{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "\n\n# Generate simulated evoked data\n\nUse :func:`~mne.simulation.simulate_sparse_stc` to simulate evoked data.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "# Author: Daniel Strohmeier <daniel.strohmeier@tu-ilmenau.de>\n#         Alexandre Gramfort <alexandre.gramfort@inria.fr>\n#\n# License: BSD-3-Clause\n# Copyright the MNE-Python contributors."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\nimport numpy as np\n\nimport mne\nfrom mne.datasets import sample\nfrom mne.simulation import simulate_evoked, simulate_sparse_stc\nfrom mne.time_frequency import fit_iir_model_raw\nfrom mne.viz import plot_sparse_source_estimates\n\nprint(__doc__)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Load real data as templates\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "data_path = sample.data_path()\nmeg_path = data_path / \"MEG\" / \"sample\"\nraw = mne.io.read_raw_fif(meg_path / \"sample_audvis_raw.fif\")\nproj = mne.read_proj(meg_path / \"sample_audvis_ecg-proj.fif\")\nraw.add_proj(proj)\nraw.info[\"bads\"] = [\"MEG 2443\", \"EEG 053\"]  # mark bad channels\n\nfwd_fname = meg_path / \"sample_audvis-meg-eeg-oct-6-fwd.fif\"\nave_fname = meg_path / \"sample_audvis-no-filter-ave.fif\"\ncov_fname = meg_path / \"sample_audvis-cov.fif\"\n\nfwd = mne.read_forward_solution(fwd_fname)\nfwd = mne.pick_types_forward(fwd, meg=True, eeg=True, exclude=raw.info[\"bads\"])\ncov = mne.read_cov(cov_fname)\ninfo = mne.io.read_info(ave_fname)\n\nlabel_names = [\"Aud-lh\", \"Aud-rh\"]\nlabels = [mne.read_label(meg_path / \"labels\" / f\"{ln}.label\") for ln in label_names]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Generate source time courses from 2 dipoles and the corresponding evoked data\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "times = np.arange(300, dtype=np.float64) / raw.info[\"sfreq\"] - 0.1\nrng = np.random.RandomState(42)\n\n\ndef data_fun(times):\n    \"\"\"Generate random source time courses.\"\"\"\n    return (\n        50e-9\n        * np.sin(30.0 * times)\n        * np.exp(-((times - 0.15 + 0.05 * rng.randn(1)) ** 2) / 0.01)\n    )\n\n\nstc = simulate_sparse_stc(\n    fwd[\"src\"],\n    n_dipoles=2,\n    times=times,\n    random_state=42,\n    labels=labels,\n    data_fun=data_fun,\n)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Generate noisy evoked data\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "picks = mne.pick_types(raw.info, meg=True, exclude=\"bads\")\niir_filter = fit_iir_model_raw(raw, order=5, picks=picks, tmin=60, tmax=180)[1]\nnave = 100  # simulate average of 100 epochs\nevoked = simulate_evoked(\n    fwd, stc, info, cov, nave=nave, use_cps=True, iir_filter=iir_filter\n)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Plot\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "plot_sparse_source_estimates(\n    fwd[\"src\"], stc, bgcolor=(1, 1, 1), opacity=0.5, high_resolution=True\n)\n\nplt.figure()\nplt.psd(evoked.data[0])\n\nevoked.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
}