mlgidbase 0.1.1

A comprehensive toolbox for grazing-incidence diffraction

mlgidBASE

mlgidBASE is a Python package for machine learning–driven analysis of grazing-incidence wide-angle X-ray scattering (GIWAXS) data. It provides a full workflow from peak detection to matching with known crystal structures.

The package builds on the following components:

• pygid — for detector image conversion
• mlgidDETECT — for peak detection
• pygidFIT — for two-dimensional peak fitting
• mlgidMATCH — for matching experimental peaks to known structures

---

Key Features

• Initialization
  Can be created from a pygid.Conversion object or loaded directly from a NeXus file.

• Methods
  Provides functions for:

  • Peak detection
  • Peak fitting
  • Peak matching

• Visualization
  Supports visualization at all stages of the analysis pipeline.

• Peak Adjustment
  Includes functions to add or delete peaks, either interactively or programmatically.

• Data Access
  Enables retrieving analysis results from the NeXus file for further processing.

---

Installation

Install using pip

pip install mlgidbase

Install from source

First, clone the repository:

git clone https://github.com/mlgid-project/mlgidBASE.git

Then navigate to the project directory and install it in editable mode:

cd mlgidBASE
pip install -e .

---

How to Use

For full details, see the dedicated tutorials.

Quick Start

from mlgidbase import mlgidBASE

# Initialize analysis from a NeXus file
filename = r'./example/BA2PbI4.h5'
analysis = mlgidBASE(filename=filename)

# Run peak detection
analysis.run_detection()

# Run peak fitting
analysis.run_fitting()

# Run peak matching with preprocessed CIFs
analysis.run_matching(
    cif_prepr='./example/prepr_cifs.pickle'
)

Data Format

The structure of the analysis results saved in the NeXus file is documented in the output file format guide.

It describes how entries, frames, and peak information are stored for further inspection or processing.