Trajectories-Users_v2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# DELIVERABLE FOR THE PROJECT:\n",
    "# \"KINEMATICS OF MOBILITY\"\n",
    "# David Pastor-Escuredo (Life D Lab)\n",
    "# Licencia MIT"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Copyright <2019> <David Pastor Escuredo>\n",
    "\n",
    "#Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the \"Software\"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:\n",
    "\n",
    "#The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.\n",
    "\n",
    "#THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import json\n",
    "from nltk.tokenize import word_tokenize\n",
    "import re\n",
    "import geopandas as gpd\n",
    "import networkx as nx\n",
    "import matplotlib.pyplot as plt\n",
    "import collections\n",
    "from sklearn.cluster import KMeans\n",
    "from PIL import Image\n",
    "import time\n",
    "from datetime import datetime, timedelta, date\n",
    "from os import listdir\n",
    "from os.path import isfile, join\n",
    "from geopandas import GeoDataFrame\n",
    "from shapely.geometry import Point\n",
    "import pickle\n",
    "import math\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "def getday(d):\n",
    "    if d<10:\n",
    "        ds='0'+str(d)\n",
    "    else:\n",
    "        ds=str(d)\n",
    "    return ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "ename": "MemoryError",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mMemoryError\u001b[0m                               Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-15-971e9a485119>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilec\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'r'\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mfl\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;31m#D_rows = pd.read_csv(fl, header=None)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m     \u001b[0mD_rows\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mjson\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfl\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      6\u001b[0m \u001b[0mrow0\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mD_rows\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3.6/json/__init__.py\u001b[0m in \u001b[0;36mload\u001b[0;34m(fp, cls, object_hook, parse_float, parse_int, parse_constant, object_pairs_hook, **kw)\u001b[0m\n\u001b[1;32m    294\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    295\u001b[0m     \"\"\"\n\u001b[0;32m--> 296\u001b[0;31m     return loads(fp.read(),\n\u001b[0m\u001b[1;32m    297\u001b[0m         \u001b[0mcls\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcls\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mobject_hook\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mobject_hook\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    298\u001b[0m         \u001b[0mparse_float\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mparse_float\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mparse_int\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mparse_int\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/usr/lib/python3.6/codecs.py\u001b[0m in \u001b[0;36mdecode\u001b[0;34m(self, input, final)\u001b[0m\n\u001b[1;32m    319\u001b[0m         \u001b[0;31m# decode input (taking the buffer into account)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    320\u001b[0m         \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuffer\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 321\u001b[0;31m         \u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconsumed\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_buffer_decode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfinal\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    322\u001b[0m         \u001b[0;31m# keep undecoded input until the next call\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    323\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbuffer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mconsumed\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mMemoryError\u001b[0m: "
