Python XMP Toolkit is a library for working with XMP metadata, as well as reading/writing XMP metadata stored in many different file formats.

Python XMP Toolkit is wrapping Exempi (using ctypes), a C/C++ XMP library based on Adobe XMP Toolkit, ensuring that future updates to the XMP standard are easily incorporated into the library with a minimum amount of work.

The proposal of this package is to provide XPath 1.0 and 2.0 selectors for Python's ElementTree XML data structures, both for the standard ElementTree library and for the lxml.etree library.

For lxml.etree this package can be useful for providing XPath 2.0 selectors, because lxml.etree already has its own implementation of XPath 1.0.

Itemloaders is a library that helps you collect data from HTML and XML sources. It comes in handy to extract data from web pages, as it supports data extraction using CSS and XPath Selectors.

It’s specially useful when you need to standardize the data from many sources. For example, it allows you to have all your casting and parsing rules in a single place.

This package provides a set of sparse matrix solvers used in geoscience.

All solvers work with scipy.sparse matricies, and a single or multiple right hand sides using numpy:

• L/U Triangular Solves

• Wrapping of SciPy matrix solvers (direct and indirect)

• Pardiso solvers now that MKL comes with conda!

• Mumps solver with nice error messages

The ItemAdapter class is a wrapper for data container objects, providing a common interface to handle objects of different types in an uniform manner, regardless of their underlying implementation.

Currently supported types are:

• scrapy.item.Item

• dict

• dataclass-based classes

• attrs-based classes

• pydantic-based classes

Additionally, interaction with arbitrary types is supported by implementing a pre-defined interface.

PickleShare is a small ‘shelve’-like datastore with concurrency support. Like shelve, a PickleShareDB object acts like a normal dictionary. Unlike shelve, many processes can access the database simultaneously. Changing a value in database is immediately visible to other processes accessing the same database. Concurrency is possible because the values are stored in separate files. Hence the “database” is a directory where all files are governed by PickleShare.

Macaroons, like cookies, are a form of bearer credential. Unlike opaque tokens, macaroons embed caveats that define specific authorization requirements for the target service, the service that issued the root macaroon and which is capable of verifying the integrity of macaroons it receives.

Macaroons allow for delegation and attenuation of authorization. They are simple and fast to verify, and decouple authorization policy from the enforcement of that policy.

Factory_boy is a fixtures replacement based on thoughtbot’s factory_girl. As a fixtures replacement tool, it aims to replace static, hard to maintain fixtures with easy-to-use factories for complex object. Instead of building an exhaustive test setup with every possible combination of corner cases, factory_boy allows you to use objects customized for the current test, while only declaring the test-specific fields.

This Python package provides high-level utilities to read and write a variety of Python types from and to HDF5 formatted files. This package also provides support for MATLAB MAT v7.3 formatted files, which are HDF5 files with a different extension and some extra metadata. Because HDF5 and MAT files might need to be read from untrusted sources, pickling is avoided in this package.

This is a implementation of immutable linked lists for Python. It contains nil (the empty linked list) and a Pair class for nodes. Since a linked list is treated as immutable, it is hashable, and its length can be retrieved in constant time. Some of the terminology is inspired by LISP. It is possible to create an improper list by creating a Pair with a non-list cdr.

Table logger using Rich, aimed at PyTorch Lightning logging.

Features

• display your training logs with pretty rich tables

• describe your fields with goal, format and name

• a field descriptor can be matched with any regex

• a field name can be computed as a regex substitution

• works in Jupyter notebooks as well as in a command line

• integrates easily with Pytorch Lightning

The fastbencode Python package implements the bencode serialization format for storing and transmitting loosely structured data, originally used by BitTorrent.

The format can encode four different types of values: byte strings, integers, lists, and dictionaries (associative arrays). It's simple and unaffected by endianness,

This package includes both a pure-Python version and an optional C extension based on Cython. Both provide the same functionality, but the C version has significantly better performance.

FFCx is a compiler for finite element variational forms.

From a high-level description of the form in the UFL, it generates efficient low-level C code that can be used to assemble the corresponding discrete operator (tensor). In particular, a bilinear form may be assembled into a matrix and a linear form may be assembled into a vector.

This package provides the CLI and Python library.

pytest-perf makes it easy to compare works by creating two installs, the control and the experiment, and measuring the performance of some Python code against each. Under the hood, it uses the pip-run command to install from the upstream main branch (e.g. https://github.com/jaraco/pytest-perf) for the control and from . for the experiment. It then runs each of the experiments against each of the environments.

