step4_percolation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "__author__ = 'leolmoss'\n",
    "# Load network, filter by minimum values\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import networkx as nx\n",
    "import numpy as np\n",
    "import networkx as nx\n",
    "import random\n",
    "import math\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction\n",
    "This notebook was created to perform the percolation of the flow network resulting from the routing of the rejection-sampling ODs. See step3."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>source</th>\n",
       "      <th>target</th>\n",
       "      <th>weight</th>\n",
       "      <th>pairs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>262391492</td>\n",
       "      <td>5728971089</td>\n",
       "      <td>1</td>\n",
       "      <td>(262391492, 5728971089)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2272948048</td>\n",
       "      <td>2272948045</td>\n",
       "      <td>1</td>\n",
       "      <td>(2272948048, 2272948045)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2272948048</td>\n",
       "      <td>2272948047</td>\n",
       "      <td>1</td>\n",
       "      <td>(2272948048, 2272948047)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>494927893</td>\n",
       "      <td>494927791</td>\n",
       "      <td>149</td>\n",
       "      <td>(494927893, 494927791)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5098176538</td>\n",
       "      <td>5098176539</td>\n",
       "      <td>316</td>\n",
       "      <td>(5098176538, 5098176539)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       source      target  weight                     pairs\n",
       "0   262391492  5728971089       1   (262391492, 5728971089)\n",
       "1  2272948048  2272948045       1  (2272948048, 2272948045)\n",
       "2  2272948048  2272948047       1  (2272948048, 2272948047)\n",
       "3   494927893   494927791     149    (494927893, 494927791)\n",
       "4  5098176538  5098176539     316  (5098176538, 5098176539)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This file is the final flow network of the potential demand. Created in step3_potential_demand.ipynb\n",
    "data=pd.read_csv('pairs_potential_bike_20190401.csv')\n",
    "data.loc[:,'pairs']=data.apply(lambda row: (row['source'],row['target']),axis=1)\n",
    "# this file is the existed bikepaths in bogota. This is easily extracted using OSMNX, see osm_bikpaths.ipynb\n",
    "osm_bikepaths=pd.read_csv('osm_bikepaths.csv')\n",
    "osm_bikepaths.loc[:,'pairs']=osm_bikepaths.apply(lambda row: (row['u'],row['v']),axis=1)\n",
    "data.head()\n",
    "#osm_bikepaths.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>source</th>\n",
       "      <th>target</th>\n",
       "      <th>weight</th>\n",
       "      <th>pairs</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>326</th>\n",
       "      <td>891289834</td>\n",
       "      <td>891289849</td>\n",
       "      <td>5413</td>\n",
       "      <td>(891289834, 891289849)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>358</th>\n",
       "      <td>891289849</td>\n",
       "      <td>5102220848</td>\n",
       "      <td>5293</td>\n",
       "      <td>(891289849, 5102220848)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19029</th>\n",
       "      <td>3236651041</td>\n",
       "      <td>6326367724</td>\n",
       "      <td>5632</td>\n",
       "      <td>(3236651041, 6326367724)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19049</th>\n",
       "      <td>3236651042</td>\n",
       "      <td>4898353696</td>\n",
       "      <td>5447</td>\n",
       "      <td>(3236651042, 4898353696)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41241</th>\n",
       "      <td>6313510427</td>\n",
       "      <td>3236651042</td>\n",
       "      <td>5632</td>\n",
       "      <td>(6313510427, 3236651042)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216573</th>\n",
       "      <td>3236651037</td>\n",
       "      <td>3236651038</td>\n",
       "      <td>5154</td>\n",
       "      <td>(3236651037, 3236651038)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216574</th>\n",
