ccp_italia_data_pipeline.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "source": [
    "### Data Processing Pipeline for the Telecom Italia Milan Dataset (TIM)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The autoreload extension is already loaded. To reload it, use:\n",
      "  %reload_ext autoreload\n"
     ]
    }
   ],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import itertools as it\n",
    "import json\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib\n",
    "import seaborn as sns\n",
    "from shapely.geometry import Point, Polygon, MultiPoint\n",
    "from shapely.ops import nearest_points\n",
    "\n",
    "from lib.problems import Generator\n",
    "\n",
    "%matplotlib inline\n",
    "sns.set()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "outputs": [],
   "source": [
    "# Telecom italia data for 1 day (Friday 2013/11/01)\n",
    "load_pth = \"./data/CCP/benchmark/telecom_italia/sms-call-internet-mi-2013-11-01.txt\"\n",
    "grid_pth = \"./data/CCP/benchmark/telecom_italia/milano-grid.geojson\"\n",
    "cellid_pth = \"./data/CCP/benchmark/telecom_italia/opencellid_2021-08-04_mcc222.csv\"\n",
    "save_grid_pth = \"./data/CCP/benchmark/telecom_italia/bs_grid.csv\"\n",
    "save_raw_pth = \"./data/CCP/benchmark/telecom_italia/data_aggr_raw.csv\"\n",
    "save_norm_pth = \"./data/CCP/benchmark/telecom_italia/\""
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### column description\n",
    "\n",
    "1. Square id: identification string of a given square of Milan/Trentino GRID;\n",
    "\n",
    "2. Time Interval: start interval time expressed in milliseconds. The end interval time can be obtained by\n",
    "adding 600,000 milliseconds (10 min) to this value;\n",
    "\n",
    "3. SMS-in activity: activity proportional to the amount of received SMSs inside a given Square id and\n",
    "during a given Time interval. The SMSs are sent from the nation identified by the Country code;\n",
    "\n",
    "4. SMS-out activity: activity proportional to the amount of sent SMSs inside a given Square id during a\n",
    "given Time interval. The SMSs are received in the nation identified by the Country code;\n",
    "\n",
    "5. Call-in activity: activity proportional to the amount of received calls inside the Square id during a given\n",
    "Time interval. The calls are issued from the nation identified by the Country code;\n",
    "\n",
    "6. Call-out activity: activity proportional to the amount of issued calls inside a given Square id during a\n",
    "given Time interval. The calls are received in the nation identified by the Country code;\n",
    "\n",
    "7. Internet traffic activity: number of CDRs generated inside a given Square id during a given Time\n",
    "interval. The Internet traffic is initiated from the nation identified by the Country code;\n",
    "\n",
    "8. Country code: the phone country code of the nation.\n"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "_________________________________________________________\n",
    "load TIM Call Detail Records (CDR) data."
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "outputs": [],
   "source": [
    "# column header\n",
    "headers = [\"square_id\", \"start_time\", \"sms_in\", \"sms_out\", \"call_in\", \"call_out\", \"internet\", \"country_code\"]\n",
    "data_io = pd.read_csv(load_pth, delimiter = \"\\t\", header=None, names=headers)     # tab delimiter"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [
    {
     "data": {
      "text/plain": "         square_id  internet\n0                1       NaN\n1                1  0.052275\n2                1  0.027300\n3                1       NaN\n4                1  0.133637\n...            ...       ...\n4842620       9999       NaN\n4842621       9999       NaN\n4842622       9999  0.014708\n4842623       9999  0.085995\n4842624       9999  0.144481\n\n[4842625 rows x 2 columns]",
      "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>square_id</th>\n      <th>internet</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>0.052275</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1</td>\n      <td>0.027300</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1</td>\n      <td>0.133637</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>4842620</th>\n      <td>9999</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>4842621</th>\n      <td>9999</td>\n      <td>NaN</td>\n    </tr>\n    <tr>\n      <th>4842622</th>\n      <td>9999</td>\n      <td>0.014708</td>\n    </tr>\n    <tr>\n      <th>4842623</th>\n      <td>9999</td>\n      <td>0.085995</td>\n    </tr>\n    <tr>\n      <th>4842624</th>\n      <td>9999</td>\n      <td>0.144481</td>\n    </tr>\n  </tbody>\n</table>\n<p>4842625 rows × 2 columns</p>\n</div>"
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# select only id and internet traffic\n",
    "traffic_df = data_io[[\"square_id\", \"internet\"]]\n",
    "traffic_df"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jones/miniconda3/envs/pam/lib/python3.8/site-packages/pandas/util/_decorators.py:311: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  return func(*args, **kwargs)\n"
     ]
    },
    {
     "data": {
      "text/plain": "         square_id  internet\n1                1  0.052275\n2                1  0.027300\n4                1  0.133637\n6                1  0.054601\n8                1  0.053438\n...            ...       ...\n4842617       9999  0.438452\n4842619       9999  0.144481\n4842622       9999  0.014708\n4842623       9999  0.085995\n4842624       9999  0.144481\n\n[2231609 rows x 2 columns]",
      "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>square_id</th>\n      <th>internet</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>0.052275</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1</td>\n      <td>0.027300</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1</td>\n      <td>0.133637</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>1</td>\n      <td>0.054601</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>1</td>\n      <td>0.053438</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>4842617</th>\n      <td>9999</td>\n      <td>0.438452</td>\n    </tr>\n    <tr>\n      <th>4842619</th>\n      <td>9999</td>\n      <td>0.144481</td>\n    </tr>\n    <tr>\n      <th>4842622</th>\n      <td>9999</td>\n      <td>0.014708</td>\n    </tr>\n    <tr>\n      <th>4842623</th>\n      <td>9999</td>\n      <td>0.085995</td>\n    </tr>\n    <tr>\n      <th>4842624</th>\n      <td>9999</td>\n      <td>0.144481</td>\n    </tr>\n  </tbody>\n</table>\n<p>2231609 rows × 2 columns</p>\n</div>"
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# remove rows with NAN\n",
    "traffic_df.dropna(axis=0, inplace=True)\n",
    "traffic_df"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "outputs": [
    {
     "data": {
      "text/plain": "10000"
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "traffic_df[\"square_id\"].nunique()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "outputs": [
    {
     "data": {
      "text/plain": "          square_id      internet\ncount  2.231609e+06  2.231609e+06\nmean   5.177475e+03  1.432492e+00\nstd    2.766707e+03  3.501098e+00\nmin    1.000000e+00  2.331498e-06\n25%    2.930000e+03  7.685247e-02\n50%    5.256000e+03  2.959635e-01\n75%    7.481000e+03  1.191590e+00\nmax    1.000000e+04  1.878688e+02",
      "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>square_id</th>\n      <th>internet</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>2.231609e+06</td>\n      <td>2.231609e+06</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>5.177475e+03</td>\n      <td>1.432492e+00</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>2.766707e+03</td>\n      <td>3.501098e+00</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>1.000000e+00</td>\n      <td>2.331498e-06</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>2.930000e+03</td>\n      <td>7.685247e-02</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>5.256000e+03</td>\n      <td>2.959635e-01</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>7.481000e+03</td>\n      <td>1.191590e+00</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>1.000000e+04</td>\n      <td>1.878688e+02</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "traffic_df.describe()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "_________________________________________________________\n",
    "load milano grid to infer square coordinates"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "outputs": [],
   "source": [
    "grid = json.load(open(grid_pth))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "outputs": [
    {
     "data": {
      "text/plain": "                                               geometry     type    id  \\\n0     {'type': 'Polygon', 'coordinates': [[[9.011491...  Feature     0   \n1     {'type': 'Polygon', 'coordinates': [[[9.014491...  Feature     1   \n2     {'type': 'Polygon', 'coordinates': [[[9.017491...  Feature     2   \n3     {'type': 'Polygon', 'coordinates': [[[9.020492...  Feature     3   \n4     {'type': 'Polygon', 'coordinates': [[[9.023492...  Feature     4   \n...                                                 ...      ...   ...   \n9995  {'type': 'Polygon', 'coordinates': [[[9.297630...  Feature  9995   \n9996  {'type': 'Polygon', 'coordinates': [[[9.300642...  Feature  9996   \n9997  {'type': 'Polygon', 'coordinates': [[[9.303653...  Feature  9997   \n9998  {'type': 'Polygon', 'coordinates': [[[9.306665...  Feature  9998   \n9999  {'type': 'Polygon', 'coordinates': [[[9.309676...  Feature  9999   \n\n             properties  \n0         {'cellId': 1}  \n1         {'cellId': 2}  \n2         {'cellId': 3}  \n3         {'cellId': 4}  \n4         {'cellId': 5}  \n...                 ...  \n9995   {'cellId': 9996}  \n9996   {'cellId': 9997}  \n9997   {'cellId': 9998}  \n9998   {'cellId': 9999}  \n9999  {'cellId': 10000}  \n\n[10000 rows x 4 columns]",
      "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>geometry</th>\n      <th>type</th>\n      <th>id</th>\n      <th>properties</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>{'type': 'Polygon', 'coordinates': [[[9.011491...</td>\n      <td>Feature</td>\n      <td>0</td>\n      <td>{'cellId': 1}</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>{'type': 'Polygon', 'coordinates': [[[9.014491...</td>\n      <td>Feature</td>\n      <td>1</td>\n      <td>{'cellId': 2}</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>{'type': 'Polygon', 'coordinates': [[[9.017491...</td>\n      <td>Feature</td>\n      <td>2</td>\n      <td>{'cellId': 3}</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>{'type': 'Polygon', 'coordinates': [[[9.020492...</td>\n      <td>Feature</td>\n      <td>3</td>\n      <td>{'cellId': 4}</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>{'type': 'Polygon', 'coordinates': [[[9.023492...</td>\n      <td>Feature</td>\n      <td>4</td>\n      <td>{'cellId': 5}</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>9995</th>\n      <td>{'type': 'Polygon', 'coordinates': [[[9.297630...</td>\n      <td>Feature</td>\n      <td>9995</td>\n      <td>{'cellId': 9996}</td>\n    </tr>\n    <tr>\n      <th>9996</th>\n      <td>{'type': 'Polygon', 'coordinates': [[[9.300642...</td>\n      <td>Feature</td>\n      <td>9996</td>\n      <td>{'cellId': 9997}</td>\n    </tr>\n    <tr>\n      <th>9997</th>\n      <td>{'type': 'Polygon', 'coordinates': [[[9.303653...</td>\n      <td>Feature</td>\n      <td>9997</td>\n      <td>{'cellId': 9998}</td>\n    </tr>\n    <tr>\n      <th>9998</th>\n      <td>{'type': 'Polygon', 'coordinates': [[[9.306665...</td>\n      <td>Feature</td>\n      <td>9998</td>\n      <td>{'cellId': 9999}</td>\n    </tr>\n    <tr>\n      <th>9999</th>\n      <td>{'type': 'Polygon', 'coordinates': [[[9.309676...</td>\n      <td>Feature</td>\n      <td>9999</td>\n      <td>{'cellId': 10000}</td>\n    </tr>\n  </tbody>\n</table>\n<p>10000 rows × 4 columns</p>\n</div>"
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_df = pd.DataFrame(grid[\"features\"])\n",
    "grid_df"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "outputs": [
    {
     "data": {
      "text/plain": "False"
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check if any is NAN\n",
    "np.any(grid_df[\"geometry\"].isna())"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "POLYGON ((9.011491047832299 45.35880131440966, 9.014491488013135 45.35880097314403, 9.014490948081299 45.35668565341486, 9.011490619692509 45.35668599465546, 9.011491047832299 45.35880131440966))\n",
      "POLYGON ((9.014491488013135 45.35880097314403, 9.017491928134044 45.35880055306028, 9.017491276410173 45.35668523336193, 9.014490948081299 45.35668565341486, 9.014491488013135 45.35880097314403))\n",
      "POLYGON ((9.017491928134044 45.35880055306028, 9.020492368182619 45.35880005415845, 9.020491604666724 45.35668473449667, 9.017491276410173 45.35668523336193, 9.017491928134044 45.35880055306028))\n",
      "POLYGON ((9.020492368182619 45.35880005415845, 9.023492808146456 45.35879947643852, 9.023491932838542 45.35668415681913, 9.020491604666724 45.35668473449667, 9.020492368182619 45.35880005415845))\n",
      "POLYGON ((9.023492808146456 45.35879947643852, 9.026493248013145 45.35879881990051, 9.02649226091323 45.35668350032926, 9.023491932838542 45.35668415681913, 9.023492808146456 45.35879947643852))\n"
     ]
    }
   ],
   "source": [
    "# unpack polygon coordinates from dict and wrap into shapely polygon object\n",
