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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Eldave93/Seizure-Detection-Tutorials/blob/master/Extra_01_Assemble_Feature_DataFrames.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w4LvkZoIQIiq"
      },
      "source": [
        "# Extra - Assemble Feature DataFrames\n",
        "\n",
        "by [David Luke Elliott](https://www.lancaster.ac.uk/psychology/about-us/people/david-elliott)\n",
        "/ [GitHub](https://github.com/Eldave93)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gHLiiH0PQPQu"
      },
      "source": [
        "Instead of running this, you can just download the datasets from my google drive as detailed in the tutorial notebooks. \n",
        "\n",
        "During making these datasets you will save things onto the disk in colab, but it will be deleted when you (re-)connect to an instance. Therefore you may want to connect a google drive (as seen below) or use the left tab to save the data (locate the file in Files, then right click the file and download).\n",
        "\n",
        "__Note__\n",
        "- Due to time changing things, the data you get out of this will likely be different from the datasets used in the tutorials currently. I aim to updated them but I haven't had time to check they don't break the tutorials so I'll leave them as is for now.\n",
        "- Also if you are planning on using some of this code for your research, go through it very carefully and make changes where needed. I threw this together from various bits of old code into one file, I've probably made a mistake somewhere (hopefully in the future I'll get time to come back and improve it and hunt down my scripts and find if/where i fixed things).\n",
        "- Some of these are not the most efficient way of doing things so they typically download more data than you need or take longer than they could... just a warning if you intend to run them."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "maCbTXcUDdwJ",
        "colab": {
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          "height": 1000
        },
        "outputId": "27f3d671-c8e6-47b4-db85-84b676819e0a"
      },
      "source": [
        "!pip install matplotlib pandas==0.23.0\n",
        "!pip install numpy scipy seaborn mne\n",
        "!pip install beautifulsoup4 requests wget\n",
        "!pip install h5py tables kaggle\n",
        "!pip install wfdb pyEDFlib PyWavelets"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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            "\u001b[31mERROR: xarray 0.15.1 has requirement pandas>=0.25, but you'll have pandas 0.23.0 which is incompatible.\u001b[0m\n",
            "\u001b[31mERROR: plotnine 0.6.0 has requirement pandas>=0.25.0, but you'll have pandas 0.23.0 which is incompatible.\u001b[0m\n",
            "\u001b[31mERROR: mizani 0.6.0 has requirement pandas>=0.25.0, but you'll have pandas 0.23.0 which is incompatible.\u001b[0m\n",
            "\u001b[31mERROR: google-colab 1.0.0 has requirement pandas~=1.1.0; python_version >= \"3.0\", but you'll have pandas 0.23.0 which is incompatible.\u001b[0m\n",
            "\u001b[31mERROR: fbprophet 0.7.1 has requirement pandas>=1.0.4, but you'll have pandas 0.23.0 which is incompatible.\u001b[0m\n",
            "Installing collected packages: pandas\n",
            "  Found existing installation: pandas 1.1.5\n",
            "    Uninstalling pandas-1.1.5:\n",
            "      Successfully uninstalled pandas-1.1.5\n",
            "Successfully installed pandas-0.23.0\n"
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          "output_type": "stream",
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            "  Downloading https://files.pythonhosted.org/packages/47/6a/62e288da7bcda82b935ff0c6cfe542970f04e29c756b0e147251b2fb251f/wget-3.2.zip\n",
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            "Building wheels for collected packages: wget\n",
            "  Building wheel for wget (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for wget: filename=wget-3.2-cp36-none-any.whl size=9682 sha256=550b90ce1e5d87f16d48c3df8f6c01d29b5bfcd88b46465da10e43bf3b34f460\n",
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            "Successfully built wget\n",
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            "\u001b[?25h  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "    Preparing wheel metadata ... \u001b[?25l\u001b[?25hdone\n",
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          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ub05E5ObK5xz"
      },
      "source": [
        "Clone and import my helper function."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tKKjzhq0Ilxb",
        "outputId": "1421b957-f391-4f55-9c5a-2b67dcd898e8"
      },
      "source": [
        "!git clone https://github.com/Eldave93/Seizure-Feature-Extraction"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'Seizure-Feature-Extraction'...\n",
            "remote: Enumerating objects: 25, done.\u001b[K\n",
            "remote: Counting objects: 100% (25/25), done.\u001b[K\n",
            "remote: Compressing objects: 100% (23/23), done.\u001b[K\n",
            "remote: Total 25 (delta 10), reused 6 (delta 2), pack-reused 0\u001b[K\n",
            "Unpacking objects: 100% (25/25), done.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "g_BjjHfPJYN7"
      },
      "source": [
        "import sys, os\r\n",
        "\r\n",
        "sys.path.append(os.path.join(os.getcwd(), \"Seizure-Feature-Extraction\"))"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xcxdURXIDvv6"
      },
      "source": [
        "#TODO: Tidy imports up\n",
        "import glob            # for file locations\n",
        "import pprint          # for pretty printing\n",
        "import re\n",
        "from getpass import getpass\n",
        "import pyedflib\n",
        "from collections import Counter\n",
        "from pywt import swt\n",
        "import math\n",
        "from bs4 import BeautifulSoup\n",
        "from Seizure_Feature_Extraction import Seizure_Features\n",
        "import sys\n",
        "import os\n",
        "from bs4 import BeautifulSoup\n",
        "import requests\n",
        "import re\n",
        "import wget\n",
        "import zipfile\n",
        "import pandas as pd     # dataframes\n",
        "from tqdm import tqdm_notebook as tqdm\n",
        "from getpass import getpass\n",
        "import os\n",
        "from scipy.io import loadmat\n",
        "import mne\n",
        "import numpy as np\n",
        "import tables\n",
        "import numpy as np\n",
        "import tables\n",
        "import os\n",
        "from urllib.request import urlretrieve\n",
        "import wfdb \n",
        "import re\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import pyedflib\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from scipy import signal\n",
        "import sys\n",
        "import warnings\n",
        "from tqdm import tqdm_notebook as tqdm\n",
        "\n",
        "# colours for printing outputs\n",
        "class color:\n",
        "   PURPLE = '\\033[95m'\n",
        "   CYAN = '\\033[96m'\n",
        "   DARKCYAN = '\\033[36m'\n",
        "   BLUE = '\\033[94m'\n",
        "   GREEN = '\\033[92m'\n",
        "   YELLOW = '\\033[93m'\n",
        "   RED = '\\033[91m'\n",
        "   BOLD = '\\033[1m'\n",
        "   UNDERLINE = '\\033[4m'\n",
        "   END = '\\033[0m'\n",
        "\n",
        "pp = pprint.PrettyPrinter()\n",
        "\n",
        "def file_list(folder_path, output=False):\n",
        "    # create an empty list\n",
        "    file_list = []\n",
        "    # for file name in the folder path...\n",
        "    for filename in glob.glob(folder_path):\n",
        "        # ... append it to the list\n",
        "        file_list.append(filename)\n",
        "        \n",
        "    # sort alphabetically\n",
        "    file_list.sort()\n",
        "    \n",
        "    # Output\n",
        "    if output:\n",
        "        print(str(len(file_list)) + \" files found\")\n",
        "        pp.pprint(file_list)\n",
        "    \n",
        "    return file_list"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m2WmZZ_orS_F"
      },
      "source": [
        "def window(a, w, o, copy = False):\r\n",
        "  # if there is no window to be applied\r\n",
        "  if w == None:\r\n",
        "      view = np.expand_dims(a.T, axis=0)\r\n",
        "\r\n",
        "  # otherwise...\r\n",
        "  else:\r\n",
        "      sh = (a.size - w + 1, w)\r\n",
        "      st = a.strides * 2\r\n",
        "      if o:\r\n",
        "          view = np.lib.stride_tricks.as_strided(a, strides = st, shape = sh)[0::o]\r\n",
        "      else:\r\n",
        "          view = np.lib.stride_tricks.as_strided(a, strides = st, shape = sh)[0::w]\r\n",
        "  if copy:\r\n",
        "      return view.copy()\r\n",
        "  else:\r\n",
        "      return view\r\n",
        "    \r\n",
        "def window_x(data, window_size, overlap):\r\n",
        "    \r\n",
        "  for i, column in enumerate(data.columns):\r\n",
        "\r\n",
        "    # window the data so each row is another epoch\r\n",
        "    channel_windowed = window(data[column], w = window_size, o = overlap, copy = True)\r\n",
        "    channel_windowed = np.reshape(channel_windowed, (*channel_windowed.shape, -1))\r\n",
        "\r\n",
        "    if i ==0:\r\n",
        "      windowed_data = channel_windowed\r\n",
        "    else:\r\n",
        "      windowed_data = np.concatenate((windowed_data, channel_windowed), axis=-1)\r\n",
        "  \r\n",
        "  return windowed_data"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "apeXm7ldNiEQ"
      },
      "source": [
        "# Save Paths\n",
        "\n",
        "Mount using the mount drive in the top left for some reason below wasnt keeping up..."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U41KsWjEQwwV",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3e376e16-888e-4885-bd2e-d3c5747edb82"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3H85zBkNQggF"
      },
      "source": [
        "## Epileptologie\n",
        "\n",
        "We're creating this combine function to take all the data from the different feature extraction methods and combine them into one dataframe.\n",
        "\n",
        "Now below is the code to take all those functions we have worked on so far and use them on all the data, and then combining the outputs into one big dataframe.\n",
        "\n",
        "**NOTES**\n",
        "- for me it takes 12 s\n",
        "- we use JSON rather than pickle (which can compress it down more) to save because JSON does not introduce a security hole and allows the data to be used by programs in different languages."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7S-HhQcxNkbq"
      },
      "source": [
        "# Run the code and create/overwite old data?\n",
        "EPIL_OVERWRITE = True\n",
        "\n",
        "# Where to save the feature data\n",
        "EPIL_SAVE_PATH = '/content/drive/My Drive/Data/Epil_features.json.gzip'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bMkY2T6bDap5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 637,
          "referenced_widgets": [
            "bea7ec62838b4814adcfa894b8d03e3a",
            "61cdca0384744951960f609e66964c7e",
            "9c0df440863f42a78f121524c4d1a547",
            "e840da4deced41d5b35db753f7d417ab",
            "24b891b3a4fa454e94edfceba3dc0616",
            "f9bf6b882b934db2829f03e56cd55161",
            "bb088419749f442ca3962684f5f964e3",
            "957e2fb1eedd4c64afd931bf88b478f9"
          ]
        },
        "outputId": "f4eb4cf4-0747-4b90-977f-9be0b844d3a2"
      },
      "source": [
        "def find_files(url):\n",
        "    # get a soup of the directory url\n",
        "    soup = BeautifulSoup(requests.get(url).text, features=\"html.parser\")\n",
        "\n",
        "    # make a list of all the links in the url\n",
        "    hrefs_list = []\n",
        "    for link in soup.find_all('a'):\n",
        "        hrefs_list.append(link.get('href'))\n",
        "\n",
        "    return hrefs_list\n",
        "    \n",
        "    \n",
        "def download_file(download_file_url, file_dir, output=False):\n",
        "    if output:\n",
        "        # print it is downloading\n",
        "        print('Downloading: '+ download_file_url)\n",
        "    # download the file to the directory\n",
        "    wget.download(download_file_url, file_dir)\n",
        "    \n",
        "    \n",
        "# needs a directory to download it to\n",
        "def download_epileptologie(DIR, output=False):\n",
        "    \n",
        "    # directory url\n",
        "    front_url = 'http://epileptologie-bonn.de/cms/front_content.php?idcat=193&lang=3&changelang=3'\n",
        "    dir_url = 'http://epileptologie-bonn.de/cms'\n",
        "\n",
        "    hrefs_dir_list = find_files(front_url)\n",
        "    \n",
        "    # for each link in the directory\n",
        "    for link in hrefs_dir_list:\n",
        "        # download the files outside of participant folders we want\n",
        "        if re.findall('zip', str(link)):\n",
        "            # if the file doesnt already exist in the directory\n",
        "            if not os.path.exists(os.path.join(DIR, link)):\n",
        "                download_file(dir_url+'/'+str(link), DIR, output)\n",
        "                zip_file_name = link.split('/')[-1]\n",
        "                zip_ref = zipfile.ZipFile(os.path.join(DIR, zip_file_name), 'r')\n",
        "                zip_ref.extractall(os.path.join(DIR, zip_file_name[0]))\n",
        "                zip_ref.close()\n",
        "                os.remove(os.path.join(DIR, zip_file_name))\n",
        "\n",
        "def data_load(file_path, output=False):\n",
        "\n",
        "    # read in the datafile\n",
        "    data = pd.read_csv(file_path,                 # file in\n",
        "                       header=None,               # no column names at top of file\n",
        "                       dtype=float)               # read data as 'floating points' (e.g. 1.0)\n",
        "\n",
        "    if output:\n",
        "        print(color.BOLD+color.UNDERLINE+'\\n'+re.findall('\\w\\d+',file_path)[0]+color.END)\n",
        "        # Output detailed information on the data\n",
        "        print(color.BOLD+'\\nData Information'+color.END)\n",
        "        data.info()\n",
        "\n",
        "        # Output first 5 rows and columns\n",
        "        print(color.BOLD+'\\nDataframe Head'+color.END)\n",
        "        display(data.head())\n",
        "        \n",
        "    return data\n",
        "\n",
        "\n",
        "def data_index(feat_data, file_name, output = False):\n",
        "    \n",
