npm package: @neurodevs/fili
Description: [![npm version](https://badge.fury.io/js/fili.svg)](http://badge.fury.io/js/fili) [![Build Status](https://travis-ci.org/markert/fili.js.svg?branch=master)](https://travis-ci.org/markert/fili.js)
Author: Florian Markert & Eric Yates
Latest version: 2.0.4

# fili

[![npm version](https://badge.fury.io/js/fili.svg)](http://badge.fury.io/js/fili)
[![Build Status](https://travis-ci.org/markert/fili.js.svg?branch=master)](https://travis-ci.org/markert/fili.js)

A digital filter library for JavaScript.

## Installation

```
$ npm install fili
```

## Usage

##### Node

```js
var Fili = require('fili');

var iirCalculator = new Fili.CalcCascades();
```

##### Browser

1. Copy `./dist/fili.min.js` into your working directory

2. Load script in your index.html

  ```html
  <script src="js/fili.min.js"></script>
  ```

3. Use `Fili` in your application

  ```js
  var iirCalculator = new Fili.CalcCascades();
  // ...
  ```

## API

### Generate IIR Filters with bilinear transform

IIR filters are composed of n Biquad filters.
The Biquad filters need a and b coefficients to work.
So, they have a backward and a forward path.
Possible filters are:

-   lowpass

-   highpass

-   bandpass

-   bandstop

-   peak

-   lowshelf

-   highshelf

-   aweighting

Possible characteristics are:

-   bessel

-   butterworth

Note: for peak, lowshelf and highshelf a gain attribute must be defined
when generating the coefficients. Gain can be positive or negative
and represents the dB value for the peak or dip.

```javascript
//  Instance of a filter coefficient calculator
var iirCalculator = new Fili.CalcCascades();

// get available filters
var availableFilters = iirCalculator.available();

// calculate filter coefficients
var iirFilterCoeffs = iirCalculator.lowpass({
    order: 3, // cascade 3 biquad filters (max: 12)
    characteristic: 'butterworth',
    Fs: 1000, // sampling frequency
    Fc: 100, // cutoff frequency / center frequency for bandpass, bandstop, peak
    BW: 1, // bandwidth only for bandstop and bandpass filters - optional
    gain: 0, // gain for peak, lowshelf and highshelf
    preGain: false // adds one constant multiplication for highpass and lowpass
    // k = (1 + cos(omega)) * 0.5 / k = 1 with preGain == false
  });

// create a filter instance from the calculated coeffs
var iirFilter = new Fili.IirFilter(iirFilterCoeffs);
```

### Generate IIR Filters with matched-z transform

IIR filters are composed of n Biquad filters.
The Biquad filters need only a coefficients to work.
So, they have a backward but no forward path.
Possible filters are:

-   lowpass

Possible characteristics are:

-   bessel

-   butterworth

-   allpass

-   tschebyscheff05

-   tschebyscheff1

-   tschebyscheff2

-   tschebyscheff3

Note: The number behind tschebyscheff defines the passband ripple.

```javascript
// calculate filter coefficients
var iirFilterCoeffs = iirCalculator.lowpass({
    order: 3, // cascade 3 biquad filters (max: 5)
    characteristic: 'tschebyscheff3',
    transform: 'matchedZ',
    Fs: 1000, // sampling frequency
    Fc: 100, // cutoff frequency / center frequency for bandpass, bandstop, peak
    preGain: false // uses k when true for gain correction b[0] otherwise
  });
```

### Generate FIR Filters

FIR filter calculation is done with a windowed sinc function
Possible filters are:
-   lowpass
-   highpass
-   bandpass
-   bandstop

```javascript
//  Instance of a filter coefficient calculator
var firCalculator = new Fili.FirCoeffs();

// calculate filter coefficients
var firFilterCoeffs = firCalculator.lowpass({
    order: 100, // filter order
    Fs: 1000, // sampling frequency
    Fc: 100 // cutoff frequency
    // forbandpass and bandstop F1 and F2 must be provided instead of Fc
  });

// filter coefficients by Kaiser-Bessel window
var firFilterCoeffsK = firCalculator.kbFilter({
    order: 101, // filter order (must be odd)
    Fs: 1000, // sampling frequency
    Fa: 50, // rise, 0 for lowpass
    Fb: 100, // fall, Fs/2 for highpass
    Att: 100 // attenuation in dB
  });

// create a filter instance from the calculated coeffs
var firFilter = new Fili.FirFilter(firFilterCoeffs);
```

