v1.0.9

In this new release we make the numcosmo dependency as optional to avoid conflict with other packages and make crow compatible with other DESC tools` dependencies. Thus, all functions that uses 'NumcosmoIntegrator' will no longer work if numcosmo is not installed.

Mainly, ExactBinnedClusterRecipe is not supported if the numcosmo package is not installed. Also the MurataBinned class will fail without the numcosmo integration.

v1.0.2-2

Added documentation to repository

v1.0.2-1

New package name changed to lsstdesc-crow in pipy and conda

v1.0.1

In this new release, purity option was added to the ExactBinnedClusterRecipe. Also, imports from the library were fixed.

v1.0.0

v1.0.0 — 2025-12-04

Overview:

crow is a standalone Python package and DESC tool developed as part of the LSST-DESC collaboration for cluster observables evaluation. It provides a modular code base for cluster modeling, enabling computation of cluster abundance, cluster lensing profiles, and flexible recipe definitions for analysis of galaxy clusters.

This release packages the core clustering functionality currently present in the repository, providing a clean, ready-to-use set of tools for cluster modeling and analysis.

What’s included in this release:

• Cluster modeling module: crow/models/cluster/ — encompasses cluster properties, profiles, mass-observable relations, selection functions, and modeling routines. Includes all code necessary to compute Cluster Lensing or Cluster Abundance.
• Cluster properties configuration: crow/properties.py allows users to specify which cluster properties to compute.
• Flexible recipe options: integration-based and grid-interpolation recipes are available in crow/recipes/. Integration recipes perform full profile integrals; grid recipes offer faster computation alternatives.
• Example scripts / test infrastructure: includes example setups (grid vs integration recipes) and tests, enabling users to validate computations and run sample configurations.

Known limitations & future work:

• Documentation is minimal: no comprehensive API reference, usage examples, or tutorials — this will be addressed in future updates.
• The purity option does not work with the integration-based recipe (ExactBinnedClusterRecipe).
• The miscentering option does not work with the vectorized shear computation and is not fully supported for all cluster configurations, i.e., should not be used in the recipes.
• Plans for future releases include expanded examples, detailed documentation, and more robust testing.

Installation:

pip install desc-crow

or

conda install -c conda-forge desc-crow

Acknowledgments:

This first release exists thanks to the contributions of @m-aguena, @eduardojsbarroso, @vitenti, @marcpaterno, @caioolivv, @henriquelettieri, @yyzhang, @beckermr, and others.

Notes:

If you make use of crow in your work, please cite this repository: https://github.com/LSSTDESC/crow