/usr/lib/python2.7/dist-packages/mne/epochs.py is in python-mne 0.7.3-1.
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# Authors: Alexandre Gramfort <gramfort@nmr.mgh.harvard.edu>
# Matti Hamalainen <msh@nmr.mgh.harvard.edu>
# Daniel Strohmeier <daniel.strohmeier@tu-ilmenau.de>
# Denis Engemann <d.engemann@fz-juelich.de>
# Mainak Jas <mainak@neuro.hut.fi>
#
# License: BSD (3-clause)
import copy as cp
import warnings
import numpy as np
from copy import deepcopy
from .fiff.write import (start_file, start_block, end_file, end_block,
write_int, write_float_matrix, write_float,
write_id, write_string)
from .fiff.meas_info import read_meas_info, write_meas_info
from .fiff.open import fiff_open
from .fiff.raw import _time_as_index, _index_as_time
from .fiff.tree import dir_tree_find
from .fiff.tag import read_tag
from .fiff import Evoked, FIFF
from .fiff.pick import (pick_types, channel_indices_by_type, channel_type,
pick_channels)
from .fiff.proj import setup_proj, ProjMixin
from .fiff.evoked import aspect_rev
from .baseline import rescale
from .utils import (check_random_state, _check_pandas_index_arguments,
_check_pandas_installed)
from .filter import resample, detrend
from .event import _read_events_fif
from .fixes import in1d
from .viz import _mutable_defaults, plot_epochs
from .utils import logger, verbose
class _BaseEpochs(ProjMixin):
"""Abstract base class for Epochs-type classes
This class provides basic functionality and should never be instantiated
directly. See Epochs below for an explanation of the parameters.
"""
def __init__(self, info, event_id, tmin, tmax, baseline=(None, 0),
picks=None, name='Unknown', reject=None, flat=None,
decim=1, reject_tmin=None, reject_tmax=None, detrend=None,
add_eeg_ref=True, verbose=None):
self.verbose = verbose
self.name = name
if isinstance(event_id, dict):
if not all([isinstance(v, int) for v in event_id.values()]):
raise ValueError('Event IDs must be of type integer')
if not all([isinstance(k, basestring) for k in event_id]):
raise ValueError('Event names must be of type str')
self.event_id = event_id
elif isinstance(event_id, list):
if not all([isinstance(v, int) for v in event_id]):
raise ValueError('Event IDs must be of type integer')
self.event_id = dict(zip((str(i) for i in event_id), event_id))
elif isinstance(event_id, int):
self.event_id = {str(event_id): event_id}
else:
raise ValueError('event_id must be dict or int.')
# check reject_tmin and reject_tmax
if (reject_tmin is not None) and (reject_tmin < tmin):
raise ValueError("reject_tmin needs to be None or >= tmin")
if (reject_tmax is not None) and (reject_tmax > tmax):
raise ValueError("reject_tmax needs to be None or <= tmax")
if (reject_tmin is not None) and (reject_tmax is not None):
if reject_tmin >= reject_tmax:
raise ValueError('reject_tmin needs to be < reject_tmax')
if not detrend in [None, 0, 1]:
raise ValueError('detrend must be None, 0, or 1')
self.tmin = tmin
self.tmax = tmax
self.baseline = baseline
self.reject = reject
self.reject_tmin = reject_tmin
self.reject_tmax = reject_tmax
self.flat = flat
self.decim = decim = int(decim)
self._bad_dropped = False
self.drop_log = None
self.detrend = detrend
# Handle measurement info
self.info = info
if picks is None:
picks = range(len(self.info['ch_names']))
else:
self.info['chs'] = [self.info['chs'][k] for k in picks]
self.info['ch_names'] = [self.info['ch_names'][k] for k in picks]
self.info['nchan'] = len(picks)
self.picks = picks
if len(picks) == 0:
raise ValueError("Picks cannot be empty.")
# Handle times
if tmin >= tmax:
raise ValueError('tmin has to be smaller than tmax')
sfreq = float(self.info['sfreq'])
n_times_min = int(round(tmin * sfreq))
n_times_max = int(round(tmax * sfreq))
times = np.arange(n_times_min, n_times_max + 1, dtype=np.float) / sfreq
self.times = times
self._raw_times = times # times before decimation
self._epoch_stop = ep_len = len(self.times)
if decim > 1:
new_sfreq = sfreq / decim
lowpass = self.info['lowpass']
if new_sfreq < 2.5 * lowpass: # nyquist says 2 but 2.5 is safer
msg = ('The measurement information indicates a low-pass '
'frequency of %g Hz. The decim=%i parameter will '
'result in a sampling frequency of %g Hz, which can '
'cause aliasing artifacts.'
% (lowpass, decim, new_sfreq))
warnings.warn(msg)
i_start = n_times_min % decim
self._decim_idx = slice(i_start, ep_len, decim)
self.times = self.times[self._decim_idx]
self.info['sfreq'] = new_sfreq
self.preload = False
self._data = None
self._offset = None
# setup epoch rejection
self._reject_setup()
def _reject_setup(self):
"""Sets self._reject_time and self._channel_type_idx (called from
__init__)
"""
if self.reject is None and self.flat is None:
return
idx = channel_indices_by_type(self.info)
for key in idx.keys():
if (self.reject is not None and key in self.reject) \
or (self.flat is not None and key in self.flat):
if len(idx[key]) == 0:
raise ValueError("No %s channel found. Cannot reject based"
" on %s." % (key.upper(), key.upper()))
self._channel_type_idx = idx
if (self.reject_tmin is None) and (self.reject_tmax is None):
self._reject_time = None
else:
if self.reject_tmin is None:
reject_imin = None
else:
idxs = np.nonzero(self.times >= self.reject_tmin)[0]
reject_imin = idxs[0]
if self.reject_tmax is None:
reject_imax = None
else:
idxs = np.nonzero(self.times <= self.reject_tmax)[0]
reject_imax = idxs[-1]
self._reject_time = slice(reject_imin, reject_imax)
@verbose
def _is_good_epoch(self, data, verbose=None):
"""Determine if epoch is good"""
if data is None:
return False, ['NO_DATA']
n_times = len(self.times)
if data.shape[1] < n_times:
# epoch is too short ie at the end of the data
return False, ['TOO_SHORT']
if self.reject is None and self.flat is None:
return True, None
else:
if self._reject_time is not None:
data = data[:, self._reject_time]
return _is_good(data, self.ch_names, self._channel_type_idx,
self.reject, self.flat, full_report=True,
ignore_chs=self.info['bads'])
def get_data(self):
"""Get all epochs as a 3D array
Returns
-------
data : array of shape [n_epochs, n_channels, n_times]
The epochs data
"""
if self.preload:
return self._data
else:
data = self._get_data_from_disk()
return data
def iter_evoked(self):
"""Iterate over Evoked objects with nave=1
"""
self._current = 0
while True:
evoked = Evoked(None)
evoked.info = cp.deepcopy(self.info)
evoked.times = self.times.copy()
evoked.nave = 1
evoked.first = int(self.times[0] * self.info['sfreq'])
evoked.last = evoked.first + len(self.times) - 1
evoked.data, event_id = self.next(True)
evoked.comment = str(event_id)
yield evoked
def subtract_evoked(self, evoked=None):
"""Subtract an evoked response from each epoch
Can be used to exclude the evoked response when analyzing induced
activity, see e.g. [1].
