lttb 0.2.1

Largest-Triangle-Three-Buckets algorithm for downsampling time series–like data

Numpy implementation of Steinarsson’s Largest-Triangle-Three-Buckets algorithm for downsampling time series–like data

It is based on the original JavaScript code at https://github.com/sveinn-steinarsson/flot-downsample and Sveinn Steinarsson’s 2013 MSc thesis Downsampling Time Series for Visual Representation.

Usage

import numpy as np
import lttb

data = np.array([range(100), np.random.random(100)]).T
small_data = lttb.downsample(data, n_out=20)
assert small_data.shape == (20, 2)

A test data set is provided in the source repo in tests/timeseries.csv. It was downloaded from http://flot.base.is/ and converted from JSON to CSV.

This is what it looks like, downsampled to 100 points:

Installation

To install the lttb package into your (virtual) environment:

pip install lttb

Development

In a virtual environment, install the dependencies and development tools:

pip install -r requirements.txt
pip install -e .
pip install -r requirements-dev.txt

The linters and tests can then be run with the commands in the Makefile:

make lint
make test
make test-all

Note that the test-all task requires the versions of Python used by tox to have already been installed with pyenv.

History

0.2.1 / 2019-11-25

• Versions are now managed with setuptools_scm rather than bumpversion.

• The code is formatted with Black.

0.2.0 / 2018-02-11

• Performance improvements

• Released on PyPI (on 2019-11-06)

0.1.0 / 2017-03-18

• Initial implementation

Contributors

• Jack Viljoen (@javiljoen) – original Numpy implementation

• Guillaume Bethouart (@guillaumeB) – performance improvements