mvpa2.mappers.wavelet.WaveletPacketMapper

class mvpa2.mappers.wavelet.WaveletPacketMapper(level=None, **kwargs)

Convert signal into an overcomplete representaion using Wavelet packet

Notes

Available conditional attributes:

• calling_time+: Time (in seconds) it took to call the node
• raw_results: Computed results before invoking postproc. Stored only if postproc is not None.
• trained_dataset: The dataset it has been trained on
• trained_nsamples+: Number of samples it has been trained on
• trained_targets+: Set of unique targets (or any other space) it has been trained on (if present in the dataset trained on)
• training_time+: Time (in seconds) it took to train the learner

(Conditional attributes enabled by default suffixed with +)

Attributes

auto_train	Whether the Learner performs automatic trainingwhen called untrained.
descr	Description of the object if any
force_train	Whether the Learner enforces training upon every call.
is_trained	Whether the Learner is currently trained.
pass_attr	Which attributes of the dataset or self.ca to pass into result dataset upon call
postproc	Node to perform post-processing of results
space	Processing space name of this node

Methods

__call__(ds)
forward(data)	Map data from input to output space.
forward1(data)	Wrapper method to map single samples.
generate(ds)	Yield processing results.
get_postproc()	Returns the post-processing node or None.
get_space()	Query the processing space name of this node.
reset()
reverse(data)	Reverse-map data from output back into input space.
reverse1(data)	Wrapper method to map single samples.
set_postproc(node)	Assigns a post-processing node
set_space(name)	Set the processing space name of this node.
train(ds)	The default implementation calls _pretrain(), _train(), and finally _posttrain().
untrain()	Reverts changes in the state of this node caused by previous training

Initialize WaveletPacketMapper mapper

Parameters:

level : int or None

What level to decompose at. If ‘None’ data for all levels is provided, but due to different sizes, they are placed in 1D row.

enable_ca : None or list of str

Names of the conditional attributes which should be enabled in addition to the default ones

disable_ca : None or list of str

Names of the conditional attributes which should be disabled

dim : int or tuple of int

dimensions to work across (for now just scalar value, ie 1D transformation) is supported

wavelet : str

one from the families available withing pywt package

mode : str

periodization mode

maxlevel : int or None

number of levels to use. If None - automatically selected by pywt

Attributes

auto_train	Whether the Learner performs automatic trainingwhen called untrained.
descr	Description of the object if any
force_train	Whether the Learner enforces training upon every call.
is_trained	Whether the Learner is currently trained.
pass_attr	Which attributes of the dataset or self.ca to pass into result dataset upon call
postproc	Node to perform post-processing of results
space	Processing space name of this node

Methods

__call__(ds)
forward(data)	Map data from input to output space.
forward1(data)	Wrapper method to map single samples.
generate(ds)	Yield processing results.
get_postproc()	Returns the post-processing node or None.
get_space()	Query the processing space name of this node.
reset()
reverse(data)	Reverse-map data from output back into input space.
reverse1(data)	Wrapper method to map single samples.
set_postproc(node)	Assigns a post-processing node
set_space(name)	Set the processing space name of this node.
train(ds)	The default implementation calls _pretrain(), _train(), and finally _posttrain().
untrain()	Reverts changes in the state of this node caused by previous training