annotate Example VamPy plugins/PySpectralCentroid.py @ 24:7d28bed0864e

* Rearrange Python plugin construction.

  Formerly, the PyPluginAdapter has retained a single plugin instance pointer
  for each plugin found, and its createPlugin method has simply returned a new
  PyPlugin object wrapping the same instance pointer.  This has a couple of
  negative consequences:
   - Because construction of the actual Python instance occurred before the
     wrapper was constructed, it was not possible to pass arguments (i.e.
     the sample rate) from the wrapper constructor to the Python plugin
     instance constructor -- they had to be passed later, to initialise,
     disadvantaging those plugins that would like to use the sample rate
     for parameter & step/block size calculations etc
   - Because there was only a single Python plugin instance, it was not
     possible to run more than one instance at once with any isolation

  This rework instead stores the Python class pointer (rather than instance
  pointer) in the PyPluginAdapter, and each PyPlugin wrapper instance creates
  its own Python plugin instance.  What could possibly go wrong?

'''PySpectralCentroid.py - Example plugin demonstrates''' 
'''how to write a C style plugin using VamPy.'''

from numpy import *

class PySpectralCentroid: 
	
	def __init__(self,inputSampleRate): 
		self.m_inputSampleRate = inputSampleRate
		self.m_stepSize = 0
		self.m_blockSize = 0
		self.m_channels = 0
		self.previousSample = 0.0
		self.threshold = 0.05
		
	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 (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,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])
		#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]