Signal_clustering.ipynb

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      "name": "python3",
      "display_name": "Python 3"
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    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.6"
    },
    "colab": {
      "name": "Signal clustering.ipynb",
      "provenance": [],
      "machine_shape": "hm",
      "include_colab_link": true
    },
    "accelerator": "TPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Akshat-Tripathi/AIHack20/blob/master/Signal_clustering.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TpGXOwTsMon9",
        "colab_type": "text"
      },
      "source": [
        "# Compressor Analytics Dataset\n",
        "\n",
        "Below are a few things to get you started with the Compressor Analytics dataset for AIHack! For more information see [here](https://drive.google.com/file/d/1ojYVHpabfkNz45nQqQLenyoaQbCmWdo4/view?usp=sharing)."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VU1imX4fMooB",
        "colab_type": "code",
        "outputId": "e5611fa9-71fc-4e80-ef8d-e0d39aa4bad0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 85
        }
      },
      "source": [
        "# Download data - please only run this once\n",
        "import urllib.request\n",
        "import zipfile\n",
        "import os\n",
        "\n",
        "print(\"Starting download ...\")\n",
        "urls = [\n",
        "    \"https://github.com/aihack20/shell_challenge/releases/download/data/clean_dataset.zip\",\n",
        "    \"https://github.com/aihack20/shell_challenge/releases/download/data/raw_dataset.zip\",\n",
        "]\n",
        "os.makedirs(\"shell_data\", exist_ok=True)\n",
        "for url in urls:\n",
        "    with urllib.request.urlopen(url) as src:\n",
        "        with open(\"tmp.zip\", \"wb\") as dest:\n",
        "            dest.write(src.read())\n",
        "    print(\"Unpacking archive ...\")\n",
        "    with zipfile.ZipFile(\"tmp.zip\") as f:\n",
        "        f.extractall(\"shell_data\")\n",
        "print(\"Done!\")"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Starting download ...\n",
            "Unpacking archive ...\n",
            "Unpacking archive ...\n",
            "Done!\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9OUbabvaMooF",
        "colab_type": "code",
        "outputId": "08db4ed5-1154-4f1e-b762-f0cf01fe30bd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        }
      },
      "source": [
        "# Run this ONLY IF YOU USE GOOGLE COLAB\n",
        "google_drive_dir = '/aihack20'\n",
        "\n",
        "try:\n",
        "    from google.colab import drive\n",
        "    drive.mount(\"/content/drive\")\n",
        "    directory = \"working_dir\"\n",
        "    if not os.path.exists(directory):\n",
        "      os.symlink(os.path.join(\"drive\", \"My Drive\", google_drive_dir), directory)\n",
        "except ModuleNotFoundError as err:\n",
        "    print(\"This only works on Google Colab. Skipping ...\")"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kin1HADiMooL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 258
        },
        "outputId": "bc25f3e1-0ce7-401b-ee81-f2496eecf484"
      },
      "source": [
        "import pandas as pd\n",
        "from matplotlib import pyplot as plt\n",
        "import numpy as np\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from tqdm import tqdm_notebook as tqdm\n",
        "from sklearn.cluster import DBSCAN\n",
        "%pip install similaritymeasures\n",
        "import similaritymeasures\n",
        "plt.style.use('ggplot')"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting similaritymeasures\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/81/e4/b1a6f89ffcba5a3394585ae3dabf8599208eefa494bae76263e53637facf/similaritymeasures-0.4.3.tar.gz (397kB)\n",
            "\r\u001b[K     |▉                               | 10kB 18.2MB/s eta 0:00:01\r\u001b[K     |█▋                              | 20kB 2.2MB/s eta 0:00:01\r\u001b[K     |██▌                             | 30kB 3.2MB/s eta 0:00:01\r\u001b[K     |███▎                            | 40kB 2.1MB/s eta 0:00:01\r\u001b[K     |████▏                           | 51kB 2.6MB/s eta 0:00:01\r\u001b[K     |█████                           | 61kB 3.1MB/s eta 0:00:01\r\u001b[K     |█████▊                          | 71kB 3.5MB/s eta 0:00:01\r\u001b[K     |██████▋                         | 81kB 4.0MB/s eta 0:00:01\r\u001b[K     |███████▍                        | 92kB 4.5MB/s eta 0:00:01\r\u001b[K     |████████▎                       | 102kB 3.4MB/s eta 0:00:01\r\u001b[K     |█████████                       | 112kB 3.4MB/s eta 0:00:01\r\u001b[K     |█████████▉                      | 122kB 3.4MB/s eta 0:00:01\r\u001b[K     |██████████▊                     | 133kB 3.4MB/s eta 0:00:01\r\u001b[K     |███████████▌                    | 143kB 3.4MB/s eta 0:00:01\r\u001b[K     |████████████▍                   | 153kB 3.4MB/s eta 0:00:01\r\u001b[K     |█████████████▏                  | 163kB 3.4MB/s eta 0:00:01\r\u001b[K     |██████████████                  | 174kB 3.4MB/s eta 0:00:01\r\u001b[K     |██████████████▉                 | 184kB 3.4MB/s eta 0:00:01\r\u001b[K     |███████████████▋                | 194kB 3.4MB/s eta 0:00:01\r\u001b[K     |████████████████▌               | 204kB 3.4MB/s eta 0:00:01\r\u001b[K     |█████████████████▎              | 215kB 3.4MB/s eta 0:00:01\r\u001b[K     |██████████████████▏             | 225kB 3.4MB/s eta 0:00:01\r\u001b[K     |███████████████████             | 235kB 3.4MB/s eta 0:00:01\r\u001b[K     |███████████████████▊            | 245kB 3.4MB/s eta 0:00:01\r\u001b[K     |████████████████████▋           | 256kB 3.4MB/s eta 0:00:01\r\u001b[K     |█████████████████████▍          | 266kB 3.4MB/s eta 0:00:01\r\u001b[K     |██████████████████████▎         | 276kB 3.4MB/s eta 0:00:01\r\u001b[K     |███████████████████████         | 286kB 3.4MB/s eta 0:00:01\r\u001b[K     |████████████████████████        | 296kB 3.4MB/s eta 0:00:01\r\u001b[K     |████████████████████████▊       | 307kB 3.4MB/s eta 0:00:01\r\u001b[K     |█████████████████████████▌      | 317kB 3.4MB/s eta 0:00:01\r\u001b[K     |██████████████████████████▍     | 327kB 3.4MB/s eta 0:00:01\r\u001b[K     |███████████████████████████▏    | 337kB 3.4MB/s eta 0:00:01\r\u001b[K     |████████████████████████████    | 348kB 3.4MB/s eta 0:00:01\r\u001b[K     |████████████████████████████▉   | 358kB 3.4MB/s eta 0:00:01\r\u001b[K     |█████████████████████████████▋  | 368kB 3.4MB/s eta 0:00:01\r\u001b[K     |██████████████████████████████▌ | 378kB 3.4MB/s eta 0:00:01\r\u001b[K     |███████████████████████████████▎| 389kB 3.4MB/s eta 0:00:01\r\u001b[K     |████████████████████████████████| 399kB 3.4MB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy>=1.14.0 in /usr/local/lib/python3.6/dist-packages (from similaritymeasures) (1.17.5)\n",
            "Requirement already satisfied: scipy>=0.19.0 in /usr/local/lib/python3.6/dist-packages (from similaritymeasures) (1.4.1)\n",
            "Requirement already satisfied: setuptools>=38.6.0 in /usr/local/lib/python3.6/dist-packages (from similaritymeasures) (45.1.0)\n",
            "Building wheels for collected packages: similaritymeasures\n",
            "  Building wheel for similaritymeasures (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for similaritymeasures: filename=similaritymeasures-0.4.3-cp36-none-any.whl size=14511 sha256=c7f94ef82fbf45ef0f3d866badb1480f8306e0bea912fcebc1cb1e7654692a4f\n",
            "  Stored in directory: /root/.cache/pip/wheels/6f/47/91/f1b5bc17659d9cb15129a6894da6e8950bc01bc21cfb65c2aa\n",
            "Successfully built similaritymeasures\n",
            "Installing collected packages: similaritymeasures\n",
            "Successfully installed similaritymeasures-0.4.3\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y3IAIKl7MooO",
        "colab_type": "code",
        "outputId": "4ecddd45-d930-4122-a78d-50a45e1150f0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        }
      },
      "source": [
        "# Load the data (will take a short while)\n",
