>\n",
       "      <td>2.65</td>\n",
       "      <td>['NH1', 'WGQ1']</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Month Year-Week       Date  Size  Tons                 Stores  NB4  NB5  \\\n",
       "0  2016-9  2016-35  2016-09-01  3.5   2.91  ['JX1']                0    0     \n",
       "1  2016-9  2016-35  2016-09-01  3.5   2.14  ['JS1', 'MH1', 'MH2']  0    0     \n",
       "2  2016-9  2016-35  2016-09-01  3.5   1.50  ['HZ1']                0    0     \n",
       "3  2016-9  2016-35  2016-09-01  3.5   2.30  ['YP2']                0    0     \n",
       "4  2016-9  2016-35  2016-09-01  3.5   2.65  ['NH1', 'WGQ1']        0    0     \n",
       "\n",
       "   NB3  ZS1  TZ2  TZ1  WH2  BB1  NJ2  MAS  WH1  AQ1  NJ3  HZ1  JX1  YP1  YP2  \\\n",
       "0  0    0    0    0    0    0    0    0    0    0    0    0    1    0    0     \n",
       "1  0    0    0    0    0    0    0    0    0    0    0    0    0    0    0     \n",
       "2  0    0    0    0    0    0    0    0    0    0    0    1    0    0    0     \n",
       "3  0    0    0    0    0    0    0    0    0    0    0    0    0    0    1     \n",
       "4  0    0    0    0    0    0    0    0    0    0    0    0    0    0    0     \n",
       "\n",
       "   JD1  JD2  JS1  MH1  MH2  NH1  WGQ1  CS1  CS2  ZJG1  CZ1  CZ2  CZ3  YZ1  \\\n",
       "0  0    0    0    0    0    0    0     0    0    0     0    0    0    0     \n",
       "1  0    0    1    1    1    0    0     0    0    0     0    0    0    0     \n",
       "2  0    0    0    0    0    0    0     0    0    0     0    0    0    0     \n",
       "3  0    0    0    0    0    0    0     0    0    0     0    0    0    0     \n",
       "4  0    0    0    0    0    1    1     0    0    0     0    0    0    0     \n",
       "\n",
       "   ZJ1  DY1  DY2  WX3  NT1  NT2  HM1  SZ1  SZ4  SZ2  KS1  SZ3  WX1  WX2  WX4  \\\n",
       "0  0    0    0    0    0    0    0    0    0    0    0    0    0    0    0     \n",
       "1  0    0    0    0    0    0    0    0    0    0    0    0    0    0    0     \n",
       "2  0    0    0    0    0    0    0    0    0    0    0    0    0    0    0     \n",
       "3  0    0    0    0    0    0    0    0    0    0    0    0    0    0    0     \n",
       "4  0    0    0    0    0    0    0    0    0    0    0    0    0    0    0     \n",
       "\n",
       "   SX1  NB6  WH3  #Stores  \n",
       "0  0    0    0    1        \n",
       "1  0    0    0    3        \n",
       "2  0    0    0    1        \n",
       "3  0    0    0    1        \n",
       "4  0    0    0    2        "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Analysis\n",
    "df_an = df_plan[['Month', 'Year-Week', 'Date', 'Capacity(T)','Total_tons(T)', 'List_Code']].copy()\n",
    "df_an.columns = ['Month', 'Year-Week', 'Date', 'Size', 'Tons', 'Stores']\n",
    "\n",
    "# Add boolean\n",
    "for store in LIST_STORES:\n",
    "    df_an[store] = df_an['Stores'].apply(lambda t: store in t)*1\n",
    "    \n",
    "# Number of stores\n",
    "df_an['#Stores'] = df_an[LIST_STORES].sum(axis =1)\n",
    "print(\"{} Months\".format(df_an['Month'].nunique()))\n",
    "df_an.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Number of routes per number of store/route"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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NyHI9+oPnGaXUKej5+NqgB/F/ie65dpBhGKVKqaHo6xBfK6V+Qn97KkB/8z0KaIV+Y1dl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\nECJpSRIUQgiRtP4fufLPwASLhYgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "my_circle = plt.Circle( (0,0), 0.8, color='white')\n",
    "\n",
    "\n",
    "df_an.groupby(['Size']).sum().plot.pie(figsize=(8, 8), y='#Stores', legend= False, pctdistance=0.7,\n",
    "                                          autopct='%1.1f%%', labeldistance=1.05, \n",
    "                                          wedgeprops = { 'linewidth' : 7, 'edgecolor' : 'white' })\n",
    "plt.xlabel('Business Vertical')\n",
    "plt.title('{:,} delivery routes per truck size (T)'.format(len(df_an)))\n",
    "p = plt.gcf()\n",
    "p.gca().add_artist(my_circle)\n",
    "plt.axis('off')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Multiple Line Plot: Stores per Route"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>3.5 T</th>\n",
       "      <th>5 T</th>\n",
       "      <th>8 T</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Month</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-10</th>\n",
       "      <td>1.866197</td>\n",
       "      <td>2.291139</td>\n",
       "      <td>2.370370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-11</th>\n",
       "      <td>1.843049</td>\n",
       "      <td>1.896825</td>\n",
       "      <td>2.116667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12</th>\n",
       "      <td>1.793269</td>\n",
       "      <td>1.917241</td>\n",
       "      <td>1.927835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-9</th>\n",
       "      <td>1.802575</td>\n",
       "      <td>2.100000</td>\n",
       "      <td>2.222222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-1</th>\n",
       "      <td>1.864706</td>\n",
       "      <td>1.816794</td>\n",
       "      <td>1.808219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-2</th>\n",
       "      <td>2.113971</td>\n",
       "      <td>2.250000</td>\n",
       "      <td>2.909091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-3</th>\n",
       "      <td>1.918288</td>\n",
       "      <td>2.080357</td>\n",
       "      <td>2.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-4</th>\n",
       "      <td>1.867384</td>\n",
       "      <td>2.213333</td>\n",
       "      <td>2.769231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-5</th>\n",
       "      <td>1.811765</td>\n",
       "      <td>2.156250</td>\n",
       "      <td>2.344828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-6</th>\n",
       "      <td>1.902834</td>\n",
       "      <td>2.082474</td>\n",
       "      <td>2.015873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-7</th>\n",
       "      <td>1.798319</td>\n",
       "      <td>2.068966</td>\n",
       "      <td>2.266667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-8</th>\n",
       "      <td>1.861751</td>\n",
       "      <td>2.259740</td>\n",
       "      <td>2.542857</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            3.5 T       5 T       8 T\n",
       "Month                                \n",
       "2016-10  1.866197  2.291139  2.370370\n",
       "2016-11  1.843049  1.896825  2.116667\n",
       "2016-12  1.793269  1.917241  1.927835\n",
       "2016-9   1.802575  2.100000  2.222222\n",
       "2017-1   1.864706  1.816794  1.808219\n",
       "2017-2   2.113971  2.250000  2.909091\n",
       "2017-3   1.918288  2.080357  2.500000\n",
       "2017-4   1.867384  2.213333  2.769231\n",
       "2017-5   1.811765  2.156250  2.344828\n",
       "2017-6   1.902834  2.082474  2.015873\n",
       "2017-7   1.798319  2.068966  2.266667\n",
       "2017-8   1.861751  2.259740  2.542857"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_strou = pd.DataFrame(df_an.groupby(['Month', 'Size'])['#Stores'].mean())\n",
    "df_strou.columns = ['stores']\n",
    "df_strou.reset_index(inplace = True)\n",
    "\n",
    "# Pivot Table\n",
    "df_strou = pd.pivot_table(df_strou, index = 'Month', values = 'stores', columns = 'Size').fillna(0)[[3.5, 5.0, 8.0]]\n",
    "df_strou.columns = ['3.5 T', '5 T', '8 T']\n",
