Ran linter.

1-phenocam.py

@@ -81,7 +81,9 @@ def load_candidate_cameras(
         sitename = str(camera["Sitename"])
         if site_filter is not None and sitename not in site_filter:
             continue
-        if not _overlaps_year(camera.get("date_first"), camera.get("date_last"), evaluation_year):
+        if not _overlaps_year(
+            camera.get("date_first"), camera.get("date_last"), evaluation_year
+        ):
             continue
         cameras.append(dict(camera))
     cameras.sort(key=lambda item: str(item["Sitename"]))
@@ -129,7 +131,9 @@ def download_site(
 
     csv_url = roi.get("one_day_summary") if roi else None
     if csv_url:
-        download_one_day_csv(csv_url, site_csv_path(cache_dir, evaluation_year, sitename))
+        download_one_day_csv(
+            csv_url, site_csv_path(cache_dir, evaluation_year, sitename)
+        )
     return sitename
 
 
@@ -199,7 +203,9 @@ def write_manifest(
     cache_dir: Path,
     evaluation_year: int,
 ) -> None:
-    rel_sites_dir = sites_dir(cache_dir, evaluation_year).relative_to(output_path.parent)
+    rel_sites_dir = sites_dir(cache_dir, evaluation_year).relative_to(
+        output_path.parent
+    )
     payload = {
         "evaluation_year": evaluation_year,
         "count": len(sitenames),

2-phenocam-screening.py

@@ -203,7 +203,9 @@ def screen_site(
     calculations["snr"] = snr
     if snr is None or snr < snr_threshold:
         calculations["failing_gate"] = "snr"
-        calculations["reason"] = "insufficient_snr" if snr is not None else "snr_undefined"
+        calculations["reason"] = (
+            "insufficient_snr" if snr is not None else "snr_undefined"
+        )
         return {"response": response, "calculations": calculations}
 
     calculations["passed_gates"].append("snr")
@@ -331,10 +333,7 @@ def print_summary(results: list[dict[str, Any]], evaluation_year: int) -> None:
         print(f"  after_{gate}: {after}, fail_at_{gate}: {fails}")
 
     print("\nPer-site table")
-    print(
+    print(f"{'site':<24} {'n':>4} {'mon':>3} {'snr':>6} {'status':>6} gate reason")
-        f"{'site':<24} {'n':>4} {'mon':>3} {'snr':>6} "
-        f"{'status':>6} gate reason"
-    )
     print("-" * 72)
     for row in sorted(
         results,

3-sentinel-data.py

@@ -388,10 +388,14 @@ def download_s2_window(
     with rasterio.Env(**_GDAL_COG_ENV):
         with ThreadPoolExecutor(max_workers=max_workers) as pool:
             futures = {
-                pool.submit(_process_item, item, bbox, bands, output_dir, ratio): item.id
+                pool.submit(
+                    _process_item, item, bbox, bands, output_dir, ratio
+                ): item.id
                 for item in items
             }
-            with tqdm(total=len(futures), unit="granule", desc="S2 COG window read") as pbar:
+            with tqdm(
+                total=len(futures), unit="granule", desc="S2 COG window read"
+            ) as pbar:
                 for fut in as_completed(futures):
                     msg = fut.result()
                     if msg:
@@ -461,9 +465,7 @@ def _netcdf_to_geotiffs(nc_path: Path, output_dir: Path, epsg: int) -> int:
             n = date_counts.get(date_str, 0)
             date_counts[date_str] = n + 1
 
-            raw = np.stack(
+            raw = np.stack([nc.variables[b][t_idx, :, :] for b in S3_BANDS], axis=0)
-                [nc.variables[b][t_idx, :, :] for b in S3_BANDS], axis=0
-            )
             stacked = (
                 np.ma.filled(raw, fill_value=np.nan).astype("float32")
                 / S3_REFLECTANCE_SCALE
@@ -523,7 +525,9 @@ def _download_with_retry(datacube: Any, nc_path: Path) -> None:
                 delay *= 2
             else:
                 print(f"[S3-OEO] All {_S3_DOWNLOAD_RETRIES} download attempts failed")
-    raise RuntimeError(f"S3 download failed after {_S3_DOWNLOAD_RETRIES} attempts") from last_exc
+    raise RuntimeError(
+        f"S3 download failed after {_S3_DOWNLOAD_RETRIES} attempts"
+    ) from last_exc
 