     ]
    }
   ],
   "source": [
    "cdr_folder='/home/davidpastor/TEF_mob/'\n",
    "filec=cdr_folder+'20140120'\n",
    "with open(filec, 'r') as fl:\n",
    "#D_rows = pd.read_csv(fl, header=None)\n",
    "    D_rows=json.load(fl)\n",
    "row0=D_rows[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#REFERENCE LOCATION -> makeHome.py and makeReference.py\n",
    "#Indetify reference location for each user\n",
    "\n",
    "datapath='/home/davidpastor/TEF_mob/'\n",
    "\n",
    "netpath=datapath+'nets/'\n",
    "netdescpath=datapath+'nets_desc/'\n",
    "trajpath=datapath+'trajs/'\n",
    "\n",
    "vector={}\n",
    "RL={}\n",
    "dayoffset=20#monday 20/01/2014\n",
    "ndays=4#monday to thrusday\n",
    "\n",
    "for yDay in range(dayoffset,dayoffset+ndays):\n",
    "    \n",
    "    with open(trajpath+'usersTracking'+'_'+str(yDay)+'.tff', 'rb') as fpp:\n",
    "        usersTracking=pickle.load(fpp)\n",
    " \n",
    "    \n",
    "    for u in usersTracking:    \n",
    "        if u not in vector:\n",
    "            vector[u]=[]\n",
    "            avisited=usersTracking[u]['ss']\n",
    "            tvisited=usersTracking[u]['ts']#we could include time\n",
    "            for i in range(1, len(avisited)+1):\n",
    "                aid=avisited[i]\n",
    "                vector[u].append(aid)\n",
    "\n",
    "for u in vector:\n",
    "    if u not in RL:\n",
    "        RL[u]={}\n",
    "        RL[u]['refloc']=-1\n",
    "        RL[u]['n_refloc']=-1\n",
    "    v=vector[u]\n",
    "    my_counter=collections.Counter(v)\n",
    "    RL[u]['refloc']=my_counter.most_common(3)[0][0]#most frequent location\n",
    "    RL[u]['n_refloc']=my_counter.most_common(3)[0][1]#ntimes of the most frequent location\n",
    "                \n",
    "with open(trajpath+'RL'+'.tff', 'wb') as handle:\n",
    "    pickle.dump(RL, handle, protocol=pickle.HIGHEST_PROTOCOL)                                      "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "0",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-10-efbea2e85970>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     13\u001b[0m         \u001b[0mtvisited\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0musersTracking\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mu\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'ts'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mavisited\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m             \u001b[0maid\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mavisited\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     16\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0maid\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mGD\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m                 \u001b[0mind\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mGD\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0maid\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'ind'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyError\u001b[0m: 0"
     ]
    }
   ],
   "source": [
    "# -> createTrajectoryNetwork.py\n",
    "#Create a descriptor vector from the net values of the trajectories\n",
    "region='_bogota'\n",
    "\n",
    "datapath='/home/davidpastor/TEF_mob/'\n",
    "\n",
    "netpath=datapath+'nets/'\n",
    "netdescpath=datapath+'nets_desc/'\n",
    "trajpath=datapath+'trajs/'\n",
    "trajdesc=datapath+'traj_net/'\n",
    "\n",
    "UD={}\n",
    "\n",
    "for yDay in range(13,18):\n",
    "    \n",
