Accidents_in_Bangalore.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Accidents in Bangalore.ipynb",
      "version": "0.3.2",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "metadata": {
        "id": "7Q-ytgSjmBia",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn.cluster import DBSCAN\n",
        "from sklearn import metrics\n",
        "from sklearn.preprocessing import Normalizer\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "0op3Jm2uFi3R",
        "colab_type": "code",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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wdXRFbGVtZW50LmRpc2FibGVkID0gdHJ1ZTsKCiAgZm9yIChjb25zdCBmaWxlIG9mIGZpbGVzKSB7CiAgICBjb25zdCBsaSA9IGRvY3VtZW50LmNyZWF0ZUVsZW1lbnQoJ2xpJyk7CiAgICBsaS5hcHBlbmQoc3BhbihmaWxlLm5hbWUsIHtmb250V2VpZ2h0OiAnYm9sZCd9KSk7CiAgICBsaS5hcHBlbmQoc3BhbigKICAgICAgICBgKCR7ZmlsZS50eXBlIHx8ICduL2EnfSkgLSAke2ZpbGUuc2l6ZX0gYnl0ZXMsIGAgKwogICAgICAgIGBsYXN0IG1vZGlmaWVkOiAkewogICAgICAgICAgICBmaWxlLmxhc3RNb2RpZmllZERhdGUgPyBmaWxlLmxhc3RNb2RpZmllZERhdGUudG9Mb2NhbGVEYXRlU3RyaW5nKCkgOgogICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAnbi9hJ30gLSBgKSk7CiAgICBjb25zdCBwZXJjZW50ID0gc3BhbignMCUgZG9uZScpOwogICAgbGkuYXBwZW5kQ2hpbGQocGVyY2VudCk7CgogICAgb3V0cHV0RWxlbWVudC5hcHBlbmRDaGlsZChsaSk7CgogICAgY29uc3QgZmlsZURhdGFQcm9taXNlID0gbmV3IFByb21pc2UoKHJlc29sdmUpID0+IHsKICAgICAgY29uc3QgcmVhZGVyID0gbmV3IEZpbGVSZWFkZXIoKTsKICAgICAgcmVhZGVyLm9ubG9hZCA9IChlKSA9PiB7CiAgICAgICAgcmVzb2x2ZShlLnRhcmdldC5yZXN1bHQpOwogICAgICB9OwogICAgICByZWFkZXIucmVhZEFzQXJyYXlCdWZmZXIoZmlsZSk7CiAgICB9KTsKICAgIC8vIFdhaXQgZm9yIHRoZSBkYXRhIHRvIGJlIHJlYWR5LgogICAgbGV0IGZpbGVEYXRhID0geWllbGQgewogICAgICBwcm9taXNlOiBmaWxlRGF0YVByb21pc2UsCiAgICAgIHJlc3BvbnNlOiB7CiAgICAgICAgYWN0aW9uOiAnY29udGludWUnLAogICAgICB9CiAgICB9OwoKICAgIC8vIFVzZSBhIGNodW5rZWQgc2VuZGluZyB0byBhdm9pZCBtZXNzYWdlIHNpemUgbGltaXRzLiBTZWUgYi82MjExNTY2MC4KICAgIGxldCBwb3NpdGlvbiA9IDA7CiAgICB3aGlsZSAocG9zaXRpb24gPCBmaWxlRGF0YS5ieXRlTGVuZ3RoKSB7CiAgICAgIGNvbnN0IGxlbmd0aCA9IE1hdGgubWluKGZpbGVEYXRhLmJ5dGVMZW5ndGggLSBwb3NpdGlvbiwgTUFYX1BBWUxPQURfU0laRSk7CiAgICAgIGNvbnN0IGNodW5rID0gbmV3IFVpbnQ4QXJyYXkoZmlsZURhdGEsIHBvc2l0aW9uLCBsZW5ndGgpOwogICAgICBwb3NpdGlvbiArPSBsZW5ndGg7CgogICAgICBjb25zdCBiYXNlNjQgPSBidG9hKFN0cmluZy5mcm9tQ2hhckNvZGUuYXBwbHkobnVsbCwgY2h1bmspKTsKICAgICAgeWllbGQgewogICAgICAgIHJlc3BvbnNlOiB7CiAgICAgICAgICBhY3Rpb246ICdhcHBlbmQnLAogICAgICAgICAgZmlsZTogZmlsZS5uYW1lLAogICAgICAgICAgZGF0YTogYmFzZTY0LAogICAgICAgIH0sCiAgICAgIH07CiAgICAgIHBlcmNlbnQudGV4dENvbnRlbnQgPQogICAgICAgICAgYCR7TWF0aC5yb3VuZCgocG9zaXRpb24gLyBmaWxlRGF0YS5ieXRlTGVuZ3RoKSAqIDEwMCl9JSBkb25lYDsKICAgIH0KICB9CgogIC8vIEFsbCBkb25lLgogIHlpZWxkIHsKICAgIHJlc3BvbnNlOiB7CiAgICAgIGFjdGlvbjogJ2NvbXBsZXRlJywKICAgIH0KICB9Owp9CgpzY29wZS5nb29nbGUgPSBzY29wZS5nb29nbGUgfHwge307CnNjb3BlLmdvb2dsZS5jb2xhYiA9IHNjb3BlLmdvb2dsZS5jb2xhYiB8fCB7fTsKc2NvcGUuZ29vZ2xlLmNvbGFiLl9maWxlcyA9IHsKICBfdXBsb2FkRmlsZXMsCiAgX3VwbG9hZEZpbGVzQ29udGludWUsCn07Cn0pKHNlbGYpOwo=",
              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 90
        },
        "outputId": "8aa7c89e-9e74-4d7e-fb5a-bb8d71b88bc3"
      },
      "cell_type": "code",
      "source": [
        "from google.colab import files\n",
        "\n",
        "uploaded = files.upload()\n",
        "\n",
        "for fn in uploaded.keys():\n",
        "  print('User uploaded file \"{name}\" with length {length} bytes'.format(\n",
