cedalion.sigdecomp package

Subpackages

• cedalion.sigdecomp.multimodal package
  • Submodules
  • cedalion.sigdecomp.multimodal.cca module
    • MultimodalSourceDecomposition
      • MultimodalSourceDecomposition.validate_inputs_fit()
      • MultimodalSourceDecomposition.validate_inputs_transform()
      • MultimodalSourceDecomposition.normalization_fit()
      • MultimodalSourceDecomposition.normalization_transform()
      • MultimodalSourceDecomposition.convert_filters_to_DataArray()
      • MultimodalSourceDecomposition.transform()
    • ElasticNetCCA
      • ElasticNetCCA.Ntx
      • ElasticNetCCA.Nty
      • ElasticNetCCA.Nx
      • ElasticNetCCA.Ny
      • ElasticNetCCA.x_mean
      • ElasticNetCCA.y_mean
      • ElasticNetCCA.x_std
      • ElasticNetCCA.y_std
      • ElasticNetCCA.latent_featureX_name
      • ElasticNetCCA.latent_featureY_name
      • ElasticNetCCA.Wx
      • ElasticNetCCA.Wy
      • ElasticNetCCA.fit()
    • StructuredSparseCCA
      • StructuredSparseCCA.Ntx
      • StructuredSparseCCA.Nty
      • StructuredSparseCCA.Nx
      • StructuredSparseCCA.Ny
      • StructuredSparseCCA.x_mean
      • StructuredSparseCCA.y_mean
      • StructuredSparseCCA.x_std
      • StructuredSparseCCA.y_std
      • StructuredSparseCCA.latent_featureX_name
      • StructuredSparseCCA.latent_featureY_name
      • StructuredSparseCCA.Wx
      • StructuredSparseCCA.Wy
      • StructuredSparseCCA.fit()
    • RidgeCCA
      • RidgeCCA.Ntx
      • RidgeCCA.Nty
      • RidgeCCA.Nx
      • RidgeCCA.Ny
      • RidgeCCA.x_mean
      • RidgeCCA.y_mean
      • RidgeCCA.x_std
      • RidgeCCA.y_std
      • RidgeCCA.latent_featureX_name
      • RidgeCCA.latent_featureY_name
      • RidgeCCA.Wx
      • RidgeCCA.Wy
    • SparseCCA
      • SparseCCA.Ntx
      • SparseCCA.Nty
      • SparseCCA.Nx
      • SparseCCA.Ny
      • SparseCCA.x_mean
      • SparseCCA.y_mean
      • SparseCCA.x_std
      • SparseCCA.y_std
      • SparseCCA.latent_featureX_name
      • SparseCCA.latent_featureY_name
      • SparseCCA.Wx
      • SparseCCA.Wy
    • CCA
      • CCA.Ntx
      • CCA.Nty
      • CCA.Nx
      • CCA.Ny
      • CCA.x_mean
      • CCA.y_mean
      • CCA.x_std
      • CCA.y_std
      • CCA.latent_featureX_name
      • CCA.latent_featureY_name
      • CCA.Wx
      • CCA.Wy
    • SparsePLS
      • SparsePLS.Ntx
      • SparsePLS.Nty
      • SparsePLS.Nx
      • SparsePLS.Ny
      • SparsePLS.x_mean
      • SparsePLS.y_mean
      • SparsePLS.x_std
      • SparsePLS.y_std
      • SparsePLS.latent_featureX_name
      • SparsePLS.latent_featureY_name
      • SparsePLS.Wx
      • SparsePLS.Wy
    • PLS
      • PLS.Ntx
      • PLS.Nty
      • PLS.Nx
      • PLS.Ny
      • PLS.x_mean
      • PLS.y_mean
      • PLS.x_std
      • PLS.y_std
      • PLS.latent_featureX_name
      • PLS.latent_featureY_name
      • PLS.Wx
      • PLS.Wy
    • estimate_filters()
    • inv_sqrt_cov()
    • get_singular_vectors()
    • leading_singular_pair_power_method()
    • soft_threshold()
  • cedalion.sigdecomp.multimodal.mspoc module
    • mSPoC
      • mSPoC.fit()
      • mSPoC.transform()
      • mSPoC.one_unit_algorithm()
      • mSPoC.deflate_data()
      • mSPoC.get_bandpower()
