Added index then blend.

.gitignore

@@ -42,3 +42,7 @@ dist/
 # OS
 .DS_Store
 Thumbs.db
+
+AGENTS.md
+METHODOLOGY.md
+.vibe

fusion.py

@@ -1,13 +1,15 @@
 """EFAST fusion: S2/S3 reflectance fusion for four scenarios."""
+
 from datetime import datetime, timedelta
 
-from preparation import _get_base_dir, RESOLUTION_RATIO
+from preparation import _get_base_dir, _get_itb_base_dir, RESOLUTION_RATIO
 
 
 def _import_efast():
     """Lazy import of efast to avoid import errors when not using efast functions."""
     try:
         import efast
+
         return efast
     except ImportError:
         raise ImportError(
@@ -15,7 +17,14 @@ def _import_efast():
         )
 
 
-def run_efast(season, site_position, site_name, cleaning_strategy="aggressive", sigma=None, date_range=None):
+def run_efast(
+    season,
+    site_position,
+    site_name,
+    cleaning_strategy="aggressive",
+    sigma=None,
+    date_range=None,
+):
     lat, lon = site_position
     datetime_range = date_range or f"{season}-01-01/{season}-12-31"
 
@@ -47,7 +56,9 @@ def run_efast(season, site_position, site_name, cleaning_strategy="aggressive",
             }
             if sigma is not None:
                 kwargs["sigma"] = sigma
-            efast.fusion(current_date, s3_output_dir, s2_output_dir, fusion_output_dir, **kwargs)
+            efast.fusion(
+                current_date, s3_output_dir, s2_output_dir, fusion_output_dir, **kwargs
+            )
             print(
                 f"[EFAST] Saved: {output_file}"
                 if output_file.exists()
@@ -60,8 +71,94 @@ def run_efast(season, site_position, site_name, cleaning_strategy="aggressive",
     print("[EFAST] Completed")
 
 
-def run_all_efast_scenarios(season, site_position, site_name, sigma_value=30, date_range=None):
+def run_all_efast_scenarios(
+    season, site_position, site_name, sigma_value=30, date_range=None
+):
     """Run EFAST fusion for all 4 scenarios. Expects prepared_*/s2 and prepared_*/s3 to exist."""
     for strategy in ["aggressive", "nonaggressive"]:
-        run_efast(season, site_position, site_name, cleaning_strategy=strategy, sigma=None, date_range=date_range)
+        run_efast(
-        run_efast(season, site_position, site_name, cleaning_strategy=strategy, sigma=sigma_value, date_range=date_range)
+            season,
+            site_position,
+            site_name,
+            cleaning_strategy=strategy,
+            sigma=None,
+            date_range=date_range,
+        )
+        run_efast(
+            season,
+            site_position,
+            site_name,
+            cleaning_strategy=strategy,
+            sigma=sigma_value,
+            date_range=date_range,
+        )
+
+
+def run_efast_itb(
+    season,
+    site_position,
+    site_name,
+    cleaning_strategy="aggressive",
+    sigma=None,
+    date_range=None,
+):
+    lat, lon = site_position
+    datetime_range = date_range or f"{season}-01-01/{season}-12-31"
+    efast_base_dir = _get_itb_base_dir(season, site_name, cleaning_strategy)
+    s2_output_dir = efast_base_dir / "s2"
+    s3_output_dir = efast_base_dir / "s3"
+    fusion_output_dir = efast_base_dir / (f"fusion_sigma{sigma}" if sigma else "fusion")
+    fusion_output_dir.mkdir(parents=True, exist_ok=True)
+    print(f"[EFAST-ITB] Fusion GCC: {site_name} ({lat:.6f}, {lon:.6f}), {season}")
+    efast = _import_efast()
+    start_str, end_str = datetime_range.split("/")
+    start_date = datetime.strptime(start_str, "%Y-%m-%d")
+    end_date = datetime.strptime(end_str, "%Y-%m-%d")
+    current_date = start_date
+    while current_date <= end_date:
+        date_str = current_date.strftime("%Y%m%d")
+        output_file = fusion_output_dir / f"GCC_{date_str}.tif"
+        try:
+            kwargs = {
+                "product": "GCC",
+                "max_days": 30,
+                "date_position": 2,
+                "minimum_acquisition_importance": 0.0,
+                "ratio": RESOLUTION_RATIO,
+            }
+            if sigma is not None:
+                kwargs["sigma"] = sigma
+            efast.fusion(
+                current_date, s3_output_dir, s2_output_dir, fusion_output_dir, **kwargs
+            )
+            print(
+                f"[EFAST-ITB] Saved: {output_file}"
+                if output_file.exists()
+                else f"[EFAST-ITB] No output for {date_str}"
+            )
+        except Exception as e:
+            print(f"[EFAST-ITB] Error {date_str}: {e}")
+        current_date += timedelta(days=1)
+    print("[EFAST-ITB] Completed")
+
+
+def run_all_efast_itb_scenarios(
+    season, site_position, site_name, sigma_value=30, date_range=None
+):
+    for strategy in ["aggressive", "nonaggressive"]:
+        run_efast_itb(
+            season,
+            site_position,
+            site_name,
+            cleaning_strategy=strategy,
+            sigma=None,
+            date_range=date_range,
+        )
+        run_efast_itb(
+            season,
+            site_position,
+            site_name,
+            cleaning_strategy=strategy,
+            sigma=sigma_value,
+            date_range=date_range,
+        )

metrics_indices.py

@@ -1,4 +1,5 @@
 """Index generation: NDVI and GCC from S2/S3/fusion GeoTIFFs."""
+
 import json
 import numpy as np
 import rasterio
@@ -67,7 +68,9 @@ def _get_ndvi_value(ndvi_file, site_position):
     return None
 