       "      <td>3236651037</td>\n",
       "      <td>3236651036</td>\n",
       "      <td>5029</td>\n",
       "      <td>(3236651037, 3236651036)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216576</th>\n",
       "      <td>3236651038</td>\n",
       "      <td>3236651039</td>\n",
       "      <td>5154</td>\n",
       "      <td>(3236651038, 3236651039)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216577</th>\n",
       "      <td>3236651038</td>\n",
       "      <td>3236651037</td>\n",
       "      <td>5057</td>\n",
       "      <td>(3236651038, 3236651037)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216579</th>\n",
       "      <td>3236651039</td>\n",
       "      <td>3236651040</td>\n",
       "      <td>5156</td>\n",
       "      <td>(3236651039, 3236651040)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216580</th>\n",
       "      <td>3236651039</td>\n",
       "      <td>3236651038</td>\n",
       "      <td>5057</td>\n",
       "      <td>(3236651039, 3236651038)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216582</th>\n",
       "      <td>3236651040</td>\n",
       "      <td>3236651041</td>\n",
       "      <td>5632</td>\n",
       "      <td>(3236651040, 3236651041)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>224268</th>\n",
       "      <td>2977661580</td>\n",
       "      <td>891289834</td>\n",
       "      <td>5512</td>\n",
       "      <td>(2977661580, 891289834)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>224270</th>\n",
       "      <td>2977661580</td>\n",
       "      <td>4898353692</td>\n",
       "      <td>5162</td>\n",
       "      <td>(2977661580, 4898353692)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>306090</th>\n",
       "      <td>6326367724</td>\n",
       "      <td>6313510427</td>\n",
       "      <td>5632</td>\n",
       "      <td>(6326367724, 6313510427)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>349379</th>\n",
       "      <td>5102220862</td>\n",
       "      <td>5102220870</td>\n",
       "      <td>5388</td>\n",
       "      <td>(5102220862, 5102220870)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>357204</th>\n",
       "      <td>5102216162</td>\n",
       "      <td>5102216170</td>\n",
       "      <td>5677</td>\n",
       "      <td>(5102216162, 5102216170)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>357221</th>\n",
       "      <td>5102216170</td>\n",
       "      <td>5102216178</td>\n",
       "      <td>5677</td>\n",
       "      <td>(5102216170, 5102216178)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>357235</th>\n",
       "      <td>5102216178</td>\n",
       "      <td>5102216201</td>\n",
       "      <td>5559</td>\n",
       "      <td>(5102216178, 5102216201)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>360686</th>\n",
       "      <td>5102220848</td>\n",
       "      <td>5102220862</td>\n",
       "      <td>5282</td>\n",
       "      <td>(5102220848, 5102220862)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>360718</th>\n",
       "      <td>5102220870</td>\n",
       "      <td>5102216162</td>\n",
       "      <td>5445</td>\n",
       "      <td>(5102220870, 5102216162)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>426163</th>\n",
       "      <td>4898353692</td>\n",
       "      <td>4898353693</td>\n",
       "      <td>5162</td>\n",
       "      <td>(4898353692, 4898353693)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>426164</th>\n",
       "      <td>4898353692</td>\n",
       "      <td>2977661580</td>\n",
       "      <td>5447</td>\n",
       "      <td>(4898353692, 2977661580)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>426165</th>\n",
       "      <td>4898353693</td>\n",
       "      <td>4898353694</td>\n",
       "      <td>5162</td>\n",
       "      <td>(4898353693, 4898353694)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>426166</th>\n",
       "      <td>4898353693</td>\n",
       "      <td>4898353692</td>\n",
       "      <td>5447</td>\n",
       "      <td>(4898353693, 4898353692)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>426167</th>\n",
       "      <td>4898353694</td>\n",
       "      <td>4898353695</td>\n",
       "      <td>5162</td>\n",