    "    with open(trajpath+'usersTracking'+'_'+str(yDay)+'.tff', 'rb') as fpp:\n",
    "        usersTracking=pickle.load(fpp)\n",
    "    \n",
    "    with open(netdescpath+'ND'+'_'+str(yDay)+region+'.cnf', 'rb') as fpp:\n",
    "        GD=pickle.load(fpp)\n",
    "        \n",
    "    with open(netdescpath+'NDcf'+'_'+str(yDay)+region+'.cnf', 'rb') as fpp:\n",
    "        GDcf=pickle.load(fpp)    \n",
    "        \n",
    "    #with open(netpath+'Net2c'+'_'+str(yDay)+region+'.cnf', 'rb') as fpp:\n",
    "    #    G=pickle.load(fpp)\n",
    "        \n",
    "    for u in usersTracking:\n",
    "        avisited=usersTracking[u]['ss']\n",
    "        tvisited=usersTracking[u]['ts']\n",
    "        if u not in UD:\n",
    "            UD[u]={}\n",
    "            UD[u]['ind']=[]\n",
    "            UD[u]['outd']=[]\n",
    "            UD[u]['in_degree']=[]\n",
    "            UD[u]['out_degree']=[]\n",
    "            UD[u]['in_eigenvalue']=[]\n",
    "            UD[u]['out_eigenvalue']=[]\n",
    "            UD[u]['in_betweenness']=[]\n",
    "            UD[u]['out_betweenness']=[]\n",
    "            UD[u]['dis_betweenness']=[]\n",
    "            UD[u]['cfbetweenness']=[]\n",
    "            UD[u]['in_closeness']=[]\n",
    "            UD[u]['out_closeness']=[]\n",
    "            UD[u]['dis_closeness']=[]\n",
    "            UD[u]['cfcloseness']=[]  \n",
    "            UD[u]['time']=[]\n",
    "            UD[u]['position']=[]\n",
    "                       \n",
    "        for i in range(1, len(avisited)+1):\n",
    "            aid=avisited[i]    \n",
    "            if aid in GD and aid in GDcf:#we impose this double condition\n",
    "                UD[u]['ind'].append(GD[aid]['ind'])\n",
    "                UD[u]['outd'].append(GD[aid]['outd'])\n",
    "                UD[u]['in_degree'].append(GD[aid]['in_degree'])\n",
    "                UD[u]['out_degree'].append(GD[aid]['out_degree'])\n",
    "                UD[u]['in_eigenvalue'].append(GD[aid]['in_eigenvalue'])\n",
    "                UD[u]['out_eigenvalue'].append(GD[aid]['out_eigenvalue'])  \n",
    "                UD[u]['in_betweenness'].append(GD[aid]['in_betweenness'])\n",
    "                UD[u]['out_betweenness'].append(GD[aid]['out_betweenness'])\n",
    "                UD[u]['dis_betweenness'].append(GD[aid]['dis_betweenness'])\n",
    "                UD[u]['cfbetweenness'].append(GDcf[aid]['cfbetweenness'])\n",
    "                UD[u]['in_closeness'].append(GD[aid]['in_closeness'])\n",
    "                UD[u]['out_closeness'].append(GD[aid]['out_closeness'])\n",
    "                UD[u]['dis_closeness'].append(GD[aid]['dis_closeness'])\n",
    "                UD[u]['cfcloseness'].append(GDcf[aid]['cfcloseness'])\n",
    "                UD[u]['time'].append(tvisited[i])\n",
    "                UD[u]['position'].append(aid)\n",
    "    del usersTracking\n",
    "                            \n",
    "with open(trajdesc+'UD'+'.cnf', 'wb') as handle:\n",
    "    pickle.dump(UD, handle, protocol=pickle.HIGHEST_PROTOCOL)       "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (<ipython-input-1-49cc27e8e2d7>, line 30)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-1-49cc27e8e2d7>\"\u001b[0;36m, line \u001b[0;32m30\u001b[0m\n\u001b[0;31m    UVD[u]['d_ind'=UD[u]['ind'][t+1]-UD[u]['ind'][t]\u001b[0m\n\u001b[0m                  ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "# -> createTrajectoryVector.py\n",
    "#Create a descriptor vector from the gradient in the trajectory\n",
    "region='_bogota'\n",
    "\n",
    "datapath='/home/davidpastor/TEF_mob/'\n",
    "\n",
    "netpath=datapath+'nets/'\n",
    "netdescpath=datapath+'nets_desc/'\n",
    "trajpath=datapath+'trajs/'\n",
    "trajdesc=datapath+'traj_net/'\n",
    "\n",
    "dayRef=14\n",
