EDA_George_Moody_Challenge_2023.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 97,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q7rpHQfvpZyg",
        "outputId": "06beff16-d64e-4fa2-f4d1-ce66249c5963"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: wget in /usr/local/lib/python3.8/dist-packages (3.2)\n",
            "Requirement already satisfied: wfdb in /usr/local/lib/python3.8/dist-packages (4.1.0)\n",
            "Requirement already satisfied: neurokit2 in /usr/local/lib/python3.8/dist-packages (0.2.3)\n",
            "Requirement already satisfied: matplotlib<4.0.0,>=3.2.2 in /usr/local/lib/python3.8/dist-packages (from wfdb) (3.5.3)\n",
            "Requirement already satisfied: pandas<2.0.0,>=1.0.0 in /usr/local/lib/python3.8/dist-packages (from wfdb) (1.3.5)\n",
            "Requirement already satisfied: requests<3.0.0,>=2.8.1 in /usr/local/lib/python3.8/dist-packages (from wfdb) (2.25.1)\n",
            "Requirement already satisfied: SoundFile<0.12.0,>=0.10.0 in /usr/local/lib/python3.8/dist-packages (from wfdb) (0.11.0)\n",
            "Requirement already satisfied: scipy<2.0.0,>=1.0.0 in /usr/local/lib/python3.8/dist-packages (from wfdb) (1.10.1)\n",
            "Requirement already satisfied: numpy<2.0.0,>=1.10.1 in /usr/local/lib/python3.8/dist-packages (from wfdb) (1.22.4)\n",
            "Requirement already satisfied: scikit-learn>=1.0.0 in /usr/local/lib/python3.8/dist-packages (from neurokit2) (1.2.1)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.8/dist-packages (from matplotlib<4.0.0,>=3.2.2->wfdb) (23.0)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.8/dist-packages (from matplotlib<4.0.0,>=3.2.2->wfdb) (1.4.4)\n",
            "Requirement already satisfied: pyparsing>=2.2.1 in /usr/local/lib/python3.8/dist-packages (from matplotlib<4.0.0,>=3.2.2->wfdb) (3.0.9)\n",
            "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.8/dist-packages (from matplotlib<4.0.0,>=3.2.2->wfdb) (2.8.2)\n",
            "Requirement already satisfied: fonttools>=4.22.0 in /usr/local/lib/python3.8/dist-packages (from matplotlib<4.0.0,>=3.2.2->wfdb) (4.38.0)\n",
            "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.8/dist-packages (from matplotlib<4.0.0,>=3.2.2->wfdb) (8.4.0)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.8/dist-packages (from matplotlib<4.0.0,>=3.2.2->wfdb) (0.11.0)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.8/dist-packages (from pandas<2.0.0,>=1.0.0->wfdb) (2022.7.1)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.8/dist-packages (from requests<3.0.0,>=2.8.1->wfdb) (2022.12.7)\n",
            "Requirement already satisfied: chardet<5,>=3.0.2 in /usr/local/lib/python3.8/dist-packages (from requests<3.0.0,>=2.8.1->wfdb) (4.0.0)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.8/dist-packages (from requests<3.0.0,>=2.8.1->wfdb) (2.10)\n",
            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.8/dist-packages (from requests<3.0.0,>=2.8.1->wfdb) (1.26.14)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.8/dist-packages (from scikit-learn>=1.0.0->neurokit2) (3.1.0)\n",
            "Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.8/dist-packages (from scikit-learn>=1.0.0->neurokit2) (1.2.0)\n",
            "Requirement already satisfied: cffi>=1.0 in /usr/local/lib/python3.8/dist-packages (from SoundFile<0.12.0,>=0.10.0->wfdb) (1.15.1)\n",
            "Requirement already satisfied: pycparser in /usr/local/lib/python3.8/dist-packages (from cffi>=1.0->SoundFile<0.12.0,>=0.10.0->wfdb) (2.21)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.8/dist-packages (from python-dateutil>=2.7->matplotlib<4.0.0,>=3.2.2->wfdb) (1.15.0)\n"
          ]
        }
      ],
      "source": [
        "!pip install wget wfdb neurokit2"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 98,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_ZJuKV5Hpb0U",
        "outputId": "f522695d-7607-44ed-b484-61dfe692cf37"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "kaggle.json\n",
            "/bin/bash: -c: line 0: unexpected EOF while looking for matching `''\n",
            "/bin/bash: -c: line 1: syntax error: unexpected end of file\n"
          ]
        }
      ],
      "source": [
        "!mkdir -p ~/.kaggle\n",
        "!cp kaggle.json ~/.kaggle/\n",
        "!ls ~/.kaggle\n",
        "!chmod 600 /root/.kaggle/kaggle.json'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 100,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NYqWe-76pgJt",
        "outputId": "27f674b8-a7d2-4ab2-9ea4-c7beea0a9775"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Warning: Your Kaggle API key is readable by other users on this system! To fix this, you can run 'chmod 600 /root/.kaggle/kaggle.json'\n",
            "george-moody-2023-complete-dataset.zip: Skipping, found more recently modified local copy (use --force to force download)\n"
          ]
        }
      ],
      "source": [
        "!kaggle datasets download -d bjoernjostein/george-moody-2023-complete-dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 158,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f9BA35CKz8AY",
        "outputId": "bf0c46d5-41ea-4ff3-b702-cd06504b6fdf"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting GitPython\n",
            "  Downloading GitPython-3.1.31-py3-none-any.whl (184 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m184.3/184.3 KB\u001b[0m \u001b[31m4.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting gitdb<5,>=4.0.1\n",
            "  Downloading gitdb-4.0.10-py3-none-any.whl (62 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m62.7/62.7 KB\u001b[0m \u001b[31m6.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting smmap<6,>=3.0.1\n",
            "  Downloading smmap-5.0.0-py3-none-any.whl (24 kB)\n",
            "Installing collected packages: smmap, gitdb, GitPython\n",
            "Successfully installed GitPython-3.1.31 gitdb-4.0.10 smmap-5.0.0\n"
          ]
        }
      ],
      "source": [
        "!pip install GitPython\n",
        "from git import Repo"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 159,
      "metadata": {
        "id": "wLv8L-uh0Awu"
      },
      "outputs": [],
      "source": [
        "HTTPS_REMOTE_URL = 'https://github.com/Bsingstad/python-example-2023.git'\n",
        "DEST_NAME = 'repo'\n",
        "cloned_repo = Repo.clone_from(HTTPS_REMOTE_URL, DEST_NAME)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 171,
      "metadata": {
        "id": "0wkCjnFB0QOW"
      },
      "outputs": [],
      "source": [
        "from repo.helper_code import *"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 273,
      "metadata": {
        "id": "quSp-rnhvG4T"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "import zipfile\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import matplotlib\n",
        "\n",
        "font = {'size'   : 16}\n",
        "\n",
        "matplotlib.rc('font', **font)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 274,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 381
        },
        "id": "vqRW-kx0psaC",
        "outputId": "9a5ca11f-4a26-4380-b6fc-641cea588641"
      },
      "outputs": [
        {
          "ename": "BadZipFile",
