{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compute probability of transfer success from delays distributions\n",
    "\n",
    "To be able to compute the probability of success of a given transfert, we use the arrival delay distribution compared with the next trip departure. To be able to do that, we need delay distributions for each trip arrival to a given station. Whenever we have a clear match, we can use an __cumulative distribution function__ to compute $P(X \\leq x)$ :\n",
    "\n",
    "$${\\displaystyle F_{X}(x)=\\operatorname {P} (T\\leq t)=\\sum _{t_{i}\\leq t}\\operatorname {P} (T=t_{i})=\\sum _{t_{i}\\leq t}p(t_{i}).}$$\n",
    "\n",
    "The strategy was to rely entirely on past data we have to compute $p(t_i)$, without the need of building a model which imply making additionnal assumptions. If we have enough data for a given transfer with known trip_id x stop_id, we use the the abovementionned formula to compute each $p(t_i)$ by simply using :\n",
    "\n",
    "$$p(t_i) = \\frac{x_i}{\\sum x_i}$$\n",
    "\n",
    "with $x_i$ being the number of delays at time $t_i$ from SBB dataset.\n",
    "\n",
    "### Recover missing data \n",
    "\n",
    "As we are using SBB data to compute delays from timetable trip_id, we may encounter problems with the translation between the two datasets (certain trip_id/stop_id have no correspondance datasets!). We may also encounter To recover missing or faulty data, the strategy is the following :\n",
    "\n",
    "1. If we have more than 100 data points in `real` group, we rely exclusively on its delay distribution to compute probabilities for a given transfer on a `trip_id x stop_id`.\n",
    "\n",
    "_Note : `real` group corresponds to arrival time with status `geschaetz` or `real`, meaning it comes from actual measurments._\n",
    "\n",
    "2. If we do not find enough data within `real` group, we use delay distributions in `all` group (contains all delays including `prognose` status), if there is more than 100 data points for a given `trip_id x stop_id`.\n",
    "\n",
    "3. If `all` group still does not have more than 100 data points, we rely on `recovery tables` to estimate delay distributions. The strategy is the following :\n",
    "    - As we will always know the `stop_id`, the `time` and the `transport_type`, we rely on arrival delays from aggregated values of similar transfer. \n",
    "        - First, we compute a table of distribution with all possible combination of `stop_id`, `time` (round to hours) and `transport_type`, and aggregate all the counts we have to compute cumulative distribution probabilities. \n",
    "        - Is there is less than 100 data points in one of these intersections, we use the last possibilities : a table with `transport_type` x `time` aggregate counts.\n",
    "        - The last values with no match are given the overall average of cumulative distribution probabilities for each `transport_type` with no limit for the minimum number of data points.\n",
    "\n",
    "Following this approach, we can find cumulative distribution probabilities for every combination of `trip_id x stop_id` as defined in `stop_times_df`. We will make a table with the same row order so that McRaptor can easily find their indexes. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 336,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle \n",
    "import gzip\n",
    "from itertools import islice\n",
    "import matplotlib as mlt \n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np \n",
    "import pandas as pd \n",
    "import math"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 337,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Functon to take a slice from a dictionnary - head equivalent\n",
    "def take(n, iterable):\n",
    "    \"Return first n items of the iterable as a list\"\n",
    "    return list(islice(iterable, n))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Load dictionnaries of distributions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 338,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "len dict_real :  6417\n",
      "[('10.TA.1-11-B-j19-1.1.R__8590314', array([ 0, 84, 54, 35, 24,  5,  3,  0,  0,  1,  1,  0,  0,  0,  0,  0,  0,\n",
      "        0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0])), ('10.TA.1-11-B-j19-1.1.R__8590317', array([ 0, 96, 69, 36, 22,  5,  4,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n",
      "        0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0])), ('10.TA.1-11-B-j19-1.1.R__8594304', array([ 0, 70, 78, 45, 22,  9,  1,  0,  0,  0,  1,  0,  0,  0,  0,  0,  0,\n",
      "        0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0])), ('10.TA.1-11-B-j19-1.1.R__8594307', array([ 0, 87, 70, 43, 16,  5,  1,  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])), ('10.TA.1-11-B-j19-1.1.R__8594310', array([ 0, 77, 65, 33, 13,  5,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,\n",
      "        0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0,  0]))]\n",
      "len dict_all :  221945\n",
      "[('1.TA.26-161-j19-1.1.H__8587347', array([  0, 214, 191,  71,  23,   5,   0,   1,   0,   0,   1,   1,   0,\n",
      "         0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
      "         0,   0,   0,   0,   0,   0])), ('1.TA.26-161-j19-1.1.H__8590671', array([  0,  82, 155, 135,  86,  28,  12,   5,   1,   0,   2,   1,   0,\n",
      "         0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
      "         0,   0,   0,   0,   0,   0])), ('1.TA.26-161-j19-1.1.H__8590677', array([  0, 118, 197, 113,  57,  16,   3,   1,   0,   0,   1,   1,   0,\n",
      "         0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
      "         0,   0,   0,   0,   0,   0])), ('1.TA.26-161-j19-1.1.H__8590678', array([  0,  71, 140, 150,  88,  36,  12,   6,   1,   0,   2,   1,   0,\n",
      "         0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
      "         0,   0,   0,   0,   0,   0])), ('1.TA.26-161-j19-1.1.H__8590680', array([  1, 216, 163,  74,  37,   9,   4,   0,   1,   0,   1,   1,   0,\n",
      "         0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,   0,\n",
      "         0,   0,   0,   0,   0,   0]))]\n"
     ]
    }
   ],
   "source": [
    "with gzip.open(\"../data/distributions_geschaetzAndReal.pkl.gz\", \"rb\") as input_file:\n",
    "    d_real = pickle.load(input_file)\n",
    "\n",
    "with gzip.open(\"../data/distributions_allDelays.pkl.gz\", \"rb\") as input_file:\n",
    "    d_all = pickle.load(input_file)\n",
    "\n",
    "# display a slice of it\n",
    "print('len dict_real : ', len(d_real))\n",
    "print(take(5, d_real.items()))\n",
    "\n",
    "# display a slice of it\n",
    "print('len dict_all : ', len(d_all))\n",
    "print(take(5, d_all.items()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Probability using cumulative distribution based on frequency of delays \n",
    "\n",
    "When we have __enough data__ and no ambiguity about `trip_id` and `stop_id` for a given distribution, then we can compute the probability $P(T \\leq t)$ for every $t$ (delay in minute). \n",
    "\n",
    "Let's take a __threshold of 100__ sample points (=number of time we could measure a delay) as a minimum number of points to use this approach. \n",
    "\n",
    "_How many keys in our distionnary of distribution have at least this number of samples ?_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 339,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_data_points_hist(dico):\n",
    "    list_tot_points = []\n",
    "    for key in dico:\n",
    "        distrib = dico[key]\n",
    "        list_tot_points.append(np.sum(distrib))\n",
    "\n",
    "    tot_per_key = np.array(list_tot_points)\n",
    "    binwidth = 100\n",
    "    n_keys_less_than_binwidth = np.sum(np.array(tot_per_key < binwidth))\n",
    "    perc_key_to_recover = round(100 * ( n_keys_less_than_binwidth / len(tot_per_key) ), 2)\n",
    "    plt.figure(figsize = (10,5))\n",
    "    plt.hist(tot_per_key, bins = range(min(tot_per_key), max(tot_per_key) + binwidth, binwidth))\n",
    "    plt.title(\"Total number of data points per trip_id / stop_id key. N keys with less than {0} points: {1} ({2}%)\"\\\n",
    "             .format(binwidth, n_keys_less_than_binwidth, perc_key_to_recover))\n",
    "    plt.xlabel('n data points')\n",
    "    plt.ylabel('n keys')\n",
    "    return plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 340,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_data_points_hist(d_all)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 341,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_data_points_hist(d_real)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we generate a dictionnary with cumulative probability based on frequency of delays, for each keys in our reference dictionnary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 342,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cumul_distri_probas_dict(dico):\n",
    "    list_tot_points = []\n",
    "    for key in dico:\n",
    "        distrib = dico[key]\n",
    "\n",
    "        # get total number of elements \n",
    "        N = np.sum(distrib)\n",