      • mSPoC.apply_cca()
      • mSPoC.split_epochs()
      • mSPoC.validate_inputs_fit()
      • mSPoC.validate_inputs_transform()
      • mSPoC.convert_filters_to_DataArray()
      • mSPoC.estimate_optimal_shift()
      • mSPoC.shift_by_optimal()
    • temporal_embedding()
    • get_orthonormal_matrix()
  • cedalion.sigdecomp.multimodal.tcca module
    • MultimodalSourceDecompositionWithTemporalEmbedding
      • MultimodalSourceDecompositionWithTemporalEmbedding.estimate_optimal_shift()
      • MultimodalSourceDecompositionWithTemporalEmbedding.shift_by_optimal()
      • MultimodalSourceDecompositionWithTemporalEmbedding.transform()
    • ElasticNetTCCA
      • ElasticNetTCCA.Ntx
      • ElasticNetTCCA.Nty
      • ElasticNetTCCA.Nx
      • ElasticNetTCCA.Ny
      • ElasticNetTCCA.x_mean
      • ElasticNetTCCA.y_mean
      • ElasticNetTCCA.x_std
      • ElasticNetTCCA.y_std
      • ElasticNetTCCA.latent_featureX_name
      • ElasticNetTCCA.latent_featureY_name
      • ElasticNetTCCA.Wx
      • ElasticNetTCCA.Wy
      • ElasticNetTCCA.optimal_shift
      • ElasticNetTCCA.fit()
    • StructuredSparseTCCA
      • StructuredSparseTCCA.Ntx
      • StructuredSparseTCCA.Nty
      • StructuredSparseTCCA.Nx
      • StructuredSparseTCCA.Ny
      • StructuredSparseTCCA.x_mean
      • StructuredSparseTCCA.y_mean
      • StructuredSparseTCCA.x_std
      • StructuredSparseTCCA.y_std
      • StructuredSparseTCCA.latent_featureX_name
      • StructuredSparseTCCA.latent_featureY_name
      • StructuredSparseTCCA.Wx
      • StructuredSparseTCCA.Wy
      • StructuredSparseTCCA.optimal_shift
      • StructuredSparseTCCA.fit()
    • tCCA
      • tCCA.Ntx
      • tCCA.Nty
      • tCCA.Nx
      • tCCA.Ny
      • tCCA.x_mean
      • tCCA.y_mean
      • tCCA.x_std
      • tCCA.y_std
      • tCCA.latent_featureX_name
      • tCCA.latent_featureY_name
      • tCCA.Wx
      • tCCA.Wy
      • tCCA.optimal_shift
    • temporal_embedding()
  • cedalion.sigdecomp.multimodal.utils module
    • validate_dimension_labels()
    • validate_dimension_sizes()
    • validate_l1_reg()
    • validate_l2_reg()
    • validate_time_shifts()
    • standardize()
  • Module contents
• cedalion.sigdecomp.unimodal package
  • Submodules
  • cedalion.sigdecomp.unimodal.ica_ebm module
    • ICA_EBM()
    • simplified_ppval()
    • inv_sqrtmH()
    • pre_processing()
    • symdecor()
  • cedalion.sigdecomp.unimodal.ica_erbm module
    • ICA_ERBM()
    • lfc()
    • simplified_ppval()
    • cnstd_and_gain()
    • calculate_cos_sin_mtx()
    • pre_processing()
    • inv_sqrtmH()
  • cedalion.sigdecomp.unimodal.spoc module
    • SPoC
      • SPoC.fit()
      • SPoC.transform()
    • standardize()
  • Module contents

Submodules

cedalion.sigdecomp.dummy module

cedalion.sigdecomp.dummy.split_frequency_bands(

ts: Annotated[DataArray, DataArraySchema(dims='time', coords='time', 'time', 'samples')],

) → Annotated[DataArray, DataArraySchema(dims='time', coords='time', 'time', 'samples')]

Extract signal components in different frequency bands.

This is not intended for real use but should act rather as an example how signal decomposition methods could be implemented.

Module contents