Skip to main content

curvicost

Name reserved. The full release accompanies a forthcoming publication.

curvicost scores curvilinear segmentations — vessels, neurites, airways, cracks — against the functional cost of their errors rather than overlap alone. Overlap metrics answer "how many voxels did you get right"; for these structures that is usually not the question being asked.

This version is a placeholder that reserves the name. It contains no implementation. The working release will provide:

  • score() — overlap and topology metrics alongside measured functional costs (traceable length, Poiseuille conductance, self-perfusion)
  • perturb() — five leak-tested graded error operators
  • a command line interface

Scope: 2D and 3D binary curvilinear masks.

MIT licensed.

Metadata

Release files for curvicost 0.0.1

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for curvicost 0.0.1
File Size Uploaded Provenance
curvicost-0.0.1.tar.gz 2.2 kB Without provenance Details

Built distribution (wheel)

Table of built distributions (wheels) for curvicost 0.0.1
File Interpreter ABI Platform
curvicost-0.0.1-py3-none-any.whl Python 3 none any Details

Total release size: 5.5 kB

Release files / curvicost-0.0.1.tar.gz

Download URL curvicost-0.0.1.tar.gz
Size 2.2 kB
Tags Source
SHA-256 checksum
How to use checksums
dbbe13819f31826eee26c63d87188e1aa1b89f0f123a4b5e0dc809b1668c5be6
BLAKE2b-256 checksum
How to use checksums
5e0c3d315dd6d2f18c210888001a9b884851bc9d8fa355ee52cba28988c0114b
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/7.0.0 CPython/3.14.6

Release files / curvicost-0.0.1-py3-none-any.whl

Download URL curvicost-0.0.1-py3-none-any.whl
Size 3.4 kB
Tags Python 3
SHA-256 checksum
How to use checksums
b7ea2b78197844d65a30d643c5f0f727761795325fe72fa79f9c51897be6925d
BLAKE2b-256 checksum
How to use checksums
41738044730305b0d9eecc285dbc0f8e34f2b17a1f28a89f7dc7d6e3bf89ed5b
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/7.0.0 CPython/3.14.6

Security for curvicost 0.0.1

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

No provenance found

None of the files in this release were uploaded with provenance metadata.

A note on package security

PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page