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# Authors: The MNE-Python contributors.
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.
import os.path as op
from ..._fiff.meas_info import create_info
from ..._fiff.utils import _file_size, _read_segments_file
from ...utils import _check_fname, fill_doc, logger, verbose, warn
from ..base import BaseRaw
@fill_doc
def read_raw_eximia(fname, preload=False, verbose=None) -> "RawEximia":
"""Reader for an eXimia EEG file.
Parameters
----------
fname : path-like
Path to the eXimia ``.nxe`` data file.
%(preload)s
%(verbose)s
Returns
-------
raw : instance of RawEximia
A Raw object containing eXimia data.
See :class:`mne.io.Raw` for documentation of attributes and methods.
See Also
--------
mne.io.Raw : Documentation of attributes and methods of RawEximia.
"""
return RawEximia(fname, preload, verbose)
@fill_doc
class RawEximia(BaseRaw):
"""Raw object from an Eximia EEG file.
Parameters
----------
fname : path-like
Path to the eXimia data file (.nxe).
%(preload)s
%(verbose)s
See Also
--------
mne.io.Raw : Documentation of attributes and methods.
"""
@verbose
def __init__(self, fname, preload=False, verbose=None):
fname = str(_check_fname(fname, "read", True, "fname"))
data_name = op.basename(fname)
logger.info(f"Loading {data_name}")
# Create vhdr and vmrk files so that we can use mne_brain_vision2fiff
n_chan = 64
sfreq = 1450.0
# 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(
f"Incorrect number of samples in file ({n_samples}), the file is likely"
" truncated"
)
for ch, cal in zip(info["chs"], cals):
ch["cal"] = cal
super().__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, dtype="<i2")
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