Python Report.add_slider_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_slider_to_section [as 别名]
def test_add_slider_to_section():
    """Test adding a slider with a series of images to mne report."""
    tempdir = _TempDir()
    from matplotlib import pyplot as plt
    report = Report(info_fname=raw_fname,
                    subject='sample', subjects_dir=subjects_dir)
    section = 'slider_section'
    figs = list()
    figs.append(plt.figure())
    plt.plot([1, 2, 3])
    plt.close('all')
    figs.append(plt.figure())
    plt.plot([3, 2, 1])
    plt.close('all')
    report.add_slider_to_section(figs, section=section)
    report.save(op.join(tempdir, 'report.html'), open_browser=False)

    assert_raises(NotImplementedError, report.add_slider_to_section,
                  [figs, figs])
    assert_raises(ValueError, report.add_slider_to_section, figs, ['wug'])
    assert_raises(TypeError, report.add_slider_to_section, figs, 'wug')
    # need at least 2
    assert_raises(ValueError, report.add_slider_to_section, figs[:1], 'wug')

    # Smoke test that SVG w/unicode can be added
    report = Report()
    fig, ax = plt.subplots()
    ax.set_xlabel(u'μ')
    report.add_slider_to_section([fig] * 2, image_format='svg')

# 需要导入模块: from mne.report import Report [as 别名]
# 或者: from mne.report.Report import add_slider_to_section [as 别名]
def test_remove():
    """Test removing figures from a report."""
    r = Report()
    fig1, fig2 = _get_example_figures()
    r.add_figs_to_section(fig1, 'figure1', 'mysection')
    r.add_slider_to_section([fig1, fig2], title='figure1',
                            section='othersection')
    r.add_figs_to_section(fig2, 'figure1', 'mysection')
    r.add_figs_to_section(fig2, 'figure2', 'mysection')

    # Test removal by caption
    r2 = copy.deepcopy(r)
    removed_index = r2.remove(caption='figure1')
    assert removed_index == 2
    assert len(r2.html) == 3
    assert r2.html[0] == r.html[0]
    assert r2.html[1] == r.html[1]
    assert r2.html[2] == r.html[3]

    # Test restricting to section
    r2 = copy.deepcopy(r)
    removed_index = r2.remove(caption='figure1', section='othersection')
    assert removed_index == 1
    assert len(r2.html) == 3
    assert r2.html[0] == r.html[0]
    assert r2.html[1] == r.html[2]
    assert r2.html[2] == r.html[3]

    # Test removal of empty sections
    r2 = copy.deepcopy(r)
    r2.remove(caption='figure1', section='othersection')
    assert r2.sections == ['mysection']
    assert r2._sectionvars == {'mysection': 'report_mysection'}

# 需要导入模块: from mne.report import Report [as 别名]
# 或者: from mne.report.Report import add_slider_to_section [as 别名]
def test_add_slider_to_section():
    """Test adding a slider with a series of images to mne report."""
    tempdir = _TempDir()
    from matplotlib import pyplot as plt
    report = Report(info_fname=raw_fname,
                    subject='sample', subjects_dir=subjects_dir)
    section = 'slider_section'
    figs = list()
    figs.append(plt.figure())
    plt.plot([1, 2, 3])
    plt.close('all')
    figs.append(plt.figure())
    plt.plot([3, 2, 1])
    plt.close('all')
    report.add_slider_to_section(figs, section=section)
    report.save(op.join(tempdir, 'report.html'), open_browser=False)

    assert_raises(NotImplementedError, report.add_slider_to_section,
                  [figs, figs])
    assert_raises(ValueError, report.add_slider_to_section, figs, ['wug'])
    assert_raises(TypeError, report.add_slider_to_section, figs, 'wug')

# 需要导入模块: from mne.report import Report [as 别名]
# 或者: from mne.report.Report import add_slider_to_section [as 别名]
def test_add_slider_to_section():
    """Test adding a slider with a series of images to mne report."""
    tempdir = _TempDir()
    report = Report(info_fname=raw_fname,
                    subject='sample', subjects_dir=subjects_dir)
    section = 'slider_section'
    figs = _get_example_figures()
    report.add_slider_to_section(figs, section=section)
    report.save(op.join(tempdir, 'report.html'), open_browser=False)

    pytest.raises(NotImplementedError, report.add_slider_to_section,
                  [figs, figs])
    pytest.raises(ValueError, report.add_slider_to_section, figs, ['wug'])
    pytest.raises(TypeError, report.add_slider_to_section, figs, 'wug')
    # need at least 2
    pytest.raises(ValueError, report.add_slider_to_section, figs[:1], 'wug')

    # Smoke test that SVG w/unicode can be added
    report = Report()
    fig, ax = plt.subplots()
    ax.set_xlabel(u'μ')
    report.add_slider_to_section([fig] * 2, image_format='svg')

# 需要导入模块: from mne.report import Report [as 别名]
# 或者: from mne.report.Report import add_slider_to_section [as 别名]
meg_path = data_path + '/MEG/sample'
subjects_dir = data_path + '/subjects'
evoked_fname = meg_path + '/sample_audvis-ave.fif'

###############################################################################
# Do standard folder parsing (this can take a couple of minutes):

report = Report(image_format='png', subjects_dir=subjects_dir,
                info_fname=evoked_fname, subject='sample', raw_psd=True)
report.parse_folder(meg_path)

###############################################################################
# Add a custom section with an evoked slider:

