# -*- coding: utf-8 -*- # Authors: Alexandre Gramfort # Matti Hämäläinen # Teon Brooks # Stefan Appelhoff # # License: BSD-3-Clause from collections import Counter, OrderedDict from collections.abc import Mapping import contextlib from copy import deepcopy import datetime from io import BytesIO import operator from textwrap import shorten import string import numpy as np from .pick import (channel_type, _get_channel_types, get_channel_type_constants, pick_types, _contains_ch_type) from .constants import FIFF, _coord_frame_named from .open import fiff_open from .tree import dir_tree_find from .tag import (read_tag, find_tag, _ch_coord_dict, _update_ch_info_named, _rename_list) from .proj import (_read_proj, _write_proj, _uniquify_projs, _normalize_proj, _proj_equal, Projection) from .ctf_comp import _read_ctf_comp, write_ctf_comp from .write import (start_and_end_file, start_block, end_block, write_string, write_dig_points, write_float, write_int, write_coord_trans, write_ch_info, write_name_list, write_julian, write_float_matrix, write_id, DATE_NONE) from .proc_history import _read_proc_history, _write_proc_history from ..transforms import (invert_transform, Transform, _coord_frame_name, _ensure_trans, _frame_to_str) from ..utils import (logger, verbose, warn, object_diff, _validate_type, _stamp_to_dt, _dt_to_stamp, _pl, _is_numeric, _check_option, _on_missing, _check_on_missing, fill_doc, _check_fname) from ._digitization import (_format_dig_points, _dig_kind_proper, DigPoint, _dig_kind_rev, _dig_kind_ints, _read_dig_fif) from ._digitization import write_dig, _get_data_as_dict_from_dig from .compensator import get_current_comp from ..defaults import _handle_default b = bytes # alias _SCALAR_CH_KEYS = ('scanno', 'logno', 'kind', 'range', 'cal', 'coil_type', 'unit', 'unit_mul', 'coord_frame') _ALL_CH_KEYS_SET = set(_SCALAR_CH_KEYS + ('loc', 'ch_name')) # XXX we need to require these except when doing simplify_info _MIN_CH_KEYS_SET = set(('kind', 'cal', 'unit', 'loc', 'ch_name')) def _get_valid_units(): """Get valid units according to the International System of Units (SI). The International System of Units (SI, :footcite:`WikipediaSI`) is the default system for describing units in the Brain Imaging Data Structure (BIDS). For more information, see the BIDS specification :footcite:`BIDSdocs` and the appendix "Units" therein. References ---------- .. footbibliography:: """ valid_prefix_names = ['yocto', 'zepto', 'atto', 'femto', 'pico', 'nano', 'micro', 'milli', 'centi', 'deci', 'deca', 'hecto', 'kilo', 'mega', 'giga', 'tera', 'peta', 'exa', 'zetta', 'yotta'] valid_prefix_symbols = ['y', 'z', 'a', 'f', 'p', 'n', u'µ', 'm', 'c', 'd', 'da', 'h', 'k', 'M', 'G', 'T', 'P', 'E', 'Z', 'Y'] valid_unit_names = ['metre', 'kilogram', 'second', 'ampere', 'kelvin', 'mole', 'candela', 'radian', 'steradian', 'hertz', 'newton', 'pascal', 'joule', 'watt', 'coulomb', 'volt', 'farad', 'ohm', 'siemens', 'weber', 'tesla', 'henry', 'degree Celsius', 'lumen', 'lux', 'becquerel', 'gray', 'sievert', 'katal'] valid_unit_symbols = ['m', 'kg', 's', 'A', 'K', 'mol', 'cd', 'rad', 'sr', 'Hz', 'N', 'Pa', 'J', 'W', 'C', 'V', 'F', u'Ω', 'S', 'Wb', 'T', 'H', u'°C', 'lm', 'lx', 'Bq', 'Gy', 'Sv', 'kat'] # Valid units are all possible combinations of either prefix name or prefix # symbol together with either unit name or unit symbol. E.g., nV for # nanovolt valid_units = [] valid_units += ([''.join([prefix, unit]) for prefix in valid_prefix_names for unit in valid_unit_names]) valid_units += ([''.join([prefix, unit]) for prefix in valid_prefix_names for unit in valid_unit_symbols]) valid_units += ([''.join([prefix, unit]) for prefix in valid_prefix_symbols for unit in valid_unit_names]) valid_units += ([''.join([prefix, unit]) for prefix in valid_prefix_symbols for unit in valid_unit_symbols]) # units are also valid without a prefix valid_units += valid_unit_names valid_units += valid_unit_symbols # we also accept "n/a" as a unit, which is the default missing value in # BIDS valid_units += ["n/a"] return tuple(valid_units) @verbose def _unique_channel_names(ch_names, max_length=None, verbose=None): """Ensure unique channel names.""" suffixes = tuple(string.ascii_lowercase) if max_length is not None: ch_names[:] = [name[:max_length] for name in ch_names] unique_ids = np.unique(ch_names, return_index=True)[1] if len(unique_ids) != len(ch_names): dups = {ch_names[x] for x in np.setdiff1d(range(len(ch_names)), unique_ids)} warn('Channel names are not unique, found duplicates for: ' '%s. Applying running numbers for duplicates.' % dups) for ch_stem in dups: overlaps = np.where(np.array(ch_names) == ch_stem)[0] # We need an extra character since we append '-'. # np.ceil(...) is the maximum number of appended digits. if max_length is not None: n_keep = ( max_length - 1 - int(np.ceil(np.log10(len(overlaps))))) else: n_keep = np.inf n_keep = min(len(ch_stem), n_keep) ch_stem = ch_stem[:n_keep] for idx, ch_idx in enumerate(overlaps): # try idx first, then loop through lower case chars for suffix in (idx,) + suffixes: ch_name = ch_stem + '-%s' % suffix if ch_name not in ch_names: break if ch_name not in ch_names: ch_names[ch_idx] = ch_name else: raise ValueError('Adding a single alphanumeric for a ' 'duplicate resulted in another ' 'duplicate name %s' % ch_name) return ch_names class MontageMixin(object): """Mixin for Montage getting and setting.""" @fill_doc def get_montage(self): """Get a DigMontage from instance. Returns ------- %(montage)s """ from ..channels.montage import make_dig_montage info = self if isinstance(self, Info) else self.info if info['dig'] is None: return None # obtain coord_frame, and landmark coords # (nasion, lpa, rpa, hsp, hpi) from DigPoints montage_bunch = _get_data_as_dict_from_dig(info['dig']) coord_frame = _frame_to_str.get(montage_bunch.coord_frame) # get the channel names and chs data structure ch_names, chs = info['ch_names'], info['chs'] picks = pick_types(info, meg=False, eeg=True, seeg=True, ecog=True, dbs=True, fnirs=True, exclude=[]) # channel positions from dig do not match ch_names one to one, # so use loc[:3] instead ch_pos = {ch_names[ii]: chs[ii]['loc'][:3] for ii in picks} # fNIRS uses multiple channels for the same sensors, we use # a private function to format these for dig montage. fnirs_picks = pick_types(info, fnirs=True, exclude=[]) if len(ch_pos) == len(fnirs_picks): ch_pos = _get_fnirs_ch_pos(info) elif len(fnirs_picks) > 0: raise ValueError("MNE does not support getting the montage " "for a mix of fNIRS and other data types. " "Please raise a GitHub issue if you " "require this feature.") # create montage montage = make_dig_montage( ch_pos=ch_pos, coord_frame=coord_frame, nasion=montage_bunch.nasion, lpa=montage_bunch.lpa, rpa=montage_bunch.rpa, hsp=montage_bunch.hsp, hpi=montage_bunch.hpi, ) return montage @verbose def set_montage(self, montage, match_case=True, match_alias=False, on_missing='raise', verbose=None): """Set %(montage_types)s channel positions and digitization points. Parameters ---------- %(montage)s %(match_case)s %(match_alias)s %(on_missing_montage)s %(verbose)s Returns ------- inst : instance of Raw | Epochs | Evoked The instance, modified in-place. See Also -------- mne.channels.make_standard_montage mne.channels.make_dig_montage mne.channels.read_custom_montage Notes ----- .. warning:: Only %(montage_types)s channels can have their positions set using a montage. Other channel types (e.g., MEG channels) should have their positions defined properly using their data reading functions. """ # How to set up a montage to old named fif file (walk through example) # https://gist.github.com/massich/f6a9f4799f1fbeb8f5e8f8bc7b07d3df from ..channels.montage import _set_montage info = self if isinstance(self, Info) else self.info _set_montage(info, montage, match_case, match_alias, on_missing) return self class ContainsMixin(object): """Mixin class for Raw, Evoked, Epochs and Info.""" def __contains__(self, ch_type): """Check channel type membership. Parameters ---------- ch_type : str Channel type to check for. Can be e.g. 'meg', 'eeg', 'stim', etc. Returns ------- in : bool Whether or not the instance contains the given channel type. Examples -------- Channel type membership can be tested as:: >>> 'meg' in inst # doctest: +SKIP True >>> 'seeg' in inst # doctest: +SKIP False """ info = self if isinstance(self, Info) else self.info if ch_type == 'meg': has_ch_type = (_contains_ch_type(info, 'mag') or _contains_ch_type(info, 'grad')) else: has_ch_type = _contains_ch_type(info, ch_type) return has_ch_type @property def compensation_grade(self): """The current gradient compensation grade.""" info = self if isinstance(self, Info) else self.info return get_current_comp(info) @fill_doc def get_channel_types(self, picks=None, unique=False, only_data_chs=False): """Get a list of channel type for each channel. Parameters ---------- %(picks_all)s unique : bool Whether to return only unique channel types. Default is ``False``. only_data_chs : bool Whether to ignore non-data channels. Default is ``False``. Returns ------- channel_types : list The channel types. """ info = self if isinstance(self, Info) else self.info return _get_channel_types(info, picks=picks, unique=unique, only_data_chs=only_data_chs) def _format_trans(obj, key): try: t = obj[key] except KeyError: pass else: if t is not None: obj[key] = Transform(t['from'], t['to'], t['trans']) def _check_ch_keys(ch, ci, name='info["chs"]', check_min=True): ch_keys = set(ch) bad = sorted(ch_keys.difference(_ALL_CH_KEYS_SET)) if bad: raise KeyError( f'key{_pl(bad)} errantly present for {name}[{ci}]: {bad}') if check_min: bad = sorted(_MIN_CH_KEYS_SET.difference(ch_keys)) if bad: raise KeyError( f'key{_pl(bad)} missing for {name}[{ci}]: {bad}',) # As options are added here, test_meas_info.py:test_info_bad should be updated def _check_bads(bads): _validate_type(bads, list, 'bads') return bads def _check_description(description): _validate_type(description, (None, str), "info['description']") return description def _check_dev_head_t(dev_head_t): _validate_type(dev_head_t, (Transform, None), "info['dev_head_t']") if dev_head_t is not None: dev_head_t = _ensure_trans(dev_head_t, 'meg', 'head') return dev_head_t def _check_experimenter(experimenter): _validate_type(experimenter, (None, str), 'experimenter') return experimenter def _check_line_freq(line_freq): _validate_type(line_freq, (None, 'numeric'), 'line_freq') line_freq = float(line_freq) if line_freq is not None else line_freq return line_freq def _check_subject_info(subject_info): _validate_type(subject_info, (None, dict), 'subject_info') return