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Setting up your local environment

You need a computer which can run python 3.7, and preferably an IDE and debugger. A good example is PyCharm, which is free for students.

You may use a native python distribution (recommended for Mac/Linux) for this course, or a docker-based python workflow. The latter may be required on windows, depending on the pset.

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Python

You need a good base python installation and terminal/CLI. Many IDE's provide the latter, or you can use a system one.

Mac/Linux users can use the default system python if it is available on their system or default package manager (just don't pip install anything there!). Mac users can also use homebrew to install python 3.7. If, however, your system does not provide the correct version of python, you should use a conda distro as instructed below. I do not recommend pyenv.

Recommended on Windows: You can use conda to create a base python environment, as well as using the shell in windows:

conda create -n py37 python=3.7
conda activate py37
pip install pipenv
pipenv install ...

Pipenv

We will use pipenv throughout the course to build repeatable, fully specified python environments. Pset 0 has no normal package requirements, so you could get away without setting up pipenv, but it would be a better idea to give it a shot now!

Instead of pip install-ing anything (eg, to run the test suite), use the included Pipfile to build a new virtual environment with your project dependencies.

Docker

This course will explore Docker, a containerization system, to help develop within fully repeatable environments. You may need to use docker immediately if you are developing on Windows (the unittests use signals). You should experiment with docker regardless, though it is not critical yet.

Older versions of Windows and Mac can install legacy Docker Toolbox or VirtualBox if necessary.

Installation and setup

You can install Docker and Docker Compose to get a fully repeatable environment. You can then set up PyCharm to use your docker-compose interpreter, or do things manually on the terminal via:

# If your Pipfile changes, or first time
docker-compose build

# Run python in the local directory
docker-compose run app python some_file.py

# Drop into an ipython shell in the container, if it is installed
docker-compose run app ipython

# Run unittests
docker-compose run app pytest

Docker is not strictly necessary to complete this initial set - just ensure you can get it running and will be ready to dive into the concepts if necessary later.