 
-def _create_timeseries_for_dir(input_dir, output_dir, site_position, source_name, pattern="*.geotiff"):
+def _create_timeseries_for_dir(
+    input_dir, output_dir, site_position, source_name, pattern="*.geotiff"
+):
     print(f"[NDVI-{source_name}] Creating timeseries.json...")
     timeseries = []
 
@@ -196,13 +199,23 @@ def create_ndvi_timeseries_post_process(season, site_position, site_name):
             processed_dir = f"processed_{strategy}_sigma{sigma}"
             for source in ["s2", "s3"]:
                 input_dir = Path(f"data/{site_name}/{season}/{processed_dir}/{source}/")
-                output_dir = Path(f"data/{site_name}/{season}/{processed_dir}/ndvi/{source}/")
+                output_dir = Path(
+                    f"data/{site_name}/{season}/{processed_dir}/ndvi/{source}/"
+                )
                 _create_timeseries_for_dir(
-                    input_dir, output_dir, site_position, f"POST-PROCESS-{source.upper()}-{strategy}-σ{sigma}"
+                    input_dir,
+                    output_dir,
+                    site_position,
+                    f"POST-PROCESS-{source.upper()}-{strategy}-σ{sigma}",
                 )
             input_dir = Path(f"data/{site_name}/{season}/{processed_dir}/fusion/")
             output_dir = Path(f"data/{site_name}/{season}/{processed_dir}/ndvi/fusion/")
-            _create_timeseries_for_dir(input_dir, output_dir, site_position, f"POST-PROCESS-FUSION-{strategy}-σ{sigma}")
+            _create_timeseries_for_dir(
+                input_dir,
+                output_dir,
+                site_position,
+                f"POST-PROCESS-FUSION-{strategy}-σ{sigma}",
+            )
 
 
 def _calculate_and_write_gcc(input_file, output_file):
@@ -261,6 +274,25 @@ def _get_gcc_from_original(input_file, site_position):
     """Calculate GCC directly from original file without creating GeoTIFF."""
     try:
         with rasterio.open(input_file) as src:
+            if src.count == 1:
+                g = src.read(1).astype(np.float32)
+                lon, lat = site_position[1], site_position[0]
+                x, y = transform_coords("EPSG:4326", src.crs, [lon], [lat])
+                if not (
+                    src.bounds.left <= x[0] <= src.bounds.right
+                    and src.bounds.bottom <= y[0] <= src.bounds.top
+                ):
+                    return None
+                row, col = src.index(x[0], y[0])
+                if row < 0 or row >= src.height or col < 0 or col >= src.width:
+                    return None
+                r0, r1 = max(0, row - 1), min(src.height, row + 2)
+                c0, c1 = max(0, col - 1), min(src.width, col + 2)
+                win = g[r0:r1, c0:c1]
+                mask = np.isfinite(win) & (win > 0)
+                if not np.any(mask):
+                    return None
+                return float(np.mean(win[mask]))
             if src.count < 3:
                 return None
 
@@ -290,11 +322,21 @@ def _get_gcc_from_original(input_file, site_position):
 
             # Calculate GCC for each pixel in window
             total = red_window + green_window + blue_window
-            mask = (total > 0) & ~np.isnan(total) & (blue_window >= 0) & (green_window >= 0) & (red_window >= 0)
+            mask = (
+                (total > 0)
+                & ~np.isnan(total)
+                & (blue_window >= 0)
+                & (green_window >= 0)
+                & (red_window >= 0)
+            )
             if not np.any(mask):
-                negative_pixels = np.sum((blue_window < 0) | (green_window < 0) | (red_window < 0))
+                negative_pixels = np.sum(
+                    (blue_window < 0) | (green_window < 0) | (red_window < 0)
+                )
                 if negative_pixels > 0:
-                    print(f"Warning: {input_file.name} excluded - all pixels have negative band values ({negative_pixels} negative pixels in window)")
+                    print(
+                        f"Warning: {input_file.name} excluded - all pixels have negative band values ({negative_pixels} negative pixels in window)"
+                    )
                 return None
 
             gcc_window = np.zeros_like(green_window, dtype=np.float32)
@@ -303,11 +345,13 @@ def _get_gcc_from_original(input_file, site_position):
             # Return mean of valid GCC values
             valid_gcc = gcc_window[mask]
             return float(np.mean(valid_gcc)) if len(valid_gcc) > 0 else None
-    except Exception as e:
+    except Exception:
         return None
 
 
-def _create_gcc_timeseries_for_dir(input_dir, output_dir, site_position, source_name, pattern="*.geotiff"):
+def _create_gcc_timeseries_for_dir(
+    input_dir, output_dir, site_position, source_name, pattern="*.geotiff"
+):
     print(f"[GCC-{source_name}] Creating timeseries.json...")
     timeseries = []
 
@@ -342,7 +386,9 @@ def _create_gcc_timeseries_for_dir(input_dir, output_dir, site_position, source_
                 f"[GCC-{source_name}] Warning: Could not sample {filename} (outside bounds or nodata)"
             )
 