subject_info def _check_device_info(device_info): _validate_type(device_info, (None, dict, ), 'device_info') return device_info def _check_helium_info(helium_info): _validate_type(helium_info, (None, dict, ), 'helium_info') return helium_info class Info(dict, MontageMixin, ContainsMixin): """Measurement information. This data structure behaves like a dictionary. It contains all metadata that is available for a recording. However, its keys are restricted to those provided by the `FIF format specification `__, so new entries should not be manually added. .. warning:: The only entries that should be manually changed by the user are ``info['bads']``, ``info['description']``, ``info['device_info']``, ``info['dev_head_t']``, ``info['experimenter']``, info['helium_info'], ``info['line_freq']``, ``info['temp']`` and ``info['subject_info']``. All other entries should be considered read-only, though they can be modified by various MNE-Python functions or methods (which have safeguards to ensure all fields remain in sync). .. warning:: This class should not be instantiated directly. To create a measurement information structure, use :func:`mne.create_info`. Parameters ---------- *args : list Arguments. **kwargs : dict Keyword arguments. Attributes ---------- acq_pars : str | None MEG system acquisition parameters. See :class:`mne.AcqParserFIF` for details. acq_stim : str | None MEG system stimulus parameters. bads : list of str List of bad (noisy/broken) channels, by name. These channels will by default be ignored by many processing steps. ch_names : list of str The names of the channels. chs : list of dict A list of channel information dictionaries, one per channel. See Notes for more information. command_line : str Contains the command and arguments used to create the source space (used for source estimation). comps : list of dict CTF software gradient compensation data. See Notes for more information. ctf_head_t : dict | None The transformation from 4D/CTF head coordinates to Neuromag head coordinates. This is only present in 4D/CTF data. custom_ref_applied : int Whether a custom (=other than average) reference has been applied to the EEG data. This flag is checked by some algorithms that require an average reference to be set. description : str | None String description of the recording. dev_ctf_t : dict | None The transformation from device coordinates to 4D/CTF head coordinates. This is only present in 4D/CTF data. dev_head_t : dict | None The device to head transformation. device_info : dict | None Information about the acquisition device. See Notes for details. .. versionadded:: 0.19 dig : list of dict | None The Polhemus digitization data in head coordinates. See Notes for more information. events : list of dict Event list, sometimes extracted from the stim channels by Neuromag systems. In general this should not be used and :func:`mne.find_events` should be used for event processing. See Notes for more information. experimenter : str | None Name of the person that ran the experiment. file_id : dict | None The FIF globally unique ID. See Notes for more information. gantry_angle : float | None Tilt angle of the gantry in degrees. helium_info : dict | None Information about the device helium. See Notes for details. .. versionadded:: 0.19 highpass : float Highpass corner frequency in Hertz. Zero indicates a DC recording. hpi_meas : list of dict HPI measurements that were taken at the start of the recording (e.g. coil frequencies). See Notes for details. hpi_results : list of dict Head position indicator (HPI) digitization points and fit information (e.g., the resulting transform). See Notes for details. hpi_subsystem : dict | None Information about the HPI subsystem that was used (e.g., event channel used for cHPI measurements). See Notes for details. kit_system_id : int Identifies the KIT system. line_freq : float | None Frequency of the power line in Hertz. lowpass : float Lowpass corner frequency in Hertz. It is automatically set to half the sampling rate if there is otherwise no low-pass applied to the data. maxshield : bool True if active shielding (IAS) was active during recording. meas_date : datetime The time (UTC) of the recording. .. versionchanged:: 0.20 This is stored as a :class:`~python:datetime.datetime` object instead of a tuple of seconds/microseconds. meas_file : str | None Raw measurement file (used for source estimation). meas_id : dict | None The ID assigned to this measurement by the acquisition system or during file conversion. Follows the same format as ``file_id``. mri_file : str | None File containing the MRI to head transformation (used for source estimation). mri_head_t : dict | None Transformation from MRI to head coordinates (used for source estimation). mri_id : dict | None MRI unique ID (used for source estimation). nchan : int Number of channels. proc_history : list of dict The MaxFilter processing history. See Notes for details. proj_id : int | None ID number of the project the experiment belongs to. proj_name : str | None Name of the project the experiment belongs to. projs : list of Projection List of SSP operators that operate on the data. See :class:`mne.Projection` for details. sfreq : float Sampling frequency in Hertz. subject_info : dict | None Information about the subject. See Notes for details. temp : object | None Can be used to store temporary objects in an Info instance. It will not survive an I/O roundtrip. .. versionadded:: 0.24 utc_offset : str "UTC offset of related meas_date (sHH:MM). .. versionadded:: 0.19 working_dir : str Working directory used when the source space was created (used for source estimation). xplotter_layout : str Layout of the Xplotter (Neuromag system only). See Also -------- mne.create_info Notes ----- The following parameters have a nested structure. * ``chs`` list of dict: cal : float The calibration factor to bring the channels to physical units. Used in product with ``range`` to scale the data read from disk. ch_name : str The channel name. coil_type : int Coil type, e.g. ``FIFFV_COIL_MEG``. coord_frame : int The coordinate frame used, e.g. ``FIFFV_COORD_HEAD``. kind : int The kind of channel, e.g. ``FIFFV_EEG_CH``. loc : array, shape (12,) Channel location. For MEG this is the position plus the normal given by a 3x3 rotation matrix. For EEG this is the position followed by reference position (with 6 unused). The values are specified in device coordinates for MEG and in head coordinates for EEG channels, respectively. logno : int Logical channel number, conventions in the usage of this number vary. range : float The hardware-oriented part of the calibration factor. This should be only applied to the continuous raw data. Used in product with ``cal`` to scale data read from disk. scanno : int Scanning order number, starting from 1. unit : int The unit to use, e.g. ``FIFF_UNIT_T_M``. unit_mul : int Unit multipliers, most commonly ``FIFF_UNITM_NONE``. * ``comps`` list of dict: ctfkind : int CTF compensation grade. colcals : ndarray Column calibrations. mat : dict A named matrix dictionary (with entries "data", "col_names", etc.) containing the compensation matrix. rowcals : ndarray Row calibrations. save_calibrated : bool Were the compensation data saved in calibrated form. * ``device_info`` dict: type : str Device type. model : str Device model. serial : str Device serial. site : str Device site. * ``dig`` list of dict: kind : int The kind of channel, e.g. ``FIFFV_POINT_EEG``, ``FIFFV_POINT_CARDINAL``. r : array, shape (3,) 3D position in m. and coord_frame. ident : int Number specifying the identity of the point. e.g. ``FIFFV_POINT_NASION`` if kind is ``FIFFV_POINT_CARDINAL``, or 42 if kind is ``FIFFV_POINT_EEG``. coord_frame : int The coordinate frame used, e.g. ``FIFFV_COORD_HEAD``. * ``events`` list of dict: channels : list of int Channel indices for the events. list : ndarray, shape (n_events * 3,) Events in triplets as number of samples, before, after. * ``file_id`` dict: version : int FIF format version, i.e. ``FIFFC_VERSION``. machid : ndarray, shape (2,) Unique machine ID, usually derived from the MAC address. secs : int Time in seconds. usecs : int Time in microseconds. * ``helium_info`` dict: he_level_raw : float Helium level (%) before position correction. helium_level : float Helium level (%) after position correction. orig_file_guid : str Original file GUID. meas_date : tuple of int The helium level meas date. * ``hpi_meas`` list of dict: creator : str Program that did the measurement. sfreq : float Sample rate. nchan : int Number of channels used. nave : int Number of averages used. ncoil : int Number of coils used. first_samp : int First sample used. last_samp : int Last sample used. hpi_coils : list of dict Coils, containing: number: int Coil number epoch : ndarray Buffer containing one epoch and channel. slopes : ndarray, shape (n_channels,) HPI data. corr_coeff : ndarray, shape (n_channels,) HPI curve fit correlations. coil_freq : float HPI coil excitation frequency * ``hpi_results`` list of dict: dig_points : list Digitization points (see ``dig`` definition) for the HPI coils. order : ndarray, shape (ncoil,) The determined digitization order. used : ndarray, shape (nused,) The indices of the used coils. moments : ndarray, shape (ncoil, 3) The coil moments. goodness : ndarray, shape (ncoil,) The goodness of fits. good_limit : float The goodness of fit limit. dist_limit : float The distance limit. accept : int Whether or not the fit was accepted. coord_trans : instance of Transformation The resulting MEG<->head transformation. * ``hpi_subsystem`` dict: ncoil : int The number of coils. event_channel : str The event channel used to encode cHPI status (e.g., STI201). hpi_coils : list of ndarray List of length ``ncoil``, each 4-element ndarray contains the event bits used on the event channel to indicate cHPI status (using the first element of these arrays is typically sufficient). * ``mri_id`` dict: version : int FIF format version, i.e. ``FIFFC_VERSION``. machid : ndarray, shape (2,) Unique machine ID, usually derived from the MAC address. secs : int Time in seconds. usecs : int Time in microseconds. * ``proc_history`` list of dict: block_id : dict See ``id`` above. date : ndarray, shape (2,) 2-element tuple of seconds and microseconds. experimenter : str Name of the person who ran the program. creator : str Program that did the processing. max_info : dict Maxwel filtering info, can contain: sss_info : dict SSS processing information. max_st tSSS processing information. sss_ctc : dict Cross-talk processing information. sss_cal : dict Fine-calibration