This is a package for image processing with Dask arrays. Features:

• Provides support for loading image files.

• Implements commonly used N-D filters.

• Includes a few N-D Fourier filters.

• Provides some functions for working with N-D label images.

• Supports a few N-D morphological operators.

Accelerate was created for PyTorch users who like to write the training loop of PyTorch models but are reluctant to write and maintain the boilerplate code needed to use multi-GPUs/TPU/fp16. It abstracts exactly and only the boilerplate code related to multi-GPUs/TPU/fp16 and leaves the rest of your code unchanged.

This module is inspired by GNU bash's variable expansion features. It can be used as an alternative to Python's os.path.expandvars function. A good use case is reading config files with the flexibility of reading values from environment variables using advanced features like returning a default value if some variable is not defined.

TensorFlow is a flexible platform for building and training machine learning models. It provides a library for high performance numerical computation and includes high level Python APIs, including both a sequential API for beginners that allows users to build models quickly by plugging together building blocks and a subclassing API with an imperative style for advanced research.

Scikit-TDA is a home for Topological Data Analysis Python libraries intended for non-topologists. This project aims to provide a curated library of TDA Python tools that are widely usable and easily approachable. It is structured so that each package can stand alone or be used as part of the scikit-tda bundle.

CellTypist is an automated cell type annotation tool for scRNA-seq datasets on the basis of logistic regression classifiers optimised by the stochastic gradient descent algorithm. CellTypist allows for cell prediction using either built-in (with a current focus on immune sub-populations) or custom models, in order to assist in the accurate classification of different cell types and subtypes.

The goal of this package is to provide a reference implementation of trait types for common data structures used in the scipy stack such as numpy arrays or pandas and xarray data structures. These are out of the scope of the main traitlets project but are a common requirement to build applications with traitlets in combination with the scipy stack.

cssselect2 is a straightforward implementation of CSS3 Selectors for markup documents (HTML, XML, etc.) that can be read by ElementTree-like parsers (including cElementTree, lxml, html5lib, etc.).

Unlike the Python package cssselect, it does not translate selectors to XPath and therefore does not have all the correctness corner cases that are hard or impossible to fix in cssselect.

Sign JSON objects with ED25519 signatures.

• More than one entity can sign the same object.

• Each entity can sign the object with more than one key making it easier to rotate keys

• ED25519 can be replaced with a different algorithm.

• Unprotected data can be added to the object under the "unsigned" key.

swiftsimio is a toolkit for reading data produced by the SWIFT astrophysics simulation code. It is used to ensure that all data have a symbolic unit attached, and can be used for visualisation. Another key feature is the use of the cell metadata in SWIFT snapshots to enable efficient reading of sub-regions.

ADARI (Astronomical DAta Reporting Infrastructure) is a system designed for creating graphical reports of astronomical data so that the quality of these products can be assessed. It has been designed from the ground up to be backend-agnostic, meaning the same ADARI code can be sent to a web plotting API, or a code-based plotting API, with no alteration.

Meshtastic Python is a small library which provides an easy API for sending and receiving messages over mesh radios. It provides access to any of the operations/data available in the device user interface or the Android applications. Events are delivered using a publish-subscribe model, and you can subscribe to only the message types you are interested in.

The zope.event package provides a simple event system, including:

- An event publishing API, intended for use by applications which are unaware of any subscribers to their events.

- A very simple synchronous event-dispatching system, on which more sophisticated event dispatching systems can be built. For example, a type-based event dispatching system that builds on zope.event can be found in zope.component.

Numarray and Numeric were the predecessors of NumPy. Numarray was created as an alternative to Numeric because the latter was cumbersome to use when efficiency for large array operations was a priority. Howver, numarray was less efficient for small arrays, and thus could not replace Numeric. Many packages of the early SciPy ecosystem supported both Numeric and numarray, with the choice made at build time.

Radio Beam is a simple toolkit for reading beam information from FITS headers and manipulating beams. Some example applications include:

• Convolution and deconvolution

• Unit conversion (Jy to/from K)

• Handle sets of beams for spectral cubes with varying resolution between channels

• Find the smallest common beam from a set of beams

• Add the beam shape to a matplotlib plot

Often when we want to label multiple points on a graph the text will start heavily overlapping with both other labels and data points. This can be a major problem requiring manual solution. However this can be largely automated by smart placing of the labels (difficult) or iterative adjustment of their positions to minimize overlaps (relatively easy). This library implements the latter option to help with matplotlib graphs.

Schematics is a Python library to combine types into structures, validate them, and transform the shapes of your data based on simple descriptions.

The internals are similar to ORM type systems, but there is no database layer in Schematics. Instead, building a database layer is easily made when Schematics handles everything except for writing the query. Schematics can be used for tasks where having a database involved is unusual.

pytreegrav is a package for computing the gravitational potential and/or field of a set of particles. It includes methods for brute-force direction summation and for the fast, approximate Barnes-Hut treecode method. For the Barnes-Hut method we implement an oct-tree as a numba jitclass to achieve much higher peformance than the equivalent pure Python implementation, without writing a single line of C or Cython.

Optimized einsum can significantly reduce the overall execution time of einsum-like expressions by optimizing the expression's contraction order and dispatching many operations to canonical BLAS, cuBLAS, or other specialized routines. Optimized einsum is agnostic to the backend and can handle NumPy, Dask, PyTorch, Tensorflow, CuPy, Sparse, Theano, JAX, and Autograd arrays as well as potentially any library which conforms to a standard API. See the documentation for more information.

This package provides a high-level, convenient API for managing internationalization/translation contexts in Python applications. There is a simple API for single-context applications, such as command line scripts which only need to translate into one language during the entire course of their execution. There is a more flexible, but still convenient API for multi-context applications, such as servers, which may need to switch language contexts for different tasks.

This is a small Python library that implements boolean algebra. It defines two base elements, TRUE and FALSE, and a Symbol class that can take on one of these two values. Calculations are done only in terms of AND, OR, and NOT---other compositions like XOR and NAND are emulated on top of them. Expressions are constructed from parsed strings or directly in Python.

The EDS-Pseudo project aims at detecting identifying entities in clinical documents, and was primarily tested on clinical reports at AP-HP's clinical data warehouse. The model is built on top of edsnlp, and consists in a hybrid model (rule-based + deep learning) for which we provide rules (eds-pseudo/pipes) and a training recipe. We also provide some fictitious templates and a script to generate a synthetic dataset.

Graph-tool is an efficient Python module for manipulation and statistical analysis of graphs (a.k.a. networks). Contrary to most other Python modules with similar functionality, the core data structures and algorithms are implemented in C++, making extensive use of template metaprogramming, based heavily on the Boost Graph Library. This confers it a level of performance that is comparable (both in memory usage and computation time) to that of a pure C/C++ library.

This package provides loaders and dumpers for PyYAML. Currently, an OrderedDict loader/dumper is implemented, allowing to keep items order when loading resp. dumping a file from/to an OrderedDict (Python 3.8+: Also regular dicts are supported and are the default items to be loaded to. As of Python 3.7 preservation of insertion order is a language feature of regular dicts.) It was originally mirrored from yamlordereddict.