Verde

Verde: Processing and gridding spatial data using Green’s functions - Published in JOSS (2018)

Science Score: 100.0%

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
Found 3 DOI reference(s) in README and JOSS metadata
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Academic publication links
✓
Committers with academic emails
1 of 15 committers (6.7%) from academic institutions
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Institutional organization owner
✓
JOSS paper metadata
Published in Journal of Open Source Software

Keywords

earth-science fatiando-a-terra geophysics geoscience geospatial interpolation machine-learning python python3 scipy scipy-stack

Keywords from Contributors

mesh bayesian-inference gravitational-lenses blackhole spatial-data open-science finite-elements finite-element-analysis fem ftp

Scientific Fields

Mathematics Computer Science - 37% confidence

Last synced: 4 months ago · JSON representation ·

Repository

Processing and gridding spatial data, machine-learning style

Basic Info

Host: GitHub
Owner: fatiando
License: bsd-3-clause
Language: Python
Default Branch: main
Homepage: https://www.fatiando.org/verde
Size: 82 MB

Statistics

Stars: 636
Watchers: 21
Forks: 74
Open Issues: 39
Releases: 14

Topics

earth-science fatiando-a-terra geophysics geoscience geospatial interpolation machine-learning python python3 scipy scipy-stack

Created over 7 years ago · Last pushed 5 months ago

Metadata Files

Readme Contributing License Code of conduct Citation Authors

Processing and gridding spatial data, machine-learning style

Documentation (latest) • Documentation (main branch) • Contributing • Contact • Ask a question

Part of the Fatiando a Terra project

About

Verde is a Python library for processing spatial data (topography, point clouds, bathymetry, geophysics surveys, etc) and interpolating them on a 2D surface (i.e., gridding) with a hint of machine learning.

Our core interpolation methods are inspired by machine-learning. As such, Verde implements an interface that is similar to the popular scikit-learn library. We also provide other analysis methods that are often used in combination with gridding, like trend removal, blocked/windowed operations, cross-validation, and more!

Project goals

Provide a machine-learning inspired interface for gridding spatial data
Integration with the Scipy stack: numpy, pandas, scikit-learn, and xarray
Include common processing and data preparation tasks, like blocked means and 2D trends
Support for gridding scalar and vector data (like wind speed or GPS velocities)
Support for both Cartesian and geographic coordinates

Project status

Verde is stable and ready for use! This means that we are careful about introducing backwards incompatible changes and will provide ample warning when doing so. Upgrading minor versions of Verde should not require making changes to your code.

The first major release of Verde was focused on meeting most of these initial goals and establishing the look and feel of the library. Later releases will focus on expanding the range of gridders available, optimizing the code, and improving algorithms so that larger-than-memory datasets can also be supported.

Getting involved

🗨️ Contact us: Find out more about how to reach us at fatiando.org/contact.

👩🏾‍💻 Contributing to project development: Please read our Contributing Guide to see how you can help and give feedback.

🧑🏾‍🤝‍🧑🏼 Code of conduct: This project is released with a Code of Conduct. By participating in this project you agree to abide by its terms.

Imposter syndrome disclaimer: We want your help. No, really. There may be a little voice inside your head that is telling you that you're not ready, that you aren't skilled enough to contribute. We assure you that the little voice in your head is wrong. Most importantly, there are many valuable ways to contribute besides writing code.

This disclaimer was adapted from the MetPy project.

License

This is free software: you can redistribute it and/or modify it under the terms of the BSD 3-clause License. A copy of this license is provided in LICENSE.txt.

Owner

Name: Fatiando a Terra
Login: fatiando
Kind: organization

Website: https://www.fatiando.org
Repositories: 46
Profile: https://github.com/fatiando

Open-source Python tools for geophysics

JOSS Publication

Verde: Processing and gridding spatial data using Green’s functions

Published

October 11, 2018

DOI

10.21105/joss.00957

Volume 3, Issue 30, Page 957

Authors

Leonardo Uieda

Department of Earth Sciences, SOEST, University of Hawai'i at Mānoa, Honolulu, Hawaii, USA

Editor

Lindsey Heagy

Citation (CITATION.rst)

Citing Verde
============

This is research software **made by scientists**. Citations help us justify the effort
that goes into building and maintaining this project.

