https://github.com/secomind/ex_lttb

An Elixir downsampling library that retains the visual characteristics of your data
https://github.com/secomind/ex_lttb

downsampling elixir

Last synced: about 1 month ago
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An Elixir downsampling library that retains the visual characteristics of your data

• Host: GitHub
• URL: https://github.com/secomind/ex_lttb
• Owner: secomind
• License: mit
• Created: 2018-03-09T11:21:39.000Z (almost 7 years ago)
• Default Branch: master
• Last Pushed: 2018-03-23T14:19:43.000Z (over 6 years ago)
• Last Synced: 2024-04-10T11:13:46.631Z (8 months ago)
• Topics: downsampling, elixir
• Language: Elixir
• Size: 38.1 KB
• Stars: 29
• Watchers: 5
• Forks: 3
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# ExLTTB [![hex.pm version](https://img.shields.io/hexpm/v/ex_lttb.svg)](https://hex.pm/packages/ex_lttb) [![Build Status](https://travis-ci.org/ispirata/ex_lttb.svg?branch=master)](https://travis-ci.org/ispirata/ex_lttb) [![Coverage Status](https://coveralls.io/repos/github/ispirata/ex_lttb/badge.svg)](https://coveralls.io/github/ispirata/ex_lttb)

An Elixir downsampling library that retains the visual characteristics of your data.

ExLTTB is based on [Largest-Triangle-Three-Buckets](https://skemman.is/handle/1946/15343) (LTTB) and it contains two implementation modules: `ExLTTB` implements the original algorithm using `Enum` and eager evaluation, `ExLTTB.Stream` implements a slightly modified version to cope with lazy evaluation and using `Stream`.

The data is assumed to be ordered by its x coordinate in both implementations.

## Installation

Add `ex_lttb` to your list of dependencies in `mix.exs`:

```elixir

def deps do

  [

    {:ex_lttb, "~> 0.3.0"}

  ]

end

```

## Documentation

You can read the documentation on [HexDocs](https://hexdocs.pm/ex_lttb).

## Examples

```elixir

# Downsample a list of data in the default shape of %{x: x, y: y}

input_samples = for x <- 1..100, do: %{x: x, y: :random.uniform() * 100}

# 30 output samples

output_samples = ExLTTB.downsample_to(input_samples, 30)

# Downsample a list of data of arbitrary shape

input_samples =

  for x <- 1..100 do

    %{nested: %{timebase: x},

      data: :random.uniform() * 100,

      untouched_other_key: :random.uniform() * 3

    }

  end

sample_to_x_fun = fn sample -> sample[:nested][:timebase] end

sample_to_y_fun = fn sample -> sample[:data] end

xy_to_sample_fun = fn x, y -> %{nested: %{timebase: x}, data: y} end

output_samples =

  ExLTTB.downsample_to(

    input_samples,

    30,

    sample_to_x_fun: sample_to_x_fun,

    sample_to_y_fun: sample_to_y_fun,

    xy_to_sample_fun: xy_to_sample_fun

  )

# Downsample a stream of data

input_stream =

  Stream.iterate(%{x: 0, y: :random.uniform() * 100}, fn %{x: x} -> %{x: x + 1, y: :random.uniform() * 100} end)

# Downsample rate of 2.3

output_samples = ExLTTB.Stream.downsample(input_stream, 2.3) |> Enum.take(20)

# The options for arbitrary shaped data are the same for the streaming version

```