        "    # get the file identifier from the file (e.g. F001)\n",
        "    file_identifier = re.findall('\\w\\d+', file_name)[0]\n",
        "    # add this identifier to a column\n",
        "    feat_data['file_id'] = file_identifier\n",
        "    \n",
        "    # if the file identifier has an S in...\n",
        "    if re.findall('S', file_identifier):\n",
        "        # make a class column with 'seizure' in\n",
        "        feat_data['class'] = 'seizure'\n",
        "    # ...otherwise...\n",
        "    else:\n",
        "        # .. make a class column with 'Baseline' in\n",
        "        feat_data['class'] = 'baseline'\n",
        "        \n",
        "    \n",
        "    # if the file identifier has a Z or O in...\n",
        "    if re.findall('Z|O', file_identifier):\n",
        "        # make a location column with 'surface' in\n",
        "        feat_data['location'] = 'surface'\n",
        "    # if the file identifier has an N in...\n",
        "    elif re.findall('N', file_identifier):\n",
        "        # make a location column with 'intracranial hippocampus' in\n",
        "        feat_data['location'] = 'intracranial hippocampus'\n",
        "    # if the file identifier has an S or F in...\n",
        "    elif re.findall('F|S', file_identifier):\n",
        "        # make a location column with 'intracranial epileptogenic zone' in\n",
        "        feat_data['location'] = 'intracranial epileptogenic zone'\n",
        "        \n",
        "    # name the index\n",
        "    feat_data.columns.name = 'feature'\n",
        "    \n",
        "    # add the file_id and class to the index\n",
        "    feat_data = feat_data.set_index(['file_id', 'class', 'location'])\n",
        "    # reorder the index so class is first, then file_id, then feature\n",
        "    feat_data = feat_data.reorder_levels(['class', 'location', 'file_id'], axis='index')\n",
        "    \n",
        "    if output:\n",
        "        display(feat_data)\n",
        "        \n",
        "    return feat_data\n",
        "\n",
        "\n",
        "if EPIL_OVERWRITE:\n",
        "  # delete the old version\n",
        "  if os.path.exists(EPIL_SAVE_PATH):\n",
        "      os.remove(EPIL_SAVE_PATH)\n",
        "\n",
        "  # --------\n",
        "  # DOWNLOAD\n",
        "  # --------\n",
        "  DOWNLOAD_DIR = \"Epileptologie Database\"\n",
        "\n",
        "  if not os.path.exists(DOWNLOAD_DIR):\n",
        "    os.makedirs(DOWNLOAD_DIR)\n",
        "    download_epileptologie(DOWNLOAD_DIR, output=True)\n",
        "\n",
        "  else:\n",
        "    print(\"Already Downloaded\")\n",
        "\n",
        "  # get a list of the downloaded files\n",
        "  EPIL_dir_file_list = file_list(os.path.join(DOWNLOAD_DIR, '*'), output=False)\n",
        "  \n",
        "  # -----------------\n",
        "  # CREATE FEATURE DF\n",
        "  # -----------------\n",
        "  # make an empty dataframe\n",
        "  feature_df = pd.DataFrame()\n",
        "  # iterate across the list of folders\n",
        "  for folder in tqdm(EPIL_dir_file_list):\n",
        "      # get a list of files in each folder\n",
        "      folder_files_list = file_list(os.path.join(folder, '*'))\n",
        "      # iteratate across the files in each folder\n",
        "      for file in folder_files_list:\n",
        "          # load the file\n",
        "          df = data_load(file)\n",
        "          # setup the feature extraction function\n",
        "          feat = Seizure_Features(sf = 173.61, \n",
        "                          window_size=None,\n",
        "                          feature_list=['power', 'power_ratio', 'mean', \n",
        "                                        'mean_abs', 'std','ratio', 'LSWT'],\n",
        "                          bandpasses = [[2,4],[4,8],[8,12], [12,30],[30,70]])\n",
        "          # transform the data using the function\n",
        "          part_x_feat = feat.transform(df.values, channel_names_list = ['CZ'])\n",
        "          # put the numpy output back into a pandas df\n",
        "          part_x_feat = pd.DataFrame(part_x_feat, columns = feat.feature_names)\n",
        "          # re-index the data\n",
        "          part_x_feat = data_index(part_x_feat, file)\n",
        "          # if there is no data in the feature data so far...\n",
        "          if feature_df.empty:\n",
        "              # then make this the feature dataframe...\n",
        "              feature_df = part_x_feat\n",
        "          else:\n",
        "              # ...otherwise combine the two dataframes together down the index\n",
        "              feature_df = pd.concat([feature_df, part_x_feat], axis='index')\n",
        "\n",
        "  # display the dataframe\n",
        "  display(feature_df)\n",
        "\n",
        "  # reset the index into columns (for easy saving)\n",
        "  feature_df_save = feature_df.reset_index()\n",
        "\n",
        "  # save the dataframe to disk for later use\n",
        "  feature_df_save.to_json(EPIL_SAVE_PATH, \n",
        "                          orient='index', \n",
        "                          compression = 'gzip') "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading: http://epileptologie-bonn.de/cms/upload/workgroup/lehnertz/Z.zip\n",
            "Downloading: http://epileptologie-bonn.de/cms/upload/workgroup/lehnertz/O.zip\n",
            "Downloading: http://epileptologie-bonn.de/cms/upload/workgroup/lehnertz/N.zip\n",
            "Downloading: http://epileptologie-bonn.de/cms/upload/workgroup/lehnertz/F.zip\n",
            "Downloading: http://epileptologie-bonn.de/cms/upload/workgroup/lehnertz/S.zip\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:143: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
            "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "bea7ec62838b4814adcfa894b8d03e3a",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=5.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>feature</th>\n",
              "      <th>CZ|2_4Hz</th>\n",
              "      <th>CZ|4_8Hz</th>\n",
              "      <th>CZ|8_12Hz</th>\n",
              "      <th>CZ|12_30Hz</th>\n",
              "      <th>CZ|30_70Hz</th>\n",
              "      <th>CZ|Ratio_3_12/2_30Hz</th>\n",
              "      <th>CZ|D6_mean</th>\n",
              "      <th>CZ|D5_mean</th>\n",
              "      <th>CZ|D4_mean</th>\n",
              "      <th>CZ|D3_mean</th>\n",
              "      <th>CZ|D2_mean</th>\n",
              "      <th>CZ|D1_mean</th>\n",
              "      <th>CZ|D6_std</th>\n",
              "      <th>CZ|D5_std</th>\n",
              "      <th>CZ|D4_std</th>\n",
              "      <th>CZ|D3_std</th>\n",
              "      <th>CZ|D2_std</th>\n",
              "      <th>CZ|D1_std</th>\n",
              "      <th>CZ|D6_ratio</th>\n",
              "      <th>CZ|D5_ratio</th>\n",
              "      <th>CZ|D4_ratio</th>\n",
              "      <th>CZ|D3_ratio</th>\n",
              "      <th>CZ|D2_ratio</th>\n",
              "      <th>CZ|D1_ratio</th>\n",
              "      <th>CZ|D6_mean_abs</th>\n",
              "      <th>CZ|D5_mean_abs</th>\n",
              "      <th>CZ|D4_mean_abs</th>\n",
              "      <th>CZ|D3_mean_abs</th>\n",
              "      <th>CZ|D2_mean_abs</th>\n",
              "      <th>CZ|D1_mean_abs</th>\n",
              "      <th>CZ|D6_LSWT</th>\n",
              "      <th>CZ|D5_LSWT</th>\n",
              "      <th>CZ|D4_LSWT</th>\n",
              "      <th>CZ|D3_LSWT</th>\n",
              "      <th>CZ|D2_LSWT</th>\n",
              "      <th>CZ|D1_LSWT</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>class</th>\n",
              "      <th>location</th>\n",
              "      <th>file_id</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"11\" valign=\"top\">baseline</th>\n",
              "      <th rowspan=\"5\" valign=\"top\">intracranial epileptogenic zone</th>\n",
              "      <th>F001</th>\n",
              "      <td>77.429564</td>\n",
              "      <td>28.734279</td>\n",
              "      <td>13.102026</td>\n",
              "      <td>2.467909</td>\n",
              "      <td>0.462172</td>\n",
              "      <td>0.138219</td>\n",
              "      <td>-1.758529</td>\n",
              "      <td>3.689356</td>\n",
              "      <td>0.572214</td>\n",
              "      <td>0.401766</td>\n",
              "      <td>-0.008293</td>\n",
              "      <td>-0.041443</td>\n",
              "      <td>94.954945</td>\n",
              "      <td>77.076934</td>\n",
              "      <td>36.437346</td>\n",
              "      <td>18.901216</td>\n",
              "      <td>6.525675</td>\n",
              "      <td>2.479045</td>\n",
              "      <td>1.272058</td>\n",
              "      <td>1.147393</td>\n",
              "      <td>0.756706</td>\n",
              "      <td>0.877359</td>\n",
              "      <td>0.631333</td>\n",
              "      <td>0.362810</td>\n",
              "      <td>76.722313</td>\n",
              "      <td>60.313528</td>\n",
              "      <td>28.409123</td>\n",
              "      <td>14.772764</td>\n",
              "      <td>5.266416</td>\n",
              "      <td>1.910710</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>6.427250</td>\n",
              "      <td>5.613190</td>\n",
              "      <td>5.805773</td>\n",
              "      <td>4.749819</td>\n",
              "      <td>3.665018</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>F002</th>\n",
              "      <td>1060.585071</td>\n",
              "      <td>480.018796</td>\n",
              "      <td>201.886582</td>\n",
              "      <td>29.713494</td>\n",
              "      <td>4.070126</td>\n",
              "      <td>0.140057</td>\n",
              "      <td>-43.528777</td>\n",
              "      <td>5.563645</td>\n",
              "      <td>-5.458138</td>\n",
              "      <td>-1.311734</td>\n",
              "      <td>0.120496</td>\n",
              "      <td>-0.002805</td>\n",
              "      <td>399.209789</td>\n",
              "      <td>281.923087</td>\n",
              "      <td>151.139973</td>\n",
              "      <td>67.289651</td>\n",
              "      <td>18.696915</td>\n",
              "      <td>3.012844</td>\n",
              "      <td>1.878949</td>\n",
              "      <td>0.824373</td>\n",
              "      <td>0.906137</td>\n",
              "      <td>0.687953</td>\n",
              "      <td>0.514802</td>\n",
              "      <td>0.208105</td>\n",
              "      <td>281.597393</td>\n",
              "      <td>149.869616</td>\n",
              "      <td>81.999218</td>\n",
              "      <td>31.116812</td>\n",
              "      <td>8.462824</td>\n",
              "      <td>1.761159</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>8.241056</td>\n",
              "      <td>7.388935</td>\n",
              "      <td>7.770027</td>\n",
              "      <td>8.062119</td>\n",
              "      <td>8.020356</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>F003</th>\n",
              "      <td>316.076591</td>\n",
              "      <td>186.372298</td>\n",
              "      <td>135.970946</td>\n",
              "      <td>20.841888</td>\n",
              "      <td>2.781560</td>\n",
              "      <td>0.148809</td>\n",
              "      <td>7.140095</td>\n",
              "      <td>-10.223593</td>\n",
              "      <td>1.088205</td>\n",
              "      <td>-1.555793</td>\n",
              "      <td>-0.034026</td>\n",
              "      <td>-0.034373</td>\n",
              "      <td>357.861759</td>\n",
              "      <td>132.001620</td>\n",
              "      <td>118.527127</td>\n",
              "      <td>55.385295</td>\n",
              "      <td>15.882118</td>\n",
              "      <td>4.972672</td>\n",
              "      <td>2.901753</td>\n",
              "      <td>0.533720</td>\n",
              "      <td>1.222865</td>\n",
              "      <td>0.809146</td>\n",
              "      <td>0.499067</td>\n",
              "      <td>0.210005</td>\n",
              "      <td>298.635855</td>\n",
              "      <td>102.915688</td>\n",
              "      <td>87.018413</td>\n",
              "      <td>39.403208</td>\n",
              "      <td>10.376156</td>\n",
              "      <td>2.179047</td>\n",
              "      <td>7.534104</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>7.412203</td>\n",
              "      <td>6.336933</td>\n",
              "      <td>7.197397</td>\n",
              "      <td>7.170449</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>F004</th>\n",
              "      <td>213.966621</td>\n",
              "      <td>91.338657</td>\n",
              "      <td>34.191639</td>\n",
              "      <td>3.157286</td>\n",
              "      <td>0.573759</td>\n",
              "      <td>0.064002</td>\n",
              "      <td>-15.857898</td>\n",
              "      <td>-3.700006</td>\n",
              "      <td>3.257209</td>\n",
              "      <td>-0.135347</td>\n",
              "      <td>0.081712</td>\n",
              "      <td>-0.032627</td>\n",
              "      <td>165.928055</td>\n",
              "      <td>119.045182</td>\n",
              "      <td>59.769483</td>\n",
              "      <td>22.377317</td>\n",
              "      <td>6.419182</td>\n",
              "      <td>2.324330</td>\n",
              "      <td>1.400790</td>\n",
              "      <td>1.052269</td>\n",
              "      <td>0.839701</td>\n",
              "      <td>0.687769</td>\n",
              "      <td>0.516294</td>\n",
              "      <td>0.365782</td>\n",
              "      <td>127.731141</td>\n",
              "      <td>91.185066</td>\n",
              "      <td>45.580184</td>\n",
              "      <td>17.377872</td>\n",
              "      <td>4.953816</td>\n",
              "      <td>1.812019</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>6.422032</td>\n",
              "      <td>7.582963</td>\n",
              "      <td>6.947882</td>\n",
              "      <td>7.086014</td>\n",
              "      <td>6.950912</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>F005</th>\n",
              "      <td>973.296659</td>\n",
              "      <td>385.347164</td>\n",
              "      <td>218.880920</td>\n",
              "      <td>47.891201</td>\n",
              "      <td>7.560384</td>\n",
              "      <td>0.183348</td>\n",
              "      <td>-21.433089</td>\n",
              "      <td>-34.709688</td>\n",
              "      <td>5.766435</td>\n",
              "      <td>-3.044324</td>\n",
