{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "\n\n# Generate simulated raw data\n\nThis example generates raw data by repeating a desired source activation\nmultiple times.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "# Authors: Yousra Bekhti <yousra.bekhti@gmail.com>\n#          Mark Wronkiewicz <wronk.mark@gmail.com>\n#          Eric Larson <larson.eric.d@gmail.com>\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 import Epochs, compute_covariance, find_events, make_ad_hoc_cov\nfrom mne.datasets import sample\nfrom mne.simulation import (\n    add_ecg,\n    add_eog,\n    add_noise,\n    simulate_raw,\n    simulate_sparse_stc,\n)\n\nprint(__doc__)\n\ndata_path = sample.data_path()\nmeg_path = data_path / \"MEG\" / \"sample\"\nraw_fname = meg_path / \"sample_audvis_raw.fif\"\nfwd_fname = meg_path / \"sample_audvis-meg-eeg-oct-6-fwd.fif\"\n\n# Load real data as the template\nraw = mne.io.read_raw_fif(raw_fname)\nraw.set_eeg_reference(projection=True)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Generate dipole time series\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "n_dipoles = 4  # number of dipoles to create\nepoch_duration = 2.0  # duration of each epoch/event\nn = 0  # harmonic number\nrng = np.random.RandomState(0)  # random state (make reproducible)\n\n\ndef data_fun(times):\n    \"\"\"Generate time-staggered sinusoids at harmonics of 10Hz.\"\"\"\n    global n\n    n_samp = len(times)\n    window = np.zeros(n_samp)\n    start, stop = (\n        int(ii * float(n_samp) / (2 * n_dipoles)) for ii in (2 * n, 2 * n + 1)\n    )\n    window[start:stop] = 1.0\n    n += 1\n    data = 25e-9 * np.sin(2.0 * np.pi * 10.0 * n * times)\n    data *= window\n    return data\n\n\ntimes = raw.times[: int(raw.info[\"sfreq\"] * epoch_duration)]\nfwd = mne.read_forward_solution(fwd_fname)\nsrc = fwd[\"src\"]\nstc = simulate_sparse_stc(\n    src, n_dipoles=n_dipoles, times=times, data_fun=data_fun, random_state=rng\n)\n# look at our source data\nfig, ax = plt.subplots(1)\nax.plot(times, 1e9 * stc.data.T)\nax.set(ylabel=\"Amplitude (nAm)\", xlabel=\"Time (s)\")\nmne.viz.utils.plt_show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Simulate raw data\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "raw_sim = simulate_raw(raw.info, [stc] * 10, forward=fwd, verbose=True)\ncov = make_ad_hoc_cov(raw_sim.info)\nadd_noise(raw_sim, cov, iir_filter=[0.2, -0.2, 0.04], random_state=rng)\nadd_ecg(raw_sim, random_state=rng)\nadd_eog(raw_sim, random_state=rng)\nraw_sim.plot()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Plot evoked data\n\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": false
      },
      "outputs": [],
      "source": [
        "events = find_events(raw_sim)  # only 1 pos, so event number == 1\nepochs = Epochs(raw_sim, events, 1, tmin=-0.2, tmax=epoch_duration)\ncov = compute_covariance(\n    epochs, tmax=0.0, method=\"empirical\", verbose=\"error\"\n)  # quick calc\nevoked = epochs.average()\nevoked.plot_white(cov, 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
}