{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "\n\n# Getting averaging info from .fif files\n\nParse averaging information defined in Elekta Vectorview/TRIUX DACQ (data acquisition).\nExtract and average epochs accordingly. Modify some averaging parameters and get epochs.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "# Author: Jussi Nurminen (jnu@iki.fi)\n#\n# License: BSD-3-Clause\n# Copyright the MNE-Python contributors."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "import os\n\nimport mne\nfrom mne.datasets import multimodal\n\nfname_raw = os.path.join(multimodal.data_path(), \"multimodal_raw.fif\")\n\n\nprint(__doc__)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Read raw file\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "raw = mne.io.read_raw_fif(fname_raw)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Check DACQ defined averaging categories and other info\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "print(raw.acqparser)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Extract epochs corresponding to a category\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "cond = raw.acqparser.get_condition(raw, \"Auditory right\")\nepochs = mne.Epochs(raw, **cond)\nepochs.average().plot_topo(background_color=\"w\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Get epochs from all conditions, average\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "evokeds = []\nfor cat in raw.acqparser.categories:\n    cond = raw.acqparser.get_condition(raw, cat)\n    # copy (supported) rejection parameters from DACQ settings\n    epochs = mne.Epochs(\n        raw, reject=raw.acqparser.reject, flat=raw.acqparser.flat, **cond\n    )\n    evoked = epochs.average()\n    evoked.comment = cat[\"comment\"]\n    evokeds.append(evoked)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Make a new averaging category\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "newcat = dict()\nnewcat[\"comment\"] = \"Visual lower left, longer epochs\"\nnewcat[\"event\"] = 3  # reference event\nnewcat[\"start\"] = -0.2  # epoch start rel. to ref. event (in seconds)\nnewcat[\"end\"] = 0.7  # epoch end\nnewcat[\"reqevent\"] = 0  # additional required event; 0 if none\nnewcat[\"reqwithin\"] = 0.5  # ...required within .5 s (before or after)\nnewcat[\"reqwhen\"] = 2  # ...required before (1) or after (2) ref. event\nnewcat[\"index\"] = 9  # can be set freely\n\ncond = raw.acqparser.get_condition(raw, newcat)\nepochs = mne.Epochs(raw, reject=raw.acqparser.reject, flat=raw.acqparser.flat, **cond)\nepochs.average().plot(time_unit=\"s\")"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "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": "3.12.2"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}