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https://github.com/iagappel/turbx

Tools for analysis of turbulent flow datasets

https://github.com/iagappel/turbx

Science Score: 23.0%

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

  • ○
    CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
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    .zenodo.json file
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    DOI references
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    Academic publication links
  • ✓
    Committers with academic emails
    1 of 2 committers (50.0%) from academic institutions
  • ○
    Institutional organization owner
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    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (7.0%) to scientific vocabulary

Keywords

h5py hdf5 mpi4py python3 scientific visualization
Last synced: 6 months ago · JSON representation

Repository

Tools for analysis of turbulent flow datasets

Basic Info
  • Host: GitHub
  • Owner: iagappel
  • License: mit
  • Language: Python
  • Default Branch: main
  • Homepage:
  • Size: 1.05 MB
Statistics
  • Stars: 1
  • Watchers: 2
  • Forks: 0
  • Open Issues: 0
  • Releases: 16
Topics
h5py hdf5 mpi4py python3 scientific visualization
Created almost 4 years ago · Last pushed 12 months ago
Metadata Files
Readme License

README.md

turbx

PyPI version Downloads

turbx is a python3 module which contains tools for organization, storage and parallelized processing of turbulent flow datasets, including super()ed wrappers of h5py.File that streamline data & metadata access.

python3 -m pip install turbx

turbx runs in python3 and uses parallel HDF5 (wrapped by h5py) for high-performance collective MPI-IO with mpi4py. This requires:

  • A python3 installation (3.11+ recommended)
  • An MPI implementation such as OpenMPI
  • A parallel HDF5 installation (must be compiled with --enable-parallel)
  • mpi4py
  • h5py compiled with parallel configuration

Visualization of HDF5 datasets in Paraview is supported through the use of XML/XDMF sidecar descriptor files. All major data classes (such as rgd) can automatically generate the descriptor files by calling .make_xdmf().

GitHub Events

Total
  • Release event: 1
  • Push event: 2
Last Year
  • Release event: 1
  • Push event: 2

Committers

Last synced: over 1 year ago

All Time
  • Total Commits: 52
  • Total Committers: 2
  • Avg Commits per committer: 26.0
  • Development Distribution Score (DDS): 0.038
Past Year
  • Commits: 8
  • Committers: 1
  • Avg Commits per committer: 8.0
  • Development Distribution Score (DDS): 0.0
Top Committers
Name Email Commits
Jason Appelbaum a****m@i****e 50
iagappel 8****l 2
Committer Domains (Top 20 + Academic)
iag.uni-stuttgart.de: 1

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Last synced: 8 months ago

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  • Total pull requests: 0
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  • Total 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
Past Year
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  • Pull requests: 0
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  • Average time to close pull requests: N/A
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  • Pull request authors: 0
  • Average comments per issue: 0
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  • Merged pull requests: 0
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  • Bot pull requests: 0
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Dependencies

setup.py pypi
  • cmasher >=1.6
  • cmocean >=2.0
  • colorcet >=3.0
  • h5py >=3.6
  • matplotlib >=3.5
  • mpi4py >=3.1
  • numpy >=1.22
  • psutil >=5.9
  • scikit-image >=0.19
  • scipy >=1.8
  • tqdm >=4.64