          "evalue": "ignored",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mBadZipFile\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-274-3aa4d8d27934>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      7\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mitem\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mendswith\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mextension\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;31m# check for \".zip\" extension\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m         \u001b[0mfile_name\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mabspath\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# get full path of files\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m         \u001b[0mzip_ref\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mzipfile\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mZipFile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfile_name\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# create zipfile object\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     10\u001b[0m         \u001b[0mzip_ref\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mextractall\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdir_name\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# extract file to dir\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m         \u001b[0mzip_ref\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;31m# close file\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.8/zipfile.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, file, mode, compression, allowZip64, compresslevel, strict_timestamps)\u001b[0m\n\u001b[1;32m   1267\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1268\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mmode\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'r'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1269\u001b[0;31m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_RealGetContents\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1270\u001b[0m             \u001b[0;32melif\u001b[0m \u001b[0mmode\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m'w'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'x'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1271\u001b[0m                 \u001b[0;31m# set the modified flag so central directory gets written\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.8/zipfile.py\u001b[0m in \u001b[0;36m_RealGetContents\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1334\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mBadZipFile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"File is not a zip file\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1335\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mendrec\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1336\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mBadZipFile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"File is not a zip file\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1337\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdebug\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1338\u001b[0m             \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mendrec\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mBadZipFile\u001b[0m: File is not a zip file"
          ]
        }
      ],
      "source": [
        "dir_name = \"./\"\n",
        "extension = \".zip\"\n",
        " \n",
        "os.chdir(dir_name) # change directory from working dir to dir with files\n",
        "\n",
        "for item in os.listdir(dir_name): # loop through items in dir\n",
        "    if item.endswith(extension): # check for \".zip\" extension\n",
        "        file_name = os.path.abspath(item) # get full path of files\n",
        "        zip_ref = zipfile.ZipFile(file_name) # create zipfile object\n",
        "        zip_ref.extractall(dir_name) # extract file to dir\n",
        "        zip_ref.close() # close file\n",
        "        os.remove(file_name) # delete zipped file"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 275,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 166
        },
        "id": "JRYZpH94sfXi",
        "outputId": "74afe999-c37c-48cf-86aa-b42d0b841e07"
      },
      "outputs": [
        {
          "ename": "FileNotFoundError",
          "evalue": "ignored",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-275-da479a7943b9>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrename\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"./i-care-international-cardiac-arrest-research-consortium-database-1.0/\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"./data/\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: './i-care-international-cardiac-arrest-research-consortium-database-1.0/' -> './data/'"
          ]
        }
      ],
      "source": [
        "os.rename(\"./i-care-international-cardiac-arrest-research-consortium-database-1.0/\", \"./data/\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 276,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QRLlceEzs05E",
        "outputId": "5a35a945-7818-4e47-fa57-25c3b1b146f2"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Total number of patients in training set:  607\n"
          ]
        }
      ],
      "source": [
        "print(\"Total number of patients in training set: \", len(os.listdir(\"./data/training/\")))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 277,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VLXy_3q2tUZI",
        "outputId": "5a0b6981-abb3-4a5c-e63a-61f7f6c18c55"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Total number of recordings:  23528\n"
          ]
        }
      ],
      "source": [
        "cnt = 0\n",
        "for root, dirs, files in os.walk(\"./data/training/\"):\n",
        "   for name in files:\n",
        "      if os.path.join(root, name).endswith(\".mat\"):\n",
        "        cnt +=1\n",
        "print(\"Total number of recordings: \", cnt)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 278,
      "metadata": {
        "id": "aailmzUcuRCe"
      },
      "outputs": [],
      "source": [
        "num_rec = []\n",
        "for root, dirs, files in os.walk(\"./data/training/\"):\n",
        "   for name in files:\n",
        "      if os.path.join(root, name).endswith(\".mat\"):\n",
        "        num_rec.append(os.path.join(root, name).split(\"/\")[-2])\n",
        "num_rec = np.asarray(num_rec)\n",
        "_, num_rec_pr_pat = np.unique(num_rec, return_counts=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 279,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Fg7W0Y-mxn9j",
        "outputId": "321fa6be-fff5-49a8-990b-843ced063980"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(23528,)"
            ]
          },
          "execution_count": 279,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "num_rec.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 280,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 310
        },
        "id": "9Qsqffe-ub6U",
        "outputId": "18d66ca7-cd01-464b-d121-850161c74976"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.hist(num_rec_pr_pat, bins=30)\n",
        "plt.xlabel(\"Number of recordings\")\n",
        "plt.ylabel(\"Number of patients\")\n",
        "plt.title(\"Number of recordings among the patients\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 281,
      "metadata": {
        "id": "YLE3HSRzv6QE"
      },
      "outputs": [],
      "source": [
        "df = 0\n",
        "cnt = 0\n",
        "for root, dirs, files in os.walk(\"./data/training/\"):\n",
        "   for name in files:\n",
        "      if os.path.join(root, name).endswith(\".tsv\"):\n",
        "        df_temp = pd.read_csv(os.path.join(root, name), sep=\"\\t\").dropna()\n",
        "        if cnt == 0:\n",
        "          df = df_temp\n",
        "        else:\n",
        "          df = pd.concat([df,df_temp])\n",
        "        cnt+=1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 282,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "3gV58SCbyQl3",
        "outputId": "5e1feac0-546f-4bb3-f82a-d378ddf4bfd4"
      },
      "outputs": [
        {
