Cookiecutter for Python Algorithm Development for vantage6

This repository is superseded by https://github.com/vantage6/v6-algorithm-template

A lean Cookiecutter template for developing algorithms for vantage6 in Python.

Requirements:

• Python >=3.7
• Cookiecutter Python package >= 2.0.0

Depending on how you manage your Python packages, Cookiecutter can be installed with pip:

$ pip install cookiecutter

or with conda (as shown in conda-forge):

$ conda install -c conda-forge cookiecutter

Creating a new algorithm (template)

To create a new algorithm template, run:

cookiecutter https://github.com/vantage6/cc_algorithm_python

For the sake of brevity, GitHub repositories can just use the gh prefix:

cookiecutter gh:vantage6/cc_algorithm_python

Afterwards, you will be prompted to enter a few values. After inputting each of them, make sure to type enter:

Field	Description
algorithm_name	Name of the algorithm
repo_name	Name of the (GitHub) repository. Please use snake_case. Spaces and dashes will be replaced by underscores (_).
author_name	Your name (or your organization, company, or team)
description	A short description of the algorithm. Please include the core details of your algorithm (e.g., if it for horizontally- or vertically-partitioned data, if it is an MPC algorithm, etc.
open_source_license	The software license for your project (more information below).

Licenses

Currently, you can choose from three different licenses when creating your algorithm template:

• MIT
• Apache License 2.0
• BSD-3-Clause
• (No license)

Adding more licenses to the template

If you are interested in adding more licenses to the template:

• See choosealicense.com (or any other resource) and choose the license that is appropriate for your use case.
• Go to cookiecutter.json and add the license name to the open_source_license list.
• Go to setup.py and add the license to the license string in setup.
• Go to {{ cookiecutter.repo_name }}/LICENSE and copy-paste the license text in its corresponding if.
• In the license text, change the important fields to Jinja delimiters (e.g., <year> to {% now 'utc', '%Y' %}; <author name> to {{ cookiecutter.author_name }})

Directory structure

The resulting directory structure for your algorithm is as follows:

├── algorithm_name     <- The core files of your algorithm.
│   └── __init__.py
│   └── central.py     <- Functions for the server. Former master.py.
│   └── remote.py      <- Functions for the nodes (which have access to the data).
|                         Former rpc.py.
│
├── docs               <- A default Sphinx project for documenting your algorithm.
|                         See https://www.sphinx-doc.org/ for more details.
|                         We suggest that the documentation includes at least
|                         the following sections:
|                          - Introduction
|                          - Algorithm (mathematical) description
|                          - Validation
│
├── examples           <- Examples of how to use the algorithm in the
|   |                     shape of Jupyter notebooks. The suggested naming
|   |                     convention is a number (for ordering), the creator's
|   |                     initials, and a short description, (e.g., `01_xyz_running_the_algorithm`).
|   |                     We also recommend you take a look into using jupytext.
│   └── 01_xyz_running_the_algorithm.ipynb
│
├── tests              <- Functions for testing your algorithm.
│   └── test_environment.py   
│
├── .gitignore
|
├── Dockerfile
|
├── LICENSE            <- If chosen, the appropriate license file.
|
├── Makefile
|
├── README.md          <- The top-level README for users of the algorithm.
|                         Make sure to provide a helpful description.
|                         Including a Quick Start guide is encouraged.
|
├── requirements.txt   <- The requirements file for reproducing the algorithm
|                         environment. This can be generated with `pip freeze > requirements.txt`
│
├── setup.py           <- Makes the algorithm pip installable (pip install -e .)
|                         so algorithm_name can be imported.
│
└── tox.ini            <- tox file with settings for running tox.
                          See https://tox.wiki/

Contributing

If you wish to contribute, first of all, thank you! Second, please take a look at our contributing guidelines.

Running tests

py.test tests