        "      name=fn, length=len(uploaded[fn])))"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-47e7e5f3-5091-40c2-b0cb-2accb6203bb0\" name=\"files[]\" multiple disabled />\n",
              "     <output id=\"result-47e7e5f3-5091-40c2-b0cb-2accb6203bb0\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving bangalore-cas-alerts.csv to bangalore-cas-alerts.csv\n",
            "User uploaded file \"bangalore-cas-alerts.csv\" with length 22720347 bytes\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "SBvpSl6-nccU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 215
        },
        "outputId": "e39d3098-1368-4b90-ed68-8e0e7140a56e"
      },
      "cell_type": "code",
      "source": [
        "datafile = pd.read_csv('bangalore-cas-alerts.csv', header='infer', sep=',', error_bad_lines=False, warn_bad_lines = False)\n",
        "datafile.head()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>deviceCode_deviceCode</th>\n",
              "      <th>deviceCode_location_latitude</th>\n",
              "      <th>deviceCode_location_longitude</th>\n",
              "      <th>deviceCode_location_wardName</th>\n",
              "      <th>deviceCode_pyld_alarmType</th>\n",
              "      <th>deviceCode_pyld_speed</th>\n",
              "      <th>deviceCode_time_recordedTime_$date</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>864504031502210</td>\n",
              "      <td>12.984595</td>\n",
              "      <td>77.744087</td>\n",
              "      <td>Kadugodi</td>\n",
              "      <td>PCW</td>\n",
              "      <td>32</td>\n",
              "      <td>2018-02-01T01:48:59.000Z</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>864504031502210</td>\n",
              "      <td>12.984595</td>\n",
              "      <td>77.744087</td>\n",
              "      <td>Kadugodi</td>\n",
              "      <td>PCW</td>\n",
              "      <td>32</td>\n",
              "      <td>2018-02-01T01:48:59.000Z</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>864504031502210</td>\n",
              "      <td>12.987233</td>\n",
              "      <td>77.741119</td>\n",
              "      <td>Garudachar Playa</td>\n",
              "      <td>FCW</td>\n",
              "      <td>41</td>\n",
              "      <td>2018-02-01T01:50:00.000Z</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>864504031502210</td>\n",
              "      <td>12.987233</td>\n",
              "      <td>77.741119</td>\n",
              "      <td>Garudachar Playa</td>\n",
              "      <td>FCW</td>\n",
              "      <td>41</td>\n",
              "      <td>2018-02-01T01:50:00.000Z</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>864504031502210</td>\n",
              "      <td>12.987503</td>\n",
              "      <td>77.740051</td>\n",
              "      <td>Hudi</td>\n",
              "      <td>Overspeed</td>\n",
              "      <td>37</td>\n",
              "      <td>2018-02-01T01:50:11.000Z</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   deviceCode_deviceCode  deviceCode_location_latitude  \\\n",
              "0        864504031502210                     12.984595   \n",
              "1        864504031502210                     12.984595   \n",
              "2        864504031502210                     12.987233   \n",
