public_transport_meeting.ipynb

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  "metadata": {
    "colab": {
      "name": "public_transport_meeting.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "mount_file_id": "1wHAaAsz0x6F_wx515ZN-vcxcRcc6f3Pq",
      "authorship_tag": "ABX9TyN6QH5svgua2T3hViUG/HHt",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ronaldexim/public_transport/blob/master/public_transport_meeting.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e3fPLbb0h3p7",
        "colab_type": "code",
        "outputId": "ec0a8b9c-7ae2-4087-e6b6-05cb1efd4844",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        }
      },
      "source": [
        "!pip install --upgrade tables                                                              # potrzebne do wczytania zawartości  plików hdf5 do colaba"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting tables\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/ed/c3/8fd9e3bb21872f9d69eb93b3014c86479864cca94e625fd03713ccacec80/tables-3.6.1-cp36-cp36m-manylinux1_x86_64.whl (4.3MB)\n",
            "\u001b[K     |████████████████████████████████| 4.3MB 2.8MB/s \n",
            "\u001b[?25hRequirement already satisfied, skipping upgrade: numexpr>=2.6.2 in /usr/local/lib/python3.6/dist-packages (from tables) (2.7.1)\n",
            "Requirement already satisfied, skipping upgrade: numpy>=1.9.3 in /usr/local/lib/python3.6/dist-packages (from tables) (1.18.1)\n",
            "Installing collected packages: tables\n",
            "  Found existing installation: tables 3.4.4\n",
            "    Uninstalling tables-3.4.4:\n",
            "      Successfully uninstalled tables-3.4.4\n",
            "Successfully installed tables-3.6.1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9daKTQMflFgW",
        "colab_type": "text"
      },
      "source": [
        "### Należy pobrać udostępnioną zawartość do katalogu na swoim Google Drive. \n",
        "### public_transport.7z to wersja spakowana plików z danymi.\n",
        "### Następnie łączymy Colaba z własnym Google Drive - ikonka katalogu po lewej stronie i \"Mount Drive\".\n",
        "### Ścieżkę w następnej komórce należy dopasować to katalogu gdzie wgrano pliki.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1BiQnjekjq1S",
        "colab_type": "code",
        "outputId": "96b7ac39-70ac-40cc-b7a0-512773622492",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "cd \"/content/drive/My Drive/public_transport\""
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/drive/My Drive/public_transport\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qRkRRG64nayc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import pickle\n",
        "import urllib.request, json\n",
        "import datetime\n",
        "import geopy.distance\n",
        "from tqdm import tqdm\n",
        "\n",
        "apikey = ''"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xKm8qrQCoFqa",
        "colab_type": "code",
        "outputId": "15982dff-ece1-4563-8147-981d859a19ea",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "stops_df = pd.read_hdf('public_transport_warsaw.h5', key='stops_df_4', mode='r')\n",
        "lines_df = pd.read_hdf('public_transport_warsaw.h5', key='lines_df', mode='r')\n",
        "schedule_df = pd.read_hdf('public_transport_warsaw2.h5', key='schedule_df_2', mode='r')\n",
        "GPS_df = pd.read_hdf('public_transport_warsaw3.h5', key='GPS_df_1', mode='r')\n",
        "print(stops_df.shape, lines_df.shape, schedule_df.shape, GPS_df.shape)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(7727, 19) (345, 3) (918719, 13) (1160129, 13)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l4c96nPvaZgl",
        "colab_type": "code",
        "outputId": "1fafe95f-d267-47ca-bbe3-6356163e2d69",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        }
      },
      "source": [
        "GPS_df.dtypes"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Lines                    object\n",
              "VehicleNumber            object\n",