    "grid_df[\"coordinates\"] = grid_df.apply(lambda row: Polygon(row[\"geometry\"][\"coordinates\"][0]), axis=1)\n",
    "for i in range(5):\n",
    "    print(grid_df[\"coordinates\"][i])"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "outputs": [
    {
     "data": {
      "text/plain": "<shapely.geometry.polygon.Polygon at 0x7f0ca6bfa0a0>",
      "image/svg+xml": "<svg xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\" width=\"100.0\" height=\"100.0\" viewBox=\"9.011370584959684 45.35656561868203 0.0032409377862769873 0.0023557304604509\" preserveAspectRatio=\"xMinYMin meet\"><g transform=\"matrix(1,0,0,-1,0,90.71548696782452)\"><path fill-rule=\"evenodd\" fill=\"#66cc99\" stroke=\"#555555\" stroke-width=\"6.481875572553974e-05\" opacity=\"0.6\" d=\"M 9.0114910478323,45.35880131440966 L 9.014491488013135,45.35880097314403 L 9.0144909480813,45.35668565341486 L 9.011490619692509,45.356685994655464 L 9.0114910478323,45.35880131440966 z\" /></g></svg>"
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p = grid_df[\"coordinates\"][0]\n",
    "p"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "outputs": [],
   "source": [
    "# calculate polygon centers\n",
    "grid_df[\"centroid\"] = grid_df.apply(lambda row: row[\"coordinates\"].centroid, axis=1)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "outputs": [],
   "source": [
    "# unpack cellid\n",
    "grid_df[\"cellid\"] = grid_df.apply(lambda row: row[\"properties\"][\"cellId\"], axis=1)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "outputs": [
    {
     "data": {
      "text/plain": "      cellid                                        coordinates  \\\n0          1  POLYGON ((9.011491047832299 45.35880131440966,...   \n1          2  POLYGON ((9.014491488013135 45.35880097314403,...   \n2          3  POLYGON ((9.017491928134044 45.35880055306028,...   \n3          4  POLYGON ((9.020492368182619 45.35880005415845,...   \n4          5  POLYGON ((9.023492808146456 45.35879947643852,...   \n...      ...                                                ...   \n9995    9996  POLYGON ((9.297630740052327 45.56782693472525,...   \n9996    9997  POLYGON ((9.300642247414926 45.56781904882199,...   \n9997    9998  POLYGON ((9.303653753522097 45.56781108353051,...   \n9998    9999  POLYGON ((9.30666525836126 45.5678030388509, 9...   \n9999   10000  POLYGON ((9.309676761919844 45.56779491478326,...   \n\n                                         centroid  \n0     POINT (9.012991025904858 45.35774349047394)  \n1     POINT (9.015991410159719 45.35774310981321)  \n2     POINT (9.018991794348462 45.35774265033727)  \n3     POINT (9.021992178458664 45.35774211204613)  \n4     POINT (9.024992562477935 45.35774149493978)  \n...                                           ...  \n9995  POINT (9.299130880320151 45.56676539179961)  \n9996  POINT (9.302142330545021 45.56675746649291)  \n9997  POINT (9.305153779508254 45.56674946180095)  \n9998   POINT (9.308165227197284 45.5667413777238)  \n9999  POINT (9.311176673599535 45.56673321426158)  \n\n[10000 rows x 3 columns]",
      "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>cellid</th>\n      <th>coordinates</th>\n      <th>centroid</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n      <td>POLYGON ((9.011491047832299 45.35880131440966,...</td>\n      <td>POINT (9.012991025904858 45.35774349047394)</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n      <td>POLYGON ((9.014491488013135 45.35880097314403,...</td>\n      <td>POINT (9.015991410159719 45.35774310981321)</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n      <td>POLYGON ((9.017491928134044 45.35880055306028,...</td>\n      <td>POINT (9.018991794348462 45.35774265033727)</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>4</td>\n      <td>POLYGON ((9.020492368182619 45.35880005415845,...</td>\n      <td>POINT (9.021992178458664 45.35774211204613)</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>5</td>\n      <td>POLYGON ((9.023492808146456 45.35879947643852,...</td>\n      <td>POINT (9.024992562477935 45.35774149493978)</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>9995</th>\n      <td>9996</td>\n      <td>POLYGON ((9.297630740052327 45.56782693472525,...</td>\n      <td>POINT (9.299130880320151 45.56676539179961)</td>\n    </tr>\n    <tr>\n      <th>9996</th>\n      <td>9997</td>\n      <td>POLYGON ((9.300642247414926 45.56781904882199,...</td>\n      <td>POINT (9.302142330545021 45.56675746649291)</td>\n    </tr>\n    <tr>\n      <th>9997</th>\n      <td>9998</td>\n      <td>POLYGON ((9.303653753522097 45.56781108353051,...</td>\n      <td>POINT (9.305153779508254 45.56674946180095)</td>\n    </tr>\n    <tr>\n      <th>9998</th>\n      <td>9999</td>\n      <td>POLYGON ((9.30666525836126 45.5678030388509, 9...</td>\n      <td>POINT (9.308165227197284 45.5667413777238)</td>\n    </tr>\n    <tr>\n      <th>9999</th>\n      <td>10000</td>\n      <td>POLYGON ((9.309676761919844 45.56779491478326,...</td>\n      <td>POINT (9.311176673599535 45.56673321426158)</td>\n    </tr>\n  </tbody>\n</table>\n<p>10000 rows × 3 columns</p>\n</div>"
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid = grid_df[[\"cellid\", \"coordinates\", \"centroid\"]]\n",
    "grid"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "outputs": [],
   "source": [
    "ppoints = grid_df.apply(lambda row: row[\"coordinates\"].exterior.coords, axis=1)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "outputs": [],
   "source": [
    "milan_polygon = MultiPoint(list(it.chain.from_iterable(ppoints)))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "outputs": [
    {
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     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hull = milan_polygon.convex_hull\n",
    "hull"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [
    "# get outer most points of all polygons\n",
    "# bounds = grid_df.apply(lambda row: row[\"coordinates\"].bounds, axis=1)\n",
    "# bounds_xy = pd.DataFrame(bounds.tolist(), columns=[\"x_min\", \"y_min\", \"x_max\", \"y_max\"])\n",
    "# x_min, y_min, x_max, y_max = bounds_xy.agg({\"x_min\": np.min, \"y_min\": np.min, \"x_max\": np.max, \"y_max\": np.max})\n",
    "# span hull between outer most points\n",
    "# p1 = (x_min, y_min)\n",
    "# p2 = (x_min, y_max)\n",
    "# p3 = (x_max, y_min)\n",
    "# p4 = (x_max, y_max)\n",
    "# hull = MultiPoint([p1, p2, p3, p4]).convex_hull\n",
    "# hull\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "_________________________________________________________\n",
    "load [OpenCellid](https://opencellid.org) data to cross reference base station locations with grid coordinates.\n",
    "Unfortunately historic data is not available so we use the base station data from 2021/08/04 as approximate locations\n",
    "for the TIM CDR data."
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "#### column description\n",
    "1. Radio: The generation of broadband cellular network technology\n",
    "2. MCC: Mobile Country Code\n",
    "3. Net: Mobile Network Code\n",
    "4. Area: Location Area Code (LAC)\n",
    "5. Cell: Cell tower code (CID)\n",
    "6. Unit: ???\n",
    "7. lat, long: Approx coordinates of the cell tower\n",
    "8. range: Approximate area within which the cell could be. (radius in meters)\n",
    "9. samples: No of measures processed to get this data\n",
    "10. changeble:\n",
    "\n",
    "     + 1 = The location is determined by processing samples\n",
    "     + 0 = We got the location directly from the telecom firm\n",
    "\n",
    "11. created: When the cell was first added to database (UNIX timestamp)\n",
    "12. updated: When the cell was last seen (UNIX timestamp)"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "outputs": [
    {
     "data": {
      "text/plain": "        radio  mcc  net   area       cell  unit        lon        lat  range  \\\n0         GSM  222   88  12031      56226     0   8.926204  44.932085   5510   \n1         GSM  222   88  12031      42650     0   8.897962  44.939257  32403   \n2         GSM  222   88  12031      42647     0   8.900029  44.948088  23649   \n3         GSM  222   10  10066       9422     0   8.924543  44.930649   1000   \n4         GSM  222   88  12032      40744     0   8.901767  44.936907  14440   \n...       ...  ...  ...    ...        ...   ...        ...        ...    ...   \n1242476   LTE  222    1  24724  128192259    -1  10.569688  45.546284   1000   \n1242477   LTE  222    1  17116  180199742    -1  10.570167  45.546645   1000   \n1242478   LTE  222    1  17113  179901502    -1  10.574132  45.549448   1000   \n1242479   LTE  222    1  17116  179547966    -1  10.570567  45.546970   1000   \n1242480   LTE  222   99  35839    3890446     0  12.006133  45.364700   1000   \n\n         samples  changeable     created     updated  averageSignal  \n0             22           1  1310029657  1441360675              0  \n1            102           1  1310029657  1441254371              0  \n2            141           1  1310029657  1443060211              0  \n3             13           1  1310029657  1310435836              0  \n4             40           1  1310029657  1448784109              0  \n...          ...         ...         ...         ...            ...  \n1242476        1           1  1590639354  1590639354              0  \n1242477        2           1  1590639405  1590639425              0  \n1242478        2           1  1590639435  1591416101              0  \n1242479        1           1  1590639455  1590639455              0  \n1242480        1           1  1591567080  1591567080              0  \n\n[1242481 rows x 14 columns]",
      "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>radio</th>\n      <th>mcc</th>\n      <th>net</th>\n      <th>area</th>\n      <th>cell</th>\n      <th>unit</th>\n      <th>lon</th>\n      <th>lat</th>\n      <th>range</th>\n      <th>samples</th>\n      <th>changeable</th>\n      <th>created</th>\n      <th>updated</th>\n      <th>averageSignal</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>GSM</td>\n      <td>222</td>\n      <td>88</td>\n      <td>12031</td>\n      <td>56226</td>\n      <td>0</td>\n      <td>8.926204</td>\n      <td>44.932085</td>\n      <td>5510</td>\n      <td>22</td>\n      <td>1</td>\n      <td>1310029657</td>\n      <td>1441360675</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>GSM</td>\n      <td>222</td>\n      <td>88</td>\n      <td>12031</td>\n      <td>42650</td>\n      <td>0</td>\n      <td>8.897962</td>\n      <td>44.939257</td>\n      <td>32403</td>\n      <td>102</td>\n      <td>1</td>\n      <td>1310029657</td>\n      <td>1441254371</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>GSM</td>\n      <td>222</td>\n      <td>88</td>\n      <td>12031</td>\n      <td>42647</td>\n      <td>0</td>\n      <td>8.900029</td>\n      <td>44.948088</td>\n      <td>23649</td>\n      <td>141</td>\n      <td>1</td>\n      <td>1310029657</td>\n      <td>1443060211</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>GSM</td>\n      <td>222</td>\n      <td>10</td>\n      <td>10066</td>\n      <td>9422</td>\n      <td>0</td>\n      <td>8.924543</td>\n      <td>44.930649</td>\n      <td>1000</td>\n      <td>13</td>\n      <td>1</td>\n      <td>1310029657</td>\n      <td>1310435836</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>GSM</td>\n      <td>222</td>\n      <td>88</td>\n      <td>12032</td>\n      <td>40744</td>\n      <td>0</td>\n      <td>8.901767</td>\n      <td>44.936907</td>\n      <td>14440</td>\n      <td>40</td>\n      <td>1</td>\n      <td>1310029657</td>\n      <td>1448784109</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>1242476</th>\n      <td>LTE</td>\n      <td>222</td>\n      <td>1</td>\n      <td>24724</td>\n      <td>128192259</td>\n      <td>-1</td>\n      <td>10.569688</td>\n      <td>45.546284</td>\n      <td>1000</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1590639354</td>\n      <td>1590639354</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1242477</th>\n      <td>LTE</td>\n      <td>222</td>\n      <td>1</td>\n      <td>17116</td>\n      <td>180199742</td>\n      <td>-1</td>\n      <td>10.570167</td>\n      <td>45.546645</td>\n      <td>1000</td>\n      <td>2</td>\n      <td>1</td>\n      <td>1590639405</td>\n      <td>1590639425</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1242478</th>\n      <td>LTE</td>\n      <td>222</td>\n      <td>1</td>\n      <td>17113</td>\n      <td>179901502</td>\n      <td>-1</td>\n      <td>10.574132</td>\n      <td>45.549448</td>\n      <td>1000</td>\n      <td>2</td>\n      <td>1</td>\n      <td>1590639435</td>\n      <td>1591416101</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1242479</th>\n      <td>LTE</td>\n      <td>222</td>\n      <td>1</td>\n      <td>17116</td>\n      <td>179547966</td>\n      <td>-1</td>\n      <td>10.570567</td>\n      <td>45.546970</td>\n      <td>1000</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1590639455</td>\n      <td>1590639455</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1242480</th>\n      <td>LTE</td>\n      <td>222</td>\n      <td>99</td>\n      <td>35839</td>\n      <td>3890446</td>\n      <td>0</td>\n      <td>12.006133</td>\n      <td>45.364700</td>\n      <td>1000</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1591567080</td>\n      <td>1591567080</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>1242481 rows × 14 columns</p>\n</div>"
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cellid_df = pd.read_csv(cellid_pth)\n",
