CRISPy
Computational Ridge Identification with SCMS for Python
Overview
CRISPy is a Python library for identifying density ridges in multidimensional data using the Subspace Constrained Mean Shift (SCMS) algorithm. While tailored for astrophysics, it offers versatile 2D and 3D post-processing tools, including gridding and skeletonization of results in image space.
Documentation
Visit CRISPy's documentation on Read the Docs (RTD) for detailed information on instructions, usage examples, and API details.
Quick Install
To install the latest version of CRISPy, clone this repository and run the following in your local directory:
git clone https://github.com/mcyc/crispy.git
cd crispy
pip install -e .
For more details, please visit CRISPy's documentation.
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Metadata
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Release files for crispy-learn 1.3.1
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Source distribution (sdist)
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| crispy_learn-1.3.1.tar.gz | 79.4 kB | Details |
Built distribution (wheel)
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|---|---|---|---|---|
| crispy_learn-1.3.1-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 150.0 kB
Release files / crispy_learn-1.3.1.tar.gz
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Release files / crispy_learn-1.3.1-py3-none-any.whl
| Download URL | crispy_learn-1.3.1-py3-none-any.whl |
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| Tags | Python 3 |
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