        "clean_data = pd.read_csv(\"shell_data/clean_dataset.csv\")\n",
        "raw_data = pd.read_csv(\"shell_data/raw_dataset.csv\")"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/IPython/core/interactiveshell.py:2718: DtypeWarning: Columns (0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,88,89,90,91,92,93,94,95,96,97,98,99,100,101,102,103,104,105,106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125,126,127,128,129,130,131,132,133,134,135,136,137,138,139,140,141,142,143,144,145,146,147,148,149,150,151,152,153,154,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,172,173,174,175,176,177,178,179,180,181,182,183,184,185,186,187,188,189,190,191,192,193,194,195,196,197,198,199,200,201,202,203,204,205,206,207,208,209,210,211,212,213,214,215,216,217,218,219,220,221,222,223,224,225,226,227,228,229,230,231,232,233,234,235,236,237,239,240,241,242,243,244,245,246,247,248,249,250,251,252,253,254,255,256,257,258,259,260,261,262,263,264,265,266,267,268,269,270,271,272,273,274,275,276,277,278,279,280,281,282,283,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,304,305,306,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338,339,340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,358,359,360,361,362,363,364,365,366,367,368,369,370,371,372,373,374,375,376,377,378,379,380,381,382,383,384,385,386,387,388,389,390,391,392,393,394,395,396,397,398,399,400,401,402,403,404) have mixed types. Specify dtype option on import or set low_memory=False.\n",
            "  interactivity=interactivity, compiler=compiler, result=result)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h2_Rf7KWMooS",
        "colab_type": "text"
      },
      "source": [
        "## Your Hack ..."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mXpdnfIHfyAG",
        "colab_type": "code",
        "outputId": "e4566134-63d4-4469-cc53-ea20c8338245",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 490
        }
      },
      "source": [
        "clean_data"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
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              "      <td>-0.050000</td>\n",
              "      <td>-0.115212</td>\n",
              "      <td>0.090000</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.030000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>733.562696</td>\n",
              "      <td>2353.957861</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>46.569746</td>\n",
              "      <td>0.310399</td>\n",
              "      <td>0.361983</td>\n",
              "      <td>0.529558</td>\n",
              "      <td>0.006196</td>\n",
              "      <td>2.292604</td>\n",
              "      <td>27.922524</td>\n",
              "      <td>44.119015</td>\n",
              "      <td>44.053790</td>\n",
              "      <td>29.662350</td>\n",
              "      <td>54.554506</td>\n",
              "      <td>64.945662</td>\n",
              "      <td>25.450283</td>\n",
              "      <td>63.617557</td>\n",
              "      <td>64.978109</td>\n",
              "      <td>43.724690</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>95.987510</td>\n",
              "      <td>134.592346</td>\n",
              "      <td>3.020742</td>\n",
              "      <td>5.600833</td>\n",
              "      <td>27179.00250</td>\n",
              "      <td>71681.450820</td>\n",
              "      <td>227.170568</td>\n",
              "      <td>5.670112</td>\n",
              "      <td>8.642001</td>\n",
              "      <td>262.580608</td>\n",
              "      <td>263.893577</td>\n",
              "      <td>37.654943</td>\n",
              "      <td>32.106802</td>\n",
              "      <td>2.966380</td>\n",
              "      <td>82.376430</td>\n",
              "      <td>0.0</td>\n",
              "      <td>11074.03344</td>\n",
              "      <td>26.263372</td>\n",
              "      <td>3.052990</td>\n",
              "      <td>8.304424</td>\n",
              "      <td>1.560687</td>\n",
              "      <td>0.111684</td>\n",
              "      <td>0.041069</td>\n",
              "      <td>0.017791</td>\n",
              "      <td>-0.016854</td>\n",
              "      <td>0.157064</td>\n",
              "      <td>0.199661</td>\n",
              "      <td>0.002104</td>\n",
              "      <td>4.281026</td>\n",
              "      <td>7.480112</td>\n",
              "      <td>2.857404</td>\n",
              "      <td>17.300145</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.291194</td>\n",
              "      <td>84.703689</td>\n",
              "      <td>28.082687</td>\n",
              "      <td>85.124370</td>\n",
              "      <td>6.632087</td>\n",
              "      <td>9.872420</td>\n",
              "      <td>7.795228</td>\n",
              "      <td>...</td>\n",
              "      <td>4.474426</td>\n",
              "      <td>11.711700</td>\n",
              "      <td>12.018494</td>\n",
              "      <td>3.159676</td>\n",
              "      <td>4.474426</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4938.391538</td>\n",
              "      <td>7019.052236</td>\n",
              "      <td>16.608910</td>\n",
              "      <td>2080.638000</td>\n",
              "      <td>149.340074</td>\n",
              "      <td>6725.873754</td>\n",
              "      <td>0.703762</td>\n",
              "      <td>0.006887</td>\n",
              "      <td>-0.050000</td>\n",
              "      <td>-0.115066</td>\n",
              "      <td>0.090000</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.030000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>736.089958</td>\n",
              "      <td>2358.574785</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>46.572425</td>\n",
              "      <td>0.307906</td>\n",
              "      <td>0.357844</td>\n",
              "      <td>0.528139</td>\n",
              "      <td>0.008542</td>\n",
              "      <td>2.297546</td>\n",
              "      <td>27.966855</td>\n",
              "      <td>44.053370</td>\n",
              "      <td>44.047508</td>\n",
              "      <td>29.694455</td>\n",
              "      <td>54.828519</td>\n",
              "      <td>64.916624</td>\n",
              "      <td>25.474713</td>\n",
              "      <td>63.724127</td>\n",
              "      <td>65.082058</td>\n",
              "      <td>43.772990</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>96.099651</td>\n",
              "      <td>133.920536</td>\n",
              "      <td>3.013397</td>\n",
              "      <td>5.797500</td>\n",
              "      <td>27383.78150</td>\n",
              "      <td>72524.834580</td>\n",
              "      <td>226.903604</td>\n",
              "      <td>5.664165</td>\n",
              "      <td>8.644843</td>\n",
              "      <td>259.783251</td>\n",
              "      <td>260.150397</td>\n",
              "      <td>37.560544</td>\n",
              "      <td>32.005114</td>\n",
              "      <td>2.959470</td>\n",
              "      <td>82.291741</td>\n",
              "      <td>0.0</td>\n",
              "      <td>11065.93960</td>\n",
              "      <td>26.272370</td>\n",
              "      <td>2.999251</td>\n",
              "      <td>8.290051</td>\n",
              "      <td>1.556959</td>\n",
              "      <td>0.111551</td>\n",
              "      <td>0.040897</td>\n",
              "      <td>0.016814</td>\n",
              "      <td>-0.022455</td>\n",
              "      <td>0.156462</td>\n",
              "      <td>0.198392</td>\n",
              "      <td>0.002085</td>\n",
              "      <td>4.279762</td>\n",
              "      <td>7.473612</td>\n",
              "      <td>2.985811</td>\n",
              "      <td>17.305048</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.286761</td>\n",
              "      <td>84.749601</td>\n",
              "      <td>28.069216</td>\n",
              "      <td>85.139617</td>\n",
              "      <td>6.594814</td>\n",