    "\n",
    "        # make cumulative distribution probabilities\n",
    "        cdf_distrib = np.empty((len(distrib)), dtype=float)\n",
    "        save_x = 0\n",
    "        for x in range(len(distrib)):\n",
    "            cdf_distrib[x] = float(distrib[x])/float(N) + float(save_x)/float(N)\n",
    "            save_x += distrib[x]\n",
    "\n",
    "        dico[key] = cdf_distrib\n",
    "    return dico"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Construct recovery tables \n",
    "\n",
    "First approach is to simple sum up similar distribution to get a new distribution we can use. For that, we need to have transport type (`route_desc`), `time` (rounded to hour) and `stop_id` which are valid. We then make all combination of these tree parameters and get the associate distributions.\n",
    "\n",
    "First we need to reformat stoptimes table in order to get time rounded to the hour, correct stop_id format and type, generate `key` column using `trip_id` and `stop_id`, and droping unnecessary columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 471,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0%, 3.84%, 7.68%, 11.52%, 15.36%, 19.2%, 23.04%, 26.88%, 30.72%, 34.55%, 38.39%, 42.23%, 46.07%, 49.91%, 53.75%, 57.59%, 61.43%, 65.27%, 69.11%, 72.95%, 76.79%, 80.63%, 84.47%, 88.31%, 92.15%, 95.98%, 99.82%, "
     ]
    }
   ],
   "source": [
    "with open(\"../data/stop_times_df_cyril.pkl\", \"rb\") as input_file:\n",
    "    stoptimes = pickle.load(input_file)\n",
    "\n",
    "# Use stop_id_general as stop_id  \n",
    "stoptimes['stop_id'] = stoptimes['stop_id_general'].apply(str)\n",
    "\n",
    "# Set same stoptimes index as distribution dict \n",
    "stoptimes['key'] = stoptimes['trip_id'] + '__' + stoptimes['stop_id']\n",
    "stoptimes = stoptimes.set_index('key')\n",
    "\n",
    "stoptimes = stoptimes[['trip_id','stop_id', 'route_desc', 'arrival_time', 'departure_time']]\n",
    "\n",
    "list_hours = []\n",
    "size_stop_times = stoptimes.shape[0]\n",
    "for x in range(size_stop_times):\n",
    "    if (x % 10000) == 0 :\n",
    "        print('{}%'.format(round(100*x/size_stop_times,2)), end = ', ')\n",
    "    \n",
    "    arr_time_hour = pd.to_datetime(stoptimes.iloc[x,:]['arrival_time']).hour\n",
    "    if math.isnan(arr_time_hour): # if arrival is NaT, use departure time\n",
    "        arr_time_hour = pd.to_datetime(stoptimes.iloc[x,:]['departure_time']).hour\n",
    "    list_hours.append(int(arr_time_hour))\n",
    "    \n",
    "stoptimes['hour'] = list_hours\n",
    "stoptimes = stoptimes.drop(columns=['arrival_time', 'departure_time'])\n",
    "\n",
    "# Write this pickle to avoid re-running this above code all the time\n",
    "with gzip.open(\"../data/stop_times_wHour.pkl\", \"wb\") as output_file:\n",
    "    pickle.dump(stoptimes, output_file)         \n",
    "              "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 344,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(16895, 32)\n"
     ]
    },
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>22</th>\n",
       "      <th>23</th>\n",
       "      <th>24</th>\n",
       "      <th>25</th>\n",
       "      <th>26</th>\n",
       "      <th>27</th>\n",
       "      <th>28</th>\n",
       "      <th>29</th>\n",
       "      <th>30</th>\n",
       "      <th>31</th>\n",
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       "    <tr>\n",
       "      <th>stop_id</th>\n",
       "      <th>hour</th>\n",
       "      <th>route_desc</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th rowspan=\"4\" valign=\"top\">8500926</th>\n",
       "      <th>7</th>\n",
       "      <th>Bus</th>\n",
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       "      <td>681</td>\n",
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       "      <td>56</td>\n",
       "      <td>38</td>\n",
       "      <td>26</td>\n",
       "      <td>18</td>\n",
       "      <td>16</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <th>Bus</th>\n",
       "      <td>63</td>\n",
       "      <td>700</td>\n",
       "      <td>115</td>\n",
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       "      <td>10</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <th>Bus</th>\n",
       "      <td>2</td>\n",
       "      <td>407</td>\n",
       "      <td>210</td>\n",
       "      <td>73</td>\n",
       "      <td>31</td>\n",
       "      <td>14</td>\n",
       "      <td>5</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <th>Bus</th>\n",
       "      <td>0</td>\n",
       "      <td>204</td>\n",
       "      <td>121</td>\n",
       "      <td>99</td>\n",
       "      <td>47</td>\n",
       "      <td>17</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>...</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",
       "<p>4 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                         0    1    2   3   4   5   6   7   8   9   ...  22  \\\n",
       "stop_id hour route_desc                                            ...       \n",
       "8500926 7    Bus         49  681  108  56  38  26  18  16   4   2  ...   0   \n",
       "        8    Bus         63  700  115  61  22  10   9   5   3   7  ...   0   \n",
       "        9    Bus          2  407  210  73  31  14   5   2   0   1  ...   0   \n",
       "        10   Bus          0  204  121  99  47  17   5   4   2   2  ...   0   \n",
       "\n",
       "                         23  24  25  26  27  28  29  30  31  \n",
       "stop_id hour route_desc                                      \n",
       "8500926 7    Bus          0   0   0   0   0   0   0   0   0  \n",
       "        8    Bus          0   0   0   1   0   0   0   0   0  \n",
       "        9    Bus          0   0   0   0   0   0   0   0   2  \n",
       "        10   Bus          0   0   0   0   0   0   0   0   0  \n",
       "\n",
       "[4 rows x 32 columns]"
      ]
     },
     "execution_count": 344,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with gzip.open(\"../data/stop_times_wHour.pkl\", \"rb\") as input_file:\n",
    "    stoptimes = pickle.load(input_file)\n",
    "\n",
    "distrib_df = pd.DataFrame(d_all).transpose()\n",
    "stoptimes_df = pd.DataFrame(stoptimes)\n",
    "\n",
    "recovery_df = distrib_df.join(stoptimes_df)\n",
    "list_bins = [x for x in range(32)]\n",
    "\n",
    "recovery_df = recovery_df.groupby(['stop_id','hour', 'route_desc'])[list_bins].apply(lambda x : x.astype(float).sum())\n",
    "recovery_df = recovery_df.astype('int')\n",
    "\n",
    "print(recovery_df.shape)\n",
    "recovery_df.head(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 345,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_df_missing(df, max_bin = 10000):\n",
    "    tot_per_key = np.array(df.sum(axis=1)).astype('int')\n",
    "    binwidth = 100\n",
    "    n_keys_less_than_binwidth = np.sum(np.array(tot_per_key < binwidth))\n",
    "    perc_key_to_recover = round(100 * ( n_keys_less_than_binwidth / len(tot_per_key) ), 2)\n",
    "    plt.figure(figsize = (10,5))\n",
    "    plt.hist(tot_per_key, bins = range(min(tot_per_key), max_bin + binwidth, binwidth))\n",
    "    plt.title(\"Total number of data points per stop_id / hour key. N keys with less than {0} points: {1} ({2}%)\"\\\n",
    "             .format(binwidth, n_keys_less_than_binwidth, perc_key_to_recover))\n",
    "    plt.xlabel('n data points')\n",
    "    plt.ylabel('n keys')\n",
    "    return plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 346,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_df_missing(recovery_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Make second recovery table\n",
    "\n",
    "Here only taking combination of `transport_type x hour`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 347,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(39, 32)\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></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>22</th>\n",
       "      <th>23</th>\n",
       "      <th>24</th>\n",
       "      <th>25</th>\n",
       "      <th>26</th>\n",
       "      <th>27</th>\n",
       "      <th>28</th>\n",
       "      <th>29</th>\n",
       "      <th>30</th>\n",
       "      <th>31</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hour</th>\n",
       "      <th>route_desc</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">7</th>\n",
       "      <th>Bus</th>\n",
       "      <td>38120</td>\n",
       "      <td>4107332</td>\n",
       "      <td>2539805</td>\n",
       "      <td>1021932</td>\n",
       "      <td>387799</td>\n",
       "      <td>157305</td>\n",
       "      <td>73998</td>\n",
       "      <td>39418</td>\n",
       "      <td>22943</td>\n",
       "      <td>13943</td>\n",
       "      <td>...</td>\n",
       "      <td>342</td>\n",
       "      <td>277</td>\n",
       "      <td>282</td>\n",
       "      <td>303</td>\n",
       "      <td>237</td>\n",
       "      <td>332</td>\n",
       "      <td>228</td>\n",
       "      <td>272</td>\n",
       "      <td>190</td>\n",
       "      <td>6931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S-Bahn</th>\n",
