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

#2 Test against latest Python & Numpy

~javiljoen filed ticket on lttb todo

9 months ago

#1 Additional documentation

~javiljoen filed ticket on lttb todo

9 months ago

lttb pypi builds.sr.ht status

Numpy implementation of Steinarsson’s Largest-Triangle-Three-Buckets algorithm for downsampling time series–like data while retaining the overall shape and variability in the data

LTTB is well suited to filtering time series data for visual representation, since it reduces the number of visually redundant data points, resulting in smaller file sizes and faster rendering of plots.

Note that it is not a technique for statistical aggregation, cf. regression models or non-parametric curve fitting / smoothing.

This implementation 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.

Licence: MIT


Install the lttb package into your (virtual) environment:

$ pip install lttb

The function lttb.downsample() can then be used in your Python code:

import numpy as np
import lttb

# Generate an example data set of 100 random points:
#  - column 0 represents time values (strictly increasing)
#  - column 1 represents the metric of interest: CPU usage, stock price, etc.
data = np.array([range(100), np.random.random(100)]).T

# Downsample it to 20 points:
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:


Input validation

By default, downsample() checks that the input data satisfies the following constraints:

  • it is a two-dimensional array of two columns;
  • the values in the first column are strictly increasing; and
  • there are no missing (NaN) values in the data.

These checks can be skipped (e.g. if you know that your data will always meet these conditions), or additional checks can be added (e.g. that the time values must be evenly spaced), by passing in a different list of validation functions, e.g.:

# No input validation:
small_data = lttb.downsample(data, n_out=20, validators=[])

# Stricter check on x values:
from lttb.validators import *
small_data = lttb.downsample(data, n_out=20, validators=[has_two_columns, x_is_regular])


0.3.1 / 2020-10-14

  • All modules and functions now have docstrings.
  • [dev] The library is now also tested against Python 3.9. No changes to the code were required.
  • [dev] The project is now hosted on SourceHut; the links on the PyPI page have been updated.
  • [dev] CI testing has been migrated from Travis to builds.sr.ht.

0.3.0 / 2020-09-15

  • Validation of input data is now configurable.
  • New default: downsample() raises ValueError if input data contains NaN values. This can be disabled by removing contains_no_nans() from the list of validators.
  • [dev] Imports are now sorted with isort.

0.2.2 / 2020-01-08

  • setup.py was fixed so that this package can be installed in Python 2 again.

0.2.1 / 2019-11-25

  • [dev] Versions are now managed with setuptools_scm rather than bumpversion.
  • [dev] 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


  • JA Viljoen – original Numpy implementation
  • Guillaume Bethouart – performance improvements
  • Jens Krüger – fix for py27


Reporting issues

If you find a bug or have an idea for improving this package, please describe it in a ticket on the issue tracker.

Submitting patches

Patches are welcome. Feel free to send them by email using git send-email, or you can send me a link to your repo if it is publically accessible.

Please ensure that the tests and linting checks listed in the Makefile all pass, and that any new features are covered by tests.

Development setup

Create a Python virtual environment, e.g. using pyenv and/or direnv. In that venv, install the dependencies and development tools:

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

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

make lint
make test

If you are using pyenv, you can run the tests on multiple versions of Python. Use pyenv to install pythons from the 2.7, 3.5, and 3.9 series; then activate them in the project folder and run the tests with, e.g.:

pyenv local 3.9.0 3.5.9 2.7.17
make test-all