view Example VamPy plugins/obsolete/PySpectralCentroid.py @ 92:a6718f9fe942

If a module appears to redefine one of our own types, refuse to load it. Also clear out the class dict for all refused modules now, so that we don't get stale names on the next scan due to not having cleared the module on unload

'''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]