xpcs-correlator 0.1.1

XPCS correlation calculations for synchrotron experiments

xpcs-correlator

Table of contents

• About
• Documentation
• Features
• Requirements
• Installation
• Quickstart
• Configuration / Logging
• Tests
• Contributing
• License
• Contact

About

This package consolidates ongoing development of correlators for XPCS data analysis at ESRF, with a focus on the ID02 and ID10-coh beamlines.

Documentation

The documentation is hosted online: https://mj.gitlab-pages.esrf.fr/xpcs_developments/xpcscorr/

Features

• Dense frames data reference and chunked correlator implementations.
• Calculates g2, g2 errors, and ttcf (2-time correlation function).
• The ttcf calculations support linear binning for t1,t2 format and hybrid linear log binning for age,lag format.
• Designed to handle large frame stacks via chunked (partitioned) processing.
• Supports Dask for both cluster (SLURM) and local parallel execution

Requirements

• Python 3.10+ (recommended: 3.10, 3.11, 3.12)
• numpy
• dask
• dask_jobqueue
• h5py
• hdf5plugin
• threadpoolctl

Installation

Install in editable/develop mode (recommended during development):

pip install -e .

Install with development extras (for running tests and linters):

pip install -e .[dev]

When published to PyPI the package can be installed with:

pip install xpcs-correlator

Quickstart

For a step-by-step walkthrough with examples and runnable code, see the Tutorial in the online documentation: Quickstart tutorial.

Basic usage example — adapt to your data shape and correlator options:

import numpy as np
from xpcscorr import correlator_dense_reference, correlator_dense_chunked

# Replace with your frames array; shape here is (n_frames, nx_pixels, ny_pixels)
frames = np.random.random((100,512, 512))
roimask= np.ones((512,512), dtype=bool)

# Run reference  correlator
result_ref = correlator_dense_reference(frames, roimask)

# Run chunked correlator (handles large data in chunks)
extra_options = {'chunks_N': 3}
result_chunked = correlator_dense_chunked(frames, roimask, extra_options=extra_options)

print(type(result_ref), type(result_chunked))

Notes:

• Replace the synthetic frames with your real dataset (HDF5 dataset or numpy array).
• Check correlator function docstrings for exact argument names and options.

Configuration / Logging

Control logging with environment variables used by the package (see src/xpcscorr/__init__.py):

• XPCSCORR_LOG_TO_CLI — set to 1 to enable logging to stdout (default in development)
• XPCSCORR_LOG_TO_FILE — set to 1 to enable logging to a file named xpcscorr.log

Example:

export XPCSCORR_LOG_TO_CLI=1
export XPCSCORR_LOG_TO_FILE=0

Tests

Run tests with pytest:

pip install -e .[dev]
pytest -q

There are unit tests under tests/ that exercise correlator behavior and core utilities.

Contributing

• Open issues for bugs or feature requests.
• Fork the repo, create a feature branch, add tests, and submit a pull request.
• Keep changes small, document API changes, and add tests for new behavior.

License

This project is licensed under the MIT License — see the LICENSE file for details.

Contact

Maintainer: Maciej Jankowski — [e-mail]