| Documentation Report | Currently Unpublished |
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• Purpose:
• Partition real space into grids to analyze and cluster crystal structures using diffraction patterns.
• Applications:
• Used for segmenting monolithic and polylithic crystals.
• Supports k-means clustering, similarity calculations, and visualization.
• Grid Partitioning:
• Divide real space into square grids.
• Map each grid to a region in diffraction space.
• Data Structures:
• Grids: Represents the grid layout.
• Features Array: Stores features like peak intensities.
• Similarity Matrix: Holds similarity scores for clustering.
• Collect Data:
• Obtain diffraction patterns for each grid.
• Peak Detection:
• Locate peaks in a defined region around expected locations.
• Calculate peak intensity, adjust for edge effects.
• Feature Extraction:
• Extract key features from diffraction data.
• Measure Similarity:
• Use distance metrics to calculate similarity between grids.
• Normalize values for consistency in clustering.
• K-Means Clustering:
• Apply k-means to group similar grids.
• Determine the optimal number of clusters using elbow method.
• Edge Cases:
• Handle grids near edges or isolated outliers.
• Color Mapping:
• Use color codes to distinguish clusters.
• Plotting:
• 2D Plotting: Represent each grid as a colored rectangle.
• 3D Plotting: Optionally visualize in 3D, excluding grids with zero similarity.
• Legend Creation:
• Generate a legend to label clusters for clear interpretation.
• Efficiency:
• Optimize for large datasets, possibly using parallel processing.
• Parameter Tuning:
• Experiment with grid sizes and clustering parameters to refine results.
• Extensions:
• Dynamic Grid Sizes: Implement adaptive grid sizing.
• Integration: Combine with other segmentation methods for better accuracy.
• Summary:
• Grid segmentation is key for detailed analysis and clustering of crystal structures.
• Future Work:
• Focus on refining techniques for better performance and new applications.