def test_resample(self, time_series):
rf = ResampleFilter(timeseries=time_series, resamplerate=50.)
new_ts = rf.filter()
assert len(new_ts) == 50
assert new_ts.samplerate == 50.
def load_eeg_full_timeseries(task,
subject,
session,
elec_scheme=None,
noise_freq=[58., 62.],
resample_freq=None,
pass_band=None):
"""
Function for loading continuous EEG data from a full session, not based on event times.
Returns a list of timeseries object.
task: str
The experiment name
subject: str
The subject number
session: int
The session number for this subject and task
elec_scheme: pandas.DataFrame
A dataframe of electrode information, returned by load_elec_info(). If the column 'contact' is in the dataframe,
monopolar electrodes will be loads. If the columns 'contact_1' and 'contact_2' are in the df, bipolar will be
loaded. You may pass in a subset of rows to only load data for electrodes in those rows.
If you do not enter an elec_scheme, all monopolar channels will be loaded (but they will not be labeled with
correct channel tags). LOADING ALL ELECTRODES AND FOR AN ENTIRE SESSION AT ONCE IS NOT REALLY RECOMMENDED.
noise_freq: list
Stop filter will be applied to the given range. Default=(58. 62)
resample_freq: float
Sampling rate to resample to after loading eeg
pass_band: list
If given, the eeg will be band pass filtered in the given range
Returns
-------
list
A TimeSeries object.
"""
# load eeg
eeg = CMLReader(subject=subject, experiment=task,
session=session).load_eeg(scheme=elec_scheme).to_ptsa()
# filter line noise
if noise_freq is not None:
if isinstance(noise_freq[0], float):
noise_freq = [noise_freq]
for this_noise_freq in noise_freq:
b_filter = ButterworthFilter(eeg,
this_noise_freq,
filt_type='stop',
order=4)
eeg = b_filter.filter()
# resample if desired. Note: can be a bit slow especially if have a lot of eeg data
if resample_freq is not None:
r_filter = ResampleFilter(eeg, resample_freq)
eeg = r_filter.filter()
# do band pass if desired.
if pass_band is not None:
eeg = band_pass_eeg(eeg, pass_band)
return eeg
def test_ResampleFilter(self):
rf = ResampleFilter(time_series=self.time_series, resamplerate=50.)
new_ts = rf.filter()
assert len(new_ts) == 50
assert new_ts.samplerate == 50.
def load_eeg(events,
rel_start_ms,
rel_stop_ms,
buf_ms=0,
elec_scheme=None,
noise_freq=[58., 62.],
resample_freq=None,
pass_band=None,
use_mirror_buf=False,
demean=False,
do_average_ref=False):
"""
Returns an EEG TimeSeries object.
Parameters
----------
events: pandas.DataFrame
An events dataframe that contains eegoffset and eegfile fields
rel_start_ms: int
Initial time (in ms), relative to the onset of each event
rel_stop_ms: int
End time (in ms), relative to the onset of each event
buf_ms:
Amount of time (in ms) of buffer to add to both the begining and end of the time interval
elec_scheme: pandas.DataFrame
A dataframe of electrode information, returned by load_elec_info(). If the column 'contact' is in the dataframe,
monopolar electrodes will be loads. If the columns 'contact_1' and 'contact_2' are in the df, bipolar will be
loaded. You may pass in a subset of rows to only load data for electrodes in those rows.
If you do not enter an elec_scheme, all monopolar channels will be loaded (but they will not be labeled with
correct channel tags). Entering a scheme is recommended.
noise_freq: list
Stop filter will be applied to the given range. Default=(58. 62)
resample_freq: float
Sampling rate to resample to after loading eeg.
pass_band: list
If given, the eeg will be band pass filtered in the given range.
use_mirror_buf: bool
If True, the buffer will be data taken from within the rel_start_ms to rel_stop_ms interval,
mirrored and prepended and appended to the timeseries. If False, data outside the rel_start_ms and rel_stop_ms
interval will be read.
demean: bool
If True, will subject the mean voltage between rel_start_ms and rel_stop_ms from each channel
do_average_ref: bool
If True, will compute the average reference based on the mean voltage across channels
Returns
-------
TimeSeries
EEG timeseries object with dimensions channels x events x time (or bipolar_pairs x events x time)
NOTE: The EEG data is returned with time buffer included. If you included a buffer and want to remove it,
you may use the .remove_buffer() method. EXTRA NOTE: INPUT SECONDS FOR REMOVING BUFFER, NOT MS!!