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              "    Hour   Time  Quality         Record\n",
              "34    35  34:55      1.0  ICARE_0829_35\n",
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              "37    38  37:35      1.0  ICARE_0829_38\n",
              "38    39  38:55      1.0  ICARE_0829_39"
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          "execution_count": 282,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 283,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 310
        },
        "id": "0kbOXKG8yIrR",
        "outputId": "13f6a82a-c7cf-408b-ee07-4a825997e4fa"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.hist(df[\"Quality\"], bins=100)\n",
        "plt.xlabel(\"Recording quality\")\n",
        "plt.ylabel(\"Number of recordings\")\n",
        "plt.title(\"Quality among the recordings\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 284,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FsemxGaqyovT",
        "outputId": "febbcb20-b0f5-47f8-e4dd-2d68788b4d59"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Number of signals with quality equal to 1.0 =  18235\n",
            "Percent of total signal with quality equal to 1.0 =  77.5\n"
          ]
        }
      ],
      "source": [
        "print(\"Number of signals with quality equal to 1.0 = \", np.where(df[\"Quality\"] == 1.0)[0].shape[0])\n",
        "print(\"Percent of total signal with quality equal to 1.0 = \", round((np.where(df[\"Quality\"] == 1.0)[0].shape[0]/df.shape[0])*100,2))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 285,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "geTMwdOFywD_",
        "outputId": "fde95c8e-d699-4023-b619-de979db6abac"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Number of signals with quality less than 1.0 =  5293\n",
            "Percent of total signal with quality less than 1.0 =  22.5\n"
          ]
        }
      ],
      "source": [
        "print(\"Number of signals with quality less than 1.0 = \", np.where(df[\"Quality\"] != 1.0)[0].shape[0])\n",
        "print(\"Percent of total signal with quality less than 1.0 = \", round((np.where(df[\"Quality\"] != 1.0)[0].shape[0]/df.shape[0])*100,2))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 286,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8BQl2hb8yCV-",
        "outputId": "bb65ad4b-b7e3-4e66-b229-26d056b2f5fb"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Number of signals with quality less than 0.75 =  2338\n",
            "Percent of total signal with quality less than 0.75 =  9.94\n"
          ]
        }
      ],
      "source": [
        "print(\"Number of signals with quality less than 0.75 = \", np.where(df[\"Quality\"] < 0.75)[0].shape[0])\n",
        "print(\"Percent of total signal with quality less than 0.75 = \", round((np.where(df[\"Quality\"] < 0.75)[0].shape[0]/df.shape[0])*100,2))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 287,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F6len2Zkxjw8",
        "outputId": "781d36da-4353-4fbf-9271-190a3b3245ab"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Number of signals with quality less than 0.5 =  1756\n",
            "Percent of total signal with quality less than 0.5 =  7.46\n"
          ]
        }
      ],
      "source": [
        "print(\"Number of signals with quality less than 0.5 = \", np.where(df[\"Quality\"] < 0.5)[0].shape[0])\n",
        "print(\"Percent of total signal with quality less than 0.5 = \", round((np.where(df[\"Quality\"] < 0.5)[0].shape[0]/df.shape[0])*100,2))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 288,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KD9kumxPxBaM",
        "outputId": "85a7b824-bee8-4cbb-e716-66ccfcef125b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Number of signals with quality less than 0.25 =  1069\n",
            "Percent of total signal with quality less than 0.25 =  4.54\n"
          ]
        }
      ],
      "source": [
        "print(\"Number of signals with quality less than 0.25 = \", np.where(df[\"Quality\"] < 0.25)[0].shape[0])\n",
        "print(\"Percent of total signal with quality less than 0.25 = \", round((np.where(df[\"Quality\"] < 0.25)[0].shape[0]/df.shape[0])*100,2))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 289,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wgPgl_dP1FDY",
        "outputId": "22e6128f-f4e0-4ac5-fa50-4f6201c1c328"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "34    ICARE_0829\n",
              "35    ICARE_0829\n",
              "36    ICARE_0829\n",
              "37    ICARE_0829\n",
              "38    ICARE_0829\n",
              "         ...    \n",
              "57    ICARE_0874\n",
              "58    ICARE_0874\n",
              "59    ICARE_0874\n",
              "60    ICARE_0874\n",
              "61    ICARE_0874\n",
              "Name: Record, Length: 23528, dtype: object"
            ]
          },
          "execution_count": 289,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df[\"Record\"].str.rsplit(\"_\",n=1).str[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 290,
      "metadata": {
        "id": "7ySnIfEM0_xO"
      },
      "outputs": [],
      "source": [
        "df[\"Patient\"] = df[\"Record\"].str.rsplit(\"_\",n=1).str[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 291,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "2R6sqfDswAV8",
        "outputId": "db61654c-b923-4cf5-c678-32a985c6175a"
      },
      "outputs": [
        {
          "data": {
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              "\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Hour</th>\n",
              "      <th>Time</th>\n",
              "      <th>Quality</th>\n",
              "      <th>Record</th>\n",
              "      <th>Patient</th>\n",
              "    </tr>\n",
              "  </thead>\n",
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              "      <th>34</th>\n",
              "      <td>35</td>\n",
              "      <td>34:55</td>\n",
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              "      <td>ICARE_0829_35</td>\n",
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              "      <td>36</td>\n",
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              "      <td>ICARE_0829_37</td>\n",
              "      <td>ICARE_0829</td>\n",
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              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>38</td>\n",
              "      <td>37:35</td>\n",
              "      <td>1.0</td>\n",
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              "      <td>ICARE_0829</td>\n",
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              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>39</td>\n",
              "      <td>38:55</td>\n",
              "      <td>1.0</td>\n",
              "      <td>ICARE_0829_39</td>\n",
              "      <td>ICARE_0829</td>\n",
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              "  </tbody>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-250ceaef-667f-4b59-addd-4b15bd8f41f1')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "    .colab-df-convert {\n",
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              "      border: none;\n",
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              "\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "        const buttonEl =\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-250ceaef-667f-4b59-addd-4b15bd8f41f1');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "    Hour   Time  Quality         Record     Patient\n",
              "34    35  34:55      1.0  ICARE_0829_35  ICARE_0829\n",
