Skip to main content
Pre-release

This release is a pre-release and may not be stable for production use.

PyEXAFS

Test Status code style: black imports: isort

Developed by Frank Foerste and Sebastian Paripsa, PyExafs aims to streamline the workflow
for XAS researchers, from data acquisition to final analysis.

Introduction

PyExafs is designed to facilitate the automated quality assessment
of X-ray Absorption Fine Structure (XAFS) measurements. Leveraging the robust capabilities of the Larch library,
PyExafs enables researchers to efficiently process, analyse, and visualise XAFS data, ensuring adherence to stringent quality criteria.

With a single execution, PyExafs provides a fast analysis of a dataset, instantly delivering:

  • Absorbance
  • Normalized Absorbance
  • $\chi(k)$
  • $\chi(R)$

These results are presented according to well-defined quality standards, enabling users to quickly evaluate
the integrity of their data.

Key features include:

  • Automated Quality Control: Implements rigorous routines to verify the quality of XAFS measurements.
  • Data Preprocessing: Efficiently handles data loading, preprocessing, and energy calibration.
  • Visualisation: Generates comprehensive plots for raw, normalized, k-space, and R-space data,
    supporting both detailed analysis and publication-quality figures.
  • Noise Estimation and Fitting: Provides tools for estimating noise and fitting the first shell,
    crucial for accurate data interpretation.

Installation

Easy:

python -m pip install pyexafs

Usage

To use the pyexafs package, run the following command and provide the path to your data file:

python -m pyexafs <path_to_data_file>

For example:

python -m pyexafs /path/to/your/datafile.txt

Metadata

Release files for pyexafs 1.0.0b9

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for pyexafs 1.0.0b9
File Size Uploaded Provenance Actions
pyexafs-1.0.0b9.tar.gz 332.8 kB Without provenance Details for pyexafs-1.0.0b9.tar.gz

Built distribution (wheel)

Table of built distributions (wheels) for pyexafs 1.0.0b9
File Interpreter ABI Platform Actions
pyexafs-1.0.0b9-py3-none-any.whl Python 3 none any Details for pyexafs-1.0.0b9-py3-none-any.whl

Total release size: 662.4 kB

Release files / pyexafs-1.0.0b9.tar.gz

Download URL pyexafs-1.0.0b9.tar.gz
Size 332.8 kB
Tags Source
SHA-256 checksum
How to use checksums
645373be13b6765014b2905d3211544582b1b0da0d15a2836b9b80a7441ea460
BLAKE2b-256 checksum
How to use checksums
80522aa7b2303b78482f2972997c4b2bbeaf49cf2acfd136dc7d5982b59f9f99
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/5.1.1 CPython/3.10.11

Release files / pyexafs-1.0.0b9-py3-none-any.whl

Download URL pyexafs-1.0.0b9-py3-none-any.whl
Size 329.7 kB
Tags Python 3
SHA-256 checksum
How to use checksums
fccfd5aaf74745cf9c8bc1f42c0949988420817d9b297666eb6fa1fbc6ececf6
BLAKE2b-256 checksum
How to use checksums
24902b0ab14249133027ad0166e598919991112e8e84b8ca13fc8672ad993c8a
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/5.1.1 CPython/3.10.11

Security for pyexafs 1.0.0b9

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

No provenance found

None of the files in this release were uploaded with provenance metadata.

A note on package security

PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.

To learn more about protecting yourself from malicious actors, visit our package security documentation.

Release history Release notifications | RSS feed

This release
Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page