CRISPy Documentation

CRISPy (Computational Ridge Identification with SCMS for Python) is a Python library for identifying density ridges in multidimensional space.

Overview

CRISPy uses the Subspace Constrained Mean Shift (SCMS) algorithm to identify density ridges (e.g., filaments) in multidimensional data, designed with a focus on scientific and astrophysical usage in 2D and 3D. CRISPy’s main features include:

• Efficient implementation of the SCMS algorithm for Python.

• Tools for gridding, pruning, and refining ridge results.

• Flexibility to tune parameters for domain-specific applications.

• Seamless integration with popular formats like .fits for input and output.

Citation

When publishing with CRISPy, please cite the following for the software:

1. Chen, M. C.-Y., et al. “Velocity-Coherent Filaments in NGC 1333: Evidence for Accretion Flow?” ApJ (2020).

and the following for the statistical framework:

2. Chen, Y.-C., et al. “Generalized Mode and Ridge Estimation.” (2014).

Navigation

To get started quickly, please see the Install and Quick Start pages. There is also a navigation bar at the top for general exploration, including, Tutorials, Guides, and API Reference.