本文整理汇总了Python中mne.preprocessing.ICA.labels_['ecg']方法的典型用法代码示例。如果您正苦于以下问题：Python ICA.labels_['ecg']方法的具体用法？Python ICA.labels_['ecg']怎么用？Python ICA.labels_['ecg']使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类mne.preprocessing.ICA的用法示例。
在下文中一共展示了ICA.labels_['ecg']方法的2个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: test_plot_ica_scores
# 需要导入模块: from mne.preprocessing import ICA [as 别名]
# 或者: from mne.preprocessing.ICA import labels_['ecg'] [as 别名]
def test_plot_ica_scores():
"""Test plotting of ICA scores."""
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)
with pytest.warns(RuntimeWarning, match='projection'):
ica.fit(raw, picks=picks)
ica.labels_ = dict()
ica.labels_['eog/0/foo'] = 0
ica.labels_['eog'] = 0
ica.labels_['ecg'] = 1
ica.plot_scores([0.3, 0.2], axhline=[0.1, -0.1])
ica.plot_scores([0.3, 0.2], axhline=[0.1, -0.1], labels='foo')
ica.plot_scores([0.3, 0.2], axhline=[0.1, -0.1], labels='eog')
ica.plot_scores([0.3, 0.2], axhline=[0.1, -0.1], labels='ecg')
pytest.raises(
ValueError,
ica.plot_scores,
[0.3, 0.2], axhline=[0.1, -0.1], labels=['one', 'one-too-many'])
pytest.raises(ValueError, ica.plot_scores, [0.2])
plt.close('all')示例2: test_plot_ica_scores
# 需要导入模块: from mne.preprocessing import ICA [as 别名]
# 或者: from mne.preprocessing.ICA import labels_['ecg'] [as 别名]
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)
with warnings.catch_warnings(record=True): # bad proj
ica.fit(raw, picks=picks)
ica.labels_ = dict()
ica.labels_['eog/0/foo'] = 0
ica.labels_['eog'] = 0
ica.labels_['ecg'] = 1
ica.plot_scores([0.3, 0.2], axhline=[0.1, -0.1])
ica.plot_scores([0.3, 0.2], axhline=[0.1, -0.1], labels='foo')
ica.plot_scores([0.3, 0.2], axhline=[0.1, -0.1], labels='eog')
ica.plot_scores([0.3, 0.2], axhline=[0.1, -0.1], labels='ecg')
assert_raises(
ValueError,
ica.plot_scores,
[0.3, 0.2], axhline=[0.1, -0.1], labels=['one', 'one-too-many'])
assert_raises(ValueError, ica.plot_scores, [0.2])
plt.close('all')