SPICY
SPICY: a Python toolbox for meshless assimilation from image velocimetry using radial basis functions - Published in JOSS (2024)
Science Score: 95.0%
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Found 1 DOI reference(s) in JOSS metadata -
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Published in Journal of Open Source Software
Scientific Fields
Repository
The repository contains the codes linked to the SPICY project (Super-resolution and Pressure from Image Velocimetry)
Basic Info
- Host: GitHub
- Owner: mendezVKI
- Language: Jupyter Notebook
- Default Branch: main
- Size: 620 MB
Statistics
- Stars: 11
- Watchers: 1
- Forks: 4
- Open Issues: 0
- Releases: 1
Metadata Files
README.md
SPICY_VKI
Installation
You can install spicy via pip:
pip install spicy_vki
This will install the package with all its mandatory dependencies, namely:
"numpy>=1.20",
"scikit-learn>=1.0",
"ipython>=7.16.1",
"scipy>=1.5",
"shapely>=1.7.0",
"matplotlib>=3.3.0",
The turotials are available in the github repository SPICY_VKI.
SPICY
The repository contains the codes linked to the SPICY project (Super-resolution and Pressure from Image veloCimetrY).
SPICY is a software developed at the von Karman Institute to perform data assimilation of image velocimetry using constrained Radial Basis Functions (RBF). The framework works for structured data (as produced by cross-correlation-based algorithms in PIV or Optical FlowS) and unstructured data (produced by tracking algorithms in PTV).
While the main scope is the assimilation of velocity fields, SPICY can also be used for the regression of other fields (e.g., temperature fields). The theoretical foundation of the constrained RBF approach is described in - P. Sperotto, S. Pieraccini, M.A. Mendez, A Meshless Method to Compute Pressure Fields from Image Velocimetry, Measurement Science and Technology 33(9), May 2022. (pre-print at https://arxiv.org/abs/2112.12752).
The GitHub folder contains four tutorials. These include regression of synthetic velocity fields as well as the solution of Poisson problems.
The documentatation can be found here: https://spicy-vki.readthedocs.io/en/latest/index.html
The list of proposed exercises is following:
1 - Solution of a Laplace problem on the unit square.
2 - Regression of the velocity field of a 2D Lamb-Oseen vortex.
3 - Regression of the velocity field and integration of the Poisson equation for the 2D flow past a cylinder.
4 - Regression of the velocity field and integration of the Poisson equation for the 3D Stokes flow past a sphere.
Tutorials 2 - 4 are taken from the article from Sperotto et al. (2022) https://arxiv.org/abs/2112.12752
Owner
- Login: mendezVKI
- Kind: user
- Company: von Karman Institute for Fluid Dynamics
- Repositories: 2
- Profile: https://github.com/mendezVKI
JOSS Publication
SPICY: a Python toolbox for meshless assimilation from image velocimetry using radial basis functions
Authors
The von Karman Institute for Fluid Dynamics (VKI), Rhode St. Genese, 1640, Belgium
The von Karman Institute for Fluid Dynamics (VKI), Rhode St. Genese, 1640, Belgium
The von Karman Institute for Fluid Dynamics (VKI), Rhode St. Genese, 1640, Belgium
Editor
Philip Cardiff
Tags
Python Radial Basis Functions Super resolution in Image Velocimetry Data Assimilation in Image Velocimetry Poisson EquationGitHub Events
Total
- Watch event: 1
- Push event: 4
Last Year
- Watch event: 1
- Push event: 4
Committers
Last synced: 9 months ago
Top Committers
| Name | Commits | |
|---|---|---|
| mendezVKI | m****z@v****e | 67 |
| ManuelRatz | m****z@t****e | 67 |
| ManuelRatz | 7****z | 15 |
| Philip Cardiff | p****f@g****m | 3 |
| Theo Käufer | 5****r | 1 |
| Kyle Niemeyer | k****r@f****m | 1 |
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: 4 months ago
All Time
- Total issues: 5
- Total pull requests: 6
- Average time to close issues: 20 days
- Average time to close pull requests: 17 days
- Total issue authors: 1
- Total pull request authors: 4
- Average comments per issue: 1.8
- Average comments per pull request: 0.17
- Merged pull requests: 5
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 0
- Pull requests: 0
- Average time to close issues: N/A
- Average time to close pull requests: N/A
- Issue authors: 0
- Pull request authors: 0
- Average comments per issue: 0
- Average comments per pull request: 0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
- MatthewFlamm (5)
Pull Request Authors
- philipcardiff (4)
- kyleniemeyer (2)
- MatthewFlamm (1)
- TKaeufer (1)
Top Labels
Issue Labels
Pull Request Labels
Packages
- Total packages: 1
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Total downloads:
- pypi 32 last-month
- Total dependent packages: 0
- Total dependent repositories: 0
- Total versions: 9
- Total maintainers: 2
pypi.org: spicy-vki
SPICY (Super-resolution and Pressure from Image veloCimetrY) is a software developed at the von Karman Institute to perform data assimilation of image velocimetry using constrained Radial Basis Functions (RBF). The framework works for structured data (as produced by cross-correlation-based algorithms in PIV or Optical Flows) and unstructured data (produced by tracking algorithms in PTV).
- Homepage: https://github.com/mendezVKI/SPICY_VKI/tree/main/
- Documentation: https://spicy-vki.readthedocs.io/
- License: BSD (3-clause)
-
Latest release: 1.1.2
published about 1 year ago
Rankings
Maintainers (2)
Dependencies
- ipython *
- matplotlib >=3.3.0
- numpy >=1.20
- numpydoc >=1.6.0
- scikit-learn >=1.0
- scipy >=1.5
- shapely >=1.7.0
- sphinx >=5.0
- sphinx_rtd_theme *
- matplotlib >=3.3.0
- numpy >=1.20
- numpydoc >=1.6.0
- scikit-learn >=1.0
- scipy >=1.5
- shapely >=1.7.0