def get_event_data(self,
channels,
events,
start_time,
end_time,
buffer_time=0.0,
resampled_rate=None,
filt_freq=None,
filt_type='stop',
filt_order=4,
keep_buffer=False,
loop_axis=None,
num_mp_procs=0,
eoffset='eoffset',
eoffset_in_time=True):
"""
Return an TimeSeries containing data for the specified channel
in the form [events,duration].
Parameters
----------
channels: {int} or {dict}
Channels from which to load data.
events: {array_like} or {recarray}
Array/list of event offsets (in time or samples as
specified by eoffset_in_time; in time by default) into
the data, specifying each event onset time.
start_time: {float}
Start of epoch to retrieve (in time-unit of the data).
end_time: {float}
End of epoch to retrieve (in time-unit of the data).
buffer_time: {float},optional
Extra buffer to add on either side of the event in order
to avoid edge effects when filtering (in time unit of the
data).
resampled_rate: {float},optional
New samplerate to resample the data to after loading.
filt_freq: {array_like},optional
The range of frequencies to filter (depends on the filter
type.)
filt_type = {scipy.signal.band_dict.keys()},optional
Filter type.
filt_order = {int},optional
The order of the filter.
keep_buffer: {boolean},optional
Whether to keep the buffer when returning the data.
eoffset_in_time: {boolean},optional
If True, the unit of the event offsets is taken to be
time (unit of the data), otherwise samples.
"""
# translate back to dur and offset
dur = end_time - start_time
offset = start_time
buf = buffer_time
# get the event offsets
if ((not (hasattr(events, 'dtype') or hasattr(events, 'columns')))
or (hasattr(events, 'dtype') and events.dtype.names is None)):
# they just passed in a list
event_offsets = events
elif ((hasattr(events, 'dtype') and (eoffset in events.dtype.names))
or (hasattr(events, 'columns') and (eoffset in events.columns))):
event_offsets = events[eoffset]
else:
raise ValueError(eoffset + ' must be a valid fieldname ' +
'specifying the offset for the data.')
# Sanity checks:
if (dur < 0):
raise ValueError('Duration must not be negative! ' +
'Specified duration: ' + str(dur))
if (np.min(event_offsets) < 0):
raise ValueError('Event offsets must not be negative!')
# make sure the events are an actual array:
event_offsets = np.asarray(event_offsets)
if eoffset_in_time:
# convert to samples
event_offsets = np.atleast_1d(
np.int64(np.round(event_offsets * self.samplerate)))
# set event durations from rate
# get the samplesize
samplesize = 1. / self.samplerate
# get the number of buffer samples
buf_samp = int(np.ceil(buf / samplesize))
# calculate the offset samples that contains the desired offset
offset_samp = int(
np.ceil((np.abs(offset) - samplesize * .5) / samplesize) *
np.sign(offset))
# finally get the duration necessary to cover the desired span
#dur_samp = int(np.ceil((dur - samplesize*.5)/samplesize))
dur_samp = (int(np.ceil(
(dur + offset - samplesize * .5) / samplesize)) - offset_samp + 1)
# add in the buffer
dur_samp += 2 * buf_samp
offset_samp -= buf_samp
# check that we have all the data we need before every event:
if (np.min(event_offsets + offset_samp) < 0):
bad_evs = ((event_offsets + offset_samp) < 0)
raise ValueError('The specified values for offset and buffer ' +
'require more data than is available before ' +
str(np.sum(bad_evs)) + ' of all ' +
str(len(bad_evs)) + ' events.')
