lttbc

KO | EN

gitlite — search

#javascript #ai #typescript #golang #android #docker #macos #java #windows #kubernetes #awesome-list #vue #security #openai

(★ 33)

Largest-Triangle-Three-Buckets (LTTB) downsampling algorithm in Python (C-Extension)

lttbc 최신버젼 다운로드

최종 버전 다운로드 (.zip)

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# lttbc: Largest-Triangle-Three-Buckets (Python using a c implementation) [![PyPi](https://img.shields.io/pypi/v/lttbc?color=blue)](https://pypi.org/project/lttbc/) [![PyPI Downloads](https://img.shields.io/pypi/dm/lttbc.svg?label=PyPI%20downloads)](https://pypi.org/project/lttbc/) [![Conda Downloads](https://img.shields.io/conda/dn/conda-forge/lttbc.svg?label=Conda%20downloads)](https://anaconda.org/conda-forge/lttbc)
This is a low-level implementation of the `Largest-Triangle-Three-Buckets` (LTTB) downsampling algorithm written in Python.

The code has been translated from the work of Sveinn Steinarsson (https://github.com/sveinn-steinarsson/flot-downsample/).

## Demo and 'Known Issues'

The examples show the efficiency of the downsampling algorithm with a data set
set of ``5000`` data points down sampled to ``500`` and ``250`` points.

![SampleView](https://raw.githubusercontent.com/dgoeries/lttbc/master/images/demo.png)

Known features and requirements:

- The algorithm requires monotonically increasing x data (finite)
- The algorithm requires finite y data (otherwise problems might occur)
- x and y data have to be of same length (of course)
- The algorithm returns arrays of **dtype** **double**

## Installing

You can also install it [from PyPI](https://pypi.org/project/lttbc/)
to use in other environments with Python 3.5 or later:

pip install lttbc

## How to use on the field

The module ``lttbc`` differs in the standard input from other largest triangle three buckets implementations.
The ``downsample`` function takes an input for ``x`` and ``y`` in addition to the ``threshold``:

import lttbc
    import numpy as np

ARRAY_SIZE = 10000
    THRESHOLD = 1000

x = np.arange(ARRAY_SIZE, dtype=np.int32)
    y = np.random.randint(1000, size=ARRAY_SIZE, dtype=np.uint64)

nx, ny = lttbc.downsample(x, y, THRESHOLD)

assert len(nx) == THRESHOLD
    assert len(ny) == THRESHOLD
    assert nx.dtype == np.double
    assert ny.dtype == np.double

# List data or a mixture is accepted as well ...
    x = list(range(ARRAY_SIZE))
    y = [np.random.uniform(0, 1000) for _ in range(ARRAY_SIZE)]

assert isinstance(x, list)
    assert isinstance(y, list)

nx, ny = lttbc.downsample(x, y, THRESHOLD)

assert len(nx) == THRESHOLD
    assert len(ny) == THRESHOLD
    assert nx.dtype == np.double
    assert ny.dtype == np.double

lttbc

lttbc 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

lttbc

lttbc 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

Download README