File size: 2,856 Bytes
9bc98d9 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 | from pathlib import Path
import numpy as np
import pandas as pd
from src.process.io import prepare_dirs, unzip, unzip_nested, upload, copy_to_local
from src.process.processor import process_shp
# === Step 1: Set up local and drive directory paths ===
local_processed_dir, drive_download_dir, drive_processed_dir = (
prepare_dirs("esdac", Path(__file__).parent.stem)
)
# === Step 2: Extract all ZIP files from drive/download to local/processed ===
unzip(local_processed_dir, drive_download_dir)
unzip_nested(local_processed_dir)
copy_to_local(local_processed_dir, drive_download_dir, files=[
"LUCAS2015_AncillaryData_20201007.csv"
])
# === Step 3: Process all SHP files under local/processed ===
move_list = []
for tif_path in sorted(local_processed_dir.rglob("*.shp")):
move_list = process_shp(tif_path, move_list)
# === Step 4: Process assets ===
spec_dir = local_processed_dir / "LUCAS2015_spectra/LUCAS2015_Soil_Spectra_EU28"
out_path = local_processed_dir / "assets/psd"
out_path.mkdir(parents=True, exist_ok=True)
for spec_path in sorted(spec_dir.rglob("*.csv")):
df = pd.read_csv(spec_path)
# Identify metadata columns to exclude
meta_cols = {"source", "SampleID", "PointID", "NUTS_0", "SampleN"}
# Extract spectral columns (handle both "spc.xxx" and plain numeric names)
spc_cols = [
c for c in df.columns
if c not in meta_cols and (c.replace(".", "", 1).isdigit() or c.startswith("spc."))
]
# Parse numeric axis values from the column names
x_vals = np.array([
float(c.replace("spc.", "")) if c.startswith("spc.") else float(c)
for c in spc_cols
])
# Sort columns by numeric x
sort_idx = np.argsort(x_vals)
x_vals = x_vals[sort_idx]
spc_cols_sorted = [spc_cols[i] for i in sort_idx]
for _, row in df.iterrows():
point_id = row["PointID"]
sample_id = '0' # main data csv does not contain sample id
y_vals = row[spc_cols_sorted].to_numpy(dtype=np.float32)
arr = np.column_stack([x_vals, y_vals])
fname = out_path / f"lucas2015_{point_id}_{sample_id}.npz"
np.savez(fname, psd=arr)
print(f"✅ PSD Spectrum data saved in assets")
# === NEW Step: Zip PSD folder and upload only the zip ===
psd_dir = out_path
zip_path = local_processed_dir / "assets/psd.zip"
import zipfile
with zipfile.ZipFile(zip_path, "w", compression=zipfile.ZIP_DEFLATED) as z:
for npz_file in psd_dir.glob("*.npz"):
z.write(npz_file, arcname=npz_file.name)
print(f"✅ Created ZIP archive: {zip_path.name}")
move_list.append(zip_path) # only zip is uploaded
# === Step 5: Collect all generated CSVs and upload to drive/processed ===
csvs = list(local_processed_dir.rglob("*.csv"))
for csv_path in csvs:
move_list.append(csv_path)
upload(local_processed_dir, drive_processed_dir, move_list)
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