              "Brigade                  object\n",
              "Time             datetime64[ns]\n",
              "Lon                     float64\n",
              "Lat                     float64\n",
              "res_type                 object\n",
              "qTime            datetime64[ns]\n",
              "d_time                    int64\n",
              "d_dist                  float64\n",
              "coord                    object\n",
              "uniqID                   object\n",
              "vehID                    object\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rD4jhtUhakmJ",
        "colab_type": "code",
        "outputId": "dd214561-5fb3-41c5-9c12-5b5f69594038",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "GPS_df.tail()\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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            "text/plain": [
              "        Lines VehicleNumber  ...                         uniqID      vehID\n",
              "1160124    14          3824  ...  14_3824_3_2020-02-12 10:54:39  14_3824_3\n",
              "1160125    13          3827  ...  13_3827_1_2020-02-12 10:54:40  13_3827_1\n",
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              "1160127     6          3829  ...   6_3829_9_2020-02-12 10:54:41   6_3829_9\n",
              "1160128     6          3830  ...   6_3830_5_2020-02-12 10:54:42   6_3830_5\n",
              "\n",
              "[5 rows x 13 columns]"
            ]
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          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W74WKr0wqsZT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "stop_coords = stops_df[stops_df.actual].set_index('id_nu')[['coord']].to_dict('dict')['coord']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ImThPqi1r2gg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# klucz (linia, brygada), zawiera listy z kolejnymi przystankami i planowanymi godzinami przyjazdu, czas i odległość od początku trasy\n",
        "\n",
        "with open('schedule_dict.pickle', 'rb') as fp:\n",
        "    schedule_dict = pickle.load(fp)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lVXttVjdcMuz",
        "colab_type": "code",
        "outputId": "bf803ac6-c9e1-4d63-9a49-d7eec04d8931",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "schedule_dict[('122', '4')]"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[['5050_08', '05:41:00', 'Gwiaździsta', 'TP-GWI', 0, 0.0, 0.0],\n",
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              " ['5051_02', '05:43:00', 'Gwiaździsta', 'TP-GWI', 0, 120.0, 0.51314],\n",
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              " ['5054_02', '05:46:00', 'Gwiaździsta', 'TP-GWI', 0, 300.0, 2.65051],\n",
              " ['5055_07', '05:47:00', 'Gwiaździsta', 'TP-GWI', 0, 360.0, 2.88631],\n",
              " ['5110_01', '05:48:00', 'Gwiaździsta', 'TP-GWI', 0, 420.0, 3.40359],\n",
              " ['5063_03', '05:50:00', 'Gwiaździsta', 'TP-GWI', 0, 540.0, 4.12568],\n",
              " ['5106_01', '05:52:00', 'Gwiaździsta', 'TP-GWI', 0, 660.0, 4.65462],\n",
              " ['5107_01', '05:53:00', 'Gwiaździsta', 'TP-GWI', 0, 720.0, 5.06485],\n",
              " ['5064_07', '05:55:00', 'Gwiaździsta', 'TP-GWI', 0, 840.0, 5.72204],\n",
              " ['5102_01', '05:56:00', 'Gwiaździsta', 'TP-GWI', 0, 900.0, 6.31435],\n",
              " ['5062_01', '05:58:00', 'Gwiaździsta', 'TP-GWI', 0, 1020.0, 6.99106],\n",
              " ['5065_01', '05:59:00', 'Gwiaździsta', 'TP-GWI', 0, 1080.0, 7.63714],\n",
              " ['6047_03', '06:00:00', 'Gwiaździsta', 'TP-GWI', 0, 1140.0, 7.87316],\n",
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              " ['6091_04', '06:08:00', 'Gwiaździsta', 'TP-GWI', 0, 1620.0, 10.38083],\n",
              " ['6088_01', '06:09:00', 'Gwiaździsta', 'TP-GWI', 0, 1680.0, 10.87806],\n",
              " ['6040_05', '06:11:00', 'Gwiaździsta', 'TP-GWI', 0, 1800.0, 11.29773],\n",
