The TraitsUI project provides a toolkit-independent GUI abstraction layer, which is used to support the “visualization” features of the Traits package. You can write a model using the Traits API and specify a GUI using the TraitsUI API (views, items, editors, etc.), and let TraitsUI and your selected toolkit back-end (Qt or Wx) take care of the details of displaying them.

This package includes Controller Area Network (CAN) related tools that can be used to:

• parse DBC, KCD, SYM, ARXML 4 and CDD files

• encode and decode CAN messages

• multiplex simple and extended signals

• diagnose DID encoding and decoding

• dump the CAN decoder output

• test CAN nodes

• generate C source code

• monitor the CAN bus

funcsigs is a backport of the PEP 362 function signature features from Python 3.3's inspect module. The backport is compatible with Python 2.6, 2.7 as well as 3.3 and up. 3.2 was supported by version 0.4, but with setuptools and pip no longer supporting 3.2, we cannot make any statement about 3.2 compatibility.

This package is a library to enable flexible and scalable operations on genomic interval dataframes in Python. Bioframe enables access to a rich set of dataframe operations. Working in Python enables rapid visualization and iteration of genomic analyses. The philosophy underlying bioframe is to enable flexible operations. Instead of creating a function for every possible use-case, we encourage users to compose functions to achieve their goals.

Liblarch is a Python library built to easily handle data structures such as lists, trees and acyclic graphs. There's also a GTK binding that will allow you to use your data structure in a Gtk.Treeview.

Liblarch support multiple views of one data structure and complex filtering. That way, you have a clear separation between your data themselves (Model) and how they are displayed (View).

emu.qmp is a QEMU Monitor Protocol (QMP) library written in Python. It is used to send QMP messages to running QEMU emulators. It can be used to communicate with QEMU emulators, the QEMU Guest Agent (QGA), the QEMU Storage Daemon (QSD), or any other utility or application that speaks QMP.

M2Crypto is a complete Python wrapper for OpenSSL featuring RSA, DSA, DH, EC, HMACs, message digests, symmetric ciphers (including AES); TLS functionality to implement clients and servers; HTTPS extensions to Python's httplib, urllib, and xmlrpclib; unforgeable HMAC'ing AuthCookies for web session management; FTP/TLS client and server; S/MIME; M2Crypto can also be used to provide TLS for Twisted. Smartcards supported through the Engine interface.

This package provides a Python module and framework for sans-io socks proxy client/server with couple io backends.

Features:

• Only TCP connect (no BIND, no UDP)

• Both client and server

• SOCKS versions: 4, 4a, 5

• SOCKSv5 auth: no auth, username/password

• Couple io backends: asyncio, trio, socketserver

• One-shot socks server (python -m siosocks)

Icegrams is a Python package that encapsulates a large trigram library for Icelandic. You can use Icegrams to obtain probabilities (relative frequencies) of over a million different unigrams (single words or tokens), or of bigrams (pairs of two words or tokens), or of trigrams. Icegrams is useful for instance in spelling correction, predictive typing, to help disabled people write text fast, and for various text generation, statistics, and modeling tasks.

nwb2bids reorganizes NWB files into a BIDS directory layout.

Features:

• Automatically renames NWB files and their directories to conform to BIDS conventions.

• Extracts relevant metadata from NWB files to populate BIDS sidecar TSV & JSON files.

• Currently supports BEP32 (micro-electrode electrophysiology) data types, such as extracellular (ecephys) and intracellular (icephys) electrophysiology, as well as associated behavioral events.

This package provides a unit tests framework backed by ML features and working in two modes:

• Testing: Test result in tmp_path is compared against a known reference. Any deviation in the files, causes a fail.

• Learning: The test result in tmp_path is taken as reference and is copied to the reference folder, which should be committed to version control and kept as reference.

The Qt for Python product enables the use of Qt6 APIs in Python applications. It lets Python developers utilize the full potential of Qt, using the PySide6 module. The PySide6 module provides access to the individual Qt modules such as QtCore, QtGui,and so on. Qt for Python also comes with the Shiboken6 CPython binding code generator, which can be used to generate Python bindings for your C or C++ code.

This Python module provides line editing functions similar to the default Emacs-style ones of GNU Readline. Unlike the Python standard library's readline package, this one allows access to those capabilities in settings outside of a standard command-line interface. It is especially well-suited to interfacing with Urwid, due to a shared syntax for describing key inputs.