    "cellid_df\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "outputs": [
    {
     "data": {
      "text/plain": "        radio  mcc  net   area       cell  unit        lon        lat  range  \\\n5         GSM  222    1  27265      57393     0   8.904054  44.907932  27303   \n7         GSM  222    1  27265       1626     0   8.908879  44.911198  18752   \n14        GSM  222    1  27265       4624     0   8.886124  44.917660  24227   \n15        GSM  222    1  27265       4143     0   8.924581  44.930649   1000   \n18        GSM  222    1  27265      57394     0   8.924606  44.930649   1000   \n...       ...  ...  ...    ...        ...   ...        ...        ...    ...   \n1242343   LTE  222    1  15110  180288318     0  12.843176  45.911442   1000   \n1242476   LTE  222    1  24724  128192259    -1  10.569688  45.546284   1000   \n1242477   LTE  222    1  17116  180199742    -1  10.570167  45.546645   1000   \n1242478   LTE  222    1  17113  179901502    -1  10.574132  45.549448   1000   \n1242479   LTE  222    1  17116  179547966    -1  10.570567  45.546970   1000   \n\n         samples  changeable     created     updated  averageSignal  \n5             44           1  1310029657  1379685914              0  \n7             30           1  1310029657  1379727348              0  \n14            88           1  1310029657  1381131915              0  \n15            11           1  1310029657  1310381575              0  \n18            20           1  1310029657  1310446187              0  \n...          ...         ...         ...         ...            ...  \n1242343        1           1  1591438977  1591438977              0  \n1242476        1           1  1590639354  1590639354              0  \n1242477        2           1  1590639405  1590639425              0  \n1242478        2           1  1590639435  1591416101              0  \n1242479        1           1  1590639455  1590639455              0  \n\n[303465 rows x 14 columns]",
      "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>radio</th>\n      <th>mcc</th>\n      <th>net</th>\n      <th>area</th>\n      <th>cell</th>\n      <th>unit</th>\n      <th>lon</th>\n      <th>lat</th>\n      <th>range</th>\n      <th>samples</th>\n      <th>changeable</th>\n      <th>created</th>\n      <th>updated</th>\n      <th>averageSignal</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>5</th>\n      <td>GSM</td>\n      <td>222</td>\n      <td>1</td>\n      <td>27265</td>\n      <td>57393</td>\n      <td>0</td>\n      <td>8.904054</td>\n      <td>44.907932</td>\n      <td>27303</td>\n      <td>44</td>\n      <td>1</td>\n      <td>1310029657</td>\n      <td>1379685914</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>GSM</td>\n      <td>222</td>\n      <td>1</td>\n      <td>27265</td>\n      <td>1626</td>\n      <td>0</td>\n      <td>8.908879</td>\n      <td>44.911198</td>\n      <td>18752</td>\n      <td>30</td>\n      <td>1</td>\n      <td>1310029657</td>\n      <td>1379727348</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>GSM</td>\n      <td>222</td>\n      <td>1</td>\n      <td>27265</td>\n      <td>4624</td>\n      <td>0</td>\n      <td>8.886124</td>\n      <td>44.917660</td>\n      <td>24227</td>\n      <td>88</td>\n      <td>1</td>\n      <td>1310029657</td>\n      <td>1381131915</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>GSM</td>\n      <td>222</td>\n      <td>1</td>\n      <td>27265</td>\n      <td>4143</td>\n      <td>0</td>\n      <td>8.924581</td>\n      <td>44.930649</td>\n      <td>1000</td>\n      <td>11</td>\n      <td>1</td>\n      <td>1310029657</td>\n      <td>1310381575</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>GSM</td>\n      <td>222</td>\n      <td>1</td>\n      <td>27265</td>\n      <td>57394</td>\n      <td>0</td>\n      <td>8.924606</td>\n      <td>44.930649</td>\n      <td>1000</td>\n      <td>20</td>\n      <td>1</td>\n      <td>1310029657</td>\n      <td>1310446187</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>1242343</th>\n      <td>LTE</td>\n      <td>222</td>\n      <td>1</td>\n      <td>15110</td>\n      <td>180288318</td>\n      <td>0</td>\n      <td>12.843176</td>\n      <td>45.911442</td>\n      <td>1000</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1591438977</td>\n      <td>1591438977</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1242476</th>\n      <td>LTE</td>\n      <td>222</td>\n      <td>1</td>\n      <td>24724</td>\n      <td>128192259</td>\n      <td>-1</td>\n      <td>10.569688</td>\n      <td>45.546284</td>\n      <td>1000</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1590639354</td>\n      <td>1590639354</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1242477</th>\n      <td>LTE</td>\n      <td>222</td>\n      <td>1</td>\n      <td>17116</td>\n      <td>180199742</td>\n      <td>-1</td>\n      <td>10.570167</td>\n      <td>45.546645</td>\n      <td>1000</td>\n      <td>2</td>\n      <td>1</td>\n      <td>1590639405</td>\n      <td>1590639425</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1242478</th>\n      <td>LTE</td>\n      <td>222</td>\n      <td>1</td>\n      <td>17113</td>\n      <td>179901502</td>\n      <td>-1</td>\n      <td>10.574132</td>\n      <td>45.549448</td>\n      <td>1000</td>\n      <td>2</td>\n      <td>1</td>\n      <td>1590639435</td>\n      <td>1591416101</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1242479</th>\n      <td>LTE</td>\n      <td>222</td>\n      <td>1</td>\n      <td>17116</td>\n      <td>179547966</td>\n      <td>-1</td>\n      <td>10.570567</td>\n      <td>45.546970</td>\n      <td>1000</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1590639455</td>\n      <td>1590639455</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n<p>303465 rows × 14 columns</p>\n</div>"
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# MNC from https://en.wikipedia.org/wiki/Mobile_Network_Codes_in_ITU_region_2xx_(Europe)#Italy_%E2%80%93_IT\n",
    "# select only base stations of telecom italia by MNC code\n",
    "tim_mnc = [1, 43, 48]\n",
    "tim_cellid = cellid_df[(cellid_df[\"net\"] == tim_mnc[0]) |\n",
    "                       (cellid_df[\"net\"] == tim_mnc[1]) |\n",
    "                       (cellid_df[\"net\"] == tim_mnc[2]) ]\n",
    "tim_cellid"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "outputs": [],
   "source": [
    "# to decrease the number of possible cell towers we only select the ones with LTE support\n",
    "tim_cellid = tim_cellid[tim_cellid[\"radio\"] == \"LTE\"]\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "outputs": [],
   "source": [
    "# wrap coordinates into geo points\n",
    "tim_milan_cellid = tim_cellid.copy(deep=True)\n",
    "tim_milan_cellid[\"point_coords\"] = tim_cellid.apply(lambda row: Point(row[[\"lon\", \"lat\"]]), axis=1)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "outputs": [],
   "source": [
    "# check which points are contained in Milan polygon hull\n",
    "tim_milan_cellid[\"contained\"] = tim_milan_cellid.apply(lambda row: hull.contains(row[\"point_coords\"]), axis=1)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "outputs": [
    {
     "data": {
      "text/plain": "        radio  mcc  net   area       cell  unit       lon        lat  range  \\\n208957    LTE  222    1  10884  128066307     0  9.260330  45.392990   1000   \n208964    LTE  222    1  10980  128046593     0  9.146805  45.401688   1000   \n211468    LTE  222    1  10853  128188941     0  9.213409  45.519791   1000   \n211472    LTE  222    1  10948  128113665     0  9.243438  45.559884   1000   \n211473    LTE  222    1  10948  128113667     0  9.232394  45.549929   1000   \n...       ...  ...  ...    ...        ...   ...       ...        ...    ...   \n1235130   LTE  222    1  24308  128265221    -1  9.163985  45.549648   1000   \n1238891   LTE  222    1  24307  128128002     0  9.083160  45.544785   1000   \n1238893   LTE  222    1  24307  128092424     0  9.083152  45.544769   1000   \n1239080   LTE  222    1  24303  128278278    -1  9.221272  45.481203   1000   \n1239081   LTE  222    1  24303  128006914    -1  9.220708  45.481380   1000   \n\n         samples  changeable     created     updated  averageSignal  \\\n208957         3           1  1422726687  1435991251              0   \n208964         1           1  1422707067  1422707067              0   \n211468         2           1  1424024597  1434740674              0   \n211472        41           1  1424025797  1477466563              0   \n211473        37           1  1424025797  1477364508              0   \n...          ...         ...         ...         ...            ...   \n1235130        1           1  1578132896  1578132896              0   \n1238891        1           1  1583192794  1583192794              0   \n1238893        1           1  1583227128  1583227128              0   \n1239080        9           1  1583643067  1583645070              0   \n1239081        1           1  1583644977  1583644977              0   \n\n                                   point_coords  contained  \n208957                 POINT (9.26033 45.39299)       True  \n208964   POINT (9.1468048095703 45.40168762207)       True  \n211468               POINT (9.213409 45.519791)       True  \n211472      POINT (9.243437999999999 45.559884)       True  \n211473      POINT (9.232393999999999 45.549929)       True  \n...                                         ...        ...  \n1235130              POINT (9.163985 45.549648)       True  \n1238891     POINT (9.083159999999999 45.544785)       True  \n1238893              POINT (9.083152 45.544769)       True  \n1239080     POINT (9.221272000000001 45.481203)       True  \n1239081               POINT (9.220708 45.48138)       True  \n\n[6578 rows x 16 columns]",
      "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>radio</th>\n      <th>mcc</th>\n      <th>net</th>\n      <th>area</th>\n      <th>cell</th>\n      <th>unit</th>\n      <th>lon</th>\n      <th>lat</th>\n      <th>range</th>\n      <th>samples</th>\n      <th>changeable</th>\n      <th>created</th>\n      <th>updated</th>\n      <th>averageSignal</th>\n      <th>point_coords</th>\n      <th>contained</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>208957</th>\n      <td>LTE</td>\n      <td>222</td>\n      <td>1</td>\n      <td>10884</td>\n      <td>128066307</td>\n      <td>0</td>\n      <td>9.260330</td>\n      <td>45.392990</td>\n      <td>1000</td>\n      <td>3</td>\n      <td>1</td>\n      <td>1422726687</td>\n      <td>1435991251</td>\n      <td>0</td>\n      <td>POINT (9.26033 45.39299)</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>208964</th>\n      <td>LTE</td>\n      <td>222</td>\n      <td>1</td>\n      <td>10980</td>\n      <td>128046593</td>\n      <td>0</td>\n      <td>9.146805</td>\n      <td>45.401688</td>\n      <td>1000</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1422707067</td>\n      <td>1422707067</td>\n      <td>0</td>\n      <td>POINT (9.1468048095703 45.40168762207)</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>211468</th>\n      <td>LTE</td>\n      <td>222</td>\n      <td>1</td>\n      <td>10853</td>\n      <td>128188941</td>\n      <td>0</td>\n      <td>9.213409</td>\n      <td>45.519791</td>\n      <td>1000</td>\n      <td>2</td>\n      <td>1</td>\n      <td>1424024597</td>\n      <td>1434740674</td>\n      <td>0</td>\n      <td>POINT (9.213409 45.519791)</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>211472</th>\n      <td>LTE</td>\n      <td>222</td>\n      <td>1</td>\n      <td>10948</td>\n      <td>128113665</td>\n      <td>0</td>\n      <td>9.243438</td>\n      <td>45.559884</td>\n      <td>1000</td>\n      <td>41</td>\n      <td>1</td>\n      <td>1424025797</td>\n      <td>1477466563</td>\n      <td>0</td>\n      <td>POINT (9.243437999999999 45.559884)</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>211473</th>\n      <td>LTE</td>\n      <td>222</td>\n      <td>1</td>\n      <td>10948</td>\n      <td>128113667</td>\n      <td>0</td>\n      <td>9.232394</td>\n      <td>45.549929</td>\n      <td>1000</td>\n      <td>37</td>\n      <td>1</td>\n      <td>1424025797</td>\n      <td>1477364508</td>\n      <td>0</td>\n      <td>POINT (9.232393999999999 45.549929)</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>1235130</th>\n      <td>LTE</td>\n      <td>222</td>\n      <td>1</td>\n      <td>24308</td>\n      <td>128265221</td>\n      <td>-1</td>\n      <td>9.163985</td>\n      <td>45.549648</td>\n      <td>1000</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1578132896</td>\n      <td>1578132896</td>\n      <td>0</td>\n      <td>POINT (9.163985 45.549648)</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>1238891</th>\n      <td>LTE</td>\n      <td>222</td>\n      <td>1</td>\n      <td>24307</td>\n      <td>128128002</td>\n      <td>0</td>\n      <td>9.083160</td>\n      <td>45.544785</td>\n      <td>1000</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1583192794</td>\n      <td>1583192794</td>\n      <td>0</td>\n      <td>POINT (9.083159999999999 45.544785)</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>1238893</th>\n      <td>LTE</td>\n      <td>222</td>\n      <td>1</td>\n      <td>24307</td>\n      <td>128092424</td>\n      <td>0</td>\n      <td>9.083152</td>\n      <td>45.544769</td>\n      <td>1000</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1583227128</td>\n      <td>1583227128</td>\n      <td>0</td>\n      <td>POINT (9.083152 45.544769)</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>1239080</th>\n      <td>LTE</td>\n      <td>222</td>\n      <td>1</td>\n      <td>24303</td>\n      <td>128278278</td>\n      <td>-1</td>\n      <td>9.221272</td>\n      <td>45.481203</td>\n      <td>1000</td>\n      <td>9</td>\n      <td>1</td>\n      <td>1583643067</td>\n      <td>1583645070</td>\n      <td>0</td>\n      <td>POINT (9.221272000000001 45.481203)</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>1239081</th>\n      <td>LTE</td>\n      <td>222</td>\n      <td>1</td>\n      <td>24303</td>\n      <td>128006914</td>\n      <td>-1</td>\n      <td>9.220708</td>\n      <td>45.481380</td>\n      <td>1000</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1583644977</td>\n      <td>1583644977</td>\n      <td>0</td>\n      <td>POINT (9.220708 45.48138)</td>\n      <td>True</td>\n    </tr>\n  </tbody>\n</table>\n<p>6578 rows × 16 columns</p>\n</div>"
