174 lines
6.8 KiB
Python
174 lines
6.8 KiB
Python
import json
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import shutil
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from pathlib import Path
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from datetime import datetime, timedelta
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from collections import defaultdict
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import numpy as np
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import rasterio
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from rasterio.warp import Resampling
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from rasterio.vrt import WarpedVRT
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from rasterio import shutil as rio_shutil
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def _import_efast():
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"""Lazy import of efast to avoid import errors when not using efast functions."""
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try:
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import efast
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from efast.s2_processing import distance_to_clouds
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from efast.s3_processing import reproject_and_crop_s3
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return efast, distance_to_clouds, reproject_and_crop_s3
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except ImportError:
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raise ImportError(
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"efast package not found. Install with: pip install git+https://github.com/DHI-GRAS/efast.git"
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)
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RESOLUTION_RATIO = 21
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def _load_clouds(clouds_file):
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clouds = {"s2": set(), "s3": set()}
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if clouds_file.exists():
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clouds_data = json.loads(clouds_file.read_text())
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clouds["s2"] = set(clouds_data.get("s2", []))
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clouds["s3"] = set(clouds_data.get("s3", []))
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return clouds
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def _reproject_raster_to_target(src_path, dst_path, target_bounds, target_crs, width, height, resampling=Resampling.cubic):
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dst_transform = rasterio.transform.from_bounds(
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target_bounds.left, target_bounds.bottom,
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target_bounds.right, target_bounds.top,
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width, height
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)
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with rasterio.open(src_path) as src:
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vrt_options = {
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"transform": dst_transform,
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"height": height,
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"width": width,
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"crs": target_crs,
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"resampling": resampling,
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}
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with WarpedVRT(src, **vrt_options) as vrt:
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profile = vrt.profile.copy()
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profile.update({"dtype": "float32", "nodata": 0, "driver": "GTiff"})
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rio_shutil.copy(vrt, dst_path, **profile)
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def prepare_s2(season, site_position, site_name, date_range=None):
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s2_dir = Path(f"data/{site_name}/{season}/raw/s2/")
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s3_dir = Path(f"data/{site_name}/{season}/raw/s3/")
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s2_output_dir = Path(f"data/{site_name}/{season}/prepared/s2/")
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clouds_file = Path(f"data/{site_name}/{season}/clouds.json")
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clouds = _load_clouds(clouds_file)
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s2_output_dir.mkdir(parents=True, exist_ok=True)
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s3_files = [f for f in s3_dir.glob("*.geotiff") if f.name not in clouds["s3"]]
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if not s3_files:
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raise ValueError("No non-cloud S3 files found for reference bounds")
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with rasterio.open(s3_files[0]) as s3_ref:
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target_bounds = s3_ref.bounds
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target_crs = s3_ref.crs
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s2_width = s3_ref.width * RESOLUTION_RATIO
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s2_height = s3_ref.height * RESOLUTION_RATIO
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for s2_file in s2_dir.glob("*.geotiff"):
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if s2_file.name in clouds["s2"]:
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continue
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date_str = s2_file.name.split("_")[0]
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refl_dst = s2_output_dir / f"S2A_MSIL2A_{date_str}_REFL.tif"
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if refl_dst.exists():
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continue
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temp_normalized = s2_output_dir / f"temp_{s2_file.name}"
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with rasterio.open(s2_file) as src:
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data = src.read().astype("float32") / 10000.0
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profile = src.profile.copy()
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profile.update({"dtype": "float32", "nodata": 0})
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with rasterio.open(temp_normalized, "w", **profile) as dst:
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dst.write(data)
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_reproject_raster_to_target(temp_normalized, refl_dst, target_bounds, target_crs, s2_width, s2_height)
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temp_normalized.unlink()
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_, distance_to_clouds, _ = _import_efast()
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distance_to_clouds(s2_output_dir, ratio=RESOLUTION_RATIO)
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def prepare_s3(season, site_position, site_name, date_range=None):
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s3_dir = Path(f"data/{site_name}/{season}/raw/s3/")
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s2_prepared_dir = Path(f"data/{site_name}/{season}/prepared/s2/")
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s3_preprocessed_dir = Path(f"data/{site_name}/{season}/prepared/s3/")
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clouds_file = Path(f"data/{site_name}/{season}/clouds.json")
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clouds = _load_clouds(clouds_file)
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s3_preprocessed_dir.mkdir(parents=True, exist_ok=True)
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s3_by_date = defaultdict(list)
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for s3_file in s3_dir.glob("*.geotiff"):
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if s3_file.name not in clouds["s3"]:
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s3_by_date[s3_file.name.split("_")[0]].append(s3_file)
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temp_composite_dir = s3_preprocessed_dir / "temp_composites"
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if temp_composite_dir.exists():
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shutil.rmtree(temp_composite_dir)
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temp_composite_dir.mkdir()
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for date_str, s3_files in s3_by_date.items():
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composite_path = temp_composite_dir / f"composite_{date_str}.tif"
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if len(s3_files) == 1:
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shutil.copy(s3_files[0], composite_path)
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else:
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s3_stack = []
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for s3_file in s3_files:
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with rasterio.open(s3_file) as src:
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data = src.read()
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data[:, np.abs(np.nanmean(data, axis=0)) >= 5] = np.nan
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s3_stack.append(data)
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composite = np.nanmean(np.array(s3_stack), axis=0).astype("float32")
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with rasterio.open(s3_files[0]) as src:
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profile = src.profile.copy()
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profile.update({"count": composite.shape[0], "dtype": "float32"})
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with rasterio.open(composite_path, "w", **profile) as dst:
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dst.write(composite)
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_, _, reproject_and_crop_s3 = _import_efast()
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reproject_and_crop_s3(temp_composite_dir, s2_prepared_dir, s3_preprocessed_dir)
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shutil.rmtree(temp_composite_dir)
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def run_efast(season, site_position, site_name, date_range=None):
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lat, lon = site_position
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datetime_range = date_range or f"{season}-01-01/{season}-12-31"
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efast_base_dir = Path(f"data/{site_name}/{season}/prepared/")
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s2_output_dir = efast_base_dir / "s2"
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s3_output_dir = efast_base_dir / "s3"
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fusion_output_dir = efast_base_dir / "fusion"
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fusion_output_dir.mkdir(parents=True, exist_ok=True)
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print(f"[EFAST] Starting fusion: {site_name} ({lat:.6f}, {lon:.6f}), {season}")
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start_str, end_str = datetime_range.split("/")
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start_date = datetime.strptime(start_str, "%Y-%m-%d")
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end_date = datetime.strptime(end_str, "%Y-%m-%d")
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current_date = start_date
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while current_date <= end_date:
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date_str = current_date.strftime("%Y%m%d")
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output_file = fusion_output_dir / f"REFL_{date_str}.tif"
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if output_file.exists():
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print(f"[EFAST] Skipping {date_str} (exists)")
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else:
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try:
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efast.fusion(
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current_date, s3_output_dir, s2_output_dir, fusion_output_dir,
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product="REFL", max_days=30, date_position=2,
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minimum_acquisition_importance=0.0, ratio=RESOLUTION_RATIO,
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)
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print(f"[EFAST] Saved: {output_file}" if output_file.exists() else f"[EFAST] No output for {date_str} (insufficient nearby data)")
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except Exception as e:
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print(f"[EFAST] Error processing {date_str}: {e}")
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current_date += timedelta(days=1)
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print("[EFAST] Completed")
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