probabilistic-piping

Python package for probabilistic piping calculations

https://github.com/hkv-products-services/probabilistic_piping

Science Score: 44.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • CITATION.cff file
    Found CITATION.cff file
  • codemeta.json file
    Found codemeta.json file
  • .zenodo.json file
    Found .zenodo.json file
  • DOI references
  • Academic publication links
  • Academic email domains
  • Institutional organization owner
  • JOSS paper metadata
  • Scientific vocabulary similarity
    Low similarity (7.5%) to scientific vocabulary
Last synced: 9 months ago · JSON representation ·

Repository

Python package for probabilistic piping calculations

Basic Info
Statistics
  • Stars: 0
  • Watchers: 3
  • Forks: 0
  • Open Issues: 0
  • Releases: 3
Created over 1 year ago · Last pushed over 1 year ago
Metadata Files
Readme License Citation

README.md

Probabilistic piping

This package contains core functionality for probabilistic piping calculations, utilizing OpenTURNS, pandas, numpy and tqdm. Probabilistic piping is developed by HKV and is published under the GNU GPL-3 license.

Installing the package

In case you just want to use the package, simply install the package from pypi:

run pip install probabilistic_piping

Setting up a development environment

In case you want to actively contribute to the source code, you need to set up a development environment. This can be done by installing Pixi.

Once Pixi is installed, checkout the source code from the repository and run the pixi command from the root of the source code:

bash pixi install

Owner

  • Name: HKV-products-services
  • Login: HKV-products-services
  • Kind: organization

Citation (CITATION.cff)

# YAML 1.2
---
authors:
  -
    affiliation: "HKV lijn in water"
    family-names: Dupuits
    given-names: Guy
  -
    affiliation: "HKV lijn in water"
    family-names: Nicolai
    given-names: Robin
  -
    affiliation: "HKV lijn in water"
    family-names: Pol
    given-names: Joost
  -
    affiliation: "HKV lijn in water"
    family-names: Haasnoot
    given-names: David

cff-version: "0.0.1"
keywords:
  - Waterveiligheid
license: "GPL-3.0-or-later"
message: "If you use this software, please cite it using these metadata."
title: Probabilistic piping

GitHub Events

Total
  • Create event: 6
  • Release event: 4
  • Issues event: 3
  • Delete event: 1
  • Issue comment event: 1
  • Member event: 2
  • Push event: 55
Last Year
  • Create event: 6
  • Release event: 4
  • Issues event: 3
  • Delete event: 1
  • Issue comment event: 1
  • Member event: 2
  • Push event: 55

Issues and Pull Requests

Last synced: 10 months ago

All Time
  • Total issues: 1
  • Total pull requests: 0
  • Average time to close issues: 11 days
  • Average time to close pull requests: N/A
  • Total issue authors: 1
  • Total pull request authors: 0
  • Average comments per issue: 1.0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 1
  • Pull requests: 0
  • Average time to close issues: 11 days
  • Average time to close pull requests: N/A
  • Issue authors: 1
  • Pull request authors: 0
  • Average comments per issue: 1.0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
Top Authors
Issue Authors
  • Daafip (1)
Pull Request Authors
Top Labels
Issue Labels
Pull Request Labels

Packages

  • Total packages: 1
  • Total downloads:
    • pypi 18 last-month
  • Total dependent packages: 0
  • Total dependent repositories: 0
  • Total versions: 4
  • Total maintainers: 2
pypi.org: probabilistic-piping

Python package for probabilistic piping calculations

  • Versions: 4
  • Dependent Packages: 0
  • Dependent Repositories: 0
  • Downloads: 18 Last month
Rankings
Dependent packages count: 9.8%
Forks count: 32.0%
Average: 34.7%
Stargazers count: 41.8%
Dependent repos count: 55.3%
Maintainers (2)
Last synced: 10 months ago

Dependencies

src/pyproject.toml pypi
  • numpy *
  • openturns >=1.23
  • pandas *
  • tqdm *