              "      <td>9.862027</td>\n",
              "      <td>7.806693</td>\n",
              "      <td>...</td>\n",
              "      <td>4.474426</td>\n",
              "      <td>11.328209</td>\n",
              "      <td>12.018494</td>\n",
              "      <td>3.163318</td>\n",
              "      <td>4.474426</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4939.808129</td>\n",
              "      <td>7039.833792</td>\n",
              "      <td>16.619297</td>\n",
              "      <td>2099.810181</td>\n",
              "      <td>149.180346</td>\n",
              "      <td>6742.504121</td>\n",
              "      <td>0.704539</td>\n",
              "      <td>0.012337</td>\n",
              "      <td>-0.050000</td>\n",
              "      <td>-0.118318</td>\n",
              "      <td>0.090000</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.030000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>733.106418</td>\n",
              "      <td>2351.183980</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>46.513494</td>\n",
              "      <td>0.309038</td>\n",
              "      <td>0.358966</td>\n",
              "      <td>0.530971</td>\n",
              "      <td>0.008130</td>\n",
              "      <td>2.293816</td>\n",
              "      <td>27.974759</td>\n",
              "      <td>44.104163</td>\n",
              "      <td>43.991800</td>\n",
              "      <td>29.750101</td>\n",
              "      <td>54.721836</td>\n",
              "      <td>64.922810</td>\n",
              "      <td>25.478107</td>\n",
              "      <td>63.597104</td>\n",
              "      <td>64.966048</td>\n",
              "      <td>43.787082</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>96.035449</td>\n",
              "      <td>134.231352</td>\n",
              "      <td>3.015035</td>\n",
              "      <td>5.701667</td>\n",
              "      <td>27379.02161</td>\n",
              "      <td>72950.024990</td>\n",
              "      <td>226.636640</td>\n",
              "      <td>5.674515</td>\n",
              "      <td>8.654543</td>\n",
              "      <td>261.888867</td>\n",
              "      <td>261.309876</td>\n",
              "      <td>37.584866</td>\n",
              "      <td>32.022100</td>\n",
              "      <td>2.961717</td>\n",
              "      <td>82.238796</td>\n",
              "      <td>0.0</td>\n",
              "      <td>11063.87551</td>\n",
              "      <td>26.169788</td>\n",
              "      <td>2.861565</td>\n",
              "      <td>8.309186</td>\n",
              "      <td>1.557559</td>\n",
              "      <td>0.111030</td>\n",
              "      <td>0.041038</td>\n",
              "      <td>0.016870</td>\n",
              "      <td>0.011505</td>\n",
              "      <td>0.157608</td>\n",
              "      <td>0.199320</td>\n",
              "      <td>0.002104</td>\n",
              "      <td>4.282709</td>\n",
              "      <td>7.491846</td>\n",
              "      <td>2.930868</td>\n",
              "      <td>17.329747</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.283244</td>\n",
              "      <td>84.728767</td>\n",
              "      <td>28.048216</td>\n",
              "      <td>85.043883</td>\n",
              "      <td>6.613800</td>\n",
              "      <td>9.865654</td>\n",
              "      <td>7.798858</td>\n",
              "      <td>...</td>\n",
              "      <td>4.474426</td>\n",
              "      <td>11.560818</td>\n",
              "      <td>12.018494</td>\n",
              "      <td>3.311425</td>\n",
              "      <td>4.474426</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4938.133208</td>\n",
              "      <td>7002.122972</td>\n",
              "      <td>16.633908</td>\n",
              "      <td>2065.648176</td>\n",
              "      <td>149.687906</td>\n",
              "      <td>6733.707667</td>\n",
              "      <td>0.704037</td>\n",
              "      <td>0.004802</td>\n",
              "      <td>-0.050000</td>\n",
              "      <td>-0.120000</td>\n",
              "      <td>0.090000</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.030000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>735.754074</td>\n",
              "      <td>2353.945151</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>46.512601</td>\n",
              "      <td>0.309708</td>\n",
              "      <td>0.356150</td>\n",
              "      <td>0.530325</td>\n",
              "      <td>0.012024</td>\n",
              "      <td>2.301982</td>\n",
              "      <td>27.968788</td>\n",
              "      <td>44.154805</td>\n",
              "      <td>43.980715</td>\n",
              "      <td>29.716741</td>\n",
              "      <td>54.590356</td>\n",
              "      <td>64.966617</td>\n",
              "      <td>25.459767</td>\n",
              "      <td>63.680943</td>\n",
              "      <td>65.015700</td>\n",
              "      <td>43.810581</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>96.067453</td>\n",
              "      <td>133.592223</td>\n",
              "      <td>3.009237</td>\n",
              "      <td>5.802500</td>\n",
              "      <td>26758.24220</td>\n",
              "      <td>71980.981910</td>\n",
              "      <td>226.369675</td>\n",
              "      <td>5.705325</td>\n",
              "      <td>8.667772</td>\n",
              "      <td>256.294601</td>\n",
              "      <td>258.136304</td>\n",
              "      <td>37.508344</td>\n",
              "      <td>31.936418</td>\n",
              "      <td>2.948506</td>\n",
              "      <td>82.205567</td>\n",
              "      <td>0.0</td>\n",
              "      <td>11059.23882</td>\n",
              "      <td>26.337421</td>\n",
              "      <td>2.875703</td>\n",
              "      <td>8.354794</td>\n",
              "      <td>1.557721</td>\n",
              "      <td>0.110957</td>\n",
              "      <td>0.040988</td>\n",
              "      <td>0.017231</td>\n",
              "      <td>0.029631</td>\n",
              "      <td>0.157631</td>\n",
              "      <td>0.198034</td>\n",
              "      <td>0.002104</td>\n",
              "      <td>4.277983</td>\n",
              "      <td>7.457638</td>\n",
              "      <td>2.872870</td>\n",
              "      <td>17.316927</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.283283</td>\n",
              "      <td>84.700847</td>\n",
              "      <td>28.057322</td>\n",
              "      <td>85.094531</td>\n",
              "      <td>6.660992</td>\n",
              "      <td>9.896783</td>\n",
              "      <td>7.800981</td>\n",
              "      <td>...</td>\n",
              "      <td>4.626564</td>\n",
              "      <td>11.502980</td>\n",
              "      <td>12.018494</td>\n",
              "      <td>3.231301</td>\n",
              "      <td>4.474426</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4913.235292</td>\n",
              "      <td>6992.154361</td>\n",
              "      <td>16.835498</td>\n",
              "      <td>2079.210657</td>\n",
              "      <td>149.395548</td>\n",
              "      <td>6710.584284</td>\n",
              "      <td>0.703496</td>\n",
              "      <td>0.011070</td>\n",
              "      <td>-0.050000</td>\n",
              "      <td>-0.120000</td>\n",
              "      <td>0.090000</td>\n",
              "      <td>0.08</td>\n",
              "      <td>0.030000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>733.787715</td>\n",
              "      <td>2349.166060</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>46.476049</td>\n",
              "      <td>0.309464</td>\n",
              "      <td>0.354819</td>\n",
              "      <td>0.533049</td>\n",
              "      <td>0.005621</td>\n",
              "      <td>2.290842</td>\n",
              "      <td>27.947436</td>\n",
              "      <td>44.042264</td>\n",
