data_usage.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "Author: Alexander Genser\n",
    "Date: 2023-03-06\n",
    "Module Name: data_usage.ipynb\n",
    "Description: This notebook read the dataset and computes basic descriptives and showcases two features\n",
    "                that can be useful for any type of model engineering. \n",
    "             Further analysis and data cleaning needs to be done as the task at hand requires. \n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "# imports \n",
    "import requests\n",
    "import zipfile\n",
    "import io\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import datetime as dt\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mlp\n",
    "mlp.rcParams['figure.figsize'] = (15, 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set the URL of the zipped file\n",
    "url = \"https://www.research-collection.ethz.ch/bitstream/handle/20.500.11850/556642/Traffic_Signal_Control_Dataset.zip?sequence=1&isAllowed=y\"\n",
    "\n",
    "# Send a request to retrieve the file\n",
    "response = requests.get(url)\n",
    "\n",
    "# Read the content of the response into a BytesIO object\n",
    "zip_content = io.BytesIO(response.content)\n",
    "\n",
    "# Open the zip file using the zipfile module\n",
    "with zipfile.ZipFile(zip_content, 'r') as zip_file:\n",
    "    # Extract all the files in the zip archive\n",
    "    zip_file.extractall()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
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       "                  time   d1   d2   d3   d4   d5   d6   d7   d8   d9  ...  sg3  \\\n",
       "0  2019-02-01 00:07:09  0.0  0.0  0.0  0.0  0.0  1.0  1.0  0.0  0.0  ...  1.0   \n",
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       "2  2019-02-01 00:07:11  0.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0  0.0  ...  1.0   \n",
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       "4  2019-02-01 00:07:13  0.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0  0.0  ...  0.0   \n",
       "\n",
       "   sg4  sg5  sg6  sg7  sg8  sg9  sg10  sg11  sg12  \n",
       "0  0.0  0.0  1.0  1.0  0.0  1.0     0   1.0   0.0  \n",
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       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load first file from dataset and print head information about dataframe\n",
    "dataset = pd.read_csv('intersection_data_set_feb_01_15.csv')\n",
    "dataset.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:8: 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",
      "  \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Detection')"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# take detector 5 at the intersection as example and retrieve a 5 minute sample to plot the detections\n",
    "detector = 'd5'\n",
    "time_from = dt.datetime(2019, 2, 1, 7, 0, 0)\n",
    "time_to = dt.datetime(2019, 2, 1, 7, 5, 0)\n",
    "\n",
    "# filtering data\n",
    "detector_snippet = dataset[['time', detector]]\n",
    "detector_snippet['time'] = pd.to_datetime(detector_snippet['time'])\n",
    "detector_snippet = detector_snippet.loc[(detector_snippet.time < time_to)]\n",
    "detector_snippet = detector_snippet.loc[(detector_snippet.time > time_from)]   \n",
    "                               \n",
    "#plot detections\n",
    "plt.plot(detector_snippet['time'], detector_snippet[detector])\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Detection')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:8: 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",
      "  \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Detection')"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# take detector 4 at the intersection as example and retrieve a multiple hours of data\n",
    "detector = 'd4'\n",
    "time_from = dt.datetime(2019, 2, 1, 7, 0, 0)\n",
    "time_to = dt.datetime(2019, 2, 1, 16, 30, 0)\n",
    "\n",
    "# filtering data\n",
    "detector_snippet = dataset[['time', detector]]\n",
    "detector_snippet['time'] = pd.to_datetime(detector_snippet['time'])\n",
    "detector_snippet = detector_snippet.loc[(detector_snippet.time < time_to)]\n",
    "detector_snippet = detector_snippet.loc[(detector_snippet.time > time_from)]   \n",
    "                               \n",
    "# plot cumulative detections as they can show if a detector (a) has periods where it shows constant detections or (b) no detections\n",
    "# further data cleaning might be done from here\n",
