https://github.com/devgru/ffp

FFP algorithm implementation
https://github.com/devgru/ffp

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FFP algorithm implementation

• Host: GitHub
• URL: https://github.com/devgru/ffp
• Owner: devgru
• License: mit
• Created: 2017-07-11T07:33:47.000Z (over 7 years ago)
• Default Branch: master
• Last Pushed: 2022-08-15T09:46:32.000Z (over 2 years ago)
• Last Synced: 2024-04-29T19:07:10.616Z (7 months ago)
• Language: JavaScript
• Size: 120 KB
• Stars: 1
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Farthest feasible point algorithm implementation

Algorithm is used to filter timeseries points to speed up rendering by defining maximum error on ordinate axis

and skipping all points that fit in resulting corridor.



FFP algorithm is described in [this](http://masc.cs.gmu.edu/wiki/uploads/GuilinLiu/ffp.pdf) paper, authored by Guilin Xinyu and Zhe Cheng.

The library is distributed as ES module.

Check [spec/ffp.spec.js](spec/ffp.spec.js) to see usage example.

Built in collaboration with [Erohina Elena](https://github.com/erohinaelena), original version of FFP implementation can be found [here](http://bl.ocks.org/erohinaelena/882e7cadc2fd687cf2b3).

## Installation

```sh

$ yarn add ffp

# or

$ npm install --save ffp

```

## Usage

FFP is instantiated and used like this:

```js

import FFP from "ffp";

const ffp = FFP()

  .maxDeltaY(0.5) // define maximum delta

  .x(({ x }) => x) // define x accessor

  .y(({ y }) => y) // define y accessor

  .result(({ value }) => value); // consume filtered points

const array = [

  { x: 0, y: 3 },

  { x: 1, y: 4 },

  { x: 2, y: 5 },

  // ...

  { x: 5, y: 0 },

];

ffp(array);

```

FFP library exports a function.

## FFP()

Creates FFP utility.

## ffp(array)

FFP utility is a function, invoke it on array of elements to filter them out.

## ffp.maxDeltaY([*delta*])

If _delta_ is specified, sets the maximum _delta_ to the specified number. If _delta_ is not specified, returns the current maximum _delta_ value, which defaults to 1.

Maximum _delta_ determines maximum variation between resulting trend and point position on ordinate axis.

## ffp.epsilon([*epsilon*])

If _epsilon_ is specified, sets the _epsilon_ to the specified number. If _epsilon_ is not specified, returns the current _epsilon_ value, which defaults to 1/2³².

_epsilon_ determines the maximum margin of error.

## ffp.x([*xAccessor*])

If _xAccessor_ is specified, sets the _x_ accessor to the specified function. If _xAccessor_ is not specified, returns the current _x_ accessor, which defaults to `(value, index) => index`.

_x_ accessor is invoked for each point.

## ffp.y([*yAccessor*])

If _yAccessor_ is specified, sets the _y_ accessor to the specified function. If _yAccessor_ is not specified, returns the current _y_ accessor, which defaults to `(value) => value`.

_x_ accessor is invoked for each point.

## ffp.result([*resultMapper*])

If _resultMapper_ is specified, sets the _result_ mapper to the specified function. If _resultMapper_ is not specified, returns the current _result_ mapper, which defaults to `({ value }) => value`.

_result_ mapper is invoked for each point. By default, `ffp(array)` returns an array of objects with `value` and `index` keys. Define custom _result_ mapper to modify this behavior.

## Development

- Run tests: `yarn test`;

## License

MIT © [Dmitriy Semyushkin](https://devg.ru)