       "      <td>(4898353694, 4898353695)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>426168</th>\n",
       "      <td>4898353694</td>\n",
       "      <td>4898353693</td>\n",
       "      <td>5447</td>\n",
       "      <td>(4898353694, 4898353693)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>426169</th>\n",
       "      <td>4898353695</td>\n",
       "      <td>4898353696</td>\n",
       "      <td>5162</td>\n",
       "      <td>(4898353695, 4898353696)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>426170</th>\n",
       "      <td>4898353695</td>\n",
       "      <td>4898353694</td>\n",
       "      <td>5447</td>\n",
       "      <td>(4898353695, 4898353694)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>426172</th>\n",
       "      <td>4898353696</td>\n",
       "      <td>3236651042</td>\n",
       "      <td>5162</td>\n",
       "      <td>(4898353696, 3236651042)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>426173</th>\n",
       "      <td>4898353696</td>\n",
       "      <td>4898353695</td>\n",
       "      <td>5447</td>\n",
       "      <td>(4898353696, 4898353695)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            source      target  weight                     pairs\n",
       "326      891289834   891289849    5413    (891289834, 891289849)\n",
       "358      891289849  5102220848    5293   (891289849, 5102220848)\n",
       "19029   3236651041  6326367724    5632  (3236651041, 6326367724)\n",
       "19049   3236651042  4898353696    5447  (3236651042, 4898353696)\n",
       "41241   6313510427  3236651042    5632  (6313510427, 3236651042)\n",
       "216573  3236651037  3236651038    5154  (3236651037, 3236651038)\n",
       "216574  3236651037  3236651036    5029  (3236651037, 3236651036)\n",
       "216576  3236651038  3236651039    5154  (3236651038, 3236651039)\n",
       "216577  3236651038  3236651037    5057  (3236651038, 3236651037)\n",
       "216579  3236651039  3236651040    5156  (3236651039, 3236651040)\n",
       "216580  3236651039  3236651038    5057  (3236651039, 3236651038)\n",
       "216582  3236651040  3236651041    5632  (3236651040, 3236651041)\n",
       "224268  2977661580   891289834    5512   (2977661580, 891289834)\n",
       "224270  2977661580  4898353692    5162  (2977661580, 4898353692)\n",
       "306090  6326367724  6313510427    5632  (6326367724, 6313510427)\n",
       "349379  5102220862  5102220870    5388  (5102220862, 5102220870)\n",
       "357204  5102216162  5102216170    5677  (5102216162, 5102216170)\n",
       "357221  5102216170  5102216178    5677  (5102216170, 5102216178)\n",
       "357235  5102216178  5102216201    5559  (5102216178, 5102216201)\n",
       "360686  5102220848  5102220862    5282  (5102220848, 5102220862)\n",
       "360718  5102220870  5102216162    5445  (5102220870, 5102216162)\n",
       "426163  4898353692  4898353693    5162  (4898353692, 4898353693)\n",
       "426164  4898353692  2977661580    5447  (4898353692, 2977661580)\n",
       "426165  4898353693  4898353694    5162  (4898353693, 4898353694)\n",
       "426166  4898353693  4898353692    5447  (4898353693, 4898353692)\n",
       "426167  4898353694  4898353695    5162  (4898353694, 4898353695)\n",
       "426168  4898353694  4898353693    5447  (4898353694, 4898353693)\n",
       "426169  4898353695  4898353696    5162  (4898353695, 4898353696)\n",
       "426170  4898353695  4898353694    5447  (4898353695, 4898353694)\n",
       "426172  4898353696  3236651042    5162  (4898353696, 3236651042)\n",
       "426173  4898353696  4898353695    5447  (4898353696, 4898353695)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data1=data[data['weight']>5000]\n",
    "data1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Join Potential flow and osm-bikepaths networks\n",
    "Bogota has one of the largest bike-path networks in the world. Thus, the idea of this step is to include the existed infrastructure in the percolated network. For cities without an existed infrastructure, one should perform the percolation only in the resulting flow network for the potential demand."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "merge=pd.merge(data,osm_bikepaths,left_on='pairs',right_on='pairs',how='outer')\n",