 
 def download_s3_openeo(
@@ -592,9 +596,7 @@ def _import_distance_to_clouds():
 
         return distance_to_clouds
     except ImportError as exc:
-        raise ImportError(
+        raise ImportError("efast not found. Install with: uv sync") from exc
-            "efast not found. Install with: uv sync"
-        ) from exc
 
 
 def _normalize_s2_grid(s2_dir: Path) -> None:
@@ -663,9 +665,7 @@ def _import_s3_processing():
 
         return s3_processing
     except ImportError as exc:
-        raise ImportError(
+        raise ImportError("efast not found. Install with: uv sync") from exc
-            "efast not found. Install with: uv sync"
-        ) from exc
 
 
 def _reproject_s3_composites_to_s2_grid(
@@ -912,7 +912,9 @@ def main(argv: list[str] | None = None) -> int:
         sitename = site["sitename"]
         site_dir = DATA_DIR / "sentinel_data" / str(year) / sitename
         if args.skip_downloaded and site_dir.exists():
-            print(f"[Sentinel-3] ({i}/{len(pass_sites)}) {sitename} — skipping (directory exists)")
+            print(
+                f"[Sentinel-3] ({i}/{len(pass_sites)}) {sitename} — skipping (directory exists)"
+            )
             continue
         print(f"[Sentinel-3] ({i}/{len(pass_sites)}) {sitename}")
         summary = process_site(sitename, site["lat"], site["lon"], year)

4-fusion.py

@@ -61,9 +61,7 @@ def _import_efast():
 
         return efast_module
     except ImportError as exc:
-        raise ImportError(
+        raise ImportError("efast not found. Install with: uv sync") from exc
-            "efast not found. Install with: uv sync"
-        ) from exc
 
 
 # ---------------------------------------------------------------------------
@@ -174,7 +172,9 @@ def fuse_site(sitename: str, year: int) -> dict[str, Any]:
 
     s2_dir = DATA_DIR / "sentinel_data" / str(year) / sitename / "prepared" / "s2"
     s3_dir = DATA_DIR / "sentinel_data" / str(year) / sitename / "prepared" / "s3"
-    gcc_s3_dir = DATA_DIR / "sentinel_data" / str(year) / sitename / "prepared" / "gcc_s3"
+    gcc_s3_dir = (
+        DATA_DIR / "sentinel_data" / str(year) / sitename / "prepared" / "gcc_s3"
+    )
     base = DATA_DIR / "fusion" / str(year) / sitename
 
     if not s2_dir.is_dir() or not any(s2_dir.glob("*_REFL.tif")):
@@ -228,11 +228,15 @@ def fuse_site(sitename: str, year: int) -> dict[str, Any]:
 
     print(f"[{sitename}] ItB: fusing GCC over {len(fusion_dates)} dates...")
     for date in fusion_dates:
-        efast.fusion(date, gcc_s3_dir, s2_dir, itb_fusion, product="GCC", **_fusion_kwargs)
+        efast.fusion(
+            date, gcc_s3_dir, s2_dir, itb_fusion, product="GCC", **_fusion_kwargs
+        )
 
     # --- BtI: fuse reflectance (3-band, matching S2 B02/B03/B04), then derive GCC ---
     # S3 composites have 4 bands; strip band 4 (Oa17/NIR) so shapes match S2 REFL.
-    s3_rgb_dir = DATA_DIR / "sentinel_data" / str(year) / sitename / "prepared" / "s3_rgb"
+    s3_rgb_dir = (
+        DATA_DIR / "sentinel_data" / str(year) / sitename / "prepared" / "s3_rgb"
+    )
     s3_rgb_dir.mkdir(parents=True, exist_ok=True)
     for p in sorted(s3_dir.glob("composite_*.tif")):
         out = s3_rgb_dir / p.name
@@ -250,7 +254,9 @@ def fuse_site(sitename: str, year: int) -> dict[str, Any]:
 
     print(f"[{sitename}] BtI: fusing REFL over {len(fusion_dates)} dates...")
     for date in fusion_dates:
-        efast.fusion(date, s3_rgb_dir, s2_dir, bti_fusion, product="REFL", **_fusion_kwargs)
+        efast.fusion(
+            date, s3_rgb_dir, s2_dir, bti_fusion, product="REFL", **_fusion_kwargs
+        )
 
