eximia.py

# Authors: Eric Larson <larson.eric.d@gmail.com>
#          Federico Raimondo <federaimondo@gmail.com>
#
# License: BSD (3-clause)

import os.path as op

from ..base import BaseRaw
from ..utils import _read_segments_file, _file_size
from ..meas_info import create_info
from ...utils import logger, verbose, warn, fill_doc


@fill_doc
def read_raw_eximia(fname, preload=False, verbose=None):
    """Reader for an eXimia EEG file.

    Parameters
    ----------
    fname : str
        Path to the eXimia data file (.nxe).
    preload : bool
        If True, all data are loaded at initialization.
    %(verbose)s

    Returns
    -------
    raw : instance of RawEximia
        A Raw object containing eXimia data.

    See Also
    --------
    mne.io.Raw : Documentation of attribute and methods.
    """
    return RawEximia(fname, preload, verbose)


@fill_doc
class RawEximia(BaseRaw):
    """Raw object from an Eximia EEG file.

    Parameters
    ----------
    fname : str
        Path to the eXimia data file (.nxe).
    preload : bool
        If True, all data are loaded at initialization.
    %(verbose)s

    See Also
    --------
    mne.io.Raw : Documentation of attribute and methods.
    """

    @verbose
    def __init__(self, fname, preload=False, verbose=None):
        data_name = op.basename(fname)
        logger.info('Loading %s' % data_name)
        # Create vhdr and vmrk files so that we can use mne_brain_vision2fiff
        n_chan = 64
        sfreq = 1450.
        # data are multiplexed int16
        ch_names = ['GateIn', 'Trig1', 'Trig2', 'EOG']
        ch_types = ['stim', 'stim', 'stim', 'eog']
        cals = [0.0015259021896696422, 0.0015259021896696422,
                0.0015259021896696422, 0.3814755474174106]
        ch_names += ('Fp1 Fpz Fp2 AF1 AFz AF2 '
                     'F7 F3 F1 Fz F2 F4 F8 '
                     'FT9 FT7 FC5 FC3 FC1 FCz FC2 FC4 FC6 FT8 FT10 '
                     'T7 C5 C3 C1 Cz C2 C4 C6 T8 '
                     'TP9 TP7 CP5 CP3 CP1 CPz CP2 CP4 CP6 TP8 TP10 '
                     'P9 P7 P3 P1 Pz P2 P4 P8 '
                     'P10 PO3 POz PO4 O1 Oz O2 Iz'.split())
        n_eeg = len(ch_names) - len(cals)
        cals += [0.07629510948348212] * n_eeg
        ch_types += ['eeg'] * n_eeg
        assert len(ch_names) == n_chan
        info = create_info(ch_names, sfreq, ch_types)
        n_bytes = _file_size(fname)
        n_samples, extra = divmod(n_bytes, (n_chan * 2))
        if extra != 0:
            warn('Incorrect number of samples in file (%s), the file is '
                 'likely truncated' % (n_samples,))
        for ch, cal in zip(info['chs'], cals):
            ch['cal'] = cal
        super(RawEximia, self).__init__(
            info, preload=preload, last_samps=(n_samples - 1,),
            filenames=[fname], orig_format='short')

    def _read_segment_file(self, data, idx, fi, start, stop, cals, mult):
        """Read a chunk of raw data."""
        _read_segments_file(self, data, idx, fi, start, stop, cals, mult)