    "merge1=pd.merge(data1,osm_bikepaths,left_on='pairs',right_on='pairs',how='outer')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#merge1\n",
    "#len(osm_bikepaths)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "495908"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "merge = merge[~pd.isnull(merge['source'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "495908"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(merge)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "#merge['weight2'] = [5000 if math.isnan(merge['u']) else merge['weight']]\n",
    "merge['u'].fillna(0,inplace=True)\n",
    "merge['v'].fillna(0,inplace=True)\n",
    "merge.loc[merge['u']!=0, 'weight'] = 5000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "merge[['source','target','weight']].to_csv('potentialdemand_V17_mod_OSM_bikepaths.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "## this file is the network + properties, third column is speed ratio, fourth column is density\n",
    "s,t,w = np.loadtxt('potentialdemand_V17_mod_OSM_bikepaths.csv',delimiter=',', unpack=True,skiprows=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "85072"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "G=nx.Graph()\n",
    "#v17 495909-> # of OSM nodes in the flow network\n",
    "for i in range(0,495908):\n",
    "     if w[i]>1:\n",
    "        if G.has_edge(int(s[i]),int (t[i])):\n",
    "            e=G[int(s[i])][int(t[i])][\"weight\"]\n",
    "            id=G[int(s[i])][int(t[i])][\"width\"]\n",
    "            G.add_edge(int(s[i]),int (t[i]),weight=(w[i]+e),width=id)\n",
    "        \n",
    "        else:\n",
    "            G.add_edge(int(s[i]),int (t[i]),weight=w[i], width=i)\n",
    "\n",
    "Gc = max(nx.connected_component_subgraphs(G), key=len)\n",
    "\n",
    "graphs2 =list(sorted(nx.connected_component_subgraphs(G),key=len, reverse=True))\n",
    "G.size()\n",
    "#w.max()\n",
    "#graphs2[2].size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "#G.edges()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Percolation "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[31299]\n",
      "[31299, 27217]\n",
      "[31299, 27217, 24975]\n",
      "[31299, 27217, 24975, 22448]\n",
      "[31299, 27217, 24975, 22448, 20662]\n",
      "[31299, 27217, 24975, 22448, 20662, 19374]\n",
      "[31299, 27217, 24975, 22448, 20662, 19374, 17944]\n",
      "[31299, 27217, 24975, 22448, 20662, 19374, 17944, 17076]\n",
      "[31299, 27217, 24975, 22448, 20662, 19374, 17944, 17076, 14873]\n",
      "[31299, 27217, 24975, 22448, 20662, 19374, 17944, 17076, 14873, 14254]\n",
      "[31299, 27217, 24975, 22448, 20662, 19374, 17944, 17076, 14873, 14254, 13377]\n",
      "[31299, 27217, 24975, 22448, 20662, 19374, 17944, 17076, 14873, 14254, 13377, 12654]\n",
      "[31299, 27217, 24975, 22448, 20662, 19374, 17944, 17076, 14873, 14254, 13377, 12654, 12264]\n",
      "[31299, 27217, 24975, 22448, 20662, 19374, 17944, 17076, 14873, 14254, 13377, 12654, 12264, 11656]\n",
      "[31299, 27217, 24975, 22448, 20662, 19374, 17944, 17076, 14873, 14254, 13377, 12654, 12264, 11656, 11215]\n",
      "[31299, 27217, 24975, 22448, 20662, 19374, 17944, 17076, 14873, 14254, 13377, 12654, 12264, 11656, 11215, 10907]\n",
      "[31299, 27217, 24975, 22448, 20662, 19374, 17944, 17076, 14873, 14254, 13377, 12654, 12264, 11656, 11215, 10907, 10701]\n",
      "[31299, 27217, 24975, 22448, 20662, 19374, 17944, 17076, 14873, 14254, 13377, 12654, 12264, 11656, 11215, 10907, 10701, 10201]\n",
      "[31299, 27217, 24975, 22448, 20662, 19374, 17944, 17076, 14873, 14254, 13377, 12654, 12264, 11656, 11215, 10907, 10701, 10201, 9451]\n",
      "[31299, 27217, 24975, 22448, 20662, 19374, 17944, 17076, 14873, 14254, 13377, 12654, 12264, 11656, 11215, 10907, 10701, 10201, 9451, 8687]\n",
      "[31299, 27217, 24975, 22448, 20662, 19374, 17944, 17076, 14873, 14254, 13377, 12654, 12264, 11656, 11215, 10907, 10701, 10201, 9451, 8687, 8582]\n",
      "[31299, 27217, 24975, 22448, 20662, 19374, 17944, 17076, 14873, 14254, 13377, 12654, 12264, 11656, 11215, 10907, 10701, 10201, 9451, 8687, 8582, 8433]\n",