    "desc='position'\n",
    "\n",
    "UVD={}\n",
    "\n",
    "with open(trajdesc+'UD.cnf', 'rb') as fpp:\n",
    "    UD=pickle.load(fpp) \n",
    "    \n",
    "with open(netpath+'Net2c'+'_'+str(dayRef)+region+'.cnf', 'rb') as fpp:\n",
    "    G=pickle.load(fpp)\n",
    "    \n",
    "for u in UD:\n",
    "    v=UD[u][desc]\n",
    "    if len(v)>1:\n",
    "        if u not in UVD:\n",
    "                UVD[u]={}\n",
    "        for t in range(0,len(v)):\n",
    "            pos=UD[u]['position'][t]\n",
    "            npos=UD[u]['position'][t+1]\n",
    "            UVD[u]['d_ind']=UD[u]['ind'][t+1]-UD[u]['ind'][t]\n",
    "            UVD[u]['d_outd']=UD[u]['outd'][t+1]-UD[u]['outd'][t]\n",
    "            UVD[u]['d_in_degree']=UD[u]['in_degree'][t+1]-UD[u]['in_degree'][t]\n",
    "            UVD[u]['d_out_degree']=UD[u]['out_degree'][t+1]-UD[u]['out_degree'][t]\n",
    "            UVD[u]['d_in_eigenvalue']=UD[u]['in_eigenvalue'][t+1]-UD[u]['in_eigenvalue'][t]\n",
    "            UVD[u]['d_out_eigenvalue']=UD[u]['out_eigenvalue'][t+1]-UD[u]['out_eigenvalue'][t]  \n",
    "            UVD[u]['d_in_betweenness']=UD[u]['in_betweenness'][t+1]-UD[u]['in_betweenness'][t]\n",
    "            UVD[u]['d_out_betweenness']=UD[u]['out_betweenness'][t+1]-UD[u]['out_betweenness'][t]\n",
    "            UVD[u]['d_dis_betweenness']=UD[u]['dis_betweenness'][t+1]-UD[u]['dis_betweenness'][t]\n",
    "            UVD[u]['d_cfbetweenness']=UD[u]['cfbetweenness'][t+1]-UD[u]['cfbetweenness'][t]\n",
    "            UVD[u]['d_in_closeness']=UD[u]['in_closeness'][t+1]-UD[u]['in_closeness'][t]\n",
    "            UVD[u]['d_out_closeness']=UD[u]['out_closeness'][t+1]-UD[u]['out_closeness'][t]\n",
    "            UVD[u]['d_dis_closeness']=UD[u]['dis_closeness'][t+1]-UD[u]['dis_closeness'][t]\n",
    "            UVD[u]['d_cfcloseness']=UD[u]['cfcloseness'][t+1]-UD[u]['cfcloseness'][t]\n",
    "            UVD[u]['d_time']=UD[u]['time'][t+1]-UD[u]['time'][t]\n",
    "    \n",
    "            #Descriptors of the vector - edge of the network\n",
    "            UVD[u]['distance']=G[pos][npos]['distance']#distance is fix\n",
    "            UVD[u]['ratio_time']=d_time/(G[pos][npos]['time'])#velocity can change\n",
    "            UVD[u]['flow']=G[pos][npos]['weight']\n",
    "            \n",
    "with open(trajdesc+'UVD'+'.cnf', 'wb') as handle:\n",
    "    pickle.dump(UVD, handle, protocol=pickle.HIGHEST_PROTOCOL) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[6, 7, 8, 5]\n",
      "[5, 6, 7, 8, 5]\n",
      "[6, 7, 8]\n"
     ]
    }
   ],
   "source": [
    "A=[5, 6, 7, 8, 5]\n",
    "B=A.copy()\n",
    "A.remove(5)\n",
    "print(A)\n",
    "print(B)\n",
    "C = [x for x in B if x != 5]\n",
    "print(C)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "region='_bogota'\n",
    "\n",
    "datapath='/home/davidpastor/TEF_mob/'\n",
    "\n",
    "netpath=datapath+'nets/'\n",
    "netdescpath=datapath+'nets_desc/'\n",
    "trajpath=datapath+'trajs/'\n",
    "trajdesc=datapath+'traj_net/'\n",
    "\n",
    "desc='cfbetweenness'\n",
    "\n",
    "TD={}\n",
    "\n",
    "with open(trajdesc+'UD.cnf', 'rb') as fpp:\n",
    "    UD=pickle.load(fpp)    \n",
    "for u in UD:\n",
    "    v=UD[u][desc]\n",
    "    if len(v)>0:\n",
    "        if u not in TD:\n",
    "            TD[u]={}\n",
    "        TD[u]['max']=np.max(v)\n",
    "        TD[u]['min']=np.min(v)\n",
    "        TD[u]['mean']=np.mean(v)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    " #Chunck getting descriptors of the edge for each displacement\n",
    "                if i<len(avisited):\n",
    "                    aid_next=avisited[i+1]\n",
    "                    if aid_next in GD:\n",
    "                        ind_n=GD[aid_next]['ind']\n",
    "                        outd_n=GD[aid_next]['outd']\n",
