Features

TACT provides a complete toolkit for processing, adjusting, and validating LiDAR turbulence intensity measurements.

Adjustment Methods

TACT includes multiple adjustment methods with an extensible framework for adding more:

SS-SF

Site-Specific Simple + FilterDirect TI regression with filtering. Avoids error propagation through WS/SD calculations.

SSWSStd

Wind Speed + Std DeviationDual adjustment approach for both wind speed and standard deviation components.

SSWS

Wind Speed AdjustmentAdjusts wind speed measurements only. Simpler single-parameter approach.

Baseline

No AdjustmentReference comparison without correction. Useful for benchmarking performance.

Performance varies by dataset characteristics. Use compare_all_methods.py to evaluate all methods on your specific data and determine which performs best for your site conditions.

DNV RP-0661 Validation

Built-in industry-standard validation ensures your adjustments meet regulatory requirements.

from tact.validation import validate_dnv_rp0661

validation = validate_dnv_rp0661(
    adjusted_data=results["adjusted_data"],
    reference_col="ref_ti",
    adjusted_col="adjTI_RSD_TI",
    wind_speed_col="ref_ws",
    bin_col="bins",
    criteria_type="LV"
)

print(validation['overall'])  # Overall metrics
print(validation['by_bin'])   # Per-bin details

Validation Metrics

MRBE - Mean Relative Bias Error measures systematic biasRRMSE - Relative Root Mean Square Error measures overall accuracyIncludes per-bin analysis and automatic pass/fail determination

Criteria Types

LV (Load Verification): MRBE ≤ 5%, RRMSE ≤ 15% for turbine load calculationsPC (Power Curve): Stricter criteria for power performance testing

Visualization

Generate validation plots with a single function call.

from tact.visualization import plot_dnv_validation

plot_dnv_validation(
    validation_results=validation,
    adjusted_data=results["adjusted_data"],
    reference_col="ref_ti",
    adjusted_col="adjTI_RSD_TI",
    unadjusted_col="rsd_ti",
    wind_speed_col="ref_ws",
    bin_col="bins",
    method_name="SS-SF",
    output_dir="output/plots"
)

Example Output

MRBE by wind speed bin with DNV acceptance limits

RRMSE by wind speed bin with acceptance thresholds

Adjusted vs reference TI with 1:1 line and regression

Before/after adjustment comparison by wind speed bin

Data Processing Pipeline

Standardized data processing ensures consistency and quality.

Data Loading

from tact.utils.load_data import load_data
data = load_data("your_data.csv")

Automatic CSV parsing
Column validation
Missing data detection

Binning

from tact.utils.setup_processors import setup_processors
bp, tp, sp = setup_processors("config.json")
data = bp.process(data)

Configurable wind speed bins
Automatic bin assignment
Statistical aggregation

TI Calculation

data = tp.process(data)

Turbulence intensity computation
Representative TI calculation
Standard deviation handling

Statistical Analysis

data = sp.process(data)

Per-bin statistics
Correlation analysis
Quality metrics

Extensible Architecture

Add your own adjustment methods with minimal code.

from tact.core.base import AdjustmentMethod
from tact.core.registry import AdjustmentRegistry

@AdjustmentRegistry.register("my-method")
class MyMethod(AdjustmentMethod):
    def required_model_parameters(self):
        return {"config_path": str}

    def required_data_columns(self):
        return ["reference.wind_speed", "rsd.height_1.wind_speed"]

    def adjust(self, data, parameters):
        # Your adjustment logic here
        adjusted_data = data.copy()
        # ... implement your algorithm ...
        return {"adjusted_data": adjusted_data}

from tact import TACT

tact = TACT()
results = tact.adjust(
    data=data,
    method="my-method",
    parameters={"config_path": "config.json"}
)

See the Adding Custom Models guide for a complete tutorial with examples.

Configuration System

Flexible JSON-based configuration for easy customization.

{
    "input_data_column_mapping": {
        "reference": {
            "wind_speed": "ref_ws",
            "wind_speed_std": "ref_sd",
            "turbulence_intensity": "ref_ti"
        },
        "rsd": {
            "primary": {
                "wind_speed": "rsd_ws",
                "wind_speed_std": "rsd_sd",
                "turbulence_intensity": "rsd_ti"
            }
        }
    },
    "binning_config": {
        "bin_size": 1.0,
        "bin_min": 4.0,
        "bin_max": 20.0
    }
}

Map your CSV columns to TACT’s expected format (supports any column names), configure wind speed bins for analysis, and define method-specific parameters. See the Configuration Guide for detailed setup instructions.

Python Package Features

Importable Module

Use TACT in your own Python scripts from any directory

from tact import TACT

Standalone Scripts

Run included example scripts directly

python main.py
python compare_all_methods.py

Jupyter Compatible

Works seamlessly in Jupyter notebooks for interactive analysis

Minimal Dependencies

Only requires standard scientific Python packages (numpy, pandas, matplotlib)

Next Steps

Getting Started

Run your first adjustment

Compare Methods

Evaluate all adjustment methods

Add Custom Method

Extend TACT for your needs

Getting Started

Guides

Advanced

Adjustment Methods

SS-SF

SSWSStd

SSWS

Baseline

DNV RP-0661 Validation

Validation Metrics

Criteria Types

Visualization

Example Output

Data Processing Pipeline

Extensible Architecture

Configuration System

Python Package Features

Importable Module

Standalone Scripts

Jupyter Compatible

Minimal Dependencies

Next Steps

Getting Started

Compare Methods

Add Custom Method

​Adjustment Methods

SS-SF

SSWSStd

SSWS

Baseline

​DNV RP-0661 Validation

Validation Metrics

Criteria Types

​Visualization

​Example Output

​Data Processing Pipeline

​Extensible Architecture

​Configuration System

​Python Package Features

Importable Module

Standalone Scripts

Jupyter Compatible

Minimal Dependencies

​Next Steps

Getting Started

Compare Methods

Add Custom Method

Adjustment Methods

DNV RP-0661 Validation

Visualization

Example Output

Data Processing Pipeline

Extensible Architecture

Configuration System

Python Package Features

Next Steps