#!/usr/bin/env python3
# -*- coding: UTF-8 -*-
import pandas as pd
import pyzh
zh = pyzh.zh()

con = pyzh.con
sch = 'zones_humides'
lst_site_old =  ['38RD0146','38RD0147']
lst_site_new =  ['38RD0164','38RD0165']
df = zh.get_usageprocess(id_site=lst_site_old)
geomsite = zh.get_sitesGeom(id_site=lst_site_new)
aut = pyzh.pers.get_auteur()

tab = 'r_site_usageprocess'
rtab_aut = 'r_rsiteusage_auteur'
id_rsiteaut = 'id_siteusage'
date_champ = 'date_usage'
nom_champ1 = 'activite_hum'
nom_champ2 = 'impact'
nom_champ3 = 'position'
id_champ1  = 'id_activ_hum'
id_champ2  = 'id_impact'
id_champ3  = 'id_position'
delim1 = zh._get_param(param_table='param_activ_hum', type_court=False)
delim2 = zh._get_param(param_table='param_impact', type_court=False)
delim3 = zh._get_param(param_table='param_position', type_court=False)

col_desc = df.columns[df.columns.str.contains('desc')]
df.drop(
    columns=['id','valid', 'date_geom', 'auteur_geom', *col_desc,'description','type','lb_hab_fr'], 
    inplace=True,
    errors='ignore')

for i, s in enumerate(lst_site_new):
    df.loc[df.id_site==lst_site_old[i], 'id_site'] = s
    df.loc[df.id_site==s, ['id_geom_site']] = geomsite.loc[geomsite.id_site==s,'id'].values[0].astype(str)

df['id_geom_site'] = df.id_geom_site.astype(int)
df[id_champ1] = df[nom_champ1]
df[id_champ2] = df[nom_champ2]
df[id_champ3] = df[nom_champ3]
df[id_champ1] = df[id_champ1].replace(delim1.nom.tolist(),delim1.id.tolist())
df[id_champ2] = df[id_champ2].replace(delim2.nom.tolist(),delim2.id.tolist())
df[id_champ3] = df[id_champ3].replace(delim3.nom.tolist(),delim3.id.tolist())
df['date'] = df[date_champ]

df.drop(columns=['id_site',nom_champ1,nom_champ2,nom_champ3,date_champ], inplace=True)
lst_idgeom = df.id_geom_site.unique().astype(str)

# check si data in bdd
sql = 'select exists(select * from {sch}.{tab} where id_geom_site in ({lst_id}));'.format(
    sch=sch, tab=tab, lst_id=','.join(lst_idgeom)
)
res = pd.read_sql(sql,con)
if res.exists[0]:
    # delete if exist
    sql = 'delete from {sch}.{tab} where id_geom_site in ({lst_id});'.format(
        sch=sch, tab=tab, lst_id=','.join(lst_idgeom)
    )
    pd.read_sql(sql,con)
# insert new line in bdd    
col = df.columns[~df.columns.str.contains('auteur')]
colaut = df.columns[df.columns.str.contains('auteur')]
df[col].to_sql(
    tab, con, schema=sch,
    if_exists='append',index=False,method='multi'
)
# Récupération des nouveaux ids insérés
sql = 'select * from {sch}.{tab} where id_geom_site in ({lst_id});'.format(
    sch=sch, tab=tab, lst_id=','.join(lst_idgeom)
)
res = pd.read_sql(sql,con)
df = df.merge(res, how='left', on=[*col])

ins_aut = df[['id',*colaut]].copy()
aut['nom_prenom'] = aut.nom + ' ' + aut.prenom
aut.loc[aut.nom_prenom.isna(), 'nom_prenom'] = aut.loc[aut.nom_prenom.isna(), 'nom']
ins_aut[colaut] = ins_aut[colaut].replace(aut.nom_prenom.tolist(), aut.id.tolist())
ins_aut.columns = [id_rsiteaut, 'id_auteur']
ins_aut.to_sql(
    rtab_aut, con, schema=sch,
    if_exists='append',index=False,method='multi')