       "      <td>97805</td>\n",
       "      <td>105802</td>\n",
       "      <td>91405</td>\n",
       "      <td>37178</td>\n",
       "      <td>13751</td>\n",
       "      <td>5179</td>\n",
       "      <td>2250</td>\n",
       "      <td>1176</td>\n",
       "      <td>812</td>\n",
       "      <td>640</td>\n",
       "      <td>...</td>\n",
       "      <td>26</td>\n",
       "      <td>34</td>\n",
       "      <td>30</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tram</th>\n",
       "      <td>28177</td>\n",
       "      <td>2773595</td>\n",
       "      <td>941928</td>\n",
       "      <td>315930</td>\n",
       "      <td>114498</td>\n",
       "      <td>47608</td>\n",
       "      <td>22713</td>\n",
       "      <td>11922</td>\n",
       "      <td>6706</td>\n",
       "      <td>3954</td>\n",
       "      <td>...</td>\n",
       "      <td>190</td>\n",
       "      <td>176</td>\n",
       "      <td>209</td>\n",
       "      <td>94</td>\n",
       "      <td>158</td>\n",
       "      <td>119</td>\n",
       "      <td>74</td>\n",
       "      <td>61</td>\n",
       "      <td>73</td>\n",
       "      <td>3004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <th>Bus</th>\n",
       "      <td>36788</td>\n",
       "      <td>3507612</td>\n",
       "      <td>2269474</td>\n",
       "      <td>1040781</td>\n",
       "      <td>451125</td>\n",
       "      <td>208801</td>\n",
       "      <td>106285</td>\n",
       "      <td>57440</td>\n",
       "      <td>33605</td>\n",
       "      <td>20332</td>\n",
       "      <td>...</td>\n",
       "      <td>355</td>\n",
       "      <td>280</td>\n",
       "      <td>277</td>\n",
       "      <td>200</td>\n",
       "      <td>181</td>\n",
       "      <td>246</td>\n",
       "      <td>273</td>\n",
       "      <td>336</td>\n",
       "      <td>220</td>\n",
       "      <td>6591</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    0        1        2        3       4       5       6   \\\n",
       "hour route_desc                                                             \n",
       "7    Bus         38120  4107332  2539805  1021932  387799  157305   73998   \n",
       "     S-Bahn      97805   105802    91405    37178   13751    5179    2250   \n",
       "     Tram        28177  2773595   941928   315930  114498   47608   22713   \n",
       "8    Bus         36788  3507612  2269474  1040781  451125  208801  106285   \n",
       "\n",
       "                    7      8      9   ...   22   23   24   25   26   27   28  \\\n",
       "hour route_desc                       ...                                      \n",
       "7    Bus         39418  22943  13943  ...  342  277  282  303  237  332  228   \n",
       "     S-Bahn       1176    812    640  ...   26   34   30   11    9    9    2   \n",
       "     Tram        11922   6706   3954  ...  190  176  209   94  158  119   74   \n",
       "8    Bus         57440  33605  20332  ...  355  280  277  200  181  246  273   \n",
       "\n",
       "                  29   30    31  \n",
       "hour route_desc                  \n",
       "7    Bus         272  190  6931  \n",
       "     S-Bahn        5    7    33  \n",
       "     Tram         61   73  3004  \n",
       "8    Bus         336  220  6591  \n",
       "\n",
       "[4 rows x 32 columns]"
      ]
     },
     "execution_count": 347,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with gzip.open(\"../data/stop_times_wHour.pkl\", \"rb\") as input_file:\n",
    "    stoptimes = pickle.load(input_file)\n",
    "    \n",
    "distrib_df = pd.DataFrame(d_all).transpose()\n",
    "distrib_to_rm = np.array(distrib_df.iloc[:,range(11)].sum(axis=1) == 11) # missing trips\n",
    "distrib_df = distrib_df.iloc[~distrib_to_rm,:]\n",
    "\n",
    "stoptimes_df = pd.DataFrame(stoptimes)\n",
    "\n",
    "recovery_df2 = distrib_df.join(stoptimes_df)\n",
    "list_bins = [x for x in range(32)]\n",
    "\n",
    "recovery_df2 = recovery_df2.groupby(['hour', 'route_desc'])[list_bins].apply(lambda x : x.astype(float).sum())\n",
    "recovery_df2 = recovery_df2.astype('int')\n",
    "print(recovery_df2.shape)\n",
    "recovery_df2.head(4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Third recovery table \n",
    "\n",
    "Takes aggregated transport type distributions to make cumulative probabilities over minutes of delays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 348,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3, 32)\n"
     ]
    },
    {
     "data": {
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       "      <th>31</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>route_desc</th>\n",
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       "      <th>Bus</th>\n",
       "      <td>363268</td>\n",
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       "      <td>23736240</td>\n",
       "      <td>10576138</td>\n",
       "      <td>4388686</td>\n",
       "      <td>1928373</td>\n",
       "      <td>951454</td>\n",
       "      <td>516108</td>\n",
       "      <td>303852</td>\n",
       "      <td>193705</td>\n",
       "      <td>...</td>\n",
       "      <td>5812</td>\n",
       "      <td>4984</td>\n",
       "      <td>4531</td>\n",
       "      <td>4406</td>\n",
       "      <td>3942</td>\n",
       "      <td>4194</td>\n",
       "      <td>3517</td>\n",
       "      <td>3662</td>\n",
       "      <td>3192</td>\n",
       "      <td>63646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S-Bahn</th>\n",
       "      <td>978390</td>\n",
       "      <td>1105562</td>\n",
       "      <td>826122</td>\n",
       "      <td>300372</td>\n",
       "      <td>108761</td>\n",
       "      <td>41031</td>\n",
       "      <td>17883</td>\n",
       "      <td>9170</td>\n",
       "      <td>5731</td>\n",
       "      <td>4066</td>\n",
       "      <td>...</td>\n",
       "      <td>183</td>\n",
       "      <td>180</td>\n",
       "      <td>115</td>\n",
       "      <td>87</td>\n",
       "      <td>73</td>\n",
       "      <td>56</td>\n",
       "      <td>49</td>\n",
       "      <td>36</td>\n",
       "      <td>42</td>\n",
       "      <td>246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tram</th>\n",
       "      <td>247447</td>\n",
       "      <td>25497756</td>\n",
       "      <td>9524532</td>\n",
       "      <td>3371924</td>\n",
       "      <td>1289451</td>\n",
       "      <td>553734</td>\n",
       "      <td>262423</td>\n",
       "      <td>135400</td>\n",
       "      <td>77638</td>\n",
       "      <td>47291</td>\n",
       "      <td>...</td>\n",
       "      <td>1986</td>\n",
       "      <td>1953</td>\n",
       "      <td>1794</td>\n",
       "      <td>1508</td>\n",
       "      <td>1278</td>\n",
       "      <td>1321</td>\n",
       "      <td>1018</td>\n",
       "      <td>1021</td>\n",
       "      <td>867</td>\n",
       "      <td>33074</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                0         1         2         3        4        5       6   \\\n",
       "route_desc                                                                   \n",
       "Bus         363268  36683792  23736240  10576138  4388686  1928373  951454   \n",
       "S-Bahn      978390   1105562    826122    300372   108761    41031   17883   \n",
       "Tram        247447  25497756   9524532   3371924  1289451   553734  262423   \n",
       "\n",
       "                7       8       9   ...    22    23    24    25    26    27  \\\n",
       "route_desc                          ...                                       \n",
       "Bus         516108  303852  193705  ...  5812  4984  4531  4406  3942  4194   \n",
       "S-Bahn        9170    5731    4066  ...   183   180   115    87    73    56   \n",
       "Tram        135400   77638   47291  ...  1986  1953  1794  1508  1278  1321   \n",
       "\n",
       "              28    29    30     31  \n",
       "route_desc                           \n",
       "Bus         3517  3662  3192  63646  \n",
       "S-Bahn        49    36    42    246  \n",
       "Tram        1018  1021   867  33074  \n",
       "\n",
       "[3 rows x 32 columns]"
      ]
     },
     "execution_count": 348,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with gzip.open(\"../data/stop_times_wHour.pkl\", \"rb\") as input_file:\n",
    "    stoptimes = pickle.load(input_file)\n",
    "    \n",
    "distrib_df = pd.DataFrame(d_all).transpose()\n",
    "\n",
    "stoptimes_df = pd.DataFrame(stoptimes)\n",
    "\n",
    "recovery_df3 = distrib_df.join(stoptimes_df)\n",
    "list_bins = [x for x in range(32)]\n",
    "\n",
    "recovery_df3 = recovery_df3.groupby(['route_desc'])[list_bins].apply(lambda x : x.astype(float).sum())\n",
    "recovery_df3 = recovery_df3.astype('int')\n",
    "print(recovery_df3.shape)\n",
    "recovery_df3.head(4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Forth Recovery table using same trip_id at different stops "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 474,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(14637, 32)\n"
     ]
    },
    {