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# only select those contained\n",
    "tim_milan_cellid = tim_milan_cellid[tim_milan_cellid[\"contained\"]]\n",
    "tim_milan_cellid"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-180-bf8f9488e0f9>:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  tim_milan_cellid[\"unq_id\"] = tim_milan_cellid.apply(lambda row: f\"{row['cell']}_{row['lon']}_{row['lat']}\", axis=1)\n"
     ]
    }
   ],
   "source": [
    "#tim_milan_cellid[\"unq_id\"] = tim_milan_cellid.apply(lambda row: f\"{row['cell']}_{row['lon']}_{row['lat']}\", axis=1)\n",
    "#tim_milan_cellid[\"unq_id\"].nunique()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "outputs": [
    {
     "data": {
      "text/plain": "        radio  mcc  net   area       cell  unit       lon        lat  range  \\\n208957    LTE  222    1  10884  128066307     0  9.260330  45.392990   1000   \n208964    LTE  222    1  10980  128046593     0  9.146805  45.401688   1000   \n211468    LTE  222    1  10853  128188941     0  9.213409  45.519791   1000   \n211472    LTE  222    1  10948  128113665     0  9.243438  45.559884   1000   \n248822    LTE  222    1  61226   80377309   490  9.132148  45.370728   1000   \n...       ...  ...  ...    ...        ...   ...       ...        ...    ...   \n1235121   LTE  222    1  24272  128337669    -1  9.188133  45.501873   1000   \n1235125   LTE  222    1  24272  128530692    -1  9.172047  45.523293   1000   \n1235127   LTE  222    1  24308  128530694    -1  9.169231  45.525436   1000   \n1235129   LTE  222    1  24308  128628742    -1  9.163039  45.547525   1000   \n1238891   LTE  222    1  24307  128128002     0  9.083160  45.544785   1000   \n\n         samples  changeable     created     updated  averageSignal  \\\n208957         3           1  1422726687  1435991251              0   \n208964         1           1  1422707067  1422707067              0   \n211468         2           1  1424024597  1434740674              0   \n211472        41           1  1424025797  1477466563              0   \n248822         2           1  1439930387  1560598701              0   \n...          ...         ...         ...         ...            ...   \n1235121        1           1  1578132602  1578132602              0   \n1235125        1           1  1578132749  1578132749              0   \n1235127        1           1  1578132762  1578132762              0   \n1235129       11           1  1578132868  1578132911              0   \n1238891        1           1  1583192794  1583192794              0   \n\n                                   point_coords  contained  \n208957                 POINT (9.26033 45.39299)       True  \n208964   POINT (9.1468048095703 45.40168762207)       True  \n211468               POINT (9.213409 45.519791)       True  \n211472      POINT (9.243437999999999 45.559884)       True  \n248822      POINT (9.132148000000001 45.370728)       True  \n...                                         ...        ...  \n1235121     POINT (9.188133000000001 45.501873)       True  \n1235125     POINT (9.172046999999999 45.523293)       True  \n1235127              POINT (9.169231 45.525436)       True  \n1235129     POINT (9.163038999999999 45.547525)       True  \n1238891     POINT (9.083159999999999 45.544785)       True  \n\n[2593 rows x 16 columns]",
      "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>radio</th>\n      <th>mcc</th>\n      <th>net</th>\n      <th>area</th>\n      <th>cell</th>\n      <th>unit</th>\n      <th>lon</th>\n      <th>lat</th>\n      <th>range</th>\n      <th>samples</th>\n      <th>changeable</th>\n      <th>created</th>\n      <th>updated</th>\n      <th>averageSignal</th>\n      <th>point_coords</th>\n      <th>contained</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>208957</th>\n      <td>LTE</td>\n      <td>222</td>\n      <td>1</td>\n      <td>10884</td>\n      <td>128066307</td>\n      <td>0</td>\n      <td>9.260330</td>\n      <td>45.392990</td>\n      <td>1000</td>\n      <td>3</td>\n      <td>1</td>\n      <td>1422726687</td>\n      <td>1435991251</td>\n      <td>0</td>\n      <td>POINT (9.26033 45.39299)</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>208964</th>\n      <td>LTE</td>\n      <td>222</td>\n      <td>1</td>\n      <td>10980</td>\n      <td>128046593</td>\n      <td>0</td>\n      <td>9.146805</td>\n      <td>45.401688</td>\n      <td>1000</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1422707067</td>\n      <td>1422707067</td>\n      <td>0</td>\n      <td>POINT (9.1468048095703 45.40168762207)</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>211468</th>\n      <td>LTE</td>\n      <td>222</td>\n      <td>1</td>\n      <td>10853</td>\n      <td>128188941</td>\n      <td>0</td>\n      <td>9.213409</td>\n      <td>45.519791</td>\n      <td>1000</td>\n      <td>2</td>\n      <td>1</td>\n      <td>1424024597</td>\n      <td>1434740674</td>\n      <td>0</td>\n      <td>POINT (9.213409 45.519791)</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>211472</th>\n      <td>LTE</td>\n      <td>222</td>\n      <td>1</td>\n      <td>10948</td>\n      <td>128113665</td>\n      <td>0</td>\n      <td>9.243438</td>\n      <td>45.559884</td>\n      <td>1000</td>\n      <td>41</td>\n      <td>1</td>\n      <td>1424025797</td>\n      <td>1477466563</td>\n      <td>0</td>\n      <td>POINT (9.243437999999999 45.559884)</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>248822</th>\n      <td>LTE</td>\n      <td>222</td>\n      <td>1</td>\n      <td>61226</td>\n      <td>80377309</td>\n      <td>490</td>\n      <td>9.132148</td>\n      <td>45.370728</td>\n      <td>1000</td>\n      <td>2</td>\n      <td>1</td>\n      <td>1439930387</td>\n      <td>1560598701</td>\n      <td>0</td>\n      <td>POINT (9.132148000000001 45.370728)</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>1235121</th>\n      <td>LTE</td>\n      <td>222</td>\n      <td>1</td>\n      <td>24272</td>\n      <td>128337669</td>\n      <td>-1</td>\n      <td>9.188133</td>\n      <td>45.501873</td>\n      <td>1000</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1578132602</td>\n      <td>1578132602</td>\n      <td>0</td>\n      <td>POINT (9.188133000000001 45.501873)</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>1235125</th>\n      <td>LTE</td>\n      <td>222</td>\n      <td>1</td>\n      <td>24272</td>\n      <td>128530692</td>\n      <td>-1</td>\n      <td>9.172047</td>\n      <td>45.523293</td>\n      <td>1000</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1578132749</td>\n      <td>1578132749</td>\n      <td>0</td>\n      <td>POINT (9.172046999999999 45.523293)</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>1235127</th>\n      <td>LTE</td>\n      <td>222</td>\n      <td>1</td>\n      <td>24308</td>\n      <td>128530694</td>\n      <td>-1</td>\n      <td>9.169231</td>\n      <td>45.525436</td>\n      <td>1000</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1578132762</td>\n      <td>1578132762</td>\n      <td>0</td>\n      <td>POINT (9.169231 45.525436)</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>1235129</th>\n      <td>LTE</td>\n      <td>222</td>\n      <td>1</td>\n      <td>24308</td>\n      <td>128628742</td>\n      <td>-1</td>\n      <td>9.163039</td>\n      <td>45.547525</td>\n      <td>1000</td>\n      <td>11</td>\n      <td>1</td>\n      <td>1578132868</td>\n      <td>1578132911</td>\n      <td>0</td>\n      <td>POINT (9.163038999999999 45.547525)</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>1238891</th>\n      <td>LTE</td>\n      <td>222</td>\n      <td>1</td>\n      <td>24307</td>\n      <td>128128002</td>\n      <td>0</td>\n      <td>9.083160</td>\n      <td>45.544785</td>\n      <td>1000</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1583192794</td>\n      <td>1583192794</td>\n      <td>0</td>\n      <td>POINT (9.083159999999999 45.544785)</td>\n      <td>True</td>\n    </tr>\n  </tbody>\n</table>\n<p>2593 rows × 16 columns</p>\n</div>"
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#tim_milan_cellid.drop_duplicates(subset=\"unq_id\", inplace=True)\n",
    "#tim_milan_cellid\n",
    "#tim_milan_cellid[tim_milan_cellid[\"radio\"] == \"LTE\"]"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "outputs": [
    {
     "data": {
      "text/plain": "2751"
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tim_milan_cellid[\"cell\"].nunique()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "outputs": [
    {
     "data": {
      "text/plain": "        radio  mcc  net   area       cell  unit       lon        lat  range  \\\n208957    LTE  222    1  10884  128066307     0  9.260330  45.392990   1000   \n208964    LTE  222    1  10980  128046593     0  9.146805  45.401688   1000   \n211468    LTE  222    1  10853  128188941     0  9.213409  45.519791   1000   \n211472    LTE  222    1  10948  128113665     0  9.243438  45.559884   1000   \n211473    LTE  222    1  10948  128113667     0  9.232394  45.549929   1000   \n...       ...  ...  ...    ...        ...   ...       ...        ...    ...   \n1235121   LTE  222    1  24272  128337669    -1  9.188133  45.501873   1000   \n1235125   LTE  222    1  24272  128530692    -1  9.172047  45.523293   1000   \n1235127   LTE  222    1  24308  128530694    -1  9.169231  45.525436   1000   \n1235129   LTE  222    1  24308  128628742    -1  9.163039  45.547525   1000   \n1238891   LTE  222    1  24307  128128002     0  9.083160  45.544785   1000   \n\n         samples  changeable     created     updated  averageSignal  \\\n208957         3           1  1422726687  1435991251              0   \n208964         1           1  1422707067  1422707067              0   \n211468         2           1  1424024597  1434740674              0   \n211472        41           1  1424025797  1477466563              0   \n211473        37           1  1424025797  1477364508              0   \n...          ...         ...         ...         ...            ...   \n1235121        1           1  1578132602  1578132602              0   \n1235125        1           1  1578132749  1578132749              0   \n1235127        1           1  1578132762  1578132762              0   \n1235129       11           1  1578132868  1578132911              0   \n1238891        1           1  1583192794  1583192794              0   \n\n                                   point_coords  contained  \n208957                 POINT (9.26033 45.39299)       True  \n208964   POINT (9.1468048095703 45.40168762207)       True  \n211468               POINT (9.213409 45.519791)       True  \n211472      POINT (9.243437999999999 45.559884)       True  \n211473      POINT (9.232393999999999 45.549929)       True  \n...                                         ...        ...  \n1235121     POINT (9.188133000000001 45.501873)       True  \n1235125     POINT (9.172046999999999 45.523293)       True  \n1235127              POINT (9.169231 45.525436)       True  \n1235129     POINT (9.163038999999999 45.547525)       True  \n1238891     POINT (9.083159999999999 45.544785)       True  \n\n[2751 rows x 16 columns]",