      "[31299, 27217, 24975, 22448, 20662, 19374, 17944, 17076, 14873, 14254, 13377, 12654, 12264, 11656, 11215, 10907, 10701, 10201, 9451, 8687, 8582, 8433, 8385]\n",
      "[31299, 27217, 24975, 22448, 20662, 19374, 17944, 17076, 14873, 14254, 13377, 12654, 12264, 11656, 11215, 10907, 10701, 10201, 9451, 8687, 8582, 8433, 8385, 7403]\n",
      "[31299, 27217, 24975, 22448, 20662, 19374, 17944, 17076, 14873, 14254, 13377, 12654, 12264, 11656, 11215, 10907, 10701, 10201, 9451, 8687, 8582, 8433, 8385, 7403, 7344]\n",
      "[31299, 27217, 24975, 22448, 20662, 19374, 17944, 17076, 14873, 14254, 13377, 12654, 12264, 11656, 11215, 10907, 10701, 10201, 9451, 8687, 8582, 8433, 8385, 7403, 7344, 7297]\n",
      "[31299, 27217, 24975, 22448, 20662, 19374, 17944, 17076, 14873, 14254, 13377, 12654, 12264, 11656, 11215, 10907, 10701, 10201, 9451, 8687, 8582, 8433, 8385, 7403, 7344, 7297, 7238]\n",
      "[31299, 27217, 24975, 22448, 20662, 19374, 17944, 17076, 14873, 14254, 13377, 12654, 12264, 11656, 11215, 10907, 10701, 10201, 9451, 8687, 8582, 8433, 8385, 7403, 7344, 7297, 7238, 7222]\n",
      "[31299, 27217, 24975, 22448, 20662, 19374, 17944, 17076, 14873, 14254, 13377, 12654, 12264, 11656, 11215, 10907, 10701, 10201, 9451, 8687, 8582, 8433, 8385, 7403, 7344, 7297, 7238, 7222, 7173]\n",
      "[31299, 27217, 24975, 22448, 20662, 19374, 17944, 17076, 14873, 14254, 13377, 12654, 12264, 11656, 11215, 10907, 10701, 10201, 9451, 8687, 8582, 8433, 8385, 7403, 7344, 7297, 7238, 7222, 7173, 7097]\n",
      "[31299, 27217, 24975, 22448, 20662, 19374, 17944, 17076, 14873, 14254, 13377, 12654, 12264, 11656, 11215, 10907, 10701, 10201, 9451, 8687, 8582, 8433, 8385, 7403, 7344, 7297, 7238, 7222, 7173, 7097, 7033]\n",
      "[31299, 27217, 24975, 22448, 20662, 19374, 17944, 17076, 14873, 14254, 13377, 12654, 12264, 11656, 11215, 10907, 10701, 10201, 9451, 8687, 8582, 8433, 8385, 7403, 7344, 7297, 7238, 7222, 7173, 7097, 7033, 6991]\n",
      "[31299, 27217, 24975, 22448, 20662, 19374, 17944, 17076, 14873, 14254, 13377, 12654, 12264, 11656, 11215, 10907, 10701, 10201, 9451, 8687, 8582, 8433, 8385, 7403, 7344, 7297, 7238, 7222, 7173, 7097, 7033, 6991, 6973]\n",
      "[31299, 27217, 24975, 22448, 20662, 19374, 17944, 17076, 14873, 14254, 13377, 12654, 12264, 11656, 11215, 10907, 10701, 10201, 9451, 8687, 8582, 8433, 8385, 7403, 7344, 7297, 7238, 7222, 7173, 7097, 7033, 6991, 6973, 6973]\n",
      "[31299, 27217, 24975, 22448, 20662, 19374, 17944, 17076, 14873, 14254, 13377, 12654, 12264, 11656, 11215, 10907, 10701, 10201, 9451, 8687, 8582, 8433, 8385, 7403, 7344, 7297, 7238, 7222, 7173, 7097, 7033, 6991, 6973, 6973, 6973]\n",
      "[31299, 27217, 24975, 22448, 20662, 19374, 17944, 17076, 14873, 14254, 13377, 12654, 12264, 11656, 11215, 10907, 10701, 10201, 9451, 8687, 8582, 8433, 8385, 7403, 7344, 7297, 7238, 7222, 7173, 7097, 7033, 6991, 6973, 6973, 6973, 3937]\n",
      "[31299, 27217, 24975, 22448, 20662, 19374, 17944, 17076, 14873, 14254, 13377, 12654, 12264, 11656, 11215, 10907, 10701, 10201, 9451, 8687, 8582, 8433, 8385, 7403, 7344, 7297, 7238, 7222, 7173, 7097, 7033, 6991, 6973, 6973, 6973, 3937, 3932]\n",
      "[31299, 27217, 24975, 22448, 20662, 19374, 17944, 17076, 14873, 14254, 13377, 12654, 12264, 11656, 11215, 10907, 10701, 10201, 9451, 8687, 8582, 8433, 8385, 7403, 7344, 7297, 7238, 7222, 7173, 7097, 7033, 6991, 6973, 6973, 6973, 3937, 3932, 3877]\n",
      "[31299, 27217, 24975, 22448, 20662, 19374, 17944, 17076, 14873, 14254, 13377, 12654, 12264, 11656, 11215, 10907, 10701, 10201, 9451, 8687, 8582, 8433, 8385, 7403, 7344, 7297, 7238, 7222, 7173, 7097, 7033, 6991, 6973, 6973, 6973, 3937, 3932, 3877, 3859]\n",
      "[31299, 27217, 24975, 22448, 20662, 19374, 17944, 17076, 14873, 14254, 13377, 12654, 12264, 11656, 11215, 10907, 10701, 10201, 9451, 8687, 8582, 8433, 8385, 7403, 7344, 7297, 7238, 7222, 7173, 7097, 7033, 6991, 6973, 6973, 6973, 3937, 3932, 3877, 3859, 3856]\n",
      "[31299, 27217, 24975, 22448, 20662, 19374, 17944, 17076, 14873, 14254, 13377, 12654, 12264, 11656, 11215, 10907, 10701, 10201, 9451, 8687, 8582, 8433, 8385, 7403, 7344, 7297, 7238, 7222, 7173, 7097, 7033, 6991, 6973, 6973, 6973, 3937, 3932, 3877, 3859, 3856, 3838]\n",