    "                        bal_n=ind_n-outd_n\n",
    "                        w=G[aid][aid_next]['weight']\n",
    "                        d=G[aid][aid_next]['distance']\n",
    "                        delta=G[aid][aid_next]['time']\n",
    "                        delta=delta/w#average time\n",
    "                        speed=d/delta#average speed\n",
    "                        UD[u]['ind_dif'].append(ind_n-ind)\n",
    "                        UD[u]['outd_dif'].append(outd_n-outd)\n",
    "                        UD[u]['balance_dif'].append(bal_n-bal)\n",
    "                        UD[u]['flow'].append(w)\n",
    "                        UD[u]['distance'].append(d)\n",
    "                        UD[u]['delta'].append(delta)\n",
    "                        UD[u]['speed'].append(speed)\n",
    "            else:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Unnamed: 0  LATITUD  LONGITUD Celda_HEX LAC_HEX antenna_id    municipio\n",
      "0           0   4.6092  -74.0718      2711    083F   083F2711  BOGOTÁ D.C.\n",
      "1           1   4.6092  -74.0718      2712    083F   083F2712  BOGOTÁ D.C.\n",
      "2           2   4.6092  -74.0718      2713    083F   083F2713  BOGOTÁ D.C.\n",
      "3           3   4.6092  -74.0718      2714    083F   083F2714  BOGOTÁ D.C.\n",
      "4           4   4.6092  -74.0718      2715    083F   083F2715  BOGOTÁ D.C.\n",
      "6757\n"
     ]
    }
   ],
   "source": [
    "#Create a descriptor vector from the net values of the trajectories just for Bogota\n",
    "#DEPRECATED\n",
    "netpath='./nets/'\n",
    "trajpath='./trajs/'\n",
    "\n",
    "ant_file='antennas/antennas_bogota.csv'\n",
    "sheet=pd.read_csv(ant_file,delimiter=',')\n",
    "print(sheet.head())\n",
    "target_aids=sheet['antenna_id'].values\n",
    "print(len(target_aids)) \n",
    "\n",
    "UD={}\n",
    "for yDay in range(1,2):\n",
    "    with open(trajpath+'usersTracking'+'_'+str(yDay)+'.tff', 'rb') as fpp:\n",
    "        usersTracking=pickle.load(fpp)\n",
    "    with open(netpath+'ND'+'_'+str(yDay)+'.cnf', 'rb') as fpp:\n",
    "        GD=pickle.load(fpp)\n",
    "    \n",
    "    for u in usersTracking:\n",
    "        avisited=usersTracking[u]['ss']\n",
    "        tvisited=usersTracking[u]['ts']\n",
    "        for i in range(1, len(avisited)+1):\n",
    "            aid=avisited[i]\n",
    "            if aid in target_aids:\n",
    "                if aid in GD:\n",
    "                    ind=GD[aid]['ind']\n",
    "                    outd=GD[aid]['outd']\n",
    "                    if u not in UD:\n",
    "                        UD[u]={}\n",
    "                        UD[u]['ind']=[]\n",
    "                        UD[u]['outd']=[]\n",
    "                        UD[u]['balance']=[]\n",
    "                    UD[u]['ind'].append(ind)\n",
    "                    UD[u]['outd'].append(outd)\n",
    "                    bal=ind-outd\n",
    "                    UD[u]['balance'].append(bal)\n",
    "                else:\n",
    "                    print(aid)\n",
    "                    print(u)\n",
    "\n",
    "with open('traj-net/UD_bogota+'.cnf', 'wb') as handle:\n",
    "    pickle.dump(UD, handle, protocol=pickle.HIGHEST_PROTOCOL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "#THIS WAS STUPID\n",
    "UDesc={}\n",
    "tag='_bogota'\n",
    "tag=''\n",
    "for yDay in range(1,2):\n",
    "    with open('traj-net/UD'+tag+'.cnf', 'rb') as fpp:\n",
    "        UD=pickle.load(fpp)\n",
    "        \n",
    "    for u in UD:\n",
    "        if u not in UDesc:\n",
    "            UDesc[u]={}\n",
    "            UDesc[u]['inave']=0\n",
    "            UDesc[u]['outave']=0\n",
    "            UDesc[u]['balave']=0\n",
    "        traj_in=UD[u]['ind']\n",
    "        traj_out=UD[u]['outd']\n",
    "        traj_bal=UD[u]['balance']\n",