      "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>radio</th>\n      <th>mcc</th>\n      <th>net</th>\n      <th>area</th>\n      <th>cell</th>\n      <th>unit</th>\n      <th>lon</th>\n      <th>lat</th>\n      <th>range</th>\n      <th>samples</th>\n      <th>changeable</th>\n      <th>created</th>\n      <th>updated</th>\n      <th>averageSignal</th>\n      <th>point_coords</th>\n      <th>contained</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>208957</th>\n      <td>LTE</td>\n      <td>222</td>\n      <td>1</td>\n      <td>10884</td>\n      <td>128066307</td>\n      <td>0</td>\n      <td>9.260330</td>\n      <td>45.392990</td>\n      <td>1000</td>\n      <td>3</td>\n      <td>1</td>\n      <td>1422726687</td>\n      <td>1435991251</td>\n      <td>0</td>\n      <td>POINT (9.26033 45.39299)</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>208964</th>\n      <td>LTE</td>\n      <td>222</td>\n      <td>1</td>\n      <td>10980</td>\n      <td>128046593</td>\n      <td>0</td>\n      <td>9.146805</td>\n      <td>45.401688</td>\n      <td>1000</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1422707067</td>\n      <td>1422707067</td>\n      <td>0</td>\n      <td>POINT (9.1468048095703 45.40168762207)</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>211468</th>\n      <td>LTE</td>\n      <td>222</td>\n      <td>1</td>\n      <td>10853</td>\n      <td>128188941</td>\n      <td>0</td>\n      <td>9.213409</td>\n      <td>45.519791</td>\n      <td>1000</td>\n      <td>2</td>\n      <td>1</td>\n      <td>1424024597</td>\n      <td>1434740674</td>\n      <td>0</td>\n      <td>POINT (9.213409 45.519791)</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>211472</th>\n      <td>LTE</td>\n      <td>222</td>\n      <td>1</td>\n      <td>10948</td>\n      <td>128113665</td>\n      <td>0</td>\n      <td>9.243438</td>\n      <td>45.559884</td>\n      <td>1000</td>\n      <td>41</td>\n      <td>1</td>\n      <td>1424025797</td>\n      <td>1477466563</td>\n      <td>0</td>\n      <td>POINT (9.243437999999999 45.559884)</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>211473</th>\n      <td>LTE</td>\n      <td>222</td>\n      <td>1</td>\n      <td>10948</td>\n      <td>128113667</td>\n      <td>0</td>\n      <td>9.232394</td>\n      <td>45.549929</td>\n      <td>1000</td>\n      <td>37</td>\n      <td>1</td>\n      <td>1424025797</td>\n      <td>1477364508</td>\n      <td>0</td>\n      <td>POINT (9.232393999999999 45.549929)</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>1235121</th>\n      <td>LTE</td>\n      <td>222</td>\n      <td>1</td>\n      <td>24272</td>\n      <td>128337669</td>\n      <td>-1</td>\n      <td>9.188133</td>\n      <td>45.501873</td>\n      <td>1000</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1578132602</td>\n      <td>1578132602</td>\n      <td>0</td>\n      <td>POINT (9.188133000000001 45.501873)</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>1235125</th>\n      <td>LTE</td>\n      <td>222</td>\n      <td>1</td>\n      <td>24272</td>\n      <td>128530692</td>\n      <td>-1</td>\n      <td>9.172047</td>\n      <td>45.523293</td>\n      <td>1000</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1578132749</td>\n      <td>1578132749</td>\n      <td>0</td>\n      <td>POINT (9.172046999999999 45.523293)</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>1235127</th>\n      <td>LTE</td>\n      <td>222</td>\n      <td>1</td>\n      <td>24308</td>\n      <td>128530694</td>\n      <td>-1</td>\n      <td>9.169231</td>\n      <td>45.525436</td>\n      <td>1000</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1578132762</td>\n      <td>1578132762</td>\n      <td>0</td>\n      <td>POINT (9.169231 45.525436)</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>1235129</th>\n      <td>LTE</td>\n      <td>222</td>\n      <td>1</td>\n      <td>24308</td>\n      <td>128628742</td>\n      <td>-1</td>\n      <td>9.163039</td>\n      <td>45.547525</td>\n      <td>1000</td>\n      <td>11</td>\n      <td>1</td>\n      <td>1578132868</td>\n      <td>1578132911</td>\n      <td>0</td>\n      <td>POINT (9.163038999999999 45.547525)</td>\n      <td>True</td>\n    </tr>\n    <tr>\n      <th>1238891</th>\n      <td>LTE</td>\n      <td>222</td>\n      <td>1</td>\n      <td>24307</td>\n      <td>128128002</td>\n      <td>0</td>\n      <td>9.083160</td>\n      <td>45.544785</td>\n      <td>1000</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1583192794</td>\n      <td>1583192794</td>\n      <td>0</td>\n      <td>POINT (9.083159999999999 45.544785)</td>\n      <td>True</td>\n    </tr>\n  </tbody>\n</table>\n<p>2751 rows × 16 columns</p>\n</div>"
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# drop duplicate entries of Cell tower code which are the same position just different radio band\n",
    "tim_milan_cellid.drop_duplicates(subset=\"cell\", inplace=True)\n",
    "tim_milan_cellid"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "outputs": [],
   "source": [
    "possible_bs_coord = tim_milan_cellid.reset_index()[[\"lon\", \"lat\", \"point_coords\"]]\n",
    "points = MultiPoint(possible_bs_coord[\"point_coords\"])\n",
    "possible_bs_coord[\"bs_id\"] = possible_bs_coord.index"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "outputs": [],
   "source": [
    "# function selecting closest base station to center of each square\n",
    "def get_base_station(orig: Point, p_union: MultiPoint, df):\n",
    "    p = nearest_points(orig, p_union)[-1]\n",
    "    msk = df[\"point_coords\"] == p\n",
    "    return df[msk].iloc[0]"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "outputs": [
    {
     "data": {
      "text/plain": "                                              coordinates  \\\ncellid                                                      \n1       POLYGON ((9.011491047832299 45.35880131440966,...   \n2       POLYGON ((9.014491488013135 45.35880097314403,...   \n3       POLYGON ((9.017491928134044 45.35880055306028,...   \n4       POLYGON ((9.020492368182619 45.35880005415845,...   \n5       POLYGON ((9.023492808146456 45.35879947643852,...   \n...                                                   ...   \n9996    POLYGON ((9.297630740052327 45.56782693472525,...   \n9997    POLYGON ((9.300642247414926 45.56781904882199,...   \n9998    POLYGON ((9.303653753522097 45.56781108353051,...   \n9999    POLYGON ((9.30666525836126 45.5678030388509, 9...   \n10000   POLYGON ((9.309676761919844 45.56779491478326,...   \n\n                                           centroid         x          y  \\\ncellid                                                                     \n1       POINT (9.012991025904858 45.35774349047394)  9.014969  45.361862   \n2       POINT (9.015991410159719 45.35774310981321)  9.014969  45.361862   \n3       POINT (9.018991794348462 45.35774265033727)  9.014969  45.361862   \n4       POINT (9.021992178458664 45.35774211204613)  9.014969  45.361862   \n5       POINT (9.024992562477935 45.35774149493978)  9.014969  45.361862   \n...                                             ...       ...        ...   \n9996    POINT (9.299130880320151 45.56676539179961)  9.303360  45.566483   \n9997    POINT (9.302142330545021 45.56675746649291)  9.303360  45.566483   \n9998    POINT (9.305153779508254 45.56674946180095)  9.303360  45.566483   \n9999     POINT (9.308165227197284 45.5667413777238)  9.309206  45.562402   \n10000   POINT (9.311176673599535 45.56673321426158)  9.309206  45.562402   \n\n                                bs_point_coords  bs_org_id  \ncellid                                                      \n1       POINT (9.0149688720703 45.361862182617)       2005  \n2       POINT (9.0149688720703 45.361862182617)       2005  \n3       POINT (9.0149688720703 45.361862182617)       2005  \n4       POINT (9.0149688720703 45.361862182617)       2005  \n5       POINT (9.0149688720703 45.361862182617)       2005  \n...                                         ...        ...  \n9996    POINT (9.3033599853516 45.566482543945)        321  \n9997    POINT (9.3033599853516 45.566482543945)        321  \n9998    POINT (9.3033599853516 45.566482543945)        321  \n9999                 POINT (9.309206 45.562402)          9  \n10000                POINT (9.309206 45.562402)          9  \n\n[10000 rows x 6 columns]",
      "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>coordinates</th>\n      <th>centroid</th>\n      <th>x</th>\n      <th>y</th>\n      <th>bs_point_coords</th>\n      <th>bs_org_id</th>\n    </tr>\n    <tr>\n      <th>cellid</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>1</th>\n      <td>POLYGON ((9.011491047832299 45.35880131440966,...</td>\n      <td>POINT (9.012991025904858 45.35774349047394)</td>\n      <td>9.014969</td>\n      <td>45.361862</td>\n      <td>POINT (9.0149688720703 45.361862182617)</td>\n      <td>2005</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>POLYGON ((9.014491488013135 45.35880097314403,...</td>\n      <td>POINT (9.015991410159719 45.35774310981321)</td>\n      <td>9.014969</td>\n      <td>45.361862</td>\n      <td>POINT (9.0149688720703 45.361862182617)</td>\n      <td>2005</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>POLYGON ((9.017491928134044 45.35880055306028,...</td>\n      <td>POINT (9.018991794348462 45.35774265033727)</td>\n      <td>9.014969</td>\n      <td>45.361862</td>\n      <td>POINT (9.0149688720703 45.361862182617)</td>\n      <td>2005</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>POLYGON ((9.020492368182619 45.35880005415845,...</td>\n      <td>POINT (9.021992178458664 45.35774211204613)</td>\n      <td>9.014969</td>\n      <td>45.361862</td>\n      <td>POINT (9.0149688720703 45.361862182617)</td>\n      <td>2005</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>POLYGON ((9.023492808146456 45.35879947643852,...</td>\n      <td>POINT (9.024992562477935 45.35774149493978)</td>\n      <td>9.014969</td>\n      <td>45.361862</td>\n      <td>POINT (9.0149688720703 45.361862182617)</td>\n      <td>2005</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>9996</th>\n      <td>POLYGON ((9.297630740052327 45.56782693472525,...</td>\n      <td>POINT (9.299130880320151 45.56676539179961)</td>\n      <td>9.303360</td>\n      <td>45.566483</td>\n      <td>POINT (9.3033599853516 45.566482543945)</td>\n      <td>321</td>\n    </tr>\n    <tr>\n      <th>9997</th>\n      <td>POLYGON ((9.300642247414926 45.56781904882199,...</td>\n      <td>POINT (9.302142330545021 45.56675746649291)</td>\n      <td>9.303360</td>\n      <td>45.566483</td>\n      <td>POINT (9.3033599853516 45.566482543945)</td>\n      <td>321</td>\n    </tr>\n    <tr>\n      <th>9998</th>\n      <td>POLYGON ((9.303653753522097 45.56781108353051,...</td>\n      <td>POINT (9.305153779508254 45.56674946180095)</td>\n      <td>9.303360</td>\n      <td>45.566483</td>\n      <td>POINT (9.3033599853516 45.566482543945)</td>\n      <td>321</td>\n    </tr>\n    <tr>\n      <th>9999</th>\n      <td>POLYGON ((9.30666525836126 45.5678030388509, 9...</td>\n      <td>POINT (9.308165227197284 45.5667413777238)</td>\n      <td>9.309206</td>\n      <td>45.562402</td>\n      <td>POINT (9.309206 45.562402)</td>\n      <td>9</td>\n    </tr>\n    <tr>\n      <th>10000</th>\n      <td>POLYGON ((9.309676761919844 45.56779491478326,...</td>\n      <td>POINT (9.311176673599535 45.56673321426158)</td>\n      <td>9.309206</td>\n      <td>45.562402</td>\n      <td>POINT (9.309206 45.562402)</td>\n      <td>9</td>\n    </tr>\n  </tbody>\n</table>\n<p>10000 rows × 6 columns</p>\n</div>"
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid[[\"x\", \"y\", \"bs_point_coords\", \"bs_org_id\"]] = grid.apply(lambda row: get_base_station(\n",
    "    orig=row[\"centroid\"], p_union=points, df=possible_bs_coord\n",
    "), axis=1)\n",
    "grid"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "outputs": [
    {
     "data": {
      "text/plain": "2020"
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid[\"bs_org_id\"].nunique()\n",
    "# -> 2020 for only LTE (5159 for all) unique base stations positioned in and around the Milan area"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "source": [
    "# save enhanced grid to be possibly reused with traffic data of different days\n",
    "grid.to_csv(save_grid_pth, index=True)\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "execution_count": 115,
   "outputs": []
  },
  {
   "cell_type": "markdown",
   "source": [
    "_________________________________________________________\n",
    "use enhanced grid to group squares by respective base station and aggregate workloads accordingly"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "outputs": [
    {
     "data": {
      "text/plain": "                                              coordinates  \\\ncellid                                                      \n1       POLYGON ((9.011491047832299 45.35880131440966,...   \n2       POLYGON ((9.014491488013135 45.35880097314403,...   \n3       POLYGON ((9.017491928134044 45.35880055306028,...   \n4       POLYGON ((9.020492368182619 45.35880005415845,...   \n5       POLYGON ((9.023492808146456 45.35879947643852,...   \n...                                                   ...   \n9996    POLYGON ((9.297630740052327 45.56782693472525,...   \n9997    POLYGON ((9.300642247414926 45.56781904882199,...   \n9998    POLYGON ((9.303653753522097 45.56781108353051,...   \n9999    POLYGON ((9.30666525836126 45.5678030388509, 9...   \n10000   POLYGON ((9.309676761919844 45.56779491478326,...   \n\n                                           centroid         x          y  \\\ncellid                                                                     \n1       POINT (9.012991025904858 45.35774349047394)  9.014969  45.361862   \n2       POINT (9.015991410159719 45.35774310981321)  9.014969  45.361862   \n3       POINT (9.018991794348462 45.35774265033727)  9.014969  45.361862   \n4       POINT (9.021992178458664 45.35774211204613)  9.014969  45.361862   \n5       POINT (9.024992562477935 45.35774149493978)  9.014969  45.361862   \n...                                             ...       ...        ...   \n9996    POINT (9.299130880320151 45.56676539179961)  9.303360  45.566483   \n9997    POINT (9.302142330545021 45.56675746649291)  9.303360  45.566483   \n9998    POINT (9.305153779508254 45.56674946180095)  9.303360  45.566483   \n9999     POINT (9.308165227197284 45.5667413777238)  9.309206  45.562402   \n10000   POINT (9.311176673599535 45.56673321426158)  9.309206  45.562402   \n\n                                bs_point_coords  bs_org_id  \ncellid                                                      \n1       POINT (9.0149688720703 45.361862182617)       2005  \n2       POINT (9.0149688720703 45.361862182617)       2005  \n3       POINT (9.0149688720703 45.361862182617)       2005  \n4       POINT (9.0149688720703 45.361862182617)       2005  \n5       POINT (9.0149688720703 45.361862182617)       2005  \n...                                         ...        ...  \n9996    POINT (9.3033599853516 45.566482543945)        321  \n9997    POINT (9.3033599853516 45.566482543945)        321  \n9998    POINT (9.3033599853516 45.566482543945)        321  \n9999                 POINT (9.309206 45.562402)          9  \n10000                POINT (9.309206 45.562402)          9  \n\n[10000 rows x 6 columns]",
