Baselining vs. Highpass filtering¶

import os
import os.path as op
import sys

import matplotlib.pyplot as plt

import mne
from mne import Epochs

sys.path.append(op.join('..', '..', 'processing'))
from library.config import (study_path, meg_dir, tmin, tmax, l_freq,
                            set_matplotlib_defaults,
                            annot_kwargs)  # noqa: E402

subject = "sub003"
event_ids = [5, 6, 7]  # Famous faces
filter_params = dict(fir_window='hamming', phase='zero',
                     h_trans_bandwidth='auto', filter_length='auto',
                     fir_design='firwin')

raw_fname = op.join(study_path, 'ds117', subject, 'MEG', 'run_01_sss.fif')
raw = mne.io.read_raw_fif(raw_fname, preload=True, verbose='error')

events = mne.find_events(raw, stim_channel='STI101', consecutive='increasing',
                         mask=4352, mask_type='not_and', min_duration=0.003,
                         verbose=True)

ica = mne.preprocessing.read_ica(op.join(meg_dir, subject,
                                         'run_concat_highpass-%sHz-ica.fif'
                                         % (l_freq,)))

set_matplotlib_defaults()

ylim = dict(mag=(-400, 400))
times = [0, 0.12, 0.4, tmax-0.1]

if not op.isdir('figures'):
    os.mkdir('figures')


def adjust_fig(fig, label, title, include_cbar=False):
    fig.set_size_inches(2.0, 2.0, forward=True)
    plt.subplots_adjust(left=.18, right=.99, bottom=.18,
                        top=1.2)
    fig.delaxes(fig.axes[1])
    for ax in fig.axes[1:-1]:
        ax.set(xticks=[0])
        ax.set(xticklabels=[ax.get_title().split()[0]], title='')
        ax.tick_params(length=0)
    fig.axes[-1].set_position([0.1, 0.73, 0.025, 0.2])
    if not include_cbar:
        fig.delaxes(fig.axes[-1])
    fig.axes[0].get_yaxis().set_label_coords(-0.15, 0.5)
    for li in range(len(times)):
        fig.lines[li] = mne.viz.evoked._connection_line(
            times[li] * 1000, fig, fig.axes[0], fig.axes[li + 1])
    fig.axes[0].annotate(label, (-0.135, 1.35), **annot_kwargs)
    fig.axes[2].set(title=title)

plt.rcParams['axes.titlesize'] = 8
plt.rcParams['lines.linewidth'] = 0.5
kwargs = dict(
    times=times, title=None, ts_args=dict(ylim=ylim, spatial_colors=True),
    topomap_args=dict(vmin=-400, vmax=400, sensors=False, outlines='head'))
raw.filter(None, 40, **filter_params)
evoked = Epochs(raw, events, event_id=event_ids,
                tmin=tmin, tmax=tmax, baseline=(None, 0)).average()
ica.apply(evoked)
evoked.pick_types(meg='mag')
fig = evoked.plot_joint(**kwargs)
adjust_fig(fig, 'A', 'No highpass', include_cbar=True)
fig.savefig(op.join('..', 'figures', 'FanningA.pdf'), bbox_to_inches='tight')

raw.filter(1, None, l_trans_bandwidth=0.5, **filter_params)
evoked = Epochs(raw, events, event_id=event_ids,
                tmin=tmin, tmax=tmax, baseline=None).average()
ica.apply(evoked)
evoked.pick_types(meg='mag')
fig = evoked.plot_joint(**kwargs)
adjust_fig(fig, 'B', '1 Hz highpass')
fig.savefig(op.join('..', 'figures', 'FanningB.pdf'), bbox_to_inches='tight')

raw_fname = op.join(meg_dir, subject, 'run_01_filt_tsss_1_raw.fif')
raw = mne.io.read_raw_fif(raw_fname, preload=True)
evoked = Epochs(raw, events, event_id=event_ids,
                tmin=tmin, tmax=tmax, baseline=(None, 0)).average()
ica.apply(evoked)
evoked.pick_types(meg='mag')
fig = evoked.plot_joint(**kwargs)
adjust_fig(fig, 'C', 'tSSS')
fig.savefig(op.join('..', 'figures', 'FanningC.pdf'), bbox_to_inches='tight')