comparison Example VamPy plugins/PySpectralCentroid.py @ 9:d2d36e7d2276

more examples and some bug fixes

+'''PySpectralCentroid.py - Example plugin demonstrates''' 
+'''how to write a C style plugin using VamPy.'''
+
+from numpy import *
+
+class PySpectralCentroid: 
+	
+	def __init__(self): 
+		self.m_imputSampleRate = 0.0 
+		self.m_stepSize = 0
+		self.m_blockSize = 0
+		self.m_channels = 0
+		self.previousSample = 0.0
+		self.threshold = 0.00
+		
+	def initialise(self,channels,stepSize,blockSize,inputSampleRate):
+		self.m_channels = channels
+		self.m_stepSize = stepSize		
+		self.m_blockSize = blockSize
+		self.m_inputSampleRate = inputSampleRate
+		return True
+	
+	def getMaker(self):
+		return 'VamPy Example Plugins'
+	
+	def getName(self):
+		return 'Spectral Centroid (VamPy Legacy Interface)'
+		
+	def getIdentifier(self):
+		return 'python-sf1'
+	
+	def getMaxChannelCount(self):
+		return 1
+		
+	def getInputDomain(self):
+		return 'FrequencyDomain'
+			
+	def getOutputDescriptors(self):
+		
+		#descriptors are python dictionaries
+		output0={
+		'identifier':'vampy-sf1',
+		'name':'Spectral Centroid',
+		'description':'Spectral Centroid (Brightness)',
+		'unit':' ',
+		'hasFixedBinCount':True,
+		'binCount':1,
+		#'binNames':['1 Hz',1.5,'2 Hz',3,'4 Hz'],
+		'hasKnownExtents':False,
+		#'minValue':0.0,
+		#'maxValue':0.0,
+		'isQuantized':True,
+		'quantizeStep':1.0,
+		'sampleType':'OneSamplePerStep'
+		#'sampleRate':48000.0
+		}
+
+		#return a list of dictionaries
+		return [output0]
+
+	def getParameterDescriptors(self):
+		paramlist1={
+		'identifier':'threshold',
+		'name':'Noise threshold: ',
+		'description':'Return null or delete this function if not needed.',
+		'unit':'v',
+		'minValue':0.0,
+		'maxValue':0.5,
+		'defaultValue':0.05,
+		'isQuantized':False
+		}
+		return [paramlist1]
+
+	def setParameter(self,paramid,newval):
+		if paramid == 'threshold' :
+			self.threshold = newval
+		return
+		
+	def getParameter(self,paramid):
+		if paramid == 'threshold' :
+			return self.threshold
+		else:
+			return 0.0
+			
+	def process(self,inbuf):
+		inArray = array(inbuf[0])
+		crossing = False
+		prev = self.previousSample
+		count = 0.0
+		numLin = 0.0
+		denom = 0.0
+		centroid = 0.0
+		
+
+		re = array(inbuf[2:len(inArray):2])
+		im = array(inbuf[3:len(inArray):2])
+		#we have two outputs defined thus we have to declare
+		#them as empty dictionaries in our output list
+		#in order to be able to return variable rate outputs
+		output0=[]
+		output1=[]
+		
+		if sum(abs(inArray)) > self.threshold : 
+			for i in range(1,(len(inArray)/2)) :
+				
+				re = inArray[i*2]
+				im = inArray[i*2+1]
+				freq = i * self.m_inputSampleRate / self.m_blockSize
+				power = sqrt (re*re + im*im) / (self.m_blockSize/2)
+				denom = denom + power
+				numLin = numLin + freq * power
+				
+			if denom != 0 :
+				centroid = numLin / denom 
+				
+		else :
+			centroid = 0.0
+
+		feature0={
+		'hasTimestamp':False,		
+		'values':[centroid],		#strictly must be a list
+		'label':str(centroid)				
+		}
+		output0.append(feature0)
+		
+		#return a LIST of list of dictionaries
+		return [output0]