              " ['6094_05', '06:12:00', 'Gwiaździsta', 'TP-GWI', 0, 1860.0, 11.59084],\n",
              " ['6098_01', '06:13:00', 'Gwiaździsta', 'TP-GWI', 0, 1920.0, 11.87256],\n",
              " ['6003_09', '06:15:00', 'Gwiaździsta', 'TP-GWI', 0, 2040.0, 12.31164],\n",
              " ['6003_04', '06:17:00', 'Gwiaździsta', 'TP-GWI', 0, 2160.0, 12.47587],\n",
              " ['6076_02', '06:18:00', 'Gwiaździsta', 'TP-GWI', 0, 2220.0, 12.84712],\n",
              " ['6078_02', '06:19:00', 'Gwiaździsta', 'TP-GWI', 0, 2280.0, 13.24607],\n",
              " ['6079_02', '06:21:00', 'Gwiaździsta', 'TP-GWI', 0, 2400.0, 13.63593],\n",
              " ['6080_02', '06:22:00', 'Gwiaździsta', 'TP-GWI', 0, 2460.0, 14.0053],\n",
              " ['6082_02', '06:23:00', 'Gwiaździsta', 'TP-GWI', 0, 2520.0, 14.80601],\n",
              " ['6081_02', '06:23:00', 'Gwiaździsta', 'TP-GWI', 0, 2520.0, 15.09508],\n",
              " ['6082_06', '06:36:00', 'os.Górczewska', 'TP-GOR', 1, 3300.0, 15.58569],\n",
              " ['6082_01', '06:37:00', 'os.Górczewska', 'TP-GOR', 1, 3360.0, 15.94626],\n",
              " ['6081_01', '06:37:00', 'os.Górczewska', 'TP-GOR', 1, 3360.0, 16.17695],\n",
              " ['6080_01', '06:38:00', 'os.Górczewska', 'TP-GOR', 1, 3420.0, 16.70639],\n",
              " ['6079_01', '06:39:00', 'os.Górczewska', 'TP-GOR', 1, 3480.0, 17.08048],\n",
              " ['6078_01', '06:40:00', 'os.Górczewska', 'TP-GOR', 1, 3540.0, 17.44519],\n",
              " ['6077_01', '06:41:00', 'os.Górczewska', 'TP-GOR', 1, 3600.0, 17.63147],\n",
              " ['6076_01', '06:42:00', 'os.Górczewska', 'TP-GOR', 1, 3660.0, 17.86617],\n",
              " ['6003_03', '06:43:00', 'os.Górczewska', 'TP-GOR', 1, 3720.0, 18.23097],\n",
              " ['6003_06', '06:44:00', 'os.Górczewska', 'TP-GOR', 1, 3780.0, 18.36909],\n",
              " ['6098_02', '06:45:00', 'os.Górczewska', 'TP-GOR', 1, 3840.0, 18.61937],\n",
              " ['6094_06', '06:46:00', 'os.Górczewska', 'TP-GOR', 1, 3900.0, 19.07291],\n",
              " ['6040_06', '06:48:00', 'os.Górczewska', 'TP-GOR', 1, 4020.0, 19.44845],\n",
              " ['6088_02', '06:49:00', 'os.Górczewska', 'TP-GOR', 1, 4080.0, 19.77968],\n",
              " ['6091_02', '06:50:00', 'os.Górczewska', 'TP-GOR', 1, 4140.0, 20.32936],\n",
              " ['6093_02', '06:51:00', 'os.Górczewska', 'TP-GOR', 1, 4200.0, 20.6355],\n",
              " ['6129_02', '06:52:00', 'os.Górczewska', 'TP-GOR', 1, 4260.0, 21.00338],\n",
              " ['6155_03', '06:53:00', 'os.Górczewska', 'TP-GOR', 1, 4320.0, 21.58388],\n",
              " ['6131_02', '06:55:00', 'os.Górczewska', 'TP-GOR', 1, 4440.0, 21.86975],\n",
              " ['6132_02', '06:56:00', 'os.Górczewska', 'TP-GOR', 1, 4500.0, 22.27519],\n",
              " ['6047_01', '06:58:00', 'os.Górczewska', 'TP-GOR', 1, 4620.0, 22.74569],\n",
              " ['5065_02', '06:59:00', 'os.Górczewska', 'TP-GOR', 1, 4680.0, 23.04751],\n",
              " ['5062_03', '07:01:00', 'os.Górczewska', 'TP-GOR', 1, 4800.0, 23.63226],\n",
              " ['5102_02', '07:03:00', 'os.Górczewska', 'TP-GOR', 1, 4920.0, 24.2504],\n",
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              " ['6131_02', '17:51:00', 'os.Górczewska', 'TP-GOR', 11, 43800.0, 168.6479],\n",
              " ['6132_02', '17:52:00', 'os.Górczewska', 'TP-GOR', 11, 43860.0, 169.05334],\n",
              " ['6047_01', '17:54:00', 'os.Górczewska', 'TP-GOR', 11, 43980.0, 169.52384],\n",
              " ['5065_02', '17:55:00', 'os.Górczewska', 'TP-GOR', 11, 44040.0, 169.82566],\n",
              " ['5062_03', '17:57:00', 'os.Górczewska', 'TP-GOR', 11, 44160.0, 170.41041],\n",
              " ['5102_02', '17:59:00', 'os.Górczewska', 'TP-GOR', 11, 44280.0, 171.02855],\n",
              " ['5064_04', '18:01:00', 'os.Górczewska', 'TP-GOR', 11, 44400.0, 171.67862],\n",
              " ['5107_02', '18:02:00', 'os.Górczewska', 'TP-GOR', 11, 44460.0, 172.06846],\n",
              " ['5106_02', '18:04:00', 'os.Górczewska', 'TP-GOR', 11, 44580.0, 172.61874],\n",
