syncopy.datatype.base_data.FauxTrial

class syncopy.datatype.base_data.FauxTrial(shape, idx, dtype, dimord)

Stand-in mockup of NumPy arrays representing trial data

Parameters:

• shape (tuple) – Shape of source trial array

• idx (tuple) – Tuple of slices for extracting trial-data from source object’s data dataset. The provided tuple has to be a proper indexing sequence, i.e., if idx refers to the k-th trial in obj, then obj.data[idx] must slice data correctly so that obj.data[idx] == obj.trials[k]

• dtype (numpy.dtype) – Datatype of source trial array

• dimord (list) – Dimensional order of source trial array

Returns:

faux_trl – An instance of FauxTrial that essentially parrots numpy.ndarray objects and can, thus, be used to feed “fake” trials into a computeFunction() to get the noCompute runs out of the way w/o actually loading trials into memory.

Return type:

FauxTrial object

See also

syncopy.continuous_data.ContinuousData._preview_trial

makes use of this class

__init__(shape, idx, dtype, dimord)

Methods

__init__(shape, idx, dtype, dimord)
squeeze()	Remove 1's from shape and return a new FauxTrial instance (parroting the NumPy original numpy.squeeze())

Attributes

T	Return a new FauxTrial instance with reversed dimensions (parroting the NumPy original numpy.transpose())

__init__(shape, idx, dtype, dimord)

squeeze()

Remove 1’s from shape and return a new FauxTrial instance (parroting the NumPy original numpy.squeeze())

property T

Return a new FauxTrial instance with reversed dimensions (parroting the NumPy original numpy.transpose())