{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cleaning"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introductory notes:\n",
    "This notebook presents cleaning functionality:\n",
    "* Resampling\n",
    "* Bandpass and notch filtering\n",
    "* Annotating bad channels \n",
    "* Annotating bad data spans\n",
    "* Interpolating bad channels\n",
    "\n",
    "Recommended reading:\n",
    "1. [MNE: The Raw data structure](https://mne.tools/stable/auto_tutorials/raw/10_raw_overview.html)\n",
    "2. [Learning eeg: artifacts](https://www.learningeeg.com/artifacts)\n",
    "3. [MNE: Overview of artifact detection](https://mne.tools/stable/auto_tutorials/preprocessing/10_preprocessing_overview.html)\n",
    "4. [MNE: Filtering and resampling data](https://mne.tools/stable/auto_tutorials/preprocessing/30_filtering_resampling.html) \n",
    "5. [MNE: Handling bad channels](https://mne.tools/stable/auto_tutorials/preprocessing/15_handling_bad_channels.html)\n",
    "6. [MNE: Annotating continuous data](https://mne.tools/stable/auto_tutorials/raw/30_annotate_raw.html)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import data"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Import module"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sleepeegpy.pipeline import CleaningPipe\n",
    "import os"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Initialize CleaningPipe object"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By default, all the input files are assumed to be saved in <b>input_files</b>, which will be created (if not already exists) in the notebook path. Change the following strings to use another path.\n",
    "Changing the output directory is also optional."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from os import makedirs\n",
    "\n",
    "output_dir  = \"output_folder\" # Output path and name can be changed here\n",
    "input_dir = \"input_files\" # input files dir can be changed here\n",
    "makedirs(input_dir, exist_ok=True)\n",
    "makedirs(output_dir, exist_ok=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Add required files\n",
    "* Put all your files in the input folder.\n",
    "* Modify your eeg file name below. The file can be any format supported by the mne.read_raw() function.\n",
    "* For more information about the supported formats, see [mne documentation](https://mne.tools/stable/generated/mne.io.Raw.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "eeg_file_name= \"resampled_raw.fif\" # add your eeg_path here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "path_to_eeg = os.path.join(input_dir,eeg_file_name)\n",
    "pipe = CleaningPipe(\n",
    "    path_to_eeg=path_to_eeg,\n",
    "    output_dir=output_dir,\n",
    ")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Resample"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Resampling might be a long process (1+ hour), be patient. If you have enough RAM to load the signal, you may want to run `pipe.mne_raw.load_data()` before resampling. This will considerably speed up the process."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pipe.resample(\n",
    "    sfreq=250,  # Desired new sampling frequency\n",
    "    n_jobs=-1,  # The number of jobs to run in parallel. If -1, it is set to the number of CPU cores.\n",
    "    verbose=False\n",
    ")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Filter"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Band-pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pipe.filter(\n",
    "    l_freq=0.3, # Lower pass-band edge in Hz.\n",
    "    h_freq=None, # Upper pass-band edge in Hz.\n",
    "    n_jobs=-1\n",
    ")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Notch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pipe.notch(\n",
    "    freqs=\"50s\", # Remove 50 Hz and its harmonics.\n",
    "    n_jobs=-1\n",
    ")  "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Select bad channels & annotate bad epochs"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create average reference projection. You can apply and remove the projection from inside the plot. Does not have an effect on the raw signal itself if `projection=True`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pipe.set_eeg_reference(ref_channels=\"average\", projection=True)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Select bad channels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pipe.plot(\n",
    "    save_bad_channels=True,  # Whether to save selected bad channels in a file\n",
    "    save_annotations=False,  # Whether to save annotations in a file.\n",
    "    # Whether to overwrite already saved bad_channels.txt or annotations.txt,\n",
    "    # if set to False and there is already bad_channels.txt, new bad channels will be added to it.\n",
    "    overwrite=False,\n",
    ")\n",
    "path = None"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another option is to use auto-detection of bad_channels (based on pyprep library).\n",
    "This method generates a bad channel text file and returns the path.\n",
    "\n",
    "To use it, uncomment the relevant line and run"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "### uncomment to find bad channels automatically ###\n",
    "# path = pipe.auto_detect_bad_channels() "
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you want to continue with previously saved bad channels, use `pipe.read_bad_channels()`. The function will import the channels from the *bad_channels.txt* file.\n",
    "Alternatively, if you already have another bad channels file, add your path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pipe.read_bad_channels(\n",
    "    # Path to the txt file with bad channel name per row.\n",
    "    # If None, will try to import '{output_dir}/CleaningPipe/bad_channels.txt'\n",
    "    path=path\n",
    ")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Interpolate bad channels"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Interpolate bad channels using [spherical spline interpolation](https://mne.tools/stable/overview/implementation.html#bad-channel-repair-via-interpolation). Can be run from each analysis pipe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pipe.interpolate_bads(\n",
    "    reset_bads=True  # Whether to set interpolated channels back as normal.\n",
    ")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Select bad epochs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pipe.plot(\n",
    "    save_bad_channels=False,  # Whether to save selected bad channels in a file\n",
    "    save_annotations=True,  # Whether to save annotations in a file.\n",
    "    # Whether to overwrite already saved bad_channels.txt or annotations.txt,\n",
    "    # if set to False and there is already bad_channels.txt, new bad channels will be added.\n",
    "    overwrite=False,\n",
    ")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you want to continue with previously saved annotations, use `pipe.read_annotations()`. The function will import the annotations from the *annotations.txt* file.\n",
    "Alternatively, if you already another annotations.txt file, you can add your path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pipe.read_annotations(\n",
    "    # Path to txt file with mne-style annotations.\n",
    "    # If None, will try to import '{output_dir}/annotations.txt'\n",
    "    path=None\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another option is to use an auto set of annotations or use an existing file.\n",
    "This method detects and sets annotations.\n",
    "\n",
    "To use it, uncomment the relevant line and run"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pipe.auto_set_annotations()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pipe.plot()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Save cleaned and annotated signal to the file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pipe.save_raw(\"cleaned_raw.fif\", overwrite=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Additional functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pipe.sf, pipe.bad_data_percent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pipe.save_bad_channels(overwrite=True)\n",
    "pipe.save_annotations(overwrite=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x640 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_ = pipe.plot_sensors()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.8"
  },
  "vscode": {
   "interpreter": {
    "hash": "e7e6a3ad0af7de53e72789e0b82b3fd5c64743c0f9fcf843fd4113b6e74b9b71"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}