Computational Ridge Identification with SCMS for Python
Project description
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.
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Filter files by name, interpreter, ABI, and platform.
If you're not sure about the file name format, learn more about wheel file names.
Copy a direct link to the current filters
File details
Details for the file crispy_learn-1.3.1.tar.gz.
File metadata
- Download URL: crispy_learn-1.3.1.tar.gz
- Upload date:
- Size: 79.4 kB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/6.1.0 CPython/3.12.8
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
6c108f3a1ec35d8dd5e5a8e03e628d03a90f6def2429c8e406734e3abf085472
|
|
| MD5 |
41d82d0d425c6dbcb4b1e2c2a40edfb6
|
|
| BLAKE2b-256 |
9343a6a3fa092c39990edc59b00108f51d97b94cb383a92a23cc652e591a2db0
|
File details
Details for the file crispy_learn-1.3.1-py3-none-any.whl.
File metadata
- Download URL: crispy_learn-1.3.1-py3-none-any.whl
- Upload date:
- Size: 70.6 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/6.1.0 CPython/3.12.8
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
3f73f70b30b17b3da8d9b93c5ac13c787a326563b051e9fd6f1315a18db48f0a
|
|
| MD5 |
49268ec4a011bce9db743e7eae7a33bb
|
|
| BLAKE2b-256 |
9cd4d9e47d6be26c4c1628b7d3aed0f47eaa47509b0975f2d2ae359e91b03285
|