"""
# add buffer is using
if (buf_ms is not None) and not use_mirror_buf:
actual_start = rel_start_ms - buf_ms
actual_stop = rel_stop_ms + buf_ms
else:
actual_start = rel_start_ms
actual_stop = rel_stop_ms
# load eeg
# Should auto convert to PTSA? Any reason not to?
eeg = CMLReader(subject=events.iloc[0].subject).load_eeg(
events,
rel_start=actual_start,
rel_stop=actual_stop,
scheme=elec_scheme).to_ptsa()
# compute average reference by subracting the mean across channels
if do_average_ref:
eeg = eeg - eeg.mean(dim='channel')
# baseline correct subracting the mean within the baseline time range
if demean:
eeg = eeg.baseline_corrected([rel_start_ms, rel_stop_ms])
# add mirror buffer if using. PTSA is expecting this to be in seconds.
if use_mirror_buf:
eeg = eeg.add_mirror_buffer(buf_ms / 1000.)
# filter line noise
if noise_freq is not None:
if isinstance(noise_freq[0], float):
noise_freq = [noise_freq]
for this_noise_freq in noise_freq:
b_filter = ButterworthFilter(eeg,
this_noise_freq,
filt_type='stop',
order=4)
eeg = b_filter.filter()
# resample if desired. Note: can be a bit slow especially if have a lot of eeg data
if resample_freq is not None:
r_filter = ResampleFilter(eeg, resample_freq)
eeg = r_filter.filter()
# do band pass if desired.
if pass_band is not None:
eeg = band_pass_eeg(eeg, pass_band)
# reorder dims to make events first
eeg = make_events_first_dim(eeg)
return eeg
def test_ResampleFilter(self):
rf = ResampleFilter(time_series = self.time_series,resamplerate=50.)
new_ts = rf.filter()
assert len(new_ts) == 50
assert new_ts.samplerate == 50.
def load_eeg_from_event_times(events,
rel_start_ms,
rel_stop_ms,
channel_list,
buf_ms=0,
noise_freq=[58., 62.],
downsample_freq=1000,
resample_freq=None,
pass_band=None,
demean=False,
do_average_ref=False):
"""
Returns an EEG TimeSeries object.
Parameters
----------
events: pandas.DataFrame
An events dataframe
rel_start_ms: int
Initial time (in ms), relative to the onset of each event
rel_stop_ms: int
End time (in ms), relative to the onset of each event
channel_list: list
list of paths to channels (ncs files)
buf_ms: int
Amount of time (in ms) of buffer to add to both the begining and end of the time interval
noise_freq: list
Stop filter will be applied to the given range. Default=(58. 62)
resample_freq: float
Sampling rate to resample to after loading eeg.
pass_band: list
If given, the eeg will be band pass filtered in the given range.
demean: bool
If True, will subject the mean voltage between rel_start_ms and rel_stop_ms from each channel
do_average_ref: bool
If True, will compute the average reference based on the mean voltage across channels
Returns
-------
TimeSeries
EEG timeseries object with dimensions event x time x channel
"""
# eeg is a PTSA timeseries
eeg = _load_eeg_timeseries(events, rel_start_ms, rel_stop_ms, channel_list,
buf_ms, downsample_freq)
# compute average reference by subracting the mean across channels
if do_average_ref:
eeg = eeg - eeg.mean(dim='channel')
# baseline correct subracting the mean within the baseline time range
if demean:
eeg = eeg.baseline_corrected([rel_start_ms, rel_stop_ms])
# filter line noise
if noise_freq is not None:
if isinstance(noise_freq[0], float):
noise_freq = [noise_freq]
for this_noise_freq in noise_freq:
b_filter = ButterworthFilter(eeg,
this_noise_freq,
filt_type='stop',
order=4)
eeg = b_filter.filter()
# resample if desired. Note: can be a bit slow especially if have a lot of eeg data
if resample_freq is not None:
r_filter = ResampleFilter(eeg, resample_freq)
eeg = r_filter.filter()
