Transforms

Instance Transforms

Classes

FixedScale([low_bound, high_bound])

Scale the input to range from low to high

MappingDeep1010(dataset[, add_scale_ind, …])

Maps various channel sets into the Deep10-10 scheme, and normalizes data between [-1, 1] with an additional scaling parameter to describe the relative scale of a trial with respect to the entire dataset.

NoisyBlankDeep1010([mask_index, purge_mask])

ZScore([only_trial_data])

Z-score normalization of trials

Functions

same_channel_sets(channel_sets)

Validate that all the channel sets are consistent, return false if not
class dn3.transforms.instance.FixedScale(low_bound=- 1, high_bound=1)

Scale the input to range from low to high

class dn3.transforms.instance.MappingDeep1010(dataset, add_scale_ind=True, return_mask=False)

Maps various channel sets into the Deep10-10 scheme, and normalizes data between [-1, 1] with an additional scaling parameter to describe the relative scale of a trial with respect to the entire dataset.

TODO - refer to eventual literature on this

Methods

new_channels(old_channels)

This is an optional method that indicates the transformation modifies the representation and/or presence of channels.
new_channels(old_channels: numpy.ndarray)

This is an optional method that indicates the transformation modifies the representation and/or presence of channels.

Parameters

old_channels (ndarray) – An array whose last two dimensions are channel names and channel types.

Returns

new_channels – An array with the channel names and types after this transformation. Supports the addition of dimensions e.g. a list of channels into a rectangular grid, but the final two dimensions must remain the channel names, and types respectively.

Return type

ndarray

class dn3.transforms.instance.NoisyBlankDeep1010(mask_index=1, purge_mask=False)
class dn3.transforms.instance.ZScore(only_trial_data=True)

Z-score normalization of trials

dn3.transforms.instance.same_channel_sets(channel_sets: list)

Validate that all the channel sets are consistent, return false if not

Preprocessors

Classes

Preprocessor()

Base class for various preprocessing actions.
class dn3.transforms.preprocessors.Preprocessor

Base class for various preprocessing actions. Sub-classes are called with a subclass of _Recording and operate on these instances in-place.

Any modifications to data specifically should be implemented through a subclass of BaseTransform, and returned by the method get_transform()

Methods

get_transform()

Generate and return any transform associated with this preprocessor.
get_transform()

Generate and return any transform associated with this preprocessor. Should be used after applying this to a dataset, i.e. through DN3ataset.preprocess()

Returns

transform

Return type

BaseTransform