# Load the evoked data
evoked = read_evokeds(evoked_fname, condition='Left Auditory',
                      baseline=(None, 0), verbose=False)
evoked.crop(0, .2)
times = evoked.times[::4]
# Create a list of figs for the slider
figs = list()
for t in times:
    figs.append(evoked.plot_topomap(t, vmin=-300, vmax=300, res=100,
                                    show=False))
    plt.close(figs[-1])
report.add_slider_to_section(figs, times, 'Evoked Response',
                             image_format='svg')

# to save report
# report.save('foobar.html', True)

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

#.........这里部分代码省略.........
                    else:
                        this_evoked = mne.read_evokeds(fname_evoked, name)
                        figs = this_evoked.plot_joint(
                            times, show=False, ts_args=dict(**time_kwargs),
                            topomap_args=dict(outlines='head', **time_kwargs))
                        if not isinstance(figs, (list, tuple)):
                            figs = [figs]
                        captions = ('%s<br>%s["%s"] (N=%d)'
                                    % (section, analysis, name,
                                       this_evoked.nave))
                        captions = [captions] + [None] * (len(figs) - 1)
                        report.add_figs_to_section(
                            figs, captions, section=section,
                            image_format='png')
                print('%5.1f sec' % ((time.time() - t0),))

            #
            # Source estimation
            #
            section = 'Source estimation'
            if p.report_params.get('source', False):
                t0 = time.time()
                print(('    %s ... ' % section).ljust(ljust), end='')
                sources = p.report_params['source']
                if not isinstance(sources, (list, tuple)):
                    sources = [sources]
                for source in sources:
                    assert isinstance(source, dict)
                    analysis = source['analysis']
                    name = source['name']
                    times = source.get('times', [0.1, 0.2])
                    # Load the inverse
                    inv_dir = op.join(p.work_dir, subj, p.inverse_dir)
                    fname_inv = op.join(inv_dir,
                                        safe_inserter(source['inv'], subj))
                    fname_evoked = op.join(inv_dir, '%s_%d%s_%s_%s-ave.fif'
                                           % (analysis, p.lp_cut, p.inv_tag,
                                              p.eq_tag, subj))
                    if not op.isfile(fname_inv):
                        print('    Missing inv: %s'
                              % op.basename(fname_inv), end='')
                    elif not op.isfile(fname_evoked):
                        print('    Missing evoked: %s'
                              % op.basename(fname_evoked), end='')
                    else:
                        inv = mne.minimum_norm.read_inverse_operator(fname_inv)
                        this_evoked = mne.read_evokeds(fname_evoked, name)
                        title = ('%s<br>%s["%s"] (N=%d)'
                                 % (section, analysis, name, this_evoked.nave))
                        stc = mne.minimum_norm.apply_inverse(
                            this_evoked, inv,
                            lambda2=source.get('lambda2', 1. / 9.),
                            method=source.get('method', 'dSPM'))
                        stc = abs(stc)
                        # get clim using the reject_tmin <->reject_tmax
                        stc_crop = stc.copy().crop(
                            p.reject_tmin, p.reject_tmax)
                        clim = source.get('clim', dict(kind='percent',
                                                       lims=[82, 90, 98]))
                        out = mne.viz._3d._limits_to_control_points(
                             clim, stc_crop.data, 'viridis',
                             transparent=True)  # dummy cmap
                        if isinstance(out[0], (list, tuple, np.ndarray)):
                            clim = out[0]  # old MNE
                        else:
                            clim = out[1]  # new MNE (0.17+)
                        clim = dict(kind='value', lims=clim)
                        if not isinstance(stc, mne.SourceEstimate):
                            print('Only surface source estimates currently '
                                  'supported')
                        else:
                            subjects_dir = mne.utils.get_subjects_dir(
                                p.subjects_dir, raise_error=True)
                            with mlab_offscreen():
                                brain = stc.plot(
                                    hemi=source.get('hemi', 'split'),
                                    views=source.get('views', ['lat', 'med']),
                                    size=source.get('size', (800, 600)),
                                    colormap=source.get('colormap', 'viridis'),
                                    transparent=source.get('transparent',
                                                           True),
                                    foreground='k', background='w',
                                    clim=clim, subjects_dir=subjects_dir,
                                    )
                                imgs = list()
                                for t in times:
                                    brain.set_time(t)
                                    imgs.append(
                                        trim_bg(brain.screenshot(), 255))
                                brain.close()
                            captions = ['%2.3f sec' % t for t in times]
                            report.add_slider_to_section(
                                imgs, captions=captions, section=section,
                                title=title, image_format='png')
                print('%5.1f sec' % ((time.time() - t0),))
            else:
                print('    %s skipped' % section)

        report_fname = get_report_fnames(p, subj)[0]
        report.save(report_fname, open_browser=False, overwrite=True)

# 需要导入模块: from mne.report import Report [as 别名]
# 或者: from mne.report.Report import add_slider_to_section [as 别名]
# Authors: Teon Brooks <[email protected]
#
# License: BSD (3-clause)

from mne.report import Report
from mne.datasets import sample
from mne import read_evokeds
from matplotlib import pyplot as plt


report = Report()
path = sample.data_path()
fname = path + '/MEG/sample/sample_audvis-ave.fif'

# Load the evoked data
evoked = read_evokeds(fname, condition='Left Auditory',
                      baseline=(None, 0), verbose=False)
evoked.crop(0, .2)
times = evoked.times[::4]
# Create a list of figs for the slider
figs = list()
for time in times:
    figs.append(evoked.plot_topomap(time, vmin=-300, vmax=300,
                                    res=100, show=False))
    plt.close(figs[-1])
report.add_slider_to_section(figs, times, 'Evoked Response')

# # to save report
# report.save('foobar.html', True)