When writing applications, finding the right location to store user data and configuration varies per platform. Even for single-platform apps, there may by plenty of nuances in figuring out the right location. This small Python module determines the appropriate platform-specific directories, e.g. the ``user data dir''.

Keyrings in this package may have security risks or other implications. These backends were extracted from the main keyring project to make them available for those who wish to employ them, but are discouraged for general production use. Include this module and use its backends at your own risk.

Scikit-build is an improved build system generator for CPython C/C++/Fortran/Cython extensions. It has support for additional compilers, build systems, cross compilation, and locating dependencies and determining their build requirements. The scikit-build package is fundamentally just glue between the setuptools Python module and CMake.

This package provides a full Python driver for the Keithley 2600B series of source measurement units. This driver provides access to base commands and higher level functions such as IV measurements, transfer and output curves, etc. Base commands replicate the functionality and syntax from the Keithley's internal TSP Lua functions.

TorchMetrics is a collection of 100+ PyTorch metrics implementations and an easy-to-use API to create custom metrics. It offers:

• A standardized interface to increase reproducibility

• Reduces boilerplate

• Automatic accumulation over batches

• Metrics optimized for distributed-training

• Automatic synchronization between multiple devices

Tortoise ORM is an easy-to-use asyncio ORM (Object Relational Mapper) inspired by Django. Tortoise ORM was built with relations in mind and admiration for the excellent and popular Django ORM. It's engraved in its design that you are working not with just tables, you work with relational data.

Tortoise ORM is an easy-to-use asyncio ORM (Object Relational Mapper) inspired by Django. Tortoise ORM was built with relations in mind and admiration for the excellent and popular Django ORM. It's engraved in its design that you are working not with just tables, you work with relational data.

Tortoise ORM is an easy-to-use asyncio ORM (Object Relational Mapper) inspired by Django. Tortoise ORM was built with relations in mind and admiration for the excellent and popular Django ORM. It's engraved in its design that you are working not with just tables, you work with relational data.

This package provides mock helpers for SQLAlchemy that makes it easy to mock an SQLAlchemy session while preserving the ability to do asserts.

Normally Normally SQLAlchemy's expressions cannot be easily compared as comparison on binary expression produces yet another binary expression, but this library provides functions to facilitate such comparisons.

Tortoise ORM is an easy-to-use asyncio ORM (Object Relational Mapper) inspired by Django. Tortoise ORM was built with relations in mind and admiration for the excellent and popular Django ORM. It's engraved in its design that you are working not with just tables, you work with relational data.

This package provides a fast implementation of the HTML5 parsing spec for Python. Parsing is done in C using a variant of the gumbo parser. The gumbo parse tree is then transformed into an lxml tree, also in C, yielding parse times that can be a thirtieth of the html5lib parse times.

This library provides ordinary differential equation (ODE) solvers implemented in PyTorch. Backpropagation through ODE solutions is supported using the adjoint method for constant memory cost. For usage of ODE solvers in deep learning applications.

As the solvers are implemented in PyTorch, algorithms in this repository are fully supported to run on the GPU.

Astro-SCRAPPY is designed to detect cosmic rays in images (numpy arrays), based on Pieter van Dokkum's L.A.Cosmic algorithm. Much of this was originally adapted from cosmics.py written by Malte Tewes. This is designed to be as fast as possible so some of the readability has been sacrificed, specifically in the C code.

This package provides a drop-in replacement for the original LooseVersion. It implements an identical interface and comparison logic to LooseVersion. The only major change is that a looseversion.LooseVersion is comparable to a distutils.version.LooseVersion, which means tools should not need to worry whether all dependencies that use LooseVersion have migrated.

Mypy is an optional static type checker for Python that aims to combine the benefits of dynamic typing and static typing. Mypy combines the expressive power and convenience of Python with a powerful type system and compile-time type checking. Mypy type checks standard Python programs; run them using any Python VM with basically no runtime overhead.

By design asyncio does not allow its event loop to be nested. This presents a practical problem: when in an environment where the event loop is already running it's impossible to run tasks and wait for the result. This module patches asyncio to allow nested use of asyncio.run and loop.run_until_complete.

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.

The pytest-xdist plugin extends py.test with some unique test execution modes: parallelization, running tests in boxed subprocesses, the ability to run tests repeatedly when failed, and the ability to run tests on multiple Python interpreters or platforms. It uses rsync to copy the existing program code to a remote location, executes there, and then syncs the result back.

PyDispatcher is an enhanced version of Patrick K. O’Brien’s original dispatcher.py module. It provides the Python programmer with a robust mechanism for event routing within various application contexts.

Included in the package are the robustapply and saferef modules, which provide the ability to selectively apply arguments to callable objects and to reference instance methods using weak-references.

pdfminer.six is a community maintained fork of the original PDFMiner. It is a tool for extracting information from PDF documents. It focuses on getting and analyzing text data. Pdfminer.six extracts the text from a page directly from the sourcecode of the PDF. It can also be used to get the exact location, font or color of the text.

The library allows a process to change its title (as displayed by system tools such as ps and top).

Changing the title is mostly useful in multi-process systems, for example when a master process is forked: changing the children's title allows identifying the task each process is busy with. The technique is used by PostgreSQL and the OpenSSH Server for example.

Aioresponses is a helper to mock/fake web requests in python aiohttp package. For requests module there are a lot of packages that help us with testing (eg. httpretty, responses, requests-mock). When it comes to testing asynchronous HTTP requests it is a bit harder (at least at the beginning). The purpose of this package is to provide an easy way to test asynchronous HTTP requests.

Openapi-core is a Python library that adds client-side and server-side support for the OpenAPI Specification v3. It has features such as:

• Validation of requests and responses

• Schema casting and unmarshalling

• Media type and parameters deserialization

• Security providers (API keys, Cookie, Basic and Bearer HTTP authentications)

• Custom deserializers and formats

• Integration with libraries and frameworks.

This package implements a functionality to solve automatic numerical differentiation problems in one or more variables. Finite differences are used in an adaptive manner, coupled with a Richardson extrapolation methodology to provide a maximally accurate result. The user can configure many options like; changing the order of the method or the extrapolation, even allowing the user to specify whether complex-step, central, forward or backward differences are used.