              " ['5063_04', '18:06:00', 'os.Górczewska', 'TP-GOR', 11, 44700.0, 173.23248],\n",
              " ['5110_02', '18:07:00', 'os.Górczewska', 'TP-GOR', 11, 44760.0, 173.75794],\n",
              " ['5055_01', '18:09:00', 'os.Górczewska', 'TP-GOR', 11, 44880.0, 174.40602],\n",
              " ['5054_01', '18:10:00', 'os.Górczewska', 'TP-GOR', 11, 44940.0, 174.7612],\n",
              " ['5053_01', '18:11:00', 'os.Górczewska', 'TP-GOR', 11, 45000.0, 175.21787],\n",
              " ['5163_01', '18:12:00', 'os.Górczewska', 'TP-GOR', 11, 45060.0, 175.5239],\n",
              " ['5052_01', '18:14:00', 'os.Górczewska', 'TP-GOR', 11, 45180.0, 175.84918],\n",
              " ['5051_01', '18:15:00', 'os.Górczewska', 'TP-GOR', 11, 45240.0, 176.35874],\n",
              " ['5050_03', '18:17:00', 'os.Górczewska', 'TP-GOR', 11, 45360.0, 176.57838],\n",
              " ['5050_08', '18:29:00', 'Gwiaździsta', 'TP-GWI', 12, 46080.0, 176.70376],\n",
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              " ['5053_02', '18:36:00', 'Gwiaździsta', 'TP-GWI', 12, 46500.0, 178.32374],\n",
              " ['5054_02', '18:37:00', 'Gwiaździsta', 'TP-GWI', 12, 46560.0, 178.71727],\n",
              " ['5055_07', '18:38:00', 'Gwiaździsta', 'TP-GWI', 12, 46620.0, 178.95307],\n",
              " ['5110_01', '18:39:00', 'Gwiaździsta', 'TP-GWI', 12, 46680.0, 179.47035],\n",
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              " ['5106_01', '18:43:00', 'Gwiaździsta', 'TP-GWI', 12, 46920.0, 180.72138],\n",
              " ['5107_01', '18:44:00', 'Gwiaździsta', 'TP-GWI', 12, 46980.0, 181.13161],\n",
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              " ['5055_01', '22:16:00', 'os.Górczewska', 'TP-GOR', 15, 59700.0, 233.4318],\n",
              " ['5054_01', '22:17:00', 'os.Górczewska', 'TP-GOR', 15, 59760.0, 233.78698],\n",
              " ['5053_01', '22:18:00', 'os.Górczewska', 'TP-GOR', 15, 59820.0, 234.24365],\n",
              " ['5163_01', '22:19:00', 'os.Górczewska', 'TP-GOR', 15, 59880.0, 234.54968],\n",
              " ['5052_01', '22:20:00', 'os.Górczewska', 'TP-GOR', 15, 59940.0, 234.87496],\n",
              " ['5051_01', '22:21:00', 'os.Górczewska', 'TP-GOR', 15, 60000.0, 235.38452],\n",
              " ['5050_03', '22:23:00', 'os.Górczewska', 'TP-GOR', 15, 60120.0, 235.60416]]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WgjMz9bH21nn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def filter_gps(list_od_tuples, x_limits, y_limits):\n",
        "    return [x for x in list_od_tuples if coord_check(x, x_limits, y_limits)] # (20.0, 22.0), (51.0, 53.0)\n",
        "#     return list(filter(lambda x: coord_check(x, x_limits, y_limits), list_od_tuples))                    # faster?\n",
        "\n",
        "def coord_check(to_check, x_limits, y_limits):\n",
        "    x, y = to_check\n",
        "    x_min, x_max = x_limits\n",
        "    y_min, y_max = y_limits\n",
        "    if((x_min < x < x_max) & (y_min < y < y_max)):\n",
        "        return True\n",
        "    else:\n",
        "        return False\n",
        "    \n",
        "def flip(coords):\n",
        "    return [(y, x) for x, y in coords]   # [x[::-1] for x in coords]\n",
        "\n",
        "def simplify_to_nr(path, nr, tolerance=0, func=simplify_coords, max_iter=100): # simplify_coords_vw\n",
        "    range_l = 0.0\n",
        "    range_h = 1.0\n",
        "    iteration = 1\n",
        "    while True:\n",
        "        param = (range_l + range_h)/2\n",
        "        result = func(path, param)\n",
        "        result_len = len(result)\n",
        "        if((result_len >= nr-tolerance) & (result_len <= nr+tolerance)):\n",
        "            print('Iteration: ', iteration, '      Number: ', result_len, ' Parameter: ', param)\n",
        "            return result\n",
        "        elif (result_len < nr): # parameter to high\n",
        "            range_h = param\n",
        "            iteration += 1\n",
        "        else:                      # parameter to low\n",
