skjerns · October 20, 2024 03:09 · datalw · Aug 21, 2020 · LunaHub · Aug 21, 2020
diff --git a/save_edf.py b/save_edf.py
 # -*- coding: utf-8 -*-
 """
 Created on Wed Dec  5 12:56:31 2018
 @author: skjerns
 Gist to save a mne.io.Raw object to an EDF file using pyEDFlib
 (https://github.com/holgern/pyedflib)
 Disclaimer:
    - Saving your data this way will result in slight 
      loss of precision (magnitude +-1e-09).
    - It is assumed that the data is presented in Volt (V), 
      it will be internally converted to microvolt
    - BDF or EDF+ is selected based on the filename extension
    - Annotations preserved
    
 Update: Since 2021, MNE also supports exporting EDF via edfio: 
 https://mne.tools/stable/generated/mne.export.export_raw.html
 """

 import pyedflib # pip install pyedflib
 from pyedflib import highlevel # new high-level interface
 from pyedflib import FILETYPE_BDF, FILETYPE_BDFPLUS, FILETYPE_EDF, FILETYPE_EDFPLUS
 from datetime import datetime, timezone, timedelta
 import mne
 import os

 def _stamp_to_dt(utc_stamp):
    """Convert timestamp to datetime object in Windows-friendly way."""
    if 'datetime' in str(type(utc_stamp)): return utc_stamp
    # The min on windows is 86400
    stamp = [int(s) for s in utc_stamp]
    if len(stamp) == 1:  # In case there is no microseconds information
        stamp.append(0)
    return (datetime.fromtimestamp(0, tz=timezone.utc) +
            timedelta(0, stamp[0], stamp[1]))  # day, sec, μs


 def write_mne_edf(mne_raw, fname, picks=None, tmin=0, tmax=None, 
                  overwrite=False):
    """
    Saves the raw content of an MNE.io.Raw and its subclasses to
    a file using the EDF+/BDF filetype
    pyEDFlib is used to save the raw contents of the RawArray to disk
    Parameters
    update 2021: edf export is now also supported in MNE:
    https://mne.tools/stable/generated/mne.export.export_raw.html
    ----------
    mne_raw : mne.io.Raw
        An object with super class mne.io.Raw that contains the data
        to save
    fname : string
        File name of the new dataset. This has to be a new filename
        unless data have been preloaded. Filenames should end with .edf
    picks : array-like of int | None
        Indices of channels to include. If None all channels are kept.
    tmin : float | None
        Time in seconds of first sample to save. If None first sample
        is used.
    tmax : float | None
        Time in seconds of last sample to save. If None last sample
        is used.
    overwrite : bool
        If True, the destination file (if it exists) will be overwritten.
        If False (default), an error will be raised if the file exists.
    """
    print('did you know EDF export is now supported in MNE via edfio? have a look at https://mne.tools/stable/generated/mne.export.export_raw.html')
    if not issubclass(type(mne_raw), mne.io.BaseRaw):
        raise TypeError('Must be mne.io.Raw type')
    if not overwrite and os.path.exists(fname):
        raise OSError('File already exists. No overwrite.')
        
    # static settings
    has_annotations = True if len(mne_raw.annotations)>0 else False
    if os.path.splitext(fname)[-1] == '.edf':
        file_type = FILETYPE_EDFPLUS if has_annotations else FILETYPE_EDF
        dmin, dmax = -32768, 32767 
    else:
        file_type = FILETYPE_BDFPLUS if has_annotations else FILETYPE_BDF
        dmin, dmax = -8388608, 8388607
    
    print('saving to {}, filetype {}'.format(fname, file_type))
    sfreq = mne_raw.info['sfreq']
    date = _stamp_to_dt(mne_raw.info['meas_date'])
    
    if tmin:
        date += timedelta(seconds=tmin)
    # no conversion necessary, as pyedflib can handle datetime.
    #date = date.strftime('%d %b %Y %H:%M:%S')
    first_sample = int(sfreq*tmin)
    last_sample  = int(sfreq*tmax) if tmax is not None else None

