This release is a pre-release and may not be stable for production use.
fpm-rs
Reconstruct and simulate image-plane Fourier ptychographic microscopy (FPM) acquisitions from Rust or Python.
fpm-rs gives microscopy researchers and algorithm developers a CPU-based core
for compiling illumination geometry, simulating measurements, and recovering a
complex object. It supports image-plane FPM only: diffraction-plane
ptychography, multislice propagation, and GPU execution are not implemented.
What it can do for you
- Compile an optical model. Describe planar, spherical, calibrated, or coded illumination and turn it into Fourier-space sampling and a pupil.
- Simulate an acquisition. Generate ideal or camera-affected intensity frames from synthetic or supplied complex objects.
- Reconstruct the object. Start with alternating projection, or use FPIE, EPRY, ADMM, or gradient descent when their calibration and regularization options fit the experiment.
- Keep a run inspectable. Record diagnostics, write checkpoints, and compare simulations with known ground truth. Optional Parquet support writes self-describing result and benchmark bundles for downstream analysis.
- Measure at the right layer. Reusable reference/estimate metrics, optimization objectives, reconstruction evaluation, and recorder-driven diagnostics are separate APIs.
A typical workflow
- Compile the optics and illumination into an
ImagePlaneModel. - Load measured intensity frames, or simulate them from a known object.
- Build a
ReconstructionProblem, run an algorithm, and inspect amplitude, phase, diagnostics, or checkpoints.
The same forward model is used for simulation and reconstruction, while algorithms consume the compiled model rather than experimental geometry.
Array layout contract
The Rust API uses native ndarray arrays and views. Pointwise utilities and
metrics accept arbitrary logical layouts, including transposed and stepped
views. Computational inputs whose kernels use flat Fourier-grid offsets—such
as pupils, synthetic objects, measurement stacks, crop operations, and direct
forward-model spectra—require standard C-style row-major layout. Those APIs
validate the layout without copying and return a NonStandardLayout error for
strided input. Owned result and diagnostic arrays are ordinary ndarray
arrays. Result bundle export is a strict persistence boundary and rejects
nonstandard result layouts rather than silently materializing a copy.
Python follows the same distinction. Metrics accept strided NumPy arrays.
Reconstruction, simulation, pupil, and measurement inputs require
C-contiguous arrays and raise an error suggesting numpy.ascontiguousarray;
the caller therefore decides whether to pay for that copy. Accepted NumPy
inputs are copied into Rust-owned storage at the binding boundary. Arrays
opened through a result bundle are loaded on first access, cached, and exposed
as immutable NumPy arrays.
Start here
Install the Python package:
python -m pip install fpm-rs
Then compile a small model, simulate it, and reconstruct it:
import numpy as np
import fpm_rs as fpm
optics = fpm.Optics(532e-9, 0.10, 4.0, 6.5e-6)
leds = fpm.LEDArray((3, 3), 4e-3, 90e-3, (1.0, 1.0))
model = fpm.compile_model(optics, leds, (32, 32))
simulation = fpm.simulate(
model,
np.ones(model.reconstruction_shape, dtype=np.complex128),
seed=1234,
)
problem = fpm.ReconstructionProblem(
simulation.measurements,
simulation.reconstruction_model,
)
result = fpm.AlternatingProjection(iterations=20).run(problem)
print(result.amplitude.shape)
For the Rust equivalent, run:
cargo run --example simulate_and_reconstruct
Go deeper
- Documentation: installation, tutorials, guides, concepts, and API references.
- Quickstart: a narrated Python workflow.
- Reconstruction guide: model sizing, algorithms, callbacks, and checkpoints.
- Measurements, simulation, and datasets: prepare real data and create test cases.
- Contributor guide: build, test, and document a checkout.
License
fpm-rs is available under the MIT License.
1 maintainer
Metadata
1 maintainer
Release files for fpm-rs 0.2.0b1
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