# Authors: Alexandre Gramfort # Matti Hamalainen # Martin Luessi # Denis Engemann # Teon Brooks # # License: BSD (3-clause) import copy import os import os.path as op import numpy as np from ..constants import FIFF from ..open import fiff_open, _fiff_get_fid, _get_next_fname from ..meas_info import read_meas_info from ..tree import dir_tree_find from ..tag import read_tag, read_tag_info from ..base import (BaseRaw, _RawShell, _check_raw_compatibility, _check_maxshield) from ..utils import _mult_cal_one from ...annotations import (Annotations, _combine_annotations, _read_annotations_fif) from ...event import AcqParserFIF from ...utils import check_fname, logger, verbose, warn class Raw(BaseRaw): """Raw data in FIF format. Parameters ---------- fname : str The raw file to load. For files that have automatically been split, the split part will be automatically loaded. Filenames should end with raw.fif, raw.fif.gz, raw_sss.fif, raw_sss.fif.gz, raw_tsss.fif or raw_tsss.fif.gz. allow_maxshield : bool | str (default False) If True, allow loading of data that has been recorded with internal active compensation (MaxShield). Data recorded with MaxShield should generally not be loaded directly, but should first be processed using SSS/tSSS to remove the compensation signals that may also affect brain activity. Can also be "yes" to load without eliciting a warning. 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). verbose : bool, str, int, or None If not None, override default verbose level (see :func:`mne.verbose` and :ref:`Logging documentation ` for more). Attributes ---------- info : dict :class:`Measurement info `. ch_names : list of string List of channels' names. n_times : int Total number of time points in the raw file. times : ndarray Time vector in seconds. Starts from 0, independently of `first_samp` value. Time interval between consecutive time samples is equal to the inverse of the sampling frequency. preload : bool Indicates whether raw data are in memory. verbose : bool, str, int, or None See above. """ @verbose def __init__(self, fname, allow_maxshield=False, preload=False, verbose=None): # noqa: D102 fnames = [op.realpath(fname)] del fname split_fnames = [] raws = [] for ii, fname in enumerate(fnames): do_check_fname = fname not in split_fnames raw, next_fname, buffer_size_sec = \ self._read_raw_file(fname, allow_maxshield, preload, do_check_fname) raws.append(raw) if next_fname is not None: if not op.exists(next_fname): warn('Split raw file detected but next file %s does not ' 'exist.' % next_fname) continue # process this file next fnames.insert(ii + 1, next_fname) split_fnames.append(next_fname) _check_raw_compatibility(raws) super(Raw, self).__init__( copy.deepcopy(raws[0].info), False, [r.first_samp for r in raws], [r.last_samp for r in raws], [r.filename for r in raws], [r._raw_extras for r in raws], raws[0].orig_format, None, buffer_size_sec=buffer_size_sec, verbose=verbose) # combine annotations self.set_annotations(raws[0].annotations, False) if any([r.annotations for r in raws[1:]]): n_samples = np.sum(self._last_samps - self._first_samps + 1) for r in raws: annotations = _combine_annotations( self.annotations, r.annotations, n_samples, self.first_samp, r.first_samp, r.info['sfreq'], self.info['meas_date']) self.set_annotations(annotations, False) n_samples += r.last_samp - r.first_samp + 1 # Add annotations for in-data skips for extra in self._raw_extras: start = np.array([e['first'] for e in extra if e['ent'] is None]) stop = np.array([e['last'] for e in extra if e['ent'] is None]) duration = (stop - start + 1.) / self.info['sfreq'] annot = Annotations(onset=(start / self.info['sfreq']), duration=duration, description='BAD_ACQ_SKIP', orig_time=self.info['meas_date']) self._annotations += annot if preload: self._preload_data(preload) else: self.preload = False @verbose def _read_raw_file(self, fname, allow_maxshield, preload, do_check_fname=True, verbose=None): """Read in header information from a raw file.""" logger.info('Opening raw data file %s...' % fname) if do_check_fname: check_fname(fname, 'raw', ('raw.fif', 'raw_sss.fif', 'raw_tsss.fif', 'raw.fif.gz', 'raw_sss.fif.gz', 'raw_tsss.fif.gz')) # Read in the whole file if preload is on and .fif.gz (saves time) ext = os.path.splitext(fname)[1].lower() whole_file = preload if '.gz' in ext else False ff, tree, _ = fiff_open(fname, preload=whole_file) with ff as fid: # Read the measurement info info, meas = read_meas_info(fid, tree, clean_bads=True) annotations = _read_annotations_fif(fid, tree) # Locate the data of interest raw_node = dir_tree_find(meas, FIFF.FIFFB_RAW_DATA) if len(raw_node) == 0: raw_node = dir_tree_find(meas, FIFF.FIFFB_CONTINUOUS_DATA) if (len(raw_node) == 0): raw_node = dir_tree_find(meas, FIFF.FIFFB_SMSH_RAW_DATA) if (len(raw_node) == 0): raise ValueError('No raw data in %s' % fname) _check_maxshield(allow_maxshield) info['maxshield'] = True if len(raw_node) == 1: raw_node = raw_node[0] # Process the directory directory = raw_node['directory'] nent = raw_node['nent'] nchan = int(info['nchan']) first = 0 first_samp = 0 first_skip = 0 # Get first sample tag if it is there if directory[first].kind == FIFF.FIFF_FIRST_SAMPLE: tag = read_tag(fid, directory[first].pos) first_samp = int(tag.data) first += 1 _check_entry(first, nent) # Omit initial skip if directory[first].kind == FIFF.FIFF_DATA_SKIP: # This first skip can be applied only after we know the bufsize tag = read_tag(fid, directory[first].pos) first_skip = int(tag.data) first += 1 _check_entry(first, nent) raw = _RawShell() raw.filename = fname raw.first_samp = first_samp raw.set_annotations(annotations) # Go through the remaining tags in the directory raw_extras = list() nskip = 0 orig_format = None for k in range(first, nent): ent = directory[k] # There can be skips in the data (e.g., if the user unclicked) # an re-clicked the button if ent.kind == FIFF.FIFF_DATA_SKIP: tag = read_tag(fid, ent.pos) nskip = int(tag.data) elif ent.kind == FIFF.FIFF_DATA_BUFFER: # Figure out the number of samples in this buffer if ent.type == FIFF.FIFFT_DAU_PACK16: nsamp = ent.size // (2 * nchan) elif ent.type == FIFF.FIFFT_SHORT: nsamp = ent.size // (2 * nchan) elif ent.type == FIFF.FIFFT_FLOAT: nsamp = ent.size // (4 * nchan) elif ent.type == FIFF.FIFFT_DOUBLE: nsamp = ent.size // (8 * nchan) elif ent.type == FIFF.FIFFT_INT: nsamp = ent.size // (4 * nchan) elif ent.type == FIFF.FIFFT_COMPLEX_FLOAT: nsamp = ent.size // (8 * nchan) elif ent.type == FIFF.FIFFT_COMPLEX_DOUBLE: nsamp = ent.size // (16 * nchan) else: raise ValueError('Cannot handle data buffers of type ' '%d' % ent.type) if orig_format is None: if ent.type == FIFF.FIFFT_DAU_PACK16: orig_format = 'short' elif ent.type == FIFF.FIFFT_SHORT: orig_format = 'short' elif ent.type == FIFF.FIFFT_FLOAT: orig_format = 'single' elif ent.type == FIFF.FIFFT_DOUBLE: orig_format = 'double' elif ent.type == FIFF.FIFFT_INT: orig_format = 'int' elif ent.type == FIFF.FIFFT_COMPLEX_FLOAT: orig_format = 'single' elif ent.type == FIFF.FIFFT_COMPLEX_DOUBLE: orig_format = 'double' # Do we have an initial skip pending? if first_skip > 0: first_samp += nsamp * first_skip raw.first_samp = first_samp first_skip = 0 # Do we have a skip pending? if nskip > 0: raw_extras.append(dict( ent=None, first=first_samp, nsamp=nskip * nsamp, last=first_samp + nskip * nsamp - 1)) first_samp += nskip * nsamp nskip = 0 # Add a data buffer raw_extras.append(dict(ent=ent, first=first_samp, last=first_samp + nsamp - 1, nsamp=nsamp)) first_samp += nsamp next_fname = _get_next_fname(fid, fname, tree) raw.last_samp = first_samp - 1 raw.orig_format = orig_format # Add the calibration factors cals = np.zeros(info['nchan']) for k in range(info['nchan']): cals[k] = info['chs'][k]['range'] * info['chs'][k]['cal'] raw._cals = cals raw._raw_extras = raw_extras logger.info(' Range : %d ... %d = %9.3f ... %9.3f secs' % ( raw.first_samp, raw.last_samp, float(raw.first_samp) / info['sfreq'], float(raw.last_samp) / info['sfreq'])) # store the original buffer size buffer_size_sec = np.median( [r['nsamp'] for r in raw_extras]) / info['sfreq'] raw.info = info raw.verbose = verbose logger.info('Ready.') return raw, next_fname, buffer_size_sec @property def _dtype(self): """Get the dtype to use to store data from disk.""" if self._dtype_ is not None: return self._dtype_ dtype = None for raw_extra, filename in zip(self._raw_extras, self._filenames): for this in raw_extra: if this['ent'] is not None: with _fiff_get_fid(filename) as fid: fid.seek(this['ent'].pos, 0) tag = read_tag_info(fid) if tag is not None: if tag.type in (FIFF.FIFFT_COMPLEX_FLOAT, FIFF.FIFFT_COMPLEX_DOUBLE): dtype = np.complex128 else: dtype = np.float64 if dtype is not None: break if dtype is not None: break if dtype is None: raise RuntimeError('bug in reading') self._dtype_ = dtype return dtype def _read_segment_file(self, data, idx, fi, start, stop, cals, mult): """Read a segment of data from a file.""" stop -= 1 offset = 0 with _fiff_get_fid(self._filenames[fi]) as fid: for this in self._raw_extras[fi]: # Do we need this buffer if this['last'] >= start: # The picking logic is a bit complicated if stop > this['last'] and start < this['first']: # We need the whole buffer first_pick = 0 last_pick = this['nsamp'] logger.debug('W') elif start >= this['first']: first_pick = start - this['first'] if stop <= this['last']: # Something from the middle last_pick = this['nsamp'] + stop - this['last'] logger.debug('M') else: # From the middle to the end last_pick = this['nsamp'] logger.debug('E') else: # From the beginning to the middle first_pick = 0 last_pick = stop - this['first'] + 1 logger.debug('B') # Now we are ready to pick picksamp = last_pick - first_pick if picksamp > 0: # only read data if it exists if this['ent'] is not None: one = read_tag(fid, this['ent'].pos, shape=(this['nsamp'], self.info['nchan']), rlims=(first_pick, last_pick)).data one.shape = (picksamp, self.info['nchan']) _mult_cal_one(data[:, offset:(offset + picksamp)], one.T, idx, cals, mult) offset += picksamp # Done? if this['last'] >= stop: break def fix_mag_coil_types(self): """Fix Elekta magnetometer coil types. Returns ------- raw : instance of Raw The raw object. Operates in place. Notes ----- This function changes magnetometer coil types 3022 (T1: SQ20483N) and 3023 (T2: SQ20483-A) to 3024 (T3: SQ20950N) in the channel definition records in the info structure. Neuromag Vectorview systems can contain magnetometers with two different coil sizes (3022 and 3023 vs. 3024). The systems incorporating coils of type 3024 were introduced last and are used at the majority of MEG sites. At some sites with 3024 magnetometers, the data files have still defined the magnetometers to be of type 3022 to ensure compatibility with older versions of Neuromag software. In the MNE software as well as in the present version of Neuromag software coil type 3024 is fully supported. Therefore, it is now safe to upgrade the data files to use the true coil type. .. note:: The effect of the difference between the coil sizes on the current estimates computed by the MNE software is very small. Therefore the use of mne_fix_mag_coil_types is not mandatory. """ from ...channels import fix_mag_coil_types fix_mag_coil_types(self.info) return self @property def acqparser(self): """The AcqParserFIF for the measurement info. See Also -------- mne.AcqParserFIF """ if getattr(self, '_acqparser', None) is None: self._acqparser = AcqParserFIF(self.info) return self._acqparser def _check_entry(first, nent): """Sanity check entries.""" if first >= nent: raise IOError('Could not read data, perhaps this is a corrupt file') def read_raw_fif(fname, allow_maxshield=False, preload=False, verbose=None): """Reader function for Raw FIF data. Parameters ---------- fname : str The raw file to load. For files that have automatically been split, the split part will be automatically loaded. Filenames should end with raw.fif, raw.fif.gz, raw_sss.fif, raw_sss.fif.gz, raw_tsss.fif or raw_tsss.fif.gz. allow_maxshield : bool | str (default False) If True, allow loading of data that has been recorded with internal active compensation (MaxShield). Data recorded with MaxShield should generally not be loaded directly, but should first be processed using SSS/tSSS to remove the compensation signals that may also affect brain activity. Can also be "yes" to load without eliciting a warning. 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). verbose : bool, str, int, or None If not None, override default verbose level (see :func:`mne.verbose` and :ref:`Logging documentation ` for more). Returns ------- raw : instance of Raw A Raw object containing FIF data. Notes ----- .. versionadded:: 0.9.0 """ return Raw(fname=fname, allow_maxshield=allow_maxshield, preload=preload, verbose=verbose)