<commit_before># Authors: <NAME> <<EMAIL>>
#          <NAME> <<EMAIL>>
#
# License: Simplified BSD

import os.path as op
import warnings

from numpy.testing import assert_raises

from mne import io, read_events, Epochs, read_cov
from mne import pick_types
from mne.utils import run_tests_if_main, requires_sklearn
from mne.viz.utils import _fake_click
from mne.preprocessing import ICA, create_ecg_epochs, create_eog_epochs

# Set our plotters to test mode
import matplotlib
matplotlib.use('Agg')  # for testing don't use X server

warnings.simplefilter('always')  # enable b/c these tests throw warnings

base_dir = op.join(op.dirname(__file__), '..', '..', 'io', 'tests', 'data')
evoked_fname = op.join(base_dir, 'test-ave.fif')
raw_fname = op.join(base_dir, 'test_raw.fif')
cov_fname = op.join(base_dir, 'test-cov.fif')
event_name = op.join(base_dir, 'test-eve.fif')
event_id, tmin, tmax = 1, -0.1, 0.2


def _get_raw(preload=False):
    return io.Raw(raw_fname, preload=preload)


def _get_events():
    return read_events(event_name)


def _get_picks(raw):
    return [0, 1, 2, 6, 7, 8, 12, 13, 14]  # take a only few channels


def _get_epochs():
    raw = _get_raw()
    events = _get_events()
    picks = _get_picks(raw)
    epochs = Epochs(raw, events[:10], event_id, tmin, tmax, picks=picks,
                    baseline=(None, 0))
    return epochs


@requires_sklearn
def test_plot_ica_components():
    """Test plotting of ICA solutions
    """
    import matplotlib.pyplot as plt
    raw = _get_raw()
    ica = ICA(noise_cov=read_cov(cov_fname), n_components=2,
              max_pca_components=3, n_pca_components=3)
    ica_picks = _get_picks(raw)
    ica.fit(raw, picks=ica_picks)
    warnings.simplefilter('always', UserWarning)
    with warnings.catch_warnings(record=True):
        for components in [0, [0], [0, 1], [0, 1] * 2, None]:
            ica.plot_components(components, image_interp='bilinear', res=16)
    ica.info = None
    assert_raises(RuntimeError, ica.plot_components, 1)
    plt.close('all')


@requires_sklearn
def test_plot_ica_sources():
    """Test plotting of ICA panel
    """
    import matplotlib.pyplot as plt
    raw = io.Raw(raw_fname, preload=False)
    raw.crop(0, 1, copy=False)
    raw.preload_data()
    picks = _get_picks(raw)
    epochs = _get_epochs()
    raw.pick_channels([raw.ch_names[k] for k in picks])
    ica_picks = pick_types(raw.info, meg=True, eeg=False, stim=False,
                           ecg=False, eog=False, exclude='bads')
    ica = ICA(n_components=2, max_pca_components=3, n_pca_components=3)
    ica.fit(raw, picks=ica_picks)
    raw.info['bads'] = ['MEG 0113']
    assert_raises(RuntimeError, ica.plot_sources, inst=raw)
    ica.plot_sources(epochs)
    epochs.info['bads'] = ['MEG 0113']
    assert_raises(RuntimeError, ica.plot_sources, inst=epochs)
    epochs.info['bads'] = []
    with warnings.catch_warnings(record=True):  # no labeled objects mpl
        ica.plot_sources(epochs.average())
        evoked = epochs.average()
        fig = ica.plot_sources(evoked)
        # Test a click
        ax = fig.get_axes()[0]
        line = ax.lines[0]
        _fake_click(fig, ax,
                    [line.get_xdata()[0], line.get_ydata()[0]], 'data')
        _fake_click(fig, ax,
                    [ax.get_xlim()[0], ax.get_ylim()[1]], 'data')
        # plot with bad channels excluded
        ica.plot_sources(evoked, exclude=[0])
        ica.exclude = [0]
        ica.plot_sources(evoked)  # does the same thing
    assert_raises(ValueError, ica.plot_sources, 'meeow')
    plt.close('all')


@requires_sklearn
def test_plot_ica_overlay():
    """Test plotting of ICA cleaning
    """
    import matplotlib.pyplot as plt
    raw = _get_raw(preload=True)
    picks = _get_picks(raw)
    ica = ICA(noise_cov=read_cov(cov_fname), n_components=2,
              max_pca_components=3, n_pca_components=3)
    ica.fit(raw, picks=picks)
    # don't test raw, needs preload ...
    ecg_epochs = create_ecg_epochs(raw, picks=picks)
    ica.plot_overlay(ecg_epochs.average())
    eog_epochs = create_eog_epochs(raw, picks=picks)
    ica.plot_overlay(eog_epochs.average())
    assert_raises(ValueError, ica.plot_overlay, raw[:2, :3][0])
    ica.plot_overlay(raw)
    plt.close('all')


