This package contains a Python module that forms the core of audit2allow, a part of the package policycoreutils. The sepolgen library contains: Reference Policy Representation, which are Objects for representing policies and the reference policy interfaces. It has objects and algorithms for representing access and sets of access in an abstract way and searching that access. It also has a parser for reference policy "headers". It contains infrastructure for parsing SELinux related messages as produced by the audit system. It has facilities for generating policy based on required access.

fMRIPrep is a fMRI data preprocessing pipeline that is designed to provide an easily accessible, state-of-the-art interface that is robust to variations in scan acquisition protocols and that requires minimal user input, while providing easily interpretable and comprehensive error and output reporting. It performs basic processing steps (coregistration, normalization, unwarping, noise component extraction, segmentation, skull-stripping, etc.) providing outputs that can be easily submitted to a variety of group level analyses, including task-based or resting-state fMRI, graph theory measures, and surface or volume-based statistics.

This package provides an object type which efficiently represents an array of booleans. Bitarrays are sequence types and behave very much like usual lists. Eight bits are represented by one byte in a contiguous block of memory. The user can select between two representations: little-endian and big-endian. All of the functionality is implemented in C. Methods for accessing the machine representation are provided. This can be useful when bit level access to binary files is required, such as portable bitmap image files. Also, when dealing with compressed data which uses variable bit length encoding, you may find this module useful.

This package provides an object type which efficiently represents an array of booleans. Bitarrays are sequence types and behave very much like usual lists. Eight bits are represented by one byte in a contiguous block of memory. The user can select between two representations: little-endian and big-endian. All of the functionality is implemented in C. Methods for accessing the machine representation are provided. This can be useful when bit level access to binary files is required, such as portable bitmap image files. Also, when dealing with compressed data which uses variable bit length encoding, you may find this module useful.

Elephant (Electrophysiology Analysis Toolkit) is an open-source, community centered library for the analysis of electrophysiological data in the Python programming language. The focus of Elephant is on generic analysis functions for spike train data and time series recordings from electrodes, such as the local field potentials (LFP) or intracellular voltages. In addition to providing a common platform for analysis code from different laboratories, the Elephant project aims to provide a consistent and homogeneous analysis framework that is built on a modular foundation. Elephant is the direct successor to Neurotools and maintains ties to complementary projects such as OpenElectrophy and spykeviewer.

This package provides a Python library intended for use in automated tests. One difficulty when testing software is that the code under test might need to read or write to files in the local file system. If the file system is not set up in just the right way, it might cause a spurious error during the test. The pyfakefs library provides a solution to problems like this by mocking file system interactions. In other words, it arranges for the code under test to interact with a fake file system instead of the real file system. The code under test requires no modification to work with pyfakefs.

Pingouin is a statistical package written in Python 3 and based mostly on Pandas and NumPy. Its features include

• ANOVAs: N-ways, repeated measures, mixed, ancova

• Pairwise post-hocs tests (parametric and non-parametric) and pairwise correlations

• Robust, partial, distance and repeated measures correlations

• Linear/logistic regression and mediation analysis

• Bayes Factors

• Multivariate tests

• Reliability and consistency

• Effect sizes and power analysis

• Parametric/bootstrapped confidence intervals around an effect size or a correlation coefficient

• Circular statistics

• Chi-squared tests

• Plotting: Bland-Altman plot, Q-Q plot, paired plot, robust correlation, and more

Eventlet is a concurrent networking library for Python that allows you to change how you run your code, not how you write it. It uses epoll or libevent for highly scalable non-blocking I/O. Coroutines ensure that the developer uses a blocking style of programming that is similar to threading, but provide the benefits of non-blocking I/O. The event dispatch is implicit, which means you can easily use Eventlet from the Python interpreter, or as a small part of a larger application.

Note: In Guix, this package assumes the environment variable EVENTLET_NO_GREENDNS defaults to yes. To try to use it, set it to anything else.

Pymodbus is a full Modbus protocol implementation using asyncio, tornado or twisted for its asynchronous communications core. It includes the following client features:

• full read/write protocol on discrete and register

• most of the extended protocol (diagnostic/file/pipe/setting/information)

• TCP, UDP, Serial ASCII, Serial RTU, and Serial Binary

• asynchronous and synchronous versions

• payload builder/decoder utilities

• pymodbus read eval print loop (REPL).

It also includes the following server features:

• can function as a fully implemented Modbus server

• TCP, UDP, Serial ASCII, Serial RTU, and Serial Binary

• asynchronous and synchronous versions

• full server control context (device information, counters, etc)

• a number of backing contexts (database, redis, sqlite, a slave device).

Grandalf is a Python package made for experimentations with graphs drawing algorithms. It is written in pure Python, and currently implements two layouts: the Sugiyama hierarchical layout and the force-driven or energy minimization approach. While not as fast or featured as graphviz or other libraries like OGDF (C++), it provides a way to walk and draw graphs no larger than thousands of nodes, while keeping the source code simple enough to tweak and hack any part of it for experimental purpose. With a total of about 1500 lines of Python, the code involved in drawing the Sugiyama (dot) layout fits in less than 600 lines. The energy minimization approach is comprised of only 250 lines!

Grandalf does only 2 not-so-simple things:

• computing the nodes (x,y) coordinates (based on provided nodes dimensions, and a chosen layout)

• routing the edges with lines or nurbs

It doesn’t depend on any GTK/Qt/whatever graphics toolkit. This means that it will help you find where to draw things like nodes and edges, but it’s up to you to actually draw things with your favorite toolkit.

PyThresh is a comprehensive and scalable Python toolkit for thresholding outlier detection likelihood scores in univariate/multivariate data. It has been written to work in tandem with PyOD and has similar syntax and data structures. However, it is not limited to this single library.

PyThresh is meant to threshold likelihood scores generated by an outlier detector. It thresholds these likelihood scores and replaces the need to set a contamination level or have the user guess the amount of outliers that may exist in the dataset beforehand. These non-parametric methods were written to reduce the user's input/guess work and rather rely on statistics instead to threshold outlier likelihood scores. For thresholding to be applied correctly, the outlier detection likelihood scores must follow this rule: the higher the score, the higher the probability that it is an outlier in the dataset. All threshold functions return a binary array where inliers and outliers are represented by a 0 and 1 respectively.

PyThresh includes more than 30 thresholding algorithms. These algorithms range from using simple statistical analysis like the Z-score to more complex mathematical methods that involve graph theory and topology.

bx-python provides tools for manipulating biological data, particularly multiple sequence alignments.

Asyncio IRC bot framework

Zope Internationalization Support

Software Heritage vault

Jalali datetime binding for python.

RFC1459 and IRCv3 protocol tokeniser

Pluggable object copying mechanism.

Vim-like file manager

Documentation at https://melpa.org/#/pythontest

Sip module support for PyQt5

Sphinx objects.inv inspection/manipulation tool.

Utilities for comparing tree sequences

Tensor-based Phase-Amplitude Coupling.