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import numpy as np
from .constants import FIFF
def get_current_comp(info):
"""Get the current compensation in effect in the data
"""
comp = None
first_comp = -1
for k, chan in enumerate(info['chs']):
if chan['kind'] == FIFF.FIFFV_MEG_CH:
comp = int(chan['coil_type']) >> 16
if first_comp < 0:
first_comp = comp
elif comp != first_comp:
raise ValueError('Compensation is not set equally on '
'all MEG channels')
return comp
def set_current_comp(info, comp):
"""Set the current compensation in effect in the data
"""
comp_now = get_current_comp(info)
for k, chan in enumerate(info['chs']):
if chan['kind'] == FIFF.FIFFV_MEG_CH:
rem = chan['coil_type'] - (comp_now << 16)
chan['coil_type'] = int(rem + (comp << 16))
def _make_compensator(info, kind):
"""Auxiliary function for make_compensator
"""
for k in range(len(info['comps'])):
if info['comps'][k]['kind'] == kind:
this_data = info['comps'][k]['data']
# Create the preselector
presel = np.zeros((this_data['ncol'], info['nchan']))
for col, col_name in enumerate(this_data['col_names']):
ind = [k for k, ch in enumerate(info['ch_names'])
if ch == col_name]
if len(ind) == 0:
raise ValueError('Channel %s is not available in '
'data' % col_name)
elif len(ind) > 1:
raise ValueError('Ambiguous channel %s' % col_name)
presel[col, ind[0]] = 1.0
# Create the postselector
postsel = np.zeros((info['nchan'], this_data['nrow']))
for c, ch_name in enumerate(info['ch_names']):
ind = [k for k, ch in enumerate(this_data['row_names'])
if ch == ch_name]
if len(ind) > 1:
raise ValueError('Ambiguous channel %s' % ch_name)
elif len(ind) == 1:
postsel[c, ind[0]] = 1.0
this_comp = np.dot(postsel, np.dot(this_data['data'], presel))
return this_comp
raise ValueError('Desired compensation matrix (kind = %d) not'
' found' % kind)
def make_compensator(info, from_, to, exclude_comp_chs=False):
"""Returns compensation matrix eg. for CTF system.
Create a compensation matrix to bring the data from one compensation
state to another.
Parameters
----------
info : dict
The measurement info.
from_ : int
Compensation in the input data.
to : int
Desired compensation in the output.
exclude_comp_chs : bool
Exclude compensation channels from the output.
Returns
-------
comp : array | None.
The compensation matrix. Might be None if no compensation
is needed (from == to).
"""
if from_ == to:
return None
if from_ == 0:
C1 = np.zeros((info['nchan'], info['nchan']))
else:
C1 = _make_compensator(info, from_)
if to == 0:
C2 = np.zeros((info['nchan'], info['nchan']))
else:
C2 = _make_compensator(info, to)
# s_orig = s_from + C1*s_from = (I + C1)*s_from
# s_to = s_orig - C2*s_orig = (I - C2)*s_orig
# s_to = (I - C2)*(I + C1)*s_from = (I + C1 - C2 - C2*C1)*s_from
comp = np.eye(info['nchan']) + C1 - C2 - np.dot(C2, C1)
if exclude_comp_chs:
pick = [k for k, c in enumerate(info['chs'])
if c['kind'] != FIFF.FIFFV_REF_MEG_CH]
if len(pick) == 0:
raise ValueError('Nothing remains after excluding the '
'compensation channels')
comp = comp[pick, :]
return comp
# @verbose
# def compensate_to(data, to, verbose=None):
# """
# %
# % [newdata] = mne_compensate_to(data,to)
# %
# % Apply compensation to the data as desired
# %
# """
#
# newdata = data.copy()
# now = get_current_comp(newdata['info'])
#
# # Are we there already?
# if now == to:
# logger.info('Data are already compensated as desired')
#
# # Make the compensator and apply it to all data sets
# comp = make_compensator(newdata['info'], now, to)
# for k in range(len(newdata['evoked'])):
# newdata['evoked'][k]['epochs'] = np.dot(comp,
# newdata['evoked'][k]['epochs'])
#
# # Update the compensation info in the channel descriptors
# newdata['info']['chs'] = set_current_comp(newdata['info']['chs'], to)
# return newdata
# def set_current_comp(chs, value):
# """Set the current compensation value in the channel info structures
# """
# new_chs = chs
#
# lower_half = int('FFFF', 16) # hex2dec('FFFF')
# for k in range(len(chs)):
# if chs[k]['kind'] == FIFF.FIFFV_MEG_CH:
# coil_type = float(chs[k]['coil_type']) & lower_half
# new_chs[k]['coil_type'] = int(coil_type | (value << 16))
#
# return new_chs
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