# do band pass if desired.
if pass_band is not None:
eeg = band_pass_eeg(eeg, pass_band)
return eeg
def load_eeg_from_event_times(events, rel_start_ms, rel_stop_ms, channel_list, buf_ms=0, noise_freq=[58., 62.],
downsample_freq=1000,
resample_freq=None, pass_band=None, demean=False, do_average_ref=False):
"""
Returns an EEG TimeSeries object.
Parameters
----------
events: pandas.DataFrame
An events dataframe
rel_start_ms: int
Initial time (in ms), relative to the onset of each event
rel_stop_ms: int
End time (in ms), relative to the onset of each event
channel_list: list
list of paths to channels (ncs files)
buf_ms: int
Amount of time (in ms) of buffer to add to both the begining and end of the time interval
noise_freq: list
Stop filter will be applied to the given range. Default=(58. 62)
resample_freq: float
Sampling rate to resample to after loading eeg.
pass_band: list
If given, the eeg will be band pass filtered in the given range.
demean: bool
If True, will subject the mean voltage between rel_start_ms and rel_stop_ms from each channel
do_average_ref: bool
If True, will compute the average reference based on the mean voltage across channels
Returns
-------
TimeSeries
EEG timeseries object with dimensions event x time x channel
"""
# eeg is a PTSA timeseries
eeg = _load_eeg_timeseries(events, rel_start_ms, rel_stop_ms, channel_list, buf_ms, downsample_freq)
# compute average reference by subracting the mean across channels
if do_average_ref:
eeg = eeg - eeg.mean(dim='channel')
# baseline correct subracting the mean within the baseline time range
if demean:
eeg = eeg.baseline_corrected([rel_start_ms, rel_stop_ms])
# filter line noise
if noise_freq is not None:
if isinstance(noise_freq[0], float):
noise_freq = [noise_freq]
for this_noise_freq in noise_freq:
b_filter = ButterworthFilter(eeg, this_noise_freq, filt_type='stop', order=4)
eeg = b_filter.filter()
# resample if desired. Note: can be a bit slow especially if have a lot of eeg data
if resample_freq is not None:
r_filter = ResampleFilter(eeg, resample_freq)
eeg = r_filter.filter()
# do band pass if desired.
if pass_band is not None:
eeg = band_pass_eeg(eeg, pass_band)
return eeg
def load_eeg(events,
rel_start_ms,
rel_stop_ms,
buf_ms=0,
elec_scheme=None,
noise_freq=[58., 62.],
resample_freq=None,
pass_band=None,
use_mirror_buf=False,
demean=False,
do_average_ref=False):
"""
Returns an EEG TimeSeries object.
Parameters
----------
events: pandas.DataFrame
An events dataframe that contains eegoffset and eegfile fields
rel_start_ms: int
Initial time (in ms), relative to the onset of each event
rel_stop_ms: int
End time (in ms), relative to the onset of each event
buf_ms:
Amount of time (in ms) of buffer to add to both the begining and end of the time interval
elec_scheme: pandas.DataFrame
A dataframe of electrode information, returned by load_elec_info(). If the column 'contact' is in the dataframe,
monopolar electrodes will be loads. If the columns 'contact_1' and 'contact_2' are in the df, bipolar will be
loaded. You may pass in a subset of rows to only load data for electrodes in those rows.
