本文整理汇总了Python中mne.io.Raw.interpolate_bads方法的典型用法代码示例。如果您正苦于以下问题：Python Raw.interpolate_bads方法的具体用法？Python Raw.interpolate_bads怎么用？Python Raw.interpolate_bads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.io.Raw的用法示例。
在下文中一共展示了Raw.interpolate_bads方法的1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: apply_create_noise_covariance
# 需要导入模块: from mne.io import Raw [as 别名]
# 或者: from mne.io.Raw import interpolate_bads [as 别名]
def apply_create_noise_covariance(fname_empty_room, verbose=None):
'''
Creates the noise covariance matrix from an empty room file.
Parameters
----------
fname_empty_room : String containing the filename
of the de-noise, empty room file (must be a fif-file)
require_filter: bool
If true, the empy room file is filtered before calculating
the covariance matrix. (Beware, filter settings are fixed.)
verbose : bool, str, int, or None
If not None, override default verbose level
(see mne.verbose).
default: verbose=None
'''
# -------------------------------------------
# import necessary modules
# -------------------------------------------
from mne import compute_raw_data_covariance as cp_covariance
from mne import write_cov, pick_types
from mne.io import Raw
from jumeg.jumeg_noise_reducer import noise_reducer
fner = get_files_from_list(fname_empty_room)
nfiles = len(fner)
ext_empty_raw = '-raw.fif'
ext_empty_cov = '-cov.fif'
# loop across all filenames
for ifile in range(nfiles):
fn_in = fner[ifile]
print ">>> create noise covariance using file: "
path_in, name = os.path.split(fn_in)
print name
fn_empty_nr = fn_in[:fn_in.rfind('-raw.fif')] + ',nr-raw.fif'
noise_reducer(fn_in, refnotch=50, detrending=False, fnout=fn_empty_nr)
noise_reducer(fn_empty_nr, refnotch=60, detrending=False, fnout=fn_empty_nr)
noise_reducer(fn_empty_nr, reflp=5, fnout=fn_empty_nr)
# file name for saving noise_cov
fn_out = fn_empty_nr[:fn_empty_nr.rfind(ext_empty_raw)] + ext_empty_cov
# read in data
raw_empty = Raw(fn_empty_nr, preload=True, verbose=verbose)
raw_empty.interpolate_bads()
# pick MEG channels only
picks = pick_types(raw_empty.info, meg=True, ref_meg=False, eeg=False,
stim=False, eog=False, exclude='bads')
# calculate noise-covariance matrix
noise_cov_mat = cp_covariance(raw_empty, picks=picks, verbose=verbose)
# write noise-covariance matrix to disk
write_cov(fn_out, noise_cov_mat)