304 lines
11 KiB
Python
304 lines
11 KiB
Python
#!/usr/bin/env python3
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# -*- coding: UTF-8 -*-
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import requests
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import numpy as np
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import pandas as pd
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import os
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def get_status(lst,con):
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sql = """
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SELECT
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t.cd_nom,
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t.cd_ref,
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t.regne,
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t.phylum,
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t.classe,
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t.ordre,
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t.famille,
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t.group1_inpn,
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t.group2_inpn,
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t.group3_inpn,
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t.nom_vern,
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t.nom_complet,
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t.nom_valide,
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t.lb_nom,
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--s.*
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s.rq_statut,
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s.code_statut,
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s.cd_type_statut,
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s.label_statut,
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s.niveau_admin,
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s.full_citation,
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s.doc_url
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FROM taxonomie.taxref t
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JOIN taxonomie.v_bdc_status s USING (cd_nom)
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WHERE t.cd_nom IN {cd_nom}
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;""".format(cd_nom = tuple(lst))
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return pd.read_sql_query(sql,con)
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def get_type_status(con):
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sql = """
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SELECT * FROM taxonomie.bdc_statut_type
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;"""
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return pd.read_sql_query(sql,con)
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def get_api_status(api,cd_nom:int):
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res = requests.api.get('%s/%i'%(api,cd_nom))
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if res.status_code == 200:
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return res.json()
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else :
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raise('Error : %i\tcd_nom : %i'%(res.status_code,cd_nom))
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def get_taxon_status(lst,api):
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from datetime import datetime as dt
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init = dt.now()
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st = [get_api_status(api,x) for x in lst] # TOO LONG
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print(dt.now()-init)
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phylo = {
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'cd_ref':[x['cd_ref'] for x in st],
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'nom_valide':[x['nom_valide'] if 'nom_valide' in x.keys() else None for x in st],
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'nom_vernac':[x['nom_vern'] if 'nom_vern' in x.keys() else None for x in st],
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'regne':[x['regne'] if 'regne' in x.keys() else None for x in st],
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'group1_inp':[x['group1_inpn'] if 'group1_inpn' in x.keys() else None for x in st],
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'group2_inp':[x['group2_inp'] if 'group2_inp' in x.keys() else None for x in st],
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'group3_inpn':[x['group3_inpn'] for x in st],
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'classe':[x['classe'] if 'classe' in x.keys() else None for x in st],
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'ordre':[x['ordre'] if 'ordre' in x.keys() else None for x in st],
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'famille':[x['famille'] if 'famille' in x.keys() else None for x in st]}
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cd_status = {
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'AL':[
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[val['values'][v]['code_statut']
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for val in x['status']['AL']['text'].values() for v in val['values'] ]
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if 'AL' in x['status'].keys() else None
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for x in st
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],
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'BERN':[
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[val['values'][v]['code_statut']
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for val in x['status']['BERN']['text'].values() for v in val['values'] ]
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if 'BERN' in x['status'].keys() else None
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for x in st
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],
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'BONN':[
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[val['values'][v]['code_statut']
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for val in x['status']['BONN']['text'].values() for v in val['values'] ]
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if 'BONN' in x['status'].keys() else None
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for x in st
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],
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'DH':[
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[val['values'][v]['code_statut']
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for val in x['status']['DH']['text'].values() for v in val['values'] ]
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if 'DH' in x['status'].keys() else None
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for x in st
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],
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'DO':[
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[val['values'][v]['code_statut']
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for val in x['status']['DO']['text'].values() for v in val['values'] ]
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if 'DO' in x['status'].keys() else None
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for x in st
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],
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'LRE':[
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[val['values'][v]['code_statut']
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for val in x['status']['LRE']['text'].values() for v in val['values'] ]