              "35    36  35:55      1.0  ICARE_0829_36  ICARE_0829\n",
              "36    37  36:55      1.0  ICARE_0829_37  ICARE_0829\n",
              "37    38  37:35      1.0  ICARE_0829_38  ICARE_0829\n",
              "38    39  38:55      1.0  ICARE_0829_39  ICARE_0829"
            ]
          },
          "execution_count": 291,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 292,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270
        },
        "id": "7V9A55sUv2B_",
        "outputId": "dcc3560a-00a5-405f-b79a-a927e07827bd"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.hist(df.groupby(\"Patient\")[\"Quality\"].mean())\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 293,
      "metadata": {
        "id": "1D9pQWmOzUC-"
      },
      "outputs": [],
      "source": [
        "patient_data =  load_text_file(\"/content/data/training/ICARE_0284/ICARE_0284.txt\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 294,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OSpWmMoe0w10",
        "outputId": "9eb14fc2-0e2a-4f0e-8223-717516528e26"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "['Patient: ICARE_0284',\n",
              " 'Age: 53',\n",
              " 'Sex: Male',\n",
              " 'ROSC: nan',\n",
              " 'OHCA: True',\n",
              " 'VFib: True',\n",
              " 'TTM: 33',\n",
              " 'Outcome: Good',\n",
              " 'CPC: 1',\n",
              " '']"
            ]
          },
          "execution_count": 294,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "patient_data.split(\"\\n\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 295,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yHFK0FD40hxL",
        "outputId": "9c67fbc1-a1c4-4974-c4f4-c26975647fa7"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "execution_count": 295,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "get_outcome(patient_data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 296,
      "metadata": {
        "id": "rON7sP3YyZ4y"
      },
      "outputs": [],
      "source": [
        "patient = []\n",
        "age = []\n",
        "sex = []\n",
        "rosc = []\n",
        "ohca = []\n",
        "vfib = []\n",
        "ttm = []\n",
        "outcome = []\n",
        "outcome_prob = []\n",
        "cpc = []\n",
        "\n",
        "for root, dirs, files in os.walk(\"./data/training/\"):\n",
        "   for name in files:\n",
        "      if os.path.join(root, name).endswith(\".txt\"):\n",
        "        patient_data = load_text_file(os.path.join(root, name))\n",
        "        patient.append(get_patient_id(patient_data))\n",
        "        age.append(get_age(patient_data))\n",
        "        sex.append(get_sex(patient_data))\n",
        "        rosc.append(get_rosc(patient_data))\n",
        "        ohca.append(get_ohca(patient_data))\n",
        "        vfib.append(get_vfib(patient_data))\n",
        "        ttm.append(get_ttm(patient_data))\n",
        "        outcome.append(get_outcome(patient_data))\n",
        "        #outcome_prob.append(get_outcome_probability(patient_data))\n",
        "        cpc.append(get_cpc(patient_data))\n",
        "patient = np.asarray(patient)\n",
        "age = np.asarray(age)\n",
        "sex = np.asarray(sex)\n",
        "rosc = np.asarray(rosc)\n",
        "ohca = np.asarray(ohca)\n",
        "vfib = np.asarray(vfib)\n",
        "ttm = np.asarray(ttm)\n",
        "outcome = np.asarray(outcome)\n",
        "outcome_prob =np.asarray(outcome_prob)\n",
        "cpc = np.asarray(cpc)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 297,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289
        },
        "id": "5yRrokjN4G5_",
        "outputId": "75951591-9712-4346-eeb8-90094e68b562"
      },
      "outputs": [
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.hist(age, bins=20)\n",
        "plt.xlabel(\"Age\")\n",
        "plt.ylabel(\"Count\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 298,
      "metadata": {
        "id": "hp4xBhJ_4sdS"
      },
      "outputs": [],
      "source": [
        "label, value = np.unique(sex, return_counts=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 299,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "IETWQMT24aD9",
        "outputId": "b2233d4f-b472-42d8-e81d-51f2bd4ae0b0"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(8,8))\n",
        "plt.title(\"Gender distribution\")\n",
        "plt.pie(value, labels = label, autopct='%1.1f%%')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 300,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "PKaJBJF77G4A",
        "outputId": "a832bc94-4621-4021-c5c8-977e819b90e2"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(8,8))\n",
        "plt.title(\"Information about return of spontaneous circulation (training data)\")\n",
        "lab,cnt = np.unique(np.isnan(rosc)*1,return_counts=True)\n",
        "plt.pie(cnt,labels=[\"Value\",\"Missing value\"],autopct='%1.1f%%')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 301,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 310
        },
        "id": "avHsh9sB6hyV",
        "outputId": "467fbd97-605a-4a1f-c340-30ebc4b81924"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.title(\"Return of spontaneous circulation\")\n",
        "plt.hist(rosc)\n",
        "plt.xlabel(\"Time to return of spontaneous circulation (min)\")\n",
        "plt.ylabel(\"Count\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 302,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hF8a4dAA8Zy1",
        "outputId": "1ecfa115-6c0c-4ffc-f6ee-0294f30073a6"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(array([ 1., nan]), array([566,  41]))"
            ]
          },
          "execution_count": 302,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "np.unique(ohca,return_counts=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 303,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "61kW72vn8v-g",
        "outputId": "e2da2aba-7d18-4986-d17e-90be6839fa66"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(8,8))\n",
        "plt.title(\"Out of hospital cardiac arrest\")\n",
        "lab,cnt = np.unique(np.isnan(ohca)*1,return_counts=True)\n",
        "plt.pie(cnt,labels=[\"Out of hospital cardiac arrest\",\"Missing value\"],autopct='%1.1f%%')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 304,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "B1bFmOpX9D0S",
        "outputId": "9dd5efd9-9b8f-4fae-9778-a95af88073a4"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(8,8))\n",
        "plt.title(\"Vfib - Shockable rythm\")\n",
        "lab,cnt = np.unique(np.isnan(vfib)*1,return_counts=True)\n",
        "plt.pie(cnt,labels=[\"Shockable\",\"Missing value\"],autopct='%1.1f%%')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 305,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JdppJcW4zCRz",
        "outputId": "b0f431e9-1409-45d6-ddc0-e6fd7cc47641"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(array([33., 36., nan]), array([448,  61,  98]))"
            ]
          },
          "execution_count": 305,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "np.unique(ttm, return_counts=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 306,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "MkdImQzk-Zn9",
        "outputId": "fb79cb4a-a619-4401-aea6-dad06e89c8f1"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(8,8))\n",
        "plt.title(\"Targeted temperature management (celcius)\")\n",
        "lab,cnt = np.unique(ttm,return_counts=True)\n",
        "plt.pie(cnt,labels=[\"33\",\"36\",\"nan\"],autopct='%1.1f%%')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 307,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "tXZsvUk9y1fz",
