cuPeriod¶

cuPeriod

Optimized, GPU-accelerated periodograms for astronomy

cuPeriod computes period-search statistics for variable stars and transiting systems — from a single light curve to millions. One Python API and one command-line tool cover seven methods, each with a fast CPU backend and a CUDA-accelerated path, plus frictionless column handling, multi-band support, raw-spectrum output, and an N-best-periods utility.

Every implementation is validated against an established reference (astropy’s LombScargle / BoxLeastSquares, and others) to floating-point round-off.

import cuperiod as cup

pg = cup.periodogram((time, mag, mag_err), "GLS")
print(pg.best_period())          # the most significant period, in days
for peak in pg.best_periods(10):
    print(peak.period, peak.power)

🚀 Get started in 5 minutes

The Quickstart runs end-to-end on a synthetic light curve — no data needed — then shows how to load your own.

📖 Learn the package

The User Guide walks through inputs, methods, results, backends, tuning, multi-band, batch, and the CLI.

⚡ Scale to millions

Batch processing over CPU pools or the GPU, written to Parquet, resumable across runs.

🔬 Trust the numbers

The Validation & benchmarks page shows parity, period recovery, and speedups on real survey data.

Which method should I use?¶

Method	What it’s for	GPU	Multi-band
GLS	general variability (generalized Lomb–Scargle)	✅	✅
BLS	eclipses / box-like transits	✅	✅
MHAOV	sharply non-sinusoidal signals (multiharmonic AOV)	✅	✅
TLS	limb-darkened transit matched filter	✅	—
PDM	non-sinusoidal folds (Stellingwerf)	✅	—
CE	sparse survey data (conditional entropy)	✅	—
String-Length	eclipsing / eccentric shapes	✅	—

All seven share one API, one CLI, and the full single/batch machinery. See Choosing a method for a decision guide.

Install¶

pip install cuperiod            # CPU (numpy, scipy, astropy, finufft)
pip install "cuperiod[gpu]"     # + CUDA 12 GPU backends (cupy, cufinufft)
pip install "cuperiod[fast]"    # + numba multicore box search (~20× astropy BLS on CPU)

See Installation for the full matrix and GPU requirements.