              "      <td>0.069729</td>\n",
              "      <td>0.081691</td>\n",
              "      <td>346.795965</td>\n",
              "      <td>216.454349</td>\n",
              "      <td>152.238202</td>\n",
              "      <td>75.145144</td>\n",
              "      <td>28.542184</td>\n",
              "      <td>6.026236</td>\n",
              "      <td>1.648172</td>\n",
              "      <td>0.853625</td>\n",
              "      <td>1.051711</td>\n",
              "      <td>0.801236</td>\n",
              "      <td>0.625299</td>\n",
              "      <td>0.198041</td>\n",
              "      <td>270.422836</td>\n",
              "      <td>164.074428</td>\n",
              "      <td>113.995056</td>\n",
              "      <td>52.705751</td>\n",
              "      <td>17.566081</td>\n",
              "      <td>3.478807</td>\n",
              "      <td>8.055723</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>8.726303</td>\n",
              "      <td>8.031105</td>\n",
              "      <td>8.460379</td>\n",
              "      <td>8.480473</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"5\" valign=\"top\">surface</th>\n",
              "      <th>Z096</th>\n",
              "      <td>84.704705</td>\n",
              "      <td>78.761643</td>\n",
              "      <td>87.675157</td>\n",
              "      <td>40.008457</td>\n",
              "      <td>4.960722</td>\n",
              "      <td>0.608763</td>\n",
              "      <td>5.887814</td>\n",
              "      <td>-3.969213</td>\n",
              "      <td>1.351992</td>\n",
              "      <td>1.485655</td>\n",
              "      <td>0.037216</td>\n",
              "      <td>-0.024188</td>\n",
              "      <td>127.120207</td>\n",
              "      <td>83.617697</td>\n",
              "      <td>84.026934</td>\n",
              "      <td>68.998630</td>\n",
              "      <td>22.854502</td>\n",
              "      <td>3.977630</td>\n",
              "      <td>1.399701</td>\n",
              "      <td>0.832908</td>\n",
              "      <td>1.101355</td>\n",
              "      <td>1.282973</td>\n",
              "      <td>0.633346</td>\n",
              "      <td>0.178012</td>\n",
              "      <td>90.686271</td>\n",
              "      <td>64.789765</td>\n",
              "      <td>64.888617</td>\n",
              "      <td>53.044436</td>\n",
              "      <td>17.801239</td>\n",
              "      <td>3.168840</td>\n",
              "      <td>6.851208</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>6.787953</td>\n",
              "      <td>7.171998</td>\n",
              "      <td>6.347687</td>\n",
              "      <td>6.180514</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Z097</th>\n",
              "      <td>94.787882</td>\n",
              "      <td>77.330884</td>\n",
              "      <td>79.944982</td>\n",
              "      <td>20.860676</td>\n",
              "      <td>3.558398</td>\n",
              "      <td>0.217532</td>\n",
              "      <td>2.011097</td>\n",
              "      <td>-10.779391</td>\n",
              "      <td>5.355695</td>\n",
              "      <td>0.940356</td>\n",
              "      <td>-0.040507</td>\n",
              "      <td>-0.075285</td>\n",
              "      <td>109.114204</td>\n",
              "      <td>77.007799</td>\n",
              "      <td>89.064524</td>\n",
              "      <td>50.774432</td>\n",
              "      <td>19.257257</td>\n",
              "      <td>4.451289</td>\n",
              "      <td>1.394748</td>\n",
              "      <td>0.795835</td>\n",
              "      <td>1.340014</td>\n",
              "      <td>0.977549</td>\n",
              "      <td>0.692304</td>\n",
              "      <td>0.219582</td>\n",
              "      <td>84.623702</td>\n",
              "      <td>60.673107</td>\n",
              "      <td>67.852810</td>\n",
              "      <td>40.598683</td>\n",
              "      <td>15.209352</td>\n",
              "      <td>3.339705</td>\n",
              "      <td>7.369106</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>7.954594</td>\n",
              "      <td>7.566592</td>\n",
              "      <td>7.246907</td>\n",
              "      <td>7.163136</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Z098</th>\n",
              "      <td>81.554287</td>\n",
              "      <td>49.704040</td>\n",
              "      <td>75.174518</td>\n",
              "      <td>25.561950</td>\n",
              "      <td>3.315037</td>\n",
              "      <td>0.369955</td>\n",
              "      <td>-5.629260</td>\n",
              "      <td>3.171042</td>\n",
              "      <td>2.649475</td>\n",
              "      <td>0.617910</td>\n",
              "      <td>-0.162761</td>\n",
              "      <td>0.029595</td>\n",
              "      <td>100.183942</td>\n",
              "      <td>75.023828</td>\n",
              "      <td>73.396206</td>\n",
              "      <td>61.581912</td>\n",
              "      <td>17.626775</td>\n",
              "      <td>4.229810</td>\n",
              "      <td>1.334503</td>\n",
              "      <td>0.861365</td>\n",
              "      <td>1.077524</td>\n",
              "      <td>1.353942</td>\n",
              "      <td>0.545801</td>\n",
              "      <td>0.232691</td>\n",
              "      <td>78.163160</td>\n",
              "      <td>58.571001</td>\n",
              "      <td>57.832559</td>\n",
              "      <td>48.772435</td>\n",
              "      <td>14.212532</td>\n",
              "      <td>3.307124</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>6.709265</td>\n",
              "      <td>6.993208</td>\n",
              "      <td>6.572458</td>\n",
              "      <td>5.427444</td>\n",
              "      <td>6.122005</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Z099</th>\n",
              "      <td>89.632144</td>\n",
              "      <td>96.726025</td>\n",
              "      <td>93.098942</td>\n",
              "      <td>39.605814</td>\n",
              "      <td>6.900507</td>\n",
              "      <td>0.426279</td>\n",
              "      <td>5.148219</td>\n",
              "      <td>4.085167</td>\n",
              "      <td>-0.842396</td>\n",
              "      <td>-0.537037</td>\n",
              "      <td>-0.073097</td>\n",
              "      <td>-0.089088</td>\n",
              "      <td>104.549202</td>\n",
              "      <td>84.509752</td>\n",
              "      <td>93.541574</td>\n",
              "      <td>66.167612</td>\n",
              "      <td>27.884251</td>\n",
              "      <td>4.996125</td>\n",
              "      <td>1.293919</td>\n",
              "      <td>0.812477</td>\n",
              "      <td>1.279047</td>\n",
              "      <td>1.090796</td>\n",
              "      <td>0.782012</td>\n",
              "      <td>0.174500</td>\n",
              "      <td>83.552973</td>\n",
              "      <td>64.573549</td>\n",
              "      <td>75.401797</td>\n",
              "      <td>53.329501</td>\n",
              "      <td>22.379102</td>\n",
              "      <td>3.905163</td>\n",
              "      <td>6.460781</td>\n",
              "      <td>6.717318</td>\n",
              "      <td>4.068638</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>5.317964</td>\n",
              "      <td>4.568267</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Z100</th>\n",
              "      <td>111.931878</td>\n",
              "      <td>61.402723</td>\n",
              "      <td>76.484853</td>\n",
              "      <td>26.954315</td>\n",
              "      <td>3.937874</td>\n",
              "      <td>0.334320</td>\n",
              "      <td>9.293238</td>\n",
              "      <td>-0.557111</td>\n",
              "      <td>2.902463</td>\n",
              "      <td>-0.446044</td>\n",
              "      <td>0.049576</td>\n",
              "      <td>0.140782</td>\n",
              "      <td>91.835636</td>\n",
              "      <td>91.846979</td>\n",
              "      <td>77.180245</td>\n",
              "      <td>59.819399</td>\n",
              "      <td>20.426899</td>\n",
              "      <td>5.369994</td>\n",
              "      <td>0.999844</td>\n",
              "      <td>1.117522</td>\n",
              "      <td>0.976147</td>\n",
              "      <td>1.231793</td>\n",
              "      <td>0.634096</td>\n",
              "      <td>0.265146</td>\n",
              "      <td>75.353533</td>\n",
              "      <td>75.365307</td>\n",
              "      <td>59.525807</td>\n",
              "      <td>46.595466</td>\n",
              "      <td>16.128865</td>\n",
              "      <td>4.276509</td>\n",
              "      <td>6.782847</td>\n",
              "      <td>5.058779</td>\n",
              "      <td>6.900223</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>5.645676</td>\n",
              "      <td>6.255625</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>500 rows × 36 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "feature                                              CZ|2_4Hz  ...  CZ|D1_LSWT\n",
              "class    location                        file_id               ...            \n",
              "baseline intracranial epileptogenic zone F001       77.429564  ...    3.665018\n",
              "                                         F002     1060.585071  ...    8.020356\n",
              "                                         F003      316.076591  ...    7.170449\n",
              "                                         F004      213.966621  ...    6.950912\n",
              "                                         F005      973.296659  ...    8.480473\n",
              "...                                                       ...  ...         ...\n",
              "         surface                         Z096       84.704705  ...    6.180514\n",
              "                                         Z097       94.787882  ...    7.163136\n",
              "                                         Z098       81.554287  ...    6.122005\n",
              "                                         Z099       89.632144  ...    4.568267\n",
              "                                         Z100      111.931878  ...    6.255625\n",
              "\n",
              "[500 rows x 36 columns]"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tIojodzoQ7N8"
      },
      "source": [
        "## UPenn and Mayo\n",
        "\n",
        "**TODO**\n",
        "- add a `save_progress` flag for the function. This makes a DF which has 4 columns (iteration number, filename, start-time, end-time). Each iteration a df is made with the three first ones filled in and the last as NA, when it ends then this is filled in with the time and the df merged with the rest of the df. You should save to drive each iteration this df and the data. As well as overwrite there is the `continue` flag, which uses the filename (in CHB it could be the iteration number?) to continue adding the missing data."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J4ZWXLT__-np"
      },
      "source": [
        "# Run the code and create/overwite old data?\n",
        "UPENN_OVERWRITE = True\n",
        "\n",
        "# Make the frame for all patients or just patient 2 (False)?\n",
        "UPENN_ALL_PART = False\n",
        "\n",
        "# Where to save the feature data\n",
        "UPENN_SAVE_PATH = '/content/drive/My Drive/Data/UPennMayo_features.hdf5'\n",
        "\n",
        "if UPENN_ALL_PART:\n",
        "  UPENN_SAVE_PATH = '/content/drive/My Drive/Data/UPennMayo_all_features.hdf5'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qtCGnOkCQ_y8"
      },
      "source": [
        "**Timings**\n",
        "- The single participant file takes about 30 minutes\n",
        "\n",
        "**NOTE**\n",
        "- hickle is an interesting way of storing data\n",
        "- origionally I used *.to_hdf()* on the pandas object and appended each to the bottom of a table. However this errored when adding in the Hill data because there was too many columns:\n",
        "\n",
        "    \"HDF5 has a header limit of 64kb for all metadata of the columns. This include name, types, etc. When you go about roughly 2000 columns, you will run out of space to store all the metadata.\"\n",
        "    \n",
        "    This doesnt exist if you do it manually like I do below"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QMts8KjlRT2k",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
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            "b98f8c6eab4e4f5eb378779772bd7d8f",
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            "441aeb81cc524de892d7ee44ebcafe38",
            "3cf1e42cbe274f239478820a2314df77",
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            "1bce6f3d00c6413182281e9b87f850ce",
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            "e4c8e5b004c44d34847871323ff743f2"
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        },
        "outputId": "5acf04cf-292f-4260-b4bf-ab598da1d208"
      },
      "source": [
        "# set mne to only output warnings\n",
        "mne.set_log_level('WARNING')\n",
        "\n",
        "def save_to_database(save_dir, part_id, combined_df, condition):\n",
        "\n",
        "  # open the file in append mode (make it if doesnt exist)\n",
        "  h5file = tables.open_file(save_dir, mode=\"a\", title=\"Upenn Features\")\n",
        "\n",
        "  # if there is already a node for the particpant...\n",
        "  if \"/\"+part_id in h5file:\n",
        "    # ...put in the directory of where it is found\n",
        "    part_x_array = h5file.get_node(\"/\" + part_id + '/Data_x')\n",
        "    part_y_array = h5file.get_node(\"/\" + part_id + '/Data_y')\n",
        "\n",
        "    # Make sure the columns are in the same order.\n",
        "    # This is for when there are electrodes which have 'died'\n",
        "    # which happens sometimes\n",
        "    data_x_labels = h5file.get_node('/'+part_id+'/Data_x_Feat_Names')\n",
        "    combined_df = combined_df.reindex(data_x_labels[:].astype(str), axis=1)\n",
        "\n",
        "  else:\n",
        "    # create the group directory\n",
        "    part_group = h5file.create_group(\"/\", part_id, 'Participant Data')\n",
        "    # make an atom which has the datatype found in the data we want to store\n",
        "    x_atom = tables.Atom.from_dtype(combined_df.values.dtype)\n",
        "    y_atom = tables.Atom.from_dtype(condition.dtype)\n",
        "\n",
        "    # create an array we can append onto later\n",
        "    part_x_array = h5file.create_earray(\"/\" + part_id,            # parentnode\n",
