{

 "cells": [

  {

   "cell_type": "code",

   "execution_count": null,

   "metadata": {},

   "outputs": [],

   "source": [

    "from clinical_ts.timeseries_utils import *\n",

    "from clinical_ts.ecg_utils import *"

   ]

  },

  {

   "cell_type": "markdown",

   "metadata": {},

   "source": [

    "# prepare data"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": null,

   "metadata": {},

   "outputs": [],

   "source": [

    "target_fs=100\n",

    "data_root=Path(\"./ecg_data/\")\n",

    "target_root=Path(\"./ecg_data_processed\")"

   ]

  },

  {

   "cell_type": "markdown",

   "metadata": {},

   "source": [

    "## Ribeiro 2020"

   ]

  },

  {

   "cell_type": "markdown",

   "metadata": {},

   "source": [

    "Download the test set from Ribeiro et al 2020 (https://www.nature.com/articles/s41467-020-15432-4) https://doi.org/10.5281/zenodo.3625006 and place it in data_folder_ribeiro_test"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": null,

   "metadata": {},

   "outputs": [],

   "source": [

    "data_folder_ribeiro_test = data_root/\"ribeiro2020_test\"\n",

    "target_folder_ribeiro_test = target_root/(\"ribeiro_fs\"+str(target_fs))"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": null,

   "metadata": {},

   "outputs": [],

   "source": [

    "\n",

    "df_ribeiro_test, lbl_itos_ribeiro_test,  mean_ribeiro_test, std_ribeiro_test = prepare_data_ribeiro_test(data_folder_ribeiro_test, target_fs=target_fs, channels=12, channel_stoi=channel_stoi_default, target_folder=target_folder_ribeiro_test)"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": null,

   "metadata": {},

   "outputs": [],

   "source": [

    "#reformat everything as memmap for efficiency\n",

    "reformat_as_memmap(df_ribeiro_test, target_folder_ribeiro_test/(\"memmap.npy\"),data_folder=target_folder_ribeiro_test,delete_npys=True)"

   ]

  },

  {

   "cell_type": "markdown",

   "metadata": {},

   "source": [

    "## Zheng 2020"

   ]

  },

  {

   "cell_type": "markdown",

   "metadata": {},

   "source": [

    "Download the dataset from Zheng et al 2020 (https://www.nature.com/articles/s41597-020-0386-x) https://figshare.com/collections/ChapmanECG/4560497/2 and place it in data_folder_zheng"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": null,

   "metadata": {},

   "outputs": [],

   "source": [

    "data_folder_zheng = data_root/\"zheng2020/\"\n",

    "target_folder_zheng = target_root/(\"zheng_fs\"+str(target_fs))"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": null,

   "metadata": {},

   "outputs": [],

   "source": [

    "\n",

    "df_zheng, lbl_itos_zheng,  mean_zheng, std_zheng = prepare_data_zheng(data_folder_zheng, denoised=False, target_fs=target_fs, channels=12, channel_stoi=channel_stoi_default, target_folder=target_folder_zheng)"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": null,

   "metadata": {},

   "outputs": [],

   "source": [

    "#reformat everything as memmap for efficiency\n",

    "reformat_as_memmap(df_zheng, target_folder_zheng/(\"memmap.npy\"),data_folder=target_folder_zheng,delete_npys=True)"

   ]

  },

  {

   "cell_type": "markdown",

   "metadata": {},

   "source": [

    "## CinC2020 Challenge"

   ]

  },

  {

   "cell_type": "markdown",

   "metadata": {},

   "source": [

    "Download the training set of the CinC Challenge 2020 https://physionetchallenges.org/2020/ and place it in data_folder_cinc"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": null,

   "metadata": {},

   "outputs": [],

   "source": [

    "data_folder_cinc = data_root/\"cinc2020/\"\n",

    "target_folder_cinc = target_root/(\"cinc_fs\"+str(target_fs))"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": null,

   "metadata": {},

   "outputs": [],

   "source": [

    "\n",

    "df_cinc, lbl_itos_cinc,  mean_cinc, std_cinc = prepare_data_cinc(data_folder_cinc, target_fs=target_fs, channels=12, channel_stoi=channel_stoi_default, target_folder=target_folder_cinc)"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": null,

   "metadata": {},

   "outputs": [],

   "source": [

    "#reformat everything as memmap for efficiency\n",

    "reformat_as_memmap(df_cinc, target_folder_cinc/(\"memmap.npy\"),data_folder=target_folder_cinc,delete_npys=True)\n"

   ]

  },

  {

   "cell_type": "markdown",

   "metadata": {},

   "source": [

    "## PTB-XL"

   ]

  },

  {

   "cell_type": "markdown",

   "metadata": {},

   "source": [

    "Download the PTB-XL dataset (https://www.nature.com/articles/s41597-020-0495-6) https://physionet.org/content/ptb-xl/1.0.1/ and place it in data_folder_ptb_xl"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": null,

   "metadata": {},

   "outputs": [],

   "source": [

    "data_folder_ptb_xl = data_root/\"ptb_xl/\"\n",

    "target_folder_ptb_xl = target_root/(\"ptb_xl_fs\"+str(target_fs))"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": null,

   "metadata": {},

   "outputs": [],

   "source": [

    "df_ptb_xl, lbl_itos_ptb_xl,  mean_ptb_xl, std_ptb_xl = prepare_data_ptb_xl(data_folder_ptb_xl, min_cnt=0, target_fs=target_fs, channels=input_channels, channel_stoi=channel_stoi_default, target_folder=target_folder_ptb_xl)"

   ]

  },

  {

   "cell_type": "code",

   "execution_count": null,

   "metadata": {},

   "outputs": [],

   "source": [

    "#reformat everything as memmap for efficiency\n",

    "reformat_as_memmap(df_ptb_xl, target_folder_ptb_xl/(\"memmap.npy\"),data_folder=target_folder_ptb_xl,delete_npys=True)"

   ]

  }

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