104 lines
3.1 KiB
Python
104 lines
3.1 KiB
Python
# Authors: The MNE-Python contributors.
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# License: BSD-3-Clause
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# Copyright the MNE-Python contributors.
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import os.path as op
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from ..._fiff.meas_info import create_info
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from ..._fiff.utils import _file_size, _read_segments_file
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from ...utils import _check_fname, fill_doc, logger, verbose, warn
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from ..base import BaseRaw
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@fill_doc
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def read_raw_eximia(fname, preload=False, verbose=None) -> "RawEximia":
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"""Reader for an eXimia EEG file.
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Parameters
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----------
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fname : path-like
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Path to the eXimia ``.nxe`` data file.
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%(preload)s
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%(verbose)s
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Returns
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-------
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raw : instance of RawEximia
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A Raw object containing eXimia data.
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See :class:`mne.io.Raw` for documentation of attributes and methods.
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See Also
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--------
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mne.io.Raw : Documentation of attributes and methods of RawEximia.
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"""
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return RawEximia(fname, preload, verbose)
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@fill_doc
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class RawEximia(BaseRaw):
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"""Raw object from an Eximia EEG file.
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Parameters
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----------
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fname : path-like
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Path to the eXimia data file (.nxe).
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%(preload)s
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%(verbose)s
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See Also
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--------
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mne.io.Raw : Documentation of attributes and methods.
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"""
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@verbose
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def __init__(self, fname, preload=False, verbose=None):
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fname = str(_check_fname(fname, "read", True, "fname"))
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data_name = op.basename(fname)
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logger.info(f"Loading {data_name}")
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# Create vhdr and vmrk files so that we can use mne_brain_vision2fiff
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n_chan = 64
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sfreq = 1450.0
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# data are multiplexed int16
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ch_names = ["GateIn", "Trig1", "Trig2", "EOG"]
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ch_types = ["stim", "stim", "stim", "eog"]
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cals = [
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0.0015259021896696422,
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0.0015259021896696422,
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0.0015259021896696422,
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0.3814755474174106,
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]
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ch_names += (
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"Fp1 Fpz Fp2 AF1 AFz AF2 "
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"F7 F3 F1 Fz F2 F4 F8 "
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"FT9 FT7 FC5 FC3 FC1 FCz FC2 FC4 FC6 FT8 FT10 "
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"T7 C5 C3 C1 Cz C2 C4 C6 T8 "
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"TP9 TP7 CP5 CP3 CP1 CPz CP2 CP4 CP6 TP8 TP10 "
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"P9 P7 P3 P1 Pz P2 P4 P8 "
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"P10 PO3 POz PO4 O1 Oz O2 Iz".split()
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)
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n_eeg = len(ch_names) - len(cals)
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cals += [0.07629510948348212] * n_eeg
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ch_types += ["eeg"] * n_eeg
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assert len(ch_names) == n_chan
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info = create_info(ch_names, sfreq, ch_types)
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n_bytes = _file_size(fname)
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n_samples, extra = divmod(n_bytes, (n_chan * 2))
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if extra != 0:
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warn(
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f"Incorrect number of samples in file ({n_samples}), the file is likely"
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" truncated"
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)
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for ch, cal in zip(info["chs"], cals):
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ch["cal"] = cal
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super().__init__(
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info,
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preload=preload,
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last_samps=(n_samples - 1,),
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filenames=[fname],
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orig_format="short",
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)
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def _read_segment_file(self, data, idx, fi, start, stop, cals, mult):
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"""Read a chunk of raw data."""
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_read_segments_file(self, data, idx, fi, start, stop, cals, mult, dtype="<i2")
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