information. smartshield : dict MaxShield information. This dictionary is (always?) empty, but its presence implies that MaxShield was used during acquisition. * ``subject_info`` dict: id : int Integer subject identifier. his_id : str String subject identifier. last_name : str Last name. first_name : str First name. middle_name : str Middle name. birthday : tuple of int Birthday in (year, month, day) format. sex : int Subject sex (0=unknown, 1=male, 2=female). hand : int Handedness (1=right, 2=left, 3=ambidextrous). """ _attributes = { 'acq_pars': 'acq_pars cannot be set directly. ' 'See mne.AcqParserFIF() for details.', 'acq_stim': 'acq_stim cannot be set directly.', 'bads': _check_bads, 'ch_names': 'ch_names cannot be set directly. ' 'Please use methods inst.add_channels(), ' 'inst.drop_channels(), inst.pick_channels(), ' 'inst.rename_channels(), inst.reorder_channels() ' 'and inst.set_channel_types() instead.', 'chs': 'chs cannot be set directly. ' 'Please use methods inst.add_channels(), ' 'inst.drop_channels(), inst.pick_channels(), ' 'inst.rename_channels(), inst.reorder_channels() ' 'and inst.set_channel_types() instead.', 'command_line': 'command_line cannot be set directly.', 'comps': 'comps cannot be set directly. ' 'Please use method Raw.apply_gradient_compensation() ' 'instead.', 'ctf_head_t': 'ctf_head_t cannot be set directly.', 'custom_ref_applied': 'custom_ref_applied cannot be set directly. ' 'Please use method inst.set_eeg_reference() ' 'instead.', 'description': _check_description, 'dev_ctf_t': 'dev_ctf_t cannot be set directly.', 'dev_head_t': _check_dev_head_t, 'device_info': _check_device_info, 'dig': 'dig cannot be set directly. ' 'Please use method inst.set_montage() instead.', 'events': 'events cannot be set directly.', 'experimenter': _check_experimenter, 'file_id': 'file_id cannot be set directly.', 'gantry_angle': 'gantry_angle cannot be set directly.', 'helium_info': _check_helium_info, 'highpass': 'highpass cannot be set directly. ' 'Please use method inst.filter() instead.', 'hpi_meas': 'hpi_meas can not be set directly.', 'hpi_results': 'hpi_results cannot be set directly.', 'hpi_subsystem': 'hpi_subsystem cannot be set directly.', 'kit_system_id': 'kit_system_id cannot be set directly.', 'line_freq': _check_line_freq, 'lowpass': 'lowpass cannot be set directly. ' 'Please use method inst.filter() instead.', 'maxshield': 'maxshield cannot be set directly.', 'meas_date': 'meas_date cannot be set directly. ' 'Please use method inst.set_meas_date() instead.', 'meas_file': 'meas_file cannot be set directly.', 'meas_id': 'meas_id cannot be set directly.', 'mri_file': 'mri_file cannot be set directly.', 'mri_head_t': 'mri_head_t cannot be set directly.', 'mri_id': 'mri_id cannot be set directly.', 'nchan': 'nchan cannot be set directly. ' 'Please use methods inst.add_channels(), ' 'inst.drop_channels(), and inst.pick_channels() instead.', 'proc_history': 'proc_history cannot be set directly.', 'proj_id': 'proj_id cannot be set directly.', 'proj_name': 'proj_name cannot be set directly.', 'projs': 'projs cannot be set directly. ' 'Please use methods inst.add_proj() and inst.del_proj() ' 'instead.', 'sfreq': 'sfreq cannot be set directly. ' 'Please use method inst.resample() instead.', 'subject_info': _check_subject_info, 'temp': lambda x: x, 'utc_offset': 'utc_offset cannot be set directly.', 'working_dir': 'working_dir cannot be set directly.', 'xplotter_layout': 'xplotter_layout cannot be set directly.' } def __init__(self, *args, **kwargs): self._unlocked = True super().__init__(*args, **kwargs) # Deal with h5io writing things as dict for key in ('dev_head_t', 'ctf_head_t', 'dev_ctf_t'): _format_trans(self, key) for res in self.get('hpi_results', []): _format_trans(res, 'coord_trans') if self.get('dig', None) is not None and len(self['dig']): if isinstance(self['dig'], dict): # needs to be unpacked self['dig'] = _dict_unpack(self['dig'], _DIG_CAST) if not isinstance(self['dig'][0], DigPoint): self['dig'] = _format_dig_points(self['dig']) if isinstance(self.get('chs', None), dict): self['chs']['ch_name'] = [str(x) for x in np.char.decode( self['chs']['ch_name'], encoding='utf8')] self['chs'] = _dict_unpack(self['chs'], _CH_CAST) for pi, proj in enumerate(self.get('projs', [])): if not isinstance(proj, Projection): self['projs'][pi] = Projection(**proj) # Old files could have meas_date as tuple instead of datetime try: meas_date = self['meas_date'] except KeyError: pass else: self['meas_date'] = _ensure_meas_date_none_or_dt(meas_date) self._unlocked = False def __getstate__(self): """Get state (for pickling).""" return {'_unlocked': self._unlocked} def __setstate__(self, state): """Set state (for pickling).""" self._unlocked = state['_unlocked'] def __setitem__(self, key, val): """Attribute setter.""" # During unpickling, the _unlocked attribute has not been set, so # let __setstate__ do it later and act unlocked now unlocked = getattr(self, '_unlocked', True) if key in self._attributes: if isinstance(self._attributes[key], str): if not unlocked: raise RuntimeError(self._attributes[key]) else: val = self._attributes[key](val) # attribute checker function else: raise RuntimeError( f"Info does not support directly setting the key {repr(key)}. " "You can set info['temp'] to store temporary objects in an " "Info instance, but these will not survive an I/O round-trip.") super().__setitem__(key, val) def update(self, other=None, **kwargs): """Update method using __setitem__().""" iterable = other.items() if isinstance(other, Mapping) else other if other is not None: for key, val in iterable: self[key] = val for key, val in kwargs.items(): self[key] = val @contextlib.contextmanager def _unlock(self, *, update_redundant=False, check_after=False): """Context manager unlocking access to attributes.""" # needed for nested _unlock() state = self._unlocked if hasattr(self, '_unlocked') else False self._unlocked = True try: yield except Exception: raise else: if update_redundant: self._update_redundant() if check_after: self._check_consistency() finally: self._unlocked = state def copy(self): """Copy the instance. Returns ------- info : instance of Info The copied info. """ return deepcopy(self) def normalize_proj(self): """(Re-)Normalize projection vectors after subselection. Applying projection after sub-selecting a set of channels that were originally used to compute the original projection vectors can be dangerous (e.g., if few channels remain, most power was in channels that are no longer picked, etc.). By default, mne will emit a warning when this is done. This function will re-normalize projectors to use only the remaining channels, thus avoiding that warning. Only use this function if you're confident that the projection vectors still adequately capture the original signal of interest. """ _normalize_proj(self) def __repr__(self): """Summarize info instead of printing all.""" MAX_WIDTH = 68 strs = [' 0: entr = ('%d item%s (%s)' % (this_len, _pl(this_len), type(v).__name__)) else: entr = '' if entr != '': non_empty += 1 strs.append('%s: %s' % (k, entr)) st = '\n '.join(sorted(strs)) st += '\n>' st %= non_empty return st def __deepcopy__(self, memodict): """Make a deepcopy.""" result = Info.__new__(Info) result._unlocked = True for k, v in self.items(): # chs is roughly half the time but most are immutable if k == 'chs': # dict shallow copy is fast, so use it then overwrite result[k] = list() for ch in v: ch = ch.copy() # shallow ch['loc'] = ch['loc'].copy() result[k].append(ch) elif k == 'ch_names': # we know it's list of str, shallow okay and saves ~100 µs result[k] = v.copy() elif k == 'hpi_meas': hms = list() for hm in v: hm = hm.copy() # the only mutable thing here is some entries in coils hm['hpi_coils'] = [coil.copy() for coil in hm['hpi_coils']] # There is a *tiny* risk here that someone could write # raw.info['hpi_meas'][0]['hpi_coils'][1]['epoch'] = ... # and assume that info.copy() will make an actual copy, # but copying these entries has a 2x slowdown penalty so # probably not worth it for such a deep corner case: # for coil in hpi_coils: # for key in ('epoch', 'slopes', 'corr_coeff'): # coil[key] = coil[key].copy() hms.append(hm) result[k] = hms else: result[k] = deepcopy(v, memodict) result._unlocked = False return result def _check_consistency(self, prepend_error=''): """Do some self-consistency checks and datatype tweaks.""" missing = [bad for bad in self['bads'] if bad not in self['ch_names']] if len(missing) > 0: msg = '%sbad channel(s) %s marked do not exist in info' raise RuntimeError(msg % (prepend_error, missing,)) meas_date = self.get('meas_date') if meas_date is not None: if (not isinstance(self['meas_date'], datetime.datetime) or self['meas_date'].tzinfo is None or self['meas_date'].tzinfo is not datetime.timezone.utc): raise RuntimeError('%sinfo["meas_date"] must be a datetime ' 'object in UTC or None, got %r' % (prepend_error, repr(self['meas_date']),)) chs = [ch['ch_name'] for ch in self['chs']] if len(self['ch_names']) != len(chs) or any( ch_1 != ch_2 for ch_1, ch_2 in zip(self['ch_names'], chs)) or \ self['nchan'] != len(chs): raise RuntimeError('%sinfo channel name inconsistency detected, ' 'please notify mne-python developers' % (prepend_error,)) # make sure we have the proper datatypes with self._unlock(): for key in ('sfreq', 'highpass', 'lowpass'): if self.get(key) is not None: self[key] = float(self[key]) for pi, proj in enumerate(self.get('projs', [])): _validate_type(proj, Projection, f'info["projs"][{pi}]') for key in ('kind', 'active', 'desc', 'data', 'explained_var'): if key not in proj: raise RuntimeError(f'Projection incomplete, missing {key}') # Ensure info['chs'] has immutable entries (copies much faster) for ci, ch in enumerate(self['chs']): _check_ch_keys(ch, ci) ch_name = ch['ch_name'] if not isinstance(ch_name, str): raise TypeError( 'Bad info: info["chs"][%d]["ch_name"] is not a string, ' 'got type %s' % (ci, type(ch_name))) for key in _SCALAR_CH_KEYS: val = ch.get(key, 1) if not _is_numeric(val): raise TypeError( 'Bad info: info["chs"][%d][%r] = %s is type %s, must ' 'be float or int' % (ci, key, val, type(val))) loc = ch['loc'] if not (isinstance(loc, np.ndarray) and loc.shape == (12,)): raise TypeError( 'Bad info: info["chs"][%d]["loc"] must be ndarray with ' '12 elements, got %r' % (ci, loc)) # make sure channel names are unique with self._unlock(): self['ch_names'] = _unique_channel_names(self['ch_names']) for idx, ch_name in enumerate(self['ch_names']): self['chs'][idx]['ch_name'] = ch_name def _update_redundant(self): """Update the redundant entries.""" with self._unlock(): self['ch_names'] = [ch['ch_name'] for ch in self['chs']] self['nchan'] = len(self['chs']) @property def ch_names(self): return self['ch_names'] def _get_chs_for_repr(self): titles = _handle_default('titles') # good channels channels = {} ch_types = [channel_type(self, idx) for idx in range(len(self['chs']))] ch_counts = Counter(ch_types) for ch_type, count in ch_counts.items(): if ch_type == 'meg': channels['mag'] = len(pick_types(self, meg='mag')) channels['grad'] = len(pick_types(self, meg='grad')) elif ch_type == 'eog': pick_eog = pick_types(self, eog=True) eog = ', '.join( np.array(self['ch_names'])[pick_eog]) elif ch_type == 'ecg': pick_ecg = pick_types(self, ecg=True) ecg = ', '.join( np.array(self['ch_names'])[pick_ecg]) channels[ch_type] = count good_channels = ', '.join( [f'{v} {titles.get(k, k.upper())}' for k, v in channels.items()]) if 'ecg' not in channels.keys(): ecg = 'Not available' if 'eog' not in channels.keys(): eog = 'Not available' # bad channels if len(self['bads']) > 0: bad_channels = ', '.join(self['bads']) else: bad_channels = 'None' return good_channels, bad_channels, ecg, eog def _repr_html_(self, caption=None): """Summarize info for HTML representation.""" from ..html_templates import repr_templates_env if isinstance(caption, str): html = f'

{caption}