Currently, all stateless Readline commands are implemented. Yanking and history are not supported.

The Intel HEX file format is widely used in microprocessors and microcontrollers area (embedded systems etc.) as the de facto standard for representation of code to be programmed into microelectronic devices. This package provides an intelhex Python library to read, write, create from scratch and manipulate data from Intel HEX file format. It also includes several convenience Python scripts, including "classic" hex2bin and bin2hex converters and more, those based on the library itself.

This project is an sklearn extension for machine learning time series or sequences. It provides an integrated pipeline for segmentation, feature extraction, feature processing, and a final estimator compatible with sklearn model evaluation and parameter optimization tools. Seglearn provides a flexible approach to multivariate time series and contextual data for classification, regression, and forecasting problems. Support and examples are provided for learning time series with classical machine learning and deep learning models.

The Qt for Python product enables the use of Qt5 APIs in Python applications. It lets Python developers utilize the full potential of Qt, using the PySide2 module. The PySide2 module provides access to the individual Qt modules such as QtCore, QtGui,and so on. Qt for Python also comes with the Shiboken2 CPython binding code generator, which can be used to generate Python bindings for your C or C++ code.

The Qt for Python product enables the use of Qt5 APIs in Python applications. It lets Python developers utilize the full potential of Qt, using the PySide2 module. The PySide2 module provides access to the individual Qt modules such as QtCore, QtGui,and so on. Qt for Python also comes with the Shiboken2 CPython binding code generator, which can be used to generate Python bindings for your C or C++ code.

Directory iteration function like os.listdir(), except that instead of returning a list of bare filenames, it yields DirEntry objects that include file type and stat information along with the name. Using scandir() increases the speed of os.walk() by 2-20 times (depending on the platform and file system) by avoiding unnecessary calls to os.stat() in most cases.

This package is part of the Python standard library since version 3.5.

ruptures is a Python library for off-line change point detection. This package provides methods for the analysis and segmentation of non-stationary signals. Implemented algorithms include exact and approximate detection for various parametric and non-parametric models. ruptures focuses on ease of use by providing a well-documented and consistent interface. In addition, thanks to its modular structure, different algorithms and models can be connected and extended within this package.

This library allows for contracting tensor networks or einsum expressions involving large numbers of tensors. It includes drop-in replacements for einsum and ncon, an explicit contraction tree object, a hyper optimizer that samples trees while tuning the generating meta-parameters, dynamic slicing for massive memory savings and parallelism, simulated annealing as an alternative strategy for optimizing and slicing, and integrations with other libraries such as numpy, opt_einsum, quimb, and others.

Envisage is a Python-based framework for building extensible applications, that is, applications whose functionality can be extended by adding 'plug-ins. Envisage provides a standard mechanism for features to be added to an application, whether by the original developer or by someone else. In fact, when you build an application using Envisage, the entire application consists primarily of plug-ins. In this respect, it is similar to the Eclipse and Netbeans frameworks for Java applications.

This package provides a Python wrapper around libevdev, taking advantage of libevdev's advanced event handling. Documentation is available at https://python-libevdev.readthedocs.io/en/latest/. libevdev makes it easy to:

• read and parse events from an input device;

• create a virtual input device and make it send events;

• duplicate an existing device and modify the event stream.

For information about libevdev, see: https://freedesktop.org/wiki/Software/libevdev/.

iniparse is a INI parser for Python which is:

• Compatible with ConfigParser: Backward compatible implementations of ConfigParser, RawConfigParser, and SafeConfigParser are included that are API-compatible with the Python standard library.

• Preserves structure of INI files: Order of sections & options, indentation, comments, and blank lines are preserved as far as possible when data is updated.

• More convenient: Values can be accessed using dotted notation (cfg.user.name), or using container syntax (cfg['user']['name']).

Autograd can automatically differentiate native Python and NumPy code. It can handle a large subset of Python's features, including loops, ifs, recursion and closures, and it can even take derivatives of derivatives of derivatives. It supports reverse-mode differentiation (a.k.a. backpropagation), which means it can efficiently take gradients of scalar-valued functions with respect to array-valued arguments, as well as forward-mode differentiation, and the two can be composed arbitrarily. The main intended application of Autograd is gradient-based optimization.