def evoked_ndvar(evoked, name='MEG'):
"Convert an mne Evoked object of a list thereof to an ndvar"
if isinstance(evoked, mne.fiff.Evoked):
x = evoked.data
dims = ()
else:
x = np.array([e.data for e in evoked])
dims = ('case',)
evoked = evoked[0]
sensor = sensor_net(evoked)
time = var(evoked.times, name='time')
properties = {'colorspace': _cs.get_MEG(2e-13)}
return ndvar(x, dims + (sensor, time), properties=properties, name=name)
def defaultColorspace(self, vmax='auto', vmin='auto', **kwargs):
"When symmetric, vmax is more important"
if vmax == 'auto':
vmax = np.max(np.abs(self.data))
kwargs['vmax'] = vmax
if vmin != 'auto':
kwargs['vmin'] = vmin
if self.ndim=='wavelet':
raise NotImplementedError
else:
return _cs.get_MEG(**kwargs)
"""
# create the time dimension
T = var(np.arange(-.2, .8, .01), name='time')
# random data
x = np.random.normal(0,1,(30,5,len(T)))
# add an effect to the random data
x[15:,:3,20:40] += np.hanning(20) * 2
# create the sensor dimension from 5 sensor locations in 3d space
sensor = sensor_net([[0,0,0],[1,0,0],[0,-1,0],[-1,0,0],[0,1,0]],
name='testnet', transform_2d=None)
# combine all these into the ndvar
Y = ndvar(x, dims=('case', sensor, T), name='Y',
properties={'samplingrate':100, 'colorspace':cs.get_MEG(2)})
# To describe the cases ('case' dimension), create a condition and a subject factor
A = factor(['a0', 'a1'], rep=15, name='A')
subject = factor(xrange(15), tile=2, random=True, name='subject')
if 01: # plot topographically an uncorrected t-test
plot.topo.array(testnd.ttest(Y, A, match=subject))
if 01: # and a butterfly plot
plot.topo.butterfly(testnd.ttest(Y, A, match=subject))
def epochs_ndvar(epochs, name='MEG', meg=True, eeg=False, exclude='bads',
mult=1, unit='T', properties=None, sensors=None):
"""
Convert an mne.Epochs object to an ndvar.
Parameters
----------
epoch : mne.Epochs
The epochs object
name : None | str
Name for the ndvar.
meg : bool or string
MEG channels to include (:func:`mne.fiff.pick_types` kwarg).
If True include all MEG channels. If False include None
If string it can be 'mag' or 'grad' to select only gradiometers
or magnetometers. It can also be 'ref_meg' to get CTF
reference channels.
eeg : bool
If True include EEG channels (:func:`mne.fiff.pick_types` kwarg).
exclude : list of string | str
Channels to exclude (:func:`mne.fiff.pick_types` kwarg).
If 'bads' (default), exclude channels in info['bads'].
If empty do not exclude any.
mult : scalar
multiply all data by a constant. If used, the ``unit`` kwarg should
specify the target unit, not the source unit.
unit : str
Unit of the data (default is 'T').
target : str
name for the new ndvar containing the epoch data
reject : None | scalar
Threshold for rejecting epochs (peak to peak). Requires a for of
mne-python which implements the Epochs.model['index'] variable.
raw : None | mne.fiff.Raw
Raw file providing the data; if ``None``, ``ds.info['raw']`` is used.
sensors : None | eelbrain.vessels.sensors.sensor_net
The default (``None``) reads the sensor locations from the fiff file.
If the fiff file contains incorrect sensor locations, a different
sensor_net can be supplied through this kwarg.
"""
props = {'proj': 'z root',
'unit': unit,
'ylim': 2e-12 * mult,
'summary_ylim': 3.5e-13 * mult,
'colorspace': _cs.get_MEG(2e-12 * mult),
'summary_colorspace': _cs.get_MEG(2e-13 * mult),
'samplingrate': epochs.info['sfreq'],
}
if properties:
props.update(properties)
picks = mne.fiff.pick_types(epochs.info, meg=meg, eeg=eeg, stim=False,
eog=False, include=[], exclude=exclude)
x = epochs.get_data()[:, picks]
if mult != 1:
x *= mult
if sensors is None:
sensor = sensor_net(epochs, picks=picks)
else:
sensor = sensors
time = var(epochs.times, name='time')
return ndvar(x, ('case', sensor, time), properties=props, name=name)