' else: html = '' good_channels, bad_channels, ecg, eog = self._get_chs_for_repr() # TODO # Most of the following checks are to ensure that we get a proper repr # for Forward['info'] (and probably others like # InverseOperator['info']??), which doesn't seem to follow our standard # Info structure used elsewhere. # Proposed solution for a future refactoring: # Forward['info'] should get its own Info subclass (with respective # repr). # meas date meas_date = self.get('meas_date') if meas_date is not None: meas_date = meas_date.strftime("%B %d, %Y %H:%M:%S") + ' GMT' projs = self.get('projs') if projs: projs = [ f'{p["desc"]} : {"on" if p["active"] else "off"}' for p in self['projs'] ] else: projs = None info_template = repr_templates_env.get_template('info.html.jinja') return html + info_template.render( caption=caption, meas_date=meas_date, projs=projs, ecg=ecg, eog=eog, good_channels=good_channels, bad_channels=bad_channels, dig=self.get('dig'), subject_info=self.get('subject_info'), lowpass=self.get('lowpass'), highpass=self.get('highpass'), sfreq=self.get('sfreq'), experimenter=self.get('experimenter'), ) def _simplify_info(info): """Return a simplified info structure to speed up picking.""" chs = [{key: ch[key] for key in ('ch_name', 'kind', 'unit', 'coil_type', 'loc', 'cal')} for ch in info['chs']] sub_info = Info(chs=chs, bads=info['bads'], comps=info['comps'], projs=info['projs'], custom_ref_applied=info['custom_ref_applied']) sub_info._update_redundant() return sub_info @verbose def read_fiducials(fname, verbose=None): """Read fiducials from a fiff file. Parameters ---------- fname : path-like The filename to read. %(verbose)s Returns ------- pts : list of dict List of digitizer points (each point in a dict). coord_frame : int The coordinate frame of the points (one of ``mne.io.constants.FIFF.FIFFV_COORD_...``). """ fname = _check_fname( fname=fname, overwrite='read', must_exist=True ) fid, tree, _ = fiff_open(fname) with fid: isotrak = dir_tree_find(tree, FIFF.FIFFB_ISOTRAK) isotrak = isotrak[0] pts = [] coord_frame = FIFF.FIFFV_COORD_HEAD for k in range(isotrak['nent']): kind = isotrak['directory'][k].kind pos = isotrak['directory'][k].pos if kind == FIFF.FIFF_DIG_POINT: tag = read_tag(fid, pos) pts.append(DigPoint(tag.data)) elif kind == FIFF.FIFF_MNE_COORD_FRAME: tag = read_tag(fid, pos) coord_frame = tag.data[0] coord_frame = _coord_frame_named.get(coord_frame, coord_frame) # coord_frame is not stored in the tag for pt in pts: pt['coord_frame'] = coord_frame return pts, coord_frame @verbose def write_fiducials(fname, pts, coord_frame='unknown', *, overwrite=False, verbose=None): """Write fiducials to a fiff file. Parameters ---------- fname : path-like Destination file name. pts : iterator of dict Iterator through digitizer points. Each point is a dictionary with the keys 'kind', 'ident' and 'r'. coord_frame : str | int The coordinate frame of the points. If a string, must be one of ``'meg'``, ``'mri'``, ``'mri_voxel'``, ``'head'``, ``'mri_tal'``, ``'ras'``, ``'fs_tal'``, ``'ctf_head'``, ``'ctf_meg'``, and ``'unknown'`` If an integer, must be one of the constants defined as ``mne.io.constants.FIFF.FIFFV_COORD_...``. %(overwrite)s .. versionadded:: 1.0 %(verbose)s """ write_dig(fname, pts, coord_frame, overwrite=overwrite) @verbose def read_info(fname, verbose=None): """Read measurement info from a file. Parameters ---------- fname : str File name. %(verbose)s Returns ------- %(info_not_none)s """ f, tree, _ = fiff_open(fname) with f as fid: info = read_meas_info(fid, tree)[0] return info def read_bad_channels(fid, node): """Read bad channels. Parameters ---------- fid : file The file descriptor. node : dict The node of the FIF tree that contains info on the bad channels. Returns ------- bads : list A list of bad channel's names. """ return _read_bad_channels(fid, node) def _read_bad_channels(fid, node, ch_names_mapping): ch_names_mapping = {} if ch_names_mapping is None else ch_names_mapping nodes = dir_tree_find(node, FIFF.FIFFB_MNE_BAD_CHANNELS) bads = [] if len(nodes) > 0: for node in nodes: tag = find_tag(fid, node, FIFF.FIFF_MNE_CH_NAME_LIST) if tag is not None and tag.data is not None: bads = tag.data.split(':') bads[:] = _rename_list(bads, ch_names_mapping) return bads @verbose def read_meas_info(fid, tree, clean_bads=False, verbose=None): """Read the measurement info. Parameters ---------- fid : file Open file descriptor. tree : tree FIF tree structure. clean_bads : bool If True, clean info['bads'] before running consistency check. Should only be needed for old files where we did not check bads before saving. %(verbose)s Returns ------- %(info_not_none)s meas : dict Node in tree that contains the info. """ # Find the desired blocks meas = dir_tree_find(tree, FIFF.FIFFB_MEAS) if len(meas) == 0: raise ValueError('Could not find measurement data') if len(meas) > 1: raise ValueError('Cannot read more that 1 measurement data') meas = meas[0] meas_info = dir_tree_find(meas, FIFF.FIFFB_MEAS_INFO) if len(meas_info) == 0: raise ValueError('Could not find measurement info') if len(meas_info) > 1: raise ValueError('Cannot read more that 1 measurement info') meas_info = meas_info[0] # Read measurement info dev_head_t = None ctf_head_t = None dev_ctf_t = None meas_date = None utc_offset = None highpass = None lowpass = None nchan = None sfreq = None chs = [] experimenter = None description = None proj_id = None proj_name = None line_freq = None gantry_angle = None custom_ref_applied = FIFF.FIFFV_MNE_CUSTOM_REF_OFF xplotter_layout = None kit_system_id = None for k in range(meas_info['nent']): kind = meas_info['directory'][k].kind pos = meas_info['directory'][k].pos if kind == FIFF.FIFF_NCHAN: tag = read_tag(fid, pos) nchan = int(tag.data) elif kind == FIFF.FIFF_SFREQ: tag = read_tag(fid, pos) sfreq = float(tag.data) elif kind == FIFF.FIFF_CH_INFO: tag = read_tag(fid, pos) chs.append(tag.data) elif kind == FIFF.FIFF_LOWPASS: tag = read_tag(fid, pos) if not np.isnan(tag.data): lowpass = float(tag.data) elif kind == FIFF.FIFF_HIGHPASS: tag = read_tag(fid, pos) if not np.isnan(tag.data): highpass = float(tag.data) elif kind == FIFF.FIFF_MEAS_DATE: tag = read_tag(fid, pos) meas_date = tuple(tag.data) if len(meas_date) == 1: # can happen from old C conversions meas_date = (meas_date[0], 0) elif kind == FIFF.FIFF_UTC_OFFSET: tag = read_tag(fid, pos) utc_offset = str(tag.data) elif kind == FIFF.FIFF_COORD_TRANS: tag = read_tag(fid, pos) cand = tag.data if cand['from'] == FIFF.FIFFV_COORD_DEVICE and \ cand['to'] == FIFF.FIFFV_COORD_HEAD: dev_head_t = cand elif cand['from'] == FIFF.FIFFV_COORD_HEAD and \ cand['to'] == FIFF.FIFFV_COORD_DEVICE: # this reversal can happen with BabyMEG data dev_head_t = invert_transform(cand) elif cand['from'] == FIFF.FIFFV_MNE_COORD_CTF_HEAD and \ cand['to'] == FIFF.FIFFV_COORD_HEAD: ctf_head_t = cand elif cand['from'] == FIFF.FIFFV_MNE_COORD_CTF_DEVICE and \ cand['to'] == FIFF.FIFFV_MNE_COORD_CTF_HEAD: dev_ctf_t = cand elif kind == FIFF.FIFF_EXPERIMENTER: tag = read_tag(fid, pos) experimenter = tag.data elif kind == FIFF.FIFF_DESCRIPTION: tag = read_tag(fid, pos) description = tag.data elif kind == FIFF.FIFF_PROJ_ID: tag = read_tag(fid, pos) proj_id = tag.data elif kind == FIFF.FIFF_PROJ_NAME: tag = read_tag(fid, pos) proj_name = tag.data elif kind == FIFF.FIFF_LINE_FREQ: tag = read_tag(fid, pos) line_freq = float(tag.data) elif kind == FIFF.FIFF_GANTRY_ANGLE: tag = read_tag(fid, pos) gantry_angle = float(tag.data) elif kind in [FIFF.FIFF_MNE_CUSTOM_REF, 236]: # 236 used before v0.11 tag = read_tag(fid, pos) custom_ref_applied = int(tag.data) elif kind == FIFF.FIFF_XPLOTTER_LAYOUT: tag = read_tag(fid, pos) xplotter_layout = str(tag.data) elif kind == FIFF.FIFF_MNE_KIT_SYSTEM_ID: tag = read_tag(fid, pos) kit_system_id = int(tag.data) ch_names_mapping = _read_extended_ch_info(chs, meas_info, fid) # Check that we have everything we need if nchan is None: raise ValueError('Number of channels is not defined') if sfreq is None: raise ValueError('Sampling frequency is not defined') if len(chs) == 0: raise ValueError('Channel information not defined') if len(chs) != nchan: raise ValueError('Incorrect number of channel definitions found') if dev_head_t is None or ctf_head_t is None: hpi_result = dir_tree_find(meas_info, FIFF.FIFFB_HPI_RESULT) if len(hpi_result) == 1: hpi_result = hpi_result[0] for k in range(hpi_result['nent']): kind = hpi_result['directory'][k].kind pos = hpi_result['directory'][k].pos if kind == FIFF.FIFF_COORD_TRANS: tag = read_tag(fid, pos) cand = tag.data if (cand['from'] == FIFF.FIFFV_COORD_DEVICE and cand['to'] == FIFF.FIFFV_COORD_HEAD and dev_head_t is None): dev_head_t = cand elif (cand['from'] == FIFF.FIFFV_MNE_COORD_CTF_HEAD and cand['to'] == FIFF.FIFFV_COORD_HEAD and ctf_head_t is None): ctf_head_t = cand # Locate the Polhemus data dig = _read_dig_fif(fid, meas_info) # Locate the acquisition information acqpars = dir_tree_find(meas_info, FIFF.FIFFB_DACQ_PARS) acq_pars = None acq_stim = None if len(acqpars) == 1: acqpars = acqpars[0] for k in range(acqpars['nent']): kind = acqpars['directory'][k].kind pos = acqpars['directory'][k].pos if kind == FIFF.FIFF_DACQ_PARS: tag = read_tag(fid, pos) acq_pars = tag.data elif kind == FIFF.FIFF_DACQ_STIM: tag = read_tag(fid, pos) acq_stim = tag.data # Load the SSP data