#!/usr/bin/env python3 """ plot_uncertainty.py — Three-panel publication figure for the MC dropout map. Run after mc_dropout_inference.py. Reads the two GeoTIFFs (or, as fallback, the .npy/parquet outputs if rasterio wasn't available) and emits: figure_uncertainty_3panel.png (300 dpi, 18 × 7 in) figure_uncertainty_3panel.pdf (vector, for journal submission) """ from __future__ import annotations import sys from pathlib import Path import matplotlib matplotlib.use('Agg') import matplotlib.pyplot as plt import numpy as np _THIS = Path(__file__).resolve() SOC_CODE_DIR = _THIS.parent.parent.parent.parent # SOCmapping/ sys.path.insert(0, str(SOC_CODE_DIR)) from _paths import SOC_CODE_DIR as _SOC_CODE_DIR, SOC_REBUTTAL_DIR # noqa: E402 OUT_DIR = SOC_REBUTTAL_DIR / 'gpu_experiments' / 'uncertainty' MEAN_TIF = OUT_DIR / 'SGT_1mil_2023_mean_mc30.tif' STD_TIF = OUT_DIR / 'SGT_1mil_2023_std_mc30.tif' # ASSUMPTION: the existing Figures 14/15 in the paper use the viridis # colormap (confirmed: SOCmapping/SpatiotemporalGatedTransformer/mapping.py # uses cmap='viridis'). Panel 1 mirrors that exactly. If the paper actually # uses a different cmap (e.g. YlOrBr, custom SOC palette), swap CMAP_MEAN. CMAP_MEAN = 'viridis' CMAP_STD = 'OrRd' CMAP_CV = 'RdYlGn_r' def load_geotiff(path: Path): try: import rasterio except ImportError: print(f'rasterio missing; cannot read {path}.', file=sys.stderr) return None, None, None with rasterio.open(path) as src: arr = src.read(1, masked=False).astype(np.float32) extent = (src.bounds.left, src.bounds.right, src.bounds.bottom, src.bounds.top) crs = src.crs.to_string() arr[~np.isfinite(arr)] = np.nan return arr, extent, crs def maybe_load_bavaria_outline(): """Try to load Bavaria boundary for overlay; return None if unavailable.""" try: import geopandas as gpd # ASSUMPTION: bavaria.geojson exists in the project dir for cand in ( _SOC_CODE_DIR / 'SpatiotemporalGatedTransformer' / 'bavaria.geojson', _SOC_CODE_DIR / 'Maps' / 'bavaria.geojson', _SOC_CODE_DIR / 'balancedDataset' / 'bavaria.geojson', ): if cand.exists(): bav = gpd.read_file(cand) if 'EPSG:32632' not in str(bav.crs): bav = bav.to_crs('EPSG:32632') return bav except Exception as e: print(f'Bavaria outline load failed: {e}', file=sys.stderr) return None def add_decorations(ax, bavaria, extent): """Bavaria outline, scale bar, north arrow, nodata-as-white.""" if bavaria is not None: bavaria.boundary.plot(ax=ax, color='black', linewidth=0.5) # ASSUMPTION: simple scale bar 50 km long-bar in lower-left. if extent is not None: x0 = extent[0] + 0.05 * (extent[1] - extent[0]) y0 = extent[2] + 0.05 * (extent[3] - extent[2]) bar_len = 50_000 # 50 km in metres (UTM) ax.plot([x0, x0 + bar_len], [y0, y0], color='black', linewidth=2) ax.text(x0 + bar_len / 2, y0 + (extent[3] - extent[2]) * 0.015, '50 km', ha='center', va='bottom', fontsize=8) # North arrow top-right nx = extent[0] + 0.94 * (extent[1] - extent[0]) ny = extent[2] + 0.88 * (extent[3] - extent[2]) dy = (extent[3] - extent[2]) * 0.05 ax.annotate('N', xy=(nx, ny + dy), xytext=(nx, ny), ha='center', fontsize=11, fontweight='bold', arrowprops=dict(arrowstyle='-|>', color='black', lw=1.5)) def