pypigeonhole-build 0.2.1

Python build & packaging tool

Python Build Utilities

This is a Python SDLC tool to shorten the time we spend on SDLC without sacrificing quality. It does so by hard-coding certain parts. Freedom could lead to confusion and low efficiency. We borrow the idea from Java's mature tool, Maven.

Our goals are:

• Make simple/routine steps efficient
• Make out of routine steps possible, i.e., our code should not add more hassle when you expand it.

A sample project is in a separate repo: Project Template

This library does the following:

• Hard code src and test folders for source code and testing code.

We don't think the freedom naming of these 2 folders help us anything.

We want to separate src and test completely, not one inside another.

• For applications, we hard code bin folder for start-up and other scripts. We hard code conf folder for configurations.

• We isolate to one place to specify dependencies (along with the name and version), this place is src To avoid duplicated dependency information in setup.py and Anaconda environment.yaml.

Now we specify The dependencies in dep_setup.py. setup.py calls this to get the needed info.

We also added scope to indicate whether dependencies are needed during runtime.

Usage

** *nix shell scripts are not fully tested! **

Add dependencies in the dep_setup.py. Each dependency has the following fields:

• name: required. If name == python, the "python_requires" field in the setup.py will be touched.
• version: default to latest. Need full format: '==2.5'
• scope: default to DEV, could be DEV/INSTALL. INSTALL dependencies show in the "install_requires" field. DEV dependencies show up in the "test_require" field.
• installer: default to PIP, could be PIP/CONDA. Extendable to other installers.
• url: this is for github+https.

If Conda is used, need to set the CONDA.env field, which is mapped to the first line in the environment.yaml. CONDA.channels can be alternated too (default to None).

Pip can be customized in setup.py, so no change.

To generate environment.yaml, add src to PYTHONPATH and run

python dep_setup.py conda

To generate environment.txt, add src to PYTHONPATH and run

python dep_setup.py pip

This file is different from the one generated from pip, it does not have dependent libraries.

To setup local dev environment, run

dbin\py_dev_env_setup

This will create a new conda environment with the name specified in CONDA.env. The old environment will be deleted.

There is a *nix port on windows,

conda install conda-build unxutils

We could use the tee command to save command output to logs.

For more info, see dep_setup.py and unit tests.