midas-integrate 0.3.2

Pure-Python radial integration for area X-ray detectors (MIDAS) — DetectorMapper + CSR integration + streaming server, CPU/GPU selectable

midas-integrate

Pure-Python radial integration for area X-ray detectors. A drop-in, pip-installable replacement for the MIDAS C/CUDA radial integration pipeline — no compilers, no native libraries, no CMake.

pip install midas-integrate

What's in the box

C/CUDA source	Python module
MapperCore.c, DetectorGeometry.c, DetectorMapper.c	midas_integrate.detector_mapper, midas_integrate.geometry
IntegratorFitPeaksGPUStream.cu (GPU streaming)	midas_integrate.kernels, midas_integrate.server, midas_integrate.pipeline
IntegratorZarrOMP.c (CPU OMP, bilinear)	midas_integrate.kernels (mode='bilinear')
PeakFit.c	midas_integrate.peakfit
PeakFitIO.c	midas_integrate.peak_io
Map.bin / nMap.bin	midas_integrate.bin_io

CPU/GPU selection

Everything that touches arrays accepts a device argument that is forwarded to PyTorch. CPU and CUDA are first-class:

from midas_integrate import build_csr, integrate, profile_1d, load_map

pixmap = load_map('Map.bin', 'nMap.bin')
geom_cpu  = build_csr(pixmap, n_r=990, n_eta=72, n_pixels_y=1475, n_pixels_z=1679, device='cpu')
geom_cuda = build_csr(pixmap, ..., device='cuda')

import torch
img = torch.from_numpy(image_2d)
profile = profile_1d(integrate(img, geom_cuda, mode='bilinear'), geom_cuda)

Three integration modes (full parity with C codes)

mode	Equivalent C kernel	Use when
'floor'	integrate_noMapMask in IntegratorFitPeaksGPUStream.cu	streaming, max throughput
'bilinear'	pixel loop in IntegratorZarrOMP.c lines 1733–1744	offline analysis, max accuracy
'gradient'	GradientCorrection=1 branch in the GPU stream	strong tilt + small R

CLI

Three entry points mirror the C binaries:

# 1. Build Map.bin / nMap.bin from a parameter file (one-shot, slow):
midas-detector-mapper params.txt -j 8

# 2. Integrate one frame (one-shot, fast):
midas-integrate params.txt --image frame.tif --device cuda

# 3. Streaming socket server (matches the C wire protocol on port 60439):
midas-integrate-server params.txt --device cuda --num-streams 4

Numerical parity

• DetectorMapper output (Map.bin/nMap.bin): byte-equivalent to the C version (entry counts, sums of frac and areaWeight per bin agree to ULP; entry order within a bin may differ).
• Per-frame integration: float32 ULP-level (median 1.7e-8 relative error vs the C/CUDA IntegratorFitPeaksGPUStream binary on PILATUS3 2M with CeO₂ data; max 2.1e-7 relative).
• Peak fitting: same model (pseudo-Voigt + global background, SNIP background subtraction), different optimizer (scipy LM vs NLopt Nelder-Mead). Fit parameters typically agree to ~1e-5 relative on noisy real data.

Performance (PILATUS3 2M, 1475×1679, NVIDIA H100)

	Throughput
C MIDAS GPU stream (per the paper)	~1,600 fps
midas-integrate (PyTorch CSR, FP32, CUDA)	~3,250 fps
midas-integrate (PyTorch CSR, FP64, CPU)	~675 fps
C MIDAS CPU (per the paper)	~262 fps
pyFAI CSR-cython (per the paper)	~7 fps

Requirements

• Python ≥ 3.9
• numpy, scipy, torch, tifffile, h5py, joblib

CUDA support is automatic if your installed torch has CUDA. macOS Metal (MPS) works for the integration kernel; sparse-CSR support on MPS is partial as of torch 2.7.

License

BSD-3-Clause. See LICENSE.