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"""Conversion tool from CTF to FIF."""
# Author: Eric Larson <larson.eric.d<gmail.com>
#
# License: BSD (3-clause)
import os
from os import path as op
import numpy as np
from ...utils import verbose, logger, _clean_names
from ...externals.six import string_types
from ..base import BaseRaw
from ..utils import _mult_cal_one, _blk_read_lims
from .res4 import _read_res4, _make_ctf_name
from .hc import _read_hc
from .eeg import _read_eeg, _read_pos
from .trans import _make_ctf_coord_trans_set
from .info import _compose_meas_info, _read_bad_chans, _annotate_bad_segments
from .constants import CTF
def read_raw_ctf(directory, system_clock='truncate', preload=False,
clean_names=False, verbose=None):
"""Raw object from CTF directory.
Parameters
----------
directory : str
Path to the CTF data (ending in ``'.ds'``).
system_clock : str
How to treat the system clock. Use "truncate" (default) to truncate
the data file when the system clock drops to zero, and use "ignore"
to ignore the system clock (e.g., if head positions are measured
multiple times during a recording).
preload : bool or str (default False)
Preload data into memory for data manipulation and faster indexing.
If True, the data will be preloaded into memory (fast, requires
large amount of memory). If preload is a string, preload is the
file name of a memory-mapped file which is used to store the data
on the hard drive (slower, requires less memory).
clean_names : bool, optional
If True main channel names and compensation channel names will
be cleaned from CTF suffixes. The default is False.
verbose : bool, str, int, or None
If not None, override default verbose level (see :func:`mne.verbose`
and :ref:`Logging documentation <tut_logging>` for more).
Returns
-------
raw : instance of RawCTF
The raw data.
See Also
--------
mne.io.Raw : Documentation of attribute and methods.
Notes
-----
.. versionadded:: 0.11
"""
return RawCTF(directory, system_clock, preload=preload,
clean_names=clean_names, verbose=verbose)
class RawCTF(BaseRaw):
"""Raw object from CTF directory.
Parameters
----------
directory : str
Path to the CTF data (ending in ``'.ds'``).
system_clock : str
How to treat the system clock. Use "truncate" (default) to truncate
the data file when the system clock drops to zero, and use "ignore"
to ignore the system clock (e.g., if head positions are measured
multiple times during a recording).
preload : bool or str (default False)
Preload data into memory for data manipulation and faster indexing.
If True, the data will be preloaded into memory (fast, requires
large amount of memory). If preload is a string, preload is the
file name of a memory-mapped file which is used to store the data
on the hard drive (slower, requires less memory).
clean_names : bool, optional
If True main channel names and compensation channel names will
be cleaned from CTF suffixes. The default is False.
verbose : bool, str, int, or None
If not None, override default verbose level (see :func:`mne.verbose`
and :ref:`Logging documentation <tut_logging>` for more).
See Also
--------
mne.io.Raw : Documentation of attribute and methods.
"""
@verbose
def __init__(self, directory, system_clock='truncate', preload=False,
verbose=None, clean_names=False): # noqa: D102
# adapted from mne_ctf2fiff.c
if not isinstance(directory, string_types) or \
not directory.endswith('.ds'):
raise TypeError('directory must be a directory ending with ".ds"')
if not op.isdir(directory):
raise ValueError('directory does not exist: "%s"' % directory)
known_types = ['ignore', 'truncate']
if not isinstance(system_clock, string_types) or \
system_clock not in known_types:
raise ValueError('system_clock must be one of %s, not %s'
% (known_types, system_clock))
logger.info('ds directory : %s' % directory)
res4 = _read_res4(directory) # Read the magical res4 file
coils = _read_hc(directory) # Read the coil locations
eeg = _read_eeg(directory) # Read the EEG electrode loc info
# Investigate the coil location data to get the coordinate trans
coord_trans = _make_ctf_coord_trans_set(res4, coils)
digs = _read_pos(directory, coord_trans)
# Compose a structure which makes fiff writing a piece of cake
info = _compose_meas_info(res4, coils, coord_trans, eeg)
info['dig'] += digs
info['bads'] += _read_bad_chans(directory, info)
# Determine how our data is distributed across files
fnames = list()
last_samps = list()
raw_extras = list()
while(True):
suffix = 'meg4' if len(fnames) == 0 else ('%d_meg4' % len(fnames))
meg4_name = _make_ctf_name(directory, suffix, raise_error=False)
if meg4_name is None:
break
# check how much data is in the file
sample_info = _get_sample_info(meg4_name, res4, system_clock)
if sample_info['n_samp'] == 0:
break
if len(fnames) == 0:
buffer_size_sec = sample_info['block_size'] / info['sfreq']
else:
buffer_size_sec = 1.
