import h5py
import numpy as np
import psycopg2
from psycopg2.extras import execute_values
from datetime import datetime, timezone, timedelta
import os
from tqdm import tqdm

DB_URL = "postgresql://postgres:seismic_pass@db:5432/seismic_data"

def fix_path(p):
    p = p.replace('\\', '/')
    if p.startswith('F:/'): return '/mnt/kingston/' + p[3:]
    if p.startswith('E:/'): return '/mnt/data_sdb1/' + p[3:]
    return p

def migrate_file(h5_path, node_id, channel, start_offset_sec=0, duration_sec=3600):
    h5_path = fix_path(h5_path)
    conn = psycopg2.connect(DB_URL)
    cur = conn.cursor()
    
    with h5py.File(h5_path, 'r') as f:
        ds = f['adc_values']
        start_ts = int(ds.attrs['timestamp'])
        # On calcule le début réel
        actual_start = start_ts + start_offset_sec
        start_idx = start_offset_sec * 200
        end_idx = start_idx + (duration_sec * 200)
        
        data = ds[start_idx:end_idx]
        print(f"Migrating {len(data)} samples...")
        
        # Préparation des tuples pour insertion par lots
        batch_size = 10000
        for i in range(0, len(data), batch_size):
            batch = data[i:i+batch_size]
            values = []
            for j, val in enumerate(batch):
                ts = datetime.fromtimestamp(actual_start + (i + j) / 200, tz=timezone.utc)
                values.append((ts, node_id, channel, float(val)))
            
            execute_values(cur, "INSERT INTO adc_samples (time, node_id, channel, value) VALUES %s", values)
            conn.commit()
            
    cur.close()
    conn.close()
    print("Done.")

if __name__ == "__main__":
    # Test sur Node 193, 1er septembre (Julian 245), 10 minutes
    # On cherche un fichier du node 193
    import sys
    migrate_file(sys.argv[1], sys.argv[2], sys.argv[3], duration_sec=600)