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argopy

argopy: A Python library for Argo ocean data analysis - Published in JOSS (2020)

https://github.com/euroargodev/argopy

Science Score: 93.0%

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
  • ✓
    .zenodo.json file
    Found .zenodo.json file
  • ✓
    DOI references
    Found 4 DOI reference(s) in README and JOSS metadata
  • ○
    Academic publication links
  • ○
    Committers with academic emails
  • ○
    Institutional organization owner
  • ✓
    JOSS paper metadata
    Published in Journal of Open Source Software

Keywords

argo argo-data argo-floats oceanography python

Keywords from Contributors

cryptocurrencies ocean mesh
Last synced: 4 months ago · JSON representation

Repository

A python library for Argo data beginners and experts

Basic Info
  • Host: GitHub
  • Owner: euroargodev
  • License: eupl-1.2
  • Language: Python
  • Default Branch: master
  • Homepage: https://argopy.readthedocs.io
  • Size: 1.49 GB
Statistics
  • Stars: 205
  • Watchers: 11
  • Forks: 42
  • Open Issues: 34
  • Releases: 25
Topics
argo argo-data argo-floats oceanography python
Created almost 6 years ago · Last pushed 4 months ago
Metadata Files
Readme Contributing License Code of conduct Citation Security

README.md

| argopy logo
argopy is a python library dedicated to Argo data access, visualisation and manipulation for regular users as well as Argo experts and operators | |:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:| | DOI Documentation Pypi Conda | | codecov CI CI Energy | | Open-SSF |

Documentation

The official documentation is hosted on ReadTheDocs.org: https://argopy.readthedocs.io

Install

Binary installers for the latest released version are available at the Python Package Index (PyPI) and on Conda.

```bash

conda

conda install -c conda-forge argopy ` bash

or PyPI

pip install argopy ````

argopy is continuously tested to work under most OS (Linux, Mac, Windows) and with python versions >= 3.8

Usage

```python

Import the main data fetcher:

from argopy import DataFetcher python

Define what you want to fetch...

a region:

ArgoSet = DataFetcher().region([-85,-45,10.,20.,0,10.])

floats:

ArgoSet = DataFetcher().float([6902746, 6902747, 6902757, 6902766])

or specific profiles:

ArgoSet = DataFetcher().profile(6902746, 34) python

then fetch and get data as xarray datasets:

ds = ArgoSet.load().data

or

ds = ArgoSet.to_xarray() python

you can even plot some information:

