本文整理汇总了Python中mne.preprocessing.ICA.score_sources方法的典型用法代码示例。如果您正苦于以下问题：Python ICA.score_sources方法的具体用法？Python ICA.score_sources怎么用？Python ICA.score_sources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.score_sources方法的3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_ica_additional
# 需要导入模块: from mne.preprocessing import ICA [as 别名]
# 或者: from mne.preprocessing.ICA import score_sources [as 别名]
#.........这里部分代码省略.........
return getattr(x, y).dtype
for attr in attrs.split():
assert_equal(f(ica_read, attr), f(ica, attr))
ica.n_pca_components = 4
ica_read.n_pca_components = 4
ica.exclude = []
ica.save(test_ica_fname)
ica_read = read_ica(test_ica_fname)
for attr in ['mixing_matrix_', 'unmixing_matrix_', 'pca_components_',
'pca_mean_', 'pca_explained_variance_',
'pre_whitener_']:
assert_array_almost_equal(getattr(ica, attr),
getattr(ica_read, attr))
assert (ica.ch_names == ica_read.ch_names)
assert (isinstance(ica_read.info, Info))
sources = ica.get_sources(raw)[:, :][0]
sources2 = ica_read.get_sources(raw)[:, :][0]
assert_array_almost_equal(sources, sources2)
_raw1 = ica.apply(raw, exclude=[1])
_raw2 = ica_read.apply(raw, exclude=[1])
assert_array_almost_equal(_raw1[:, :][0], _raw2[:, :][0])
os.remove(test_ica_fname)
# check score funcs
for name, func in get_score_funcs().items():
if name in score_funcs_unsuited:
continue
scores = ica.score_sources(raw, target='EOG 061', score_func=func,
start=0, stop=10)
assert (ica.n_components_ == len(scores))
# check univariate stats
scores = ica.score_sources(raw, start=0, stop=50, score_func=stats.skew)
# check exception handling
pytest.raises(ValueError, ica.score_sources, raw,
target=np.arange(1))
params = []
params += [(None, -1, slice(2), [0, 1])] # variance, kurtosis params
params += [(None, 'MEG 1531')] # ECG / EOG channel params
for idx, ch_name in product(*params):
ica.detect_artifacts(raw, start_find=0, stop_find=50, ecg_ch=ch_name,
eog_ch=ch_name, skew_criterion=idx,
var_criterion=idx, kurt_criterion=idx)
# Make sure detect_artifacts marks the right components.
# For int criterion, the doc says "E.g. range(2) would return the two
# sources with the highest score". Assert that's what it does.
# Only test for skew, since it's always the same code.
ica.exclude = []
ica.detect_artifacts(raw, start_find=0, stop_find=50, ecg_ch=None,
eog_ch=None, skew_criterion=0,
var_criterion=None, kurt_criterion=None)
assert np.abs(scores[ica.exclude]) == np.max(np.abs(scores))
evoked = epochs.average()
evoked_data = evoked.data.copy()
raw_data = raw[:][0].copy()
epochs_data = epochs.get_data().copy()
示例2: test_ica_additional
# 需要导入模块: from mne.preprocessing import ICA [as 别名]
# 或者: from mne.preprocessing.ICA import score_sources [as 别名]
#.........这里部分代码省略.........