This package provides:

• PEP 561 typing stubs packages for the Trio project packages:

  • trio (trio-stubs)

  • outcome (outcome-stubs)

  • async_generator (async_generator-stubs)

• A package trio_typing containing types that Trio programs often want to refer to (AsyncGenerator[Y, S] and TaskStatus[T]) and a mypy plugin that smooths over some limitations in the basic type hints.

Advanced Python Scheduler (APScheduler) is a Python library that lets you schedule your Python code to be executed later, either just once or periodically.

You can add new jobs or remove old ones on the fly as you please. If you store your jobs in a database, they will also survive scheduler restarts and maintain their state. When the scheduler is restarted, it will then run all the jobs it should have run while it was offline.

Advanced Python Scheduler (APScheduler) is a Python library that lets you schedule your Python code to be executed later, either just once or periodically.

You can add new jobs or remove old ones on the fly as you please. If you store your jobs in a database, they will also survive scheduler restarts and maintain their state. When the scheduler is restarted, it will then run all the jobs it should have run while it was offline.

This package implements Jupyter/IPython magic commands for interacting with the SLURM workload manager. SLURM magic simply wraps command-line executables and the commands themselves should look like their command-line counterparts. Commands are spawned via subprocess and output captured in the notebook. Whatever arguments are accepted by a SLURM command line executable are also accepted by the corresponding magic command---e.g., %salloc, %sbatch, etc.

The FInite element Automatic Tabulator (FIAT) supports generation of arbitrary order instances of the Lagrange elements on lines, triangles, and tetrahedra. It is also capable of generating arbitrary order instances of Jacobi-type quadrature rules on the same element shapes. Further, H(div) and H(curl) conforming finite element spaces such as the families of Raviart-Thomas, Brezzi-Douglas-Marini and Nedelec are supported on triangles and tetrahedra. Upcoming versions will also support Hermite and nonconforming elements.

FIAT is part of the FEniCS Project.

This package provides Numba-accelerated implementations of common SciPy probability distributions and others used in particle physics.

The supported distributions are:

• Uniform

• (Truncated) Normal

• Log-normal

• Poisson

• Binomial

• (Truncated) Exponential

• Student's t

• Voigtian

• Crystal Ball

• Generalised double-sided Crystal Ball

• Tsallis-Hagedorn, a model for the minimum bias pT distribution

• Q-Gaussian

• Bernstein density (not normalized to unity)

• Cruijff density (not normalized to unity)

• CMS-Shape

• Generalized Argus

Fiona is GDAL’s neat and nimble vector API for Python programmers. Fiona is designed to be simple and dependable. It focuses on reading and writing data in standard Python IO style and relies upon familiar Python types and protocols such as files, dictionaries, mappings, and iterators instead of classes specific to OGR. Fiona can read and write real-world data using multi-layered GIS formats and zipped virtual file systems and integrates readily with other Python GIS packages such as pyproj, Rtree, and Shapely.

The pyjson-tricks package brings several pieces of functionality to python handling of JSON files:

• Store and load numpy arrays in human-readable format.

• Store and load class instances both generic and customized.

• Store and load date/times as a dictionary (including timezone).

• Preserve map order using OrderedDict.

• Allow for comments in json files by starting lines with #.

• Sets, complex numbers, Decimal, Fraction, enums, compression, duplicate keys, pathlib Paths, bytes, ...

As well as compression and disallowing duplicate keys.

OpenStack Object Storage (code-named Swift) creates redundant, scalable object storage using clusters of standardized servers to store petabytes of accessible data. It is not a file system or real-time data storage system, but rather a long-term storage system for a more permanent type of static data that can be retrieved, leveraged, and then updated if necessary. Primary examples of data that best fit this type of storage model are virtual machine images, photo storage, email storage and backup archiving. Having no central "brain" or master point of control provides greater scalability, redundancy and permanence.

TensorStore is a C++ and Python software library designed for storage and manipulation of large multi-dimensional arrays that:

• Provides advanced, fully composable indexing operations and virtual views.

• Provides a uniform API for reading and writing multiple array formats, including zarr and N5.

• Natively supports multiple storage systems, such as local and network filesystems, Google Cloud Storage, Amazon S3-compatible object stores, HTTP servers, and in-memory storage.

• Offers an asynchronous API to enable high-throughput access even to high-latency remote storage.

• Supports read caching and transactions, with strong atomicity, isolation, consistency, and durability (ACID) guarantees.

• Supports safe, efficient access from multiple processes and machines via optimistic concurrency.