              "      <td>43.994559</td>\n",
              "      <td>29.790411</td>\n",
              "      <td>54.697244</td>\n",
              "      <td>64.887116</td>\n",
              "      <td>25.441427</td>\n",
              "      <td>63.648130</td>\n",
              "      <td>64.955505</td>\n",
              "      <td>43.746895</td>\n",
              "      <td>4</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",
              "      <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",
              "      <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",
              "      <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",
              "      <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",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>106705</th>\n",
              "      <td>102.668322</td>\n",
              "      <td>134.525475</td>\n",
              "      <td>3.019441</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>35342.37838</td>\n",
              "      <td>2470.361816</td>\n",
              "      <td>198.122769</td>\n",
              "      <td>6.369088</td>\n",
              "      <td>7.595532</td>\n",
              "      <td>214.456317</td>\n",
              "      <td>212.795302</td>\n",
              "      <td>37.597032</td>\n",
              "      <td>32.738456</td>\n",
              "      <td>12.419254</td>\n",
              "      <td>82.905072</td>\n",
              "      <td>0.0</td>\n",
              "      <td>11148.53597</td>\n",
              "      <td>28.876352</td>\n",
              "      <td>2.227985</td>\n",
              "      <td>20.000000</td>\n",
              "      <td>0.321258</td>\n",
              "      <td>0.081408</td>\n",
              "      <td>0.041661</td>\n",
              "      <td>0.036645</td>\n",
              "      <td>0.081246</td>\n",
              "      <td>0.127869</td>\n",
              "      <td>0.154755</td>\n",
              "      <td>0.007085</td>\n",
              "      <td>4.290908</td>\n",
              "      <td>7.503941</td>\n",
              "      <td>2.656044</td>\n",
              "      <td>16.242882</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.088365</td>\n",
              "      <td>88.100123</td>\n",
              "      <td>28.091424</td>\n",
              "      <td>88.458612</td>\n",
              "      <td>7.718779</td>\n",
              "      <td>9.683025</td>\n",
              "      <td>7.802643</td>\n",
              "      <td>...</td>\n",
              "      <td>7.492050</td>\n",
              "      <td>10.509674</td>\n",
              "      <td>19.510544</td>\n",
              "      <td>4.991533</td>\n",
              "      <td>4.625307</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4037.824280</td>\n",
              "      <td>5762.722924</td>\n",
              "      <td>17.724978</td>\n",
              "      <td>1753.730240</td>\n",
              "      <td>142.009308</td>\n",
              "      <td>5603.960621</td>\n",
              "      <td>0.707521</td>\n",
              "      <td>0.012391</td>\n",
              "      <td>-0.043648</td>\n",
              "      <td>-0.105919</td>\n",
              "      <td>0.105000</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.010000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>600.245110</td>\n",
              "      <td>1944.448668</td>\n",
              "      <td>21.737323</td>\n",
              "      <td>41.638794</td>\n",
              "      <td>0.303389</td>\n",
              "      <td>0.348074</td>\n",
              "      <td>1.871696</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.442396</td>\n",
              "      <td>27.981711</td>\n",
              "      <td>39.874854</td>\n",
              "      <td>39.751213</td>\n",
              "      <td>38.251081</td>\n",
              "      <td>54.073089</td>\n",
              "      <td>64.149514</td>\n",
              "      <td>29.292260</td>\n",
              "      <td>65.151241</td>\n",
              "      <td>39.939657</td>\n",
              "      <td>39.212746</td>\n",
              "      <td>131899</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>106706</th>\n",
              "      <td>102.612860</td>\n",
              "      <td>134.581917</td>\n",
              "      <td>3.021966</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>35559.26054</td>\n",
              "      <td>2463.582243</td>\n",
              "      <td>198.004638</td>\n",
              "      <td>6.369728</td>\n",
              "      <td>7.647181</td>\n",
              "      <td>220.028956</td>\n",
              "      <td>220.552348</td>\n",
              "      <td>37.631157</td>\n",
              "      <td>32.650664</td>\n",
              "      <td>12.177823</td>\n",
              "      <td>82.773147</td>\n",
              "      <td>0.0</td>\n",
              "      <td>11150.70412</td>\n",
              "      <td>28.942347</td>\n",
              "      <td>2.261013</td>\n",
              "      <td>20.000000</td>\n",
              "      <td>0.321099</td>\n",
              "      <td>0.082223</td>\n",
              "      <td>0.041773</td>\n",
              "      <td>0.037526</td>\n",
              "      <td>0.079419</td>\n",
              "      <td>0.129136</td>\n",
              "      <td>0.154072</td>\n",
              "      <td>0.007121</td>\n",
              "      <td>4.286932</td>\n",
              "      <td>7.448294</td>\n",
              "      <td>2.665108</td>\n",
              "      <td>16.306197</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.088145</td>\n",
              "      <td>88.103645</td>\n",
              "      <td>28.051037</td>\n",
              "      <td>88.541541</td>\n",
              "      <td>7.701116</td>\n",
              "      <td>9.707081</td>\n",
              "      <td>7.804577</td>\n",
              "      <td>...</td>\n",
              "      <td>7.492050</td>\n",
              "      <td>10.509674</td>\n",
              "      <td>19.510544</td>\n",
              "      <td>4.862602</td>\n",
              "      <td>4.474426</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4028.974124</td>\n",
              "      <td>5750.004520</td>\n",
              "      <td>17.637870</td>\n",
              "      <td>1728.799788</td>\n",
              "      <td>142.009308</td>\n",
              "      <td>5575.601850</td>\n",
              "      <td>0.707577</td>\n",
              "      <td>0.012757</td>\n",
              "      <td>-0.045919</td>\n",
              "      <td>-0.110000</td>\n",
              "      <td>0.108648</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.010000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>590.652615</td>\n",
              "      <td>1947.626071</td>\n",
              "      <td>21.806505</td>\n",
              "      <td>41.638794</td>\n",
              "      <td>0.302730</td>\n",
              "      <td>0.347738</td>\n",
              "      <td>1.855698</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.445131</td>\n",
              "      <td>27.993688</td>\n",
              "      <td>39.825465</td>\n",
              "      <td>39.751213</td>\n",
              "      <td>38.369474</td>\n",
              "      <td>53.789299</td>\n",
              "      <td>64.119730</td>\n",
              "      <td>29.295746</td>\n",
              "      <td>65.136760</td>\n",
              "      <td>40.026059</td>\n",
              "      <td>39.212746</td>\n",
              "      <td>131900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>106707</th>\n",
              "      <td>102.727730</td>\n",
              "      <td>134.407348</td>\n",
              "      <td>3.019845</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>35934.04115</td>\n",
              "      <td>2469.585920</td>\n",
              "      <td>197.882666</td>\n",
              "      <td>6.369479</td>\n",
              "      <td>7.765437</td>\n",
              "      <td>219.691205</td>\n",
