aeon is an open-source toolkit for time series machine learning. Fully compatible with scikit-learn, it brings together the latest machine learning methods alongside a wide range of classical approaches for tasks such as forecasting, clustering, and classification.

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

sktime is a library for time series analysis in Python. It provides a unified interface for multiple time series learning tasks. Currently, this includes forecasting, time series classification, clustering, anomaly/changepoint detection, and other tasks. It comes with time series algorithms and scikit-learn compatible tools to build, tune, and validate time series models.

This is a Python library for time series data mining. It provides tools for time series classification, clustering and forecasting.

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.

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 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 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.

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.

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.

This package provides the SSH/SFTP plugin for DVC.

Pytest test utilities used by DVC.

Common library for sending telemetry.

This package provides the data management subsystem for 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.

S3 plugin for DVC.

Turn a Git repository into an Artifact or Model Registry.

This package provides the filesystem and object-db level abstractions used by DVC.

This package provides the Celery-based task queue used by DVC.

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.

This package provides the HTTP plugin for DVC.