Wind TMCAD

A Python application for processing and visualizing terrain point cloud data from DXF files, with a focus on wind turbine micro-siting analysis.

Features

Point Cloud Processing

• Import 3D point data from DXF files
• Memory-efficient point cloud storage using HDF5
• Spatial indexing with KD-trees for fast neighbor queries
• Point interpolation using various methods:
  • Inverse Distance Weighting (IDW)
  • Barycentric interpolation
  • Natural Neighbor interpolation

Elevation Analysis

• Generate isometric contour lines at multiple intervals:
  • 25 cm (fine detail)
  • 50 cm (medium detail)
  • 1 meter (major contours)
• Interactive 2D visualization with:
  • Points colored by elevation
  • Contour lines with different styles per interval
  • Hover information showing exact elevations
  • Equal aspect ratio for accurate representation

Web Interface

• Flask-based web server
• Interactive visualization using Plotly
• File upload for DXF processing
• Real-time point cloud and contour display
• Responsive layout with legend and controls

Installation

1. Clone the repository:

git clone https://github.com/crunchy-devops/wind-tmcad.git
cd wind-tmcad

2. Install dependencies:

pip install -r requirements.txt

Usage

1. Start the web server:

python app.py

2. Open a web browser and navigate to:

http://localhost:5000

3. Upload a DXF file containing point cloud data

4. View and interact with the visualization:

   • Toggle contour levels using the legend
   • Hover over points to see exact elevations
   • Use the modebar for zoom, pan, and other controls
   • Download the plot as PNG if needed

Technical Details

Point Cloud Class

• Efficient point storage using dictionary mapping
• Spatial indexing with scipy's cKDTree
• Support for various interpolation methods
• HDF5-based file storage for large datasets

Contour Generation

• Uses matplotlib's contour generator
• Cubic interpolation for smooth contours
• Grid-based approach with customizable resolution
• Multiple elevation intervals with distinct styling

Visualization

• Points:
  • Colored using Viridis colorscale
  • Size and opacity optimized for clarity
  • Hover information showing coordinates and elevation
• Contours:
  • 25cm: Thin orange lines (semi-transparent)
  • 50cm: Medium green lines (more opaque)
  • 1m: Thick blue lines (fully opaque)
  • Interactive legend for toggling visibility

Dependencies

• Flask: Web framework
• Plotly: Interactive visualization
• NumPy: Numerical operations
• SciPy: Spatial operations and interpolation
• Matplotlib: Contour generation
• ezdxf: DXF file processing
• h5py: HDF5 file handling

Contributing

1. Fork the repository
2. Create a feature branch
3. Commit your changes
4. Push to the branch
5. Create a Pull Request

License

This project is licensed under the MIT License - see the LICENSE file for details.