Usage

Supported interfaces

McSAS3 currently supports three maintained user-facing paths:

  • 1D CLI optimization via mcsas3-runner

  • histogramming of stored results via mcsas3-histogrammer

  • canonical Python workflows built on ProcessingData and DataBundle

CLI workflows

The CLI uses YAML configuration files:

  • a read configuration for source data

  • a run configuration for the optimizer

  • a histogram configuration for post-processing

See Quickstart for a minimal example command sequence.

Python workflows

New scripts and notebooks should use the top-level canonical workflow API:

from mcsas3 import (
    load_result_processing_data,
    optimize_processing_data,
    prepare_1d_processing_data,
    prepare_1d_processing_data_from_file,
    prepare_2d_processing_data,
    prepare_2d_processing_data_from_file,
    selected_bundle_from_processing,
)

Result files

McSAS3 result files now store canonical ProcessingData at:

  • /analyses/MCResult*/mcdata/processingData

The maintained load/store helpers are:

  • load_result_processing_data(...)

  • store_result_processing_data(...)

Reusable preprocessing helpers

Canonical preprocessing helpers live in mcsas3.preprocessing:

  • clipping

  • omission

  • 1D rebinning

  • 2D reconstruction from clipped bundles

See also