asyncio testing in notebooks | Prototyping to tested code | Causality and function approximations | Keyword detection from scratch
Support | Getting started | View on nbviewer
How can you test asyncio code inside notebooks? This blog post sketches how to test asyncio code using pytest inside nobteooks. The post also discusses how threading can help to run multiple asyncio event loops inside the same interpreter.
How can pytest be used in Jupyter notebooks? And why does it make sense? This talk discusses how Jupyter notebooks form an effective environment for prototyping and how code can be refactored code into modules. A particular emphasis is placed on testing and the use of ipytest.
How do causal analysis and function approximations interact? This blog post demonstrates how results for tabular methods break down for models with finite capacity.
Detecting keywords in speech from data-collection to modelling, includes code to listen continuously for commands.
- chmp: support code as a python package
This project uses pipenv to organize dependencies and common scripts. To setup a virtual environment with all requirements use:
pipenv sync --dev
After that the following tasks can be performed:
# run all pre-commit tasks (docs, formatting, tests)
pipenv run precommit
# run pre-commit tasks and integration tests
pipenv run precommit-full
# run notebook integration tests
pipenv run integration
# run tests
pipenv run test
# update the documentation
pipenv run docs