        "                                        'Data_x',                 # name \n",
        "                                        x_atom,                   # atom\n",
        "                                        (0,combined_df.shape[1]), # shape\n",
        "                                        'Feature Array')          # title\n",
        "\n",
        "    part_y_array = h5file.create_earray(\"/\" + part_id, \n",
        "                                        'Data_y', \n",
        "                                        y_atom, \n",
        "                                        (0,1),\n",
        "                                        'Events Array')\n",
        "\n",
        "    # create the feature names array (we only need to do this once)\n",
        "    h5file.create_array(\"/\" + part_id,                               # where\n",
        "                        'Data_x_Feat_Names',                         # name \n",
        "                        np.array(combined_df.columns, dtype='unicode'),  # obj\n",
        "                        \"Names of Each Feature\")                     # title\n",
        "\n",
        "  # append the data to the array directory\n",
        "  part_x_array.append(combined_df.values)\n",
        "  part_y_array.append(condition)\n",
        "\n",
        "  # flush the data to disk\n",
        "  h5file.flush()\n",
        "  # close the file\n",
        "  h5file.close()\n",
        "\n",
        "def mat_to_df(file_path, output = False):\n",
        "  mat = loadmat(file_path)    # load mat-file\n",
        "\n",
        "  data = mat['data']          # variable in mat file\n",
        "  channels = mat['channels']  # dtypes of structures are \"unsized objects\"\n",
        "  freq = mat['freq'][0]\n",
        "\n",
        "  channels_list = []\n",
        "  for channel_array in channels[0][0]:\n",
        "    channels_list.append(channel_array[0])\n",
        "\n",
        "  df = pd.DataFrame(data,\n",
        "                    index=channels_list)\n",
        "\n",
        "  df = df.T\n",
        "\n",
        "  # remove columns that do not change value\n",
        "  df = df.loc[:, (df != df.iloc[0]).any()]\n",
        "\n",
        "  if output:\n",
        "    display(df.head())\n",
        "\n",
        "  return df, freq\n",
        "\n",
        "def file_class(file_name):\n",
        "\n",
        "  if re.findall('interictal', file_name):\n",
        "    return 0\n",
        "  elif re.findall('ictal', file_name):\n",
        "    return 1\n",
        "  else:\n",
        "    return 2\n",
        "\n",
        "\n",
        "if UPENN_OVERWRITE:\n",
        "\n",
        "  if os.path.exists(UPENN_SAVE_PATH):\n",
        "    os.remove(UPENN_SAVE_PATH)\n",
        "\n",
        "  # -------------\n",
        "  # SET UP KAGGLE\n",
        "  # -------------\n",
        "  user = getpass('Kaggle Username: ')\n",
        "  key = getpass('Kaggle API key: ')\n",
        "\n",
        "  if '.kaggle' not in os.listdir('/root'):\n",
        "      !mkdir ~/.kaggle\n",
        "  !touch /root/.kaggle/kaggle.json\n",
        "  !chmod 666 /root/.kaggle/kaggle.json\n",
        "  with open('/root/.kaggle/kaggle.json', 'w') as f:\n",
        "      f.write('{\"username\":\"%s\",\"key\":\"%s\"}' % (user, key))\n",
        "  !chmod 600 /root/.kaggle/kaggle.json\n",
        "\n",
        "  # ----------------\n",
        "  # DOWNLOAD & UNZIP\n",
        "  # ----------------\n",
        "  if not os.path.exists('clips.tar.gz'):\n",
        "    !kaggle competitions download -c seizure-detection\n",
        "  \n",
        "  if not os.path.exists(\"Volumes\"):\n",
        "    if UPENN_ALL_PART:\n",
        "      !tar xvzf clips.tar.gz\n",
        "    else:\n",
        "      !tar xvzf clips.tar.gz Volumes/Seagate/seizure_detection/competition_data/clips/Patient_2/\n",
        "  \n",
        "  upenn_dir = os.path.join(os.getcwd(), 'Volumes', 'Seagate', 'seizure_detection', \n",
        "                           'competition_data', 'clips')\n",
        "  # ---------------\n",
        "  # CREATE FEATURES\n",
        "  # ---------------\n",
        "  for part_dir in tqdm(file_list(os.path.join(upenn_dir, '*')), desc='Patient Folders'):\n",
        "    part_file_list = file_list(os.path.join(part_dir, '*'), output=False)\n",
        "    part_id = re.findall('Patient_\\d+', part_file_list[0])[0]\n",
        "\n",
        "    for file in tqdm(part_file_list, desc='Patient Files'):\n",
        "      df, freq = mat_to_df(file)\n",
        "      class_name = file_class(file)\n",
        "      \n",
        "      feat = Seizure_Features(sf = freq, \n",
        "                              window_size=None,\n",
        "                              bandpasses = [[2,4],[4,8],[8,12],\n",
        "                                            [12,30],[30,70]],\n",
        "                              feature_list=['power', 'power_ratio', 'mean', 'mean_abs', \n",
        "                                            'std', 'ratio', 'LSWT', 'fft_corr', 'fft_eigen',\n",
        "                                            'time_corr', 'time_eigen'])\n",
        "      part_x_feat = feat.transform(df.values, \n",
        "                                   channel_names_list = list(df.columns))\n",
        "      \n",
        "      part_x_feat = pd.DataFrame(part_x_feat, columns = feat.feature_names)\n",
        "      \n",
        "      part_y_feat = np.expand_dims(np.array([class_name]), axis=1)\n",
        "        \n",
        "      save_to_database(UPENN_SAVE_PATH, part_id, \n",
        "                       part_x_feat, part_y_feat)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
            "Volumes/Seagate/seizure_detection/competition_data/clips/Patient_2/Patient_2_interictal_segment_2695.mat\n",
            "Volumes/Seagate/seizure_detection/competition_data/clips/Patient_2/Patient_2_interictal_segment_2696.mat\n",
            "Volumes/Seagate/seizure_detection/competition_data/clips/Patient_2/Patient_2_interictal_segment_2697.mat\n",
            "Volumes/Seagate/seizure_detection/competition_data/clips/Patient_2/Patient_2_interictal_segment_2698.mat\n",
            "Volumes/Seagate/seizure_detection/competition_data/clips/Patient_2/Patient_2_interictal_segment_2699.mat\n",
            "Volumes/Seagate/seizure_detection/competition_data/clips/Patient_2/Patient_2_interictal_segment_27.mat\n",
            "Volumes/Seagate/seizure_detection/competition_data/clips/Patient_2/Patient_2_interictal_segment_270.mat\n",
            "Volumes/Seagate/seizure_detection/competition_data/clips/Patient_2/Patient_2_interictal_segment_2700.mat\n",
            "Volumes/Seagate/seizure_detection/competition_data/clips/Patient_2/Patient_2_interictal_segment_2701.mat\n",
            "Volumes/Seagate/seizure_detection/competition_data/clips/Patient_2/Patient_2_interictal_segment_2702.mat\n",
            "Volumes/Seagate/seizure_detection/competition_data/clips/Patient_2/Patient_2_interictal_segment_2703.mat\n",
            "Volumes/Seagate/seizure_detection/competition_data/clips/Patient_2/Patient_2_interictal_segment_2704.mat\n",
            "Volumes/Seagate/seizure_detection/competition_data/clips/Patient_2/Patient_2_interictal_segment_2705.mat\n",
            "Volumes/Seagate/seizure_detection/competition_data/clips/Patient_2/Patient_2_interictal_segment_2706.mat\n",
            "Volumes/Seagate/seizure_detection/competition_data/clips/Patient_2/Patient_2_interictal_segment_2707.mat\n",
            "Volumes/Seagate/seizure_detection/competition_data/clips/Patient_2/Patient_2_interictal_segment_2708.mat\n",
            "Volumes/Seagate/seizure_detection/competition_data/clips/Patient_2/Patient_2_interictal_segment_2709.mat\n",
            "Volumes/Seagate/seizure_detection/competition_data/clips/Patient_2/Patient_2_interictal_segment_271.mat\n",
            "Volumes/Seagate/seizure_detection/competition_data/clips/Patient_2/Patient_2_interictal_segment_2710.mat\n",
            "Volumes/Seagate/seizure_detection/competition_data/clips/Patient_2/Patient_2_interictal_segment_2711.mat\n",
            "Volumes/Seagate/seizure_detection/competition_data/clips/Patient_2/Patient_2_interictal_segment_2712.mat\n",
            "Volumes/Seagate/seizure_detection/competition_data/clips/Patient_2/Patient_2_interictal_segment_2713.mat\n",
            "Volumes/Seagate/seizure_detection/competition_data/clips/Patient_2/Patient_2_interictal_segment_2714.mat\n",
            "Volumes/Seagate/seizure_detection/competition_data/clips/Patient_2/Patient_2_interictal_segment_2715.mat\n",
            "Volumes/Seagate/seizure_detection/competition_data/clips/Patient_2/Patient_2_interictal_segment_2716.mat\n",
            "Volumes/Seagate/seizure_detection/competition_data/clips/Patient_2/Patient_2_interictal_segment_2717.mat\n",
            "Volumes/Seagate/seizure_detection/competition_data/clips/Patient_2/Patient_2_interictal_segment_2718.mat\n",
            "Volumes/Seagate/seizure_detection/competition_data/clips/Patient_2/Patient_2_interictal_segment_2719.mat\n",
            "Volumes/Seagate/seizure_detection/competition_data/clips/Patient_2/Patient_2_interictal_segment_272.mat\n",
            "Volumes/Seagate/seizure_detection/competition_data/clips/Patient_2/Patient_2_interictal_segment_2720.mat\n",
            "Volumes/Seagate/seizure_detection/competition_data/clips/Patient_2/Patient_2_interictal_segment_2721.mat\n",
            "Volumes/Seagate/seizure_detection/competition_data/clips/Patient_2/Patient_2_interictal_segment_2722.mat\n",
            "Volumes/Seagate/seizure_detection/competition_data/clips/Patient_2/Patient_2_interictal_segment_2723.mat\n",
            "Volumes/Seagate/seizure_detection/competition_data/clips/Patient_2/Patient_2_interictal_segment_2724.mat\n",
            "Volumes/Seagate/seizure_detection/competition_data/clips/Patient_2/Patient_2_interictal_segment_2725.mat\n",
            "Volumes/Seagate/seizure_detection/competition_data/clips/Patient_2/Patient_2_interictal_segment_2726.mat\n",
            "Volumes/Seagate/seizure_detection/competition_data/clips/Patient_2/Patient_2_interictal_segment_2727.mat\n",
            "Volumes/Seagate/seizure_detection/competition_data/clips/Patient_2/Patient_2_interictal_segment_2728.mat\n",
            "Volumes/Seagate/seizure_detection/competition_data/clips/Patient_2/Patient_2_interictal_segment_2729.mat\n",
            "Volumes/Seagate/seizure_detection/competition_data/clips/Patient_2/Patient_2_interictal_segment_273.mat\n",
            "Volumes/Seagate/seizure_detection/competition_data/clips/Patient_2/Patient_2_interictal_segment_2730.mat\n",
            "Volumes/Seagate/seizure_detection/competition_data/clips/Patient_2/Patient_2_interictal_segment_2731.mat\n",
            "Volumes/Seagate/seizure_detection/competition_data/clips/Patient_2/Patient_2_interictal_segment_2732.mat\n",
            "Volumes/Seagate/seizure_detection/competition_data/clips/Patient_2/Patient_2_interictal_segment_2733.mat\n",
            "Volumes/Seagate/seizure_detection/competition_data/clips/Patient_2/Patient_2_interictal_segment_2734.mat\n",
            "Volumes/Seagate/seizure_detection/competition_data/clips/Patient_2/Patient_2_interictal_segment_2735.mat\n",
            "Volumes/Seagate/seizure_detection/competition_data/clips/Patient_2/Patient_2_interictal_segment_2736.mat\n",
            "Volumes/Seagate/seizure_detection/competition_data/clips/Patient_2/Patient_2_interictal_segment_2737.mat\n",
            "Volumes/Seagate/seizure_detection/competition_data/clips/Patient_2/Patient_2_interictal_segment_2738.mat\n",
            "Volumes/Seagate/seizure_detection/competition_data/clips/Patient_2/Patient_2_interictal_segment_2739.mat\n",
            "Volumes/Seagate/seizure_detection/competition_data/clips/Patient_2/Patient_2_interictal_segment_274.mat\n",
            "Volumes/Seagate/seizure_detection/competition_data/clips/Patient_2/Patient_2_interictal_segment_2740.mat\n",
            "Volumes/Seagate/seizure_detection/competition_data/clips/Patient_2/Patient_2_interictal_segment_2741.mat\n",
            "Volumes/Seagate/seizure_detection/competition_data/clips/Patient_2/Patient_2_interictal_segment_2742.mat\n",
            "Volumes/Seagate/seizure_detection/competition_data/clips/Patient_2/Patient_2_interictal_segment_2743.mat\n",
            "Volumes/Seagate/seizure_detection/competition_data/clips/Patient_2/Patient_2_interictal_segment_2744.mat\n",
            "Volumes/Seagate/seizure_detection/competition_data/clips/Patient_2/Patient_2_interictal_segment_2745.mat\n",
            "Volumes/Seagate/seizure_detection/competition_data/clips/Patient_2/Patient_2_interictal_segment_2746.mat\n",
            "Volumes/Seagate/seizure_detection/competition_data/clips/Patient_2/Patient_2_interictal_segment_2747.mat\n",
            "Volumes/Seagate/seizure_detection/competition_data/clips/Patient_2/Patient_2_interictal_segment_2748.mat\n",
            "Volumes/Seagate/seizure_detection/competition_data/clips/Patient_2/Patient_2_interictal_segment_2749.mat\n",
            "Volumes/Seagate/seizure_detection/competition_data/clips/Patient_2/Patient_2_interictal_segment_275.mat\n",
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            "Volumes/Seagate/seizure_detection/competition_data/clips/Patient_2/Patient_2_interictal_segment_2751.mat\n",
            "Volumes/Seagate/seizure_detection/competition_data/clips/Patient_2/Patient_2_interictal_segment_2752.mat\n",
            "Volumes/Seagate/seizure_detection/competition_data/clips/Patient_2/Patient_2_interictal_segment_2753.mat\n",
            "Volumes/Seagate/seizure_detection/competition_data/clips/Patient_2/Patient_2_interictal_segment_2754.mat\n",
            "Volumes/Seagate/seizure_detection/competition_data/clips/Patient_2/Patient_2_interictal_segment_2755.mat\n",
            "Volumes/Seagate/seizure_detection/competition_data/clips/Patient_2/Patient_2_interictal_segment_2756.mat\n",
            "Volumes/Seagate/seizure_detection/competition_data/clips/Patient_2/Patient_2_interictal_segment_2757.mat\n",
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            "Volumes/Seagate/seizure_detection/competition_data/clips/Patient_2/Patient_2_interictal_segment_276.mat\n",
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            "Volumes/Seagate/seizure_detection/competition_data/clips/Patient_2/Patient_2_interictal_segment_277.mat\n",
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            "Volumes/Seagate/seizure_detection/competition_data/clips/Patient_2/Patient_2_interictal_segment_278.mat\n",
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            "Volumes/Seagate/seizure_detection/competition_data/clips/Patient_2/Patient_2_interictal_segment_28.mat\n",