    "df_strou"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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GG4cdykBsu+22LF++nEWLFs2rvcW0JEmSZrRhwwZ22GEHVq1axaSfAqSquPLKK9mwYQN3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\nemQxLUmSJPXIYlqSJEnq0f8POXoxm8rKWosAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = df_strou.plot.bar(figsize = (12, 6), y = ['3.5 T', '5 T', '8 T'],\n",
    "                           edgecolor = 'black',\n",
    "                            color = ['tab:red', 'yellow', 'tab:blue'], \n",
    "                            legend = True)\n",
    "plt.xlabel('(Month)'\n",
    "           , fontsize = 12)\n",
    "plt.ylabel('(Deliveries/Route)'\n",
    "           , fontsize = 12)\n",
    "plt.title('Number of Store Deliveries per Route for each Truck Type'\n",
    "          , fontsize = 12)\n",
    "plt.ylim([1.5, 3])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Build the Graphs\n",
    "### Create the nodes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1,176 combinations\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Source</th>\n",
       "      <th>Target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NB4</td>\n",
       "      <td>NB5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NB4</td>\n",
       "      <td>NB3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NB4</td>\n",
       "      <td>ZS1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NB4</td>\n",
       "      <td>TZ2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NB4</td>\n",
       "      <td>TZ1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Source Target\n",
       "0  NB4    NB5  \n",
       "1  NB4    NB3  \n",
       "2  NB4    ZS1  \n",
       "3  NB4    TZ2  \n",
       "4  NB4    TZ1  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# All stores combinations\n",
    "L1 = [i for i in combinations(LIST_STORES,2)]\n",
    "df_nodes = pd.DataFrame({'Pair': L1})\n",
    "# Source/Target\n",
    "df_nodes['Source'] = df_nodes['Pair'].apply(lambda t: t[0])\n",
    "df_nodes['Target'] = df_nodes['Pair'].apply(lambda t: t[1])\n",
    "df_nodes.drop(['Pair'], axis =1, inplace = True)\n",
    "\n",
    "print(\"{:,} combinations\".format(len(df_nodes)))\n",
    "df_nodes.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Number of deliveries\n",
    "#### All Size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "94/1176 non zero\n"
     ]
    },
    {
     "data": {
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       "      <th>Source</th>\n",
       "      <th>Target</th>\n",
       "      <th>2016-9</th>\n",
       "      <th>2016-10</th>\n",
       "      <th>2016-11</th>\n",
       "      <th>2016-12</th>\n",
       "      <th>2017-1</th>\n",
       "      <th>2017-2</th>\n",
       "      <th>2017-3</th>\n",
       "      <th>2017-4</th>\n",
       "      <th>2017-5</th>\n",
       "      <th>2017-6</th>\n",
       "      <th>2017-7</th>\n",
       "      <th>2017-8</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NB4</td>\n",
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       "      <th>1</th>\n",
       "      <td>NB4</td>\n",
       "      <td>NB3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NB4</td>\n",
       "      <td>ZS1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NB4</td>\n",
       "      <td>TZ2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NB4</td>\n",
       "      <td>TZ1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Source Target  2016-9  2016-10  2016-11  2016-12  2017-1  2017-2  2017-3  \\\n",
       "0  NB4    NB5    0       0        0        0        0       0       0        \n",
       "1  NB4    NB3    0       0        0        0        0       0       0        \n",
       "2  NB4    ZS1    0       0        1        0        1       0       0        \n",
       "3  NB4    TZ2    0       0        0        0        0       0       0        \n",
       "4  NB4    TZ1    0       0        0        0        0       0       0        \n",
       "\n",
       "   2017-4  2017-5  2017-6  2017-7  2017-8  Total  \n",
       "0  0       0       0       0       0       0      \n",
       "1  0       0       0       0       0       0      \n",
       "2  0       1       2       0       0       5      \n",
       "3  0       0       0       0       0       0      \n",
       "4  0       0       0       0       0       0      "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# By Month\n",
    "df_del1 = df_nodes.copy()\n",
    "for m in df_an['Month'].unique():\n",
    "    df_temp = df_an[df_an['Month'] == m]\n",
    "    df_del1[m] = df_del1[['Source', 'Target']].apply(\n",
    "        lambda t: (df_temp[t['Source']] * df_temp[t['Target']]).sum().sum()/2, \n",
    "                                                       axis = 1).astype(int)\n",
    "df_del1['Total'] = df_del1[df_an['Month'].unique()].sum(axis=1)\n",
    "print(\"{}/{} non zero\".format((df_del1['Total']>0).sum(), len(df_del1)))\n",
    "# Process\n",
    "df_del1.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Deliveries 3.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "89/1176 non zero\n"
     ]
    },
    {
     "data": {
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NB4</td>\n",
       "      <td>ZS1</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NB4</td>\n",
       "      <td>TZ2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NB4</td>\n",
       "      <td>TZ1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Source Target  2016-9  2016-10  2016-11  2016-12  2017-1  2017-2  2017-3  \\\n",
       "0  NB4    NB5    0       0        0        0        0       0       0        \n",
       "1  NB4    NB3    0       0        0        0        0       0       0        \n",
       "2  NB4    ZS1    0       0        1        0        0       0       0        \n",
       "3  NB4    TZ2    0       0        0        0        0       0       0        \n",
       "4  NB4    TZ1    0       0        0        0        0       0       0        \n",
       "\n",
       "   2017-4  2017-5  2017-6  2017-7  2017-8  Total  \n",
       "0  0       0       0       0       0       0      \n",
       "1  0       0       0       0       0       0      \n",
       "2  0       1       1       0       0       3      \n",
       "3  0       0       0       0       0       0      \n",
       "4  0       0       0       0       0       0      "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# By Month\n",
    "df_del2 = df_nodes.copy()\n",
    "for m in df_an['Month'].unique():\n",
    "    df_temp = df_an[(df_an['Month'] == m) & (df_an['Size'] == 3.5)]\n",
    "    df_del2[m] = df_del2[['Source', 'Target']].apply(\n",
    "        lambda t: (df_temp[t['Source']] * df_temp[t['Target']]).sum().sum()/2, \n",
    "                                                       axis = 1).astype(int)\n",
    "df_del2['Total'] = df_del2[df_an['Month'].unique()].sum(axis=1)\n",
    "print(\"{}/{} non zero\".format((df_del2['Total']>0).sum(), len(df_del2)))\n",
    "# Process\n",
    "df_del2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Deliveries 5.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "73/1176 non zero\n"
     ]
    },
    {
     "data": {
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       "      <th>2</th>\n",
       "      <td>NB4</td>\n",
       "      <td>ZS1</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NB4</td>\n",
       "      <td>TZ2</td>\n",
       "      <td>0</td>\n",
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       "      <th>4</th>\n",
       "      <td>NB4</td>\n",
       "      <td>TZ1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Source Target  2016-9  2016-10  2016-11  2016-12  2017-1  2017-2  2017-3  \\\n",
