本文整理汇总了Python中mne.fiff.Raw.time_to_index方法的典型用法代码示例。如果您正苦于以下问题：Python Raw.time_to_index方法的具体用法？Python Raw.time_to_index怎么用？Python Raw.time_to_index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.fiff.Raw的用法示例。
在下文中一共展示了Raw.time_to_index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_io_raw
# 需要导入模块: from mne.fiff import Raw [as 别名]
# 或者: from mne.fiff.Raw import time_to_index [as 别名]
def test_io_raw():
"""Test IO for raw data (Neuromag + CTF)
"""
for fname in [fif_fname, ctf_fname]:
raw = Raw(fname)
nchan = raw.info['nchan']
ch_names = raw.info['ch_names']
meg_channels_idx = [k for k in range(nchan)
if ch_names[k][0] == 'M']
n_channels = 100
meg_channels_idx = meg_channels_idx[:n_channels]
start, stop = raw.time_to_index(0, 5)
data, times = raw[meg_channels_idx, start:(stop + 1)]
meg_ch_names = [ch_names[k] for k in meg_channels_idx]
# Set up pick list: MEG + STI 014 - bad channels
include = ['STI 014']
include += meg_ch_names
picks = pick_types(raw.info, meg=True, eeg=False,
stim=True, misc=True, include=include,
exclude=raw.info['bads'])
print "Number of picked channels : %d" % len(picks)
# Writing with drop_small_buffer True
raw.save('raw.fif', picks, tmin=0, tmax=4, buffer_size_sec=3,
drop_small_buffer=True)
raw2 = Raw('raw.fif')
sel = pick_channels(raw2.ch_names, meg_ch_names)
data2, times2 = raw2[sel, :]
assert_true(times2.max() <= 3)
# Writing
raw.save('raw.fif', picks, tmin=0, tmax=5)
if fname == fif_fname:
assert_true(len(raw.info['dig']) == 146)
raw2 = Raw('raw.fif')
sel = pick_channels(raw2.ch_names, meg_ch_names)
data2, times2 = raw2[sel, :]
assert_array_almost_equal(data, data2)
assert_array_almost_equal(times, times2)
assert_array_almost_equal(raw.info['dev_head_t']['trans'],
raw2.info['dev_head_t']['trans'])
assert_array_almost_equal(raw.info['sfreq'], raw2.info['sfreq'])
if fname == fif_fname:
assert_array_almost_equal(raw.info['dig'][0]['r'],
raw2.info['dig'][0]['r'])
fname = op.join(op.dirname(__file__), 'data', 'test_raw.fif')示例2: test_io_complex
# 需要导入模块: from mne.fiff import Raw [as 别名]
# 或者: from mne.fiff.Raw import time_to_index [as 别名]
def test_io_complex():
""" Test IO with complex data types """
dtypes = [np.complex64, np.complex128]
raw = Raw(fif_fname, preload=True)
picks = np.arange(5)
start, stop = raw.time_to_index(0, 5)
data_orig, _ = raw[picks, start:stop]
for dtype in dtypes:
imag_rand = np.array(1j * np.random.randn(data_orig.shape[0],
data_orig.shape[1]), dtype)
raw_cp = deepcopy(raw)
raw_cp._data = np.array(raw_cp._data, dtype)
raw_cp._data[picks, start:stop] += imag_rand
raw_cp.save('raw.fif', picks, tmin=0, tmax=5)
raw2 = Raw('raw.fif')
raw2_data, _ = raw2[picks, :]
n_samp = raw2_data.shape[1]
assert_array_almost_equal(raw2_data[:, :n_samp],
raw_cp._data[picks, :n_samp])示例3: Raw
# 需要导入模块: from mne.fiff import Raw [as 别名]
# 或者: from mne.fiff.Raw import time_to_index [as 别名]
data_path = sample.data_path('..')
fname_inv = data_path + '/MEG/sample/sample_audvis-meg-oct-6-meg-inv.fif'
fname_raw = data_path + '/MEG/sample/sample_audvis_raw.fif'
label_name = 'Aud-lh'
fname_label = data_path + '/MEG/sample/labels/%s.label' % label_name
snr = 3.0
lambda2 = 1.0 / snr ** 2
dSPM = True
# Load data
raw = Raw(fname_raw)
inverse_operator = read_inverse_operator(fname_inv)
label = mne.read_label(fname_label)
start, stop = raw.time_to_index(0, 15) # read the first 15s of data
# Compute inverse solution
stc = apply_inverse_raw(raw, inverse_operator, lambda2, dSPM, label,
start, stop, pick_normal=False)
# Save result in stc files
stc.save('mne_dSPM_raw_inverse_%s' % label_name)
###############################################################################
# View activation time-series
pl.plot(1e3 * stc.times, stc.data[::100, :].T)
pl.xlabel('time (ms)')
pl.ylabel('dSPM value')
pl.show()