main(): mean_arr, extent, _ = load_geotiff(MEAN_TIF) std_arr, _, _ = load_geotiff(STD_TIF) if mean_arr is None or std_arr is None: # Fallback to the parquet (point scatter rendering instead of raster) # ASSUMPTION: at least one of GeoTIFF or parquet is present after # running mc_dropout_inference.py. import pandas as pd df = pd.read_parquet(OUT_DIR / 'mc_dropout_points.parquet') # Fall back to a scatter-render at low DPI; this branch only fires # if rasterio failed at write time, which would be a setup bug. print('GeoTIFF missing — falling back to scatter; result may be sparse.', file=sys.stderr) mean_arr = std_arr = None cv_arr = None # We'll just plot scatter points in original lon/lat space. scatter_mode = True else: scatter_mode = False with np.errstate(invalid='ignore', divide='ignore'): cv_arr = np.where(mean_arr > 1e-6, np.clip(100 * std_arr / mean_arr, 0, 100), np.nan) bavaria = maybe_load_bavaria_outline() if not scatter_mode else None fig, axes = plt.subplots(1, 3, figsize=(18, 7), dpi=300) plt.subplots_adjust(wspace=0.15) if scatter_mode: # --- scatter fallback (rasterio missing) --- import pandas as pd df = pd.read_parquet(OUT_DIR / 'mc_dropout_points.parquet') for ax, key, cmap, label, title in [ (axes[0], 'mean_pred_g_per_kg', CMAP_MEAN, 'Mean predicted SOC (g/kg)', 'SGT 1.1M — 2023 SOC prediction (MC mean, n=30)'), (axes[1], 'std_pred_g_per_kg', CMAP_STD, 'Prediction uncertainty — std (g/kg)', 'MC Dropout uncertainty (n=30 passes)'), (axes[2], 'cv_pct', CMAP_CV, 'Coefficient of variation (%)', 'Relative uncertainty (CV = std/mean × 100%)'), ]: sc = ax.scatter(df['longitude'], df['latitude'], c=df[key], cmap=cmap, s=1, marker='s') ax.set_title(title, fontsize=11, fontweight='bold') ax.set_xlabel('Longitude (°E)') ax.set_ylabel('Latitude (°N)') cbar = fig.colorbar(sc, ax=ax, fraction=0.046, pad=0.04) cbar.set_label(label, fontsize=9) ax.set_aspect('equal', adjustable='box') else: panel_specs = [ (axes[0], mean_arr, CMAP_MEAN, 'Mean predicted SOC (g/kg)', 'SGT 1.1M — 2023 SOC prediction (MC mean, n=30)', dict(vmin=0, vmax=float(np.nanpercentile(mean_arr, 99)))), (axes[1], std_arr, CMAP_STD, 'Prediction uncertainty — std (g/kg)', 'MC Dropout uncertainty (n=30 passes)', dict(vmin=0, vmax=float(np.nanpercentile(std_arr, 99)))), (axes[2], cv_arr, CMAP_CV, 'Coefficient of variation (%)', 'Relative uncertainty (CV = std/mean × 100%)', dict(vmin=0, vmax=100)), ] for ax, arr, cmap, label, title, kwargs in panel_specs: im = ax.imshow(arr, extent=extent, cmap=cmap, origin='upper', interpolation='nearest', **kwargs) ax.set_title(title, fontsize=11, fontweight='bold') ax.set_xlabel('UTM Easting (m)') ax.set_ylabel('UTM Northing (m)') cbar = fig.colorbar(im, ax=ax, fraction=0.046, pad=0.04) cbar.set_label(label, fontsize=9) ax.set_facecolor('white') add_decorations(ax, bavaria, extent) out_png = OUT_DIR / 'figure_uncertainty_3panel.png' out_pdf = OUT_DIR / 'figure_uncertainty_3panel.pdf' fig.savefig(out_png, dpi=300, bbox_inches='tight') fig.savefig(out_pdf, bbox_inches='tight') print(f'Wrote {out_png}', flush=True) print(f'Wrote {out_pdf}', flush=True) plt.close(fig) if __name__ == '__main__': main()