@requires_sklearn
def test_plot_ica_scores():
    """Test plotting of ICA scores
    """
    import matplotlib.pyplot as plt
    raw = _get_raw()
    picks = _get_picks(raw)
    ica = ICA(noise_cov=read_cov(cov_fname), n_components=2,
              max_pca_components=3, n_pca_components=3)
    ica.fit(raw, picks=picks)
    ica.plot_scores([0.3, 0.2], axhline=[0.1, -0.1])
    assert_raises(ValueError, ica.plot_scores, [0.2])
    plt.close('all')


@requires_sklearn
def test_plot_raw_components():
    """Test plotting of raw components."""
    import matplotlib.pyplot as plt
    raw = _get_raw()
    picks = _get_picks(raw)
    ica = ICA(noise_cov=read_cov(cov_fname), n_components=2,
              max_pca_components=3, n_pca_components=3)
    ica.fit(raw, picks=picks)
    fig = ica.plot_sources(raw, exclude=[0], title='Components')
    fig.canvas.key_press_event('down')
    fig.canvas.key_press_event('up')
    fig.canvas.key_press_event('right')
    fig.canvas.key_press_event('left')
    fig.canvas.key_press_event('o')
    fig.canvas.key_press_event('-')
    fig.canvas.key_press_event('+')
    fig.canvas.key_press_event('=')
    fig.canvas.key_press_event('pageup')
    fig.canvas.key_press_event('pagedown')
    fig.canvas.key_press_event('home')
    fig.canvas.key_press_event('end')
    fig.canvas.key_press_event('f11')
    fig.canvas.key_press_event('escape')
    plt.close('all')


run_tests_if_main()<commit_msg>Fix to whats_new. Tests.<commit_after># Authors: <NAME> <<EMAIL>>
#          <NAME> <<EMAIL>>
#
# License: Simplified BSD

import os.path as op
import warnings

from numpy.testing import assert_raises

from mne import io, read_events, Epochs, read_cov
from mne import pick_types
from mne.utils import run_tests_if_main, requires_sklearn
from mne.viz.utils import _fake_click
from mne.preprocessing import ICA, create_ecg_epochs, create_eog_epochs

# Set our plotters to test mode
import matplotlib
matplotlib.use('Agg')  # for testing don't use X server

warnings.simplefilter('always')  # enable b/c these tests throw warnings

base_dir = op.join(op.dirname(__file__), '..', '..', 'io', 'tests', 'data')
evoked_fname = op.join(base_dir, 'test-ave.fif')
raw_fname = op.join(base_dir, 'test_raw.fif')
cov_fname = op.join(base_dir, 'test-cov.fif')
event_name = op.join(base_dir, 'test-eve.fif')
event_id, tmin, tmax = 1, -0.1, 0.2


def _get_raw(preload=False):
    return io.Raw(raw_fname, preload=preload)


def _get_events():
    return read_events(event_name)


def _get_picks(raw):
    return [0, 1, 2, 6, 7, 8, 12, 13, 14]  # take a only few channels


def _get_epochs():
    raw = _get_raw()
    events = _get_events()
    picks = _get_picks(raw)
    epochs = Epochs(raw, events[:10], event_id, tmin, tmax, picks=picks,
                    baseline=(None, 0))
    return epochs


@requires_sklearn
def test_plot_ica_components():
    """Test plotting of ICA solutions
    """
    import matplotlib.pyplot as plt
    raw = _get_raw()
    ica = ICA(noise_cov=read_cov(cov_fname), n_components=2,
              max_pca_components=3, n_pca_components=3)
    ica_picks = _get_picks(raw)
    ica.fit(raw, picks=ica_picks)
    warnings.simplefilter('always', UserWarning)
    with warnings.catch_warnings(record=True):
        for components in [0, [0], [0, 1], [0, 1] * 2, None]:
            ica.plot_components(components, image_interp='bilinear', res=16)
    ica.info = None
    assert_raises(RuntimeError, ica.plot_components, 1)
    plt.close('all')


@requires_sklearn
def test_plot_ica_sources():
    """Test plotting of ICA panel
    """
    import matplotlib.pyplot as plt
    raw = io.Raw(raw_fname, preload=False)
    raw.crop(0, 1, copy=False)
    raw.preload_data()
    picks = _get_picks(raw)
    epochs = _get_epochs()
    raw.pick_channels([raw.ch_names[k] for k in picks])
    ica_picks = pick_types(raw.info, meg=True, eeg=False, stim=False,
                           ecg=False, eog=False, exclude='bads')
    ica = ICA(n_components=2, max_pca_components=3, n_pca_components=3)
    ica.fit(raw, picks=ica_picks)
    raw.info['bads'] = ['MEG 0113']
    assert_raises(RuntimeError, ica.plot_sources, inst=raw)
    ica.plot_sources(epochs)
    epochs.info['bads'] = ['MEG 0113']
    assert_raises(RuntimeError, ica.plot_sources, inst=epochs)
    epochs.info['bads'] = []
    with warnings.catch_warnings(record=True):  # no labeled objects mpl
        ica.plot_sources(epochs.average())
        evoked = epochs.average()
        fig = ica.plot_sources(evoked)
        # Test a click
        ax = fig.get_axes()[0]
        line = ax.lines[0]
        _fake_click(fig, ax,
                    [line.get_xdata()[0], line.get_ydata()[0]], 'data')
        _fake_click(fig, ax,
                    [ax.get_xlim()[0], ax.get_ylim()[1]], 'data')
        # plot with bad channels excluded
        ica.plot_sources(evoked, exclude=[0])
        ica.exclude = [0]
        ica.plot_sources(evoked)  # does the same thing
    assert_raises(ValueError, ica.plot_sources, 'meeow')
    plt.close('all')