# process the channels
if isinstance(channels, dict):
# turn into indices
ch_info = self.channels
key = channels.keys()[0]
channels = [
np.nonzero(ch_info[key] == c)[0][0] for c in channels[key]
]
elif isinstance(channels, str):
# find that channel by name
channels = np.nonzero(self.channels['name'] == channels)[0][0]
if channels is None or len(np.atleast_1d(channels)) == 0:
channels = np.arange(self.nchannels)
channels = np.atleast_1d(channels)
channels.sort()
# load the timeseries (this must be implemented by subclasses)
eventdata = self._load_data(channels, event_offsets, dur_samp,
offset_samp)
# calc the time range
# get the samplesize
samp_start = offset_samp * samplesize
samp_end = samp_start + (dur_samp - 1) * samplesize
time_range = np.linspace(samp_start, samp_end, dur_samp)
# make it a timeseries
dims = [
Dim(self.channels[channels],
'channels'), # can index into channels
Dim(events, 'events'),
Dim(time_range, 'time')
]
eventdata = TimeSeries(np.asarray(eventdata),
'time',
self.samplerate,
dims=dims)
# filter if desired
if not (filt_freq is None):
# filter that data
eventdata = eventdata.filtered(filt_freq,
filt_type=filt_type,
order=filt_order)
# resample if desired
if (not (resampled_rate is None)
and not (resampled_rate == eventdata.samplerate)):
# resample the data
eventdata = eventdata.resampled(resampled_rate,
loop_axis=loop_axis,
num_mp_procs=num_mp_procs)
# remove the buffer and set the time range
if buf > 0 and not (keep_buffer):
# remove the buffer
eventdata = eventdata.remove_buffer(buf)
# return the timeseries
return eventdata
def get_event_data(self,channels,events,
start_time,end_time,buffer_time=0.0,
resampled_rate=None,
filt_freq=None,filt_type='stop',filt_order=4,
keep_buffer=False,
loop_axis=None,num_mp_procs=0,eoffset='eoffset',
eoffset_in_time=True):
"""
Return an TimeSeries containing data for the specified channel
in the form [events,duration].
Parameters
----------
channels: {int} or {dict}
Channels from which to load data.
events: {array_like} or {recarray}
Array/list of event offsets (in time or samples as
specified by eoffset_in_time; in time by default) into
the data, specifying each event onset time.
start_time: {float}
Start of epoch to retrieve (in time-unit of the data).
end_time: {float}
End of epoch to retrieve (in time-unit of the data).
buffer_time: {float},optional
Extra buffer to add on either side of the event in order
to avoid edge effects when filtering (in time unit of the
data).
resampled_rate: {float},optional
New samplerate to resample the data to after loading.
filt_freq: {array_like},optional
The range of frequencies to filter (depends on the filter
type.)
filt_type = {scipy.signal.band_dict.keys()},optional
Filter type.
filt_order = {int},optional
The order of the filter.
keep_buffer: {boolean},optional
Whether to keep the buffer when returning the data.
eoffset_in_time: {boolean},optional
If True, the unit of the event offsets is taken to be
time (unit of the data), otherwise samples.
"""
# translate back to dur and offset
dur = end_time - start_time
offset = start_time
buf = buffer_time
# get the event offsets
if ((not (hasattr(events,'dtype') or hasattr(events,'columns'))) or
(hasattr(events,'dtype') and events.dtype.names is None)):
# they just passed in a list
event_offsets = events
elif ((hasattr(events, 'dtype') and (eoffset in events.dtype.names)) or
(hasattr(events, 'columns') and (eoffset in events.columns))):
event_offsets = events[eoffset]
else:
raise ValueError(eoffset+' must be a valid fieldname '+
'specifying the offset for the data.')
# Sanity checks:
if(dur<0):
raise ValueError('Duration must not be negative! '+
'Specified duration: '+str(dur))
if(np.min(event_offsets)<0):
raise ValueError('Event offsets must not be negative!')
# make sure the events are an actual array:
event_offsets = np.asarray(event_offsets)
if eoffset_in_time:
# convert to samples
event_offsets = np.atleast_1d(np.int64(
np.round(event_offsets*self.samplerate)))
# set event durations from rate
# get the samplesize
samplesize = 1./self.samplerate
# get the number of buffer samples
buf_samp = int(np.ceil(buf/samplesize))
# calculate the offset samples that contains the desired offset
offset_samp = int(np.ceil((np.abs(offset)-samplesize*.5)/samplesize)*
np.sign(offset))
# finally get the duration necessary to cover the desired span
#dur_samp = int(np.ceil((dur - samplesize*.5)/samplesize))
dur_samp = (int(np.ceil((dur+offset - samplesize*.5)/samplesize)) -
offset_samp + 1)
# add in the buffer
dur_samp += 2*buf_samp
offset_samp -= buf_samp
# check that we have all the data we need before every event:
if(np.min(event_offsets+offset_samp)<0):
bad_evs = ((event_offsets+offset_samp)<0)
raise ValueError('The specified values for offset and buffer '+
'require more data than is available before '+
str(np.sum(bad_evs))+' of all '+
str(len(bad_evs))+' events.')