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       "    <tr>\n",
       "      <th>trip_id</th>\n",
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       "      <th>1.TA.26-161-j19-1.1.H</th>\n",
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       "      <td>3</td>\n",
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       "      <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",
       "<p>4 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                       0     1     2     3    4    5   6   7   8   9   ...  \\\n",
       "trip_id                                                                ...   \n",
       "1.TA.26-161-j19-1.1.H   1  2413  3065  1643  675  186  56  27   5   2  ...   \n",
       "1.TA.26-162-j19-1.1.R   1  2357  1686   290   34    3   0   2   1   0  ...   \n",
       "1.TA.26-163-j19-1.1.R   3  1216  1627   457   63   17   8   7   1   0  ...   \n",
       "1.TA.26-165-j19-1.1.H  23  4860  3453  1676  749  225  82  56  25  26  ...   \n",
       "\n",
       "                       22  23  24  25  26  27  28  29  30  31  \n",
       "trip_id                                                        \n",
       "1.TA.26-161-j19-1.1.H   0   0   0   0   0   0   0   0   0   0  \n",
       "1.TA.26-162-j19-1.1.R   0   0   0   0   0   0   0   0   0   0  \n",
       "1.TA.26-163-j19-1.1.R   0   0   0   0   0   0   0   0   0   0  \n",
       "1.TA.26-165-j19-1.1.H   0   0   0   0   0   0   0   0   0   0  \n",
       "\n",
       "[4 rows x 32 columns]"
      ]
     },
     "execution_count": 474,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with gzip.open(\"../data/stop_times_wHour.pkl\", \"rb\") as input_file:\n",
    "    stoptimes = pickle.load(input_file)\n",
    "    \n",
    "distrib_df = pd.DataFrame(d_all).transpose()\n",
    "\n",
    "stoptimes_df = pd.DataFrame(stoptimes)\n",
    "\n",
    "recovery_df4 = distrib_df.join(stoptimes_df)\n",
    "list_bins = [x for x in range(32)]\n",
    "\n",
    "recovery_df4 = recovery_df4.groupby(['trip_id'])[list_bins].apply(lambda x : x.astype(float).sum())\n",
    "recovery_df4 = recovery_df4.astype('int')\n",
    "print(recovery_df4.shape)\n",
    "recovery_df4.head(4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Fifth recovery table using stop_id and time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 477,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(15535, 32)\n"
     ]
    },
    {
     "data": {
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       "      <th>31</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stop_id</th>\n",
       "      <th>hour</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th>7</th>\n",
       "      <td>49</td>\n",
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       "      <td>108</td>\n",
       "      <td>56</td>\n",
       "      <td>38</td>\n",
       "      <td>26</td>\n",
       "      <td>18</td>\n",
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       "      <td>115</td>\n",
       "      <td>61</td>\n",
       "      <td>22</td>\n",
       "      <td>10</td>\n",
       "      <td>9</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2</td>\n",
       "      <td>407</td>\n",
       "      <td>210</td>\n",
       "      <td>73</td>\n",
       "      <td>31</td>\n",
       "      <td>14</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <th>10</th>\n",
       "      <td>0</td>\n",
       "      <td>204</td>\n",
       "      <td>121</td>\n",
       "      <td>99</td>\n",
       "      <td>47</td>\n",
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       "      <td>5</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "</table>\n",
       "<p>4 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              0    1    2   3   4   5   6   7   8   9   ...  22  23  24  25  \\\n",
       "stop_id hour                                            ...                   \n",
       "8500926 7     49  681  108  56  38  26  18  16   4   2  ...   0   0   0   0   \n",
       "        8     63  700  115  61  22  10   9   5   3   7  ...   0   0   0   0   \n",
       "        9      2  407  210  73  31  14   5   2   0   1  ...   0   0   0   0   \n",
       "        10     0  204  121  99  47  17   5   4   2   2  ...   0   0   0   0   \n",
       "\n",
       "              26  27  28  29  30  31  \n",
       "stop_id hour                          \n",
       "8500926 7      0   0   0   0   0   0  \n",
       "        8      1   0   0   0   0   0  \n",
       "        9      0   0   0   0   0   2  \n",
       "        10     0   0   0   0   0   0  \n",
       "\n",
       "[4 rows x 32 columns]"
      ]
     },
     "execution_count": 477,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with gzip.open(\"../data/stop_times_wHour.pkl\", \"rb\") as input_file:\n",
    "    stoptimes = pickle.load(input_file)\n",
    "    \n",
    "distrib_df = pd.DataFrame(d_all).transpose()\n",
    "\n",
    "stoptimes_df = pd.DataFrame(stoptimes)\n",
    "\n",
    "recovery_df5 = distrib_df.join(stoptimes_df)\n",
    "list_bins = [x for x in range(32)]\n",
    "\n",
    "recovery_df5 = recovery_df5.groupby(['stop_id', 'hour'])[list_bins].apply(lambda x : x.astype(float).sum())\n",
    "recovery_df5 = recovery_df5.astype('int')\n",
    "print(recovery_df5.shape)\n",
    "recovery_df5.head(4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### sixth recovery table using stop_id and type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 485,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1338, 32)\n"
     ]
    },
    {
     "data": {
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       "      <th>stop_id</th>\n",
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       "      <td>431</td>\n",
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       "      <td>34189</td>\n",
       "      <td>6373</td>\n",
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       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>8502187</th>\n",
       "      <th>S-Bahn</th>\n",
       "      <td>426</td>\n",
       "      <td>46737</td>\n",
       "      <td>18913</td>\n",
       "      <td>5894</td>\n",
       "      <td>1519</td>\n",
       "      <td>356</td>\n",
       "      <td>110</td>\n",
       "      <td>53</td>\n",
       "      <td>34</td>\n",
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       "      <td>0</td>\n",
       "      <td>4</td>\n",
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       "<p>4 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     0      1      2      3     4     5    6    7   8   9   \\\n",
       "stop_id route_desc                                                           \n",
       "8500926 Bus         189   4636   2132   1357   649   392  243  160  91  60   \n",
       "8502186 S-Bahn      431  30455  34189   6373  1851   475  167   78  38  20   \n",
       "8502187 S-Bahn      426  46737  18913   5894  1519   356  110   53  34  23   \n",
       "8502188 S-Bahn      501  34142  23814  10243  3826  1162  237   85  33  35   \n",
       "\n",
       "                    ...  22  23  24  25  26  27  28  29  30  31  \n",
       "stop_id route_desc  ...                                          \n",
       "8500926 Bus         ...   2   1   0   0   1   1   0   0   0   6  \n",
       "8502186 S-Bahn      ...   4   3   0   0   0   1   0   0   1   2  \n",
       "8502187 S-Bahn      ...   2   0   1   1   0   0   1   0   1   4  \n",
       "8502188 S-Bahn      ...   3   2   1   1   3   0   1   0   0   4  \n",
       "\n",
       "[4 rows x 32 columns]"
      ]
     },
     "execution_count": 485,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with gzip.open(\"../data/stop_times_wHour.pkl\", \"rb\") as input_file:\n",
    "    stoptimes = pickle.load(input_file)\n",
    "    \n",
    "distrib_df = pd.DataFrame(d_all).transpose()\n",
    "\n",
    "stoptimes_df = pd.DataFrame(stoptimes)\n",
    "\n",
    "recovery_df6 = distrib_df.join(stoptimes_df)\n",
    "list_bins = [x for x in range(32)]\n",
    "\n",
    "recovery_df6 = recovery_df6.groupby(['stop_id', 'route_desc'])[list_bins].apply(lambda x : x.astype(float).sum())\n",
    "recovery_df6 = recovery_df6.astype('int')\n",
    "print(recovery_df6.shape)\n",
    "recovery_df6.head(4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Recov table using stop_id only"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 482,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1233, 32)\n"
     ]
    },
    {
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8500926</th>\n",
       "      <td>189</td>\n",
       "      <td>4636</td>\n",
       "      <td>2132</td>\n",
       "      <td>1357</td>\n",
       "      <td>649</td>\n",
       "      <td>392</td>\n",
       "      <td>243</td>\n",
       "      <td>160</td>\n",
       "      <td>91</td>\n",
       "      <td>60</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>1</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>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8502186</th>\n",
       "      <td>431</td>\n",
       "      <td>30455</td>\n",
       "      <td>34189</td>\n",
       "      <td>6373</td>\n",
       "      <td>1851</td>\n",