              "3        864504031502210                     12.987233   \n",
              "4        864504031502210                     12.987503   \n",
              "\n",
              "   deviceCode_location_longitude deviceCode_location_wardName  \\\n",
              "0                      77.744087                     Kadugodi   \n",
              "1                      77.744087                     Kadugodi   \n",
              "2                      77.741119             Garudachar Playa   \n",
              "3                      77.741119             Garudachar Playa   \n",
              "4                      77.740051                         Hudi   \n",
              "\n",
              "  deviceCode_pyld_alarmType  deviceCode_pyld_speed  \\\n",
              "0                       PCW                     32   \n",
              "1                       PCW                     32   \n",
              "2                       FCW                     41   \n",
              "3                       FCW                     41   \n",
              "4                 Overspeed                     37   \n",
              "\n",
              "  deviceCode_time_recordedTime_$date  \n",
              "0           2018-02-01T01:48:59.000Z  \n",
              "1           2018-02-01T01:48:59.000Z  \n",
              "2           2018-02-01T01:50:00.000Z  \n",
              "3           2018-02-01T01:50:00.000Z  \n",
              "4           2018-02-01T01:50:11.000Z  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "metadata": {
        "id": "o5rYN0A3pYu4",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "gpsdata = datafile[['deviceCode_location_latitude','deviceCode_location_longitude']]\n",
        "gpsdata.head()\n",
        "gpsdataarray = gpsdata.values"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "ffXaqdTiqk_X",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 140
        },
        "outputId": "f1c43d7d-5ab9-4ddc-d8ca-1ede6ea2452d"
      },
      "cell_type": "code",
      "source": [
        "print(gpsdataarray)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[12.9845953  77.74408722]\n",
            " [12.9845953  77.74408722]\n",
            " [12.98723316 77.74111938]\n",
            " ...\n",
            " [12.96939564 77.74988556]\n",
            " [12.974123   77.74684143]\n",
            " [12.97548008 77.74412537]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "ZtQ1zgWHqoAu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 379
        },
        "outputId": "17b29e28-27ef-47c9-c05e-c796dcb1c576"
      },
      "cell_type": "code",
      "source": [
        "db = DBSCAN(eps=0.0001, min_samples=200).fit(gpsdataarray)\n",
        "samples_initial = np.zeros_like(db.labels_, dtype=bool)\n",
        "samples_initial[db.core_sample_indices_] = True\n",
        "labels = db.labels_\n",
        "n_clusters_ = len(set(labels)) - (1 if -1 in labels else 0)\n",
        "print('Number of clusters: %d' % n_clusters_)\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "unique_labels = set(labels)\n",
        "colors = [plt.cm.Spectral(each)\n",
        "          for each in np.linspace(0, 1, len(unique_labels))]\n",
        "for k, col in zip(unique_labels, colors):\n",
        "    if k == -1:\n",
        "        col = [0, 0, 0, 1]\n",
        "\n",
        "    class_member_mask = (labels == k)\n",
        "\n",
