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

Project description

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

Based on the original JavaScript code:

https://github.com/sveinn-steinarsson/flot-downsample

and:

Sveinn Steinarsson. 2013. Downsampling Time Series for Visual Representation. MSc thesis. University of Iceland.

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

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)

For example, here is the data set provided in tests downsampled to 100 points:

http://github.com/javiljoen/lttb.py/raw/master/tests/timeseries.png

Installation

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

pip install lttb

Requirements

  • Python 3
  • Numpy

Licence: MIT

Contributors

  • Jack Viljoen (@javiljoen) – original Numpy implementation
  • Guillaume Bethouart (@guillaumeB) – performance improvements

Project details


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This version

0.2.0

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