# [ANONYMIZED] LP Dataset for DANCE-ST Testing

This synthetic dataset contains corrosion data for 500 turbine blades over 10 time points, with a 50x50 spatial grid for each blade.

## Directory Structure

- `[ANONYMIZED]_lp_dataset/`: Main dataset directory
  - `[ANONYMIZED]_lp_materials.csv`: Material properties for each blade
  - `[ANONYMIZED]_lp_operations.csv`: Operational conditions for each blade at each time point
  - `[ANONYMIZED]_lp_corrosion.csv`: Corrosion depth at each spatial point, blade, and time point
  - `[ANONYMIZED]_lp_ndt.csv`: Non-destructive testing results for each blade at each time point
  - `[ANONYMIZED]_lp_rul.csv`: Remaining Useful Life predictions for each blade
  - `[ANONYMIZED]_lp_spatial_grid.csv`: Spatial grid coordinates
  - `[ANONYMIZED]_lp_metadata.json`: Dataset metadata
  - `train/`: Training data subset
  - `test/`: Testing data subset
  - `knowledge_graph/`: Knowledge graph files for DANCE-ST
    - `[ANONYMIZED]_lp_vertices.json`: Knowledge graph vertices
    - `[ANONYMIZED]_lp_edges.json`: Knowledge graph edges
    - `[ANONYMIZED]_lp_kg_config.json`: Knowledge graph configuration
  - `constraints/`: Physical constraint definitions
    - `[ANONYMIZED]_lp_constraints.json`: Constraint definitions for DANCE-ST
  - `visualizations/`: Corrosion pattern visualizations

## Usage with DANCE-ST

```python
from dance_st import DatasetLoader, DANCESTModel

# Load the [ANONYMIZED] LP dataset
dataset = DatasetLoader(
    data_dir='C:\Users\[user]\Documents\[ANONYMIZED] DANCE-ST\[ANONYMIZED]_lp_dataset',
    kg_config='C:\Users\\Documents\[ANONYMIZED] DANCE-ST\[ANONYMIZED]_lp_dataset\knowledge_graph\[ANONYMIZED]_lp_kg_config.json',
    constraints_file='C:\Users\\Documents\[ANONYMIZED] DANCE-ST\[ANONYMIZED]_lp_dataset\constraints\[ANONYMIZED]_lp_constraints.json',
    train_data_pattern='**/train/[ANONYMIZED]_lp_*.csv',
    test_data_pattern='**/test/[ANONYMIZED]_lp_*.csv'
)

# Initialize and train the DANCE-ST model
model = DANCESTModel(
    subgraph_threshold=0.3,
    fusion_temperature=0.1,
    alpha=0.4,
    beta=0.4,
    gamma=0.2
)

model.train(dataset.train_data, epochs=100)

# Evaluate model
results = model.evaluate(dataset.test_data)
print(f"RMSE: {results['rmse']}")
print(f"MAE: {results['mae']}")
print(f"Consistency: {results['consistency']}")
```

## Dataset Generation

This dataset was generated using the [ANONYMIZED]LPGenerator with the following parameters:
- Number of blades: 500
- Number of temporal points: 10
- Spatial grid size: 50x50
- Random seed: 42

Generation date: 2025-05-05
