Renaming.

postprocessing.py

@@ -0,0 +1,108 @@
+"""Post-processing: crop fusion/S2/S3 to valid pixels."""
+from pathlib import Path
+import numpy as np
+import rasterio
+from rasterio import windows
+from rasterio.warp import reproject, Resampling
+from rasterio.io import MemoryFile
+
+
+def process_cropped(season, site_position, site_name, cleaning_strategy="aggressive", sigma=None):
+    """Crop fusion to valid data, then crop S2/S3 to match."""
+    base = Path(f"data/{site_name}/{season}")
+    prepared = base / f"prepared_{cleaning_strategy}"
+    processed_dir = f"processed_{cleaning_strategy}_sigma{sigma}" if sigma else f"processed_{cleaning_strategy}_sigma20"
+    processed = base / processed_dir
+
+    s2_prep = prepared / "s2"
+    s3_prep = prepared / "s3"
+    fusion_prep = prepared / (f"fusion_sigma{sigma}" if sigma else "fusion")
+
+    for output_dir in [processed / "s2", processed / "s3", processed / "fusion"]:
+        output_dir.mkdir(parents=True, exist_ok=True)
+
+    print(f"[PROCESS] Processing files: {site_name}, {season}, {cleaning_strategy}, sigma={sigma or 20}")
+
+    # Crop fusion to valid data and get dimensions
+    fusion_dims = {}
+    for fusion_file in fusion_prep.glob("REFL_*.tif"):
+        date_str = fusion_file.stem.split("_")[1]
+        with rasterio.open(fusion_file) as src:
+            data = src.read()
+            valid = ~np.isnan(data) & (data > 0.001)
+            rows = np.any(valid, axis=(0, 2))
+            cols = np.any(valid, axis=(0, 1))
+            row_idx = np.where(rows)[0]
+            col_idx = np.where(cols)[0]
+            if len(row_idx) == 0 or len(col_idx) == 0:
+                print(f"[PROCESS] Skipping {fusion_file.name} (no valid pixels)")
+                continue
+            r0, r1 = row_idx[0], row_idx[-1]
+            c0, c1 = col_idx[0], col_idx[-1]
+            w, h = c1 - c0 + 1, r1 - r0 + 1
+            window = windows.Window(c0, r0, w, h)
+            data_crop = src.read(window=window)
+            transform = rasterio.windows.transform(window, src.transform)
+            p = src.profile.copy()
+            p.update({"width": w, "height": h, "transform": transform})
+            output_file = processed / "fusion" / f"{date_str}_0.geotiff"
+            with rasterio.open(output_file, "w", **p) as dst:
+                dst.write(data_crop)
+            fusion_dims[date_str] = (c0, r0, w, h, transform, src.transform, src.crs, src.profile)
+        print(f"[PROCESS] Cropped fusion: {output_file}")
+
+    # Crop S2 and S3 to fusion size
+    for date_str, (c0, r0, w, h, transform, fusion_transform, crs, fusion_profile) in fusion_dims.items():
+        window = windows.Window(c0, r0, w, h)
+        # S2
+        for s2_file in s2_prep.glob("*REFL.tif"):
+            if s2_file.stem.split("_")[2] == date_str:
+                output_file = processed / "s2" / f"{date_str}_0.geotiff"
+                with rasterio.open(s2_file) as src:
+                    data = src.read(window=window)
+                    p2 = src.profile.copy()
+                    p2.update({"width": w, "height": h, "transform": transform, "crs": crs})
+                    with rasterio.open(output_file, "w", **p2) as dst:
+                        dst.write(data)
+                print(f"[PROCESS] Cropped: {output_file}")
+        # S3: resample to fusion pixel size, then crop
+        s3_file = s3_prep / f"composite_{date_str}.tif"
+        if s3_file.exists():
+            output_file = processed / "s3" / f"{date_str}_0.geotiff"
+            with rasterio.open(s3_file) as src:
+                # Resample to fusion pixel size
+                temp_profile = fusion_profile.copy()
+                temp_profile.update({"dtype": src.profile["dtype"], "count": src.count})
+                with rasterio.MemoryFile() as memfile:
+                    with memfile.open(**temp_profile) as resampled:
+                        for i in range(1, src.count + 1):
+                            reproject(
+                                source=rasterio.band(src, i),
+                                destination=rasterio.band(resampled, i),
+                                src_transform=src.transform,
+                                src_crs=src.crs,
+                                dst_transform=fusion_transform,
+                                dst_crs=crs,
+                                resampling=Resampling.nearest
+                            )
+                        # Crop using same window
+                        data = resampled.read(window=window)
+                        p2 = resampled.profile.copy()
+                        p2.update({"width": w, "height": h, "transform": transform})
+                        with rasterio.open(output_file, "w", **p2) as dst:
+                            dst.write(data)
+            print(f"[PROCESS] Cropped: {output_file}")
+
+    print("[PROCESS] Completed")
+
+
+def process_all_scenarios(season, site_position, site_name):
+    """Process all 4 EFAST scenarios."""
+    for strategy in ["aggressive", "nonaggressive"]:
+        for sigma in [None, 30]:
+            process_cropped(season, site_position, site_name, cleaning_strategy=strategy, sigma=sigma)
+
+
+# Aliases
+postprocess = process_cropped
+postprocess_all_scenarios = process_all_scenarios