99-make_reports.py

"""
================
99. Make reports
================

Builds an HTML report for each subject containing all the relevant analysis
plots.
"""

from mayavi import mlab
import os.path as op

import mne
from mne.parallel import parallel_func

import config


def run_report(subject):
    print("processing %s" % subject)

    meg_subject_dir = op.join(config.meg_dir, subject)
    ave_fname = op.join(meg_subject_dir, "%s-ave.fif" % subject)

    rep = mne.Report(info_fname=ave_fname, subject=subject,
                     subjects_dir=config.subjects_dir)
    rep.parse_folder(meg_subject_dir)

    evokeds = mne.read_evokeds(ave_fname)

    figs = list()
    captions = list()

    for evoked in evokeds:
        fig = evoked.plot(spatial_colors=True, show=False, gfp=True)
        figs.append(fig)
        captions.append(evoked.condition)

    fname_trans = op.join(config.meg_subject_dir, '%s-trans.fif' % subject)
    mne.viz.plot_trans(evoked.info, fname_trans, subject=subject,
                       subjects_dir=config.subjects_dir, meg_sensors=True,
                       eeg_sensors=True)
    fig = mlab.gcf()
    figs.append(fig)
    captions.append('Coregistration')

    # rep.add_figs_to_section(figs, captions)
    # for evoked in evokeds:
    #     fname = op.join(meg_path, 'mne_dSPM_inverse-%s'
    #                     % evoked.condition)
    #     stc = mne.read_source_estimate(fname, subject)
    #     brain = stc.plot(views=['ven'], hemi='both')

    #     brain.set_data_time_index(112)

    #     fig = mlab.gcf()
    #     rep._add_figs_to_section(fig, cond)

    rep.save(fname=op.join(config.meg_subject_dir, 'report_%s.html' % subject),
             open_browser=False, overwrite=True)

parallel, run_func, _ = parallel_func(run_report, n_jobs=config.N_JOBS)
parallel(run_func(subject) for subject in config.subjects)


# # Group report
# faces_fname = op.join(meg_dir, 'eeg_faces_highpass-%sHz-ave.fif' % l_freq)
# rep = Report(info_fname=faces_fname, subject='fsaverage',
#              subjects_dir=subjects_dir)
# faces = mne.read_evokeds(faces_fname)[0]
# rep.add_figs_to_section(faces.plot(spatial_colors=True, gfp=True, show=False),
#                         'Average faces')

# scrambled = mne.read_evokeds(op.join(meg_dir, 'eeg_scrambled-ave.fif'))[0]
# rep.add_figs_to_section(scrambled.plot(spatial_colors=True, gfp=True,
#                                        show=False), 'Average scrambled')

# fname = op.join(meg_dir, 'contrast-average_highpass-%sHz' % l_freq)
# stc = mne.read_source_estimate(fname, subject='fsaverage')
# brain = stc.plot(views=['ven'], hemi='both', subject='fsaverage',
#                  subjects_dir=subjects_dir)
# brain.set_data_time_index(165)

# fig = mlab.gcf()
# rep.add_figs_to_section(fig, 'Average faces - scrambled')

# rep.save(fname=op.join(meg_dir, 'report_average.html'),
#          open_browser=False, overwrite=True)