References
----------
[1] David et al. "Mechanisms of evoked and induced responses in
MEG/EEG", NeuroImage, vol. 31, no. 4, pp. 1580-1591, July 2006.
Parameters
----------
evoked : instance of mne.fiff.Evoked | None
The evoked response to subtract. If None, the evoked response
is computed from Epochs itself.
Returns
-------
self : instance of mne.Epochs
The modified instance (instance is also modified inplace).
"""
logger.info('Subtracting Evoked from Epochs')
if evoked is None:
picks = pick_types(self.info, meg=True, eeg=True,
stim=False, eog=False, ecg=False,
emg=False, exclude=[])
evoked = self.average(picks)
# find the indices of the channels to use
picks = pick_channels(evoked.ch_names, include=self.ch_names)
# make sure the omitted channels are not data channels
if len(picks) < len(self.ch_names):
sel_ch = [evoked.ch_names[ii] for ii in picks]
diff_ch = list(set(self.ch_names).difference(sel_ch))
diff_idx = [self.ch_names.index(ch) for ch in diff_ch]
diff_types = [channel_type(self.info, idx) for idx in diff_idx]
bad_idx = [diff_types.index(t) for t in diff_types if t in
['grad', 'mag', 'eeg']]
if len(bad_idx) > 0:
bad_str = ', '.join([diff_ch[ii] for ii in bad_idx])
raise ValueError('The following data channels are missing '
'in the evoked response: %s' % bad_str)
logger.info(' The following channels are not included in the '
'subtraction: %s' % ', '.join(diff_ch))
# make sure the times match
if (len(self.times) != len(evoked.times) or
np.max(np.abs(self.times - evoked.times)) >= 1e-7):
raise ValueError('Epochs and Evoked object do not contain '
'the same time points.')
# handle SSPs
if not self.proj and evoked.proj:
warnings.warn('Evoked has SSP applied while Epochs has not.')
if self.proj and not evoked.proj:
evoked = evoked.copy().apply_proj()
# find the indices of the channels to use in Epochs
ep_picks = [self.ch_names.index(evoked.ch_names[ii]) for ii in picks]
# do the subtraction
if self.preload:
self._data[:, ep_picks, :] -= evoked.data[picks][None, :, :]
else:
if self._offset is None:
self._offset = np.zeros((len(self.ch_names), len(self.times)),
dtype=np.float)
self._offset[ep_picks] -= evoked.data[picks]
logger.info('[done]')
return self
def _get_data_from_disk(self, out=True, verbose=None):
raise NotImplementedError('_get_data_from_disk() must be implemented '
'in derived class.')
def __iter__(self):
"""To make iteration over epochs easy.
"""
self._current = 0
return self
def next(self, return_event_id=False):
raise NotImplementedError('next() must be implemented in derived '
'class.')
def average(self, picks=None):
"""Compute average of epochs
Parameters
----------
picks : None | array of int
If None only MEG and EEG channels are kept
otherwise the channels indices in picks are kept.
Returns
-------
evoked : Evoked instance
The averaged epochs
"""
return self._compute_mean_or_stderr(picks, 'ave')
def standard_error(self, picks=None):
"""Compute standard error over epochs
Parameters
----------
picks : None | array of int
If None only MEG and EEG channels are kept
otherwise the channels indices in picks are kept.
Returns
-------
evoked : Evoked instance
The standard error over epochs
"""
return self._compute_mean_or_stderr(picks, 'stderr')
def _compute_mean_or_stderr(self, picks, mode='ave'):
"""Compute the mean or std over epochs and return Evoked"""
_do_std = True if mode == 'stderr' else False
evoked = Evoked(None)
evoked.info = cp.deepcopy(self.info)
# make sure projs are really copied.
evoked.info['projs'] = [cp.deepcopy(p) for p in self.info['projs']]
n_channels = len(self.ch_names)
n_times = len(self.times)
if self.preload:
n_events = len(self.events)
if not _do_std:
data = np.mean(self._data, axis=0)
else:
data = np.std(self._data, axis=0)
assert len(self.events) == len(self._data)
else:
data = np.zeros((n_channels, n_times))
n_events = 0
for e in self:
data += e
n_events += 1
data /= n_events
# convert to stderr if requested, could do in one pass but do in
# two (slower) in case there are large numbers
if _do_std:
data_mean = cp.copy(data)
data.fill(0.)
for e in self:
data += (e - data_mean) ** 2
data = np.sqrt(data / n_events)
evoked.data = data
evoked.times = self.times.copy()
evoked.comment = self.name
evoked.nave = n_events
evoked.first = int(self.times[0] * self.info['sfreq'])
evoked.last = evoked.first + len(self.times) - 1
if not _do_std:
evoked._aspect_kind = FIFF.FIFFV_ASPECT_AVERAGE
else:
evoked._aspect_kind = FIFF.FIFFV_ASPECT_STD_ERR
evoked.data /= np.sqrt(evoked.nave)
evoked.kind = aspect_rev.get(str(evoked._aspect_kind), 'Unknown')
# dropping EOG, ECG and STIM channels. Keeping only data
if picks is None:
picks = pick_types(evoked.info, meg=True, eeg=True, ref_meg=True,
stim=False, eog=False, ecg=False,
emg=False, exclude=[])
if len(picks) == 0:
raise ValueError('No data channel found when averaging.')
picks = np.sort(picks) # make sure channel order does not change
evoked.info['chs'] = [evoked.info['chs'][k] for k in picks]
evoked.info['ch_names'] = [evoked.info['ch_names'][k]
for k in picks]
evoked.info['nchan'] = len(picks)
evoked.data = evoked.data[picks]
# otherwise the apply_proj will be confused
evoked.proj = True if self.proj is True else None
evoked.verbose = self.verbose
return evoked
@property
def ch_names(self):
return self.info['ch_names']
def plot(self, epoch_idx=None, picks=None, scalings=None,
title_str='#%003i', show=True, block=False):
""" Visualize single trials using Trellis plot.
Parameters
----------
epoch_idx : array-like | int | None
The epochs to visualize. If None, the frist 20 epochs are shoen.
Defaults to None.
picks : array-like | None
Channels to be included. If None only good data channels are used.
