|
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': 2555123112}, |
|
'ANNEE': {'min_val': 1777, 'max_val': 2024}, |
|
'MOIS': {'min_val': 1, 'max_val': 12}, |
|
'JOUR': {'min_val': 1, 'max_val': 31}, |
|
'HEURE': {'min_val': 0, 'max_val': 23}, |
|
} |
|
|
|
def normalize_column(column, min_val, max_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 |