      "[31299, 27217, 24975, 22448, 20662, 19374, 17944, 17076, 14873, 14254, 13377, 12654, 12264, 11656, 11215, 10907, 10701, 10201, 9451, 8687, 8582, 8433, 8385, 7403, 7344, 7297, 7238, 7222, 7173, 7097, 7033, 6991, 6973, 6973, 6973, 3937, 3932, 3877, 3859, 3856, 3838, 3838]\n",
      "[31299, 27217, 24975, 22448, 20662, 19374, 17944, 17076, 14873, 14254, 13377, 12654, 12264, 11656, 11215, 10907, 10701, 10201, 9451, 8687, 8582, 8433, 8385, 7403, 7344, 7297, 7238, 7222, 7173, 7097, 7033, 6991, 6973, 6973, 6973, 3937, 3932, 3877, 3859, 3856, 3838, 3838, 3818]\n",
      "[31299, 27217, 24975, 22448, 20662, 19374, 17944, 17076, 14873, 14254, 13377, 12654, 12264, 11656, 11215, 10907, 10701, 10201, 9451, 8687, 8582, 8433, 8385, 7403, 7344, 7297, 7238, 7222, 7173, 7097, 7033, 6991, 6973, 6973, 6973, 3937, 3932, 3877, 3859, 3856, 3838, 3838, 3818, 3818]\n",
      "[31299, 27217, 24975, 22448, 20662, 19374, 17944, 17076, 14873, 14254, 13377, 12654, 12264, 11656, 11215, 10907, 10701, 10201, 9451, 8687, 8582, 8433, 8385, 7403, 7344, 7297, 7238, 7222, 7173, 7097, 7033, 6991, 6973, 6973, 6973, 3937, 3932, 3877, 3859, 3856, 3838, 3838, 3818, 3818, 3798]\n",
      "[31299, 27217, 24975, 22448, 20662, 19374, 17944, 17076, 14873, 14254, 13377, 12654, 12264, 11656, 11215, 10907, 10701, 10201, 9451, 8687, 8582, 8433, 8385, 7403, 7344, 7297, 7238, 7222, 7173, 7097, 7033, 6991, 6973, 6973, 6973, 3937, 3932, 3877, 3859, 3856, 3838, 3838, 3818, 3818, 3798, 3798]\n",
      "[31299, 27217, 24975, 22448, 20662, 19374, 17944, 17076, 14873, 14254, 13377, 12654, 12264, 11656, 11215, 10907, 10701, 10201, 9451, 8687, 8582, 8433, 8385, 7403, 7344, 7297, 7238, 7222, 7173, 7097, 7033, 6991, 6973, 6973, 6973, 3937, 3932, 3877, 3859, 3856, 3838, 3838, 3818, 3818, 3798, 3798, 3757]\n",
      "[31299, 27217, 24975, 22448, 20662, 19374, 17944, 17076, 14873, 14254, 13377, 12654, 12264, 11656, 11215, 10907, 10701, 10201, 9451, 8687, 8582, 8433, 8385, 7403, 7344, 7297, 7238, 7222, 7173, 7097, 7033, 6991, 6973, 6973, 6973, 3937, 3932, 3877, 3859, 3856, 3838, 3838, 3818, 3818, 3798, 3798, 3757, 3757]\n",
      "[31299, 27217, 24975, 22448, 20662, 19374, 17944, 17076, 14873, 14254, 13377, 12654, 12264, 11656, 11215, 10907, 10701, 10201, 9451, 8687, 8582, 8433, 8385, 7403, 7344, 7297, 7238, 7222, 7173, 7097, 7033, 6991, 6973, 6973, 6973, 3937, 3932, 3877, 3859, 3856, 3838, 3838, 3818, 3818, 3798, 3798, 3757, 3757, 3756]\n",
      "[31299, 27217, 24975, 22448, 20662, 19374, 17944, 17076, 14873, 14254, 13377, 12654, 12264, 11656, 11215, 10907, 10701, 10201, 9451, 8687, 8582, 8433, 8385, 7403, 7344, 7297, 7238, 7222, 7173, 7097, 7033, 6991, 6973, 6973, 6973, 3937, 3932, 3877, 3859, 3856, 3838, 3838, 3818, 3818, 3798, 3798, 3757, 3757, 3756, 3756]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[31299, 27217, 24975, 22448, 20662, 19374, 17944, 17076, 14873, 14254, 13377, 12654, 12264, 11656, 11215, 10907, 10701, 10201, 9451, 8687, 8582, 8433, 8385, 7403, 7344, 7297, 7238, 7222, 7173, 7097, 7033, 6991, 6973, 6973, 6973, 3937, 3932, 3877, 3859, 3856, 3838, 3838, 3818, 3818, 3798, 3798, 3757, 3757, 3756, 3756, 3754]\n",
      "[31299, 27217, 24975, 22448, 20662, 19374, 17944, 17076, 14873, 14254, 13377, 12654, 12264, 11656, 11215, 10907, 10701, 10201, 9451, 8687, 8582, 8433, 8385, 7403, 7344, 7297, 7238, 7222, 7173, 7097, 7033, 6991, 6973, 6973, 6973, 3937, 3932, 3877, 3859, 3856, 3838, 3838, 3818, 3818, 3798, 3798, 3757, 3757, 3756, 3756, 3754, 3754]\n",
      "[31299, 27217, 24975, 22448, 20662, 19374, 17944, 17076, 14873, 14254, 13377, 12654, 12264, 11656, 11215, 10907, 10701, 10201, 9451, 8687, 8582, 8433, 8385, 7403, 7344, 7297, 7238, 7222, 7173, 7097, 7033, 6991, 6973, 6973, 6973, 3937, 3932, 3877, 3859, 3856, 3838, 3838, 3818, 3818, 3798, 3798, 3757, 3757, 3756, 3756, 3754, 3754, 3754]\n",
      "[31299, 27217, 24975, 22448, 20662, 19374, 17944, 17076, 14873, 14254, 13377, 12654, 12264, 11656, 11215, 10907, 10701, 10201, 9451, 8687, 8582, 8433, 8385, 7403, 7344, 7297, 7238, 7222, 7173, 7097, 7033, 6991, 6973, 6973, 6973, 3937, 3932, 3877, 3859, 3856, 3838, 3838, 3818, 3818, 3798, 3798, 3757, 3757, 3756, 3756, 3754, 3754, 3754, 3754]\n",
