ContextLab/hypertools

What it solves

High-dimensional data is difficult to visualize and interpret. HyperTools provides a streamlined pipeline to reduce the dimensionality of complex datasets and create visual explorations, allowing users to gain geometric insights into the structure of their data.

How it works

The library integrates several scientific Python tools (such as scikit-learn, matplotlib, and seaborn) to provide a single-function call interface for dimensionality reduction and plotting. It can handle multiple datasets simultaneously, align them into a shared space, and support various input types including text embeddings and market data. It also offers advanced visualization features like 2D/3D animations, morphing between shapes, and the overlay of smooth surfaces over convex hulls.

Who it’s for

Data scientists and researchers who need to visualize high-dimensional datasets, such as text embeddings, financial market data, or time-series trajectories, to identify patterns and structures.

Highlights

• Unified Pipeline: Reduces dimensionality and plots data in a single function call.
• Text Embedding Support: Directly embeds strings and projects them into 2D or 3D space.
• Dynamic Visualizations: Supports animations, morphing between point clouds, and serial revelation of data over time.
• Extensible Backend: Offers an optional interactive Plotly backend for interactive exploration.
• Analytical Tools: Includes built-in support for forecasting (via Kalman, ARIMA, and Chronos), missing-data imputation, and clustering.