topoconvert 0.1.3

A Python CLI tool for converting geospatial survey data between KML, CSV, and DXF formats with specialized topographical operations

TopoConvert

A unified geospatial conversion toolkit for survey data processing and transformation.

Overview

TopoConvert is a Python-based command-line tool that provides a comprehensive set of utilities for converting and processing geospatial survey data between various formats including KML, CSV, and DXF. It's designed specifically for surveyors, GIS professionals, and engineers who need to transform topographical data efficiently.

Features

• Multiple Format Support: Convert between KML, CSV, and DXF formats
• Contour Generation: Create contour lines from point data with customizable intervals
• Mesh Generation: Generate triangulated meshes from survey points (with optional wireframe)
• Slope Analysis: Create slope heatmaps with contours, target slope settings, and color-blind friendly options
• GPS Grid Generation: Generate GPS grid layouts within property boundaries (supports convex/concave hulls)
• Batch Processing: Combine multiple CSV files into DXF (separate layers) or KML
• Coordinate System Support: Handle various projection systems via pyproj
• Smart Defaults: Optional output files automatically named based on input
• No Progress Bars: Clean, distraction-free command execution
• Production-Ready Robustness: Comprehensive edge case testing ensures reliable handling of real-world data quality issues

Requirements

• Python 3.9 or higher
• All dependencies automatically installed via pip

Installation

From Source (Development)

# Clone the repository
git clone https://github.com/DavGarcia/topoconvert.git
cd topoconvert

# Create a virtual environment (recommended)
python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

# Install in development mode
pip install -e .

# Install development dependencies
pip install -e ".[dev]"

From PyPI

pip install topoconvert

Quick Start

TopoConvert provides a single CLI with multiple subcommands:

# Convert KML points to DXF contours (output file optional)
topoconvert kml-to-dxf-contours input.kml --interval 1.0

# Convert CSV to KML
topoconvert csv-to-kml survey_data.csv output.kml --add-labels

# Convert KML to CSV format
topoconvert kml-to-points input.kml output.csv --format csv

# Generate slope heatmap (output file optional, contours shown by default)
topoconvert slope-heatmap terrain.kml --target-slope 5.0

# Combine multiple CSV files to DXF on separate layers
topoconvert multi-csv-to-dxf file1.csv file2.csv file3.csv --output merged.dxf

# Combine multiple CSV files to KML
topoconvert multi-csv-to-kml file1.csv file2.csv file3.csv output.kml

Available Commands

Data Conversion

• csv-to-kml - Convert CSV survey data to KML format
• kml-to-points - Extract points from KML (supports DXF, CSV, JSON, TXT output)

DXF Generation

• kml-to-dxf-contours - Generate contour lines from KML point data
• kml-to-dxf-mesh - Generate triangulated mesh from KML points (wireframe by default)
• kml-contours-to-dxf - Convert KML contour lines to DXF format
• multi-csv-to-dxf - Merge multiple CSV files to DXF on separate layers

KML Generation

• multi-csv-to-kml - Combine multiple CSV files into a single KML
• gps-grid - Generate GPS grid points within property boundaries

Analysis & Visualization

• slope-heatmap - Generate slope analysis PNG (contours by default, color-blind friendly options)

For detailed usage of each command:

topoconvert <command> --help

Reliability & Testing

TopoConvert v0.1.2+ includes a comprehensive edge case testing system that ensures robust handling of real-world data challenges:

Data Quality Resilience

• Corrupted File Handling: Graceful failure with clear error messages for malformed KML, CSV, and DXF files
• Invalid Data Detection: Systematic handling of corrupted coordinates, missing headers, encoding issues, and inconsistent data structures
• Boundary Condition Testing: Validation with extreme coordinate values, minimal datasets, and edge cases
• Memory Efficiency: Stress testing with large datasets (50k+ points) and memory leak detection

Quality Assurance

• 88 Comprehensive Edge Case Tests: Covering corruption scenarios, memory limits, and boundary conditions
• Cross-Platform Reliability: Tests validated on Linux, Windows, and macOS with platform-specific optimizations
• Error Message Consistency: User-friendly error reporting with consistent exit codes across all commands
• Performance Regression Prevention: Automated benchmarking with threshold-based regression detection
• Production-Ready Validation: No crashes on corrupted input, proper cleanup, and informative feedback

Testing Infrastructure

# Run edge case tests
pytest tests/test_*corruption* tests/test_*memory* tests/test_*benchmark*

# Memory stress testing
pytest tests/test_memory_utils.py -v

# Performance benchmarking
pytest tests/test_benchmark_config.py -v

Development

Running Tests

# Run all tests (450+ tests including edge cases)
pytest

# Run with coverage
pytest --cov=topoconvert

# Run core functionality tests only
pytest tests/test_*.py -k "not (corruption or memory or benchmark)"

# Run specific test categories
pytest tests/test_kml_to_contours.py  # Core functionality
pytest tests/test_*corruption*        # Corruption handling
pytest tests/test_*memory*           # Memory efficiency (cross-platform optimized)
pytest tests/test_*benchmark*        # Performance testing

Note: All tests are optimized for cross-platform reliability and will adapt to different system environments automatically.

Code Quality

# Format code
black topoconvert/

# Lint code
flake8 topoconvert/

# Type checking
mypy topoconvert/

Contributing

Please see CONTRIBUTING.md for guidelines on how to contribute to this project.

Documentation

Full documentation is available in the docs/ directory.

License

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

Acknowledgments

• Built with Shapely for geometric operations
• Uses ezdxf for DXF file handling
• Powered by pandas for data manipulation
• Coordinate transformations via pyproj
• Quality assurance enhanced by:
  • pytest-benchmark for performance regression testing
  • Hypothesis for property-based testing
  • psutil for system memory monitoring

Support

For bug reports and feature requests, please use the GitHub issue tracker.