Python Report.add_htmls_to_section方法代码示例

# 需要导入模块: from mne.report import Report [as 别名]
# 或者: from mne.report.Report import add_htmls_to_section [as 别名]
def test_add_htmls_to_section():
    """Test adding html str to mne report.
    """
    report = Report(info_fname=raw_fname, subject="sample", subjects_dir=subjects_dir)
    html = "<b>MNE-Python is AWESOME</b>"
    caption, section = "html", "html_section"
    report.add_htmls_to_section(html, caption, section)
    idx = report._sectionlabels.index("report_" + section)
    html_compare = report.html[idx]
    assert_true(html in html_compare)

# 需要导入模块: from mne.report import Report [as 别名]
# 或者: from mne.report.Report import add_htmls_to_section [as 别名]
def test_add_htmls_to_section():
    """Test adding html str to mne report."""
    report = Report(info_fname=raw_fname,
                    subject='sample', subjects_dir=subjects_dir)
    html = '<b>MNE-Python is AWESOME</b>'
    caption, section = 'html', 'html_section'
    report.add_htmls_to_section(html, caption, section)
    idx = report._sectionlabels.index('report_' + section)
    html_compare = report.html[idx]
    assert_true(html in html_compare)
    assert_true(repr(report))

# 需要导入模块: from mne.report import Report [as 别名]
# 或者: from mne.report.Report import add_htmls_to_section [as 别名]

#.........这里部分代码省略.........

        fig = ica.plot_scores(scores, exclude=ecg_inds, labels='ecg',
                              title='', show=show)

        report.add_figs_to_section(fig, 'scores ({})'.format(subject),
                                   section=comment + 'ECG',
                                   scale=img_scale)

        current_exclude = [e for e in ica.exclude]  # issue #2608 MNE
        fig = ica.plot_sources(raw, ecg_inds, exclude=ecg_inds,
                               title=title % ('components', 'ecg'), show=show)

        report.add_figs_to_section(fig, 'sources ({})'.format(subject),
                                   section=comment + 'ECG',
                                   scale=img_scale)
        ica.exclude = current_exclude

        fig = ica.plot_components(ecg_inds, ch_type=topo_ch_type,
                                  title='', colorbar=True, show=show)
        report.add_figs_to_section(fig, title % ('sources', 'ecg'),
                                   section=comment + 'ECG', scale=img_scale)
        ica.exclude = current_exclude

        ecg_inds = ecg_inds[:n_max_ecg]
        ica.exclude += ecg_inds
        fig = ica.plot_sources(ecg_evoked, exclude=ecg_inds, show=show)
        report.add_figs_to_section(fig, 'evoked sources ({})'.format(subject),
                                   section=comment + 'ECG',
                                   scale=img_scale)

        fig = ica.plot_overlay(ecg_evoked, exclude=ecg_inds, show=show)
        report.add_figs_to_section(fig,
                                   'rejection overlay ({})'.format(subject),
                                   section=comment + 'ECG',
                                   scale=img_scale)

    # detect EOG by correlation
    picks_eog = np.concatenate(
        [picks_, pick_types(raw.info, meg=False, eeg=False, ecg=False,
                            eog=True)])

    eog_epochs = create_eog_epochs(raw, tmin=eog_tmin, tmax=eog_tmax,
                                   picks=picks_eog, reject=reject_)
    artifact_stats['eog_n_events'] = len(eog_epochs.events)
    artifact_stats['eog_n_used'] = artifact_stats['eog_n_events']
    eog_ave = eog_epochs.average()
    report.add_figs_to_section(eog_ave.plot(), 'EOG-used', 'artifacts')
    _put_artifact_range(artifact_stats, eog_ave, kind='eog')

    eog_inds = None
    if len(eog_epochs.events) > 0:
        eog_inds, scores = ica.find_bads_eog(eog_epochs)

    if eog_inds is not None and len(eog_epochs.events) > 0:
        fig = ica.plot_scores(scores, exclude=eog_inds, labels='eog',
                              show=show, title='')
        report.add_figs_to_section(fig, 'scores ({})'.format(subject),
                                   section=comment + 'EOG',
                                   scale=img_scale)

        current_exclude = [e for e in ica.exclude]  # issue #2608 MNE
        fig = ica.plot_sources(raw, eog_inds, exclude=ecg_inds,
                               title=title % ('sources', 'eog'), show=show)
        report.add_figs_to_section(fig, 'sources', section=comment + 'EOG',
                                   scale=img_scale)
        ica.exclude = current_exclude

        fig = ica.plot_components(eog_inds, ch_type=topo_ch_type,
                                  title='', colorbar=True, show=show)
        report.add_figs_to_section(fig, title % ('components', 'eog'),
                                   section=comment + 'EOG', scale=img_scale)
        ica.exclude = current_exclude

        eog_inds = eog_inds[:n_max_eog]
        ica.exclude += eog_inds

        eog_evoked = eog_epochs.average()
        fig = ica.plot_sources(eog_evoked, exclude=eog_inds, show=show)
        report.add_figs_to_section(
            fig, 'evoked sources ({})'.format(subject),
            section=comment + 'EOG', scale=img_scale)

        fig = ica.plot_overlay(eog_evoked, exclude=eog_inds, show=show)
        report.add_figs_to_section(
            fig, 'rejection overlay({})'.format(subject),
            section=comment + 'EOG', scale=img_scale)
    else:
        del eog_epochs