       "      <td>475</td>\n",
       "      <td>167</td>\n",
       "      <td>78</td>\n",
       "      <td>38</td>\n",
       "      <td>20</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>3</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>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8502187</th>\n",
       "      <td>426</td>\n",
       "      <td>46737</td>\n",
       "      <td>18913</td>\n",
       "      <td>5894</td>\n",
       "      <td>1519</td>\n",
       "      <td>356</td>\n",
       "      <td>110</td>\n",
       "      <td>53</td>\n",
       "      <td>34</td>\n",
       "      <td>23</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8502188</th>\n",
       "      <td>501</td>\n",
       "      <td>34142</td>\n",
       "      <td>23814</td>\n",
       "      <td>10243</td>\n",
       "      <td>3826</td>\n",
       "      <td>1162</td>\n",
       "      <td>237</td>\n",
       "      <td>85</td>\n",
       "      <td>33</td>\n",
       "      <td>35</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          0      1      2      3     4     5    6    7   8   9   ...  22  23  \\\n",
       "stop_id                                                          ...           \n",
       "8500926  189   4636   2132   1357   649   392  243  160  91  60  ...   2   1   \n",
       "8502186  431  30455  34189   6373  1851   475  167   78  38  20  ...   4   3   \n",
       "8502187  426  46737  18913   5894  1519   356  110   53  34  23  ...   2   0   \n",
       "8502188  501  34142  23814  10243  3826  1162  237   85  33  35  ...   3   2   \n",
       "\n",
       "         24  25  26  27  28  29  30  31  \n",
       "stop_id                                  \n",
       "8500926   0   0   1   1   0   0   0   6  \n",
       "8502186   0   0   0   1   0   0   1   2  \n",
       "8502187   1   1   0   0   1   0   1   4  \n",
       "8502188   1   1   3   0   1   0   0   4  \n",
       "\n",
       "[4 rows x 32 columns]"
      ]
     },
     "execution_count": 482,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with gzip.open(\"../data/stop_times_wHour.pkl\", \"rb\") as input_file:\n",
    "    stoptimes = pickle.load(input_file)\n",
    "    \n",
    "distrib_df = pd.DataFrame(d_all).transpose()\n",
    "\n",
    "stoptimes_df = pd.DataFrame(stoptimes)\n",
    "\n",
    "recovery_stop_id = distrib_df.join(stoptimes_df)\n",
    "list_bins = [x for x in range(32)]\n",
    "\n",
    "recovery_stop_id = recovery_stop_id.groupby(['stop_id'])[list_bins].apply(lambda x : x.astype(float).sum())\n",
    "recovery_stop_id = recovery_stop_id.astype('int')\n",
    "print(recovery_stop_id.shape)\n",
    "recovery_stop_id.head(4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Recov table using time only"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 489,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(13, 32)\n"
     ]
    },
    {
     "data": {
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
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       "      <th>6</th>\n",
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       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>22</th>\n",
       "      <th>23</th>\n",
       "      <th>24</th>\n",
       "      <th>25</th>\n",
       "      <th>26</th>\n",
       "      <th>27</th>\n",
       "      <th>28</th>\n",
       "      <th>29</th>\n",
       "      <th>30</th>\n",
       "      <th>31</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hour</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>164103</td>\n",
       "      <td>6986742</td>\n",
       "      <td>3573143</td>\n",
       "      <td>1375043</td>\n",
       "      <td>516048</td>\n",
       "      <td>210092</td>\n",
       "      <td>98961</td>\n",
       "      <td>52516</td>\n",
       "      <td>30461</td>\n",
       "      <td>18537</td>\n",
       "      <td>...</td>\n",
       "      <td>558</td>\n",
       "      <td>487</td>\n",
       "      <td>521</td>\n",
       "      <td>408</td>\n",
       "      <td>404</td>\n",
       "      <td>460</td>\n",
       "      <td>304</td>\n",
       "      <td>338</td>\n",
       "      <td>270</td>\n",
       "      <td>9968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>137004</td>\n",
       "      <td>5723582</td>\n",
       "      <td>3283417</td>\n",
       "      <td>1447115</td>\n",
       "      <td>616263</td>\n",
       "      <td>279548</td>\n",
       "      <td>139668</td>\n",
       "      <td>74172</td>\n",
       "      <td>43183</td>\n",
       "      <td>25815</td>\n",
       "      <td>...</td>\n",
       "      <td>619</td>\n",
       "      <td>548</td>\n",
       "      <td>456</td>\n",
       "      <td>387</td>\n",
       "      <td>386</td>\n",
       "      <td>417</td>\n",
       "      <td>369</td>\n",
       "      <td>427</td>\n",
       "      <td>310</td>\n",
       "      <td>10096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>117208</td>\n",
       "      <td>4978388</td>\n",
       "      <td>2725734</td>\n",
       "      <td>1077274</td>\n",
       "      <td>403992</td>\n",
       "      <td>158442</td>\n",
       "      <td>72168</td>\n",
       "      <td>36291</td>\n",
       "      <td>19152</td>\n",
       "      <td>11239</td>\n",
       "      <td>...</td>\n",
       "      <td>427</td>\n",
       "      <td>349</td>\n",
       "      <td>275</td>\n",
       "      <td>424</td>\n",
       "      <td>401</td>\n",
       "      <td>559</td>\n",
       "      <td>339</td>\n",
       "      <td>558</td>\n",
       "      <td>239</td>\n",
       "      <td>8298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>116266</td>\n",
       "      <td>4756715</td>\n",
       "      <td>2529807</td>\n",
       "      <td>982727</td>\n",
       "      <td>358404</td>\n",
       "      <td>132813</td>\n",
       "      <td>55631</td>\n",
       "      <td>25103</td>\n",
       "      <td>12635</td>\n",
       "      <td>6925</td>\n",
       "      <td>...</td>\n",
       "      <td>244</td>\n",
       "      <td>239</td>\n",
       "      <td>175</td>\n",
       "      <td>310</td>\n",
       "      <td>229</td>\n",
       "      <td>293</td>\n",
       "      <td>214</td>\n",
       "      <td>271</td>\n",
       "      <td>162</td>\n",
       "      <td>6701</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          0        1        2        3       4       5       6      7      8   \\\n",
       "hour                                                                            \n",
       "7     164103  6986742  3573143  1375043  516048  210092   98961  52516  30461   \n",
       "8     137004  5723582  3283417  1447115  616263  279548  139668  74172  43183   \n",
       "9     117208  4978388  2725734  1077274  403992  158442   72168  36291  19152   \n",
       "10    116266  4756715  2529807   982727  358404  132813   55631  25103  12635   \n",
       "\n",
       "         9   ...   22   23   24   25   26   27   28   29   30     31  \n",
       "hour         ...                                                      \n",
       "7     18537  ...  558  487  521  408  404  460  304  338  270   9968  \n",
       "8     25815  ...  619  548  456  387  386  417  369  427  310  10096  \n",
       "9     11239  ...  427  349  275  424  401  559  339  558  239   8298  \n",
       "10     6925  ...  244  239  175  310  229  293  214  271  162   6701  \n",
       "\n",
       "[4 rows x 32 columns]"
      ]
     },
     "execution_count": 489,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with gzip.open(\"../data/stop_times_wHour.pkl\", \"rb\") as input_file:\n",
    "    stoptimes = pickle.load(input_file)\n",
    "    \n",
    "distrib_df = pd.DataFrame(d_all).transpose()\n",
    "\n",
    "stoptimes_df = pd.DataFrame(stoptimes)\n",
    "\n",
    "recovery_time = distrib_df.join(stoptimes_df)\n",
    "list_bins = [x for x in range(32)]\n",
    "\n",
    "recovery_time = recovery_time.groupby(['hour'])[list_bins].apply(lambda x : x.astype(float).sum())\n",
    "recovery_time = recovery_time.astype('int')\n",
    "print(recovery_time.shape)\n",
    "recovery_time.head(4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Overall aggregate distribution (used when it fails)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 349,
   "metadata": {},
   "outputs": [],
   "source": [
    "last_chance_distrib = np.array(recovery_df3.sum(axis=0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reconstruct cumulative distribution probabilities from multiple distributions to recover data with few/missing points \n",
    "\n",
    "At this point, we have 2 dictionnaries of distributions and 3 recovery dataframes :\n",
    "\n",
    " - `d_real` : contains delay distribution for each keys in form `trip_id + __ + stop_id` calculated from delays with status `geschaetz` or `real` in sbb datasets.\n",
    " - `d_all` : contains delay distributions for each keys in form `trip_id + __ + stop_id`. No filter was applied on status (contains `geschaetz`, `real` __and__ `prognose` = evaluated delay).\n",
    " - `recovery_df` : contains aggregated delay distributions for each combination of `stop_id`, `route_desc` (transport type) and `hour` (time rounded to hour). \n",