       "0  NB4    NB5    0       0        0        0        0       0       0        \n",
       "1  NB4    NB3    0       0        0        0        0       0       0        \n",
       "2  NB4    ZS1    0       0        0        0        0       0       0        \n",
       "3  NB4    TZ2    0       0        0        0        0       0       0        \n",
       "4  NB4    TZ1    0       0        0        0        0       0       0        \n",
       "\n",
       "   2017-4  2017-5  2017-6  2017-7  2017-8  Total  \n",
       "0  0       0       0       0       0       0      \n",
       "1  0       0       0       0       0       0      \n",
       "2  0       0       1       0       0       1      \n",
       "3  0       0       0       0       0       0      \n",
       "4  0       0       0       0       0       0      "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# By Month\n",
    "df_del3 = df_nodes.copy()\n",
    "for m in df_an['Month'].unique():\n",
    "    df_temp = df_an[(df_an['Month'] == m) & (df_an['Size'] == 5.0)]\n",
    "    df_del3[m] = df_del3[['Source', 'Target']].apply(\n",
    "        lambda t: (df_temp[t['Source']] * df_temp[t['Target']]).sum().sum()/2, \n",
    "                                                       axis = 1).astype(int)\n",
    "df_del3['Total'] = df_del3[df_an['Month'].unique()].sum(axis=1)\n",
    "print(\"{}/{} non zero\".format((df_del3['Total']>0).sum(), len(df_del2)))\n",
    "# Process\n",
    "df_del3.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Deliveries 8.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "60/1176 non zero\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Source</th>\n",
       "      <th>Target</th>\n",
       "      <th>2016-9</th>\n",
       "      <th>2016-10</th>\n",
       "      <th>2016-11</th>\n",
       "      <th>2016-12</th>\n",
       "      <th>2017-1</th>\n",
       "      <th>2017-2</th>\n",
       "      <th>2017-3</th>\n",
       "      <th>2017-4</th>\n",
       "      <th>2017-5</th>\n",
       "      <th>2017-6</th>\n",
       "      <th>2017-7</th>\n",
       "      <th>2017-8</th>\n",
       "      <th>Total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NB4</td>\n",
       "      <td>NB5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NB4</td>\n",
       "      <td>NB3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NB4</td>\n",
       "      <td>ZS1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NB4</td>\n",
       "      <td>TZ2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NB4</td>\n",
       "      <td>TZ1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Source Target  2016-9  2016-10  2016-11  2016-12  2017-1  2017-2  2017-3  \\\n",
       "0  NB4    NB5    0       0        0        0        0       0       0        \n",
       "1  NB4    NB3    0       0        0        0        0       0       0        \n",
       "2  NB4    ZS1    0       0        0        0        0       0       0        \n",
       "3  NB4    TZ2    0       0        0        0        0       0       0        \n",
       "4  NB4    TZ1    0       0        0        0        0       0       0        \n",
       "\n",
       "   2017-4  2017-5  2017-6  2017-7  2017-8  Total  \n",
       "0  0       0       0       0       0       0      \n",
       "1  0       0       0       0       0       0      \n",
       "2  0       0       0       0       0       0      \n",
       "3  0       0       0       0       0       0      \n",
       "4  0       0       0       0       0       0      "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# By Month\n",
    "df_del4 = df_nodes.copy()\n",
    "for m in df_an['Month'].unique():\n",
    "    df_temp = df_an[(df_an['Month'] == m) & (df_an['Size'] == 8.0)]\n",
    "    df_del4[m] = df_del4[['Source', 'Target']].apply(\n",
    "        lambda t: (df_temp[t['Source']] * df_temp[t['Target']]).sum().sum()/2, \n",
    "                                                       axis = 1).astype(int)\n",
    "df_del4['Total'] = df_del4[df_an['Month'].unique()].sum(axis=1)\n",
    "print(\"{}/{} non zero\".format((df_del4['Total']>0).sum(), len(df_del2)))\n",
    "# Process\n",
    "df_del4.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "scrolled": false
   },
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Network Graphs (All Sizes)\n",
    "#### Full Scope"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# Month \n",
    "df_graph = df_del1.copy().reset_index()\n",
    "df_graph = df_graph[df_graph['Total']>0].copy().reset_index()\n",
    "\n",
    "# Add the city\n",
    "for dic, loc in zip([dict_prov], ['Province']):\n",
    "    df_graph['Source ' + loc] = df_graph['Source'].map(dic)\n",
    "    df_graph['Target ' + loc] = df_graph['Target'].map(dic)\n",
    "# Unique cities\n",
    "PROVINCES = list(df_graph['Source Province'].unique()) + list(df_graph['Target Province'].unique()) \n",
    "n_prov = len(set(PROVINCES))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ePTrD823cuJG3Pnsr0+smxCZgZ2v3yP2KiF4tISLyWF6f8DpHvj9C4PbANI//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\nTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot\n",
    "fig, ax = plt.subplots(figsize = (10, 7))\n",
    "G = nx.Graph()\n",
    "for link in df_graph.index:\n",
    "    G.add_edge(df_graph.iloc[link]['Source'],\n",
    "             df_graph.iloc[link]['Target'])\n",
    "# Add Colors by city\n",
    "df_cat = df_city.set_index('Code')\n",
    "df_cat = df_cat.reindex(G.nodes())\n",
    "df_cat['Province'] = pd.Categorical(df_cat['Province'])\n",
    "df_cat['Province'].cat.codes\n",
    "\n",
    "dict_color = dict(zip(['ZHEJIANG', 'ANHUI', 'JIANGSU', 'SHANGHAI'], [\"black\", \"red\", \"lightgreen\", \"orange\"]))\n",
    "\n",
    "# Position of labels\n",
    "def nudge(pos, x_shift, y_shift):\n",
    "    return {n:(x + x_shift, y + y_shift) for n,(x,y) in pos.items()}\n",
    "pos = nx.spring_layout(G)\n",
    "pos_nodes = nudge(pos, 0, 0.1)  \n",
    "\n",
    "# Set node_color\n",
    "nx.draw(G, with_labels=False, node_color=df_cat['Province'].map(dict_color),\n",
    "       node_size=100, edgecolors='black', alpha = 1, \n",
    "        font_size = 11, font_color = 'black', width=1 , verticalalignment='top')\n",
    "# plt.title(\"{}\".format('FULL SCOPE'), fontsize=40)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.5 T Trucks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Month \n",
    "df_graph = df_del2.copy().reset_index()\n",
    "df_graph = df_graph[df_graph['Total']>0].copy().reset_index()\n",
    "\n",
    "# Add the city\n",
    "for dic, loc in zip([dict_prov], ['Province']):\n",
    "    df_graph['Source ' + loc] = df_graph['Source'].map(dic)\n",
    "    df_graph['Target ' + loc] = df_graph['Target'].map(dic)\n",
    "# Unique cities\n",
    "PROVINCES = list(df_graph['Source Province'].unique()) + list(df_graph['Target Province'].unique()) \n",
    "n_prov = len(set(PROVINCES))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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HMcEiIgZ09OhRit1TjKRnkyCt3Tp2QdSiKOLi43A3dUP5dG76PVABLOcshGwM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\n9/9zDxN6QQQjMQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot\n",
    "fig, ax = plt.subplots(figsize = (10, 7))\n",
    "G = nx.Graph()\n",
    "for link in df_graph.index:\n",
    "    G.add_edge(df_graph.iloc[link]['Source'],\n",
    "             df_graph.iloc[link]['Target'])\n",
    "# Add Colors by city\n",
    "df_cat = df_city.set_index('Code')\n",
    "df_cat = df_cat.reindex(G.nodes())\n",
