light-curve processing toolbox for Python
A Python wrapper for the light-curve-feature and
light-curve-dmdt Rust crates, providing high-performance
time-series feature extraction for astrophysics.
Full documentation: light-curve.snad.space
Quick start
pip install 'light-curve[full]'
import light_curve as licu
import numpy as np
rng = np.random.default_rng(0)
t = np.sort(rng.uniform(0, 100, 100))
m = 15.0 + 0.01 * t + rng.normal(0, 0.1, 100)
err = np.full(100, 0.1)
# Statistical and variability feature extraction
extractor = licu.Extractor(licu.Amplitude(), licu.BeyondNStd(nstd=1), licu.LinearFit())
result = extractor(t, m, err)
print("\n".join(f"{name} = {value:.4f}" for name, value in zip(extractor.names, result)))
# Multi-band: per-band features and cross-band color in one Extractor
band = np.tile(["g", "r"], 50) # 50 interleaved g/r observations
ext_mb = licu.Extractor(licu.WeightedMean(bands=["g", "r"]), licu.ColorOfMedian(["g", "r"]))
result_mb = ext_mb(t, m, err, band=band)
print(dict(zip(ext_mb.names, result_mb)))
# dm-dt map — 2D histogram of Δmag vs log-Δt for CNN classifiers
dmdt = licu.DmDt.from_borders(min_lgdt=0, max_lgdt=2, max_abs_dm=1.0, lgdt_size=16, dm_size=16, norm=[])
matrix = dmdt.points(t, m)
print(f"dm-dt map shape: {matrix.shape}") # (16, 16)
Embed the light curve with a pretrained Astromer2 transformer (model downloads on first use):
from light_curve.embed import Astromer2
import numpy as np
rng = np.random.default_rng(0)
t = np.sort(rng.uniform(0, 100, 100))
m = 15.0 + 0.01 * t + rng.normal(0, 0.1, 100)
model = Astromer2.from_hf(output="mean") # cached after first download
embedding = model(t, m).squeeze() # shape (256,)
print(f"Embedding shape: {embedding.shape}")
The package provides:
- 40+ feature extractors — amplitude, period, variability indices, parametric fits, and more. See the Features docs for a full list.
- ML embeddings — embed light curves with pretrained ONNX models (AstraCLR, Astromer2, ATAT, ATCAT). See the Embeddings docs.
- dm-dt maps — 2D histograms of Δm vs log-Δt for CNN input. See the dm-dt docs.
Installation
pip install 'light-curve[full]'
# or
conda install -c conda-forge light-curve-python
The full extra installs optional Python dependencies required by some features
(iminuit, scipy).
Binary wheels are available for Linux (x86-64, aarch64), macOS, and Windows. For the full platform support table and build-from-source instructions, see the Installation docs.
Developer guide
Prepare environment
Install a recent Rust toolchain and Python 3.10+.
Use rustup to install Rust and keep it updated with rustup update.
Clone the repository, then create and activate a virtual environment:
git clone https://github.com/light-curve/light-curve-python.git
cd light-curve-python/light-curve
python3 -m venv venv
source venv/bin/activate
Install the package in editable mode:
python -mpip install maturin
maturin develop --group dev
Run this command on initial setup. On subsequent runs, activate the environment with
source venv/bin/activate and rebuild Rust code with maturin develop.
Python-only changes require no rebuild.
Install pre-commit hooks:
pre-commit install
Run tests
python -mpytest
Benchmarks are disabled by default; enable them with --benchmark-enable.
Citation
If you found this project useful for your research, please cite Malanchev et al., 2021:
@ARTICLE{2021MNRAS.502.5147M,
author = {{Malanchev}, K.~L. and {Pruzhinskaya}, M.~V. and {Korolev}, V.~S. and {Aleo}, P.~D. and {Kornilov}, M.~V. and {Ishida}, E.~E.~O. and {Krushinsky}, V.~V. and {Mondon}, F. and {Sreejith}, S. and {Volnova}, A.~A. and {Belinski}, A.~A. and {Dodin}, A.~V. and {Tatarnikov}, A.~M. and {Zheltoukhov}, S.~G. and {(The SNAD Team)}},
title = "{Anomaly detection in the Zwicky Transient Facility DR3}",
journal = {\mnras},
keywords = {methods: data analysis, astronomical data bases: miscellaneous, stars: variables: general, Astrophysics - Instrumentation and Methods for Astrophysics, Astrophysics - Solar and Stellar Astrophysics},
year = 2021,
month = apr,
volume = {502},
number = {4},
pages = {5147-5175},
doi = {10.1093/mnras/stab316},
archivePrefix = {arXiv},
eprint = {2012.01419},
primaryClass = {astro-ph.IM},
adsurl = {https://ui.adsabs.harvard.edu/abs/2021MNRAS.502.5147M},
adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
1 maintainer
Metadata
1 maintainer
Release files for light-curve 0.13.3
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Source distribution (sdist)
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| light_curve-0.13.3-cp310-abi3-musllinux_1_2_x86_64.whl | CPython 3.10 | abi3 | Linux musl 1.2+ x86-64 | Details |
| light_curve-0.13.3-cp310-abi3-musllinux_1_2_aarch64.whl | CPython 3.10 | abi3 | Linux musl 1.2+ ARM64 | Details |
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| light_curve-0.13.3-cp310-abi3-manylinux2014_aarch64.manylinux_2_17_aarch64.whl | CPython 3.10 | abi3 | Linux glibc 2.17+ ARM64 | Details |
| light_curve-0.13.3-cp310-abi3-macosx_15_0_x86_64.whl | CPython 3.10 | abi3 | macOS 15.0+ x86-64 | Details |
| light_curve-0.13.3-cp310-abi3-macosx_14_0_arm64.whl | CPython 3.10 | abi3 | macOS 14.0+ ARM64 | Details |
Total release size: 116.9 MB
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