If you do not enter an elec_scheme, all monopolar channels will be loaded (but they will not be labeled with
correct channel tags). Entering a scheme is recommended.
noise_freq: list
Stop filter will be applied to the given range. Default=(58. 62)
resample_freq: float
Sampling rate to resample to after loading eeg.
pass_band: list
If given, the eeg will be band pass filtered in the given range.
use_mirror_buf: bool
If True, the buffer will be data taken from within the rel_start_ms to rel_stop_ms interval,
mirrored and prepended and appended to the timeseries. If False, data outside the rel_start_ms and rel_stop_ms
interval will be read.
demean: bool
If True, will subject the mean voltage between rel_start_ms and rel_stop_ms from each channel
do_average_ref: bool
If True, will compute the average reference based on the mean voltage across channels
Returns
-------
TimeSeries
EEG timeseries object with dimensions channels x events x time (or bipolar_pairs x events x time)
NOTE: The EEG data is returned with time buffer included. If you included a buffer and want to remove it,
you may use the .remove_buffer() method. EXTRA NOTE: INPUT SECONDS FOR REMOVING BUFFER, NOT MS!!
"""
# check if monopolar is possible for this subject
if 'contact' in elec_scheme:
eegfile = np.unique(events.eegfile)[0]
if os.path.splitext(eegfile)[1] == '.h5':
eegfile = f'/protocols/r1/subjects/{events.iloc[0].subject}/experiments/{events.iloc[0].experiment}/sessions/{events.iloc[0].session}/ephys/current_processed/noreref/{eegfile}'
with h5py.File(eegfile, 'r') as f:
if not np.array(f['monopolar_possible'])[0] == 1:
print('Monopolar referencing not possible for {}'.format(
events.iloc[0].subject))
return
# add buffer is using
if (buf_ms is not None) and not use_mirror_buf:
actual_start = rel_start_ms - buf_ms
actual_stop = rel_stop_ms + buf_ms
else:
actual_start = rel_start_ms
actual_stop = rel_stop_ms
# load eeg
eeg = CMLReader(subject=events.iloc[0].subject).load_eeg(
events,
rel_start=actual_start,
rel_stop=actual_stop,
scheme=elec_scheme).to_ptsa()
# now auto cast to float32 to help with memory issues with high sample rate data
eeg.data = eeg.data.astype('float32')
# baseline correct subracting the mean within the baseline time range
if demean:
eeg = eeg.baseline_corrected([rel_start_ms, rel_stop_ms])
# compute average reference by subracting the mean across channels
if do_average_ref:
eeg = eeg - eeg.mean(dim='channel')
# add mirror buffer if using. PTSA is expecting this to be in seconds.
if use_mirror_buf:
eeg = eeg.add_mirror_buffer(buf_ms / 1000.)
# filter line noise
if noise_freq is not None:
if isinstance(noise_freq[0], float):
noise_freq = [noise_freq]
for this_noise_freq in noise_freq:
for this_chan in range(eeg.shape[1]):
b_filter = ButterworthFilter(eeg[:, this_chan:this_chan + 1],
this_noise_freq,
filt_type='stop',
order=4)
eeg[:, this_chan:this_chan + 1] = b_filter.filter()
# resample if desired. Note: can be a bit slow especially if have a lot of eeg data
# pdb.set_trace()
# if resample_freq is not None:
# for this_chan in range(eeg.shape[1]):
# r_filter = ResampleFilter(eeg[:, this_chan:this_chan+1], resample_freq)
# eeg[:, this_chan:this_chan + 1] = r_filter.filter()
if resample_freq is not None:
eeg_resamp = []
for this_chan in range(eeg.shape[1]):
r_filter = ResampleFilter(eeg[:, this_chan:this_chan + 1],
resample_freq)
eeg_resamp.append(r_filter.filter())
coords = {x: eeg[x] for x in eeg.coords.keys()}
coords['time'] = eeg_resamp[0]['time']
coords['samplerate'] = resample_freq
dims = eeg.dims
eeg = TimeSeries.create(np.concatenate(eeg_resamp, axis=1),
resample_freq,
coords=coords,
dims=dims)
# do band pass if desired.
if pass_band is not None:
eeg = band_pass_eeg(eeg, pass_band)
# reorder dims to make events first
eeg = make_events_first_dim(eeg)
return eeg