Python for Atmosphere and Ocean Scientists: Glossary

xarray and iris are the core Python libraries used in the atmosphere and ocean sciences.

Use conda to install and manage your Python environments.

Libraries such as xarray can make loading, processing and visualising netCDF data much easier.

The cmocean library contains colormaps custom made for the ocean sciences.

Define a function using def name(...params...).

The body of a function must be indented.

Call a function using name(...values...).

Use help(thing) to view help for something.

Put docstrings in functions to provide help for that function.

Specify default values for parameters when defining a function using name=value in the parameter list.

The readability of your code can be greatly enhanced by using numerous short functions.

Write (and import) modules to avoid code duplication.

Libraries such as argparse can be used the efficiently handle command line arguments.

Most Python scripts have a similar structure that can be used as a template.

Use git config to configure a user name, email address, editor, and other preferences once per machine.

git init initializes a repository.

git status shows the status of a repository.

Files can be stored in a project’s working directory (which users see), the staging area (where the next commit is being built up) and the local repository (where commits are permanently recorded).

git add puts files in the staging area.

git commit saves the staged content as a new commit in the local repository.

Always write a log message when committing changes.

git diff displays differences between commits.

git restore recovers old versions of files.

A local Git repository can be connected to one or more remote repositories.

You can use the SSH protocol to connect to remote repositories.

git push copies changes from a local repository to a remote repository.

git pull copies changes from a remote repository to a local repository.

For large arrays, looping over each element can be slow in high-level languages like Python.

Vectorised operations can be used to avoid looping over array elements.

Program defensively, i.e., assume that errors are going to arise, and write code to detect them when they do.

You can raise exceptions in your own code.

Put try-except blocks in programs to catch and handle exceptions.

Put assertions in programs to check their state as they run.

Use a logging framework instead of print statements to report program activity.

The are more advanced lessons you can read to learn about code testing.

It is possible (in only a few lines of code) to record the provenance of a data file or image.

Libraries such as dask and xarray can make loading, processing and visualising netCDF data much easier.

Dask can speed up processing through parallelism but care may be needed particularly with data chunking.