268 lines
11 KiB
Python
268 lines
11 KiB
Python
"""Post-processing: crop fusion/S2/S3 to valid pixels."""
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from pathlib import Path
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import numpy as np
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import rasterio
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from rasterio import windows
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from rasterio.warp import reproject, Resampling
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def process_cropped(
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season, site_position, site_name, cleaning_strategy="aggressive", sigma=None
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):
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"""Crop fusion to valid data, then crop S2/S3 to match."""
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base = Path(f"data/{site_name}/{season}")
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prepared = base / f"prepared_{cleaning_strategy}"
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processed_dir = (
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f"processed_{cleaning_strategy}_sigma{sigma}"
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if sigma
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else f"processed_{cleaning_strategy}_sigma20"
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)
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processed = base / processed_dir
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s2_prep = prepared / "s2"
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s3_prep = prepared / "s3"
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fusion_prep = prepared / (f"fusion_sigma{sigma}" if sigma else "fusion")
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for output_dir in [processed / "s2", processed / "s3", processed / "fusion"]:
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output_dir.mkdir(parents=True, exist_ok=True)
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print(
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f"[PROCESS] Processing files: {site_name}, {season}, {cleaning_strategy}, sigma={sigma or 20}"
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)
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# Crop fusion to valid data and get dimensions
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fusion_dims = {}
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for fusion_file in fusion_prep.glob("REFL_*.tif"):
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date_str = fusion_file.stem.split("_")[1]
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with rasterio.open(fusion_file) as src:
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data = src.read()
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valid = ~np.isnan(data) & (data > 0.001)
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rows = np.any(valid, axis=(0, 2))
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cols = np.any(valid, axis=(0, 1))
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row_idx = np.where(rows)[0]
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col_idx = np.where(cols)[0]
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if len(row_idx) == 0 or len(col_idx) == 0:
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print(f"[PROCESS] Skipping {fusion_file.name} (no valid pixels)")
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continue
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r0, r1 = row_idx[0], row_idx[-1]
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c0, c1 = col_idx[0], col_idx[-1]
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w, h = c1 - c0 + 1, r1 - r0 + 1
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window = windows.Window(c0, r0, w, h)
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data_crop = src.read(window=window)
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transform = rasterio.windows.transform(window, src.transform)
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p = src.profile.copy()
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p.update({"width": w, "height": h, "transform": transform})
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output_file = processed / "fusion" / f"{date_str}_0.geotiff"
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with rasterio.open(output_file, "w", **p) as dst:
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dst.write(data_crop)
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fusion_dims[date_str] = (
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c0,
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r0,
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w,
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h,
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transform,
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src.transform,
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src.crs,
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src.profile,
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)
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print(f"[PROCESS] Cropped fusion: {output_file}")
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# Crop S2 and S3 to fusion size
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for date_str, (
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c0,
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r0,
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w,
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h,
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transform,
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fusion_transform,
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crs,
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fusion_profile,
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) in fusion_dims.items():
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window = windows.Window(c0, r0, w, h)
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# S2
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for s2_file in s2_prep.glob("*REFL.tif"):
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if s2_file.stem.split("_")[2] == date_str:
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output_file = processed / "s2" / f"{date_str}_0.geotiff"
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with rasterio.open(s2_file) as src:
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data = src.read(window=window)
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p2 = src.profile.copy()
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p2.update(
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{"width": w, "height": h, "transform": transform, "crs": crs}
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)
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with rasterio.open(output_file, "w", **p2) as dst:
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dst.write(data)
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print(f"[PROCESS] Cropped: {output_file}")
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# S3: resample to fusion pixel size, then crop
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s3_file = s3_prep / f"composite_{date_str}.tif"
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if s3_file.exists():
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output_file = processed / "s3" / f"{date_str}_0.geotiff"
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with rasterio.open(s3_file) as src:
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# Resample to fusion pixel size
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temp_profile = fusion_profile.copy()
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temp_profile.update({"dtype": src.profile["dtype"], "count": src.count})
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with rasterio.MemoryFile() as memfile:
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with memfile.open(**temp_profile) as resampled:
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for i in range(1, src.count + 1):
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reproject(
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source=rasterio.band(src, i),
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destination=rasterio.band(resampled, i),
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src_transform=src.transform,
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src_crs=src.crs,
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dst_transform=fusion_transform,
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dst_crs=crs,
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resampling=Resampling.nearest,
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)
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# Crop using same window
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data = resampled.read(window=window)
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p2 = resampled.profile.copy()
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p2.update({"width": w, "height": h, "transform": transform})
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with rasterio.open(output_file, "w", **p2) as dst:
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dst.write(data)
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print(f"[PROCESS] Cropped: {output_file}")
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print("[PROCESS] Completed")
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def process_cropped_itb(
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season, site_position, site_name, cleaning_strategy="aggressive", sigma=None
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):
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base = Path(f"data/{site_name}/{season}")
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prepared = base / f"prepared_{cleaning_strategy}_itb"
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processed_dir = (
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f"processed_{cleaning_strategy}_itb_sigma{sigma}"
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if sigma
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else f"processed_{cleaning_strategy}_itb_sigma20"
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)
