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scaleinvariance

Simulate and analyze multifractal fields with Hurst exponent estimation and GSI scaling.

1|Updated Jul 24, 2025
Search Tags:#time-series#hurst#fbm#multifractal#scaleinvariance#fif#scaling-analysis
One-click install
npx skills add https://github.com/thomasdewitt/scaleinvariance --skill scaleinvariance
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: scaleinvariance
Source: https://github.com/thomasdewitt/scaleinvariance/tree/main/agent-skills/scaleinvariance
Command: npx skills add https://github.com/thomasdewitt/scaleinvariance --skill scaleinvariance

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Use when working with scaleinvariance package for multifractal field simulation and analysis - Hurst exponent estimation, fractional Brownian motion (fBm), fractionally integrated flux (multiplicative cascades) (FIF), intermittency analysis, and Generalized Scale Invariance (GSI). Also use for scaling analysis of time series, turbulence data, atmospheric fields, or any task involving self-similar, cascade, or multifractal processes or fields.

Core Features & Use Cases

  • Hurst exponent estimation (structure_function_hurst, haar_fluctuation_hurst, spectral_hurst) with multi-dimensional support.
  • FIF_ND and fBm simulations for N-D fields and 2D/3D turbulence-like datasets.
  • Intermittency analysis including C1, K(q) analytics, and empirical fits.
  • Generalized Scale Invariance (GSI) metrics and anisotropic scaling controls for target-driven simulations.

Quick Start

Install the scaleinvariance package and run a basic FIF_1D example to simulate a multifractal field and estimate the Hurst exponent.

Frequently Asked Questions about scaleinvariance

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I estimate the Hurst exponent for a multifractal time series?▼

This Skill simulates fractionally integrated flux multiplicative cascades and fractional Brownian motion for N-D fields. It generates turbulence-like multifractal datasets in 2D and 3D, providing simulated fields for testing scaling analysis and intermittency metrics.

Can I analyze intermittency and K(q) metrics on atmospheric spatial fields?▼

This Skill performs intermittency analysis including C1 and K(q) empirical fits on spatial fields and atmospheric data. It calculates intermittency metrics to characterize the multifractal nature of your atmospheric datasets.

Does this multifractal analysis toolkit support PyTorch backends?▼

Yes, the toolkit supports optional numpy or torch backends for core analyses. You can select your preferred backend for multifractal field simulation and scaling analysis, enabling hardware acceleration for large datasets.

What is Generalized Scale Invariance and how do I apply it to turbulence data?▼

Generalized Scale Invariance (GSI) extends scaling to anisotropic systems. This Skill provides GSI metrics and anisotropic scaling controls, allowing you to apply target-driven simulations and analyze anisotropic scaling on turbulence data.

Why do I need to specify axis parameters carefully when running scaling analysis?▼

Careful handling of inputs and axis specifications is required because multifractal analysis depends on correctly identifying the dimension of scale-invariance. Incorrect axis specifications will yield invalid Hurst exponent estimations or skewed intermittency metrics.

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