-        timeseries.append({"date": date, "filename": filename, "greenness_index": gcc_value})
+        timeseries.append(
+            {"date": date, "filename": filename, "greenness_index": gcc_value}
+        )
 
     timeseries.sort(key=lambda x: x["date"])
     output_dir.mkdir(parents=True, exist_ok=True)
@@ -400,13 +446,41 @@ def create_gcc_timeseries_post_process(season, site_position, site_name):
             processed_dir = f"processed_{strategy}_sigma{sigma}"
             for source in ["s2", "s3"]:
                 input_dir = Path(f"data/{site_name}/{season}/{processed_dir}/{source}/")
-                output_dir = Path(f"data/{site_name}/{season}/{processed_dir}/gcc/{source}/")
+                output_dir = Path(
+                    f"data/{site_name}/{season}/{processed_dir}/gcc/{source}/"
+                )
                 _create_gcc_timeseries_for_dir(
-                    input_dir, output_dir, site_position, f"POST-PROCESS-{source.upper()}-{strategy}-σ{sigma}"
+                    input_dir,
+                    output_dir,
+                    site_position,
+                    f"POST-PROCESS-{source.upper()}-{strategy}-σ{sigma}",
                 )
             input_dir = Path(f"data/{site_name}/{season}/{processed_dir}/fusion/")
             output_dir = Path(f"data/{site_name}/{season}/{processed_dir}/gcc/fusion/")
-            _create_gcc_timeseries_for_dir(input_dir, output_dir, site_position, f"POST-PROCESS-FUSION-{strategy}-σ{sigma}")
+            _create_gcc_timeseries_for_dir(
+                input_dir,
+                output_dir,
+                site_position,
+                f"POST-PROCESS-FUSION-{strategy}-σ{sigma}",
+            )
+            itb_dir = f"processed_{strategy}_itb_sigma{sigma}"
+            base_itb = Path(f"data/{site_name}/{season}/{itb_dir}")
+            if not base_itb.exists():
+                continue
+            for source in ["s2", "s3"]:
+                inp, out = base_itb / source, base_itb / "gcc" / source
+                _create_gcc_timeseries_for_dir(
+                    inp,
+                    out,
+                    site_position,
+                    f"POST-ITB-{source.upper()}-{strategy}-σ{sigma}",
+                )
+            _create_gcc_timeseries_for_dir(
+                base_itb / "fusion",
+                base_itb / "gcc" / "fusion",
+                site_position,
+                f"POST-ITB-FUSION-{strategy}-σ{sigma}",
+            )
 
 
 def _get_bands_from_original(input_file, site_position):
@@ -425,7 +499,10 @@ def _get_bands_from_original(input_file, site_position):
             row, col = src.index(x[0], y[0])
             r0, r1 = max(0, row - 1), min(src.height, row + 2)
             c0, c1 = max(0, col - 1), min(src.width, col + 2)
-            bands = [src.read(i + 1, window=((r0, r1), (c0, c1))).astype(np.float32) for i in range(4)]
+            bands = [
+                src.read(i + 1, window=((r0, r1), (c0, c1))).astype(np.float32)
+                for i in range(4)
+            ]
             mask = ~np.any([np.isnan(b) for b in bands], axis=0)
             mask &= np.all([b > 0 for b in bands], axis=0)
             if not np.any(mask):
@@ -440,7 +517,9 @@ def _get_bands_from_original(input_file, site_position):
         return None
 
 
-def _create_bands_timeseries_for_dir(input_dir, output_dir, site_position, source_name, pattern="*.geotiff"):
+def _create_bands_timeseries_for_dir(
+    input_dir, output_dir, site_position, source_name, pattern="*.geotiff"
+):
     print(f"[BANDS-{source_name}] Creating timeseries.json...")
     timeseries = []
     for f in sorted(input_dir.glob(pattern)):
@@ -456,11 +535,14 @@ def _create_bands_timeseries_for_dir(input_dir, output_dir, site_position, sourc
     timeseries.sort(key=lambda x: x["date"])
     output_dir.mkdir(parents=True, exist_ok=True)
     (output_dir / "timeseries.json").write_text(json.dumps(timeseries, indent=2))
-    print(f"[BANDS-{source_name}] Saved: {output_dir / 'timeseries.json'} ({len(timeseries)} entries)")
+    print(
+        f"[BANDS-{source_name}] Saved: {output_dir / 'timeseries.json'} ({len(timeseries)} entries)"
+    )
 
 
 def _write_export(ndvi_dir, gcc_dir, bands_dir, export_dir):
     """Merge ndvi, gcc, bands into combined timeseries.json and timeseries.csv."""
+
     def load(p):
         p = Path(p)
         if not p.exists():
@@ -469,6 +551,7 @@ def _write_export(ndvi_dir, gcc_dir, bands_dir, export_dir):
             return json.loads((p / "timeseries.json").read_text())
         except Exception:
             return []
+
     ndvi = {str(t.get("date", ""))[:10]: t for t in load(ndvi_dir)}
     gcc = {str(t.get("date", ""))[:10]: t for t in load(gcc_dir)}
     bands = {str(t.get("date", ""))[:10]: t for t in load(bands_dir)}
@@ -482,12 +565,16 @@ def _write_export(ndvi_dir, gcc_dir, bands_dir, export_dir):
     export_dir.mkdir(parents=True, exist_ok=True)
     (export_dir / "timeseries.json").write_text(json.dumps(merged, indent=2))
     cols = ["date", "filename", "ndvi", "greenness_index", "b02", "b03", "b04", "b8a"]
+
     def esc(v):
         s = str(v) if v is not None else ""
         return f'"{s}"' if "," in s or '"' in s else s
+
     rows = [cols] + [[esc(r.get(c)) for c in cols] for r in merged]
     (export_dir / "timeseries.csv").write_text("\n".join(",".join(x) for x in rows))
-    print(f"[EXPORT] Saved {export_dir / 'timeseries.json'} and timeseries.csv ({len(merged)} entries)")
+    print(
+        f"[EXPORT] Saved {export_dir / 'timeseries.json'} and timeseries.csv ({len(merged)} entries)"
+    )
 