    
    # convert data
    channels = mne_raw.get_data(picks, 
                                start = first_sample,
                                stop  = last_sample)
    
    # convert to microvolts to scale up precision
    channels *= 1e6

    # set conversion parameters
    n_channels = len(channels)
    
    # create channel from this   
    try:
        f = pyedflib.EdfWriter(fname,
                               n_channels=n_channels, 
                               file_type=file_type)
        
        channel_info = []
        
        ch_idx = range(n_channels) if picks is None else picks
        keys = list(mne_raw._orig_units.keys())
        for i in ch_idx:
            try:
                ch_dict = {'label': mne_raw.ch_names[i], 
                           'dimension': mne_raw._orig_units[keys[i]], 
                           'sample_rate': mne_raw._raw_extras[0]['n_samps'][i], 
                           'physical_min': mne_raw._raw_extras[0]['physical_min'][i], 
                           'physical_max': mne_raw._raw_extras[0]['physical_max'][i], 
                           'digital_min':  mne_raw._raw_extras[0]['digital_min'][i], 
                           'digital_max':  mne_raw._raw_extras[0]['digital_max'][i], 
                           'transducer': '', 
                           'prefilter': ''}
            except:
                ch_dict = {'label': mne_raw.ch_names[i], 
                           'dimension': mne_raw._orig_units[keys[i]], 
                           'sample_rate': sfreq, 
                           'physical_min': channels.min(), 
                           'physical_max': channels.max(), 
                           'digital_min':  dmin, 
                           'digital_max':  dmax, 
                           'transducer': '', 
                           'prefilter': ''}
        
            channel_info.append(ch_dict)
        f.setPatientCode(mne_raw._raw_extras[0]['subject_info'].get('id', '0'))
        f.setPatientName(mne_raw._raw_extras[0]['subject_info'].get('name', 'noname'))
        f.setTechnician('mne-gist-save-edf-skjerns')
        f.setSignalHeaders(channel_info)
        f.setStartdatetime(date)
        f.writeSamples(channels)
        for annotation in mne_raw.annotations:
            onset = annotation['onset']
            duration = annotation['duration']
            description = annotation['description']
            f.writeAnnotation(onset, duration, description)
        
    except Exception as e:
        raise e
    finally:
        f.close()    
    return True
	# -- coding: utf-8 --
	"""
	Created on Wed Dec 5 12:56:31 2018
	@author: skjerns
	Gist to save a mne.io.Raw object to an EDF file using pyEDFlib
	(https://github.com/holgern/pyedflib)
	Disclaimer:
	- Saving your data this way will result in slight
	loss of precision (magnitude +-1e-09).
	- It is assumed that the data is presented in Volt (V),
	it will be internally converted to microvolt
	- BDF or EDF+ is selected based on the filename extension
	- Annotations preserved

	Update: Since 2021, MNE also supports exporting EDF via edfio:
	https://mne.tools/stable/generated/mne.export.export_raw.html
	"""

	import pyedflib # pip install pyedflib
	from pyedflib import highlevel # new high-level interface
	from pyedflib import FILETYPE_BDF, FILETYPE_BDFPLUS, FILETYPE_EDF, FILETYPE_EDFPLUS
	from datetime import datetime, timezone, timedelta
	import mne
	import os

	def _stamp_to_dt(utc_stamp):
	"""Convert timestamp to datetime object in Windows-friendly way."""
	if 'datetime' in str(type(utc_stamp)): return utc_stamp
	# The min on windows is 86400
	stamp = [int(s) for s in utc_stamp]
	if len(stamp) == 1: # In case there is no microseconds information
	stamp.append(0)
	return (datetime.fromtimestamp(0, tz=timezone.utc) +
	timedelta(0, stamp[0], stamp[1])) # day, sec, μs


