Metadata-Version: 2.1
Name: tlpytools
Version: 0.1.3
Summary: A set of tools for building models at the TransLink Forecasting Team
Home-page: https://github.com/TransLinkForecasting/tlpytools
Author: Forecasting TransLink
Author-email: forecasting@translink.ca
Project-URL: Bug Tracker, https://github.com/TransLinkForecasting/tlpytools/issues
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown

# TransLink's Python Tools

Python tools to read data, yaml configuration, logging, and much more.

## Getting Started

To use the tools within this package, you must first install this package:

### Method 1: precompiled wheel

Install with precompiled wheels from PyPI is the fastest way to get started:

```bash
pip install tlpytools --upgrade
```

### Method 2: build from source

Build and install from source allow you to access the latest version or a specific version without any available precompiled wheels:

```bash
git clone https://github.com/TransLinkForecasting/tlpytools.git --depth=1
cd tlpytools
python -m pip install --upgrade build
python -m build
pip install dist/tlpytools-0.1.3-py3-none-any.whl --upgrade --force-reinstall
```

### Method 3: editable install for development

If you are developing and testing tlpytools to work with downstream code, you may install the package as an [editable install](https://pip.pypa.io/en/stable/cli/pip_install/?highlight=edit%20mode#editable-installs). This allows you to continuously make changes to the `tlpytools` package in your local development folder, while integrating these changes in real time with your downstream code.

```bash
git clone https://github.com/TransLinkForecasting/tlpytools.git --depth=1
cd tlpytools
pip install -e ./
```

Note that after this installation, any code changes you make within the `tlpytools` folder will take effect immeidately witin your development environment.

## Usage

After installation, you can access the `tlpytools` namespace within your data project and importing parts of the package in your python script:

```python
from tlpytools.log import logger
from tlpytools.config import run_yaml
```

## Build and Distribution Process

1. Review `setup.cfg` and `pyproject.toml` to ensure dependencies and versions are up to date.
2. Generate distribution archives
```
conda activate base
python -m pip install --upgrade build
python -m build
```
3. Upload to PyPI
```
python -m pip install --upgrade twine
python -m twine upload dist/*
```
Review the newly upload package on `https://test.pypi.org/project/tlpytools/`

If you are having problem authenticating to PyPI, you should register on [PyPI](https://pypi.org/account/register/) and create a new token. Set up the token in `~/.pypirc` by following [package distribution guide](https://packaging.python.org/en/latest/guides/distributing-packages-using-setuptools/#create-an-account)
