本文整理汇总了Python中mne.Epochs.resample方法的典型用法代码示例。如果您正苦于以下问题：Python Epochs.resample方法的具体用法？Python Epochs.resample怎么用？Python Epochs.resample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.resample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_resample
# 需要导入模块: from mne import Epochs [as 别名]
# 或者: from mne.Epochs import resample [as 别名]
def test_resample():
"""Test of resample of epochs
"""
epochs = Epochs(raw, events[:5], event_id, tmin, tmax, picks=picks,
baseline=(None, 0), preload=True,
reject=reject, flat=flat)
data_normal = cp.deepcopy(epochs.get_data())
times_normal = cp.deepcopy(epochs.times)
sfreq_normal = epochs.info['sfreq']
# upsample by 2
epochs.resample(sfreq_normal * 2)
data_up = cp.deepcopy(epochs.get_data())
times_up = cp.deepcopy(epochs.times)
sfreq_up = epochs.info['sfreq']
# downsamply by 2, which should match
epochs.resample(sfreq_normal)
data_new = cp.deepcopy(epochs.get_data())
times_new = cp.deepcopy(epochs.times)
sfreq_new = epochs.info['sfreq']
assert_true(data_up.shape[2] == 2 * data_normal.shape[2])
assert_true(sfreq_up == 2 * sfreq_normal)
assert_true(sfreq_new == sfreq_normal)
assert_true(len(times_up) == 2 * len(times_normal))
assert_array_almost_equal(times_new, times_normal, 10)
assert_true(data_up.shape[2] == 2 * data_normal.shape[2])
assert_array_almost_equal(data_new, data_normal, 2)示例2: test_resample
# 需要导入模块: from mne import Epochs [as 别名]
# 或者: from mne.Epochs import resample [as 别名]
def test_resample():
"""Test of resample of epochs
"""
epochs = Epochs(
raw, events[:10], event_id, tmin, tmax, picks=picks, baseline=(None, 0), preload=True, reject=reject, flat=flat
)
data_normal = cp.deepcopy(epochs.get_data())
times_normal = cp.deepcopy(epochs.times)
sfreq_normal = epochs.info["sfreq"]
# upsample by 2
epochs.resample(sfreq_normal * 2, npad=0)
data_up = cp.deepcopy(epochs.get_data())
times_up = cp.deepcopy(epochs.times)
sfreq_up = epochs.info["sfreq"]
# downsamply by 2, which should match
epochs.resample(sfreq_normal, npad=0)
data_new = cp.deepcopy(epochs.get_data())
times_new = cp.deepcopy(epochs.times)
sfreq_new = epochs.info["sfreq"]
assert_true(data_up.shape[2] == 2 * data_normal.shape[2])
assert_true(sfreq_up == 2 * sfreq_normal)
assert_true(sfreq_new == sfreq_normal)
assert_true(len(times_up) == 2 * len(times_normal))
assert_array_almost_equal(times_new, times_normal, 10)
assert_true(data_up.shape[2] == 2 * data_normal.shape[2])
assert_array_almost_equal(data_new, data_normal, 5)
# use parallel
epochs = Epochs(
raw, events[:10], event_id, tmin, tmax, picks=picks, baseline=(None, 0), preload=True, reject=reject, flat=flat
)
epochs.resample(sfreq_normal * 2, n_jobs=2, npad=0)
assert_true(np.allclose(data_up, epochs._data, rtol=1e-8, atol=1e-16))示例3: fix_triggers
# 需要导入模块: from mne import Epochs [as 别名]
# 或者: from mne.Epochs import resample [as 别名]
for event_type in event_types:
events_behavior_type = fix_triggers(events_meg, events_behavior,
event_type='trigg' + event_type)
# Epoch raw data
epochs_list = list()
for run in range(1, n_runs):
fname_raw = op.join(path_data, subject, 'run%02i.fif' % run)
raw = Raw(fname_raw, preload=True)
raw.filter(.75, h_freq=30.0)
sel = events_behavior_type['meg_file'] == run
time_sample = events_behavior_type['meg_event_tsample'][sel]
trigger_value = events_behavior_type['meg_event_value'][sel]
events_meg = np.vstack((time_sample.astype(int),
np.zeros_like(time_sample, int),
trigger_value.astype(int))).T
event_id = {'ttl_%i' % ii: ii for ii in np.unique(events_meg[:, 2])}
epochs = Epochs(raw, events_meg, event_id=event_id,
tmin=-1.0, tmax=.500, preload=True)
# epochs.resample(128) # XXX BUG MNE when concatenate afterwards
epochs_list.append(epochs)
epochs = concatenate_epochs(epochs_list)
epochs.resample(128)
# Save data
fname = op.join(path_data, subject, 'epochs_%s.fif' % event_type)
epochs.save(fname)
fname = op.join(path_data, subject, 'behavior_%s.pkl' % event_type)
with open(fname, 'wb') as f:
pickle.dump(events_behavior_type, f)