      "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>coordinates</th>\n      <th>centroid</th>\n      <th>x</th>\n      <th>y</th>\n      <th>bs_point_coords</th>\n      <th>bs_org_id</th>\n    </tr>\n    <tr>\n      <th>cellid</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>1</th>\n      <td>POLYGON ((9.011491047832299 45.35880131440966,...</td>\n      <td>POINT (9.012991025904858 45.35774349047394)</td>\n      <td>9.014969</td>\n      <td>45.361862</td>\n      <td>POINT (9.0149688720703 45.361862182617)</td>\n      <td>2005</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>POLYGON ((9.014491488013135 45.35880097314403,...</td>\n      <td>POINT (9.015991410159719 45.35774310981321)</td>\n      <td>9.014969</td>\n      <td>45.361862</td>\n      <td>POINT (9.0149688720703 45.361862182617)</td>\n      <td>2005</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>POLYGON ((9.017491928134044 45.35880055306028,...</td>\n      <td>POINT (9.018991794348462 45.35774265033727)</td>\n      <td>9.014969</td>\n      <td>45.361862</td>\n      <td>POINT (9.0149688720703 45.361862182617)</td>\n      <td>2005</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>POLYGON ((9.020492368182619 45.35880005415845,...</td>\n      <td>POINT (9.021992178458664 45.35774211204613)</td>\n      <td>9.014969</td>\n      <td>45.361862</td>\n      <td>POINT (9.0149688720703 45.361862182617)</td>\n      <td>2005</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>POLYGON ((9.023492808146456 45.35879947643852,...</td>\n      <td>POINT (9.024992562477935 45.35774149493978)</td>\n      <td>9.014969</td>\n      <td>45.361862</td>\n      <td>POINT (9.0149688720703 45.361862182617)</td>\n      <td>2005</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>9996</th>\n      <td>POLYGON ((9.297630740052327 45.56782693472525,...</td>\n      <td>POINT (9.299130880320151 45.56676539179961)</td>\n      <td>9.303360</td>\n      <td>45.566483</td>\n      <td>POINT (9.3033599853516 45.566482543945)</td>\n      <td>321</td>\n    </tr>\n    <tr>\n      <th>9997</th>\n      <td>POLYGON ((9.300642247414926 45.56781904882199,...</td>\n      <td>POINT (9.302142330545021 45.56675746649291)</td>\n      <td>9.303360</td>\n      <td>45.566483</td>\n      <td>POINT (9.3033599853516 45.566482543945)</td>\n      <td>321</td>\n    </tr>\n    <tr>\n      <th>9998</th>\n      <td>POLYGON ((9.303653753522097 45.56781108353051,...</td>\n      <td>POINT (9.305153779508254 45.56674946180095)</td>\n      <td>9.303360</td>\n      <td>45.566483</td>\n      <td>POINT (9.3033599853516 45.566482543945)</td>\n      <td>321</td>\n    </tr>\n    <tr>\n      <th>9999</th>\n      <td>POLYGON ((9.30666525836126 45.5678030388509, 9...</td>\n      <td>POINT (9.308165227197284 45.5667413777238)</td>\n      <td>9.309206</td>\n      <td>45.562402</td>\n      <td>POINT (9.309206 45.562402)</td>\n      <td>9</td>\n    </tr>\n    <tr>\n      <th>10000</th>\n      <td>POLYGON ((9.309676761919844 45.56779491478326,...</td>\n      <td>POINT (9.311176673599535 45.56673321426158)</td>\n      <td>9.309206</td>\n      <td>45.562402</td>\n      <td>POINT (9.309206 45.562402)</td>\n      <td>9</td>\n    </tr>\n  </tbody>\n</table>\n<p>10000 rows × 6 columns</p>\n</div>"
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "outputs": [
    {
     "data": {
      "text/plain": "         square_id  internet\n1                1  0.052275\n2                1  0.027300\n4                1  0.133637\n6                1  0.054601\n8                1  0.053438\n...            ...       ...\n4842617       9999  0.438452\n4842619       9999  0.144481\n4842622       9999  0.014708\n4842623       9999  0.085995\n4842624       9999  0.144481\n\n[2231609 rows x 2 columns]",
      "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>square_id</th>\n      <th>internet</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>0.052275</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1</td>\n      <td>0.027300</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1</td>\n      <td>0.133637</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>1</td>\n      <td>0.054601</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>1</td>\n      <td>0.053438</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>4842617</th>\n      <td>9999</td>\n      <td>0.438452</td>\n    </tr>\n    <tr>\n      <th>4842619</th>\n      <td>9999</td>\n      <td>0.144481</td>\n    </tr>\n    <tr>\n      <th>4842622</th>\n      <td>9999</td>\n      <td>0.014708</td>\n    </tr>\n    <tr>\n      <th>4842623</th>\n      <td>9999</td>\n      <td>0.085995</td>\n    </tr>\n    <tr>\n      <th>4842624</th>\n      <td>9999</td>\n      <td>0.144481</td>\n    </tr>\n  </tbody>\n</table>\n<p>2231609 rows × 2 columns</p>\n</div>"
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "traffic_df"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "\"None of ['cellid'] are in the columns\"",
     "output_type": "error",
     "traceback": [
      "\u001B[0;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[0;31mKeyError\u001B[0m                                  Traceback (most recent call last)",
      "\u001B[0;32m<ipython-input-118-949f3b94e0ff>\u001B[0m in \u001B[0;36m<module>\u001B[0;34m\u001B[0m\n\u001B[1;32m      1\u001B[0m \u001B[0;31m# replace index with cellid for join (which uses index)\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m----> 2\u001B[0;31m \u001B[0mgrid\u001B[0m\u001B[0;34m.\u001B[0m\u001B[0mset_index\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m\"cellid\"\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0minplace\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0;32mTrue\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      3\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m~/miniconda3/envs/pam/lib/python3.8/site-packages/pandas/util/_decorators.py\u001B[0m in \u001B[0;36mwrapper\u001B[0;34m(*args, **kwargs)\u001B[0m\n\u001B[1;32m    309\u001B[0m                     \u001B[0mstacklevel\u001B[0m\u001B[0;34m=\u001B[0m\u001B[0mstacklevel\u001B[0m\u001B[0;34m,\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    310\u001B[0m                 )\n\u001B[0;32m--> 311\u001B[0;31m             \u001B[0;32mreturn\u001B[0m \u001B[0mfunc\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34m*\u001B[0m\u001B[0margs\u001B[0m\u001B[0;34m,\u001B[0m \u001B[0;34m**\u001B[0m\u001B[0mkwargs\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    312\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m    313\u001B[0m         \u001B[0;32mreturn\u001B[0m \u001B[0mwrapper\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;32m~/miniconda3/envs/pam/lib/python3.8/site-packages/pandas/core/frame.py\u001B[0m in \u001B[0;36mset_index\u001B[0;34m(self, keys, drop, append, inplace, verify_integrity)\u001B[0m\n\u001B[1;32m   5444\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m   5445\u001B[0m         \u001B[0;32mif\u001B[0m \u001B[0mmissing\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n\u001B[0;32m-> 5446\u001B[0;31m             \u001B[0;32mraise\u001B[0m \u001B[0mKeyError\u001B[0m\u001B[0;34m(\u001B[0m\u001B[0;34mf\"None of {missing} are in the columns\"\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   5447\u001B[0m \u001B[0;34m\u001B[0m\u001B[0m\n\u001B[1;32m   5448\u001B[0m         \u001B[0;32mif\u001B[0m \u001B[0minplace\u001B[0m\u001B[0;34m:\u001B[0m\u001B[0;34m\u001B[0m\u001B[0;34m\u001B[0m\u001B[0m\n",
      "\u001B[0;31mKeyError\u001B[0m: \"None of ['cellid'] are in the columns\""
     ]
    }
   ],
   "source": [
    "# replace index with cellid for join (which uses index)\n",
    "grid.set_index(\"cellid\", inplace=True)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "outputs": [
    {
     "data": {
      "text/plain": "         square_id  internet         x          y  bs_org_id\n1                1  0.052275  9.014969  45.361862       2005\n2                1  0.027300  9.014969  45.361862       2005\n4                1  0.133637  9.014969  45.361862       2005\n6                1  0.054601  9.014969  45.361862       2005\n8                1  0.053438  9.014969  45.361862       2005\n...            ...       ...       ...        ...        ...\n4842617       9999  0.438452  9.309206  45.562402          9\n4842619       9999  0.144481  9.309206  45.562402          9\n4842622       9999  0.014708  9.309206  45.562402          9\n4842623       9999  0.085995  9.309206  45.562402          9\n4842624       9999  0.144481  9.309206  45.562402          9\n\n[2231609 rows x 5 columns]",
      "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>square_id</th>\n      <th>internet</th>\n      <th>x</th>\n      <th>y</th>\n      <th>bs_org_id</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>0.052275</td>\n      <td>9.014969</td>\n      <td>45.361862</td>\n      <td>2005</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1</td>\n      <td>0.027300</td>\n      <td>9.014969</td>\n      <td>45.361862</td>\n      <td>2005</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1</td>\n      <td>0.133637</td>\n      <td>9.014969</td>\n      <td>45.361862</td>\n      <td>2005</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>1</td>\n      <td>0.054601</td>\n      <td>9.014969</td>\n      <td>45.361862</td>\n      <td>2005</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>1</td>\n      <td>0.053438</td>\n      <td>9.014969</td>\n      <td>45.361862</td>\n      <td>2005</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>4842617</th>\n      <td>9999</td>\n      <td>0.438452</td>\n      <td>9.309206</td>\n      <td>45.562402</td>\n      <td>9</td>\n    </tr>\n    <tr>\n      <th>4842619</th>\n      <td>9999</td>\n      <td>0.144481</td>\n      <td>9.309206</td>\n      <td>45.562402</td>\n      <td>9</td>\n    </tr>\n    <tr>\n      <th>4842622</th>\n      <td>9999</td>\n      <td>0.014708</td>\n      <td>9.309206</td>\n      <td>45.562402</td>\n      <td>9</td>\n    </tr>\n    <tr>\n      <th>4842623</th>\n      <td>9999</td>\n      <td>0.085995</td>\n      <td>9.309206</td>\n      <td>45.562402</td>\n      <td>9</td>\n    </tr>\n    <tr>\n      <th>4842624</th>\n      <td>9999</td>\n      <td>0.144481</td>\n      <td>9.309206</td>\n      <td>45.562402</td>\n      <td>9</td>\n    </tr>\n  </tbody>\n</table>\n<p>2231609 rows × 5 columns</p>\n</div>"
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# join grid and traffic data\n",
    "bs_traffic_data = traffic_df.join(grid[[\"x\", \"y\", \"bs_org_id\"]], on=\"square_id\")\n",
    "bs_traffic_data"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "outputs": [],
   "source": [
    "# group df by location id to get different base stations\n",
    "grp_data = bs_traffic_data.groupby([\"bs_org_id\"])"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "outputs": [
    {
     "data": {
      "text/plain": "          square_id     internet                   x          y\n           <lambda>          sum      mean  <lambda>   <lambda>\nbs_org_id                                                      \n0               798  1000.980770  1.254362  9.260330  45.392990\n1              1664  3206.357522  1.926898  9.146805  45.401688\n2               559  1960.867676  3.507813  9.213409  45.519791\n3               346   381.208988  1.101760  9.243438  45.559884\n4               325   837.206373  2.576020  9.232394  45.549929\n...             ...          ...       ...       ...        ...\n2742           1004  3778.433320  3.763380  9.187517  45.494183\n2744            955  4153.051634  4.348745  9.186849  45.498079\n2747            644  1553.643933  2.412491  9.172047  45.523293\n2748            345   739.342531  2.143022  9.169231  45.525436\n2750           3612  2430.388491  0.672865  9.083160  45.544785\n\n[2020 rows x 5 columns]",
      "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 tr th {\n        text-align: left;\n    }\n\n    .dataframe thead tr:last-of-type th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr>\n      <th></th>\n      <th>square_id</th>\n      <th colspan=\"2\" halign=\"left\">internet</th>\n      <th>x</th>\n      <th>y</th>\n    </tr>\n    <tr>\n      <th></th>\n      <th>&lt;lambda&gt;</th>\n      <th>sum</th>\n      <th>mean</th>\n      <th>&lt;lambda&gt;</th>\n      <th>&lt;lambda&gt;</th>\n    </tr>\n    <tr>\n      <th>bs_org_id</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>0</th>\n      <td>798</td>\n      <td>1000.980770</td>\n      <td>1.254362</td>\n      <td>9.260330</td>\n      <td>45.392990</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1664</td>\n      <td>3206.357522</td>\n      <td>1.926898</td>\n      <td>9.146805</td>\n      <td>45.401688</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>559</td>\n      <td>1960.867676</td>\n      <td>3.507813</td>\n      <td>9.213409</td>\n      <td>45.519791</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>346</td>\n      <td>381.208988</td>\n      <td>1.101760</td>\n      <td>9.243438</td>\n      <td>45.559884</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>325</td>\n      <td>837.206373</td>\n      <td>2.576020</td>\n      <td>9.232394</td>\n      <td>45.549929</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>2742</th>\n      <td>1004</td>\n      <td>3778.433320</td>\n      <td>3.763380</td>\n      <td>9.187517</td>\n      <td>45.494183</td>\n    </tr>\n    <tr>\n      <th>2744</th>\n      <td>955</td>\n      <td>4153.051634</td>\n      <td>4.348745</td>\n      <td>9.186849</td>\n      <td>45.498079</td>\n    </tr>\n    <tr>\n      <th>2747</th>\n      <td>644</td>\n      <td>1553.643933</td>\n      <td>2.412491</td>\n      <td>9.172047</td>\n      <td>45.523293</td>\n    </tr>\n    <tr>\n      <th>2748</th>\n      <td>345</td>\n      <td>739.342531</td>\n      <td>2.143022</td>\n      <td>9.169231</td>\n      <td>45.525436</td>\n    </tr>\n    <tr>\n      <th>2750</th>\n      <td>3612</td>\n      <td>2430.388491</td>\n      <td>0.672865</td>\n      <td>9.083160</td>\n      <td>45.544785</td>\n    </tr>\n  </tbody>\n</table>\n<p>2020 rows × 5 columns</p>\n</div>"