        "            range_l = param\n",
        "            iteration += 1\n",
        "        if(iteration > max_iter):\n",
        "            print('calculation to long, Number: ', result_len, ' Parameter: ', param)\n",
        "            return result  \n",
        "\n",
        "def draw_simpl_path(line, brig_idx, nr_points, tolerance=0, rdp=True, wv=True):\n",
        "    brigades = GPS_df[GPS_df.Lines.isin(['122'])].Brigade.value_counts().keys().tolist()\n",
        "    print(line, brigades)\n",
        "    line_stops = stops_coords_list([line])\n",
        "    route = GPS_df[GPS_df.Lines.isin([line]) & \n",
        "                   GPS_df.Brigade.isin([brigades[brig_idx]])].sort_values(['Time'])[['Lon', 'Lat']].values\n",
        "    route = filter_gps(route, (20.0, 22.0), (51.0, 53.0))\n",
        "    route = np.ascontiguousarray(route)\n",
        "    simpl_rdp = simplify_to_nr(route, nr_points)\n",
        "    simpl_vw = simplify_to_nr(route, nr_points, tolerance, func=simplify_coords_vw)\n",
        "    print(len(line_stops), route.shape, simpl_rdp.shape, simpl_vw.shape)\n",
        "    print('stops ', path_dist(line_stops), \n",
        "          ' gps ', path_dist(route), \n",
        "          ' rdp ', path_dist(simpl_rdp), \n",
        "          ' wv', path_dist(simpl_vw))\n",
        "    plt.figure(figsize=(10,6))\n",
        "    plt.scatter(*zip(*route))\n",
        "    if(rdp):\n",
        "        plt.scatter(*zip(*simpl_rdp))\n",
        "    if(wv):\n",
        "        plt.scatter(*zip(*simpl_vw))\n",
        "    plt.show()\n",
        "\n",
        "    def path_dist(to_check_distance):\n",
        "    sumed = 0.0\n",
        "    last_first, last_second = to_check_distance[0]\n",
        "    for first, second in to_check_distance:\n",
        "        dist = round(geopy.distance.distance((last_second, last_first), (second, first)).kilometers, 5)\n",
        "        sumed += dist\n",
        "        last_first, last_second = first, second\n",
        "    return sumed\n",
        "\n",
        "def stops_coords_list(line):\n",
        "    a = lines_df[lines_df.line_nr.isin(line)]['stops'].values[0]\n",
        "    b = [stop_coords[x] for x in a]\n",
        "    b = flip(b)          \n",
        "    b = filter_gps(b, (20.0, 22.0), (51.0, 53.0))\n",
        "    return b\n",
        "\n",
        "def draw_stops(b):         \n",
        "    #b = stops_coords_list(line)\n",
        "    print('stops nr: ', len(b))\n",
        "    plt.figure(figsize=(10,6))\n",
        "    plt.scatter(*zip(*b)); "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NhgZ0HLM5m9b",
        "colab_type": "text"
      },
      "source": [
        "### Ramer–Douglas–Peucker or Visvalingam-Whyatt algorithms\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ql2g64345pMB",
        "colab_type": "code",
        "outputId": "0bf58471-e6fa-43b0-f03e-084927254b7b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        }
      },
      "source": [
        "print(GPS_df.shape)\n",
        "with open('GPS_df.pickle', 'rb') as fp:\n",
        "    GPS_df = pickle.load(fp)\n",
        "GPS_df.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(1160129, 13)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1136854, 18)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zgV1pxEMuM1z",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "with open('stops_dist_dict.pickle', 'rb') as fp:\n",
        "    stops_dist_dict = pickle.load(fp)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y8wXRkPpvqFm",
        "colab_type": "code",
        "outputId": "f6607328-dfc2-4faa-bc2e-a5d431d0918c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "len(stops_dist_dict)         # odległości w lini prostej między kolejnymi przystankami - wyszukiwanie w słowniku jest szybsze niż obliczanie z geopy, klucz: wartość -> ('6074_02', '6073_02'): 0.42795"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "10718"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    }
  ]
}