| import rasterio | |
| import numpy as np | |
| f = r'D:\RockGlacier_Project\features\feature_0000.tif' | |
| import os | |
| if not os.path.exists(f): | |
| f = r'D:\RockGlacier_Project\features\feature_0100.tif' | |
| bands = ['B2','B3','B4','B8','B11','B12','NDVI','NDWI','NDSI','Elevation','Slope','Aspect'] | |
| with rasterio.open(f) as src: | |
| data = src.read() | |
| print('\n## Actual Dataset Values (Sample)') | |
| print(f'Below are the actual numbers (Min, Max, and Mean) calculated directly from the pixels inside `{os.path.basename(f)}`:') | |
| print('\n| Band Index | Feature Name | Min Value | Max Value | Mean Value |') | |
| print('| :---: | :--- | :--- | :--- | :--- |') | |
| for i in range(12): | |
| b = data[i] | |
| # Mask out completely invalid data (often 0 or extremely low negative values from GEE) | |
| b_valid = b[np.isfinite(b)] | |
| min_val = b_valid.min() | |
| max_val = b_valid.max() | |
| mean_val = b_valid.mean() | |
| print(f'| **{i+1}** | `{bands[i]}` | {min_val:.4f} | {max_val:.4f} | {mean_val:.4f} |') | |