By encapsulating just a few fuctions from two library modules, we get a clear picture of what a digital filter looks like. Also, by splitting __init__ into two section, the filter's creation parameters and internal condition can be reset on the fly.
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""" Encapulates a scipy filter. Given type, freq and order,
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create the coefficients and internal conditions, etc."""
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import scipy.signal.filter_design as fd
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import scipy.signal.signaltools as st
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_filterTypeDict = {'elliptic':'ellip', 'Butterworth':'butter',
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'Chebyshev I':'cheby1', 'Chebyshev II':'cheby2',
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'Bessel':'bessel'}
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DefaultFilterType = 'Butterworth'
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DefaultFilterOrder = 2
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DefaultFilterFreq = 0.1
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class FilterObject(object):
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def __init__(self, channelNumber, nTaps, freq,
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bType='lowpass', fType="butter", output='ba'):
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self.channelNumber = channelNumber
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self.InitFilter(nTaps, freq, bType, fType, output)
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def InitFilter(self, nTaps, freq, bType, fType, output):
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print "taps = %d: freq = %f" % (nTaps, freq)
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b, a = fd.iirfilter(nTaps, freq, btype=bType, ftype=fType, output=output)
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self.b, self.a = b, a
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self.ic = st.lfiltic(b, a, (0.0,))
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def Filter(self, dataArray):
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resTuple = st.lfilter(self.b, self.a, dataArray, zi=self.ic)
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self.ic = resTuple[-1]
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return resTuple[0]
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if __name__ == "__main__":
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x = (1.5, 1.4, 1.5, 1.6, 1.5, 1.4, 1.5, 1.6)
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f = FilterObject(2, .05)
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for a in range(20):
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print f.Filter(x)
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Here's another good example.
_filterTypeDict is an example of "name mangling". This is a way to keep module scope variables semi-private.
When you import myModule
module scope classes, functions and variables are referenced by
myModule.myClass(), myModule.myFunction(), myModule.myVariable, respectively.
When you import the object of a module with
from myModule import *
all the names in that module are put into the scope of the importing module except _semiprivate variables. They are semi-private because you can still do
a = myModule._semiprivate (the name has been imported into the current scope with the name of the module prepended onto it).
