TACT Documentation
TACT (Turbulence Adjustment Computation Tool) is a Python package for processing, adjusting, and comparing LiDAR-based turbulence intensity measurements with traditional anemometer-based measurements.π Getting Started
New to TACT? Start here:- Installation Guide - Install TACT as a Python package
- Getting Started Guide - Complete walkthrough from installation to first results
- Data Import Guide - How to prepare and load your data
- Quick Start - Basic usage examples
β¨ Features
- Standardized Data Processing: Consistent formatting and validation for LiDAR and anemometer data
- Multiple Adjustment Methods:
- Baseline (no adjustment reference)
- Site-Specific Simple + Filter (SS-SF) - recommended
- Site-Specific Wind Speed (SSWS)
- Site-Specific Wind Speed + Std Dev (SSWSStd)
- DNV RP-0661 Validation: Industry-standard validation with MRBE/RRMSE metrics
- Professional Visualization: Publication-ready plots with DNV acceptance criteria
- Method Comparison Framework: Automated benchmarking of all methods
- Extensible Architecture: Easy to add custom adjustment methods
- Statistical Analysis Tools: Comprehensive metrics and regression analysis
π User Guides
Core Workflows
- Getting Started - Installation, first run, and basic usage
- Data Import Guide - Preparing and loading your data
- Adding Custom Models - Extending TACT with your own methods
Example Scripts
- main.py - Complete pipeline with DNV validation and visualization
- compare_all_methods.py - Compare all adjustment methods
- Example Test Scripts - Method-specific examples
π API Documentation
Core Components
- TACT Core - Main TACT class and factory
- Base Classes - Abstract base classes for methods
- Registry System - Method registration
Adjustment Methods
- Baseline - No adjustment (reference comparison)
- SS-SF - Site-Specific Simple + Filter (recommended)
- SSWS - Site-Specific Wind Speed
- SSWSStd - SS Wind Speed + Standard Deviation
Validation & Visualization
- DNV RP-0661 Validation (
tact/validation/dnv_rp0661.py) - Industry-standard validation - Visualization Suite (
tact/visualization/dnv_plots.py) - Professional plotting
Utilities
- Data Processing - Loading and processing data
- Statistics - Statistical computations
- Setup - Configuration and processors
π Method Comparison
See Method Comparison Results for detailed performance analysis of all implemented methods on example data. Quick Summary:- SS-SF: Best overall performance (MRBE: 37.8%, RRMSE: 111.1%)
- SSWSStd: Second best (MRBE: 37.7%, RRMSE: 121.6%)
- SSWS: Third (MRBE: 92.1%, RRMSE: 188.0%)
- Baseline: Reference (MRBE: 90.4%, RRMSE: 183.0%)
π§ Configuration
TACT uses JSON configuration files to map your data columns and set processing parameters:π― Quick Examples
Run Adjustment with Validation
Compare All Methods
Add Custom Method
π¦ Project Structure
π€ Contributing
We welcome contributions! See the Contributing Guide for:- Code style and standards
- Testing requirements
- Pull request process
- Development setup
π License
This project is licensed under the BSD 3-Clause License - see the LICENSE file for details.π Support
- Documentation: Youβre reading it!
- Issues: GitHub Issues
- Email: [email protected]
π Additional Resources
- Status Report - Current implementation status and comparison with legacy
- Method Comparison Results - Detailed performance analysis
- README - Project overview and quick start
Ready to get started? β Getting Started Guide

