pyEntropy is a lightweight library built on top of NumPy that provides functions for computing various types of entropy for time series analysis.

The package provides systematic time-series feature extraction by combining established algorithms from statistics, time-series analysis, signal processing, and nonlinear dynamics with a robust feature selection algorithm. In this context, the term time-series is interpreted in the broadest possible sense, such that any types of sampled data or even event sequences can be characterised.

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

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.

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.

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.

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

This package provides the HTTP plugin for DVC.

Pytest test utilities used by 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.

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.

Turn a Git repository into an Artifact or Model Registry.

This package provides a client to interact with DVC Studio.

S3 plugin for DVC.

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

This package provides the SSH/SFTP plugin for DVC.

Common library for sending telemetry.

This package provides the data management subsystem for DVC.

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

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.