Defaults to None
scalings : dict | None
scalings : dict | None
Scale factors for the traces. If None, defaults to:
`dict(mag=1e-12, grad=4e-11, eeg=20e-6, eog=150e-6, ecg=5e-4,
emg=1e-3, ref_meg=1e-12, misc=1e-3, stim=1, resp=1,
chpi=1e-4)`
title_str : None | str
The string formatting to use for axes titles. If None, no titles
will be shown. Defaults expand to ``#001, #002, ...``
show : bool
Whether to show the figure or not.
block : bool
Whether to halt program execution until the figure is closed.
Useful for rejecting bad trials on the fly by clicking on a
sub plot.
Returns
-------
fig : Instance of matplotlib.figure.Figure
The figure.
"""
plot_epochs(self, epoch_idx=epoch_idx, picks=picks, scalings=scalings,
title_str=title_str, show=show, block=block)
class Epochs(_BaseEpochs):
"""List of Epochs
Parameters
----------
raw : Raw object
An instance of Raw.
events : array, of shape [n_events, 3]
Returned by the read_events function.
event_id : int | list of int | dict | None
The id of the event to consider. If dict,
the keys can later be used to acces associated events. Example:
dict(auditory=1, visual=3). If int, a dict will be created with
the id as string. If a list, all events with the IDs specified
in the list are used. If None, all events will be used with
and a dict is created with string integer names corresponding
to the event id integers.
tmin : float
Start time before event.
tmax : float
End time after event.
name : string
Comment that describes the Evoked data created.
baseline : None or tuple of length 2 (default (None, 0))
The time interval to apply baseline correction.
If None do not apply it. If baseline is (a, b)
the interval is between "a (s)" and "b (s)".
If a is None the beginning of the data is used
and if b is None then b is set to the end of the interval.
If baseline is equal to (None, None) all the time
interval is used.
picks : None (default) or array of int
Indices of channels to include (if None, all channels
are used).
preload : boolean
Load all epochs from disk when creating the object
or wait before accessing each epoch (more memory
efficient but can be slower).
reject : dict
Epoch rejection parameters based on peak to peak amplitude.
Valid keys are 'grad' | 'mag' | 'eeg' | 'eog' | 'ecg'.
If reject is None then no rejection is done.
Values are float. Example::
reject = dict(grad=4000e-13, # T / m (gradiometers)
mag=4e-12, # T (magnetometers)
eeg=40e-6, # uV (EEG channels)
eog=250e-6 # uV (EOG channels)
)
flat : dict
Epoch rejection parameters based on flatness of signal
Valid keys are 'grad' | 'mag' | 'eeg' | 'eog' | 'ecg'
If flat is None then no rejection is done.
proj : bool | 'delayed'
Apply SSP projection vectors. If proj is 'delayed' and reject is not
None the single epochs will be projected before the rejection
decision, but used in unprojected state if they are kept.
This way deciding which projection vectors are good can be postponed
to the evoked stage without resulting in lower epoch counts and
without producing results different from early SSP application
given comparable parameters. Note that in this case baselining,
detrending and temporal decimation will be postponed.
If proj is False no projections will be applied which is the
recommended value if SSPs are not used for cleaning the data.
decim : int
Factor by which to downsample the data from the raw file upon import.
Warning: This simply selects every nth sample, data is not filtered
here. If data is not properly filtered, aliasing artifacts may occur.
reject_tmin : scalar | None
Start of the time window used to reject epochs (with the default None,
the window will start with tmin).
reject_tmax : scalar | None
End of the time window used to reject epochs (with the default None,
the window will end with tmax).
detrend : int | None
If 0 or 1, the data channels (MEG and EEG) will be detrended when
loaded. 0 is a constant (DC) detrend, 1 is a linear detrend. None
is no detrending. Note that detrending is performed before baseline
correction. If no DC offset is preferred (zeroth order detrending),
either turn off baseline correction, as this may introduce a DC
shift, or set baseline correction to use the entire time interval
(will yield equivalent results but be slower).
add_eeg_ref : bool
If True, an EEG average reference will be added (unless one
already exists).
verbose : bool, str, int, or None
If not None, override default verbose level (see mne.verbose).
Defaults to raw.verbose.
Attributes
----------
info: dict
Measurement info.
event_id : dict
Names of of conditions corresponding to event_ids.
ch_names : list of string
List of channels' names.
drop_log : list of lists
This list (same length as events) contains the channel(s),
or the reasons (count equalization, not reaching minimum duration),
if any, that caused an event in the original event list to be dropped
by drop_bad_epochs(). Caveat. The drop log will only know about the
events passed to epochs. If the events represent a selection the
drop log can be misaligned with regard to other external logs (e.g.,
behavioral responses) that still refer to the complete list of events.
verbose : bool, str, int, or None
See above.
Notes
-----
For indexing and slicing:
epochs[idx] : Epochs
Return Epochs object with a subset of epochs (supports single
index and python-style slicing)
For subset selection using categorial labels:
epochs['name'] : Epochs
Return Epochs object with a subset of epochs corresponding to an
experimental condition as specified by 'name'.
epochs[['name_1', 'name_2', ... ]] : Epochs
Return Epochs object with a subset of epochs corresponding to multiple
experimental conditions as specified by 'name_1', 'name_2', ... .
See also
--------
mne.epochs.combine_event_ids
mne.Epochs.equalize_event_counts
"""
@verbose
def __init__(self, raw, events, event_id, tmin, tmax, baseline=(None, 0),
picks=None, name='Unknown', preload=False, reject=None,
flat=None, proj=True, decim=1, reject_tmin=None,
reject_tmax=None, detrend=None, add_eeg_ref=True, verbose=None):
if raw is None:
return
# prepare for calling the base constructor
# Handle measurement info
info = cp.deepcopy(raw.info)
# make sure projs are really copied.
info['projs'] = [cp.deepcopy(p) for p in info['projs']]
if event_id is None: # convert to int to make typing-checks happy
event_id = dict((str(e), int(e)) for e in np.unique(events[:, 2]))
proj = proj or raw.proj # proj is on when applied in Raw
# call _BaseEpochs constructor
super(Epochs, self).__init__(info, event_id, tmin, tmax,
baseline=baseline, picks=picks, name=name,
reject=reject, flat=flat, decim=decim,
reject_tmin=reject_tmin,
reject_tmax=reject_tmax, detrend=detrend,
add_eeg_ref=add_eeg_ref, verbose=verbose)
# do the rest
self.raw = raw
proj = proj or raw.proj # proj is on when applied in Raw
if proj not in [True, 'delayed', False]:
raise ValueError(r"'proj' must either be 'True', 'False' or "
"'delayed'")
self.proj = proj
if self._check_delayed():
logger.info('Entering delayed SSP mode.')
activate = False if self._check_delayed() else self.proj
self._projector, self.info = setup_proj(self.info, add_eeg_ref,
activate=activate)
# Select the desired events
selected = in1d(events[:, 2], self.event_id.values())
self.events = events[selected]
if len(self.events) > 1:
if np.diff(self.events.astype(np.int64)[:, 0]).min() <= 0:
warnings.warn('The events passed to the Epochs constructor '
'are not chronologically ordered.',
RuntimeWarning)
n_events = len(self.events)
if n_events > 0:
logger.info('%d matching events found' % n_events)
else:
raise ValueError('No desired events found.')
self.preload = preload
if self.preload:
self._data = self._get_data_from_disk()
self.raw = None
else:
self._data = None
def drop_picks(self, bad_picks):
"""Drop some picks
Allows to discard some channels.
