# Authors: Alexandre Gramfort # Denis Engemann # Martin Luessi # Eric Larson # Cathy Nangini # Mainak Jas # # License: Simplified BSD import os.path as op import numpy as np import pytest from mne import (read_events, read_cov, read_source_spaces, read_evokeds, read_dipole, SourceEstimate) from mne.datasets import testing from mne.filter import create_filter from mne.io import read_raw_fif from mne.minimum_norm import read_inverse_operator from mne.viz import (plot_bem, plot_events, plot_source_spectrogram, plot_snr_estimate, plot_filter, plot_csd) from mne.utils import requires_nibabel, run_tests_if_main, requires_version from mne.time_frequency import CrossSpectralDensity # Set our plotters to test mode import matplotlib matplotlib.use('Agg') # for testing don't use X server data_path = testing.data_path(download=False) subjects_dir = op.join(data_path, 'subjects') src_fname = op.join(subjects_dir, 'sample', 'bem', 'sample-oct-6-src.fif') inv_fname = op.join(data_path, 'MEG', 'sample', 'sample_audvis_trunc-meg-eeg-oct-4-meg-inv.fif') evoked_fname = op.join(data_path, 'MEG', 'sample', 'sample_audvis-ave.fif') dip_fname = op.join(data_path, 'MEG', 'sample', 'sample_audvis_trunc_set1.dip') base_dir = op.join(op.dirname(__file__), '..', '..', 'io', 'tests', 'data') raw_fname = op.join(base_dir, 'test_raw.fif') cov_fname = op.join(base_dir, 'test-cov.fif') event_fname = op.join(base_dir, 'test-eve.fif') def _get_raw(): """Get raw data.""" return read_raw_fif(raw_fname, preload=True) def _get_events(): """Get events.""" return read_events(event_fname) @requires_version('scipy', '0.16') def test_plot_filter(): """Test filter plotting.""" import matplotlib.pyplot as plt l_freq, h_freq, sfreq = 2., 40., 1000. data = np.zeros(5000) freq = [0, 2, 40, 50, 500] gain = [0, 1, 1, 0, 0] h = create_filter(data, sfreq, l_freq, h_freq, fir_design='firwin2') plot_filter(h, sfreq) plt.close('all') plot_filter(h, sfreq, freq, gain) plt.close('all') iir = create_filter(data, sfreq, l_freq, h_freq, method='iir') plot_filter(iir, sfreq) plt.close('all') plot_filter(iir, sfreq, freq, gain) plt.close('all') iir_ba = create_filter(data, sfreq, l_freq, h_freq, method='iir', iir_params=dict(output='ba')) plot_filter(iir_ba, sfreq, freq, gain) plt.close('all') plot_filter(h, sfreq, freq, gain, fscale='linear') plt.close('all') def test_plot_cov(): """Test plotting of covariances.""" import matplotlib.pyplot as plt raw = _get_raw() cov = read_cov(cov_fname) with pytest.warns(RuntimeWarning, match='projection'): fig1, fig2 = cov.plot(raw.info, proj=True, exclude=raw.ch_names[6:]) plt.close('all') @testing.requires_testing_data @requires_nibabel() def test_plot_bem(): """Test plotting of BEM contours.""" pytest.raises(IOError, plot_bem, subject='bad-subject', subjects_dir=subjects_dir) pytest.raises(ValueError, plot_bem, subject='sample', subjects_dir=subjects_dir, orientation='bad-ori') plot_bem(subject='sample', subjects_dir=subjects_dir, orientation='sagittal', slices=[25, 50]) plot_bem(subject='sample', subjects_dir=subjects_dir, orientation='coronal', slices=[25, 50], brain_surfaces='white') plot_bem(subject='sample', subjects_dir=subjects_dir, orientation='coronal', slices=[25, 50], src=src_fname) def test_plot_events(): """Test plotting events.""" import matplotlib.pyplot as plt event_labels = {'aud_l': 1, 'aud_r': 2, 'vis_l': 3, 'vis_r': 4} color = {1: 'green', 2: 'yellow', 3: 'red', 4: 'c'} raw = _get_raw() events = _get_events() plot_events(events, raw.info['sfreq'], raw.first_samp) plot_events(events, raw.info['sfreq'], raw.first_samp, equal_spacing=False) # Test plotting events without sfreq plot_events(events, first_samp=raw.first_samp) with pytest.warns(RuntimeWarning, match='will be ignored'): plot_events(events, raw.info['sfreq'], raw.first_samp, event_id=event_labels) with pytest.warns(RuntimeWarning, match='Color is not available'): plot_events(events, raw.info['sfreq'], raw.first_samp, color=color) with pytest.warns(RuntimeWarning, match='event .* missing'): plot_events(events, raw.info['sfreq'], raw.first_samp, event_id=event_labels, color=color) with pytest.warns(RuntimeWarning, match='event .* missing'): pytest.raises(ValueError, plot_events, events, raw.info['sfreq'], raw.first_samp, event_id={'aud_l': 1}, color=color) pytest.raises(ValueError, plot_events, events, raw.info['sfreq'], raw.first_samp, event_id={'aud_l': 111}, color=color) plt.close('all') @testing.requires_testing_data def test_plot_source_spectrogram(): """Test plotting of source spectrogram.""" import matplotlib.pyplot as plt sample_src = read_source_spaces(op.join(subjects_dir, 'sample', 'bem', 'sample-oct-6-src.fif')) # dense version vertices = [s['vertno'] for s in sample_src] n_times = 5 n_verts = sum(len(v) for v in vertices) stc_data = np.ones((n_verts, n_times)) stc = SourceEstimate(stc_data, vertices, 1, 1) plot_source_spectrogram([stc, stc], [[1, 2], [3, 4]]) pytest.raises(ValueError, plot_source_spectrogram, [], []) pytest.raises(ValueError, plot_source_spectrogram, [stc, stc], [[1, 2], [3, 4]], tmin=0) pytest.raises(ValueError, plot_source_spectrogram, [stc, stc], [[1, 2], [3, 4]], tmax=7) plt.close('all') @pytest.mark.slowtest @testing.requires_testing_data def test_plot_snr(): """Test plotting SNR estimate.""" import matplotlib.pyplot as plt inv = read_inverse_operator(inv_fname) evoked = read_evokeds(evoked_fname, baseline=(None, 0))[0] plot_snr_estimate(evoked, inv) plt.close('all') @testing.requires_testing_data def test_plot_dipole_amplitudes(): """Test plotting dipole amplitudes.""" import matplotlib.pyplot as plt dipoles = read_dipole(dip_fname) dipoles.plot_amplitudes(show=False) plt.close('all') def test_plot_csd(): """Test plotting of CSD matrices.""" import matplotlib.pyplot as plt csd = CrossSpectralDensity([1, 2, 3], ['CH1', 'CH2'], frequencies=[(10, 20)], n_fft=1, tmin=0, tmax=1,) plot_csd(csd, mode='csd') # Plot cross-spectral density plot_csd(csd, mode='coh') # Plot coherence plt.close('all') run_tests_if_main()