Apply a digital filter (direct form II transposed): Difference between revisions

From Rosetta Code
Content added Content deleted
Line 51: Line 51:
0.0259303398477,0.490105989562,0.549391221511,0.9047198589};
0.0259303398477,0.490105989562,0.549391221511,0.9047198589};


//Constants for a Butterworth filter (order 3, low pass)
vector<float> a = {1.00000000, -2.77555756e-16, 3.33333333e-01, -1.85037171e-17};
vector<float> a = {1.00000000, -2.77555756e-16, 3.33333333e-01, -1.85037171e-17};
vector<float> b = {0.16666667, 0.5, 0.5, 0.16666667};
vector<float> b = {0.16666667, 0.5, 0.5, 0.16666667};

Revision as of 14:36, 17 December 2016

Apply a digital filter (direct form II transposed) is a draft programming task. It is not yet considered ready to be promoted as a complete task, for reasons that should be found in its talk page.

Digital filters are used to apply a mathematical operation to a sampled signal. One of the common formulations is the "direct form II transposed" which can represent both infinite impulse response (IIR) and finite impulse response (FIR) filters, as well as being more numerically stable than other forms. [1]

Task

Filter a signal using an order 3 lowpass butterworth filter. The coefficients for the filter are a=[1.00000000, -2.77555756e-16, 3.33333333e-01, -1.85037171e-17] and b = [0.16666667, 0.5, 0.5, 0.16666667]

The signal that needs filtering is the following vector: [-0.917843918645, 0.141984778794, 1.20536903482, 0.190286794412, -0.662370894973, -1.00700480494, -0.404707073677 ,0.800482325044, 0.743500089861, 1.01090520172, 0.741527555207, 0.277841675195, 0.400833448236, -0.2085993586, -0.172842103641, -0.134316096293, 0.0259303398477, 0.490105989562, 0.549391221511, 0.9047198589]

C++

This uses the C++11 method of initializing vectors. In g++, use the -std=c++0x compiler switch.

<lang cpp>#include <vector>

  1. include <iostream>

using namespace std;

void Filter(const vector<float> &b, const vector<float> &a, const vector<float> &in, vector<float> &out) {

out.resize(0); out.resize(in.size());

for(int i=0; i < in.size(); i++) { float tmp = 0.; int j=0; out[i] = 0.f; for(j=0; j < b.size(); j++) { if(i - j < 0) continue; tmp += b[j] * in[i-j]; }

for(j=1; j < a.size(); j++) { if(i - j < 0) continue; tmp -= a[j]*out[i-j]; }

tmp /= a[0]; out[i] = tmp; } }

int main() { vector<float> sig = {-0.917843918645,0.141984778794,1.20536903482,0.190286794412,-0.662370894973,-1.00700480494,\ -0.404707073677,0.800482325044,0.743500089861,1.01090520172,0.741527555207,\ 0.277841675195,0.400833448236,-0.2085993586,-0.172842103641,-0.134316096293,\ 0.0259303398477,0.490105989562,0.549391221511,0.9047198589};

//Constants for a Butterworth filter (order 3, low pass) vector<float> a = {1.00000000, -2.77555756e-16, 3.33333333e-01, -1.85037171e-17}; vector<float> b = {0.16666667, 0.5, 0.5, 0.16666667};

vector<float> result; Filter(b, a, sig, result);

for(size_t i=0;i<result.size();i++) cout << result[i] << ","; cout << endl;

return 0; }</lang>

Output:
-0.152974,-0.435258,-0.136043,0.697503,0.656445,-0.435483,-1.08924,-0.537677,0.51705,1.05225,0.961854,0.69569,0.424356,0.196262,-0.0278351,-0.211722,-0.174746,0.0692584,0.385446,0.651771,

Python

<lang python>#!/bin/python from __future__ import print_function from scipy import signal import matplotlib.pyplot as plt

if __name__=="__main__": sig = [-0.917843918645,0.141984778794,1.20536903482,0.190286794412,-0.662370894973,-1.00700480494, -0.404707073677,0.800482325044,0.743500089861,1.01090520172,0.741527555207, 0.277841675195,0.400833448236,-0.2085993586,-0.172842103641,-0.134316096293, 0.0259303398477,0.490105989562,0.549391221511,0.9047198589]

#Create an order 3 lowpass butterworth filter #Generated using b, a = signal.butter(3, 0.5) a = [1.00000000, -2.77555756e-16, 3.33333333e-01, -1.85037171e-17] b = [0.16666667, 0.5, 0.5, 0.16666667]

#Apply the filter to xn. Use lfilter_zi to choose the initial condition of the filter: filt = signal.lfilter(b, a, sig) print (filt)

plt.plot(sig, 'b') plt.plot(filt, 'r--') plt.show()</lang>

Output:
[-0.15297399 -0.43525783 -0.1360434   0.69750333  0.65644469 -0.43548245
 -1.08923946 -0.53767655  0.51704999  1.05224975  0.9618543   0.69569009
  0.4243563   0.19626223 -0.02783512 -0.21172192 -0.17474556  0.06925841
  0.38544587  0.65177084]

zkl

Translation of: C++

<lang zkl>fcn butterworthFilter(b,a,signal){

  out:=List.createLong(signal.len(),0.0);  // vector of zeros
  foreach i in (signal.len()){
     tmp:=0.0;
     foreach j in (b.len()){ if(i-j >=0) tmp += b[j]*signal[i-j] }
     foreach j in (a.len()){ if(i-j >=0) tmp -= a[j]*out[i-j]    }
     out[i] = tmp/a[0];
  }
  out

}</lang> <lang zkl>signal:=T(-0.917843918645, 0.141984778794, 1.20536903482, 0.190286794412, -0.662370894973,-1.00700480494, -0.404707073677, 0.800482325044, 0.743500089861, 1.01090520172, 0.741527555207, 0.277841675195, 0.400833448236,-0.2085993586, -0.172842103641,-0.134316096293, 0.0259303398477,0.490105989562, 0.549391221511, 0.9047198589 ); a:=T(1.0, -2.77555756e-16, 3.33333333e-01, -1.85037171e-17 ); b:=T(0.16666667, 0.5, 0.5, 0.16666667 ); result:=butterworthFilter(b,a,signal); println(result);</lang>

Output:
L(-0.152974,-0.435258,-0.136043,  0.697503, 0.656445,-0.435482,
  -1.08924, -0.537677, 0.51705,   1.05225,  0.961854, 0.69569,
   0.424356, 0.196262,-0.0278351,-0.211722,-0.174746, 0.0692584,
   0.385446, 0.651771)

References