"""
self.picks = list(self.picks)
idx = [k for k, p in enumerate(self.picks) if p not in bad_picks]
self.picks = [self.picks[k] for k in idx]
# XXX : could maybe be factorized
self.info['chs'] = [self.info['chs'][k] for k in idx]
self.info['ch_names'] = [self.info['ch_names'][k] for k in idx]
self.info['nchan'] = len(idx)
if self._projector is not None:
self._projector = self._projector[idx][:, idx]
if self.preload:
self._data = self._data[:, idx, :]
def drop_bad_epochs(self):
"""Drop bad epochs without retaining the epochs data.
Should be used before slicing operations.
.. Warning:: Operation is slow since all epochs have to be read from
disk. To avoid reading epochs form disk multiple times, initialize
Epochs object with preload=True.
"""
self._get_data_from_disk(out=False)
def _check_delayed(self):
""" Aux method
"""
is_delayed = False
if self.proj == 'delayed':
if self.reject is None:
raise RuntimeError('The delayed SSP mode was requested '
'but no rejection parameters are present. '
'Please add rejection parameters before '
'using this option.')
is_delayed = True
return is_delayed
@verbose
def drop_epochs(self, indices, verbose=None):
"""Drop epochs based on indices or boolean mask
Parameters
----------
indices : array of ints or bools
Set epochs to remove by specifying indices to remove or a boolean
mask to apply (where True values get removed). Events are
correspondingly modified.
verbose : bool, str, int, or None
If not None, override default verbose level (see mne.verbose).
Defaults to raw.verbose.
"""
indices = np.asarray(indices)
if indices.dtype == bool:
indices = np.where(indices)[0]
self.events = np.delete(self.events, indices, axis=0)
if(self.preload):
self._data = np.delete(self._data, indices, axis=0)
count = len(indices)
logger.info('Dropped %d epoch%s' % (count, '' if count == 1 else 's'))
@verbose
def _get_epoch_from_disk(self, idx, proj, verbose=None):
"""Load one epoch from disk"""
if self.raw is None:
# This should never happen, as raw=None only if preload=True
raise ValueError('An error has occurred, no valid raw file found.'
' Please report this to the mne-python '
'developers.')
sfreq = self.raw.info['sfreq']
if self.events.ndim == 1:
# single event
event_samp = self.events[0]
else:
event_samp = self.events[idx, 0]
# Read a data segment
first_samp = self.raw.first_samp
start = int(round(event_samp + self.tmin * sfreq)) - first_samp
stop = start + self._epoch_stop
if start < 0:
return None, None
epoch_raw, _ = self.raw[self.picks, start:stop]
# setup list of epochs to handle delayed SSP
epochs = []
# whenever requested, the first epoch is being projected.
if self._projector is not None and proj is True:
epochs += [np.dot(self._projector, epoch_raw)]
else:
epochs += [epoch_raw]
# in case the proj passed is True but self proj is not we
# have delayed SSP
if self.proj != proj: # so append another unprojected epoch
epochs += [epoch_raw.copy()]
# only preprocess first candidate, to make delayed SSP working
# we need to postpone the preprocessing since projection comes
# first.
epochs[0] = self._preprocess(epochs[0], verbose)
# return a second None if nothing is projected
if len(epochs) == 1:
epochs += [None]
return epochs
@verbose
def _preprocess(self, epoch, verbose=None):
""" Aux Function
"""
if self.detrend is not None:
picks = pick_types(self.info, meg=True, eeg=True, stim=False,
ref_meg=False, eog=False, ecg=False,
emg=False, exclude=[])
epoch[picks] = detrend(epoch[picks], self.detrend, axis=1)
# Baseline correct
epoch = rescale(epoch, self._raw_times, self.baseline, 'mean',
copy=False, verbose=verbose)
# handle offset
if self._offset is not None:
epoch += self._offset
# Decimate
if self.decim > 1:
epoch = epoch[:, self._decim_idx]
return epoch
@verbose
def _get_data_from_disk(self, out=True, verbose=None):
"""Load all data from disk
Parameters
----------
out : bool
Return the data. Setting this to False is used to reject bad
epochs without caching all the data, which saves memory.
verbose : bool, str, int, or None
If not None, override default verbose level (see mne.verbose).
Defaults to self.verbose.
"""
n_events = len(self.events)
data = np.array([])
if self._bad_dropped:
proj = False if self._check_delayed() else self.proj
if not out:
return
for ii in xrange(n_events):
# faster to pre-allocate memory here
epoch, epoch_raw = self._get_epoch_from_disk(ii, proj=proj)
if ii == 0:
data = np.empty((n_events, epoch.shape[0],
epoch.shape[1]), dtype=epoch.dtype)
if self._check_delayed():
epoch = epoch_raw
data[ii] = epoch
else:
proj = True if self._check_delayed() else self.proj
good_events = []
drop_log = [[] for _ in range(n_events)]
n_out = 0
for idx in xrange(n_events):
epoch, epoch_raw = self._get_epoch_from_disk(idx, proj=proj)
is_good, offenders = self._is_good_epoch(epoch)
if is_good:
good_events.append(idx)
if self._check_delayed():
epoch = epoch_raw
if out:
# faster to pre-allocate, then trim as necessary
if n_out == 0:
data = np.empty((n_events, epoch.shape[0],
epoch.shape[1]),
dtype=epoch.dtype, order='C')
data[n_out] = epoch
n_out += 1
else:
drop_log[idx] = offenders
self.drop_log = drop_log
self.events = np.atleast_2d(self.events[good_events])
self._bad_dropped = True
logger.info("%d bad epochs dropped"
% (n_events - len(good_events)))
if not out:
return
# just take the good events
assert len(good_events) == n_out
if n_out > 0:
# slicing won't free the space, so we resize
# we have ensured the C-contiguity of the array in allocation
# so this operation will be safe unless np is very broken
data.resize((n_out,) + data.shape[1:], refcheck=False)
return data
@verbose
def _is_good_epoch(self, data, verbose=None):
"""Determine if epoch is good"""
if data is None:
return False, ['NO_DATA']
n_times = len(self.times)
if data.shape[1] < n_times:
# epoch is too short ie at the end of the data
return False, ['TOO_SHORT']
if self.reject is None and self.flat is None:
return True, None
else:
if self._reject_time is not None:
data = data[:, self._reject_time]
return _is_good(data, self.ch_names, self._channel_type_idx,
self.reject, self.flat, full_report=True,
ignore_chs=self.info['bads'])
def get_data(self):
"""Get all epochs as a 3D array
Returns
-------
data : array of shape [n_epochs, n_channels, n_times]
The epochs data
"""
if self.preload:
data_ = self._data
else:
data_ = self._get_data_from_disk()
if self._check_delayed():
data = np.zeros_like(data_)
for ii, e in enumerate(data_):
data[ii] = self._preprocess(e.copy(), self.verbose)
else:
data = data_
return data
def _reject_setup(self):
"""Sets self._reject_time and self._channel_type_idx (called from
__init__)
"""
if self.reject is None and self.flat is None:
return
idx = channel_indices_by_type(self.info)
for key in idx.keys():
if (self.reject is not None and key in self.reject) \
or (self.flat is not None and key in self.flat):
if len(idx[key]) == 0:
raise ValueError("No %s channel found. Cannot reject based"
" on %s." % (key.upper(), key.upper()))
self._channel_type_idx = idx
if (self.reject_tmin is None) and (self.reject_tmax is None):
self._reject_time = None
else:
if self.reject_tmin is None:
reject_imin = None
else:
idxs = np.nonzero(self.times >= self.reject_tmin)[0]
reject_imin = idxs[0]
if self.reject_tmax is None:
reject_imax = None
else:
idxs = np.nonzero(self.times <= self.reject_tmax)[0]
reject_imax = idxs[-1]
self._reject_time = slice(reject_imin, reject_imax)
def __len__(self):
"""Number of epochs.