      "[31299, 27217, 24975, 22448, 20662, 19374, 17944, 17076, 14873, 14254, 13377, 12654, 12264, 11656, 11215, 10907, 10701, 10201, 9451, 8687, 8582, 8433, 8385, 7403, 7344, 7297, 7238, 7222, 7173, 7097, 7033, 6991, 6973, 6973, 6973, 3937, 3932, 3877, 3859, 3856, 3838, 3838, 3818, 3818, 3798, 3798, 3757, 3757, 3756, 3756, 3754, 3754, 3754, 3754, 3754]\n",
      "[31299, 27217, 24975, 22448, 20662, 19374, 17944, 17076, 14873, 14254, 13377, 12654, 12264, 11656, 11215, 10907, 10701, 10201, 9451, 8687, 8582, 8433, 8385, 7403, 7344, 7297, 7238, 7222, 7173, 7097, 7033, 6991, 6973, 6973, 6973, 3937, 3932, 3877, 3859, 3856, 3838, 3838, 3818, 3818, 3798, 3798, 3757, 3757, 3756, 3756, 3754, 3754, 3754, 3754, 3754, 3751]\n",
      "[31299, 27217, 24975, 22448, 20662, 19374, 17944, 17076, 14873, 14254, 13377, 12654, 12264, 11656, 11215, 10907, 10701, 10201, 9451, 8687, 8582, 8433, 8385, 7403, 7344, 7297, 7238, 7222, 7173, 7097, 7033, 6991, 6973, 6973, 6973, 3937, 3932, 3877, 3859, 3856, 3838, 3838, 3818, 3818, 3798, 3798, 3757, 3757, 3756, 3756, 3754, 3754, 3754, 3754, 3754, 3751, 3751]\n",
      "[31299, 27217, 24975, 22448, 20662, 19374, 17944, 17076, 14873, 14254, 13377, 12654, 12264, 11656, 11215, 10907, 10701, 10201, 9451, 8687, 8582, 8433, 8385, 7403, 7344, 7297, 7238, 7222, 7173, 7097, 7033, 6991, 6973, 6973, 6973, 3937, 3932, 3877, 3859, 3856, 3838, 3838, 3818, 3818, 3798, 3798, 3757, 3757, 3756, 3756, 3754, 3754, 3754, 3754, 3754, 3751, 3751, 3751]\n",
      "[31299, 27217, 24975, 22448, 20662, 19374, 17944, 17076, 14873, 14254, 13377, 12654, 12264, 11656, 11215, 10907, 10701, 10201, 9451, 8687, 8582, 8433, 8385, 7403, 7344, 7297, 7238, 7222, 7173, 7097, 7033, 6991, 6973, 6973, 6973, 3937, 3932, 3877, 3859, 3856, 3838, 3838, 3818, 3818, 3798, 3798, 3757, 3757, 3756, 3756, 3754, 3754, 3754, 3754, 3754, 3751, 3751, 3751, 3751]\n",
      "[31299, 27217, 24975, 22448, 20662, 19374, 17944, 17076, 14873, 14254, 13377, 12654, 12264, 11656, 11215, 10907, 10701, 10201, 9451, 8687, 8582, 8433, 8385, 7403, 7344, 7297, 7238, 7222, 7173, 7097, 7033, 6991, 6973, 6973, 6973, 3937, 3932, 3877, 3859, 3856, 3838, 3838, 3818, 3818, 3798, 3798, 3757, 3757, 3756, 3756, 3754, 3754, 3754, 3754, 3754, 3751, 3751, 3751, 3751, 3751]\n",
      "[31299, 27217, 24975, 22448, 20662, 19374, 17944, 17076, 14873, 14254, 13377, 12654, 12264, 11656, 11215, 10907, 10701, 10201, 9451, 8687, 8582, 8433, 8385, 7403, 7344, 7297, 7238, 7222, 7173, 7097, 7033, 6991, 6973, 6973, 6973, 3937, 3932, 3877, 3859, 3856, 3838, 3838, 3818, 3818, 3798, 3798, 3757, 3757, 3756, 3756, 3754, 3754, 3754, 3754, 3754, 3751, 3751, 3751, 3751, 3751, 3751]\n",
      "[31299, 27217, 24975, 22448, 20662, 19374, 17944, 17076, 14873, 14254, 13377, 12654, 12264, 11656, 11215, 10907, 10701, 10201, 9451, 8687, 8582, 8433, 8385, 7403, 7344, 7297, 7238, 7222, 7173, 7097, 7033, 6991, 6973, 6973, 6973, 3937, 3932, 3877, 3859, 3856, 3838, 3838, 3818, 3818, 3798, 3798, 3757, 3757, 3756, 3756, 3754, 3754, 3754, 3754, 3754, 3751, 3751, 3751, 3751, 3751, 3751, 3751]\n",
      "[31299, 27217, 24975, 22448, 20662, 19374, 17944, 17076, 14873, 14254, 13377, 12654, 12264, 11656, 11215, 10907, 10701, 10201, 9451, 8687, 8582, 8433, 8385, 7403, 7344, 7297, 7238, 7222, 7173, 7097, 7033, 6991, 6973, 6973, 6973, 3937, 3932, 3877, 3859, 3856, 3838, 3838, 3818, 3818, 3798, 3798, 3757, 3757, 3756, 3756, 3754, 3754, 3754, 3754, 3754, 3751, 3751, 3751, 3751, 3751, 3751, 3751, 3751]\n",
      "[31299, 27217, 24975, 22448, 20662, 19374, 17944, 17076, 14873, 14254, 13377, 12654, 12264, 11656, 11215, 10907, 10701, 10201, 9451, 8687, 8582, 8433, 8385, 7403, 7344, 7297, 7238, 7222, 7173, 7097, 7033, 6991, 6973, 6973, 6973, 3937, 3932, 3877, 3859, 3856, 3838, 3838, 3818, 3818, 3798, 3798, 3757, 3757, 3756, 3756, 3754, 3754, 3754, 3754, 3754, 3751, 3751, 3751, 3751, 3751, 3751, 3751, 3751, 3724]\n",
      "[31299, 27217, 24975, 22448, 20662, 19374, 17944, 17076, 14873, 14254, 13377, 12654, 12264, 11656, 11215, 10907, 10701, 10201, 9451, 8687, 8582, 8433, 8385, 7403, 7344, 7297, 7238, 7222, 7173, 7097, 7033, 6991, 6973, 6973, 6973, 3937, 3932, 3877, 3859, 3856, 3838, 3838, 3818, 3818, 3798, 3798, 3757, 3757, 3756, 3756, 3754, 3754, 3754, 3754, 3754, 3751, 3751, 3751, 3751, 3751, 3751, 3751, 3751, 3724, 3614]\n",
