denspp.offline.pipeline.pipeline_cmds#

Module Contents#

Classes#

PipelineLibrary

Class for searching all Pipeline Processors in repository to get an overview

DataloaderLibrary

Class for searching all Pipeline Processors in repository to get an overview

PipelineCMD

Class for handling the pipeline processing

API#

class denspp.offline.pipeline.pipeline_cmds.PipelineLibrary[source]#

Class for searching all Pipeline Processors in repository to get an overview

get_registry(package: str = 'src_pipe') → denspp.offline.dnn.model_library.ModuleRegistryManager[source]#
class denspp.offline.pipeline.pipeline_cmds.DataloaderLibrary[source]#

Class for searching all Pipeline Processors in repository to get an overview

get_registry(package: str = 'src_pipe') → denspp.offline.dnn.model_library.ModuleRegistryManager[source]#
class denspp.offline.pipeline.pipeline_cmds.PipelineCMD[source]#

Class for handling the pipeline processing

path2save: str = <Multiline-String>#
get_pipeline_name() → str[source]#

Getting the name of the pipeline

generate_run_folder(path2runs: str, addon: str) → None[source]#

Generating the default folder for saving figures and data

Parameters:

  • path2runs – Main folder in which the figures and data is stored

  • addon – Name of new folder for saving results

Returns:

None

apply_mapping(data: numpy.ndarray, electrode_id: list, mapping: numpy.ndarray) → numpy.ndarray[source]#

Transforming the input data to 2D array using electrode mapping configuration

Parameters:

  • data – Input data with shape (num_channels, num_samples)

  • electrode_id – List with name/numbers of electrodes used on data

  • mapping – Numpy array with electrode ID localisation

Returns:

Numpy array with transformed data to 2D

deploy_mapping(data: numpy.ndarray, electrode_id: list, mapping: numpy.ndarray) → numpy.ndarray[source]#

Transforming the 2D data to normal electrode orientation using electrode mapping configuration

Parameters:

  • data – Input data with shape (num_rows, num_cols, num_samples)

  • electrode_id – List with name/numbers of electrodes used on data

  • mapping – Numpy array with electrode ID localisation

Returns:

Numpy array with original data format

save_results(name: str, data: dict) → None[source]#

Saving the data with a dictionary

Parameters:

  • name – File name for saving results

  • data – Dictionary with data content

Returns:

None