        "outputId": "57d4ebc0-f08e-4386-dbc8-148e21fe02a1"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(8,8))\n",
        "plt.title(\"Outcome\")\n",
        "lab,cnt = np.unique(outcome,return_counts=True)\n",
        "plt.pie(cnt,labels=[\"Good (CPC score of 1-2)\", \"Poor (CPC score of 3-5)\"],autopct='%1.1f%%')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 308,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 509
        },
        "id": "qQUJc2hd-8YX",
        "outputId": "432cfc81-4583-4beb-c57b-f8180b50bb27"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(8,8))\n",
        "plt.title(\"CPC score\")\n",
        "lab,cnt = np.unique(cpc,return_counts=True)\n",
        "plt.bar(x=lab , height=cnt)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 313,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Plj2YwnfARon",
        "outputId": "f7e3e251-2bb5-4d1e-8050-bc43b2c5469f"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
              "        18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34,\n",
              "        35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51,\n",
              "        52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68,\n",
              "        69, 70, 71, 72]),\n",
              " array([  3,   6,  24,  54,  94, 150, 175, 189, 214, 240, 266, 281, 298,\n",
              "        323, 335, 351, 368, 399, 408, 421, 443, 458, 457, 465, 468, 469,\n",
              "        464, 472, 469, 466, 469, 466, 462, 449, 439, 436, 428, 426, 416,\n",
              "        418, 417, 415, 404, 401, 386, 382, 369, 357, 355, 340, 331, 323,\n",
              "        317, 315, 312, 306, 302, 292, 294, 288, 285, 278, 275, 268, 262,\n",
              "        250, 242, 236, 232, 226, 216, 213]))"
            ]
          },
          "execution_count": 313,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "np.unique(df[\"Record\"].str.rsplit(\"_\",n=1).str[1].astype(int),return_counts=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 314,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 509
        },
        "id": "NDjPFeuMAhQl",
        "outputId": "e8c770da-dcec-42ae-b1c5-995bfac19990"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(8,8))\n",
        "plt.title(\"num recordings from 0 to 72 hours \")\n",
        "lab,cnt = np.unique(df[\"Record\"].str.rsplit(\"_\",n=1).str[1].astype(int),return_counts=True)\n",
        "plt.bar(x=lab , height=cnt)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 317,
      "metadata": {
        "id": "yQAaO2ZmBHcB"
      },
      "outputs": [],
      "source": [
        "timeslots = np.asarray([(0,12),(12,24),(24,48),(48,72)])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 332,
      "metadata": {
        "id": "kL4GsFN_CUnv"
      },
      "outputs": [],
      "source": [
        "rec_after_time = np.zeros((timeslots.shape[0],607))\n",
        "for num,i in enumerate(timeslots):\n",
        "  cnt = 0\n",
        "  for root, dirs, files in os.walk(\"./data/training/\"):\n",
        "    for name in files:\n",
        "        if os.path.join(root, name).endswith(\".tsv\"):\n",
        "          df_temp = pd.read_csv(os.path.join(root, name), sep=\"\\t\")\n",
        "          rec_after_time[num,cnt] = df_temp[i[0]:i[1]].dropna().shape[0]\n",
        "          cnt+=1"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "btsC3iQZGCNs"
      },
      "source": [
        "## 0 to 12 hours"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 340,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "j6JydpKtFh7d",
        "outputId": "1299be27-2c73-4c69-e7b7-d9a0554211a0"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(8,8))\n",
        "plt.title(\"Recordings from 0 to 12 hours\")\n",
        "lab,cnt = np.unique(rec_after_time[0] == 0,return_counts=True)\n",
        "plt.pie(cnt,labels=[\"Yes\", \"No\"],autopct='%1.1f%%')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 336,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 509
        },
        "id": "gZVFcr7zFKU3",
        "outputId": "14275efb-70bb-46b5-943d-23639caf6a55"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(8,8))\n",
        "plt.title(\"num recordings from 0 to 12 hours\")\n",
        "lab,cnt = np.unique(rec_after_time[0],return_counts=True)\n",
        "plt.bar(x=lab , height=cnt)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CCmV-CWTGFE1"
      },
      "source": [
        "## 12 to 24 hours"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 342,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "_zyfZikpCn2i",
        "outputId": "5f014593-8a82-44d4-b762-2943ffc267fa"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(8,8))\n",
        "plt.title(\"Recordings from 12 to 24 hours\")\n",
        "lab,cnt = np.unique(rec_after_time[1] == 0,return_counts=True)\n",
        "plt.pie(cnt,labels=[\"Yes\", \"No\"],autopct='%1.1f%%')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 358,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 509
        },
        "id": "8rd_vsTAF2wl",
        "outputId": "424f282c-1db9-4968-cb72-ccb62347728a"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(8,8))\n",
        "plt.title(\"num recordings from 12 to 24 hours\")\n",
        "lab,cnt = np.unique(rec_after_time[1],return_counts=True)\n",
        "plt.bar(x=lab , height=cnt)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "byRKE0F-GKED"
      },
      "source": [
        "## 24 to 48 hours"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 346,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "8TV4DD-7F_Hc",
        "outputId": "dc34d311-475d-457a-fd5f-c61875e6c92b"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(8,8))\n",
        "plt.title(\"Recordings from 24 to 48 hours\")\n",
        "lab,cnt = np.unique(rec_after_time[2] == 0,return_counts=True)\n",
        "plt.pie(cnt,labels=[\"Yes\", \"No\"],autopct='%1.1f%%')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 357,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 509
        },
        "id": "P0AJsCwdGO59",
        "outputId": "a31eed1d-dfd1-48e8-b169-785895c245cb"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(12,8))\n",
        "plt.title(\"num recordings from 24 to 48 hours\")\n",
        "lab,cnt = np.unique(rec_after_time[2],return_counts=True)\n",
        "plt.bar(x=lab , height=cnt)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g_0DfjyHG1i1"
      },
      "source": [
        "## 48 to 72 hours"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 353,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 487
        },
        "id": "Du7zdhUQGX18",
        "outputId": "47cf04e2-5a36-45fa-b340-97596b77e29d"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(8,8))\n",
        "plt.title(\"Recordings from 48 to 72 hours\")\n",
        "lab,cnt = np.unique(rec_after_time[3] == 0,return_counts=True)\n",
        "plt.pie(cnt,labels=[\"Yes\", \"No\"],autopct='%1.1f%%')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 356,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 509
        },
        "id": "EZ8Dt8tDG-Zx",
