bird-cloud-gnn
This package generates graph representations from point cloud data and fits graph neural networks for classification problem
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
-
○Committers with academic emails
-
○Institutional organization owner
-
○JOSS paper metadata
-
○Scientific vocabulary similarity
Low similarity (15.3%) to scientific vocabulary
Keywords
Repository
This package generates graph representations from point cloud data and fits graph neural networks for classification problem
Basic Info
Statistics
- Stars: 8
- Watchers: 3
- Forks: 3
- Open Issues: 39
- Releases: 1
Topics
Metadata Files
README.dev.md
bird_cloud_gnn developer documentation
If you're looking for user documentation, go here.
Development install
```shell
Create a virtual environment, e.g. with
python3 -m venv env
activate virtual environment
source env/bin/activate
make sure to have a recent version of pip and setuptools
python3 -m pip install --upgrade pip setuptools
(from the project root directory)
install birdcloudgnn as an editable package
python3 -m pip install --no-cache-dir --editable .
install development dependencies
python3 -m pip install --no-cache-dir --editable .[dev] ```
Afterwards check that the install directory is present in the PATH environment variable.
Running the tests
There are two ways to run tests.
The first way requires an activated virtual environment with the development tools installed:
shell
pytest -v
The second is to use tox, which can be installed separately (e.g. with pip install tox), i.e. not necessarily inside the virtual environment you use for installing bird_cloud_gnn, but then builds the necessary virtual environments itself by simply running:
shell
tox
Testing with tox allows for keeping the testing environment separate from your development environment.
The development environment will typically accumulate (old) packages during development that interfere with testing; this problem is avoided by testing with tox.
Test coverage
In addition to just running the tests to see if they pass, they can be used for coverage statistics, i.e. to determine how much of the package's code is actually executed during tests. In an activated virtual environment with the development tools installed, inside the package directory, run:
shell
coverage run
This runs tests and stores the result in a .coverage file.
To see the results on the command line, run
shell
coverage report
coverage can also generate output in HTML and other formats; see coverage help for more information.
Running linters locally
For linting we will use prospector and to sort imports we will use isort. Running the linters requires an activated virtual environment with the development tools installed.
```shell
linter
prospector
recursively check import style for the birdcloudgnn module only
isort --check-only birdcloudgnn
recursively check import style for the birdcloudgnn module only and show
any proposed changes as a diff
isort --check-only --diff birdcloudgnn
recursively fix import style for the birdcloudgnn module only
isort birdcloudgnn ```
To fix readability of your code style you can use yapf.
Pre-commit
You can enable automatic linting by install pre-commit.
shell
pre-commit install
You can also run the linters with pre-commit run -a.
Generating the API docs
shell
cd docs
make html
The documentation will be in docs/_build/html
If you do not have make use
shell
sphinx-build -b html docs/ docs/_build/html
To find undocumented Python objects run
shell
cd docs
make coverage
cat _build/coverage/python.txt
To test snippets in documentation run
shell
cd docs
make doctest
Making a release
This section describes how to make a release in 3 parts:
- preparation
- making a release on PyPI
- making a release on GitHub
(1/3) Preparation
- Update the
(don't forget to update links at bottom of page) - Verify that the information in
CITATION.cffis correct - Make sure the version has been updated in
bird_cloud_gnn/__init__.py,setup.cfg, anddocs/conf.py - Run the unit tests with
pytest -v
(2/3) PyPI
In a new terminal, without an activated virtual environment or an env directory:
```shell
prepare a new directory
cd $(mktemp -d birdcloudgnn.XXXXXX)
fresh git clone ensures the release has the state of origin/main branch
git clone https://github.com/point-cloud-radar/bird-cloud-gnn .
prepare a clean virtual environment and activate it
python3 -m venv env source env/bin/activate
make sure to have a recent version of pip and setuptools
python3 -m pip install --upgrade pip setuptools
install runtime dependencies and publishing dependencies
python3 -m pip install --no-cache-dir . python3 -m pip install --no-cache-dir .[publishing]
clean up any previously generated artefacts
rm -rf birdcloudgnn.egg-info rm -rf dist
create the source distribution and the wheel
python3 -m build
upload to test pypi instance (requires credentials)
twine upload -u token -p THETOKEN -r testpypi dist/*
```
Visit https://test.pypi.org/project/birdcloudgnn and verify that your package was uploaded successfully. Keep the terminal open, we'll need it later.
