Code

Our convention is to follow PEP8 unless there is a good reason to do otherwise.

One good reason is to get closer to mathematical notation in a given lecture.

Hence it’s fine to use capitals for matrices, etc.

Operators are typically surrounded by spaces, as in a * b and a + b, but we write a**b for \(a^b\).

Guiding principle

QuantEcon lecture’s should run in a base installation of Anaconda python.

Any packages (that are not included in anaconda) need to be installed at the top of the lecture.

An example:

In addition to what’s in Anaconda, this lecture will need the following libraries:

In the example above we install the quantecon and yfinance packages.

We use tags: [hide-output] as the output is not central to the lecture.

There are a couple of exceptions to this guideline.

• when the software involves specific configuration for the hardware (i.e. gpu computing), or

• if additional software needs to be installed on your system via apt or some other binary source.

JAX

When using jax you should not install jax at the top of your lecture.

This may install jax[cpu] which will run but is not the optimal configuration for executing the lecture.

The following admonition can be used.

GPU

This lecture is accelerated via hardware that has access to a GPU and JAX for GPU programming.

Free GPUs are available on Google Colab. To use this option, please click on the play icon top right, select Colab, and set the runtime environment to include a GPU.

Alternatively, if you have your own GPU, you can follow the instructions for installing JAX with GPU support. If you would like to install JAX running on the cpu only you can use pip install jax[cpu]

which will render as

GPU

This lecture is accelerated via hardware that has access to a GPU and JAX for GPU programming.

Free GPUs are available on Google Colab. To use this option, please click on the play icon top right, select Colab, and set the runtime environment to include a GPU.

Alternatively, if you have your own GPU, you can follow the instructions for installing JAX with GPU support. If you would like to install jax running on the cpu only you can use pip install jax[cpu]

The jax[gpu] package needs to be properly installed via Docker or GitHub Actions.

See also

Make sure the repo is compliant with the following configuration requirements such as Support files

Please consult with Matt McKay should you need to update these settings.

Binary packages with Python frontends

The graphviz package is a python interface to a local installation of graphviz and is useful for rendering DOT source code.

If you need to use graphviz you should:

1. Install pip install graphviz at the top of your lecture

2. Check if graphviz is getting installed in .github/workflows/ci.yml for preview builds

3. Add the below note admonition to your lecture.

graphviz

If you are running this lecture locally it requires graphviz to be installed on your computer. Installation instructions for graphviz can be found here

which will render as

graphviz

If you are running this lecture locally it requires graphviz to be installed on your computer. Installation instructions for graphviz can be found here