    "df_cat['Province'] = pd.Categorical(df_cat['Province'])\n",
    "df_cat['Province'].cat.codes\n",
    "\n",
    "# Specify colors\n",
    "cmap = matplotlib.colors.ListedColormap([\"black\", \"blue\", \"red\", \"green\", \"orange\"])\n",
    "dict_color = dict(zip(['ZHEJIANG', 'ANHUI', 'JIANGSU', 'SHANGHAI'], [\"black\", \"blue\", \"green\", \"orange\"]))\n",
    "\n",
    "# Position of labels\n",
    "def nudge(pos, x_shift, y_shift):\n",
    "    return {n:(x + x_shift, y + y_shift) for n,(x,y) in pos.items()}\n",
    "pos = nx.spring_layout(G)\n",
    "pos_nodes = nudge(pos, 0, 0.1)  \n",
    "\n",
    "# Set node_color\n",
    "nx.draw(G, with_labels=False, node_color=df_cat['Province'].map(dict_color),\n",
    "       node_size=100, edgecolors='black', alpha = 1, \n",
    "        font_size = 11, font_color = 'black', width=1 , verticalalignment='top')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Network Graph (Monthly)\n",
    "#### Month (All Size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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A0tPT8x0XFRXFsWPHTs3yVKpUiSNHjhR4/djYWOLi4golqwho0bKIiFyA//73vwWWHQCv\n18tLL71EtWrVSE9P5+jRo+d0/Z07d15sRJHTqPCIiMh5S0tLO6dxXq+XzZs343K5cDgcud7+yo3N\nZruYeCJnUeEREZHzduWVV7J8+XJ8Pl++40JCQhg7diz169cHYOnSpWzYsCHfcywWC61atSq0rCKg\nRcsiInIBBg0ahMPhKHBc7dq1T5UdgOHDhxMREZHvOU6nk+eee+6iM4r8kwqPiIict0aNGtGhQ4fT\nHjk/U3h4OGPGjDnts3vuuYeOHTvidDpzPcfpdNKnTx/atGlTqHlF9JSWiIhckKysLO6++26WLVt2\n6gWCkFN0AD777DN69ux51nk+n4+RI0cyduxYLBYLhmFgsViw2Wy88MILPP3001gsloB+FzE/FR4R\nEblgf20RMXbsWLZs2UJoaCh33303Dz30EGXKlMn33OzsbJYsWcLJkycpX748bdq0KXBLCpELpcIj\nIiIipqc1PCIiImJ6KjwiIiJieio8IiIiYnpaHWYSbrebOXPmsHjxYjweD82bN6dPnz5ER0cHO5qI\niEjQadGyCSxcuJCePXvi9XpP7W0TERGB3+/nzTffZOjQoUFOKCIiElwqPMXc0qVL6dKlC1lZWbke\ndzqdvPnmmwwZMiTAyURERC4dKjzFmGEY1K1bl127duU7Ljw8nGPHjhEZGRmgZCIiIpcWLVouxn79\n9VeOHDlS4Dir1crUqVMDkEhEROTSpMJTjG3ZsuWcxmVkZLB+/foiTiMiInLp0lNaxZjNZjvn/WZC\nQkJO++ft27ezYsUKfD4fjRo14sYbb9TeNSIiYloqPMVYy5Yt8Xq9BY5zOp20b98egJ07d9K3b1/i\n4uJObdpns9koU6YMEyZMoGPHjkUdW0REJOC0aLmYa926NT///POpXYpzY7FY6N69O4MGDeLOO+8k\nNTU11/Hh4eFMnz6drl27FmVkERGRgFPhKeb27NnDNddcQ0pKSq4lxul0Mn36dH799Vdee+01fD4f\n+f1fHh0dzfHjx3E4HEUZW0REJKC0aLmYu+KKK/j1119p1aoVYWFhREZGEhkZidPppEGDBixcuJBb\nb72Vvn37YrPZ8i07kPOo++zZswOUXkREJDC0hscErrjiCpYuXcrevXtZtWoVXq+X2NhYmjRpcmrM\nqlWrcDgcZGdn53uttLQ0fvzxR3r27FnEqUVERAJHhcdEatasSc2aNXM9VtCtrDPHioiImIluaV0m\nmjRpku/C5r9ERERw/fXXByCRiIhI4GjR8mWkcePGbNq0Kd8xTqeTxMREbUMhIiKmohmey8iECRNw\nOp15Hnc6nYwePVplR0RETEczPJeZFStW0L17dzIzM0lLSwMgMjISwzAYPXo0gwYNCnJCERGRwqfC\ncxny+XwsXLiQxYsX4/V6adq0KT169Mh39kdERORcbNiw4dQLcZs0aULr1q0via2LVHhERETkom3c\nuJE+D/Yh4WAC/tp+DKtByL4QSoaV5LPxn9GhQ4eg5lPhERERkYsSFxdHy3YtSW+TDrH8vULYAHaC\nc76TWV/PonPnzkHLqMIjIiIiFyW2eSybK2+GpnkM2Aul/leKxIOJ2O3BeQWgntISERGRCxYXF8fu\nvbtzZnbyUhM8UR6+//77gOU6kwqPiIiIXLDVq1dDLcCW/7i0amms/HllQDLlRoVHRERELpjf78ew\nnMPqGAv4/MHbukiFR0RERC7Y1VdfjTXBCgXsXhR1MIprr7k2MKFyoUXLIiIicsEMw6BebD12XrkT\nGuYx6BBEz47m+OHjOByOgOb7i2Z4RERE5IJZLBYmj5+Mc5ETdpDzKPo/HQLnLCcTPpwQtLIDmuER\nERGRQrBs2TJ69u1JOumk10jPmVLZDpwAu8VOg9gGPPfkc/To0YOQkJCA51PhERERkULh9/v53//+\nx9PPPc2uA7vw1vNCayAE2AsR6yNoUK4BPy38KeAbVeuWloiIiBQKq9XK0hVL2e/Zj3eIF24FooAw\n4ErI6J3BJvcmevXtFfBsmuERERGRQpGRkUG5SuXIfDATSuYxyA1hH4Tx+8bfqVmzZsCyaYZHRERE\nCsX8+fOxVbHlXXYAHOBr6OPrr78OWC5Q4REREZFCcvz4cTzRngLHeaI8HDp6KACJ/qbCIyIiIoWi\ndOnShKQX/ASWPd1O+bLlA5Dobyo8IiIiUii6dOmCd58XUvIZ5AF7vJ1ePQO7cFmFR0RERApFVFQU\nAwYMwLnQCd5cBhjg+MlBqxatqFOnTkCz6SktERERKTQej4eud3bl5/ifyWieAfUAC5AAzl+d1Ayr\nycqfVlKiRImA5lLhERERkULl8/mYPn06b499m03rN4EBNerV4Nmhz9KvXz/Cw8MDnkmFR0RERIqM\nYRgYhoHVGtxVNCo8IiIiYnpatCwiIiKmZw92gEA5ePAgH330EUuWLMEwDFq3bs3gwYOpXr16sKOJ\niIhIEbssbmmNGjWK1157DcMwyM7OBsDhcGC1Wnn66ad59dVXsVgsQU4pIiIiRcX0MzwffPABr7/+\nOi6X67TP3W43AO+++y6RkZGMGDEiGPFEREQkAEw9w5OdnU25cuVITU3Nd5zT6eTYsWNEREQEKJmI\niIgEkqkXLc+bN49z6XNWq5VZs2YFIJGIiIgEg6kLz549e8jKyipwXHp6Ort378712JYtWxgwYABV\nqlShQoUKtG/fnvnz5+P3+ws7roiIiBQRU6/hCQsLw2634/XmtqHH32w2G2FhYWd9PnLkSN566y3c\nbjc+nw+AxMRE1q5dS9OmTVmwYIFug4mIiBQDpl7Ds2PHDpo0aVLgLI/FYqFcuXLcf//99OzZk2bN\nmjF58mSGDBlCZmZmrueEhYXRvn17vvvuu6KILiIiIoXI1IUHoGXLlqxZsybPWR6r1Urjxo358ssv\nmTZtGtOmTcNisXDkyBEyMjLyvXZ4eDi//fYb9erVK4roIiIiUkhMvYYHYNq0aZQuXRq7/ey7dzab\njZIlSzJr1iwaNWrEa6+9xs6dOxk+fDgej6fAa3s8Hj799NOiiC0iIiKFyPSFp0qVKmzcuJH77ruP\n8PBwYmJiiImJISwsjJ49e7Jx40Zq1qx5arzFYiEyMpLQ0NACr+31etmzZ09RxhcREZFCYOpFy3+p\nUKECkydP5r333uP3338HoH79+pQoUSLX8dHR0ef05mWLxULp0qULM6qIiIgUAdOv4bkQWVlZlC1b\ntsA1PJGRkcybN4+2bdsGJpiIiIhcENPf0roQ4eHhDBw4kPDw8DzHWK1WKlSoQJs2bQKYTERERC6E\nZnjy4Ha76dy5M2vWrDnr0fSQkBBiYmJYvXo1tWrVClJCEREROVea4cmDw+Fg4cKFjB49murVqxMa\nGkp4eDhRUVE8/vjjbNmyRWVHRESkmNAMzzkwDIMTJ07g9XopU6YMISEhwY4kIiIi50GFR0RERExP\nt7RERETE9FR4RERExPRUeERERMT0VHhERETE9FR4RERExPRUeERERMT0VHhERETE9FR4RERExPRU\neERERMT0VHhERETE9FR4RERExPRUeERERMT0VHhERETE9FR4RERExPRUeERERMT0VHhERETE9FR4\nRERExPRUeERERMT0VHhERETE9FR4RERExPRUeERERMT0VHhERETE9FR4RERExPRUeERERMT0VHhE\nRETE9FR4RERExPRUeERERMT0VHhERETE9OzBDiAiIiLBl5iYyObNm7FYLDRp0oTSpUsHO1KhUuER\nERG5jO3evZsnn32SRYsWEVYlDAzIPpRN19u6Mnb0WCpXrlzgNXbs2MG4j8axev1q7DY7XTp04ZGH\nH6F8+fIB+AbnxmIYhhHsECIiIhJ4W7du5YbWN5DWJA3/NX4I+/NAJtjW2Ci5oyTrV6+nWrVquZ7v\n9/t5bOhjfPnVl3ibePFU9YAPwvaEQTz8e/S/GfTooMB9oXyo8IiIiFyGDMOgQZMGbK++HePq3KuA\n7Wcb13mu4+clP+d6fNizw/jkm0/I7J75d1n6SxI4pzn59P1P6dWzVyGnP38qPCIiIpeh1atX06Fb\nBzIGZuT9CJMXwseFs3HtRurWrXvaoePHj1O1ZlWyH8uGiDzO3wcVf6rIwT0HsVqD+5yUntISERG5\nDP3www9k1c3KvwnYwV/Xz/Tp00lKSiIjIwO/3w/AlClTsFxpybvsAFSDNH8ay5cvL9TsF0KFR0RE\n5DLkynbht/sLHJdNNqNHj6ZOnTqULVsWu91OaGgoI14cgausK/+TLWBUNNizZ08hpb5wKjwiIiKX\noXp16xF5PLLAcVEno/jPf/7DyZMnyczMxOfzkZKSwoAHB0ABfQfAkm0hLOzMBT6Bp8IjIiJyGere\nvTvGAQOS8hl0CBwZDjp37nzqI4slp8B0v7s7kTsjIb+VwFng2eWhffv2hZb7QqnwiIiIXIacTicj\n/zUS5ywnpOYy4CQ45zj591v/xm4/+7V97dq1o3R4aYjL+9dwrHTQ5dYul8T7ePTiQRERkcvUsKeG\nkZWdxajXR0FDcFVzgQHhCeFkb8qmTYc29O3bl82bN7Nr1y7CwsJo0aIF0dHRWCwW7r3zXt4Z+w6k\nkvMeH+efF04Gxy8OKqdUZuK3E4P6Hf+ix9JFREQuc0ePHmXCxAksX70cq9VKh9Yd6HJLF1q1akXJ\nCiVJPJGIvZId3OA56KFX7140rNeQcePG8eWXXzL2w7F8/933hFUIw/AZ+JJ9PNDvAV4b+RolSpQI\n9tcDVHhEREQkFzNnzqRP/z64b3FDff5eBJMK9qV22ArrV68nNjYWgKSkJHbs2IHdbqdhw4Y4nc48\nrx0MKjwiIiJympSUFCpVrURm70yomMsAAxxzHTzS5hHGvTsu4PkuhBYti4iIyGm++OILqE3uZQfA\nAu5WbiZPnkxmZmYgo10wFR4RERE5zZwFc8isXUCRKQW2UjY2btwYkEwXS4VHRERETpOdnQ0hBY+z\nhFhwu91FH6gQqPCIiIjIaZpe1RT7oQLeXOOG7CPZ1KlTJzChLpIKj4iIiJxmyGNDCIkLgex8Bm2E\nFi1bULly5UDFuigqPCIiInKaevXq0f2u7jlvYc5tv6ydEPFLBGPeGBPwbBdKj6WLiIjIWbxeLwMf\nH8jUqVPxNPDgq+QDN0TuiCQkJYR5s+fRokWLYMc8Zyo8IiIikqeEhARuu+M2rA4rV9a/krtvv5tu\n3boREnIOq5ovIdpLS0RERHLl9XqJiIjAbrXz8biPuf7664Md6YJpDY+IiIicZt++fQx6fBAxpWOo\nVrsaGzdtZODQgcycOZPiemNIt7RERC5hHo+HuXPnMvPbmaSlp3FlnSsZ+PBAateuHexoYlIbNmyg\n3c3tyGyQifdqL5QAfMA2iFgVQa+uvZjw0QQsFkuQk54fFR4RkUvUunXruPWOW3FFuEirmwZhEHI8\nBFucjW63d+OLSV/gcDiCHVNMxOVyUaVmFZJaJ0GD3AZAxNQIxr40loceeijg+S6GCo+ISBCtX7+e\nadNmkpSUQs2alenb9z6qV6/O1q1bubbFtaR3Ss/Zqfqf3BD+bTi3NLyFWf+dFZTcYk5ffvklg98e\nTHr39LwH7YXqq6qzd9veYjXLo8IjIhIEhw4domvXnuzceZCsrPvx+8vhcGzHap3KbbfdRob7OAsy\nF2C0yOOPaA84P3Gy8oeVNG3aNLDhxbTadmrLsuhl0CifQQZEfBjBryt+pX79M9v4pUuLlkVEAiwp\nKYnrrmvHli23kJGxC7//FeBx3O5xuFz7mTcvnQXfLcNoms/fR0Mgu3E273/0fsByi/klJydDRAGD\nLGCPtpOSkhKQTIVFhUdEJMDGjHmfEyda4vW+ANjOOBqByzUVw18eDud/HV9lHxs3byyilHI5qlql\nKiQVMMgL2UnZVKxYMSCZCosKj4hIALndbj76aALZ2cPyGeUA4zn4uYC/anvRomUpVIMfHkzk5kjI\nb7HLNmjUsBHVqlULWK7CoMIjIlLE/H4/X3/9NU2ubUJYeBgpqUch5GbgHSAtj7NugqP5LwgN3xHO\nHbfcUdhx5TLWqVMnqsRUwb7cnnvpSYLwn8J5/f9eD3i2i6VFyyIiRcjr9XJnjztZ8tsSMq7NgHrk\n/FXzILAsHPZVAM8qoPwZZ/4OtuvhxTQ4s/ckA9shZGkI61avo3HjxgH4JnK5OHr0KC1vakmiP5H0\nJulQAciGkN9DsMfZeW/0ezz88MPBjnneNMMj52Xv3r08/ezTXHHlFVSqWYlW7Vsxe/ZsvF5vsKOJ\nXJJefPlFftr6Exn3ZeS818RGToGpCvTJguYHIeS2XM6cA34nTLfDH39+dAyYFAnjIuB/bbFb2nPd\nde3o2PFOdu/eHZDvI+ZXoUIFtmzYwscvfkzTfU0p/d/SVFpYiYFNBxK3Lq5Ylh3QDI+ch0mTJvH4\