volumes/miovision/dev_notebooks/01 - Test of Miovision Data Completeness With Cadence.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Test of Miovision Data Completeness With Cadence\n",
    "\n",
    "In the `pull_data` function in `intersection_tmc.py`, data is retrieved using the Miovision API with a cadence given by `time_delta`. Recently Aakash and Raph [discovered](https://github.com/CityofToronto/bdit_data-sources/issues/323#issuecomment-681085745) that if this cadence were set too large, the Miovision server would return incomplete data. In this notebook, we check for the largest cadence that returns complete data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "from requests import exceptions\n",
    "import pytz\n",
    "from datetime import timedelta, datetime\n",
    "import math\n",
    "import time\n",
    "import json\n",
    "import pathlib\n",
    "import configparser\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import missingno as msno"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "config = configparser.ConfigParser()\n",
    "config.read(pathlib.Path.home().joinpath('.charlesconfig').as_posix());\n",
    "miov_token = config['MIOVISION']['key']\n",
    "\n",
    "local_tz = pytz.timezone('US/Eastern')\n",
    "url='https://api.miovision.com/intersections/' \n",
    "tmc_endpoint = '/tmc'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "class MiovisionAPIException(Exception):\n",
    "    \"\"\"Base class for exceptions.\"\"\"\n",
    "    pass\n",
    "  \n",
    "\n",
    "class TimeoutException(Exception):\n",
    "    \"\"\"Exception if API gives a 504 error\"\"\"\n",
    "    pass\n",
    "    \n",
    "\n",
    "class NotFoundError(Exception):\n",
    "    \"\"\"Exception for a 404 error.\"\"\"\n",
    "    pass\n",
    "\n",
    "\n",
    "def poll_miovision_api(start_time, end_time,\n",
    "                       intersection_id='6174c193-13a9-4056-832b-f80783eaf333'):\n",
    "\n",
    "    headers = {'Content-Type':'application/json', 'Authorization': miov_token} \n",
    "    params = {'endTime': end_time, 'startTime' : start_time}\n",
    "\n",
    "    response = requests.get(url + intersection_id + tmc_endpoint,\n",
    "                            params=params, headers=headers)\n",
    "\n",
    "    if response.status_code == 200:\n",
    "        tmc = json.loads(response.content.decode('utf-8'))\n",
    "        if len(tmc) == 0:\n",
    "            raise ValueError(\"Successfully returned nothing...\")\n",
    "        tmc = pd.DataFrame(tmc)\n",
    "        # [tmc['class'] == \"Light\"]\n",
    "        tmc_counts = (tmc.loc[:]\n",
    "                      .groupby(\"timestamp\")['timestamp'].count().sort_index())\n",
    "        tmc_counts.index = pd.to_datetime(tmc_counts.index)\n",
    "        return tmc_counts\n",
    "    elif response.status_code == 404:\n",
    "        error = json.loads(response.content.decode('utf-8'))\n",
    "        raise NotFoundError(error['error'])\n",
    "    elif response.status_code == 400:\n",
    "        error=json.loads(response.content.decode('utf-8'))\n",
    "        raise MiovisionAPIException(error['error'])\n",
    "    elif response.status_code == 504:\n",
    "        raise TimeoutException('Error'+str(response.status_code))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reproduce Raph's Comment\n",
    "\n",
    "Reproduce Raph's comment [here](https://github.com/CityofToronto/bdit_data-sources/issues/323#issuecomment-681085745)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "start_time = local_tz.localize(datetime(2020, 8, 20))\n",
    "end_time = local_tz.localize(datetime(2020, 8, 21, 1))\n",
    "\n",
    "tlc_full_day = poll_miovision_api(start_time, end_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Series([], Name: timestamp, dtype: int64)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tlc_full_day[tlc_full_day.index > pd.Timestamp(\"2020-08-20 17:00:00\", tz=local_tz)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "start_time = local_tz.localize(datetime(2020, 8, 20, 17))\n",
    "end_time = local_tz.localize(datetime(2020, 8, 21, 1))\n",
    "\n",
    "tlc_evening_only = poll_miovision_api(start_time, end_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4500"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tlc_evening_only.sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is the same number of rows Raph reports."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Check Data Completeness with Cadence\n",
    "\n",
    "Now we find the largest cadence that returns all available data in the day."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def timerange(start_time, end_time, dt):\n",
    "    for i in range(math.ceil((end_time - start_time) / dt)):\n",
    "        c_start = start_time + i * dt\n",
    "        yield (c_start, c_start + dt)\n",
    "\n",
    "\n",
    "def compare_cadences(start_time, end_time, dt_range,\n",
