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# -*- coding: utf-8 -*-
# Authors: MNE Developers
#
# License: BSD-3-Clause
import os
import shutil
import datetime
import os.path as op
import numpy as np
from ..io.egi.egimff import _import_mffpy
from ..io.pick import pick_types, pick_channels
from ..utils import verbose, warn, _check_fname
@verbose
def export_evokeds_mff(fname, evoked, history=None, *, overwrite=False,
verbose=None):
"""Export evoked dataset to MFF.
%(export_warning)s
Parameters
----------
%(fname_export_params)s
evoked : list of Evoked instances
List of evoked datasets to export to one file. Note that the
measurement info from the first evoked instance is used, so be sure
that information matches.
history : None (default) | list of dict
Optional list of history entries (dictionaries) to be written to
history.xml. This must adhere to the format described in
mffpy.xml_files.History.content. If None, no history.xml will be
written.
%(overwrite)s
.. versionadded:: 0.24.1
%(verbose)s
Notes
-----
.. versionadded:: 0.24
%(export_warning_note_evoked)s
Only EEG channels are written to the output file.
``info['device_info']['type']`` must be a valid MFF recording device
(e.g. 'HydroCel GSN 256 1.0'). This field is automatically populated when
using MFF read functions.
"""
mffpy = _import_mffpy('Export evokeds to MFF.')
import pytz
info = evoked[0].info
if np.round(info['sfreq']) != info['sfreq']:
raise ValueError('Sampling frequency must be a whole number. '
f'sfreq: {info["sfreq"]}')
sampling_rate = int(info['sfreq'])
# check for unapplied projectors
if any(not proj['active'] for proj in evoked[0].info['projs']):
warn('Evoked instance has unapplied projectors. Consider applying '
'them before exporting with evoked.apply_proj().')
# Initialize writer
# Future changes: conditions based on version or mffpy requirement if
# https://github.com/BEL-Public/mffpy/pull/92 is merged and released.
fname = _check_fname(fname, overwrite=overwrite)
if op.exists(fname):
os.remove(fname) if op.isfile(fname) else shutil.rmtree(fname)
writer = mffpy.Writer(fname)
current_time = pytz.utc.localize(datetime.datetime.utcnow())
writer.addxml('fileInfo', recordTime=current_time)
try:
device = info['device_info']['type']
except (TypeError, KeyError):
raise ValueError('No device type. Cannot determine sensor layout.')
writer.add_coordinates_and_sensor_layout(device)
# Add EEG data
eeg_channels = pick_types(info, eeg=True, exclude=[])
eeg_bin = mffpy.bin_writer.BinWriter(sampling_rate)
for ave in evoked:
# Signals are converted to µV
block = (ave.data[eeg_channels] * 1e6).astype(np.float32)
eeg_bin.add_block(block, offset_us=0)
writer.addbin(eeg_bin)
# Add categories
categories_content = _categories_content_from_evokeds(evoked)
writer.addxml('categories', categories=categories_content)
# Add history
if history:
writer.addxml('historyEntries', entries=history)
writer.write()
def _categories_content_from_evokeds(evoked):
"""Return categories.xml content for evoked dataset."""
content = dict()
begin_time = 0
for ave in evoked:
# Times are converted to microseconds
sfreq = ave.info['sfreq']
duration = np.round(len(ave.times) / sfreq * 1e6).astype(int)
end_time = begin_time + duration
event_time = begin_time - np.round(ave.tmin * 1e6).astype(int)
eeg_bads = _get_bad_eeg_channels(ave.info)
content[ave.comment] = [
_build_segment_content(begin_time, end_time, event_time, eeg_bads,
name='Average', nsegs=ave.nave)
]
begin_time += duration
return content
def _get_bad_eeg_channels(info):
"""Return a list of bad EEG channels formatted for categories.xml.
Given a list of only the EEG channels in file, return the indices of this
list (starting at 1) that correspond to bad channels.
"""
if len(info['bads']) == 0:
return []
eeg_channels = pick_types(info, eeg=True, exclude=[])
bad_channels = pick_channels(info['ch_names'], info['bads'])
bads_elementwise = np.isin(eeg_channels, bad_channels)
return list(np.flatnonzero(bads_elementwise) + 1)
def _build_segment_content(begin_time, end_time, event_time, eeg_bads,
status='unedited', name=None, pns_bads=None,
nsegs=None):
"""Build content for a single segment in categories.xml.
Segments are sorted into categories in categories.xml. In a segmented MFF
each category can contain multiple segments, but in an averaged MFF each
category only contains one segment (the average).
"""
channel_status = [{
'signalBin': 1,
'exclusion': 'badChannels',
'channels': eeg_bads
}]
if pns_bads:
channel_status.append({
'signalBin': 2,
'exclusion': 'badChannels',
'channels': pns_bads
})
content = {
'status': status,
'beginTime': begin_time,
'endTime': end_time,
'evtBegin': event_time,
'evtEnd': event_time,
'channelStatus': channel_status,
}
if name:
content['name'] = name
if nsegs:
content['keys'] = {
'#seg': {
'type': 'long',
'data': nsegs
}
}
return content
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