If you used Verde in your research, please consider citing our paper:

    Uieda, L. (2018). Verde: Processing and gridding spatial data using Green's
    functions. Journal of Open Source Software, 3(29), 957. doi:10.21105/joss.00957

This is an open-access publication and can be freely downloaded from
https://doi.org/10.21105/joss.00957

Here is a Bibtex entry to make things easier if you're using Latex:

.. code:: bibtex

    @article{uieda2018,
      title = {{Verde}: {Processing} and gridding spatial data using {Green's} functions},
      author = {Uieda, Leonardo},
      year = {2018},
      journal = {Journal of Open Source Software},
      volume = {3},
      number = {29},
      pages = {957},
      issn = {2475-9066},
      doi = {10.21105/joss.00957},
    }

GitHub Events

Total

Issues event: 20
Watch event: 36
Delete event: 34
Issue comment event: 22
Push event: 102
Pull request event: 64
Fork event: 3
Create event: 35

Last Year

Issues event: 20
Watch event: 36
Delete event: 34
Issue comment event: 22
Push event: 102
Pull request event: 65
Fork event: 3
Create event: 36

Committers

Last synced: 5 months ago

All Time

Total Commits: 333
Total Committers: 15
Avg Commits per committer: 22.2
Development Distribution Score (DDS): 0.285

Past Year

Commits: 30
Committers: 5
Avg Commits per committer: 6.0
Development Distribution Score (DDS): 0.6

Top Committers

Name	Email	Commits
Leonardo Uieda	l**a@g**m	238
Santiago Soler	s**r@g**m	52
dependabot[bot]	4****]	15
Fatiando a Terra Bot	5****t	10
Jesse Pisel	j****l	5
David Hoese	d**e@s**u	2
Matt Tankersley	8****r	2
Souza-junior	9****r	2
Arfon Smith	a****n	1
DC Slagel	d**s@m**g	1
Federico Esteban	f**n@g**m	1
Goto15	4****5	1
James Sample	j**e@g**m	1
Lindsey Heagy	l**y@g**m	1
Rowan Cockett	r**1@g**m	1

Committer Domains (Top 20 + Academic)

mailworks.org: 1 ssec.wisc.edu: 1

Issues and Pull Requests

Last synced: 4 months ago

All Time

Total issues: 63
Total pull requests: 172
Average time to close issues: over 1 year
Average time to close pull requests: about 1 month
Total issue authors: 12
Total pull request authors: 7
Average comments per issue: 1.11
Average comments per pull request: 0.67
Merged pull requests: 148
Bot issues: 0
Bot pull requests: 20

Past Year

Issues: 11
Pull requests: 67
Average time to close issues: 6 days
Average time to close pull requests: 4 days
Issue authors: 3
Pull request authors: 4
Average comments per issue: 0.36
Average comments per pull request: 0.22
Merged pull requests: 56
Bot issues: 0
Bot pull requests: 11

View more stats

Top Authors

Issue Authors

leouieda (45)
santisoler (5)
mdtanker (2)
ckohnke (1)
ThomasMGeo (1)
Esteban82 (1)
dependabot[bot] (1)
joshdunnlime (1)
mtb-za (1)
elfring (1)
gabrahamastro (1)
ianpdavies (1)

Pull Request Authors

leouieda (132)
santisoler (38)
dependabot[bot] (35)
Souza-junior (4)
mdtanker (3)
Phssilva (1)
JamesSample (1)

Top Labels

Issue Labels

enhancement (25) maintenance (19) documentation (5) bug (4) good first issue (4) question (2) dependencies (1)

Pull Request Labels

dependencies (35) documentation (1) github_actions (1)

Packages

Total packages: 3
Total downloads:
- pypi 6,236 last-month
Total docker downloads: 120

Total dependent packages: 8
(may contain duplicates)
Total dependent repositories: 35
(may contain duplicates)
Total versions: 38
Total maintainers: 2

pypi.org: verde

Processing and gridding spatial data, machine-learning style

Homepage: https://github.com/fatiando/verde
Documentation: https://www.fatiando.org/verde
License: BSD 3-Clause License
Latest release: 1.8.1
published over 1 year ago

Versions: 16
Dependent Packages: 6
Dependent Repositories: 13
Downloads: 6,236 Last month
Docker Downloads: 120

Rankings

Dependent packages count: 1.3%

Stargazers count: 2.8%

Dependent repos count: 4.1%

Average: 4.1%

Docker downloads count: 4.2%

Forks count: 5.3%

Downloads: 7.1%

Maintainers (2)

Leonardo.Uieda santisoler

Last synced: 4 months ago

proxy.golang.org: github.com/fatiando/verde

Documentation: https://pkg.go.dev/github.com/fatiando/verde#section-documentation
License: bsd-3-clause
Latest release: v1.8.1
published over 1 year ago

Versions: 12
Dependent Packages: 0
Dependent Repositories: 0

Rankings

Dependent packages count: 5.4%

Average: 5.6%

Dependent repos count: 5.8%

Last synced: 4 months ago

conda-forge.org: verde

Verde is a Python library for processing spatial data (topography, point clouds, bathymetry, geophysics surveys, etc) and interpolating them on a 2D surface (i.e., gridding) with a hint of machine learning. Our core interpolation methods are inspired by machine-learning. As such, Verde implements an interface that is similar to the popular scikit-learn library. We also provide other analysis methods that are often used in combination with gridding, like trend removal, blocked/windowed operations, cross-validation, and more!