### Run Filter

```javascript
// run the filter with 10 samples from a ramp
// returns single value
for (var cnt = 0; cnt < 10; cnt++) {
  console.log(filter.singleStep(cnt));
}

// run the filter from input array
// returns array
console.log(filter.multiStep([1,10,-5,3,1.112,17]));

// simulate the filter
// does not change the internal state
// returns array
console.log(filter.simulate([-3,-2,-1,5,6,33]));
```

### Evaluate Filter

```javascript
// get the filter impact on magnitude, phase, unwrapped phase, phase delay and group delay
// returns array of n objects
// Fs = 1000 n = 100, so the array represents 0Hz, 10Hz, 20Hz....
// returns array of objects
// {dBmagnitude: -4, groupDelay: 2, magnitude: 0, phase: -7, phaseDelay: 12, unwrappedPhase: 7}
var response = filter.response(100);

// get the filter impact on magnitude, phase, unwrapped phase, phase delay and group delay
// for a defined frequency
// returns one object
var responsePoint = filter.responsePoint({
    Fs: 1000,  // sampling frequency
    Fr: 123 // frequency of interest
  });
```

### Evaluate stability

```javascript

// initialize filter for testing
// note: changes internal state of filter -> create a new filter from
// the calculated coefficients for evaluation
var filterTester = new Fili.FilterTester(testFilter);

// check if filter is stable for the specified input range
// returns true for stable filter
var stable = filterTester.directedRandomStability({
    steps: 10000, // filter steps per test
    tests: 100, // numbers of tests (random, ramp, impulses, steps)
    offset: 5, // offset of input
    pp: 10, // peak to peak of input
    maxStable: 20, // values over this border will be considered as unstable
    minStable: -10, // values under this border will be considered as unstable
    setup: 1000 // steps until initial setup of filter is complete
  });
```

### Calculate FFT

An FFT is always useful to evaluate filter responses.
The algorithm uses precalculated twiddle factors and a
lookup table for sine and cosine values.
It also reuses all calculation buffers and precalculated window functions.
This minimizes garbage collection and improves calculation speed.

Generate a new FFT calculator:

```javascript
// Fft radix must be 2^n
var fft = new Fili.Fft(8192);
```

Frequency<--->Time Domain:

```javascript
var buffer = [];
for (var cnt = 0; cnt < 8192; cnt++) {
  buffer.push(cnt);
}

// Supported window functions are
// none, hanning, hamming, rectangular
// tukery, cosine, lanczos,
// triangular, bartlett, gaussian,
// bartlettHanning, blackman, blackmanHarris,
// nuttall3, nuttall3a, nuttall3b,
// nuttall4, nuttall4a, nuttall4b, nuttall4c
// sft3f, sft4f, sft5f, sft3m, sft4m, sft5m
// nift, hpft, srft, hft70, hft95, hft90d
// hft116d, hft144d, hft196d, hft223d, hft248d

// get available window functions
var availableWindows = fft.windows();

// buffer.length must be greater or equal fft radix
var fftResult = fft.forward(buffer, 'hanning');

// fftResult = {re: [], im: []}. The array length equals the FFT radix

var magnitude = fft.magnitude(fftResult); // magnitude
var dB = fft.magToDb(magnitude); // magnitude in dB
var phase = fft.phase(fftResult); // phase

// Note: magnitude, dB and phase are arrays.
// The length equals the FFT radix.
// For exact phase evaluation, the phase must be unwrapped.

var originalBuffer = fft.inverse(fftResult.re, fftResult.im);
```


## Test

```
$ make test
```

## TODO
- add travis
- add wavelet transform
- add Parks-McClellan FIR algorithm
- add iir filters other than biquad
- add stability evaluation for fix-point arithmetic

## License

MIT