comparison Example VamPy plugins/obsolete/PySpectralCentroid.py @ 37:27bab3a16c9a vampy2final

new branch Vampy2final

+'''PySpectralCentroid.py - Example plugin demonstrates 
+how to write a C style plugin using VamPy.
+
+Obsolete warning: this plugin will no longer be supported 
+since the legacy interface should pass a list of complex
+numbers for frequency domain plugins and a list of floats
+for time domin plugins.
+
+'''
+
+from numpy import *
+
+class PySpectralCentroid: 
+	
+	def __init__(self,inputSampleRate): 
+		self.m_imputSampleRate = 0.0 
+		self.m_stepSize = 0
+		self.m_blockSize = 0
+		self.m_channels = 0
+		self.previousSample = 0.0
+		self.m_inputSampleRate = inputSampleRate
+		self.threshold = 0.00
+		
+	def initialise(self,channels,stepSize,blockSize):
+		self.m_channels = channels
+		self.m_stepSize = stepSize		
+		self.m_blockSize = blockSize
+		return True
+	
+	def getMaker(self):
+		return 'Vampy Example Plugins'
+	
+	def getName(self):
+		return 'Spectral Centroid (legacy process interface)'
+		
+	def getIdentifier(self):
+		return 'vampy-sc2'
+	
+	def getMaxChannelCount(self):
+		return 1
+		
+	def getInputDomain(self):
+		return 'FrequencyDomain'
+			
+	def getOutputDescriptors(self):
+		
+		output0={
+		'identifier':'vampy-sf1',
+		'name':'Spectral Centroid',
+		'description':'Spectral Centroid (Brightness)',
+		'unit':' ',
+		'hasFixedBinCount':True,
+		'binCount':1,
+		'hasKnownExtents':False,
+		'isQuantized':True,
+		'quantizeStep':1.0,
+		'sampleType':'OneSamplePerStep'
+		}
+
+		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,timestamp):
+		
+		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])
+
+		output0=[]
+		output1=[]
+
+		# pw = 0
+		# for i in xrange(1,len(inbuf[0])) : 
+		# 	pw = pw + abs(inbuf[0][i])
+		
+		if sum(abs(inArray)) > self.threshold : 
+			for i in range(1,(len(inArray)/2)) :
+			# for i in range(1,len(inbuf[0])) :
+				
+				re = inArray[i*2]
+				im = inArray[i*2+1]
+				# re = inbuf[0][i].real
+				# im = inbuf[0][i].imag
+				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 [output0]