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.

The indexed_gzip project is a Python extension which aims to provide a drop-in replacement for the built-in Python gzip.GzipFile class, the IndexedGzipFile. indexed_gzip was written to allow fast random access of compressed NIFTI image files (for which GZIP is the de-facto compression standard), but will work with any GZIP file.

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.

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.

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 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.

Salad is a schema language for describing JSON or YAML structured linked data documents. Salad schema describes rules for preprocessing, structural validation, and hyperlink checking for documents described by a Salad schema. Salad supports rich data modeling with inheritance, template specialization, object identifiers, object references, documentation generation, code generation, and transformation to RDF. Salad provides a bridge between document and record oriented data modeling and the Semantic Web.

This package contains public type stubs for python-pandas, following the convention of providing stubs in a separate package, as specified in PEP 561. The stubs cover the most typical use cases of python-pandas. In general, these stubs are narrower than what is possibly allowed by python-pandas, but follow a convention of suggesting best recommended practices for using python-pandas.

This Python package processes and generates instances for UFO files, glyphs and other data. It can, among other things:

• Collect source materials.

• Provide mutators for specific glyphs, font info, kerning so that other tools can generate partial instances.

• Support designspace format 4 with layers.

• Apply avar-like designspace bending.

• Apply rules.

• Generate actual UFO instances in formats 2 and 3.

• Round geometry as requested.

gcvb (generate compute validate benchmark) is a Python 3 module aiming at facilitating non-regression, validation and benchmarking of simulation codes. gcvb is not a complete tool of continuous integration (CI). It is rather a component of the testing part of a CI workflow. It can compare the different metrics of your computation with references that can be a file, depends of the 'configuration' or are absolute. This is a minimal version without the dashboard functionality.

This package provides a CLI tool and Python utility functions for manipulating SQLite databases. It's main features are:

• Pipe JSON (or CSV or TSV) directly into a new SQLite database file, automatically creating a table with the appropriate schema.

• Run in-memory SQL queries, including joins, directly against data in CSV, TSV or JSON files and view the results.

• Configure SQLite full-text search against your database tables and run search queries against them, ordered by relevance.

• Run transformations against your tables to make schema changes that SQLite ALTER TABLE does not directly support, such as changing the type of a column.

• Extract columns into separate tables to better normalize your existing data.

In order to be compatible with legacy web content when interpreting something like Content-Type: text/html; charset=latin1, tools need to use a particular set of aliases for encoding labels as well as some overriding rules. For example, US-ASCII and iso-8859-1 on the web are actually aliases for windows-1252, and a UTF-8 or UTF-16 BOM takes precedence over any other encoding declaration. The WHATWG Encoding standard defines all such details so that implementations do not have to reverse-engineer each other.

This module implements the Encoding standard and has encoding labels and BOM detection, but the actual implementation for encoders and decoders is Python’s.

This package provides easy download of thousands of pretrained models to perform tasks on different modalities such as text, vision, and audio.

These models can be applied on:

• Text, for tasks like text classification, information extraction, question answering, summarization, translation, and text generation, in over 100 languages.

• Images, for tasks like image classification, object detection, and segmentation.

• Audio, for tasks like speech recognition and audio classification.

Transformer models can also perform tasks on several modalities combined, such as table question answering, optical character recognition, information extraction from scanned documents, video classification, and visual question answering.

This package provides APIs to quickly download and use those pretrained models on a given text, fine-tune them on your own datasets and then share them with the community. At the same time, each Python module defining an architecture is fully standalone and can be modified to enable quick research experiments.

Transformers is backed by the three most popular deep learning libraries — Jax, PyTorch and TensorFlow — with a seamless integration between them.

PyCryptodome is a self-contained Python package of low-level cryptographic primitives. It's not a wrapper to a separate C library like OpenSSL. To the largest possible extent, algorithms are implemented in pure Python. Only the pieces that are extremely critical to performance (e.g., block ciphers) are implemented as C extensions.

You are expected to have a solid understanding of cryptography and security engineering to successfully use these primitives. You must also be able to recognize that some are obsolete (e.g., TDES) or even insecure (RC4).

It provides many enhancements over the last release of PyCrypto (2.6.1):

• Authenticated encryption modes (GCM, CCM, EAX, SIV, OCB)

• Accelerated AES on Intel platforms via AES-NI

• First-class support for PyPy

• Elliptic curves cryptography (NIST P-256 curve only)

• Better and more compact API (nonce and iv attributes for ciphers, automatic generation of random nonces and IVs, simplified CTR cipher mode, and more)

• SHA-3 (including SHAKE XOFs) and BLAKE2 hash algorithms

• Salsa20 and ChaCha20 stream ciphers

• scrypt and HKDF

• Deterministic (EC)DSA

• Password-protected PKCS#8 key containers

• Shamir’s Secret Sharing scheme

• Random numbers get sourced directly from the OS (and not from a CSPRNG in userspace)

• Cleaner RSA and DSA key generation (largely based on FIPS 186-4)

• Major clean-ups and simplification of the code base

This package provides drop-in compatibility with PyCrypto. It is one of two PyCryptodome variants, the other being python-pycryptodomex.

This package provides a Python library to communicate with Ledger Nano dongle.

Zope datetime.

Zope Location

grammars for babi

Zope Security Framework