Update normalization_parameters

normalization_parameters

@@ -2,7 +2,7 @@ norm_params = {
     'LAT': {'min_val': -66.817333, 'max_val': 51.055833},
     'LON': {'min_val': -178.116667, 'max_val': 171.358333},
     'ALTI': {'min_val': 0.0, 'max_val': 3845.0},
-    'AAAAMMJJHH': {'min_val': 1777010107, 'max_val': 2024030803},
+    'AAAAMMJJHH': {'min_val': 1777010107, 'max_val': 2024031203},
     'ANNEE': {'min_val': 1777, 'max_val': 2024},
     'MOIS': {'min_val': 1, 'max_val': 12},
     'JOUR': {'min_val': 1, 'max_val': 31},
@@ -10,4 +10,14 @@ norm_params = {
 }
 
 def normalize_column(column, min_val, max_val):
-    return (column.astype('float64') - min_val) / (max_val - min_val)
+    """Normalize pandas Series from [min_val, max_val] to [0, 1]."""
+    return (column.astype('float64') - min_val) / (max_val - min_val)
+
+def extract_date_parts(chunk):
+    chunk['AAAAMMJJHH'] = chunk['AAAAMMJJHH'].astype(int)
+    chunk['ANNEE'] = (chunk['AAAAMMJJHH'] // 1000000).astype(int)
+    chunk['MOIS'] = ((chunk['AAAAMMJJHH'] // 10000) % 100).astype(int)
+    chunk['JOUR'] = ((chunk['AAAAMMJJHH'] // 100) % 100).astype(int)
+    chunk['HEURE'] = (chunk['AAAAMMJJHH'] % 100).astype(int)
+    chunk = chunk[(chunk['ANNEE'] <= 2024) & (chunk['MOIS'] <= 12) & (chunk['JOUR'] <= 31)]
+    return chunk