     print(f"[{sitename}] BtI: deriving GCC from fused REFL...")
     compute_gcc_from_refl(bti_fusion, bti_gcc)
@@ -318,7 +324,9 @@ def main(argv: list[str] | None = None) -> int:
     for i, sitename in enumerate(sites, 1):
         fusion_dir = DATA_DIR / "fusion" / str(year) / sitename
         if args.skip_blended and fusion_dir.exists():
-            print(f"[Fusion] ({i}/{len(sites)}) {sitename} — skipping (fusion directory exists)")
+            print(
+                f"[Fusion] ({i}/{len(sites)}) {sitename} — skipping (fusion directory exists)"
+            )
             continue
         print(f"[Fusion] ({i}/{len(sites)}) {sitename}")
         summary = fuse_site(sitename, year)

5-metrics.py

@@ -72,7 +72,9 @@ WHITTAKER_LAMBDA = 400.0
 SPATIAL_CV_HALF_M = 150
 
 # PhenoCam archive image URL pattern
-PHENOCAM_IMAGE_URL = "https://phenocam.nau.edu/data/archive/{site}/{year}/{month}/{filename}"
+PHENOCAM_IMAGE_URL = (
+    "https://phenocam.nau.edu/data/archive/{site}/{year}/{month}/{filename}"
+)
 
 
 # ---------------------------------------------------------------------------
@@ -133,7 +135,9 @@ def _date_from_s2_tif(path: Path) -> str | None:
 # ---------------------------------------------------------------------------
 
 
-def _whittaker_smooth(values: list[float | None], lam: float = WHITTAKER_LAMBDA) -> list[float | None]:
+def _whittaker_smooth(
+    values: list[float | None], lam: float = WHITTAKER_LAMBDA
+) -> list[float | None]:
     """Penalised least-squares smoother (Whittaker, 2nd-order differences).
 
     Masked (None) values are filled via the smooth and then re-set to None in
@@ -211,9 +215,7 @@ def _parse_phenocam_csv(
 # ---------------------------------------------------------------------------
 
 
-def _moving_average(
+def _moving_average(series: list[dict], value_key: str, window: int) -> list[dict]:
-    series: list[dict], value_key: str, window: int
-) -> list[dict]:
     """Compute centred moving average; returns new list with ``_smooth`` suffix key."""
     if not series:
         return []
@@ -221,11 +223,13 @@ def _moving_average(
     half = window // 2
     smoothed = []
     for i, pt in enumerate(series):
-        chunk = [v for v in vals[max(0, i - half): i + half + 1] if v is not None]
+        chunk = [v for v in vals[max(0, i - half) : i + half + 1] if v is not None]
-        smoothed.append({
+        smoothed.append(
-            "date": pt["date"],
+            {
-            value_key + "_smooth": (sum(chunk) / len(chunk)) if chunk else None,
+                "date": pt["date"],
-        })
+                value_key + "_smooth": (sum(chunk) / len(chunk)) if chunk else None,
+            }
+        )
     return smoothed
 
 
@@ -237,8 +241,10 @@ MATCH_TOLERANCE_DAYS = 5
 
 
 def compute_metrics(
-    ref: list[dict], ref_key: str,
+    ref: list[dict],
-    pred: list[dict], pred_key: str,
+    ref_key: str,
+    pred: list[dict],
+    pred_key: str,
 ) -> dict | None:
     """Compute NSE, RMSE, nRMSE, Pearson r between pred and ref.
 
@@ -246,7 +252,9 @@ def compute_metrics(
     ``MATCH_TOLERANCE_DAYS``.  Returns a dict or ``None`` if fewer than
     2 matched pairs exist.
     """
-    ref_lookup: dict[str, float] = {p["date"]: p[ref_key] for p in ref if p.get(ref_key) is not None}
+    ref_lookup: dict[str, float] = {
+        p["date"]: p[ref_key] for p in ref if p.get(ref_key) is not None
+    }
     if not ref_lookup:
         return None
 