 
 def create_prepared_fusion_timeseries(season, site_position, site_name):
@@ -497,17 +584,86 @@ def create_prepared_fusion_timeseries(season, site_position, site_name):
         for source in ["s2", "s3"]:
             inp = base / source
             if inp.exists():
-                _create_timeseries_for_dir(inp, base / "ndvi" / source, site_position, f"PREPARED-{source.upper()}-{strategy}", "*.tif")
+                _create_timeseries_for_dir(
-                _create_gcc_timeseries_for_dir(inp, base / "gcc" / source, site_position, f"PREPARED-{source.upper()}-{strategy}", "*.tif")
+                    inp,
-                _create_bands_timeseries_for_dir(inp, base / "bands" / source, site_position, f"PREPARED-{source.upper()}-{strategy}", "*.tif")
+                    base / "ndvi" / source,
-                _write_export(base / "ndvi" / source, base / "gcc" / source, base / "bands" / source, base / "export" / source)
+                    site_position,
+                    f"PREPARED-{source.upper()}-{strategy}",
+                    "*.tif",
+                )
+                _create_gcc_timeseries_for_dir(
+                    inp,
+                    base / "gcc" / source,
+                    site_position,
+                    f"PREPARED-{source.upper()}-{strategy}",
+                    "*.tif",
+                )
+                _create_bands_timeseries_for_dir(
+                    inp,
+                    base / "bands" / source,
+                    site_position,
+                    f"PREPARED-{source.upper()}-{strategy}",
+                    "*.tif",
+                )
+                _write_export(
+                    base / "ndvi" / source,
+                    base / "gcc" / source,
+                    base / "bands" / source,
+                    base / "export" / source,
+                )
         for sig, fusion_sub in [(None, "fusion"), (30, "fusion_sigma30")]:
             inp = base / fusion_sub
             if inp.exists():
-                _create_timeseries_for_dir(inp, base / "ndvi" / fusion_sub, site_position, f"FUSION-{strategy}-σ{sig or 20}", "*.tif")
+                _create_timeseries_for_dir(
-                _create_gcc_timeseries_for_dir(inp, base / "gcc" / fusion_sub, site_position, f"FUSION-{strategy}-σ{sig or 20}", "*.tif")
+                    inp,
-                _create_bands_timeseries_for_dir(inp, base / "bands" / fusion_sub, site_position, f"FUSION-{strategy}-σ{sig or 20}", "*.tif")
+                    base / "ndvi" / fusion_sub,
-                _write_export(base / "ndvi" / fusion_sub, base / "gcc" / fusion_sub, base / "bands" / fusion_sub, base / "export" / fusion_sub)
+                    site_position,
+                    f"FUSION-{strategy}-σ{sig or 20}",
+                    "*.tif",
+                )
+                _create_gcc_timeseries_for_dir(
+                    inp,
+                    base / "gcc" / fusion_sub,
+                    site_position,
+                    f"FUSION-{strategy}-σ{sig or 20}",
+                    "*.tif",
+                )
+                _create_bands_timeseries_for_dir(
+                    inp,
+                    base / "bands" / fusion_sub,
+                    site_position,
+                    f"FUSION-{strategy}-σ{sig or 20}",
+                    "*.tif",
+                )
+                _write_export(
+                    base / "ndvi" / fusion_sub,
+                    base / "gcc" / fusion_sub,
+                    base / "bands" / fusion_sub,
+                    base / "export" / fusion_sub,
+                )
+        itb = Path(f"data/{site_name}/{season}/prepared_{strategy}_itb")
+        if not itb.exists():
+            continue
+        for source in ["s2", "s3"]:
+            inp = itb / source
+            if inp.exists():
+                _create_gcc_timeseries_for_dir(
+                    inp,
+                    itb / "gcc" / source,
+                    site_position,
+                    f"PREPARED-ITB-{source.upper()}-{strategy}",
+                    "*.tif",
+                )
+        for sig, fusion_sub in [(None, "fusion"), (30, "fusion_sigma30")]:
+            inp = itb / fusion_sub
+            if inp.exists():
+                _create_gcc_timeseries_for_dir(
+                    inp,
+                    itb / "gcc" / fusion_sub,
+                    site_position,
+                    f"FUSION-ITB-{strategy}-σ{sig or 20}",
+                    "*.tif",
+                )
 
 
 def create_bands_timeseries_post_process(season, site_position, site_name):
@@ -518,5 +674,16 @@ def create_bands_timeseries_post_process(season, site_position, site_name):
             for source in ["s2", "s3", "fusion"]:
                 inp, out = base / source, base / "bands" / source
                 if inp.exists():
-                    _create_bands_timeseries_for_dir(inp, out, site_position, f"POST-{source.upper()}-{strategy}-σ{sigma}", "*.geotiff")
+                    _create_bands_timeseries_for_dir(
-                    _write_export(base / "ndvi" / source, base / "gcc" / source, base / "bands" / source, base / "export" / source)
+                        inp,
+                        out,
+                        site_position,
+                        f"POST-{source.upper()}-{strategy}-σ{sigma}",
+                        "*.geotiff",
+                    )
+                    _write_export(
+                        base / "ndvi" / source,
+                        base / "gcc" / source,
+                        base / "bands" / source,
+                        base / "export" / source,
+                    )

