@verbose
def make_ad_hoc_cov(info, verbose=None):
 info = pick_info(info, pick_types(info, meg=True, eeg=True, exclude=[]))
 info._check_consistency()
 grad_std = 5e-13
 mag_std = 2e-14
 eeg_std = 2e-07
 logger.info(('Using standard noise values (MEG grad : %6.1f fT/cm MEG mag : %6.1f fT EEG : %6.1f uV)' % ((10000000000000.0 * grad_std), (1000000000000000.0 * mag_std), (1000000.0 * eeg_std))))
 data = np.zeros(len(info['ch_names']))
 for (meg, eeg, val) in zip(('grad', 'mag', False), (False, False, True), (grad_std, mag_std, eeg_std)):
  data[pick_types(info, meg=meg, eeg=eeg)] = (val * val)
 return Covariance(data, info['ch_names'], info['bads'], info['projs'], nfree=0)

@ verbose def make_ad_hoc_cov ( info , verbose = None ) : info = pick_info ( info , pick_types ( info , meg = True , eeg = True , exclude = [ ] ) ) info . _check_consistency ( ) grad_std = 5e-13 mag_std = 2e-14 eeg_std = 2e-07 logger . info ( ( 'Using standard noise values (MEG grad : %6.1f fT/cm MEG mag : %6.1f fT EEG : %6.1f uV)' % ( ( 10000000000000.0 * grad_std ) , ( 1000000000000000.0 * mag_std ) , ( 1000000.0 * eeg_std ) ) ) ) data = np . zeros ( len ( info [ 'ch_names' ] ) ) for ( meg , eeg , val ) in zip ( ( 'grad' , 'mag' , False ) , ( False , False , True ) , ( grad_std , mag_std , eeg_std ) ) : data [ pick_types ( info , meg = meg , eeg = eeg ) ] = ( val * val ) return Covariance ( data , info [ 'ch_names' ] , info [ 'bads' ] , info [ 'projs' ] , nfree = 0 )
create an ad hoc noise covariance .