Source code for proc

import scipy.signal as sig
import pylab as pl

import wf

#------------------------------------------------------------
### FIR filter class ###
#------------------------------------------------------------

class FIRFilter :
    """Finite Response Filter based on the Kaiser window"""

    """fc - cutoff, bwt - transition region width, ripple in dB"""

#------------------------------------------------------------

    def __init__(self,ns,fs,fc,bwt,ripple) :

        self.ns = ns
        self.fs = fs
        self.fc = fc
        self.bwt = bwt
        self.ripple = ripple

        self.fn = self.fs/2. # Nyquist frequency

        # Normalise the desired width of the transition band
        self.bwtn = self.bwt/self.fn

        # Compute the order and Kaiser parameter for the FIR filter.
        self.N, self.beta = sig.kaiserord(self.ripple,self.bwtn)

        print "Filter order:", self.N
        print "Filter beta:", self.beta

        # Use firwin with a Kaiser window to create a lowpass FIR filter.
        self.coeff = sig.firwin(self.N, self.fc/self.fn, window=('kaiser', self.beta))

#------------------------------------------------------------

    def filter(self,wfin) :
        """Filter the waveform"""

        wfout = wf.WfDouble(wfin.getNs(),wfin.getFs(),wfin.getT0())
        # Apply the FIR filter
        wfout.setValues(sig.lfilter(self.coeff, 1.0, wfin.getWf()))

        return wfout

#------------------------------------------------------------

    def plotCoeff(self) :
        """Plot the filter coefficients"""

        pl.plot(self.coeff, 'bo-')
        pl.title('Filter Coefficients')
        pl.grid(True)

#------------------------------------------------------------

    def plotResponse(self) :
        """Plot the filter response"""

        w, h = sig.freqz(self.coeff, worN=8000)
        pl.plot( (w/pl.pi)*self.fn, pl.absolute(h) )
        pl.xlabel('Frequency')
        pl.ylabel('Gain')
        pl.title('Frequency Response')
        pl.grid(True)


#------------------------------------------------------------
###  ###
#------------------------------------------------------------

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