    "        lenTraj=len(traj_in)\n",
    "        inave=0\n",
    "        outave=0\n",
    "        balave=0\n",
    "        for i in range(0,lenTraj):\n",
    "            inave=inave+traj_in[i]\n",
    "            outave=outave+traj_out[i]\n",
    "            balave=balave+traj_bal[i]\n",
    "        inave=inave/lenTraj    \n",
    "        outave=outave/lenTraj\n",
    "        balave=balave/lenTraj\n",
    "        UDesc[u]['inave']=inave\n",
    "        UDesc[u]['outave']=outave\n",
    "        UDesc[u]['balave']=balave\n",
    "    \n",
    "with open('traj-net/UD_Desc'+tag+'.cnf', 'wb') as handle:\n",
    "    pickle.dump(UDesc, handle, protocol=pickle.HIGHEST_PROTOCOL)  \n",
    "        \n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "198391\n",
      "198391\n",
      "198391\n",
      "(array([13]),)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x133be65f8>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAY4AAAEKCAYAAAAFJbKyAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nicPMzGrixGFmZjX5/6kL2icCFwHMAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x124cadcc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tag='_bogota'\n",
    "#tag=''\n",
    "with open('traj-net/UD_Desc'+tag+'.cnf', 'rb') as fpp:\n",
    "    UDesc=pickle.load(fpp)\n",
    "\n",
    "print(len(UDesc.keys()))\n",
    "vector=[]\n",
    "for u in UDesc:\n",
    "    vector.append(UDesc[u]['inave'])\n",
    "    \n",
    "print(len(UDesc.keys()))\n",
    "print(len(vector))\n",
    "n, b, patches=plt.hist(vector,bins=100,color='khaki')\n",
    "print(bin_max)\n",
    "bin_max = np.where(n == n.max())\n",
    "plt.axvline(x=b[bin_max][0], color='k', linestyle='--')\n",
    "plt.xlabel(\"Average in-degree\")\n",
    "plt.ylabel(\"#people\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1822272\n",
      "1822272\n",
      "1822272\n",
      "(array([12]),)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x17f497f28>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZcAAAEKCAYAAADenhiQAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x13adab940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tag='_bogota'\n",
    "tag=''\n",
    "with open('traj-net/UD_Desc'+tag+'.cnf', 'rb') as fpp:\n",
    "    UDesc=pickle.load(fpp)\n",
    "\n",
    "print(len(UDesc.keys()))\n",
    "vector=[]\n",
    "for u in UDesc:\n",
    "    vector.append(UDesc[u]['outave'])\n",
    "    \n",
    "print(len(UDesc.keys()))\n",
    "print(len(vector))\n",
    "n, b, patches=plt.hist(vector,bins=100,color='khaki')\n",
    "print(bin_max)\n",
    "bin_max = np.where(n == n.max())\n",
    "plt.axvline(x=b[bin_max][0], color='k', linestyle='--')\n",
    "plt.xlabel(\"Average out-degree\")\n",
    "plt.ylabel(\"#people\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1822272\n",
      "1822272\n",
      "1822272\n",
      "(array([50]),)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x14252f588>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZsAAAEKCAYAAADEovgeAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x14362a5c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tag='_bogota'\n",
    "tag=''\n",
    "with open('traj-net/UD_Desc'+tag+'.cnf', 'rb') as fpp:\n",
    "    UDesc=pickle.load(fpp)\n",
    "\n",
    "print(len(UDesc.keys()))\n",
    "vector=[]\n",
    "for u in UDesc:\n",
    "    vector.append(UDesc[u]['balave'])\n",
    "    \n",
    "print(len(UDesc.keys()))\n",
    "print(len(vector))\n",
    "n, b, patches=plt.hist(vector,bins=100,color='khaki')\n",
    "print(bin_max)\n",
    "bin_max = np.where(n == n.max())\n",
    "plt.axvline(x=b[bin_max][0], color='k', linestyle='--')\n",
    "plt.xlabel(\"Average in vs out-degree\")\n",
    "plt.ylabel(\"#people\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.6.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}