Py4research provides a thorough tutorial on how to build and upload a Python package for research, which is going to be presented at the "X Workshop do Programa de Pós-Graduação em Ciência da Computação". Apart from the tutorial itself, we also implemented a straightforward package to illustrate how to build its package.
- Every needed piece of information is elucidated in this README;
- Installation is also straightforward and well-explained;
- If there is a problem, please do not hesitate and call us.
The installation process is straightforward as the package is hosted at PyPI, and the dependencies are listed on the requirements.txt file. One can install the package with either command, as follows:
pip install py4researchpip install -r requirements.txtAdditionally, the source files for the lecture are presented in LaTeX. Thus, one might need an additional compiler or even Overleaf to build the files into a PDF file.
This section provides an overview of the "build your python package" tutorial and the main package description.
The tutorial is written in Portuguese in a slide-based format. The contents are available in the slides folder and compiled to PDF using a LaTeX compiler.
Py4research is a standard package composed of a single math module composed of two sub-modules: clustering and random. Essentially, the idea is to provide a simple framework that can foster research and help the community deploy their official implementations to PyPI.
Every good code should come with information on how to use it, correct? Documentation is pretty straightforward to be built, as follows:
cd docs
pip install -r requirements.txt
After entering the documentation folder and installing the required dependencies, one can build its HTML version:
make clean
make html
The HTML will be available inside a _build folder, inside the docs folder.
The first method that we offer is running solo the pytest command. This will realize all the implemented tests and return an output declaring whether they passed or failed.
pytest tests/
An exciting addition to the solo pytest is the coverage module. Despite offering the same outputs from the pytest, it will also offer a report that documents how much the tests cover percent of the code.
coverage run -m pytest tests/
coverage report -m
It is inevitable to make mistakes or create some bugs. If there is any problem or concern, we will be available at this repository.