metrics_stats.py

@@ -1,4 +1,5 @@
 """Metrics and statistics: temporal/spatial metrics and PhenoCam stats."""
+
 import json
 import numpy as np
 from pathlib import Path
@@ -132,6 +133,31 @@ def _get_spatial_stats_from_raster(raster_file, site_position):
     """Extract spatial statistics (mean, std, min, max) from GCC raster in 3x3 window."""
     try:
         with rasterio.open(raster_file) as src:
+            if src.count == 1:
+                g = src.read(1).astype(np.float32)
+                lon, lat = site_position[1], site_position[0]
+                x, y = transform_coords("EPSG:4326", src.crs, [lon], [lat])
+                if not (
+                    src.bounds.left <= x[0] <= src.bounds.right
+                    and src.bounds.bottom <= y[0] <= src.bounds.top
+                ):
+                    return None
+                row, col = src.index(x[0], y[0])
+                if row < 0 or row >= src.height or col < 0 or col >= src.width:
+                    return None
+                r0, r1 = max(0, row - 1), min(src.height, row + 2)
+                c0, c1 = max(0, col - 1), min(src.width, col + 2)
+                win = g[r0:r1, c0:c1]
+                mask = np.isfinite(win) & (win > 0)
+                if not np.any(mask):
+                    return None
+                valid = win[mask]
+                return {
+                    "mean": float(np.mean(valid)),
+                    "std": float(np.std(valid)),
+                    "min": float(np.min(valid)),
+                    "max": float(np.max(valid)),
+                }
             if src.count < 3:
                 return None
 
@@ -161,7 +187,13 @@ def _get_spatial_stats_from_raster(raster_file, site_position):
 
             # Calculate GCC for each pixel in window
             total = red_window + green_window + blue_window
-            mask = (total > 0) & ~np.isnan(total) & (blue_window >= 0) & (green_window >= 0) & (red_window >= 0)
+            mask = (
+                (total > 0)
+                & ~np.isnan(total)
+                & (blue_window >= 0)
+                & (green_window >= 0)
+                & (red_window >= 0)
+            )
             if not np.any(mask):
                 return None
 
@@ -259,7 +291,9 @@ def calculate_scenario_metrics(season, site_name, strategy, sigma, site_position
 
     # Calculate spatial metrics
     fusion_raster_dir = base / processed_dir / "fusion"
-    spatial_metrics = calculate_spatial_metrics(fusion_raster_dir, phenocam_ts, site_position)
+    spatial_metrics = calculate_spatial_metrics(
+        fusion_raster_dir, phenocam_ts, site_position
+    )
 
     return temporal_metrics, spatial_metrics
 
@@ -283,7 +317,9 @@ def calculate_all_metrics(season, site_name, site_position):
         results["phenocam_stats"] = phenocam_stats
 
     # Calculate S2 baseline metrics once (S2 data is identical across scenarios)
-    s2_ts_path = base / "processed_aggressive_sigma20" / "gcc" / "s2" / "timeseries.json"
+    s2_ts_path = (
+        base / "processed_aggressive_sigma20" / "gcc" / "s2" / "timeseries.json"
+    )
     s2_ts = load_timeseries(s2_ts_path)
     if s2_ts:
         s2_metrics = calculate_temporal_metrics(s2_ts, phenocam_ts)
@@ -303,7 +339,9 @@ def calculate_all_metrics(season, site_name, site_position):
             fusion_ts = load_timeseries(fusion_ts_path)
 
             if not fusion_ts:
-                print(f"[METRICS] Warning: Missing fusion data for {scenario_name}, skipping")
+                print(
+                    f"[METRICS] Warning: Missing fusion data for {scenario_name}, skipping"
+                )
                 continue
 
             # Calculate temporal metrics
@@ -313,7 +351,30 @@ def calculate_all_metrics(season, site_name, site_position):
 
             # Calculate spatial metrics
             fusion_raster_dir = base / processed_dir / "fusion"
-            spatial_metrics = calculate_spatial_metrics(fusion_raster_dir, phenocam_ts, site_position)
+            spatial_metrics = calculate_spatial_metrics(
+                fusion_raster_dir, phenocam_ts, site_position
+            )
+            if spatial_metrics:
+                results["spatial"][scenario_name] = spatial_metrics
+
+    for strategy in ["aggressive", "nonaggressive"]:
+        for sigma in [20, 30]:
+            scenario_name = f"{strategy}_sigma{sigma}_itb"
+            processed_dir = f"processed_{strategy}_itb_sigma{sigma}"
+            fusion_ts_path = base / processed_dir / "gcc" / "fusion" / "timeseries.json"
+            fusion_ts = load_timeseries(fusion_ts_path)
+            if not fusion_ts:
+                print(
+                    f"[METRICS] Warning: Missing ItB fusion data for {scenario_name}, skipping"
+                )
+                continue
+            temporal_metrics = calculate_temporal_metrics(fusion_ts, phenocam_ts)
+            if temporal_metrics:
+                results["temporal"][scenario_name] = temporal_metrics
+            fusion_raster_dir = base / processed_dir / "fusion"
+            spatial_metrics = calculate_spatial_metrics(
+                fusion_raster_dir, phenocam_ts, site_position
+            )
             if spatial_metrics:
                 results["spatial"][scenario_name] = spatial_metrics
 
@@ -321,14 +382,18 @@ def calculate_all_metrics(season, site_name, site_position):
     if results["temporal"]:
         best_temporal = max(
             results["temporal"].items(),
-            key=lambda x: x[1].get("r_squared", -1) if x[1].get("r_squared") is not None else -1
+            key=lambda x: x[1].get("r_squared", -1)
+            if x[1].get("r_squared") is not None
+            else -1,
         )
         results["summary"] = {"best_temporal_scenario": best_temporal[0]}
 
     if results["spatial"]:
         best_spatial = max(
             results["spatial"].items(),
-            key=lambda x: x[1].get("r_squared", -1) if x[1].get("r_squared") is not None else -1
+            key=lambda x: x[1].get("r_squared", -1)
+            if x[1].get("r_squared") is not None
+            else -1,
         )
         if "summary" not in results:
             results["summary"] = {}

postprocessing.py

@@ -1,17 +1,23 @@
 """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):
+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_dir = (
+        f"processed_{cleaning_strategy}_sigma{sigma}"
+        if sigma
+        else f"processed_{cleaning_strategy}_sigma20"
+    )
     processed = base / processed_dir
 
     s2_prep = prepared / "s2"
@@ -21,7 +27,9 @@ def process_cropped(season, site_position, site_name, cleaning_strategy="aggress
     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}")
+    print(
+        f"[PROCESS] Processing files: {site_name}, {season}, {cleaning_strategy}, sigma={sigma or 20}"
+    )
 
