https://github.com/dgoeries/downsample
Downsampling algorithms for Python written in C
https://github.com/dgoeries/downsample
c-implementation downsampling ltd ltob lttb performance python
Last synced: 5 days ago
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Downsampling algorithms for Python written in C
- Host: GitHub
- URL: https://github.com/dgoeries/downsample
- Owner: dgoeries
- License: mit
- Created: 2024-10-31T16:46:10.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-12-29T08:28:27.000Z (5 months ago)
- Last Synced: 2026-01-01T06:32:28.931Z (5 months ago)
- Topics: c-implementation, downsampling, ltd, ltob, lttb, performance, python
- Language: Python
- Homepage:
- Size: 481 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE.txt
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README
# downsample: Collection of downsample algorithms for Python (Python using a C implementation) [](https://pypi.org/project/downsample/) [](https://pypi.org/project/downsample/)
This packages includes low level implementations written in C-Python of:
- The `Largest Triangle Dynamic Buckets` (`LTD`) downsampling algorithm
- The `Largest Triangle Three Buckets` (`LTTB`) downsampling algorithm
- The `Largest Triangle One Bucket` (`LTOB`) downsampling algorithm
The algorithm of `LTTB` was initially developed in (https://github.com/dgoeries/lttbc.git)
Parts of this code have been translated and refers to the work of:
- Ján Jakub Naništa (https://github.com/janjakubnanista/downsample) (Typescript)
- Hao Chen (https://github.com/haoel/downsampling) (Go)
The algorithms are described in 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 of ``7500``
data points down sampled to ``500`` points.
Known features and requirements:
- The algorithm requires that x data is increasing and finite.
- y data must be finite; otherwise, issues may arise.
- x and y data must have the same length.
- The downsample algorithm returns a tuple of two arrays with data type *double*
## Installing
You can also install it [from PyPI](https://pypi.org/project/downsample/)
to use in other environments with Python 3.10 or later:
pip install downsample
## How to use on the field
All functions take an input for ``x`` and ``y`` in addition to the ``threshold``:
from downsample import ltob, lttb, ltd
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)
x_l = x.tolist()
y_l = y.tolist()
for func in {ltd, ltob, lttb}:
nx, ny = func(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!
nx, ny = func(x_l, y_l, threshold)
assert len(nx) == threshold
assert len(ny) == threshold
assert nx.dtype == np.double
assert ny.dtype == np.double
## Performance Overview
For a performance overview, a sample of 7.500 data points was analyzed with a
threshold set at 500 points. The performance test was conducted on a single core,
utilizing a base clock speed of 3.70 GHz and 32 MB of L3 cache.
- LTD: 977.2 us
- LTOB: 61.0 us
- LTTB: 63.1 us