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processed = base / processed_dir
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s2_prep = prepared / "s2"
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s3_prep = prepared / "s3"
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fusion_prep = prepared / (f"fusion_sigma{sigma}" if sigma else "fusion")
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for output_dir in [processed / "s2", processed / "s3", processed / "fusion"]:
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output_dir.mkdir(parents=True, exist_ok=True)
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print(
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f"[PROCESS-ITB] {site_name}, {season}, {cleaning_strategy}, sigma={sigma or 20}"
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)
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fusion_dims = {}
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for fusion_file in fusion_prep.glob("GCC_*.tif"):
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date_str = fusion_file.stem.split("_")[1]
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with rasterio.open(fusion_file) as src:
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data = src.read()
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valid = ~np.isnan(data) & (data > 0.001)
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rows = np.any(valid, axis=(0, 2))
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cols = np.any(valid, axis=(0, 1))
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row_idx = np.where(rows)[0]
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col_idx = np.where(cols)[0]
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if len(row_idx) == 0 or len(col_idx) == 0:
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print(f"[PROCESS-ITB] Skip {fusion_file.name} (no valid pixels)")
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continue
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r0, r1 = row_idx[0], row_idx[-1]
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c0, c1 = col_idx[0], col_idx[-1]
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w, h = c1 - c0 + 1, r1 - r0 + 1
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window = windows.Window(c0, r0, w, h)
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data_crop = src.read(window=window)
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transform = rasterio.windows.transform(window, src.transform)
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p = src.profile.copy()
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p.update({"width": w, "height": h, "transform": transform})
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output_file = processed / "fusion" / f"{date_str}_0.geotiff"
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with rasterio.open(output_file, "w", **p) as dst:
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dst.write(data_crop)
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fusion_dims[date_str] = (
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c0,
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r0,
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w,
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h,
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transform,
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src.transform,
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src.crs,
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src.profile,
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)
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print(f"[PROCESS-ITB] Cropped fusion: {output_file}")
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for date_str, (
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c0,
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r0,
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w,
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h,
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transform,
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fusion_transform,
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crs,
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fusion_profile,
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) in fusion_dims.items():
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window = windows.Window(c0, r0, w, h)
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for s2_file in s2_prep.glob("*GCC.tif"):
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parts = s2_file.stem.split("_")
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if len(parts) > 2 and parts[2] == date_str:
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output_file = processed / "s2" / f"{date_str}_0.geotiff"
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with rasterio.open(s2_file) as src:
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data = src.read(window=window)
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p2 = src.profile.copy()
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p2.update(
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{"width": w, "height": h, "transform": transform, "crs": crs}
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)
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with rasterio.open(output_file, "w", **p2) as dst:
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dst.write(data)
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print(f"[PROCESS-ITB] Cropped: {output_file}")
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break
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s3_file = s3_prep / f"composite_{date_str}.tif"
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if s3_file.exists():
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output_file = processed / "s3" / f"{date_str}_0.geotiff"
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with rasterio.open(s3_file) as src:
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temp_profile = fusion_profile.copy()
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temp_profile.update({"dtype": src.profile["dtype"], "count": src.count})
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with rasterio.MemoryFile() as memfile:
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with memfile.open(**temp_profile) as resampled:
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for i in range(1, src.count + 1):
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reproject(
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source=rasterio.band(src, i),
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destination=rasterio.band(resampled, i),
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src_transform=src.transform,
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src_crs=src.crs,
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dst_transform=fusion_transform,
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dst_crs=crs,
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resampling=Resampling.nearest,
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)
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data = resampled.read(window=window)
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p2 = resampled.profile.copy()
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p2.update({"width": w, "height": h, "transform": transform})
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with rasterio.open(output_file, "w", **p2) as dst:
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dst.write(data)
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print(f"[PROCESS-ITB] Cropped: {output_file}")
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print("[PROCESS-ITB] Completed")
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def post_process_all_itb_scenarios(season, site_position, site_name):
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for strategy in ["aggressive", "nonaggressive"]:
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for sigma in [None, 30]:
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process_cropped_itb(
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season,
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site_position,
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site_name,
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cleaning_strategy=strategy,
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sigma=sigma,
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)
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def post_process_all_scenarios(season, site_position, site_name):
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"""Crop fusion/S2/S3 to valid pixels for all 4 scenarios."""
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for strategy in ["aggressive", "nonaggressive"]:
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for sigma in [None, 30]:
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process_cropped(
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season,
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site_position,
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site_name,
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cleaning_strategy=strategy,
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sigma=sigma,
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)
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def post_process_timeseries(season, site_position, site_name):
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"""Generate NDVI, GCC, and S2 bands timeseries for all 4 scenarios."""
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from metrics_indices import (
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create_ndvi_timeseries_post_process,
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create_gcc_timeseries_post_process,
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create_bands_timeseries_post_process,
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)
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create_ndvi_timeseries_post_process(season, site_position, site_name)
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create_gcc_timeseries_post_process(season, site_position, site_name)
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create_bands_timeseries_post_process(season, site_position, site_name)
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