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            "Volumes/Seagate/seizure_detection/competition_data/clips/Patient_2/Patient_2_test_segment_999.mat\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:133: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
            "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "dbdbc4615706476bbf5d3cb4e6d5403b",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Patient Folders', max=1.0, style=ProgressStyle(descriptio…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:137: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
            "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "7fbd3976b26d4a58b0286e1475fa3f1f",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Patient Files', max=7035.0, style=ProgressStyle(descripti…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FrAsVH8ERVZV"
      },
      "source": [
        "## CHB-MIT Scalp EEG Database\n",
        "\n",
        "Here are some settings. This just allows control over whether to create a raw or feature dataframe, and whether to extract for all patients or just the first.\n",
        "\n",
        "**Timing**\n",
        "- For both the filtered and feature dataframe (for 1 patient) it takes about 2hrs (depends on the machine you get)\n",
        "- The filtered df is much quicker to create\n",
        "\n",
        "**NOTE**\n",
        "- we only use the first patient for the tutorials"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nqlulngeAB89"
      },
      "source": [
        "# Run the code and create/overwite old data?\n",
        "CHB_FILT_OVERWRITE = True\n",
        "CHB_FEAT_OVERWRITE = True\n",
        "\n",
        "# Make the frame for all patients or just 1 (False)?\n",
        "CHB_ALL_PART = False\n",
        "\n",
        "# Where to save the feature data\n",
        "CHB_FILT_SAVE_PATH = '/content/drive/My Drive/Data/CHB_filt.hdf5'\n",
        "CHB_FEAT_SAVE_PATH = '/content/drive/My Drive/Data/CHB_feat.hdf5'\n",
        "\n",
        "if CHB_ALL_PART:\n",
        "  CHB_FILT_SAVE_PATH = '/content/drive/My Drive/Data/CHB_all_filt.hdf5'\n",
        "  CHB_FEAT_SAVE_PATH = '/content/drive/My Drive/Data/CHB_all_feat.hdf5'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "klS4CosNRazF",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 849,
          "referenced_widgets": [
            "d5fcd08d68d74d3cafc0f6c9fd2d33d8",
            "d6cc65250a9a42b9b36253195e75e496",
            "0166e2b974b548c9a5d4bb80209810de",
            "58923540f6904a76bf09c04b9a8dc78d",
            "3450cd124c884398ad2b43f55336a1a8",
            "6b3c5bf191bc473cb2dfedd6717085e6",
            "1f8b18d42a934089a53ebda8a51b9ccb",
            "18ba49601b7848cf8176660b85797e42"
          ]
        },
        "outputId": "29bd5532-a030-42ac-9649-7387aee9a242"
      },
      "source": [
        "def get_content(part_code):\n",
        "  url = \"https://physionet.org/physiobank/database/chbmit/\"+part_code+'/'+part_code+'-summary.txt'\n",
        "  filename = \"./chbmit.txt\"\n",
        "\n",
        "  urlretrieve(url,filename)\n",
        "\n",
        "  # read the file into a list\n",
        "  with open(filename, encoding='UTF-8') as f:\n",
        "      # read all the document into a list of strings (each line a new string)\n",
        "      content = f.readlines()\n",
        "      os.remove(filename)\n",
        "\n",
        "  return content\n",
        "\n",
        "  import re\n",
        "\n",
        "def info_dict(content):\n",
        "  \n",
        "  line_nos=len(content)\n",
        "  line_no=1\n",
        "\n",
        "  channels = []\n",
        "  file_name = []\n",
        "  file_info_dict={}\n",
        "\n",
        "  for line in content:\n",
        "\n",
        "    # if there is Channel in the line...\n",
        "    if re.findall('Channel \\d+', line):\n",
        "      # split the line into channel number and channel reference\n",
        "      channel = line.split(': ')\n",
        "      # get the channel reference and remove any new lines\n",
        "      channel = channel[-1].replace(\"\\n\", \"\")\n",
        "      # put into the channel list\n",
        "      channels.append(channel)\n",
        "\n",
        "    # if the line is the file name\n",
        "    elif re.findall('File Name', line):\n",
        "      # if there is already a file_name\n",
        "      if file_name:\n",
        "        # flush the current file info to it\n",
        "        part_info_dict[file_name] = file_info_dict\n",
        "\n",
        "      # get the file name\n",
        "      file_name = re.findall('\\w+\\d+_\\d+|\\w+\\d+\\w+_\\d+', line)[0]\n",
        "\n",
        "      file_info_dict = {}\n",
        "      # put the channel list in the file info dict and remove duplicates\n",
        "      file_info_dict['Channels'] = list(set(channels))\n",
        "      # reset the rest of the options\n",
        "      file_info_dict['Start Time'] = ''\n",
        "      file_info_dict['End Time'] = ''\n",
        "      file_info_dict['Seizures Window'] = []\n",
        "\n",
        "    # if the line is about the file start time\n",
        "    elif re.findall('File Start Time', line):\n",
        "      # get the start time\n",
        "      file_info_dict['Start Time'] = re.findall('\\d+:\\d+:\\d+', line)[0]\n",
        "\n",
        "    # if the line is about the file end time\n",
        "    elif re.findall('File End Time', line):\n",
        "      # get the start time\n",
        "      file_info_dict['End Time'] = re.findall('\\d+:\\d+:\\d+', line)[0]\n",
        "\n",
        "    elif re.findall('Seizure Start Time|Seizure End Time|Seizure \\d+ Start Time|Seizure \\d+ End Time', line):\n",
        "      file_info_dict['Seizures Window'].append(int(re.findall('\\d+', line)[-1]))\n",
        "\n",
        "    # if last line in the list...\n",
        "    if line_no == line_nos:\n",
        "      # flush the file info to it\n",
        "      part_info_dict[file_name] = file_info_dict\n",
        "\n",
        "    line_no+=1\n",
        "    \n",
        "\n",
        "def data_load(file, selected_channels=[]):\n",
        "\n",
        "  try: \n",
        "    url = \"https://physionet.org/physiobank/database/chbmit/\"+file\n",
        "    filename = \"./chbmit.edf\"\n",
        "\n",
        "    urlretrieve(url,filename)\n",
        "    # use the reader to get an EdfReader file\n",
        "    f = pyedflib.EdfReader(filename)\n",
        "    os.remove(filename)\n",
        "    \n",
        "    # get a list of the EEG channels\n",
        "    if len(selected_channels) == 0:\n",
        "      selected_channels = f.getSignalLabels()\n",
        "\n",
        "    # get the names of the signals\n",
        "    channel_names = f.getSignalLabels()\n",
        "    # get the sampling frequencies of each signal\n",
        "    channel_freq = f.getSampleFrequencies()\n",
        "\n",
        "    # make an empty file of 0's\n",
        "    sigbufs = np.zeros((f.getNSamples()[0],len(selected_channels)))\n",
        "    # for each of the channels in the selected channels\n",
        "    for i, channel in enumerate(selected_channels):\n",
        "      # add the channel data into the array\n",
        "      sigbufs[:, i] = f.readSignal(channel_names.index(channel))\n",
        "    \n",
        "    # turn to a pandas df and save a little space\n",
        "    df = pd.DataFrame(sigbufs, columns = selected_channels).astype('float32')\n",
        "    \n",
        "    # get equally increasing numbers upto the length of the data depending\n",
        "    # on the length of the data divided by the sampling frequency\n",
        "    index_increase = np.linspace(0,\n",
        "                                  len(df)/channel_freq[0],\n",
        "                                  len(df), endpoint=False)\n",
        "\n",
        "    # round these to the lowest nearest decimal to get the seconds\n",
        "    seconds = np.floor(index_increase).astype('uint16')\n",
        "\n",
        "    # make a column the timestamp\n",
        "    df['Time'] = seconds\n",
        "\n",
        "    # make the time stamp the index\n",
        "    df = df.set_index('Time')\n",
        "\n",
        "    # name the columns as channel\n",
        "    df.columns.name = 'Channel'\n",
        "\n",
        "    return df, channel_freq[0]\n",
        "\n",
        "  except:\n",
        "    OSError\n",
        "    return pd.DataFrame(), None\n",
        "\n",
        "\n",
        "def create_events(file_name, df):\n",
        "\n",
        "  # default df with all 0's the length of the last time datapoint\n",
        "  events_df = pd.DataFrame([0]*int(df.iloc[-1].name+1))\n",
        "\n",
        "  if file_name in part_info_dict:\n",
        "    seizures_window = part_info_dict[file_name]['Seizures Window']\n",
        "  else:\n",
        "    seizures_window = []\n",
        "\n",
        "  # if not empty\n",
        "  if seizures_window:\n",
        "    # just get the start values\n",
        "    seizures_start = seizures_window[0::2]\n",
        "    # just get the end values\n",
        "    seizures_end = seizures_window[1::2]\n",
        "    # get index values where seizures occour\n",
        "    seizures_index = []\n",
        "    for index, value in enumerate(seizures_start):\n",
        "      seizures_index.extend(list(range(value,seizures_end[index])))\n",
        "\n",
        "    # make a df of 1's with the index where there are seizures\n",
        "    seizure_events_df = pd.DataFrame([1]*len(seizures_index),seizures_index)\n",
        "\n",
        "    # update values where seizure\n",
        "    events_df.update(seizure_events_df, overwrite = True)\n",
        "\n",
        "  # name the index 'time'\n",
        "  events_df.index.name='Time'\n",
        "  # name the column event\n",
        "  events_df.columns=['Class']\n",
        "\n",
        "  events_df = events_df.astype('uint8')\n",
        "  \n",
        "  # join the events to match the time they are associated with\n",
        "  df = df.join(events_df, on='Time')\n",
        "\n",
        "  # get the events\n",
        "  data_y = df['Class']\n",
        "\n",
        "  return data_y\n",
        "\n",
        "def window_y(events, window_size, overlap):\n",
        "    \n",
        "  # window the data so each row is another epoch\n",
        "  events_windowed = window(events, w = window_size, o = overlap, copy = True)\n",
        "\n",
        "  # get the value most frequent in the window\n",
        "  #data_y_mode = stats.mode(events_windowed, axis=1)[0].flatten()\n",
        "  \n",
        "  # turn to array of bools if seizure in the\n",
        "  # windowed data\n",
        "  bools = events_windowed == 1\n",
        "  # are there any seizure seconds in the data?\n",
        "  data_y = np.any(bools,axis=1)\n",
        "  # turn to 0's and 1's\n",
        "  data_y = data_y.astype(int)\n",
        "\n",
        "  \n",
        "  return data_y\n",
        "\n",
        "def save_to_database(save_dir, file_title, group, data_x, data_y, feature_columns):\n",
        "\n",
        "    # open the file in append mode (make it if doesnt exist)\n",
        "    h5file = tables.open_file(save_dir, mode=\"a\", title=file_title)\n",
        "    \n",
        "    # save space\n",
        "    data_x = data_x.astype(np.float32)\n",
        "    data_y = data_y.astype(np.int16)\n",
        "    \n",
        "    # get filters to compress file\n",
        "    filters = tables.Filters(complevel=1, complib='zlib')\n",
        "    \n",
        "    # if there is already a node for the particpant...\n",
        "    if \"/\"+group in h5file:\n",
        "        # ...put in the directory of where it is found\n",
        "        part_x_array = h5file.get_node(\"/\" + group + '/Data_x')\n",
        "        part_y_array = h5file.get_node(\"/\" + group + '/Data_y')\n",
        "    \n",
        "    else:\n",
        "        # create the group directory\n",
        "        part_group = h5file.create_group(\"/\", group, 'Group Data')\n",
        "        # make an atom which has the datatype found in the data we want to store\n",
        "        x_atom = tables.Atom.from_dtype(data_x.dtype)\n",
        "        y_atom = tables.Atom.from_dtype(data_y.dtype)\n",
        "\n",
        "        # create an array we can append onto later\n",
        "        part_x_array = h5file.create_earray(\"/\" + group,                   # parentnode\n",
        "                                            'Data_x',                        # name \n",
        "                                            x_atom,                          # atom\n",
        "                                            (0,data_x.shape[1], data_x.shape[2]), # shape\n",
        "                                            'Feature Array',\n",
        "                                            filters=filters\n",
        "                                           )                 # title\n",
        "\n",
        "        part_y_array = h5file.create_earray(\"/\" + group, \n",
        "                                            'Data_y', \n",
        "                                            y_atom, \n",
        "                                            (0,),\n",
        "                                            'Events Array',\n",
        "                                            filters=filters\n",
        "                                           )\n",
        "        \n",
        "        # create the feature names array (we only need to do this once)\n",
        "        h5file.create_array(\"/\" + group,                                   # where\n",
        "                            'Feature_Names',                             # name \n",
        "                            np.array(feature_columns, dtype='unicode'),  # obj\n",
        "                            \"Names of Each Feature\")                         # title\n",
        "    \n",
        "    # append the data to the array directory\n",
        "    part_x_array.append(data_x)\n",
        "    part_y_array.append(data_y)\n",
        "    \n",
        "    # flush the data to disk\n",
        "    h5file.flush()\n",
        "    # close the file\n",
        "    h5file.close()\n",
        "\n",
        "\n",
        "if CHB_FILT_OVERWRITE or CHB_FEAT_OVERWRITE:\n",
        "\n",
        "  if CHB_FILT_OVERWRITE and os.path.exists(CHB_FILT_SAVE_PATH):\n",
        "    os.remove(CHB_FILT_SAVE_PATH)\n",
        "\n",
        "  if CHB_FEAT_OVERWRITE and os.path.exists(CHB_FEAT_SAVE_PATH):\n",
        "    os.remove(CHB_FEAT_SAVE_PATH)\n",
        "\n",
        "  dbs = wfdb.get_dbs()\n",
        "\n",
        "  records_list = wfdb.io.get_record_list('chbmit', records='all')\n",
        "  part_codes = sorted(list(set([record.split('/')[0] for record in records_list])))\n",
