Library of Descriptive and Predictive Models for Brazilian Asphalt Materials Data
Project description
Pysphalt
Library of machine learning models for Brazilian asphalt material data.
Installation
pip install pysphalt
Local Quickstart
The fastest way to get Pysphalt up and running locally for development.
1. Install dependencies
There are three things to install
- Conda
- Python libraries
- Pre-commit hooks
Create a new miniconda environment.
conda create -n pysphalt python=3.10
conda activate pysphalt
Install all python libraries. Libraries related to development are kept separate, in requirements-dev.txt
. Make sure to add any dependencies you introduce into these files!
pip install -r requirements.txt -r requirements-dev.txt
Install pre-commit
and spin it up:
pre-commit install
pre-commit
⚠️ Whenever you work on this codebase, remember to activate the conda environment:
conda activate pysphalt
Building Docs
cd docs
make html
You can access the generated docs on docs/build/html/index.html
Deploy to PyPi
Deploys to PyPi are managed automatically by Github Actions. To upload a new version of the library, just bump the version field on pyproject.toml
and push a new tag to main
.
The Action to publish a new version to PyPi will be triggered by the pushing the tag.
Project details
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Source Distribution
Built Distribution
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