        "outputId": "da7c3350-281d-44fb-d254-445775d247e3"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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5pN9WVU/N8A3Bvhm28x5JXjPnPvO5DPvHb1TVXhnH3rfWXtlau2E8p/kpSf7nuH3/NcNpHg/N8C3Mq1fZ7hV/jDgOffq7DD8WPCXJY6vqscuqnddaO2mV657HaveNt2c47/5xY6/2P2X4QfL9MvyY87DMb7ckn62qr2U4Br6bYdjIkzL86PS4m/2qYAsRqGEdtNbeUsPlhH8jQ+/P9zL82OqVGQLWVmU8XdgvV9XHM7T3yAwXDbkww6m8fi3DOYCX6l9YwxUcX5LhnMX/b5IrM/QuvvLGS88LknwxyX/JEKxvGB8/v7X2V6ts5/+oqisy/Njt2UkuSfLeDJdzXn4WlEX6zxneh2dleK3fHttwYYaAMh0a8MYMvX2HZrhoxQ0ZLmrzsiSvb8NV8dbEGBR/PkMofcbY1h9nCJd/PD1F3NaqtfaRsbf3dzIMj/nZDD9K/FKG0ym+e87lfL+qjsoQ1p6TIdQl4/7ZWvvk2Gt6bIZtuHuSf8nwLdIft1Vc1GX89uUXM+wHJ2+m6h4ZQmwy/HjxiBl1Ppnhb8VCrXbfGI/xf5+hc+CxGb5N+vsM+/XLsrpA/cMkx2TYlodl+ID0/QyXkv8vrbUPd7w02CJq+D8JwKJV1fEZwsUTWmsfW6k+ANsmgRqgU1XdYRw7Pi27V4arSFaSA2acJxyA7YQhHwD9XlZV/yHDj9kuzvCjrCdlGHv6HGEaYPsmUAP0+3iS+2S4rPReGcaEfibJn7TWPrqeDQNg7RnyAQAAHZyHGgAAOmzzQz722WefduCBB653MwAA2I6dddZZF7fW9p01b5sP1AceeGDOPPPM9W4GAADbsar6zqbmGfIBAAAdBGoAAOggUAMAQAeBGgAAOgjUAADQQaAGAIAOAjUAAHQQqAEAoINADQAAHQRqAADoIFADAEAHgRoAADoI1AAA0EGgBgCADgI1AAB0EKgBAKCDQA0AAB0EagAA6CBQAwBAh13WuwHbqgOP+ehCl3feCU9Y6PIAANgy9FADAEAHgRoAADoI1AAA0EGgBgCADgI1AAB0EKgBAKCDQA0AAB0EagAA6CBQAwBAB4EaAAA6CNQAANBBoAYAgA4CNQAAdBCoAQCgg0ANAAAdBGoAAOggUAMAQAeBGgAAOgjUAADQQaAGAIAOAjUAAHQQqAEAoINADQAAHQRqAADoIFADAEAHgRoAADoI1AAA0GHFQF1VP1dV76yqc6vq0qq6sqq+WlUnVNXtl9W9dVW9oqq+UVVXV9WGqjq5qu47Y7k7V9VvVtVXqupH47I/XlWPWOQLBACAtbTLHHVemORxy8oOHm+/VFUPbq1dXlW7JDklyaMm9fZJclSSI6rq8NbaWZN570rytMnjWyU5IsljqurI1trHVvlaAABgi5tnyMfVSd6Q5CFJdktyaJLzx3kHJfmVcfr52Rim/zwbw/T1SW6b5M1LC6yqJ2ZjmD41yQFJDkvywwwh/61VtevNekUAALAFzROof7m19oLW2hdaaz9urZ2R5HWT+fcc74+elB3TWruktfaBJJ8cyx5SVfefUfflrbULWmufSvK+seyA3LRXHAAAtjorBurW2hUzim81mf7u2Jv8gPHxD1pr50/mnzOZfuiy++XzZ9UFAICt1qrP8lFVB2QYV50kVyV5Z5K9s3E89uXLnjJ9vN94v/8m5s+qCwAAW61VBeqqukuGMc/7J7khyTNba99d6WmrWcWc7XhuVZ1ZVWdu2LBhFYsHAIDFmjtQV9W9k3wmyb2TXJfkP7fW3j/OvmQsS5I9lz11j8n0ReP9hZOyaf1ZdW+itXZia+2Q1toh++6771ztBwCAtTBXoK6qQ5KcnuQuGYZ5HNla+4ul+a21a5J8eXy4e1XdefL0gyfTn192nyT3XaEuAABstea5sMujMwzz2CdDT/TPbuIc0SdNpk+oqr2r6qgMp8NLkrNaa1+ZUfcVVbV/VR2W5Clj2feSfGLuVwEAAOtknh7qY5PsPk7vneRzVdUmt9PGeW/M0IudJE9PcnGSk5PsnOTKJM9bWmBr7cNJ3jM+fHSSC5KcluQ2GYaOPGfs9QYAgK3aqs/ysSmttesyXOnw+CTfSnJNhh7t9yd52LKrJCbJM5K8OMlXM1w85vIMV1o8rLX20UW1CwAA1tKKlx5vrR0+78Jaa1dl6NE+do661yV57XgDAIBt0sJ6qAEAYEckUAMAQAeBGgAAOgjUAADQQaAGAIAOAjUAAHQQqAEAoINADQAAHQRqAADoIFADAEAHgRoAADoI1AAA0EGgBgCADgI1AAB0EKgBAKCDQA0AAB0EagAA6CBQAwBAB4EaAAA6CNQAANBBoAYAgA4CNQAAdBCoAQCgg0ANAAAdBGoAAOggUAMAQAeBGgAAOgjUAADQQaAGAIAOAjUAAHQQqAEAoINADQAAHQRqAADoIFADAEAHgRoAADoI1AAA0EGgBgCADgI1AAB0EKgBAKCDQA0AAB0EagAA6CBQAwBAB4EaAAA6CNQAANBBoAYAgA4CNQAAdBCoAQCgg0ANAAAdBGoAAOggUAMAQAeBGgAAOgjUAADQQaAGAIAOAjUAAHQQqAEAoINADQAAHQRqAADoIFADAEAHgRoAADoI1AAA0EGgBgCADgI1AAB0EKgBAKCDQA0AAB0EagAA6CBQAwBAB4EaAAA6CNQAANBBoAYAgA4CNQAAdBCoAQCgg0ANAAAdBGoAAOggUAMAQAeBGgAAOgjUAADQQaAGAIAOAjUAAHQQqAEAoINADQAAHQRqAADoIFADAECHuQJ1Ve1bVa+rqjOq6uqqauPthcvqHT6ZN+t2xLL6O1fVb1bVV6rqR1V1aVV9vKoescgXCQAAa2WXOevdKcmL1mD970rytMnjWyU5IsljqurI1trH1mCdAACwMPMO+bgsyWuTPDXJm+Z8zkGttVp2O2VpZlU9MRvD9KlJDkhyWJIfZgj6b62qXedcFwAArIu5AnVr7bzW2otba+9LcuGC1n30ZPrlrbULWmufSvK+seyAJI9b0LoAAGBNrOWPEs+oqmur6uKq+nBVPXLZ/IdOps/ZxPS0DgAAbHXWMlDvl2Hoxt5Jfi7Jp8ZhHkv2n0xfvonp/daueQAA0G/RgfrCJL+V5OAkt0ly1yTvmKzrtXMso1asUPXcqjqzqs7csGHDzW0rAAB0W2igbq2d21r7k9ba/26tXdVa+26S5yT50Vjl7lW1zzg9HYu952R6j8n0RZtYz4mttUNaa4fsu+++i2o+AACs2rynzZtLVe3UWrthxqw2Y/rzSe4yTt83yafH6YMndT+/yPYBALBlHXjMRxe6vPNOeMJCl7cI817YZaeq2mfsXb71ZNZtJuVJ8raqemVV3b+qbllVd07ylslzzm2tXTJOnzRZziuqav+qOizJU8ay7yX5xM15UQAAsKXM20N91yTfnlF+wnhLhrHPeyZ5ZpLfn1H36iQ/ubJia+3DVfWeDOeifnSSCyZ1r0vynNbaNXO2DwAA1sWif5T4miQnZjj13WVJrk1yfpJ3JzmktXbqsvrPSPLiJF/NELgvT3JKksNaa4v9fgAAANbAXD3UrbXzMsfZN1prpyc5fd6Vt9auy3Dmj3nO/gEAAFudtTwPNQAAbPcEagAA6CBQAwBAB4EaAAA6CNQAANBBoAYAgA4CNQAAdBCoAQCgg0ANAAAdBGoAAOggUAMAQAeBGgAAOgjUAADQQaAGAIAOAjUAAHQQqAEAoINADQAAHQRqAADoIFADAEAHgRoAADoI1AAA0EGgBgCADgI1AAB0EKgBAKCDQA0AAB0EagAA6CBQAwBAB4EaAAA6CNQAANBBoAYAgA4CNQAAdBCoAQCgg0ANAAAdBGoAAOggUAMAQAeBGgAAOgjUAADQQaAGAIAOAjUAAHQQqAEAoINADQAAHQRqAADoIFADAEAHgRoAADoI1AAA0EGgBgCADgI1AAB0EKgBAKCDQA0AAB0EagAA6CBQAwBAB4EaAAA6CNQAANBBoAYAgA4CNQAAdBCoAQCgg0ANAAAdBGoAAOggUAMAQAeBGgAAOgjUAADQQaAGAIAOAjUAAHQQqAEAoINADQAAHQRqAADoIFADAEAHgRoAADoI1AAA0EGgBgCADgI1AAB0EKgBAKCDQA0AAB0EagAA6CBQAwBAB4EaAAA6CNQAANBBoAYAgA4CNQAAdBCoAQCgg0ANAAAdBGoAAOgwV6Cuqn2r6nVVdUZVXV1Vbby9cEbdW1fVK6rqG2PdDVV1clXdd0bdnavqN6vqK1X1o6q6tKo+XlWPWMSLAwCAtbbLnPXulORFK1Wqql2SnJLkUZPifZIcleSIqjq8tXbWZN67kjxt8vhWSY5I8piqOrK19rE52wcAAOti3iEflyV5bZKnJnnTZuo9PxvD9J9nY5i+Psltk7x5qWJVPTEbw/SpSQ5IcliSH2YI+m+tql3nbB8AAKyLuQJ1a+281tqLW2vvS3LhZqoePZk+prV2SWvtA0k+OZY9pKruP6Puy1trF7TWPpXkfWPZAUkeN0/7AABgvSzsR4ljb/IDxoc/aK2dP5l9zmT6ocvul8+fVRcAALZKizzLx97ZOCb78mXzpo/3G+/338T8WXUBAGCrtKVOm1eLrFtVz62qM6vqzA0bNnQ0CwAA+iwyUF+S5Lpxes9l8/aYTF803k/HYk/rz6p7I621E1trh7TWDtl3331X31IAAFiQhQXq1to1Sb48Pty9qu48mX3wZPrzy+6T5L4r1AUAgK3SvBd22amq9qmqfZLcejLrNpPyJDlpMu+Eqtq7qo7KcDq8JDmrtfaVGXVfUVX7V9VhSZ4yln0vySdW8VoAAGCLm7eH+q5JNoy335mUnzApT5I3Jjl9nH56kouTnJxk5yRXJnne0hNbax9O8p7x4aOTXJDktCS3yTB05DljrzcAAGy1FvqjxNbadRmudHh8km8luSbD2Or3J3nYsqskJskzkrw4yVeTXJ3hDB+nJDmstfbRRbYNAADWwlyXHm+tnZc5z9TRWrsqybHjbaW612W4AuNr51k2AABsbbbUafMAAGC7JFADAEAHgRoAADoI1AAA0EGgBgCADgI1AAB0EKgBAKCDQA0AAB0EagAA6CBQAwBAB4EaAAA6CNQAANBBoAYAgA4CNQAAdBCoAQCgg0ANAAAdBGoAAOggUAMAQAeBGgAAOgjUAADQQaAGAIAOAjUAAHQQqAEAoINADQAAHQRqAADoIFADAEAHgRoAADoI1AAA0EGgBgCADgI1AAB0EKgBAKCDQA0AAB0EagAA6CBQAwBAB4EaAAA6CNQAANBBoAYAgA4CNQAAdBCoAQCgg0ANAAAdBGoAAOggUAMAQAeBGgAAOgjUAADQQaAGAIAOAjUAAHQQqAEAoINADQAAHQRqAADoIFADAEAHgRoAADoI1AAA0EGgBgCADgI1AAB0EKgBAKCDQA0AAB0EagAA6CBQAwBAB4EaAAA6CNQAANBBoAYAgA4CNQAAdBCoAQCgg0ANAAAdBGoAAOggUAMAQAeBGgAAOgjUAADQQaAGAIAOAjUAAHQQqAEAoINADQAAHQRqAADoIFADAEAHgRoAADoI1AAA0EGgBgCADgI1AAB0EKgBAKCDQA0AAB0EagAA6CBQAwBAh4UH6qo6uqraZm73ntS9dVW9oqq+UVVXV9WGqjq5qu676HYBAMBa2GW9VlxVuyQ5JcmjJsX7JDkqyRFVdXhr7ax1aRwAAMxpTYd8tNZqxu1r4+znZ2OY/vNsDNPXJ7ltkjevZdsAAGAR1nMM9dGT6WNaa5e01j6Q5JNj2UOq6v5bvlkAADC/NQ3UVXVBVV073r+3qu43lu+a5AFjtR+01s6fPO2cyfRD17J9AADQa617qPfPME57/yRPSfKPVfXQJHtn4/jty5c9Z/p4vzVuHwAAdFmLQP2tDOOj75FktyT3TPLxcd5uSV61wvNrpRVU1XOr6syqOnPDhg09bQUAgC4LD9SttU+31t7UWvtWa+3HrbVvJnnOpMqhSS5Jct34eM9li9hjMn3RJtZxYmvtkNbaIfvuu++img4AAKu2FuehnrXMNp1urV2T5Mvj492r6s6T+QdPpj+/6PYBAMAircWQj49U1Uur6p5VtWtV3SPJWyfzTx/vT5qUnVBVe1fVUUkOG8vOaq19ZQ3aBwAAC7MWgfqOSU5I8vUkVyf5RpLHj/MuTfI74/QbszFcPz3JxUlOTrJzkiuTPG8N2gYAAAu1FoH6ZUnelSFIX5HkmiTfznChlge11s5JktbadUmOSHJ8hh8yXpNhbPX7kzzMVRIBANgWLPzS4621jyT5yJx1r0py7HgDAIBtznpeKREAALZ5AjUAAHQQqAEAoINADQAAHQRqAADoIFADAEAHgRoAADoI1AAA0EGgBgCADgI1AAB0EKgBAKCDQA0AAB0EagAA6CBQAwBAB4EaAAA6CNQAANBBoAYAgA4CNQAAdBCoAQCgg0ANAAAdBGoAAOggUAMAQAeBGgAAOgjUAADQQaAGAIAOAjUAAHQQqAEAoINADQAAHQRqAADosMt6NwAAgPVz4DEfXdiyzjvhCQtb1rZEDzUAAHQQqAEAoINADQAAHQRqAADoIFADAEAHgRoAADoI1AAA0EGgBgCADgI1AAB0EKgBAKCDQA0AAB0EagAA6CBQAwBAB4EaAAA6CNQAANBBoAYAgA4CNQAAdBCoAQCgwy7r3QAAYHUOPOajC13eeSc8YaHLgx2NHmoAAOggUAMAQAeBGgAAOgjUAADQQaAGAIAOAjUAAHQQqAEAoIPzUG/FFnmeUecYBQBYG3qoAQCgg0ANAAAdDPkAYIfist3AoumhBgCADnqod2B6aQBg6+Z/9bZBDzUAAHQQqAEAoINADQAAHYyhZk25OA0AsL3TQw0AAB30UAMAN7E9fMPoDBlsKXqoAQCggx5qAFiw7aF3l5XpAWeJHmoAAOighxpgC9GbBbB90kMNAAAd9FDDCoyFBAA2Rw81AAB00EPNNs2Y1PlsD73stvXWwXYAuCmBGtaZgAI3tj18AGRl/vaxPTHkAwAAOuihBrrpaQJgR6aHGgAAOgjUAADQQaAGAIAO6z6Guqpun+TYJD+f5A5JLklySpKXt9a+u55tA3Yc28s4cGfIANjy1jVQV9Xtknwmyb0nxQckeVaSI6rq4a2176xL4wAAYA7rPeTj2GwM069OsneSF42PD0jymvVoFAAAzGvdAnVVVZJnjg+vSvIHrbXvt9Zen+Sfx/Ijq2qvdWkgAADMYT17qA/K0COdJN9qrV0zmXfOeL9Lkgdv0VYBAMAqrGeg3n8yffmyedPH+22BtgAAwM1SrbX1WXHVw5N8dnx4emvtZybz3p3k6ePDp7XW3rvsuc9N8tzx4b2SfH2Nm9tjnyQXr3cj2CJs6x2D7bzjsK13HLb1jqF3O/9frbV9Z81Yz7N8XDiZ3nPZvD0m0xctf2Jr7cQkJ65Bmxauqs5srR2y3u1g7dnWOwbbecdhW+84bOsdw1pu5/Uc8vHtDOecTpKfqqpdJ/MOHu+vS/LFLdoqAABYhXUL1G0Ya/KO8eFuSY6vqr2q6teT3G0s/+vW2qXr0kAAAJjDep+H+g+TfG2cfkmS7yf50/HxBUl+az0atWDbxNAUFsK23jHYzjsO23rHYVvvGNZsO6/bjxJ/0oDh0uMvT/LkDBdzWbr0+LEuPQ4AwNZu3QM1AABsy9Z7yMd2qapuX1X/f1V9p6qurqp/q6q3VdVd1rttLEZVHV1VbTO3e693G1m9qtq3ql5XVWeMx+7S9nzhjLq3rqpXVNU3xrobqurkqrrverSd1Zl3W1fV4Ssc60es12tgZVX1c1X1zqo6t6ouraorq+qrVXXC+A35tK5jehs277Zeq2N6PU+bt12qqtsl+UySaaA6IMmzkhxRVQ9vrX1nXRoHrOROSV60UqWq2iXD0LRHTYr3SXJUhuP88NbaWWvTRBZkrm3NNu+FSR63rOzg8fZLVfXg1trljuntwlzbeq1Wrod68Y7NxjD96gyXV1/6o31AktesR6NYO621mnH72srPZCt0WZLXJnlqkjdtpt7zs/Ef759n4z/e65PcNsmb166JLMhlmW9bTx0041g/Zc1ayCJcneQNSR6S4YxihyY5f5x3UJJfGacd09u+ebf11MKOaYF6gaqqkjxzfHhVkj9orX2/tfb6JP88lh9ZVXutSwOBzWqtnddae3Fr7X258cWnljt6Mn1Ma+2S1toHknxyLHtIVd1/rdpJv1Vsa7Ztv9xae0Fr7QuttR+31