nU4/jj/XjvtINocARiNwYSc2SNVmycAmlS5cOdkyRS0ZWVhblKpUjvW86lMpjkB94xwmZK4Bmf36Y\nBDQGpoFtKlgmQ4wLkp3gfwN4GAj/c2w6VutHREePZc2apdStW7dIv5NIcaUZHjknP/zwA0OeG4Kr\nnwt3RzdUAcoCsZB+fzrbw7bTqWunYrvHikhR+PHHH7GWt+ZddiDnT+FmLrB+9ecHm4H2wH1AS/B9\nBN7jkFQR/B8DQ/m77ABE4vc/R0rKC/TqVTz/5i0SCCo8ck6GvzScrPZZUCaXgxZwt3ezff92VqxY\nEfBsIpeqkydP4o/yFzywhB+sc4AWQGdgAPDGPwbEAZHklKDcGcZAtm7dxZYtWy4isYh5qfBIgXbv\n3s32HdvPfr39P1khIzaDDyd8GLBcIpe68uXLY/3jHP6YPQH4rgBeBBKAIZy+Uvkn4C7y/yM7BMO4\nnSVLllxoXBFTU+GRAh06dAhHWcfZ70c7g1HGYM++PYEJJVIM3HTTTVj+sOQsNs6LF/jNAsajwC1A\nSC6DPOQsmsuf3x+G2+2+oKwiZqfCIwWKjo7Gl+7L/0VUABlQIrpEICKJFAsOh4Phzw7HucAJrlwG\n+IF5oeC/AliUz5WuBAqeuQkN/ZkGDXLb4lpEVHikQLGxsUTYI+BQAQN/hayULNavXx+QXCLFwYhn\nR9Dr5l5EfB4Ba4BUIBPYCkyIgN8bgGc+MAdY8OdZO4FhQE1yFs6NBH4F8luf8wthYce4+eabi+ib\niBRvKjxSIKvVyvCnh+Nc4syZWc/NViiZXZIuXbpw55130rJlS2bMmFHg+3mys7P55ptveO+99/js\ns89ITEws/C8gEkQWi4WJH09k3tR5OFdFw/tOGOOEbxrD0QngWQ3UBb4BHgA6ANcDDuB74HdgPDZb\nQ+BmILf37WzB6byXDz4Yjc1WwL1nkcuU3sMj58Tv93Nvn3uZv3o+mTdmQm1y6nIq2DbYcG5ysuSH\nJVx99dV4vV7mzJnDe++9x759+xg8eDAPP/wwpUr9/WyuYRiMHjOa1954DcqBu5Qbe5Yd304fXW/r\nyqTxk4iOjg7a9xUpCldeeT3btr0DtMxjxDxySs9yoGEux/sAs3E6byczsxPgJyJiPoaxlI8+eo9+\n/e4vktwiZqDCI+csMzOTxx57jKmzpuJxecAOFp+F1q1bM/HjidSpU+esc9avX8/777/P3Llzuffe\nexk6dCgNGjTg6Wef5pPpn5Bxe8bpj7pnQejSUGpl12LtyrVERBSweaJIMfLkk8/x0UcGHs/oPEbc\nBXQEBuV5jYiITtx2W2k8HgdWq4WbbrqePn16ExUVVRSRRUxDhUfOSUpKCq07tGZX+i4yr87M2fbH\nAxyCiA0RtGzUknmz5xESktsTJjl7s4wfP57x48dTs2ZNNvy+AfejbnDmMtiAsG/CeKH7C7z04ktF\n+bXOiWEYLF++nO++/460jDSurHsl999//2kzViLnYs+ePTRseC0u10Zy3t75Tynk7DdxmJx37uRl\nLs2avcv69Xr8XOR8qPDIOel8W2eWnlhK9s3ZZ6/88kL47HAG3DyAce+Oy/c62dnZdOjcgZXGSmiX\nz8DDUHpuaRIPJhbqmoSDBw+ya9cuHA4HTZs2JTw8PN/x8fHx3H737RxLO0ZGvQwMh4HzhBP/dj9D\nhwzljdfewGrVUjg5d2+++Q6vvjqBzMwpwHX/OLIW6EZO4clPPJUrd+fgwd+LKqKIKanwSIF2795N\no2aNcA1x5f6KEIBUCJ8YTuKhxHyn1n0+HzXr1+TATQegUv6/rvN9J1t/20q1atUuPPyffvvtN4aN\nGMbqVasJrRSK4TbwJ/vp/2B/Rr0yisjIs/9GvXv3bq6+/mpSW6ZiNDZOfw9cGkTMjuDB2x9k3Nj8\nS57ImSZP/oIRI0aSlVUSv78RVutJ3O7leDxe/P6T5P/OnUU0bPgyW7b8Eqi4IqagwiMFGjVqFCO/\nH4mnU16PaOUImxbGvdfdS82aNTl58iRJSUmn/vevn6empuJ3+DH6GVAh/1/X9o6Nfj36ce2111K/\nfn3q1atH+fLlsVgs+Z94huXLl9Plji5ktMjI2Y/R8eeBkxC6MpRa/lqsWbHmrNJzZ487mXtyLv5W\neWwNkAlh48PYsmELtWrVOq9MIj6fj2XLlrFv3z4iIiLo2LEjnTt3Z+3aAUCvPM9zOnszatR1PPnk\nE4ELK2ICKjxSoCFPDuGD3z/I2eYnH9Z5Vlo4W9C2bVtKly5NqVKlzvrfEiVK0PP+nsw+ORv/Dfns\nMXQCwr8M58XhL+ZsbbF9O9u2bcPr9VKvXr1TP/4qQrVr1yYsLOysy2RnZ1OhSgX+uOUPyK2TGBD6\nXSj9ruvHJx9+8vcvf+IEVWtWxTXYdfo+jWcIWRzC4GsH8+477+b/L0fkHMyfP5/u3R8nM/NnoGIu\nIxYQHf0g+/Zto0SJEgFOJ1K82YMdQC59lcpXwrHGgZv8X1kfkR3B4GcHc++99+Y7btiQYSy4cwEZ\nV2f8PdtyBsdaB4MfHcwLL7xw2udJSUmnys/27duZMmUK27ZtIyEhgcqVK59VhOLj4/GW9uZedgAs\nkN06mymfTmHMW2OIjIzEMAzWrVuHvaw937ID4KnqYfX61fkPEjlHXbp04fnnB/LGG9eTmfkCOY+h\nRwL7CAn5mNDQz1iwYI7KjsgF0AyPFGjv3r00aNIA1+OuPAsKf0D4pHCOHT6W63qYfzIMg5739eS7\n9d+ReUfm6U9q+cD2s43yu8uzaf0mSpcufU4ZPR4Pe/fuPVWE/vqxduNa3O3ccHX+59sn2rki4goy\nMzM5duwYdrsdV4QL/+ACdrreBlFLovjg7Q/o0aNHrrNMIudr6dKlvP76+/z00/dYLHYcjlD69evH\ns88OpWbNmsGOJ1IsqfDIObnjnjtYuG8h2V1yeUrLA85ZTh697VHGvD3mnK7n9XoZOmwokydPxlLP\nQlbJLGwuG7ZNNuxWO9s2baNq1aoXnbvzHZ1ZGLIQrsp/XNjUMF59+FW6d+9OuXLl8Pl8lKtUjqyH\nsiAmnxPnAD6I9EcSlhTG4oWLiY2NvejcIpDz+8TlcuF0OvU0oMhFUuGRc5Kens5NnW7i92O/59yK\nuoKcjQ+3Q8SvEXS4vgMzv56J3X5+d0mPHz/O119/TcK+BErElKBbt24MGjSI/v37M2DAgIvO/ezw\nZ3nvl/fwdMhnwbUPLO9YiHZE07ZtW1q1akWrVq34ZNInTImbgueWPM5NAiYCj5Nz12EzlFhWgvi4\neCpVKuARNBERCSgVHjlnbrebGTNm8NbYt9gevx2LxUJYZBgP3f8Qb7/9dqH9DXT9+vV07dqVbdu2\nEROT3/RKwU49Uj/YlfeTvlug8b7GfP/N96xcuZLly5ezYsUK9u7di8fiwXOVB39rP/x1t8oADgCz\ngFbANX9fyrHQwROtn+DtN9++qNwiIlK4VHjkonz99dd8/vnnLFy4sFCvO2DAAEqWLMk777xDcnIy\naWlplC5d+oK2mujbvy8zV80k666ss9cgHYbw6eF8P/t72rU7/U2IJ0+eZP78+bz0yksk7E3I2bg6\nAjgKZAM3cfatsuMQ/XU0f5z447wfnxcRkaKjwiMXZffu3TS8qiElypYgIy2D0mVK88iDj/DIw49Q\npkyZgi+Qh6NHj1K3bl2q16nO9q3bCXGG4Mvycettt/LyCy+f1zoZj8fDg488yOxvZ+OJ9eCt5AUP\nhO8IJ+v3LCZ+PJGHHnooz/PHjRvHkiVL+G7Bd3hae3LeH1SDs9cy/ck2ykZaSlqBb3EWEZHAUeGR\nC7Z48WK63dONzNqZ+Jv5IYqcp7U2hePY62DR/EU0b978gq793PPPMXbiWDztPFCfnBcoZIJ1o5Ww\ntWHMnDqTW2655byuuXXrVj74+AM2xm8k1BHK3V3v5uDBg2zfvp3Zs2fneV7Tpk0ZPXo0Pfr0ILln\nMuS3hZYbrG9Z8bg9WmQqInIJUeGRC7Jr1y6aXNOEjDszcmY7zrQNohdFsyN+B+XLlz+va8+dO5de\nA3uReX9mzi2kMx2AiFkR7Nq6iwoVCnhdcwFcLhexsbGMGTOG22677azjv/32G926dWPv3r30f6Q/\nX+39Cl9rX94XXA/t3O346X8/XVQuEREpXPorqFyQ0f8eTXaT7NzLDkB9cNdyM/6T8ed97VfefIXM\nlnmUHYCq4KvnY8LECed97TOFhYUxfvx4hgwZQkZGxlnHJ0+ezIMPPojVamXY0GHY19khOY+LZYBz\njZMRw0ZcdC4RESlcmuGR82YYBhExEWQNyIIS+Qw8BBV/qMjhhMOnzvP7/ad++Hy+s/755MmTNIxt\niOcZT/7vAd8HtdfWZueWnYXyne6//34qVKjAyJEjWbRoEcnJyZQsWZL+/fuzfv16atSowXfffUfP\n3j3x2r1kt8/++1abH9gFziVOBvcbzNtv6AktEZFLjQqPnLfs7GycEU78LxXwFuIs4B2wYcPv92MY\nBhaLBZvNhtVqxWq1nvbzv9a8JLuSYXgBIU5AyRkl2bx+M5UqVbroJ6IOHTpEnfp1MDBwVHHgi/Th\nP+HHfdTNa6+8RpWKVXjmmWeYO3cuSUlJvPTqS8THx+Mo6cCT6qF61eq8/PzL9OzZ86JyiIhI0VDh\nkfNmGAah4aF4hnryvu0EcBxKzSrF0f1HsdlsWCyWAotJZmYmpcqWIvvx7NO3nDjTNohcFEm4NRy3\n203Dhg1p2LAhjRo1OvXzc91Z3efz0fXOrvy07SfcXdxQ8h8Hj4FjroOQ1BBW/7yaRo0anTp04MAB\njh07RqlSpfS6fxGRS5w2D5XzZrFYuOOuO5gdNxv/jXnP8oRsCuG+XvcREhJyztd2Op10vb0r32z8\nJt9rR26K5IMxH9CvXz+OHz9OfHw88fHxbNmyhZkzZxIfHw9wWgH6qxCd+bj81KlTWRG/Andv99m/\nI8qB+z43tkk2Tp48edqhqlWrFsr2FyIiUvQ0wyMXZP369bTu0JrM+zIht9ftHAbnf51sWr+JWrXy\n2qo8d5s2beKG1jeQeU8m5NInrGusVNpWiR3xO/J8141hGCQmJrJly5ZTZeivQhQWFnZaAXrr3bfY\n23RvzpqcPFjWWLg17FbmzZ53Xt9FREQuDSo8csE+nfQpTzz7BFnXZWE0NiAcSP/zXTm/hvGfyf+h\nW7duF3TtBQsW0L1Xd3z1fLgaunJunR2HyM2RlMgqwfLFyy/oNpJhGBw6dOhUAYqLi+PLKV/CS+T/\nzGIKRH8ZTcqJlAv6PiIiElzFsvB4PB7++9//8vbYt9m6eSsAja9uzPCnhnPXXXdhs9mCnPDysXbt\nWl5/53Xmz52PxWYBA+7pfg8jnhnBVVcVsEV5ARITE5kwcQJfTvuS9PR0KlWqxNCBQ+nRo0ehvcU4\nKyuLqJgofC/4IL/lPukQ+WkkaclphfLriohIYBW7wpORkUGHWzqw+chmMq75c9duA9iZs2t3i4Yt\nmDd7Hg7HmZsmSVHyer2kp6cTFRVVrAqnYRhUql6Jo52OQpV8BsbDNYeuYd3P6wKWTURECk+xe/Hg\nAw8/wMaMjWT0zshZc+EgZxfsRpBxXwYr9q3giaefCHLKy4/dbqdEiRLFquxAzgLsp4Y8Rfi68Jzi\nnBs/RG6I5LknnwtoNhERKTxBKTwHDhzgmeeeoVT5UtjsNqJLRfPwow+zbdu2fM87ePAg8+bNw9XJ\nlXtyO2R1zuLzzz/njz/+KJLsYj6PDnyU8lnlsS+zw5m7RnghdEEoV1a48oLXI4mISPAFvPCsWLGC\nBo0bMO7ncSTfk4z/eT9pfdP4fMfnNLu+GdOnT8/z3BkzZkADcmZ08hIJ9lp2vv3220LPLuYUHR3N\n6uWraepuivNjJ9YlVlgHth9sWN+x0q5COxYvWHxej9eLiMilJaDv4Tly5Ahd7uhCetd0+OeTyiXB\n28aLt56XBx95kHr16tG4ceOzzj90+BDZkdkF/jrZkdkcP368EJOL2ZUvX561P69lw4YNfD3ta44l\nHaNSbCUm7ZzE6NdHExUVFeyIIiJyEQJaeD7+5GM8dT2nl51/qgCu5i7efOdN3h39LnFxcWzatIm4\nuDji4uLYtm0blkYWjDwXW+QITQ896+VyIueiWbNmNGvW7NQ/h9hD+OSTTxg3blwQU4mIyMUK6FNa\nla+ozOEOh6FyPoMygDFQIroETZo0oXHjxqd+REdHc1Wzq3A97sr7tlY6hI0P4/D+w5QsWTKPQSLn\nZv/+/TRp0oQDBw4QEZHfPhoiInIpC+gMT8rJlPx31waIAHuInX379hEdHX3W4S5dujD/h/m4bs1l\n4bIPwheG07dvX5UdKRTVqlWjRYsWTJs2jQEDBgQ7joiIXKCALlqOKRkDBb2oNgMsWIiMjARg7969\nDHt2GHWvqkvN+jXJzMqkQmoFmAhsBzyAG4iHiK8iuLHyjbz/7vtF+0XksvLoo4/yySefBDuGiIhc\nhIAWnv59+xO6Kb9HrIAN0KZdG6xWK++Pe5+GTRry4ZoP2XndThLaJrDQu5D9J/ZTMaQi9ePrY3vb\nhm20jab7mzLpjUks/G6hXjoohapz584kJiayfv36YEcREZELFNA1PEeOHKFuw7qk35ae84bkMyWC\nY4qD6LBoGjRswLr4dWT1yYIz7075IGxeGDfXupk5M+YAOS+QEykqo0aNYt++fUyYMCHYUURE5AIE\nfGuJZcuW0bVbV7IbZeNp4skpM+lgi7MRuj6UT8d/SpdbulCtdjVSu6TmXowAPBD+YTgbVm2gfv18\ntrkWKQRHjhyhQYMGJCQkEBMTE+w4IiJyngL+4sE2bdqwZeMWBl83mJhpMVhesxDxWQR9a/Vl7c9r\n6dWzFzt27MAf4of8NsMOAU+sh4mTJgYsu1y+KlasSIcOHfjPf/4T7CgiInIBgr55qGEYZ92OmjVr\nFv1f7U/qnan5nxwHt1lvY+7MuUWYUCTH4sWLeeqpp4iLi9MtVBGRYibom4fm9h+OqKgoyDqHk7Mg\nJkq3FyQw2rVrh8vlYtWqVcGOIiIi5ynohSc3LVu2xJfog5P5DDIgcmskvXv0DlguubxZrVYGDhyo\nR9RFRIqhS7LwOJ1OHnn4EcKXhIM/9zGWTRZKWEvQqVOnwIaTy1q/fv349ttvOXkyvzYuIiL/5PV6\nCfIKmkuz8AC88dobXF3masKnh8MBOLV9VgrYfrIRszKGhd8txGq9ZL+CmFCZMmXo2rUrX3zxRbCj\niIhc0lJSUnhn9NvUqVmR0FAHDoedjm2vZ86cOUEpP0FftJwft9vNuA/GMea9MfyR9gc2hw1fpo/e\nvXvzrxf+RbVq1YIdUS5Ds2bN4uGBDxMRE0FGegblK5bn8Ycfp2/fvtpVXUQEOHToEO3b3ECTCid4\nomMW19WCbC/MXgdvzo/gurZ3MGHSlIBOWlzShecvfr+fAwcO4Ha7qVy5Mk6nM9iR5DL17bff0rtf\nbzLrZEJTIAJIgoi4CCKSIljx0wrq1q0b7JgiIkFjGAY3NI/lttrb+H+3e886nuGCTmOcdOv3Es88\nNyJguYpF4RG5FPz222+0vKklmd0zofLZxy3rLZT7rRy7t+3WzuoictlauXIlD/XuzNY3MsjrDR6b\n9kOX90qScOAYdntg9jHXAhiRc/Tqm6+SdV1WrmUHwLjaIL1kOlOnTg1sMBGRADAMgzVr1vD0U0Po\n3/denh/+LPHx8WeNm/afz+nfMjPPsgMQWw0qxvhYuXJlESY+nQqPyDnIzMzk+3nfYzTOf0I046oM\nxk0YF6BUIiKBcfjwYVrd0Ized7WnxMGPuDF0OmwfS4c2zbnj1g6kpv79ouCk40epXLLgm0dVSkFS\nUlJRxj5NYOaRRIq5kydPYg+343a68x9YGhKPJgYmlIhIAKSkpNC+zQ30bnqY//e4l7/XGXsZeZeX\nIV+tpGvndixetpqQkBDsDie7j1n4+/Hq3O09ZlCuXLmijn+KZnhEzkF0dDSeTA94ChiYDlHRelJL\nRMxj/Mcf0qziMV7q9s+yk8Nhh4/7ZuP7YxsPPPAAjRs35ofFy/hkiR1fHu/RA1i3G1Kyw7jxxhuL\nNvw/qPCInIPo6Gia39Acfs9/XFh8GA/0fiAgmUREipphGIz/cCxP3ezKc4zVCk91zGTVih94//33\nOXLkCPUaNGXE9BByeywqOQMe+cLJcyP+hc1mK8L0p9NTWiLnaOHChdx1/11k9s2E3CZxEiBiTgR7\ntu8J6DStiEhRycjIoEzpEmR9dvbj5f+UmAJXvRjJsaQ0AE6cOEGn9i0pYzvIEx0yuLEOZLlh5joL\n7/7g5K57H2T0v98P6EbMmuEROUedOnVixBMjcH7phA3AX8t50sC23IZlqoXhw4ar7IiIadjtdnw+\nP15f/uNcbrDb/56tKVOmDCtX/8a9j7/PyB/rU+2pUK56MYIVqbcw+evveefdcQEtO6AZHpHztmjR\nIno/0JuTx05iC7XhzfZy3333cWvnWxkyZAg//vgjsbGxwY4pIlIobmx+FcNbbeGOa/IeM/Z/Fta6\nbmPq9G8DF+w86SktkfPUunVrPBke9uzaQ3Z2Ntdccw1ffPYFFosFv9/P7bffzpo1ayhfvnywo4qI\nXLTBT47g9VcH0rlxBqEhZx9PzoD3fgznqxnPBT7cedAtLZFz5PF4SElJYdGiRVx11VVUr16dunXr\nEh0dzb59+wDo1asXffv25a677uLYsWN8+OGHNLm+CdXrVqd5y+ZMnjyZrKysIH8TEZFz16tXL6o3\naMvt7znZdfT0YxsToP1bTu6+tz8tWrQISr5zpVtaIgVYuXIlo94exY8Lf8Ris+D3+WnarClfff4V\n9erVo0uXLjz66KPcfvvtQM7ebx07dmTF6hU46jjIaJgB0UAyRG6OxJniZNmPy6hfv35wv5iIyDny\ner28+n8v8fHHH9CgipWKMX72HLdyOMXOc8Nf5PGhTwZ8Tc75UuERyce7773Li6++SOZ1mRALhAGp\nYPvNRuiGUObMmMPixYuJjIzkxRdfBODAgQM0bNKQtJvTIJdOY/nNQum1pdkRv4OSJUsG9PuIiFwM\nl8vFTz/9RHJyMuXLl6dt27YB2wvrYumWlkgeli1bxouvvEjm/ZlwLTllByAafG18ZN6VyZ097qRq\n1aps2rTp1Hnvvv8u2Vdm51p2AIymBhkVM/hs8mdF/h1ERApTWFgYXbp0oU+fPnTo0KHYlB3QDI9I\nnjre2pEfrT9CPk8mhC0I44GrH2DJ4iVs27YNwzCIKR1D2n1pUDqfi++H6iurk7A9obBji4hILopP\nNRMJoKysLJYuXgrD8h/nauRixpwZ/JH4B4899hh+v5/0lPT8yw5AWUg8pD23REQCRYVHJBfp6enY\nQ+14Q/N/uyhRcDLpJIbN4NOZn+LP9GNggIu/b4HlJhPCnPkNEBGRwqTCI5KLmJgY/B4/ZAAR+Qw8\nCUakAYPAY/eAH/gCiAOuy/s0+xY7d991d6FmFhGRvGnRskguHA4Hd9x5B9aNBfwW+RVozt9/dbAC\nNwE/A6l5nHMCQjaEMGxoAffLRESk0KjwiOThxeEvErYuDA7lMWATcABofMbn1cmZ3fn0zzF/3RVz\nAxvA+R8n48aMo0GDBkURW0REcqGntETyMXfuXHr17YW3kRf3VW6IBJKA1eSUnfuBvHaQ+B34Fhx2\nB44oB+5UNze2vJH/e+H/aNOmTYC+gYiIgAqPSIH27dvHBx99wFfTviI9LZ2y5cpycN9BPA97Cnwa\ny/66nZ3bd+J2uylTpgylSpUKTGgRETmNCo/IBajTqA67mu2CWvkMOgGRX0WSejL1kn/luoiI2WkN\nj8gFeHLQkzg3OvMd49jg4JGHHlHZERG5BGiGR+QCpKWlcWXslRypfwT/9f6zB2yBmKUxxG+Mp3Ll\nyoEPKCIip1HhEblACQkJtO3YlpOWk6Q1SoOSQBpExkcSejKUHxf8SJMmTYIdU0REUOERuSgej4d5\n8+bx4cQPOXL0CKVKleLhvg/To0cPwsPDgx1PRET+pMIjIiIipqdFyyIiImJ6KjwiIiJieio8IiIi\nYnoqPCIiImJ6KjwiIiJieio8IiIiYnoqPCIiImJ6KjwiIiJieio8IiIiYnoqPCIiImJ6KjwiIiJi\neio8IiIiYnoqPCIiImJ6KjwiIiJieio8IiIiYnoqPCIiImJ6KjwiIiJieio8IiIiYnoqPCIiImJ6\nKjwiIiJieio8IiIiYnoqPCIiImJ6KjwiIiJieio8IiIiYnoqPCIiImJ6KjwiIiJieio8IiIiYnoq\nPCIiImJ6KjwiIiJieio8IiIiYnoqPCIiImJ6KjwiIiJieio8IiIiYnoqPCIiImJ6KjwiIiJieio8\nIiIiYnoqPCIiImJ6KjwiIiJieio8IiIiYnoqPCIiImJ6KjwiIiJieio8IiIiYnoqPCIiImJ6Kjwi\nIiJieio8IiIiYnoqPCIiImJ6KjwiIiJieio8IiIiYnoqPCIiImJ6KjwiIiJieio8IiIiYnoqPCIi\nImJ6KjwiIiJieio8IiIiYnoqPCIiImJ6KjwiIiJieio8IiIiYnoqPCIiImJ6KjwiIiJieio8IiIi\nYnoqPCIiImJ6KjwiIiJieio8IiIiYnoqPCIiImJ6KjwiIiJieio8IiIiYnoqPCIiImJ6KjwiIiJi\neio8IiIiYnoqPCIiImJ6KjwiIiJieio8IiIiYnr2YAcQERE5VwkJCUyaOJ7fN68nJMTBTTffTu8+\nfYiMjAx2NLnEaYZHREQueX6/n6effJyrm1xJ2sZ36VnzR24tN58Fnz9N9aoV+Pbbb4MdUS5xFsMw\njGCHEBERyc9zTz/BL//7lHlPZlIy4vRj63bDbWOdTJ0xj5tuuik4AeWSp8IjIiJB4/V6OXLkCACV\nKlXCZrOdNebQoUM0urIWu0ZnUzoq9+vMWgujVzRg9fr4oowrxZhuaYmISMClpqbyyv/9ixpVy3Fd\ns/pc16w+NaqW49WRL5OWlnba2M8/m0TPG8iz7AB0uwaOHEpg06ZNRZxciisVHhERCagTJ07Q8vqm\n7FjyNgueSObwe5kcfi+T74acZOuPb9PqhmacPHny1PgdW+O4rkZ2vte0WeHqK+zs2LGjqONLMaXC\nIyIiAfXQA73oVPsAUx7J5qpqf3/euDr851EXN9XYzyP9+wDg8Xj4IzWVjPz7DgCZbggNDS2i1FLc\naQ2PiIgEzJ49e7ju6obsf9dFuCP3MRkuqDzUzrU3tmPt2rWUKlWK8iEHWfWyJ8/rHkuBusPD2JNw\niFKlShVReinONMMjIiIBM3fuXO66xsiz7ABEhMHtzQwqVqzIjh072LZtG/tTIvkhn+U5r811cPed\nd6rsSJ5UeEREJGBSU1MpG+kucFylkgb16tWjXLlyOBwOvp4+hz4TnEz4CbL+cfrhZBj0uYOf9lZi\n9LsfFGFyKe5UeETksmIYBlu3bmX16tXs27cv2HEuO1WqVGFrorPAcVsTnVSpUuXUP7du3Zr/LVrO\nNwktqD4snJvHxND6zWgavRCOpWYfVqzaoNkdyZfW8IjIZcEwDCZMmMDrr79OUlISdrud7OxsGjRo\nwBtvvMHNN98c7IiXhZSUFGpUq8iWUVlUzqOfHEiCxi+Gs+9gIlFRZz+LnpCQwLZt2wgJCaF58+ZE\nR0cXcWoxA83wiEixc+LECV599VUqVKiAzWbD6XTSvXt31q1bl+t4wzAYMGAAw4YNY//+/WRkZJCS\nkoLL5WLDhg1069aNCRMmBPhbXJ5iYmIY/Nhgeo13kpZ19vHUTOg13snQoU/mWnYAatSoQefOnWnf\nvr3KjpwzzfCISLHy+++/06pVKzIzM3G5XKc+t1qthIWF8corr/D000+fds6MGTN48MEHycjIyPO6\n4eHhbNq0idq1axdZdsnh9/t5bGB//vfdDB5r56JzrB+ABZusfPRTGF279WTcRxOxWvV3cik8Kjwi\nUmy4XC6qV6/O8ePHyeuPLqfTycyZM7nllltOfda0aVM2btyY77VDQkIYOHAg48aNK8zIko+1a9fy\n8bgxrFu7CoBrr2vBoCHDaN68eZCTiRmp8IhIsfHFF1/w+OOPk56enu+45s2bs3btWgDS0tIoVaoU\nXq+3wOtXrlyZgwcPFkpWEbm02IMdQETkXI0fP77AsgPw22+/MWTIEDIyMti/fz8+n++crv/PW2Qi\nYi66QSoixcbx48fPadxfaz9uvPFGBg8efM7bDVSvXv2Cs4nIpU0zPCJSbJQtW5bdu3cXOM5isfDC\nCy9QsWJFAPr168ekSZPyva0VGRnJsGHDCi2riFxaNMMjIsXGwIEDiYyMLHDcVVdddarsAIwYMQKn\nM++X3dntdipUqMA999xTKDlF5NKjwiMixca9996L0+nEYrHkOcbpdDJy5MjTPqtRowZLliyhVKlS\nZxWmyMhI6tSpw4oVK7TTtoiJ6SktESlW4uPjad269Vnv4bFYLISHhzNy5EieeeaZXM/NyspixowZ\nTJ48meTkZKpWrcrgwYO5+eab9c4XEZNT4RGRYuf48eN8/PHHfPjhhxw/fhyHw0HXrl157rnnuPba\na4MdT0QuQSo8IlKsGYaR7y0uERHQGh4RKeZUdkTkXKjwiIiIiOmp8IiIiIjpqfCIiIiI6anwiIiI\niOmp8IiIiIjpqfCIiIiI6anwiIiIiOmp8IiIiIjpqfCIiIiI6anwiIiIiOmp8IiIiIjpqfCIiIiI\n6anwiIiIiOmp8IiIiIjpqfCIiIiI6anwiIiIiOmp8IiIiIjp/X8bAIWYPIYMwwAAAABJRU5ErkJg\ngg==\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Month \n",
    "MTH = '2016-10'\n",
    "df_del = df_del1.copy()\n",
    "\n",
    "df_graph = df_del[df_del[MTH]>0].copy().reset_index()\n",
    "# Add the city\n",
    "for dic, loc in zip([dict_prov], ['Province']):\n",
    "    df_graph['Source ' + loc] = df_graph['Source'].map(dic)\n",
    "    df_graph['Target ' + loc] = df_graph['Target'].map(dic)\n",
    "# Unique cities\n",
    "PROVINCES = list(df_graph['Source Province'].unique()) + list(df_graph['Target Province'].unique()) \n",
    "n_prov = len(set(PROVINCES))\n",
    "\n",
    "# Plot\n",
    "fig, ax = plt.subplots(figsize = (10, 7))\n",
    "plt.rc('axes', titlesize=20)     # fontsize of the axes title\n",
    "G = nx.Graph()\n",
    "for link in df_graph.index:\n",
    "    G.add_edge(df_graph.iloc[link]['Source'],\n",
    "             df_graph.iloc[link]['Target'])\n",
    "\n",
    "# Add Colors by city\n",
    "df_cat = df_city.set_index('Code')\n",
    "df_cat = df_cat.reindex(G.nodes())\n",
    "df_cat['Province'] = pd.Categorical(df_cat['Province'])\n",
    "df_cat['Province'].cat.codes\n",
    "\n",
    "\n",
    "# Position of labels\n",
    "def nudge(pos, x_shift, y_shift):\n",
    "    return {n:(x + x_shift, y + y_shift) for n,(x,y) in pos.items()}\n",
    "pos = nx.spring_layout(G)\n",
    "pos_nodes = nudge(pos, 0, 0.1)  \n",
    "\n",
    "# Set node_color\n",
    "nx.draw(G, with_labels=False, node_color=df_cat['Province'].map(dict_color),\n",
    "       node_size=100, edgecolors='black', alpha = 1, \n",
    "        font_size = 11, font_color = 'black', width=1 , verticalalignment='top')\n",
    "plt.title(\"{}\".format(MTH), fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "#### Month (3.5T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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R01PhEREREdNT4RERERHTU+ERERER01PhEREREdMLSOHx+XzMnz+f227rTdOm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\nHDIiGDzG6nQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Month \n",
    "df_del = df_del2.copy()\n",
    "MTH = '2016-10'\n",
    "df_graph = df_del[df_del[MTH]>0].copy().reset_index()\n",
    "# Add the city\n",
    "for dic, loc in zip([dict_prov], ['Province']):\n",