        "\n",
        "  # these are channels common to all records\n",
        "  channel_keeps = ['P7-O1','FP2-F4','F7-T7','FP1-F3','F4-C4','FP2-F8','P8-O2',\n",
        "                  'C4-P4','F3-C3','T8-P8','P3-O1','FZ-CZ','FP1-F7','P4-O2',\n",
        "                  'T7-P7','CZ-PZ','F8-T8','C3-P3']\n",
        "  \n",
        "  # ----------------------------\n",
        "  # PARTICIPANT INFORMATION DICT\n",
        "  # ----------------------------\n",
        "  part_info_dict = {}\n",
        "  for part_code in part_codes:\n",
        "    content = get_content(part_code)\n",
        "    info_dict(content)\n",
        "\n",
        "  # fix wrong times\n",
        "  start_time_24 = part_info_dict['chb03_24']['Start Time']\n",
        "  end_time_24 = part_info_dict['chb03_24']['End Time']\n",
        "  start_time_25 = part_info_dict['chb03_25']['Start Time']\n",
        "  end_time_25 = part_info_dict['chb03_25']['End Time']\n",
        "  part_info_dict['chb03_24']['Start Time'] = start_time_25\n",
        "  part_info_dict['chb03_24']['End Time'] = end_time_25\n",
        "  part_info_dict['chb03_25']['Start Time'] = start_time_24\n",
        "  part_info_dict['chb03_25']['End Time'] = end_time_24\n",
        "\n",
        "  # ---------------\n",
        "  # CREATE FEATURES\n",
        "  # ---------------\n",
        "  # reduce the list to just the first participant\n",
        "  if not CHB_ALL_PART:\n",
        "    regex = re.compile('chb01')\n",
        "    records_list = [i for i in records_list if regex.search(i)]\n",
        "\n",
        "  for record in tqdm(records_list):\n",
        "    part_id = record.split('/')[0]\n",
        "    file_name = record.split('/')[1].split('.')[0]\n",
        "    raw_data, freq = data_load(record, channel_keeps)\n",
        "    if raw_data.empty:\n",
        "      print('Skipped: '+file_name)\n",
        "    else:\n",
        "      raw_events = create_events(file_name, raw_data)\n",
        "      \n",
        "      if CHB_FILT_OVERWRITE:\n",
        "        # filter the data\n",
        "        b, a = signal.butter(4, [1/(freq/2), 30/(freq/2)], 'bandpass', analog=False)\n",
        "        filt_x = signal.filtfilt(b, a, raw_data.T).T\n",
        "        \n",
        "        # scale the data\n",
        "        SS = StandardScaler()\n",
        "        scaled_data = SS.fit_transform(filt_x)\n",
        "        scaled_data = pd.DataFrame(scaled_data, columns = raw_data.columns, \n",
        "                                   index = raw_events)\n",
        "        # drop na\n",
        "        scaled_data = scaled_data.dropna()\n",
        "        \n",
        "        # window the data\n",
        "        window_size = 256*2\n",
        "        overlap = 256\n",
        "        data_x = window_x(scaled_data, window_size, overlap)\n",
        "        data_y = window_y(raw_events, window_size, overlap)\n",
        "        \n",
        "        # save the data\n",
        "        save_to_database(CHB_FILT_SAVE_PATH,\n",
        "                        'CHB_filt',\n",
        "                        part_id,\n",
        "                        data_x,\n",
        "                        data_y, \n",
        "                        list(scaled_data.columns))\n",
        "      \n",
        "      if CHB_FEAT_OVERWRITE:\n",
        "        feat = Seizure_Features(sf = freq,\n",
        "                                window_size=2,\n",
        "                                overlap=1,\n",
        "                                levels=6,\n",
        "                                bandpasses = [[1,4],[4,8],[8,12],\n",
        "                                              [12,30],[30,70]],\n",
        "                                feature_list=['power', 'power_ratio', \n",
        "                                              'mean', \n",
        "                                              'mean_abs', 'std', 'ratio', \n",
        "                                              'LSWT', \n",
        "                                              'fft_corr', 'fft_eigen', 'time_corr', \n",
        "                                              'time_eigen'],\n",
        "                                scale = True)\n",
        "        with warnings.catch_warnings():\n",
        "          warnings.filterwarnings(\"ignore\", category=RuntimeWarning)\n",
        "          x_feat, y_feat = feat.transform(raw_data.values, \n",
        "                                          raw_events.values,\n",
        "                                          channel_names_list = list(raw_data.columns))\n",
        "\n",
        "        \n",
        "        # TODO: sometimes the feats come out as Float64Index...\n",
        "        # should probably look into why that is, but for now, I'll just\n",
        "        # turn them to ints\n",
        "        feat_df = pd.DataFrame(x_feat,\n",
        "                               index=y_feat[:,0].astype(int), \n",
        "                               columns = feat.feature_names)\n",
        "\n",
        "        feat_df = feat_df.dropna()\n",
        "        \n",
        "        feat_df.to_hdf(CHB_FEAT_SAVE_PATH, part_id, format='table', append=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:343: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
            "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d5fcd08d68d74d3cafc0f6c9fd2d33d8",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=42.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:254: FutureWarning: Series.strides is deprecated and will be removed in a future version\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:254: FutureWarning: Series.strides is deprecated and will be removed in a future version\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:254: FutureWarning: Series.strides is deprecated and will be removed in a future version\n",
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            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:254: FutureWarning: Series.strides is deprecated and will be removed in a future version\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:254: FutureWarning: Series.strides is deprecated and will be removed in a future version\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:254: FutureWarning: Series.strides is deprecated and will be removed in a future version\n",
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            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:254: FutureWarning: Series.strides is deprecated and will be removed in a future version\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:254: FutureWarning: Series.strides is deprecated and will be removed in a future version\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:254: FutureWarning: Series.strides is deprecated and will be removed in a future version\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:254: FutureWarning: Series.strides is deprecated and will be removed in a future version\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:254: FutureWarning: Series.strides is deprecated and will be removed in a future version\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:254: FutureWarning: Series.strides is deprecated and will be removed in a future version\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:254: FutureWarning: Series.strides is deprecated and will be removed in a future version\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:254: FutureWarning: Series.strides is deprecated and will be removed in a future version\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:254: FutureWarning: Series.strides is deprecated and will be removed in a future version\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:254: FutureWarning: Series.strides is deprecated and will be removed in a future version\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:254: FutureWarning: Series.strides is deprecated and will be removed in a future version\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:254: FutureWarning: Series.strides is deprecated and will be removed in a future version\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:254: FutureWarning: Series.strides is deprecated and will be removed in a future version\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jVux0dpARiTt"
      },
      "source": [
        "## NEDC TUH EEG Seizure corpus\n",
        "\n",
        "**Timings**\n",
        "- For the UDWT, filtered, and feature dataframes (for absence seizures) it takes about 20 mins\n",
        "\n",
        "**TODO**\n",
        "- change the way data is downloaded to be alike to the CHB data (download it, load it, remove it), rather than currently where it downloads all the files. This is fine for the smaller sets but for largers ones (such as all seizure types) it will fill the disk and crash."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ApvsgfT9ASnX"
      },
      "source": [
        "# Run the code and create/overwite old data?\n",
        "TUH_FILT_OVERWRITE = True\n",
        "TUH_FEAT_OVERWRITE = True\n",
        "TUH_UDWT_OVERWRITE = True\n",
        "\n",
        "# What type of seizure in TUH (If `None` will do all)?\n",
        "TUH_code = 'ABSZ'\n",
        "\n",
        "# Where to save the feature data\n",
        "TUH_FILT_SAVE_PATH = '/content/drive/My Drive/Data/TUH_filt.hdf5'\n",
        "TUH_FEAT_SAVE_PATH = '/content/drive/My Drive/Data/TUH_feat.hdf5'\n",
        "TUH_UDWT_SAVE_PATH = '/content/drive/My Drive/Data/TUH_udwt.hdf5'\n",
        "\n",
        "if not TUH_code:\n",
        "  TUH_FILT_SAVE_PATH = '/content/drive/My Drive/Data/TUH_all_filt.hdf5'\n",
        "  TUH_FEAT_SAVE_PATH = '/content/drive/My Drive/Data/TUH_all_feat.hdf5'\n",
        "  TUH_UDWT_SAVE_PATH = '/content/drive/My Drive/Data/TUH_all_udwt.hdf5'"
      ],
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zYYskVfUUoEU"
      },
      "source": [
        "**NOTES**\n",
        "- I don't re-reference, instead leaving the origional references in. This means that I select files with the most common type of reference for that seizure.\n",
        "- I merge the files below to the same participants together. They are still windowed separately but it just means when you split the training and test its easier to ensure data from the same patient doesnt fall into separate test and training.\n",
        "- Getting a warning for getting features on records like 00003281:\n",
        "`RuntimeWarning: All-NaN slice encountered`\n",
        "but this is just at for a couple of seconds at the start of the records. Probably to do with electrode noise so I'm just going to ignore it for now."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jfVzO-rWRru8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
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            "e77ace13225943748ab38660d387a245",
            "7258b27722f941a6b7111b49ed464074",
            "f9f8757e58fa4086b67d5ab88fa8b6dd"
          ]
        },
        "outputId": "cdef6cca-f885-4615-b48a-27305cec76b5"
      },
      "source": [
        "def find_files(url, headers):\n",
        "    # get a soup of the directory url\n",
        "    soup = BeautifulSoup(requests.get(url, auth=(headers['user'], headers['passwd'])).text, \n",
        "                         features=\"html.parser\")\n",
        "    # make a list of all the links in the url\n",
        "    hrefs_list = []\n",
        "    for link in soup.find_all('a'):\n",
        "        hrefs_list.append(link.get('href'))\n",
        "\n",
        "    return hrefs_list\n",
        "    \n",
        "    \n",
        "def download_file(download_file_url, file_path, headers, output=False):\n",
        "    if output:\n",
        "        # print it is downloading\n",
        "        print('Downloading: '+ download_file_url)\n",
        "    # download the file to the directory\n",
        "    r = requests.get(download_file_url, auth=(headers['user'], headers['passwd']))\n",
        "    with open(file_path, 'wb') as f:\n",
        "      f.write(r.content)\n",
        "\n",
        "# needs a directory to download it to\n",
        "def download_TUH(DIR, headers, sub_dir, output=False):\n",
        "    \n",
        "    # directory url\n",
        "    dir_url = 'https://www.isip.piconepress.com/projects/tuh_eeg/downloads/tuh_eeg_seizure/v1.5.0/'+sub_dir\n",
        "\n",
        "    hrefs_dir_list = find_files(dir_url, headers)\n",
        "    \n",
        "    # for each link in the directory\n",
        "    for link in hrefs_dir_list:\n",
        "        # download the files outside of participant folders we want\n",
        "        if re.findall('.xlsx|\\.edf|\\.tse(?!_)', str(link)):\n",
        "            # if the file doesnt already exist in the directory\n",
        "            if not os.path.exists(os.path.join(DIR, link)):\n",
        "                download_file(dir_url+'/'+str(link), DIR+'/'+str(link), headers, output)\n",
        "\n",
        "def data_load(data_file, selected_channels=[]):\n",
        "\n",
        "    try:\n",
        "        # use the reader to get an EdfReader file\n",
        "        f = pyedflib.EdfReader(data_file)\n",
        "\n",
        "        # get the names of the signals\n",
        "        channel_names = f.getSignalLabels()\n",
        "        # get the sampling frequencies of each signal\n",
        "        channel_freq = f.getSampleFrequencies()\n",
        "        \n",
        "        # get a list of the EEG channels\n",
        "        if len(selected_channels) == 0:\n",
        "            selected_channels = channel_names\n",
        "\n",
        "        # make an empty file of 0's\n",
        "        sigbufs = np.zeros((f.getNSamples()[0],len(selected_channels)))\n",
        "        # for each of the channels in the selected channels\n",
        "        for i, channel in enumerate(selected_channels):\n",
        "        \n",
        "            try:\n",
        "              # add the channel data into the array\n",
        "              sigbufs[:, i] = f.readSignal(channel_names.index(channel))\n",
        "            \n",
        "            except:\n",
        "              ValueError\n",
        "              # This happens if the sampling rate of that channel is \n",
        "              # different to the others.\n",
        "              # For simplicity, in this case we just make it na.\n",
        "              sigbufs[:, i] = np.nan\n",
        "\n",
        "        # turn to a pandas df and save a little space\n",
        "        df = pd.DataFrame(sigbufs, columns = selected_channels).astype('float32')\n",
        "\n",
        "        # get equally increasing numbers upto the length of the data depending\n",
        "        # on the length of the data divided by the sampling frequency\n",