return getattr(x, y).dtype
for attr in attrs.split():
assert_equal(f(ica_read, attr), f(ica, attr))
ica.n_pca_components = 4
ica_read.n_pca_components = 4
ica.exclude = []
ica.save(test_ica_fname)
ica_read = read_ica(test_ica_fname)
for attr in ['mixing_matrix_', 'unmixing_matrix_', 'pca_components_',
'pca_mean_', 'pca_explained_variance_',
'_pre_whitener']:
assert_array_almost_equal(getattr(ica, attr),
getattr(ica_read, attr))
assert_true(ica.ch_names == ica_read.ch_names)
assert_true(isinstance(ica_read.info, Info))
sources = ica.get_sources(raw)[:, :][0]
sources2 = ica_read.get_sources(raw)[:, :][0]
assert_array_almost_equal(sources, sources2)
_raw1 = ica.apply(raw, exclude=[1])
_raw2 = ica_read.apply(raw, exclude=[1])
assert_array_almost_equal(_raw1[:, :][0], _raw2[:, :][0])
os.remove(test_ica_fname)
# check scrore funcs
for name, func in get_score_funcs().items():
if name in score_funcs_unsuited:
continue
scores = ica.score_sources(raw, target='EOG 061', score_func=func,
start=0, stop=10)
assert_true(ica.n_components_ == len(scores))
# check univariate stats
scores = ica.score_sources(raw, score_func=stats.skew)
# check exception handling
assert_raises(ValueError, ica.score_sources, raw,
target=np.arange(1))
params = []
params += [(None, -1, slice(2), [0, 1])] # varicance, kurtosis idx params
params += [(None, 'MEG 1531')] # ECG / EOG channel params
for idx, ch_name in product(*params):
ica.detect_artifacts(raw, start_find=0, stop_find=50, ecg_ch=ch_name,
eog_ch=ch_name, skew_criterion=idx,
var_criterion=idx, kurt_criterion=idx)
with warnings.catch_warnings(record=True):
idx, scores = ica.find_bads_ecg(raw, method='ctps')
assert_equal(len(scores), ica.n_components_)
idx, scores = ica.find_bads_ecg(raw, method='correlation')
assert_equal(len(scores), ica.n_components_)
idx, scores = ica.find_bads_ecg(epochs, method='ctps')
assert_equal(len(scores), ica.n_components_)
assert_raises(ValueError, ica.find_bads_ecg, epochs.average(),
method='ctps')
assert_raises(ValueError, ica.find_bads_ecg, raw,
method='crazy-coupling')
idx, scores = ica.find_bads_eog(raw)
assert_equal(len(scores), ica.n_components_)
raw.info['chs'][raw.ch_names.index('EOG 061') - 1]['kind'] = 202
idx, scores = ica.find_bads_eog(raw)示例3: test_ica_additional
# 需要导入模块: from mne.preprocessing import ICA [as 别名]
# 或者: from mne.preprocessing.ICA import score_sources [as 别名]
#.........这里部分代码省略.........
ica.n_pca_components = 4
ica_read.n_pca_components = 4
ica.exclude = []
ica.save(test_ica_fname)
ica_read = read_ica(test_ica_fname)
for attr in [
"mixing_matrix_",
"unmixing_matrix_",
"pca_components_",
"pca_mean_",
"pca_explained_variance_",
"_pre_whitener",
]:
assert_array_almost_equal(getattr(ica, attr), getattr(ica_read, attr))
assert_true(ica.ch_names == ica_read.ch_names)
assert_true(isinstance(ica_read.info, Info))
sources = ica.get_sources(raw)[:, :][0]
sources2 = ica_read.get_sources(raw)[:, :][0]
assert_array_almost_equal(sources, sources2)
_raw1 = ica.apply(raw, exclude=[1])
_raw2 = ica_read.apply(raw, exclude=[1])
assert_array_almost_equal(_raw1[:, :][0], _raw2[:, :][0])
os.remove(test_ica_fname)
# check scrore funcs
for name, func in score_funcs.items():
if name in score_funcs_unsuited:
continue
scores = ica.score_sources(raw, target="EOG 061", score_func=func, start=0, stop=10)
assert_true(ica.n_components_ == len(scores))
# check univariate stats
scores = ica.score_sources(raw, score_func=stats.skew)
# check exception handling
assert_raises(ValueError, ica.score_sources, raw, target=np.arange(1))
params = []
params += [(None, -1, slice(2), [0, 1])] # varicance, kurtosis idx params
params += [(None, "MEG 1531")] # ECG / EOG channel params
for idx, ch_name in product(*params):
ica.detect_artifacts(
raw,
start_find=0,
stop_find=50,
ecg_ch=ch_name,
eog_ch=ch_name,
skew_criterion=idx,
var_criterion=idx,
kurt_criterion=idx,
)
idx, scores = ica.find_bads_ecg(raw, method="ctps")
assert_equal(len(scores), ica.n_components_)
idx, scores = ica.find_bads_ecg(raw, method="correlation")
assert_equal(len(scores), ica.n_components_)
idx, scores = ica.find_bads_ecg(epochs, method="ctps")
assert_equal(len(scores), ica.n_components_)
assert_raises(ValueError, ica.find_bads_ecg, epochs.average(), method="ctps")
assert_raises(ValueError, ica.find_bads_ecg, raw, method="crazy-coupling")
idx, scores = ica.find_bads_eog(raw)