      "[31299, 27217, 24975, 22448, 20662, 19374, 17944, 17076, 14873, 14254, 13377, 12654, 12264, 11656, 11215, 10907, 10701, 10201, 9451, 8687, 8582, 8433, 8385, 7403, 7344, 7297, 7238, 7222, 7173, 7097, 7033, 6991, 6973, 6973, 6973, 3937, 3932, 3877, 3859, 3856, 3838, 3838, 3818, 3818, 3798, 3798, 3757, 3757, 3756, 3756, 3754, 3754, 3754, 3754, 3754, 3751, 3751, 3751, 3751, 3751, 3751, 3751, 3751, 3724, 3614, 3614]\n",
      "[31299, 27217, 24975, 22448, 20662, 19374, 17944, 17076, 14873, 14254, 13377, 12654, 12264, 11656, 11215, 10907, 10701, 10201, 9451, 8687, 8582, 8433, 8385, 7403, 7344, 7297, 7238, 7222, 7173, 7097, 7033, 6991, 6973, 6973, 6973, 3937, 3932, 3877, 3859, 3856, 3838, 3838, 3818, 3818, 3798, 3798, 3757, 3757, 3756, 3756, 3754, 3754, 3754, 3754, 3754, 3751, 3751, 3751, 3751, 3751, 3751, 3751, 3751, 3724, 3614, 3614, 2801]\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "##Plot Number of links in the giant cluster, and in the second largest cluster (orange).\n",
    "def my_range(start, end, step):\n",
    "    while start <= end:\n",
    "        yield start\n",
    "        start += step\n",
    "a = []\n",
    "b = []\n",
    "c = []\n",
    "Gc = max(nx.connected_component_subgraphs(Gc), key=len)\n",
    "Gc.size()\n",
    "\n",
    "\n",
    "##This the the percolation process.\n",
    "for x in my_range(200,3500,50):\n",
    "    for (u,v,w) in G.edges(data=True): \n",
    "        if w['weight'] < x:\n",
    "            G.remove_edge(u,v)\n",
    "    graphs2 =list(sorted(nx.connected_component_subgraphs(G),key=len, reverse=True))\n",
    "    c.append(graphs2[1].size(weight=None))\n",
    "    Gc = max(nx.connected_component_subgraphs(G), key=len)\n",
    "    a.append(round(x,2))\n",
    "    #b.append(Gc.order())\n",
    "    b.append(Gc.size(weight=None))\n",
    "    print(b)\n",
    " #####################DATA#########\n",
    "\n",
    "# Create some mock data\n",
    "fig, ax1 = plt.subplots()\n",
    "plt.xticks(fontsize = 15)\n",
    "plt.yticks(fontsize = 15)\n",
    "color = 'tab:red'\n",
    "#ax1.set_xlabel('time (s)')\n",
    "#ax1.set_ylabel('exp', color=color)\n",
    "\n",
    "#ax1.plot(t, data1, color=color)\n",
    "ax1.plot(a, b,'o', color=color, markersize=10)\n",
    "ax1.tick_params(axis='y')\n",
    "\n",
    "ax2 = ax1.twinx()  # instantiate a second axes that shares the same x-axis\n",
    "color = 'tab:orange'\n",
    "#ax2.set_ylabel('sin', color=color)  # we already handled the x-label with ax1\n",
    "\n",
    "plt.yticks(fontsize = 15)\n",
    "ax2.plot(a,c,'o',color=color, markersize=10)\n",
    "#x2.tick_params(axis='y', labelcolor=color)\n",
    "fig.tight_layout()  # otherwise the right y-label is slightly clipped\n",
    "plt.savefig('./percolation_test.pdf', dpi = 300)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def scatterplot(x_dim, y_dim,z_dim):\n",
    "   #f=plt.figure()\n",
    "   x = x_dim\n",
    "   y = y_dim\n",
    "   z = z_dim \n",
    "\n",
    "   fig, ax = plt.subplots(figsize=(8, 5))\n",
    "   colors = ['#2300A8', '#00A658']\n",
    "\n",
    "   ax.set_xlabel('Flow',size=20)\n",
    "\n",
    "   ax.set_ylabel('G',size=20)\n",
    "   ax.plot(x,y,'o',linestyle='-',linewidth=2,color='#E43576',markersize=10,markeredgewidth=1.5,label='G')\n",
    "\n",
    "   ax.legend(bbox_to_anchor=(0.37, 0.07, 0.5, 0.5),frameon=False, fontsize='xx-large')\n",
    "   plt.xticks(fontsize = 15)\n",
    "   plt.yticks(fontsize = 15)\n",
    "   plt.xlim(200,3400)\n",
    "\n",
    "   ax2 = ax.twinx()  # instantiate a second axes that shares the same x-axis\n",
    "   color = 'tab:orange'\n",
    "   ax2.set_ylabel('SG',size=20)\n",
    "   plt.yticks(fontsize = 15)\n",
    "   ax2.plot(x,z,'o',linestyle='-',linewidth=2,color='#009669', markersize=10,markeredgewidth=1.5,label='SG')\n",
    "   ax2.legend(bbox_to_anchor=(0.398, -0.05, 0.5, 0.5), frameon=False,fontsize='xx-large')\n",
    "\n",
    "   fig.tight_layout()  # otherwise the right y-label is slightly clipped\n",
    "\n",
    "   fig.savefig(\"t\", bbox_inches='tight')\n",
    "   plt.savefig('./percolation_old_verde.pdf', dpi = 300)\n",
    "   plt.show()\n",
    "\n",
    "\n",
    "scatterplot(a,b,c)"
   ]
  }
 ],
 "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": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}