     # Crop fusion to valid data and get dimensions
     fusion_dims = {}
@@ -48,11 +56,29 @@ def process_cropped(season, site_position, site_name, cleaning_strategy="aggress
             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)
+            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():
+    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"):
@@ -61,7 +87,9 @@ def process_cropped(season, site_position, site_name, cleaning_strategy="aggress
                 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})
+                    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}")
@@ -83,7 +111,7 @@ def process_cropped(season, site_position, site_name, cleaning_strategy="aggress
                                 src_crs=src.crs,
                                 dst_transform=fusion_transform,
                                 dst_crs=crs,
-                                resampling=Resampling.nearest
+                                resampling=Resampling.nearest,
                             )
                         # Crop using same window
                         data = resampled.read(window=window)
@@ -96,11 +124,135 @@ def process_cropped(season, site_position, site_name, cleaning_strategy="aggress
     print("[PROCESS] Completed")
 
 
+def process_cropped_itb(
+    season, site_position, site_name, cleaning_strategy="aggressive", sigma=None
+):
+    base = Path(f"data/{site_name}/{season}")
+    prepared = base / f"prepared_{cleaning_strategy}_itb"
+    processed_dir = (
+        f"processed_{cleaning_strategy}_itb_sigma{sigma}"
+        if sigma
+        else f"processed_{cleaning_strategy}_itb_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-ITB] {site_name}, {season}, {cleaning_strategy}, sigma={sigma or 20}"
+    )
+    fusion_dims = {}
+    for fusion_file in fusion_prep.glob("GCC_*.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-ITB] Skip {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-ITB] Cropped fusion: {output_file}")
+    for date_str, (
+        c0,
+        r0,
+        w,
+        h,
+        transform,
+        fusion_transform,
+        crs,
+        fusion_profile,
+    ) in fusion_dims.items():
+        window = windows.Window(c0, r0, w, h)
+        for s2_file in s2_prep.glob("*GCC.tif"):
+            parts = s2_file.stem.split("_")
+            if len(parts) > 2 and parts[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-ITB] Cropped: {output_file}")
+                break
+        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:
+                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,
+                            )
+                        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-ITB] Cropped: {output_file}")
+    print("[PROCESS-ITB] Completed")
+
+
+def post_process_all_itb_scenarios(season, site_position, site_name):
+    for strategy in ["aggressive", "nonaggressive"]:
+        for sigma in [None, 30]:
+            process_cropped_itb(
+                season,
+                site_position,
+                site_name,
+                cleaning_strategy=strategy,
+                sigma=sigma,
+            )
+
+
 def post_process_all_scenarios(season, site_position, site_name):
     """Crop fusion/S2/S3 to valid pixels for all 4 scenarios."""
     for strategy in ["aggressive", "nonaggressive"]:
         for sigma in [None, 30]:
-            process_cropped(season, site_position, site_name, cleaning_strategy=strategy, sigma=sigma)
+            process_cropped(
+                season,
+                site_position,
+                site_name,
+                cleaning_strategy=strategy,
+                sigma=sigma,
+            )
 
 
 def post_process_timeseries(season, site_position, site_name):
@@ -110,6 +262,7 @@ def post_process_timeseries(season, site_position, site_name):
         create_gcc_timeseries_post_process,
         create_bands_timeseries_post_process,
     )
+
     create_ndvi_timeseries_post_process(season, site_position, site_name)
     create_gcc_timeseries_post_process(season, site_position, site_name)
     create_bands_timeseries_post_process(season, site_position, site_name)

preparation.py

@@ -1,4 +1,5 @@
 """Data preparation: S2/S3 preprocessing for fusion."""
+
 import json
 import shutil
 from pathlib import Path
@@ -16,6 +17,7 @@ def _import_distance_to_clouds():
     """Lazy import of efast.distance_to_clouds."""
     try:
         from efast.s2_processing import distance_to_clouds
+
         return distance_to_clouds
     except ImportError:
         raise ImportError(
@@ -40,6 +42,76 @@ def _get_base_dir(season, site_name, cleaning_strategy):
     return Path(f"data/{site_name}/{season}/prepared_{cleaning_strategy}/")
 
