The processes functions¶

AdaptiveWhitening¶

Whitening

createsegments¶

createsegmentsMinMax¶

class processes.createsegmentsMinMax.createSegmentsMinMax(parameters)[source]¶

DWhitening¶

Whitening

StateVectorSegments¶

class processes.StateVectorSegments.createSegments(parameters)[source]¶

wdf_reconstruct¶

class processes.wdf_reconstruct.wdf_reconstruct(parameters, wTh=<sphinx.ext.autodoc.importer._MockObject object>)[source]¶

The main WDF class responsible for the communication with the p4TSA library regarding the application of WDF onto data

FindEvents()[source]¶

This method calls wdf2reconstruct function from pytsa to search for triggers in the data

Returns:	trigger

Process()[source]¶: This method calls wdf2reconstruct function from pytsa to search for triggers in the data If the triggers are found, they are stored in tosend_triggers variable that is later on used for further processing

SetData(data)[source]¶

This methods sets sets the data for the p4TSA wdf2reconstruct class for further search of triggers

Parameters:	data (pytsa.SeqViewDouble) – An pytsa.SeqViewDouble object storing data to be processed

wdf¶

class processes.wdf.wdf(WdfParams: <sphinx.ext.autodoc.importer._MockObject object at 0x7f3f5a65f0d0>, wTh=<sphinx.ext.autodoc.importer._MockObject object>)[source]¶

The main WDF class responsible for the communication with the p4TSA library regarding the application of WDF onto data

FindEvents()[source]¶

This method calls wdf2classify function from pytsa to search for triggers in the data

Returns:	trigger

Process()[source]¶: This method calls wdf2classify function from pytsa to search for triggers in the data If the triggers are found, they are stored in tosend_triggers variable that is later on used for further processing

SetData(data)[source]¶

This methods sets sets the data for the p4TSA wdf2classify class for further search of triggers

Parameters:	data (pytsa.SeqViewDouble) – An pytsa.SeqViewDouble object storing data to be processed

wdfUnitWorker¶

wdfUnitDSWorker¶

Whitening¶

class processes.Whitening.Whitening(ARorder)[source]¶

This class is responsible for the communiction with whitening functions from pytsa

GetLV()[source]¶

This method returns LV object

Returns:	LV object

GetSigma()[source]¶

This method returns the sigma parameter of the Whitening process

Returns:	The sigma parameter of the whitened data

ParametersEstimate(data)[source]¶

This method estimates parameters of data by calling proper methods from pytsa

Parameters:	data (pytsa.SeqViewDouble) – The Sequence View object containing the data to be processed

ParametersLoad(ARfile, LVfile)[source]¶

This method loads the calculated AR and LV parameter from the file

Parameters:	ARfile (basestring) – file for AutoRegressive parameters LVfile (basestring) – file for Lattice View parameters
Returns:	Autoregressive and Lattice View

ParametersSave(ARfile, LVfile)[source]¶

This method saves the calculated AR and LV parameter to the file

Parameters:	ARfile (basestring) – file for AutoRegressive parameters LVfile (basestring) – file for Lattice View parameters

Process(data, dataw)[source]¶

This method whitens the data by calling proper function from pytsa

Parameters:	data – pytsa.SeqViewDouble dataw – pytsa.SeqViewDouble

DWhitening¶

Whitening