Homepage: http://github.com/fatiando/verde
License: BSD-3-Clause
Latest release: 1.7.0
published over 3 years ago

Versions: 10
Dependent Packages: 2
Dependent Repositories: 22

Rankings

Dependent repos count: 7.6%

Average: 16.9%

Stargazers count: 17.5%

Dependent packages count: 19.6%

Forks count: 22.8%

Last synced: 4 months ago

Dependencies

env/requirements-build.txt pypi

build *
twine *

env/requirements-docs.txt pypi

cartopy >=0.18
matplotlib *
pyproj *
sphinx ==4.5.
sphinx-book-theme ==0.3.
sphinx-copybutton ==0.5.
sphinx-design ==0.1.
sphinx-gallery ==0.10.

env/requirements-style.txt pypi

black *
flake8 *
flake8-bugbear *
flake8-builtins *
flake8-functions *
flake8-mutable *
flake8-rst-docstrings *
flake8-simplify *
flake8-unused-arguments *
isort *
pathspec *
pep8-naming *

env/requirements-test.txt pypi

cartopy >=0.18 test
coverage * test
matplotlib * test
pytest * test
pytest-cov * test
pytest-mpl * test

.github/workflows/docs.yml actions

actions/cache v3 composite
actions/checkout v2 composite
actions/checkout 5a4ac9002d0be2fb38bd78e4b4dbde5606d7042f composite
actions/download-artifact v2 composite
actions/upload-artifact v2 composite
conda-incubator/setup-miniconda v2 composite
styfle/cancel-workflow-action 148d9a848c6acaf90a3ec30bc5062f646f8a4163 composite

.github/workflows/pypi.yml actions

actions/checkout v2 composite
actions/download-artifact v2 composite
actions/setup-python v2 composite
actions/upload-artifact v2 composite
pypa/gh-action-pypi-publish bce3b74dbf8cc32833ffba9d15f83425c1a736e0 composite

.github/workflows/style.yml actions

actions/checkout v2 composite
actions/setup-python v2 composite

.github/workflows/test.yml actions

actions/cache v3 composite
actions/checkout v2 composite
codecov/codecov-action v1 composite
conda-incubator/setup-miniconda v2 composite
styfle/cancel-workflow-action 148d9a848c6acaf90a3ec30bc5062f646f8a4163 composite

environment.yml conda

black
build
cartopy >=0.20
coverage
dask !=2021.03.0
flake8
flake8-bugbear
flake8-builtins
flake8-functions
flake8-mutable
flake8-rst-docstrings
flake8-simplify
gmt 6.3.*
ipython
isort
matplotlib 3.5.*
numba
numpy
pandas
pathspec
pep8-naming
pip
pooch
pygmt 0.6.*
pykdtree
pyproj
pytest
pytest-cov
pytest-mpl
python 3.10
scikit-learn
scipy
sphinx 4.5.*
sphinx-book-theme 0.3.*
sphinx-copybutton 0.5.*
sphinx-design 0.1.*
sphinx-gallery 0.10.*
twine
xarray

Verde

Science Score: 100.0%

Keywords

Keywords from Contributors

Scientific Fields

Repository

Basic Info

Statistics

Topics

Metadata Files

README.md

Processing and gridding spatial data, machine-learning style

About

Project goals

Project status

Getting involved

License

Owner

JOSS Publication

Verde: Processing and gridding spatial data using Green’s functions

Authors

Editor

Tags

Citation (CITATION.rst)

GitHub Events

Total

Last Year

Committers

All Time

Past Year

Top Committers

Committer Domains (Top 20 + Academic)

Issues and Pull Requests

All Time

Past Year

Top Authors

Issue Authors

Pull Request Authors

Top Labels

Issue Labels

Pull Request Labels

Packages

pypi.org: verde

Rankings

Maintainers (2)

proxy.golang.org: github.com/fatiando/verde

Rankings

conda-forge.org: verde

Rankings

Dependencies