 
+def _get_itb_base_dir(season, site_name, cleaning_strategy):
+    return Path(f"data/{site_name}/{season}/prepared_{cleaning_strategy}_itb")
+
+
+def _compute_gcc_from_refl_array(blue, green, red):
+    total = red.astype(np.float32) + green.astype(np.float32) + red.astype(np.float32)
+    mask = (total > 0) & np.isfinite(total)
+    gcc = np.zeros_like(green, dtype=np.float32)
+    gcc[mask] = green[mask].astype(np.float32) / total[mask]
+    return gcc
+
+
+def _link_dist_cloud_from_prepared(src_s2_dir, dst_s2_dir):
+    dst_s2_dir.mkdir(parents=True, exist_ok=True)
+    for src in src_s2_dir.glob("*DIST_CLOUD.tif"):
+        dst = dst_s2_dir / src.name
+        if dst.exists():
+            continue
+        try:
+            dst.symlink_to(src.resolve())
+        except OSError:
+            shutil.copy2(src, dst)
+
+
+def prepare_s2_gcc_for_itb(
+    season, site_position, site_name, cleaning_strategy="aggressive"
+):
+    base = _get_base_dir(season, site_name, cleaning_strategy)
+    itb_s2 = _get_itb_base_dir(season, site_name, cleaning_strategy) / "s2"
+    s2_prep = base / "s2"
+    itb_s2.mkdir(parents=True, exist_ok=True)
+    for refl in sorted(s2_prep.glob("*REFL.tif")):
+        out = itb_s2 / refl.name.replace("_REFL.tif", "_GCC.tif")
+        if out.exists():
+            continue
+        with rasterio.open(refl) as src:
+            if src.count < 4:
+                continue
+            b, g, r = (src.read(i).astype(np.float32) for i in range(1, 4))
+            gcc = _compute_gcc_from_refl_array(b, g, r)
+            profile = src.profile.copy()
+            profile.update({"count": 1, "dtype": "float32", "nodata": 0})
+            with rasterio.open(out, "w", **profile) as dst:
+                dst.write(gcc, 1)
+        print(f"[S2-ITB] Saved {out.name}")
+    _link_dist_cloud_from_prepared(s2_prep, itb_s2)
+
+
+def prepare_s3_gcc_for_itb(
+    season, site_position, site_name, cleaning_strategy="aggressive"
+):
+    base = _get_base_dir(season, site_name, cleaning_strategy)
+    itb_s3 = _get_itb_base_dir(season, site_name, cleaning_strategy) / "s3"
+    itb_s3.mkdir(parents=True, exist_ok=True)
+    for comp in sorted((base / "s3").glob("composite_*.tif")):
+        out = itb_s3 / comp.name
+        if out.exists():
+            continue
+        with rasterio.open(comp) as src:
+            if src.count < 4:
+                continue
+            b, g, r = (src.read(i).astype(np.float32) for i in range(1, 4))
+            gcc = _compute_gcc_from_refl_array(b, g, r)
+            profile = src.profile.copy()
+            profile.update({"count": 1, "dtype": "float32", "nodata": 0})
+            with rasterio.open(out, "w", **profile) as dst:
+                dst.write(gcc, 1)
+        print(f"[S3-ITB] Saved {out.name}")
+
+
 def _reproject_raster_to_target(
     src_path,
     dst_path,
@@ -90,7 +162,9 @@ def _rescale_dist_cloud_for_small_roi(s2_output_dir):
             print(f"[S2-PREP] Rescaled DIST_CLOUD for {dc_path.name} (max was {d_max})")
 
 
-def prepare_s2(season, site_position, site_name, cleaning_strategy="aggressive", date_range=None):
+def prepare_s2(
+    season, site_position, site_name, cleaning_strategy="aggressive", date_range=None
+):
     lat, lon = site_position
     s2_dir = Path(f"data/{site_name}/{season}/raw/s2/")
     s3_dir = Path(f"data/{site_name}/{season}/raw/s3/")
@@ -99,7 +173,9 @@ def prepare_s2(season, site_position, site_name, cleaning_strategy="aggressive",
     clouds = _load_excluded(season, site_name, cleaning_strategy)
     s2_output_dir.mkdir(parents=True, exist_ok=True)
 
-    print(f"[S2-PREP] Starting preparation: {site_name} ({lat:.6f}, {lon:.6f}), {season}, strategy={cleaning_strategy}")
+    print(
+        f"[S2-PREP] Starting preparation: {site_name} ({lat:.6f}, {lon:.6f}), {season}, strategy={cleaning_strategy}"
+    )
 
     s3_files = [f for f in s3_dir.glob("*.geotiff") if f.name not in clouds["s3"]]
     if not s3_files:
@@ -113,7 +189,9 @@ def prepare_s2(season, site_position, site_name, cleaning_strategy="aggressive",
 
     for s2_file in sorted(s2_dir.glob("*.geotiff")):
         if s2_file.name in clouds["s2"]:
-            print(f"[S2-PREP] Skipping {s2_file.name} (excluded by {cleaning_strategy})")
+            print(
+                f"[S2-PREP] Skipping {s2_file.name} (excluded by {cleaning_strategy})"
+            )
             continue
         date_str = s2_file.name.split("_")[0]
         refl_dst = s2_output_dir / f"S2A_MSIL2A_{date_str}_REFL.tif"
@@ -136,14 +214,16 @@ def prepare_s2(season, site_position, site_name, cleaning_strategy="aggressive",
         temp_normalized.unlink()
         print(f"[S2-PREP] Saved: {refl_dst}")
 
-    print(f"[S2-PREP] Computing distance-to-clouds...")
+    print("[S2-PREP] Computing distance-to-clouds...")
     distance_to_clouds = _import_distance_to_clouds()
     distance_to_clouds(s2_output_dir, ratio=RESOLUTION_RATIO)
     _rescale_dist_cloud_for_small_roi(s2_output_dir)
     print("[S2-PREP] Completed")
 
 
-def prepare_s3(season, site_position, site_name, cleaning_strategy="aggressive", date_range=None):
+def prepare_s3(
+    season, site_position, site_name, cleaning_strategy="aggressive", date_range=None
+):
     lat, lon = site_position
     s3_dir = Path(f"data/{site_name}/{season}/raw/s3/")
     base_dir = _get_base_dir(season, site_name, cleaning_strategy)
@@ -153,16 +233,22 @@ def prepare_s3(season, site_position, site_name, cleaning_strategy="aggressive",
     clouds = _load_excluded(season, site_name, cleaning_strategy)
     s3_preprocessed_dir.mkdir(parents=True, exist_ok=True)
 
