本文整理汇总了Python中mne.Epochs.plot_topomap方法的典型用法代码示例。如果您正苦于以下问题：Python Epochs.plot_topomap方法的具体用法？Python Epochs.plot_topomap怎么用？Python Epochs.plot_topomap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.Epochs的用法示例。
在下文中一共展示了Epochs.plot_topomap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_ctf_plotting
# 需要导入模块: from mne import Epochs [as 别名]
# 或者: from mne.Epochs import plot_topomap [as 别名]
def test_ctf_plotting():
"""Test CTF topomap plotting."""
raw = read_raw_fif(ctf_fname, preload=True)
events = make_fixed_length_events(raw, duration=0.01)
assert len(events) > 10
evoked = Epochs(raw, events, tmin=0, tmax=0.01, baseline=None).average()
assert get_current_comp(evoked.info) == 3
# smoke test that compensation does not matter
evoked.plot_topomap(time_unit='s')示例2: test_ctf_plotting
# 需要导入模块: from mne import Epochs [as 别名]
# 或者: from mne.Epochs import plot_topomap [as 别名]
def test_ctf_plotting():
"""Test CTF topomap plotting."""
raw = read_raw_fif(ctf_fname, preload=True)
assert raw.compensation_grade == 3
events = make_fixed_length_events(raw, duration=0.01)
assert len(events) > 10
evoked = Epochs(raw, events, tmin=0, tmax=0.01, baseline=None).average()
assert get_current_comp(evoked.info) == 3
# smoke test that compensation does not matter
evoked.plot_topomap(time_unit='s')
# better test that topomaps can still be used without plotting ref
evoked.pick_types(meg=True, ref_meg=False)
evoked.plot_topomap()示例3: Epochs
# 需要导入模块: from mne import Epochs [as 别名]
# 或者: from mne.Epochs import plot_topomap [as 别名]
###############################################################################
# Read in raw data and prepare for epoching
raw_fname = op.join(study_path, 'ds117', subject, 'MEG', 'run_01_sss.fif')
raw = mne.io.read_raw_fif(raw_fname, preload=True, add_eeg_ref=False)
picks = mne.pick_types(raw.info, meg=True, exclude='bads')
events = mne.find_events(raw, stim_channel='STI101', consecutive='increasing',
mask=4352, mask_type='not_and', min_duration=0.003,
verbose=True)
###############################################################################
# First, we don't highpass filter and only baseline. Note how it creates a
# spatially varying distortation of the time-domain signal in the form
# of "fanning"
raw.filter(None, 40, **filter_params)
evoked = Epochs(raw, events, event_id=event_ids, picks=picks,
baseline=(None, 0)).average()
evoked.plot()
evoked.plot_topomap()
###############################################################################
# Next, we highpass filter (but no lowpass filter as we have already done it)
# but don't baseline. Now, the late effects in the topography are no longer
# visible and the "fanning" has disappeared.
raw.filter(1, None, l_trans_bandwidth=0.5, **filter_params)
evoked = Epochs(raw, events, event_id=event_ids, picks=picks,
baseline=None).average()
evoked.plot()
evoked.plot_topomap()