    # check the amplitudes do not change
    if len(ica.exclude) > 0:
        fig = ica.plot_overlay(raw, show=show)  # EOG artifacts remain
        html = _render_components_table(ica)
        report.add_htmls_to_section(
            html, captions='excluded components',
            section='ICA rejection summary (%s)' % ch_type)
        report.add_figs_to_section(
            fig, 'rejection overlay({})'.format(subject),
            section=comment + 'RAW', scale=img_scale)
    return ica, dict(html=report, stats=artifact_stats)

# 需要导入模块: from mne.report import Report [as 别名]
# 或者: from mne.report.Report import add_htmls_to_section [as 别名]
def generateReport(raw, ica, subj_name, subj_path, basename,
                   ecg_evoked, ecg_scores, ecg_inds, ECG_ch_name,
                   eog_evoked, eog_scores, eog_inds, EoG_ch_name):
    from mne.report import Report
    import numpy as np
    import os
    import HTML
    report = Report()

    ICA_title = 'Sources related to %s artifacts (red)'
    is_show = False # visualization

    File_length = str(round(raw.times[-1], 0))
    # report.add_htmls_to_section(htmls=name_html, captions='File path', section='General')
    name_html =  '<h4 style="text-align:left;"> Path:  ' + subj_path + '/' + basename + '.fif' + '</h4>'
    ex_comps_table = [['', 'ICs to exclude'],['ECG', ecg_inds], ['EOG', eog_inds]]
    ex_comps_html = '<h4>' + HTML.table(ex_comps_table) + '</h4>'
    File_length_html = '<h4 style="text-align:left;">' + 'File length: ' + File_length + ' seconds' + '</h4>'
    report.add_htmls_to_section(htmls=name_html + File_length_html + ex_comps_html, captions='General info', section='General info')
    # --------------------- Generate report for ECG ---------------------------------------- #
    fig1 = ica.plot_scores(
        ecg_scores, exclude=ecg_inds, title=ICA_title % 'ecg', show=is_show)
    # Pick the five largest ecg_scores and plot them
    show_picks = np.abs(ecg_scores).argsort()[::-1][:5]

    # Plot estimated latent sources given the unmixing matrix.

    # topoplot of unmixing matrix columns
    fig2 = ica.plot_components(
        show_picks, title=ICA_title % 'ecg', colorbar=True, show=is_show)

    

    # plot ECG sources + selection
    fig3 = ica.plot_sources(ecg_evoked, exclude=ecg_inds, show=is_show)
    fig = [fig1, fig2, fig3]
    report.add_figs_to_section(fig, captions=['Scores of ICs related to ECG',
                                              'TopoMap of ICs (ECG)',
                                              'Time-locked ECG sources'], section='ICA - ECG')
    # ----------------------------------- end generate report for ECG ------------------------------- #


    # --------------------------------- Generate report for EoG --------------------------------------------- #

    # check how many EoG ch we have
    if set(EoG_ch_name.split(',')).issubset(set(raw.info['ch_names'])):
        fig4 = ica.plot_scores(
            eog_scores, exclude=eog_inds, title=ICA_title % 'eog', show=is_show)
        report.add_figs_to_section(fig4, captions=['Scores of ICs related to EOG'],
                                   section='ICA - EOG')
        rs = np.shape(eog_scores)
        if len(rs) > 1:
            rr = rs[0]
            show_picks = [np.abs(eog_scores[i][:]).argsort()[::-1][:5]
                          for i in range(rr)]
            for i in range(rr):
                fig5 = ica.plot_components(show_picks[i][:], title=ICA_title % 'eog',
                                           colorbar=True, show=is_show)  # ICA nel tempo
                fig = [fig5]
                report.add_figs_to_section(fig, captions=['Scores of ICs related to EOG'],
                                           section='ICA - EOG')
        else:
            show_picks = np.abs(eog_scores).argsort()[::-1][:5]
            fig5 = ica.plot_components(
                show_picks, title=ICA_title % 'eog', colorbar=True, show=is_show)
            fig = [fig5]
            report.add_figs_to_section(fig, captions=['TopoMap of ICs (EOG)', ],
                                       section='ICA - EOG')
        fig9 = ica.plot_sources(eog_evoked, exclude=eog_inds, show=is_show)  # plot EOG sources + selection
        # fig10 = ica.plot_overlay(eog_evoked, exclude=eog_inds, show=is_show)  # plot EOG cleaning

        # fig = [fig9, fig10]
        fig = [fig9]
        report.add_figs_to_section(fig, captions=['Time-locked EOG sources'], section = 'ICA - EOG')
   # -------------------- end generate report for EoG -------------------------------------------------------- #
    # import ipdb; ipdb.set_trace()
    IC_nums = range(ica.n_components_)
    fig = ica.plot_components(picks=IC_nums, show=False)
    report.add_figs_to_section(fig, captions=['All IC topographies'], section='ICA - muscles')

    psds = []
    captions_psd = []
    ica_src = ica.get_sources(raw)
    for iIC in IC_nums:
        fig = ica_src.plot_psd(tmax=None, picks=[iIC], fmax=140, show=False)
        fig.set_figheight(3)
        fig.set_figwidth(5)
        psds.append(fig)
        captions_psd.append('IC #' + str(iIC))
    # report.add_slider_to_section(figs=psds, captions=captions_psd, title='', section='ICA - muscles')
    report.add_figs_to_section(figs=psds, captions=captions_psd, section='ICA - muscles')
    report_filename = os.path.join(subj_path, basename + "-report.html")
    print '******* ' + report_filename
    report.save(report_filename, open_browser=False, overwrite=True)