    " - `recovery_df2` : contains aggregated delay distributions for each combination of `route_desc` (transport type) and `hour` (time rounded to hour). \n",
    " - `recovery_df3` : contains aggregated delay distributions for `route_desc` (transport type) \n",
    " - `last_chance_distrib` : contains aggregated delay from all lines\n",
    " \n",
    "We will now use these in order to reconstruct the final table with $P(T\\leq t_i)$ for each time points between -1 and +30, using a cumulative probability function as mentionned above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 350,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.0%, 3.84%, 7.68%, 11.52%, 15.36%, 19.2%, 23.04%, 26.88%, 30.72%, 34.55%, 38.39%, 42.23%, 46.07%, 49.91%, 53.75%, 57.59%, 61.43%, 65.27%, 69.11%, 72.95%, 76.79%, 80.63%, 84.47%, 88.31%, 92.15%, 95.98%, 99.82%, "
     ]
    }
   ],
   "source": [
    "###################### MAKE CUMULATIVE PROBABILITY TABLE #######################\n",
    "\n",
    "# Load stop_time table, to use its order as a template for our final table \n",
    "with open(\"../data/stop_times_df_cyril.pkl\", \"rb\") as input_file:\n",
    "    stoptimes = pickle.load(input_file)\n",
    "    \n",
    "# declare empty variables \n",
    "size_stop_times = stoptimes.shape[0]\n",
    "n_fail = n_real = n_all = n_recov1 = n_recov2 = n_recov3 = i = 0\n",
    "all_distrib, all_transport_type, all_hours, all_keys, all_failure_keys, all_data_origin = \\\n",
    "    ([] for i in range(6))\n",
    "\n",
    "# Get useful indexes\n",
    "stop_id_idx = stoptimes.columns.get_loc(\"stop_id_general\")\n",
    "trip_id_idx = stoptimes.columns.get_loc(\"trip_id\")\n",
    "transport_type_idx = stoptimes.columns.get_loc(\"route_desc\")\n",
    "\n",
    "for index, row in stoptimes.iterrows():\n",
    "    \n",
    "    trip_id = row[trip_id_idx]\n",
    "    stop_id = str(row[stop_id_idx])\n",
    "    transport_type = row[transport_type_idx]\n",
    "    key = str(trip_id) + '__' + str(stop_id)\n",
    "\n",
    "    # Compute rounded hour using arrival if possible - recover with departure\n",
    "    hour = pd.to_datetime(stoptimes.loc[index]['arrival_time']).hour\n",
    "    if math.isnan(hour): # if arrival is NaT, use departure time\n",
    "        hour = pd.to_datetime(stoptimes.loc[index]['departure_time']).hour\n",
    "    \n",
    "    distrib = np.zeros(31)\n",
    "    keep_trying = True\n",
    "    \n",
    "    # 1) try d_real to get distribution from measured delays\n",
    "    if key in d_real:\n",
    "        distrib = d_real[key]\n",
    "        sum_distrib = np.sum(distrib)\n",
    "        if sum_distrib > 100 :\n",
    "            #summary_df.loc[index, 'distribution'] = distrib\n",
    "            all_distrib.append(distrib)\n",
    "            keep_trying = False \n",
    "            n_real += 1\n",
    "            all_data_origin.append('d_real')\n",
    "    \n",
    "    # 2) try d_all to get distribution from measured + estimated delays\n",
    "    if keep_trying and key in d_all:\n",
    "        distrib = d_all[key]\n",
    "        sum_distrib = np.sum(distrib)\n",
    "        if sum_distrib > 100 :\n",
    "            #summary_df.loc[index, 'distribution'] = distrib\n",
    "            all_distrib.append(distrib)\n",
    "            keep_trying = False\n",
    "            n_all += 1\n",
    "            all_data_origin.append('d_all')\n",
    "\n",
    "            \n",
    "    # 3) try first recovery table using stop_id, transport_type and hour\n",
    "    if keep_trying and (stop_id, hour, transport_type) in recovery_df.index:\n",
    "        distrib = np.array(recovery_df.loc[(stop_id, hour, transport_type)])\n",
    "        sum_distrib = np.sum(distrib)\n",
    "        if sum_distrib > 100 :\n",
    "            #summary_df.loc[index, 'distribution'] = distrib\n",
    "            all_distrib.append(distrib)\n",
    "            keep_trying = False \n",
    "            n_recov1 += 1\n",
    "            all_data_origin.append('recov_tab1')\n",
    "            \n",
    "    # 4) use second recovery table using transport_type and hour \n",
    "    if keep_trying and (hour, transport_type) in recovery_df2.index:\n",
    "        distrib = np.array(recovery_df2.loc[(hour, transport_type)])\n",
    "        sum_distrib = np.sum(distrib)\n",
    "        if sum_distrib > 100 :\n",
    "            #summary_df.loc[index, 'distribution'] = distrib\n",
    "            all_distrib.append(distrib)\n",
    "            keep_trying = False \n",
    "            n_recov2 += 1\n",
    "            all_data_origin.append('recov_tab2')\n",
    "    \n",
    "    # 5) use third recovery table using transport_type only \n",
    "    if keep_trying and (transport_type) in recovery_df3.index:\n",
    "        distrib = np.array(recovery_df3.loc[(transport_type)])\n",
    "        sum_distrib = np.sum(distrib)\n",
    "        #summary_df.loc[index, 'distribution'] = distrib\n",
    "        all_distrib.append(distrib)\n",
    "        keep_trying = False \n",
    "        n_recov3 += 1\n",
    "        all_data_origin.append('recov_tab3')\n",
    "\n",
    "    # Record results in summary\n",
    "    all_keys.append(key)\n",
    "    all_transport_type.append(transport_type)\n",
    "    all_hours.append(hour)\n",
    "\n",
    "    # save number of failure for recovery\n",
    "    if keep_trying:\n",
    "        #print('fail{}'.format(index), end = ', ')\n",
    "        all_distrib.append(last_chance_distrib)\n",
    "        all_failure_keys.append(key)\n",
    "        n_fail += 1 \n",
    "        all_data_origin.append('fail')\n",
    "        \n",
    "    # print progression \n",
    "    if (index % 10000) == 0 :\n",
    "        print('{}%'.format(round(100*index/size_stop_times,2)), end = ', ')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 351,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.barh(np.array(['real / geschaetz','real / geschaetz / prognose','recov_tab1 (stop_id x hour x type)','recov_tab2 (hour x type)','recov_tab3 (type)', 'fail'])[::-1],\\\n",
    "         np.array([n_real,n_all,n_recov1,n_recov2, n_recov3, n_fail])[::-1])\n",
    "plt.title('where the distribution data come from')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 352,
   "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>key</th>\n",
       "      <th>hour</th>\n",
       "      <th>transport_type</th>\n",
       "      <th>distribution</th>\n",
       "      <th>origin</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2064.TA.26-13-j19-1.24.H__8576240</td>\n",
       "      <td>7</td>\n",
       "      <td>Tram</td>\n",
       "      <td>[15, 445, 31, 4, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0,...</td>\n",
       "      <td>d_all</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2064.TA.26-13-j19-1.24.H__8591353</td>\n",
       "      <td>7</td>\n",
       "      <td>Tram</td>\n",
       "      <td>[1, 442, 41, 11, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0,...</td>\n",
       "      <td>d_all</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2064.TA.26-13-j19-1.24.H__8591039</td>\n",
       "      <td>7</td>\n",
       "      <td>Tram</td>\n",
       "      <td>[0, 415, 68, 11, 2, 0, 0, 1, 0, 0, 1, 0, 0, 0,...</td>\n",
       "      <td>d_all</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2064.TA.26-13-j19-1.24.H__8591121</td>\n",
       "      <td>7</td>\n",
       "      <td>Tram</td>\n",
       "      <td>[0, 403, 79, 12, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0,...</td>\n",
       "      <td>d_all</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2064.TA.26-13-j19-1.24.H__8591417</td>\n",
       "      <td>7</td>\n",
       "      <td>Tram</td>\n",
       "      <td>[0, 388, 79, 24, 4, 1, 0, 1, 0, 0, 0, 0, 1, 0,...</td>\n",
       "      <td>d_all</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 key hour transport_type  \\\n",
       "0  2064.TA.26-13-j19-1.24.H__8576240    7           Tram   \n",
       "1  2064.TA.26-13-j19-1.24.H__8591353    7           Tram   \n",
       "2  2064.TA.26-13-j19-1.24.H__8591039    7           Tram   \n",
       "3  2064.TA.26-13-j19-1.24.H__8591121    7           Tram   \n",
       "4  2064.TA.26-13-j19-1.24.H__8591417    7           Tram   \n",
       "\n",
       "                                        distribution origin  \n",
       "0  [15, 445, 31, 4, 1, 0, 1, 0, 0, 1, 0, 0, 0, 0,...  d_all  \n",
       "1  [1, 442, 41, 11, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0,...  d_all  \n",
       "2  [0, 415, 68, 11, 2, 0, 0, 1, 0, 0, 1, 0, 0, 0,...  d_all  \n",
       "3  [0, 403, 79, 12, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0,...  d_all  \n",
       "4  [0, 388, 79, 24, 4, 1, 0, 1, 0, 0, 0, 0, 1, 0,...  d_all  "
      ]
     },
     "execution_count": 352,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summary_df = pd.DataFrame([all_keys, all_hours, all_transport_type, all_distrib, all_data_origin],\\\n",
    "                          index = ['key','hour','transport_type','distribution', 'origin']).transpose()\n",
