midas-nf-process-images

Differentiable PyTorch port of NF_HEDM/src/ProcessImagesCombined.c for CPU, CUDA, and MPS.

Notebooks

Worked-example Jupyter notebooks live in notebooks/. They are not shipped with pip install — get them by cloning the MIDAS repository.

Pipeline

For one layer of a near-field HEDM scan:

  1. Load NrFilesPerLayer raw TIFF frames into a [N, Z, Y] tensor.
  2. Temporal median across frames → per-pixel background [Z, Y].
  3. Per-frame:
    • Subtract median + BlanketSubtraction, clamp at 0.
    • Spatial median (3x3 or 5x5).
    • LoG response at one or more scales.
    • Threshold and label connected components → integer spot labels (detached).
    • Sigmoid surrogate → continuous spot-probability map (autograd path).
  4. Accumulate spot pixels into a bit-packed SpotsInfo.bin mmap (drop-in compatible with FitOrientationOMP / simulateNF).

Phases 1, 2, and 3 (filtered, log_response, soft spot probability) are differentiable end-to-end. Connected components and the binary SpotsInfo.bin output are computed on detached tensors and do not participate in autograd.

Quickstart

from midas_nf_process_images import ProcessParams, ProcessImagesPipeline

params = ProcessParams.from_paramfile("ps.txt")
pipe = ProcessImagesPipeline(params, device="cuda")  # or "mps", "cpu"
spots = pipe.process_layer(layer_nr=1)
spots.write("/path/to/SpotsInfo.bin")

# Or all layers in one call:
spots = pipe.process_all(layers=range(1, params.n_distances + 1))

CLI

midas-nf-process-images <ParameterFile> <LayerNr> [--device cuda] [--dtype fp32] [--n-cpus 8]

Behaves like the C ProcessImagesCombined executable.

Backend selection

Same contract as midas-transforms:

  • Default device: cuda -> mps -> cpu.
  • Default dtype: float64 on CPU, float32 on CUDA/MPS.
  • Override with device= / dtype= kwargs, or env vars MIDAS_NF_PROCESS_IMAGES_DEVICE / MIDAS_NF_PROCESS_IMAGES_DTYPE.

Differentiability

The end-to-end pipeline produces three tensors:

  • filtered: median-subtracted, spatial-median-filtered image (autograd).
  • log_response: LoG convolution output (autograd).
  • spot_prob: soft, continuous spot-probability map via sigmoid(L / temperature) (autograd).

Plus one detached output:

  • labels: integer connected-component IDs from hard-thresholded LoG (no gradient).

Optimization through the discrete spot mask uses the soft spot_prob surrogate.

Project links

Data verified by PyPI on Aug 4, 2026
Data provided by the project maintainers, verified at the time the release was uploaded to PyPI.

Key dates

PyPI data
Data sourced directly from PyPI's database.
  • Released:
Newer release available (0.7.1)

1 maintainer

PyPI data
Data sourced directly from PyPI's database.
Avatar for marinerhemant from gravatar.com marinerhemant

Credits

Author: Hemant Sharma

GitHub Statistics

Data verified by PyPI on Aug 4, 2026
The GitHub source repository was provided by the project maintainers and verified by PyPI at the time of upload. Stars, forks, and open issues/PRs are derived from that repository and have not been independently verified.

License expression

BSD-3-Clause
View SPDX License List

Requires

Python >=3.9

Provides Extra

dev parity

Tags

MIDAS NF-HEDM diffraction PyTorch differentiable image processing peak detection near-field preprocessing hex grid tomography

Classifiers

Report project as malware

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

midas_nf_preprocess-0.5.1.tar.gz (82.5 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

midas_nf_preprocess-0.5.1-py3-none-any.whl (100.6 kB view details)

Uploaded Python 3

File details

Details for the file midas_nf_preprocess-0.5.1.tar.gz.

File metadata

  • Download URL: midas_nf_preprocess-0.5.1.tar.gz
  • Upload date:
  • Size: 82.5 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? Yes
  • Uploaded via: twine/7.0.0 CPython/3.13.14

File hashes

Hashes for midas_nf_preprocess-0.5.1.tar.gz
Algorithm Hash digest
SHA256 5865e737112eb2fd2ab073f177a45ee67dfae2c2862bcecc27b6c4e2cf18c949
MD5 b1b6326c7c5ab5fa1642ac768bd24cd5
BLAKE2b-256 89af60176ce29da9aa6145cf5243f141f1dfb20becb7cad0623ff0dd9863ecc1

See more details on using hashes here.

Provenance

The following attestation bundles were made for midas_nf_preprocess-0.5.1.tar.gz:

Publisher: python-packages.yml on marinerhemant/MIDAS

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

File details

Details for the file midas_nf_preprocess-0.5.1-py3-none-any.whl.

File metadata

File hashes

Hashes for midas_nf_preprocess-0.5.1-py3-none-any.whl
Algorithm Hash digest
SHA256 da71e72c99a60e66e0515190aa936b3a1d852586c1e480f34a75a5c166b314b3
MD5 0cf1123c27bcf0168b2e8d921914a73d
BLAKE2b-256 2345eb6f3aa3ed609d4e6938ed270db716bd793b3144e179dcde0834e252d375

See more details on using hashes here.

Provenance

The following attestation bundles were made for midas_nf_preprocess-0.5.1-py3-none-any.whl:

Publisher: python-packages.yml on marinerhemant/MIDAS

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

Release history Release notifications | RSS feed

0.7.1

2 files

0.7.0

2 files

0.6.1

2 files

0.6.0

2 files

This release

0.5.1 This release

2 files

0.5.0

2 files

0.4.0

2 files

0.3.0

2 files

0.2.0

2 files

0.1.2

2 files

0.1.1

2 files

0.1.0

2 files

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page