mainanalytics – Python Development Guide

ToC

• mainanalytics – Python Development Guide
  • Introduction
  • How to use the template
  • Install enviroment from pyproject.toml file (poetry) - Recomended!
  • Add new dependencies and packages
  • Virtual Enviroment handling with poetry
  • VS Code COnfigurations:
  • Formatter and Linter
  • Unit Tests
  • Compilation (pyinstaller)
  • Basic code quality agreements
  • Commit style:
  • Conda Enviroments (not recommended)
    • Install enviroment from enviroment.yml file
    • Install new dependencies

Introduction

The mainanalytics team develops code that is developed, maintained, and used by multiple users. Therefore, we place higher demands on code quality that go beyond validation and testing to allow continuous development, deployment and maintenance.

How to use the template

Use this as tempate repository

Follow the instructions here: https://docs.github.com/en/repositories/creating-and-managing-repositories/creating-a-repository-from-a-template

By hand (more complicated)

• Create a new git repository

• Clone template :

git clone https://github.com/biontech-qm/gbs-pytemp-simple.git

• remove git history from local repo

cd my-project
rm -rf .git 

• initialse new local repo and push to origin

git init           # start a fresh repo
git add .
git commit -m "Initial commit"
git branch -M main
git remote add origin <link to new repo>
git push -u origin main

• next step:
  • Create a new venv using Poetry (recommended)
  • Alternative: Create a new venv using conda (see next section)

Install enviroment from pyproject.toml file (poetry) - Recomended!

1. Ensure that poetry is installed, if not do so by following information on the webside: https://python-poetry.org/ or use

   pip install poetry

2. Recommendation: Ensure to create venv in project folder for easier handling of multiple projects

   poetry config virtualenvs.in-project true

3. Lock poetry file and install venv

   poetry lock
   poetry install

Add new dependencies and packages

General bash poetry add <mypackage>

Into groups (e.g. for dev) bash poetry add --group dev <mypackage>

• Run scrip:

  poetry run <my-script>

• Run pytest:

  poetry run pytest

Automatic validation reports are created via pytest-html. Configuration is handled by pytest.ini and the test/conftest.py file

• (Not required) Extract requirements.txt (toml -> requirements (requires plugin))

  poetry export -f requirements.txt --without-hashes -o requirements.txt

Virtual Enviroment handling with poetry

Install Virtal Enviroment the first time bash poetry ini_options

Activate Poetry Venv bash poetry env activate

Install VENV in workfolder bash poetry config virtualenvs.in-project true

Virtual Environment Informations

    poetry env info
    poetry env info --path
    poetry env info --executable
    poetry env list
    poetry env remove python

VS Code COnfigurations:

Ensure to select the correct interpreter in VS Code by Ctrl+Shift+P >Python: Select Interpreter. Maybe you neet to set the User settings by Ctrl+Shift+P >Preferences: Open User Settings (JSON) and add a correct pylint and interpreter path: e.g. like

{
   "python.defaultInterpreterPath": "${workspaceFolder}/.venv/Scripts/python.exe",
    "python.analysis.autoImportCompletions": true,
    "python.analysis.autoFormatStrings": true,
    "python.analysis.completeFunctionParens": true,
    "python.analysis.extraPaths": [
        "${workspaceFolder}/.venv/Lib",
        "${workspaceFolder}/.venv/Lib/site-packages",
    ],
    "pylint.interpreter": [
        "${workspaceFolder}/.venv/Scripts/python.exe"
    ],
    "pylint.lintOnChange": true,
    "pylint.path": [
        "${workspaceFolder}/.venv/Lib",
        "${workspaceFolder}/.venv/Lib/site-packages",

    ],
}

Formatter and Linter

Basic formatter and linter must be used. The pipeline enforces formatted code. We will use ruff as formatter (can also be used as linter and allows for auto-fixing). We will expect a pylint score of >0.9 and no critical issue

Run linter:

poetry run pylint <myfile|module>.py 

Configuration file: .pylintrc

Run formatter:

poetry run python -m ruff format <myfile|module>

Run ruff as linter

poetry run ruff check <myfile|module>

Run ruff as linter with auto-fixing

poetry run ruff check --fix <myfile|module>

Configuration file: pyproject.toml

Unit Tests

Unit tests are stored in test folder and can be executed via pytest

poetry run pytest 

Configuration file: pyproject.toml

Compilation (pyinstaller)

Two different options.

1. Onefile compilation.
   • Advantage: all binaries and resources are packed into a zip file.
   • Disadvantage: longer start time, as the file is first unpacked into a temporary folder. This requires all local resources to be searched for in the temporary folder. See template resource_path function

   pyinstaller main_onefile.spec

2. Onedir compilation
   • Advantage: Application starts faster
   • Disadvantage: Application is a folder. Best practice to create a shortcut for endusers in a safe enviroment

   pyinstaller main_standard.spec

Basic code quality agreements

1. Typing has to be used! Type of arguments and return values
2. Docstring documentation of all important functions and classes
3. Separate logic & GUI & IO
4. Avoid usage of global variables, no plain text variable declaration
5. Avoid side effects
6. Functional style with return values
7. Always start in main.py with in if name == "main": block
8. All import at the start of the file

Commit style:

Use specific tag

• feat:
• fix:
• refactor:
• release:

Conda Enviroments (not recommended)

Install enviroment from enviroment.yml file

Create Conda enviroment from enviroment.yml and overwritte venv name:

conda env create -f environment.yml -n <myenv>

Activate enviroment:

conda activate <my-env>

Create new enviroment.yml

conda env export --no-builds > environment.yml 

Install new dependencies

All new dependencies should be installed via conda to ensure reusability

Ensure the correct venv

conda activate <my-env>

Install via conda

conda install package