        "        index_increase = np.linspace(0,\n",
        "                                      len(df)/channel_freq[0],\n",
        "                                      len(df), endpoint=False)\n",
        "\n",
        "        # round these to the lowest nearest decimal to get the seconds\n",
        "        seconds = np.floor(index_increase).astype('uint16')\n",
        "\n",
        "        seconds = index_increase\n",
        "        \n",
        "        # make a column the timestamp\n",
        "        df['Time'] = seconds\n",
        "\n",
        "        # make the time stamp the index\n",
        "        df = df.set_index('Time')\n",
        "\n",
        "        # name the columns as channel\n",
        "        df.columns.name = 'Channel'\n",
        "\n",
        "        return df, channel_freq[0]\n",
        "\n",
        "    except OSError as error:\n",
        "        print('Error '+data_file+': '+str(error))\n",
        "        return pd.DataFrame(), None\n",
        "    except ValueError as error:\n",
        "        print('Error '+data_file+': '+str(error))\n",
        "        return pd.DataFrame(), None\n",
        "\n",
        "def create_events(file_name, df, code = None):\n",
        "\n",
        "    data_y = pd.Series(index=df.index)\n",
        "    data_y.name = 'Events'\n",
        "\n",
        "    events_tse = pd.read_csv(file_name, \n",
        "                             skiprows=1,\n",
        "                             sep = ' ',\n",
        "                             header=None,\n",
        "                             names =['Start', 'End', 'Code', 'Certainty'])\n",
        "    \n",
        "    data_y = data_y.fillna('bckg')\n",
        "    \n",
        "    for pos, row in events_tse.iterrows():\n",
        "        # if you want to manually set the code\n",
        "        if code != None:\n",
        "          if row['Code'] == code:\n",
        "              data_y[row['Start']:row['End']] = code\n",
        "        # let it be the code it is in the event file\n",
        "        else:\n",
        "          data_y[row['Start']:row['End']] = row['Code']\n",
        "\n",
        "    return data_y\n",
        "\n",
        "def window_y(events, window_size, overlap, target=None, baseline=None):\n",
        "    \n",
        "  # window the data so each row is another epoch\n",
        "  events_windowed = window(events, w = window_size, o = overlap, copy = True)\n",
        "  \n",
        "  if target:\n",
        "    # turn to array of bools if seizure in the\n",
        "    # windowed data\n",
        "    bools = events_windowed == target\n",
        "    # are there any seizure seconds in the data?\n",
        "    data_y = np.any(bools,axis=1)\n",
        "    # turn to 0's and 1's\n",
        "    data_y = data_y.astype(int)\n",
        "    # expand the dimensions so running down one column\n",
        "    data_y = np.expand_dims(data_y, axis=1)\n",
        "  \n",
        "  elif baseline:\n",
        "    # replace all baseline labels to nan\n",
        "    data_y = pd.DataFrame(events_windowed).replace(baseline, np.nan)\n",
        "    # get the most common other than baseline\n",
        "    data_y = data_y.mode(1)\n",
        "    # change nan back to baseline class\n",
        "    data_y = data_y.fillna(baseline).values\n",
        "    # if there was nothing but baseline there will be an empty array\n",
        "    if data_y.size == 0:\n",
        "        data_y = np.array([baseline]*data_y.shape[0])\n",
        "        data_y = np.expand_dims(data_y, -1)\n",
        "  \n",
        "  else:\n",
        "    # get the value most frequent in the window\n",
        "    data_y = pd.DataFrame(events_windowed).mode(1).values\n",
        "\n",
        "  return data_y\n",
        "\n",
        "def downsample(data_x, data_y, freq):\n",
        "    if freq > 256:\n",
        "        if freq >= 1000:\n",
        "          subsample = 4\n",
        "        else:\n",
        "          subsample = 2\n",
        "\n",
        "        freq = freq/subsample\n",
        "        data_x = data_x[::subsample]\n",
        "        data_y = data_y[::subsample]\n",
        "\n",
        "    return data_x, data_y, freq\n",
        "\n",
        "def sel_file_list(set_name, seiz_type):\n",
        "  # load the training information\n",
        "  train_info = pd.read_excel('/content/TUH Database/seizures_v32r.xlsx', set_name)\n",
        "  # just want the info per file here\n",
        "  file_info = train_info.iloc[1:6101,1:15]\n",
        "  # cleans some of the names\n",
        "  file_info_cols = ['File No.', 'Patient', 'Session', 'File', \n",
        "                        'EEG Type', 'EEG SubType', 'LTM or Routine', \n",
        "                        'Normal/Abnormal', 'No. Seizures File', \n",
        "                        'No. Seizures/Session', 'Filename', 'Seizure Start', \n",
        "                        'Seizure Stop', 'Seizure Type']\n",
        "  file_info.columns = file_info_cols\n",
        "\n",
        "  # we forward fill as there are gaps in the excel file to represent the info \n",
        "  # is the same as above (apart from in the filename, seizure start, seizure stop \n",
        "  # and seizure type columns)\n",
        "  for col_name in file_info.columns[:-4]:\n",
        "    file_info[col_name] = file_info[col_name].ffill()\n",
        "\n",
        "  # patient ID is an integer rather than float\n",
        "  file_info['Patient'] = file_info['Patient'].astype(int)\n",
        "\n",
        "  if seiz_type:\n",
        "    # Get a list of patient event files that have a specifc type of seizure\n",
        "    return list(file_info[file_info['Seizure Type']==seiz_type]['Filename'])\n",
        "  else:\n",
        "    return list(file_info['Filename'])\n",
        "\n",
        "def save_to_database(save_dir, file_title, group, data_x, data_y, \n",
        "                     feature_columns=None):\n",
        "\n",
        "    # open the file in append mode (make it if doesnt exist)\n",
        "    h5file = tables.open_file(save_dir, mode=\"a\", title=file_title)\n",
        "    \n",
        "    # save space\n",
        "    data_x = data_x.astype(np.float32)\n",
        "    #data_y = data_y.astype(np.int16)\n",
        "    \n",
        "    # get filters to compress file\n",
        "    filters = tables.Filters(complevel=1, complib='zlib')\n",
        "    \n",
        "    # if there is already a node for the particpant...\n",
        "    if \"/\"+group in h5file:\n",
        "        # ...put in the directory of where it is found\n",
        "        part_x_array = h5file.get_node(\"/\" + group + '/Data_x')\n",
        "        part_y_array = h5file.get_node(\"/\" + group + '/Data_y')\n",
        "    \n",
        "    else:\n",
        "        # create the group directory\n",
        "        part_group = h5file.create_group(\"/\", group, 'Group Data')\n",
        "        # make an atom which has the datatype found in the data we want to store\n",
        "        x_atom = tables.Atom.from_dtype(data_x.dtype)\n",
        "        y_atom = tables.Atom.from_dtype(data_y.dtype)\n",
        "\n",
        "        if file_title == 'UDWT_Data':\n",
        "          shape = (0,data_x.shape[1], data_x.shape[2], data_x.shape[3])\n",
        "        else:\n",
        "          shape = (0,data_x.shape[1], data_x.shape[2])\n",
        "        \n",
        "        # create an array we can append onto later\n",
        "        part_x_array = h5file.create_earray(\"/\" + group,                   # parentnode\n",
        "                                            'Data_x',                        # name \n",
        "                                            x_atom,                          # atom\n",
        "                                            shape, # shape\n",
        "                                            'Feature Array',\n",
        "                                            filters=filters\n",
        "                                           )                 # title\n",
        "\n",
        "        part_y_array = h5file.create_earray(\"/\" + group, \n",
        "                                            'Data_y', \n",
        "                                            y_atom, \n",
        "                                            (0,),\n",
        "                                            'Events Array',\n",
        "                                            filters=filters\n",
        "                                           )\n",
        "        \n",
        "        if feature_columns:\n",
        "          # create the feature names array (we only need to do this once)\n",
        "          h5file.create_array(\"/\" + group,                                   # where\n",
        "                              'Feature_Names',                             # name \n",
        "                              np.array(feature_columns, dtype='unicode'),  # obj\n",
        "                              \"Names of Each Feature\")                         # title\n",
        "    \n",
        "    # append the data to the array directory\n",
        "    part_x_array.append(data_x)\n",
        "    part_y_array.append(data_y)\n",
        "    \n",
        "    # flush the data to disk\n",
        "    h5file.flush()\n",
        "    # close the file\n",
        "    h5file.close()\n",
        "\n",
        "def udwt_spectrogram(data, waveletname, level, window_size):\n",
        "  data_ucwt = np.ndarray(shape=(data.shape[0], level, data.shape[1], data.shape[2]))\n",
        "  \n",
        "  for ii in range(data.shape[0]):\n",
        "    for jj in range(data.shape[-1]):\n",
        "      signal = data[ii, :, jj]\n",
        "      coeffs_list = swt(signal, waveletname, level=level)\n",
        "      \n",
        "      coeffs_array = np.zeros((len(coeffs_list), window_size))\n",
        "      \n",
        "      for i, array_tuple in enumerate(coeffs_list[::-1]):\n",
        "\n",
        "        coeffs_array[i,:] = np.array(array_tuple)[1,:]\n",
        "      \n",
        "      power = np.abs(coeffs_array)**2\n",
        "      \n",
        "      data_ucwt[ii, :, :, jj] = power\n",
        "      \n",
        "  return data_ucwt\n",
        "\n",
        "\n",
        "if TUH_FILT_OVERWRITE or TUH_FEAT_OVERWRITE or TUH_UDWT_OVERWRITE:\n",
        "\n",
        "  if TUH_FILT_OVERWRITE and os.path.exists(TUH_FILT_SAVE_PATH):\n",
        "    os.remove(TUH_FILT_SAVE_PATH)\n",
        "\n",
        "  if TUH_FEAT_OVERWRITE and os.path.exists(TUH_FEAT_SAVE_PATH):\n",
        "    os.remove(TUH_FEAT_SAVE_PATH)\n",
        "\n",
        "  if TUH_UDWT_OVERWRITE and os.path.exists(TUH_UDWT_SAVE_PATH):\n",
        "    os.remove(TUH_UDWT_SAVE_PATH)\n",
        "\n",
        "  # ---------\n",
        "  # TUH SETUP\n",
        "  # ---------\n",
        "  DOWNLOAD_DIR = \"TUH Database\"\n",
        "  if not os.path.exists(DOWNLOAD_DIR):\n",
        "    os.makedirs(DOWNLOAD_DIR)\n",
        "\n",
        "  user = getpass('TUH Username: ')\n",
        "  key = getpass('TUH Password: ')\n",
        "  auth_dict = {'user': user, 'passwd': key}\n",
        "\n",
        "  # --------------\n",
        "  # GET FILE PATHS\n",
        "  # --------------\n",
        "  # download info files\n",
        "  download_TUH(DOWNLOAD_DIR, auth_dict, '_DOCS')\n",
        "\n",
        "  seiz_types_path = '/content/TUH Database/seizures_types_v01.xlsx'\n",
        "  seiz_types = pd.read_excel(seiz_types_path)\n",
        "\n",
        "  seiz_types = seiz_types.set_index('Class Code')\n",
        "\n",
        "  int_code = seiz_types.to_dict()['Class No.']\n",
        "  # change to lower case\n",
        "  int_code = { k.lower() : v for k,v in int_code.items() if not isinstance(k, float)}\n",
        "\n",
        "  # get a list of files\n",
        "  tuh_file_list = sel_file_list('train', TUH_code)+sel_file_list('dev_test', TUH_code)\n",
        "\n",
        "  # get a list of the montages\n",
        "  montage = []\n",
        "  for file in tuh_file_list:\n",
        "    montage.append(file.split('/')[3])\n",
        "\n",
        "  # count how many times the montages appear in the data\n",
        "  montage_counts = pd.Series(montage).value_counts()\n",
        "\n",
        "  # remove all files apart from those in the most common montage\n",
        "  regex = re.compile(montage_counts.index[0])\n",
        "  tuh_file_list = [i for i in tuh_file_list if regex.search(i)]\n",
        "  # remove duplicates\n",
        "  tuh_file_list = list(set(tuh_file_list))\n",
        "\n",
        "  # They changed \"dev_test\" to just \"dev\" in their file structure so \n",
        "  # I need to account for this now...\n",
        "  for i, string in enumerate(tuh_file_list):\n",
        "    tuh_file_list[i] = re.sub('_test', '', string)\n",
        "\n",
        "\n",
        "  # --------------------\n",
        "  # GET SIMILAR CHANNELS\n",
        "  # --------------------\n",
        "  # this is to make sure all the data have the same channels\n",
        "  all_channels = []\n",
        "  for events_path in tqdm(tuh_file_list, desc = 'Finding Channels'):\n",
        "    file_ID = events_path.split('/')[-1][:-4]\n",
        "    # we use the above to get the file directory this file is in\n",
        "    pat_file_dir = '/'.join(events_path.split('/')[1:-1])\n",
        "    # this will download all edf and event files for the selected patient\n",
        "    download_TUH(DOWNLOAD_DIR, auth_dict, pat_file_dir, output=True)\n",
        "    \n",
        "    with pyedflib.EdfReader(DOWNLOAD_DIR+'/'+file_ID+'.edf') as f:\n",
        "        # get the names of the signals\n",
        "        all_channels.extend(f.getSignalLabels())\n",
        "\n",
        "  # turn the list into a pandas series\n",
        "  all_channels = pd.Series(all_channels)\n",
        "\n",
        "  # count how many times the channels appear in each participant\n",
        "  channel_counts = all_channels.value_counts()\n",
        "  \n",
        "  # threshold the channels to only those found in all raw data\n",
        "  channel_keeps = list(channel_counts[channel_counts >= channel_counts[0]].index)\n",
        "  regex = re.compile('30|PHOTIC|EKG|PG')\n",
        "  channel_keeps = [i for i in channel_keeps if not regex.search(i)]\n",
        "\n",
        "  # ---------------\n",
        "  # CREATE FEATURES\n",
        "  # ---------------\n",
        "  for events_path in tqdm(tuh_file_list, desc='Creating Features'):\n",
        "    file_ID = events_path.split('/')[-1][:-4]\n",
        "    # we use the above to get the file directory this file is in\n",
        "    pat_file_dir = '/'.join(events_path.split('/')[1:-1])\n",