              "      <td>219.158746</td>\n",
              "      <td>37.606366</td>\n",
              "      <td>32.635398</td>\n",
              "      <td>12.240028</td>\n",
              "      <td>82.720306</td>\n",
              "      <td>0.0</td>\n",
              "      <td>11152.12507</td>\n",
              "      <td>29.006455</td>\n",
              "      <td>2.201698</td>\n",
              "      <td>20.000000</td>\n",
              "      <td>0.321449</td>\n",
              "      <td>0.082137</td>\n",
              "      <td>0.041744</td>\n",
              "      <td>0.039043</td>\n",
              "      <td>0.082216</td>\n",
              "      <td>0.131384</td>\n",
              "      <td>0.154169</td>\n",
              "      <td>0.007137</td>\n",
              "      <td>4.290933</td>\n",
              "      <td>7.366300</td>\n",
              "      <td>2.667783</td>\n",
              "      <td>16.345817</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.086986</td>\n",
              "      <td>88.071811</td>\n",
              "      <td>28.085066</td>\n",
              "      <td>88.551835</td>\n",
              "      <td>7.706220</td>\n",
              "      <td>9.720662</td>\n",
              "      <td>7.799564</td>\n",
              "      <td>...</td>\n",
              "      <td>7.492050</td>\n",
              "      <td>10.509674</td>\n",
              "      <td>19.510544</td>\n",
              "      <td>5.077948</td>\n",
              "      <td>4.474426</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4054.407486</td>\n",
              "      <td>5767.295706</td>\n",
              "      <td>17.418973</td>\n",
              "      <td>1679.350890</td>\n",
              "      <td>142.009308</td>\n",
              "      <td>5616.817938</td>\n",
              "      <td>0.707022</td>\n",
              "      <td>0.011815</td>\n",
              "      <td>-0.050000</td>\n",
              "      <td>-0.106352</td>\n",
              "      <td>0.106352</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.010000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>602.266822</td>\n",
              "      <td>1954.110891</td>\n",
              "      <td>21.706772</td>\n",
              "      <td>41.638794</td>\n",
              "      <td>0.303123</td>\n",
              "      <td>0.347017</td>\n",
              "      <td>1.859746</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.446731</td>\n",
              "      <td>27.994097</td>\n",
              "      <td>39.827782</td>\n",
              "      <td>39.751213</td>\n",
              "      <td>38.414607</td>\n",
              "      <td>53.837684</td>\n",
              "      <td>64.114728</td>\n",
              "      <td>29.181410</td>\n",
              "      <td>65.102921</td>\n",
              "      <td>40.009821</td>\n",
              "      <td>39.212746</td>\n",
              "      <td>131901</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>106708</th>\n",
              "      <td>102.733250</td>\n",
              "      <td>134.722928</td>\n",
              "      <td>3.019742</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>35340.80704</td>\n",
              "      <td>2472.703972</td>\n",
              "      <td>197.749874</td>\n",
              "      <td>6.369905</td>\n",
              "      <td>7.668778</td>\n",
              "      <td>222.626131</td>\n",
              "      <td>221.687887</td>\n",
              "      <td>37.600795</td>\n",
              "      <td>32.682506</td>\n",
              "      <td>12.235383</td>\n",
              "      <td>82.801579</td>\n",
              "      <td>0.0</td>\n",
              "      <td>11151.53755</td>\n",
              "      <td>28.763769</td>\n",
              "      <td>2.215250</td>\n",
              "      <td>20.000000</td>\n",
              "      <td>0.321373</td>\n",
              "      <td>0.082411</td>\n",
              "      <td>0.041782</td>\n",
              "      <td>0.037485</td>\n",
              "      <td>0.079447</td>\n",
              "      <td>0.129138</td>\n",
              "      <td>0.154339</td>\n",
              "      <td>0.007048</td>\n",
              "      <td>4.291267</td>\n",
              "      <td>7.454727</td>\n",
              "      <td>2.647071</td>\n",
              "      <td>16.279545</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.087236</td>\n",
              "      <td>88.118027</td>\n",
              "      <td>28.067715</td>\n",
              "      <td>88.530151</td>\n",
              "      <td>7.748643</td>\n",
              "      <td>9.734761</td>\n",
              "      <td>7.793506</td>\n",
              "      <td>...</td>\n",
              "      <td>7.492050</td>\n",
              "      <td>10.509674</td>\n",
              "      <td>19.510544</td>\n",
              "      <td>5.293635</td>\n",
              "      <td>4.474426</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4040.065085</td>\n",
              "      <td>5773.169689</td>\n",
              "      <td>17.462800</td>\n",
              "      <td>1713.433376</td>\n",
              "      <td>142.009308</td>\n",
              "      <td>5614.427881</td>\n",
              "      <td>0.706827</td>\n",
              "      <td>0.011315</td>\n",
              "      <td>-0.050000</td>\n",
              "      <td>-0.108648</td>\n",
              "      <td>0.108648</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.010000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>600.669163</td>\n",
              "      <td>1941.955564</td>\n",
              "      <td>21.609023</td>\n",
              "      <td>41.638794</td>\n",
              "      <td>0.303084</td>\n",
              "      <td>0.343201</td>\n",
              "      <td>1.866820</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.444872</td>\n",
              "      <td>28.011976</td>\n",
              "      <td>39.837981</td>\n",
              "      <td>39.751213</td>\n",
              "      <td>38.504729</td>\n",
              "      <td>53.879363</td>\n",
              "      <td>64.064254</td>\n",
              "      <td>29.135779</td>\n",
              "      <td>65.113767</td>\n",
              "      <td>40.046685</td>\n",
              "      <td>39.212746</td>\n",
              "      <td>131902</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>106709</th>\n",
              "      <td>102.626251</td>\n",
              "      <td>134.541245</td>\n",
              "      <td>3.019225</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>35644.90450</td>\n",
              "      <td>2468.475468</td>\n",
              "      <td>197.616431</td>\n",
              "      <td>6.369142</td>\n",
              "      <td>7.711804</td>\n",
              "      <td>217.666310</td>\n",
              "      <td>216.750750</td>\n",
              "      <td>37.595197</td>\n",
              "      <td>32.640062</td>\n",
              "      <td>12.410933</td>\n",
              "      <td>82.765578</td>\n",
              "      <td>0.0</td>\n",
              "      <td>11148.03030</td>\n",
              "      <td>28.836972</td>\n",
              "      <td>2.272496</td>\n",
              "      <td>20.000000</td>\n",
              "      <td>0.321812</td>\n",
              "      <td>0.081436</td>\n",
              "      <td>0.041734</td>\n",
              "      <td>0.036258</td>\n",
              "      <td>0.076395</td>\n",
              "      <td>0.129694</td>\n",
              "      <td>0.154681</td>\n",
              "      <td>0.007029</td>\n",
              "      <td>4.290846</td>\n",
              "      <td>7.350617</td>\n",
              "      <td>2.644106</td>\n",
              "      <td>16.252381</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2.090294</td>\n",
              "      <td>88.140913</td>\n",
              "      <td>28.069131</td>\n",
              "      <td>88.538423</td>\n",
              "      <td>7.703490</td>\n",
              "      <td>9.712448</td>\n",
              "      <td>7.809624</td>\n",
