本文整理汇总了Python中mne.datasets.sample.data_path方法的典型用法代码示例。如果您正苦于以下问题：Python sample.data_path方法的具体用法？Python sample.data_path怎么用？Python sample.data_path使用的例子？那么, 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.datasets.sample的用法示例。
在下文中一共展示了sample.data_path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: mne_python
# 需要导入模块: from mne.datasets import sample [as 别名]
# 或者: from mne.datasets.sample import data_path [as 别名]
def mne_python():
subjects_dir = data_path + '/subjects'
# Read data
evoked = mne.read_evokeds(fname_evoked, condition='Left Auditory',
baseline=(None, 0))
fwd = mne.read_forward_solution(fname_fwd)
cov = mne.read_cov(fname_cov)
inv = make_inverse_operator(evoked.info, fwd, cov, loose=0., depth=0.8,
verbose=True)
snr = 3.0
lambda2 = 1.0 / snr ** 2
kwargs = dict(initial_time=0.08, hemi='both', subjects_dir=subjects_dir,
size=(600, 600))
stc = abs(apply_inverse(evoked, inv, lambda2, 'MNE', verbose=True))
stc = mne.SourceEstimate(stc.data * 1e10, stc.vertices, stc.tmin , stc.tstep, subject='sample')
stc.save(data_path + '/MEG/sample/sample_audvis_MNE')
# brain = stc.plot(figure=1, **kwargs)
# brain.add_text(0.1, 0.9, 'MNE', 'title', font_size=14) 示例2: init_data
# 需要导入模块: from mne.datasets import sample [as 别名]
# 或者: from mne.datasets.sample import data_path [as 别名]
def init_data():
data_path = sample.data_path()
raw_fname = data_path + '/MEG/sample/sample_audvis_raw.fif'
fname_inv = data_path + '/MEG/sample/sample_audvis-meg-oct-6-meg-inv.fif'
tmin, tmax, event_id = -0.2, 0.5, 1
# Setup for reading the raw data
raw = io.read_raw_fif(raw_fname)
events = mne.find_events(raw, stim_channel='STI 014')
inverse_operator = read_inverse_operator(fname_inv)
# Setting the label
label = mne.read_label(data_path + '/MEG/sample/labels/Aud-lh.label')
include = []
raw.info['bads'] += ['MEG 2443', 'EEG 053'] # bads + 2 more
# picks MEG gradiometers
picks = mne.pick_types(raw.info, meg=True, eeg=False, eog=True,
stim=False, include=include, exclude='bads')
# Load condition 1
event_id = 1
# Use linear detrend to reduce any edge artifacts
epochs = mne.Epochs(raw, events, event_id, tmin, tmax, picks=picks,
baseline=(None, 0), reject=dict(grad=4000e-13, eog=150e-6),
preload=True, detrend=1)
return epochs, inverse_operator, label