@@ -257,9 +265,16 @@ def compute_metrics(
         v = pt.get(pred_key)
         if v is None:
             continue
-        nearest = min(ref_dates, key=lambda d: abs((
+        nearest = min(
-            np.datetime64(pt["date"]) - np.datetime64(d)) / np.timedelta64(1, "D")))
+            ref_dates,
-        gap = abs((np.datetime64(pt["date"]) - np.datetime64(nearest)) / np.timedelta64(1, "D"))
+            key=lambda d: abs(
+                (np.datetime64(pt["date"]) - np.datetime64(d)) / np.timedelta64(1, "D")
+            ),
+        )
+        gap = abs(
+            (np.datetime64(pt["date"]) - np.datetime64(nearest))
+            / np.timedelta64(1, "D")
+        )
         if gap <= MATCH_TOLERANCE_DAYS and nearest in ref_lookup:
             obs.append(ref_lookup[nearest])
             sim.append(v)
@@ -281,7 +296,13 @@ def compute_metrics(
     def _r4(v: float | None) -> float | None:
         return round(v, 4) if v is not None else None
 
-    return {"n": len(obs), "rmse": _r4(rmse), "nrmse": _r4(nrmse), "nse": _r4(nse), "r": _r4(float(r))}
+    return {
+        "n": len(obs),
+        "rmse": _r4(rmse),
+        "nrmse": _r4(nrmse),
+        "nse": _r4(nse),
+        "r": _r4(float(r)),
+    }
 
 
 S2_BAND_NAMES = ["B02", "B03", "B04"]
@@ -294,7 +315,9 @@ def _read_multiband_center(
     """Return 3×3 mean per band at (lat, lon). Keys are ``band_names``, values float or None."""
     try:
         with rasterio.open(path) as src:
-            transformer = Transformer.from_crs(CRS.from_epsg(4326), src.crs, always_xy=True)
+            transformer = Transformer.from_crs(
+                CRS.from_epsg(4326), src.crs, always_xy=True
+            )
             x, y = transformer.transform(lon, lat)
             row, col = rowcol(src.transform, x, y)
             h, w = src.height, src.width
@@ -353,7 +376,9 @@ def _read_footprint_stats(
     """
     try:
         with rasterio.open(path) as src:
-            transformer = Transformer.from_crs(CRS.from_epsg(4326), src.crs, always_xy=True)
+            transformer = Transformer.from_crs(
+                CRS.from_epsg(4326), src.crs, always_xy=True
+            )
             x, y = transformer.transform(lon, lat)
             res = abs(src.transform.a)  # pixel size in CRS units (metres for UTM)
             half_px = max(1, int(round(half_m / res)))
@@ -392,7 +417,11 @@ def compute_covariates(
             means.append(m)
             stds.append(s)
 
-    spatial_gcc_cv = round(float(np.mean([s / m for s, m in zip(stds, means)])), 4) if means else None
+    spatial_gcc_cv = (
+        round(float(np.mean([s / m for s, m in zip(stds, means)])), 4)
+        if means
+        else None
+    )
     spatial_gcc_std = round(float(np.mean(stds)), 4) if stds else None
 
     # S2 temporal gap statistics
@@ -406,13 +435,13 @@ def compute_covariates(
         s2_max_gap = None
 
     return {
-        "spatial_gcc_cv":    spatial_gcc_cv,
+        "spatial_gcc_cv": spatial_gcc_cv,
-        "spatial_gcc_std":   spatial_gcc_std,
+        "spatial_gcc_std": spatial_gcc_std,
-        "s2_scene_count":    len(s2_series),
+        "s2_scene_count": len(s2_series),
-        "s2_mean_gap_days":  s2_mean_gap,
+        "s2_mean_gap_days": s2_mean_gap,
-        "s2_max_gap_days":   s2_max_gap,
+        "s2_max_gap_days": s2_max_gap,
         "s3_composite_count": len(s3_series),
-        "n_gcc_points":      n_gcc_points,
+        "n_gcc_points": n_gcc_points,
     }
 
 
@@ -521,8 +550,22 @@ def export_site(
     ]
 
