Python_scripts/1_SICEN/update_dataONsicen.py

63 lines
1.3 KiB
Python

#!/usr/bin/env python3
# -*- coding: UTF-8 -*-
from sqlalchemy import create_engine
from sqlalchemy.engine import URL
from datetime import datetime as dt
import pandas as pd
import pycen
# Parametres bdd
user = 'cgeier'
pwd = 'adm1n*bdCen'
adr = '91.134.194.221'
port = '5432'
base = 'sicen2'
url = URL.create('postgresql+psycopg2',
username=user,
password=pwd,
host=adr,
database=base,
)
con = create_engine(url)
sch = 'saisie'
tab = 'saisie_observation'
ids = 'id_obs'
col_updt = 'phenologie'
sql = """
SELECT
{ids},
{col}
FROM {sch}.{tab}
WHERE {col} = 'Inderterminé'
""".format(sch=sch,tab=tab,ids=ids,col=col_updt)
df = pd.read_sql_query(sql,con)
df[col_updt] = 'Indéterminé'
pycen.update_to_sql(df,con,tab,sch,ids,geom_col=None)
tab_suivi = 'suivi_saisie_observation'
ids_suivi = ['operation','date_operation','id_obs']
col_updtSuivi = 'utilisateur'
date = dt.today().date().isoformat()
sql = """
SELECT
{ids},
{col}
FROM {sch}.{tab}
WHERE {col} = 'inconnu'
AND date_operation > '{date}'
""".format(sch=sch,tab=tab_suivi,ids=','.join(ids_suivi),col=col_updtSuivi, date=date)
df = pd.read_sql_query(sql,con)
df[col_updtSuivi] = 'colas.geier@cen-isere.org'
pycen.update_to_sql(
df,
con,
table_name=tab_suivi,
schema_name=sch,
key_name = ids_suivi,
geom_col=None)