src/esdac/lucas-2009-topsoil-data/process.py

import re
from pathlib import Path

import numpy as np
import pandas as pd

from src.process.io import prepare_dirs, unzip, copy_to_local, upload
from src.process.processor import process_excel

# === 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 and copy specific files from drive/download to local/processed ===
unzip(local_processed_dir, drive_download_dir)
copy_to_local(local_processed_dir, drive_download_dir, files=[
    "LUCAS.SOIL_corr.csv"
])

# === Step 3: Convert Excel file to CSV(s) in place ===
process_excel(local_processed_dir / "LUCAS_TOPSOIL_v1/LUCAS_TOPSOIL_v1.xlsx")

# === Step 4: Collect all generated CSVs and standardize delimiters
move_list = []
for csv_path in local_processed_dir.rglob("*.csv"):
    try:
        # Read with automatic delimiter detection (comma, pipe, etc.)
        with open(csv_path, "r", encoding="utf-8") as f:
            sample = f.read(2048)

        delimiter = "|" if sample.count("|") > sample.count(",") else ","
        df = pd.read_csv(csv_path, delimiter=delimiter)
        df.to_csv(csv_path, index=False)  # overwrite with standard comma separator

        move_list.append(csv_path)
        print(f"✅ Standardized CSV: {csv_path.name}")

    except Exception as e:
        print(f"❌ Failed to process CSV {csv_path.name}: {e}")

# === Step 5: Process assets ===
spec_path = local_processed_dir / "LUCAS.SOIL_corr.csv"
out_path = local_processed_dir / "assets/psd"
out_path.mkdir(parents=True, exist_ok=True)

df = pd.read_csv(spec_path).drop(columns=["Unnamed: 0"])

# Extract all "spc." columns
spc_cols = [c for c in df.columns if c.startswith("spc.")]
# Parse numeric axis values from the column names
x_vals = np.array([float(c.replace("spc.", "")) 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["POINT_ID"]
    sample_id = row["sample.ID"]
    sample_id = re.sub(r"\D", "", str(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"lucas2009_{point_id}_{sample_id}.npz"
    np.savez(fname, arr=arr)
print(f"✅ PSD Spectrum data saved in assets")

# === Step 6: Zip PSD folder and upload only the zip ===
psd_dir = local_processed_dir / "assets/psd"
zip_path = local_processed_dir / "assets/psd.zip"

# Create zip archive
import zipfile
with zipfile.ZipFile(zip_path, "w", compression=zipfile.ZIP_DEFLATED) as z:
    for npz_file in psd_dir.glob("*.npz"):
        # arcname removes full path → keeps folder structure clean
        z.write(npz_file, arcname=npz_file.name)

print(f"✅ Created ZIP archive: {zip_path.name}")
move_list.append(zip_path)

# === Step 7: Upload to drive/processed ===
upload(local_processed_dir, drive_processed_dir, move_list)