Pydigitalwavetools is a Python library to parse, write and format digital wave files in VCD format, a standardized ASCII format used to store simulation data from Verilog and other hardware description languages.

This plugin makes it simple to test general data, images, files, and numeric tables by saving expected data in a data directory (courtesy of pytest-datadir) that can be used to verify that future runs produce the same data.

This project does not implement the parsing of pyproject.toml containing PEP 621 metadata. Instead, given a Python data structure representing PEP 621 metadata (already parsed), it will validate this input and generate a PEP 643-compliant metadata file (e.g. PKG-INFO).

This is the Cython-coded accelerator module for PyOpenGL.

This package provides a pytest plugin for writing tests for mypy plugins.

This package provides an API wrapper for programmatic management of PythonAnywhere services.

This package provides a Bootstrap-based Sphinx theme from the PyData community.

This package provides a pytest plugin to test that mypy produces a given output. As mypy can be told to display the type of an expression this allows you to check mypys type interference.

Python asyncio code is usually written in the form of coroutines, which makes it slightly more difficult to test using normal testing tools. pytest-asyncio provides useful fixtures and markers to make testing async code easier.

PyTorch Forecasting is a PyTorch-based package for forecasting with state-of-the-art deep learning architectures. It provides a high-level API and uses PyTorch Lightning to scale training on GPU or CPU, with automatic logging.

This package is a set of opinionated helpers for creating py.test fixtures for your smoke testing and integration testing. It strives to keep your environment definition declarative, like a docker-compose.yml. It embraces py.test fixture overloading.

PyTorch is a Python package that provides two high-level features:

• tensor computation (like NumPy) with strong GPU acceleration;

• deep neural networks (DNNs) built on a tape-based autograd system.

You can reuse Python packages such as NumPy, SciPy, and Cython to extend PyTorch when needed.

Note: currently this package does not provide GPU support.

PyTorch is a Python package that provides two high-level features:

• tensor computation (like NumPy) with strong GPU acceleration;

• deep neural networks (DNNs) built on a tape-based autograd system.

You can reuse Python packages such as NumPy, SciPy, and Cython to extend PyTorch when needed.

Note: currently this package does not provide GPU support.

PyTorch is a Python package that provides two high-level features:

• tensor computation (like NumPy) with strong GPU acceleration;

• deep neural networks (DNNs) built on a tape-based autograd system.

You can reuse Python packages such as NumPy, SciPy, and Cython to extend PyTorch when needed.

Note: currently this package does not provide GPU support.

pytest-random-order is a Pytest plugin that randomizes the order of tests. This can be useful to detect a test that passes just because it happens to run after an unrelated test that leaves the system in a favourable state. The plugin allows user to control the level of randomness they want to introduce and to disable reordering on subsets of tests. Tests can be rerun in a specific order by passing a seed value reported in a previous test run.

This package provides an interactive HTML debugger for Pyramid application development.

A Lexer for Pygments, following what could be a standardized pseudocode.

This package provides a pytest plugin to re-run tests to eliminate flaky failures.

This plugin helps to use fixtures in pytest.mark.parametrize, inspied by pytest-lazy-fixture.

This package provides fixture configuration utilities for the py.test testing framework.

pyxDamerauLevenshtein implements the Damerau-Levenshtein (DL) edit distance algorithm for Python in Cython for high performance.

This package provides a Pytest plugin for customizing string representations of doctest results. It can change the display hook used by doctest to render the object representations.

This package aims to make the transition away from pytz easier. It is intended for temporary usage only, and should allow you to drop your dependency on pytz while also giving your users notice that eventually you will remove support for the pytz-specific interface.

This plugin package provides a way to include information about the system, Python installation, and select dependencies in the header of the output when running pytest. It can be used with packages that are not affiliated with the Astropy project, but is optimized for use with astropy-related projects.