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# aggregate session lengths\n",
    "bs_table = grp_data.agg({\n",
    "    \"square_id\": lambda x: x.count(),   # interpret count of connections as approx number of users\n",
    "    \"internet\": [np.sum, np.mean],\n",
    "    \"x\": lambda x: x.iloc[0],\n",
    "    \"y\": lambda x: x.iloc[0],\n",
    "})\n",
    "bs_table"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "outputs": [
    {
     "data": {
      "text/plain": "     bs_org_id square_id     internet                   x          y\n                <lambda>          sum      mean  <lambda>   <lambda>\n0            0       798  1000.980770  1.254362  9.260330  45.392990\n1            1      1664  3206.357522  1.926898  9.146805  45.401688\n2            2       559  1960.867676  3.507813  9.213409  45.519791\n3            3       346   381.208988  1.101760  9.243438  45.559884\n4            4       325   837.206373  2.576020  9.232394  45.549929\n...        ...       ...          ...       ...       ...        ...\n2015      2742      1004  3778.433320  3.763380  9.187517  45.494183\n2016      2744       955  4153.051634  4.348745  9.186849  45.498079\n2017      2747       644  1553.643933  2.412491  9.172047  45.523293\n2018      2748       345   739.342531  2.143022  9.169231  45.525436\n2019      2750      3612  2430.388491  0.672865  9.083160  45.544785\n\n[2020 rows x 6 columns]",
      "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 tr th {\n        text-align: left;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr>\n      <th></th>\n      <th>bs_org_id</th>\n      <th>square_id</th>\n      <th colspan=\"2\" halign=\"left\">internet</th>\n      <th>x</th>\n      <th>y</th>\n    </tr>\n    <tr>\n      <th></th>\n      <th></th>\n      <th>&lt;lambda&gt;</th>\n      <th>sum</th>\n      <th>mean</th>\n      <th>&lt;lambda&gt;</th>\n      <th>&lt;lambda&gt;</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>798</td>\n      <td>1000.980770</td>\n      <td>1.254362</td>\n      <td>9.260330</td>\n      <td>45.392990</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>1664</td>\n      <td>3206.357522</td>\n      <td>1.926898</td>\n      <td>9.146805</td>\n      <td>45.401688</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2</td>\n      <td>559</td>\n      <td>1960.867676</td>\n      <td>3.507813</td>\n      <td>9.213409</td>\n      <td>45.519791</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3</td>\n      <td>346</td>\n      <td>381.208988</td>\n      <td>1.101760</td>\n      <td>9.243438</td>\n      <td>45.559884</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>4</td>\n      <td>325</td>\n      <td>837.206373</td>\n      <td>2.576020</td>\n      <td>9.232394</td>\n      <td>45.549929</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>2015</th>\n      <td>2742</td>\n      <td>1004</td>\n      <td>3778.433320</td>\n      <td>3.763380</td>\n      <td>9.187517</td>\n      <td>45.494183</td>\n    </tr>\n    <tr>\n      <th>2016</th>\n      <td>2744</td>\n      <td>955</td>\n      <td>4153.051634</td>\n      <td>4.348745</td>\n      <td>9.186849</td>\n      <td>45.498079</td>\n    </tr>\n    <tr>\n      <th>2017</th>\n      <td>2747</td>\n      <td>644</td>\n      <td>1553.643933</td>\n      <td>2.412491</td>\n      <td>9.172047</td>\n      <td>45.523293</td>\n    </tr>\n    <tr>\n      <th>2018</th>\n      <td>2748</td>\n      <td>345</td>\n      <td>739.342531</td>\n      <td>2.143022</td>\n      <td>9.169231</td>\n      <td>45.525436</td>\n    </tr>\n    <tr>\n      <th>2019</th>\n      <td>2750</td>\n      <td>3612</td>\n      <td>2430.388491</td>\n      <td>0.672865</td>\n      <td>9.083160</td>\n      <td>45.544785</td>\n    </tr>\n  </tbody>\n</table>\n<p>2020 rows × 6 columns</p>\n</div>"
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bs_table.reset_index(inplace=True)\n",
    "bs_table"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "outputs": [
    {
     "data": {
      "text/plain": "         bs_org_id     square_id      internet                         x  \\\n                        <lambda>           sum         mean     <lambda>   \ncount  2020.000000   2020.000000   2020.000000  2020.000000  2020.000000   \nmean   1328.403960   1104.756931   1582.555599     2.249301     9.181048   \nstd     791.473984   1772.120093   1312.033596     1.840666     0.065195   \nmin       0.000000     48.000000     13.466486     0.070629     9.011536   \n25%     613.750000    341.750000    678.233962     0.910364     9.138822   \n50%    1349.000000    641.000000   1286.638581     1.694617     9.184459   \n75%    2003.250000   1273.250000   2060.797545     3.121914     9.227872   \nmax    2750.000000  38090.000000  11880.566761    17.459901     9.311943   \n\n                 y  \n          <lambda>  \ncount  2020.000000  \nmean     45.479163  \nstd       0.045153  \nmin      45.356369  \n25%      45.451105  \n50%      45.479965  \n75%      45.512924  \nmax      45.567959  ",
      "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 tr th {\n        text-align: left;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr>\n      <th></th>\n      <th>bs_org_id</th>\n      <th>square_id</th>\n      <th colspan=\"2\" halign=\"left\">internet</th>\n      <th>x</th>\n      <th>y</th>\n    </tr>\n    <tr>\n      <th></th>\n      <th></th>\n      <th>&lt;lambda&gt;</th>\n      <th>sum</th>\n      <th>mean</th>\n      <th>&lt;lambda&gt;</th>\n      <th>&lt;lambda&gt;</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>2020.000000</td>\n      <td>2020.000000</td>\n      <td>2020.000000</td>\n      <td>2020.000000</td>\n      <td>2020.000000</td>\n      <td>2020.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>1328.403960</td>\n      <td>1104.756931</td>\n      <td>1582.555599</td>\n      <td>2.249301</td>\n      <td>9.181048</td>\n      <td>45.479163</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>791.473984</td>\n      <td>1772.120093</td>\n      <td>1312.033596</td>\n      <td>1.840666</td>\n      <td>0.065195</td>\n      <td>0.045153</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>0.000000</td>\n      <td>48.000000</td>\n      <td>13.466486</td>\n      <td>0.070629</td>\n      <td>9.011536</td>\n      <td>45.356369</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>613.750000</td>\n      <td>341.750000</td>\n      <td>678.233962</td>\n      <td>0.910364</td>\n      <td>9.138822</td>\n      <td>45.451105</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>1349.000000</td>\n      <td>641.000000</td>\n      <td>1286.638581</td>\n      <td>1.694617</td>\n      <td>9.184459</td>\n      <td>45.479965</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>2003.250000</td>\n      <td>1273.250000</td>\n      <td>2060.797545</td>\n      <td>3.121914</td>\n      <td>9.227872</td>\n      <td>45.512924</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>2750.000000</td>\n      <td>38090.000000</td>\n      <td>11880.566761</td>\n      <td>17.459901</td>\n      <td>9.311943</td>\n      <td>45.567959</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bs_table.describe()\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "outputs": [],
   "source": [
    "# rename columns\n",
    "bs_table.columns = [\n",
    "    \"bs_id\",\n",
    "    \"num_users\",\n",
    "    \"workload\",\n",
    "    \"workload_mean\",\n",
    "    \"x_coord\",\n",
    "    \"y_coord\"\n",
    "]"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "outputs": [
    {
     "data": {
      "text/plain": "      bs_id  num_users     workload  workload_mean   x_coord    y_coord\n0         0        798  1000.980770       1.254362  9.260330  45.392990\n1         1       1664  3206.357522       1.926898  9.146805  45.401688\n2         2        559  1960.867676       3.507813  9.213409  45.519791\n3         3        346   381.208988       1.101760  9.243438  45.559884\n4         4        325   837.206373       2.576020  9.232394  45.549929\n...     ...        ...          ...            ...       ...        ...\n2015   2015       1004  3778.433320       3.763380  9.187517  45.494183\n2016   2016        955  4153.051634       4.348745  9.186849  45.498079\n2017   2017        644  1553.643933       2.412491  9.172047  45.523293\n2018   2018        345   739.342531       2.143022  9.169231  45.525436\n2019   2019       3612  2430.388491       0.672865  9.083160  45.544785\n\n[2020 rows x 6 columns]",
      "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>bs_id</th>\n      <th>num_users</th>\n      <th>workload</th>\n      <th>workload_mean</th>\n      <th>x_coord</th>\n      <th>y_coord</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>798</td>\n      <td>1000.980770</td>\n      <td>1.254362</td>\n      <td>9.260330</td>\n      <td>45.392990</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>1664</td>\n      <td>3206.357522</td>\n      <td>1.926898</td>\n      <td>9.146805</td>\n      <td>45.401688</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2</td>\n      <td>559</td>\n      <td>1960.867676</td>\n      <td>3.507813</td>\n      <td>9.213409</td>\n      <td>45.519791</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3</td>\n      <td>346</td>\n      <td>381.208988</td>\n      <td>1.101760</td>\n      <td>9.243438</td>\n      <td>45.559884</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>4</td>\n      <td>325</td>\n      <td>837.206373</td>\n      <td>2.576020</td>\n      <td>9.232394</td>\n      <td>45.549929</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>2015</th>\n      <td>2015</td>\n      <td>1004</td>\n      <td>3778.433320</td>\n      <td>3.763380</td>\n      <td>9.187517</td>\n      <td>45.494183</td>\n    </tr>\n    <tr>\n      <th>2016</th>\n      <td>2016</td>\n      <td>955</td>\n      <td>4153.051634</td>\n      <td>4.348745</td>\n      <td>9.186849</td>\n      <td>45.498079</td>\n    </tr>\n    <tr>\n      <th>2017</th>\n      <td>2017</td>\n      <td>644</td>\n      <td>1553.643933</td>\n      <td>2.412491</td>\n      <td>9.172047</td>\n      <td>45.523293</td>\n    </tr>\n    <tr>\n      <th>2018</th>\n      <td>2018</td>\n      <td>345</td>\n      <td>739.342531</td>\n      <td>2.143022</td>\n      <td>9.169231</td>\n      <td>45.525436</td>\n    </tr>\n    <tr>\n      <th>2019</th>\n      <td>2019</td>\n      <td>3612</td>\n      <td>2430.388491</td>\n      <td>0.672865</td>\n      <td>9.083160</td>\n      <td>45.544785</td>\n    </tr>\n  </tbody>\n</table>\n<p>2020 rows × 6 columns</p>\n</div>"
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# replace orig_bs_id with index from 0 to N-1\n",
    "bs_table[\"bs_id\"] = bs_table.index\n",
    "bs_table"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "outputs": [],
   "source": [
    "# save aggregated raw data\n",
    "bs_table.to_csv(save_raw_pth, index=False)\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Do some prior analysis and plots"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "outputs": [
    {
     "data": {
      "text/plain": "      bs_id  num_users     workload  workload_mean   x_coord    y_coord\n0         0        798  1000.980770       1.254362  9.260330  45.392990\n1         1       1664  3206.357522       1.926898  9.146805  45.401688\n2         2        559  1960.867676       3.507813  9.213409  45.519791\n3         3        346   381.208988       1.101760  9.243438  45.559884\n4         4        325   837.206373       2.576020  9.232394  45.549929\n...     ...        ...          ...            ...       ...        ...\n2015   2015       1004  3778.433320       3.763380  9.187517  45.494183\n2016   2016        955  4153.051634       4.348745  9.186849  45.498079\n2017   2017        644  1553.643933       2.412491  9.172047  45.523293\n2018   2018        345   739.342531       2.143022  9.169231  45.525436\n2019   2019       3612  2430.388491       0.672865  9.083160  45.544785\n\n[2020 rows x 6 columns]",