projs = _read_proj( fid, meas_info, ch_names_mapping=ch_names_mapping) # Load the CTF compensation data comps = _read_ctf_comp( fid, meas_info, chs, ch_names_mapping=ch_names_mapping) # Load the bad channel list bads = _read_bad_channels( fid, meas_info, ch_names_mapping=ch_names_mapping) # # Put the data together # info = Info(file_id=tree['id']) info._unlocked = True # Locate events list events = dir_tree_find(meas_info, FIFF.FIFFB_EVENTS) evs = list() for event in events: ev = dict() for k in range(event['nent']): kind = event['directory'][k].kind pos = event['directory'][k].pos if kind == FIFF.FIFF_EVENT_CHANNELS: ev['channels'] = read_tag(fid, pos).data elif kind == FIFF.FIFF_EVENT_LIST: ev['list'] = read_tag(fid, pos).data evs.append(ev) info['events'] = evs # Locate HPI result hpi_results = dir_tree_find(meas_info, FIFF.FIFFB_HPI_RESULT) hrs = list() for hpi_result in hpi_results: hr = dict() hr['dig_points'] = [] for k in range(hpi_result['nent']): kind = hpi_result['directory'][k].kind pos = hpi_result['directory'][k].pos if kind == FIFF.FIFF_DIG_POINT: hr['dig_points'].append(read_tag(fid, pos).data) elif kind == FIFF.FIFF_HPI_DIGITIZATION_ORDER: hr['order'] = read_tag(fid, pos).data elif kind == FIFF.FIFF_HPI_COILS_USED: hr['used'] = read_tag(fid, pos).data elif kind == FIFF.FIFF_HPI_COIL_MOMENTS: hr['moments'] = read_tag(fid, pos).data elif kind == FIFF.FIFF_HPI_FIT_GOODNESS: hr['goodness'] = read_tag(fid, pos).data elif kind == FIFF.FIFF_HPI_FIT_GOOD_LIMIT: hr['good_limit'] = float(read_tag(fid, pos).data) elif kind == FIFF.FIFF_HPI_FIT_DIST_LIMIT: hr['dist_limit'] = float(read_tag(fid, pos).data) elif kind == FIFF.FIFF_HPI_FIT_ACCEPT: hr['accept'] = int(read_tag(fid, pos).data) elif kind == FIFF.FIFF_COORD_TRANS: hr['coord_trans'] = read_tag(fid, pos).data hrs.append(hr) info['hpi_results'] = hrs # Locate HPI Measurement hpi_meass = dir_tree_find(meas_info, FIFF.FIFFB_HPI_MEAS) hms = list() for hpi_meas in hpi_meass: hm = dict() for k in range(hpi_meas['nent']): kind = hpi_meas['directory'][k].kind pos = hpi_meas['directory'][k].pos if kind == FIFF.FIFF_CREATOR: hm['creator'] = str(read_tag(fid, pos).data) elif kind == FIFF.FIFF_SFREQ: hm['sfreq'] = float(read_tag(fid, pos).data) elif kind == FIFF.FIFF_NCHAN: hm['nchan'] = int(read_tag(fid, pos).data) elif kind == FIFF.FIFF_NAVE: hm['nave'] = int(read_tag(fid, pos).data) elif kind == FIFF.FIFF_HPI_NCOIL: hm['ncoil'] = int(read_tag(fid, pos).data) elif kind == FIFF.FIFF_FIRST_SAMPLE: hm['first_samp'] = int(read_tag(fid, pos).data) elif kind == FIFF.FIFF_LAST_SAMPLE: hm['last_samp'] = int(read_tag(fid, pos).data) hpi_coils = dir_tree_find(hpi_meas, FIFF.FIFFB_HPI_COIL) hcs = [] for hpi_coil in hpi_coils: hc = dict() for k in range(hpi_coil['nent']): kind = hpi_coil['directory'][k].kind pos = hpi_coil['directory'][k].pos if kind == FIFF.FIFF_HPI_COIL_NO: hc['number'] = int(read_tag(fid, pos).data) elif kind == FIFF.FIFF_EPOCH: hc['epoch'] = read_tag(fid, pos).data hc['epoch'].flags.writeable = False elif kind == FIFF.FIFF_HPI_SLOPES: hc['slopes'] = read_tag(fid, pos).data hc['slopes'].flags.writeable = False elif kind == FIFF.FIFF_HPI_CORR_COEFF: hc['corr_coeff'] = read_tag(fid, pos).data hc['corr_coeff'].flags.writeable = False elif kind == FIFF.FIFF_HPI_COIL_FREQ: hc['coil_freq'] = float(read_tag(fid, pos).data) hcs.append(hc) hm['hpi_coils'] = hcs hms.append(hm) info['hpi_meas'] = hms del hms subject_info = dir_tree_find(meas_info, FIFF.FIFFB_SUBJECT) si = None if len(subject_info) == 1: subject_info = subject_info[0] si = dict() for k in range(subject_info['nent']): kind = subject_info['directory'][k].kind pos = subject_info['directory'][k].pos if kind == FIFF.FIFF_SUBJ_ID: tag = read_tag(fid, pos) si['id'] = int(tag.data) elif kind == FIFF.FIFF_SUBJ_HIS_ID: tag = read_tag(fid, pos) si['his_id'] = str(tag.data) elif kind == FIFF.FIFF_SUBJ_LAST_NAME: tag = read_tag(fid, pos) si['last_name'] = str(tag.data) elif kind == FIFF.FIFF_SUBJ_FIRST_NAME: tag = read_tag(fid, pos) si['first_name'] = str(tag.data) elif kind == FIFF.FIFF_SUBJ_MIDDLE_NAME: tag = read_tag(fid, pos) si['middle_name'] = str(tag.data) elif kind == FIFF.FIFF_SUBJ_BIRTH_DAY: try: tag = read_tag(fid, pos) except OverflowError: warn('Encountered an error while trying to read the ' 'birthday from the input data. No birthday will be ' 'set. Please check the integrity of the birthday ' 'information in the input data.') continue si['birthday'] = tag.data elif kind == FIFF.FIFF_SUBJ_SEX: tag = read_tag(fid, pos) si['sex'] = int(tag.data) elif kind == FIFF.FIFF_SUBJ_HAND: tag = read_tag(fid, pos) si['hand'] = int(tag.data) elif kind == FIFF.FIFF_SUBJ_WEIGHT: tag = read_tag(fid, pos) si['weight'] = tag.data elif kind == FIFF.FIFF_SUBJ_HEIGHT: tag = read_tag(fid, pos) si['height'] = tag.data info['subject_info'] = si del si device_info = dir_tree_find(meas_info, FIFF.FIFFB_DEVICE) di = None if len(device_info) == 1: device_info = device_info[0] di = dict() for k in range(device_info['nent']): kind = device_info['directory'][k].kind pos = device_info['directory'][k].pos if kind == FIFF.FIFF_DEVICE_TYPE: tag = read_tag(fid, pos) di['type'] = str(tag.data) elif kind == FIFF.FIFF_DEVICE_MODEL: tag = read_tag(fid, pos) di['model'] = str(tag.data) elif kind == FIFF.FIFF_DEVICE_SERIAL: tag = read_tag(fid, pos) di['serial'] = str(tag.data) elif kind == FIFF.FIFF_DEVICE_SITE: tag = read_tag(fid, pos) di['site'] = str(tag.data) info['device_info'] = di del di helium_info = dir_tree_find(meas_info, FIFF.FIFFB_HELIUM) hi = None if len(helium_info) == 1: helium_info = helium_info[0] hi = dict() for k in range(helium_info['nent']): kind = helium_info['directory'][k].kind pos = helium_info['directory'][k].pos if kind == FIFF.FIFF_HE_LEVEL_RAW: tag = read_tag(fid, pos) hi['he_level_raw'] = float(tag.data) elif kind == FIFF.FIFF_HELIUM_LEVEL: tag = read_tag(fid, pos) hi['helium_level'] = float(tag.data) elif kind == FIFF.FIFF_ORIG_FILE_GUID: tag = read_tag(fid, pos) hi['orig_file_guid'] = str(tag.data) elif kind == FIFF.FIFF_MEAS_DATE: tag = read_tag(fid, pos) hi['meas_date'] = tuple(int(t) for t in tag.data) info['helium_info'] = hi del hi hpi_subsystem = dir_tree_find(meas_info, FIFF.FIFFB_HPI_SUBSYSTEM) hs = None if len(hpi_subsystem) == 1: hpi_subsystem = hpi_subsystem[0] hs = dict() for k in range(hpi_subsystem['nent']): kind = hpi_subsystem['directory'][k].kind pos = hpi_subsystem['directory'][k].pos if kind == FIFF.FIFF_HPI_NCOIL: tag = read_tag(fid, pos) hs['ncoil'] = int(tag.data) elif kind == FIFF.FIFF_EVENT_CHANNEL: tag = read_tag(fid, pos) hs['event_channel'] = str(tag.data) hpi_coils = dir_tree_find(hpi_subsystem, FIFF.FIFFB_HPI_COIL) hc = [] for coil in hpi_coils: this_coil = dict() for j in range(coil['nent']): kind = coil['directory'][j].kind pos = coil['directory'][j].pos if kind == FIFF.FIFF_EVENT_BITS: tag = read_tag(fid, pos) this_coil['event_bits'] = np.array(tag.data) hc.append(this_coil) hs['hpi_coils'] = hc info['hpi_subsystem'] = hs # Read processing history info['proc_history'] = _read_proc_history(fid, tree) # Make the most appropriate selection for the measurement id if meas_info['parent_id'] is None: if meas_info['id'] is None: if meas['id'] is None: if meas['parent_id'] is None: info['meas_id'] = info['file_id'] else: info['meas_id'] = meas['parent_id'] else: info['meas_id'] = meas['id'] else: info['meas_id'] = meas_info['id'] else: info['meas_id'] = meas_info['parent_id'] info['experimenter'] = experimenter info['description'] = description info['proj_id'] = proj_id info['proj_name'] = proj_name if meas_date is None: meas_date = (info['meas_id']['secs'], info['meas_id']['usecs']) info['meas_date'] = _ensure_meas_date_none_or_dt(meas_date) info['utc_offset'] = utc_offset info['sfreq'] = sfreq info['highpass'] = highpass if highpass is not None else 0. info['lowpass'] = lowpass if lowpass is not None else info['sfreq'] / 2.0 info['line_freq'] = line_freq info['gantry_angle'] = gantry_angle # Add the channel information and make a list of channel names # for convenience info['chs'] = chs # # Add the coordinate transformations # info['dev_head_t'] = dev_head_t info['ctf_head_t'] = ctf_head_t info['dev_ctf_t'] = dev_ctf_t if dev_head_t is not None and ctf_head_t is not None and dev_ctf_t is None: from ..transforms import Transform head_ctf_trans = np.linalg.inv(ctf_head_t['trans']) dev_ctf_trans = np.dot(head_ctf_trans, info['dev_head_t']['trans']) info['dev_ctf_t'] = Transform('meg', 'ctf_head', dev_ctf_trans) # All kinds of auxliary stuff info['dig'] = _format_dig_points(dig) info['bads'] = bads info._update_redundant() if clean_bads: info['bads'] = [b for b in bads if b in info['ch_names']] info['projs'] = projs info['comps'] = comps info['acq_pars'] = acq_pars info['acq_stim'] = acq_stim info['custom_ref_applied'] = custom_ref_applied info['xplotter_layout'] = xplotter_layout info['kit_system_id'] = kit_system_id info._check_consistency() info._unlocked = False return info, meas def _read_extended_ch_info(chs, parent, fid): ch_infos = dir_tree_find(parent, FIFF.FIFFB_CH_INFO) if len(ch_infos) == 0: return _check_option('length of channel infos', len(ch_infos), [len(chs)]) logger.info(' Reading extended channel information') # Here we assume that ``remap`` is in the same order as the channels # themselves, which is hopefully safe enough. ch_names_mapping = dict() for new, ch in zip(ch_infos, chs): for k in range(new['nent']): kind = new['directory'][k].kind try: key, cast = _CH_READ_MAP[kind] except KeyError: # This shouldn't happen if we're up to date with the FIFF # spec warn(f'Discarding extra channel information kind {kind}') continue assert key in ch data = read_tag(fid, new['directory'][k].pos).data if data is not None: data = cast(data) if key == 'ch_name': ch_names_mapping[ch[key]] = data ch[key] = data _update_ch_info_named(ch) # we need to return ch_names_mapping so that we can also rename the # bad channels return ch_names_mapping def _rename_comps(comps, ch_names_mapping): if not (comps and ch_names_mapping): return for comp in comps: data = comp['data'] for key in ('row_names', 'col_names'): data[key][:] = _rename_list(data[key], ch_names_mapping) def _ensure_meas_date_none_or_dt(meas_date): if meas_date is None or np.array_equal(meas_date, DATE_NONE): meas_date = None elif not isinstance(meas_date, datetime.datetime): meas_date = _stamp_to_dt(meas_date) return meas_date def _check_dates(info, prepend_error=''): """Check dates before writing as fif files. It's needed because of the limited integer precision of the fix standard. """ for key in ('file_id', 'meas_id'): value = info.get(key) if value is