__author__ = "Elena Cuoco"
__project__ = "wdf"
from pytsa.tsa import WaveletThreshold, WDF2Classify, EventFullFeatured
from wdf.observers.observable import Observable
from wdf.config import Parameters
[docs]class wdf(Observable):
"""
The main WDF class responsible for the communication with the p4TSA library regarding the application of WDF onto data
"""
def __init__(self, WdfParams: Parameters, wTh=WaveletThreshold.dohonojohnston):
"""
The main WDF class responsible for the communication with the p4TSA library regarding the application of WDF onto data
:type parameters: WdfParam
:param parameters: Set of WDF parameters
:type wth: pytsa.WaveletThreshold
:param wavThresh: Type of wavelet thresholding function; default value = WaveletThreshold.dohonojohnston
"""
Observable.__init__(self)
self.parameters = WdfParams
self.wdf2classify = WDF2Classify(
self.parameters.window,
self.parameters.overlap,
self.parameters.threshold,
self.parameters.sigma,
self.parameters.Ncoeff,
wTh,
)
self.trigger = EventFullFeatured(self.parameters.Ncoeff)
[docs] def SetData(self, data):
"""
This methods sets sets the data for the p4TSA wdf2classify class for further search of triggers
:type data: pytsa.SeqViewDouble
:param data: An pytsa.SeqViewDouble object storing data to be processed
"""
# to be multiplied by central frequency
self.wdf2classify(data, self.parameters.sigma)
[docs] def FindEvents(self):
"""
This method calls wdf2classify function from pytsa to search for triggers in the data
:return: trigger
"""
# to be multiplied by central frequency
self.wdf2classify(self.trigger)
return self.trigger
[docs] def Process(self):
"""
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
"""
while self.wdf2classify.GetDataNeeded() > 0:
m = self.wdf2classify(self.trigger)
if m == 1:
self.update_observers(self.trigger)