Alex Alex - 1 year ago 217
Python Question

Filtering signal with Python lfilter

I'm new with Python and I'm completely stuck when filtering a signal. This is the code:

import numpy as np
import matplotlib.pyplot as plt
from scipy import signal



# Create a input sin signal of 70.1 MHz sampled at 105 MHz

# Define the "b" and "a" polynomials to create a CIC filter (R=8,M=2,N=6)

plt.title('CIC Filter Response')


plt.title('Input Signal')
plt.title('Filtered Signal')

And the plots:

Input, filter and output signals

As you can see, although the filter transfer function is correct, the output isn't. And I can't see why my code isn't working. I've coded the same in Matlab and the output looks ok.

Thaks for the help!

Answer Source

I don't find it confusing that this didn't work with Python, but I do find it confusing that it worked with Matlab.

CIC filters don't work with floating point numbers.


Interestingly, at least with the version of scipy I have, lfilter doesn't work with integer arrays -- I get a NotImplemented error. Here is a numpy version of a CIC filter that is about twice as fast as a pure Python implementation on my machine:

# Implements an in-memory CIC decimator using numpy.

from math import log
from numpy import int32, int64, array

def cic_decimator(source, decimation_factor=32, order=5, ibits=1, obits=16):

    # Calculate the total number of bits used internally, and the output
    # shift and mask required.
    numbits = order * int(round(log(decimation_factor) / log(2))) + ibits
    outshift = numbits - obits
    outmask  = (1 << obits) - 1

    # If we need more than 64 bits, we can't do it...
    assert numbits <= 64

    # Create a numpy array with the source
    result = array(source, int64 if numbits > 32 else int32)

    # Do the integration stages
    for i in range(order):

    # Decimate
    result = array(result[decimation_factor - 1 : : decimation_factor])

    # Do the comb stages.  Iterate backwards through the array,
    # because we use each value before we replace it.
    for i in range(order):
        result[len(result) - 1 : 0 : -1] -= result[len(result) - 2 : : -1]

    # Normalize the output
    result >>= outshift
    result &= outmask
    return result
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