not None: assert 'msecs' not in value for key_2 in ('secs', 'usecs'): if (value[key_2] < np.iinfo('>i4').min or value[key_2] > np.iinfo('>i4').max): raise RuntimeError('%sinfo[%s][%s] must be between ' '"%r" and "%r", got "%r"' % (prepend_error, key, key_2, np.iinfo('>i4').min, np.iinfo('>i4').max, value[key_2]),) meas_date = info.get('meas_date') if meas_date is None: return meas_date_stamp = _dt_to_stamp(meas_date) if (meas_date_stamp[0] < np.iinfo('>i4').min or meas_date_stamp[0] > np.iinfo('>i4').max): raise RuntimeError( '%sinfo["meas_date"] seconds must be between "%r" ' 'and "%r", got "%r"' % (prepend_error, (np.iinfo('>i4').min, 0), (np.iinfo('>i4').max, 0), meas_date_stamp[0],)) @fill_doc def write_meas_info(fid, info, data_type=None, reset_range=True): """Write measurement info into a file id (from a fif file). Parameters ---------- fid : file Open file descriptor. %(info_not_none)s data_type : int The data_type in case it is necessary. Should be 4 (FIFFT_FLOAT), 5 (FIFFT_DOUBLE), or 16 (FIFFT_DAU_PACK16) for raw data. reset_range : bool If True, info['chs'][k]['range'] will be set to unity. Notes ----- Tags are written in a particular order for compatibility with maxfilter. """ info._check_consistency() _check_dates(info) # Measurement info start_block(fid, FIFF.FIFFB_MEAS_INFO) # Add measurement id if info['meas_id'] is not None: write_id(fid, FIFF.FIFF_PARENT_BLOCK_ID, info['meas_id']) for event in info['events']: start_block(fid, FIFF.FIFFB_EVENTS) if event.get('channels') is not None: write_int(fid, FIFF.FIFF_EVENT_CHANNELS, event['channels']) if event.get('list') is not None: write_int(fid, FIFF.FIFF_EVENT_LIST, event['list']) end_block(fid, FIFF.FIFFB_EVENTS) # HPI Result for hpi_result in info['hpi_results']: start_block(fid, FIFF.FIFFB_HPI_RESULT) write_dig_points(fid, hpi_result['dig_points']) if 'order' in hpi_result: write_int(fid, FIFF.FIFF_HPI_DIGITIZATION_ORDER, hpi_result['order']) if 'used' in hpi_result: write_int(fid, FIFF.FIFF_HPI_COILS_USED, hpi_result['used']) if 'moments' in hpi_result: write_float_matrix(fid, FIFF.FIFF_HPI_COIL_MOMENTS, hpi_result['moments']) if 'goodness' in hpi_result: write_float(fid, FIFF.FIFF_HPI_FIT_GOODNESS, hpi_result['goodness']) if 'good_limit' in hpi_result: write_float(fid, FIFF.FIFF_HPI_FIT_GOOD_LIMIT, hpi_result['good_limit']) if 'dist_limit' in hpi_result: write_float(fid, FIFF.FIFF_HPI_FIT_DIST_LIMIT, hpi_result['dist_limit']) if 'accept' in hpi_result: write_int(fid, FIFF.FIFF_HPI_FIT_ACCEPT, hpi_result['accept']) if 'coord_trans' in hpi_result: write_coord_trans(fid, hpi_result['coord_trans']) end_block(fid, FIFF.FIFFB_HPI_RESULT) # HPI Measurement for hpi_meas in info['hpi_meas']: start_block(fid, FIFF.FIFFB_HPI_MEAS) if hpi_meas.get('creator') is not None: write_string(fid, FIFF.FIFF_CREATOR, hpi_meas['creator']) if hpi_meas.get('sfreq') is not None: write_float(fid, FIFF.FIFF_SFREQ, hpi_meas['sfreq']) if hpi_meas.get('nchan') is not None: write_int(fid, FIFF.FIFF_NCHAN, hpi_meas['nchan']) if hpi_meas.get('nave') is not None: write_int(fid, FIFF.FIFF_NAVE, hpi_meas['nave']) if hpi_meas.get('ncoil') is not None: write_int(fid, FIFF.FIFF_HPI_NCOIL, hpi_meas['ncoil']) if hpi_meas.get('first_samp') is not None: write_int(fid, FIFF.FIFF_FIRST_SAMPLE, hpi_meas['first_samp']) if hpi_meas.get('last_samp') is not None: write_int(fid, FIFF.FIFF_LAST_SAMPLE, hpi_meas['last_samp']) for hpi_coil in hpi_meas['hpi_coils']: start_block(fid, FIFF.FIFFB_HPI_COIL) if hpi_coil.get('number') is not None: write_int(fid, FIFF.FIFF_HPI_COIL_NO, hpi_coil['number']) if hpi_coil.get('epoch') is not None: write_float_matrix(fid, FIFF.FIFF_EPOCH, hpi_coil['epoch']) if hpi_coil.get('slopes') is not None: write_float(fid, FIFF.FIFF_HPI_SLOPES, hpi_coil['slopes']) if hpi_coil.get('corr_coeff') is not None: write_float(fid, FIFF.FIFF_HPI_CORR_COEFF, hpi_coil['corr_coeff']) if hpi_coil.get('coil_freq') is not None: write_float(fid, FIFF.FIFF_HPI_COIL_FREQ, hpi_coil['coil_freq']) end_block(fid, FIFF.FIFFB_HPI_COIL) end_block(fid, FIFF.FIFFB_HPI_MEAS) # Polhemus data write_dig_points(fid, info['dig'], block=True) # megacq parameters if info['acq_pars'] is not None or info['acq_stim'] is not None: start_block(fid, FIFF.FIFFB_DACQ_PARS) if info['acq_pars'] is not None: write_string(fid, FIFF.FIFF_DACQ_PARS, info['acq_pars']) if info['acq_stim'] is not None: write_string(fid, FIFF.FIFF_DACQ_STIM, info['acq_stim']) end_block(fid, FIFF.FIFFB_DACQ_PARS) # Coordinate transformations if the HPI result block was not there if info['dev_head_t'] is not None: write_coord_trans(fid, info['dev_head_t']) if info['ctf_head_t'] is not None: write_coord_trans(fid, info['ctf_head_t']) if info['dev_ctf_t'] is not None: write_coord_trans(fid, info['dev_ctf_t']) # Projectors ch_names_mapping = _make_ch_names_mapping(info['chs']) _write_proj(fid, info['projs'], ch_names_mapping=ch_names_mapping) # Bad channels if len(info['bads']) > 0: bads = _rename_list(info['bads'], ch_names_mapping) start_block(fid, FIFF.FIFFB_MNE_BAD_CHANNELS) write_name_list(fid, FIFF.FIFF_MNE_CH_NAME_LIST, bads) end_block(fid, FIFF.FIFFB_MNE_BAD_CHANNELS) # General if info.get('experimenter') is not None: write_string(fid, FIFF.FIFF_EXPERIMENTER, info['experimenter']) if info.get('description') is not None: write_string(fid, FIFF.FIFF_DESCRIPTION, info['description']) if info.get('proj_id') is not None: write_int(fid, FIFF.FIFF_PROJ_ID, info['proj_id']) if info.get('proj_name') is not None: write_string(fid, FIFF.FIFF_PROJ_NAME, info['proj_name']) if info.get('meas_date') is not None: write_int(fid, FIFF.FIFF_MEAS_DATE, _dt_to_stamp(info['meas_date'])) if info.get('utc_offset') is not None: write_string(fid, FIFF.FIFF_UTC_OFFSET, info['utc_offset']) write_int(fid, FIFF.FIFF_NCHAN, info['nchan']) write_float(fid, FIFF.FIFF_SFREQ, info['sfreq']) if info['lowpass'] is not None: write_float(fid, FIFF.FIFF_LOWPASS, info['lowpass']) if info['highpass'] is not None: write_float(fid, FIFF.FIFF_HIGHPASS, info['highpass']) if info.get('line_freq') is not None: write_float(fid, FIFF.FIFF_LINE_FREQ, info['line_freq']) if info.get('gantry_angle') is not None: write_float(fid, FIFF.FIFF_GANTRY_ANGLE, info['gantry_angle']) if data_type is not None: write_int(fid, FIFF.FIFF_DATA_PACK, data_type) if info.get('custom_ref_applied'): write_int(fid, FIFF.FIFF_MNE_CUSTOM_REF, info['custom_ref_applied']) if info.get('xplotter_layout'): write_string(fid, FIFF.FIFF_XPLOTTER_LAYOUT, info['xplotter_layout']) # Channel information _write_ch_infos(fid, info['chs'], reset_range, ch_names_mapping) # Subject information if info.get('subject_info') is not None: start_block(fid, FIFF.FIFFB_SUBJECT) si = info['subject_info'] if si.get('id') is not None: write_int(fid, FIFF.FIFF_SUBJ_ID, si['id']) if si.get('his_id') is not None: write_string(fid, FIFF.FIFF_SUBJ_HIS_ID, si['his_id']) if si.get('last_name') is not None: write_string(fid, FIFF.FIFF_SUBJ_LAST_NAME, si['last_name']) if si.get('first_name') is not None: write_string(fid, FIFF.FIFF_SUBJ_FIRST_NAME, si['first_name']) if si.get('middle_name') is not None: write_string(fid, FIFF.FIFF_SUBJ_MIDDLE_NAME, si['middle_name']) if si.get('birthday') is not None: write_julian(fid, FIFF.FIFF_SUBJ_BIRTH_DAY, si['birthday']) if si.get('sex') is not None: write_int(fid, FIFF.FIFF_SUBJ_SEX, si['sex']) if si.get('hand') is not None: write_int(fid, FIFF.FIFF_SUBJ_HAND, si['hand']) if si.get('weight') is not None: write_float(fid, FIFF.FIFF_SUBJ_WEIGHT, si['weight']) if si.get('height') is not None: write_float(fid, FIFF.FIFF_SUBJ_HEIGHT, si['height']) end_block(fid, FIFF.FIFFB_SUBJECT) del si if info.get('device_info') is not None: start_block(fid, FIFF.FIFFB_DEVICE) di = info['device_info'] write_string(fid, FIFF.FIFF_DEVICE_TYPE, di['type']) for key in ('model', 'serial', 'site'): if di.get(key) is not None: write_string(fid, getattr(FIFF, 'FIFF_DEVICE_' + key.upper()), di[key]) end_block(fid, FIFF.FIFFB_DEVICE) del di if info.get('helium_info') is not None: start_block(fid, FIFF.FIFFB_HELIUM) hi = info['helium_info'] if hi.get('he_level_raw') is not None: write_float(fid, FIFF.FIFF_HE_LEVEL_RAW, hi['he_level_raw']) if hi.get('helium_level') is not None: write_float(fid, FIFF.FIFF_HELIUM_LEVEL, hi['helium_level']) if hi.get('orig_file_guid') is not None: write_string(fid, FIFF.FIFF_ORIG_FILE_GUID, hi['orig_file_guid']) write_int(fid, FIFF.FIFF_MEAS_DATE, hi['meas_date']) end_block(fid, FIFF.FIFFB_HELIUM) del hi if info.get('hpi_subsystem') is not None: hs = info['hpi_subsystem'] start_block(fid, FIFF.FIFFB_HPI_SUBSYSTEM) if hs.get('ncoil') is not None: write_int(fid, FIFF.FIFF_HPI_NCOIL, hs['ncoil']) if hs.get('event_channel') is not None: write_string(fid, FIFF.FIFF_EVENT_CHANNEL, hs['event_channel']) if hs.get('hpi_coils') is not None: for coil in hs['hpi_coils']: start_block(fid, FIFF.FIFFB_HPI_COIL) if coil.get('event_bits') is not None: write_int(fid, FIFF.FIFF_EVENT_BITS, coil['event_bits']) end_block(fid, FIFF.FIFFB_HPI_COIL) end_block(fid, FIFF.FIFFB_HPI_SUBSYSTEM) del hs # CTF compensation info comps = info['comps'] if ch_names_mapping: comps = deepcopy(comps) _rename_comps(comps, ch_names_mapping) write_ctf_comp(fid, comps) # KIT system ID if info.get('kit_system_id') is not None: write_int(fid, FIFF.FIFF_MNE_KIT_SYSTEM_ID, info['kit_system_id']) end_block(fid, FIFF.FIFFB_MEAS_INFO) # Processing history _write_proc_history(fid, info) @fill_doc def write_info(fname, info, data_type=None, reset_range=True): """Write measurement info in fif file. Parameters ---------- fname : str The name of the file. Should end by -info.fif. %(info_not_none)s data_type : int The data_type in case it is necessary. Should be 4 (FIFFT_FLOAT), 5 (FIFFT_DOUBLE), or 16 (FIFFT_DAU_PACK16) for raw data. reset_range : bool If True, info['chs'][k]['range'] will be set to unity. """ with start_and_end_file(fname) as fid: start_block(fid, FIFF.FIFFB_MEAS) write_meas_info(fid, info, data_type, reset_range) end_block(fid, FIFF.FIFFB_MEAS) @verbose def _merge_info_values(infos, key, verbose=None): """Merge things together. Fork for {'dict', 'list', 'array', 'other'} and consider cases where one or all are of the same type. Does special things for "projs", "bads", and "meas_date". """ values = [d[key] for d in infos] msg = ("Don't know how to merge '%s'. Make sure values are " "compatible, got types:\n %s" % (key, [type(v) for v in values])) def _flatten(lists): return [item for sublist in lists for item in sublist] def _check_isinstance(values, kind, func): return func([isinstance(v, kind) for v in values]) def _where_isinstance(values, kind): """Get indices of