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if 'LRE' in x['status'].keys() else None
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for x in st
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],
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'LRM':[
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[val['values'][v]['code_statut']
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for val in x['status']['LRM']['text'].values() for v in val['values'] ]
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if 'LRM' in x['status'].keys() else None
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for x in st
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],
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'LRN':[
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[val['values'][v]['code_statut']
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for val in x['status']['LRN']['text'].values() for v in val['values'] ]
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if 'LRN' in x['status'].keys() else None
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for x in st
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],
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'LRR':[
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[val['values'][v]['code_statut']
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for val in x['status']['LRR']['text'].values() for v in val['values'] ]
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if 'LRR' in x['status'].keys() else None
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for x in st
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],
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'PAPNAT':[
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[val['values'][v]['code_statut']
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for val in x['status']['PAPNAT']['text'].values() for v in val['values'] ]
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if 'PAPNAT' in x['status'].keys() else None
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for x in st
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],
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'PD':[
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[val['values'][v]['code_statut']
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for val in x['status']['PD']['text'].values() for v in val['values'] ]
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if 'PD' in x['status'].keys() else None
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for x in st
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],
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'PNA':[
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[val['values'][v]['code_statut']
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for val in x['status']['PNA']['text'].values() for v in val['values'] ]
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if 'PNA' in x['status'].keys() else None
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for x in st
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],
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'PR':[
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[val['values'][v]['code_statut']
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for val in x['status']['PR']['text'].values() for v in val['values'] ]
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if 'PR' in x['status'].keys() else None
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for x in st
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],
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'REGL':[
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[val['values'][v]['code_statut']
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for val in x['status']['REGL']['text'].values() for v in val['values'] ]
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if 'REGL' in x['status'].keys() else None
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for x in st
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],
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'REGLII':[
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[val['values'][v]['code_statut']
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for val in x['status']['REGLII']['text'].values() for v in val['values'] ]
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if 'REGLII' in x['status'].keys() else None
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for x in st
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],
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'REGLLUTTE':[
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[val['values'][v]['code_statut']
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for val in x['status']['REGLLUTTE']['text'].values() for v in val['values'] ]
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if 'REGLLUTTE' in x['status'].keys() else None
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for x in st
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],
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'REGLSO':[
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[val['values'][v]['code_statut']
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for val in x['status']['REGLSO']['text'].values() for v in val['values'] ]
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if 'REGLSO' in x['status'].keys() else None
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for x in st
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],
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'SCAP NAT':[
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[val['values'][v]['code_statut']
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for val in x['status']['SCAP NAT']['text'].values() for v in val['values'] ]
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if 'SCAP NAT' in x['status'].keys() else None
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for x in st
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],
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'SCAP REG':[
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[val['values'][v]['code_statut']
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for val in x['status']['SCAP REG']['text'].values() for v in val['values'] ]
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if 'SCAP REG' in x['status'].keys() else None
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for x in st
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],
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'SENSNAT':[
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[val['values'][v]['code_statut']
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for val in x['status']['SENSNAT']['text'].values() for v in val['values'] ]
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if 'SENSNAT' in x['status'].keys() else None
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for x in st
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],
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'ZDET':[
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[val['values'][v]['code_statut']
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for val in x['status']['ZDET']['text'].values() for v in val['values'] ]
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if 'ZDET' in x['status'].keys() else None
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for x in st
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],
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'exPNA':[
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[val['values'][v]['code_statut']
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for val in x['status']['exPNA']['text'].values() for v in val['values'] ]