fnames.append(meg4_name)
last_samps.append(sample_info['n_samp'] - 1)
raw_extras.append(sample_info)
first_samps = [0] * len(last_samps)
super(RawCTF, self).__init__(
info, preload, first_samps=first_samps,
last_samps=last_samps, filenames=fnames,
raw_extras=raw_extras, orig_format='int',
buffer_size_sec=buffer_size_sec, verbose=verbose)
# Add bad segments as Annotations (correct for start time)
start_time = -res4['pre_trig_pts'] / float(info['sfreq'])
self.set_annotations(_annotate_bad_segments(directory, start_time))
if clean_names:
self._clean_names()
@verbose
def _read_segment_file(self, data, idx, fi, start, stop, cals, mult):
"""Read a chunk of raw data."""
si = self._raw_extras[fi]
offset = 0
trial_start_idx, r_lims, d_lims = _blk_read_lims(start, stop,
int(si['block_size']))
with open(self._filenames[fi], 'rb') as fid:
for bi in range(len(r_lims)):
samp_offset = (bi + trial_start_idx) * si['res4_nsamp']
n_read = min(si['n_samp_tot'] - samp_offset, si['block_size'])
# read the chunk of data
pos = CTF.HEADER_SIZE
pos += samp_offset * si['n_chan'] * 4
fid.seek(pos, 0)
this_data = np.fromfile(fid, '>i4',
count=si['n_chan'] * n_read)
this_data.shape = (si['n_chan'], n_read)
this_data = this_data[:, r_lims[bi, 0]:r_lims[bi, 1]]
data_view = data[:, d_lims[bi, 0]:d_lims[bi, 1]]
_mult_cal_one(data_view, this_data, idx, cals, mult)
offset += n_read
def _clean_names(self):
"""Clean up CTF suffixes from channel names."""
mapping = dict(zip(self.ch_names, _clean_names(self.ch_names)))
self.rename_channels(mapping)
for comp in self.info['comps']:
for key in ('row_names', 'col_names'):
comp['data'][key] = _clean_names(comp['data'][key])
def _get_sample_info(fname, res4, system_clock):
"""Determine the number of valid samples."""
logger.info('Finding samples for %s: ' % (fname,))
if CTF.SYSTEM_CLOCK_CH in res4['ch_names']:
clock_ch = res4['ch_names'].index(CTF.SYSTEM_CLOCK_CH)
else:
clock_ch = None
for k, ch in enumerate(res4['chs']):
if ch['ch_name'] == CTF.SYSTEM_CLOCK_CH:
clock_ch = k
break
with open(fname, 'rb') as fid:
fid.seek(0, os.SEEK_END)
st_size = fid.tell()
fid.seek(0, 0)
if (st_size - CTF.HEADER_SIZE) % (4 * res4['nsamp'] *
res4['nchan']) != 0:
raise RuntimeError('The number of samples is not an even multiple '
'of the trial size')
n_samp_tot = (st_size - CTF.HEADER_SIZE) // (4 * res4['nchan'])
n_trial = n_samp_tot // res4['nsamp']
n_samp = n_samp_tot
if clock_ch is None:
logger.info(' System clock channel is not available, assuming '
'all samples to be valid.')
elif system_clock == 'ignore':
logger.info(' System clock channel is available, but ignored.')
else: # use it
logger.info(' System clock channel is available, checking '
'which samples are valid.')
for t in range(n_trial):
# Skip to the correct trial
samp_offset = t * res4['nsamp']
offset = CTF.HEADER_SIZE + (samp_offset * res4['nchan'] +
(clock_ch * res4['nsamp'])) * 4
fid.seek(offset, 0)
this_data = np.fromfile(fid, '>i4', res4['nsamp'])
if len(this_data) != res4['nsamp']:
raise RuntimeError('Cannot read data for trial %d'
% (t + 1))
end = np.where(this_data == 0)[0]
if len(end) > 0:
n_samp = samp_offset + end[0]
break
if n_samp < res4['nsamp']:
n_trial = 1
logger.info(' %d x %d = %d samples from %d chs'
% (n_trial, n_samp, n_samp, res4['nchan']))
else:
n_trial = n_samp // res4['nsamp']
n_omit = n_samp_tot - n_samp
logger.info(' %d x %d = %d samples from %d chs'
% (n_trial, res4['nsamp'], n_samp, res4['nchan']))
if n_omit != 0:
logger.info(' %d samples omitted at the end' % n_omit)
return dict(n_samp=n_samp, n_samp_tot=n_samp_tot, block_size=res4['nsamp'],
res4_nsamp=res4['nsamp'], n_chan=res4['nchan'])
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