Using the Blanking Plugin

We are now going to add the blanking pipeline to remove the blanking artifacts. Apart from the additional plugin and additional parameters, the code is identical to example15.
The new parameter blankingEndOffsetInSeconds is set via
aSweepInterBlankingPlugin.setEndOffsetInSeconds(blankingEndOffsetInSeconds)
and allows extending the blanking duration by some fixed value in seconds. This may be needed because after filtering the artifacts, the segment contaminated with artifacts may extend beyond the original stimulation duration. To make sure that all artifacts are properly blanked and don't affect the automatic scaling of the plot, we simply add a few milliseconds to the actual stimulation duration.

After making the modification, this is what the code looks like:

def example16():
    sampleRate = 24414
    numChannels = 32
    sweepLen = int(sampleRate/2)
    enableCISimulation = True
    enableBlanking = True
    #set up the stimulation parameter object    
    #set stimulation parameters     
    level_dB=5  # in dB in relation to 100 uA
    clickRate=50  # 300, 900, 1800
    ears='left' # other options are  'right' and 'both' Must be set to 'left' or 'right' during simulation
    duration = 0.01 # This defines the duration of the stimulation
    numClicks = 1
    nloop = 0
    reference_Simulation=0.100 # set reference to 100 uA
    levelRePeak = True  # normally we set levels relative to RMS If you want to set them relative to abs max instead, set this to True
    clickShape_CI_Simulation = np.array([0,1,0,-1,0]) # use a biphasic pulse
    blankingEndOffsetInSeconds = 0.012 # This offset shifts the end of the blanking. This can be useful if the filter artifacts occur beyond the actual duration of the stimulation.
    #set up the stimulation parameter object    
    stimulatorModule.soundPlayer=stimulatorModule.pygameSoundHardware()
    stimulatorModule.soundPlayer.sampleRate = 25000
    stim=stimulatorModule.clickTrainObject()
    stim.clickShape= clickShape_CI_Simulation
    stim.stimParams['duration (s)']=duration # this parameter controls the duration of the stimulation.
    stim.stimParams['ABL (dB)']=level_dB #ABL means Average Binaural Level
    stim.stimParams['clickRate (Hz)']=clickRate
    stim.stimParams['Nloop']=nloop
    stim.stimParams['numClicks']=numClicks
    stim.reference=reference_Simulation
    stim.clickShape=clickShape_CI_Simulation
    stim.levelRePeak=levelRePeak
    stim.ears=ears
    stim.ready()  

    aDatasource = pipeline.Datasource()  
    aDatasource.setName("Eyphs PushDataSource")
    sweepSpikeFiltFiltPlugin = pipeline.SweepSpikeFiltFiltPlugin()
    sweepSpikeFiltFiltPlugin.setName("Eyphs SweepSpikeFiltFiltPlugin")
    
    aSpikeSimulationPlugin = pipeline.SpikeSimulationPlugin()   
    aSpikeSimulationPlugin.setName("Eyphs SpikeSimulationPlugin")
    aSpikeSimulationPlugin.loadSpikeTemplates("FilteredSpikeTemplates.npy")
    debugSpikeAmplitudes = [0.0001, 0.0001, 0.0001, 0.0001]
    debugSpikePositions = [0.1, 0.2, 0.3, 0.4]
    aSpikeSimulationPlugin.setDebug(False)
    aSpikeSimulationPlugin.setDebugSpikeAmplitudes(debugSpikeAmplitudes)
    aSpikeSimulationPlugin.setDebugSpikePositions(debugSpikePositions)
    
    aSweepInterBlankingPlugin = pipeline.SweepInterBlankingPlugin()
    aSweepInterBlankingPlugin.setName("Eyphs SweepInterBlankingPlugin")
    aSweepInterBlankingPlugin.setEndOffsetInSeconds(blankingEndOffsetInSeconds)
    aSweepInterBlankingPlugin.setEnabled(enableBlanking)
    
    aArtifactSimulationPlugin = pipeline.ArtifactSimulationPlugin()
    aArtifactSimulationPlugin.setName("Eyphs ArtifactSimulationPlugin")
    aArtifactSimulationPlugin.setEnabled(enableCISimulation)
    aSpikeSimulationPlugin = pipeline.SpikeSimulationPlugin()
    
    aDisplay = pipeline.MplSweepDataDisplay(plt)
    aDatasink = pipeline.Datasink()
    aDatasink.setName("Eyphs DataSink")

    #assemble the pipeline
    aDatasource.setOutput(sweepSpikeFiltFiltPlugin)
    sweepSpikeFiltFiltPlugin.setOutput(aSpikeSimulationPlugin)
    aSpikeSimulationPlugin.setOutput(aArtifactSimulationPlugin)
    aArtifactSimulationPlugin.setOutput(aSweepInterBlankingPlugin)
    aSweepInterBlankingPlugin.setOutput(aDisplay)
    aDisplay.setOutput(aDatasink)
    
    for n in range(100):
        #generate multichannel data and random background noise
        sigTupel=tuple(0.000015*np.random.normal(0,1.0, numChannels*sweepLen))
        data=RZ2ephys.MCsweep(sigTupel,sweepLen,sampleRate,0)
        stimObjectCopy = copy.deepcopy(stim)
        aDataframe = pipeline.Dataframe()
        aDataframe.setStimObject(stimObjectCopy)
        aDataframe.setFrameNumber(n)
        aDataframe.setData(data)
        aDatasource.addDataframe(aDataframe)
        aDatasource.run()

And this what the output looks like:

The plot looks almost identical to what we have seen in Fig 24. The result of the blanking can be seen in Fig 27.