In a new terminal, without an activated virtual environment or an env directory:
```shell cd $(mktemp -d birdcloudgnn-test.XXXXXX)
prepare a clean virtual environment and activate it
python3 -m venv env source env/bin/activate
make sure to have a recent version of pip and setuptools
pip install --upgrade pip setuptools
install from test pypi instance:
python3 -m pip -v install --no-cache-dir \ --index-url https://test.pypi.org/simple/ \ --extra-index-url https://pypi.org/simple birdcloudgnn ```
Check that the package works as it should when installed from pypitest.
Then upload to pypi.org with:
```shell
Back to the first terminal,
FINAL STEP: upload to PyPI (requires credentials)
twine upload -u token -p THETOKEN dist/* ```
(3/3) GitHub
Don't forget to also make a release on GitHub. If your repository uses the GitHub-Zenodo integration this will also trigger Zenodo into making a snapshot of your repository and sticking a DOI on it.
Owner
- Name: point-cloud-radar
- Login: point-cloud-radar
- Kind: organization
- Repositories: 2
- Profile: https://github.com/point-cloud-radar
Citation (CITATION.cff)
# This CITATION.cff file was generated with cffinit.
# Visit https://bit.ly/cffinit to generate yours today!
cff-version: 1.2.0
title: bird-cloud-gnn
message: >-
If you use this software, please cite it using the
metadata from this file.
type: software
authors:
- given-names: Olga
family-names: Lyashevska
email: o.lyashevska@esciencecenter.nl
affiliation: Netherlands eScience Center
orcid: 'https://orcid.org/0000-0002-8686-8550'
- given-names: Abel
family-names: Soares Siqueira
email: abel.s.siqueira@gmail.com
affiliation: Netherlands eScience Center
orcid: 'https://orcid.org/0000-0003-4451-281X'
- given-names: Bart
email: b.kranstauber@uva.nl
family-names: Kranstauber
affiliation: University of Amsterdam
orcid: 'https://orcid.org/0000-0001-8303-780X'
- given-names: 'Bart '
family-names: Hoekstra
email: b.d.hoekstra@uva.nl
affiliation: University of Amsterdam
orcid: 'https://orcid.org/0000-0002-7085-3805'
repository-code: 'https://github.com/point-cloud-radar/bird-cloud-gnn'
keywords:
- point cloud
- graph neural network
- bird identification
license: Apache-2.0
version: 0.1.0
date-released: '2023-11-28'
GitHub Events
Total
- Issue comment event: 4
- Push event: 3
- Fork event: 1
Last Year
- Issue comment event: 4
- Push event: 3
- Fork event: 1
Committers
Last synced: about 2 years ago
Top Committers
| Name | Commits | |
|---|---|---|
| Abel Soares Siqueira | a****a@g****m | 42 |
| Olga Lyashevska | o****a@h****l | 29 |
| Bart | b****t@m****s | 12 |
| bart1 | 1****1 | 5 |
| Olga Lyashevska | o****a@g****m | 3 |
| pre-commit-ci[bot] | 6****] | 2 |
| NLeSC Python template | n****e | 1 |
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: about 2 years ago
All Time
- Total issues: 69
- Total pull requests: 35
- Average time to close issues: 29 days
- Average time to close pull requests: 5 days
- Total issue authors: 4
- Total pull request authors: 4
- Average comments per issue: 0.96
- Average comments per pull request: 1.57
- Merged pull requests: 30
- Bot issues: 1
- Bot pull requests: 2
Past Year
- Issues: 69
- Pull requests: 35
- Average time to close issues: 29 days
- Average time to close pull requests: 5 days
- Issue authors: 4
- Pull request authors: 4
- Average comments per issue: 0.96
- Average comments per pull request: 1.57
- Merged pull requests: 30
- Bot issues: 1
- Bot pull requests: 2
Top Authors
Issue Authors
- abelsiqueira (35)
- lyashevska (24)
- bart1 (12)
- github-actions[bot] (1)
Pull Request Authors
- abelsiqueira (19)
- bart1 (9)
- lyashevska (5)
- pre-commit-ci[bot] (2)
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: 1
- Total maintainers: 1
pypi.org: bird-cloud-gnn
Graph Neural Network for bird point cloud data
- Homepage: https://github.com/point-cloud-radar/bird-cloud-gnn
- Documentation: https://bird-cloud-gnn.readthedocs.io/
- License: Apache Software License
-
Latest release: 0.1.0
published about 2 years ago
Rankings
Maintainers (1)
Dependencies
- actions/checkout v3 composite
- actions/setup-python v3 composite
- actions/checkout v3 composite
- citation-file-format/cffconvert-github-action main composite
- actions/checkout v2 composite
- actions/setup-python v2 composite
- actions/checkout v3 composite
- gaurav-nelson/github-action-markdown-link-check v1 composite
- SonarSource/sonarcloud-github-action master composite
- actions/checkout v3 composite
- actions/setup-python v3 composite