        "    xy = gpsdataarray[class_member_mask & samples_initial]\n",
        "    plt.plot(xy[:, 0], xy[:, 1], 'o', markerfacecolor=tuple(col),\n",
        "             markeredgecolor='k', markersize=14)\n",
        "\n",
        "    xy = gpsdataarray[class_member_mask & ~samples_initial]\n",
        "    plt.plot(xy[:, 0], xy[:, 1], 'o', markerfacecolor=tuple(col),\n",
        "            markeredgecolor='k', markersize=1)\n",
        "\n",
        "plt.title('Number of clusters: %d' % n_clusters_)\n",
        "plt.show()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Number of clusters: 36\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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SiYXd8mS3/ADmzlO0E4gkKk+xlIrV+xl9r6KiXJw89jp+sONEl9fUVeJKd779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\nyAL+f4+LhSTG07OzAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f17a3ae9f60>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "VICfAxz4jbfA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 668
        },
        "outputId": "0e978d02-9d3e-47cd-eb9a-2f2538d14ebf"
      },
      "cell_type": "code",
      "source": [
        "f = np.column_stack((gpsdataarray,labels))\n",
        "LabelDF= pd.DataFrame(data=f, columns=['lon','lat','lab'])\n",
        "EachLabelPrint = LabelDF.groupby('lab',as_index=False).first()\n",
        "pd.set_option('display.max_rows', 500)\n",
        "print(EachLabelPrint.head(40)[1:])"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "     lab        lon        lat\n",
            "1    0.0  12.978786  77.742203\n",
            "2    1.0  12.975768  77.742432\n",
            "3    2.0  12.985925  77.744621\n",
            "4    3.0  12.987546  77.740494\n",
            "5    4.0  12.987633  77.735977\n",
            "6    5.0  12.988061  77.732338\n",
            "7    6.0  12.988720  77.728699\n",
            "8    7.0  12.990313  77.723686\n",
            "9    8.0  12.989051  77.727257\n",
            "10   9.0  12.983611  77.743828\n",
            "11  10.0  12.988010  77.733452\n",
            "12  11.0  12.987665  77.737122\n",
            "13  12.0  12.987523  77.736702\n",
            "14  13.0  12.976376  77.741486\n",
            "15  14.0  13.003273  77.757149\n",
            "16  15.0  12.955878  77.723160\n",
            "17  16.0  12.956163  77.730621\n",
            "18  17.0  12.956956  77.740120\n",
            "19  18.0  12.955770  77.720749\n",
            "20  19.0  12.989065  77.726936\n",
            "21  20.0  12.988876  77.727745\n",
            "22  21.0  12.987380  77.741058\n",
            "23  22.0  12.976850  77.741714\n",
            "24  23.0  12.972576  77.749840\n",
            "25  24.0  12.984163  77.743942\n",
            "26  25.0  12.964371  77.748642\n",
            "27  26.0  12.970830  77.750069\n",
            "28  27.0  12.974451  77.750526\n",
            "29  28.0  12.955745  77.716087\n",
            "30  29.0  12.956965  77.738487\n",
            "31  30.0  12.956148  77.731323\n",
            "32  31.0  12.956048  77.730064\n",
            "33  32.0  12.970218  77.750046\n",
            "34  33.0  12.969193  77.749832\n",
            "35  34.0  12.987545  77.739670\n",
            "36  35.0  12.983851  77.743813\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "OHsA8Htf_2oM",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}