"""
.. _ex-sim-evoked:

==============================
Generate simulated evoked data
==============================

Use :func:`~mne.simulation.simulate_sparse_stc` to simulate evoked data.
"""
# Author: Daniel Strohmeier <daniel.strohmeier@tu-ilmenau.de>
#         Alexandre Gramfort <alexandre.gramfort@inria.fr>
#
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.

# %%

import matplotlib.pyplot as plt
import numpy as np

import mne
from mne.datasets import sample
from mne.simulation import simulate_evoked, simulate_sparse_stc
from mne.time_frequency import fit_iir_model_raw
from mne.viz import plot_sparse_source_estimates

print(__doc__)

# %%
# Load real data as templates
data_path = sample.data_path()
meg_path = data_path / "MEG" / "sample"
raw = mne.io.read_raw_fif(meg_path / "sample_audvis_raw.fif")
proj = mne.read_proj(meg_path / "sample_audvis_ecg-proj.fif")
raw.add_proj(proj)
raw.info["bads"] = ["MEG 2443", "EEG 053"]  # mark bad channels

fwd_fname = meg_path / "sample_audvis-meg-eeg-oct-6-fwd.fif"
ave_fname = meg_path / "sample_audvis-no-filter-ave.fif"
cov_fname = meg_path / "sample_audvis-cov.fif"

fwd = mne.read_forward_solution(fwd_fname)
fwd = mne.pick_types_forward(fwd, meg=True, eeg=True, exclude=raw.info["bads"])
cov = mne.read_cov(cov_fname)
info = mne.io.read_info(ave_fname)

label_names = ["Aud-lh", "Aud-rh"]
labels = [mne.read_label(meg_path / "labels" / f"{ln}.label") for ln in label_names]

# %%
# Generate source time courses from 2 dipoles and the corresponding evoked data

times = np.arange(300, dtype=np.float64) / raw.info["sfreq"] - 0.1
rng = np.random.RandomState(42)


def data_fun(times):
    """Generate random source time courses."""
    return (
        50e-9
        * np.sin(30.0 * times)
        * np.exp(-((times - 0.15 + 0.05 * rng.randn(1)) ** 2) / 0.01)
    )


stc = simulate_sparse_stc(
    fwd["src"],
    n_dipoles=2,
    times=times,
    random_state=42,
    labels=labels,
    data_fun=data_fun,
)

# %%
# Generate noisy evoked data
picks = mne.pick_types(raw.info, meg=True, exclude="bads")
iir_filter = fit_iir_model_raw(raw, order=5, picks=picks, tmin=60, tmax=180)[1]
nave = 100  # simulate average of 100 epochs
evoked = simulate_evoked(
    fwd, stc, info, cov, nave=nave, use_cps=True, iir_filter=iir_filter
)

# %%
# Plot
plot_sparse_source_estimates(
    fwd["src"], stc, bgcolor=(1, 1, 1), opacity=0.5, high_resolution=True
)

plt.figure()
plt.psd(evoked.data[0])

evoked.plot(time_unit="s")