#!/usr/bin/env python
# -*- coding: UTF-8 -*-

from pycen import con_fon
import pandas as pd

dico_data = '/media/colas/SRV/FICHIERS/OUTILS/BASES DE DONNEES/BILAN_FEDE_CEN/2024/TDB2024_enquete_SIG/Dico_DATA_sites_CEN_v2024.xlsx'
bilan_2023 = '/media/colas/SRV/FICHIERS/OUTILS/BASES DE DONNEES/BILAN_FEDE_CEN/2024/TDB2024_enquete_SIG/DATA N-1/Sites_CEN_38_2023.csv'

dic = pd.read_excel(dico_data,sheet_name='sites_cen_xx_2024',header=0, usecols='F',nrows=50)
dic_head_name = dic.columns[0]
bil2023 = pd.read_csv(bilan_2023,sep=',',header=0,encoding='utf-8')
# Mise en forme des dates
date_cols = bil2023.columns[bil2023.columns.str.contains('date')]
bil2023[date_cols] = bil2023[date_cols].apply(pd.to_datetime)
# Mise en forme de la colonne remq_sensibilite

vm_site = pd.read_sql_table('vm_sites_cen_2024_csv',con_fon,'_tdbfcen')


dic_missing = (dic.loc[~dic[dic_head_name].isin(vm_site.columns),dic_head_name]
    .tolist())

(bil2023[['id_site_cen',*dic_missing]]
    .merge(vm_site[['id_site_cen']],how='inner',on='id_site_cen')
    .to_sql(
    '_sites_cen_2023_csv_complement',
    con_fon,
    schema='_tdbfcen',
    if_exists='replace',
    index=False))