comparison Example VamPy plugins/PySpectralCentroid.py @ 52:d56f48aafb99

Updated some example plugins.

+'''PySpectralCentroid.py - Example plugin demonstrates 
+how to write a C style plugin using VamPy in pure Python.
+This plugin also introduces the use of the builtin vampy
+extension module.
+
+The plugin has frequency domain input and is using the
+legacy interface: the FFT outpout is passed as a list
+of complex numbers.
+
+Outputs: 
+1) Spectral centroid
+
+Note: This is not the adviced way of writing Vampy plugins now,
+since the interfaces provided for Numpy are at least 5 times
+faster. However, this is still a nice and easy to understand
+example, which also shows how can one write a reasonable
+plugin without having Numpy installed.
+
+Warning: Earlier versions of this plugin are now obsolete.
+(They were using the legacy interface of Vampy 1 which
+did not distinquish between time and frequency domain inputs.)
+
+Centre for Digital Music, Queen Mary University of London.
+Copyright (C) 2009 Gyorgy Fazekas, QMUL. (See Vamp sources 
+for licence information.)
+
+'''
+
+# import the names we use from vampy 
+from vampy import Feature,FeatureSet,ParameterDescriptor
+from vampy import OutputDescriptor,FrequencyDomain,OneSamplePerStep
+
+from math import sqrt
+
+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 (using legacy process interface)'
+		
+	def getIdentifier(self):
+		return 'vampy-sc3'
+	
+	def getMaxChannelCount(self):
+		return 1
+		
+	def getInputDomain(self):
+		return FrequencyDomain
+			
+	def getOutputDescriptors(self):
+		
+		cod = OutputDescriptor()
+		cod.identifier='vampy-sc3'
+		cod.name='Spectral Centroid'
+		cod.description='Spectral Centroid (Brightness)'
+		cod.unit=''
+		cod.hasFixedBinCount=True
+		cod.binCount=1
+		cod.hasKnownExtents=False
+		cod.isQuantized=True
+		cod.quantizeStep=1.0
+		cod.sampleType=OneSamplePerStep
+		return cod
+
+	def getParameterDescriptors(self):
+		thd = ParameterDescriptor()
+		thd.identifier='threshold'
+		thd.name='Noise threshold'
+		thd.description='Return null or delete this function if not needed.'
+		thd.unit='v'
+		thd.minValue=0.0
+		thd.maxValue=0.5
+		thd.defaultValue=0.05
+		thd.isQuantized=False
+		return thd
+
+	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,inputbuffers,timestamp):
+		
+		# this is a 1 channel frequency domain plugin, therefore
+		# inputbuffers contain (block size / 2) + 1 complex numbers
+		# corresponding to the FFT output from DC to Nyquist inclusive
+		
+		cplxArray = inputbuffers[0][:-1]
+
+		prev = self.previousSample
+		numLin = 0.0
+		denom = 0.0
+		centroid = 0.0		
+
+		output = FeatureSet()
+
+		pw = 0
+		for i in xrange(1,len(cplxArray)) : 
+			pw = pw + abs(cplxArray[i])
+		
+		if pw > self.threshold : 
+			for i in range(1,(len(cplxArray))) :
+				
+				re = cplxArray[i].real
+				im = cplxArray[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
+			
+		output[0] = Feature()
+		output[0].values = centroid
+		output[0].label = str(centroid)
+		
+		return output