# process the channels
if isinstance(channels, dict):
# turn into indices
ch_info = self.channels
key = channels.keys()[0]
channels = [np.nonzero(ch_info[key]==c)[0][0] for c in channels[key]]
elif isinstance(channels, str):
# find that channel by name
channels = np.nonzero(self.channels['name']==channels)[0][0]
if channels is None or len(np.atleast_1d(channels))==0:
channels = np.arange(self.nchannels)
channels = np.atleast_1d(channels)
channels.sort()
# load the timeseries (this must be implemented by subclasses)
eventdata = self._load_data(channels,event_offsets,dur_samp,offset_samp)
# calc the time range
# get the samplesize
samp_start = offset_samp*samplesize
samp_end = samp_start + (dur_samp-1)*samplesize
time_range = np.linspace(samp_start,samp_end,dur_samp)
# make it a timeseries
dims = [Dim(self.channels[channels],'channels'), # can index into channels
Dim(events,'events'),
Dim(time_range,'time')]
eventdata = TimeSeries(np.asarray(eventdata),
'time',
self.samplerate,dims=dims)
# filter if desired
if not(filt_freq is None):
# filter that data
eventdata = eventdata.filtered(filt_freq,
filt_type=filt_type,
order=filt_order)
# resample if desired
if (not(resampled_rate is None) and
not(resampled_rate == eventdata.samplerate)):
# resample the data
eventdata = eventdata.resampled(resampled_rate,
loop_axis=loop_axis,
num_mp_procs=num_mp_procs)
# remove the buffer and set the time range
if buf > 0 and not(keep_buffer):
# remove the buffer
eventdata = eventdata.remove_buffer(buf)
# return the timeseries
return eventdata
def get_event_data(self,channels,event_offsets,
start_time,end_time,buffer_time=0.0,
resampled_rate=None,
filt_freq=None,filt_type='stop',filt_order=4,
keep_buffer=False):
"""
Return an TimeSeries containing data for the specified channel
in the form [events,duration].
Parameters
----------
channels: {int}
Channels from which to load data.
event_offsets: {array_like}
Array/list of event offsets (in samples) into the data,
specifying each event onset time.
start_time: {float}
Start of epoch to retrieve (in time-unit of the data).
end_time: {float}
End of epoch to retrieve (in time-unit of the data).
buffer_time: {float},optional
Extra buffer to add on either side of the event in order
to avoid edge effects when filtering (in time unit of the
data).
resampled_rate: {float},optional
New samplerate to resample the data to after loading.
filt_freq: {array_like},optional
The range of frequencies to filter (depends on the filter
type.)
filt_type = {scipy.signal.band_dict.keys()},optional
Filter type.
filt_order = {int},optional
The order of the filter.
keep_buffer: {boolean},optional
Whether to keep the buffer when returning the data.
"""
# translate back to dur and offset
dur = end_time - start_time
offset = start_time
buf = buffer_time
# Sanity checks:
if(dur<0):
raise ValueError('Duration must not be negative! '+
'Specified duration: '+str(dur))
if(np.min(event_offsets)<0):
raise ValueError('Event offsets must not be negative!')
# make sure the events are an actual array
event_offsets = np.asarray(event_offsets)
# set event durations from rate
# get the samplesize
samplesize = 1./self.samplerate
# get the number of buffer samples
buf_samp = int(np.ceil(buf/samplesize))
# calculate the offset samples that contains the desired offset
offset_samp = int(np.ceil((np.abs(offset)-samplesize*.5)/samplesize)*
np.sign(offset))
# finally get the duration necessary to cover the desired span
#dur_samp = int(np.ceil((dur - samplesize*.5)/samplesize))
dur_samp = (int(np.ceil((dur+offset - samplesize*.5)/samplesize)) -
offset_samp + 1)
# add in the buffer
dur_samp += 2*buf_samp
offset_samp -= buf_samp
# check that we have all the data we need before every event:
if(np.min(event_offsets+offset_samp)<0):
bad_evs = ((event_offsets+offset_samp)<0)
raise ValueError('The specified values for offset and buffer '+
'require more data than is available before '+
str(np.sum(bad_evs))+' of all '+
str(len(bad_evs))+' events.')