     # Band reflectance timeseries (multi-band center-pixel)
-    bands_s2 = _multiband_series(sorted(s2_refl_dir.glob("*_REFL.tif")), _date_from_s2_tif, lat, lon, S2_BAND_NAMES, f"{site} S2 bands")
+    bands_s2 = _multiband_series(
-    bands_s3 = _multiband_series(sorted(s3_comp_dir.glob("composite_*.tif")), _date_from_gcc_tif, lat, lon, S3_BAND_NAMES, f"{site} S3 bands")
+        sorted(s2_refl_dir.glob("*_REFL.tif")),
+        _date_from_s2_tif,
+        lat,
+        lon,
+        S2_BAND_NAMES,
+        f"{site} S2 bands",
+    )
+    bands_s3 = _multiband_series(
+        sorted(s3_comp_dir.glob("composite_*.tif")),
+        _date_from_gcc_tif,
+        lat,
+        lon,
+        S3_BAND_NAMES,
+        f"{site} S3 bands",
+    )
 
     # --- Per-metric JSON outputs ---
     _write_json(out_dir / "gcc_phenocam.json", phenocam_series)
@@ -545,16 +588,40 @@ def export_site(
     s2_valid_dates = {p["date"].replace("-", "") for p in s2_series}
     s3_valid_dates = {p["date"].replace("-", "") for p in s3_series}
 
-    s2_refl = [r for r in _raster_index(sorted(s2_refl_dir.glob("*_REFL.tif")), _date_from_s2_tif, rel_root)
+    s2_refl = [
-               if r["date"] in s2_valid_dates]
+        r
-    s3_comp = [r for r in _raster_index(sorted(s3_comp_dir.glob("composite_*.tif")), _date_from_gcc_tif, rel_root)
+        for r in _raster_index(
-               if r["date"] in s3_valid_dates]
+            sorted(s2_refl_dir.glob("*_REFL.tif")), _date_from_s2_tif, rel_root
-    s2_gcc = [r for r in _raster_index(sorted(s2_gcc_dir.glob("*_GCC.tif")), _date_from_s2_tif, rel_root)
+        )
-              if r["date"] in s2_valid_dates]
+        if r["date"] in s2_valid_dates
-    s3_gcc = [r for r in _raster_index(sorted(s3_gcc_dir.glob("composite_*.tif")), _date_from_gcc_tif, rel_root)
+    ]
-              if r["date"] in s3_valid_dates]
+    s3_comp = [
-    bti_refl = _raster_index(sorted(bti_refl_dir.glob("REFL_*.tif")), _date_from_gcc_tif, rel_root)
+        r
-    itb_gcc = _raster_index(sorted(itb_gcc_dir.glob("GCC_*.tif")), _date_from_gcc_tif, rel_root)
+        for r in _raster_index(
+            sorted(s3_comp_dir.glob("composite_*.tif")), _date_from_gcc_tif, rel_root
+        )
+        if r["date"] in s3_valid_dates
+    ]
+    s2_gcc = [
+        r
+        for r in _raster_index(
+            sorted(s2_gcc_dir.glob("*_GCC.tif")), _date_from_s2_tif, rel_root
+        )
+        if r["date"] in s2_valid_dates
+    ]
+    s3_gcc = [
+        r
+        for r in _raster_index(
+            sorted(s3_gcc_dir.glob("composite_*.tif")), _date_from_gcc_tif, rel_root
+        )
+        if r["date"] in s3_valid_dates
+    ]
+    bti_refl = _raster_index(
+        sorted(bti_refl_dir.glob("REFL_*.tif")), _date_from_gcc_tif, rel_root
+    )
+    itb_gcc = _raster_index(
+        sorted(itb_gcc_dir.glob("GCC_*.tif")), _date_from_gcc_tif, rel_root
+    )
 
     _write_json(out_dir / "rasters_s2_refl.json", s2_refl)
     _write_json(out_dir / "rasters_s3_composite.json", s3_comp)
@@ -564,19 +631,27 @@ def export_site(
     _write_json(out_dir / "rasters_fusion_itb_gcc.json", itb_gcc)
 
     # --- Site covariates (heterogeneity + observation density) ---
-    _write_json(out_dir / "covariates.json", compute_covariates(
+    _write_json(
-        s2_gcc_paths, s2_series, s3_series, n_gcc_points, lat, lon
+        out_dir / "covariates.json",
-    ))
+        compute_covariates(s2_gcc_paths, s2_series, s3_series, n_gcc_points, lat, lon),
+    )
 
