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from collections import OrderedDict
from pathlib import Path
import numpy as np
from ...utils import logger, fill_doc, verbose
from ..meas_info import create_info
from ...evoked import EvokedArray
@fill_doc
@verbose
def read_evoked_besa(fname, verbose=None):
"""Reader function for BESA .avr or .mul files.
When a .elp sidecar file is present, it will be used to determine electrode
information.
Parameters
----------
fname : str | Path
Path to the .avr or .mul file.
%(verbose)s
Returns
-------
ev : Evoked
The evoked data in the .avr or .mul file.
"""
fname = Path(fname)
if fname.suffix == '.avr':
return _read_evoked_besa_avr(fname, verbose)
elif fname.suffix == '.mul':
return _read_evoked_besa_mul(fname, verbose)
else:
raise ValueError('Filename must end in either .avr or .mul')
@verbose
def _read_evoked_besa_avr(fname, verbose):
"""Create EvokedArray from a BESA .avr file."""
with open(fname) as f:
header = f.readline().strip()
# There are two versions of .avr files. The old style, generated by
# BESA 1, 2 and 3 does not define Nchan and does not have channel names
# in the file.
new_style = 'Nchan=' in header
if new_style:
ch_names = f.readline().strip().split()
else:
ch_names = None
fields = _parse_header(header)
data = np.loadtxt(fname, skiprows=2 if new_style else 1, ndmin=2)
ch_types = _read_elp_sidecar(fname)
# Consolidate channel names
if new_style:
if len(ch_names) != len(data):
raise RuntimeError(
'Mismatch between the number of channel names defined in '
f'the .avr file ({len(ch_names)}) and the number of rows '
f'in the data matrix ({len(data)}).')
else:
# Determine channel names from the .elp sidecar file
if ch_types is not None:
ch_names = list(ch_types.keys())
if len(ch_names) != len(data):
raise RuntimeError('Mismatch between the number of channels '
f'defined in the .avr file ({len(data)}) '
f'and .elp file ({len(ch_names)}).')
else:
logger.info('No .elp file found and no channel names present in '
'the .avr file. Falling back to generic names. ')
ch_names = [f'CH{i + 1:02d}' for i in range(len(data))]
# Consolidate channel types
if ch_types is None:
logger.info('Marking all channels as EEG.')
ch_types = ['eeg'] * len(ch_names)
else:
ch_types = [ch_types[ch] for ch in ch_names]
# Go over all the header fields and make sure they are all defined to
# something sensible.
if 'Npts' in fields:
fields['Npts'] = int(fields['Npts'])
if fields['Npts'] != data.shape[1]:
logger.warn(f'The size of the data matrix ({data.shape}) does not '
f'match the "Npts" field ({fields["Npts"]}).')
if 'Nchan' in fields:
fields['Nchan'] = int(fields['Nchan'])
if fields['Nchan'] != data.shape[0]:
logger.warn(f'The size of the data matrix ({data.shape}) does not '
f'match the "Nchan" field ({fields["Nchan"]}).')
if 'DI' in fields:
fields['DI'] = float(fields['DI'])
else:
raise RuntimeError('No "DI" field present. Could not determine '
'sampling frequency.')
if 'TSB' in fields:
fields['TSB'] = float(fields['TSB'])
else:
fields['TSB'] = 0
if 'SB' in fields:
fields['SB'] = float(fields['SB'])
else:
fields['SB'] = 1.0
if 'SegmentName' not in fields:
fields['SegmentName'] = ''
# Build the Evoked object based on the header fields.
info = create_info(ch_names, sfreq=1000 / fields['DI'], ch_types='eeg')
return EvokedArray(data / fields['SB'] / 1E6, info,
tmin=fields['TSB'] / 1000,
comment=fields['SegmentName'], verbose=verbose)
@verbose
def _read_evoked_besa_mul(fname, verbose):
"""Create EvokedArray from a BESA .mul file."""
with open(fname) as f:
header = f.readline().strip()
ch_names = f.readline().strip().split()
fields = _parse_header(header)
data = np.loadtxt(fname, skiprows=2, ndmin=2)
if len(ch_names) != data.shape[1]:
raise RuntimeError('Mismatch between the number of channel names '
f'defined in the .mul file ({len(ch_names)}) '
'and the number of columns in the data matrix '
f'({data.shape[1]}).')