"""
if not self._bad_dropped:
err = ("Since bad epochs have not been dropped, the length of the "
"Epochs is not known. Load the Epochs with preload=True, "
"or call Epochs.drop_bad_epochs(). To find the number of "
"events in the Epochs, use len(Epochs.events).")
raise RuntimeError(err)
return len(self.events)
def __iter__(self):
"""To make iteration over epochs easy.
"""
self._current = 0
return self
def next(self, return_event_id=False):
"""To make iteration over epochs easy.
"""
if self.preload:
if self._current >= len(self._data):
raise StopIteration
epoch = self._data[self._current]
if self._check_delayed():
epoch = self._preprocess(epoch.copy())
self._current += 1
else:
proj = True if self._check_delayed() else self.proj
is_good = False
while not is_good:
if self._current >= len(self.events):
raise StopIteration
epoch, epoch_raw = self._get_epoch_from_disk(self._current,
proj=proj)
self._current += 1
is_good, _ = self._is_good_epoch(epoch)
# If delayed-ssp mode, pass 'virgin' data after rejection decision.
if self._check_delayed():
epoch = self._preprocess(epoch_raw)
if not return_event_id:
return epoch
else:
return epoch, self.events[self._current - 1][-1]
return epoch if not return_event_id else epoch, self.event_id
def __repr__(self):
""" Build string representation
"""
if not self._bad_dropped:
s = 'n_events : %s (good & bad)' % len(self.events)
else:
s = 'n_events : %s (all good)' % len(self.events)
s += ', tmin : %s (s)' % self.tmin
s += ', tmax : %s (s)' % self.tmax
s += ', baseline : %s' % str(self.baseline)
if len(self.event_id) > 1:
counts = ['%r: %i' % (k, sum(self.events[:, 2] == v))
for k, v in self.event_id.items()]
s += ',\n %s' % ', '.join(counts)
return '<Epochs | %s>' % s
def _key_match(self, key):
"""Helper function for event dict use"""
if key not in self.event_id:
raise KeyError('Event "%s" is not in Epochs.' % key)
return self.events[:, 2] == self.event_id[key]
def __getitem__(self, key):
"""Return an Epochs object with a subset of epochs
"""
data = self._data
del self._data
epochs = self.copy()
self._data, epochs._data = data, data
if isinstance(key, basestring):
key = [key]
if isinstance(key, list) and isinstance(key[0], basestring):
key_match = np.any(np.atleast_2d([epochs._key_match(k)
for k in key]), axis=0)
select = key_match
epochs.name = ('-'.join(key) if epochs.name == 'Unknown'
else 'epochs_%s' % '-'.join(key))
else:
key_match = key
select = key if isinstance(key, slice) else np.atleast_1d(key)
if not epochs._bad_dropped:
# Only matters if preload is not true, since bad epochs are
# dropped on preload; doesn't mater for key lookup, either
warnings.warn("Bad epochs have not been dropped, indexing will"
" be inaccurate. Use drop_bad_epochs() or"
" preload=True")
epochs.events = np.atleast_2d(epochs.events[key_match])
if epochs.preload:
epochs._data = epochs._data[select]
return epochs
def crop(self, tmin=None, tmax=None, copy=False):
"""Crops a time interval from epochs object.
Parameters
----------
tmin : float
Start time of selection in seconds.
tmax : float
End time of selection in seconds.
copy : bool
If False epochs is cropped in place.
Returns
-------
epochs : Epochs instance
The cropped epochs.
"""
if not self.preload:
raise RuntimeError('Modifying data of epochs is only supported '
'when preloading is used. Use preload=True '
'in the constructor.')
if tmin is None:
tmin = self.tmin
elif tmin < self.tmin:
warnings.warn("tmin is not in epochs' time interval."
"tmin is set to epochs.tmin")
tmin = self.tmin
if tmax is None:
tmax = self.tmax
elif tmax > self.tmax:
warnings.warn("tmax is not in epochs' time interval."
"tmax is set to epochs.tmax")
tmax = self.tmax
tmask = (self.times >= tmin) & (self.times <= tmax)
tidx = np.where(tmask)[0]
this_epochs = self if not copy else self.copy()
this_epochs.tmin = this_epochs.times[tidx[0]]
this_epochs.tmax = this_epochs.times[tidx[-1]]
this_epochs.times = this_epochs.times[tmask]
this_epochs._data = this_epochs._data[:, :, tmask]
return this_epochs
@verbose
def resample(self, sfreq, npad=100, window='boxcar', n_jobs=1,
verbose=None):
"""Resample preloaded data
Parameters
----------
sfreq : float
New sample rate to use
npad : int
Amount to pad the start and end of the data.
window : string or tuple
Window to use in resampling. See scipy.signal.resample.
n_jobs : int
Number of jobs to run in parallel.
verbose : bool, str, int, or None
If not None, override default verbose level (see mne.verbose).
Defaults to self.verbose.
Notes
-----
For some data, it may be more accurate to use npad=0 to reduce
artifacts. This is dataset dependent -- check your data!
