mvpa2.mappers.wavelet.WaveletTransformationMapper

Inheritance diagram of WaveletTransformationMapper

class mvpa2.mappers.wavelet.WaveletTransformationMapper(dim=1, wavelet='sym4', mode='per', maxlevel=None)

Convert signal into wavelet representaion

Notes

Available conditional attributes:

  • calling_time+: None
  • raw_results: None
  • trained_dataset: None
  • trained_nsamples+: None
  • trained_targets+: None
  • training_time+: None

(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 everycalled.
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

Initialize _WaveletMapper mapper

Parameters:

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

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

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 everycalled.
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