              "      <td>...</td>\n",
              "      <td>7.492050</td>\n",
              "      <td>10.509674</td>\n",
              "      <td>19.510544</td>\n",
              "      <td>5.228823</td>\n",
              "      <td>4.625307</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4038.688693</td>\n",
              "      <td>5742.158084</td>\n",
              "      <td>17.687251</td>\n",
              "      <td>1734.693234</td>\n",
              "      <td>142.009308</td>\n",
              "      <td>5581.485398</td>\n",
              "      <td>0.706515</td>\n",
              "      <td>0.009918</td>\n",
              "      <td>-0.049081</td>\n",
              "      <td>-0.110000</td>\n",
              "      <td>0.110000</td>\n",
              "      <td>0.10</td>\n",
              "      <td>0.009081</td>\n",
              "      <td>0.0</td>\n",
              "      <td>598.496659</td>\n",
              "      <td>1938.081316</td>\n",
              "      <td>21.707749</td>\n",
              "      <td>41.638794</td>\n",
              "      <td>0.303101</td>\n",
              "      <td>0.345860</td>\n",
              "      <td>1.869885</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.444329</td>\n",
              "      <td>27.978399</td>\n",
              "      <td>39.832216</td>\n",
              "      <td>39.751093</td>\n",
              "      <td>38.364494</td>\n",
              "      <td>54.005345</td>\n",
              "      <td>64.072414</td>\n",
              "      <td>29.116479</td>\n",
              "      <td>65.067852</td>\n",
              "      <td>40.001147</td>\n",
              "      <td>39.212746</td>\n",
              "      <td>131903</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>106710 rows × 363 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        Temperature Indicating 0  ...  original_index\n",
              "0                      96.026969  ...               0\n",
              "1                      95.987510  ...               1\n",
              "2                      96.099651  ...               2\n",
              "3                      96.035449  ...               3\n",
              "4                      96.067453  ...               4\n",
              "...                          ...  ...             ...\n",
              "106705                102.668322  ...          131899\n",
              "106706                102.612860  ...          131900\n",
              "106707                102.727730  ...          131901\n",
              "106708                102.733250  ...          131902\n",
              "106709                102.626251  ...          131903\n",
              "\n",
              "[106710 rows x 363 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jvDNSh2RMooT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "anomalies = np.array([10634, 36136, 57280, 57618, 60545, 63144, 118665, 128524, 131118])\n",
        "anomalies_idx = np.array(np.where(clean_data['original_index'].isin(anomalies))).flatten()\n",
        "s = set(clean_data['original_index'])\n",
        "t = set(raw_data.index)\n",
        "idx_during_trip = np.array(list(t.difference(s)))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nCqcmJJC-EJ8",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "outputId": "7d57a7e4-2809-4dca-cb2b-952ed6423b74"
      },
      "source": [
        "idx_where_trip_starts = idx_during_trip[np.where(np.diff(idx_during_trip) != 1)]\n",
        "idx_where_trip_starts"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([   838,   1090,   3179,   4257,   4286,   5404,   6015,   6483,\n",
              "         6751,   6909,   7807,  10699,  10765,  10836,  13016,  15924,\n",
              "        16153,  17990,  18089,  18888,  23887,  25522,  26289,  31767,\n",
              "        31799,  31906,  31935,  33692,  34583,  36019,  36111,  36240,\n",
              "        38518,  39765,  39814,  40429,  42451,  42791,  43156,  45344,\n",
              "        47561,  47693,  48274,  51855,  52379,  52451,  52705,  52830,\n",
              "        54758,  56248,  57403,  57670,  57918,  58106,  60665,  60930,\n",
              "        61121,  63314,  65675,  68885,  71086,  73967,  74206,  75292,\n",
              "        76414,  77481,  87539,  87571,  87669,  87747,  87799,  89645,\n",
              "        95729,  98932,  98967,  99487, 102133, 105309, 106676, 106910,\n",
              "       107237, 108394, 108471, 110322, 110939, 111349, 112342, 114274,\n",
              "       115072, 115144, 119996, 120111, 121031, 121064, 121313, 124372,\n",
              "       124420, 128499, 128548, 129453])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Fxwodzl4_onX",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 187
        },
        "outputId": "3fada3d9-e13f-4bcf-af45-df920fc9fa1c"
      },
      "source": [
        "anomalous_signals_at_trip = clean_data.loc[clean_data['original_index'].isin(anomalies)]\n",
        "anomalous_signals_at_trip.dropna(axis = 1, inplace = True)\n",
        "anomalous_signals_b4_trip = clean_data.loc[clean_data['original_index'].isin(anomalies-1)]\n",
        "anomalous_signals_b4_trip.dropna(axis = 1, inplace = True)"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:2: 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: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:4: 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: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  after removing the cwd from sys.path.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZaF8X-FiGZbC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "1aa07811-0f30-47d7-e605-c42e54c9b8b5"
      },
      "source": [
        "len(anomalous_signals_at_trip)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "9"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ih799fNREYbG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "42eacd7b-4c64-4717-e3fb-9edb77216eaa"
      },
      "source": [
        "fig,ax = plt.subplots(2,1)\n",
        "ax[0].plot(anomalous_signals_at_trip.iloc[1].values)\n",
        "ax[1].plot(anomalous_signals_b4_trip.iloc[1].values)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f9f115d8588>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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p6fHrjORzj6uYQayzevVqHA4Hzc3NvPzyy/3mHddCNFd+qIlVXQDz589n8eLF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\n3Ur1AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qS88MBr3F2BU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "a570d5e7-f681-473b-ab03-b3982de37f3a"
      },
      "source": [
        "fig,ax = plt.subplots(2,1)\n",
        "ax[0].plot(anomalous_signals_at_trip.iloc[2].values)\n",
        "ax[1].plot(anomalous_signals_b4_trip.iloc[2].values)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f9f110e5ac8>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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UVFSwaNGiQLzYTzzVabjOeKxTXdd58cUXaWtr46WXXrqmuU6iQbjOr776ivvv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\nQBAEQQD+fyW+kaWDMiFkAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d3RZlQIqLVhl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def signal_near_trip(window):\n",
        "\n",
        "  signals = []\n",
        "  for i in range(len(anomalies_idx)):\n",
        "    df = clean_data.loc[anomalies_idx[i]-window: anomalies_idx[i]]\n",
        "    _ = []\n",
        "    for j in range(window):\n",
        "      _.append(df.iloc[j])\n",
        "    signals.append(_)\n",
        "\n",
        "  return np.array(signals)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W6HMUJZNRjTU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def similarity_measure(x,y):\n",
        "\n",
        "  t0 = np.zeros((len(x),2))           #similaritymeasures requires input data in this form\n",
        "  t0[:,0] = np.arange(len(x))\n",
        "  t0[:,1] = x\n",
        "\n",
        "  t1 = np.zeros((len(y),2))       #similaritymeasures form\n",
        "  t1[:,0] = np.arange(len(y))\n",
