---

name: hypertools description: "Plot, load, analyze, text, and forecast with HyperTools." disable-model-invocation: true metadata: disco-role: operating license: MIT

HyperTools

Use this skill when a task involves the HyperTools package, its public APIs, or a workflow that combines plotting, loading, stage pipelines, text conversion, forecasting, or imputation.

Start here

• Read references/repo-provenance.md to confirm the generated skill matches the current package state.
• Read references/api-overview.md for the exported surface, common return shapes, and shared conventions.
• Read references/troubleshooting.md when imports, extras, file formats, or backend selection fail.
• Run scripts/check-install.py for a tiny import/config/plot/save-load smoke.
• Run scripts/check-backends.py when you need optional plotly, gensim, density3d, xlsx, or LSL coverage.

Install

pip install hypertools

Add only the extra that matches the task:

• hypertools[interactive] for plotly rendering and image export
• hypertools[io] for Excel support
• hypertools[gensim] for gensim text models
• hypertools[lsl] for Lab Streaming Layer streams
• hypertools[density3d] for 3-D density iso-surfaces
• hypertools[predict] for the Laplace forecaster
• hypertools[predict-hf] for Chronos forecasting
• hypertools[kaggle] for Kaggle datasets
• hypertools[text] for Hugging Face text embeddings
• hypertools[torch] for torch-backed autoencoders

Route map

sub-skills/visualization/SKILL.md

Use for hyp.plot(...), backend selection, animation, styling, save_path, streaming plots, surface/density, MultiIndex rendering, and forecast overlays.

sub-skills/io/SKILL.md

Use for hyp.load, hyp.save, source resolution, trust decisions, built-in datasets, and hyp.io.lsl_stream(...).

sub-skills/pipeline/SKILL.md

Use for manip, normalize, reduce, align, cluster, apply_model, Pipeline, stage ordering, model-spec grammar, and fitted-model reuse.

sub-skills/text/SKILL.md

Use for text2mat, text-aware hyp.plot(...), vectorizer=, semantic=, corpus=, sklearn text models, gensim wrappers, and Hugging Face fallback behavior.

sub-skills/forecasting/SKILL.md

Use for hyp.predict, hyp.impute, reusable forecasters/imputers, horizon rules, and forecast-overlay model selection.

Shared behavior

• HyperTools 1.0 uses a unified public surface: plot, analyze, reduce, align, normalize, describe, cluster, manip, predict, impute, load, save, apply_model, Pipeline, set_interactive_backend, HyperAnimation, io, and supported_models.
• The canonical analysis order is manip -> normalize -> reduce -> align -> cluster.
• plot returns a matplotlib Figure, a plotly Figure, or a HyperAnimation depending on backend and animation settings.
• analyze(..., cluster=...) returns transformed data; recover labels from the fitted cluster step when return_model=True.
• load returns raw data or a stream object, while save writes by filename extension.
• If a request spans routing boundaries, start with the owning sub-skill and hand off the adjacent concern there instead of widening this root router.

Quick smoke habits

• Prefer show=False for batch checks.
• Prefer tiny synthetic arrays, DataFrames, or corpora over repo examples when you only need to confirm package behavior.
• Keep backend-specific checks local to the matching sub-skill or the bundled smoke scripts.