"""
if self.preload:
o_sfreq = self.info['sfreq']
self._data = resample(self._data, sfreq, o_sfreq, npad,
n_jobs=n_jobs)
# adjust indirectly affected variables
self.info['sfreq'] = sfreq
self.times = (np.arange(self._data.shape[2], dtype=np.float)
/ sfreq + self.times[0])
else:
raise RuntimeError('Can only resample preloaded data')
def copy(self):
"""Return copy of Epochs instance"""
raw = self.raw
del self.raw
new = deepcopy(self)
self.raw = raw
new.raw = raw
return new
def save(self, fname):
"""Save epochs in a fif file
Parameters
----------
fname : str
The name of the file.
"""
# Create the file and save the essentials
fid = start_file(fname)
start_block(fid, FIFF.FIFFB_MEAS)
write_id(fid, FIFF.FIFF_BLOCK_ID)
if self.info['meas_id'] is not None:
write_id(fid, FIFF.FIFF_PARENT_BLOCK_ID, self.info['meas_id'])
# Write measurement info
write_meas_info(fid, self.info)
# One or more evoked data sets
start_block(fid, FIFF.FIFFB_PROCESSED_DATA)
start_block(fid, FIFF.FIFFB_EPOCHS)
# write events out after getting data to ensure bad events are dropped
data = self.get_data()
start_block(fid, FIFF.FIFFB_MNE_EVENTS)
write_int(fid, FIFF.FIFF_MNE_EVENT_LIST, self.events.T)
mapping_ = ';'.join([k + ':' + str(v) for k, v in
self.event_id.items()])
write_string(fid, FIFF.FIFF_DESCRIPTION, mapping_)
end_block(fid, FIFF.FIFFB_MNE_EVENTS)
# First and last sample
first = int(self.times[0] * self.info['sfreq'])
last = first + len(self.times) - 1
write_int(fid, FIFF.FIFF_FIRST_SAMPLE, first)
write_int(fid, FIFF.FIFF_LAST_SAMPLE, last)
# save baseline
if self.baseline is not None:
bmin, bmax = self.baseline
bmin = self.times[0] if bmin is None else bmin
bmax = self.times[-1] if bmax is None else bmax
write_float(fid, FIFF.FIFF_MNE_BASELINE_MIN, bmin)
write_float(fid, FIFF.FIFF_MNE_BASELINE_MAX, bmax)
# The epochs itself
decal = np.empty(self.info['nchan'])
for k in range(self.info['nchan']):
decal[k] = 1.0 / (self.info['chs'][k]['cal']
* self.info['chs'][k].get('scale', 1.0))
data *= decal[np.newaxis, :, np.newaxis]
write_float_matrix(fid, FIFF.FIFF_EPOCH, data)
# undo modifications to data
data /= decal[np.newaxis, :, np.newaxis]
end_block(fid, FIFF.FIFFB_EPOCHS)
end_block(fid, FIFF.FIFFB_PROCESSED_DATA)
end_block(fid, FIFF.FIFFB_MEAS)
end_file(fid)
def as_data_frame(self, picks=None, index=None, scale_time=1e3,
scalings=None, copy=True):
"""Get the epochs as Pandas DataFrame
Export epochs data in tabular structure with MEG channels as columns
and three additional info columns 'epoch', 'condition', and 'time'.
The format matches a long table format commonly used to represent
repeated measures in within-subject designs.
Parameters
----------
picks : None | array of int
If None only MEG and EEG channels are kept
otherwise the channels indices in picks are kept.
index : tuple of str | None
Column to be used as index for the data. Valid string options
are 'epoch', 'time' and 'condition'. If None, all three info
columns will be included in the table as categorial data.
scale_time : float
Scaling to be applied to time units.
scalings : dict | None
Scaling to be applied to the channels picked. If None, defaults to
``scalings=dict(eeg=1e6, grad=1e13, mag=1e15, misc=1.0)`.
copy : bool
If true, data will be copied. Else data may be modified in place.
Returns
-------
df : instance of pandas.core.DataFrame
Epochs exported into tabular data structure.
"""
pd = _check_pandas_installed()
default_index = ['condition', 'epoch', 'time']
if index is not None:
_check_pandas_index_arguments(index, default_index)
else:
index = default_index
if picks is None:
picks = range(self.info['nchan'])
else:
if not in1d(picks, np.arange(len(self.events))).all():
raise ValueError('At least one picked channel is not present '
'in this eppochs instance.')
data = self.get_data()[:, picks, :]
shape = data.shape
data = np.hstack(data).T
if copy:
data = data.copy()
types = [channel_type(self.info, idx) for idx in picks]
n_channel_types = 0
ch_types_used = []
scalings = _mutable_defaults(('scalings', scalings))[0]
for t in scalings.keys():
if t in types:
n_channel_types += 1
ch_types_used.append(t)
for t in ch_types_used:
scaling = scalings[t]
idx = [picks[i] for i in range(len(picks)) if types[i] == t]
if len(idx) > 0:
data[:, idx] *= scaling
id_swapped = dict((v, k) for k, v in self.event_id.items())
names = [id_swapped[k] for k in self.events[:, 2]]
mindex = list()
mindex.append(('condition', np.repeat(names, shape[2])))
mindex.append(('time', np.tile(self.times, shape[0]) *
scale_time)) # if 'epoch' in index:
mindex.append(('epoch', np.repeat(np.arange(shape[0]),
shape[2])))
assert all(len(mdx) == len(mindex[0]) for mdx in mindex)
col_names = [self.ch_names[k] for k in picks]
df = pd.DataFrame(data, columns=col_names)
[df.insert(i, k, v) for i, (k, v) in enumerate(mindex)]
if index is not None:
with warnings.catch_warnings(True):
if 'time' in index:
df['time'] = df['time'].astype(np.int64)
df.set_index(index, inplace=True)
return df
def to_nitime(self, picks=None, epochs_idx=None, collapse=False,
copy=True, first_samp=0):
""" 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.
first_samp : int
Number of samples to offset the times by. Use raw.first_samp to
have the time returned relative to the session onset, or zero
(default) for time 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='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 = _time_as_index(abs(self.tmin), self.info['sfreq'],
first_samp, True)
t0 = _index_as_time(self.events[0, 0] - offset, self.info['sfreq'],
first_samp, True)[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 equalize_event_counts(self, event_ids, method='mintime', copy=True):
"""Equalize the number of trials in each condition
It tries to make the remaining epochs occurring as close as possible in
time. This method works based on the idea that if there happened to be
some time-varying (like on the scale of minutes) noise characteristics
during a recording, they could be compensated for (to some extent) in
the equalization process. This method thus seeks to reduce any of
those effects by minimizing the differences in the times of the events
in the two sets of epochs. For example, if one had event times
[1, 2, 3, 4, 120, 121] and the other one had [3.5, 4.5, 120.5, 121.5],
it would remove events at times [1, 2] in the first epochs and not
[20, 21].