instances.""" return np.where([isinstance(v, type) for v in values])[0] # list if _check_isinstance(values, list, all): lists = (d[key] for d in infos) if key == 'projs': return _uniquify_projs(_flatten(lists)) elif key == 'bads': return sorted(set(_flatten(lists))) else: return _flatten(lists) elif _check_isinstance(values, list, any): idx = _where_isinstance(values, list) if len(idx) == 1: return values[int(idx)] elif len(idx) > 1: lists = (d[key] for d in infos if isinstance(d[key], list)) return _flatten(lists) # dict elif _check_isinstance(values, dict, all): is_qual = all(object_diff(values[0], v) == '' for v in values[1:]) if is_qual: return values[0] else: RuntimeError(msg) elif _check_isinstance(values, dict, any): idx = _where_isinstance(values, dict) if len(idx) == 1: return values[int(idx)] elif len(idx) > 1: raise RuntimeError(msg) # ndarray elif _check_isinstance(values, np.ndarray, all) or \ _check_isinstance(values, tuple, all): is_qual = all(np.array_equal(values[0], x) for x in values[1:]) if is_qual: return values[0] elif key == 'meas_date': logger.info('Found multiple entries for %s. ' 'Setting value to `None`' % key) return None else: raise RuntimeError(msg) elif _check_isinstance(values, (np.ndarray, tuple), any): idx = _where_isinstance(values, np.ndarray) if len(idx) == 1: return values[int(idx)] elif len(idx) > 1: raise RuntimeError(msg) # other else: unique_values = set(values) if len(unique_values) == 1: return list(values)[0] elif isinstance(list(unique_values)[0], BytesIO): logger.info('Found multiple StringIO instances. ' 'Setting value to `None`') return None elif isinstance(list(unique_values)[0], str): logger.info('Found multiple filenames. ' 'Setting value to `None`') return None else: raise RuntimeError(msg) @verbose def _merge_info(infos, force_update_to_first=False, verbose=None): """Merge multiple measurement info dictionaries. - Fields that are present in only one info object will be used in the merged info. - Fields that are present in multiple info objects and are the same will be used in the merged info. - Fields that are present in multiple info objects and are different will result in a None value in the merged info. - Channels will be concatenated. If multiple info objects contain channels with the same name, an exception is raised. Parameters ---------- infos | list of instance of Info Info objects to merge into one info object. force_update_to_first : bool If True, force the fields for objects in `info` will be updated to match those in the first item. Use at your own risk, as this may overwrite important metadata. %(verbose)s Returns ------- info : instance of Info The merged info object. """ for info in infos: info._check_consistency() if force_update_to_first is True: infos = deepcopy(infos) _force_update_info(infos[0], infos[1:]) info = Info() info._unlocked = True info['chs'] = [] for this_info in infos: info['chs'].extend(this_info['chs']) info._update_redundant() duplicates = {ch for ch in info['ch_names'] if info['ch_names'].count(ch) > 1} if len(duplicates) > 0: msg = ("The following channels are present in more than one input " "measurement info objects: %s" % list(duplicates)) raise ValueError(msg) transforms = ['ctf_head_t', 'dev_head_t', 'dev_ctf_t'] for trans_name in transforms: trans = [i[trans_name] for i in infos if i[trans_name]] if len(trans) == 0: info[trans_name] = None elif len(trans) == 1: info[trans_name] = trans[0] elif all(np.all(trans[0]['trans'] == x['trans']) and trans[0]['from'] == x['from'] and trans[0]['to'] == x['to'] for x in trans[1:]): info[trans_name] = trans[0] else: msg = ("Measurement infos provide mutually inconsistent %s" % trans_name) raise ValueError(msg) # KIT system-IDs kit_sys_ids = [i['kit_system_id'] for i in infos if i['kit_system_id']] if len(kit_sys_ids) == 0: info['kit_system_id'] = None elif len(set(kit_sys_ids)) == 1: info['kit_system_id'] = kit_sys_ids[0] else: raise ValueError("Trying to merge channels from different KIT systems") # hpi infos and digitization data: fields = ['hpi_results', 'hpi_meas', 'dig'] for k in fields: values = [i[k] for i in infos if i[k]] if len(values) == 0: info[k] = [] elif len(values) == 1: info[k] = values[0] elif all(object_diff(values[0], v) == '' for v in values[1:]): info[k] = values[0] else: msg = ("Measurement infos are inconsistent for %s" % k) raise ValueError(msg) # other fields other_fields = ['acq_pars', 'acq_stim', 'bads', 'comps', 'custom_ref_applied', 'description', 'experimenter', 'file_id', 'highpass', 'utc_offset', 'hpi_subsystem', 'events', 'device_info', 'helium_info', 'line_freq', 'lowpass', 'meas_id', 'proj_id', 'proj_name', 'projs', 'sfreq', 'gantry_angle', 'subject_info', 'sfreq', 'xplotter_layout', 'proc_history'] for k in other_fields: info[k] = _merge_info_values(infos, k) info['meas_date'] = infos[0]['meas_date'] info._unlocked = False return info @verbose def create_info(ch_names, sfreq, ch_types='misc', verbose=None): """Create a basic Info instance suitable for use with create_raw. Parameters ---------- ch_names : list of str | int Channel names. If an int, a list of channel names will be created from ``range(ch_names)``. sfreq : float Sample rate of the data. ch_types : list of str | str Channel types, default is ``'misc'`` which is not a :term:`data channel `. Currently supported fields are 'ecg', 'bio', 'stim', 'eog', 'misc', 'seeg', 'dbs', 'ecog', 'mag', 'eeg', 'ref_meg', 'grad', 'emg', 'hbr' or 'hbo'. If str, then all channels are assumed to be of the same type. %(verbose)s Returns ------- %(info_not_none)s Notes ----- The info dictionary will be sparsely populated to enable functionality within the rest of the package. Advanced functionality such as source localization can only be obtained through substantial, proper modifications of the info structure (not recommended). Note that the MEG device-to-head transform ``info['dev_head_t']`` will be initialized to the identity transform. Proper units of measure: * V: eeg, eog, seeg, dbs, emg, ecg, bio, ecog * T: mag * T/m: grad * M: hbo, hbr * Am: dipole * AU: misc """ try: ch_names = operator.index(ch_names) # int-like except TypeError: pass else: ch_names = list(np.arange(ch_names).astype(str)) _validate_type(ch_names, (list, tuple), "ch_names", ("list, tuple, or int")) sfreq = float(sfreq) if sfreq <= 0: raise ValueError('sfreq must be positive') nchan = len(ch_names) if isinstance(ch_types, str): ch_types = [ch_types] * nchan ch_types = np.atleast_1d(np.array(ch_types, np.str_)) if ch_types.ndim != 1 or len(ch_types) != nchan: raise ValueError('ch_types and ch_names must be the same length ' '(%s != %s) for ch_types=%s' % (len(ch_types), nchan, ch_types)) info = _empty_info(sfreq) ch_types_dict = get_channel_type_constants(include_defaults=True) for ci, (ch_name, ch_type) in enumerate(zip(ch_names, ch_types)): _validate_type(ch_name, 'str', "each entry in ch_names") _validate_type(ch_type, 'str', "each entry in ch_types") if ch_type not in ch_types_dict: raise KeyError(f'kind must be one of {list(ch_types_dict)}, ' f'not {ch_type}') this_ch_dict = ch_types_dict[ch_type] kind = this_ch_dict['kind'] # handle chpi, where kind is a *list* of FIFF constants: kind = kind[0] if isinstance(kind, (list, tuple)) else kind # mirror what tag.py does here coord_frame = _ch_coord_dict.get(kind, FIFF.FIFFV_COORD_UNKNOWN) coil_type = this_ch_dict.get('coil_type', FIFF.FIFFV_COIL_NONE) unit = this_ch_dict.get('unit', FIFF.FIFF_UNIT_NONE) chan_info = dict(loc=np.full(12, np.nan), unit_mul=FIFF.FIFF_UNITM_NONE, range=1., cal=1., kind=kind, coil_type=coil_type, unit=unit, coord_frame=coord_frame, ch_name=str(ch_name), scanno=ci + 1, logno=ci + 1) info['chs'].append(chan_info) info._update_redundant() info._check_consistency() info._unlocked = False return info RAW_INFO_FIELDS = ( 'acq_pars', 'acq_stim', 'bads', 'ch_names', 'chs', 'comps', 'ctf_head_t', 'custom_ref_applied', 'description', 'dev_ctf_t', 'dev_head_t', 'dig', 'experimenter', 'events', 'utc_offset', 'device_info', 'file_id', 'highpass', 'hpi_meas', 'hpi_results', 'helium_info', 'hpi_subsystem', 'kit_system_id', 'line_freq', 'lowpass', 'meas_date', 'meas_id', 'nchan', 'proj_id', 'proj_name', 'projs', 'sfreq', 'subject_info', 'xplotter_layout', 'proc_history', 'gantry_angle', ) def _empty_info(sfreq): """Create an empty info dictionary.""" _none_keys = ( 'acq_pars', 'acq_stim', 'ctf_head_t', 'description', 'dev_ctf_t', 'dig', 'experimenter', 'utc_offset', 'device_info', 'file_id', 'highpass', 'hpi_subsystem', 'kit_system_id', 'helium_info', 'line_freq', 'lowpass', 'meas_date', 'meas_id', 'proj_id', 'proj_name', 'subject_info', 'xplotter_layout', 'gantry_angle', ) _list_keys = ('bads', 'chs', 'comps', 'events', 'hpi_meas', 'hpi_results', 'projs', 'proc_history') info = Info() info._unlocked = True for k in _none_keys: info[k] = None for k in _list_keys: info[k] = list() info['custom_ref_applied'] = FIFF.FIFFV_MNE_CUSTOM_REF_OFF info['highpass'] = 0. info['sfreq'] = float(sfreq) info['lowpass'] = info['sfreq'] / 2. info['dev_head_t'] = Transform('meg', 'head') info._update_redundant() info._check_consistency() return info def _force_update_info(info_base, info_target): """Update target info objects with values from info base. Note that values in info_target will be overwritten by those in info_base. This will overwrite all fields except for: 'chs', 'ch_names', 'nchan'. Parameters ---------- info_base : mne.Info The Info object you want to use for overwriting values in target Info objects. info_target : mne.Info | list of mne.Info The Info object(s) you wish to overwrite using info_base. These objects will be modified in-place. """ exclude_keys = ['chs', 'ch_names', 'nchan'] info_target = np.atleast_1d(info_target).ravel() all_infos = np.hstack([info_base, info_target]) for ii in all_infos: if not isinstance(ii, Info): raise ValueError('Inputs must be of type Info. ' 'Found type %s' % type(ii)) for key, val in info_base.items(): if key in exclude_keys: continue for i_targ in info_target: with i_targ._unlock(): i_targ[key] = val def _add_timedelta_to_stamp(meas_date_stamp, delta_t): """Add a timedelta to a meas_date tuple.""" if meas_date_stamp is not None: meas_date_stamp = _dt_to_stamp(_stamp_to_dt(meas_date_stamp) + delta_t) return meas_date_stamp @verbose def anonymize_info(info, daysback=None, keep_his=False, verbose=None): """Anonymize measurement information in place. .. warning:: If ``info`` is part of an object like :class:`raw.info `, you should directly use the method :meth:`raw.anonymize() ` to ensure that all parts of the data are anonymized and stay synchronized (e.g., :class:`raw.annotations `). Parameters ---------- %(info_not_none)s %(daysback_anonymize_info)s %(keep_his_anonymize_info)s %(verbose)s Returns ------- info : instance of Info The anonymized measurement information. Notes ----- %(anonymize_info_notes)s """ _validate_type(info, 'info', "self") default_anon_dos = datetime.datetime(2000, 1, 1, 0, 0, 0, tzinfo=datetime.timezone.utc) default_str = "mne_anonymize" default_subject_id = 0 default_sex = 0 default_desc = ("Anonymized using a time shift" " to preserve age at acquisition") none_meas_date = info['meas_date'] is None if none_meas_date: if daysback is not None: warn('Input info has "meas_date" set to None. ' 'Removing all information from time/date structures, ' '*NOT* performing any time shifts!') else: # compute timeshift delta if daysback is None: delta_t = info['meas_date'] - default_anon_dos else: delta_t = datetime.timedelta(days=daysback) with info._unlock(): info['meas_date'] = info['meas_date'] - delta_t # file_id and meas_id for key in ('file_id', 'meas_id'): value = info.get(key) if value is not None: assert 'msecs' not in value if (none_meas_date or ((value['secs'], value['usecs']) == DATE_NONE)): # Don't try to shift backwards in time when no measurement # date is available or when file_id is already a place holder tmp = DATE_NONE else: tmp = _add_timedelta_to_stamp( (value['secs'], value['usecs']), -delta_t) value['secs'] = tmp[0] value['usecs'] = tmp[1] # The following copy is needed for a test CTF dataset # otherwise value['machid'][:] = 0 would suffice _tmp = value['machid'].copy() _tmp[:] = 0 value['machid'] = _tmp # subject info subject_info = info.get('subject_info') if subject_info is not None: if subject_info.get('id') is not None: subject_info['id'] = default_subject_id if keep_his: logger.info('Not fully anonymizing info - keeping ' 'his_id, sex, and hand info') else: if subject_info.get('his_id') is not None: subject_info['his_id'] = str(default_subject_id) if subject_info.get('sex') is not None: subject_info['sex'] = default_sex if subject_info.get('hand') is not None: del subject_info['hand'] # there's no "unknown" setting for key in ('last_name', 'first_name', 'middle_name'): if subject_info.get(key) is not None: subject_info[key] = default_str # anonymize the subject birthday if none_meas_date: subject_info.pop('birthday', None) elif subject_info.get('birthday') is not None: dob = datetime.datetime(subject_info['birthday'][0], subject_info['birthday'][1], subject_info['birthday'][2]) dob -= delta_t subject_info['birthday'] = dob.year, dob.month, dob.day for key in ('weight', 'height'): if subject_info.get(key) is not None: subject_info[key] = 0 info['experimenter'] = default_str info['description'] = default_desc with info._unlock(): if info['proj_id'] is not None: info['proj_id'] = np.zeros_like(info['proj_id']) if info['proj_name'] is not None: info['proj_name'] = default_str if info['utc_offset'] is not None: info['utc_offset'] = None proc_hist = info.get('proc_history') if proc_hist is not None: for record in proc_hist: record['block_id']['machid'][:] = 0 record['experimenter'] = default_str if none_meas_date: record['block_id']['secs'] = DATE_NONE[0] record['block_id']['usecs'] = DATE_NONE[1] record['date'] = DATE_NONE else: this_t0 = (record['block_id']['secs'], record['block_id']['usecs']) this_t1 = _add_timedelta_to_stamp( this_t0, -delta_t) record['block_id']['secs'] = this_t1[0] record['block_id']['usecs'] = this_t1[1] record['date'] = _add_timedelta_to_stamp( record['date'], -delta_t) hi = info.get('helium_info') if hi is not None: if hi.get('orig_file_guid') is not None: hi['orig_file_guid'] = default_str if none_meas_date and hi.get('meas_date') is not None: hi['meas_date'] = DATE_NONE elif hi.get('meas_date') is not None: hi['meas_date'] = _add_timedelta_to_stamp( hi['meas_date'], -delta_t) di = info.get('device_info') if di is not None: for k in ('serial', 'site'): if di.get(k) is not None: di[k] = default_str err_mesg = ('anonymize_info generated an inconsistent info object. ' 'Underlying Error:\n') info._check_consistency(prepend_error=err_mesg) err_mesg = ('anonymize_info generated an inconsistent info object. ' 'daysback parameter was too large. ' 'Underlying Error:\n') _check_dates(info, prepend_error=err_mesg) return info @fill_doc def _bad_chans_comp(info, ch_names): """Check if channel names are consistent with current compensation status. Parameters ---------- %(info_not_none)s ch_names : list of str The channel names to check. Returns ------- status : bool True if compensation is *currently* in use but some compensation channels are not included in picks False if compensation is *currently* not being used or if compensation is being used and all compensation channels in info and included in picks. missing_ch_names: array-like of str, shape (n_missing,) The names of compensation channels not included in picks. Returns [] if no channels are missing. """ if 'comps' not in info: # should this be thought of as a bug? return False, [] # only include compensation channels that would affect selected channels ch_names_s = set(ch_names) comp_names = [] for comp in info['comps']: if len(ch_names_s.intersection(comp['data']['row_names'])) > 0: comp_names.extend(comp['data']['col_names']) comp_names = sorted(set(comp_names)) missing_ch_names = sorted(set(comp_names).difference(ch_names)) if get_current_comp(info) != 0 and len(missing_ch_names) > 0: return True, missing_ch_names return False, missing_ch_names _DIG_CAST = dict( kind=int, ident=int, r=lambda x: x, coord_frame=int) # key -> const, cast, write _CH_INFO_MAP = OrderedDict( scanno=(FIFF.FIFF_CH_SCAN_NO, int, write_int), logno=(FIFF.FIFF_CH_LOGICAL_NO, int, write_int), kind=(FIFF.FIFF_CH_KIND, int, write_int), range=(FIFF.FIFF_CH_RANGE, float, write_float), cal=(FIFF.FIFF_CH_CAL, float, write_float), coil_type=(FIFF.FIFF_CH_COIL_TYPE, int, write_int), loc=(FIFF.FIFF_CH_LOC, lambda x: x, write_float), unit=(FIFF.FIFF_CH_UNIT, int, write_int), unit_mul=(FIFF.FIFF_CH_UNIT_MUL, int, write_int), ch_name=(FIFF.FIFF_CH_DACQ_NAME, str, write_string), coord_frame=(FIFF.FIFF_CH_COORD_FRAME, int, write_int), ) # key -> cast _CH_CAST = OrderedDict((key, val[1]) for key, val in _CH_INFO_MAP.items()) # const -> key, cast _CH_READ_MAP = OrderedDict((val[0], (key, val[1])) for key, val in _CH_INFO_MAP.items()) @contextlib.contextmanager def _writing_info_hdf5(info): # Make info writing faster by packing chs and dig into numpy arrays orig_dig = info.get('dig', None) orig_chs = info['chs'] with info._unlock(): try: if orig_dig is not None and len(orig_dig) > 0: info['dig'] = _dict_pack(info['dig'], _DIG_CAST) info['chs'] = _dict_pack(info['chs'], _CH_CAST) info['chs']['ch_name'] = np.char.encode( info['chs']['ch_name'], encoding='utf8') yield finally: if orig_dig is not None: info['dig'] = orig_dig info['chs'] = orig_chs def _dict_pack(obj, casts): # pack a list of dict into dict of array return {key: np.array([o[key] for o in obj]) for key in casts} def _dict_unpack(obj, casts): # unpack a dict of array into a list of dict n = len(obj[list(casts)[0]]) return [{key: cast(obj[key][ii]) for key, cast in casts.items()} for ii in range(n)] def _make_ch_names_mapping(chs): orig_ch_names = [c['ch_name'] for c in chs] ch_names = orig_ch_names.copy() _unique_channel_names(ch_names, max_length=15, verbose='error') ch_names_mapping = dict() if orig_ch_names != ch_names: ch_names_mapping.update(zip(orig_ch_names, ch_names)) return ch_names_mapping def _write_ch_infos(fid, chs, reset_range, ch_names_mapping): ch_names_mapping = dict() if ch_names_mapping is None else ch_names_mapping for k, c in enumerate(chs): # Scan numbers may have been messed up c = c.copy() c['ch_name'] = ch_names_mapping.get(c['ch_name'], c['ch_name']) assert len(c['ch_name']) <= 15 c['scanno'] = k + 1 # for float/double, the "range" param is unnecessary if reset_range: c['range'] = 1.0 write_ch_info(fid, c) # only write new-style channel information if necessary if len(ch_names_mapping): logger.info( ' Writing channel names to FIF truncated to 15 characters ' 'with remapping') for ch in chs: start_block(fid, FIFF.FIFFB_CH_INFO) assert set(ch) == set(_CH_INFO_MAP) for (key, (const, _, write)) in _CH_INFO_MAP.items(): write(fid, const, ch[key]) end_block(fid, FIFF.FIFFB_CH_INFO) def _ensure_infos_match(info1, info2, name, *, on_mismatch='raise'): """Check if infos match. Parameters ---------- info1, info2 : instance of Info The infos to compare. name : str The name of the object appearing in the error message of the comparison fails. on_mismatch : 'raise' | 'warn' | 'ignore' What to do in case of a mismatch of ``dev_head_t`` between ``info1`` and ``info2``. """ _check_on_missing(on_missing=on_mismatch, name='on_mismatch') info1._check_consistency() info2._check_consistency() if info1['nchan'] != info2['nchan']: raise ValueError(f'{name}.info[\'nchan\'] must match') if set(info1['bads']) != set(info2['bads']): raise ValueError(f'{name}.info[\'bads\'] must match') if info1['sfreq'] != info2['sfreq']: raise ValueError(f'{name}.info[\'sfreq\'] must match') if set(info1['ch_names']) != set(info2['ch_names']): raise ValueError(f'{name}.info[\'ch_names\'] must match') if len(info2['projs']) != len(info1['projs']): raise ValueError(f'SSP projectors in {name} must be the same') if any(not _proj_equal(p1, p2) for p1, p2 in zip(info2['projs'], info1['projs'])): raise ValueError(f'SSP projectors in {name} must be the same') if (info1['dev_head_t'] is None) != (info2['dev_head_t'] is None) or \ (info1['dev_head_t'] is not None and not np.allclose(info1['dev_head_t']['trans'], info2['dev_head_t']['trans'], rtol=1e-6)): msg = (f"{name}.info['dev_head_t'] differs. The " f"instances probably come from different runs, and " f"are therefore associated with different head " f"positions. Manually change info['dev_head_t'] to " f"avoid this message but beware that this means the " f"MEG sensors will not be properly spatially aligned. " f"See mne.preprocessing.maxwell_filter to realign the " f"runs to a common head position.") _on_missing(on_missing=on_mismatch, msg=msg, name='on_mismatch') def _get_fnirs_ch_pos(info): """Return positions of each fNIRS optode. fNIRS uses two types of optodes, sources and detectors. There can be multiple connections between each source and detector at different wavelengths. This function returns the location of each source and detector. """ from ..preprocessing.nirs import _fnirs_optode_names, _optode_position srcs, dets = _fnirs_optode_names(info) ch_pos = {} for optode in [*srcs, *dets]: ch_pos[optode] = _optode_position(info, optode) return ch_pos