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Development environments

Apple Mac OS uses Python for its own operations, so it is very important to isolate the development environment from the operation of the OS to avoid compromising the integrity of the whole system. So virtual env must be used.

Virtual env

The goals are:

  • avoid installing softwares and libraries not often used on the native OS
  • describe the dependencies on library so programs developed 5 years ago should still run
  • easy to switch laptop
  • quicker provisioning than a VM running with Vagrant, still offering mounting host folder, running under localhost
  • use docker compose for each project to manage component dependencies
  • if needed pipenv can be used to set up virtual environment to isolate dependencies

Classical venv creation:

python -m venv .venv
# for MAC / Linux users
source ./venv/bin/activate
# for Windows
source ./venv/Scripts/activate

Then for each projects define a requirement.txt

Use the different docker images

The DockerfileForEnv in the current project defines an image for running python 3.7 with Flask, pytest, panda and other basic libraries.

To build the image you need docker engine and do the following

docker build -t jbcodeforce/python37 .

Then start the image as container with the command below, which also mount your local folder to the /home folder inside the docker container:

docker run -e DISPLAY=192.168.1.89:0 --name pythonenv -v $(pwd):/home/ -it --rm -p 5000:5000 jbcodeforce/python37 bash

Note

The script named `startPythonDocker.sh` performs this command.

The docker image includes pipenv for improving the dependency management.

The other Dockerfile for astrophysic is Here

Using graphics inside the python container

The approach is to run graphics program inside python interpreter, but the windows will appear on the host machine (the Mac). To do so we need a bidirectional communication between the docker container and the Mac. This is supported by the socat tool. To install it the first time do the following:

brew install socat

When installed, open a new terminal and start socat with the command:

socat TCP-LISTEN:6000,reuseaddr,fork UNIX-CLIENT:\"$DISPLAY\"

As the container is running X window system, we need to also install a X system on Mac. This is done via the Xquartz program:

brew install xquartz

Then start Xquartz from the application or using

open -a Xquartz

A white terminal window will pop up. The first time Xquartz is started, open up the preferences menu and go to the security tab. Then select “allow connections from network clients” to check it on.

See this note from Nils De Moor for more information.

Use pipenv

Pipenv offers the last best practices from other language to manage virtual environment and dependencies for Python. Adding and removing packages is also updating the dependencies descriptor file: Pipfile. It basically combine pip and virtualenv. It helps addressing build inconsistency that requirements.txt brings.

To install it on the mac:

brew install pipenv

When using the docker image you do not need to install pipenv on the host. It is also available in the docker image so the following commands should work from the bash inside the docker container.

# Create a new project using Python 3.7
pipenv --python 3.7
# start the virtual env shell
pipenv shell
# or start a python interpreter
pipenv run python
# or run a program with python interpreter
pipenv run python FaustEasiestApp.py
# install dependencies including dev
pipenv install --dev
#Check your installed dependencies for security vulnerabilities:
pipenv check
# print a pretty graph of all your installed dependencies.
pipenv graph 
# Uninstalls all packages not specified in Pipfile.lock.
pipenv clean
# lock dependencies
pipenv lock

Run the python interpreter

Start python in the container shell:

root@485cff8245df:/home#  python
Python 3.7.4 (default, Jul  9 2019, 00:06:43) 
[GCC 6.3.0 20170516] on linux
>>>

Use exit() to get out of the python interpreter, and Ctrl D for getting out of the Docker container.

vscode

To start a new Python project:

  1. create folder on the OS file system, then start code <created_dir_name>
  2. Verify Python extensions: The microsoft extension includes debugger and pylance. Install also pylint as a linter.

  3. In case of change the Python environment: in footer bar

  4. Product doc

  5. Tricks

  6. Ctrl+shift P to open command palette

  7. Ctrl K + ctrl T for changing the theme for all windows

Settings are at user level, so at the workspaces and windows level, or at workspace level.