        "  t1[:,1] = y\n",
        "\n",
        "  return similaritymeasures.dtw(t0,t1)[0]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8EVkfbVPStTO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def similarities(profiles):\n",
        "  sim = []\n",
        "  for i in range(profiles.shape[0]):\n",
        "    _ = []\n",
        "    for j in range(profiles.shape[1]-1):\n",
        "      _.append(similarity_measure(profiles[i,j],profiles[i,j+1]))\n",
        "    sim.append(_)\n",
        "  return np.array(sim)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nvhMnH5nlkou",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "45481afb-2a95-4cca-a8a3-6179ee77152a"
      },
      "source": [
        "window = 10\n",
        "signals = signal_near_trip(window)\n",
        "\n",
        "ten_window_similarities = similarities(signals)\n",
        "\n",
        "for i in range(len(_)):\n",
        "  plt.figure()\n",
        "  plt.plot(ten_window_similarities[i],label = '%s th trip' %i)\n",
        "  plt.legend()\n",
        "  plt.xlabel('Timestamp (approaching trip)')\n",
        "  plt.ylabel('Dissimilarity')"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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0aZJXQwohhMfkZgNyh7w3tHjZcHp6OnfccUeDYgLQr18/pk+fztdff+21cEII\n4Wmq7govGZD3vBYLSn5+fpMTQA4aNIj8/HyPhxJCCG9RuVkQHYMWYmp5Y+GWFguK3W4nODi40feC\ng4Ox2+0eDyWEEF6Tmw295P4Tb2hxDKW2tpYdO3Y0+b4UFCFER6GOl8PRArTRE/WO4pNaLChhYWG8\n/vrrTb5fv3SvEEK0e3n7AdDkCi+vaLGgLFmypC1yCCGE16mcugF56fLyCpcnhxRCiA4vNwvCI9G6\nhuudxCdJQRFCdBoqN1tmGPYiKShCiE5BnaiCgoNo0t3lNVJQhBCdQ/4BUHYZkPciKShCiE7BeYe8\nFBSvkYIihOgccrIgNAwiIvVO4rOkoAghOgWVmwW94mV2dC+SgiKE8HmqpgYO5cqAvJdJQRFC+L5D\nuVBbKwPyXiYFRQjh82RAvm1IQRFC+L7cLAjuAtExeifxaVJQhBA+T+XIgHxbkIIihPBpqrYW8g/I\ngHwbkIIihPBtBflQUy0zDLcBKShCCJ+mcrMBWQOlLUhBEUL4ttwsCAiAmFi9k/g8KShCCJ+mcrMg\nLh7N4Kd3FJ8nBUUI4bOU3Q652TIg30akoAghfNfRAqiqlBsa20iLa8p726xZswgKCsJgMODn58eC\nBQsoLy9n4cKFHD16lOjoaB5++GFMJhNKKVavXs3WrVsJDAxk5syZxMc7fvPYuHEjH374IQDXXXcd\n48aN0/FTCSHag/o75KWF0jZ0LygAzz77LF27dnU+X7duHYMHD2bKlCmsW7eOdevWcdttt7F161YK\nCgpITU1l7969vPnmm7z44ouUl5fzwQcfsGDBAgDmzp1LUlISJpNJr48khGgPcrPBzwg9eumdpFNo\nl11eGRkZjB07FoCxY8eSkZEBwObNmxkzZgyappGQkEBFRQVWq5Vt27YxZMgQTCYTJpOJIUOGsG3b\nNj0/ghCiHVC5WRDbG83or3eUTqFdtFDmz58PwMSJE0lOTqa0tJSIiAgAwsPDKS0tBcBisRAVFeXc\nLzIyEovFgsViITLy5KI5ZrMZi8XS6LnS0tJIS0sDYMGCBQ2O5w6j0djqfb1JcrlHcrmnI+VSSnE0\nbz9BI8bQVafMHen78shxPX5ENz3//POYzWZKS0t54YUX6NGjR4P3NU3z6Pw7ycnJJCcnO58XFRW1\n6jhRUVGt3tebJJd7JJd7OlIuVXwUVVZKVfdYqnXK3JG+L3ec/nO6nu5dXmazGYCwsDCGDx/Ovn37\nCAsLw2q1AmC1Wp3jK2azucGXUFxcjNlsxmw2U1xc7HzdYrE4jyuE6KScA/JyhVdb0bWgVFVVUVlZ\n6Xz8888/06tXL5KSkti0aa3H3wIAABgtSURBVBMAmzZtYvjw4QAkJSWRnp6OUorMzExCQkKIiIgg\nMTGR7du3U15eTnl5Odu3bycxMVG3zyWE0J/KzQLNALF99I7Saeja5VVaWsrLL78MQG1tLaNHjyYx\nMZFzzz2XhQsXsn79eudlwwBDhw5ly5YtzJkzh4CAAGbOnAmAyWTi+uuv58knnwRg6tSpcoWXEJ2c\nys2Gc3qiBQbqHaXT0LWgdO/enZdeeumM10NDQ3nmmWfOeF3TNO6+++5GjzV+/HjGjx/v8YxCiA4q\nNwtt4AV6p+hUdB9DEUIIT1OlViixQG+5obEtSUERQvgembJeF1JQhBA+p37KFeKkhdKWpKAIIXyO\nys2CbuegBYfoHaVTkYIihPA9OVnS3aUDKShCCJ+iKsqhuFCmrNeBFBQhhG+pv0NervBqc1JQhBA+\nRdVd4UWctFDamhQUIYRvyc0CczRaaNeWtxUeJQVFCOFTVG6WjJ/oRAqKEMJnqKpKOHJIlvzViRQU\nIYTvyN8PSsklwzqRgiKE8Bkqp25AXq7w0oUUFCGE78jNgq7hECYL7OlBCooQwmfUD8h7ctlw4Top\nKEIIn6BqquFQroyf6EgKihDCN+TngN0uV3jpSAqKEMInqLy6KeuloOhG1yWAReuo6hOOye+KjqCO\nFtT9fQSKjmAxmbD3jIf4AWjxCY47hqU/WXQGOdkQ0gWiuuudpNOSgtIOKbsdSoobFAqKClBFR+Do\nESi1NNwhIAAiuzv+R6qpRm36N6R9hAIIi4C+juKixQ+A3v3QgoL1+FhCeJUMyOtPCopO1PFyR3Eo\nOoIqKjiltVEIxUfAZju5saZBRBREdUc7f6ijcETFoEXHOB53DXf+T2SOiuJowWHIP4DanwnZe1DZ\nmahtPzgKjGaA2F6O4tK3rsjE9EQzSO+n6LiUzQb5B9DGX6V3lE5NCoqXKFsNFB+Fo3Uti6KCBq0N\njlc03CHEBNExaD37QOIIx+Oo7hDd3dFtZfR3+dya0R/69Efr0x8uu9KRp+wYHMh0FJfsPajN30D6\nF44iExzi2D5+QF2hGSAT64kOxZZ/AGw1Mn6iMykoraSUQpVYGnZFndLawFoMSp3cwWisa1l0d/zQ\njuqOFt395GshJq/m1UK7wuAktMFJjvx2Oxw5hMreA/v3OIrM5x+glN2xQ3QMWt8BdWMxAyCuj1tF\nTYi2ZMveAyCXDOtMCkor1L7yNIVZv0L1iYZvhJsdXVEJgx0ti6juaFF13VLh5nbVraQZDHBOT7Rz\nesKoCQCoE1WQs89RXLL3oPb8Aj9ucrRijP7Q+9y6IlPXVSYD/qKdqMnOhMAg6H6O3lE6NSkoraD1\nHUBw/99QaQo7WTCiuqH5B+gd7axogUGQcD5awvmAoxWGtQj2Z54sMqcO+HcNd7ZgtL4J0KcfWlCI\nrp9BdE627EyI64tm8NM7SqcmBaUVDNfeRmhUFCeKivSO4lWapoE52tESuXAUUDf4efAAKrt+wH8P\natv/Ozng3yPO0XqJH+BozZwjA/7Cu5Tdjm1/JtrICXpH6fSkoAi3aEaj49Lj3v3gsskAqPJjsH+v\no7js34P66Vv4+r8NBvzL+g3EHhAEYWa0cLPjcuZwMwR3kW4zcXYKD6GqKmX8pB2QgiLOmmbqCoMv\nRBt8IVA34F9YN+CfvQe1P5PKLz92LH4EqFN39g84WVzCItDCzI0+pkuoTxQeZa91XOFXfgzKy6Ci\nzFGQy8scr1WUocqOYcVObXUNaDguG0er+7vu+amPqX986rag1e9z+rb1X2OD5/Xbnvr4zHMcCwnG\nXl0DBgMY/Br+7WdwtFINfo7HjW3j/NuA5ufn2N6v6W0aHrvx46rMnY6YMmW97qSgCI/TDAbHvS0x\nPaGuGyIqKoqj+blQYoVSK6qkGEodjymxoEotcDAXtWsbVB4HTis8RqNjSvK64qOFRTien/aYLqFt\n1sWmamuhouxkcSg/hjr1ecUxVHnD51SUN7z671R+RjB1BVMoqosJamrqTqRO7qMUUPdcgfNbOvX9\nusfKue1p+zk/QGPHPe0xDbep0jRHt6e9Fuz2U/62u//9ub1HMwICICbOk0cUrSAFRbQZLSgEYkIg\nJpbm2hrqRJVjNoASK6rUCqXFJwtRqQUO56N+/QWOlzu2P3VnPz/oemqLp/7xKV1tYWYI7dpgAFfV\n1Dh+4Ne1Gig/rRg0KBaOlsQZ9xKdyj/AURy6hDrOFRflLBZ0CQVTVzRTaINtCAxucINqUTsco4tq\nIpdSCpQdauuKi6o9+fj04lNb69j21Me1pxeoUwpV3WNV2/jr2O2EDRhEmVF+nOlN/gVEu6MFBkG3\nHtCtR/OFp/pEXSvHUtfqOVl8VKnVcVPpvl2OgsBphcdggK7hHA0IxF5aAicqmz5RYLCjENT98Nei\nYxoWgi6hjm4/5zZd0QIDPfFVdBiapjm6pLx4lVVz/y0ERkVR1g4LcGcjBUV0WFpAIETHOP7Q9A8c\nVVMDxxxda5RaHMWmrvgEGI2c8A88WSzqCkSD4uAvN3QK4QopKMLnaf7+ENnN8YeGhSesnXYtCdER\nyQ0CQgghPEIKihBCCI+QgiKEEMIjfGoMZdu2baxevRq73c6ECROYMmWK3pGEEKLT8JkWit1uZ+XK\nlcybN4+FCxfy7bffkp+fr3csIYToNHymoOzbt4+YmBi6d++O0Whk5MiRZGRk6B1LCCE6DZ/p8rJY\nLERGRjqfR0ZGsnfv3jO2S0tLIy0tDYAFCxYQFRXVqvMZjcZW7+tNkss9kss9kss9nS2XzxQUVyUn\nJ5OcnOx83tp7EJqagkJvkss9kss9kss9vpqrR48ejb7uMwXFbDZTXFzsfF5cXIzZbG5xv6a+GFec\nzb7eJLncI7ncI7nc05ly+cwYyrnnnsvhw4cpLCzEZrPx3XffkZSU5LXzzZ0712vHPhuSyz2Syz2S\nyz2dLZfPtFD8/Py48847mT9/Pna7ncsuu4y4OJnOWggh2orPFBSAYcOGMWzYML1jCCFEp+T33HPP\nPad3iI4qPr59rhAnudwjudwjudzTmXJpSjW1fJwQQgjhOp8ZlBdCCKEvKShCCCE8wqcG5dtCe52A\ncunSpWzZsoWwsDBSUlL0juNUVFTEkiVLKCkpQdM0kpOTmTx5st6xqK6u5tlnn8Vms1FbW8vFF1/M\ntGnT9I4FOOalmzt3LmazuV1ddjpr1iyCgoIwGAz4+fmxYMECvSMBUFFRwbJly8jLy0PTNO6//34S\nEhJ0zXTo0CEWLlzofF5YWMi0adO48sordUzl8Omnn7J+/Xo0TSMuLo6ZM2cSEBDgmYMr4bLa2lo1\ne/ZsVVBQoGpqatRjjz2m8vLy9I6llFJq586dKisrSz3yyCN6R2nAYrGorKwspZRSx48fV3PmzGkX\n35ndbleVlZVKKaVqamrUk08+qfbs2aNzKodPPvlELVq0SP3lL3/RO0oDM2fOVKWlpXrHOMOrr76q\n0tLSlFKOf8vy8nKdEzVUW1ur7r77blVYWKh3FFVcXKxmzpypTpw4oZRSKiUlRW3YsMFjx5cuLze0\n5wkoBw0ahMlk0jvGGSIiIpxXkwQHBxMbG4vFYtE5FWiaRlBQEAC1tbXU1taiaU2tSt92iouL2bJl\nCxMmTNA7Sodw/Phxdu/ezfjx4wHHHFVdunTROVVDv/zyCzExMURHR+sdBXC0gKurq6mtraW6upqI\niAiPHVu6vNzg6gSUonGFhYXs37+ffv366R0FcPyP9cQTT1BQUMDll19O//799Y7EmjVruO2226is\nrNQ7SqPmz58PwMSJExvMiaeXwsJCunbtytKlS8nJySE+Pp7p06c7f1loD7799ltGjRqldwzAMUXV\n1Vdfzf33309AQAAXXHABF1xwgceOLy0U0SaqqqpISUlh+vTphISE6B0HAIPBwEsvvcSyZcvIysoi\nNzdX1zw//fQTYWFh7fa+heeff56//vWvzJs3jy+++IJdu3bpHYna2lr279/PpEmT+Nvf/kZgYCDr\n1q3TO5aTzWbjp59+4uKLL9Y7CgDl5eVkZGSwZMkSli9fTlVVFenp6R47vhQUN7R2AsrOzmazkZKS\nwqWXXsqIESP0jnOGLl26cN5557Ft2zZdc+zZs4fNmzcza9YsFi1axI4dO0hNTdU106nq/1sPCwtj\n+PDh7Nu3T+dEjl6CyMhIZ+vy4osvZv/+/TqnOmnr1q307duX8PBwvaMAju63bt260bVrV4xGIyNG\njCAzM9Njx5eC4oa2noDSFyilWLZsGbGxsVx11VV6x3E6duwYFRUVgOOKr59//pnY2FhdM91yyy0s\nW7aMJUuW8NBDD3H++eczZ84cXTPVq6qqcnbDVVVV8fPPP9OrVy+dU0F4eDiRkZEcOnQIcPzA7Nmz\np86pTmpP3V3gmLZ+7969nDhxAqUUv/zyi0f/u5cxFDe05wkoFy1axK5duygrK+O+++5j2rRpzoFK\nPe3Zs4f09HR69erF448/DsDNN9+s+5xrVquVJUuWYLfbUUpxySWXcOGFF+qaqT0rLS3l5ZdfBhzd\nTKNHjyYxMVHnVA533nknqamp2Gw2unXrxsyZM/WOBJwsvPfee6/eUZz69+/PxRdfzBNPPIGfnx99\n+vTx6FiYTL0ihBDCI6TLSwghhEdIQRFCCOERUlCEEEJ4hBQUIYQQHiEFRQghhEdIQREe9eGHH7Js\n2TK9Y+ju6aefblc32J2t9957r9mbLB955BF27tzZhokaeuONN/jggw9c2nbt2rX897//9XKizknu\nQxFuuf32252Pq6urMRqNGAyO30vuvfderrvuujbPtGTJEiIjI7npppva/NyN2bx5M0FBQfTt21fv\nKG3mlVde8cpxN27cyFdffcXzzz/f7Hbu3Otx9dVXM2/ePMaPH4/RKD8CPUm+TeGWv//9787Hs2bN\nYsaMGQwZMkTHRO3Pl19+yZgxY9r8vEoplFLOAt9Z2O12tz5zREQEPXr0YPPmze1mji1fIQVFeNR7\n771HQUEBc+bMobCwkNmzZ3P//ffz3nvvUVVVxc0330x8fDzLli2jqKiISy+9lLvuusu5//r16/nk\nk08oKSmhX79+3HvvvURHR6OU4q233uKbb76hpqaGqKgoHnzwQTIzM/nmm28A+OyzzzjvvPOYO3cu\n69at46uvvqK0tJTIyEhuvvlmLrroIuDkb73nnnsuGzduxGQy8cADD3D48GHeffddampquO222xg3\nbhzgaAH5+/tz5MgR9u7dS9++fZk9e3aj05HbbDZ27NjR4Dfmffv2sXr1ag4ePEhAQAAjRozgD3/4\ng/O342nTpjF9+nQ+//xzKisrGTduHLfeeisGg8GZtU+fPqSnpxMREcFdd93F4MGDAXjuuecYMGAA\nu3btIjs7m5SUFAICAlixYgW//vorJpOJa665xnk3dEtZ8vLyWLNmDdnZ2RiNRn772986W502m43X\nXnuNH3/8kaioKGbNmsW5554LNPzl4r333iM/P5+AgIBGt83OzmbZsmUUFBSQmJiIpmmcc845Z7Qw\n8/PzWbFiBTabjdtvvx0/Pz/WrFnDkiVLCAgIoKioiF27dvH444/z9ddfO1upO3fu5NVXX2XSpEl8\n9tlnBAUFcdNNN3HppZc6j33eeeexZcsWKSge1rl+lRG62Lt3L4sXL+ahhx7irbfe4sMPP+Tpp5/m\nlVde4fvvv3fOWpuRkcG//vUvHn30Ud58800GDhzI4sWLAdi+fTu7d+9m8eLFrFmzhocffpjQ0FCS\nk5MZPXo011xzDX//+9+dKxx2796dP//5z6xZs4YbbriBV199FavV2iBT7969WbVqFaNHj2bRokXs\n27eP1NRUHnjgAVatWkVVVZVz+2+++Ybrr7+elStX0qdPnybHEw4fPozBYGiwzIHBYOAPf/gDK1eu\n5IUXXmDHjh188cUXDfbLyMhgwYIF/PWvf2Xz5s1s2LChQdbu3buzcuVKpk2bxssvv0x5ebnz/fT0\ndO69917Wrl1LVFQUixcvJjIykuXLl/Poo4/y9ttvs2PHjhazVFZW8vzzz5OYmMjy5ctJTU11Fi5w\nzIY8cuRI1qxZQ1JSEqtWrWry37ypbW02Gy+//DLjxo1j1apVjBo1ih9//LHRY/Ts2ZN77rmHhIQE\n/v73v7NmzZoG/x7XXnstb731FgMHDjxj35KSEsrKyli2bBmzZs3ijTfecM73BRAbG0tOTk6T+UXr\nSEERXjd16lTn2guBgYGMHj2asLAwzGYzAwcOdA5ef/nll1x77bX07NkTPz8/rr32Wg4cOMDRo0cx\nGo1UVVVx8OBBlFL07Nmz2YWBLrnkEsxmMwaDgZEjRxITE9Ngdtxu3bpx2WWXOd8vLi5m6tSp+Pv7\nc8EFF2A0GikoKHBuP2zYMAYNGoS/vz8333wzmZmZFBUVnXHeioqKM9biiI+PJyEhAT8/P7p160Zy\ncvIZU79fc801mEwmoqKimDx5Mt9++63zvbCwMK688krnom49evRgy5YtzvfHjRtHXFwcfn5+lJSU\n8Ouvv3LrrbcSEBBAnz59mDBhAps2bWoxy08//UR4eDhXX301AQEBBAcHN1gjZuDAgQwbNgyDwcCY\nMWM4cOBAk99/U9tmZmZSW1vLb3/7W+dst61ZH2f48OEMHDgQg8HQ5PK1N954I/7+/gwaNIihQ4fy\n3XffOd8LDg52Tg4qPEe6vITXhYWFOR8HBASc8by+JXD06FFWr17N2rVrne8rpbBYLJx//vlcfvnl\nrFy5kqKiIi666CJuv/32JtdW2bRpE59++ilHjx4FHBP1lZWVNZkJaDDF+Km5gAYtjqCgIEwmE1ar\nlaioqAbnNZlMDfYDx/ria9euJSsry7lS3ulrnpx6/Ojo6AatKbPZ3GA1yejo6AarXp66r9VqxWQy\nERwc7HwtKiqKrKysFrMUFxfTvXt3mnL6d1ZTU0NtbS1+fn4ub2u1Ws/4PKfmd1VL+3Tp0qVBYT/9\nO62srGx3Kzv6Aikoot2Iioriuuuua9DXfarJkyczefJkSktLWbhwIR9//DE33XTTGUv3Hj16lOXL\nl/PMM8+QkJCAwWDg8ccf52zmQT11HZyqqirKy8sbbSHFxMQ4i2D9+iFvvvkmffr04cEHHyQ4OJjP\nPvuMH3744Yzj189cXVRU1ODYFosFpZTzcxYVFTVYNuHUzx8REUF5eTmVlZXOolJUVORSlsjIyAa/\nxXtDRETEGZ+nuLiYmJgYt47T0nLNFRUVVFVVOYtKUVFRg5nBDx48SO/evd1ML1oiXV6i3Zg4cSLr\n1q0jLy8PcKwX/v333wOOweS9e/dis9kIDAzE39/feWVPWFgYR44ccR7nxIkTaJpG165dAdiwYYPz\nmK21detWfv31V2w2G++88w4JCQlntE7Asab54MGDG3RpVVZWEhISQlBQEAcPHmz0HoiPP/6Y8vJy\nioqK+Pzzzxk5cqTzvdLSUv79739js9n4/vvvOXjwIEOHDm00Z1RUFAMGDOCf//wn1dXV5OTksGHD\nBmeRbi7LhRdeiNVq5bPPPqOmpobKykqPL3FdX+D/85//UFtbS0ZGRrMLdYWHh2OxWLDZbG6f6733\n3sNms7F79262bNnCJZdc4nxv165d7Wb6fV8iLRTRblx00UVUVVWxaNEiioqKCAkJYfDgwVxyySVU\nVlby1ltvceTIEed4zO9+9zsAxo8fzyuvvML06dMZNGgQf/zjH7nqqqt46qmnnH34AwYMOKtso0aN\n4v333yczM5P4+HgeeOCBJredOHEi//nPfxg9ejTguHfnjTfe4KOPPqJv376MHDnSOUheLykpiblz\n53L8+HHGjRvXYC2b/v37c/jwYe666y7Cw8N55JFHCA0NbfL8Dz74ICtWrGDGjBmYTCZuuOEG56Xd\nzWUJDg7mT3/6E2vWrOGDDz7AaDRy5ZVXNhhHOVtGo5HHHnuMZcuW8c9//pOhQ4dy4YUXNnk/yPnn\nn+8cnDcYDKxcudKl84SHh2MymZgxYwYBAQHcc889zoWkrFYr+fn5zqv+hOfIeihCtKA1N04+/fTT\n3HnnnS7d3Dht2jRSU1Mb7fZx9ca+jmzevHlMnDiRyy67zCPHq79suKkZG9auXUv37t25/PLLPXI+\ncZK0UITwAl8uAGdr165d9OjRg9DQUL7++mtycnLatPvp97//fZudq7ORgiKEaFOHDh1i4cKFVFVV\n0b17dx599NFmLwEXHYd0eQkhhPAIucpLCCGER0hBEUII4RFSUIQQQniEFBQhhBAeIQVFCCGER/x/\np4yRoNNqVWwAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SDiBBwanYATW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        },
        "outputId": "585e5721-e13b-4bd2-9c1d-5f0546fdd925"
      },
      "source": [
        "np.random.seed(11)\n",
        "window = 10\n",
        "ten_window_similarities = similarities(signal_near_trip(window))\n",
        "control_similarities = []\n",
        "signals = []\n",
        "rand = np.random.random_integers(0,109709,9)\n",
        "for r in rand:\n",
        "  df = clean_data.loc[r:r+window]\n",
        "  _ = []\n",
        "  for j in range(window):\n",
        "    _.append(df.iloc[j])\n",
        "  signals.append(_)\n",
        "signals = np.array(signals)\n",
        "ten_window_control_similarities = similarities(signals)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:6: DeprecationWarning: This function is deprecated. Please call randint(0, 109709 + 1) instead\n",
            "  \n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uyJYlPMbNoSo",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 581
        },
        "outputId": "0b47fb64-a109-4186-b604-293d157b15e8"
      },
      "source": [
        "plt.figure()\n",
        "for i in range(len(ten_window_control_similarities)):\n",
        "  plt.plot(ten_window_control_similarities[i],label = '%s th random' %i)\n",
        "  plt.legend()\n",
        "  plt.xlabel('Random point timestamp')\n",
        "  plt.ylabel('Dissimilarity')\n",
        "  plt.title('Random timestamps')\n",
        "\n",
        "plt.figure()\n",
        "for i in range(len(ten_window_similarities)):\n",
        "  plt.plot(ten_window_similarities[i],label = '%s th trip' %i)\n",
        "  plt.legend()\n",
        "  plt.xlabel('Timestamp (approaching trip)')\n",
        "  plt.ylabel('Dissimilarity')\n",
        "  plt.title('Near trip')"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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2LU7D9bU61aNphVdpRGPLwx5+U4AE+jiwx8aVy1XfOdbFVj08Ukm33v6t9z91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\nv9+P3+9v0yi63W78f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W3KCfz6178G4IorrmD69OldbI1+TWVlZfHcc89RVVVFeXk58+bN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\nLSocIiJiiwqHiIjYosIhIiK2/AE2ExTWGyCktgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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7duxg6tSpzvh4AsFlzx/+8IeuNsGlqSiXMddb9buq6iy8uPMstRaFv8T1JMDO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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zymeO72IxCYh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "np.save('/content/drive/My Drive/aihack20/control_similarity',ten_window_control_similarities)\n",
        "np.save('/content/drive/My Drive/aihack20/similarity',ten_window_similarities)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cYsX3VqEQoUW",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 476
        },
        "outputId": "6437cd5a-6ccc-4b08-941f-6d84156e4709"
      },
      "source": [
        "ten_window_control_similarities"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[2576.6623951 , 1766.5414309 , 1292.8116446 , 2221.3446895 ,\n",
              "        1470.8841216 , 1434.1860376 , 1122.7756179 , 1932.1813649 ,\n",
              "        2431.2148583 ],\n",
              "       [3055.7469807 , 3481.0131101 , 3605.195311  , 2737.7677296 ,\n",
              "        2390.0932751 , 3003.1556884 , 2762.2160027 , 2379.2427088 ,\n",
              "        2626.1685532 ],\n",
              "       [1213.7006615 , 1409.7882395 , 1867.9644887 , 1612.5085634 ,\n",
              "        1421.3886333 , 1535.8461283 , 1244.1124955 , 1423.1440588 ,\n",
              "        1238.3894998 ],\n",
              "       [2535.0511294 ,  884.3030578 , 2038.6693285 , 1272.0092593 ,\n",
              "        2866.1396932 ,  661.4941856 , 1914.7433234 , 1242.3963233 ,\n",
              "         939.9812236 ],\n",
              "       [1691.6093236 , 4637.8615811 , 7538.1297861 , 2177.2866195 ,\n",
              "        1705.6211911 , 1188.5685547 , 8042.7260243 , 5836.6383613 ,\n",
              "        1895.4727171 ],\n",
              "       [1608.9175455 , 1538.30500833,  733.8464745 ,  809.0154647 ,\n",
              "        2223.4708692 , 1002.489384  ,  670.3697997 , 1079.9501178 ,\n",
              "         848.9204646 ],\n",
              "       [1270.6820465 , 1489.4619464 ,  943.9358936 , 1225.6328762 ,\n",
              "        1438.6308537 , 1400.1729278 ,  822.8363245 , 1458.1197259 ,\n",
              "        1271.76988121],\n",
              "       [ 798.272456  , 1302.31889075, 1395.9189124 , 1515.1924473 ,\n",
              "        1416.0131488 , 1332.2643198 ,  693.6561553 , 4413.82317811,\n",
              "         988.4908628 ],\n",
              "       [1747.8012435 , 1545.4095614 , 2563.0316364 , 1543.925339  ,\n",
              "        2270.6155754 , 3004.9713764 ,  879.4925699 , 2029.4777685 ,\n",
              "        2741.1667284 ]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j32RUpfKr6ZR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}