-    print(f"[S3-PREP] Starting preparation: {site_name} ({lat:.6f}, {lon:.6f}), {season}, strategy={cleaning_strategy}")
+    print(
+        f"[S3-PREP] Starting preparation: {site_name} ({lat:.6f}, {lon:.6f}), {season}, strategy={cleaning_strategy}"
+    )
 
     s3_by_date = defaultdict(list)
     for s3_file in s3_dir.glob("*.geotiff"):
         if s3_file.name not in clouds["s3"]:
             s3_by_date[s3_file.name.split("_")[0]].append(s3_file)
         else:
-            print(f"[S3-PREP] Skipping {s3_file.name} (excluded by {cleaning_strategy})")
+            print(
+                f"[S3-PREP] Skipping {s3_file.name} (excluded by {cleaning_strategy})"
+            )
 
-    print(f"[S3-PREP] Found {sum(len(v) for v in s3_by_date.values())} acquisitions across {len(s3_by_date)} dates")
+    print(
+        f"[S3-PREP] Found {sum(len(v) for v in s3_by_date.values())} acquisitions across {len(s3_by_date)} dates"
+    )
 
     temp_composite_dir = s3_preprocessed_dir / "temp_composites"
     if temp_composite_dir.exists():
@@ -187,7 +273,9 @@ def prepare_s3(season, site_position, site_name, cleaning_strategy="aggressive",
                 profile.update({"count": composite.shape[0], "dtype": "float32"})
             with rasterio.open(composite_path, "w", **profile) as dst:
                 dst.write(composite)
-            print(f"[S3-PREP] Composite {date_str}: {len(s3_files)} acquisitions merged")
+            print(
+                f"[S3-PREP] Composite {date_str}: {len(s3_files)} acquisitions merged"
+            )
 
     # Reproject S3 to match S2 REFL bounds (full coverage) instead of DIST_CLOUD bounds
     # This ensures fusion covers the same area as S2 and dimensions match
@@ -212,7 +300,9 @@ def prepare_s3(season, site_position, site_name, cleaning_strategy="aggressive",
             height,
         )
 
-    print(f"[S3-PREP] Reprojecting {len(list(temp_composite_dir.glob('*.tif')))} composites to S2 grid ({width}×{height} px)...")
+    print(
+        f"[S3-PREP] Reprojecting {len(list(temp_composite_dir.glob('*.tif')))} composites to S2 grid ({width}×{height} px)..."
+    )
 
     # Reproject each S3 composite to match S2 REFL bounds
     sen3_paths = sorted(temp_composite_dir.glob("*.tif"))

pyproject.toml

@@ -0,0 +1,2 @@
+[tool.ruff.lint.per-file-ignores]
+"run.py" = ["F401"]

run.py

@@ -1,10 +1,19 @@
-from fusion import run_all_efast_scenarios
+from fusion import run_all_efast_scenarios, run_all_efast_itb_scenarios
-from postprocessing import post_process_all_scenarios, post_process_timeseries
+from postprocessing import (
+    post_process_all_scenarios,
+    post_process_all_itb_scenarios,
+    post_process_timeseries,
+)
 from acquisition_s2 import download_s2
 from acquisition_s3 import download_s3
 from acquisition_phenocam import download_phenocam
 from preselection import create_timeseries
-from preparation import prepare_s2, prepare_s3
+from preparation import (
+    prepare_s2,
+    prepare_s3,
+    prepare_s2_gcc_for_itb,
+    prepare_s3_gcc_for_itb,
+)
 from metrics_indices import create_prepared_fusion_timeseries
 from metrics_stats import calculate_all_metrics
 
@@ -12,10 +21,10 @@ from metrics_stats import calculate_all_metrics
 def run_pipeline(season, site_position, site_name):
     """Run pipeline."""
     try:
-        #print(f"Downloading S2, S3, and PhenoCam: {site_name}, {season}")
+        # print(f"Downloading S2, S3, and PhenoCam: {site_name}, {season}")
-        #download_s2(season, site_position, site_name)
+        # download_s2(season, site_position, site_name)
-        #download_s3(season, site_position, site_name)
+        # download_s3(season, site_position, site_name)
-        #download_phenocam(season, site_position, site_name)
+        # download_phenocam(season, site_position, site_name)
 
         # print(f"Creating preselection timeseries: {site_name}, {season}")
         # create_timeseries(season, site_position, site_name)
@@ -28,13 +37,20 @@ def run_pipeline(season, site_position, site_name):
         # print(f"Running EFAST fusion for all scenarios: {site_name}, {season}")
         # run_all_efast_scenarios(season, site_position, site_name)
 
+        # Index-then-Blend (ItB): GCC stacks, EFAST fusion with product=GCC
+        # for strategy in ["aggressive", "nonaggressive"]:
+        #     prepare_s2_gcc_for_itb(season, site_position, site_name, cleaning_strategy=strategy)
+        #     prepare_s3_gcc_for_itb(season, site_position, site_name, cleaning_strategy=strategy)
+        # run_all_efast_itb_scenarios(season, site_position, site_name)
+        # post_process_all_itb_scenarios(season, site_position, site_name)
+
         print(f"Creating prepared/fusion timeseries: {site_name}, {season}")
         create_prepared_fusion_timeseries(season, site_position, site_name)
 
         print(f"Post-processing: {site_name}, {season}")
         # post_process_all_scenarios(season, site_position, site_name)
         post_process_timeseries(season, site_position, site_name)
-        
+
         print(f"Calculating metrics: {site_name}, {season}")
         # calculate_all_metrics(season, site_name, site_position)
 
@@ -51,4 +67,3 @@ if __name__ == "__main__":
     run_pipeline(2023, (64.2437, 19.7673), "vindeln2")
     run_pipeline(2024, (36.7455, -6.0033), "sunflowerjerez1")
     run_pipeline(2024, (42.6558, 26.9837), "institutekarnobat")
-