makewfs

CI PyPI Python License: MIT

makewfs turns a configured pupil-plane phase/OPD map into a realistic adaptive-optics wavefront-sensor image. The supported sensors are Shack–Hartmann and four-face pyramid sensors.

The package owns the wavefront-sensor optics. It deliberately reuses pyturb for atmospheric OPD and getframes for detector response and noise; neither model will be reimplemented here.

Status: stable. makewfs 1.0 freezes the configuration-driven Shack–Hartmann and four-face pyramid API under Semantic Versioning. Broadband source morphology, wavelength-resolved detector QE, Shack–Hartmann sodium-layer elongation, and optional end-to-end CuPy execution are supported. The documented mean-altitude LGS OPD approximation remains intentionally distinct from future range-resolved turbulence.

The minimal API is:

import makewfs

wfs = makewfs.WavefrontSensor.from_toml("wfs.toml")
frame = wfs.expose(opd_m, seed=0)  # getframes.Frame, data in ADU

Install the CPU package from PyPI with:

python -m pip install makewfs

For the optional CUDA 12.x path:

python -m pip install "makewfs[gpu]"

Set device = "gpu" under [numerics] and pass a CuPy OPD array for the device-resident path. frame.data is then a CuPy ADU array; use getframes.to_numpy(frame.data) only at an intentional host boundary.

The repository includes a runnable starter configuration at examples/configs/shack_hartmann_minimal.toml. The corresponding pyramid starter is at examples/configs/pyramid_minimal.toml. For an ideal photon-rate map, use wfs.photon_rate(opd_m); for one detector exposure containing several temporal OPD samples, use wfs.expose_integrated(samples).

For closed-loop work, construct the sensor once and call expose() with each new residual wavefront. The only per-frame inputs are the wavefront and, optionally, a detector-noise seed; telescope, source, sensor, sampling, and camera choices live in the configuration.

CPU/GPU throughput

Warm end-to-end frame throughput on an AMD Ryzen 9 9950X3D and NVIDIA RTX 5090 includes optics, getframes detector noise, truth, and ADU, with device-resident input/output and no host transfers:

Workflow Output Work samples CPU (frames/s) GPU (frames/s) Speedup
20x20 SH, float32 160x160 1 143.4 2,011.2 14.03x
60x60 SH, float64 360x360 1 26.3 945.2 35.94x
Broadband/range-sampled SH 64x64 9 99.8 557.1 5.58x
Pyramid, 8-point modulation, float32 80x80 8 668.5 1,579.4 2.36x
Pyramid, 32-point modulation, float64 108x108 32 33.3 903.9 27.18x

Higher is better. A tiny unmodulated 54x54 pyramid case remains CPU-faster (3,570 versus 1,541 frames/s) because GPU launch overhead dominates its small optical workload. See the full snapshot, raw JSON, and benchmark methodology.

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Author: Jacob Taylor

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License

MIT License (MIT)

Requires

Python >=3.10

Provides Extra

dev docs examples gpu interop validation

Tags

adaptive-optics pyramid shack-hartmann wavefront-sensor

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