annotate Example VamPy plugins/PyZeroCrossing.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

'''PyZeroCrossing.py - Example plugin demonstrates
how to write a Vampy plugin in pure Python without
using Numpy or the extensions provided by the embedded 
vampy module. 

This plugin is compatible with provious versions of vampy, 
apart from moving the inputSampleRate
argument from initialise to __init__()

Outputs: 
1) Zero crossing counts
2) Zero crossing locations

Centre for Digital Music, Queen Mary University of London.
Copyright (C) 2009 Gyorgy Fazekas, QMUL. (See Vamp sources 
for licence information.)

'''

class PyZeroCrossing: 
	
	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 = 0.005
		self.counter = 0
		
	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 'Vampy Zero Crossings'

        def getDescription(self):
                return 'Count the number of simple zero-crossings for the signal within each processing block'

        def getCopyright(self):
                return 'Plugin By George Fazekas. Freely redistributable example plugin (BSD license)'
		
	def getIdentifier(self):
		return 'vampy-zc2'
	
	def getMaxChannelCount(self):
		return 1
		
	def getInputDomain(self):
		return 'TimeDomain'
			
	def getOutputDescriptors(self):
		
		#descriptors can be returned as python dictionaries
		output0={
		'identifier':'vampy-counts',
		'name':'Number of Zero Crossings',
		'description':'Number of zero crossings per audio frame',
		'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
		}

		output1={
		'identifier':'vampy-crossings',
		'name':'Zero Crossing Locations',
		'description':'The locations of zero crossing points',
		'unit':'discrete',
		'hasFixedBinCount':True,
		'binCount':0,
		'sampleType':'VariableSampleRate'
		}

		return [output0,output1]


	def getParameterDescriptors(self):
		paramlist1={
		'identifier':'threshold',
		'name':'Noise threshold',
		'description':'Magnitude below which a process block will be considered to be all zero',
		'unit':'v',
		'minValue':0.0,
		'maxValue':0.5,
		'defaultValue':0.005,
		'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


	# legacy process type: the input is a python list of samples
	def process(self,inbuf,timestamp):
		crossing = False
		prev = self.previousSample
		count = 0.0;
		channel = inbuf[0]

		#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(s) for s in channel]) > self.threshold : 

			for x in range(len(channel)-1) :
				crossing = False
				sample = channel[x]
				if sample <= 0.0 : 
					if prev > 0.0 : crossing = True
				else :
					if sample > 0.0 :
						if prev <= 0.0 : crossing = True		
			
				if crossing == True : 
					count = count + 1
					feature1={
					'hasTimestamp':True,	
					'timeStamp':long(timestamp + x),				
					'values':[count],			
					'label':str(count),				
					}				
					output1.append(feature1)
			
				prev = sample	
			self.previousSample = prev

		else :
			count = 0.0
			self.previousSample = channel[len(channel)-1]

		feature0={
		'hasTimestamp':False,		
		'values':[count],
		'label':str(count)				
		}
		output0.append(feature0)
		
		#return a LIST of list of dictionaries
		return [output0,output1]