     # --- Validation metrics vs PhenoCam gcc_90 ---
-    _write_json(out_dir / "metrics.json", {
+    _write_json(
-        "bti":          compute_metrics(phenocam_series, "gcc_90", bti_series,       "gcc"),
+        out_dir / "metrics.json",
-        "itb":          compute_metrics(phenocam_series, "gcc_90", itb_series,       "gcc"),
+        {
-        "s2_whittaker": compute_metrics(phenocam_series, "gcc_90", s2_whittaker,     "gcc"),
+            "bti": compute_metrics(phenocam_series, "gcc_90", bti_series, "gcc"),
-        "s3_smooth":    compute_metrics(phenocam_series, "gcc_90", s3_smooth_series, "gcc"),
+            "itb": compute_metrics(phenocam_series, "gcc_90", itb_series, "gcc"),
-        "s2":           compute_metrics(phenocam_series, "gcc_90", s2_series,        "gcc"),
+            "s2_whittaker": compute_metrics(
-        "s3":           compute_metrics(phenocam_series, "gcc_90", s3_series,        "gcc"),
+                phenocam_series, "gcc_90", s2_whittaker, "gcc"
-    })
+            ),
+            "s3_smooth": compute_metrics(
+                phenocam_series, "gcc_90", s3_smooth_series, "gcc"
+            ),
+            "s2": compute_metrics(phenocam_series, "gcc_90", s2_series, "gcc"),
+            "s3": compute_metrics(phenocam_series, "gcc_90", s3_series, "gcc"),
+        },
+    )
 
     # Remove legacy bundled outputs if present
     for legacy in ("timeseries.json", "rasters.json"):
@@ -682,7 +757,9 @@ def main() -> None:
     print(f"Exporting {len(fusion_sites)} site(s) with fusion data for {year}")
     for site, meta in tqdm(fusion_sites.items(), desc="Sites"):
         out_dir = out_base / str(year) / site
-        ok = export_site(site, year, meta["lat"], meta["lon"], out_dir, meta.get("n_gcc_points"))
+        ok = export_site(
+            site, year, meta["lat"], meta["lon"], out_dir, meta.get("n_gcc_points")
+        )
         if ok:
             print(f"  ✓ {site}")
         else:

fix-tile-boundary.py

@@ -121,12 +121,16 @@ def repair(site_dir: Path) -> None:
             refl_path.unlink(missing_ok=True)
             n_removed += 1
 
-    print(f"[repair] {name}: removed {n_removed} minority-shape file-sets (kept {ref_shape[0]}×{ref_shape[1]})")
+    print(
+        f"[repair] {name}: removed {n_removed} minority-shape file-sets (kept {ref_shape[0]}×{ref_shape[1]})"
+    )
 
     # --- 3. Remove stale GCC files from prepared/s2 ---------------------------
     gcc_removed = sum(1 for f in s2_dir.glob("*_GCC.tif") if f.unlink() or True)
     if gcc_removed:
-        print(f"[repair] {name}: removed {gcc_removed} stale GCC files from prepared/s2")
+        print(
+            f"[repair] {name}: removed {gcc_removed} stale GCC files from prepared/s2"
+        )
 
     # --- 4. Wipe stale S3 composites ------------------------------------------
     for d in (s3_out, gcc_s3_out):
@@ -136,15 +140,21 @@ def repair(site_dir: Path) -> None:
 
     # --- 5. Regenerate S3 composites with the correct reference ---------------
     if not s3_raw.exists() or not any(s3_raw.glob("S3*.tif")):
-        print(f"[repair] {name}: WARNING — no raw S3 data in {s3_raw}; skipping S3 regeneration.")
+        print(
+            f"[repair] {name}: WARNING — no raw S3 data in {s3_raw}; skipping S3 regeneration."
+        )
         return
 
     s2_refl_path = next(iter(sorted(s2_dir.glob("*_REFL.tif"))), None)
     if s2_refl_path is None:
-        print(f"[repair] {name}: WARNING — no REFL files left; cannot regenerate S3 composites.")
+        print(
+            f"[repair] {name}: WARNING — no REFL files left; cannot regenerate S3 composites."
+        )
         return
 
-    print(f"[repair] {name}: regenerating S3 composites (reference: {s2_refl_path.name})...")
+    print(
+        f"[repair] {name}: regenerating S3 composites (reference: {s2_refl_path.name})..."
+    )
     step3 = _load_step3()
     s3_out.mkdir(parents=True, exist_ok=True)
     step3._prepare_s3(s3_raw, s2_refl_path, s3_out)