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

Implements iterative statistics operators for mean, variance, high-order moments, extrema, covariance, threshold, quantile (experimental) and Sobol' indices.

The Enable project provides two related multi-platform packages for drawing GUI objects.

• Enable: an object drawing library that supports containment and event notification.

• Kiva: a multi-platform DisplayPDF vector drawing engine.

Quantities is designed to handle arithmetic and conversions of physical quantities, which have a magnitude, dimensionality specified by various units, and possibly an uncertainty.

This package introduces a function decorator preventing a function from being called more often than that allowed by the API provider. This should prevent API providers from banning your applications by conforming to their rate limits.

Compute a single hash of the file contents of a directory.

This package provides functions for 3D coordinate transformations.

h5io is a package designed to facilitate saving some standard Python objects into the forward-compatible HDF5 format. It is a higher-level package than h5py.

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.

Backport of pathlib ABCs.

This package provides a Zarr I/O backend for the HDMF.

Chaco is a Python package for building interactive and custom 2-D plots and visualizations. Chaco facilitates writing plotting applications at all levels of complexity, from simple scripts with hard-coded data to large plotting programs with complex data interrelationships and a multitude of interactive tools. While Chaco generates attractive static plots for publication and presentation, Chaco differs from tools like Matplotlib in that it also works well for dynamic interactive data visualization and exploration.

Mayavi is a general purpose, cross-platform tool for 2-D and 3-D scientific data visualization.

This package provides common CLI patterns on top of Click.

The Traits library is designed to enhance object-oriented programming in Python by providing a way to define and manage attributes of objects more effectively.

Confit is a complete and easy-to-use configuration framework aimed at improving the reproducibility of experiments by relying on the Python typing system, minimal configuration files and command line interfaces.

This package provides classes and functions for performing customizable robust ICA.

Protocol Buffers are a way of encoding structured data in an efficient yet extensible format. Google uses Protocol Buffers for almost all of its internal RPC protocols and file formats.

This module contains classes for the object model defined by the Static Analysis Results Interchange Format (SARIF) file format.

TensorFlow Probability is a library for probabilistic reasoning and statistical analysis in TensorFlow. As part of the TensorFlow ecosystem, TensorFlow Probability provides integration of probabilistic methods with deep networks, gradient-based inference via automatic differentiation, and scalability to large datasets and models via hardware acceleration (e.g., GPUs) and distributed computation.

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.

ScotchPy is a python module to interface the Scotch/PT-Scotch graph partitioner library.

JAX is Autograd and XLA, brought together for high-performance numerical computing, including large-scale machine learning research. With its updated version of Autograd, JAX can automatically differentiate native Python and NumPy functions. It can differentiate through loops, branches, recursion, and closures, and it can take derivatives of derivatives of derivatives. It supports reverse-mode differentiation (a.k.a. backpropagation) via grad as well as forward-mode differentiation, and the two can be composed arbitrarily to any order.

This package provides procedures to calculate statistics for Oxford Nanopore sequencing data and alignments.