    "summary_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Write down summary table used to make final distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 353,
   "metadata": {},
   "outputs": [],
   "source": [
    "with gzip.open(\"../data/join_distribution_all.pkl.gz\", \"wb\") as out_file:\n",
    "    pickle.dump(summary_df, out_file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 354,
   "metadata": {},
   "outputs": [],
   "source": [
    "list_all_rows = []\n",
    "for index, row in summary_df.iterrows():\n",
    "    distrib = np.array(row['distribution'])\n",
    "    \n",
    "    # get total number of elements \n",
    "    N = np.sum(distrib)\n",
    "        \n",
    "    # make cumulative distribution probabilities\n",
    "    cdf_distrib = np.empty((len(distrib)), dtype=float)\n",
    "    save_x = 0\n",
    "    for x in range(len(distrib)):\n",
    "        cdf_distrib[x] = float(distrib[x])/float(N) + float(save_x)/float(N)\n",
    "        save_x += distrib[x]\n",
    "        \n",
    "    list_all_rows.append(cdf_distrib)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 355,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.03 , 0.92 , 0.982, 0.99 , 0.992, 0.992, 0.994, 0.994, 0.994,\n",
       "        0.996, 0.996, 0.996, 0.996, 0.996, 0.996, 0.996, 0.996, 0.996,\n",
       "        0.996, 0.996, 0.996, 0.996, 0.996, 0.996, 1.   , 1.   , 1.   ,\n",
       "        1.   , 1.   , 1.   , 1.   , 1.   ],\n",
       "       [0.002, 0.886, 0.968, 0.99 , 0.992, 0.992, 0.992, 0.994, 0.994,\n",
       "        0.994, 0.996, 0.996, 0.996, 0.996, 0.996, 0.996, 0.996, 0.996,\n",
       "        0.996, 0.996, 0.996, 0.996, 0.996, 0.996, 1.   , 1.   , 1.   ,\n",
       "        1.   , 1.   , 1.   , 1.   , 1.   ],\n",
       "       [0.   , 0.83 , 0.966, 0.988, 0.992, 0.992, 0.992, 0.994, 0.994,\n",
       "        0.994, 0.996, 0.996, 0.996, 0.996, 0.996, 0.996, 0.996, 0.996,\n",
       "        0.996, 0.996, 0.996, 0.996, 0.996, 1.   , 1.   , 1.   , 1.   ,\n",
       "        1.   , 1.   , 1.   , 1.   , 1.   ],\n",
       "       [0.   , 0.806, 0.964, 0.988, 0.99 , 0.992, 0.992, 0.994, 0.994,\n",
       "        0.994, 0.994, 0.996, 0.996, 0.996, 0.996, 0.996, 0.996, 0.996,\n",
       "        0.996, 0.996, 0.996, 0.996, 1.   , 1.   , 1.   , 1.   , 1.   ,\n",
       "        1.   , 1.   , 1.   , 1.   , 1.   ],\n",
       "       [0.   , 0.776, 0.934, 0.982, 0.99 , 0.992, 0.992, 0.994, 0.994,\n",
       "        0.994, 0.994, 0.994, 0.996, 0.996, 0.996, 0.996, 0.996, 0.996,\n",
       "        0.996, 0.996, 0.996, 0.996, 1.   , 1.   , 1.   , 1.   , 1.   ,\n",
       "        1.   , 1.   , 1.   , 1.   , 1.   ]])"
      ]
     },
     "execution_count": 355,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df = pd.DataFrame(list_all_rows)\n",
    "final_df.index = summary_df.index\n",
    "final_np = final_df.to_numpy()\n",
    "final_np[0:5,:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Last check if all indexes corresponds to `stoptimes` indexes :"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 243,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(np.array(final_df.index == stoptimes.index)) == stoptimes.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {},
   "outputs": [],
   "source": [
    "################################## WRITE FINAL NUMPY ##################################\n",
    "with gzip.open(\"../data/join_distribution_cumulative_p_3.pkl.gz\", \"wb\") as output_file:\n",
    "    pickle.dump(final_np, output_file)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluate precision of recovery tables for missing trip_id \n",
    "\n",
    "We use only data with `geschaetz` or `real` status to evaluate perfomence of our recovery tables - namely probabilities coming from aggregated delay distribution from similar transfer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 397,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(6417, 5)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "d_real        5716\n",
       "recov_tab1     685\n",
       "recov_tab2       9\n",
       "d_all            7\n",
       "Name: origin, dtype: int64"
      ]
     },
     "execution_count": 397,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summary_df_real = summary_df.iloc[np.array(summary_df['key'].isin(d_real.keys())),:]\n",
    "print(summary_df_real.shape)\n",
    "summary_df_real.head(3)\n",
    "summary_df_real['origin'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 466,
   "metadata": {},
   "outputs": [],
   "source": [
    "def cdf_distrib(distr_arr):\n",
    "    # get total number of elements \n",
    "    N_tot = np.sum(distr_arr)\n",
    "        \n",
    "    # make cumulative distribution probabilities\n",
    "    cdf_distrib = np.empty((len(distr_arr)), dtype=float)\n",
    "    save_x = 0\n",
    "    for x in range(len(distrib)):\n",
    "        cdf_distrib[x] = float(distr_arr[x])/float(N_tot) + float(save_x)/float(N_tot)\n",
    "        save_x += distr_arr[x]\n",
    "    \n",
    "    return cdf_distrib\n",
    "\n",
    "def residual_in_cdf(cdf1, cdf2):\n",
    "    res = N_tot = 0.0\n",
    "    for i in range(len(cdf1)):\n",
    "        res += abs(float(cdf1[i]) - float(cdf2[i]))\n",
    "        N_tot += 1.0\n",
    "    \n",
    "    return res/N_tot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 490,
   "metadata": {},
   "outputs": [],
   "source": [
    "all_res_recov1 = []\n",
    "all_res_recov2 = []\n",
    "all_res_recov3 = []\n",
    "all_res_recov4 = []\n",
    "all_res_recov5 = []\n",
    "all_res_recov6 = []\n",
    "all_res_recov_stop = []\n",
    "all_res_recov_time = []\n",
    "all_res_fail = []\n",
    "\n",
    "for index, row in summary_df_real.iterrows():\n",
    "    \n",
    "    key            = str(row[0])\n",
    "    trip_id        = key.split('__')[0]\n",
    "    stop_id        = key.split('__')[1]\n",
    "    hour           = row[1]\n",
    "    transport_type = str(row[2])\n",
    "    distrib_real   = np.array(row[3])\n",
    "    origin         = str(row[4])\n",
    "    \n",
    "    cdf_distrib_real = cdf_distrib(distrib_real)\n",
    "    \n",
    "    if origin == 'd_real':\n",
    "        # Evaluate recov_tab1 - stop_time_type\n",
    "        distrib_recov = np.array(recovery_df.loc[(stop_id, hour, transport_type)])\n",
    "        cdf_distrib_recov1 = cdf_distrib(distrib_recov)\n",
    "        all_res_recov1.append(residual_in_cdf(cdf_distrib_recov1, cdf_distrib_real))\n",
    "        \n",
    "        # Evaluate recov_tab2 - time_type\n",
    "        distrib_recov = np.array(recovery_df2.loc[(hour, transport_type)])\n",
    "        cdf_distrib_recov2 = cdf_distrib(distrib_recov)\n",
    "        all_res_recov2.append(residual_in_cdf(cdf_distrib_recov2, cdf_distrib_real))\n",
    "        \n",
    "        # Evaluate recov_tab3 - type\n",
    "        distrib_recov = np.array(recovery_df3.loc[(transport_type)])\n",
    "        cdf_distrib_recov3 = cdf_distrib(distrib_recov)\n",
    "        all_res_recov3.append(residual_in_cdf(cdf_distrib_recov3, cdf_distrib_real))\n",
    "        \n",
    "        # Evaluate recov_tab4 - trip_id \n",
    "        distrib_recov4 = np.array(recovery_df4.loc[(trip_id)])\n",
    "        cdf_distrib_recov4 = cdf_distrib(distrib_recov4)\n",
    "        all_res_recov4.append(residual_in_cdf(cdf_distrib_recov4, cdf_distrib_real))\n",
    "        \n",
    "        # Evaluate recov_tab5 - stop_time\n",
    "        distrib_recov5 = np.array(recovery_df5.loc[(stop_id, hour)])\n",
    "        cdf_distrib_recov5 = cdf_distrib(distrib_recov5)\n",
    "        all_res_recov5.append(residual_in_cdf(cdf_distrib_recov5, cdf_distrib_real))\n",
    "              \n",
    "        # Evaluate recov_tab6 - stop_type\n",
    "        distrib_recov6 = np.array(recovery_df6.loc[(stop_id, transport_type)])\n",
    "        cdf_distrib_recov6 = cdf_distrib(distrib_recov6)\n",
    "        all_res_recov6.append(residual_in_cdf(cdf_distrib_recov6, cdf_distrib_real))\n",
    "        \n",
    "        # Evaluate recov_stop_id - stop\n",
    "        distrib_recov_stop = np.array(recovery_stop_id.loc[(stop_id)])\n",
    "        cdf_distrib_recov_stop = cdf_distrib(distrib_recov_stop)\n",
    "        all_res_recov_stop.append(residual_in_cdf(cdf_distrib_recov_stop, cdf_distrib_real))\n",
    "        \n",
    "        # Evaluate recov_time - time\n",
    "        distrib_recov_time = np.array(recovery_time.loc[(hour)])\n",
    "        cdf_distrib_recov_time = cdf_distrib(distrib_recov_time)\n",
    "        all_res_recov_time.append(residual_in_cdf(cdf_distrib_recov_time, cdf_distrib_real))\n",
    "        \n",
    "        recovery_time\n",
    "                \n",
    "        # Evaluate compare with overall_average\n",
    "        cdf_overall = cdf_distrib(last_chance_distrib)\n",
    "        all_res_fail.append(residual_in_cdf(cdf_overall, cdf_distrib_real))\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 495,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "all_residuals = pd.DataFrame([all_res_recov1, all_res_recov2, all_res_recov3, all_res_recov4, \\\n",