	def write_mne_edf(mne_raw, fname, picks=None, tmin=0, tmax=None,
	overwrite=False):
	"""
	Saves the raw content of an MNE.io.Raw and its subclasses to
	a file using the EDF+/BDF filetype
	pyEDFlib is used to save the raw contents of the RawArray to disk
	Parameters
	update 2021: edf export is now also supported in MNE:
	https://mne.tools/stable/generated/mne.export.export_raw.html
	----------
	mne_raw : mne.io.Raw
	An object with super class mne.io.Raw that contains the data
	to save
	fname : string
	File name of the new dataset. This has to be a new filename
	unless data have been preloaded. Filenames should end with .edf
	picks : array-like of int \| None
	Indices of channels to include. If None all channels are kept.
	tmin : float \| None
	Time in seconds of first sample to save. If None first sample
	is used.
	tmax : float \| None
	Time in seconds of last sample to save. If None last sample
	is used.
	overwrite : bool
	If True, the destination file (if it exists) will be overwritten.
	If False (default), an error will be raised if the file exists.
	"""
	print('did you know EDF export is now supported in MNE via edfio? have a look at https://mne.tools/stable/generated/mne.export.export_raw.html')
	if not issubclass(type(mne_raw), mne.io.BaseRaw):
	raise TypeError('Must be mne.io.Raw type')
	if not overwrite and os.path.exists(fname):
	raise OSError('File already exists. No overwrite.')

	# static settings
	has_annotations = True if len(mne_raw.annotations)>0 else False
	if os.path.splitext(fname)[-1] == '.edf':
	file_type = FILETYPE_EDFPLUS if has_annotations else FILETYPE_EDF
	dmin, dmax = -32768, 32767
	else:
	file_type = FILETYPE_BDFPLUS if has_annotations else FILETYPE_BDF
	dmin, dmax = -8388608, 8388607

	print('saving to {}, filetype {}'.format(fname, file_type))
	sfreq = mne_raw.info['sfreq']
	date = _stamp_to_dt(mne_raw.info['meas_date'])

	if tmin:
	date += timedelta(seconds=tmin)
	# no conversion necessary, as pyedflib can handle datetime.
	#date = date.strftime('%d %b %Y %H:%M:%S')
	first_sample = int(sfreq*tmin)
	last_sample = int(sfreq*tmax) if tmax is not None else None


	# convert data
	channels = mne_raw.get_data(picks,
	start = first_sample,
	stop = last_sample)

	# convert to microvolts to scale up precision
	channels *= 1e6

	# set conversion parameters
	n_channels = len(channels)

	# create channel from this
	try:
	f = pyedflib.EdfWriter(fname,
	n_channels=n_channels,
	file_type=file_type)

	channel_info = []

	ch_idx = range(n_channels) if picks is None else picks
	keys = list(mne_raw._orig_units.keys())
	for i in ch_idx:
	try:
	ch_dict = {'label': mne_raw.ch_names[i],
	'dimension': mne_raw._orig_units[keys[i]],
	'sample_rate': mne_raw._raw_extras[0]['n_samps'][i],
	'physical_min': mne_raw._raw_extras[0]['physical_min'][i],
	'physical_max': mne_raw._raw_extras[0]['physical_max'][i],
	'digital_min': mne_raw._raw_extras[0]['digital_min'][i],
	'digital_max': mne_raw._raw_extras[0]['digital_max'][i],
	'transducer': '',
	'prefilter': ''}
	except:
	ch_dict = {'label': mne_raw.ch_names[i],
	'dimension': mne_raw._orig_units[keys[i]],
	'sample_rate': sfreq,
	'physical_min': channels.min(),
	'physical_max': channels.max(),
	'digital_min': dmin,
	'digital_max': dmax,
	'transducer': '',
	'prefilter': ''}

	channel_info.append(ch_dict)
	f.setPatientCode(mne_raw._raw_extras[0]['subject_info'].get('id', '0'))
	f.setPatientName(mne_raw._raw_extras[0]['subject_info'].get('name', 'noname'))
	f.setTechnician('mne-gist-save-edf-skjerns')
	f.setSignalHeaders(channel_info)
	f.setStartdatetime(date)
	f.writeSamples(channels)
	for annotation in mne_raw.annotations:
	onset = annotation['onset']
	duration = annotation['duration']
	description = annotation['description']
	f.writeAnnotation(onset, duration, description)

	except Exception as e:
	raise e
	finally:
	f.close()
	return True