Dispersive_XAS

Dispersive_XAS is a Python package for dispersive X-ray absorption spectroscopy (DXAS) data reduction. It covers the common APS-style workflow from raw areaDetector HDF5 files to calibrated spectra, interactive ROI selection, batch preview HTML files, and time-resolved transition diagnostics.

The installable package name is dispersive-xas; the Python import name remains Dispersive_XAS for compatibility with existing notebooks and legacy analysis scripts.

What is included

• HDF5, NeXus, Bluesky, and legacy processed-data loaders.
• Raw detector preprocessing into transmission and absorption maps.
• Horizontal and tilted-band ROI tools, including notebook editors.
• Spectrum extraction, normalization, interpolation, peak finding, and edge point interpolation.
• Pixel-to-energy calibration from a reference foil and standard spectrum.
• Chunked calibration for large scans that cannot be loaded fully into memory.
• Interactive Plotly HTML previews and summary dashboards for batch workflows.
• Crystal geometry calculators used for DXAS beamline design checks.

Installation

From this repository:

python -m pip install -e .

For notebook and interactive ROI widgets:

python -m pip install -e ".[notebook]"

For development and tests:

python -m pip install -e ".[dev,notebook]"
pytest

Quick Start

import Dispersive_XAS as dxas

roi = dxas.make_tilted_band_roi(
    shape=(512, 2048),
    left_center_row=190,
    right_center_row=210,
    half_width=35,
)

calibration = dxas.load_calibration_model("calibration.json")

result = dxas.apply_calibration_to_scan(
    data_path="scan.h5",
    flat_path="flat.h5",
    calibration=calibration,
    roi=roi,
)

For interactive tilted-band ROI selection in a notebook:

editor = dxas.select_tilted_band_roi(mux_image, save_path="roi.json")
roi = editor.get_spec()

For a full large-quantity workflow:

from pathlib import Path

import Dispersive_XAS as dxas

cfg = dxas.BatchAnalysisConfig(
    data_dir=Path("/path/to/data"),
    scan_file="20251011_1915_sample_001.h5",
    foil_file="20251011_1857_Cu_foil_002.h5",
    roi=roi,
    analysis_dirname="analysis_run",
)

result = dxas.run_large_quantity_analysis(cfg, make_previews=True)
print(result["summary_dashboard_html"])

Documentation

• Workflow guide: practical recipes for ROI selection, preprocessing, calibration, scan application, and large-batch analysis.
• API reference: public functions/classes grouped by task and module.

Data conventions

• Image stacks use shape (frames, rows, columns).
• A single image uses shape (rows, columns).
• Spectra use shape (2, N) where row 0 is the pixel/energy axis and row 1 is the intensity axis.
• ROI specs are dictionaries. The most common forms are:

{"kind": "row_range", "row_start": 155, "row_stop": 235}

{
    "kind": "tilted_band",
    "center_row_at_col0": 190.0,
    "slope_per_col": 0.01,
    "half_width": 35.0,
}

Package layout

• Dispersive_XAS.core: numerical loading, preprocessing, ROI, spectra, calibration, and crystal calculations.
• Dispersive_XAS.web: Plotly and ipywidgets helpers for interactive viewing.
• Dispersive_XAS.batch: high-level large-quantity analysis orchestration.
• Dispersive_XAS.progress: console and JSON progress reporting.

Repository Notes

• Keep raw detector data (.h5, .hdf5, .nxs) outside git.
• Generated preview HTML, CSV, JSON, and analysis folders should stay untracked.
• The example/ folder contains a workflow snapshot, not raw data.