This package provides a Python implementation of catch22, a collection of 22 time-series features.

PyTorch Forecasting is a PyTorch-based package for forecasting with state-of-the-art deep learning architectures. It provides a high-level API and uses PyTorch Lightning to scale training on GPU or CPU, with automatic logging.

skbase provides base classes for creating scikit-learn-like parametric objects, along with tools to make it easier to build your own packages that follow these design patterns.

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.

This package provides a library implementing the Mapper algorithm in Python. KeplerMapper can be used for visualization of high-dimensional data and 3D point cloud data. KeplerMapper can make use of Scikit-Learn API compatible cluster and scaling algorithms.

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.

This package provides Python bindings for PHAT, a software library which contains methods for computing the persistence pairs of a filtered cell complex represented by an ordered boundary matrix with Z2 coefficients.

Tadasets provides various utilities for creating and loading data sets that are useful for Topological Data Analysis. Currently, we provide several synthetic data sets with particular topological features.

This library provides easy to use constructors for custom filtrations that are suitable for use with Phat. Phat currently provides a clean interface for persistence reduction algorithms for boundary matrices. This tool helps bridge the gap between data and boundary matrices. Currently, we support construction of Alpha, Rips, and Cech filtrations.

Typst is a markup-based typesetting system that is designed to be as powerful as LaTeX while being much easier to learn and use. Features include built-in markup for math typesetting, bibliography management and other common tasks, an extensible scripting system for uncommon tasks, incremental compilation, and intuitive error messages.

Pytest test utilities used by DVC.

SSH/SFTP plugin for DVC.

Extensible task queue used in DVC.

Client to interact with DVC Studio.

S3 plugin for DVC.

SCM wrapper and fsspec filesystem for Git for use in DVC.

dvc-render is a library for rendering data stored in DVC plots format into different output formats, like Vega. It can also generate HTML and MarkDown reports containing multiple plots.

Turn a Git repository into an Artifact or Model Registry.

HTTP plugin for DVC.

Common library for sending telemetry.

DVC is a free, open-source tool for data management, ML pipeline automation, and experiment management. This helps data science and machine learning teams manage large datasets.

Xbae is a Motif-based widget set consisting of the XbaeMatrix, XbaeCaption, and XbaeInput widgets. The XbaeMatrix widget displays a grid of cells like a spreadsheet. The cell array is scrollable, editable, and configurable in appearance. Each cell usually displays text. Pixmaps can also be displayed but are not editable.

uacme is a lightweight client for the RFC8555 ACMEv2 protocol, written in plain C with minimal dependencies. The ACMEv2 protocol allows a Certificate Authority such as Let's Encrypt and an applicant to automate the process of verification and certificate issuance.

uacme calls an external hook program with the tokens required for domain authorization. The hook program can be an executable, shell script, perl script, python script, or any file that the operating system can execute.

uacme is ACME challenge agnostic. It provides the user or hook program with all tokens and information required to complete any challenge type but leaves the task of setting up and cleaning up the challenge environment to the user or hook.

uacme also includes ualpn, a lightweight proxying tls-alpn-01 challenge responder compliant with RFC8737 and RFC8738.