s5I8rrJ/HuO90dPyhzT26Z5t/WaEKgX66AMPdJJ8q3W2jWTeeeM97skefAWbRVrqqouqKprx/v3VtX91rtNrJ3xIlQPGB/+oLV2/mT2OZPph265VrGFnDEe6xdX1Yer6pHr3SA2r7V2xYziW02mv+uY3j7Ms61nzF/YMS1QL9b+k+nl43Smj/fbAm1hy9k/wwel/ZM8Jck/VpU/vNuvvbPx9yeO8x3Lfhm2/d5Jfi7Jp6rqievbJFajqg7IMNY2Gb5Jfmcc09ulTWzr5RZ2TAvUW06tdwNYqG9lGHN3jwxjte6Z5OPjvN2SvGqd2sX6cpxvfy7McJGxg5PcJslds/EqvztlGIfNNmA809apGTo/bkjyzDmud+GY3gatsK3X5Jh2lo/Fmo7D23PZvD0m0xetfVNYS621Tyf59KTom1X1nGz8AcShW75VbCGXJLkuw9/PPZfNc5xvZ1pr5yY5d1J01Xis/1KGD893r6p9WmsXr0sDmct4KtO/TXKXDMfvM1tr7x9nO6a3Iyts6zU7pvVQL9a3MxyYSfJT47isJQeP99cl+eIWbRULV1Wzjp22iWm2I+NvI748Pty9qu48mX3wZPrzW65VrJVNHOuJ432bUVWHJDk9Q8C6KsmRrbW/WJrvmN5+rLStxzprckwL1AvUhstOLn1tsFuS46tqr6r69SR3G8v/urV26bo0kEX6SFW9tKruWVW7VtU9krx1Mv/09WoYN19V7VRV+1TVPkluPZl1m0l5kpw0mXdCVe1dVUclOWwsO6u19pUt0GRuplVs67dV1Sur6v5VdcsxbL1l8pxzW2uXhK1SVT06w1f/+2To8PrZ1trHZlQ9aTLtmN4GrWJbr8kx7dLjCzZe2OUfcuMLuyy5IMmhLuyy7auqs5M8cBOzL03yqNbaOZuYz1aqqg7M8E3TJrXWarwIxKm58UUgllyZxEUgtnKr2NYfSnLkJqpcneQ/ttZOXWzrWJSqOi0bQ/Esn2ytHe6Y3vatYlt/KGtwTOuhXrDxKjyPTPKnSf4lybUZgvRJSf6dML3deFmSdyX5RpIrklyT4Z/zm5M8SJjevrXWrktyRJLjM/xA9ZoMPSLvT/Iw/3i3K69JcmKG06ddluFv+vlJ3p3kEGF6++CY3qGsyTGthxoAADrooQYAgA4CNQAAdBCoAQCgg0ANAAAdBGoAAOggUAMAQAeBGgAAOgjUAADQQaAGAIAO/wcX/mokA+u92wAAAABJRU5ErkJggg==",
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize=(12,8))\n",
        "plt.title(\"num recordings from 48 to 72 hours\")\n",
        "lab,cnt = np.unique(rec_after_time[3],return_counts=True)\n",
        "plt.bar(x=lab , height=cnt)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 359,
      "metadata": {
        "id": "xY0IugSRHJX_"
      },
      "outputs": [],
      "source": [
        "for num,i in enumerate(timeslots):\n",
        "  for root, dirs, files in os.walk(\"./data/training/\"):\n",
        "    for name in files:\n",
        "        if os.path.join(root, name).endswith(\".tsv\"):\n",
        "          df_temp = pd.read_csv(os.path.join(root, name), sep=\"\\t\")\n",
        "          break"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "unKswHghIZih"
      },
      "outputs": [],
      "source": [
        "\n",
        "for num,i in enumerate(timeslots):\n",
        "  for root, dirs, files in os.walk(\"./data/training/\"):\n",
        "    for name in files:\n",
        "        if os.path.join(root, name).endswith(\".tsv\"):\n",
        "          df_temp = pd.read_csv(os.path.join(root, name), sep=\"\\t\")\n",
        "          df_temp[i[0]:i[1]].dropna()\n",
        "          "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 363,
      "metadata": {
        "id": "SbVJLt-VJJRK"
      },
      "outputs": [],
      "source": [
        "patient_id=\"ICARE_0874\"\n",
        "data_folder=\"./data/training/\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 365,
      "metadata": {
        "id": "Zf6L4kiiJGfj"
      },
      "outputs": [],
      "source": [
        "patient_metadata, recording_metadata, recording_data = load_challenge_data(data_folder, patient_id)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 403,
      "metadata": {
        "id": "KKuHWuqJLRQ3"
      },
      "outputs": [],
      "source": [
        "def get_signal_indx(recording_metadata, task, signal_len = 30):\n",
        "  \"\"\"\n",
        "  task: 0,1,2,3 - 0: 0,12 hours, 1: 12-24 hours, 2: 24-48 hours, 3: 48-72 hours\n",
        "  signal_len: desired signal lenght to extract from the total signal\n",
        "  \"\"\"\n",
        "  TIMESLOTS = np.asarray([(0,12),(12,24),(24,48),(48,72)])\n",
        "  time = TIMESLOTS[task]\n",
        "  valid_signals = pd.read_csv(StringIO(recording_metadata), sep='\\t')[time[0]:time[1]].dropna()\n",
        "  if valid_signals.shape[0] > 0:\n",
        "    indx = valid_signals[\"Quality\"].argmax()\n",
        "  else:\n",
        "    indx = np.nan\n",
        "  return indx"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 390,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZDqtTKLiKfY3",
        "outputId": "281b9b5c-782e-499f-f2d7-1d954a06e9a2"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([0., 0., 0., ..., 0., 0., 0.], dtype=float32)"
            ]
          },
          "execution_count": 390,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "\n",
        "recording_data[17][0][0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 367,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        },
        "id": "jOGKcWG9JgD1",
        "outputId": "dc6240b9-48a9-4153-81f2-e185fc266633"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'Hour\\tTime\\tQuality\\tRecord\\n01\\tnan\\tnan\\tnan\\n02\\tnan\\tnan\\tnan\\n03\\tnan\\tnan\\tnan\\n04\\tnan\\tnan\\tnan\\n05\\tnan\\tnan\\tnan\\n06\\tnan\\tnan\\tnan\\n07\\tnan\\tnan\\tnan\\n08\\tnan\\tnan\\tnan\\n09\\tnan\\tnan\\tnan\\n10\\tnan\\tnan\\tnan\\n11\\tnan\\tnan\\tnan\\n12\\tnan\\tnan\\tnan\\n13\\tnan\\tnan\\tnan\\n14\\tnan\\tnan\\tnan\\n15\\tnan\\tnan\\tnan\\n16\\tnan\\tnan\\tnan\\n17\\tnan\\tnan\\tnan\\n18\\t17:55\\t0.000\\tICARE_0874_18\\n19\\t18:55\\t0.817\\tICARE_0874_19\\n20\\t19:45\\t1.000\\tICARE_0874_20\\n21\\t20:55\\t1.000\\tICARE_0874_21\\n22\\t21:00\\t0.983\\tICARE_0874_22\\n23\\t22:20\\t0.967\\tICARE_0874_23\\n24\\t23:55\\t1.000\\tICARE_0874_24\\n25\\t24:20\\t1.000\\tICARE_0874_25\\n26\\t25:25\\t1.000\\tICARE_0874_26\\n27\\tnan\\tnan\\tnan\\n28\\t27:20\\t0.517\\tICARE_0874_28\\n29\\t28:55\\t0.000\\tICARE_0874_29\\n30\\t29:55\\t0.000\\tICARE_0874_30\\n31\\t30:40\\t0.900\\tICARE_0874_31\\n32\\t31:30\\t1.000\\tICARE_0874_32\\n33\\t32:20\\t0.983\\tICARE_0874_33\\n34\\t33:00\\t0.967\\tICARE_0874_34\\n35\\t34:30\\t1.000\\tICARE_0874_35\\n36\\t35:55\\t1.000\\tICARE_0874_36\\n37\\t36:40\\t1.000\\tICARE_0874_37\\n38\\t37:45\\t1.000\\tICARE_0874_38\\n39\\tnan\\tnan\\tnan\\n40\\tnan\\tnan\\tnan\\n41\\tnan\\tnan\\tnan\\n42\\tnan\\tnan\\tnan\\n43\\tnan\\tnan\\tnan\\n44\\t43:25\\t1.000\\tICARE_0874_44\\n45\\t44:45\\t1.000\\tICARE_0874_45\\n46\\t45:55\\t1.000\\tICARE_0874_46\\n47\\t46:50\\t1.000\\tICARE_0874_47\\n48\\t47:40\\t1.000\\tICARE_0874_48\\n49\\t48:30\\t1.000\\tICARE_0874_49\\n50\\t49:25\\t1.000\\tICARE_0874_50\\n51\\t50:10\\t0.950\\tICARE_0874_51\\n52\\t51:35\\t1.000\\tICARE_0874_52\\n53\\t52:55\\t1.000\\tICARE_0874_53\\n54\\t53:50\\t1.000\\tICARE_0874_54\\n55\\t54:15\\t1.000\\tICARE_0874_55\\n56\\t55:55\\t1.000\\tICARE_0874_56\\n57\\t56:40\\t1.000\\tICARE_0874_57\\n58\\t57:55\\t1.000\\tICARE_0874_58\\n59\\t58:15\\t1.000\\tICARE_0874_59\\n60\\t59:35\\t1.000\\tICARE_0874_60\\n61\\t60:20\\t1.000\\tICARE_0874_61\\n62\\t61:35\\t0.983\\tICARE_0874_62\\n63\\tnan\\tnan\\tnan\\n64\\tnan\\tnan\\tnan\\n65\\tnan\\tnan\\tnan\\n66\\tnan\\tnan\\tnan\\n67\\tnan\\tnan\\tnan\\n68\\tnan\\tnan\\tnan\\n69\\tnan\\tnan\\tnan\\n70\\tnan\\tnan\\tnan\\n71\\tnan\\tnan\\tnan\\n72\\tnan\\tnan\\tnan\\n'"
            ]
          },
          "execution_count": 367,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "recording_metadata"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 402,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gVDD9efgJ91d",
        "outputId": "b7b77418-32b8-49e0-ea78-d6857d252494"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "execution_count": 402,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from io import StringIO\n",
        "pd.read_csv(StringIO(recording_metadata), sep='\\t').dropna()[\"Quality\"].argmax()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 361,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "9OrdzGPHIPn6",
        "outputId": "f6e778cd-aa2a-42c7-d8c1-8381ac9a1826"
      },
      "outputs": [
        {
          "data": {
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Hour</th>\n",
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              "      <th>Record</th>\n",
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              "      <th>17</th>\n",