Parameters
----------
event_ids : list
The event types to equalize. Each entry in the list can either be
a str (single event) or a list of str. In the case where one of
the entries is a list of str, event_ids in that list will be
grouped together before equalizing trial counts across conditions.
method : str
If 'truncate', events will be truncated from the end of each event
list. If 'mintime', timing differences between each event list will
be minimized.
copy : bool
If True, a copy of epochs will be returned. Otherwise, the
function will operate in-place.
Returns
-------
epochs : instance of Epochs
The modified Epochs instance.
indices : array of int
Indices from the original events list that were dropped.
Notes
----
For example (if epochs.event_id was {'Left': 1, 'Right': 2,
'Nonspatial':3}:
epochs.equalize_event_counts([['Left', 'Right'], 'Nonspatial'])
would equalize the number of trials in the 'Nonspatial' condition with
the total number of trials in the 'Left' and 'Right' conditions.
"""
if copy is True:
epochs = self.copy()
else:
epochs = self
if len(event_ids) == 0:
raise ValueError('event_ids must have at least one element')
if not epochs._bad_dropped:
epochs.drop_bad_epochs()
# figure out how to equalize
eq_inds = list()
for eq in event_ids:
eq = np.atleast_1d(eq)
# eq is now a list of types
key_match = np.zeros(epochs.events.shape[0])
for key in eq:
key_match = np.logical_or(key_match, epochs._key_match(key))
eq_inds.append(np.where(key_match)[0])
event_times = [epochs.events[eq, 0] for eq in eq_inds]
indices = _get_drop_indices(event_times, method)
# need to re-index indices
indices = np.concatenate([eq[inds]
for eq, inds in zip(eq_inds, indices)])
epochs = _check_add_drop_log(epochs, indices)
epochs.drop_epochs(indices)
# actually remove the indices
return epochs, indices
def combine_event_ids(epochs, old_event_ids, new_event_id, copy=True):
"""Collapse event_ids from an epochs instance into a new event_id
Parameters
----------
epochs : instance of Epochs
The epochs to operate on.
old_event_ids : str, or list
Conditions to collapse together.
new_event_id : dict, or int
A one-element dict (or a single integer) for the new
condition. Note that for safety, this cannot be any
existing id (in epochs.event_id.values()).
copy : bool
If True, a copy of epochs will be returned. Otherwise, the
function will operate in-place.
Notes
-----
This For example (if epochs.event_id was {'Left': 1, 'Right': 2}:
combine_event_ids(epochs, ['Left', 'Right'], {'Directional': 12})
would create a 'Directional' entry in epochs.event_id replacing
'Left' and 'Right' (combining their trials).
"""
if copy:
epochs = epochs.copy()
old_event_ids = np.asanyarray(old_event_ids)
if isinstance(new_event_id, int):
new_event_id = {str(new_event_id): new_event_id}
else:
if not isinstance(new_event_id, dict):
raise ValueError('new_event_id must be a dict or int')
if not len(new_event_id.keys()) == 1:
raise ValueError('new_event_id dict must have one entry')
new_event_num = new_event_id.values()[0]
if not isinstance(new_event_num, int):
raise ValueError('new_event_id value must be an integer')
if new_event_num in epochs.event_id.values():
raise ValueError('new_event_id value must not already exist')
# could use .pop() here, but if a latter one doesn't exist, we're
# in trouble, so run them all here and pop() later
old_event_nums = np.array([epochs.event_id[key] for key in old_event_ids])
# find the ones to replace
inds = np.any(epochs.events[:, 2][:, np.newaxis] ==
old_event_nums[np.newaxis, :], axis=1)
# replace the event numbers in the events list
epochs.events[inds, 2] = new_event_num
# delete old entries
[epochs.event_id.pop(key) for key in old_event_ids]
# add the new entry
epochs.event_id.update(new_event_id)
return epochs
def equalize_epoch_counts(epochs_list, method='mintime'):
"""Equalize the number of trials in multiple Epoch instances
It tries to make the remaining epochs occurring as close as possible in
time. This method works based on the idea that if there happened to be some
time-varying (like on the scale of minutes) noise characteristics during
a recording, they could be compensated for (to some extent) in the
equalization process. This method thus seeks to reduce any of those effects
by minimizing the differences in the times of the events in the two sets of
epochs. For example, if one had event times [1, 2, 3, 4, 120, 121] and the
other one had [3.5, 4.5, 120.5, 121.5], it would remove events at times
[1, 2] in the first epochs and not [20, 21].
Note that this operates on the Epochs instances in-place.
Example:
equalize_epoch_counts(epochs1, epochs2)
Parameters
----------
epochs_list : list of Epochs instances
The Epochs instances to equalize trial counts for.
method : str
If 'truncate', events will be truncated from the end of each event
list. If 'mintime', timing differences between each event list will be
minimized.
"""
if not all([isinstance(e, Epochs) for e in epochs_list]):
raise ValueError('All inputs must be Epochs instances')
# make sure bad epochs are dropped
[e.drop_bad_epochs() if not e._bad_dropped else None for e in epochs_list]
event_times = [e.events[:, 0] for e in epochs_list]
indices = _get_drop_indices(event_times, method)
for e, inds in zip(epochs_list, indices):
e = _check_add_drop_log(e, inds)
e.drop_epochs(inds)
def _get_drop_indices(event_times, method):
"""Helper to get indices to drop from multiple event timing lists"""
small_idx = np.argmin([e.shape[0] for e in event_times])
small_e_times = event_times[small_idx]
if not method in ['mintime', 'truncate']:
raise ValueError('method must be either mintime or truncate, not '
'%s' % method)
indices = list()
for e in event_times:
if method == 'mintime':
mask = _minimize_time_diff(small_e_times, e)
else:
mask = np.ones(e.shape[0], dtype=bool)
mask[small_e_times.shape[0]:] = False
indices.append(np.where(np.logical_not(mask))[0])
return indices
def _minimize_time_diff(t_shorter, t_longer):
"""Find a boolean mask to minimize timing differences"""
keep = np.ones((len(t_longer)), dtype=bool)
scores = np.ones((len(t_longer)))
for iter in range(len(t_longer) - len(t_shorter)):
scores.fill(np.inf)
# Check every possible removal to see if it minimizes
for idx in np.where(keep)[0]:
keep[idx] = False
scores[idx] = _area_between_times(t_shorter, t_longer[keep])
keep[idx] = True
keep[np.argmin(scores)] = False
return keep
def _area_between_times(t1, t2):
"""Quantify the difference between two timing sets"""
x1 = range(len(t1))
x2 = range(len(t2))
xs = np.concatenate((x1, x2))
return np.sum(np.abs(np.interp(xs, x1, t1) - np.interp(xs, x2, t2)))
@verbose
def _is_good(e, ch_names, channel_type_idx, reject, flat, full_report=False,
ignore_chs=[], verbose=None):
"""Test if data segment e is good according to the criteria
defined in reject and flat. If full_report=True, it will give
True/False as well as a list of all offending channels.