        "    # patient ID\n",
        "    pat_ID = events_path.split('/')[-3]\n",
        "\n",
        "    # this will download all edf and event files for the selected patient\n",
        "    #download_TUH(DOWNLOAD_DIR, auth_dict, pat_file_dir, output=True)\n",
        "\n",
        "    # load data\n",
        "    raw_data, freq = data_load(DOWNLOAD_DIR+'/'+file_ID+'.edf', channel_keeps)\n",
        "\n",
        "    if raw_data.empty:\n",
        "      print('Skipped: '+file_ID)\n",
        "    else:\n",
        "      raw_events = create_events(DOWNLOAD_DIR+'/'+file_ID+'.tse', raw_data)\n",
        "      # change to integer representation\n",
        "      raw_events = raw_events.replace(int_code)\n",
        "\n",
        "      if TUH_FILT_OVERWRITE or TUH_UDWT_OVERWRITE:\n",
        "\n",
        "        # downsample\n",
        "        down_x, down_y, down_freq = downsample(raw_data.values, raw_events.values, freq)\n",
        "\n",
        "        # window y\n",
        "        window_size = 256*2\n",
        "        overlap = 256\n",
        "        \n",
        "        if TUH_FILT_OVERWRITE:\n",
        "          # filter the data\n",
        "          b, a = signal.butter(4, [1/(down_freq/2), 30/(down_freq/2)], 'bandpass', analog=False)\n",
        "          filt_data = signal.filtfilt(b, a, down_x.T).T\n",
        "          \n",
        "          # scale the data over each channel\n",
        "          SS = StandardScaler()\n",
        "          scaled_data = SS.fit_transform(filt_data)\n",
        "          scaled_data = pd.DataFrame(scaled_data, columns = raw_data.columns, index = down_y)\n",
        "          \n",
        "          # drop na\n",
        "          scaled_data = scaled_data.dropna()\n",
        "          \n",
        "          # window x\n",
        "          data_x = window_x(scaled_data, window_size, overlap)\n",
        "          data_y = window_y(scaled_data.index.values, window_size, overlap, target=None, \n",
        "                            baseline=6)\n",
        "          \n",
        "          # to stop it printing warnings like: object name is not a valid \n",
        "          # Python identifier: '00001113'\n",
        "          with warnings.catch_warnings():\n",
        "            warnings.filterwarnings(\"ignore\", category=tables.NaturalNameWarning)\n",
        "            # save the data\n",
        "            save_to_database(TUH_FILT_SAVE_PATH, 'Filt_Data', pat_ID, data_x, \n",
        "                            data_y[:,0], list(scaled_data.columns))\n",
        "          \n",
        "        # TODO: add in a dropna bit\n",
        "        if TUH_UDWT_OVERWRITE:\n",
        "          # window x\n",
        "          data_x = window_x(pd.DataFrame(down_x, columns = raw_data.columns), \n",
        "                            window_size, overlap)\n",
        "          data_y = window_y(down_y, window_size, overlap, target=None, \n",
        "                          baseline=6)\n",
        "\n",
        "          udwt_data = udwt_spectrogram(data_x, 'db4', 6, window_size)\n",
        "\n",
        "          # get the shape of this data\n",
        "          orig_shape = udwt_data.shape\n",
        "          # reshape to merge the levels and data\n",
        "          udwt_data_reshape = np.reshape(udwt_data, (-1, orig_shape[-1]))\n",
        "          # scale across channels\n",
        "          SS = StandardScaler()\n",
        "          udwt_data_scaled = SS.fit_transform(udwt_data_reshape)\n",
        "          # shape the data back\n",
        "          udwt_data_scaled = np.reshape(udwt_data_scaled, orig_shape)\n",
        "\n",
        "          #display(udwt_data_scaled.shape)\n",
        "\n",
        "          # to stop it printing warnings like: object name is not a valid \n",
        "          # Python identifier: '00001113'\n",
        "          with warnings.catch_warnings():\n",
        "            warnings.filterwarnings(\"ignore\", category=tables.NaturalNameWarning)\n",
        "            # save the data\n",
        "            save_to_database(TUH_UDWT_SAVE_PATH, 'UDWT_Data', \n",
        "                            pat_ID,\n",
        "                            #file_ID, # had to use file ID as appending to an existing array was not working \n",
        "                            udwt_data_scaled, data_y[:,0])\n",
        "\n",
        "        \n",
        "      if TUH_FEAT_OVERWRITE:\n",
        "        # TODO: in 00001795 relative_power = bandpass_2/bandpass_1 creates an inf\n",
        "        # due to dividing by 0. Fix this with a checks in the function, but for now,\n",
        "        # for a quick patch I'll just not use power_ratio.\n",
        "        feat = Seizure_Features(sf = freq,\n",
        "                                window_size=2,\n",
        "                                overlap=1,\n",
        "                                levels=6,\n",
        "                                bandpasses = [[1,4],[4,8],[8,12],\n",
        "                                              [12,30],[30,70]],\n",
        "                                feature_list=['power', #'power_ratio', \n",
        "                                              'mean', 'mean_abs', \n",
        "                                              'std', 'ratio', 'LSWT', 'fft_corr', \n",
        "                                              'fft_eigen', 'time_corr', 'time_eigen'],\n",
        "                                scale = True,\n",
        "                                baseline=6)\n",
        "        \n",
        "\n",
        "        \n",
        "        # just to ignore the runtime warnings about na's\n",
        "        with warnings.catch_warnings():\n",
        "          warnings.filterwarnings(\"ignore\", category=RuntimeWarning)\n",
        "          \n",
        "          x_feat, y_feat = feat.transform(raw_data.values, \n",
        "                                          raw_events.values,\n",
        "                                          channel_names_list = list(raw_data.columns))\n",
        "\n",
        "        \n",
        "        feat_df = pd.DataFrame(x_feat,\n",
        "                                index=y_feat[:,0], \n",
        "                                columns = feat.feature_names)\n",
        "\n",
        "        feat_df = feat_df.dropna()\n",
        "        \n",
        "        \n",
        "        # to stop it printing warnings like: object name is not a valid \n",
        "        # Python identifier: '00001113'\n",
        "        with warnings.catch_warnings():\n",
        "          warnings.filterwarnings(\"ignore\", category=tables.NaturalNameWarning)\n",
        "          feat_df.to_hdf(TUH_FEAT_SAVE_PATH, \n",
        "                        pat_ID,\n",
        "                        format='table', append=True)"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "TUH Username: ··········\n",
            "TUH Password: ··········\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:350: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
            "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "f838748e2abd43d68fc15071f12583d6",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Finding Channels', max=19.0, style=ProgressStyle(descript…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:375: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n",
            "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "9b40af8bcf4e486694c2a2c068e2b87e",
              "version_minor": 0,
              "version_major": 2
            },
            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, description='Creating Features', max=19.0, style=ProgressStyle(descrip…"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:9: FutureWarning: Series.strides is deprecated and will be removed in a future version\n",
            "  if __name__ == '__main__':\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:9: FutureWarning: Series.strides is deprecated and will be removed in a future version\n",
            "  if __name__ == '__main__':\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:9: FutureWarning: Series.strides is deprecated and will be removed in a future version\n",
            "  if __name__ == '__main__':\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:9: FutureWarning: Series.strides is deprecated and will be removed in a future version\n",
            "  if __name__ == '__main__':\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:9: FutureWarning: Series.strides is deprecated and will be removed in a future version\n",
            "  if __name__ == '__main__':\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:9: FutureWarning: Series.strides is deprecated and will be removed in a future version\n",
            "  if __name__ == '__main__':\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:9: FutureWarning: Series.strides is deprecated and will be removed in a future version\n",
            "  if __name__ == '__main__':\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:9: FutureWarning: Series.strides is deprecated and will be removed in a future version\n",
            "  if __name__ == '__main__':\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:9: FutureWarning: Series.strides is deprecated and will be removed in a future version\n",
            "  if __name__ == '__main__':\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:9: FutureWarning: Series.strides is deprecated and will be removed in a future version\n",
            "  if __name__ == '__main__':\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:9: FutureWarning: Series.strides is deprecated and will be removed in a future version\n",
            "  if __name__ == '__main__':\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:9: FutureWarning: Series.strides is deprecated and will be removed in a future version\n",
            "  if __name__ == '__main__':\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:9: FutureWarning: Series.strides is deprecated and will be removed in a future version\n",
            "  if __name__ == '__main__':\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:9: FutureWarning: Series.strides is deprecated and will be removed in a future version\n",
            "  if __name__ == '__main__':\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:9: FutureWarning: Series.strides is deprecated and will be removed in a future version\n",
            "  if __name__ == '__main__':\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:9: FutureWarning: Series.strides is deprecated and will be removed in a future version\n",
            "  if __name__ == '__main__':\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:9: FutureWarning: Series.strides is deprecated and will be removed in a future version\n",
            "  if __name__ == '__main__':\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:9: FutureWarning: Series.strides is deprecated and will be removed in a future version\n",
            "  if __name__ == '__main__':\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:9: FutureWarning: Series.strides is deprecated and will be removed in a future version\n",
            "  if __name__ == '__main__':\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:9: FutureWarning: Series.strides is deprecated and will be removed in a future version\n",
            "  if __name__ == '__main__':\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:9: FutureWarning: Series.strides is deprecated and will be removed in a future version\n",
            "  if __name__ == '__main__':\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:9: FutureWarning: Series.strides is deprecated and will be removed in a future version\n",
            "  if __name__ == '__main__':\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:9: FutureWarning: Series.strides is deprecated and will be removed in a future version\n",
            "  if __name__ == '__main__':\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:9: FutureWarning: Series.strides is deprecated and will be removed in a future version\n",
            "  if __name__ == '__main__':\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:9: FutureWarning: Series.strides is deprecated and will be removed in a future version\n",
            "  if __name__ == '__main__':\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:9: FutureWarning: Series.strides is deprecated and will be removed in a future version\n",
            "  if __name__ == '__main__':\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:9: FutureWarning: Series.strides is deprecated and will be removed in a future version\n",
            "  if __name__ == '__main__':\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:9: FutureWarning: Series.strides is deprecated and will be removed in a future version\n",
            "  if __name__ == '__main__':\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:9: FutureWarning: Series.strides is deprecated and will be removed in a future version\n",
            "  if __name__ == '__main__':\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:9: FutureWarning: Series.strides is deprecated and will be removed in a future version\n",
            "  if __name__ == '__main__':\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:9: FutureWarning: Series.strides is deprecated and will be removed in a future version\n",
            "  if __name__ == '__main__':\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:9: FutureWarning: Series.strides is deprecated and will be removed in a future version\n",
            "  if __name__ == '__main__':\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:9: FutureWarning: Series.strides is deprecated and will be removed in a future version\n",
            "  if __name__ == '__main__':\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:9: FutureWarning: Series.strides is deprecated and will be removed in a future version\n",
            "  if __name__ == '__main__':\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:9: FutureWarning: Series.strides is deprecated and will be removed in a future version\n",
            "  if __name__ == '__main__':\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:9: FutureWarning: Series.strides is deprecated and will be removed in a future version\n",
            "  if __name__ == '__main__':\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:9: FutureWarning: Series.strides is deprecated and will be removed in a future version\n",
            "  if __name__ == '__main__':\n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:9: FutureWarning: Series.strides is deprecated and will be removed in a future version\n",
            "  if __name__ == '__main__':\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}