# Consolidate channel types
ch_types = _read_elp_sidecar(fname)
if ch_types is None:
logger.info('Marking all channels as EEG.')
ch_types = ['eeg'] * len(ch_names)
else:
ch_types = [ch_types[ch] for ch in ch_names]
# Go over all the header fields and make sure they are all defined to
# something sensible.
if 'TimePoints' in fields:
fields['TimePoints'] = int(fields['TimePoints'])
if fields['TimePoints'] != data.shape[0]:
logger.warn(
f'The size of the data matrix ({data.shape}) does not '
f'match the "TimePoints" field ({fields["TimePoints"]}).')
if 'Channels' in fields:
fields['Channels'] = int(fields['Channels'])
if fields['Channels'] != data.shape[1]:
logger.warn(f'The size of the data matrix ({data.shape}) does not '
f'match the "Channels" field ({fields["Channels"]}).')
if 'SamplingInterval[ms]' in fields:
fields['SamplingInterval[ms]'] = float(fields['SamplingInterval[ms]'])
else:
raise RuntimeError('No "SamplingInterval[ms]" field present. Could '
'not determine sampling frequency.')
if 'BeginSweep[ms]' in fields:
fields['BeginSweep[ms]'] = float(fields['BeginSweep[ms]'])
else:
fields['BeginSweep[ms]'] = 0.0
if 'Bins/uV' in fields:
fields['Bins/uV'] = float(fields['Bins/uV'])
else:
fields['Bins/uV'] = 1
if 'SegmentName' not in fields:
fields['SegmentName'] = ''
# Build the Evoked object based on the header fields.
info = create_info(ch_names, sfreq=1000 / fields['SamplingInterval[ms]'],
ch_types=ch_types)
return EvokedArray(data.T / fields['Bins/uV'] / 1E6, info,
tmin=fields['BeginSweep[ms]'] / 1000,
comment=fields['SegmentName'], verbose=verbose)
def _parse_header(header):
"""Parse an .avr or .mul header string into name/val pairs.
The header line looks like:
Npts= 256 TSB= 0.000 DI= 4.000000 SB= 1.000 SC= 200.0 Nchan= 27
No consistent use of separation chars, so parsing this is a bit iffy.
Parameters
----------
header : str
The first line of the file.
Returns
-------
fields : dict
The parsed header fields
"""
parts = header.split() # Splits on one or more spaces
name_val_pairs = zip(parts[::2], parts[1::2])
return dict((name.replace('=', ''), val) for name, val in name_val_pairs)
def _read_elp_sidecar(fname):
"""Read a possible .elp sidecar file with electrode information.
The reason we don't use the read_custom_montage for this is that we are
interested in the channel types, which a DigMontage object does not provide
us.
Parameters
----------
fname : Path
The path of the .avr or .mul file. The corresponding .elp file will be
derived from this path.
Returns
-------
ch_type : OrderedDict | None
If the sidecar file exists, return a dictionary mapping channel names
to channel types. Otherwise returns ``None``.
"""
fname_elp = fname.parent / (fname.stem + '.elp')
if not fname_elp.exists():
logger.info(f'No {fname_elp} file present containing electrode '
'information.')
return None
logger.info(f'Reading electrode names and types from {fname_elp}')
ch_types = OrderedDict()
with open(fname_elp) as f:
lines = f.readlines()
if len(lines[0].split()) > 3:
# Channel types present
for line in lines:
ch_type, ch_name = line.split()[:2]
ch_types[ch_name] = ch_type.lower()
else:
# No channel types present
logger.info('No channel types present in .elp file. Marking all '
'channels as EEG.')
for line in lines:
ch_name = line.split()[:1]
ch_types[ch_name] = 'eeg'
return ch_types
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