"""
bad_list = list()
has_printed = False
checkable = np.ones(len(ch_names), dtype=bool)
checkable[np.array([c in ignore_chs
for c in ch_names], dtype=bool)] = False
for refl, f, t in zip([reject, flat], [np.greater, np.less], ['', 'flat']):
if refl is not None:
for key, thresh in refl.iteritems():
idx = channel_type_idx[key]
name = key.upper()
if len(idx) > 0:
e_idx = e[idx]
deltas = np.max(e_idx, axis=1) - np.min(e_idx, axis=1)
checkable_idx = checkable[idx]
idx_deltas = np.where(np.logical_and(f(deltas, thresh),
checkable_idx))[0]
if len(idx_deltas) > 0:
ch_name = [ch_names[idx[i]] for i in idx_deltas]
if (not has_printed):
logger.info(' Rejecting %s epoch based on %s : '
'%s' % (t, name, ch_name))
has_printed = True
if not full_report:
return False
else:
bad_list.extend(ch_name)
if not full_report:
return True
else:
if bad_list == []:
return True, None
else:
return False, bad_list
@verbose
def read_epochs(fname, proj=True, add_eeg_ref=True, verbose=None):
"""Read epochs from a fif file
Parameters
----------
fname : str
The name of the file.
proj : bool | 'delayed'
Apply SSP projection vectors. If proj is 'delayed' and reject is not
None the single epochs will be projected before the rejection
decision, but used in unprojected state if they are kept.
This way deciding which projection vectors are good can be postponed
to the evoked stage without resulting in lower epoch counts and
without producing results different from early SSP application
given comparable parameters. Note that in this case baselining,
detrending and temporal decimation will be postponed.
If proj is False no projections will be applied which is the
recommended value if SSPs are not used for cleaning the data.
add_eeg_ref : bool
If True, an EEG average reference will be added (unless one
already exists).
verbose : bool, str, int, or None
If not None, override default verbose level (see mne.verbose).
Defaults to raw.verbose.
Returns
-------
epochs : instance of Epochs
The epochs
"""
epochs = Epochs(None, None, None, None, None)
logger.info('Reading %s ...' % fname)
fid, tree, _ = fiff_open(fname)
# Read the measurement info
info, meas = read_meas_info(fid, tree)
info['filename'] = fname
events, mappings = _read_events_fif(fid, tree)
# Locate the data of interest
processed = dir_tree_find(meas, FIFF.FIFFB_PROCESSED_DATA)
if len(processed) == 0:
fid.close()
raise ValueError('Could not find processed data')
epochs_node = dir_tree_find(tree, FIFF.FIFFB_EPOCHS)
if len(epochs_node) == 0:
fid.close()
raise ValueError('Could not find epochs data')
my_epochs = epochs_node[0]
# Now find the data in the block
comment = None
data = None
bmin, bmax = None, None
baseline = None
for k in range(my_epochs['nent']):
kind = my_epochs['directory'][k].kind
pos = my_epochs['directory'][k].pos
if kind == FIFF.FIFF_FIRST_SAMPLE:
tag = read_tag(fid, pos)
first = int(tag.data)
elif kind == FIFF.FIFF_LAST_SAMPLE:
tag = read_tag(fid, pos)
last = int(tag.data)
elif kind == FIFF.FIFF_COMMENT:
tag = read_tag(fid, pos)
comment = tag.data
elif kind == FIFF.FIFF_EPOCH:
tag = read_tag(fid, pos)
data = tag.data.astype(np.float)
elif kind == FIFF.FIFF_MNE_BASELINE_MIN:
tag = read_tag(fid, pos)
bmin = float(tag.data)
elif kind == FIFF.FIFF_MNE_BASELINE_MAX:
tag = read_tag(fid, pos)
bmax = float(tag.data)
if bmin is not None or bmax is not None:
baseline = (bmin, bmax)
nsamp = last - first + 1
logger.info(' Found the data of interest:')
logger.info(' t = %10.2f ... %10.2f ms (%s)'
% (1000 * first / info['sfreq'],
1000 * last / info['sfreq'], comment))
if info['comps'] is not None:
logger.info(' %d CTF compensation matrices available'
% len(info['comps']))
# Read the data
if data is None:
raise ValueError('Epochs data not found')
if data.shape[2] != nsamp:
fid.close()
raise ValueError('Incorrect number of samples (%d instead of %d)'
% (data.shape[2], nsamp))
# Calibrate
cals = np.array([info['chs'][k]['cal'] * info['chs'][k].get('scale', 1.0)
for k in range(info['nchan'])])
data *= cals[np.newaxis, :, np.newaxis]
times = np.arange(first, last + 1, dtype=np.float) / info['sfreq']
tmin = times[0]
tmax = times[-1]
# Put it all together
epochs.preload = True
epochs.raw = None
epochs._bad_dropped = True
epochs.events = events
epochs._data = data
epochs.info = info
epochs.tmin = tmin
epochs.tmax = tmax
epochs.name = comment
epochs.times = times
epochs._data = data
epochs.proj = proj
activate = False if epochs._check_delayed() else proj
epochs._projector, epochs.info = setup_proj(info, add_eeg_ref,
activate=activate)
epochs.baseline = baseline
epochs.event_id = (dict((str(e), e) for e in np.unique(events[:, 2]))
if mappings is None else mappings)
epochs.verbose = verbose
epochs.drop_log = []
fid.close()
return epochs
def bootstrap(epochs, random_state=None):
"""Compute epochs selected by bootstrapping
Parameters
----------
epochs : Epochs instance
epochs data to be bootstrapped
random_state : None | int | np.random.RandomState
To specify the random generator state
Returns
-------
epochs : Epochs instance
The bootstrap samples
"""
if not epochs.preload:
raise RuntimeError('Modifying data of epochs is only supported '
'when preloading is used. Use preload=True '
'in the constructor.')
rng = check_random_state(random_state)
epochs_bootstrap = epochs.copy()
n_events = len(epochs_bootstrap.events)
idx = rng.randint(0, n_events, n_events)
epochs_bootstrap = epochs_bootstrap[idx]
return epochs_bootstrap
def _check_add_drop_log(epochs, inds):
"""Aux Function"""
new_idx, new_drop_log = 0, []
for idx, log in enumerate(epochs.drop_log):
if not log:
new_idx += 1
if new_idx in inds:
new_log = ['EQUALIZED_COUNT']
elif log:
new_log = log
else:
new_log = []
new_drop_log.append(new_log)
epochs.drop_log = new_drop_log
return epochs
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