    "    df_graph['Source ' + loc] = df_graph['Source'].map(dic)\n",
    "    df_graph['Target ' + loc] = df_graph['Target'].map(dic)\n",
    "# Unique cities\n",
    "PROVINCES = list(df_graph['Source Province'].unique()) + list(df_graph['Target Province'].unique()) \n",
    "n_prov = len(set(PROVINCES))\n",
    "\n",
    "# Plot\n",
    "fig, ax = plt.subplots(figsize = (10, 7))\n",
    "plt.rc('axes', titlesize=20)     # fontsize of the axes title\n",
    "G = nx.Graph()\n",
    "for link in df_graph.index:\n",
    "    G.add_edge(df_graph.iloc[link]['Source'],\n",
    "             df_graph.iloc[link]['Target'])\n",
    "\n",
    "# Add Colors by city\n",
    "df_cat = df_city.set_index('Code')\n",
    "df_cat = df_cat.reindex(G.nodes())\n",
    "df_cat['Province'] = pd.Categorical(df_cat['Province'])\n",
    "df_cat['Province'].cat.codes\n",
    "\n",
    "# Position of labels\n",
    "def nudge(pos, x_shift, y_shift):\n",
    "    return {n:(x + x_shift, y + y_shift) for n,(x,y) in pos.items()}\n",
    "pos = nx.spring_layout(G)\n",
    "pos_nodes = nudge(pos, 0, 0.1)  \n",
    "\n",
    "# Set node_color\n",
    "nx.draw(G, with_labels=False, node_color=df_cat['Province'].map(dict_color),\n",
    "       node_size=100, edgecolors='black', alpha = 1, \n",
    "        font_size = 11, font_color = 'black', width=1 , verticalalignment='top')\n",
    "plt.title(\"{} 3.5T\".format(MTH), fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "#### Month (5T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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B2uN2/8LEic256aaOxMXFsWnTJhYuXMjChQsJBBzASuD6HN7hN8qXr8nPP8/n\nzz//PO8jISGBVatWkR6ZnmvWjGIZ7D+0/7KOV0RE5FKp8BRiY8ZMwON5mnPLztlKkZb2NH36PIBh\npOJyuejYsSP3338/7dpdz/Dhb+N2t4Nsngtjt79OUtJBhg0bxpAhQ+jSpcs52xcvXszs2fMJ2HM/\nK2pLs1G2VFkCgQALFizgrffeImFXAk6nk17dezHwgYGUL1/+oo5fRETkQmkNTyHmdMbg9e4k+8ID\ncAqbLZbt2zdRo0aNM6+mpqbSsGFL9u/vic/3EueWHgMYSsmSk4mJKcvu3RsBA5erLA891JfXXnuN\nH3/8kT59HsLjeQPsD8DjHiiWTQQ/RLwTwZIfl/DEM0+w7cA2Uhulnn4qsRcc2x1Ytlj45ONP6NWr\n12V+VURERM6nwlOI2e3h+P3JnH9p1NkysVgcHDx4gAoVKpyz5fDhw3Tpchu7dp3E7b4Pw6iKxbIP\np/MjvN6jWK1X4vP9E+j438/4CYvlZRyO3fj9mWRkLAaage0xqDoJ+nrg7zdoNsC22IZ1nZXiJYqT\nVCmJjM4Z5z/F7Qg4v3Qy/9v5tG3b9nK+LCIiIudR4SnEqlRpwP79HwJtchj1G+HhNxEVBRERETRt\n2pQmTZqc+W9sbCwrVqxg0qTPOXjwOBUqlGbfvv2sXFmK9PQpnN9g/FitLQgESgEL/vtaBoR1hVK/\nwfVpUIvTE0YHwPEfBxV9FRkxdAQDHhuA7xFf9o+s3QCtEluxYumKy/myiIiInEeFpxB7441xDB26\nCo/ny2zHOJ39ePnlxjzzzFPs3buXdevWsXbt2jP/tVgs5xSgihUr0q7djXi9e8j+HNVtwC1A/7Ne\n8wGfQ/hoyNwBFgvYLPS5vRcjR47kqf97ijnJczBa5vDt9t/L13ds2kHlypUv8qshIiKSPRWeQiwp\nKYkGDZpz+PCDBAJPnbfdan2HcuXeYcuWNRQvXvy87YZhcPDgwXNK0C+//EJycldgeg7vfBtwB3B7\nNtvTgQAQR0TE76dfsaRDL6B6zscU81kMP079kdatW+c8UERE5CLoKq1CLCYmhuXLF9K+/T84dmw6\nqan3A1WBfURHT6JUKTdLlizMsuwAWCwWKlWqRKVKlejWrRsAL7/8Mq+84s/lnesDy8i+8EQAHmAL\n9erVo27duvy66lcOug/mvFsD/G6/HkEhIiJ5ToWnkKtatSo7dqznp59+4sMPP+fIkdmUL1+aBx54\niS5dumCz/X0NTs7Kli2L0/kbHk9Oo+4HrgJGACWy2H4U+AcWC2zaVJ3t22MwjNIwOxH8adAom90e\nBJfNxVVXXXVRmUVERHKjU1pyjmPHjlG1ah283j/IuswAHAeu4PTq5EWc+3iK40AzTp+/egmIOmvb\nb2DrDp2PQ4u/fdv5wDXDxSsPvcLTQ57Om4MRERH5LxUeOc/ddz/MzJnH8XimA2F/25qB03k7/fpV\nxWq1MGnSFPz+2zCM6wA3NtsY/P6bgfey2fsusF4JD3tP3z7IAPaA698u2jdsz5yv5lz0rJSIiEhu\nVHjkPOnp6XTt2pNVq46QljYE6PzfLfOIjHyDNm2q8t13MwgPD+fQoUNMmvQJK1asY//+vWzevAnY\nBcRmu3+rrR8W60xcpR34vD7KlCrDs08+y0MPPqSyIyIi+UKFR7Lk9/uZPXs2Y8ZMZOPGtQA0btyC\nZ599mG7dumVZTJYvX07XrkNITl6Vy97n0KrV+0ye/BYOh4OqVatm+UR3ERGRvKJFy5Ilm81Gz549\n6dmz5wV/TiAQwGK5kG8pOxaLlbp16156QBERkYuQ3T1vRS5avXr18Ho3A6dyHBcevoTWrRsHJZOI\niAio8EgeKl26NDfeeBNWa3YLlgFOYLVO5ZFHBgYtl4iIiAqP5KmxY4cTHT0ei2Uyp++2fLaDuFw3\n8sgjA6lWrVoI0omISFGlRcuS57Zs2UL37n04fNiD290bw4jG5VpDILCAp58ewvDhL2qRsohIEZCY\nmMi0zz5j25Z4wiMctL+hCzfeeGNIrshV4ZF8YRgGy5cv5/vv55GW5qV+/Zrceeed2T7mQkREQicz\nM5O5c+fyxacfcOL4UcqULU+fux6kW7du2O0Xf32TYRiMGT2K0aNH0bmhldZXuPFmwqz10ZzwuJg2\n/ZugPzNRhUdERKQI27lzJzd1uZ5yziTua5NC1dKw9wR8/O9ojnuL88NPS6lRo8ZF7fPVka/w5eQx\n/PCkm8qlzt323Tq4d3IkPy36hSZNmuThkeRMhUdERKSIOnnyJE0b1+O5jsd5qMP5dWDCQitvLCnD\n2g1bKVEiu8cNnevYsWPUqVmVza95ic3mUz5eCjN3tmTB0pWXE/+iaNGyiIhIEfXRh+/TtkZKlmUH\n4JGOAVpXS2bSxx9d8D4/mTKJHs3JtuwA9GsDG+I3sHPnzouNfMl040EREZEi6uMP3uXzez05jnm0\nvYe73n+bIU89jcfjwe12Z/nx17bvZs/ggau9Oe7TEQ4ta4ezadMmatasmZeHlC0VHhERkSLIMAz+\n2HeUJtVyHnd1Ndi5+xB2ux2Hw4HL5crx41RiIpn+3N8/088lLYi+VCo8IiIiRZDFYsHpCCPJnUGp\n6OzHJbkhKtLBqaQ0rNbcV8JMmjSJuR8N5r52qdmOOZUGv23P4NMWLS4l+iXRGh4REZEi6uYbOzF9\nZc73RZu+0sJNN3a6oLIDcOedd7J8u8Ha3dmPeWOena43dqFs2bIXE/eyqPCIiIgUUY8Ofo7X5zs5\nmpT19iOJ8MYCJ48Ofu6C9+lyufhw0lRuHufkxw1w9rXgqV54Zbadz9eU5PVxEy4r+8XSZekiIiJF\n2Mjhw/jsozd48w43XRqBzQr+AMzbAE9+6eKeh57lhRdfuuj9zp8/n2eHDCI99RitaoIn08LC3zNp\nF9eWdydOoWLFinl/MDlQ4RERESnivvrqK15/dRgHD+yjUukwDpzIpFLlqjzzwnB69ux5yfs1DINV\nq1axZcsWwsPDiYuLo3LlynmY/MKp8IiIiAgACQkJnDhxgjJlygTtcvFgUeERERER09OiZRERETE9\nFR4RERExPRUeERERMT0VHhERETE9FR4RERExPRUeERERMT0VHhERETE9FR4RERExPRUeERERMT0V\nHhERETE9FR4RERExPRUeERERMT0VHhERETE9FR4RERExPRUeERERMT0VHhERETE9FR4RERExPRUe\nERERMT0VHhERETE9FR4RERExPRUeERERMT0VHhERETE9FR4RERExPRUeERERMT0VHhERETE9FR4R\nERExPRUeERERMT0VHhERETE9FR4RERExPRUeERERMT0VHhERETE9FR4RERExPRUeERERMT0VHhER\nETE9FR4RERExPXso3zwQCLBkyRK+n/sNnrQUrqhZjwF33U1sbGwoY4mIiIjJWAzDMELxxps2baJX\nj5sI85+kT4tUYpyw4YCDmatgwIC7GDtuPHZ7SPuYiIiImERICs8ff/xBm5ZNGHNbEv2uBYvlf9sS\n06DnBBfVm97Gh5OmBjuaiIiImFBICs/d/XtT3TuLYbcGstye6oU6zzmZv2QVV111VZDTiYiIiNkE\nfdFyYmIic76dy6Absi47AFEOeCAugw/eezuIyURERMSsgl54du3aRfXy4ZSOznlcXB0/m+LXBCeU\niIiImFrQC4/NZiM9M/ezaOk+tGhZRERE8kTQC0+9evU4kQI7Duc8bs56B23bdw1OKBERETG1oBee\niIgI7r1vIMO/jSC75dK7jsKM3+CBBx8ObjgRERExpZDcafmFF19iS1JVBk4O43jy/143DPh5C3QY\n4+K10WOpUKFCKOKJiIiIyQR9kYxhGHwy+WOOHj3GvIMG05dD8+oQ44Ith8OwOssy5q2x9Op9R7Cj\niYiIiEkFvfA8+9QTLJ47iR8ed9O4GpxMhSWbYfdxOJRspWqDRvS8vVewY4mIiIiJBfXGg8uXL6dv\nz06sH+6mROT529Mzoe1rkTw+9AP69u0brFgiIiJickEtPH1730LziLkM7pL9W85ZA2N+vZIVqzcG\nK5aIiIiYXFALT2y54qx8IYmqZbIf4/OD814raWkewsPDgxVNRERETCyoV2n5fH4cuXQYuw3sNis+\nny84oURERMT0glp4GtSrza/bch6z5g8oV6YETqczOKFERETE9IJaeB589BneWhRJIPvnhjJugYOH\nBj2BxWIJXjARERExtaCu4cnMzKRDXEvqRW5mfP90ws66KD4QgFe/szNtbQV+W/M7xYsXD1YsERGR\nAmv79u0cOXKEEiVKcNVVV2lC4BIFtfAAJCcn0//OHqz+zwrubpNJjbI+Dp2y8skKJ+Ur1WTmNz9Q\nsWLFYEYSEREpcL755hteeOUF9h/cT1ipMPxJfopHFmfo/w3lgfsfUPG5SEEvPH/ZvHkz06ZO4ejh\n/RQvWYbed/anRYsW+g0UEZEib/Troxnx+gjcHd1Qk9MLUAxgL0QuiaTvzX15f/z7+jfzIoSs8IiI\niMj51q9fT5sObfDc7YGYLAZ4IXJqJF9M/IJu3boFPV9hFZKHh4qIiEjW3nj7DTKaZGRddgAckHZN\nGqPHjQ5qrsJOMzwiIiIFSMlyJTl1xykomcOgTLCOtpLuTcduz/2xmD6fj02bNuHxeKhWrRoVKlTI\nu8CFhGZ4RERECpCM9AzI7UEDYWC1WcnIyMhxWGZmJqNeHUX5yuVpe1Nbbux/I9XrVOf6ztezevXq\nvAtdCGiGR0REpACp17ge267cBrVyGHQMLB9beODuB7jxxhvp0KED0dHR5wzJzMyk082dWLVvFZ44\nD/w1qZMB/A6uf7v4evrXdOnSJb8OpUDRDI+IiEgBMuSRIUSujzx9VVY2ItZFcP8991OnTh0mTJhA\nbGws7du3Z8yYMWzcuBHDMBjz+pjTZef2s8oOnJ49agbuHm563tGT5OTk/D6kAkGFR0REpADp27cv\nZf1lsf9qP7/0HASmAxuhxhU1uPfee1m4cCFHjhxhyJAh7Nu3j1tuuYVKlSox4rURp2d2bNm8URXg\nCpg6dWq+Hk9BoVNaIiIiBcyhQ4fo0KUDB5IOkHplKoQB/wa8QCPABa4/XQQSAgx+YjCjho/Caj09\nh2EYBnPnzqX3A71JfyQ95zfaAm1OtOHfi/+dz0cUerkv7RYREZGgio2NZfOGzSxevJg333mThQsW\n4o/zQwvOnJtx44YUeOeLd0hJSWH82+MBsFgslC1blohiEaSTS+FxgNvjzt+DKSBUeERERAogq9VK\nx44dmfblNGgGtMxiUDS4e7mZ/MFkHh74MKmpqSxevJgffviB5H3Jpxco53DFl/WIlbo16+bTERQs\nWsMjIiJSQKWkpPDVV1/hb+HPfpATvA28NGnRhPvvv5/jx4/z/PPPc13b6+D3HHbuB2e8k8cHPZ7n\nuQsizfCIiIgUUDt27CCsZBieaE+O44yaBrW8tdi4ZuOZ18qVK0e7Tu1wl3WfXqB8Nj9E/BDBNVdf\nwzXXXJMPyQseFR4REZECymq15nh5+hkBCA8/99xV8+bN+ebLb7it921wBaTVTgPH6dNYzngnLRq3\nYO6suUXmAaQqPCIiIgVU3bp18Sf54RRQIvtx4bvC6RjX8bzXO3fuzKF9h/jss8+YPns6breberXq\n8fjwx2nRokWRKTugy9JFREQKtMcGP8aHKz8ko2s2j5FIAsfHDrZv2k6VKn8/dyV/0aJlERGRAuyV\nYa9Q4WQFwhaHcd5V5kfA9aWLYf8cprKTC83wiIiIFHAnTpxgwH0DWLp0KbbaNnzhPsKPh2NPtvPa\niNd4cOCDoY5Y4KnwiIiIFBIHDhxg/vz5uN1uqlevTpcuXbDbtRz3QqjwiIiIiOlpDY+IiIiYngqP\niIiImJ4Kj4iIiJieCo+IiIiYngqPiIiImJ4Kj4iIiJieCo+IiIiYngqPiIiImJ4Kj4iIiJieCo+I\niIiYngqPiIiImJ4Kj4iIiJieCo+IiIiYngqPiIiImJ4Kj4iIiJieCo+IiIiYngqPiIiImJ4Kj4iI\niJieCo+IiIiYngqPiIiImJ4Kj4iIiJieCo+IiIiYngqPiIiImJ4Kj4iIiJieCo+IiIiYngqPiIiI\nmJ4Kj4iIiJieCo+IiIiYngqPiIiImJ4Kj4iIiJieCo+IiIiYngqPiIiImJ4Kj4iIiJieCo+IiIiY\nngqPiIiImJ4Kj4iIiJieCo+IiIiYnj2Ub75v3z5Wr15NIBDg6quvpmbNmqGMIyIiIiZlMQzDCPab\n7tq1iyGPD+Tfy1dwXb1wrBaDf2/LpHGjxox9+wMaNmwY7EgiIiJiYkEvPAkJCcRd24InOyQz6IYA\nkY7Tr3sz4JNfYNi3Ucxb8DNNmzYNZiwRERExsaAXnuuva06PGmt5rFPWbztjJYxYWI2NW//AYrEE\nM1qR4ff7+eGHH5gybQonTp6gSsUqDLx3IG3bttXXXERETCmohWfLli3cENeMvW94CMtm9ZBhQIN/\nRjHx0++Ji4sLVrQiY8eOHXTo0oEkSxIp9VMgCiynLLg2uqgZW5MFPyygbNmyoY4pIiKSp4J6ldbS\npUu5ubGRbdkBsFigx9VpLFm8OHjBioijR4/SOq41B686SMqAFGgG1AWjlUHaA2lscW3hug7X4fV6\nQx1VREQkTwW18GRmZuIIy31CyWE38Pkyg5CoaHnzrTdJqZaC0SSL3wMLZF6fyaHMQ8ycOTP44URE\nRPJRUAtPgwYN+HdCOLmdRPv3H1HUb3BlcEIVEYZh8MHHH5DRNCP7QRZIvTqVN8a/EbxgIiIiQRDU\n+/B06NCBpAwXv2xLIa5e1mM2H4B1e2B2jx4cOHAAr9dLbGwsLpcrmFFNJyUlBXeqG8rkMrAi7Fmw\nJxiRREwnISGBjRs3YrPZaNmyJeXKlQt1JBH5r6DO8FitVt54ayJ93neyYc/523cegc5jbHTqcjPN\nGtelacPadIq7mooVSvPg/QPYtWtXMOOaSnh4OAF/APy5DMyAsIiwoGQSMYu1a9dyzXXX0KhFI+4Z\nfg8DXhxA1ZpV6XZbNw4ePBjqeCJCiG48+OX06Twy6AFa14Kbr0rDaoGFW10s3BgAoHZ5H6Nv99Gu\n/ulFzEcSYeJiK+8vi+THn5bqHj2XqGmrpqyrtA5yOFto+9VGn8p9mDplavCCiRRiy5cvp9NNnXDH\nuaEh/5s394BtlY2SO0qybtU6KlWqFMqYIkVeSAoPQFpaGl9++SW//XsJgYCfq5u14cTxo/z67ZvM\ne8pDeBYn22avhsdnlGLXnkOEh4cHP3QhN2vWLAYMHoBngAcishiQDM4pTlYsWUHjxo2DHU+k0PH7\n/VSoUoHj7Y5D7azH2H6x0d7RngU/LAhuOBE5R8gKz9/5fD6qVS7LD4+folHV7Me1/1cUD77wMb17\n9w5eOJPYuXMnjZo1whftI6NLBlQELEAA+ANcC13888l/8sJzL4Q4qUjh8N1339H3ib6k3JWS/aB0\niHg3goQtCVSuXDl44UTkHCF9eOjZ1q5dSymXL8eyA9C/ZSqzZ36mwnORdu3aRYcOHXhj9Bt4M7yM\nHjuatEAatmI2fH/6KF+6PK++8yq9evUKdVSRQuOnRT+RUiOHsgMQAWG1wvjll1/o27dvcIJJUB07\ndoxvvvmGEydOUKpUKW677TbdwLUAKjCFJyUlhVLRua+hLh0NqbuTgpDIPHbu3En79u158cUXGThw\nIACPP/o469atIzExkXLlynHllVfqsRIiF8mb7r2gv0UNu0FGRg63hJBCyev1MnDQQL766iusdax4\nXB6cbidDnh3C7bffzofvfYjD4Qh1TPmvAlN4KleuzPaDGfj8YLdlP27zQSuVq9UIXrBCLiEhgQ4d\nOpxTduD0FXPNmjULYTKRwq/xlY1xrXLhxp39oACwH+rXrx+0XJL/fD4fnW/uzOoTq/E+4gXn6dfd\nuOEGmDV/Fntu3sOS+Uuw2wvMP7VFWlAvS89JnTp1qFqtBnPXZT/GH4APlzm45/5BwQtWwLndbiZN\nmsQ1ba+h5pU1adO+DdOmTcPr9ZKQkED79u0ZNmzYOWVHRPJGv379COwMQGIOgxKgXIlytGjRIlix\nJAi++uor1u5ei6eb50zZOcMJnm4e1u1epzvXFyAFZtEywLx583jgrp4sftZNndhzt/kD8PAnEey3\ntODHBct0+gWIj4+nQ5cOeEt7SWuQBsWARIjaFIUjyYHNb2PUqFHcd999oY4qYlqjRo/i1fGv4u7t\nPv1n8GwHwfmVkzkz5tCpU6eQ5JP80bhFY+KviIecJu62QKPdjdjwnw3BiiU5KFCFB+CTyZN5asij\n9G3lp3eLDKIcsGY3jF8SRYnY+sz5fiHFiv39b5Wi5/Dhw9RvWJ/EdolZ31dnDUSviGbvzr2UKFEi\n2PFEigzDMBj52khefe1VLHUteCp6IADOBCfeXV6+mPoFd9xxR6hjSh4LiwjD97QPcrpDSibYxtjw\nZfiClkuyV2BOaf3l7nvvZf3v24hp8iSDv61F/6lV+OFwR14b/xWLfl6psvNfb7/7Np5anuxvItgM\nfNV8TJo8Kai5RIoai8XC0BeGsn/3fl7u9TK3Om6lV0wvJjw7gdu638a2bdtCHVFEKIAzPHJhSpQt\nQeLtiZDTlY/7oPIvldmXsC9YsUTkLHv37qVJkybEx8frTssmc6GntBr+0ZD41fFByyXZK3AzPJK7\nzMxMkv5Myv1BoGXh6MGjQckkIuerWrUqAwcO5IEHHqBH7x5UqlGJKrWq0GdAH9auXRvqeHIZnh38\nLJFrIrN/PqEfItdE8uzgZ4OaS7KnGZ5CyDAMwh3h+Ib4IKdbPCRC9NRokv9MDlY0ETmLz+ejd9/e\nfPPjN1jaWDCqG2CAdacVx3oHd915FxPemaCLMAohn89H2/ZtWXtoLRktMqAy4PrvRg84f3LSrGQz\nlvyky9ILCv0uFEIWi4VON3Zi3u/zMFpk31dtG23c0v2W4AUTkXM8+cyTzN8wHx4HI/x/f1YD5QO4\nm7r5dManlC9fnmH/HBbClHKx9uzZw5DnhrBu/TqMEgYsAU4CpcFR2gG7oeftPfnwvQ9VdgoQzfAU\nUr/++iudb+2M524PRGcx4BQ4P3Wy8ueVNGrUKOj5RIq6P//8k0rVKuF92AuR2Qw6CVFTozh26BhO\n599v5iIFUUJCAtdcew1JDZIItAj87x48aWD5zULkpkh+XvgzTZs2DWlOOZ/W8BRStWrVwmVzETY5\nDDbzv/PImUA8uKa5eH3U6yo7IiEyY8YMLLUt2ZcdgJJgibUwd+7coOWSy9Pjjh4kNU8iEBc494aD\nkWB0MPA09/DokEdDlk+yp8JTCB0/fpwbbriBxx95nFmfzOLqfVcT8VYE0R9FE/F2BC3/bMmc6XN4\nZNAjoY4qUmTt278PT4wn13HeEl4OHDgQhERyudauXcsf+/4g0DSQ7Rh/Cz/xv8frdgQFkE4uFlBe\nr5eVK1eSlpZGtWrVuPLK0zfcOXnyJJ06daJ79+4MHToUi8VCt27dOHToECdPnqRMmTKUK1cuxOlF\npHhMccK8YWSSmeO4MG8Y0dFZnZeWgmbBggWk10nPearADoE6ARYsWEDdunWDlk1yp8JTwKSnp/PP\nYf/kg48+wFrKisVhIfNIJlViqzD8xeGMGTOG9u3bM3LkyHOu7IiNjSU2NjaHPYtIMN1yyy0M/9dw\nMjtkZv83rRd823z84x//OH+T18uaNWv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      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Month \n",
    "df_del = df_del3.copy()\n",
    "df_graph = df_del[df_del[MTH]>0].copy().reset_index()\n",
    "# Add the city\n",
    "for dic, loc in zip([dict_prov], ['Province']):\n",
    "    df_graph['Source ' + loc] = df_graph['Source'].map(dic)\n",
    "    df_graph['Target ' + loc] = df_graph['Target'].map(dic)\n",
    "# Unique cities\n",
    "PROVINCES = list(df_graph['Source Province'].unique()) + list(df_graph['Target Province'].unique()) \n",
    "n_prov = len(set(PROVINCES))\n",
    "\n",
    "# Plot\n",
    "fig, ax = plt.subplots(figsize = (10, 7))\n",
    "plt.rc('axes', titlesize=20)     # fontsize of the axes title\n",
    "G = nx.Graph()\n",
    "for link in df_graph.index:\n",
    "    G.add_edge(df_graph.iloc[link]['Source'],\n",
    "             df_graph.iloc[link]['Target'])\n",
    "\n",
    "# Add Colors by city\n",
    "df_cat = df_city.set_index('Code')\n",
    "df_cat = df_cat.reindex(G.nodes())\n",
    "df_cat['Province'] = pd.Categorical(df_cat['Province'])\n",
    "df_cat['Province'].cat.codes\n",
    "\n",
    "# Position of labels\n",
    "def nudge(pos, x_shift, y_shift):\n",
    "    return {n:(x + x_shift, y + y_shift) for n,(x,y) in pos.items()}\n",
    "pos = nx.spring_layout(G)\n",
    "pos_nodes = nudge(pos, 0, 0.1)  \n",
    "\n",
    "# Set node_color\n",
    "nx.draw(G, with_labels=False, node_color=df_cat['Province'].map(dict_color),\n",
    "       node_size=100, edgecolors='black', alpha = 1, \n",
    "        font_size = 11, font_color = 'black', width=1 , verticalalignment='top')\n",
    "plt.title(\"{} 5.0T\".format(MTH), fontsize=12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "#### Month (8T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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hAKCYIniAIiQ+Pl61ajVQZuYxSYF5rLLLZquprVu/UpMmTW7aLBkZGYqPj5eXl5eqVq0q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\n4AEAAKb3/wEMqTcZH9jeMQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Month \n",
    "df_del = df_del4.copy()\n",
    "df_graph = df_del[df_del[MTH]>0].copy().reset_index()\n",
    "# Add the city\n",
    "for dic, loc in zip([dict_prov], ['Province']):\n",
    "    df_graph['Source ' + loc] = df_graph['Source'].map(dic)\n",
    "    df_graph['Target ' + loc] = df_graph['Target'].map(dic)\n",
    "# Unique cities\n",
    "PROVINCES = list(df_graph['Source Province'].unique()) + list(df_graph['Target Province'].unique()) \n",
    "n_prov = len(set(PROVINCES))\n",
    "\n",
    "# Plot\n",
    "fig, ax = plt.subplots(figsize = (10, 7))\n",
    "plt.rc('axes', titlesize=20)     # fontsize of the axes title\n",
    "G = nx.Graph()\n",
    "for link in df_graph.index:\n",
    "    G.add_edge(df_graph.iloc[link]['Source'],\n",
    "             df_graph.iloc[link]['Target'])\n",
    "\n",
    "# Add Colors by city\n",
    "df_cat = df_city.set_index('Code')\n",
    "df_cat = df_cat.reindex(G.nodes())\n",
    "df_cat['Province'] = pd.Categorical(df_cat['Province'])\n",
    "df_cat['Province'].cat.codes\n",
    "\n",
    "# Position of labels\n",
    "def nudge(pos, x_shift, y_shift):\n",
    "    return {n:(x + x_shift, y + y_shift) for n,(x,y) in pos.items()}\n",
    "pos = nx.spring_layout(G)\n",
    "pos_nodes = nudge(pos, 0, 0.1)  \n",
    "\n",
    "# Set node_color\n",
    "nx.draw(G, with_labels=False, node_color=df_cat['Province'].map(dict_color),\n",
    "       node_size=100, edgecolors='black', alpha = 1, \n",
    "        font_size = 11, font_color = 'black', width=1 , verticalalignment='top')\n",
    "plt.title(\"{} 8.0T\".format(MTH), fontsize=12)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}