pytype

A static type analyzer for Python code

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View the Project on GitHub google/pytype

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Development process

GitHub

  1. Fork https://github.com/google/pytype.

  2. Follow the instructions for installing from source, using your fork instead of the original repository. Now the pytype and pytype-single command-line tools are running from your local copy of the pytype source code. Make sure to use pip install -e . so that the tools will automatically pick up code edits.

  3. Make your change, adding tests as appropriate.

  4. Make sure the code still passes all tests and is free of lint and type errors.

  5. Push your change to your fork and open a PR against the original repo. If it’s your first time contributing to a Google open source project, please sign the Contributor License Agreement when prompted. Depending on what files your PR touches, it will be either merged directly or closed after being copied into the Google-internal codebase and re-exported to GitHub. You will be credited as the author either way.

Running pytype locally

Run the single-file analysis tool as

pytype-single some_file.py

to check some_file.py for type errors, or

pytype-single some_file.py -o -

to infer a pyi (dumped to stdout via -). The default target Python version is the version that pytype is running under; pass in -V<major>.<minor> to select a different version.

Note that the single-file tool does not handle dependency resolution, so you’ll have to supply .pyi files for all non-stdlib imports.

If you’re using the GitHub-installed tools, you can run the whole-project analysis tool, pytype, over the file to generate a .pytype directory that includes the necessary .pyi files. Then add

--module-name <module> --imports_info .pytype/imports/<module>.imports

to your pytype-single invocation, replacing <module> with the fully qualified module name.

Debugging

Use the --verbosity (or -v) option to print debugging output. The possible values range from -1 (“quiet”, log nothing) to 4 (“debug”, log everything).

pytype can be run under pdb, the Python debugger. Add:

import pdb; pdb.set_trace()

at the point in the code at which you want to enter debugging.

Profiling

For profiling a single file, pass in --profile <path> to turn on profiling and write the generated profile to <path>. The profile can then be analyzed using the pstats module.

Note that numbers are not directly comparable between runs; differences of 100% for different machines on otherwise identical code have happened. The relative rank of functions in the profile is stable between runs.