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if 'exPNA' in x['status'].keys() else None
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for x in st
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]
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}
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return pd.DataFrame({**phylo,**cd_status})
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dict_dep = {
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'38':'Isère',
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'42':'Loire',
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'07':'Ardèche',
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'26':'Drôme',
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}
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if __name__ == "__main__":
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# Définition de la connection à la bdd GéoNature
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from pycen import con_gn
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# NOT USE FOR NOW - API Taxref
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api_taxref = 'https://geonature.cen-isere.fr/taxhub/api/taxref'
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# Paramètres de chargement du fichier des taxons
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PATH0 = '/media/colas/SRV/FICHIERS'
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PATH = 'SITES/SITES GERES/PLAN_PLANCHETTES/Scientifique et technique/Flore et habitats/Suivi flore patrimoniale 2025'
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file = 'donnes_sp_suivi2025.xlsx'
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sheet = 'liste sp'
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# Liste des CD_NOM en entrée
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cd_col = 'cd_ref' # Nom de la colonne à utiliser dans le feuillet ``sheet``
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# Lecture des données
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taxlist = pd.read_excel(os.path.join(PATH0,PATH,file),sheet,usecols=[cd_col],header=0)
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tab_sp = pd.read_excel(os.path.join(PATH0,PATH,file),sheet,index_col=cd_col)
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lst = taxlist[cd_col]
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# Récupération des statuts
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df = get_status(taxlist[cd_col].astype(str),con_gn)
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typ = get_type_status(con_gn)
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typ = typ[typ.cd_type_statut.isin(df.cd_type_statut.unique())]
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# Distinction LRR [old vs new] région
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is_lrr = df.cd_type_statut == 'LRR'
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df.loc[is_lrr & (df.niveau_admin == 'Région'),'cd_type_statut'] = 'LRR_AURA'
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df.loc[is_lrr & (df.niveau_admin == 'Ancienne région'),'cd_type_statut'] = 'LRR_RA'
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del df['niveau_admin']
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for c in ['cd_ref','cd_nom','lb_nom']:
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if c in tab_sp.columns:
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# if 'cd_nom' not in df.columns and c == 'cd_ref': continue
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tab_sp.drop(c,axis=1,inplace=True)
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pivot = pd.pivot_table(
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df,
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values='code_statut',
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index=['cd_nom', 'cd_ref','lb_nom'#,'niveau_admin','lb_adm_tr'
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],
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columns=['cd_type_statut'],
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aggfunc=list,fill_value=None)
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for c in pivot.columns:
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pivot[c] = [x[0] if x is not np.NaN and len(x)==1 else x for x in pivot[c]]
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if 'DH' in pivot.columns:
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pivot['DH'] = [','.join(x) if (x is not np.NaN) and (len(x)==2) else x for x in pivot['DH']]
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pivot.DH.replace({'CDH':''},regex=True,inplace=True)
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pivot = tab_sp.merge(pivot,on=[cd_col],how='left')
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pivlib = pd.pivot_table(
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df,
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values='label_statut',
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index=[
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'cd_nom', 'cd_ref','lb_nom'#,'niveau_admin','lb_adm_tr'
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],
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columns=['cd_type_statut'],
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aggfunc=list,fill_value=None)
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for c in pivlib.columns:
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pivlib[c] = [x[0] if x is not np.NaN and len(x)==1 else x for x in pivlib[c]]
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if 'DH' in pivot.columns:
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pivlib['DH'] = [','.join(x) if (x is not np.NaN) and (len(x)==2) else x for x in pivlib['DH']]
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pivlib.DH.replace({'CDH':''},regex=True,inplace=True)
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pivlib = tab_sp.merge(pivlib,on=[cd_col],how='left')
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print('INIT writer')
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NAME_OUT = os.path.join(PATH0,PATH,sheet+'_status.xlsx')
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with pd.ExcelWriter(NAME_OUT) as writer:
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df.to_excel(
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writer,sheet_name='v_bdc_status',index=False
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)
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# writer.save()
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print('v_bdc_status OK !')
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pivot.to_excel(
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writer,sheet_name='pivot_table'
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)
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# writer.save()
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print('pivot_table OK !')
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pivlib.to_excel(
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writer,sheet_name='pivot_libel'
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)
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# writer.save()
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print('pivot_libel OK !')
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typ.to_excel(
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writer,sheet_name='dic_type_statut',index=False
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)
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# writer.save()
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print('dic_type_statut OK !')
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