      "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>bs_id</th>\n      <th>num_users</th>\n      <th>workload</th>\n      <th>workload_mean</th>\n      <th>x_coord</th>\n      <th>y_coord</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>798</td>\n      <td>1000.980770</td>\n      <td>1.254362</td>\n      <td>9.260330</td>\n      <td>45.392990</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>1664</td>\n      <td>3206.357522</td>\n      <td>1.926898</td>\n      <td>9.146805</td>\n      <td>45.401688</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2</td>\n      <td>559</td>\n      <td>1960.867676</td>\n      <td>3.507813</td>\n      <td>9.213409</td>\n      <td>45.519791</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3</td>\n      <td>346</td>\n      <td>381.208988</td>\n      <td>1.101760</td>\n      <td>9.243438</td>\n      <td>45.559884</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>4</td>\n      <td>325</td>\n      <td>837.206373</td>\n      <td>2.576020</td>\n      <td>9.232394</td>\n      <td>45.549929</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>2015</th>\n      <td>2015</td>\n      <td>1004</td>\n      <td>3778.433320</td>\n      <td>3.763380</td>\n      <td>9.187517</td>\n      <td>45.494183</td>\n    </tr>\n    <tr>\n      <th>2016</th>\n      <td>2016</td>\n      <td>955</td>\n      <td>4153.051634</td>\n      <td>4.348745</td>\n      <td>9.186849</td>\n      <td>45.498079</td>\n    </tr>\n    <tr>\n      <th>2017</th>\n      <td>2017</td>\n      <td>644</td>\n      <td>1553.643933</td>\n      <td>2.412491</td>\n      <td>9.172047</td>\n      <td>45.523293</td>\n    </tr>\n    <tr>\n      <th>2018</th>\n      <td>2018</td>\n      <td>345</td>\n      <td>739.342531</td>\n      <td>2.143022</td>\n      <td>9.169231</td>\n      <td>45.525436</td>\n    </tr>\n    <tr>\n      <th>2019</th>\n      <td>2019</td>\n      <td>3612</td>\n      <td>2430.388491</td>\n      <td>0.672865</td>\n      <td>9.083160</td>\n      <td>45.544785</td>\n    </tr>\n  </tbody>\n</table>\n<p>2020 rows × 6 columns</p>\n</div>"
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bs_table = pd.read_csv(save_raw_pth)\n",
    "bs_table\n",
    "\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "outputs": [
    {
     "data": {
      "text/plain": "             bs_id     num_users      workload  workload_mean      x_coord  \\\ncount  2020.000000   2020.000000   2020.000000    2020.000000  2020.000000   \nmean   1009.500000   1104.756931   1582.555599       2.249301     9.181048   \nstd     583.268092   1772.120093   1312.033596       1.840666     0.065195   \nmin       0.000000     48.000000     13.466486       0.070629     9.011536   \n25%     504.750000    341.750000    678.233962       0.910364     9.138822   \n50%    1009.500000    641.000000   1286.638581       1.694617     9.184459   \n75%    1514.250000   1273.250000   2060.797545       3.121914     9.227872   \nmax    2019.000000  38090.000000  11880.566761      17.459901     9.311943   \n\n           y_coord  \ncount  2020.000000  \nmean     45.479163  \nstd       0.045153  \nmin      45.356369  \n25%      45.451105  \n50%      45.479965  \n75%      45.512924  \nmax      45.567959  ",
      "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>bs_id</th>\n      <th>num_users</th>\n      <th>workload</th>\n      <th>workload_mean</th>\n      <th>x_coord</th>\n      <th>y_coord</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>2020.000000</td>\n      <td>2020.000000</td>\n      <td>2020.000000</td>\n      <td>2020.000000</td>\n      <td>2020.000000</td>\n      <td>2020.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>1009.500000</td>\n      <td>1104.756931</td>\n      <td>1582.555599</td>\n      <td>2.249301</td>\n      <td>9.181048</td>\n      <td>45.479163</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>583.268092</td>\n      <td>1772.120093</td>\n      <td>1312.033596</td>\n      <td>1.840666</td>\n      <td>0.065195</td>\n      <td>0.045153</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>0.000000</td>\n      <td>48.000000</td>\n      <td>13.466486</td>\n      <td>0.070629</td>\n      <td>9.011536</td>\n      <td>45.356369</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>504.750000</td>\n      <td>341.750000</td>\n      <td>678.233962</td>\n      <td>0.910364</td>\n      <td>9.138822</td>\n      <td>45.451105</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>1009.500000</td>\n      <td>641.000000</td>\n      <td>1286.638581</td>\n      <td>1.694617</td>\n      <td>9.184459</td>\n      <td>45.479965</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>1514.250000</td>\n      <td>1273.250000</td>\n      <td>2060.797545</td>\n      <td>3.121914</td>\n      <td>9.227872</td>\n      <td>45.512924</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>2019.000000</td>\n      <td>38090.000000</td>\n      <td>11880.566761</td>\n      <td>17.459901</td>\n      <td>9.311943</td>\n      <td>45.567959</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bs_table.describe()\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "outputs": [
    {
     "data": {
      "text/plain": "<seaborn.axisgrid.FacetGrid at 0x7f0c60d989d0>"
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 506.6x432 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.relplot(x=\"x_coord\", y=\"y_coord\", size=\"workload\",\n",
    "            sizes=(10, 100), alpha=.6, palette=\"muted\",\n",
    "            height=6, data=bs_table)\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "Normalize data\n"
   ],
   "metadata": {
    "collapsed": false
   }
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "outputs": [
    {
     "data": {
      "text/plain": "      bs_id  num_users     workload  workload_mean   x_coord    y_coord\n0         0        798  1000.980770       1.254362  9.260330  45.392990\n1         1       1664  3206.357522       1.926898  9.146805  45.401688\n2         2        559  1960.867676       3.507813  9.213409  45.519791\n3         3        346   381.208988       1.101760  9.243438  45.559884\n4         4        325   837.206373       2.576020  9.232394  45.549929\n...     ...        ...          ...            ...       ...        ...\n2015   2015       1004  3778.433320       3.763380  9.187517  45.494183\n2016   2016        955  4153.051634       4.348745  9.186849  45.498079\n2017   2017        644  1553.643933       2.412491  9.172047  45.523293\n2018   2018        345   739.342531       2.143022  9.169231  45.525436\n2019   2019       3612  2430.388491       0.672865  9.083160  45.544785\n\n[2020 rows x 6 columns]",
      "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>bs_id</th>\n      <th>num_users</th>\n      <th>workload</th>\n      <th>workload_mean</th>\n      <th>x_coord</th>\n      <th>y_coord</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>798</td>\n      <td>1000.980770</td>\n      <td>1.254362</td>\n      <td>9.260330</td>\n      <td>45.392990</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>1664</td>\n      <td>3206.357522</td>\n      <td>1.926898</td>\n      <td>9.146805</td>\n      <td>45.401688</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2</td>\n      <td>559</td>\n      <td>1960.867676</td>\n      <td>3.507813</td>\n      <td>9.213409</td>\n      <td>45.519791</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3</td>\n      <td>346</td>\n      <td>381.208988</td>\n      <td>1.101760</td>\n      <td>9.243438</td>\n      <td>45.559884</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>4</td>\n      <td>325</td>\n      <td>837.206373</td>\n      <td>2.576020</td>\n      <td>9.232394</td>\n      <td>45.549929</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>2015</th>\n      <td>2015</td>\n      <td>1004</td>\n      <td>3778.433320</td>\n      <td>3.763380</td>\n      <td>9.187517</td>\n      <td>45.494183</td>\n    </tr>\n    <tr>\n      <th>2016</th>\n      <td>2016</td>\n      <td>955</td>\n      <td>4153.051634</td>\n      <td>4.348745</td>\n      <td>9.186849</td>\n      <td>45.498079</td>\n    </tr>\n    <tr>\n      <th>2017</th>\n      <td>2017</td>\n      <td>644</td>\n      <td>1553.643933</td>\n      <td>2.412491</td>\n      <td>9.172047</td>\n      <td>45.523293</td>\n    </tr>\n    <tr>\n      <th>2018</th>\n      <td>2018</td>\n      <td>345</td>\n      <td>739.342531</td>\n      <td>2.143022</td>\n      <td>9.169231</td>\n      <td>45.525436</td>\n    </tr>\n    <tr>\n      <th>2019</th>\n      <td>2019</td>\n      <td>3612</td>\n      <td>2430.388491</td>\n      <td>0.672865</td>\n      <td>9.083160</td>\n      <td>45.544785</td>\n    </tr>\n  </tbody>\n</table>\n<p>2020 rows × 6 columns</p>\n</div>"
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bs_table = pd.read_csv(save_raw_pth)\n",
    "bs_table"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "outputs": [
    {
     "data": {
      "text/plain": "             bs_id     num_users      workload  workload_mean      x_coord  \\\ncount  2020.000000   2020.000000   2020.000000    2020.000000  2020.000000   \nmean   1009.500000   1104.756931   1582.555599       2.249301     0.564275   \nstd     583.268092   1772.120093   1312.033596       1.840666     0.217022   \nmin       0.000000     48.000000     13.466486       0.070629     0.000000   \n25%     504.750000    341.750000    678.233962       0.910364     0.423713   \n50%    1009.500000    641.000000   1286.638581       1.694617     0.575629   \n75%    1514.250000   1273.250000   2060.797545       3.121914     0.720143   \nmax    2019.000000  38090.000000  11880.566761      17.459901     1.000000   \n\n           y_coord  \ncount  2020.000000  \nmean      0.408759  \nstd       0.150305  \nmin       0.000000  \n25%       0.315357  \n50%       0.411429  \n75%       0.521144  \nmax       0.704344  ",
      "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>bs_id</th>\n      <th>num_users</th>\n      <th>workload</th>\n      <th>workload_mean</th>\n      <th>x_coord</th>\n      <th>y_coord</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>2020.000000</td>\n      <td>2020.000000</td>\n      <td>2020.000000</td>\n      <td>2020.000000</td>\n      <td>2020.000000</td>\n      <td>2020.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>1009.500000</td>\n      <td>1104.756931</td>\n      <td>1582.555599</td>\n      <td>2.249301</td>\n      <td>0.564275</td>\n      <td>0.408759</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>583.268092</td>\n      <td>1772.120093</td>\n      <td>1312.033596</td>\n      <td>1.840666</td>\n      <td>0.217022</td>\n      <td>0.150305</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>0.000000</td>\n      <td>48.000000</td>\n      <td>13.466486</td>\n      <td>0.070629</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>504.750000</td>\n      <td>341.750000</td>\n      <td>678.233962</td>\n      <td>0.910364</td>\n      <td>0.423713</td>\n      <td>0.315357</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>1009.500000</td>\n      <td>641.000000</td>\n      <td>1286.638581</td>\n      <td>1.694617</td>\n      <td>0.575629</td>\n      <td>0.411429</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>1514.250000</td>\n      <td>1273.250000</td>\n      <td>2060.797545</td>\n      <td>3.121914</td>\n      <td>0.720143</td>\n      <td>0.521144</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>2019.000000</td>\n      <td>38090.000000</td>\n      <td>11880.566761</td>\n      <td>17.459901</td>\n      <td>1.000000</td>\n      <td>0.704344</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# normalize coordinates\n",
    "bs_table[\"x_coord\"] = bs_table[\"x_coord\"] - bs_table[\"x_coord\"].min()\n",
    "bs_table[\"y_coord\"] = bs_table[\"y_coord\"] - bs_table[\"y_coord\"].min()\n",
    "max_val = max(bs_table[\"x_coord\"].max(), bs_table[\"y_coord\"].max())\n",
    "bs_table[\"x_coord\"] = bs_table[\"x_coord\"] / max_val\n",
    "bs_table[\"y_coord\"] = bs_table[\"y_coord\"] / max_val\n",
    "bs_table.describe()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "outputs": [],
   "source": [
    "# The maximum capacity is given by total workload divided by number of edge servers times a factor\n",
    "# the factor controls how tight the capacity constraint is\n",
    "max_cap_factors = [1.05, 1.1, 1.2, 1.5]\n",
    "k = 25  # we fix the number of ES to 25\n",
    "total_workload = bs_table[\"workload\"].sum()\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "outputs": [
    {
     "data": {
      "text/plain": "<seaborn.axisgrid.FacetGrid at 0x7f0c60e2ae80>"
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 568.55x432 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "normalizer = np.ceil((total_workload / k) * 1.05)\n",
    "bs_table[\"normalized_workload\"] = bs_table[\"workload\"] / normalizer\n",
    "sns.relplot(x=\"x_coord\", y=\"y_coord\", size=\"normalized_workload\",\n",
    "            sizes=(10, 100), alpha=.6, palette=\"muted\",\n",
    "            height=6, data=bs_table)\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saving dataset to:  data/CCP/benchmark/telecom_italia/data_norm_cap1.05_.dat\n",
      "Saving dataset to:  data/CCP/benchmark/telecom_italia/data_norm_cap1.1_.dat\n",
      "Saving dataset to:  data/CCP/benchmark/telecom_italia/data_norm_cap1.2_.dat\n",
      "Saving dataset to:  data/CCP/benchmark/telecom_italia/data_norm_cap1.5_.dat\n"
     ]
    }
   ],
   "source": [
    "# normalize with different capacity factors and save as np array\n",
    "for f in max_cap_factors:\n",
    "    # normalize workload\n",
    "    normalizer = np.ceil((total_workload / k) * f)\n",
    "    bs_table[\"normalized_workload\"] = bs_table[\"workload\"] / normalizer\n",
    "    save_table = bs_table[[\"num_users\", \"x_coord\", \"y_coord\", \"normalized_workload\"]]\n",
    "    # save as csv\n",
    "    save_table.to_csv(os.path.join(save_norm_pth, f\"data_norm_cap{f}_.csv\"))\n",
    "    # convert to numpy array\n",
    "    save_array = save_table.to_numpy(copy=True)\n",
    "    # save as serialized data\n",
    "    Generator.save_dataset(save_array, filepath=os.path.join(save_norm_pth, f\"data_norm_cap{f}_.dat\"))\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "name": "pycharm-5881f2c3",
   "language": "python",
   "display_name": "PyCharm (cluster2route)"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}