              "      <td>18</td>\n",
              "      <td>17:55</td>\n",
              "      <td>0.000</td>\n",
              "      <td>ICARE_0874_18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>19</td>\n",
              "      <td>18:55</td>\n",
              "      <td>0.817</td>\n",
              "      <td>ICARE_0874_19</td>\n",
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              "      <td>0.983</td>\n",
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              "      <td>1.000</td>\n",
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              "      <th>24</th>\n",
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              "      <th>25</th>\n",
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              "      <td>1.000</td>\n",
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              "      <th>27</th>\n",
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              "      <td>0.000</td>\n",
              "      <td>ICARE_0874_30</td>\n",
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              "      <th>30</th>\n",
              "      <td>31</td>\n",
              "      <td>30:40</td>\n",
              "      <td>0.900</td>\n",
              "      <td>ICARE_0874_31</td>\n",
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              "      <th>32</th>\n",
              "      <td>33</td>\n",
              "      <td>32:20</td>\n",
              "      <td>0.983</td>\n",
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              "      <td>ICARE_0874_36</td>\n",
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              "      <td>ICARE_0874_37</td>\n",
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              "      <td>1.000</td>\n",
              "      <td>ICARE_0874_38</td>\n",
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              "    <tr>\n",
              "      <th>48</th>\n",
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              "      <td>1.000</td>\n",
              "      <td>ICARE_0874_50</td>\n",
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              "      <th>50</th>\n",
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              "      <td>ICARE_0874_51</td>\n",
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              "      <th>51</th>\n",
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              "      <td>51:35</td>\n",
              "      <td>1.000</td>\n",
              "      <td>ICARE_0874_52</td>\n",
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              "    <tr>\n",
              "      <th>52</th>\n",
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              "      <td>1.000</td>\n",
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              "      <th>53</th>\n",
              "      <td>54</td>\n",
              "      <td>53:50</td>\n",
              "      <td>1.000</td>\n",
              "      <td>ICARE_0874_54</td>\n",
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              "      <td>1.000</td>\n",
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              "      <th>55</th>\n",
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              "      <td>55:55</td>\n",
              "      <td>1.000</td>\n",
              "      <td>ICARE_0874_56</td>\n",
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              "      <th>56</th>\n",
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              "      <td>56:40</td>\n",
              "      <td>1.000</td>\n",
              "      <td>ICARE_0874_57</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>57</th>\n",
              "      <td>58</td>\n",
              "      <td>57:55</td>\n",
              "      <td>1.000</td>\n",
              "      <td>ICARE_0874_58</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>58</th>\n",
              "      <td>59</td>\n",
              "      <td>58:15</td>\n",
              "      <td>1.000</td>\n",
              "      <td>ICARE_0874_59</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59</th>\n",
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              "      <td>59:35</td>\n",
              "      <td>1.000</td>\n",
              "      <td>ICARE_0874_60</td>\n",
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              "    <tr>\n",
              "      <th>60</th>\n",
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              "      <td>60:20</td>\n",
              "      <td>1.000</td>\n",
              "      <td>ICARE_0874_61</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>61</th>\n",
              "      <td>62</td>\n",
              "      <td>61:35</td>\n",
              "      <td>0.983</td>\n",
              "      <td>ICARE_0874_62</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e9161fb2-3adc-4d49-8b62-5ddef99f46a6')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-e9161fb2-3adc-4d49-8b62-5ddef99f46a6');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "    Hour   Time  Quality         Record\n",
              "17    18  17:55    0.000  ICARE_0874_18\n",
              "18    19  18:55    0.817  ICARE_0874_19\n",
              "19    20  19:45    1.000  ICARE_0874_20\n",
              "20    21  20:55    1.000  ICARE_0874_21\n",
              "21    22  21:00    0.983  ICARE_0874_22\n",
              "22    23  22:20    0.967  ICARE_0874_23\n",
              "23    24  23:55    1.000  ICARE_0874_24\n",
              "24    25  24:20    1.000  ICARE_0874_25\n",
              "25    26  25:25    1.000  ICARE_0874_26\n",
              "27    28  27:20    0.517  ICARE_0874_28\n",
              "28    29  28:55    0.000  ICARE_0874_29\n",
              "29    30  29:55    0.000  ICARE_0874_30\n",
              "30    31  30:40    0.900  ICARE_0874_31\n",
              "31    32  31:30    1.000  ICARE_0874_32\n",
              "32    33  32:20    0.983  ICARE_0874_33\n",
              "33    34  33:00    0.967  ICARE_0874_34\n",
              "34    35  34:30    1.000  ICARE_0874_35\n",
              "35    36  35:55    1.000  ICARE_0874_36\n",
              "36    37  36:40    1.000  ICARE_0874_37\n",
              "37    38  37:45    1.000  ICARE_0874_38\n",
              "43    44  43:25    1.000  ICARE_0874_44\n",
              "44    45  44:45    1.000  ICARE_0874_45\n",
              "45    46  45:55    1.000  ICARE_0874_46\n",
              "46    47  46:50    1.000  ICARE_0874_47\n",
              "47    48  47:40    1.000  ICARE_0874_48\n",
              "48    49  48:30    1.000  ICARE_0874_49\n",
              "49    50  49:25    1.000  ICARE_0874_50\n",
              "50    51  50:10    0.950  ICARE_0874_51\n",
              "51    52  51:35    1.000  ICARE_0874_52\n",
              "52    53  52:55    1.000  ICARE_0874_53\n",
              "53    54  53:50    1.000  ICARE_0874_54\n",
              "54    55  54:15    1.000  ICARE_0874_55\n",
              "55    56  55:55    1.000  ICARE_0874_56\n",
              "56    57  56:40    1.000  ICARE_0874_57\n",
              "57    58  57:55    1.000  ICARE_0874_58\n",
              "58    59  58:15    1.000  ICARE_0874_59\n",
              "59    60  59:35    1.000  ICARE_0874_60\n",
              "60    61  60:20    1.000  ICARE_0874_61\n",
              "61    62  61:35    0.983  ICARE_0874_62"
            ]
          },
          "execution_count": 361,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df_temp.dropna()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "KhlWg48aIQ-8"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python",
      "version": "3.8.5"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}