# process the channels
if channels is None or len(np.atleast_1d(channels))==0:
channels = np.arange(self.nchannels)
channels = np.atleast_1d(channels)
# load the timeseries (this must be implemented by subclasses)
eventdata = self._load_data(channels,event_offsets,dur_samp,offset_samp)
# calc the time range
# get the samplesize
samp_start = offset_samp*samplesize
samp_end = samp_start + (dur_samp-1)*samplesize
time_range = np.linspace(samp_start,samp_end,dur_samp)
# make it a timeseries
dims = [Dim(channels,'channels'),
Dim(event_offsets,'event_offsets'),
Dim(time_range,'time')]
eventdata = TimeSeries(np.asarray(eventdata),
'time',
self.samplerate,dims=dims)
# filter if desired
if not(filt_freq is None):
# filter that data
eventdata = eventdata.filtered(filt_freq,
filt_type=filt_type,
order=filt_order)
# resample if desired
if (not(resampled_rate is None) and
not(resampled_rate == eventdata.samplerate)):
# resample the data
eventdata = eventdata.resampled(resampled_rate)
# remove the buffer and set the time range
if buf > 0 and not(keep_buffer):
# remove the buffer
eventdata = eventdata.remove_buffer(buf)
# return the timeseries
return eventdata
def get_event_data(self,
channels,
event_offsets,
start_time,
end_time,
buffer_time=0.0,
resampled_rate=None,
filt_freq=None,
filt_type='stop',
filt_order=4,
keep_buffer=False):
"""
Return an TimeSeries containing data for the specified channel
in the form [events,duration].
Parameters
----------
channels: {int}
Channels from which to load data.
event_offsets: {array_like}
Array/list of event offsets (in samples) into the data,
specifying each event onset time.
start_time: {float}
Start of epoch to retrieve (in time-unit of the data).
end_time: {float}
End of epoch to retrieve (in time-unit of the data).
buffer_time: {float},optional
Extra buffer to add on either side of the event in order
to avoid edge effects when filtering (in time unit of the
data).
resampled_rate: {float},optional
New samplerate to resample the data to after loading.
filt_freq: {array_like},optional
The range of frequencies to filter (depends on the filter
type.)
filt_type = {scipy.signal.band_dict.keys()},optional
Filter type.
filt_order = {int},optional
The order of the filter.
keep_buffer: {boolean},optional
Whether to keep the buffer when returning the data.
"""
# translate back to dur and offset
dur = end_time - start_time
offset = start_time
buf = buffer_time
# Sanity checks:
if (dur < 0):
raise ValueError('Duration must not be negative! ' +
'Specified duration: ' + str(dur))
if (np.min(event_offsets) < 0):
raise ValueError('Event offsets must not be negative!')
# make sure the events are an actual array
event_offsets = np.asarray(event_offsets)
# set event durations from rate
# get the samplesize
samplesize = 1. / self.samplerate
# get the number of buffer samples
buf_samp = int(np.ceil(buf / samplesize))
# calculate the offset samples that contains the desired offset
offset_samp = int(
np.ceil((np.abs(offset) - samplesize * .5) / samplesize) *
np.sign(offset))
# finally get the duration necessary to cover the desired span
#dur_samp = int(np.ceil((dur - samplesize*.5)/samplesize))
dur_samp = (int(np.ceil(
(dur + offset - samplesize * .5) / samplesize)) - offset_samp + 1)
# add in the buffer
dur_samp += 2 * buf_samp
offset_samp -= buf_samp
# check that we have all the data we need before every event:
if (np.min(event_offsets + offset_samp) < 0):
bad_evs = ((event_offsets + offset_samp) < 0)
raise ValueError('The specified values for offset and buffer ' +
'require more data than is available before ' +
str(np.sum(bad_evs)) + ' of all ' +
str(len(bad_evs)) + ' events.')
# process the channels
if channels is None or len(np.atleast_1d(channels)) == 0:
channels = np.arange(self.nchannels)
channels = np.atleast_1d(channels)
# load the timeseries (this must be implemented by subclasses)
eventdata = self._load_data(channels, event_offsets, dur_samp,
offset_samp)
# calc the time range
# get the samplesize
samp_start = offset_samp * samplesize
samp_end = samp_start + (dur_samp - 1) * samplesize
time_range = np.linspace(samp_start, samp_end, dur_samp)
# make it a timeseries
dims = [
Dim(channels, 'channels'),
Dim(event_offsets, 'event_offsets'),
Dim(time_range, 'time')
]
eventdata = TimeSeries(np.asarray(eventdata),
'time',
self.samplerate,
dims=dims)
# filter if desired
if not (filt_freq is None):
# filter that data
eventdata = eventdata.filtered(filt_freq,
filt_type=filt_type,
order=filt_order)
# resample if desired
if (not (resampled_rate is None)
and not (resampled_rate == eventdata.samplerate)):
# resample the data
eventdata = eventdata.resampled(resampled_rate)
# remove the buffer and set the time range
if buf > 0 and not (keep_buffer):
# remove the buffer
eventdata = eventdata.remove_buffer(buf)
# return the timeseries
return eventdata