    "                     intersection_id='6174c193-13a9-4056-832b-f80783eaf333',\n",
    "                     retry_times=3):\n",
    "\n",
    "    df = pd.DataFrame(index=pd.date_range(start_time, end_time, freq=\"60 s\")[:-1])\n",
    "\n",
    "    for cdtval in dt_range:\n",
    "        cdt = timedelta(hours=cdtval)\n",
    "        \n",
    "        joined_data = []\n",
    "        for (c_start, c_end) in timerange(start_time, end_time, cdt):\n",
    "            for i in range(retry_times):\n",
    "                cjd = None\n",
    "                try:\n",
    "                    cjd = poll_miovision_api(c_start, c_end,\n",
    "                                             intersection_id=intersection_id)\n",
    "                except exceptions.ProxyError as prox:\n",
    "                    print(\"ProxyError; retrying in 2 minutes\")\n",
    "                    time.sleep(120)\n",
    "                except exceptions.RequestException as err:\n",
    "                    print(\"RequestException; retrying in 1:15 minutes\")\n",
    "                    time.sleep(75)\n",
    "                except NotFoundError:\n",
    "                    break\n",
    "                except TimeoutException as exc_504:\n",
    "                    print(exc_504)\n",
    "                    sleep(60)\n",
    "                except MiovisionAPIException as miovision_exc:\n",
    "                    print(miovision_exc)\n",
    "                    break\n",
    "                else:\n",
    "                    break\n",
    "        \n",
    "            if cjd is None:\n",
    "                raise ValueError(\"API didn't return anything.\")\n",
    "            joined_data.append(cjd)\n",
    "                \n",
    "        joined_data = pd.concat(joined_data)\n",
    "\n",
    "        df[int(cdtval)] = joined_data\n",
    "    \n",
    "    return df.sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "start_time = local_tz.localize(datetime(2020, 8, 20))\n",
    "end_time = local_tz.localize(datetime(2020, 8, 24))\n",
    "dt_range = [4., 6., 12., 24., 48.]\n",
    "\n",
    "df_cc = compare_cadences(start_time, end_time, dt_range)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7ffdbb34de80>"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1800x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "msno.matrix(df_cc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have substantial data gaps when using a cadence of 24 hours or more. It appears that only the first ~950 or so data points are returned (this changes from block to block)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2020-08-20 03:52:00-04:00   NaN\n",
       "2020-08-20 04:03:00-04:00   NaN\n",
       "2020-08-20 05:09:00-04:00   NaN\n",
       "2020-08-21 01:48:00-04:00   NaN\n",
       "2020-08-22 03:02:00-04:00   NaN\n",
       "2020-08-22 04:34:00-04:00   NaN\n",
       "2020-08-23 02:12:00-04:00   NaN\n",
       "2020-08-23 04:00:00-04:00   NaN\n",
       "2020-08-23 06:13:00-04:00   NaN\n",
       "Name: 4, dtype: float64"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cc.sort_index().loc[df_cc[4].isna(), 4]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Even in the case of 4 hour cadence there is missing data, but these are isolated minutes in the middle of the night, so are probably just minutes where no vehicles were detected.\n",
    "\n",
    "We see the same pattern on another day."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "start_time = local_tz.localize(datetime(2020, 7, 15))\n",
    "end_time = local_tz.localize(datetime(2020, 7, 19))\n",
    "dt_range = [4., 6., 12., 24., 48.]\n",
    "\n",
    "df_cc = compare_cadences(start_time, end_time, dt_range)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7ffdde5b9ef0>"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "msno.matrix(df_cc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Though this day has an extended period of time when there were no vehicles, perhaps indicating a camera failure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2020-07-15 02:57:00-04:00   NaN\n",
       "2020-07-15 03:36:00-04:00   NaN\n",
       "2020-07-15 11:03:00-04:00   NaN\n",
       "2020-07-15 11:04:00-04:00   NaN\n",
       "2020-07-15 11:05:00-04:00   NaN\n",
       "2020-07-15 11:06:00-04:00   NaN\n",
       "2020-07-15 11:07:00-04:00   NaN\n",
       "2020-07-15 11:08:00-04:00   NaN\n",
       "2020-07-15 11:09:00-04:00   NaN\n",
       "2020-07-15 11:10:00-04:00   NaN\n",
       "2020-07-15 11:11:00-04:00   NaN\n",
       "2020-07-15 11:12:00-04:00   NaN\n",
       "2020-07-15 11:13:00-04:00   NaN\n",
       "2020-07-15 11:14:00-04:00   NaN\n",
       "2020-07-15 11:15:00-04:00   NaN\n",
       "2020-07-15 11:16:00-04:00   NaN\n",
       "2020-07-15 11:17:00-04:00   NaN\n",
       "2020-07-15 11:18:00-04:00   NaN\n",
       "2020-07-15 11:19:00-04:00   NaN\n",
       "2020-07-15 11:20:00-04:00   NaN\n",
