本文整理汇总了Python中mne.SourceEstimate._data方法的典型用法代码示例。如果您正苦于以下问题：Python SourceEstimate._data方法的具体用法？Python SourceEstimate._data怎么用？Python SourceEstimate._data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.SourceEstimate的用法示例。
在下文中一共展示了SourceEstimate._data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_limits_to_control_points
# 需要导入模块: from mne import SourceEstimate [as 别名]
# 或者: from mne.SourceEstimate import _data [as 别名]
def test_limits_to_control_points():
"""Test functionality for determing control points
"""
sample_src = read_source_spaces(src_fname)
vertices = [s['vertno'] for s in sample_src]
n_time = 5
n_verts = sum(len(v) for v in vertices)
stc_data = np.random.rand((n_verts * n_time))
stc_data.shape = (n_verts, n_time)
stc = SourceEstimate(stc_data, vertices, 1, 1, 'sample')
# Test for simple use cases
from mayavi import mlab
mlab.close()
stc.plot(clim='auto', subjects_dir=subjects_dir)
stc.plot(clim=dict(pos_lims=(10, 50, 90)), subjects_dir=subjects_dir)
stc.plot(clim=dict(kind='value', lims=(10, 50, 90)), figure=99,
subjects_dir=subjects_dir)
with warnings.catch_warnings(record=True): # dep
stc.plot(fmin=1, subjects_dir=subjects_dir)
stc.plot(colormap='hot', clim='auto', subjects_dir=subjects_dir)
stc.plot(colormap='mne', clim='auto', subjects_dir=subjects_dir)
figs = [mlab.figure(), mlab.figure()]
assert_raises(RuntimeError, stc.plot, clim='auto', figure=figs)
# Test both types of incorrect limits key (lims/pos_lims)
assert_raises(KeyError, plot_source_estimates, stc, colormap='mne',
clim=dict(kind='value', lims=(5, 10, 15)))
assert_raises(KeyError, plot_source_estimates, stc, colormap='hot',
clim=dict(kind='value', pos_lims=(5, 10, 15)))
# Test for correct clim values
colormap = 'mne'
assert_raises(ValueError, stc.plot, colormap=colormap,
clim=dict(pos_lims=(5, 10, 15, 20)))
assert_raises(ValueError, stc.plot, colormap=colormap,
clim=dict(pos_lims=(5, 10, 15), kind='foo'))
assert_raises(ValueError, stc.plot, colormap=colormap,
clim=dict(kind='value', pos_lims=(5, 10, 15)), fmin=1)
assert_raises(ValueError, stc.plot, colormap=colormap, clim='foo')
assert_raises(ValueError, stc.plot, colormap=colormap, clim=(5, 10, 15))
assert_raises(ValueError, plot_source_estimates, 'foo', clim='auto')
assert_raises(ValueError, stc.plot, hemi='foo', clim='auto')
# Test that stc.data contains enough unique values to use percentages
clim = 'auto'
stc._data = np.zeros_like(stc.data)
assert_raises(ValueError, plot_source_estimates, stc,
colormap=colormap, clim=clim)
mlab.close()示例2: test_limits_to_control_points
# 需要导入模块: from mne import SourceEstimate [as 别名]
# 或者: from mne.SourceEstimate import _data [as 别名]
def test_limits_to_control_points():
"""Test functionality for determing control points
"""
sample_src = read_source_spaces(src_fname)
vertices = [s['vertno'] for s in sample_src]
n_time = 5
n_verts = sum(len(v) for v in vertices)
stc_data = np.zeros((n_verts * n_time))
stc_data[(np.random.rand(20) * n_verts * n_time).astype(int)] = 1
stc_data.shape = (n_verts, n_time)
stc = SourceEstimate(stc_data, vertices, 1, 1)
# Test both types of incorrect limits key (lims/pos_lims)
clim = dict(kind='value', lims=(5, 10, 15))
colormap = 'mne_analyze'
assert_raises(KeyError, plot_source_estimates, stc, 'sample',
colormap=colormap, clim=clim)
clim = dict(kind='value', pos_lims=(5, 10, 15))
colormap = 'hot'
assert_raises(KeyError, plot_source_estimates, stc, 'sample',
colormap=colormap, clim=clim)
# Test for correct clim values
clim['pos_lims'] = (5, 10, 15, 20)
colormap = 'mne_analyze'
assert_raises(ValueError, plot_source_estimates, stc, 'sample',
colormap=colormap, clim=clim)
clim = 'foo'
assert_raises(ValueError, plot_source_estimates, stc, 'sample',
colormap=colormap, clim=clim)
clim = (5, 10, 15)
assert_raises(ValueError, plot_source_estimates, stc, 'sample',
colormap=colormap, clim=clim)
# Test that stc.data contains enough unique values to use percentages
clim = 'auto'
stc._data = np.zeros_like(stc.data)
assert_raises(ValueError, plot_source_estimates, stc, 'sample',
colormap=colormap, clim=clim)