README.md

signal

Matlab based signal processing workflows in Go. Design in Matlab and use in Go

Filter Signals

Package filter provides ability to infer filters created in Matlab.

Create a filter

Design a filter in Matlab using Filter Design Tool and export it as an object on the workspace.

Create a JSON encoding of that object to use with this repo.

For instance a discrete time FIR low-pass filter can be created as follows:

function Hd = lowPassFilter
%LOWPASSFILTER Returns a discrete-time filter object.

% MATLAB Code
% Generated by MATLAB(R) 24.2 and Signal Processing Toolbox 24.2.
% Generated on: 24-Dec-2024 14:56:02

% Equiripple Lowpass filter designed using the FIRPM function.

% All frequency values are normalized to 1.

Fpass = 0.02;            % Passband Frequency
Fstop = 0.1;             % Stopband Frequency
Dpass = 0.057501127785;  % Passband Ripple
Dstop = 0.0001;          % Stopband Attenuation
dens  = 20;              % Density Factor

% Calculate the order from the parameters using FIRPMORD.
[N, Fo, Ao, W] = firpmord([Fpass, Fstop], [1 0], [Dpass, Dstop]);

% Calculate the coefficients using the FIRPM function.
b  = firpm(N, Fo, Ao, W, {dens});
Hd = dfilt.dffir(b);

% [EOF]

Encode the filter to JSON string

Hd = lowPassFilter();
js = jsonencode(Hd);

Use filter in Go

package main

import (
	"github.com/kubetrail/signal/pkg/filter"
	"math"
	"math/rand/v2"
)

// js represents the JSON encoding of filter object from Matlab
var js string = `
{
  "Arithmetic": "double",
  "Numerator": [
    -3.178415946962565e-05,
    0.00012243197558829932,
    0.00020044046800350558,
    0.0003488471460655481,
    0.0005671834873047258,
    0.0008686374904649395,
    0.0012697519972841237,
    0.0017879347298591607,
    0.0024404612047465553,
    0.00324370926934789,
    0.004212343496976391,
    0.005358457175976007,
    0.006690726110359709,
    0.008213604807760608,
    0.009926598151788674,
    0.011823664168543389,
    0.01389279947716996,
    0.016115826951839628,
    0.01846838883421013,
    0.02092016735730781,
    0.023435371170089436,
    0.025973497770710112,
    0.028490327966822312,
    0.03093908790591826,
    0.03327174706042746,
    0.03544045152939104,
    0.03739905943817371,
    0.03910467033841596,
    0.040519017370098276,
    0.041609677925945006,
    0.04235122582902602,
    0.04272655856897903,
    0.04272655856897903,
    0.04235122582902602,
    0.041609677925945006,
    0.040519017370098276,
    0.03910467033841596,
    0.03739905943817371,
    0.03544045152939104,
    0.03327174706042746,
    0.03093908790591826,
    0.028490327966822312,
    0.025973497770710112,
    0.023435371170089436,
    0.02092016735730781,
    0.01846838883421013,
    0.016115826951839628,
    0.01389279947716996,
    0.011823664168543389,
    0.009926598151788674,
    0.008213604807760608,
    0.006690726110359709,
    0.005358457175976007,
    0.004212343496976391,
    0.00324370926934789,
    0.0024404612047465553,
    0.0017879347298591607,
    0.0012697519972841237,
    0.0008686374904649395,
    0.0005671834873047258,
    0.0003488471460655481,
    0.00020044046800350558,
    0.00012243197558829932,
    -3.178415946962565e-05
  ],
  "FilterStructure": "Direct-Form FIR",
  "States": [
    0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
  ],
  "NumSamplesProcessed": 0,
  "PersistentMemory": false,
  "RateChangeFactor": [
    1,
    1
  ]
}
`

func main() {
	f, _ := filter.NewDffirFromJSON([]byte(js))
	// handle error
	
	// similarly, create direct form II SOS filter 
	// instead of FIR filter depending
	// on which filter was designed in Matlab
	// f, _ = filter.NewDf2sosFromJSON([]byte(js))

	// create some signal with noise that will be filtered using f
	x := make([]float64, 1024);
	for i := range x {
		x[i] = math.Sin(float64(i)/1024*10*math.Pi) + rand.Float64()/5
	}
	
	// filter signal
	y, _ := f.Filter(x)
	// handle error
	
	// do something with y
	_ = y;
}