    "                              all_res_recov5, all_res_recov6, all_res_recov_stop, all_res_recov_time, all_res_fail], \\\n",
    "                             index = ['recov_stop_time_type', 'recov_time_type', 'recov_type', \\\n",
    "                                      'recov_trip_id', 'recov_stop_type', 'recov_stop_time', \\\n",
    "                                      'recov_stop', 'recov_time', 'baseline average error']).transpose()\n",
    "meds = all_residuals.median()\n",
    "meds.sort_values(ascending=False, inplace=True)\n",
    "all_residuals = all_residuals[meds.index]\n",
    "\n",
    "plt.figure(figsize = (12,6))\n",
    "plt.gcf().subplots_adjust(bottom=0.5, left = 0.2)\n",
    "all_residuals.boxplot(vert=False)\n",
    "plt.xlabel('residual error $\\sum $ | $P(T \\leq t_i)_{true} - P(T \\leq t_i)_{estimated} $ |')\n",
    "plt.savefig(\"../figs/recov_tab_residuals.pdf\", dpi=300)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Poisson cumulative distribution\n",
    "\n",
    "The Poisson distribution is popular for modeling the number of times an event occurs in an interval of time or space. We modeled a poisson distribution for delays assuming parameter $k$ is the time in minutes (as it was done [here](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0126137), formulas $(4),(5),(6)$).\n",
    "\n",
    "A discrete random variable X is said to have a Poisson distribution with parameter λ > 0, if, for k = 0, 1, 2, ..., the probability mass function of X is given by:\n",
    "\n",
    "$${\\displaystyle \\!f(k;\\lambda )=\\Pr(X=k)={\\frac {\\lambda ^{k}e^{-\\lambda }}{k!}},}$$\n",
    "where\n",
    "\n",
    "e is Euler's number (e = 2.71828...)\n",
    "k! is the factorial of k.\n",
    "The positive real number λ is equal to the expected value of X __and__ to its variance.\n",
    "\n",
    "$${\\displaystyle \\lambda =\\operatorname {E} (X)=\\operatorname {Var} (X)}$$\n",
    "\n",
    "We can approximate E[𝑋]∼$\\mu_i$ for our data $X_i$, if we assume the sample $X_i$ of size N follow the distribution of $X$ meaning $X_i$∼$X$.\n",
    "\n",
    "Poisson-related __assumptions__ :\n",
    "- $k$ is the __delay time in minutes__ and can take values 0, 1, 2, ... (strictly positive and discrete)\n",
    "- We assume our sampling $X_i$ of $X$ is good enough to approximate E[X] ~ $\\mu_i$\n",
    "- The occurrence of one event does not affect probability of others. That is, events occur independently.\n",
    "    - __We assume being late one day is not affecting the delay of the day after__ \n",
    "- The average rate at which events occur is independent of any occurrences. For simplicity, this is usually assumed to be constant, but may in practice vary with time.\n",
    "    - __we assumes delays occurs with a constant rate over time__\n",
    "- Two events cannot occur at exactly the same instant\n",
    "\n",
    "We made a function `poisson_proba` that takes a `trip_id`, a `stop_id`, an `arrival time` and a `departure time` and a dictionnary {key : distribution} to compute a __probability to be at least 2 minutes before departure of next trip__. \n",
    "\n",
    "We make a few __assumptions__ on our side :\n",
    "- We assume that if we have less than 2 minutes for the transfer, we miss it.\n",
    "- We assume the next train is on time.\n",
    "- As for poisson distribution $k$ is strictly positive, we assume trains ahead of schedule were on time ($k=0$)\n",
    "\n",
    "\n",
    "_Question we should address :_\n",
    "- _Is the poisson a reasonable approximation of the binomial distribution in our case ?_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's first test the poisson distribution and compare it with our distribution to see how well it fits the data. We will compute $Pr(X = k)$ for each values of k and look at the shape of the poisson distribution compared to the shape of our scaled data. Then, we will compare $\\sum_{k=0}^T Pr(X = k)$ with the cumulative distribution function which directly gives $Pr(k \\leq X)$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "An error was encountered:\n",
      "Invalid status code '404' from http://iccluster044.iccluster.epfl.ch:8998/sessions/6821 with error payload: \"Session '6821' not found.\"\n"
     ]
    }
   ],
   "source": [
    "################################# POISSON FIT TEST #########################################\n",
    "\n",
    "# to do .. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here are all the functions needed to calculate probability of success for a given transfer. We need the `trip_id`, `stop_id`, `departure_time`, `arrival_time` and dictionnary `d` (pickled load at the beginning of the cell) to be able to compute a probability of success with following function : \n",
    "\n",
    "`poisson_proba(trip_id, stop_id, arrival_time, departure_time, d)`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lambda (expectation given distribution):  1.0194769059543685 \n",
      "\n",
      "Probability of success for transfer time = 13.0 minutes :  0.999999999994185\n"
     ]
    }
   ],
   "source": [
    "%local\n",
    "################################# POISSON FUNCTIONS ########################################\n",
    "\n",
    "import pickle \n",
    "import gzip\n",
    "import time\n",
    "import math \n",
    "import datetime\n",
    "import time\n",
    "from scipy.stats import poisson\n",
    "\n",
    "# Load dictionnary\n",
    "with gzip.open(\"../data/distributions.pickle\", \"rb\") as input_file:\n",
    "    d = pickle.load(input_file)\n",
    "\n",
    "# Load dictionnary\n",
    "with open(\"../data/stop_times_array.pkl\", \"rb\") as input_file:\n",
    "    times = pickle.load(input_file)\n",
    "\n",
    "# we take two exemple time in format numpy.datetime64\n",
    "arr_time = times[4][1]\n",
    "dep_time = times[0][1]\n",
    "\n",
    "# Load distribution in dictinonary given a key\n",
    "def get_distrib(key, dico):\n",
    "    if key in dico:\n",
    "        return dico[key]\n",
    "    else:\n",
    "        raise ValueError(\"KEY ERROR: {} not found un distribution dictionnary\".format(key))\n",
    "    \n",
    "# Evaluate lambda parameter assuming it is equal to average \n",
    "def evaluate_lamda(distrib):\n",
    "    # First calculate total number of measures N\n",
    "    N = 0 # by starting at -1 we ignore trains ahead of schedule\n",
    "    for x in distrib:\n",
    "        N += x\n",
    "\n",
    "    lambda_p = 0 # expectation - we want to calculate it\n",
    "    t        = -1 # time = index - 1\n",
    "\n",
    "    for x in distrib:\n",
    "        if t>0:\n",
    "            lambda_p += t*x\n",
    "        t += 1\n",
    "\n",
    "    # calculate lambda - the expectation of x\n",
    "    if N > 0:\n",
    "        lambda_p /= N \n",
    "        print('lambda (expectation given distribution): ',lambda_p, '\\n')\n",
    "        return lambda_p\n",
    "    else : \n",
    "        raise ValueError(\"ERROR : {} distribution has 0 counts\".format(key))\n",
    "        #print('Returning 1 to avoid later problem... \\n')\n",
    "        return 1\n",
    "\n",
    "# process time given as string in format 'hh:mm' - not needed\n",
    "def process_time_str(str_time):\n",
    "    x = time.strptime(str_time,'%H:%M')\n",
    "    return datetime.timedelta(hours=x.tm_hour,minutes=x.tm_min,seconds=x.tm_sec).total_seconds()\n",
    "\n",
    "# Calculate transfer time given two times in string format 'hh:mm'\n",
    "def get_transfer_time(arr_time, dep_time, delta=2.0):\n",
    "    diff_time_min = (arr_time - dep_time).astype('timedelta64[m]') / np.timedelta64(1, 'm')\n",
    "    return diff_time_min - delta\n",
    "\n",
    "# Calculate poisson probability of success for a given transfert \n",
    "# for a given trip_id, stop_id, arrival/departure times and dict\n",
    "def poisson_proba(trip_id, stop_id, arr_time, dep_time, dico):\n",
    "    # Generate key from trip_id / stop_id \n",
    "    key = str(trip_id) + '__' + str(stop_id[0:7]) # 7 first char to be sbb-compatible\n",
    "\n",
    "    # Get distribution from dictionnary\n",
    "    distrib = get_distrib(key, dico)\n",
    "    \n",
    "    # Calculate transfer time at disposal \n",
    "    T = get_transfer_time(arr_time, dep_time)\n",
    "    \n",
    "    # Get lambda value to calculate proba\n",
    "    lambda_p = evaluate_lamda(distrib)\n",
    "\n",
    "    # Get proba\n",
    "    if T > 2:\n",
    "        poisson_p = poisson.cdf(T, lambda_p)\n",
    "    else : \n",
    "        poisson_p = 0.0 # if we have less than 2 minutes, we miss it\n",
    "        \n",
    "    print('Probability of success for transfer time = {} minutes : '.format(T),poisson_p)\n",
    "    return poisson_p\n",
    "\n",
    "# Mock exemple of probability calculations with given inputs\n",
    "trip_id = '1286.TA.26-32-j19-1.12.H'\n",
    "stop_id = '8591184'\n",
    "\n",
    "# we take two exemple time from stop_times_array in format numpy.datetime64\n",
    "arr_time = times[3][1]\n",
    "dep_time = times[0][1]\n",
    "\n",
    "Pr = poisson_proba(trip_id, stop_id, arr_time, dep_time, d)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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