ArgoSet.plot('trajectory')
```

They are many more usages and fine-tuning to allow you to access and manipulate Argo data: - filters at fetch time (standard vs expert users, automatically select QC flags or data mode, ...) - select data sources (erddap, ftp, local, argovis, ...) - manipulate data (points, profiles, interpolations, binning, ...) - visualisation (trajectories, topography, histograms, ...) - tools for Quality Control (OWC, figures, ...) - access meta-data and other Argo-related datasets (reference tables, deployment plans, topography, DOIs, ...) - improve performances (caching, parallel data fetching)

Just check out the documentation for more !

Energy impact of argopy development

The argopy team is concerned about the environmental impact of your favorite software development. Starting June 1st 2024, we're experimenting with the Green Metrics Tools from Green Coding to get an estimate of the energy used and CO2eq emitted by our development activities on Github infrastructure. Results:

| Activity | Green Coding tool | |----------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------| | CI tests running on each commit | CI Energy CI Energy | | Upstream CI tests, running daily | CI Energy CI Energy |

Development and contributions

See our software management dashboard here: https://github.com/orgs/euroargodev/projects/19

And if you want to get involved and help maintain or develop argopy, please checkout the contribution page.

Tutorials

Some tutorials, as jupyter notebooks, are available to get you started:

  • https://github.com/euroargodev/argopy/blob/master/docs/tutorials/basicfeaturescore_01.ipynb

Owner

  • Name: Euro-Argo ERIC
  • Login: euroargodev
  • Kind: organization
  • Email: contact@euro-argo.eu

Euro-Argo is the European infrastructure for the Argo programme that aims at sustaining 1/4 of the global network and enhance coverage in European seas.

JOSS Publication

argopy: A Python library for Argo ocean data analysis
Published
September 01, 2020
DOI
Volume 5, Issue 53, Page 2425
Authors
Guillaume Maze ORCID
Univ Brest, Ifremer, CNRS, IRD, LOPS, F‐29280 Plouzané, France
Kevin Balem ORCID
Univ Brest, Ifremer, CNRS, IRD, LOPS, F‐29280 Plouzané, France
Editor
Kristen Thyng ORCID
Tags
ocean oceanography observation
View PDF Review Thread Software Archive

GitHub Events

Total
  • Create event: 75
  • Issues event: 40
  • Release event: 3
  • Watch event: 24
  • Delete event: 70
  • Issue comment event: 102
  • Push event: 399
  • Pull request review event: 8
  • Pull request review comment event: 6
  • Pull request event: 127
Last Year
  • Create event: 75
  • Issues event: 41
  • Release event: 3
  • Watch event: 24
  • Delete event: 70
  • Issue comment event: 102
  • Push event: 401
  • Pull request review event: 8
  • Pull request review comment event: 6
  • Pull request event: 127

Committers

Last synced: 8 months ago

All Time
  • Total Commits: 2,841
  • Total Committers: 12
  • Avg Commits per committer: 236.75
  • Development Distribution Score (DDS): 0.057
Past Year
  • Commits: 798
  • Committers: 4
  • Avg Commits per committer: 199.5
  • Development Distribution Score (DDS): 0.031
Top Committers
Name Email Commits
Guillaume Maze g****e@i****r 2,679
dependabot[bot] 4****] 82
quai20 k****m@g****m 54
Filipe Fernandes o****f@g****m 7
bkatiemills y****u@e****m 4
Andrew Barna a****a@g****m 4
Arne Tarara a****e@d****e 3
tylertucker202 t****2@g****m 2
Dhruv Balwada d****a@g****m 2
Damien Irving i****n@g****m 2
Ryan Abernathey r****y@g****m 1
Kurt Schwehr s****r@g****m 1
Committer Domains (Top 20 + Academic)
google.com: 1 datafuse.de: 1 ifremer.fr: 1

Issues and Pull Requests

Last synced: 4 months ago

All Time
  • Total issues: 163
  • Total pull requests: 427
  • Average time to close issues: 5 months
  • Average time to close pull requests: 19 days
  • Total issue authors: 53
  • Total pull request authors: 18
  • Average comments per issue: 3.67
  • Average comments per pull request: 0.92
  • Merged pull requests: 278
  • Bot issues: 4
  • Bot pull requests: 223
Past Year
  • Issues: 34
  • Pull requests: 147
  • Average time to close issues: about 1 month
  • Average time to close pull requests: 8 days
  • Issue authors: 10
  • Pull request authors: 2
  • Average comments per issue: 0.91
  • Average comments per pull request: 0.48
  • Merged pull requests: 87
  • Bot issues: 1
  • Bot pull requests: 73
View more stats
Top Authors
Issue Authors
  • gmaze (85)
  • iuryt (4)
  • cywhale (3)
  • cgrdn (3)
  • github-actions[bot] (3)
  • ctroupin (3)
  • jpivarski (3)
  • PedroVelez (3)
  • dcherian (2)
  • mayursapkal (2)
  • andrewfagerheim (2)
  • apatlpo (2)
  • kamwal (2)
  • rcaneill (2)
  • DamienIrving (2)
Pull Request Authors
  • dependabot[bot] (223)
  • gmaze (178)
  • ocefpaf (5)
  • quai20 (4)
  • DocOtak (2)
  • bkatiemills (2)
  • ArneTR (2)
  • RaphaelBajon (1)
  • pyup-bot (1)
  • schwehr (1)
  • xeulha (1)
  • tylertucker202 (1)
  • dhruvbalwada (1)
  • DamienIrving (1)
  • rabernat (1)
Top Labels
Issue Labels
enhancement (36) stale (33) internals (32) invalid (31) bug (17) performance (13) help wanted (13) documentation (12) good first issue (12) argo-BGC (11) argo-core (11) backends (11) question (9) CI (8) closed-as-stale (6) argo-traj (5) forQCexpert (4) wontfix (3) argo-deep (3) breaking-change (3) plot (2) dependencies (2) good-practices (2) ignore-for-release (1) github_actions (1) design (1) python-environment (1)
Pull Request Labels
dependencies (228) ignore-for-release (196) github_actions (95) enhancement (56) internals (39) bug (24) performance (22) release (22) documentation (16) backends (16) argo-BGC (14) CI (8) argo-core (7) stale (6) forQCexpert (3) plot (3) python-environment (3) invalid (2) argo-traj (1) argo-deep (1) design (1)

Packages

  • Total packages: 2
  • Total downloads:
    • pypi 4,324 last-month
  • Total docker downloads: 211
  • Total dependent packages: 1
    (may contain duplicates)
  • Total dependent repositories: 14
    (may contain duplicates)
  • Total versions: 32
  • Total maintainers: 2
pypi.org: argopy

A python library for Argo data beginners and experts

  • Versions: 23
  • Dependent Packages: 1
  • Dependent Repositories: 5
  • Downloads: 4,324 Last month
  • Docker Downloads: 211
Rankings
Docker downloads count: 2.8%
Dependent packages count: 3.2%
Stargazers count: 5.6%
Average: 5.7%
Forks count: 6.6%
Dependent repos count: 6.7%
Downloads: 9.2%
Maintainers (2)
balemkevin euroargodev
Last synced: 4 months ago
conda-forge.org: argopy
  • Versions: 9
  • Dependent Packages: 0
  • Dependent Repositories: 9
Rankings
Dependent repos count: 11.5%
Forks count: 28.7%
Stargazers count: 29.2%
Average: 30.2%
Dependent packages count: 51.5%
Last synced: 4 months ago

Dependencies

.github/workflows/codeql-analysis.yml actions
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.github/workflows/pytests-upstream.yml actions
  • actions/checkout v3 composite
  • mamba-org/provision-with-micromamba v15 composite
  • xarray-contrib/ci-trigger v1 composite
  • xarray-contrib/issue-from-pytest-log v1 composite
.github/workflows/pytests.yml actions
  • actions/checkout v3 composite
  • codecov/codecov-action v3.1.1 composite
  • mamba-org/provision-with-micromamba v15 composite
  • xarray-contrib/ci-trigger v1.2 composite
.github/workflows/pythonpublish.yml actions
  • actions/checkout v3 composite
  • actions/setup-python v4 composite
.github/workflows/stale.yml actions
  • actions/stale v7 composite
.github/workflows/pytests-upstream-windows.yml actions
  • actions/checkout v4 composite
  • mamba-org/setup-micromamba v1 composite
  • xarray-contrib/ci-trigger v1 composite
  • xarray-contrib/ci-trigger v1.2 composite