def to_nitime(self,
picks=None,
epochs_idx=None,
collapse=False,
copy=True,
use_first_samp=False):
""" Export epochs as nitime TimeSeries
Parameters
----------
picks : array-like | None
Indices for exporting subsets of the epochs channels. If None
all good channels will be used.
epochs_idx : slice | array-like | None
Epochs index for single or selective epochs exports. If None, all
epochs will be used.
collapse : boolean
If True export epochs and time slices will be collapsed to 2D
array. This may be required by some nitime functions.
copy : boolean
If True exports copy of epochs data.
use_first_samp: boolean
If True, the time returned is relative to the session onset, else
relative to the recording onset.
Returns
-------
epochs_ts : instance of nitime.TimeSeries
The Epochs as nitime TimeSeries object
"""
try:
from nitime import TimeSeries # to avoid strong dependency
except ImportError:
raise Exception('the nitime package is missing')
if picks is None:
picks = pick_types(self.info,
include=self.ch_names,
exclude=self.info['bads'])
if epochs_idx is None:
epochs_idx = slice(len(self.events))
data = self.get_data()[epochs_idx, picks]
if copy is True:
data = data.copy()
if collapse is True:
data = np.hstack(data).copy()
offset = self.raw.time_as_index(abs(self.tmin), use_first_samp)
t0 = self.raw.index_as_time(self.events[0, 0] - offset)[0]
epochs_ts = TimeSeries(data, sampling_rate=self.info['sfreq'], t0=t0)
epochs_ts.ch_names = np.array(self.ch_names)[picks].tolist()
return epochs_ts
def to_nitime(self, picks=None, epochs_idx=None, collapse=False, copy=True, use_first_samp=False):
""" Export epochs as nitime TimeSeries
Parameters
----------
picks : array-like | None
Indices for exporting subsets of the epochs channels. If None
all good channels will be used.
epochs_idx : slice | array-like | None
Epochs index for single or selective epochs exports. If None, all
epochs will be used.
collapse : boolean
If True export epochs and time slices will be collapsed to 2D
array. This may be required by some nitime functions.
copy : boolean
If True exports copy of epochs data.
use_first_samp: boolean
If True, the time returned is relative to the session onset, else
relative to the recording onset.
Returns
-------
epochs_ts : instance of nitime.TimeSeries
The Epochs as nitime TimeSeries object
"""
try:
from nitime import TimeSeries # to avoid strong dependency
except ImportError:
raise Exception("the nitime package is missing")
if picks is None:
picks = pick_types(self.info, include=self.ch_names, exclude=self.info["bads"])
if epochs_idx is None:
epochs_idx = slice(len(self.events))
data = self.get_data()[epochs_idx, picks]
if copy is True:
data = data.copy()
if collapse is True:
data = np.hstack(data).copy()
offset = self.raw.time_as_index(abs(self.tmin), use_first_samp)
t0 = self.raw.index_as_time(self.events[0, 0] - offset)[0]
epochs_ts = TimeSeries(data, sampling_rate=self.info["sfreq"], t0=t0)
epochs_ts.ch_names = np.array(self.ch_names)[picks].tolist()
return epochs_ts
