midas-suite 0.3.12

Meta-package: installs the MIDAS Python pipeline (FF/NF/PF HEDM, calibration, peakfit, integration, transforms, ODF, stress).

midas-suite

A meta-package that installs the MIDAS Python pipeline in a single command.

pip install midas-suite

This pulls in the currently published MIDAS sub-packages — the FF/NF HEDM analysis chain, calibration, peak fitting, radial integration, forward model, indexing, transforms, grain processing, and stress/strain analysis.

midas-suite itself contains no scientific code. It's a thin meta-package whose only job is to declare the sub-packages as dependencies so users don't have to install them one at a time.

What you get

pip install midas-suite installs 19 sub-packages (as of v0.3.1):

Top-level orchestrators (the entry points most users want):

Sub-package	Role
midas-pipeline	Unified FF + PF orchestrator (--scan-mode {ff,pf,auto}). End-to-end from raw data through grain reconstruction; single source for both scan modes.
midas-ff-pipeline	Independent FF-HEDM workflow orchestrator (1-N detectors). Co-exists with midas-pipeline; same kernels under the hood.
midas-nf-pipeline	Pure-Python NF-HEDM pipeline orchestrator (single + multi-resolution, multi-layer). Drop-in for nf_MIDAS.py / nf_MIDAS_Multiple_Resolutions.py.
midas-parsl-configs	Bundled + user-extensible Parsl configs for running MIDAS pipelines on laptops, workstations, clusters.

FF-HEDM building blocks:

Sub-package	Role
midas-peakfit	Differentiable PyTorch peak fitting for FF-HEDM Zarr
midas-transforms	FF-HEDM peak transforms (merge / radius / fit-setup / save-bin)
midas-index	Pure-Python/PyTorch FF-HEDM indexer (drop-in for IndexerOMP)
midas-fit-grain	Single/multi-grain refiner
midas-process-grains	FF-HEDM grain-determination + strain pipeline

NF-HEDM building blocks:

Sub-package	Role
midas-nf-preprocess	NF-HEDM preprocessing (hex grid, tomo filter, spot prediction)
midas-nf-fitorientation	NF-HEDM orientation/calibration fitter

Shared foundations:

Sub-package	Role
midas-stress	Crystallographic stress/strain analysis (Voigt-Mandel, Cij inversion, slip/Schmid/Taylor)
midas-params	Parameter-file registry, validator, wizard for FF/NF/PF/RI
midas-hkls	Pure-Python crystallography & HKL list generator (sginfo-equivalent)
midas-diffract	End-to-end differentiable HEDM forward model (FF + NF + pf-HEDM)
midas-integrate	Pure-Python radial integration (DetectorMapper + CSR + streaming server)
midas-integrate-v2	Differentiable, autograd-clean integration kernels (torch); companion to v1
midas-calibrate	Native Python/Torch detector geometry calibration (LM-based)
midas-calibrate-v2	Torch-native Bayesian/Laplace calibration (LM + L-BFGS); companion to v1

You then import midas_stress, import midas_diffract, etc. directly — each sub-package retains its own API. midas-suite does not re-export them.

To check what was installed:

import midas_suite
print(midas_suite.installed())

Modality bundles

If you don't want everything, the optional extras let you pick a workflow:

pip install "midas-suite[ff]"        # FF-HEDM stack
pip install "midas-suite[pf]"        # PF-HEDM stack (scanning / point-focus)
pip install "midas-suite[nf]"        # NF-HEDM stack
pip install "midas-suite[calib]"     # v1 calibration + integration
pip install "midas-suite[calib-v2]"  # v2 (torch differentiable) calibration + integration
pip install "midas-suite[ff,plots]"

Extra	What it pulls
ff	midas-ff-pipeline (transitively pulls hkls, peakfit, transforms, index, fit-grain, process-grains, diffract, parsl-configs) + stress, params, calibrate, integrate
pf	midas-pipeline[fast] (numba) + stress, params, calibrate, integrate (scan-mode pf pulls index + fit-grain + transforms + stress transitively)
nf	midas-nf-pipeline (transitively pulls hkls, stress, nf-preprocess, nf-fitorientation) + params
calib	hkls, integrate, peakfit, calibrate (v1 C-backed stack)
calib-v2	hkls, calibrate-v2, integrate-v2, peakfit (torch differentiable stack)
plots	matplotlib (for sub-package plotting helpers)

What pip install midas-suite does NOT include

Be aware:

• The MIDAS C executables (IndexerOMP, ProcessGrains, MakeDiffrSpots, …) still need to be built from source via cmake --build . from the MIDAS monorepo. The pure-Python pipeline (calibration → integration → indexing → grain processing) is now end-to-end in PyTorch and does not require them.
• The PyQt FF viewer GUI needs PyQt5 or PySide6 installed separately. Not declared here because it's optional and platform-sensitive.
• Optional crystallography backends for midas-hkls: install gemmi or pycifrw separately for CIF I/O via pip install midas-hkls[cif].
• GPU acceleration is a runtime backend selected by PyTorch device string. CUDA/MPS just work if your torch install supports them; no separate *-gpu package needed.
• In-tree-only packages (midas-grain-odf, midas-joint-ff-calibrate, midas-pf-odf, midas-pink, midas-propagate, midas-uq) are intentionally not published to PyPI — they live in the monorepo for ongoing research and only build / install from a local checkout.

Cross-platform

All MIDAS Python sub-packages are pure Python or PyTorch and ship as py3-none-any wheels. Tested install paths: Linux, macOS, Windows. See packages/RELEASE_READINESS.md for the detailed cross-platform readiness matrix.

Versioning

midas-suite versions are independent of the sub-package versions. The rule:

Change	Bump
Floors tightened (no new sub-package added)	patch (0.1.0 → 0.1.1)
New sub-package added to the dep list	minor (0.1.0 → 0.2.0)
Backwards-incompatible reorganisation of bundles	major (0.x.y → 1.0.0)

Floors are pinned with >=, never ==, so a sub-package patch release doesn't break midas-suite.

Releasing a new version

See RELEASING.md for the full release flow. TL;DR:

cd packages/midas_suite
./release.sh 0.3.2 --publish

License

BSD-3-Clause, same as the sub-packages.