       "2020-07-15 11:21:00-04:00   NaN\n",
       "2020-07-15 11:22:00-04:00   NaN\n",
       "2020-07-15 11:23:00-04:00   NaN\n",
       "2020-07-15 11:24:00-04:00   NaN\n",
       "2020-07-15 11:25:00-04:00   NaN\n",
       "2020-07-15 11:26:00-04:00   NaN\n",
       "2020-07-15 11:27:00-04:00   NaN\n",
       "2020-07-15 11:28:00-04:00   NaN\n",
       "2020-07-15 11:29:00-04:00   NaN\n",
       "2020-07-15 11:30:00-04:00   NaN\n",
       "2020-07-15 11:31:00-04:00   NaN\n",
       "2020-07-15 11:32:00-04:00   NaN\n",
       "2020-07-15 11:33:00-04:00   NaN\n",
       "2020-07-15 11:34:00-04:00   NaN\n",
       "2020-07-15 11:35:00-04:00   NaN\n",
       "2020-07-15 11:36:00-04:00   NaN\n",
       "2020-07-15 11:37:00-04:00   NaN\n",
       "2020-07-15 11:38:00-04:00   NaN\n",
       "2020-07-15 11:39:00-04:00   NaN\n",
       "2020-07-15 11:40:00-04:00   NaN\n",
       "2020-07-15 11:41:00-04:00   NaN\n",
       "2020-07-15 11:42:00-04:00   NaN\n",
       "2020-07-15 11:43:00-04:00   NaN\n",
       "2020-07-15 11:44:00-04:00   NaN\n",
       "2020-07-15 11:45:00-04:00   NaN\n",
       "2020-07-15 11:46:00-04:00   NaN\n",
       "2020-07-16 03:26:00-04:00   NaN\n",
       "2020-07-16 03:41:00-04:00   NaN\n",
       "2020-07-16 03:54:00-04:00   NaN\n",
       "2020-07-16 04:03:00-04:00   NaN\n",
       "2020-07-17 02:03:00-04:00   NaN\n",
       "2020-07-17 03:26:00-04:00   NaN\n",
       "2020-07-17 03:27:00-04:00   NaN\n",
       "2020-07-17 04:23:00-04:00   NaN\n",
       "2020-07-18 01:50:00-04:00   NaN\n",
       "2020-07-18 02:29:00-04:00   NaN\n",
       "Name: 4, dtype: float64"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cc.sort_index().loc[df_cc[4].isna(), 4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4     15.0\n",
       "6     15.0\n",
       "12    15.0\n",
       "24     NaN\n",
       "48     NaN\n",
       "Name: 2020-07-15 16:56:00-04:00, dtype: float64"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cc.iloc[1016, :]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at another station, we see the same thing:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "start_time = local_tz.localize(datetime(2020, 7, 15))\n",
    "end_time = local_tz.localize(datetime(2020, 7, 19))\n",
    "dt_range = [4., 6., 12., 24., 48.]\n",
    "\n",
    "df_cc = compare_cadences(start_time, end_time, dt_range, intersection_id=\"169471e1-c460-489a-9765-352c0803990b\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7ffdb9a04358>"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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XPsGkqm0ZbJx5IMkfk7yY5LwkVyY5I8nPk1zVWnt57LrXJ/lzBpt3vmmB9cPnz/9Sku1Jnk1yT5LZJJdnsPzJr5J8sLU2O2/+qgx+Af7+JI8meSDJuUmuzmADz82ttUdO+AUBAAAAAFj2pg3ilyXZmuTdSc7JYL3wI0l+k2RPkj1twgdV1aeSfDvJXa2166Z6oKqPJflskk1JXpvB0ig/SHJba+2fE+avTnJLkusyiOEvJNmf5NbW2hPT3BMAAAAAgOVvqiAOAAAAAACnu6XYVBMAAAAAAF71BHEAAAAAALogiAMAAAAA0AVBHAAAAACALgjiAAAAAAB0QRAHAAAAAKALgjgAAAAAAF0QxAEAAAAA6IIgDgAAAABAFwRxAAAAAAC68G9hSS/wyPluLgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1800x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "msno.matrix(df_cc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another station at another time of year:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [],
   "source": [
    "start_time = local_tz.localize(datetime(2019, 12, 3))\n",
    "end_time = local_tz.localize(datetime(2019, 12, 7))\n",
    "dt_range = [4., 6., 12., 24., 48.]\n",
    "\n",
    "df_cc = compare_cadences(start_time, end_time, dt_range, intersection_id=\"151ee827-dd49-4ad4-af21-dde5d7032c21\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7ffdb35ff048>"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1800x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "msno.matrix(df_cc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And yet another."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [],
   "source": [
    "start_time = local_tz.localize(datetime(2019, 5, 25))\n",
    "end_time = local_tz.localize(datetime(2019, 5, 29))\n",
    "dt_range = [4., 6., 12., 24., 48.]\n",
    "\n",
    "df_cc = compare_cadences(start_time, end_time, dt_range, intersection_id=\"02a90c2e-b428-4f89-9422-327c8c91671d\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7ffdb353fb00>"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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DAAAAADCEfwF3mzYE4jFU0AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1800x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "msno.matrix(df_cc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So it looks like picking any cadence of 12 hours or less will return all available data, though we'll need to test this hypothesis at scale."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.6.9 64-bit ('bditto': venv)",
   "language": "python",
   "name": "python36964bitbdittovenvd56842b7ca0c4929925d70f8c4c7f13a"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}