    "plt.plot(detector_snippet['time'], detector_snippet[detector].cumsum())\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Detection')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\cbook\\__init__.py:1402: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  x[:, None]\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_base.py:276: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  x = x[:, np.newaxis]\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_base.py:278: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  y = y[:, np.newaxis]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2652d132fc8>]"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# sample feature computation of the Time last occupation (TLO) of detector x\n",
    " # if detector = 1 -> TLO = 0 \n",
    " # else count ++\n",
    "\n",
    "# doing the work for all detectors in the 5 minute data snippet\n",
    "filter_col = [col for col in detector_snippet if col.startswith('d')]\n",
    "for det in detector_snippet.loc[:,filter_col].columns: \n",
    "    detector_snippet['TLO_' + str(det)] = np.where(detector_snippet[det]== 1, \n",
    "                                 0, detector_snippet.groupby((detector_snippet[det] != detector_snippet[det].shift(1)).cumsum()).cumcount())\n",
    "\n",
    "\n",
    "# plot the feature for detector 5 from above\n",
    "f, (ax1, ax2) = plt.subplots(2,1)\n",
    "ax1.plot(detector_snippet['time'], detector_snippet['TLO_' + detector])\n",
    "ax1.set_ylabel('TLO')\n",
    "ax2.plot(detector_snippet['time'], detector_snippet[detector])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "# sample fature computation of the traffic flow during a red and green phase of a corresponding traffic light\n",
    "# create the traffic flow since the last red; \n",
    "# if signal is red F == 0\n",
    "# else if signal green\n",
    "#  cumulate detections\n",
    "\n",
    "#create map of devices (as we need to know which detector serves which signal for traffic flow)\n",
    "mapping = {'sg':[col for col in dataset if col.startswith('sg')],\n",
    "           'det':['NA', 'd4', 'd4', 'd5', 'd5', 'd6', 'NA', 'NA', 'NA', 'NA', 'd7', 'd2']}\n",
    "infra_map = pd.DataFrame(mapping)\n",
    "identifier_red = 0\n",
    "identifier_green = 1\n",
    "\n",
    "#flow features is only constructed with a detector serves that traffic light\n",
    "for sg, det in infra_map.itertuples(index=False):\n",
    "    if det != 'NA':\n",
    "        dataset['Count_FR'] = np.cumsum((dataset[sg] != dataset[sg].shift(1)) & (dataset[sg] == identifier_red))\n",
    "        dataset['FR_' + str(sg)] = np.where((dataset[sg] == identifier_red), \n",
    "                                                     dataset[det].groupby(dataset['Count_FR']).apply(lambda x:  ((x - x.shift(-1)) > 0).cumsum()), 0)\n",
    "        dataset['Count_FG'] = np.cumsum((dataset[sg] != dataset[sg].shift(1)) & (dataset[sg] == identifier_green))\n",
    "        dataset['FG_' + str(sg)] = np.where((dataset[sg] == identifier_green), \n",
    "                                                     dataset[det].groupby(dataset['Count_FG']).apply(lambda x:  ((x - x.shift(-1)) > 0).cumsum()), 0)\n",
    "\n",
    "# eliminate counting columns (remove if these are needed)\n",
    "dataset = dataset.drop(['Count_FR'],axis=1)\n",
    "dataset = dataset.drop(['Count_FG'],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['time', 'd5'], dtype='object')"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_snippet.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\cbook\\__init__.py:1402: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  x[:, None]\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_base.py:276: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  x = x[:, np.newaxis]\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_base.py:278: FutureWarning: Support for multi-dimensional indexing (e.g. `obj[:, None]`) is deprecated and will be removed in a future version.  Convert to a numpy array before indexing instead.\n",
      "  y = y[:, np.newaxis]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x2657231fe48>]"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the feature for detector 5 from above\n",
    "time_from = dt.datetime(2019, 2, 1, 7, 0, 0)\n",
    "time_to = dt.datetime(2019, 2, 1, 7, 5, 0)\n",
    "\n",
    "# filtering data\n",
    "dataset['time'] = pd.to_datetime(dataset['time'])\n",
    "data_snippet = dataset.loc[(dataset.time > time_from)]\n",
    "data_snippet = data_snippet.loc[(data_snippet.time < time_to)]   \n",
    "\n",
    "f, (ax1, ax2) = plt.subplots(2,1)\n",
    "ax1.plot(data_snippet['time'], data_snippet['FR_sg5'])\n",
    "ax1.set_ylabel('FR')\n",
    "ax2.plot(data_snippet['time'], data_snippet[detector])"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}