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 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.

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.

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.

CRDS is a package used for working with astronomical reference files for the HST and JWST telescopes. CRDS is useful for performing various operations on reference files or reference file assignment rules. CRDS is used to assign, check, and compare reference files and rules, and also to predict those datasets which should potentially be reprocessed due to changes in reference files or assignment rules. CRDS has versioned rules which define the assignment of references for each type and instrument configuration. CRDS has web sites corresponding to each project http://hst-crds.stsci.edu or https://jwst-crds.stsci.edu/ which record information about reference files and provide related services.

Basix is a finite element definition and tabulation runtime library.

Basix allows users to:

• evaluate finite element basis functions and their derivatives at a set of points;

• access geometric and topological information about reference cells;

• apply push forward and pull back operations to map data between a reference cell and a physical cell;

• permute and transform DOFs to allow higher-order elements to be use on arbitrary meshes;

• interpolate into and between finite element spaces.

Basix includes a range of built-in elements, and also allows the user to define their own custom elements.

This package provides the Python wrapper for Basix.

MARTINI is a modular package for the creation of synthetic resolved HI line observations (data cubes) of smoothed-particle hydrodynamics simulations of galaxies. The various aspects of the mock-observing process are divided logically into sub-modules handling the data cube, source, beam, noise,spectral model and SPH kernel. MARTINI is object-oriented: each sub-module provides a class (or classes) which can be configured as desired. For most sub-modules, base classes are provided to allow for straightforward customization. Instances of each sub-module class are given as parameters to the Martini class; a mock observation is then constructed by calling a handful of functions to execute the desired steps in the mock-observing process.

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.