@requires_sklearn
def test_plot_ica_overlay():
    """Test plotting of ICA cleaning
    """
    import matplotlib.pyplot as plt
    raw = _get_raw(preload=True)
    picks = _get_picks(raw)
    ica = ICA(noise_cov=read_cov(cov_fname), n_components=2,
              max_pca_components=3, n_pca_components=3)
    ica.fit(raw, picks=picks)
    # don't test raw, needs preload ...
    ecg_epochs = create_ecg_epochs(raw, picks=picks)
    ica.plot_overlay(ecg_epochs.average())
    eog_epochs = create_eog_epochs(raw, picks=picks)
    ica.plot_overlay(eog_epochs.average())
    assert_raises(ValueError, ica.plot_overlay, raw[:2, :3][0])
    ica.plot_overlay(raw)
    plt.close('all')


@requires_sklearn
def test_plot_ica_scores():
    """Test plotting of ICA scores
    """
    import matplotlib.pyplot as plt
    raw = _get_raw()
    picks = _get_picks(raw)
    ica = ICA(noise_cov=read_cov(cov_fname), n_components=2,
              max_pca_components=3, n_pca_components=3)
    ica.fit(raw, picks=picks)
    ica.plot_scores([0.3, 0.2], axhline=[0.1, -0.1])
    assert_raises(ValueError, ica.plot_scores, [0.2])
    plt.close('all')


@requires_sklearn
def test_plot_instance_components():
    """Test plotting of components as instances of raw and epochs."""
    import matplotlib.pyplot as plt
    raw = _get_raw()
    picks = _get_picks(raw)
    ica = ICA(noise_cov=read_cov(cov_fname), n_components=2,
              max_pca_components=3, n_pca_components=3)
    ica.fit(raw, picks=picks)
    fig = ica.plot_sources(raw, exclude=[0], title='Components')
    fig.canvas.key_press_event('down')
    fig.canvas.key_press_event('up')
    fig.canvas.key_press_event('right')
    fig.canvas.key_press_event('left')
    fig.canvas.key_press_event('o')
    fig.canvas.key_press_event('-')
    fig.canvas.key_press_event('+')
    fig.canvas.key_press_event('=')
    fig.canvas.key_press_event('pageup')
    fig.canvas.key_press_event('pagedown')
    fig.canvas.key_press_event('home')
    fig.canvas.key_press_event('end')
    fig.canvas.key_press_event('f11')
    ax = fig.get_axes()[0]
    line = ax.lines[0]
    _fake_click(fig, ax, [line.get_xdata()[0], line.get_ydata()[0]], 'data')
    fig.canvas.key_press_event('escape')
    plt.close('all')
    epochs = _get_epochs()
    fig = ica.plot_sources(epochs, exclude=[0], title='Components')
    fig.canvas.key_press_event('down')
    fig.canvas.key_press_event('up')
    fig.canvas.key_press_event('right')
    fig.canvas.key_press_event('left')
    fig.canvas.key_press_event('o')
    fig.canvas.key_press_event('-')
    fig.canvas.key_press_event('+')
    fig.canvas.key_press_event('=')
    fig.canvas.key_press_event('pageup')
    fig.canvas.key_press_event('pagedown')
    fig.canvas.key_press_event('home')
    fig.canvas.key_press_event('end')
    fig.canvas.key_press_event('f11')
    # Test a click
    ax = fig.get_axes()[0]
    line = ax.lines[0]
    _fake_click(fig, ax, [line.get_xdata()[0], line.get_ydata()[0]], 'data')
    fig.canvas.key_press_event('escape')
    plt.close('all')


run_tests_if_main()
{'fraction_non_alphanumeric': 0.13611718054543984, 'fraction_numerical': 0.01367645868738367, 'mean_word_length': 3.6371482176360224, 'pattern_counts': {'":': 0, '<': 15, '<?xml version=': 0, '>': 15, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'dir': 'git-commits-cleaned', 'id': '7428672', 'n_tokens_mistral': 4682, 'n_tokens_neox': 4595, 'n_words': 849}